Homework_19.6.ipynb

homework_19-6.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOEopJtuhfU/lq69fV2ExNx",
      "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/gist/cvergel1/913802049a01f23c28f9921039ad77cb/homework_19-6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Claireen Vergel de Dios\n",
        "CS 4410"
      ],
      "metadata": {
        "id": "WWJJyO-Jxqtt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A. Download diamonds.csv from one of the dataset repositories."
      ],
      "metadata": {
        "id": "9T2LgrmdvxlT"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vYpZJ0NZrNg1"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "url = \"https://raw.githubusercontent.com/selva86/datasets/master/diamonds.csv\"\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "B. Load the dataset into a pandas DataFrame with the following statement, which uses the first column of each record as the row index:"
      ],
      "metadata": {
        "id": "ejqKTpx6v0SR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(url, index_col=0)"
      ],
      "metadata": {
        "id": "H1-29NBSwcRz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "C. Display the first seven rows of the DataFrame."
      ],
      "metadata": {
        "id": "43OumuoXw8fY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.head(7))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZznYjaIZw-JC",
        "outputId": "54e51abe-1211-461e-f011-4e081f88b561"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             cut color clarity  depth  table  price     x     y     z\n",
            "carat                                                                \n",
            "0.23       Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
            "0.21     Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
            "0.23        Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
            "0.29     Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
            "0.31        Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
            "0.24   Very Good     J    VVS2   62.8   57.0    336  3.94  3.96  2.48\n",
            "0.24   Very Good     I    VVS1   62.3   57.0    336  3.95  3.98  2.47\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "D. Display the last seven rows of the DataFrame."
      ],
      "metadata": {
        "id": "-lKgf47nxD-E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.tail(7))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkPhMTFaxF4J",
        "outputId": "83d6d57f-0224-4981-ce3b-0f4a819f97fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             cut color clarity  depth  table  price     x     y     z\n",
            "carat                                                                \n",
            "0.70   Very Good     E     VS2   61.2   59.0   2757  5.69  5.72  3.49\n",
            "0.72     Premium     D     SI1   62.7   59.0   2757  5.69  5.73  3.58\n",
            "0.72       Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
            "0.72        Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
            "0.70   Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
            "0.86     Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
            "0.75       Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "E. Use the DataFrame method describe (which looks only at the numerical columns) to calculate the descriptive statistics for the numerical columns—carat, depth, table, price, x, y and z."
      ],
      "metadata": {
        "id": "n1khfwplxNrq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "url = \"https://raw.githubusercontent.com/selva86/datasets/master/diamonds.csv\"\n",
        "df = pd.read_csv(url, index_col=0)\n",
        "print(df.reset_index()[['carat', 'depth', 'table', 'price', 'x', 'y', 'z']].describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LApsNrRMcJkG",
        "outputId": "b81428da-6fa6-4e4d-a6a5-70b2c5642519"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              carat         depth         table         price             x  \\\n",
            "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
            "mean       0.797940     61.749405     57.457184   3932.799722      5.731157   \n",
            "std        0.474011      1.432621      2.234491   3989.439738      1.121761   \n",
            "min        0.200000     43.000000     43.000000    326.000000      0.000000   \n",
            "25%        0.400000     61.000000     56.000000    950.000000      4.710000   \n",
            "50%        0.700000     61.800000     57.000000   2401.000000      5.700000   \n",
            "75%        1.040000     62.500000     59.000000   5324.250000      6.540000   \n",
            "max        5.010000     79.000000     95.000000  18823.000000     10.740000   \n",
            "\n",
            "                  y             z  \n",
            "count  53940.000000  53940.000000  \n",
            "mean       5.734526      3.538734  \n",
            "std        1.142135      0.705699  \n",
            "min        0.000000      0.000000  \n",
            "25%        4.720000      2.910000  \n",
            "50%        5.710000      3.530000  \n",
            "75%        6.540000      4.040000  \n",
            "max       58.900000     31.800000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "F. Use Series method describe to calculate the descriptive statistics for the categorical data (text) columns—cut, color and clarity."
      ],
      "metadata": {
        "id": "9COTcsrBbnUo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['cut'].describe())\n",
        "print(df['color'].describe())\n",
        "print(df['clarity'].describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OU5t4w3NbrOa",
        "outputId": "b6aa9b2e-3263-49f3-a5b7-b2b204c2e658"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count     53940\n",
            "unique        5\n",
            "top       Ideal\n",
            "freq      21551\n",
            "Name: cut, dtype: object\n",
            "count     53940\n",
            "unique        7\n",
            "top           G\n",
            "freq      11292\n",
            "Name: color, dtype: object\n",
            "count     53940\n",
            "unique        8\n",
            "top         SI1\n",
            "freq      13065\n",
            "Name: clarity, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "G. What are the unique category values (use the Series method unique)?\n",
        "\n"
      ],
      "metadata": {
        "id": "UpZxFMihbrfo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['cut'].unique())\n",
        "print(df['color'].unique())\n",
        "print(df['clarity'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s7O4MUXRbw-C",
        "outputId": "40fc220f-a4db-47e9-e1ea-28c2fe344eaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Ideal' 'Premium' 'Good' 'Very Good' 'Fair']\n",
            "['E' 'I' 'J' 'H' 'F' 'G' 'D']\n",
            "['SI2' 'SI1' 'VS1' 'VS2' 'VVS2' 'VVS1' 'I1' 'IF']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "H. Pandas has many built-in graphing capabilities. Execute the %matplotlib magic to enable Matplotlib support in IPython. Then, to view histograms of each numerical data column, call your DataFrame’s hist method. The following figure shows the results for the DataFrame’s seven numerical columns:"
      ],
      "metadata": {
        "id": "5pN5PxbGbxPK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "df.hist(bins=10, figsize=(10,10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 942
        },
        "id": "dN64E9ABb3tJ",
        "outputId": "f5dc744f-5086-445d-a42a-ed327ef7cbfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'depth'}>,\n",
              "        <Axes: title={'center': 'table'}>],\n",
              "       [<Axes: title={'center': 'price'}>, <Axes: title={'center': 'x'}>],\n",
              "       [<Axes: title={'center': 'y'}>, <Axes: title={'center': 'z'}>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 6 Axes>"
            ],
            "image/png": 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5uXzMmDFKTU1Vs2bNZLPZ9NhjjykuLk59+/aVJCUkJKhLly66//77NXv2bDkcDk2ZMkXJycnm1aFHH31Ur776qiZNmqSHHnpI69ev13vvvafVq38ZKSc1NVWjRo1S79691adPH82fP1+nT582R+UDAAAAAHer0ih8lzNv3jz5+/tr2LBhKigoUGJiol577TVzfUBAgFatWqVx48YpLi5ODRo00KhRozRjxgwzJjo6WqtXr9aECRO0YMECtWrVSm+88YY5hLkkDR8+XD/99JOmTp0qh8OhXr16ac2aNaUGlgAAAAAAd7niAmrDhg0ur0NCQpSenq709PRyt2nTpk2pEb4u1b9/f+3cufOyMSkpKUpJSbGcKwAAAABciUo9AwUAAAAAdRkFFAAAAABYRAEFAAAAABZRQAEAAACARRRQAAAAAGARBRQAAAAAWEQBBQAAAAAWUUABAAAAgEUUUAAAAABgEQUUAAAAAFhEAQUAAAAAFlFAAQAAAIBFFFAAAAAAYBEFFAAAAABYRAEFAAAAABZRQAEAAACARRRQAAAAAGARBRQAAAAAWEQBBQAAAAAWUUABAAAAgEUUUAAAAABgEQUUAAAAAFhEAQUAAAAAFlFAAQAAAIBFFFAAAAAAYBEFFAAAAABYRAEFAAAAABZRQAEAAACARRRQAIA6a+HCherRo4dsNptsNpvi4uL06aefmuvPnTun5ORkNW/eXA0bNtSwYcOUm5vrso9Dhw4pKSlJoaGhCgsL08SJE3X+/HmXmA0bNuj6669XcHCw2rdvryVLlpTKJT09XW3btlVISIhiY2O1bdu2amkzAODKUEABAOqsVq1a6fnnn1d2drZ27Nih2267TXfeeaf27t0rSZowYYI+/vhjrVixQhs3btThw4c1dOhQc/uioiIlJSWpsLBQmzdv1ltvvaUlS5Zo6tSpZsyBAweUlJSkW2+9VTk5ORo/frwefvhhrV271oxZvny5UlNTNW3aNH311Vfq2bOnEhMTdfTo0ZrrDACAJRRQAIA664477tDtt9+uDh066Nprr9Wzzz6rhg0basuWLTpx4oTefPNNzZ07V7fddptiYmK0ePFibd68WVu2bJEkrVu3Tl9//bXeeecd9erVS4MHD9bMmTOVnp6uwsJCSVJGRoaio6M1Z84cde7cWSkpKfrNb36jefPmmXnMnTtXY8eO1ejRo9WlSxdlZGQoNDRUixYt8ki/AADKV8/TCQAA4A2Kioq0YsUKnT59WnFxccrOzpbT6VR8fLwZ06lTJ7Vu3VpZWVnq27evsrKy1L17d4WHh5sxiYmJGjdunPbu3avrrrtOWVlZLvsoiRk/frwkqbCwUNnZ2Zo8ebK53t/fX/Hx8crKyrpszgUFBSooKDBf5+fnS5KcTqecTmeV+8JblbTJnW0LDjDctq/Kulw7qqOt3oq21k6+2FaruVJAAQDqtN27dysuLk7nzp1Tw4YN9cEHH6hLly7KyclRUFCQmjRp4hIfHh4uh8MhSXI4HC7FU8n6knWXi8nPz9fZs2d1/PhxFRUVlRmzb9++y+Y+a9YsTZ8+vdTydevWKTQ0tOLG+yi73e62fc3u47ZdVdonn3xSYYw72+rtaGvt5EttPXPmjKU4CigAQJ3WsWNH5eTk6MSJE/rb3/6mUaNGaePGjZ5Oy5LJkycrNTXVfJ2fn6+oqCglJCTIZrN5MLPq4XQ6ZbfbNXDgQAUGBrpln93S1lYcVE32pCWWu6462uqtaGvt5IttLbmKXxEKKABAnRYUFKT27dtLkmJiYrR9+3YtWLBAw4cPV2FhofLy8lyuQuXm5ioiIkKSFBERUWq0vJJR+i6OuXTkvtzcXNlsNtWvX18BAQEKCAgoM6ZkH+UJDg5WcHBwqeWBgYE+84WlKtzZvoIiP7fspyqstKG2/y4vRltrJ19qq9U8GUQCAICLFBcXq6CgQDExMQoMDFRmZqa5bv/+/Tp06JDi4uIkSXFxcdq9e7fLaHl2u102m01dunQxYy7eR0lMyT6CgoIUExPjElNcXKzMzEwzBgDgPbgCBQCosyZPnqzBgwerdevWOnnypJYtW6YNGzZo7dq1aty4scaMGaPU1FQ1a9ZMNptNjz32mOLi4tS3b19JUkJCgrp06aL7779fs2fPlsPh0JQpU5ScnGxeGXr00Uf16quvatKkSXrooYe0fv16vffee1q9erWZR2pqqkaNGqXevXurT58+mj9/vk6fPq3Ro0d7pF8AAOWjgAIA1FlHjx7VAw88oCNHjqhx48bq0aOH1q5dq4EDB0qS5s2bJ39/fw0bNkwFBQVKTEzUa6+9Zm4fEBCgVatWady4cYqLi1ODBg00atQozZgxw4yJjo7W6tWrNWHCBC1YsECtWrXSG2+8ocTEX55/GT58uH766SdNnTpVDodDvXr10po1a0oNLAEA8DwKKABAnfXmm29edn1ISIjS09OVnp5ebkybNm0qHE2tf//+2rlz52VjUlJSlJKSctkYAIDnVeoZqIULF6pHjx6y2Wyy2WyKi4vTp59+aq4/d+6ckpOT1bx5czVs2FDDhg0r9VDsoUOHlJSUpNDQUIWFhWnixIk6f/68S8yGDRt0/fXXKzg4WO3bt9eSJUtK5ZKenq62bdsqJCREsbGxpR7iBQAAAAB3q1QB1apVKz3//PPKzs7Wjh07dNttt+nOO+/U3r17JUkTJkzQxx9/rBUrVmjjxo06fPiwhg4dam5fVFSkpKQkFRYWavPmzXrrrbe0ZMkSTZ061Yw5cOCAkpKSdOuttyonJ0fjx4/Xww8/rLVrfxlmdPny5UpNTdW0adP01VdfqWfPnkpMTHR5iBcAAAAA3K1SBdQdd9yh22+/XR06dNC1116rZ599Vg0bNtSWLVt04sQJvfnmm5o7d65uu+02xcTEaPHixdq8ebO2bNki6cLEfl9//bXeeecd9erVS4MHD9bMmTOVnp6uwsJCSVJGRoaio6M1Z84cde7cWSkpKfrNb36jefPmmXnMnTtXY8eO1ejRo9WlSxdlZGQoNDRUixYtcmPXAAAAAICrKg9jXlRUpHfffVenT59WXFycsrOz5XQ6FR8fb8Z06tRJrVu3VlZWliQpKytL3bt3d3koNjExUfn5+eZVrKysLJd9lMSU7KOwsFDZ2dkuMf7+/oqPjzdjAAAAAKA6VHoQid27dysuLk7nzp1Tw4YN9cEHH6hLly7KyclRUFCQy2SDkhQeHi6HwyFJcjgcpUYUKnldUUx+fr7Onj2r48ePq6ioqMyYffv2XTb3goICFRQUmK9LZht2Op1yOp0We8B9So7piWNXhq/kKdW+XIMDjJpKp5RL86stfeoNfCVP6fK5+kL+AAC4W6ULqI4dOyonJ0cnTpzQ3/72N40aNUobN26sjtzcbtasWZo+fXqp5evWrVNoaKgHMrrAbrd77NiV4St5SrUn19l9ajCRS1w6qlht6VNv4it5SmXneubMGQ9kAgCAZ1W6gAoKClL79u0lSTExMdq+fbsWLFig4cOHq7CwUHl5eS5XoXJzcxURESFJioiIKDVaXskofRfHXDpyX25urmw2m+rXr6+AgAAFBASUGVOyj/JMnjxZqamp5uv8/HxFRUUpISFBNputEr3gHk6nU3a7XQMHDlRgYGCNH98qX8lTqn25dktbW+bymrAn7cIcNbWtT72Br+QpXT7Xkqv4AADUJVc8D1RxcbEKCgoUExOjwMBAZWZmatiwYZKk/fv369ChQ4qLi5MkxcXF6dlnn9XRo0cVFhYm6cJfNW02m7p06WLGlPWX75J9BAUFKSYmRpmZmRoyZIiZQ2ZmZoXzZwQHB5szw18sMDDQo19iPH18q3wlT6n25FpQ5FfD2fzi0pxqS596E1/JUyo7V1/JHQAAd6pUATV58mQNHjxYrVu31smTJ7Vs2TJt2LBBa9euVePGjTVmzBilpqaqWbNmstlseuyxxxQXF6e+fftKkhISEtSlSxfdf//9mj17thwOh6ZMmaLk5GSzsHn00Uf16quvatKkSXrooYe0fv16vffee1q9erWZR2pqqkaNGqXevXurT58+mj9/vk6fPq3Ro0e7sWsAAAAAwFWlCqijR4/qgQce0JEjR9S4cWP16NFDa9eu1cCBAyVJ8+bNk7+/v4YNG6aCggIlJibqtddeM7cPCAjQqlWrNG7cOMXFxalBgwYaNWqUZsyYYcZER0dr9erVmjBhghYsWKBWrVrpjTfeUGJiohkzfPhw/fTTT5o6daocDod69eqlNWvWlBpYAgAAAADcqVIF1JtvvnnZ9SEhIUpPT1d6enq5MW3atCl1i96l+vfvr507d142JiUlpcJb9gAAAADAnao8DxQAAAAA1DUUUAAAAABgEQUUAAAAAFhEAQUAAAAAFlFAAQAAAIBFFFAAAAAAYBEFFAAAAABYRAEFAAAAABZRQAEAAACARRRQAAAAAGARBRQAAAAAWEQBBQAAAAAWUUABAAAAgEUUUAAAAABgEQUUAAAAAFhEAQUAAAAAFlFAAQAAAIBFFFAAAAAAYBEFFAAAAABYRAEFAAAAABZRQAEAAACARRRQAAAAAGARBRQAAAAAWEQBBQAAAAAWUUABAAAAgEUUUAAAAABgEQUUAAAAAFhEAQUAAAAAFlFAAQAAAIBFFFAAAAAAYBEFFAAAAABYRAEFAAAAABZRQAEA6qxZs2bpV7/6lRo1aqSwsDANGTJE+/fvd4k5d+6ckpOT1bx5czVs2FDDhg1Tbm6uS8yhQ4eUlJSk0NBQhYWFaeLEiTp//rxLzIYNG3T99dcrODhY7du315IlS0rlk56errZt2yokJESxsbHatm2b29sMALgyFFAAgDpr48aNSk5O1pYtW2S32+V0OpWQkKDTp0+bMRMmTNDHH3+sFStWaOPGjTp8+LCGDh1qri8qKlJSUpIKCwu1efNmvfXWW1qyZImmTp1qxhw4cEBJSUm69dZblZOTo/Hjx+vhhx/W2rVrzZjly5crNTVV06ZN01dffaWePXsqMTFRR48erZnOAABYUs/TCQAA4Clr1qxxeb1kyRKFhYUpOztb/fr104kTJ/Tmm29q2bJluu222yRJixcvVufOnbVlyxb17dtX69at09dff63PPvtM4eHh6tWrl2bOnKknnnhCaWlpCgoKUkZGhqKjozVnzhxJUufOnfXFF19o3rx5SkxMlCTNnTtXY8eO1ejRoyVJGRkZWr16tRYtWqQnn3yyBnsFAHA5XIECAOD/O3HihCSpWbNmkqTs7Gw5nU7Fx8ebMZ06dVLr1q2VlZUlScrKylL37t0VHh5uxiQmJio/P1979+41Yy7eR0lMyT4KCwuVnZ3tEuPv76/4+HgzBgDgHbgCBQCApOLiYo0fP1433nijunXrJklyOBwKCgpSkyZNXGLDw8PlcDjMmIuLp5L1JesuF5Ofn6+zZ8/q+PHjKioqKjNm37595eZcUFCggoIC83V+fr4kyel0yul0Wm26zyhpkzvbFhxguG1flXW5dlRHW70Vba2dfLGtVnOlgAIAQFJycrL27NmjL774wtOpWDZr1ixNnz691PJ169YpNDTUAxnVDLvd7rZ9ze7jtl1V2ieffFJhjDvb6u1oa+3kS209c+aMpTgKKABAnZeSkqJVq1Zp06ZNatWqlbk8IiJChYWFysvLc7kKlZubq4iICDPm0tHySkbpuzjm0pH7cnNzZbPZVL9+fQUEBCggIKDMmJJ9lGXy5MlKTU01X+fn5ysqKkoJCQmy2WyV6AHf4HQ6ZbfbNXDgQAUGBrpln93S1lYcVE32pCWWu6462uqtaGvt5IttLbmKXxEKKABAnWUYhh577DF98MEH2rBhg6Kjo13Wx8TEKDAwUJmZmRo2bJgkaf/+/Tp06JDi4uIkSXFxcXr22Wd19OhRhYWFSbrwF1ebzaYuXbqYMZdebbDb7eY+goKCFBMTo8zMTA0ZMkTShVsKMzMzlZKSUm7+wcHBCg4OLrU8MDDQZ76wVIU721dQ5OeW/VSFlTbU9t/lxWhr7eRLbbWaZ6UGkWC+DABAbZKcnKx33nlHy5YtU6NGjeRwOORwOHT27FlJUuPGjTVmzBilpqbq888/V3Z2tkaPHq24uDj17dtXkpSQkKAuXbro/vvv1z/+8Q+tXbtWU6ZMUXJyslncPProo/rhhx80adIk7du3T6+99pree+89TZgwwcwlNTVVf/nLX/TWW2/pm2++0bhx43T69GlzVD4AgHeoVAHFfBkAgNpk4cKFOnHihPr376+WLVuaP8uXLzdj5s2bp1//+tcaNmyY+vXrp4iICL3//vvm+oCAAK1atUoBAQGKi4vTb3/7Wz3wwAOaMWOGGRMdHa3Vq1fLbrerZ8+emjNnjt544w1zCHNJGj58uF566SVNnTpVvXr1Uk5OjtasWVNqYAkAgGdV6hY+5ssAANQmhlHxCGwhISFKT09Xenp6uTFt2rSpcECA/v37a+fOnZeNSUlJuewtewAAz7uiZ6AqO19G3759y50vY9y4cdq7d6+uu+66cufLGD9+vKRf5suYPHmyud7KfBneNtyrrwzv6Ct5SrUvV28YXre29ak38JU8pcvn6gv5AwDgblUuoHxxvgxvHe7VV4Z39JU8pdqTqzcNr1tb+tSb+EqeUtm5Wh3uFQCA2qTKBZQvzpfhbcO9+srwjr6Sp1T7cvWG4XVrW596A1/JU7p8rlaHewUAoDapUgHlq/NleOtwr54+vlW+kqdUe3L1puF1a0ufehNfyVMqO1dfyR0AAHeq1Ch8hmEoJSVFH3zwgdavX3/Z+TJKlDVfxu7du11GyytrvoyL91ESU9Z8GSVK5ssoiQEAAAAAd6vUFajk5GQtW7ZMH374oTlfhnRhnoz69eu7zJfRrFkz2Ww2PfbYY+XOlzF79mw5HI4y58t49dVXNWnSJD300ENav3693nvvPa1evdrMJTU1VaNGjVLv3r3Vp08fzZ8/n/kyAAAAAFSrShVQCxculHRhKNaLLV68WA8++KCkC/Nl+Pv7a9iwYSooKFBiYqJee+01M7Zkvoxx48YpLi5ODRo00KhRo8qcL2PChAlasGCBWrVqVeZ8GT/99JOmTp0qh8OhXr16MV8GAAAAgGpVqQKK+TIAAAAA1GWVegYKAAAAAOoyCigAAAAAsIgCCgAAAAAsooACAAAAAIsooAAAAADAIgooAAAAALCIAgoAAAAALKKAAgAAAACLKKAAAAAAwCIKKAAAAACwiAIKAAAAACyigAIAAAAAi+p5OgEAAIC6qO2Tq8tdFxxgaHYfqVvaWhUU+bn1uAefT3Lr/oC6hitQAAAAAGARBRQAAAAAWEQBBQAAAAAWUUABAAAAgEUUUAAAAABgEQUUAAAAAFhEAQUAAAAAFlFAAQAAAIBFFFAAAAAAYBEFFAAAAABYRAEFAAAAABZRQAEAAACARRRQAAAAAGARBRQAAAAAWEQBBQAAAAAWUUABAAAAgEUUUAAAAABgEQUUAAAAAFhEAQUAAAAAFlFAAQAAAIBF9TydAODt2j65ulr2GxxgaHYfqVvaWhUU+VXLMQAAAOBeXIECAAAAAIsooAAAddqmTZt0xx13KDIyUn5+flq5cqXLesMwNHXqVLVs2VL169dXfHy8vv32W5eYY8eOaeTIkbLZbGrSpInGjBmjU6dOucTs2rVLN998s0JCQhQVFaXZs2eXymXFihXq1KmTQkJC1L17d33yySduby8A4MpQQAEA6rTTp0+rZ8+eSk9PL3P97Nmz9fLLLysjI0Nbt25VgwYNlJiYqHPnzpkxI0eO1N69e2W327Vq1Spt2rRJjzzyiLk+Pz9fCQkJatOmjbKzs/Xiiy8qLS1Nr7/+uhmzefNm3XvvvRozZox27typIUOGaMiQIdqzZ0/1NR4AUGk8AwUAqNMGDx6swYMHl7nOMAzNnz9fU6ZM0Z133ilJevvttxUeHq6VK1dqxIgR+uabb7RmzRpt375dvXv3liS98soruv322/XSSy8pMjJSS5cuVWFhoRYtWqSgoCB17dpVOTk5mjt3rlloLViwQIMGDdLEiRMlSTNnzpTdbterr76qjIyMGugJAIAVXIECAKAcBw4ckMPhUHx8vLmscePGio2NVVZWliQpKytLTZo0MYsnSYqPj5e/v7+2bt1qxvTr109BQUFmTGJiovbv36/jx4+bMRcfpySm5DgAAO9Q6StQmzZt0osvvqjs7GwdOXJEH3zwgYYMGWKuNwxD06ZN01/+8hfl5eXpxhtv1MKFC9WhQwcz5tixY3rsscf08ccfy9/fX8OGDdOCBQvUsGFDM2bXrl1KTk7W9u3bddVVV+mxxx7TpEmTXHJZsWKFnnnmGR08eFAdOnTQCy+8oNtvv70K3QAAQGkOh0OSFB4e7rI8PDzcXOdwOBQWFuayvl69emrWrJlLTHR0dKl9lKxr2rSpHA7HZY9TloKCAhUUFJiv8/PzJUlOp1NOp9NyO31FSZvc2bbgAMNt+3KnYH/D5b/u5G3vjer4vXor2urdrOZa6QKq5F7xhx56SEOHDi21vuRe8bfeekvR0dF65plnlJiYqK+//lohISGSLtwrfuTIEdntdjmdTo0ePVqPPPKIli1bJumXe8Xj4+OVkZGh3bt366GHHlKTJk3MWx1K7hWfNWuWfv3rX2vZsmUaMmSIvvrqK3Xr1q2yzQIAwOfMmjVL06dPL7V83bp1Cg0N9UBGNcNut7ttX7P7uG1X1WJm72K379NbBydx5+/V29FW73TmzBlLcZUuoLhXHABQV0REREiScnNz1bJlS3N5bm6uevXqZcYcPXrUZbvz58/r2LFj5vYRERHKzc11iSl5XVFMyfqyTJ48Wampqebr/Px8RUVFKSEhQTabrTJN9QlOp1N2u10DBw5UYGCgW/bZLW2tW/bjbsH+hmb2LtYzO/xVUOzeuQL3pCW6dX9Xqjp+r96Ktnq3kqv4FXHrIBIV3Ss+YsSICu8Vv+uuu8q9V/yFF17Q8ePH1bRpU2VlZbmcNEpiLh1+FgCAqoqOjlZERIQyMzPNgik/P19bt27VuHHjJElxcXHKy8tTdna2YmJiJEnr169XcXGxYmNjzZinn35aTqfT/CJht9vVsWNHNW3a1IzJzMzU+PHjzePb7XbFxcWVm19wcLCCg4NLLQ8MDPSZLyxV4c72eftE5gXFfm7P0VvfG7X9fXsx2uqdrObp1gKKe8Urx1fuDfWVPCXfuj++Ou9vd4dL+7Ku/v6rg6/kKV0+V1/I34pTp07pu+++M18fOHBAOTk5atasmVq3bq3x48frT3/6kzp06GDemh4ZGWk+/9u5c2cNGjRIY8eOVUZGhpxOp1JSUjRixAhFRkZKku677z5Nnz5dY8aM0RNPPKE9e/ZowYIFmjdvnnncxx9/XLfccovmzJmjpKQkvfvuu9qxY4fLUOcAAM+rU8OYe+u94r5yb6iv5Cn51v3x1XF/uztceo98Xf39VydfyVMqO1er94p7ux07dujWW281X5fc3TBq1CgtWbJEkyZN0unTp/XII48oLy9PN910k9asWWM+1ytJS5cuVUpKigYMGGAOjvTyyy+b6xs3bqx169YpOTlZMTExatGihaZOneoyV9QNN9ygZcuWacqUKXrqqafUoUMHrVy5kud6AcDLuLWA4l7xyvGVe0N9JU/Jt+6Pr877292h5B75uv77rw6+kqd0+Vyt3ivu7fr37y/DKP9KsJ+fn2bMmKEZM2aUG9OsWTNzIKTy9OjRQ3//+98vG3P33Xfr7rvvvnzCAACPcmsBxb3iVePp41vlK3lKvnV/fHXc3+4Ol/ZfXf39VydfyVMqO1dfyR0AAHeq9ES6p06dUk5OjnJyciT9cq/4oUOH5OfnZ94r/tFHH2n37t164IEHyr1XfNu2bfryyy/LvFc8KChIY8aM0d69e7V8+XItWLDA5erR448/rjVr1mjOnDnat2+f0tLStGPHDqWkpFx5rwAAAABAGSp9BYp7xQEAAADUVZUuoLhXHAAAAEBdVelb+AAAAACgrqKAAgAAAACLKKAAAAAAwCIKKAAAAACwiAIKAAAAACyigAIAAAAAiyigAAAAAMAiCigAAAAAsIgCCgAAAAAsooACAAAAAIsooAAAAADAIgooAAAAALCIAgoAAAAALKKAAgAAAACLKKAAAAAAwCIKKAAAAACwiAIKAAAAACyigAIAAAAAiyigAAAAAMAiCigAAAAAsIgCCgAAAAAsooACAAAAAIsooAAAAADAIgooAAAAALCIAgoAAAAALKKAAgAAAACLKKAAAAAAwCIKKAAAAACwiAIKAAAAACyigAIAAAAAiyigAAAAAMAiCigAAAAAsIgCCgAAAAAsooACAAAAAIsooAAAAADAIgooAAAAALCIAgoAAAAALKKAAgAAAACL6nk6AQDeqe2TqyVJwQGGZveRuqWtVUGRX7Uf9+DzSdV+DAAAgKriChQAAAAAWOTzBVR6erratm2rkJAQxcbGatu2bZ5OCQCAKuO8BgDezadv4Vu+fLlSU1OVkZGh2NhYzZ8/X4mJidq/f7/CwsI8nR4AAJVSl89rJbcNX05N31JcW1np6+rCbdqoDXz6CtTcuXM1duxYjR49Wl26dFFGRoZCQ0O1aNEiT6cGAEClcV4DAO/ns1egCgsLlZ2drcmTJ5vL/P39FR8fr6ysrDK3KSgoUEFBgfn6xIkTkqRjx47J6XRWb8JlcDqdOnPmjH7++WcFBgbW+PGt8pY8Y2dlVhgT7G9oynXF6vX0+yoods9fJ6vrH0m9YkNnzhSrntNfRW7KtTrUdJ4///xzlbf1lvdqRXwlT+nyuZ48eVKSZBiGJ1KrdWrDee1K1Dt/uuIYH/ncdIfa2tayPuN96TPxStFW72b1vOazBdR//vMfFRUVKTw83GV5eHi49u3bV+Y2s2bN0vTp00stj46OrpYc4Rn3eTqBSvCVXGsyzxZzavBgcIuTJ0+qcePGnk7D53Fes8ZXPjfdoTa2lc94+IKKzms+W0BVxeTJk5Wammq+Li4u1rFjx9S8eXP5+dX8X3fy8/MVFRWlH3/8UTabrcaPb5Wv5CmRa3XwlTwl38nVV/KULp+rYRg6efKkIiMjPZQdvO28Vt186d/OlaKttRNt9W5Wz2s+W0C1aNFCAQEBys3NdVmem5uriIiIMrcJDg5WcHCwy7ImTZpUV4qW2Ww2n3hj+UqeErlWB1/JU/KdXH0lT6n8XLny5D616bxW3Xzp386Voq21E231XlbOaz47iERQUJBiYmKUmfnLczHFxcXKzMxUXFycBzMDAKDyOK8BgG/w2StQkpSamqpRo0apd+/e6tOnj+bPn6/Tp09r9OjRnk4NAIBK47wGAN7Ppwuo4cOH66efftLUqVPlcDjUq1cvrVmzptQDuN4qODhY06ZNK3X7hbfxlTwlcq0OvpKn5Du5+kqekm/lWhv4+nmtutWl9yNtrZ1oa+3gZzD+LAAAAABY4rPPQAEAAABATaOAAgAAAACLKKAAAAAAwCIKKAAAAACwiAKqhj3//PPy8/PT+PHjzWX9+/eXn5+fy8+jjz5a47mlpaWVyqNTp07m+nPnzik5OVnNmzdXw4YNNWzYsFITPnpDnt7SnyX+/e9/67e//a2aN2+u+vXrq3v37tqxY4e53jAMTZ06VS1btlT9+vUVHx+vb7/91uvyfPDBB0v166BBg2o8z7Zt25bKw8/PT8nJyZK8531qJVdvea8WFRXpmWeeUXR0tOrXr6927dpp5syZuniMIW95n6Ju8JXzkTv4yjniSvnSZ/eVqmufqSdPntT48ePVpk0b1a9fXzfccIO2b99urq9NbTUZqDHbtm0z2rZta/To0cN4/PHHzeW33HKLMXbsWOPIkSPmz4kTJ2o8v2nTphldu3Z1yeOnn34y1z/66KNGVFSUkZmZaezYscPo27evccMNN3hdnt7Sn4ZhGMeOHTPatGljPPjgg8bWrVuNH374wVi7dq3x3XffmTHPP/+80bhxY2PlypXGP/7xD+O//uu/jOjoaOPs2bNeleeoUaOMQYMGufTrsWPHaizHEkePHnXJwW63G5KMzz//3DAM73mfWsnVW96rzz77rNG8eXNj1apVxoEDB4wVK1YYDRs2NBYsWGDGeMP7FHWHr5yPrpSvnCPcwZc+u69UXftMveeee4wuXboYGzduNL799ltj2rRphs1mM/71r38ZhlG72lqCAqqGnDx50ujQoYNht9uNW265pVQBdfFrT5k2bZrRs2fPMtfl5eUZgYGBxooVK8xl33zzjSHJyMrKqqEML7hcnobhPf1pGIbxxBNPGDfddFO564uLi42IiAjjxRdfNJfl5eUZwcHBxl//+teaSNEwjIrzNIwLBdSdd95ZMwlVwuOPP260a9fOKC4u9qr3aVkuztUwvOe9mpSUZDz00EMuy4YOHWqMHDnSMAzveZ+i7vCV89GV8pVzRHXwpc/uyqpLn6lnzpwxAgICjFWrVrksv/76642nn366VrX1YtzCV0OSk5OVlJSk+Pj4MtcvXbpULVq0ULdu3TR58mSdOXOmhjO84Ntvv1VkZKSuueYajRw5UocOHZIkZWdny+l0uuTfqVMntW7dWllZWV6TZwlv6c+PPvpIvXv31t13362wsDBdd911+stf/mKuP3DggBwOh0u/Nm7cWLGxsTXarxXlWWLDhg0KCwtTx44dNW7cOP388881lmNZCgsL9c477+ihhx6Sn5+f171PL3ZpriW84b16ww03KDMzU//85z8lSf/4xz/0xRdfaPDgwZK8532KusVXzkdXwlfOEe7mS5/dVVGXPlPPnz+voqIihYSEuCyvX7++vvjii1rV1ovV83QCdcG7776rr776yuV+0Ivdd999atOmjSIjI7Vr1y498cQT2r9/v95///0azTM2NlZLlixRx44ddeTIEU2fPl0333yz9uzZI4fDoaCgIDVp0sRlm/DwcDkcDq/Js1GjRl7Tn5L0ww8/aOHChUpNTdVTTz2l7du367//+78VFBSkUaNGmX0XHh7usl1N92tFeUrSoEGDNHToUEVHR+v777/XU089pcGDBysrK0sBAQE1luvFVq5cqby8PD344IOS5FXv00tdmqvkPf/2n3zySeXn56tTp04KCAhQUVGRnn32WY0cOVKSvOZ9irrDV85HV8pXzhHu5kuf3VVRlz5TGzVqpLi4OM2cOVOdO3dWeHi4/vrXvyorK0vt27evVW29GAVUNfvxxx/1+OOPy263l6rOSzzyyCPm/3fv3l0tW7bUgAED9P3336tdu3Y1lar5lxFJ6tGjh2JjY9WmTRu99957ql+/fo3lUZHL5TlmzBiv6U9JKi4uVu/evfXcc89Jkq677jrt2bNHGRkZZmHiDazkOWLECDO+e/fu6tGjh9q1a6cNGzZowIABHsn7zTff1ODBgxUZGemR41dGWbl6y3v1vffe09KlS7Vs2TJ17dpVOTk5Gj9+vCIjI73qfYq6w1fOR1fKV84R7uZLn91VUdc+U//nf/5HDz30kK6++moFBATo+uuv17333qvs7GxPp1ZtuIWvmmVnZ+vo0aO6/vrrVa9ePdWrV08bN27Uyy+/rHr16qmoqKjUNrGxsZKk7777rqbTddGkSRNde+21+u677xQREaHCwkLl5eW5xOTm5ioiIsIzCf5/F+dZFk/2Z8uWLdWlSxeXZZ07dzZvRSnpu0tHGqrpfq0oz7Jcc801atGihcfep//3f/+nzz77TA8//LC5zFvfp2XlWhZPvVcnTpyoJ598UiNGjFD37t11//33a8KECZo1a5Yk73mfou7ylfNRZfnKOcKdfOmzu6rq2mdqu3bttHHjRp06dUo//vijtm3bJqfTqWuuuabWtbUEBVQ1GzBggHbv3q2cnBzzp3fv3ho5cqRycnLKvPUpJydH0oUPVk86deqUvv/+e7Vs2VIxMTEKDAxUZmamuX7//v06dOiQ4uLiPJila55l8WR/3njjjdq/f7/Lsn/+859q06aNJCk6OloREREu/Zqfn6+tW7fWaL9WlGdZ/vWvf+nnn3/22Pt08eLFCgsLU1JSkrnMW9+nZeVaFk+9V8+cOSN/f9fTQUBAgIqLiyV5z/sUdZevnI8qy1fOEe7kS5/dVVVXP1MbNGigli1b6vjx41q7dq3uvPPOWttWRuHzgItH3vruu++MGTNmGDt27DAOHDhgfPjhh8Y111xj9OvXr8bz+sMf/mBs2LDBOHDggPHll18a8fHxRosWLYyjR48ahnFhiNHWrVsb69evN3bs2GHExcUZcXFxXpWnN/WnYVwYur5evXrGs88+a3z77bfG0qVLjdDQUOOdd94xY55//nmjSZMmxocffmjs2rXLuPPOO2t8eM+K8jx58qTxxz/+0cjKyjIOHDhgfPbZZ8b1119vdOjQwTh37lyN5VmiqKjIaN26tfHEE0+UWuct79MS5eXqTe/VUaNGGVdffbU55O77779vtGjRwpg0aZIZ4w3vU9QdvnI+ulK+co5wF1/67L4Sde0zdc2aNcann35q/PDDD8a6deuMnj17GrGxsUZhYaFhGLWrrSUooDzg4gLq0KFDRr9+/YxmzZoZwcHBRvv27Y2JEyd6ZC6Y4cOHGy1btjSCgoKMq6++2hg+fLjLXBRnz541fv/73xtNmzY1QkNDjbvuuss4cuSIV+XpTf1Z4uOPPza6detmBAcHG506dTJef/11l/XFxcXGM888Y4SHhxvBwcHGgAEDjP3793tVnmfOnDESEhKMq666yggMDDTatGljjB071nA4HDWep2EYxtq1aw1JZfaTt7xPS5SXqze9V/Pz843HH3/caN26tRESEmJcc801xtNPP20UFBSYMd7yPkXd4CvnI3fwlXOEO/jSZ/eVqGufqcuXLzeuueYaIygoyIiIiDCSk5ONvLw8c31tamsJP8O4aFpkAAAAAEC5eAYKAAAAACyigAIAAAAAiyigAAAAAMAiCigAAAAAsIgCCgAAAAAsooACAAAAAIsooAAAAADAIgooAAAAALCIAgoAAAAALKKAAgAAAACLKKAAAAAAwCIKKAAAAACwiAIKAAAAACyigAIAAAAAiyigAAAAAMAiCigAAAAAsIgCCgAAAAAsooACAAAAAIsooAAAAADAIgooAAAAALCIAgrwQn5+fkpLS/N0GgAAALgEBRQAAAAAWFTP0wkAKO3s2bOqV49/ngAAAN7GzzAMw9NJAJCKi4tVWFiokJAQT6cCAACAcnALH+BmaWlp8vPz0759+3TPPffIZrOpefPmevzxx3Xu3Dkzzs/PTykpKVq6dKm6du2q4OBgrVmzxlx36TNQ//73vzVmzBhFRkYqODhY0dHRGjdunAoLC82YvLw8jR8/XlFRUQoODlb79u31wgsvqLi4uEbaDgDAxc6ePatOnTqpU6dOOnv2rLn82LFjatmypW644QYVFRV5MEOg8rhHCKgm99xzj9q2batZs2Zpy5Ytevnll3X8+HG9/fbbZsz69ev13nvvKSUlRS1atFDbtm3L3Nfhw4fVp08f5eXl6ZFHHlGnTp3073//W3/729905swZBQUF6cyZM7rlllv073//W7/73e/UunVrbd68WZMnT9aRI0c0f/78mmk4AAD/X/369fXWW2/pxhtv1NNPP625c+dKkpKTk3XixAktWbJEAQEBHs4SqBwKKKCaREdH68MPP5R04URhs9n02muv6Y9//KN69OghSdq/f792796tLl26XHZfkydPlsPh0NatW9W7d29z+YwZM1RyF+7cuXP1/fffa+fOnerQoYMk6Xe/+50iIyP14osv6g9/+IOioqKqo6kAAJQrNjZWkyZN0gsvvKC77rpLubm5evfddzV//nxde+21nk4PqDRu4QOqSXJyssvrxx57TJL0ySefmMtuueWWCoun4uJirVy5UnfccYdL8VTCz89PkrRixQrdfPPNatq0qf7zn/+YP/Hx8SoqKtKmTZuutEkAAFRJWlqaunbtqlGjRun3v/+9brnlFv33f/+3p9MCqoQrUEA1KbkKVKJdu3by9/fXwYMHzWXR0dEV7uenn35Sfn6+unXrdtm4b7/9Vrt27dJVV11V5vqjR49WnDQAANUgKChIixYt0q9+9SuFhIRo8eLF5h8AAV9DAQXUkLJOFPXr13fb/ouLizVw4EBNmjSpzPXcJgEA8KS1a9dKks6dO6dvv/3W0h8RAW9EAQVUk0tPDt99952Ki4vLHSiiPFdddZVsNpv27Nlz2bh27drp1KlTio+Pr0q6AABUm127dmnGjBkaPXq0cnJy9PDDD2v37t1q3Lixp1MDKo1noIBqkp6e7vL6lVdekSQNHjy4Uvvx9/fXkCFD9PHHH2vHjh2l1pcMInHPPfcoKyvL/AvfxfLy8nT+/PlKHRcAAHdwOp168MEHFRkZqQULFmjJkiXKzc3VhAkTPJ0aUCVcgQKqyYEDB/Rf//VfGjRokLKysvTOO+/ovvvuU8+ePSu9r+eee07r1q3TLbfcokceeUSdO3fWkSNHtGLFCn3xxRdq0qSJJk6cqI8++ki//vWv9eCDDyomJkanT5/W7t279be//U0HDx5UixYtqqGlAACU709/+pNycnKUmZmpRo0aqUePHpo6daqmTJmi3/zmN7r99ts9nSJQKRRQQDVZvny5pk6dqieffFL16tVTSkqKXnzxxSrt6+qrr9bWrVv1zDPPaOnSpcrPz9fVV1+twYMHKzQ0VJIUGhqqjRs36rnnntOKFSv09ttvy2az6dprr9X06dO5TQIAUOO++uorPffcc0pJSdGtt95qLn/yySf14YcfauzYsdq7d6+aNGniuSSBSvIzSu7/AeAWaWlpmj59un766Seu+AAAANQyPAMFAAAAABZRQAEAAACARRRQAAAAAGARz0ABAAAAgEVcgQIAAAAAiyigAAAAAMCiOj0PVHFxsQ4fPqxGjRrJz8/P0+kAgE8xDEMnT55UZGSk/P35e5w34LwGAFVn9bxWpwuow4cPKyoqytNpAIBP+/HHH9WqVStPpwFxXgMAd6jovFanC6hGjRpJutBJNput3Din06l169YpISFBgYGBNZVerUM/ug996T70ZdXl5+crKirK/CyF51k9r5WlLvxboI21A22sHbyxjVbPa3W6gCq5vcFms1VYQIWGhspms3nNL9gX0Y/uQ1+6D3155bhVzHtYPa+VpS78W6CNtQNtrB28uY0Vnde4aR0AAAAALKKAAgAAAACLKKAAAAAAwCIKKAAAAACwiAIKAAAAACyigAIAAAAAiypVQM2aNUu/+tWv1KhRI4WFhWnIkCHav3+/S0z//v3l5+fn8vPoo4+6xBw6dEhJSUkKDQ1VWFiYJk6cqPPnz7vEbNiwQddff72Cg4PVvn17LVmypFQ+6enpatu2rUJCQhQbG6tt27ZVpjkAAAAAUCmVKqA2btyo5ORkbdmyRXa7XU6nUwkJCTp9+rRL3NixY3XkyBHzZ/bs2ea6oqIiJSUlqbCwUJs3b9Zbb72lJUuWaOrUqWbMgQMHlJSUpFtvvVU5OTkaP368Hn74Ya1du9aMWb58uVJTUzVt2jR99dVX6tmzpxITE3X06NGq9gUAAAAAXFalJtJds2aNy+slS5YoLCxM2dnZ6tevn7k8NDRUERERZe5j3bp1+vrrr/XZZ58pPDxcvXr10syZM/XEE08oLS1NQUFBysjIUHR0tObMmSNJ6ty5s7744gvNmzdPiYmJkqS5c+dq7NixGj16tCQpIyNDq1ev1qJFi/Tkk09WpllV1vbJ1TVynLIcfD7JY8cGAAC+i+8vwJWpVAF1qRMnTkiSmjVr5rJ86dKleueddxQREaE77rhDzzzzjEJDQyVJWVlZ6t69u8LDw834xMREjRs3Tnv37tV1112nrKwsxcfHu+wzMTFR48ePlyQVFhYqOztbkydPNtf7+/srPj5eWVlZ5eZbUFCggoIC83V+fr6kCzMhO53OcrcrWXdpTHCAUe421e1y+Xqr8voRlUdfug99WXX0GQCgLqpyAVVcXKzx48frxhtvVLdu3czl9913n9q0aaPIyEjt2rVLTzzxhPbv36/3339fkuRwOFyKJ0nma4fDcdmY/Px8nT17VsePH1dRUVGZMfv27Ss351mzZmn69Omllq9bt84s8C7Hbre7vJ7dp8JNqs0nn3ziuYNfoUv7EVVHX7oPfVl5Z86c8XQKAADUuCoXUMnJydqzZ4+++OILl+WPPPKI+f/du3dXy5YtNWDAAH3//fdq165d1TN1g8mTJys1NdV8nZ+fr6ioKCUkJMhms5W7ndPplN1u18CBAxUYGGgu75a2ttxtqtuetESPHbuqyutHVB596T70ZdWVXMX3ZbNmzdL777+vffv2qX79+rrhhhv0wgsvqGPHjmbMuXPn9Ic//EHvvvuuCgoKlJiYqNdee83lj3iHDh3SuHHj9Pnnn6thw4YaNWqUZs2apXr1fjnNbtiwQampqdq7d6+ioqI0ZcoUPfjggy75pKen68UXX5TD4VDPnj31yiuvqE8fD/61DgBQSpUKqJSUFK1atUqbNm1Sq1atLhsbGxsrSfruu+/Url07RURElBotLzc3V5LM56YiIiLMZRfH2Gw21a9fXwEBAQoICCgzprxnryQpODhYwcHBpZYHBgZa+uJ0aVxBkV+F21QXX/6iZ7W/UTH60n3oy8qrDf1VMjjSr371K50/f15PPfWUEhIS9PXXX6tBgwaSpAkTJmj16tVasWKFGjdurJSUFA0dOlRffvmlpF8GR4qIiNDmzZt15MgRPfDAAwoMDNRzzz0n6ZfBkR599FEtXbpUmZmZevjhh9WyZUvz2d6SwZEyMjIUGxur+fPnKzExUfv371dYWJhnOggAUEqlRuEzDEMpKSn64IMPtH79ekVHR1e4TU5OjiSpZcuWkqS4uDjt3r3bZbQ8u90um82mLl26mDGZmZku+7Hb7YqLi5MkBQUFKSYmxiWmuLhYmZmZZgwAABVZs2aNHnzwQXXt2lU9e/bUkiVLdOjQIWVnZ0u68Kzvm2++qblz5+q2225TTEyMFi9erM2bN2vLli2Sfhkc6Z133lGvXr00ePBgzZw5U+np6SosLJQkl8GROnfurJSUFP3mN7/RvHnzzFwuHhypS5cuysjIUGhoqBYtWlTzHQMAKFelrkAlJydr2bJl+vDDD9WoUSPzmaXGjRurfv36+v7777Vs2TLdfvvtat68uXbt2qUJEyaoX79+6tGjhyQpISFBXbp00f3336/Zs2fL4XBoypQpSk5ONq8OPfroo3r11Vc1adIkPfTQQ1q/fr3ee+89rV79y6gxqampGjVqlHr37q0+ffpo/vz5On36tDkqHwAAlXXp4EjZ2dlyOp0uAxt16tRJrVu3VlZWlvr27euTgyOVpS4MqEIbL/D1QbD4PdYO3thGq7lUqoBauHChpAuT5V5s8eLFevDBBxUUFKTPPvvMLGaioqI0bNgwTZkyxYwNCAjQqlWrNG7cOMXFxalBgwYaNWqUZsyYYcZER0dr9erVmjBhghYsWKBWrVrpjTfeMG9zkKThw4frp59+0tSpU+VwONSrVy+tWbOm1MASAABYUdbgSA6HQ0FBQWrSpIlLbHh4eIUDH5Wsu1yMpwdHKktdGFClrrextgyCVdd/j7WFN7XR6uBIlSqgDOPyf7GIiorSxo0bK9xPmzZtKvwH1L9/f+3cufOyMSkpKUpJSanweAAAVKS8wZG8WVUHRypLXRhQhTZe4OuDYPF7rB28sY1WB0e6onmgAACoDcobHCkiIkKFhYXKy8tzuQp18aBFvjw4UlnqwoAqdb2NtWUQrLr+e6wtvKmNVvOo1CASAADUJhUNjhQTE6PAwECXQYv279+vQ4cOmYMWMTgSANQtXIECANRZFQ2O1LhxY40ZM0apqalq1qyZbDabHnvsMcXFxalv376SGBwJAOoaCigAQJ1V0eBIkjRv3jz5+/tr2LBhLhPplmBwJACoWyigAAB1VkWDI0lSSEiI0tPTlZ6eXm4MgyMBQN3BM1AAAAAAYBEFFAAAAABYRAEFAAAAABZRQAEAAACARRRQAAAAAGARBRQAAAAAWEQBBQAAAAAWUUABAAAAgEUUUAAAAABgEQUUAAAAAFhEAQUAAAAAFlFAAQAAAIBFFFAAAAAAYBEFFAAAAABYRAEFAAAAABZRQAEAAACARRRQAAAAAGARBRQAAAAAWEQBBQAAAAAWUUABAAAAgEUUUAAAAABgEQUUAAAAAFhEAQUAAAAAFlFAAQAAAIBFlSqgZs2apV/96ldq1KiRwsLCNGTIEO3fv98l5ty5c0pOTlbz5s3VsGFDDRs2TLm5uS4xhw4dUlJSkkJDQxUWFqaJEyfq/PnzLjEbNmzQ9ddfr+DgYLVv315LliwplU96erratm2rkJAQxcbGatu2bZVpDgAAAABUSqUKqI0bNyo5OVlbtmyR3W6X0+lUQkKCTp8+bcZMmDBBH3/8sVasWKGNGzfq8OHDGjp0qLm+qKhISUlJKiws1ObNm/XWW29pyZIlmjp1qhlz4MABJSUl6dZbb1VOTo7Gjx+vhx9+WGvXrjVjli9frtTUVE2bNk1fffWVevbsqcTERB09evRK+gMAAAAAylWvMsFr1qxxeb1kyRKFhYUpOztb/fr104kTJ/Tmm29q2bJluu222yRJixcvVufOnbVlyxb17dtX69at09dff63PPvtM4eHh6tWrl2bOnKknnnhCaWlpCgoKUkZGhqKjozVnzhxJUufOnfXFF19o3rx5SkxMlCTNnTtXY8eO1ejRoyVJGRkZWr16tRYtWqQnn3zyijsGAAAAAC5VqQLqUidOnJAkNWvWTJKUnZ0tp9Op+Ph4M6ZTp05q3bq1srKy1LdvX2VlZal79+4KDw83YxITEzVu3Djt3btX1113nbKyslz2URIzfvx4SVJhYaGys7M1efJkc72/v7/i4+OVlZVVbr4FBQUqKCgwX+fn50uSnE6nnE5nuduVrLs0JjjAKHeb6na5fL1Vef2IyqMv3Ye+rDr6DABQF1W5gCouLtb48eN14403qlu3bpIkh8OhoKAgNWnSxCU2PDxcDofDjLm4eCpZX7LucjH5+fk6e/asjh8/rqKiojJj9u3bV27Os2bN0vTp00stX7dunUJDQytss91ud3k9u0+Fm1SbTz75xHMHv0KX9iOqjr50H/qy8s6cOePpFAAAqHFVLqCSk5O1Z88effHFF+7Mp1pNnjxZqamp5uv8/HxFRUUpISFBNput3O2cTqfsdrsGDhyowMBAc3m3tLXlblPd9qQleuzYVVVeP6Ly6Ev3oS+rruQqPgAAdUmVCqiUlBStWrVKmzZtUqtWrczlERERKiwsVF5enstVqNzcXEVERJgxl46WVzJK38Uxl47cl5ubK5vNpvr16ysgIEABAQFlxpTsoyzBwcEKDg4utTwwMNDSF6dL4wqK/Crcprr48hc9q/2NitGX7kNfVh79BQCoiyo1Cp9hGEpJSdEHH3yg9evXKzo62mV9TEyMAgMDlZmZaS7bv3+/Dh06pLi4OElSXFycdu/e7TJant1ul81mU5cuXcyYi/dRElOyj6CgIMXExLjEFBcXKzMz04wBAAAAAHer1BWo5ORkLVu2TB9++KEaNWpkPrPUuHFj1a9fX40bN9aYMWOUmpqqZs2ayWaz6bHHHlNcXJz69u0rSUpISFCXLl10//33a/bs2XI4HJoyZYqSk5PNq0OPPvqoXn31VU2aNEkPPfSQ1q9fr/fee0+rV682c0lNTdWoUaPUu3dv9enTR/Pnz9fp06fNUfkAAAAAwN0qVUAtXLhQktS/f3+X5YsXL9aDDz4oSZo3b578/f01bNgwFRQUKDExUa+99poZGxAQoFWrVmncuHGKi4tTgwYNNGrUKM2YMcOMiY6O1urVqzVhwgQtWLBArVq10htvvGEOYS5Jw4cP108//aSpU6fK4XCoV69eWrNmTamBJQAAAADAXSpVQBlGxcN2h4SEKD09Xenp6eXGtGnTpsJR5Pr376+dO3deNiYlJUUpKSkV5gQAAAAA7lCpZ6AAAAAAoC6jgAIAAAAAiyigAAAAAMAiCigAQJ22adMm3XHHHYqMjJSfn59Wrlzpsv7BBx+Un5+fy8+gQYNcYo4dO6aRI0fKZrOpSZMmGjNmjE6dOuUSs2vXLt18880KCQlRVFSUZs+eXSqXFStWqFOnTgoJCVH37t0rfF4YAFDzKKAAAHXa6dOn1bNnz8sOfjRo0CAdOXLE/PnrX//qsn7kyJHau3ev7Ha7OdH8I488Yq7Pz89XQkKC2rRpo+zsbL344otKS0vT66+/bsZs3rxZ9957r8aMGaOdO3dqyJAhGjJkiPbs2eP+RgMAqqxSo/ABAFDbDB48WIMHD75sTHBwsCIiIspc980332jNmjXavn27evfuLUl65ZVXdPvtt+ull15SZGSkli5dqsLCQi1atEhBQUHq2rWrcnJyNHfuXLPQWrBggQYNGqSJEydKkmbOnCm73a5XX31VGRkZbmwxAOBKUEABAFCBDRs2KCwsTE2bNtVtt92mP/3pT2revLkkKSsrS02aNDGLJ0mKj4+Xv7+/tm7dqrvuuktZWVnq16+fgoKCzJjExES98MILOn78uJo2baqsrCylpqa6HDcxMbHULYUXKygoUEFBgfk6Pz9fkuR0OuV0OivVxpL4ym7nS2jjBcEBFU9LU13c0ff8HmsHb2yj1VwooAAAuIxBgwZp6NChio6O1vfff6+nnnpKgwcPVlZWlgICAuRwOBQWFuayTb169dSsWTM5HA5JksPhUHR0tEtMycTvDodDTZs2lcPhKDUZfHh4uLmPssyaNUvTp08vtXzdunUKDQ2tUnvtdnuVtvMldb2Ns/vUYCKXcOdzfXX991hbeFMbz5w5YymOAgoAgMsYMWKE+f/du3dXjx491K5dO23YsEEDBgzwYGbS5MmTXa5a5efnKyoqSgkJCbLZbJXal9PplN1u18CBAxUYGOjuVL0CbbygW9raGs7qF3vSEq94H/weawdvbGPJVfyKUEABAFAJ11xzjVq0aKHvvvtOAwYMUEREhI4ePeoSc/78eR07dsx8bioiIkK5ubkuMSWvK4op79kr6cKzWcHBwaWWBwYGVvkLyZVs6yvqehsLivxqOJtfuLPf6/rvsbbwpjZazYNR+AAAqIR//etf+vnnn9WyZUtJUlxcnPLy8pSdnW3GrF+/XsXFxYqNjTVjNm3a5HJ/vd1uV8eOHdW0aVMzJjMz0+VYdrtdcXFx1d0kAEAlUEABAOq0U6dOKScnRzk5OZKkAwcOKCcnR4cOHdKpU6c0ceJEbdmyRQcPHlRmZqbuvPNOtW/fXomJF25F6ty5swYNGqSxY8dq27Zt+vLLL5WSkqIRI0YoMjJSknTfffcpKChIY8aM0d69e7V8+XItWLDA5fa7xx9/XGvWrNGcOXO0b98+paWlaceOHUpJSanxPgEAlI9b+AAAddqOHTt06623mq9LippRo0Zp4cKF2rVrl9566y3l5eUpMjJSCQkJmjlzpsutc0uXLlVKSooGDBggf39/DRs2TC+//LK5vnHjxlq3bp2Sk5MVExOjFi1aaOrUqS5zRd1www1atmyZpkyZoqeeekodOnTQypUr1a1btxroBaBmtH1y9RXvIzjA0Ow+F57lqsztiAefT7riYwMSBRQAoI7r37+/DKP8YZ3Xrq34gftmzZpp2bJll43p0aOH/v73v1825u6779bdd99d4fEAAJ7DLXwAAAAAYBEFFAAAAABYRAEFAAAAABZRQAEAAACARRRQAAAAAGARBRQAAAAAWEQBBQAAAAAWUUABAAAAgEUUUAAAAABgEQUUAAAAAFhEAQUAAAAAFlFAAQAAAIBFFFAAAAAAYBEFFAAAAABYRAEFAAAAABZVuoDatGmT7rjjDkVGRsrPz08rV650Wf/ggw/Kz8/P5WfQoEEuMceOHdPIkSNls9nUpEkTjRkzRqdOnXKJ2bVrl26++WaFhIQoKipKs2fPLpXLihUr1KlTJ4WEhKh79+765JNPKtscAAAAALCs0gXU6dOn1bNnT6Wnp5cbM2jQIB05csT8+etf/+qyfuTIkdq7d6/sdrtWrVqlTZs26ZFHHjHX5+fnKyEhQW3atFF2drZefPFFpaWl6fXXXzdjNm/erHvvvVdjxozRzp07NWTIEA0ZMkR79uypbJMAAAAAwJJ6ld1g8ODBGjx48GVjgoODFRERUea6b775RmvWrNH27dvVu3dvSdIrr7yi22+/XS+99JIiIyO1dOlSFRYWatGiRQoKClLXrl2Vk5OjuXPnmoXWggULNGjQIE2cOFGSNHPmTNntdr366qvKyMiobLMAAAAAoELV8gzUhg0bFBYWpo4dO2rcuHH6+eefzXVZWVlq0qSJWTxJUnx8vPz9/bV161Yzpl+/fgoKCjJjEhMTtX//fh0/ftyMiY+PdzluYmKisrKyqqNJAAAAAFD5K1AVGTRokIYOHaro6Gh9//33euqppzR48GBlZWUpICBADodDYWFhrknUq6dmzZrJ4XBIkhwOh6Kjo11iwsPDzXVNmzaVw+Ewl10cU7KPshQUFKigoMB8nZ+fL0lyOp1yOp3lbley7tKY4ACj3G2q2+Xy9Vbl9SMqj750H/qy6ugzAEBd5PYCasSIEeb/d+/eXT169FC7du20YcMGDRgwwN2Hq5RZs2Zp+vTppZavW7dOoaGhFW5vt9tdXs/u47bUKs2XB8y4tB9RdfSl+9CXlXfmzBlPpwAAQI1zewF1qWuuuUYtWrTQd999pwEDBigiIkJHjx51iTl//ryOHTtmPjcVERGh3Nxcl5iS1xXFlPfslSRNnjxZqamp5uv8/HxFRUUpISFBNput3O2cTqfsdrsGDhyowMBAc3m3tLWXa3q12pOW6LFjV1V5/YjKoy/dh76supKr+ADgC9o+udojxz34fJJHjovqU+0F1L/+9S/9/PPPatmypSQpLi5OeXl5ys7OVkxMjCRp/fr1Ki4uVmxsrBnz9NNPy+l0ml9o7Ha7OnbsqKZNm5oxmZmZGj9+vHksu92uuLi4cnMJDg5WcHBwqeWBgYGWvjhdGldQ5FfhNtXFl7/oWe1vVIy+dB/6svLoLwBAXVTpQSROnTqlnJwc5eTkSJIOHDignJwcHTp0SKdOndLEiRO1ZcsWHTx4UJmZmbrzzjvVvn17JSZeuGLSuXNnDRo0SGPHjtW2bdv05ZdfKiUlRSNGjFBkZKQk6b777lNQUJDGjBmjvXv3avny5VqwYIHL1aPHH39ca9as0Zw5c7Rv3z6lpaVpx44dSklJcUO3AAAAAEBplS6gduzYoeuuu07XXXedJCk1NVXXXXedpk6dqoCAAO3atUv/9V//pWuvvVZjxoxRTEyM/v73v7tc+Vm6dKk6deqkAQMG6Pbbb9dNN93kMsdT48aNtW7dOh04cEAxMTH6wx/+oKlTp7rMFXXDDTdo2bJlev3119WzZ0/97W9/08qVK9WtW7cr6Q8AAAAAKFelb+Hr37+/DKP80efWrq34uaBmzZpp2bJll43p0aOH/v73v1825u6779bdd99d4fEAAAAAwB2qZR4oAAAAAKiNKKAAAAAAwCIKKAAAAACwiAIKAAAAACyigAIAAAAAiyigAAAAAMAiCigAAAAAsIgCCgAAAAAsooACAAAAAIsooAAAAADAIgooAAAAALCIAgoAAAAALKKAAgDUaZs2bdIdd9yhyMhI+fn5aeXKlS7rDcPQ1KlT1bJlS9WvX1/x8fH69ttvXWKOHTumkSNHymazqUmTJhozZoxOnTrlErNr1y7dfPPNCgkJUVRUlGbPnl0qlxUrVqhTp04KCQlR9+7d9cknn7i9vQCAK0MBBQCo006fPq2ePXsqPT29zPWzZ8/Wyy+/rIyMDG3dulUNGjRQYmKizp07Z8aMHDlSe/fuld1u16pVq7Rp0yY98sgj5vr8/HwlJCSoTZs2ys7O1osvvqi0tDS9/vrrZszmzZt17733asyYMdq5c6eGDBmiIUOGaM+ePdXXeABApdXzdAIAAHjS4MGDNXjw4DLXGYah+fPna8qUKbrzzjslSW+//bbCw8O1cuVKjRgxQt98843WrFmj7du3q3fv3pKkV155RbfffrteeuklRUZGaunSpSosLNSiRYsUFBSkrl27KicnR3PnzjULrQULFmjQoEGaOHGiJGnmzJmy2+169dVXlZGRUQM9AQCwggIKAIByHDhwQA6HQ/Hx8eayxo0bKzY2VllZWRoxYoSysrLUpEkTs3iSpPj4ePn7+2vr1q266667lJWVpX79+ikoKMiMSUxM1AsvvKDjx4+radOmysrKUmpqqsvxExMTS91SeLGCggIVFBSYr/Pz8yVJTqdTTqezUm0tia/sdr6ENl4QHGDUVDrVItjfcPmvt6vK+433qmdYzYUCCgCAcjgcDklSeHi4y/Lw8HBzncPhUFhYmMv6evXqqVmzZi4x0dHRpfZRsq5p06ZyOByXPU5ZZs2apenTp5davm7dOoWGhlppYil2u71K2/mSut7G2X1qMJFqNLN3sadTsORKnmWs6+/VmnbmzBlLcRRQAAD4qMmTJ7tctcrPz1dUVJQSEhJks9kqtS+n0ym73a6BAwcqMDDQ3al6Bdp4Qbe0tTWclXsF+xua2btYz+zwV0Gxn6fTqdCetMRKb8N71TNKruJXhAIKAIByRERESJJyc3PVsmVLc3lubq569eplxhw9etRlu/Pnz+vYsWPm9hEREcrNzXWJKXldUUzJ+rIEBwcrODi41PLAwMAqfyG5km19RV1vY0GR9xcdVhQU+/lEW67kvVbX36s1zWoejMIHAEA5oqOjFRERoczMTHNZfn6+tm7dqri4OElSXFyc8vLylJ2dbcasX79excXFio2NNWM2bdrkcn+93W5Xx44d1bRpUzPm4uOUxJQcBwDgHSigAAB12qlTp5STk6OcnBxJFwaOyMnJ0aFDh+Tn56fx48frT3/6kz766CPt3r1bDzzwgCIjIzVkyBBJUufOnTVo0CCNHTtW27Zt05dffqmUlBSNGDFCkZGRkqT77rtPQUFBGjNmjPbu3avly5drwYIFLrffPf7441qzZo3mzJmjffv2KS0tTTt27FBKSkpNdwkA4DK4hQ8AUKft2LFDt956q/m6pKgZNWqUlixZokmTJun06dN65JFHlJeXp5tuuklr1qxRSEiIuc3SpUuVkpKiAQMGyN/fX8OGDdPLL79srm/cuLHWrVun5ORkxcTEqEWLFpo6darLXFE33HCDli1bpilTpuipp55Shw4dtHLlSnXr1q0GegEAYBUFFACgTuvfv78Mo/zhkP38/DRjxgzNmDGj3JhmzZpp2bJllz1Ojx499Pe///2yMXfffbfuvvvuyycMAPAobuEDAAAAAIsooAAAAADAIgooAAAAALCIAgoAAAAALKKAAgAAAACLKKAAAAAAwKJKF1CbNm3SHXfcocjISPn5+WnlypUu6w3D0NSpU9WyZUvVr19f8fHx+vbbb11ijh07ppEjR8pms6lJkyYaM2aMTp065RKza9cu3XzzzQoJCVFUVJRmz55dKpcVK1aoU6dOCgkJUffu3fXJJ59UtjkAAAAAYFmlC6jTp0+rZ8+eSk9PL3P97Nmz9fLLLysjI0Nbt25VgwYNlJiYqHPnzpkxI0eO1N69e2W327Vq1Spt2rTJZTLB/Px8JSQkqE2bNsrOztaLL76otLQ0vf7662bM5s2bde+992rMmDHauXOnhgwZoiFDhmjPnj2VbRIAAAAAWFLpiXQHDx6swYMHl7nOMAzNnz9fU6ZM0Z133ilJevvttxUeHq6VK1dqxIgR+uabb7RmzRpt375dvXv3liS98soruv322/XSSy8pMjJSS5cuVWFhoRYtWqSgoCB17dpVOTk5mjt3rlloLViwQIMGDdLEiRMlSTNnzpTdbterr76qjIyMKnUGAAAAAFyOW5+BOnDggBwOh+Lj481ljRs3VmxsrLKysiRJWVlZatKkiVk8SVJ8fLz8/f21detWM6Zfv34KCgoyYxITE7V//34dP37cjLn4OCUxJccBAAAAAHer9BWoy3E4HJKk8PBwl+Xh4eHmOofDobCwMNck6tVTs2bNXGKio6NL7aNkXdOmTeVwOC57nLIUFBSooKDAfJ2fny9Jcjqdcjqd5W5Xsu7SmOAAo9xtqtvl8vVW5fUjKo++dB/6suroMwBAXeTWAsrbzZo1S9OnTy+1fN26dQoNDa1we7vd7vJ6dh+3pVZpvjxgxqX9iKqjL92Hvqy8M2fOeDoFAABqnFsLqIiICElSbm6uWrZsaS7Pzc1Vr169zJijR4+6bHf+/HkdO3bM3D4iIkK5ubkuMSWvK4opWV+WyZMnKzU11Xydn5+vqKgoJSQkyGazlbud0+mU3W7XwIEDFRgYaC7vlra23G2q2560RI8du6rK60dUHn3pPvRl1ZVcxQcAoC5xawEVHR2tiIgIZWZmmgVTfn6+tm7dqnHjxkmS4uLilJeXp+zsbMXExEiS1q9fr+LiYsXGxpoxTz/9tJxOp/mFxm63q2PHjmratKkZk5mZqfHjx5vHt9vtiouLKze/4OBgBQcHl1oeGBho6YvTpXEFRX4VblNdfPmLntX+RsXoS/ehLyuP/gIA1EWVHkTi1KlTysnJUU5OjqQLA0fk5OTo0KFD8vPz0/jx4/WnP/1JH330kXbv3q0HHnhAkZGRGjJkiCSpc+fOGjRokMaOHatt27bpyy+/VEpKikaMGKHIyEhJ0n333aegoCCNGTNGe/fu1fLly7VgwQKXq0ePP/641qxZozlz5mjfvn1KS0vTjh07lJKScuW9AgAAAABlqPQVqB07dujWW281X5cUNaNGjdKSJUs0adIknT59Wo888ojy8vJ00003ac2aNQoJCTG3Wbp0qVJSUjRgwAD5+/tr2LBhevnll831jRs31rp165ScnKyYmBi1aNFCU6dOdZkr6oYbbtCyZcs0ZcoUPfXUU+rQoYNWrlypbt26VakjAAAAAKAilS6g+vfvL8Mof/Q5Pz8/zZgxQzNmzCg3plmzZlq2bNllj9OjRw/9/e9/v2zM3XffrbvvvvvyCddSbZ9c7ZHjHnw+ySPHBQAAALyBW+eBAgAAAIDajAIKAAAAACyigAIAAAAAiyigAAAAAMAiCigAAAAAsIgCCgAAAAAsooACAAAAAIsooAAAAADAIgooAAAAALCIAgoAAAAALKKAAgAAAACLKKAAAAAAwCIKKAAAAACwiAIKAAAAACyigAIAAAAAi+p5OgH4lrZPrq7ytsEBhmb3kbqlrVVBkV+ltz/4fFKVjw0AAAC4A1egAAAAAMAiCigAAAAAsIgCCgAAAAAsooACAAAAAIsooAAAAADAIgooAAAuIy0tTX5+fi4/nTp1MtefO3dOycnJat68uRo2bKhhw4YpNzfXZR+HDh1SUlKSQkNDFRYWpokTJ+r8+fMuMRs2bND111+v4OBgtW/fXkuWLKmJ5gEAKokCCgCACnTt2lVHjhwxf7744gtz3YQJE/Txxx9rxYoV2rhxow4fPqyhQ4ea64uKipSUlKTCwkJt3rxZb731lpYsWaKpU6eaMQcOHFBSUpJuvfVW5eTkaPz48Xr44Ye1du3aGm0nAKBizAMFAEAF6tWrp4iIiFLLT5w4oTfffFPLli3TbbfdJklavHixOnfurC1btqhv375at26dvv76a3322WcKDw9Xr169NHPmTD3xxBNKS0tTUFCQMjIyFB0drTlz5kiSOnfurC+++ELz5s1TYmJijbYVAHB5FFAAAFTg22+/VWRkpEJCQhQXF6dZs2apdevWys7OltPpVHx8vBnbqVMntW7dWllZWerbt6+ysrLUvXt3hYeHmzGJiYkaN26c9u7dq+uuu05ZWVku+yiJGT9+/GXzKigoUEFBgfk6Pz9fkuR0OuV0OivVxpL4ym7nS2jjBcEBRk2lUy2C/Q2X/3q7qrzfeK96htVcKKAAALiM2NhYLVmyRB07dtSRI0c0ffp03XzzzdqzZ48cDoeCgoLUpEkTl23Cw8PlcDgkSQ6Hw6V4Kllfsu5yMfn5+Tp79qzq169fZm6zZs3S9OnTSy1ft26dQkNDq9Reu91epe18SV1v4+w+NZhINZrZu9jTKVjyySefVHnbuv5erWlnzpyxFEcBBQDAZQwePNj8/x49eig2NlZt2rTRe++9V25hU1MmT56s1NRU83V+fr6ioqKUkJAgm81WqX05nU7Z7XYNHDhQgYGB7k7VK9DGC7ql+fazdcH+hmb2LtYzO/xVUOzn6XQqtCet8rfh8l71jJKr+BWhgAIAoBKaNGmia6+9Vt99950GDhyowsJC5eXluVyFys3NNZ+ZioiI0LZt21z2UTJK38Uxl47cl5ubK5vNdtkiLTg4WMHBwaWWBwYGVvkLyZVs6yvqehsLiry/6LCioNjPJ9pyJe+1uv5erWlW82AUPgAAKuHUqVP6/vvv1bJlS8XExCgwMFCZmZnm+v379+vQoUOKi4uTJMXFxWn37t06evSoGWO322Wz2dSlSxcz5uJ9lMSU7AMA4D0ooAAAuIw//vGP2rhxow4ePKjNmzfrrrvuUkBAgO699141btxYY8aMUWpqqj7//HNlZ2dr9OjRiouLU9++fSVJCQkJ6tKli+6//3794x//0Nq1azVlyhQlJyebV48effRR/fDDD5o0aZL27dun1157Te+9954mTJjgyaYDAMrg9gKKCQcBALXJv/71L917773q2LGj7rnnHjVv3lxbtmzRVVddJUmaN2+efv3rX2vYsGHq16+fIiIi9P7775vbBwQEaNWqVQoICFBcXJx++9vf6oEHHtCMGTPMmOjoaK1evVp2u109e/bUnDlz9MYbbzCEOQB4oWp5Bqpr16767LPPfjlIvV8OM2HCBK1evVorVqxQ48aNlZKSoqFDh+rLL7+U9MuEgxEREdq8ebOOHDmiBx54QIGBgXruueck/TLh4KOPPqqlS5cqMzNTDz/8sFq2bMnJBgDgVu++++5l14eEhCg9PV3p6enlxrRp06bCkbj69++vnTt3VilHAEDNqZYCigkHAQAAANRG1VJA1bYJB8ub6MvXJ6KraVc68Z03TbTmad44+Zyvoi+rjj4DANRFbi+gavOEg5dO9FVbJqKraVWd+O5KJqKrrbxp8jlfR19WntUJBwEAqE3cXkDVxgkHy5voy9cnoqtpVzrxXVUmoqutvHHyOV9FX1ad1QkHAQCoTap9It3aNOHgpXG+MHmbN6rqxHd8uS3Nmyaf83X0ZeXRXwCAuqja54FiwkEAAAAAtYXbCygmHAQAAABQW7n9Fr6SCQd//vlnXXXVVbrppptKTTjo7++vYcOGqaCgQImJiXrttdfM7UsmHBw3bpzi4uLUoEEDjRo1qswJBydMmKAFCxaoVatWTDgIAAAAoNq5vYBiwkEAAAAAtVW1PwMFAAAAALVFtY/CBwAAANRVbZ9cXeltggMMze5zYcqcKxn1+eDzSVXeFuWjgILPqMoHkDvw4QMAAIAS3MIHAAAAABZxBQoAAMADquPOCnfd+gWgfFyBAgAAAACLKKAAAAAAwCIKKAAAAACwiAIKAAAAACyigAIAAAAAiyigAAAAAMAiCigAAAAAsIgCCgAAAAAsooACAAAAAIvqeToBwNtVx0zxVh18PsljxwYAAEBpXIECAAAAAIsooAAAAADAIgooAAAAALCIAgoAAAAALGIQCcCLlTeARXCAodl9pG5pa1VQ5Fctx2YACwAAgNK4AgUAAAAAFlFAAQAAAIBFFFAAAAAAYBHPQAEok6cmEObZKwAA4M24AgUAAAAAFnEFCoBXqekrXxePaLj/2V/X6LEBAIDvoYACgP/PU7ctSty6CACAr6CAAgAvwDNnAAD4Bp6BAgAAAACLfL6ASk9PV9u2bRUSEqLY2Fht27bN0ykBAFBlnNcAwLv5dAG1fPlypaamatq0afrqq6/Us2dPJSYm6ujRo55ODQCASuO8BgDez6efgZo7d67Gjh2r0aNHS5IyMjK0evVqLVq0SE8++aSHswMAoHI4rwFwJwZHqh4+W0AVFhYqOztbkydPNpf5+/srPj5eWVlZZW5TUFCggoIC8/WJEyckSceOHZPT6Sz3WE6nU2fOnNHPP/+swMBAc3m986evtBl1Sr1iQ2fOFKue019FxX6eTsen0ZfuU9f78ueff67ytidPnpQkGYbhrnTqtJo8r5WlvHNdbeJtbayO7xF14TONNvqGis4v3vbvUbJ+XvPZAuo///mPioqKFB4e7rI8PDxc+/btK3ObWbNmafr06aWWR0dHV0uOKO0+TydQi9CX7lOX+7LFnCvfx8mTJ9W4ceMr31Edx3kN7lIXPtNoo/dzx/nFUyo6r/lsAVUVkydPVmpqqvm6uLhYx44dU/PmzeXnV351n5+fr6ioKP3444+y2Ww1kWqtRD+6D33pPvRl1RmGoZMnTyoyMtLTqdRZVT2vlaUu/FugjbUDbawdvLGNVs9rPltAtWjRQgEBAcrNzXVZnpubq4iIiDK3CQ4OVnBwsMuyJk2aWD6mzWbzml+wL6Mf3Ye+dB/6smq48uQ+njivlaUu/FugjbUDbawdvK2NVs5rPjsKX1BQkGJiYpSZmWkuKy4uVmZmpuLi4jyYGQAAlcd5DQB8g89egZKk1NRUjRo1Sr1791afPn00f/58nT592hy9CAAAX8J5DQC8n08XUMOHD9dPP/2kqVOnyuFwqFevXlqzZk2pB3CvVHBwsKZNm1bqNglUDv3oPvSl+9CX8CY1dV4rS134t0AbawfaWDv4chv9DMafBQAAAABLfPYZKAAAAACoaRRQAAAAAGARBRQAAAAAWEQBBQAAAAAWUUBVID09XW3btlVISIhiY2O1bds2T6fkUWlpafLz83P56dSpk7n+3LlzSk5OVvPmzdWwYUMNGzas1KSQhw4dUlJSkkJDQxUWFqaJEyfq/PnzLjEbNmzQ9ddfr+DgYLVv315LliypieZVq02bNumOO+5QZGSk/Pz8tHLlSpf1hmFo6tSpatmyperXr6/4+Hh9++23LjHHjh3TyJEjZbPZ1KRJE40ZM0anTp1yidm1a5duvvlmhYSEKCoqSrNnzy6Vy4oVK9SpUyeFhISoe/fu+uSTT9ze3upUUV8++OCDpd6ngwYNcomhL4Ff1PZz3axZs/SrX/1KjRo1UlhYmIYMGaL9+/d7Oq1q8/zzz8vPz0/jx4/3dCpu9+9//1u//e1v1bx5c9WvX1/du3fXjh07PJ2W2xQVFemZZ55RdHS06tevr3bt2mnmzJny5THf3PH9x9tQQF3G8uXLlZqaqmnTpumrr75Sz549lZiYqKNHj3o6NY/q2rWrjhw5Yv588cUX5roJEybo448/1ooVK7Rx40YdPnxYQ4cONdcXFRUpKSlJhYWF2rx5s9566y0tWbJEU6dONWMOHDigpKQk3XrrrcrJydH48eP18MMPa+3atTXaTnc7ffq0evbsqfT09DLXz549Wy+//LIyMjK0detWNWjQQImJiTp37pwZM3LkSO3du1d2u12rVq3Spk2b9Mgjj5jr8/PzlZCQoDZt2ig7O1svvvii0tLS9Prrr5sxmzdv1r333qsxY8Zo586dGjJkiIYMGaI9e/ZUX+PdrKK+lKRBgwa5vE//+te/uqynL4EL6sK5buPGjUpOTtaWLVtkt9vldDqVkJCg06dPezo1t9u+fbv+/Oc/q0ePHp5Oxe2OHz+uG2+8UYGBgfr000/19ddfa86cOWratKmnU3ObF154QQsXLtSrr76qb775Ri+88IJmz56tV155xdOpVZk7vv94HQPl6tOnj5GcnGy+LioqMiIjI41Zs2Z5MCvPmjZtmtGzZ88y1+Xl5RmBgYHGihUrzGXffPONIcnIysoyDMMwPvnkE8Pf399wOBxmzMKFCw2bzWYUFBQYhmEYkyZNMrp27eqy7+HDhxuJiYlubo3nSDI++OAD83VxcbERERFhvPjii+ayvLw8Izg42PjrX/9qGIZhfP3114YkY/v27WbMp59+avj5+Rn//ve/DcMwjNdee81o2rSp2ZeGYRhPPPGE0bFjR/P1PffcYyQlJbnkExsba/zud79zaxtryqV9aRiGMWrUKOPOO+8sdxv6EvhFXTzXHT161JBkbNy40dOpuNXJkyeNDh06GHa73bjllluMxx9/3NMpudUTTzxh3HTTTZ5Oo1olJSUZDz30kMuyoUOHGiNHjvRQRu5Vle8/3ogrUOUoLCxUdna24uPjzWX+/v6Kj49XVlaWBzPzvG+//VaRkZG65pprNHLkSB06dEiSlJ2dLafT6dJnnTp1UuvWrc0+y8rKUvfu3V0mhUxMTFR+fr727t1rxly8j5KY2tzvBw4ckMPhcGl348aNFRsb69J3TZo0Ue/evc2Y+Ph4+fv7a+vWrWZMv379FBQUZMYkJiZq//79On78uBlTF/p3w4YNCgsLU8eOHTVu3Dj9/PPP5jr6Erigrp7rTpw4IUlq1qyZhzNxr+TkZCUlJZX6XKotPvroI/Xu3Vt33323wsLCdN111+kvf/mLp9NyqxtuuEGZmZn65z//KUn6xz/+oS+++EKDBw/2cGbVw8r3H29Uz9MJeKv//Oc/KioqKjX7e3h4uPbt2+ehrDwvNjZWS5YsUceOHXXkyBFNnz5dN998s/bs2SOHw6GgoCA1adLEZZvw8HA5HA5JksPhKLNPS9ZdLiY/P19nz55V/fr1q6l1nlPS9rLafXG/hIWFuayvV6+emjVr5hITHR1dah8l65o2bVpu/5bsozYYNGiQhg4dqujoaH3//fd66qmnNHjwYGVlZSkgIIC+BP6/uniuKy4u1vjx43XjjTeqW7dunk7Hbd5991199dVX2r59u6dTqTY//PCDFi5cqNTUVD311FPavn27/vu//1tBQUEaNWqUp9NziyeffFL5+fnq1KmTAgICVFRUpGeffVYjR470dGrVwsr3H29EAYVKufgvID169FBsbKzatGmj9957r1YWNvBNI0aMMP+/e/fu6tGjh9q1a6cNGzZowIABHswMgKclJydrz549Ls/v+roff/xRjz/+uOx2u0JCQjydTrUpLi5W79699dxzz0mSrrvuOu3Zs0cZGRm1poB67733tHTpUi1btkxdu3Y1nwWPjIysNW2sDbiFrxwtWrRQQEBAqRHkcnNzFRER4aGsvE+TJk107bXX6rvvvlNERIQKCwuVl5fnEnNxn0VERJTZpyXrLhdjs9lqbZFW0vbLvd8iIiJKPdR9/vx5HTt2zC39W5vf19dcc41atGih7777ThJ9CZSoa+e6lJQUrVq1Sp9//rlatWrl6XTcJjs7W0ePHtX111+vevXqqV69etq4caNefvll1atXT0VFRZ5O0S1atmypLl26uCzr3Lmz+ShBbTBx4kQ9+eSTGjFihLp37677779fEyZM0KxZszydWrWw8v3HG1FAlSMoKEgxMTHKzMw0lxUXFyszM1NxcXEezMy7nDp1St9//71atmypmJgYBQYGuvTZ/v37dejQIbPP4uLitHv3bpcvr3a7XTabzfxQjIuLc9lHSUxt7vfo6GhFRES4tDs/P19bt2516bu8vDxlZ2ebMevXr1dxcbFiY2PNmE2bNsnpdJoxdrtdHTt2NEcpqov9+69//Us///yzWrZsKYm+BErUlXOdYRhKSUnRBx98oPXr15e6PdfXDRgwQLt371ZOTo7507t3b40cOVI5OTkKCAjwdIpuceONN5Yafv6f//yn2rRp46GM3O/MmTPy93f9eh4QEKDi4mIPZVS9rHz/8UqeHsXCm7377rtGcHCwsWTJEuPrr782HnnkEaNJkyYuI8jVNX/4wx+MDRs2GAcOHDC+/PJLIz4+3mjRooVx9OhRwzAM49FHHzVat25trF+/3tixY4cRFxdnxMXFmdufP3/e6Natm5GQkGDk5OQYa9asMa666ipj8uTJZswPP/xghIaGGhMnTjS++eYbIz093QgICDDWrFlT4+11p5MnTxo7d+40du7caUgy5s6da+zcudP4v//7P8MwDOP55583mjRpYnz44YfGrl27jDvvvNOIjo42zp49a+5j0KBBxnXXXWds3brV+OKLL4wOHToY9957r7k+Ly/PCA8PN+6//35jz549xrvvvmuEhoYaf/7zn82YL7/80qhXr57x0ksvGd98840xbdo0IzAw0Ni9e3fNdcYVulxfnjx50vjjH/9oZGVlGQcOHDA+++wz4/rrrzc6dOhgnDt3ztwHfQlcUBfOdePGjTMaN25sbNiwwThy5Ij5c+bMGU+nVm1q4yh827ZtM+rVq2c8++yzxrfffmssXbrUCA0NNd555x1Pp+Y2o0aNMq6++mpj1apVxoEDB4z333/faNGihTFp0iRPp1Zl7vj+420ooCrwyiuvGK1btzaCgoKMPn36GFu2bPF0Sh41fPhwo2XLlkZQUJBx9dVXG8OHDze+++47c/3Zs2eN3//+90bTpk2N0NBQ46677jKOHDniso+DBw8agwcPNurXr2+0aNHC+MMf/mA4nU6XmM8//9zo1auXERQUZFxzzTXG4sWLa6J51erzzz83JJX6GTVqlGEYF4byfOaZZ4zw8HAjODjYGDBggLF//36Xffz888/GvffeazRs2NCw2WzG6NGjjZMnT7rE/OMf/zBuuukmIzg42Lj66quN559/vlQu7733nnHttdcaQUFBRteuXY3Vq1dXW7urw+X68syZM0ZCQoJx1VVXGYGBgUabNm2MsWPHlvoySF8Cv6jt57qyPi8k1YpzS3lqYwFlGIbx8ccfG926dTOCg4ONTp06Ga+//rqnU3Kr/Px84/HHHzdat25thISEGNdcc43x9NNPu0yp4Wvc8f3H2/gZhg9PbQwAAAAANYhnoAAAAADAIgooAAAAALCIAgoAAAAALKKAAgAAAACLKKAAAAAAwCIKKAAAAACwiAIKAAAAACyigAIAAAAAiyigAAAAAMAiCigAAAAAsIgCCgAAAAAsooACAAAAAIsooAAAAADAIgooAAAAALCIAgoAAAAALKKAAgAAAACLKKAAAAAAwCIKKAAAAACwiAIKAAAAACyigAIAAAAAiyigAAAAAMAiCigAAAAAsIgCCgAAAAAsooACAAAAAIsooAAv8vnnn8vPz08ffPBBqXXLli2Tn5+fsrKyPJAZAACVd/DgQfn5+ZX7A/giP8MwDE8nAeACwzDUpk0b9enTR3/7299c1iUlJWn//v367rvvPJQdAACVc/r06VJ/FHQ6nZowYYKCgoJ09OhRD2UGVF09TycA4Bd+fn767W9/q7lz5+rEiRNq3LixJOmnn37SunXr9PTTT3s4QwAArGvQoIF++9vfuixLTk7WqVOnZLfbPZQVcGW4hQ/wMg888IAKCgpcrkAtX75c58+fL3USAgDAl7z99tt67bXXNHv2bN16662eTgeoEm7hA7xQnz591LBhQ61fv16SFBcXJ0k8/wQA8Fk5OTm64YYbNGTIEC1btszT6QBVxhUowAs98MAD2rhxo/71r3/p+++/15YtW7j6BADwWcePH9ewYcN07bXX6o033vB0OsAVoYACvNCIESMUEBCgv/71r1q6dKkCAwM1fPhwT6cFAEClFRcXa+TIkcrLy9MHH3yg0NBQT6cEXBEGkQC8UIsWLTR48GC98847OnfunAYNGqQWLVp4Oi0AACpt+vTpWrt2rT799FNFR0d7Oh3givEMFOCl/vd//1e/+c1vJF0YROKee+7xcEYAAFTO7t271bNnT/Xr108PP/xwqfXcng5fRAEFeKnCwkJFRESouLhYDodDISEhnk4JAIBK2bBhw2VH2+NrKHwRt/ABXsrf31/16tXTHXfcQfEEAPBJ/fv3p0hCrcMgEoCXWrlypX766Sc98MADnk4FAAAA/x+38AFeZuvWrdq1a5dmzpypFi1a6KuvvvJ0SgAAAPj/uAIFeJmFCxdq3LhxCgsL09tvv+3pdAAAAHARrkABAAAAgEVcgQIAAAAAiyigAAAAAMCiOj2MeXFxsQ4fPqxGjRrJz8/P0+kAgE8xDEMnT55UZGSk/P35e5w34LwGAFVn9bxWpwuow4cPKyoqytNpAIBP+/HHH9WqVStPpwFxXgMAd6jovFanC6hGjRpJutBJNputUts6nU6tW7dOCQkJCgwMrI70ahTt8W60x7vV1fbk5+crKirK/CyF59Xl8xr5e5av5y/5fhvI/8pZPa/V6QKq5PYGm81WpRNNaGiobDabT75JL0V7vBvt8W51vT3cKuY96vJ5jfw9y9fzl3y/DeTvPhWd17hpHQAAAAAsooACAAAAAIsooAAAAADAIgooAAAAALCIAgoAAAAALKKAAgAAAACLKKAAAAAAwKJKFVALFy5Ujx49zPkl4uLi9Omnn5rrz507p+TkZDVv3lwNGzbUsGHDlJub67KPQ4cOKSkpSaGhoQoLC9PEiRN1/vx5l5gNGzbo+uuvV3BwsNq3b68lS5aUyiU9PV1t27ZVSEiIYmNjtW3btso0BQAAAAAqrVIT6bZq1UrPP/+8OnToIMMw9NZbb+nOO+/Uzp071bVrV02YMEGrV6/WihUr1LhxY6WkpGjo0KH68ssvJUlFRUVKSkpSRESENm/erCNHjuiBBx5QYGCgnnvuOUnSgQMHlJSUpEcffVRLly5VZmamHn74YbVs2VKJiYmSpOXLlys1NVUZGRmKjY3V/PnzlZiYqP379yssLMzNXXR53dLWqqCo5ieRPPh8Uo0fEwCA6tL2ydVXtH1wgKHZfSp/XuZ8CqCyKnUF6o477tDtt9+uDh066Nprr9Wzzz6rhg0basuWLTpx4v+1d/9hUdb5/sdfgMwg6oBagB6VKEslfwaJs5XHFEHj6mSxXfbjtGRmlx7opOxq2TH8tR1bd9VsozidUjpXualda23aKhMuuK6YSXJMXT3VuofdkwNtqaOowwT394++3OuIwgygMyPPx3Vx5dz3+77n/b6n4b7f3DOfz0m98cYbWrlypcaPH6+UlBStXbtWu3bt0u7duyVJJSUlOnTokN566y2NHDlSkydP1tKlS1VYWKj6+npJUlFRkZKSkrRixQoNGTJEeXl5+uEPf6hVq1aZeaxcuVIzZszQtGnTlJycrKKiIkVHR2vNmjUdeGgAAAAAwJtfd6DO19DQoI0bN6qurk52u12VlZXyeDxKT083YwYPHqwBAwaooqJCY8aMUUVFhYYNG6b4+HgzJjMzU7NmzdLBgwc1atQoVVRUeO2jKWb27NmSpPr6elVWVmr+/Pnm+vDwcKWnp6uioqLFnN1ut9xut/nY5XJJkjwejzwej1/1N8Vbww2/tuso/ubr6/46er+BQj3BjXqCm6/1XC31AgDgD78bqM8++0x2u13nzp1T9+7dtWnTJiUnJ6uqqkoWi0WxsbFe8fHx8XI6nZIkp9Pp1Tw1rW9a11KMy+XS2bNndfz4cTU0NFw05vDhwy3mvmzZMi1evLjZ8pKSEkVHR7de/EUsTW1s03bt9eGHH16W/Tocjsuy30ChnuBGPcGttXrOnDlzhTIBACB4+N1ADRo0SFVVVTp58qTeffdd5eTkqLy8/HLk1uHmz5+v/Px887HL5VL//v2VkZEhm83m1748Ho8cDoee2xsud+OV/w7UgUWZHbq/pnomTpyoyMjIDt13IFBPcKOe4OZrPU138QEA6Ez8bqAsFosGDhwoSUpJSdEnn3yi1atXa+rUqaqvr9eJEye87kLV1NQoISFBkpSQkNBstLymUfrOj7lw5L6amhrZbDZ17dpVERERioiIuGhM0z4uxWq1ymq1NlseGRnZ5osed2NYQAaRuFwXae05FsGIeoIb9QS31uq5mmoFAMBX7Z4HqrGxUW63WykpKYqMjFRpaam57siRI6qurpbdbpck2e12ffbZZ6qtrTVjHA6HbDabkpOTzZjz99EU07QPi8WilJQUr5jGxkaVlpaaMQAAAABwOfh1B2r+/PmaPHmyBgwYoFOnTmndunUqKyvTtm3bFBMTo+nTpys/P1+9evWSzWbTk08+KbvdrjFjxkiSMjIylJycrEceeUTLly+X0+nUggULlJuba94Zmjlzpl5++WXNmzdPjz32mLZv364NGzZoy5a/D2+an5+vnJwcpaamavTo0XrxxRdVV1enadOmdeChAQAAAABvfjVQtbW1+tGPfqRjx44pJiZGw4cP17Zt2zRx4kRJ0qpVqxQeHq7s7Gy53W5lZmbqlVdeMbePiIjQ5s2bNWvWLNntdnXr1k05OTlasmSJGZOUlKQtW7Zozpw5Wr16tfr166fXX3/dnANKkqZOnaqvv/5aBQUFcjqdGjlypLZu3dpsYAkAAAAA6Eh+NVBvvPFGi+ujoqJUWFiowsLCS8YkJia2OoLcuHHjtG/fvhZj8vLylJeX12IMAAAAAHSkdn8HCgAAAAA6CxooAAAAAPARDRQAAAAA+IgGCgAAAAB8RAMFAAAAAD6igQIAAAAAH9FAAQAAAICPaKAAAAAAwEc0UAAAAADgIxooAAAAAPARDRQAAAAA+IgGCgDQab366qsaPny4bDabbDab7Ha7fvvb35rrz507p9zcXPXu3Vvdu3dXdna2ampqvPZRXV2trKwsRUdHKy4uTnPnztV3333nFVNWVqZbbrlFVqtVAwcOVHFxcbNcCgsLdd111ykqKkppaWnas2fPZakZANA+NFAAgE6rX79+euGFF1RZWam9e/dq/Pjxuueee3Tw4EFJ0pw5c/TBBx9o48aNKi8v11dffaX77rvP3L6hoUFZWVmqr6/Xrl279Oabb6q4uFgFBQVmzNGjR5WVlaU777xTVVVVmj17th5//HFt27bNjFm/fr3y8/O1cOFCffrppxoxYoQyMzNVW1t75Q4GAMAnNFAAgE7r7rvv1l133aUbb7xRN910k55//nl1795du3fv1smTJ/XGG29o5cqVGj9+vFJSUrR27Vrt2rVLu3fvliSVlJTo0KFDeuuttzRy5EhNnjxZS5cuVWFhoerr6yVJRUVFSkpK0ooVKzRkyBDl5eXphz/8oVatWmXmsXLlSs2YMUPTpk1TcnKyioqKFB0drTVr1gTkuAAALo0GCgAAfX836Z133lFdXZ3sdrsqKyvl8XiUnp5uxgwePFgDBgxQRUWFJKmiokLDhg1TfHy8GZOZmSmXy2XexaqoqPDaR1NM0z7q6+tVWVnpFRMeHq709HQzBgAQPLoEOgEAAALps88+k91u17lz59S9e3dt2rRJycnJqqqqksViUWxsrFd8fHy8nE6nJMnpdHo1T03rm9a1FONyuXT27FkdP35cDQ0NF405fPhwi7m73W653W7zscvlkiR5PB55PB4fj4DMbc7/75VmjTDat3244fVfXwWq3gsF+vi3V6jnL4V+DeTfcTm0hgYKANCpDRo0SFVVVTp58qTeffdd5eTkqLy8PNBp+WTZsmVavHhxs+UlJSWKjo5u0z4dDkd702qT5aM7Zj9LUxv9iv/www875ok7SKCOf0cJ9fyl0K+B/NvuzJkzPsXRQAEAOjWLxaKBAwdKklJSUvTJJ59o9erVmjp1qurr63XixAmvu1A1NTVKSEiQJCUkJDQbLa9plL7zYy4cua+mpkY2m01du3ZVRESEIiIiLhrTtI9LmT9/vvLz883HLpdL/fv3V0ZGhmw2mx9H4fu/vDocDk2cOFGRkZF+bdsRhi7a1npQC6zhhpamNuq5veFyN4b5vN2BRZntet6OEujj316hnr8U+jWQf/s13cVvDQ0UAADnaWxslNvtVkpKiiIjI1VaWqrs7GxJ0pEjR1RdXS273S5Jstvtev7551VbW6u4uDhJ3//11GazKTk52Yy58C6Hw+Ew92GxWJSSkqLS0lJNmTLFzKG0tFR5eXkt5mq1WmW1Wpstj4yMbPMFSHu2bQ93g+9NT4v7aQzza1/BdqEZqOPfUUI9fyn0ayD/9j23L2igAACd1vz58zV58mQNGDBAp06d0rp161RWVqZt27YpJiZG06dPV35+vnr16iWbzaYnn3xSdrtdY8aMkSRlZGQoOTlZjzzyiJYvXy6n06kFCxYoNzfXbGxmzpypl19+WfPmzdNjjz2m7du3a8OGDdqyZYuZR35+vnJycpSamqrRo0frxRdfVF1dnaZNmxaQ4wIAuDQaKABAp1VbW6sf/ehHOnbsmGJiYjR8+HBt27ZNEydOlCStWrVK4eHhys7OltvtVmZmpl555RVz+4iICG3evFmzZs2S3W5Xt27dlJOToyVLlpgxSUlJ2rJli+bMmaPVq1erX79+ev3115WZ+fePjk2dOlVff/21CgoK5HQ6NXLkSG3durXZwBIAgMCjgQIAdFpvvPFGi+ujoqJUWFiowsLCS8YkJia2OhDBuHHjtG/fvhZj8vLyWv3IHgAg8PyaB2rZsmW69dZb1aNHD8XFxWnKlCk6cuSIV8y4ceMUFhbm9TNz5kyvmOrqamVlZSk6OlpxcXGaO3euvvvuO6+YsrIy3XLLLbJarRo4cKCKi4ub5VNYWKjrrrtOUVFRSktLa/ZFXgAAAADoSH41UOXl5crNzdXu3bvlcDjk8XiUkZGhuro6r7gZM2bo2LFj5s/y5cvNdQ0NDcrKylJ9fb127dqlN998U8XFxSooKDBjjh49qqysLN15552qqqrS7Nmz9fjjj2vbtr+P0LN+/Xrl5+dr4cKF+vTTTzVixAhlZmaqtra2rccCAAAAAFrk10f4tm7d6vW4uLhYcXFxqqys1NixY83l0dHRlxx6taSkRIcOHdJHH32k+Ph4jRw5UkuXLtXTTz+tRYsWyWKxqKioSElJSVqxYoUkaciQIdq5c6dWrVplfmZ85cqVmjFjhvkF26KiIm3ZskVr1qzRM888409ZAAAAAOCTdn0H6uTJk5KkXr16eS1/++239dZbbykhIUF33323nnvuOXNCv4qKCg0bNszri7GZmZmaNWuWDh48qFGjRqmiokLp6ele+8zMzNTs2bMlSfX19aqsrNT8+fPN9eHh4UpPT1dFRcUl870cM7b7O+N5R+noWZqDYfbnjkQ9wY16gpuv9Vwt9QIA4I82N1CNjY2aPXu2brvtNg0dOtRc/tBDDykxMVF9+/bV/v379fTTT+vIkSP69a9/LUlyOp3NRhVqeux0OluMcblcOnv2rI4fP66GhoaLxhw+fPiSOV+OGdv9nfG8o1yumdNDffbqC1FPcKOe4NZaPb7O2A4AwNWkzQ1Ubm6uDhw4oJ07d3otf+KJJ8x/Dxs2TH369NGECRP05Zdf6oYbbmh7ph3gcszY7u+M5x2lo2dOD4bZnzsS9QQ36gluvtbj64ztAABcTdrUQOXl5Wnz5s3asWOH+vXr12JsWlqaJOmLL77QDTfcoISEhGaj5dXU1EiS+b2phIQEc9n5MTabTV27dlVERIQiIiIuGnOp715Jl2fGdn9nPO8ol+siLdRnr74Q9QQ36glurdVzNdUKAICv/BqFzzAM5eXladOmTdq+fbuSkpJa3aaqqkqS1KdPH0mS3W7XZ5995jVansPhkM1mU3JyshlTWlrqtR+HwyG73S5JslgsSklJ8YppbGxUaWmpGQMAAAAAHc2vO1C5ublat26d3n//ffXo0cP8zlJMTIy6du2qL7/8UuvWrdNdd92l3r17a//+/ZozZ47Gjh2r4cOHS5IyMjKUnJysRx55RMuXL5fT6dSCBQuUm5tr3h2aOXOmXn75Zc2bN0+PPfaYtm/frg0bNmjLli1mLvn5+crJyVFqaqpGjx6tF198UXV1deaofAAAAADQ0fxqoF599VVJ30+We761a9fq0UcflcVi0UcffWQ2M/3791d2drYWLFhgxkZERGjz5s2aNWuW7Ha7unXrppycHC1ZssSMSUpK0pYtWzRnzhytXr1a/fr10+uvv24OYS5JU6dO1ddff62CggI5nU6NHDlSW7dubTawBAAAAAB0FL8aKMNoecju/v37q7y8vNX9JCYmtjqK3Lhx47Rv374WY/Ly8pSXl9fq8wEAAABAR/DrO1AAAAAA0JnRQAEAAACAj2igAAAAAMBHNFAAAAAA4KM2TaSLwLvumS2tB/nBGmFo+Whp6KJtLU4M/OcXsjr0eQEAAIBQwh0oAAAAAPARDRQAAAAA+IgGCgAAAAB8RAMFAAAAAD6igQIAAAAAH9FAAQAAAICPaKAAAAAAwEc0UAAAAADgIxooAAAAAPARDRQAAAAA+IgGCgAAAAB8RAMFAAAAAD6igQIAAAAAH9FAAQAAAICPaKAAAAAAwEc0UAAAAADgIxooAAAAAPCRXw3UsmXLdOutt6pHjx6Ki4vTlClTdOTIEa+Yc+fOKTc3V71791b37t2VnZ2tmpoar5jq6mplZWUpOjpacXFxmjt3rr777juvmLKyMt1yyy2yWq0aOHCgiouLm+VTWFio6667TlFRUUpLS9OePXv8KQcAAAAA/OJXA1VeXq7c3Fzt3r1bDodDHo9HGRkZqqurM2PmzJmjDz74QBs3blR5ebm++uor3Xfffeb6hoYGZWVlqb6+Xrt27dKbb76p4uJiFRQUmDFHjx5VVlaW7rzzTlVVVWn27Nl6/PHHtW3bNjNm/fr1ys/P18KFC/Xpp59qxIgRyszMVG1tbXuOBwAAAABcUhd/grdu3er1uLi4WHFxcaqsrNTYsWN18uRJvfHGG1q3bp3Gjx8vSVq7dq2GDBmi3bt3a8yYMSopKdGhQ4f00UcfKT4+XiNHjtTSpUv19NNPa9GiRbJYLCoqKlJSUpJWrFghSRoyZIh27typVatWKTMzU5K0cuVKzZgxQ9OmTZMkFRUVacuWLVqzZo2eeeaZdh8YAAAAALhQu74DdfLkSUlSr169JEmVlZXyeDxKT083YwYPHqwBAwaooqJCklRRUaFhw4YpPj7ejMnMzJTL5dLBgwfNmPP30RTTtI/6+npVVlZ6xYSHhys9Pd2MAQAAAICO5tcdqPM1NjZq9uzZuu222zR06FBJktPplMViUWxsrFdsfHy8nE6nGXN+89S0vmldSzEul0tnz57V8ePH1dDQcNGYw4cPXzJnt9stt9ttPna5XJIkj8cjj8fja+nmNpJkDTf82i5YNdXRWj3+HqdAacozVPJtDfUEt85az9VSLwAA/mhzA5Wbm6sDBw5o586dHZnPZbVs2TItXry42fKSkhJFR0e3aZ9LUxvbm1ZQaa2eDz/88Apl0jEcDkegU+hQ1BPcOls9Z86cuUKZAAAQPNrUQOXl5Wnz5s3asWOH+vXrZy5PSEhQfX29Tpw44XUXqqamRgkJCWbMhaPlNY3Sd37MhSP31dTUyGazqWvXroqIiFBERMRFY5r2cTHz589Xfn6++djlcql///7KyMiQzWbz4wh8/5dXh8Oh5/aGy90Y5te2wcgabmhpamOr9RxYlHkFs2q7ptdn4sSJioyMDHQ67UY9wa2z1tN0Fx8AgM7ErwbKMAw9+eST2rRpk8rKypSUlOS1PiUlRZGRkSotLVV2drYk6ciRI6qurpbdbpck2e12Pf/886qtrVVcXJyk7//KabPZlJycbMZceKfD4XCY+7BYLEpJSVFpaammTJki6fuPFJaWliovL++S+VutVlmt1mbLIyMj23zR424Mk7sh9BuoJq3VE2oXh+15bYMR9QS3zlbP1VQrAAC+8quBys3N1bp16/T++++rR48e5neWYmJi1LVrV8XExGj69OnKz89Xr169ZLPZ9OSTT8put2vMmDGSpIyMDCUnJ+uRRx7R8uXL5XQ6tWDBAuXm5prNzcyZM/Xyyy9r3rx5euyxx7R9+3Zt2LBBW7ZsMXPJz89XTk6OUlNTNXr0aL344ouqq6szR+UDAAAAgI7mVwP16quvSpLGjRvntXzt2rV69NFHJUmrVq1SeHi4srOz5Xa7lZmZqVdeecWMjYiI0ObNmzVr1izZ7XZ169ZNOTk5WrJkiRmTlJSkLVu2aM6cOVq9erX69eun119/3RzCXJKmTp2qr7/+WgUFBXI6nRo5cqS2bt3abGAJAAAAAOgofn+ErzVRUVEqLCxUYWHhJWMSExNbHYxg3Lhx2rdvX4sxeXl5LX5kDwAAAAA6UrvmgQIAAACAzoQGCgAAAAB8RAMFAAAAAD6igQIAAAAAH9FAAQA6rWXLlunWW29Vjx49FBcXpylTpujIkSNeMefOnVNubq569+6t7t27Kzs7u9lE7tXV1crKylJ0dLTi4uI0d+5cfffdd14xZWVluuWWW2S1WjVw4EAVFxc3y6ewsFDXXXedoqKilJaW1mzieQBA4NFAAQA6rfLycuXm5mr37t1yOBzyeDzKyMhQXV2dGTNnzhx98MEH2rhxo8rLy/XVV1/pvvvuM9c3NDQoKytL9fX12rVrl958800VFxeroKDAjDl69KiysrJ05513qqqqSrNnz9bjjz+ubdu2mTHr169Xfn6+Fi5cqE8//VQjRoxQZmamamtrr8zBAAD4xK9hzAEAuJps3brV63FxcbHi4uJUWVmpsWPH6uTJk3rjjTe0bt06jR8/XtL3cx8OGTJEu3fv1pgxY1RSUqJDhw7po48+Unx8vEaOHKmlS5fq6aef1qJFi2SxWFRUVKSkpCStWLFCkjRkyBDt3LlTq1atMuc4XLlypWbMmGFOCF9UVKQtW7ZozZo1euaZZ67gUQEAtIQ7UAAA/H8nT56UJPXq1UuSVFlZKY/Ho/T0dDNm8ODBGjBggCoqKiRJFRUVGjZsmNdE7pmZmXK5XDp48KAZc/4+mmKa9lFfX6/KykqvmPDwcKWnp5sxAIDgwB0oAAAkNTY2avbs2brttts0dOhQSZLT6ZTFYlFsbKxXbHx8vJxOpxlzfvPUtL5pXUsxLpdLZ8+e1fHjx9XQ0HDRmMOHD18yZ7fbLbfbbT52uVySJI/HI4/H42vp5jbn//dKs0YY7ds+3PD6r68CVe+FAn382yvU85dCvwby77gcWkMDBQCApNzcXB04cEA7d+4MdCo+W7ZsmRYvXtxseUlJiaKjo9u0T4fD0d602mT56I7Zz9LURr/iP/zww4554g4SqOPfUUI9fyn0ayD/tjtz5oxPcTRQAIBOLy8vT5s3b9aOHTvUr18/c3lCQoLq6+t14sQJr7tQNTU1SkhIMGMuHC2vaZS+82MuHLmvpqZGNptNXbt2VUREhCIiIi4a07SPi5k/f77y8/PNxy6XS/3791dGRoZsNpsfR+D7v7w6HA5NnDhRkZGRfm3bEYYu2tZ6UAus4YaWpjbqub3hcjeG+bzdgUWZ7XrejhLo499eoZ6/FPo1kH/7Nd3Fbw0NFACg0zIMQ08++aQ2bdqksrIyJSUlea1PSUlRZGSkSktLlZ2dLUk6cuSIqqurZbfbJUl2u13PP/+8amtrFRcXJ+n7v6DabDYlJyebMRfe6XA4HOY+LBaLUlJSVFpaqilTpkj6/iOFpaWlysvLu2T+VqtVVqu12fLIyMg2X4C0Z9v2cDf43vS0uJ/GML/2FWwXmoE6/h0l1POXQr8G8m/fc/uCBgoA0Gnl5uZq3bp1ev/999WjRw/zO0sxMTHq2rWrYmJiNH36dOXn56tXr16y2Wx68sknZbfbNWbMGElSRkaGkpOT9cgjj2j58uVyOp1asGCBcnNzzeZm5syZevnllzVv3jw99thj2r59uzZs2KAtW7aYueTn5ysnJ0epqakaPXq0XnzxRdXV1Zmj8l0pQxdt67BmBgCuRjRQAIBO69VXX5UkjRs3zmv52rVr9eijj0qSVq1apfDwcGVnZ8vtdiszM1OvvPKKGRsREaHNmzdr1qxZstvt6tatm3JycrRkyRIzJikpSVu2bNGcOXO0evVq9evXT6+//ro5hLkkTZ06VV9//bUKCgrkdDo1cuRIbd26tdnAEgCAwKKBAgB0WobR+ohtUVFRKiwsVGFh4SVjEhMTWx2MYNy4cdq3b1+LMXl5eS1+ZA8AEHjMAwUAAAAAPqKBAgAAAAAf0UABAAAAgI9ooAAAAADARzRQAAAAAOAjGigAAAAA8BENFAAAAAD4iAYKAAAAAHzkdwO1Y8cO3X333erbt6/CwsL03nvvea1/9NFHFRYW5vUzadIkr5hvv/1WDz/8sGw2m2JjYzV9+nSdPn3aK2b//v264447FBUVpf79+2v58uXNctm4caMGDx6sqKgoDRs2rNVJDAEAAACgPfxuoOrq6jRixIgWZ2SfNGmSjh07Zv786le/8lr/8MMP6+DBg3I4HNq8ebN27NihJ554wlzvcrmUkZGhxMREVVZW6uc//7kWLVqk1157zYzZtWuXHnzwQU2fPl379u3TlClTNGXKFB04cMDfkgAAAADAJ1383WDy5MmaPHlyizFWq1UJCQkXXffHP/5RW7du1SeffKLU1FRJ0i9/+Uvddddd+sUvfqG+ffvq7bffVn19vdasWSOLxaKbb75ZVVVVWrlypdlorV69WpMmTdLcuXMlSUuXLpXD4dDLL7+soqIif8sCAAAAgFb53UD5oqysTHFxcerZs6fGjx+vn/70p+rdu7ckqaKiQrGxsWbzJEnp6ekKDw/Xxx9/rHvvvVcVFRUaO3asLBaLGZOZmamf/exnOn78uHr27KmKigrl5+d7PW9mZmazjxSez+12y+12m49dLpckyePxyOPx+FVjU7w13PBru2DVVEdr9fh7nAKlKc9Qybc11BPcOms9V0u9AAD4o8MbqEmTJum+++5TUlKSvvzySz377LOaPHmyKioqFBERIafTqbi4OO8kunRRr1695HQ6JUlOp1NJSUleMfHx8ea6nj17yul0msvOj2nax8UsW7ZMixcvbra8pKRE0dHRbap3aWpjm7YLVq3VE2rfM3M4HIFOoUNRT3DrbPWcOXPmCmUCAEDw6PAG6oEHHjD/PWzYMA0fPlw33HCDysrKNGHChI5+Or/Mnz/f666Vy+VS//79lZGRIZvN5te+PB6PHA6HntsbLndjWEenesVZww0tTW1stZ4DizKvYFZt1/T6TJw4UZGRkYFOp92oJ7h11nqa7uIDANCZXJaP8J3v+uuv1zXXXKMvvvhCEyZMUEJCgmpra71ivvvuO3377bfm96YSEhJUU1PjFdP0uLWYS333Svr+u1lWq7XZ8sjIyDZf9Lgbw+RuCP0Gqklr9YTaxWF7XttgRD3BrbPVczXVCgCAry77PFB//etf9c0336hPnz6SJLvdrhMnTqiystKM2b59uxobG5WWlmbG7Nixw+vz9Q6HQ4MGDVLPnj3NmNLSUq/ncjgcstvtl7skAAAAAJ2U3w3U6dOnVVVVpaqqKknS0aNHVVVVperqap0+fVpz587V7t279ec//1mlpaW65557NHDgQGVmfv/RryFDhmjSpEmaMWOG9uzZoz/84Q/Ky8vTAw88oL59+0qSHnroIVksFk2fPl0HDx7U+vXrtXr1aq+P3z311FPaunWrVqxYocOHD2vRokXau3ev8vLyOuCwAAAAAEBzfjdQe/fu1ahRozRq1ChJUn5+vkaNGqWCggJFRERo//79+qd/+ifddNNNmj59ulJSUvT73//e66Nzb7/9tgYPHqwJEyborrvu0u233+41x1NMTIxKSkp09OhRpaSk6Mc//rEKCgq85or6wQ9+oHXr1um1117TiBEj9O677+q9997T0KFD23M8AAAAAOCS/P4O1Lhx42QYlx7qetu2ba3uo1evXlq3bl2LMcOHD9fvf//7FmPuv/9+3X///a0+HwAAAAB0hMv+HSgAAAAAuFrQQAEAAACAj2igAAAAAMBHNFAAAAAA4CMaKAAAAADwEQ0UAAAAAPiIBgoAAAAAfEQDBQAAAAA+ooECAAAAAB/RQAEAAACAj2igAAAAAMBHNFAAAAAA4CMaKAAAAADwEQ0UAAAAAPiIBgoAAAAAfEQDBQAAAAA+ooECAAAAAB/RQAEAAACAj2igAAAAAMBHNFAAAAAA4CMaKAAAAADwEQ0UAAAAAPjI7wZqx44duvvuu9W3b1+FhYXpvffe81pvGIYKCgrUp08fde3aVenp6fr888+9Yr799ls9/PDDstlsio2N1fTp03X69GmvmP379+uOO+5QVFSU+vfvr+XLlzfLZePGjRo8eLCioqI0bNgwffjhh/6WAwAAAAA+87uBqqur04gRI1RYWHjR9cuXL9dLL72koqIiffzxx+rWrZsyMzN17tw5M+bhhx/WwYMH5XA4tHnzZu3YsUNPPPGEud7lcikjI0OJiYmqrKzUz3/+cy1atEivvfaaGbNr1y49+OCDmj59uvbt26cpU6ZoypQpOnDggL8lAQAAAIBPuvi7weTJkzV58uSLrjMMQy+++KIWLFige+65R5L0X//1X4qPj9d7772nBx54QH/84x+1detWffLJJ0pNTZUk/fKXv9Rdd92lX/ziF+rbt6/efvtt1dfXa82aNbJYLLr55ptVVVWllStXmo3W6tWrNWnSJM2dO1eStHTpUjkcDr388ssqKipq08EAAAAAgJZ06Hegjh49KqfTqfT0dHNZTEyM0tLSVFFRIUmqqKhQbGys2TxJUnp6usLDw/Xxxx+bMWPHjpXFYjFjMjMzdeTIER0/ftyMOf95mmKangcAAAAAOprfd6Ba4nQ6JUnx8fFey+Pj4811TqdTcXFx3kl06aJevXp5xSQlJTXbR9O6nj17yul0tvg8F+N2u+V2u83HLpdLkuTxeOTxeHyus2kbSbKGG35tF6ya6mitHn+PU6A05Rkq+baGeoJbZ63naqkXAAB/dGgDFeyWLVumxYsXN1teUlKi6OjoNu1zaWpje9MKKq3VE2oDdTgcjkCn0KGoJ7h1tnrOnDlzhTIBACB4dGgDlZCQIEmqqalRnz59zOU1NTUaOXKkGVNbW+u13Xfffadvv/3W3D4hIUE1NTVeMU2PW4tpWn8x8+fPV35+vvnY5XKpf//+ysjIkM1m86dUeTweORwOPbc3XO7GML+2DUbWcENLUxtbrefAoswrmFXbNb0+EydOVGRkZKDTaTfqCW6dtZ6mu/gAAHQmHdpAJSUlKSEhQaWlpWbD5HK59PHHH2vWrFmSJLvdrhMnTqiyslIpKSmSpO3bt6uxsVFpaWlmzL/927/J4/GYJ2+Hw6FBgwapZ8+eZkxpaalmz55tPr/D4ZDdbr9kflarVVartdnyyMjINl/0uBvD5G4I/QaqSWv1hNrFYXte22BEPcGts9VzNdUKAICv/B5E4vTp06qqqlJVVZWk7weOqKqqUnV1tcLCwjR79mz99Kc/1W9+8xt99tln+tGPfqS+fftqypQpkqQhQ4Zo0qRJmjFjhvbs2aM//OEPysvL0wMPPKC+fftKkh566CFZLBZNnz5dBw8e1Pr167V69Wqvu0dPPfWUtm7dqhUrVujw4cNatGiR9u7dq7y8vPYfFQBAp8H8hgAAf/jdQO3du1ejRo3SqFGjJEn5+fkaNWqUCgoKJEnz5s3Tk08+qSeeeEK33nqrTp8+ra1btyoqKsrcx9tvv63BgwdrwoQJuuuuu3T77bd7zfEUExOjkpISHT16VCkpKfrxj3+sgoICr7mifvCDH2jdunV67bXXNGLECL377rt67733NHTo0DYfDABA58P8hgAAf/j9Eb5x48bJMC49UltYWJiWLFmiJUuWXDKmV69eWrduXYvPM3z4cP3+979vMeb+++/X/fff33LCAAC0gPkNAQD+6FSj8AEA4I/W5jd84IEHWp3f8N57773k/IY/+9nPdPz4cfXs2VMVFRVeH1VvirnwI4XnY3qOv/N1Oo4LBctw/KE+HUKo5y+Ffg3k33E5tIYGCgCASwj2+Q2ZnqM5f/MPtu+Zhfp0CKGevxT6NZB/2/k6PQcNFAAAIYrpOf7O1+k4LhQs03OE+nQIoZ6/FPo1kH/7+To9Bw0UAACXEOzzGzI9R3P+5h9sF5qhPh1CqOcvhX4N5N++5/aF36PwAQDQWZw/v2GTpvkNm+YdPH9+wyYXm99wx44dXp+vv9T8hudrbX5DAMCVRwMFAOjUmN8QAOAPPsIHAOjU9u7dqzvvvNN83NTU5OTkqLi4WPPmzVNdXZ2eeOIJnThxQrfffvtF5zfMy8vThAkTFB4eruzsbL300kvm+qb5DXNzc5WSkqJrrrnmkvMbLliwQM8++6xuvPFG5jcEgCBEAwUA6NSY3xAA4A8+wgcAAAAAPqKBAgAAAAAf0UABAAAAgI9ooAAAAADARzRQAAAAAOAjGigAAAAA8BENFAAAAAD4iAYKAAAAAHxEAwUAAAAAPqKBAgAAAAAf0UABAAAAgI9ooAAAAADARzRQAAAAAOAjGigAAAAA8FGHN1CLFi1SWFiY18/gwYPN9efOnVNubq569+6t7t27Kzs7WzU1NV77qK6uVlZWlqKjoxUXF6e5c+fqu+++84opKyvTLbfcIqvVqoEDB6q4uLijSwEAAAAAL5flDtTNN9+sY8eOmT87d+40182ZM0cffPCBNm7cqPLycn311Ve67777zPUNDQ3KyspSfX29du3apTfffFPFxcUqKCgwY44ePaqsrCzdeeedqqqq0uzZs/X4449r27Ztl6McAAAAAJAkdbksO+3SRQkJCc2Wnzx5Um+88YbWrVun8ePHS5LWrl2rIUOGaPfu3RozZoxKSkp06NAhffTRR4qPj9fIkSO1dOlSPf3001q0aJEsFouKioqUlJSkFStWSJKGDBminTt3atWqVcrMzLwcJQEAAADA5bkD9fnnn6tv3766/vrr9fDDD6u6ulqSVFlZKY/Ho/T0dDN28ODBGjBggCoqKiRJFRUVGjZsmOLj482YzMxMuVwuHTx40Iw5fx9NMU37AAAAAIDLocPvQKWlpam4uFiDBg3SsWPHtHjxYt1xxx06cOCAnE6nLBaLYmNjvbaJj4+X0+mUJDmdTq/mqWl907qWYlwul86ePauuXbteNDe32y23220+drlckiSPxyOPx+NXnU3x1nDDr+2CVVMdrdXj73EKlKY8QyXf1lBPcOus9Vwt9QIA4I8Ob6AmT55s/nv48OFKS0tTYmKiNmzYcMnG5kpZtmyZFi9e3Gx5SUmJoqOj27TPpamN7U0rqLRWz4cffniFMukYDocj0Cl0KOoJbp2tnjNnzlyhTAAACB6X5TtQ54uNjdVNN92kL774QhMnTlR9fb1OnDjhdReqpqbG/M5UQkKC9uzZ47WPplH6zo+5cOS+mpoa2Wy2Fpu0+fPnKz8/33zscrnUv39/ZWRkyGaz+VWXx+ORw+HQc3vD5W4M82vbYGQNN7Q0tbHVeg4sCo3vmDW9PhMnTlRkZGSg02k36glunbWeprv4AAB0Jpe9gTp9+rS+/PJLPfLII0pJSVFkZKRKS0uVnZ0tSTpy5Iiqq6tlt9slSXa7Xc8//7xqa2sVFxcn6fu/gtpsNiUnJ5sxF94JcTgc5j4uxWq1ymq1NlseGRnZ5osed2OY3A2h30A1aa2eULs4bM9rG4yoJ7h1tnquploBAPBVhw8i8ZOf/ETl5eX685//rF27dunee+9VRESEHnzwQcXExGj69OnKz8/X7373O1VWVmratGmy2+0aM2aMJCkjI0PJycl65JFH9N///d/atm2bFixYoNzcXLP5mTlzpv70pz9p3rx5Onz4sF555RVt2LBBc+bM6ehyAAAAAMDU4Xeg/vrXv+rBBx/UN998o2uvvVa33367du/erWuvvVaStGrVKoWHhys7O1tut1uZmZl65ZVXzO0jIiK0efNmzZo1S3a7Xd26dVNOTo6WLFlixiQlJWnLli2aM2eOVq9erX79+un1119nCHMAAAAAl1WHN1DvvPNOi+ujoqJUWFiowsLCS8YkJia2OljBuHHjtG/fvjblCAAAAABtcVnmgQIAAACAqxENFAAAAAD4iAYKAAAAAHxEAwUAAAAAPqKBAgAAAAAf0UABAAAAgI9ooAAAAADARzRQAAAAAOAjGigAAAAA8BENFAAAAAD4iAYKAAAAAHxEAwUAAAAAPqKBAgAAAAAf0UABAAAAgI9ooAAAAADARzRQAAAAAOAjGigAAAAA8BENFAAAAAD4iAYKAAAAAHxEAwUAAAAAPqKBAgAAAAAf0UABAAAAgI9ooAAAAADARyHfQBUWFuq6665TVFSU0tLStGfPnkCnBABAm3FeA4DgFtIN1Pr165Wfn6+FCxfq008/1YgRI5SZmana2tpApwYAgN84rwFA8AvpBmrlypWaMWOGpk2bpuTkZBUVFSk6Olpr1qwJdGoAAPiN8xoABL8ugU6grerr61VZWan58+eby8LDw5Wenq6KioqLbuN2u+V2u83HJ0+elCR9++238ng8fj2/x+PRmTNn1MUTrobGsDZUEFy6NBo6c6ax1Xq++eabK5hV2zW9Pt98840iIyMDnU67UU9w66z1nDp1SpJkGMaVSu2qxnmtfXw9j10oWM5rof57JNTzl0K/BvJvP1/PayHbQP3tb39TQ0OD4uPjvZbHx8fr8OHDF91m2bJlWrx4cbPlSUlJlyXHUPOQDzHXrLjsaQAIMadOnVJMTEyg0wh5nNfaz5fz2IU4rwG4UGvntZBtoNpi/vz5ys/PNx83Njbq22+/Ve/evRUW5t9f21wul/r376+//OUvstlsHZ3qFUc9wY16gltnrccwDJ06dUp9+/a9gtnhfJzX/o78AyvU85dCvwbybz9fz2sh20Bdc801ioiIUE1NjdfympoaJSQkXHQbq9Uqq9XqtSw2NrZdedhstpD8n/RSqCe4UU9w64z1cOep43Be6xjkH1ihnr8U+jWQf/v4cl4L2UEkLBaLUlJSVFpaai5rbGxUaWmp7HZ7ADMDAMB/nNcAIDSE7B0oScrPz1dOTo5SU1M1evRovfjii6qrq9O0adMCnRoAAH7jvAYAwS+kG6ipU6fq66+/VkFBgZxOp0aOHKmtW7c2+wLu5WC1WrVw4cJmH50IVdQT3KgnuFEPOgrntbYj/8AK9fyl0K+B/K+cMIPxZwEAAADAJyH7HSgAAAAAuNJooAAAAADARzRQAAAAAOAjGigAAAAA8BENVBsUFhbquuuuU1RUlNLS0rRnz55Ap+SzHTt26O6771bfvn0VFham9957z2u9YRgqKChQnz591LVrV6Wnp+vzzz8PTLKtWLZsmW699Vb16NFDcXFxmjJlio4cOeIVc+7cOeXm5qp3797q3r27srOzm01SGSxeffVVDR8+3JxAzm6367e//a25PpRquZgXXnhBYWFhmj17trkslGpatGiRwsLCvH4GDx5srg+lWpr83//9n/75n/9ZvXv3VteuXTVs2DDt3bvXXB9Kvw/QPqF8XmvtvRlsQv083Fr+jz76aLPXY9KkSYFJ9iJC/drBl/zHjRvX7DWYOXNmgDL2drVc69BA+Wn9+vXKz8/XwoUL9emnn2rEiBHKzMxUbW1toFPzSV1dnUaMGKHCwsKLrl++fLleeuklFRUV6eOPP1a3bt2UmZmpc+fOXeFMW1deXq7c3Fzt3r1bDodDHo9HGRkZqqurM2PmzJmjDz74QBs3blR5ebm++uor3XfffQHM+tL69eunF154QZWVldq7d6/Gjx+ve+65RwcPHpQUWrVc6JNPPtF//Md/aPjw4V7LQ62mm2++WceOHTN/du7caa4LtVqOHz+u2267TZGRkfrtb3+rQ4cOacWKFerZs6cZE0q/D9B2oX5ek1p+bwabUD8Pt5a/JE2aNMnr9fjVr351BTNsWahfO/iSvyTNmDHD6zVYvnx5gDL2dtVc6xjwy+jRo43c3FzzcUNDg9G3b19j2bJlAcyqbSQZmzZtMh83NjYaCQkJxs9//nNz2YkTJwyr1Wr86le/CkCG/qmtrTUkGeXl5YZhfJ97ZGSksXHjRjPmj3/8oyHJqKioCFSafunZs6fx+uuvh3Qtp06dMm688UbD4XAY//iP/2g89dRThmGE3uuzcOFCY8SIERddF2q1GIZhPP3008btt99+yfWh/vsAvgv181pL781gF+rn4QvzNwzDyMnJMe65556A5NMWoX7tcGH+hmF4nWtDQShe63AHyg/19fWqrKxUenq6uSw8PFzp6emqqKgIYGYd4+jRo3I6nV71xcTEKC0tLSTqO3nypCSpV69ekqTKykp5PB6vegYPHqwBAwYEfT0NDQ165513VFdXJ7vdHtK15ObmKisryyt3KTRfn88//1x9+/bV9ddfr4cffljV1dWSQrOW3/zmN0pNTdX999+vuLg4jRo1Sv/5n/9prg/13wfwzdVyXrvUezPUXC3vu7KyMsXFxWnQoEGaNWuWvvnmm0CndEmhfu1wYf5N3n77bV1zzTUaOnSo5s+frzNnzgQivRaF8rVOl0AnEEr+9re/qaGhodmM8PHx8Tp8+HCAsuo4TqdTki5aX9O6YNXY2KjZs2frtttu09ChQyV9X4/FYlFsbKxXbDDX89lnn8lut+vcuXPq3r27Nm3apOTkZFVVVYVcLZL0zjvv6NNPP9Unn3zSbF2ovT5paWkqLi7WoEGDdOzYMS1evFh33HGHDhw4EHK1SNKf/vQnvfrqq8rPz9ezzz6rTz75RP/6r/8qi8WinJyckP59AN9dDee1lt6bPXr0CHR6frka3neTJk3Sfffdp6SkJH355Zd69tlnNXnyZFVUVCgiIiLQ6XkJ9WuHi+UvSQ899JASExPVt29f7d+/X08//bSOHDmiX//61wHM9u+uhmsdGihcFXJzc3XgwIGg/ty7LwYNGqSqqiqdPHlS7777rnJyclReXh7otNrkL3/5i5566ik5HA5FRUUFOp12mzx5svnv4cOHKy0tTYmJidqwYYO6du0awMzaprGxUampqfr3f/93SdKoUaN04MABFRUVKScnJ8DZAb5r6b05ffr0AGbWOT3wwAPmv4cNG6bhw4frhhtuUFlZmSZMmBDAzJoL9WuHS+X/xBNPmP8eNmyY+vTpowkTJujLL7/UDTfccKXTbOZquNbhI3x+uOaaaxQREdFsNJCamholJCQEKKuO01RDqNWXl5enzZs363e/+5369etnLk9ISFB9fb1OnDjhFR/M9VgsFg0cOFApKSlatmyZRowYodWrV4dkLZWVlaqtrdUtt9yiLl26qEuXLiovL9dLL72kLl26KD4+PuRqOl9sbKxuuukmffHFFyH5+vTp00fJycley4YMGWJ+9ClUfx/AP1fjee3892aouRrfd9dff72uueaaoHs9Qv3a4VL5X0xaWpokBc1rcDVc69BA+cFisSglJUWlpaXmssbGRpWWlsputwcws46RlJSkhIQEr/pcLpc+/vjjoKzPMAzl5eVp06ZN2r59u5KSkrzWp6SkKDIy0queI0eOqLq6OijruZjGxka53e6QrGXChAn67LPPVFVVZf6kpqbq4YcfNv8dajWd7/Tp0/ryyy/Vp0+fkHx9brvttmZD3/7P//yPEhMTJYXe7wO0zdV4Xjv/vRlqrsb33V//+ld98803QfN6hPq1Q2v5X0xVVZUkBc1rcKGQvNYJ8CAWIeedd94xrFarUVxcbBw6dMh44oknjNjYWMPpdAY6NZ+cOnXK2Ldvn7Fv3z5DkrFy5Upj3759xv/+7/8ahmEYL7zwghEbG2u8//77xv79+4177rnHSEpKMs6ePRvgzJubNWuWERMTY5SVlRnHjh0zf86cOWPGzJw50xgwYICxfft2Y+/evYbdbjfsdnsAs760Z555xigvLzeOHj1q7N+/33jmmWeMsLAwo6SkxDCM0KrlUi4cGSiUavrxj39slJWVGUePHjX+8Ic/GOnp6cY111xj1NbWGoYRWrUYhmHs2bPH6NKli/H8888bn3/+ufH2228b0dHRxltvvWXGhNLvA7RdqJ/XWntvBptQPw+3lP+pU6eMn/zkJ0ZFRYVx9OhR46OPPjJuueUW48YbbzTOnTsX6NQNwwj9a4fW8v/iiy+MJUuWGHv37jWOHj1qvP/++8b1119vjB07NsCZf+9qudahgWqDX/7yl8aAAQMMi8VijB492ti9e3egU/LZ7373O0NSs5+cnBzDML4fQvW5554z4uPjDavVakyYMME4cuRIYJO+hIvVIclYu3atGXP27FnjX/7lX4yePXsa0dHRxr333mscO3YscEm34LHHHjMSExMNi8ViXHvttcaECRPMXyiGEVq1XMqFDVQo1TR16lSjT58+hsViMf7hH/7BmDp1qvHFF1+Y60OpliYffPCBMXToUMNqtRqDBw82XnvtNa/1ofT7AO0Tyue11t6bwSbUz8Mt5X/mzBkjIyPDuPbaa43IyEgjMTHRmDFjRlA146F+7dBa/tXV1cbYsWONXr16GVar1Rg4cKAxd+5c4+TJk4FN/P+7Wq51wgzDMC7vPS4AAAAAuDrwHSgAAAAA8BENFAAAAAD4iAYKAAAAAHxEAwUAAAAAPqKBAgAAAAAf0UABAAAAgI9ooAAAAADARzRQAAAAAOAjGigAAAAA8BENFAAAAAD4iAYKAAAAAHxEAwUAAAAAPvp/hsrKE5uS98IAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}