Supervised_Learning_with_Python_CH2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Supervised_Learning_with_Python_CH2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyM1nekB68bl2z+DsjlTD6Ug",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/christophergaughan/ChristopherGaughan.io/blob/master/Supervised_Learning_with_Python_CH2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oVYHDCuJbomS",
        "outputId": "a96f1443-4e7a-4d23-f495-fe8edbafcce1"
      },
      "source": [
        "!pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JnG6HLCVbptv",
        "outputId": "3e90f4c0-9bd1-45bd-9036-99d3f38e9bac"
      },
      "source": [
        "!ls -la"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 16\n",
            "drwxr-xr-x 1 root root 4096 Mar 25 13:38 .\n",
            "drwxr-xr-x 1 root root 4096 Apr  6 19:17 ..\n",
            "drwxr-xr-x 4 root root 4096 Mar 25 13:38 .config\n",
            "drwxr-xr-x 1 root root 4096 Mar 25 13:38 sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZSYQu8qWbwY9"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvC9RTYxb8iv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ece9b89d-c4eb-41a0-87a0-ee1d5bfe266a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oa8OG8z7b8ix"
      },
      "source": [
        "with open('/gdrive/My Drive/foo.txt', 'w') as f:\n",
        "  f.write('Hello Google Drive!')\n",
        "!cat '/gdrive/My Drive/foo.txt'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yfm1KUstcO4c"
      },
      "source": [
        "!ls -la"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ksJX8gNXcWZO"
      },
      "source": [
        "cd sample_data/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGjsD0I3ccYs"
      },
      "source": [
        "!ls -la"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "92nUosgecez9",
        "outputId": "6a1fd798-4792-41f0-c814-5f6a3e4ba731"
      },
      "source": [
        "cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L121H4YSck-Y",
        "outputId": "ff499bf8-82ee-4da2-cd52-404de3b5a77e"
      },
      "source": [
        "cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gj_cnao7crRw",
        "outputId": "6942a6c2-105e-4c33-fee2-8fb092ee60ff"
      },
      "source": [
        "ls -la"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 104\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[01;34m..\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:29 \u001b[01;34mbin\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Apr 24  2018 \u001b[01;34mboot\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:38 \u001b[01;34mcontent\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 29 13:57 \u001b[01;34mdatalab\u001b[0m/\n",
            "drwxr-xr-x   5 root root  360 Apr  6 02:30 \u001b[01;34mdev\u001b[0m/\n",
            "-rwxr-xr-x   1 root root    0 Apr  6 02:30 \u001b[01;32m.dockerenv\u001b[0m*\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[01;34metc\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Apr 24  2018 \u001b[01;34mhome\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:31 \u001b[01;34mlib\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Mar 25 13:20 \u001b[01;34mlib32\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:20 \u001b[01;34mlib64\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34mmedia\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34mmnt\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:32 \u001b[01;34mopt\u001b[0m/\n",
            "dr-xr-xr-x 121 root root    0 Apr  6 02:30 \u001b[01;34mproc\u001b[0m/\n",
            "drwx------   1 root root 4096 Apr  6 02:30 \u001b[01;34mroot\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:23 \u001b[01;34mrun\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:29 \u001b[01;34msbin\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34msrv\u001b[0m/\n",
            "dr-xr-xr-x  12 root root    0 Apr  6 02:30 \u001b[01;34msys\u001b[0m/\n",
            "drwxr-xr-x   4 root root 4096 Mar 29 13:53 \u001b[01;34mtensorflow-1.15.2\u001b[0m/\n",
            "drwxrwxrwt   1 root root 4096 Apr  6 02:45 \u001b[30;42mtmp\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 29 13:57 \u001b[01;34mtools\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:32 \u001b[01;34musr\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[01;34mvar\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xF5-d-Jmctal",
        "outputId": "6f4f1524-6880-4e73-a942-e7d256808ea8"
      },
      "source": [
        "cd gdrive/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Errno 2] No such file or directory: 'gdrive/'\n",
            "/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RoOgTMCOc3tL",
        "outputId": "91580d3c-ddfd-4048-c80e-758f73698061"
      },
      "source": [
        "ls -la\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 104\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[01;34m..\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:29 \u001b[01;34mbin\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Apr 24  2018 \u001b[01;34mboot\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:38 \u001b[01;34mcontent\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 29 13:57 \u001b[01;34mdatalab\u001b[0m/\n",
            "drwxr-xr-x   5 root root  360 Apr  6 02:30 \u001b[01;34mdev\u001b[0m/\n",
            "-rwxr-xr-x   1 root root    0 Apr  6 02:30 \u001b[01;32m.dockerenv\u001b[0m*\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[01;34metc\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Apr 24  2018 \u001b[01;34mhome\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:31 \u001b[01;34mlib\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Mar 25 13:20 \u001b[01;34mlib32\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:20 \u001b[01;34mlib64\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34mmedia\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34mmnt\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:32 \u001b[01;34mopt\u001b[0m/\n",
            "dr-xr-xr-x 121 root root    0 Apr  6 02:30 \u001b[01;34mproc\u001b[0m/\n",
            "drwx------   1 root root 4096 Apr  6 02:30 \u001b[01;34mroot\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:23 \u001b[01;34mrun\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:29 \u001b[01;34msbin\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34msrv\u001b[0m/\n",
            "dr-xr-xr-x  12 root root    0 Apr  6 02:45 \u001b[01;34msys\u001b[0m/\n",
            "drwxr-xr-x   4 root root 4096 Mar 29 13:53 \u001b[01;34mtensorflow-1.15.2\u001b[0m/\n",
            "drwxrwxrwt   1 root root 4096 Apr  6 02:45 \u001b[30;42mtmp\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 29 13:57 \u001b[01;34mtools\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:32 \u001b[01;34musr\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[01;34mvar\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wO1vqruWc403",
        "outputId": "f4f3637c-1ae9-43af-f96d-4e627112efa7"
      },
      "source": [
        "cd MyDrive/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Errno 2] No such file or directory: 'MyDrive/'\n",
            "/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BOafsvzc8Zz",
        "outputId": "72f743c9-482b-4196-fbb5-784c93e9a7bc"
      },
      "source": [
        "ls -la\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 104\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[01;34m..\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:29 \u001b[01;34mbin\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Apr 24  2018 \u001b[01;34mboot\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:38 \u001b[01;34mcontent\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 29 13:57 \u001b[01;34mdatalab\u001b[0m/\n",
            "drwxr-xr-x   5 root root  360 Apr  6 02:30 \u001b[01;34mdev\u001b[0m/\n",
            "-rwxr-xr-x   1 root root    0 Apr  6 02:30 \u001b[01;32m.dockerenv\u001b[0m*\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[01;34metc\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Apr 24  2018 \u001b[01;34mhome\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:31 \u001b[01;34mlib\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Mar 25 13:20 \u001b[01;34mlib32\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:20 \u001b[01;34mlib64\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34mmedia\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34mmnt\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:32 \u001b[01;34mopt\u001b[0m/\n",
            "dr-xr-xr-x 121 root root    0 Apr  6 02:30 \u001b[01;34mproc\u001b[0m/\n",
            "drwx------   1 root root 4096 Apr  6 02:30 \u001b[01;34mroot\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:23 \u001b[01;34mrun\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:29 \u001b[01;34msbin\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34msrv\u001b[0m/\n",
            "dr-xr-xr-x  12 root root    0 Apr  6 02:45 \u001b[01;34msys\u001b[0m/\n",
            "drwxr-xr-x   4 root root 4096 Mar 29 13:53 \u001b[01;34mtensorflow-1.15.2\u001b[0m/\n",
            "drwxrwxrwt   1 root root 4096 Apr  6 02:45 \u001b[30;42mtmp\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 29 13:57 \u001b[01;34mtools\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:32 \u001b[01;34musr\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[01;34mvar\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcRUpzmdc9ol",
        "outputId": "2cedb63b-7b3e-4fb5-95e9-d49559362186"
      },
      "source": [
        "cd 'supervised-learning-w-python-master.zip (Unzipped Files)'/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Errno 2] No such file or directory: 'supervised-learning-w-python-master.zip (Unzipped Files)/'\n",
            "/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s1shclkldkMJ",
        "outputId": "9aa4e1ac-194c-4854-947d-265f7f7ba899"
      },
      "source": [
        "ls -la\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 104\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[01;34m..\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:29 \u001b[01;34mbin\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Apr 24  2018 \u001b[01;34mboot\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:38 \u001b[01;34mcontent\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 29 13:57 \u001b[01;34mdatalab\u001b[0m/\n",
            "drwxr-xr-x   5 root root  360 Apr  6 02:30 \u001b[01;34mdev\u001b[0m/\n",
            "-rwxr-xr-x   1 root root    0 Apr  6 02:30 \u001b[01;32m.dockerenv\u001b[0m*\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[01;34metc\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Apr 24  2018 \u001b[01;34mhome\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:31 \u001b[01;34mlib\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Mar 25 13:20 \u001b[01;34mlib32\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:20 \u001b[01;34mlib64\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34mmedia\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34mmnt\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:32 \u001b[01;34mopt\u001b[0m/\n",
            "dr-xr-xr-x 120 root root    0 Apr  6 02:30 \u001b[01;34mproc\u001b[0m/\n",
            "drwx------   1 root root 4096 Apr  6 02:30 \u001b[01;34mroot\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:23 \u001b[01;34mrun\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:29 \u001b[01;34msbin\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34msrv\u001b[0m/\n",
            "dr-xr-xr-x  12 root root    0 Apr  6 02:45 \u001b[01;34msys\u001b[0m/\n",
            "drwxr-xr-x   4 root root 4096 Mar 29 13:53 \u001b[01;34mtensorflow-1.15.2\u001b[0m/\n",
            "drwxrwxrwt   1 root root 4096 Apr  6 02:45 \u001b[30;42mtmp\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 29 13:57 \u001b[01;34mtools\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:32 \u001b[01;34musr\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[01;34mvar\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pl7ILsl1dlnX",
        "outputId": "a27add88-d15b-4a4e-cc00-d66a705b2c36"
      },
      "source": [
        "cd supervised-learning-w-python-master/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Errno 2] No such file or directory: 'supervised-learning-w-python-master/'\n",
            "/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-EAzWwmDduNr",
        "outputId": "945c799f-86a0-4183-ee8b-d703351033c3"
      },
      "source": [
        "ls -la"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 104\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[01;34m..\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:29 \u001b[01;34mbin\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Apr 24  2018 \u001b[01;34mboot\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:38 \u001b[01;34mcontent\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 29 13:57 \u001b[01;34mdatalab\u001b[0m/\n",
            "drwxr-xr-x   5 root root  360 Apr  6 02:30 \u001b[01;34mdev\u001b[0m/\n",
            "-rwxr-xr-x   1 root root    0 Apr  6 02:30 \u001b[01;32m.dockerenv\u001b[0m*\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[01;34metc\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Apr 24  2018 \u001b[01;34mhome\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:31 \u001b[01;34mlib\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Mar 25 13:20 \u001b[01;34mlib32\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:20 \u001b[01;34mlib64\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34mmedia\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34mmnt\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:32 \u001b[01;34mopt\u001b[0m/\n",
            "dr-xr-xr-x 120 root root    0 Apr  6 02:30 \u001b[01;34mproc\u001b[0m/\n",
            "drwx------   1 root root 4096 Apr  6 02:30 \u001b[01;34mroot\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:23 \u001b[01;34mrun\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:29 \u001b[01;34msbin\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34msrv\u001b[0m/\n",
            "dr-xr-xr-x  12 root root    0 Apr  6 02:45 \u001b[01;34msys\u001b[0m/\n",
            "drwxr-xr-x   4 root root 4096 Mar 29 13:53 \u001b[01;34mtensorflow-1.15.2\u001b[0m/\n",
            "drwxrwxrwt   1 root root 4096 Apr  6 02:45 \u001b[30;42mtmp\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 29 13:57 \u001b[01;34mtools\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:32 \u001b[01;34musr\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:30 \u001b[01;34mvar\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cW2XUdnEdv1Q"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.datasets import make_regression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bywk4G7YBvdP"
      },
      "source": [
        "X,Y = make_regression(n_features=1, noise=5, n_samples=5000)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "v-d_CaZWCSKZ",
        "outputId": "832bf5f5-d01d-4f07-deb5-4ac2b7ddf68d"
      },
      "source": [
        "plt.xlabel('Feature - X')\n",
        "plt.ylabel('Target - Y')\n",
        "plt.scatter(X,Y,s=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f48de9bf890>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "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": "code",
      "metadata": {
        "id": "E9EhdSKlCsJG"
      },
      "source": [
        "linear_model = LinearRegression()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "anOVJ8meDVrZ",
        "outputId": "a200f304-6fd8-4568-ffe6-4a824a1f7fe2"
      },
      "source": [
        "linear_model.fit(X,Y)\n"
      ],
      "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": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vHYZ5BUXDfUZ",
        "outputId": "a208afca-f4e1-4b5c-9581-2be526dfdeab"
      },
      "source": [
        "linear_model.coef_\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([37.52930285])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XiP4FhhiDovR",
        "outputId": "e36f82b0-0157-40e1-f95e-34afa597ccc3"
      },
      "source": [
        "linear_model.intercept_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06685539288556938"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UnnZtzMD5Ln",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "bd5e6569-a299-4435-9b08-886e57e68894"
      },
      "source": [
        "pred = linear_model.predict(X)\n",
        "plt.scatter(X,Y,s=25, label=\"training\")\n",
        "plt.scatter(X,pred,s=25, label=\"prediction\")\n",
        "plt.xlabel('Feature - X')\n",
        "plt.ylabel('Target - Y')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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13GSlVGMXsg2X/PVzNu4qqRIwMvCzynsZLkyNSe4DrQ6n37m/pZ/lomRNEZkbl8dyJABuS3h82ggyPFZalAVJVjoGjRhjjBGRpMaRjDEzgZkQnnJbLw1TSjVKIduwcE0Rb68M/6x5+lFdGT+gMy5LqiTBITwcNc5awc2ulx0FjL0mixeGzOKayLT8cf07Mbxn2yqlR04e0gWXJY0i8V1ZOgaNouiwk4h0BYoj57cA5Qse9YicU0qpGoVsw0XPLuGz73fFEttzvirk2H6H8ffLjqmQBHcT5EbXy1zh/m/c2VBR5XskIWBs8Cke7dEhds5lCX+/7Ji0KTZYF9IxaMwB/ge4P/L7G+XOzxCRl4FjgL3J5jOUUs3XooJilm3YXWEmlG1g2YbdLCooZmDXVmS64QT7U57wPB77cEzUu4Dwjnrz7VHcGLyazm1bV8lNRHsUjbFXEU9Kg4aIvEQ46d1BRDYDdxEOFq+IyGXABuD8yO1zgdOAdUAJcGmDN1gp1Wit3LIXf6hqr8EXtPnjuwXs37ObD6yr6OgK15qLFywq5zr2kkmufyZB3FjAnWcObtS9CCdSPXvqJ9VcqrL1bWTW1DX12yKlVFMUsg3fFh+Ie83CpnfxfJ72PFZtnajyDhovJcbLP+1xPBo6l2DkYzQzw+LERjID6lCk4/CUUkrVmWhpkC/W76pyrSUH+Mx7Ldn4gMQBwwAleDnK/1zctRbGwEdrtzeZYajqpHpxn1JK1auFa4pYtmE3gXJDU26C3OJ6gZXeK8jGV2MPIzosdazv8bgBA6AsYDeaVd2HQnsaSqkmK2Qb7nkrD1/Qjp1rzT6+9F4VmxXlJFiUGDd/PfotQp/vhThlRqBxreo+FNrTUEo1CSHb8N6aIh57by3vrSmKlTgv3u+L3dOeXXzlvTK25qKmgGGAKwNXM9Q/m4C3PcN7tiU7w4UQLo0ezXlnN7JV3YdCexpKqUYjZBsW5hfx9teRxXlDuzI+kkMov393RqSk+che7QgEg0y2FvOAaxatLT9QfbAoPzvKj8UI30xKyI5dK7/mYkDXVmAgf9v+JrH+wikNGkqpRiGa0C6/EdKcrwo5pm97po/tG7ek+ZbdB3necwdjrA2x96kpYKyxuzPfPprHQ2fHZkYBuCwr7pqLSYO71O03muY0aCilGoXqFud9sX43u0r8cct/TLEWMMbaUOM0WmPAh4tc39McoGWV69kZLoZ0b/r5Cic0aCilGoXVhfsqJLSjgrahYNsPazDC1Wivp4O1j5oGi2K9C9OFKf4/VOhZRHndVrPJVzihQUMp1SgM7NoKjyUEKu+IFJGBn4fdj3C6awXgbFYUwLWBnzPXHhebSmtJuAfjEujWNos7zxwUK2qoNGgopdJcNPl995zV1QaM1uxjuffK2HRQJwFjqX04FwfuoIyKm4dNGdaNwzu2bFbJ7WRo0FBKpZXoVNmVW/YStG3+8+UWtu3zEYwTMLIp4T+eX5FjhYthO8ldABzne5hi6ULlwa7sDBdnDuvW5Fd1HwoNGkqplIgGh9WF+xjc7YfqsBc/9xnLNuyOm7+IsrCZai3gYc/s2Dmnw1HH+B6hmE64BFp7XQRCplHuoJcqGjSUUg0quhHSPW/lUbzfhz8Y/sAe1qMNI3u149NvdybYwQI6sIMl3uuI7uTtJFjYBtaZ7pwVuDu27iJkQ9CGy4/vh8dl6XCUQxo0lFINJrrWonJPosQf4tPvdrHku13VBgw3QX7jepIL3Z8BzoeiZgSm8197fNyaUaX+EB6XxbUTcmrz7TRLGjSUUg0muqVqdUNP1QWMLmzjY++NSSW6IZy72Eb1i++aS72ouqRBQynVYMpvqepEJmX8zXM7o60iwHnvotBuySmBh+Mu1LMkfJ/mMGpHg4ZSql6VT3gHQzZet1AWTJS1CK+5eNx9Pye78mPnnPYuRvmeYBftq9zjsoScTi25YWIO3xQd0BxGLWnQUErVm2gOI1pI0OMSgnG2XC2vC9v41Htj7Nhp7+ILuw8/C9xZYd2FWyAan0K2YeOuEtyawzgkGjSUUvUmmsOI1oWKt0d3VCZlvOj5NSOsQsB5sNhlPIzx/6XKIr0OLTPYecBf4VypP0Re4T5dh3EIdD8NpVS9cZrDOJJvWOOd7ihgGPNDwLgjcD5H+/9aJWD0aJvFfT8eSlaGq8J5TXwfOu1pKKXqXDSP8e32A1gC1XUwWrOP9z0zaG8FAee9i212NpMCj8RNdPfv1II3rzsBlyUM79k2NjSmie+6oUFDKVWn/EGbMx//iLXFB6imVBQAA8jnv957YsdOE90n+u5nI73i3nfq4E48cWFuLLldftMkTXzXDQ0aSqlDUn521MCurfjjO/l8U3yw2vvbsoePPFfTMjI47qR3YYDng+O4NzQ9bvnyqAFd21YICvE2TVKHRoOGUiopFYJEl1b89ZP1sWS3UP0CvQz8POP+PSe4CmLnnPYuhvv+zD4S5yJ0o6SGoUFDKVVB5UKCx+d05KO126sEiehe3IGQHRuGqi5gHME65nvvjB07zV2ss9szNfCHWL2o6mR5dKOkhqJBQykVU3ldRVaGC49L8AdtygJ2+OtyWe1ElWgh3Lt4zn03Y13fA86DBfxQjTYetyWcNrQLfQ5rgcsShnRvo/mKBqJBQykVU3ldRUml6bKJ1llUNog83vbeGzt2OhS1MDSIq4K34Cej2vuDtmH5pj386YIRGigaWNoGDRFZD+wHQkDQGJMrIu2BfwJ9gPXA+caY3alqo1JNTbK1oeJpyx4+9FxNqyQS3QClNowNxC8BEk/xPh+LCoo1yd3A0n1x30nGmOHGmNzI8W3Ae8aYHOC9yLFSqo4M7ta6yoI4p9wE+aPrQZZ7nQWM8ov0pvmuY1DgRccBA8JDY3mF+2rVVlV76R40KpsK/C3y9d+AH6ewLUo1OeP6d2JYj7YkO+DTg80UeH/Gue4vgXCwcDIc5bPhaN9jfMaxSbc1W1d3p0TaDk8RnogxT0QM8BdjzEygszFma+T6NqBKv1RErgCuAOjVK/4CIKVUWLyZUrsO+hLunFdeNiXM9VxNbytc4ymZRPck3z2s44i497ksIZRgZaDbQmdLpUg6B42xxpgtItIJmC8i+eUvGmNMJKBQ6fxMYCZAbm6u86ydUs1MvJlSvdplUVB0wNHrazuN9uPQ4UwP/l+1ie7sDBe92mezcVcJpf5QlQDmtuDqcUfwy4lHahI8BdI2aBhjtkR+LxaR14HRQJGIdDXGbBWRrkBxShupVCNSuVdhG8PyjbspDYSnzZb4Q+Q7CBjdKORDz024kkx0A5zi+w3fcGS192ZH6kPNvnQ0H63dzqot+/jvqq1s2HmQsoAdqx+lASN1xJj0+2FcRFoAljFmf+Tr+cA9wARgpzHmfhG5DWhvjLmluvfJzc01S5cubZhGK5XG4vUq2mW52bLXl9T7nMA8/uadHTt2Oo32gcAUZtrnx92nO2pAl1bcfEr/KustosFO60c1HBFZVm4CUgXp2tPoDLwu4X+RbuBFY8w7IvIF8IqIXAZsAM5PYRuVajTirb+ovAYjkT6s5z3Pr7CS7F3YNhwfeJBCuiW8X4D/PfnIuNNntX5UeknLoGGM+Q4YFuf8TsK9DaVUDcoPR323/UBSQSLKwuYK61lu9SyKnXPau7jIdwUfM87Rc3q0y2L8AA0KjUFaBg2l1KGpPBzlrsXk+kHk8WbGvURHgpz2LvbbcELgKfbQNu59llChZHqGS7jzjEE65NRIaNBQqglamF/Esg27Y7WhAolLRFXQnl187JlBZi0S3dN819W45qJbay+7SoMVNkYar0NPjYYGDaWamJBtuOfNvBqLCcZzLJ/wkveJ2LHzabQdmR78Y8J6UVE/HtmDEb3aaWK7kdKgoVQTs3BNEVv3liX1mkzKeNVzFYOt8GyqupxGW5nH5dLEdiOmQUOpRqzyhkhlviA3vPoVyXQyjuJr3vDeHzt2muj+ym7BtMDjlJHp+Fm6UVLjp0FDqUaqfLK7NjOjBpDP2557kp5GG7LhhBqm0bbKsLj4R31YvnEvX23eUyF/oaU/Grdqg4aI3Ao8aIw5tDrJSql6sXBNxWS3Uxn4meX+OWNcwdg5p72LS30XsYjTEr6/S+DhC0YwaXAXXZjXBCXqafQElonINcaYjxuqQUqpmoVsw91vrk46YIzkS17zPhg7dtq7CNgwNlD9Tnrl2Qbyt+1n0uAuujCvCao2aBhjZojISOAJEVkDPA3Y5a5/2QDtU0rFsXBNEVv2OE92Z1LGHM8vyLECQHKJbie9i/KytGR5k5Ywp2GM+VJEfgW8BhzOD/vGG2B8PbdNKRVHyDY889G3jsuXn8g7zPY+Hzt2OhTlJHcRleESAiGjeYtmIFFOoxPwENAPGG+M+arBWqWUionmBVZu2UswZPPGikI27S6t8XVH8g3/9fymVvWiTg/cST4DanyG2xJG9WrDz084nPyt+zVv0Qwk6ml8Bvwe+JlJx1K4SjUD/qDNlCcWs654f1LTaM9mNg9558WOnfYu7vCdwItc6egZHVpm8MA5R8WCxKRBXZw3UDVaiYLGaGPM9gZriVKqgpBtmPrEYvK37Xf8ml5s5H3PbbXqXUwN/IpVDHH8rCM6ttQEdzOUKBGuAUOpFFqYX0RBkbOAkYGf2e6rOM71w7CV097F9b5TeYOLk27fpWP6JP0a1fjp4j6l0lCpP8T//vMrEmyTHZNMohsq9i5ODPyBzfRIun3H9m3HRB2OapaSChoi0sUYs62+GqOUCucxRv12PiWBxOtq27KHjzxX07IW1Wiv853Om1xYY1syXMKo3u255Ee9+e+q8H/904d2ZfzAzprsbqaS7WnMBUbWR0OUau5CtmHB6m385s3VNQaM8czhOe/LsWOnAcNvw5jAY+ygQ43t8biEK088PLYf9ylDutb4GtX0JRs09EcLpepYyDYszC/i7jmr2VzDgr3aJroBLvRdySec4LhdwZDB47K0R6EqSDZoPFMvrVCqmSm/9uKdldtYW7yfUA35i9pOo/045GJ68DlHe12Upyu7VTxJBQ1jzFP11RClmouQbbjo2SUs27Abf02RAhjN57zkeaRW02hPCtzPRno5apcAGW4Lf9DWld2qWjp7SqkGUH7fi7JAkE+/21Xja9qzi8WeGWSV29/bae/iFt8E/sVljtuXneFiWI82TB/bV1d2q4Q0aChVz6L7XizfuJtSh5t1j2Muf/X+I3acTO/i1IDznfS8botTh3ThzGHddGW3cqTGoCEiDxhjbq3pnFKqolJ/iFte/YpPv9vJrgN+nISLbhTyoecmXLVIdP/CdzbzONdx+7xui1G92/HQ+cO1R6Ecc9LTmARUDhCnxjmnlCLcs3jn60KueXlFUq87lX/ylPeN2LHTgLHXhhMCf2YfzpLWLkvo3iaT/ztjkK63UElLVOX2KuBqoJ+IfF3uUitAN2VSKo6QbbjwmU9Z8v1ux6+pbTVagHN8N/FlEkunvG6Lx6aNYOIgDRaqdhL1NF4E/ku40u1t5c7vN8bUnMVTqhlakLctqYDxU/7Mfd4PY8dOE91rbDg7MIsyMmt8Rvc2Xnof1pLcPu2YMT6HDLdV42uUqk6igoV7gb3AT0RkLJBjjPmriHQQkb7GmO8brJVKpbHyM6Pe/rrQ0WuGsIo3PL+r1TTanwau4zOOTXi/BZw5rCtnHNVNh6BUnXKSCL8LyAX6A38FMoB/AGPqt2nVtmcy8CjgAp41xtyfinYoFbINC9cUcc9beRTv9znar9tNkN+7ZnCue1/snNPexW2+k/gnlztq25Un9eOWUwY6ulepZDhJhJ8FjAC+BDDGFIpIq3ptVTVExAU8STg5vxn4QkTmGGPyUtEe1XxFp9Eu27DbUbAAOIm3eNb7ItHBoWR6F5MC9/Id/Rw9x+u2uH5Cf0f3KpUsJ0HDb4wxImIARKRFPbcpkdHAOmPMd5G2vAxMBTRoqHpXfhgqGLJZvtFZwOhEMR97rsddi0T3rb7xvMLPHbfR4xIemzZC8xaq3jgJGq+IyF+AtiJyOTCd1NWg6g5sKne8GTim/A0icgVwBUCvXs7KJyhVk2jPYsWmPZT6Q7hdQsBBCZDTeJknvXNix9YK94UAAB42SURBVE4DRsiGsYGH2YbzhXbZkdIfEwfpbnqq/tQYNIwxD4rIJGAf4bzGncaY+fXesloyxswEZgLk5ubq3uaqVsr3KgZ3a00wZFcYiqopYNQ20Q3wM990PmKi47a2znQzfUxfhvZoo6U/VL1zVEYkEiTSIVBsAXqWO+4ROadUnfEHbaY+sZh12w8QDBmyMlyEQjY+Bz0LgCv5A7d6f1jY5zTR/XqwB7eEfkcwieo+XrfFQ+cNY9JgLf2hGoaT2VP7gcr/W/YCS4H/jeYXGsgXQI6I9CUcLKYBP23A56smLmQbpjyxmPxtP+zNXeJPvCFS1BgW8bxnZq2m0Z4SuId1HOHoOdG3jVaiHT9Qh6NUw3HyI80jhHMHLxL+9zoNOJzwbKpZwLj6alxlxpigiMwA3iU85XaWMWZ1Qz1fNX2LCor5dvuBpF7TkgO857mCTrWoRvsr34m8xC8cP2vKUV04c3h3rUSrUsZJ0JhijBlW7nimiKwwxtwqIr+qr4ZVxxgzl/C2s0rVuZVb9jpKcEfVdttV24YzA78mj0GOn5Wd4WLqiB5MGNhZK9GqlHESNEpE5Hzg1cjxuUB0T0pNNKtGr/wueh99s93Razqwg8We6/DWItE93fdT3ueMpNqYrZsiqTThJGhcSHgF9lOEg8QS4CIRyQJm1GPblKp35afSOs1dTOA/POt9JXZcX9VoszMsHjpnGOt2HNShKJU2EgaNyArsq40xZ1Zzy+K6b5JS9csftHli4VqWbtjNYS0yHAeMLmxjsefGWu11caHvSj7hBEft69+5JacM7qLFBVVaShg0jDGhSLFCpZoEf9Dm6Pvms7c0mNTrpvAPHvX+kEpzmujOs92cE5jpqBothGeanHFUN66dkJNU+5RqKE6Gp5aLyBzgX8DB6EljzL/rrVVK1aHyC/UKtu1LKmB0YRsfeW5MugRIstNoo7IyXAzq5mz4SqlUcBI0MoGdwPhy5wygQUOlvcrlP5zO3MimhP96fk6vWkyjvcY3hblMS7qtmuxWjYGTMiKXNkRDlKoPiwqKk0pyA5zAPP7mnR07TqZ3cWLgD2ymR8L7LaiwX7jXbXHqkC6cOaybJrtV2nOyIjwTuAwYDD8MzBpjptdju5Sqlco1o77a7Dxg1HYoCuB/fJfwISfX+IzWme5Iu/ZS6g/FVnU/dP5wDRaqUXAyPPV3IB84BbiH8BTcNfXZKKVqo/JQVHiWk7MP4lP5J09534gdOx2K2m7D+MBMDtDS0XN+dlxvbpjUn0UFxeQV7tOptKrRqTZoiIjbGBMEjjDGnCciU40xfxORF4GPGq6JSiUW7V28+VVhhUq04d8SZzEOJdE9IfA71tPHcTtbZ7q5bsKRuCxhwsDOTNCaUaoRStTT+BwYCQQix3tEZAiwDdBMnUoLtVmcB2Bhc7P1OFd6Poudc9q7uMk3iddwnurzui0uG9OX6ycdqesuVKPnZHhqpoi0A34NzAFaAv9Xr61SyqFFBcUs37ib0oCzLVcBRvM5//Q+EjtOpndxeuBO8hng6DkCXDP+cG6Y2F+Hn1STkShodBKRGyNfR3+sejLyeyq3fFUKCPcy/rNii+OAUXkabTKJ7ht8k/kPP3PcNq/bYlTvdhowVJOTKGi4CPcq4v2L10KFKqWiw1Kffb/L0f3Hs4DnvbNix04Dhs+GsYHH2EGHGp/R2uvij+cN45uiA5rgVk1WoqCx1RhzT4O1RKkkRNdfhOzEP7+0Zxcfe2aQWYvexRW+c5jPOY7aI8Afzx3GKUO6csoQRy9RqlFKFDT0RySVlkK24d9fbq4x8T2J15jpfS127DTRfcCG4wNPsYe2jtrjsmB0n/ZM1C1XVTOQKGhMaLBWKJVAyDYsXFPE2yu3YoD8wr0UFB+s9v4ebOYDzy212nb1rMBtfM1RjtvmcQlXnXg4v5x4pA5FqWah2qBhjHE2WKxUPQrZhoueXcKS73Y5SqSdyQs85n07duy0d5HMUFRUlsdiRK92GjBUs+Jkyq1SKfPu6m18+l3NP7/04zvme36ddO+i1IaxgSfYRfuk2uVxCZcf308Dhmp2NGiolKlcJ+r4nI58tHZ77PhHh3fgly8vT/geGfiZ5b6JMa4dsXNOexc/803nIybWqu3BkMHjsjRgqGZHg4ZKicp1orIyXHhcgj9oUxawyfRYeCyLQKj6QalB5PG2997YcX1Mo62O7nuhmisNGqrBlO9ZBEN2hdIflWdClQZsSom/aC+8SG8GvawyILlptBN89/Id/WpsqwCZbgu/bQjZhmiHwja674Vq3jRoqAZRuWfhdknCXkR1JvAfnvW+Ejt2OhT1TGAU99s3YFNz7adoUJh96Wg+WrudvMJ9DOjaCgzkb9uvC/dUs6ZBQzWIypshJRsw+rCe9zy/qtU02lMDv+EbjqzxGU9eMJzvdpVUCAqVq9FO0rUYqpnToKEaxOrCfZTGWYzncQnBkCErwwUGSgJV7xnHXP7q/Ufs2Gnv4lLfRSziNEfty3QLmZlurp2Q4+h+pZorDRqqQQzs2ooMtxXb6wLC6xwmDuxM8f4y8gr3s98frPCaDuxgsec6vEn2LopsmJjExkgAvqAhr3Cf7nGhVA3SLmiIyG+Ay4HtkVO/MsbMjVy7nfDWsyHgOmPMuylppEpKyDY899F3+MsFDAE8Lot5q7fiq9S5cBPk964HOdf99Q/3O+xdTPLdwzqOSLqNOhtKKWfSLmhE/MkY82D5EyIyCJhGeK/ybsACETnSGON85x2VEgvXFPH5+t0VVnQbYF9ZsMq9nShmiff6WOEzp72Lb21hSuAZSsh21CaJvLcxxPbp1tlQStUsXYNGPFOBl40xPuB7EVkHjAY+TW2zVE1eX7GZGorR4ibI3a6n+ak7/NfpONENnOS7n430ctyeH/Vrz6Vj+oJA/ladDaVUMtI1aMwQkZ8BS4H/NcbsBroDS8rdszlyrgIRuQK4AqBXL+cfJKruhWzDvFVbmbuyKOF9PdjMB95bYpNhnQ5FvRocwu2hWwgm8c84wyVcdny/WO5i0iCdDaVUMlISNERkARDvf+sdwNPAbwmPYPwWeAiY7vS9jTEzgZkAubm5ullUCkSr0t7zVh6bd5dWe19r9vG+5xraW+ERxmSm0Z4USK53AWAJjOrdToehlDoEKQkaxhhHBX9E5BngrcjhFqBnucs9IudUGogGije/LuSTb3ey+6Cf6pZiWNhcYL3F7zwvx8457V1M9TkvXW4J5HRqyYAurRGB04d2ZfzAzjoMpdQhSLvhKRHpaozZGjk8C1gV+XoO8KKIPEw4EZ4DfJ6CJqpKkilf3oliPvVe72goCn4IGNttL+MDjzuaRutxCZOHdGHKUd00SChVx9IuaAB/EJHhhIen1gO/ADDGrBaRV4A8IAhcozOn0sPC/KIaA0a4XtT19LIOAMnVi3Lau+jRNouzR3ZnxvgcMtw1lwtRSiUv7YKGMebiBNfuA+5rwOaoGoRswzMffpswYAxhFW96fxc7dhowCm2LUwJ/dtS7yM5wsfCmcRoslKpnaRc0VOMRsg0XPvMpn6/fE/d6Sw4wz3MVXZNNdBs43v8ghXRz1I7DWmSw+NbxGjCUagAaNFSthGzDn+YXsOT73XGv9+M73vP+OnbsNNF9e+AsXrHPcVSN1mUJOZ1aMmfGWA0YSjUQDRoqaf6gzZTHPyK/6ECVa5mU8bLn1wyzCoH63RjpzKO68tD5wzXRrVQD0qChkuIP2pz04CK27Km6/qIbhXzsvSl27LR3cYrPWeny8rxuizOHddOAoVQD06ChahRdgzHn60IW5BVTWql8eVv28IFnBq2tcEFCp72LjXZrTg08XGO9qB5tsyjcWxorRWIJjOyltaKUSgUNGiohf9DmzMc+pKD4YJVrboLc4ZrJJe7FsXNOehc2cJzvEYqp+UM/O8Ni/o0nsnjddt7+Orx8RxfpKZU6GjRUtfxBm/EPLYpbCqQ1+/jSeyWuyLHToagFoX5cE7wTPxkJn20JdGubxfwbTiQrw8WkQV20TpRSaUCDhoorZBumPL64SsDIwM/D7gc43bUGcD4UVWoLYwOPs4v21d4rQP8urTh1SBeGdG+jlWeVSkMaNJqZkG1YVFDM6sJ9DE5QEnzeqm3kF+2vcC6ZvS7K9y7G+BKvuejRNose7bK4dExfJg7SYSel0pkGjWYkZBsufu4zVmzaQ6k/FNt8aPalo/lo7XZWF+5jYJdW2Aauf2VF7HXZlPAfz63kWDsB572LhaGBXBW8NeFQlABb9pSyq8TP3z5dz8RBut2qUulMg0YzsqigmBWb9lDiD89+KvGHWL5xN1OeWMz3Ow5W2L87qgM7+MJ7XezYae/iJN/vWE+fau91WYJtm1j5kRJ/iBWb9rCooFj36VYqjeky2mZkdeE+Sv0Vp8uWBmwKtu2vEjDcBPm1a1YsYIg4Cxhb7WyG+mYmDBgDOrfkzKO6VqlXVeoPkVe4z+m3o5RKAe1pNCODu7UmK8MV62lA+Cf+ULm9WMOJ7sc5zbUsqdyFDYzyPcUe2gLQtY2XO04bxHUvL6+w1asl8L+n9McSYV5eUYW2ZGW4GNSt9aF+m0qpeqQ9jWZkXP9ODO/ZluwMF0K4Mmy7LE/selv2kO+9hNMjASNR78KYHwLGX4MT6e97PhYwAM4b1ZNTh3bl2H6H4Y3UhfK6LY7tdxjjB3SO25bhPXXBnlLpTnsazYjLEmZfOponFq7li/W76NDSCwbeXrmFydZinvT8GXCe6J4XGsK1wZuqTXS7LOHvlx3DooJi8gr3MajSbK1E15RS6UmDRjMRsg0L84u45808tu4tJZrCyKSMz7zX0oHwim+nie5RvicSrrmIfvi7LGHCwM5xk9uJriml0pMGjWYgOtV22YbdsYS3myA3uv7Jle63Y0NR1SkfLOaHhjMjeH3CabRZHosh3dvUUeuVUulEg0YzEJ1q6wvaWNicbH3KE54nkyoBstPOZHzgEfYRP1HtdVv4g3Zs7YfmJpRqmjRoNFHR4ai3v95K/rb9lPpDuAkyx3MrA61w4T+nvYthvj9XGywAjuvXnulj+5K/db/mJpRq4jRoNEHR4agl3+2MTXd1E+T9jBvoITsdJboNsNTux88Cv6aMzGrvPfOorjwybQQuS7SgoFLNgAaNJiRaV+rNrwr5Yv0ubAMWNuOtpTzomUkbShz1LspwM8I3M2GwAGiT5dad85RqZjRoNBH+oM2Zj3/Euu0HCNnhWVH/8PyeXFmHJeFo4CRgPB08jYdD0wjW8E/j2L7tef6yY3RvbqWaGQ0ajZA/aPPEwrUs3bCbkb3bMrhza+54YyW7SoIAtOQAK71XxO53Eiy22y05IfBYjb0LCC/Eu/yEfhowlGqGNGg0Mv6gzdH3zWdvaThAfPLtzti1bEp43fN/HJlkontG4Cr+a4/BjhQIiE7BtU14VpTXbREI2ZQFdHaUUs2dBo1G5omFa2MBI8rCZrL1EU96/hI7V1PAsIGldg7/E7i9Su/CRO7xuIS+HVrw+tVj+OTbHbpyWymlQaOxWbphd4VjC5vnPfcyxsoHnJUA2WR3ZHzgIYK4wx/+duV6s2GBkGHjrhI++XaHrtxWSgFasLDRye3dDggHi4nWMl7w/JYxVr6j0uXGQL7pGQsY2RkuvO7EUUbLlSulyktJ0BCR80RktYjYIpJb6drtIrJORApE5JRy5ydHzq0TkdsavtXp4apxR9A+I8ASz9XM9DzEsVZBwvujwWIvmfwi8EtO9/8+FjB6t8/GrqaXEaXlypVS5aVqeGoVcDbwl/InRWQQMA0YDHQDFojIkZHLTwKTgM3AFyIyxxiT13BNTj1/0ObiR+ayVP6nxp4FhINFicnghuDVLLBz8brdXD2uLx7LxZDurVm5ZS+PLlhb5XUelxAMGU16K6WqSEnQMMasAZCqn3pTgZeNMT7gexFZB4yOXFtnjPku8rqXI/c2m6ARsg3THp7Lvw5e5LjA4Bq7K1MCD8TWXFx+Qj9uPLl/hXsrb8qU5bG4/Ph+eFyWJr2VUlWkWyK8O7Ck3PHmyDmATZXOHxPvDUTkCuAKgF69etVDE+tWdBX36sJ9DC73IR09n7dlN8fLClru/Jp/HnwKi5oDxkG83BD4Be/Zo2PTaDNcwrCebSvcG90IacWmPZT6Q7GexS8nHqmBQikVV70FDRFZAMQrRnSHMeaN+nquMWYmMBMgNzc38YB9ikVrREU/tDM9Fr0Pa8GkQZ15/cstFO87yCzX78mxviVbyoD4ASPaszBAvt2LKYF7K6zotgRG9W5XZZippk2SlFKqsnoLGsaYibV42RagZ7njHpFzJDjfaEVLlkeHh0oDNvnb9pO/bT8WNr90vcZoK58MiQwfJQgYr4eOYa49loX2iFjvAmBgl1bcOOlIxg/sHDcY6EZISqlkpNvw1BzgRRF5mHAiPAf4nPDHZY6I9CUcLKYBP01ZK+vI6sJ9FfIJ4eKCyznD+oRx1gpaSWnC6W3RRXrDfDM5QMsq17MzXNx0Sn8NCEqpOpOSoCEiZwGPAx2Bt0VkhTHmFGPMahF5hXCCOwhcY4wJRV4zA3gXcAGzjDGrU9H2ujS4W2u8biu2OdLfPb/nWCsPi+oLDJYv/+HHYoRvJiVkh+8HMnQzJKVUPRJj0nrY/5Dk5uaapUuXproZ1fIHbcb+bh5Dyz7j5665HG0V4Jb4fx/GQAAX20x7NpqOLLP783jorFjuIstjMaJXOy79UR/yt+lmSEqp2hORZcaY3HjX0m14qlkI2YYFq7dx1+vLmR24hf6eTTWusgwhPB2cwqOhc7Cx8LiEI7q05IYJOXxTfKBCkJg0WDdDUkrVDw0aDSxkGy5+5mNabJjPO56ZtLESb4wE4V5Ggd2TR0PnYLAYc/hhTB/bNxYkTkn8cqWUqjMaNBpA+bUYoWCAGVtu5hhPXrVrLgzh/ES02my+6cnUwL3YWGRnuJg+tq8mt5VSKaFBo55F12J8vWkXxwSXMdX1CcdYebgS9C5ELOgwANN5EA8XDmb2jgGEMGRrclsplWIaNOrZooJiVm3awSvczhGeLbgJxVtuARCdM4X0GQsX/wfLcnGDbRihi++UUmlCg0Y9y9uym5fM7Qy0NiYu/wHgbYNMfQoGnAqWC9DFd0qp9KJBo54dLyvIsQprrBe1L7MrbW7+GtwZDdc4pZRKkgaNenaUawNIsMp5Y8K9ixAW6+hB4ZlzmeDOqLaAoVJKpQMNGvXM6jqMoCsLK1QaO2cMbDIdeTV0Imtd/djXYxzPD+pRpYBhdFX33y87RgOHUiotaNCoI6FgkJUfvErpxuVk9RrB0BPPxeV2Q84kXL1GU/b9EjzGRwgX39Kdm9v+iUlH9ebc7j/0Jt5bU1ShgGGJP8SKTXtYVFCsOQ2lVFrQoFEHQsEga/4wgRxfPpn4KFvvZc1nTzPwlvdwud3Ixa/j+WYe61ctIc/uTfbgybwxsGuV3sPqwn2UlitgCD/s0a1BQymVDjRo1IGVH7xKji+fFpE9L1pQRl/fGlZ+8CrDJ0wDy4VrwKkcPuBUDk/wPoO7ta66k57u0a2USiM1lTxS8dghKHgHPvgDFLxD2YZlZOKrcEsmfko3Lk/qbaM76WVnuBDQxXxKqbSjPY1k2CEoeBfm3Q77t0HQBxnZDM7qSRkZtCgXOMrIIKvXiKTeXnfSU0qlOw0aTtkh+PtZsGlJOFhE+Q/S0t7IRk83CBSSiZ8yMvjeO5ChJ56b9GN0MZ9SKp1p0KhBdFZUZv5/yNm5BJftq3KPBEvpeeI0vrb7VJ09pZRSTYh+qsURXWC3avMuTljyC44MFpBFWbhmVLyqtJ5srG7DGd5/MuGdaJVSqmnSoFFJyDb87NlPaL15ESfbixnoyiNTAnHvNQZ8eFgnOQw8fCKuBm6rUko1NA0alSxas5UZW27hKFlLlstXpWMRLf/hx02Rac9vgxezxOTy6NqdmodQSjV5GjQqObj6HY5lLS2kau4Cwj2LuaHRvGUfxyJ7ODYW4je6AE8p1Sxo0KhksLWeTPwVzkV7F6V4WWEfwU3Bq7DLLXHRBXhKqeZCF/dV0mfIcfgtb4VzfvFQ0OlUvjn+UQ6c9wo92rXA67Z0AZ5SqtnRnkYlriNPJrPPMQQ3fYEVLMV2Z5HR82gGXvxCbGOkiUO66wI8pVSzJMaYVLeh3uTm5pqlS5cm/0I7BGvnw7aV0GUo5EyKBQyllGrqRGSZMSY33jXtacRjuaD/5PAvpZRSMZrTUEop5ZgGDaWUUo5p0FBKKeWYBg2llFKOadBQSinlWJOecisi24ENqW6HAx2AHaluhAONpZ3QeNqq7axbjaWdkN5t7W2M6RjvQpMOGo2FiCytbk50Omks7YTG01ZtZ91qLO2ExtXW8nR4SimllGMaNJRSSjmmQSM9zEx1AxxqLO2ExtNWbWfdaizthMbV1hjNaSillHJMexpKKaUc06ChlFLKMQ0aaUJEfisiX4vIChGZJyLdUt2meETkjyKSH2nr6yLSNtVtikdEzhOR1SJii0jaTWsUkckiUiAi60TktlS3pzoiMktEikVkVarbkoiI9BSR90UkL/L3/stUtykeEckUkc9F5KtIO+9OdZuSpTmNNCEirY0x+yJfXwcMMsZcmeJmVSEiJwMLjTFBEXkAwBhza4qbVYWIDARs4C/ATcaYWmysUj9ExAV8A0wCNgNfAD8xxuSltGFxiMgJwAHgeWPMkFS3pzoi0hXoaoz5UkRaAcuAH6fbn6mICNDCGHNARDzAYuCXxpglKW6aY9rTSBPRgBHRgvC25GnHGDPPGBOMHC4BeqSyPdUxxqwxxhSkuh3VGA2sM8Z8Z4zxAy8DU1PcpriMMR8Cu1LdjpoYY7YaY76MfL0fWAN0T22rqjJhByKHnsivtPy/Xh0NGmlERO4TkU3AhcCdqW6PA9OB/6a6EY1Qd2BTuePNpOEHXGMlIn2AEcBnqW1JfCLiEpEVQDEw3xiTlu2sjgaNBiQiC0RkVZxfUwGMMXcYY3oCLwAz0rWdkXvuAIKRtqZtO1XzIiItgdeA6yv13tOGMSZkjBlOuJc+WkTSdtgvHt3utQEZYyY6vPUFYC5wVz02p1o1tVNELgHOACaYFCbFkvjzTDdbgJ7ljntEzqlDEMkRvAa8YIz5d6rbUxNjzB4ReR+YDKT1RIPytKeRJkQkp9zhVCA/VW1JREQmA7cAU4wxJaluTyP1BZAjIn1FJAOYBsxJcZsatUiC+TlgjTHm4VS3pzoi0jE641BEsghPhkjL/+vV0dlTaUJEXgP6E57xswG40hiTdj99isg6wAvsjJxakqazvM4CHgc6AnuAFcaYU1Lbqh+IyGnAI4ALmGWMuS/FTYpLRF4CxhEu410E3GWMeS6ljYpDRMYCHwErCf8fAviVMWZu6lpVlYgcBfyN8N+7BbxijLknta1KjgYNpZRSjunwlFJKKcc0aCillHJMg4ZSSinHNGgopZRyTIOGUkopxzRoKFWJiIQi1Yajv/rU4j1+LCKD6r51NT73RhGZVe74QhF5u6HboZounXKrVCUicsAY0/IQ32M28JYx5tUkXuMuVwyyts91A0uBa4DVwHLCK/e/O5T3VSpKexpKOSAio0TkAxFZJiLvRkpxIyKXi8gXkf0RXhORbBH5ETAF+GOkp3K4iCyK7ushIh1EZH3k60tEZI6ILATeE5EWkT0sPheR5cnW0YoEnauBJ4E/EF44qAFD1RmtPaVUVVmRKqQA3wPnE15dPtUYs11ELgDuI1zl99/GmGcARORe4DJjzOMiModyPY1wlYtqjQSOMsbsEpHfEd6vZHqk3MTnIrLAGHPQaeONMZ+IyBpgIjAwmW9cqZpo0FCqqtJIFVIAIlVIhwDzIx/+LmBr5PKQSLBoC7QE3q3F8+YbY6J7VpwMTBGRmyLHmUAvwvtDOBKp9JpLeK+GjoRLrytVJzRoKFUzAVYbY46Lc2024R3ivopU/x1XzXsE+WE4OLPStfK9CAHOSbSBlIjcB5wOUD64lXM38A/CtaL+BJxX3XsplSzNaShVswKgo4gcB+ES3CIyOHKtFbA1Upb7wnKv2R+5FrUeGBX5+twEz3oXuDZStRURGVH5hsi+K8PjBQwRGUo4oDwAzAT6iMikmr9FpZzRoKFUDSJbsp4LPCAiXwErgB9FLv8f4R3iPqZiieuXgZsjyezDgQeBq0RkOeGKsdX5LeFhpa9FZHXk2JFIoHkauMEYU2aMsYGrgEcjJdiVOmQ65VYppZRj2tNQSinlmAYNpZRSjmnQUEop5ZgGDaWUUo5p0FBKKeWYBg2llFKOadBQSinl2P8DMZZ1wJ/Z90cAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jFcxZCoiHJaQ"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings(action=\"ignore\", module=\"scipy\", message=\"^internal gelsd\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "njok8DGoH_Nv",
        "outputId": "c1401ebf-aee3-4af6-fc7c-84bc13b6c095"
      },
      "source": [
        "pwd\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n11OcIpyIJuf",
        "outputId": "c198f7b1-4f79-46ee-f912-665fedd81a2f"
      },
      "source": [
        "cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lVwoD5vkIZtG",
        "outputId": "e3921e25-2db1-4d73-d85c-35624ea688be"
      },
      "source": [
        "cd tmp/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/tmp\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_tO08GQBIanm",
        "outputId": "1b8496d2-3a55-4587-8e25-ffd6836b158d"
      },
      "source": [
        "ls -la\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 20\n",
            "drwxrwxrwt 1 root root 4096 Apr  6 02:59 \u001b[0m\u001b[30;42m.\u001b[0m/\n",
            "drwxr-xr-x 1 root root 4096 Apr  6 02:59 \u001b[01;34m..\u001b[0m/\n",
            "-rw-r--r-- 1 root root 1502 Apr  6 02:59 dap_multiplexer.d36bf09a4f12.root.log.INFO.20210406-025939.47\n",
            "lrwxrwxrwx 1 root root   61 Apr  6 02:59 \u001b[01;36mdap_multiplexer.INFO\u001b[0m -> dap_multiplexer.d36bf09a4f12.root.log.INFO.20210406-025939.47\n",
            "srwxr-xr-x 1 root root    0 Apr  6 02:59 \u001b[01;35mdebugger_1yg0vbnuu6\u001b[0m=\n",
            "drwx------ 2 root root 4096 Apr  6 02:59 \u001b[01;34minitgoogle_syslog_dir.0\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0XBAKi-8JRXx",
        "outputId": "04290098-2594-416f-8596-143e1a82e7e6"
      },
      "source": [
        "cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1YcgPw0JWGU",
        "outputId": "d1be76a8-0a2a-441f-a791-43dfb1793396"
      },
      "source": [
        "ls -la\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 100\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:59 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:59 \u001b[01;34m..\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:29 \u001b[01;34mbin\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Apr 24  2018 \u001b[01;34mboot\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:38 \u001b[01;34mcontent\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 29 13:57 \u001b[01;34mdatalab\u001b[0m/\n",
            "drwxr-xr-x   5 root root  360 Apr  6 02:59 \u001b[01;34mdev\u001b[0m/\n",
            "-rwxr-xr-x   1 root root    0 Apr  6 02:59 \u001b[01;32m.dockerenv\u001b[0m*\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:59 \u001b[01;34metc\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Apr 24  2018 \u001b[01;34mhome\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:31 \u001b[01;34mlib\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Mar 25 13:20 \u001b[01;34mlib32\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:20 \u001b[01;34mlib64\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34mmedia\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34mmnt\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:32 \u001b[01;34mopt\u001b[0m/\n",
            "dr-xr-xr-x 113 root root    0 Apr  6 02:59 \u001b[01;34mproc\u001b[0m/\n",
            "drwx------   1 root root 4096 Apr  6 02:59 \u001b[01;34mroot\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:23 \u001b[01;34mrun\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:29 \u001b[01;34msbin\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34msrv\u001b[0m/\n",
            "dr-xr-xr-x  12 root root    0 Apr  6 02:59 \u001b[01;34msys\u001b[0m/\n",
            "drwxr-xr-x   4 root root 4096 Mar 29 13:53 \u001b[01;34mtensorflow-1.15.2\u001b[0m/\n",
            "drwxrwxrwt   1 root root 4096 Apr  6 02:59 \u001b[30;42mtmp\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 29 13:57 \u001b[01;34mtools\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:32 \u001b[01;34musr\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:59 \u001b[01;34mvar\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fRutQPOvJXcA",
        "outputId": "d1cdc59c-6e29-4741-a1e0-ba1610d08364"
      },
      "source": [
        "cd content/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HMCfzmmxJmX-",
        "outputId": "8a75ffe6-f930-475c-8c78-1967f8a09b06"
      },
      "source": [
        "ls -la"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 16\n",
            "drwxr-xr-x 1 root root 4096 Mar 25 13:38 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x 1 root root 4096 Apr  6 02:59 \u001b[01;34m..\u001b[0m/\n",
            "drwxr-xr-x 4 root root 4096 Mar 25 13:38 \u001b[01;34m.config\u001b[0m/\n",
            "drwxr-xr-x 1 root root 4096 Mar 25 13:38 \u001b[01;34msample_data\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zlV4yqp7Jncu",
        "outputId": "bdb5a704-8788-488f-c8b9-2d6b9a781eef"
      },
      "source": [
        "cd sample_data/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4re8S9yXJqWg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7159283f-1a44-4d8d-c9da-5c5f7f8bd406"
      },
      "source": [
        "ls -la\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 55512\n",
            "drwxr-xr-x 1 root root     4096 Mar 25 13:38 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x 1 root root     4096 Mar 25 13:38 \u001b[01;34m..\u001b[0m/\n",
            "-rwxr-xr-x 1 root root     1697 Jan  1  2000 \u001b[01;32manscombe.json\u001b[0m*\n",
            "-rw-r--r-- 1 root root   301141 Mar 25 13:38 california_housing_test.csv\n",
            "-rw-r--r-- 1 root root  1706430 Mar 25 13:38 california_housing_train.csv\n",
            "-rw-r--r-- 1 root root 18289443 Mar 25 13:38 mnist_test.csv\n",
            "-rw-r--r-- 1 root root 36523880 Mar 25 13:38 mnist_train_small.csv\n",
            "-rwxr-xr-x 1 root root      930 Jan  1  2000 \u001b[01;32mREADME.md\u001b[0m*\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQeu21qYJrnB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70200a1a-7f98-4a8d-8c5f-115cf432a7e9"
      },
      "source": [
        "\\ google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KTPAOOvWKFc8",
        "outputId": "ad3f1ce2-bad8-4d9f-cacb-d19eddd004bf"
      },
      "source": [
        "ls -la"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 55512\n",
            "drwxr-xr-x 1 root root     4096 Mar 25 13:38 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x 1 root root     4096 Mar 25 13:38 \u001b[01;34m..\u001b[0m/\n",
            "-rwxr-xr-x 1 root root     1697 Jan  1  2000 \u001b[01;32manscombe.json\u001b[0m*\n",
            "-rw-r--r-- 1 root root   301141 Mar 25 13:38 california_housing_test.csv\n",
            "-rw-r--r-- 1 root root  1706430 Mar 25 13:38 california_housing_train.csv\n",
            "-rw-r--r-- 1 root root 18289443 Mar 25 13:38 mnist_test.csv\n",
            "-rw-r--r-- 1 root root 36523880 Mar 25 13:38 mnist_train_small.csv\n",
            "-rwxr-xr-x 1 root root      930 Jan  1  2000 \u001b[01;32mREADME.md\u001b[0m*\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "taXLziS6KRtu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e7d3d5b-9a57-4d32-db47-1c788b2b1eb4"
      },
      "source": [
        "cd ..\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ug2qIWG1MCgc",
        "outputId": "f2430231-1db1-4595-910b-a46b59b4bcfd"
      },
      "source": [
        "ls -la"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 16\n",
            "drwxr-xr-x 1 root root 4096 Mar 25 13:38 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x 1 root root 4096 Apr  6 03:10 \u001b[01;34m..\u001b[0m/\n",
            "drwxr-xr-x 4 root root 4096 Mar 25 13:38 \u001b[01;34m.config\u001b[0m/\n",
            "drwxr-xr-x 1 root root 4096 Mar 25 13:38 \u001b[01;34msample_data\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QX5mMl4IMD3Q",
        "outputId": "785a7106-2fd6-4262-c0ab-324db0e7b7c1"
      },
      "source": [
        "cd sample_data/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SdMML1tMMGev",
        "outputId": "1fef2443-6770-4686-d61e-bef46066e8e3"
      },
      "source": [
        "ls -la"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 55512\n",
            "drwxr-xr-x 1 root root     4096 Mar 25 13:38 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x 1 root root     4096 Mar 25 13:38 \u001b[01;34m..\u001b[0m/\n",
            "-rwxr-xr-x 1 root root     1697 Jan  1  2000 \u001b[01;32manscombe.json\u001b[0m*\n",
            "-rw-r--r-- 1 root root   301141 Mar 25 13:38 california_housing_test.csv\n",
            "-rw-r--r-- 1 root root  1706430 Mar 25 13:38 california_housing_train.csv\n",
            "-rw-r--r-- 1 root root 18289443 Mar 25 13:38 mnist_test.csv\n",
            "-rw-r--r-- 1 root root 36523880 Mar 25 13:38 mnist_train_small.csv\n",
            "-rwxr-xr-x 1 root root      930 Jan  1  2000 \u001b[01;32mREADME.md\u001b[0m*\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kv7OoV7eMHW1",
        "outputId": "38ac4dd5-d197-4af0-b04e-518b1b144637"
      },
      "source": [
        "cd ..\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-0B6ZgsMJjg",
        "outputId": "915d5020-26cd-4276-a80c-fe7d6e6a7e45"
      },
      "source": [
        "cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "ImYk00c0MKWF",
        "outputId": "2e0ad700-70fd-481d-9508-e146ea86abaa"
      },
      "source": [
        "pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6tLfCTVxMLbO",
        "outputId": "cfef908d-0084-41c8-94b0-1084bee165e8"
      },
      "source": [
        "ls -la"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 108\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 03:10 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 03:10 \u001b[01;34m..\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:29 \u001b[01;34mbin\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Apr 24  2018 \u001b[01;34mboot\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:38 \u001b[01;34mcontent\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 29 13:57 \u001b[01;34mdatalab\u001b[0m/\n",
            "drwxr-xr-x   5 root root  360 Apr  6 02:59 \u001b[01;34mdev\u001b[0m/\n",
            "-rwxr-xr-x   1 root root    0 Apr  6 02:59 \u001b[01;32m.dockerenv\u001b[0m*\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:59 \u001b[01;34metc\u001b[0m/\n",
            "drwx------   5 root root 4096 Apr  6 03:10 \u001b[01;34mgdrive\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Apr 24  2018 \u001b[01;34mhome\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:31 \u001b[01;34mlib\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Mar 25 13:20 \u001b[01;34mlib32\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:20 \u001b[01;34mlib64\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34mmedia\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34mmnt\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:32 \u001b[01;34mopt\u001b[0m/\n",
            "dr-xr-xr-x 121 root root    0 Apr  6 02:59 \u001b[01;34mproc\u001b[0m/\n",
            "drwx------   1 root root 4096 Apr  6 02:59 \u001b[01;34mroot\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:23 \u001b[01;34mrun\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:29 \u001b[01;34msbin\u001b[0m/\n",
            "drwxr-xr-x   2 root root 4096 Sep 21  2020 \u001b[01;34msrv\u001b[0m/\n",
            "dr-xr-xr-x  12 root root    0 Apr  6 03:06 \u001b[01;34msys\u001b[0m/\n",
            "drwxr-xr-x   4 root root 4096 Mar 29 13:53 \u001b[01;34mtensorflow-1.15.2\u001b[0m/\n",
            "drwxrwxrwt   1 root root 4096 Apr  6 03:10 \u001b[30;42mtmp\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 29 13:57 \u001b[01;34mtools\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Mar 25 13:32 \u001b[01;34musr\u001b[0m/\n",
            "drwxr-xr-x   1 root root 4096 Apr  6 02:59 \u001b[01;34mvar\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LfYDd7D4MM8x",
        "outputId": "11f295a2-dc9c-423a-b310-3f9b29c13e4a"
      },
      "source": [
        "cd var/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/var\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8HZm1-RMebX",
        "outputId": "b3477cce-4d8e-4c7b-949d-5956bc7b0016"
      },
      "source": [
        "ls -la"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 52\n",
            "drwxr-xr-x 1 root root  4096 Apr  6 02:59 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x 1 root root  4096 Apr  6 03:10 \u001b[01;34m..\u001b[0m/\n",
            "drwxr-xr-x 2 root root  4096 Apr 24  2018 \u001b[01;34mbackups\u001b[0m/\n",
            "drwxr-xr-x 1 root root  4096 Mar 25 13:21 \u001b[01;34mcache\u001b[0m/\n",
            "drwxr-xr-x 3 root root   120 Apr  6 02:59 \u001b[01;34mcolab\u001b[0m/\n",
            "drwxr-xr-x 1 root root  4096 Mar 25 13:29 \u001b[01;34mlib\u001b[0m/\n",
            "drwxrwsr-x 2 root staff 4096 Apr 24  2018 \u001b[01;34mlocal\u001b[0m/\n",
            "lrwxrwxrwx 1 root root     9 Sep 21  2020 \u001b[01;36mlock\u001b[0m -> \u001b[30;42m/run/lock\u001b[0m/\n",
            "drwxr-xr-x 1 root root  4096 Apr  6 02:59 \u001b[01;34mlog\u001b[0m/\n",
            "drwxrwsr-x 2 root mail  4096 Sep 21  2020 \u001b[01;34mmail\u001b[0m/\n",
            "drwxr-xr-x 2 root root  4096 Sep 21  2020 \u001b[01;34mopt\u001b[0m/\n",
            "lrwxrwxrwx 1 root root     4 Sep 21  2020 \u001b[01;36mrun\u001b[0m -> \u001b[01;34m/run\u001b[0m/\n",
            "drwxr-xr-x 2 root root  4096 Sep 21  2020 \u001b[01;34mspool\u001b[0m/\n",
            "drwxrwxrwt 2 root root  4096 Sep 21  2020 \u001b[30;42mtmp\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8AmSUueyMf58",
        "outputId": "77019aa2-9297-4b3c-a092-6c167aa5cee5"
      },
      "source": [
        "cd colab/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/var/colab\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FsT-GZusMkVR",
        "outputId": "7a95f9dd-abb1-4090-ba8a-643690f02cc4"
      },
      "source": [
        "ls -la"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 20\n",
            "drwxr-xr-x 3 root root  120 Apr  6 02:59 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x 1 root root 4096 Apr  6 02:59 \u001b[01;34m..\u001b[0m/\n",
            "-rw-r--r-- 1 root root 2852 Apr  6 03:11 app.log\n",
            "-rw-r--r-- 1 root root   18 Apr  6 02:59 hostname\n",
            "-rw-r--r-- 1 root root    1 Apr  6 02:59 ooms\n",
            "drwxr-xr-x 2 root root   40 Apr  6 02:59 \u001b[01;34mshared\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_5CXhcSMmgh",
        "outputId": "bfeb92bb-6c9b-4ee6-a3e0-4e3d1255dc88"
      },
      "source": [
        "cd shared/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/var/colab/shared\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GdwAR841Mp5i",
        "outputId": "79d3e4b3-831d-4f3e-95ff-4cab77405ff4"
      },
      "source": [
        "ls -la"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 0\n",
            "drwxr-xr-x 2 root root  40 Apr  6 02:59 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x 3 root root 120 Apr  6 02:59 \u001b[01;34m..\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wc5YJHKFMrNz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0944903-8461-4725-a644-1ce12317be73"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "%cd /gdrive"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n",
            "/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kga1XLyjoeQh",
        "outputId": "48bce353-1df8-4b57-86c9-eb03e937d326"
      },
      "source": [
        "ls -la"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 16\n",
            "dr-x------  2 root root 4096 Apr  6 19:19 \u001b[0m\u001b[01;34m.file-revisions-by-id\u001b[0m/\n",
            "drwx------ 11 root root 4096 Apr  6 19:19 \u001b[01;34mMyDrive\u001b[0m/\n",
            "dr-x------  2 root root 4096 Apr  6 19:19 \u001b[01;34m.shortcut-targets-by-id\u001b[0m/\n",
            "drwx------  2 root root 4096 Apr  6 19:19 \u001b[01;34m.Trash\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTXxXRz2ol7G",
        "outputId": "8e146336-0900-44cf-8ba9-529067983053"
      },
      "source": [
        "cd MyDrive/"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/gdrive/MyDrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBqtas-momwJ",
        "outputId": "26d2bed9-fc72-492d-9263-977459690dd6"
      },
      "source": [
        "ls -la\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 221268\n",
            "-rw------- 1 root root   257215 Nov  7  2014 '2014-07-03 11.26.24.jpg'\n",
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            "-rw------- 1 root root  8018612 Jun 20  2017  90DB1D42-9616-46D4-B8E9-807F9F779FEE-8298803d-4113-4fc2-8224-d7029a51c504.mov\n",
            "-rw------- 1 root root  7926493 Jun 20  2017  90DB1D42-9616-46D4-B8E9-807F9F779FEE-d94c3902-3536-4bc1-96e9-6d615b34a99b.mov\n",
            "-rw------- 1 root root      151 May 31  2010  AccountSetupInfo_Chris.gdoc\n",
            "-rw------- 1 root root      162 Feb 20  2014 '~$ail this.docx'\n",
            "drwx------ 2 root root     4096 Feb 11 06:39  \u001b[0m\u001b[01;34mBioinformatics\u001b[0m/\n",
            "-rw------- 1 root root    16556 Feb 17 06:00 'Chapter 3- Classification.ipynb'\n",
            "-rw------- 1 root root      151 Dec  9  2009  chris_250_v1.gdoc\n",
            "-rw------- 1 root root    50176 Dec  4  2014 'Chris_NIH biosketch Aug 2013.doc'\n",
            "-rw------- 1 root root      151 Sep  8  2009  Christopher_Gaughan_CV_Latest.gdoc\n",
            "-rw------- 1 root root   172591 May 20  2017  CHRISTOPHER_GAUGHAN.docx\n",
            "-rw------- 1 root root      151 Dec 18  2016  CHRISTOPHER_GAUGHAN.docx.gdoc\n",
            "-rw------- 1 root root    47616 Sep 15  2014  christopher_gaughan_phd.doc\n",
            "-rw------- 1 root root      151 Oct 19  2018  christopher_gaughan_phd.doc.gdoc\n",
            "-rw------- 1 root root    22380 Feb 12  2018 'Christopher Gaughan_Resume_Feb_2018.docx'\n",
            "-rw------- 1 root root    47063 May 19  2017 'C_L_Gaughan__May_Updated copy.docx'\n",
            "drwx------ 2 root root     4096 Aug  3  2019  \u001b[01;34mCode_Acad\u001b[0m/\n",
            "-rw------- 1 root root  7901872 Mar 10 22:05  CoffeeBreakPythonSlicing.html\n",
            "-rw------- 1 root root  3653102 Mar 10 22:06  CoffeeBreakPythonSlicing.pdf\n",
            "-rw------- 1 root root  1238703 May 15  2016  coi_disclosure.pdf\n",
            "drwx------ 2 root root     4096 May 15  2019 \u001b[01;34m'Colab Notebooks'\u001b[0m/\n",
            "-rw------- 1 root root    38189 May 23  2017 'Copy of C_L_Gaughan__May_Updated copy.docx'\n",
            "-rw------- 1 root root      151 Jul 28  2009 'Copy of Scientific Article.gdoc'\n",
            "-rw------- 1 root root      151 Jul 27  2009 'Copy of to-do-list.gsheet'\n",
            "-rw------- 1 root root      151 Sep 21  2009 'Copy of Vaxinnate - Sr. Research Associate, Molecular Biology and.gdoc'\n",
            "-rw------- 1 root root 34709209 Feb  6  2016 'dane idea.band.zip'\n",
            "-rw------- 1 root root   930626 Aug  3  2019  Data_Cortex_Nuclear.xlsx\n",
            "-rw------- 1 root root      151 Mar 26  2020  Data.gsheet\n",
            "-rw------- 1 root root      151 Aug  4  2020  Dibyendu_code.gsheet\n",
            "-rw------- 1 root root    12100 Aug  4  2020  Dibyendu_code.xlsx\n",
            "-rw------- 1 root root    23452 Jan  2 06:02  Fermi.ipynb\n",
            "-rw------- 1 root root       19 Apr  5 23:49  foo.txt\n",
            "-rw------- 1 root root      151 Jan 26  2018 'Gaughan_Christopher_RESUME_January_2018 (1).docx.gdoc'\n",
            "-rw------- 1 root root    34429 Jan 26  2018  Gaughan_Christopher_RESUME_January_2018.docx\n",
            "-rw------- 1 root root      151 Dec 15  2018  Gaughan_Christopher_RESUME_January_2018.docx.gdoc\n",
            "-rw------- 1 root root   165375 Dec 20  2019 'Helix Consultant Agreement Christopher Gaughan Dec 18 2019.pdf'\n",
            "-rw------- 1 root root    80286 Aug  3  2019  his_plots_pie_plots_etc.ipynb\n",
            "-rw------- 1 root root    72374 Nov  8  2014  holy_mackerel.jpg\n",
            "-rw------- 1 root root   853324 Dec 27 22:32  Housing.ipynb\n",
            "-rw------- 1 root root   436266 Aug 10  2020  Image_209.pdf\n",
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            "-rw------- 1 root root   244980 Oct 10 23:45 'IMG-0985 (1).jpeg'\n",
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            "drwx------ 2 root root     4096 Mar 10 03:23  \u001b[01;34mIntermediate_Python\u001b[0m/\n",
            "-rw------- 1 root root    32793 Jul  8  2014  Intuit.pdf\n",
            "-rw------- 1 root root      151 May 11  2019 'Invoice_INVJBA1110554 (1).gdoc'\n",
            "-rw------- 1 root root      151 May 11  2019  Invoice_INVJBA1110554.gdoc\n",
            "-rw------- 1 root root   111678 May 11  2019  Invoice_INVJBA1110554.pdf\n",
            "-rw------- 1 root root  1558695 Apr  3  2017 'IPM Np Packet.pdf'\n",
            "-rw------- 1 root root   107328 Jul 16  2015 'JJ Candidate Rep Agreement--Christopher Gaughan.docx'\n",
            "-rw------- 1 root root      151 Jul 16  2015 'JJ Candidate Rep Agreement--Christopher Gaughan.docx.gdoc'\n",
            "-rw------- 1 root root      151 Jul 17  2020 'Kidnapped!.gdoc'\n",
            "-rw------- 1 root root      151 Jul 28  2009  man2v2.gdoc\n",
            "-rw------- 1 root root      151 Mar 16  2010  March17_Program1.gdoc\n",
            "-rw------- 1 root root   508489 Mar  7  2016 'Membrane proteins.docx'\n",
            "-rw------- 1 root root   463850 Sep 29  2014 'Molecular Biology Research Associate Position.pdf'\n",
            "-rw------- 1 root root 12930302 Mar 14  2018 'MTF debridements.key'\n",
            "drwx------ 2 root root     4096 Mar 10 03:07  \u001b[01;34mMy_WAR\u001b[0m/\n",
            "-rw------- 1 root root  2114840 Dec  7 08:14  National_Poverty_Data_1607328804170.zip\n",
            "drwx------ 2 root root     4096 Jan 19 21:27  \u001b[01;34mnotebooks\u001b[0m/\n",
            "-rw------- 1 root root    17068 Nov 18  2014  Order_Form_8498-DN.html\n",
            "-rw------- 1 root root     3698 Aug  3  2019  pandas_merging_files.ipynb\n",
            "-rw------- 1 root root     3676 Dec 28 02:29  Pandas_Notebook_Lambdas_change_columns.ipynb\n",
            "-rw------- 1 root root   337946 Jul 23  2014  photo.JPG\n",
            "-rw------- 1 root root      151 Aug  3  2019  photo.JPG.gdoc\n",
            "-rw------- 1 root root     1113 May 15  2019  PipelineOpt.ipynb\n",
            "-rw------- 1 root root     3132 Jul  9  2018 'PLAN OF ACTION.txt'\n",
            "-rw------- 1 root root      151 Nov 27  2014 'PLAN OF ACTION.txt.gdoc'\n",
            "-rw------- 1 root root   227696 Dec 10 00:35  plot_linear_model_coefficient_interpretation.ipynb\n",
            "-rw------- 1 root root    92071 Aug  3  2019 'plotting 2.ipynb'\n",
            "-rw------- 1 root root      151 Dec  8  2009 'Position Profile - Assoc Medical Strategist Final.gdoc'\n",
            "-rw------- 1 root root    13660 Aug  3  2019  power_generated.png\n",
            "-rw------- 1 root root      151 Jan 26  2019 'Resume (1).gdoc'\n",
            "-rw------- 1 root root      151 Jan 26  2019 'Resume (2).gdoc'\n",
            "-rw------- 1 root root   108763 Mar 20  2019  Resume320_C_Gaughan.docx\n",
            "-rw------- 1 root root      151 Mar 20  2019  Resume320_C_Gaughan.gdoc\n",
            "-rw------- 1 root root      151 Jan 26  2019 'Resume (3).gdoc'\n",
            "-rw------- 1 root root      151 Jan 26  2019  Resume.gdoc\n",
            "-rw------- 1 root root      151 Dec  4  2014 'SBIR_Phase I_BBB_2014_Introduction.gdoc'\n",
            "-rw------- 1 root root      151 Sep 21  2020 'Script I.gdoc'\n",
            "-rw------- 1 root root  3429690 Jul 17  2020  seaweed.mp3\n",
            "-rw------- 1 root root 18883662 Sep 27  2020  seaweed.wav\n",
            "-rw------- 1 root root      151 May 23  2020  Story.gdoc\n",
            "-rw------- 1 root root      574 Dec  8 01:45  student.zip\n",
            "-rw------- 1 root root      151 Jan 22 04:27  summ_eptot.gsheet\n",
            "-rw------- 1 root root  5099794 Apr  1 03:28  supervised-learning-w-python-master.zip\n",
            "drwx------ 2 root root     4096 Apr  1 03:30 \u001b[01;34m'supervised-learning-w-python-master.zip (Unzipped Files)'\u001b[0m/\n",
            "-rw------- 1 root root      151 Aug  3  2019 'the lord.gdoc'\n",
            "-rw------- 1 root root   723014 Dec 18  2018 'the lord.htm'\n",
            "-rw------- 1 root root   386731 Oct 20 19:36  The_Modern_Python_Challenge.ipynb\n",
            "-rw------- 1 root root  2478951 Oct 19  2018  thing.ogg\n",
            "-rw------- 1 root root      151 May 27  2010 'Timesheet - Lab Support - Piscataway ju14.05.gdoc'\n",
            "-rw------- 1 root root   102400 Jun  7  2020  Titanic_data.tar\n",
            "drwx------ 2 root root     4096 Jun  7  2020 \u001b[01;34m'Titanic_data.zip (Unzipped Files)'\u001b[0m/\n",
            "-rw------- 1 root root   574809 Dec 20 07:02  titanic.ipynb\n",
            "-rw------- 1 root root  2621357 Feb 10  2016 'train - 2:10:16, 3.26 PM (1).mp3'\n",
            "-rw------- 1 root root  2621357 Feb 18  2016 'train - 2:10:16, 3.26 PM.mp3'\n",
            "drwx------ 2 root root     4096 Feb 19 04:33  \u001b[01;34mUDEMY_Nerual_Networks_Deep_learning\u001b[0m/\n",
            "-rw------- 1 root root   775807 Feb 19 01:03  UDEMY_OOP.ipynb\n",
            "-rw------- 1 root root      860 Nov 30 05:37  Untitled0.ipynb\n",
            "-rw------- 1 root root     4622 Feb 18  2016 'untitled-[1.2].html'\n",
            "-rw------- 1 root root     1508 Aug  3  2019  Untitled1.ipynb\n",
            "-rw------- 1 root root       89 Aug  3  2019  untitled-1.py\n",
            "-rw------- 1 root root    11196 Aug  3  2019  Untitled2.ipynb\n",
            "-rw------- 1 root root      864 Nov 30 23:48  Untitled3.ipynb\n",
            "-rw------- 1 root root      317 Feb 23 07:21  Untitled4.ipynb\n",
            "-rw------- 1 root root     3637 Mar 31 22:43  Untitled5.ipynb\n",
            "-rw------- 1 root root      151 Jun 14  2020 'Untitled document.gdoc'\n",
            "-rw------- 1 root root      151 Feb 22 05:52 'Untitled Jam.gjam'\n",
            "-rw------- 1 root root      151 Mar 26  2020 'Untitled spreadsheet.gsheet'\n",
            "-rw------- 1 root root   175957 Dec 18 06:39  Untitled.zip\n",
            "-rw------- 1 root root      151 Sep 21  2009 'Vaxinnate - Sr. Research Associate, Molecular Biology and Protein.gdoc'\n",
            "-rw------- 1 root root   119331 Jan 11  2017 'W9 Form.pdf'\n",
            "-rw------- 1 root root      151 Feb  8  2010 'What are we trying to accomplish rec.gdoc'\n",
            "-rw------- 1 root root    14227 Aug  3  2019  winning_word_lengths.png\n",
            "-rw------- 1 root root 37238757 Sep 29  2017 'wsayyyyyy out (1).band.zip'\n",
            "-rw------- 1 root root 37238757 Oct 14  2018 'wsayyyyyy out.band.zip'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RlO6iYf4o1F9",
        "outputId": "b5e3b965-3c5b-41a5-986c-f505e41552c2"
      },
      "source": [
        "cd 'supervised-learning-w-python-master.zip (Unzipped Files)'/"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/gdrive/My Drive/supervised-learning-w-python-master.zip (Unzipped Files)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UCcV_g9zpBWU",
        "outputId": "93d42b9e-5478-4032-c7e3-2c4929337564"
      },
      "source": [
        "ls -la"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 4985\n",
            "drwx------ 2 root root    4096 Apr  1 03:30  \u001b[0m\u001b[01;34msupervised-learning-w-python-master\u001b[0m/\n",
            "-rw------- 1 root root 5099794 Apr  5 23:36 'supervised-learning-w-python-master (1).zip'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9MQpGXpppDIb",
        "outputId": "bc646d6e-1c91-4063-e200-d7f902a9ebbf"
      },
      "source": [
        "cd supervised-learning-w-python-master/"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/gdrive/My Drive/supervised-learning-w-python-master.zip (Unzipped Files)/supervised-learning-w-python-master\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PWEjlJ5xpPFd"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1euI5wNlpJ19",
        "outputId": "2c328f8a-edf8-42ff-88df-8df328c73179"
      },
      "source": [
        "ls -la"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 116\n",
            "-rw------- 1 root root 27237 Apr  1 03:30  9781484261552.jpg\n",
            "drwx------ 2 root root  4096 Apr  1 03:30 \u001b[0m\u001b[01;34m'Chapter 1'\u001b[0m/\n",
            "drwx------ 2 root root  4096 Apr  1 03:30  \u001b[01;34mChapter2\u001b[0m/\n",
            "drwx------ 2 root root  4096 Apr  1 03:30 \u001b[01;34m'Chapter 3'\u001b[0m/\n",
            "drwx------ 2 root root  4096 Apr  1 03:30 \u001b[01;34m'Chapter 4'\u001b[0m/\n",
            "drwx------ 2 root root  4096 Apr  1 03:30 \u001b[01;34m'Chapter 5'\u001b[0m/\n",
            "-rw------- 1 root root   690 Apr  1 03:30  Contributing.md\n",
            "-rw------- 1 root root   237 Apr  1 03:30  errata.md\n",
            "-rw------- 1 root root  1373 Apr  1 03:30  LICENSE.txt\n",
            "-rw------- 1 root root   536 Apr  1 03:30  README.md\n",
            "-rw------- 1 root root 66346 Apr  6 02:57  Untitled0.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "utlxk0KbpLRy",
        "outputId": "aae29942-fa50-4c86-eb5d-43837bc6a431"
      },
      "source": [
        "cd Chapter2/"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/gdrive/My Drive/supervised-learning-w-python-master.zip (Unzipped Files)/supervised-learning-w-python-master/Chapter2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5V2E038TpSos",
        "outputId": "fdbcb067-b8f0-4d06-e7d2-52f8dc66481a"
      },
      "source": [
        "ls -la"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 3804\n",
            "-rw------- 1 root root   18131 Apr  1 03:30 auto-mpg.csv\n",
            "-rw------- 1 root root  392468 Apr  1 03:49 Chapter2_PythonCode.ipynb\n",
            "-rw------- 1 root root 2515206 Apr  1 03:30 House_data.csv\n",
            "-rw------- 1 root root  421218 Apr  1 03:30 House_data_LR.csv\n",
            "-rw------- 1 root root  544963 Apr  1 03:30 House_data_MLR.csv\n",
            "-rw------- 1 root root    1389 Apr  1 03:30 petrol_consumption.csv\n",
            "-rw------- 1 root root     160 Apr  1 03:30 ReadMe\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IyICguZ2pT71"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings(action=\"ignore\", module=\"scipy\", message=\"^internal gelsd\")"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ad0St-qNpmK5"
      },
      "source": [
        "house_df= pd.read_csv('House_data_LR.csv')"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "k4T-djArpurK",
        "outputId": "1226e515-f465-4bca-cf03-8bdf3e2eb26b"
      },
      "source": [
        "house_df.head()"
      ],
      "execution_count": 19,
      "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>Unnamed: 0</th>\n",
              "      <th>sqft_living</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1180</td>\n",
              "      <td>221900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2570</td>\n",
              "      <td>538000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>770</td>\n",
              "      <td>180000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1960</td>\n",
              "      <td>604000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1680</td>\n",
              "      <td>510000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  sqft_living     price\n",
              "0           0         1180  221900.0\n",
              "1           1         2570  538000.0\n",
              "2           2          770  180000.0\n",
              "3           3         1960  604000.0\n",
              "4           4         1680  510000.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "--D054SYp0qo",
        "outputId": "b53cb657-177c-4ae0-bb79-572495990e41"
      },
      "source": [
        "# Check if there is any null value present in the dataset:\n",
        "house_df.isnull().any()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0     False\n",
              "sqft_living    False\n",
              "price          False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLcIv-gQqaWt"
      },
      "source": [
        "# Let's drop a column (axis = 1) called \"Unamed\"\n",
        "house_df.drop('Unnamed: 0', axis = 1, inplace = True)\n"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "M5ZH9Iqnr4Sm",
        "outputId": "b2e1a3a4-0103-409f-a126-6f2fb6e6c8eb"
      },
      "source": [
        "house_df.head()"
      ],
      "execution_count": 22,
      "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>sqft_living</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1180</td>\n",
              "      <td>221900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2570</td>\n",
              "      <td>538000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>770</td>\n",
              "      <td>180000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1960</td>\n",
              "      <td>604000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1680</td>\n",
              "      <td>510000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sqft_living     price\n",
              "0         1180  221900.0\n",
              "1         2570  538000.0\n",
              "2          770  180000.0\n",
              "3         1960  604000.0\n",
              "4         1680  510000.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hkdl2i_HsaJS"
      },
      "source": [
        "# Here we are just taking the FIRST column as the X\n",
        "X = house_df.iloc[:, :1].values"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6SslpzMytHGf"
      },
      "source": [
        "y = house_df.iloc[:, -1].values"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZJyOhoTtOf-"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 5)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZYhVs2_wDbk",
        "outputId": "2e993b43-47da-4590-be01-3cb73ea7174d"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "simple_lr= LinearRegression()\n",
        "simple_lr.fit(X_train, y_train)\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1QVrBdHwTor"
      },
      "source": [
        "y_pred = simple_lr.predict(X_test)\n"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "5RIollgZwa_i",
        "outputId": "db82d0fb-65b6-414b-9017-f3afdaff633e"
      },
      "source": [
        "plt.scatter(X_train, y_train, color = 'r')\n",
        "plt.plot(X_train, simple_lr.predict(X_train), color = 'b')\n",
        "plt.title('Sqft Living vs Price for Training')\n",
        "plt.xlabel('Square feet')\n",
        "plt.ylabel('House Price')\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "m80qjWNcwust",
        "outputId": "b701eace-e829-4bf5-a8eb-892abddce93e"
      },
      "source": [
        "plt.scatter(X_test, y_test, color = 'r')\n",
        "plt.plot(X_train, simple_lr.predict(X_train), color = 'b')\n",
        "plt.title('Sqft Living vs Price for Test')\n",
        "plt.xlabel('Square feet')\n",
        "plt.ylabel('House Price')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'House Price')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GQFzG2AfyI14",
        "outputId": "46434b5c-cced-4d3b-c1fd-d6d38f446079"
      },
      "source": [
        "# Now let’s figure out how good or how bad we are doing in the predictions. We will calculate the MSE and R2.\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "rmse = sqrt(mean_squared_error(y_test, y_pred))\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "adj_r2 = 1 - float(len(y)-1)/(len(y)-len(simple_lr.coef_)-1)*(1 - r2)\n",
        "rmse, r2, adj_r2, simple_lr.coef_, simple_lr.intercept_\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(257125.13804007217,\n",
              " 0.5020612063135523,\n",
              " 0.5020381653254589,\n",
              " array([281.4054356]),\n",
              " -45441.30813530844)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dfVNM6NMy10N",
        "outputId": "37b7f9da-0b74-4b3f-db7c-dfc53d3731e4"
      },
      "source": [
        "import numpy as np\n",
        "x_unseen=np.array([1500]).reshape(1,1)\n",
        "simple_lr.predict(x_unseen)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([376666.84526037])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gglfUcs4zqtL"
      },
      "source": [
        "## **Multiple linear regression or multiple regression** can be said as an extension of simple linear regression where instead of one independent variable we have more than one ***independent variable.*** For example, Figure 2-11 shows the representation of more than one variable of a similar dataset in the vector-space diagram:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKQzfLE58-Yx"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMf-rRVF9Uw4"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sbvV78d--sG2"
      },
      "source": [
        "The multiple linear regression model depicted by x1 and x2 and the residual shown with respect to a sample observation. The **residual** is the difference between actual and predicted values."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cJe0iAql_LFg"
      },
      "source": [
        "***MULTIPLE LINEAR REGRESSION FOR HOUSING*** **DATASET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4M6TI7EzzXky"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings(action=\"ignore\", module=\"scipy\", message=\"^internal gelsd\")              "
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRBpH_txABr9"
      },
      "source": [
        "house_mlr = pd.read_csv('House_data.csv')"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        },
        "id": "avFu2-EwAMgo",
        "outputId": "52a6d001-30ae-4472-d4e5-b37658c32026"
      },
      "source": [
        "house_mlr.head(20)"
      ],
      "execution_count": 60,
      "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>id</th>\n",
              "      <th>date</th>\n",
              "      <th>price</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>sqft_living</th>\n",
              "      <th>sqft_lot</th>\n",
              "      <th>floors</th>\n",
              "      <th>waterfront</th>\n",
              "      <th>view</th>\n",
              "      <th>condition</th>\n",
              "      <th>grade</th>\n",
              "      <th>sqft_above</th>\n",
              "      <th>sqft_basement</th>\n",
              "      <th>yr_built</th>\n",
              "      <th>yr_renovated</th>\n",
              "      <th>zipcode</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "      <th>sqft_living15</th>\n",
              "      <th>sqft_lot15</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7129300520</td>\n",
              "      <td>20141013T000000</td>\n",
              "      <td>221900.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1180</td>\n",
              "      <td>5650</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1180</td>\n",
              "      <td>0</td>\n",
              "      <td>1955</td>\n",
              "      <td>0</td>\n",
              "      <td>98178</td>\n",
              "      <td>47.5112</td>\n",
              "      <td>-122.257</td>\n",
              "      <td>1340</td>\n",
              "      <td>5650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6414100192</td>\n",
              "      <td>20141209T000000</td>\n",
              "      <td>538000.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.25</td>\n",
              "      <td>2570</td>\n",
              "      <td>7242</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>2170</td>\n",
              "      <td>400</td>\n",
              "      <td>1951</td>\n",
              "      <td>1991</td>\n",
              "      <td>98125</td>\n",
              "      <td>47.7210</td>\n",
              "      <td>-122.319</td>\n",
              "      <td>1690</td>\n",
              "      <td>7639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5631500400</td>\n",
              "      <td>20150225T000000</td>\n",
              "      <td>180000.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.00</td>\n",
              "      <td>770</td>\n",
              "      <td>10000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>770</td>\n",
              "      <td>0</td>\n",
              "      <td>1933</td>\n",
              "      <td>0</td>\n",
              "      <td>98028</td>\n",
              "      <td>47.7379</td>\n",
              "      <td>-122.233</td>\n",
              "      <td>2720</td>\n",
              "      <td>8062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2487200875</td>\n",
              "      <td>20141209T000000</td>\n",
              "      <td>604000.0</td>\n",
              "      <td>4</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1960</td>\n",
              "      <td>5000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>1050</td>\n",
              "      <td>910</td>\n",
              "      <td>1965</td>\n",
              "      <td>0</td>\n",
              "      <td>98136</td>\n",
              "      <td>47.5208</td>\n",
              "      <td>-122.393</td>\n",
              "      <td>1360</td>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1954400510</td>\n",
              "      <td>20150218T000000</td>\n",
              "      <td>510000.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1680</td>\n",
              "      <td>8080</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>1680</td>\n",
              "      <td>0</td>\n",
              "      <td>1987</td>\n",
              "      <td>0</td>\n",
              "      <td>98074</td>\n",
              "      <td>47.6168</td>\n",
              "      <td>-122.045</td>\n",
              "      <td>1800</td>\n",
              "      <td>7503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7237550310</td>\n",
              "      <td>20140512T000000</td>\n",
              "      <td>1225000.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4.50</td>\n",
              "      <td>5420</td>\n",
              "      <td>101930</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>3890</td>\n",
              "      <td>1530</td>\n",
              "      <td>2001</td>\n",
              "      <td>0</td>\n",
              "      <td>98053</td>\n",
              "      <td>47.6561</td>\n",
              "      <td>-122.005</td>\n",
              "      <td>4760</td>\n",
              "      <td>101930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1321400060</td>\n",
              "      <td>20140627T000000</td>\n",
              "      <td>257500.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.25</td>\n",
              "      <td>1715</td>\n",
              "      <td>6819</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1715</td>\n",
              "      <td>0</td>\n",
              "      <td>1995</td>\n",
              "      <td>0</td>\n",
              "      <td>98003</td>\n",
              "      <td>47.3097</td>\n",
              "      <td>-122.327</td>\n",
              "      <td>2238</td>\n",
              "      <td>6819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2008000270</td>\n",
              "      <td>20150115T000000</td>\n",
              "      <td>291850.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1.50</td>\n",
              "      <td>1060</td>\n",
              "      <td>9711</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1060</td>\n",
              "      <td>0</td>\n",
              "      <td>1963</td>\n",
              "      <td>0</td>\n",
              "      <td>98198</td>\n",
              "      <td>47.4095</td>\n",
              "      <td>-122.315</td>\n",
              "      <td>1650</td>\n",
              "      <td>9711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2414600126</td>\n",
              "      <td>20150415T000000</td>\n",
              "      <td>229500.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1780</td>\n",
              "      <td>7470</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1050</td>\n",
              "      <td>730</td>\n",
              "      <td>1960</td>\n",
              "      <td>0</td>\n",
              "      <td>98146</td>\n",
              "      <td>47.5123</td>\n",
              "      <td>-122.337</td>\n",
              "      <td>1780</td>\n",
              "      <td>8113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>3793500160</td>\n",
              "      <td>20150312T000000</td>\n",
              "      <td>323000.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.50</td>\n",
              "      <td>1890</td>\n",
              "      <td>6560</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1890</td>\n",
              "      <td>0</td>\n",
              "      <td>2003</td>\n",
              "      <td>0</td>\n",
              "      <td>98038</td>\n",
              "      <td>47.3684</td>\n",
              "      <td>-122.031</td>\n",
              "      <td>2390</td>\n",
              "      <td>7570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1736800520</td>\n",
              "      <td>20150403T000000</td>\n",
              "      <td>662500.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.50</td>\n",
              "      <td>3560</td>\n",
              "      <td>9796</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>1860</td>\n",
              "      <td>1700</td>\n",
              "      <td>1965</td>\n",
              "      <td>0</td>\n",
              "      <td>98007</td>\n",
              "      <td>47.6007</td>\n",
              "      <td>-122.145</td>\n",
              "      <td>2210</td>\n",
              "      <td>8925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>9212900260</td>\n",
              "      <td>20140527T000000</td>\n",
              "      <td>468000.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1160</td>\n",
              "      <td>6000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>860</td>\n",
              "      <td>300</td>\n",
              "      <td>1942</td>\n",
              "      <td>0</td>\n",
              "      <td>98115</td>\n",
              "      <td>47.6900</td>\n",
              "      <td>-122.292</td>\n",
              "      <td>1330</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>114101516</td>\n",
              "      <td>20140528T000000</td>\n",
              "      <td>310000.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1430</td>\n",
              "      <td>19901</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>1430</td>\n",
              "      <td>0</td>\n",
              "      <td>1927</td>\n",
              "      <td>0</td>\n",
              "      <td>98028</td>\n",
              "      <td>47.7558</td>\n",
              "      <td>-122.229</td>\n",
              "      <td>1780</td>\n",
              "      <td>12697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>6054650070</td>\n",
              "      <td>20141007T000000</td>\n",
              "      <td>400000.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1.75</td>\n",
              "      <td>1370</td>\n",
              "      <td>9680</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>1370</td>\n",
              "      <td>0</td>\n",
              "      <td>1977</td>\n",
              "      <td>0</td>\n",
              "      <td>98074</td>\n",
              "      <td>47.6127</td>\n",
              "      <td>-122.045</td>\n",
              "      <td>1370</td>\n",
              "      <td>10208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1175000570</td>\n",
              "      <td>20150312T000000</td>\n",
              "      <td>530000.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1810</td>\n",
              "      <td>4850</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1810</td>\n",
              "      <td>0</td>\n",
              "      <td>1900</td>\n",
              "      <td>0</td>\n",
              "      <td>98107</td>\n",
              "      <td>47.6700</td>\n",
              "      <td>-122.394</td>\n",
              "      <td>1360</td>\n",
              "      <td>4850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>9297300055</td>\n",
              "      <td>20150124T000000</td>\n",
              "      <td>650000.0</td>\n",
              "      <td>4</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2950</td>\n",
              "      <td>5000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>1980</td>\n",
              "      <td>970</td>\n",
              "      <td>1979</td>\n",
              "      <td>0</td>\n",
              "      <td>98126</td>\n",
              "      <td>47.5714</td>\n",
              "      <td>-122.375</td>\n",
              "      <td>2140</td>\n",
              "      <td>4000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1875500060</td>\n",
              "      <td>20140731T000000</td>\n",
              "      <td>395000.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1890</td>\n",
              "      <td>14040</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1890</td>\n",
              "      <td>0</td>\n",
              "      <td>1994</td>\n",
              "      <td>0</td>\n",
              "      <td>98019</td>\n",
              "      <td>47.7277</td>\n",
              "      <td>-121.962</td>\n",
              "      <td>1890</td>\n",
              "      <td>14018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>6865200140</td>\n",
              "      <td>20140529T000000</td>\n",
              "      <td>485000.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1600</td>\n",
              "      <td>4300</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>1600</td>\n",
              "      <td>0</td>\n",
              "      <td>1916</td>\n",
              "      <td>0</td>\n",
              "      <td>98103</td>\n",
              "      <td>47.6648</td>\n",
              "      <td>-122.343</td>\n",
              "      <td>1610</td>\n",
              "      <td>4300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>16000397</td>\n",
              "      <td>20141205T000000</td>\n",
              "      <td>189000.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1200</td>\n",
              "      <td>9850</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>1200</td>\n",
              "      <td>0</td>\n",
              "      <td>1921</td>\n",
              "      <td>0</td>\n",
              "      <td>98002</td>\n",
              "      <td>47.3089</td>\n",
              "      <td>-122.210</td>\n",
              "      <td>1060</td>\n",
              "      <td>5095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>7983200060</td>\n",
              "      <td>20150424T000000</td>\n",
              "      <td>230000.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1250</td>\n",
              "      <td>9774</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>1250</td>\n",
              "      <td>0</td>\n",
              "      <td>1969</td>\n",
              "      <td>0</td>\n",
              "      <td>98003</td>\n",
              "      <td>47.3343</td>\n",
              "      <td>-122.306</td>\n",
              "      <td>1280</td>\n",
              "      <td>8850</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            id             date      price  ...     long  sqft_living15  sqft_lot15\n",
              "0   7129300520  20141013T000000   221900.0  ... -122.257           1340        5650\n",
              "1   6414100192  20141209T000000   538000.0  ... -122.319           1690        7639\n",
              "2   5631500400  20150225T000000   180000.0  ... -122.233           2720        8062\n",
              "3   2487200875  20141209T000000   604000.0  ... -122.393           1360        5000\n",
              "4   1954400510  20150218T000000   510000.0  ... -122.045           1800        7503\n",
              "5   7237550310  20140512T000000  1225000.0  ... -122.005           4760      101930\n",
              "6   1321400060  20140627T000000   257500.0  ... -122.327           2238        6819\n",
              "7   2008000270  20150115T000000   291850.0  ... -122.315           1650        9711\n",
              "8   2414600126  20150415T000000   229500.0  ... -122.337           1780        8113\n",
              "9   3793500160  20150312T000000   323000.0  ... -122.031           2390        7570\n",
              "10  1736800520  20150403T000000   662500.0  ... -122.145           2210        8925\n",
              "11  9212900260  20140527T000000   468000.0  ... -122.292           1330        6000\n",
              "12   114101516  20140528T000000   310000.0  ... -122.229           1780       12697\n",
              "13  6054650070  20141007T000000   400000.0  ... -122.045           1370       10208\n",
              "14  1175000570  20150312T000000   530000.0  ... -122.394           1360        4850\n",
              "15  9297300055  20150124T000000   650000.0  ... -122.375           2140        4000\n",
              "16  1875500060  20140731T000000   395000.0  ... -121.962           1890       14018\n",
              "17  6865200140  20140529T000000   485000.0  ... -122.343           1610        4300\n",
              "18    16000397  20141205T000000   189000.0  ... -122.210           1060        5095\n",
              "19  7983200060  20150424T000000   230000.0  ... -122.306           1280        8850\n",
              "\n",
              "[20 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2J83-Oz2AgkI",
        "outputId": "c85b3deb-e27b-4773-b7b8-64aa2fb68829"
      },
      "source": [
        "house_mlr.info()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 21613 entries, 0 to 21612\n",
            "Data columns (total 21 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   id             21613 non-null  int64  \n",
            " 1   date           21613 non-null  object \n",
            " 2   price          21613 non-null  float64\n",
            " 3   bedrooms       21613 non-null  int64  \n",
            " 4   bathrooms      21613 non-null  float64\n",
            " 5   sqft_living    21613 non-null  int64  \n",
            " 6   sqft_lot       21613 non-null  int64  \n",
            " 7   floors         21613 non-null  float64\n",
            " 8   waterfront     21613 non-null  int64  \n",
            " 9   view           21613 non-null  int64  \n",
            " 10  condition      21613 non-null  int64  \n",
            " 11  grade          21613 non-null  int64  \n",
            " 12  sqft_above     21613 non-null  int64  \n",
            " 13  sqft_basement  21613 non-null  int64  \n",
            " 14  yr_built       21613 non-null  int64  \n",
            " 15  yr_renovated   21613 non-null  int64  \n",
            " 16  zipcode        21613 non-null  int64  \n",
            " 17  lat            21613 non-null  float64\n",
            " 18  long           21613 non-null  float64\n",
            " 19  sqft_living15  21613 non-null  int64  \n",
            " 20  sqft_lot15     21613 non-null  int64  \n",
            "dtypes: float64(5), int64(15), object(1)\n",
            "memory usage: 3.5+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "Zu9yjl4TDOl9",
        "outputId": "c4fe442d-b16c-4509-ce03-540c7fe9a56b"
      },
      "source": [
        "house_mlr.describe()"
      ],
      "execution_count": 62,
      "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>id</th>\n",
              "      <th>price</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>sqft_living</th>\n",
              "      <th>sqft_lot</th>\n",
              "      <th>floors</th>\n",
              "      <th>waterfront</th>\n",
              "      <th>view</th>\n",
              "      <th>condition</th>\n",
              "      <th>grade</th>\n",
              "      <th>sqft_above</th>\n",
              "      <th>sqft_basement</th>\n",
              "      <th>yr_built</th>\n",
              "      <th>yr_renovated</th>\n",
              "      <th>zipcode</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "      <th>sqft_living15</th>\n",
              "      <th>sqft_lot15</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.161300e+04</td>\n",
              "      <td>2.161300e+04</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>2.161300e+04</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "      <td>21613.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.580302e+09</td>\n",
              "      <td>5.400881e+05</td>\n",
              "      <td>3.370842</td>\n",
              "      <td>2.114757</td>\n",
              "      <td>2079.899736</td>\n",
              "      <td>1.510697e+04</td>\n",
              "      <td>1.494309</td>\n",
              "      <td>0.007542</td>\n",
              "      <td>0.234303</td>\n",
              "      <td>3.409430</td>\n",
              "      <td>7.656873</td>\n",
              "      <td>1788.390691</td>\n",
              "      <td>291.509045</td>\n",
              "      <td>1971.005136</td>\n",
              "      <td>84.402258</td>\n",
              "      <td>98077.939805</td>\n",
              "      <td>47.560053</td>\n",
              "      <td>-122.213896</td>\n",
              "      <td>1986.552492</td>\n",
              "      <td>12768.455652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.876566e+09</td>\n",
              "      <td>3.671272e+05</td>\n",
              "      <td>0.930062</td>\n",
              "      <td>0.770163</td>\n",
              "      <td>918.440897</td>\n",
              "      <td>4.142051e+04</td>\n",
              "      <td>0.539989</td>\n",
              "      <td>0.086517</td>\n",
              "      <td>0.766318</td>\n",
              "      <td>0.650743</td>\n",
              "      <td>1.175459</td>\n",
              "      <td>828.090978</td>\n",
              "      <td>442.575043</td>\n",
              "      <td>29.373411</td>\n",
              "      <td>401.679240</td>\n",
              "      <td>53.505026</td>\n",
              "      <td>0.138564</td>\n",
              "      <td>0.140828</td>\n",
              "      <td>685.391304</td>\n",
              "      <td>27304.179631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000102e+06</td>\n",
              "      <td>7.500000e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>290.000000</td>\n",
              "      <td>5.200000e+02</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>290.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1900.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98001.000000</td>\n",
              "      <td>47.155900</td>\n",
              "      <td>-122.519000</td>\n",
              "      <td>399.000000</td>\n",
              "      <td>651.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.123049e+09</td>\n",
              "      <td>3.219500e+05</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.750000</td>\n",
              "      <td>1427.000000</td>\n",
              "      <td>5.040000e+03</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1190.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1951.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98033.000000</td>\n",
              "      <td>47.471000</td>\n",
              "      <td>-122.328000</td>\n",
              "      <td>1490.000000</td>\n",
              "      <td>5100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.904930e+09</td>\n",
              "      <td>4.500000e+05</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>1910.000000</td>\n",
              "      <td>7.618000e+03</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1560.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1975.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98065.000000</td>\n",
              "      <td>47.571800</td>\n",
              "      <td>-122.230000</td>\n",
              "      <td>1840.000000</td>\n",
              "      <td>7620.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.308900e+09</td>\n",
              "      <td>6.450000e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2550.000000</td>\n",
              "      <td>1.068800e+04</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2210.000000</td>\n",
              "      <td>560.000000</td>\n",
              "      <td>1997.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98118.000000</td>\n",
              "      <td>47.678000</td>\n",
              "      <td>-122.125000</td>\n",
              "      <td>2360.000000</td>\n",
              "      <td>10083.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.900000e+09</td>\n",
              "      <td>7.700000e+06</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>13540.000000</td>\n",
              "      <td>1.651359e+06</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>9410.000000</td>\n",
              "      <td>4820.000000</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>98199.000000</td>\n",
              "      <td>47.777600</td>\n",
              "      <td>-121.315000</td>\n",
              "      <td>6210.000000</td>\n",
              "      <td>871200.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id         price  ...  sqft_living15     sqft_lot15\n",
              "count  2.161300e+04  2.161300e+04  ...   21613.000000   21613.000000\n",
              "mean   4.580302e+09  5.400881e+05  ...    1986.552492   12768.455652\n",
              "std    2.876566e+09  3.671272e+05  ...     685.391304   27304.179631\n",
              "min    1.000102e+06  7.500000e+04  ...     399.000000     651.000000\n",
              "25%    2.123049e+09  3.219500e+05  ...    1490.000000    5100.000000\n",
              "50%    3.904930e+09  4.500000e+05  ...    1840.000000    7620.000000\n",
              "75%    7.308900e+09  6.450000e+05  ...    2360.000000   10083.000000\n",
              "max    9.900000e+09  7.700000e+06  ...    6210.000000  871200.000000\n",
              "\n",
              "[8 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhOXLxHAD6eM"
      },
      "source": [
        "**A very good way to visualize the variables is using a box-and-whisker plot using the following code.**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "gGYdwnxkDWwX",
        "outputId": "dd96ff0a-9dd3-4b9f-d84d-eff900893503"
      },
      "source": [
        "fig = plt.figure(1, figsize=(9, 6))\n",
        "ax = fig.add_subplot(111)\n",
        "ax.boxplot(house_mlr['sqft_living15'])"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boxes': [<matplotlib.lines.Line2D at 0x7fe82b89a3d0>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7fe82b8a0450>,\n",
              "  <matplotlib.lines.Line2D at 0x7fe82b8a0990>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7fe82b8a7490>],\n",
              " 'means': [],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7fe82b8a0f10>],\n",
              " 'whiskers': [<matplotlib.lines.Line2D at 0x7fe82b8919d0>,\n",
              "  <matplotlib.lines.Line2D at 0x7fe82b89aed0>]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_GfxLCqHYBR"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5b4A2OTFQY3"
      },
      "source": [
        "# Step 5: Now we are going to check for the correlations between the variables. This will be done using a correlation matrix which is developed using the following code:\n",
        "\n",
        "# *The* analysis of the correlation matrix shows that there is some correlation between a few variables. For example, between sqft_above and sqft_living there is a correlation of 0.88. And that is quite expected. **For this first simple example, we are not treating the correlated variables.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "id": "S5dxtJWNEz5i",
        "outputId": "6eae8944-aa05-422f-eb0c-ac4d6a6fe96a"
      },
      "source": [
        "house_mlr.drop(['id', 'date'], axis = 1, inplace = True)\n",
        "fig, ax = plt.subplots(figsize = (12,12))\n",
        "ax = sns.heatmap(house_mlr.corr(),annot = True)\n"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8u0dRXGN+iKjRRiNG9uT8d7b5IWXtPkIvtlEh0Lq7omhUNHoXrBVmTMWllGUVkH8kEQBzSRlFZ9NwD/O7rOuW/L38IzzYiqJEAhuAA0AP4BgwBTgOrAKuA15WFOV6YK0Q4ntFUXoDbwEeQDlwLbbfqH8R22/auwLvCCEc3bBXGdrgQMwZ2ZXfzZnZuHZp5yTnPWksPlMmouh0pN0357/Wp/oEYM3PqfxuLchF07yN83V16Y8mqiPWrDTKV3+IKLCdo/gG4jb1WdTAJpSv+fiyvdcAWYYyQr2qvIch3m4cSa39/LRCI2kFJfSJDL7s/AHcQv0pTc2t/G5MzyOge5SDjHuoH8Y0m15hsWIqMuLi70nB8STCR/Qg+adduDcJwK9LC9zDA8g7eK5enbrQAEzp1WyYnoNbt7Z1yvvdMoLirQcAcGkRjqWohKbvzcMlIoTinQfJfPmzer0O2pAATOlVNjRn5ODWtW59vjePoHjrfqfjPjcMJvejn+otW03UgECs2VXhK9acbLTt2jvIaFq1Rg0KxrRvD9zk7OmsDY8wP4rTq9pCSUYewbXYrSS9ym4VRUZc/Twpzy8mqHsUQ159AM+IQGIfWYGwWPFqGkRpnoEhr0/Fv0Mzco4ksvvZL5x0ZxWXE1K9XXrqOZpR6CCTVFACwN3f7MEqBNP6tWJAZJCDzMbT6dzZI/KSZVU8/Rz6jTDkoTZxLKvqH4oV0E9+BhQF047VWM4duWTedaENCsScUWU3S1Y2Lp2cxxnPW8bhfcdNKDotmdOcxxn3awdRcfIsmEzOaaF+lKRVs2F6HkG12TDN2YYAns2CGLtxMSZDKXEvf0/WPltYUfcnbuLY++uxlNa9ONSH+lGaVtXvy9Jy8e3Rqk55p/Ob+NP7q7l4RIZwYuFXtXqvAbxC/SmqpseQkYdXiB/FWVVP2rxC/CjKqKoHQ3oeXqFVC8reU0bQ5cZBpB05x6ZFX1Fmv+kL7xbF2Fem4hseSM68lxy91zTOXOER6kdxNRsWp+cRUsOG1WWExUqFwYjez5Oy/OJKmZaje5N9JLHyxuUiLt7uNB/encMfb4CRjroVL//KsEQAUZSHGu6oG0DTvjduzdthzc2gIuYLRFEe1sxkdENuxLR7Hehc0ER2wJrt7EF2D/XHmFZjfujhqMOtxvxQYZ8fktfuI3xkTyYcfAetmwtxz31JhX1cqKybiED8OjUnJy7BSbfk6uOf5MFuC7wrhGgPFAEz7cdzhRA9hBCVAYOKorhgW3g/IoToCgwHSoH7gEIhRG+gN/CAoiiXdkH+P6Dom19IGX03uW98iN/UO/5SXeaj+yhZeB/Glx/Gcvog+tsfrUwTBTkYX36YksVT0fW+FsXTt56c/ns2HkthePtwNPV4kxqa8yu3YkzP47oNi+m+cDI5+89UepUaCp/xQ3Hr3Iqc//wAgKLV4NG7IxlLPyJhwr9xaRaK303XNqC+Yeg7tybXru8i2iA/XNtEUrw9rsF0AaAoeE59kJIP3m3YfC9BdnwC31/7JKvHPEvXWWPRuOpQtRoCO0Vy/Ivf+en6ZzAby+n64JXF81/EYhUkF5Twn5v78MLorizadAxDWdUiM7u4jDM5BvpdKjzkclE1qH6hlH31AuU/v4fLqHvA1b1h8q6H4m/XkDZ+Cvlvf4jP/Y7jjK5lc3wffoC8JZcf9325GLMK+L7Po/wy8hn+eP4rhrwzE52nG/4dm+HVPITkDc43iA1JWVoe24fNZUv0v4m4dTAuQT5/iZ79X/7GssH/5v1R8yjOKuC6+VV1nHowgRXXzeXDcfPxvf9WFBfnpwSXQ2POFbXh1yacfvMmsfWpjx2OKxqV65Y/yJFPNlKUnF3H2fVjPh1H6duPUvr+U1jOHcF1/HQALOeOYDlzEP29C9BPnIX1wpkG/4nwgO5RCIuV1d1nsabvv2k3fTQezapusrXurgz88FHinv0Cc3FpPTk1MsL69/9dpfyTFtgpQoiLuzq+BC4+d65tN0RbIF0I8QeAEKJICGEGRgBTFEU5COwFAoDWNU9WFGWqoij7FUXZ/+HnKxu6HFeEOSsHbWi1ThoShCUzt075kvWxeFzTv870S2EtzEX1q1oEqL4BDl4DwPY42h4KYtodg6apsydIFOVhzUhCE9XhsnUHe+nJMFQNPJlFpQR7udUqu+H4Ba7v2PSy875IaUYebuEBld/dw/wpzXD0Rhkz8nFvYvMoKRoVnbc7FXnFCIuVg899Scx189hxz+u4eLtjOJdxSZ2mjFx0YdVsGBaIqRYbegzoStCDt5I0dRHC7tkxpedQdvwcppRMsFgxxOxB39HZa1Mdc2YuurAqG2pD69DXvxuBM28lZdrCSn0X8R4zCMOm3WC2XLJ81bHm5qAGVT1VUAODsOZUedMVN3c0kS3weflN/D77Bm37Dng9vxRt67o97AAl6fl4Vnts7BHqT0m6s90uPlpWNCou3u6UV/OaARScTcNcUoZf2whK0vMoSc8jO97mTTr/6z4CO0c66Q72dCWzerssLiPI07WGjJ4hUcHoNCrhPu4093MnuaDqcfym0xlcExWCTnPpIVsU56N4V5VV8fJHGBzLKorysJyJB6sFUZiDyMtA9a9/42R9mLNz0IZW2U0THIQlq+5xxrhxC+7VQkg0wYEEvfY8uc++hPlC7WFdxox8PJpUs2GYP8Za+p5HE2cbWivMlbbMPZKIITEL75ahBPVsTWCXFty05w1GrX4W75ZhXP+dc5x2WUY+bk2q+r2+SQBlGbV7oeujPDMfw8kL+Petaq/N77mOqeuWMnXdUoqzCvCupscr1B9DDW+3ITMf72oea68wfwx2j3ZJTpEtREUI4lZuIbyrc1/POZuGMJahaxXpcLwx5oqSjHw8q9nQM8yfkhr1WF1G0ai4eLlXeq89Qv0Z9Z9H+f3RFRQlOW7UHvrSfRSez+BwHRsAhSEPxaeqbhVv535BaXHl3GSO34IaVuU/M+34mbIP5lH25YuAgsh1bqfGjDzcm9SYH2qMM6U15gcX+/zQ/F/9Sd9yGGG2UJ5bRM4fp/HvatsMrWg1DPzwURJ/3MmF9X/tzaCk4fgnLbBrBp1d/F5SU7AeFOAhIUQ3+18LIUSMkyIhPhBC9BJC9Lp/ym3/7fU2COVHT6FrHo42PBS0WjxGDaEkdreDjLZZk8rP7oP7Ykr+7zdPWJPPoAY2QfEPAY0WbffBmI/uc5BRvKvix7Sd+lRugFR8AkDnYktw80DTogPWrMu/lo5N/EjOKya1oASTxcrG4xcY0ibMSe58joGiMhNdw53j9C5F3sFzeLUIxaNpEKpOQ7Px0aRuPOAgk7YxjshbBgMQcUMfMnfYNuJp3FzQuNkWViGDO2G1WB02R9ZF6eHTuEY2QRcRgqLT4nPDYAy/7XWQ0XdoSfjiWSRPXYQlt7DauWdQvT3R+Ns2enr07+KwObIufS6R4egiQsCur/h3Z31hi2eRMm2hg76LeN8whMJftl6ybDUxnzqJJjwCNcTWXl2HXkPFnqq3HQhjCXm3jCf/rknk3zUJ84njGJ6bd8m3iGQfOod3i1C87HaLGh9N8iZH73rSpjja3DwIgBZj+pBmj5f3ahqEYl/YeoYH4BPVBENKNqXZhZSk5eHT0tbGmgzsSH4tG486hvqQnG8ktdBoa5enMhja0jE0aVirYPan2BZJ+aUVJOUbCfepujnccCqd69s5t+XasKadR/ULQfEJBFWDpn1fzGccN5pazsShNrc//nfzRPEPxVpQ/5tl6qPi2Cm0TcPRNrGPMyOHUrp1l4OMtml45We3QX0xpVwAQPH0IPjtJeQv+5DyQ3VvWs05aLOhp92GLcZHkxLjaMOUmDha2W0YOaYP6XYbuvp7VcY+ezYLwqtFCIbkLE59/jvf9nyI76P/zfoJCyk6l86Gm5c46S6MT8CjZShuzYJQdBqaTOhHZo1+Xxf6MH9Ue2y91scDvz5tKUmoWpwlfbKJD0bP44PR8zgVs5+uE23XH969FeWGUofwEIDirALKi0sJ725zTHSdOIhTm2zXUj1eu93IXmSdstWxb7U27BMeiK5FU8xpjvsAGmOuyDp0Dp/Iqn7Yalw052v0w8RNcbS7yVYHUWP6kGq3oYu3O2M+m82eF1aRsf+Mwzl95tyEi5cbOxZ8Wadua+o5VP9QFN8gW7/oGI35tKMNqz8x1bTpiTXHvvlSUcDNFmqkBDdFDWmKJcE5pKq2+eFCjKOO1Jg4Wtxsmx+aVpsfjKk5hAy0OZQ0bq4E9GiN4axNf9/XHqDoTCqnPlhfZ/n+NqyWv//vKuUfEYNtp5miKP2EELuB24EdQPc6ZE8BYYqi9BZC/KEoihe2EJGNwAxFUTYLIUyKorQBUoUQV7JIr5c5z73IH/GHKSgo4toJdzLzvslMHDvy0ifWhcVKztLlhK5YiqJRMfy0EVNCEn4PTqH82GmMsXvwuW08btHdEWYL1iIDWU+/Unl60w2fo3q6o+h0eFzTn/SpTznsKnfCaqXshxW4T38eVBXT3t+wZiTjMuoOLMlnsBzbh27wWLQd+9q8Z0YDZV+/BYAa0hTXCffabn0UqNjyE9b0pLp11UCrqjw5shszVu7EahWM79qcVkHevLv1OB3CfBnaxjY5bDiewvUdIlCUKw8PERYrcfM+ZcjKubbX9H2zlaLTqXSaM5G8Q+dJi4nj3MpYopfNYPSu16goKGH39GUAuAZ4M2TlXBACY3o+ex967/KUWqykLVhB5GcLUVSV/O82UX4mmeBH76D0yBkMv+8j9Kl7UT30NF3+JACmtGySpy4Cq5WMFz6ixZdLQFEoPXKW/G8u8Yoni5WM59+j2aeLUFSVgu9t+oIevZPSI2co/n0vwU/eh+qhJ2LZU5X6UqYtBEAXHowuLBDj3v8iptdqofidN/FZ+iqoKmUx67AkJeI+5V7Mp09SsWfXpfOoBWGxsmv+Z4z66gkUVeXUqq3kn06l5+MTyT50nuRNcZz6ZitD35rOLTteo7ygmM0zbW+fCOnThpEzx2I1WxBWwc6nP630hu6c/xnDls1AddFiSMpi6+wPaP2Q41MTraoy95oOzPxxP1YhGN8xgqhAL97ddYYOIT4MjQqmf/NAdiflcONn29EoCo8Obouvm+1mM63QSIahjJ4Rl3lDKKxUbPoC/aQ5oKiYD29D5KSiG/QvrOmJWM7GYzl3BE2LTrg9sBSsVio2r4JS2zCmv3MeakAY6PS4PfgGFes+wnL+aP06LVbyXlpG8DsvgqpSvGYDpnNJ+Ey/i4rjpyndthuvW8ej79sDzGasRcXkPmt7y433rRPQNm2C7wN34vvAnQBkznwSa77jwlJYrOx55jOu+9pmw7OrtlJwOpVuj08k99B5UjbFceabrQx6ezo32m241W7D0Oh2dHt8IsJuw91PfeIU31pvlVqsHH3qU/p88xSKRuXCyliKT12gzRM3UXDoPFkbD+DTrSU9P3kMna8HISN60GbOzWwbMgfP1uG0f/5O26ZOReHce2sxnKj9JvfM5oO0GtaNWdtex1RawZrHq7b4TF23lA9GzwNg3TOfMP61aWj1LpyNPcTZLbY3Zgx/6jZCOjQHISi4kM2v82xhFE17tWXSzLFYTRaEsJKzZBnWgiInG/7Vc4WwWNk+/zPGfvkEikblpL0f9p49kezD50ncFMeJb7Zy7ZvTuWP7a5QVFLPpQZsNO999HT6RIfR69F/0evRfAPxyx0uoOi29Hp5A/plUblm/GIAjn24CttQwopWK9Z+iv2OurV8c3IrITkU3dCLWtPNYTseh7TMSbZseCKsFykoo/9n+qlxVi9vdz9qyKS+l/Kf3ag1NEBYr+5/+lKFfO84Pne3zQ2pMHAkrY+n39gxu2GmbH3bOsM0PZz7ZRN83pjF6y0u2drJqKwUnUgjs04YWNw+i4Hgy129aCsChF1aRvvmQk37J1YUiLvWKsP8Bqm1y3A/0xLa5cbL9fy8hRI5d7lMcNzkuw/bOrVJscdhGYDEwFps3OxuYIIRwduHZMeWca9QKThk2vTHVEXSt89sO/kq0PTo1qr41c+u5mfgL6OhWZ1P6y1CVxh0DglsVX1qoAfnp2JWHAv0Z7ngm4NJCDYjIv7JXTVH/EXcAACAASURBVDYEOd817ivCtmSGNqq+oCsMbfqzHNA37sPkO73/uxjlP8OGgivbUP5nueuBxh3X1jTMz1ZcEbelfdV4m4jqoCIp7m9fRLo07/G310Nt/JM82GYhxJ01jkVW/yKEuLva5z+AaJyZZ/+TSCQSiUQi+edyFW8y/Lv5J8VgSyQSiUQikUgkfzn/CA+2ECIRaNzYAolEIpFIJJL/ZeQvOdaJ9GBLJBKJRCKRSCQNiFxgSyQSiUQikUgkDcg/IkREIpFIJBKJRNKwCLnJsU6kB1sikUgkEolEImlApAdbIpFIJBKJRHLlyE2OdSI92BKJRCKRSCQSSQMiF9gSiUQikUgkEkkDIkNEJBKJRCKRSCRXjtzkWCfSgy2RSCQSiUQikTQg0oMtkUgkEolEIrlyrJa/+wquWuQC+y8mZdj0RtXXdMuKRtVXtmBWo+qznj3fqPoKNI3bRfKMbo2qD+CAi2uj6gs94tuo+iyaRlVHweeHG1WfR9tGLiBgrmhcneFmU6PqO+Oia1R9qUp5o+r7qTC4UfUBtKto3IWYcXtao+orUps2qj7J1Y8MEZFIJBKJRCKRSBoQ6cGWSCQSiUQikVw5cpNjnUgPtkQikUgkEolE0oBID7ZEIpFIJBKJ5MqRv+RYJ9KDLZFIJBKJRCKRNCBygS2RSCQSiUQikTQgMkREIpFIJBKJRHLlyE2OdSI92BKJRCKRSCQSSQMiF9gSiUQikUgkEkkDIkNEJBKJRCKRSCRXjnyLSJ1ID7ZEIpFIJBKJRNKASA+2RCKRSCQSieSKEcLyd1/CVYtcYP8NuA3oRcDcGSgalaIfN1D40SqHdK+bx+Bz2ziExYowlpL9/JuYziWj+ngR8vp8XDu1xfBzDLlL3/nT1/LM0tfZtnMf/n6+rP5yxZ/OD0DTvif6m6aBqmLatZGKTd85pGv7Dsd1wn2IwhwATFvXYtq9ETW8JfpJD4LeHaxWKjauwhy37dL6WnXFZfRdoKiY4zZj2r7GUV+3IbiMvANrUR4A5r0bMcdtAUA34na0bbqDomJJOEzFus9q1dF0aBf6Pz8ZRaNycmUsB9/5xSFdddFyzZvTCezSgrJ8A7/NWE7xhRyCurVk8Ev3AaAosP/1n0jcsB+PMH+GvTUd90AfhBCc+HoLRz/aWGcZ/YZ1I2rRPSgalYyvfidl+WqHdJ/o9rRceDeeHZpzYvqb5KzdU5nWYv6d+A/vgaIo5G87TMIznzicO/T5ybQY1g1TaTkxsz8g62iik/7gzpGMfG0aWr0L57ccJPa5LwBw9fFgzLuz8I4IouhCNr/OXEZ5oRGAiOj2DHnuTjQ6DaV5Br67ZYmtzr3d6fPqA/i2i0AIwd7HPsDFy50eiyajqCoJK2M5sdy5fqPfnoF/50jK84vZNX0ZJRdyULQa+r56P36dW6BoVRK/28Hx5Y72r27DAQtsNjyxMpaD79Zuw6DOdhvOXI7hQg4RgzrR98lbUV20WCvM7F6ykrRdxwFoNb4f3WeNAyEoySxg88Pv1qrbtW9vfB6dBRoNxl9+pfiLlQ7p7hPG4jFxAlisWEtLKXzpNcyJSQBoo1riO/cxFHcPEFay75sOFaZa9VRH26k3+ttn2vrhtvWUr/umdrmeg/CY9RzFz8/EkngaNBrc7pmNpnlrex/+jfJfV9Z6bl24DehF4JPTUTQain5YT8FH3zqke98yBu9JY8FqxWosJXvBW5jOJV+RjoBhXWm7+G4UjUrqV5tJXPazQ7pvdHvaLroLzw7NODLtLbLW7q1MG562kuITNn1lqTkcnPJKrTqaDu3CwAWTUTUqx1fGEl9Lmxlerc3E2NuMq68n17//MMFdW3Lyu21sn/955Tmtxval50PjUVSVxN/j2fPCqppqK7n1uXvoNKwHFaXlfPr4O6QcO+8kM/7x24i+cTDuPp480nFy5fF+Nw1l4lOTKci0jXtbPlvPzlWbARhm7/Pm0nI21NPnr6/W57fY+7zex4MbqvX5X+x93sXLjdFvzcCrSQCqVsP+99dx7Dvb+D3oqVtpP6wbAGde/5H0n23jU9CwrnRYPAVFo5Ly1RYSljn2Xf/odnRYNAWvDs2In/Y2GWv3AeAWEUjPTx4DVUHVakn8aCPJn/9WZz1eRNerD54zH0JRVUrX/0rpqq8d0vU3jMNt3L/AakGUlmJ441UsyUmg1eL16ONo27QFq5Xid5dhOnywVh3/7VwRPqgTfZ+qGmf2LK4aZ6LG9qX7w7Y2k/x7PHuX1t1mJFcPf/sCW1GUSGCtEKJTY577t6GqBD49i/SpT2LOyCH8m2UYt+x2mFyK123B8N2vALgPjSZgzjQyZjyNqDCRt/wzXFpF4tI6skEuZ8Lo67h94jjmLXq1QfJDUdHfMhPj8qcRBTm4z3kT85E9WDNSHMTMcdso/+49h2PCVE7p568hstNQfPxxf+JtzCcOQGlJPfoUXG64l7LPliCKctFPW4r55AFEdqqjvqO7qfjVcWGpNm2DpllbSt95AgD9/c+jRnbAmni8RpEUBiy+i19vf5GS9Dxu/HUhiTEHKDiTVinTbtJQygtL+GbgbKLGRRM9bxK/zVxO/skL/Dh6PsJixT3Yl5tilpC0KQ5hsbJn4dfkHE1E56HnxvWLuLDtCJxwnkBRVVq9cB9HbllEeXoe3Te8QG7MfoynL1SKlKXmcPqRd4iYOc7hVO9ebfDu3ZYDwx4HoNuaRfj070ChfeD2u7Y7npGhfDJ4NqHdo7hmyd18M36B0yVcu+QeNs39kIz4BCZ8NofIoV1IjD1MnwfHkrLzOH+8+wu9Z46l98yx7HhhFa7e7lyz5G5+mvwyhrRc3AK8K/PquXAy6bGH2Dn1LVSdBo2HnpHrFrFl0guUpucxYt0iUjfGUXSmyoYtbxtKRUEJawfMptn4aLo+cxu7pi+j2di+qK461l/7JBo3F0bHvkzS6l2UXMhxsuHAxXex9qIN1y4kadMB8qvZsP2koZQXlLBykM2Gfe02LM0zsP7e1zBmFuDXNoIbvnyCL3o/jKJRGbDgTlZdM5ey/GKi502i090j4PtTTvbzefwRch+ZgyUrm6CPVlC2fVflAhqgNOZ3jKttE7HrwP54PzyTvMfmgkbF77l55C98AfPZBBRvbzBfhsdIUdFPfoiSV+ci8rLxfPYdTAd3YU2rsYjVu+F63b8wJ5yoPKTrPQS0OornPwAurngt+YiKPZsRuZmX1msvb9AzD5L2wFOYM3KIWLWMki17HMY4w69bKPq2aowLfGIa6dOfvrz8AVSFdi/eS9wtSyhLy6XvxhfI3rifktNVbaYsNYdjj7xL8xljnU63lFWw59q59apQVIXBi+/il9tfpDg9j5vWLiSxjjbz1aDZtBoXTb95k4iZuRxLuYm9r35PQNsI/NtGVMq7+nrS7+nb+G70fMryDFzz+jTCB3Tk0J44J/2dhnYnuEUY84c+RIvurbljyQO8OGGek9zh3/ez5bP1LIpd5pS2f+0uvnnuI6d8/SJD+XjwbMK6RzF8yd18XUufH27v8+nxCdxYo88n7zzOvnd/oc/MsfSZOZbtL6yi25TryD2Tyup7X8fN34t7Yl/hxOqdNB/UmZBOkWy/5klUVx3RP84n+/dDmI1ldHzxHvbespSytFwGblxC5sYDFFezYWlqDoceWUHLGWMcrq0sM59dY57FWmFG4+7K4K2vkLnxAOWZ+XUbVFXxeuhRCubOxpqTjd/y96nYvdO2gLZTvvk3ytbaFvku/frjOf1BCuc9gX70DQDkT70HxdcXnyUvUzBrGgjhoOLPzBVleQY23FM1zoz56gm+7PUwrr6e9H3mNn4cZWszQ9+wtZnUncfqLqvkquB/MgZbUZS//cahLlw7t8WUnIb5QgaYzZSs34rHsP4OMqLEWPlZcdNXHS8tozz+GKKiosGup1e3zvh4ezVYfmpkG6w5aYjcDLCYMcdtQ9ul32WdK7JSEdm2gUgU5iEMBSiePvXri2iFNS8DkZ8FFguWI7vQtut1mVcrQKsDjdb2X9UgigucpIK7RVGUmIkhORurycLZn/cQOaKng0zkiB6c/m47AOd+3UeTgR0BMJdVICy2TSAaV13leGzMKiDH7jUylZRRcCYNj1D/Wq/Sq3srSs9nUJachTCZyV69k4CRjmUsT8mm5EQywuo44AsBqqsLqosW1VWLotNQkV1YmR44sjcnftgBQEZ8Aq7eHngE+zrk4RHsi4unGxnxCQCc+GEHUXb9La/ryfHvbeU+/v12okbYjrcd35+z6//AkJYLQGluEQAuXm4ERbfj3NexAFhNFryjwihOzKTEXr/JP+8hYqRj/UaM7Ml5uzcsZe0+Qu31ixBo3V1RNCoavQvWCjOm4lKnOqxpw4Q1ddjw+yobhg+w6cg9loQx09Yu8k9dQKO31aeiKKAoaN1dAdB5ulFSywSv69AO84U0LGnpYDZT+ttm9IMGONrJWNXnVTd95cTt2qc3poRzmM/a6l4UFV3WpiJNy7ZYs9IQ2elgMWPaF4uu+wAnOf2/7qZ83SowVRtThEBx1YOqouhcEWYzlBmdzq2LmmNc8fpYPK5xHAOqj3HVy3u5+PRohfF8JqVJWQiThYzVuwi6vreDTFlKNsXHk//rTVjB3aIoTMyk6GK/X7OHFjXaTIsRPThpbzMJ1dqMubScjD9OYy53fNLg0zyYwvOZlOUZALiw4yhRox2v+yJdR/Rmz49bATgffwY3Lw+8g3yd5M7Hn6Eo23ncqouuI3pz3N7n0+vp866ebqTb+/zxH3bQyt7no67ryTF7mY99v51WIy6ORQIXDzcAdB56ygpKsJqtBLQO58LeUwiLFYuxHMOJZIKu6Ypvj1YYz2dU2jBt9W5Crncc10pTcjAcr2VcM1mwVpgBUF11KKpyyXJr27bHkpaKNcPWD8tiN+PSf6BjvtX6oaJ3A7tabfNIKg7aboJEQQGipNjmza7Bn5kr6hpnvJsHU1StzaTuOEqLOtrM34Kw/v1/VylXy0JUqyjKV0AP4BgwBWgPvA54AjnA3UKIdEVRegIf28+LuZiBoih3Azfa5TWKovzLLtcSMAJThRCHFUXxr+P4AqCF/Xgz4N9ANDAKSAXGCiFMiqK8CIwDzECMEOLxKypocCDmjOzK7+bMbFy7tHOS8540Fp8pE1F0OtLum3MlKv5WVJ8ArPlV3kNrfg6aSOeBSNttAJpWnbBmpVL+wweIAkePo9q8DWi1iJz0evUpXv6IwtzK76IoDzWilZOcpkMf3Jq3w5qbQcX6zxFFuVhTzmA9fxz3OStAUTDt3YjISXM61z3Mj+L0vMrvJRl5BHePcpDxCK2SERYrFUVG9H6elOUXE9w9iiGvPoBXRCCbH1lRueC+iGdEIAGdmpMVn4A3zriG+VOeVlXG8vQ8vHq0rrdeLmI4cJqCXUeJPvQBKAppH2+gtJpn2CXMH0N6Vd7FGXl4hvpRklU1YXuG+lGckeckA+Ae6F0pW5JVgHugrQR+LUNRtRpuWvU0Lp564j/eyIkfduDTNIjyXAN935iGX8dm5B0+T+bO4xirlc+YnkdAD8f6dQv1w5jmWL8u/p4kr91H+MieTDj4Dlo3F+Ke+5KKAucnHh6hfhSnVStDeh4htdmwug5DlQ0v0nJ0b3KOJFZO7tvnfcItm17EZCynMDGDHc98SutwR92aoEAsmVmV3y3Z2bh0aO90je43TsDztptQtDpyHnoMAG3TCBAC/zdeRvX1oey3LRR/VXuoR3UUv0BEXpVOa142mijHcUZt3grVPxjz4b24jrql8rhp/za03fvj9ea3KC6ulK5cgSgxXFLnRbTBATXGuBz0nWsf43zvutE2xt37xGXnD+AaWqNPpOXi3cO539eF6qqj78alWC1WEpetJnv9fieZhmoz1SlMzMA3KgyviECK0/NoMbInGl3t07BviD951cpYkJGLX6j/FS2me4zqS+s+7ck8n853iz4lPz0X3xB/0qr1eUMdfd5Qrc8bLqPPx3+6iQkfPca0/ctx8dCz9sHlIATZx5Po9+8bObz8FzRurgQM6IDhVCrCaqW0WvnK0nLxvQIb6pv40/uruXhEhnBi4Vf1e68BNTAQS3a1PpGTja6dcz/Uj5uA+8RbQKuj8IlHATAnJODabwDlm39HDQ5C27oNalAwnDrpcO6fnSsu0mJM1ThTmJiBT1QYnhGBlKTnEVlPm5FcXVwtVmoL3CeE2KkoysfAg8C/gPFCiGxFUW4FlgD3Ap8As4QQ2xRFqRk41wPoIoTIUxRlGRAvhJigKMo1wOdAN+D5Oo4DRAHDgA7AbmCiEOIJRVF+AsYoirLdfl3thBBCURRndwKgKMpUYCrA4ibtuc0/ojaxein65heKvvkFj9HD8Jt6B9nP1B4j+P8R89G9mA/EgtmMbsAo9JNnU7rsqcp0xdsPtymPU/rFa1fs2apV36kDmI/sBIsZba9rcb1xBmWfLkbxD0EJaoLxtZkA6O96GsvZdliTTl4ixysjKz6B7659Et9WTRj25jRSthzCYvdsad1dGfHBI+xe8GWtntc/iz4yFPfWEezpPh2ALt/Ox7tvO4r2NmwZa6JqVEI6t+D7215Aq9cxafUC0uPOomo1+HWO5MAzn5Ebn0CPhZOJGNULU9Hle0irE9A9CmGxsrr7LFx8PBi+ej4Z249Skpx96ZOvEL824fSdN4lf73gJAFWrocPk4Xw/6mmKkrIYuGiKLR77p9rjsC+F8cfVGH9cjdt11+J192QKFr8IGg0uXTqTc990RFk5Acteo+LkaSoOOIcUXBGKgtukGRg/fNkpSdOiHVitGP59K4q7Fx5PvYH5eJzNG96AXBzjPEcPw2/a7WQ93UBhapfBjp4PUp6Rj1vzYHp+P5/i4ymUJl1mCMyfoLzQyNZ5nzDi3VkIqyDjwBm8mwf/JboO/7afP9bswFxhZtDtw7n7tVm8cfvzf4kugMghnck+nsR3k5bi2zyEm76ay+f7TpG0/SihXVvSf+3zVOQayN9/BtEAr3YrS8tj+7C5uIb40euzx0hfu8/h6dx/ne+a1ZStWY3rsOG43z4FwysvULZhHZpmzfB7930smZmYjh/7y15P59cmnL5PTWKdfZypKDSy46lPGP7eLLAKMvb/dW3mv0K+pq9OrpYQkRQhxE775y+BkUAnYJOiKAeBZ4AI+4LWVwhxcefbFzXy2SSEuHj7OPBiuhBiMxCgKIp3PccB1gshTMARQANssB8/AkQChUAZ8JGiKDdi84A7IYT4QAjRSwjRq+bi2pyVgzY0qPK7NiQIS2ZuzSwqKVkfi8c1/etMv9qwFuai+gVWflf9Ah08zACUGMBs8wCadm1E06ya10LvhtuM5yn/5TOsiTViWWtBGPJQfAIqvyve/oiiPEeh0mKw2PSZD2xGbdISAG373lhTzkJFOVSUYzlzEE1TZ8+wMT0fz7Cq8A2PUH9K0h29JSUZVTKKRsXF293Ji1VwNg1TSRl+9phMVathxAePcOanXZyvxYN2kfL0PFybVJXRNcyfivS620x1Akf3wXDgNFZjGVZjGXmb44mYfgM9fnuFHr+9QkVmPl5hVXl7hvpTnOFYtuKMfDyrha9UlzHmFFU+XvYI9sWYU1R5TtK2w5hLyynLLyZ170mCOjTDkJ6HMT2PXPuj55S1+3AL8cW9Wvncw/wprVG/pRn5uDdxrN+KvGKa/6s/6VsOI8wWynOLyPnjNP5dWzrVQ0lGPp5NqpUhzJ+SjFpsWF2HV5UNPUL9GfmfR9ny6AqKkmxesICOzQEqvyes3UtoT+f2Y8nOQRNSNSFqgoKwZOc4yVWW9bfN6AcPsJ+bTcXBw1gLixDl5ZTt2ouu7aWfXoj8HBT/Kp2qfxAiv1qb0bujhkfi+eRreL3yJZqo9rg/vBBNZBt00ddgPvIHWCwIQwGWs8fQRra5pM6LmLNya4xxgZiz6i5v8fpY3K9wjCvPqNEnmgRQnlG/B9PxfJtsaVIW+buO49U50knmz7aZukj6LZ4fxi3gxwnPU5CQTuG5jMq0oZNH8sy6V3hm3SsUZuXjX62MvqEB5Gfk1ZZlrZQUFGO2P2nR6LS06duhMt/qfd6rjj7vVa3Pe11Gn+908xDObLCNYwVJmRSmZOMfFQbA3uVr2HHtU+y7ZSkoCiUJ6ZRl5ONWrXz6JgGUXYENL1KemY/h5AX8+zo/Ka2ONScHTVC1PhEYhCWn7nZZHvs7LgPsISRWCyUr3iF/+v0UPfc0qocnlgspTuf82bnCI8yfER86jjNgazOrxy5g9fjnKTyXTuH5DCRXP1fLArumm9IAHBNCdLP/dRZCjLiMfOrZDXdZlAMIIayASYhK96kV0AohzEAf4HvgBqoW4Jev4OgpdM3D0YaHglaLx6ghlMTudpDRNmtS+dl9cF9Myak1s7lqsSadRg1qghIQAhot2h6DMR/e4yCjePtVftZ27lu1AVKjxe2B+Zj2/o754E4uB2tqAqp/KIpvEGg0aDr3x3zygKM+z6oHDZp2vbDaN0BaC3PRRLYHVQVVgyayQ2VadbIOncOnRSheTYNQdRpajY8maZOjBzFpUxxtbh4EQMsxfUjbadtE6NU0CEVj62ae4QH4RjWhOMXmXR3y6v0UnE3jyH/W11tGw8GzuLUMQ98sGEWnJWjCAHJj6l6QV6c8NQeffh1Ao6JoNfj060DG11uIGz6HuOFzyN3wB+0n2iaR0O5RVBiMDo+KwfYYuKK4lFD7o872EweSEGOr43Ob4uhwk63cHW4axLlNtuMJMQdo0rstikZFq3chtHsUeWfSMGYXYkzLxcs+8YYM6kjOgbN4tQjFw16/zcZHcyHG0YapMXG0uHkwAE1v6EPmDtsGH2NqDiEDOwDYHj/3aI3hrHOYT9ahc/hEVtkwalw0iTVsmLgpjjY3OdvQxdudUZ/NZu8Lq8jYf6aqXjLy8Gsdjt7ftochYlBn8mvRbTpxEm1EOJowW593G34NZTt2OchoIqriSlz7R2NOsbXD8r1/oI1qgeLqChoV1+5dHTZH1oXl/Ck0weEogaGg0aLrMxRTfDWdpSUYHp6IYc6dGObciSXhBMa3n8WSeBprXhba9vaHei56NC3bY0m//Dd8lB89ha5ZONrwENBq8Rw1lJItjmOAzmGM63PFY1xRfALuLUPRNwtC0WkIndCf7I2X1ye0Ph4oLraHtzp/L3z7tKWk2obhi9RsM63GRXO+ljbTzt5mosb0IXXncad8anJxw6+rjzudpgzn+MrYyrTYLzayePQcFo+ew8GYP4i+cQgALbq3ptRgvKLwkOrx2gUZeSQePluZbwd7nw/rHkV5HX2+vLiUMHuf71CtzydsiqOjvcwdbxpEgr3PF6Xl0Mweg+4e6I1fVBiFyVkoqoLe1xMArw7N8O7QjJzYwxTGJ+DRMhQ3uw2bTOhH5kbHfl8X+jB/VL0OsNnTr09bShLqf8JiPnUSTXgEaqitH+qHXkPFbsd5RhNe1Q9d+vbDkmpvF66uoLfth9L16IWwWBw2R17kz8wVF8eZfS+sIrPaOAOgt7cZFx93OkwZzsmvY+stq+TqQBEN8Aj+T12A7U0g54H+QojdiqJ8CJwBHgAm24/pgDZCiGOKohwGZgohdiiK8hIwRgjRyR6D3UsIMcue79tAthBikaIoQ4E3hBDd6zm+ACgWQrxqP79YCOFp/7wAKAZWAO5CiCxFUXyAc0KIqlvwWjjXeYRTBbsN6k3AE7bX9Bl+2kjBf1bi9+AUyo+dxhi7h4C5M3CL7o4wW7AWGchZ+g6mBFtnbrrhc1RPdxSdDquhmPSpTznszm+65cpetTfnuRf5I/4wBQVFBPj7MvO+yUwcO/Kyzy9bMMvpmKZDL9tr+hQV054YKjauwmXMnViSz2A5sheXcXej7dzX5iEzGihf9Q7WzAtoew9Df+e/saZXDVxlX7yBNfVc5XfV3zlKWdO6Gy6j7gJVxRy3BdO21eiuuRlr6jkspw6gGz4JbbuetseSpcWU//KRLdZaUXC54T7bIlsILGcPUbHB8aHIlx/bJuKm13Sl/4I7UVSVU6u2Er9sDb0en0j2ofMkbYpD46pj2FvTCewUSXlBse0Vb8nZtJ44gG4zx2I1WxBWQdybP5G48QChvdsw/qdnya22MXHfS9/iGbOv1nr2u7Y7UQttryTLWLmFlLd+pPkTt2I4mEBezH48u0XR8eM5aH09sJaZqMgu4MCQx0BVaf3i/fhEt0cA+ZsPcm6B46sIvV+aSuTQLphLK4h5/AMyD9veZHLH+iV8Ncr2ZoeQLi0Y8dpUtHoXErccYsuztteO6X09GfPeQ3g1CcCQmsPaGcsoL7Td5/acNoaOtwxGWK0c/SaWePtrCNu1bUafV+9Ho9NSnJzFnn+/T2DP1vSwv9rq3DdbOf72z3SeM5G8Q+dJjYlDddXR7+0Z+HVqTkVBCTtnLKMkORutuyt935iGT5twUBTOrfo/9s47vIpqe9jvPiW990AIJRCkhx6aFGmiFMWOKIIFsCuiYrmKXa9KE1DvtSMqVjpBJfTeewtJSO/1JKfNfH+cQ5JDThIiIXB/336fh4czs9fsNWtmr9l71qy9s5ETi1Y52FeotU2Aihxsv4da2z3cN385PZ4dT/ahyns4ZE7lPVz/qO0edntiLF0fHU3hucoUgpUT3qM8t4j29w6h0+QRKBYrxSk5bHjmM0Z4V//y4tqnN75PPgpaDYaVayj5egneDz6A6cRJjFu24fPUY7j26A4WC0pxMYUfzcNyLhEA9xFD8Zo4AVAxbttJ0cJPHer2bKt12mZ0nXvhdrd9mb7NazGu/B7XcfdjTTyF5YDjS73n8x9S/uOntmX6XN3wmPIcmibNAYFpyzpMax2X2cvZ7VznBTwG9CTw+am2pUh/i6Pgs4uecS9MxSO2G6rFglJUQvZblc84ZyRkV8/GC7ohjCj07gAAIABJREFUhug37kdoNaQtjefcnN+Imnk7RQcTyF63F5+YKLp8+Sx6P0+s5WZMWQVsHzgD3x7RtPv3Q6CooBEkf7aatO83ONR92sU2eIsc3IX+9jZz4seN7J2/nJ72NpNobzM3zJlKcMcWlNvbTJE9PenebR/j4u2OVq/DWGRgxYR3yT+dxrAFjxLYLhKAPXN/48zyHRzUGp3afffsKXQYGIOpzMTXz31C0mHbs/Dl1R/w5ijb3JxbX7iXXmP74xvqT2FmPlt+/IuVc5YxbuY9dBnaA6vViqGghCUvf07mWdsL4LTXbT5vLjOxrorPT1zzFt9W8fmRdp8/t+Egf1fx+ZsXPY5Pk0CK7D5fXliKZ6gfIz98BM8QP4SAXQtXcvy3rWhd9Uxc/SYuioqlpIwjz/2XoqO2ex18Qwzt37At05eyNJ4zc34neuZtFBw8R9a6vfjGtKL7l8+gtz/XjFmFbBr4HEHXd6Ld6/faUgiFIPGLdZz/9m+Ha9erc/WXXZdevfGcZlumr3zdagzff4fH/ZOxnDqBafs2PKc/jkvX7mC1oBSXULJgDtakRDShYfi+8wGoKkpONsUfvo+S5ZhS9OvxZsA/7yu6PjGWro85PmdW3WN7ztyw4FEC29vazN45v3F2ue2F9ZGU7+qe3XmFKd/7+9UdRAJu3cdd9evgjGtlgL0W2AN0B44BE4FoYB7giy1XfI6qqp9XmeSoYpvkOKqGAXZNkxlrm+RY1wB7CfAH4AYI4N+qqjpfONmOswH2laS+A+zLxdkA+0ribIB9JbkwwG4s2lmcd7RXkr0uro2qL8zSuM+cCwPsxmJMs8b94lTTAPtKUtcAu6FxNsC+klwYYDcWNQ2wrxTRSuP6PMB1xsb9gyTOBthXkgsD7MZEDrBtXKsD7Ks+yVFV1USg+hRzOABc70R+L9Clyq6Z9v1fAV9VkcsDxjk5vqb9r1207VVDWS8n5yqRSCQSiUTy/xeK/EuONXGt5GBLJBKJRCKRSCT/J5ADbIlEIpFIJBKJpAG56ikiEolEIpFIJJL/Qa7hv6R4tZERbIlEIpFIJBKJpAGREWyJRCKRSCQSSf2Rf8mxRmQEWyKRSCQSiUQiaUDkAFsikUgkEolEImlAZIqIRCKRSCQSiaT+yEmONSIj2BKJRCKRSCQSSQMiI9gSiUQikUgkkvojJznWiIxgSyQSiUQikUgkDYiMYF9hgm9wbVR95a891qj63F5b0Kj6LJt/alR9ftbDjaqv67jiRtUHkLiicdtopGJsVH0/umgbVd9993VuVH1Cr29UfQCW7UcbVV/s+KJG1ZfyR2Cj6ivSmBtVn6fauD4PEKova1R9HrGhjarP74jaqPok1z5ygC2RSCQSiUQiqT8yRaRGZIqIRCKRSCQSiUTSgMgBtkQikUgkEolE0oDIFBGJRCKRSCQSSb1RVevVPoVrFhnBlkgkEolEIpFIGhAZwZZIJBKJRCKR1B85ybFGZARbIpFIJBKJRCJpQOQAWyKRSCQSiUQiaUBkiohEIpFIJBKJpP6oMkWkJmQEWyKRSCQSiUQiaUBkBFsikUgkEolEUn/kJMcakRFsiUQikUgkEsn/SYQQI4UQJ4UQZ4QQLzgpjxRCbBBC7BdCHBJCjGoIvTKCfRXQXtcNt1sfAqHBvGM9pr9+dijX9boB1zEPoBbmAmDevArzjjiEfzDuk18CjQCNDvPmFZi3ra1bX7vuuN32CGg0mLetw7R+maO+3kNxHTcFtTDHpm/jSszb16Fp2gq3ux4FNw9QFEzrfsSyb9Nl2f7y2x+xaesuAvz9+P27xZdV1wW2nkzh/RU7UVSVW3pGM3lQZ4fyD1bsZHdCBgDlZgt5JeVseW0CAN1e/IrWYf4AhPt5Mvf+oU51hA3uTNfZExFaDQnfx3NiwQqHco2Ljt7zpuHfuQWm/BK2PTIfQ0oOQqel54cP4t+pJRqdhsRlWzg+fzkA0Q+PpNU9g0FVKTh+nl1Pf3ZJ9mrbd8ftjmm2+7l1LaZ1PzmU6/oMw/XWKagF9vYTvwLz1rrbSZNBnek5eyJCo+HM0niOfFLdxv5zpxLQqSXG/GI2TVtAaYqtzfi1a0af9yaj93JHVVRW3fQqitFccezgL5/BKzKYFTe86FS33+AYWr3xAGg1ZC75i9QFvzuU+8S2o+XsB/Bs35yTUz8md+WOirLmL9+L/9BuAKR8/DM5f2yr01aA8f+aRPvBXTGVGVkyYxEpR89Vk7lpxp30uvV6PHy9eK7D/ZX2TLmJPncNwWqxUpJXxPczF5OfmlOjrq2J2XwQfxxFgXEdI5jcq1U1mbiT6SzecQaBIDrYm3dGdQEgvaiM2euPkFlSDsCCcd1p4utRq21bE7J4/68jNp/oHMnk2DbVZNadSOPTrScBiA7x5d3Rtms4J/4YmxOyAHi4TxtGtGtaq64LePTvTuhLU0GjofDnteR97vic8Z90C763jQSrFUteIRkvfYwlzaYneMZkPAf2Ao3AsG0/WW/V/WzQtu+O2+1Tbc/RbWsxxV30XIsdiustD6IWXHiurcC8bR0A7o++gbbldVjPHqVs0Ws16mg6qDO97T5xamk8h534xPVzpxJo94n4aQsoScnBKyKIW+LfpzAhHYDsfWfY/sKXAHR7/nZa39YfF19Pvot+sE47L3D/aw8SM7g7pjIji2bMI/FIgkO5i5sLTy2aSUhkGKqisPfP3fzw3re11tlsUGf6vWZ7rh1fGs+BhdXtGzJnKsGdWlKeX8yf0xdQnJJDxICO9H7hTjQuOhSThe1vLSVt2zEAxvz0Eh4hfljKTQCsnPAe5blFTvX7DOpK5OsPglZDztL1ZHzyq0N56ENjCLp7GKrViiW3iMRn52NKzca9fUuav/MIWi8PVEUhfd4y8ldsrfMaalt3weWmB0BosOz9C/PmPxzKdV0H4jJiIkpRHgCWnWux7P0bTcsOuNxY6f+aoCYYl83Fenx3NR0N3Vd4R4XTZ/HjFcd7NQ/hyAc/c+rzup/pEhBCaIFPgGFACrBbCLFcVdVjVcReBn5SVXWREKI9sBpocbm6G32ALYRoAaxUVbXjJcpPAuJUVU2zbycCPVRVrbk3u5YRGtxum4ph0SuoBbl4PPMRliM7UTLPO4hZ9m/G+MunDvvUonwMc2aA1QIubni+sADLkV2o9odBjfrumI5hwUuoBTl4PDcHy+EdKBkX6du3CeOyRY76zEbKvvkQNTsN4RuAx8x5WI7vhbLSf2z+uFHDuGf8GGa98e9/XEdVrIrCO3/sYPGUEYT6ejBhwQoGtoskKtSvQua50b0rfi/deowTaZXXy1Wv5acnx9aqQ2gE3d+eRPyd71CWnsewNW+QFrePolOpFTKt7h6EqbCU1X2fpdnYWLq8fDfbp86n2ejeaF30rBvyAlp3F27c+D5Jv21DtVhpM2UEawfOxFpups+njxM5tg8Yf6vdYKHB7e5HMcydhZqfg8eL87Ac2oGSnuwgZtm7CeMPCy/lElbY2Put+1l/97sY0vMYtXo25+P2Ung6rUKmzd2DMBaW8nv/Z2kxJpbuL93FpmkLEFoNA+ZNY8uTi8k/loyrvxeq2VJxXOSNPbCUltesXKOh1TsPcvSO2ZjS8+iy9l3y4vZQdiqlQsSYmsPpJz+h6fQxDof6D+2GV6eWHLhhBhpXPR1/fZ38v/ZjLSmr1d72g2IIbhnGG4OepEXXNtzx1hQ+GvdyNbmjf+1j89freCV+rsP+lGOJfDD6RczlJvrfO4yxL07gq8fmVjsewKqovPv3MRbd2pNQbzcmfL+dgVEhRAV6Vcgk5Zfyxe4EvrozFh83PXkGY0XZK+sO8WCvKGKbB2EwWRBC1GqbVVF558/DLL4jllBvdyZ8s5mBrcOICvKu1JdXwhc7TvPVhH74uLmQV2rTt+lsJsczC/lx0vWYLQpTfthGv1YheLnqa9WJRkPoq4+SMnkW5swcmi+bS8nfOzGdrWyX5cfPUnDbE6jlRvzuuongGZNJf+Zd3Lq2w71bexLHTgcg8vt/496rE2W7DtesT2hwu/NRDPNm2Z5rz8/FcmgnSsbFfrAR40+Lqh1u+vMXcHHFpf+NtagQxL51P+vsPjF69WySL/KJaLtP/NL/WVqOiaXHS3cRP20BAMVJmSwf/lK1es+v38fxL9czfsulPwNjBncnrGU4Tw+cRuuu0Ux5cyqvjJtZTW7lZ79zbPsRtHodL38/my6DunEwfl+N9vV/835W3vMupel53LpyNknr95Jfxb52dw3CWFDK0gHPEjUmlt6z7uLP6QsoyytmzeQPMWQW4N82gpu/m8m3PZ+oOO6vJxaSfaj6C6sDGg2Rbz7CqXv+hTk9l3arPqAgbhflpyv93nA0geOjnkUpNxE8cSQRL91PwvR/o5QZOffUXIzn0tGH+tNu9YcUbTyAtaiWvkkIXEZPofyrN1GLcnGb+g6WE3tQs1MdxCyHt2Fa9YXDPuXcUcoX2q+3uyceT83Heuag02va0H1F8dl04obNqqh/9P4FpKzZU/u1bUyu/UmOvYAzqqomAAghfgDGAlUH2CrgY//tC6TRAPwvpIhMAprU5wAhxDUbmdc0b4OSk46amwlWC5b9m9B16l33gWAbWFvtAxedHkTdt0/TIholJw01N8Omb98mdJ37XJI6NSsVNdvWztTCPNTiAoSX76Wdaw30iOmEr4933YKXyJHzOTQL9CYi0Bu9TsuILq2IP5Zco/yagwmMjGlZLx0BXaMoTsykNDkbxWwl+Y8dNB3R3UGmycjuJP5ki+6nrNxF6IAOtgJVRevhitBq0Lq5oJgsWOyDP41Wi9bNBaHVoHN3pSwzv85z0bRoi5KVjppjv5+7N17y/ayNQLuNJXYbE//YQbOLbGw2vBtnl20GIGnVLsL622xsMrAT+cfPk2+/7sb8ElRFBUDn4Ur7h2/k0FzHiHRVvLu2pvxcBsbkLFSzhezftxIwoqeDjPF8NobjSagX5ft5REdQuOM4WBUUgxHDsST8hsTUaW+n4T3Z9avtfiXuP427tyc+wX7V5BL3n6You6Da/tPbj2K2R+gS95/GLyywRl1HMgpo5udBhJ8Heq2GEW3DiD+b6SDz2+EU7ugSiY+bbSAb4OEKwNncEqyKSmzzIJu9Ljrc9dpabTuSnk8zP08i/Dxt+to1If5MhoPMr4eSubNrC3zcXGz6PG36EnKK6d4sEJ1Gg7uLjuhgH7aey65VH4Bb52jMyWmYUzLAbKF49Ua8boh1kCnbeQi13DaQLzt4An2YzSZUFeHqgtDrEC56hE6LNaf6Na+KpkU0SnaV59rejei6xNZ6TFWsJw9AuaFWmaCLfCLhjx1EXuQTkcO7ccbuE4mrdhFu94nayN53lrKs2u27mO7DerH5l3gAzuw/hYePJ34h/g4ypnITx7YfAcBqtnDuyFkCa2mXITFRFCVmUmy37+zyHbQY7mhfi+HdOPWzzb6EVbto2s9mX+7RJAyZNhvyT6agdXNB41K/btczpg3GxHRMyZmoZgt5f2zBb7hjX1i87QiK3c9K9p3EJdxmj/FcGsZztq8D5sx8LLmF6AJ9qA1NRGuU3AzU/CywWrEe3oauXc9aj3GGrkMs1tP7wWyqVnal+ooLhAzoSGliFoaU/8344pVCCPGwEGJPlX8PVyluClSNKKbY91XlNeBeIUQKtuj14zQAV2sgqhNCLAG6AUeB+4AZwGjAHdgGPAKMB3oAS4QQZcCFkcTjQojRgB64XVXVE0KI14AooBWQLIR4EfgCCAKygQdUVU22R9Cd7f8KKAO6AiHAZPt59QF2qqo6yf6p4b/2c1KBL1RV/bg+hmt8A1HyK51DKchF2zy6+gXq3BdtVAeUrDSMv/+n4jOn8AvC/eFXbZ+oln9Re/Tamb78HLQt2lbXF9MPbeuOKFmpGH/5rEJfRT3No0GnQ81Jr4+5V5ysIgNhvp4V26G+Hhw+73xAkJZfQlp+Cb2iwiv2mSxW7pm/HK1GwwODOjGkQ/Nqx7mHBVCWmluxbUjPI7BrlIOMR5g/BntkXLUqmIsMuAR4cX7lLpqO6M6Yg5+gc3dh/7++w1RQCpRyYvEqbt4zD2u5icyNh8nceNj2Xl0LGv9AlPxK+5SCHLQtndzPrv3Rtu6EkpWCcdmnqPm1P5A9wvwprRLZN6TnEXSRje5ObHT198KnVRgqKkOXzMQ10IfEP7ZzdNEqAGJm3sbRT9dgKaveGV3AJTwAU1rl+ZnSc/HuVj2lwRmlR5No9uztpC1ejsbdFd9+HTFUiXzXhG+oPwVplfe0ICMX37AAp4Ppuoi9YzDH4g/UWJ5VYiTU271iO9TLjSMZhQ4ySQW2yNukH3agqCqP9GlNvxbBJOeX4u2q59kV+0ktNNA7MpAn+rdFq6k5ip1VUk5YVX3ebhxOc7QrKa8EgPuXbEFRVKb2a0u/ViFEh/jw6bZTTOzZinKzld3JubQKrPuFWBcahDm9sl1aMnJw61K9XV7A97bhlGyyReHKD5zAsPMQUZuXgBAULFmBKeF8jccCaPyCHP2gpuda1/5o23RCyUzF+EvdflAVZz4R7MTvS6v4hMnuEwBekcGMWfcm5uIy9r3/M5m7Tl6y7osJCAsgt4qP5GXkEhAaQEGW85dyDx9Pug3tydovVtZYp2eYPyVV7CtJzyP0IvuqyqhWBVOxATd/L8rzSypkWo3qSc7hRBRT5VerQR8+jGpVSFizm301vFy7hAdgSq/i9xm5eHWt2e+D7x5K4Ybq0XjPmDYIvQ5jYoaToyoRPgEVaZcAamEumojq+rQdeuPeoh1Kbjqm1V+jFuU6lOs69cO81fl1vTJ9RSWRY2NJ+v3SUuAajWtgkqOqqp8Bl5Zj6Zy7ga9UVf1QCNEH+FYI0VFVLy88f7UG2G2BKaqqbhVCfAFMBxaoqjobQAjxLXCzqqo/CyEeA2aoqrrHXgaQo6pqNyHEdGwD8wuJbO2B/qqqlgkhVgBfq6r6tRBiMjAPGAfMr2E/gD+2AfUYYDnQz173biFEDKAFml5IbxFCVA95NQCWI7uw7N0IVgv6viNxu+cpyhbaPl+rBTkY3n8C4ROA+5SXsBzYhlpS/0GBo76dWPbGg8WCvt+NuE18lrL5lbmywscf9/tmUPbth6Cql6XrarLuYAJDO7ZAq6mM/K9+/nZCfT1JyS3moc/X0ibMn2Z1RELqQ2DXKFRFYXnMY7j4ejLk91fI3HQEU2EpTUd0Z1XvpzAVGuj7+RM0H98PLMsvW6fl0A4su+PBYkY/YBRu98+gbE61eR0NhtBqCekZzepRr2IpMzH8pxfJPZyIMb8Y7+ah7HltCZ4RQVdEd8HGg3jFRNFpxVtYcoso3nMK1dp4D/we4/oT2TmKeXe+dln1WBWV5IJSPr+9F1kl5Uz5aRfLJvbDoqjsT81n6YS+hPm48fyqgyw/lsotHSMuX19+Kf+5qy9ZxeVMXrqVZQ8Mom/LEI5mFHD/kq34u7vQuYk/mloG8/8En9GDcesQzfmJtk/u+shwXFo14+ygiQA0++Jt3Lt3oGzv0cvSYzm8E8uejTY/6H8jbvc9S9lc53MAGhpDVgHLej2FMb+EwE4tuOGLp/lt8AuY60hdagg0Wg2Pz3+GdV+uIut8Zt0HXAb+0U3pPesuVk14r2LfX08spDQjH72nG8M/e5Lo8f059cuWy9ITcOtAPDq35uRtjik3+hB/Ws59inNPz22QvslyYi+WQ1vBakHXYyiu4x+l/MvZFeXCyw9NaKTT9JDLpaa+ojTZ9iKp0WtpOqI7h97+scF1/x8nFWhWZTvCvq8qU4CRAKqqbhdCuGELwmZdjuKrlSJyXlXVCzMSvgP6A4OFEDuFEIeBIUBt39ouzITYi2Mi+nJVVS88wfoA39t/f2vXUdt+gBWqqqrAYSBTVdXD9jeYo3Y9CUArIcR8IcRIwOnMjaqfK748nORQphTmovGvHGxo/AId3qoBMBRXpIKYt8ehbda6mg61KA8lIwltVHtnp1CzPv+g6vpKi8Fi17dtHdrIKvrc3HGf9jrGFV+jJP7zCMyVIsTHg4zCyrf8zEIDIT6eTmXXHjxXLT0k1B79jgj0pkerMIf87AuUZeTh3rTyU6tHeABlGY6RI0NGPh5NAgAQWg16Hw9MeSVE3tKX9A2HUC1WjLlF5Ow+RUCXVoQO6EhpcjbG3GJUi5XU1bsJ6lF31FbJz0XjH1yxrfELQs13dj9tEwzNW9aibV53vYaMfDzt53/BRsNFNpY5sdGYX4IhPY+snScx5pdgLTeR8vdBAju2ILh7GwI7t+TWHR8z8vdX8WkVzvBl1XNSTel5uDSpbKMu4YEY02v/MlOVlLm/cnDocxy98w0QUJ7gPH1uwMThzFz9HjNXv0dRVgF+TSrvqV9YIIUZl64TILpfJ4Y/diufPfg+lirRu4sJ8XIls7hyYJVZUk6wl+tFMm4MjApBr9XQ1NeD5v4eJBcYCPV2IzrYmwg/D3QaDYOjQjiR5XzCWNW6MqrqKy4nxNvNQSbU252BrUNt+vw8aO7vRXK+zY8e6hPNT5MG8umdfVCB5v7O/akqlswc9OGV7VIXFoQlM7eanEefGAKm3kXq9NdQzbY26jW0L+UHT6AaylEN5ZRu2oN7TLta9SkFOY5+UONzze4HW9ehjby0ryIXcOYTpU783rOKT7jYfUIxWTDao7y5hxMpSszCp1VYvfQPu+9G3ln9Me+s/piCrHwCq/hIQFggeZnO2+tD704n41w6a75Y4bT8AqUZ+XhVsc/LiX1VZYRWg4u3R0X02jMsgBGfP8WGpxZTlJTlcAyAubScM79vIySm+oResPt9eBW/DwvE5MTvvft3Jvzx2zjzwNuoVfxM4+VO669fJvX97yjdd6pWW8HWZwrfSp8XvoGoxRfpKyup6Hste/9C08Tx3LUd+2A5tgsUq1MdV6KvuEDYkBjyDydizKnd/yXV2A20EUK0FEK4AHdhC6BWJRm4AUAI0Q5ww5bhcFlcrQH2xa+aKrAQuE1V1U7A59gMrIkLM4CsOEbh//nsO8d6lSq/L2zrVFXNB7oA8cBU4D/OKlFV9TNVVXuoqtrjgU6OKQdK8mk0QU0QAaGg1aHrej2WI7scZIRPZW6drmOvigmQwjcQ9LacSdw90bZsj5J18YuYI0rSKTTBTRCBdn3drsdyaIeDjIO+Tr0rJ0Bqdbg/9ArmnX9hOVD3DO2rQYeIIJJzi0jNK8ZssbLuYAID2zerJncuq4CiMhNdIkMq9hUZjJgstgdlfmk5B5IyaRVS/aNE3oEEvFuG4dksGI1eS+TYWFLX7XWQSVu3jxZ3XA9AxM29yNxii74ZUnMI7Wd7CdK6uxLYvQ1FZ9IwpOYS2L01Wnfb/Qzp34Gi03XPq1CSTqIJqXI/ew50cj8rO01dl9hqEyCdkWu30ctuY4uxsZyPc/wcez5uH1G3DwCg+U29yNhqmyOStvEQftc1q8gnD4u9joLTqZz65i9+7v44v8Y+zdpxsylKSCfu9req6S4+cAb3VuG4RoYg9DqCx/UjL6767HynaDTo7J/kPdo1x6N9c/LjnUeXNn8bx/ujnuf9Uc9zKG43vW613a8WXdtQXmyoV3pIRIcW3PX2g3z+4PuU1LBCwgU6hPmSnG8gtdCA2aqw7mQGg1qFOMgMbh3CnvO2zj6/zERSvoGmvu50CPWl2Gghz2BLsdl9Po9WAbUPeDuE+5GcX0pqgV3f8TQGtnYc3A1uE8aeZNuANN9gJCm/hAg/D6yKSoE9nedUVhGns4vo0zK4mo6LKT98Cn3zJuibhoJeh/eogZT87dguXdtFEfr6E6ROfx1rXmWKjCU9G/eenUCrAZ0W956dMNaRIqIknXL0g+7O/KDKc61zbLWJ3XWRcyABnyo+0cqJTyTH7aO13Sda3NSLdLtPuAZ4I+yRf6/IYHxahlKcXL9g2Ppv1vDiqKd5cdTT7InbyYDxgwBo3TUaQ3Gp0/SQO2bcg7u3J9+8/t866886mIBvizC87fZFjYklcb2jfYnr9xF9m82+Vjf1Is1un4uPBzd+/Sw73/mRjD2nK+SFVoOb3R81Oi2RN3Ql76TzlK3Sg6dxaxmOSzOb3weM7U/Bese+0L1DS5q/O50zk9/GklvZZoReR+v/vEjuz/Hkr9pep60ASupZNIHhCL9g0GrRduqL5YTjZEHhVfn8117XAyXb8dx1nfthOVxzX3gl+ooLNB/Xh+TfrrH0ELBNcrza/2o7PVW1AI8B64Dj2FYLOSqEmC2EuDBr/lngISHEQWApMMkebL0srlaKSKQQoo+qqtuBe4AtQF8gRwjhBdwGXFi7rhj4J7PitmF7U/kWmABsrmN/nQghggCTqqq/CCFOYou+1w9FofyXxXhMfd22zNrOP1EyknG5cQLW5NNYj+5Cf/1odB16g2JFNRRT/r1tdQJNaDNcx022vY4IMG34DSU9qW59Py3C49E37csCxtn03XSvTd/hnegHjbVNtLTa9X33EQC6bgPQtu6I8PRGH2tbvq78249RUhNq01grz/3rXXbvP0RBQRE3jLuX6VMmMn70iH9cn06r4YUxsUz7Ig5FURnbow2tQ/1ZGLeP9hFBDGofCdij111aOqzAkJBdwJu/bkMjBIqqMnlQZ4fVRy6gWhX2zfqKgUufty299MNGik6l0vG58eQdPEda3D4SlsYTO38ao7Z9iKmglO1T5wNw5sv19JrzCCPj3wMhOPfDRgqP2zr68yt3MTzuLVSLlfwjSZz97m/a1LyogQ1FofzHhXg88ZZ92cU4lPQkXEZPxJp0GuuhHeiHjEXXOdbWfkqLKf/6wzqvo2pV2PXy1wz9fqZtmb4fN1J4KpUuM8aTe/AcKev3cfqHjfSfN5VxWz7EVFDCpum21RJMhQaOfbaGm1bPRlVVUv8+SOpfNeckV8OqkDDrP3RY+jJoNWQt/Zs/I2KXAAAgAElEQVSykylEzryTkgNnyYvbg1dMFNd9MROdnycBw3oQ+dyd7B/4NEKvpdMfb9iqKS7j9KPz4BJSRI5t2E+HwV15deNcTGUmljxXudLEzNXv8f6o5wEY88IEeozth97dhdnbF7L9x79ZM+dnxr54Ly4ebjyw8GkA8lNz+PyhD5zq0mk0PD+kPdN/3YOiqoztEEFUkDcLt52mfagvg6JC6Ns8iO1JOdz69Wa0QvDU9W3xs798PXN9W6b+sgtVhXahPtzaqfoL5MX6XhjakWnLbPncYzs1o3WQNws3n6B9mB+D2oTRt2Uw2xOzufW/G9AIwdOD2uPn7oLRYmXy97YBhKeLjrdu6opOcwlxGKtC1huLiPjvm6DRUvhLHKYzyQQ+PpHyI6co3bCT4OemoPFwo8kc24oIlvRsUqe/TvG6LXjEdqHF8kWgQumWPZRu2Fm7PkWh/MdFeDxm02feHoeSnozLzROxJp2yPdcGj0XXKbbyOfpNpR+4P/MBmtBmCFc3PN/6lvLvPsZ63HFwqVoVdrz8NcPtPnH6x40UnEql64zx5Bw8x3m7TwyYN5XxWz7EWFBCvN0nwmKvo+uM8SgWKygq21/8siKftsdLd9Hqlr7o3F24Y888Tn0fT9y8b2o1d//fe4kZ3J05mxZjLDPy6Yx5FWXvrP6YF0c9TUBYILc8fgepZ87z9irbMzzum1Vs+OFPp3WqVoUtr3zNTd/NRGg1nPxxI/mnUunx7HiyD50jaf0+TvywkSFzpnL3Zpt96x+12ddx0jB8W4TS/alb6P7ULYBtOT6LwchN3z2PRq9FaDSkbjnK8e83ODfKqpD8yudEL/kXaLTk/vgn5afO02TG3ZQePEPh+t00e3kSWk83ohbb0olMqdmcmfw2/qP74dW7PTp/b4LuGALAuafnUXaslpVLFAXTyi9wu/8l0Giw7NuAmpWCfsgdKGlnsZ7Yi67Pjeiu64GqWMFQgvHXypWYhF8wwjcIJfFYjSquVF+hdXcl9PqO7JlZ94uTpDqqqq7GNnmx6r5Xq/w+hi0luEERDTBIr59C2yTDtcAeoDu2pVImArOwJZpnAKeAJFVVXxNCjAfexjYBsQ+2N5AeqqrmCCF6AP9WVXWQfZJjiaqq/7braQ58SfXJjDXt/wrb8oE/X7yU4IUy4LT92As9zouqqq6pzd7ip0Y37gW2OP90daVwe21Bo+qzbP6pbqEGZPn0WpYKuwKMGlv9s/qV5rcVVyY/uiairLUs2XcF+NGt9lU3Gpp3X6hfKsDlIvR1LKF3BUj+4PLyo+tLkyGNqo6f/6h59Y0rQZyupG6hBmSg4lW3UAPTQ21cG9s94NKo+lb+p/H98M70JQ07QeIfULZm3lWfmOV+4xNX/To4o9Ej2KqqJgLXOSl62f7vYvlfgF+q7GpRpWwPMMj++7WLjkvClst9cX017Z900Tl2dFaGbeUTiUQikUgkEonEKf8L62BLJBKJRCKRSCT/M1yzf5BFIpFIJBKJRHINcw2sg32tIiPYEolEIpFIJBJJAyIH2BKJRCKRSCQSSQMiU0QkEolEIpFIJPXn8v6a+P9pZARbIpFIJBKJRCJpQGQEWyKRSCQSiURSf+QkxxqREWyJRCKRSCQSiaQBkQNsiUQikUgkEomkAZEpIhKJRCKRSCSS+iMnOdaIjGBLJBKJRCKRSCQNiIxgX2F03To2qj7lzLlG1WfZ/FOj6tMNuKNR9YUqextVX/EhY6PqA/CyNm4EIsivtFH1BZQFNKo+0aRZ4+qLiG5UfQAhHfY0qr7cbfpG1ScaVRuIRtboehWCjjpd4yrV9OjRqPqafL6rUfVdM8hJjjUiI9gSiUQikUgkEkkDIgfYEolEIpFIJBJJAyJTRCQSiUQikUgk9UdOcqwRGcGWSCQSiUQikUgaEBnBlkgkEolEIpHUHznJsUZkBFsikUgkEolEImlA5ABbIpFIJBKJRCJpQGSKiEQikUgkEomk/sgUkRqREWyJRCKRSCQSiaQBkRFsiUQikUgkEkn9UdWrfQbXLDKCLZFIJBKJRCKRNCBygC2RSCQSiUQikTQgMkXkKrP1bAbvxx1CUVVuiWnB5L5tHco/WH+I3YnZAJRbrOSVGtkyY3S9dGhbd8Fl1P0gNFj2/Y1583KHcl3MQFxGTEApygPAsnMdln0bANAPvwdddFcQGqxnD2Fa/XXdNp1M4f0VO2029Yxm8qDOjjat2MnuhAybTWYLeSXlbHltAgDdXvyK1mH+AIT7eTL3/qH1stUZL7/9EZu27iLA34/fv1t82fUFDI6h9ZsPILQa0pf8RfL83x3KfWPb0fqNSXi1b86xR+aQvXIHAH79OtB69qQKOY/WTTg2dQ45a3bXqdO1d098n3oMtFoMK1ZR8u1Sh3KPcaPxHD8OrApKWRmF732IJTEJbVgoIUu/xpJ0HgDT0WMUfvBxvewNHdyZzm/ch9BqSFyygVMLVjiUB8ZeR5fZE/FpH8muqfNJW7mrXvUDePTvTuhLU0GjofDnteR9vsyh3H/SLfjeNhKsVix5hWS89DGWtCwAgmdMxnNgL9AIDNv2k/VWzff4xtfuo83gLpjLTPw+41PSjyRWkwnv2IJxH05F76bn9IaDrHntGwAGPXUr3e4ejCG3GIC/PviR0xsOotVrufntKTTp3ApVUdDk70DJPe9Q59YT53l/+XYUReWWXm2ZPCTGofyD5dvZfSYNqOITb9wPQHp+Ca8v20RmYSkCmD9lJE0DvOu8plsPnOC9b5ajKAq3DO7FlLFDHMrTc/J5edGPFJeWoSgKT949igFd22G2WHn9s2UcT0zFalUYPaA7U8YNqUFLJbqYXnhMfgw0Wox/rcL42/dO5fSx1+P13GyKZj6C9ezJiv0iKATfOV9T9tNXGJf/WKc+9749CJg5HTQaSn5bQ+GXjsd433Yz3neOAUVBMZSR+8bHmBOScYvthv8TUxB6ParZTP7Hn1O++4BTHU0HdabX7IkIjYbTS+M5/Ilj29e46BgwdyqBnVpizC9m47QFlKTk4BURxLj49ylKSAcge98Ztr/wJQAjl72Ee6gf1nITAHF3vweFpXXae99rU4gZ3B1TmZHFM+aTeCTBodzFzYUnFz1HaGQYiqKw7889/PDet7XW2XRQZ3rb7TtVg33XV7Evvop9t8S/T6ET+4Z9NxOPUF+EVkvmrpPsmPUVquI8jcB7YDea/utBhFZL7g9xZC36xaE8+MGxBN41DNWiYMkrJPm5eZhTsyvPz8ud6/78hMK4naS++mmd13DryRTe/2MHiqrY/HBwF4fyD5bvYPdZm00Vfjh7ImD3w5832/1QMH/y8Dr90H9wDK3esPUVGUv+ImWBY1/hE9uOqNkP4Nm+OSemfkyOva8AaPHKvQQM7Y4QgvxNh0h4+Ys67bsqyEmONSIH2FcRq6LyztqDLL6nP6E+7kz4YgMD24QTFexTIfPcsMrB6dLdZzmRUVA/JULgcvNkyr9+C7UoF7dH3sZyYi9qdqqDmOXIdkyrvnTYp2kWjTayLWWfzATA7cHX0bRoj5J4rBabFN75YweLp4wg1NeDCQtWMLBdJFGhfpU2je5dadPWY5xIy6vYdtVr+enJsfWzsQ7GjRrGPePHMOuNf19+ZRoNbd6dwsE73sCYlkf3de+Qs24PhlMpFSLG1BxOPPkJzaaNcTi0YOtR9tzwHAA6Py9675hPXvzBS9LpO+NJcp98DmtWNsH/XUz55m1YEpMqRMri/sLwu61zdO3fF58nppP3zPMAWFLTyJ700D+0V9DlnQfYcsc7lKXnMnjtm6TH7aP4VGX7KUvNYc+Ti2kz/eZ/qEND6KuPkjJ5FubMHJovm0vJ3zsxnU2uECk/fpaC255ALTfid9dNBM+YTPoz7+LWtR3u3dqTOHY6AJHf/xv3Xp0o23W4mpo2g7sQ0DKMeQOfJaJra2568wH+M+5f1eRufmsyK174Dyn7zzDh65m0HtSFM/b7tOO/a9j22WoH+W532wafi0a8gGegD8/+/S+Mm76rKLcqCu/8tpXFD48i1NeTCfN+Z2CH5kSF+lfIPDemT8XvpVuOcCItt2L75R/iefCGGPpER2AwmhFC1HlJrYrC21/+xqezHiY00Jd7XprHoO4diIoIrZD5/Le/GBHbmTuG9eVsSiaPvfdf1sxvx/qdhzBZLPzy/rOUGU3cOuPfjOwXQ9PggJoVajR4PPQkJbNnoORm4/3eYsy7t6KkJDnKubnjetN4LKeqP0M8Jj2Kef/OOm27oC/gxcfJnPo8lswcmixZgGHjdswJlW2mZM3fFP+8EgD3gX0IeHYqmY/OQskvJOvJV7Fm56KPakHoondIGX53NRVCI+j91v3E3f0uhvQ8bl49m+S4vRSeTquQaXP3IEyFpfza/1lajoml+0t3sXHaAgCKkzJZPvwlp6e/6bGF5B46V7mjjp44ZnA3wlo24ZmB02ndNZrJbz7Cq+Oerya36rM/OLb9CFq9jpe+f50ug7pxMH6f0zqFRhD71v2ss9s32ol90XcPwlhYyi92+3q8dBfxddgXP3U+5pIyAAZ/9gQtbu7NueU7qsmh0RDxxiOcnfAq5oxcopd/SOGfuzCernw5LTuawMmbn0EtNxF47400eXESSY99UFEe/uwESncdrf3i2bH54TYWPzTS5ofzlzOwfeRFfhhb8Xvp1qOcSK3ihz9u5MEhMfSJbnppfqjREPXOgxy5YzbG9Dxi1r5LXlz1vuLkk58QMd2xr/Du0Rafntexb/CzAHRZ/ga+fTtQuO3SbJVcG/xPpIgIIVyFEH8KIQ4IIe4UQsy6hGNK7P83EUL8XIfsGCHECw11vpfKkbQ8mgV4EuHviV6rYUT7COJPpdcov+boeUZ2iKiXDk1Ea5S8DNT8LLBasR7ehu66Hpd4tAo6PWh1tv81WtSS2gf4R87n0CzQm4hAb/Q6LSO6tCL+WHKN8msOJjAypmU9LKo/PWI64etTd8TvUvDp1pqycxmUJ2Whmi1k/b6VoJGO17P8fDalx5KhhqgNQPDoWPL+3o9SZqpTp779dVhS0rCmpYPFQtmff+M2oJ+DjGowVPzWuLs12MSTgK6tKT2XiSE5C9VsJeX37YSP6O4gYzifQ9Hx8/84kuHWORpzchrmlAwwWyhevRGvG2IdZMp2HkItN9p+HzyBPizIVqCqCFcXhF6HcNEjdFqsOc7baNth3Tn4y2YAUvafwc3HA68QPwcZrxA/XL3cSdl/BoCDv2zmuuHdq9VVleA2TTm3zTZgLM0tQjUbEX5hFeVHkrNpFuRDRKCPzSdioog/mlRTdaw5cJaRMVEAnM3Mx6oo9Im2+b2Hqx53l7rjIkfOJNMsLIiI0ED0Oh0j+8QQv+eizllASZntmpYYygj297mwmzKjCYvVitFkRqfT4uXuVqs+bevrUDJSUTJtbdS85W9cevarJud+9xTKf1uKanJs9/pe/VGy0rGeT6zTNgDXjm2xnE/DkpoBFgul6+LxGNTXQUYtdfQJ1e4TppNnsWbbBk7ms4kIVxfQ66vpCOoaRXFiJiXJ2ShmK+f+2EHkRW0/cng3ziyztanEVbsI79/hks6/vnQf1ovNv9i+Kp7ZfwoPH0/8QvwdZEzlJo5tPwKA1Wwh8UgCAWGBNdZ5sX0JDWTfhcG10GnRuOgA588ij5g2GBPTMZ3PRDVbyF+xGd9hvR1kSrYfRrVH+g37T6IPD6ooc+8YhS7Ij+JN++s8J4Aj5y/ywy6tiD9aS990IOEiP1TpE93Udu6X4IfeXVtTfi6D8mRbX5H9+1YCRvR0kDGez8ZwPKn6s1NV0bjq0bjo0LjqEHodpux6BtcaC0W5+v+uUf5XIthdAVRVjYGKwfPbl3KgqqppwG11yCwHltcmcyXIKi4nzNu9YjvUx53DqXlOZdMKDaQVlNKrRUi9dAjvANTCyrdwtSgPTUTranLa9r1wb34dSm4GpjXfoBblopw/jXLuGB7PLQYhMO9ch5qTVu1YB5uKDIT5elba5OvB4fPZTmXT8ktIyy+hV1R4xT6Txco985ej1Wh4YFAnhnRoXi97rzSuYQEYq0QXjWl5+HRrU+96Qsb1I2XxiroFAW1wENbMrIpta3Y2Lu3bVZPzuHUcXnffhtDpyXn8mcrjw8MI/uozlFIDxZ/9F9PB6tHdmnAL96esir1l6XkEdKvefi4HXWgQ5vTKNmLJyMGtS9sa5X1vG07Jpj0AlB84gWHnIaI2LwEhKFiyAlPCeafH+YQFUFTFlqKMPHxC/SnJquy4fEL9Kcqo9MGi9Dx8wiojt73uG06XWweQdjiBdW8sobzIQOaxJNoO68aR5dvwaRKIxi8U4e6NWmBLg8oqKiXMz6uijlBfTw4nV97PqqTlF5OWV0yv1k0ASMouxNvdhWe+Xk9qXjG92zTlyVE90Wpqj41k5RcRFlj58hAS6MvhM46DiWnjhzP1nc9Zum4rZUYTn816GIChvTuzYe9Rhk57gzKTiecmjsHXy6NWfZqAYJScynuo5GWjbdPeQUbbsg2aoGAs+3bA2LsqC9zccRt3N8WzZ+A25s5a9VTUFRKEJaNKm8nMwbXTddXkvO8cg8+94xF6HRkPz6xW7jF0AKbjZ8Bsrl4W5k9pla9rpel5BHeNqlFGtSqYigy4+tvutVdkMKPXvYm5uIx97/9M1q7KdJj+Hz2Mqigkrt7NoTmOaQPO8A8LJK9K283LyMU/NICCrHyn8h4+HnQb2oO1X6yssc6L7TP8A/vGVLEvs4p9w5fMJCgmipQNB0msIWVMHxaIOT2nYtucnoNH15r9PuDOYRTH77VtCEHTlyeT9NRHePfvUuMxVckqrE/fdMEPbX1TUnYh3m4uPPPNnzY/bN2UJ0f1qNUPXcMDMKZV2mdKz8X7EvuK4r2nKNx2lN4HPwcBaV+spex0at0HSq4prloEWwjhKYRYJYQ4KIQ4Yo9MjxRCnBBC7BNCzBNCrBRChADfAT3tEexlgLv995JL0NNCCHHE/nuHEKJDlbJ4IUQPIcQkIcQC+76v7Lq3CSEShBC32fdrhBAL7ee3Xgix+kJZY7Du6HmGtmuKVlP35+H6Yjm5l7KPHqds4fNYzx7C9dZpAIiAUERwEwwfTsfw72loW3VA07x6J/ZPWXcwgaEdWzg8pFY/fzvfPz6Gd+4ayAcrdnE+t6jB9F0ruIT44XldJHkbLiE9pB4Yfv2drNvvpWjhZ3hPsuUNWnPzyLzlLrInPUzRvIX4v/YywqP2wdK1jM/owbh1iCb/v7ZcTX1kOC6tmnF20ETODrwXj9guuHe/MlHE3d/9ydzrn2bxjbMozipgxCu2eQP7f9pIUXoeD694k5GvTkTJS/vHXxDWHTjL0M4tK3zCqijsP5fBMzf3ZskT40jNLWL5nlMNYs+abfsZc30P1n/yMp/MnMxLC5eiKApHziaj1WhYv/AVVs+dxTerNpGSmVt3hbUhBO6THqXsq0XVitzvmET5ymVQXnZ5OpxQ/ONyUkffT/7c/+D30D0OZfqo5vg/+SC5b85pcL2GrAJ+7vUUK0a8zO7XlzDwk+novWzBlE2PL+SPoS+y+pY3CO3Vlqjb+jeobo1Ww2Pzn2Xtl6vIOp/ZoHVfwJBVwLJeT7F8xMvsusg+gLgJ7/Njt8fQuugI73f5/uh/yyA8OrUm69NfAQi6bxRFG/ZizrjMdlkD6w4kMLRTVT9U2Z+YwTM39WLJ42NJzStm+Z7TV0Q3gFuLMDzaNGVn10fYGfMIfv074tO7elBFcm1zNVNERgJpqqp2UVW1I7AW+BwYDXQHwgBUVc0CHgQ2q6oao6rq7UCZ/feEeur8EbgDQAgRDoSrqrrHiVw40B+4GXjXvu9WoAXQHpgI9HFyHPa6HxZC7BFC7PnvBueTZwBCvN3IKK7sVDKLygipEtGuytpjKYzs0KzGumpCLc5D+FZ+JhQ+AahFF0XJy0rAagHAsvdvNE1aAaBr1xPl/BkwGcFkxHr6ANpmtb+Bh/h4kFFlwk5moYEQH0+nsmsPnquWHhJqjzBEBHrTo1WYQ372tYAxIw/XJpXX07VJAMZ6PuSDx/YlZ80uVIv1kuSt2TloQyu/XGiDg7Fm59QoX/bn37hdb/88bzajFtleUswnT2FJTUMXeelpRuXp+bhXsdc9PICy9Ia9J5bMHPThwRXburAgLE4GdB59YgiYehep019DtUccvYb2pfzgCVRDOaqhnNJNe3CPqeyI/O65mamr32bq6rcpzirAp4otPmEBFGU6RgCLMvMdItY+4QEVEe3SnCJURUVVVfYt3UDTLrZon2JVWPfGdyweNYsfHvoI9K6oJZX1hvh4klFQUrGdWVhKiG8NPnEggZExlV8IQn09adskkIhAH3RaDYM7tuB4St3tLcTfh4zcysh8Vm4hof6+DjK/bdjNiD626F+X6BYYzRbyiw2s2bqfvl3aotdpCfT1Iia6BUcTUqgNJS8bTVDlPdQEBKPmVokOunugjWyJ1+w5+Cz6AV10e7xeeAttVFu0bdrhPnEqPot+wPXm23C7dQKuN95Sqz5rVg66sCptJjQIa1bNPlG6Nh6PQZUpK9qQIEI+eo2cV97HkuI8Lc+QkY9nk8q24BkegCEjv0YZodXg4uOBMb8ExWTBmG+757mHEylOzMKnVVjFMQCW0nLO/b6NoJhWTvUPu+9G3l79EW+v/oiCrHwCqrTdgLBA8jOd++GD704n41xardFrZ/Z5hAdQ+g/tK6pi3wWsRjPJcfuIHNHNqX5zRq5Dyoc+PMjpgNmrXxdCH7udcw++iWqy9VMe3doSdP9NtN/yOU1emkzArYMJf/6+Wu0N8a1P35TAyCr3JdTXk7bhVfywQyTHU2v3Q2N6Hq5NKu1zCQ/EeInPzsBRvSjaexrFUI5iKCf/7/349Ii+pGMbHVW5+v+uUa7mAPswMEwI8Z4QYgDQEjinqupp1ZYs913th/8jfqIyXeQOoKbc7N9VVVVUVT0GXJgV1B9YZt+fAWyoSYmqqp+pqtpDVdUeUwbH1CRGhyb+JOeVkFpQitmqsO5YCgOjw6vJncsppqjcTJemtUwyqgEl9SyagDCEXzBotWg79cVyYq+DjPCq/JSsva4Hin0CpFKYi7ZFO9BoQKNF26J9RVmNNkUEkZxbRGpeMWaLlXUHExjYvvqLwbmsAorKTHSJrBw4FhmMmOyDzvzScg4kZdLqohzZq03x/jO4twrHLTIEodcRMq4fOeucvaPVTOgt/cj6bcsly5uPn0AX0RRteBjodLgPHUL5lm0OMtqIphW/XfvGYjlvu08aP1/b/QO0TcLRNWuKJbXmPP+LyT9wFq9WYXhEBiP0WiLG9SE9bm/dB9aD8sOn0Ddvgr5pKOh1eI8aSMnfjpOiXNtFEfr6E6ROfx1rXmHFfkt6Nu49O4FWAzot7j07YaySIlLw/UoWj5rF4lGzOBG3hy7jBwAQ0bU1xuIyh/QQgJKsAowlZUR0tQ1yu4wfwMn1Nnur5mtfN6IHWSdtg069mwt6d1cAWvXvCKqCWlLZ+XZoFkxyThGpeUU2nzhwloHtI6tdB5tPGOnSPMTh2OIyE3n2vNZdZ9JoFVq3T3SIakZyRg4pWXmYLRbWbj/AwO6OKRvhQX7sPGKLwiWkZmIyWQjw8SQsyJ9dR2056IZyE4fPJNGySXA1HVWxnjmJJjwCTYitjer7D8G0p0obNZRS+MBYiqbdRdG0u7CcOkbJuy9hPXuSkleeqNhvXPkz5b8uwbjmt1r1GY+eRBfZFF0Tmz7PEYMwbNzuIKOLrPQJ9wG9MSfbfcLbk9D5b5I/978YD9Q8aSznQAI+LcPwahaMRq+l5dhYzsc5Thg8H7eP1rfb2lSLm3qRvtWWi+8a4I2wf230igzGu2UoxclZCK2mIsVC6LREDO1KwUnnLy/rv1nDrFHPMGvUM+yJ28mA8YMBaN01mrJig9P0kNtn3IOHtwffvl73ihMX29fKiX3Jl2ifj90+nYcr7nY/EVoNzW6IofBMDS8wB0/j2rIJLs1CEXod/qMHULTecZKre4dWNHtnOglT3sSSW+n3yU9+xLG+UzjW/yHS3vqCvF83kP7eN7Xa2yHigh9W7Ztq8kPTRX4YRHF5FT88m16nHxYfOINbq3Bc7X1F8Lh+5MXVvWIU2CY/+vZpD1oNQqfFt08Hh8mRkv8NrloOtqqqp4QQ3YBRwJvAX42gM1UIkSuE6AzcCUytQdRY5XfD52TY0Wk0vDAihmlLt6IoKmO7NKd1sA8LNx6jfbgfg6JteZhrj51nZPuIS1o9oBqKgmnVl7jdNws0Giz7NqBmp6AfcjtKagLWk3vRxY5Ed113VEWBshKMv9k+4/4/9s47PKria8Dv3d303iAJAdLoLaGE0CRIFWkCIooIWGgiFjoiIkWwoRSBH3YRsaAiRUhACNJbQuglkBDSe29b7vfHLsluCkVCUL95n4eH7L0zc+6Ze+bO2XPPzGrPH0Xp0wKrlz8AWUYbHYX2ctUr0st0UiqYPSiYSV+G6XVq3wj/uk6sCYuguZcrIYYH2q6oGPq18THR6XpaNot/PYxCktDJMs+HtDbZfeTvMuPtZZyIPEN2di49hzzL5BdGM2xg37/VlqzVcXXOF7T+4U39Nn2b9lF4OR7vmU+RF3WNjNCT2AX40fKrGagcbXDp0w7vGSM40V2fE21Z3w0LT1eyD1e/E0sltDpylq/E5eP3QamgcPtONDGx2L04jtJLlyk5eBib4U9g0b4daDTo8vLIXqx/8WIe0Aa7F8eBRoMs68h+/2PkvLx70vf03K/psmk2klLBjU3h5F1OoNnM4WSfvk5SWAROAb4Ef/k6Zo42uPduS/MZw9nTvXK+6+30S120Fq8vFoNCSc4vYZRGx+HyymiKz12hYN8x3Ga8gMLaEs9P9OubNUlpJEx+h7zQg1gHt8F761qQoYrTc5cAACAASURBVODgSQr2Vb0TxdW9p2nUI4Cpfy1HXVTK79PLt/Wa+Me7rOuvb3vHvK8Y8tEEVJbmRIdHcdWQytN7ztO4N28Iskx2fBrb5uqdGBtXe579dhayLJOXnIU6YqeJXJVSwewhnZn02U79mAhqgr+7M2tCT9Lcy40QwzqDXYbFjcZjQqlQ8PqAjkz43x/IyDSr58qwjndO01IplcwZO4RJSz9Dp9MxJCQI//rufPpzKC18vAhp34Jpzw5k4Wc/890fB5AkWDhpBJIkMbJPZ+av+4knpn8IyAzu3oHGDT1vL1CnpfDzFdi+9QEoFJTu3YnuZiyWI8ehjb6M+uTh29e/V7Q6Mpetpu7apfpt+n4PRX3tBo6TxlBy4QpF+49gP3Iwlh0DQaNFm5tH+vz3AbB7ajCqBp44TngWxwnPApA8cTa6LNMvW7JWx9F539D7+5lICgXRP+4n+0oCAdOHkREVw83dEVz9YT/dVk5k6MGPKMnOZ/9k/Q4b7sFNCZg+DFmjRdbJHJnzFaXZBaisLOj9/SwUKqX++XHgPFc27rtjqOv03lME9GjHx3+tpaSohP9NX1V27t0/ljO3/xs4u7vwxCtPkhAdz5IdHwEQ9u0fhP+wp8o2b+nXx6DfVYN+gdOHkV5Bv2EG/cKN9AucPgydRgtG+lm62tPzqzdQmquQFBJJhy9yaUM1U7tWR/z8/+H77QIkpYLMn/ZQfPUm7m88Q+GZaHL3HMdz7lgU1lb4rNHvmFKamEbMi0tu31nVoFIqmD24E5M+36Ufhx0a4+/uxJrQU/q5qWwcXqdfG9/K4/DxICas34kM+nEYVH2++C39rs39nJab5iEpFaRs2kvh5XgaznyKvNPXyAw7iW2AH82/nInK0Qbn3u1pMOMpIrq/Tvq2ozh2aUm7fcsBmcy9p8ncXbOBDcGDR5If0s9cSpLkCWTKslwsSdIAYAr69IsesixfkyRpE2Any/IASZJCgOmyLA8w1M0C6siyXHllSnn7+bIs20qS5A1sN6ShIEnSy+jTOwJlWW5hODYWaC/L8hRJkr42lN9coZ0ngTHAIMANuAiMv1WuOoq+nVOrHayLjrlzoRpEEVh9hP5BoOo2olblHWpReSusB0ljv+pfcz8ojl6v/NbkQdLCseqFWQ+KTUX3/ubnfpi1KrBW5Uletf/quGjJ8lqVl3Ot8i4fD5LwtLp3LlSDhKnuvA92TdJbU3VqxIMkUFW762marKzZ3PY7cXLCve//f790S978wAKAd0tt+zhVYfXc0ofeD1XxMHcRaQV8IEmSDlADkwBXYIckSYXAAaC6vdXWA2ckSYr4G3nYm4EVwKJ7rPcL0BO4ANwEIoCc29YQCAQCgUAgEPy/42GmiIQCoVWcagpwK2ptKBsOhBvVnQXcNrQoy7Kt4f9YoKXR8RQq6C3L8tfA14a/x1bTjk6SpOmyLOdLkuQCHEefRy4QCAQCgUDw/4+HlAXxb+Dfsg/2P4XtkiQ5AubAIsNiR4FAIBAIBAKBoIx/rINdMWpdFYZIclUrKHrKslzjG2TKshxS020KBAKBQCAQCP5b/GMd7LvB4ETX7io7gUAgEAgEAsE/+qfKHzYPcx9sgUAgEAgEAoHgP8e/OoItEAgEAoFAIHhIiAh2tYgItkAgEAgEAoFAUIMIB1sgEAgEAoFAIKhBRIqIQCAQCAQCgeDekUWKSHWICLZAIBAIBAKBQFCDiAi2QCAQCAQCgeCekXXilxyrQ0SwBQKBQCAQCASCGkREsB8wW2fF1aq8bGXt3lJH7dlalVdXd6pW5XU5/16tylvddn6tygOwMKtdeUW5LrUqz66Wn3Kh4yNqVZ4LR2tVHkCUmVetyrOt5TTPvFoOPblK5rUq74pZ7efNttFKtSpvfy2Pw+7Lm9WqPME/H+FgCwQCgUAgEAjuHbEPdrWIFBGBQCAQCAQCgaAGERFsgUAgEAgEAsG9I7bpqxYRwRYIBAKBQCAQCGoQ4WALBAKBQCAQCAQ1iEgREQgEAoFAIBDcO2If7GoREWyBQCAQCAQCgaAGERFsgUAgEAgEAsG9I7bpqxYRwRYIBAKBQCAQCGoQ4WALBAKBQCAQCAQ1iEgREQgEAoFAIBDcOyJFpFpEBFsgEAgEAoFAIKhBRAT7IeDeozWBC0cjKRVc/z6cS6u3mZxXmKvouHISTq29Kc3K5/CEVRTGp6MwU9L+/RdwauMLOh0Rb20g7cjFKmXUD2lN53f0Mi5tCuf0p5VlPPrJRFxb+1CclceeSavJj0/HLcCXR957AQBJgpPLfyN210lsPJzpsWIi1q4OyLLMxe/3ce6L0PvWSVIp6fDRizi18kGhUhD780EurtoKQOPx/fB9pgfIMtkXb3L89fVQVFpJV+ceAfgvHoekVJC08U/iVm0xOe8Q3Az/RWOxbd6QCxM+IW37UQAcu7TAf+HYsnLW/p5cmPgJ6TtPVHfr7op57y7nr0PHcXZyZMt36+6pbsg7o/HpEYC6qISwaetJPRdbqUydVt70/WgCKktzYvadJvztDQBYONjw+Jop2Hu5kRufxo7JqyjJKcQruBmDPn+dnJtpAETvOsGxFVuw9XCm38cTsXF1AFkm8fhl6nVujkKp4MKmcCLXVL6HvT6ZiFsrvc2ETV5NXnw6Xt1aEjz7KZTmKrSlGo4s2UTC4QuoLM3pu24q9g3rIGt1xO6J5OiyH8va8whpTftFo5EUCqI3hXOhCpvpvHIizq18KMnK4+DE1RTEp+P9RGeaTX68rJxTs/rs7DuPrPNx9Nr8JlZ1HdEU6+1k78j3ICe3rGwPQ/9qikrYdZv+7WfUv/sM/WvpYMMAo/7dZuhfCwdr+n4wHseGddCUqLnw2v/IuxRv2maP1rRa9BySUsGNjfu4WkFXl+CmtFo4GvvmDTg5cRWJ24+Xnev0/Syc2/mTcfwyR0d/WOl6q8OpRwC+i/TjInnjn8SvNh0X9sHN8Fs4DpvmDbk08WPSDeMCwPutZ3Hu1Q5Jksj66wzX531ZpYwGIa3ptkA/7i9sCieiCpvpbWQzoQabsXS0pd//plKnjS+Xfv6Lv976tqzOEz+9iXWd8nu4ddR7kKa/h/VCWhO0UG8zVzeFc7aK51q3FRNxMdjMfsNzzdbLlSHh75N7PQmAtIhojsz+CoDe383Eqq4DklJJ6vHLxIWdor2RTqeq0KmPkU67DDoBtHt5IM1HhiBrdfz19rfE7T+Lo68H/dZMKavv0KAORz/aTJTh+dl6bG9GjO2FTqvj/N5Iti7bWFZ22Ntjad4jkNKiEjZOX0v8+ZhK9+Dx6U8RNPQRrB1smdFiTNnxLqN60W10X3Q6HSUFxfw4Zz3J0QmV6g98+zma9AigtKiUzdPXkXg+tlIZz5Y+PPnhBMwszbm87zTb3tHfL4/mDRmy5HlUFmboNDp+f+sr4qOuAeAT3IwB80ejVKkoyMoje/jsSu3ahwTiteAlUCrI2LSblDW/mJyv89IgXEb2Aa0WdUYOcdNXUZqQhnk9N3w/mwMKCUmlIu3rHaR/t6tS+xVx7dGGZovHgFJB/Ma9xBjmmls4BTel6aIx2DVvQNSElaRsP2ZyXmlrRbcDH5Ky8yQX5351R3mHopN4P/Q0Op3ME4E+PN+1mcn5D0IjORGrfy4XqzVkFpRwcNYTXErO4t0dEeSXqlFKEi92a0bfFg3uKE/wz0I42LWMpJBo9+5Ywp9aSlFSJr13LiIxLILcK+UPPt+nQyjNKeCPztOoPziYNvOe5sjEVfiOehSA0EdnY+FizyPfz2R3v7dAlivJ6LJ4DDueWUZBUiZDdywkNuwU2VcTy8o0HRlCSU4BP3Sdht+gYILnjmTP5NVkXYrn1/5vIWt1WNdxZHjYEm7sjkDW6ji68HvSz8ViZmPJ0J2LiP/rLFxKuC+d6g/siNLcjNBHZ6O0Muex/e9z47fDyBotjV7oy67uM9EWq+n0v1doMLgTRT/sMe1QhYJGy14gasQiShIzaRe6lPTQkxReKXdwShLSufTqp9SfNMikavah85zsOQMAlaMtHY+uIjM86v5uMDCkf2+eGTaIuYvu3hkCkMyscPR256tHpuEe6MejS8byw+AFlcr1XDKO3bM+JznyGkO+mYF3SGtiw88Q9PJAbh66wIk12+gweSAdJg/k4FK9Q5tw4jK/j/vIpB1Zq+Ovxd+TczYWczsrxkWtZcfYD0k8cpHh2xcSu/sUWUY202xkCCXZBWzsNg3/QcF0mjuSsMmrKc7M44/nP6IwJRvnJl4M+G4m33aYCkDk/3aQeOQiCjMlg36YS4OQ1uT8eQZJIdHh3THsHbmMwqRM+v2xkPjQU+QayfN7OoTS7AK2dplGw8HBBM4bycGJq4n97TCxvx0GwLGpF498+TpZ5+PK6h16eQ2ZZ4wcEcNTzqdHG5y83fnykWl4BPrRa8lYvq+if3sZ+jcp8hpDK/Rv3KELHF+zjaDJAwmaPJADS3+k48uDSbtwg63jP8HZz4MBi8Zw6Ml3yxtUSLRZOo5DI5ZSlJRByK7FJIdFkGc0PooS0ol4dR3+kwdUup6ra7ajsrLA+7lHK52rFoUCv6Uvcm7EQkqSMgnYtYzMsMrj4vKrn+I12XRc2LVvgn2HpkT0mAZAm62LcOjcgpzD503KSQqJ7ovH8Pszy8hPymTE9oXEVLCZ5gab+a7bNBoNCqbz3JGETl6NpkTNsQ8349zEC5cmXpUuf/fUNaQa3UNbg7yOS8YQ9rTeZgb8sZC4sFPkGMlrZHjO/Np1Gj6Dgmn35kj2T1oNQN6NFLb2ebOSrPCJq1DnFwEQsn4qXZdP4KchC8hPyuSp7Qu5XkGnFiNDKM4uYINBpy5zR7Jr8mqcGnnSeFAwG3vOwrauE0M2zWbDI9PJvp7ED/3eLOuzcSdWcX3XSQDqdWqGb592vPfYTDSlGmxd7Mv7LiQANx93FoW8indgI0YseYHlQ+ZVuv7zf0Zw4JtQ3gpfYXL81O+HOLRR/7xs2asdT7z1HGvHLDUp0yQkABcfdz4MeYP6gf4MWfI8a4bMryRjyOLn+XXO59yMjGbs1zNpHNKGK+FRPDb7af5c8StXwqNoEhLAY3Oe5rORi7G0t2bwonF8NeY9chIzsHGxp3/FRhUK6i+ewNVn3kadlEGT7R+Ss/s4xVdvlhUpPBdD2uNvIBeX4jq6H/XeHEvM5A9Qp2ZxechM5FINCmtLmu1ZSc7u46hTMitde7k8iebLnufEiCUUJ2bQKfRdUkNPUWA0DosTMjj76lp8JlUehwCNZo8g8+il6mUYodXpWLozgnXPdqeuvRWjPt9D9yae+Lk5lJWZ0Tew7O9Nx69yKTkLACszFYuGBNHQxY7UvCKe+Ww3nfzcsbc0vyvZtYos9sGujv98iogkSRaSJO2RJOm0JElPSZI09y7q5N/hvLckSc/8netxDvQjLzaFgrg0dGotcb8fpV7fdiZlPPu1I/anvwCI336cut1aAGDfuB4phy4AUJKRizqnAOc2PpVk1AnwIzc2hTyDjOjfj+Ldx1SGd5+2XPn5AADXdxzHs6tehqa4FFmrz6lSWpiVjZ3C1GzSDdE+dUEx2VcTsXF3vm+dkGWU1hZISgVKS3N0pRo0hslOoVSitDRHUipQWVlQlJJVSVf7tv4UxSRTfCMVWa0hdcshXPu1NylTfDONggtxt90Q321gMJl7I9FVESG/V9oHtMLB3u6e60nmNlz85SAAyZHXsLC3waaOo0kZmzqOmNtakRypjxJd/OUgfn31+vr2bseFzfp7emHzAfz6mPZDRQpSs8siuE6N6lGSUwAyepvZehSfCjbj06ctlwztX9txnHpd9Pcw/fwNClOyAci8HI/K0hyFuQpNcSmJhjcsOrWW9LOx2HjobcbFYDP5Bpu58ftR6lewGa++bblusNG47cepa7BRYxoO6cyN349WOl4Vfn3accHQv0m36V8LWyuSDP174ZeD+Bv61693O84b9D+/+QD+hv51aVSPuMP6cZl5LQnr+m5YuJY7Sk6B/uTHpFAYl4qs1hK/5QjuFXQtvJlO7sWbVeYzph88j6ag6K50vIVdoD/FMckUx+nHRdqWQzj37WBSpuRmGoUXb1SWKcsoLMxQmKtQWKiQzFSUpmVXklE3wI+c2BRyDffw6taj+FawGV8jm4necRwvg81oikpIOnEFbYn6rnVyrWAzMb8fpUGFfmzQpy3RBpuJ3XEcjypspiK3nGtJpcTKzYGitOwyna5UoZNPNTr59mnHla1H0ZVqyL2ZRnZsCnUD/EzqenVtQc6NVPISMgBoNboXp9ZsQ1OqASA/o/xtS6s+HTj+q/6ZGRt5FSs7G+zdTO311rncKu5PcX65zZhbWyBX4Qg169OOyF/1utyMjMbSzhq7CjLs3ByxsLPiZmQ0AJG/HqC5wfZlwMLWCgBLeytyDc/ogEGdOb/rBDmJej0LjPS6hU1AI0pikymNS0FWa8jaegCHPkEmZfKPnEU2vMkoiLiMmbuLXq5ag2zoM8ncDElxZ1fGsa0/hTHJFN3Qj8PkLYepW2GuKLqZRn41c4V9ax8s3BzICD9zR1kA5xIyqe9ki5eTLWZKJX1bNCD8cmK15Xeei6OfIUrd0MWOhi76OaSOnRXONhZkFZTclVzBP4f/vIMNBALIshwgy/KPwB0d7LvAG/hbDraVuzNFhocrQGFSJlbuTiZlrN2dKEzUfxOXtTrUuYWYO9uSfeEG9fq0RVIqsKnvhlNrH6zruVSSYe3hRH5S+Tf5guRMbDxMZdi4l5eRtTpKcwuxdLIFoE6gH0/+uYwn9yzlwJyvyhzuW9h6ueLSsiGpBifkfnS6uf042sISBkV9ysCTK7i0bgel2QUUJWdxad0OBpxcyaCoT1HnFZKy/2wlXS3cnSlJLJddkpiJhXvlPrkTdYZ0IfW3g/dcryaRlEryksp1yU/OxLZCP9q6O5GfnFllGWtXewpS9RNtQWo21kZOnkdbf57dtYQh38zApXG9SrJdWzREZWVBiuGe5idlYuNehc0Y3cPSvHKbuYVv/w6knY1FZ5j8bmFub03DXoEkHNJHQa2M7AEMNuNR2WYKKtiMhbOpvIaDOhK75YjJsU4fj+ex3Uto+doQk+O27k4m/ZtXTf/mGfVv3l30b9rFOBoZJmr3Nr5Yebli6Vlug1YeThQZ2WhxUiZWhi8aDwoLD2dKEtPLPpcmZWBxlzLzTl0h5/B5OkZ9Rseoz8jad5qiq5VTC2zcncgzuofV2UzeHWymKnp+NJ6ndi2h/avl99DYHgAKkjKxruI5Y2wzpbmFWBjk2TZwY2DoYvptfpM6QU1M6vXeOJORUWtAhvSo8sh5flI1NlKFTrZG46O6/mg8qBNXfy+3V0dfdzyDmvDGlsVM/fFtGrQud8gd6jqRbWQ32ckZOLjfm910G92H+ftXMHj2KH5Z8HWl83oZ5deck5yJfYVrtnd3ItdoPslJysShrr7M9ne+pf+cZ5h1eBWPzR1F6Pv6N2auvh5YOdjw0g/zmLJtCYFDu1WSbebuQqmRjaqTMsoc6KpwGdmb3PBT5fU9XGkWtoJWx78gee2vt49eo58rTMZhYiYWd9ufkkTTBaO5tOC7uysPpOYV4e5gXfa5rr0VqXlVf1FOzC4gMbuAIJ86lc6dTchArdVR3/nO4+ahoNM9/H//UP6VDrYkSTaSJO2QJClKkqRzhsh0P0mSLkmSFCFJ0kpJkrZLklQH+A7oYIhg/wxYGf7eeAcxSHo+MMg4K0nSU4ZTy4BuhnZer6LeeEmSTkqSdHJPYXSN6R2zaT+FSZn03rWYwIWjST95tZLzWxOkRl7j556z+fXx+QROGYjSwqzsnMragj7rX+XIgu/KIj/3g0ugH7JOx9aAKWwPep0mE/pj08ANMwdr6vVtx46Or7E1YApKawsaDuty3/KqwryOIzZNG5C57/7TQ/6JpJ6L5YtOr/Fdvzc5/XUYAz8zNVmVtQUBE/qTEhF9X/fUqXE9Os0dyf45pvm6klJB79Uvc/arUHLj0v52+xVxCfRDW1RKzuXytIdDU9awo+ccdg9ZRJ2OTfAZ3rXG5FXH8TXbsLC3YfTOJQSO60POuVh4AOOytrD0dse6UT2OBU7gWMAEHLu2xL5jsztXrCHCpq5hU+85/DpsEZ5BTWgy7P7vYWFqNpuDXmNb33mceGcj3T+djJkh8gqwe9T7/NR2CpJKgXVdp9u09PdRmCnx6d2WqzvK83oVKgUWjrYsHzKPLe9+x7hPX6tRmQc2hLGw+6tsXfY9fV4ZWqNtAwQ/24vtizbwXudX2LFoA8PeGw/o3z7Wa+XD1+M+4MvnlvHoK09g4eP5t+U4P9Edm9b+pKz7reyYOimdi31e5Xy3ibgM74HK1eE2LdwfDcb1Ie3PSEqSbu/E/11Cz8fRq5kXygqR+LS8IuZtOc47g4JQSNIDkS14cPxbc7D7AYmyLD8OIEmSA3AOeBSIBn4EkGU5VZKkF4HpsiwPMJTNl2U54C7lDAUCgDaAK3BCkqS/gNnGbVZEluX1wHqAHz1GmbxrKkrOxMoo6mzt4UxRsmnqQ2FyFtaezhQlZSIpFZjZW1Oaqc9aOf12+TfonlvfJu96ciX5hUlZ2BpFq2zcnSlIMpVRkKwvU2CQYW5vTXGWaWZMdnQi6oJinJp4kX4mBoVKSZ/1r3L1t8PE7DxZIzo1mN6ZpH1nkDVaSjJyST9xBec2vsiyTEFcGiUZeQAk/HEC1/aNiP95n0m7JcmZWBhFCy08nSlJzuBecBvcmfSdx5E12nuqVxMoLO1RWOpfBcqaEuw8ynWxdXcmv0I/5idnYWsUdTEuU5iei00dRwpSs7Gp40hhuv61bKmR0xy7LwrF4rFYOtlSnJWPQqWk3/pXufHnaRz9PMrb9XCmILkKm/F0piDZYDN25TZj4+7MY5+9xp+vrSP3RqpJvZD3XiAnJpkzRotiiwz2cAtrD2eKkirbjE0FmynJLLfRhoODK0Wvb9mdpqCY2N8O4/d0d/wnPAZA8pnrJv1rV03/2hn1r91d9m/o9PVldSYd+pgCoz4oSsrCyshGLT30Oj1ISpIysfB0Lfts7uFy186BS/8gck9dRVdYDEDW3kjs2zcm95jpguqC5CzsjO5hdTZjV43NVMetNtQFxVzZcpi6Ab4k/HywzB5uYePhTGEVzxkbT2cKjZ5rJQZ5JaX6/zPOxpIXm4q9rzsZRnne2hI1iX+dw39EebTV1qMaG6lCp3zD+KiuPxr2aEPauViK0svTJfKTsrhmWFQdF3UNC2tLZod+gE6rIy7qGo5GduPo7kJO8t+zm4hthxmx+EU2oo9qd3m6JwDxUddx9HTmhqGcg7szuRX0zU3Owt5oPnHwcCbHkArSdtgjZQsez+44xtBlLwGQk5xBYXYe6qIS1EUlxBy/iEtzb0piylMk1MkZmBvZqJmHC+oqnt12Xdvg/sqTXHnyzbK0EGPUKZkUXY7DNqgF2X8crrYPSpIzTcehpzMld9mfju0b4dSxKQ3G9kFpY4HCXIW2sJgrizdVW6eOnRXJOYVln1Nyi6hjZ1Vl2V3nbzLnsbYmx/JL1Lyy6QBTerSktde9v5UVPHz+lRFs4CzQW5Kk9yRJ6gb4ADGyLF+V9Ylmd/8e5/Z0BTbJsqyVZTkF2A90uEOd25J5+jp2Pu7Y1HdDYaakweBgEkJPmZRJDI3Ae8QjAHgNCCLloP61utLKHKWVBQB1H2mJTqszWUh4i9So6zj4uGNnkOE/OJgbuyNMytzYHUHjJ/UTie/jQSQacrvt6rshKfVmYVvPBUc/T/INu090//BFsqMTOfvZzhrTqTAhnbpdmhv0s8ClXSNyoxMpTMjApZ0/Siv9oo46XVuYLIC7RV5kNFa+Hlg2qINkpqLOkC6kh56sVO521H3i4aWH6Ipz0WQnoMlOQFdSQDNDtM490I/SvMKylIRbFKRmU5pfhHug/lVys2FduRam7+vruyNoPlx/T5sP78b13frj1kaLauq28UVSSGVOTu8PXiTraiKHFm7EwdvIZgYFE1PBZmJ3R9DU0L7f40EkGGzG3N6ax7+ZxtGlP5J88qpJnaAZwzG3s+JghVerGRVspuHgYOLDTOUlhEXga7DRBgOCSDl4ofykJNFwYEduGL1ul5SKshQSSaWkXq9Abmw5wobH3mTDY28SHXqK5ob+9Qj0o6Sa/i3JL8LD0L/Njfr32u4IWhj0bzG8G9cM/Wthb43CTAlAq6dDSD96qWwdAUD26WvY+rpj3cANyUyJ15BOJIeZjo+aJu90NJa+HlgYxoXbkC5kht3d7jglCek4dGoOSgWSSolDpxYmiyNvkRJ13cRmGlVhMzFGNuP/eBDxhy5UascYSakoSyFRqJR49wwkw/CGIv30dex93LE1yPMZHMzNCjZzMywCf4PNeD8eRJJBnoWzHZJCHwG0beCGnU9d8uJSUVlbYGXIw5eUChz8PVBZWWBvkNH4HnSK2R1B40HBKMxV2Nd3w9HbnZTT18rqNR7ciSu/m34hvB56Eq/O+uefm48H6pJSlvWdwfv9Z3Em7ARBQ/XPTO/ARhTnFVaZa10dbt7uZX+3eDSQtFj9DioHNoSxqv9cVvWfy4Wwk2XpG/UD/SnOKyKvgoy8tGxK8oqoH+gPQODQblw02G9uahY+wfq3G36dW5ARmwLAhbBTNGzfBIVSgZmlOfUD/CmONrWhgqirWHh7YF5fb6NOg7qRs/u4SRmrFj40WDaJa88vQZORU3bczN0FybDgT+lgg22HZhRfrzwXGpMTeQ1rX3esDOPQfUhnUkPvbhyembya/e2msL/DK1x+ZyMJPx24rXMN0KKeM3GZ+SRk5aPWagk9H0f3xpWj+DHpueQWldLGyIlWa7W88eMhBrT2pnfz+nd1jQ8Nnfzw//1Dkapa+PBvQJIkZ6A/8BLwJ9BLluVHDOcGAeNlta34ewAAIABJREFUWR4gSVIIlSPYt01mulVGkqSPgbOyLH9pOL4B+BnI5TYRbGMqRrABPB5tU76l3Q/7ubjid1rOGEZmVAyJYREoLMwIXjUJx5YNKc0u4MjEVRTEpWHt5Ur3TbNAlilMyuLEtM8ojE83aTtbqZ9E6j/ahs4LnkVSKLj8434iV22l/fRhpEXFcGN3BEoLM3qsmIhrS29KsvPZM3k1eXFpNBrWhYDJA9FptMg6mYhPfiM29BTuHRoz+Lf5ZFyMQzYY9PH3fiJv9+n70kllbUHQJxOwb1wPJImYH/Zzee0OAFpMH0aDwcHIGi1Z525wYtpnuBUXUhHnnoH4Lxqr36Zv0z7iPvkV75lPkRd1jYzQk9gF+NHyqxmoHG3QFaspTc3mRPc3ALCs70bgtsUcCZxY5WroLuffu9MtrsSMt5dxIvIM2dm5uDg7MvmF0Qwb2Peu6p7fcQ3vkNZoikoJm76eFEOEbdTOJWx8TL8TQd3WPvT5aDwqS3Ni90Wxb74+gmTpaMvja1/BztOFvIR0tk9aRUlOAW3G9KbN6J7oNFo0xWr2L9pI0qmreHZozFO/lN9TM1tLlBZmaEvUXPpxP6dWbaXDtGGknYkh1mAzPT+ZiFtLb4qz89n98mpy49JoN3UwbV8eSE5MSpke20a9h8JMxZgTK8m6moDWEHk6+/VuEjeGA+D5aBvavfMsklLBtR/2c37lVlrPGEZGVAwJBpvpvHIizgYbPTRpNfmGFJM6nZoROPcpQgcuKJOptLKg92/zUKiU+q3pDpwnYsF3JCnK72vPRWPwDmmNuqiUUKP+Hb1zCRuM+refoX9j9kWx16h/B6x9BXtPF3IN/VucU4BHW3/6LZ8AMmRciefKa+tR5xSY3Ne6PQNoZRgfNzaFc2XF7zSdOZzs09dJDovAMcCXjl++jpnBRovTctjbfSYAXbfMx66RJyprS0qz8oh84zNSjRZauVD1wlynnoH4LtRv05eyaS83V/xKw5lPkXf6GplhJ7EN8KP5lzPLx0VaNhHdXweFAv9lL+IQ3ByQydx7mpgF35i0HWVmCeijst0W6O/hBYPNBE0bRqqRzfT+pPw5E2qwGYDnDn+MuZ0VCjMVpbmF/D5qGXnxGQzdPA+FmRJJoSD+4HkOLvwOG43+HtZ7tA1B7+ifa9E/7ufMyq0ETNfbzE2DvG4rJ+LcQi9v/2S9zTTs34GA6cOQDc+1yI9+IX53JJau9vT6ZjoKcxWSQiL58EUS9p+h3duj9NtV/rifk6u20tGgU4yRTm4GnXYZ6dT+lUE0f6o7Oo2OAws2cMNwn1RWFow99gnfdnmDUqM8XIWZkp4fjse+RQO0ag1blmzg6pHy3VqeXPg8zbq3obSolI0z1nLz7HUAZv7xHu/3nwXAoNmjaD+4C/Z1nchNyeLIj3vZ+clmhr49hiZdWqHVaCnKKeDn+V+SfFXv5NqgLJMxaOFYGndvg7qohM0z/kfCWf2YeOWPd1nVX79kqV4rH4Z/OBEzS3OuhEex9e2v9fe/fRMGvv0cCpUCTYmaLfO+IvGcvn638QNo9+QjyDqZkz/uw/Mz0y34AOx7tMNrwQtISgUZP/5J8qqf8Zj2DIVnosnZfRz/7xdi1bQh6lR9pLk0MZ3rzy/BrlsbvN56HlmWkSSJ1K93kPF9mEnbqaWVo8WuPQNotmgMklJB/KZ9XP9kC/4znyQn6jppoaewD/Cl7VfTysZESWo2h7rPMGmj3lPdsW/jW2mbvu7LG1WSd+BqEh+ERqKTZQYH+PBSt+as2XeO5p5OhDTRr4VZG36OUo2OV3u1Lqu348wN3t56HF+j4MjCwR1oWiE/3mrUooeeN1L44YsP3Ym0nv75Q++HqvhXOtiSJHkCmbIsF0uSNACYAjQHesiyfE2SpE2AXTUOdhZQR5blapevGznYQ4EJ6B15Z+Ak0BGoByyXZbn7na61Kgf7QXLLwa4tHLW1az91dfe/y8e98Hcc7PthddvKW2Q9aCxq+RHgUMuZOMm1nAjnV1q7+dfVOdgPklsOdm1hW8sp7Xm1/G73qvLud1OpCYwd7NriSW3BnQvVIFU52A+SqhzsB80/wsH+4PmH7kRaz/jyofdDVfxbc7BbAR9IkqQD1MAk9DnSOyRJKgQOANXtk7YeOCNJUoQsy6PuIOc3oBMQhX5HopmyLCdLkpQBaCVJigK+lmX54/tXSSAQCAQCgUDwX+Bf6WDLshwKhFZxqinArai1oWw4EG5UdxYw6w7t2xr+l4EZhn/G59XoF1QKBAKBQCAQCAQm/CsdbIFAIBAIBALBQ+YfvMjwYfOfdLArRq2rQpIkF/SLIyvSU5ble9vnTSAQCAQCgUAgMPCfdLDvBoMTfbf7YQsEAoFAIBAIjJD/wb+keAtJkvoBKwAl8Lksy8uqKDMCWIB+vV2ULMt/69e6jfl/62ALBAKBQCAQCP67SJKkBD4FegPx6H8wcKssyxeMyjQC5gBdZFnOMvwK+H3zb/2hGYFAIBAIBAKB4HYEAdGyLF+XZbkU+AEYXKHMS8Cnsixngf5XwGtCsIhgCwQCgUAgEAjunX/+Isd6wE2jz/Hof8/EmMYAkiQdQp9GskCW5V33K1g42AKBQCAQCASCfyWSJI0HxhsdWi/L8vp7aEIFNAJCAC/gL0mSWsmynH0/1yUcbIFAIBAIBALBvSM//EWOBme6Ooc6Aahv9NnLcMyYeOCY4TdOYiRJuoLe4T5xP9clcrAFAoFAIBAIBP9FTgCNJEnykSTJHBgJbK1QZgv66DWSJLmiTxm5fr+ChYMtEAgEAoFAIPjPIcuyBpiC/te/LwI/ybJ8XpKkhZIkDTIUCwUyJEm6AOwDZtTE76FI+l8DFzwozvkOqNUOziy0qk1xBA7Jq1V5eWdKalXej/H1alXelIiFtSoP4IvA+bUqT6rlR87visxalfd9t+Jalad0s6lVeQDP/lq7N3FD39JalffNrhrZpeuuyVDUbn821Ei1Kg/AVautVXmNHe8rffae2V7oUqvyAKbFfVf7N7ICBQtHPXQn0mb+xofeD1UhItgCgUAgEAgEAkENIhxsgUAgEAgEAoGgBhG7iAgEAoFAIBAI7p1/wU+lPyxEBFsgEAgEAoFAIKhBRARbIBAIBAKBQHDv/PN/yfGhISLYAoFAIBAIBAJBDSIcbIFAIBAIBAKBoAYRKSICgUAgEAgEgnvnH/BT6f9URARbIBAIBAKBQCCoQUQEWyAQCAQCgUBw74hFjtUiItgCgUAgEAgEAkENIhxsgUAgEAgEAoGgBhEpIg8B20fa4jF/PCgUZP0URvq6zSbnXV4YgtOIPqDVosnMJWHmJ6gT0wAw83Sj3tJXUHm4gSxz4/kFqBNSbyvPqUcAfovGISkVJG/8k5urt5icdwhuhu/Csdg2b8jFiZ+Qvv1o2Tmft57FuVdbJEki668zXJv31T3pqmzeDssRk0ChQH1oF6WhP5mcV3XqjcXQF5CzMwBQh29DfWjXPcmw6NgBh9emgFJJ4bYd5G/YZHLeeshAbIYNAa0OXVEROe99hCb2Bkr3utTZ9A2aGzcBKD1/gZwPPjapG/LOaHx6BKAuKiFs2npSz8VWkl+nlTd9P5qAytKcmH2nCX97g/66HGx4fM0U7L3cyI1PY8fkVZTkFOIV3IxBn79Ozk39PY3edYJjK7Zg6+GM0t4DSaEEQFeci6449476z3t3OX8dOo6zkyNbvlt3T31nTP2Q1nRZMBpJqeDipnBOr9lmcl5hruLRTybi1sqH4qw89kxeTV58Ol7dWtJx9lMozFXoSjUcWbKJxMMXTOr2+/IN7Bu48VOvOSbyOr+jl3dpUzinP61anmtrg7xJq8mPT6det5Z0nFMu7+jicnl+AzsSOHUwkkJB3J+RHHv3x2r1nfDOBDr06EBJUQnLpy3n2rlrJuctLC2Ys3YOHg090Ol0HNtzjK+XfV12vtuAbox6fRSyLBNzIYb3p75frSxV6w5YjZ4CCgWl4X9Qsm1TleXMOnTD5rV3yJs3EW3MFX0/1PfF+oXXkaxsQNaR99YkUKurlQWgbNoWy6EvgaRAfXQ3pX+aPmNUQT2xGDQOOccw7g7sQH00DEU9HyyfnAwW1iBrKd39E5rIg7eVZcxL74ynXY/2lBSVsGLaJ1yv0KfmlhbMWjsb94bu6HQ6Tuw5zrfLvgHg0eE9Gfvm82Qk66/pj2+2s/uHsOp1bNEey5GTkBQKSg/sonRX1fda1bYr1pPmk7/4ZXQ3roJSheXoV1E2bAyyjuIf1qK9csak3Wdnv4KkVHBhUzinqhgHfYzGwS7DOABo9/JAmo8MQdbq+Ovtb4nbf1avt701Pd9/EZcmXsiyzJ/TPyM5IpqO04fj26ctGp1MQUYuW6ato+vkQTTq0QZ1USlbpv+PpCqeOR4tvRny0UTMLM24ui+KnQu+BSDktaG0fboHhRl5APz5wY9c3ReF0kzJgHdfwLO1L7JOR+T870g+crFSu/VCWtNx4WgkhYIrm8I5W8WYfGTFRFxa+VCSlUe4YUzaernyRPj75FxPAiAtIpojs+88V7j1aEPLRc8hKRXEbdxH9OqtJuedg5vScuFz2DVvQMTElSRtPw6AlZcrHb58AxQSCjMVMV+EcuPbPXeUZ921HXXfnAgKBTmbd5H52c8m553GPoHD8H6GuTeH5Dc/RpOon1/dpj+PTfcgUEgUHo4kdUn1z9oehnlDU1TCrtvMG/2M5o19hnmj8eNBdHp9KC7+nmwc9DYpZ2IAsHS0ZeC6qbi38eX8z3+xd/63d9S3NpDFLzlWy3/WwZYkaSowCbAHfpNlecpDviQ9CgWe70wi5rl5aJIz8N3yMXl7jlESfbOsSPH5a1wb/DpycQnOox7DffY4bhomb68P3yB1zY8UHDyNwtoS+U75TwoF/ktf4OyIRZQkZRK4aykZYScpvBJfLi8hnSuvforX5EEmVe3bN8a+QxNO9ZgOQMDWRTh0bk5OBeepWiQFlk+/TOGKuchZ6VjPWYnmzFF0SXEmxTSn/qLkhzV312YV+jlMf5WMV2egTU3D7Yt1FB84jCb2RlmRorA/KdyinygsunbGfupkMt+YpZedkEja2JeqbNqiU0ccvd356pFpuAf68eiSsfwweEGlcj2XjGP3rM9JjrzGkG9m4B3SmtjwMwS9PJCbhy5wYs02OkweSIfJAzm4VO8EJJy4zO/jPjJpR9bq0BZkgLYUJAmVYz106iLQ3t6ZGtK/N88MG8TcRR/edbdVRFJIdF08hu3PLKMgKZOh2xdyY/cpsq4mlpVpNjKEkuwCNnWbht+gYDrOHcmeyaspysxj5/MfUZiSjVMTLwZ8N5MNHaaW1fPp1x51QXEleV0Wj2HHLXk7FhIbdopsI3lNR4ZQklPAD1318oIN8ooz89g1rlze4xtn8l37qVg42tJx3tP8+thbFGfmEfLxBOp1aQFHDlTSt32P9tTzrseLj7xIk8AmTFkyhdcHv16p3K/rf+XMkTOozFS8u+ld2oe052T4STy9PRkxeQTTh04nPycfBxeH23UuVmNfpWDpDHSZadgtWos64jC6hBum5SytsOg3DE200fhSKLCZPIeCtUvRxV1HsrUHjbZ6WQZ5lsMnUrj2LeTsDKzfWI7m3DF0KTdNimkiD1Dyy/9MjsmlJRR9txw5PQnJ3hnraR+juRQJRQW3lwm069EeD29PJj4ynsaBTZi0ZDIzBk+rVG7L+l85e+QsKjMVCzctoW1IOyLCTwFwcNsB1s+/iy+JkgKrZ6ZQ8PFs5Kx0bN5chSbqSKVnCxZWmPd8As31cmfSrNtjABS8MwHJzhHrV5dQsGQKyHJZu5ufeJ/8pEye2r6Q6xXGQYuRIRRnF7Ch2zQaDQqmy9yR7Jq8GqdGnjQeFMzGnrOwrevEkE2z2fDIdGSdzCMLRnMj/Aw7J65EYaZEZWUBQMS6HRz7cDMZCpmOY/sy6L2XQCGxsvs0vAL9eXzxOD4f8nYl9QcseZ5tsz8nPjKaUd/MxD+kDdHhUQAc/WInh9f/YVK+7dOPArC272xsXOwZ9/VMtvWfr9e5rEslgpeMIfTpZRQmZTLwj4XEhZ0ix0j3xk/rx+QvXafhMyiY9m+OJHzSagDybqSwtc+bd753t1BItFo6jqMj3qUoKYNuu5aQHHaK/CsJZUWKEtKJfHUdfpMfN6lanJLFwQHz0ZVqUFpbELL/A5JDT1GSknUbeQrqzn+Z+Ofnok5Jp+HPK8jfe4zSa+U2U3zxGtnDpyIXl+A48nHcpj9P0hvLsAxshlXb5sQOngxAg+8/xCqoFUXHz1YS49OjDU7e7nz5yDQ8Av3otWQs31cxb/QyzBtJkdcYajRvpF+OZ+v4FfRe+rxJeU2JmsMfbcaliReujb3upocFD5n/corIZKA3cA8jvnokSaqRLyNWbRpTciMJ9c0UZLWGnO1/Ydc72KRMwdGzyMUlABRGXkbl7gqAhX99UCkoOHgaAF1hcVm56rAL9KcoJpniuFRktYa0LYdw6dvepEzJzTQKLsZVctZlGRQW5ijMVSgsVEhmSkrTcu5aV4V3E3SpScjpyaDVoDmxH1XrTndd/24wa94UTXwi2sQk0Ggo2rMXy25dTPUoLCy/JitLk0nldlh268LFX/TRu+TIa1jY22BTx9GkjE0dR8xtrUiO1EfqLv5yED9D//r2bseFzXrn7sLmA/j1Me33ihSkZuudawBZRtaokRR3Nrv2Aa1wsLe7K52qo06AH7mxKeTFpaFTa7m29SjefdqZlPHu05YrBn2u7ziud16BjPM3KEzJBiDrcjxKS73NAKisLWj90mNErDR9a1JRXvTv1cj7uVyeZ9fby7NvWIfcmBSKM/XRu4SD5/Dp36FKfYP7BPPnL38CcDnyMjb2NjjVcTIpU1Jcwpkj+simRq3h2rlruHi4ANDvmX5s/3Y7+Tn5AORkVD8ulH5N0aUkoEtLAq2G0qN7MWvXuVI5q+HPU7xtE5SWlh1TteqANu46urjrAMj5uXfcFkvRsBG69CTkjBT9uIv8C1Wrjretcws5LRE5XR+FlHMzkfNzkGzs76puUJ+O7PtlLwBXqunT0uISzh7ROyUatYbr567h4uF6V+0bo/Rpgi4tsezZoj6xH1VA5T61GDJGH9lWl/ep0rMh2kv6Z6icl41cmI+iYWOTdnMNdnll61F8K9ilT5+2XDKMg+gdx/EyjAPfPu24svUoulINuTfTyI5NoW6AH+Z2Vnh2bMKFH8IB0Km1lObqn0nq/KKyds2sLXDwciXqF33b8ZHRWNpbY1vhmWNbxxELWyviI6MBiPrlAE0rXGNF3BrVI8YQGCnIyKU0txDXNj4mZVwD/ciLTSHfoPv134/SoK9puw36tCXaMCZjdxzHwzAm/w5Ogf4UxCRTGJeKrNaSuOUI7hXmpqKb6eRdjKu0kE5Wa9GVagBQWJghSdId5Vm2bow6LhF1fDKoNeT9sR/bnqZzb9GxM2VzalHUJcwMcy+yjGRhjmSmQjI3Q1Ip0aZnVynHr087LhjmjaTbzBsWtlYkGeaNC78cxN+ge2Z0IlmGNwHGaIpKSDhxBW3x7QMutY5Ofvj//qH8Jx1sSZLWAb7ATsDJ6Li3JEl7JUk6I0nSn5IkNbjD8a8lSVonSdIx4H1JkrpLknTa8C9SkqR79mrM3F1QJ6WVfdYkpWNW16Xa8k4j+pC/Xx/dMfephza3gPpr5+K3bQV1Z48Dxe1voYWHMyWJGWWfS5IyMfeoXp4xeaeukH34HMFR6wmO+oysfVEUXU24c0UDCicXdFnluuqy05GcKstWBXbFet5aLMe/ieR0b5Ot0s0VbUp5iow2LQ2lW+U2rIcOoc7P32E/eQI5H68qr+/hjtvX63H59BPM27Sq1HZeUnnf5SdnYutu6jDYujuRn5xZZRlrV3u904zeebZ2LXdUPNr68+yuJQz5ZgYujetVVkyhQlJZIGuKK597ANi4O5GfaKRHUiY2FXQ1LiNrdZTmFWLpZGtSxrd/B9LPxpZNfkEzhhP12U40RaUm5aw9nMhPKpdXkJyJjUcV8pKM5OVWlufzeLm8nNhkHPw8sPVyRVIq8O7bDltP5yr1dXV3Jc1oHKYnp+PqXr3t2djbENQriKhD+ihhPZ961POtx4e/fsjyLctp1716B0fh7Iouo9xGdZnpKJzcTMoovRshubihOX3M9LiHPlJlM+s9bBf/D4sBT1Urp0yegwu6rPRyedkZSA5VjLvWnbGeuRLLsbORHCvrrmjQCFQq5IzkO8oEcHF3IT2pXG56cgYu7tU/a2zsbejQK4gzh06XHevUvzMrQlcxa90cXG/jeEuOrugyy++fnJWGwtFUlqKBPwonNzRnj5sc1968jqpNJ1AokFzdUTZshMLZrcp285OqHvN5VYwD22rGkH19N4oz8+i1fDwjdy7m0fdfLItgAwTPfJLXj6yk9ZDO5CVnkmv0vM5NzsS+rql8+7pO5Bo9c3KTMrF3L7fzoOf6MGnXUgZ/8BKW9tYApFy4QZPebVEoFTjWd8OllTc2nqb9Ze3uRIHR9RdW8QwwLnNrTFoYxqRtAzcGhS7msc1vUjeoCXfC0sOJIiNdi5MysKzwDLhtfU9nuu99j96nVhP96dbbR68BVV1X07k3OR3VbeZeh+F9yP/rpP7aTl+i8NgZ/A5sxO/ARgoORlB6/WaV9WzdnUzmjbxq5o08o3tYVRnBv5//pIMty/JEIBHoARiPulXAN7IstwY2AivvcBzAC+gsy/IbwHTgZVmWA4BuQBEPEIfBIVi18if9s18AkFRKbDq0IPndL7g25HXMG7jjNLznA5Nv6e2OdSMvjgZO5GjABBy7tsS+Y9MalaE5c5SCN8dQuHgS2ouRWI6ZXqPt36Lw1y2kPvksuWvWYzd2NADajExSnhhJ2tjx5K5cg9OCeUjW1g9EvjGp52L5otNrfNfvTU5/HcbAzyqmJkio7OuiLUi/62j7PwGnxvXoOHckf835EgCX5g2wb1iX2F0nH5y8OSM5MFsvrzSnkINzvqLX2ikM/vUt8m6mo9Pef36gQqlg1qpZbP1qK8lxemdTqVLi6e3JrBGzeO+V95j63lRs7G3+ngBJwmrUJIo3rq1KOMrGLSn8dAn5C6di1r4rqhaB96GNHs254xQsfIHC96eivXIay2deM70keyesnn2D4u9XPBAbVCgVTFs1g+1fbSUlLgWAE3uO81Ln53m17yucPhDJq8srp+zcNZKE5YgJFP+8vtIp9aFd6LLSsZn3KZZPTURz7QI8wDxShUqJW0tvzn77Jz88Ng91YQntXh5Ydv7o+z/zcaepnNlyGEcvt9u0dGdOfLeHFY+8zrrH5pKXmk3ft0YBEPnTfnKTMhm/bTH95o8m7eRV5BoYG7coTM3m56DX2Np3Hsff2Uj3TydjZmtVY+1XRXFiJvsfncWfnV7Ha8QjmLveJk3rHrEf2APLFo3J+kI/95o18MDctz7XQkZzrfuzWAe3ward34/eC/5/8J/Nwa6GTsBQw98bgPfvcBzgZ1mWbyU9HgKWS5K0EfhVluV4qkCSpPHAeID5Lq140r5B2Tl1cgZmHuUPUZWHK+qUjEpt2HRpg9vLTxHz9GxkQzRQnZRO8YXrqG/qJ6S8sKNYBTYBdlercElSJhZGkQoLD2dKkyrLqwrX/kHknbqCrlAfRc3cG4l9+8bkHrt0V/V1WRmYGUXqFI6uyFkVZBfklf2pPrgLi6Ev3FXbt9CmpaOsW6fss9LNDW1aerXli/bsxWGGwZlQq5ENi8XUl6+gSUjEduyzWAbp0wpKL13Czijab+vuTH6yaZQkPzkLW6PokXGZwvRcbOo4UpCajU0dRwrT9QsWS41eC8fui0KxeCyWTrYUZ+nTDZT2ddEV5yOXFlJbFCRnmUR7bT2cKaig660yBcmZSEoF5nbWZdds4+5M389eY99r68i9oY/W1m3XCLfWPow6/DGSSomViz2DfnqTbU8uoTApC1uPcnk27s4UJFUhz8OZgiSDPHsjeR7O9PncVB7AjT2R3NgTCUCzUT1MFuAMeG4AfZ/uC8DVM1dxMxqHru6upCdXbTdTl00lITaB37/4vexYelI6lyMvo9VoSbmZQkJMAp7enlw9c7VSfV1mOgqXchtVOLuavNnB0hpFfR9s5+kX2EoOzthMW0zBR/PQZaahvXRGnxoCqE8fQ+ndGM35yCqvFUCXk4GZ0ZsghaNL2WLGMgqNxt2RMCwGji0/Z2GF1UtvU7JjA7obl6uVA9D/ucfpbejT6DNXTaLOru4uZQsW/4+98w6Pquga+G92N72QQsgmtITQWwg19A7SUVBRREVUIgIvCiLNRlFRQRRUFBGxASK+gtQgEKr0KkgJSUgP6W3Tdvd+f+yS7KbQDAHfb37Pk4e9986dM+fMzJm5585cSvPy+5NIiIrn95Ulm9qyM0rKtHNNKM/MHFuhXCUjpTjqDCDcvTBmWMiyd0Dl64fTtA9N16t54DhxLrplb2K8doWCn5dzY3Gd4+sfY0yKLTdfZ5/y+7xLOf0gp4I+lJOQRk5CGkmnTcsBrm49SpsJJRPsFs/0ofETPVHZqHH0cMHVwl+7aj3IKhWZzUpKt4pYu/p4FEe0c1NKNkWfXLOHJ78xBSyMBiM75v1QfG3ChreKNyTeQJeYjpNF+R3L8QE30ugs+mSBuU8WFJr+TT0XRVbUdVzraUk1b9Arj/yEdBwsdLX38SQ/4eZR6PIoSEon+2IsnsGNijdBloc+KcV67NVWR1/O2OvYsRUeIaOIGTO9eHxw7tOJ/DMXUcxjYe6+4zi0akLeifMAuD05mDEjBgOQeDbCatxwqWDccLGow/LS/Gt4gJdo3G/+JyPYlUzx7h5FUd4HngccgINCiHLDuYqifKUoSltFUdpaTq4B8s5exs7PF5ta3ggbDdUGdyP7D+vXwvZprtR1AAAgAElEQVRN61Fz/kSiX5yHwWJtZ97ZK6hcnVF7mJYaOHVqabU5sjyyT4fjUM8H+zo1EDYavIZ3JjX09iKKBXEpVOvYFNQqhEZNtY5N0V2+/SUixmuXUNXwRXh6g1qDpl139GcPW6URriVORhMYXHaT0i0o+vsimlo1UftoQaPBoU8v8g8cskqjrlWyBMOuUzD6GJMOKrdqxUts1L4+aGrXJOf7NSQ/+wLJz75A/r6DNBnRBQBtUACF2briJR83yL2eQWFOHtqgAACajOjC1VDTkp6InSdpOrIrAE1HdiVip+m8o1dJpMU7sB5CJUom185eKIYijPm3v9a9Mrh+JoJqflpcanuhslETMDSYqJ0nrdJE7TxJQ7M+9Qa1J/6gaU2nrasjA1ZP5ch760g8XjLBvPD9Lr5vO4kfO73CxkfmkhmZwKbHFpTI8y+RV39YMNdKybu28yQNH61Y3tH31pF03HpCa+9p6hu21Rxp+nQfLv4UVnxt83ebmTRgEpMGTOLPHX/Se4Tp7U+joEbkZueSfr3sAPf0tKdxcnHiq7etI6F/7viTFh1NS4pc3V2p6V+zOLpdGkPERVTamqi8tKDWYBvci6ITf5YkyMslK+RhsqY8SdaUJzGEXyB30RwMkZfRnz2GqnY9sLUDlQpNk0AMcVHlyrmBMfoKquq+CA9zvwvqhv4v64mHcC15Ha1p3r5kA6Rag8O42RQd343+jHU/Ko+t323hlQGTeWXAZA7v+JOeI0yb6RoGNSI3W1euTUdPewpHF0e+fnuF1XnL9drt+3Yg9ia+zRB1CVWNmojqJpvatOuO/oylTXXkvPooOTOfJmfm0xgi/i6eXGNrB7b2JnWbtAajsdjv3MjX1dwuGw4NJrJUu4zceZLG5n5Qf1B7Ys3tMnLnSRoODTbtB6jthZuflqTTV9ElZ5KTkIZbPR8AanVuRpp5qV01P2/Orf6D5QNncfzHXSRfiSNwhCnvWkH1KcjOI6eUz8m5nkFBTh61guoDEDiiK5fMvsVyvXbj/m25fsn04GBjb4uNeVlKvS7NMeqNVpsXAVJOR+Dqr8XZrHu9YcHEhFrrHh16kvrmPuk3qD0JZt3tPFwQKtM6aOc6Xrj6e5MdffOvW2WcvopTPS0OdbwQNmp8h3ck0ew7b4W9jwcqexuTbtWc8GjfiJzwsuuWLck/dxmbur7Y1PQGGw0uA7uTs9t6PLJrEoD3O5OJm/AOhrQSH6xPSMahXQtQq0CjxqFdCwoslohk/LSZ7wfM5vsBswnfcYKm5nHDJyiAggrGjYKcPHzM40ZTi3FD8r/D/7cI9iFgFKYo9Whg/y3OWyGECFAU5RxwTgjRDmgM3F449wYGI/FvL8dv9VyESkX6+p0UXImmxpTR5J27Qvauo2hnPofKyZ7ay2YAUBSfTPSL88BoJPG9lfj/sACEIO9cOOlrd9xSXvislTRfM9v0mb41e9BdiqXu9MfJPn2VtNDjOLcKoNk3r6Fxc8KzbxvqvvYYJ7q/SvLvh3Hr3Jy2exahAOm7T5O28w6cgNFI/rrPcZy8wPSZvkOhGBOuYTtkDIZrVzCcPYxNr2FoWgaD0YCSm03+6kW3zreUfpmLP8Xz4w9ArUK3eRv6yChcnh9L4cVLFBw4hNPIh7Fr2wb0eozZ2WTMfx8A21aBuDw/FvR6FMVIxgcfo2SXRNEKDh0ms0Uvxu5fhD6vkNBpJZOs0dsW8OMA0/7Z3XO+pd+iF9HY2xK15wxRe0zrdI99/juDvphEs8e7kx2XwuaXTGu/GwxsT+CY3hj1BvT5RWyd+BkAvu0aorJ3QdEXoHIzPRQYctNQim6+Eum1t97n2KmzZGRk0Xv4U0wYN4YRQ/rfkRkVg5EDb6xm0A/TEWoVl9btJf1yHG2njiD5bCTXdp7k4tq99FoSwhP7F1GQkcPOl01fD2j+bF+q+XnTZsrDtJnyMACbRy8kP7XiTwzekDfwx+kIlYW8aSNIPlMir+cnIYw6YJL3xwSTvGbP9sXVz5vWUx6mtVnelidN8jq/MwbPpqaH2hNL/ktmZGK5YYRju4/Rrmc7Vu5fSUFeAR9PK/k849JtS5k0YBKeWk9GTR5F9JVoPt1qWjW2efVmdqzdwYm9J2jdrTXLdy3HaDCycsFKqwisFUYjed8uxen1haBSU7h3G8a4KOxHPIs+8jL6kxVPZBVdDgXb1uMy7wtQFIrOHCmzTrs8efkbluMY8o6p3x35A2NiNLYDRmOIvoLh/FFsug1B06yDqd/psk1LQQBNqy6oA5ohnFywaW96AMn/aQnGuIojkTc4sfs4bXu2Zfn+FRTkFbB02pLiax9v+5RXBkzGU+vJY5NHEXMlhsVbTTJvfI5v8NihtO/bHoPeSE5GNp9MXVKRKJOOPy3Dccq7CKGi8OAOjPHXsBv6NIZrl9GfOVzhrcLFDccp75o2EqenkLdyYZl8h/4wHZVaxYV1e0m7HEeHqSO4fjaSyJ0nubB2L32XhDDG3A+2m/tB2uU4rmw+wlO7F2LUG9k759vijeN731hNv6UvobbRkBV9nT+mmnxJp5mP4x7gg96okBGXwvqXl9J14jAm71tMUV4hG6eVfOUlZOu7LB84C4Atc1Yx3PyJt/CwM1wx+5y+M59A27QuKAoZscn8Psu0fMqpuitPffc6iqKQnZjOvslllyMpBiOH56ym30+mPnll3V4yLscRNG0EKWciidl5kitr99L10xBGmPtkmLlPaoMbEzRtBEa9AYwKf85cRWHGzb88oxiM/DXrW4LXzESoVcSsCSPnUiyNpo8k43QkSaEnqNaqHu2+eRUbNye8+7am0WuPEtb9NZwb1KTZ20+hKApCCK5+sZnsizcPNmEwcn3eF9RaOR9UajI3hFIYHo3npDHk/3WZ3D1H8HptHCpHe3yXmOysT0gmbsI7ZO84gGNwIH6bvgAFcg8cJ3dP+f0wcvdp6vUMZNz+RRTlFbLDYtwYs20B35vHjV1zvuUh87gRuecMkeY6rN+/Lb3mPo2DhwsPr5pG8oVrbBhjeqn+/MGPsXVxQG2jubEpsilwm5/1ukfcYtP1/2eE8i9a43knCCGigLbAYKCtoigThRB1gVVAdSAZGKsoSvRNzn8LbFYU5Rdznksxres2AueBZxVFuelnPP6qN7hKDZymu7fr3koTNLyCScU9Ivvszb+aUtmsiy1nA+I9ZOLJuVUqD2Bl0JtVKk9UscvZqEq7daJK5KeuVbMx9QZqr7tc+/0PeOrXqq3E7/sX3jpRJbJ6e41bJ6pEUlVVa8+6+lt/daOyqW64xeclK5mGbuV/5eNesVl3ex8PqEymRv9Q9RVZipxpw+77JNL5o4333Q7l8T8bwVYUxc/881vzH4qiXAN6lZO2ovPPljqeVLmllEgkEolEIpH8r/E/O8GWSCQSiUQikdxD5CbHCpGbHCUSiUQikUgkkkpERrAlEolEIpFIJHdM6f8BWlKCjGBLJBKJRCKRSCSViJxgSyQSiUQikUgklYhcIiKRSCQSiUQiuXPkEpEKkRFsiUQikUgkEomkEpETbIlEIpFIJBKJpBKRS0QkEolEIpFIJHeOUf5X6RUhI9gSiUQikUgkEkklIiPYEolEIpFIJJI7R25yrBA5wb7HqETVNr4TtnZVKi/q96qV52yo2tdRdjZVKo6VQW9WrUBg3Km5VSovc8zYKpVn+Kt2lcrbtqdq+7wRUaXyAEZRtTouD61aHf31Vaufrbpq9UtVV6k4AOI1VfvCXJfpUaXynO+DTSUPNnKJiEQikUgkEolEUonICLZEIpFIJBKJ5M6RS0QqREawJRKJRCKRSCSSSkRGsCUSiUQikUgkd4yiyAh2RcgItkQikUgkEolEUonICbZEIpFIJBKJRFKJyCUiEolEIpFIJJI7R25yrBAZwZZIJBKJRCKRSCoRGcGWSCQSiUQikdw5MoJdITKCLZFIJBKJRCKRVCJygi2RSCQSiUQikVQicomIRCKRSCQSieSOUeQSkQqRE+z7gFO3NmjfeBGhVpG+LpTUL9dbXfd4bjjuj/VHMRgwpGUS//oSiuKTcQxuiXb2C8XpbANqEfefhWTvPFx8rsc7Y/Dv2YqivAJCp37F9b+iysiv0cKP/ovGo7G3JXLPacLe+h4Au2pODPp8Iq61vMiKTWbLhKUUZOoAqBXchO5vPYXaRk1eWjbrH1sAgI2rI50+ep7qQQHYV3clPzWbS6t28tdnv1vJVNlq6PJJCB4t/ClIz2bfS8vIjU0BwK1JbToufA4bZwcUo8KWQW9iLCgqvrfnqldxruPF771n3tK23j1b0nLe0wi1iqgf93B5mXU5PIMbEzh3DK5N63A0ZCnxm4/eMk+A2j1a0uXtMajUKi6sCePU52X167MkBK8W/uSnZxM6YRnZsSnU6tqc4BmPo7bVYCjU8+eCNcQduoDG3pb+yyfjWrcGisFI1B+nOPz+Oit5nd8eg1Cr+HtNGKfLkdfLQt4fFvI6zHgcla0Go1le/KELVvc+9M2ruNbx4uc+t7Znecx5dzH7Dh7Fw92N335Yfld53AybNu1xCpmEUKnI376FvPU/lZvOtnM3XOfMI2Pyi+ivXLplvndbh3Zuzjz05WRqBNbj4vp97H/jO1M5nex5eMMbxfc7+Xhw+deDXHjT1J98erSk9bwxCJWKq2vC+HtZWXnBn76ERws/CtJzOBSylNzYFIRGTYePnse9hT9CoyJq/QEuLNuEys6GPr++gcpWg0qjJnrLUf76aENxfj49WtLWLC98TRgXypHX6dOSPnggxNQH/R7uRJMJg4rTuTepzbb+c8iKSKTrl5Nx9jO10bidpzj97jqrPCtbR4AOi1/At08Q+SlZbOs1o0w99n57DPXMPm7btK9IKsfHeTf3Y6DZx0XsOc2ut0110mPWEwT0DsJQpCfj2nW2vfYVBVkmH+fVuDb93nsOO2cHbAwKfwx4A69OTQiaa+qHET+Fcakc/dp/+hLuLU36HR6/FF1sCsJGTZsPxuERWA/FaOT0G9+T/OffAHT9aTr2NdwQGjUpRy5xcuaqMuUHU3vt9I5J9sU1YZwux6f2WhJC9ZZmH/DSMnJiU/BqVY9uC8cBIAQcX/xforYfL1cGQC/zmKHPK2BbBWOGdws/HrIYM3abx4yGg9rT6ZVH8Kzvyw9D3yLpbKTVfS6+nozdtZBDH//KwRVbis/3e/tpAnoGUpRXyOZpX5JYjkxtcz+GLApBY2/D1T1nCH37O6vrHV4YSJ85o1ncajx56Tk0G96JjiFDEEJQmJvH+emryLgQjbZnS6s6vFhOHXYw12Fheg6HbtShRk27RaY2qjK30b+Xmtqojasj7Ra9QLXGtUBROPrKV2ScvnrP6q37Ry9Qt08r8lKyWH+XfltS9fyrlogIIaYIIRzv4r7GQojTQohTQoiA+1UOAFQqfN5+iejn3iK8/0tUG9IN2/q1rZLkX4ggYvgUIgZNJGvbQWrMeA4A3eGzRAyZRMSQSUQ9NRMlr4Cc/aeK73Pu0RY3Py2ruk3ljxkr6bXg2XKL0HvBWHa+/jWruk3FzU+LX4+WALR/eQgxBy/wbfdpxBy8QLsJQwCwc3Wk14Jn2TRuMd/1mcHml5YW59V+7hjiws5hLNTze++ZbO4/G7/hwVRr4Gsls8ETPSjIzOW3LlP5e8V22sweZbKlWkXXT1/i8IxVbOo1g9BHF6AU6YvvqzOgLfrc/Nu0rSDwvbEcfPIDdnZ7jVoPd8KlYU2rJHlxKRz/z3Ji/nvo9vIEhErQbf4zbHn6A9b0mk6DYcG4l9KvyageFGTk8mPXqZz5ejsdZ5n0y0/LZutzi1jXdya7X/2S3p+EFN9z6sstrOk5nZ8HzEbbriF1zPUgVIIuZnnrek2n/k3krek6lbNfb6eDWV5eWjbbnlvE+nLkAfg/1Jai27VnBQwf2Jfli+f/ozwqRKXC+eUpZL0xnfTxz2DXozfqOnXLJBMODjgMG0nRxfO3le0/qUNDQRFHPvqFQ/OtJ/pFufn8/NDs4r/s2BQith8rltfm3WcJG/0BW3tMp+6wjrg2sG6L9Z7oQWFGLps7T+XSim0EznkCgDpDOqCys2Fb7xnseGgOAWN64VSrOsaCInY/uoDtfWexre8sfHq0xLN1/WJ57d59hj2jP2Bzj+n4DQvGtZR+AWZ5mzpP5eKK7QTNMekX9d9DbOs7m219Z/PnpC/IiU4m/Xw0AH8v38LmbtPZ1m82Xu0a4tuzpZVNK1tHgIh1+wkb/UG59VivZyDu/lpWdJ/Kjpkr6Tv/2XLT9Vswlu0zvmZF96m4+2vxN/etqP3n+KbfDL59aBbpkQkEm32cUKsYtOQlQmet4pu+MwgbMR+jwUDrd59l/+gP2N59OnWGdyzjT/yf6EFhZi7bOk3lylfbaGnWr97oXgCE9prBvsffJ/Dt0aZZE/Dni0vZ2WcWoT1ex87ThdpDOpQpv1AJOs9/hq1jPuDnniYf4FaqPhuPMvnUtV2mcm7FdoLN7TX9Yiy/DnyDDf1ns/WpD+n2/liEuvyh3r9nIO5+WlZ2m0rojJX0rWDM6LNgLKGvf83KblNx9yuxZ8qlWDa++AmxR8p/wO355mgiw85YnQvoGYiHv5Yvuk9l68yVPDR/bLn3DljwHFtmfM0X3afi4a8loEdg8TUXHw/8u7Yg0xykAciISeaHx+axov8MDnz6G20/HFfcRveZ67Du8I64NiynjWbmsrXTVC59VdJGaw/pgNrWhh29ZhDa39RGHc1tNGjeGBL3nGFb19fY0XsmWVfigXtXb5fX72PrUx+Wa6f7jlG5/38PKP+qCTYwBbijia0QQg0MB35RFCVIUZSrFteEEOJubHDH5biBQ2BDCq/FUxSTCEV6Mjfvw6VPsFUa3eGzKPkFAOSdvoiNtnqZfFwHdCFn7/HidAAufYL5e8MBABJPXcXO1QmnGm5W9znVcMPW2YHEUyYz/L3hAAH92wJQr28bLvyyH4ALv+wnoJ/pfKNhnQjfdozs+FRTmVKzALB1caBGh0ZkXIohOyqJrIhEClKzidp4mNr921jJrd2vNVfXm/K+tuUo2i7NAPDt3oL0v2NIv2Aa0AvSc4pfOWkc7Wj64gDOfvLbbdnWI6g+uZFJ6KKvoxQZiP3tT3xKlUMXk0LW3zFgNN5WngA1WgWQGZVEVnQyxiID4ZsO49/POl//fq25aLbd1S1HqdnZpF/K+WvokjIASLsUi8beFpWtBn1+IfHmiJaxyEDKuSicfDyK5WVFJZFtlnd102H8Ssnz69eay2Z5ERbyUi3kpV+KRW2WByZ7tnxhACc/vT17VkTbVi2o5uryj/KoCE3DJhji4zAmJoBeT8He3dgGdymTzvHpcejW/wSFhbeV7z+pQ31eAYnHLqO3eKtSmmr+Whyru5Jgnmh4BAWQE5VErlle9MbD1CrVFmv1b0Pk+n0AxGwu6RMoChpHO4RahdreFmOhnqKcPFNZdKb+rrJRo7JRg/m/KfYMCiA7Kokcs7xr5fTBWv1bE2Hug9Gbj+J9Q54FdYd34tpG0xsxQ14hSYdK2mjauSgczG30XuqYfOQihek55dq5ft82nDf7uIRTV7G/iY9LMPu48xsO0MDsy6L2/4ViMPX9+FNXcTHr49+tBckXY0j+2+SHCtNz8AisV6yfUmQgZuNhapbSz/ehNkT9bNIvdvNRanQ16efasCbXD5reHBWkZlGYmYt7oD8AerOeQqNGZaOhvP9purQPCN9YgQ9YX+IDfM221ecXFuuotrMpN/9ie/aztufNxgxLe9Y3jxlp4fGkRyRUmHdmdDKpl+Oszjfs24azG0zljj8Vjr2rI86lZDqbZcafCgfg7Ib9NLTQv++bY9j93hqr/6Y77sQV8s1vI+JOXsHBxwMPc7+wbKO3qkPvriVtVF2qjepz8rBxccAruDERP4UBpr5RZJZ7r+ot4cgl8jPK7xOSB5f7MsEWQrwmhJhs/v2xEGK3+XcvIcSPQogvhBDHhRDnhRDvmK9NBnyBPUKIPeZz/YQQfwohTgoh1gshnM3no4QQC4UQJ4HHMU2IXxJC7BFC+AkhLgkhvgP+AmoLIT4UQvwlhDgnhHjcnEcPIUSYEOIXIcRFc7lEeeW4EzTenhQllDx16xNTsPH2rDC926P9yNlb9vVetcHdyPx9b5m8sxNSi49zEtNw1rpbpXHWupOTmFZuGsfqruReN03Ocq9n4FjdFQD3elrsqjkxct1sntwyjyYjTBOearW9KEjNJmjGY3i09Kfjh8+jcbBDl5CGYym5Dlp3dPEmuYrBSFGWDjt3Z1zraVFQ6PPjdAZtn0+zl0peVbeaPpLzX25Dn3d7kyh7H3fy4kv0z0tIs5oQ3C1OWndy4i1slpCGUyn9LNMoBiOF2Trs3Z2t0tQb2I7kc1EYC/VW521dHanbJ4i4g+crXV6Khbz2r43kzIrbt+f9QFW9Osbk68XHxpRkVJ7WD5jqgAaoqteg6Njh0rdXSGXZtCIaDA0m/PeS8jhqPdBZtEVdQhoOPjfvE4VZOmw9nInefBS9roDhpz9j2LFPuLh8C4UZuYApQvbQznd5+OwXJO77i1TzpMcyr4rkOWrdyS3dBz2s9as7tANRv/1ZRj8bV0dq9g0i6UDJG4N7pePNcNG6k2UhMzsxDRdva5ku3u5kW/i47IQ0XErVNUCLx7oREXYWAHd/LSgKj343nWe2zKfRhME4aD3QxZXSrxy/llfKprYezmRcuIZvv9YItQrH2l64t/THsWaJn++65nWGnvsCfU4+sZuPlCmbo487OQklOuQmpuHkU057TbC27Y32WiMogEd3vc+jf7zH/pmriidupXHWuluNGdm3MWaUl6Y0No52tH9pMIeW/FrmmovWw6oOs267Dk2+vGHfNmQnpnHd/DBUHoGjepC4+wwOWg/yblGHjuWMTbYezsRsPopBV8DQM58x5HhJG3WqU4OC1GzaLxlPv9AFtPvoedQOdqa8qqjeJP8O7lcEez/Q1fy7LeAshLAxn9sHzFYUpS3QEuguhGipKMqnQDzQU1GUnkKI6sAcoI+iKK2B48CrFjJSFUVprSjKT8By4GNFUXqarzUAPlcUpZlZfisgEOgDfCiE8DGnC8I0OW8K1AM6ly5HJdvFimrDemLfogGpKzZYndd4uWPX0I+c/SfvpfhiVGoV3i38+e3Zj/j1qYV0mDwcN38tKo0ajxZ+JOw9R8z2E+h1BTSfOOSO8hZqNTXaNWT/xM/ZPnwudQa0RdulGe7N6uBS15uYm6wd/Dfh3rAmHWeNYu/Mb6zOC7WKvste5tyqHWRFJ1eqvA6zRrHPLM+zaR1c63rfdC3mvwIhcH7xZXJXfH6/S2JF/aEdubKx7MT0bvAMCkAxGPktaCKbOrxC45CBONXxAkwbirb3ncXGNpPwbBVAtUa1KkXmDbmGvEIyL8VanRdqFV0+f5lLK3eQU0lt9GY6VgXBE4di1Bu58N+DAKg0amq2a8jm/3zOjyPmUnNAW6o1qX2LXComas1e8hLS6LN9Pq3mjiH1+BWrydL+Jxbye6uXUdlpqFHOm4R/yvVTV1nfewa/DnqToIlDUNvZVLqMm9HplUc4sXI7RbqCWye+AzT2tnR6eSj7Fv9SYZq6HZvS6vEenFmw9h/J8gwKQDEa2dRqIpvbv0Kj8aY2KjQq3Fv4Eb76D0L7zUafV0CTSXc27lXE/a63u8L4APw9oNyvTY4ngDZCCFegADiJaaLbFZgMPCaEeNFcPh9ME9yzpfIINp8/KExr22wByxFuHRVzTVGUG+GmLsAaRVEMQJIQYi/QDsgCjiqKEgsghDgN+AEHbqWcuewvArxVvTmPudYpvqZPSsXGpyQip9FWpygptUweTp1aUX3C40Q9+TpKqYin66CuZO/8E/QG3J8ahPvjDwGQd+4yLj4lURJnrQc5ielW9+YkpuOs9Sg3jS4lC6cabuRez8Cphhu6lKzie/IzzqLPK0CfV0DckYt4Na1D3NFL6BLSSPzzb7Qdm3D2k400nzgEfV4BulJy8xLTcfT1QJeQhlCrsHF1pCA9B11CGtePXKLA/Eo4dvcZPJv7UaTLx7OlP48c/hihUWPv6Uq/9bM59Mi8Cu2en5COg2+J/g4+HuRZRBPultzEdJx9LWzm40FuKf1upMlNNOln6+JIvlknJ60HA1ZMYdeU5WRdu251X4+F48iMTOTsyh2VKq//iinssZDn3aYBXi39GX3IZE8HT1eG/jybTebNqg8KxpQUVF41io9V1b0wppa88REOjqjr+lPtgyWm6+4euLz1LtnvzLrpRsd/atOb4dmkDiqNiuRzUcXndIlpOFq0RUcfD/ISyu8TeeY+YevqSGFaDnWndSJhz1kUvYGC1CxSjl3GI7AeuRaT26IsHUmHLuDTsyXpl+KK87qZPF1iOk4W8mxcHSlIK9Gv7rDgcqPXHT4cR1ZkIpe+3mF1/l7reIMGz/al12hTLCPxbASuvp7cWHTgovUgO8laZnZSenG0E0zrdbMt6rr5yK4E9A5i3RPvldyTkEbskUvkmes7Yfdp7KpXs4o6O/p4kFeOX3MoZdNCs03PvPVDcbqem94iOyLR6l5jQRHxO05Qs38bLh603kugS0jH2eLtm5PWg9yEctqrjwe5FrYt3V4zwuMpys3HvVEtUswbEJs904eGT5bY03LMcLmNMaO8NKXxCapPw4Ht6TZzFHaujqhsNLQe9xB5adnEm+vwBq63XYdpuNf1xq22F89vM9Wdq48H47YsYNWwN8lNzqRG49oMWvg8a5/5AI/0HPIS03C4RR3qSrXRG3VYp4I2mnz4InkJaaSZ3x7FbD5KE3Ng6V7Wm+Tfx32JYCuKUgREAs8ChzBFtHsC9YE8YBrQW1GUlsAWwL6cbASwU1GUVua/poqijLO4frP3jbd+F2nC8vHbwG0+kCiK8pWiKK+yL3gAACAASURBVG0VRWlrObkGyDt7GVu/mtjU8gYbDdUGdyNnl/UrQvum9fCZP5GY8XMxpGaWyd91cPfi5SHpP2wp3viYHXq4ePmGNiiAwmxd8ZKPG+Rez6AwJw9tkGmvZ5MRXbgaegKAiJ0naTrS9GKh6ciuROw0nb8aegLfdo0QahUae1u0QQGkXYlHl5xJbnwaRdl5uPhrqTuwHVnh8fgNCyYm1Dq6HhN6koBHTXnXHdSeRPP6xPi9Z3FrXBu1vS1CrUIb3JiMK3Fc/m4Xv7SZxK/Br7B9+FyyIhIIffTmk8H001dxrqfFsY4XwkZNreEdSTDr9k+4fiaCan5aXGp7obJRU39oMJE7rfWL2nmSxmbbBQxqT5xZP1tXRwatnsrh99aRePyK1T3tXxuJrYsDB97+wep8aXkBQ4OJKkdeQ7O8eoPaE28hb8DqqRwpJe/C97v4vu0kfuz0ChsfmUtmZMIDN7kG0F++iNq3FipvLWg02HXvReHhg8XXFV0uaaOGkf7sKNKfHYX+4oVbTq7hn9XhrWgwrGz0Ou10BC7+WpzM8uoMCya2VFuMCz2J/6PdAKg9uH3x8gtdXAreXZoCoHaww7N1A7LD47HzcMHG1bT1Q21vg7Zbc7LCTetfU0vJqzssmNhSfTAu9CT1zH2wzuD2JB2w0E8I6g7pwLVSegROH4mNiwMn3rRuo/dKx/K48u1OVg+czeqBs7kSeoJmZh/nExRAwU18nI/ZxzUb0YVwsy/z796S9iGD+XXcYvT5JUulIveexatxbTRmP+QV3ITEsLM4+2txrG3yJ7WHBRO/w1q/+B0n8XvMpF+twe25btZP7WBbvGygRrfmKAYj2ZfjUDvaYW9ebyzUKnx6B5FVjt7Xz0RQzd+ivQ4L5lqp9npt50kaPlrWB7jU9ireHOdc0xO3AF9yYkoeXM6v/oPvBszmuwGzCd9xl/a8hV9dO3IeKzq/worOr3Dymx0cWryBpcGT+HrgLC6HHqflCFO5fYPqU5CdR04pmTlmmb5Bpk28LUd05fLOEyRfimFJmwl81mUKn3WZQlZCGisHzSY3ORNXX09GfDmFja98QVqk6WGmvDYad4s6tGqjnS3aaJsGZIXHk5+ciS4+FZcA04tu7y7NyDKvM7+X9fagohiV+/73oHI/P9O3H9NE+jngHLAYU2TbFdMEOFMI4Q0MAMLM92QDLkAKcBj4TAhRX1GUcCGEE1BTUZTLd1GO8UKI1YAH0A14DWh8k3ssy3FnGIwkvvMFdb6dh1CpyPhlJwVXovGa8hR5566Qs+sINWaMQ+VkT62lps/xFMUnEzN+LgA2NWtg41Md3ZFzZbLOCTtGYbfOjN2/CH1eIaHTviq+NnrbAn4cMBuA3XO+pd+iF9HY2xK15wxRe0y7vI99/juDvphEs8e7kx2XUvy1kLTweKLCzjIm9D0Uo5G/1oaRetn0GvnoG6vpvGQ8CKj/ZA8KUrO5smYPmZfjCJw2gtQzkcTuPMmVtXvp8mkIww8sojAjh30TlgFQmKnjwlfbGLR1LoqiELf7DHG7Tt+xWcG0nu30rG/pvGYGQq3i2powsi/F0WT6SDJOR5AQehL3VvUI/uYVbNyc0PZtTdPXRvJH9+m3zHf/G6sZ8sN006eX1u0l/XIc7aaOIPlsJFE7T/L32r30XhLC6P2LyM/IYefLJv1aPNuXan7etJ3yMG2nPAzA76MXorLR0HbycNKvxPHYNtMXOc59u5NLa8NQDEYOvLGaQWZ5l8zy2prlXdt5kotr99JrSQhP7F9EgYW85mZ5baY8TBuzvM2jF5Jv3phaGbz21vscO3WWjIwseg9/ignjxjBiSP/KydxoIOeLJVSb/xGoVeSHbsUQHYXjmOfQX75I4ZHb//qLJf+kDgGeOvQxti4OqG00+Pdvy++j3yfd/OWAgMEd2PLMh2XkHZ/9LT1+et30ebC1e8m6HEeL10aQdiaSuNCTXF0TRsdPX2LwwUUUZuRy0NzfrqzaSYePxzNwz0IQgoh1e8n4Owa3JrUJ/iQEoVKBShD9+xHi/zgFCLO81fT6yaTf1bV7ybwcR8vXTH0wLvQk4Wv20unTEIYeNLWZgy+V6FcjuDG6+DSrJSAOPh40nzKczCtxDAg1tdHLq3Zy9aewe6YjQKfPX6ZGxybYebgw7PhSzi36hZhfTJvQInafpl7PQF7YZ/Jx2yx83DNbF7B6oMnH7ZzzLQPMPi4y7AwRZh/XZ+4zqG01PPaD6fN/CafCCZ29ioIsHce+3sbTv5v8UOofZ0jceYpTBiPd1pj0izTr18ysX0LoSSLXhNF+6UsMOGTS73CIST87T1e6rXkdRVHIS0jn6KQvANNG486rX0Vla4NQCa4fvEDEd7vKba8H3ljNwB+nI1QWPmDaCJLPlPiAnp+EMOqAqT7/MPtUbfuGtJowBKPegGJUODD72wrfxETsPo1/z0Ce37+IorxCtlvY8+ltC/jOPGb8YWnPPWeINNuzfv+29J77NA4eLjyyahrXL1xjw5jyvwBzg/Ddpwno2YoJ+xYXf6bvBs9vfZevB84CYPucVQxeNB4be1uuhp3h6p4zFWUJQNf/PIyDuwsD5pm+SmKjN7DzoTc4Oetbuq+xbqPNzXUYH3qSiDVhBC99iYHmOvzTXIfhq3bSfsl4HgoztdHItXvJNLfRk7O/I/izCahsNOREX+folC/vab31XvYyPh2bYO/hzOhjn3J80QYurd1b2gSSBwyh3GyL8b0ULERvYDvgpihKrhDiMrBcUZTFQohvgU5ADJAJbFIU5VshxCRgIhBvXofdC1gI2JmznaMoyiYhRBTQVlGUFLOst4EcRVE+EkL4AZsVRWluviaADzBN5BVgvqIo64QQPYBpiqIMNqdbBhwvrxw30/NCwKAqNfCOoptvPqlsPA1VKg7nKt70kWhTtS957scrpXGn5lapvMwx5X+W617x8193v5b2bnA3VK1PNSKqVB6AiqrVMcamanX0L6xa/dLUVatf9n1wNAWiam1av4r3cmdUcR0CjI/9oeqFliJjdK/7HkJ2+3H3fbdDedy3CLaiKLsAG4vjhha/n63gnqXAUovj3ZjWS5dO51fq+G2L31FAc4tjBVPE+rVS94RREjlHUZSJFZVDIpFIJBKJ5P8dD/ASjfvNv+072BKJRCKRSCQSyQONnGBLJBKJRCKRSCSVyP3c5CiRSCQSiUQi+bfyAH+H+n4jI9gSiUQikUgkEkklIiPYEolEIpFIJJI75kH+DvX9RkawJRKJRCKRSCSSSkROsCUSiUQikUgkkkpELhGRSCQSiUQikdw5cpNjhcgItkQikUgkEolEUonICLZEIpFIJBKJ5I6RmxwrRkawJRKJRCKRSCSSSkROsCUSiUQikUgkkkpELhG5x9Son1Ol8rTn3KpUXh1jQZXKq+6WW6Xy8rI8q1Rezn145M0cM7ZK5VX7flWVyrNr9WaVymtul1Wl8pJ1DlUqD8C3WtX6tSsF7lUqT61U7WtvN0OViqNQiKoVCKSrqtamraqlV6m841U8VjwwyE2OFSIj2BKJRCKRSCQSSSUiI9gSiUQikUgkkjtGkRHsCpERbIlEIpFIJBKJpBKRE2yJRCKRSCQSiaQSkUtEJBKJRCKRSCR3jlwiUiEygi2RSCQSiUQi+Z9ECPGQEOKSECJcCDHjJulGCCEUIUTbypArI9gSiUQikUgkkjvmQd/kKIRQA58BfYFY4JgQYpOiKBdKpXMB/gMcqSzZMoItkUgkEolEIvlfpD0QrihKhKIohcBaYFg56eYBC4H8yhIsJ9gSiUQikUgkkv9FagIxFsex5nPFCCFaA7UVRdlSmYLlEhGJRCKRSCQSyZ3zACwREUK8CLxoceorRVG+us17VcBi4NnKLpecYEskEolEIpFI/pWYJ9MVTajjgNoWx7XM527gAjQHwoQQAFpgkxBiqKIox/9JueQEWyKRSCQSiURyxzzomxyBY0ADIYQ/pon1KODJGxcVRckEqt84FkKEAdP+6eQa5AT7vmPTtj1OIZMQahX527aQ9/NP5aaz7dIN1zfmkTHxRfRXLt2RDJ8eLWk9bwxCpeLqmjD+Xva71XWVrYbgT1/Co4UfBek5HApZSm5sCkKjpsNHz+Pewh+hURG1/gAXlm26pTy3nq2oN28sqFUk/biLuGW/WV13DW6C/9yxODWty6WQj0ndfLj4Wt05T+HepzUAsR//QsrGQ7eU59ilDd6zQ0ClIvOX7aStWG913f3Zh6k28iEwGNCnZZI4+2P08dcB8Jr2HE7d24NKoDt0iusLlldow7ZmG4avCeNCOTbs9GkIHi38KUjP5kDIMnJjU/B7uBNNJgwqKUuT2mzrP4f089H0+WU2Dt5u6PMLAdg9aiE56VkA1O7Rkk7vjEGoVVxcE8bpz8rK67UkhOot/clPz+aPl5aRE5tCza7N6TDzcVS2GoyFeg7PX0P8IdNm6YAhHQiaPAyhUhG96xRH3l13S9sC2LQxt1GVivztW8hbX0Eb7dwN1znzyJh85230Zsx5dzH7Dh7Fw92N334ov35uh5o9WhL8zhhUahWX1oRxthybdrew6R6zTW/g5OvJiD0LObn4V/76cisAtq6OdPnwedwb1QJFYf/UFXDuRBnZzt1a4/vWC6BSkb5uJ8nLf7G6Xn3cMNwf74diMGBIzSL29U8oiksGoHn4b+RfugZAUXwy116Yf0tdPXoG0mD+WIRaRcKPu7i2dKPVdbfgJjSY9wxOTetyfvwSkjebNs67dW5Gg7nPFKdzrO/L+ZBPSNl27JYynbq2ocbs8Qi1ioz1O0j7qlQ/HPswbo/2R9EbMKRnkjBzSUk/fO05nHu0A5Ug9+Aprs//skI5/d9+mgY9AynKK2TjtC9J/CuqTBqf5n4MXRSCjb0NV/acYcfb3wHQfcojBD3RE11qNgC7P1xH+J4z+AbWY/B7z5tuFhD14Qbit1U8vnr3bEnLeU8j1CqiftzD5VL+wDO4MYFzx+DatA5HQ5YSv/noLe0HoO3ZkqC5pn4f8VMYF8vxMx0+fQn3ln4UpudwaPxSdGZf3W6RyVerzL7676UmXz346BKKcvJRDEYUg4FzC9fTz1z2C2vCOPF5WRn9loTg1cLUD7ZPWEa2uR+0eXkITUf1QDEY2ffWd0TvPQeY+kHvD57Hs1EtFEVh17QVJJ4Mp/PsJ/DvE4ShSM/16CQ2vPYl+Vk6Br31NI16tqIor5AN05YTf75sHfo292fER+Oxsbfl0p7TbHnHVIePL5uEVz0fAOxdncjPymXZwFmoNGoeXvgCvs38UGnUiE2hpH31s1We/9axoueP03GoUQ2hUZN85BLHZn2LYlTKlS+xRlEUvRBiIrADUAPfKIpyXggxFziuKMqtJzV3yf/rCbYQwhf4VFGUkfelACoVzi9PIXPmVIwpybgt/ZLCwwcxRF+zLqeDAw7DR1L09/k7FiFUgjbvPsueUe+Rl5BGv63ziNtxkqwrJW9I6j3Rg8KMXDZ3nkqdYcEEznmCQyFLqTOkAyo7G7b1noHawZaBYR9w7bdD5FpMOsrTqd57z3P+sbkUJqQRuP190kKPk3c5tjhJQVwKV/7zGTUnDLW61b1Pa5xb+HO69zRUdjY0//Ud0nedwpCTd1N53m++TOxzsyhKSqHu+k/I2X2EwqvRxUny/75KxsjJKPkFuI0ahNe050h49X3sg5rg0LopUcMmAFDnp49waN+CvKPnytiw3bvPsHvU++gS0nho61xid5wg60p8cZoAsw03dZ5K3WHBBM0ZxYGQZUT99xBR/zU9JLg1rkW3b14h/XxJ2Q6+/DlpZyMt9DHJ6zz/GbY8+T65CWk8smUuUaEnyLCQ13hUDwoyc1nbZSoBQ4MJnjWKPyYsIz8tm+1jF6FLysC9US0G/TidH9pOxs7NmQ5znuDXAW+Qn5ZNj4/HU7NzM+IO3qJN3Wijs8xt9JMvKTxSQRsdNpKii3feRm/F8IF9eXLEUGbN++iu8xAqQaf5z7DdbNOhW+YSXcqmjcw2Xd9lKvWGBtNu1ij2TFhWfL3DW6OJ3XPGKt/gd8YQG3aW3eM/RWWjRuNgV1a4SoXv3BAix7yBPjGVgI2LyfrjCAXhJftu8s5HkDr0VZT8AjxGD0A7Yywxkz4AwJhfSPig/9y+sipBo/fHceqx+RTEp9J2x3sk7ziO7nJJn8+PS+HCfz6nzktDrG7NOHieY72nA6Bxc6Lj4aWkhVnrXL5MFd5vTSBm7GyKElPw27CEnF2HKbxaomPBhatEPfIfUz98YiA1pj9H/JT3cTD3w8ghLwNQd82HOLZvga5UPwSo3zMQT38ty7pPpWZQfQbNH8vK4W+VSTdwwXNsnvE1cafCeXL1dOr3CCTcrMeRldv486utVumvX4plxZA5KAYjzjXcmLDtXRJCT6IYygnPqQSB743lwGPvkZeQSs/t80kIPUm2hX3z4lI4/p/lNJgw+Na2M3PDV4c9bvLVfbfNIz70JFmXS/nqzFy2dppKbbOv/jNkKbWHdEBta8OOXiZfPWDvB1z77yF0Zl+9Z+R8CtNyECrBwIOL2DD6fXIS0nh881widp4g3aIfNBvVg/yMXL7vOpUGQ4PpPGsU2ycsw72BLw2HBvNj79dx9nZn+JoZfN9tGopRodvbY7gWdpZtIdb9IHr/OQ69vw7FYCRw9mN0nzCUyMMXqe6vZXGPV6kdVJ+hC55j+fA3y9hj2Pzn+G3m18ScCueZb6fTsEcgl8POsG7i0uI0A2aPJj9bB0DzgR3Q2Nqw9KEZ2NjbMnvXQrK2hKGPu26ut3/vWLF//FL05nGw64rJ1BnSgWsbDyO5PRRF2QpsLXWubKMzne9RWXL/X39FRFGU+Ps2uQY0jZpgiI/DmJgAej0FYbux7dilTDrHZ8ah+/knKCy8YxkeQQHkRCWRG52MschA9MbD1OrfxipNrf5tiFy/D4CYzUfRdmlmuqAoaBztEGoVantbjIV6im422QVcguqTH5lIQfR1lCI9yb8dxKN/O6s0BTHJ6P6+hmK0HrwcG9Yi8/DfYDBi1BWgu3ANt16tbirPvmVDiqLjKYpNhCI92Vv34tw72CpN3pGzKPkFpt9nLmKjrV6sn7CzRdhoELY2CI0aQ0pGGRmeQQFkRyWRY7bhtY2HqV3Ghq2JWL8fgOjNR/G+YUML6g7vdFtOsUarALKiksg2ywvfeBi/ftby/Pq15rJZXsSWo/ia5aWev4YuyaRD+qVY1Pa2qGw1uNatQVZkEvlppshd3IG/8B9oXS/loWlYqo3u3Y1tcDlt9Olx6NbfXRu9FW1btaCaq8s/ysOrlE0jNh6mTimb1unXmnCzTSMtbApQt38bsmOSSbeY7Ni4OKDt0IjLa8IAMBYZKMzSlZHtGNiAwmsJFMUkoRTpyfx9H659O1ilyT18rriN6k5dwkbrede6uraujy4ykfxr11GKDFz/7RBeD1nXdX5MMrkXouEmUbAaQ4JJ3X0KY96t69S+ZUMKr8VTFGPqh1lb9uHcp6NVGp1lPzx9EY23qR8qioLKzqa4H6LRoE8t2w8BGvVtw5kNpjqKOxWOnasjzjXcrNI413DDztmBuFPhAJzZsJ9Gpeq6NPr8wuLJtMbOBm4SHPQIqk9uZBK6aJN9Y3/7E59S/kAXk0LW3zFgvP335x5mP2Ppq2uWytf3oTZE/Wzy1bGbj+LdtcRXq0v5an05vvqGjCyzjMubDlOvlG38+7Xm4i8mG4dvOUqtziYZ9fq14fKmwxgL9WTFJJMRlYR3qwBsXRzw7dCIC2vDAOt+ELPvr2K7xpwKx1XrSZN+bTj16/7ic/Yujrh4Wdehi5cbdi4OxJjr8NSv+2nSr+z//dF8UDBnN/1pPlKwdbBDpVahsbdFKSrCmFPSH//NY8WNuhQaNSpbDSgPTvRaMd7/vweV/zcTbCHE+0KIly2O3xZCTBNC/GU+VgshPhRCHBNCnBVCjDef/0wIMdT8+79CiG/Mv58TQiz4J2VSeVbHmHy9+NiYkoyqenWrNOr6DVB51aDo6N09rTpqPdDFpxYf6xLScPBxt0rjoHVHF58GgGIwUpilw9bDmejNR9HrChh++jOGHfuEi8u3UJiRe1N5tj4eFMaXRLgLE1Kx8/G4rbLmnr+Ge89WqBxs0Xi4UK1zc+x8q9/0Ho13dYoSkouP9YkpaLwrnpxUG9mPnH2mV7/5py+iO3KWgP0/ErD/R3IPnKQwIqbMPZb2gfJt6Kh1J9fChkVZOuw8nK3S1B3agajf/rQ61/HjFxmwcwHNpwwvycvHnZyEEnm5iWk4lZLnpC1Jc6PO7N2t5fkPakfKuSiMhXoyoxKpFuCDc63qCLUKv/5tcPa9db2oqpfTRj1LtdGABqiq16Do2IMbUXH0cSfXwqa627SpnbszGkc7Wk4YzKnFv1qld6ntRX5aNl0Xv8jw7fPp8uHz5UawNVpPihJK+kRRYupNJ9Aej/cle2/JMhOVnS0BGxcT8OuHuPYNrvC+G9hpPSiw6PMF8anYaW+vD1riPbwzSf89eFtpbbw90SeW6KhPTMHmJv3Q7dH+5Fr0w9wjZ6l/8AfqH/yB3AMnrCLflrhoPciy0C07MQ0Xb+t6dPF2JyuxpK6zE9JwsdC/3dP9GL/9PYZ8+AL2ro7F52u2CiBk50JCdrzP6ekry49eA/Y+7uRZlCEvIQ2H2/RxN8NB60FeXClfrS3rZ3Sl/IythzMxm49i0BUw9MxnDDlu7asVRaHH2hn03TEfv8e6WsnISUjDuZQMZ6072ZbjQbbJtzhr3cmx8IM5CWk4ad1xNfeDPotfZNS2+fT6oPx+0ObRHlwOO42rtzuZFvlkJabhWqoMrlp3Mi36a2ZCGq6l6tmvfWNyUzJJjUoE4K+tRynMK2DG0c+ZfuhT0r75FWNmTnH6f/tY0fOn6Yw4+zn6nHyib3PJkeT+8v9mgg2sAx6zOH4M6/+xZxyQqShKO6Ad8IJ5Ufx+oKs5TU2gqfl3V2BfeYKEEC8KIY4LIY5/F5tw9yUWAucXXyb3q8/vPo9/gGdQAIrByG9BE9nU4RUahwzEqY7XPZOXsfcM6btO0uL3BTT6YgrZxy9XOMjdDa5DemLfrCHpKzcAYFPHB9t6tbnaYwxXuz+FY3AgDm3KRhMqA8+gAAx5hWReKlkqc3Di52zpPZOdw+dRo0Mj/EeWjQzfLe4Na9Jh5ij2z/gGgMJMHQdmrqLPFxMZ9usbZMekYKwM295ooyvuTxutClq/+gh/rdiOXldgdV6lUePZ3I+L3+/it4fmoNcV0PLlIRXkcnu4De+BQ4v6pHxVMpm/2OU5rg57lej/fITPm89jW0f7j2TcDrY13HBqXIe0PbexPOQOcR3aE/vmDUj72rQO3aaOD3YBtQnv9jThXcfgFByIQ9t70w+P//AHS7u9wpcDZpFzPYO+b4wuvhZ3+irL+77O10PfoOHkYajsbO5JGe4FnkEBKEYjm1pNZHP7V2g0vsRX7x42l9B+c9j35Af49AzEvlTE/5+i0qjxau7Hue92sXbAHIp0BbQp1Q/aThqK0WDgzG+398B2O7Qc2okzm0r26NQKDMBoMPJ+h5f5qOsUPMY+gk2tu+srD9pYAbDnyQ/4NWgiKltNuVFvyYPH/5sJtqIop4AaQghfIUQgkI71x8f7AU8LIU5jmnh7Ag0wT7CFEE2BC0CSEMIH6AiUuwNPUZSvFEVpqyhK26dr+VRYJmNqCiqvGsXHqupeGFNKokDCwRG1nz/VPliC++q1aJo0xeWdd9E0aHTbeusS03D0LXlKd/TxIC8h3SpNXmI6juZoplCrsHV1pDAth7oPdyJhz1kUvYGC1CxSjl3GI7DeTeUVJqRhaxF1tvXxpMAiEnErYj/5lTN9XuP84/NAQH5E/E3T65NSsPEpmfRrtNXRJ6WWSefYsRUeIaOIm/A2SlERAM59OpF/5iKKLh9Fl0/uvuM4tGpS5l5L+0D5NtQlpuNkYUMbV0cK0kqiJ3WHBZeJSOQlmvLQ5+YT9d9DeAaZbKtLSMfZIiLmpPUgt5S83MSSNDfqLD/dJM/Jx4N+X09hz5TlZF0riT5f++MUvw15m9+GvUNmRAKZkYlldC2NMaWcNppaqo3WNbfRb9eiadwUl7furI1WBbqEdJwsbOp4mzYtSM/BK6g+7WaP4rE/P6bZuP60mjSUJs/2JTchjdyENJJPXQVMy0qqt/ArI1ufmIqNT0mfsNF6UpRYto06dQ7E6+XHiHphPkqhvuT+JFP/KYpJIvfwX9g3u3kfLEhMw86iz9v5elKQePt9EKDGsI4kbzuKojfcVvqipFQ02hIdNdrqFJXXDzu1wvOlx4kNeQelyKSjS99O5J2+VNwPc0r1Q7fRg3lx67u8uPVdcq5n4Gqhm4vWg+wk63rMTkrH1SJi7eLjQbZZ/9yULNPmMEXh5Jo91AwMKFPGlPB49Ln5uDauVa6u+QnpOFiUwcHHg7w78HEVkZeYhkPNUr46sayfcSzlZwrTcqhzE199I4+C1CwS9/2Fs793cX7OPh7klJKRk5j+f+ydd3hURduH77Ob3nsDAikUCRBCDVISelG6Aoo0CwKKhQ7ii9IFRRQVxA4ooog0KaEFBaSZhBZKCIGQ3nvfPd8fu242ySYBDYH3/ea+rr2ye84z53dmzjwzc6YFa/36wFpTtuQlZVYY9bJydyA/KZO8xAzyEjNIjtD4QfS+s7i0aqKza/F0d1qOCcbB05VX9y0nNyULW73r2Lg5kFPpHnKSMrHV81dbdwdy9J6zQqnAr39HLuktkPcf+jhRxy+gLlORn55DYVgkZq2a6s7/N9cVf6MuLiXuYBgN+7czeP5h8LCnh4gpIo8OPwNPAaPR9GjrIwHTZVluq/14ybIcIstyPGAHDEDTY/0Hmt7vPFmWc//NzZRdv4ayQUMUrm5gZIRpcC9KxdvSnQAAIABJREFUTpe/4csF+WSMGkrmhDFkThhD2dVIchctuK8dGjIibmHt5YZlI2cUxko8hwYSF1Jxl4P4kDC8nu4BQKMnO5F8QrNQrSA+Dddumg57pbkpju2aknuz5gZvbsRNzL3dMfV0QTI2wnlYVzJCat+BAACFAiPtNAeLxxpj0bIxmbUssCq6dAPjxh4YN3AFYyOsBwWRd7TiVAXTx3xwffc14qe9iyojW3e8LDEV846tQakAIyXmHVtTbGDYL71SGjYeGkhcSFgFm/iQMLyf1gx0eD7ZieQTkeUnJYnGgztzZ1d5oSkpFbphQclISYM+AWRf0/RYpFy4ha2XG9ZaPd+hgdw5VFHvzqEwmmn1vJ/oRMJJjZ6JjQUDv5vJ2RXbSD4fVSGMmaONxsbWgpbj+3Dth9Aa0xag7MY1lB56eTTIQB4dM5TMiWPInDiGsmuR5L57f3m0Pki9cAsbLzestGnqPTSQ2EppGnsoDF9tmnrppelvI5fwU5c3+anLm1z56iAR63Zz9dtDFKZmk5+Qga12RwOPbn5kRsVTmYKLUZg28cC4oSuSsRG2g3uQc7jiEK9ZS28aLHuFOy8tQZVenkcVNpZIJpq16Ep7GyzaP0ZxlOHpE3+TGx6Nhbc7Zp7OSMZKXIY9TtrB+9txynX4vU8PAY0fmmjjiLERNk/0IO9IZT/0xm3xdOKmLK7gh6WJqVh0aqXzQ4tOrSssPMv6fi8bBy1g46AFXA85j/9IzTNqEOBLcW4heSkV58LmpWRRnFdIgwBfAPxHduf6IU2Zpz9fu0X/DqRoewntGjkjKTXVoW0DJ6x9PSi4a3gxd2ZENFbeblho07fhsC4khlTdOeZ+MVRWxx+seN2Eg2E0GaUpqxtWLqu76pXV7ZuSczMBpbkpRpZmuuO2zRpgbGOBjVaj2ZBAYir5QcyhMFo8pUlj3yc6Eaf1g5hDYTQbEqhZ09HIGbsmbiRHRFOQmk1eYgZ2Wj9o2NWPDK0feAa3of2UJ/lp8CLWDZzHJ4MWcDXkPAEjNNdvpH2GuakVn2FuahbFuYU00j7DgBHduaqXxj7dWpF6K6HCVKCshHS8H9f07Bqbm2Lm36LCNI7/1rrCyMJUN+ogKRU06NOWnJv/YmRcUG/8f9tFZBvwBZo9D4MA/YliB4GpkiQdlWW5VJKkZkC8LMv5wGngDaAXmp7t7drPv0OtIu/Ttdgufx8UCopC9qG6cxuL8c9TduMaJadr36KuNmSVmvNvfUvwD3M1Wz/9eJycG/G0nj2SjAsxxIeEEb01lC4fT+XJkx9QkpXPyamaVdpR3xyi84cvM+jYeyBJ3Np2nKyrNVfuqNTcWvAlflsXglJBytajFF6Pw3POaPIioskIOY9VWx9afD0HIztLHPp2wHP2aMKD3kQyVtJ61xLNZXILiXrlY6htGoNKTcqS9TT8aikolGT/EkLJzVgcp4+j6PIN8o+dwXn2CygszPBYuwDQFJbx094l9+AJLAL9abJ7PciQf+I8+cfOVJHQpOF39PphDpJSQfSPx8m+EU+b2SNJ16bhza3HefzjKQw5+QHFWXmcnFq++4RLYAsKEjLIiy2f/6cwMabnD3M120kpFST9cYWb3x/T6Z14+zsGfT8HSaHg+rbjZN6Ip8OskaReiOHOoTCu/Xicnh9NYcwJjd5h7W4XfhP7YtPElXZvDKfdG8MB+O3Z9yhKz6Hru+NwbOkJwF9rf72nHmzUKvLWr8V26fug1ObR2NtYjNPm0TP/Po/WxuxFKzkXfpGsrBx6D3uOaS+MY+Tg/vd1DVml5s+3v2OANk1vbDtO1o142s0aSdqFGGIPhXHjx+MEfTSFp7Vpqr+DSHX8+fZ3BK2bitLEiNw7Kfw+cyNVluWq1CQs2oDXpnc12/T9fJjiqFhc3hxL4aUocg+fxX3+JBSWZnh+Og8o347PzLcRDZa9gizLSJJE6obtFXYfqS6uN+Z/Tdsf30JSKkjYeoz863F4zRlF7oVo0g7+hXVbH1p/MwtjO0uc+rXHa/YozgbNBMCskTNmHk5knYqsUadyHJMXr6fRV0tBqSB7u8YPnV57jqLLUeQdPYPLXI0fNvh4vi6O8VMXk3vgBBaBbfDa+5nGD//4i7xjhueYRh2NwLdnW179fQ2lhSXsnlW+nd/kfcvZOEjj4/sWfsPQD17GyMyEm6EXuKmd6tJn/jO4tmwMskxWXCq/LdBMoWrUoTljpg1GXapCltVEzPuGkgzD/SeySk3Egm/punUeklLBna2h5F6P57E5T5EVcYvEkDDs23oT+PWbGNtZ4ta3HS1nP8XhoDm1PrewBd8StLViWd1KW1YnhIRxa2sogeumMuiUpqz+c4qmrL75zSE6rX2ZAaGasjrmx+NkX72Lpacz3b5+E9C8yN/59RRZl24zZMscFEoFkduOk3Ejns4zR5JyMYaYQ2FE/nicvmunMO4PjR8ceEXjBxk34onae4bnjr6HukzN8YXlW8Udf/s7+q2bitLYiJzYFA7P1PzPj6AlE1CaGDHsh3mUSTJ3w2+y662vadazLTOOf0hpYTE7Zpc/w1f3LecT7TPc/fbXjHx/CkZmJkSFXuBGaITOrs3gLlzcXbHsObMphBGrp/BayCokCbJ3HKD4xu1yg//SusLIwpTgb2egMDFCUkgkn7pK1KYjNealekWWHvYdPLJI8iO0GrU+kCTpEpAmy3JPSZKaAHtlWW6l/XeZS4HBaHqzU4FhsixnS5L0ArBElmUPSZKMgSxgnCzLOwyrlJPWP6heE/jQJcPDmg8KT3Vx7UZ1iJNdzYss65rzOf98N4d/Qt5DGFMa2bqWl6Y6xnbzN/Wqt6mtwd2YHhidjLNrN6pDUgvM61UPwMM2r3ajOuSnYvvajeqQVsX1Wy+WSvXbSEkxqv9GUZKyfsfyx5tl1m5Uh9R3XQEwNmHLQ2/dJgcHP/RGpGto6ENPB0P8f+vBRpbl1nrfb6P5F5nIsqwGFmg/lcN8BXyl/V4KWNbHvQoEAoFAIBAI/vv4f9fAFggEAoFAIBD8ex7lRYYPm/9vixwFAoFAIBAIBIIHiujBFggEAoFAIBDcN7L6kZz+/EggerAFAoFAIBAIBII6RDSwBQKBQCAQCASCOkRMEREIBAKBQCAQ3DdikWP1iB5sgUAgEAgEAoGgDhE92AKBQCAQCASC+0YW/8mxWkQPtkAgEAgEAoFAUIeIBrZAIBAIBAKBQFCHiCkiAoFAIBAIBIL7RixyrB7RwH7A/HqlUb3qqZT1Ksc2k/oVdCh0qFc963r2kCNk1K8goLpcv3nUtO1/6lVvfMTietV7rcO8etUrM5frVQ9gbLZlvertVNypVz21aeN61bOv53mspfWqpqGQ+m2JfV1kV696bqI1JaiEyBICgUAgEAgEgvtG/CfH6hFzsAUCgUAgEAgEgjpENLAFAoFAIBAIBII6REwREQgEAoFAIBDcN3L9LwH5r0H0YAsEAoFAIBAIBHWIaGALBAKBQCAQCAR1iJgiIhAIBAKBQCC4b8QuItUjerAFAoFAIBAIBII6RPRgCwQCgUAgEAjuG9GDXT2iB1sgEAgEAoFAIKhDRANbIBAIBAKBQCCoQ8QUEYFAIBAIBALBfSP2wa4e0cCuBxoGt6HLu+OQlAqubw3lwqd7KpxXmBgRvHYKTm28KM7M5cjUT8iLS8O5rTfd33tBYyRB2JpfuX3gPAAmNhZ0X/0iDs0bIssyv8/8gpSwm7prNgpuQ9d3NJpXt4YS8VlVzV5rp+Dc2ouizFwOT/uE3Lg0GnZvRed5o1GYGKEuKePPZVtJOBUJgO/QLgS8OgRkmfzkLI6+9hnkZBqM88hFE2nZM4CSwmK+n7WeuCsxVWyemDWaTiN6YGFrxWy/CbrjPV94gi5jeqEqU5GXkcMPczaQGZ9WJfzAd8bTtKc/pYUl7Jz1OYmXb1excW/VhGEfTMHYzJioYxfY/84mAILfGEG7Z3pSkJ4LwJHV24g6dgGlsZInl7+ARxtvZLWa4+9sIe70Vc19vTsOr55tKSss5sDMjaQY0HNp3YQBH7yMkZkJMcciOLZoMwBmtpY8+dmr2DR0JiculT3T1lGcXYCprQX9V0/GrrELZcWl3Jz1Pndu3DGYpi+/+zIde3akuLCYNTPXEH05usJ5UzNT5q+fj3tjd9RqNWcOn+Hbld/qznd/sjtj3xyLLMvERMaw6rVVFcI3Cm5Dt3fGoVAqiNwaSriBPNNHL8+EaPOMqZ0VAz5/DRd/b679/Dt/vK1JY2NLM4b/8rYuvKW7Azd2nOT8O1sAaBDchsB3NXrXt4Zy0YBfBGn9oigzl2Nav9Bdz8ORkcfeI2zNDi5/vg/Q+EW31S9i37whyDJ/zPzCYFrWxsLla/j95Fkc7O3YuWXDP7pGZUYtmoSf1ic2zfqMuwZ8YsisMXTW+sSbfuOrnA8Y0JnJG2ayYvA8Yi/dqlVzzKJJtO7ZjpLCYr6Z9SmxBjSHzXqGLlrN6X7jKpzr8EQXBr8xCmSZu1fv8OXrH1Wr5dCzLb5LJyEpFSR+f4TYdTsrnLcNfAzfJROxatmYyJfXkrr3NAB2Xf3wXTxRZ2fh60HklLWk7T9Xa/xmL3mdbr27UFRYxKI3lnPt0o0qNp/88AFOLo4ojZSEn7nAyvlrUKvVAIx+fiSjJo1ArVJz4vApPlq6vkr4f1POAHSa2I9O4/qiVquJOhrBoRVbMbezYtSG12nQxpuI7b/zp9ZnGge1IUhbbl/5MZTzlXxQaWJEvw+n4KL1wX2vaHwQoMMrg/EbHYysUhO6aBOxv19CaWrMUz8vRGlihMJIyc19Zzm9ZkeFa/bSK9f2V1OuuVYq145qy7VmT3Ti8TdH4OjrwZYhi0i+qMlfNg2dmHR0FZnRiQAkhN9k00LDvjh80QQe0/rF1lnrib9SVX/grNF0GNEDC1tL5vtN1B337tSCYf+ZgHsLTzZP/5iL+88Y1BiyaALNe7altLCEn2atJ8GARoNWXjz9/hSMzUy4fiyC3e9+B4D7Y54MX/YCJhZmZMal8uMbn1KcV0hDfx9GrnhRE1iSKErIwMHHg9LCYkJqqB/666VjqDYdTW0teUKvfvhNWz94923H47OeQlbLyCoVoe9uIeFchTxuA0QCO4FXDUZe8ND4n58iIknSt5IkPaX9/qUkSS213xdUsjv1QPQVEl2XTuDAuFVs7zkHn6GB2DX1qGDTfEwwJdn5/NRtJpe+OECnBWMAyLgWx6+D3mZH/7fY/9xquq3UVF4AXd4dR1zoRX4OnsOOfgvIuplQQbPb0gn8Nn4V23rNwXdoIPaVNB8bE0xxVj5bu8/k4pcH6KzVLMzIZf/zH/Bz3/kcnfE5vT+aormmUkHXd55jz6hl/NxvARlXY2k1sZ/BOLcMbouzlxtLgl9n24IvGLXsBYN2V46E8cHQt6ocj4u8zerB83lv4Bwu7D/D0Pljq9g07emPg5cbHwfNZM/8r3hi6SSDGk8ue549877k46CZOHi54Rvsrzt3+qv9bBi0gA2DFhB17AIA7Z7pBcD6/vPY/NxKgt9+FiQJr57+2Ddx4+seMzk07yv6LJtoUK/PskkcmvslX/eYiX0TN5oEtwGg0yuDiT0ZyddBs4g9GUmnaYMB6PzKUFIj77Cp/wIOvLmBl9992eB1O/TsQIMmDXixx4t8PO9jXl1muCzdsXEHL/d6mekDp9OyQ0s6BHcAwKOJB6OmjWLWiFlM7TOVz9/9vEI4SSHRQ5tntvaaQ9Ma8sz33Wdy4csDdNHmGVVxKWfe386ppT9UsC/NL+KnAW/pPrlxadw6cE6n9/jSCYSMW8UvPefgXY1fFGfn83O3mVz54gAdtXp/03nRWOK0z+1vArV+8UvwHH6t5Bf3w7BBfdmwZuk/CmsIv+AAXLzcWBT8Gj8s2Mgzy140aHfpyF+8N3SBwXOmlmb0nDSQmPCqjUhDtAoOwMXLnbeCp7N5weeMXfaSQbuLR86zfOj8KsddmrgxcNpw3hu5kEX9ZrBt8TfViykUNF35AhefXcbZ7m/iMrwrFs0aVjApjk/j2uufkrzjRIXjWSevcL73bM73nk3EyHdRFZaQEVrxuRqia69APL0bMfTxMSydvZr5K2cZtJs7+W3G9JnI08HjsHe0o8/gngB0eDyA4P7dGdNbc27T+q1Vwv7bcqZJl5a06Nue9QPn81nfuZza+BsAZcWlHHv/Z0KWlfuMpJAIXjqBnRNWsbn3HJoNCcShkk/4jdb4xHc9ZhL+5QG6zdf4hENTD5oNDmRLn7nsHL+KnssmIikkVMWl7BiznB8GvMUPA96icVAb3AJ8dNf7u1z7qsdMQuZ9Rd8ayrWQuV/ylbZc89KWa2nX49g1+SPizlyvEib7TjKbBr7FpoFvcXiB4bzzWHBbnLzcWR78Bj8v+IKnqvGLyCN/sdZAXZGZkM7WWesJ23XSYDiA5sFtcfJyY3Xwm+xY8AXDq6mPhi99nh3zv2B18Js4ebnRXPsMR66czP73fmTtgLlcOXieoMlPApB8/S7rBr/FR4Pmc3zDHhp3b8W3PWdzeN5X9KomHXtr64dveszErlL9cPdkJN8GzeLuyUg6auuHuyevsKX/Ar4f+BYhs76g73tV0mcJ8Hu1ka8HZLX00D+PKv/zDWx9ZFl+UZblSO3PBZXOPf4gNJ3b+pBzO5nc2FTUpSqid52mcb/2FWya9GvHjZ//ACDmt7M06OYHgKqoBFml6WkxMjXWDcUYW5vj3rk517eGAqAuVVGSU6C7nktlzd2naWJIc7tG89ZvZ2nQVaOZfuUOBclZAGRej0NpZoLCxAhJkkCSMLIw1dyDlTn5yYZ7r1v368jZHRqfvx0ehbm1JTbOdlXsbodHkZOaVeV41J9XKC0q0dnYuTlWsWnetz0XftHcf1z4TcxsLLByqahh5WKHqZU5ceGanv0Lv/xBi0rpUBnnpg2I0fbY56fnUJRTgFsbL3z6tSfyF03DIDE8GlMbSywr6Vlq9RLDNT3Lkb+cwLe/poHr07c9V7TpfWX7H/j20xx3bNqAWK1eRnQirg1dsXOqmlaB/QI58ssRAK6HX8fSxhJ7F/sKNsVFxVz88yIAZaVlRF+OxtFdk3YDnh3A3k17ycvOAyA7PbtCWJe2PmTfTiZHm2du7j6NV6W08urXjmvaOETr5ZmywmKSzt2grLi02nS19XLDwsmGRG1FXNkvbu06jWclPc9+7bip5xceWr8AaNy/Pbl3U8m8Ea87Zmxtjlvn5tyoxi/uhw5tW2NrY/2PwhrCv18HTmt9IiY8CotqfCKmGp8AGDJzNCEbdlFaQzrr07ZfR07vOA7ALa2mrQHNW+FRZBvQ7D6mD8c2HaAgJx+A3PScarVs2vlSGJNE0Z0U5NIyUnaexGlAhwo2RXdTyY+MBXX1Y8rOgwPJOBqOurCk1vgFD+jO3p8PAHAp7ArWNlY4uVQtK/LzNHnAyEiJsbGxbkz7qQnD+eaTLZSWaNIzM71qGvzbcqbjc7058dluVCVlmnvRpmFpYTGx5yv6jGslH7yx5zTelXzCu187IrU+GLXvLI20Pujdrz039pxGVVJGzt1Usm8n49pW05AuLSgGQGGkRGFkVGFI37dfe67cQ7lmoleuXdEr1zJuJpB5K7FKut0rrfp14LzWL+6E38Tc2gJrA3n0TvhNcg3k0cy4VBKvxSLXME/Br197/tqhSbPYajSsne0wtTYnVvsM/9rxB37aMtrZy52YM5pRzKgTF2k1sBMApUUlqLX1c4teAZQWaZ5lUi3pmKRNx6u/nMBHm47efdvrnmvk9j/w0Wr//ewAjC1MK8TTpXUTAFcgpNrICx4qj1wDW5Kk8ZIkXZQk6YIkSZslSWoiSdJR7bEjkiR5au2+lSTpY0mSTkmSdEuvl1qSJOkTSZKuS5J0GHDRu3aoJEkdJElaCZhLkhQhSdL32nN5euFXS5J0WZKkS5IkjdYeD9aG3y5J0jVJkr6XJKnWVydLd3vyEjN0v/OTMrB0r9gwsnCzJ19rI6vUlOQUYGpvBYBzgA9PHVnJyMMrODn/G2SVGutGzhRm5BK0ZjLDDyyl++oXMTI3Ldd0sycvoVwzLzEDS7eKmvo2skpNSW4BZlrNv/Ee1JG0S7dRl5ShLlPxx4JvGHVoJePOf4J9swZc+zHUYJxtXe3JSkjX/c5KSsfWzaG2pDJI4KieRIZGVDlu4+ZAjp5GTlIGNq4V42jjak9OUnk65CRmYKN3H53G92PqgRUMXf0SZjYWACRH3qF533YolArsGjnj2qoJ1h6OWLnZk5tYrpeblIFVpTS1crMnV09P38bCyYb8FE0FkZ+ShYWTDQCpV2Npqm2IuPl749LABSd3pyrxdXJzIjUxVfc7LSkNJ7eqdn9jaWNJpz6duHBS0xPYwKsBDbwb8P6O91mzcw3tgypW3HWVZ6qj6ZBAbu45rftt4V6e5wEKDPiFpVu57+j7hZGFKW2mPUl4paFu60bOFGXk0n3NZIYdWEq3Sn7xMLFzdSAzoXx6S2ZSOnb34RON/Lywd3fi8rHwew5j7+pAhp6P3K+mq7c7rl4ezN2+hPm/LsMvqG21tqZuDhTraRUnZGBq4MW4NlyGdSXl1xO1GwIubk4kJ6TofqckpuBswHcAPt36AYcv7SU/r4DDe0MBaOzdiHad2/Ddbxv5Ysc6Wvq3qBLu35Yzjl7ueHZqwYs732XitoV4tPGuNj5WbvbkVvJBK9eafbBY64NWrgbCav1XUkg8u38ZL4V/RuyJSyRHlE8tu9dyLa+acq0mbBs5M27fUkb/9BYNOjU3aGPj6lCprsj4x3VFddi4OpCtp5GdVLEeAM1zztYrj7IT07Fx1dgkR8XRUtvgbTMoEDv38nzdqK0PM0JW0+aJQM6v36vrEMu7h3TMu4f6AcCnfwcmHF3FsG9ncWi2dpqNJNFj4VgAw8M2gkeCR6qBLUmSH7AQ6CXLsj/wOrAO+E6W5TbA98DHekHcgW7Ak8BK7bHhQHOgJTAeqNIzLcvyPKBQluW2sixXnn8wAmgL+AN9gNWSJLlrzwUAb2iv7Q10/VcRvgdSw6PZ3nseO5/4D/6vDkZpaozCSIlTqyZEbj7CrwMWUlZQjP8rg+tU175ZAzovGMPv878GNL0fLcf1YfvAt9jc4VUyrsZq5mM/QDoM64ZnGx+Obtxd59c+t+UwH/V4kw0DF5CbkkX/tzXZIPyn4+QkZjB5z1IG/GccCX9F6QrNB8HZz/ZgamPJuP3LCJjUj+gr0bpekX+KQqlg7rq57P5mN0mxSQAojZR4NPFg7qi5vDf9PV577zUsbSzrIgr3hO+QLkTt+rNOrtVuxgguf3GAMr3eHdDkUcdWTbi2+Qg7tX7Rpo794mEgSRJPvT2e7cs21W5chyiVSly93Hl/zDt8Mf0jxq94GXPti+iDwMTFDssWnmQcq316yP3yyjMz6dd2KCamxnTs1g7Q+ISNnQ0TnpjM2sWf8d7GxXWuqzBSYG5nyZfDFnFo+Q88/dn0OteoDVkt88PAt/iq82u4+vvgWGnqzoMgPyWLzwPfYPOghYQu+Z4nPp6GqZX5A9d9EPw853O6PNeX6XuWYWplTllpme7c3Yho1vSbze3z13lsRFeUpsZ1rh998Dzf9ZrD7hc/5PFZTwHgP74Pt49FAMTVueB9IsvSQ/88qjxqixx7AT/LspwGIMtyhiRJXdA0egE2A/ors3bKsqwGIiVJctUe6wFslWVZBSRIknT0Pu+hm174ZEmSjgMdgRzgrCzLcQCSJEUATYAq3S2SJE0GJgO80/d5Xnimp+6cpZsD+YkVp1YUJGVi6e5AfmIGklKBiY0FxZl5FWyybiZQll+EffOG5CdmkJ+YQap2qCnmt7MVGtj5SZlYeZS/oVu5O5CfVFHzb5v8JK2mtQVFWk1LNwf6f/EGx97YQM4dTQ+Ro19jAN3v6L1nCJhWrtl9XD+6PNMbgNgL0dh5lL/l27k5kq335n4vNOvamn6vjuDj0e9Qph1e7T6uH93H9AEg/uItbPQ0bNwcyKk0ZSUnObNCT4WNu4Oupyk/rXy4O2zrMZ79WtMRoFapObhEswiv4/i+9Js/BltPF+LPXcdar+fC2s2BvEppmpeUibWenr5NQVoOli525KdkYeliR4FWvySvkIOzNurCPHfyAxJjNUOuT45/kv7P9Acg6mIUzu7OOjsnNyfSkqou/AR4beVrxN+OZ9dXu3TH0hLTuB5+HVWZiuS7ycTHxOPRxIOoi1Ga9PiXeaYmHB/zRGGkIPXSbd2xgkRNnv8bCwN+kZ+UiZW7AwWV/MI5wJcmT3Si41tjMLGxAFlGVVzK7d/O1ugX9U3QuP501frEnQvR2Hs4AZopMvZujmTdo0+YWpnh0awRM35cBICNsx1Tv5zD+hdXVVnoGDyuPz2e0fhIzIWbOOj5yP1ogqbH+1ZEFKoyFWlxKSTHJOLaxJ3bF6Or2BYnZWCqp2Xq4UBxUnoVu5pwHvo4afvPIpepqrUZNXEEw8dqnumVC1dx9dANUOLi7kJqomGfACgpLiH04AmC+3fnzO/nSUlM5eg+zRSaKxFXUatl7Bzt6De4FxOfGwn8+3ImJzGDq9qF6fEXbiGrZSwcrCnIyK1yf3lJmVhX8sG8ZMM+mKf1QVOtD+YlGwhbyX9LcgqI+zOSwJkjsfHUpFvSxVv3VK5ZVVOuVYeqpAxViaZsSL50m+w7KTh7uRN36RZdx/UjULvW5W6VusLhvusKQ3Qd148uWo24C7ew1dOwdXOoMOIAmpEJW73yyNbdkZxkjU1qdAJfjV8BgJOXGy16akZyuozrSyd9DXtrnJo3JPliDFb3kI5W91A/6BN/9jq2ni6Y2Vvh3s7371GB24AVYALkAfPuM6kED5BHqgf7H6DfhVUfrzH6eiqqeUGRZXmjLMsdZFnu0OB6KTZeblg3ckZhrMTXofajAAAgAElEQVRnaCCxh8Iq2N85FEazp7sD4PVEJxJOaubkWjdy1i1qtGrgiK2PB7l3UylMzSY/IQNbb03Hukc3PzKjyueiply4hW0TPc0hgdyupHn7UBjNntJoeutpmthYMPC7mZxZsY2k81E6+/ykDOybNsDMQTMvtWH31mTqLSD7Y3MIqwbNZdWguVwMOUenET0AaBLQlKLcgmrnlRqioV8Txix/kS9eXEWe3rzPPzaH6BYlXgs5j/9Izf03DPClOLeQvJSKGnkpWZrV3gG+APiP7M71Q39p0lNvflyL/h1Iua7pCDA2M8FYO60g/VYiyZdu822vOdw8+BctR3YDwD3Ah+LcAt2Qni6NtHru2kVELUd2IzpEoxd9KAw/bXr7PdWdaO19mNpYoDBWAtD6mWAun71MYV4hAHs37WX6wOlMHzidPw/+Se+RmsZa84Dm5Ofmk5lStZIbP2s8ltaWbHxnY4Xjfx78k9ZdWgNgY29DA68Gut5tqJpnfIcEEmMgz7TQxsHniU7En4zkXmg6tGrvdeqFW9h4uWGl1fM24Bexh8LwNeAXv41cwk9d3uSnLm9y5auDRKzbzdVvD9XqF/XN8c0HWT5oDssHzeFCyFkCtT7hFdCUwvvwiaLcQma3e5GF3V5lYbdXiQmPMti4BgjdfJDFg2azeNBsIkLOETgiCABvraahudbVER5yjuaBmjm+VvbWuHq5kxqbbNA2N/wm5t7umHm6IBkb4TKsK2kHz9+zFoDr8Nqnh/z07Q6e6TuJZ/pOInT/Hzz59AAAWrfzIy83j7SUio16cwtz3bxspVJJ995duH1Ts0vPsQO/06Grpjfb07sRxsZGZKVn8dO3O+qsnLkW8hdeXR4DwNHLDaWxkcHGNUDyhVvYeblho/WJZoMDuVXJJ24dCqOl1gebDurEXe36jVuHwmg2OBCliRE2jZyx83IjOSIacwdrzUsooDQ1xrN7ayJ/Oq5bfHjz4F/43UO5VqJXrvmN7MZNbblWHeYO1kgKTZVs6+mMnZcrGdq8c3JzCB8MmscHg+ZxKeQ8HbR+0TjAl6LcAoNzre+Xk5tD+GjQfD4aNJ8rIedpP0KTZp7VaOSmZlGcW4in9hm2H9GdK9o4WjpqpmtIkkSvV4dz+nvNWphrxyJ0ixxv/3UDuyZuZN9NxS3Ah5Ia0vHvRaaP6dUP+s+15VPduaXNP7aNXXXhXVo1QWliRFFmHgdeX89XXd4ATSffLGATD6lxLasf/udRRappcUB9o50i8ivQRZbldEmSHIBv0fRqb5YkaSIwVJbl4ZIkfQvslWV5uzZsnizLVpIkjQBeBgahmX8dCbwky/J2SZJCgVmyLJ+XJCkTcJFlubSG8A7AeaAz0EIb9kmt/SfAeVmWv60pTl80fE5u1MufLu88h6RQcH3bcSLW7ab9rJGkXogh9lAYSlNjgj+agmOrJhRn5XF02ifkxqbiO7IrbacNRl2mQlbLhK39lTsHNY7n0NKTHqtfRGFiRO6dFI7P3EhJdgEq7WuGZ09/Hn/nOc3WgNuOE7ZuNx1mjiT1Ygx3tJq91k7BSat56BWNZrvXhhLwymCyY8or0r1j36MoPYeWz/Wi9fP9UZepyI1L49iMjYTnGu7JeHrx8zwW5E9JYQnfz17PXW1jYM6+91g1aC4AQ+aNpcPQrpo5jMmZ/LntKPvXbueVLQtxb95I1wDJjE/ji5dWa+Itl7/TDFoyEd+gNpQWlrBr1uckXNJsETVl33I2DNKsYfVo7cUw7bZIN0MvsO8/mq2Xhn84FbeWjUGWyYpLZc+Cr8lLycKuoRPPbZqLLMvkJmVyePYX5MZrKu3eSybQJFijd3DWRt2WVOP2L2PzQM0Kd9c2Xgz4YLJ2G6YLHP2PZljfzM6KJ9dPx8bDkZz4NPZOXUdRdj7u7XwZsOZlkCH9RhxvzVmtW4hYmWlLptE+uD3FhcV8OOtDXe/zuv3rmD5wOo5ujmw+u5nYqFjdwq293+3l4I8HAXjp7ZdoH9wetUrNj+t+5Pc9msVFT8oOujzTTZtnrm07zl/rdtNRm2dua/NM77VTcG7VhCJtnsmJ1cwLf+7Uh5hYm6M0NqI4p4A9Y1eSGaV5ARt7Yg2/TVhNlnbLLlNtkdOwlz+BWr+4se04F9btpt2skaTp+UWQnl8c0/qFPgEzRlCaX6Tbps+hpSfdVr+IUusXv8/cyJjj91/vzF60knPhF8nKysHRwY5pL4xj5OD+9xT2tQ6G9cYsfoGWWp/YNPszXQN5wb5VLB80B4Dh88bScWg3bF3tyU7O5OS2o/y29ucK13nzx0X8smyzLnwZ1Zfhzy5+Ab+gtpQUlvDt7E+5ow3zn32rWTxoNgAj5z1HZz3NP7YdYY9Wc9TCCfgFtUWtUrPv0184t0ez2dLYwqpaDr0D8F0yUbNN39ZjxK7dQZM5o8m9EE36wfNYt/Wh1TezMbKzRF1USklKFueCZgBg1siZgD1L+TNgisGNdWcoDC+km7d8Bl16dqaosIh33lzO1QuaEYKth77hmb6TcHCy56PNqzAxMUZSKDh/MowPFq1DpVJhZGzEOx/Op5lfU0pLS1n77qecO6lp0A4xaazT+DfljNJYydDVk3Fr2RhVaRkhy37QLaJ+48RaTPV8ZudzK7Fp6EyPRRofjNx2nHOf7CZwxkiSL8UQo/WJ/mun4Oyn8cH9r5b7YMdXh9BydBBymZrj727mTuhFnFo0ou+al1EoFaCQiNp7hrMf7UR/mWzvJRPw0pZrB/TKtfH7l7FJr1wbqFeuHdGWa779O9B78XjMHawpzikgJfIOv4xbRdOBHek6cyTqUk3ddfLDXzhx1PC2iyMWT6JFUFtKC4vZOnsDcdo8OnPfSj4YpPGlJ+c9Szu9uuLMtmMcXLudRm28mfT5TMxtLSkrLiU3NYtV/TT52livz23o4kk0D/KnpLCYn2d/TrxW4/V9K/hokGYHnQatvRn19zZ9oRHsWvQtAF0nDaDLOM2OWZcPnuXAez8CEDC8Gz2nDkVVVoaslilJycbex52ywhJC9NJx7P5lfK+Xjv206Xj72AWO6dUPT6yfjrWHI7na+qE4O58OU5+k5chuqEpVlBWV8MfyrRW26XszdosETAQ68JC26bvZsv9Db0T6Rh58JOeJPFINbABJkiYAs9H0EIcDi4BvACcgFZgky3JsDQ1sCc287b5ALFAKfG2ggf0eMAQIk2V5bKXwq4CBgAwslWV5myRJwfzDBnadJc49oKrnbBapvLcdDeoK/QZ2fWBdz/O7jvDvh0fvl78b2PWFaT0XOeMj6n5ubU1U18B+UNTUwH5QGGpgP0iqa2A/KPQb2PWBfT2XM/VbamtIUJTVblSHGNfLoHY5bmplveqBroH9UBEN7Op51OZgI8vyd8B3lQ73MmA3sdJvK+1fmWre5GRZDtb7PheYW0342dqPfthQIFTvt9jUXSAQCAQCwf9b1I/wIsOHzX/7HGyBQCAQCAQCgeCR4pHrwRYIBAKBQCAQPPo8ytvkPWxED7ZAIBAIBAKBQFCHiAa2QCAQCAQCgUBQh4gpIgKBQCAQCASC+0ZWiyki1SF6sAUCgUAgEAgEgjpE9GALBAKBQCAQCO6bR+xfqTxSiB5sgUAgEAgEAoGgDhENbIFAIBAIBAKBoA4RU0QEAoFAIBAIBPeNWORYPaIHWyAQCAQCgUAgqENED/YDZuxCx3rVy9p0sV71xo9vU696kkejetU7ODmsXvVe6F1Ur3oA+4/V7yqVVqY59ar3Wod59ar38fmV9apXdnJ7veoB/Dn5fL3qHe1hXK96W07Vb6/cOWVhveoNLzKtVz2Ax6XSetULeL9lveqFzIyuVz3Bo49oYAsEAoFAIBAI7hu1+Ffp1SKmiAgEAoFAIBAIBHWI6MEWCAQCgUAgENw3sujBrhbRgy0QCAQCgUAgENQhooEtEAgEAoFAIBDUIWKKiEAgEAgEAoHgvhH/Kr16RA+2QCAQCAQCgUBQh4gebIFAIBAIBALBfSO26ase0YMtEAgEAoFAIBDUIaKBLRAIBAKBQCAQ1CFiiohAIBAIBAKB4L4R+2BXj+jBFggEAoFAIBAI6hDRg/0QOHk7ldWhV1GrYVirhjzfybuKTcj1RDacvomERDNna1YM8ufc3XTeP35NZ3M7I5+Vg/zp6etao55p547YvvEqKJUU7PmNvM1bK5y3GDYYy5HDQKVGXVhI9nsfUHb7DgBGPt7YzZ2BZGEJsprUF6ZASekDiR9AYk4hiw9dJjmvCIBPhrXHw9aiZr1rd1m1+0/UapnhnZrzfK+2Fc6v3v0n524mAFBUWkZGXhEnlkzQ6GXm8e7Pv5OcnY8ErHthAA0crGvUc+nZhtZLxiMpFdz5/hhRn+ypcN4xsAWtF4/DpqUn56esI2HvWd25Lj/MxaG9L+lnr3N63Ps16uhj1KYj5uNeBYWCktB9FO/ZatDOuGN3LN94l9yFU1DF3ABA0cgbixfeRDLXPMPct6dCadVn6B7chnZLxiEpFERvDeVqpXgpTIwI/HgqDq2bUJyZx6kp68iPS0MyUtL5/Rexb+2FZKTg9s8niPxkNwpTY/rseBuFiREKIyWxv53l8vu/GLxvqx7t8Fj0EigUZG47ROqG7RXOO70wFPvR/ZBVKlTpOcTN/YjS+FQAWt3cSdF1TX4tTUjlzktL7ylNRy2ahF/PAEoKi9k06zPuXompYjNk1hg6j+iBha0Vb/qNr3I+YEBnJm+YyYrB84i9dOuedA2xcPkafj95Fgd7O3Zu2fCPr/M3J6/HsWrXadSyWuMTPf0rnF+9+zTnohMBPZ9YPA7Q+sT2P7Q+IbHu+X61+gSAQ09/mi6dhKRUkPj9Ee6s21XhvF3gYzRdMgHLlo258vJaUvee0Rzv6kfTxRN0dha+HlyZ8hFp+8/VqGfk3xHzia+CQknJ0d8o3lWNT3TqgeXMd8md/zKqWzdQOLtiveY71Al3ASiLiqTwyw8NhvUMbkP3d8YhKRVEbg0l7LOqPtF37RScW3tRlJnLwWmfkBuXhpmdFQM+fw0Xf2+u/fw7v7+9qcq1n/h6BjaezmztM7/aOI5d9Dz+PdtRUljCF7PWccdAHh0561m6jgjC0taSl/2e0x138HBi8gfTsbCxQKFQ8tN7W7gYGlat1sMo1/Sx69kWr8XPg1JByg9HiP/k1wrn3V8ejOuzvZHL1JSmZxM94zOK41LvS+PkzURWHYzQ1BUBXjzf7bEK51cfDOfcbc01i0rLyMgv5sTc4QBM+/53LsalE+DpxLpnut+T3t9pilJBbDVp2kovTRO1aWrj1xj/957HyNocWaXmxkc7Sdh1+r7iWl+IbfqqRzSwa0GSpNtAB1mW0+rieiq1zMqjkawf0RFXazPG/vAnQT4u+Dha6WzuZObz9blbfDs6EBszYzIKigHo2MiRbc91BSC7qIQhX/9BYGOnmgUVCmxnvU7667NRpaTi/NUGiv44pWtAAxSGHKFgp8bxTbs9js1r08iYMReUCuwXLSBz8QrKbkYj2dhAmeqBxQ/g7YMXebGTD4GNnSgoKUOSah5+UqnVrPj1JBsmD8LV1pKxH+8kyK8xPq72OpvZQ7rovm89cZlrCem63wt/DOXF3m3p0qwhBcWlteqhkPBfMYmTo1ZQmJhO8IGlJIWEkXsjvjw949MIe30DvtOerBI86rO9GJmb0mR8r5p19JEUmE98nfwVs1FnpGK9ZD2lYadQx9+paGdmjumAkZTdjNS7XwWW0+aTv34F6thbSFaGn6GkkGi/fCLHxqygMDGDfvuWEH8wjJyo8nh5PxNMSVY+e7vOxHNoIP4Ln+HUlHV4Du6MwtSY/b3noTQ3YVDoKu7sPEV+XBpHn15GWUExkpGSPjv/Q+LRC3ClUiWvUOCxeAox496mLCkdn11ryDl8huKbd8vT9Mot0ofMQC4qxmHsQNzmTeLu9FUAqItKuPnE6/eenoBfcAAuXm4sCn4Nr4CmPLPsRVYNe6uK3aUjfxH63QHeDf24yjlTSzN6ThpITPiN+9I2xLBBfXl25BAWLPlnjRN9ND5xig0vDdD4xLrdBLX0rOQTgbrvW09e4Vq8nk9sO86LvdrSpVmDe/MJAIVE85UvED5qKcUJ6XQ4uILUg+cp0POLovg0Il//DM+pgysEzTp5hXO95wBgZGdJl9PryAi9ULOepMD8+dfJXzYbdXoq1is2UHq+Gp8YNIKyqMgKh9XJCeTOfakWCYmgpRPY9exK8hIzGLV3MTGH/iIzKkFn03JMMMVZ+WzpPpOmQwJ5fMEYDk77hLLiUs68vx2H5g1xbN6wyrW9B3SgNL+oRv02we1w83JnTvCr+AQ0ZcKyySweVrUxHnHkHIe/28eq0E8qHB/66lOc/e0UR7ccxMO3ITO+fYtZ3aYaFnsY5VoFfQXey1/iyujFlCSm02b/e2SEnKPwRpzOJP9SDBcHzEFdWILr+P40XjiOG1PW3LOESq1mxf4wNjwXhKuNOWO/PExQcw98nG11NrP7B+i+bz0bxbWkTN3vCV2aU1SqYntY9D3GSaLNikmc0qZpkIE0LYhPI9xAmqoKiwmbvp78mCTMXO0ICllGyrGLlOUU3HN8BQ+f/5dTRCRJemgvFpeTsmhkZ0FDOwuMlQr6N3cjNDq5gs2vl+IY5e+JjZkxAA4WplWuc/hGMl29nDA3VtaoZ9yyBWVxCagSEqGsjMLDRzHr3rWCjVxQ7rQKczPdK6lpp46URt+i7KamQJFzckCtfmDxi07PQ6WWdS8NFiZGtcbvcmwqjZxsaOhog7GRkv5tfQi9cqda+/0R0Qxo66PRS85EpVbTpZmmArQwNcbcpOasYR/gS15MMgWxKcilKuJ2/olb//YVbAruppFz9a7BtEo7cYWy/MIaNSqj9GmBOjkedWoiqMooOX0U4/aPV7Ezf+p5ivZshZIS3TGj1h1Rxd5CHavpXZXzckCuel8OAT7k3U4mPzYVdamK2F2naVgpXg37tyfm598BuLv3LG7d/DQnZBkjC1MkpQKlmQnqkjJK8zRxLNO+PCmMlSiMlQa7Oyz8m1JyJ5HSu8nIpWVk7/kdm76dK9jkn76EXKS5VkH4dYzdHO8p7arDv18HTu/QxCUmPAoLa0tsnO2q2MWER5GTmmXwGkNmjiZkwy5Ki2se0bkXOrRtja1N7b3E98Llu5V8wt+b0Cux1drvj7hVySdkujRrANybTwDYtPOlICaJojsav0jZeQrnAR0r2BTdTSU/MhbU1Xd5uQwOJP1oOOrCkmptAJS+LVAnJ6BO0frEqaMYd+xaxc589PMU7fqxgk/cK65tfci+nUyO1ieidp/Gu19Fn/Du145r2/8A4OZvZ2nYVeMTZYXFJJ67gcpA3jC2MKXtSwM59/HOGvXb9evIyR3HAYjW5lFbA3k0OjyKbAN5VEbGzMocAHMbC7KSM6rVehjlmj5WAb4U3k6iOFZTBqTtOoFD/4r5J+fUZV2+yAu7gYn7/ZUBl+MzaGRvRUN7K4yVSvr7eRJ6PaFa+/2XYxng56n73dnbFQvTe2862Af4kq+XpvEG0rRQm6ZypTTNv5VEfkwSAEXJWRSn5WDqaHPP2oJHg//JBrYkSW9LknRdkqQTkiRtlSRpliRJoZIkrZUk6TzwuiRJgyVJOiNJUrgkSYclSXLVhnWUJClEkqQrkiR9CUh6131OkqSzkiRFSJL0uSRJNbf+DJCSV4yrtbnut6uVGal5xRVs7mTlE5tZwMQfTzN+65+cvF11GOzgjUQGNHevVU/p7IQqOUX3W5WaitK5aq+3xYhhuPy8BZtpL5P94ToAjBo1BFnG4cNVOH3zOVZjxzzQ+MVm5mNtaszMPeGM2XKSD3+/hqqGyhggJScfN7vy3nFXW0tSsvMN2iZk5pKQkUsnXw/NfaRmY21uwozvDjH6wx2s2XsGVS0vEObu9hTq9YAXJWZg7u5QY5h/i8LBCXV6+TNUZ6ShsHeuYKNs0hTJ0ZmyiDMVj7trXh4s576H1dLPMX1ytEENCzcHCvTiVZCYgbm7fQUbczd7ChI0lbSsUlOSU4CJgxWxe89SVlDMsIhPGXruI65t+I2SLM0zkBQSAw4tZ/jF9ST9fpn08Kq9P0ZujpQmlg8QlSal19iAdhjdl9zjf5Wnj6kJPrvW4LNjNTZ9A6sNp4+dqwOZCeWamUnp2Lnd+3Ns5OeFvbsTl4+F33OY+iIluwA3W0vdb1dbC1JyavMJTVlyJzUbazMTZmw6zOi1v7Jm79lafQLA1M2BYr38U5yQjul9pKfuXod1JfnXk7XaVfGJ9FQU9hXLNaVXUyRHF8rCqw6tK5zdsFq5EatFa1G2aG1Qw9LNntyE8kZpXmIGlm721drIKjUluQWY2VtRE51nP0XEF/spq+Ulwt7VgXS9PJqRlI79fbxY/vrhNh4f1oMP/9zIzG/eYsuir6q1fRjlmj6mbg6UxJfHtSQxA5Ma4uryTG+yjlU/3cUQKbmFuOlNN3S1MScl1/BLQUJWPglZ+XTycrkvDX3MKqVpYWIGZv8gTe0CfFAYG5F/O7l244eAWpYe+udR5X+ugS1JUkdgJOAPDAQ66J02kWW5gyzLHwAngEBZlgOAH4E5WptFwAlZlv2AXwFP7XUfA0YDXWVZbguogLEPIg4qtUxsVj5fPN2JFYP8WXLoCrlF5T0hqXlFRKXl0qW26SH3QcGOnaQ8/Rw5n23EeqJmLiZKJSZtWpP1zlLSp7yGWVA3TNq3+9da1cWvTC0THp/Jm92bs+XZLsRlF7I7Mr72C94jByOi6dPGC6VCob0PNeExScx4sjPfvzaM+PQcdp//98P99Y4kYT52KkXfr696TqFE2awVBZ8uI2/xaxh36IaRX0BVu3+BY4APskrNzoBX2d35TVpMGYSlp+YFQFbLHOi7gF3tp+PY1gdbA8Pl94PdsGDMW/uStnGH7ti1bs8TPXQGsa+/j/t/XsTE0+1fadSGJEk89fZ4ti+rOq/2v42DEbfo01rfJ2TCbycx44lOfD99KPEZuew+H1Uv92LiYodlC08yjtUyPeRekCTMx02jaPNnVU6pMzPIeWUMefMmU7jpMyynLwTzmtd51BVOLT2xbezKrQPnH7hW4JDunNh+jDe7TOaDScuY/OFr9zbd5xHHaWQPrPx9iP9sV+3G/5CDV2Lp81hDnV88LExd7Gi/birhb3wuJjv/F/I/18AGugK7ZFkukmU5F9BfVbBN73tD4KAkSZeA2YB2vJsewBYAWZZ/A/6ehNUbaA+ckyQpQvu76uo9QJKkyZIknZck6fzXf1yucM7FypRkvbfm5LwinK1MK9mYEeTjgrFSQQNbCxrbWxCbVT6N49CNJHr5uGKsrP3xqVLTULqWv4UrnZ1RpVY/nbzw8FHMenTVhk2lJOIi6uwc5OJiik6dwbh50xr1/k38XK3NaOZsTUM7C4wUCnr6uHAtJadmPRtLkrLyyvWy83HR673T50DELQa09dX9drW1pLmHIw0dbTBSKujZqglX49INhv2bwsRMzD3Ke1bM3B0oTKx+6LUuUGekoXAsf4YKByfUmXqjGmYWKBp5YbXwQ2zW/oDStyWWM5ei9GqGOiMV1bWLmqkhJcWURpxB2aRZFY2CpAws9OJl4e5AYWJmBZvCpEwsPDQ9MJJSgYmNBSUZeTQe/jiJxy4il6koTs8h7dwNHPwrukZpTgHJpyJx79mminZZUjrG7uUvi8ZujpQmVX0Oll39cX5lFLdfWopcUlYeXjv0XXo3mfzTlzHzM+iWBI3rz4J9q1iwbxXZKVnYe5Rr2rs5kpV0b8/R1MoMj2aNmPHjIpae+ASvgKZM/XIOnq0N69Y3LrYWJOmN4iRnF+BiU41PXLjFgLbl9+1qa0lzdz2f8PPkanzNPgFQnJSBqV7+MfVwpPge01N330O7kLr/LHIt6zzAgE84OqPO1CvX/vaJ/6zFZt1WlE1bYjl7GUrvZlBWqvEHQBVzA3Vygm6kR5/8pEysPcp7HK3cHchPyqzWRlIqMLG2oCgzj+pwa98UlzZejD/1ISN3/Ac7L3eG/1Q+97/3uAEs3vc+i/e9T1ZKJo56edTBzZFMA35RHUGje3P2t1MARIfdwNjUBKtqFqs+jHJNn+KkDEwalMfVxN2BEgNxte3ehoavj+TahBUVyoB7wcXanKTs8no0OacQF73RVn0OXLnLgFaeBs/dK0WV0tTc3YGi+0hTIytzArfMJnLlT2SG3fxX9/IgkWXpoX8eVf4XG9g1oT9Oug74RJbl1sDLgFktYSXgO1mW22o/zWVZfseQoSzLG7U95R2e796qwjk/N1tiMwuIzy6gVKXm4PUkgr0rDkP19HXh/F2NI2YWlnAns4AGtuUFwYHriQxoUfv0EIDSq9cwatgApbsbGBlh3qcXRSdOVbBRNmyg+276eCBldzW9xsVnzmHk44VkagpKBaYB/hUWRxri38TPz9WW3OIyMgo0Q6fn7mbg7WC4YaDTa+RMbFoO8Rk5lJapOBgRTVDLqgVjTEoWOYXF+Dd2qRA2t7CEDO184bM3E/B2rTrHUZ+siGisvN2w8HRGMlbScFgXkkL+qjHMv0V16xoKtwYonN1AaYRJYC9K//qz3KAwn5wpw8l541ly3ngW1c1I8j9YiCrmBmUXz6Fo5A0mpqBQYPSYP6r421U0MiJuYe3lhmUjZxTGSjyHBhJXKV7xIWF4Pd0DgEZPdiL5xBVAs1DHtVtLAJTmpji2a0ruzQRMHawxttH0DCrNjHHr0Yqcm4lVtAsuRmHaxAPjhq5IxkbYDu5BzuGzFWzMWnrTYNkr3HlpCar0bN1xhY0lknaOsNLeBov2j1EcdRdDHN98kOWD5rB80P+xd97hURVtH75nd9N7IYWakNBrqKEHpYnSEbHRVJpiA5RiRRDsKIiIXUFARARpoSO9JfROSALplZRNNsVmKpYAACAASURBVNnd8/2xS5JNJRIDr9/c18VF9sxz5rczZ+bMnOc8M/sap7YdJXioqSz+QQ3IydSWGWtdnNzMHKa3eZY3ur7AG11f4Hr4Fb569sO72kWkKmlW+3afyDT1iVMR5fSJvGJ9wpPM3CJ94lpchX0CIDP8Gvb1fbE19wuvwZ1JDq2cl9Z7yJ2FhwAYrhXrE50fIP94kftaTjYZzw0mY8rjZEx5HMOV82R/NBtDxGWEkwsI09Cn8vJF5VsLY0LJdplwKgIXPx+czH2iwcBgrm+3DEu4vj2MxsNNO0oEPtyBmwfOl8inKGd/2ckP7abwc+dXWDt0DunX41g3Yl5B+s5ftvJW/2m81X8aYduO0mVoDwACzG20tFjrskiJTaJpF9MDrW9ALaxsrMhMKd1hcS/ua0XJOnkVO39fbOp4Iaw0eA7qSmqx9uPQ3J+ADydwcfQC8ssoR3k0q+VOdGoWMWlZ5BsMhJ6LpkfDmiXsridnmPpF7btb55F+8hoOReq0ViXqVFip6fDDK9xYs69gZxHJ/x7/xV1EDgBfCyHmYyrfI8CyUuxcgNvxB6OLHP8beAKYK4R4CLgddLcTWC+E+ExRlEQhhDvgpChK+TPOYmhUKl5/oCmT/ziOUVEY1Kw2AZ5OLDl4habeLoQEeNG5nieHopIZ+tM+1ELwcvdGuNpZAxB7S0t8Zi5ta99hLJfByK1Pv8Djsw9BrUK7cQv665E4PTuWvIuX0O0/iMPwIdi0awt6PcbMTNLnLgBAycwie9UaPL9bCijoDh5Bd7D8rYLutnyvdm/ExLVHURRo4u3M0BZ1ytdTq5gxuDOTvtmC0agwqEMjAn3cWRJ6nKa1axDSrB4AW82LG4u+IlWrVLzySEcmfL0ZBYUmtTwZ1rFxuXqKwcjpWT/SeeUM03ZWK/eQeSmGxq8NJ/1kBPHbwnBtXZ+O37+ClasDPr3b0Hj6cHb1MEUgdf3zLZwa1ERjb0vfsEWEv/oNiXtOl6uJ0UjOj4tweP0D05Zke7dgjInEdtgY9Ncvow87WOapijYL3ZY1OL33FSgK+aeOlIjTvl2u47N/JOTX1xFqFRGr9pJxOYYW04eReuo6MdvCuLZyD52+mMQjBz4hLz2bA5NMsfpXfthOx88m0H/3ByAEEav3kn7hBq5N6hD8+USESgUqQfRfR4jdEY578TW7BiOxby/F/+d3Tdv0rdmB7ko0Xq88Sc6ZK2TuOIrvzLGoHGyp++UMoHA7PtvAOtSa9zyKoiCEIGnp7xa7j5TF2d3hNO/Zhjl7vyAvJ4+fpxeGEsza/CHv9zddryEznqT9oK5Y21nz/qGvOLB6F5sWrqkw/8oy/e0FHAs/TXp6Bg8OforJzzzNsAF9/1FeGrWKGYM6MenbraY+0b4hgT5uLAk9QdPankX6RAT9WtUv2Sce7sCEZVtQwNQnOjSqUFMxGLk883tar5qNUKuIXbmb7Es38X9tBJmnrpEcegKn1gG0+GEaVq4OePZpi//0ERztMRUA2zo1sK3pSfrB8ieoBRiN5Hz/BQ6zPjRvXbkF481IbB8diz7iEvoTZfcJTZNW2I4YCwY9imJE+81nKNmZpZbp7zd/YtDy10zb9K3eS+rlGDpMHUbi6etEbg/j/Kq99F44kaf2fYIuPYvQ5wt38hh18DOsnexQWWmo37cd659cYLEDSUWc2h1Gy55t+Gjvl+hydHw7/cuCtDmbP+at/tMAGDHjaToN6oa1nQ2fHVrG3tU7+HPhb6yc+xPjFkyi7zOPoCgK305bXJbUvbmvFcVgJGLWtzRd+SZCrSJh1S5yLt+gzvSRZJ26Stq249R7cxQqB1saLTO1GV1MMhfHLLhjCY1KxYyH2jBpxd+msam1P4FeLizZfZamNd0IaWRyNG01L24sHk4z9oddRKZkos3T0+ezv3hnQHs6B5Ydjna7TjuZ6zS6jDrtUKxOd/d4jVoDg/EIboy1myN1HzM5AsJe+pqMchbwS+4/hPIfjOsRQryDaZKcACQCWzHFS09TFOW42WYQ8BmmEJBdQHtFUUKEEB7ASqAWcBDoA7RVFCVZCPEYMBOT5z8feF5RlHJnnNqlL1VrBaf/XImbWhXgOqrkK/9/E1Gz/Al3VRM6vnILae6WkAfjq1UPYMvuO3sbUlU0t6m89+luWKKU/hr43+KL43c+6FcF+gO/V2xUxRwa/+/HEBelTUjl9ju+W5YfrFWxURVyTP3Pd+D4JwzJLbkz1b+Nl9BVbFSFBH3ctFr1tk29w+37qpBB8b/e8/iIIzWH3vNJZMfYP+55PZTGf9GDDfCxoijvCCHsMXmkTyiK8k1RA0VR1gMlVkkoipKCaVJdAkVRVmMZxy2RSCQSiUQiuU8RQvQDPgfUwLeKoiwolv4q8CygB5KAcZWNTiiN/2oM9jLzQsQwYK2iKNXrhpRIJBKJRCL5j6PcB//Kw7yd8peYdpVrCjwuhCj+eiMc0w8KtgR+Bz6sdEWUwn/Sg60oyhP3+jtIJBKJRCKRSO4pHYCriqJEAAghVgGDgIIFH4qi7C5ifxh4qiqE/6sebIlEIpFIJBLJf5yiWyOb/40vklwLKLry/ab5WFk8A2ypiu/1n/RgSyQSiUQikUj+Xe6HX1JUFGUZpe8WVymEEE9h+nHCHnf9pZATbIlEIpFIJBLJf5MYoOj2Y7Up3KK5ACFEL2A20ENRlCrZ8kaGiEgkEolEIpFI/oscAxoIIfyFENbASGBDUQMhRBDwNTBQUZTEqhKWHmyJRCKRSCQSSaW5n3+qHEBRFL0Q4gUgFNM2fd8rinJOCDEHOK4oygbgI8ARWGP+gaFoRVEG3q22nGBLJBKJRCKRSP6TKIqyGdhc7NhbRf7u9W/oygm2RCKRSCQSiaTSGO/1F7iPkTHYEolEIpFIJBJJFSIn2BKJRCKRSCQSSRUiQ0QkEolEIpFIJJVG4f5e5HgvkRPsfxklLb1a9RwaqatVT1hZVa9e7YbVqufB4WrVU9dwqFY9AGM13yCTtHbVqqe3U6pX78Dv1aqn6TK8WvUA9tueqla91rnVG+lZ3QNjkjGnWvWirKu3DwK0802rXsFqHptqiCrZOlnyH0JOsCUSiUQikUgklcZYvf6L/ylkDLZEIpFIJBKJRFKFyAm2RCKRSCQSiURShcgQEYlEIpFIJBJJpanuNTz/S0gPtkQikUgkEolEUoVID7ZEIpFIJBKJpNLIbfrKRnqwJRKJRCKRSCSSKkROsCUSiUQikUgkkipEhohIJBKJRCKRSCpN9f4E1P8W0oMtkUgkEolEIpFUIdKDLZFIJBKJRCKpNHKRY9lID7ZEIpFIJBKJRFKFSA/2PUBdvwXWvZ4ElQr9yb3kH95U0qZxB6y7DQYFjInR6DYsBcDmsamoawZguHkF3ZrP7khP07w9tk9MBpWK/L+3oNu8qnS7tt1weOFtst6djCHyMqjV2I2dirpeA9O5B3eg27SyQr0DEYl8uPMsRkVhSMu6jAtuUMIm9GIsXx+4BEBDLxcWDGgDwMI959kXkQjA+E4N6NukVsV6Jy/ywc8bMBqNDOnZgWcGPWCRHpecxhtfrSYzOwej0chLj/enW1AT8vUG3l22hguRMRgMRgZ0a8szgx8oQ6UQt56tqf/eWIRaRfyKndxc/KdFunNwEwLmjMWhaT0uTvyM5I2HC9L83nwK915tEUKQ9vdpIt74vkI9AHXjNtgOfQ6EivzD28nb+btFuqbDg9gMHItyKwWA/H2byD+8DVUtf2wfnQw29qAYyNv+G/rw/aVq+Ia0pN17TyNUKq6u3MP5xX9ZpKusNXT+YiLuLfzRpWWyf+Jism8m4zekM00mP1xYP03qsKXvG2RExNPt6xdx9PNCMRiJ2R7OyfdXl6rt3rMVDeaa6jRuxU6iFq23SHcNbkKD90bj0LQe5yYsJGnjEdPxLs1oMGd0gZ19YE3OTfyc5C3HKqzTkW+PpUXPNuTl6Phh2pdEn7tewmbwtMfpNLQ79i6OTGn2tEVau4c7MeDlEaAo3LgQxbcvfV6m1oFLN/lw/WGMipEhHRoxrmcri/SPNhzm2LU4AHLz9aRm5bJ/jkkvLi2Ld3/fR8KtbASCReP6UMvdqcLylccb73/K3weO4u7myp/Ll95VXn3fGUWDnq3Iz8lj/bSviT8bWcLGt7kfAz+ZiJWtFVd2nyL0nZ8B6PHyUIIe74k2JROAXR+t5uruU9Tv2pwHZoxEbaXBkK9Hs/pz9GfCLfK0CuqA/XNTQKVCt30TuWt/LfX7WXXqjtOM97g1dTyGq5fQtGqH/ajxoLECfT7aH78qkfdt6oS0pMs7TyPUKi6s3MPJJSX7xAMLJ1KjhT+5aZnsmLyYzJvJ1O7WnI4zHkNlrcGYp+fQvJXEHjwPQP9fXsPeywWVWk3c0Uvsf+NHUEqv2wnvTqB9z/bocnR8OvVTrp29ZpFuY2vDzK9m4lvPF6PRyJEdR/hxwY8F6d0e6caTrzyJoiio1WpUahW6HB2Hpn5HUinXyauFH70/mYDG1prI3SfZ+/YvJh0XB/oveQHn2jXIuJnE5smL0N3Smq7hu0/j17M1+hwd26YuK8i3y6yR+D/QGiEE0fvPwtIPEDY2uM9/G02tmmA0knctEuuGAQiViuwNm8n82XJ8cRgyAMfhg1CMRpScHNLmf4r+elRButrbC+9VP5Dx7U9krfit9EoswoErsXy46bhpbGobyLjuzSzSP9p8gmPXEwBzP8zOZf/sEcSmZ/Hqr39jVEBvMPJ4cEMe7dCwQj3Xnq3xnzMO1CoSf91JzOJ1FunOwU3xmzMWhyb1uDzxU1I2FY4V9WY/hVuvtgDc+GwNKRsOVqgnub+QE+zqRgis+4wid9WHKBmp2I55B/2VcJSU2EITN2+sOj1Czi9zIVcL9oWDaf7hLeitrNEE9bxDPRW2T08h++PXUVKTcHzrS/JPHsQYG21pZ2uHTe8h6K9dKDhk1b4HaKzIevM5sLbBad535B3ehZKSUKacwagwf8cZlo4IxtvJjid/3kePQB8CPAvLEJWaxfeHr/Djk11wtrUmNVsHwN/XEriQcIvVY7qTrzfyzKqDdKnvhaONVTl6Rt7/YR1fzxqPt4cLT8z+gpC2zQio7V1g8826nfQNbsmI3p25djOBFz74ji2LmrD9yGny9HrWfjiVHF0eQ6d9TL8uralVw73s+lSpCJj/LGdHzEEXl0rrrQtI3XYc7eWbBSa6mGQuvfQltScPtDjVqV0jnNs3JqznVABabXgPl87NuHXwXNl6YLqGwyei/epNlPQU7F/9FP3ZIxgTbliY6cP3oVv7tcUxJU9HzvJPUZLjEM7u2E/9DP3FcMjJLiYhaP/+aHaNXIA2LpV+m+dwM/QEGVcK22XA4yHkpWezoctU6g0KJuiNkeyfuJjIdQeJXGe6+bs2rk33718h7Vw0ajtrLizdRMLBC6is1Dz42yxq9myJsuN4sToVNFrwDOEj5qKLTaFd6HySQo+jvRxTYJIbk8z5l5ZQd9IAi1PTD5zj2IOvAaBxdaDT4UWk7jlVfn0CzUOC8PL3ZXbIFOoHNeDJec8xf/CsEnandx5n909bmLtnkcVxLz8fHpo8hA+GvYE2IxsnD+cytQxGI/PXHWTpc/3wdnHgyUUb6NG0LgHebgU20wcGF/y98sA5LsakFHx+Y/Venn2gNZ0a1kKry0eIu38lO7h/b54YNpBZ7318V/kE9myFh78Pi3tMpVZQIA/PHct3g98uYdd/3jg2zviWmPCrPPHTawSGtOKq+Tod+W4Lh5ZttrDXpmWyatzHZCWmU6NhbSasmk36uOGFBioV9hNeJvPtqRhTknD++Gvyjh7AeCPKIh/s7LAdMBz9pcI+pmTcInPeTJTUFNR1/XF65yPLvM0IlaDr3NFsfGIB2XGpDN04h6jtJ0gr0ieajAxBl57Nym5TCRgYTMdZI9kxeTE5qZlsGfcJ2oR03BrV5pHlr/FL+xcB2D5pEflZOQD0+fpF6j/SEf7aXEK/Xc921PKrxbPdn6VRUCNemPcCrwx6pYTdH8v+4PSh02isNLy/8n3ahbTj+J7j1PSryYjJI5g2dBqN2zRm6PihzHp8Fo2CGvH6vOdZPeidEnn1nDeWna9/S3z4NQb9NJ16IS2J2nOads8P4MaB8xxf8hftJg+g3eQBHJi/Gr+erXD18+Gn7lPxCQrggXljWD3oHXzbNqBmu4as6DMTgEfXvoWmTSvyzl0ka8Vv6E6cBGtrau1YT9qHn6PdugOvH78iZ99Biwm0dttOsteZHmpsu3XG9aVJJL88oyDd5eVJ5B46WqIcpWEwGpn/1zGWjnkAb2d7nly6lR6NaxPg5VJgM71/24K/Vx6+xMW4VABqONrx8/i+WGvUaHX5DFu8iR6Na+PlbF+2oEpF/fef49xjc8iLS6Hllg9I3XaMnKJjxc0krr60mJqTLMcKtwfb4NCiPid7TUVlbUXzP+aQviscg7nd3E/IRY5lc9+GiAghbIQQO4QQJ4UQjwkhSo5+d55XVlV+t7tBVbM+xrQElPQkMBowXDiCpmEbCxtN6x7ow3aaJtcA2syCNGPUeZS83DvWU9dvhDExFiUpDgx68o/uwSqoSwk72yFj0G1eDfl5hQcVBWFjCyoVwsoGRa8v/E5lcDYujTquDtR2dcBKraJvk5rsuRpvYfPH6WgeC/LD2dYaAHcHGwAikjNpW8cDjUqFnbWGhjWcOXA9qXy9q9HU8fGktrcHVhoN/Tq1Zs/xYhNWAVk5pkl8ljaHGm7Otw+To8tDbzCgy8tHo1HjaGdbrp5TUCC51+PJjU5EydeT9OcB3Pu2t7DR3UhCeyEKjMVuPYqCysYKlbUGlY0GYaUhLym9XD0AVb0GGJPjTA82Bj368L/RtOhY4XkASlIsSrLJM6pkpKJk3UI4lJwMegQFkBmZQFZ0EsZ8A1HrD1Onb1sLm9p92xCxZh8A0RuP4t21WYl86g3uTNR6kxfGkJNHwkHTA5sx30DqmUjsfEs+vDi3CUR7PZ7cqESUfAOJfx6kRj/LOs29kUT2+WgwluHqA7wGBJOyKxxjTl6ZNrdp3ac9h//YC0BE+BXsnRxwqeFawi4i/Aq3SrlG3Ub2YvfPW9FmmB5UMlMyytQ6eyOJOp7O1PZwxkqjpm+r+uw5F12m/ZaTEfRrHQDAtYQ0DEaFTg1Nb3Lsbayws757v0i71i1wcb47LzhAo95tObXW1CZiwq9i42yPo5dlPTp6uWLjaEdM+FUATq3dR6M+bUvkVZT4c1FkJZrqPenyTbC2MXmczWgaNMEYH4MxIQ70evL27cK6Q9cS+dg/8Qy5a39FyStsE4brV1BSTQ8whujrJfK+jVfrADIiE8g094lrGw7jV+x7+/Vpw+XfTeWP2HSUWl1MfSLlXBTaBNP3T7t0E7WtNSrzdbs9uVZp1KisNKCU3qaD+wSzc+1OAC6FX8LB2QE3LzcLG12ujtOHTgOgz9dz7ew1PHw9AOj3RD82/ryRrFtZBPcJJnRlaEFeNs4O2Be7TvZerlg72hEfbvKSX1i7n4C+7QAI6N2W8+Zynv99HwF9TMfr92nLhbWmN2Lx4dcK8lUUBbWNFSorDWprK1RWagypaSg6nWlyDVg3DMSQmo5Qq0GvJ2f7Luy6d7b4Tkp24Xgj7Gwt6sq2excMsfHoIyJLrb/inL2ZQh0PJ2q7O5n6YYt67Llwo0z7Lacj6dfCDwArjRprjRqAPIMRpYxrVhTHoEByIuPRRSeg5OtJXr+/5Fhx8/ZYYZmfXcM6ZBw5DwYjxhwd2eejcO0ZdEfllNw/3LcTbCAIQFGU1oqirAb+8QT7fkI4uqFkpBZ8VjJTEU6WN02Vuw/C3Rvbp9/AdtSbqOu3+Od6bp4oqYkFn42pSQg3D0u9eoGo3L3Qnz5icTz/+N8oulycFv6G0ycr0G1dg5KdSXkkZuXi42RX8NnbyZbETMsHgqjULKLSshm9Yj9P/7KPA+aQkIZezhy4nkhOvp40rY5j0SkkZJT/xJ6YloGPR+FA4eXhQkLaLQubScP6sGl/GL2fn8vzH37PjDGDAejVsSV2Ntb0mvQefafMY/QjPXBxLMcjAdj4uqOLTS74nBeXgk0pk8bSyDxxmVsHz9Hx1Dd0PPUNabtPknMlpsLzVC4eGNMKNY3pKQgXjxJ2mpadsX/tC2zHzEC4epbMp24D0GhQUuJLpNn5uKGNLWyX2rhU7Hwt26W9jxvZZhvFYCQ/Q4uNu6OFTb2BHYn881CJ/K2c7anVO4iE/SW99TY+7uhiCz22utgUbHzurE6L4j24CwnrDtyRrZu3O6lFNNPiU3CthKZ3fV+8/Wvy+u/vMXPdPJr1aF2mbeItLT4uDoXnutiTmJFdqm1sWiaxqZl0CPQFICrpFk621rz68w4eW7iOTzcexVD8we0e4uTjTkaResyMT8XJ27LdOHm7kRFf2LYy41JxKlLX7Uf1YcLW+Qz46DlsS/EINunfAUPEZdDnFxwTHp4Ykovc11KSUHlYtnl1/QaoPL3IP3GYsrDq3KNE3rdx8HEjq0ifyIpLxcHHrUwbxWAkL1OLrZtln6jfvz3JZyIx5ukLjj28/DVGhy8hPzuXiE2le2A9fTxJiit0MCTHJ+PpU7JfF3wXZwc69OrAqQOmNwO1/GtRq34tPv7jY7o/0h1378I6z4pPxbFYWRx93Mgqcp2K2th7OqM1P/BoE9Ox93QuPCcupcQ58WFXuXnwPM8dX8yzxxcTtfcM+kjLh0p13VqonB3JPRYGgCExGXWNGiXLNXwQPmuX4/LCeNI/XQyYJttOo0aS8e1PZdZHcRIzcvBxKWxf3i72JGaWPr7EpmcRm5ZFh/qFb0Ljb2Xz6OJN9Pt4HWO6NS3fe43pvpYXU3SsSMXap+R9uzSyz0fiGhKEys4ajbsTLl2aY1Pzzs6V3D9U6wRbCOEghNgkhDglhDhr9kz3E0JcFEKECSG+EEJsFEJ4AcuB9mYP9hrAzvz3inLy/1MIcUIIcU4IMb5Y2mfm4zuFEDXMx1oLIQ4LIU4LIdYJIdyEEI2FEEeLnOcnhDhj/rutEGKvWSNUCOFbxvcYL4Q4LoQ4/v3Ry5WvKJUalZsPuSvmo1v/FdYPjTXF0P4bCIHdyEnkrCoZh6n2bwxGI5mvPEbm9Kex6TscUaPUIlcKg1EhOi2bb0d2ZsGAtswJPUVGbj6d/b3oWt+L0SsOMOOvMFrWdEOluvvX4VsOhjOwezu2f/kGX742jtlLVmI0Gjl7LRq1SsX2JW+y+fNZ/Lzpb24mpFSc4T/E1s8H+wa1OBI0gSOtJ+DatTnOHZtUSd76s0fJnvMM2g9fxHD5JLZPvGyRLpzdsHvqVXJ//bxMj9nd4hEUgCEnj1uXblocF2oVXZc8z6XvQsmKLv+NxD/F2ssVh8Z1Sd1dcXhIVaBWq/H29+Xjke/wzZTPGTV/AnYVDLh3QujJCHq18EetMt2aDUaF8Mh4Xn24AyumDCImNZMNx6/ctc79wvHlO1jU/RW+fmgWWYnp9H7zSYv0Gg1q8eCMkWQv+aRyGQuB/bjn0f6wpEwTdR0/7EdNqHzelcCtYS06zhrJ3zMt11pseupDfm73AmprTYHX+25QqVW8vuh1Nvywgfho0wO0WqOmpl9NXh/xOlfPXmX4pOE4ODtUkNOdUdEdxKWeN+6Btfiu44t812EKdTo3xbp1EUeRWoXTyOHkR0RhiI0rN6/s39cTP+wpbi1ehtPYpwBwfm4MWSt/R8m587e5lSH0dBS9mtUt6IcAPi4OrHnhYTa8PJC/wq+T8i+Ga9zae4q0XWG02PA+DZe8QuaJSyj30YN1UYz3wb/7ler2YPcDYhVFaaUoSnNgK/ANMABoC/gAKIqSCDwL7DN7sB8Fcsx/P1lG3gDjFEVpC7QDXhRC3H7kcwCOK4rSDNgL3A4S/Bl4XVGUlsAZ4G1FUS4C1kIIf7PNY8BqIYQVsAgYbtb4HphX2pdQFGWZoijtFEVpN67YQgglKw3hXOhJEE7uKJlpljYZqRiuhIPRgHIrGSU1HpW7N/8EJS0Z4e5V8FnlXgMlrcgk0tYeVS0/HGd8gtNHy1EHNMH+xTmo/RpiFfwA+jPHwGBAyUzHcPUcGr/yF3Z4OdoSX8QrkJCZi5eTZdiFt5MdPQK9sVKrqOVqTz03R6LTTB695zo15LcxPfj6sU4oQD238gcELzdn4lMKX+EnptzC283Fwmbd7mP07WRaVNaqoR+6fD1pmVq2HAinc6tGWGnUeLg40rqhH+ciLCeHxdHFpWJTs9CLZO3rgS4utZwzCvHo34GME1cwanMxanNJ2xWOc7uKF8oYb6WgcivUVLl6FCxmLECbCQaThyz/0DbUdQIL02zssHvubXSbfsEYdalUjZz4NOxrFrZLe193cuIs26U2Pg0Hs41Qq7BytkeXWhh9VW9QcKne644fPUPG9XgufRtaqrYuPtXCO2NT0wNd/J3V6W28BnUiactRFL2hTJuQp/vy1uaPeGvzR6QnpuFeRNPNx4P0SmimxadwcscxDHoDyTcTSbgeh7df6Q+fXi72xN8q9Fgn3NLiVcZEZ+upCPq1rl/w2dvFgUa+HtT2cEajVtGzWV0uxPx7D4F3gsrWmfGb32f85vfJSkzHuUg9Ovm4k5lg2W4yE9JwLuKxdvJ1J9Nc19nJGShGBRSFsJW7qdUqwCKvEcteYf2rSzHGx1rkqaQko/Yscl/zqIExpdBbKOzsUdfzx2nuQlyWrULTqClOs99HHdjIlO5RA8eZc8le+H6JvG+THZ+GY5E+4ejrTnZ8Wpk2Qq3C2sme3DRTn3DwcafvNy+z++WlZEQlUhyDLp/IbWH49SkMEXxk1CMs2rKIRVsWkZqYSg3fQo+up48nyfHJJfIBeHHBi8RExrD+u8LFwQ5ODgS2CGThXwuJj47nVsotavrVNJXFx52siAiiuAAAIABJREFUYmXJik/Dsch1KmqjTc4oCCmx93IlJzmj8BxfjxLnBPRrR3z4VfK1OvK1OiL3nMK6edMCO7eZU8m/EYOSXdgv1F6eGJLKfgDP2b4bux6m8EbrZo1xeWECPut+xXHkMJxHP4HD8MFlngvg5WxH/K3CkJOEW1q8irxtLcrWM1H0a+lXRj72BHq7EBZZvrNAF5+Kda2iY4U7efF33ndjPl/Lqd7TOD9yDiDIuVb+g4jk/qO6J9hngN5CiA+EEN0Af+C6oihXFFNQ0/K7zP9FIcQp4DBQB7i9fYURuL19wXKgqxDCBXBVFGWv+fhPQHfz379hmlhj/n810AhoDmwXQpwE3gBqV/YLGmOvo3LzRrh4gkqNuklH9FcsV7AbroShqtfY9MHOEeHugzG95A36TjBcv4TaqxbC0wfUGqw6hJAfXmQ1ck42mS8OI3P6U2ROfwrDtQtov3gLQ+RljKmJaJqYX31b26Ku3wRDXNmxowDNfF2JTssmJl1LvsFI6IVYegT6WNj0bODD8WjTjSZNqyMqLYvarvYYjArp5vjZy4kZXEnKoJN/yVeGFnoBdYiOT+ZmYir5ej1bD52kR9umFja+nq4cOWvy+kXEJJCXp8fd2QEfTzeOnjPFhWpz8zhzNQr/muXrZZ68im19X2zqeiGsNNQY3IXUbRXvWAGmxY8unZqCWoXQqHHp1MxicWRZGKOvoPKsiXD3BrUGTVB39GctXysL58LXvZrmHQoXQKo12D0zm/zju9CfKnsVesrJCJz8fXCoUwOVlZp6g4K5uS3MwiZmWxj1H+0GQN1HOpCw/3yRLyCoN6AjUestJ9itXhuOlZMdJ94qu2tnhl/Dvr4vtnVrIKzUeA3uTHLo8TLtS8N7SMXhIXt+CWVO/+nM6T+dk9uOETy0BwD1gxqQk6ktNda6LMK3HaNRsMnz6OjmhLe/L0nRpS/+bVa7BtHJGcSkZpKvNxB6KoIeTeuWsLuemE5GTh6t6hVOHJvV8SQzN49Us7fs6LU46nuXjBWvToy5GSzrP4tl/WdxadtxWg0ztYlaQYHoMnMKYqdvk5WYji4rh1pBpoe+VsO6cWn7CQCLeO3GfduRaH77YeNsz+M/TGPnB6u4cbzkW0D9lYuofGuj8vIBjQbrbg+Qf7Tw+ivabNKfHsSt8SO5NX4k+kvnyZw3C8PVSwgHR5zeXID256/RXzxbZjkTT0Xg4ueDk7lPBAwMJnK7ZZ+I3B5Gw+Gm8td/uAOxB0x9wtrZnod+msqR+auJL/LGQWNvUzBRFWoVdR9sTdrVwonTxp83MuWhKUx5aAqHQg/x4LAHAWgU1IjszGzSEi0nxQCjpo3CwcmBZe8sszi+atEqTh86zZSHpnDqwClq1qtJfHQ8jYIaocvUFoR83EabmE5eVg4+QaaHnCbDuhKxzXSdIraH0dRczqbDu3Fte+HxJsNMse8+QQEF+WbGJlMruDFCrUKlUVM7uElBiIjzhHEIRwfS3p6Hpk4t1L6ma2jX+wFy/ra8f2jqFO4iZdslGP0NU0hd0oSXiR/yBPFDniBr1VoyfvqV7N8td3MqTrNaHkSnZBKTlmXqh2ei6NG45BB+PekWGbl5tKpTODlOuKUlN9/kwMjI0REelYSfZ/lrGLJOXsXO3xebOqaxwnNQV1Lv9L6mUqExhxrZN6mHQ9N6pO89eWfnVjMK4p7/u1+p1l1EFEW5LIRoA/QH5gI7qypvIUQI0AvopCiKVgixByhrxVpFb7hWA2uEEH8AiqIoV4QQLYBziqJ0uqsvqhjJ2/4LtiOng1ChP/03SnIMVt2GYIyLxHA1HEPEGdT+zbF77n3TVka7Vhfs+mD71CxUHr5gZYvd85+Rt/k7DNfLHiQwGslZsQiHqQtMW+3t24oxNgqbwaMxRF5Gf7Kkx/E2eTvXY//MdBznfgsI8vaHYrxZciuzomhUKmb0as6kNYcxKgqDWtQh0NOJJfsu0tTHlZAGPnT2r8GhyCSGfrcblRC8EtIUVztrdHoD4341DZIO1hrmPRyERlX+M6BGrWbmmMFMmv8NRqORwSEdCKzjw5drQmnmX5uQds2Y+tQA5nyzhuWb9yEEzJk0AiEEI/t05q2lvzFk2seAwqAe7WlYr2a5ehiMXJv1Lc1XvoFQq0hYuQvtpZvUe+0xMk9eI3XbcRxbB9D0+9fQuDrg3rsddac/RliPV0j+6zCuXZrTdvengELqrpOkmgeqcjEayV27FPuJ75qu4ZEdGOOjsX7oSQzRVzCcO4pV9wFomnU0vfXQZppCQQBN666oA5ohHJyw6mAarHN/XYgxxvI6KgYjx2f/xAO/voZQq7i2ai+3LsfQcvowUk5dJ2ZbGFdX7qXzFxMZeOATdOlZHJi0uOB8r+DGaGNTLUJA7Hzdaf7yYG5dieGhbXMBuPzDdrS/bCuhfXnm97ReNRuhVhG7cjfZl27i/9oIMk9dIzn0BE6tA2jxwzSsXB3w7NMW/+kjONrDtBuLbZ0a2Nb0JP3gee6UM7vDaNEziHl7F5GXk8eP078sSHtr80fM6T8dgGEznqLjoK5Y21nz4aGl7Fu9k78WruHc3pM069aKd7d/htFg5Pf5v5CdXvpaao1axYxBnZj07VaMRoVB7RsS6OPGktATNK3tSUizegBsPRlBv1b1LXYJUatUvPJwByYs24ICNKnlybAOje64nGUx/e0FHAs/TXp6Bg8OforJzzzNsAF9K53PlV0nCezZmhf+/pT8nDw2TCvcxWb85vdZ1t+0dGbzGz8wyLz929U9p7hqDuXpNfNxvJvWA0Uh/WYSm2aZQik6jO6Du5833V8cSvcXh+Jsl0fmO9NQbpknhUYD2mULcXrnY9M2fTs3Y7gRid0T49BfvUj+0bIfJm36D0HtWwu7x0Zj95hpi0eLvM0oBiP73/yJh5eb+sSl1XtJuxxDu6nDSDp9najtYVxctZcHFk7k8X2mPrH9eVOfaD6mNy5+3rR9eQhtXx4CwMYnP0AI6Pf9q6itNQiVIObgBc4vL30YPLbrGO17tue7fd+hy9Hx2bTCbVkXbVnElIem4OHjwcgXRxJ9JZovNn9h0vlpI6GrQjmx9wRturdh6c6lGA1Gzhw5w8K/FqLL0bF72ncFeT2xZR6/PjQbgN1v/EjvT8ajsbUmavcpIs3X6fiSv+j/1RSaPdaDjJhkNk8y7aoTueskfj1bMXrfJ+hz8tg+zTTJv7rpKHU6N+OpbfNRgKg9p/Hafwi1lyfO454i/3oUXj98hQJ4fbcYJSeX7L+2oL8eifP4MeRduEzuvoM4PDoY2/ZtUfR6jJmZpL77QZnXtSI0ahUzHmnHpJ92mfphmwACvV1ZsvMUTWt6ENLENNneeiaKfi3qWfTDiKRbfLo1DIFp8jCqSxMaFIthL4HBSMSsb2m68k3TWLFqFzmXb1Bn+kiyTl0lbdtxHFsF0Oj719G4OuDWux11po/kZMjLCCs1zf803TMNmTlcfuFzMNzPwRCS0hB3shq2ysSEqAmkKoqSK4R4BHgBaAr0VBTlmhBiJeCkKMoj5gnzNEVRHjGfmwZ4KYpScjWKKX0Q8KyiKAOEEI2Bk0A/RVH2CCEU4HFFUVYJId4AvBVFmWL2dr+gKMo+IcQ7gIuiKK+Y8zsGXATOKIryoRDCGjgPPK0oyiFzyEhDRVHK3WMte/7o6qtgQH+54kVzVYl117IXd/0biKDuFRtVIcf6/1yteq1H6qpVD2DDby4VG1UhvoaKd/moSlbaVWsX5IvP21dsVIVoupTcYu7fZn7bN6tV74XWFb/pqUp+C6tTrXobROVCou6WvlR+EfHdMsy39FCcfwuPqSHVqhf+UvWs/yhK57i199x9u8n78eq9wZbCwwkr73k9lEZ174PdAvhICGEE8oFJgCewSQihBfYBZb13WQacFkKElRGHvRWYKIS4AFzCFCZym2ygg3lynUhh+MdoYKkQwh6IAMYWOWc18BGmMBYURckTQgwHvjCHl2iAhUAFmxhLJBKJRCKR/Pcw3pdT2/uD6g4RCQVKW+nUGArCPKaZbfcAe4qc+zrwejl564CHykhzLOP4SSC4jLSPgY9Lsa9eF6pEIpFIJBKJ5H8K+UuOEolEIpFIJJJKY7yPFxnea+6rCXZxr3VpmLfeK21VyIOKotzb/askEolEIpFIJP/vua8m2HeCeRJdvSvrJBKJRCKRSCSSO+R/boItkUgkEolEIrn33PMtRO5jqvuHZiQSiUQikUgkkv800oMtkUgkEolEIqk08udvykZ6sCUSiUQikUgkkipETrAlEolEIpFIJJIqRIaISCQSiUQikUgqjVHIfbDLQnqwJRKJRCKRSCSSKkQoitxk5d8kqk2vaq1gfZ66OuXQ66v3Gc2rWU616q04Wrta9baL9GrVAxipd6lWvSDHtGrVi7/lUK161f3LZvttrapVD2DmifeqVe+VdjOrVS84r3rrtLp9gA7G6l+aFmqrr1a90XnVqxemtq9WPYDJN5bfc/fxGt8n7/kk8tG4Ffe8HkpDerAlEolEIpFIJJIqRE6wJRKJRCKRSCSSKkQucpRIJBKJRCKRVBq5D3bZSA+2RCKRSCQSiURShUgPtkQikUgkEomk0hjvy+WF9wfSgy2RSCQSiUQikVQhcoItkUgkEolEIpFUITJERCKRSCQSiURSaap73///JaQHWyKRSCQSiUQiqULkBFsikUgkEolEIqlCZIiIRCKRSCQSiaTS3PPfSb+PkR5siUQikUgkEomkCpEe7HuAbef2uE+bDGoVWeu2kPHjKot0x2GP4DRiEBgNGLW5pM79lPzr0dh2bIPri88iNFYo+nzSFy4j99jJSmnbdWmH54yJCLWajLVbSP/uN4t05xEP4zxyABiNGLU5JL3zOfkR0ZXSsO/aFu/ZE0Gl4tbvW0n9Zo1FutuYIbgM7wcGA/rUW8TP/gx9bCIANaaNw6FHB1AJtAfDSZy3tEI9TesO2I97AVRqdDs3oVv3a6l2VsHdcZw+h4zXJmC4dqnguPD0wmXhT+T89iO6DatLPbduSEu6vfM0Qq3i/Mo9hC35yyJdZa2h98KJ1GjhT25aJqGTF5N5MxlbV0f6ff0iXq3qc3HN3/z95s8F5wz5bTb2Xq7oc/MA2PDkB5CaXmY5n3t3PG17tkOXo+PzqQuJOHvNIt3a1obXv5qBTz0fjEYjx3Yc5ecFPwHwwPAHGTN7HCnxKQBs/mkj21dtszjfN6Qlbd57GqFScW3lHi4sLlnG4C8m4d7CD11aFgcnLiL7ZjJCo6bjx8/i1sIfoVERuWY/5xdvAKDjp89Rs1cQuckZbHlgRpllc+jWFq/ZExBqFelrQkldVqzNjB2C66N9UfQGDGm3iJu5sLDNTB+HY0h7UAmyD4STOPfrMnVu496zNYFzxyLUKuJW7CR60Z8W6S7BTQh8bwyOTetxfsJCkjYeBsC1SzMC54wpsLMPrMn5iQtJ3nKsAr1WNCiiF7VovUW6a3ATGrw3Goem9Tg3YSFJG48U6DWYM9pC79zEz8vU6/vOKBr0bEV+Th7rp31N/NnIEja+zf0Y+MlErGytuLL7FKHvmNpkj5eHEvR4T7QpmQDs+mg1V3efon7X5jwwYyRqKw2GfD073i+9f5XHG+9/yt8HjuLu5sqfyyvu03fK8LfH0KxnEHk5On6Z9hU3z10vYTNg2mN0GNodexdHpjYrrMuuT/ai+9N9MRqN6LJzWTlzGfFXYyzO9Q1pSXtzn7i6cg/nSukTnb+YiEcLf3RpmeybuJjsm8n4DelM08kPF9i5NanD5r5vkHYumlavP0r9R7ti7eLA6gbPltBrV0TvfBl67ma9/UX0mhTT22LW6/X7bOy8C+8zu0Z+AEkl7zNePVvS4r1RCLWKqBW7uVJM2yO4MS3mPI1z07ocn7iI2I1HC9I6/fo67m0DSTl6icNPf1wi7/IY8fbYgmv487Ql3CjlGg6cNpKO5mv4SrNRJdKD+nVk/NKpzB8wg+gzEXek6xIShN974xAqFYkrdxC7eJ1Fus/4AXg90QtFb0CfksG1V78kLyapwnzrhLSk6ztPozKPFeGljBW9iowV28xjhU2xsWKfeaywcrBlyNo3C8538HXn8h8HOPDu8jsq57+N3Ae7bOQEu7pRqXB/fQqJk19Hn5CE7/Ivydl7kPzrhZPY7K27yFq7EQC77p1wmzqJxBdmYkjPIOmlNzEkp2AV4IfXlwuI6TeyUto13nie2Odmoo9PpvbqRWTvPmwxgc7ctJuM3zYBYB8SjOdrE4ibOLtSGt5vPc/NcbPIT0im3prPydp1hLxrhRq5F66RPvxFlFwdriMfpsa0ccS9ugDboCbYtWlK5KDJANT99WPsOrQg5+iZcvXsn3uJrDnTMKYk4fTBUvKPHcB4M8rSztYOm4eHob98vkQW9mOeJz/8SJkSQiXoMXc0659YQFZcKiM2zuH69hOkXYktsGk6MgRdejbLu02lwcBgOs8aSejkxeh1+Rz5+HfcG9XGo1HtEnlvf3EJiaeLDChl3Kza9myHr19NJnYfT8OgRkyaN5npg6aWsPtz2R+cOXQGjZWGOSvn0SakLWF7TgCw/699LHur9MmNUAnavj+G3SPnkxOXSp/N7xETGkbGlcIJR/3HQ8hLz2Zjl6nUHRRMqzce5+DERdQd0BGVjRVbHpyB2s6a/ns+JOrPg2TfTCZi9T4u/7Cd4M8nllm/qFR4vz2ZG2Nnkx+fjN/ahWTtPEzetRsFJrrz14gc+pKpzTzeH6/XxhH78gLszG3m+oDnAai38iPsO7RAW0GbabDgGU6NeA9dbCptQ+eTHHoc7eWbhXoxyVx86UvqTBpocWr6gXMcf3A6ABpXRzoeXkTqnlNlawGoBI0WPEP4iLnoYlNoFzqfpNDjaC8X1m1uTDLnX1pC3UkDSugde/A1s54DncrRC+zZCg9/Hxb3mEqtoEAenjuW7wa/XcKu/7xxbJzxLTHhV3nip9cIDGnFVXOeR77bwqFlmy3stWmZrBr3MVmJ6dRoWJsnf3kdKPtBsDQG9+/NE8MGMuu9yk2+yqNpSGtq+PvwbshL+AU1YOS8Z/h48Bsl7M7sDGPvT6G8vedzi+PH1x9g/4odALTo1Zahb45iyej5BelCJejw/mh2jlyANi6VhzbP4WboCW4V6feB5j6xvstU6g0KJuiNkeyfuJjIdQeJXHcQANfGtenx/SuknTPdA2O2h3H5h+0MPGBZF0IlaP/+aHaZ9fqZ9TKK6AWY9TZUoNe9iB7AgeeXkFrkPlNi4FcJWs0fy4ER88mJSyFk61zit4WRWaSN5sQkE/bSUgInP1Kijq8s2YjGzga/UQ+USCuPZiFBePn78HbIi/gHNeDxec/y4eCS482ZnSfY89NW3t3zRYk0Gwdbeo59iOvhl+9cWKXC//3nuDDyXfLiUmi++UPSQo+Rc6XwHqA9e52zD03HmJOH96i+1HtzFFcmflJutkIl6D53NH+Zx4rhG+cQWWysaGIeK1Z0m0rgwGA6zRrJtsmLMZjHCo9GtXEvMlbkZ+fyW7/COhm+6T0itpb/QC+5P6jSEBEhhI0QYocQ4qQQ4jEhxKwK7P2EEGer8jtUB+bv/cQ/Ode6eSP0N2PRx8SBXk926B7sQrpY2CjZ2kItO1tQTFFO+ZeuYkg2eSDzr0UibKzByuqOtW1aNCI/Ohb9zXjQ68nasgeHBzqVqa0qon2n2LZsSH50LPk34yFfT+bmvTg+GGxhk3PkNEquzvT3qYtY+XiaxRWEjTXCSoOwtkJo1BiSyx/I1YGNMcbHYEww1Wf+/l1Yt+9Sws7u8WfIXbcSJS/P4rhVh64YE+Mw3IgsU8O7dQC3IhPIiE7CmG/gyobD1O/T1sKmfp82XPx9HwBXNx2ldpdmAOhzdMQdu4xBl19uOSqiQ5+O7F67C4DL4ZdwcHbAzcvNwiYvV8eZQ6aJpT5fT8TZa3j4et5R/u5BAWRFJpBtLmP0+sPU7mtZxtp923J9zd8A3Nh4FJ+upjKiKGjsbRBqFWpba4x5evKzcgBIOnKRvLSscrVtWzYkLyqW/BumNpOx6W8ce1m2S23RNnPyIhpvT7O0gsrGqqDNoNGgTym/zTi3CSTnejy5UYko+XoS/zyAZ792Fja5N5LIPh8NxrLbf40BwaTuCseYk1emzW09bYGegcQ/D1KjX/tK63kNCCalHL1Gvdtyaq2pDcaEX8XG2R5HL1cLG0cvV2wc7YgJvwrAqbX7aFSsLRcn/lwUWYmmOk26fBMrW+ty7UujXesWuDg7Vfq88mjZpz1H/zC1x8jwK9g5OeBcw7WEXWT4FTJK8djmmtsogLW9DUqxe51HUACZkQlkmftEZKl9og0Ra0x1Hl20TxTBb3BnItcfLvicHHaNnMSS36e4XtT6w9SpQM+7FL16gzsTVUTvTnALCiTregLaaFMbvfnnIXyKaWtvJJNx4QYYjSXOT95/Dn12TonjFdGqTzsOm6/h9fAr2JdxDa+XcQ0BBk59jG1L15NfiXusY1AguZFx6KITUPL1pKzfj1vfDhY2GQfPFvS1zLDLWPt6VJivV7Gx4uqGw/gX61/+RcaKa5uOUqvIWBF/7DL6csrh4u+DvaczcUculWkjuX+o6hjsIABFUVorirIaKHeC/T+MH/CPJtiaGp7o4xMLPhsSk1B7ley4jiMGUnP9z7i99BypH35ZIt3+wW7kXbwK+Xd+U9F4eaCPL3zFpU9IRuNVcgLmPHIAdbf8gMfUZ0mev+SO8wfQeHuSH1dEIz4ZjXfZNyaX4X3I+vs4ALknL6I9cpqAfSsI2LeC7P1h5EXcKPNcAJV7DYzJhXrG1CSERw0LG7V/A1SeNdCHFRt0bO2wHfw4Ob/9VK6Gg48bmbGpBZ+z4lJx8HEr00YxGMnL1GLr5lhuvgAPfjKex7bOo91Lg8u18/DxIDkuueBzcnwKHj5l16uDswPte3Xg9IHCEKJO/TvzeegiXl86E89iE297H3e0sSkFn7Vxqdj5WpbRzscNbdEyZmixdnckeuNR9Fodg09+yaBjn3Nx6Sby0rMrLPttrLw90McXlk0fn4xVOW3G9dG+ZBdpM9lHThN4YDmBB5aTvf+Ehee7NGx83NEVKasuNhWbcuqyLLwGdyFx3f4K7UrqpWDj415pPe/BXUhYd6DMdCcfdzKK6GTGp+LkbXkNnbzdyIgvbMuZcak4Ffku7Uf1YcLW+Qz46Dlsne1LaDTp34G4UsJO7gWu3m6kFSlvenwKrpWs1+5P9+HtvZ8zeMaT/P7OjxZp9kXaO5j6hH2xPmFfrE/kZ2ixcbfs9/UGdiTyz0MVfhe7UvSK90F7Hzey/4Fep8/G89D2eTR/ufT7jJ2vGzlF6jI3LhU738q30cri6u1OWmxh30+r5DWs08wfN19Pzu4Or5SutY8HeUXKmxeXgnU55fV6/EHSd4VVmK+DjxtZdzBWZP2DsQKgwcBgrv5VuYenfxvjffDvfqXCCbYQwkEIsUkIcUoIcdbsme4nhLgohAgTQnwhhNgohPAClgPtzR7sNYCd+e8V5UhohBArhBAXhBC/CyHszbpvCSGOmTWXCSGE+fiLQojzQojTQohVRb7j90KIo0KIcCHEIPPxMUKIP4UQ24UQkUKIF4QQr5ptDgsh3M12AUKIrUKIE0KIfUKIxubjP5rLd1AIESGEGG7+zguAbuayvfLPqr58sn7bQOygUaR98S0uzz5pkWZVvx6uLz5H6rzP/g1pMlb9RfRDY0n59DvcJvyj54g7wnlAT2ybNSTtu7UAWNX1xbp+Ha6FPM21Hk9hH9wKu7YlPTSVQgjsxjxPzo9flUiyGzGG3I1rILfynpeqYNuLS1jZeyZ/DHuPmh0a0WhY1yrJV6VWMXXRdDb+sIGE6AQAju04ynOdx/FS3ymc3BfOS59WXbP1CApAMRj5M+gFNnR8hcYT++NQt0bFJ/4DnAf2xLZ5A1K//R0wtRmbgDpc7T6Kq92exiG4FXbt7rLN3AHWXq44NK5L6u4KwkP+h/SOL9/Bou6v8PVDs8hKTKf3m5b3nRoNavHgjJFsmvndv/Ydqpu/f9nGuz1eYv2CX+k3ZWiV5+8RFIA+J49bl25WbFxFeoZiegdeWMKmB2eyffB7eHVshP/wqrnP3GuEEAx/cxS/z/u5YuO7wHNodxxaBhL71Z8VG//LBA7sxJX1FT+sSe4P7sSD3Q+IVRSllaIozYGtwDfAAKAt4AOgKEoi8Cywz+zBfhTIMf/9ZBl5AzQCliiK0gTIACabjy9WFKW9WdMOuB34NQMIUhSlJXA7sHM2sEtRlA5AT+AjIYSDOa05MBRoD8wDtIqiBAGHgNurJZYBUxRFaQtMA4q6bX2Brmb9BUW+w+1ylpjlCiHGCyGOCyGO/5psuWhGn5SMxser4LPaqwaGxJTiWRSgDd2NfZEQErWXJzU+eZeUtz5AfzOuzPNKQ5+YgsancOKj8fZEn5hcpn3Wlj3YP9C5choJyVj5FtHw8USfULJ89p1a4z5xJDGT30Exe+Ede3Um99RFFG0uijaX7L+PY9e6Sbl6xtQkVJ6Feir3GigpRRai2NmjruuP45yFOH+1Ck3DpjjOmIc6oNH/sXfm8TUd7x9/z703+yIRicQSiaVVagmxVC2xl1qLovYuamu1tQYtVXu/WlWlVa1WKUpbSwVRxK6qYikagiAksu/bXc7vj3sluclNBBHa37xfLy/3njNzPmdmnufM3DnPTFDXega7IaNwXrEBm259sX1pEDZdehfSSI9OxKlS3uyGo1d50qMTi0wj1CqsnezJukdoxN1raNOzuLTlKBUbVjc733Xoi3y6cymf7lxKYkyi2axzBU+33AWLBRm74C2iIm6z/ZttucdSk1LR5egA2LM+mBr1aprlyYhOwL5S3iyuvVd5MqPMy5gZnYh9/jI625OTkEY07EW+AAAgAElEQVS13i2I2n8WRacnOz6FuD8vUb6BeVmKQ3snHo1nXtk0nhXQWrKZFg1xG92fyFEfomiNZXHq2ILM02G5NpNWApvJjk7AJl9ZbSqVJ7uIuiwK954tiNt5AkWnv2fawnpuZOebRS4JHj2fI9aCXuURnRkZNI+RQfNIi0nCOZ+Ok2d5Uu+Yt2HqnUSc880QOnmVJ9V0L+lxKSgGBRSFU+v3U7lBDbNrvbzyXba+9yWJN2J4XLQe0ompQQuZGrSQ5JgkXPOV18XTjaT7rNe7/LX9KPU7moftZOSzdzD6REYBn8go4BNWzvZkJ+T5vU/P5iWavQZz/7qrV9AHM6ITcShGr5oFvUzTc0aXnkXEr0dx8yvsm5lRidjlq0tbr/JkRj1YXd6LNkM6My1oEdOCFpnaMM/3Xe+jDW0cban0VFXe2zCTOYeX4etXi9GrJuNd797PnpzoeKzzldfay40cC+V1blWfyuP7EjZ8Porp+Vkc6dGJOJagr3C8z74CwO0Zb1QaFbHnIu6ZtixRnoB/TyolGWCfAzoKIRYKIVoBvsA1RVEuK8agtYddynpTUZS77z3XYhzMArQVQvwhhDgHtAPuTkudBdYJIQYDdy2+EzBVCHEaCAFsAW/Tuf2KoqQqihILJAN3l/SeA3yEEI5AC2CTKf9XGAfVd9miKIpBUZQLQMWSFEhRlJWKovgriuL/SoXKZudyzoehqVoZTSVP0Ghw6BxA5oGjZmk0VfPy2LVqhvamcTZCODrgsXQuiZ+vIvvM+ZLcihnZf4dh5V0ZTeWKoNHg2CWA9P3mr5usvCvlfrZv3RTtjVsFL1MsWecuYVWtElaVK4KVBqeubUjbZ65h80wNKn74NrfGfIg+ITn3uC4qFrsm9UCtAo0auyb1yL5HiIg+PAyVVxVUHsb6tGrZjpyT+eozI53kET1JGT2AlNED0F26QNqC6eivhJH2/tu5x7N/20zWL+vI3vlrIY07Z65SzscTp6ruqKzU1OrRnGt7zF8XXttzitp9WwFQ88WmRB4pvJgyP0Ktyn0tqNKo8WnvR3yBWa6gNTt4t8vbvNvlbY7vPkbbPsYFRE/5PU16agaJMYmFrjto4mDsnexZNetrs+P547WbdmxGZLh5vSacvoqTrycOpjJ692xOZPBfZmluBZ/Ct19rAKp2a8qdw0YbzLgVR8WWdQBQ29ng1qgWqeG3KSlZ5y5h7VMJqypGm3F+sTVpewvaTHU8Z79F5KjZZjajjYrFvumzuTZj37Se2YJaS6SGhmNX3Qtbbw+ElQaPXs8Tt/tkie8XoGLvkoWHGPWuYF/dC1tvd4SVGo9eLR5Iz1J4yK3Vu1nZdRoru04jLPgkDfoYbbCyX02yUzNzY6fvkhaTRHZaJpX9jD+wGvRpRdgeYzvnj9eu3dmfGJM92jjbM3D1RPYu3MDNk/exkOwRcPCHYBZ0ncKCrlM4G/wnTV8y2qOPXy0yUzOKjNO1hLuPZ+7nuu38iI0wn7CIL+ATPj2bExls7veRwaeo3s9Y597dmnLncD6/F4Jq3ZtxvYQzjgX1qlnQu3WfekKtyg0hERo1lTv4kfxP4dn0pNNXcKzuib3JRqv0eo7oAv5fWhz4YTfzuk5mXtfJnAk+QXNTG/reZxtmpWYyqdHrzGg5jhktx3Et9DIrXl9Uol1E0k6HY+vrhU1V4zPArWdLEoPNFw7aP+tL9YWjCBs+H118chFXMiemQF9R00JfEZGvr6jxYlNu3aOvuEutnnL2+t/GPXcRURTlkhCiEdAVmAPsLeV7KPgDRBFC2GKcRfZXFOWmEGIWxkEzwItAa4wz6NOFEPUw7r3QR1EUs8h/IUQzIDvfIUO+7waM5VcBSYqiNCzi/vLnf/gNafQGEhZ+jscXC0ClIm3bLrRXr1Nu1DByLlwi8+AxnPr3xLZZI9DpMKSkEf/BIgCc+/dCU7USLm8MxuWNwQDcGTMVQ2IJOxW9gbh5X+D11TyEWkXKr8For1zHdexQss9fIiPkOM6v9MC+eSMUk3bMtPtc9a83EPPRCqp8MwdUapJ/DiYn/AZubw0h6+9LpO//A/dJr6Gyt6XSEmOIvi4qlltjPiR192HsmzfAZ9sKUCD98EnS9xe9uwcABj0Zqz7D8f2PQaUiZ99ODDcjsB0wAn14GNqTR4vPXwIUvYGD739Pz7WTjdv0bTxAwqVbNJ3Qh5iz14jYc4oLGw7QcckoBh9aTHZSGrvHLsvNP/Top1g72aGy0lC9sz9bBy0gNTKeHmunoLJSI1QqIg+f58KP+4u8h7/2ncS/rT9fHvqa7MxsPp+4JPfcpzuX8m6Xt3HzdOPltwdw8/JNPgky7phwdzu+biN60LRjU/Q6A2lJqXw2YYnZ9RW9gZPTvyPgxykItYqrGw6QcukW9Sb1IeHMNW4Fn+LK+hCeWzqabkcWk5OUzpHRnwNwefUemn36Jl33LwQhuLrxAEkXjQP4FsvH4vHcM9iUd6Lnyc85t3gzbC/wqlVv4M7sFVT9Zg6oVSRvNtpMhbcHk/X3ZdL2/YHHFKPNVF4aCID2diy3Rs8mdddh7JvXx/e35UabOfQXaftPUByK3sDlwG+ov2G6cdu89fvJCIvEZ3J/Us9cIX73SZwa1uDZ1ZPQuDjg1qkxPpNe5s827wFgW9Udm0oVSDpaso5R0Ru4FPgtDU16t9fvJz0sEt/JL5N65gpxu//CqWEN6q2eiJWLAxU6NcZ30sucaDMhV8+2BHqX952mZtuGjDv4CdrMHLZNzNuucGTQPFZ2Nfpb0IzV9Fz8Jhpba8JDzhBuCjvpEDiQinWqgaKQFBnLjmnfAtB0WCfK+1Sk9dsv0fptUxiFUEApefTjpJkL+DP0LElJKbTvNZgxrw2hT/fOJc5vifP7Q6nb1o+ZBz5Dm5nD2kl5YWBTgxayoOsUAHpOHYR/z+exsrPmo2PLObZxH0FLNtN6WGdqP18PvU5PRnI6ayaYrzdR9Ab+nP497X80+v2VDQdIvnSL+iafiAw+Rfj6Azy/dBQ9jxj9/vDoPL+v2Lw26bcTSLthvrWb34wB+PRqgcbOmt4nl3JlfQjnFv9i8sHvaWdBL97kg+HrD9Bi6Sh6mPSO5NPzaF6bjAJ6Kmsr2v44BZVGjVCriD50nvB1+ykYXa/oDZyd9h0t1k81btO3PoTUsFvUntyXpNNXiQ4+hUvD6jT79l2sXBzw7NiI2pP6sq+NcYeblls+wKlWJTT2tnQ+9Tmh731NTMjZe7bh3/tDebZtI2YfWEpOZg5rJuW1wbSgRczrarx+76mDaNKzJdZ21sw7toIjG/exY8mmoi57b/QGIqavovaPHyDUKmI27CXz0k2qTBpA+pkrJAb/SbX3h6JysKXWyomAcWehS8PnF3tZRW/g0Pvf093UV/yz8QCJl27RZEIfYk19xcUNB2i/ZBSDDi0mKymNPfn6isGmvkJtpcG3sz/bBy3I3YGkRrdm7Bj28YOXWVLmiIIrpwslEKISkKAoSpYQohswDqgDtFUU5YoQYj3gpChKNyFEADBRUZRupryJgIeiKBZX4gkhfIBrQAtFUY4JIVYBF4FvgDCMiwnVwHFgMzAb8FYUJUIIYQVcN93LZMAZY5iHIoTwUxQlVAgxHOMgfZxJL8L0PS7/OSHEUeBTRVE2mWK96yuKckYI8R3wm6Iom0350xRFcRRCNAY+URSlzb0q+HqjDmX6BkOXoy5LOXS6sv1bRR51yzZeet2JwlvrPUr2iPvb/qw0GKArV6Z6fo6FZ94fJdHJDvdOVIoYSuF3+P1w2LbkOwmVFoF/fVSmeu/6B5apXvOcsq3Tst5K2MHCTiCPmt229w6xKE2GlSCkozQ5pS68KPhRM+bm2se+C/U3VQY/9iiN1yIffz1YoiSjo3rACVP4xExgBjAS2CGEOAUUF5C3Ejh7j0WOYcBYIcRFwBVYoShKEsY477+B3cDddzdqYK0pbCQUWGpK+xFgZdI6b/p+PwwCXhNCnAHOAz3vkf4soDct/HwkixwlEolEIpFIJP9OShIishvjILcgd3faCMC4MBBFUUIwxkDfzTsFmFLMtSPuXsfCuRkYB/MFKbQEWlGUTOBNC8e/A77L993H0jlFUa5hXMxZMP/wAt8dTf9rMcaFSyQSiUQikfy/5EneJu9xU7bv9yUSiUQikUgkkv84D/2n0gvOWltCCOGG5cWR7RVFub/9sSQSiUQikUgkkieYhx5glwTTILqoXTokEolEIpFIJP8yZIhI0cgQEYlEIpFIJBKJpBQpkxlsiUQikUgkEsl/C+WJ3CDvyUDOYEskEolEIpFIJKWIHGBLJBKJRCKRSCSliAwRkUgkEolEIpHcN3KRY9HIGWyJRCKRSCQSiaQUkTPYEolEIpFIJJL7Rs5gF40cYD9i9t/xLFO9yjptmeo175NSpnrxR63KVM+xjJ8eP3TNKVtB4Mvgsl0GfjnbtUz1tqiul6nevtZla6MNs8q+i3vXP7BM9T49Ob9M9QY2fqdM9a5rk8pUz9+2bPslgDl175SpXlpU2fqhc7R9mepJnnxkiIhEIpFIJBKJRFKKyAG2RCKRSCQSieS+UZ6Af/dCCPGCECJMCBEuhJhq4byNEGKj6fwfQgif+64IC8gBtkQikUgkEonkP4cQQg18AXQB6gADhRB1CiR7DUhUFKUm8CmwsDS05QBbIpFIJBKJRPJfpCkQrijKVUVRcoANQM8CaXoC35s+bwbaCyEeenGSXOQokUgkEolEIrlvDE/+n0qvDNzM9z0SaFZUGkVRdEKIZMANiHsYYTmDLZFIJBKJRCL5VyKEGCmEOJnv38jHfU8gZ7AlEolEIpFIJA/Ak7APtqIoK4GVRZy+BVTN972K6ZilNJFCCA1QDoh/2PuSM9gSiUQikUgkkv8ifwK1hBC+QghrYACwrUCabcAw0+e+wD5FUUqyQUmxyBlsiUQikUgkEsl/DlNM9ThgN6AGvlUU5bwQYjZwUlGUbcA3wA9CiHAgAeMg/KGRA2yJRCKRSCQSyX3zJISI3AtFUYKAoALHPsj3OQvoV9q6MkREIpFIJBKJRCIpReQMtkQikUgkEonkvnnoQOX/MHKAXQZUDqhP09lDECoVl9eHcO6L7WbnVdYaWn02Crd6vmQnpnJg9DLSIuNwrFKBXiGLSLkaBUDsqXCOTV1tlrfd6vdw8nZna/vAIvXd2jbg6TnDEWoVt9btI+LzrWbnXZo/w9MfDcOxjjfn3vyMmN/+yD3X4fZ60i7eACDrVhynh358z/Kq6zTGtt8oECq0R3eRE7zJ7LymeQdser+OkmTcYlJ7YDvao7sBsBv7EWrf2uivnCdzxax7agHYtfCn/OQxoFKR9utOkldvNDvv1LcbTv17gMGAISOT+I8+RXv1BrbNG+H69msIKysUrZbET78m68/TFjUeRRt2XDsZu4rlEGo1MSfCOD7tu6LrtK4/tgNGI1Qqcg7tImfXRovpNI1aYj/6A9LmjMVw/TKoNdgOGY+62lOgGMjasAL9pbNF6rSfNYTqbRuizcxm58SV3Pk7olCais/60HXxm2hsrbm6/zR7Z/0AQMC0gdRo74deqyPpegw7J60kOyUDAPfaVek0/1VsHO3QGxRW9XgffbaWzrOGUqttA7SZOWyd+BXRFvS8nvWhx+JRWNlacXn/GXbPWgNAm3dewm9gWzLiUwHY9/FGwvefoVKD6nSb/7oxs4Dk/33F/p0HC1130kfjadn+ObIys5j5zjz+OXepUJplPy6mgocbao2a0D/OsCDwEwwG40vR/q/24eURL2HQGzj8+1E+m7OiyHrVNGiC3fBxoFKTs28H2VvXW0xn1bQ1DhM+JDXwTfRXL6Fyr4jTJ99juG3cxlV3+QKZqz4tUsfsWn5NsX/jLVCpyN6zg6yff7Sc7rnWOE39iOQJI9GHh6Fp4I/90JGgsQKdlozvVqA7F1oizb4zh1O3rR85mdn8MHEFkeevFUrTfWJ/mr7UGvtyjkyoOyz3eMtBHWg9pDMGg4Hs9CzWB64kOrzgYv+SMWPeJxw8coLyri5sWfvlA13DEq/OegO/tv7kZGazbOISrv191ey8ta01E1ZMwdPbC4PBwMnfT7BuodFeu73ek/YDOmLQGUhJSOaLSUuJuxVbrN6Ej97m+XbNycrM5sN35xNmwUaXrvsYNw83NBo1oX+cZdG0TzEYDLwxYQS9XulGUkISAF/M/5qj+44Xq/fyzBG57bdm4nJuWmi/HhMH0MzUfu/WHZp7vHnfNrwUOISkOwkAHPh+F0c27itWz6pxUxxGGm00K3gHWZss26h1i9Y4Tf+IpPFGGxVOzjhNm42m1tNk/76L9C8/K1bnLrYtmlB+4hhQG/uKlO82mJ137NMNp5d7gkGPISOLhDmfoL12A+u6T+M2411jIiFI+moNmfuPWNTwCqhPk4+MfUX4+hDOLyvcV7RYmtdXHBq1jPTIOHx6t6DOmBdz07k+U5WgzjNIPH+DBlP6Ub1fS6zLObCx1uslKqvk8SMH2I8YoRI0mzuM4IELyIhKoFvQbG4E/0Xy5du5aWoNDCAnOZ1fWk7At0dzGk8fwIHRywBIvX6HbZ2mW7y2dxd/dOlZxd+ASlB7waucenkuWbfjabZ7PrG7T5J+Ka/jyroVx/nxy6k2unuh7PqsHI63n3I/Bca2/1gylk5DSYrDfspn6M7+gSH6hlky3V8HyP6p8IAk5/efwdoG65ZdSqanUlE+8C3ujJqC7k4cldYtI+PAMbRX8/TSdu4jdfNvANi1eY7yE0ZxZ+w0DInJxIz/AH1sPFY1fKi4Yj6RnQZaKNKjacOQUZ+jTcsEIGDl2/h0awa6goubAaHC7pVxpH86FSUxDofpn6M7cwxDlHmdYmOHdfve6K5ezD1k1cpYj+kfvolwcsF+/FzS544DCwukq7dtgKuvJ1+3mYCXXw06zhnO2l6zCqXrNHcEu6auIir0Cn2/n4RvQH2uhZwl4tA5DizciKI30GZqf5qP6c6BBRsRahUvLhnNjne/JPbiDYSLAwatjpptG+Dm68myNhOo7FeTF+eM4JteMwvpdZ37Kr9NXcWt0HBe+X4yNQMaEB5yBoA/vtnJsZVmoXXEhEXydfcZKHoDjh4uTN85h4PBR9Dr9blpnm/XHO/qVenZYgD1GtUlcMFEhr1YeOvUKSPfJz3N+CPh41Vz6NC9LcFb9+Lfwo+Azq0Y0H442hwtrm4uhdvtLkKF3avjSZ87CUN8LE7zv0R78iiGW9fN09naYdP1JXSXL5gdNty5TeqUN4q+viVUKuzffIfUmRMwxMfi/L+vyDlxBMPNApp2dth274su7HzuISUlmdS5gSgJ8ai9fXGa9TFJr/a9p2SdgIa4+3ryYcB4fPxqMWDua/yv14xC6c7tPcWB73czM8R8UHRy6xEOr/sdgHodGvPS+0NZPmz+/ZXbRK+uHXmlTw+mffS/B8pvCb+2jfHyrcRbbd6klt/TjJwzmsBekwql27ZyC+ePnUNjpWHmjx/hF9CI0JBTXDt/lSnd3iMnK4dOg7swJHA4n44resKiRbvmePtW4aXnX+HZRnWYOv89RnQbVShd4Jszc2104dcf0b57AHu2Gge267/exNovNxTKY4m6AX54+HoyM+BtfP1qMXDu6yzqVfi5dW7vX4R8v4sPQ5YWOvfXb0fZOPPbEumhUuEw+h1SZkzAEBdLuU+/Qnv8CHpLNtqzL9p/8tloTg4ZP3yDupovmmq+JdYrP+UtYsZMQXcnFq+1X5B54Cjaa3nP0fRd+0j72dRXtH4O1wmjiRkXiPZKBFGDx4DegLpCebw2fEXkwWOgN49AFipB03nD2DvA2Fd0CZpN5G7zvqLmwAByktLZ+vwEqvVsjt+MARwetYyIX48S8etRAFxqV6HNt++SeN54b7f2nOLS6j30OFJ69ix59Py/icEWQvgIIf6+zzw9hBBTTZ9nCSEmmj4PF0JUKsk1KvjVIDXiDmk3YjFo9Vzbehzvzo3N0nh3akT4pkMAROw4gVfLuve8rsbehroju3Dmsy3FpivXqCYZ1+6QeT0GRasnestR3F9oYpYm62YsaRdugOHhlyuofJ7CEHsbJT4a9Dp0fx1A06B5ifPrw05DVkaJ09s8+zS6m7fR3YoGnY703SHYB7QwS6Ok511PZWfL3d13csKuoI81bnWpvRKBsLEGK6tCGo+qDe8OroVGjdpag1LEyza179PGOo0z1qn2zwNoGrYolM6m1zDjzLY2Jy9vpWro/zHOyiupSSgZaaiqPWVRp2bHxpz/+TAAUaFXsHV2wMHDfODo4OGCtaMdUaFXADj/82FqdfI3lvvQ3yimDud26BWcvMoD4Nu6HrH/3CTW9CYkMykNxaDwdMfGnPnZWGe3QsOxcbbHsYCeo4cLNo523AoNB+DMz4d4upN53RdEl5WTex8aGyss7bYU8EIrftu0C4Bzp87j5OxIBQ+3QunuDlw0GjVWVla5P0z6DuvN6mVr0eZoAUiMTyryftQ1a2O4cxtDTBTodeQc3YdVk+cLpbPr/ypZWzdATo6Fq9wfmlrPYIi+heFOFOh05Bzah3XTloXS2b/yGlk//4iST1N/7TJKgtEv9DeugbWNcTb7HtTv1IQTvxjfFESEXsbOyQFn98I/PCJCL5MSW7i+skz+AGBtb2Ox3UqKf8N6lHN2euD8lmjSsRkhP+8H4HJoGPbODrh4uJqlycnK4fyxcwDotDqu/n0FN88KAJw/do6crJzc/G5eFYrVa9O5JTs2G9/s/X3qAk7lHHErxkbVGjVW1poHfmffoJM/x03tdy30MvZFtN+1ItrvftE89Qz627cwRBttNPvgPqyaW7DRwa+RuflHc7/IzkJ34ZzZs+5eWD/7NLrI2+huReX2FXYB5n6Yv68Qdra5/q5kZecOpoW1dZF17Fagr4jYepwqBfqKKp0bcdXUV9z47QSeFvoKn14tiNia97Yh7tQVMmMevs4fBQbx+P89qfznBtimTcJLBUVRtimKssDCqeFAiQbY9p6upN9OyP2eHpWAvadrkWkUvYGclAxsXB0BcPR2p/vuObyweToeTZ/OzeM3uS/nv9qJPrP4B4yNZ3myb+ftl559Ox6bAvrFobKxotnueTQJmoN7F/97p3epgCEx77WnITEOUa5wp6Dxa4n99OXYvj4d4Vp8R1Mcao8K6KLz9HR34lB7FL6eU/8eVN7+Pa7vvE7CouWFztt3aEXOxXDQagufe0RtCNBx3WQGnFmONi2L67+dsFhG4VIBQ0JeGZXEWFQu5nWq8q6JytUd3Tnza+hvXkXT4DlQqRAVPFFXq4WqvLtFHSdPV1Ly2UpqdAJOFc3L6VTRldTovLpIjUrAyYI91Xu5NVdDjKEorr6eoCj0WzOZYTvm0OLNbia98iXSSymkVz73e5OhnXhz13y6f/wGts72uccrN6zBqD0LGbV7AfOm/M9s9hrAw7MCd27H5H6PiYrBvYgBzxfrF/P7ud9IT8vg999CAKhWvSqNmtXn+x0r+fqXz6nToLbFvACq8hUwxOdpGeJjURWwebVvLYSbB7rQwq/wVe6eOC5YiePMJahr1ytSJz/CrQL6uAKabgU0q9dCVcED7V9Fhw1YtWiD/uol0BX2i4K4VHQlMV97JkXH45KvrUpC6yGdmHngM3pNHcTmWd/dV95HjZunG/G38/wwIToet4qFn213sXd2wL9DU84eOVPoXLv+HQkN+atYPfeCNno7Fg9Pyza69Mf/EXx2G+lpGew12ShAvxG9+fH31bz/yRScyjkWq+dSsTyJt/P+MnTiA7SfX5dmTN/5MW8sfw9Xr6LrBkDlVgFDfhuNi0Vd0EZr1ELl7oH2z+JDW0qCxr0Cuug8PX1MLGoLP1gcX+5Bpa1rcB3/BgmLvsg9bv1sbbw2rcLrp6+Jn7ek0Ow1GPuBjHx9RUZUAvZehfuKjHx9hTYlA5vy5m1TrUczIrYce7CCSp4Y/nUDbCHEbCHEO/m+zxVCjBdCHBJCbAMuFJNdI4RYJ4S4KITYLISwN10jQghRwfTZXwgRYvo8XAixrIB+X8AfWCeEOC2EsCvlIuaSEZPE5qbvsL3zDP78cB1tvhiDlaMd5et641StIjd2nXxU0rkcbjyWPzpP4+/RS3l69jDsqlV86Gvqzv1B+vvDyZg7Bv0/p7AdOqEU7rR4Ujdu41b3YSR+tgqXN14xO2dVoxqu418nfs6SUtctqg3vsmfQIn5qNA6VtQbP5+89620RIbB9+U2yNhX+Q1baI7swJMbhMOMLbPuPQnflQqm8qSiO5uN6YNAZuPCrMUZRpVFTuclT/DZ+Oev6zKb2C/74PmhZ83Fy7e983vpdvuoyjbSYJDq+Pyj33K3TV/iy4xRW9XifEW8NxtrG+oF1xg6cQKeGPbG2saJJy0aAcbbQ2cWZYS+OZMns5SxcOfvBCyIEdkPGkPVD4R9+hsQEUsYOIG3qSDLXLMfhrRlgZ2/hIvevaf/qWDJWF9a8i7qqD/ZD3yR9+eKH1yshB38I5sM249m64EdeeOulMtMtbVRqFe9+PpGg1b8Rc/OO2blWvQOoUa8mW7/6pdT03n5lIl38emNtbY2/yUZ//n4LvZ8byKCOrxJ3J553Zo4tNT1LnPv9L2a0HMvcLpO4ePgswxY/pJ4QOLw+loxVRdvooyDtp23c7jmUxKWrKPd63jMl5+9/iOr3OlFDxlJuxECwvvdbnQfBza8GuswcksMiH8n1SxvDE/DvSeVfN8AGvgWGAgghVBg3BI8EGgHjFUWx/P7byNPAckVRngFSgDH3K64oymbgJDBIUZSGiqJkFkwjhBgphDgphDh5+NpZHCrlzQI4eJUnIzrRLH1GdGJuGqFWYe1sT3ZiGoYcHdmJaQDEn4sgNSIG5+qeuDeuRYX6vvQ9/ildtnyAc3UvXthkOU47OzoBm0p5vyBMmGYAACAASURBVNJtKrmRXUC/OO6mzbweQ+LRCzjV8yk2vSEpDpVr3gypyrUCSnKBvzianpo7I6Y9shu1d60S309B9DFxaDzz9DQVK6CPiSsyffquEOzzvRZUe1TA45NZxL2/CF1klMU8+dsHSqcNzcqQreVm8Cm8OzeyqK8kxZnNOgtXdwxJ+erU1g5VJR8cJn6M4/w1qKs/g/242aiq1QKDgeyfviR99mgyv5iFsHPAcCfvwW0V0J1hQXMZFjSXtJgknPPZipNneVLvmJcz9U6i2Qyyk1d5UvPVxbN9W1GjvR+/jc/rFFOjEoj8I4zMxDTqvdyaclXc6fXp6BLrORfSM87+pMeloBgUUBROrd9P5QY1CtVdXPhtMtMzqVHbl5eHv8T6PatZv2c1sTHxVKzkkZvOw8uD2Kii7SYnO4eQ3YcJ6NwKgJioWPYFHQDg/OmLGAwKLkXEYRsS4lC55Wmp3NwxJObTsrVHVdUXxw+W4Pz5etS16uAwaS7q6k+BTouSlgKA/tolDHduo/aqUuR93kWJj0NdoYBmfJ6msLNHXc0XpzlLKLdyA5qn6+A0fR7qmsY3LMLNHcfAOaQvmYch+nah69+l9ZBOTA1ayNSghSTHJOGarz1dPN1Iyvf24X74a/tR6ndscu+Ej5gXhnbl46AlfBy0hMSYBNwq5flheU834u9Y/mvKoxaMI+rabXZ8a76mot7zDegzrh8LXp+DLkdXKF+/4b1Zt+cb1u35hviCNlrJnZjo4m30wO7DtOlsDLNIiEvEYDCgKApb1v1G3YbPFMrTZkhnpgUtYlrQIlP75c0gu95n+6UnpeWW6ciGvXg/W73Y9Ib4OFT5bbSCO3oLNuq8YAku325AU7sOzh/k2ej9oouNQ+OZp6f2cEcfU/Rfw87Yvd+sr8i9zrUbKJmZWNcoHPudEZ2Ifb6+wt6rPBlRhfsK+3x9hZWzPdkJabnnfXo2l7PX/xH+dQNsRVEigHghhB/QCQjF+DfjTyiKUnjJszk3FUW5u/R3LVA44Kt07nGloij+iqL4e4dpcfb1xLGqOyorNb49m3Mz+JT5TQWfomY/Y8ft82JToo4YJ+FtyjshVMYAI0dvd5x8K5J6I4awNXv5qfFbbG7+Ljt7zSblahS7+s21eC8poVewr+6Jrbc7wkqNZ68WxO4u2cy3ppwDwtoYcWNV3gmXpk+Tfqn4X9WG65dQeVRCuFUEtQZN4zbozpq/3hPOea/MNPWbY4i+WaL7sUT2+TA03pXRVPIEjQaHzgFkHDB/OGm8K+d+tmvVDO0N4wJPlZMDFT+fQ+Jn35B9+jxFEXf6aqm3ocbeBjtTvLFQq6jSviHJ4ZYH+PqIMFQelREVPEGtwapJG3Rn8pUxM4O09/qRFjiUtMCh6K9eJGPZB8ZdRKxtwNoWAPUzjYw7qeRbHKkN2c73XafzfdfpXA7+i7p9jC7h5VeD7NQM0gvE/aXHJJGTlomXn3EwW7dPS8L3GF91+7apT9NR3fjltU/QZeWFLl07cBb32lXR2Fpzet0+Yi9F8tvUVYQFn6RBH2OdVfarSXZqJmkF9NJikshOy6SyX00AGvRpRZhJL3+8du3O/sSYZnxcqroj1MZHW7nKFfCpWY2om9H89N0vDOw4goEdRxCy8xDd+r0AQL1GdUlLTSOuQGdrZ2+XG5etVqtp1f45IsKNC7D27zqI//PGH0Te1atiZaUhqYg4bP2Vf1B5Vkblbmw/6xbt0J48mq/90kl5oxcpbw0k5a2B6C9fIP3j6eivXkI4lQNhLIvKwwuVV2VjXPU90F3+B5VXFVQeRr+wbtUO7Ym8XQ+UjHSShvQkeeQAkkcOQBd2gdS504w7NDg44vT+AjLWfIXun+KXrRz8IZgFXaewoOsUzgb/SdOXWgPg41eLzNSM+4rVdffJ++FZt50fsRH3LuejZteaICZ1fYdJXd/hRPAfBPRpC0Atv6fJSM0gKabwZMWAiYOwd7Jn9YerzI771q3Om/PHsOC1OaTEJ1vU2/Tdrwzq+BqDOr5GyK5DvNi3MwDPNqpDWko68RZs1C2fjbbs8BwR4Ub/zh+vHdClFVfCCnePB37Yzbyuk5nXdTJngk/Q3NR+vg/Qfvnjtet39Cf6SvF9he7SP6grV0FV0WijNq3bof3D3EYTX+lJ0qsDSHp1ALp/LpAy22ijD0LO+TA0Vc37iswDR83SaKoW6CtuGsugqeQJpmeK2ssDjU9VdFHRhTTiT1/FydcTB1Nf4dOzOZEF+orI4FNUN/UV3t2acudwvpfuQlCtezOub5UD7P8C/9ZdRFZhjIP2xDijDZBegnwFlybc/a4j78eG7cPenJmA3sDxGd/T8cfJxm17Nh4g6dItGk7sQ/yZa9zcc4rLGw7QaukoXjq8mOykNA6MMUaleDavTcOJfVB0ehSDwrHA1eQklaSY5vphgd/SaMM0hFrF7fUhpIdFUmNyP1LOXCV29184N6xBg9UTsHJxoEKnxtSY1I9jbSbiUKsyz/zvDTAooBJc+3yr2e4jFjEYyNq4Avtxc0ClRnssGEPUDay7DUF//RL6c39g1bYnmnrNwaBHyUgla03eK2i79z5GVbEqwsYWh7k/kLX2U/QXTxWtpzeQsGAZFVfMN27Tt3U32ivXcRk9jOwLl8g8cAznAT2xbeYHOj36lFTiPlgEgFP/nmi8K+Hy5mBc3hwMQPSoqRgSzTuVR9GGthWcab/6PVTWGoRKEH30ImE/7KVO5yLq9Mdl2L8zDyFU5BzZjeH2dWx6DEV//RK6M0XHJwonF+zfmQeKgpIYR+Y3C4tMe3Xfaaq3bcAbBxejy8xh58S8kJNhQXP5vqvxLcmeGd/RZfFINLbWXAs5w9X9xhjTDrOHobbW8PLaqQBEhYYTPH012SkZ/LlqJ0O3z0ZRFC7vP8PlfcaFlzXbNmTcwU/QZuawbeJXuXojg+axsus0AIJmrKanaVvA8JAzhN/VCxxIxTrVQFFIioxlxzTjo6Cq/9MMGNMdg1aPohiYH7iYpATzAc3hvcdo2f45th7bSFZmFrPenZd7bv2e1QzsOAI7e1s+/X4B1tZWCJWKk0dOsXmNcYvLret3MOvTQH7avwatVsvM8ZZ/4N5tv8xvl+IwbRGoVOSE7MQQGYFtvxHoroah++tokVk1zzTA9uURoNehKAYyvv4UJT21aK1cTT0ZK5fgNOt/xm369gahvxmB3Suvogv/B+2JojVtuvZG7VUZu/7DsOtv3EYvddZElOTiB1vn94dSt60fMw98hjYzh7WT8nYJmhq0kAVdjbsR9Zw6CP+ez2NlZ81Hx5ZzbOM+gpZspvWwztR+vh56nZ6M5HTWTHjw0IBJMxfwZ+hZkpJSaN9rMGNeG0Kf7pacq+Sc2neSRm0bs+zgV2RnZrN8Yt4uGh8HLWFS13co7+lG37f6Exl+k0U7jNsp7lqzg70b9jBk2nBs7e2YsNxYD3G3Y1n4etF2c2TvcZ5v/xy/Hl1PVmY2s9/N21Fl3Z5vGNTxNezsbfnku3lYWVujUglOHg3lF5ONvj1jFE/VrYWiKERFRjNvcvE7UPy9P5Rn2zZi9oGl5GTmsGZSXv1PC1rEvK6TAeg9dRBNerbE2s6aecdWcGTjPnYs2UTbEV2o38Efg15PelIa30+8R/sZ9KSvWILzRyYb3ROE/kYEdoNfRXf5H7R/FG2jAC7fbkDYOyA0Gqyea0nqjImFdyDJj95AwsLP8fhigbGv2LYL7dXrlBs1jJwLl8g8eAyn/j2xbdYIdDoMKWnEm/oKG79ncR8+AHQ6FINCwvylGJJSCkkoegN/Tv+e9j9ORqhVXNlwgORLt6g/qQ8JZ64RGXyK8PUHeH7pKHoeMfYVh0fnRaFWbF6b9NsJpN0w377Rb8YAfHq1QGNnTe+TS7myPoSzi0svxOhhkPtgF414mJXajwshhDVwDrACagGtgImKonQrJo8PcA1ooSjKMSHEKuCioiiLhRC/A4sVRdkphPgU8FMUJUAIMRzwVxRlnBBiFpCmKMr/hBDbgU8URdl/r3v9rvLgMq3gyiVYjFSaNO9T+CHzKIk/Wvi16qMkJPbhY87vh5e6xtw7USnzZbDHvROVIlll/EjeklNMp/sI2Nf60cRmFoUhq+yjED8443nvRKXIpycfbLu+B2Vg43funagUua4t2x0i/K3Ltv0A5tS9c+9EpUhaVNn64aHosq/TwbfXPvY9NOZXK9sxjiUCrz/+erDEvy5EBEBRlBxgP/CToij6e6XPRxgwVghxEXAF7k6xfAh8JoQ4CZTket8BXz7qRY4SiUQikUgkTyoGlMf+70nlXxkiYlrc2BzoB6AoSggQUlweU+y2xb20FEU5BBRaHKkoyncYB9MoijIr3/GfgZ/v+8YlEolEIpFIJP95/nUz2EKIOkA4sFdRlMuP+34kEolEIpFIJJL8/OtmsBVFuQAUuf+PEMIN2GvhVHtFUYrek0cikUgkEolEUmKe5H2oHzf/ugH2vTANohs+7vuQSCQSiUQikfz/5F8XIiKRSCQSiUQikTzJ/OdmsCUSiUQikUgkj54ndw+Px4+cwZZIJBKJRCKRSEoROYMtkUgkEolEIrlv5CLHopEz2BKJRCKRSCQSSSkiB9gSiUQikUgkEkkpIkNEJBKJRCKRSCT3jUE87jt4cpEz2BKJRCKRSCQSSSkiZ7AfMe46fZnqXba2KlO9yK1uZapX1j+WU8v4J+j3uzzKVhDw1ZXtRktqpWz1DDbVylRv7dGytdLH8RBvXraPNQY2fqdM9db/taRs9Rp8UKZ6mboylQPgs/NVylTPtYynVjXqMpV7YjDIjfqKRM5gSyQSiUQikUgkpYgcYEskEolEIpFIJKWIDBGRSCQSiUQikdw3MkCkaOQMtkQikUgkEolEUorIGWyJRCKRSCQSyX0j/5Jj0cgZbIlEIpFIJBKJpBSRA2yJRCKRSCQSiaQUkSEiEolEIpFIJJL7Ru6DXTRyBlsikUgkEolEIilF5Ay2RCKRSCQSieS+kfPXRSNnsCUSiUQikUgkklJEDrAlEolEIpFIJJJSRIaIPAbc2zagzpyhCLWKm+v2c+XzbWbnyzevTZ2PhuJUx5vQN5cS/dsJAOyqVKDx6vdAJVBpNER8s5sba363qFE1oD4tZw1BpVZxYX0Iocu3m51XWWvosGQU7vV8yUpMJXjMMlIj47BxceSFr97Go0F1/tl0kEPvr8nNU7N7Mxq/1ROhUhGxN5Tj8zfmnqscUJ9ms4cgVCourQ/h3BeF9Vp/Ngq3er5kJ6YSMnoZaZFxOFapQO+QRSRfjQIg9lQ4x6auBqDRlH7U7NsS63IOrH3qdbPrVQ6oT1OT3uUi9Frl0zuQT69XyCJSLOi9sGk6dhVd0GflAHB22XYaTOyDMNXhXxbqsFO+OtxlqkOAxmO7U2dAAIrewMGZa7hx4Bwu1b14Yfm43PzlvD04vngzZ77ZDUCbOcOoO7AtAFGnLvNrv7kPpQdg7WxP+0Wv4/Z0FRRFYe/Er4k+FU6ziX2p3qkRikFBH5fCn+O/pFwdb/xmD0GoVVz9MYSwZYXL23TpaFzr+5CdmMbxNz8nIzIOYaWm8aLXKN+gOorBwOn3fyD22EUAWv04GVsPF4RGTdwfYZwKXA364l8qVmxbn/ofGf0jYt1+LhW4D7fmtWkwewjOdbw5Mepzbpv8oyR0mTWUWm0boM3MYcvEr4j6O6JQGq9nfei1eBRWtlZc3n+GnbPyfKDp8E40HdIRg8HA5X2n2TN/PXYujrz85Xgq16/O6c0HOTHDmN47oD6tZg3JtZ9TFuynY7723G1qT9sCPngwnw/e5cVv38PZ2531HQJzj1UNqM/zJr2L60M4bUGvXT693016VVo9S7Op/VFZazDk6Dg2dz23j14AoOsPk7H3KIdKrSbqRBiHZ3yHYshrP6+A+jT5yOiH4etDOG/BZloszfPDQ6OWkR4Zh0/vFtQZ82JuOtdnqhLUeQaJ52/QYEo/qvcz+v3GWuZ+X5BXZ72BX1t/cjKzWTZxCdf+vmp23trWmgkrpuDp7YXBYODk7ydYt9BYn91e70n7AR0x6AykJCTzxaSlxN2KLVavOGbM+4SDR05Q3tWFLWu/fKBrVAqoT5PZefX5t4XnWsvPRlHeVJ8HRxvrE8Dlmao8t/BVrBztUAwKO178AEO2Njdv29Xv4ejtzvb25jbT4kOjzfyzPoTTFvTaLRlFhfommzE9Ryu3epZmgXk2c3xOns3U6N4Mv7eN/cSNvaH8MW8jBek6M88Pf534FVHnIwql8XrWh5f+NwqNyQ+DPszzg2bDOtF0aEcUvYFL+04TvGA9Ko2angtfp1JdX1QaFdqkdOwrlEObmU3whJXEWPB1j3o+dF78Jhpba67tP03IzB8AsCnnwIvLx+FcxZ2UyFh2jPmc7OQMavdqgf/obgghyEnLZO/074i7eINnBwTQ9qNhCCAzKQ1re1uzZ3tp9hUVnvEmYP4IrBxsAbYDg4CUQoUrI+Q+2EUjZ7DLGpWg7oIRnHhlIQdaTaRS7xY4PlXZLEnmrTjOjP+S278cMTuedSeRoy9+wOH2gRzpMoMab/XApqJrIQmhErSeM4wdQxexvt1kavVsjmutSmZpnhkQQHZSOutaTeDMql08N20AAPpsLX/8bzNH5/xolt7GxZHnpg9k64D5bOgwFXt3Fyo/XzdXr/ncYQQPXsSvbSdTvVdzyhXQe2pgANnJ6fzccgLnv96F//QBuedSr99hW6fpbOs0PXewC3Bzzym2vzjTYvmazR3GnsGL2NJ2Mr4W9GoNDCAnOZ1fWk7gwte7aFwCPYCD45azrdN0tr8wg0ZT+rFt6CLWtZvMUxbqsO6AALKS0vmh1QROr9rF86Y6dK1Viad6NGdd+ylsG7KIgLnDESpB0tUoNrwwnQ0vTGdj1xloM7O5uuskAJVb1KHOy234sVMgX9Z+DTtXp4fWA2g9awjXQ86ytu1k1neeRkL4bQBOfbmD9Z2mseGF6UTtCaXOhJdoNG84hwYtYlebyXj3eg6nAnbpa6rTnS0mcHnlTurPGAhA9UHtAAhuN5WD/RfQYNYgEEb9YyM/Z0+HaQQHTMHGzYmq3ZsVak8zVIIG80dw5JVF7Gk9iSq9WxS6j8xbcZwc/yU3fz1a/LUKUKttA8r7erK0zQS2B37Di3NGWEzXbe6rbJ+6iqVtJlDe15OaAQ0A8HmuDrU7NmZFl0CWd5zC0ZU7ANBla9n/v00Ez83zGaEStJkzjO1DF/FjEfZTx+SDa00+2MLUnjqTDx4p4IN3qf6CP9r0LLNjQiVoafL5je0mU7MYn1/fagJnV+2imUkvMyGVna8uZlPHQPa99xXtPxuVm2fP6M/Z3Hk6P3WYip2bE9W7NTPTbDpvGPsGLWJ7wGR8ehb2w5oDA8hJSmfr8xO4+PUu/GYYNSN+PUpQx+kEdZzO0bdWkHYjlsTzNwC4tecUu7oW9vuC+LVtjJdvJd5q8yZfBn7ByDmjLabbtnIL49uPYVLXd6jt/wx+AY0AuHb+KlO6vceEF97mWNBRhgQOv6dmcfTq2pEvP5nzwPnvPtf2Dl7EtraT8SniuZadnM6Wlsb6vPtcE2oVrZaO5vjU1WxrN5XgfnNRtLrcfN5d/NFZsJnn5wwjaMgifmprtBmXAnq1Bxj1NrScwLmvd9HcZDNZCansGrGYzR0C2f/uV7RbarQZGxdHms0YyG/957Op/VTs8vUTuWUIaICbryefBUxg27Rv6D7Xsh92n/MqWwNX8VnABNx8Pall8kNfkx8u7xLIsk5TOPK10Q/rdm2GxtqKL16Yyu+LfsK9rg8/vzKf36d+Q7u5wy1qtJ87gj1TVrG69QRcfDzxCagPQNOx3bl55ALftZnIzSMXaDKmOwDJN2PZ9PIcfugUyB9Lt9BhwasIlaDJ2O6sazeZL595naz4VHQ52txnO5RuX9Hu49c5umAj6zsGAvwKTLJYOMlj5//9AFsIMVwIUeneKc3y+Agh/n4QPZdGNcm4Fk3m9RgUrZ7bW45R8QV/szSZN+NIvXDDbKYIQNHqMeQYH5oqG6vcQVRBPBrWIDniDik3YjFo9YRvO45vp8ZmaXw7NeKfzYcAuLLjRO5DUJeZTfSfl9Dlm/kAKFfNg+Rrd8hKSAUg8vDf1OjaBIAKfjVIjbhDmknv6tbjeHc21/Pu1IjwTUa9iB0n8Gpp/tC1ROypK2TGJBU6XlDvWinpWdK4W4eXth2nejF1GL7jBFVMdVi9U2MubTuOIUdHys1YkiLuULFhDbO8VVrWJfl6DKm34gFo8lZP4i9FknQlCoNWT9ivRx5az9rJjkrNnubChhAADFo9OSkZAGjTMnOvq7G3wcbNmbSIO6TfiEXR6rm59TiVC9RppRcaE/HTQQAifzuBRyujvvNTlYk5Ypy9yo5PISc5HdcGvgDoTDpCo0ZlpUG5x4qY8n41Sb92h4wbRv+I3HIMrwL3kXEzjpSLN8Fwf3MnT3dszJmfjfUXGRqOrbM9jh4uZmkcPVywcbQjMjQcgDM/H6K2qR2aDG7P4eXb0Jt8MD3eOGmkzczmxklzn6lYwAcvW7Cf6kW0py4zm6g/L6Ev4IMAVvY2NHyjC38u3WJ23KNhDVIi7pBq0ruy7Tg+BfR8OjXikknvaj6fjz9/nYw7Rj9LDItEbWuNytr4cvOunahM7Ze/Ad0K+GHE1uNUKdBWVTo34qrJD2/8dgJPC37o06sFEVuP536PK8LvC9KkYzNCft4PwOXQMOydHXDxMJ9wyMnK4fwx49scnVbH1b+v4OZZAYDzx86RY3pbdTk0DDevCvfULA7/hvUo5+z0wPkt1WfVAvVZtVMjrpjq8/qOvPqs1KYeiRdvknjB+CMlOzEtt//Q2NtQZ2QXzn5WvM2Eby3CZjbl2UyllsXbjHM1D1Ly9RO3Dv+Nr6mfuEvtTo05/Us+P3Syx9G9gB+6u2DjlOeHp3/J54eD2nNoRWE/BAVrOxtUahW1OzUmOyWd7NRMokOvYOPsgEMBX3fwcMHa0Y7o0CsAXPz5MDU6G/vi6h0bc8HkKxc2H6JGJ+PxqL8uk51sfIZGhYbj5FUez4Y1SMrn63fOXUWXmZ37bIfS7StcfD25ffyfu6f3AH14jBhQHvu/J5VHOsAWQtwzBEUYeZwD/eHAfQ2wHwZbT1cyb+c5XtbteGw9C89CF5m/Unla7V9I+1PLuLJsG9l3EgulcfB0Je12Qu73tKgEHApo5E+j6A3kpGZg6+pYpG5yRDQuNbxwqlIBoVbh27kxjl7lAbD3dCU9n16GBb38aRS9gZyUDGxMeo7e7vTYPYcum6dTsenT96yDgnrpUQnY36de991zeGHzdDwK6LX8ZCQ9gufyzGudzTTSohJwLKDh6OlKqoU6dCxB/T/V4zkubz2W+925mgdCpaLftlm8tGk6KivNQ+s5V3UnKyGVDp+MZMDOObRb9DoaO5vcdM0n92P4H5/h/VILbu8JJSNfh5ARlYBdAX07T1cy8+lrUzKwLu9I0oXrVOrUCKFWYV/VHdf6vthXdsvN12r9FHqcW4EuLYvI3/6gOGy9zP0jMyoBO5OdPSzOnuVJyXftlOgEnAu8AXKu6EpKdF5dpkQl4Oxp1Hfz9cK7aW1e3/IhwzfOoFL96kVqOeRrKyjaBy21Z3E0m9SX01/vRJeZU+hapeHz1bs2Ie5cRO4PeYAX105mWOhytOlZXN2RF45j7+lKRgG/t/cq7IcZBWzGpry5ZrUezYjYcoz7xc3TjfjbeSEdCdHxuFV0KzK9vbMD/h2acvbImULn2vXvSGjIX/d9D6WJpedoweeanaX6dHXEubonCgod1k3mxV1zqDs6L/ym4eS+nP+qsM3Ye7mSFpXvORqdgIOXBZuJMn+OFrQZ3xfzbCY5IppyNbxwNPUTPp0b41jJ3H+dK5YnuaAfFiins6crKVEF/LCiyQ+re1GtaW1GbvmQV/P54fmgE+RkZjPpxBc06P08F385QnZyOgBp0Zaf32n5fD1/GvsKzqSbfuSlxyRhX8GZgjzbP4Br+8+aPZcBXKpVJDUy3ixtafYVCZciqZ73w6sfULXQzUmeCB5qYCuEmC2EeCff97lCiPFCiENCiG3AhSLy+QghwoQQa4C/gapCiElCiD+FEGeFEB/mS3dRCPG1EOK8ECJYCGFnOtdQCHHclP5XIYSrEKK2EOJEAZ1zps8fmK7/txBipWlg3xfwB9YJIU4LIeyEEI2FEAeEEH8JIXYLIbxM+RsLIc4IIc4AY+9RLyOFECeFECd3ZYY/TBUXIut2AofaTmF/83ep0r811u7lSvX6RZGdnMGBaavptHwcvX9+n9TIOAz3OYNoiYyYJDY1fYdtnWdw4sN1tPliDFaOdqVwx0XrbW76Dts7z+DPAnoH31rO1g6BBPX+iHI1K+HsW/GR3IPKSo1vx0Zc3pE32FSpBGobDZt6zOLI3PXUH9bx4XU0atyf9eHcmr1s6DIDbUY2jcd2zz1/fNEmvms2nhu/HMWrbf0H1olYf4DMqAQ67JpDw9lDiD95GUWfZxuHBi5ke8OxqGw0eNznm4QnCZVGhZ2LA6t6zWTPvB/pt/ytMtWvUMebctUqmr16Lk1cn6pMs2kDOBj4rdnxHYMXscZ/HGprTaHX/Q+Lm18NdJk5JIdFlup1C6JSq3j384kErf6NmJt3zM616h1AjXo12frVL4/0Hh4lQq3Go8lTHBq3nF29ZuPdxR/PlnVxreuNU7WK3HyUNhM4gENTjTaTk5zB4cDVdFgxjp6/vE/qzTgM+tKN0lWpVdiVc2Blr5n8H3vnHVbF8f7tezkUqVJEDtjAmlhBUbGDBRV7SWKJmuI3lmgSI3YTo1GjKcZEE42pllgSo7ErNuzGKIhGY0FBRYoovQvM+8dZ4Rw4qETE5PfOfV1ewu6z89mZfZ7Z2WdnVR3xDwAAIABJREFUhz3z1/LSV7o4rNqkFvl5+XzSchzXj1+kXm8fKlZ3LlPtB1Rt9TwNXurA0Y/WG56bma7PTYqMLRMdY/eK/YHf0mh4Z17a8SGALZBT0vGSZ8uTZo5/AIYDqFnoQUAU0BR4WwhR9yHH1gG+FkI0AOqpv7cAPIFmiqK017P7SrVLovB1yCpgihCiMXAemCWEuASYK4riodq8BDz4wmKpEKK5EKIhYAn0FEJsBE4DQ4UQnkAusAQYKIRoptbvwZdmPwLjhRBNHtUoQogVQghvIYR3N8vaBvuyYhOxdCvMslRwcyIrtngW+lFkxyWSeikKx5bFM77psYkGWQMbV0fSi2jo2ygaE8xtrchKTHuo5o19ofzW+wM29Z1N0rUYkq/rOpGM2ESs9fSsjOjp2ygaE8ztrMhOTCM/J5dsVffe+UhSIu9gV1P70PMoqmft6kjGP9RL1dN7UEZuehZRe0Ox0eucbVwdSSuikRabiK2RNkx7RPvX8GtC/F+RZN4t/C4lLS6J3IxsAOLOXsdEo5Clvor8p3ppMQmkxSQQd1b3CvTazlNUbuherD1vbDpWLOts5epIZpH6ZsYmYqmnb2ZnRU5CGiIvn7BZa9jbZTrHX12EmZ0VqdcNbzD52feJ3nOm2LSTomTFGMaHpasjmXqZrNJS89UujN45n9E755N6Jwk7vbLttI6kFHkDlBKXWJCxBrBzdSzIaKfEJPC3OlC5HXYdkS+wcjQ+JSBd71pByTFo7HqWhLZZHSo39mD48c8ZsOl97D1c6ffLjIKyniTmrbWOdP32HQ6+s5yUG3eKaedl3ycyKAR3/6YF2zJiE7EqEvcZMcXj0KqIz2QnFNbRvY9PqbLX3YYH8MnOxXyyczGJdxJwciuMUUetE/fi7hk9bvSCccRERLPjB8MPyhu1acKAcS+wYORccvWy9s8CY/1o0X4t01h7JqaREZPAnT8uk52YRl5WDlEHwnBq6I5zszo4Nfag/8nP6fb7+9jVdMX/V53PZMQkFryFBJ0PpMcY8RlXw360wGdcHfH/rrjP3NgXyu+9PuD3PrNJvh5DckQsDUZ0ZszO+YxR47Bi0TgsUs+U2ETsXIvEYZwah7EJ/L2neBx2nvwiNZrXY9TWD0mKukvyzTu4qNltG63x/ttGL9b1bTLuphRMKbGubE+GXl9d6blqdPl4JFtHfk5WUppBv1zDrwkZ8ckkRxo+xJXlvSLxWgxbhi5kQ4/3ANYB13iGiH/Bv38rTzTAFkJEAvcURfEC/IFQ4B5wSggR8YjDbwghHky889c7PgR4Dt3AGiBCCHFW/fkM4K4oSkXAXghxSN2+EngwIP8F3cAaDAfYfoqi/KFmtDsCxlIx9YCGwF5FUc4CM4GqiqLYq3qHVbvVj6hbiSSHXsO6phbL6s4oZhrc+rYibs/jvZqs4OqISQUzAEwrWuPQoh7p12KK2d0Ju05Fdy221ZwxMdNQu7cPEXtDDGwi94bw3MB2ANTq0YLbx4y+bDDA0kn3msyiohUNh3fm4rpgAO6evY6dhxYbVa9mHx9uBRnq3QwKofYLOj33Hi2IUfUsHG0L5pLbVHfGzsOF1JvFb/D6FNXzMKJ36zH1bFU9RWNSMIVEMdVQsa4bphUssFM16hppwwi9NqzdowVRqkbE3hDq9vbRzUms5oy9u7ZgkAtQt08rrmwxHFRc/u0IFT202FVzxrFOFcxsLLm61dCmtHoZ8cmkxSRgX9MVgKptGpBw9TYAFd0Ls/NVujYj6cINbDy0WFXT+WW1Pj5EF/HL6D0huL+oC7OqPVtw5+gFADSW5mjUqSeV2zdE5OWTeuU2GisLKqg3KUVjgmsnL1LUjyxLIvHsNWxqarFS46Nq31bEBP3zV/fXf9zL8oDpLA+YzqWg0zQZoGu/ql61yU7NJK3IXN+0O0lkp2VS1Uv3YNxkQDsu79XpXwo6g0er5wFw8tCiMTMlQ51rWpS4IjFYpxT+UxJ/rd7Pj97jWdV6Ar/1n0NSRAybX9Q9/xeN+Vq9fYg0EvN1Vb2aPVoQreqZ21nRfeVE/vhoA7GnrxbYm1pZYKV3/ap38iQxvLC/uXf2OrYeWqxVTfc+PkQVicOooBBqqnFYvWcL4o7q1VFRqNGrJTe2PP4Ae/eqnUwKeIdJAe9wKugPfAfoVt2p41WPjNQMku4UT1YMChyKla0VP87+zmC7R4OajPpoLAten0vKveTHPoenxYP2tNFrT2P9Wi21PWv0aEGseg2jD53D/rlqaCqYo2hM0Po8R9LV21xZtZ+NzcazyWcCu/vOIeV6DEEv6PmMh959oo8PN4r4zI29IdR9oWSfOfXRBuL0fAaggnqfMK9oRf3hnbm0NpgLK/exLGA6y9Q49OxfGIdZqZmkxReJw/gkslML49Czfzsuqf3A30Fn8PApHodXDp7l+vELLAuYztVDYbg0rklCeDRar1rkpGYUTPl4QPqdJHLSMtF66eY8Pz+gLddUjet7Q6ivxkr9ge24rvYBtm5O9FrxDrvfWU5ShC6JEBt2HQe1767btzUaC7Oneq94cB9WPySfCfyzJWskTx1FPOqro0cVoCgvAa0BLbqBbgYQKITo+ZBj3IHtajYZRVE+A64IIb55hF0gYAN8DpwXQlRXt9cCfhVCNH3wM7ps+johRDNFUSoANwBvIcQtRVE+ABBCfKAoSrB6vqcVRWkErBBCtCpyHvbAOT29xsDaB+f1MHa4DC7WwM6dPKmvLkMWtS6Y8MW/U3fyQJLCIriz5wwVPWvS7Md3MbO3Jj/rPtl3kjncYRKV2jfi+dkv6z40UhQif9jDrdUHDMq+Ya4BoLpfE9p+8LJu+aUNhzizZCvNJw4g/lwEkXtD0FiY0WnxaJwbupOVlMbeN5eSclM3n/Hl459jbmuJxsyU7JQMtg1dQOLVaLosfROn56sDcPqLzYRvPYml+vavascmtJj9sm7ZvA2HOPflVrwCB3A3LIJbql67L0fj1MCd7KQ0gscuJe1mPDUCmuMVOID83DzIF4R+9hu39oYC4D1jEDX7tcbKxZ6MuCSurA0mbJHuNW4VPb1wVc8zcAD3iug5qnqH9PQ8AwcgcvMQql7U3lBMLS3otmkmJqYaFI0JMUcuEH34PM0+GKpb6nDDIU4v2UrLiQO4cy6CCFWji9qG2Ulp7NZrQ+/xvan/Ugfyc/M58sFqbgSfA8DU0oJX/ljMqjbvkpNa+KGhiZmG3qun4Na8Dgi4tPkYByZ998R6lepXp+MnI9GYmZJy8w77Jq4gOzmD7t+8hUMtV0S+IOfWXc5M+QH7+tXxVJfpi1h/iEtfbKHBpAEkhEUQExSCiYUZLZaMwaFhDXKS0jk5egnpN+OxqlqJ9uumIIQgMyaR0xO/JSPqLhaV7Gi7OhATc90HuXeOXSRs1hpMcvMeGjMunTxprJ7HjXXBXP5iC89PHkjS2evEBIXg4FkTnx8mYGZvTV7WfbLjk9nXYXKJ5Z2zKPwYOODDV6jdoTH3M3PYEvgN0ed1eYDRO+ezPGA6AG6NPOirLt0VHhzGzvdXAqAx09DnkzfQ1q9B3v1cguatJUJdmuydo4uxUGMmJyWDLUMXYFfVmXZqDF5UY7CFej0j9a5nJfV67tG7nsPVGDTRKy/xauHDiW3VSvT8aSLrOk8rWGu1ul8TWqt6lzccImTJVrzVmL+h6nXU09v75lJSb8bT9K0+eL3Zi+SIwqzb9qELURTo/lMgGnNTFBOF28f/5vjsNYi8fGzUS+jWsQnes3Wa19Yf4q8vt9JY9Zko1WfafDkaR1Xz6BhdHAK4tHoez+kvsafXBwbXy2vmINz7tsZKa09GbBLX1gUz58ufjF7bkR+OwrNDU7Izs/k68EuunddNyftk52ImBbyDo9aJFX/8SFT4Le6rH43uXrWD/ev38v7Pc6hez53EO7rM6N3oeBaO1A0+151ZXKI/lcSkWQv4M/QcSUkpODnaM/b1YQzo1fWxjl3X5H1A16811+vXzn+5lSZqvxa1V9eebdV+LScpjcNjC9vTo38bGo3rhRCC2wfCCJlnOH3BumolOq6cyLZO08hUQ6JaR9VnTHQ+E7pkK96BA4gPK/QZvy8KfWbfWJ3PeL3VB69xhj6zY8hCsu6l0GnpmzjV190nzizezLWtujzabdPCW2GPOa9QR43DzZMK43DMzvks04vDfp+OwqyCOVeDw9gxqzAO+35cGId75q0l4sRFzK0s6PvJKCrXqQKKQm5yOlbOFcnNzCEocAVx53QaQ3fN4+fuuiy+S2MP/D97A9MK5kQeDOPg+7qlACvY29Bj2Xhs3ZxIvX2X7WOWkJ2cTueFI6kT0JwUdYk9kZfH2p7v4+7XBL8PhmFXvTKnl27hj09/e2r3iiavdaXRiM4AONR0XQhM4xkmcie4D3rmSeTPI9cbX/HhGVMWA2xzdFM0zNBlndtR+gG2P/Ah0EkIkaYoShXgPmCFkQG2OjAOA8YJIY6oA+aKQogJqt2fwCV0g/CP1QHyZcAd0AAngY1qOduARUKIg2pdLgLDhBAnFEUxA+oKIS4oinIOGCuEOKooykKgxz8dYD9NHgywywvLcl4Es7yjKPX/g3V2tLnl2z9qnrDPKS36A+zyoFJ++eo9iz9mYPPwZ6Qy53ez8l3m958MsJ9ITx1glxeZz2A4oj/ALg8cyjsOn8Ewc/ytNc98YPn2v2CA/cW/dID9xH2zECJHUZSDQJIQIk9RSl9PIUSQoijPAyfU49OAl4GHdeMjgOWKolgB1wH9xTQ3AJ8AHmr5SYqifIvug8pY4E8925/UcjKBVsBA4Et1GoopsBi4oJb/g6IoAggqdSUlEolEIpFIJP9f8MQDbPXjRh90y8UghAgGgh92jDp3u2GRbV8AXxgxb6hn86nez2dVXWPlfwp8WmTbTHTzlYra/gb8prfpLIXzufXtzgD6HziW/E5aIpFIJBKJ5P844l/9meGz5UmX6asPhAP7hRBXH2UvkUgkEolEIpH8X+eJMthCiItAiX9tQVEUJ2C/kV2dhBDG11OSSCQSiUQikUj+wzzV72PUQbTn09SQSCQSiUQikZQ/5bzOwX+K/w/WSJBIJBKJRCKRSMqPZ7HCk0QikUgkEonkP06+/MixRGQGWyKRSCQSiUQiKUPkAFsikUgkEolEIilD5BQRiUQikUgkEkmpkRNESkZmsCUSiUQikUgkkjJEZrAlEolEIpFIJKVGfuRYMnKA/ZQ5U6F8XxLcVrLLVS/F5H656iko5apXSTEvVz1HUf4haa4p3za1zytXORxE+dbvT01muerF55evHsBQHMtV78b9pHLVW9fk/XLVGxw2p1z1wluNK1c9gEY3wspVr6NLo3LVq6WxK1c9yb8fOUVEIpFIJBKJRCIpQ2QGWyKRSCQSiURSauRfciwZmcGWSCQSiUQikUjKEJnBlkgkEolEIpGUGiE/ciwRmcGWSCQSiUQikUjKEDnAlkgkEolEIpFIyhA5RUQikUgkEolEUmrkR44lIzPYEolEIpFIJBJJGSIz2BKJRCKRSCSSUiM/ciwZmcGWSCQSiUQikUjKEDnAlkgkEolEIpFIyhA5RUQikUgkEolEUmrkR44lIwfY5UjXD4ZTx68J9zNz2BL4DbF/RRazcW3oTu/PRmNWwYyrB8PY88EqADq80x+vwX5k3EsF4MAnGwg/GIZbk5r0/Gik7mAFNn/xC2f3nCpW7kuzXqWhX1NyMrP5KfArbl2IKGbTJ3AwPv3bY1XRhrcbDCvY3mqgLwOmDSMpLgGAgyt3cWzDgceu94gPRuLp14yczGyWBX5J5F/XDfabVzDnnWWTqVxdi8jP58y+P1m/cPVjl1+U4R+8XqC3PHCJUb23l03CpbqW/Px8QvadLrXegFmvUN/Pi5zMbH4OXEaUkfbsEfgSLdT2nNRgRMH2NkM7025YV/Lz88lOz2LDtBXEht8udnx3PX/5PfAbYkrwl756/rJL9Rffd/rTVM9f9n+ygasHw9CYaeg5/3XcGtdE5Oeze/Zqso9dKlZuNd/GtJ49DEVjwqV1wZz9apvBfhNzUzouHk2lxh5kJaayb8xS0qLu4uxZk/YLXwdAUeD0os1E7j5ttA21fo3xmqPTuL42mEtLi2u0/HIMDo3dyUlM4/ioJWRE3UUx1dD8s5E4NPLAxNSEyF+P8veSrQD0PLWY+2lZiLx8RF4ee7u9V1BejQ6N6fCBTu/C+mBOf22opzE3xf/z0VRupKvTzjeXkhp1FwDvN3vR4CVfRF4+wbNWcfPweTQWZgz8dSYac1NMTDWE7zzFyUWbjNYVYOis12ji15SczBy+DVzCDSM+MyBwCG36d8C6ojWjGrxcsN3RrRJvfDYeKzsrTEw0/LJwDeeCQ0rUesCo2aNo7tec7MxsFk1cxLW/rhnst6hgwbRl03Ct4Up+fj5/7PuDnxb8VLC/Xc92DJ0wFCEEERcj+Pitjw2Od/VtjPeHw1BMTAhfF8xFI9ew9ZejcWzkQXZiKkdHLyU96i7u/Vrz/NgeBXYOz1djV9eZJF64SeeNM7B0sSc3KweAA4MWwj3j9Zv44Vu06ehDVmY2syd8xOXzV4rZfPnzJzhVdsLUVEPoH+f4ePrn5Ofn87+Jr9J3SE+SEpIA+Oqjbzl+4KTBsW6+jWk+p7B+fxmJg7ZfFNbv8Bhd/QDsn69Gq4WvYWZjicgX7OjxPvnZ9wuO9fvxXWyqO7Ot0zTjlXsEM+cv4vCxUzg62PP7muX/qAx9rNs1w2XmKBSNCUm/7OHeil8N9ju+2g/7F7sicvPIS0gmetpicqPvYNWyMS4z/ldgZ16zGrffWUjavhOPpfv5ojl079aRjMxMXn99AqFn/ypms3/vr2hdXcjMzAKge8Bg4uN1TjFwYC/ef+9dhBCcO3eRYcPHPVRv7OwxNO+oi4lP3/2M8L/CDfZbVLBg5vIZuNVwJS8vn5P7TvLDgh8BcHZzZtLngdjYWWOi0fD9Rz/w58E/H6r34qxXaaDeK1YFfm303ts7cBAt1XvFhAbDC7b7DOxAf71776GVu0t175U8W/7zA2xFUXYCQ4QQSU+p/GAgUAhhfJTwmNT2a4KTh5alHSZSxas2Pea+yvd9ZxWzC5j3Gtunfsft0HCGrJxMbd8mhAeHAfDH97s4sWKngf2dy1F822smIi8fm8r2jNw1n3P7TpOfV/hc2dDXi8oerrznOx4PrzoMnfc/FvSdXkz73P7THFy5iw+DlxTbd3r7cdbP+r7U9fb0a4bWw5UJHcZQ26sur88dzXt9Jxez277idy6e+AuNmSkz186hiW9Twh5jAFFcrylaDzfe7TCW2l51eW3uKN7vO6WY3Y4VWwr0ZqydXSq9+r6eOHto+dD3bdy96vDivNdZ1HdmMbsL+0M4snIP7wV/YbD9zJZjHPt5HwANOzej33vDWTbiIwObOn5NcPTQ8mWHiVRV/eU7I/7Sc95rbJv6HVGh4Qwt4i8nv9/F8SL+0nRwRwCWdZ2KtZMdQ1dOZlvA+yAKP1RRTBTazB3BjiELSI9JoP+OOUQGnSHpanSBzXODfMlOTmd924nU6u2Dz/RB7Bu7lMRLUWwKeA+Rl49VZXsGBs3jxt4QRJ5hnkMxUWg2/xWCX/qIzJgEuuz6kOigEFKuFD5o1BzsS05yOjtbT6RaHx+azBzMidFLqNarJRpzM/Z0nIrG0pzuhz7mxubjZKgDm4MD55KTkFZMz3fuCDYPXUBaTAKDts3h+t4zJOjVqcFLujqtbD+Rur18aDttELveXIpjHTfq9vJhTecpWLs40G/tVFZ1CCQv+z6bBs3nfkY2JqYaXvjtPSIPhkFY8QFCY9+maD1cmew7jlpedRgx7w3m9C0+sDq7/0/2rdzJx8FLDbb3GTeQUzuOc2DNHtxqV+Xdn2YQ2HZMseP18fbzpop7FUa2H0k9r3qMmzeOCX0mFLPbtGIT506cw9TMlPnr5uPt683p4NO4ubvx4tgXCewfSFpyGhWdKhZr0+bzR3Bg0AIyYhLotnMOUXvOkKLXprUG+5KTlM7WNhOp0ccHr5mDODp6KZGbjxO5+TgA9s9Vpf0PE0i8cLPguGNvfk3CueIDEX1ad/ShukdV+rcZQsOm9Zn60bu82nN0Mbtpo2aRnpYBwMJvP6RTL1/2btENUtZ9+ytrlq83Wr5iotBy3gj2DtbVL2DnHG4FnSFZr351But85ve2E3Hv7UOzGYM4PGYpisaEdl+O4ejby0m8eBMLBxvE/dyC46p39yY3Peuh9XsUfQO6MGRAb6Z/+OkTlQOAiQnaD8Zy85UZ3I+9i8dvi0k9cJKc8FsFJlkXrxHR721EVjb2QwJwmfwat99ZQMYf54joPV5XTEUbau/7nvSjj9eXdu/WkTq1PXiufltatmjKV0s/onXbXkZthw8fx5mQcwbbatf2YMrkcbTv0JekpGScnZ0eqtfcrzlVPNx4td1rPOf1HG/NH8dbvd8pZrfxm42EqTGxcP0Cmvt682fwaYa+NZjD2w+zffUOqtepztyVHzK89QgjSjoa+HpR2UPLLN+38PCqw+B5I/m474xiduf3nyF45W5mB39ZbN+Z7cfZMOuHh9ZL8u/kPz8HWwgR8LQG12VJvS7NCPvtCAC3Q8OxsLPCprK9gY1NZXssbCy5Hap7og777Qj1/Js9tNzcrJyCwYuphZnBQOkBTfybc3LTIQAiQq9iaWuNnbN9MbuI0KukxJdtUzbr0oIjvwUDEB56BSs7a+wrOxjY5GTlcPGEblCSdz+XiL+u4aR9eEf5cL2DpdKL/Os6jqXQa+TfnFObDgMQ+ZD2jCyhPbPSMgt+NreyQBi5Zvr+EhUaToWH+EuUnr889wh/ca5ThYjjFwFIv5dCVko6zk08DGwqe9YiJTKO1Jvx5N/PI3zLSdyLlOvu35Qrv+rO7/qOU7i1bQAY+qPGwsyYOwLg6FWL1Mg40lWNm1tOUqWroYZbt2ZE/qJr56jtp3Bpp9NACDRWFigaEzQVzMnPySVXr02N4eJZi+TIOFJUvSvbTlKzSJ1q+jfl4kZdna7uPEW1Ng3U7c24su0keTm5pNyKJzkyDhfPWgDcz8gGwMRUg4mpaYn1berfnGNqDF4LvYqVrTUVjfjMtdCrJBvxGYGggo0lAJZ2VgUZrYfh4+/D/t/2A3A59DLWdtY4FImF7Kxszp3QDVpy7+dy7a9rOLnqYqHbkG5sX7WdtGTdw0ryvWSDY53Ua5imtumNLSepVuQaVu3alOuqn9zcfgoX1U/0qdG3NTe2nCy2/VF06NqWHRv3APBXyEVsK9rgVLl4HD8YXGtMNZiZm/K4ix4UrV+kkfpV82/KNbV+N3acQqvWz61DIxL/vkXiRd1DQ3ZiGiJfJ2xqZUH9N7pz7ovfS11nfbw9G1HRzvaJyniAZeO65NyI5v6tWLifS8qOw9h2amVgk/HHOUSWzt8zz17CVFupWDl23dqSdvh0gd2j6NWrK6t/3gjAH6dCqGhfEa228mOf98jXh7Bs2U8kJel880FWuyRa+7dirxoTl0IvYW1ng2NlRwOb7KxswvRiIvx8OJVcdXUVAqxsrACwtrXmXtzD9Zr4e3NSvVdEqHFfXvfe8iJfiGf+79/Kf2qArSjKaEVRzqr/IhRFOagoSqSiKJUURXFXFOWSoig/K4ryt6IoGxVFsVKPa64oynFFUcIURTmlKIqtoigVFEX5UVGU84qihCqK4qfaWiqKsl4tYzNgqafvryjKCUVRQhRF+VVRFJvHPXdbrSMp0YXBmBqbgK2L4c3O1sWBlNjCG2dqTAK22sLgbz7cn1G7P6LXJ/+jgp1VwfYqnrUYvXcho/cs4OeZ3xpkrwHsXRxJ0NNOir2Hg9awU3kUTbu35L1dn/LG1xNxcH38waij1pF70XcLfk+IvYejS8naVnbWNO3cnL+OnSvR5mE4aJ0M6poQew+Hh+pZ0bSzNxdKoVfRxYGkIu1ZsZTt2W6YP+8f+oI+U4fy2wc/FdtvV8RfUmITsCviL3ZF/CUlJgE7vfNoMdyfMbs/oo+ev8RdvEG9Lk0x0ZhgX80Zt4Ye2LgZXk8rVwfSYgrLTY9NwNrVUNtaW2gj8vLJScmggoMuHCp71eKF/Qt4Yd9HHJn2Y7HsNYCl1pHM24X1y4hJwFJrqGGldSAjulDjfkoG5o423Np+iryMbHqHfUWv019wafkOcpLSdXZC4Lt+Kl32zKXmy34FZdloHUiNLqxTWkwCNi5G6qSnl52qq5ONi5Fj1XNVTBSG7JrH/0K/5ubR88SdNZyC8QAHl+Jx4FCKh7rNn2+gdd/2fH5iBRN/nMGax3ibVElbifiY+ILf78bepZKRQdEDrO2sadG5BWHHdG9AqnhUoUrNKny66VMW/b6IZh0MB5eWetcH1GvoWvwaphe5hhaOht1mjd4tifzdcDpBq8/foPveeTR8p2+J5+usrURc9J2C3+9Ex1O5hPp9ufZTgs5tJT0tg/3bgwu2v/BqP9bu+5H3Fk3BtqLheemf+4P6WRXxUUsjPmrhYINdTS0CQeefJ9Nj91wajCmcDuM5eSAXvtlFbmZOiXUrb0y1TuTGFPrn/di7mLqU7J/2A7uSdrj4S127Hh1I2X7osXWruGmJulX4RuB2VAxV3LRGbb/7bhGn/wxixvTCjHOdOjWpW7cmh4N/59iRbXT1932onpPWifhovZiIiX9oMsfazhqfzi0JPXYWgNWfr6FT/478fGo1c1fO4ev3v36onr2LI4l6cZ8Yew/7Ut4rvLq3ZMauT/jf1++W6t4refb8pwbYQojlQghPoDkQBSwqYlIP+FoI8TyQAoxVFMUc2AC8LYRoAnQGMoE3dUWKRsBgYKWiKBWAMUCGWsYsoBmAoiiVgJlAZyFEU+A08O5TrbAep9fsY0n7CXzTfTppd5Lo8t7Qgn23z15jeZcpfNf7PbqN6afLZJe6HtCOAAAgAElEQVQh5/adZnrbsXzYPZC/j4bxymcPn+P2TzHRmDB+ybvs+XEHd27FPRWNonrjlkxkdznp6XNkdRBzOrzN1gVr8R/fv8zL/3PNPr5oP4Hl3aeTeieJrqq/hP5yiJSYBN7YNpdu7w/jVshVowPgJ+FO6DV+7TSVTT3ex2tcLzRl7I9OXrUQ+fls9RzH9hYTqDcqAOvqzgAc6DOHIP+ZHB7yMXVe6YKzz3Nlql0UkS9Y230G37d8C5cmtXCqW/Wp6Pj0bsfRjQeZ0OoNPnt1Hm98/haKopRZ+SYaE6YsmcLWH7cSezMW0GV83dzdmPLiFBaOX8hbC9/C2s66zDRBdy3zMnNIvhxVsO3YuK/Z0Wkae/t+SOWW9fAY2PaJdd4aEkh3r36Ym5vj3bYpAL+t/J1+rQYztMtr3I27xzuz3nxinQcoGg2Vm9flyLiv2d13DtW7e6Nt2wCHBtWxreHCrRK+S/gvYNfbD8tGdUj4bqPBdlNnByzquZN25EyZaw4bMR6vpp3x9etH2zYtePnlgTpNjSm1a3vQsfNAhg4by/Jln1Cxol2ZaJpoTJi+dCq//7ilICb8+vgS9OtehrYYxswR7zN58aQyjcOinN93hplt32Re90n8ffQcIz4rOx8tK8S/4N+ToCiKo6IoexVFuar+72DExlNNrl5QFOWcoigvPU7Z/9U52F8AB4QQ2xRF0Z8wfEsIcUz9eQ3wFrAHiBFC/AkghEgBUBSlLbBE3XZJUZQbQF2gPfCluv2coigPUps+QH3gmBpQ5oDRrzgURXkDeGPq1KnO/9s+CysTC6LPXcdOL1Noq3UkNS7R4LjUuESDDKStqyOpaoYy/W5KwfaQdQcZ/ENgMd274dFkZ2RRpW41PDzr0HZwZwAiw8JxdHPiQW7NXutEYuyjXzE/ID2pcD7r0fUHGDB12EOsocvw7nQc5A/A9XNXcXIrzCo5ap1IKOH19v8WjCU2IoZdP2wzuv9hen6Duqh6urrq6yWWoDdywVhiI6LZ/cP2R2q0G+ZPq8GdALgZdg17PQ17rRPJpWhPfUK2HefFuSP5WdVoN0h3zW4X8Rc7rSMpRfwlpYi/2Lk6FmS0i/rLENVf8vPy2fPhmoJ9r2+aRdL1GINyM2ISsXEtLNda60h6jKF2eqzOJj0mAUVjgrmdFVmJhvOek8KjuZ+ehUO9qtwtMp82MzYByyqF9bNydSQz1lAjIzYRKzdHMlUNMzsrchLSqB7YmpiD5xC5eWTfS+Hun1dwbFKT9JvxBWVk30shatdpHD1rwpnLpMUmYutWWCcbV0fS4ozUyc2RtFidnoWtrk5pcUaOLXKuOSkZRJ24SA3fxhB+FYBOw7rRQY3BiLBwnNwqcVW1d9Q6kRj78NfL+nR4qROfjvgQgGshVzCzMMfG0ZbUeykGdj2H96Tr4K4AXD13FWdX54J9lbSVuBt7F2O8teAtbkfeZsv3Wwq23Y25y+XQy+Tl5hF3K47bEbdxc3fj6jldLTLV6/MAK1dHMmOKX0PrItcwW29+fI0+PsWy1w+uYW56FpGbj+PkVRPUsdwLr/Sj79CeAFw8ewkXt8LpBJXdnLlTQv0AcrJzOLTnKB26tuXU4dMk3C08199/3s7nqxYYPXf9+mUUue4P2iBDv36JaWTEJHDnj8tkqzERdSAMp4bu3M/IwqmxB/1Pfo5iqqGCkx3+vxafk1ve5Mbew9S1sJ8201Yi18j0B6vWnlQa+xI3hkxB5OQa7LMNaE9q0HHIzXuo1pjRI3j9dd0D/+nTZ6laza1gX5WqrtyOji12TLS6LS0tnXXrf6e5tydr1mwk6nYMp06FkJubS2TkLa5evU6d2h6cPhNWcGyvEb0IGNwNgMthV3B204sJV2fulRCH7yx8m9sR0Wz+vnAqT9eXujJjmO56/R3yN+YW5lR0tCNJb/pUh2FdaaPeK26EXcPBrRJwGdC9YU36h/feY+v303/qyw+xlvxDpgL7hRALFEWZqv5e9MOtDGC4EOKqoihuwBlFUfY8anryfyqDDaAoyitADWC2kd1FH2bKcnKOAuwVQniq/+oLIV43ZiiEWCGE8P7oo49qrOk5mxUB07kcdJomA9oBUMWrNtmpmaTdMbw2aXeSyE7LpIpXbQCaDGjH5b26bID+/NvnunpzR8342FdzRtHoLmPFKpXQ1nLjblQ8wav3MDdgEnMDJnE26E98+ncAwMOrDpmpGaWa76U/Z6xJF29irkU9xBr2rtrFtIAJTAuYwOmgP2g3wBeA2l51yUhNJ+lOYrFjXgwcgqWtNatml/5Dyr2rdjE94F2mB7yr6vkV6GWmZhjVeyFwCFa2Vqye/XgfjxxZHcTHAVP4OGAK54L+pEX/9gC4e9Uhq5Tt6exe+Aq0QUcv4iNjCjSWB0xnecB0Lun5S9VH+EvVUviLWQVzzCwtAKjZtiH5ufkGHy8C3Am7TkUPLbbVnDEx01C7jw839hp+tHRjbwh1X9CdX80eLYg+ppvXbavnjzZVnLCv5UbarXiKknD2OrYeWqxVjep9fLi9xzDzFb0nBPcXde1ctWcL4o5eACDj9l1c2tQHQGNpgVOzOqSER6OxtMDUukLBdm2HRgWZ0biw69h7aLFT9er28uF6kTpd3xtC/YG6OtUJaMEtda769b0h1O3lg8bcFLtqzth7aIk7ew1LR1vM1ak3GgszqrdrROK1wrbcv3o37wcE8n5AICFBp2ijxmAtNQaNzbUuiXvR8dRv0xgA11pVMLMwKza4Bti+ajvju49nfPfxnNhzgk4DdDf6el71SE9NJ9FILAwPHI61rTUrPlhhsP3EnhM0atUIADsHO6p4VCnI5AHcK3INa/TxISrIsE1vB4VQU/WT6j1bEHf0YuFORaFGr5bc2FI4wFY0JgVTSBRTDVU6e5F8qbC/+fWnzQzt8jpDu7xO8O4j9Bioe5ho2LQ+aSnp3LtjOFiytLIsmJet0Who27kVkeG6edH687V9u7fj2mXDh8AH9bNR6+fex4dbRep3KyiEWmr9avRoQawaB9GHzmH/XDU0FcxRNCZofZ4j6eptrqzaz8Zm49nkM4HdfeeQcj2GoBfm8azJPH8Fc3c3zKq6gJkpdj3ak7rfcF68Rf2auH44nluj5pCXkFysjIo9H296yLLlK/Fu7o93c3+2bt3DsKG6bHTLFk1JSU4hNvaOgb1Go8HJSZdUNDU1pUePzly4oBuwbt26mw4dWgPg5ORAnTo1uR5x0+D4bSu3Mabbm4zp9ibH95ygixoTz3k9R3pqOgl3ig94X5k0Amtba5Z9YLg6S3z0HTzbegFQrXY1zCuYGwyuAQ6t3sP8gMnMD5hMWNApfNR7xZPeext38Sb2EfdeyT+iD7BS/XklUGxemhDiihDiqvpzNHAHcC5qV5T/VAZbUZRmQCDQTghh7L12dUVRWgkhTgBDgKPoHh1dFUVpLoT4U1EUW3RTRI4AQ4EDiqLUBaqrtofVYw8oitIQaKyWfRL4SlGU2kKIcEVRrIEqQoji60IZ4eqBs9T282Tc4UXcz8xha+A3Bfve2DmfFQG6VT12zvyRPp+NwrSCOeHBYYQf1D2Jd542GJf6NUAIkqLi2TFdNzCs5l2PQWN7kX8/DyHyWfved6Qnphpo/3UwhEZ+Xsw9tISczBxWTvqqYN/MnZ8wN2ASAP2nvkyLPm0xtzRnwYnlHN2wn+2Lf6XjqwE06exNXl4eGUlp/BT4FY9L6IEzePo1Y/Hh5WRnZvNNYOFX0h/t/JxpARNw1DrRb/yL3A6/xfwdulk/Qat2cHD9vsfWecBZVe/zw8tUvcIXHPN3LmJ6wLuq3gvcDo9i3o7PVL2dBD+m3sWDoTTw8+L9Q1+Qk5nDz5OWFeybvHMhHwfoHn57Tx2Kd582mFmaM+fE15zYcIBdizfSbkRX6rVpRF5uHpnJ6ayZWHwe39UDZ6nj58lbqr9s0fOX0Tvns1z1lx0zf6Svnr9cVf2ly7TBaPX8ZZvqL9aV7Hh51RSEEKTGJrJpwjKsimiLvHyOvreSgJ8no5iYcHnDIRKv3MY7cADxYRHc2BvCpfWH8PtiNIOOfkZ2Uhr7xupWvdC2qIvn2F7k5+Yh8gVHZ/xULLP9QCNk+k90WDdFt0zf+kOkXLlNw0kDSAiLIDoohOvrgvFZMoaA45+Rk5TOidG6axn+415aLB5Ft+CFoChErD9E8t+3sK7uTNsfdKtkKKYabmw+TuzBc2Cm6JbXe28lfVdPRtGYcHHDIRKu3Mbn3QHEnY8gYm8IFzYcouvi0Yw4/BlZSWnsGqerU8KV21zd/gcv71+IyM3n4MyfEPkC68r2dFk0ChONCZgoXN3+BxH7zxpNW4QdDKGxX1M+OfQV2ZnZfKcXg3N2fsr7Abo3DC9OHUarPu0wt7Tg8xMrOLRhH78v/oV1c1fy2oIxdH29J0IIvgtcWlykCH8e+JPmfs35/sj3ZGdm83ng5wX7luxawvju43HSOjHorUHcvHqTL3fqYnP7yu3sWb+HM4fO0LR9U5bvX05+Xj7fz/ue1KTCvkXk5XN6xko6rtW16bX1h0i+cpvGkwZwLyyC20EhhK87ROsvR9P7mM5Pjo0pPO/KPs+REZ1A2s3CBzATczP81k7BxFSDojEh9sgFwn8+aLR+x/afpE2nVmw+vo6szGzmTChciefnvd8ztMvrWFpVYNFP8zEzN8fEROH08VA2rdJl6d+aOZq6DeoghCAmKpb5kw1X4xB5+ZyauZLOa3VxEL5BV78mgbr6Re0N4er6Q7T9cjR9j35GTlIah9U4yEnO4OKKXfTYOQchBLcPhHF7/9lHXrPSMGnWAv4MPUdSUgqd+r7M2NeHMaBX139WWF4+sbOXUe2Hubpl+jYGkRN+k0pvv0zW+aukHfgDl8mvY2JVgapLdKvf3I+OJ2r0HADMqlTGVFuJjFPnSyW7c9d+unXryOW/j5GRmcnIkYWzLk//GYR3c38sLMzZuWMtZmamaDQa9u8/wnff/wzAnqBgunTuwLmwg+Tl5TFl2ockJBR/iHzAqQOnaNGxOT8d/UG3TN/Ewlmmy3Z/xZhub1JJW4khbw3m5tWbfL1Ldz23/LSN3et3882H3zJh4dv0H9kPhODTdz97aP3+OhhKQ7+mzDn0JTmZOayaVNjXT9/5MfMDdCtq9Zs6lObqvXf+iWUc23CAHYt/xe/V7jTu7E1+Xh7pSWmsDHz4nO9nQf6/4E+lP5g1oLdphRBiRUn2RXARQjx4jRsLuDxCqwW6GQzGP7jRtzW2gsG/FUVRfgS6ont6AN086M6AN2AD7Fa3NQMuAsOEEBmKojRHNx3EEt3gujOQCyxTj80F3hVCHFQUxRL4EWgC/A1UAd4UQpxWFKUjsBCwUPVnCiG2Puyc59QYWq4NfFsp3w9nUsT9RxuVIQpPb76bMSop5uWq5yjK/5nXNa9829Q+r3z7nFiz8q1fiMnDVzQpa+Lzy1cPYGhu6T7UelIWc/PRRmXIeKV6ueoNDptTrnrhrZ7OdzQPo9GNsEcblSEdXRqVq14tTdnM/S4NyyJ/Kd/OzQhDavR75oPItTc2P7QdFEXZBxj7enYGsFIIYa9nmyiEKDYPW93nCgQDI4QQj1z66D+VwRZCvFrSPnVFj1whRLFJSur8ax8jhxUrTwiRCQwqQf8Aug8sJRKJRCKRSP6/RvwLMtiPQgjRuaR9iqLEKYriKoSIUQfQd0qwswN2ADMeZ3AN/8E52BKJRCKRSCQSSRmwFXjw14JGAFuKGqir0W0GVgkhNhbdXxL/ZwbYQohIIUTDZ30eEolEIpFIJJL/BAuALoqiXEU3fXgBgKIo3oqifKfavIhuhblX9P4Wi+ejCv5PTRGRSCQSiUQikfw7KNu/olD+CCHuAZ2MbD8NjFR/XoNu6edS8X8mgy2RSCQSiUQikfwbkBlsiUQikUgkEkmp+Tcs0/dvRWawJRKJRCKRSCSSMkQOsCUSiUQikUgkkjJEThGRSCQSiUQikZSa/8I62M8KmcGWSCQSiUQikUjKEJnBlkgkEolEIpGUmv/6Mn1PE5nBlkgkEolEIpFIyhCZwX7KvGwXX656m5Mrl6uetbAoVz2Lcn5cvmJWvoI1cpVy1QO4pylfvRylfOt4v1zVoF9W+cbEDXPLctUDsM4v37jwrqAtV73M3HKVI7zVuHLVq31iabnqAYz2nlaues3um5Wr3hWNzOVKDJEDbIlEIpFIJBJJqRFCfuRYEnKKiEQikUgkEolEUobIDLZEIpFIJBKJpNTIv+RYMjKDLZFIJBKJRCKRlCFygC2RSCQSiUQikZQhcoqIRCKRSCQSiaTUyLVTSkZmsCUSiUQikUgkkjJEDrAlEolEIpFIJJIyRE4RkUgkEolEIpGUGiFXESkRmcGWSCQSiUQikUjKEJnBlkgkEolEIpGUGrkOdsnIDLZEIpFIJBKJRFKGyAz2M8CyjTdOU8agaExI2bSb5O83GOy3faEHFQf3RuTlIzIyiZ+9mPvXb2JS0RaXRe9h0bAeqVuCuDf/q2Jl+80ehoefJ7mZ2eyeuII7f0UWs6ncyJ1un43CtII5EQfPcnDWagAqVLSm59fjsKvqTEpUPNvGLiE7OQNzW0sCvhiDrZsTJqYaTn+zkwu/Hgag5bSXqNHJEzOrCpiYm5KbmcPf64I5+/U2A00Tc1M6Lh6NcyMPshJT2Td2KalRd6nariEtp76Eibkp+Tm5nJi3jujjFwHo/csMrCrbk5uVA8D2oQsR8SkAVPFtTMs5w1BMTLiyLpjzXxXXa//FaJwaeZCdmErwmKWkRd3Fpmol+gV/TPL1GADiQ8I5MfVHALqsmYyVS0UUjYa4U5c5Of0ngzJ7zRpOPT9PcjJz2Bi4nOgLxdvWraEHL3w6CrMK5lw+eJZts1cB4Fq/Bn3nvYaphRn5uflsee9HosKuAeDh8zw93x+GxtQUcS+VXQPnFSv3adTXGB31/GdXCf7jUsR/Dqj+U7dHC1pP6I9TbTfW9J5F3LkIg+Ns3Zx4LfgT7qdnkZOaycV1wZwx4if+en6yW/UTgGZv9qL+IF9EXj6HZ63i5qHzAJjbWdHp45E41auKEIL9gd8SGxJOmxmD8ejsRe79XJJu3GF34AraTBxQ5vWzq1qJVw98TOI1XRunn75K2JQfDMqs7NeYRh8OR9GYcOPng1xdalhvJ5/naDRnGHb1q3N69BKit58q2Ndq7RQcm9Xm3qnLnBz2KQCN5g7HpZMneZk57AhcQXwJcd5FrUfkwbMcUuthUdGaAL0436nGOUCH2cNwV9snaGJhuW2mD8KjoyeKonDz6F9ETluJxtKc5t++jXUNF0R+PimXblGxQY0yq+Pj8OKsV2ng50VOZjarAr/m1oWIYja9AwfRsn97rCraMKHB8ILtPgM70H/aMJLiEgA4tHI3xzYcMDi2mm9jWs8ehqIx4dK6YM4aibuOi0dTqbHar6lxV6VdQ1pOK+zXTs4t7Ndq9WqJ11t9UExMuLk/lD/mG/b/D7Bu1wyXmaNQNCYk/bKHeyt+Ndjv+Go/7F/sisjNIy8hmehpi8mNvoNVy8a4zPhfgZ15zWrcfmchaftOPHa7GmPm/EUcPnYKRwd7fl+z/InK0mfgrFcKruHqwGVEGbmGvQJfooV6DSc2GFFsv2e3FoxcPpGPe03j5vnrBvv+ad/5AGs3J/oFL+TsZ5v465udANR/vSt1h/iConBl7UEufrfH8HzL4V6Rnpj66MaVPFNkBtsIiqKkPWK/vaIoY/9R4SYmVJoxjtixM7jV53/YdPfFrGZ1A5O0nQeJ6j+K2y+MIenHX3CaNAoAkXOfhKUruffpCqNFW7ZrjoO7lh/aT2Tv1O/pPO8Vo3ad573K3inf8UP7iTi4a3H3bQxAizd7cfPYRX7oEMjNYxdpMbYXAJ7Du3Dv6m1Wd5vBLy/Oo8N7QzAx0+DR0RPnhu5s7D4TIQSZCals6j2L2n18cKjjZqD5/CBfspPSWdduIue+203L6YMAyExIZddrn/Frl2kcePcbOn0x2uC4/W99zcZuM9jYbQZZ93SDa8VEwWfeCIJe/pjNfpOp2deHikX06g72JTs5nd/aTuTCt7vxnjGoYF/qjTi2+s9gq/8Mg8Fm8OglbOkyg987TqWCoy3uPVsW7Kvn64mTh5ZPfd9l8/Tv6DvvNaNt23fua2ya9h2f+r6Lk4eWur5NAOg+dTD7v9jEkoDp7Fu0ke7TBgNQwc6KPh++yqqRn7HYfzIHRy0pVubTqm9RPPya4OCu5fv2Ewma+j1dHuI/QVO+43vVfzxU/7l7OYotb3xB1B+XjR7n9/5QRF4+F9Yd5OeOk6lrxE8aDPIlKymd1e0mcva73bRR/cShjht1e/vwc6cpbB32Mb7zXkExUQBo/8EwbgSfY43fZNZ1nU5CeDQAN4+c5+fOU1nZdTqJETH4L3z9qdUv+UYcq7rPYFX3GcUG15goNPnoVU4M+Zj97SdRtV9rbOtWMTDJvH2XkLeXE7X5eLGyr369nTPjlhX87tLJE5uaWva1epezgd/RsYR6+M17lf1TvmNl+4nYu2upodbD+81e3Dp2kZUdArl17CLeapy7+zXB3l3LyvYT2T/1+4JyXZvVwc27Lj/7T2NNl6m4NK5JpdbPAxC+bAf72wVy0H86rl2bcWXp1jKp4+PQwNeLyh5aZvm+xdrpKxg8b6RRu/P7z7Cwz3Sj+85sP878gMnMD5hcbHCtmCi0mTuCncM+5he/ydTu44N9EX99bpAu7ta3ncj5b3fjo/prVkIqu1/9jI2dp3Fwwjd0/FLXr1nY29By5mC2v/QRv3aaiqWzPVXaNCh+YiYmaD8Yy62R73Ot+2jsenbAvHY1A5Osi9eI6Pc2Eb3eJGXPUVwm6/qkjD/OEdF7PBG9x3Nj2DREZjbpR0Me3aCPoG9AF5YvmvvE5ehT39cTZw8ts33fZt30bxk073Wjduf3h/BJnxlG91lYV8D31QAiQq8W2/ekfSdAiw+GEnUwrOB3+3pVqTvEl209ZrGly3SqdfbC1t2lYH953SvWjv3CaLnljRDimf/7tyIH2P8Me+AfDbAtGtXj/s1ocqNiITeX9F2HsPZrbWAj0jMKflYsKxRuz8wiO/QCIifHaNnWfq25+NtRAGJCr2FhZ411ZXtDm8r2WNhYEhOqeyK++NtRanf1BqBWl2Zc2HgEgAsbj1Db3/uBMubWlgCYWVcgKymd/Nx8nOpUIfrUZZwbeZAcEUt82HWqtmnAta0ncfdvZqDr7t+UK2rZ13ecKrip3Ltwg4y4JAASL0ehqWCOifnDX6xU8qpFamQcaTfjyb+fx/UtJ6ne1VCvun9Twn/V6UXuOIVrWyM3sSLcT8sEQDHVqOdQGLjP+zcjdJOuvFuh4VSwtcLW2bBtbZ3tsbC15FZoOAChm45QX21DAVjY6Nqwgp0lKXGJAHj2bs2F3X+SHH0PoOAhojzqW5Ta/s248Bj+Y67nPxf0/CchPJpENVNurOzc7PukxyeTnZRO/v08rmw9Sc0ifuLh35RLqp+E7zhFVdVPavo348rWk+Tn5JJyK56kyDhcPGthbmuJW8t6XFwfDED+/TxyUnTxc+vwX4g83Z9BiA65RuX6NZ5a/R6Gg1dt0iLiyLh5B3E/j6jfT6Atcv0ybt0l5e9bkF/8zzbcPXqB3PTMgt+1XZtx8xddGyWGhGNhZ41VkXpYqfWIVevx929HqaUX5xfVNr648Qi1VB+t6d+Mv9X2iVXbx6qyPUIINBZmmJiZojE3w8RMQ1Z8MnmZOdw9psvK2jf0IPtuCiYaTZnU8XFo4u/NyU26N2kRoVexsrXGrkhMPtiXEp9UqrIBKnvWIiUyjlQ17sK3lNCv/VrYr7m1fXi/ZlejMikRcWQl6LKPt4/+hUdA82Lalo3rknMjmvu3YuF+Lik7DmPbqZWBTcYf5xBZ2QBknr2EqbZSsXLsurUl7fDpArsnwduzERXtbJ+4HH0a+zfnlHoNI0OvYlnCNYx8yDXsOfEl9i7fQm528fvik/ad1bs2I/VmPEmXbxdss6/jRnzoNfKychB5+cSevESN7t4F+8vrXpFu5F4h+XchB9gPQVEUG0VR9iuKEqIoynlFUfqouxYAtRRFOasoyielKdO0ciVyY+MLfs+Ni0fj4lTMzm5QL6rt/Amnd//H3Y+KTwUxhqayE6kx9wp+T41NwEbrYGBjo3UgNTbBqI1VJTvS7+g6sfQ7SVhVsgMg9Ke9ONZ2Y9TppYwI+oiDH6wGIYi/eIPqHRpjW92ZzHspVGlVH2s3R9JiErAuomutdSAtWqcr8vLJSc2ggoONgU3NgObcPR9Jfk5uwTbfz95g4O55NH27b8E2K60D6dGFdcgwoqdvI/LyyUnJwELVs6nuTO89c+m+cQYuLeoZHOf/82QGh33N/bQsIvVeYVd0cSBJTzM5NgG7Ipp2WgdSYvRsYhKo6KKz2T57FQHThjDl+BK6Tx/Kno91r4Ur1XTFsqI1/1s/k3Hb5lFrYFuK8jTrq4+N1uGx/CetBP8pCTMrC1qM6UlEcBg5qYWDqLSYEvzTiJ/Y6PnPg2OttQ7YVXMmKyGVzoveYNCuuXT8eCSmlhbFzqHRS+3Jych6KvUDqFjNmWE75/LSLzNwamnYxpauDmRGF+pmxSRg6er4yDJLQleeXls8Rj3SisR5hhrnGXpxbqN1IE2vfR4cExsSTtTxi/zv9FJGnl7KjUPnSbsabahXU4tZRWvij1wokzo+DvYujiRGF77KT4y9h722dJpe3VsyY9cn/O/rd3FwNeyHrVwdSNOL5/TYBKxdjfRrMYZxV7Rf8+hR2K8lR8ZSsZYrNlUroWhMcO/aDBu34udsqnUiN6awbvdj72Jq5D7xAPuBXUk7fLrYdrseHUjZfughLfBssXdxIFEvNpJKeQ2rNvDAwdWJCwdDje5/kr7T1DtcS/gAACAASURBVMqCRm/25OyiTQb2iZeicGlZDwsHGzQVzKnasQnWboXXprzuFV792z12Oz1N8v8F//6tyDnYDycL6CeESFEUpRJwUlGUrcBUoKEQwvNpCaes30bK+m1YB/jh8MZQ4meWahxfprh3aET8xRv/j73zDo+iev/2PbubTe8JSeiBUEMLhColKB1pgoogoEgvAlJFFAUpYhcQVCwoUr5iQ0AILYAUaSF0CJBCyqb3bJIt8/6xS5JNgwhEfq/n5uLK7pwz85nntHnmmXNm+Wn4clzqeDHsx/l8f+o6UUcvUbtlPdrNew6lSkncyauFEcPK4tqwBu0XDmfXyPcKtx149XNyNGlY2dvQ68sZNBzamaif/nogW3IT0/mp3Uzy07Jxb16Xp76Zxa/dFxRGr4NHrkJpbUXX1ZPxecKf6ycvPJDeXTq82IOdS3/g8p7TNO/fnqHvTeDrF5ejUCqp0dyXDSOWY2WjZsYv75B07iaZtzUPRfde9lYFnWY9w9mv9yCXEbl8UBQqJZ7N6nL4ze9JOH+LLm+Pos3UAfz9wfbCPO2nDcSoN5KtSXvo+mC6Gf2iw0zy0rPxal6XoV/O4mC3eeirsIwfhHs9YHWu44WbXw2+bv8qAEN+XEB++0akmKfKSEoF9Sf0Iet6DLnRiY/4bB8eF/ef5cyOY+gL9HQe0YMxH07lkxFLHqqGa8MatH99OLvN41pBRi5/vf4tPdZNA6OM5kw4TnWqPZCG08Du2DZvQNTIeRbbVZ6uWDeqS/bRsw90/McVSZIY+uYofphTualF90vA7Ge4/NUe9LmW0f+Mm3FcXLuTXpvno8/NJ/Vy1EMd2+73WjH5l3cAGgI3Hpq44KEiHOyKkYDlkiR1xXSjVAPwqngXkCRpAjAB4N3qTXjBrWZhmj4xGZW3Z+F3lZcnhoSUUse4S86fIXguepWkctKdhg/AcWg/APIvXcexWBTG0dutlFORrUnDsViEoHie3ORM7Ku5kJOYjn01F3KTTY+gmj3bjVPrTAtD0qMSyLiThFt9HzRhtzm3egexx64Q+NozIElk3Nbg3rQ2OSV0czRpOFR3I0eTiqRUoHa0Iy/NNNXd3tuN3l/N5NDM9WRGJVrsA6DLyePmb8ep1qoeUT/9Ra4mDftiUR87H7dSenfz5Mab9ZzsyDfr5ReY/qZcjCQzMhGnet6kFFuQZ8jXER18jpYzBtH8LdN8vJiw27hUdyPKnMfZ243MEpqZmjScikXtnH3cyDA/3ms9tGvhIpaLu/7mmZWmRUgZmhRy07PQafPRafPRnLyGW9PaFg72o7S38Zge1B/ZHQDNhdv31X4cymk/5eET4EfDfu1QqlXYujniXGsghjwdVg42ZbfPMtpJtrn93MXBXAbZ8alkx6eScN40FeLW7lO0Mc8pBujxyST8+rYlPSqB+PO3Hol9hgI9BnMZJ1yMJDcqAYf63qSHmcpYG5+GbbEIl42PG9pi0av7wadPIO4dGtN9/3LSzt/GtnhZ3IcdDiX6uV01F1P0upoLWnM/z9ak4VCsfO7u03jIE2hCb6IzOxqRIWG4BjYodLBbfTCO7IgErBztHsjG+6HbqN488cJTAESF3cK1ugdgOg9Xb3fSNfevmZNetNTm2NYDPLPgRYv03Pg0HIr1Z3tvN3LiyxjXfNzIKdbvCsc1Hzd6bSg9rkXtDyVqvyni2mRkd2SjkZLPXPSaFFQ+RVM+rLw90JdxnbDr1AqPKc8TNWI+crEnfwCO/bqSFXwc9Ib7KI2qo+uoXnSyqMOiNudSiTq0drDBp2EtZmx9CwAnTxcmbpjLF+PeL1zo+CBjp0eAH3X6tyPwjeGonezAKGPI13H1u32Ebz1M+FbTk4HWC57DsU41pu9eDlTdtSLi1FU8fL1bIhzsxxYxRaRiRgKeQBtztDoBsKl4F5Bl+UtZlgNlWQ4s7lyDyQm2qlMDVQ1vUKmw79uNnBDL1d2q2kWLMOy6tkcXHUt5ZG79g9hnJxP77GRyDh6n6VDTFAOfgPrkZ+UWTvm4S05iOvnZWnwC6gPQdGhnbgWbIhy39p3Df5jpsZP/sC7c2mfanhmXTG3zXFg7Dydc6/uQEZ2IpJCwdnEgMew2rg1r4tmiLrEnrlB/YAci91kuqoncd46G5mPX69+OOPPcTbWTHX03zubvFdvQnClapCIpFYWPWhUqJbWfCiD1egwAyedv4+TrjUMtTxRWSuoN6sCdYEu96OBz+D1r0qvbvx3xZj1rN8fCxXEOtT1x8vUiKzoRlZ01tuZ5rJJSQa2nWhG58xSr+y1kdb+FXAk+U/hIrlaAH3lZWrJKzAnMSkonP0tLrQA/AAKe6cJVc9lmJqbh28G0MKx+J39SIhMAuBJ8ljqBjVAoFVjZqPEMqE96icfvj8Leu1zbuL9wcd7NvWfxv4/2U1Cs/fgP7czN4IojZFuHLeWrJ2bxRYcZ6HLzubjpAJc2H6ThwA5ElGgnEfvO0djcTvz6tyPGbEfEvnM0HNjBNI+1licudb1JOH+L3KQMsuNTcannA0DNJ/xJDTf1l9pBLfBq7suXnWaysffCR2afbbEydq7tib2vNznFHKr087dwqOeNXW1PJCslNQd3RHOPY5Ykfs8ZUk5e41CPhcTvOUPt50xl5Nraj/ys3MIpH3fJNdvhbbajydDO3DZr3t53jqbmMm5arJ/f3neOJuby8TaXT25iOllxydTo0BhJqUChUlKzQxOyb5jaaJP5z2LlaMfZKWsf2Mb74fAPewsXJYYFn6LDM10B8A1ogDYrt1JzrYvP9W3RMxDNrRiL9MSw2zj7euNo7nd+gzoQVaK9Ru07R8Nnyx/XTq3YRsIZy8V3Nu6mKTlqZzuaju7Btc0hpc5Ne/EG6rrVsarpBVYqnPp3JevASYs81k3r4bN0OncmLsGQmlHqGM5PP57TQ478EMzKfvNZ2W8+F4JP085ch3UrWYd5WVoWtB7P4s7TWdx5OpGh4RbONTzY2PnnM0vZ3mEW2zvM4sqGvVxYvYOr3+0DiurQvro7dfoGcmL+N1V+rajVyg/g6n0V1iNEfgz+Pa5Ij/MKzH8LSZKyZVl2kCRpBuAny/J0SZK6AwcBXyALOCfLcp17Het2816lCti2S1vc55le05f1617Sv9qC69TR5F++QW7ISdznT8a2QwCy3oAxM4vk5WvR3TLdD9fa8z0KBzskKyuMWdnET3gd3e3owmNnzplL3aAW6LQF7J3zZeGrxEb9uYwf+ppWYXu18KXPhxPMryEL4+BbprtlGxcHnl43Hafq7mTGJrNz8mryMnKw93Khz4cTsa/mgiTBqc93cvXXYyitrRi9y7SqXFJIqGzUGA0Grm87zLnVOwicPZSkCxFE7TuH0trK9DqrZnXJT89m39Q1ZEUn0frVQQRMHUBGREKhDTtHvoc+N59B2xehsFIiKRTE/nWZ40s2odabirPmky1p986LSAoF4dsOc+GzHQTMGUpyWAR3zHpdPpuEu79JL2TKGrKjk6jTry0Bc4Zi1BvAKBP64c/c2ReKjYcTPTbOQalWISkk4o9f5dTbm7iuKIoKDVzyEg27tUSnzWf73C+IvWgq2+m7l7O6n+ktBTWa+zLsg0lY2ai5ERLGjsXfAVAnsBEDFo9GoVKgz9fx26Jvibtk2r/LhKdp82xXZKNM1OaQUq98ehT2FidFWfT5qaVj8DW3nz3F2s/oP5fxfbH207dY+zlgbj9+vQN5aslobN0cyc/MJfFKFD+PWmWh1e/Tyfh2bU5BtpYr2w5zZvUO2s8eSuKFCCLMdvT8ZBKe5nayZ+oaMqNNz28Cpw+k6fPdMOqNHH37B6JCTNN3PJrW5sn3x6G0UpEZncj+2V+Sn5HLqKMfolSr0JojinGhN5ENxoduX4O+bXli9lCMOgOyUSZi1XY0JRwxr6da0XyJ6XVvUVtCuPHp7zSeN4z087fRBJ/DpVU92n8zCysXe4x5OvKSMjjYzfTIv/Nvb+HYoDoqOxsK0rIIfe0rvHu3xqt7S/TafHbP/YpEsx0j/lzGZrMd1Vr40tNsR9ShMEKK9fN+66bjaO7nuyevJj8jB4CgpWOoE9QCvbaAfXO+JPFCBJJCovuyl6nRrhEyEBVygeg3f8DGx40+oWvIuhGLsUCHysEWpZ01htz8h2JjYkjR9Ky9NpbR2bsMX/IKTbu1pEBbwPdzPy90rhbuXsXyfqZjD1kwkraDOuPs5UpGQhrHth1k1yc/MWjeC7ToEYjRYCAnPZstizaQcMt049BKrwag1pMt6fS2qd9d33aY0NU7CJwzlKSwonGt+6dF49r+KaZxLeDVQQRMsxzXdo14j7yUTJ5aMxX3pqY3R5395Fdu7ThJV9vSUVv7boF4vWF+Td/2YFLWbcNjxovkXQwn++Df1P5uGdaN6qJPMu2ri0siZpJpiotVjWrU2foBN7uOgTKu8X4n1pRZnhUxd/FKTodeID09E3c3F6a8MoqhA3rf9/6zAl8vc/tzS8bSpFtLdNoCNs1dV1iHC3a/x8p+8wEYtGAkgYOeKKzDE9sOsvuT7RbHmbH1LX5dtqlw/zY6K+Cfj53FafXaM+hz8gpf09f3lzexcXXAqNdz6p3NxP91mRtWRdNEquJacWbbIZ5+a7R03xXwiOhVq8+/7kQG39nzr5dDWQgHuwyKOdgewB+AA3AG6AD0lWU5UpKkzUAL4E9ZlueWd6yyHOxHya8ZDzafr7LYV3Hzsa7iFQ3FB82qoKGu6h8qFXewqwLrKm4z+VU89PoVVG2biVJXfZupU8U2ludgPyruOthVRVkO9qPknzjYD0p5Dvaj4q6DXVVU9bUCYEXk5n/dsexRq/e/7kTuv7P3Xy+HshBzsMtAlmUH899koGM5eUZU6UkJBAKBQCAQCP5PIOZgCwQCgUAgEAgEDxERwRYIBAKBQCAQVBoxzbh8RARbIBAIBAKBQCB4iIgItkAgEAgEAoGg0hgf49fk/duICLZAIBAIBAKBQPAQEQ62QCAQCAQCgUDwEBFTRAQCgUAgEAgEleZx/iXFfxsRwRYIBAKBQCAQCB4iwsEWCAQCgUAgEAgeImKKiEAgEAgEAoGg0hjFe7DLRUSwBQKBQCAQCASCh4iIYAsEAoFAIBAIKo2IX5ePcLAfMXvSq1WpXuMCQ5XqeVlpq1RPpTJWqV5Lg1SlerEGuyrVA4hTVe2DrDRF1Q7JWqq2zXSSdFWqF+iTVqV6AMsSPKpU713/hCrV+/RyzSrVax4VVqV6kwJfr1I9gI/PrKhSPU3f8VWqF5hc9WO34PFGTBERCAQCgUAgEAgeIiKCLRAIBAKBQCCoNOKn0stHRLAFAoFAIBAIBIKHiIhgCwQCgUAgEAgqjYhgl4+IYAsEAoFAIBAIBA8R4WALBAKBQCAQCAQPETFFRCAQCAQCgUBQaWTxS47lIiLYAoFAIBAIBALBQ0REsAUCgUAgEAgElUYsciwfEcEWCAQCgUAgEAgeIsLBFggEAoFAIBAIHiJiiohAIBAIBAKBoNLIYopIuQgHuwqoFdSCzm+PQqFUcGVLCKGf/2GRrlCr6PHJJDyb+5KXlkXwlDVkxSRTs0szOix4HqVahaFAz4llW4g9fgWVjZre61/FqU41ZIORyP2hnFy5rVx9z+4tafruaCSlgjs/HuLW6h0W6W4dGtN06Wgcm9YmdOJnaHaeAsC2pgdtvn0NFBIKlYrIr/cS/f3+e9rrFBRA7XfGgVJB8pZ9aNb+YpHuNX4gHi/0RDYY0KdkEjl7NQWxSdg29aXOiokoHeyQjUbiP/uJtD+O3VPPsVtraiweh6RUkrI1mMR1P1vaP24Q7sN7IuuN6FMziJ77GbrYpKLyd7Cl8f61ZAT/TexbX9yXfTXfHg9KBSlb9pHwuaVetfEDcR/eCwwGdCkZRM8x2aeu4Um9r14HhYSkUpH03S6SN+25px6Y6rDZUlMdRv94iJtrStdhsyWmOjw36TPii9Vh22/MdWilIuLrvURVUIe93h5N/e4t0WkL2DnnCzSXIkvl8W5WlwEfTkJlY8WtQ2EEv/29RXr78f3osWgkH7WaiDYtG//Bneg4aQCSJFGQo+XXRd+guRoNQP/Fo2nUvRU6bQE/z1lP3OXSetWb+TL0g4lY2ai5fug8u94x6T2/Zjqe9XwAsHGyJy8zhzX9FqJQKRny3niq+9dFoVJy+pcjHPj891LHHbJ4DE26B1CgzWfLnHXElqHdd87zBD7TFTtne173f6lwe712jRn81hh8Gtfmh+mfceHPv8st05K4dG+F75KxoFSQuPkAsWt+tUj3mTgArxFPIeuN6FIyuPXa5+THJJVztPKx7tAWl9emISkU5OzYTdb3WyzS7YcMwGHYIGSjEVmrJW3FR+gjogrTlV7V8Nr6LZkbNpL94//uS/O5xS/jby7T7+d8zp3LEaXyDJwznPbPdMXO2YFZ/qNLpQf0ac+E9bNZMWAB0Rdvl6tl1aYd9hOmg0JBXvAu8n7aXGY+daeuOL6xlPQZEzDcvI7k6ITjwiWoGjQif/8ectZ/WqFN/RaPpoG5T/w65wviy2gnPs3q8swHpj4RfiiM3e8U9Yn2Y3rRbnRPZIORGwfPE7xyCwqVkkHvjaO6vy8KlQLDd9/z3qo1pY778UdL6NvnSXK1Wl55ZRah5y+VynNg3094+3ih1eYB0LffCyQlpQAwbNgA3nrzNWRZ5sKFK4waPa1CWwGGLX6psA5/mLOOmDLqcMCc52lnrsPZ/mNKpbfq045x62ezasDrFdZhRSxa/hFHjp3CzdWF3zat/0fHKIlNx7a4zJ4KCgU5v+8ma+NWi3T7Z57G4dlBYDQi52pJXf4x+ogo1E0b4frGa+ZcEplfbUQbcu9rk0PX1lRfPB4UCtK27SNp/XaLdI9XBuH6fC9kgwFDSiYx8z8tvDY1u/kbeddN/VEXl0TU+HcfvAAEVcp/zsGWJClblmWHKtNTSHR9dwx/jFhJdnwqw3YuIXLfWdLC4wrzNBkeRH56Dj92mY3fwA50XDic4ClryEvNYvfYD8lNSMetUU2e3jSP79u+CkDoF7uIO3EVhZWSgVsXUjuoBdEhF0qfgELCf+XL/P3ccvLiUui8dxkJe8+SfSO2MIs2NpmwGeupN7m/xa55CWkc7/8WxgI9Sjtruh5+n4S9Z8lPSCvfYIWC2u9O5MaIxejiU2iy633Sg0+RFx5TmCX38m2u9puNMa8Az1F9qPnGGG5P+QCjNp+ImZ+SHxGPlZcrTXZ/SObh8xgycyrUq7l0IrdGvoVOk0LDHR+Ssf8U+eF3iuy7fJvrT7+GnFeA+4t9qf76S0RNe78w3Wf2SHJOXS5fo4RerXcnEm62r9HOD8jYd4q8Ynq5lyJI6m/S8xjVhxpvvETElPfRJaZxffA85AI9Cjsbmuz/jIx9p9AlpN5DU6L5ipc5+dxytPEpdNmzDE1w6ToMnbGe+lNK1+FfTxfVYdDh99GUU4f1u7fEzdebdd1mUz3Ajz7vvsx3gxeXytd32Vh2LdhAXOhNhm+cR/2gltwKCQPA0ccN3y7NyYhJLsyffieJTc8tJS8zl/pBLRm8YhzrB79Fw6BWePh681HQa9QK8GPgsrGsH/xWKb1B747lt9c3cCf0JmO+m0fDoJbcCAlj27TVRef0xkjysnIBaNavPSq1Fav7LMDKRs2r+9/n3I7jpBVzUpsEtcLD14flQTOpE+DHsGXj+HTwolLaVw6c5a+Ne1kY8onF9rS4FLbMWUfQ+KdL7VMhCgX1lo/n8vNLKIhPocWf75EafBrtjaL+kXMxggt95mHUFuA1ujd1Fo3ixqSPKq3jOncGSdPnYkhMotp369AePW7hQOcGHyDnV9PNvk2XTrjMmEzyzAWF6c4zJ5N34tR9S/oHBVDN15vFQa/iG9CAF5aNY9XgN0rlu3jgLCEb9/BOyGel0qztbej+cl8iQm/c0z77yTPJXDQbY3ISzh9/ge7kMQx3oizz2dpiM2gYumtF/VsuKCD3h69R1vFFVce3QpkGQS1x9/Xm06DZ1AzwY8Cyl/myjD4x4N2x/P76BmJCbzLqu3k0CGpJeEgYvh2b0rhnGz7v+zqGAj327k6msjK30bXmNjp+zxK2bvuNqKiidtC3z5M08POlcdPOtG/XmrVrVtCp84Ayz3P06GmcPWc5/vv5+TJ/3jS6dhtMenoGnp7uFdoK0DSoFZ6+3rwTNIO6AQ0YvuwVPiijX1w8cI7DG/eyOKT0zYm1vQ1BL/cjIjT8nnoVMbhfT0YMHcjCpR880HEKUShwnfcqidPmYUhIwmvj52iPnLDsE3sPkvPLTgBsunbEZdYkkl99Hd2tSBJGTwaDEYW7G96bv0R79AQYjBXqVV8yiYhRb6LXpFD/94/I3P83+Tctr00pA19DzsvHbWRfvBe8zJ3pqwAw5hVws/+Mh2P7I0S8pq98xBzsR0y1VvXJiEwgMzoJo87AzR0n8e3VxiKPb6/WXNt+FIBbu05R4wl/AJIvR5GbkA5A6vUYVDZqFGoV+rwC4k5cBcCoM5B8MRJ7H7cy9V1a+5EboUEblYisMxD32wm8+gRa5NHeSSbrSjSy0bKjyDoDxgI9AAprKySFdE977Vs1ID8ynoLoBGSdntTf/8KlV3uLPFnHL2HMKwAg+9x11D6mgT8/Io78iHgAdAlp6FMyUJkvSOVhd1fvjkkv7Y+jOPe01Ms+cRHZrJcbeh0rH4/CNNtm9VF5uJB1JPSethXZpym0L23HUZx7tStXL+fcday8TfbJOj2yuTwltRWS4v66n2uAHzkRGnKji+rQu3cZdXg1Gu5Vh1L5ddiwZxsu/Gxqh3GhN7FxssOhmotFHodqLqgdbIkLvQnAhZ+P0rBYe+751igOrthiMejGng0nL9Pk/MaeC8fZ29RWm/RqQ+gvJr07oTexcbTD0dNSz9HTBWtHW+6Y9UJ/OUqTXpa2AzTr34ELO07ctRq1rTUKpQKVjRp9gZ58s/NdmL9XIGd+OQJAVOhNbMvQvpuWlZReantaTBLx16IrfXFxCPBDG6kh39x+kn//C7febS3yZB6/hFF7t3/cKOwflUHdtDH6mFgMcfGg16PddxDbrp0s8sg5RWUi2dpAMVtsuj6BIU6D/nbkfWu27BXISXOZRoSGY+doj1MZZRoRGk5mGWUKMHD28wSv/x1dvq5CLVXDJhjiYjFqTPblHzmIVYfOpfLZvfgK2u2boaCgaGN+HvorF0FXUCp/SRr3asN5cxuNMbdRhxI2OZjbaIy5jZ7/5SiNzX2i7cinOLpuBwZzH8xJyTTvZdlGC3Q6MjOzLY47YEBvfvjRFPH8+9Q5nF2c8fauds9zvsu4V0awbt13pKdnABRGtSuiRa+2nDLXYWRoOLbl1GFkBXX49Ozn2bf+d/T59y7fighs1RxnJ8cHOkZx1P6N0d2JxRBrajO5+w5h2638PqGwseHu7Ac5P7/QmZas1dzPrAi7lg0oiIpHZ742ZfxxBKcS16ackxeR8/IB87XJu/J9XfD48p91sCUT70uSdEmSpIuSJD1v3h4kSVKIJEnbJUm6JknSj5LZK5EkqZ9521lJkj6TJGnnvXTsvV3JjiuKUGbHp2Lv7VpuHtlgpCArFxtXyyB7vX5tSboYWegs3UXtZEedHgHEHis7Amvj7Yo2rmhgzYtLwaaEfkXYVHejy6H3eOrcGm6t2VFx9BpQ+7hREF8UvSzQpKAux/kH8HyhBxmHzpXabt+qAZKVivxITYV6Vt7u6Irp6eKTKxyk3J7vSVbIWdMXSaLGorHELfu2Qo2SegVxxfVSKtRzH96TzLt6gJWPB02CP6X5qa/RrPvl3tFrwManRB3Gp2DjU7k67HbwPXqeXcPNteXXoaO3G5nFdDI1qTh6Weo4ermSpSk656z4VBzNDnPDnm3I0qSSaJ7+URYthwdxwxztdvJyJaNY38jUpOJUom06ebuSEV+UJyM+FacS51S3XWNykjNIMbeVS7tPUaDNZ8Gpz5l3/DNCvtpJboblUxAnLzfSi9markktdPwfJdbebhTEFusf8amoK2g/1V54ivQy+se9UFbzwJCQWPjdkJiM0tOzVD77YYPw/nkTztMmkP6RaYqCZGuD4+jhZG7YWClNFy830or1jTRNCi6VKNNa/r64+nhw6dC9b3YV7h4Yk4vsMyYnoXT3sMijrN8AhWc1dKdP3vc5lMTJy42MEn2irDaaWayNZsan4uRlstu9ng912jVmwm/vMHbbIqq3qAfAZXMbnXtqLbOPf8pHH60nLc3SYa1R3ZuYO0VPOmNj4qlR3bvM89yw4SPOnA7mjYUzC7c1aFCPhg3rcSTkN44d/YPevYLuaa+LlytpFv2icnVY098XVx93Lt9HHVY1Sk8PDAlFT7EMCUkoPT1K5XN4dhA+v/6A86sTSP+gaNqO2r8x3tu+xnvLBtJWflxx9BpQlbw2aSq+Vrg935Osw0XXCoW1mvq/f0T9X97HqWeH+7JR8Hjxn3WwgWeAVkBLoAfwviRJPua0AGAm0BSoBzwhSZIN8AXQV5blNkDpq5UZSZImSJJ0RpKkM9fyKnYQ7wfXhjXouHA4h1//xlJHqaDnmqlc/HYvmdGVn6N5P+TFpXK0+3wOdZhFzee7ovZ0fmjHdnumG3Yt/NCst5yDalXNFd9PZxI5e7VFVO1BcR0ShF1zPxK/MM0J9xjdj8xDZ9Fp7h3Z+Se4DemGfQs/EorZp4tP5mqvGVzuMgn3Yd1ReTy88iyPvLhUDj85nwMdZ1Hzua6oH4GmykZNp6kDOfLR9nLz1OnYlFbPB7Fn5ZZy8/wTWgzsRNiO44Xfa7asj9FgZGX7qXzQZSZB4/rjVuv+I3+PCx5Du+LQsj6xZcwff1jkbP8dzdAXyVjzJY4vvwiA0/iXyN6yHdk8p7cqkCSJYW+OZvuy7++d+f4OiP24qeRu+PzhHO8f/uR8EAAAIABJREFUolAqsHW258vBi9m7fDPPr50OFLXR99tP4+Mus5g1ayK+vrX/kcaoMdMJaN2DoO5D6PxEO158cRgAKqUKPz9fnuwxjJGjprB+3fs4O1f8RPBBkCSJoW+O4pdlPzwyjaog+6ffiR8yiozVX+E09sXC7QWXr6F5/hUSxkzB8aURoLZ6aJoug4Owbe5H8pdF65WudR7LrUGvET3jA3zeGoe6dtk3V/82RuR//f/jyn9uDnYxOgNbZFk2AAmSJB0G2gKZwClZlmMAJEk6D9QFsoHbsizfXfGxBZhQ1oFlWf4S+BLg50Fvyw7ViyIADj5u5GgsI4g5mjQcqruRo0lFUipQO9qRl2Z6XGjv7Ubfr2ZyYOZ6MqMSLfYLeu8VMiI0XPh6b7lG5mnSsK1edNdsU92dPE3FUeiyyE9II+taDG7tGxUugiyLgvhU1MWmYKi93SmILx2ldezcAp/pw7g+bFHhtAkwLTj027iI2FWbyDl3j3mYmKMCxfSsfDzKdJgdnmiJ17RnufncwkI9u9aNcGjrj8eovijsbZGsVBhztMS/V/5FXqdJQV29uJ57mXqOnVviPf1Zbjz7hoV9hcdJSEV7PRqHdv6k7z5eKr04efEl6tDHnbz4f16H7h0aFS6CrPtyT9q9+BQAcRdu41RMx8nbjawS0e6shLTCiDWY5lxnaVJxreOFSy1Pxv25wrSvjxuv7FrGt4PeIicpg2qNa9H/vXFc/PUYr2xeCEBM2G2ci/UNJ283Mku0zUxNGs7FnoA4+7iRWeycFEoF/r3bsnZA0VzfloM6EX44DKPeQE5KJhFnr1OrRT2aBLWiwwtPAnAn7BYuxWx18XYjQ3PvpwkPSr4mFXWNYv3Dx42CMtqPc5cW1JwxlMtD3iyz/dwLQ2IySq+imwplNQ8MSeXfhGv3HcJ1/kzSMEXqbLt3xXnaRBSODqYFX/kF5Gz/rdR+3Ub15okXTO0nKuwWrtU9gOsAuHq7k36fZWrtYEP1hrV4batpfrOTpwuTN8xj3bhVZS6SM6Yko/Aosk/h4YkhpShaKNnaoazji9NK09x5hasbTm8tJ3PJQgw3r1d8Lv0HM/n9ZwCIDbuNc4k+UVYbdSrWRp183Mg0P5nK1KRyde+ZwmPJRhk7N0eaD+rEzcMXCtvo8eOnadOmJX16d+eVV0YCcObMeWrWql543Bo1fYiNKx2wiTNvy87OYcvW32gb2IpNm7YTExvPqVPn0Ov1REbeITz8Ng38fDlzNsxi/66jetHJog6L94vK1aFPw1rM2GpaR+Hk6cLEDXP5Ytz7/3ih48PEkJSM0qsoLqb08sSQlFxu/tzgQ7gumAHvWG7XR0Yj52qxqu+L7mr51yh9yWuTd9nXCvsnWuI59TluD3/doq/rzW1IdyeBnJOXsPGvR0H0gwfsBFXHfzmCXRH5xT4beIAbkcSw2zjX9caxlicKKyV+AzsQsc/ykW/kvnM0HtYFgPr92xF77Apgmv7Rf+NsTq7YhuaM5YKRdnOHoXa05a+3N1WonxF6C/t63tjW9kSyUlJ9cEcS9p6tcJ+72Pi4obAx3aWrnO1xbdeInFvxFe6TExaOja8P6lrVkKxUuA3qTPo+S4fc1t+XOiuncHPscvQpGYXbJSsVfhteJ2V7CGm7TpQ8dJnkhoVj7VsddS0vJCsVrgO6kLnP8o0Otv71qLViCrdfeddCL3rGR1zp9ApXOo8nbtk3pP5yqELn+q591nWL7HMd2IWMMuyrvXIyt8Yus9Cz8nZHslEDoHS2x6FtE/Jux3Iv0s+XrkNNcOXr0MrZHrd2jci+WVSHkd/uY0O/hWzot5AbwWdoMdTUDqsH+JGfpSU70fKxdXZiOgXZWqoH+AHQYmgXbuw7S9L1O3zSZgprO89kbeeZZMan8nX/N8hJysCpujtDv5jJ77PWcfTjn1nTbyFr+i3kavAZAp4x6dUy65Wc75yVlE5+lpZaZr2AZ7pwtZjt9Ts3I+l2HJnFnID0uBTqdTKtY7CytaZOQAMSb8Vx7IdgPuy3gA/7LeBi8BkCn+kKQJ0AP/Kycsuca/2wyT5/E1tfH6zN7cdjUGdSzQ7YXeyb+VJ/1USujVmJrnDObuUouHoNVa0aKH28QaXCtueTaI9Y9ilVrRqFn22e6ID+jqktJk2ciWbICDRDRpC99WcyN24u07kGOPzDXpb3m8fyfvMICz5FB3OZ+gY0QJuVW+483ZLkZWmZ23ocizpPY1HnaUSEhpfrXAPob1xDWaMmCi+TfdZdn0T3d9FbHeTcHNJGDCJ97HDSxw5Hf+3KfTnXAPm7fmNdv4Ws67eQa8FnaGVuozUD/MjL0pJdwqZscxutaW6jrZ7pwjVzG70afBbfDk0AcPf1RmmlIjc1i4y4ZHw7NQVMbbR9+9Zcv36Tdes3Eti2F4Fte7Fjx15GjTRFo9u3a01mRiYajWWQRalU4u5umrKiUqno378Hly+bbNyxYw/dzHOM3d1dadCgHrcjSk/fOvJDMCv7zWdlv/lcCD5NO3Md1v0Hdbig9XgWd57O4s7TiQwNf2yca4CCK9ewql0DZXVTm7Hr2R3tEcvghkWf6NwBfbSpTyire4PS5C4pvathVbcWhjJudoqTeyEc67rVsappujY5D+hK5n7La4VN03rUWDaVqPFLMRS7Viic7JHUJrdD6eqEXZsmFgv3HydkWf7X/z+u/Jcj2EeBiZIkbQTcgK7AXKBxOfmvA/UkSaory3Ik8Pz9iMgGI0ff3MiATfOQlAqubTtM2o1Y2s4eStKFCCL3nePq1sM89ckkRh79kLz0bPZNNc37av5ST5zrehE4cwiBM4cA8MfI91BYqQh8dTBp4bE896fp1T0Xv9vH1a0hZepfev072m19HUmpIGZLCNnXY2g4bxjpYREk7j2Lc6t6tPn2Naxc7PHq1ZqGc5/lSLe5ODSoQZN3XjRN05Akbq/bSdbVe3Ryg5HoN7+i4Y+LQaEkZdt+8m7cofqcF8gJu0nGvtPUWvQSSnsb6q+fB0BBbBI3xy7HdcATOLRvisrVEY/nTJHGiFmfob1S+jVRxfVi3vqCet+/jaRUkPq//eSF38H7tRHkXrhJ5v5TVF/4Ego7W3w/n2/Si0siYtyy+6m+MvXuvPklfptMeinbDpB34w4+s016GftOUeONl016d+2LS+b22GXYNKhJzTfHIssykiSR8MVv5F2LqlgPcx0u/I4OW0x1eMdch43mDSP9fAQJwaY6bPuNuQ57tqbR3GcJMdeh/9svFmreWreTrGtl1+HNg+ep370VU458VPiavruM272cDf1Mkec9i77l6Q9Nr827FRLGrUNhZR7vLl1mDMHW1ZG+S18GQG8w8PnARVw/dJ6G3Vvx2uGP0Wnz+WVukd603ctZY9bb8eY3DP1gEiobNeEhYdwIOV+Yr8WAjlzYYXmR/Pv7YJ55fxKvBq9CkuDvn0KIv2bpWFw9FEqT7q1YePhTdNp8tswteg3Y7N0r+bCf6W0aTy8YQetBT2Blq+atE2v5e9sh9n6ynVot6vHyF7OxdbbH/6nW9Jk1jFW95lZYDgAYjNxeuIGmW95EUipI2HoQ7Y071Jo7nOywm6QFn6HOm6NR2NvQ6MvZAOTHJnPtpZX3PnYJnfQPVuPx2XtICiU5f/yJPiISpwkvUXD1BnlHj2P/7GBs2rZB1usxZmWR+s57ldMowaVDoTTr3polhz+jQFvA93OLpmcs3L2K5f1M/WHIgpG0HdQZta2a5SfWcWzbQXZ98lPlxIwGctZ9gtPSD0ChIH/fbgzRkdi+OBZ9+DV0f1f8VMjlm61IdvZIKhVWHTuTtWhO6TeQADcOnadB91bMPGzqE78Wa6OTdy9nnbmN7nzzW4aYXyUZHhJGuHmdQej/Qhi8agJT967EoNPzy2xTOzv1/T4Gvz+RacHvgSTx4dffcPHiVQvt3X8eoE+fJ7l+9Ri5Wi3jxr1WmHbmdDCBbXthba1m967NWFmpUCqVHDhwlA1f/wjA3uAQevboxoWwQxgMBua/vpTU1IqffF0+FIp/9wAWH/4UnbaATXPXFaYt2P0eK/uZxtBBC0YSaO4XS098zoltB9n9SfnTw/4Jcxev5HToBdLTM3lq8ItMeWUUQwf0/ucHNBhJW7Uaz8/eQ1IqyN7xJ/rbUThNfImCq9fJO3ICh+cGY9OutalPZGaTYu4T1i2b4fTSC8h6PRhl0t77DGPGPW5+DUbiFq/H9/t3TK/p+2k/+eHRVJs1Eu3FcLL2n8Ln9ZdR2NtQe61pvLn7Oj4bv1rUWDa1cNxOWr/d4u0jgv8bSI+z9/8ouPuaPvPCxVVAX0xrgt+VZXmbJElBwBxZlp82518DnJFl+TtJkgYA7wM5wGnAUZblkRXpfV7rxSot4DoFhqqUw8tKW6V6KmXFC0seNkbjvd+c8jCJ1dlVqR7ABeuqfZCVK1XtmKOlatvMsHu8AeNhU7t25acLPSjLEkovDnuUvOufUKV6n16uWaV6y+NCqlRvUvXSb1x51Hx8ZkWV6mn6jq9SvfTkqh+7m0f8UbUXqDII8H7iX3ciQzXH/vVyKIv/XAT77juwZdOdxVzz/+LpIUBIse/F38x/SJblxmbnfC1g+WxXIBAIBAKB4D/C47zI8N9GzMGuHOPNix4vA86Y3ioiEAgEAoFAIBAU8p+LYD8Isix/DHz8b5+HQCAQCAQCgeDxRTjYAoFAIBAIBIJKI4spIuUipogIBAKBQCAQCAQPERHBFggEAoFAIBBUGuN/7E10lUFEsAUCgUAgEAgEgoeIcLAFAoFAIBAIBIKHiJgiIhAIBAKBQCCoNGKRY/mICLZAIBAIBAKB4D+HJElukiTtkyQp3PzXtYK8TpIkxZh/4fueCAdbIBAIBAKBQFBpjLL8r/9/QBYAB2RZbgAcMH8vj6XAkfs9sHCwBQKBQCAQCAT/RQYBG82fNwKDy8okSVIbwAsIvt8DiznYj5gx46t2flLu0bgq1bPr4FWleorAwCrVOzzhXJXqNXRNr1I9gNwMtyrVa+WcVqV63+S5VKlewAdNq1QPK6uq1QPGzDxfpXrZ8VVro6tRqlK9J72aV6leG13VtxlN3/FVquf951dVqhfZbF6V6gkeGl6yLMebP2swOdEWSJKkAD4EXgR63O+BhYMtEAgEAoFAIKg0j8MiR0mSJgATim36UpblL4ul7we8y9j1jeJfZFmWJUkqy6ApwG5ZlmMk6f5vvoWDLRAIBAKBQCD4P4nZmf6ygvRyo86SJCVIkuQjy3K8JEk+QGIZ2ToCXSRJmgI4AGpJkrJlWa5ovrZwsAUCgUAgEAgElef/g19y3AGMAVaa//5eMoMsyyPvfpYk6SUg8F7ONYhFjgKBQCAQCASC/yYrgZ6SJIVjml+9EkCSpEBJkjY8yIFFBFsgEAgEAoFA8J9DluUU4Kkytp8BxpWx/Tvgu/s5tnCwBQKBQCAQCASV5nFY5Pi4IqaICAQCgUAgEAgEDxERwRYIBAKBQCAQVJr/DxY5PjJEBFsgEAgEAoFAIHiICAdbIBAIBAKBQCB4iIgpIgKBQCAQCASCSiMWOZaPiGALBAKBQCAQCAQPERHB/hdQ1m+BuvcoUCjQh4agO/aHRbqqZVfUPV7AmJUGgP50MPrQEACsnhqOqkErAAqO/Ibhysl76lkFtsNhynQkhQLtn7vQbttskW7z9EBsBw4BowFZqyXr4w8wREeBSoXjzDmoGjYCo5Hsz1eju3D+3vb5tUTd/2WQFOjPHkB31PKHkVQB3VD3HoUxM9Vk39970J89iMLXH3XfMYX5FB7Vyf/pUwxXT1eod+x6DKt+P4lRNjKkXSPGdm9pkf7+jpOcvhUPQJ5OT2p2Hn8tGQVAfFo272w/SkJGDhISq8f2ooabY4V6Ht1b0uTdMaBUEPPjQSJW77BId+3QmMZLx+DYtDZhEz8jYeffluXjYEuXox+Q8OcZri78tkKtu9h1boPXG5NAoSBj+x5Sv/rJUvOlITgP6wMGA/rUDDRvfIw+zvSLr55zxmLfrR0oJHKPh5K4bH2ZGt7dWxCwZBSSUsHtzSFcW2PZLhVqFe0/m4xri7oUpGVzfOJqcmOSkVRK2n44DtfmvihUCiJ/+our5jKxcrKj7YfjcW5cE2SZU7O+hJun/hX7AAYuHkOj7q3QaQv435x1xF2OLJWnRjNfnv1gElY2aq4fOs+OdzYC4NOkNkOWvYLazoa0mCS2zlxLfraWmi3rM3SF+XWpkoQi8RjGmKsWxzx2M55Ve89jNMoMCfBlbOcmFunv7w3ldGQSYG6jOfn8NX8IAFN+PMKFmBQCanuw+oUu5dpWkmPhcazadQajLDOkjR9ju/pbau4+y+mIhGKaefz1xnPEpWfz2uYjGGXQG4y80KEhz7ZreN+6AM5BAdRdOhZJoSBxy37i1vxqke49YQDVRvRA1hvQp2Ry67W1FMQmVUrDplNb3OZMAaWC7F//JPO7rRbpDkOfxvG5QWA0YMzNI/Xdj9BFRKP2b4T7olmmTJJE+hffoz10zGLfoHdG4du9FTptPsGzvyTxUmQp/WrN69L7w4mobNREHDpPyOIfALB2tqf/59NwqulJZkwSu6asJj8jl8aDOxE4+WkkSaIgW8uBN74j+Wo0rvV8WLf2naKyqe3N9x/+wK9f/8aUdybT9sm25Gvz+eC1D7l56abFOVjbWLNo/RtUr+ODwWDk5P6TfLPSNKZ4Vvdk7sdzcHCyR6FU8vWKbzh9yHIsrRHUgvZLRiEpFNzYEsLFtaX7fNdPJ+He3Jf8tCxCJq8hOya5MN2+ujtDQt7j/Ie/cOmL3QA0faU3DUcEgSRxY/MhrmzYW34ddmyLy+ypoFCQ8/tusjZa1qH9M0/j8OwgMBqRc7WkLv8YfUQU6qaNcH3jNXMuicyvNqINOVZaoBIsWv4RR46dws3Vhd82lT+GVAbX7q2ot/RlJKUCzY8HiFnzm0W6U4cm1F/yMvZN63Bt0sck7yy6ntd980XcerRBkiTSjlzg9qJvHso5PWxk2fhvn8Jji3CwqxpJQt33JfI2rUDOTMVm3FL0188hJ8daZNNfPknBno0W25QNWqH0qYv2i4WgssJm9BsYboZBgbZ8PYUCx+kzSZ8/G2NyEq5rvqDgxDGTA20m/+B+8naaHCJ1x044TJpKxsJ52PR7GoC0CS8jubjgvGwV6dMmQkWrhiUJ9YBXyPvuXeTMFGwmrUB/7QxyUgn7Lh6nYJflgGGMuEze5/NMX2ztsZu52mRfBRiMRlb8epz14/vg5WzPyNU76Na0NvW9XAvzzB3YofDzlmOXuRabUvh90bbDjHuyFR0b1iA3X4ckSRXqoZBounIsp59bRl5cCh33Lidx71lybhTZlxebwsUZ6/Cd/HSZh2iw4DlST16rWMdCU4HXW1OJGbsQXUIydX76lOyDf1NwK7pI8+ot0oe9ipyXj8vw/njOGUv8ayuxCWiCbeumRA6aAkDtzR9g26452lMXLSQkhUSb5S8R8vwKtPGp9PxzKXHB58gsZle9F4IoyMhhd6fZ1BrUgZaLXuDEpNXUGtAepdqKvU8uQGmrpu/hVUT9epzcmGQClo5CcyiM4+M/RWGlRGlrDSWLuArsA2gU1AoPX2/eD5pF7QA/hix7hbWD3yyVb8i7Y/nl9a+IDr3J2O/m0yioJddDwhi6cgK7lv9IxN9XCXw2iG4Tnib4o59IuH6H1QPewGgw4ujpwhtH3ycv9jqYLzwGo5EVf55j/Yvd8HKyZeSG/XRrVJ36ns6FmnN7BxR+3nIqnGuatMLvYzo2Ik9nYPu5W+U2kZIYjEZW/HGa9S89iZeTHSPX76Fb45rUr1ZMs1+bIs2T17kWb7rh9XSw5fsJvVGrlOTm6xi6ZhfdGtekmpPd/YkrFPguH8/V4e9QEJ9Cs92rSNt7Gm14TGGW3EsRXOo7F6O2AK/Rvanz5mjCJ3143/ahUOA2fzqJU+ajT0jCZ9NatIePo4soajM5ew6S/fNOAGy7dsR19mQSp72O7lYk8S9OAYMRpYcbPlu/IObICTCY6svmiXa41PXm266z8Q6oz5PLXmLroLdLncJTy15m3/wNaEJvMXjjXOoGtSAy5ALtpg7gzrErnP78D9pOGUDbKQP4a8U2Mu4k8dNz75KfkUvdoBb0WDmWrYPeJu12PJP7TDWbpWDz6U0c23Octt3bUsO3Oi93GUvjgMa8unwarw6cWeo8tn+xnbATF1BZqXhv60raBgVyOuQMI199gSM7j7Dzh13UblCbdzcuZXSnogCGpJDosGwMe19YSW58KgN2LyE6+CwZ4XGFeRq+EER+Rg4/d56N78AOBL4xnJDJawrT2709kphDRWO0S6OaNBwRxB/9F2PU6en14zzu7D9PVmRCmXXoOu9VEqfNw5CQhNfGz9EeOYE+oujalLv3IDm/mOrQpmtHXGZNIvlVUx0mjJ4MBiMKdze8N3+J9mhRHf4TBvfryYihA1m49IN/fAwLFArqrxjHpeeWkB+fSqs9K0kNPkPujaJ+kB+bzPUZa6k5ZaDFro6BjXBq25hz3WcD0HLHUpw7+ZNx/PLDOTdBlfCvTBGRJMlakqT9kiSdlyTpeUmSFt7HPtnmv9UlSdp+j7wDJUm65+/EV7D/NEmSbkqSJEuS5FFse5AkSRnm8z4vSdJblT22okZ9jGkJyOlJYDRguHwSVaM2994RUHjUwBB1zXTh1uVjTLyD0q9FhfuoGjXBEBeLURMPej15IQdRd+pskUfOzS38LNnYcndKlapOXQrOnzPlSU9Hzsk2RbMrOseafhhTNMhpiWAwYLh4HFWTtvdln8V5+3fAEB4KuoIK8126k0QtDydqujthpVLSu2U9Qi5Hl5v/z/O36dOqPgC3EtIwGGU6NqwBgJ21Fbbqiu85XVr7kRuhQRuViKwzoPntOF59Ai3yaO8kkX0lGoylb0ScWvhi7elMSsiFCnWKY9OiIbroOHQxGtDpydp9GIenOljk0f59ATkv3/Q57BpW3uZmK8tI1mokKxWS2gpJpcSQnF5Kwy2gPlmRCeREJ2HUGYj+/SQ1elu2y+p92hD5vyMAxOw8hVcX/0INpZ01klKB0kaNsUCPPluLlaMtnh0ac3tzCABGnQFdZi4lqQr7APx7teHsL0cBiA69ia2jHY6eLhZ5HD1dsHa0JTrUFCk8+8tR/HuZ6tfT14eIv02R6fC/LtCsbzsAdHkFGM0XdpW1FSWnJF6KTaWWqwM1XR2wUirp7V+bkOtxlMefl6Lp41+78Hv7el7YWVcuFnIpJoVa7o7UdHM09YvmdQi5eqd8zQuR9GleFwArlRK1SglAgcGIXMnXcDkE+JEXGU9+dAKyTk/K73/h2rudRZ7M45cwak19O+vcDdQ+7pXSUDdrhD4mDn2saVzL2RuCbdATFnnknGLjmq1NYWBAzssvdMQktbpUfdkFdeLqz38BoAm9hbWTPfbVLNuJfTUX1A62aEJNNz1Xf/6L+r1N7aRezzZc2W5qZ1e2H6W+uf3Enw0nP8N0TvGhN3H0cStlV0DnVsRHxZMYm0inXh3Z9/MBAK6FXsPeyQG3apb75OflE3bCNJbodXpuXryJh4+pb8gy2DmYborsHe1JSUix2NfD3OezzX3+9u8nqV2iz9fu1ZqbP5lsidx1Cp/ORU9BavduQ1Z0EunXi27CXRpUJyn0Foa8AmSDEc3Ja9Tpazk+3kXt3xjdnVgM5jrM3XcI226dLPIUr0OFjU1hXcn5xerQunQd/hMCWzXH2anip5eVwTHAj7wIDXnRicg6PUm/HcOtt+W1MP9OErlXo8BY4sZAllFYW6FQq1BYq5CsVBQklT2uCR5f/q0IdgCALMutoNB5Xn4/O8qyHAcMu0eeHcCOivLcg2PATiCkjLSjsiyXHZq8DyRHN+SMooFOzkxFUaN+qXzKJm2xrdMYY4qGguAfkDNTMSZEY9XtGXQndoOVGmXdphhLRIZLovDwwJCUWPjdmJyEVeMmpfLZDByM3dDnQGVFxjxTlER/6xbWHZ8g/+ABFNU8UTVoiMKzGlwvP/oqOZWwLyMFRc0Gpe3zb49t3SYYU+Ip2L0ROdNy8Fc1fwLdsZ0V2gaQmJGLt7N94XcvZzsu3in7UXNcWhZxqVm08/MBICopA0cbNa99v5/Y1Cza+9VgRr9AlIry7zutvd3QxhWda15cKs6t/e55ngBIEo3fHkXY1DV4dG1+f/sAKi8PdPFFNuk1ydi0LP9Gx3lYL7KPnDGd3/lr5P59gfpHfzQ9Dv/xDwpul3a0bL3d0BaL7OfGp+IeYNku7bxdyY0zRTllgxFdZi5qNwfu7DxFjd5tGBi2FpWtmtDFmyhIz8HFvw75KVm0+2QiLk1rk3YhgnNv/vCv2Afg5OVGRrG6y9Ck4uTtRlaxC5eTtxsZ5kguQEZ8Ck5eJqcmITyGpr0CuRJ8hhb9OuBSzCms1ao+z66ahEsNDwpO/VIYvQZIzNLi7VwU/fVysuVibJFGceLSc4hLz6Gdb7Vy7b8fEjNLaDrbcTEmpcy8cenZxKVl066eV+E2TUYO038I4U5qFjN7B9x/9BpQe7tTUKycC+JTcGhdegy4S7UXniL94Ln7Pj6AytMDvaZoXDMkJqFu1rhUPofnBuI0chiSlYqEiXOLzrFZY9wXz0Hl40XymystIp/Kah5kxRedT7YmFQdvV3ISi9qJg7cr2ZrUUnkA7DycCvPmJKZj5+FU6ryaPR9ExKHSN9ndBnbj0O8hALh7u5MUV9QvkuOTcPd2JzWx7LZj72RPhx7t+fUb0zSEHz7exIoflzHo5YHY2NqwYMTrFvntvF3JiSs6Vm58Kp5l9PmcYn2+IDMXa1cHDPk6mk99mr3DV9JsUv/C/GnXYmg9/1msXR3Qawuo+WRLksMiyjxfpacHhoQi+wwJSaiblb42OTw7CMcRw8C5zE1hAAAgAElEQVRKRdLkOYXb1f6NcXtrLkpvL1IXr3ig6PWjwNrHjfy4ouk0BfEpOFbQD4qTdfYGGccv0z7sK5Ag7ps9aMMrvtb/WxjFIsdyeWgRbEmS7CVJ2iVJUpgkSZfMkek+kiRdkyTpnCRJn0mStFOSpGrAJqCtOQr8E2Br/vzjfejUlSTpkvnzSUmS/IulhUiSFChJ0kuSJK0xb/vOrH1ckqTbkiQNM29XSJL0ufn89kmStPtumizLobIsRz6ssqks+hvn0H42E+0Xr2O4fRHrQZMAMNy+iCH8/P9j77zDo6jWP/45u+kJqRCSUEISpLdQQ5MiRUCKoIIggh2woFdAsVwVbGC5Ioj1ClZErIjSVDoiJfTeQgkJJKT3suf3x0yS3WSTgO5s8P7O53l4yMycOd89M2dm3nnPe97B4+7n8Rj1EJbzx20e5H+HvOU/kDJhLNkfvY/X2Du1dat+oTjpEgEL38dn8sMUHjpY8U37L1B0ZBe5bzxI7jvTKT6xD/dRD9psFz7+mOo2rDY85GpZvecU/VpHlBrQxRbJ7rhE/jWkM188PJz4lEyW7zzuUE1rGt41gKTfdpOfYP8B6Qh8h/bBo2UTUv/7LQCuDUNxi2zAyd7jOdnrDrxi2uLZoWU1tVwdQdFRSIuF5e0eYkXnx2j6wGC8G9ZBuJgIaN2IE5/8ypoBT1OUm0/zh4f+La2aaF8Jy2a8T9c7+vPwTy/h7uNJUWFR6bZze07y5oDpLBj2NK4trwfTX/NdrD54ln7N61f5kudoVu87Q7+WDW00Q/y8WfbQEJY/Ooyfdp/mclYVYWh/g9ojr8e7TWMuvPtD9YX/AllfL+fC8DtJffsj/O4dV7q+4MAREm69l4TxD+J31+3g5mqIvj3qd21Oy9G92PyKbbyxi6sLXfvHsPHnTVddp8ls4qkFT/LDoh9JPJsIQJ/hvVmzbC3jOo/nmQn/ZsZb06sPgbtCoh8fycEPV1GUk2+zPv3EBfa/s4IBXz7BgC9mkHLwDPJvPjOylv1Iws3jSZ//Ib5331G6vuDgERJH38PFCVOoNXGsU8+h0Xg0CsHrunr8Gf0Af7Z7AP8erfDtUvHlQ3Ft40gP9o3ABSnlEAAhhB9wAOgLnACWAkgpLwkh7gWmlXiChRBZJd7sq2QpcBvwnBAiFAiVUu4UQrQqVy4U6AE0Q/NsfwOMBBoBLYBg4DBwJbMIugoh9gIX9DZUCIoSQtwP3A/w9tDO3N2xzMMpM1MQfmWeL+EbiMxMta0gN6v0z6Ld63Drd3vpcuHmHyncrE0adL/5QeTlhCp/rCU5GXOdMm+YqXYdipOTKy2fv/43fKY+Bq8BlmKy33uHbH2b/1vvUHy+8mFm0DzyNu3zC0JmljMordu36zfcBt5hs9ncqitFh7aDpbhKLYBgPy8S07NLly+m5xDs62237Kq9p5g5omwIsq6fN01Dg6gfpHmY+rRsyL6zSdxchV5+YgqeYWXt8wgLJD/xygxm/47XEdClGQ0nDsDs7Y7JzYXinDyOvbikyv2KLibjGlqndNklpDZFFyt6I726tiNw0hjOjZ+BLCwEwKdfN/L2HkHm5AGQvXEnnu2ak7vLttvmJqbgWa+sXV6hgeQm2vbLnMRUvMICyU1IQZhNuPp6UZCSRcNp3UhYtw9ZVEz+5QySdxwjsG0kSduOkJuQQoo+jH5uxXaaPzSUTCe2z3/sTUy9RfOwnd97Cj+rc+cXEkhGuXOXkZiCn9XQvV9oEBkXtTJJJy/w3ztfAaB2RAjN+lS8ZV06eQFZVIDwD0amaGEgwbU8SUwvG+q+mJFLcC3PCvsCrDp4jpmD2tvddjUE+5bTTM+pXHP/GWYOtR/GFezrReO6fsTGJdG/VUO7ZcpTkHgZN6vj7BYaRIGdl0rfnm2oN/UWDo58FllQVGF7VRQlJeMSUnZfMwfXofiSfQ89QM7qdQTNnEr5EkWnzyJzc/GfNAHPrlooQ/7BY9SyGp3wCQkkq9y1kJWYik9IoN0yOckZeAf7k30pDe9gf3KSM0rL1W7WgP5z7+X7O18jLy3Lps5OfTqSfjmdVz5/CYCje49RJ6zsuqgdWofLifbb+OicqcSfvsD3/y17URk4eiBPj38agMOxh3Fzd8Mv0Je0y+na70xMxTusrA1eoYFk27nmvcMCydGveTdfL/JTs6gd3ZjwIZ3p+PQY3Hy9wCIpzi/k8OK1HP9qA8e/2gBA+ydvI6cSh0JxUjLmumXtM9etQ3FS5c+mnDXrCHhyKrxgu74o7iwyJxfXqAgKDx+rdH9nk5+QgntYaYQpbqFBV+xcCRrcmYxdx7Ho97XU33fj27EJGX8ermZPxbWEI90k+4H+Qog5QoieQARwWkp5XGpBfJ87UKuErykLF7kNzXC2xw9SSouU8hBQMg7aA1imr08E1l2BXiwQLqVsC8wH7LpdpJQfSCk7Sik7WhvXAJb4U5gCQxD+dcBkxtwyhqJju2zKCJ+yeD9zkw5YkvV4TSHA00f7M7gBproNKD5ZcTKXNUVHj2CuVx9TSAi4uODRuy8Ff9jOtjbXq1f6t1uXrhTH65Mw3N3BwwMA1/YdkcXFNpMj7WGJP4kpKFRrn9mMuXU3io7srLx9zTpiSTpvs92lTXeK9l/ZjPCW9etwNjmD+JRMCouKWb33FL1aVDQETl9KIyO3gLbhZQ/llg1qk5lXQIrundt+MoHIuv4V9rUmffdJvCJD8GxYB+FqJmRENy6t3lXlPiXsm7KADR0eYkOnhzn6whfEf72pWuMaIG//MVzDw3CtVxdcXag1uBdZv9tmj3FvHkXdFx4hfsoLFKekl64vSkjCs1NrMJvAxYxnp9bk2wmhSNlziloRIXg3qIPJ1UzD4THEl2vXhdWxNLrtegDq39SZi5s1IzYnPpm63VsAYPZ0J6jDdWScuEBeUjo5Fy5TK0oLyanbo6XNpElntC/tyxXMGzyTeYNncnDNTjqM1LJwNIxuTF5mjk14CEBmUhr5mbk0jNau2w4je3JwjXYcvPUXMSEEfR+6mW1faPGxAfXrYDJrt1L/erUx+dZGZpfV27JeIGdTsohPzaKwuJjVB8/Sq0lYheNwOjlD66P1ry4e2R4t6wVx9nKmpllUzOr9Z+jVrH5FzaR0MvIKaNugzBC4mJ5Dnu6dz8jNZ/eZJBrVvvLY1Kw9J/CICMW9QTDC1YWg4T1IXWObvcKrVQSRcyZxdOIrFF1Or6Smyik4eBSXBvVwCdPua94De5O7YatNGZcGZfc1z55dKDyn3WdcwkK0/gKYQ4NxadSAjE+/JuH2SSTcPonc9VtoPkqbpxISHUVBZo5NeAhooR8FWbmE6CEVzUf14KTeT06tjaXFLVo/a3FLT06t1dbXCgti6AePsurR90g7nVihTX2G9+br95Yx+cYHmXzjg2xd/Qf9R90AQLPoZmRnZtsND5k4fQLetbx593nbzBdJFy7Rroc2ebZB4wa4ebiVGtcAyXtO4RsRgo9+zUcOj+HcGttQnbNrYml8q9aWRkM6k7DlEAArR87mm5jH+CbmMQ59tJp985dzePFaADz068Q7LIjwQR059b3teSmh4NARXBvWw6yfQ6/+fcjdWPk59OgRQ9FZ7f5htj6HIcG4NmpA8YWKx7QmydxzAo/IUNwbatdBnRHdSVlTdUasEvLjk/Hr2gLMJoSLGb+uLW0mR15LSClr/N+1isM82FLKY0KI9sBg4EXgN0fVXYVmvBDishCiDTAamFRJUetxrL88RialzLD6+xc9xKS2lLLy1+4KlVgoWLkYj3FPaGns9mxAJsXj2nsUlgunKT4Wi0vngbg0aY+0FENeNvk/6jdOkwueE7V5lTI/l/zv360+RMRSTNaCt/B75XWEyUTe6l8oPhOH14S7KTp2hII/tuIxfCRu0R2guAhLZhaZczUvnck/AL9XXgMpsSQnkTnnperbZ7FQsOJjPCY8raUhjF2HvHQe1763YblwkuIju3DpOgiXZh219uVkkf/dwtLdhX8dhF9tLHGHruhwuphNPDm8K5M/WoXFIhneqQmNQwJYuHoXLerXpnfLcABW7TnFjW0jbYZIzSYTjw3pzAMfrEQCzevVZlTnqidxymILh2YuouNXTyHMJs4vWUfW0fM0nnEr6XtPkbR6F77tImm/6HFc/L2pM6A9jaffwpZe06ust0qKLVya/S71//simMykf7uGghNnCXp4PHkHjpG97k/qTL8Hk5cHYW9p84WLEpKIn/ICmas34xXTlkbL3wUJ2Zt3kr3uzwoSsthC7FOL6bXkCS1N31cbyDgWT6vpo0jZe5oLa2I5tWQ9MfMnM3jrGxSkZfPHpPkAnFi0ls5vPcCN6+eAEJz+agPp+oS62Kc/JeadKZhcXcg6e4ntj75PhQAOJ7QP4Mi63TTt044ZG96iIDefZdPfL9029ZdXmDdYi1H9/tlF3FaSpm/9Ho6u11JTthvWja7jBwBwYPV2di5bD0CjTk3pM3k4xUVFSIukYMcKyC/zHruYTDw5qD2Tv9iIRUqGt4ugcbAfC9cdoEVYAL2bakbEKn1yY/lh/LsW/U7c5UxyCooY8J+feH5oJ7o1Dqmyy7iYTTx5U0cmf/K7dl20j6JxXX8W/raXFmFB9G6uGdur9p/hxtbhNpqnktJ5c1UsAm3u2J3dm3NdSIB9IXsUW4h7+iOafflvhNnEpa9+I/fYOepPH0P23pOkrtlB+LN3YvL24LoPtJja/Phkjk185ao0UubMJ/idV8FkImv5KgpPncFv0gQKDh0jd+Mf1Bo9HI8u7aGoCEtGFpf/PRcA9+hW1Jk4BvTzlfLK21jSyrzMuZv/JL1Db+7a9AZFuQWsmfZB6bZxK1/ii0GaV/j3ZxYz4I37cfFwI27dXuL0bBo7Fv7EkHcfpuXoXmTGJ7NisnaddJl6Mx4BPvR9cSIAsriYL2/S7ucenu6079met558u1Rr++/b6dy3E4s3f6yl6Xv8zdJt7656h8k3PkjtkNqMfeR2zh4/y8KVWnaPHxf/xKqvVvH+7A95bM5URt57M0jJ6/+yzdIiiy1se+YTBnw5A2EycXzpBtKOxRM9bRTJe09zbm0sx7/aQM+3JzFq8xvkp2WxfsoCqqPPh1PxCPDBUlTEtqc/ocDOxOaSc5g6dz513p6DMJvIWr6SolNn8H1gIgWHj5K38Q98bhuBR+f2yJJz+MIc7Ry2bYXvxNuRRUVgkaTOeRtLeoZ9nStk+nOvsmP3PtLSMrhhxB1MuWc8o4YO/OsVFls4+dRHtFryDMJs4uKS38k5ep7wGaPJ3HOSlDU78WkXRYuPZ+Di701g/440nD6a2F6PkfzTNvy7t6LDujcBScrve0hZe2WOHMW1g3CU9S+ECANSpJR5QoibgIfQwi/6SClPCiGWALWklDcJIXpjGyKSCgRLKQurqD9LSukjhGgErJBSttLXPwh0BaKllC31dROBjlLKh4QQi/Xy35Sr51ZgAjAMqIMWInJ/STm9bJxeT7K+HAJclFJKIURnNI95uKziIGbPGufU16ucTc59y/WKqVt9IQdi6mh/RrpRbLj/6iZf/V0iAq7em/d32ZNeMZuBkbTzMy4G3R4f51U9KuFonn/pCie9OgpX58ee7n20+nz4jiQ05O8ZT1fLd8lVv8A4mpVX4aNxBLdbaldfyMH0C6s6nNHRhKz80Kl621rNcKoeQM/EbxwTVP83qB/YqsZdyOdTDtT4cbCHI2OwWwOvCSEsQCEwGagN/CyEyAE2AZWNM34A7BNCxEopx1VSpjK+AeYBs69yv2+BG4BDwDm08I90ACHEI8AMIET/Xb9IKe9FC0eZLIQoAnKBMVUZ1wqFQqFQKBSK/384MkRkNWDvk03NQMshDUzTy67HKgWelPIJ4Ilq6vfR/48DWlmtv0i5dkgpFwOL9b8nVlKPRQgxTUqZJYQIArajxZEjpXwbeJtySCkXANWPkSkUCoVCoVAo/t/y//1LjiuEEP6AGzBbn+yoUCgUCoVCoagGNYhfOU4zsMt7re2he5LtTY68QUpZeQ6mv/6beju6ToVCoVAoFArF/2+uKQ+2bkT/lXzYCoVCoVAoFAonYlEe7Epx3ufCFAqFQqFQKBSK/wcoA1uhUCgUCoVCoXAg11SIiEKhUCgUCoXin4FEhYhUhvJgKxQKhUKhUCgUDkR5sBUKhUKhUCgUV41K01c5yoOtUCgUCoVCoVA4EGVgKxQKhUKhUCgUDkQo976xLAkb59QDnGESzpTDv9i5/SdM5jtVr+MbzZ2qt3DmaafqAfhY/rf1Ljk5EC6ywLkNrCOce00A7HHxdKqeb7FT5Ug1O1fvkLnAqXr+NRAdOlZkOlUvLdfdqXoxB+Y6VQ/AtXakcx/4dqjj17TGjcik9KM1fhzsoTzYCoVCoVAoFAqFA1GTHBUKhUKhUCgUV42Kgqgc5cFWKBQKhUKhUCgciDKwFQqFQqFQKBQKB6JCRBQKhUKhUCgUV41FhYhUivJgKxQKhUKhUCgUDkQZ2AqFQqFQKBQKhQNRISIKhUKhUCgUiqtGZRGpHOXBVigUCoVCoVAoHIjyYCsUCoVCoVAorhoLyoNdGcqDrVAoFAqFQqFQOBBlYCsUCoVCoVAoFA5EhYg4gdDebWg/ezzCZOLkkvUcXvCTzXaTmwsxb08msHUj8lOz2DppPtnnkxEuZrq8fi8BrSMQLibilm3m0ILleIUFEjNvMh51/EBKTnz+O8f+u9qmzga929DthfEIs4kjS9az552Kmn3fmkTtNhHkpWby6+QFZJ1Ppl7PVnSZORqTmwuWgiK2vbiEC1sPARA1tAvRjwxHmEyc/W03f768tLS+kD5tiJ6l6Z36cj1H7LSxy9uTCWjTiILULLY+MJ8cvY2d3tDaaNLbeHj+cmpFhdL1vYdL9/cJD+bAa9+Q9cGPFY5vQJ92RM6+C2E2kfjFb5xf8IPNdt+Y5kTNugvvFuEcmfQfkldsK93W6Nk7COzXASEEqRv3ceqZj6s9n1tOJDB39R4sFsnN0RHc3aO5zfbXVu9mR1wSAHmFRaRk57P5iZs5kpjKyz/HklVQiFkI7u3ZnIEtG1aq0+eF8UT0aUdRbj6rHv+ASwfiKpQJbt2IG994ABcPN06v28O65z4DoMmQznR9bCRBjcP4YthzXNx3GgAPfx+GvvcIIW0jObhsI7//+9PSuv5qn6nTLpLr59wDgBCw883viVu1E4Ber99HeL925CZnsKzfTJv6Qnu3oaN+XZxYsp5DdvpMt7cnEdg6gvzUTDZPWkD2+WQa3dyN5lOGlJYLaN6AlQOfIfXgWfp8MQPPYD+Ei5mkP4+y46nF9H7hDiL6tKMwN581VRzHgVbHcb1+HN39vBmy8CF869ch43wSP0+ZT356DpH929Nt2i1Ii0QWF7P+hc+5sONYaX0uPp703TiXhFW72P/UYoL7tKH17DvBbOLsF+s4Xq6tQTHNaDVrPL4tGrJz0nwSVmwHwLdlOG3n3I1LLU9ksYVj837gwo/bqA7/Pu2ImHU3mE1c+vI34hd8b7PdN6YFjWbdhXfzcI5NepPLP5fVGf70HQT06wDAuf8s4/LyrZXqNOjdhh7Pj8dkNnFoyXp2L6x4Dvu9NYk6rbU+s2bKAjLPJ+Pu78ON7z9CcNtIjizbyKZntX7o6u3Bzd8+W7q/d2ggx77bwv5/fw5ofaaTVZ85WEmfCdL7zCarPtOiXJ/5Re8zbZ+4lchbe+Dm583S6+4ltHcbhszWroNDS9azy06bBli1aZXeJoAODw6lxZjeyGILG5/7lLMb9uMfGcqNCx8q3d+vYTDb3viGvf9dTe3mDen9yl1YvN25fD6JRY++TV5WbmnZ2567i5Z9oinIzefTaQs5d/B0hXMwbNoYuoy8Hi8/Hx5reWfp+phbejFy5njSLqYAsOGTVWxZ+nuF/Yc+dydN+7SjILeAb6a9x4WDcRXKhLWK4NbXH8DVw42j6/bw0wva+QptEc6Il+7Gxd0VS5GFH59dxPm9JwGIiGnOTf8ej9nFhezUTLLHzKhQr8/17Ql77j4wmUhdupak976x2V77nuEEjB6ALC6m+HIG55+YR2G8dm9tdeIH8o6eAaDwQhJn7nuxQv3lcfazoiqeeflNNm7ZTmCAPz98/t7fqqsmUZMcK0cZ2AYjTIIOL09k3ZhXyE1IYcAvs4lfHUvG8fjSMpG396YgLZsV3R+n4fAY2j5zO1snzafh0C6Y3F1ZecOTmD3dGLx+Lmd+2EpxQRG7Z31B6v44XLw9GLjqRRI3HiitU5gE3V+cwM9jXyU7IYWRP88ibs0u0o5fKNVsNqY3+enZfNXjcaKGxRDz1Bh+nbKAvJRMVt31BjkX0whoWp8hX8zg846P4O7vQ5dnbue7Qc+Sl5JJ7/88QL3uLcneeKC0jetHa23sv3I2F9bEknGsXBvTs/ml2+M00Nv4x6T5NBjaBbObK6v7am0ctGEuZ77fSubJBNb0f6q0PUN3L+D8yp34lz/AJhNRr9zLgdtmkZ+QQrtVr5KyZic5x86XFsmPT+bo1HeoP2WYza61OjbFt1MzYvs8DkDb5bPx69aS9K0HKz2fxRYLr6yM5b07elHX15NxH/1Kr6ZhRNXxKy0zfWB06d9Lth/nSGIqAJ6uLswe0ZnwoFpcysxl7Idr6RoVgq+HWwWdiD5tCWgUwsfXP05odBT9XprIl8Ofr1Cu30t3sfaJj0jYfZKRn0ynUe82xK3fR/LR8yy/fx79X7nbpnxRfiFb3/iGoKb1qd2kfun6v9NnUo+c57vBzyKLLXgF+3PLmpc4szZWMwiXbeTg4rX0eesBm98hTIJOL0/g9zGvkpOQwo2/zOL86l1kWOlF6dfF8u6PEz48huhnxrB50gLivt9K3Pea0effrD7Xf/wYqQfPArDpgfkU6QZKzw8foe2Tt+HVKIRF1z9OSHQUfV+ayFd2juMN+nFM3H2SEVbHsfODQzm35RA7Fv5EpylD6TRlKJtfWcq5LQf5fG0sALWbNWDIwof5pG+ZAdHsiVu5vO2ItmAStHnlLrbe9gq5CZfptepFEtfEkml1feTEJ7N76ns0nnKTze8qzs0n9uF3yT6diEddf3qteYlL6/ZRlJFToQ2lmExEvnwfB0fPoiDhMm1WziFlzQ5yra+J80mcmLqAsMm210TADe3xbh3Jnn6PY3JzpdV3s0j7fTfFVkaf9Tm8/sUJ/DT2VbISUrhlxSzi1u4i1eocNh/Tm/y0bL7o+TiNh8XQ9akxrJmygOL8Qv58XeuHgU3L+mFhdh5f3/h06fItP8/m1KodpXqdX57Ab3qfGaT3mXQrvcZ6n/mxmj7Ty6rPxK+N5diitQzb8nqpxnfjtDaNXjGLU+Xa1HJMb/LSsvms5+NcNyyG7k+NYdWUBQRcF0aTYTF8ccMT+NQNYMSSJ/ns+mmknUrgK71NwiS4a8d8TukvoH1fu5fNL37Jrzv20fXWPvS/fxg/vak5Llr2jiY4IoTnej9CRPR13P7SvcwdUXZsStj/2y7Wf7KKF9a/XWHbrhVbWfpc5YZg097tCIoI4fXe/6JBdGNGvHQ3C0f8u0K5ES/ezXczP+Lc7hNMXDyDJr3bcmz9XgY9eTu/zfuOY+v30rR3OwbNvJ0Px7yIh68Xw2ffxaIJc0i/cBnvIF+Gl6/UZCJs1iROj3+WosTLRP34Jhm//kn+iXOlRXIPnuLysH8h8/IJHDeIkCfv4tzDcwGw5BVwYsjUSttWASc/K6pjxOD+jB01jKdmv/6X61Bc2/yjQkSEEO5CiF+FEHuEEKOFEE9dwT5Z1WxvJIQYa7UcJIRYJ4TIEkIsKFd2vRDiqK6/RwgRXJ1+YHQUWXEXyT6bhKWwmLM/bqP+wA42ZeoP7MDpZRsBOLdiOyE9WmobpMTFyx1hNmH2cMNSUERhVi55l9JI3R8HQFF2HhknLuAVGlBaX3C7KDLiLpKpa574cRuNBthqNhrQnmPLNgFw6ufthOmalw+eIediGgCpR89j9nDD5OaCb3gwGacvkpeSCUD85gNEDO5U2sbMcm2sV66NYTd2IO5rrY3nV2ynbs+yNprLtbGo3MM8uGcrsuMukaN7iaypFd2YvNOJ5J29hCwsIumHLQQO7GRTJv9cEjmHz4DFYruzlJjcXTG5uWByd0G4ulCQlFZBw5oD8Sk0CPChfoAPrmYzA1s2ZP3RC5WWX3ngLDfqXurwoFqEB9XS2lTLk0Bvd1Kz8+3uFzWgA4e+3QxAwu6TuPt64x1s+3rhHeyPu48nCbs1j9GhbzfTeGBHAFJOXCD1VEKFeoty84nfcYzivEKb9X+nzxTlFSCLtWNrdnfF2qGR8OdR8tIqXoJBep/J0vXO/LiNBhWui/ac0vXOrthO3ZLrworwEd04Y+XRLek7wsWMyc2FwDYRHNaPY2IVx9HNx5NE/Tge/nYzUfpxjOzfgUPfaL/h0DebiBqgrS/MKTtvrl7uNl4cvzYRuNfx49KG/QAERDcm+/RFcs5eQhYWE//DH4SUa2vuuWQyDp9Dluuj2acSyT6dCEDexTTykzNwD/KtcBys8YluTG5cIvlnLyILi0j+cXPFa+J8yTVh633ybNKAjD8PQbEFS24+2YfO4N8nGnsEt4siPe4iGSV9Zvk2Isr1mYgB7TmiH7+TP2+nXne9z+Tmk7jjGEX5hRXqLcEvIgSv2r4k/HkUqNhn4uzeS237TIidPtNoRDfirPpMcuxJci+l2WiUtOnY8m1EVtGmEz9vp77epsgBHTi2fBuWgiIyziWRFneRuu2ibH9fj5akn7lEZvxlAPwjQrigv4gd2byP6EFdSsu2HdCRbd9p98zTu4/jVcsb3zoVXAyc3n2cjGruW5XRfEAHdn+nteXc7hN41PKiVjmNWnX8ca/lybndJwDY/d0mWujXgQTcfTwB8PD1JOOi5kxoN6wbB1ftILU+UawAACAASURBVP2C1s7syxkVtL3aXkfBmQQKz2n9NP2njfj272JTJnvbfmSedq3l7D6Ka0jQX2onOP9ZUR0d27XGz7fW36rjWsAiZY3/u1b5RxnYQDSAlLKdlHIpUK2BfQU0AsZaLecBzwLTKik/TtdvJ6W8VF3lXiGB5Og3GYCchBQ8rYxhAM+QAHIuaMN4sthCQUYOboE+nF2xnaKcfEbseYfhO+Zx5L2fKUjLttnXu35tAlqFkxx7skwzNICshJTS5ezEFLzLaXqHlJUp0fQI8LEpEzGkE8n747AUFJEel4hfVCg+9WsjzCYaDeyAT1ig/vsDyY0v18YQWz2vcm0s1Nt4bsV2inPyGbb3HYbutN/GhsNjOPOD/WFq99BA8i+UGd4FCZdxDw20W7Y8mbuOkb71IF32fkiXvR+Sum4PuVYjC/a4lJlLiJ9X6XJdX08uZVb07gFcSMvmQlo2nSMqvoftj79MYbGFBoE+dvYEn5AAMhPKjmlmYgo+5Y6pT0gAmYkpVZa5Uv5unwmOjuLW317l1l9fYdPMRaUGd2VY93mwf114hQSQXa7PuJc7XuHDuhD3wx826/p8OYNR+xZSlJWHpaDQ5jhmVXIcs6yOo3UZr9q+ZOvGV/alNLxqlxm3UQM7MuH3uYxYPI210z/UVgpBq+fHcfCFL0rLeYQGkGt1D8hNSMHjCvuoNf7RUZhcXciOu1hlOfeQQArira+JFNyu0DDJPhSHf+9oTJ5uuATWwq97K9zD7O/rHRJAltU5zEpIwTvETp+xvrdlVrzPVMZ1w2I48VOZIexlp8942ekz5e8zV9JnKtPISqjkurPTJp8rOB5NhnXl+I9l2inHzhOpvyS0HxxDQGjZsfavG0iq1b0tNfEy/iFX12+iB3Xh6ZWvcd/Cf9nUXYJf3QDSrH5zemIKvuV+s29IABlW94b0hBT86mplVrzwKYNnjuWJrfMZ9NQ4Vs/VvO+1I0Px9PPmvq+e4aGfXiJ6ZM8K2i4hQRQmlLWvMPFylQZ04Oj+ZG7YVbpscncj6sc3ifruNXz7x1R3KJz+rFAoajxERAjhDXwN1AfMwGwgHXgLyAE2A5HA3cDnQB0hxB7gOOCp/31QSjmuGh0BzAUGob14v6gb6a8CzfV6PpFS/gfYLIRo7PDGXiVB0VHIYgs/RD+Em583/X54lsRNB8g+q8WguXi50+OjR4n992cVvL5/l4Am9egycwy/jJsDQEF6DptnLqLfuw+BRZK48zi+4dU68KslKDoKabGwvJ3Wxr4/PMvFjWVtNLmaqTewA/us4r0dhUejELyuq8ef0Vr4QuuvnyW1S3My/jzskPpXHzxLv+b1MZts32OTMnN55oftzB7eGZMQDtGqaS7tPsmyG57Ev3EYfd56gHPr9lJchXfSEQRFR1GcW0D60fM269eNnYvJ3ZXuCybjHmich+jk6p2cXL2Tep2b0m3aLXw79lXa3tmPi7/tIc/KIHEE7sH+dJg/mdhH3gMDPTbpG/bi064xrZe/TOHlDDJ3Ha3gVXcWjYd15bdH33VonUHRURTZ6TPOwORqJqJ/e7a+WnYv+23ah1w/605aTx3Ovl93UlRY5DC9/b/uYufyLRQVFNFjbD8mvPEgb42d5bD6AWLu6MeK2Z9xcNUOWg/pwqg59/PfO17GZDZTr3UEH419GVcPNyZ/9wKX9+yh4HTlo31V4T+iN56tG5Mwpmwex5Eed1N0MQXXBnWJ/PIl8o7GUXA20VFNs8HoZ4Xif5MaN7CBG4ELUsohAEIIP+AA0Bc4ASwFkFJeEkLcC0yTUt6kl82SUra7Qp2RQDugLVAb2CGE2Ag8aV3nFbBICFEMfItmpFd42gkh7gfuB3ip393cFdandJtXaCC5Cak25XMTU/EKCyQ3IQVhNuHm60VBShbh07qRsG4fsqiY/MsZJO84RmDbSLLPJiFczPT46FHivtvC+ZU7berLSUjFx+rN3DskkOxymtmJWplsK828VG0o3zs0kAEfPcq6R98j40yZk/7Mr7s58+tuAJqP61P64M1NTMGzXpnnwSs0kNxEW72ccm101dvYsIo2AoT0bUfq/jjykysOMQLkJ6TgHla7dNktNIj8KzRuggZ3JmPXcSw5eQCk/r4b345NqrxpBtfyJDG9LP71YkYuwbU87ZZddfAcMwe1t1mXlV/Iw0s28VCfVrSpb+utMTfpzPiV9wKQuO8Utaw8TrVCAskqd0yzElOpZeXRslfmSvm7faaEtBMXKMzOI6BpfZL3VZyQVUJJny/B3nWRk5iKd7k+k59Sphc+PMauJ7LJxH5EjeuDu583hdl5NsfRp5Lj6GN1HK3L5CRn4B3sT/alNLyD/cmx0w/jtx/Fr2EwHgE+hLZvTHinpkRM7I/ZywOTmxnXWp641y6L0fcMDbwqA9zFx5OYz6dz6NWvSY09UW35/MQU3OpZXxOBFCRermKPcu2Z9y3x874F4Lp3HiX3ZMVQI9D7g9U59AkNJDvRTp8JCyQ7Ue8ztSr2GXsENW+IycVEkh4KB2X3kBK8QgPJsdNnvMICyamkzzSqpM9UpuETWsl1Z6dNWdUcj/A+bUk6EEeuVR9KPZnAj+PmcMhcQHBEKF1v7cNTv2gxxmf2niQgrDaghcgEhASRlnjl/SbbKjRry1e/MfLJOwDoNX4g199+AwDn957CPyyQM3o5v5BAMsq1NyMxFV+re4NfaCDpeihI+1HXl0543P/zn4x89T4A0hMvk5OWSWFuPoW5+Zzefpjg5hE2BnZR4mVcQ8v6qWtIEIV2+ql397bUefA2To2ZiSwoewEp0idvFp67SPa2A3i0jKzSwHb2s+L/C1Llwa6UayFEZD/QXwgxRwjRE4gATkspj+vG6+cO0ukBLJFSFkspLwIbgE7V7FOecVLK1kBP/d94e4WklB9IKTtKKTtGHimgVkQI3g3qYHI103B4DOfX7LIpH78mlohbrwegwU2dubhZmziRE59M3R4tADB7uhPU/joyT2g3qC5v3EfG8XiOfrCygv6lvafwiwihlq7ZeHgMZ/QJWSWcWRtLk1u1YbvIIZ25sEXLFOLm68WgTx5n+ytLubjzuM0+Hnrsp5ufFy3u7MeRL9cDkLLnVIU2xq+2beOF1bE0uk1rY/3ybexu1cYO15FxouwmHD6iK2e/rzyLQeaeE3hEhuLeMBjh6kKdEd1JWbOj0vLW5Mcn49e1BZhNCBczfl1b2kx4sUfLeoGcTckiPjWLwuJiVh88S68mYRXKnU7OICO3gLZWRnRhcTH/WrqFm9o0on+LBhX2KT62nc8GPc1ng57mxOpdtBjVA4DQ6CjyM3NKQxVKyL6URn5WLqHRWpxni1E9OFmub10pf6fP1GpQB2HWbiU+9YLwjwoj61xSlXqXy/WZ8OExnF9jqxe/JpZIXa/hTZ25uPlQ2UYhCB/ahTNWw+0uXu54BPtzbPGvrLrxWZJjT3DpzyM0149jSHQUBZUcx4KsXEL049jc6jieWhtLi1u039Dilp6cWqut9wuvW7p/cKtGmN1cyEvNYtXUd1nb8RHWdprKwVlfcG7ZZnY/+j7ekSF4NayDcDVTb0RXEq/wPAlXM50XPca5ZZtKM4tUR9aeE3hGhOLeQLsmag/vQcrqndXvCGAy4aKHcHg1D8e7RThpG/bYLXpp7yn8Gln1mWExnC7XZ+LWxtJMP35RQzoTv+WQvaoqcN1w21AKqNhnGtnpM+evss+Up0TDV9doYqdNp63a1HhIZ87rbTq9NpYmw2K0OSsN6uDfKISLe8pC95oM78qxctqe+j1VCMGgh0by87xveHnwDF4ePIO9a7YTM1K7Z0ZEX0duZs5VxVpbx2u36d+RxJPavW3DZ6uZP/gp5g9+ikNrdpaGbzSIbkxeZi6Z5TQyk9LIz8ylQbQ2qBs9sieH9f6bcSmViBgti1JUt5Zc1sOXDq3ZRXjHppjMJlw93GjQrrHN5EWAnH3HcW8Uhmv9ughXF/yGXk/Gr7Z93KNFJPVeepAz982m+HJ66XqTrzfCTfMPmgN88erQnPzjtvWXx9nPCoVCXAspVoQQgcBg4D7gN6CflPJ6fdsw4H4p5U1CiN5U9GBXGdBXUkYI8R9gv5TyY339Z8AyIAM7HmwhxESgo5TyofJ1Xsn2EpaEjZOhfdvSXk9/duqrDRx6+0daTx9Fyt7TxK+JxeTuSte3JxPQKpyCtGy2TJ5P9tkkXLzc6fKfB/BrUg+E4NTSDRx592dqd25C/x+eI+3Q2dLJVXtfWUrC73vJMGkhBw36tqXb83cgTCaOLt3A7vnL6ThtFEl7T3NmbSxmd1f6zJtE7VaNyE/L4tcpC8g8m0T0I8OJfmgo6afL4jx/HjuHvMsZ3LDgQYJaaBP2dr31PSeXb8O/WNMP7du2LE3fVxs4PO9HWultvKC3MWb+ZPz1Nv4xqayNnd96AF+9jae/2sDRd38GNIN76M55/BzzGIV6nHOYrDgpMOCGaCJnaamXLi75nXPzviN8xmgy95wkZc1OfNpF0eLjGbj4e2PJK6QgKY3YXo+ByUTjV+/FL6YFIEn5fQ+nn//Epu6ObzSvoLfpeAKvrd6NRUqGt4vgvp4tWLjuAC3CAujdtB4A764/QEGRhan92pQdx31neG75diKtMo7MGt6JZlYxjwtnlnl9b5g9gUa921CYW8DqaR+Uptobv/IlPhukZROo2yaCG9+4X08vt7c07V7jgR3pO+tOPANrkZ+RQ9KhM3w7XvOM3bvlP7jV8sTs6kJ+Rg6/jH2VtOMX/nKfuW5Ud9pNGYqlqBhpkcS+9T1x+gvWDQseJLRrczwCfchNzmDnG98S/+UG7Vz2bUuHF+5AmE2c/GoDB99eTpvpo7hsdV10e3sSgbrelskLyNJHNoK7Nif6qdGsHvp86fHyqO1L70+nYXJzQZgEF7ceZtdzn9PixfE06t2GotwC1lgdx3ErX+ILq+M4QD+Ocev2sk4/jh7+Pgx592FqhQWRGZ/MisnzyU/PpuPkm2gxqgfFhcUU5RWw6eUlpWn6Igu0kZ0Go6/Hv22klqbvhna01q+Ps0vWc2zejzSbcQtpe06RuCYW/3aRdP74MVz1PpqXlM66XjOoP6o70W89QKZVSEPs1PfJOHimdLmOqHhN+PdtT0TJNfHV78TP+5YG08eQtfcEqWt24tM2iqYfP1F6TRQmpbGn96MId1farnkNgOLMXE4+8T45dtK27XHRRm0a9mlLj+e1c3hk6QZ2zV9Op8dHkbTvNHF6n7nhrUnUadWIvLQs1j64gAz9HN6x1bYf/jTu1dJsHeM2v8nPE14jTfee+xZT2mc6WvWZA3qfSdl7mvN6n+lu1Wc2W/WZul2b065cnwGIfmYMjUZ0wyvEn5zENC5tO4x/u0gt9eDSDeycv5wuj4/i0r7TnNbb1F9vU35aFqus2tTx4WG0GN0LS5GFTc9/xpn1+wBw8XRn4p9v8Wn3f1FgNWej7d0DaT2hHwVI9qzezg9zvrT5bWNm3UOLXm0pyC3g0+kLObv/FABP/TKXlwdrWWtufnIcnYb3wK9uAOkXU9my9Hd+fmsZw2fcTpt+HbEUF5OdlsWSZz7i4knt+PpbDV4PmzWRJr3aUpibzzfT3yd+v3Z9PPzLy8wfrE1zqtc6glten4SrhxvH1u9l+XOLAQjv2JShz92JycVEUX4hPzyziAsHtP173n8THW69HmmR7Fy6jvBFyyr0o1q9OxD6bz1N37JfSXrna4IfG0fu/uNk/rqdiM9m494snKJLmse8JB2fV/tm1HvpQaSUCCFIXrSc1K/X2tSdluteQc/IZ0XMgbkV9Kpi+nOvsmP3PtLSMggK9GfKPeMZNXTgVdXhWjuyxmMMPT3Da9yIzM09U+PHwR41bmALIcKAFCllnhDiJuAhoAXQR0p5UgixBKhViYGdCgRLKSsN9rQysEcCD6AZ8oHATqALUA94U0rZq9x+E7EyoIUQLoC/lDJZCOEKLAF+lVJWmcBySdg4px7gEgPbWZQY2M7CnoFtJPYMbCOxNrCdhY+TQ2ydrXfJyYFwJQa2s7BnYBtNiYHtLEoMbGeRanau3iFzgVP1/GsgOnSsyHSqnj0D20iu1sB2BMrA1rhWDexrIQa7NfCaEMICFAKT0WKkfxZC5ACbgMpmKn0A7BNCxFY3yRH4HugK7EWb5DhDSpkohLgMFAsh9gKLpZT/EULEAb6AmxBiBDAAOAOs1o1rM/Ar8OFfbrVCoVAoFAqF4n+SGjewpZSrgdV2NjUDKPFa62XXA+ut9n0CeKKa+n30/yUwXf9nvb0QbUKl9bpGlVTXoZL1CoVCoVAoFP+vqOkoiGuZa2GSo0KhUCgUCoVC8T9DjXuwq6O819oeQoggtMmR5blBSnnl+akUCoVCoVAoFFeEStNXOde8gX0l6Eb0lebDVigUCoVCoVAoDEOFiCgUCoVCoVAoFA7kf8KDrVAoFAqFQqFwLmqSY+UoD7ZCoVAoFAqFQuFAlAdboVAoFAqFQnHVKA925SgPtkKhUCgUCoVC4UCUga1QKBQKhUKhUDgQFSKiUCgUCoVCobhqVIBI5SgPtkKhUCgUCoVC4UCEClC/NhFC3C+l/EDp/XM1ld4/W68mNJXeP1uvJjSV3j9br6Y0FcajPNjXLvcrvX+8ptL7Z+vVhKbS+2fr1YSm0vtn69WUpsJglIGtUCgUCoVCoVA4EGVgKxQKhUKhUCgUDkQZ2Ncuzo7H+l/XqwlNpffP1qsJTaX3z9arCU2l98/WqylNhcGoSY4KhUKhUCgUCoUDUR5shUKhUCgUCoXCgSgDW6FQKBQKhUKhcCDKwFYoFAqFQqFQKByIMrCvMYQQ4UKIfvrfnkKIWjX9mxRXjhBi6pWsc5CWtxDCpP/dRAgxTAjhaoSWQvFXceY1odftfiXrFFeOEGLOlaxTKBRlqEmO1xBCiPvQEs4HSimjhBDXAe9JKW8wSG8u8CKQC6wC2gCPSSk/N0hvKrAIyAQ+AqKBJ6WUa4zQ0zVH2lmdDuyXUl4yQC9WStm+3LrdUspoA7R2AT2BAGALsAMokFKOc7SWleZv5fujvXUO1JsNbAS2SimzjdCwo9kNaAS4lKyTUn5qkJYAxgGRUspZQoiGQIiUcrsRerpmXeBlIExKOUgI0QLoKqX8r0F6TrsmqtCrsM4BOvuBSh+gUso2jtSz0vUCHgcaSinv058TTaWUK4zQ0zXtHdN9BrbxJyoe23RgJ/C+lDLPCF1dO1BKmWJg/bWllMlWy3cAnYEDwIdSGWX/M7hUX0ThRB5Eu9D+BJBSHhdCBBuoN0BKOUMIcTMQB4xEM2YMMbCBu6WU84QQA9GMwvHAZ4BhBjZwD9AVWKcv9wZ2ARFCiFlSys8cISKEuB0Yq9e73GpTLcCom7WQUuYIIe4BFkop5woh9hgiJIQH4AXUFkIEAELf5AvUM0JT5xRwO/C2ECIT2ARslFL+aISYEOIzIArYAxTrqyVgiIENLAQsQF9gFtrL57dAJ4P0ABajveg+rS8fA5YCDjWwnX1NCCFC0PqipxAiGts+6uVoPeAm/f8H9f9L7iWGveDqLEK7h3XVl+OBZYDDDWwhxGRgChAphNhntakW2ku9UZwC6gBL9OXRaNdGE+BDtGfH30YI8YyU8kX97xbAD4Cr/uI7Wkr5pyN0yrEGaF+ij+Yk+RKtPzUHHjNAU1EDKAP72iJfSlmgXdsghHChCg+JAyg5/0OAZVLK9BJtgyipfDDwmZTyoDBYEK2NzaWUF6HUe/cp0AXtZcIhBjawFUgAagNvWK3PBPbZ3ePvI4QQXdEe6Pfo68wGaT0APAqEoT3cS85bBrDAIE2klIuARbrxdBswDW2Ux6jQqY5ACyd6kbpIKdsLIXYDSClThRBuBmvWllJ+LYSYqWsWCSGKq9vpL+Dsa2IgMBGoD7xZTu8pR4tJKc8ACCH6l/PGPymEiAWedLSmTpSUcrT+AoP+km3UffRLYCXwCrbtyTTSywt0k1Jav2T+JITYIaXsJIQ46ECdkWijuACvAVOllCuFEJ2Bt4BuDtQqwfpcjQR6SimzhRBfArEG6ClqCGVgX1tsEEI8heaB6Y/mOfjJQL0VQogjaCEik4UQdQDDht6AXUKINUAEMFOPL7cYqAfQoMS41rmkr0sRQhQ6SkR/2J4BuupGfMnD4bCUsshROuWYCswEvtdfViIp89Q7FCnlPGCeEOJhKeV8IzTsIYT4CGgBXETzXt+CsQ+hA0AImmHoDAqFEGb0F2n9GjT6msgWQgRZacagDb87FOtrwtF1V6L3CfCJEGKUlPJbZ2jqCCFEdynlFn2hG8bObyoQQnhSdv6igHwjhKSU6Wh943ZdKxjwAHyEED5SyrNG6Or1NyypXw+d8tG3FRikGSalXAkgpdyuH2MjKBlhMQHmktA3KWWhQS+6ihpCGdjXFk+ieSL3o3kMf0GLVTYEKeWTehx2upSyWAiRDQw3Sg+tbe2AU7rXJQi4y0A9gPVCiBVoQ6gAo/R13kCao8WEELcCrwPr0TwV84UQ06WU3zhYxwwMk1IOK1knpTwFPOJInfJIKec7M0YZCELzyqehhRUkG/jCApq39ZAQYjtWRov1cXYwbwPfA8FCiJfQXiCeMUirhMeB5UCUEGIL2lD8LUaJ6fMg5gDBaNeEAKSU0tfBOv+y93cJUso3y69zEPcAHwsh/PTlNOBug7QAnkObM9NACPEF0B3Nc28YQoihaKMCYWhOinDgMNDSIMnHgc1CiJNo/SUCmKLftz9xoE6kHr4kgPpCCC8pZY6+zagJ4wmUjbCkCCFCpZQJ+vPQyHubwsmoSY7XEPrNI09KWawvmwF3qwve0XpmtPCQRtgaS0Y9iBBCtLGj952BegLNqO6ur9oCfGtUCIAQYi/Qv2QCpe6R/FVK2dYArW1SyhhH11uNpt0YZSmloYa9EKI5WgjAY2hen/oG6fSyt15KucEIPV2zGXAD2kP+NynlYaO0rDRdgKa65lEppcNGc+xonQCGGt0uIcRzVW2XUr5gsL6fruPw0QA7WkFADNr522Y9ac4gvb1o8wR+lVJGCyH6AHdIKe+pZte/o+kONNMXjxoxsdHO9b5LSpmlj0LeIqV8x9GaVfwWQ5/3CuejDOxrCCHENqCflDJLX/YB1kgpjYgDQwjxC1pIyH6shqWNehAJIT5Gy1Ry0EpPSimN9PY4FSHEfilla6tlE7DXep0Dtd5Fm9S1DCjNsGHwC8thnBijLIS4CW0S0PWAP7AN2CSl/NhATesQn+0GZZsJrGq7wVkM9gFfAUullCeN0rHS2yKl7F59yX8mwklZWYQQVWZBkVIaFjolhNgppeyoG9rRUkqLEGKvEY4DK01njpRdEwghmkkpj9T071A4BhUicm3hUWJcA+hv0kbMfi+hvlFpliohRkrZwol6ThuetmKVEGI1trPffzFIywO4jOZZKkEChhnYOD9G+Ua02Ot5UsoLRosJIW5Dm+y0HgNDfNAmikpdoyGQqv/tD5xFGxI3iqFo/fJrIYQFLYPI1wbG0+4UQixFy9BgHXbj0H4qhJihZ9KZj53J4QaOsizGCVlZKJso6oE2GXcvWp9pg5a+zshY9zTd4bMR+EIIcQmrl3pHU9lIGcZl87H3Gz6QUt7vLD2dNWj3A8X/AMrAvrbIFkK0L/FECCE6oE1ANIqVQogB0sA81OX4QwjRQkp5yEl6AHNxwvB0CVLK6UII65CUD6SU3xukZXT8uj2cGqMspXxICBGONtHxgj7xyEVKmWmEHpqR1Kl8iA/gUANbShmh1/8h2iTVX/TlQcAIR2rZ0T6Ddl3MFVoO5WfRXkKNykDjC+QAA6x/Bo5/ESy5xnc6uN7qcEpWFillHwAhxHdAeynlfn25FfC8o/XKMRxttPMxtKxFfmhpJY3CKdl8qhhJEmjZrozQfLsKTX8jNBU1gzKwry0eBZYJIS6gXWwhaJ4mo9gGfK+HMRRivHf3UzQjOxHNOCvRM9KLftFZxnUJegYDw7MYCCHqA/MpM+Y3oaWZOm+g7PMG1l0BYfXxJTSPVn3gPbSYZSMwlQsJuYyxGSFipJT3lSzoKcLmGqgHgP7SMlr/VwzMMErLWS+CUsqf9P8dOQnuSnBKVhYrmpYY1wBSygP6HAXDkLYfeXLG8XXWSFkSWqYb69R5JSNLRn2D4i60SZz2Mr/cbpCmogZQBvY1hJRyhz7hqam+ytDJR2gzmbuifdXQGTG1/0X7QIBNzLfBOGt4OhP7OcuNfGlZhJan9lZ9+Q59XX8DtABtsp8zYpStcPbHl5wZ4gOaV/4Zyj7uNA4wNBRGCPEnWoaEZcCtevYZI/WaAO8CdaWUrfSJzsOk/oEPA/TWYT9EpK+d4o7gX1TMynJr1bv8LfYJLX2ldZ8xJNd+Dd3XwHkjZaeAG+yFRwkhzjlYq4QdwAEp5VY7ms8bpKmoAdQkx2sAIURfKeXvwv5nvQ2btCaE2Aj0llI6xdgVQvwhpXRKTlwrzUV2VtfYxEohRICUMtVBde2RUrarbp0jsROj3BMwIka5RO9PKWUXoX9aW89+EWvkqEe5EJ9NRoX46FqBaGnXrtdXbQReMHiSY1Mp5VGj6rejtwGYjvaJ62h93QEpZSuD9DpYLXqgZREqklIa4qXXs10UY5WVBW0kxJDc1EL7qupkbPvMu0Zk2agpnJXNRwjxILBZSrnXzjZDcv7r13yeyhbyv48ysK8BhBAvSCmfc7YxKIRYDESifanL2ktgSJo+IcRCtBiznzDQm3wtI4SIlVJWmQ3gKur6Dc1jXeJtvR24S0ppVPiEU9MQ6vXPRcsrfCfwMNrHlw5JKZ+uckdFpejp5KyN+g3ALGlQejlR9gW+3VYGtqEvgnZ+w3YpZWeD6q5wTTvyOq9E0w3NoJcYP9JZIzh5pMzpCCGmSu0DXlWuU/xzUSEi1wC6cW0CVkopAX/x3gAAGAxJREFUv3ai9Gn9n5v+z2g80Qxroyc71WRGgepw5CeN70aLwf6PvrwF4z/c4+wYZad8fEkIsVlK2cPOkLihQ+H6C8oMtA92eJSsNzCcAeBjtBjX2/Tl8WgvanZH0BxAstC+NlgSo3wLBsbWlpu4ZgI6oE3Kc7ROCFqazJIv85Vc276AYdmfhBC90eKg43TNBkKICVLKjUZpOhsnZvMp0bPX99PRwieNMuwnAOWN6Yl21in+oSgP9jVESa7RGtD1AS0toLO1jUIIMVRK+ZMQYoK97TUwEQow3rNlNEKI19DSglnHKO+TUj5Rc7/qn4sQYg1aSrdpwCS0h26SkcfT2aFFQohI4AOgG1o6wtNoHymJM0jvNGUT1Yp0vVlSys0O1pmAZhB1xDZzSSaw2MDQvl3A2JIwHz3GfYmUskPVe/5zqIGRsp/R5iOt01f1RkulGYHWdz5zoNbtwFigB9rE9BJqARYjRyAVzkV5sK8tfhVCTEN74Fp/OMSQeEw9vdNnaBkaEEIkA3dKKQ8apOe0rBclGQXQPBCGfYChJnF2FhEhhED7tHcntIcDGJSGUAjxtZTyNiHEfuyPQBgSgy2E+ExKOb66dQ4kSEr5X31oeAOwQQixwyCtEnKFED1KDE4hRHcMTAeqT6LsJ7Qv1ZqkcSkWS/SMzCFurfMJ8IkQYpSeOchZuFrH0EspjwkhjPqsd03h7JEyF6C5lPIilIanfAp0QYtxd5iBDWxFG8GpTVluc9BezAyZrKqoGZSBfW0xGs2YmFJufaRBeh8A/5JSroPSoccP0TxNRuD0rBfAG/pQ7jdoX647YKDWleDIEBGnHk8ppRRC/CK1r1IaHTc/Vf9/MVo6SSNTD1rT0npBn1RppGewJHY2QQgxBC2DSJVfeXQAk9EMQz+0/piC5jk3BCGEP1oMfSPARXtPMy5MS58EOAXtJVCivXi+Z9QkQCnlt/q5Kx/mY1Se6J12sog4O/e30Tg7m0+DEuNa55K+LkUI4dD4dqnloT8DdC0XZ35YSlnkSC1FzaJCRK4hhPYRDXsPBkO8S8LOp27trXOgntOzXugaIWjxpqPR4iOXSuNShFXpARVCBDpqRKKGsoh8wv+1d+5BllXVGf99IA8ZGQSDAVQQ1IAK+ADkpalgRcWAKZVXASoQ0SAqoAiVkaAiilFBC5gSFUEFxfBS5CFBQlTe8hgQBDHK+JaHQnBAsEbgyx9rn+nbTU/PDHP2Od33rl9VV/c597F29+17z9prr/19MNd27SprE+/DxGt3P7Gyc/aEC2FbceYAHyT2CTS7+wUsJKr0c9qOWeLuTLzPn0OsRswmVETOrxFvQuzZALYXVI5zNTFJGifPWatNS9JZRDWwSUD3Ap5uu4p0nqTPEz3XOxD7A3YlNuW9vVK8VQj5ymYV6Qrgc7VUS/qiYzWfzxEOimeXU7sQk/rDgAtdTH5ajrkbcCwdKTIl3ZMJ9jSiXBgWAF8vp/YC1rC9++IftVzxvgXMY2z56y3AFrbfVCle56oXE+JvRmwo28N2lU2dE3usJa1ItKm0bhHfk4rIHcDziQrMn6ETsyAU2sl7UC58tv+xUpxP1EqmJ4m1InCQ7c8u8c7txn0GoSLSTOSvJPpM76sUr9N9B5Jun/h+m+xci/Fusb35wPenERvWX1Up3ixC5u2xcrwisIpT9u1JU9rfBhP6q4BzXTFB6rrPPOmebBGZXmw64SLwPUk1bcX/BTiKseX+K8q5mvE6Vb1QOJw1idl9RBX00ApxFlVAJTUVwUUV0LbjFQb/niZ6+6r8PSVtaPsXwOtqPP9ScC9wN/Eatm40I2kT23cQTqpPSAZr9PHbfqxseOo0wQb+k+gr3aUc7028L6pMWoDTFY6cFzJenrOW1vc8SdvYvhZA0tbUbaFoVhgflrQe8T+6bsV4lxGvVbMp/anAd6nX2tcZk6j4LLqJimo+JZE+p3x1Rdd95knHZII9vej0wuAwPDlI0upxWFdFpPSete3EtSROJZKH19mu6ZD3c9urN5vzKsZZRMd/z3OIXuRTu9zlLulAokVkbWL59h22a0w6DwXewfhNRw0GasnmXSVpLk/c2FxzY+66to8eOP6YpD0qxltISK4dwVjyZFreWzKwIXYl4GpJvy7HGwB3tBlrAheWPvNPEyuCJvay1GLVwc9q2w9JqiYL2CW2V+8jbpHp+yQxeReVE/pC133mScdki8g0QtJPCPOAxrZ1fcIV7FEqLMOXlonTGNtU9Udgn1obAbtWveiSZhm8i+VwSSdMdXuNzWOSbiIS3HcxScXV9cyJPkH0zN9c4/n7RmHrDWOJZ3Nhr6aDLekzwHVAo7m/K/AK2x+oFG9+ef4/1nj+gTgbTHV7mZCiFt1UJxnDKkQCXMW0p8S4CnhvMwlTOFfOdccuucOEpJ8Db7D9k47jdtZnnnRPJtjTiKW9QLQY72rgiAkqIsfYrrLUKOlSQvVisOd7b9utq15MIfNWpWe4/G4mdoRfMfF2261VmiX9lqgGrknoCk+M1frmMUkbA28EDgE+P0nMo9qO2SWa3GhiEa6naXwoY5rNlJ8XADe0PakYWH4XMIuxDYcrAA/VqtYptL7fOF16hNueBEu6hWi7OdP2nW097xTxtirxfk+8lusQ+0purB17WJF0le3tl3zPJFl6MsEeYYZZRUTSurbvWtykpcJkZWXg5cTkYf9J4v2gxVi3Ez2YFxOGCOOk/2r1tircRvew/Y0l3nmGIenL5cdnEr2s/1OOdwCutr1zpbhnEEYl5xOv486EFu5zCcWUT9WI2yVlM/WLCROPwR7sXtxUNWDZ3tLzbUAs7+9BTFrOBM6y/espH7h8MVciVjthSK3Su0TS8cRE5TzG/4/WcBnupc886Z5MsEeYUVMR6QJJa9v+Q+UYBxGtGhsBvxu8ifiArqWbjnpyG+2KUm3dx/Zd5XhdwpWvyuZOSZcD/9T01BYFiouAHYEbKypfrAm8gPG6zVWstjVCbqqSXgAcSazMrVgpRqc636PAwAR7ENuuuel/Smq2MiXdkAn2CFMuskcx/oP6qIr9iRsQPdjbMqZ6cVCNSk9fVQJJFywmLtB6q8hJtt/V1vMtZcz/IHr1O3Eb7RpJP7H9woHjFYDbBs+1HO8OYLOmAll6eH9ke5O2K60DMfcnjHyeDdwMbANcU7nv+6nA+h5wIOyLGgn2hCr2Y0S7yGQbZtuI1anOd9IPXctbJu2TKiIjStFO/aYrCOhPEe+YNhPMqVja3egVqgTziaXG5uK3J3APsfTYKl0n14VGbeLdg0Ohntto11w2yc7+/64Y7+vADyV9uxy/ATijaB3Xkug8mNgrcK3tHSRtAhxTKRaS3kAYaqwMbCjppYTudteKQouG1OqTST8klEvOBnZzWMPXpGs516FF0uG2PyXpRCYpjPTVxlRo9f806Z5MsEcUhwbv45LWqLnjfUK8DSStbHth7XjLwGVE73RbbD+hheKC0lbxvhZj9IbtDfseQ01sv6dseGxMQr5Yc2e/7aMlXcyYksABthtpzr0rhf2L7b9IQtIqtu8om1hr8RHgFYRjHbZvllRlQlYm8rfZ3mSKu7Xdkva2jivzXet8DzONash0/Ptle8EMJxPs0eYh4NaigDG43F9r1j6f0P09f0K8KhJvS0nbVYJZkjZqqlglkZjVcozeKHq77yeW+99Zek43tn1hz0NrjbKxqYpqyGLi3UC3F/jfFt3m84BLJf0f4cxZi7/a/pM07q32+OLuvDyUifxPJa2/uNazCu1Mdxfpw78vxz8gKvS1ChdbMKbzDUXOtVFMalshaZixfUH58VbX1Z5PRpBMsEebThMJ4M7ytQLQi6HAJLRdJTgE+H7R/oVQg3hnyzH65MvAjYy5xv2OWBofigRb/RhOdMrAJuaPFB3uNYD/am6v0DZ1m6S9gBXLhOwgYv9FLdYsMa9j/ES+VkvKqcCPCUMkgLcS75MppR+Xgx0rPe8oc5ykdQhDrTNdyQtiGckWkRlObnJMRpoKmri7AZcAGxIui9sRWuNDUR1pVEQGN+DVlHbsmr4MJ6YTFd4TqxG67a8lkoZLgKNrqV4UlZ3fAOMq1W1KZU6I14n8qKTZthdIWmuy24dlo3FflAR7d2LfxWwi0f5YxXin237r4s5JWitf05lNVrBHED3RfGUctZYYJf0d8AGiqrvof6+mesFS0HaV4EjbZyvs519NbO46Cdi65Th9sbAoQhhA0vMY0I0dAu4Z5eS60Op7wmEwcwRwROmRnlVZUu6ZRJV8HlFdvsR1K0mPSHql7SsBJG0PPFIhzhmETvqNjDcnguHaaNwLtu8GTiirOocDHwKqJdiENvwiyntji4HxZHI9w8kEezRpTDMaJYhBHeyaF6KzCRfALxFSVtVZUpWA9jc8Nb/XTsDJti+SVPNDums+QrQTPEfS14nNefv1OqJ2uUHSmXRgODGNafUzoJjpHEC8N64HZks63van24zTYPvfJR1JVMz3A+YWabtTXMdp8QDgNElrEEnv/cC+bQfxmNnRL4DjbF/U3Cbp5LbjjRKSXkhUrncB7iNkSA+tFGsO8EHgqZIWNKeBhcAXa8RM+iFbREaYyXR2K5sw3Gh7iyXfs9WY436fUiW4taKBx4VEX/JrCHWSR4DrhqWFAkDSMwjtZBFSb3/seUitMR0NJ7qmQovIzbZfKmlv4j3xb4SJTtXNeJJeQiTYOxIuktsAl9o+vFK82QC2FyzpvssZZz7RAnOZ7Y+Wc6mZvBxIuoYxB87fV461W1nlPMv27kt+RDJTyQr2aCNJ29u+qhxsR2xAbDtI0zN4gaQDgW8xvjrY+lJYj1WC3YkL+rG2H1A4AR5WMV6nSLrM4bx50STnZjy2h6ka/2Rpu21qJYW19xuBubb/KqlaZUfSwcDbCEOkLwGHlZgrAD8jlv/bjLcKUfl8LvCURi2lSX4r8ACx8naCwtjqLZXijAy2t+0w3BxiNff5HcZMeiAT7NHm7cCpZWkT4oO7RqVuYs/gYMJZq3fw57ZX77pKUPpNvzlwfBdwV1fxa6GwZ14N+BuFA2jzWs4GntXbwFpG0rMJt9FGl/oK4GDbv+1vVO3SQ9vUF4BfAj8CLle4Htas8q4FvNn2OOlB249L2nkxj1kevg38ific62I/gmw/ChwoaV/gSkI5JVlGmuvDJPuSGvWgGqss90n6LmG6dP7EG3s0YEpaJltEEpoEu7bhjKRVJ25umuxcS7Hm2X55Lp22Q6kKHgKsR7TANAn2AqLXfG5fY2uTogl/BuP3Jext+zX9japdum6bWswYnlKSxBmPpB/b3rTDeP9q+wsDx1sA7x6lNqa2kLSu7bvKpO8JTJyktRRzZaJV6nRg/0liVlG7SbonE+wRRtLfEhbJ69l+vaQXAdvaPqVSvCcku7US4JIombCEvmLi7VkleHJIeq/tE/seRy26klzrg8G2KeDh5jSlbcr2nIqxdyJUE1ZtzlVsoegUSV8ETrR9a99jSWYOkta2/Ye+x5HUI1tERpuvEIYIR5Tj/yU2erSaYBd90WcR/dAvY3x7wWptxhpgJ8aqBMdVijFy2D5R0qbAixifLJ3W36ha5T5JbwG+UY73JFQFhoFe2qYkfZ54n+9A9ETvClzXVfwOeCWwr6RfEC0iNdsLkhaR9CCTq+Z0YTB16lR7EbIINPPJCvYII+l621tNMA2pYZCwDyFbtSXjLaEfBL5SUwItqwTtIunDwD8QCfZ3gNcDV9retc9xtUVZKj4R2Ja48F4NvNf2b3odWAv01TYl6Rbbmw98fxpwse1XdTWGmnTZXpD0g9p3N0XS8cA6wNfKqT2BewiJ0GwVGQKygj3a/LlIrjWmIdsQm3VaxfZXga9K2sX2uW0//xLIKkG77Aq8BLjJ9n6lzehrS3jMTOKjwD7NxbQo4BxLnc2/XdPX5qrGdOVhSesRKwLrVorVObZ/JemVwAtsf1nS2sDT+h5X0iqXESuibbK97S0Hji9QOOW+r+U4SU9kgj3avB84H9hI0lXA2kQCVQXb5/bQizmfKaoEyTLzl6LG8GjR/b0XeE7fg2qRzQcrVbbvL21Nw0BfbVMXSno68GnCXdHA0BijlFWdLYGNiZa7lYjPm+2nelwyo2hbuhJglqSNbM8HkLQRMKtCnKQnMsEebW4nNKkfJto1ziP6sKvQUy9mVgna5fqSLJ1MyJI9BFzT75BaZYXB5eBSwR6Kz0nbC4FrJW3XZduU7aPLj+cqjJhWra1Y1DFvAl5GTB6w/XtJq/c7pKRlavTSHgJ8vxgHQeiov7NCnKQnhuLCkTxpTiNk1o4px3sR1a3dKsXbbqAX8yhJxwEXV4rVkFWCdplN/H98n7BMn237ll5H1C7HAddIOrsc7wZ8vMfx1KDTtqmioX4gsRnQwJWSTqohz9kTC227+ZtKys+XZGmYDWwKbAj8M7AdYY6UDAmZYI82m07Qvv2epNsrxuujFzOrBO1yCvAqYiPg84CbJF1u+/h+h9UOtk+TdAPw6nLqzbZrvif6oOu2qdOIFbJG3rH2RL5rzpL0BeDpkt5B9OsPTQtMAtRpETmyWKavTnzeHAucBGxdIVbSA5lgjzbzJG1j+1oASVszXuWjbZpezE8R7QUQrSI1ySpBi9j+nqTLCX3xHYADiJ76oUiwAUpCPWxJ9SBdt011PZHvDEkipE03IVYDNwY+ZPvSXgeWLBM9uJsCPFa+70SYdV0k6WMV4iQ9kQn2CDJgC7sScLWkX5fjDYA7KoY+FngXUQG9hjCAOaliPMgqQatIuoxosWlev61s39vvqJJlpOu2qa4n8p1RWkO+Y3szIJPqmcuLBw+Ku+kWzbHt+yvE/F1Z+XgN8ElJqwArVIiT9EQm2KPJzj3F/SqxVHxCOd6LWD6uaXqRVYJ2uYW48GxKSDo+IOka249M/bBkGtFJ21SPE/mumSdpK9vX9z2QZNkYdDeVtKA5TXE3rRx+d2BH4FjbD0haFziscsykQzLBHkF6NEDoY6k4qwQt0rQRlBWBfQlZsnWAVXocVrJsdNU2tVQT+RomHh2zNbC3pF8BfyadHGcSvbibAth+GPjmwPFdwF1djiGpSybYSZf0sVScVYIWkfQeosVnC+CXwKlEq0gyc+ikbWoZJvI1TDy65HVT3TgEE4hhZg5wNvD8vgeSDB+ZYCfV6XOpOKsErbMq8BngRtuP9j2Y5Ekx3dqmaig0dMZSTCRm+gRimOnL3TQZATLBTrqgr57vpGVsH9v3GJLlZrq1TdUw8ZhOzOgJxJDTl7tpMgLIHvbPtiRJkqRB0mpE29Sttn9W2qY2s/3dnsYzz/bQVniH/fcbBiSt3aW7aTIaZAU7SZJkhJiGbVNZ4U36plN302Q0SDWFJEmSpBqSTl/CuRomHtOJnEBMf+YTTsMnl6+HgDuJtpFsHUmeFNkikiRJklRjYotEMfG4dYJk54yk/C632d5kivusVcmoJGmJ4mS65ZLOJcmykBXsJEmSpHUkzZH0ILC5pAXl60HgXuDbPQ+vFWw/BvxU0vpT3CeT6+nPrOJoCnTibpqMANmDnSRJktSgNxOPjlkTuE3SdYTRDJB9uzOMTtxNk9EiE+wkSZKkBqNi4nEO8FkgK9Uzl67cTZMRIhPsJEmSpAajYuLxTOAgYB7hbHqJc3PTTKMTd9NktMhNjkmSJEnrSFqZMROP/SfebvsHnQ+qEpIEvBbYD9gSOAs4xfadvQ4sWSok3WT7ZZI+QWzAPaM51/fYkplLVrCTJEmS1rG9ELhW0nbDbuJh25LuBu4GHiX6ss+RdKntw/sdXbIUTDd302QIyAp2kiRJUg1JFzCFHfpMbxWRdDDwNqJn90vAebb/KmkF4Ge2n9frAJMlMt3cTZPhICvYSZIkSU3mA+sAXyvHewL3AOf1NqJ2WQt4s+1fDZ60/biknXsaU7IMTEN302QIyAp2kiRJUo008UiSZBTJHqMkSZKkJmnikSTJyJEtIkmSJElN0sQjSZKRIxPsJEmSpCZp4pEkyciRLSJJkiRJTY60vQBoTDzmEiYeSZIkQ0sm2EmSJElNHivfdwJOtn0RsHKP40mSJKlOJthJkiRJTRoTjz2A76SJR5Iko0DK9CVJkiTVSBOPJElGkUywkyRJkiRJkqRFcpkuSZIkSZIkSVokE+wkSZIkSZIkaZFMsJMkSZIkSZKkRTLBTpIkSZIkSZIWyQQ7SZIkSZIkSVrk/wGLZeEIIYbOAgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "guV1pr_uIf2A"
      },
      "source": [
        "# Step 6: Now we will clean the data a little. There are a few null values present in the dataset. We are dropping those null values now. We are examining the concepts of missing value treatment in Chapter 5.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "lwYOZ-_QJkkR",
        "outputId": "dd344397-f8a5-41d8-cf83-c5c3a82c1db1"
      },
      "source": [
        "house_mlr.isnull().any()\n",
        "house_mlr ['basement'] = (house_mlr ['sqft_basement'] > 0).astype(int)\n",
        "house_mlr ['renovated'] = (house_mlr ['yr_renovated'] > 0).astype(int)\n",
        "to_drop = ['sqft_basement', 'yr_renovated']\n",
        "house_mlr.drop(to_drop, axis = 1, inplace = True)\n",
        "house_mlr.head()"
      ],
      "execution_count": 65,
      "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>price</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>sqft_living</th>\n",
              "      <th>sqft_lot</th>\n",
              "      <th>floors</th>\n",
              "      <th>waterfront</th>\n",
              "      <th>view</th>\n",
              "      <th>condition</th>\n",
              "      <th>grade</th>\n",
              "      <th>sqft_above</th>\n",
              "      <th>yr_built</th>\n",
              "      <th>zipcode</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "      <th>sqft_living15</th>\n",
              "      <th>sqft_lot15</th>\n",
              "      <th>basement</th>\n",
              "      <th>renovated</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>221900.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1180</td>\n",
              "      <td>5650</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1180</td>\n",
              "      <td>1955</td>\n",
              "      <td>98178</td>\n",
              "      <td>47.5112</td>\n",
              "      <td>-122.257</td>\n",
              "      <td>1340</td>\n",
              "      <td>5650</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>538000.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.25</td>\n",
              "      <td>2570</td>\n",
              "      <td>7242</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>2170</td>\n",
              "      <td>1951</td>\n",
              "      <td>98125</td>\n",
              "      <td>47.7210</td>\n",
              "      <td>-122.319</td>\n",
              "      <td>1690</td>\n",
              "      <td>7639</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>180000.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.00</td>\n",
              "      <td>770</td>\n",
              "      <td>10000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>770</td>\n",
              "      <td>1933</td>\n",
              "      <td>98028</td>\n",
              "      <td>47.7379</td>\n",
              "      <td>-122.233</td>\n",
              "      <td>2720</td>\n",
              "      <td>8062</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>604000.0</td>\n",
              "      <td>4</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1960</td>\n",
              "      <td>5000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>1050</td>\n",
              "      <td>1965</td>\n",
              "      <td>98136</td>\n",
              "      <td>47.5208</td>\n",
              "      <td>-122.393</td>\n",
              "      <td>1360</td>\n",
              "      <td>5000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>510000.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1680</td>\n",
              "      <td>8080</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>1680</td>\n",
              "      <td>1987</td>\n",
              "      <td>98074</td>\n",
              "      <td>47.6168</td>\n",
              "      <td>-122.045</td>\n",
              "      <td>1800</td>\n",
              "      <td>7503</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      price  bedrooms  bathrooms  ...  sqft_lot15  basement  renovated\n",
              "0  221900.0         3       1.00  ...        5650         0          0\n",
              "1  538000.0         3       2.25  ...        7639         1          1\n",
              "2  180000.0         2       1.00  ...        8062         0          0\n",
              "3  604000.0         4       3.00  ...        5000         1          0\n",
              "4  510000.0         3       2.00  ...        7503         0          0\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "id": "KMqIN2IjFsyV",
        "outputId": "bf4b2c07-f57c-47ab-ad23-39f043b2154f"
      },
      "source": [
        "categorical_variables = ['waterfront', 'view', 'condition', 'grade', 'floors','zipcode']\n",
        "house_mlr = pd.get_dummies(house_mlr, columns = categorical_variables, drop_first=True)\n",
        "house_mlr.head()\n"
      ],
      "execution_count": 66,
      "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>price</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>sqft_living</th>\n",
              "      <th>sqft_lot</th>\n",
              "      <th>sqft_above</th>\n",
              "      <th>yr_built</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "      <th>sqft_living15</th>\n",
              "      <th>sqft_lot15</th>\n",
              "      <th>basement</th>\n",
              "      <th>renovated</th>\n",
              "      <th>waterfront_1</th>\n",
              "      <th>view_1</th>\n",
              "      <th>view_2</th>\n",
              "      <th>view_3</th>\n",
              "      <th>view_4</th>\n",
              "      <th>condition_2</th>\n",
              "      <th>condition_3</th>\n",
              "      <th>condition_4</th>\n",
              "      <th>condition_5</th>\n",
              "      <th>grade_3</th>\n",
              "      <th>grade_4</th>\n",
              "      <th>grade_5</th>\n",
              "      <th>grade_6</th>\n",
              "      <th>grade_7</th>\n",
              "      <th>grade_8</th>\n",
              "      <th>grade_9</th>\n",
              "      <th>grade_10</th>\n",
              "      <th>grade_11</th>\n",
              "      <th>grade_12</th>\n",
              "      <th>grade_13</th>\n",
              "      <th>floors_1.5</th>\n",
              "      <th>floors_2.0</th>\n",
              "      <th>floors_2.5</th>\n",
              "      <th>floors_3.0</th>\n",
              "      <th>floors_3.5</th>\n",
              "      <th>zipcode_98002</th>\n",
              "      <th>zipcode_98003</th>\n",
              "      <th>...</th>\n",
              "      <th>zipcode_98055</th>\n",
              "      <th>zipcode_98056</th>\n",
              "      <th>zipcode_98058</th>\n",
              "      <th>zipcode_98059</th>\n",
              "      <th>zipcode_98065</th>\n",
              "      <th>zipcode_98070</th>\n",
              "      <th>zipcode_98072</th>\n",
              "      <th>zipcode_98074</th>\n",
              "      <th>zipcode_98075</th>\n",
              "      <th>zipcode_98077</th>\n",
              "      <th>zipcode_98092</th>\n",
              "      <th>zipcode_98102</th>\n",
              "      <th>zipcode_98103</th>\n",
              "      <th>zipcode_98105</th>\n",
              "      <th>zipcode_98106</th>\n",
              "      <th>zipcode_98107</th>\n",
              "      <th>zipcode_98108</th>\n",
              "      <th>zipcode_98109</th>\n",
              "      <th>zipcode_98112</th>\n",
              "      <th>zipcode_98115</th>\n",
              "      <th>zipcode_98116</th>\n",
              "      <th>zipcode_98117</th>\n",
              "      <th>zipcode_98118</th>\n",
              "      <th>zipcode_98119</th>\n",
              "      <th>zipcode_98122</th>\n",
              "      <th>zipcode_98125</th>\n",
              "      <th>zipcode_98126</th>\n",
              "      <th>zipcode_98133</th>\n",
              "      <th>zipcode_98136</th>\n",
              "      <th>zipcode_98144</th>\n",
              "      <th>zipcode_98146</th>\n",
              "      <th>zipcode_98148</th>\n",
              "      <th>zipcode_98155</th>\n",
              "      <th>zipcode_98166</th>\n",
              "      <th>zipcode_98168</th>\n",
              "      <th>zipcode_98177</th>\n",
              "      <th>zipcode_98178</th>\n",
              "      <th>zipcode_98188</th>\n",
              "      <th>zipcode_98198</th>\n",
              "      <th>zipcode_98199</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>221900.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1180</td>\n",
              "      <td>5650</td>\n",
              "      <td>1180</td>\n",
              "      <td>1955</td>\n",
              "      <td>47.5112</td>\n",
              "      <td>-122.257</td>\n",
              "      <td>1340</td>\n",
              "      <td>5650</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>538000.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.25</td>\n",
              "      <td>2570</td>\n",
              "      <td>7242</td>\n",
              "      <td>2170</td>\n",
              "      <td>1951</td>\n",
              "      <td>47.7210</td>\n",
              "      <td>-122.319</td>\n",
              "      <td>1690</td>\n",
              "      <td>7639</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>180000.0</td>\n",
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              "      <td>1.00</td>\n",
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              "      <td>10000</td>\n",
              "      <td>770</td>\n",
              "      <td>1933</td>\n",
              "      <td>47.7379</td>\n",
              "      <td>-122.233</td>\n",
              "      <td>2720</td>\n",
              "      <td>8062</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>2.00</td>\n",
              "      <td>1680</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 107 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      price  bedrooms  bathrooms  ...  zipcode_98188  zipcode_98198  zipcode_98199\n",
              "0  221900.0         3       1.00  ...              0              0              0\n",
              "1  538000.0         3       2.25  ...              0              0              0\n",
              "2  180000.0         2       1.00  ...              0              0              0\n",
              "3  604000.0         4       3.00  ...              0              0              0\n",
              "4  510000.0         3       2.00  ...              0              0              0\n",
              "\n",
              "[5 rows x 107 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TrFkt81bLWQn"
      },
      "source": [
        "# The categorical variables are converted to numeric ones using one-hot encoding.\n",
        "\n",
        "One-hot encoding converts categorical variables to numeric ones. Simply put, it adds new columns to the dataset with 0 or assigned depending on the value of the categorical variable, as shown in the following:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PSLFVDRfI1lh",
        "outputId": "a3a511de-d7c9-4f85-ac6a-7a5b032588e1"
      },
      "source": [
        "X = house_mlr.iloc[:, 1:].values\n",
        "y = house_mlr.iloc[:, 0].values\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 5)\n",
        "from sklearn.linear_model import LinearRegression\n",
        "multiple_regression = LinearRegression()\n",
        "multiple_regression.fit(X_train, y_train)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tYze7kLKMDIm"
      },
      "source": [
        "y_pred = multiple_regression.predict(X_test)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "porDzsCbOKEe",
        "outputId": "8c9c6184-fe21-4549-d043-34cc0bb90ace"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "rmse = sqrt(mean_squared_error(y_test, y_pred))\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "adj_r2 = 1 - float(len(y)-1)/(len(y)-len(multiple_regression.coef_)-1)*(1 - r2)\n",
        "rmse, r2, adj_r2"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(147274.98522605025, 0.8366403242153613, 0.8358351477235371)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7l_R5MNHOlsa"
      },
      "source": [
        "# **The steps used in this example can be extended to any example where we want to predict a continuous variable. In this problem, we have predicted the value of a continuous variable but we have not selected the significant variables from the list of available variables. Significant variables are the ones which are more important than other independent variables in making the predictions. There are multiple ways to shortlist the variables.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LocEv3ZkOPhR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}