Horse_Vs_Human.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Horse Vs Human.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ikramooe/DataScience-1/blob/master/Horse_Vs_Human.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VggJ0sp-GYeA",
        "colab_type": "code",
        "outputId": "4cf5a963-c558-4fde-c95e-6c29e73e7c15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip \\\n",
        "    -O /tmp/horse-or-human.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-05-17 23:23:10--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.141.128, 2607:f8b0:400c:c06::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.141.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 149574867 (143M) [application/zip]\n",
            "Saving to: ‘/tmp/horse-or-human.zip’\n",
            "\n",
            "/tmp/horse-or-human 100%[===================>] 142.65M   121MB/s    in 1.2s    \n",
            "\n",
            "2019-05-17 23:23:11 (121 MB/s) - ‘/tmp/horse-or-human.zip’ saved [149574867/149574867]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zFucUwaOAf8w",
        "colab_type": "code",
        "outputId": "78baf67b-2033-4c7d-de57-89e06f46e244",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/validation-horse-or-human.zip \\\n",
        "    -O /tmp/validation-horse-or-human.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-05-17 23:23:17--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/validation-horse-or-human.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.141.128, 2607:f8b0:400c:c06::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.141.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 11480187 (11M) [application/zip]\n",
            "Saving to: ‘/tmp/validation-horse-or-human.zip’\n",
            "\n",
            "\r          /tmp/vali   0%[                    ]       0  --.-KB/s               \r         /tmp/valid  36%[======>             ]   4.01M  10.0MB/s               \r/tmp/validation-hor 100%[===================>]  10.95M  25.7MB/s    in 0.4s    \n",
            "\n",
            "2019-05-17 23:23:17 (25.7 MB/s) - ‘/tmp/validation-horse-or-human.zip’ saved [11480187/11480187]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BnU2ljyjEh81",
        "colab_type": "code",
        "outputId": "59afbedf-4f21-48ab-fc92-55b1aff440a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1357
        }
      },
      "source": [
        "#imageGenerator\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing import image \n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "import requests\n",
        "import zipfile\n",
        "import os \n",
        "import matplotlib.pyplot as plt \n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "local_zip = '/tmp/horse-or-human.zip'\n",
        "zip_ref=zipfile.ZipFile(local_zip,'r')\n",
        "zip_ref.extractall('/tmp/horse-or-human')\n",
        "loc_zip = '/tmp/validation-horse-or-human.zip'\n",
        "zip_rep = zipfile.ZipFile(local_zip,'r')\n",
        "zip_ref.extractall('/tmp/validation-horse-or-human')\n",
        "zip_ref.close()\n",
        "\n",
        "train_horse_dir = os.path.join('/tmp/horse-or-human/horses')\n",
        "train_human_dir = os.path.join('/tmp/horse-or-human/humans')\n",
        "\n",
        "validation_horse_dir=os.path.join('/tmp/validation-horse-or-human/horses')\n",
        "validation_human_dir=os.path.join('/tmp/validation-horse-or-human/humans')\n",
        "\n",
        "print(train_horse_dir)\n",
        "\n",
        "train_horse_names = os.listdir(train_horse_dir)\n",
        "print(train_horse_names[:10])\n",
        "\n",
        "train_human_names = os.listdir(train_human_dir)\n",
        "print(train_human_names[:10])\n",
        "\n",
        "validation_human_names=os.listdir(validation_human_dir)\n",
        "print(validation_human_names[:10])\n",
        "\n",
        "validation_horse_names = os.listdir(validation_horse_dir)\n",
        "print(validation_horse_names[:10])\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "fig , axarr =plt.subplots(2,5)\n",
        "img_human = [os.path.join(train_human_dir,fname)\n",
        "      for fname in train_human_names[190:195] ]\n",
        "img_horse = [os.path.join(train_horse_dir,fname)\n",
        "      for fname in train_horse_names[100:105] ]\n",
        "for img_path in enumerate(img_human):\n",
        "\ti = img_path[0]\n",
        "\tprint(i)\n",
        "\tprint(img_path)\n",
        "\tx = mpimg.imread(img_path[1])\n",
        "\taxarr[0][i].imshow(x)\n",
        "\taxarr[0,1].grid(False)\n",
        "       \n",
        "for img_path in enumerate(img_horse):\n",
        "\ti=img_path[0]\n",
        "\tx=mpimg.imread(img_path[1])\n",
        "\taxarr[1,i].imshow(x)\n",
        "\taxarr[1,i].grid(False)\n",
        "plt.show()\n",
        "\n",
        "print('total training horse images:', len(os.listdir(train_horse_dir)))\n",
        "print('total training human images:', len(os.listdir(train_human_dir)))\n",
        "print('total validation human images:', len(os.listdir(validation_human_dir)))\n",
        "print('total training horse images:', len(os.listdir(validation_horse_dir)))\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale= 1./255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "\t'/tmp/horse-or-human/',\n",
        "\ttarget_size=(150,150),\n",
        "\tbatch_size=128,\n",
        "\tclass_mode='binary')\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "validation_generator = test_datagen.flow_from_directory(\n",
        "\t'/tmp/validation-horse-or-human/',\n",
        "\ttarget_size=(150,150),\n",
        "\tbatch_size=32,\n",
        "\tclass_mode='binary')\n",
        "\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "\ttf.keras.layers.Conv2D(16,(3,3),activation = 'relu', input_shape=(150,150,3)),\n",
        "\ttf.keras.layers.MaxPooling2D(2, 2),\n",
        "\ttf.keras.layers.Conv2D(32,(3,3),activation='relu'),\n",
        "\ttf.keras.layers.MaxPooling2D(2,2),\n",
        "\ttf.keras.layers.Conv2D(64,(3,3),activation='relu'),\n",
        "\ttf.keras.layers.MaxPooling2D(2,2),\n",
        "  tf.keras.layers.Conv2D(16,(3,3),activation='relu'),\n",
        "  tf.keras.layers.MaxPooling2D(2,2),\n",
        "\ttf.keras.layers.Flatten(),\n",
        "\ttf.keras.layers.Dense(512,activation='relu'),\n",
        "\ttf.keras.layers.Dense(1,activation='sigmoid')\n",
        "\n",
        "\t])\n",
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "\toptimizer=RMSprop(lr=0.001),\n",
        "\tmetrics=['accuracy'])\n",
        "\n",
        "history = model.fit_generator(\n",
        "\ttrain_generator,\n",
        "\tsteps_per_epoch=8, # we walk through 8 batches to say that one epoch is done \n",
        "\tepochs=15,#how many times we walk through all the training data\n",
        "  validation_data=validation_generator,\n",
        "  validation_steps = 8 ) #0 silent 1 progress  2 one line per epoch \n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/tmp/horse-or-human/horses\n",
            "['horse12-2.png', 'horse38-1.png', 'horse43-6.png', 'horse29-6.png', 'horse37-9.png', 'horse24-4.png', 'horse35-0.png', 'horse30-6.png', 'horse40-7.png', 'horse50-8.png']\n",
            "['human07-23.png', 'human10-15.png', 'human14-28.png', 'human05-04.png', 'human07-05.png', 'human13-24.png', 'human05-18.png', 'human15-17.png', 'human06-07.png', 'human15-05.png']\n",
            "['human07-23.png', 'valhuman03-06.png', 'human10-15.png', 'valhuman03-18.png', 'valhuman05-11.png', 'human14-28.png', 'human05-04.png', 'human07-05.png', 'human13-24.png', 'human05-18.png']\n",
            "['horse5-458.png', 'horse1-122.png', 'horse12-2.png', 'horse38-1.png', 'horse43-6.png', 'horse29-6.png', 'horse4-072.png', 'horse5-400.png', 'horse2-224.png', 'horse37-9.png']\n",
            "0\n",
            "(0, '/tmp/horse-or-human/humans/human05-06.png')\n",
            "1\n",
            "(1, '/tmp/horse-or-human/humans/human09-26.png')\n",
            "2\n",
            "(2, '/tmp/horse-or-human/humans/human06-01.png')\n",
            "3\n",
            "(3, '/tmp/horse-or-human/humans/human17-23.png')\n",
            "4\n",
            "(4, '/tmp/horse-or-human/humans/human14-24.png')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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G3HhyGfCm193Ju7772zl37jIf+I9/xL2n7uTo4gy1qUmiALwIuXzlIvecOkWn0+IjH/s4\nb/vaNzEa7XDsjpBLZy5WkA/Xczz+FYb8Bix/18PnRo+fKmK/lbHiwZUFoc1QvYtsn3uOfn+Lw5MR\njUaMFNVz2N4ZUIs0KlRYZ5HeEgiF9orNwZDpiRaDNOfaygDhHKmVnLm4xWRLc89ih81+Ri1UoDyT\n9TqD3pBOLeabDjf44IUh1hiU1kRRRDoaEUURRZFx7vISRw/M0m1ErO8MKYqSqU4d72C1N8Q5xygr\n6Y9KlC4YZGD8MlmxSRIcpjBNtkfDLzYsXiFfFozee/8R4CM3vwNY5whUQFYW4B1eBJW3YwUyBOEr\nj0sg8S7HC4FzIFyFNwY6oCgKBBKpwFtBmuc4b3DeYz0wsHgVoqVCC4kMFJGOkAqe/Nyz/OIv/ioz\nc7P85Pt+FAEEkUKqEO8sSkpUEhJi8C4AZ3FaUboq4kixJImmKA3eO6wV2NIjNQRSI6qM8Y73/vU3\noxKpAtCTzM4kRJHBby2zMchQUpJvrRAGmoaWlM0uG9vrTE43UV5ACTLLGAx3SOI2Qkhy6Xj5/Iuk\nI0lqLQePnEAFMXhYe/lZ3nLyACuFZvWJLXTpOSU79KyBbhMRNmnIklprCoHFhwnTkwmXl7c4sriI\ncY6iv05vc4U4brB8/jmELbC2Ri1QFIXmyPEjdFstpLQ4L2i1msxMdlheXuXZ08/ftE6g8uS7zZhW\nLaAVSxpxQKepaTYCkigijkIajTpxLSEIQhAC5/wYurmOiVvrcM6MjXwV3Ac6QCqFc9XiXRYlxhis\nTAi1BFFFeAIIAk04NvJBoDGmJM8LnHfjEF1grcVZR5plpFleQTtSoWSIlFU+xzhTRYrO4Z3HSkGS\nxHjvb79ZncSB4tSBLoO8JCssUgq2RgUH2wn/+bOXUFIwyC0HOgkLnYRRYVjvpWyNStLSkhaWzWHB\n0akGw7Tgo89dI1SSwjhCLXny/ArH7r0TrSRvuXeRRitkfaPHG48dwguFwNEfjNgZbHLs2G38g+8/\nSpoO+dBHP8F0q87U/ASbK5t7Rnw3Ufsqw34jRg973v8NmdCbHyvDddpnPohUoLylNRUgJ1oM85yN\nrS2iKKTIS7QWiLLESUFhLcEeXcATJyGnzy8hpSSKNFJqDnZitje2EcCTL63QaNSoR4LbFrpkmUFI\nSVYYFrsh7SsjilqLYb9PWZYYaxFC0Gy2yNMR2/0h3UZCogXtRoSxJZdXN1nefpEXz12i0fZEqkVZ\nDPDOYzBYI9neeQlFTGsy/FJa2JP/ZsnYV4l1pGZYeV7Sg8iRIqhC3xK8qIx4YUdjNogCWbF0lPTk\nRYGUCofBuSrLnUSaUEdYqSgyA8KhtWY3nZ0ZQ25zPviBD/GZxz4HIuLcy2f55r/1bj7wG79AKCTC\nF4SBB68p8xFIRaCqDLg1Dq0VpXWEgSRPDaXyaKmQaKSEYjSgMB6lb4ZfcF08lqTTZDjKuJQNKZxk\n6o7XUNMx0zspjdl5zjzy20x22/T7bcpSk1vDRLNB3s+wIqKfRYx8Dj7nyMJhtte2Wc4kaTok0GAL\nS3tqkg88do7ve/NhPvWnzxOpBF+zfM+77+d9n7jIRLsBApavvEyrO0UgSs5d2CIIQ4a5RylBZ/YQ\nhWqxtbXDtmkwOTXF2vY2QaNGQ/bIRgNeuHoBqUKazTrTswd5/rnP0enO3PIw0VKQaE8oLEmoaSQB\n7WaNdqtJHIWEYUAcV4nQiiVUaRN2mTVyDJt4vN99L8ZsmcqQg8faygsvipJ6AKUDLUBqQVYadKAr\nvN87rDHVmFQS4XaZE+wdw5gxJCTGDB8pkOI6Z8N7v7coSKVujopygwzyks9e3GAnM68wlle20r2k\nbz1U4/tyGOtZ6+dMN2OEqqAlhWeYl1zeyXjriRmeubLFQjvBOE8z1uxcuUDY6nLyjjs5v7zGwZk2\nhRGUxZC5+YOkWc5UZ5J6o0GejWiHXU7deYwry2s88uzLPP/sWVpxwOaowHnP1e2UUCtec2SWe48f\npFmP9xLV4wmwZ+T9LmB/C+K85/SFZby3WFWwupnRroWsbY+oJQIjSgJCbpvtUljHgYkm569toANJ\nFGp2ejnOGXZ6AyYmm4Qy5vnVTU4EU9S7dYwxpLZklPcZpR6hFGeXrnFodoZfe/wa//Kdx/nw+ZwL\nBeggYGpqmiiK2NraZHJyElHTPL98CetAErLd76MCQ3/0HMurQ0TeYnszY2rGs3q1cjhqzZh2J6Ds\nSwrnuHpu66b18RVh6KUUxHEEwqHQCGWRIgQhsbagtA7vxBivr2FtgbMlIBHeVQPBG0prUUJihSdQ\nFc2xtBZnBToI8UKQG8Og1x9PTI1whk89+Ci1OGKzt4O1jjzPGI2yveQv3qGEqiaRhnoQYZxDC4uz\nmoqGE+KUocyKKr8gNFJA6SXD4c44h3Dz4p0ljAR9o3j53FlO3vUaZKNLrDxhXrJ69klK1eby5csE\necn2qEHqhixdu0y9VsOYAiuHRBRkNmBSlOQopIS4FrO9uYXSMY2ZQ7whHuGc5Fu+835+81c+TZGA\ncZJTr70XrSWlddQaHZqtSbwKWWwWXLpwlrIs2VzfIR/GrF66gMNz5PgJpuYOMH/gCI984g9YPHqc\nF557iiCIOXjwAFcuXmBra4tmo4UU7pbHilaCwOe4wqNFRBxHNBsNut0WgdYopSsjT0WPFWKXGsue\noa3eS5SqDLxWFV6/S4t0zo0TrlUkEHQPIHHUA40VAuM8QRDgxrCcMQYhRMXMcdfhGjtO4EIVAeye\nR0nBmK27F2F45ymNJZDyVWzOmxHrPaESFNYTjpN+17Fwz4FujXYScGalR2k9U/WQMys9nPdoKZlr\nx3gP/90dM3zq3Do/8I5T/OYjZ3jTsWmEEHzuxWu85p4ajekZ5Mo6KqnxwnPPE0YBYRSQxBGTM9Os\nXrvKYJQxPzfDPafuxDp4+fwy64Oc5V6GsR4pIVKS3Fgee+kaj710jVpQPSMlBG84OstGVnKkHbO4\neJAw0OhAfbHbf5XsWMWj/mBFgbSg2oIrAvwMiCqbjxBwIa3O6VeAcLoaL8YT1ADnyEPDFSHxwuO1\n4/kVAXSrhxe6cXLfI4YC0TnA2VyyFM2hpeJ/+9oD/I47yXPPP0e/1+MbvuEbeeqpJ/EOnk4XmZiZ\nJdSKbrvB1yxMce/J26ltPMPTj32Y3nDESj/l7qOHOTjTZXV9i9IZVtaX2Rn18Diy0vLxj3/2pvTx\nFWHovQdjfcU39kWVQBXZGMerEqBCCKTySEqEd0gMwiu8rOysEIJAVniftJLSWjJvq9DYVlxwvAMv\nCENFWRRQpoRRjelWC+MdUaTwhSMtoFYLUaHGeYcWFQ87y0ukc2R5ifeGkbFIV1Sc+TBDqQClYnQk\niQNFUWZoNMnUDAj7pRVxg1hnWF26ys5gh0O3z5BtvMhjj36Uk697O2tnzzNx+G7WeueZm5hjqtth\nZWmTZjJHPx8SKol2jmuXXiAtc1rNBhe3HL2tVbqT8/Q2t+j1eiQNzdWr5+nj+dYGLMx1uaZHzDY7\n/N6gy6FDJzh+5ynOv/gs9VrEqN/jyuXLXDp3BiUFjWabUBjW1jboD4YEYUBmz9BcWqW3s0Gepbxw\n+mkajSYnjixyeWWDpFYnUhYd1RmNRrc8VqSEVk2jlaDdqtNsNWk26tTr9THuXRkM53eJeZXVfAWN\ncReCEdeTslLe4IVbQ1GUe/ulG8vEQZVv8cKxeGAOpRTGGLyrXM8gCNCBwrlqDBoM1vm9xUIqWRl5\nVTF/8NcZJX7Mx3dud+G7NUu/CyfVAkUz1sRakYSKVhKwvFNBNBc2htRDxUwzJtAVLDPXiklCxSA3\nBEoyzA29tOSd9x3mw0+c454DXdZ6GdPthONHZ5iemx3rxXNkcYFved29ENd55NFPs+0Nz6XPsLax\nzsHDi7x09iK/9O9+lwfuv5ft/ohRafc44d5Whj7SktJWOjDOY5xHS8ELS5t06yHLPcvW6Zc4Mddm\nca57i+NEEgUVECOVqjKxu7U0UiGlBm8rts04f1PlBKqcCb7KtSRBOK7FqZ5Nxc4RVanpbk2Jl9U4\nEgKlYO7gUc5cfIHjizM8/7FP0F08TjpKef7508wvHGaUpqQrlzl412sRecHRmQb3v/YuZmZjduLX\nct83nSLLUlYvX8SVA8og5c6TtxHp3ZySoywLvLP88P/1gZvSx1eEocd7rC3HIa0A57DO4bwFqQm0\nJ9QK4cYJNKFBalAevMJ5gaAq6LEOEA7vFMJ7FAEIh7EFINBaEGlFpzXDcDDE4Xnp0hW0FMy0E4Y5\nbA8L1jbWqpSPrfi2EoHz4LwhTBK0lDjrCcMA5wvKXCFViZIaFcSU5RBhJIXJEErjbHFLKrEO+i5k\n6vAxVLlJXkiOLd7G7OG7OHTHm3n+4d+jU5d4GdAfDajVQp596nP0RmtMz02yubrD1Ow0kwvH6W2v\nsr68wn2njrO0VTLc6TE70abW7jDZafLkU08TtbqsbW5x8s4Z7j48y6A7RRInXLl4llGaMdhepd7u\noqOEII6o15t4D0dPfg39QZ/N9WVqUURvaw2ZpYhhRqwEKmlQWFhfWyHGYa0h8wkLUwmo4JaHipKS\nI4sHaTQaTHTbNOs1klpCGAZ7Rh6oaiuoPHcpxZgBo/boe0rdaOCve/FlWTAapqRptjce77r9IE99\nsjLcpYOZKBrDMgalNFqPqZnO4WyVVK0onGKPnaOUrIq2lMI7R1kajDUVtFMajK28Q63VGD66NTnY\nrVELNa0kwFiPGtNPG1FAYRzdRoSSVb4i1BItBcFuQthXxVZSCLSS1CLNX/+aY8SRJgoUR+65g+nj\nd5B0pwmkoNFoVzmGeh0mpnjL3383EAEONpbYvnSO0Dkevfs4H/nYQ7SURzciHHCok7CTliSh4lAn\n4ZmrOxzoJoAAKaiHARPNmEBJGlFVvBgGmp1Rfkv6EALiROOtRYkKIhNKoqgYd4F0IMYMK6pcr5Rq\nPNPHzgEegRt/cuAq2qzwHikUwlmc7dN0a7h4AeMjtK5x6Nu+k9/5hR/m3Y2Ud98R8cyojz64wNLq\nGo8//hivve91rK08yA//L++hUQ+Ynp5gblrw2CeeYLSzhnA5NtuhFkmatYCmlGTDISNriaOYZqvO\n1PQEQt18lPOVYegFCOFw1uNwVOUNquIni0r1piz2ElhCCKx3UEqcMNcpYMZVid3dwg78eNJ5lNDV\nWJLgSstop4cMJKHStGsxnU6bzfUVRnlF++u0uzgvUIKxobZYwFlPvR5XbBx8FZqLBFN68rIcJ9cM\n0iusdwRBgtaAvzVVCyGRjRY2s+wMrqLrNY4dvo9+f4tBb536zCIP/vbP8roHvo5rV9eoa8PCgWmO\ni0mWM093LkEGMS888wxzC/MsHD7Gy1d32NncQAcBWWZQYcH58y+Q5z1+9vkml597hnYoefDjz7Hz\nx5/jXX/vu8lyS7vdpVbr4KTixJHDnDi2yNraOlevrdAfDMBaJiZm2NhcJ0yabCxdrrzUMKSTNEjC\ngCTcZbdYelnGMDWEwa0PP6UkU5MTtNstWs06cRzvGe3dKte9qtexFy+FRI6xeUHF15Zjowrs8duL\noiDLUvI8xxhTeYJK8vY33M6V9Af5+K//DKGGi5eushsw7NIkd4+z+6qqskGq6jy70NAunFiWJaWp\nksF27NUqpcaV2bdm6OuRZpiVtCKNpDJaG72U7cyQaMlSP0MIwYnpBtZ7lnsZcaBoxgEzzZgD3RpR\noJhq1uhlBWluWNke4bwnCRRTJ28nSUtkPCDzhrnZWdbX1uHIUQhCoKAyJQFMHqJTi+HKeXo7PXye\nMSqrWX3qQJfNQcapQxNkpaFdj/inf+sOnj67jHOebiPm2EKXP3jiPE+eX+PIVIO0MLz7G17DixfX\nbkknsQ45PnXgeiTnHR6Hd+CcGdsSB1LgXVktxEKgdFgVhpkS5ww4hbUW6T3Cl3gZUuYZQlSFZ1rP\nItwREu+oKei0Qr79b34d3/bRf4/Sl5msKVqrlxBbnqnFU6x0Opx+7lm0Vjz9sZ/iu//u30QMwQ0l\nbzwm8X4Kb0ucncaME/nWCbyvCrGGgx1Gq0OuLV2DGxybLyVfEYbee1/RFIVHS43Ao7RGKnCFG8Me\nHusd1lucUeArBoejigDKMSyDl3hhkF7slS9X4dq44IOKJ+uFwuCxJfRGKd6VrA9zBlazPshJ0xSh\nBEkYYcqUSAfESiEjibcFUgQa9djdAAAgAElEQVRYkxMISNMCqRSxNMggRIoxa9g6CmHxVmHtreHR\nDsG1pUsszi+g4xa1qMHQeopixE6vz3NPfJITJ0+ydOUsaVqiZmbAl9gClPeUztIfDDh66DDNSLE8\nGKIQhLUWzpYk9QQE6NoUHREBhvU0Z2PocLWY1/3c77PameDKe76DidlF3vimtxBFNXAlg2FOnuVk\neYbAkyQ1trdWSaKQmfmDDDUY4chFhA40ubFcWllDWQM6ot6aqLynV7UB+NIShgEz05PEcbxnqHel\nYtXstqqoji1FxbbZbftQ4fTXjXNl4EuKIifPc4qiiixrtQSlFUJI/stDT7B0eQcpNaUpKJzjx3/q\n5/mn/+T7cc6Pi6Cq8WVt5aHvbrvRaJvS4Jwbn6+svpeCIKggnUAHRHH4+V0/vqQY64i0YrmXkaQl\nuXW04gDjHOc2U2qBYjstefzS1lgnN9IbIVACY68ncaWoish35dBdx9kyikMLswzTkpN3HOOeyYMQ\nJFWhilWgdjndxRjSiFiwKQsnZ3nnvQcqz1xXEVVvmFGaCsopjONNdx9mlBVoKVnfGXL/XQc5Ptch\nLysG0dlrWxyavzXoJpBwsCar6/MOVRSUZYHM1slknaDcxJQpwzxFqyYqbCJsjnEeiyczJYQtvC9w\ng3Xq3SO02h1ec89hymGVH2x22tx39ywXzm1TWksYN6gnknRgeOCNb+NXnvssj37qIb7v7in+x0XL\nez75DG964K2cPfMC/cGIf/XBP8AFHb7lHW+lmVQwoHNVbqesoAmMG7daERKcJ4ibOFci8yFRcPMR\n8VeEoRdCoJUaFzU4ZKCwxmAsCFcZdzEutnC+6p1iMUgqapfzVHRJKfE4lFc4UbEulNwt+lAI4bBO\n4pDjNgrVSv9vf+mneOff/UG8dSSx4nve+zN03/ZW/Gd+Hy8EtbBK8lkrkErgpUPh8VLgHURRgnMe\noUO884RxTBQFICqKnjOG8hY9emMMcQhptkPeT3GlYWXjYeL6PNeurTDRrdOcnGdaCZ5/9nOkRtKe\nXmC4sczyyir9bMipxXmsy9gZSgZbWziT0TOCqZk50hICV5CwiVA5n3vmHG84coBnVrdpv+WvUZ+f\n4jPf9nbqjTqjiy8wNzvLRCNkoGCjP8SFkzTqTYpixNbmCs1WmzCKGAyG0Giiha+osYBWivb8EeSY\n8VQWOUU+RMlbtGiA1pput/NKjByuV7Tuete+CrmFrLz5MTQ/ZtaMvXsP1ljyIiPLsnHVq+H81XX+\n2Y//HFKHJJ1JBqvXiFsd6MyRba4gAs3jL63wze/6AX7nV3666p00xnCtsZRjg767uFQX6CmNIc+r\n1gllaVBKEoYBWmuiKCAKq4XxVi29B1q1kJ20YCst0VIwKgz1SDPbiplp1+iNiioJLGCtn9Ophcy0\nE2bbCVc3hkw0Yq5tDZltJ5xY6OKcQytJGChC7RkOR9SSGhtbS7z50EloRbC5CZ15UAeoQOsSEJB0\n4ECCSQ1HZltkRQVNjfICOc6PxIHGeUjikPWdEWVpSSKNdZ6NcTQxO9EYM6IEWXlrOS4vJEWYVAu/\n95QqxKkCUzqm6hEn77iDB954ncHqLVW1ez1+xXGch7XlbZ5+5iVWV9Z54VNPEzdqVQsJI3jm0+O+\nCXgUHpzjyI99L6G3dLDccfcpDjaWmO4mfP3th3jyzIu0Wi3edfsUp559lo996kEOzs8Rakc9CWjW\nE7rdDtIbQlnlWvAWKRxxkuAdJLNHoCwgeeW1fjH5ijD04HHOIKTDWPCmGjACA77yMGzVRgvvKy9N\nSYnxHil0tRLCuGxakjuBcWY8++W4h07VhEiICucV4QQzX/vNGAOPP/ISSsBkp8W/+P2nuP22Nlc2\nQN39N4hi8Cs9mlufQQWVhycI8MIibVD14FEeayVCaKwAKTVFXnFmvVB4KYluAU+D6p63N5fAzBC6\njNW1HYwbkDQXGPVX0ckUzaDBMB2xvb2N954dXdJqRDRadeJGgzzLWV1fpRmF/I3bpvmNpwfEtTqz\n2vDy5pBeb5v77jzAL//27/OT33I/cRzy2O88xNp/+lXu/Tc/R62WkNQSdrYH/Ml/+Qjf8+3vpCwN\ncxNdVoYeFUZMJBInOqTDHnfefjcrq2sM+pIsHTEhRzSaDS4ubVBGTbAjWp0JbFmidMRUp3HLI6Vq\nMzCGQaTCC3cdkrnBS0VUfZHkrgMwhnOc83jh2G2gV7UroCq6iwRhGPL//uvfqnIRSZ28v413VdGL\nR+DKjCBsY9IBxhi+5e/9Q379599fJRrHlbXWVA3MwkAjVQXvOe8piirJuxdlKoXWFcYvpapaNIyv\n+1akU4v41jcfJy8shbHkpSHSms+eWyErLUmguOfOeaY7NepJhPOOPDcM0xxjHJP1kNI6JuuaUWFZ\n2thhkJUVc01KpkrPT7z3f+WT557gUKMO+RB6FuaOwsWzrH/615h66wNw7C2ABFey8+EP8E1v+6uc\nv/Bp8tJUC0aQcGV1myQO6Y9ySuPY6qdkeUmjFrLVK8dRF8x061UELiVFWcFqtyLOGobra3uJcp+P\niHxOq1Zja2OZJ57Y4enPPEu92aTfHzLRaaNFztz8HHffcwcTkwkf+9CjFLlFjluitBs1Oq06QTCu\nuxMVO056i5IeiWP+738nW9bxFuH5jdk5TuV9alGVN6llGxw9eor15SXycwVv/0dv4d5BwuaZ36Pz\nhrch2gtEcUgcCLRqgDfgLCqsYU2OKUriJGbn8jlqSYOgduCm9fEVYuir4lM8SFV1ZRPeV2uk90hV\n4fTeVBisE9WkUkphnMBh8Q4MEmctXux2rbTjFgYCPW6pZ72krB/l0FveCkCg4UO/+n5cmTN33zez\ntb3GyLWZn6wyBRHQn25x0TzAxOqfILzA+KrTZcWgEEgjUBJQBmEFhTdIITHGU9qSUIVYU96SPso8\nJR/12XaObmea7sLtrF5+gbQMWbzz9eTpDudOP8bGxgpzU1McPH6SokiptdvM6haffvghLtkReZYR\naslrDkzwPW8+jFMJ8906f/v9v4wOBLf90A/zz4tNGu0Oo36Pr3v8LA89cAdKwPbWNo1mA4TDec+K\nbbCxsYIWefUcttfxtuTu4wsMihErS9ew3jPZCnlp6TKNdkKZZXQaCaMsxQjB5oUX6KWKsuyTDW4t\nHIfrfWg8Ff5d9WTZFT/25MXeTJSq8uitc+B8leD3ntIayrKsOORSEgSVR/1v/t3/R3/pPCppEtQa\neO/pHLqNzXNVEaZAYEb9G6IDwZNPn+bo4mHyohgXW/mqXqOWoJwizwuyPMdYS2kMSRSRxHFVbKU1\nWleUUHFjBHAL4rxnq5+RFoZWLaqSuQIeOHmAONKYMUyy1RuRZiVSCIZpThRWydZ2I6KehHggH3vf\n2/2M0jia9QgVVfdyrwxZbrSg04XZOXjxKZg/STy5AMubMHMZjKH/6GfYuv3tnP/Yr7O6NSAvDP1R\nTj2JCENNllddVvvDDAd0WwmDYb53/tJWC2ZpLO1GVd18q1oR3tMQEh0GIDxOgsxGBJllcbbB9FSX\nuYMHOXS4astlsoLl8y+CGbF19gmGF+DeBY8UnlA5lKpooXgYjjLyIqcoMqIgoF6rkZeG1W/6bmpK\n4IB/Xm/Smpjku+4yTNbq9NOSHQpq9Sbl2mf5wbSktnCCnQsBr7/8Ei9/z/tRH/xpcAX5yJEikFiE\nUIgsJYg0xlSN1ZI4Zmtrk3p58zblK8LQC0AjsdjKeFpfFUNRJV+d99hxx2ArBd4WGCfxxlKOaVDe\ng8AQaI8QIda7yptDYXwVD8iKGEXtyJtxaZVHssCb/8o3cvbhP+SNrz/F/a+/DU+lGAFspYCDTjdi\neMWjyKveKBp2y+ZlVSiLLSv6lXIOA2RFjvSOXPoKyrgF8UKy0RvSMZYDiyeIpSWJa1BkCJcTRE2u\nXL4MylMWKctXzoFQnH/xNFeuXuD197+dy5eX6Pe3caZgaXOT+068lqQWo6TlR/6Hb+Q/TH4NrV/5\nF8wfm+b06iZ/UruTzb/9DQih+ONj87zxsRd49u2vp8pwl/S21rj96AIvnD2PLSzCO44tHsDkOY1a\nQhjXWF+5BKFGiZKoMUVWWC5du4xxnlpctUyIm4peFpGlN1/C/YrxMvbghZTjVsnjBKj3COcruEZW\nDbSc8yDdDYlSez1h6q+jJEVZ8nu//4c8+cJFWvWEXjqiHPZxpqAcDdBxDZMO8XgiAd1Ys1FUDsX8\n3CzlGEaqmpNVTdCyLEdISWkqdg0CwqCqpq28+Iq2Z8c8fCHAGnMDzfImx4qHUV4SBZqpTr1qX1AY\nglCT5tV1JXFVOdxshKRZSadVqzj0SiKU5OJyjyBQBFphjSWKAlpNTS0KUUrCVo/2/d9A8fDHKkaD\nFnDv/WBDGq0YXv+6Kie7tkZzfpKm2uahlav0hvmYFOHZGaRkWZUDyQtDEgcUhSFNi8prt5ZmEtIO\n4vGCqBllBXEYoPWt/euMeqT52q9ZpN1uM9nt0p6s2DXnLqRsbm5RlJatbc/W1sUq8Hcl1mjwdeQ4\nJ+isJZQGSUnoc0KR004giALqjSa+byhNQX9o2Pq/f5YJ4Vkpc1oq4hNZztfnK0TBNEIHNBsBc3ff\nzocefoj/6fAJWmdfQv3gQxz+rlOkv3uV2MOLD36a7utOYa0lDAJCqYjiOkmkSUcZ3hlqtQb9oaE5\nMcH6yupN6+Mrw9ALiQwkzuo9CMa53Z7MVasDK6oe49ZapIjRCpx2hCLAuxKBwniHFI7SCrTSOG+r\nxmJCV1WLXmK9Z+n8i2T9eRaOTqIDSOoJ/8ev/Sl33nsbimq8lsDWch/nHa2pNnjIFt5Oe+ljOGsQ\nSJzIsd5jTAjComTVc8fZiuNfD6pJJBx7zIqbFaU0CIWXDtIha+tbJIlEBh6pAkSkuO3ue1lfusjO\nsCB3Q1bX1vE2Zf7QCYKozvHb72SnP+Tpz3yCj18c8fjLH+Ynv+/biMIYl/WYevYRnumXLA4K/mSl\n5B0f/Qk+eP8b8QjqD3wjg0c+TZHnHDx4gO1ej4cf/jhH3vV38EQIhuRlSaJ8hYubamGNTEEsLHcc\nP0Zvc404iNFhyNEjJwBLFCVcvbZMXm4S1lp/3gEzxtiryM/s9avZLYRSgKJqQlsl6XcblhljKkbN\nmHK5exytFM9fWqZ/+SyRdwR40s0VAGyR73V1PHx4nq2lFaRSLNQl66Xbq2yVSoCTeyyeUXYjriwI\ndUAYBte7eQqB2W1/AHtQ0u693KwY6/jE6WssTjf53PlVstJxfLZFVhped9s82/2UB5+/Sj0KmGhE\nHJpu02klrG8NkELy8tImCEFhLCfmu1xZ73Hy8DRF6Vje2KTTiHn/z/yf/MiP/Qw6qENhYTCEqQ7o\nAN7616oV0wxgYgqkh1aHMk/pDTLUONm7vjPEe89Es8bq1oBuI6Y3KtBK0KpFTEY1pBSkWcWwS8dG\nXu7C4LcgzVaNe197lJdeWOHs+SXcSw4keCdwlJUtsLZamHfpsCIAoTDeVy1OvMNYjSBkSIxwJTuu\nZJ4B0o8Ah5KCnadOM/fUizjpCQx8REccmp3iO940y8J8kyQKCLVk7cHHUCrk+69eIhDVmBz94mcx\nlaeL+cXfZua33oazns3+DkaEFKMhaS6QQhOFkkGaImVAurqB5y8bvRKPs4DUVfJSyD2KmhN+D3st\nd/8pyZgXK52gdBlQdQr0OJzXaGErbJzKY3LeYn1VLauUZrITs7J8memDk0gBq6tXiQLJfW+4DcOY\nnunAq4BPPfiHvOHND6DjNkk7xq9F4C0ahVManMC6sqLTOYv0DiFB62q8l+PWyErdmkeS5xkuH7Cy\nldEOErJhSJiMaM15rAiIao7lq0uMhgWanLm64NidC+Re8dkXr+LcaRq1hO3tAf3+gGajxqXtPv/z\nL36Yt0wKHj+/wvx//y7W/+hD/Ob2JllekAGj0QBrDa9//0+x8tnHMdZjrCNLq1D+uRdeBoLqn6wI\ngTU5wzxjpxBcfOwRnCk4PN2inefEk9OMskFVtRooarUWeVEyPz+HFob+4M/p0Y9fCPEF5v/Yq3ZV\nCA2V42CNraCTssSUZdXbe7dVgXdcXumhZIASkFpHIKsiPuvBpUOOHjnAYK0kXV8nDvQePPK+9/7Q\nHsWy4uRXdEnnqiZmQgi01gS6+icoYaDHY6Hqt2Ot2SueclaSF2UFM92CjApDZjRnV3ukRQUrJqHi\npZUeT13aZH2QUws1oZY0I81zVzaJAk1hLItTTZx3BEpyeKqF957b5rtEYVWUFoVN4jBgttvAnzlD\no9GF4U7FoR/+/9S9d5BmV3nu+1tr7fjljpM0I43SjAIIkMnRRAMiGYMxyccBjK8TzgHOtQ8YDDYY\nc505OIC5Bi4Y4wPYREvIJEmgnDU59nT88k4rnD/W7hb3Xte1mrqnSt5VKpWqRzXdX6+91rve93l+\nTw7NBogW0IfjJ+DA5WAKKCoOnTxFMw1ZH0zptVP2zHcYTnJwvhW4Mc6xztFrNWpzmWUw8m3BRhyy\nONfaQkeMJ9vT0Y+GU66/9lakcEhk7W2q9w+3qSp6kK1P7aB2bCqyvIpe2ACH8VF8QpAZwX3rM2gt\nOdA5RrV6jvPe9hdUriIzltUo4l9muvzKhZfw+FNHcQd3IRD8802H6O27grnb7iCKGwinWckNk6pk\nNgjAOdqjsj5wC7qtFkVZYIVEG0duS5KogcBR6pIyH9PYRqH0sNjoHQJbD02FCsAaJAIj62oficUD\nzVTtTbPW1r340A/XhEI6z8kxTqBtWcvxFcIFGO1wyjFdfDqNZkg8yvzNQcMVj3gM3fmdaAuB9Drk\nqoR83OfSy65Ck7B3NmAMDC59Puq+z1I5TWAUTjiC2iItoLah+zCKEkcUeRlgqR86O9p/Jo5BfwNE\nwDDL0Fji5iJLK+tMB31aMx0sgoUde1g6e5hJNMvO8y5nvLHGZY+cRZcWYcYYNyJOGpy37yIOHjzI\ndDzm3zLD8YUO5bOuYferfpL40P3ISw9y40AzvegKnvonH+S67zvIvre+l5e87If51y9/ARVEGFsS\nJG1SZ0AmFI0ed53c4JnPeg6LxRSTF4wHK5QuRHW7jCd9lEyIGaOtrxiFEH7DdYrzzr/4e1wxfoN2\n9Vu6aUz67sda73oE6r/T9+SrsqKqKgKranyGVzgtdn3S1tgI0tk5ImM5f2GeuR07OXzXnUxXVnyl\nDkRK+RlFEJKmCYFSWwEktjb1WeeDPVSgSOOYuG6BSOnnTlpXtcNRb2mktZQUVbUlDd3Oc9FCB6UE\n3TQiChWhkix2UkZ5xeMaEcY6slIz104JlCCJAkKlaCYhDvx/B4o4UoRKUVSaaV7VtxXHWTfi7R9+\nP2/5sZ+AuX2+kikm0OwCFSwvwcICyB7ow7A2YjDNObkyII1DhlnJBTtniMOATishjAKm05K1Ucba\nKNvKA2i3YsJaNpvlmij07dY42p65zjpHkZXfxTdiS3lV93m3+v5bJFMnateyLyC0drVSyfkDGYtw\nFbHM2dvuY61m8Wd/DyEcOVAqRTdt8JjZeV515jQMx3DXtRhreUqrw3WDkzyv12M800MdG3Fclxwr\nNc+WilgqEjw6utNokBcFiVK4AMZZTjMKfFqVsP4wUlEtwXxoz8Nio7fWUOYFTtQtC2cJo4DQhTis\nZ95YgXC1Bh4vrRfS85696URTWQ1aegkmBqUilLf+oWJBM05JZ3oIO6aVKsZnV4k7bbCaRmipJhAk\nftg9Xlujct4YffrY/czNPZogBF04EE2aQVabLQABSsWE0recrAyIoxRdGYytvKyM7S3UMIjYsXMH\nWrU5fm6JPY0GKzpndeMcwhlWhxsMNlY4VVZc/ohHMb9zN5Nxzmh1mdlde7GJpdVo46IGJ8+cYWZx\ngSRKsNVJHtmJOPYvX+fYm17LpJmSNFIEcPCGb7PjDb/A9c97BmWZ88C738oVz30h5+29gKWzRygL\nzdq5U1Bpms0WjVaLC/fuxujKB8HEMQ+cOMtjrn4848kYwhbTbEhWWarRgEDNoAJvSzflmMHge1l+\n/h7v6ip4M2hmUxfv15PvwQu3yUUU6KpCl5XvgRvjB/p1i7DSFaPMUMmY/ZddiZDerBe15rwwIBsT\nKElpvORQSoESgt/6rV/ckkdqrakq7bVh9cahlCRUnnIZx9HW92KM3pJhFjXa2GiNkpLK6G1v9EJA\nqQ2dKPIogUIz10nYMdOiW1YY653lSeiDfRpx5A8q66iMbz9Y5/v4WVHVbSoIQ8U0K4mVpCw1s90G\namE3hAmkka+IzIZ3OHdTiHtQnoX1DU7efy/aWDrN2BsctSEvK0bTgpXBhDQKSaKAi/bM+gOwbo1l\neUlZWT80lgJj/MHYSB86qRGAGikhBVhht1QyQtbxkRYQbqtYsBa0tfVB/d1rjRqN4IfskXK0mgKt\nBb3X/gqOikIIcgGn45AnpZLf6Z+utbwOkhiKCmfhEcWYX2rvx54+zEDCRAqiVoI2jhRLU0mSMKXU\nll4vJcumTPMMIWE4mRCHikpbGkmIcQGVyf/9n/3feR4WG72UkjBKAFdX6Ynvz0tX62BByPBBI4e1\nKKX8n8FzpW1gSFyKs16lI4iwTmGFwzqBJGSqA4rV40RpQp4XyKBgZsc8d938TQ487pdIUogl5EB7\ndg49Kjhz4jBXtPoMlvcgwpTeTBt95fcj7v0CQlhEFNb6ekluLMJKlBQURY5xNRRLJDV75aE/KoxI\nertopxFmvE68sEhbVKQmotnxU/65A49klBluvuM2BivH2bH7QqJWj0r7hbyykRElARdfdCm9mZ2c\nOX2cxswCRX+NxblFTp9bYiIFKooJpGP9nuMc/omXYrRlcddunvPS11OO+uzetZ8jh++m1W5z3/33\n8IgrHu3ZP3mBdgGD4ZDxaEij2SYUjvF0gi410+nYx/S15jl27BjxubPMzsxy4sQRpErZFbe/p/Wi\n9aZO3aODN7nyviqrX2hfulFVJdZYdFlijPGa9STaGugiBJFIyKsBozOHyCdjRht9lKuRGsZ6Pj2C\nOFTEgX9lLrryClrNRk249Pp7rT0nB+rfexgRhqHHDNQqMSUl1nrkQlmVZFlGpTWBCupozO0rTNIw\n4LK9s2yMc7qNmHYaUWp/2J230GVpY4zAIaWgmSZM84pJXrJjts00K4hTH5M4HOcMJzmdVkJZGirt\nDUvVpMA6x3v++S5+9ldSOHIYDlzqnZmhhJOHYNgAuQ6DIXQN//Lla0nCgDgKKCpNo5ngrKPbTIhC\nRVFoksTzg6SUpElIuxHXkssprWaMALL6VnFufXttPuf87ELgat9EnVlsebC6d99VMDiLcJ6OujUD\nkp4jhPOxilIqRJEz89rX0xKWdiBZLy0bEsR730r3gWOkn/y8N6xK5TEtTqNEwIXS8kuLizA5g4xj\niixDKsWOX/hx8j/4S1pRSM8Z7n3zb3L5n/w+zjmiIGCgbU0F8IKBJAqZ5hnWChqNh/7+PCw2evAp\nTgiBs2KrT7ZlacfRiAOs2/oN4YzDYsB5tYKuIoytECogr0pCKbHCm1QEIIVltPNpdOOKbLDMYPkE\nSXcveW7R0wGx8Yk6Gq+g0RYmowHF+llsq0Fw7lbsnicQRv7rfbGDRrmExhDVgxVjDYEMfG/XBj6j\nEt8y2u7L66sRSVGWZJMBR49M2NFNCIRAhAnn7Vrg5NoG5893meSWcmWNjfGYSw5cwbEjR0nimKrU\n9GZ79GZ7rK0s023Psra+Shg06HRmWFpZJp9OmJufo7+xRv5zP0GRl8wvLCKf91J6ScLt95zgwj0X\n8JznvoSvfPnTKBmwMD9PYQTj4YAqG7J0KuPIsROEccJkPGL/xRXNRoMoirDWcuudd7Dv4oP018+R\nl4YiK5mWBcPh6HtaLZvuUxUGXiO/aViqK+lNrb2zFquNr+YrjXWWKI6Ik2Sr4hNSEqiAXTsiputr\n7N61yLOf/gT+7O/+yXdpBcwmIe12m2I8QgrH0lTzrte9gigMMcZX5lWlMdb3xz1I60H0gf+ebQ3T\nEls/Q1VVFHlOXpQEdaRhEm+zcsXvtztn2uycaXFqdcjGpKg3CsXxc312zrTqQ8twdn3EpXvnSWO/\nyeIck7yk0oYkDEjjEOdgkpU0Uu/SFYFgSMuH58gAHvsYimuvJT5wEIIKdu6BOQ3DDGaasDrk7MpJ\n5nsNABo6IElCqsqQJCGBkiRxSBgoBNBpJnRbib/RVBptvB9AG8v8jGfk75jdXlEghCAMfPNO1BW5\nD6Gp1VbO1aIPWPz2H7P6uJ9HSH84KCHqIlJsDXB3/v3fsPPaz5IIRxwIxIUX03/gPs4pxdxH/xSV\nxJS79jD4+OdooQgEYDTWVRQWYltBFAECawzrQpIEivOf8WQe+IuPMFMWzL71Mr54/YDzJ2OUEGRl\nzng6IisNOp+wOL8AQiGDtFbmPPTt++Gx0TtBZZ1Xjjr/oqhAYKyuN0jlnYSB8r372g0prMNQ4YzY\nkrVVVYmQAofEWQhrDbvGsLCzx84e/NOnbuSyTsZw1KfZbTGZ9DEIitz36DVQ5gXD4ZDLdym0tqQ7\nD9LY1SHXgIa+6BFzjkgprDMI43k6vkIQqFB56ZqzGO0n/tt5gjAgTCK+dd2Xme916bQEzZkejWab\nxdkeN95yK+1mxA3LyxC2EGHMUy7fz8mVPnPNpidvRjk33HgDUgrO272b0SRndnaGww/ci5Mhe3Zf\nQPUDL8R86X8QhhGdn/llxE+8ivW1VRaKjC/982cYTaacOXUKV2XEcco0yzl9dolWq01/OGKj38ch\nmYwHBEFIFAiu/+q1FHmJVH6WEoeSb33lX2i2IkoDw8GIwXC8XSFFvVbqNCZnMcV3D+hEzR7xuvRA\nKXA+q9WHghRobTxaY8spW5MmdYmuNL//7v9KNs1Z3xhw2bNewQNf/TTOVKznmo1ig1gKdOlvkJsO\n183gkjAMCAkeNGXVV+TImqwAACAASURBVH3fi39wWBsE3tFblAV5UVBWlf++pMRZQ1WW27/9Sckk\nLzHGD1XTKGSSlzSTiLDuxw8mOb12ShR6aNpoUngpZqGZ6zVZH049rhcYjHOkFISBIstLzmYwd16T\nqNlh7sk/xC1/+Q72veB5cPIInB7DBSEkHWjMwLnT3HXTzWTaMd8M/e+g6bX83Vbq8wTigGYS0Uhj\nwjDAGMOhk6tEoSIvNUJAp5GwOpjw0Wvv4raTG/zk91+2rc9ECEcQSqTwQg9fmGtPC6xzBT36HFYe\n9VPsuv0v2JA7EVe+xCv1nKF17z3s/6PfpWk0oYCgmVL1ekyWTjE4foi+dCz+w38HPBtHKLjnjT/M\nvr/6GDuVQsnAS7qrin5Z0bMSiWEsYOgMMg4pdMXOP3krR77xaThlmG+VaOPl2UU+QaFpJBEmaDEe\nD1BRjEMx7G+wc8dDz3N4WGz0DkepLQKFUAoVCEqtsSYgVALC2s2KxNjKyy1ReEaxxFhNUVX+qikk\ncRDXbAg/AI3CgNJYzp5ZZ7E3y+Oe8ULsPZ8hS5royQirK6psQneuyaSEUMFkOsUWUyIVInc+ms6+\n3UwKr5VfWzpDNlwmaEmc0/56JxWWOsii3mTyrCAOBShwentI3sloxFc++ym6c+eRkdAQAefWRxxY\nmOUjn/os440RndYsG5MVFub30mqnyM4iPW1pd+cZ9Je56+gRFvZczMFHPJYkjThy93e487abaHfm\nmI77HF46zo7rE5qBIjCK3sWXbA02e099Ns17HyAWhtF4iAw9sVGpgHvvuZO5uQX6gyFhGDKdjGi2\ne8ShpD8aIAKJs9Lz4cOICRqjcyBkOPULd6bbYDzZvurGx076f4qy9ARJKesqPvWtmTDyBiSBx7ra\n0F/Hy/JBSaXwxYDWfkCbTaZs9Iesbmxw/PQK4/4K8cwC+eqZepOAogaVzbZbTLMcY33wTFA7XDeT\npZxzaON19JuZBl59o3CR9V8rCvI8pygqjDWEVvl2jtZYuz15pQCaacT6YMpgUjCYlrTSiPXRlEpb\nkijbwk200oiV9bHfyENFXumtoXUQSFb7E/qTgnYj5utH+vRVizRpsLSkOXDwUq68+EKe+5Y/Yvhz\nb8NZwx+94ZW84ClX0N5/AArL6NwS1/zu3xKnCZ22pMgKnCk4MBPw4qv3Mt9rEAYBZeVnB4mU3H9i\nmflek3MbY46eXqcyhvlukxvvP8ue2SYX7+xyrr/dteJwVUZhQIUhkZTeOe/Aar2lqFJrS/Tu/SST\nxNJRQ/a/5UcRGxVOCQJT4QIQaUJZFgyrKdXZCWtFwfIrX8iFP/xSsjynMt6nY4wmvfxSdBBjo3o+\nKCRJGJCkEVlRsZEbKiXZlcSs/PyPUVaGeHYHq5OCuV6DRhpRDJexUYPhZEpVlsQNVd/4YoIoQlcV\nOy+6aCvr4KE8D4uNHiAIIqR0mMognA9mrhwoobB4XjfCt1RwiiAQWCtBhmAgchLjSibTDERAFCmU\niCmtIa8qVCCZbqwwqWZptGFVa5yuCBozWKORcQMnIA6gLCFtz5Dnh6DbZnbfbiyQFVD2V9DDJS5K\n+wAIofxwD0+sBEelC5y1lGVBNjGkcUS4TdWArkqs1ayvnWZxx3mcXS6Ym53lU//0ZcpsinaaleFp\nqrJidfU006Lkm+QcfeABtKvYv28f99x/PyqMOHHoDjozO+nOeCdqf32JHTv3QAThxgqm2cJqw/T4\nMYQQLMzPMTx6lOnSaUIJlTZMpxn9/phmqwFozj9/P4/etZdbbvw3NtaWCYKAZz79BVz8yCdx7MR9\nXHDBFaytnebQ4SOsHr+H5TNn6HS6LK2d5BFPeC6T4Tqj4THOLp3b9loRNd/dqyPdlrxRbPVTLcJ5\nN3XdiPfqEWsJ3GYlbzHWUJV+w51mOZMsJ8sKji6PqfIJVz3nFXzro++vUSb1vAjI8qK+CXg9Pkpu\nOXY3B8XSflezzvmtdJPlnueFvwlsavusxRkDVhFGIVJuE5dRb+K9dkozjTDGUmqPPmg1Ys6sjtg1\n26I/ycmKil3zHW/oKrxLdjDO6TQTyspwuG84UzVJXIugFzITxVhryaYZabvB3ceOc/DgQWKl6M72\nOJou8Py3f4SvfeA9XP6KH+PZz3giF11yIcvnVpiZ6TKUY6xMOVyU/N5XTpBNp/zIVYtctm+GPYs9\nltcGLPSarA8zDp1aY22U0YhDbj1yjv2LHS8BXexu3zHsAOv7885oCuPzgJVnoBBIhzCO4J4v0Eg1\nZdKhHQfkF84yc8oxOXWcodFMnWMqc/pPfQyNa55Lc/ciAbCgK0b5CKygqDx2Ik0iuo0UHYeEcQRh\njW5GQJKSjoekbUdR5ZzMKjqXXkRpKkQpKcoxc7MtDi4obvj1PyR+5y9iHYRJ6n0eWEaTgsRBmrQ5\nuXSOTrPxkD+Oh8lG7yWJERFOGsrK69Sds5TWt2J0ZbESosB7aKvSa+XLKsdYTVU5DIZ2s9bkOkNl\nK3SpUcrLpqqNkxh7gE4Ma9YSNVoUo3WkVOgyx5We6NhsQjaAXRcdYH5fZ2uTt+MN+sduZnc3wGiL\nrQxxGHkShtVUxiKlxr/uutZlO5zT5MU205SEYDKZ0Gh2OHX6KN1mm5OnjmPKkt179rG+do6qMjSb\nDS67/CCnlsecPHmaKAk4f2EXS8vrPP3ZL2NjnNFstdh13n7Onj7O/Xd+CxWknDxxlGd9+Vvc96Mv\nojKaSTal95lPMdfrYirN5a9+PYe/+lWyo/cx205ZP9OnyDWNhqEsMh44dA/F7bfRiCN27zufZz7n\nhRw9fZjy9hsZ5RukUZMvfeb/4uonPIP1qIuMNzh9doVLL72APft28IV//CKzs83vabX4gBGPDTDG\nB2B7JvyDyOHNXr3AK0q0sehKEwYhAoexBl2WFJlPExtnBXndNtm/c4ab7z/Jd/7xg1vOWVHb8IUQ\nTPPCtwedq+cDbguJ4Bz1YNg+SMvED/zyomCSZXX0YM1sYlND7zf+JEn/X1LR/+iptGWS+QCctWHm\nEQbTgl4zYW2cc8Fij7XRFG0s7STizOrQ97CVoNdp8Y1lgYojj/zozNK2GqkkM3MzCOu9KL/21l/l\nt9/xXpxwlLYkiFNKa1jtD8nynN6Tno+UgrsPHUdXmkajyW+++ad56zv/kGaa+IMiadBsNfnM4Yy/\n+/YZ3vaSy+mPp5xZGdJMY+JAIYFWEtKIOsSBYqbdYLbbZLhNHr1zvmL3+HMPPrSGLby4s74YqJ7y\nWtY+9xZacxUn+hHZD/4WcjRELs7TcMvEcoSSinYgMc6HtNh6/tEIAgSCOPSBKkHgybu3HIi56Eyw\nKRDzxaiQ3lxmDU6HDK55Cl0ZEErf3tXnZlGf/CZjqdjn4HiR02z30Now0/WGzcJs0IwUxhYszMxt\n5lA/pOdhsdE7/AdndEWgJAhHVeR1C8T3QKWSSOcwlcYIhy4NRlZgfKivEDVGwWwy4q3XzuOQTlKV\nlt3RKqMNy7t++nkopXjtf/s+L93UmslgjW56HivLp3FBg4++/c284Q8/RAWsn1pleuo2eqFhd1Mi\ndI6uKpw2TKoCaS3aaZQIsUqgTYmVIWIrxFyyTQ8MQRCyuPtCnC7RkzHnVs6xuLiTsiwYjPrMLMwz\n2+oRRBGtuMlkcAxnMq68/FGcPH2a8y99LCqdJxneyh03fIP7b0kYZ1PS9gw6z0lDSWa1X6B+7E15\nx0087mNfYulT/yfrr3kOp4+eZf/iTm67914OXn4pL/uhV/MPn/hrhBC87OU/zLdvvJG8v8qgv8bZ\n4yeZCZqcPXaYkyePc+bO28lGQ0ajASbrc8mF53PqyH1M+6t8/hMf47x9FyOD7b28ULdAVIAVhjDw\nUtwtFkqNea1qtypstmjAGlNLMN2WnLEsS6bTnMFowmSaUWlDEIYcXl6hzEYY7cNqNvvtDtgkWVRl\nhdfW+kG7NZaKaitesKy0by19l8vVSzArfw0PQ0TssNr3pINAEdbB5sE2N/pACgaTnLVRzukN3+Jo\nRAGVntJKQ5YHEwaTglYSctuxFfYttCEIuX0joFulNGZ6nhArJFEUUeSWmd4M2SjzYeeB4uonP5u4\n+WcEQjEtM8qiJHNTjg6P8MD99yOUpNFoghTESUKlKw5eegCA6SgjSALiIKbb7TIcDQjjkPdcd5py\nMuIHr5xlaW3EztkWM62EUCmSNCCOFHEUcHZtxL9njfv/ehxQaI0QcsvfgAAlrL+BW4vWvsVmnvgb\nCKcJpSW2BbQixmurzHdbELaYS4ZEgfJrAYWrvSCl8QWlreWroRSEgY8ZpacgD8FowICeeLBW5SiV\npPecZ/o2NQ5nBPOvfhkrX72RC3bvgbNnOP+8/awNhsz22uR5hpIQxSlOBMx0W5w9t0wzTR/6GtnW\np/e/6BEIpM8awWHrnrdF4KFkRWWQTmNFfVJb4zfOTYZ27aS1xhGGjsoZbGkARaQ84SYKPQztnT/7\nQzSKnElVcts3Ps8jH/U4nveqNxFiOHPyAb71sT/lq9ddx/l7dvGOVz6en3rL25mPK7qB8/bo0iCE\nBut1+8oGVBgcnq9unUPJyIvxCTz+QDqs2d7V0zrH8vJJkmYTbXLCOObs0hmEVLQaCf31AevLqzRa\nTfrra0gVUFaaW++4mVAptL0DKaYcOXSE2YXdrC2fwllNno1otbuETuOaXbTWlJVmz84dHPjajXxh\n/16sdezbswuKCSdOnSSOYo4dPsnJYx9h/pWvx+26gP/x139KGCqMLukPJ9xw47UMNgYs7NqNrTRX\nP+3JnPviV/n6tZ+nmaacOnqISZYRNRPitMsVV1zJN7/1pe9trSi/YYeBN8P5373BIBBSYI2XLtpN\nQ4kQhGFIFEUIIWu4mKGqDFlRMZpMyKYZQRgQxzEPLI/AOGToU5H8ywpbPnwBK6srzM7M+EpeiPpz\ndFRlRV7kFGVJkfuNfssoVUtA4ygkiUK08goMqSQqCIjTlEazUf98D/0Z5RW3HVtl12yTK/fOAn6m\nFYeKOAzQxtBpxGwMp1y4s8sw6rBcRqi4QtRtIi9HrCiqnG674zcXJSmrConi1m98gXf+1zfz6297\nL+12i2ajxf4L9tPf2OBL8l9ptVr0h30arQb52EsHG40muxfnOX12BQxUznNulPCt2WbaZJxEfPru\nDa65tE1WVCz0mlvS1zQOGWclO+fanFx66EHY4Em1DaFB+M24MNpXz9IPYjWaWAHSYQKJ9TlTdQUO\n3VaArTI67SkOhcFnSlhrEYECqRgORrQaEXHoC1JjNUJ7xr6Z1oor6er4KsApkAJrHCoMqYoMpSKE\nsJhYoB2YyQgJnD63xNzcIhKY6XSpdEUYSRAK6xRXPfrRlPl/Mh39lqfdOZwzWOtNUdZWGO0RoR5E\n7OP7cBKklx96u7shCEKMrLDWECmFw5BEMVEt26xMibSSN//ENfzh296DVgGf+uCf8/7hH/DTv/IO\n3vXxX2KUFUgLe13J0omjjCrHtV/4R17+gmdT4qf0Aqh0gTagEcRBHQxgS8+sEB45qyuLoPIoBu2/\nv+081lqUEoQInvS0Z7P3gv18/fovsvOCA0Syyef/6W+JopjpWBPECXsXZimLCZ0IQHJiY507bvUH\nAKM+1oGSik6rTRwH2ApUkTOdZAgp0U5w9LrvUGoPdDpxZgkhJFk+QSJq56Kl+NwnuHK+xbcm4xq/\n61UMgYaZTgdRZLSSmJuv+xoqSnjuk7+f22/8V6585g96KWTgzUJfvfazDIfbI3rCgxW9cWA24WW2\nbpfU13RjDFZ7BIFnmEiPlpXS80sqS1lVNdK3Is+8xLFVowqy0RCHQ4URJp9stW9sbbKRSD79uX/l\nTT/+I0gh64Oloqy8Eqeo1TTe9QrOKQK1ucw9Gyish7jUKrKgpiA2mg2+OxLxoTzNJOR5j9mPlJ6d\nk5e+vSB8EUuoFFGo2DHb5rhuMMx9YE8YUg+RFXESM51OiaMUp0HrChlI4iCiyAo+/o//wu/9wR9x\n8Xl/x9LaFBz0+30WFha58Px93PfAIYJYcd6OXdyzcT9CSM6tnOPqR11Jmd/EsbPnaKdNcmMJooBY\neFpmHEWo+TnajVrSWPOsms2YQCkaScg0L2k14v/oY/i/rxMJcbyJKHeY2pm9ybcPwhBXD1EjSZ1P\nwZaRbDytWGxXWzgL6+qBu7A4W7E2GNBMfURolDQwWmKMZeXm23j8uRg1k0AUAHEdSSp88WcCEgST\n4QgZx8SBZDgaoExBZh1mMvVO5brd44SgrBwqSOg0YtLY6/ydsUTpQ8d8Pzw2eud59Mb5ExPnKI3G\n+ouSN5g4gRQWUed+BjJCW70FsXIC4jDBaQgjDxbDabSG0vgT3WqDQxA3G+y/6CI+/IXvsNBN+av3\n/zZKCmQQgwq4aXnIE8/rcShz/OwjD2K0wRiBcxVRoqisQEpLgNfPU7N1BP4F9kM6SeUcofKXe7NN\nBAIInAmZTKbceeu3OHbfzWRZyaH1bzLOK+bmF1hbXqaSEYvzc2RWec6JjEgbDfbtatCf5L6C1RWt\nNPLcGifJCkcQt0hPH6PbaZAVmpXVNQ6kM17rLbyhp9tus6YNCwvnbW0cf/KqqwnDiAf+8V6MKYgC\nRTusY9WUwOmcosgZZyVlKTh5/A4Wzt/LoTu+zuLcItNJn6yqaMQtojnH0tkT2/tUhJcygoPSM2xs\nLcl1zmKNV+EEjdS3dHzSfK2lr77LlVpRlQ8CxDYVM0IIsv4KxXCduDNDOdqonZK+fSPw0szDx0/T\n7bbJspzpNCPL/c9tjI+zVEo9GHNY59Zaa3DG+JAdBEEU0klTwiAgTmKSJCFJ4q2D5SE/DtaHUwIl\naSYRvXZKXlQEgcIaLwdVSnLfJMSIgEGeo+r3yAmw0hGlcQ3ScwgLRZnhCn/7idOYW+64h8GZY7zn\n3X/E33/kb/m7T/wTp0+dotFocsUlF3Ls6DGsgbnZWe99sY7z9+7i99/3F9x/183867Vf4g0/+6vc\n8Y3reNJzryEKQjYGA3bt2EHabHBio2B3O0AKUGEADqa1qev42Y2tgfNDXidsxn07osgfdN7jUOMM\nhPUUTgDh8yS01litcbZiTyej2XAoFREqhXMC63Q9ZylpNiLaSUpWlDjjccYISetz17OrlUIaQBhC\n2PT9Pl2AjqDIiMKAsx/6BJf8zOvIszHT6cRHTNbBMOAxLlhNEKdEyrG83ieOY+bnuujKsd4/SaUf\nejvrYbHROwd5pb0TlsDz5QHhrJcsSlAiRFhbu9YcTtYuN2exCIQRaFvhrKbMA6x15PkUYx1xGOFU\nTLsRYq3m1/7336DUFdfefoz5nfs4c/wQSnlmuXCOOE759nJBJAWLcwsIKXz1PDFYqzC22ooos0KS\nRDFWW0+ytAJkhArAVg4lBFZJjNjeRt/rdXnxK15DVRXc8Z3vcOzkXTjrWJyboaE0pTbs3rHAuLA4\nVeEqQ1EJRsMBlz/yUu6960asM3Q6HZrNFs5aAmuJoxAVBOR5jrn1ZuI4pigLirzyodJKoivjB+DW\nt84qa4lFRZVnNJoNiqxg/eQ9VHjly2rU9Cx1vBZcWz/0lIFideDohB2izgyHTp2l0eogcOR2TJlv\nT3IKmzLFwFdgQmxpzjcHoXbL/ep570p5b8NoPCTLMnTlWTda+/aN0ab+/71E01qHznN0PiZsNLcc\nts3dFzE6ddg3b5xX+jQbKUVRMs0zxqMJ02mGChRxHJOGMc1mgzgMty6sRldorQkCr6yJooi0kZKk\nCUkcE0Xbq1o3HykFO+daCCEoimqLk++spdNOPAo4r7jvzJR2d45Gq0leFMRR5FOtVOAxIYkkcAHj\n6QhrHTIQOOuYjjM+9IH38aM//SY+8bcf5K677+SfP/FhPvKxT/M3H/0YrUYDLfxg+vNf+BJ5nrO0\nskTUatNut5hb2InTlms/+xk6vQY/8Kxn8Fu/8mZe+qrX0Wx1OHL0OPlMh9fuaDKZlkShZ/NHYbDl\nmdkuFNA5izM+b8CYOqYPh6qzCZwT9ebsb1nSQRIKkqRktmkRIkYonzCytr5BIw2ZZJo4CokDSTOK\nsM7RaTZwtQ9IlgWzjRiZJhAlPmRDGv/vMIFGF1bPIYOKi1bWGE/H6KpgPBwxv7jA+nwPOfY37Lm5\nDoW2bAxHzHc6HLj0YooiR8kAG1Q4U1Do/2ysG/ywRFuHpKKsLFJ6W7KUkko7lMjASX8EODCUWCSm\n1DigKj2S1oc4aKSDdrtFoBRKKJwzGOe/jrP86Yc+x74Lr2DlzFFcOUJLiVUpO/deSquzyplTp/2B\noxzT0lAOzxLHTXIdEkYxyllKYzwDpbLkVUEjSrz6pnL1JiSxQhEEErfNLkVVGa679st0ZwKas/D6\nZ7+ZL3zu4/QHGY++6ml88cufJE0iqsohphZnJAcPPgLyPutnD3HJ/vMZVZZRf41jR4/hsDQCwZ4L\nLuLIA0cI0oQ9d92CQRFFCcZYzn3kg3UOq6+ekiT27tJiTNhIed1Tr+Atn7iJ37nmSn72WY/gv3/n\nLEoIYunQBHQaiuX+lGZjxlv+BeSTdc7ecQyARrPN+vJJwigkVIJO66HLwzafTYWKEIJGmhKF4RaP\n3hhdm6LU/yOw21DmOdPxhKqsKMqKUvuBaV4U6ErX2AQ4s7KBKacIIck3lv1NAS//3fwzm9/HRn/I\nYDhiPJ5irCVJExqNlFazRbOR0m7UQLui8pgEKXw0ZbNJq9UgimPCmpXjf+deObNdHr1SAq0tQfjg\n726m65G/k6xkbTClqAyX7N3J2bFFOEkgQzppBxkKgjhAWEFZlj5LOBMI4U2Kf/Pn7+LNv/q7/PJv\nvo1Lzt9LmKa84w/+gAMHruKG6z9Pmgre+rZ389mP/z2PfeKTaXbnwE1ANHnCY7/Pc3/KnMo6nvuy\nH+T4Pbdz8f4LmEyG/MNHP8bFV17Io656AkWWce/SmMt2t70gA0ca+bZNt52gt7GpbT4+fN1TcI2r\nk+qc9gIP59k2Ho1gCdyEOCp9eLhrMhiOGI1KWp0mDss0r7wyKAxwSeiRFSr0e5YShM6w/rWb2Hd+\nEwYOGi3QlXdgSuWllrqCTg/yKXtR3PBvNxI++iCtVsxgMMRdeRHiW3dgrSWv/Oxn744ew3HOYGOD\n9fVVWs0GxgWoMMHqwUP+LB4WGz3OYWwJzlI5iZJs9X99e0aQO4c1nleS5b76x0KjlRCEKXFsUDKs\n5VN1gmUNOxOI2vpcMyyQ3HL3/XSaLcBSWsdgY53ZuV2kScz60rhWdOB5Oa4iTFq+YtWVz7RFEAUR\npa5wWNIgQhufdp+mMdYUBFJSVaausranpMjzEeVoifkLn8hjr/4+Vs6ssLS8wkte+Wog4Konfj/7\n9u5iZWmF3vwC/Y2c03d9nWFRkoYB06xieX2VTjelM9uh1U24YMeFFONVrrryALecXMZo7U08Nia2\nhv5XPuVbWFJ6CqjWqJkFwumIymie+Lgn8XuffhfiBx9NrxkRoNGVZuQcURgymfqkpmw8QAUhMk5x\nQcrMfMNXjQg63RmPgK5GlHabdmHqYbzxCV6N1A8ujdZoXdWh2+UWq8TVLUGtvSFqNBh4o1JRkBcV\neVFhnCcUxrHfyO85voLJs62/zx8gliBOPT7Nef2NEIITp85QFiX94YhWq0mr2aDX7dLrtmm3GjTT\nxG+24ynjsZ8ROCFptZt0e12vvKljDv0Qt6Cqqm0HyTsH7VaCtZYkColD35ZZXR9vEUPvnjZphjHN\nlmJ1eY1Gq0k2zUhaCU0VM8kmWAwJFd1mi9MnTnH85Gle+qo38O3v3Mo1L3guf//RD/Pud7yT3Ytz\n3PfAfXzukx/nF37rtzn9wGFe87rXMxoNuOnmO/jLv/5b0kbCo6+8jJ//qR9HNNq85tWv4Nfe+jbe\n8KY3cc0LXsC3b72d177q5XzzK9fxsb/9EG988y9wz2TK5cLnPE+yijjSdFspq/0xrW1CzZxzKDRB\n4Ns4disUxqKNpdQW5zTdeMpMJ0CXJSoISRNP+pzttpnpwiTLKQrHzoVZSv2g0kobizE5SZz623IY\nEhw5RfzUhPI2DaHzvHMBqLrVKIEohkASWNh5y/2sXnUprfYMO5tNlh9zBeabd4KUxFHMYLyOWcmI\n44TpRFNUBcPVjH179hJJRyb+k1X0DkdZbW7IGq1tTY5WGKc9lKoyNQfHkKYRYSRphhEykCjhEboe\nd+LlThU+/MMXOLWszvhwkOF4hBKOsipJ0gglFSqMCcIIXeZYawjDgEBYwjBAFx6TWpTe1q8dpLFg\nmhU4K1EiROMlnk4bJtMSQUAYmpqY58MttvNIEfDsa34MZzNOnl5mcXGWZz7nRRw5dpJ22uTMobtZ\nOXsn437Bzn07sIVgdX0VEUY4kaLLzN9uEsl0XBFOY870zzFdHrI6WCeKE0y744M4jB9SlXlJM1X+\nqqu9byF98jM49sE/IwgUL37L7/OTT7+KqjL8/pfuAiEpKx/kcf75XYajMdPpmEAGuCInDDRa574t\nEVjysmA8LgmCECeDbadugQ/9zjJ/vU2c20IK+P67qYdplqIotmBnuqooC18kWOd5l1EUEkbRZgGM\nUh7PO868Rh67qejatM/bmkkOm+GFS+eW2bG4wGyvQ7vTpt1q0Wo2aKS+kjfW+GQ0KYnThDBxpNaR\npL53bx24OmikqjxZUym57X60koJ2GnvRQaWZ5KUHiLUTKuMPKlXEZFVJI2qyc88uVldXWZxf9JJP\nWxGEkjtvuYu9z3k+t998A9/+9i0842mP511vfzt33fANPvvPX+Q5z34qR094p/Bjv+9qXviDP8Cb\n77uPo4du5xlPfTyv/sn/DWMMcRRx2QUHuPveB3jsux8LVBB1+Orn/4X3vO+9/PArXsRXvnItb3zT\nL/K0Jz+FJzyxRyADSiF5/BXnM8lyvn3PabS2rA0mjKfltjHFAi94sc635pz1EZOBskgsiZjSbjl6\njTbjbOpziAX0bInFxAAAIABJREFUR1NCJWmkMVVZoYKAhVYLpCUIA9/Dx1JUFWEQU1ZeEi4RLIYB\n0xOG9uM6cLODbgMqARhvt08a0I5gdQJJg04zYVVIn+IlQ1Rla7OfY6W/wuJMl1IL4jhBopmZ2cvJ\nM2tgHdpq0m1AzbZfUv2veByUWjPOMyZZyTArGAyn9DcGTIcZ1jiaacLcfIed8z3mei16jRZRJD2D\n3tVRbFIgrPHDL+E5FmAQ0qCkIgi95Om9f/UZpBQkaciwv4aQklazTagUG+tLGFOBc+zdtcPLKUMQ\nKBppjBKORAny3PdiAuWQCoKaNqekxDpJGMfIMPQvupOobUahdXuzdGLH6bMnOHbqBo4cO4SKEo7d\nfw+r6w/QmmmyurzBhZfuwZqSstqgvbvHtChppzHWOoIwIm6kzPVm+dXfeB8/8/P/jUk55Ufe+OsI\nFdB79Rv84LjGyGJzopm5eqDptfwbn/wwSdriyiddilIBf/ONe/ihP/onxtMSU5bMtmL2LfbIi4JC\nG6w1FGXOeJqzsjGoiY6GwXiMNdYPbE1Fu5kQyu1fx521TKdTppMJeZ5RFCVFUZAXuY/zcx4YVuQ5\n08mE6WjMdDymKIq6N+sP3jiO6bRbdNtt2s2mz3BVyiM3qFEL37XTTzdWcN89JRWC+w8dJ44jut0O\ns72u3/BbDZLEFw+VNt4NKwRRktBoNul0OyRJsqUAKsuSPC8oCi/FDJTyLJ5tPIHykYqRkizOtNgx\n02LXfIc4CjHaUhkIw5Rms0UYRAQqYO+evfQHGxDAfGeWG7/9HV58zUv5P/74j3n8Y64G4Lrrb+BV\n/+WNvOi1r6aanOPdb/8dHnX5JTjr2LVrN8g5fvUtb2M0ynn/B/6GJzzmkQBYLNdd+29cuP8CLrn8\nUVxy4HJWl05x8ZVX0W43GI8GvOkXfhPjLE9+wtV84EMfJcszgjDkk9ffT39csHexy0V75tgz3+HR\nB3bz2Mv2bXel4PCHp65K736noBeXzKUjZjuOXrOJQGNMRaU1URjSSSNPn9UGqRQzrZYfrosAXfl3\nRUlFEkaEQUCaxMSxh+TNLszTtLPw4h6ksR/GNlOIY7gohte04DwNSQDtlE6zSTA8SSAjinyCPL1E\n52UvBwTdtMmkMGhdIGzFNJvgnOSSi/fSbrVpttpI99BRGQ+Lit46R1H6uD2rNVJAq9MgjGMaYYiq\ncZ/OOnD+uo11GOEt5JVnF3udu3Ao59U1QkAkIv/yi4pAqroP6+PRdOXNWNYYZJD6gIGyRAiJMwWv\nffnzQQoUgR+0Vg4VxQgcca0MqEpTUzM9M99Yi5IBOi+91ToM0GWOEttr3QjhGFVTkmbFkx/7Cu6+\n7xtc9egnYpjyfY98CqsrZ/j7v/ljjh87xyMOPJKL9h8kn065qXEzScsxnGTosmTj9ICoNc9bf+ON\n7Nu3l6Q5ixtP2LF7EfGoxxAGXptb5j5Or7fvEiary1jrAzHaT/kBnn/hIzlx4xeI1QlKB2maUuQZ\no7wiNwJFhopiZuYXedRjLmft1DFUq8W5M6e4+Lw9OGEZlgWH776XUAms0ayvrjI7N7f9tVL3sIMg\nrFscJWVZUhaFH6gqRVWWZNOMsixxxksuhZQ0G02yosAWbLkKvaNV1m0eSyuO6r68LxRc7XrN1paI\nmj2qyYav553jznsP8QPPfippmpImCUmNuvCZCq5OkDJ+Jlc3+GWdp2CM8Y5NW5MVhV+vfovf3k3H\nWk+gDANFp5UymPg0sONn1xlNC+7JmizubRFEMcZqpFRIJVhYWGCj3+eLX/wir3nVq3n723+X9aVT\nvO+P/5yP/d0HeNXr3shtN30TXEXYXOAxT3wWf/q+PTzhOc/n99/zXt7xnj/lrb/6C6ytrXHnrTfy\nyEc9nrf80pv55V/6OWDKn7z33UDIA/cdgWIdgKc84Ylc9aTn0e20ePE1L+Jd730fJ1YGKKVoNFK+\ncW7MVRd4I93uhRkmWc4oK7j32NL2FkoNrVNKoqQDkzHfUXSaTcIgpSxz1gbrFKUhjBQqgCiMGE7G\ndNpd+oM+UZLSbrY5de6sF3lojYoT4iD2bmvnkDKkqjJkcIbm02ehKaGnIdFwoAErQKrgGW3YF8I/\nG2gDM4Z4mtA8e4Tg4NMIwpD4c9czRNC3lplGg43hGjsWd9BIE+QoQEYRVV5Q5gWduVl64v9HTLEQ\nYi/wYWAHfgV+wDn3fiHELPBx4ALgGPBK59yG8DKF9wMvAKbAf3HO3fwf/E6IY0GoUmQYEqFwdViA\nM5u6ep8YJEWAcAIjfXXmkIBEihBtK7S2deKUxCl//Q4DhTIh0vkUUXSBFBHtTofxaIB2gkYQIQOv\nynHWoKThwv37UEJitUEGFiUdCsnxM8v82jv/mJX1PgJ4+QufxRt/5MVs9If8/O+8j9NLK+zaMc+7\nfv2naaYJcSh5y/s/CHClEOL2h/KZDPp9rv/ax+iGV+LMddzytZtwtk0x1pw8d4Z777qJ8y5/BJdf\nfBHn7djNfScP841vXk+S9Ogli1TlYVrtHsP+Gnq0yq6988zv2MGLXvJS8v6Uq5/weO7/yIcRQFFX\nnQhJ9+ons3LHDVR7L0ffdxvF1z/PxVc/jsHiAnPn72CyvE6rlXImGyNESBAn2GJCVU657+5buffO\nmxFCsmvP+US6otg5xw033uQVJq02c90OYws/9PJX/k/q3jPKsqM8276qaqeTO6fpmZ6gmVFklCVQ\nIkiIIIEEJgmDBIhgMGAQ2QGbYMDG5sWATTDYJtgEkyVEEBIgkJA0CqM0mhx7pnP3yTtWvT9qdyP7\ns5dm1vp+8NZavbrXOd19TlfvXfXU89zPdfO1Lx/fnIBd1KRyctSwJkkT4iii27HERUdJoiii1eoQ\nRVGOJ5BUKmXKvodstm03ZJbZVFyutgFr6D02UMGYPA2Xp9sEgtf+4VV8/ef3k7QXc20+RLlJh+95\neK6lZi4zd3ROsZyemeNTX/gP6o0mQkqec9nFXHXFpTSbbT7y959jemae4aF+3ve266mVikidMTM7\njxBi97HeP9oYfEexdrSfI7NLdHLP1TM2reK3jx1FZAUSk9FpN6gUynYRlAJhBOVSheGRYb793W/z\nwQ9+iL6RcR57cCsvfcXr2Lh5A7t27EE3j7C8BQ2vOxHPcXjW5U8D4B1vfzM9o9Yp7MGHHuXD738P\nynFJu5oNp53zuHepuOqKy7nimZeilIeUHm9827tQUjIwPMRJJ53I4uwCe/cd5k8e28mpawb5s8Ee\nvnjTVl5/xdn84L6Dx3WtCATKRJS9kHJB4bkBEkMcd2i2Y+bqHapFl95qEaUcUp0RJSFJCp1Om3Kp\nTBiHzC5MYwzEibbrSA7LS+LYuscp0Klk+PAURAAZqADKAgYEtIChgl3kfSwO4TQffjsFvSVqbsT0\nwhwDPVUOPHuC0T0Nwlmr5Cr4BaJuSG+tTFbw8HRCNw6ZWVii2lOj2/n/t2EqBW4wxtwnhKgA9woh\nfgZcB/zcGPNRIcR7gPcA7waeDWzMP84D/in//L8OKRVlP8CIvGwqDBj7NUi0SBHGRsomR9BJBLGx\nYKJMW+VNpk2uhQapNI70bPpGSxxHWPeevDCb6Ywky9A6oVwsIaWw1Me5SUyWWBMIrEQr1RmBKpGl\nseXmAH/x1us4ae0E7ajLc1/9bi486zS++5Nfcv4Zp/Km617EZ778n3z1ez/lXX90Lbfd/huOziwC\nPAz88bHMiZGwenwza9etZ93EyVz8lOfg+AUWpyZJtGb1ms3M7j1AqVjku7f8O9VaD33jGpHZDl/l\nAzLCDwKKfoFGa4nN1YCF+Xn6Bnq458bb0PdvxUfgOg5JBMYInM1PQgpJsvN+PM8nimO++K+fZHBo\nnHLNY3jkbHwTcOrZFX7zy59hdJcwiZBICkGJvr5+Wq06Ryf30VPr5567tyKlw7rxDQyvW0+nVeey\ns5/C9PQijnGPa06Wh+NadnuSxDbHrTVpliJSQyog7EZ0u13iJEHkBiCe5xIUAqLI2stl2hpxCylX\nlEZpphnsKSHdAJ2EKyYmAGPDg+gk4XcZenLSpD1hJDlSQWXK+hfn0DNjBH/44itZv3Y1aZryzr/4\nOFtO3cwtv7iT0087kWtedCXf+M6P+PYPfsZrr3ked979AHGScDz3j+soTlk/xnd/9TCzSy08V7Gq\nr8xCvctDrYBytUYa2dSE67ikxrLz3cAlIOCO/fs4fHCS39xyE2eeeRrbd+3lbTe8k+/+x5dYs241\nX/v8p3nZ9W9k252/pn9sHcMjo1x02fMA2L33IGePLltCZpy1ZQtvf8ef8uTzzuaaV732d2/S9xkZ\nGWdgZIQ3XX8NE6s3cPVVz+HHN/+Yl7zqjQwND7L/wAH6+vsY7N/ACYU2n7v5fgpKcqQRc3iueVzX\niiM1gzWBq4pIkZEkId04JklTWq2I1WPDpGliLSCTCNcrUAp8ut0ZfK9ImiWUfDd3dPJWTmSe4+U2\npgKFRbIYJ8G/cADGHEgyUAlcVrKo26cCHnalNQ6c5EBFg/Bg0CXZYRi9eIDt//oxBkb7mB+okZzy\nDGo6o51mOH7A7gOTrFk1ZqW5XsDE+AiNeoNKMTiW28XOxxN9gzHmKHA0/7ophNgOrAKej/0zAP4N\n+AV2oX8+8GVjw6HfCiF6hBCj+e/5317F2v8to6KFtoU68bsIyRiRq2ZsS7PRGUpLUpMhpV36PQeM\nkLmiyUHlzS3GkJsISBzl4gjJ6tUTNJpNlHJItKDoKMLWIo4QJEbzoisvwxiFcCQitRpoV3roTLNq\npJ8krSBcgW98Nq4dZ3a+zq133MvXP/NB4iTheZdewnVv/yv+9I+v49Y7t3HV5U/lV3dvwxhzTHOS\nhjE7H9jHvof2cXdwG+0wojAgSWOHJE3wXI9Kr8PCQ0dpzGboZIax1RvZ99gulAdpmFHvdukdLNJt\nJwyVejm0fyd79j/G3KEWJ59yLqVaH489fCtBEICQBIUCPXu2274FFaykN5bm6khpOPnUs3nswYfw\ngwLKGae3t5fG0hITw2MUe/rp6JjJ/fuIoxgpHYTj0qkv0NM3wNziFFNz00xPTxLOzNHSGQ6d5Wvs\nGK8TUI4i8P28q9S1N2DOm4nCkG4nJEpsn0NQdAl8Hz+wH3aBN0RxspITX87ZW8yxQ7EY4PcM0J2d\ntKkbmwSkEHi2e1RIMNnKct9qtuh0OisMfM/z8Dw//+xSqZSoVEoWp6wUY6NDzM4tcNfWB/nkR99L\ntVTkhc+9lNe9/f1ce/Uz+fmvtxJ4Hu1OeMz3j5KS3z6yH2EMTz19HfP1Dgj48f6IgbG1eJ5vT8Mq\nN97QhjAMCYKA+fo8F11wAVP7HuGCS5/NI3f/kqtfeDVXv/DFtGYmObhvL62FKa55/lX88Ac/ZHqp\nxfTRKV5+zR/ytX//JgcPHOQVr9nI9sd2AYqzzzmdX91xO3//qc/wrW9/m3e88wYuuOQyvvGvX+TW\n227lxz/7KRMT44yPPMTU0SmESCmXyiRxwtGjU5x97pkII9g2Oc/c3Cz/8pbL+bvvbeXJm0b47Y4j\nx3ytCGHwHUOUZkRJTDeM6asWceKMwdW9NNttjDFUSmXA0IkiojikVLBeor7nWXyFlBQKRWsEZGwD\nVmoMnnIxjocwmuqm3TBQBhzwXEDCSQr8DOtAYq/RH92wj3BggoOz8+zOHJZ21PmbcUUSdWjFGaOO\not7skkQxhUKRtNO0UuIsZdfevThKsWbVGEYLkixjqXXsfSjHVfURQqwFzgDuAoYfN9FT2NQO2E3g\n0ON+7HD+2H//Xa8TQmwVQmxdrDcRuSWglUh6GOVhRE4DTC2EyOgMdIbC0gsd1zJDfEcSuArP9yg4\nLlK4eNLSDR3p4kh3RT3Rzc2XwygmCHzbCQhIJejt7aEbtvAcyTMuusB2WGIt5DINQqUkuVWcq2zX\n4dTsIo/s2seWk9Yzv1hnqN8qCEYH+5lfWkIYwczcAmOD/yUf/YRzkpmEemMer9oidNsMTPSAiBlb\nM8Lw6iJPv/hZlAsVPF9zwthmXLGaxlTKxpPHGFs9zuDEMKWyjzJFhleXWWxF7N8zQ2OhzekXns+m\nLScS1DKE45Fpy/HvdtvM/OaHpKmlP/qOh5QOJzxpDaV+h337t1Hrq2BMwqGpvURphON54BganSUm\nDxzCD6oUqr3W7KPbIstSXEdRLBToHRgiy1J2Hd7Hrl2PMrvUecI5+e/z0mi0VqSq4nfP53l2q522\n6R1l+Ta+XXDBSnazzKIRtLH2cJk2tnEqs6dBx3V400ufZWmVy/RB7GJ69YWnsmISjA0gWq02s7Pz\nTB6Z4uChSY4cmWZ2bp56o0EUJytArSzTTE/Pse/AYU7auJ6lRoOx0WEC32fVyCCLSw2SOGZ6buG/\ngNCO5VqZWWrTVy0yOlDBGEOnG3PrniZBpcdC/nRm8d/aFgEKfoFSqcjc9Bw6tT0gb73h3Zyx5VSe\n9aJXcNWVNlovD60CXMp9q5mvN7j82Zfz7ve8jx07dnLRk89jwwnr6IYdYp3x4F0/4faffh/pl0mj\nlD971x/z3Rt/xIa1G/jip/6OVePr+bfPfYKRgQF++Ysf88+f/SwPPHgvP7j5p9QbDSqVCnEcUy6V\n8VyPcv8gS+2Io7N1ZusdThgfOK45mV9qMrPU4sDkLIWgRJJkxHGENhmdrm1s6ymX6SYpRgiy1CKb\nAz/AdW3U7kqF5zi2+11r4sxYoUZmm9KE0cRpSKU3wIbtNviAAEoOOF5+rTiwZ44f6mF+Uc/Y59XQ\nysMJCnRGUh752c2se8GriboRUZTak0QS2w3aaFItUI7HyMAAWhtm5maQWjM7M/k/3S7/4zjmhV4I\nUQa+DfyJMabx+OfMMt7vOIYx5vPGmLONMWf31qqkqPwGtJ2VFjtgC51p/qutabOP63r4rosrDJ5y\nUI5tU1bCoITBdTwLD9Ipi4063bBtmxDS1Po5kjFz9AjNZot2u4UjFQjJkcnDoDPOetJGwiTGkNHq\ndtBaWAhVpnA8RaeToJEsNlq8/k8/xvveeC2Vms19hlGGFAbHVQgEURLmzfNPrDB5/JyMjK7m9Cdd\nxMK0wdE1hPCQosLhXYcZqp3Kow/dg68rXHzOc3jW85/LljM3MTDcy2L9IK3mYU4YqbJxeJhV68Zp\nLHYwaBwpiTsZW2+/hYcf+g2Th3bR/4wryNIMISUYwf59u2x3a5JQKRWtK4/IGKgO0G5FdOM2xd4i\nE+PjXPuCq1FOwNOf81zazRjX8agvLrAwfZha7zCl2iBgfVcPHDrEvt3brZLFcZFKUi5Wj/taqVXL\nhN0OzVaTRqNBo15nfm6B6ek5ms02QljppOdY85dOu8vCQp3ZuUXmFpbohBGuZzER5XKZSrVMrVql\nUilTLBYJPI81IwMIx7cCX2VPhZ1Oh75qCeUFeWM9IAyNZgulFL21GqMjwwyNDDLQ30e1UsH3nBW2\nShInfPJzX+F1176IoeFBBIJSsWibpYQELNRquWB7PHNSLXo8tGeK6YUW9+86aomk5T6UE+AFHlIK\niuWSPbUIhRHWDS0oB0RhyMzUDLf9+i6mZhdAwz33b2PdhnX/5fVe+gfPZ3TC0iif+cxLGR8f51Mf\n/wjv/fMP8chvb2PN6rX09VboGxrnJS97GZc+8yoeuf9uRiYmeM2bb+DCS5/NrkMzjIyPc/9d91Do\nG+Z5V1zJ7Xds5ZQnnUytUgOwDY5S4noumTZ0k5QwSbnnoX3HNyflIr4rWTXSS6tVpxRYi0/XsQYk\nRb9As9PFZAlCG1zXJUkTwth2L2faIKSDdFzINEJaU3ujBVI66CwjDEN0pnnpKx8hF8yzstDj8buc\njYINq+npH6S31g/CwwhJmhl0WRJ4ikJQ4t7pjHYUURodpb+3F9f1GRsbZaC/FxfN4lKdmdlZpOOR\nGhjoGzym+weOUXUjhHCxi/zXjDHfyR+eXj4+CSFGgZn88Ulg9eN+fDx/7H8d1mU9zSVtBoy9QRAG\nKVy7cDoKpRyk1sic4ZGiQRh85VkzCmEw2pLktJDo1GpUE3s+XynkCuVT66lhhAVOWfaFg04jpBRc\nd83VKOUSpRme56907SZxho9LtVah1Wnylvd/kudfdjHPvOQ8hIHBvl6azZDiYJHZhQX6e6u4SjAy\n2MfRmfnH/8lPPCe6zZ/+2dsoBB633f1Vtv1mK0e1R6+qkdTnaOkZutrlJ3d+lY0TTyfpLNKMHiFs\nJySZ4cCRRZTnE4RthsaH2fnAPkqlMu1mnYLnMRQIdhyapeeSy9A/+bbVGDsOhbJHOzYMFAu2yOcX\nuOGNHybVgnYr5MjRg7TrIa2lOnfes4eTTtrEkd07OP2kVfz6nocJO3U8v4hyPfqLPSz6Lq6JueiC\np+On09z0y3vothokScJiY+G45gRsgbTT6axo46O8YWlxaYlatUpPrYrr2Bx5FCe02h063W6+zYqV\ntMoy10bk6gwrx5U2XaM1Jo0BLBYDQbfTta8xuoaF/TtYjmt+eed9PO/yi6lUy1QrVYLAx/PcnCVj\nvydNMz75+a/w1AvP5WkXn0+pWGSgv49mq8NAbw9Hpmbo7amSaKhVKxw+OvP4P/kJ50Ubw4bxfu58\n6CAIeKTpMLC6lzixsmLXd1FCEhR9WmELmUk836NSrCBRyJZjaxZRBr5AuZKwG7L5pBO49cc/ZtXE\nCdx7//1MrF3LunWrKRZLXHH1SwBw3/E+Pvx3n+GDH/4besY2s+3uX3LWU57G1O5HOOWM8/nml/+J\nF7/yj4CQV77qOv7gykv58c9+SRz+iDfd8F56alXWrV5HqVqiUCjYTlZpiMMY5XrsODDHQKVA9F+b\nyJ5wTpSUVAo+rhuQeC5g8gU6RSpFN7RY6lqlTLPdwvP8nPNk0EIShjGe5xFGCcVCEYQi7DTza8Vu\n/hqJTmKcYIBrX78XqSTvfeMQm07thxXwhYs1H0n5yJ8XYe8UM7fO0iFj7cvG2LMtZHjiqRzZtQO/\nNcuWN7+bdpzRanfxRMbe3TtxpSQISkzNz3HWGVs4tG8/hWqAUseOKX7C0CFX0XwR2G6M+fvHPfUD\n4Nr862uB7z/u8VcKO84H6k+UdzXGcih0XmT1HPAchaes0YjnOCihkChrIJIkJJktjHlOgMDy3tMM\nEq2tnjszCCXxghKudNBoPGVzd6ApV2s0FhdxvSKYjIG+fnQWWWSsKqCkBRUpISxGNI4tgVAassTw\nvo9+lk3rx3nNi56L57soJXn6k8/iP2/+KQbFN394K5dddC4SxTOefC7f++mvlufzGOckotr8DT//\nxieZunMnrvZoLxoqvRUOLj7GqZuewvlbLuZJGy7kwORPOTx3L2G7y5rhC2nPp1x55aVcftmFLE0f\nJYoaSGFTF64bkGq46969aGk48rF3grGE0FKxgOuV6L/mjSzW2yzU6ziOw8c+8n6EEhQrAaXeMhtO\n2chJ5z+JF776Ol583Zv59T2PcMuvttJqNJhYs47XvOHtCBJOO+N0skzbFIZf4aH9LU4+7Ryec8UL\nOee8i6j19B7XnICVVzYa1nO22bJ8mTiOcvqgYTnbYgvzNr/a09tHb08vtVqP1bALkRMkLa3SkXJF\n3mh/ViO93xW6lJKMDA+ydmKcZ5z2uxjGAJNTs6waH2egf4BarUIhb4Zatg5USvKFL3+LsZEhXnDl\nZbiOgxCSp118Hv/5g5/Q7nb51g9+wjlnnEonjDnlxA2EtmB8zPdPFGcUfJdnn7eRJ588TlDtJ9W2\nwSeOY9qNFqVKmXKljO/5tKO2hbxltv/kke2P4vt+vjEZkjRBC0290eS0s87lph98g/f8xYfZdNrZ\n3PrTn/O9732fqZ0PsDi5m89/2i7yy+MXt/2KW2/+Ho/s2MGDd9/OtoceIW4v8pprryeam+fdf/7X\nzM7O8dTnvoD5xUXGxkbxApf6Up1VY2NMTh6hUq0wMzvPwMAAndSwqlbg3v1zx3WtKCmRQhImMQLQ\nmTUHarQ7dKMIEDieRzsMkY6Doxx837MS7TSlWPAxWYbnucRxRLO1mNc4BJnRJDojjkIyBK5c7n3Q\nfOxzC7zqjTv4yw/sgDDCalkEVnITw/p+hq7fwNrr14LqEndOpiA1IxvGOev1N1Aq1Wi2O3SjJkcW\n6tRqNdygRDdK6Kv1sP2x3Xh+kUoxYM+BJz7lLI9jiegvAF4BPCSEeCB/7H3AR4FvCiFeAxwAXpw/\n9yOstHJZHvaqJ3oBIYTFNksHV9l/kJQKKRQI27igtSaKG0jpoLAyOgykWYoULpok51QYpLIySSkV\ngpRCoUiSJpbkGEeILCLqtgmKRVSziVAuR/bvQAnJ2159Db6jiLMM13EBgdESqUC6Lmmiuf/RB/ne\nT29n8/o1XPnadwHwjte+nDdc83ze8oFP8M0bb2FseIB//PC7iJOES84/jVvvvBvgVOALxzInccdw\n7Xu+SE+txuz8JGeeeQIDA1X2HnqAnoECDzx4O6efdAnrN52C5/TSrE/T1nsZ6Btkpulw40234jgS\nnTnMT8YUixWkmyApEIcx3U6LQqdKpzFLqeDgei7Vapkgikjv/A5ZltGOY6qVMkenJnn7m1/NG9/6\nPg5P7aRaW4WSDqWgwc9++A0+/P73sHf/IT704Q9y5MhBPvfpv8aVcNPN3yHwy3SiLvff9XMKpRIv\nedU7OLh/D04xINOGI4cPHPOcgI2OZ+cWSJLE0gmNscbx+WK9TE5MM23VXKWitXE01hyk0+nS6nSR\nUuK5joWNZWlOTSVvkbfUwLTbWnndVSNDuRUg/PCOEcL5KZb7moYGByiXizhK5RZ9kTWYdhy279zL\n7Xfey+pVI7zl3R9GKsUfv+4VvPrlf8Db3vfXfPO7P6K/t4fXXfsi6u0Omzaus56qcXLM90/Bd9h/\nZAGdae5acCn09GN03h+gDJVihVKxxOrx1Rw5eoR6o85SvU6GptvsWBaMTnEchyRNLTPeESihKPiK\nnTvu47X449JtAAAgAElEQVSr/oR/+LuP8gcvtbHd2Zc+l8MHJzkxtvWEb3/9K2zb9jAf+MjHeN87\n/4TvfP8mbvjj6/nw336aTZs3sGHdWtacsgXHUfzHt75D2I1wXIcdu/awY9ceTj99C5s2n8Cdd97F\nnj17cB2XM888gzsnD3P+aJHAWTqu+2c5vSaMptVtI5SDj4fryrxPIjeMMQbPDYhzkxLXC1C6a8UE\nJrOdw3GIlp41FscGkyazbKGFhTp+oYBBEod1AqWRCh7dschzXrCbj7/1Kt70N99k3YmnMNhbQaqI\nTjuj00mYnz7KB1+7iQTw/Aqu4zC9sEilVGZuqYHruBSCAs3mHMNDI/ieQ6fdIopDHtu9l2Jw7A5t\nx6K6+TW/q3v99/GM/+H7DfCmY34H+S+3ZgsSpVwL93ddhAZXWelakuic060QEtIkw/M8q8bRFnug\nlEI60joNAWmmcaXEkNj8dKbxPQ+w3Iuw0wahkFmEySU/p512gjUQ0IKU1Dq5K8fS79KEVMOZW7aw\n59ffzh2vQhtNOi5ZGvEfn/oAOhMYkyGlIbWJV97/1tfzHz+85WFjzNnHMietMOIpp53Cm//ojXzl\ny3/DxPoJ1k5czP3bt9IIE/oLg6wZn0C6Fcq1GvfcdxeHDk5ydPoQp6x7FtUtqznt9NMRjuKdb3k9\nfiHAK4KrXOZn5+itVZmdOsxQ/yCq5CJyA5KZToOerEX/819K/cav0wlDhgb6mV9Y4p//8eOc8/Rz\neWT7g0zu3kutOsgbX/sW0BGDPT386xe/xG/uuJetD2/F0Q479+xibGKCa1/1RsoDo7hCMDM3zf69\nuzn7vIsZG1/H3XfcesxzAjaK9jwP3/Ps7Zp7wWqt8+sBK31znLyt3QYEyzJIwJ7SwKIBpFgxF7cL\nHgghcQpFWMpTL1m2cs0IIegfHmFyYYplqsU/fvFrvPdtb7DpQ6mtxaHIMNpw0qb1fOcr/2AhbMWC\nRRfn6OSP/uUNNBtN6s0W7XabTsdijmvVMq12Z8OxzgnA5jWD1FtdvNglSzI8x6dvoI8oDjGJZn5u\nnok1E+zZvQvXdWm325DAzMwMcRpb7ryJQBuEEZb7JQzFYpFCaTUIwVvf+V7e/q4/pVQK+OZXvsBb\n3/Q6PvmZzzM2McqH/vS9fPO73+X9f3YDm07cxMF/+jzv/auP8aV//xatdoe77ruPolvACPAKNvcc\nhiGnn74F5VmoWqlQ5vzzz6Ovr5cwDGm12vT09rKtmXD22kEePLR47NeKMbhuQNhewvMC0ixDI3Ck\nsqC5NLEeC66V6Vq4mSLstpAiQ2eKRMfWy1q51v5RsJL+Efnp56E90+jYUChXESZASsiymCRJeMvV\nZ1MoVCgUy2RpRqsbg3BpNbponfHsM9fgKkm1WiVKBQhNT9EC0wq+RykoMHnkMDqn+3Y7DRw3QArB\n+jXjzC/8PwY1M1gFg3IEWRaSJJoo7hB4HklslQJKWT9GDCRJgjaCOLbmzFYOKBBKYLLUYoulgzTa\ntr1rjesIPE8gjMO6VcPMzs2xsFTHcQsokZCkhkq5iM4kSRKhvAIl3yfOYus2JOwCIByNJw0pCkca\npCyRpjFJmpBlwnZoKrvgdLoJnu+gMAgTH9ecuJ7PplPO5Be/up/qwKko81v2H6hy1x0/odP0KJcK\n3C66VHt8qtUSUSYoyBqiU6B/eB1DA4M0Wktsf/heLrn4PLSImFqY4/wLX0Jjbpaf//CrRFFIO+6y\n6uoX07zpWzY/Grm0O4qpr/0rYvU6yvWjzM7PMz46xvzSIlt/eRfVSpFVIxNc/9o/oRAUmN6/nZ7+\nfoQbcMHTLua3D93O3FSbWrlMfeEo/+fjf8nZFz6dCy58FouNOaanJqnVSjjy+OZkeZSLBTzfW6FW\nLvu26mX/VsB13bwL1Ub8WWaRxFrrFWRAliYIx6pzpCCH0uXmF9GyzaHdFNI0JY5itNY8/Ulr+fKj\n969ILB946DHy4wDk3Zhgcuy1sB2zvodUln/T7XYJw5AwjOmGEd1uSBiGxFFkFU/6+DpjPddhoKfE\nAweWkKpg0cwSom5omTpohAN333MX3W6I9CQ95R6OTh3l7FrE948YpGcLtVpZow1HSJSjCKOQm27+\ncc7mUpBBpxPzsle/gTRK+NYPxlBC8bFP/AOtTod1J5/JwsKS5VelCfVm2/6sULiBSxJbx6o4ixkf\nG6VeX6JSreG4Dq1Wi1KtiMFYhEQSUyyX8bKUh5rHx/+xFoIpUigc5eBIQaJtz4UrrXG8MRqdWpct\nW5vJ7ClPeighrbyaZVeqDKNTe/rLUpQjyVLNA/vqqPIQmbbyWaNj2s02cbfDhvWrScM6CIWrHBzh\n0mjXbf9O1Oas006iWChYvlHYopNGGOERtZYQQjLY1w/1Ap7UTE0fpeAoBvt9Ig37j86ydmzomOfj\n94J1IzAokZLGlh+vHGmlRVKghbQLrACNQJsE+7YzG82jyQwYI3NEgj2u2eKLyNncEp2BTmzX64Xn\nnYEXBAgV5M9Zv8w/f9v1tqCLZVV3u5HFJEid/84MgT1hKJMSdhPaURuhl9+fQUqfLEtI0xilPHwl\nEdraHx7PSKKInTt30E73cPedP+LgXkkQHKQsBjh5w0ZWj03QXkhIoxZHp48Qz7kUnF42nLCeuaP7\nuOeOX/Dgb28jbi5Bpnnwnh0Ui6P09o2y7sTTGVq1GZNldJeWGH759Rbk5ihWlTKmjh5icGSQdX0d\n1j35ElwlOTIzzZrRYVuwIqFrDvOut7+Jj//th1BBESMd4iTh/3zq42Q6AimoDAwjUGRJytrhHnqd\nJqesGecPX/wC9u3eRb3ZesJ5+O9DCkEQ+LbomSMHXM+xZiQC4iQhSqxyYiWCzx2dfN/F91w8z80d\ns2yax+hsxSbOaGtRmITWe1XkNbUoSuhGMZk2rB7pQwVlmxwQy4GKRSgYk5d9hbTyXEeh8hzwMqWy\n0+lQr9s6Q6fTJU4scG3ZVOV4TJ8Bmp2Im+54jO3dsgXG5Rq4MImIdUJiErIoy8mfGSY1LC0uUagf\notWNMTqlG4U23ZHPwYqhixDs27sHN3Dz2oZCuSonjkgC3+flL7maZr2NyQyjI2PWqN3YAGz16Ij1\ncMawfvUErmP/X6+//rXsP3iQKycUC9PTJHEMEtIoo9lqEYUx7VabQrGI43gWQHc814mUpFmM5yiS\nOLI1GyQF3wdtkROOVLhK4kkFOsNxnfzet0gMJQVJEpIldgO2TC6DRhB2Y7qdkJlWZgOOJEE6PkLY\nOuIHXv00kighyuwG6RUCFpcaSCTCpHQ7HQq+T6fdoBu28QtFKsUqcdTAd31q5SrdsMFAr1VmDff2\noKVLUC5TrVToLflMzcwe83z8XkT0gO2EFQ5xmljMqrYcCWtoAWkaY4RcUUb4XmBlgLkdmJbWVWrZ\nVQfscdtxPFulJUM6LnEUMTbYw8jYamYXOlTLDq1FuzH09vWSGRshxVmGKyVKOrnRibC0QZMAMY70\ncVyB6WqMJ3CEl6d5rIlB5haQWMhXO2yv4JOPdRRKkvO2eBw9dJhnX9HDwJBh90OHWbu6xs4D+6l3\nWiRek87CKkbH1tDb10ffwCAG6HYa9PaUaYZt7t92N5Dg9bu025Jf3fIdnvHcl3Dupc/h0QfvoFzt\nQQ8Mr5Ae/UAxUa0w3Vyi21VM7ryd6pYLaDxwO7v3H8LzXVJdY+lok6ExH92a5W8/8Q+cd8m5PHrP\nPXTaEW6hRG/fINNTBxDCoadW4Oe//hG3/+Y3DI+Pc2DnAS578lmMq/XHfZkopfB83zo2ZdkKUyZN\nM8IwotXpQG5yXSwUKJeL+IGP69o0ThRFRN3QWv3FNucfhVGuq7cLW6VURMePay830OqGtMOQzIBy\nXIp9QzSP5JsBeWrILKNw7Ybk+hYgJqTILQwTiyMOY3vKyHX9Jk87FopFPMdZ4dMf60gzzc6wSP/4\nAFEcohBIR1IqFomjmEKhgFKK9lIbIQVhN2R+bpZq2OFwnOGZjCi1QZVdxDUI8JRLlES02h1LuFSO\nbVZMbYFaGMt7+vb3byYoBkgp8XNDDpOjJ/oHB20/i3DZ+sADnHbyOrbvOMhnP/85jDHEmeYlp1b5\n2c5p5sMYr+AjkdSbDc459xx2796DEILCcRhhg627CGOjdiNMTgQVpEmEkh6u61lKK9a5znUDpLKB\n5HKtJcms2s5ogzEpUWp9qRH2lBgUA6SSeQOjxJOSxXabbqtBmqXExpqDa21YmJtjoLeXTpqgBIgs\noVSoEhR9GktzdDodRoZHKHdLOJ4lkWo8Gq0GaI2QMDE2SKuxRKvdYnqhxaqRkWOej9+LiB5yuJSA\ngheAEWgkxkh0mloUgrCoX2Fdwe1EJpllBWBIyXCUdXoChTDk+uUYoRSO5yIweI7LwNAgMzPTCCFI\now4G69wk8mKedD08AdIY4iRCp0nevJXmN7EkwzbnqEKRNE2we6YkSyOMFHgyI9MRUWibM47XBzSO\nJPNTRzjxjGlGa8McvltRFCdweG+dLC6wdvRkzjnlMk48+UmMrR6n1lOl1Zhj/+E9fO+mn3L3zp+z\n8/A2VGmJxLRpt5s0WztoNOc4sG87vgNCuaRoFh7YmrfsZ3RiKPXUGPU9No6MgIDWQ3dRKBXQ2jA2\nMMjiwhTDwwNMHV1C9Y4ghOaeX95FohWOG3DJRU+l066zdnwDxmiUV+LEEy5A+bB/x2M0lmbZduBR\n/vmmrx33dSKlQDlqJd2njSHNLCWyG4a025Zs2e12bWFRCDzfw/etVZ/nuShHrUTbAourjqKQLLWO\nWr7v2U0EG6kbDFFiI3qNjdDKtT6WJZYGm05Mk9R6HGudF3tdXM9Gwjp/j51uaLtyl/tE0hSts5VF\nslgooI4zKPBch0LfIJ7rERQCMpMhEYwNr6JcqZAmKa12kziLSLOU+YV51sl51o/0cM7GES49ccjO\nhQX82FSH42KkAZ1RKBZwpIMrHaSjcF2Hgl/EK7j4vm/lyYbcojFXueQ6ukJQIs0dnvzAZ8eeSUvu\n1JZShTHMLDZ54ZnDJJ0GSZTg+C6jo2Ps3rWbQhDguR6l8rEXHu0QKz05nuPZ4rhU1sVLQBxH1rzb\nQJLbLZJv9lEcgbJ/q6Mci5s2uZuZsC5mmTZEYUSaaQq+i+s6RIlNv73uspOQwhB4Lm5+DRSDgIV6\nHZ1o4jjkqqdsZnHpKO2lWYqFEq7j0u0mOEFgjXJS6IYdRodHrIgkyTg8NUurExFlHuNj44Sd/9dy\n9AaMyXCdwB4djSBLQ9IkIfB9jHTAxLkdmN2dlnOGtnIOJoNERNYj1IBJE5Sxbe1oTZLG4BVARAwP\nDjG32KW34tFaWEAYw+f+9i/pdLvEiQVlZUlii0RCI2SAyWy2Nk4TpOcRdmMgP/JnGcZJUbmLVckL\niMKUJMuNMBC4x7mljvRX6K+uxZnfgnADekZbLC3WufxZF/LogUXA8jZ+fcddnLh+Pfc88ACVagmq\nMLZWknU0pR5BubqKB/ft4sSNm1nsNqgvzfLbX32DuSNtevr70WlI98EH0FrT6XY4MDvP6MAAmzet\nZXLfQfqchChVCFMkSVscODpFrVZl/65drN+wijjr5AXxBK09xkYn2LPrMRxPMLMwQ6lQwpMud93x\na7Ru0WnGbN5yEod2T/KKV7+ZD73/Hcd5rRi63dByagCTaTqdkHqjaU93UlIslqiUrYuT73t545TV\n3oe5x6sgj0oF9v/j+VQqFUqlIoXAt97E2bJkUyCVi3JcNBlKOVzypHV8Y/u9NlUooNFoEwQ2leT7\nPo7rgBA2tx/HK5r/MIxWirvaaOsqlaV4josR1tt22R7xWEczAYNdqD3XJ1aWwHpo8hAjQ8MIbWh3\nOwR+gaXFJa5aKwn8VfSUixydrbN2uMZN2+dsr4l0SdKUciHAGIPj+oDhzC2n84bXv5KZWYsNcejw\n8GMH+dy/fAHf8cmyDNd3abVtc6LIdeRHDu/FdwO6UdcCAo1BOYoszfClTaGUCz5zi21ef+EqvnR/\nA0c6NOp1lOvkfQyQJceX+gRb1zP5PauUQ2rAEQqVF8zt8xYvTpYgDMSZtjygNEEYa05itCaJUzzX\noZkLOCrVEq1Wh1q1bIuv2iWLu4TdJrWeMsVSmXanQ6VgT1WZNvT3D9JozJFpuOS80+h222RGMjl9\nhFq1Dx130cYw345Z1edwaHoOaTJKvqIbdohMgBf4lJRhsK9EVDx2Rv/vxUJvW80lcdIBFEIoHMfF\n9QLiKEGYhCTJcF2J7/rESUyYJnjKapI1tugijUTnJwOki0Fbxrcj0VJBHGGExFUanSXEXTuxrufS\nDjsUAp9u0rVZH+mRJSHCcdFI0jS2aSTpE6cxYGxkbwSe69qTh+viGdfy4eOQdtJBJxmB74M4vqmO\nY82GVasYHq4wNjrBjr17OTJf5T9vvIVt2w6gdUan1eAFVz2fWq1EVxaITR26CbOL8zi+wQ/6WT+y\nmfUjZ1EsF5hvxdy37XbiNKPTbVGplVla6PLU9/4Rhy+5jOKWE5gc7WNyZoaSLxkaHMTvxkRhxJp1\nJ9O/YSPf+fq/WLyugE43RCqPQsHDlwU0gvr0bjqZYNNpJ3Hf1m15uiKiVinzlHMvZ2S8n699/Rt4\njsv2nY8c97VigCiKieMEN4/IumHEUr25Ai4rFYtUKiUKhcD6yxpjZZXNNnEUEUWhjZ5z/o2QEuW6\nFIoFSpUyjpIU+kfozFjjcmsGYnPt2tj00VmnbODrmJzDBPVmE9fttY3wjsJx8iakJCUKY7rdkCi2\nSGW7OVhP1ySJyZIUJQRoRZqS9wQc+3AcBykkC/UFKpUKExMT7N2/D2JDY6lBs92k1W6TJAmV+kHS\n3n4e2HWU8eFeZhdb1DsRRSUI8y4Ex3FI0wQpLGdqabHOnVvvYuu2+xDaQgEd6ZCaFE95CCXs45nh\npM0b2bNvr01X6YwDk9N24TdihRCn8zpGb8GlGyUIAWvGhnhs3xS+9DFZjHQVvuvjux5RGudso+O9\nUkApD4E1c1G5B4DWtr7nOA6CzHpaSEtvLUsfLRQ6i0m0JtEKjSBJM5JMEyWGoT6fxaUmYTeyHHvj\nUSx4hGmEVA6TcxGjgwM25YPEVQbHkcRJnNcKEzrtJVphiOsWGR1eQyu0pwhfSVYViqSZoRhUyIyi\n02zQSQW+J4naEUoKtu+pc/L6Y2f0/34s9AibSxcyL3rY42OWpQiRkmFVBFI5JMYWdlReMNEmxVG2\ne9aYDIF1fI+imMzkrByz7NIO6JQf3nY/BUcTdixrxQ+KKBzSzGJbm60mSmZoU7RHbJ0Q+AWidh0t\nrUKDLAPXpndMJvEcq+PW5DZjwh4VjWtPIFF6fAoT3/eYbXf5yAe+yWJjkdGJIdr1I0xNJfSUBoji\niCufdRWLi2127t6JQTB5dI7A8zn3KU/msV33sWvbIej4jK9ZxcjIKOFcSLU8yt7HtiNw6O+vMTw4\nwGOf+hL7P/I+4m4Xv1AmS1Lq3ZD5epNTN01wsN1lIVxig5vh+T5RFLJqZJgoEbgyQMuIxBjWbl7D\nI9sexXM89u7Zx0h/LyifNEu45upng+OhpCRLuzztOS/kJzffeNzXynIxFrCGHXmEXCgW6Onpoben\nSqVc+h3ELImJw5iw3aHbadvUTN4dq1zPeo9J6zGbpimdlv2ecHEasKfNQmBrAr7rIYXEUQ6lom9P\njnnEr5RDoWBBcFEUEXbD3Bowx3nk6i8lpd380pQ4CklzJY/JHCsBfFwR+XiG67gExYAsyzhy5Cil\noMT09DRKKMIwpN1qs8ldZNW6fvZPL7Fnrs3RpS5rBsqM9Ja5eLPLzY/OWC8GYVsXQRAncZ7SAVuG\nT/FcjzC0NQyBIMkSil4RFJx79lO4+ZZblh33CDyfRa3xA484tiA5FwetNSev6qFU9Gm0uuzaP02m\nDWf0GB5sdalVK0RxRrvTQcjjjpNAWE8Bo2McIaz6SGdkSYpUDnESoTKXwLMWjLZZ0rNrShoRpzFh\nbO0m/cDH8xTNVmRP9VrSV3FJih5KTJHFMYnvIZ2AoFjmSz/ZxukbhzA6JYwNF504yMNzGpVleQFf\nUSj10FdTCNdHSBc37jI5M025WMCVkma7QaFYw/cE/T3jLLVaNDsR/QP9RO0WhZJj18xjHL8fOXph\n1TPCmJXd3hbYkhxbkBs3mIyo07HwM/RKPlVgLdQ0CiMEUX5UU9JYFyqsiYaSBiEVX7/xF5ikne+4\nlnGe5Tl4Vzl4nkuaxWAyGzUKQZJkGCFwpL3RhZJI4aGUgxvkloZSI7AyLCUlnTAkcBXNrs0bH8/Y\nf2iav/67rzI/HzE2djIjg30YVaBYKtFOWnTiiCiOKfXVOHB4jt7VZzGx/kTa3Q5r16zlgjMv4sor\nrmDV+AgPPfIIn/7MZ/nRf/4bz7vqGjafcgJnnnYyQ9Uh5FOvxvvGFwgchyyJCTwHz5c0uxnS8Th6\n4AAnr+lh6eBeHr7nF2w68VwcqZiam2OxXKX3oisYGhjHkLHz4b2U/CJxt0WtWCQxLkenjrCwuAiO\nh+cH6Bw3fctt38EJjj/OEEISBAGu5xLFCUvNBmmWERQK9PXUGBoapK+/n2q1SiEIQGuidptuu0W3\n3bR6e9/D8z2U6+B4HoVCgOu6pHFCq9liqd5AKm+F3nTq5vUripjA82zBXxuc3MrNYKhWygQFq+IK\nuxGLSw2mp2eZmpqm3qgTRdaiUknb9xGFHaJOlySKVhb3TFt+yvF6xgpjSFJLX3QdFz/wyYtJzC8s\nsHf3Hp450qXPtzWNxW7Myat62TBSQ0jJrsPzJGGEFCLHkUCaWK9dpWxXulXkYFU3ed9BwS8gpURj\niJKIOIx59LGHMdhaytDAEDNzs0gpSBPbIbpcuM6yjMCRLNTbLLVCXM9lqL9K1RckUUgS20g/yzJc\nz0Ue51IlAGW0LSAjbROlMSuSW+W4pGmMNinScVDKI00idJbRjbp0o9CSJDNod2LCMMF3BT21gMAx\nJMaj1tPLC7fUSOIWncUF4qiFQFCp1XjDJ24kywylYomnnHUaruNhdILCHmzSNGOh1SVLUkhD9h04\nmvN1XITyGBvoR5mI6bk5Gq0mOu7ikNBYnCGOOzhSsbQ4/0TTsDJ+PxZ6TJ54d/DdAGF7npBS4To2\nleMphckEXlDKux4dpFTEYWKPkl6ApyxDXkmrHZZKYVIN0i4QRgtu+fV9eJ4iyewi341tURVsBT7J\nQhzPwRibY83SCGUEUoco4eI4Aq1jDBKl7GuJLMbkONQ0jewxL+5SDhzacYRSAh0du0kAAMLwvBdd\nxzOf93Ke++xTeP6LnsvckZBOXaMTyStf8Bwq5Ro33vgjTjj1NM4761TOOud8Ln/WcyhXSvilEnP1\n/dyz9VdEyRwjYz2csHEjN37r8/ilBgfnd7Fj9w72/ssn2L59O41Gg0wonnzBpfzRm9/Luo1bODw1\nQ1sWmDo6Q6kYMDM9z+yurax+yfUAPPOeO3js0bvZf2Qf1b4h0rhLq9VCSMXM7CLTU4dxhSRNE77/\n41tpNOtMz89z2pbNeL5H3ImeYBL+v0NrbQuuHWsdqJRDuVxhoK+PcrmI5zpgNHEY5ot7x+b0M6u6\ncj1bmPWD3xVnbbOUJk4Sm2KJEoLeQZZZfSesX5NjjG1KxnPzj3LPymZQLPjEUUTY7dJud2i3WkRh\nlzSJyNIEnWXW+SqP9KMoIU5TUq3JtFkhakqljlteqcFy9V1Lac0yq+nuqdXoNFv4ZHTChL3TdY4s\ntFhshNRbXaqlAN+RnHPiKsb6KytpDTd3dTMYlJIkWWKb0aQ9aSeJVbQkSYJwBIH0MVqTkfHwww+D\nMZjM8OpXXEccxyRZSpIkGKMp+gWryMEQuFbeeObJqykX8gY4YMBJyBLrElcKirjSpuSOZxgDqd3r\nbI4/jTHGnrjRmoJfxHc9RJYRdtsstRvEaUKz26bVjfI6g0EYTa3sE3gO4BB2UxqtGNdThN2QjetG\n+auXX0C326LdaoOBSqVIT/8AN3z2NprNFtWiVS/Z5muBlIqC79PX2wdSESUxY8M9uCTMLy6hpMQv\n1iiXyowNDlMtl5hYO0F/tcKa8dVUaoPoXM9/rOP3InVDjiJGOaRphnJchJI40pAZgyEjzQRSGit9\ntJgbQFi5X6pRWIyoo6TtShVOnkt0yUREkqU4TsCXv/crPNOyxcMsQ6kCxWIBhZXAeULZ4qLQpDrF\ncVyUK0gjCa7VimfaHr8cDDoLiTKJkAnSGLRIMdrquDud0HrF6tQWgo9rCL7/3X+nVK7ypHdcxCOP\nPooSgnKpyIfedR37Jud44DcP01vr4dH77mJy726SNOWFf/AypiYPsthcZN/kI/SPBSg3IFoq8Ixn\nXE5f/yjdMOUfP/1XLNUXKZdK+J7P6JpTQCj2H9jHnn37WHfCiTiOw+xShC476GSRcqVKGDeoPftF\nvOjvP8jD9+4jeegeogyysIPGQacJIz0VlJQIUQHXkLVa7N+7j2/MHmVhsc6nvvQ1vvqVr7P3kbuO\n+1JJs4y5/8vemcdpdpV1/nvOXd/9rb2qq6r37qS70032kIQlJBABkbCJCDiIC4osAuqMyIwLDo7i\nOCqOoigogsgiRiCJECD7vqe7053et9r3d3/vds78cd+3qnpJ6BIzCf2530+/Xe9y3/ee+9xzf+c5\nz3nOuXMLND0frTWumyKfy1IsFkilnFiYvCbVSoV6pUa9NRlJIbBsF8txsV0Xp7V8cRgE8Y3FwyiO\n/QcBlmVxwZoeHhg9hGWaXHLRjtbNvHVrcp6B49hxxlZr4FRFEbVGk0ajQb1Rx2s241tUitjjRkV4\nrfRKPwgJwpAwUoQ6XnE1VBpLg2XZZ7V65al1JQoUKlRoIxbj0I/DWflCkRNzc9y/d5RVXVlOzFTo\nzLl0dWSoewGTC3UqXlwWQ0g0Ai/wcS0HrWU8iU4pdKRRUqGCeM6J4Rh49TiUIYlXhFSBYvfevXGG\nnDiYOtIAACAASURBVNA0g2acJhjGSQqhH+fmSxGvGZNybfwwYnKqhGOZzNQ9ugtpXrze5aZDdXQU\nIbJx5kt7gbizR7cG7OPGROtYO5SSmFLGEQQBjWaTqLW+vBcGeH6I1/TIFrMYmXgS3vz8AqEfMji0\nCsuObyM4Oj5F4AdsPW8Td9zzEH/0S6/iv/3NdzENGxUq0m6GoBjxxN6jXPviCwmbdXDiG5hIw+CD\nf/YN/uCXX0uzUSbtOhTyBaIwYsv5Gxg9cRhRyJHPdyMtK+791ZrY6Sz7Riv0ZSGTSi1mhp0NLwih\njwfETFASbUDT83Bdm0gbaBQqAsuSaAVhqJDEa+NIk9ZyxhpHtiZWaYkWGgiRWhBEHkKCiYUX+PjV\nKbRULFQbFIu96KDJqp5OQgWGBJTANgUqVJi2TRgG+EETsJBatdZCD0iZPmCgiFDoVlZAPPgbRnGF\nzuTTzE7MovwQu7BCoReCzVteRHV+jHpFc/TIOFu3bOe9b3slKUcgpKLmj1Ps7KBvaIhNG9ez96md\nGLrJw0/fj23XuOyit7N67Sb8Zrw+hpYWSktyxQLvevdHOLL3CE8c/FfWD16GIQLqdR8h0sxVPR66\n62YcN01HymV2foGh/iLzpSqDHSnkR3+K2z6oUDoEHWEYgkq1xnk7NlJMDTE6ehijM8Qq91CePkom\nVUSpkJGxCaIo4iO/9AvUGtX4fqkrRLXvDCUg3bpXaz6XIZ12MYRAhSFeo0GtUqVareOHYbxsr2Vj\nWPFkKWlIojCkEYaxl92Mw2BRKzb+8JNPse+xR+hKx3coKxTyRFFr0E6AZZo4rs3PvvE6/ukLX13s\nRURhQBT6hL5H4PutO0zFk48CM87AanoBhmViOQ6WHU9gAuLeZ2s9/JUKvVIKN+MiTYGQ8d2ztKHx\nGh7CiEMJ69IivluSbWAaksAPWWjGd24a6s4x2JMnZ81SieLebxiF8ZLJoY4XEowCDB17n4Y00J5G\nSEHohQgjXlq5v6+PmldHh/F8gs9+/u8JwyA+Jl+DoXEdBz8I0KHmW48d5+euiZc+7urI8PTxadKW\nQVchzWXFKg/Og1eP4/mmtdLB2HiyJFqhdGsmdOAhMKiHAaYUoAWGZaJDhR+FhEFEyrVoND2q9QaW\nKQlCjZtO4xYdenoGWJifoyOfwZD96NCnUq1y0Y4taCz+93t/nA9/6t+wUxkc1yWXdvjnew7y1tde\nw6ZOydG6AMPGFgZBEFHMuDRMTRT6TExNEgYeo2MncEwDpRXj0+NoIVF+nWImSzqbJUuVZkPTaFro\n4OzDwS+Q0E28PgQyQmiNbUlEa1EmIolr2/FKezJeeVJHEYYdNwLSMnBNCKIQS7QmT4UBISJOx2wP\nykjFffc+SH8ui6vAtl38wEMIxaU7tsR5/IaJtGU8OQoVdzuDCEHcOzCkRCuPlBUPKioFwnCgtQiu\nF/oESuMYgmImRdD06e7OUOjOI8TKZvalUg6b1hmgF7jnrl3MTo9x2fo+3JTF/pFJytEtmOkK1doC\ntfokxw4c4ZLLL+bO+2+ktlCm2fCoVsYJvSYp22ZocDUDQ8PY6SxK2BS6Bti8tgciQbarl007ro9z\nhJXP7MQR3HRA30CefDZNV0eB2VKNlGsyW1dUGhEdv/JReq6+ns6ODhzHIZ3KoJTGztq4GUinXcpT\nR3Ck4IrrXknf2vX09PQihKDerFModmOt+OKNnYKU65LPZinmcxTyWVKug9mKffvNBl4zvjm2QsT5\n0JaN7TiLg6pEEbVqjcmJKcYnp1koV2j6PodHp/jDv/h7bvz6TWjAD0LMlgNhGCamacQrXrbuD/uF\nf/wKQgjc1hIKliHjqe5mvFhao9FkoVxmoVRifmGBcrVGw/MwTZNcLku+kCdXyJPOZrAcG9FazG/l\nNgERgePEE3gkEsuM774VZ5MI9o/M4Qch4/N1hnvybF7bhy0l6/qLWIakWvfozbRm1bZmAxstwWkP\nxoZhGK8dpOJou2M7mIaJoeOZssNDg8SzVeJ5BCqKw6foOCgjpaTpNQm8AGkYVIL4eu3qyDIyWWL7\nxgGUUpyYWKBcrlEIStQbtbh3GK1wCQQ0BnG6bRh4mAKCMEJrhWXGkYMgiBefazSbzC3UqNQaWKZB\npCWeF1Itx7dlLORyZDIFjh07RKVeZWRiiijUaGHFx2WYmIZNEMGvvulKvEaDRr2OjgJsx+bpQwd5\n/bUXE+nW+JJpY9omloxn7vpek8H+1fR0dpFyXHylGRk5jNcs0dPZSXf3AIblUq5U8UOffL6LVasG\nSGeKZ22PF4RHryG+QIx4gMS0LILQw5ZplNkk0hGOKVEinjFrWEa86JIShCrAEkZrgEODEd8tSuh4\nyQSpFZq4y3/TzXcglSbn2vzKz72dj3/yL8mn02zdsonZUgVLgpPOUirNErYWdbKt+M5FrmlgSIEw\nHUyiVlonhL4Xx0VVs3WrwzhnP4gCVODRjEy8QMVe34qMojh0/CFefk0/Dz88Rl92DZdeeRGGiLj1\n7ifYvkPFN9WYbvL6q95MqD3uffC71Btz2CpLuig5fOwJhoa3kS+sQgtJFEVUy3OU5ueRpsOTe/fR\nW9zCE4/fwuzUXq56xQ2cODbC04cPUq85aLGAiDppynlWb9nA0aePYkuLer2B/anfpzg4zMZb7uW+\nSzeSyXYxOzrOy666ngsu2MZTu3ch5ARKKSYn7iGo2tSbTfKFIkEQcs01L8UwHP7+7/7viswihSDX\nisW389XjWbIhgecRND2aDS/25KElnhJptG7aHSn8yKNarTIzM0sQRfGCb7ZDdyHNDa9+GYePjjI9\nM4eUEtM0+dO/+tziKplB676xpYlxIk286qopefCRJyjmskgpiMJ4ULXebOD5IZGtYvFvTY83LWtx\nLR4ZRkRGvA4PsHjrzJUhCHUErcHSUASoQOOmXdx0Cr/hsdrpZGahxsUb+hifqzBTqjNb97BMA0tA\nRy5Nb0fAoWoJQbxulCEk0raIVJwtYgmToLW0sRZx6qg0494BwN3338/H33wtH/vq97jummt54OEH\nsA2LSMfLO7QnuQkpsEwLLwiYK9coVRt0FzPksy7zC1WGB7tJOSZrBXx1bx1VjHsoKzOJIFIRYRiH\naJTvY0hBREDkB3FGnlJ49Qa1egNTQLGYp9H0SDtQb2gKHd1I2+XAsWl6CjaG7ZKyHKq1KjVdJgiJ\n72rnOjSaNeZK83QUHFLao1QLyeaKoCW//hff4g/f/wYa1Rq2aRBpQej7NCOL0K+QdmxMQ5BOZzEN\nCEObYs7BD5qUZiawbZfu7i6Oj8yhEFRqdRSCxgrmFoh2jPH5RAhRAfY93+X4IegGZs5iuzVa67O6\nLcw5YBM4O7uctU3gnLBLYpPTSa6f0/lPtckLwqMH9q1kqdoXGkKIR56D8v9I2wQSu5yJxCank9jk\ndP6zbfKCidEnJCQkJDw3JEKfkJCQcI7zQhH6zzzfBfgheS7K/6NuE0jsciYSm5xOYpPT+U8t/wti\nMDYhISEh4bnjheLRJyQkJCQ8RyRCn5CQkHCO87wLvRDi1UKIfUKIg0KI33y+y3MmhBDDQojbhRB7\nhBBPCSF+tfX+7wohRoUQT7Qer132nY+2jmmfEOLHVri/xCan7y+xyen7S2xy5n0mdjkV3bq/4/Px\nAAzgELAesIEnga3PZ5meoZwDwMWt5zlgP7AV+F3g18+w/dbWsTjAutYxGolNEpskNnnubJLY5Zkf\nz7dHfzlwUGt9WGvtA18Gbniey3QaWutxrfVjrecVYC8w+CxfuQH4stba01ofAQ4SH+vZkNjkdBKb\nnE5ikzOT2OUMPN9CPwicWPZ6hGc/2OcdIcRa4CKgvcbu+4UQO4UQnxNCdLTe+2GOK7HJ6SQ2OZ3E\nJmcmscsZeL6F/kcKIUQW+DrwIa11Gfg0sAG4EBgH/uR5LN7zQmKT00lscjqJTc7M/y+7PN9CPwoM\nL3s91HrvBYcQwiI+If+ktf5XAK31pNY60lor4G9Z6kr9MMeV2OR0EpucTmKTM5PY5Uw8zwMSJnCY\neHChPXCy7fks0zOUUwD/CPzZKe8PLHv+YeIYGsA2Th44OczZD7IlNklsktjkP2CTxC7Psr8XwAG/\nlnjE+RDwsee7PM9QxpcQL5u/E3ii9Xgt8AVgV+v9b55ykj7WOqZ9wGsSmyQ2SWzy3NskscuZH8kS\nCAkJCQnnOM93jD4hISEh4TkmEfqEhISEc5xE6BMSEhLOcRKhT0hISDjHSYQ+ISEh4RwnEfqEhISE\nc5xE6BMSEhLOcRKhT0hISDjHSYQ+ISEh4RwnEfqEhISEc5xE6BMSEhLOcRKhT0hISDjHSYQ+ISEh\n4RwnEfqEhISEc5xE6BMSEhLOcRKhT0hISDjHSYQ+ISEh4RwnEfqEhISEc5xE6BMSEhLOcRKhT0hI\nSDjHSYQ+ISEh4RwnEfqEhISEc5xE6BMSEhLOcRKhT0hISDjHSYQ+ISEh4RwnEfqEhISEc5xE6BMS\nEhLOcRKhT0hISDjHSYQ+ISEh4RwnEfqEhISEc5xE6BMSEhLOcRKhT0hISDjHSYQ+ISEh4RwnEfqE\nhISEc5xE6BMSEhLOcRKhT0hISDjHSYQ+ISEh4RwnEfqEhISEc5xE6BMSEhLOcRKhT0hISDjHSYQ+\nISEh4RznORF6IcSrhRD7hBAHhRC/+Vzs40eRxC6nk9jkdBKbnJnELv9xhNb6P/cHhTCA/cCrgBHg\nYeCntdZ7/lN39CNGYpfTSWxyOolNzkxilx+O58Kjvxw4qLU+rLX2gS8DNzwH+/lRI7HL6SQ2OZ3E\nJmcmscsPgfkc/OYgcGLZ6xHgilM3EkK8B3gPgO1mLukd2oQQAtXqYYjW/5YhCSKFRnPGzocGxLN8\ndvKTM3Lqp2Lxfx3/fHs3p2wrzvL3AIq9q1mYOt5c9tZpdlluE9M0L8nk0wRmJ2npEylNpASGYaD9\nKioKUQqk7WKaNjRmaCoLHYUIKRFS46ZzaAyiKEJHAUop0CGu61Kr1Qj8ACeVRasALUyws4TCRNXm\nMJ0sGAa27eKHIao6y/CqHmZnZ2nW64BGSgMhBEEQYNk2adchk7IZnZxFCIlhSMIgpL+nA9M0GJ2Y\nIV8oUCqXEVJiOy6+13xWm5xmFyd1SWff+pNPggYhnu08PsN50oq50X04qSzZ7uHFH1uYPAZhE6UU\nncNbT/m9U37j2fZwhg+WvyWlIO5RL9Wszr7VlOcml3/lB9rEsu1Lenr6TiqN1hp0bBgRf2Fx30LK\npeeG2f7B5b8N0jj9QE+5Hn4QYvnRnmaLdrnO9LFo/1ukd2CIqfGRs75+DMO6pKN3mCj08b0KgdcE\nNIZpkSsOYGUKJ+2jfTwnlUOffr0/43H/RwMjp9hFCE4+drHs/RbNcoXALyOUx9zM/IzWuucH7ea5\nEPqzQmv9GeAzAEObLtIf/vM70fH7aARSCHoLGTYPSzauhi/dWqfmBSgFWimU1u26F/8eGq1Atc+O\nbot/+wxoTvkC7Qvj1JMsWtYWaNRiicXilvHHcUUVaLQAreLXliXxw6i1tUYpTaTh6Qdv4sZP/WL1\nbG1S7F+rN17/Sxj5Aa4emmLvvTfjXPUx1kR30zyym4kyDGzcxP7H7iY/uJ1jj96L4Vi4dpaqN055\n2qdrdT8btqyir2sH816KvQ/8O9s29TFfrnLi+DGaDYGV76JemsLzBXTvwAtDjHQn2ZTCPP/V+I99\ng0985G3c+OUvctf+MlecJ7jhjW9h98P38q2bbsJJ55mdnWLzRZdxxXCO++9/lLCzwPt/4Z2ct2kT\nv/HfP44APvJzN3B8dIrSQpl3/df/xfc//6f85Vf/nTGv+aw2OdUufWsu0G/72L8ihF4SEtE+Z5wk\nHu16JAAlQOr4vGnd+q6Ar/zmS6g2arz6dW+nZ3gbN/7DJygJSU8xTyMIeceH/hKzczVStxv+Zftc\nrEEnq5VYVg6BQKJRAoTWpF0Lw4CmFyJanyMFsvV7Tz14C9/8zEd/kElOssmqwWH93vd9BNMwMU0L\nbdoEXpMo8GIrCIllOximhWWa2Kk0ViaPblQxCt1xhW61lFqAlBIhDbRo13A4yZNqXXtLjdTJzhAn\nnQuxdG4ECCEBgSHEon1E+/yJ2A5SCISIGyMh4nN4353f4Y9/+8Nnff30DKzXH/jTe3ng+18k5c9y\ncOdtXHTd28kOvIj5vXez/fUfwenPQNQquF46xEXxP8N7p8mIXjp2vey9lv+59GUdv1YChFyqI1oA\nEgwZt63SBMOA+YN7qB+/A6dnI6aRJrfqQsxUlmYTbv3UddRKc2Rzk3z7mxx7Npu0eS6EfhQYXvZ6\nqPXeMyJYdD5ASAYKaa69zGDAXtrmA9enuWUXPD1ao+kFCNWuaEt+hl7WrGqILXtaS9u+GlvVUrer\n8ilN57KLtFUdESLeh5QCxzbYMFCg0fDJuA77RueRhuD8wSKluo8A8lmXXYem8SJFrnMAYNkR/QC7\nSIOulMFoEDLTKPDiV/86T05XOVruZJ2IGFr7CqasTuriKPO776HerLPlyndTPXgXI+MaN5ujMjGH\ndeWbeXr3Y9RnDpO2Jccmqrhmk5nZJl7TI5qZwTQF2shRaBxjw+bLmVN5pp/8EtcNRLz9197Ir3/i\nExQdg005ySMP7OHBB+5Dawk6ZKizByU0I/t38ZKhy5gr1/jwB36Z7976PS7Ycj6/8l/ewv/5239m\n7dAaTsxHjB6d5Hc/+G72HptE2CmgfPY2aZ0fIWVLJNtvLWus2+cIjRZyUdCFBi01snVGaWnbm//n\n7dz4O6/kjpu/hJvNMTs9TrbQSSPwsCyTJ2//Epe++aMICWiJatUU0VLypdhn+2pu1xZ9UgNkCMil\nLC7d3M89u0cXBUwDpiG5YLiT6y91+IK+gBv/yl9+xD/4+hEC07RxM1ksJ4Wb76BZKSN0RNCoIqVB\nqqMHw7ZRvo+qlzHdNEHgYVgmwrCg1YS1GzLRVqPF60ogWgKvTu0+n8GbFWLpepItMZdtB0qAbjUA\ncUMcnyMpZNzgiSXxbz96+1bBCq4fN9/JLf/3Z+nsW0+DAM+rMjs1ysLYU1z9M5/EtlLxvkxQGlAs\nivii566Xjk1qOLLnadZsPT8+5HYDsExi2mJ/kn8pln5HCzA0YLSdjtZvSzCsWOBnD+9h712fo3rs\nK0SRJIxCunsLZNe8FUuPUa6ECH2MTNqjWn3m3uqpPBdC/zCwSQixjvhEvA14+w/6kmEIbrg6x7rc\nM29z5XbwwwwHxioEoUIr1fIuxOLFS8vzEgK0Xt4xE8ua2Hg70e7zL6vMhhQMdOaoewHVpo+K1KKn\n8caX9fDYnibr+lwsAVvOh5RIMVuHtf1dIGGhBLZhI4Rkw2qwRDePH55leOOFAO7Z2iUMPI6lNlC/\n66+4f/P1jB49jOF0YOQGuT/zFqLUAANP/gW5oIL5ovfgjNzH3PhRpkcn6C7a1KIGHZ1refK2L3LR\nS9/G0ZkTzJVLDPZu5YnHnyRrS3K5LDOzZV558VYePXgEx6qj5x9BLjTYONDP/PwCj+zcxbt/8o3U\nyvNsXr2Ku+65l96BYXbv3cvkzBz/7aMf4zc+/KsMrerlxtsfxXVt5k4cwrIsPvuPX+JVL7uSSy96\nER/6+J/zBx94K7d8e5QgbVENItJpd0U2aSOlsaxdjp9IaAnnkmC1GwMtBFIrFBKEwFjWX7bdNNtv\n+O889M0/oTxRRhoS2zQxLIeuzk7GDu9m7smv033xWxEa2kGP5R57G3FKHYv3rxcFLVRw71PjIOWi\nA5FPmVx7cQ9Cw9fuLFNLr8f3PVZiEyEkGCaR0njzMwTNBp1rNuCmsi1PvSXQoU/QqOI3SgTTI8hU\nDsd2FsM3S4kZou0Dxb0hvdzLbfek9Un6viiOtGzUMlC717L0Vy41gMu9d7HkvYt2b1m2nwu27ljZ\n9dMoz7L+xVfhDl3Fnjs+S60eUTl+gPU7XsHYvkcxtGD3w3fzmvf/Dpaz5N/ROt62R952QlUEj336\nnWz4q0diO7Ak8otCr5e+v9wu7S8oEdfTRSvLWOS1gvrYHHd85rUszB0mkjn8Wki+WMd2LEbHffK1\nf0BoE8MMsFlFU46i1fMo9FrrUAjxfuA7gAF8Tmv91LN9x5CSD74694wjww1grBS3eMenG0gpFmNZ\nSAFKIzUoBLplfX2qsANLtfeUYI2GC7sdGlNHacxO49QdDGUykE/T0dmBF5p4dpbOYI4n/vQ15F73\nK9zwnndRIz5xnWlIDcNcGboKMFeRWAbUm+DYgqxrIIQDcPxs7aL8JsFdf4s9dBmN+VFGt78K88iD\n+JUx7NkjmN2rebrzNWzvv4/msX+muO4NrNuwmW888O8EoUN37ypKsxNYjs1Td36J8ZEJhjdvB38C\nuzGBNi3e8Yvv43WvvJZ777mTwvDTPHTfPTz9xD5s22TBMNhomtz1ve8wPzdHEPhEUUQQKjrzaUq1\nJmEY8tGPvI/y3CwHyzOUGgE/fvVFRCriQ+95Jx19g+x+9CHe+tqX8XtP76USGnzi136RX//jT7Nx\n82amp6ZXZBNoeXnSWDr/rU5wu9GOBUfHdSl2w1vCY8SNASLuPi/zuks7v01Prkg9AhXVyKU6cBwX\ny7UwI82hXQ/TfeFbENJa1uGL9yTFMi+flq62Gpv26/ivJojajUTs3mmhqXshNz8w3iqLQX/BpaNn\nkOmxIyuwCZiGgdCaXLELaZg0ZibJri+0QiWxQCspKU+cIGh6GIYkl8tj2g4AUeAvhmxOUj1oNYx6\nUfD18hjFsp70Yu9p2blaPGdiqVd8srcen6d2iI1l7xmLDYLAjMcMzrqu+H6TbW/5H5RnJ4ke7OeV\nb/9ltr/q9VgpCycFfhWK67bz1+84j5f/wp9x0Wtes/hdudxFbx3mv/7Rf2XP0SM8+K2vc9Ub3rwU\n6mn3FtubLwv36GVm1Dru1bU9+9YhEvlwzxc+SW32YUJPYZkWGddntBQyP2cTBDa9/TVKpSJuqoHw\nBML2OLi/RG/vDwzNL/KcxOi11rcAt5zt9t058azpPw2gVIHjE/D6K1Pc/EAdLwiBuE4ahjip36RE\nO56+6NstLx2LHovWOKZkuz6BKzWDa0PSW3qJpEM2m0MpH41idnKE4498i59679/xrte9ihe/410o\noN35MAFHQrYIMz44DVgoxyc0imCoK0OlGQKUtNaXno1NDCEZGMpzwnDInH8J4dP3kFq3hfObRxmJ\nCswtTKE3nsejx4bpVBWCch1jdheDw4NMjI9h2y5brv1pjtz7L3jNMrVmmaC5wMixSeyUw8DgIE8+\neDe3ffNr5PIFdu98EqMwjNAR2VyOS6/YxC1PFVltHmf8og9i3vsnbD3/fMZnQ8rzRykUiuw9fAyD\nGs1AI0KF69gUe3rYuG4tkdLcf/8DvPjSixk7eojf+dAv8MV/+Qa/96vvYmKugtvVwLbkimwSEwu9\nFKJ1wbTFhFY7LjDQEAdv2l9pxdjj58ZivZDc/g//g2Z1gS3nvYjB1et5+KHbaXohCOgbXMuRJ77H\nfOhRmp2ms381RsuZiC9uhWg1IIttzmIj07qY20FcsTSQGRdWIYREtcYLRKvOTlaaOOk8WuvNZ20R\nIUmlUqSKndiWjWGZWLaLKeOSzU2PU56ZRqiIyG9gSgPDMHHSGaJ6GWHE9lShxrRcQr+Jlc7HYTDd\nbrjEYlx+ecx+UdTb52axl8yiZ77owdMO13CSZy/a27bj+WJpu3j8YjHOf9Z1JVvsZPWWHmqlHi65\n4Re49JqXY5rgNWKHMVWAVL4H1zb5+h+/kz3fuZqf/MNvkk4vE2qWfMLRR7+KUppHvvxbvPRNb178\nfLGKnSL2EUspje3eTnzWW7+tYx918sABxp/6HlIexDY7GD0RUuwB27LwfIF0FBEWke/R9CFtm/j1\ncbK5DJXSGWJmz8DzNhi7nEbw7J93ArccV7zzKsln7/DxI4UhY3GXhqSYdnjJdof9x+CpkQWEVOh2\nzE204+xLr9tn4yd29LL7wbvIDNukzZCu/rVorQgaFdAeXr1O0Jjh6J1f48DOh3jHm95A/hW/zUDm\nzOVUwPQ0PHmoyUTZI4w0UWvg+ExxzGfDN1xGKkXE6n68g09gWDbWwgk6r7yG8ccfo7N5DH/nPyER\nlKeP88b3/x+qpWnCu+9HmNMseHXkwfuw8jm8eQ/HtZBGxIahYSZG5lm3apDvfvf7pDuKNCtFfuwt\nP8Pex/fQ2RXR2ZHh2ITPFZu7mBs5xivM23m00E+hq4+du+/n/NU97Np/jHS+C79Rx8IDCU0voFKu\nYFZneODRGj15i+rCDKVqk5TtsPPACM0IfvKVV3D3nqOUyuWVGQVicTDM1nk9RUzazbsQqLYnDyf9\nv/QnvvyKzVGsvgGufOn1NIKI19ywmn/7yt9RWphlg70V03SoVitMHN1D16o420cuxTEQsl274v21\nn8dlioO/i41RSwoEGuRSGZYOIG6gVophGNiOSz6TJdPdC0pTmh4nnSuAEIR+HLJx3DSpXJGoWae4\naph0Nkfo20S+h+mkEFKCUtjp9GJp0SfH7i0ZN2Se0rSimstEXywf4lpsjNtHKoRAi6WB2GUC3hJ5\nuZjoIEWcOSZb3v6pvYwfRCqd4tiuEVZvGmLX7d/gJT/2cqIIHDf+vB3qTa9+Mc78d9j1xD0s/MrL\ned/f3Inlstj5b8ftG76PH4TUG41YdyxAtQRetMYY9ZKYt9r2pX2xLEbfeq80VcYvjdKoTFBpCDLp\nMVJpi65Oh2oQYjYiAi2plALSnSFdhTyTYzUG12bpXyNwzRxP7Tw7e7wghD5l/eBt3nlVfAH8/DU2\n33jU5sBkBSHguotzXDIQb7OtB4ZXFbEMODoGI7N15irNuJppMEwIfcVQZ4Zi/Qh+dZ5CsU5ffz+N\nSonRo4cpjR3BtCwU0N3XhUbS0Bk61l3BWCPL0e/+C/lqgUO7H+M7t97J5YOSD33lcQwBCw3ofIDC\npgAAIABJREFU74JAuzT3R8zVwlZKpDg5cHcWaDRi+AJEugPzgg1IKfBdB5UfoLBBMT18MVcNWPhW\ngSf+8d3c83c/Q3UOFkaPs3HjJrJ961m/ppc7vn87q9deyPvf8z6OHj/Bv33tK0zPl5icnsLX4BhF\nRHWU22/cw8tfegGh2cOu3aP0r3kR3sIhhoZX8dh9jxEKi4ljDvnONHsOj6HDgHzWZrZZwzQtwsjj\nTW96I0efegJVhG1XXs+n/vbzfHzHxWzbvoPp8cO84zVX8/S+g2zbsJpvP7yXG378av76776xwtoi\nEIa1KPJL3uKScApE3E1e9m5bbNtxUoEgDALKtVm8hQW+9+2vUyx0snfv4ziWQgULBNU5TCuFV6ow\n9tQd7HjpGxe9W8SSlyvbYSNN7O3DMsFXi6/aQh9/V8Yi2u59tpXhtHDjWVhECkzLoFKax3BsnFSG\nzv5VLMxOMjcxgdbQ2TuAZZl0DgwBAsN20IBpmJDOLPNDlkrfbjqV1hhC4JqxoKVNuKTPQPZmYMFn\n73gTS8LGPheKWXbunqURKUItiJSgoXQ8+AqLot3OrmF5XF60Gwe56PUvxu7lyoReCNhx+RAKUM15\ndACWGR+a0otDebz3zz/LJ37iAhq+x8iRXfzbH/wym656I6NH91Kf3M+Jo3tZs/4CBBBqjRdqlAIr\nHu4hY8BVW+B7O1veuo7jSsuzddqh9Hbj0qxDKgNhbY7Gwgks1ycVGdgpm57OfrxgDtV06ez2sGwD\nqTXTtRDLiFi1Jk3TC4l8g9ApnbU9XhBCDzDnLxnPMWC2ArYNpgX5U5ycn7gEyuT43HeqFNPQBAJg\nugydeUDDsakaNS/EMgykAENowjDi0sEOKpMP0dPjElTnGejpZH5qgkOP3EUhl0brALtnNcWObg48\n+TDpYheeyOI7HYS1MsWBYb757SfJT97BB37z01z2uquZmINSCQIFlgGRgNXdGfywSk1AGCnCFc5A\nlnYKe9VWGk/dhXXBy2Ovohlw5xP7kShSIqKWMRm777eImgEdG/uYn5hEaqgtzFMvP8bOu8YItWZ+\n/DiP3HMrSI3t5ih2FMjlM1QrNSQhkV8mtfrF3HX/g3R0FSnPL5AeyrB18w4m9TAXXbbA0RNVRiZO\nkM/Y1GyHRr3GzMw8Eo2MmhRyOR588AG2DnRx175xXrl1hmuuvpyx6XkWpqbo6Miy7bwN/Nnnb2R4\nVR+bt66nbrgrrifxRW/GQiDjvKh2OGTJH9aLoYLF7Cpi2ZKLz8A2LDDTWAiKuSwTE8cIEbimQ7ds\nUl6Yo6e3l1Ktyvz0MaRpLvUaWvtp7Q3R/l/EnXOJbsWy4xBSnA66mDaw9Fy0n4MUJw9wni2+58fj\nHVKSKhSYnZnBNE2q87OEzTodvQOs27KNsBW+WsqKPFnUQS96qO0N46hovI0lNSlTYwvFwbmAYa9B\nqmCyZRVxwDnvAHl2XJA/pYRN7tw11Wrm4jPQFncpl7x5STw3o3WiFz9b7vmfLYGvqCyAUnDextWM\nHzpOd/9qUplY8Nted86Cf7xnN2+4aD2NZpVdj95NuTzD3Nwck2MHUFozPTlKrdpEGhbSMLn7c79P\n1+arueJV11K0YP/hWEgH8uCFMFVrefkt67ZnJCjAq3k0F+YoFgaYP7yTXXd9Dmkoih2CkBSHj0/i\nOAbrN8DkPPQOKLxAUchIAprUSpqNGzupVatxcsFZ8oIR+s5W4lQdqIexwHc6EBLHuyabkLIhJeHg\nJHR3wXUXZ/n2Yx69aZvtGwWPPh1R8kIqzYAoavleAl59aR4BHHvsKK5/DD+aoz4nME2HiUN7Ccvj\nuCKg7mkue+WPMzs1xcLcPDJdIFscoG/jRTx0281k7Dp+bQ7hdnH+Rx5gv6/Z/Z0KjinRWtOZsZgo\n+URaE0WgVDsdrRUyWglKEU0fILXjWnToIc140Mxw0vHYgq7w2KFxVnVdSK52gPHKBlav76Bj1QBP\n3nMva9b1kO/qxc5nmR0Z5cKLL+Kpp/eAgMDzCcQA0prFsgVudh1+WCGX7SAyPbKpLE7lMQ5MXseO\noQNEHTmaai1rzhM8dPeDuCmb+ZmAUGquumI7t9/xCFYepsYmeM/2ATYMns/Yoac4L5vhwbvu4Cff\n+pMI5bNhyzZqjS/zS297HV++/3EevfuhFdcTgcA0Ldr558szOhZj9svCM+1QTjsmH9P2wAVK2FRq\nMxQKRR58/AE68h14zSZWpgdTh0QiRS6VYrw0g2U5SBmHbsKo9TuAbQqagWLpDMeXuCZa6v8vhnFa\nnr3QGIu5eXHDZEiBbWh6Cyu7LJWKqFXLmIbJ6KFDDAyuontggBkiamVJR0eejpSFIzUNJfCVwFPt\nYa1lzdZJzoheHF+QaAKl8bUi8BUmiqxUTFR8MtUqGVORMkF6VegxgOIpJXR5+fbVp5V7/9Ep0pZk\nvKlb3n07FCcQUmAIGeec/weE3pZQ2XsH3/zib1EvT3D4yc+D6OY3/voRMjaYEqYrcf56rQGfvuMw\n+w9O88fvfTm7dz5AttBDGASEkY+MAoRhUchk6C0WqNeqGPvv4PtHH8IXLj/1cx9CRTA1o1jbL5lv\nJY54QUhXymS6oVmVFewZmSRf7Ka0cIBv/q/3Uao8hmGZFFI2jXrIzEQFjWb12lU0Gj69PQukLFC+\nhXANRGSxbo3B6Mg0q/qKGMHZ15MXhNBrYo/c05AVMO7BwVHwA4g0+BHMVCIavsKQGqUFKVvGAxoI\nji80OPaIQKl4yFu1xoO0EERKc/MjZS5MNwn1MWbG5mlWq1g9XcyNHaVZnacz7dKoVlm3dQdH9u0G\npfCaDpOzTb79719kcO0GrMgjl07TVcxiugXmG9GiV+CHGtcSjJd8QhXvPx60Wppw1c5+OFtUs0rl\niX+n47Vb8WePYw+ctxT/04oyOZydf8T4Zb+Krt5DeWwCz9Gksjkuu+xyDhzdQ600j1WtEzR9Dh87\njJPKIAobcKJp8tmA0pzJ/NQY6dQCnu+h02lq0xVMU5LvSlM9+G387osZO1YjDKeYnQnID2yjy5mm\nv8fAq2uq9TKu61KaKfHbN1zJFUMdWCmL7p4i33lqBmolKvfeTHjeZRhS8PNvfiUTU5MUvBoLswsr\nryxCYBgWS9kZLWGglc0glp7TOj/LojitLvtSLNk2BOlUhn37nyLtZpgvTVNIO/QPDBFGAUQ+Hfks\nY+UZXrwJNg7CiRmYmoeUI9g9ApECw2z3JWiF3XUcnddtv67l24k4lGO0xV63egFCYBhw3kCGUn1l\nfr0hJZvWrKG/v5dCxiXX3UPDcOnq6yMKAizLBAGKAFtEuIamqsEXNlrEuUjtYPTSEGu7Z6LiGdhE\nCC2RSoHSBIFPxfPBUmQMkDm3dYynivwzs3ltLwBDhKhyhb1liBBIKVuhOdlqWAUrvHyYmjzMv/3T\nezl6dB5LuJhSc+krfgLDC5mrxmMRCgNfCfx6LBi9aQPTMLAcG61C7FSKNFmE4dDtWNS8JqSK+NU5\nyrqL/p4MXm2ez/7px3j9e36HAM3IhCBlwGxDobVmxPNASG67/Q6m9t3Otpe+jdmxSXzZSWXeJmNb\nHD42i2EE9HebaCPEco7Q2T3Mof2C/vUW2UIaoT1qdU2jnGHz4CaEH2FXNLD7rOzxghD6ch2+9bCi\nkJZUmzBZ9gmUjme5trvdMh7ICVQ88uE144qotI5n8ak4dz4W31Y+tY5nrV7oVKlX96FqE0xMzpBx\nUkwdP4oMG7imROuArjXrqM5OoJQJIgcqwpuf4vDBXejQY+1ADis7CCogN7gJJQWOARnXYKYaUvHi\nmbhKtyaU6HhAMPY0T877ORsM22XV9muZO7Ebpo/Q8OpkV28HKdGGiVKKxoa3gNvJ5qFVHDxwkLIv\nmTsxwrv/y7vYf3Q/uXyeRqjoHxykq9PhvM2b+Po3byVd6KU6W4VAY5o2QRBiCMH8/BzFYhE3bbJn\n92HWDg/ywH13E6gCV122ittvexQ3H7JntsJ5mweRmU50ZRrDh5s/9mYytosKmjiug+d7vGxDBiPK\nUii6MPU481UBlQafvPE2fvntryX9yEFgfmWGEQJpGoseIMsEve3Ma8Fy332pEVj22WJ4wrBBVZhf\nmMeQEtuycQ2J16ihLQvbmyfyAyzTIJ+FkVnIpMGswlPjIIzW4KxeFhJRsZNhaGMxuBM/VBxqEopl\nzUI84EhcrgOTTaKl6dhnhWNbXHTeIDJTQLk5Ih1hK4/ITseNohaEaILIAKURSqP9BiZNQg2mm0FJ\nC2G0A1Atw+rWIKwEW4FUIYYOSVkR2lfsO3SIkcMH+Ku/+VsGci6/94Gf5eXv6Qf6VnYAmMh8B9ta\nEZ8DU5V4slsrNi+FiHs/KyD06+zbV0IAhS7N1LjH7bd8hpmZf8GxUhhCU2tGZB3w6iFmJsPsRAqp\nBEJJjFbIqL9/PY16naxjI20PN5PFNCzytkGpVKanu5NU0+Nbn/4tVGU3jfoC3b1FLHWCSmmGgUFN\nuW6x92BIPmsQzd/Bw48cp+ErMlmwuuCKKyIef8JAOwFD/RqEpNE4wRWXGqAUk7Me0xMOnQNQ8LMU\nq4LDx8cxjB+x0I0XacYrPmNljRQGkVZo3Z4E1ZpBGPfBW7HY1uXTjoGK1jIEnLxoAcC2tGZh+gls\n3aC0UMLWPgYWjco0tmnQkU4RBBGGofECSbOpsYwqhCHDfUXOW7+eSCtUFJJ2DQrFLNHAIL5rUPMU\n1VaDs+S3LSmPQC6KDyuUeksEuK6DW/dg6n4afRtQloNoVlFRA2k7lKdOsOb4zdS7eunuyLF373FW\n5Ry+9OWv0ijX8YMA3CxSR0zM+hy/7W60MpmYGqeztw8/mMUxczipIlWvieMo0rkCtikYXicZ7O3m\ngQd3MrymSLVmcd6WzYxPlenrTlGztrH//q9hhZLbPv7TREKhfJ9Aa9JCUSw4ZF2b2akFlLTpzhYo\ndkc4KUHv0JupRrCur8CBwyurK4JWOq1gMaWyLdzthKp2euNJXzrlR9pthAqaWJZJqTKDbbkUMi6F\nTIZqfQHX7MKVisBKYegqt+2Lu+RALIJGLO5aspiBIYhfy1asW7bFEgFCLqZ+tmP0cackDuM4BgiM\nFYcpXEvSqJXwq1UiJTBNA6TEyHUSRgo7V4yHhFWcJYQGZToEtTI68Il8D2HZqMBHa42ZSmMYJpab\nQmiFQCNlRBR4/NEf/D633PgvvGrbMBu6s+TTDr94+TDFdIZHb/82t938DW564gSPPvkNKF7WKmHI\nSqSm2xWUIkC0ppVJyQrmBgGQy8D11zpMj0fkOzSvujaF7wtMs04Q1hAiQ8q0GR1T5HIRuUKZ0thW\nOrssDFPgpHLMzYzT9H2KxW6kamC6OXq7+1Ghx8LsBLnObqanJrCdNKuHhjg+arEq9SWKhaP4oaZn\nSNFsePQWfIYHXOpRlXWrRti+tcFsM6RRcak2PLIp2HGJjSMkhQ4LQ4UMDAgmJrooNabI6E7SPZrV\nqwoc211l5/g089UA42yyWFq8IIQeHQ+aKCSRXpp+oVtDbUsC2h4CW/rbniDVniwjlgIcdDoCw38U\n3Zyl6gdkHJdSeZZao4ahIlK2Q7VWxZQBvh/iNRSGMNBRgIWgs7OXa666ktLCNIbQuKkCjUZIJbRR\nTUWkNCpiWaO05A8thQ6WUsxWZBItqMyeILvlGkq7AvplnfLRx1m1ei1Hdj3M9YP7uHViFnd4NbJx\nGCeXp6cnhYnBBRdczCNPPEBlocbaV/w8u278JEObzqf//FdSfvS7uIZFUK+zeeNmxqamqDaqRGET\ny0lTmpvh/C0XMD09zl33PU7WtTh4aD8nRo6xYesW5uZLzE5OwOEbWZsu8A8fvBYPTXehwPGDB6gL\ngSEDhGcgMRHZNPUFn8fGRsjZBtKwOLIgma1FvOTCzdx6/66V1RURe9GLY62tv0brM03sYbeFf/E7\nnPy87R9KIUin0tQsG0NqMm4aPwwJoohO18WvzOOkUuTz3WgFl66DravBAf7pIagHrdomY6E3ZBxu\njBpgpVu7FBDUFIYrQQlka96HaKXditaAp49GaEG0whFZpTX1hoevJSGCqB4RBBFyoYowTXJhSBCE\nSMvEtN24bxH6NEslCJqtfHUDhMS0XLxaFR0GSENimSaWafIXf/YnzO5+kEvW9fKbr7kIKSN0GBJF\nET4wU61gGAJDa161dYifvu4djFdq3HHrF2Dtta2Snp3gd+RDqvMmYXmSMNuJnp2kQzRWZhShWdXn\nM9AHOnKoNkMyaZNGIyLtamynSmUuYvU6E9ewuf/hl1Ip11i1ehsbzttO5Fe5/XvfJGqWCNMFhDLo\n7u1ieO1W9u+6l7SbwS/NEpppVBQRZbMM9/dyfPynmK1ILly7kwMTF1OtNFE6wjAMLMtmtCSxzTrZ\n6Gtk0yV6eixQPsO2g2H7mJaN1xRMjofUwgk67FWMVCvk6OTQoZBSY45i93oGNi+wft0ct5/lMNcL\nQ+gFqJOCcLIdfGExJ2G5l7OYexu7TbFHF/edhRYULIurNgj23HczpeosWRd8bVCdm8E0IAxDDAHe\nwhT5nl6iQOM3AwwVXzRagOfNo0WaodXr6OlMU6+XcPMdHJlp4hvu4syHuGvbDm7KVgy2tSBTqwGS\nLPP0z9YkhkkzN4x+5OusuvztlEsL7EhPkZl5GndoHY8+eoQOM2DN+j4eum2SF112PqMnLCYnF3jT\n5k3cduetGKbB0dv/hj/6n7/HJ//680weeZTenhzjJ0a49g3X8v2b7uP87RvZtesgtmmgwiZRFLAw\nPcHMxETcZGoDQ0SQ6uHo3r2sXjdAZ/eL+fPXrMIxBa5jkS/aHN37NDUVUW6GWHaelGMgNcxNzfK9\nxw6zbsMweVsymlmNk8lhNyZ4eM+xlVcVEafJLnrOomV2lkS/NU67bBLVstx3uST0ArClplFfIJ/N\n4NoOadeN87eDOYyFw5QaIbbdQS6XozI9x858J7um4p9SEkwHwhr4tRDpmphpmNu1CzvbQ/fmfupl\nuOnj19O39gKklWPjpa8l3ztIYdUQQSTi8STdXgemVc4Veq9RpKg1aniBxoviMQOlAdlEC4NK3UOa\nJkrHwUQpBI5tIw0JQYBQCiUlSgvQtdiEUiKEZHp6kt/4wPt42+UbGBgskBUKr17FlJJ02kWagu6c\nxeRsjSPjZWzXwbEMLl7bye7jAa977c9w0wM3Qf6iZzmw40CaOL7vA02GOxzoWAOjB3msViUkXJFN\ngkBQns0izDnGJkO683kOjFUJfZtC1gThkssusHDcZs+edZiuh2HYvPy6n8DzQ3y/TiadhVQWoUKC\nsEGtOk1p5jDFjm4MNHNTx1Bmmq6+YUwJM7PT9HR0MjM/y879G/GFjw6aSNPCMASZlItlWmidpi5+\nES8y6Ar3M5D5DmlXMjKToTzvcGLMJJB1wrEOhGlT7LcZaYzTbEasu0BzZNdxvDkLr3H2NnlhCD0g\nhMHJuV1iMT+5nToXO8bi5FRjAYYQFBsLOMEoWcenPDHG3kkPYWToKGZoNJpUFqYImnUCv07adXAt\nG6VKSNUE1aCxEGKnighCwmYdI6wQCgUiRBgmERZK2uiBHXGZZOx1i3ZJRTvD+sxxAqFXdvWGZppt\nPWkOPnWEhZf8HL3eDEdzA1wx/inSwZ2MNhaIjF4m9k5y3dUvw+kwOZg6imf57LjwQiIVL+xlu5Lv\nfvd7OMKnPDOF66bo7u/kezffy8UXbOXB4wE6iggVZAo9rB50GR2dIFPMslCpo7TCTHfS40RMNU1G\nDs5x469djg7ruFYKN2UzMTJNxYsw0gZTo7P8xY0PcOW21dTrPg8cniRlCPaOzTPvR5R4kgtf9CJ2\n7dn7/5h78xhLs/O873fO+fa71a29eu+e6Z7pWTjDZcghJc5w0UqOKCmhJWs3EgdCJCMWmFg2YNgO\nEAWBk8ByYNmRYzt0ZNmSJZqEJJOSSEokTXJIzr709Mz0Nr1V13ar7vrtZ8kfX/UMqSDhlIAAc4AC\nbqEW3HrrfO95z/M+7/Pwiz/9Uf7oK08dcKM0AlD7W6GphuX+ePnthC95XUfk27S5GiG8fUw934Mo\ngTCKcM4i/Yh26BMITZoNWYwcMmxRuAphKtpBwParX+fQPT/y+lSjt/97ixRunvsG157/DPe88yPk\nqoXYuMLCiR/lz/63n0CFEb3V++iolJ/96EMkbcleCuc3Yax5vQkqb/9NB4sIxhryoqQ2UBuHtg7j\n9sEhIXFVDVI1E7A0lElVCny/CWRT/Fgq08RMI3AWTJ3xP/7qr/AD9x3FR+OpgNpUKE8R+Qrlg68k\ntzanbOyN0V7IUqTwAo8Xb+7x4JmTfOIf/g/7SR5ghyahfxv9cvoSjFJYnWf4yotYY1hYm4NODMkc\nHL6TB6rnqA/Yt7AONBpXxYSBZpLV9HvgC8Nk0iJJEvaGJ2nFFyjTCZ63gDE1ezu3OHTiNMImZNmE\nJGwjlYerc25ev0moHIeOnMbkE5zOKYqULIwp+wt4wjHc26Hd6jIZV0iT4fwYP4zwRJMvrDXUVYES\nCltXFOFZzm89Q15s0GrXdOcmHD4xx3L7BL93cca7H95ikjs+8q6CwZ7D2g8z6L7Au+72DnTze2sk\neiEQ6o0qvqm49uuu2ywK+M4r+D72eUhpwvE3qdIxS2trDAc7JF5GmCwiTElZCGyVY6uSyXCHTuyh\nK0WpCwRgihk63yXPxiycfTdVnoObUhuHkZYiy6i1JZCgZIBePtNwffdPntff1+vym7yBC7wO2Ry0\nngfnLDdtD//sD1PnU46FO0RuxHSaI13BI4+cZVAVVGPDcHiBatDloXvezqdf+yzPPfMkc0nMOM2Q\nxmOWFYzHY3oLa9x//yIvPH8NbR3Pv3gO6QTe3FGi/hrj177BxalAI5iNJkgp8X2JlDVB0mHJi/jf\nP34PVpcoEWClIM8yamvx2wF1ZQjCmLsPL3LDxWyPM04cP8KjP/QDpOMxu5s3OHX8GFkxI6zX+Pd/\n8Pm/zFZpRKh4o1IXYl/i9fa/xTZVv6lh8+mnMHpK2FmmynO2Xv5DWv2TRN15rBHousLzFGme4qse\nRmjs6DpyfhlPKVqho5SKIAp44cv/ljPf8yPoeobvtbEmZ+fycyyffi/zy0fJjr2NzZ0tFtdOYmOf\nL/+zX2Th5Pcw3Fnntec+xdzCaT7xiV9C5Vscv//DPPixv0HQCEdi4I3B2ANuFk9CmuUY49C2afla\nmtuCkwrXcGZwODwhWEo8FmKPR463yDXUzvHsdslAG8pa4KTg9/7dv+XaU1/l0TtX6QSCSCkCaxvs\nPgwJXUmgHNNpivQML+9WnF4NUBICo7FC8KVLm3z86g7HjlwFTgBLNM33Gtg/rTv3Ai+Bfyf9+324\ndL3hrm6NIH6WT39rk4+dXuLJne+qZv0dy5ceXnYEK0ZEMsAFGaZYxvklc92EwXbNiWN7ZGWHzd1t\n7mgvkBeWx7/yWX6g+3MsLMxx+uzDbN84TxJ4DDYHxO1F1i8+y1J/mV4nYs9pqCvKdI+t9SuoIMLz\nI5I4xrl5rLUYZ6gsWOlhrWFWFMx1OwR+xGR3k+mwRMkfpNX6M+aDBXx7BW/hEDtb8L737+Ezj1+s\n8vgTioceOMunPvefUCLhG094PPaT6rsH4vYeOVD0/n9atxtsbyT5/U2/n9n31Q5uQ/GvJ9Iz5mXu\nPLLApLtEOpZIkXLm3nu5cfElsDOmsxydp4yGQ/I0xzM5YbiIUBHO5PS682ArLJLcO8azh36F+175\n2zgriDt9Bjs7KG8P6Sm0kSgihPL3qy63z6Fo1hv8oDf+qu9QUjxgTJxUvHbsAZJjb0NUORe/9Jtk\ng3XO3nWC6Tjl2auX+Z5Hz1L0ArRN2N0bUOcFp88cI52lHDl1HHFtg+UjfS5cvAxOY4sJrzxfMtod\nc2JtAeMCru7k+MU21c4uQeixuLBCnqcsLq4w2h1QFiXzy11UpPj17ztJqBS+56OEptv22d2dkZcW\ng+O1jT0sPudszDFjOXvvHVjtcGWKKTO2CsXg3BWmwx1OHFvj808c3AXO6YrBhXMcOnsfym8qdudg\n++Iuw1vn8E3GxoU/pr1ykpCK7fWn2dt8kv7ifUStw4w2X6WavEBRS/Qob2ApZ5G2ojYVs7pmPuk0\ncKITWGNpBZbJdMLy0jx//L88SLjwDuZX7mKyfo1DZ7+HuWN3Mxhc4O0f/Ws887nfZnj5mzz4sV9m\n7Y47eeXZJ3nvez9KsbfNK1//DCtrR7lybpvtay+ircH3FcI0ae876oMDrKIyTGY5noDEVzgEuWnU\nMp3QGCeobAPpOGCaws5E0hKGh491eWErJ7QWl2V85vc+xaFiizPC8s63HUUoyTTNqbQmUD7GVPjW\nQ7VblOkMZS3nNqckkUI6Q15CpSx39NvkzrG21MddWUf4W82IaD+GcAfCRZpOR8Tnv3oBX11mc1Jz\nciFmY5Rzc1yR5hVpUfL186/hB8F3icJ3LiVCRrOQMDxF7AuK0tGfD9gb3sLzYg6txkynhjwf8Nij\na1y66aHQTLMRc3NdLl18mXS0SSuKqYs9QgVJehPXX6HOh8jOGmHUQlgNrmY23CLpLQOSbLJL1JoD\n6VGXOb0kYTQak5WOQErqqvmZpNVtINNixu7uPKuru9jyHuobkJYZOj2BbEU45zNOc55+7kXOnOlx\n8eoOP/7YAi8+7dP4r3z39ZZI9NA0sRzf1sR0DQx+G7O8rQ2FgLAsePv8Or4Xku5eI4wiwoU5er15\nBpvXWJjvkmcZeZYhw5Aqz5iNduj4jjKb4WRNy7d4qoW1OdoI8g/+zygpuXTnP6DzrV9lzusQt+cJ\no5DaOoTaH7nx5Os87NtwqnW8PkH4nSn9drL/fx4D33U5RywFhfJ59+AZVk7NsdfJSeKUVnuJ4ydW\n2dhNUbFmb2PG3HKHbKTY2dnlheefZf3WLcbjKWtrC4zHQ979vge58tomWZ6zvNAnmIuepvPQAAAg\nAElEQVTZuTFGRfO0IsVcf5ErF16irGYEkcf2xi0EChkqHnzP/XxEpPT7HTzdDAEpUzMaFZRFxdbe\niG4cUGnNf/+5Zzl28jijWcbZ08cZDEZcXd8GU3Pr5k1euXKNu44sM3z1Gp04YPOA7MrZzgVe+YOP\nc+nzPVZO/xird/8oyfI9fO23f4ZsMmJlYZvhsMSc90lngkOHJUoKyvQSG1efZ24+ZG/9Fq3uGpsb\nV0lMw+XeGY7pWsOyX1AUJUJIAlGCiijLHOcUnXaHoTmD3XmB5178PP25iLKaMn/sHVx59nMEVrB6\n6h2s3H0PV578Eksn76fWmhsvP8Xi0eMcu+9Rwnab+77/51k+voz0wJk39rz7S2Z6bR3TrMYiWB9v\nsXPpPOiGAWXn1zhx5n6c9HBSoW0j711UgvMbFmFrXh5UXLp+iwtf/ixHEks7lkjhEyso94kGofQQ\nxkDgo5zGFhbp+UymBec3JyRxh35LEGAJasPKsoBaMassOs/5j+e2SGvBkbmQ4z3FHz73ZeaSgL/5\nw3dzJFEc6yf8k5de4bmLFuVsA7HkNUVt0Pb2xMqbX7XVOG9CpoeM8hLfj5iuN/pTaTklJKCqLXgV\ngpI4XCSz89x7z31YB08+/qcIF3DyyBGuXRkRJgtkdcbpxSU8V6LLvHn2jcZVGVYGlOmQuq7pdOcI\nPcn27g5Rq8vq2jGy2ct0OgmeUqTTMe0kRMiA4dY11pbXmM1OMMsNUnhgNG0RsnjsKONsRnjycaLN\nk7Q7GWk95CP3+Vzeuslo+uaxm7dEou96JY/MXeDajQHXgwcRSdJgsbexUAuH1Rbe7BJRAFmtWb9W\nEfuS2CvxAp92r89wa4IfthgPLhMkC0Shz2w2JvAUrs6opEfsK4p0REVIXqQESrInj+N5HiBwrT6j\nGexm2xw9dprReIeofYRKFwSEKPmdVhNWfHsQ30jzr6d2J/4SwE1zRLTzEba7iH3tz1nfe4XeqQ8z\nl7xCVrTZHdwg8mtGuwmrRxXTsmI0G/DuB+7mK19+gbDvU7iKq1fXWVpc5uVXruH54FRCkWe0WGZ9\n4wIL/ZK0NIyHAxb6Kyz2fbYHEzxfsaw8/t6PvYvjq3PoKqQuyoa+Ki3SOYpyihMWH7iyOeQ3vvAi\ntTYIV5J0ljB1xeZwystPvkgSRSx2AqpK4wcBsyxDH1D/B0Brw2B7i95cysVnfpedK/8BSwtX3aQV\nVgzHDj9SWG2JOjVbmxLPF0S+ZThwFLOa/nzE9vpNLBWzdIpzlqosqesKbSaYukZGEdpJnDMYITBG\nk01HHL/jJJefucbKapdb25aefYon/v3P4pTkT/6vz/PYf/1JLn7lDzj+wPv5rb/7Azz0vY9RbW/w\n3/6tf04cw9jA169DIqE0UIn9xql74yZ70CHqfDLm0pf+A6HTCKtpex5zgSQMfTqexd0YsbEzZSy6\nVFZghMTUNQNR81ydIWyF0DWne4pAKXAGYypqT+GhmO92wFoKbZGmxjhB4EuywqCx/P1/9E/ZHQz4\nZ7/2DzjUjTm83EOKEOU5/vy5K2wOMxSOzWHG9rrEHp/nkVM9sqLmk198lSIvKKoaUWuOJx47oxnz\nrZCWc6RoZnnNpPguyod/YSVhwoff/oOMcs14ssFwUuJKgwjbdCIoqhlptoEfLBOgyGeKQPu86z2P\ncvHVZymLkiSJ2Ny8hdGaXqsFtaOTJMSBxeoMXWYEnqR2DuEsZTFFOUGZTaiqivm5BWTgkxcZnd4c\neTpjOp4iHExnI1pRSNLqMdpdR5c5tdfmvfe9jVFRI9WUsv1FxoOM2eAYebrD6vKMwUAgujHlMOT0\nneWbjsdbItHjHJFvObraYbG+hB+ErB4+yvXLL+MJR6fnU6QZu/mAhRMPMTz/Ivl4hIx9kn5MkZdU\n5QaeH2AZk8ytUKZjlChQnqPMpvhxTDv2iQKfJOiCFVTFjJ3JGP+HfxO8N5J0/b6/y60//V/pZRXd\nzlHSrMCP5knz6nWdDNiHnPi2Soz95qDjjYaxgAP2kYDGzq1eOcwHxuexC6e464zHbHqLykkQOXMr\nh/AYo8MUYxzFoGT16CoCzR133sG03MGvFa04JisMa5HP+mhIFHocOXmc69deQzhL7SdIM8ETHqPh\nBn68QltI/tUvfj8YUDiM1pQGZG0wkYdvambakleGPM0pleC3v/Yq0dIR5PVrLCz3WZ3vcu7yOkeO\nHue5V65w19oyN29tsLbQYzad8q4H7+WZc6/y2ubeASPjUddd6jqg0DNKX5KlO2STBD/RFKklivep\nhFawPRR4Xo1nLF6ikMqSlxXDYUUUdugsBEwGBUkoWRFjYk/ghyFlXVFai69CnPLJtYLpjGPhUT78\nS/+Y81ef5+1K8tLX/5DR3ph3/OCvcOd7dimmNxjtXOXB1R/lb/+bp9CFY7h5g5/7xV8lH1xmunuT\nR/7qJzjzzkc5fGyVfgvi/QZxEkFdQ3zAp1I6jdQpxgliX9AODEEo8AMIRUmeFXSEwcsm1KKhf4a+\nbIobYRopCdc0YHVd4SsPKwV1ZRlUBf0wwBceibREykM7i64VxsCZn/wEo9GEMAj5hb/53/Gv/8mv\nU3k5C22PdrvLuzoey15ApS33rfSZZjU7wxHp3hBjLR6w6AvwHOtpwTi1+AjSmaaoDRJoe9BNDjgw\nZSqujXawUyjJuXVti1a3x3IQks5gb3fCOx58B+VswqzykWyTTncIk4CVtRPksxGhF7A+uEa3FREq\nWFw9Tl7M8JzAC33aylDWmkqXiKCFqTTOTZgMB1jpc/T4nYynU0LPo/A8WkmIKTM6vXm2Nq7hdEK/\nv8D5l77Og297O/cdPcnK2hrtrGRnmrL92km2b11FaYcIlqjJOHO3T1oPWT1pCdIPA6++yafmLbAc\njs7a3YwvnieQhlanxfprFwgDxe61l6lbMfH8YWrrGNy8TjocYuqa0WSPMFijzKf4QYuo3cLzFV7U\np67BCyqilqTdSdB1AQqckFRFiq5r4tijJqIVh2/wqgF56AQvXrzK0sohhKypCZnlAYGeodR+v8x9\n55X79bz+bdATt+GngxeuxOWQ5atPo1uSldWEwdY6klUGG1ucPvVO1tdvUDNhLooZm4LLV25xotKk\nic+Z03fx3MtjOsvzFLM9OrbgJ95+N2lxiE+fW+fG9XXmektkGWR7G0hnOX1ylY+eOMn77j3BfDdC\nG0Pc89G1YJZWhIGiNAU2KymDkKpMEdbw8o1dRkXG4oNnuPHEZYwxTIYlc8cifN3muXPn8X2fK5tD\n3n//Kb76wlWsdVx87QbvuOsYXz/32sH2inPsDiusyImjmKzoU7uCbl/w8ksZS8sxWebT73mkhSSK\nJKNRQW9eMJl42FIwnTiiJCEtKuYPPcohfx07uYkuUya5oR15JJGHsg1sV1mBlD62LvGl49xLLzHf\nbnP+tW+iGVPJmKf+5O+AP2N++V5efuo8z335/+S//LXf5da187zw5J8T+JI8f5nV+x/lc7/1d7jw\n4ntZOfQAydwKP/hX/hrCQj1r9kplDrpbHEJrYl+RCIknJEHg0058/EAynRZMsxJjHYEnCJVCYnG6\nwhiHUhJfCaQHFR6m1hhrqZ2g5ykGs5wFTxHECVoIOoGH8xQqWWRvMkPsM3k8FaCdxAjBODPIoObG\nrR2UgHbsU2eOEMdKvE8BtY20dTprBrUSDMPJjFlWsz6csdRLmGQVr23PqMzBgiKc5ES3zws3XuXk\niWPkixlVUXD+lWvo0nB0ZZ7ti+e598G3c/rMnQxnj5PakDhMGO5us7J0gsl0hyiKaMUxgaiJopi9\n6RY27CCcQeqMyhhCTzHNp2gRgtGMzCbd+UPsDrawQuGMZnF+icHOFrYqqdMZ/d4CYKmLGd1IIZ3l\n7JkTZHlNv+XT75ymFx4i7qwwG91inG7SiSToJQ51U9IwYzR580XSWyPRO8Huxi2UkFiTMty8iqlr\nhnsbjPdGWKfYGV1jsjuiSC1VNkVXJWmRo3ZS+ot9BjcusnDYR3khtd6kKia4utGU7/X7BGFEmpU4\napQf4UuYzTJ6vUWcfAOGgSYoFkddluR1hy988Q/5zx77MSalT1u+MU4PbyT426/F7Rf7X/jLJHmA\nqta0R9e4oyu4trFHx/UY3XyZXKdcfO15UJooDqmp2N6Z0E1CAhS+7bE432eh1+bn7lzh6OIdtH2P\nMApQkcfPfPhBdnb22NubIcUJWp2Y0AuJogBrasqyBmMwuqJIJZ40KGMoS4tnQcumeWRrg4sDLmwO\nubwzZtSzeEoBgizPee7FVzm21OHHH/t+Ll28jDIl56/eIolCHj57lJcuXWewNz1wXJTvmF9TlFOP\nwpXE9YQwbmNVzeJyTBS0SLOKGzc0nhDcuDlgrhexs6GxpsK2EqQSVFWJtg5vNiBwhlQETHWBDw01\n0QmcMfhBwiwzWOkhPY/hYJelQ8eodY0pZhi7hUBTlz51rvDcBfrLCp3W/M4//Bid3jzt+TlMqTD+\nUc5/9TP0lxe59eoz7N58mbse+hiBD1XV9HmW/z+sNP/fVjMgLvHF7YHD/d1pK3YGmuEow9QOlKJC\nESqHkwKlJIEvkVY3XH7p4+HITcOzrI3B1pp2GJCbGleWeJHPqHQkUlAjmY2m4Cx1VXPpxg6HH/wQ\nW9de5MUsoOMlPP7Zp5ACKm2aXknduHHXptGC8eT+iKNzDSX49jyBFNzanaBkYxQTq4PhWdY6Ou0W\nH3r/e0naLe6/+w7OnjjKrdGMx594ioV2xPGTxzl15DizdMpir8PFnRor4JnHv0BRphhricMI6Qzz\n80uM9tYJpCPwmuZKoARSKkxd4bSjdo1xS21mSAez8ZjlQ0cosozhcI/pbILveWAN7SRmOBqCNMy1\nOqTpiG53nsW+IK0180mH+fkhvZWaQL+PF0e/hao7zC2VqOGPsXVjxF33XX3T8XhLJHqEYLa3TTbZ\nRQiLEz5lOmWwNSIIA/LaZzLYQQqo64p8Oma4NyRI2tT5DC84yuLhO5jubRElPSZ7M1pzC+TTCdZC\nq7/EyskVrlx4kXR3B7Qm8kKCJKCuKpJ9OMbjDehlrtNiOk3Z3toFoB07LLqBbgT8RekN9xc+uS0t\nfrvaP2i+r1sLiIceY+uZf040Xae1cBhWSvbOX8KZlLQoWTqyxPxCjzP9HpfS10j6EaUrefybT/D3\nP3iUKAgJlaAzl+BHAkrN3ngG2rG80EIBQio0PsIU+KIxlCjKFPwQrTMq08jgpjWEWiM8SSUcQvmM\nZyWXtkfcmNX0F1pMpzcIw5Byd5cqXEQC3cBw+vgKvvK4cmuH4SSn1jXf971v54+/9vzBtwoQ+5Ko\nr9ACpKuR+6T5E3f2kEqTZyE7m4JbN0YY7ZhNNK12wCQtMAKKrEBKSaubYKsZtSuwDpIwZJaXBFZh\n8HBKMitLnPNIWh3mF5bJZyOkO4yTMcu9M1y4sYWQY6Llw8yuPkGlPFo+DK0j8MFzA8rxBnXlc3h1\njbfd+zO8+My3GI+vMBnvor/6SfLHfoYTd55BCuh1ITkYwQQhBIGA0loCwDqLEpYiqxmMK6rKNfIH\naAKpqISlJUHUGqTCiEYp0lmH8D0oakRZoYCpAV+XdGKfaVUTKdcMWyHxpeLc1U0ms5yirFCikfEb\nT6b0WotcuXaD5FCPQDb684bmRlZUGs9rrAKNtY0RiWsUJ51wGG33bQ0biW/HbbmTN7+6nTaHVhYR\nQrKyvALWkmvJYr/Px3/ksYaaax1h3CJqdWm3EvLJZWbjEelkGyE8PApaUYKoC9IsxZQz8qqk2+li\nrSNPUzwvwiqBtTWhHxAHkmlWUqa79OaWKLOcsO0x1+uRZSlpOqYVt1DtkFBBFMes35jiJS3WltcY\nTnPuXOtR64ql1aMcnR7l1Z2v8sj8LzOzW5R1xdG1uzh1dMLC3EeAP35T8XhLJHqjNVVlyGdjUDGm\nmlHnE4IoQakEndfoqqYuMjCWTpIwHmwQ4+NLQba3gzYlVToAWyO9mDobEyQJXp0TxS2iWPHQ+z/E\npUs3ee3pL9CK55BiikvW+PDZxqqwsvDgvonJp7TGFhntKOBdZ88w2LrJPb/wP5HVjSOWAO46Aqs9\n2NiDbrtJ6nUJm7uNEqcn4Pqooc7pgwL16Zgo3aVonUANnuC1yzu02m2iuMuoDHnH/R2oSrZvbRJ0\nVlg5sYLddcwvH+JnVwYIC8LUeFGCCgJcVTIY5pSFptUO0dY2JhxCoMoxtefjOUddVVSVxjMaLX08\nmhtXqDVaBdRVQWUFyocsy5gWNUl/gXe/524unDuPVJJBWvHBY4eY73gcWezzRy9f5fqtAadOHmeW\nXeDGIOXa9issLcwfeK845zDG0un7FLmPFzqEMmhTNdPR0mM2yRkNCxbXWgx3C7RxzGYVzgnKUiOB\ntRMtEv80yhYIp6m1xtXNrWRaVLSiZnLUGgcSzpw5CeEcUnkINKacYauKpcNnGV16nDmzgRd3kZVl\nlGW0lCAoE+oaevMhXd9nsDlBz75If/4IvjxBp+vRnevz2d//R/zS3/pNjIDNjaa6P2BQGpoeAiks\nQioK7VjtxLx6c0ZhDIHvIZ1AiQpfSAqtCJXCGoeSjrp0eD4Yq/GkIBeStKyRSIwx5JUk8Ztq01Me\nVVmh45KrVy/S8TWdQDLJSiqtmV+c48ULVzh9+k4Cu4OvQpTfobKNwqwfKZQU+L5HXVeNm5RofG+N\nNdTeviYKDrGv9ikOaDwSBgG9/iKZS9C2JPJ9hArxlERKief5jU+FlGgLSZJQ65rzz38Dh8FoTSsQ\neM5Su5oiHZDlOZ0oxBpNUaRUuqIdxuQywJclFkNWlPgKqnzK9sYVOnOLUNcUqQVn8ZXbd+kSjYyE\nyXCmQglNZ26BOGkUcFXURiYtYulzR/gYxWyXO+fOIpwF6XHEOaK51Tcdj7dEorfWMh5sUWYGYfdA\nBigVkk93KXSOFJooCFAiwVcSqzX9/jwqSED6pOMdBAalAqrJJsqPqGcOr7OE1TXr157BD0KWDx+n\nu3CI+x95jFe/+eeESLT0ubVlCH0FFs7farjZf+83Ps1zX/ost77xO0gV8/B/9Rv4Hix7DRW03YK8\ngK3dhiJ3/XpTtc8yw9K8okobA5KFEMYlB6bMqTBm6fqXufbCf+LU6iKrqx66KClGimQ8ZP1Wxn13\nvpt2dJinX/oWa2dWGOzu8ewXX+Gv/PTD+IGi1BWxcxSTKaPZjDqTmH0dmkBYrDU4qbAiBKuxwsPg\nqKoakyRIoK5qRBhSlTWiyhHSo6oLtIWdUYp2cN8dxxiNU9790EM889yzGGPIpmOmXof/43f+iI29\nlIV+h6vXbvKhhx/gD774DR6+9wTru2/eIef2ktLRX7Rs3JghIo871/oI3+DJgHQyQRnD6Ts73Hfy\nOJe217n/9CM89cSr7O5NKLKSs/f1UErR781DPYceXqeqSoqyJvElVhukbCSw68qiRDNsNNjZwe9o\nFhcPYaqUWmvaScCxWy/RCgTS1fQX5xkMJ3QcdObmGE0yTi51mOzNGNcV01TQWu7QVVO8KuVY5zj5\nbMRwss2/+Ve/ztLRdxIqx2x6QEhLQFVbolAQ+R6esygcs1JyuB/y5NUhvTb4StFSkqo2+2yRZoS+\nRhKKhmsvjMU6i3CQV5rcOHqhh5QW5flUlWm0W6yiZ0uqdBfTbbM1qlDKQ0pJt9PjgfvvaQ6DuqLM\nZnTaFc5YVNBFW4GxYOoCIQS1MQjRaP1Y29hvOtsMDTa3YfedjIc3uVpRSMtZoqiF9AI8KZFScXMw\n4dShLsbV+H6EyVJeuXSVLBtT5yW+kCivkR5HNsOQDstqr0eWTzG6pJxsoDxFaTRuf6rf1BXKj6m0\noTYFc0HNcHcLoTw6cwtURU4YxbSjkKKY0Y4SJtNtnLUszc9jTI0THr4v8TxBPt2jtIpW6JGEq+RF\nzvb2LZZXD2OtgDx907F4SyR6U9fkoxHSCwAPTwqkUCSteeoqBecxm45oJxFVugGq1Yy8mwqhC6yQ\nSJO9PshEPSWIIrKtl3GqSxR4CFuTbVxiZ2udIEqwcUKVDii1Y31kkKqRRL3NvXHAkfd+lGPv+ygI\n2Ng3KaCRssbtNA0ls78Hm2spWKkYD/dfA9gG6jnoNg2rAf72l3j3CcnW3oA93WY6Kbh5c5vICylv\njfmTq39Ge83j8JFVzDTj2S9f5Zc/eIowBGMMSvgUVc4016SpAQOe5+F0Te4EUjlMVuM8i7U1nqjQ\n2qCRUKSIMMYKhbQVlQRTGcR+RVJow5OXt/CE5AxD/t3jl3jgvnfxUz/1U3zyk5/k/JVb/PyPPsLn\nvnYOqTxmsxTf99jeHfHIu+5htd+i1WnD1w8maiaEIFKGpeUAP4TZsMKTMf1un7vmH8LPRuSzipXF\nFdaW1nj18lV+8N0P0At9pqMhwgt56pXLVHmJak2JPY9hXpMEHloXaGOwKNKyIgwTQgwems3rl3FR\nl8k4pdVu0+/N0V9b47r26bS7dOd6JKFPmIw5stJnZaHDk89dpNvrcPToGt989gKjwTaynFKUmmNH\nlvnaV59o9PPDFmcPPcUXP/8pvu8dR6lnWweKibWNQkzoBL4C6TnyCox1zC/NkV3cIR8ZFtsxwliU\nL+k4h/QVWjuktBAE5FmOw1HUdt/b1FGUFbHvEWlLgaWfxGRFQegJhHF0WwnWWVpRQBIoZllJ4jWu\nSNZUaGORSrE3GpMECk828yF5oamMpR15hCqg1BaHw2qLQjTw074frxQCaw92JRZCksQNTVsiSDod\nyqLCCwJOHolpHkyFdZaw3eHitS2wNZdf+RZh4OF0SacVU9UGIxXduE1dzogDGO5tgvDItMYzJdaC\nNgYlFUbXeEohpYdSHnk6RpuadhKTjXeJegnj8YAobJHZHM8WeMrjr//8T5OmGSpIaEURZVU3Ezi2\nYjKtiZKY3tw8rShkVAh6UTMR/WbXWyLRCyxhFCFdgRVBo3Gjc4RQBErgXE03CTHWoaucXFcopYgj\nn6QVkY0HDRbuygZbUxJtA1qdVXLr8JzE2ZrAa/wuh+MtZFkwKT1Eb4mb18+htU/U7tPuzKE8nzAM\n98WmaE51AXLf/vM2jm+bN/+6HezruDxvJPb95+XAiV4pRVHF6HaLZF5w7oVrSCQLi22+9z2P8pk/\n+hz3PHCEbtRlPL7BH3/qeYQKObrQohfGTLSlTGcYESBE8/B6WEztME4TBgG1FtRViTKNjVtpBcbU\nGCGpawhchcQhvAhRFuTWEAooqppKWG4MZmTacGqlT/ncLcoi545jRxFC0O72uHjlJp3EJ251cXXJ\nJCtQUrIw10FKRxy8+RHu2ysKHZ1A4gUhG685un3DUTlHVATcvPw4vbkFfFuzkVdUsyEnT53i4rUd\nzg93Gc5yam0onSSODUe6oPMK6cz+hK1DSg/TKIJhbU1aVvSSBKcNZT5ltH0TafrMtUKy8Q7Xb21y\n9Ngx1nemLM932R7llGXF8uoa99xzFzfXN/jcl55C4DAyZPnwUX7kA+9ga3fCj/9Ql2sbI9aW51js\ndfihDzzEP/3dL5MXBxPwMs4xSXOkVUReTG18fFdTVIZIOBbbARd3MkJPouIQH0fqLIWDph0gqLMC\nX0FZWbS1lPtso3GhicKaSW5ZCmpiXzLNa1wMt/ZKlBSEgcdgb0gd+PhKoo0hCiRp1vRTLOCEoRVF\npGWNpxrBNF8JisrihY7Kwu7uHnHgE4QBi702g+EEQUM1VgeEbpSn8P3G+s84hzYCz5NUZQG2xg8i\n/DDCWNtM9FaOwA9JR1v05xZQoUJiiIOAdLLHLHNYJDpvbra+B844nJB4yiOUisoaAk+RG0cYeEhf\nkqdD/CAgnQyoijF1JGn5islwHelqup0A5QkOHz7KdLJDoJomddRtQx3Tcpq8qImTCFCo/iGWiiEy\n6WLSyZuOx1si0TtrCKRuTsZqBlhMnRNFESU50mqqsiBstym9kMAWGARh6KOzvUavRBiCOAbZOBAF\ncQtdGfwyxzqFlyyjXEbSigFF4Jdom1EuLTGbZiglqHYzJoMNVCDxlMQYD/CIWm36S6u0Wv7+VW7/\nfe9X83b/4/XP7Xd+rfnmg0ZFEVRbvHbV5x0P3MNz+iKn7zjG9lbBc08/wZmzC9xx6gwXL57nS599\nBSENs3KPY4f7FLognzVNSmsNSnrkZUGIphYeQQV1VTVibZXGqABhKwwSXVZYA5U1dFsRSkryvTFp\nbZvbQG3JK832LKUwlkfOHiXXmvv6Ac+/8ByPfvCDdDpttveG7LQEf/Wjj/L1F6+y0gn55vOvcPzQ\nAlJ5eKHHqaWDY/RCCKysOOafobcwY67b4oVzjTfuLNPMxxVJtcRSEjLSPnubM4bTlKC9iB1fY25+\nmcc+8A6y2YzrW1MGuSD2vf2JUYN2htoIBD7GNTfLvG4GxEIl6UYSYWpm4yGtUPA3fv7HGIyndLtd\nSm34+JFFfuv3v8BnvvAEv/YrP0lt7uWHP/QwVa05cXiR9e0xSgre+c4OrSjhvabmpQvXOX3HUZyD\nT/wXP8Kff+3rB4qJ24dZEl8yy5upb6kEEs00N1zbnZKVlq1Zc1+NAw9rBLIyzIQkChSec2R1I4Zm\ntcFah64N+T7Mk+cVMxRBqUnzijAQvDJNMQh2dmaUlWbooDaG00dX2RvOGKclnSRkqRtTyoC9WcFs\nljLfbVEZx+L8HGlWUGUpSjU0xFlWsxgoLlxdp9dtY4zBUx6j2QFlit2+oYqpUdYiqRHKI1IKFc2h\n8wzrDJ4fgICssk1TOoiJOvNE0mCKKbPpGM/3yMqCULqmb2EFfq2b+QNpqW1N4Twav4GmANTGQDZF\nKY+szGknMTgNptH+D9CEsc906yZYRTbeJAhjDJIwUKS7G8T9VURpiFsxZZYStrvgCqo8w3N2HwF5\nc+stkeilBF9WFNoRKIcua/ygQ1XlKCcQfkyro5hOJiwuzjPe3WYwydBxiB/5dPX/+v8AACAASURB\nVNoxzlQEcRdhLbUpKGcp6BlVoZESjM3wWgtobeh0Ykw5o+3BZAyIGislCNMkx0JSS4lUFQhFWubM\nhoPGtEEq5L5QlFA+c/PLeGGI8vx9dsDtj8ZS0PxlynmgLAuWj9/LxrMvcGv9GsePr9LpdTh37hZ3\n3nmIYlry4rnn+dqfPs1kllJbw/d9+GGksTjhIaWhMhBbsa9XUjDDUpQz4sAjiSKM0GAMwmUIoSi1\noawtUkiySiN8jyqt0Lqm8kLa0ifPx9wapjx+8TqhFHzk7cfZHGZszkpKbfmX/+JfkGU5wgUsLy+C\nn3D/PXcz2LzFfXccIo4CkiThzLFDfPErXzlwXKyVLPaWuf7qNXTeZjOtsHMtlls97p4zzK/d4Gvf\nkLz09KvMzff5bz7yMINpxnQ8JLv3JMZA3OrSjTzanS6Pb61T72PAan8zOmv39ewlgScorQGhiEKf\nvb1tFpeP0El8qrpieanHoeU5hBT8zue+wcpCxE/+8Hv5x//6jyiM4vBSH7PQR0iJco6zpxeoqhxP\n+rRbMc5JHn3vctNktoYwCFhefPN2fNDsNc/3sQhi4VCqoVj25jpI4bg4yGj5HlEYsJdXdLWl8iVO\nQKAURVnhSYEVAl8KSu2otWYnrRgVNQqYa4Uc6Ua0owCnJOPKMJ5ljNOC5fku/V6bvXHKStJiMByR\nVwZPymZWoRPheYp8b0IYheS1QdcVs+mEG9tD1pb6rPU7hKo5aLaHU9pJRFmWJFGItYbQP1iqElKg\ny5S41cFo3cTf86nKAleV6LpGOYFUClMbKl3jnCVOOlS6IkhiaueBH2CtxhqLNgYHGNfIoqp904O6\nNghhqIREKUHiSQyW6WgXL4yIo5ginVDXNcoqbJHheRZFSZYXhEmHIPAJQm9fmlUStzpIX4AIIe7j\nlTNAgCeJ+kvgScxo8Kbj8ZZI9AKB0SW+0FgRIqiR0kMIh3ai4cCPS8IgpK5qjC6p0ynxYockikEJ\ntPERotGRDj1JZTPCVhffn1FlY4KkjfIsaVazl09RdU6v2+fF3fWGyiWaRlLgN9OzzgnK2jaxjXwq\n3eh4SyGbCl56OCHY2rgBVqA8hbUOKxp55UprKm2ptabWNaU+2HUcAbtbWwz2Uo4fW0Y5nwuv3OJt\n99/PhYsvc8+xQ/z+H3yJurYg4f0P3cVdZ+bIK4OjSbqBH4MHWTFlMs1I84pplrHQaxF4OUiJJxta\nXW0M1koCDIVT7E2mHFvuktuczDhcMWNzb4/dWcYzN3YZpBULUUBhHZdubjPyIpZWQsqZwVMKYQzL\ny4t8+ZvP8Z73PMSZU0d4/vk9eu2Ywd4Iq2vy6uAnYD4TXHohwm87SrVN6Rxxp8XObsqgCOjtRWxs\nX+ejH3qYyUjz0oXLzf80DnnbkRWub2zTDgJOHVtlfXOPRAkKoRGmRkrwpKPShqysmfMkUmiM36FI\nMwrpCDuHcaYi8R2rKy0qq7m8OeCJp1/lP//YB/jak+fZHqzzCz/xAe44fgScYzzLUHjEoSLwIArm\n8AMf4VxT+WExxuJ5IXlRHLzvKKB2MK4M8+2QRHngeSgJUeIxNhIRBRTjDCUcrcBjLlLE+5aMSkn8\n/YlZRYPtl8aynVZoIdgtNO1A4YSg1U2Y7m7y9T2Ppbk2UimUlNzcHrLQa7M1nBEGIcuLC8zSDIHj\n1esDTqx0CXyF70E3jtAu4tL6Jg+dOcb13ZStUcZ4mhHFMZ0kaJqS2tKJIzaHY2azN994vL2idgdn\nG0q2lI06buD71LUl6rTRVYWQik//x88jpaCsajzlNUwc50iLDF3X3HFkkZdemSCVQFiHdBZPQG0d\npW2Svi8hVLaRQxAAGs9TeFKAM41yqKuRUqHLCYEvePrJZ1ns9zh+6iQGwFNMxxNacYBFIbXFWomY\nbqOWjkJZ4IoU4fm4dIrq9t50LN4Sid5ZQ7q9TpgkwJQwalGVk0bYK+kCujnhC3CmoK4NvqwYbG8S\nTkf0ej3aC0dQnsJzFU55dLs9inSC5wmiTkwtFPlsxCTVuLDDJEtZCCXbzmOiffbbqciqgszgaLi8\nSggo68ZDUjaGIkpKcI04FEisaxpF2mica5Km1hXGmqZRaw32gJN9OLi+uYVXlxRV8zuXl9psbl/h\nne96G7/9Lz+N0RYnDQ9/79tZOtSlO5qhwy6TcYZTTQPb8ywXNvb4zLeuUNjmin96scWhTkQcePiB\njzUaXzYbfFZWZEWNs4Y4CTjZWiCdpNwa5nzl0jrreylpWZEoyVaac31jyF//+MNs/O436c21uHTx\nFkNniOOEZ16+xse+/1FOnznF7/7ep5mf65EkEQ/ducLmOCP0D64BVFswnS2ShYp75yWT7P9m7r2j\nLLvqO9/P3iefe26sHDsHtRLKCSGihIgimDBgwNgIsAljsAeGMY4PG78Zj22eZx4DY4IxtvFzEEEy\nQkYSEghagLLUklqdqrrirZvvuSef/f44ZS+YNWN1z1qzlvdavbrq1L0VfmfvfX6/3/4GSb9dYVDP\nOXCoy6OPZ1xz+Q4uOecQB/fsYBQE2OVxomETy3YYn5qg5Lh0hj7HVlYIwhGOFLSyFITE0RVBBCqP\nAYcozRFZlwibRtkmR6fWaLC+tsI1V5/DkWeOMgx9LrpwH7ff+S2q3jgVt8rSWos7vncYU1Ps27uH\nRrlCnOVkaOiaQOSCMMqwDAMlJK5nEgRD7FKlqBzPYuS5ojsc4bkWcZ6TAKbMSdNio7no/H1IFF7J\n5aGHnySNUnpRhq0JdCm2JbglphQIKRglGaNUEWcZGZJBUhyUrvVD6mWbkd1gx7QgThRCN4mznPnp\nSZIkwbIcPKfob3u2JEgEU+NVglxnEPp4AlJbEaUZC9OTrPVjLMtEihzXtYjjiJJtEicpSmoYts3e\nBZduv88zJ5fOPChKFSgfTaAZFqapkaVAdQrD7wApmm6i0oQ//OI/EEYRucrxfZ/+yGdqaraYb5ni\nQ+96PTf/yn8izYtkTWgmuUrIt1FCxT3I0TWDHEUUheimjUBHkxpIgzTN0Miw3RJp0OSJx4+ByskU\n3Hj9C5FSIxz5OJZBrhUVEEInHrWxLUGyfhwlNEzHK0AopkXYbp5xOP5VbPRCSryqS+z3EFaNqLtO\nio6hwSgcIqREFfoFSN2gVJkgzRJ6rSZTzgR5FqPiLggXwxAkYZ8kHSLSDE2HVJkkcUQmytgVEz/K\nyfUqxzZSnqMe40eVq0EKdK3os+WZQkjQZIH3zdLigERqgjyOisw9K/w3syxBk8UNNgwbkGiqUNtL\n0nibMKWRZmeX0UdRQmNqHxfNjdjAJOl28f2U48dW+dF3n2RqrMJat80NL7uMUyc6XGrv4tJGiKVJ\nRknG8aV1emHM/cc22PBjlrohuqFhoHh0Y8CJlr9tjA66Vsjxym15UCkFjmmABnqaYlomwoxY6wwx\npMQzNMJccc2BaSZrkl5nxAeevx9NM0gu3kVi2iz7LieHio2hYOu++zln7w6majYz0zNEvk8tU1TK\npbOeK2Uv5XkXVHnymSqd4T7WV0/RC20GvS02u4tc95KjLNiHaHc3ufuBp2k4u9k1P+Tkxian17YY\njCLWtjZYbfa4/uqrcKem6Jw+haMXR+hRBq4p6AcxjgGdVhfbq3Ptc68kTMAplaiWJ3j6/m9x3w8P\n43njdH2dbu8YJ5ebNCZS9u06wFNPPchgtIgtDVp9n9nJGo2xccqlMuPVOvEooFTyijmVZHQHQzTd\nRJNW4XdwFiPJclbbQ+Y1jX6YY8kUEkhqEt8XWLpRZP1JzkUXHsIPwwJGqmloUtEfBMRxgioZVL0K\nZpgz6xgkacyJJ5/Gj1M0dNajkNlKiBQuQZhQ9RziNMI2LAQFdr1SBUNoJEphmDa2SHEsh77vMzHW\nKJIeIdA0nYrnEkYxcRQRJEUV7FgFAmcUJniOhT/oEogCX382QwhJlmYgdITKQOnkaUC2eZJcSOIg\nwDANdCnRRYwmMvbsPMj6xil67U0efuBerr70WuIkYX5uEUPXSZMEUxZJXaaKlrMUhdFLriBPEyyZ\nk2sGaAaWaREnEbZukqUhrm0Q9Nt87XuPsK+qUS6XMU0Hw3JRefFgSzJFFm0hdRdNFXpAseYiRQE5\nTUadouLLU0zvzE3Y/3Vs9KLQozdtm2C4ySgIMTRJrBfWW53WBuXqJKE/QjMdNLuEbpSZnbMJ45j2\nVpuRP0QIqNWqePUpklyhWYokydFNi1FzGatWJwoUURiAdBB5h4EqMVu1uOEFz+Xq/S6/9tk7GQY+\nYRQgpIXtWLz+ZS/BsDXmx8HQ4Bt3n6Q/DDi5vkmW5Riajue4ZCpDV5Ikz6h6Hn6UEIQZSRqRiTM3\n8gWYn5/lwIRNpI3znle8kNvv+A53f/eHtNZ8yhWHZqfLO9/1Nk6cWMOyJHJyEZk9QZKZxGTopuSH\npyI+9PGP8ZGPfYJxMyrwz0Kga5CrlPXUYa5eLqSbBWh5Rpbn5AjaUUbsRyiV04/g1FqrYClKRYpC\nE4I333gNRhaRpTGgoWRxFCVUzqmNJq3MxnZhlLlctLNeHBZ2NrCcCqtbQ2566bV84ou3n1VcVO6x\n2nw+uv000l7Fj7ss7hzHdSu4loEavoyV9EFM6WCqHQwZ8MPlhzhyakicpSSdIUdOdCD1OLp8ilEI\nColpmgUzOMkZxhFSCMI4YcdElXZmMfADIiz6QZeg1SoSCg1SmXNo/zzHTx3jTTe9iu//+Ptsbm5y\n+cXPo9Vdx/cHBE0fTYdev0uU5izMzSOlhZA5UtOpeBWQOnNjEzhu6azlK3Up2FUxyYxCRCaOE9yS\ng6UJTrVDNKmT5zlRmpButxJd0yBKM5SSeCWX0Egp2Ra+X2DbldIwdI3FfXvZOnmSaqOMJnKGcU4n\nKrRp/FFI2XZAiO1zMEUWCyplizzLCZOUKAWp5TiOS5wk1FyHOE3IVI4/CoiTlChJsAwDTRabc5oX\nqJUoSfB0kzCOKTlnRxfOsgxBSr/TJcszyrUJEBqGoZOlKZV6jSzLSAIf14RqeZIdOxdZXztKkoRU\nKmMFyVHFpNGQLE+Ik5iaJRgEKWlWrJNM6piyyOwlYOiCVGzblSLQhURSnHsFoxFxMmIYJCQVgyiK\nMAwL2xREwYAojnHLteJML4vAsNCI6TU3qU5MkpPT7w2pVKtkaYiejc58jpxV9P4PDZUXN0XTwK3W\nybImKg0Y9IbU6lXGJ+aLjLpcYmNjE01roRkWWRJTsTSMqodh1xgNunR6A/IcTMcmjlOkMGgPmzhu\nQbYql3QM6RBkGrYzz1Zrk+suew4v2O8igD9+1wv/5d8VeO+Ld25/dg4PrEVstbbQhMlgMGBhbgdP\nH3uUYyceY7C5RM2r4Uc+6iyZsSsr63zp7++kUvX44eMn6TdXWDpyDN3WCcIYJeD7d91DFGeMVxw6\n992Bdsk8lgWtLOXRsMKb3/02cs0gKbvo4Wg71nlBhBIGjuNwcqPNi669DITkH+85zPTCLk6fPM7u\nMYdREIMSHF1rcvtjp9nMDK67/DK+edd9vPC6a/nsLd/jxst2sdYc8NTyFpWKiYbg6a2II+t9du7e\nzYG9BzBtl6ePnuDCgztpTE2zurRKo2Jz933/GxIIIqbcOEEerGDXLGb396g0ltGMGpsbETV7iWCU\ns2OqQ0hA278H4nHOmboOXU9oT9/F869d5JmlgCsu/CH//TP7AZNUCXQVokuolAyCXkwQ+ozXPfw+\n+P4IPw0xhGDQPMrE+DSbrSarq6tsNFewnBK3fOdWqlLHKGXc/YOv43p1UIoLDp3Pg0ceYueOvSxO\nzXLbHbdx4w2vxNItqp7LM0vLnLd/L/94+Ls855xzUWc5WQwpqDk6oUgwNQvbtNCEIhzG3N+3Cbd7\n9ypOij55pqMBhq4VnrJDH02CH4a4toah64RxWBDnMoVmSCKVM1lxGCYptUoNRxQsY6FJuoOAetmh\n1R0wVquQpSl+lOPZkiiOQAmyrDiL86MI2zToDcNCZ0cIsjTDdCwMXeAHSfHABjSp0/dHTNSr+MGZ\nS/IW8wS63S7VWqNoteYxYZJiaFZxKCpT8lRh2C6/9aF38tuf+ktcESBVgh8mTDbqDAZbfPq330u5\nMYGjQyYUfpSRI0hVcWYn8pQUhZIGYZYh4hRNExhmRpoL0lxRtcdI8xG6yPjm9x9GN2xs14Y8I88z\nPK8KQqKbNp3mOo2JaYb9DtVagzROiPOcTmsDy3SoeqUCYh4kpFn3jOPxrBu9EGIB+DNgimKf+4xS\n6o+FEA3gKxQeYSeBNyilOkIIAfwx8DJgBLxDKfXAv/QzhsMB6+tr1EoWmu1Rq7uEviAaNfEHPWqG\nRZqmCAwqrmQURKTRCMOu0PMDrDjGjSNqjSmErpOnKdGgh0IySgYYUqJJjzTsoUsTz5QY4RBNmIhG\nnUfv/RZZcg3jk1MgBLWaw3CQcM5CIQEw4p+Nz0iAreVl3v62t7G2sYEmBDfffDPv+uAHGbY1nnvN\neXRaLbxqhQ/+h4/T6fXo9jd55I67Ac4TQjxyJjFRgK5JOp0Bg8HjtE6voFkmlhT88vt+lkGvx9vf\n9fP8461fJRsMaGweQynFRqfH5297gA9+/Dc5ceIk9QP7ECkESY6tFygBTZd0I3jlc6/k1m/dyVar\nwyPH1vDDmLX1DfI856OvuYJkGNEZBNx1ZIW0McV8yeZb997PYBTy4wceJFSSr33hPoQQvPb6q/jK\nvT8mygWaFAwHQyxziamxCe754R0M/RGzYyU++NaXsXz0KNOzs9z+g0fPKiYAmpHxzPKjhH6ZeVzq\nTgctGGNzNcOtgGWZ5H7KkcerHDrYY2NJcsX5MVV9heMnqqxsTLGnfhH99Qf52poDWVYYdAuJn2rE\ncch4rUIsFDvPmSRYDclzhWUaCE2w1dzCyBJGcUYwaKNrkqCfMDdxDtOlfUgD2u0Wuxf3sLq5zqUX\nXMHn/uI21je3kPJRXvfyF/H+n7uZta0OH/u9/8Jms82O+Vk+9bsf46YXvIgsU6ysbyKEeOZM148m\nBaau4+eKOEowSzoiUTzeS+n7CZalMRzEVGtlgiCiZAnCFLxyhbWtHpZlMgpCquUSnb5PyQGBxHNs\nwiRHNiYYdNqUxj38OMUW8OTpTcYbdZrNFjMTNZrdIVXPIwhCQGHpemGyHaUFI7fk0un5jMIYic8z\nS6tEcYomBVNjNTQJaaY4trzGKIywDIMDu+apV8usNrtsbHXOaq5IKdB1gzyNMRwb3w8wbadokShF\npzPA9Uo4hsGVlz6HpeVPsHz6JAjBVLWEaRkoJIcOHmBzZZkPv+8tfOI/fgYdSaby7ZanKryu2Wb3\nUoA0pBAkaYJmmnglhzzzqZdtTCFRKkWScv55B/nODx5kTEhq9QpQVNv1eoNwNEI3XXrdDo7jUnYt\nup029fo4WRqTb5vHGPqZdwnOJKNPgQ8rpR4QQpSBHwsh7gDeAXxbKfVJIcRHgY8CHwFuBPZt/7sC\n+H+3//9fDtPQGbVPEw1cpGHiWQYiD9C0jHKlhNANRBqRxgFCZTTqVXKlSFNBqV4EOggCmhubSJGh\nhE44aCMMi6mpSfq9DkKzEbrALumkcYShSSoixECipQaPH/4BkTKZm5ulVK0zHPjc+43jTM9PU66O\nYZc8WqsrjM3MEUQhH/jo71P1JjBsh7e+/rmUJw/x7Ttu4TmXXcOePQt89567+avPfppLr7mE5jMn\n6TWbAI8B7zuTmCiltg9yFUmnTW28SpIJ6vUa9//4CH/y6T+gtbmJ0h0GRx9gvKqxutnji995jN/9\nfz5FnOSUxyKam5tIqYMQlNyCBDaKEvwo4e9uuxOVhjx2/DSGlLiOhUpjMEuUctiQOuv9gI5RoTJW\n58lHn8by6njSIrLq+K214pBZaHzn/keZHG9w0aVXcO455/CRj/86SZbR6m6xY26Gyy++iB89+CBf\nu/tBrtk/yaPHVlne7JxVTABUahFv1pmZmMNRKROLBZt3cqeGMaqxNfDYMTvGcWOJzmbKoXMWOfZk\niesufx6XXGgxu7XAWqvDjsUD7Jl7Gbd17mJ1rWhdISWmZZHpLrv3Nhgfq7DaXyPtJ/ijCMMtEQ03\nyZKQukq58srnUdJdDuzdhWPZeKUqP3jgQWqNKQ7uWOTp46fw/ZBfeucbefE1VxEnOTe8+WZe98qb\nuOX2+7numqv56PvexR98+vN84a++we/+2q9y+133EoUxZ7N+4iyn7dSRwyGarqOTk+SSx4aS3iig\nobmkSpG1+3i2hRTgOBbLy6cplUrkGJimw8ZWj4mxOprMiaKIbFtGOI4TklzRzTR0XWIrRaNeYzga\n4TgWg1GMEDlpGuFHCVmaUy1r+EGI2K4Uap6DJnLGqg6DYcDu+Vk0mZPlgieOLSGlpDcY4bk2CzPT\njAKf0xtbNKolwjDAD4KzXD9QrVRotZpEcYhjO2RCkqY5hqEjcHBsi9AfEUc+f/6p3+Ltv/w7RElE\nKjSkVNz5lf+KROGWy7zgec/j6PFTfPVvb8OQMFk2GYZJ0a7KM3RNYBoaQmho5ChNYJsanlvAM33f\nJ7ZL5FmObdrc/PZ/w7cPP0Jq2FS90jasWaHrgiQvjMO92gRhGGAZinq9hqZiTLcC+RDHNtEt59mW\nyz+PZ93olVJrwNr2xwMhxBFgDng18Pztl30RuJtio3818GeqkJv7gRCiJoSY2f4+/9MhNQ3bqZAk\nIa2WTxMTwx3DlX2GayuYTg/LcghDnzTL6Q76eK6LbtpkKYzCiGG/R47CK3m4tolX9hASVk8vE4QJ\nUbzB/kMXk6cxVqlE4I/QZYwIIjw9xzQshkJn+XSTecMjjBWZu0BzoLHa2sI0upQ9m+Xl45ApFA69\nfgshNaZmdnL82Anu+tZt/F9/+GWUZuE1LuNTn3gPL371R9j47qe56Lmv58TRT6KUOqOYCGBxpsFk\no4ImcqbGG1x8wSEsy2R9q8fv/Nrv8d3HTvG6Q2M0VMiJVZ+7Hj/Na9/yM1TqU2xsbpCpnNWVFfrr\nazi64Npd49z51AZrqYnrFJ6o0jQJwgRUTsmxidOMkq6xaVSI/DY/OrnBoSsu4/B3f4ztlWhtrIIQ\nhP4AKQubOanpbPUjmt0R5eozXHv1VeiaTrVa40cPP8YbXnE9UZxywwtu4A8/81/ZO2by0DMbnLt3\nF0+d2jjjmABUvBrPfdF5rB+N6G4dY/f+Gro7zdKxHqXxDvsnL8SsHaPUOI+94y+nOxzw8ksm6PcT\nqvU6S2srHNy9i8ePPswVV1/HI48+ycrKMnGWUXYtpOVy/NRpPvn7H+eBB77H6254Ff/21z/NAIdZ\n22Njc4OqYyMNgze/4o34oxGe6wEF1f8V178cXTfI0phzzrkETbewbKdgUGcJB/buod3pcfudd3Hr\nX/x3DMPgnW/+GW5808/xiY/9Cl+//R+p16v0BoMzXj9OqYQVjLBM0EXOIIRbjrcxLJc8zwnDiMXp\nBlsdn1EYEEQatZrFBQd3gmbw+BPP4Do2uYKVlVVilTM7MYFrGwgRUXZtpOdy3M/YNVYizLKCgGca\nDP2AXMswDOOfEShjVZd+mGzr3GsIlZFmOaubHWzbxnMMDF2SZgqvZONYJoah4wcBuxcmqVWrrGxk\nLK+3iNOUZrvPVKNOt++fxVxRCMOgMT6OQifLMgzHI499Rv0Otu2SpTmmZWPoggvP3cv9t3wa3a2S\npyOyXAIF/HVldYnduw/yxte8mr/+uztwbI04B1uHJElJRY4mdWSuUDIvgBwqRSMmTXz8UUiiTDy9\nhGubVD2Tnbv2keWCIAHy4h5mSYBeqiOyAZZWwLe9SrnwyBAZQz/Ckz69/oj6eKPo45/hOKsevRBi\nJ3ARcBiY+olAr1O0dqB4CCz/xNtOb1/7F26KoD42zdbWJl5JopTOUEmufvkvcGorIE4y4jTHFgJD\nNwr1OcPEMAw0qdMQaputqmg/8U2GrSWSJCQME0zbwm7sZu8Vb0DqFvkzt5D4KXGUYbo1HDchzxPM\nfA1Ln8J0yzz58IM870UvRNMNNM0CitI9Dn26Gwl+b4mZnTuQGjz99BOcOPooCzPvpdveRKU53a1N\nHD2n329TrY8xGvRoVMZ+8g9+1phIKdhstjlxapWpiQZPntjk8aOnWdro0uoPSZOYctljRvd4em3E\nNx88yhve9QuMT9T44Y9/XGRelsXn/vTLXH/uHLZQ3LE0YmhWsEVKvVYBBHE4YqM9oOS6TI9VsG2T\nXbNzrA4Vo+6Ixzsx9R8/Rr1eQ+QZ3Va3KFG3qcC5ArFdypLnLJ1e4SMf/00UimDYozcMcEzJo48/\nwAff+WE++V/+hPHJaQYPL1GreGcVEwBDN3jpJa/HuMLle8/8CavP7ODk+pO86aXvI1cKlfo45Vdg\nNWzGqjW8Uplcd9H0LVCKG198PSeWj3OedjGDUcBauEIidcJRRKVSIc4lgzRnszXgDW98NzKL8Sbm\n2eq08bwKkYIgzXjz299ElmZYtoddqqJJSRiOSKIRhmlhOw6arpFmBew2CUc02z0eeeIpLn3OuWw2\nW1RKLijF/OwMm1stLNdhaXUD2/ypkvxZ4xIGIZ4OSANLSg5vJQig1W4xOzWNqUta7T6WoRWSAFlK\nrz9A5gmtbpsojJmdrDLwfUyvyigWGKYgjGMaZY80iVltB0ghaIcKp14i64xQWo5panilEq3uAFOT\nmJogzXJIE0ZxyljVZRSm4AeM18s4tollmQz9gLJhcXqjzSiM8FyHKElJEkW3N2DX7DhPHlsqetJZ\nTq38U9nrs88VIUiThDxLSfME27RQ0QjDsXBVgzQaITRJmsRIkZNkAsMtEfhdpK6j6zpSgqbpLCzs\noddubRPabMgTBoMOjmVg6QoyDSEEhkwRolBANSyLNIkYJjmZMMnyDA3BlRccpFKu0G63SHONOCt0\neILARwqdZNhBpAHSdHBtQbvTojE2i99vE4chouRQnxgnjSN088y37zN+M/QmQwAAIABJREFUpRDC\nA/4W+LdKqb74CWSAUkoJ8T8qtD/r97sZuBlgfnqCJFVomg5awSTLVYX9u6rs31nZZpbmqCRD5Yoc\nQZjkBFFOLwjpjXL8KCOIYpi5GDnokWQmueiR5AZBb0h90AIhkZPX4C8/TFXvk0RDUuFgaBpKZVgM\nybIRVtjitr///7jxFa8mkxG5EkRDhdQ0rMoUhj3OKIVeu83//fuf5D0f+G32nnc1UmpMVTSq2QjT\ndNA1yaGpEY6RMu4+u/bsT8bEMAxMy8Z1XUbb2dGptRblxhjTC/OoUZeSFCyFcHipSVKfKoSgTJP7\nDz/G8SeP8JKXvowLZytcdmCO3/jaY5x7YAevueQQTx1b4Zvfe5Dz9u5kqddlslGmVq1QdkwWpsYY\nm5wjVwFPnm7SG/pceNVzWDu1Snujtd2qEYXglMoxDQNNk2SZIlU5ozBEobhw3yIly4DlJjvmJhn1\nOvzJH30UpXJOLK0ThBGj0ZmhBn4yLgsz08xN7WG1P2Df7A2cu+DiGjdQ8xo4toVdqtAaKbJRC88r\nMdaYApGTTc4TRQmWLZiemGfn3G76fp/n7HsOR358G1muaLa7lBuTJFnGeft2kwU9YiVYX13GLZVp\ntrYQCKIcLrn4EpIwRjf0QvsIhWmaOK5DliYEQSGPLQWoPCUH3nzz+/m9X/sw9WoVhaJUriI1SRTF\nCODo8VOgctLs2ZfST8bENTQCNBq2TqibdMIRSmpkuSIIfAKlEJrOZLWEruUkQrLZWUPXpjhyap0r\nLjzEYJQwPz1OexCRxwOi0GFlY4uaY2JXanimwdbAp+uPmBqvEoUBo1HOrrlJTm90KLsGK80uliEL\nW0KKB4UkJ1OSIEwYr5fZ6g7I8pwwTNE0xdJai8Xpse3EAbI0xjQchqOIPM+3WacQnYHt1k/Nk9kp\n0shHs0qoeEC4DW9WoyKzd0sOYRRv61ZlbG60mJ2dxbRclMoxahWUP0LTddrtLfI8J6M4y9DzGFNK\nkigm1QrJgj/+z7/D12/9R/bv2cnQH/GFz/8FjuvhlT26/QGOW8Z1bPbs2FsIn4mYOAkpKQd/0CXP\nExzLJNYsSiWDNBeoLGFjdZkkipmamcMwdJSQNJeOMjazgN8bntH6gTPc6IUQBsUm/2Wl1N9tX974\np/JJCDEDbG5fXwEWfuLt89vXfmoopT4DfAbggoN7lT8aYlglTFGIKp13cJHUDwCFpusITUNoegFJ\nkxoWCXmWMOnBXN3A0IvyWCn462NldEMjTgS5UCS54PnnlDF0CUqRHricPIdOt8vRI0dYa/nMzi7Q\nanewbBdTJlgq5+//5iv86gfegZIGeS5RKBxvDCkKFuxb3/xuXv/6N/HqV95IFA1pjI0TZxmVmT00\nNzepVusY5iTj43PE0U9N1GeNSa1SVgd2zTAcBmx2Bvh+gOfoDFsbxN1NGlWP6V27+eHyCpuJRs8P\n+MKXb2Fmss6h8y+k0+vzjVtvY//B81hVLldevJ+nT6zxh5//KkopTENneW0dy9ARKFQao3KdVrdH\nuRbx6MoyD59uMzc7Q9DzGXb6OJbJzHiN1WZnW1pWAIrZiTpxkrDR6tHudNixuMjs/ALRoINtGowS\njUsvewmLu/Zzyw8eoeLqeI6FwbPH5H+My8Xnn6dMt8S8ZVEvXUOOjmkUGGkMjzhVODoFa1CzaHc7\nWJZOvV6m1W2hSQ/pVLAsk4pZ4XU3vYU7vnkvy6s+IgVNk0ihMT41j1epEwU+UeCTJhGaEGS5QuRg\nOSU0qRMGI+pjMwxa6yhV/D1ZFG23awqWZI7JW97zfv7N627ilTe8AD9KmZoYZ211henJBs2tNvVa\nldmJGlPjDY48ffSs5krFNtVGd4iQJbzJSXI6OIbOVpah8pwszzGFoNPvU/dcNKfMeGOR5Y1lxsct\nRmFC1S2M25XUKLk2x1eaJOGIsmOTxjGOa6ENfcIoJs0FuiYJU8XSehuFoN0PqZZKjI9V6A9D4kzh\nOAmGoVHaRtmsNTtM1j2iVKGJgBMrTRanx6iVHbJcYRgahlVIS4ziFMPQWW12kFKy1RucVUwuOe+A\nynJJGvhIoYGmM+x0qFZcIn9AP06YmJ1GJQlKWEzPWAUnRtfIc8mouYVmWuTRgEq5Sq/XxdEFrg5h\nXPyucQqObXD++YfYsbDA+9/786wsn6A+McUXv/hX9MIUJXpYQhKOfLQ0wLVcZnfOohk2ORp/9Bvv\nww9GuLZNfzgCGZNIRRqlhLrGgQOHiMIR0aDNcBQTx6sMRgnVRFGqnLkd2bNS8LZRNH8KHFFK/eef\n+NLXgLdvf/x24Ks/cf1tohhXAr1n67sKIcmFjdBcUqXRD+DAvj20Oj2iJOfxJ5+gvdXku99/iObm\nJu1mk2A0RBMZpgZ5mpKlCVmcEAUBcpuJlqLjliewSw2SNGbg+wxGAX4QkmYpjuNw/qWXYGsho+4m\nlUoZP4ghGrJ3fpIdc5N89k//kmjl+6jN72N1H0SsHyY7fS8ffvcr2T9f4903XY0zPELaX+fKK67l\nH77+t2jS5K67bueaa1+C6Za57oUv5d7vfuuf4nlGMUnSjHZvxNL6FmtbPfqjiM2OT5wJOqOMZi+k\n7FU4vbrK5Redy00vuZxrLj0HpXKeeOwx9u9cQNMky+tNelg8cXSZ0+stDF3j373rtfyHX3oTH/75\nm3jXm27k9S+5ile98Epe8aIrufw555ELwUqzy8G9O3j+NVfQ3mzx2te+iv07ZnAtA02Kn4J6L69v\n4QcRnmOiSckLn38dpmGyf2GSiw8u8ujjR4jSlK/e/k2uuuRSNE3nuksO8ujxlbOKSfFaCu3yJKda\nrVLxbDzPw7ZLeJUyjmUipcQfhdhumbHxCaqVCnEcMtWYxDZ15qemKVfqOJZBuVzh1/79RynbGsMg\nYtfOReJMsXR6DakKvLSmUpLARwU90gwUOuFwUOjj6JLW+inSNC4qPsvEKpUxTA3ShF5/wHs/9BH2\n7VrkPT/3FqSQlD2Xl734Ov7i77+ObXv8+d98jVfe8GIEilfe8CJ6vT5ns35M22KQKU72ErphwmX7\nd9Md+iyMlUEpJApNgsoVjbpLGMcEYcC+hUUa5VlG/pDhoMtqs0046JErgWNIDuxeBKGhaxqOadGo\neZimTZZluKZBxdHxgxjDtCiXS0jDoNsfsrzRpuwUpD8dQWcYkWYZFc/BjxI0DU6ttfFci3LJpdUP\nSbKMRrXMxlYXx9I5vbbBzFgFQyrKjsXA989qrgghsE2B55UxdJ3YH+J6LqbtUPJcGvUqWRSTJgki\ni1AUxujdTo88U9imhqYyhGaRkVOpV2nMzINpEsUplqYV+vaazsF9u5GaQS5gYmIaw7B4zc+8iomK\nQxxF+MEIKXMW5xtc/4LnsnthBkvLybOEuakJXNtkMOhBntJtFR7O1UaDas2j1dwkijMGfoTj2MSp\nYt+enZDFhGchC3EmGf01wM8CjwohHtq+9jHgk8BfCyF+HjgFvGH7a7dRQCv/CR72c8/2A4SAdi9k\nx959eE7KymOHefzJJzi47wD9fp+6V0G3XP761ruZX9hNc6vD8lqXP/jYm7ZV6Qx0zSSKRgz8EUJq\nJGmGlIKtrS2s6gyd/hDLsuj1e4UgkWYUkDkUl15zLffddQdllRCNoOw59NaexrDrjE8s8uffPMG7\nX70XkYXYlsZ9Dz7K3976Lc49sJdXv/09gOLXP/SL/KdfeSs/+8GPcfM7/obpyWk+/mu/C1nIVZc/\njx8evhfgPOCzZxKTRr3CTS+5soDNBQFCSNrdHo16HaVynjm5SbvX46rLLiSJIkajjDCIufSCg6xv\nDTh58iS7dixy7PgxojjmpdddzdSkx8zEOLoU3PKte1FZzvzMOAJYWW9ydHmdc869kJJts9lqsd4x\naHX71G2dw/feS57G9P2QeqVEbzgCCp3wPM8Z+CPCKMJ1LL5x2z+QpQmXHVzgqkM7uPVHx/n24Udx\nTIOJisGLXn4p8zOTnFpv89DR02ccEygqtt5whMoT4lRDkwZCCrK88IQVhompJHrJIo19JALDtEgz\nie2aEA1B6kSjNrZtg8pY2LGDJMnJkHzgF9/D3/7Dt5kabyB1A89V2HqORCFVXFSNeUqWhORJAJqO\nQAJFpTkc9EiiiHJjHLtU5gcPPMpXbvkG5+zbzbUvfz0IwUd/8Z2892ffwM2/+ht86a9vYX52mr/6\nb3+Eadm89hUv4X3//jcZ+KMzXj9ZrpiYW2DY69H2Q7SKxQX79/D4U89gWiZSM9C3HaiiKGIwjJG6\nVdzDJKFkGZzY2CSMFNdcdBCRZaw2t6jXFnjpcw9x5Nhj3PvggzTK5UIPRzPwHJsxSwfpk2UxSEkU\nhgyDlMWZOkEE9XoJw6oyWa2ggKefPo4goz8Y0ekPiGKT7mBEmuXMTDaYalQ5trzO/VtdLMvAcqpo\nRvHw1P3grNZPkmWgmwT+kOGgR5zEjDvjrG9sMlavkmU5bqWM5pQAgb++TpKmjI/VieKYXHdYPX0C\n3bCIwxHjExNsnD7FoQO7uOf7PaSmQZoTJRnnH9qLzEMypWGYJna5ylvf+DpuveVbxFFChMDRNG68\n/rmMNWpUd+5E03WkZjBW9bBNHa1SIclyJiYnUbkkDYZs9PqMj0/Q7XZo1Kt0OgOyNGF1+ST1iSmk\ncebM8jNB3XyX/7U/0ov+J69XwC+d8W9QvAvTLpEpQaezxStffj0H9izgD3vohgFWiUG3xZteeQV3\n37/E1NQMzzm4B882SDPJYNinXK5Rr5RR6JxzcJ7v3bOM4U3QH6wzVqmShH0G/QQhJVLqJJFPrTxL\nEIYkacg11z0fTdf52t99FSUd6hWXUsVGM0a4ucaf3foMb7rhIEYuuOKSC9l46E50w8IwdHIlCnkG\nAbd+6b8V5C4hEEKiSEApvvj7H6J2y188ppS69EwiYmgaw1FAp9PDNiTSdP6ZMfnI40/hlBocPnyY\n3bvmmWjUOXV6nUzB0ydXWZyf5tTKOrZTYhimfPXb9/GL7/w5vvSVv2Tn3ASzE3WGwxEbrR4/evwY\n1bJLo1JicXqcnVPzbPW6fOTm17O22eOeww8gTYeKYxAnKa897wBxkrG60eQ7PzpCohRSFo4srquT\nZTlvf+ubiTurNJtblMplPvVbH8LvtvjK7Y9x3UXTOGmHzXafX7jpBXztngfPOCZQaJPXalWEUaAU\nNNNCxRGl2jiQbj/EBYZtIyydZBQCElMlhVdkYU2DbXvE0YhMgalBM0gZRhm2pSOEYGxiCo2cXBZS\nz6iMyy/YS2sYoUkdw7TRDQMpRaFR4nqkCGzhIIRAUzmjfodLDu2l9dRhTMOk3WpiOw66EGRK8Y0v\nfbrQSo9CcpWRJSEpMDc1yWaztedMY5KmGVmWMjUzRb83YLM/oOY57Nq5k2dOnMQyDRJNMhyNGAUh\nuxcX2OgFdEdJ0UMXipmpBaI054ePPkmjOs51l1yNZrmk0YB7H3iENM3xRz4XHrqQ6y65gju/fxfr\nrS0qts6xlR6GLsgUuLZDkuqUvRJl12TCFSy122jARNVCNwyCOGJ2ZoL1ZgfH1JibHiOKE8IgZP+O\n6YLTEIwol2xqZRtTl8RpRqs3POO5okvJ0smTzM7PkSvB7Pw84XCEUtDt+3R6TeqDGnmWMjU7z3AU\nMFYtEcUpuiaI/T5jjTGGwz5BHJJmMFGv8P53v4U773uIVj/C0BVC6lx8+dWYJQ9bagRba5AEmJZF\nrhtkIkQIHUWhKuqWKqyuL1N2HWzbK/wR4pCtdo84VcxMjpPnIUapyqQ3x+bpk1QqddJtf99qxaXb\nHTLsdSlXzlxW5V8FM7YQedfod1v4vs/evXtIgVKljum6hIMhCMn5+w/Q8yuEcUJvGBInOY6tM5I6\npkYha2pB3qiQYtLd2CDXLFaWlzAvmSNOIlSuGIUBhq7hD3vkWYKum2giJw59bnrNy3noxw9xanmF\nMOhQrzSwc5Nz953HN37Q4eWXVZioW2i6DUAUjHDc0rYKoSRXGUIphNQQZMUBU55zlib2tLp9Pvvl\nr2MYGvNTY2x1+mx1h8RJym988N9x2SWX86277kGXGvWqh5RT3Hnfw7R7A2xDcWjfIhXP5uLzriLU\nXF7xgpu46SWv5XNf/o8kWc7VC3OEcc7S8jLTkw1W17d4zy98jIlajW/cfivLmyf4wQNPkAvJxkaT\njW0Hlt4woOa5XHLeXnYtzvKnf3NHISBFwXzUpeBzn/8SH3jHTZjZkL4fUK6PkaUxV5/foOq6mEKw\nudVhGJ2loidQ+IfqoEXIXJKMehglrzABcDwYtNEMm2Gni+d6GLYGOmi6BM1g0Fyl3CjkZ7UsIfYH\nCKkTpIo8yzi1tIpim3MgBanv81ef/wxve9e7ufzKq3jk8ScLhUNNgzQkVTlCNwo6fRyjSYGGKg6q\nnQpaSZHEBQyuUq1hmCZJGOJ5Ht1mk8kdu1FJRBgEKCAenbmZxD8NIQQDP6Y73EJqOmVHZzQKqdeq\nXHDuOTz+1FGyKEZKDT9KOb2+iWvqSAQxkmEQYsQxUpPs37HA9NQc+xYXCUdDPvfVrxJlGXEUYZoG\n440p4jTnra/+GX7/C5+jHyTYls3s7Cy6SGi1mmy1mizOTqKRsbQ1ZKJisdzsszA9SXcwYKPdp+Rk\nTDc84jTl5MoWJduiUXMYq5axTJNmp0d7EBAGCbnS8Rz7rGKS5TnTk2M0N9bx3BJ5rqGbBuNjNaRu\nMjFeY3Nzi5mduyEXzO/aCSonHg6I45wMMAwTr1wFodHa2iCr1egNh+i6JElSpIKKY6KTEnWaGF4N\nTUpGgwGD0Yh2b4CmaWQo5qbHqdUaZEnAjoVdpGmEIqdeK4NmUa/kWLZbcHz0QqZB5VCp1imXy2C4\nOI2UPCgkLWzHKcR2znD8q9jou70h0pmguX6M2uJl6FpBFkJa+EOfPIeS6zIKI8hHSGmT5ZIHn9pk\nbSvg+ZdOsdbsMkokG62YsjEkyUAzDJrtPlIzyZSGaXmM/C6OZWLbLjkCIU3yNMZPBJaho+smhy68\ngPm9e/jB3ffgD7vM7DiXQX+DA/PzfPuRiGop5xWXF4bGpq4h8gxLA7Zx5UIJ8jxFKYHUJEpl5Ors\nFAnDKKZRLdHsDHhmaZ3+MCz0gHTJ7h27WV9b5cYXXM7S8gr3P/w0eRpz00uuwvU8ZqfHSKIYKcDc\nFlZ6/MjDXHrRFcxNjZMqxfETp9kxN8GVFx/CK3ksLsyzY3YGTTNZXX+a/XtmeN5VF/HU0ZMIqTEc\nhdimye6FaWamZ5iYqLKwYPOO12R84e/vQii1XfdJkjTlR488xVzDJRcawaBfbDJBwq45j0F3xOTk\nJOH/hkxxIYwEpPl2v16ShyHSq4LfA6dMHoV48ztImyvkmo1pO4xaHUxDo9yogTSI/R5SZJQqFfxB\nH00oUiF56lghayykYDToEwz7vOpt76dmwZf/7pu0/Zx3/sz16KaOVDpRFKCShFxlBVrDtEhihaYJ\n0ihC6Tq27SI1gaWsYl7oBnme4lbKtFaXKFVqtFpbOLbJ6uppztbAIE4zNtst0G0mxmoESYJjaAyD\nkJddcy2vveRiPn/3d1haXcPUBX6UECcpVc9la6uFZ0uONYfMzU4jNJt+9yjdYcjq5iYz01N0+gM6\naUK71+eRp55k7/wsozSjVh8DBaVyRBxFoAsuOHSIY0vrPHHkSRbnZ6m7klY/xDUkzXYP1zLZNTNG\nbzhktVnYg7quzeZWl8mGhy4l/eGQJEkgT/FDQdlzCcPwrGKiGwYnTi2jaTA9OU0SDlAohDTJ0hRN\nL6rme79zDxcdOojhFZuwLgGhGEY5Wb9DpWRuS5YHbHUlSeRz9UX7eeKJY/SHIblSrJ0+iVuqkg6H\ndNvbWk4bmxgaeCWHXhDz4V/+ecbGGmhKkpPT3OoyO7+XMI4ZP3QFydOH0XSJrtvkQoc0Io4jLLMQ\nWlx6+jEaYw10KdnaXGPH4gKnT6+feTzOKnr/p4YQxCnk5jjLqxucWhun7GhYhkHJszm9sUql2qBR\nL3P+vjEeOjrEtD02OyGp0rj7gQ4qy9C0Ios+2jpNkJmIbMDUeI04g8GgTRgGaFISx4UFWpT0MQyb\nWnUcK0/JVcbA7+HaLiVL48U3PI8gjDm1EmObOUF3kzG3itIkn/52xBsvjdClwCs5GIaJrqtt27PC\noETl2xJ3ulZshGcxpBRstgdESYplaOyYbXDDcy+iXClz349u5YYXvInFuUmqtQbzzTUmJ8eZmRhH\nIIuMwzQReUarP+TwQ0e4/nm72Wxt0er5lFwbu+T9/+29aYxl6Xnf93vPvt391l7V+/T07BwORVJc\nTNmWTJmyYksQICOKI8dIYsdx4AjZnDiAFCT+kA+C/UWQ4wgxIEYwZZoUJMGSKHFEhaAoDjkkhz09\n09M9vXdV13rXs2/vmw+ntFgzkrsiMT0c3B9wgaqLqrrv+de5zz3nfZ7n/2DYPtMw5XNf/AbLS30e\nO/sG7W6Xlm+w1O0wXNnCNAyWl4d84ctf57EzG2xsbRJHIf/m11/hiccfY9AboFSzT6/rjeuipusY\nhslrN7f53o99GGG6eIZOXUsmR7t0h0tUZYmhTmjdTOPVk8dz7E4PJBiB0ey7ywJcj8M7N1laWaU6\n3MXoDqAGmeToutF4yEiBLMJmpqfRjJIzLZfAMsiKlBeefYZaSuqySSBaVmOhmxYVhgaBpVjqtSiz\n9HjcpEI3bXQN/MDDsE2qLG1q58sSzxCkYYRmOXi2BVLhOGZTpqdr+EELQxd0Wm1MS+Pi40+d2L1S\nypoLZ9YwTYt7O4d0Ap/pNOa9Tz8HmkVna5Mf/USXX/2dF8mzmLoqibKaoq5YWl7h1p07tHyH+Tzi\naDSm0+owr7cxTL0x6rIsNjc2UXXFPAn5xd/8NX7o+/4a/9kP/yg//yufpswVjq2jiWZ8pedYxHHE\nvfs7qLWl46S1SZnllHpzbk9mcTMFyhA4usZzFzZBwDSMqeqKKE7ZnybomsbeOKR+iJLTf/dEUayu\nLNMfDKgVuK5DmSYoDfKsQOssYcxnfPRjf6HxwSnAdNvodUw0S0km+ywtbzKZHKAUPH7uAuN5SuCY\n/Cf/8Q/zn/6D/w0pBEdZzWQ2Qbe8xlE3TnCqiiiOySpFR2i4tsnm+gbxdEx97HL7qV/+Teo84bWr\n13jBtsnLGo8Sw3apqwrH9ajrmCxJKbOUdruL0HQUgv5wBTNos7T08Jq8IwK9ZQhsvaCz2qPC5NO/\nvcff/6GzGIaGktDrDSkqySwqaA/W+Giv4rd/7zaaZuHUAiUsdE00b8g0Z3J01HirpxVd08REYBkW\nBQlVmdPpDkBo2JZNM7YwIStLDGrivEJTgqqqkUCn1eL5Z1wuX7mDqgvCbESrO+TSaptf+qbgha2I\njV5BrxWAVWE5DlJyvEcvmxJEAYZ+sjevrmmsDbuc3VxmdbnP7sGYqzfv49gWf/s/+oe4nk8cp2jC\nZjBc5sLWCpodUOcJqq7wWm2iNKfTAteyaHcGzGdHPPPYOSrdYtg54u6DA3odn7XlPuNZxC//xi/y\nj37iJzGcNgdhRbsYs7aySq/d4f3PPcWd7QfMw5iiKPnLH/tujsZTZknBx//ih/mtL36VqiqRsub7\nP/Z+njy/RfHYaYKWR7fb4+b115thCsEq9+7dZnN95Q/HLJ4AITQs26KKptRSYJgWQkKVjzFMh6W1\nDbIkRlUF6f4+liEwXRchFLoO6AaapiPTkDKZowkNpxWQyyancuHieXzfpyoyZFXQarU4tTYgnE4I\n44QoirFMA9txmiBv2agixTAd0nhKcjBHKYEftHAdBykg6A2oy5KiUpi2g5IFlgZS6AgkeZqhqNEM\nnzt3blFV5Yk0kVLRafmMpyFn17pomsa+VNzaeUC73WWt12Y2m/M3vu8/YLkT8C8/80kQCeNp4+Pz\n3c8/jaYqvv7GbcqqIkoSyrrGc5ur37rMyLMcy3ZYGgwwDcHOwREXTp8hjiO6nsV0HqEQ7B5OsGyT\nU5urXLl+DyFgbWUJ37Y4HE/ZPzri8YuPsb7cJ84ydg7m1DKkrnvMkhJdKHodn067zXCpQxgXeJZF\nnOZs7z28/7qSksFwyGw8Qrdc8iSiP+iRxBlJHOE6Np7f4mB7m5bvIYROXDYNlp5jMNctJpMDdN2g\nrCWXX3+Vsxeeobe6xcaphEkpKaqatmaxtnoKw9CoSsXycA1NKF780mWUgkmUYLkud2+9ztrqFi3L\n5Obt63zgmdP86ld3WFvqcP3Ga6AUZ0+dp5xPkXXTkStUhe+3KCuBQJFnkrLI6HVbHN6/T7v78JPI\n3hGB3vU8tpYt7h5k6IZkra3xzz9zlb//o08zHh3RbnebKy9RMZlGtAObj7x3kyLPqWSFo0nu7I44\nPIowsfBtSVmUtD0dS6943wefo91uI4SkKHOKMiOch3heY15UZHEzJNi06PgetmVBAUkak6YSUyrO\nnO4xHs2YhSnxbA+lh2x0l7my1+LL93V++OlDHMvBTVMC30MzbISmIaoC0BDiZI6Enuvwlz/6Pqqq\nIopCDKFQSlBXFT/zs/8UxxJ89bWr/L0f+2tgO7xxZ4e8AksXTbv7PKLle4zDlMfOXGIw6HHv3g6/\n9vkvcOXaTaZhjG02xlOarlPXNb/92RdRCHa2Q/7FJ/8VpmGgaCYQKSkpy+rYklajKGukrCjKEhDE\nSUpdN8f4hS+/zJe+9i2KosC2TD75mc8xDCwczyORgvneA3zfp1Qns56Fxuk0z3N0TUPUGWWVUFfN\n4ObRaMza1uZx5UCNMCx0odBMC8uCKi+p0wRNgzia01lapa4UdVayvrLE9bu7iDIiiVPubh9wam0J\nJQz+95/8H/ix//y/wvFbjGdzbt3fp8wK5LEFre36TA73CHpLCE3DNO3GGjtqAkpRVAgkSklmB9sM\nN8+iJKi6JEti0uPjsW2LU5urJ9YEAVGUEc4TzLbD9jhl2HK4evMhVP01AAAf0UlEQVQmZRaz7Oj8\n8hdf4oUnL/He9zxJFM3p+C0cQ2N/POP+g32euNR8wJ3aWKMoC2rZ7P1HtcRvtbl4fkheloRxTpqV\nfOF3P89XvvoF1lY30EWFLhS1lHhuU364tdTntZvbjGcx57dWmoQ2EOU1V964wdb6kDgpObXapSwr\nilrRcgx0HaqyZBblVFKyvtxqBnSccJ6DlIrd7W2k0Bl6Hna7Q1lUWKZJr9tBKkmUS3qDJQzbpUjm\nmJpOlWe4fsDS8go7uw/wdAPHcbl08SniLGH/1qsMh0sUUiOvJVIpXrl8mWu37tDrdLmzs8c3rlzl\ni1fG+Loi0AQtL+D/+IV/y6nVPn/vx/8mm+tbHEwbl0vHcXAck267SzQbYwRDRJ1SFCn9bg/fd8nz\nAk0zQVO0fIc4TUkrRbT7HbZ1Y+gaz7/wXt5vSF78wiskZcnp1Taf/q3bVDj80Mf8ZkB4IdENnbqG\nvMzptUzS0kaVMf1+j9XlIY4XwHed4lvffBndsHj80tO4to5ExxQBUgZoms6w0yFOE9AcsiKn1+2i\nC5jO50xmUwLfZ9jpUJYFVZVTliV+y+DxxwMe7GlM5jGa3Ge93ccazXnp/gbvWdunKDKKIiPwfAzb\npioljm2i6pPt0ZuGjmsbVIaOLsAyTOw4wTANTMPg+s27WKagrBWurWNbJks9l6ysycuapX6f8WTE\n2tomlr/FL37m56irjPWVPh98/nFKKej4LodhydZym06rzU/+k/8Ox+lw7e7r/NP/6b9EaQZ5Jbl5\n6yZ3t/fZ3jtia2uLM2t9Wi2fWrOaPepaMo8ifvb//hXOrw/58R/7Ee7cvolQksfOn+c9732Bb339\nJY5GM86f3uI3rr3Bz3z6RbYG7ROfK4qmC9pUOSAbGwxHR6QZfuAzH48wTJuqKDBMiTQcVFmQpQlo\nOnWZI6sSxwtQtYQiRRgaq8tDrt3dJcsrCjQuX/4Wuztt1tZP8dxTj/PN3/m3WI7PxtMf4s6dO81g\n+rrC1E3C6QTXb0NV0moPmk7YqsCyDNKixJI1VVUiNB076JJEIQJJVTUThnrdPlVdc7B3wGDYP65i\nengEgpvbhyx3XdJScWq1S1UUPHVug/F0zr/+7RfRDYvfeen3uHr9KuvDPi/fuM+zj51C0zTysuDo\ncMSZzTXyMgPN4d7OLsvDAe+5uIlj21y+eoMawdb6OpOjfVaXOliGzs7+NksdH1M36Lct9sdzrt/b\npxUEfNfTF/jG67f52pUbdFoBaZ6zurTE6zduc2p9wKAXUJYVhmkyjUJWBk0VTF03E94cy+TuboTv\naHSCkyVjhYBur818HjaWKUIRFeBaOpPRhOXVVXyZgyxJZxFVrRBIpJLIMj2eb1FjuG0MmZOlMdOj\nfQ6nMf/PV7/Z/E1NJy8lP//Zz2GaemOPbpkUlSLNU4ShqKkhFvzguU0++MKzxGlJmuf83Z/6ObzO\ngDdubnP21ApJmhHmJfHsNqdXtxiurFGmEeNJSJHH2K6HgULrDum0m655dYJhLO+IQF/XNWVVURcV\nf+mj72Eyj/ndl2+g4eEa8K9f3ONvf+JM4+QYjY5H/EFVgZQVFSa1zLBMG1WmZKXkiWe+C003mluy\nIiPP5+RZiuf3iKIIQUEUp/hOhaoVt25fo9UdMhysMbTNphU9SzF0G9MwseyAWTglrzKG/ZwoNYjS\nhDKP2Ri00eyYr95rvK8/cuYAKWP0LKeuFXlpI8TJpK6lJI5Sev1+k/S1DS5dfBzXqCgryfrykA++\n8CyW69MPbES/j65KqsmESZSwP43pBBb5+AAOt/n4h5/FdlxkkVLoAWGh2L33JoPhKrbf5nAy5vnn\nHkezAh47ZeP1lplNp1iWzrOXznPx9AYS8II+WZbhW4JZFBEaGuPxiHv39lhf6vOxD72Po8MjLMPE\n9Vu0Wm3qPAHdRBgmyxubvP+FZ/jRc6d56Ssv09gmPTxKKlwy5nGMbuhoWocyS7AtA89zqdKYskhw\nTAPN9tB1k7pIyLOcVssHq4MqUqQqKbIES5OUeUHLdzF0nZ37d6jyjG9ducZzT57j+WeeJpqOsV2f\n7VtX8f2AD7/vaabjEb5nUdY6nm9TSY2yaAKC7bgoVVMmEZZtUxZg2S55GlGR0W41jXlRNMPSoDQM\nNN3AsU3u37lx4kDv2o2h2M7hlLVui70owtQER7O4GSaSF03TUFnyxr27JMUS77t0mqyQFHWFY7vE\naUaUhPQCH9MuGfQ6xLMJX9x5QDfw+P6/8AJXbu0xj5OmeiZKaHdbPNg/Ik5yDC3jG1cfsDLs0um5\nmIbBJKpZWh7SbfkMen0ODg7o+h4//Fc+ws7+EWEUIlDYtsugG7C9d8TSMEAJnSyv6PguS/2AOM05\nnJx8ZmxeKNIsR9UlhQTfsRnv76MZFmWWImuF6XoUeUaRRQTtDjKriMIZeVawvLzB/ft36LY7TCZH\nXHntOq/dvM/9vTFt32cSRniWQCpFXUqUJgjDiMs3Z4jjEmvTMJFlyue/9DK/8vkvs9LvcvPeDllh\nEu7c5+c+/bv8zP/ydxAaBEGP5RUfUURcf+Mm3U6H9aVl5jjYhsFsNsLQFaOkYh7PWR0M/v0iHPOO\nCPSapuPYDlQ5umUy7Ll870cu8bnfuYxp9Wk5Jp968QF/40N9Wo6O2/LYPxiDlJimDkqy1Gmh6QaT\nKMY1DNBAVRG1ajLZutsicF3KSmJogijO0Q2TuFS4uqLf7+N7HZAVUZgyGu+z0u+TVzWmJpAKur7D\njBrT0LhwyuTrl0fYFox2j9CDTdrugNzo87lbm3z36i7DVtFMjlcVnuudSJOqqsiKktF4jGWZrA66\nROmIe6OIooZe28HQm9bm+/tHdIKCB/sHDIcDLE3j4qk+GHbTem343D4YUdfg2SaOq1B1zsXHLnBv\n95CwTimFyzMX1inLGm3jElU8whSCstY5nEf02208vaLKY7Q6p65t2r5P36sZtBwunFoFVbKyscXa\n6io3rr5KrTRAkGcZ7f4q+6MZlVJcv3kf3XaasvaTohQKA9tySZKElpNj2CaO66HqEt3r4OhAVVIV\nFXVdYBga7VbQ+IjXBULTiOcJ3YFPnafMZvPmils3+Ve/9GuklaTdDvieD38AN2hh2xZhlLA8XELp\nFj/9yc/xgx//i8i6pCxKTE2gWzaT+Yy8zPEzG8sNEKKZtVpkMTKLML02dZVxdHhIbzDE1NpomkmW\nJpgO9JYG+O3eiSURxzMGfNtmHCV0fA9dND404vhcbzk6ZVURJjl5nvPG7Qec3Vqn22ozi2KyCiyh\nMwhMcmlxcDSjVKLxXykrPvd7r7I+bCOKnDCqyIuanf0j7u01s3jPrHepNIuD+RzHNDm1tcrWyhII\njel8StuU+OurWJbZmPIVJTfv7bPUbdHrWQxXNpC6R57muLakKEtsx2qsSuYpWXaywSNKSTSZ0+/4\nxEmChkDXBe3BgLqsKKua6SzEyktUmdPpdKiqmoODewwGKwwHLYRhsL66gaxydh7s842rbzKaxERx\njG0ZVFXJ2rCHoWkgmlGKYZpxccvF0lu8cqPJKQhVIJWFqWsczWagueiahunZHM4L0rzANQ1sQzWl\nmkLnzOZp7u/vce3WNfrdAZYeYBomUZzR6XTodTts724/tB7viECvlESVCZphUBWNsX4nCPj4x57h\nV3/ra/j9s+RpyJv3DS5umNzfPcDUDXQhQRgga3YOj3BsjyBoYVADAmm5FGmKrkpkrSjqJvDWqqbS\nLHqBh6ZglqZM9w7YXDdR0icvEnrdPgAGTalnmmWYmsCxHYSscQ3JM5d6RGGJYQpev3VAZUr6Qxs9\nL3nplsn3PZkgUWRp/gcDxB8WTdOxLYMkz0jzgijNaXtWM9dV1U31hmWw0gsIHZMkz3j8iSe5e+cW\nwrAZZyWulmPZHrd3D7Asi8B3MC0bVRcUUvBgb4/1QRfP1InTnDhXOLZDksRYloOwdDzTJzAVeVmi\nGy1KmVIKG8qC1rBLPo0xhKSsKtK0sa6Nk4zZdIwwXWTRR5kbFPmEMkt59coVbEtnOpnS7z98MukP\ndDEMDNcHCmzTRhg2umVTlzFCKIQymjnvtothKepkjhIGmmWhyYK60phPDul028gy5+7ObnP1Ow2p\n64rPvvhNwOAzv/El/osf/5GmiUlqWKaOtH1m84S82m8mJBlN8BiHGa22g2vrDPurlEqgyZIci2tv\nvoHr2mwsrzeOqkmK7Tjs7h3Q63ik8QTP9ymKkiKOULrelBaegLw89rTRDFquxu5oggLOr+oUZY3v\n+8dTj0w81+VonvHgaMrWWoFBTRZOWO0F3NubcTip6HZ6fPDZS/z6l17Cd12SrODZx8/x2o27nDu1\nzqn1ZQxN46uXr3N6c4tBL+DO9gM+8T0f5EsvX8Y2dB7bWibOFGE4ox+4JGnIziTk1LBFHk2YJiUf\neO9z5HmFOs7xHByOaDk2ca3Ra3WpipIsjhj4Nmavz83t/YfWRAgNy3WhqpC6jmcZjGYJthMQzSbc\nf3Afx/HYWt8kL1Nms5p5khKmOVuuSZSVzKa7aLJmGsXkeYUmDNrt5oJtU+ls72u0fBvfd6hrRVkW\nmBp02x3arkl5dRvbNOj3VjG1BM0QfOS7nuMHvvejvH71GsIMWB36BI6JoemM5iGu61GVEqEJnnr8\nIqPRmHk4wy5rNMPAcX081+Hg4ADfffitz3dEoNc1gVAVVdlMU3dNAaLCMQV/64c+xKd+/VWORgXf\n0AIurOksdRsPjywvUVVJXdcsd9uU0iBKMmytZpZKPMdougYllGWGa1sIzWAyb3wldpMUJSWu67Cy\ntkoYjnHsmE5nSFFVGL/f/ahqWkGLPMtwhEIYFgpFty5wTEVWCF54csCbey18LeS3vvIV9mY5v/kl\nm5/+u+9B0zW0k5l7HidKS9qBTy0VQauFkAUSh+D41j5MM+KdfdKs4MzpLWSZs7G6Qhhn7B+OuHT+\nHGGSYBoWj60PKWqNKAppd312RxGaZhFlNWVR0x90yZOMPI0whWQ0GTGNUpZ77cboSymkSjDdgCI7\nIpc1xjRGp8Z2febxEU8//RThZETIIXvjiPMX1jA0CEdHbN+6zngyZml1lW5/QDgZs7q5duJzRUlJ\nHk0wTAvHDxBKoVAgLcoyw9QVSlZoZfNhKISgzFP0qkA3DGSVYZmC6zfv8srlK7x5+y73HuzyrTe3\nSfOMrNIpqoq7+1N+4n/+J/i+y/rKgCKrePHLX8N2PAzL5XA8YbnfZjyJ6XYDZDrjweEYbzbH8zyE\nYSFUBZpgZWkT3dQxNYHmOJi2jaYp3KCN7zooYaKrim+9/iZPXHoCTTtZd51Uim7gkqQ5Ya5QQufU\ncpdJGBJ4LoIa1/XIy5owzpBS8tyZPrZe4FoOTz9xgWvX3qTlO1Rlzb39CVfuHDAcrLE3mnBxa5P5\nZIzvWKwt9fFtnSxJ+cBTp3np1btsH4SsLS2zfzhhdXkNWYRMo5xXr15v7ngMi8fPrlHEM7JAYxam\nVLXg1r0HOJaFH7RIoxxLFxxMJri2jWWAJlqUlYSswDJPNnMZYDw+QgidqsgpOh1m8xlycsRwMGAw\nWKFUOoejQwa9HtOwqVYb9vrHpmuKsq7pBS5RkrI3GjMNQ7KswjB1Wq0Aw7DotVtkWU5d1+SlPC6b\nFUTKZtD1mScVviNYHg44d/YU3/uh5/Ecj/c+/yyyjNF0j7KqMCzB6lKfo9GIg8N90qKp+DI0hawF\n+wcPELqO73g4K2s4tkN5gt6cd0Sgr+saYdiookAqULpFVdZcu32P9aUuH3hywFferMhlxVFYsKwb\npEWF7zcty7qpYbsuyWxO37dI8pphx8TQJbW0AI1YSaq6KWvfXF5GUGGbFlI2ntWGZZJakGYp6ng2\naJEXZFWJYxnYMsXQNSzToShShNBxbRvQsMyK3bHF7u51bj+YYJsmba/pmPxnn73Nf/Mj51AnrBpA\nCM6f3sR3moReklV4QZej8RjNsciyggsbfdK0pNRMqqrCdAyiMMGxTbqddW7cvc88StjYWGd7NMcw\nbNBdxuMZbcfl7v6Msphi6BpRkbPabXMwjVhZGuJYktMrHpWCrGhq+VE10/EBnmMQxgVaXiKEwJIJ\nUV5wfqOHa9toukEYRpgCdMPBdF16G+eIckWRpziuTq+7xXw0OvG5IoSgfeFpqr1bCFkzGR/iez5l\nLZmND8lrjW63jW9p2C2PMiuxvYBkPmf78IDf/dIXuXz9DkUWotCpyhK3PUDWNZqm8wPf/Ri+a5Eq\nk6osSJOY116fYNkWTz52mv/1v/8ET156okmGmS6G5XI0OqTle6wM+ty+e4N23acqCwb9IRe2tsjy\nCFP3KUqJ59hkRYnrtCijKV67S54k1EryxMWLaEpiGCd7W5qGgWGaPH16naKqsVSNaerY+ip5rbAc\nF13XebB7SMuCfndIGo7I0xQlFWVV4LV8TMOgKmvsqmI2zxjHIUVecOXGbZ6+eJ4LpweYovH9sU2d\nUmks9X1uXb5OOLPwHIcz633CWlKUBY9fOMvVG9s4js1omhL4AYbbpbdkUIwjwmjOzt4R7ZYPNPvp\nT1+8QFbk6CjCJKc76CGUpJQnuyOuqpIkTtja2gLVeDIFroduaByNxkhgfXXI4UHOne0dDAM8y0LX\ndIQEhc7myiqjyYRvXLnON69ca1w5iwLPdbDNJm7JskQzDDSh0Q5sDE2S5M3c22HbZR6H9AObD73w\nDE89cZ6qyjE00DSDUSxZGfpYmqBUcO31V5CqsUswTYvlQZft3T1Wl9bJ4ikIRbvVYe/oEMO0aHc6\nD63HOyPQy8aRcBbF+J7bWGqqinOnz2BoEs/voG5HJONDfumLkr/5l5Zw/QBZ5nQ9h6iwGE9DkDVJ\nOKJWQK1TCwPTcsnyBFOHLMuQSmLpAsNyyNOQCoOyrpkdTamPC7ujLMTUJL4dUNYFq4NTyKqiUpIo\niWj7HnvjEVFWYega/cBHEPOaXrLWd9E0g2BWIOsYPZ3yL39pxn/4A0+dSJOqrtk9PETXDYqi5OzW\nBgY1j20sU1QS1VY4XhdLTChqgaFBLAWr3YA4SeiZNd5Sh3LQwTANDMNGKEkYzfF8n1pKNod9NCS6\nbqLrkJXQC1zmcULHa3EwOWKepJRFRavTQ5QJphC0vBaaJrh9fwdV1ZQIWu0W0yRmqaOQdcXWmXPs\n37/N/niCk2Tsbd/F1CSBLmn7baIkYm116cTnilKS5M5VdvcP2Fwb4voO42nE8lIXd/MMZZogdB3T\ndZmND7G8HlUaEecFd27f4Nb2LrIqmvLQqrnLq+IjLCdAqgmubVBWJb6jUwFoNitbm1gq4UPve47T\np86SFwWu71HkCZosWFvb5GBvG9PQee7Z96KERhLFmKaJqgsUzWxUgcb9B/usra6ilKQWOuFkTFaU\ndLs9hJDYltk02p1MFfbHIctLA1a7HlVZkGQ5kzQjKyRWmuPZBhdPL/Py5ZsEasLqcp84Laiqgtl0\nzjytcC0dzbKZRhmGpphkGXVZUVQ1l69eo996hqos6PjLSBQqj7AMnYtbq9SiaQqMMsnV29u89uZd\nPM/BcVziJGavrui1POIoxu+0qYoM17Y5t7mKZeoczBJODfqMp1OSNGsCp2tweDRnY21AlqQnUsQw\nDFpBQJrEuI5LGCekaYzteCwvL5NEM669cQXfb9PzXaq6RNMt4nAGEkzbZm8y4ytf+yZX3rxDmpd0\nAocs10nyEtsyEJpA6AaBbzMPE5IkBQSebZMWNZ7roGlNoYAu4OjggKWlPkpopEXK6c0zTEaHaN0+\n48kR3Xa3SdQnOb7vMJmO6XouuoC90RhZK0bjKZvrW0TRHPcEeb93RKDXDR0lBf1OF8uAMIoxTAtL\nk+S14hc+v4OjQ7fbYT6bouqMMgM0SdDyKedzPAsmkSRVOpoCWzPQNYHMExzTpFaCtCiwDJNpWqNl\nUeP3oeskWQmagaFB29Xwgz5JEjIPxziWjawL6qpsXCQ1SLOEYbuHa+WNa6JUtFyXT3z0PEop2p7b\neJVnCUJTaJpOfcIrEk3omEazx7qyNGAa52wMW0ySHNfQkUjK8IhE6khVN5n/asrOvGnquXhmHc92\nqMsc0wBTF0S5TiVM8kqh0dTk+8M1svkhR9MKpSI2h108WVOLCk1z8G0QlsLTJbbfRSqNsq64s3NA\npUCXNabeVBvUVcn9NMPRJK6Xcu70Krfu7iMxcG0d1+9gmxqW3kzu+X0PmJMghEA3DbY2NwjncyxD\nZ6nnc7i3i27o+EEPVVXodYXbWkbVGXVdcfXaNS5ffZM4ikjSBAEYlkmUV4RRiNIMikqgaQpFM9x8\nFoV0goC9w33Wl4fc377PY2dPsba2yuSoyXu4rkuWRXSPS3HHo+ZDw9QEQpU4nodpW6iqxLQsTFPH\ncWzCMMFzLWzPxsgUtVTYjk2eJtT1yTqG86LEMjSEKsjzZntO12B/EhEnOc9c2CTwHK7fuMm502tc\nufomQhMYhs69nQNWugG+Y7AX1pxqBVxa2eTm9h7rS0Neeu1NlFKUVU0YhSx324xnCbuHMwbtplfE\n9X3mUcI0TLBMjfWVFbqugWa53N09ZKnXY9gL0DVBmGV4ts1yv0VRVoRpgaWZvOfSWe7tjlnq+1y/\ns8PqMCArJUvLS0yTnOjhZtT8AUVR0O702H6wgybGGKaNrluMRntkeZd2K2DYG+C6DkkcYtoeURLT\n9lsE7Raj0YjX3rjB11+7SZYlxwN2dOI0xLIdWr0BZXWN8bw57y3HoaoL6ipHCXHcKS4wTZuqUhiG\nwfr6OmiSg8MHDAdD0jShUIKjOzdYHq5guh626xEEPYoyA6URtLrsT8YozaLbcYhmM8qqQCpFPDv4\n9wtxzDsi0AslkVWCZRkUaY5jWSgkZZEyms557crX8Byb2XzCZJ7xd/7q32Ke5piGTV3m+K5BXhls\nLtvM0gpV5Bimhqyb4cNpXnA4i+kFDsiKQkgqKQjzGiUTFDWmbiBlTS51RDInyyJ8t0XgB0glcPw2\npmmhqMmzhDAJKWuJJnQUOVIYVFJR1xVpXlLXTfu2jkATAv2E5ZXi+DbN91yEgJ4Gd7Z3WR4MuLd3\ngNB12r6LlDWB5zKeh4SzOTWwuraKcFa4sXOdTstnZzJHR1GYPZbcGk9zGc1mGJaDNh9TVrDc9yny\nijLPmObNh1LL0ZhVimC4jlWGKNOhSiKyJOX5i6cat9Eo5nCWobIQ3w1I05RZUmAaOi3bptVuk5cZ\nvqHjWgIMk+2jKe3A43AU/ukivA1SSgwqilrDc02OxjPu797DNB1c08TzMkzToipSbKMiLTLubO/w\nlZe/yYMHDyiqCk3TyZKISrPQZEVWWYTTbXTTJE6b5qYg8JlHISBYHfQo8px5kqIZFmGc4VgmUZLy\nYO8B6ysr1EpHSEmSV/S6JvuHB9Q1LA+gUgLPc8jykkGnRZhkZElIOE3ZXFvl8GBMmsec2tzAMMw/\ncCl9+HOlaWpL4wTqilZngGboPLmyRTKPKIqEWiqSEjxbxzI0qGviNOPBKKLX7VIJg5WhTa8TcHd3\nH8dxqOuKH/yeD/DgKMQyTY6ODgnDCMcy6XVavHE/ZtDxcS2DYGOdXhJhGhq2afH6jXsYWXMRVFQ5\njhmg6pJTg4DLb+4wC0Mcx2V1aYl2K2B3FLIyaHM0GdNut6hqQb/XR9MFMi5Y7tgn0kTTNK7fvI4m\nBI7V3LXnUmKbJmWRYBpdZJWTlzqeF6BZAVmeUOQJV6/tce/ePV55/QZxOAehkdcplt1phh8pyZs3\n74KCvXFM23eZhhFFkbO+vsbh3h6O6zHamxH4bdIiR9ZNVVdZpuiGjmfbGKbO2lKfssiaTn3bYj6f\nEXjecewq0YSAuuSJM6e49+AeT116klEmsMUU3Xp4TYQ6oQfLtwMhRAhce9Tr+DMwBI4e4udOK6Ue\nar/iXaAJPJwuD60JvCt0WWjyVhbvn7fy56rJO+KKHrh2Ek/ydxpCiJe/Dev/jtYEFrq8HQtN3spC\nk7fy563JyVrwFixYsGDBdxyLQL9gwYIF73LeKYH+XzzqBfwZ+Xas/ztdE1jo8nYsNHkrC03eyp/r\n+t8RydgFCxYsWPDt451yRb9gwYIFC75NPPJAL4T4fiHENSHEDSHEP3rU63k7hBBbQogvCCFeF0K8\nJoT4h8fP/5QQYkcI8crx4xN/5Hf+x+NjuiaE+PgJX2+hyVtfb6HJW19vocnbv+ZClz+OUuqRPWjG\nPN8EzgEW8C3gyUe5pj9hnWvAe4+/bgHXgSeBnwL+27f5+SePj8UGzh4fo77QZKHJQpNvnyYLXf7k\nx6O+on8/cEMpdUspVQCfAv76I17TW1BK7SqlvnH8dQhcBTb+lF/568CnlFK5Uuo2cIPmWB+GhSZv\nZaHJW1lo8vYsdHkbHnWg3wDu/5Hvt/nTD/aRI4Q4AzzPH45G+gdCiMtCiP9LCPH7UyP+LMe10OSt\nLDR5KwtN3p6FLm/Dow7031EIIQLgM8B/rZSaAz8LnAfeA+wCP/0Il/dIWGjyVhaavJWFJm/P/1+6\nPOpAvwNs/ZHvN4+fe8chhDBp/iG/oJT6LIBSal8pVSulJPB/8oe3Un+W41po8lYWmryVhSZvz0KX\nt+MRJyQM4BZNcuH3EydPPco1/QnrFMDPA//sjz2/9ke+/gmaPTSAp/h3Eye3ePgk20KThSYLTf4/\naLLQ5U95vXfAAX+CJuN8E/jHj3o9f8IaPwIo4DLwyvHjE8AngVePn/+VP/ZP+sfHx3QN+KsLTRaa\nLDT59muy0OXtH4vO2AULFix4l/Oo9+gXLFiwYMG3mUWgX7BgwYJ3OYtAv2DBggXvchaBfsGCBQve\n5SwC/YIFCxa8y1kE+gULFix4l7MI9AsWLFjwLmcR6BcsWLDgXc7/C2TXw5XzUgZbAAAAAElFTkSu\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "total training horse images: 500\n",
            "total training human images: 527\n",
            "total validation human images: 655\n",
            "total training horse images: 628\n",
            "Found 1027 images belonging to 2 classes.\n",
            "Found 1283 images belonging to 2 classes.\n",
            "Epoch 1/15\n",
            "41/41 [==============================] - 10s 249ms/step - loss: 0.3692 - acc: 0.9088\n",
            "9/9 [==============================] - 17s 2s/step - loss: 0.6567 - acc: 0.6972 - val_loss: 0.3692 - val_acc: 0.9088\n",
            "Epoch 2/15\n",
            "41/41 [==============================] - 10s 237ms/step - loss: 0.5576 - acc: 0.7334\n",
            "9/9 [==============================] - 10s 1s/step - loss: 0.7683 - acc: 0.8491 - val_loss: 0.5576 - val_acc: 0.7334\n",
            "Epoch 3/15\n",
            "41/41 [==============================] - 10s 236ms/step - loss: 0.6752 - acc: 0.6422\n",
            "9/9 [==============================] - 10s 1s/step - loss: 0.2961 - acc: 0.9133 - val_loss: 0.6752 - val_acc: 0.6422\n",
            "Epoch 4/15\n",
            "41/41 [==============================] - 10s 233ms/step - loss: 0.3398 - acc: 0.9236\n",
            "9/9 [==============================] - 10s 1s/step - loss: 0.2491 - acc: 0.8802 - val_loss: 0.3398 - val_acc: 0.9236\n",
            "Epoch 5/15\n",
            "41/41 [==============================] - 10s 235ms/step - loss: 0.3488 - acc: 0.8636\n",
            "9/9 [==============================] - 10s 1s/step - loss: 0.1841 - acc: 0.9270 - val_loss: 0.3488 - val_acc: 0.8636\n",
            "Epoch 6/15\n",
            "41/41 [==============================] - 10s 235ms/step - loss: 0.8408 - acc: 0.8332\n",
            "9/9 [==============================] - 10s 1s/step - loss: 0.1812 - acc: 0.9279 - val_loss: 0.8408 - val_acc: 0.8332\n",
            "Epoch 7/15\n",
            "41/41 [==============================] - 11s 257ms/step - loss: 0.2536 - acc: 0.9454\n",
            "9/9 [==============================] - 11s 1s/step - loss: 0.2853 - acc: 0.8832 - val_loss: 0.2536 - val_acc: 0.9454\n",
            "Epoch 8/15\n",
            "41/41 [==============================] - 11s 258ms/step - loss: 0.2728 - acc: 0.9548\n",
            "9/9 [==============================] - 11s 1s/step - loss: 0.0541 - acc: 0.9776 - val_loss: 0.2728 - val_acc: 0.9548\n",
            "Epoch 9/15\n",
            "41/41 [==============================] - 9s 231ms/step - loss: 0.3115 - acc: 0.9236\n",
            "9/9 [==============================] - 10s 1s/step - loss: 0.0313 - acc: 0.9893 - val_loss: 0.3115 - val_acc: 0.9236\n",
            "Epoch 10/15\n",
            "41/41 [==============================] - 10s 236ms/step - loss: 0.3059 - acc: 0.9486\n",
            "9/9 [==============================] - 10s 1s/step - loss: 0.3006 - acc: 0.9133 - val_loss: 0.3059 - val_acc: 0.9486\n",
            "Epoch 11/15\n",
            "41/41 [==============================] - 9s 232ms/step - loss: 0.2393 - acc: 0.9634\n",
            "9/9 [==============================] - 10s 1s/step - loss: 0.0774 - acc: 0.9708 - val_loss: 0.2393 - val_acc: 0.9634\n",
            "Epoch 12/15\n",
            "41/41 [==============================] - 10s 243ms/step - loss: 0.3433 - acc: 0.9556\n",
            "9/9 [==============================] - 10s 1s/step - loss: 0.0151 - acc: 0.9961 - val_loss: 0.3433 - val_acc: 0.9556\n",
            "Epoch 13/15\n",
            "41/41 [==============================] - 10s 234ms/step - loss: 0.1937 - acc: 0.9556\n",
            "9/9 [==============================] - 10s 1s/step - loss: 0.1188 - acc: 0.9718 - val_loss: 0.1937 - val_acc: 0.9556\n",
            "Epoch 14/15\n",
            "41/41 [==============================] - 10s 232ms/step - loss: 0.3980 - acc: 0.9462\n",
            "9/9 [==============================] - 10s 1s/step - loss: 0.0319 - acc: 0.9912 - val_loss: 0.3980 - val_acc: 0.9462\n",
            "Epoch 15/15\n",
            "41/41 [==============================] - 10s 237ms/step - loss: 0.3581 - acc: 0.9626\n",
            "9/9 [==============================] - 10s 1s/step - loss: 0.0098 - acc: 0.9971 - val_loss: 0.3581 - val_acc: 0.9626\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9q1qtFYAeJL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "30UrHNfxRUNK",
        "colab_type": "text"
      },
      "source": [
        "**Running The Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8ohAq8hRomM",
        "colab_type": "code",
        "outputId": "4ef4b6c0-1cd1-4c93-c65e-2d1594eb3dce",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "source": [
        "from google.colab import files\n",
        "from keras.preprocessing import image \n",
        "import numpy as np\n",
        "from keras.applications.imagenet_utils import preprocess_input, decode_predictions\n",
        "\n",
        "uploaded = files.upload()\n",
        "for fn in uploaded.keys():\n",
        "  #predicting images\n",
        "  path = '/content/'+fn\n",
        "  img = image.load_img(path,target_size=(300,300))\n",
        "  x = image.img_to_array(img)\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "  images = np.vstack([x])\n",
        "   \n",
        "  classes = model.predict_proba(x)\n",
        "  print(classes)\n",
        " \n",
        " if classes[0]>0.5: \n",
        "#then class 1 \n",
        "    print(fn + \"this is a human\")\n",
        "  else:#or class 0 \n",
        "    print(fn + \"this is a horse\")\n",
        "      "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-3349bdbe-d50a-482e-8c48-a36414c2faeb\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-3349bdbe-d50a-482e-8c48-a36414c2faeb\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving large.png to large.png\n",
            "[[1. 0.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zI-AzR9MhjkW",
        "colab_type": "text"
      },
      "source": [
        "Clean Up"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVGQsBPrhqIp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os,signal\n",
        "os.kill(os.getpid(),signal.SIGKILL)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}