GellyML.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GellyML.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "16zX-yyNjOx1_9kuP-RabletjsqZU0ke0",
      "authorship_tag": "ABX9TyNsFDzqG0OrUvgh/xxRUO8G",
      "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/RoyalSWiSH/User-login/blob/master/GellyML.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": "6wyWTnZYdGva",
        "outputId": "f2917736-89cf-477c-beca-f4f4b3ae9134"
      },
      "source": [
        "print(\"Hello\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUfn2VvA-mWh"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nc3uJyiYdKg3",
        "outputId": "d4e9289b-02d4-4dbd-fb68-01e082199793"
      },
      "source": [
        "from os import listdir\n",
        "location_of_data=\"/content/drive/MyDrive/Colab Notebooks/xml/\"\n",
        "listdir(location_of_data)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['11177ta-w.xml', '11177ta-w.png']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LWt670oYfHFm",
        "outputId": "5ad7c063-b902-45eb-8345-dcc3080f0830"
      },
      "source": [
        "!pip install detecto"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting detecto\n",
            "  Downloading detecto-1.2.1-py3-none-any.whl (25 kB)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from detecto) (0.11.1+cu111)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from detecto) (1.1.5)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from detecto) (4.62.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from detecto) (3.2.2)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from detecto) (4.1.2.30)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from detecto) (1.10.0+cu111)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->detecto) (3.0.6)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->detecto) (1.3.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->detecto) (0.11.0)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib->detecto) (1.19.5)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->detecto) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->detecto) (1.15.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->detecto) (2018.9)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->detecto) (3.10.0.2)\n",
            "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->detecto) (7.1.2)\n",
            "Installing collected packages: detecto\n",
            "Successfully installed detecto-1.2.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ZUb7QlffbaP"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from detecto.utils import read_image\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "SLda8Cn4fnVl",
        "outputId": "26e1068a-f772-462c-9a18-8e4ca4aabf5c"
      },
      "source": [
        "sample_image_path = \"11177ta-w.png\"\n",
        "sample_image = read_image(location_of_data + sample_image_path)\n",
        "plt.imshow(sample_image)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb96c52c050>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vn/3sZzE3N4dGo4Hp6elKv4X60m8jlyT9dnHQ4Nff9ra34ezZszh79ixGo1FFCvel9joGEgI4v+70ezweY2FhAddffz127tyJ1dXVWubAGVOoT0PlhxgTJTIJfeUrX0GW5RCbYAMHPTVnShRFbqRZKd3xZfZrRFQ3iTRNkaYp1tbWsLHRxezsHKubBdEqwNr20IKpEKRlwKl7CKCHwyH+6I/+CD/2Yz+GOE4cOqtnLdCr6/WukLZ+cD6pjXbsCVP2eDzCNdccxE033YRTp045feMzKV4vmj/cG8i20QJ8ldldfLpiwJ1SaILyZ0g1LopC2xCtVEDPctMOqUwnT54095TEpDqcFlTjJAakBPeIFL5DOkkazEeWSxNq0kRoNFK84hWvwOzsLP7rb/9XPHP0GfzJn/wJ8jx3eHodsPi0Cal2IXPCP/3TP+F973sfABivBv6sRBWkreuiqQFrS9h1kpd5/PhxHD9+HPPz8zh48CC+8Y1vOH3nr6Pwa3XmjFC/h77/8z//Mz7wgQ8gTVN8+tOfxng8rjzn19+ncWj3bSgPTq8///M/x6/92q+hKAr8/u///sT8XUbF83TNBX7ZXCqk7NrtNmZmZnDffV/1wMlnvGHfbHLxrGNm3JxA/bS4uIivfvWr+IM/+ANsbGzgf/7Pr5n60/O2/TZPl6kCNOf43gq3Dio/C/ICP/uzP4s777wT//2//zdkWebV2wI6Z1Z+nuoV11mA0532AUgpceLEcUgpkWU5/vIvP43f+I0PYDQa4bd/+7cDNCNmxTUh0kb8PR/cNBVeS7yU9LyH/L2UtFn4gZ/8yZ/EcDh0wMMHL/q+c+dOzM3NI88zM2j9SUXXCdjJ93g8Hhu1nQZFnhfI8xx5nqOUJbJxjrKsqva0eEt5cb92u3hjTS+pNokAioEMRyMzpAlQSylR5LmjRdT1ly+9U4r05ix6Ty14CWZD9F0ebR0IrELmEkCZwLjfekh65vVL09QArN8f/Dd992lMz3HA3Sy1223EcYxer+fk7QOuX6dQm+uYTEi67nQ6KMvSjFtert923rchWvA2c0CnInlejUYDo9EYrumBGI8FrpCW4iYfmN16EcAKLW13Oh2Mx2MMhyNGI6ofQAAaRS6oc+nZN0NxSdu3UUuptA3l2ZZP7E9fCKlqflxY8bUG1c4oUuN3NBqb+dxud5DnObJsbNro50F0V2OEt5NrKmFtrNVq4Z577tks/MA3pZR3hO5dMZI7mU785C/yARK7d+/C7NysWTilODIE5GTjKwoF3MPhEKPRCKPxGJkGeHKJzLIMRaEWVLLcnRz+QCLgFpHQoQV058HKIkIoJiClMs+QtBR70lWpJaPSkXCqgBaSJvm1OIkVABUlpPZ/DpkTePLL8ye3P3g5SISkeXqPS86hAe1LLpzR8vf8CRoCT0oUkyhEs7r3QozMZ1p1IELXiXn6QFWldb2ZDbBMNlQnUt/pd1lKjEYjLx/1XJ3kzEHaTdZtOJQ4KBVFiW63a8pz2w0A7mKiL8mH6GzfdUGdv1sUOZO4oeeVyzjofmicW1qENV2iI2neo9HYebbX6zEac0ZUOvWCWeC1G8Pi2GU07lyLnEXsS03/5sGdpGJa/CNJyZn0zP4rRIRbb70Vr3rVqzAcDJDlOZIkcYiXJAmSJEGe5xiPx1hdXcXi4hK63XX0+n0M+n30BwMMB0MMhwNkWYalpSWsra0hE8qUQRKxytSqfLF2t4y1vZ+7swkBxHGCRqOBLMuUNsBcH9V/CRFJRBqQQwPTB86KRKnXBSK24g8BRLB1rkjFEGbzFKWQlKOe9yeh+1zIm6kOMHlZXFPwpeFJNPABMVTnSddDz4UkqjqA8PthEsOr1gcAXB9+vvOUxn8d/bjmVLXfhhmtrXcEIWjPgmu64HXz21qlQRg0SRDQbzrmIf85qjeBKQGmqhcXj6yEHcqHCx+KNLZ9VA9LBq4RcDdIZR7yaVgFYbf+1AdEZ67ZciGUCyiAQJoqGFZee9bJo06g3Wr6Nw/uRISiKEyDkyRBWZSmbySsq1Gr1cJrXvMa/PiP/3jFRa5ODR2NRtjY2MDGxgbW1tawsrKClZUVLC8vY21tDePxGIcffRSPPPywu7tS8DxVvq1WC51OB3EcI45jNBoNp2PTNNWqs3LBVJpBgaLI9WfpgP5wOEQ2ziogEZpkPBplHCXKF0AIRBEgJYVgsHTlNIbUDEuqHbKuZGefqyu7ltEEADpkVgkBEAG8b5cPMYW6utSpvJs9F5LYuV069Gyojb7mUTWvuGNStTnStlpXiAmBWXhs2zEJR/CpMmm1UG6lZC6Z0pi2jFeavH2mwSVk66ViNQchqvVX+VgtEIABNlU/y3w4bblUTPXizNB+V2VbBmHNLL4mo2hP6xOS0UP95msXAIzAqHbjUhgEty1W87RMi2hMtnwhpHHFDm7AvIz0bx7cATjSLY/NYbxSCjdeCVfTtpI6nQ7a7Tb27NljyiCpPssylGWJ+++/H2dOn0a/31cdxqUV3QdRFGHXrl247rrrsGPHDiRJgjiO0Ww2Vb5SotVsIUkTjIZD5HmOfr+PwWCA4WiI0WiM8WiE8XhsyiYmI/Ow6YBSHMVopKkKlhaRLz+XZl1wU8xQGnsgByKiAS1S+VIj/SbNhAOemXRQMXaM/z7IxbQGqEppYu7USfa8jyvlBcrw8/CTEAJJkjjhAUJAzbWKuvzcAFrVcnymNkkT4UC4GdPkgFtHl61oLDx+S325QEiS1znwqxpMLQMoSysAuYyBnld5uABJ40w4efOyqP3qdE+3HrwMKt9tB1+QdiVx/7szzkybJGx9qqYtwiolXPGx5buK2rqRUBjHcUWoudh0RYA7V9PJFg6E1f8sy3DhwoWLLoN3HpmBWq2WuX/bbbdh586deO65E+o5DST8/SRJ8Ja3vAV33XUXVldXkec5ZmdnsW/fPvMcdRpx9cXFRSwuLmJ9fR29Xg/dbhfr3S663S7GoxGOHTuGhx9+WNn3ynoVP0kTzM3Po9FoWF9/5hXEGR9fUyAtoSgKlEWhwjKUJUajEdbX1ytg7AOI8etHdZWfmCsvuyxLFGUBySIdUv9yxuqrwJQPAaiv5lJZspSQQhrGT/0khNBaHsBVZ66NbPVAGF+DCXk3TAZJ+54vnYe8c0L52t9AHeCGAL3KVHwtjj6r7p7+uOMMj9PQr5MrTVcZCC+3WmXfDTMyQG1B0QV8Xl+uWfPn1Pui0l4X7ENrEU4JBvipL6vtcvvSMg6745vK061FkijTLddiLiVdMeAe+u0PNlKTTp48idFoiFar/byVT9I0DVpfQgQEms0mXvnKV+K2226r5BHqJCklrrrqKvOdzE8EvFJKPPDAA/jQhz6EQX+AEmWFFoAa8PPz8/jBl74UnXYbU1NT2LFzJ5qNhvHgoTrEcWw0ho2NHkYjtZg8HI2QjTOtqRRYX1/HY489VnGP88tvNBpGM4njCEli/edJc1HhHhRTy7IMo9HIAXP6L4rCmMjqzB+Ud2insg8cNmSC3psgoTalwUpSQsDQhOfvt5dP0jiKTZgKP3YM/+4zKH+sCrUQEtxs42pJwmymCYGuv7N4M28iAiJbhiuBu+AWXiPx21mVyOu1TARs2vSclFLvMHclaC7tWmB36e27fXI6qnYLlKUNc2zzs1FjbT+EaRZqo/+bM3zXpKXa4dPFMhR1m4RLJTh9j4M7T0SoClZq4uR5jmPHjuHkyVO4/rrrlJligiSz1bS4uIh+v++YOsjeTxxeRAJra2tbLickkfmmpKuuukpt/HHURDf/KIrwohe9CP/X7/wO5ufnkSQJms3mxHoURYHxeOyAKw3eoijwd3//9/jdD3/Y8b7gn5T37OwsXvSiF0FKtaGl3WojbaTaHNVCp9NGs9lEu91Go9HA2toalpeXldkpz5CNM4zHYwP4586dw9GjR009eFk0CUhSb7fbkJBq8TqOEBubZWTWZpT0ExnPJO6BU5aFmXTD4dB4P9T1EzGMOEkUUzAbUexznPmo07s8E5HmLIU2JdIaDvEpH+jUe1WzQKgv6sCVA7n/Sfm77QU46PL6+4yLt5cS1d3XslxQVcBugVR5iRCDI1pb5uPWkYMeVZOfblUnTLnv0vOuJkNMR7/l0JbaygUmfx0lxNi5tgjj8snrpessbVkqyN6lm2auCHD3B7NrS5Zm0pDk/vTRo/j0pz+NW265Bbt378aO+XnMzs2pSHTNJlqtFlqtVmUBI1QuddqxY8fQ7XaR58zHXQCC7dvPsgwnTpzAaDQy0Q8vJ0kp0ev1Ahs0UBlMU1PTmJubw+zs7JbKpcXeunJfdMMNxuRCIEvAyifynj178Pa3vx3XXnsIu3ftRksHQTNAqMGVu58SUyHJvSgKDAYDLC4u4uMf/ziOHTvm2MEpGSYq1ML1LbfcgpmZGd2fbS3tJGbRutVuo9NuI01TbGxsYDAYYMTWNPI8R5bnKPICzzxzFE888QRra9jun6Yp5ubmHNMX0ZPU6WazqTyi8kxF/tPWAKJLQeFe9Xjp9XqGFnU7cUkLAtywwb6gw0MymDWp0tX4aJ740q9Pa9/kxMcbr0dI2+H/dM3ubLWukVLykMd286A7vlVr3HFgf0eRWz8CS554+0jjUmMJzvPuc1GwzWRa9bUnQIUZkZqHk4nR0kC1WdWRDiNxmTaZen3N9FLSFQHuIRWLkqOKa3er+bk57N27F3meY2lpCd1uF43z5xFFEVrNFqampzDV6SDVnixZNgYFTIojBXqNZsNw6DNnzuD+++83i6mqUqpECDsB8zzHkSNHcPLkSbzgBS+otONiwb4sSzzzzDPodrsVe7A/wFdWlnHixAnceuutwYXkiymb2kyBvrgkUpbeYNQAMDc3j87UFPPxp8kCs2OXJgW9GzOtKi+KSmCxUL1owDebTVx77bW48cYbsWPHTrTbLRNSgdYdGmmK6ZkZNJtNbGxsGA8lYijKPDXA6uoyVldXgt4wPv3a7TZe8IIXYMeOHUZQEELtb0iSBM1GA61Wy2zYGo8zmAMZCBQ0uC8tLeHCwgUN7uHzYancVquFvXv3qrGpBQcV9yhybAhSSuOBReBCWss4y1DoNZbBYGCeC7WTvkdxhCROKmYI+ufrLWTeIkArpYT0XH25CcplnNWySYLmQ9cwlEidz8DzrhPQrGYqnXyrtnorPKRpivn5eayurlpaaMBN0xSA9YYxdBOMoRaW9i5dw2Yrn7ZxHBsB9FLTpuAuhPgEgNcBuCClvE1f2wngMwCuA3AcwJullCtC1fAPAbwWQB/AL0kpv3XJtbN1qJWuHYkEEnGc4M4778TrX/96I5nySUPeK3Q6k5QSGxsb6Ha7GA4G2Bh0MRyNMB5nyDJlLlhaWsLi4qI7ESIBISVkqesQK6n0sccew+c+9znceOONmJqaQqvVQrPZNP+AGlI00eMoQqI1imazaQaQlBLr610cOXIE/X7fjEHflkffl5eX8cADD2A4GGBmdtaUS4DHV+ClLI1ZQOVpB3xRFFheXsajjz5a8SLx1U4A6Ha7ePzxx5HnOaanp5VEq8Muq+BtMdJUAdLU1BSyLEO/10cUq0kSCbVhazgc4umnn8aFCxcqbePjgFJZluj1ehgMBmg0NpDnmWEWtCciTVJ0NzbQbDYx1N5JkrlNqIXjITY2eoZxh8wW/jiM9WI7Segk9wkoJsXdXElL8ccxrXsUWhN0VfQAyEYRpqamMTM7jVazZQzDvL60vlGydRujIUnl/TQejbG2toZTp08DnnbEAcaYBtIGZmamEUXWi4Pqw8cBCRSqvYXxwiKAI+8zivHDx5UqE5XEA4r5AgHFtCEmzYUfkvQpb9LWuKSunrFeLpLNrzRVZsUsy5A2Gsyl2GppuvbGbES/6YMYGW8jZ1Tc1KS0FrftnMaXmrYiuf85gP8HwCfZtQ8CuFdK+XtCiA/q378B4D8BuEH/3wngT/TnZSfXBumZZLzn4jjG9PS0MVFsRqBWq4Xdu3cDgJmQRVFgOBxiMBhgMBjgkUcewZEjj2kOrUaDlNJofzRWRqMRnnjySZw+cwatVgttbQLqdDoGaLlaRtyf1Hy+wer8+fN48KGHMBoNjW0/pMWUZYm1tTV88wMEh7gAACAASURBVJvfwurqqjFVkJRHUhX9JnVeHRIujMovNciePXsWTz75pCNJ15mFut0uHn/iCXS7XePfTwyK/pM4RpKmaLVayPMco9EIJqxyLCBLidF4jJOnTuHChQtViUgnDj7j8Rhnz51DkqaYnppCmjYUk4qs7Z2kd3J3JE8gmjRqR+cQS8vLWF5enigFUn1o05sQQJKkSDTdaG5GNCmjCCUHHiEcQ0FRFFhdXTVrGn4beZnEDHq9DVWHJoVwYJKnEGoDnQY/azrUErZmduSllDGmw00nPlOLogjNZssIRFFkTUOktfHNes1m0zA7bnYjt167hmOld1ZV0161TtPS/WkZCi22crMiaWO+ACKlNGOJBJWQsGC1BtcEk2WZPr3N9pnNQzoH+UgpYRqOasRSXi+rQXBzle0DinLJBYNLSZuCu5Tya0KI67zLPwPgx/T3vwBwHxS4/wyAT0rV+n8RQswLIQ5IKc9ecg1Z8iW3kCvkeDzGF77wBfR6Pfzoj/4o9u/fj2azaaRoLsXSQOHvEwACwNzcnCnrzjvvxNe+9jV0N3pG2vQBQAiBgwcP4tZbb0Wr2UKz2UC73Ua71cbU9JSWNhoQAkaS7PV62NjYMN4rWTbGeDTGYDTEoN9HaY4Y02UwF0w+EQeDAY4ceQwnTz6nJPZGQ/m9m8moDgw3UgebUQaQhECWZVheWcHxZ581G8dCEi2pu71eD089+RSWl5fRajYNiHF7LDGXNE1B9t44SYwHDXnRnD9/Hmtraw49aeL4Jossy7C0uAhIaU7H8SW8Tqdj3CqVJEs7gJUpo9R12VhfR7fbdfoyZB4ikFheXsJ4NNIHcSgayrKE1O2ODfMoUZQlJC1oQoE/0YDGgD+GQiA1Ho/1hjrFGENqPZcu+ZiOE7uhLc9zrK+vOwu9lEK7cmk9pCgKs37Cx53PyCn5brBRFDlASdIyyUoAP0uWhJGms65h22dR1F8348ybgDKs8Ztv+rvFE9J6OP19uvjXqB944t41vE7U5pDgAiiTDgmZk8yUm6VLtbnvY4B9DgA5cl8N4CR77pS+9ryAu2+bmvTcmbNn8eUvf9mozq1mC41WE01tHpiensb09LT2J1VAsGPHDqPu0XtFUeDChQt44IEHtDcFtHth5HByKSWazSbe9KY34a677nJUytAEMHZJxqXzPEd3fR1dDfaDwQBxHKtF2uHICTXsp0ajgZ07d5oNWe1225h5lAlGGikuz3PkOvxBkecYl6X6rU/HyfPcmJA4rUPSbFkWWF1dwcZG15yCZdrJgD2JE0SRMO6OlB/RTjESFYCKJy5JRSIy7qBZlmFhcQHdbtcBAPK6oHJ5OT6DoHIHgwG63W5wwvrtVX3UxZgFkKJXfKmfzCG0QQt6QU1qtX2s+4DnT+OFTEl0rSgKvbg+NgBbYbiRYB481gOD09AePBEeSxy4qb29Xk9rWxaJI7PYiAqzIYD2hbEBi/PjmyUAiuGiGDCZcbh2ahdeS5a/kqD5Ii0BP80vrh2p/ASsx4prluGAXAfuNp+qeZR/t89A0ym8puMz6rzQgQrL8PrPVtNlL6hKKaWgvb0XkYQQ7wbwbgA4dOjQZs8GJZpqZRQRr77matz58pdjempaqXJpYjgnmWzabeUDT14a3fWu8m7QoEvPj0YjnD9/Ho888ogZbEVpY5/zwTAcjfC5z30OeZ7j+uuvV+W0WspuFwB50h46nQ5mZ9VpLvv27XOk05MnT6qtzqiqzJSiKMLBgwfxxje+UYG6Nge1Wy0kOnIe/ZNJYzDoY329a+zDg8HA2EQBdbTYyZMnncHom0qEEGg0mmY3rvFrZ2aRJEmRJLHxYBkMBuj3+45JIM9zFJrBnD/vSu687FK64SQEhOkvkirVe6VZDLdmitKc0tXr9TAcDrVtWK0/cKDkZfrXpVQmpDzP4e+cJKCg76Usze5pf6CSacw3N1JZPuhLqYKCGe8bVj+lzUGjiKvdqXxtnYje/H16ldOdyrTB8wpdbtXWb2hGC4qwAE+J8rFt5S6FrhlIShWNlbybJvUNpw//zu9xMx9pJ+QJw9usyrH9w/OgZ/izfIHW1xBU27SmqC9TPRRdqu6ivB1qLcGNPnux6VLB/TyZW4QQBwDQltDTAA6y567R1ypJSvlxAB8HVMjfSYVx9aqOSwKA1AGGbrrxJrz2ta9FmqZKLTfb8SOzCk1SXZZl6Pf7xjyy0e1io9dDv9dDX9vbSSoH1OAtC/cMVSMplCWefPJJ3H333dixYwfa7bZeeGshTRNjJyQJl7QEvuhKrnRFUeD06dPqtPW+DVfrDwJKWZZh165duPnmm9HpdAzYUpuJkRDNSGMwB4KXpdqhWpbo9/u4++67cd999zllcOZCfTE/P4877ngZms2Gts3aHbJk4iIXwU6ng42NDSwvL6t+ATDWR5WRffHBB7+BhYWFWkZmTA1xjNnZWSRpilQzFUEumEKYRTFacxiORsYM1Gw2HZsmSaAkKdeZZKj8VNNWMWy1lMrNQcS8rbeEXbijvKQEytJdDBRCmMU43/2V8vbngDGlSKuZGayU0NqeYm68XX4buazkMxurWQVMQbQT2NMifNr5QOqXwz8BqWnj5uunELj7yQd112PGbQuVLSUqtKc83PpW/fwNkyglokgiz0M05/S1v3lbVCjhyd5jm6VLBfcvAngHgN/Tn19g139VCPFXUAupa/J5sLf7HewDPJfs8zzHAw88gBe96EV4yUtegp07dyKNUralNzacNooiA0BkXyei0gr7cDjE+vo6Dh8+jKNHj9rJaBfAwVXB6elpzMzM6LNSYYKCkWRJG2nIPY17VpR6Eimb2xi9Xg9nzpxR0hpT/X2QLcsSJ557Dh/96Edx4MABzM7OotVum8VEarvyWkmV257Z5KMZTqoWB4fDIY4fP4577rknGHudMxcp1QJslo1x8OA1mJqaMszEaidqEY7Kmp6eNsyV1jaUa94QzzxzFL1ez9GGeJt53zcaDRw4cBX27N2jtCMN5rxs2swV63YVZWni4wMw8XuyLMPhw4exvLzitDWUGo0Gdu3aZctj9mwF/CkazYZ1C9QMM/aAgfz9n332WdcXmi2s8aRMh211ZGQcKenckfisq59ZXKSAetIKSARw6r9e7edzyweukNTOTV7clELX/DALfvIBH97OVVO+Zia2TKHbXmNm0ruTiaEKwceTXcjk9fD/6Xm+iGznIDlHqETreFmWGa2Nl2d35trFVF9ronv+eszFpq24Qt4NtXi6WwhxCsD/CQXqnxVC/BcAJwC8WT/+JSg3yKNQrpD/+bJqp1NISp/07LlzZ/H5z38eX7//fszOzGBmZgZzc3OYm5szJpmZmRns2rVLTX7tjkj+yVEUYXpqCmJmBlJKsxjrqH/OjjMrTd5yyy14yUteosE0NZJ4q9lCo9lAkqQG1Hu9DcVABgMMtPQ6Go0wGo2M1rCwsIDz58+bcAe+KUhXBpAS/X4fp06dMi6Qyn0tYiqj9dThbnKAtTXSQs7KyoozufguR07rlZUVfOUrX3EWNQFvIY0BvjJpcekn0gM5Q7fbxcrKinmf97cv5Q0GA5w4cRyDQQ/NZouZgyI72YTrQULSrw3KJJBlY5w7dw7nzp11JOhQe6keURRhenraeD/xcSGEMAxDSmVKKkupF8ZdhkzeMj4Y8nby1G6r9ZSYFo9B9m7XXs/zo6MeibHRQu7Cghvn3i+Pm9bI35oHs7L9BzO2AOZRAqni/EhrQ8+yvHKGbUjaV/1kPXD8OtH3JEmMV5LZzWwzMfnaxUnXe8UFeVt2RB46kVevKELheeZQXpSIUfP8OSOw/WO9ZXidqA5KWwqHG9lq2oq3zM/X3PqPgWclgF+55NpMSP6gouSrT0IoO/Ds7Cya2u1v0O/rTSPLSJNEg2ziclltn421+xyZSPr9AZ555igeeOCByq5JOzhsPI7p6Wm86lWvwr59+5AksXH5C/ms9vt9FvK3MItmy8vLWF9fV2YVIXDixAmMR2NTJh9UAgIiFmi3O9i/fz86nY52I+ugaSTIwhnYmV5QLYrCmGNII8myDJlezCKg9dVi/lsdZWgDoXFfYCltQC+hUACZtlfzNhDz4HZLd/K5JgIh1ALj2toaer0N7bUi0G63jblJaPuworuyWviSI4FvkRf69KKwqYC/MxqNcPr0aZw7d063CUYy9mlUkVR1RTiTDh3YTSYQLjVmWYbFxUXHO6TuWDxH8oQwcXDIxj0aDR1ahjQkSiSIJHGCTFgzgYREhEhvUKN+dqVcqksUxUjTCONx5tSXax4h5tJqNREnCSjMR5ooM+ZgOFCL1NSxqG4AA+xJZna8qbpWvYLYeNDjMEfBGFfIjFVl6v5zLrBrqklbJm3o4jvuudBVt+i91XRF7FCtc+b3zTVExJmZGVxz9TXYuWunAek4TpAksbEHk4Se5zm63Q0MBn1jhx30+8Y3dzgcYmVlxUpiJF2RmYRJ8FmW47777sOpU6ewb98+TE1NqZgqug60g5EYC9nbp6amnI1O8/PzmJ6exnPPPWfVdm+RxxJB0WHnzh14xStegb1795o4Lu222pIPKSGiyEjXChR7xuaeJIlhLEtLSyhLiUcfPYwzZ84EJVgOCI1GE7t27TKSlNnkEscQEEj0WkOizTDD4RCD4VC5DurJQQyGaE5H4vlgyb/TmgVfxKW+obg6JG0SEIYkrrIsIRNr55yUfCBU5HfNRWqiUiwVtVjIcUH1IZnILUC7eVTPlaX8iKFDQPmsB2zlk5IjGARAvcL8Snen5SS6TCrf0HoCGPJ6KK8eT6oXMOaozbT5kFRNJhG/DoEWOYAfameI+YfcUHke4XlULYczve+q5P6vnbh673NL5zntISCEQKfTwcFDB3Ho0CF0Oh20mkoC4PE/yIRANvXBYIDhYKB3p46Nvb3f7ysgbqROhwmhy9N7E+lOnufK+0a76BkA0uXT2alOIH9IRIJOcFLtpO3pTz/9NIq86rHCf5dlifPnz+Ohhx7Cddddi5kZu0M1SRMkceK4d5L9n5jA9NQ0kkaKsiiwtLSE48ePY3FpMbDDzlWjhVCbTa6//nrsmN+Bht7owmNS8/80bSDLxtpTpVRSLKzJgMIeLy0tBUGIpzRNsXPnTrOOEnI9Jd96oicBlR/XJo5jrCyvYDAYmDLrtAZap+GuljZOiDIzkeBAAoHVaCgGuNX0/GBmdW0mhhmJCFGsFk2NpqMXTqn9pdQLrNLa8Plzoc01zrhmYFSnMft1AwJn2+qv5MLLmaNflg96gDVTmXf89nj5cM1xUj1d7dBK7mSiIYmdj3Ou5ZgyWH38OnGN1K+Dra/KxKeD3/5LTf/mwZ1zfAFLWLoWRVHF9avVapnNQzMzM5iZmUanM2Xsh9wfmxa2lJ95huFwpBc5x+j3B1hfX8fMzAw67U64fpDOsky73VYmIW2/V4uaqQZZ67GSZznysrD+5oUCuLE2kYyzsWO3pRSSkKRUC5tPPfUUTpw4oZlX6jAL2rkZ08YtDax8RyWk3Sxz/vz5oMThD+D19XV85ztHsGPHThPzhP9zF1Qy1Ugp1YYqBhxZluHcuXNYWFhw1rj88nyAmJmZUQCvTVgQQu+8VZtmiN5WareTmZjKYDAwYQ94Ozmo0LU4jjA1PYVmo2nGEe8Pbvu3knt4EVxFouxXxlQQ2KMILRZOgrsd2gdhTEQ+s+BgQ7uv68Iq+xIo9SHXoDkw8foaQCL1BHCYqb9exCXUkGmmjiZEa7dvw4JAWUoURXU/gf4FWotSbXSBPKSV+N9D8zGUqgBOv4lc9jfXci41/ZsHd0pFUaAUJSJptyPXqWXNZhMvfvGLcejQIccFcHV11ZgfSIIngI8TZTaYnp5xylxZXsZwMJwoEahKqME4MzOLvXv3otPpGE+cNE2RGKBXJCevHDpLlWzveaEAfzweY6O1gbNnzxqfekoh9S7SZg/rNWJ3SgKALEsU+h1u4+fMkyZ+SJLkA57bxik429raesUtkCRZDg48fxrg1BSih+/EEJpY4/EYZ8+exeLiou5fgGwUUSTQbLaQ5fr4Mr3hpSgKD6BK5HlhtLT6rrW0yDK1iWnUGFWkMBMgTNOB0zWKIrOhidrB3d18UPXBSgkhoyCDo2shrxYeyI3qwg/vDkmHPlMXmkn62rMv0arfOjCYBkk6x7coC2TjzBlbIZMG/x1rTYWbPnm7bX0CoZV1Un1AwG69VeyYck1TvlZj+8ICcIiBhISuEC15P1v68fftnPh3A+7U0ElucoACm3/5l3/Bb/7mb+K2227DoUOHcPDgQczOzmJ+fh779u0zoM5NC/x9KiPX7onXXnetjlPjcWrdF5JUYClNNLmdO3ei3e6gpXfFpo0Gmg0dPEyoLcbjTAUny7LM7BBVWsQIw+EI3fUujh49asrwTQUAjL28kaaYnp5WPvWN1Phi+5MmjmPNRHKUhdpkQ65iBO7j8djENq945sCdPCKKkKbku2/tzVzSI8mdAFZNcJpENmofry9nJL50zCXRvChMR9hqCeRFYZiiYURFrtYfhKVJSELyAcwH0vFojGycmfUOO/GtVkBrANx0AsPMql5PHCh5f/Fys0x5nPjgUlf/0HfAlQr5ePJDehB9SAAJpTqtLpRcpm7pZN+PALib2+oYmd/ukATN+1Hd871XAF5lC+wuEyBBhZfFNQ6uQfB6cQbm08ivLxeYKGTIvwtwD3F6/7cQ2gdWKo+Go0eP4vTp0yaAVqvdRrvV0gBoY7rPzs5hx455zMzMYH5+Hnv37sWePXuMi6SUEo88chjnzp0zkRSN9EoStQb5JEmwf/8+zMzO6sGZYTwmYCnMLr+yLDEcDJDzwS4EGmmKRtpA0W4jbfS1z3fYF5naTao1xRnvdKbQaKTG3h/HsYmDQyYY8r8u8tyR3nn0vvX1dUcio3b7Ay7VUTb5wiYls6Cp/erJDs2PSqS+JRDhYZX9T1/S83fE2slhmQxF/3R3EFptoSwL3VdRZaMOT8SsTF3iyBhF4tj6Kjump4AjgJTM3CCBUoTd6vz3QpKrf88tJ7wvhH77zISu10mmvqbIn6dUF8fFf5/e9ZkbjXUfEP06h9rsl+0z5xCG+O22bXfdSkO0pO+hZ+roqvqenwRF2pQNvSCEwPT0tNkLczkAf0WAO1AvFTjXmdpEkpPaZJPpU5TcxT7a0ShgO4pAkaT78TjDhQvnce7cOUVoIUwgKMAdtHme47mTz+HQoUO45uqrMTc3ZxYx7UEhbTSbDdOxWTZWTEOrsmVZYn19HRvdLqanpvHAAw9UBi1PJG3xcL6StSeKIkRQUpGSoG38balPcKd8CWB5nJWQ9OHQX3vhNGktQyiTRBTZELRUbhzFZvMWz5821NDidoiJ87bThOUMzE4+6PLdaIXcW4Yn0iKUVFyNHCil1OGKOyhLiUF/oGK4xG50RCu5W43Flkfqth2nkEARKQ2Kj2NeNvcAUsw5MtoCZaR1AVuE/nTAhUwPImwfrtMaOHPayvyr0yjqxq/fz37iWmcduPJnN9MgQoCr8nfvhxhCXftCEnkdU6VF9cjzn3dOdwOwsaGif1Lc+EtNVwy4U9pURYuUBN1sNZEmDSSJG3+aNvbYCagWWziX5AHBKLCUuS9lYK6qBd08z/H0U0/j/Lnzxr3R2Ny1jb/R1GYaFqCsrc89pVC9aZpiNBziyHe+g1OnT1fc5UITIUkS7NixQy3m6qiQZlu+iIwXC0nuGQUKKwqAAftwNEKSJJiamqrQlqujvC/iONY+QzALmSCpn0gluceTopoCI2lU3pAKHipXXYRhGjwiJOXDwZ2bSDhToXyLInfa6kteSkNIUBRjSEgksdUYeL3I68mCQ6kPrNAAaryrdNsmxIUK0TlijFnfAKRkcWXcJVZLL2sOkmxtgPLeitbgAFZg/BvzJO+fwPmf1BchOvOyN5PU/edD30PaRjgPPa0D2lZISwmBfIgBuEICkKZxZT3Lai30LI8DX1vlLaUrBtwnSQh+ajQa2DG/Q289V7ZgAptYe4YoSUgdeUY2xSIvwON9EKhalzc9QfQkJVA3Cz5CBXxSR7r1QRuYqAP5P+XnDwr6pM0ja2trFUnSnxRlWWJhYQGPPPIIZmZm0Gy10DBumCnS1PrVE6OhQ6vJXZAvPC8uLGBhYSEo6XJVl2gzMzOD6ZkZfUhHDAovnHhhX9M0NScuaUQyGgMtdC8sLARpU5GiIoofY33rCdQBK3Vyqd7pQ5pgnEExCZZP5uFwqAOquagmIVGUhV401Osuxq4LABGELHXMI2nkbMqDq/W+21/QDc4DTMUwoA6N0fcRMYAn1FI/DIgQkPltnSR508KmzsrWQQM7fbIbRnvx+9DvX39M+X3vMzouCU/SDkL5hTQVP1+enHqIyLichp7h5ftzFoCJF+QyXbovnd/SdOilpysC3P3Y0zRggj64+tFmU+1SVTGhrY87xVCh99WOvRGyPEc2ZgucJN3mOdI0xeraGrIsNwVwkFfFSkCbSMgVjpsFjPTFXDk5eNIEpwkhpd5U4w0Uai8foMq8o3YwLi0t6XK03VkwlR5qvtOWf3d3oBpQ5NVAcWW4TdFfnBVCYGNjA0888YQxY5F3hnoAxlUVUFK98NpAtCANiZ+IZLo0MIHJnS9JUic/V9ugyH32UAkiAtWpKHJs6A1doQnpg20cqyijjdT1dSfaRlFsvWM85shBjOrvg1YIYOhanFDoBKUPiUgLGGxxOpTsGFNeJXkOxwQ1SUrmDJLWmOxz9kBrCIHIKYvoUTWt8DL8fiYwrwN+X7vzxxGgy2SMNjTXJpmBeL7OrmlYoYDTIWTvr7YvJKlXNQuq1iby65bSFQHu1NCQikTJEBw2hrMBKGaGSfQCYJqqzTwSaqAXTFon/2cVkyLDwsIFJGfOOIJQRVLR9Wo0UhNHnXyvyY+cvhOQ06e/DV39ziFlbBofai9vN71HE74sASEKo6qXCC1WKVD3A06FJoOYUA/FFDML4sLuOwhJOc4uX2G/lmyBeZIUReVSzHsOThFJsxwQUFMfCeMp5IcB4OVwCbEoCvQ2ehiIgXH343kmaaI1wLK2Lb72QC6iIQ2D053C39bRg5ug/Gc4DerCFvB3+FhU4Y2rB3cL4ZpXJkm0dYyujrn5wpC6z5guac0BiT70GWLcXLipW5D1+4G/T6liMgzQscq8q7Tg7fx3BO71W5t9AsZxjLm5OXPEXlEq8wYFLpJS6miBwhxnR/En+K5RQZ4eIqtoDkZq5sgkCNybJqiUs3grBOKE/HZhmAkd/cbPvaSVcloIJpcwoB7kzclSwvp124lhVT3OCGiwKeGTJF2t0RRVgODfHeDhC3qsTkIIe11/UgxzmpxCWskmNBn88kMTzZGuWP1oAZJ+T9II6sYX0cr/LYUkrz2TbxzHGA6Glbzq2kV14iEe6oDSlkNn4BYVTSq0o9gfN5u1NVRXn9ED4VOb/HI4bepoEpLEeVs46PkmHp5/CNz9OhFocgGL14O3rY5mlMckgafax3Sd36NJ4V4T3hy6nHRFgPukhvpcOY5jvOxlL8NP//RPo9/vq0OIte82HURBduE0Tc2GDgo1Ox6PVZhWDbbKZGN9mimR2yWtGPJ7pK4nidr6r+KrRKBDhimVpT2GjU+iolAHV/QHfWfjiCk7ALAUQpdA3S4gW4nHbq4pDMPi0gm5BapF5gKirNq6eR3oPxKRsQUHn9OnEJEbWFl6NkpA2Y3ZP73r/w6BnhDCkUzNu1DBrVinVaR4AuW62Nl+XfzyObj4z/gARI0N5VUPSP7mJlcjmCTt83z8Zxw6BZgor3dIog6NR34vJCnX0Y3e9UHVp4Nf39DvUB/ZZ908654LuXTaelbrFErupsc66d2ts73MNetLT1cEuANVSaGOWxZFgVOnTkEIgR/+4R82O0X5ealW0imwvLyiY8v0sbCgbNZ0Us/S0hJGoxEOHDiAxYUFDHoDA2ShnWzQ0gXZM0saRBFNfpIWACH0Ap7nxWEbZANP+SnUdghlDyZzEy3mmvtw7YghG6Q1EeXm2D/+XmiSA8oWbNcWoGeABXahJRJbNpWrYrJQHSiWPf32Gabf9sp9ukVmPG2XBmCCbJHWoC7avuF5+mX44BWSNBUzcVX9YPJMjCGA4P3kJwIeX1oNgVGd1ExpEkiG6hS6VjcnN8uP58mZus+Y/J3hfFNbiMHV0n1CHbZS75BGU0d/OrtXRScNC0ikAbjvutp1aO5fTLoiwP1i1DpZSnz729/Ghz70IVx11VXYs2cPdu3ejQMHDmDnjh3YuXMn9u7di/3796vNTa0W5ubmzMERPD8KcLW4uIDvfOc7uHDhAmgnqipMf9caViNNzYHclFfMIkGmaYq00cCg31fb3YXyKiGNwQCclMbm74+z2kkjoaXi0vEeCUnCJCWFVF9AoijCdmL+LF0jLSHkjhj6V9oB5UemIFsPYowhKZLTAADTTlwm5kjTmrkUZaEXH20+pVqYcN71F+v5xDQTlDEFR2uMIggJZyHPoZ3nMhj6nAQ2IQcCX2MIAU4I+FWs8Ko0W5dPXX/UMbxJ2oXfV9Q2n5mF7fz1bQoBLmcQvC/9dQk+BuvmCeU1WTuwO95pTqvquLSgUNm+kGXbfHlSO3CFgHso1RFXQvmtr6ysYGNjA88++6wJsUu+5nyHKvmXk51+x44d2Lt3L+bn55EkCbrdLv7+7/8eJ06cUDZ5kjpNaVb1bzQbuOuuu/DSl74UBw4cCHrKkJ/5qVOnsLCwgIE+lKPX62F1bQ0b3S4GgyHyXIU+GI/HJqhVqO10nbvy0W/SVvguVPpNwKoGn5roJLmHdsZNAg0hdOQ/CScWCLf9E7ATA6K6U750nqkP0JP6nFRXqVFKeM8KhaUQQgVII6lICCU5RVFk1jz8dtZpKQDXxHT7jeeNijBJxzAahlaUxjTle1hxs0fmkwAAIABJREFUphICQ9sWW5YPjBRSou59PxFjFaIKsiEp2BcUOLMOMSJeB799/Dn+PmeqobEXYh6he6ExGmKe/v06JsDzmrRjlTONlZUV73oYr1zmLwDj3eXa6i81XTHgzgnqfwdcSZEmPJlI6DOKIgwGg8rOO/6vpNDYHACRZRlWV1fVrjEmlUkGCOT72u/1cc8992B5eRmHDh3CzMyMOUeVIlV2Oh1MTU3h2muvxQtf+EJ2WlPD1Jvimn/rW9/CsWeeMWeK8rbzdqdJimuvvRZ79+xFUZZI0gSp1hScCaQDV/X7fQxHQ+S5DUGQFznyjA6bVmsDFHq3TloCiGFpX3IBuxbB7qtPgShqaG3EMwmF/KcDzIS339KjhJTKxbJks4HU9yhS5hJ7S6IsXVMAB3Jevv/dAR4zFpQ7IIWjKMvSeHHQZJeQ7HAJuC51tLAcACM/1QG3H91xK+/WgZ9vBuHvb5Z36BkumfP7id4dzcc1F0DqJHGe5yRmslmygOpqRX7ePHGTmF/+ZlqVTw9+jSR4HoKAmMLlpCsG3EOd5g8MdwC7EgYl/zkrtVlOyaUJWlTlk7ooi+BEyfMCzz77LM6dO2fijKepCr2bxIk509M/rKPVaqHT6ZgjAKMoRq+3gSPf+Q7Onj3rgE9lAknSVgocuOqADljWdiJEktmEQLssSmR5hjynGC/SRGQcjUdYW13DV7/2VSwvL5syQ1KaEAKzs7M4cOAAhIj0yVN2ExEtLFummRgX1ZCPP2k06+vrEwEDUNIqqb980ZnqRVoLLWDneWY8dUhD4W2zR7FVd8OGJD7y4U/TVHk+eXSxGklpICSKIqP5kSRfljZSpD+efInTARDCJlZHuheiWd38maShhCTckFYRystPvuRO1/w2blbnrbRrK9oL2DoQvRPKj9oVMsdMEkD8evCxqW677/n52+cuPW3lDNWDAD4JYJ+u0cellH8ohNgJ4DMArgNwHMCbpZQrQtXuD6HOUu0D+CUp5bcup5I+wUNbhDlRFKgQmNkNRfxdDgh2UMqg5wRxUan/KpKkUZV1uILhAKPRqKIh2IBWipHww3F9iVRJ72MMBoOK7dYHmaIocPz4cSwtLTkLyEmijg2kwzpodyoBPT8gm9JwOMSJEydw5rR7ClMd2OV5jrTRwPzcnMNMuN3fgHucKO8gqWzdQqhdwmVRYpxnOH3qlBN9MMRMqC5xHGN+xzymdJz+OIlNP5g9BnoDlRA2zLGSpGEWsomBnzt3ziyA1SUqVzFg1bftThvZODO0UhqKayKTkq9tWGmxKEqsra0FT2Py7c/EsHjIA1NXAgtPCveB1ox1SKOlTQInus6PSlQamuCqkGOuIOAy7TcykbpfF+NHMvs/B9TQ3Od15mXz+vug7Yc84NmSFC+0Bubvag5pM6EyN/tu83PnvZ9vSCi9lLQVyT0H8H9IKb8lhJgB8E0hxFcA/BKAe6WUvyeE+CCADwL4DQD/CcAN+v9OAH+iPy8rhdQbX5oggnD/cu4WyH9zcCebNH3yyH5S0m7DqsSsBjpVsFpnLnHRwKd5oYDFPscHrFXtw22ndzhtiqJAt9tFt9s19SKfepJQ7CYjVWH6BCS4ayS5jvp09geeEALr6+t47NFHKwuqgNCul7aOfFD7ZZOGFLL3V7UytVi1tLiE0bTSqqI4YkzWlSgNSMiqvZzCRVCwpjoak12bpOxGI4UQaj8AmRh4W12AIcCWBkBoEZ381WlskvYQkqIl1Ia8OIorwgCV67bf9bGWUoe1GI8q7eN0Dmkuav0ksqCoM5TmpwL9SHcrrXFQPUtJ7rXhjU9CVCVZf57SgjSvu8lDAoi0uZQxFDOfvHHsjxE7P6uLq6E1GapDneYS0gBCghJnQlS+bd/lie5bOSD7LICz+ntXCPE4gKsB/AyAH9OP/QWA+6DA/WcAfFKq2v2LEGJeCHFA53NZyVdxQinPCzRbTXN+KF/U5AOG7+bLshxlWTgdZ6ULiSgaO51iBgqzrTodDQ9UQJ1raIo4dhmGnQRWSufShj8ROS3MdncKSRwJdSo894lm9XJVROnabGskdbB3ufQYksbMZGNd5HiKBEwKIQknNEE4vcnE47cn9K4qyC2XEkWl9KU9n85SqlgyGxsb6Pf7zjOVCa8ZicrflcL5szyPkABD98qyRDYeIwtoF0aIEBEKWTr9HMqf+sqXgukaMTJLnxIlytpAZz7dfXqE2r7ZewCc0NB+W32Bw6fXJDr77/hM0rf7+/3gCxD+Ozxfoql6ZjPAlloQMCxzwrObp4uyuQshrgPwAwAeALCPAfY5KLMNoID/JHvtlL7mgLsQ4t0A3g0Ahw4dmlgugQlQVXkq0ksk0Gq2sHPnTjSbLVDoWUJWdTqRDvQE6CPt9ClIWY6iyM2gsqp2bAELFrxIbuEeEEKr5b75pypVAVFkB44xG0EHaJJKnfcBjacKIAo4ZdEkhwCEr3jQwAcbxADgTSaf1vSdb2GvrZe0oF7K0roi1tUl0LehtnJ6EGMzknUNqNiXJ7eNtyk0eel5+k0aIjflGTAoq54tPHGp0H+fxo4Pvj49+PVCFqYffYD13QpDUif9rgvF4Ned14O/T5ovubX67/ifXLIOSbf+2AvVLVSPzeoeajvgmpn8OnAp39dM64Qw21dkfgrvY7BtVAAvRI2QssW0ZXAXQkwD+P8AvE9Kue5NPCmEDx+Tk5Ty4wA+DgB33HHHxHdDPqi19YRSXTudjj1fM0701n9hokQq0BMqSFY2VueXZpmSjvIceZaZTyklNnobjqSuAFODNIuYFzOtIGR3pvbYzpV6oa/U9+wAoYMcNL1sGz2Jx5dYlBSnqEEDyl8rcGjGB1sNM/GBtiJVeztE/XeEK8YH3/UnWmgC+O9RXlEUoaDFyxrJ2091TKlOEgylkHTJ85s0Vvk9XyKM9f4H3o7QGOB9FeqjYF8FaBIaQ0R/H+zqFpypHf5ehTo6b2Ve10nmmzGfujy2krf6beRBp0wrpIW1hpDA6Zfnl2XXttwyNxs/m6UtgbsQIoUC9r+UUn5OXz4vtLlFCHEAwAV9/TSAg+z1a/S1y0pWiq5GjKPv/mccx+bs0jSlGOrWa0REkYorUxSQ2luDTgpS3iM5hiO1kYlC0QYnBvPMaTSaaDRSI71HxsYbMXBXG3lIYinLwpzyFEWl3mBSjcLIkz9IyN5MNCKpgiwR/FzPyGzA0Xl7IBCiZYihOANYMztOCyeOeWQ1H2ICJk+BygYjv3+D5ZIGK4FsnEHC9WrwJweBpn/cWwjU6ybVpLG3WZoEuP5zvnvjVpkET6F9AyFhgVKoPZVxtgVGESo/BGp+/euEllB5IUFgUp0n5eV/V3MvzEzdelswDtWjTjjidVeP0D2JKIq1wCDZ9UtLW/GWEQD+XwCPSyn/b3briwDeAeD39OcX2PVfFUL8FdRC6pq8THs7n2z+9mP+jJE2itK4tkEwmVHYzojiGBHYafUQKNICzWbT2SGa9GLjL143MelaFEXGn13lG2v3QB44TO0OpBOAeLAwupbnhfGWITfMOsBi/cSA3d08RW03i8Das0LFc7EAG4vYKWtTaT1QhgkERmNTgIUAFoAsLRMgyZuNYyrfB8+Jk0fAkfx5/fz6T09Po9vtVtYI6jQkv73+980Avq7ufrkh8OP9UAeMW637JAFhEqOi6yGm6NexLn9fIAvRxWc+ftoMnCfd26ztflsAizMhGgHhHbV1ZdTVzfapubMlk9hW01Yk91cC+N8BPCqEeFhf+00oUP+sEOK/ADgB4M363peg3CCPQrlC/ufno6KcyNT4ungzUkodUXFgoj6mjQYaWYai1UKr2YKU6vg0CW1j9BYvlUSt46oXebAcMyhhJ0CSJGi328btkLsGJvowC9qNyWO85DmBfWmAfb27DiHWnbL87/Q7SRPjweHb+906k8aggVRvsAGU/z4N4pB7Xh3IUFnEJOgaHe0HpoUUhTSALwGIUurDLMISdB24hYCfjwl/w4l6GFhbW6u0yy+bu4DyZ2nC++WH+iRUhp9CGpFfH/+7X1aIZvSb6uwIPp4pxM8/LKGGI0Nu1j6/jDrGUdeWOubu05wLfKF28DHE61FXHytRh01zNi81nzhD8Nvu7zzm5fP3fDqqcXZ5IL8Vb5l/Qr1+8B8Dz0sAv3JZtarmWeGek6Q0io5IEjHFoy4itZOx1WqaY/D6gwG63S5GOo6MNZWUyIsC/X5fnXRP5bCt42aQmAVQ+k0V1+9EwrjgKfOI3dgjpUQpSyRJrL+rcMBxHGmPDLHp4ACgQys0HV/o0H4AwA5C7o1jN/ZIjMdqvcHf6BOiO3fjc0GWnquuN6jBzPtWA4iUyLbg/cI1NyGsH7YBLuhNTJ5G4HaO7VEgDLLhCVdN/Pk0TSs2Z59Z+IubHAT8PEP1qJPi67QAXj8OgtT3W7Hvc7PopCiaF5PIDTQkzfP2tVotR4P1xyOnp88QNps3fhsBcuCoChecmahx54YjCEn2fkgDSjw/SwNev8uX3q+YHapAeDD7gzdJElx99dXYv3+/GYBRFJkzRaenp7F7927Mzc2i05nC8vKyWjwdZ1Zq1kfu5bk6U9TxDEFYAlH/erNMniNn3F9V1k4+uyXdnrEJqSzRiiHxqI31frF+4r79PuCGJDUukXGAL8sS8KTKoASjgZtMT7TVn2LK6Icc/3PeLgJWVbayM+civIjKgZEmDk1sLk2rPtISFduFaOLIe8DFAaNO8g6BPp/wRAsI2DAD7F3/YOsQkw1tfw+lOmDygbGOfiEJ33f9Cz3H8+HzoY4Bh8oPCQahdvh50Tz221lXVymVVt5ut7Cx0ZvIhELAH2KMvA0hzSLUXv+ezdvdREX7H2icTiDjRaUrCtx58juZvs/NzeE973kPDh48iPPnz2M4HJqgSnSSTaPRQKfTwfz8PHbs2IEkSdDprGCj18NGtwvZ76PU0RklV+91P0X6mDrXO0SZIaS0LmCqXkCUxCilVCYIXdckSTEejZBnmQo0ZTYS6U1EWYYszyqDI4qiYCwRNUCr7nr+AKV79h1XsiGg9ZM/CXSTea4A9OHYIHWVVEu+8ck9NpGAPo7dE4LqGCgHlyiKENHh3J7Ex5uszEPcrOBOWN/O6Utq/FlfOuOePsTADWhJuxZgFo2Z26zfl5tJwj6DniTd82c2A/5Q+SFpdCt1rKvDJJr69ffbWLepqI4eRNderx9kACFQDt2fRFP6HhJ+NtMSCEiqjNN+Go++y0hXDLiHBnMIAKIowr59+3DrrbfixS9+sXOf4pfQyUYU6+TGG2/E+vo6Fi4s4MzZM1heXkGvt4GNXg95lmFqagrLy0tqw0ypQZ1pTlJKCIQHbBzHaGoPGvoNIfQZnMIspCppnZ3ElGUocvfYOQJs3n5Ok6IoEceu65oFVfvvS6sEMFS2H+7AbxP9dsv31WEYApFUwnpTP8skZ3ItC3jNhMYAtcMsfDEbjMqTgzyfhF6erO7+xKwDdpWvDiNhPHRUbk70RyGMIGCKkTDH88kKkwuPa/963aQPahh885iXl69BhACWLyyG6hSi28VoEKH86p7xPaEmvcvHtz/meX51Y72Oifpl1o0Z/70qkLvzRD0bZhCXmq4YcPdT3U6xlZUVfOQjH8Hb3vY23P6S27Fr5y4T5jeOYhOFkd5L0xTT09MAAHmLNU3QVnghBBYWFvDe974Xjz32mAJ3JrXzzUvtdhv79x9AmqbaNBKh3W5jdnYWzWbTHCK9uLiIPM/QaXfQbDTNjscss2YbwJogfOmOh3jldaA20eKtP9Dpu09Hf3GXS9t1E42Ay4JsAbNQ6jEWAgcyOfkD1zIt6ZgvQpJVJUkJAbv7z5fm9COGkVBetE4CaTUeHxBCyebN3Az1QSAAA4wSalcnvYcqU6X/kM17EoCZNkyQ3h2Gwp53SRc2VYXKpHFF4SFCZU0CbD+F8vD7m6/TAG70S9+M5belTuPYjIH4wotvz/ev+3UOMcpJiR6j8auEEnr/34nkDoSkpyr45HmObz/8bRw/fhy7d+82Mdp3796N+fl5zM/PY/fu3di/fz+mpqYwOzuLXbt2o91umTwpyBaV+eyzx9HvKxWv9I45o7LjOMYdd9yBd73zXZCwqn4URWg0moj1xqokSbC4uICTJ09pQM+wtraGxcVFDAYDjMcZhkPl5bO+rmLF2LNdVXm+WUZEQi+oKsY1NTWFdqeNJFYAP84yjPXaAS0w0y5CS1vOLCNH2gtNFAOQ/HpZotCeL9zubnYJQx2xR+9TDPhSEmOpTnLe147nAdQiNfh1aEnTMBWS56tSm4RkUvXkCR8abyYoGZ3KJRWAF3nhjFEDOpEKzZwxMxynJ38eqHpQcBMbZ5z8/boUAnXeLr/NIXDkazOhPEMMyZ8nIe3Btr3qHbIZc69jwpwunHH6+DGJiYe8b/zPULv8XcUhOtA9lxkAYAIAl+QvNV1R4M4HNP8OMA5eSuQaMPv9Pot+qKIkNhoNcwKT8knvYHZ2BtMzM5iemsLc3JwOXdCEEAKLi4v46le/itOnT7tcmehuOG+JjY0NzM3P4YYbvk8zh6pJRNX1JrNZikwhvV4P4/HYhN4FgAsXLuC3fuu3cOzYsUpYWE6TdruNF77whca/nkIIt9ptI9XmuYqfMx6PsbKygixXIENmKh41kQKHFVs4tIOiT/JNWnTPun8q/38hBHq9HvI8qwB5nucoysJIhnVqrKG/tmFHbAwIsIihAo4Go+IH+SYtqmsYPHxPjgrtIy9EgDaBCKHMIQT6UioTjAmKJqwkX5u3V5+Q51NIWvXNApOAPQR2IQD2v/v14+03JqdA3pPaSODm19UHSP5eHbDzsrgrqP9MHVBvVQPxscj8ZjunJ/mtGyGHHdBRNxYvJV0R4O5LKb704nBKoQ7Apnjq9F5RKHDL8xyDwQDr6+uVbdWmkyIV/U5ALcJ2u111LJ6EU6Yxh0g1iB599FH8zu/8Dn7gB34Au3btwtTUFDqdjgmFQMHM6EQotaKvDvOYnp5WcW/0JB6NRjh+/Lg5wNuXkjlt9uzZg3e9853Yt3+/sZFSvHNbVwBCIM9zrK2vYzgYmMBbdGbscDSC0LT6/Oe/gGPHjhn6hcxgSZJg7769mJ6aNr/JLEGeO41mE0kcm1DECwsL2NjYMHSnfzILnT17VjOAvFb9BaBMbI3E7AS2m7RgGA2Z4MqyVEcmyhJl4Z4fK4Q96iwk4VHi400ddK76Cp7qrhiKWlfJxmPkeYFIuvHmiYa87RwgfG8heo/WawRcExLPzwEcVLWrUHs201iiKCJLXQUkQ995Gf69SVqG//wkDcrfZOTfD0nPoTpMaj/HHc5cg5sEvWRdi8PMzWXO9prX2tr8t5KuCHAH6idc6HqapgZMzKQAYCVpmEmSF/Y0Ir6gSB1Dki1Xxykv452igWU4HOLw4cN44oknzCamKI6R6s1MZO5R56o20Ww2DPh3Oh3MTM+g1W4hiiIsLCzgwQcfxOLiYpAWgJ3Aq6ureOAb38BP/dRPmcM6ms2mcofUzIr7vvvukpzJDYdDPPTQQ/gf/+PztjwEJDco89Wtt9yKH/qhH0Kn3UGr3VKx5OMEc/NzaDabTplxHOtTrWiHqI1FMhwOcfz4cXzmM58xG43qJgUAzM3P4ZprDiJNVax6cjfk5RETlVKi1+9rsK32NQCcPXvWnDzlmx/4+CKtT0QCcRRricsCgWJoDcRRbLQzOn6Pt0HlL7GysorBYOCM5xDYKI+uNgA3hrkvwdNYnLQoTv3MzVx15hQatzaUhnY7jSIk2uRBTFhK1/OIf9JzdDLaVlJFImbgSp9kXizyHAYlpTTf6R0KrxzScPx2q35MlGZWoy3R+AWq44X3pd+eunb67+oabYlOdemKAXegKrX7E49PCg6mSaxAlkeIpE4zE70sjW87edWQeYJimwPeLjTNWUtZAkIDVZ6hlKU5+IEkH1K3hKjuGqU6GylZqroMh0NkWe60iychVJzs9fV1/PVf/zX+7u/+Dq1WC81mA2naMNIrHaJBYJc2UjQbTaRpw5iqoihClmVYWlrCU089iZMnT1YAzpkUUADx8MMPQ0qJvXv3Gm2JXBQT7xSmEIMRWptYXFzE/fffb2L41KnH1G4pgT17duP66683G9LiyB7SYU6g0kHjFPAo/30JtcaQZTkGgz6eO/kcFhYWVNuEa2rx69LpdPCCF7wArVYLaZoa7Yjux3Gs4q1DIopjtQ5RFCYEg9AxfvI8w/LyMtbWHgmOB7/sKI4xOzuHdqeDRqpiF6nDvwEIAVlK2JhBtDGsdFR+ip3U6/WcTUEuXW1fk5ATxxGSJHUOvzESPXvX7qqsmk4pXv/q2mrFVMJBk+9ZUEJKQ48TK6VziZ2ENbV3hOYktUt9H4/GRkirS76WWBQ5RCmQebtfJ/UR/16HTZxe/JM/Y72vLi9dMeBepwKGAJ/ML+TtYiQaxIgiqUCAAQ/lo87jbKMoS5RFgTwvMBwOTOxuUnE5wDl1jCKkSeocXBH6t5PK+pT7HiskWcrS8+kl9Vir3CpkgdIaxuMxojiqLGbqDOw1migm9IBedmQAEJoI/gAtigJnz57F4uIilCkkIGlR8XAHs+mzSBjbfzYOlxuqw8rKMv75n/4Z3/rWt1SZxDgCqjRXo82mJy3h0oJ2t9tVedcwNPre7XbxzDPPoNPpVHaauiYSGFD17wNKI+z3+3ZseSDrf8/0QekU5yi0W9Y1Fbi+/gDM4uJgMHT2CtQBFpUxHqsjGUnb5QLOJJMOrxNJ9dS/3A3RL5feozHN21VnXuEpZNYKbfEP1dnXhujduucqGq03vkMLrHXmP56XENV1lotNVwy48xTyvTXfpTqwYzRSp81wtVi5ELqSnd9JygtCAWAc8YkJk39QqlTrZI6k7AMKn5AE4qQqclAnqWVhYcGJie0PbOMbrkFfSqlsykLquPUCeUFhcGmi6TMXhNBHzVkWZXbMFmXtgKWy+UClzWEU+pgvKNF2US1PwbnBfprJxHx9/QntT7rBcIDhaGjuxUmi1wwUcJHLqx9XnKvTZsGT0XKSIFEUBdbW1rC+vo7NEvcxDwFRnTuv7xnD6Vx3OMlWUkj95y61/rNUb26WnASIoev0TqhNIXrzd7nA4zNaf0yE2ue3wy+njqHW0TREA37NFdzqU6jfCCd8c9rF9K+frghwJ3AlmoZAhhJXA2lS86PM4iRBHMVKwtXubH54TzX37XFzRZFrKcyV3usmhX+OKJcgAXdnIh3kzMG91Wph7969WF1drQyYkKSggE23SQgNplXpTtWXtA6ghPj/23vTYNuS6kzsW/ucc6d3331D8V5VUVVAMaixhNyAS7LU3dFhiaElWSGkCEmNpDBIxkGE3WG1rR/dKPTDdoTDbdkOq9XhFjQh1AEddAuEW1IFaMKSkKOnMoVASEVRULyiqCqoNw93PsNO/8hcuVeuvTL3Pvc+ePe+OKvi1T1779yZK6dvDTtzJYiEhlJVqJyvoTwRKkcJiMsypAAk0W/WBiVu03DbmkDWOnBNLgilOlojwHQy9as3xN6EpP1C2cxzTjtMBD9RAjgyndlGRp4WWUCTAyn9jgY8q3xeo873NNDqOZUDT6t/cqBZ0pSlZZDj2QJOK/+c9iwVga4yZd31PX3fsuq0hdYlfK226fstoi8dC3BnjZN/ZyeKVAwD1bWKbjidwlU1Bhj6CRpeq9VGIdZq9vf3g6+2m09perJVoOOgyI0g/I7UXqrKn6N59epVs67JIA6uGRZQwwHHeGEh0rgH2Ez3efrGqgfsimo0JeccJtOJPzkX+cntc/BlywOUWUsPDKagyR0kJp2ciJY57fuwfVKPpWHXdQ1e0eHgj8SrHIcbVq4hBzgKJ3OBksifeuJJILf8xaPRyDz7VPexRdZYLikO/NcCEb0UkfOqKr+Zbnvbx1lhYWlptV1gJLVwi+eSgJS/tcKi//KY4jmVK4fvdWnV1jyyAFa7i3KCo9Qe+rcuy2qjum6+jzRj7eBaO3AMwJ2Bp7kuTAYWAML3S5SuDhmy26RqAlCNg4YvAznxv7iBSPnbdflc9nQ2RTVtB7fiv3rda/u398Pz8kRCc66qrm/kCXyaE/+r4ePcNO8wwEtfbHMPiVbKfmtrEnNaOcna/zh/igPUD1pe5aHzbPjn8oajEeoY4z4FSe3HtLRQCeTeYgCSYwurpo4WYEiSwC7LYxoOh2bYhgSsqgGWV5awu7tnasu6PjmytGcL0OSKGv63tLQUlJVZqy45jTPX/5onCyiLipiRtyUElpaWMB6Pk2WFZn+r92Qb6DqWNGsp9DhvmY/mtdRmOUsqL0DTvOUxfgehIw/uUpL563Yjyi/sRD5ui49UWCWukOFohBNra1hfP4nV1RUMBgNsbm1he2sL+/ve/TKra1TQJjHM8uW9+BHT8caFGnXt10RzZ0mwlBOaibfgc4TE2WyWXaesqQ7+ctQz1DWBqG4tv7NIa3k+vo09saUmXdKQnDO0fCGk9V9dznA4xBte/3pcuPAMrly53Jo8elJLwQSi1HgjbylEQRae+4/fPraQdAvl2qgEunt7ewlo8MoYFvpslezt7SftlLJp+6Jb2r9L35HtYd3j6729vegCzGm6uf7OgXC7j/NuoZIWbaXlbwGDwQCrq6vJ5ro+ZUnBVwJeiR36DAM9R0sCL2cJWBZDSbjkyj4IHXlwl5RrDClFAWB9fR3LKythUjdL8UajEU6cOIGzZ8/gxIkTYdUEYRZjlwd3DLQ2EjoiA3qsXQ+QgilvppGSX5rEcj291rJ4aVeuHazfrnaYobFi5MfN6B93jY87WhzhN6+esdZIZyelAG0+w5TIL9Ek8Z7UfkOGsX9kNEi2br74xS9id3c3q93Kfgfgt/Q7B1D0QwX+lMUVXFIIHyibauTBtqRJc52SydoMGwDAcDREPa1bdciO+aXNAAAgAElEQVT9zrU1L21lF1Bu8nNMefmclQWZV6y7AUCSF81j7r0S5bRcKx8JbpMQuI9XcGklid/X40ySXt2jy+dxpzez6U2OzqVLNVuCF+05kxtHnBfXR4K+fPcwdKzAnSmnNVZVhdOnT+OnfuqnUFUVnn32a9jd3QlagF+qt7S8jKXRCKdOncKJ9fWwDn6A7e1t7OzsYm9vD/v7e9Evzi4dXwjMj4LxoxynCVqi3EwDIH7QRejY/f19TCZj6FjjsU6AOWiSstXAqgYVOyQaoAkrYqjyyyT9tvhmtQjQHMwt/1nlWBNfUwLgBu+yfs0EQeKLj0sTFcnBzw2u/eAcyMvkj8tDWTOyQKA0iUsAWc/q6FqTfVyyYHIA1Eejk/syciTBsNTOuj7yWZdWLgErB7yaH03OuRBzaZzkZ6WTYzInMPgvtyULCqlg6DytcnL8557l6so854TFYQC+zxmqKwD+XwDLIf3HnHP/AxE9DOA3AdwD4DMA/gvn3JiIlgF8CMB/AuAqgL/rnPvqgTls+GjdswbDqVOn8La3vQ0PPvggrl+/gb09PzD29vawt7cX17+vLK+gGlS4ePEirl27hmvXrsU12xznhd+bTCbY39/Pfs32vIVOQvN7MBjEiJQ8ObkecqLOZtNYH60t+SWWFM17WaauP2/SIqjBEtZp8o5KYADn/MYt1mS1ZtUFWAkgOUQt1QsUsbOPgpuErQc0yR0QDrdgLpWWDXvSpAOfBYXgQyru6WXiguEP0ppKoNU14TX45sA4B6gl4JZ7AGQflbTgHM/6mQXCXYCsyyv1W5cg0Pdk3XjDlSzHWj8OpOGL+f1cCAtrp6/FS46/HKDnBIzVxtYHep/2cMAO9NPc9wF8v3Nui4hGAP4NEf0+gF8A8CvOud8kovcBeBeA94a/151zryaitwP4ZQB/91BcouynlR158eJFvO9978NP/MRP4OUvfznOnj0bN5wQkY/dIvLlxuWYM/v7+3j22Wdx7eo17AUN/lOf+hT+6I/+CNvqVJe0s4DRaIi1tbVmd+RwiBMnTmBlZcXvDl1ewmTsN81QVWF9/SRWVlexG6JDNrHdp6hrsZO14DLg6yrEOvEbtIZi+WDzsZLTDgaAcxWAWXTFOFenvmukEzSn5cnlkB7ECUtLo3iaFMEfRk7k482DKGrPBMTVHbqfdX3NTS+uaRu9LDNOFrRBRwpKtsQssNQgl0zCpNJtfhM2M9qsppyVkLvHoZ/1uxYPmnjbvm7T3KYmzjOxSDo2BuUAUtalJDRlf1iCLAeiJZ6tci3ec5ZAl/VxEOtE1zVwkFUM+lKfM1QdgK1wOQr/HIDvB/DT4f4HAfyP8OD+tvAbAD4G4P8iInKH4TKQlm56oBARdnd28YlPfAKf/vSncc9LXuLD/d5zT4z2eP7ee3HP2bM4efJkDAXM2+83NjZQVf6wD6bJZIKLFy/ik5/8ZNAtlU8tzO7RaITXv/4N+LZv+zYP1GEJ5iAsTVxdXcXZs2exv7+P559/Hjdv3oRz3qfIgcnYr8jLKWtXN+e3crHUHmhE/iPecDDEaLQUY8fzc4426X387HetAjhMMaPU709VOkEt4IwDVbs/yMfrcY5X4KAJNVDNUM9mINiHLjCItyLpuWZLdl2LTTdk8BOWA1ZEjYXgXAskoLT2LtBoaWGwAVqCfwwHnCFL45dLaHUb6fLkktpcnvoe5yM3L3VZGtIfnQO7Plq+VV6XtaK1bktjllpwTnjkyusSRjllo+ueVEhy9ZX9rJUKXbeDUC+fOxEN4F0vrwbwTwF8BcAN5xzbic8DeCD8fgDAc4H5KRHdhHfdXFF5vhvAuwHgZS97WVf5yT+rw6SWu7e3h0uXLuHmzZvR5z0cjTCK0RiXsba2itXVtRC5cbU5W/X0aZw8sY4T6ycwnU7xla98Bb/zO7+TfHwLdYvl+Y4c4HWvex1+/ud/HisrKzEda+Qc62YymWC8P8bevt8Cvru7ixs3fPCovb39uGlqb28Pn/zkJ/GpT/0Zxryio/FCRB6IKH4o5p2xPpTxKobDUeRzMvF+2L29Pezu7oogSiNUdQ0MHaazGQjAZEKYkgi9SzZoeCuAd+IC7I5yzmEwoBDPx/v5NzY2Yiz76ZQFTDPInfM7i+u6HXiKwUhP6Pj9g+DdP8EqGESA9EsQnQs7gX2mALwLi1fXwDlg1tRxUA1i3H49zuRv3pGbpBGCsOzGswEh91drkRZIaW3T4tvXvUo+5ndRDrj1KjVpSemyLYvF4lc/ZwDUJNNpgWilte5b7dglpLJ9Iyw5/6yZrKWjFLXikUt3EOoF7s65GYDXE9FpAL8N4LWHLdg5934A7weARx55pFdN+gxe2VjTsApmOp1iMBljj5oVLHqZoAxoNQxRHKfTKba2tnD16tVWzBPpryX4QFC/93u/h62tLbzqVa/CPS95CU6fOhVD/HLQrpXlFaysrvgDNVZXcerUKTzwwAMxPC27U65evYovfvGLfq2rBHUF8FVV4aGHXobv/77vw4n1E6EOwxDUaim0iccwqirs7uxgc3MTu7s+Zs6uCP07Ho9RVRVu3bqFJ554wq+zLwy4wWCAkydPNm1XVd6P7vyH3eFgGAKGDXDy5Hp0I8iPY/LfZDLB1tZWbNPEd+6atpZ9RkQxnk4DBmF/w3CA4WAodit7IeKjefI4cmHJauOikAeyWHWPLj7DJ+P3Vfjyp1MrhnwbBC3N2prwLFDk9w252zny6QBUBJJaeBSGtgYuydpHEH/LcZ8AHIJl1d6ToTVUWR9df6vcVvvJ9lCALnnSeFHS2DVphVLf01aR1r6dUA5K1oK+T5SGajgMzbVaxjl3g4j+FMD3AjhNRMOgvT8I4IWQ7AUADwF4noiGAE7Bf1g9FJXMt5ZGR/YzBE1PL42S+cpNDBxEyy8r4xzCQCGXSOu6rnHx4kV8/OMfjxEDh8NhjEq4tLQU7o2wtDQK0RuXsbKyEk+EOnPmDNbW1jAej/HUU0/hz/7sz/zKB6Wty3o55zAe7+PUqQ1879/4G9jY2IgavIxp01z7yH6DwQDT2RT7e/uYTaeYhA/Nm5ubePTRR/Hkk0+22l/T6uoqvuM7vgP3338/VldXfbjfcDCKj7EzjNEgOZja5uYmxuMJHH/rmE0xDqF4L1y4gMcff9zHsC8sPK+owvr6Os6cOR0OYWkCtTGwc51Ho1G0kNiNwVYAb+ZxzuHatWtx6aXVxlx/XlKbm/Rc18GgwmxWtywOPW75472+r4mIsLyyHCKcVvGjuQVicqmt1kpZY+eQv7nyZL38GA7hOqhq1d3/RSi72UQny2S+ZFC6Ls29qryCwNpwUib5uTgYVADaISE0gHK5JReJ7kdpnXE9NJjn+k32i1YMJOjL/ioJgYNQn9Uy5wBMArCvAngL/EfSPwXw4/ArZt4J4HfDK4+G638fnv+Juw02Ru8snFByRSdZJwVZ+WqtiTdPIGM+yaWRPlzBHqbTSQwDwDysr5/EZLIfBljKA/+WZY/H+81uRqWt64H79a9/HR/4jd/AR3/rtxJLYTAcYFANI8j6f164sF+ewaoaDDAZj/Hiiy/iiSeeiDHGS7S7u4vnn38+fqvgGD5Nm6dadiNoOO7OAMOR5+/ixUt44YUX4soIIu9uavmVyWu9o9EIL33pAzh37pz/YB2WtMryGWi5neR6Yrbodvf2cPHFF/HZz34Oe8buUf0hd21tDefPn/eCmsNJVyJukIrKmdPe6nqGmzdv4cJXLmTbV48zfwbAcgyCp8eMBoMIQGL8sKCT/VvS5J3j70ze1UAk8nVeyeFNW8wziWu/gmsUATZnpWg+Yv+Nhq15K/ujOXvUL2porLFGcHFf56wCCexcN2tppNVWWsBa7ZdTTHPr6S0r5SDUR3O/H8AHyfvdKwAfdc59nIi+AOA3ieh/BvBZAB8I6T8A4F8Q0dMArgF4+6E4RBv4dOMyxcaG1gZSn19uSaKe2LJ8KTC4LKDxuRMvcBfP5b/NzVtNXmEg+ixcBHufp1/dMp1NzSO6rA6fzWbY3NzE9va2cFF41wMoPc80lBQnoK+bi8G1ZrMZxsE/PxwOhH9ctIVr3F5f+9rX8PWvfx2D4QBwXH3hf5SrSlxTHqHx03NeOj6LPis2cu8cbly/gc997nMYLTHAplvNq4r9ng1QyLZg7W86meLWrVs+7orxgVVPvN3dXVy6dAlLSz6mOpCGcJV+/ZTndBzOZrP4Eb0PzWofjVJrppayAaRun/hO5QPLlWK1WMAiN0DlgErzklOarGeaT07DgshKo0FTa9MWKOd4zQkb+Z7V7lbeufax5q3GtD75zEN9Vst8HsAbjPsXAHy3cX8PwE8ciqsM6QEov6THRke7E4FyXGTrWdtkDCYBNe8kHRx2PYaU4Rsfp/H/5IEDzslB0QB8REGh3fPOWEua+2tqlrbNaiytjLC/PwMQzGE0h/A6oHUeK/NRe6aaqIrTWfR9y7oOBgPUs+ZEenk+KK8SSSZdaBRS+bD/ltsDZt+127qqvKm8v7cfww3H9CQ84dxWmXxkORZY+DaYJu9Kt4I0q/XH1SQvcHygRoXOgWCOWAO17mtwyuVrgWEfEJRtk7NINDDnytXXOc02x6tMZ8WVt+piz5k2WTzmLCNTAVTpLAGs85SW4aAaYFbnz1ydl478DlUGWD2ILGnLGjS7UqLGFkhq7Lk8rEnk+DkZE0Z9VAvGq7AMqLlLWmNAAHmAqJEcua3hUaiFj2rMaxU+3nE7efcIYTZzAd+8YPEbePJaF5QF498M5rcTVg9XVL9vpGdNtgFw0VLUwCER4DKLN7IT13JVOQGxxnvRL+tsRUBrgDlqgSDym4mactLnpTK6NFGZrsiXAchd5cr3SvPF+m2NV90uRE0YDquOuetcPS3hUaIuzdrqmxxvpXdk+uzYEO1sWVuHoSMP7kDbR1ZO7BuMQ7iyD1bGdI+rKcSKmWJnCtBrDThK33HqXnrRAHzzrA73GqG1tLQUd60CiG4MB7EsTGj2UbMO0mI2a1ZosB3h82BB1eCeC2BYAgn5OwmuxCtaUo+P8NEi7kBN2xMe2PVcoMaqiO6FgsbWuHy61zPHvCAmErdB7VpAr0lOag0EJZO7qVozeTVwzqN1lsjScLW12zfP0mYmXabkV5Kl8ebej9fK8tP5ld7PadP6t3Wty7Dmg25L67rUZoxHOr/bpalrOhbg3mcySGLtuUmbdiovk6uoEismaszECUTOhdjsM7HeW5ejrIhoZSTAlDcLm4Hkz55kTdwPgiaetRwI5q5AsXKCAPDB3T4+dPBtO8ARWwkECuBKAumlaycHjvG0JVl3lhhwgKxvVMcJIEr8vSlAItafaTDwJqoJVOzOCUIP4jnHlYnpieJRhVVVRXdSTO+c9Ja0KKf1tjRb17SFXCoolQD++J7TdHNkAWQJSHLAkQO7RHCBWoKuBNCWFqrJsga6NnflKKdha8C1ytb3usDYqpOe8zLvnEbeVbdc3x4W9I8NuMvlQpZWkQzQqkpWExBVcbUIL0FcWlqCcy5sHtrDdMouDMSt8xxgq9VJqs3jAKB0677/sNl84POPGq1W8840Ho8xnU192ezztgAjssOrF+SkbnaIJhRveERjbGa/Oag9uZOJwxiugAswNBfmP1DN2g0agcWaPMdXBzzfayfWsL21nQTcymmkzEQEagGiHPkzTha2ckI36A+QuXEl2yItt+kDSzuPvKMdl1+2rXVf51N6VwNQYu53KUO6L50Nhn0opyF3CTSp1er5ZZXfZRGU0uSsMKsOfS2YEq+SF1m2XHadE7aHoSMP7jwp67o2AwBpTWM4HGJ1bRWDqjkdiJfeLa+sYGNjAxsbG1hZXcV0OsW1a9di406n7K9uypZSVGqMJq91DUcEB7/zsabGf+43Q3Jnpr5GAMnEd3Wzo1ICYayz+u239svld4InNKKkDVQhLWudxgDTZcb7LlgM+ptDVbF5EP6mE8QDfIVmBhNAaVvw6h9ZdqKNwoFce621CzzJjwK8np55j1YSiYaSIK8mVl+Qi4JJfFhNLK7cBwWkYMKkTXhdvgbvkpmfu983TZeAsdL0EU5AGzytuljabx/g02vKS3XLWSBWv7TGY0ZL18+66mWlOQwdeXAH0oHS0gKUi2B5ZRkPPfgQhsNhsrSOY2GfOHECq2trOLWx4T+8Tv0a9vF4HHdqcuRIL1nbAbx4N2TCo1h2x0BZKQuiOY7Ou0y4DNa6OUcrHgWXwfVN2oKC6yH85rbyWjKalTviY2f0pAS3RrPxpb06wmp3xFddov3G78LiHXaMRT5Yw+bnNSJvcgVNUnf5N2aW16DjdQT2JgUAkBMKoujKkqZbMvtTWRW+j6gDw63JXXJP6DX2luDp0s5zWvSB6mhQ7rSuLgtIvq/HWRf1FTQl3rSm3BdQ5ZLbPrz0aRv9Xna+zUnHAtwtoLO0EyLCQw8+hHe84x1YW1vDzZs347FiMngWAGxsbGA2m+HkxgZu3ryJzc1N3LxxAzs7O3H98WQyARElcWVa/l5xjxDcIOT9y7yJhndQsiY2HA7jLkHe2OFc6FSkfnpdX16nPXOzCHT1rI4badraU2rlMCDWIdyvcxo88iZiAgokBmJ0GTUHkySDU+QlNX/BWLyWbhjJc9Lf1NRHarcpkHvrITptKLIB9otbwCeFleUe0KZ1/I1GeMZ2o/Zk7QPIXHcZ8bEEFjmeSnkf9HlX+lLZOc1Va7Y561HmkwvjW+JDa8a59JaFlMt3Hu26SxDI/A4L7MAxAXdJ1gCRDby0tIQ3vvGNeM23fRuGYjVMPLZOTLRbt27h8uXL2NrawqVLl/C1557DzRs3cOnSJdy6dQt7e3u4fv06Lly40Gwosvoy9IN3/wyTre/s32fQk4eArKysoKqqRIOf1bPg2pglgBHBAtbkdYDYnNNYD4gadNqGYnNOuEFVFVaqpNqMJUQTEAwAtry83AgqlT4nkHmn5Xg8jpaFrp8ETOlj4jNJp9NZbE/fx3zoOUVryJfPW9UDaAoQlmV2KQ/yfhI3RrkG5RZ72W6ybWXe/FdabTpgln5Hg2BJcHSBvqQ+WqMFQrr8kkZe0pRle8h3LQHXVde+dZb89BHAXXXg+6V2zAmOkhCah44FuFsDRWrCTHVd4+mnn8b/8o/+Ed7y5jfjda97HTY2TmFj42SMsy416fPnz+P8+fOxDI6LceXKFVy/fh07Ozt47rnn8N73vhdPPvmkB2bWUpXpz0G0VldXEw19bW0tHNixguHQn/i0tbUVY7xwlEYG+KEbYDarMcEEs2oGhJWH3sRPNUImCpYCEcWgZ6zF82qg2tXe/wxgOBjEaxInMMmPkVxH+SGX6xp/i2eD4SDZyRgexGeudqgB4fZpDiIfDodR8ElqrdwIGn418B9cx+Nx3MFIRBiORpDzIe4aBuDqGrOZaDcCUDc+cnalwHnrobXXINyXwlMK++iyE2Z7XASg6mEBu2xbad1Y31z0uyUtVf4uaaCahyJfsLVtzU+XMMpp/VY+JbDOWlMF4JT10r91Obn6d1Fu4YcWUiUBdxg6FuAugVxLda0Zbm9v49//u3+Hv/z853HixAmsr6/j5MmTOHXqVPz7kpecw6nTp3D2zBncd999OHv2bAzgdeLECZw7dw7nzp3zWiBREt2vYSoFn/X1dbz5zW/Gq1/96sgfkdcWnfNBtpaXl7Gzs4OrV69ia2sLW1tbMWzA3t4+9vZ3MZ34bfg7Ozu4cuWKBy/u56hpp53fBKsaJDFjPIC6uKmrDueG1nXtQ+FSharij7veReNjdaTuFlkfGRJBtv3u7l70qcs+I/IhdKthFZeWsjLOSxRr57ygMZbhSX++7O/JZIK9vb2YzjmHejaLLi8K7VINqihYtJKQTKDgK69hH0CRcyNEF5Gj+FuPSxYKmjQ4avDQddbUx9rQZen7+v1cXjlhkBMiXRq5xb/OI/duq50UEOfI0vxlHjnwzr2Xy0tfy+8K8wjYvlZHjo4FuAO2lLUGCfuyx+Mxbty8gUE1iJrsYFBhOBwlIXiXl5exurqKtbU1nDp1ChsbGzhx4gSWRkvY3tnGE088gWeffTZZHx0bH+mgOnnyJN70pjfh7NmzIgrkMEauk4PIORfBncPubm9v++udHbzw9a/jIx/5CJ555hl/+LPynUvTfX19HadOnUpi1y+NRhiNRgl/zjns7O5ib3c3An7U7IN2X8/qEO891aKtwS+jPVKVrpwhakLx8nGDfNqVXy0EuBDvhQDUddhzMBVCNAq1thYvvxXIj9nNR2sfeGx1dRW7uzs+VryrYwwdIuHLV3MoBzCx38OmJ6ooatcyrXc3VZhMpn7cUJNv0ofCBSbL1bxwGvlNJaepWnnE+WIIT5mXTNt6NyMoZLk6P0tQaCDNae+ajxwg536XhKHOryTUrLrz3/b3Hr+nxBI2uXpquh0aO9OxAPeSZmF1UvOx0KGuwiEUQdMm2gsd4/2wvFQPCB8rwwEPrKX6A7P3oz841/jb29v4xCc+gS996Uu49957cfLkBtbXveWwvLziIzEuLWN5xQsTtir8uvslnDx5Eg8++CCqyrtqHnvsMUym09buSfkRlwHsVa96Fb7ru74LG6dOYSVo7qOlJYxCREQZHXEymWBndzfGdZ/OZtjZ3o4CcX9/H08++SSeeeaZ4iEDBF/2Pffc46NKVhUGw2F0D/E3B/arLy0tYTweY3t7G9OZP5HJCxSHWfgOcWvzFjZvbbZP/WHwdPDukboGMIx7Fdj3z8K0dj4kgz+YxadpTqKqo1UiY/ZMJhPTdRLzB7u/qmbMBDeR1upZ4A0GLjnOToMEC1hLU9Pjm8FE/ouWg2vy5Hyb/LxVxnlyvnzil547FojK8w84jU/XWHk8h3ye4luHa8qV/yTJslv1BsUdq1ZUVyKAqHs5JfeB5RbRbc5UVVVjJQtFg9s8d8RhSVjrts2VfTvoWIC7NdBLDdd0qg9jycsEpYZV13KiAbL3nEOUwPzBLjyJna3Lrusaly9fxo0bN1ANqni8HrsG+Fof3iEth7UTJzCoKmxvb+OZZ57BxRdfTCZJ5AHpoL92/Rpu3LiBjY0NLC0tYXV1NYSkHWJptBQPAuHyBoMBQIT9vT0Mh0N/OtR4H5ubW7hw4QKeeeaZogbJtLq6ile+8pU4f/48lldWvDtIfNfgZaDDAP6z6RST8SQGR2Jw29/fx9bWFv7iL/4CW5tb9goZ1dWrq6s4ffp0FCpDIUz4wJKlJW+5cFx+/pgdrZagzXOkx/G+HVddtvlw5DfC+R3OFHcSyzDDft9BA2y1qxMBwXlPJhNcu3rVC3FDq9QgxX3IY8fXRYSDSALTOfF+k9c01FuvxLEUKObFW2b+MJbQEI0lEAQMj0eONS/Tcd9xP1jLO3PCZTAcYDgc8CT1808eA8mmn0vzk+0ohahVx5ybJ7YfKBl/OvCe5EVb8zq/kmYu8a0rbR86FuBeoqz5XHkTaTarg8sAcWUIoDsR/DRodU0+rm7Mdp7A3OFyOZ1zzeEPqXaDZvATm23tIGYgbznEVTPhHFWIgZeslqHmg+RzX3sOly5eihNfH9IhQcfHeB9gGLXqKloq+/v7uHr1Ci5dupxqmpIFoU1vbW3iS1/6Ei5fvoLhyLufeNIzrwB//G74oGAhVV7tQj2b4dq1a7h48aKvvwBTKZArNKEPOFbQyY2TWF1ZjfwNVHx1IsLSaAm0FngK7e+cw6yusbe7i2984xtN2ALWggVANePCa49ra2se4IMgAewTiLh8rbUz2EwmEx+JU1DJFcHLetk6GA6H2Nvfy0YNtQCTx1buEJGclTydTFAbdU00YfF9hPOS86V2zdF+GsC0IGOSc0q2q6acK0jOc30ohtXWLWDPKJHmfRLjxuA1B9alcg5DxwbcZYfojstp9kCIyhgHX3vyyID5sxBuAETxYx9rkB6c2SxMgQCQg6mKHwslP7VadRIPKGa04TzCdYwRDpcMGIvkAQymdSPKkHWPHzzDkYJSw0n8iWE1SWyLUMZ0MsPly5dx5cqVVOsI9ZdHwumyLcundXIQbA2rrmtsb29jd3fXH0QyGiXaqH83mOxIJzM3Bd/j04Hk8kg4+AigUvaG9Pt7+7h86XJ0zWh3QKybUBJywOREP0dzHykwcdA7II2rDrQ3OWmFI7faSdcpp7XzPdnuOQGi39HzTIKlFHglfnT58p65n0LVrw84y3kix73MK1dHnZ+8XxIyVlvJv7cL5I8NuAMpeOeeAe0BQfCRzavW+6lmw5okADg1cVhT9pguOkRplxpEmZ+YLw9qTusaJ14cTMFaaC0DDHnFLe6uecaDow6rTpoY7g2fMg8p2BgYtUnJgJMd2EHA5QZtTWn0xriOPZGKDf9Mo9HIg/2sTg/LIMFXKGNvdx/VeBK/s+g6JozKMmO/iEmM5q8UCrELWDDXDdjKsjRQMlkn7eg0liBh8M/xoYVhNDb4fXQDhsVLCVysuWcpV6Xn8nCTnOUg380BpQbfXPvn6miVnVgiBbyx2oCIYkz2LsrlfTsB/liAe5/G5XSs7Zzc2AAB2NraikCqm8uSqh54UwDxP/z/aqTmWjWoEoCT78ZBBQAh9K3kNVoHjUMdvApEamAxLwYvxioFZJKHnEaTDPQgQGo0PuGGj0b7s9q5lS8LOeGSkfxEUNfaKfuig9nOQqcKm6py2/Ml6EUXgzKJG564WqKPw4fqiqokkiQ/z2mz8btLlboTdHtIsoBdu7q0Fmppu7n8D0KWdp1LkwNYyWdiKRr5S5J55dLpesuydHkaKFttrd4vWSpd7ZSjyA/a9e8D1l1C7iBUdSeJhQ2I6LNE9PFw/TARPUZETxPRR4hoKdxfDtdPh+evOCyTehB0NVRVVfiPXvtavPrVr/Z+USJUwvcs6hQ1Nxfy9REhU797/MAH+2u/zI/I+5wb3zgFdw2uPlkAACAASURBVFBII/z28h0QpQADsbQw4oFw3/gbDT9SGLkUoKK/X38YYkHUGA+NxifeiUKzdVyfyqu5SPLg59wmCRgGHjjsAIM1f/xM0jvRLg4RlK3+SICe/4v5iXLrut3uDP5Otbeqaw5EWm2jwJ/bWLaXRRowrbLimDMErsU73yvxnMvTAty+ZQL2YTlWGX1p3rS59K25KO539e28/Gfnz23S1iX1BncAfx/Ak+L6lwH8inPu1QCuA3hXuP8uANfD/V8J6Q5NbKICjQSW15LqusaTTz6JL3/5y/E5x3ohQlzz7j8m8kab8KWfmvQN8KZgIcvTWmMERw5hqzQLAgsMNK4aNPiSaGnsNmJBYYA5IJblAYnmzMBdu2b5X0sRD5aA/pCZCBQGVUJS1yaLNpDG3xrIRX/q+7HuUuDo9CT+6TZTVgE/SwQeKFpHJiCykEMZzPQpQla9+pJlaUnKKTUt4Wek0QpNLu8SdQGSFkI5YWP57HU5WruW71t108JP82xZRJpHq4/189yzXFmWEJtXGz+s9t4L3InoQQD/OYBfD9cE4PsBfCwk+SCAHw2/3xauEZ6/iW6DjZGT+roDuIG3trb8ocdBK/ehZvk9mPnwRKgEUEh/sTW4YplIAYQBSmqACHywlQAeDGiwWmt2Mu9YbnA3RLB0CpxiWcLnzfcZONH8zWmm/G7t/Iqj1bW1uBeA0GjyCa8FcJNCoKJmyZwGL6lp++dV49ZRJLV6vtZ9lnxopLRPE/6IkraXddJAGvtZ1zEDNhZZwFwC7Mifshj0fc2LFEQS6PoIJw3Y+p2+U3ueNukCfwtsNa/6HYtXq+270ul7VnvoZ32sGlmHktCah/pq7v8YwD8An7gM3APghnOOtzE+D+CB8PsBAM8FRqcAbob0ByarkeQzJum/lcSKnnOyU/y/pIxadK5Ltb54v8Qjg7DQLqMGH96NFoRYqtcAe6ZO7EYIz5LIiS51aTR47xrglRpwvKaopZa0Cs5nNpt5YRmuoztLDWzdB4kGpsA08U8K908CIg5e05bCCY21wm2QCF+VNilTCU7Ju+bXqlf6oH0rl4fFh2x3DkltmfmWVqzTdZXZ950SUOp8Shp96XdJOPQBb40HvPy3y8LQv3MAmsOaLu3byq/U5jkLQNb1MNQJ7kT0wwAuOec+c+jS0nzfTUSPE9Hjly9f7koLoFszbHwbjTkqQ+FSODsVEUg4f/9Sk7fSmg1zX/Pmc0g/xkKAOsm0IR95yAaF5HETjPJXW8T8xckmyqcU0VONHR4webBxeZxHrr35dz0Tbp5Wg3CR6UTWebKrSL7HWmZSRzQmPV+3tNECH/YjGzysOks++mhgFnVaNBnQ0BM/B1K6LP2ufMb56P7onFtz1qsl9DN1LIGrrq/Oj+/LDUp9+bUAvqsPcspAThhZaXPP++QzL/XR3P8mgB8hoq8C+E14d8yvAjhNRLza5kEAL4TfLwB4KDA3BHAKwFWdqXPu/c65R5xzj5w7d64Xs3qw6yVmMQ0JQA8gXg3SjT3+ehDBtap8kC8KYQlaHYK25pO4Q3yi9K8yDxxEevnXCXiKEgeNxYBUc8xqCOxyyABb1EBdUx/tguG6WYOtCKZo94/km/nV7piuyVZRe92x/q37SEb+1Nq1Nn+TenUAlQW2fajPxK3rOkbF1NpxX+DVoGc973OvdL8P5bT7vu1VshpkX7WsvB5lyM1tXRaIlV9X/uYc6CE0SgLmoNQJ7s65X3TOPeicewWAtwP4E+fczwD4UwA/HpK9E8Dvht+PhmuE53/iDmlj6E5I8NRqDNnZrbzC3wCCRITBYBjPXeWt46xFy0xaHc0fAtkS0Nqy+ik3NulnrNknoFUFcLI8ArlBQI0G37iE0P4XCicokFJWSmNt8GMB2nzP0sqc5595SAY5pX2qQVsKBAe1csdojKavXRQGMx/f19Z+0e3GkJSdoKHtci4B05UjeM5p4abCouqg71uabcm9wGTxrvMvCQD9L1eOxWfOaugCXlk/TdYmpDZ+lAVBqf7y3T5jSFt8FmWVtUPSPKtlNP1DAL9ARE/D+9Q/EO5/AMA94f4vAHjP4Vi0Bq7/LVfL5BqDP2i6sMmlrr1LoHZ1dFU4x6AQgoaJ9xkcrTMwI1/CFWEBWEUEYncLeR87Wxj817H27lwUXktLSz40r4ufGBPtW/MhQU4/87wIU7ISmrtD4ouPYOba2noS1yPw4ttXARRcsj2/eak9qaQmr7XtBOArseNSWjLyP6Jmtytv8hECi99jV1QOSJMJbUxQKSTkZqKkvaldNw3UOs+kHdR967elrc+jCVplWtp/l2VjPbfaxMq7xCNRiD46GMZrDkZXqk8u3xwoW31hCRzpEdDP+tRHlymtkNY8PpxOPN8mJufcpwB8Kvy+AOC7jTR7AH7iUFwZJAdFrtPkzk0fl9wHDKvCBPMbXwCC2BYfgJv8ickgag+a6Ms2eMpqK2IwOASFmaNPqvQO8OFvgQD8nv/x/jgCWwLYaD7epn5+Cyx8vXy7xQya/BzgSJbRgCnIC0cd2rSl2cDviOXYL0xTFRALLjPwCYkVYE3Q2oX8ueyK4gEbsky509YhhE1QeUVrhZp4NUmbyL7JTDA9GUvAaF3ntM/ShLYAgIXLgSgIb8vAkPlr8LF4yQGmfFe3TU6IMFkhBpxzcWewhQm59teWovWOjj2TbTbXhD8AwvhXyp+s82g0ah9kg3Y8Iv3uYenI71DNgbru8KiFAtGdobURr7wGEEcjgVkz44HjgvbsXHvSR9CT/LhGixUMRo08N6ite7JLLWshqa9LgbhibRu6HCWAQljj2XQGVGEwiQBpkhFLo2GtPslTtL+VhoUHP2O/+P7+fuQ5Bx6Sj9zkbdhOQy0kbifXpJF8yXt9tCbNmxR+80zMnJap8ynVWQISX/fhWfZXn/QWj9Z9SRr8NaCW6qeFhzXfc+X3af/SHCzlac3lbJuHscVKjlVnnVfJQpqXDuOW+ZZQ1wDLNcRgwCtlKPmAOqgGqIJZJ4+jayZI4xZxzsWwuly25fLwXhIFCkR+TbviUV57cG4w0Dm/5j05BKPDZOXyZUYlX2+agagHe23EPT25ZN7SPZK4i9AIOnbhEFHizon3hDCSgjLXrzkwGg6HSdlJPvB9yEsndR65PLs0N01S4zvM5CyNd30tgWbecg8KgH2vS+l0O2X72wDC1jO087LSJmM4009dAr0rT+ZHKhExjcq6D5+HpSOvufehZMCDQORPAJIxtlmzJaoC8A9iQzbREFm7qOMReyWJCygLImqN1GjuaeKW5OePrLVKV1UUA2jpsKdFTYktFh/JPjyQjZWatvE5KbBAs0xS7ix0rnF1yPK0li7lCz/3y1CbSIzyTNpEq0Nba+Tn0fcuJkEMIaA1cyf6RPFiCTFtXeSoSzu3xk10GWaoj/ao87eAMsdfX6tCa9f6nlZULD7l/Vx9cla4rqf+3dLoUe4r+Y5lOVj1LeXTdd/BJX0tsSHnerTy7lLs+tCRB/dUq07v5waFP+GH4pYr52oMBsMQ+H8UD3VIwr36lOHMUYJzwS0S82gmB4ey9Zp2269IQPSvgxpoIem7BlCR3dGz2Qwz8MYWf+iF5LOiKh4AkQwe/yOAmZj8aHzqkl8mHnj8kUoKEg5NLAGcr1tRCVHQ9giABgOXptPWAQcP06CozWCOhBn7SVgNiVangUCyJ3jSoKKBk8eObKecvzaCCPLgWtJMu+7n0lngVcrTAjgN5DnQL6XLCumCwiTL7iMENK8lQWHlURIuWnDmQgIn5RDFoIDSsrV4lfe0UDysBn/kwV2bXLLietCy+c9nd1bieC5/1NtyONbOn0a0t7cX1xYD/BHWwcceafzGuhMt/7AGFC2Umr6lqNi7oN1XLU3PD46ZEyfm1E2euVjviXYoNFmZxtK8uA6z2azl0uEDp6OWHurJJzhFAad4kb5/3X9NEqXdOOEvDyDrKDVptWYtwZPr6uDg6uaIO24P7X9PLI6e84jPrJ1OpzG8hTUOJVngKmOx6xhJMh/dZlpzzQGoLtuirvz5OokZXxAUrf7tKfhz7ZcTGhavfeqSK6ME9l0CyuIDAsw50qkp8A3ec3PlIHTkwZ2pVFkJpMPhMIK717KaA5pX19awsbGBlZXlOJnl0WsA/Pme4dAOBt/caplYvvQ1E6+SkeDOZ7aG1BSWZNZeiDjnDxWp61AeAY7U4A7atSnNHZJlgo2pEP4EIRfBN2Meyut4wtBs5t35EpiCa2s8Fu9yGwnNN13JE4rWE164QvSpRvFAE9HGzKfMT+Yh3S3JqgohbOTviiqMlofY3x/30pTqusbOzk4RxKzr1sEetd3u+jo38a2yzPkh660fKT6ZR7YMrTS58nP86XYqAWdJKGrA7wPaOStJWyAla0Cm1+XLxRWcj3XaE1vYuk21dXU7AF3SkQd3CZiWVNaNsbTkzwxlarT2JSyNRhgNh1hZXsFwOAwAUGMwGGA8HmMymYDGFE8lqshhBg+OVActj9LyE+3TSdPfu3aClebXuRN/vEU4m7WGcwQimUfqewbSAGCWiZe2DQkwN9qxIsyms3b7ibpxvr7s4OYQZTv4s2VlGxBR9MVHUGdN3wB5WT8guIScCwHeDA3G2Zoh85Nvj9TNhNCf0iU0mXjrjUHNAhOZ/3g8btpPCH5ZtvWtIslHWTV9NV6+LgFoIkioaWcrX3lPh3iweJJtm+PTAvouIC29I92FOr0us4+A1u9ZvOi0LPQSQUn5+iWgbYz9HE99+O9LRx7ccyYR0O6Y4XCI8+fP4/Tp0/7Q58k4rtP2/waoa4fhcIj19fWgJRP29vawvb2d5OtjigPAtNGy1DhINIsE/JpJ5s/0bEIf8OSoa4fZjOCENHf+R8y7JTzQnqhauyESbPJgEfXiD86+nJAbYx0PSPVBU/PAH3jTjkECtBFAKZipUvOWmnbIdzgcxjNjs31uWACsLcn6AWFdfNitKlfqaABgF46upwbteF+DiHR/Ka2wa+yWgD1xh4U2s8DEyssCwb5kCcec9mrxngN/6x39nP/pjW/WUkPNE9/TIFkCb8uiyqXTSoQus1WeUB76Us56OygdeXAHfKXj6TyGGcgdffrMafz0T/803vrWt2Jz8xYuXryEra2teL7o1tY2xpOxj77ngGvXr+HW5iZu3byJS5cvYzqdqlPSfZn1rO1zNHfHRg2dMBgOMBIfb7ke/A4vdZzNZpjVs7jDc+bEEArAoeuqJ4UENQlAoOYQahLpQfCgW4eTXaO3ogE5a6Dl2j68FP3uqXsmZt7wjDSfuq4xHo9bddJjoAHdKK+CljyACx+YmYc4MdGs7mHAl/55Xd/SRJZ9KNuaBY8Mhia/5cj0Oh8LfCSgyz6x2iP7blUlgjInbLq0WKs8DaJWX+UoB+xaKPTR8M16K6HURfIM5Vy+WV7COJu5ZuWZHHd6COWsIfluy2I9BB0LcJdkmT18PZvOMBqN8MADD2B19dXJe978nkRtaDqd4ubNm9jb28MTTzyBp556Ci+88AKuXb+OzVu3sLu7i83NTVy9ehXbs+3khHnJSzOZvCk+CJEoh4MhlpaWsLKyEnmcTqeYTCaoqgqrq6uYzWbY398HTQlu4LwQIR91sSZK/LIAWho1a3MU1HUebINBhcFwEF0ZGpgHRDGuvK9UG9CT+pUGKT8Ll/wBidgcqLyAqWvvJoplOKUph/pqt9NgMIibnaJgElSLVQmyDeTa0ug2UtZDI/GMe6odLOK24Rg6mDV10SuJNKhp7ZFASSjnetYcnpIbc9wP+oMsBWFeZzRn6xoAVlZWsL+/n2jKJeHA9wfhlLPJZFJsN2016TR9AE0LGa5/CfRLeVrCplimErgWYGcxImNpyHd1uYehYwHulpmod+Y553Dz5k186EMfwle/+iy+8z/+Tjzw0pfivvvuw+nTp7GysoK1tTUsLS3FdzY2NgAADz/8MN7ylrdE9wz/+9rXvoYPf/jD+PM///MGXAQoyY4dDAZYW1vDcOibtKoqLC8vYXl5GcPhECsrK5hOZ9jcvIXpdBpXcvg6AK72IDFA8D3X8GZ4nWo1CcCLscKTbDQaRavAORe3PTPY+Ino8ayugo85WA1xUrs07+i3FXXnMqXGHvsqfpgABhxygT1BnAchCQ0QgVuMZ67DdDrNm7/yfdE+2pWk84XgRZvyuvwSsWUgdxNbPHZp6lq46qWTevxbmrQE/S7tVmqqnNfe3l7yfcJyceTy1d81LLKAnC1yoFlBZOWT02otAZorr9QefS0Qy6LLCb15qUuzn5eOBbgDqbtBAzvTdDrFs199Fh+79Fv4gz/8fWyc3MCZM2dw5swZnD17FufOncPZs2dx+vRpnDt3HhsbJ3Hy5EmcPn0aq6urWF9fx5kzZ+LAv++++/CJT3yiAQO0XQrOeR/+vffei+/8zu/EyZMn4+TxPna/smR1dRXOOdy6dQvXr9/A9vY2dnd3sLOzg/39fUwmk7ixZzKZYG9vD5PxJDvQ9TV/OB4Oh8EVVIHHBn8g9t8RJnACVOHCpHIOA9G+2r+dXTEkQJoQjioEYVbXccUQT97ZbOZj0FQswOqYtxYcXG4nmFMjSOQY4W8LvBy2nql9AWGJZZfGJsuxgC5aTa6xHJgXqVHmQN2iPhM8Jzi6XCISIHVefcqy8pCrmnKk28BqFw3aEuxL4JwrqwS6jWJF5v1c/a1ytJD1115TYYs0F0qkVM5BBISkYwHulgaRo+lsiu2tbezu7uLK5StRox0MKlSDQXSXrK6sYmV1BRsbG7jnnntw7tw53H///bj//vuxsbGB/fEYj3/60/jsZz+L8Xgc3Q2SJ6aqqvDSl74UP/ZjP4Y3vOENWF1dxWg08u4IwTO7YXZ2drC1tYXr16/h1q1N3Lp1C7du3cKVK1ewubmJS5cu40tfegrPP/883MQhZ/5x2UtLSxgOh1Fz9/+GHkThJx+vCNre3m7i58DBx9gBhvATaSYAN1mFkvmYRKAYxMIDXVhZUDXmOlszqWaauhF0bJs+JrqluTYWhHdNsSsoupeEK4YqQlWXd46WTOlkMxtRA/Bhcle8SZhs14oFbLEeZLtbYh7iA2sJqDXIaSFlPRtUA8xq70fu2hmtAZvzlMAn66bTExFOnDiBvd09/+1JuSks8LXKL1k4+ndOY+875jiNV1oaVx6PtcYEDTuy0QZ2azxY7XMYOvLgbmnokiwTrUYNN+N1qBTWO1Psh2bVCoJ2VyVANBhUqGsXgZg1k9zAm81m+MpXvoKPfvS38PTTT0dX0MbGBtbX1wXgjrCyvIwzZ87gvvvua62imc38gQ1/+Zefx6/92q/h61//esu9kGiS8BPj4Ve+Ei+55x6/3DMsBWXAZz6rqsJkOsXW5iZ2dnawu7sbLYbxeByXgu7s7ODmzZtJRMecFhSPhmMNnaokFMBoyS89HQ790tS9vT3sj/e9JlOLsMuBdDvntLV44AIINKiA2glQRbPHIRy8ErW/oEkNh4O4/FG6vkqapCQek+aKGlA8AMbViN8adH45ELa0Tg2OcU6wgNJr6AtKUE4YyOcSZOXKlRxA57RdDew6uii/Mx6PkzLN+R7qauFBS2CIfsn1n26HnJbP+XnFBUJY+39ELg13HfiMy0oNgWLxbV3f9W4Z2TiW9M4NYMBrmxVVcDUANsONdFRVIOt910SJlINGd1Bd17h+/Tr+7b/9N/jzz34Gy0tLGA4bgGWw5Q+sKysrWF9fx4n1dZza2MDGxgZOnTqFlZUV7O7u4vOf/zy+8IUvYDppVly0NGnnQBVheXkZr3j5y/HXX/963H/ffThx4oTfhbu0hGrgl2Lyih3mdTweY293F3v7+9jb3cV2sCSuXLmCz33uL/D445/GrVu3Yv30wOT2OHlyHefOn8fa6lpiPbB7aDgaYSAOg9jd3cXO7g6mk1l0EU2n0yh0bty4gf29/WT1ge5XBgi2UjhOEPPnBSV8kLgQGM6XNW1i2ThgOBr5CV7XmE5nfo9DB8A3gnIQj2/04wwYDgbwZngVz/TUB1Prreuz2Qy7u7vtdfgZLZXbdjDw7r4c0HmrDGELvAsgFMJyBCDi3cW6fWU9JQDGk61EORrA5TtW3lH5Eh+beTzm6sxl8YozuZyY25SiGyyveXOoEUuA5pS2eA/C8gOas5aF9cR8xpAZSmjrEMEla/F20ZEHdybLvLTS6LSyU5j0h0NyLkZwlHmx7zkOPONDqpTY+/v7yUCNZ6SG/KjyAqIKZ7kSwce8EREq67r2ILizk+5oU4OIy7x18xYee+wxXLhwAadOnYofcPnDqvy3vLyM0WgJy8tL0ZJgIBqPx7h06RIuXnzRh+EN5SwvL8drrXmORku45+w9OH/+PFZWVgLYDj2gczROYZ3EtiKv+jDIbG1v4+mnn8bm5mZ0B1hChS0RADh58iRecu4c1k+c8IJlNMJQlSfL5Y/K/G9W16hnM+zs7ODFF1/ElStXWssX9TjydR7hxIkToS2b9pOHR2jQ9YAC8PYC5xymkwm2trawv7fv/f9qDFsgJz+Yy6B4nrfGHYQ4Ttt5sqDjY/0s94AlMFIrs3HByW9RVRjTsb6iHfyH+xqTybTo6rGE3GA4jEpC/NAsgNyDOreZi20s85FLnGV7aIGr+bFCL8T0cMmCh5JFkAgVraRlBNJh6diAO5OlSVmmrEzvfyD6YxOp6dQmCddIaq8BpRtwACQrVjQAtO7P/AEhJUomgdD25HPThCRgPBnj8uXLuH7tuv+YKSZNYsJT2K1bDRJg4H+z2Qzj8T62t3eSJW2TySTxVUuN6/r16/jCF76ACxcutEIUO3BgtKoRZmGpqCzbOb/j8+rVq9jZ2SmaolVVRQAej8fY2txEPZslYCfbn0L5XrD6/QfOVwKAFxZbW95NJXeT6naWPLEGSOR3MmtXg/wty5LrnusgWMaTSRRmut4a5FkQ8sqhFBA8oOcsy2aO+LRNFNT+2qMutw8oJc+DzJHjWz4vfTidTaewbTmbSjigx0gv7DDeayfuPn9B52uNr9tJvcCd/OHYmwBmAKbOuUeI6CyAjwB4BYCvAvhJ59x18rX4VQA/BGAHwM865/78MExaoGlJeKuTkrTxT7PZRa91bl4s8IP8REx+e7PAx4xhK0EOEmXW8Ttd65tl3YBwuLKbwk1TQZSs76ZUU4kTTPEQNbFwHSNmsiaGRhObTCa4efMmbt26lSx/lCtfGsARIYjRnlj6lBrZrswPb3QiovjdIAow2ZcGeVzjceIxd2VlBePxuFnmKsqSoCh5mc1m2N7exs7OTqsfLK0752bgZ6WJrZ/pnZoH1frkeGs0/7y/V8+/HBiWyOKTyx0MBkn/628IfZZZ6jyt8nMAf5B6yDECNAphl0dB58EkT526XTSP5v59zrkr4vo9AP7YOfe/EtF7wvU/BPCDAF4T/v2nAN4b/t426hpcOUkJBLCJ/ncB8CQAGQSH9rmYCQ8aFFX5AJLldtFqcE3Z6UuBB7gkfSvPzACKWoMA12gmCq1RfkPwxbp4XF2smzC3+VoLHgnOeslfq73hQJSCRY76AMdBNB4iSlxQUqBoTVJPPguMc2ClBXIuH/5bEtqWkNF10u/l+OL0XcKmC+y6xqHOtziHlAVi5WnNY7b8cmDYBbIHqY/VJ7ky++Ylry3BfVg6zElMbwPwwfD7gwB+VNz/kPP0HwCcJqL7D1FOi6zGkRO0JJkl0DmEw7L5A4/jvOrWuwk4Z/jQ1oRcx82ChF0kIVNEdxEhGaxSe7cAIzt4BLCX2kgO1sQNZUyYJK1K0/VXunNy7+rnTPGg64IAzb1rXXvXUxP9kcNN5NqzdC9Xh75kgQWDVq6srvxK4KXL4vx13nqVh+Sl9e0EZevDmoPWuLQ0c+uayweQuJWsNrPqYPEq276PFh8Vph44ULJWSm0i+ToM9QV3B+CPiOgzRPTucO9e59w3wu8XAdwbfj8A4Dnx7vPh3oFJN771V2tXVvqkQ6AaWWnTDPbxOf/pOQCAMOiEhRAHhco3fo1XZIG6VU7yDvKg19ICVZF6wEnrgSj/hb8ofJydzipPU9cA5/ykz71Ecqeu1rQ4H4sH/VuPLWm1aP4sEMgJIkuD66KcEmPxL8vSAC/z0c/5tz6gpEtDtXjU/MryLcXM6iddLytcgi5D1sVq+7710HxqXruUjlK63DsHpb5umb/lnHuBiM4D+CQRfVE+dM45knZ3DwpC4t0A8LKXvaxP+t4DXw4Ka9BE10XQqqW7IcZGKWhogOFW4fu6UyLGNYBKCG6PxBUkfhvjzNJCrMGa5cN4R38sTt5x+TqWqE87dU0k3X/ynib5IbU0IXKCRU5s7ffPgUnL8jHAssQHr6zRVoMFAl1kgXcuz1zdrPy0ZaF56tPmufwtgLd4yglMa3xY+XN6nU+ujl0g3yXUtKDqQzKdrtNhAb6X5u6ceyH8vQTgtwF8N4CLFNwt4e+lkPwFAA+J1x8M93Se73fOPeKce+TcuXPF8vsO9Ow7rrlHRMm182gb02kLIHmvg6yJFV0xMh3/Z2l1cK2yS+WV2sXk2yGpf84ikb51yV/MW60YMq0jNUhzaaz6yhU9uf7ntHpJX0kr0mms/s61a580XdS0Vd61oeubGwPz8NAFpDLP3MlLJVDT961rzYOVTyJMkGrIWpkpKTAlslZGyfetvxafsi7zzNk+1BdzStQJ7kR0gohO8m8AbwXwVwAeBfDOkOydAH43/H4UwDvI0/cAuOka982hqKvC2ediLkmNlf9L0mF+bacvtcorUUdR85iRkmpXJ9q0Ca4ZV4q1EsdKZwGqJSxzmplzLlpWVn78W+7u7dM2ubIk77zZq/R+LrZRLr3WfL2vv71xqaTBlsqwBIEleIma8adjppeoJIzl6ivr+bz5J/fF9zFNXQJ/HsXM0upLwkmO21I9D6KQWmUflPq4TluQlAAAHGhJREFUZe4F8NuhwCGAf+mc+wMi+jSAjxLRuwA8C+AnQ/rfg18G+TT8UsifOwyD81Q0p+1IDT7ep/Q9orbbJzHXYHduFx8OfnMHbwbSpiCAJNRrdIcYgkZrLVqD0L9zZqu16kXzbmkhbFVoYNZL2ZjkcW1W25QOSpjVsxg5MycEOOJljt8cyZjufTQ9a/Lrts5pkbIM3Sc5a8PKx6q/XMYoNVt+bo0NvRzXEiQlSyEnsHT7yGurffTZDDo9/7aibcr6y/ululjjrg/4+jb1y3h1+VbdrXbPCYJcG0Y+qfs7Uok6wd05dwHAXzfuXwXwJuO+A/D3DsxRO7/kL1NpwvXOV7Rb60NRAMDYiSiDRwKYym/OG1+sOlkagXR5cJnWYJTAlwMKWUYsFzbIJAdZzKH98gYX/bzrcOAckOT6WoMBb77qa8Jy2ZIvOdGJKPGDa/60Nphrf4vn3DjW93R5ubpb70l++ggRS+BowMzlz1T6DlbSgltpC/sUcm2geZlXSFvzxL5OLUc93qz5qeuea0+LPxkL6TB0mKWQ3xIyB4LRMX2Bop2ZfiHtmINITqkV8l+9I1DmnVvT3oeK/Ln84DJ56ahqLg8L8EtgJu9rwJT5paditcsqbW4pTVyr/K53OH6NpL7WW1darnsOBEvP+2igOQ05x2uuTSQvuTJK+RNRPPqwpdig3B+y7NL40u1Rwoa+87skfK37WrBaAiFXjhbO82CBpiMP7s7lF/h3gU3S+M2yldazJB9C4xqBmmDIN7iWslbHtwAVqRZr1Sl3r1UvmS8PDtgTrmvZoORnXuFW6hMesLx0UZfXBYh9hbc1kbL9ltHGdd6zWfvYPAs0+oKfxfO81AUacix0abiWcJXP+gK+1e5y/PP3LjkGc3Uoab2aZGTXnGZfGmclYWfdk+Mml2+pz7vqdBhQZzry4A7kB6OmnDYoTb4EtJ29pVt/cNT355mIuQ6va3GMmlhzX5qoZv7Gh82StsxH/FVV+8NhThOJIXYNgNBaSo5f+UyvbrH47AOQOY1cx+fpqzEBjeDTURD7vFvStkqa+UGo77t9QILzsvYA6D7v0kB1n5T6kdDE/Jd8SuC3/M7WOOvzIdyqU07Tz+XRlX9XWV15lgTlvHQswL0F1mR/qCgBYLL8kSVtRtvOaQyauoROSavm/IlSU1Wmk++V6mhpVaUB6mPU5wFWD8iu/QUlnnKTTmo7enKXgEVelz7IcpqSRVDS8FkAWWa9ld9oNGrx3TV+Stqwda8ktCUvubwSRcW4PqjAds6hGrTdVqbL2AEgD+DD0bBZs5YpUy4ZtuYH3+Ogan35LQGnZVXqcjWfEpdKYyYqm7qeGeF2GDoW4J6YdXNK1fge2iBSSt833657OfCQg6F29hZ7K8+u+lvaoXRJ5bbzl6hLU5P10dQ1UbSg1nlbZUmecgK+i2edn3Wt86kojSUuicPocj6lfi/x1dWXVj3k81xfmOOiAH7yHRN0Fa+18WHb+iDIYO2c8/HmCf5wccVHSbBZ/WaR1T5ascqNW0sByFk1WljKZ7psS6nUxOPosCB/LMDdor7Aa2n4Op+DgPi8zxNgUr77kjYwGo3MTrbALDsghfsH6DdocpMnJ2C10JAalRXGuG++VlqgCSXQlbZP/8q0JZKTkvO1VpfIGCjZvDJjsUvrlu/mLBznXC8hrttJ81xSXkrWmPWuvq7rGpNwgEo9s4P0WX2XG2caeEuKgbzuq4xZ1yVBXBJAOUtAp5s3FIWmIw/uB5FcXRqhJquzBoMBBsLXXFrRktWSCLZp2qNKPDj0IQPWhNYDtWTh5FwVVj10/vpeiSwtUg9mq482Njbw8MMPxy36fYBaa1OHGTO5fu4Sdro/rIkpwbuPhVHiteu51kotjVOSpXnmwKur73NkjVHn0sOjS1o0kCom1vjKlZuboyXrxMrTmnf6PavtrLbMgXxJSM5DRx7cgW4JV3qv61lOE5nNZpjJte+ZlScWxcEU/Iu6PAsoJahInnIaRpO4eWdeC8J6zuVqIOoCz5Kw6KOhs+Z4/tx5vPKVr0RFhPX1dSwvLyd56TwS0EIb0PS7OYrartLOc3XMCfeS8MyV2Ye6xpsFYF39bKXJab85jbWLTw3A81DXeC1ZOhZPOcVI/y4Bax+lxkqXq0eXUDlIuzEd+ZOYuho6N7HmMcV1B+eAxNJ8i+VQeTCQ2Pikvwl0fXSJ99RO0751twYh85OrWynvvhM+p3UxXXjmAi48c8GvYR+P48lLlnbeGhuEeL4lpysBnDxjtTS5tLDV7dHVRznAt8auzlun0+VYfMq2lTyWNFj5W+dt9VVOmFhuqsNo+tYYyVlAXW1WAt5SGp13Ll/9Xt+5mFMULF7noSOvuVsTtASsc0k6xoQ5gDFnSuXS6evkA6OxWieCOvJgkExIFZaXqFk/r9+TfJgWRAbYnXPgDVF9LQTrr1UXPRlkSIH9/X0zcqIlcK18i4I1WAqj0SjLV18tzbJschNevmc9k2m6xmIOyLqALacR57TNXNvrezw+dAjenDJUysuqh0yrvxvlgN3Kv0QWTxYv2kLMjXG9oMBKY5Xfh9cuOvLgritpTR6ZVv/OArZwmXRp4dZkzWkmfSaulWfDVjcQJGXD0KDmWBmky2G+hsNhcuiz5V5KyixMyshLzrNUAGv9PFc282xpdLn6TqdT7O3tZetlgmQmRHJfQZC731fLm4dyQpb7Q39AzWnuMopiqR4VVa2yqqoCjLlTUtikEqHT58AvN2Y0CFt8W8pBF864Ov8BV9ZBfgDOCf7b3e9MRx7cmbq0QRP4CpqjXJIF5LUd/YENaHy7MU0HoFt5aM2D07T4MDCjr+lmaRa5d3W7TSaTJBhYl1Zl3ZOHXxBRDKKmJ5PZPwcQTOzCyfFj5T+3hqSEXK48Pdl1++aEWG825gAE0+JTvy0h09XXuh65A6KXl5ZMQMuNvZKSwDTPsXT6WXauG2PRepeIiophH6Hfp/8OC/rHAtz7SLt5GqLvJHLOXpOa3OuTlWQtuDd4iWMXb6yFd2kg2aIzEzurmbj2gM0Jsi7etXDg+sh7rEFqQdSlJeYsh75KQBfvpbSSD52uS6O0eM6lLSknJcpZQTlerHLkuzlBlBPMRIRKbF6TRxvy8z6A27deJSug9LyPRThv2/e1xErPb5cmfyzAHeivrXLaHHhJrVtTbqCW7llukaQs5f9mi2F/fz/RNOOHVWPQa/OuiyyQ08+J2uuaA9NZU7NrsGttTP7W4N1Hu+E85V8rP+t5Kd8+7agnqS4vV4YliGN7U9v/2mX15e6VgMiyFCxi7dcSADlBWeJHlj/ocUJWCcRygqNUH/muhReW9aSvdb9boTdyJPva4slKU6rnYelYgLvW5qQJZWk+FoDw/Vx88a6Bq6lrUgLtgzE0WeCqB4Y1AUvp+V7X/bqui8fKzUu5wWqBoNVnJW1cUx+Lh+vq4+hUrUnVVdcuAWm1r1VHWV7ObZGzQrMKSkZ46HdlepnO0oBzWruVV2l8Ad5ikzt2df59SAsL+XdebZuv9UHaFsBbikhf/uXcyj23rI6cQDgs6B95cNeABLTNM90BJamdm0jWsxL11Tx1HZhWVlawtrZmTlTJiwWWXeApr3P5a/5kWVrgzQOEubx1OTlN+CBlWGXqSTqPtSbzyYFr7t3c2MyVbYGxxVuXcNfAXUqb4zdXtjX+SsIgR6U5WBqXXfO79A7Q3vhUSpsTkn0FfO5+1xyZB0/60pFf5y6pb2NaWpxFfSZP7lnXxNTv687jVRol60OXUyprHgDS9ZB/cxOvCzS0dZXj3xJCOa1d16EvQMuy9/b2erehLjfXhpZWnsujRKV2sfjqQ1Z7dgm5rvpY9yzlw5oPckxYec0jAC2y3i+NE+ZlOBwm1kXOEimNvb7jwpr/XfW+HdRLcyei00T0MSL6IhE9SUTfS0RnieiTRPTl8PdMSEtE9E+I6Gki+jwRvfGwTFqaXlf61rXreD4H9Z28TFYIWu1D7wIcfT3vwLDesbSvkhaT40enl/l1AZhOUwINqx5Wmbo8LWxkPiUt0rKk+lCuHTVx/taa7Vy+uftWu+XKzJXT97vOPIqQFPx98p5nbOf6J1eexBCOINmFLV3KVE4QdmniVVUlq8m+GdTXLfOrAP7AOfda+CP3ngTwHgB/7Jx7DYA/DtcA8IMAXhP+vRvAew/L5DxaTU4LtFZpaDDI5W1JXdm5BwXf3MAEED/EWiBJ1P4Y2geAYxqn3AeFYDe5SVKqv75vTTb9PJeX7Cvdb1adS0JM/+Xfui4lwE/KdvnJLfPWIKT5zwn6VhvNqeiVBHYfbTenrZbGd6kvc/WyNN0ushQTzV9Xv2u++ghPzf+8go7/1nV96MBgXdQJ7kR0CsDfBvABAHDOjZ1zNwC8DcAHQ7IPAvjR8PttAD7kPP0HAKeJ6P6DMmgNAoPHVnr93ErTJV1LZZWkfg58Ss/1YNGrcEragHMqRkmmKvEdUm0Ge6LltM/SxAWQ3aFY0rKtMji6ogZXq/8s7d8i3faW0NBkle1vtPO2/lo85QSJLo//+c1A2WolxKcScV6W8NL/ZLqusavrkCOdb07A6fsl4O2jTCVthvJ4kHzl6mPxZClcuXwtPuWc7asMzkt9NPeHAVwG8M+J6LNE9OtEdALAvc65b4Q0LwK4N/x+AMBz4v3nw70DUUlLK6WX6foAfilPi7hzSkInl7elXZS0JMlzjiRIWYJBvl9qO0vj0b9zg9rSyEqCrKteuVDBml8rT6k19wGIFnAbZIGAJeC7wEKXmauDLLeUl1V3a2xWanmiLt8qk/ks8S/TaKEl+yAn4Kx8c3WUwN1n/nbNH0u45Orah7/c8xKffeb+QagPuA8BvBHAe51zbwCwjcYFw4w4zGk0EtG7iehxInr88uXL87yXlfCWJmhNvJK2PS8PfYBDvmdRaYKXAEznkQNQS0NqCSXXpNX8drVLrt1zg9jSnDXPJQ2qT7+VhJvFY07DLNWzlL9VXq5eXfnL9fG5svvkpwG7S5jk3pP3c2POEjilOnT2vdqfkgupLH/3AUdLOesSQn2EcYk369qivniUoz7g/jyA551zj4Xrj8GD/UUK7pbw91J4/gKAh8T7D4Z7CTnn3u+ce8Q598i5c+eyhWvg62pQrTkE/uKzeSSw1Di6BsrS0hKWwjbrCFygFj9d2pCuQ2kCSeoCns70HS6Geag04UsD3xKWknR/6Lw4yqNVti6/BPhWeosv55wpFK3yc6syusYBkN/WbwGTlSanOZf4yd3PaaglbbSvJWNp5/FdI1xIX2WnS9BYCmAX9dG+S+3TNa4OC+xAD3B3zr0I4Dki+mvh1psAfAHAowDeGe69E8Dvht+PAngHefoeADdd476Zm+RktgZcl7ZwEE1a5yHTlqT4dDpLyw3ukdb5ms6eJBrkNP9dGkVXO1i/E34LQkWny/GgLQRJEmBkOfNqfDIf6X6oBlUEBav83D1+nw/F1v90WyXvZnYoazAtCaVSXfs8K/VTqcy+9/SzPu1qjZUc2OYESk6xsQR0SWj34fV2kGU5yLkvn3VZcl2CsA/1XYvz3wL4MBEtAbgA4OfgBcNHiehdAJ4F8JMh7e8B+CEATwPYCWkPTHqQyvvzSDc5yHVeVVWZy8Bi/g7IBYzivCaTSfu9QDJvLotguwR03tYgcM6Om63fs+pj1Z/If73PWTsWWfetnaAyvTVZSxoY86QFgvXbOYfJeJLUX9c9R/yM+zDHU05rlLz00cSte74OQFW1rb2DaHGlNuvSfksCvs1zeyWT5MHSUEtryzWP1vtWvXS9+5DFr0W6fla75QSR3AXeJdx1usNSL3B3zn0OwCPGozcZaR2Av3dIvhKSHZvTBgw+kndy7wP2+t5kUFF+wOeATKaV+c/CQcI5fq18ZD2tiSXr1gXQpQnV3EDrEJDcuzJ/3hhi1alvP+WAwnpH9qVuB/5dEuol8CrlO692mxs3kjyvbYFWolL7lMbNQfPtY6FYz7V/vHS+rOa3qiqcOXMGt27dwmQyKY5FPW76gGiOLOVhXoWnL5jn8jmIUJd0rHaoAvZEsdL0oS7w4XuDqoIDWtqtNWH0YM9pYFkrQbxjaSk5TaM0gHKDjNDW4GVeXe2o+ZxOpy2+c3zmeM89z4GIJVhlna1n8t2c0C6Bc9fkzr2bK79Un67yrPTWdwddVh8hmrMccu1q9ZNzrrUsU7dlThtmGo/HprbfBfQ53ucRnDm8sZTGHA+lNKV3Dqu9H4vYMpJyFbYmfx/JLd/LTUrKaBolzckCA76fGwiluvVN27JsjJOaxEOz3bTG0pecc8nJSbxOXeedK4/z0Pf1O11gwPl0aZp9gE2n6xKimgedx0CEwi3lc1DNvQQOJeHYpdnLsVsSgjoPDYJ9y5RpNjc3O+O3d5Vh8duVX5dmn3u3i26HYtqHjjy4A21Q7OqgnOndtxMYEHlgTqdT85zNLs2/V1lCwOQ0JVmepdVbIBzvi6Pzcm2o260lIIz2k+Xm2n0eDZfvd2n0Fr85sOFrnafmzZrIufaUeVpAUgI+FnilcqVG3UVFwZ25p+uXGw9Wv3YpGaXnXWBYmlt83Uf71fwcBiz79EMfBVKn6xJUtwvgjwW4awC0GrS07rUvCEtAzL1XAqy+ppfMMwc2XWWVrIAcv7nB3sfUbZ0cRf12AFqCIycsugBKp9cflTUAl4RUSePMtWuJl1yZ+rf0G+vn1nWJHNK68WqfvmRZRJa1ZfUdp9e/51F4SlZTzlLrGmsybU7p6cNLX6uoj+Vn3cu1aR9e+9KR97lbHdTVcPJd+cya/H0adl7pfBAtQ743zyDIAY7kq0/Zuh4ESoQc/+ujWeu6lyandV/ey2mWWtDP249WXXKWE5eT+9iZa/9S/5cEnFWHnJXDf61YJX3GnzVH5DX/7gLtkiKUa2d9TwvrUrvmlASLZ5lfH+GXG1N6rJbmpK6b1dZWGV3jeR468pp7Duy63pF/c++VACE3iSvyflMLYJikltunkzSfevDmNBn9js5Pa6JWOvlPPperZXK8lu7nBq0mfV/Hpcm1sU7bla/kSdY5l8a67qpTKd8+PHYJb4ssxaWUplRWqT1LSkSprD5CV/dxyXKSY6JLaPThu68mr++X+qVkvZXIwpHD0JEHd0nzSjVL67U6pavTkwGC8iQo5Smf5Uy8nNTn5/PQPO92CT9+LkOVWkLHyqtv3n15s8C+jxZVut8HAA7Shvq9krCy3stprV3lyLIs3uTqkz6KSJ8+7RJWWpOW7ZoTlCUe9byZd37MC+wHpZKCkKNSP/elYwXuloTv+x7TPI1mpZXmueQhZ4JamkZOE++aMF3aifWs1FaWlSDztEzJZEu4kXffSXaQiaWFlW7PnMlfshosi0byoetf6rcuTS6nOc8jaPq+Y13LNsv51XP8W3lZPOl+mDesrXy3S/vtwgOrrzidnJdW+lxZB6G+Wvw8Ck8fOvI+dyLC8vJy657uIKnllkzDHEBqsvLTz0s8l8q30g4Gg2RFjszjMIOqVAedznpPE4P7cDhstbUuo65r04/a1UeaN0k50LKEVMli6NvO82hauTL4Xi6NxVPX+OrK2xLMEgSlsMpZRho4c5ZAScDNO36J2rulu/LR47yr/awy+wr3Ej8lwSqvSyTrsry8fCgr4siD+8MPP4yPf/zj3/TA9nea5h2QuTyA2yP1F9Sm29FHC1pQXyIiPPzwwwd+/8iD+8rKCl772tfeaTYWtKAFLehY0bHyuS9oQQta0IL60QLcF7SgBS3oLqQFuC9oQQta0F1IC3Bf0IIWtKC7kBbgvqAFLWhBdyEtwH1BC1rQgu5CoqOwbpeINgE8daf5uM30EgBX7jQTt5EW9TnadLfVB7j76vTNqM/LnXPnrAdHZZ37U8456xi/Y0tE9PjdVKdFfY423W31Ae6+On2r67NwyyxoQQta0F1IC3Bf0IIWtKC7kI4KuL//TjPwTaC7rU6L+hxtutvqA9x9dfqW1udIfFBd0IIWtKAF3V46Kpr7gha0oAUt6DbSHQd3IvoBInqKiJ4movfcaX76EBE9RER/SkRfIKIniOjvh/tnieiTRPTl8PdMuE9E9E9CHT9PRG+8szWwiYgGRPRZIvp4uH6YiB4LfH+EiJbC/eVw/XR4/oo7ybdFRHSaiD5GRF8koieJ6Hvvgv7578N4+ysi+ldEtHKc+oiIfoOILhHRX4l7c/cJEb0zpP8yEb3zTtRF8GLV6X8P4+7zRPTbRHRaPPvFUKeniOjviPu3Hwf1aTTfyn8ABgC+AuCVAJYA/AWAb7+TPPXk+34Abwy/TwL4EoBvB/C/AXhPuP8eAL8cfv8QgN8HQAC+B8Bjd7oOmXr9AoB/CeDj4fqjAN4efr8PwH8dfv83AN4Xfr8dwEfuNO9GXT4I4L8Kv5cAnD7O/QPgAQDPAFgVffOzx6mPAPxtAG8E8Ffi3lx9AuAsgAvh75nw+8wRq9NbAQzD718Wdfr2gHHLAB4O2Df4ZuHgne7s7wXwh+L6FwH84p0ehAeox+8CeAv8Rqz7w7374dfvA8A/A/BTIn1Md1T+AXgQwB8D+H4AHw+T6ooYpLGvAPwhgO8Nv4chHd3pOoi6nApASOr+ce6fBwA8F0BtGPro7xy3PgLwCgWEc/UJgJ8C8M/E/STdUaiTevZjAD4cfif4xn30zcLBO+2W4QHL9Hy4d2womLtvAPAYgHudc98Ij14EcG/4fRzq+Y8B/AMAfOTVPQBuOOem4VryHOsTnt8M6Y8KPQzgMoB/HtxMv05EJ3CM+8c59wKA/wPA1wB8A77NP4Pj20dM8/bJke8rRf8lvAUCfIvrdKfB/VgTEa0D+L8B/HfOuVvymfMi+FgsRSKiHwZwyTn3mTvNy22iIbyp/F7n3BsAbMOb/JGOU/8AQPBFvw1ecL0UwAkAP3BHmbrNdNz6pIuI6JcATAF8+E6Uf6fB/QUAD4nrB8O9I09ENIIH9g875/51uH2RiO4Pz+8HcCncP+r1/JsAfoSIvgrgN+FdM78K4DQRcYgKyXOsT3h+CsDVbyXDHfQ8gOedc4+F64/Bg/1x7R8AeDOAZ5xzl51zEwD/Gr7fjmsfMc3bJ8ehr0BEPwvghwH8TBBawLe4Tnca3D8N4DXhi/8S/IefR+8wT51ERATgAwCedM79n+LRowD46/074X3xfP8dYQXA9wC4KUzRO07OuV90zj3onHsFfB/8iXPuZwD8KYAfD8l0fbiePx7SHxmNyzn3IoDniOivhVtvAvAFHNP+CfQ1AN9DRGth/HGdjmUfCZq3T/4QwFuJ6EywZt4a7h0ZIqIfgHdx/ohzbkc8ehTA28NKpocBvAbA/4dvFg7eyQ8RYaz9EPxqk68A+KU7zU9Pnv8WvPn4eQCfC/9+CN6n+ccAvgzg/wFwNqQnAP801PEvATxyp+tQqNt/hma1zCvD4HsawG8BWA73V8L10+H5K+8030Y9Xg/g8dBHvwO/suJY9w+A/wnAFwH8FYB/Ab/q4tj0EYB/Bf+9YAJvXb3rIH0C78d+Ovz7uSNYp6fhfeiMDe8T6X8p1OkpAD8o7t92HFzsUF3Qgha0oLuQ7rRbZkELWtCCFvRNoAW4L2hBC1rQXUgLcF/Qgha0oLuQFuC+oAUtaEF3IS3AfUELWtCC7kJagPuCFrSgBd2FtAD3BS1oQQu6C2kB7gta0IIWdBfS/w9p+XpKfXliRgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "bb6vEGyVgDNY",
        "outputId": "391f72d8-a642-412a-fc02-4997ab9ccee1"
      },
      "source": [
        "from detecto.core import Dataset\n",
        "dataset = Dataset(location_of_data)\n",
        "\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-951b97fc3379>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdetecto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation_of_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/detecto/core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, label_data, image_folder, transform)\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_csv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_csv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxml_to_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m         \u001b[0;31m# If image folder not given, set it to labels folder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/detecto/utils.py\u001b[0m in \u001b[0;36mxml_to_csv\u001b[0;34m(xml_folder, output_file)\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0mroot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetroot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 258\u001b[0;31m         \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'filename'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'width'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'text'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "bQ4LDXBUkwhn",
        "outputId": "166e9298-8f9b-47c0-8910-74ba2ec4ff61"
      },
      "source": [
        "from detecto.utils import xml_to_csv\n",
        "\n",
        "xml_to_csv(location_of_data)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-bf4d02d3d4f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdetecto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mxml_to_csv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mxml_to_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation_of_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/detecto/utils.py\u001b[0m in \u001b[0;36mxml_to_csv\u001b[0;34m(xml_folder, output_file)\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0mroot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetroot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 258\u001b[0;31m         \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'filename'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'width'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'text'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pnTMogOykruf"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_ARBjvKgvB1"
      },
      "source": [
        "from detecto.visualize import show_labeled_image\n",
        "\n",
        "image, targets = dataset[0]\n",
        "show_labeled_image(image, targets['boxes'], targets[\"labels\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "D1mshxKIi-GU",
        "outputId": "251d0d82-b437-419c-9e9f-0f82e228df98"
      },
      "source": [
        "root = tree.getroot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-2f46a7f56f4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mroot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetroot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'tree' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vku8hiDzi_1P"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}