subset_BraTS19_colab.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "subset_BraTS19_colab.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iF5C7dO16Y5x",
        "outputId": "dcc8117d-777a-49fb-9d79-227c39e2f316"
      },
      "source": [
        "from keras.models import Model\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose\n",
        "from keras.layers import Input, merge, UpSampling2D,BatchNormalization\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.optimizers import Adam\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras import backend as K\n",
        "import tensorflow as tf\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import skimage.io as io\n",
        "from glob import glob\n",
        "\n",
        "import numpy as np\n",
        "import random as r\n",
        "import cv2\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "R7RoqzxW7QyJ",
        "outputId": "f58277a1-bf4d-4639-a90e-54146284376d"
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.4.1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x0Yjxz5_6kWo",
        "outputId": "98fbe8b7-3fed-470f-cca9-0df3724e53cb"
      },
      "source": [
        "!pip install simpleitk"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting simpleitk\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/cc/85/6a7ce61f07cdaca722dd64f028b5678fb0a9e1bf66f534c2f8dd2eb78490/SimpleITK-2.0.2-cp36-cp36m-manylinux2010_x86_64.whl (47.4MB)\n",
            "\u001b[K     |████████████████████████████████| 47.4MB 67kB/s \n",
            "\u001b[?25hInstalling collected packages: simpleitk\n",
            "Successfully installed simpleitk-2.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ea5n2ZFO6kLh"
      },
      "source": [
        "path = \"/content/drive/MyDrive/BraTS/subset/HGG/\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "80kdrwcu6Y57",
        "scrolled": true,
        "outputId": "52ff72d3-c15f-42ff-e653-baf821cad441"
      },
      "source": [
        "files = glob(path+\"**/*flair.nii.gz\")\n",
        "\n",
        "len(files)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ghlBMdNp6Y58"
      },
      "source": [
        "# Convert to Array"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WvxUKT2g6Y59",
        "outputId": "58831680-c976-47db-f6df-256914102033"
      },
      "source": [
        "first_img = io.imread(files[1], plugin=\"simpleitk\")\n",
        "\n",
        "print(f\"shape: {first_img.shape}\")\n",
        "print(f\"dtype: {first_img.dtype}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "shape: (155, 240, 240)\n",
            "dtype: int16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDAJ13WW6Y59"
      },
      "source": [
        "## Visualize Flair Image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "bRs6xlTM6Y5-",
        "outputId": "b33e3bdb-2206-45ae-e6e3-bbb990227bea"
      },
      "source": [
        "fig = plt.figure(figsize=(20,15))\n",
        "\n",
        "ax1 = fig.add_subplot(1,3,1)\n",
        "ax1.imshow(first_img[100,:,:])\n",
        "plt.title(\"axial (transverse) plane\", fontsize=20)\n",
        "\n",
        "ax2 = fig.add_subplot(1,3,2)\n",
        "ax2.imshow(first_img[:,100,:])\n",
        "plt.title(\"coronal plane\", fontsize=20)\n",
        "\n",
        "ax3 = fig.add_subplot(1,3,3)\n",
        "ax3.imshow(first_img[:,:,100])\n",
        "plt.title(\"sagittal plane\", fontsize=20);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhERERERERERE1hgFhEREREQOgpm928zczE5cwX24mV21UtsXETlaHY5z9CLbsaLncTN7YdzHC1dqH3L0UkBIREREREREjnpmdmIMnrx7gflnx/kXHt6WiawOBYRERERERETkaPM64IHAravdEJEjVbbaDRARERERERE5lNz9duD21W6HyJFMGUIiIiKyaGZ2ppl9wMxuNbOWmd1uZp80s3P7LHuumX3OzPaZ2YyZfdvMXmdmtT7L7oivUTP7i/hzpzdt38yeYGafMLPdcd8/MLOLzWysz/auimn/mZn9XzP7YVznZjP7EzOr9lnn583sn+J2G/H1VTN7mZkt+2+mnjbVzOwPzeyG2KbrzOyN/dq0wHa2mtnvm9kXzOwOM2ub2W1mdpmZPajP8rNdJOLP7zezu82saWZfMbOf28++nmtmnzGzvXH5a83s9f2+QxERM3u6mV0Zrw2teG76rJm9dN5yp5vZW83sm/Gc3ozn6T83s3ULbHvMzP7KzG6Jy3/PzF5pZif36wY2v4ZQvJ7cEGefF+d1Xy+M638mzn/jvPln97ThVWb26diOtpndZWZXmNmjD8Hx6z1fP8DM/j0en4aZXW1mP7OEbZ1jZpeY2XfNbCJeh/83Xm/qfZa/sPtZzewXzOwaM5uO+3+/mW1bYD/rzezN8fowY+Gaf+VS2iqrRxlCIiIisihm9mLg74ACuAL4IbAJeCTwUuCDPcv+MSFd/27gMmAKeArwx8CTzOxn3L09bxdV4NPAeuCTwATxj3czuyDuuwH8C7ATOBt4DfA0M3uMu+/t0+zLgMcBH4/beyrw6tjuX5m37MVACXyJ0MVgDPgp4K3AGcALFnOcFuGDcXv/CnSAZwAXAo80s6e7ux9g/Z8EXku4cbmccGxPA34BeHo8Ft/ss94JwDXA9cB7Ccf5F4EPm9lPu/tnehc2s3cRjtEtcT97gUcBFwFPMLMnunu+xM8uIkcpMzsfeCdwB/ARwvl/E/BQwrnk7T2Lvxh4JvBZ4FOERIXTgVcCTzGzs9x9smfbdcL14RHA14H3Ec7Rv0c4xy/GVcA48HLgm8C/98z7BuEcB3BebNdVPfN3xPcHAn8EfA74KLAHOB54emz309z9E4tsz/6cBPw38G3CMd1COF9/3Mx+yd0/sIhtvAZ4APDF2NY68BjC9ebseN4v+qz3UsLnuYJwHM6K+36Ymf24u7e6C5rZCYTjdCLweeATwBDwc8AnzOwCd/9/S/rkclgpICQiIiIHFDNP3k4IqjzO3b8zb/72np8fTQgG3Qyc6e53xOmvAz5E+EPxdwnBoV5bgO8Cj3f3Rs/2TgDeRgh8nOnu3+uZ93bg14G3AOf3afopwIPdfXdc/vcINwK/bGav67Yt+ll3v27e50qAf4zL/427f2nho7RoD4xt2tPTps8QjsvzCcGa/fk0sLn3Zilu52HAFwiBraf0We9s4EJ3f1PPOpcR/oB/FXNPxrEwWs2vEL6v57n7TM+8C4E3Ar9BCJaJiABcALSBh7n7zt4ZZrZx3rJvBn5jfkDCzH4N+HtCUOJPema9ihAMej/wS93AuZn9EfC1xTTO3a8ysx2EgNA33P3CeYt8w8z2EgJCV/WZD3AtsNXd757X7u2EgPtfEs6py/WTwJ+5+6t69vE3hCDRO8zs4+4+cYBtvBS4Yf5DBjO7CHg94SFCv8DSk4Ez3P3bPetcBjyX8ADjgz3LXkp42PBcd39/z/LjhEDR28zsCne/8wBtlVWiLmMiIiKyGL9OeJB00fxgEIC739Lzz1+N73/YG3CJ2SS/Q8jCedEC+/md3mBQ9HxC9tDf9AaDot8DJoEXLNCN6TXdYFBsQ4PwZDkhZDb1fobr5q2Lu5fMBT2etECbl+qibjAo7qNJCKDB3LFbkLvvnB8MitO/SQgWnWNmlT6r3gj84bx1/hO4CThz3rIvB3LgV3uDQd32A7uA5x2orSKy5uSEzMd7mB9AcfcbF8hOeRfhwcP88+15hGvH63oDHO5+M/BXy230Yrn7vvmfJU6/hZD1+QAzO/4Q7Gof8Afz9vEVwvVrnJBddaC2Xr9AxulfxveFrmlv6w0GRd0sn9lrRXwI8Xjg8t5gUNz3XsKDgzrw7AO1VVaPMoRERERkMR4V3z++iGUfEd8/PX+Gu//AzG4BTjKzMXff1zO7CXxridvbY2ZfJzxNfQAh+6fXV/ps7+b4fo86FWa2gfAU+qnAyYS091596ycchM/2mXY1oSvewxezATP7WeAlhKDWRu79N91G7l1M9RsL3IDdDMzWvjCzQeBhhO4erzCzfk1oETKdRES63gf8OfBdM3s/4Vz3BXe/a/6CMWh9AfAc4EGE7l+9yQrbepYdJWR73uzuO/rs9+pD9QEWw8weQwiaP5rQJW5+/bdthED7cnytX+CfkHVzHuFacekB2jkU2/lM4H7ACNB7Ql/omrbY62b3ujFmPfX+ehwT33WtOIIpICQiIiKLMR7fFzN8b7fI80Kju9xOqLkwTngK2rVzgaeZi9lebxtnLVBXqFv3Ju1OiOntXybUbbgGeA+wOy7brTlxqAop3yt13t1zM+vW29gvM3s54Yn4HuC/CDce04ADP08I5vRra79jAeEz9t6IrSPcNBxDeMIrInJA7v4X8Tz2UuBlwCsAN7PPAq+KGS5dHyAEKq4HPkyoO9StTfMK7nkOG43vC3U7OmzdkczsmYRMoCbh/HsdobZdSeiW+3gOzbVioc/Uzbq912AK89pZITxEORP4X8Lxvou57K03snA7F3XdBDbE9yfG10KG99dWWV0KCImIiMhidP9A3AbM77Y1XzfIcyzhj+X5tsxbrmuhYsq927tXd7X9bG8pXkQIBr1pft2IWBPp5cvY9nybmff02MwyQlbPfmtCxOUuJNwUPCIOqzy/rcvVPY5fd/dH7HdJEZEe7v4e4D0xyP4ThKDPrwL/aWYPcPe7zOyRcfqngKf0FqePddtePW+z3fPi5gV2u9D0lXARoU7SI9392t4ZZvZOQkDoUFjoMx0b3w90vXsGIRj0bne/xwAKZraFQxPs77bh5e7+tkOwPVkFqiEkIiIii/E/8b1fseL5vh7fz54/w8xOBbYTCl0ulLGylO2NAz9OeFp77fz5S3BqfL+8z7xD9Qf+/rb3WMKT16/3mddrIyFj6Yt9gkHDzHWvO2juPkUIvD3YzNYvd3sisva4+153/5i7vxh4N2FUw5+Ms7vn2yv6jFR4JjAwb1sThEyibRaHkJ/nsUtoWrfbbHqQ808FvtsnGJQssR0H8ggzG+kz/ez4fqBrRfcY/1ufeYfqmtb9u2Cxo7zJEUgBIREREVmMvyOkjL8hjjh2D72jjBGKggK83syO6VkmBf6M8PfHPyxh3/9ESHP/rRhQ6nURoTvBP/UOhXsQdsT3s3snmtnDmSv4fKi8wcxm6zDE4ZTfHP/5jwdYdyehe9jpMQDU3UaFUPx6/kg+B+svCHUx3hWDbvdgZuvMTNlDIjLLzM6x/kXHul1hp+P7jvh+9rz1NwF/u8Dm30O4dry5dx9mdhyhi9li7SFkoy5U+HlXfF9o/g7gNDPb2tMGI2Ru3uvauAxjwO/3ToiZVc8jZOZ86ADr74jvZ8/bxsncc/S2gxa7AH4eeJaZ9R0Qwcx+LH6vcoRSlzERERE5IHf/rpm9FHgH8HUz+zDwQ0INgTMIKf3nxGW/aGZvIaT9/6+Z/SuhxsJTgIcQCoD+6RL2vcPMXkG4UfiamX2QUAvh8YSilt8DXrPMj/geQkHpvzKzc+JnO40wFPy/Ab+4zO33uhb4TjwuHUJq/ynARznAkPPuXprZ24DXAt+O30OVcOzXE4aOP2e5DXT3d5nZ6YRaINeZWXc0svWErnU/SQhevWS5+xKRo8aHgCkz+x9CQMII2SNnAF8ldBGDUK/tC4RAwhcJ14TNhGvE94Hb+mz7LYQaac8B7m9mnyQETc4FPhfnlQdqoLtPmdmXgMeZ2fuAHxCygq5w92/F/d8KPMfMOoTRGR14r7vfSBihq3sdvJxwDn8MIRj0EeBpizpSB/Y54EVmdhbhWG0hXIcS4IJFDDn/EeBHwCvN7McIGUXHE65pH2XhgNdS/RKhVtE/mNnLgC8RuphvBx5KuOY/mvAwQ45AyhASERGRRXH3/0dIif8PwlPHVwFPJwRn/nbesq8BnksIrPwyocBoArweeKK7t5e477cThsj9H8IQtq8kPHX+U+DRvUPLHwx3v41w4/JRwmf8TeAEQkDktcvZdh/nErKonhb3kxCeLj97gaLa870B+B1ghjBKz7MIo8KcyfJHtpnl7r8R2/jfwE8TjvnTCTdhf8phHOpZRO4TXksI9jyCcO78FaBCCNif4+4dgDja4dMJmadbCdeHxwJ/TzjP9xu2foYQ7P5rQh2d347//mPmMiwPFCTpegHhXP9kQi2di2Kbu217JiFI9X+AN8X5J8X574yf63bCaF/PI4zAdRbwtUXufzFuINRg2kMIvJ8bt/9Ud//AgVZ29wbwU8BlwIMJx/ih8bM8/1A10t1vAU4Hfo8QWHte3NdPEK5HFwDzh7CXI4gt7u+OlTVq6/0se8JqN0NE5IjzJb+SCd/dd8xnEblvMbOrgMe7u/5Pi4gcImb2YuAS4CUxYHOfFWsk3QBc6u4vXNXGyJqgDCERERERERE5ovXW7emZdjwhazIndJMSkSVQDSERERERERE50l0eC+h/lVCn5kRCTZxB4HWx66+ILIEyhERE5LAzsyeb2ffN7Edmdqjrs4iIyH2crhPSx3sJ9YW6deR+hlDE+NnufvFqNkzkvko1hEREjmBHYw2hOPT4D4AnArcQClA+192/u6oNExGRI4KuEyIih4cyhERE5HA7E/iRu18fR5p6P2HYbREREdB1QkTksFANIREROdy2EYZo7bqFMFzrPZjZ+cD5ACnp6YOMHp7WiYjchzRp0PbWUZVJiq4TIiKHzP6uEwoIiYjIEcndLyEMI6uuxSIiC/iSX7naTVg1uk6IiBzY/q4T6jImIiKH263AcT3/3h6niYiIgK4TIiKHhQJCIiJyuH0ZOM3MTjKzKvAc4IpVbpOIiBw5dJ0QETkM1GVMREQOK3fPzew3gf8EUuBd7v6dVW6WiIgcIXSdEBE5PBQQEhGRw87dPwZ8bLXbISIiRyZdJ0REVp66jImIiIiIiIiIrDHLyhAysx3AJFAAubs/0szWAx8ATgR2AOe6+57lNVNERERERERERA6VQ9Fl7Bx3v7vn368FrnT3i83stfHfrzkE+xHZvyQlqddI1q/DhwbwSoY1W5Q7bgZLSAbqUKuBl3irTTk5uazdpRvWY9UqpGmckIBZeOVF+DeA+9wy7Q75LRokQ0RERERERFbXStQQegZwdvz5UuAqFBCSQ8wqVaySYVmGt9uQJFi1gg0P4/UquGPtDpiRbtyAu2NmMFCHvMDqdZLREfLbbg8BmyXt3EhqtRD4cYeiCAGfdmduW2ZQ9vTITJIwzz20PU3wPMfz/NAdFBEREREREZFFWm5AyIFPmpkD73T3S4DN7n57nH8HsHmZ+xC5F6vXSIYGoV6DqQZYgtWq+OgQdHJsugl5DrUqPjYCZQmdHB+sQ6sNlYxyuI7duXPJQRlLU2x4CIoSL9phYprgM00oCtydpFabC0IlhhV13B3yPGQqVSsw01RASERERERE7rMsiw/pex6ymxlAmFYUuuc5gi03IPRYd7/VzDYB/2Vm3+ud6e4eg0X3YmbnA+cD1BlcZjNkLbBaDcsyym7gpSgxM6xWw1stvDENjWlsdBiy0EWrvHs3FAVWr8HAAAwNgBnW6pDOtPB16ygnJvBWa2mNKUqsXsPLMmQIEWICelgAACAASURBVIJUmGFpGvaZpnMnw04HkhSyDEqfzRYSERERERG5ryrPegj7ThmgMl3iBkXNyOuGp1CddIZvapJc/Y3VbqYsYFkBIXe/Nb7vNLMPAWcCd5rZFne/3cy2ADsXWPcS4BKAUVuvO2M5sNKxSka69QTKsUHKLMEKJ901iZUlVIBaFYoSEvDBOp1T78/MpiqeQNp2hm5qkHS7diVJyDTyYTxNKaenF9eONMWGBiFLsVY7RLw7eYiAlyVAqC2U53iaYpUKVo9ZQWY409DuYNUKaTaK5znlzIwCRCIiIiIicsRLN26g9bATSdolzWOqIRBUNToDRlEzygpgYCVMb60z9Jgfp6wkZFNtkuk2FCXl9TfhnfZqf5Q176ADQmY2BCTuPhl//hngD4ArgPOAi+P7hw9FQ2VtsywjGR+DY9Yx8cB1tMYSrISs6QzVMyo7J7FmazaoUo4N0d44xN7TqjTXhyydbAaS9iD1xEimO1hRYJ08dDfLMqxWIxkZxqdn7hkcMsOyCng5u+xs8egsw9wp252QDVSJGUBFAUlcplLBaxW8XsMrKUmtgk3NQKeD5zkGJAMDeCfXSVFERERERFZfkpIesyHc+yTJPQbQyTeOMLm9StpxytTAoMyMssrsC4e8blAC2+oUFahNZFSmqiTNgvTGBO+s9oeU5WQIbQY+FLvEZMBl7v4JM/sy8EEz+zXgRuDc5TdT1rpkeIjyhM1MnDrMnY+CcqQDnYRsX0pzXZ0N34XKrQXcvRtbN87McSPsPbnC5Ckl4FBCkhtlljE8OkR9d0Ht7hnSiQaUoSh0un4dnRM3U7ljL35zGAnMiwKrVkmGh0KQxxJIDG+3saQWAkCVDGu1sVocccyMcs9erFLD6nV8eACvZuTjdTpDGdgg9Z11kr0N2DsJiZGMjuCdDsWu3at7oEVEREREZM1L6jWaP3YcndGMomoUFaPMwDMoUygrRpJDkhMqCxt4GpYpKnEjTpiRENZNU4qqUd8FabxvUi+J1XXQASF3vx54WJ/pu4AnLKdRsnYl9To2MoI3GmH0sDQlGR+j9ZDj2HdylcnjIT22gRUpZaUkr5TsHUgpKwOMbKgy/IMaU6eOMrk1Ix+CwVsT8kEoao5n0F7n7B4zkk6FtFVh7PohRm5okOxtUA7VSfKSyYdtpvHErQBsumYfyV178WYTGxqarV3k0zPQaoVRzQZqsGkDNGbwxjTl9DRWrYZuY4DtmaBz/63suV+dxhajqDv1XSPU7x5mcOcGBm+aCIGhVptkZAQbHMD3TVA2m6v5VYiIiIiIyBplWUZzY4XWmFFmocdF2Q30WAjwFG6kLUhyp6iFukFlJWYJVTw8SM+YnZcPQGcooaxUsac+hNEfTFB+89rV+5CyIsPOiyxZUq+H7BrAGw0oyxBUGajD2AhFPaEzZORDJdXUyduGdxLIDVJorQMrU9zGaA8nJLlTmTSKOiQdsNxm95UPO52BknYK+WBKZ2iEodvr1O9okEw1qUxWqQwlVKYdr6SUx4xTDFRobK+HIec7Tm1vh+rOBkw0sMkGPjIU6xJZ6N42OhIyirKUYvM4d54+wMyxTj7WgdIoawmtcWNmU4WZY9YzcNco9btmSG+9m3JiMhyTwUFsoE6xe48i5yIiIiIiclhkx22nfcJGOoNzBaKtBE/Bk55XChazgIpanJaBJ05Zc8pO6DIWx9mhqIXMIjcjyR1/4BiD6x9B+pmvrebHXdMUEJLVZxYKNadpKLDcbJHUa1i1glWrlANVilpCPgDlYElZxmBQJ8EKA4diwGmNG5BSpqG2UNoKkeqkDUkBlCFa3UmgrDteLWmN5ExQpaxUqExUyRpN0pmc2kRKbVeLzkiVzkhKczxl6njDE0jaUN2XMjKaMXhrhfSufVhRhoLSSRpGG6tkUK1QjA8zcdoIUycW+EhOkpWUzYxiLKcYSshHEop6Qj6QUdYGGZ4ehX0T4XhUslCQOqvgeUdBIRERERERWVHp+Bj5tvU0ttfJ66EukCchIASx61cM/JSZk+Q2GywqMyBhNoPI0/DAHCdU8ajOBZOSjtEZMWbW19n6w21hQB53yHOV0TiMFBCSI0I50wQvsTQNwaDBgVDALMuwvCSvGfmwkwx36MxUsKks9FctDc8cN8gHPUSdq1DbbVQmQ5/WypRjRTjxNDcaZdXxzKE00qGc9klN9oxVqTQGGG+0SDollcmcbOcEu37qWKaOh/bmnKSe4254YUy3UiZOTRi+aZSxHYOMfPNOAKxWxetVmGnROu1Y9p5WY9cZOdlwh3yyAnsrpCUUowXUCspqSbsIZ9Uyy3Bbx+h0E983ic/MUE41SAYH8XaiLmQiIiIiIrKiZs46jfZoqPVTDISsHoxQk7XoBoII91RJ6BThBnjMIoo/d2sLeXhmj+WhjEdZjQ/ti7jOqHHzL55A1nCyJlQaJSMf+moYyVlWnAJCctjNjhhmCd5ohBG9vOhZIGQM+fox8rEB2uuqFLUQWS7bKXSMtBOKRBPiOpQVx1JI2jYbwU5bTjoVN1k67qELmRVgrQSAopqS1XOK8Q77TqoxsqNGdtcktTvaeL1Ka73RGS0gdbw0vJVCzEoqhwsm7lfS2JYyfsxWxn/YItvXwjoFUw85hrsemtHaFNYt76iTdWzuJJnHEchSDyfMDlgR+95WK9jgQIimN6YpWy0sy0jHxyAL/2W91aacnDzM35yIiIiIiBw1khQ7/UEU9YyillDWEmY2ZCHgk4VgUD4YeikkOVhzrgwHFh64F9UQ9cmmw6DMpYMlRtIdQSyJCULp3AtzSkJRaiugqIaC1PkQtMZT/NmPZN01t5PfcOPhPiJrjgJCcvilKTYwgE818CIGguLQ7snwELZujM7mMZqbarSHQ+0gDKwwbCbFE8fK2B+1BDLDqyV0jGzG8FYYYj5tQ9p2ksIpqkZnCNqjHgrdx4BzURhlaWBOPuJ4JcGKEm+2YHQIy0M6Y9Gx8L+lNJJWMptx5BWnHCiZPCHB8hoDezKyqYLJ7Rmd0RLPHJtOSduGxSg4Sahp5JZADpWphMokVBqQNUvIUqhVsSQO7Tg9ExpbhhRKSg81lmo1vNU6/N+fiIiIiIjcZ2UnHo8PhvqoncGMzlAWarYOGO0RCw/TzXALARsAK222hhAQRxCLXcUqHmqt5oRAURzo2bx3ufAg3IqwLYujQHe3X1SAWvh5ukxIH34sQ+uH8a9+57Ack7VKASE5bKxSne0WRpbiMzNQFKFrWJqSDA7CxnW0to0zeXyN5noLdYMqUN0XsmjSRkIxWM6eYKwwsBKvlFieUpkEDLJpJ2s5aavECugMJLRHjWIkJ20kWMcwYkCoE85q+VBJmSahVk9ZUNYz0hakTaOsJ+AFlhtJO9QlsiKMGFbUnfbGgkYrIx/MqE6kTG8Oo5pZ28imE5KWYd2USfMQ3CoM6xjVvVDbV1KdKqlO5HiSQL0ahrNPEyjKMLqZOzTbeFFiaYJVq2EkNotn5bJY6NCLiIiIiMgaldTr0H3Y7E7rxI20xzPcjOpETlmJw8pXwv1XN1BjJaTtmBXkobsYEG7EPLx7CqWH4FFShF1YGpdzm71vK7PQ84w8dBez3MLD93gLU1ZCYKlMAYzdQykz60fZdP06ir17VU91hSggJIeHGTzsfqS7p/B9E+Q7bgoZQYODUKthaUL7tK00ttWY2pbQHu2u5yFlcToMaWh7jFZqs9HlJIdiOFQ4yxrG8K0FncGE+t6CymQBBvtOqtLYajS35FhhpDNG2gonrc4o0EqhWjKwdYqkU4WZJt7ukI/UyKaddNrojIDtqZI0Q/pj2jTSNmQz4cTZWgedYaeoQmudUVactGlYbqTNUM+orIahFvMhhxIqU0ZtlzG2I6e6LyebbJHuaVAO1ylrldhPt4BjN0JRknRyfNcefKZJ2e5Aq0UyPEyyYR0A+Y6bVue7FRERERGRI1brsQ+mM5LhaRgi3vJQ08fMyQdSyszmij0XhCyfNHYTaxODP9AeDSOI0c0c8lDPFYt1gTpx/RZ0xggPwssQXEpSo6jE3hqtsKx1M4aK0DWtTMEzDyNFt43pY40bX/JATnjHtRR79qzmITxqKSAkKy5dtw4/YQvt0RqWlyRlSVp6yG6BEAw6dQt7T6vTGjfywVBwrMuAfHCusn1lMiGvd6PRQFZiMymVCWPothaehSh2Xk/Zd3KFiZOhGM4hddKJlGw6nJRC/1WwjuEVOH3rzeycPg6fnsHMyAfSkGXUsdj31SmGnLII6ZJZM2QKpU1i6f0YBE8hbcVIuoUMIk+NYsBnK/HXdhv1u52hOzsM7pjAGjPQyUPxtME6ZTWjM1ph+sdGSTrO8G0tqjvupmy1SAbqeFFQTk/jzRblzrvDCGciIiIiIiLzpJ2SvHSKakJRicGf1ELWThmCRBB7M5SEe5+UuVqneajJWtRCBlHvqGMWu4d1i0Wn3RGeu7dzscC0p/FeKYu9JUpmRyPL8tiNzD3UayV2TbN4X5gYsjIUEJIVlY6PwbEbmd4+HApDM0AVSFrtUJJ+oI6vG6WxvU5zfahkX1biKGBusxHjbpQaCLWDZivWhx+SZhJq8Nw9RTlco7l5kMaxGVPHQzFchBPRdEplMmQauYWMHVKPBZ5he30vO8vjwgkny2ZrDVmskG/dEc1Sx7NwAk3zOJJZg9nia2U1tjmBMulOD5/JSqO6xxi6rWTg7oLandPY7n3hc1UrMDxIe9MQ7bGM5njC1PEWahhV64x31pPetQuqlZBimYYD0q3DZFmmavxyRDGz44D3AJsJfxZc4u5vNbP1wAeAE4EdwLnursc+IiJrjK4TIodHmSVh1LBquIcpK1BWjaIW7lsqU8wWgbY4aI8nzA4XnxQhqyjJe+7DrGdUMeaGovdiLusnFCHqaUhP3SGKue30jlIG3fs/p0zAKzBzxilU924nmelg0y2s3cEnppQ1dAgoICQra+tmpk8YY2pbihsU1QqeGQP7GtBuw9gIra2jTJyQ0Bn2EI3upijGNMKkE/qXegXK1Em6EWWI6UNGNm1UJxz2TsDIJiaOz5g41bGtTdhbpTKRUtlnVCfCOnk9RJuTgZyyrGCJU0mKUFS6XodKJXZL81j9PtT76aZLeuIU9VgMbSbULCorRl5nttja7AkuDRHzpG1kU8bojSUjO6ZJd03BxBTe7mDrxig2jDBz7ABTW1I6I0Z7zGkdF4pGd0arFLVBNn+/HgI/7qH2EmDVKiSGdzOMRI4cOfA77v41MxsBvmpm/wW8ELjS3S82s9cCrwVes4rtFBGR1aHrhMhh0B7N6AwlFFVCvaB6uB/Kh2P3sdLIGj4XvIn3Mb2FoZOCUEe1DDM8icEhN4jdyMqKh5qrFkpszN4XJbMdKqAI5TWSPGYCWU9tojLuOu8+iIcyhZuenFKZGKK22xi4u6S2r2Tw5ilsclL3P8ukgJCsqMYp40wcl9EeCyN/+WRMR0wTyDLyjcNMbq/SWudxGMIQFEraNjvCl8VIdJmEZZKZeJJIQsphOp3EqLbjx27g9seOMnlyAeMduKvO0O1hfjrjWAlTxxmdsZJiqGRoqE0zgbIwLr/ux9k8XCXZFGryVKZz8qHqPT5P2rTZjKWiFtIdkxyy6TAso8WUyM5IKLTWPVEmbWPwDmPk5pLxr9wRRg6rVfGN62huG2F6U0ZrLKE9Fod2tHCSzXZWyUcLOsd02DOScuzHB8MIaFlGsmE93myGhnVyykbjcH61Igfk7rcDt8efJ83sWmAb8Azg7LjYpcBV6A99EZE1R9cJkcOjOZ7QHrPZYd/zQegMlxTjOdZKqTTSUK+16NYWCut50vuAHNKmx0F9QiAnbRo+EGr/WBl6RiRZmFfdB52Rua5fnnlIOIoBoNleFbHg9GyPjCT0rCiznq5pefg5H4SZYxLaI0Zz3RgDWx9O7eNfUcHpZVBASFaEVar4w+/P9DEp+WB3OMIQkS5Tw4qScqpBWU1prTfy0ZykFUbL8jQEfognBIv9SJNOyMixcq77WCgSHaLVRdWYOnmUxrbYPWuiQv3uhOq+cBIrqzCzyWhu60DiJFMZjTuHqOxLqTSN3OoknRm8VsFTI2kVlLFv7Wy2Y7frWiyGBlBUIa/bXFYQIZKdzYT2lhUnmzaGby0ZujmMrFZs20h7XZ3mhgqtMaOsGmWle6LtDToBlpKPOz5Q0LzfZuo/uDMEhQCSeCCqRrpxA8Wu3TohyhHJzE4EHg58CdgcbwIA7iB0FRARkTVM1wmRQy/duIGJs0+luTHca7j1dMvKDWul0DNQcaj1E4aQLwGvztUIgjiKWCyp4dVYYBpC3SGYqz1kUGk4xcBcnaLuw3xPgTzc5+GxJ4jNZSKVsbSHp4TMI0LNVwj3kx2DfCBkOVmZUrMwGrQcHAWE5JCzWo103TgT2wdpj4S+qWU1/IcvqoSiz2WJtzsUtYT2CNhQjneqsb+pzRUh627TCScrv/e0rBOq1JcVaBybUIwUWMfIGgnZdCj6XNSgM2y0NhRYvcBbKVnD8OmU+i6jMhmCRkmnoMwSvJqQzOSzfWzDzmyuLy2xn613u4Qx2++1NCCJo6B1QoHp+t1O/e4OaaOFDw/SPHaQmfUZrXVzfXfNIW13C7uFfXgKNm0UQwlUc6a2VandVIWZEFiaPRaVCj40QNruUM408U57Zb5ckYNgZsPA5cAr3H3Cuo+dAHd3M+sbxTSz84HzAeoMHo6miojIKtB1QuTQSzdvojjpWPaemlJUuVcXMCvBugPhdLuFdZwyM8ycxIwy1gvqZvlQhvubpB1HFwvP82frCs1mAFlPMeq4X2yu6xh0s4961u/5X15WYymRGBDKOj2FpitQpI6bkY4Y2Qnbod3Bm83wcFyWRAEhOeTSTcfQPvkYOoNhuPbOsIcaOtUw1HpRNcgLrFohH0rpjJYMjTZp7K2Stm22aHT3hOVpjMH4XHS5240sFEHzkCFUN6a3OCRO2gg1g7rF0TqjxvSWEh8uSO6uUp1MqE6E7J76Lqe+pyDpOMlMGG6+M5xRzZ18MGbulPEklIauYJaH4tRlFgNCGVQnPRRrq8codxGKTdf2lgzf0iad7lAOVpk5ts7UlrRn23PBoyQPJ+EkD9HzzqCRNSGvJ+RpytQ2Y32tihUlXrTBS6jVQvez4QEY3k56xy7yO+5cra9f5B7MrEL4I/997v5vcfKdZrbF3W83sy3Azn7ruvslwCUAo7ZeqW8iIkchXSdEVkbjzBPZ9aCMok7I4OkZ9Wv2AXTLZotDWwmV6XAvAlCaYx56MoDPLpPNELuAhXspiA/0k56d29zD7tm6RN39m8/1Seud1xOo8qrHTCXCIECNJJbj6OmyVoHWuHHHE7dSmXaGbu+QXamA0FIlB15EZGl8sM7Mxir7Tklobi7IxwvKwZJ8tGDmWKexJcVHh0jGx7DCqUwluNtsgCdtG9W9RtYwkpaFk03N8ZjmmM1AdRKyBqStEHTpjBjTxzrtLZ0wmlgjBFKsDCmFeT2cWGw6JW11u3JBUYf2iNEeTsJJbrpFWUuZ2ZBRVpKQjjjglNWSYqSgjMPdpzEBJynmKvLDPQNZRT2cJNM2WF7SXlejsX2AxuaUznDow2tF6F6GQdp2BnaV1CbKsE4R1i2zeN5sx2BTPcNq1TgSWhIyhTo51upgjSZUKqQb1q/GVy9yDxYe8f4DcK27/0XPrCuA8+LP5wEfPtxtExGR1afrhMjKsdxDL4puYIV4r0LMtHEjaYf7rW7tU2J3rd4RyYo6FANGPhDu19KWk01DNm092/O57CNzygrkA3MPvsuYhmKFYcU9e10wbwj7JLdQmLqZkMwkWDuZHaK+m+UU6g45+aDTGTZmNibsPaVK6ylnYLXaih/bo4kyhOSQK4drzGxI6IyWeLUMYcfCMDfy0ZLpPGHm+DEGvzNNbXeHgTtTpvMErzhlaqTN0M0riVHnIgWyeKKJw71ThgBKEgMm7XHIR8IQ8tlUyN6x2O2qrBDbEDN9LJxAQhcwp6gZed3APQxhaFBUQgE1K8K+MKBW4M0Uw2azgpIc0k7vh4+ZPp2Q6pgPGe0hIxuv0h4JBdBa4xaKqOVzgaVulD6bKbEyoTMY6i3NFqbOIZ1OwuetpqT1GtZs4WRYtQLVCl5JSVodKMMZNRkZoZyaUk0hWU2PAV4AfNvMvhGn/V/gYuCDZvZrwI3AuavUPhERWV26TogcQunmTbQefBwYtNalpO0QYJntntWt9dNbhqMID7kh9E6YHZa+wlz9H5t7MB9KW4RMorQaHp672WytIe8GlWpz9V+73cVgbnsQ2xN7h1gZmxVrtXaDTWUe6qt2R3AOGU2x+HS3dAfgiTFzTEb9IaeS3rST4q67VuYgH2UUEJJDKqnX6YzUaI8ZxUABmc91Ji3AhwraG5yJEysMXFclm2gydEeVvc0KnpWUVcOnbbamDnQLkM39PNvNqglZ02mtS+iMlJS1kmQmpTJlJLFKvqdQ1ubSC7v9VD0No5bh8WTXHUwsL2bnW1GSNj0WeTaSWkHRTkJ7YpHsJJ8LPFlJ6G/bCdH2zkhJPgTtMSPJUzpDoY5RUQ9dzxK3uf64MTsqaZckuVNU4weO+7IyBLnSGBDyagVrtiAxqGR4vYqnac8XkZIMVfB2G2+3FRSSVeHuVzOXDDzfEw5nW0RE5Mij64TIITY+yu4H1Siqoahz0naS2MugG3SxnjpC8++R8oGYGVSNpTHig3jvFoVOweLD72zGKWuhN8dswMnjA/dqKKXR7bHRrSEUfja6I4rNBoyYew8jTkMS68omcdRmkjhKWeLhIX+8zez2tnCD9qgxecoIY1MtUEBoURQQkkPDDKtWKR96Go0t1VgVnvi/1LDSyKYTOhWHgYI9jy4YvGsTQzdMMfzDfdSuW09ze4dyqKBTGFkzjh7Wgmob2haHSbQYqS7m0hob25yy5iTNhIE7EiqNkCJpDnnN6AyFVEcsjlTWExRKOrH/a83w1MC675DM5Ixf3yJrVSnqCUPHzzBlTseqMJ3NDtsIUNsXK+BngDmVKaM9Dp3BAk9CvaDZfrcNQv2gqofgUAJZwzAPbc4m2rRHUjzpFuH22SEXrbDQrzdN8E4HOjnUa+H4u+O1CtQqUJTQapNuOgafalDs2XPYfyVEREREROTwsU5O1nCaG420aVSnnNpep7XO4mjGhN4Ss/dqc2U7IIycXAzM1UkN9VMNi8WgO0NGdcJDT414P1ZmRpmFIE3SDl3FyloYYaxbBiPpEAcPYjYYlLZ6Hq7HQXmwEHxK2za3Tgl5HcgMUp/tbtbdBj0jPbdHwJOE4R0Dh/vQ32cpICTLlm7ehNVrlHftwqY7VKZL0pkEaxtOErpRzRjWgcq+lKKV4Ovb3HlGwob6COPfnWDLf7e585FVmhtLitGcZpFR3WdUJ6EyGVISi3oYLawY8HCiqhr5oJEPFaTTCVnDQhpjJwRnyoqRD0A+VM7W60lbMb0wnkjCkPJO0g0IZSn5YKjxk+6eIN1ZkO1dhxUj7KqspzNWQLWks7kD7YS8kVCpJ2QzIXsnVOEP3d6yKaMoE8qKkw+EE5v9f/beJNaWLDvP+9be0Zz2dq/JvmVmVrKKriKrSLOR1dENZJsQBFs2DBiEBgIEeGIDhmFDc08MD2xN3AjywDMCMmAYMmDDkGANDFGiRNsUKZVJVlUyq7J/zX23OV1E7L08WHtHxM3KYlUx+8r4gYt377vnxImIc+5u/vX//+oAb3ayUEOsIxKFMJP0vIC0oWfSJViANYVdkzoLnpZ9Q9xsUVVcXdHeWbJ9rOawFtq1sfr1Q+X4tYby4oC/PCP8wbc/tc/IhAkTJkyYMGHChAkTPj50v/oNHj1dcTgVmpd2SJhT7GD+wHJ2XGOqoZyxqpqK436wc7ku2b8SERMTYZQtZmEO4WA2tOKglJvUIj4V7yWROTmpOLeT9wfLHMp7MN+OiKIOcPSd0CQ3nB69bka2jalXCIJLJJOWVuRXD4caHnxtxfH6GxR/77c//hv/OcdECE34UChefB4tC2g7NIRe8ysRimtHtzKTqGvF/KuqKEK3KWhPA5fPFbjuiNV3t6y/a3rDXa2EVaQLDheE8toS70lt38MCQqU9+SKdBVBbyLQNcqG0Dl6hHlnM2kzImEerb3vojBgKlZ19qIRuAbrdQ9vgrmpm5wtWrxfs7hQ0J5F42iKzQABQaNcOeTQasTQFrUUhloofh7X1ap+k+KkjYe4IJbhtO0S9j8PfwqCmlmT/klmNWy7ZP3PC1dMVmycTaTZXQq00R4JrK+YLT7UsKeUl9I23iZvNx/RpmDBhwoQJEyZMmDBhwicK5/EvPc/FEyW720JzDGcnGx6e1lSPHLNHEdekMOi97ZVcCnZ2ne2VMimULWWA2bU6RoocUr6QoF6RoJaneoCiNGeDBCN0LHIj29MU6VKr+uSucAd6ZZJE2x+KjhqRZfuYjPKGcoYQ2aWhaBBcyiiKhdnftFAOx8LuTsn6Y7/5n39MhNCEPzlEuP7KXWb39hRvn0MIaOlRb/ao+pGg3oGYMseydgTfKHLu6Z5s2D6vdKuCxdueo+/scN2cbuVoHmtpzSRK9cjYbABcImuWSigU9Y5iK1SXUGxTkn4KQjMFjoJXS6pvBzmktTpUs2OpoKUx5WhKxV8otA2IsTOuiay/F/AHz65xbBYeWbUwg6BwOHEUm5se2XIDxd4IpmKX5JOVDWquTd5ZIC6VdqnECtzVlni8+D43fWbpe7a98MhySfPCXc5fqdg8Dc1jDXQOigheaVbCZlMRS0+ohc3jtznd7idCaMKECRMmTJgwYcKEnxC4quTyq7fZnznaFTSngcdWVzy4teJwMWP9hjXj8U1S5MRE0EhyOPicDyS4ZtQtLOcMpewf10JXmRIn+iGTyB8s1ycHS/sImvdco1by/WtJOg8dMUDvQ59zNCKEHdD7sAAAIABJREFU+lDpdE54eheIOk2Zs1b0j5UFZE/44ZgIoQkfCovXLpG2g6jgPf5iw+y9kuhr9meO6M2+hCRyRnOeEMT7JeEo0D594I1/ec7Tf2/H+o+2uHbOO79cEE46mlLZ7QvK19Q8otHaHF47R5gp6q1tvd9rHyLdrsTIoES++I115/KN0B6NBjnyIGL2ratnHKfHS8qNsnjbIasV7bO3uXxhzsXLzljy1o5ZnnvaTNAINGeBYuspr82yFqtBaimdha6pNya+aIysksS2l+uGrnGoeDi/gPXipkRSueGfVZe0lFXJ239qwe6xSFxE5ODtOcFbaPWq7cOyy+tIt3BTuPSECRMmTJgwYcKECT9JcI7DkaNdQ3sUkdOGx2ZXPHpszpvhjOt7tiEIle1HXJsK5amQ7fe2N1JvLgvXjQ7d0iuJit1AwPQQs5kVu6FZjo54GNdZ1pB0ZvPS5HqQmOMwpM8AGj9vTBSNu5PRpW5m2e1RWLSIdURTRCwLNszUukhP+KGYCKEJfyK42Qx35zacX6LHK8JjJ+gTp2xuz2iOPM1KCPXQGQuFbik3/qD9TlDniQr7ZxsevTzj6PWG9bevuHr6hMulQ5YdhzNH965Ywn2SGZaXZhNTsRR6dVm6OGKf/c20+lCZJKcPM0vtD3Mbw/ZY2T+5xrXK6q2ArhdcPT9n86SjXRohFQs7VqwUSZ3TiJbzky1olvtjr2XnlQbSNv3BRUvLz4+v6w7dOoqdIlXVq4BErQVkSJ3VSNcaS0c8mqOF43CmaKlIK7i9I9YRLU0VpZ2z+38AfwgUm479K49TPnaC/vY/++Q+LBMmTJgwYcKECRMmTPhI4b76Koe7S3Z3SpojoZsndcyu4A8v7hBUKBcNh9MKf8D2Lpr2SqMIC3KznmzIaEckTLC9RLZ2ldeCa80qlgmcfJxip5YD5KQnivLvXIAYgNRERwImGBAlOrlBBmnuKpaIp1xElzY/gGQNs/1drAf1UCztNfMebMIPx0QITfix4ZZLZLVE6xJpO9qzBbvH6sRMDwRIT44EbvhA++N0Fr6M8/ijHZsnK+rLgtlrW1ZvH7F51qFrJR53dLMK36TcIG+sNiP2OhZ2cB/UOowF6dU5ffK8t5/zueSQM8TY5VDC4cRTXQbq80BYz9jdcrRrUyIh0mcAMRqkUGsz3yMfN6WhSW4vn89HsrTRiKUYUwbSPqLLObEuBn9tA6EcXrN+pHQLR/RzYm35RGCDousAJ0SPZTWl+1vso7Wz37a0TywIswWLr3yJ8M1vpZF5woQJEyZMmDBhwoQJnyeE9Yzd3ZLNE45uZu4IHEjreHC9YFa1eK/JVTF0PO7JoC4pdZSUsZp+TvsoTXsWr9g+JtCTQa7LdguSJUwtL9ZDTLkY0g22Mom2J8qF+T6bKFnIGBXLcaARXM4tkoGwcqkYT2exHVLS74dQeveG7e/Av/QC8fU30bb5xN6XzxvcD3/IhAk3IU89DrdOkOst7ZNnnP/0nPtfc9z/xcDF1xqufiqwv22dtfrOYJUpdmKpxFJ7L6rfC8WV0G5Ldk93XD3l0bpk9UcbqnOHdo71rY11FUsDVajTgJE4mFilr0Rv5sHOGHBTFtkvUnv3OKh5XGPfx1rRWaBZ2UGLTcvh1ozmyF7PddJbt1xjgdF+75Boj/f7RFJJCmZL/tb+XIS+rXwme2KtxHXHfltRP4RyGwm317RHFe3cEb3lIpHIJL+H4++07M48Fz9VcvF8Ya+brlEC+K0gB7GRs3OU10p5bWSQhEB9f0+oHa/9O7dwy8Un8GmZMGHChAkTJkyYMGHCRwrnLbID2wO1ayUsklOgE3ZXM7b7mpCUNO1aaVdKt1DCzMiSPhsoETz2ZXlDVmC3//eH1Ga+y84D+x5JRE8ckT5JDWTB1aY2GiuFxsTSOMDaMowGUmisUMqKJVEjmVwHxdayWokpzygOGa1E2xt1M3jvzz2OOztJ+bETPgiTQmjCjw25vCY8dZvLP/sCD74qtCcRLSLFRYHfeyTYH2OsLKDZnmReTus4NkgL7Y9dKN8taU8CzSlcfvmM9bevWLyttEcV68fPeTizP3zXDBLD7DV1rf2/69S8r6lbmG9MxOMkdTdLAWNhpkSP0c5AnEcLYo5JWqigIlw9W3C4HYnziAQhzMHvXHo9QYLaoJZYdtJAFlV69h3o5Zea5ZAR4hxCrUihFG/WzM4j6oWLlxbGmHsb7DLrPr+vLN+NNMcFV8+bPLLYCrGyg/uDUJ9b4Ha3UqgitEmu2UbcZo8cGtzDyPr1d1n93R3x6urj/qhMmDBhwoQJEyZMmDDhI4S/c4f3/tJLrN7ojKTZQ3cSoE45GAcHe8++mUEQqmBBzxZrIfi0n8qOBn8YCtrFQa0IXwBOe5UPpL2Xp3dJqBPbf6kSc5cwl3iqtD/yrSmQsiopx3qEOqmJ/NBdzMQDljWEyijM2ron96HSAn5vNg9/ELS0fVHeN8XsrsBsbFe/8gKr79wi/s43P8m36XODiRCa8GOje/YuFy8vuXjZ0R4HVBRp3c2AsYyRkkei9KqZHC4W6pT1o+AODhXYnzpWpafcKsW1ow2+Z4yzHSvKQCr1PlQGG5iknGuqRNZESbavxDQDEcEF0K1Da0GrSDcXwtwR9579mRBnwYijVpDWLiSWEGYxBbKN2OZIr0bKgWuZKR8PYKLGZKOC7j31AxvgmpWwPxXmD2Kf5O+CMr9nIW6hFC6fd5ZfBCmjyMigYmP2sG5GH7iGCu1K2N8u8e0RxcUBd34F14eJDJowYcJnFu5nv8zuiWUarxXXKsV1A7/1u5/2qU2YMGHChAmfGYiCbyLlZtSGKwjSuEFVE1O0xChEuid5UnSG7VfEtiqjYrfCEHWRyCAFfHptTfM0SJ/z47pE5uSg6NwhOe8J3bCny9ms48eqV2KROpV5e4zLVrPkxJBUPM8Nf7JqSEwJQFuYSECLwakxKYR+MCZCaMKPBbdccvn8ksvnHfsnrOWWNEaM9F5TBqtUb+NSLPNHRoNR3wJeh1bsQLcQQu3xrQU5H9rCWgn6IZQ6H9MlHyqYqifbq2D4u8/dx3qPa2d+LhGQFjxCENCZSSi7WvAzT3OsyTQLRME3YocolbCMuEfeVDz59d4ne+y7jIVBltmHWBfp/PeOYmuWulAL7QpmD8V8uAHKXaTYCbEUmrWwvxstQDrQE1yusfBu12VvrlgCfxC6GexPBLTG3a5YvFVSOgePLj7CT8WECRMmfDi42Qx3coyqcvXimqunrRBgikylviw5ffNJCDlhUkBTtlvTEB48/HQv4LMKEYrnnhlNiGL3sAvo4UC4/+DTPb8JEyZMmPDjo22Y34+gRuQUe/tXWyOE/EFwh+HhuTX8eA/Wt3WHPuenR/75/Z2/hBuxHeNc1JxFJCI41UTmSB/5kW1g0ZuySETNPZEzf/rXln5vqM72kuPsI/VGFsUiB1dbtEefW5v2ZpoCpmMhaKPEyuPW66ko/gGYCKEJPzKkrOh+/hUeftmxf9yi5f2VH4iQaH+QxS5JFxdCu0ykRSaMsiUrSQJdAPYCNUSnvdSwW5WEyp5zfT2jwAaQPKj0A9L75IexMGtZ/hlGSiKfu3QNv+utZ4Xi5x3RV8TSVELdrc5IpGBSRr8TYpEkkbllYpeyg3Rgyunzg7Qnq/JrZYWQeu1zhsJ86MCWBzu/h3ITWXzvivZ0zsWLNdfPCOqUcp+Is7neCO3O98a14De+H+y7hXC9FLbf2KLvzjn55oLbf+uNKVB6woQJnxnIs09x75fv4lvlcCRJTp7mCS/EQtj/xefwaYGrHiPjBZbvBmZ/57c+zdP/zEKqitd+/ek+uw+B8loor2DxXmD1tydCaMKECRM+bwiPLlj8z/+I9l/7edsvBSgfOcJMkE4orwW/Y7BwleY2kKCpYJ9cG5riM7z03/fZPWlvN94zZUWRETl6U2GUM4LyMYIQC/r4kF4dVFgDHDfq0JxJoRyXYT+rPXZ84Zkk0mEfmaNDkJHrpEsxITXsb1l+7P6xOXXxIvIPfudjelc+v5gIoQk/MsQ7tncrmiPL3HGXhZEPBzE2FmhOTGXjd4kYOiSSohsGGM0SwRLL4Im5dTz9wLO9U9Au7XHxujS7WRpIstd1zF7f6B6W0+rf31YxCmFuMkR1RgI1x9FaxpeR4/WWy6M57dIeOzvZs7+szW5WKuU1dDMjgKoL37eC19S9LHpwKZgtD5agvUVOE1vtWsXv7HehFpqjfIONUQoz2+DU97YQlKtnanZ37dz9QQi1DcCuoU/rV0keXZJaK1ilIId6W3v7lvZx5ZHMqf/dX+D0N98k3ntA3G4/9s/OhAkTvlho/sIvcPV0QbsUnvzv/2/ifv8DHxv+/Ne5eLIizNLirjO+2uy52qsgJVhOWq4QFhsj1NtVwfov/yLr//V3/tjX+UlE8czTfPffe/aGehaybJ5hvvNZ2m/zYajh+inP5j/8FYpNCgcFDifCU//7e4Tf/9Ynfi0TJkyY8Emj/Ve+gXqhvGxBoFsUxNKl7x0Pv+Q53ImUF44X/5tvEd59DykK3Csv0p0uKB7tCP/s9z+184+l0M2FbiZUF0LXJnLoEqqrlN1TwOHU9gZ9Pqk3MqgncQR0LNPJxe0cszEqsvdZP9kNUgquUTt+UBDXR3241vZOmucikX5eioycFWk/5RrMrqZCN9eeRIol/b4LTfvDGjTt/1TSfkduHhOvHE7FmgE1Qll5fFYYT+gxEUITfjQ4j8znvTyPFJycpXk94eNMmielUr0L3XyQAnbzgQHOrQPToVLCvSlxQgXNWuwPvaBvId9bvjJzzMAqu/Ef/8j32r+egDq1bmeF2sBU2sGKa48Ex3m9ZvWeUF3Zk8/WG966rG3E0mEhbYonhlwjGHKR3nce/ciVfbrYwCnJuiaF9sHQ9hzFtVBsOty+o7mzZH8mhJqeBIpFlnYmSWXyyPb3NKR71kG30N6b2+4qNAiUysULntV3zyj2B5gIoQkTJnxI6K98jTAvkKgU1y3XTxbsbxmBE77+JYpHO7MpzSsQQa53cLVBypLr2yXtyqyxqC1YJS1au4XiCiPY/SGT+anFrArtSnFB2O0ci2+8Svmdd+jefufTvh0fL5xn82/9vN2fmXA4VQZlKkMFNyll3YE+98F1aT6skmzfYxXlVNU9nCg6K3/wa0+YMGHC5wzFc8+gqwXESFzWZjUW0NKzebKyUOTCxsFY2r7FdcmyVEJcBg7zwMWfeYHlG0/gH27QwhFqj57OqV58nu611z9RksEtFsSf+SlC7YheCLUVrN1h2JOMXQrjwjkkFwN5/0U/bwC9Qid3DVOEHPkhcWjenAvfsZDUMUxxTUyOBek7f/WqnrSHzEUKBAunHsUfQTr3FqQeXCFAHzxtD0r7oawq6gsgenOfKFZYUgfldaR+/QHdRAZ9HyZCaMKPBFeVSGpTnhecffu/eHOQUW95P/MHkWbprO38TNAVxEJ7lU+WCtqT0gYghYV1q2QRK7A8nFE7Qxu0bsoMh8GJ/hxFhwyj3lI2U7RUy9/xiuwd9UOh2MJGKk6+3VFcB/a3Sp5YXvKWnBnxkgPJ+hvCsPgmkUFhuC9GjjEMsCNCKNTDOboOa/2YbGtaKuVG8LsWFWH7eEW7tvtmA72kY2p/fSqmBopFOsF0r3yT7mNm4i9LC/+OsL8T2Tw14/jeCt5978N9OCZMmPCFhpQV735jSbs2srx+WHM4NSI7VsrDLy+YP5zh95HmyKMO5vcX1O/UdOsZ7dIRKkkku41xLqkctdC+iigx2XZLiLUph7qVVSW7uePBz8y5He7iLi5/IpWP/vYtpK6hKnnzV9Usy4eBzOmnqJGlOhZpfsiS/xT0adVWm5Q6kX6uCjNoz+bUTzwO3hPu3R9ymwDtuk/qcidMmDDhQ8MtFjTP32Z324if3Znv1+ihFrNSbUGixx8UUU1K/ogErGi8d8hpw72fm7G7veDo9Yr5m9eIKmHmaZ45xb/5Nto0nxgpJMsl9392RXWlfWSGhJQTNMozJWX2jDsXA0PMRR8CTb+PyM1vnI44oqQHyAHS+Wd1QvTaW7WsEUQkFB71+UmgXgbr2Ki1vBudj32Tz4mbzYpy0SPvs5LbJEfH5jDqXPwY79ms8C5UF40RdxO+DxMhNOFHgiwXxJN18pzaCBHnEa59H/gFaYHpQVvh6JuP2D57xP7M26Bbaf84UxTZaCTZf5rb0It1y8p/zOWV+WBdO5xPHgxyflCYpfPMIc4j9Y7ZppQ4U2IVKU4aiwZ6VLF6zc5/fwbtnY76fktxuSdWa15ZvcfvLp+kaWb4jWN3R62zWJeud8SkgymbMkHmd0qsbCCMBX3oMyJ0dfK6ahogi4gmlZA7CPP7LbHyhOOa3W3r3lbshnwgiZintjBrXh7w2/VwDyCdYxwYdXdw+J2kSUHY3oX10fwj+XxMmDDhiwk3m8GrL3I4tfG/6oQwE7qVqXtiqbRLh+scbm5VTFP+OPzpnN2dinaVVJACzZH0lcvqCsK56/MLYmljsKgiCGE2lpna4v781SWL069Q/2//+FO7Jx8X/ug/+BLNqzt8EeA8NRTIc0le+JfDfcrW6JjmSZsbpCfUYmnPDYtohYMygoPv/NslWj3D4taWp//LW/jzLcSIhEj43lto23yq92HChAkTfhRIUbD781/hcOyJXigOmgoVybaU9g+hsuDjcqsUe6VqA+XFHmkDizunNMcO/0TLfllzOHFstwV+v8DvO3xnY6N75kn03fufWGCxeEe3EIo95E5bWtzcKyFZCToQPTeyTTP0fSRSKs6LgsZUUMhETSp+j2M6ogixNZVvLAqbVyqzseU9YiySutePXjYLC/omQ7aXyuearc+5yNE/TkeF/sQIxVIHa3S+DswxgSjFHlwbP/DyJ0yE0IQfAe6rr7J57ojLZws2TylhZaOABKtO5sEozOiJDQkgDx4RXz6mW8gQAAqDNLBPpJcbBI69KESvfZhzP/CkTmLjrmF9x7LEQvfKHGzw6RZJFVQobt3ifKQ5n7H+VkF1qWyfEA63Ijjl4qU5q7cK/CHyG//056nmLW7ZEttqyGHobCE+vpaYBmMS4x7q98krS2Pe1VtgdJZQxgi+EdpVsE5m5w6/7YgzT3NcGHse3seGB3A78Hsjnbr5SAVU5Hb1mTATVAdZaP++Tev5CRMmfEi4xQJ3fEQ3K9BisC2FOpHwSY3ZLYCUbwZQ7E1i3s0L9qfOrLxpDtjdtTHKN1CfK+WVDMer6LOEJEC3tGB9F4ZcujYI3dJRf4r35aNE/LM/x5v/0pywUJo7LU4hBoe/dvi94DohVJrk8onkKRTaVGxRy24AhkX/2F4cISL2HJ/LwYLsHbs3V7jrC2S7h7YlXl3j795GNxvC1KlywoQJn2H4W2foM4/TzRyhzASQEObSNyyIpUVJuCopXWohbEGCY95FpIvMHgXm9wounpohTumWyv6WUG0KFm91uG2L7A9I+OTIhu5Xv8F7P22zXLb+5sJ6Lqhkta19nxrq7M1iNbaE2fdm+cqP1V7Ro72NS30Kch7FZ4zbyMdCCLWpfX2jhEoIVcoCTARcTBEXdkB7fnDmEMl7qKwi6hVDOrx+3xktgCayyHKC0mN7FVP6OZNCae3RHFfMXn6R8Iff+Ujeh58kTITQhB+K7bNHXD5TsL8DYaGmOgkOd7D259LZH2jngDrAwVPsBO06QuXoZkKsuCHf6xnc9O+YbYYsI7RFv895PWFQEfWhYXmBmwen3MUskSPdXIl1NNWSV4oy0FzU1O95Fu9G9rcczUlEj1pEYHdHqK488/sNy9+bc/3TDlcFtI5oIVjSmUDUZHkzX6rkdoeZ6KkGNY8FeA6Sfd9oP1D37RELhUYoN4JrOtqj0pj1EZMONpDKAcqNUl8oh2N6SWb0DINfam2f2zbi9MY5vt9bPGHChAk/Ctx6jTzzBLLZoWVBXNS0q9LUnlGHymK07pKaFmY5DLLvICJ5ASnD3CBG4JvyU6gubBztrb+FyUlzd0UWQJR+EakCUkM7d/jH7hLuPfjcdVMsnn+W7vETmpOaMBMefqng8OqOsu6QXUnce2LnmF05mztyNTeRQVnlcyNzzytEueF67ufcnDlEOlaaK/xWKK8EidbWWEME59DlHI6W+Lu3kaYlvvPeFy7Ie8KECZ99yHzO/vbcyIgyqVQqU6AMRYs0rzghqCJqhYtYCnFe4sSIkmKjyLXv57duAc3SsRBBmha53sKsRuoKFxYfu2W5W3ratQVHjzt0WUMZ+o3VuK38uDMzWIFdVIe28+N9VCqwR2+t4V1n3zuUGIYcoD4b1duXFGL2bwehzOqgFChdDKqe8f4jlIpXsSwh8jmoPUQlNdBhsKTlqI7k1pAkIHg/pEv7IDdk58XKoYuflHLRR4uJEJrwx8N5Lp8v2J+BOuuO5fdm0Myhny63GPRQzlu6TUH1yKSa7TyFis5ussI4chROT4z4rLDJvtJ2yOZpV8lPqkp00g8KrlOCEyQPTtjjurm9ZreOFh7trPqp0bH8dsnqjcjsYeD+10Ge2LNeHNhuZuxvKfN7jsVbkSf+rw3fXS05PNHiVy2xdUQpUHGWKxRGg5skAigRMWFhSiB/sMG5m+XztcfkDU4fAO0UjVA/tDyMWDraufSVcxgphFojhGb3W7pZRSzUUvuT+6uXWebBt7BKfZkS9nNHOFGzsDGl7U+YMOFHgfPIY7d571dusXq76wn6Zu3TgmvIdnMHC7js5eFZYdkT51YoiAU9OaE+WZEBda63l+VFbc68yeNoP9Zp7qaoSExzzguP466uP1dZQlIUXH3tce79XMHh+QPrky3zIrAQJUThcFXjrguKa6F6lOaaOi90TQmLN49wLrQQ6Tt6wlBMydl4/X1NN1mKiDsI9UNh/UZEnUMKjwSPrJbERc3+sQW72wX1RWS528NECE2YMOGzBOfRxYzDmRVXQ2XzjbpEBs2VUGvvagiOnnyI3hQtzUlNsbMJrNwp5YWjPY7EWukitCuxIOouEDdbXF0hqyVSlh/rvONmMyQoxRaKnQ55qgIuDvsqGIrSAymUJoBMsGR7mWRlUZrDvQzKGycQrBlPdFb4sdzTNG2kwoR6IaoprkI1kEZ9Zl0mhUZdgESzNc2y7vK55I5jYPbnvrlPEiDoqLhkqth0HTo8v9jbtUax4pQ14RG0GNtRJmRMhNCEHwh/64yHf+EVNk/a6rLYCO7AjUDl8tpImVhY2Kf3CleO1VuB8Mxd9reE9sh8nd0ipu5XRkr4Q0r5TyFgvpGBocYGjggUB2svbDYouaG+AQammlRNLocBX0ulOt0TOk+4Kjn+h3PW320Ic8fDL5fEO3tKH+k6Tz1r2D3uuAgVsOD23/l9nuUZHr2y4PwrBeVzG1oBLRxt7Qg7e1GTXMookT9ZGEoImpP36TvnxEr6gdQ3VmnQxuEODlG4fmHN/sTRrm1jYwFqOvhlK6GrhTBzlNtIs/J0czjcitTnriemLCBUCfMIAtWV2H1srSJQbpSrF5bUZ9+g/D/+ySf74ZrwhYKIeOCfAG+q6q+JyAvAbwC3gN8Gfl1VJyPjZxzxT3+VR8/OaJfC5TOFZZmlbBoJgB/I7mKXnlOa/SvLzvN43dUCM1s0+kN+LLi95c1pobQr6St70g2Ws5wl4HfDz7HSfhGsArH0OLmhiflsQ4Ttr32d85cK2nVEO2GzrdkAqBA7Qa4L/Nba57oWgh+uXWc2zhNBGsueI4ITIfg8/4y6g6ZFc2/BTpLdogoUl0YGHX/zAp0VRL9EurkphbzNaeVOKXbhRuD0hAkfBtM8MeGjQvgzX+PiqcpyOCOE+VCcyEVWMfEj6hXxph6KpfTdqpojTzd3FjLdwey+EBbJ+lQp3VzYn1W43ZrikD6WzoH3H3xSHwWc57v/8detrfy1OQ66mfQF8kEdNNojgc0FLf38eyMvSJN6KpFA5rpIBWPo1aY5c45oyqJY2uuWrdJV1tI+d6DuZjLYuTLBU9ieRHKkhSp05oSw3CCl2AjFNabYqqFdWydm19icV+y4EaCdrzemwkgmhGIJbOz3TqF6lDKWHMRZyedoZfCJYSKEJnwgpK6R1fJG2/hQ2wjiU1cTUWOnY2kDqXpoW0+1EcrrwP7OjG45PK8/dlb3NJkFTp1k8mCTfLDqsXaPZVIhRXp5POTFrLFH/UDnTU3UrpTuKFCf7Qidh3dqTl5znH5zy/bJGVdPO65eafFe6RpPqxaCpmHkud1sKd94wIkTYjHn4WKO1hGKiJQdUbwNjK2krCJ6xj6TXdHbufk0V+QW8Tc6lLVAFLRUDqcOCY5mLbTp3hVbQRCT/WOv4w9CNzeZVbeEbp3em/1wL0IFYRlBobj01OdKsUstnaMRQ7OHDcWjPXz1VfT3X0MPh4/h0zRhAv8R8E3gKP38XwD/lar+hoj8d8BfBf7bT+vkJvxwuOWSkBaI6qE5Nhm+OoZ28IlgcA19Tpn6YSGWH2OhngNJ5A9GmsfWSAsqyx4KtREQkhVBnT2hD+/PRFJSyoyroN3C4195nuK9c7o33/oE79SfHLszT3OqhJQpF3Y2LxEEguCTurNvp5vnE4dZ5yJIa9lC2SKclbZ5Ud/bBvp7lWwFnUDjaK8q6vR+dUczk9h7s0n7Q8QdOlwTKK8Fv+3g9JhibmRR9+Zbk9p0wofBNE9M+FCQssK9+Cz7dUE3F8LciBP75agwkYiNLKc0W20KJpZseSJ13Rq6+EoniNde9XI4chS7GX6zRjZWBdF5jX/lp5CmhbZD93vCg4d/7Hn701N0t0NWS9789S8hAc7+v+ZGsVa+8RX+4K+sQCP1A2fkiEt2LJfX9fRh2WPWo1fgZBtYirsIVS4SpK6eo4J/zn7tM4kyuePth7xnk7nNv74ByZEYbnQeDKqebq4WKZLPq7Pn5ZgPUSi+aZo3AAAgAElEQVSvbU4KVdoHVTZPldeC6zSpjKUPn+5zZHNWrdr3WYEs0Qrg1XXEtUqoPcXkjPg+TLqpCR8IKQrwZgMAjLCpjIXtyQwd+TJT162wL6yjVqscTrxl+FSaSBC5mVmgo8qy5s4nWfo/+hoFUquMBqXRwrYPxywSGXQccOuWsgyE+zXLNxxHf9QhUbl+wrF5JrJ+7NokhMGhrUM7N5xTZtbbFn+xZ/1mR/XAI439ybhCkTpCYZXs6C1oLiTbVva85glIwiCxzBaJ/Fo2iBoR1c0xMmhlgamx0v5e569uoTRraFaOWEqyx2lPsklM70mtUEYkii3eD/QbglBCu8gbiEBze4m/ewe3XH6sn6sJXzyIyNPAvwn8rfSzAL8K/E/pIf8j8Jc+nbOb8MMgRYFbLpH5LMnN6a1KYabWrWpcWsrVyZxdMJovcqBxLLOEfyD0i33uApKVLLbQjSkwuZ8roJ97/CF3cBxeNzcWiKWjO67RxeyTuVEfAlIU+JOTNPYrbtHZdSSSRg6mIIU07yUSyIK4reIqreAahz9ImoPTfd2autcfBBessJDnrDyfSkz3vBHcxizh3Uw43Cppjgv2ZwW7OyWHWyXdqiLW3jYilSOcLgl3j4m3j/G3byP1lM8w4cfHNE9M+LBw6zX+ycdonjyiXbrUOWwg0O1B9s+NdX7GSDGpqTNXV1unrNwFs9/DpHmwXYopiU7maFnYV13R3V4Rbq3RkzVytMYfHYEblENusRi+lkvkaIV74jG6l5/m8qdbLl7tePhqhfvZL+N+9svoL3+Nd3/pmH/1l/4pnDbEWvu9kDWskWSj0iEjKBdp8jysI6VOZcqcWOYvu8Y+t7R/jvb5Pfke5U5heX8WZmn/5rHGPyPLV7aK9fN/sjRb4yBAUufQpPTJBabqSpmdR3OGZMFAvnfB9pg9OQVGduXXSGuFWGnfrMJ1UOyidXue8IGYFEITPhghwKFh9VbL+aulERMpCMx1tuCMlREYub26a0E2RiKFuWN/5ggza6muUfEb32cCIWbp6quYjREg2V+qXqwbVlK7QPo3q4NyhVOGTUIsTV7Y3WpZnO4ofOTqu0c8/feU+tGOUHte/9cXtM/tmS8PqFrIpisjVEpZBtrWEwsbedztWzTP3yEsClRg9sCuqysd0amRy2KySlE43AqE2iNBqC7s/03OL0OnsRysmhVPmB2uvu9TjoMpftojJSxif90mB7WA1uZWSEGsjuqRtV5WAd/KkOeUpJkAbmfhrM1a6JZmQ2uOI1oomyfnHP1RxdE3L3j0y0+z/N4t5Dd/5+P7XE34IuK/Bv5TYJ1+vgU8UtVEN/MG8NQHPVFE/hrw1wBmLD7m05zwQXCrJdw+Q3YHYuVSPkCqpOaF9lh/rQM5n3+XVaaQForjxV0LxUHxjZEUxdYP1UWxeUY6KNqUA9ArTLUf+3VkDctyclMmRaT77Fua3GpJePlpmmPQVcds3rIPDt17U/wcjLyPs9S1LVV2Y5mu82DzpUSQ1qx0LoA7QLlVQm1FhpCqppDmzS4FinYmq9dgxFFWgHXzAtcqXbJJVFdCucw7KpDoKXYV/hDx+whnzzL7g3fo3njz07qVEz6/mOaJCR8OLzzF5UvHhFI4HAthBtk+pKO9A5CUL3CjuUr6PnfF8ntrUZ+t0Tnb1CIyIqFS2rWwD4Jra5aHRXq+J9aeMCvwdYerCuR4ifvD14mbjWXxPf0ExAgiUHhiXXL1U2suXrRgH4nC5UuBy5fW4KG6s+W5W9/ludlDbt+64sGDqndI5DHdByusmOsiWcBSXs9gj0v3oxjuSZ8DlBo2uDAUolXkhtooZzDJSK3aLvJ9tuiQvglEsoLlzD8ikPZ1XQFaKrGVpOASQiJ+fKvUjwLlVcfl83O6hfbKrVAJxVbxDTRHWT1En5squXmC5PfO7GaxAL8PSJfu+YTvw0QITfhAyHKBzmuqBztcWxJb6RfjPYus9KGfrknt4Y+MZd6f+KHrlagNXKIpsT5nAMnNzJ3WWqSrSN/By1pDCqW3Ach1UF7Rk0Nj6XyojVQqly3NoWR7UfHYbwnbO44HXy7YPR4p7m5x0bG7rtHGW0eVIuJLZVa1iCjaCLOHHfHikm75BJvHSw4nQnNi51o8KpBYWPDccYdbtxyy/N4rzbFdW3mlfch0N5d+QI0FtEu7ltxmsdjZ9cd6UPv4rTPiLIXejTddcRHZPhvZPWn3yXKZkp0jSy+DIBuPROFwBofbwe5ZGSlWLTEK175MPulTbv+f3yO8d29qPjbhI4OI/Brwnqr+toj8uR/3+ar6N4G/CXAkZ9NH89NAVCTYIirMhG5hKpZeUg7fR9r3i8YsPX+fohPo/f/+oEMnEoH6kVUMY5VJ9CFzwI+yi/vgy1SlzDbmPBeozyvYz8HHxnvCoiTMLVPhsC+RIloDg2DXLVHonM0x2XanYiSZa+g7u0mX8pvUyKD6UaRZW7dPU/MmEigtnF2TFvxJ7VvsoF0qzYmdWnEtyboHYS7sz4aJSNQKGuXGUV9EyuswLbYn/NiY5okJHxb+Sy/Rns5Rgd0tx+HU1tLqbeyEgQABTC2pits7YtRBJZksV2EOXSepGxmDJasRXKlWCMf+v5sLuzOHxEVSx6SMHS/4g8fvC4pth/78K6ayBdh1SBsS0V/QnFQcjp2t3fduWM97RYtI1xac7+ecdwteOb3HPzg9onlUI51Zp6yJDf38q8IQs4H9f47kyHulbOd2reBGbgtNe7N8z/Icm5VBOJCkurFzNEdC1wmi0jsRsoIpzGyPgjO1qgS7PwpDi/nOihmhUtqFUG4d5WY4P/WKC3A4s/+bXUTajedwyxRH5WWe7+1Y1VY4nChdrXTryPbxgvUbjuJg/mn5xldw3/oe4dHFR/xJ/PxiIoQmfB+K55+lfeKU5qyim7k+HyhirdZjbQRPzgDyrUJnC3pJYcqWvg85/yBbAMZWKaDfSNjGALQdiBOTHhoz3C0kLYrBBwszG7qRmWc1FomgAbptQXFlYctXz0F7GmDdoirE1qGdWC6DKJomi2Xd0HTeTrkWDr/4ChcvlDQnQrs0S1hW6hQbe04jnhDF2sanHKBYDNkarkv7kbHVLYVlZzIsp+WrwyaHCrIlgqwMyhuB1L0nAlrF1IbeKsgSU2t7SRzcAVRsNgi1+aNtwvN0YNLNUmmPI/tbHm2aKUNowkeNPwX8RRH5N4AZlg3xN4ATESlS9fdpYJIUfFZRFma7alq62hlJXyt+L/2YpC5nzWW2x0gGjdzMvXFYa9ms8ml1CHpM84XfJ0Ip28xGuW6uSwtTMcInj7OxGOzLWWEaSusAE24f4RevEP75H3zSd+5HR1HQzZNKNApx7ymXLVFlaMSQ1bXkjUK63W3KbMo6ivdZIayanarCPuUFJTWpC9J3ioPcuCHNUYV1bMuy+161lSx8ecGf7Qquc/iDEs/W+P0dwr17n9z9m/B5xzRPTPhweHBOURcUi4JYe7q19mNYLhgAw/5DSS3mNakdkzqmsz1I9OaAyJajvplOymXzh5uq1DAT9qe+J50sq9P2KmEm4Cz3M5ZC9EK5LfC7gAtGHHVLR7dIqiZsrY6kPJ7UXfPQFnxvd8rct8xWB/a3CiwN26InXHfTMgajfceIDMt2boVhb5Hzg2Aotuvo55HyVl3imfo8UuvqHEvrqOwbm0OygyPMQZz296/PBAwM10i69xhhdThyqKuMiOvSOiCasyRbzcttak2f9zzJeuYPVlhSZyHgOu/Y37V777qItIHuuMafneLLCmL4vown/9MvI5ebz03+4EeBiRCacBMi7F66w9UzFYeznOsTrWU7gqYBTiWpUnT4w/aFDWKW4zMEfkkQXO4g1ucGpa5cDKx03gjkhae4YfDpZhZ4lnNwYm73jg2COfV/frLnsCtxlwXFVtjdgfbpA76y2aA7eGidhcnlwS4RQiezHeebOeqUZuV4+GpNt1ZCHYkzywuSveutbK4B1NF1QnsSoIioOrRIBFWZWHC5OSirUygETeogEhGUK9y4rIYaNgExhcYhRggRheAUUcHvrIsPomgh/aBbbNOEkqShxbXDN1YlDnshrCNaB3QeaRcemdVIUaBd3llMmPDhoKp/HfjrAKny+5+o6r8vIn8b+MtYB5m/Avwvn9pJTviBsGyDNe3JHLcvCLXcGIty5TAWyrj7pAVuat/VaqwcGreLd+mrJ5TUCgza0CtNdDzWt/1wba/rRwvPKOCGsE87V0c8rom3ZlTflM+sWkicI9ZGvkgQFIcvIu14fm0hpKykfhHcq6zygRgyI8jvgyTSJt3HmOavRAb1mwfNC/T0lTL/+q4zIztyfs8zKSVR6HZWKW9P51TdGW67tdbLn9F7PuGzg2memPBhEe4/oJjNKE/nqJSEyroMW+FaetKARITkrBnXypBFmoiKfh+Src3vEz3ac/RGp65YWixDPpZrUjZOGh9zLptl50E395Rbh2+s8YtlFQ1NeFwAxQKsXR3wRUCBd7drnlmds5of2J9UHIBi69CNBS67gyLR9gaM9h3DuM6ghnI6zOPjOT3P5aPi/Y25XugL6ZkQAvqChguDZVyCzdOSrkm6VIxoraiTu0z3c5EOooJQO1MxdZBb1OcCkHqh2KspvdJ15QYKxR7qi0gsHPvb4OuA3oqEOm1Ku0goHeF4iSsLCAHXtBAj4j3MZ2xfPGH2ToXcf/CFKZRPhNCE78Ojlyuunofu1Fbf/spTXBupE2s1u0BJ3wI4L1bZKdVFJoTSgjF1i4nYQFAcJBEpFiDtWpOcF3sdNhu5u1hlXWZipdaSvTPVSyzMWgVDcKYuTAL5M3fu8Xu/9SKLd4w8un5G4bKgqzwU1pZXDkbqqJjKBgUR5fnlQ14/P2W/UjZPOvav7HH3KuIy8NSzD3jrnVM02rr4oI5yIxRbqB8JzWXB7smAlpGwinQHIdRms/P7QQGUWezs421X+f+GCWrcOlmg9+9KFPx2IIhc5/EHIXrz7GoBPEoh1Z1Sn9sEFeuh+q4ONCm+dB7ws47YOiSWbL7yOIuqJPzhdz7Jj9uELyb+M+A3ROQ/B/4f4H/4lM9nwgeg+/orHG6VhMrhupo2Zc77nSQS21bZltWgyb40yPNl1Bigrz6mcc66mqQqajEoPnNeTs4IUpfsYx58ykfIx78hYQ+kvAMlOKWbWbB0fuxnGt7T1Q6tItIIEhzeR9zOpTDo1EFyJkSnfRHBdVaYKHaDhc8IO+kX96G8mRnhAvgmhXhmQddIuRsLLIcoV8HTxgDS71srFLnc2Saptpoje0H1Fe2qoLyzpPyH/5y43/+Ai54w4Ydimicm/MjQ3Y7iwY76fMbuMcuk+b4MhGjz0pBFx4gQGikxcwEjDj/36qIOfO6yxUAIxdRURiK4UobmMtCrg0KVcvEC1uxlLxQHJaRQ5zgzUkW69FwRVqsdy7pBgfvXS+7Mr1mULcv1np1X2qqkvLS8t+pRQ7ue2/yXlLNkRW0i8P3BzrGLlvkDozl6RHL1hNC4oYMMc7kKuDQPiebcP3OShLy/28PBJUEB9MV05/NczUBWZddEAW3q8Jbt6VkMUF7ZZH5YC7OLyOy+o5vZORX7XAxX6vMO33p2jxV0wO1bVxyO7jBbFBRNR7f0SJzh5wWujYTHj8EL7bLg4asl5UZRt2S5e+6zrS7+CDERQhN6FE89ycUvP8PurhCriLSO4tJa2Lo2KXFawR3EEts3tkDvq4gBqktri97NGewBWZJYKkFt0e73SQXTmm0gZ0Rk9jfWZtGKswhOcRvXV5qztSoz00OGhPDm1TFhFWiOCopdWjBfujTYeTQPfmmzQhHRzqEqOIlsNzX1uWP1puIPMyTC1Qvw5OqCN+MZlIoS0YOFM4MRS8UWZu96uqWzMOdxYF22Q+QJJdBnML2ffSfJ8HMMg3TD8/JGqmfCO0k2AVu0Z8tAqIAqdUUY3aswM5IulpZBhCjhusRtPf4AxS5Ym8wJEz4GqOrfB/5++v47wL/4aZ7PhB+O/e2K7R1HyPkzFSBQ7IewyogQZ9HIoXYYh8byb4nD4jIT4HlOUSc9EdHNrWviuKrZL0TBFJBws0OMG8ZTk5+nx44WuH2e0GcU4Z13Ofm7O+59/Us2fxRKjJLsC5pUPvbYvqvniBSCQc4fi4EMEic9cdYXjLNkP889KZ+vr5CLqUvzxqGvLgM5n8F1FlgqavkQljEkJtGfCf5IKDeOs6qCiRCa8GNgmicm/Engb51BUeCutyzfXbN9vKRVd6PD2A371Ch7zuUOVXEgcPquXHmtnuahvouVDqoaGD03PT83kelVL0UqbKRCt7kpxFqwX9tYO7+vlNeuJ/BtnFfKIvD0+hFN8FzuZlwc5kQVqqIj1I59ELqlp1k5qqOyVyLlcV8lzd8lvTrH70yN2hfiC7WLGU2V0Q/kmW/S3DFS5fZdlElRFtlO7O14Go00KzbSB19bTl0OvrZ8urE1re+Elo6TGwtZgx7LkM3WcYDqkeIWQre098WnBhUSobromN0ruDqacV+FxZlQ7GtmpUO90K49Ye5Skd0cHe3CWQOGmVBdORalx331Vfj29ywQ/CcYEyE0YUBVsr3t7I96LC3MdqbO8iAsEHrMPI/UL43iCsElebo7ODRoX/3NnbRyR5QxG92HlhWk3KLkOW2cBVbHm5VmSVXmoe0gXG5mUEe65ShJXkbkjCY/sZj3VJz2XIsTJe4K6nNYvtXg2pKr5xy67CgkmrzSKYoRTFFT4HOWTna52iA35PgqQJbW57FPh3s49uWSJqAIuGgDtC2+033y73tPxhsjoWfKe+Isb45cItgK0DLCLCICcu2Z3XMs3o0Ujw7o/oshjZwwYcIPhpQV8qUX2Z84umWqXObMGtI456UnYYIf2cNGC2r1oFWq7sXhyzeKby3vIBaDfTfn1eXnIzeDpGMmfvJY2quPEtGfF+ipouj3keK6wW1b4mfYuqRdRzi/wO9tUR29EULoQADFChvPBSO+vPQLcgvSln4eGQdtu1RkyMUDUYZun1jnsUwGuVElPHcp67ODsmU5gI4qx92cfkORLWpubhuNwy+8zOw79+lee/3TuK0TJkz4okDEuiPvD9QPD1SXJbEUuoX2duY8p1jLc9uLOEZk0CjXs7eXjdbucJNU6nPbxnuM9HMmMLINLdZmB+vV/Io5IYKN3f6glNdKdWnjaLO2Bg7qhbazSdGJEoLj7as1MTpCcHStR4MRL82RsD8UNCvLIrL5YGB41KfxPpiyZ8hSwrouh2H+yIRPnvc1dwpTQbE5yqUOyz7Z44zkuVmUCclNoqL9fgRGSqHUrKif18PN18+OilwYqS70xt6q2FssRreQvqCuIoSZo9gGyq1SPXLsliWhtvvqm4JQ2Zto9vW855WUM2RzWrsQ2rM50kbK556ieHhB9867H+Wn9jOFiRCacAN9O/REMsQyZeUwEBB96LOH4KQfOLPSxx/UBmcZKr/WaUttsMjhobtsnZJe6p7VQb2Pdu8oroXqajgfYKg2R0YySGF/WVMsW7q1oM7bYJJko7mrGQE7v6TeGUN2jsV7kfm37oG7yzt/2nHniQv2ocCVEeciQRR1BZIGKNyo44DQp+W7UT5DLOz/+wkm0HcZi8VA+Eigz8HQdKPdAaSwSnuYa58t5Fr6iU4LaNfQrmLvVw6r2JNqKEYCFRHnFOeUblcwv+c4/k7k6A+u8O88IF7/ZDPgEyZM+OMhRYE7O+HBz53SnEiyXlll0zWDWlTHUnq5SVIDg4WpVAu9zyR4sGpjruLFAiTanGHtfm8uzF07WoQXN39nOULDgj8HHTuF6loprhuKtx5+btqg1+dWmYxLJQbXb16s46b0mxhbqOd51lokt0sGgqiwgop0gk+2B9fSK06zygdN9zvkfId0yDQ/+Sa1EPbSL9LHWQ+us/OLqRNmLEEqO1aohfv/Qs3d7gw3EUITJkz4OJGyL7Xr8PevmJ0vCDNnkQlee+LH5olRUD5pfsoW2jTE9qqg3Bwn/V8mHfJYOC5gZHUL2FwUZmqOCrVziGXa/2CPjV6RpBxCLCS5uuhwIbK7U7E/dYRa2OwqzvcLIkLbFOzO52kThBWko6n/m2ObMMJ8KDD3ZFgmqUr6/UNvpU7X2lvlGF2Hh+hs7s/Zf0CfJYeaQ6KY2f+FXDzQIR/IXCYyKlAP4c+6lV5xNXZQ5HvsGitAWLMea0KRcwhjIfhWCYlUsv2q2eC6ucPvoxFtV0L7yNs+aSH4g7tB+oHgQlqPeIsbUZ+6x90uqS4D7fqY2bLGpa5ksWmNzfsJwkQITQDAn56iixn1hRLfEg4njnathJktCKW0hWe3SANpZBhESYTMbhgw/N4Y41ClcM+U+YMORExYWJUzFub9jJWRHtYyHQtv7sxjW59r2iwM0nkwMom02PV7cNeeOOvS5kGRg1h+9EzRIuA2nurSRqVupoiLaA1lGdiFkvqhxzcdOq9N6dM49m1BVMds3nDYl8TWI6JItAV7LEBr26G4YGSQFmnDhC2yw5IhUHVErGU7BTBacKd7lMJZjb1P15tlpOke9iRapXQrJc5D35HAXRQU+3yOiialVXklzO8p8weRxRtXFG+fE967R4iKzGr8rbPvS9yfMGHCFwPy6ktcvnrM9vHUGCAt4lw7kEGuBZqk6HFCcWm20z7gXsekvfRdsCQkFWl7sxNKrkSGygoCPYnN0M0lq16yYkgL+nbo9h/0xQoJMHvQUty7RC+vPs7b9dEhBh7/G7/Jg7/6Szz4FaVrPcwj6q3TWJjFXo2qkqrLHly6b+2xDvuWFPjsUji3P9j3kizcfq+DxXg+bJQkWPHB3rtk5a5Sc4lqVLnN5zAK/BZN5F8QNNr7+PRvfOcnuqI6YcKEzwb69uEi+Kgs3zolFjWhNpWQBR5ntQpp/ayQSAQZrbHHxdxsWRojz0VF+l1ez0sAPMQ8TibyCSf4RujWFp2BguxsTxUrpVMrvCzei1T3N7jLLdHfoatL/EHQ31vx7dNFIkSE+UPX27LCDMIsopVyuBtoTlIhuRHKzZBzFFNuaVhomjtTwT43/0lFguDsGvJ1qdfe/pat3v4gdKtIm6I6ZucBFxzXles7s0mwY8eSPh4k37t2nUikrXUr7dL+CEbEVFL+uiC2/4kQKyN7XJqvuvmwNsjvXRYgmDrMIwrlRpk9GDJqu5ldXyaXgBu9D4qtmu1uDrvbdnCJcP3sAp77GhLg6P995ydO+ToRQhPwR0fIrCaWnlClyuDBFvquNRo11GpkTTXIJHUkfRzLJXPrxlwx9k1+Jatw9i1tUz4CIv1gleWGklhvSa/VzdJgkAa38eAiDFYCfxDaJq1Q02JVFDgI0vgUdqYDYQT4IlAUgQeHJbN7UFwHtCrY3i2IqTvZPiRFUBTopN8c5cr5YJ0wCao/DLL8vh1zIox6+1cfopoqqqmioF6R1D6xt+Tlgc6lDVOS/qs31VCote8GJzuPv3KsX0+D4tzklKEesevJhna4PSPO71Cu5uaBU0W6ABMhNGHCFw7+5RfZPH/E5nHP9olo4cPjjocjabdvUicTxdqON2ksa7HA6ChmV3JDIKRETXJs6XPVQmnH10J6pYkWifPOFdhsgYUhS2dUoewD+QXE2fHKR3v08oq4+xxl2KjaPbwscI30Kp9+oZ6scb1dLhcFSvruL1ZQENTHYSHfSAqgTgGdmRxKaiEdWcJuyPc1L66TOnekwsrFCssxSpssdHie/8ArnDBhwoSPFapK+XDLfOlpVmUqSqsRHanDYhRFRAjLaGvsRAKFGbAHH25m1WTlpaRQ6jz2DhlB9u/7VTfAEO4cMevUaA8ARu63R8L2boG6I6qLObu7Jc2R7Xnm7ymz+4IWFqA8e2jjrBawuyO0K0esjfQqtmLF+hxKnc+lJ7yM4JGZZcXFQpOC1GxrEiXZ2Ei2ONunqbciN2pFmW4F3VHAtZ5iFyg3HfuTGYezQU2cQ7v9yGXiWiOEQmW/mz+I7MTZ66X9Wt7fZSdHVrdKWmto1KSKsjcm399YkuyAdu6hlr4Q7/dAPXpPvJhVTOk7neZNrEsZe+qMdAqVWOMkErE2F66/8hjz20foP/7dj+ZD+xnARAh90SGCHB8R7h6zv7tIbRMTwxzA7eg7wOBAY/pDDzcXhOMFam4Xn/+o8rG0S7+Dnn2WOISdxVJvLEot/Cz5OVOoZkzVSx0x+P2laKqItnZiJgu14LTxwNhnLDhQFcoyULjI+WFBfRHxeyOE9rckhXs69l1pqqAk0fQ7+9JV2sA40NLsWlGAK/n/2XuzWFm2NL/r9621IiIz93Sme+5Y1beq27Qx7QGPICwLyyAEBlkCyxJPNiBZYrB4xOKFV79aQrJkEENLCIN4wDwgXkCGB2NL2EbtiXZ3Vw915+mcffbemRkRa62Ph2+tFbFvV1VXV1fXrXtPftLWOXvv3JGRkRlr+H//oQFkwOKZlGmTQauVOWp9jb5qk1fHaIh9NVGFJsnQ5vskyCSEvdDdKeNVuZ6FXpo7m/CmS9PYHh863BzYPuzsmkcl7BP+W7/ym/kUnepUp/oKVHp8znjlmC4g7zKSPK56KsBieF+6oT7bAlJD9QOgsX8kl8FrtRhtlP0aGV/AnswCbCw+BiWZpNLHVxR4laUJ0HwQqpeb0yLbTQZyf4nKP3kMmAlnuBWmK8qi2pom2mmjujfJ3ArMkWQbAT8LEVeaMou30733oV7D8q9bz+GrubWygOpc2ubOwOIlVDq6smIwZQ/prVfw83xinJ7qVKf6kZWIwH6ke7FleOGZrtw90FuSNW61xa4r1XM099pYRG5WUvHKEzWCpqymlMbgX/maLr9kkTY7RcWYnjaGLuO4HccYKeMDQcUTd47DE8d8ZsBMOEC3z6iD8YGjv1UkKakXxish3AkpA9n+H/b23LljkWOxmls7m4j90fvHI7gAACAASURBVBXgyvZ04QB5sua/OlnUGmtfusIkkijgtahAHP2zCT9a0zn1ti+R0izwxwK0qbFvpgdSCABCfxuZzksgwfo56vWtqgqgpZFVlmoQcrb30K0BMFnmsvq+uGiKFLcOqXDLewSAkwbk1ea9loh7TXbwHIw5vX/qUb9l+1v4nP641QkQetlLHOnJFR/8C1ccXjVAxk00dk/YQ/cC/GyI7HwpZjQpxhbyB23m0tOl+RhopSh+LiGrdhZRkNkGrkq1nK+03ZR1setGIRygf2HGo5LK/bqiqVfUXRyNDiqToD3m7dMpoUoOKrW9LI7V26l5nxFRPrk94+om42Im7QKHVxWCMs+e68OGTReJw8zhpmPzsRAOyhiFSSCeZ9gmVAUdDT2r1FM30aLnJReZWBnk6iDWFve9ol0mJ9cW8v5Y2EUeXH//GqXe0P61AbdRNZUXbwvjk2SDv9j1wCkzcHy9tiZsMvB3vi34u5uOr/0NgR9jE9ZTnepUP/yKu464KxJfr0vs7KruGRYnWzRnXxgo0RaEKM200qVVrHwZ423BKA0A8qhJhrcZv3fGdin+DjlI89GRuDpOXSzm+4CHqCwdyd0WiRGdJ74MlX7qTdRDf23mmXFHk9x5J0QPhBKIIFqkyQUIi8ZaDXsh3C2sHb/q0NYEMlEtrNwSgzxA9WtoEoJkk/U69KD6O1SZ8tozqkbQN9AuKJ/8vgue8MaJcXqqU53qR1feI8eJ8OLI5rPAeOVKAmLZ48yY7QVCzNDM+rFxLU+LzLYCGWCPUZZ9jPqyTK6Ay7oJXJoU6hdFgkvAVGws/GovIpA3yuSEuBWmo3B8VJg82Hg9PI8YE6nDj3mRqWXobyBNZiTdv4DhOqMiTJc2X7pYwIxOmncRxefHFxlYOEB/Y6CTBRQIuitzrFSzaLHzKaENMdnPD48D/fOJ7qCEO0tarvI2JpNspY00+RZqa4w4Q9gnwuhLApsFWHQ10ayANy7q0vRZXe8cgCwmb47l+ep8VEIXKhhU50NRbXNga4BUJYcue9/qsVTZsJT3vTau0laQ7NmFgMbawf9y1wkQesnL9R3v/vErjk9LFGK7CW10nC8sRr5KBsaH2rqOboLpSsxAucjM/LRoOG3hqIUCKA0tzhTZWG9ASTWGXjiNsqDCNQllYKESToXO2NFiCWtiV6qjtbNRobtxDJ8K8yWMjxN+74wi30E+S3TOwKCYHXc3G64E5vOO+SLYuXszYRZRDpOxhAh2bEnQX9tAczcIWiF/p6SdEu7K4FTBsNLprl4aa5ldLn4YeZuhz7hbb14/H+kim8AW/RUMijslbQ1AQgxk65+5xrpK2zJAztJaGzWG0g6muNHZ9VOIA820zVoCXy3DtFOd6lTfu8ZHgbixcctfe/xBGsBDYYVksTlBohSqtS2a3R2LeXFeUrBqVdAbKcbJ3dINFDVgQreZHAXtC7szCHlcFm6hGH8KthD3x9UTVGZpBH/A6OBdQMKXZ5kTfvUjzi/fQnKHH5XDE9fi312y61wXqK6CYwFqqlrYC90LGF5kXHIFELJ5Pe6k+TuklS/QXFiuQQ14mx4o4dauazhU+TjUhE4tYM+6Y1ylAThpErLNx8L2s4TbT5xmklOd6lQ/snJGZZU50V/PbD/1qHPMWRrA46Mtcf3BNZDbUpRtrZw7WfzVVsxJi4KHyjSqexOZQVcMfi0eRXnNlpmkpWotqWY21qZdJp5ZV8NNJmWrrM+7NzzzWW/Pt7F51Y8Gkti+SwkHe139rdJfJ2PPdJ64k5X3j5ktk4W8ycVjrs7byuaTCYmZtA2kricNAlUWV/YeVWngD6X57pW7NxxX3xL6F5nNZ8LxiQFctXEkZU6uxIFwZ/OWKOyf9m3+cGFJrfSjvaY1cOYnA8n8VBg/bmm0S6YZdsMiG5OkOBVc4F6Te80MauFHUCTnGPGhHKdaqagr82Znnk/z14TpP/jDvPGz/3DxsPoS15dnpXSqH3r5J4/Jb79O3GGLuHJzu2iLuhaLXm+sQtUzhkt5nBq4Up3la4JMTuXmLIvXjC5GboUJE/aLCfU6Ul6D0l07/Fxv1OLC/7nHrtH45nmQaaAMyQyU1RmTRjttA6CuEPJaOTqyF9LGm1mZChod8+yBnmkMOKfglcNrygFbgLsZuheOyQUzlxYl7kq6wLTCuTwtfp4qncPOp+p/ESAWRlMwFHr2tgGzyQJkEmrijhbPDZmE7sbRvbBrlTaFRjlVbqVNLLmnGY6SpbColvfBRbHPwR/5GcI//pWvxCB3qlOd6nuXDAP+9VcZL1yLq/WjdUmrd1AqkfDqtXXzclkg1rG7+gSJQix6fnUrjzhnaR6141aTQapBNHUeCAa860bhWW9zDQv432j/hYVaQZLmQRCVLxPD0T95zPS7f4Lw/71H9sK8s2vli3dCOFjKmKt+P51JEDTY3OqPgl+BQX4yvyDbONhifz6T1kjJwf6NGyVeJfydA7G5Ml5kJDr8VGQTk+JLylgKa71EkVeU6F4322P8Ebo7uHgncfH3P4aPP/3iLuypTnWql6+aXFkNFLpJxI2xXmrATWWBhDshbrWANtbgBloiVw13aalbTY5kHkQmbS4sGr+sr6s8LXdK3uSyBvclWGFZl9dzUcHYn17NdsKoQWhQpitn7E6tsmph81kF+7V5GbkZUiekrWv7trXywo1Kd2fXIam7l4pmjX5TSOiU6O8y4wPfAKB7BsxVSRINWBof2v6uu4sM19aUJrE0xItaIpd4eJdocuN5Z0lhlclj0e+YmfSs9/Z2Lak0mjIlHMp6o1qTTCxWJvUaQpGvyz2285JwaixmTYUt6+raQpuEzADD+0Ae2M/HB4D/ahjmnQChl7QkBHj6mOt/6txu1hIjnFUa7VsydLcGRrSbMrJ8aipgVMyg00ZwN7T42lgHRbcMjgrNC6JK09Y3LkXfWv0omlN9Zcj4FbK70sbeM7ZuPkRCdwPTZRnYRc3pf6vNjFRVliZ2LgPlALE48DM7svfMQD4EcsiIwPzKTHc+cfx0y/Cxp7sBRIhnjnSWyeeJfCew6mDX7jg1+heaJ1AaDFEHkNmZpnhrr0GDATypxj7X4znbnMnoCAehf24buLUBqB/LRS9ygIiS1Sax5kdUWVxHaZvAz37Xjlffu4QTIHSqU33lS/qe9MqVmc/3Nq5UVmdjM5Zo3Or/s057vKfXrwB4SSBDtchhi8HjkfsL5ipJ6mggNV6RPuNDbgb+LbWsmCE7WAD+wpRJ63PqPOKcLeq+4PKXl8jZDj0cIAR49ID45By/n9HgGC8H7t7oGP6JM9r8xuhOZtSt+HLNanIn3poeWcwLQ5JJu/sbpbsrwQzJJhkXtRiA08AzQvGe6xTZJLjxLXZZh0TubENBafLoDK40JtYef6lTcqHy14VyOMLwXDn7tTv4+FPy7d0Xd+FPdapTvXSlMd4LBnOzmr/NtKyH636muy3MS2dNCT8VkKd4erqqNpZlzmo+anVNLwYQtfAXv4zB2ikUFn+enDWK47I3ssZFaQKXua/B7g6zeTjXxbS6U/zoCPsCZDhjPUkCX8yf5yztnNf/+tmYOr7nHpsG6l5Myv+VcMi42TZeGhTm5bybHUYUMtiep/f4MdHdJSQFUzMU2wstlh5gjaVwoM03uWPx/6sN/1BYOSsQqjZ6msoiK2HtVUhpSqxNwF05dmkONUBIi5S9+EPZ67Kmi4ayP0ogsYJ+y56p+iM2JcaZwmuv4KaZfPMlSTT9LnUChF7Sct/4OnfffMDtm7aKr0jp8LzcmNEWdttPMscH5SYR6F8I8qzQDwsVPQ1SvljMpUv6Ve6XbrA/Cv2tGYz5oznaj4+E42MlXiZkNMRaJvOAkAKcpKEsSitDaUOjqRvV0GRVicWTiCQFKFH2ry3AyWJYCiUQxUAwUVyfSH1oVPwluUbp+0gUZb7pzbQ6ZIYhEi9mplkIe0d/beyaUaD/2h3HeQt4NuX1VPPPujDvbpXNM2W6FG5+R8JdzOjR4+4CaZdJV8qsIJNr5neiDh2lbY66zzzDZ0J3q7iovPimdXi1U/qPvZnLFZ8iA9lK6ljVxNbUmuIH4sdiLNcBrkJPpzrVqb7KJX3HeNUTz8q4HWhSMdFVx02XnzVwoY0ly2PUWzrHEj9b5EpF6pT65e/TdpU8mcr80ye6IaJqngh+XBgxraNZomxF7Xe5K2mU3uak6cmOPiluf/jRXszvUPPv+SbXP7Xl8ldHxocdH/xzjn/lj/9d/sa3f4pHZ3e88/7AW/9zhuBJvZQNio3r3V7xRSYtlT3aZTIOFUWPjt2dcP5+IuwTLirjwxrFRgNv/Gis3MWIuoRDKHQ3RuO3uF/rUqsXUif4SXGz4rwszZzCItaSbJYzqNg8F+7UYoh/+T148gjX9aSPP/6iLv2pTnWql6lUyTc3uKtL1Hty71mCcooxfmlsyASbT5X5wqLp02YxlJYKfMyrPU3xWrPxEXLpfjTWUFf2PMEAexmdhc30GRcyKQrqnPmMOoVsbBlXMQ3BLCkCtkh3ioSMXs7EjbM9QJ855h4/CTk45guT+RqTVNr5mvXDqmlOBXqseZ+HZX6u4Ewalmvlj5lur8yXZc7oaLK0ajLtZnCTI/eZ+dLjPs34YyYcpEnsTCanlq5W5vDK8K1qDwOFaKyt3Jdrje0Xw9E2a5LA5QWYcTFT0TMNEEYDeVoCp68LkuoftAA6frKE0yqXkwTTua1NqpVJa0BV8E8XEkSdk+ODyPt//DFP/58B/tbP/XZ+sn/b6wQIvWTlNhvSP/vTjNvA4bFnvliSveoNELfQH80wOlU50VwGvlBjiLUBDRUY8lPxhSjsHVco5zXatgItcWveRPM55F1ChwTRFY2rpXeFO5q0wB+KEdoq5r7SObUkm+SyCQGQsexSnHL3piEw3a3gZtfOB6/m1VNGSwH6TST7jS1+ywBJp4hTpqncKs7Sy0jC3fUWjYL2meMjIRyWCMdx3+EPrtHoq9YVaEliJp/IDUnXJA2pcUcHR1Ypa0bPr/5KlepKeanjI2G+VOaHEZkc4dpbh7/KKpQmBZHSGWlgnVc0m+GeREsoMNO9z7nJnupUp/pKlvQ9+9c6pgvT6EMZb7AI2kavTmWMLknuNTmxxs2rg7gxaWqVHLu5MCCLl8KcbfHdfBZ6pSaHtQ5oZ2PPfNfz4JnikhYj6rrIK53BOm+Vtm81PY5buHu9w8Ut/d13B4Tmf+kPcPdGz+O/8x1Mj8cJ+o54tUX+9j8wHfRvovzjR4y/7xuMDwL7p84kX/8ksXsvsv3wnH98/SrHQ887LzZ03+7ZfLhHz7bMO9d8LoZrpbvLSFLGy0AzJ91GdBBL5dXOGDnv7CFn8hDgYaCabodRCfvMcC2NUTVdlWuogn7Sc/aeEo6WWiN7m+vSxtJgqryhLojrey4J3N41iUQe1LygBmE+c/D4oXk4+VNj4VSnOtWPtvKDC+ZXdowPOuYzafHhTfZUvGE2o9K9KHKhi2zjdAJVJQ/CXPcc3nw77xlMu4V1IkDc5sYYwhsDs4HoLpOH3CwswBhEOcq94GFxiusyaXYwOXQyIIhifq1JSBeJ/auecCXMZ0q6ipCFuagFJFnoTVVTpK5Orq7Ny1KBjdX1MOaLnZuLmc3zxHQZmK5Ko2hfwyPs8f4gFvTgheODcv4Rhmd2RWpyWThYgyGnKjWrgFBpKBSVipSmdN3bxTOagqUBXGtlCMu+D12AnSrxao2P2sAqb401SrRJ0VNvUnY/0Romi2m3NtkgDmQydpA/KL4X3FlkvOpI2/DrAqS/bHUChF7CEjXfGPOBWJg49yJny51TTSxbrdHmz8m1pKbNVMnAigpIxrwLuhW1supqDx53dGaOdrBkMRe1dJClDRhVx7swd5bnbiBLlZQVA7S4Na2podTl9SqQxCRi5cV5l+m62AZFSxhwyJAIfSIlZ4BNAVBIgh5r1JkZwgnOxhkHeghGDS2D2Pr6VZRaPcw7Z3rZgyOF0GJ9G5gzF8DLa9MYVwmEZOuIp03x9BgUmR1+75o/U6O1rnTQ997P+p5KeQ2zgAgPvjWiJ6r/qU71clQI5jGzwbqSqXYFFWkLKlsI1cXZfLaAPn4CKQB+3NrcUiNo/Qx5UmIUCMXHplDtRa2r2Yz3nRg78hAsyfx5KD4Hq3SxFQ2+pk+Kavkyv6IUlOMjx3DdIW88xJ9tITjkW++QXrwA5/no3/8jqDemZroYbK5x0sbHcOOsm9s7wje/jhxGdH8gPXv2PS+lf+UV5OKM9PiCwysdxwfCfCaLlC4YU/Xj2zPS0ZtZZxbiWSAP58akKnNe21w4W7S2tC8BFxTnMrMPRu+/PaLeo1tD9LI3k9RwyKC6bATUgH8/loX2KGw/TbhZGa8cMi8y5bboLz5SNemlLsr9sXxmyvMxKPO5cIzC8esP6F5MhOvTMvNUpzrVj6YkBPxbbzBfDsxngenCMV1Is1JYR41Xg/114zRvFDmWOUkUHWhr5DzoChRfLaS1DNNH10yj004Kg9KayiIgwQZOTQ5xikYB59DZgBqZBCWQRsXdedxkY21NR6MkfdFl4gNISdAhEXaRnIUcPCkH4k5Kw3kBW2zfoE2GVUGdlnDcLa9HxdhCVVbnZoi9PXdt3EABcLL9QfZK6h1OFDdpY7PWub0eyxKXtV3vz0vvaiJYjaC3REshzzVgR9C1BGz1NpgXqpZ9nTU4qiqjNjQy4EpAgpTjWFpc/RwUKdkKdVoHAC2ehWVNksT2tl5OgNCpvlylMRLe/Yy0fVoWnYqfrb+KXyiSSFmAhgUwWn+p2A23XqCbOZfdkFJuXFgW8eN2lXIlLCDQUdrg4OZC11NDtWsSSiqDch3Ia1y7eRFZNHHuKs2QNpBSdLyVFtnYSxHS7NCNgUIiyhCS+TRECMdM2DvYzOw2E3eHgWnsEJ9t0zA55OCtCxAUHTJRlwQwt3eNvmkXp4BZ2PP7ZJPT8ZFNHt0dSPakbW4Tlh/N4K4NlkFRLZu0JCSMKprOk13PvaP/zFkyUDQwr/p8yOeb22JvjGRtIJHsYqG0err/+x+RxvGH+Mk71alO9WNbTiwavisU7DJ24mnaeSjAf0mUnK4KMF619tkWlWljndfhuVG9VRaD6go0tE5flOYn0Ew8nSOpjd/dC3fPz0G9df0MoFro58ac1LLAVLSD6QLGS0fqB3htIHfCg8NT/HYDF2cMf/IjPv25V3j8D5S46xrApE7KvKFIypCU+fUHhJsR9zzAs2dICGgyvZV0PThBxBb3PH3E8dVzxkeB8UqYL2SRHnSOuDWmz93NBkZjz6hTpgehhBqUebMrnctZjHmzpQQPGKPThYzztsHwE8g4oxeBtPH2OsqiVhLFF2F5D13U4u9g79vm4xG8ELe9GYUOZa6uxNhpBQrVxXEBhKrROMULr8rNbsaeq19WQviyL5NPdapT/biXf3AFWcF75jcfMZ8H4s4xn5kiocWHu2UvUBULBiQYMJ+HjMweVxQTabOwgrQrAELxSQU7HsVE2R8tyEYyzLNjvspoD+ItqViqRYNPOKckcWiW5g/nj4KWZXc4lL3RDOPDwo4F8IIMCRnsGD4kQsjE6JgBHX1h4MrS7C++fypCKCwgN0vzZq2hD7l3JY5diDsL2amPrbYVde5WWMyc5wW8aeE/91g8ts9xUZt9RW0q5Bom1ACh5XjJV7CqfCWhei7VJlWVuDXApoBCjpWfkK7IDuX7vCI+ZG/rB5cUnQuzaOWntJbO159rJSnM5ufkxy+/ouIECL1kpSmRn1/TfXyO//pA3pjRWsEHmC+MPimFMuePKzZRV9DitqCEeL7cNOEO1iCRilEFq1RKojRpQNOfVmOxfmEBpY050eeaalNo8uHOBsfjE6W/rtI1Q4/TUAcNez1+lOYzBCWmsIAz8bywYTYJHwwpURW2/cSnT4Sz95Xdr7xgunjA9EdH3rx8wbf1AdO+Wy5kAZA10zTJSJVBKC66tmiuFMbmTl8GJouaxwaTgx1UQ6F73i7SrbSxwSd3gg7K9HBBdyQK4drjR3s8UlhF6yhgVig8tJQDlWquXZDxLpMfTtxKz7N/6/fx+P/8NvGdd3+on79TnepUP4YVE/2NJSOq17bxr+mSFt9KYy2Si7fbrI19idg4Pl9A9spwneluIuOjjv4mk4NDgxB3ls5YO4/Dc21yszRY5y3sBYpBsXkP2SItbZdu49pXzqjpJe0qCrm0bKcLIW4Wz7X3/+Wn7F97hf6fueb2gx3nn5hHzvjIMmdrYyEOwkWCcDvjYsbtJ2PgHEwr519/Db29M6+Kb36d9HBH9g5Uyb1n/1rH8YGzuab6JWU4PuqZz4qfwYcDIRkjKnewf+KIZ8VrKSixt4ZB6k1OPV9UKruQD558pgTBmhLOGSPp1S23b3jrqAaIKuyfhuKrZOfhjwbC+SmXuOJM99ENuhtwj/sF5Aml2eGkMMXso+JWC/2wLyDfYJ+bdJ5JXpkfCmnrOX/PU41KT3WqU53qt6s++tO/i26vbD+JlrzVO+LG2JnzmbFOmty1NjzE9hrVQwcF2Sby5FojOQ+Z6pMnsZgLe2n+m9ZkNkZM/8wxPFfC3uat284xbzPeZ3Iy8EfKXiFnQWfXUoXB2Jj+WJUBFhIwPM88DwbyiApJBDZC6CJdl4jRkZIjl2PnLhPPhNwvLB0DWUqDP0sB8+3cm0Q8wO0bHd2dN0DrzBk7p1uChtZVPXQqiJN7IQ7gG+OoXM9CAqhrClGTMVfWjfMQixKk2ouEfXm/NrS9Z9zVBrYsZIC4MHol0wAsoHkJtufWRXli28KFXVxBs+oR1MKJKgDk1dLnCoDUUrE9MDm+/rO/RPzgw9/yZ/iLrhMg9DJWSrhnL+hvryAJ8TwTbp0xhZxtBjSYqgpK6kywG8yJDQSw3BBSDCVTb55DLhV6+rzoQmGhKNaBOA8FaS+DsYvLAv8evTOtJGMV4S+DU/WVqMfneD/KvjrcryVlKjYoahRCSDiXCT7zdHfDe49fJW4Ftz8yPEvczHbwoTNUS/cm6xIp3YIEMgkuFf8kl23wSNKkFG42UE2DNRIs0pkGKrVrM4LrBV9NtSvuIwbgdLeQj9KugRbwx/yRynHm+n4sLv1rqqP/fCwjUiKMIWfh8uLA8OgG+ZtP+DLFNp/qVKf6wUuPR87em5guBqZLi+aVmoQideytPPEyhh2lpUGa5l+Im8VnwR8z4S4Szz0qNka5EcKtjWV+Mmnu5pkZUeIgbh1x51tDQT3MZ9ZIqF3Mds7FELOZXa9MKe2PMfPMvpx2XlKybj/bMbzbEfYwb4XxgWtsptrNnC4c6jvcrPRzQoNHNgPh7a+TnlwiD89xx0cQPHnwpM6BE9LgzLNiZ5LeeGZzqvfC4ZFjurK5YPOxK1JfW7xPV47pgbbXnc7NvwER/FGJW20LZjl6dGNzlxQPn7QLHB95Dq9K2/S4HtLWXo86A9HcrAzPssnMpmydzWLMLUnZfCLEu4Vq392aPDCeKfODjN44a7jULm9JcdEAdBnX28Q1R2H7S5+S3nn/R/UxPtWpTvWSVXj9NT79E29bnHmE+czmj9QbA8bNBoYArZGwZgitmSMIZuLsFMEay921t+CDTs1SoaMwIuvivRw6g74w8H64yWw/U9QHxpuOeGGgvO4yeUi4TSIXjyA3lbAbbyBIfwPDs0zcCsN1ZvfBxPHRhiNCKlSXuBO0SNZUxZbq9d+g6CaZ/2u1xpjrHqoARFPZm+X7jZXp0uYvyabQmC+WaxT20gARYJGdra6jBsgq5M58Am0+FlxU0srsWV1hpypQj1P3NHXvlsyndj4vEubBmu+BhbGTqvpDyx5vZVOSSxCSsY4pYJQtFtakBNEqR7e9qylRFF1p0XKQxm6SvIRf2F50kbB92es3BIRE5L8C/nXgI1X9mfKzR8D/ALwN/ArwZ1T1mYgI8JeBfw3YA39OVf/ub8+pn+oHKnHIxTl6PNLfJLqbjulJbqyVGjWsgknIOho1rjqtNwf3ivxC8SUqjykocEXjm940F3pflX6tTSrrQPO5OOP7517+1eWYFJlBBT7cGsleSR8amFQ73bOgxewyZ/v3qjuSLhPzLqDBEw6JaQxMybMJ0eieSUzn60H73LoILloXtXoMrVHrCtY0amS3/AwWkArKIF068OvfSSrdA6movGnTpJi01U1Z7dS3SEldvm+Stfp+JBsAs4hF3mfhYjPyk1ef8PPpCSdT6VOd6uUoPY4MH+85vwjsZwMt0GXsb2D6Km2jetBIrgwRA/lzv1Ds3ZxwoxJ30jpr1WTSjyYpC/uEH7N5A9Tkq9I4WMf71ucF2phWF/ZCWaiV7mcF0ysYpM6AlFyp+c8CwzMbdOcLYXys5NtlAehGSqqLJaSE24CMBY3qAvG8x40eb67OxG0gbQQVYyTNu8patdSZmlyjJbq9pcDUOaqcazPIFNtw5M58EFBBfQkgyObTl5IzrwQxyXTqHPMOpquMiwbWpfJ7Ny6x8JKgu8uEQ0SmbEkt2BzuR2V4bu+NvRfK8ELJwTE9AM4j+dgZuwu5562ga+qQlPnxMKLzxKlOdapT/bArvPUm09uvcHjsWsqkJJM95VAZMYsEqfmdVpaQ0PY11bhZ48IOkmwNDHWQShS5OmOMEJYGiSES0sbr7KF/nth9JISDY35h88H42BEvKNJsM2Su9hZKAfuj4osNhivGy8N1mQcwZj/RkbOxglQp4QIrJmZ5XcZ0UbKAFG/qiO0j7jVSChjTQKMqj6r7D7UGUGXarD2EmpUIxUdOl73W0ky6f255pZhonqqrc3JlLpfCRM51z9lBjsv7mDqTwCnL81RpdK5riJXvnhZ5n2hRcyhVgVb2YAubeHmRkN0CZhkLqQJhlsh5/ce+wdXPXZJ+/hd/S5/nL7q+H4bQfwP858DPrn72F4H/XVX/koj8xfL92F54gQAAIABJREFUfwL8q8DvKF9/BPgr5d9T/ZiUdIH8tae4X3mf4aM9l9+64pNXlTQobjK2SZUzNU+HlfSpMm3aCOBARjP7isX7oDLEq4N8G3jqOSjIaIvuKj2rkqjUVylCGRxdXTwvRpZukvugz+q4dWBYHOILK6cAWm42aUEqD5wnM6TwLrD1M+F8ZnzUMT+9QJIy7zuOseO8H8353y8mq1ovQ5lIlhOBvM3k4BuLp25g7Lrq4nzP4ntUQZx67rpiRbmiWa6MJ1SaG78UemNFr3OwHZIvHdzc20RZB2ekmG3XSc+BDhlR2HUTb28/5RdiHSlPdapTfdVLpxn/0TPOARcvuMueNCxS3DoOazCc2Jc0kBZXW42Qu5KsUinXSeluI9Pl0Lx/ulslHM3suNtnwj7ZuFU1SVTG0TJe5a4kTo6lK+pMlubqAk4X+bFLpQ9RUmQqoOSm8rgRNkdHd6MmJ7iE6VEmd67I4oROi1TNW5e1fxGQmHFe0M6TNr6YaNs5TxeOuHErybOlsmjAGJhRGls2nmkz+vTjymy6W2j0zdjNrRoksiygVSHNQs421sdByJ0jbYV0Ecmzw4028eZgLB9/KN3hIoXzhxmZV+Zy0Rhdm+eWVmPeREr/PHJ4MpC2meFsYrwJ33kOrhT75Kx73mUIvu5Wfpgf11Od6lQvYUkINhkVz7bD73yNF2/3VFVBPJPmA2rGwPZ3bl4t0VceaA28qMePQh4dfjKwBoX+1uRQuStgfLTESwmGClRABq/kwSLs571j937i7N3IZhuYzz2pF16IR8URt2Yp4ebCsq3SrrmCERhbBfP12XwWSX1nTyTCPFs6WQqG5CgFFMnGDqU0RqypUxrVrmTpVGNqpKk97MUvez4DgGjAkNRme16+B5vPpAA8xhw2uXZtwNS5IVWWrmIN6M72Hyq6hBWsgaZsDQqXIE22N6SsP1K/am6ooqWxsiYrNKWJXzWT4tI8AjvXVOYsySaJzqs5tn1gCriWvc1j4orCg3LckHn/Tyq5e8LlVx0QUtX/S0Te/tyP/xTwL5b//7fA38AAoT8F/Kwaf+pvicgDEXldVU+c4R+TkhA4vLLl7IMNWYp2toAMaYDpsjBOqpQLWDNvXFnUa7lJqnl08isAo8T91RjbFrs4281Ub1CLHJbl+FXiJeapI0U2ls4UP0pDsCuARDnHcCxAVWU5Vc+ivDBnRIEi4Yo7vTcJoELKjo/Gc7a7kfHBjsOrA91dhmwUyI2fSaOloUkU8jYbNdMr2Tm6m7IZEHOc1yEznztDuXUlrfAwXxpIUzdTeaDJHcJdiXR2y2BWB98qn6vXq7tVkx+45X2KO6kvCZy9n5WVlPqyQfHaJCCtSzIkLq4OfLo/4699+gd4+5duIAT85aWl8pzqVKf6apcq7vqO4dOeHDaMV846btV4slci4MXAaD9ic4fHPBpKQiJRrHuawe1H3DiTvz4sAA+0YIBwl/CHGQ0OdZ64MVPq+ULLHLLEBEPp9NaGQ+1ClmZFYxLV52hsm3L+Q5HyFunafC7M5wbQqFPiRS7HEtzkmC+EWLrEfuroe0e3N1aNxGwNDwfT4y23b3nrTqvNReOjjPaKO5oM24/QvYD+Vs0cemeSAf+xNxlCWYiHwlLK3Urq1UHqaDJkKZ1oOXrG2wGScHhFCKO35sHBm/dPYSTpkInZwK48i7GIHvT0Ivj9hDvMyBxBlVAC1Ny5LQ27mxlJasbcl5Hz7chxN6AHu8iVcSTYvEJ0qGTrkwjc/dOvcqZK/NVv/1A/qqc61alevop/9Pewf7Vn3gnjwyWMJodiQRGqVUJpTheQoYbQVLZLYwSxgAjVmgHnzcenNFT9qHS3tqA2GZQgk81XBIXZGWsn2Jw1PTB2y/l7ge3ff4fOOTZ9BzGRu7fIwZHOvEnEJmnJyv64yKgbq0kgbj2b9/eQFT927HFMe0ccPKmwlHKUBk4RLSlyYdOqhUMERYMYY6h3aHDwQhZbjR760fZRdb/mp+LBkxc28Foi5mZ7EsdiUA229+tuFtlx7tb7shICFLSpSBYp2SI/lwR+rrKzgs+INZ/qvKPe9kvtmN39NM40VOat4lcMqsrIanLyVNiuJWV6LSGsvRkNxhQyoE0XiduQTLa3IkF9WesH9RB6dQXyfAC8Wv7/JrCe+d8pP/t1gJCI/HngzwNs2P2Ap3Gq30zJMCC7HcOzEd30zI82HJ8U+qMrMHeJFgYa06Z1Jcviu8rJ1IHLpTtc3eUL+i6FNVMlZ+UMWud36YTab3JH04L6kYXqVwbu1Csegbii2suqk7ymg34uUcvFhfEE9eYvpthjwPcJEWUfe7xoS0k5PvT43Yh3mX3s7fLU4yexrz6jIZE25qug3pE2uTB8lLi1112TdHwCvZM20NiLp3WPG5toBfJIoqHjqhbjXDXAsFyj5rW0BtxKZ71GSNcusx/N88lP1lHIvWc8C9w839G92yOHj9GzLdIFfBdIn372Q/kMnurlKxF5APyXwM9gd+6/C/w830F2/AWd4qmcwHbD/MZDjk96pnPHfC7EMxYT51mKH8D672hpVO5gC2p/FNJWCYcZOVorrcrBWtKLUOLPDVzJvSf1jvnMEXcwX2bw4KLDx9Kt9ffHTCnpLH7kHnuyhRo4FjSdxQS7UlirH5E/COE2cHwlo9tkDM7nPcfzwnDNMF06zr8tnH0Iw92BcAeSMrkPHB8F5p2l0bgkxK2SLxLSZbKE5innR0t3yduMP4ukg0dmj68sIGdpk+ogJXAlgayO4WSaSag1TYTkPeqU8YltWtSDOwrprJy7s7k9bzNpMrPQSYS75JnPHMN1YPvtFxCTJaqJ4MaOUBJC/ZhIu0DuwfWJKfpqLrhcY7fae5QIXpyis7MudzpJj0/1G9dpnjjVdysZBvLv/52MjzriVszrZgNaAaHOmC8iYpY+6xSwMh9ItOGw+so0IEjr9yXWvBgw+2mxY+hubAyPO5qFA6NDtRo3C1o8cXJnEunD48Dm4swotV2AlBdv1aMrex2bT8PepLl+0sXoGJs3pwvP5mNHOCT6W8fxYWGVruVi0S2soEzxDrLXrWBzkCvJY87YRFHAzc5AsMoO8rTGPKysLRojSG2tUIC1uj/M2LWv5WYLHKjXGWjgUN1LVqWJpALafP5xbnkOV/dAsuxxasZ72gAqSzx9WY+kzuZk7ez0XUkkFV2FZlRgq4KFjhY/3zyOakOpkCEkmUQebA8WsyBe2+O+zPVbNpVWVRVZB8x933/3V4G/CnApj0584h9BuWFAhh7/2Z1FM154xgeKhOXDLFnumYw1Kt+6KphR6fzF2Lki6vfAjrrYrYBFt7rh0zLw5GCHI9OkUOsBey1VqF2BBfQop5Xqa1jO/V7UYBnwWlfTCXr0aAGExhRwLrfHHh85NtuJ4DIvxk05yAKQMdvi1/WJuA1s7mwSSbECRaXTq8ZwquyecGCJUVxraas52jrMTO9vdLRsUH4dgu0LEcvbIFiR71wMR9v7O1uHPNwJXNimQiLkzjNuBvynHWfvgHYB3XYQB3zO8NkzTrT/U/2A9ZeB/01V/7SI9MAO+E/5zrLjU30RlRWmmcPTgfHSlWSWImfVBax28zLOrr3koHQ4R3v8fCmEuxligqEnbpdFn6bitdAJuffkTSDuAvO5PW8eFO0NyFDnlkYDNECbjJl4lvGwMkzXpdXjoTUdLKUll25C3CjhYOmMw2fK+BgYEt0mEjcd8UGyZkkS5kfQ3QWGG8cAuCminSdvPFORKeAgOyVtM24TcV6JkyspYEbTT701YER0tai2yUqhzRFrpuva+LRJhqV0kA+OPGTmq8T0INjiNmKS6DqHey1yBkdSWvxw6q2Du3nPPBWIEfEONyVb9GdFDjN63pF76LrEHL1tPFiaLC0oQoHiKSGuyOTmZOjWqU71G9dpnnjJK7z+GoQyocwz8aNPcJsB9+gh+6cDcSi2EpuF3VH3AjaA6hIXXsaiCrZX1YOwAipkeVz1n2PQ5s0ZDorJxiwJ8/AajTEjk9hQWMbDe/uMXg20enRmstwaVV6YsTU0pgJOZmWh+KmoEnoDXVJhvMTzDndM+GNuc+y9yqVBXX9egZfyrTg1iVsWm3uGwoo9umLSLMUPZzFprg2cNShy/zmXPYtLy3tQ93bGHpJFoVFPr07LvmBWFeSp75uar6mr5IRKHFhhLk1KXhIu62dgHYCUttrYQWiVtZnPX0uIWzGTWJ0bAjKvvi0sNFUjQWhX5uhJ0MkzvNux++jL75X3gwJCH1YpmIi8DnxUfv4u8LXV494qPzvVj0M5Ae+QwwjBG4Iu4PtEDHZ3hv3q8fUmXnVc06bIuQrDp0q4fLINQe64Z0gtyQa/XMyiG7qroFsW0GkVky6pRg3baZijuy7Icks2q+ekizFbWa+ae/wix6qL49zTFtwa1Ayik9gkoYKrjwtw+3XlrYtbsgofPT9HSxSlHScTXnhSFvJWkAcT+XqwGPhbT3otmpfGKLixRCb39rzd3ZKOpqFEPYsNMmlb3qppxWwq1zEUZtB0ZRs1MzFdgLHKDIpbyLuM2zvy1gZ+ScLu294i7ifFTcrxlaWLEA4Q3ulKYply/TMPcFHZfDLjfu3uBAad6gcqEbkC/hjw5wBUdQImEflusuNTfQGl80R89z32T3/CUsY68xnTQIuV94fFB6eC7bkvcqYIwzNleGHpVceHHvf8DkSYX7vi+EgaqGFjuBTzz0A884xXjulCmC4rk7GsZuuifcUorYzJaj7tErio+Hn5szVQ1Ragg5JTMWT2St4ociMMz5WH/+TI8981MJxNvP7gBd+6Hjh/5Y7OJ8Y5sBtmrp8/pr92bD8MuONMHjzTVbB0mxkQIQ2KbjPeK85nZJMQp6RtYL4oQQbREY8BJlc6ntqYNutmQO3OtnmwmmmWeTkcbM6LTnBPDxz3OzP+rN1UjO0q3jYDeXaoONgp8UKIOyEHx9UvWLdF59m62H2Hn6L9/ziir56RNsr5MHFzu0WKf5AGLb5/Wuj/5rekYt1oiQ6J8TR3nOo3rNM8cSqc58U//xPNi7S7y5z9H3vcK485fPMxUNb3vZC22Fgri2zMreLbq5ecCkjZe1gzw4CduC0AjVv2HGCb/fnK/t5PSn+jpEHYPLMHvPipYGljuRrqCy1tTKWpIbKH8aFw99YWf1RLzCpzpov3Ezr9WFj/RR5lciRH6oS4FeYzYf+05+zdI/6Y6G9zmxTMUkipUmeioENekJPaCA6Z0CVyKn/XJRhgnh2SPC6VpOHezkez7TGa7LoN4QtAU9OkmypkxZIRLQ0LZ9d/XQtwVmRWhdHTKlmDJXlDo+o+te5F6z7QEs2WplUDhIoawgAhO3GJQgupCHbsBnRJu5zLywz2N5Wp1fa6CsSFAOGPIHvPN/7KL5I+/Igve/2ggND/AvxZ4C+Vf//66uf/kYj8NcxM+vrkH/RjVilDjOjhwOaTK3bvnXP9lhnV5F7JfhnUKhDkJhp9Mm2gRfglSDvaIjX3yyLcFVpdM/qsN1Q1JUvWTKweOX5agUUs4Ea9UashZh1Umxv9XOIQK9OoN3lZKGwdFQOFKlUzDTQ5GlhXQLN5CGUVpmhG0HEjpFcnnCj7uWM+dMjoFlf9kijmJiHjjYJ5pm2Qj7F0hr3JCGrlAPM5rTMhKqRNkW5hA5mbS6pBv7xOXZ1z7RBIAgaIHS3hzAY8myC6GyFP5nkU9jA81/Z3lu5jMdGpr1HRtrgfH0A4OCTC7sLxysevINcv0LjWipzqVN9XfQP4GPivReT3An8H+I/57rLje3WSFv8IS8yLZnqQ2xzgyrjk5kXWWhmjcWNfuYy3u08imw8P+Od7NpdbZH9EL8+4/fqWw5sJdzQ5mZsroGBGyGCL57SxRT4K7uDvNQoonTl/YIm5393vyrbkLmjmkK0rWbyNaqRtlT9bSAF0H90g84bz7cgffPxrfHB9wTcefUbvIrfzwO9+8B5//WbLzXhGfzMwXJtR6Hhp1P+whwjEM3DbiPN2Ys4roYuM5x3TLKgX3MGhZU6YL8uiVWojZPFM8kc7eRdsTlsnd/rDkoIjSZj3PaFeV4+lYao9VpPYotwp2hc5c5+YBgfOM18N+E8Ho+BmRcYJ7hJ6PJJe3MJPPiX1ytX2yPNnZ9a1lVWDp3TgqYmbIdN1iVHBzek0b5zq+6nTPPGyl2b660jY25zg5oz+9E8Qg8NFY6/ETQk68ObXUyOiWqNiBYRIUig+qVq9YgqAUc2Ua8KUW80zdS/hIgwvEvtXApsPD7i7Ef8HX7mXgryEvCyN62oXMV3C3avegJ61lUXZi9Q9lZ+0eAWJNTWKF5+B7TYv3r3hkDzQ3VoQQ3fryZ03NW6nCzuomPnr6AzryEASnFOGYSYlC9Lp+8TQzVxnR8oDue4/erU5Ota5RJfEtcqkWqk0msF0XuwugNaortexkguafK8kTmshDiwKFRr4YkCNWOKaX13nMq/nTovnTwH3PMTz4p0XzOC7nn81mM5hOe+8Ul1oZTHVXlRpzpgHLIuaRYqyI9ic70r6M/mr0fT4fmLn/3sMoX8iIu8A/xkGBP2PIvLvAb8K/Jny8P8Vi5z/RSx2/t/5bTjnU/0A5XY75PwcvdgZjf/ZNf76wOW3N9x9vSee5RKJq8WboaC9xbeg+f5ku7GcLgNcvVFb93JFu1uzi2r6S735KwhUQQ+gGZO1rujn7rNK16yysyqlkjoWzwsiXYGjCjo1LyTKY6O9Tk2OGB1z8gvj0kO/m0jZmW8CGN2yUfa1nZ+LkEdng1uggWCILoNiQcurDtbFSt23BbUcyhNXTyRZBqEmGWteHsuAW6MVG8qtZRM3Ct0dRJUmMUvDQgFNGyFuDQxKWy2x89ret7SrIJvj+Po5w68MaFbIJ/r/qX5TFYDfD/wFVf3bIvKXMdp/q+8lOz5Ji3/EVRmHQVvKSpPI6jIOVbaoevNb6G+KQfTtiLy4xXlnUrGHOw6PHP7RnviiB9zCgnFi0cDOTKS1JDBSTPhNNrVEm9eEsnqirgQi2HkVk8poc4FUNtKqO+lHk8dKsq5u9Vybz4X5tQszx46eT8ZzUnLcTANnnbDxESeKD7kltNTztvFWoSsL6qD4kNAsZBVjJA2KbCNxEvzBm3+bgvZqHgd9AY+OzpoD5Tz9sYzVA6hI81JQKUBZlUBE61I2KbVbLcIRNBRmUl2QZ0zSVYIPjo87us/OcX1n70kfkCmavLzrmAX6F45vf/QIbjsDmZxCkCIXXy28Vezai+IODhnTyUPoVN9PneaJUwHLniENDrkwJKeySVJv7FVr6iqaBVU135dVClaTOVfAKCxjpityJnWyBEmt1to1ydLkSM7WzmcdGhzd7cKcabYauaIZZU9RGJ8a6hxZUrEKO0mq71D5WV2f51CaL1UOVYN6Cuv/+NCROyEclf6FXah49PYcxfajSqFwxp6SZKE29YbwPpN8xrlM5w24P26SmU2XvYJ619LOUq8LIWC26+XEgLS4k2IWra1hdG/ftiJs2ZvIsk8p16vKpstU1axIWtPfl31L3edBm/PBmv1z2asaIJSWY1dv3GzNpdwXEKoQGmryWTunFQu5NVZYnqsxb1ekBbDP08f/xk/x+O89Rf/eP/wun+ovR30/KWP/9nf51Z/4Do9V4D/8rZ7UqX745V55THp0STq3u6A/TnCz5+xbjovXH3HztiOeZ+J5MQMraVT+WFDcuigv4EQOhm6vb/K6OIUFyGlSpoBFza/Q17AHKabTbcPB/UX8Wm9ajytxOS7QIgxltsQxUWkL4xrJ3o5Vb+7i0ZA2QBJS9CQ16Vg9991mYkyeOXmj3deJpqDua1d5N4p1ejtDnoHiHUGLnr+HPCegg+RW104hF38NKciZn2GZtcqkFQ3RToMNiLaJqr8TpJishr3RXXOZmNKwvFf1OcyITdEul7Z6Oe+QcSFzlIHbN3s252dIjOiUOUkATvWbqHeAd1T1b5fv/ydsof/dZMen+iKrgj5eLalk9XOwsTpXb7Rt9VoQhueZsJ+Rw4juD/Dwkny5Y3rYM1/CZjtxO3pykfaCMWpqkqJ18ygAC60L2eaUMvZ1e5MpqbdFtStRsnU+cNG6wm2Bt1rU2aK1glxCjraQjmfC3esDGpRxDrx/uCRGz+044ER5OOw5pI5UkkjsOcoxk8JYPQmKybVX4uzI0ZnZJ+CCWsqjt9fjnJBCGXeLx0Fl3bjZ5snWxV7PERGTy1VPoWQLdb83c1Bj1urKVNS6x7ryl6gdVdcn8jkcHnVsHwz4waPekuXcGJFNj9v0SFb6ZzB/MODn1YbH2/uvBRAim2EpQEqO7lZarL2EcGIKnep71WmeeIlLQsBdXTJ1jtxL27irEwNNWNbOLSBGlrFRnSLI0pBesTo8ik6Cn01W7KLi4pJE1Wwr1uxLNYAmbmzcnM8Dsg2EO8VdCrk0e1viZcVhEpZEVQZb9dZnlQwiy2tx0R7bTJOjpWTdZ9CsXp/HJMdlPxH2Nt760Rq78bxcl2KJUWPmqf1bFXJ2dD7hnOLFfOxCSPhtbIzVPHtjznRiHkmbDLhFIkfZjmiZq0qimz+YKXb2svg5aXvqJZSofK8iiyfqMp0uTYsq4Vr7+6xL6zkoulELnPCKv5hb4hrFp09nZ6BY0JZOXdNRKc9prFr7zGh9Pys4VRljldBQzmdNZti/Jlw+2vzWTZm/4Pqyn/+pvs+6/kNvcPuGZ7qy7x/94zfZvXuke/8ZT//mM8LxITc/4Tl8YyLlYGkyR2mLzLxi+TQWTVixfDAfmnsosJonTmOx5GUQpDBj6qDsi9EyBTy6519U6l5EYWe00XpzStPhmqRqPivHK95GUDYxg94Dl2zkB1XhvJ8Y52ASq4NyebbnZhxIyYEahT8U6Vp+Zi86b7QMmiCzdSlcFAticWrR9G4B0dpguXpNLhpYc88no7KRCvupUWKB+cKu63yeLaVgLN1yiv63MJXu3oTpcYlWqP4OobTaHfjnoUkOwnVoHYG8y/izZBua88Snv9fz5G+e47KixyP55uYH/Rie6iUrVf1ARL4tIj+tqj+PNRL+Ufn6s/x62fGpvsiqnTW/sAWbzr5o9PNg/kJpm+mfW9f07P0Z/+II0wzeEx/uOL4ysH/iiTslHnqYy2q2SrmCmUcCNj4VoFnSis1Tqehl/gijMgVpHgUuqpkjB0gihDvFPBjkvqlmNmnx2jyydjTnc+X6m468jUxT4NeePSQdAnd9Tx8iYZt573DFfNtzdisMzyOptwQtP5YNhzfpGF7pu0icvYFBk2OaAnm2J27SXgFKmomxXqUZnrbXXBaiFRwLt3YdUjHnXndka/pb6oF+LZ0TZHb3WK0kk0lvNjP+bOT261dsP+sJ+0DuBD9l3Oxxc8YdA/5u5uLdDhc98zmMjyEPucwlDt3YiWthM0kS0n7g8XsmP5PdFr8ZiO9/8EP9qJ7qq1OneeLlLv/6a7z4Q28SN0L2lQoCcaMFPDHJWE20vMdEWa2Xa8O0SrIkawE7lHDQNmfkIGSviJf7DWNK0qM3+4nxytiq46UvzKKyxq5AxQp8t3G7gj5iwPvn2LW5L2B8BInLHOun0rwtUocmv6pAFcUbtBNyJwzXyuZ5LtfIpN7xTIkCqTQhjMUJ2mdj9ESHdxlfGEIAm85AelUzm56mQOw9eSqdmgqKeANf8NbMQMwiA+w1hyDsPs5IZ/OwS0Vq5pXsVv6BsLyPFYxjxb6pBIIKjNX3pYJq2UCvyuSKO4XLSBgioUtcnR04Th1z8qTkyFmY9qGlv6XBGkk+ch9UDIorJ7OWHrbkMVc+V5S9cChMsXK+P/Ff/ALp449/4M//j0udAKGXoUS4ectzeGpGWwDPf9IzXu44e9Cz+4VPePRz12yenfNh6Blfmy0sJDrCjUNaFLos5JC0Qr7Lz6rBNGDobV8Xu9ji1oFUAzctdMoV4NOiB0vnsznEFypmowl29/+uUfjEbtJKta96z+YlsZJXlVO0RW2h+V/1B959dkV3C/1t5q2z5/xyeswNg0XoTgvw7CfaSag4dMhopzZYzJijP3b+GpbXJmrnF3c0w+iqeZUSdcnKbC0LbWMkKzDJTdDd2nuz9sXIvZls18HKjc6AuFiO6RaAqkVgFjO+uFMzwg7ZqLgHD0Hpv3aHDj0yT+S7tev4qU71fdVfAP67khzzLUxK7PjOsuNTfVGlytt/9Rf54N/8SZ794Yg6b+OXA7BFXNzZHJJ2GTYZvXGEozK8c41c36DHI4hjetCzf8UzPjLmZLrpzOeggNPUjmFhx5iBo/3cT4I/LjLb5leQlTgUyYCWBBhYGharzqKUTq8txLVJe2uHFWhebblXxqcR2UbSMXD3ojdQ49zhRdn6mV++e4y/Dmw+UYYPbtm/fdnOwc0ZxKKQ1SnbfmacAlEsqCHddgZ4RWmyYaT4SsyCVtbSLPiD4MfSqaxefmV+6+6U+XwBtcJdkT10NA+M6ndgHk31dVq3uzGwIqRZUBW2/cztqzO3r/f0N9IW2i4q4eAYpkx45xnnKRP2O27fDIyPsHliSDY/FcmbJrEGyNETPgucvxORlNGrc/K2gxMgdKrvXad54iWp8PprzN94lcOrlt6bA8SNszAZb3sNWMBwNxvI4GZwIziPAeoVhJlpoIFL4EctoASo2mRjY+Sy/gVa06AqG+4xMQXmM8Ft7HHhaGC+m8CLNTCis7HbJVMJGBBfQJ8CIK197lLP4rmDoLnMa5tlQ2PNY1nAEVfG8OL1OV/Y98O1AUk5VEmavcY8y7JfEujOp7ZJUyC4jHeKE6UPkZgcSW0+aA8qjNK6f6gNbQGy0+b9Y0oDm4fmncNPSrfPbQ6aC2s2e8EXY21YXlcFZUKRiK8TqWvjxI+rfWZhJlk8vK0Vcp94cLnn6dktuzDxYtpwOw3cjb01+DeJnASZba5Mm2UoUxoIAAAgAElEQVSe1GAfgsoyqw2r5tfX2A/l+dNK5VL2dblbdfi/5HUChL7i5TYb5GtvWDLVTsl9RrIwPbSBJA2B4dkF/tNbdt+GB5eXfLoJxMuE9pm0ERwVfdcl5hdBVxG59yRa9aavi3loN5TROM2nqFI/zd9mAVuk+u18p1pTNK0ZfO+xjWJZx7a1SXVNc6nnU0GqYoJ50Y3E2TMcIOwzWz/jXeVJSjPTbN3d1WuiUekVenu8loG/elmU/Q5e6vkUllM9vwzUzrAvv/Pgy4tpbvuULvpxWfyDDZKpxNk3SUXpXrhYr4W0jVOVG0j5XQ6FcaWCRlc6y5ntMJGutnSfbJDbu0ahPNWpvp9S1f8X+IPf4Ve/TnZ8qi+20ocf4Y8/adKfIoOCsk4qUrGWCJJs4elmRe4OJhXzHjk/Y7zyTJdiwLcHmdwCxssCYLfgAq3Ad/HPGWnm0ZK0+QXlTpq/Wk1hlKRIAdHXEgA/fW5zETEJ2QoQr80CAE0OYmFSesUV+dOYAp/tt3Q3zhJexsnOo8gZXCysJA8MGe8yzikuZLLzyGGlgXbLa25AWGmYtA1EBXTqnFG84ly0hXe9fn6Guc5BdW6oi9jVYtuie8vOpy1whXn27Mce6TLxDEQF1zYYlpiG9IQPO9wx0l/PdA+KB9Lo0MqunR3NS0MVt/cMz4XNB3tjTh0n/N2Bk2DsVN+rTvPEy1Hh9dfQy3MkK/OZ3AM/bFyljY+oecMhIKPcA4isuUCLPaest9esmjbOarVvKPPZ+jlYmtq1UeHmsl7uFwsIV+YlP5W/RZDNAga4uDAz65hshtGysF9qkzfbet/N1lhv81o9WJ0jyv+rPFh8abBshbmcY9wsKo06d7hZmizq4vzAOHfE6FA1H6A5emJy9CGZ1102No2Wr3Zd6qDtillzsdVw0PY2YPP0dCEML0yW58eMekeezQJDw6qDX69/3TutqlqSNHlW8WSVuFh2uF5IBTjyB/n/2XuzX9m2LL3rN+ZcK5q99+nuuU3e7Cqrk6tSLmGMDcgGGQEPiAd4LSHxgDCWeEGCv4B3kHikeePNAgkkJKPCEsggYWRKpnG6VLjJysr23rz3nv7sHRFrrTkHD2OMOVecmxY2lVmZ9ziGdHTO2TtixVorIkbzjW98g2VOlCrs8swglV1e0FFYqunCylCpY0WW3NhAdfDlDRUQQVw/NTSmoikT57r+fEQtZbmFj6Gl8+v7otoFEHrLLb33Lh/9Sx+wXLvwo1oSv1xVliuY3hHScs3jb8Hww6e881c/JS2/yrPfGDh+uFD3BU3JVw9LA1E0aPkrMbYQSV47s2Dr1BxMlhUAMtCcfd34WmNH1Jcr+rcw0GF5w1n7AGoIfYaD1zeEqo0dpH1tYDCbiiFQaazsNzPvjLcscyYflPHVzFwzSZSYauj6GtZVXotbp0Oi7IxGr1nhaEJmpu1AQ9QByuLIcrCfHBlX0VYgBJspRKqTr5zXTKNMxkazsrFrNoaQHTNNwnDb74kEsl76PYH+/+LsIBU1cEuTd9CF28OWqy9tuffiEWmeKU+e/pQ/pRe72MV+YUww0eFW5Psa2C1NLyZW3BqTR9HThE4T8uA+9fF9jo8S841R/kOgWkftSa7/8wycXqQlvvlkYpaIbwZbup8ET9omCwq1GBhuALuDP9WSuqY3gQNIwnlToEBS2/xVXehARWE0an1V4W7ZcPt6x/Ur2LwuSFXKJjX9Az1aQl+2MF7NVLWtLikrdajk59l9ud/TWBMchUw0VkoX0AYftZb+dyq94WEjxOpgT09So0lRk8W8GEuLsYp1x7McBw7FRgWWaztWTjbubBR509DY/+CKNC3k24nxdsNwO5guXc32noT2XAUlMb4Udp8p+dPn6P1rONxSPv3sZ/NZvdjFLvbFMBFks6F8+TFSlPzsDvTKtj+NmPj9yj+HDIJmoWSLM+Hrzpj0PiZ2ph9UO+gABgSp+9FobgQ4xPp5Gv5VbfX6ro87pVkZVFn2fcxgjp5JnNfJL9Ufb0wXpTjQVT1PD02d7EGwukZpgELrCYg2FeB6eTIaCEQVylZcVJo+XVGljRwrcH934nlNDRBaqgFAqsBuckBIWJZkP3eJjGgmRMyyxUIeIwnQpoNd0wNjl453Sj5V6mjNorQIy7iKu/4eR/PjTCZ+BeZJhKsa8c5/vgA+ATEc4HgYOM1WXFWEIRXGnNiPM4dptCUPmwTH5ONvBqrZdjAvV8XelJiayCf1es9JAAEUevOmNeK9tnxb7AIIve02ZJYroWz9k78Iw2sxbZ/RqP9Pf0s4vnPDve9f8ej/esrDv/YHPPw/7zF/eJ8nv7nj+J6tI48162E6YEjt0cCJ1nl1ca683kImlsCaIKh9uTbP7QsX85hA09mxeU86On4ysEOzrVMvO1qSm0/dwZJgceBHCgzuSMpGmjMLip91ZqVRJ5MoOpkWRN0kXsxGZxVRY/ykDsI0UToxMKpsPTmf3bHEZrbVysZYb1g2RnsdXPdh2cP0IEbLlOSdECvMaE4yUPPoBmuCfLQOREnAAPmuC+u1jTTuUM+0nHx8omxX42zFnbsow7PBgLdTZl72tv7ywY7x9h5cAKGLXeztNbVOoIaQvjibdK/mOxXyMbF9Iuw/VXbPC3JzBacTPH7I61+5x+3XuvBwY+P4WvQ0d30fCYbQvNIMWmibTeqorWsXY8b5aGBQnr2ZMLio88a2XoVOhCzm9yOGBPulLSTwAgBfHauDJ4xJSZtCSpWiwotpB59s2T1RhrvC/MED5isx+n8V6jDw+suJw1cKX3/8gqd3exPsHAtlTB18r1Hk9EIkrUS2G7N19fhYybtmUwXY1US3HUArGzF2KufU+7RALdKAp/D1ekzUKdkGtGt7n/LOOvLFO9/DnfDo72xIn03I64ntkw37TzN5MrAolQ5YxTVc/0i594MZloV6vWV5/wZ+7QPyX/s/fmYf2Ytd7GK/2JZubjj+uT/B7sd3yN0JamU4+rhUgCpFG6Mz1rFX3yxWTzSflo/0Db3FgAFxnaHWGA7AyC1qizUTKWITGNjR2CHelB0O3kitIKpsXlkNMzubNfJqKUI6weZVxCsfV/PxL9SWF0QjWKp4PWMnOBzOzyXGwIJ1FNpINuYkbcOnFEzTb6cGbMS4mwNDFLXtkCvUpVZboqOLsYJy1rOfsQixYEa8bNRqtcF6AkQWGwMLse/5vm3IHI7C/seFsktnLNzlWloMi2vNJyVP+Khgj4ttmsRHx9ZAkQFtViMNr2HzJHO337I8NibuUjObtHBzdeJ22lBqQmti2WRrdidanhD3KlUoV7TaNB8j/iolWGVBSFA1BpY/ntVn7ItuF0DoLbb8+B3mLz1kvg+6qW3GtFMKBa02Rnb4oLJcJebrxzz6/SuG50fGT295Jwl3zzacHgjTQxsB6F1OLxqc7hh6PWlpk0e2DcUZLurbX2K1/NlqQk9UgxUji5BWxURazBHWQc2pBtsn2EmDTyQsmGBYJNbhWGLGdnaQJuiKYyUPFQGeTtfIyWiFy1WmqtEOS0k2kytKbLCx69WWnEtQW0fX4clxTdJHLFLvAEAH1OrYO8VxblRgEZK30s+KhaCfJit4ojNMsWtvot5h0u950yZazVHH3LNtG7PjJ5/JVnAFf1iuB+SDB6QH30R//9vo6fTT+Jhe7GIX+wUy8z/S9H00uS7Zzua8Ium8+rFy9cnC9rOjtdBypl5vOTxKzI8X5JDtWIMiR2OmtiRPaMxLFTtegD4hqBnsy4gZLbmk+z9YgeWx4bJq6zz2JLJ3nJt/pXdi8ySmq+MMS9MoEJaSeV0yu08TV5/ODC9PLPe3zNfCfM+BF00c31PyoxPv7G756Nl9cjYhT1QoO20x6Iwmr734qNlYmiB9dGzF+olxuXVxUAdakp5m35iJFQJ5cgbv4joLa2Bp9LXHGRRF52TCo4OwXINulOG+rTibnmztfh5O6O2BMWd271l7Vm6E8fWKyl8sHu6eLGyenWC3pVwNjE/vkO//uC28udjFLvaPp0lRytVIGlwEpuWzHcgR15oJkX3NCoM0cL8xO2faZqh8UtdKs8ZqjCRHrhx6dG2zlAMdrSnQzs/ZIdFo8GawNZNhuF3YDEJaEnOBzTPzrXkyjbfNq0qetI04la2fN8nAm+yjxaFbE01aOuCgEsCRnZ8JUNt56WKNiLK3Oqg9b1BobFFv7KJQMf2+oXo8t4BX5wRTYq4b6s4KJl0SOvWx6SaWrfE++E1asalklnYjTeMITo8Sd6cti1976Jk2EWYHeOpgDKnh4A0nr03WY3ziDYc0YHpL/rpNAkOsCT6/Gvn49h7vXt3amJgoixZO88A0ZUosdRiwmgojBuTFF/IsMD/wBpYI8hl9RDsYWsU/m+1v+50Oyh/+O7/Gh3/9awz/09/8aXxNfm52AYTeYpOrK+YHG6b77gyKI79gYsTVkF8dhHpVOe0rZZ+h7rn/3cz2k1s2P35NOu0ZjlukZmr25HKFvtcBxDVo0mJCaaF7INX93dzR+eYAu38ya0DHSrkfmgNajz61VfNO4dOsNt8a6HiAJ07vSwXUk/KzOd9BydmqgxfzjnQ0Bzdfm/crTrWUWdr5R7HRNI+CRhgAStArfSQtxhgiEAQkFCuXwQCwM4vjLqFN1Dsa0ZGN7kADi95wlA2N98AYHYsotmKErHcfrKMQI3+aaCMiZZMo2wQ3I+W9HVd/b7gAQhe72Ftm+YP37Xt/Z44pLd2PSWgGLUENr4yvFvLrE5RqMSE7S2e/GNCwEkxOvoGxUa3D5SXtGgkrwCjGw9oIsvvzNWPmbGvY7CDICuxZd3/X41JvCkyf+W/XDlIVpsUaA01TQIRll1muYL6n1rl+Asv9wsPrI5tc0CoUUlvAoJsKU+qAULWTi2YCCrql6cjFOfVRL4tZTUfIj6NZmt9vDRW8eCjSO9ulXy8xDbBiKkX3V/26ZVe4uT5SVHj1fANV7f2tBZkX8rEynCwebV/VxsZKs93s8dWMTAt6tWPZZca5UJ49+0f/MF7sYhf7wlq6vka++iEMFkt0cPb9NtO2QCbpeXCkns1H+oEi/w//FgW6Nz9ts69toCxVOvAcOWy23H8tn5BQ88+h/bIChYZT5Oz2d6LHp3RaGO6S66Am6pgoWxuNGu+U8baSjwVZql+HofZ18PhZxeKLX2PUEeuNWxH7NK/G1cKHq8l+QH9O07rxGNkmA3zsVyZBq7FfCyBRCxYLojUbi1WrGBg0px6PQ1oD6AGtgzXx2ilZQ1yzsX0OjwOA6Vqx1Te31dGYRCZKbfd53eg5050tdAHqDO3Nrf13UkEm4eXdjuvNRKmJqsJxUeYlU+ZsOneALdWx2rIOkBJ93bz48Ua1WDuff57FGx6hD9X0/sbK4asLx8cDNz/pi/AFsgsg9DbbPBsN/MMD9W5s3jb5WsKKQoL8OtmGrI2y3C88/SeFw/tbrj/a8ODbR6RUtk9nhkNl/zRz+0FivjGkGvEvdgi/+Ze50TPpjiu2w0TXtyXhnuS3BDj5OsEoAqCNiKWTjWfVLWhRMiH2bILO1RkzebKxLNs2AItK1ziiBxXEAsNUMnfLhvGFwdfHh8ImFQ7zyDINpEW8G6zkIoyvzWEvO6Xu7LXTKRmgNAl1q76GXlZ6QNqd/srG29U65ytluJNGD5UC8w5kYx1wKXYv0gLDnTnf9fWUlX5pK5y8EJDVv1Wsy9AFTb2bfEjU0TQljE2kkG27zfI6tTnui13sYm+fzb/+ZSv0Px0oW5uzDyBExQAdWUzQOM1KPi7I3RFq7QdRfN2sklRgFt9cRff57o/AksZIeC251w4GOXjUurkBmIidU10lzXlyHwvdz0byujo9KTAcXYchdyClbRoRy31rNS21ec7sMD2J5d7GmLT3lPletedLJt+febA/slTTaaBCVV8ssHW2UCSlDgbJAsNddIqF2D4WJ2+dcmMF5aOBT7LQurWtiFgB/231cgVxzTobl3MG6+DvwSKdJeXrkREgm6bebjNzmgfSlEhzgWT6H1HI5cniwOb5Qj5V0lxIx4WyH0nTAiKU+zvKLqHjOihd7GIXe5tNxg1oRT58n0/++fdY9ubPhqNy/zsTdZuRLKvmrrTmZAj1A40hKiu/v9YJCuBAFhP2z1MAC16TRK67YgZFc7hmfJ24gT7iYIdm87XitY2spx4WJZ0WRoV8yuRTRmWgbCx+DYfK+HIiHRZkLn4N+x7fQgc0ipBV8yOAjWDz10Ga5qgBEH0EzQ7sNY/QGuOabARNJtCNxbCkNg2ivihBJcG20pbkVM6WAqQp9eUOMVXhcXs9jrduNCDABNnvcdnC4T3pgIrXhTpA3frq9wJs/TMwrj88q3/6+1w9PmrVs8+BSYHYY9MsnA4jd9cjqsJcTFB6mgbqlGFODsZhUy1+PToYWJj8eLH4omyFPPcYqxkkmkJqoOFyNF3dtC2uo7cey/hi2gUQeotNT0b7/pUPPuPv/L0vQ5tpVf/CCzIZcl0LxoIZlXJdufv6wuFD4dlv7BhfCeMtjK+U4WRf5uG2z+0ue6HsjBqP0OZkU+nf7hoMnzVVf3Df7sls3WlDk4ejUfqq6+poEpKvJtRsGXv8frgT0mSFQblSZKY5u1TEgC9o2hQxchbi07FN5unhit1n9vrHxyZOJqKmIeQiq1WFNEQy7wEtnG6sH1SaOLRAA7tE3CmPECtXRF1A25+Xj8JwoBVCZWPdeNT+PRy7Av5ab2N9nLaBLeQrHPRBOBfc9o57iIVL3I/J9EJ0dHrXYttnVGDZJ+YrYfrXfosHf/cV+jd/76f2eb3YxS7287X017/FO+VPIvWK6YHpIogvENi86Ns4AN9MmEjjAFc7JCVkLlx9Wjn83StGaCO9sRFMR6gpuqW0LuCbFjoSadZWEKSiVHFgP5kTK9uVH5yVsu0dyaZ5EN1mD0dpgc1L+0HZWDw4YyElRZI2QKgsmbKDu3cz81Vi2QnzjY1djc8S+6eVw7iwzQtPjtekXFnmjBZBsvbutP9pgI32LqSm86UKa72gNCv5BPONMJ60awutiqNoOpggpsXl+UZWDFSQJFDVFkTEZkkEtiAqNmKgIKJsc2Ep2eOqUO9dW1PjygCxmm01vSXmtel3zA82pNnSyvkmM18ndHNJMy92sX8cLO12fPwX/7T57WDuHLWNP9VNMtAip56jagcXNEP1rU9SVg1letMzxlNZaWSaHIW2hm+AUL15qc3vpsXqn2CtJHrebjp1Qhlt+xli4PdwqAyHBTlMyHEmqTKIIPV+X2SgkA6LAeKq6Gagjna9NVsczSehuv+O0WgwUCyftGnYtCU6wegc7Z40f15pE3dg/h2X1ABpUg/LXikPFuSUmzaQOjtIPO8vKbVmRDp2OQ0pVnPl0NqBDghFXPWfSbZ/xLKg+ca2WcfIcvLNbLLY1mQDgpQlCcdHycb0VuNkLV57bDPQyH6gWfra+1bLKXVJHCdDl+aSOR5Hyu2InBIydQByPSIYtTBYzWlNLzv2sqPF7MYkDjCsQnbB7JwrNa02WH+B7RKp32Krd3fkk63hkyX1FbdiaHIKpxD08UFRBD0YY0gHE51OxWDSOgiTSu+6upZE6CCE/s2yg7qD5aa27uTwWmwzgL8U0Cn7CV9N2J1/muyBKtEt8NWFK00IDdZP0ADdmaRFmlDycPRAE9tp/N+x9pFqW1aSKKd5YL4P8z2Yv2LjUIHmN+2fFHPA6+0D5vCWq5VTW11no19KXBNnxUrd9H/HaEB7n97YDNZG1lY6FO38PAAbLZPmVNfFhXUgdBWE7TNRN9qESK2Y0jZznI6JzSt7obIVZ0ZB3Qw/ifB0sYtd7Atq+Vd/iTkL+yeFNGcbUco+73/StjFy2RlQUK4G5PENdZsZhoTmxHhb2X2SKXv66vQUCZwl7bEhbL2hEu9oEp3FdeLpfi6YQ01XqERiRjvXGK+tQyTq2pPBarGlaU6MXeiyFQ8CKVmgUQWtwnJtAItUK3CGW2M9bZ9bZzhnWzd/OO3sOcXW96ahoiU1vYGzP9i9yc4AGu76/bBrlPZ3Y5e+kZiGzkH1DT2pGhj0OcfsRYZWW/YQo2Znm3kwRpOIUn1cLh9Bx0y5n6ibTNkm5r0Fw7QIZZNIS7KmyOLF1NbFPfcGHPU1nRe72MXeJkvX1xz+wjdJi3pn1ICN0IST6mO87ruWK0uO28ZDNcC76fQQ0ghC0lVeLyCL67s4WGQnsM5rxf2cCT+v9YYqrsfp4JEUz23VS4oVEFFi8U2wMJ2hkw4LMs3mz1QhJdKxIJvUN5YNCZ1XjKPBfbMLWcfK+rrpwMN6/LkGU3Rt/ngb3abl9W+OWVUg+VZQwRi6daPkfbFJq+Qd4hIaQV6zzcmbv95AiBrL64mYtIj7bC9KB6ySyYS0RozXYGs5kAbqxetWbCpl9Liq0p57xth1jdX2WiIEiyrykurHSUPt+kFLopwycky2pCca9MvqGujHIOqvYCpXzjSTYqRMHKBr0yYKZUmNMPBFtwsg9Babnk7kY2HRhJzE9BvCAqwIcTYAFZtRncWV/bWhqXVr41hlqwxHA0ACtJHFEto0RwJriu31/gJFqIdMPgly7I6vjTPlN/5EB7VgOkcJqoukxXawtAjpaJRK3Wr73Xq2c7lSGIGn4om/glhXs9au4K+L+PSBcjiNcKXMXz3xp375+1RNLCWhVc7z67RCpsW7sgdpbCYbs4Km17TSgmiObVBzzE71tN+bs6wb2hpNTdr1haKjEg4rknx3aLEusbqQW6xdbp3pycEzv89pkhZY6wbXKqLRKhkVfH399kVh2Yuv3DxH2S92sYu9HVYe30AWxlelbf2IlbnjoVoyPTqzZoD5noEEZZfYVUCVNFe2Lyonko36Dp5ADc4AzRYrWnez0mn9S/jJ3rVt/4zVwdofHwmcrbdVZHO+tWsNeNTB/F1sKIuxqTi+gTHiGj3SRKEBytaYsMMB8gtl+ywx3CnbF0qaujOcS1pl+H6FroeHd0b79WCsnsWS7XInZ1oSrVsacXIdvhXXsXAtjnz+u+hux//jXsZocLB5ga4RQbyeMpXM8TQyHAzg0cF8vzUD7PE5QdklUskt3C3b1MeXVxtFL3axi719JuPA7ZeGzvZU39Ckeg44Y/5s2UkruKHn+qHTU0WbNlqMKDWZh4pxX3SV/66aBW3sig7mnDVP20n7sVQNKCorskvEItdIjRhigvlqQJDr5VmsK9QArTDGbM79pOMY6mBLms7PpzWaxeObS228ec51gOTVeugORTyx+yDeHKAvzBlAN8pmO3MK4GtJ5qg91oVAcxXTKm3Ahjf+0+Lx8kRbVBDvSR1wBEE76B+1xmK/C0mKN5vj4OeXTAKjhByp9Fim2aU0fEIkQmoKXSHPKTRj9ZbAstjIdikJTpl0krbBNBWh5t607+NgamxepS90UHvPGmgZo32ByQUrrEKdMzJUDu8mHv3mr1N+/+/xRbULIPSWW5oK33v+kOHgDsoT6hCR1EHAkWBRH4maoR7tQ58WOL5v7jIdbTwrAJGyhdNGG4I63MHDb1fSR8qyFZ5+c2R+YM8tG2U7+5daoYTjqnhHWFCfHW5zpYLR8l/HF9h/v5heRDCbluva6I3ja3e8s13H/mn1TVzSWDQWgPzLfTswP8y81i3lD274lf/+lo/+/A1/+Ogdvv7gObd3W/QwhM5oCzBlC/PNagvaAJuXQtkK871K3RVkzg1wW7NvNNb6Oig13BnQhs+plr0r2S9COomPNJjnl6NTOANJVwdzPMgF6Bed8BBxU7FxNM3OBtooOSejri4w3BrAVnZQriuMShoLepe5+ljYfXLg+MGesrVu/HQ/UbdWCFzsYhd7O0xz8k5ZQWpuWgZla7+vo1DcN89XwrzP1rEbQGXDcDCR4e2LyrIX6lZYRgMg6hgJnKMc7UWxUTLp69QrK0AEmu+0Fb72pADQbaRKret7RS8YEqRJvVNt17B5qQwHe2yeoCyY/JFibFkBlcQyZiTb6FjKxpStL31UqigPvlMYbwv5bmG5HpingcNi+gW1rmYcoiNbO6i/ZgiZUDRIUVv3vu1d4raGWPu/IyE926IW8dLvUx2kjw/Xfi+6SCjMNz3xTsdE3VZ7X5KSkvL89Z7p2Y6rVwb6LbvU1kPP9xwkm+xeaYJhlDay165tjKQ9XZikF7vYW2haKtcfL64D5GyYFICMJ/uR9zqzPMcYskhbz96mDgQYMWaIN1sDvDnb1FhpIMxaIqGdl3Sfafpy2n1l7sdNamPIa1JOMDPPwAnrGluHmgopoeNgS1jW4JeYv7OfdfZmMFuDRdPGfhNtSUDE0dhato5jdQvF9Y/iooP1oqHN6mC/VEj4a2Zlv53IuXLKI/OL7eoG0aQsElb7tUUEq0mMfKIJZutg8aVsjCFsPZMA8PApDxtLrpyPhsfm5TxFHSQ2ajV43HALbag6eD2yARksf8gnQWNjnLOE62garhRhOoymiTcn0jGRD0KOxrdiY9PJj73VBmBZnF7FywC2gjk060q7sDOV0wJMiWE/8/qfPfB3fvkxv/YfXAChi/2CmkwLr55cs7+TDiJgCXpsmsoOEAGGHC8d8M1HGF+uBEULlFEbxVIWT+YzTBvl9ksmSlY3ML9T0LEih8xwK4yvlOVKKN7Zbbo2nhRH0UGVM2ccwmvBuIEVcj+B7N25VQOwlk0Hbqabvp1LFMreu7DV6PXDQZhOI+8+esXjP/0RP/7Rl5mv4dWn97i/OzG/2pBvU0OaoSP0KdbbYyBLmt2DxNjbSZouRAQCK4as+LB/+D2PTnUANwHw+HY06Ah26zDnzlIKq2MH5/IE86rDEiMWphlk5zbe2ntcRzg9dJBQlOF6Znk1svsk8+DbMyThdD9x974VAKd3lLLd8eDRP8PVf/s3/v9+PC92sYv9ApnMBXIwQRLTtVD2xoDPykQAACAASURBVDhMc2bZWWIvVX08yP2KQtlmUsmt3bpcdWA/H4R8sOeWvZKnDhyErl0OceNVZ7l1Q/1PY5zE6IBY9zZPFVmUOg5nSXcTxPSRteFgya2Ny6qvd++jWuYbE1VH6tVCGqq99qiUvbLcCHfvZd7/X5/YKvbNyHL9kPrDPR8PlS8/esGLF1dIrlCFejfY8b1BESO6YNdRR0GqgWgpRiKSjU+XbZyXdu0Lv2epaCchrbqvKr1Zkw89uQbz8ym2fY5exLSRYbF5vU2lLJnl9cjw3N7Lw+PsnVqLnfM9e2/sWGIsoTkZM3ffi5jlyt7jS+PgYhf74pqMGz7+d/+MLxPQBmgPJwPibfRJmhgy9AZojIbF9kkT1LdEVzNw7ABSdSZNHbtY8OjFuPnOALXNCbYtizhgVAMIstGsYNqEnzef2bdb5Ul9gYGxa4A2+iVFTfNTxC5WvKk8Dsi8WONkyD7OJsQYmVR/7Bib1c6vp21Zrut7dH6/jUnVfXcDP3zhY0hthBCy+IREWjzGFEwT1hvJm6GQk21hnuvW75fft8VH81zvx9g4tI1hmsXBE7ETlwBhjM1U82q8LuNyGjaWnKYQprbrLFlJODsJnzBR6a/pbKB8sK2emq05XrZd4kJdvxX/zJSr2thBWlLfknYShoOYvuzcgbQYNVuLea91DtvmMOz3Ndn9lRPWsF+fR7FrSMcED5WUC0sstfiC2gUQestNlorc5TMkOhLftW5CJJerJScNpEinzjoBo96prp5YtCWZp0fSEkIAmRP5KK3z276Eq2LgLLF94/vUKJXrpvIK5FgXDlE8lI22QFR2JogcekfrLkIASvPrgdf7Ld/44Cnf+9UvNdR3zC4/r4ZMD3de5Ay+PafSNncFst8Klio9iMU8qgQCbcEjfhZ0JU1WZK23Kayvc01/Xa9MXtNv6wZiJeP6vgQrDI35aQfTFvtdcWFVHYBdJeXK+GTg6mNl83Lm7sM9tx8mju+6psgHJ169lzm9M/Crv/er6Pd+SD0e/78+jhe72MV+gS2dFup+pI7ZGED3hLLz8bBraWzEPNnYmHXwtFGs16MDNfwb5+B22a/8OpYErkUq11pyuvL50IGeWCNvSahtG0t1NfLqjJizrWbq9PdjdVCrJ4ENhHJdBc2gOzENuSLIWClbZb6xUSi5O8JpgnEgzcrmWeL47obr9ydUTZcOjRW+sWVG+mwC/bya5pEzchFjLYUQ6bpZoFF7CE3Hrt2f1WOAFn8aE8g7zMVHjPsmmYidpsNQlkS6y8ag2kMNoe5s51S2fu/9/S67YGr189BsLCRZ4Nmvb7m//bNsfud3fyqf0Ytd7GI/Gxu+8XWmrz1GFhuLUgEdkzFHHAiI73jdQN4Iw6FvSqyrcZ+mRwndfzeBYPdlsY14pf3Wx4GgTgFad1A8jqfrmmDFGlI6ON5G0hb1deyrkTS/lFRWRYcjJX27ma5qJIEho7U6Uwjz51XdTytyWs16JWfUbmxy4Ay8j8NG3Ennv6ve7F03RJp+noNKjS1a41xXuX6/FHbDYiLLrosnLajGuJi/dvzYwaCyMUZX1CY1d1JBXb9vb/4JCY+2rt6vL2F1o3gzaFXTWI1kkxNRnxpI5MtttI8fpyxd+mLrAdXZvKqCTNLkMD53T2q/1jT1TW6wOpcAi2KkzGvWyFGaoHd85irUkkm59MVCX1C7AEJvsckwoFnId4nlWtuHfbgV8pGePK46r1K74wH7UgTlPpg/6QS4+BsJtAgpQQFOjwtNXf42+TYuYTj2YFCH1Uxm+5k2IKRR9jh3iOHhNEtP9APA905wjD2BkjaQZ2F3h4u8deGydo8WYXg+cCtXPHtwxaNfecqzZzdwN/DB/hXfvXnEcjuQT7B9bjoSOmIX6/cnNHeWPW0OV6Y+u2rB0042BKjZGaUynCii3tmttqL5ZBobMWYRGwEk5mfdWSU/ftyLZe/v11qYtVjy3zQ1fJVkcqR73sL00D4fdVe5fnjgcLfhnb8P9787oUl48s3M4WsL44MTqPBP/9L32OeZH9w+5JPvfJUPfudI/cEPf3of3otd7GJ/7CZLRZOw7DPLXpivoW4t4S/b6PI6O8QTv/BtoYUGkYRLT3jpAE1o56jncnVjrMhI3kLkcd2cUGhdwegUJx/drYNYJ5Z0plFnug+0OJcmA4RSUZZNb3KsRxFS+MrBsZsq6JSQXUG3ygzoQ88IvSmSpsL2ORwOmavBNieouqi0g0BtRW+7mLjh9A08g7GXonGgzv5sjNCWVK/idCSswrmtxiRK6r9syW3TkaA1KBBIWVnusr0fizgjbHWuvvzB9FmVUi2pFrWYkz3u1Gwj1WmGV/fh7ksj3/hfrqiHQ7tvF7vYxX5xLF1fM/3SY5785q4zCZ0l0sZ8okh2sMP8YAcKGpC/0g5reTqR7zsgIKClM1GiyVkDDBr1TKslQKEWI940feMPNN/bxqDCt64BmLL6NytfGgU/4TcF3QxIiQ6siUenpXrtpMawTdhIWRUfr7IY10an3gQo/P40/Z+Kjc2FWw7/7xqf7dxXTfRogJ9pKvnFbPNCEiXLhphI6Bf7xu1LoEOFBKUIy2zXGZu93mRnNWbQ2NlBOrg2kwfw9tmIWnM1vmz3X1bjdno+djgoZEXjJiUog7aaS/YFXWJcxGNnE4Z+Myh2UKfVTivQUlo8VL+3dk/jXGIc7833URTqIqQkNG2RL6hdAKG32J7/9p/h2Z+Qs/WGmg0w2bzEkvPB2SErxwM0gebhtiv2v5l4NsEwB1nSSUjZqOPDna+29S/bfE2j2I0FpyE6s2bQRgVMsY1FaIrwdTDqYKjAS7HXrOsRLP/yW3Hh1H9/nelB78zmSRutFaKDas7k41f37Du+2FaY++OR7XZhyZ2OKtUS33QC3Vm2Hvek3FRzYEB+nTqbJ5vIdT6CHA2MOz3qiX/NNh9bt0rdV3QQ0mAi4DL7tbpDzBLO1449vqZRUutAu+fFA9Bw24Nt3dKLnsUeE0EmH4XT+wv5/sSyJL703225+d4tp3e2/OBf2DB9eUIOmfLRnrqvfPflI371wRO+cfOU733jq7x/tfujfVgvdrGL/dyt3Nty+GDH4Z3EcgXlShuter3yNtg1+Wj+2mj4naEjsyB3fay2XPXksFxV0pLaJpKyVfKdeAcXNEkTWg4Lmn8dLSblk40slI39bNmntqK+JdvBzvQE9PqjSp4qZZNY9sme21g4vnQhQHcB8Q1hw/OBcp2s4XBVuXrvlnrvmlQq1Eq+ndm8VNIhsUmLJZElwWSjxrYIwJoD7R5qP7dIgOsG5DUtgY6lANEAOGMHJWfthMinU9hb00edkr8q0pYdja0qZ8lw77oDyDG3RRPLtbYRhzZyHkVSttHz2K4pBfKdOHAEy6NoP2PC43/q1xn+9ncoL1/+VD+zF7vYxf7odvgL3+T0INu4TpM4kDYS3LZIsfp9iCUv2vJ2OP9d+3mwS2IDrgLbLt5sG3z9ZILlXiE2dq0ZQpKkjQqFSVXEGxPr7WTtfNzNxZhYY9AvDjzF2JmDHTaK7P5sKyx1gCxkEaS4ErUqMi0+xaC2hQxMcX8ztHGrsvO6ZKGtcA95hxhTCpAr2P1lG3Gpgy2NAQVtxXwDJlbs3LhfOEsniTIOBTYVPSXILtFx02sAwCQ+doU0KCUpiw7oCOUkpJ012BuIpDTZirKzkerqS34kA5MzZVegUMSGwWvSiM3qjJ3hVfJaRTt7Z+wvqPg1ZdM43ewWpuPQWLlRD1rdqCRnucXkX9Nlkj5GFoyns2mTAlUV/LNgo93qjCwbz4vrkUXQKVOSIstqBOULaBdA6C22ZWeOqHVUlw6SNMexohtaF7hrDRgbxY4RDn04utOOBFUBd3AyAEhbVxgARSSpi6+rDeuIrKCogUH1/HetCz2aUNmacg+Aypm2Qox3NZQ8d3AEBR1Waw/Xx6nCaRq43p9Im0KdEj+6e2Bq9Z6E26wxhMBSWkCroL5ak+KXJ97xduAqtJGCQrlc+eOq3eeYnQ0wad25SLOp/4vrKp0F5fXfDi7lI20DWh39+ASKr7TtAkH9xwu2hwW2FX64Z/+Hwr1vv+D5b9zj1dcT0wezOeEVkv/ybsf30iPub4/MXzvx9M++x6PNSP3b/88/xCfzYhe72C+inR5vuXs/cXyn+xupprXW/TUE8zAfnXo9YCMGDtIHk4eKbRMZLQHLR9g+yZ3pOOp51y3yqVUnr3WTHRhJJTafWAFQs8CobYvYekOLjapZsbL/dLYCZy8sPgbFKlFcd5MRRbKih8TuU6E+y5QdTI8KX334gttf+zLXfwDp1QESHB8LdV/4/utHpE+2q00l0bSQdl1NPw9W99i6sOLMI9uGhmk0OGi0Hl8IvQ5rqJivzw70p1lbg8Q6vj2WRhKeZmnvU4vJi7DcjuSDbQprv1vR7mWhLS6I+GNNCN/aJqvjKTBY9i1FyN/6A8rt3R/l43mxi13sp2Ei1H/uT1G2CR0SdRCm+6kB7HWQDiZL+DEai0UFW8G99tcrCzCojpwB+00+wZ8TLFHxf6dVs0Fzl5o4iz0EqION9EYdAmdjWe011QF0tNU6MUJmDFFlGfoGyLrKv/PJDlw2tlCgbhKbCvm4IJOJrcpcaNvHYiQqJep2aFu6yrQ6Ke0xqm22WmDe91opYqGKjUHh41MBQEQjPjYvS8U2Jsd9CoCowvPjnt2wkFMl7xdKHX1SQinJ4kDz22Nl2BSGsbDkwlwEFdeA8+Kmvf+NUGBgULmpLvqk6CmjkkjOopKT5xOuCxQjYW3awe93LNhZkw9k6B8gEaVKahq31esiwe9T6M+miOP989DGyLWnGbIA2Yk9KzaxCY7TRtziOyGlf4aqM78QYKg8+Os7PvwfPopy+AtpF0DoLbY6WsKNJ2qho4N3Hc+oi+GE4nFOyQ8EGFbsnHXHclmNLa0omVJANwFqOCB07SemtK1mZ4FkPQYGrXPQKJIBYoWz8P/rCtllFTRiPKwBJg6GNVafrJJehfk0ULczkhSGymeHa6P++7aYWGsZK+FxZ9IE5eJcak+aazg87c56iU04MZ4QQtQRM4qzg5ZYCy8dtQ4kO85/NecawUWLv8fre+TOWFLD2a0DUeyJOlbys4F7fyi8+7cP1O3Aq68n7r6+kK4W6utxVQgKh9stTwWKCo/eec107zG6Xw3kXuxiF/vC2elB5vRImB+YuGOjYM+rxD6AG+m+P1ioTdNnS+uEsnpempXNSx9F8jiUfBw2EkbJ5sOagPLi//cYdabFEPo3SagpxsSkZXyxnSYtynAoLNdDZ9eEP18l0PFvzSAOcmyfGnAy3ReWG+HL1y/41rtfY/fJ1gAhEeYbYKw8vb1i89wKGRJM9xXdGmACK5FNVnFu1VS0RFb82qxJEtp/63XNbdTYO+usz9/rElbxcz36EAVUS7rj+bMl1DGO1jrzZwCbnI1YtN+rtGZI+4xU6+SS1IC/L71H+v6PqMfVAS92sYv98ZskTu+MbVuUbeJdMRJXsgTNIsdegQ1txOZzx++1w7o+OFunrt0vsfZDFVgiNqhvBo5xMQfFxX2pP6fVEWKxoOXib55TXFIASMl+oZ7Hq4/Otqf49WkSqmsBpSmDKrn6yYdPTH4RycD9uh1AjU1i4P6bx/VzCJ8cTRFWtZE4O2jdEAkfL3G9vS6pzhQNsAWBacnkVFEVcq7UsXoD28e8V4CQDKYfKqLkrMxZTVNoNPDlLLYo1lTw6QY2lbQpSLJRYlXQmtBFen2TfCQscw4IeS0pXjvVzXmzQrwGE7G4DA4G0sqv/p6+CQqu7mWrI/0edd3A87pqPVoWSynOvhd4U8blTvKmcnoonL7+DvkP/pAvql0AobfYliuh7hfGl4Mxheae2IeKfZodTHCwaOA88Wz6AItR2DWZ0GRxtHp8LbB6fBNxm0E2RltfrxGEntjbf1bPC4TWwSorGAwsSZN0fYdkneH2RV8BRjEyFV/afHKG0LJylL7Ssg6w7I19hAr19cgrp5amXeHFYdfPUfmck0kFSvJkX2gMH1lchC1W8K7ub4inxT2oPo4mc7+e8bW4hoM/N1Tyq917mfyU1Mb/1gKs8X7Fe7UODIiBQGf0UBUoMD4b+PrvnNh88hodM9/+7YcsH57Im0I55eZE80kYXyemWbirwpgL79+8Rv/vPfq73/ojfFovdrGL/bzt+DAxPVTm+wWZcwO3k6/BDVtv60gTTtd31srGehBtswc03yYFNs+V+q5tG9NkmnYN6IkGhCPcxjYNJ6qU0hkzmtVjlW+PcW26dTEQYFCelDQV2Oc+tgANAOoAvT9/ULIoOgtXnxVLoEkc3xW+vn/K7z4WlpuREStA5htFNpXb2x33npkWUNn4pq3UW5KtKBKaFs+6i1mzJ70BABUDpTJq74FwlkjX0P6LpLZ2tmyMfETBECKk0QG3WGgNo9Bx0NhsYzWPn0fQ43t3NICkKETappfU76FMyTUgKnVXefVb73Hv+Su4LB+42MV+fiZC2u9YdqmxdWJsJsZQ143S8Pup0Bk9pfuy5kOhNSHPmrl0H5sW22hl4JKeAzkSjU9tjMYQxjdQSTtg5Ii2uCB00xZaN4sDzBFsk5aIMUlW/lCzwLBmYK6E+tXAlzyrTVtsLLYNh2xTCcXEpBGBpVrQi/s5Zsre/iOLxUjd9ePGPQSaWP9ZXUTUCh4ncF+dsIZvbCle+fA6WjNEUp8SYFCWmpgWO8g4FkpJ1DmhYoiIuCCzJB+JCuDFdXmMSRqAYZz86txHA/yTM4tSUrSIvRdF0JOYLEjy2mNQ21gpXjslLAbFZ2wBdT1WzdrKrjgnSbTPkP3c3ttOKfInRMxrnxn7c9aUcVDoTEza58uk2uddtOcXUlbC6c7Q1a2y3c7c/Xrmh+OOr/81vrB2AYTeYisbjN2xGRjupAkND3chvEwXi1vpQ5QtZxT+AHhCo0GziWWGE4uRqHw0zZqgyodDq+6ghzsINosOUHwlsXWAV0FIlHQUB4d8zG105ftsiXHreip9e4v2ABTA0rRzJ7lyCEp/3Pa5oDmxDCYGV1fK+MfDxnyOH3fZ+/kG0yqcy2ICanVrYtDjSyHPBroZmAVp7KCXqds7sj5oy9LTJMhB2lY3E5k2rzesVjG2LQ0JQ7dXXeP5mhYM87T6MLzhGDUp+cFMOWTGT0a+8VcOpNPCs3/iHZ78llC+diQlpc4JTpntp9nYRAHAVdBj5uXLPVWFD5f6Jmv4Yhe72BfJRFiuYLmqsK1IHRiOkE50kdEVyFwDaK/KeGeJvLo2Qds4Bs03B+1697xy9yXxrYzKcLdaXbsuIqogizN8sC6tLJaIlY2v2V20s1zER6zcL6e5i0inRSm7gbpNlNHHs2oX6ZQCklf+8bpQVcgHYft0NrHtvOPwQWaumfm+Mt9kdpuR5d6G5UEhZ6U+33DzcWF8XZivM8fHGc3aRr/y1ONiz3RpbJ2ykQaC2bgbMCnM9txlJ411uj5GL5pozNg3RyeANoIdI9+BElks7SyfOjj4M50fpI3UJX+JBbL6auWtd3GLxbgsFjPz1rQrppsdMuTPndPFLnaxPz4bvvF1nvy5D5lvDOhfL3gB8+exEOBzJr3INk1PbYz5AJyDCSq+abcB1QEer0a31lvJxMHt7I3rGA+28S47mbYePnXWfD9n87P4OTRGaQOptb3WOStT+tju6udS1UDxAL2cFVVHYbnKFkMWazSkpdqGsewjZCkx3cuUrZ1nntRWt6/u6VqrqTXevaFRlIZs6eq9UVHYgK60fMRHxUim5YN67XBdydczYy6clkytic2wwA4mGVhm2OysKAyWzTh2CldK2Nh0MjkLI4L1Jvf6b5mEOmUYC+NoXYUlK0uFOlvhtb7vy24l8xHd7WSxzXTpKrozPSNJDiJWZdwVZjJaTLQ7JUW1Ipp90kJ7TFzVcm2b8tb7M3OAjtJq0LR4PuAjYuJ5hfp8WoCmZUsjF9QRuJm5f3Vk9z/e473//G/8hC/NF8cugNBbbMMR6pSpjxbSx6Mhx0AOsEe6HlANJ78CVdbAjiXvKwfvXcGGYksfx1K60y1bOvV/7q975pCDXbNSrE6lCyXrsBrHAj6nHq+cO5eVBVsmlc//nGogVqPAbyrjdqEsmXLK1JVzqdvVdSfsm+NdiVC0D2FsUT/3jQcx3zgGK0Q6rsedfmwGy5M0dk/NwJbeXXaWUN30jWy6dAe93hS3vi/1jW6NnRSUKSG3BhZO90de/tKeuy/D/N6CLIm6CHLMbF4k8kGacGxsPgOoc2bIPqN2sYtd7AtpMm7Ijx/ZWvnYlFgCVDkH/kNborEtR+sADnMk3T4yFSPE2fwaWBwpwc4pNGbkemMJdFBjODnzSKT7PI81y1YY73QlcNpHaqXac/PUu9B1k1h2qY1HBHO1AeXQYtPmemI+jGwOlinnVyc2zwd2nyV+5/u/Sb4T5ivh8MuPePW1AVkq9cmG/UeZ8eWJ4W5BirJ9nji9kyzn9Q58G6ULtk0wXL1REl14u0dq2kCtUy89Gff/xr1s/9cYneuv0fSSoriIOLSsfr86J0n9sXWjNO25snrcG2ysAJlia6aOxppKzuwajmormy92sYv98ZoI+eFDePyQ6cOHBiy7UG77/q7zaD3f0Aif95FtGmAl3h/s/lwwH5BpefJ6TKoBRCGojxXp7biVpg269mMNAEo91zUZCQdnspJn0GOPJRBAk11cSXK2WKaxLqWl2u0XNWtbGZ+KorEIYSNoskUGQatJVdFsujl1TATbpDE5V6O7QF/5HoxPgcE3YZadUD13Nz/c17g35ow3aBswtqoB6lbRTWUcC7txIfsY15grQ+4nsdksDD5OttTE9XZiLoml5M/Jta5lOfpF4AUGcEro3qYGxly4Y2vaS/tKPvU3ozUUPKZFE0LTCoQZFdkVtvuZUoSyONtKlGEoLIBWE5IWEVKqaLYb3BhiruEan7dUfGpGQIrYe+efjcg37NwMWExvjA+yyncS2uqhNFY2/8U7PP5bH7HUL/Y49KWKe0tNtlvGl4rcZjZfukM/G6gYWl3X86vDCqVnFRCgawwItmrdE9bYQHImALpOZldJp3onMfSGGhhC/xunOKZJ+4sqnY4YyfvaIa2ZQPFlXgUviODRuyBNCC2S5tpZT4h1Mq92E69vd7DYKuMGnI3ueBRwbaUWPNTZOF60hOr+WsxzTQ+NGxJOSsXYW7GmvjGgBijl3FmlBWZnHlFWiby/T9GNj/vcTjGrU0rt95oUuRtsPG2C118deP5bFW5mUlbqy5F0Sgx3wuZZ7+DXTVxbP/hmKNQxISnDF9whXuynbyLy7wN/Efvkfwv4t4APgb8MPAb+JvBvqur0DzzIxX6mJjmh965NJNETs2Cd2JixdfDWGj4NgBlXQEwBVH2MzB5XNvK5/4MnZdG1Td13rwGS9npxns2PShPUZBWn7Jc4Q7I2fQv1JD1EU9cM0/DPwTTSBNf7E8+e7axhoJDujmxejOyejnz0owfcTLYk4faDgbsvCTIL46vE1Y+V4XYh302gsH0xko+dbi+jdOamx7L1yG+7jkiQI2nW83sEHYwRuq9XkQbmnWlrsHoNfz3rvjtQl7W9jrHBDOQjW3f2rKDT/t5IoY24tZMSabE7bQw1qlNmfF0soF3sYj/BLnHiZ2MyDMh+j2w3lHtXzPeGlvenAFbOwKD+XQ/x6Pj52XEDuIkRLwfuW2ENprOptPGwz72GI/xOOreRK3986IzGGKyNdHUkvJ1zjG3Rc+R8su3IzbcmaSBU/UlSl81/rn4WzfBhdexiDretRveYaDZYozULOqTmJ2N0t/nM9T1YjS414C3Gl2IMzsfCpEjr3EcdsQaYmgaUj3CRlZQqm1zIolQVhlSpKk3jZ8yF/bhQVDhOIzlVSrWbVkoUb6v3q9ABobh0D0DplKg1MWZ7zdM8koZKyXpWl1DlTMvu7HhxH7KShso4LiT/kFbfJDYMigjUxhBSimL/FqyRz/p98cOvtlazpW/fXp2L+vuiA2e6RNFYJ9hcAYAmyLly73d/wPLDH/2ED9YXyy6A0NtoKVP/qd9g/7Ry79uZ05dsfWw++AauSLxlNX4UAG7xdbee5JMg+2KQOvgY1D6SdkPP2/xuBl0xglon1L9IrZMgDt6Mfg6DUQS3R9OqKTtleght9aSfZ+sIrLR4AnzJJ/GxNjHmz2zPL8GwmWGYbBa2hDK8wnwtlG2FbWG3nxiHQi2CnBLDvYllzs0hBhDWgkybQ7Z/h5B2U8n3LS7LjZ1zEjvHfLLjaVBUPSjWjZrgNJ2JNRwEXekwpdmU+NVFo8tViO71ALkO5tGVNkCnV1dShfF5QqoJnx7fxWagX49UUXYfD4y3tG1AwXiqG+s8IJCuZ65uTpzmgc3jLTcfvMfy0cc/rU/xxd4CE5GvAP8e8E1VPYjIfwX8NvCvAv+Jqv5lEfnPgH8b+E9/jqf6j7fljF7vTBvOt3ok141Ls3ZAnZ44qXdPITY7amO0DEfxETITzc9HaeDGciU+OosjT3YKou5HzxJpWmIdjJY2HuY6QiFSCZ0JKlVJJ/XjmL8sWRoghKziExFP4rrgvetbXt4+YnxlRYi+fEVW5fpq4Oq7e9LkItM7OHxlYXyW2X8K9743MTy/Q04zMheufjzw+itbpvvqYJvdi9hwY9s448b22Gmb08KXW0LdutpRCKz8fEuqEyhdW8Mu7o332quvNAnDkXNNJdeLCnZrdSZsjm2hC03Top8/Ddxqo2gCuq1stzPTaUReD2yeHSDWMl/sYiu7xImfnaUH9+Gdh/D67my0Nv6WbDlk2/C7Lv6ls1vWtgacxRuroVeGGhun+ab182p0br05ibT16bmumtXqQFP4c9UVeNIBmrVytGYHklwntbhPr65/k4qSZqWO2bRrzs/MjrEC421cTChjB73WRRq8UgAAIABJREFU+mkBCllxg1+/HyfAfFZxLF5mBTIQ15fP8YvQTqojkBTdKOKbPiP2xpivgukgjUrdK7othl4kpZTENi8Mo7251YEVgJyUnCr3t8cOAtXEUhLzkpmmAV18fKSKA1JWOwRTFxycypBvhWVOiCjv7O54fdpwYrRYM3vc9fcxVt0rnT281qUjKTlXxlwag6kUO8fNYGK36tdiH4FElYpWoWRtTaU1c7Y1gAYog9dWwWqNpnu8Z9nqMwMjHYxrGls0tpY4ENXWnn3B7QIIvYUmSTi8t6WORtN+9clVS5TDQTeBZzVtn6Aa2g/9rwr50H8WgaLs7FuTVl/kSOLbiKl6ct4BfQNHHEwK8U9z3rbOsTlpXT2mijNhtAFUZatnwEeseG+Udk9OmwYD7gRiVjgQeIXpAZR7hf29E199+Jxvf/we+nRrzJivLCxT5nyFr1/7vJqJ9WsLxfk2trV0uqh1WewmqTOijPrpnZpF2306vaOMryxZTxPM95x1tIHhYGNuJBu9qBsHg0qcmzltrXGvesI+vE4t2K2n7qQI6aTkg4mA1myFYKMMZxPfXq4UBmV4mdGvHfjae894b/+a3/srf4J3n9xexgEu9g+yAdiLyAxcAR8B/yLwb/jv/0vgP+SS6P98balsnwp1HKlXtY2IaQgMeDIbTJa6ARWlbmmAT2g95JPpPlQX7Bxfu68bhLIz/5vdz9fck2PB/FXNIBsbC8u++MCYmL5uOBvIM18LZTRnNt4pm9fVEnWPSWUrvp7dApOJbUaQ6slgACJ1o6Z7kyq7TxPXnxTSVKjPXyCniXHI3P/uluOjxN37yvThzJc+fMbL737Ave8v7H//I/TugOaEHEa2S+HqK++z7IX5UaEUYXwljUkrpXey18sXbGRLbORNfEnBahyO5EyedhwrttYi0nVwRqfrUzRmkG9Ai6Q+zR7L23H885AwtpFKizFpsWQaPH67Pl6Nx6u0rna6Xrh/deTHt1t2nyWGJ6/R6ULuuNg/0C5x4qdswy99Dd2MDTiR2RLi4nm4VEykeOm5cmejayukGyMmgOsG1ODgDVS0j+E6WCN0X4zaCK+BJboq1gV1JmcbKVvXDcmYRylhzBvoWnFexCvqwtB+3t5QDQZrGXucyKfa1547sJO0CyeDxwO1xrJNUBg7JTRn3lzYUofkW5jVR6hXDJX149Pqfq2WvchiEwtgx0yza+QVoaAGCsU4nW8gBq83Ns7YHyt5V9juJk7HDVphWTK7vHAzntj6m/Ns2pNEGXPhdtow18xSE6Umbo8b5jlTlkw9ZlhS23Ysb4BBUb8gNK05PWZe3u342r3nPL6+4ziNlGW7Ypdas9tYVNZY5uR6tK43EnVHSsroG89KTaTkjKfBQKIkSlHh7rRhqQZ+aRLmXaXMieFWrJFRPYcRbTqsy5Uy30hnKAVTyN+vBnZGrUhvPJWdx74TvPN7yqP/6HssL17+w30hf8HtAgi9pTbdJJa9sFyZSHEIj5052qzUZA4mVsqvx4wi+Vyr7jfKOCv0NR5fu8Oj9u9XS7yH7kApbxy3dBS9o8/9NUXExMVWoBNyTnsPWmIwippGwvq8pf/RZLO2sq1sR5ulrZ/sGF/bF/9md+Jwu6F6Mp6PtPG3CAaNQrm6r8GIivvdAiKr64/L+wlOSF2np7o+ULsfKdDp8/cnXkd85Ax68Kmj9q7NUdo5iHc/+ny2C7ZWgbEDVWAFRdlZUIp7+M6DW67HiY9v7/PutxbGj19Q7wI9vNjFzFT1hyLyHwPfAw7AX8Wo/89VY6cRPwC+8nM6xYsBlEK6O3Lv+xXRxPFxMrZQNYA5FTkTtwTMx4e/x7WBVLpIaeoCz3l6g+Wp/ViNBRTmsaKqPSet/F1yoLpsLPEOPSBxUch8Mh+l2UCsOpiIdLBryti72F37zrbHSHUA5V7h09sbdk+U3ZOJdDdRJVkMKpXNq8rxYWLZKzeP7tgNC4db2D6f0eMRXRaEAZWCHE7snywc3h2ZH/SCoxcJfayh+W4fz12PA9g4sjF/EkJJMBw9TuQO5jTQZ/b7HVtqIjaLxcuIC8FuXQNkDKvCZfJxv3WsjzgasXbA1wRrizGxbefpy2vkyYbdZ4rMC/Vzu6AvdrFLnPiZWalIsTXjWitynJHFwWVnAclC18JZfT3blkhvaK7HWdePXeeKXWNMz/N41vmqNtajDF0QuY0M+9hrzQ7CBPNIorg3/15zF+lvm4alA12i2pvEvtBAFPJRSXMlWKZ2DULFZCHWMgzx77aWPhglqyUIYK9RgPHORqJja1nTswlA5A2LPH1df7WGsr8/aRbqIdvSGTUATxbz+Qxqo8j7hXG3sNvOXG0nXgLznFFn/oypsM0LCeWJXhsglArZUSj1MbJ5GljmjM4JOZlQsw7qY2jSdYxiO5iDPA3Im4XjYQPA490tTzZXHFJnV0XtY9vEpL9vzhZCaY2IGHcDXKcUkqiNjaVq5156QpKSFVBpU4xgkFPbLt22zc39fSg+Nhb9rmhMreu1Rmzw8fnQKywbGqOuPH/x+Tf2C2oXQOhtNEnMN8LxsbBcW4KcFnciK/AiWC1zUkNTTxYc2hjZyiGuwYcAYJo+TqCp3pUkGsoBcAQyHuZfpOpfqgBu1FH5sy6l9C/om6J2FpQsYV0XFcHUkSpsn9NGzNZFQFP3zyCOQt/OG65+kNABju9VHu4OPMk3xk7aKem5U0IDKNn4qNoSugv9evHuqXEie/cX7zI0AEz6KBru46l2fmVnSXs+rre/0Ga6TZfJ0bHq2xlOXtRsfcRr7O9Z23AjUCdpDj26xz0ICcuVMviMSNkrdV9bZ2K5Kfzaw8/40e0Dvv/t9/jNv/EdysuXl+7vxT5nIvII+NeBXwaeA/818K/8Izz/LwF/CWDH1c/iFC8GaKkwzdz84Egdd2hOnB75JsQK1efv11o9DUx2AKhspCXizd/HY11rJnmCL9pyya5RQT9+T7xt5Kx3ot1/Rgzx+JS84yyLWic5YsFg4qnRjW0dzRKjEL4haxMUfSXfn3j24povPa0MTw7I3RGSQM6gyvh6QTRT95X3770GYHyt5NdT18jRCrXAPLN5NrF9PjDfNxCpFzUO1ngBkIr2ZoPY5so370kqQFFkMPaQSi+aotjS5NvXUnKGlpKKCaSKgzz4hrcaybx4ki/SextejNg4YLxBPQ5H3IgVxyqQTtKWQNQlMb0e2X+W2H9WYLnoB13sJ9slTvyMbFkgJ0QTWhWZZtNWK95MnZXsQsZtFCuK4+q1A9LYiYrnt7VjQjULMgSjv/vqz1nUCepgUABD0d1dNUdtzXf4rQBuLKdvvjPFc7Tl3ZEnhw+FOCfpgBDatlMmpY3sSuq+Dez5Gvo361FYf/06uKhxBfHmx3A0IKttLVvHvzcAoWBShh7om4sBQpNPZshIn7gQaewnuwfKuFu42k1cbyf248y0ZERgmqBiIsiDlMYSAovFm1xI/mapCrUmtAjEhq5BkW0hjTaOpYsVfJIUVUGnBEuCSbwWE8ph4FhGPti94moz88zH1VrDQqwmS8nubzTZ8QkRE4u2fCEAq5yUWg0MymKjboNUyip5SPHYoVA32eKQA4IxwSJFO7tqtM8FS88L7PO9QjHjM+uAUAxB1BG2z2Hz8u2airgAQm+jJWG+Fqb7StkraYrOZMxWGoiyfSpsXyh3HwinR8r0jjvJSRgO4hoPDmZEEigwRHEQo092aMBAiGCc1OhyhlMM4CfBfM+32DgdMWVB1eBZKXYccfX6ujVgw/R37LyW61hxrI0NZCdgAEryAFMHO+dwyGuEuI42GjUdBm43W3M6I8z3lfrexC4v7K9OvJ4y3GbygTZulSajDrauxODAyfobtQKo1l0VWaKTKq2rGs/VbCBTPkpbvSnFQa6tB+hZzoOLo+u1wvKAphdUI0gvxv4p2w6OpeIdfS+eNEO96u9Jmm20o26VZe+CcdeFzYMjf/LDj/jf/tav8+7/nvnm//wRDBn5jV8h3R4pf/87P53P8MXeFvuXge+o6qcAIvLfAH8eeCgig3d/vwr88Cc9Wf9f9t6lV5YkSQ/7zNwjIjPP4z7q3Q92T2Omh5JAShAkkVxJgAABWksL7bTVXj+AG/0GLQgI0kaAFtxwMRtCgjYCycGMeiRyOEP0zPSzuqq67q37OI/MjAh3Ny3MzN3zVI0ISix295k0oFD3nJMZD49MM/PPPvtM5O8B+HsAcE3Pz/SCr9NyxvCzl7iK76GEDeanBqxnqmyTWiDI2vvfC/WXESiZjA5vwHn1/UqBd0q/Fh2s2tixV1QsEo1F9IAV6WOFPVnup914+wCvYq1UZOwg1Cp2G62siZ8XRfJGEN4Q8rbgm89v8PkPPsDuswP49Q1knkHjCAQGSsHw+gihDfhyxe8++Rx//OojjLcFfHuEzea1ixVISohf3OPq4wGcBuw/NI2ewc+LkxZmjxFsGysFWqi2tYUVtTjCK8Au9j90AJ1VNavIaAQKSdvI2XpLBGBtxsrwAYSkav1R0jzAp14Clgt0BRlfO49NYQGyl1zvI3YfB1z9rODi46P+rpy/wmf7SjvHia/LUgaK9ZemjHi74OKzEWmjUxrjURDmgjxxBWxkw9p6Jfq1pREQ99cE03BBE9sFAQYuqS9WgMmxnhN2vrMRXWjZjlkZk/47b1OrcafLe/v3OaPDGUlEzUcBFSjXyWAqxYC+dunHKNDpUa6nQBozuO+SsNeW2l5moIa9pQxUmUn9XoCK1Da19jsAVpT2CcxapNYic22TnqlJOKDFQ45AFkFmYLdZcLWZsRsWZGFsbPQ7kWAxFs02rNjwijEkpKQsnifjEUkYc44QW0giAGNBuFxxfbXH890BT8YDUgl4ddxVcepUGK/vtzgeRpT7CNyzFgRuAz69vcZ70x2ebg54eT1DeDy5/rBoG3RPIdNnXVFD3ZeYkLQzg0RaoQRQwKhqItnvpymh5IA8DnXtPX/g7GwssonJomBeAmpKYuBfxSdt30pJwSReNXa+/4d3wD/5p3hMdgaEHqnVBNn6/ikr0KNgC7A8VQHjvFEnNd4o+yPugfW6JazOGqoJZAeeUlFNmxKdni4nQIXT8B1drW1T5mhDR3EvoUNz7Qvr6KxP3/J7Wq9a7yqKiZJJcyhUrLUgqz5SceCDVNtCr8GPSaCFkWx8+vxuQZl0fGVkVeeXlTG95tPAkWxdHHSStgY1MIFAhSodlxdNsjm1hJ2KAT0OWkmb0ALR1xYTwebV2FBdxbgXustb1b9wQbr6nGwDkTeiQqXZwSZbc3tGPhmujFJ1hyQAiILwzgG7KWFdA/6vf/I7+NY/Lth9fAdhQvruB1ivB0wvexrY2c4GQFsA/jYR7aCtAP8pgD8E8L8D+C+hE2T+awD/4Fd2hWcDBQY2E3A4In5xwMUmYP/hWLUmShWnV/9JLk7vPj2qXg8v+gsXX/TXlKisFV4BLMBKVFk9OgEMdcPA5nu8mBB8yhm05cvjmk+S8ekxwkDa6kkVQGlAlm44vmJD4YDJABX0D4L7ecTzPxHEl3eqBzTPoMkEleYFfLOHxKe4uDrib178HP/4k+/i6U0G3R90rxNt3KZNHaC3t9j8uGB4cwFOF1guCetV2zCEA5qvRfPpJQAQqvG1jLox4NTrbzTQzLWVSrBJbtKO5ZuRk5YEY5ZWAVk7Xji2CW5iccmZpGG2tbbzpJ0yZSVIyy+GBhBdfCK4+njG8NkbIAZlWp3tbF+2c5z4OiwqciOr0dBTAt/P2H2+wXIdEI6CsBSEQwZdRPOV1DQtgeaLgdriI8biqX6UjWEkwLCW+j44MEQd0+iE6W9VAFgc6FuS7TVAt59xBhI6LMFBJmfViBi7R4HpJovQYpGDXzBwx6+LExChY98l+EmkXS854G6AxQN/lisg5MfubuUhS8j3Rv1SO/vI9YngsU7BdmFgvdCYRclkIILg3ct7XMQFBYRDYgQSbIeE7ZAgQoicseMFT+Iem7BiyRGLnWDOEWthZCGEUEAjwKHgyeUB/+67n+C3d5/jW+MrBBT8+fwB9nlEBmMuET/bPcPn+yu8GC6xEMB3AbwSXn78FL/Y3WMTEt69vseL8VKZqQJl7qwAk7bpwQCubOLUda2FkAojsnZwBBYsiSsARCRgKFsI1hpXhCqgV6aCtA3a0eIi59bFQRkq5B2s6NUVVBSFa6Cit5Kx7aV5Bb7zP/0U+cXLryTC/SbbGRB6rNYl494y5ECBTwmRaEmw2Ae9APEgSBekcKkBDlVrIOCEPklFQY5Tp0cd0gsDOpooWf1CwnytgRAnQJO0jQag5yQPRF4xfsjUk3atgDna1arRPsXFXqfnkCpaTYkgmTBwQd5ab7FRJrMQYACKU/sVpNI1PZmEI+349d4N0PKxkWwV3tIBWq7xQHZ93pssQZfbqbPkIzx97dCdk7waLEBSIIrqSOduEg/pAks3+tHBKE5UxbuVTmmMr6sV05Swv5+Azye8/0eCy7+4AbKgPLvE8f1JN2K0w+4730b62cfAWSvibABE5PeJ6O8D+AF0CO0fQSu5vwfgfyGi/85+9z/86q7ybGCGDBG0BNCyYrhdMb4dcByo+f7OKffaENX31ORdapJf28zEQHhRuj4nnQ6iTB57vb9lQvVrvQbDiX6cxazyIIMpA9Vqs7eInbQre+Egdv+2eJUuAFoZbz65xvc+WUHzApGilKUu8RdW9uSTzYwPh7e4P4x4ZynaLsak/3XdUZIScHOHuCZsnk0AIspoVeA+rgWyOGDr3N1DnqgWBuqGxFlP1jrRpt7YOpamTeFsU6DFJI8/5CxSr6KuMBpsew4nenUmsklENZdQcKkxrsqkAS0eBeF+1eli06jv+YqP39n+ats5TnxNFlhZecbOEyqgeUW8T8gTV6Dd9wsKpHTvJ1TGy5fMi5LS/l1BHvs70PL1VsikBip1rVi1pdfzf3cUonsTB/57HdQvgUNoQFVlC9mBnLlUAgFj20O47yWlpCAsLbfXqWjdLbs/rPsPOfm9BGi7VX/9Dp51uXo9nncY08mvH7Cb0LRK7Z7Xrcp8+O/f2dxjG1bcrhMOynVB5IJNXDHniKVErBJQ7MBMBUyMAsKaA5YUVZRZFAyKMWMTE/6ti0/xb29+gW/EppPzJu+wSkQGYccLLocZWQhfFMIqI7APCLcBn95c4/nFHkPIOggoP7g3j+t+nxE2Mv5L27XKCioGEnEOYBKkwpVBVNfOPgwSpU7LDqxxLfeM4qwsJdsSoQRCWAUwFhG6z2XpgSSBgkHzjMdmZ0DokZpPl/Jknu9thLAxQabXVKui87OiYw0TYXnCjWWzqhPzKjGgjKBk1Vxh08kxo6wVzDxZe5I7Tum+hPa9DXtCmUQrC4IqVlYDiztOS1rTVirINNyyMn+sTStPOjaTs7Y69VZbDDpKvmssxD1OnPZuWJTRMxNKGXDMA+ZZR6iUCcDBRsbb8fxeyyCgRdu8fMx7vXboGkmn1aATw0zPIVurnJnqAtm/ozThMwO40oVUMWinlTqzR5lRbdJamElb6iKAjdQqb5+Ri41oDJl0+gysd3vV9gl6uuB3v/VL/OlPPsKz3x/x7v+9R/zRp1i//03MzwcVL99pa8bhHcbNX/sW3vsffwlZz3pCZ1MTkb8L4O8++PWPAPxHv4LLOdtXGREwRP2vFIT9ivFWMD/rACGhtoGo7zPdGxPn1CqaNOAa+ErAn7OggKr+kHhLE1osaFo18qVNQhXSt79BL88mlFETjHYQ/AFQ34uFOoiBbUG4Y2xeEKZPXwPzAx/mfnqISBvg+XaP5+EO62EAJb/YDpEpAkQCcoHMCzDPmL64Rhl3SJMOfMijvZZrrQXiQJgB9SUSisdzvxerdDewroFftZJuSa+zUn3zpa0RbU24A69qW3VshZ6HmyDqNiZ1Q2jTZ/JojNexgGIBpwG0ZgXFdhuc7Wx/mZ3jxNdg7H2ijsoXQAS0ZlAS+Ph0ytwVfm1DzZ2/lwf/PbTOBz/Uj/MJT14jaBO3HgBHHSAE80PuZ6gb+94DQl+6jC7vrsf1PYCziww894mY/QQwElQmq1Rl4dN77EHy3h+7hip3APpXWrd+Op0NJzGyajj5OuRTHw1Yi/ORQCuATHg+7jFywiEPdbR84ILLYcbdMuE+jbjLE3Z8GtOWHDDngDmFKkLNMSMEFW3+3vQ5vje8wgeBUUTwKr7FhlcsErChFe/FWzwb7pFKwJIC3gohC2H8IuD2rU4ze36xR94VhFkr3CpI3cV42wfVIcUPnmsqjRUkQlhSQyxLhwQWIZPOsFH03u1gGoKhey6coeLcDnQKlC2cvC1aWnHJPpe8qtaWJhaPs6xxBoQeo+WMJz8qCEfG/ByY38/IE2O4JYSDajxsvijaU0uEITLm9xPoyQL5KIP/9BLbzwTbVwXxUPDmexHrpSV6QlVcDmjaBbxoa5OPqS82Er1Ysg8ibS8bUBkoMCCoaijUCgJaa4K1kYUZdTxvGQXJEHf9Qqv4aTb9nQq8GHDlWkglAmlLlXqZdgooCQvimPCfvf+n+OE/+zbCgUAr4X4dsc4RlFSQM8w25ngC5BKnYtAGeKURSFcZ46ugGxsPYCY6XSYLHCbc7b3FNbhwa7uTopWRMGviv24F6UIQ76z9D76ZkZNNQSEFk+o4Rb/MB8GcV9tQsGlNJevrBZDeXfHsvVvsjyP+4h99B7/ze3vEl28AItz9ne/i5tsR6UI3AHkrGG6B8QbYvrJWibOd7Wy/OcYMGYICQgBgDEoALVHNOGXK1CosajtumAW82Hhhays6YXT6ezxhNsDffbKEVmRwvaEetPek2zcJXgDoNYYUpEcTHXWApIjqUoQWG3RiiEC2eoDt5wEf/P4e+PRzSAgN4CmiZcYQICFgvRJ8a/cGT/kIuouAzCo6HRjIpSWMIWgLWbDK7Ns9piGA0og4B8zXKnrtbWtgQEaq9wpInc4WZiCsAs6CzK0drlbbBxgbyIVi9ZjBJ2T6puNBLitsm5hs74kAkrZ0t2qqrndlZgl0TDB8BLLG0fUKKLuMuE24vDhiuXyGMkVEIkgMtkE929nO9m/EioJBNE3AOEDGAflqg+X5Buslw0csaW7t+aBpw5FONORVOqC5A2Rya8fitYEWEjoxZQftzddWoIN639x8fwWKROOJF4ep2Ga8BwuYKkh/wgK1e6ri0524dA/cZG6Tl6uuaNBi8XBfEBadYpnHds8nII80OY4Tof1gl+X3VoF1addi/6Yk4ELVx+tFoOq2uT/3MfVeeJEoKgFBBJoZr5YdtmHFzbLBzXGDYGPZj3nAnCJeHC5RhDBcZKSi7JpIBfd5xH4ecTwOSMdBi/NcUArj1f0On61P8N34BRAKJtLcYEMrnoc7fDPoQIU346f4IL7FXfoPEFjwhgvKzQXwdsANbxFDAe0y8oFB1rqcN2KsVAJ3hQZKBBn1FyLKXgKANQXt1gBwXAZlNYWMaUgoBhgx6ZCEkk0Ym6QNjJgI8WDxa1UwrX62HMz0gRPF42UHCFrx4/qnK4Z/+IePluV6BoQeoUkRbF6uoBIRjoy0Y+QnGSuCKuEfCOulObuNOvPhdUDZM5ZdwRi1bWxeTbdmbU47XRhV0ZL5KoppDlC6KmYNCn1l4CGC75C9U1atpxNAE09mA45IgEGFpPtNRt+G5setY3vt8J4Mh6O9p9LdBQh6jS/XS4SDMn0wCXJhR7Ts+qmKarqQtbdYcURT0Y/dJsZOwVqYgXiVY9L7AhvD6EEFoKfAquO3TcGxG/3oa0l6nFrA8H9HaVPIfL37Ko8DWaJMojRokJFNBgS4/2fPsf0l4emfr+C14PBbzzA/i7j/gLFe20ZqaPeoYtkCeaTo+dnO9phNiMC5QIaIMkWkDVXA5qTl15grCkIoiMxJwQQHLMIimkgTqc+zCTTVujjAWUxYmlBgOm/dZuGr2wL0ekMFjzRpliLaXhVb64MyJqm2OrtV3SIGkLSqef3TjOHPPoEcZ9B2qy8MwRx3VEAjEPJVxvPhXifWLKSaAzEAMwApkJwBZr1Fm06GIqDDjPgmgJeE4XZAfH/C4Rkjb6kymhTM0vXEhqqOz3Cr6+pjlOtmx3y5r1cVJIUCajqgQCpA5tV211XKG1RNPF5s3HPfqiD6jDzOK0hHNcZqMcdG2YcCFEJOjHcv7/HiyXPkiwExRgPYvqKsf7azne3rsXlR/0OEcr1DvpywXg2YnwakyYESqjl3zeMZAFTjRUj3CrUwUDr2oOXYrvNWC5sOUgMnObgEqkTL2ibVFSpry5qgDUWRTjeoMksE6H7vI+HdqDwAt0r/NznpSIBfAxqwFFaXlGhtyH3HQ91T4AFI5mvTgQ2Khxl4xtJACDJNPShjprJZoX7YOxnCrDFxvaBWSM7WYRGAeM/4gx9/B8yCkgkUBLvdDJqkAip384Q1B2xCQuoWSqS18IoASIycAlYCMACfrk/xZtoCuMdEETdFWZ4f0owPQsRRMhgLvju+xGWc8Wyj4tQvrzcId4y8jzhcDDoKfoiwGgLyCARvRfTPmxEEBIBkwroG7OcRQ8hYUkDODGZBzoycGYkDAmvbmD/CUvRvkrl+wMqo+9QyohY3eAGwOwUy/fmXYLHXAL8wa/xLG8LdRwPG/+Jv4fIf/J/Ken1kdgaEHqlREUyvVTDo+E7A8tGMDCDnAM4KCCmLRxD3pC1kkZDM6SzX6kyHvYuBapKYJz8+qhaDbxao6BddwRoTV0NzoicoPpoTQDAf6wDLYLQ981TFzlHfF61CWYOX1GllFSQyUCiIByIAZBpJ205Px94vAvxo/27Fp0CoiHR/vRUcs5YEsKL1ei82prILrvV40tZIUWrVFSoB+i30oFXs4knqdZQBjTp66Ck/7f8CVFAN3n7BDazzNoQq7ZGV8eWUyRL1syBTAaJgeDHgvR8UXP70HnxzwP6MuONpAAAgAElEQVR3nuPtbw04vtMAuSrUfbRrW4GwnNlBZzvbb6QxVAdniJDIdUokr8aGtLamXgMA1pbEi/tGA/NN36cCC+VB8t4BA6311ZJgaxUAcAJe98B+TZxd/FQM6HCB0gfablVfzjcqFYjXNuPhJmD3GWH7YoHc3nVrQqAQlOkTGAgMiQyMBasEfJYvgUJI24hhtwGlDLG4B2K9HiLTzlFxVzrMCPMKvmEgPEGapgrulFHjW/YpmxY7OGm7Nq8eW1wkuq+8tMtu7Xf2XAyQE7JNVh0UoVN1nI3VV8tbq5jURBqw5+mxxtfWny1Dq8ALYxtX5A2QR2dH3SE/wiT6bGf7dTXJpYLSMgSsVwOW64DlQguiPaBLRdSfuw8wwNgnHJ60TFH3M9q/ndXSDz45uZ6+IIxuD2DHINFJUt6Oitz+TgKULuc90RlFd67++sTvR04AHc5tCtpDPbwqqm1DD2Bj0F1D5uRcDwClk1Yy9922HjX/d+MGRpRkmmxdwcXP7y1O7frQmLJBEO8I9NkEZELIwPo8Yw4FQ8gooxbS1xRQJGKfBkSj5JQHvVlE0MJMYmQWDEPG63WHN2WHt+UG4Bn78g4yGDeyx1HucRTB0RbjIi5YxoAkjFeXK+jtBrQyliWAuSBH0y5l3duoTquAOiHp1npCKEnfS5MgpYCSGYXaAmcBUmZrEdP7zM4Osu4M39+VqKCmtnxZrCvUGF7SsYrt+hR8VM3D8a4gDwF5Ao4j45IefLAfiZ0BoUdoNER8+nc2mF4Lhnvg6ueC138jgy4EycCI8S0h3qO2kAkBmNTR5W2xvlvGeqFjGqko6LI87amhwHDTgkcJxhiyBJbfkjJISnNgVfMAqDR3DI16quPbC3BgCEn9olYxaxbwbKhyFEhUPaJ4oKqzQ0Wv40vjeBMw7E0Xw2j08UBITwglB/zzzz/E+uGCddEv+2EZ6ppybhUSbTNAFXTj1XSHJgWzKKhIAy369xKt53fRnl/ORkkMBBoF66U6ReqEv0u0imu0ey5N9LOMgn66mj501AlnkMbq8r95cHbdojAD6QIAi4Jw767AXcTmk4jLnwve/19/DpkGLN96hs//42vc/k4CSrJrpCowzqutvfU5h8MDqtPZzna2X3sjIpTAOqJYfwFAK2lUtEKZJwLGDhSyxNWnOYbVpoaZ9gSJiUhnOkneK6gDNCAHgIhWSePcJtN44l9Y6fzVl1VAyBNkYwh1jNO+3QCEJoDMGnfKAMRVgZbpFeHJj1cML+7VpQ7N92OIOq0nBEgMKGMEDxmvlgv84PBdAMByHTA82yHmoiBQzoqShE6hVQqQBHI4AilBlhUDM6ZLZc+QAGu0qWIjILNOXnENuXgsOsXN7qvqV3RFkFaJ97UFgrVK+Fpy1vaLEgnZnmGdXCYNZHNx1gbqdc8NqH+rFW+BDmBYCIWASNkGHRAggvTxV04MP9vZzvZ1mRTVSgEAIqyXjPlKC8Ig1MErgDHyIRXUl/Bw7HsDkOqEMWkTHgV+TPcfqGxDwMAejxu+JwhtQ84JKBngigCh+Ra36vtbK9uJODX8Xv0crTjr4DbQABcEgjxsg/bYgQbGeCzhLFWs+gSLdy07+z0EtSALUYzhJFfvY5OctkxX8MgL3p1Pb4ARAZuCwgLZB2w/Zww3gngE3nw/YN0MWKcVqTCGkDGvESUHLCViDHPVGSISBNd3MqBFEqGwijW/Wi7w8+UdPA93uKa5CUoL4ZpmZBDuZcSLdI1tWIARYBK8uLzAQTaglZBTwDAmIApERPUCSRdIki563YN2wJpkQloiYiwKBmWCCCMOWYWjTU+oFBXDLoX134mBTKCVT7pIssl1VBDIp297W7oNH6pTVUfbxybB+HrBcrWxqab/8q/db6qdAaFHZuHf+V18+p+8g/m5oIyEdAeMbwTpaAmuJWxpAxOVVrr/5gVhuHdonTVpDoLlmjC+1W9pmI1+PuuXpbaPSTuW08gZJmQJ2LEseTe2Son67S+jqKD1QgizorpkuhB5486b1Jl0wQRoAErcf3mcMKDnTDvb1JhznZ+YXkMAyoU5W1KG0DxHbK9mLEtEWQJu7raQlZXlyUrvzBtC2gLpUn926mw8AsuoG6CS+bS9wft9GQigKiLno5yd4VN1/6xyC0YFwgBzmKGtQ56kBRczbWXTFoywnE4mq2ygIDi+C6SnGfFNwOYlY/jRhMtPM+JhBRXBm7/9Tbz9XsD8TJCvMsId6/hGu540FMhGUIKAU8T4hnDxWUL8wQ9RyhkUOtvZfqOMCWVSQWmne4TVRPRFNw6VVQkbLMAAHxVQGW+kJuglWsuWJ/Rdq5gYMOMtv1SkEzFFq+RmqyLGluV7ogbgZFSvFw0pC1zjqITGUPJW3WIaOyUC61VBGQTTq4Ddp4Krn88YP34Nuj8A06Rv8JHIIYA2k7aLTSOWpyPCMONnd8/w8/unkCDYv8egssGWCfFuAkpRoellBa1JK5YhNAYTkTKOcsb4ZgWvCtAsVwFpw1ivdOqZsnsMnMswoKsVJxwgo4LafuGtYJxRK+wnk12spU83Hrqxqnp43LWcsWqLVAaw1xtq7CLbJBFysOe2aEyWwa55BXgukMVPcLazne3flFGMkFwgxyP4fgaVnf6+mB8OyhShIhCvBRiQXKCaayWQaq85KOKAj7EaKUOHljh5w3WLqQMwKluDbC/gQJJN9yqCgA6EekjAkO4/BvJo19xNLDt5ORkTsrT22NOhBA0h99aweh67Dr+HXkyfV9PGwwNGjwENyfxnLcCWdg19p4QEHVHv8ZFXAg8CJC2K5En9Mhk7a54IaUt1YI/vWxAF6brgyZ8zrn+6YHy5x3rxDDfbAftxwjsXewQSjLHl5A64uSBzDAUhFNR5PElfcTyM+JOXH+DF4RJ/sPsO3hvvsErAQBnbcI1AgoCCowz4ZHmGwUToihCuNzNun1kgWRklMGiTddgAYKCTKOWLADly6wLxh5UJMgcssUAyQTJDEgFjApFACiOZwHTOjLJyPR+Z1InHT9dtzTYFm5OCZ71YudheqZj2obZSK2DIS0YeCHlDmmc8UjsDQo/M8tWE/Yc6kStvAUBHkMsxQAWLlbJXRusbtgRd27SUORIWZacA+qVYnXGStZ/V2wcAc/iG7pZJQSQdPysK7FgiqYJhaJozxv7pqaa+gfDpZoCdxzYo+sWlOoXLDqOVX0d8vTXAglo9tr22JrgWdDgBvDDKHCCFEOMC5lLF2xAEMmhifBKUigFSLphnYnm8EMqRaxCCAUa89gwf1LKHj/ytItoPqgJ44Hva6Hi0ijDQgm8QSCbdMBV9proGUjd1ZQDSdQYlZYptfynYvs7gWZC2jOWSsf+AMD8TlI2CdbyYfhLapqw+A6s4CwH0zQ+BP/sRWg/B2c52tl97I9Yk2ISRVbfGk2ZUVqePL3cx+3CgJiLtGhFBE/YTJhCav/LJjyetXGZejf1SbOhAkB78LzY+nqirRFublANP7q9KAMSmdfFCGN4yti8E25cJw8s96Kg7GdpMkFJquxdCgOw2ABHKbsT8VKuSn95eIaUAGQTzM4Ayg/KEcQya5C8Z4T4A+1ldtoMipi0kxKBcEO4XncQVGOEYcfetSVu5RBeNQ1ujPGkrn2948tC0M2oLM3tloa1TZeXaWhVjcnnu7RXaGncIJ+tdmUDds6zjqU9aoS0mG2KnBZuEcnPz//75O9vZzvb1mOvu7I8KMji4Kw7qNMC/AcfK8KxAfPC8VX181cus/qLlxyEa0F/Bb30duOX12orW2Eh+PZyktqn1fo3o9GcvPPSs/Z5NpPHD7tUKBdTnpKT3iGxtREADi+RBrPL9i4MHlRokLa/vzldP0bXYsovy27U6G7a1JqHtK7r7Yztn2pK138I6B6yleCHEO1ZtuSJAFuxeFMzPGYfdgOW5OunABZELRkviixByYaw5IFvLle6ZrKUrE/LKePt2h/vDhE+nK1xMC55MR1yPRzwb99jwe5h4xVwGfL5c4ZBHFCEcsm7yeJdQEoNIz8dRwSDigrwGBXhcQyhp8CEjBIj1p9PKyIeI2k6WCWKj5gHbZggpYLToJoySDgVSIMg6R1w6Izihlk7AINjzrXmGnU/lUwiHj7ZYLwnrhX14OlbZY7IzIPTITALrJKusoI5XSvnAyjqBgjLFxM3CbF+SEciirUy0AixNDT5vlIUz3KlWhIp+ttah1oOp4AnEQInFgSRH1w186tBzToRirWASYcrz9uUtLUn119JqIAcp00UCkDY6XcynojjoVMe3e+JqQcyroWmjjoJnggwBkgjJ9A6IBcwFmLIOjRnVS7hzD4sr0fsGCArALEC8VVGkvjVOxaABkCnsi14fJ4Jk0TWwe+Xav91V1mEgVugwIvdJxuaSIPC2gLqOTsX03m4PtJuC4ZMBu88EV79YwUvB/TdG7N9jHN8THTm/EuKdMrf6nmlyMbgVwKLtcmERrFcBb/+993D1o58+SsG1s53t0VtgzUgdYPEkKjZxY20daoB2GzpAVeOhDF2Si/b61moAnIiQ+kvs56q/Bjv3V7Ts15aEbtKMJ3iclOzkIpzerpwu9LXxjjC9AjZvCobbFTQvet/TWAEbQ0+0TexiAoiQLgesO0I+RtwlhqysxcyNYL0kLPcMkmDty2ytbLZWAOr0MVLxTwCgeUVYtcUsHCIoGSDElqB3bdYOAJFvHnxyjVXOneD7cJ1qDLQKdek3dBZfCUDmDoDjB6/pMf5ug1bBO7GNYgQQgGMesH1ZEF/vkef5yw/wbGc729dq4iBICMCalA1oqVnVofHvfECdDMlJGuvG/IeL3Qt3w1l6No/57Dx+Wc6gsuD7a6voshd8Xez54Ss7oNqt92lQf1WZG10x4WHRoRaJfd8C+dJQ3MpYteuu7WBox2DRawxLi2O99pKvb9MOkgp2+fU/1ECqE9mEGpPWC9t2/xKlAXdWlI531AAhBsa3CePbEctTE2U2dpAASBJwzOqblxKQCiMX0+ExWQqCnT8zZM9YaMASR9yNWxyfDJh3sbKLJk5IEvByvkA0NGyxtok4Zo0ntbCREYJgGBJmAnKWxvxh0/8TBYR66YtyDLq3YV2bUqhp2BVlM5XEQLI92kq6V/SieG57QY+P/vz7te8ZaP6chYF1Rzg+DTaVGijR2GF4fHYGhB6ZUS76JRqAtC3AIMiJwEc27R0DekbVtOG9slPWS2UNUQGGffuyLFeE+b2iYtFE2L4UDAdBnFVTSAWFBXkglIGN0thax/SiLIgsgjJ1FeeObVOTzyiV7s6JAPsi8wJNOD2hHaQGseFegaKT3k7zu+FoFEvrCU07A6+CJvHJNIscFb/YLrjfT8j3AzKAcLGCzAkDqIHBgSVOeg3zUwVJKBtriNVxOKgW7xuoLFHAB2Nu2b2gWOvdZBpKgIIuBciXRae3QIE9H1Pv1+IAGwSgWTV9wkGdahltg7QQ0qXUEZrTz0c8/WFRsbSJ8fr7ozLLbFz9+IZrsM+jtOcDPV68JcQ9Ix4VWDs+UyfMSXD1SAXXzna2R2tSWkJtY3udcl8GFTt2BqZXEkO2CRw+ebETh8ym3Va1G0TjhzNBK03fc3/HNLKCMF7hhbTJKu1a23mUoUra3hQIwRJvXqGxzsAU99Prkww+Mq5+DFx9nDDcJYQ5Q6aximnLEFCm2GlpMNbLiDLYxM4tgd9o1ZJTA87KAKwXAAkba5JAeUBkQtizTiFLGZS9j03/o+Oi/05ZNZfWpxoHISiJUNa2Saktc6u29JWxbVz6oQHVbI2yAXR13LF0m8BIoFIsfhLSjtq0ULJWO3tm+pBaBb9pYRgw57E5CD67vcJH/8cnSD/52f+PD+bZzna2/8+2rMA4gIYBKAXxkBCPrCPn0efT3dRdUG2VqRo9psFSRmdVNKZ+nXxluEmeTEMu18N9CVBW9ryBOBZvql6wFQtgGnSVpeQsfbvMXp/IiwEPNYd67Z0qnyAGzAhAWcAn0y/J9EdbQYMyTpj5YS4KQFVtpPb2CvoYENTvG/RvXuBoent6jA4Ec2BtY+t8lKrhp9PTCmAdGOFIGN8Cw30GrUVz99czti8j1kvG/TcmXOxUM0iFpQkDF2QhLCliTgHrGpFtKhcl0lY0BpAJ8TbYXjCgROAtgCUFHNYBr+cdIhUUEO6WEc82B0QuKEKqXTRkhFB0fPwSQQQMQ8LVZkbkgnmNWJeIlFnZaB5HMiC5m3RZdAqy7g0FZQkgVoCISHSimLWJUdYCthfCvf3RSQDFpnMS2Z7yoXWfVd9Hly0wEyFdaKt53hVtH3+EdgaEHpmVyEhb/ULTyoBPYSFN4qs+wAikreg0E2OUlEFHGyrNW/tlw2LgAoC8FcxPqGozlEDARpCYqp4AJYCDVho9mQSa03SnnC4M4FiVSURzJ6I8FWB2yh/gk7ckaNXUJ56RTd2KezRfapuROnJ4aH9z505GyyTRL7gMCpzxmDHEjPXthM2nEcdvrMoSGoG8UTFRSkA4AGEgLM8ckbFzeSWCm36FO5Z++hkVFdsuaECLBg/TVIoCnhm0aBJeVnXUKFCdJatEcCKsF/oekFZXwqJBIszdNVl1p9jGKMwaRABgfhKQNsD8xNZ1tmdEel8WrvR6oyYD4xuqlX0XX1ujvi/2U9DOdraz/cYY5aKgxGFGGCPCPIJTQBmt1d8ZKObTeFW2qBcE+oqtTyCsLH1qItEtKUdNwCSYJkQhA46kggx1IhkZjR4KWvcJn29sfPhBSwCNhWTtD3y1osiA3eeEzWd7BX/GAERt3ypTRNoFpIvQUfwJZSCkiap+w+al9y+0e9LhCwq+sF1buggoI4N3EWHVEZ20FHAqqjPU6xGIQMao4NuqFU9neWo1v200eBVwhC1CiyFkrQk1pvhmarKNjReEDKBTAMeW19sr0CYL6aQ51Q3EIlUX8EvMgOLXiDoE4vXHT/DRevuv5bN5trOd7V/d8ps3CFdXwJNroBSEuwXjJiBtdZ8gRIAPX3HQntSHuhZZn6NqUaBj8feg8UMWTZf39oxP/YVhO0yWV1KTWQA0xpgYvseJ0rV9VUC725f7MIMTUIhcE4ZMt0jaZEpn7rg4deTWGp0FBbrXibMgj/rvNBGm18UKJQwSrq23gLL7pUg9b21z4+58ovGOs762MjItlqEDrGBs1+GVPisHtyQ23dXNq6I6dEuGbActZlixZn8z4laU/QMAt7xBjBlsYMq6BpQcVH/HWTVJz61+XupDIQHkLmK/MI77Ea/HDCKdUplTgDwnbKLq+6xZb3yIGWPMuAMwDgnbccWT6YgxZByHiH0YcZd0ehiBKqPHPwOun+fFbQyALMooQhD9DCUCJQYlXY8KCAVf83ZMHyzhsydqV4Y0END3TGERgAnJiARlEJRJEK5XfLny8jjsDAg9IuO/+dfx5ntblE1WB2uIKQBUiqKBIRAA0cEEZZ4ACvBIUOYPWQtUOFAdrZ63AFxIDtobWtxJ1VHu2vuKopsI/9L1Pb9whpCN/uPVApC1PZVo4mdCYANWyoMAUKsUPbjOVjWtekloQBDQxtkP1kawzaAg4KHg2ZN75EIItwGbL4Dld7q2p6gsqWiB0J2G6HIoRbY3d+5ApUJWKx4MUbWDwEYFHdoLK9XfN1HFA4beO8Reb61lDTxraxJmY0htpa4zL3qcPGrlP2016PPivc6oDC7fGFACwkptQlrUz0LeasueA1+cH6ejPNvZHrOV+wOGn76AzDOIGbx4vy1O/GsDo9UPeNJfRgMP7PWNbm9v636nfoXa4euGgxor50EbQmWkuGZdpYyjJnYaehpAAnT+yF5XVgYvDF4LJDDyJiJPbNoagrwJWC8Z647Ri2HDKrZ50I3FcNsmp/l9+9QS17QD9Jx5wygjIeegTNlZwKuCQZQsINumIO30Nbzn6u/DUeNxibrJYLSqeW0lMMbqSZtw9zdvG/YWQAkNoPONn3RtgP4M80bjaOxAH9eK+pKJnkOGoq0LL8O5dfhsZ/tVWm0ZU4fIx4R4nzDsA/LI4KTAhwTNrxmmL2PM8mLtSZQtP1y8YGwARVRwGej8DGDTmE41dQCcaAvpL4zpUydddWCONEDJ9w5Cp36rhiXzbz1TqC6B/b4EAgZ/PYFYIDDNHO4AMJgfNf/Ji0pvIJJO2vRrs+lUVERf7MWSGsNsghlJBWQ4i7VVn/rPEw0kAWDtcw6AhUVZQmxgjYMlTYZCCw/apcFIG58ETMj7bptfCJkjeMoIseiY9sT1+rzFrS6uFxt8m3LUNumyMJYYAduvAcC8RkTWcfc6j0FUuyhkbMYV05CwHVbs4gImwcgZTIL7+019dhKgLXy+R4XKgUiy9Yy6LtLdDwqAZG1iFv9cu9Y/J/pMuz2hfQb0SUjLQbrPcoltb9smZAtCJ9D92OwMCD0i+4v/6hnyt4/AElTbwIW1cudknbI/q96DDAXxakWiAcIBwoS8LQhHRjyoIxru1BGWURPEEqn74pjz8y+d6A+8aH9t7f01MMmF1GhR1ktYDKRYAR4AgFAEqqEQtf2KjBXT6yMokm8I7q4BPUQ67Uw3Lfom75lWzSRtgfBWrrDVP242K/7WBz/F//aT72N6pVWA3/7oBX784rmi52wUzm6DFO+aU3cVe+fN8qogkQYvORG7q2whQME1acwgkID3RtMk0cltgzkyE/0mG+MsQVA2RfWCEgHm7Lw9DgDGvVV6B50k5+d2Oqo/t3BEY08VAItWofOGsF6Lam68UVHxtNU1n58Kkk0S4ANDiMAj8FgF1852tsdqklaU129A4wCkpEyh0sWNzlwPLixSK28lQGNLOU2qxF5fhRpPQIgO1LCefvfVDob3lV7XTmjgT3ttP6HRk7hWcVWgWwTAISAc9QB5NyBd6MYoLAWUgbRjrFtG2kBp7Ga8olaJ4wGILs7a6RTVFt7Y6OhCpO1aRPWewijgZCBUlgoGlYGwXLLGlr1uVHhVMCgcxaa36WKUoC1y/VSefqJNBYNCWzegJckOYjXx1BaXwmrThWySXBmA0uvd+SZRumdr4FUVpBZlkiI/3uT5bGf7jTCflhhVRygcE+J+AF2x6jz7ppkr2b1OZHQghYrqZlJWMGjYC/KgxUaeqOoLgTQG1AJxl3vXombP6qkxoSIAJ23GX5ok1sUOb1HTN3XANrsPbPmts0trDAJ0NDkEEr160PnJ4jm7trVxJh3AaXp6IUsTqRYDFUDW8qu/L0HsGtrQGNh0sxNNpP6a3Kdy11ZN0OMmB/013/fibYmE9TLYcyONXzu0gQvHUPdptOrClULALunepiMNgPWZUenQNpamA7RY8SKxgiSjtq8hCoo0oCZwAZOykJhEAaGQsY0rRksavN2MQ0YOUVvCCiCZavECDOCABvQIdIAQ7FylAWNk/+6BNS+oe5t6lfx4sEWpn13/2YEg6zYpo4JGPDPWF1s81qE5Z0DoEVmZBOMmYQ2Ccj+BZwVDqCgA5K1M69OiSP9MoBQglwkYdbQf7wNKVGr8cm0Iaunon6YE7yweHVtPlUmULuzbmNGqtTaRpvbdErRFbVQKHhVC2CvIwRmgoybHnrHm0bQlxBhLxwaKUAZCapWMMqJOQatOV3Sj4U7Dx8aXbalfgCFkfDhpH9V6Lbj7NuM/f/dH+MXbJ1j3I3BU7502du8DMBgbXmxEYRnsGlNjUPmauz60MJB3CuS4in44km4ASJ+hAkNk1WGGWIChRIj3hLQVpA2QLzLCPWO4YfCiQFDaaZ+t938DGsjjPVWUm4q1BC5Sq+8JCvihBh1BWDTgpwtRXajIoAQc3y8qYj0V0CaDWFAmndRGPlbtbGc722+MhSfXWP/G9zD+2Sd10hhlOfGbTS9GNDGUxloprhnUAT2NRWKtRgbWAABiS7oATWodEPLRvoD6KAd6JHRVS7Kfs1SQhAigQZNi93O8oDKahIHppQJCyxVj3Y22kSHEg8YeB32AVrzomZKqpaHXOtwLhn2poEweCWmr/48HqQBNiaRT16IeM4ytSENZqpBrnoD1UmP29Frj73APDHeq25c2VIcylGhV/R7w8c1EV7TQ2EsVIPJ7IQPvAItJpptBGXrtHgekTcMpA2om7YzQmmAHjcnaMqZC25svRMHFs53tbL86c8bKEIFSwPsF4+uA4/OAbOL2JHQC5ueRquaNRMBbVcMs2Lwu2HyRkC4C5mtlP/atUU0eoQdn7Foc5HGWUGVr6OtrZ4GYr/Sx5LYd8C6BysLsCgZAA7Yru166nweLM4ODKaaT5+BDB8y4UD8nmLaq5sMlOIuTlOXZM6AcdEkK9AcmLJdtXSQSRNgmcarMhq9FBdKBev9ELfaliW3fpMWCknTfQAUm+RBqjFquVO/GByjwTJURqoVzQgmar0MArHpsjEVf4+17tvYlinV0KDAmMMbMVIApgwcdWz/GjMGmmUUuBgrpLV2OCwbO2IQVbCCRPn7BZrMirwGFAoQ9OLXPS2TTZs2C7IMLfK2zFsIriNgxhjm3HENbwCzO1QJKV9Byzbyg/y9DP9FNyQMyCJ78ScD7//0/Qv/YH5OdAaFHZBKAEArWNWiLVzRKpCVvlBQ8CcfGmpFCSK9GYCxAFKxPMiirKJhEZe8oKwWgFTWxLBHK4nFWDKhqCKizpUbxr6we97r6Gj7QSWAoXZtA4yia1kRSfRydzKUBoQwCmYDhrjFfSlCxNjLHBwseYg7Dq6xCAC2MZHTKW2zwx7ff0LWbBOsV8Aevv4NlCYY4E/KmjZkMs92Xg2UC5Hw6AtjpnJVmz7pmEG0JgN+mb3gcNXL6JqMi0cKADLZ5GBSxBgPjK8Z4o9V6HfVMJ44xXahjSzsBD8BwSxjugOlt0VGW1jbm2hghaaDME2G90vf7ZIPlSam6TlQIUoJWCqYMGgpkLEg7Qvn3/zrin32M/PKLfw2f6rOd7Wxft+WbO8Qf/BCy3WhefMUfezgAACAASURBVJgx3Kw4PgsGGlNLmoHqSyW01oF4kNPE2qu6ojoRClyYbzOdBaAVDsCa3MauF6CO5vVNQWmvV10G05azZHcd3L+fTrh0fzjcqv9aLqm2/Tr9XqeSUQV++lH3aUQFdPKo/n+6EUxvM4SA9TIgbQjLtcYAnT5jN2XXjQydhOl6BgwARkuPuvGQAPBRQbG0pRpblA2EyoZqgtCoMefErGBTWUUObFFXOfaai4FGmkQLwgqM9wLcA8M9cHiXKm3eY6uLxoYZHWCmizXeMngFtq8TJD/W1PlsZ/vNMBEBFf0eyuUWeTdgeToqkB9RwYLKCPWCrzHpXRdMC8MKpsS9irRp8Vidfp70dV6EBtDihf/bgB//udcNesiir36OTv9WY0F37ApEkTOaVNdO9WM8h24i2c54wgp8VQ2zb18rgxcrBPFYdE0mgoRwAoD5PWmBQM/F2UT8izGUYDGm+tMu1+/uh7Jtm0hjTtpS1WmKB6CYTh6vqN0bfhzP5SlBhxnY8QiAy03QAgABEgv6UfMAWreYh3OxLUkUZLL9wKjF4DgmxFhAJLgYF0xBNYQiK0BUhFCEEKggF8ZeRhQLWMccsZSAzZCwTAkr6R6wUFsXiO5dnC1UwUUverDt9UxCw/dcJ3lKAGjUmEqJEIqCdtNbQViVabteqJh4n294d0YFv4KgDI+74H0GhB6RVQp9aUBIgdQR5GHWL1TeGIOFFVQZbhjrE/vQbzNkDoqIMiGQfYmyB4QWHB6e26dRwSvKlqySsWZ6EdGqdeA0TYZtLuyAjtoD1QFQRhMH8+qnn6NjpnOmOhGn9g8bS6e4oy/aD12CfsGzEH705h2UJehkNgH+7JfvIa/Bzic2sUvXg42VxE5P7CiNFQTrNiM9fd/P3fdBl9Ammfmz6XU0dO2kAkMwCmo86EYsLFpNTxdUA6jQafuECtCpI4yHgrQJioRPDj6hBrS0A9YrZXDVDdMAUBBtuTBnXBKQAwNjAQ8ZZSLcf2uDp59eAGdA6Gxn+82wklHu7xHGEZIXIBfE2x04bZofdoAfp334VZ+mNL9zYp7Yd6zF2q8vqEKPnmjzKh3l+0H19EEl11kwQNe2Vmn/bYPjekC8tqS8UtK9xYE7pumDUfd508WcUSvqlIF4qxujvFUKfd7qNWa7D2fR1I2W/9vvRVrC3Quqeqz1+6Ju0prr8ZUuN61tegQtsNYiA1r87DZVJ0yrfg3tecZDBi8Fwz1huRyQdu01PoaZBFV3TgiQWdkAYVXm1MWPbiDH87j5s53tV2myLJC7e1AIyGPAejVgfmLDAiJ132tUH15zTvt9bddxv0IKuvAqiEcxEN0GzlRWqTRtH2NfFGd5uv9I0iQGHAgqAMRkEjqWZn3fw1bmrgDhP/e+r8CHEzRfp2ANtHUK/bFbAbaun4tVO9PU7oO6WKbr1ISfT64X7ZhloFMt1T5WPoht/SCaYsUIzePt7w68WTHdNUTZQRHoeUo8fZaqf+TgD2nrnOsG2YucIXViBBQjDtCkYNA0pTpZbAj5pFUscAGJ7iGZlN2ThXGfRgycMeeINQcMISPGgpKLsqiy6dnZ9eSpaVE9BODExKj7z8PDlvL6eQimyZQUDBzvCsJcLNYHzE+pvhYAykb0fhlAEFz96YB3/vSrRpM9HjsDQo/IKAMpsfaFFv3yEjHGe8LmpdSKaIkKaKSd/hfvCOGedfLITpqz8B5YA1S8ohiOALOCBEJWNTBKfoY0wenRwKhVv4DesoQISGkUfAlSdXBk0FYuREWsKesEMhI9nraENafrGg5tAg4BpQmelqkFh7SFTdlSlFyiQFykDYxXby4hSR29MLD+cgu5yIr6DIIyCKKJfVaxMcA2AtJo++bL4qKhoLatZVQaZm3BgP6tRAAsdWxi2ikKLwBo8d2TvrY6ugKsV+ro4h6Y3hTE+zYymKHtBjqiWPDkh3cIr+6AccD9957W9gJvIfPqwvouML+rJQpaCMMbNk0LtA1UtOfrInwkSj3eZIw3AsyP23Ge7WyP0nLWDYQIwm6DsF4rwG5iyV5hrQLFDwEYr/j2CRrjBCXq/R4X1zPzhPm0ypxtSmQVgOyTVOn+Zq0JJ8m5JcQkLaE8KUK0/LfpU3h71NjdA+lETjI2Utqp0CUnQXx70Nd8MCFP6rfJ2gyqroZda23nqhsiva7MVEGaqlEnGr+EAImETNa+Z9fkrW79JqqKaHK77n6t6pAD2wD2wF0P1AnraOXhZgGKIHwYkTfdKHpp1xls8phvpOJRMNwlDF/sUf7pv8DZzna2X63JPCOvCXG7RRkD1suA43NuRcquUMsd+NOzMYKP8bYWWInKfOQkiAdl45QBoAm1HetEy8wKAn48zl1bat9CJkAdQZ8FadAuAgdbqh/vASA8AAEIdcKXY/5VJ8fNXtMY+AZwmQZNH9P643MSZCtsFO6ACAOwTgGoL58zR2cpUT1uvW+050DU/LXruDm7lhd/Fvo+f4aUbTIWOk0dIchWTuIOYHuRTBDmJgWy9uCRBsW+FauQ7oNoKAhDwTQlbIaEwAVrZjCUGQQATKJkAgDRQKIMIBXGmlWWZMkBuXAVos6RUQojx2KVe73YskXVBjpFevzh2tLVwrm9syuYCOnexvdhKoyeEe9XK1RN2H/ALcdhoGyzds4Ige4CvvkPX6H88eOOaWdA6BHZ5iXj8HzE7vqI9Mk1AHUW6bLg8AFX9N+rlmG2EeLmOMLMkBuuFdPqiKI7b33tcNNQ6nSJiig/DAA8N/S9RPvCWhWChdTBAJ03Nye2cBt/bk6pjnGvSHEbW5w3Vv1dVHOBDAH2+4iL6QZdAGkShIPpNLwipA0hXQoyA/JiUhDlhqrmzuGvFfBFBk8ZKRPKvWovcTYwaADKpJsEpyv29NUq1Llo2wQikAcrCXvyXrq1AeqoZPiUOIFGCDFAayGlbk6C40dJdYTeMoZ7wnAn3QZBsH2RML2awa/vQEVw+N472H8w4PAu6z1Mxp6yzVDeamsYCiHethH28aAONZuwOEiR+3xZgLFge7FgGlYsKWJ8G1Hee4q43SD96Cdfwyf9bGc729dlNI4K9IaA4U6z0uGOcHiXaxL+UFOReuYLDOAx2j9glWBP1EOLQ1SA/Fz9tRce6jn8Z0ATPwJOmvephY5ec6BVWMUq0jqcoGrKdfnkiRAzLAn3pLhL0l3EXyIgg2C9EKQtQ4YAOq44PglYngD5oiDcsRYMqE2JwYMWOR1/rMfmrCKftfJp16j6FRYqWMVNa8XdNnMkXYtGv3nxRN43ACuqqKazfP162KayeDxKO2C5CgBG8FJOdD60XdlEZmdg/GWu4tiUBXGfEF/cAq/f4mxnO9uviUmB3NyC8rtYd4z7bwrioTHpYQCE6q11uWyPaVg+WwKQt8FAH0E4FgwR8Kqsg0EncgiRauuZ5rGEMAtw9PNLPb4D3ZwakOGMlV7sucYG0vdIkebaO3aIF4yrj+8Y+8WOqwVmghiY1UaQm/5dIOSeNWSAU+s4aBO//Fx+L8JQbRruGFLAaazytbJllNAYoiSARM3n/Xj1+YQGgjnoH48a93QQgi5CnuTkfS790O+7OKnuUI2TLG1apug9ChdQ0Baxh7aUgEEyRk6IVLBC2T8uIu3vWXJQ6aIckDJjFmUK5UwomcDRHxRALMiIrb4kAN1F1Jax4BPCDKgkqfp8Ik2rTwkBVDUJdQ0EfFgBEUxEGG8C5meEstUCP20zhilhuRvx/f/2j1DWx1/kPgNCj8guPhGslyMO3wDkeUbY66QxXgnrpTQaurTkk00XiBcoQydq61fVGOoEKys7xRJSZQudUusptZYt/UX3/96H9E6sGNskawTyySsAWvU2QpPWrLm1A1aCLtF1qqq9p04zcOdhPdAuWFcGE0zeFMioYsxggdwNiHuj/xPALOCgwEcZA2QGaIY6eaefrjY5IGrfqRScqt8nvR6PfSfVDWcWlR4Fbz+3QGbn8CXM7QeJgvWitSn4e9IugGRCjAww4f4bI/bvE5ZnYmJzekEeaMqgbK1oIuG+pg8r0WmjlEpE/cXlRhHCN28m8OEIvj1A9od/6Wf2bGc726+RMQExqrB0KRhfzwiHiLwNWK6HmtBSQp1EUzVx3Kc/KIzWRD+c/l0sr+uZir2/8UT/4fh3P2ZfJaWCNuUKuoFw7QgA6nh9shba/z2J9nPQg6povf5EkKkrQlwVzE8D1udbxDeM+blOY5TQpsH4+TVRRd2caMHANmMem/zeLHb1zKJ+Hb/UauA+uQO2ausHa1WXivvxNu2ybXjQgCSLZXmj084kKAuqVo0JVW+ORDcb8VDAS6mbQ0oFtD8ivzkDQmc726+NiaDs94g3R8TjBgBrnh+otkL1QAWv5qf8/d3fVDeIVTxZALC+Px6ltmT1zJoe4Pf2NG1ptb1D79/Nr3tc6MEbnRps15dt2rH7f5/iRd15/Xjd/qT3tSdMV69Js76pjyOUDcxn+tJ0xnoPAmVe9gBLbtfubcg9uO6AjwP4FRQSA87GB+vYX7ffnv9c46U+C4agWJGeEhDsnpzB61OZOaKCWbUAYV0b/fr0ayeFUAojpQAMyZ4n4ZgitnGtjJ+lhJNJY2zaQkPIyIUhQhAhBYYSo+TQwJqgrycWndZmnxnXV3WigFjVQ9lnYp8TF0g3sXRbW2UI2R53AxzeH0EiCPcr+Lhi2G+wXuia5VHwnf+ZMdwQeD1C/gqAQcAZEHpUdv2TGcITbsoI/t07HF9tEG4i+Ais72TwoqPBHazxsYWuwC5kwv07Zfe4iHSl8LuYczA/QdqalU1wUynkp/o91cF3wDigiK4E1IRWTMVeASFCiUU3J5bBFtakXM8rJ73FvJrYtIEvJUApl4aAe9VZJ8MY02ULLE8LZGe7mSCYLhXUKC8GhFlHroOg7VAk4CEj7wJ4YYxvVETPry8cVHS6ECBRvW6WAp5ZJ9gkQKJdsKnit6AlOEn86yL5/6km+mJi3gAU7FvasZZrIB4JPOs0CBAhbQmH5wxOA6gA9x8pGLQ+yeCZrd9br722jx2UEVDP70GN2vWUba4jJ3koeLo54MX9BcIvNqDDDcrnL1Hu7//VP8RnO9vZfmUmawINI2gYgHkBv7kHTSN4HcGrlner1EA33eMrwQr7t7QcrracNS2HBpoDGldqMcIPIdAED6iMG7GEsLZh9efzNta/xKdS9/8TEMVPKq3IUFspLFnWexDQLmO9DFieRPAxY70E8k79YR0JbMl3FapkIG3atVGUmoj37WJ929iXWu/QXlN1ifx45fQY4kCRg0HldE3IJ+34BDhqbFzVDCJworqRceZvnrRgwwPAS0E4Jt00+TNOCXKeLna2s/1amRQB7Y8Y7gviQXVCC4v5EKotqkDLnfWNqLm2+7E8kclGOACkhchIqOLy3vpU+OGV9IDKA1Aczc+V0FqyxIsDDmp3QDiA6sP04k+Pp8fS9uTq//z8f8m5PU45u8Rz7BIf6NXw6XkdMILYmPqOzVnv2Zmja3e+brkr0BTt2dg1oPftf0nBQAIBqYvRHiNT2xMJNVZoWOhkLXkBMGrbm7O2WvHB9gSibKycGEWASAIRwpK0BYxJsAkrkj34yAXRVLJzYQRrHysCFZ0uhJyCTr00AIftRskDnegUUc8d9Hq6++f2z46Mi1KslQ/WCRH181gicHxGiIcBUxLENwfEY0GYm3TJ7p9/ivTxL75ya/ZY7QwIPSIbf3mL9z69wXu/d4Mf/ze/jbgV5F2BfLQgsCDfDRBm5Etg/CJAFlQ9Aa9WUrEpZKslqlZ55QzwrEBKGfVLd8LIAVrA6ISM9eD6Hy3QqqVNaslbsfPYpqBjvAAGfHglNVE9DwsUuarOto1B9kkHfj9CQLmwe0tAOAqWp4Tl3Yzth3fYv9qBZi2FlC0j/IsLXH4uiAdg/w0AmZDWgAjdhMg2Iy2kLWn3emNiTqtMoloWC0OmArnKyEPAcBcRDnotESb63Il6py1UKNqcPN8yYFN5wND3ejK+VW9HyVq59jZ1LArWa0HaKUDGq45S9opu2gmGW8J6JcijgFZS7aiZKtAHaNUiHLTas160yRHYaKtYmQT5MqvGk2JO+A9/66dIhfHDn30L3//7t6Bl/SvlRM92tsdi5f4eYbsBhgi5uwPu9+DdFiRPEZYdgG7aVddeUMe2OvjfJ/IOiHTtYjXpNWp81UuwuOLVSc4Gmog8SPTldIMgqGKf6JJldc0tsX04paRnGPWV2qrfU6vT9lpWP729OmJ5MuL4NGC4iVivpBYXfPqLtixTjYcl6DjgyrgU0gS885ZVfyOrDgOYGqBmbClnUimjtVVaekZTBZZcV0La35s+htQ4XyJArAGzRFTff1o11jhTNoKyKZobJB1jTalAxghEPq34n+1sZ/u1MAoBsj9gejVj8zLi7tuojB1AIHMT8K8abF9hEoE0EYL5X0DjQFhER4Nnbjos0XJ620eQg9+l86+FUIWXO19XBtdN00RTB8MQKAk4qd5PY492LWT6I/rLl6AgRvu5ncsL4/X8X8VksvfW4rKDEQ5QkPvHupz12CdrYdcm0PalXvRfDECqgw0GqrFAAlUWJgq1iY8doFUCIJPHho65lHES5+r1eyzs4iov9kPU/U4ZDVwypmmtmxTdF4ktYhHCsgyYp4gihKthBpOg2N+ZBCUzgo2jR24CP6UoKCTF2Dx2HhJtIXOQqFLHfDqQoDKG9Ll50mFrLAQW2JQ91ME4JahUxiESwhzAS0T85YJwFMSjyY189Uf/0dsZEHpElq82AIA4L7j6iRgQEHC43yBPAvbqbCw6st0cX1gIZQJ8hDovqBT8MAPL0DlKR/Wd2eMbAaA6RQDVEVWaoaGyrkkEspYkQ6p7pXin1AMGRiUbzUsKKNFiPsx+FvuCk2sh4P9h781CZUuz/L7f+r69d0Sc4d6bU2VVdbXdUqvt1gC2MFggDBb4xYNA+EVIL56E2wYJgfGDhxcZhIwNHjAYZNpYtPVgDeAHCSMwtrAlsNSSraHkbqkNPdRclfdm5p3OiWHv/X3LD2t9Q5xMdaequ6urMmPB5Z4TJ2LH3jvOWcN//dd/tXPIDobEEwyLsNwI607RIRvlcXDh5n1k+rvXvPPlhf07A3c/LBy/uEAWdAnO+vHLLF3XUuj4Wnsd/QmrQFSGTSIJpG10hXs/R6fUKlD1mgQDlgLkjVbWU2Fo5VEr8CUu4m3O25N0v68BIQf7Hnyl/FZJW2XY2zGHJEgW4qExj0jULQY6wOmJbRpTB37yZB1wndp87+1b9/zmNz5kCit/+6/8dr7wdzLxO8/Rqy0SP93rGS92biLyp4DfCzxV1d/hj70J/DngR4CvAL9fVZ+LiAD/DfAvA3vg31DVv/0bcd4X+xhTtX+bDbo/IDGSp8GSdF8MoMHGiUqsoIsPCFV7oAmK0pLfrrOnAuO+CUKnyRNfPIkvjYoCGHmsKdoNhXaPv7aCQYWh2iXu9oZ+mI4Z1Med8lpJio4NzOmZTbJNDDFzHNXX1FviHKaE1o0tfii/X2lja23nR2rsS3+/KDhl3zR4goNEYYVU1sOXU48t7jbBaT3b3FMBufJ/sgQ3bc4/4r6gyVMBvjx5T+39UmhjbvXXY1AX2wQdA3JaISUIAX7250nrwsUu9nF2iRO/cSYxoHf3DE9fcfv1LYfPDdZgxHLsAnqrmC/tWSa9HyL7z2PzI1ZIK+GkqGhdWd+PjIlSNxJKD0Y4QGJARWEAmY5PGr1uKAtp/BjBtXLKewyrbbEqYIp0dUAP2JRJgV7kOosSyvODATNlg6Nk6rr3fuKgB2J6Xbq0Pb9v6nqtdRSqY/zkfmFCsGu3eCMVGJIkdbNZ9dkPWaEF6B9sCUM8ShVHRrpxwAdxtI6tdaBWKLqtydg1Gr2uiZYSsAYDx6I1v+8PG06LwQjLEnl9mtjEHbfDibc3NiGQVLhfNzY2lmz9/BIiWQXVTIyZxc+pNCU0i02F1N85afEtOlRZdJvKJZVN1pQ6VI2dpu0+Jim1qJJulH0KoCObD29YrwJphN0z5Yf+u59h3e/5rNnHkPku9oNq8xsbDl/ccfzRdyzZPMHmufLoF2H7LBD3ct5N3TjrYzTWiEaqAnv/h9brCIUObQbqqt+KkEd1gMIEOOkKAdsq1lDc/n3qH37p5HZrLh+CPMXjSpaq3ZMmNeE0B0/OugND2wBWxTTVaIphcJq/wuaFMr5e0ADzY2V85MrZSyDNEU0BlkCoW9gegmEG2hAUGTISMvhoXC6gmkIVxOa8IBF1uqLfowq6FWdeQLJyHzw4aQmW5XrPOhatC2GjD3bPJMlHirUC2q1bWB4py21muVHWa2W9SehVIlythKuVq8cH3rw6MITE3/z6P86bP6vc/uIdxIAcTpeRgc+e/RTwLz547D8E/rKq/hjwl/17gH8J+DH/9xPAn/wenePFPoHlu3v0xUvk+goZBhgH0vXIcmXARh7lzN8ADWjpEuWQtLJlJGH6ZnNj20iyrnKcqVpEcTkXJK2dzS75LQlwHjweWQ7ZfGy25LoWGLk8Bv3K9D6OlZGEMh4gZZV68Yu+EZMAEjOneSAeheHggsor1ul2ML40P9atLS5IW0vWy2IGKEB/AVyk3aNOCPOjI3fSYmS9H+2a+s+jFEBh0bPHy6hYY2qdAz5VYDpyVli0AkjtOUsw0emU7BwCaEp8RHH8Yhdr9lNc4sRviOV5QU8n9PU9u+/s2b4P26fC5n1hemXMcGP50PRjOv/TWwEf0ijOguzW12dvKHycG+j9bhcryrjVw+fUcasClHSC//GUz4CfcpwKQHUNaTDfWbaP2feFjdN8qokpd8cp/lGkabuJsT1z2Rg2+PV7/Kjn2z3/I/dR/XVBKMt7+vXwfU1VgDAVadfWXffZKHB5Tcnpz2IHtbbqx7Trtjd/PDiTq9QKZ03oY7CJiiXY9MQcmU8jyxLJOXCcR17NGw5pZJDELsxswsoYEgHTBYquI2Rr6pUQ1MbZXDPIaD1+brVbg4FC+cE9SO37ngFVPrdWH2klQRSACBXWnTW/91/csVwJj78y89bfeGZyF5/BOHZhCH2KbP/uyP7zQo4DGmHzHHbvZx79zGte/NZbXv9wIG1N48AAG0UnJc/R/oYSDHf2B1ScU5pMMyAeLZENJ84cdV0dH7SxheofpFYRZxXIW60snoAzk0YDcXpgxcAi38pVTLQq44vaHGhxXGwaVbOAP5Ig5kLd79Fld3ynQNoExu2KplDng+cnI8utkHaJJ1cnnr/YIrPAOsCgxPtAPNh5J9Pmq3PUOMNHgThmNAdjGEXrmPTnAOqdYdNq0lEhwXAfWK9z1VySBCLYOkxnEynAgM1/095bVqlOsY7fFeCpdNq925C2im5LIPBOyagWIAfbHibbZIAWEIISh8Q4Jjbjyo+/9ZQX846//97nufnfbnjz736IHE6s7z6B//tnPpPO9LNsqvpXReRHHjz8+4Df41//j8D/CfwH/vifVlUFflpEnojIF1T129+bs73YL2d6OhkmcLNFXr1Gh0i6GlivS3Z5Dpg81IAoQEMd9SqJpgJJG8PUXxcWbXT60qXuOrQ9GHFWTNAB5sW3O+hTjtGzgIpehHDum/rRsKod8ZCRtGnd6BCV+Tiy3QvDMTtj1JJVGUtDRIzyPvjIlYP28UjrUEclT4K6XmXRTsLjpUZp43fazqUk+5YQm4vPnOtGlOdBY/z0P6vjBh/58D2+VtZqO04RkDWWlxcJpxVZE6SMHBdMCOJiF/t4u8SJ30DLycaS9nviB6/ZvX9TmT694DBg+WAHPCOdi3fwRpyBmWmgCWi38vzcx1QQo/PdD0eLVahsHXGWUBm30gAy+OiV2NhYHtVolrTjnAEGfWPYY9UZ+N2DNmfn0ZypVB/YLdDpH+v8bg+ulxhJwSycfVTuSx4aw7UwNAu4X85F8Dj2sL/6MAb2DM54fk1n1yoQcnuDUg/V+1Ma9ILF4gWbOqh1hQMt2aQy8hpsJMs3wy3zwH2cuFs3PJkOVUy6gEFFWHoI2UbSYiKsPkdeNsn5L4Xm7n4XMOjB9ZeR5nL/a41UgTW1Rk0HlBXdPkkW15db4fBmZPsys/nqh6Sf/yU+q3YBhD5Fdnoi7L+QydeJ8f0BjWKrceMt+3cD600RcjbBsiJOPDhzKCyWTK9T8UT2RxZnaZu83GGUzqYliM5ocV2cPoFNW0vO40FgbMBG2jliK+Z04iJ1nblkGO4Nva3aNmtzBnk0p5kHRTzpL2uLCx2wOvV6XQ28kgzxJKS7geUYjSE0KPNjO+94UIb7wM1m5vmUYY6EoxCWULWKju/Y+VjxIN419ex9yGy3C/NpMOrjAMutja1JahtnynkPB+H+Sw5mRYhH82aV4lrmrxWWbbnXhtjn0YAyY/3YNcajMOzts0hevBnA5kVZtnGzPkCmrbLeJpgyYZMYfXsYKmQV0osJud+S9kJ6Ab/03hvcfP3Eb/rKM9Dn5Lcekd+8IZxWLiXBxdze7ZL37wDv+tc/BHy9e943/LGPJPoi8hNYd5gtV79+Z3qxM5MYWd/YMT0d0GlkuY6cnijxJJXRYxpAWv/eezZPHgr9nObDnKmSRjlLzExnwZ4YOnHi8ro+uS3iyGExDTV7kKb/U5lAkMuGsp5p2SfJXWfRNCvaccLcPUdg9dEKnK6udwPjHYx3mbBkW9mbhLDJ1lDxWJM2lnSWJsd4Z4/VWDWpjxNr3ewDONu2sKns9eKjYaXzGU/dfSmAWPHvqdHjPyJAqp1Okx+islBLPdIBQeWzVem6+Nk6x+HlHo4n8v2e/PXXn+h362IXe2CXOPE9tHw4Ih885+brT8hTJE+BNIU2/uqgS5poPlKoTP2ib1P8cxAlrTZCbI0AZxlF6Qp3Z2cEaSzNwvZ0oLlnRBadtDRxxlTMKqSsLFdtOgBoYsPFbwVjO+XOQgArHwAAIABJREFU4as3a4PauZ75uh5rKACTtp+dgQzlfpQY1QHu/Wv6e1ebJ+U+quf3S7uvDchq6H8diXNdG0W84dFtwyxjeKnFgR7YOxufG0CO7s+zLc4pOkwhST0Vx/ps/Gy1Jki5z4hpzK7RGumaTFSaIaMpsM+Bb8ZHZBVuxhO7uDgglBlECChj16WYYyKETMqxsmw1hbJjCHUwqDS56wSFYo2YSb2wkVqLVZZvXVrhv4uiPolhtdd6pSw3cHxbePvP/b+kz+CYWG8XQOhTZF/4K8+ZXj3h2e+yhHu9VvIE9zmYc3cgwASQ7TWSDDwoYlunJ1IRV7A/vrRxzYJg+gYf11kUBVnORULXaxuhEjXwgpPUzm0/QwsNOS/Od7kpKDZnCX8NMLP42JuNuwVnx5ROb5rsxR8pAnJznPEQyJOSHy/uZCLj/Uqc7c8iFxEzf5/hPtQubh5NzyesIDNwENbrgO4SRBPWCzETtytJIUkknoIVGg5UEWz9YaUw+v0u11E+nzID3LOvUK0U0rJm3rae2fdxhtObPu9cP3fOigKN2sRCHVjiFMm+PSCcgnWBF9i+smJmeqXcfHNmuF/IY+TwWz/PuguMd4l4WInHy6jYxT5qqqoiH+c5fsXX/STwkwCP5M1/5Ndf7LuzvN8z/I1/YEyh22tL6HsGTVccFJOlS/IHS8ibXgPEpYEbeTDwpwAeIYmh19WHW7IfV1ufW7uh3n2Oi6KnljwX9qPktvq4+juhioFW2r1qHb0tv5XGii2JuDFAa4xyqjkKaQlsnw5sP1DGVyaoHA8Ci63SxcexUYuBaWP6beHe/Of+C1IbHdl1gsIKw0lZdlLFSatod7nXfeEhNpKX5YFuQm5NmwLIpbHTWvDr7bvXmq0rb+CeNW6Kle50ZX0pyGxvGI8Cz1/aeHDqT+5iF/vu7BInvgemmbzfM/7id2C7QXcb1idXHD+3MeH7jfugAvq47ysgCkLVHQMIzmwJ67nQtCRlOFFHqaqsQh3b8f/VAIXCBAplk2SZQhiadp2Z+PHD2ahU+V+DnXPxn2EVVJWyxKAHYs7YqA4QVdarYP5cXZOz/63SFgMruBQcVHFASsN56VHz+nIvO1Fs8UZ5vZbsZUJ/3bUp4gCbs5gkK0Fccye39/i4UTUD9e0eS2kMdABYSO00gscGksXNyhytH0Oo4+MqoBvj3qY58EKvURVuNjM304knk8lLGEsos+SJ0zowp8i8DqzzgM7BNIrGXKc6NAu9WHSZgkClssmKlqos3qwqzCJtLwWri4oYd8hGhFivlN0z4Us/9XOfeTAILoDQp8ryl/8Bj5/8Tt7/ZyYHSzJ5MkcTyx9NcYACogKrr5v0dfLpSs9E39JWDTRZ7I9s2Evr/LoV9FW6RDTMOPpMTaQlcYaW985KYqO8l/GyMEujq4aS8FOvo4xn6ag2DlAohWLjawmpUHd9n2zPK51jMoQxV1qoLNm61Nop6vtmmarM33UzyjHL/K2WFYdZCEEZhgRbIZ0Ckn07GQ6ITT6KUM7LnZzg71EqlvSQqqrWoQVb0bjaZ6y7FpjKWFjt4jxcy1mCRWggVDi1qGpAoQGAcbb58nGvjPtM3K9oDBzenbj7QiRt4Pbrge1zQdYLP+hi1d4rFH8R+QLw1B//JvDD3fO+5I9d7PvI8vGIbDaElJHV2EFVa6ACQtIy6T77LSB+8elR0FRash1wpB3A5EnqOnQgRwdii/+8FB8FCK9vWanvHfj9sCHQm3a+WxUNodN+kMaCFW0MpCTkw8DmA9i+SITDiuTMcISwD6ybSIyWrIeEi26Lbe70bV55gBybVlLZ5nnW4ZZWMFHGDJK269IH164YcCadPl3B5uXc/xf9JqKcx0VAgtZ4brpMtqgAP1aYhShWOIyvBOYFneeLZtzFfjV2iRPfK3OHrFnJ93skGYcmbCc0bEmTsO6EKm1Q8uIiDeaAw5k+TqCyinSw9yjAugHa3cKYXJq158eugtPeDDA/1on6xwZGmM+yCYI461lNUJq90AFWq6Xt0p271T+WR/fbyc7GbR081yB8RAKhr39KPCy0mq5x3U9LSBePVFodVQD6Ag5psNiRCyDTxYVeRy9H37Sp/rxuvLoHt4J259vXAF1NVq+pjytdI0T83pyxjfZia9yLvIiLY2uAvB+4i1uWFFl8Ff0gmVUDd/OG4zowr5ElReZ5QNcAcyDMwQApC2jG+Indua3U5nmN34vY+fkkhAFvXrOtPr2BfV3H0cq9TMY0Th98yMUugNCn1vJVRn2D1hICeh8qRT3vsv0RuWBYTYIHZd0pw73RMcG1ZgY10GUjaAgmFNoh2hWoUe8cFBCiSMdjj0t25zGYx1IM/VWhAldC51xrsLGgYgKlhuqWDmucy2ulCqLp4AGt6Akp1oFwoAoBBhOiJsA4rYSg5GlL2tqB48lGpSpQpV1yjDvAjSKjqehL55xUAnkTUG1IVNEKiif7Z6vm7dryRon7gAvse8DQukGtdhoqQuVaSis2u+uAT1h8JvYRrDf29ALqVGDOA0seDBALC+BCp+Uzf1hQ5NHYR/Nj4Z7Iix+9Jm1heZRJjxbkEIDI6fHIdDfw+G/HS4FwMYC/CPzrwH/m//+F7vE/IiJ/FvhdwMuLLsT3seVMPGWGPc3vdMmrdl1hoCaYGqwzCyZAajpC0pJxf64lwA0s0m7VOnTPhZawCmevAWfMFHFTB1Ra8u+v9XXK1V+nB76xKzzyVLY1UrN5SRDvAldPM9OLhXBaQJVhbyygeRtbsrnCsEoFZMICaVeKpvKcBmz1xUq5njOBbfUxuO5+1MKsPCTtOHVbmN+r2lH249aiK1C3fQaTA6p6QYVpG8q1nzCA6wTbDxRNiXw6fbRgutjFPrld4sT3yqRROvV4QmKAZUSWRGH+pN2D17ivtG1YWsGevrlrotK+8bDkrAph9lXp0o6TxyaboBFronqx3/xX0zaq/wbHPIp/3OB1QWsUSPJGqZ+TNQ/Md6fi56E1Goqv669VMZZPaVR4jAIafaa/pd2YckhUGbV+AUIB2EujVwSkY39WllGJex6bPL2vmm7l/Iv2UHbkJyS1uqe7jtpcyO06HjZHJLm+aflRf8653Y9a63XaeoPaPc7RahmNwWq7ILAqSxhJKdi0hChRlCUH7k8TKQXWNZJSQJPAIoRTIJ7Exbbts5VZKkoh2Zbu1Nsgdl6hxsjWsNJgcdZ+p5xNtNarPNMolPUSu4pdAKFPkX3n3/vdvP5NycaW5sBDMeW6BUuUuI9G11+lahqA/WGVtYVpp6RbCxQ4AFQSxezbs+LBWC8FaEk7c0xpYwk1OBiSDEDJG+oWMI3ZEWVFkxBOBlJJWaleGs9JGO7FR6yU9SaDBPtjV2PIFCpqodBX2mfpZEsDluykQDe2IjPGTM6B8U44vTEQVtg9VV7tt0xXM3MY0buB5VYt+CTXXDo276oRphfWYU67QLpa0dmqiTBmeLywHAJxEaYXyvqmCVPLKkx7Obv/YRUfV3DR78EeK6NfefDA5+Lgaack7H6XoCMZppeWuBfx70q79QhYWGMFIDKkv4mD5409L8zCeCd1FCOPNuoRD0KYBxA4vq0c3wFR4fH/HOECCH2mTET+DCYM+raIfAP4Y1iC/+dF5A8BXwV+vz/9L2GrhH8eWyf8b37PT/hin8yyoocj4/Mju6dTtyqXOtpEcI0D7Ge5sHtCwwnyCHkVZ+JIHe2qo1juLoqIaOvUNn9tP7exJvPx1rntwaMC/nwkxdPGkhHRNq6QqbpFqCXeZc16KrEKIGLi2IswvhZuvrEnvj7aWx5nNi+V5SaQx+jjxDDel0KKqiu03LSGiWRhem1Fk90jZxGVjLcvXqRseLMH4tG2QZZzLWLapWgDKqOoank8vCVlc87g15btcyiMYQ2QbjJ5EeLBmgvjnT2+ea7cfnNB5+UCBl3sE9slTvwGW04QImG7IR8O5IMBKGGIhPkRaRTmR+ZDh73l9WGB8c591ADrNQ3g6YDoHG0dffleEkSaVk8d6x2xpShFmNqBlLho1Zeruj2xA4aiFzMrDqwY+6PEgbCaD8y504PD/Fqc1ZlA0prNDqSIufEGxmfa2GxtbnRATjd+W0EX2mNVmLkwMbFzDdpGkDVYM7uyWBMulm2vDatNPah2AEZ3LrY5smvg+vsWHbo6jeFgv3hDwGJPOSd/fuyO0ek7mejz+evLawGCM3HLtIOKtHiEkBchHyKHaWA+jYSY0BxIi90w9U1lqAE/8STEvZBjQEetjB+dpeYJJodhdY9tGbVGe7FYPkuXDemZyGFtOUvZPlZ0DC9mdgGEPkU2PwK9Nm8k6tByEuLBUVVxga19rGwbWe0PI3k3VB3tTRvTPihgUGUTdSi3Ds6QcQqfrS90IKfbXHA23xudcRTVIOboSatCztE6pp4Rly5uEUzL3r3t522LSn4Vzxy0BanShE5O309i1+SOucyqhqDWyc62Jng4Ktvn8PzZNdefu0d2C8fZGVbRmT4HaSj9aOCJqP1sfCksT0ZLzKNaVzdoDV5lpIsMw2wMnsJ6KmNitbNdtIVKoHDxbAvAWh2aCmei3bWzEZsTbMWSmGZE97kQaB2acvt6emn5MlM3zlnXRpvwn2+uW/6538H2Z7/B+p33vsvf5Iv9oJmq/sF/yI/+hY95rgJ/+Nf3jC72a2G6zOj+QHxxx+03tqxX0Vap70yXrviQ4UAFivpOpmkPmN/Lg/lISz6l+hpRrToGZ6uHe2JQ8e9qopStc9wYriVxxfPqonsA1MS1UuSlgSm5jDRIe88yZou0bZfFH8YToErejhCE4f7AcFCGexiuhdXvRymi0gTLxkYc8kat8eH6dOOd1gIiRxvLFTXdn7NrH2C98vjn/j2ldv89bDQACK2jcxq8UPPPpYBu/b+S+IcE8WTbzyT5GHE5roNE8QibV5nNd+6MBXuxi31Cu8SJ7wPLiXw4gAR0nskpISmz+fBNNo8D61UgJJheqq2hX2B6nUCt2fn6KtYxnrPNYcFADcnueyJoCHWDpCrEpMRFWYP4plwBbxCHxUelvMnQfD4NHHDfbU2D5pdzcKHoBDG3eGINBQOLrPDvpgZKTr3QmhzSrqf6RV9KU9Pl8r4FIPHz6XXrzkAIweqcDEHOhbDrMSsTqb2/jbrZBq9aG3gN0q+or8cpLCtfLqB9/aTYdrGikVfAMi0AUWFTteNKUpicgbVa7VbZtFAbDznafY5H/4w8RmkU37gZSHMgeXNFnDVbRrbKZxtOzkI9grpWkI3VeX3pvwfxaMcQB3wqE7b8rnQsp77xMxy86e2EheW2fV4XM7sAQp8m6xFu7P8wmxZM3qij5cYaKWBQSCbgnLbO6IlqukFe3JOEcLTtWlKctYMBeSxgjLGChjvOaJ9FFwcaiFG7woMim0QY1Dq2oixLQJfiYLUCP6EmugqBKuaswY5lf/Tm9Sp9viT27kRktevNvoAibTOySejB/gRE7NhpI0z3mWGf2Twd4HOw28wsu4HkAnloMKFsR9fBx8+AcbYuanwdSdfGrtI1IKN5raJPoa7VEE/4CIaQxfSaCOdzr3YRCuJK+2KAmo52TTYbCwmtYxD4eF6593UDDc1h1tlcuq+1A+NK8eTzyWUWOZ6sOEhjAdYMZFMBHTNPf+eGL73/JlwAoYtd7AfflgUOR8aXJyRNSDKnVwTvVUwQOY1YwukAvz3Jk373QbXB2P2cLOSo9Xt5iDGUhLtnHoUO1Cj079TFCk/CK0NHumN3QHk9174YcDOxSvthGtUO7voOOgbb0DMIcRopY3AlwS2JaBkPw2NVXWrgvrQC/OU9k28d67UavNAqTF7bLgl5bht7Hia18uCaapJPKwZaTBYbBelGtus43VIOYDE5j8J4pwz7THh9YM0P3vhiF7vY97+pEjYjeV7QlOB4Ynh5ZPt8quDx5lUmHpU4Z4bXC2FeWW833H0+nhXf2vnnwmYvviU5YA/N9xZphww+BuY5Z8fmsBec++SyxbiOX0Fl7VQGT+eO6rKA6KBG8WmF4dM1rVUwiQaoGnWNta/no2LSPV7Bkc7HKmdLcnrQwRoWDQk6O+eHcejB/ShlAHSgWeffRa1eq6dZ8vwOZOrPv0v328h3biAVznA6A1hyd4yiGQvk3OqCXn8pR1tSJClUTdN+O1xpvIS5jXtxkrpAodQy/T06Y/RI+/wq28qfJ2t7rmRlONpIo7F+hdMT5eZr8PiXZi5m9isCQiLyp4DfCzxV1d/hj/0nwL8NPPOn/ceq+pf8Z/8R8IewX5c/qqr/66/DeV/sY2y9UobtynqKhuY6eFCAm8FFQePJ0FIwmvrhXSVvta7zW68y4RQYXgvDoVA2DV3NW9MfQkCnDKdoK+QnZdgHo5QP1hWNJ6nUvEK9D4vYJFsUwpgZhsQwZKZh5WUKJB2RJZIeJ/I+2kjaSVhuHKASHJwyD5ChgkKSzx0tePBxZ4Ma84lB0V3i+vbE/TFyOtmfgb6pjK/NicWc2Xwg3N1PxNvM1fWRu+XKHB6Z9UoY7qQThGtBsQS8FBWGDEsga2RY25a1Anrl0TolVfh5cGCuoOEnu5i8gbTLpB3GzFptnjZdK7K1yKMDqOs5Bd8ORud4G+3WP4dBjem0Ospfx8Zo2hW50HlNWDrO1i04vm0gY9op6clKfDHYe+A6Exdx6Ytd7FNhmjJ6PBJeHRjE9IIk2briogcRZ2sRZx/pkq4BkOtWSYtHD8EO+168wFDXu6H6rtyBP2DgSAGCcixdaUteS6OgZxoVAKiMLNgxGqhUxh0qYF46vDPelLCGCR4HJQnrbrDtN5MwPb5i3UrthFdx6wETaPXrJEP0LngpTsKq9XrgYwqApHXLTtpqvR5JrkW0inV+u2S4HwmoCXIppMrXvaZE6UzHBo6Vc4lzO04ZowsLjPsVvdvbCMrFLnaxHziT3c7YmfOMpoS8vGP77YGw7mxz7OtEPK6Ew0p4vYfX90xPHhF/y8a2gZUx3wE04Vt5u8Um7jdyl9/maLlkdh+YB8PZVUvN0M5PXaS4Mjd7sKj8vLDpu8ZAe0LXMBCPLc6EsVpAWq7rhy8jbDKrn3vPVIEioF2Of85i7cCY0mjtx8ko79WjVtRxOrumLlb5NZevFf8+aze63TGytPh+b8CU8/YGSs9ssrfWyuYp5/Jw0UOvGRR826QxshrgVK43zqCFbVUaFwPklUoo0NixfhwxC7NvIC1xUVtYqbG4b3pkmlaQb+kUaDGtvG71UUG/n/GkRIHU/d5+7q8/J/+9n+NiZp+EIfRTwH8L/OkHj//Xqvpf9A+IyG8D/gDw24EvAv+7iPwTqnrJGr4HlkclhgzzaCNeJ1vLuN4qm2fBNqHM5Y/adGqWG0jXmfjIMr/8YuL6a9GKe6epr1tLtIeDEB0ASluFMaMhtGTRgafiQIuzUAcolhutY0ayTcSYiTFXhtA4rZzGCBKJr3x8zIWgwyqs164h9MjQjeI81iufH07OAhpp4JGrzGffbBBmQ581BubTAIOSveOdbzIQCKsyHBPTKyV8a8urtyLjzUyYjKOfo5CTkKdgbCtnIOmoLLdW+IyvBGUgXWV0mwlTQscBDdatiCcbX1uv+s6Fa0Nca72nffCJh1B1hXSwf+Hk92iVNkrngXi9avdAVmz0QW2sLMyty5AH+x2RzlGnrYOIe+HqO8L0ysCitIH5TWF+Q2vRJXcDZEFQ5Bi5epaR/fF79Wt/sYtd7NfRdJlJLxeG21uCCHIaGF4Ho+WHQB4CeRMdJBLSKCwxfGQsqbAjJWFjuuX7XHTSHExO2Or2buSpHztebszPDQdlPMLxbUjRqOTDsXQ0hdx1aFWsWIlLt8QA6iaxPMBw5KyzGJzhI1lYb4U8ZXQQwmwgzbqz0bn4zo7lWmrjJfhmlDQZq8cYuQ6wHBsIVrrW5bpUYN2IgWyhnUcdu67Nhw5kitZhD96tLuBQGltybc0JakJdCqdekDoPZay53esiei3gY96WY2xeZcb396RnpR94sYtd7AfN0vPnECISbQlI/vA54XBg9/4VeuPK0mtClhWOJ/R0QvZHrp4ljm8PTW9NgDo+3ED/4l/yKMSTPxaE6eXCMAUOIXJ4x7TWVMWBIz3zU338MM1TP/kOzAaMER+NKd8vLKjXOjXApDA0czIAKEcDM0IhAkkDpvKoVYumTASk0cCVAoiUJkRwliXOiM2ugdRfU7/EwJrL59dTgIzSQLDXKIxSfXmcrdFQZCCGQ5HSoDURfPsYFWTR+nlU8MT9fnQNu7MpDsFqpg7cix6zqn5fAZDKORZgxoG0wtoJi495+Tr4MoJXN9HN3mj281+3Qug0k8rna09uo+GSm65eZYr5/+XGls9ABU6PAtsXmcHjaVjknPl1sV8ZEFLVvyoiP/IJj/f7gD+rqifgl0Tk54F/Fvjr3/UZXuwTm0YlRmVxTnihpZdkLk2O1vsfSxqdjSLKOCaWJTK8DOyeKcu1sDwyACbdZPRkgENdO7yCnOJ58th1NhVHh8eGktexJU9oY8yEoARR1hQJQZHB2EZlBboxZgpg4s7StY7K2nmiuvia2rGrqCYV2CqOt3SjZRaWowkil9WOumn8yjwETm+aM5d9ZGEibJOt5a3FDRX8CsnuZ9oqaZfZvB99xXwgRdBNK276TsLDGVYTj+6EUrvOsaH1YvO15cepe72j6VU3aGzztUXXyYAzmjh4GSvzbWdWdJhe1HAvTC+E3fuZkJTj40DaCcsNrDv1ismOk64zYQ5MLwK7pyc4XAChi13sU2Oq5GfvE959B3K2IuE0wzAQthPr5x4RT4Jkc8rrjkbjLtZ1jVOUmnyGJE6xd+2H1f1QEUkup+CFwbqzpDK8hs3rxOGdgbSBtLUFB5aslzYntRAoOhKFsZQ21PhVWEUlwa/NjNKZ9H+FtVk21+QBjm9FlmvfalIKoWhxxRYBGKOTYAl10dkIzlSveg+0IqiMoNmmsRbPbdMKZ1pIZVyh76amjSf6qT3exjqsQKsriHuwrmxZe9DZtpthgp63/99L+Ppl2dPFLvaDbhKjARgpoXM3OrMZIQQkZVjWui2SZWHzwUw8DuiVy0bkVldoAWy0FO4Q7nJl2KRJGPYJnTN5FF6NsTE6g/08zlpHa4uPzCVWFJZKn0KXOiecf91bPqs9tDFvUntuBXcibcNjpmkgdSzVXqsmLOp6OXbN6gt3KMxPNRZnrRmgadv0zJfyddf86PWISrwswtAFYCksJig/646JNx5Sez3S3kIHYGnPr5IbYkCe+PYz+97ApxJL0tTFqtS+LmBR/9mERRkO9qC4+Hepg0psLp9JPKnHwXbfq87eyBlDCPw4PcO2/mJY46du0Q5CvreG/HSX+ZG/cI987VtcrNmvRkPoj4jIvwb8P8C/r6rPgR8Cfrp7zjf8sY+YiPwE8BMAW65+FadxsWI62MYsgrNIjmW0yrZ/rTcGGsnSaRQIkMUom6fI7beE3Ycry+3A/Mg2eoU3ZtIhgg7EgzmfeDSx6vW6gQgVtFCQAOvG2C7lDxKx2dwwC2kVYsyM0f7S98fJwKEpka4C8TSYIFm0Ma/h3tHkshq9gkBQ9A1UbE08ULV1yBjQUZzrKnWMLN8P6FUjr8kmIWqjAPPjgdc/thIOgXAKcAqkNyDu0hmIU5T9y3vpJhGuF5Z5w3hnrCyNgTRGE3wOnM/Zump+pbuWrmzv/CsYZp1gPVjkq4ygYONpMnQb4iYD1uKdtBGGYAUFXrTYlofWTQm3i8XK48D4bGT3VNl9mNk9nTm+NZJ2toFieZQNPAt+X4fMuFtYP9hx9W1l840X5Pv9r90v9sUudrHfcMvHI2FN1lE+HNG7e2SaXPRS2uhTSTzpEt6S6HZJcR0Bg7YquNNhKH4KuoQuqgE5DvKMrxPIQJ6scdAnj/12m6rdk12kU1qjwZ5sflmWllD3RUXR0wmLEBdF1oyobZWcb6WCS2EB2dI0LYrov/tKSRgjNndgfXeuPQD2UBtIvddRutVnJl0toa0Z0Gs/VEBoNIp+iS91vM7foxR5BYRqRYxtG5NnH5JevfrY35GLXexiP0CmGVuhiGkJJWcEFcvZNgkCZEXXleHVkeGwI229oC85f2kUp+b34qyM+1zHmvIQzAcfM+MrqXp0Z2wQCjhjgDYfszm4+LkK1DsoZbhV24hbASLfEmkAutgTO6AccK0gOff72nxo0+LUetzixzNaFwUUgL+CVCWO9YBQqb8KsKRVkoc6stX/3MetAMoW4Y8IUff38SHQ1F1Pf6/75T9nDX21+xm7hnNpHpfxbETOxMWtzvP6TNo1lnsY5rYoyIJ+1/ju7kNY/NdS2vKIWsNEOXt+WEBKY6cHz/w5hZBQ4t70OjG+WgmnFf1bP0sXgi/Gdw8I/Ungj2O3/o8D/yXwb/2jHEBVfxL4SYBH8qb+Ck+/2CexXeLN6z2H/US+H5zxYaNFeTKhaKLpGGg0Novuko1N/eINj74jPPrqyge/beDwbiY/WmF1bxOVvLPZ1ZuvBoaDkibh9ZsZVjlzOoWGn7a2qUwn0ycKh2DoboZ8jMzzwHS1shtXlhQJPjp2UmE9RuvoLnK2RcwckLiQsXVew6l1mwmQdwlZA+Li2caYMS87eMBSBF1AV0FDQIZMHLJTG2G5Em7efc3dB1fI0cbXhvdH1jcEBiXuQ31ummC9zsas2Qc0TehG4a4APh6LPJjlohOUzzd2FUc9HFxk1b8Pc2MXFYqopICskK78HpdV8T62IErVWtIIeavWPXfR7/XKOsSVHSVKCEp6OXH1tYHP/Z2FeEyIKmkMHJ8Ejm/B/Eb24kaRyUbhBFjuJm6+Evn8//EURBB5EOEvdrGL/eDbEK0jNy/ouiJXV+h2w3I9MN/GCv5XUMGtJskdI6f47JqARgf0S3JXwBPfTKYDJGzUNrge2/hqBjbkSVm3whSxRkGsPhP0AAAgAElEQVSf3HoBEZx5ZMWCUc2Dv0/VuqiJZ/uHGBg/rKYbF0+ZeMrYaJYwP3YAxvX5CmAF3uEuiWoByTrthbNE1llBSissilWdDE+044mm/5AtEa/aFA525VWaWKgXJzqYyGc+teKg15EoiXNlIJXPyrUedu8rrJc9vRe72KfBdF3P/57LJqpphJTt3+kE02gsodNM+OAV06snpsEjvp4+WcEfT7Y+3pjzynBQNs9nA5OGwLrbkMfA8PrEdFzZvNhUUMG0ZLRqcDbQxMEkH2EKRWg4t43CJYcuPrxu/Mot5605dpkS8PGxPDiwkcpadWnb0kQaoFPYkwUM0ubLA+W4zUTPARcogEbz0wVQCqsaq7bQdx6kz2FVsotR11XwRTuoO2R7bwt0+uA9P8LkAdeD0rPRZHvcaog6hjz5MpzVRbdLE6WEmJ7NG8+nE6osRv95ZdqYV3c/Q1avyzoQrovRtameH8TSEssr6KZVIF2D1VzXX/4W6ze+ycU+3r4rQEhV6/ogEfnvgf/Fv/0m8MPdU7/kj13se2Fz4LCM9gcd1USfVWDI5E37Y023VtRLEuKryPTCxKAlwwe/bWD/pWSaMPcDvDHXjTEa1Sl9lvzOj0C3CU4BnWPbNjBA3hjokG+T/aEe4hkCTbYiQN1p7aaFlIWUrQ2gm0wW8YTdQQ3XzhF3RnUEYOQMUCELKgojdX2uZDlzdBpdL8HH1yQoIWbGfSbOmflmYDct3Pl166BMLwbyIaJBGV+Gs/GssnFHkqCL2JheUBgbik7nwMo59Z1be01L+u15Uke5isPLI7aqE5oA9eqxblSCypljLN3xampjGilk8gYYFLaZ9Gpi8yyye2rI/bpr3Zu0E9adkjeZuA+kwUbXTBw8IcfI+Frhw5cs/+QPMb18DZcu8sUu9qmy9WvfwGZms40b+NpxjcLhnWC+Sd03e2FQAIcz6nthAxWKukK/RrixGqWutS1jz/EkDPcw7jNp00CoQmuHj4JRZ6BMSRBHtYaD+3HTWGsNiDw2/x5nA+/H16ZdFJZktPkdLLe+7ng2faGwSl3hHpK293RQp5xbjoCvl+870eUeJRfs1mDvL0s3zqac3TcVIU0+irZQR+Ikm64SDqrl0e7hEMWKNF9pLEXMtQBjBQzyZko8Crtnylt//sukw+HX5pfpYhe72PeV6TKT70HG2Mgm62q+HiBn8ouX3H7lwHjYcLqNVYMmrOpaQeZXJCvDPjG8OMBqGprDk5G0CQasv3jNo69ds38rooPrxzn7pLBFih4cKEuQCoDHRVs+3QPnFeyg1QMds6bWMoE21aCdb9b2vUap+Xg7vvvr3JoLaXJAXssYlbTcWwtQJR2jxxa6QItNYDEvYL68aN4BtdYp4IgO0o1VexioLKHGfmqjXyCx3bvytHJ+Vj/ZqF4PCg0Hj19+rDrKlQuY5s2F0DVgcol3Pm7WHa/fDlZG8uz+Sat3tLzGxcnVplfKZxCSso4WUFXUF9g4iOm/g2XEr9Z7o8V6+524NKp/OfuuACER+YKqliHyfxX4Gf/6LwL/k4j8V5io9I8Bf/NXfZYX+0R2/YsjH5zesG8G9X8ZiQ1IIYGONjY23AU2L2xdfN7A/BiOn0/oLhFeDjZWFZS8hMoUKtvG+tlbW6lr3kcHSyjTpOhGCduVPEcTUd5lY7s40KE5sOZAVmGMCdXBOsIAU0ZzQJPAQhVRVhdGLk6nFBxle5YksS1oPpOcRzWmDHjnusynanXaRu80RzEcDAHXAI+3R57x2N5rk1AZbGxAheHYtCigBDNP4FVZR7yj4eLLXfCxzoDQC6UVK+dWxvoqAOQgkVFa20Y46I4V/LrKsTrfJ9mDUBcIEAw4jApJiPeBYW+BIe1CDcxFtyP7hjZbrSxoEjQFwmZFFtPvYJkJa0bTg3bJxS52sR98U6XshNWUYF6Q00xYlNk15yQZYBNLolh9VwN9inN6qPXw8LHK0gnFf1tiOextXCxdmbhpz2bp6fb1rQoIJLYUoAomdwlrTUpL4m5T0mgw4Gg4wHhva5jJphuRJmqXtOozpPP37lPQ0g0FqoZb0YEr28HqdQytAIknJc7iwv9URijQklyxxsBwMH2JPBpLqBY10v4v66FR2x457C0erlshTF1R5A2ZsJjodt5fRoEvdrFPs+m6EL71PnJz5Q5AIDlCH8w5Dx/csUuZ8WaqejCSFVm8QTAG8y37xY4RAzpGBwwEksLhyPbpiXWzI2261fBKQ27AhZSFfMKAhaRNRsFjiobz5mq9Fgd6igm0Bm15gAbGl+PY1zSmUQfc6IBt0lRFFgPuexCq14HrtXmKT5USJ3qtuhIXnBXbfLv/y9R7d7Y9UrvTq+8ptqnTwaiq8ZQtjpwjXHY9COQkBPrms9bmQ93Q2dVeNirX3cOkbUx77WJpia1946cDiEoTRyMo/nug3UfkSyaKJqGoy12I1Sq2CdPiV9EVqtMVi9W2YRa++H+dyC9ecrF/uH2StfN/Bvg9wNsi8g3gjwG/R0T+aewz+wrw7wCo6s+KyJ8H/j6wAn/4smHse2c/9J//NeKP/WY+/F2f4+WPRo7vrqaLExSVgKzBxqv2cPVt4fo904f58Mc3vP7RxPjOgcfbmRffflTXBOb9gJxCZdLoJnN608aaJIPsY6OyO0KeNjZaplMmFLDhIPClE3neMr4OhBnSEljXyBwj29G8g4gSh4TslEVHNAucmqJ+dbBTJoudV95kA0UK6LIGdHQAaVSGV8GKiY2yPiqtZ79pfn5pDoSQGfb28zzAjz16xs/zLgDjbmF5NFb9obDAck3VENo8F9ZtG5fLu0zeOUA1C+EYDETzoqYPDKjPzWoZFbPEPyxeAHQi1L0vR7D7ihc7gxcNi4NEU4sWfTF1JqTn6+vDURjvrUA4vmWz3tdPV2RV1qvIcmvsoOKpZREIwVhjpeAD2Gzgp//eZTb3Yhf7lJtMk60rPs0MhxWNo4Hg2UB4OE8EQ+n+AeBJfE0wsa6oOGtFWnLfJ9g6QDgY62V8deLwhauWtOPHoCWy+jFgkTE6DeTp9QYky8cIMJsPlSPEgzLulTBnCOKjvw98Mi0Jtvd0mn33fVGQrhtfeoFR/HyDCUMH0TqKFk/lPFuCr74qviTfcTbmFBq8MdN8vXbAUaHcm9gnTPfaFgyUzrivk0ZheqVsP7x49Ytd7FNvqqRnzxiGz9v3IRj4H4JJAQRBXt0xLCvx9YSOER2NhiOrzQHV708GCOlmJG+HenzJGV0Whud7pjcmFo0VUIAOwHaAJqzqS1qozKDggsd1eUHDkPwgtPjSATPlZ/V96PCVjsFZftCD+KJK9q1VVe9mgI80Nor2jY+rFeJOf8zQsWIqqKJ+fQ9iSr2GjBF0u7EoFW+AS3f+ZbV6+d7PIayQekAJKsNWfGNl9MZEA3Cocaf/bHrgDKV9PtLEv6uubP08Wg5QWFZVHLuMZydnF/klh6SkfhGP+v31UTJb8EDdJhbKOQbX2vPPafjLf+uyVOxXsE+yZewPfszD/8Mv8/w/AfyJX81JXey7t/QLX+XND1/w6t/9cWQJ6NFGuUhSneZwFOYncHxHSJuJ9NbMsF1JKfDivVvENWskw/Zbo41+jQauIMr8ViLfBTYfCPEQbLX6ZKvK08bR7aAQlXQcYBXypKQ5MviqPw2gKZCzEASuxoUoypoCOQVSCi6OnVl3go4ZUTGgo/zFdwwbdWaR5sasURF0m5ifSGPcHAwsYlBkSuj94CNpyhqVYb+wXpuAckCJu0Q6Qc6h6v0A7N+17Wvh5GCXe9iwOJCT40eKkbItphdVLUl4Uc0ftAizqrGsdrb6vQA5pbObpoZ81/naQisd7H4MeyGcymMePAuTK9gYQC5aGaOyXsGQbSzi+r2V8dXCejNy//nAeq3IEgw8u86Eo436yaAMQyIf7Bx5+w14+oy+w3Oxi13s02fhN/9jyIvX6OHA+K2XPPqlDacngXVLBTUs9lAZO/3SAe0p/NIeS86ILIlz0Xgo4FBIMN1n4nsvWH7LjVPevTLoEmUDThww9/WzJemMM95ZpI2oud6EJCwzKmCUj7wNRxjvM+GUSNvBVv+OyvTS1+mmVpwUMeY8AaeiTUETBv24AsYfV7Gu83oFA7YRbNxnhrtYWUvrtp2/KKZVd1I2rxObDxfAhLYln2sCNaDLrjUkuH66Mr5cSNtIeGMgzDb6lzaeMwi8/eU98te+/Gv563Oxi13s+9h0WcwpP7QY0f0B9geIkfDoBt1MhmJk04KQYM5d1oTGgF5vjM05Cpu7FVmSNT5f3TO9uK66P+brpfl+F4YOq+umFX/nm6mqn63UFbPqi+kelgePdb66B6J6gD8sLUYU350jHh/EtFRHm4wAY2eWpkOafFrt/NR8OYAzjJwtUzcPF+BFm6+uI2fJx/GQCsIYGcY3jg1N40geyLzFk2k8Ta+U+YambVRiom8gLrEurA20qvfOAaMKYqnXWCXmdgsaQmkcZwN01PWPlKZ1V5ojYVXWjcXu7OPZPUMoLA7ClXrKa9OwKLsPEmExBC2PwnIdzkbMx72y3MrZ/b/YP9x+NVvGLvb9aDmh93vSrnDrWlKnk5KikjcGgBRghEVYs3PQk21SCUtjwsgEOHW+MoXg3NEGAzCs+2gi1gByFwmLJc56iBUxz5uMRPM2azLtozUHkkrdHiZRUXIVI9VOg8Lo/kJdfS7U0SfK+JgaQAT2swJ8FAqmzpF4H8lX2V7zakROJ9a3t5yewLcOj+q2gHSKtpygrCy+zuiUSUGMfVVGEJzGGg82X6uD1lWJ0ND0s004Pc1VyoPmOHPsRsY6MKl8CDqoMYgyVUAbX+F4tolGBUXrCEPwUQJxqmUOppGxeQlX72dQyJvIugukjbGOogeMdJUdjbd7nx2EiyeF959fwKCLXewzYPkXv0a4vUGur9EXr7n96mPG+4nT42Cbt7b2vIei+cEk6oyl42O/JfxUMeei1ZDUtIhWCMH86/hKCSdFtxPzjVQ9vL6T2YtYliS/bizJtv0mLMbyOeuiAoVBkz1m5NES7rAYOyismdNbG9atVSfbD5TlRpq+T8ntxXV8zoJlOz/tdDLK80sCLr4woepARKPH58HG3spx6sr40i2d1bv0FvPyKL7qWc/YVkUEW1bYffU1YX8kX20J6YpxZ35/ep3Yfvlrdm6v7y7d1Ytd7LNk81LZ6bLbmsC02rjsmWUDflCFNcGyeLJqW8lkHJHrbWWEkEHHSLi5RoeILInhIKSNIS5Z3P9LAz4KANCPvRaTTJWIqIweOpDHn3cmofCA/XKuE9Rei5pPLmVUrw+kYnGkLr1xACJNJrGwXlnjtvr7EgN7pg0d+OH+3Jq+tKZIWYAwGygUkp7F03J/ikhzr+cTT+INAa0NDcTZox2QBVSmUCygS8+iKudSPw9vAIeiydriGD5Q0jYle5wd7Brl2N0PbTVTr+9UPldJFsMqu9djqjXeYXoxEw+Ln3+Ad238MA+QRHj3L321jjteViH8ynYBhD6FpmrskPVKzFFmAw40qs1ixpKJYg5vb78G6lpAYelAoTKr2wExLUk1IEjlvPtZNX2SMNzbX/d6nQlHmysuIs3BgZs1Bw6Ln4OK6QiF4lBMnFlTMGq9O005hso0kmwAlyEdhqBXiqevg7dur9YRMbJU0CSNGQZlfBqQnFluAvNj5Vt3jw1QygJL8O6ygV26TQZYiZAF0hqJR7WT9jEyAiR3bmFp97DSLUvQOXO45uzDamg6aBVEC1FqMKmBLFALonAs19wCypm+hmvAam7zuzUYCQz3sHme2T2bSZtIHkN1xnGmdgjKOYuajlDOwYXvlPTe0+/21/ZiF7vYD5Dp6YTGCOuKzgvjB/eelE+kTSBP3QrfYImtZq2ZmWkmeCexrJMF8zkFPFIDQsxJqY9EWac0P9qx7qzTWhiUlYkjXXKcW3JarXSHC2g0dIVDNwJdu7vexS1rb9PWt94A2w+1Pl4T3aJDFJpGArT3C0kttjzoXPbvW9hU/aaVIB2Q1G1Ey0W7bxPIm9j0G6CKY1fXndt55RHkeELv94ScGTYRyRPbZyvh+d3Fn1/sYp9Ry6dT901GpgmGAVK2jWO4SyljsDlbLDjN3qRUSAlVRQ4z8X5giEI4rZAUHSKMQ40DYcnkIl5NAwbK+M+Z/67O2nNuuudK50dpTYEe5Km+XTnTGOqP0zZieYO2xIKH47kYYJ8HYTjZtmENVusUmYbepPuijEmXmqBcax0J9hy+MDoLeNLfn6rbo1jtkboYsgJ10YISj8kYphuvZ3K7P+1eWcDrl0BUdmm5L5mzUeeeWWVNFDtGTPhInJBGi9W62FazAmqFVa1JNAhFcLs0aQrLtwJgpWnk8TXMiXDwscQxInnL1bdOhDmhQyC999Q26V3sE9kFEPoUmp5OfOk//Wu890d/Ny9/u3EewyFUEU0dlOFeqobCeqPVyaFCPNpftkZYizNJUvVm8jaTJLMuRi2XJTg13cWrs42SqceTPIBuMnKIRmNXiIdAvgqkEKs/v97MjHFmGlZOy0jykbKcAmty2L50AgqaXNT6y0gcmFBydYhSwSCNihyio+ZKvl1Z3lE2j4/Mh5HHvwDroy37dwLpycKzD2/RNVSHna8yOhh4JFHRU7BzGdT0e+jAMteWiLMw3sN4Z12DtPGucaJuSWOmgjfizJtaqMQ+gXfdC2cjDQ485aEUGY59Lfaa9aqAfHb9uTC4ekaSFiaRML1WNq8yw92CLNkD4ch0O7JeC/N1Ju1sdC8ehLwIKSgi6sXFg+rmYhe72Kfa8n4PrjMcnz1nBHIMnG4nGx3zDKMkn2QhOOgNpQtI7VQWfZwqILA23bOQDPgZDpYRz29sSTssvi1t7LZ2VT2j7zfRlK9zlAqqaAYZqNtoSvIpZQ2xJ+BpA+uVgd9pkqoFt/1gYb6dWhLvzYySwOZS44gRWiuLSZ21E8r4QLuvRrWngUrRRDxTPyqQmx/Po4FUy5UQT9FFV80flw6uSkuybWsnBqitycC9rITBtTy+8k3SRYDzYhf7zJp2gJCeTsRxMEepq7EuYjTgp2zESqYLpKeT/QzseaqE+wPRN3HJaUGW1UD9aejAdD3butWPdxnDsyAODtJ06WYv6q9iTeUK+PRMmg5Ir5MFtPjQAyPlvauv9cdCMqAlHpXppV9HDLaIZVVkDeRBCUlYr+xNQg9C0YE/fl0FAGn+3BshHUAUHoxmVebOA6CriHOjto2t3zAdTxkdAjmqa+Y5u1ba60vM6cfOGsNWHINT++izHafeywJgjVjDuDsXW7IjxKNaqO2Au550kAePdyUmdjWLuqh2aYKIYkw28DFFGH/pPdZvf6d+xBf75HYBhD7FJsnAmnDyrVBB2/hRksokyVPrQqqDB3VVO10y6Qkmm4wOQt4HppdSNXjmJybuXHRt0k5JO2P4FEHrMsYkGLtEQmYaV6YhMUXzdDFkgsAQE6rC3XFDmqMxZhRwllIdAcOBnyR1FrmCODsbB5NDYHwREYXlUUZ3iXi1ollYlwG9H9AA8+OxjnjFIRGCkpOQ10C4XRBgPUWG70xsPjSdh/XKRrHS1pxxOAlpaxo9pcu7XnWiaQ7C2GpmmgPuga7SPFYY7m38Yt0pqBIPcgYWxVND7aVoCGHHjrMLi145Oj8UcEwY74Qwt9nh6VUmj8L+izvComw+PDE9P3ELbF9E7n4ocnxbmJ9k8mTMJaKyroHhKMTTZajgYhf7rFp69owhBsbrDVfvB0QHB8EdlB8cE/JuYFmR3q8aDkuLQyVBDosynJR4VMb7FVky6/XA4Z3RhP0VwgnzZWXl+mTxS7xjCqZNFF0TwpJTe99Q2D8DZwl7mKFUDHmA+y8K8+PI1bcjm5eJVxJYnlgn0vSCfLPXQE2CKx2+A8DqlpyKNbW0NY9St1eejYItNp5gG9Isia+jBwOs11YRrTthPQbiUZFr8W6tAVjg57OW51PFvwFYV3h9T/6Fr3AZ+73YxS7WWw8Qy2ZDGAYI0cbEUkJX2zpJzvYPjEmUEro/IDkT5wXmBV1Xw2veumW9Gq2ZKGLMy8E2QeZRmmhy38hU8+M9S8hGes1t5fjAdzmoUIGHfpS4Yx6V76GBLqU5EZKxXNaOISQJpqf3sKwwDqyPthCEtI0W8zo2f5m0ALzu0vqe0v0vzrzRyZsOrgtUGhk9e7RISPTTBqK2Lr40IWxdvdZaT1KumkS2cKCcQKtFClBzph9UGheuzVdYSlbP2c08Y/EMNoghK3BwYGqwGLtkIS5ds8ZHuNNGqo5UnA0Mqls1C+vIwbk0gj4Wjp/bsl2dpXU1GrhYkLOL/SPbBRD6NFqIpH/+n2K5xcSUV/GRMXMOBFsPXGZ0NVLFhQkGGthqcZtBrV3VstJ8FWQOzjKy5NoojQYygHclg6PQSYzKOEM8OaizU2QfWYeBdUxcbWaie6CcItOwEkVJPkJWtYJc56iOTJUOQI/on60ewJ6/NpCqgEjpFIlTNvohNvNryDzE15F8a5vHYgCJSl4Duh8YXkZ27xmYkurKxNL1Fsja5pD9/FL5S+scd4+qtwLAPh8ClRZZkvizMTPOv+5XZPYFVdU0OnqxMdpnjGgTWqW9Lg+CjkKMtu0nnBKbD09sPgS4QnIkTaHdR4G0RB5/Vbn+yt0Fkb/YxT7Dlj54TjjNbORLqOw4PQ51KwsOeudBqji+ac/Zmtww2/awzfN2vBJ7NIgDG8GmoKfAunOQYy1sS23sl7Jmt1sBb8dTyoawCkgpxKDk3MSrwYGk2YAlDXB6N7E4e3Z8uQAjcrOSNgN5EtKGlhAH871l80o95kzrxJZz6jq8dTwidM2YwWj0UquHTh+uMIh8LKyMbU93yvxEyKH9vLxPSD7CffKTXVaQgM4zend/AYMudrGL/bKm8wxXV8h2A8tiAtTLakDPunpnsiAiwR6bxVgcOSPjCNPI/HhiuR3qmHBZCV+E78PaAOv+51nL2FHbDDkcTW+G0ZuiXVO1tyJKXcSrCyulvMdHtHnkARBS8vWIgVHLiqoS94G8G1GJNh7dTTNIBwiFbuyraiLRMUn9+VVPrz832jnUY1TGlNRzDx73QlKmVwbIDPtMvJ/hjaltQx6shrHmtFaWvwFF6ksdSgyyOqLUFQUkq9pC5V4XsK2Aaasxk45vBJYRlhubnIgne8/KXPX7UuvJJD5OVhA9+y+e7NzXHZweR6bnA4gwP+lmsy/2XdkFEPoUWphGfuFfmUCNrWIU+QY26JRZ8bW2s1SmTQFwdFB3dDY+FlZp42AqyCEyvha2HxgrZrk1JlA8CrunwnIN8xOFCDJTBT/jLAwHd7JRGF4H1jByGpRwfSCIkt3rbYeVlANLDkZPjJ6wK208LBvg1QNCZUSKMve7GFAVZutSpycun78GZC/oG56xR2V+BHwLxjtl+37g/o0B2a3EISGSyR9u2H07snkBm5eZ0+OAjpimkK8nzkAR3i6dhF4DogrQiRdH2Plmd/w1CCTX4/cgYd1zB8ICdSSuFhAufora51fEUsdgjnc4gqiwXLtIm29ByGPrVMy3wrC34JFE0DEge0Ve7pHX99zo20i+Zr6NHK5zBZbyKfL233pO/ns/9+v8m32xi13s+9l0mUkvE/H5PdNuIE8Ty01L3GsXNjS/mCcIe08cZ62MHmisyhxxbQNLtks3sY6Tle1g/bl0ujnQgfDYsUJqSX7ZJnOmlZC8D+EMIW4W0sZGxoa7GeSKq5uTbeqcHJQpLM+SvBe9JD+HSuenJfhn51y64NK+z9Ea8ckPVDvFpZgosaB0hZMy3sOwt6S5sn874MkSdYvH1t03HShdZi52sYtd7Jc1F4xmnuF0QlM2YGhdTSeO5owlOGNInL0RAgwR3W3+f/beJFa2Lc3v+n1rrb13RJzu3vv6ly8r02VXqcpGNEKycIGEERMsAZ4wgBmNZFlCYoIEshjAxAMLiQmMLIFsJMB4ABYCBLgExha4KAOuqqwmq8v2dffd7nQRsZu11sfgW2vvOC+rTNmVmfXyvf2Xru45cSJ27L3jnNX8v//3/xPPPdOZ+cvUIurcEnXqWcNS6Jx91Sp5o4vKRErROPrlZ79rlbIWY0+IptPHXNaS0V6efppcWT3YzjpcTEZyjREJDmge2jKwEB52Ict71RS1On4vwQha2vEWhdDD+39yXeW+nfL4VXErCqHP+GMm7CNyHBdl6YlB9vy60yLzSXEil3na9ywKqUriOHu/6q9U5+9acEYz7W0kHDxpK4wXSjwKVdmlrqiB1JRMUk2kT+5hPa9c9lHVi3baCmkT8MdI+3I0BVTf/y4f+Ir/P6yE0OcRzpHOM+Ha097aoq9/zf441UG8tDh3bSDvCotRB8/MbFKMWJuSL61e4c4TjhZlDnbM4Y2ENkYebZ5bD616sZSzhJEyefG6iTsbaNJObUwchXTX8FQueXK1p/UJJ0rjEo1LnLcDXzq/4TvNYw5Dyzh68taTX7Szp9FsOOcKMdPkxThawM/G1srmaqB/tsX1zogyFVzI0GZSZ8fpbnOpTLQMTxpyhuZGuHiujBd23bc/AXkTzaTaqammem/JbNEW234sbQzJWPFPV4ZrVcJa9uyeh3thuqiJMOb7E3dLFUQSpE4XHyVdqgipU7SeE0B0pCcZBkdz49k8F9o7ITUQL9Se3yraZML5xKvLls3HgfPvwvZlYrxoSI2jvfHIprGBd8ycfehAPNOFI20KUZjWqvKKFSsAzfD8FWHbEjYeeexmQqLK3NUBxaQ+N2VBPyl+UPyYkFjUhxvHcOlnc/u4EXwtYpRqIbViGZcNg3na2WLz1DuiLpzniq8uhBBiRLiUjYcraY2oLXrb7URuTeYk90fUPeLtyzvudpezN5w6K4zMaY6pLJbrOdSo5LqZ+TQpJHzPZoLSjibFQ+n0ufP5j8tr3KiEQ2Q6E9sSzBEAACAASURBVMZzI8/SdiGg/AiuF5obYfs8oymT9wfr5VuxYsWK3wPy3R3c3dk3rrDRufQyncbVi0O8R5oG3RQn/iaQdy3jmWM8FwsgkBNip/inVcxKFV+KwSymxIqN1eGgheR31npWSB6XmdfLs/mxW7ojzBdHl2JBJZ6SqU4rUaPYPJE2tZVMOL61ZauK2w8wRdxRkKvNQzIjM89Rp8Xr2QOn/DyH4ulTC8knLcEkZvLmtLAxE0YlUKH6EflxIZH8MdPejPi7ATn0+DEj2c/k19wB0sj8GjOCXt5Dvc1vTX1+nXOxSb3uZVz15CseprUtrXl2YPOkIZ550ptlT1MUQNYuaNfkR2ZlkXWuWOGm3iMaZiWRJJvX0tbRfXwk/epvAKdU5Iq/X6yE0OcQuR/4yb/Uc/2TZ/RPKPL1ZXHsevcwIn2UpSUJIyhq7+Z0UQcoKYOu/UHGLQyvp5lwqYvg49vCdJ7JbcYf3FINdcwqGkmmPLJeX1sB56njNiQuz3qebA+8s7slSMaVA7zqdqQspORIEdzgbPBxkLZ1FCs3INWRDmSTQHzxyhH6u84IpM4IKT16knhkdCZFDEJ7nwh7JfSeu/e8SVcT3PxhyF02NVWXqX5Gkkzq82ChD3O6jDuW++lODdfKfRFIxexZnB0gt6VVL8rii1F9JRxG5DU6q3vcKHO7nnvZzJWIuFPSzqFOiTvl+AZzC6Ab7XWpFfJGiICcRYY3BEmedu9o7hN+ysiU0DZYHmW2ZIXtJw4/CNOFtTMwrU7+K1asAFRJr17hvvQWotAcdW4VrmbJtZWpVmb9WNQzSek+vkd6iy5OV1vG8zNSA2lbWs0mmSugvi/V3OIzNBXynIS1DlQZfvXy+ZSkvqaRuaRoOmlDqPNWaT9TgbFv0MkhcUJSpr2Bb330Gm8dzexfA7Mnw0Lgn/gknXgvnJpNf7oqe2oyOqt7HWXOXmT8Ltq84tVeM17ZJikcEs3zAxeqDI8ajk88hzNZ2th62H7sOP8wc/HNI+nZsx/0b8SKFSs+z8gJaVrkbFe+z6CKptonVQbeJtjjwZEbx3Rma0j11bONxRen+q3VcfuEeHjwdXlNmIMJ1MbKE3XRqZ9PLRrkwFxo0DJe2+OCtLY3aarXzayqLO9XAnfu3/W4aUOnin85wt0B92QH2tjcU8d7V0iWWsQtyhsF66TIxd+zMFwuKn5SNMjcVWBJksacuFp4D9bp4ft6nuYTKwfmJK/FjNvIm1oMqa3Hc5pbVSjl5Rzr/ayp0+ZFp9BbBkMlhVwsbdjJlFUuCvHcmLjplcC3P+DCC+ouOb5Zghm2RvCldiGwJFlBo867KHNMvWTzIZp9cBX6J/Du//iM+M1v/95/V1f8rlgJoc8jcoKf+yW6t/84h7cC45UucnbReVEuJcZdklVa/WB/jGlrKiBLPrGWo9RC7opZZ6pqlIyMDplK61mA6SKTNwvxc9pPakoSeeCF4xLIIGgUpjEQNw4nykXoaSSRcIw50LiEd4pz2Qb6yrgX6SBg7WxFGWReSVpazE7arUYHIaPFVJTJyDHXy0y8SFSaO9uQoN4GJ4H4+nRCNikkV/yRSjuEU6t6e4GslhiWFhb+NIK5Ri9Xv6D5WorXk13Pcp/qZqp+rQ60tWvM4vBZcAdh+4nSlMlguBLi1hG3pgiK52YG7oopt++NfdfJPKHYJHSbGJ8Iw6XQ3GNpY2NEmw4y+DHDYfEBAWHzHGR//MH8Lq/4kYCI/GfAPw98oqr/UHnsPwT+BSxH77eBf01Vr8vP/hzwb2Bb939LVf/nP5ATX/EDg3zwlI0q48Uj2hsti1mrAIPMC/P2VuluEmGf8H1CjkX6vWmJFy1xayqX6nlgFVBBaiW5kidl41Bjd0/qAuWEWL4pVeMHmwEKYRV1aUuWhdiv84XrI8REe6fwvKPZZ/zoij8bc7VzNpY+ldhnfZAuI6oPWscexMOXueuBrH9+7cn3ZWGfN0oODokZd+hphhF3PCM3O/bv+dnTKByUzSvl4ptHwm99uFZUV/zQsM4Tn19onOC49DZZ6xhIE8xXMyZIGVxROw7R9hzRHB5mXzQejtuSwCdlyjJ7AwGzshTK0Lx1i7/O6Xr5lBgq39c1uZusXckPUNX2FgAjliTslhao6tsjEXRjipnhiTDcBty0wfUROVr7RLV2WC5o+f805n1pDZb5uk0dI+SiFqr7r5kU8iVIphY5SmuWpQLX9url/XInpG3A9QGZpmX/4EtQQVHoLHOgFt8fU+2qM58hUVnShFUJfbk3tXWszl1i98hNtg8bLwV5722Ime3zid2HG3JXrqUtvqW97T1r2E6sbXmi5Gife/XpSxu7meEgvPezd+Snz1h9774/WAmhzzGGC8/hnczuD92yv9ugvYcouNGRSgqXOoiqNDcOF2FzrUwDtHslHDO7Z4I64f4dx/61TLpI+NuidSwLZhvAbGBJ22wDwiQn/jnVqNrOa047wyYBN1o8o0bHlDwpOzoXySqMOfBJf46erJp9SKSgUIio2iKlyeLs1QGl/UsOgbAv5xIAUZrzkRAyOQvDqw0kwU1GfKnYhsANkfFiw/7HMrlTwr3DbRL6ssUNzlrtqgS0xNxXj6ZUMhXrBDdXqeeqsS3Ml8qvkTNQBrsTIii3StrYufmZc7HNVO5AtsnY9iw0t8LldyKbjw64uwN6vkWmxPjmGdc/3vHiH1fSZba26CRwbSdWqwI6OfBKuorsv9Syeya4IZr6R1vcEK1SED3TzhRCflAe/+W/zaoP+sLjLwH/CfCfnzz214E/p6pRRP4C8OeAf1dE/ijwLwN/DHgX+FkR+UlVXfemnyOkV6/wIbB9fUfoA/0jT9wIYaiVS1v8bV8k2rsJd4y4ISLjBM6hjWe8CsRNWThW0+RavTyR39dWgKU1TGe1zmmSSzXBfBj7e3LSVcVUBrQspsasiZsouGGCaaLZZ9prTzhMSAymWC2V51M10Px1HdeF+fprW/N8DafeGJws+qtPhHzv5mG+nmDzLF5szL494oHmcYdEjyuFn9Ar5+8PNN99TlzVQSt+uPhLrPPE5xPVV+jTyB6NEXFiMfR4UxQNQuiVaRAkFINht4TYVFgrccb3jumsFJfLODsrPZ0pZerY/+mx9kGwgD4ck11SwtH2Q6kpE0ZtJ/NALN5EblHvuCQkUeIOhivBD4Fw3+LnvmhMOV/PQ5l96+YxW9UUQacoP8uAK3uearcxB8bMBWOd/UrtGmVp9TopKuQgxI3Hb4wQErW0Sg3g9g+LDioWxOBHaI6ZsM+2p+msVXp4JEUxW4ruZX5yyfxfazS8ZCy0oN6jdy9pnx8I9xPnHzccHzumC0tP1gBJy/lnXfaJNfSmeKKaostSoiUKYS/w819joSBX/H6xEkKfY9x/Wdh+9RYRI1tIRgb5YyEUGoWgZK+MT0ZGhbsfb/BH4fK3hfPSHnb3JUf/hpLObXDIXbb+z6NfjKdHitKlLrjNR0hDZTbAYnFLC1q0n5s/kZI2imsyIsoxNnxwfMTWG6MSXKYLkbN2xImSsuP52Qbfe8IRhkaNpZ4c4SDEMyWHjHTZlEcX5mGRN+YVFIfAdHAwCQRFdiPTzjPuW84+NoY8bxruvuzRN4/o6PHPPPFVi26ykV6T0N75wnIviqjZy6gxwmYmb2r1WIsxGwtLHw6y9Bf3S9JNDlpIF2bmH0p1IApu9IxipBBXmd4HPvonA2F/Rffyknf+h+/axkWEs/PA+NuB4Yna/dklptci0pvKSXcJCZnQJnzIjLfBGPubA9zt7dS7FoJDhsT01Q3TuXyPkeuKLyZU9W+KyFc/9dj/cvLtzwH/Uvn6TwN/RVUH4Jsi8lvAHwf+9g/hVFf8EJGePcP978/ogPZP/CPc/9gWP9aFvxJ6Zff+PTJYSo0UskXPd6Tzjv2btnCcDUdPiJbFAwI0CHFrFc+a7JWbMk6qLsr9soH4tAknnFSUWdp0EdAGVBXxtgCXuwM6jLR3me6lp7kb8VNLimLy/6r4zKfnvLzZLOMvC/zT1rHq82dPZI4nrlXjuZ2sqpcCNu/G8n0D01mg9SZ9kuNA96Jn+9wqMDXJLfydXyOu5psrfshY54kvHnQa0WlExoC/uizhACBTpL1PxG3xkUtWLK6Yi6fHTHs9mlXFLjAFU4q4SdBYxlYxxY4R6PKASJ8LspV7STYG+lFnk38/ZCNDWkf/xM2Ju6kFPyjV6FnU3tf3piIar5T+NWOdwnFD68TUOmpjsRsfFhjme+Js3+OSlnEfoj8h+qvnXvG382UPocVy4tQrT6p3XrlIl9R8hXzx9HFC2jpSH3D9UHxi7Th2D2RJMitzSThmmrtE96JHgyNtApID03kwUm4D+dparGc1li7ElWQlHC21Obdw+5WWq6yE64Hzb9zhv3TGXgODE+LZidpLpbTKLZ+blta+XKwy5PFIHjzpfk0V+35jJYQ+x5jOlTd3R17tt/aAlj/2eyF3tqJMQQn3njSJxYhvMvEssT+25BBAYHjNCBuZBOndnFw2qyCdQk2z2jvztwlAUFPNZHDRnUjoS9uYL88rTLhmyNmRVLiP3WwuvfUTY/JsgxFE14ctiC7Mf23bmkzpQ2HOtfYM1F7YJOjg0SaX0q8gPuODqZriudLsE27M5MYxPFFTJqkpd8LeoXu3VHhra0CUuXzryoSEK212QY3AydUzCZyTpY2uJLvNiTHCA5JFauxm/XlpN6veT82NJ0ZBvZmZpk6ZrjL9m8LFB+/SPTcJa9gn2ltP3JV+7SEQrxK6yWaEukk4l2naiAh0Lxxuskq9hGBS31JZyG0gbeDsaebiG2vU/IrfE/514L8uX38JW/hXvF8e+x6IyJ8B/gzAht0P8vxW/IDh/t+vc/VLpbzrPfLuW8TXz5Fvf2TV15TI44S0DTy+ZLxsOL5tlUg3mc+dL55qNYqeMiZmIG5g8wrCIReyxCENIIK4YrgZ9UEy2Sn5cqoakhKpa54GpTrZe3xNR+ksxTP0ihwnmnuby9JGZvWrFMbJ3sPmkTkAobxnbosvg5hk/kHBWBcCC5vi5qqwnCzqEfAR1Cupg7hzphByHmLEv7xn++KMi195gX741O7zSgat+GxinSc+p9AYid/9kPCld6AJKNBeW3Jj7EpwSloIBjcqvk809xP+5ki47RkvHqHOMzwxc2dXU4xL6xSBxXy/rNNziZevnkOVCGruM+EYLcCgqIrUC/5tT9zKrMbJQRZiXm3e8L2iIqSNFbn71wQXA5ut7Q+mrXmuzsrSMqfU4kVubB5rjnauqcwDleyvbV91OvDDyX0s/klm6WFzRGrLe2jZRwV5UIgwr1JH9+gKNyWaozIehdiZyl/TMg/5SWkOxYj6o5fQtbiLLcgWedujzq457sq9KJ0QFuSw+DK5kZkkOrwl+KFl1zi6Tw6EfeLsKTR7z3BlLeFAUSPJQnph55XapV1cvPLa/9Xw5n/z62u78/cZKyH0OUZu4LwdeHF3BmAeO1nsD9gVyZ842hshDc5MiB9FuvOB4Q2HBvMdmM6tFUomS3ixP3QbfVJXiI0I4d4kl/ECcpuLp07tKysvyTJXTmu6WfU20uRIyZFViNn+D5LZ+pEpe1qfCC4bIeSXtgMmu65qlE25To0yr76r0aiMYhUKlYXIEcU5JZ5lmvuIO0zkRx3T4zyTSqlTmjs3t5XFs0JInbQb1MHZ9zZg5/NsC/biw3T6ucApq88Sx4yZX2sxmD6NQ673qk4SEqG5FchuNuVLW0XPIr5LvPijO84+8nQ3ieY+EQ5qaqSSgHYUT7yKuG0yFZkK0+TJyfP4Q5uM8c4m7+DtvjlH2gZyI2yej+j//cvfp9/WFZ9XiMi/h7lm/Rd/v69V1b8I/EWAS3myco8/wtBhQIdlIPRPHc2hJ9c2g5TQlBDpQJYWLQopX6ulLpbFu1uIkaqgcSmbz1lWRMNiQHmirNGyqJdkvkanpBDLlLEQ/knMr200BSopQdfOhJH0A+1dJnW+JI0tKiBLfjk5PjwgfaovBFhFd1Yvfc/N48QMtLQK1DaCejync6FChwHEodMEL3sufq2Bjz6xZKAVKz6DWOeJLwByQm9ukYtz2G1ovvOc1L2FO/OzItLahhQ3ZnwfcfsBuT/CNLF9dkbqTPUPzOviRZkCiJBRqk9bHX9zW59fg1Uy7hiRKVubrQjZ+Yfr8rrHkFJoLgVbU+2Ygj9u7djj1bKITxuW/QilLq3L8Wr7c/UxUilBCaVY7NKJP08hqyTVvUy5tlpkOPUnLfekklnqS9eCFzSIkTvHyZLGkis+QoUPE7P9cBHckHGH0Vr9RJA+IHEz+/OZIquEPJSgg1Pl7jzv5oWwGi8EPwXcuCFuPW5SNi8mQu85PvHFI1Bmf6E6X7/xS+aVkYMjbRz9ow2PfvWW9OLl9/VXc8VKCH2uIRlUhRDqqGT/zcacg9DcQXujDI+NlU2jjSzucmIMir+1KPUZ+YSRd0LaJSMYevOvuQmBeI6lcOUyCoIlc4kikzNpaBLCvVsGr02GJMTJPITOm4FH7ZEnzZ532msOm5asjpu4ZT+13N5uyZ0RE+HOm/dOUelIpsQ0Gvkxt3F5I6Q0m5qouXPER0oczKlOHo+EF0fczT15+xpn7/bsbzc2UJ4l9CCzpP9BO5xYdVayoMclHcz3RebYVpM2G8CncyNmLFXB5K+n/kLA7MHkkklh6+c3/7w8PxzMg6ky6i4KSEO8cKSfHth/1dN9Erj8pqe7yVx+K+GS0l5Hjm803PzhhsM7nthmwp3HHYXtNbzxtz5a2hxEiK+d4ybzK5ou/QMj1BUrfjeIyL+KmYj+s6pzk84HwJdPnvZeeWzFFwjp+gaub3C7k2QaMBJaTU5vyY827tlCXEtEPKgqmZOo3OI/4I9m5uNGW5gnf+IJlBfzTRfL3ENZ5KqtdOfUlUI0SbT5y/WOsBc0ZyPJq5n1caC9m5jOHQMyb1IqWaO12ltVSJ/acFTVkLqF+Dr1Fap+GjUIwqrMxauuWkZkTDlUNxLjZOc4TkYCXd983z+/FSu+X1jniS8O0u0t3gnSBOJ336d9fI6btqbO6c1hWrIiY0SGiBx69HAg3+9pnz1ic+bpH7t53TyPsW5ZQztk8YirY2k1Wy4khpsybkwWmhLMs06d7SkqwZF99cXRWZUq2TyEGM3+Ibem0J/OljfTOQp+IVDA1D1V6a+FZKpE/9w5UUVFaakM1GJzLVLMSWxUFdDDBbkVGoxkwl5Cah3aBOQ44gZdihVlX2GqKlNP+Slb0idAjMhgQQ/1mqxroiiCYj2P8ual+2E+93JNaSsMl+BiQ2qEzYuJ5nakvVZyc8a0c8SNEs+LT1GZt/3P/So6DDj76OiWQ6/4PmMlhD7H+Mn/+Dtc/4n3ePfPfsAnLnPbbomuMXragT8KYW9s9+al0uyF6S4wHM/MBBobpFw00qGaJMdd8cjxtlA2tlgZLx05GPGRbz2501mNQ8ZyCjlR0oymvEmdIm1GfMb5bCSWZB6FA281t3y5ecFGJu7ylg/dY97Z3fLh5op+05A2hYUP1jc7XZTF/mTEjzojbqwiYOlpsfYYAN124vhqCxlee/0O3bRo35C2gZ947UN+4fbLEI2qjufK9ChbG5xXi5yfzIzbTc5M5LB7EQ5CeGlEzXTxcCMwG5+eJCTUNgbJFmdpx7T7FLcLWz6/oEhkpzMeemlUn4vJzYRVbpTxQtg9zYRjwk1WQb/65Tt2H53Rv9FxfOLZXGdCn3FDhq4tqRAJ6Sf8YSyTZiB2ji/99x+QP3m+Grqt+F0hIv8c8O8A/7SqHk5+9N8B/6WI/EeYWehPAD//B3CKKz4DyIfyq+E8btOB9xATYR/ZPG+IW5krs75fFsZVPq6FKGn2SnMX8YcRmRLnH264+XHbOOTGZPcLOWOEUzphtms1eTZtPqk6p22mfeXZvFSIEdrG0lYA7XvCqyPNZYN6G+RnxacTcpH81MQYYPYOUneycXFWIddcIpMLwXWaUlZTX2zuWMiwcFS2HwTOPlC2H/fQdeRXr35nk9cVKz5DWOeJLx5qMQAg/9LXaR5doT/2Du7uWOQqCuOE5oyOEzqO6BThk5dsG0/cXHB83c1EeiqJVZXoJ554s+USa34oXx+VcJ/wfUSmBDHhDj16tiVedNYKNTAnhc1eck5IDYTByBpfCrK5sZantLGYeKjjvy7+oHzv+t8PZe/UlpYpWc6X6jtUVavO3rOS/6fhA66YQNd9AZT5RUGCeQrFrhQ6gofnr/DDoxMfu2V+au6huU/4+xE5DmhKaPk8/GGydu1yPZb4JbNy99NK2Fn5mkvbtYfpXMiNI26FMHi6jyf49gdszn8CdxVw0XF4W0qBHxjWyvMPEysh9DlGfP8DHv0/Dd/+6z9G+zMv6DYTx8GTWzebnqWN0LelMqn2h92+dOQWNCzeDW4qyVi13am+yQk5cf+uI54X469UqOxU2rmq3LFUMsH8deae2SS4VvE+U4UpjUt0bsJL5jW/Z+MmEsI7mxvOt28ybBvStsr4AQ9pV7yOop33HFNZT7gafpbKgnOFhS/nFC9agipx5wkuo5NDBjtJN5iJncUFl2MWRZJEkFIdFpb3ABsIQ1H8oObh9GBTU6sacWHf7WaV3mAWskg9c0VZclF7NZg/UZGi5ta8m8RnmDy+F8KxGuJlXD8hQxnVZ8WT0DsrV6uDuy8/IQxqxqnPB2qiT27KhufFK/J+//v/JV3xuYCI/FfAnwReF5H3gX8fS4vpgL8u1gL0c6r6Z1X1V0TkrwK/irUI/JtrcswKciIfj8g44p3Dn23YvOoYJzcvNv2oD9K2VIFoyqHuNtHejLjbIzJFNs/PuP7DnfnlaUlyPCktZl8l+Cwy/rqole9V89icohhbc7JQDQHtGuLGvA7c9KlqaX3uSbJn9tjmYRG2GlmVl6QVKf4MknRWi/oxzyku1fPIYpMV38Oj3zriv/YN8vG4kkErPnNY54kVvxPS7T3ut75DSol5A6AZ99oTU4w6Z+1Ud3e45y27XYO6TSFTikrnRE0p0XzVoJBAveInmz8sHXLAHUakHyFn9H5vgcEXJsfPLZZaPJbIdym+qcH2KrYetwCDcLD3jcVbZyZ+TtrFrFhbCw52ft2d4sdM9ublk9plPpKsi7XGCeo8Ua2J6nzmJp2v9WH6M1BUo36yc5BkRWE/KVPxFK1eP1ZUmXC3R/R4hBjRlO18Yl5IK11eY7YYLIra2hrnl/NefP8Wn73hwtO8dcbm/jFuSDR3lVwq+8iwJI6t+OFgJYQ+54jf+BZf+gvf5qP/9qfYdiND16DB4uc1wHSpxF2e2766V0J7awNiNSaDRcHCbJhp3+doRs7q4PBeLrHttng236ETM2ldfH4U0BOyg8nhdublU+FQfKG8L9zERhMEeKe94cn2wOG84TA5ODS4ySqxeZtw2Zd4dKwC7O2ELe5eTEFTNwF5WdgP0TM8bshemM4ch9hCtHh1shjxVCIl3UTpdV0qtrPcsyigcijG0huFo6mkJEOzL2RcaxNMbYeoJFK95/NgW+6/i8bK1wG8Dvhpo/N5qMfa9bqEOMUfHO0tbK7rQRWZEm5/ZHr3Mfu3Ow5vOg7vqMk/u2x+GaK4g6d9FTj/rmf3LOH7ZJuPUVlU3StWgKr+K7/Dw//p3+P5fx748z+4M1rxI4kSXaz7A+52QzicgSzSfTNZLgbRTuckFj8q/piNDLo/oDHR3I1AZ5XiE4+DGZ9aVNdKMJTF7emYTjXQVBD3gJSRpmG6aJl2Rlz5fiF5pB6/EFg1QleDPGiFk7hcR31fI4BKO0EyRZMbbCVu7QW28/BjZvvhEfWO5sOXxNUnaMVnFOs8seJ3RE6LUvQEejhaK3FK4D2aMno8Eq6PtJetmTpHV9QyZaCu4+h8ECN22n0mHDLN7Yi7OSDjBLHKXTJMEddHpHjeOAGGEwJGlzYsp0sh149q/nUBZMOs0nlQFChF3Fk1kyz8wI+Z1JUCvP8UofPg/hQSxi9zYb22uYDwqc6D6k8UBrPrCH0uYQkOiRk/wLRbzk2DefC5ISL9iKbixScC3pF3DWlOOJMl6dILNKU17aTV+RTuU8SOZNvLjJeB9nyHpIwfEqLKa79mZJE6IQxqn/2KHwpWQugLguAymybSdpHBt8hkkkbrdS0KHgfTOQ8GsLqwjVtK7KGWOHVbxLpRaG4dboR4Zkld6jDTZ1FcMQRdfBXsGz+eDB5eSGfgQ8a7jBPoU+CQWxKO19wejzIBLYnXwy1vbO85xoaUHNN9mNvBsnOzzDML1tLGCXs9Wsy7BmW6yKRDC0GhyfR9g3/Hs9kIOQjffPEE2SRiBr/35NZIE1FwN/Z+ksrCPhuBVT2Dpl1NHLN7VD2GyAupU9u85qScQgDNkYtihJMvUfQxnFaTrdpsJM5DPyNJAveBcOs4/w5sXyS6l1YucWMEEaa3H/HhP7Xj+HYmX4024fQekuB622zlbaY/T/Rfgt23GnZPPRffGdn9tZ8nr4TQihUrfkCoPhPh+MQUpEEWwkYBscRDq3jaYjccJri+Jd+bctHd98CFzQdOZ++7ClOn2oCqslRv5/atBPlUmj8Vmb8rxY6sZnDdNgxPAtNFSXD8lHxesi7ec21Jrwk23jux6nWV4s8EUNkAuElPjFbBD6Wa0he1Zlaa2xF+/msIJqNYsWLFis8DTk3w3WaDomg/4G7uaW62uDHgWyUMwji4eXx1Ew+IFT8qzX2iuR7wL+/h+nYparoiZUnJ/HVGiDtIDppswQNVqVNDAKpPqUuKToIXG+NzvwTCPGgVq68v3jrW4hxxvVXZcwizosaUn7b5UuBB8vBJe/GsiEqWguaKUtZPYB584IeML0SQGxNMEQneksb2meGRX1LYqu/RlGCaQKunn0c2G8YnG9J22e+cdl3kRua5rt7307n200ofpzYXLEjX3AAAIABJREFUjheOdLUxEmqI+H3G/2+/yvbkuetO44eHlRD6IkCV1//Clm//qSdc/WPP6Tcdsm+s1avRmTTJRcrupqoLLGS3mG9Q3Cl+FGsJy0WCXxbS1VWe01aqVEurdVFti2lxJwMrZcB01i7WNZHWJzY+snETrUQaSaQysjvJPPIHLpueXTOy7SbGxyNxH0yNNJWB+HTQzMzx7ZKNQMmhkCj7AE1GWkXESC3/VNk9nXjxrQv0yYTsEsmBNg51WggemSX7iC7kjpqx87yoL4kERgLpbNDtYvHbVjOorpGPaWMLfzeUY6SSNFY2Edaep/PmqD6X0T4sN9nn5wfYfqKcPY34Phvzf7TtQjrvuH9vY35LgBxtJnKDmW27ZNVsDovcNTdw85PQv97x7s+uQ/SKFSt+wMhqKTNDMkIllMCDIRV1pCN1zpQzY8b1hQ5xDnJGjgO7p2YAmjoh9La4h6LWOVEE1XZcLQrQ3IG7LwvZQu4099DdJKRpUBGrvvZKvtiyf9MzXC3HmluwY2kTo4yjLbPsv47hzZ1J9UOvZaxWKCSSHzMSFUkZN2VTd17fE7/93WrJt2LFihWfe+S+x11cQErEj57iX7/CxTwTI5vWE88CcWvE0EziiHmshbsJfz8g+6ORDFmN9EgJmtYStW7u8OMbpG5Zv+tmadEyol/mQkL1+rQDVuK/dBGU0ALsJfN6XZKdj0wZbZ0le0067ymAUsSw16iAL5YSmXI+tTic7bmUa9y8iviDFX0lK+4wISkhUzLVz/3BPEv3A5uXLf0T81N1JbChuTNipqqDcGIt0ZuW4coznS0tYrPHUbm+2pZX08pOCywSmedRSotz6oTxQhgfNWz/5m+T7+9X8ucPGCsh9AWB/B+/QPszP8OT7YFnXOF7ezwG0CabL06qrLbM6SenLUxAGZHq4zaQIWpkxYM3VBtFq+TeVZm7vXYe7CikShlxvctsm4nOGxHkyEzq6TWTi6RoIxMXoeey7bnrOoazQK+C9iC9m8kfYL6m+ZFTc+cSS69ekRI9nz2EY6Z9tqd7/oTDhYMuFcVTOdlC3LiT9zl166fIN+cB8VQCqrNIys4hl2Swk75biUbo+FKpOD32A2ZeC3kXdZ4U3WCvDQfY3GSa28mSFLLi9gP5rGM6DwyPZI5IJpX7FIuqKQp4nTdJuTHlV3xtIvvm9/gbt2LFihX/4MiHA/7Xv2uL0sdX5KudLV4PxZnZO/yuBVUkllSUWeLuYRjZPUtI9kxnMhtbqi9hBk1NjMHmBSkLfYosv6h4cgB3dLS3SvtqgJTQ4ErVVamtCrXNy9qmdVb55BMVaPWEm6OSy8I69Ep7Gwl347yRycHhRjM+lWgtDby6IR+K8eqKFStWfIGQ96WtLCfkmx8g3kMIcHWOTxl/ucNfbhivmpP0LsUPCX+cbAytptVgxHtKSGMDs6rOyb1VWToXDmpxtBRqJVvLsotGhEiWJRGYUnxueVAs9sfqY6T440Te2Hq6kkinqGSSOtujzIKm6eGcQy7qnqyE+4nw/B6aYGqgu73dn2rOvd8jISB3B5rG073egJjCyPfWTif9YG3bqoh4m3/FlFc1/Wv2SFXmFr3moPP8lw7m6zS3a6uiJ31kRgbZsfwQ2JVk0RV/sFgJoS8Q4ha+fHbNb/Tv0ZUErNSBbJIlck0OekvNyiUFhQCut0Qy68U96V/NNghqOFHIwGJ6nJj9hao6ZjZ0PpFRSgYZhZxNerkNE62L7NxIK4nrvGNkwJNJOFpJvN7ccddtOMQWAa6dcvQtOrXWmlWPXc4B0Vn6D5gSZizJAFlQFZomohM09xH37Jr29gn9wc9JWrMpqYPpMuOPrkj9TSbqi0pnNlxrqsHzouTxY1EM1Y0BViGuKpxcKgjtnRKOtnkZL41h92OZZBqZ5a5+wEgtZwqiqkyyWE3FxYy/65G7AzqM5EfvMl56xishb1JJYBPc8fSzMYJPCyGUdhkuJja7ieF+HTJWrFjxg4fGSHr1CgCfFZ+yLRr7wRarzuHimS2Oc0amiGou5g+g00T3ckRdi6RFT6Pe5Oq5LeS36AP/t1Ny3tp3rW23vc+4+x6NaU4Tk6SQzaDTnySinJo+z/HztUqcmZMlXbRqaTgk/CHi92M5R8FFj7/Z2yYmRjQm8s0dOo0/+Ju/YsWKFZ815KX3KN3eAiAh4DSjMeLGiaafcMOWtGssTXJIyGDtYDJOpn4B5pYo1flrESleQ75YZMjSbVANocOyTq9pYJLBJwvhmfc/dS6RpQDuRyUcleaQcfdW2JDU2Np9LvpWP56T966pYSfeRfak8vxaEJ6ykUBtAzGh+wN0nV1fjJbUBnA84m497d2FFTAmSyD2dz1aCCFgnkvRsn8qJNlpMXw21+6tvVkdpM4Ti7+FdWqcGGor7D7JhL2RTJvrvHwmK/5Ase7uvkDYfaz8r7/5k1z8pqfZK/1rQrxIcAwlQcVahgCq309qoLkVfILQC/1ragQBAG52j6cY0LtB5kGrGqjhdE73mhfJJ+kt1d+hayKXXc+jdjGXG9VznXbz1702bGTiy81LGkmc+YGn3QXX2x0vdzs+ac5JaUfYy9yeJdEGdm2WVit74zoIGxk2joFHH2Wms0D/T3yF6z8WLV4+CzIKmxeWIpO2JncEUwqpqwleYm1g+XTgFsTrbLKd2kVxlTbWvsatzB4SbhKmq8wwOmvfG8sgeiq/LCouxEzBwd7TDzJ7HKWpMPqNxwN6e4dcXjC81nF4y3N8M8PVBEePOzjaG3t+6pS0sUP6aiSujiQNb/1Vz/ZvfG2Nml+xYsUPFenVKyjk0AwRfHwdCR6cs0VsyjZYAjqMhE9u2U5nuGnDcGWDf6105mocWuPb67wgRgTlplREAzQ3Qnsdkds9Oo1o4+eFtBxH2lsbh1MjD9JVaovaXIgoyTV1LgwHZfOqpKPdjzCM5tmAOYymDz5Ch+EHdFdXrFix4kcbGiPp+Qv7pkTZAzT/8E+h3uPuLXWSKdocUdvEYFa/EKMpaUTYfDKwvdwyXsis4pyJmVAUQgE0WWE2R8FVz55JiRtTl1b/n/o6dQ/VoDx/iR8vCOctqWsIeyOMXLTnxs2iLspBcOgSOBAXNY2k8jwp+5x+MA+gilyS2yrRlBL0A4jQveitVW1KuMMIz16ihyM6RaQt3QApIcV8+0G0fC1wlBawZm/pZeosMS0HU/1o8RhKHbYPjHD1136B3PfLKX6ffhdW/P6wEkJfILz5f77i4v0Lth9e8+GffMTwSK01aHQL86snssfSTpUbGzdzgHimlmKlQk4mXTx1tkewQSsVEqO2g33KYM0NFG8HNVnlo4mrbc/GT8Ts6ZojTaHB97mjkcQ+t9zlLUNucJIZcsO5H8itsPUTuzASXOY7QyBqS6ikTJbZaPo0RpiS1qUhg1OG6w27pxP96w03P+6Qsx7dB/NCEhugc2ekUtjbJiIVOac7qQ5DUVdRNgC9PBy0ZYlhzCUNraqyanU6bu213XUxlS4bGCl9yaL2deqYNxtGGClZ7fn+mGleHkwd1ATykwvGC8d0DukqWQtf7039xbJJyhtTP4WDXWP28N7fyOx+5SPiGjW/YsWKzwJUSc+fE959B91tkJRRDrbYT9lUQ4ce3wSaLjBeeltIO1nUOqW9t85bKnWuMF+32gp99qHSPt+Tb+8gJVLnUS+EPsHtPdvnjxFtmHYwnRdfPS/mRTSdVJezzX3hqDRHpXsV2Tw9IGXTok1Au2BtAF/7jQcV8RUrVqxY8XtD/uXfRJyQsuLOdkjXIiHA2Q49LGSEhGDzRcqoJJqXB3bPGlz0TDshIuRSlK2u/bO3ZhB0az51EpmLwdktBfHqF7ckBiv+aEmaOk40TeDso86Uor0p+9NGOLzhmQqRIsnmE3Fm2uyHh/sZ3xsZpU4WVQ9Y69inU7pSMrJqGPHPb9EmLCrbEIwYA/Pim0rrWAizDUadN+s9kAyhzzTXA5IyOEdqHXHr5qJ7UmW8FM4+Ul7/K7/4gAxa8dnBSgh9gZB/+et0v2xs7Pinf4b4OBbGVpaUKi3+OBMzIaReLLHLYWRQUEgsZFFRqzzwGlJLf+HEJ2F2n69qmql4Ogh0ZyO7ZqT1ieASj5sDjUQyjru8oZHITTrjZTzj2XhB5yY6F+lc5DwMBJdpJDMmzwfNI1JYkl2M1NB5oS/Fr0idSS1pM+KU8CLQvrjl9qsth69OiApudDOZkzsldSaDbI7Fe2hWGc2XTSk6M/v+lP8zUJ1ALR2gCKWKqsixVI5TZ7LMWlFOUgihk/eh+DzVx9JGZ6JIIjS3E3Jzjw4jstkwXW2ZzsTMwc8m0r4hDIIfqjG4XZ9uMrJ3VrEYzCBu8ze+RlwH8RUrVnyWoEq+vkH2e+v2GifzS1C1he9ovhFuiPihpTlAmgCBFJfxNp+shBRTl7oJmIzQ3z2bcHdHcqm85sYqyxIVhgF/iDQbhzpP3ED1DIKlVax68vmpkEHXie6TI+7Frcn0AabRFuWwkkErVqxY8Q+KnJausOMRHUfzHPJ+UdA0De7ywuaJGG38PQ4097FEzDtbf7dF7SkLCbP4hFrhQIKpe+wNmZMq3QREI23CUQlHC0CQtjWy5dBz9qGdj+8jbsqkzjOebUitEKsHXW1pZnkPO5Hl+9lrrgmWnibY95UkclJM7TCp0xQRkbkFmqaBZgI1L755Hk0ZPyi+t7nPlZY5P5R/fZ4NrNV7wrFBksdRfPoiXH47svnwjnw4sOKziZUQ+oKif2/i4o177l7tcFOwlKpCbuTGmBvJWvpo7TWzfDKJtVEV5U1urSUqHI1Fr4tsyUIKxmi7SYr/0EPjMBeLMTUwZc/WT3x1+4Kf3n7AIXdcpx0fjY8YQsNd2nA97fjG/WtkFZ50B760vaZzZkA9qeNm3JJedIS9ETm5hVz6X5U6mBsRpAIalGY7kSbP5W+Bf3bNeHXFl7/ynI//7ts14RiJljyTW2sMTtEtRnF9aU8rKWqzQqqmzZTUs0r8iJpZtLUj2L3rrinmbGZol1slntlz/HHpI66JBmmrjOeKdkpz7VCB/G6P3rR0LzwXHySabz1FpwnZbslX5wxPGo5vCOOTRNck8rHD9+Xa2qJ+2iX82YR/v+Hig0TYZ8ZLv/b4rlix4jOJ/PdSLZYFvm8C4dCBeHyolc0lGhhYlJbZ/ODcUDx+RqV73sOxn73b1JXW6JTRKeKPk/lOBMFvvSk6y6K6FhSq3N8PFlzQ3I74Z9fkm1vboDTNg5jlFStWrFjx+4fG4sP2qcclRri8gOJBxDQhxx5/P9I6QVJAj7UV2JSlqZM5bEBLi7ACeCuCS7Jibm5sbe3KfiAczDvIHyIyTNYanBI6jHRP79HG2+NTRDYd7estcWdMUC1snwYfzNdWVTu5zEcxIk1V+ZRJLWsxiRbwJwqiWMijisYUQpLSHNCgADkRDsVbqahe3WDtb6ZsSrhDbyoj52iuHZszmw9dNNXT5u9+m/Ts2ffzY13xfcZKCH1B8Uf/gw94+qe+wuN/8SU3N49pboqZdIDmTohbJXdQI09miX00ZUxFuBfSVsmbTMTN3jmVCHGDzB479UDqlLwtCh5vipth3zJcBpIKXjK/cnyPPjccU8Mxtbwcz9jHlj4FgsvcDhue6Tl9Cry7veXluOPlsOPlfge5GJgVU+cak2gxwEIOOhu/VWVUHj275wlEmM7gq5cv+Ji30UaRyVQ0ZhBUoo+nZSCeDazj0v7lxiIRlaV3GKyXODYQN/YiUUg7MyQNR2julOyF6QpyUPrXHJuXpW3MWyvZ+DiTH01cPD5w/PVHXP0GtPeZu6dbNi+V8w8Hug9v0eMR3nmT7D14k6Ae387I1ciwb2mvHf5obWFaiD1Ctn2RwvbZSLgZ0HD+g/o1XLFixYofGNL1DVzfICHQ7X4KN3XktpL5vhhyWlRwNbwES2n0R0tO6W4S4ZMb9EQhab4L2dLNmgDHkRAcErWkwpSWsUZRMe85DTYvbF4l2luLQI7vf7Cc7KrAXLFixYofGjRG4gcfPngsf9wTNp0R+FPGjZYqWYsA41XDdObsX8DCsebOB/AHKxrHrREvbrKigh+V9noi3JaQl/qGwwAfP8M1jRUcsuKOA92LLXHjGC8LuZTAqT5s3Sqm1dWiwvWRnCxVeFaoRouhn9EsScGashFMqubDt+2sdbm2XYdgqipg+9Ge8eqS8cyUUuGoJTHNSC6922P+TJn8jW/R/cLDe73qXT/7WAmhLyjiBx/ih6/w3tU1v3hxge4bGI3QCEeLDEzeVCFzG5RifkBFNqNB8YMjt5h5mFdTD1VU1rrGDZ4YkeXA7KYvCfToeXXYsg2XnIeRrbMklaiefbRIrttpwzE2NC7R+UhG2E8dQxd40Z/x8rhjf2znUVIyyCTkTYl1iYKfyjUUz6TUQRwDsveEY4JpotnDd+6eMF0m3OjQ0mLgJpmN2STK0hpW/Cf8WCoCJ+qgSkwBxQdIS3RjMZpWI6XSBiSZ0sgPkEarQk+XSjjI3Lc7t94l4XDoeOMXlYtvHfGHifZuZ9WHpOSzDvfOm6SrbfHFEPbvCno22XnfB/xR5pjImohAtrS3tFOGxw052MS3/f7++q1YsWLFDw0aI+6Xf4v2p36ceGUmebmpTs9K9m5p8y0r9fYu072KdB/foXf3tngO5slw9je/botmVeg65NDjckbGlo0qeCF7R9q4EnMvZC+EIdO+Gmme3pK/88HvfLIrVqxYseIPDOn9D3H3V8iX3kQOw2KqHDxuPCMcG0IfCEdX1s5a1v2mhrGUYSFunSlojhk/mD+R3Nyjt59Sgk4RzbokZB4izfN7urPA8YljeGzK0ppeaU+y/2oCMQp52+DOS/KmZmNhRKztqypWa3tY/pReSpXcBrwTm9dSQh5d2rlNE+5mz/aTLeHCkxvB90o4JtyYTTDw+BLddfbaTwdArPiRwEoIfYGRWvjq+Qu+fvEm6UWDp0gBS1vSAwNmmP1yVE0to17ndCxzSmahgU97XuvrKmPtjATJSeYBzh8c+7sNH4kiovyRi+cEZwc7xNZMpFNgiAHXKGdhZMyeY2wYsud27Lg/dsTBfqWlpG5JUvIWO7ds7VjMCiEheUV7T3NwSLKBb/Nc+c5HT/CPRvLLDsQkom4CX0icUxPt2Sj7JD1Gw/Kz3Nr/qaiVcmPxlKfxkakYRvvSpuAGUzKlrRJ3MiuMjLATUg5wHXjyt95H7/fghO2zDbrtmN6+4vDWDok7Y/+zVbT7dyKuS+TJ09w5Ql88nJaCASQhTw49z9y/4+l2pZp+alS3YsWKFT9iyH1PePqS9tZiFMOLDfHRhnjeMOec1EV2Etq7RHM3IofeHk7JYns1PzDFdABtg5SFdACrtgaHHwK5cVY4CYLvM83TW3j+ak0PW7FixYrPIDRG8t09/pMGHUcj/0OAN55AzLghEURwY8bF0iZWEraoLV1ekNRYpPsh4aZkfj4poSnNypvT9zwla+R2T3N3RugDgzvZd3zKR6i2NUtWNDikacziQU9sHpyzQrD3FkMPRvh4Z+bRUFRC9lycR3WC4OetHDHhj5HcCOo8Z9+4tva2mMyPaBiRYYSsa2rYjyhWQugLjDAo37h/nRAyU2vynfZGmS6EtDGfHX804gCMtJjbxYqHkO9BH4F2GendTJSoNyVMNUaWTHHFXxQ2NS3LTYAT0iGwlw0fAa9v9jQucTd1fLy/4L2La97Z3dBI5tW4JTh7o+QcfWo4b0fGjWc4NISDI9yLseZY/GHaKrnNs4yz0hvhbCLetkiE26+0vPFLnke/cWA6P2P4Z245bgOWvG7R7POALIuyRjJz3DtYnHz/mlUM1JlBtL3O7kW4F9q7og7yzGkBuVOmM2HzUmnvBD8JQzle9Bbf6Efh/DvQ3UB3ndC7uxIR2UIT6L/6hJc/3bH/svkmtTcyVw9oR/RlR7MXmjszTU0thXRS5PFo7XbHgDaZ+684wq8Jj//y3/6e3usVK1as+FFD/OjjB983f+QPIW9d4UZbnFd/htw42lcD7vY4R/RqSug0fs8x8+GA76xwICnbAlkEDR7pWrzDYuobjwwJ/eDj1VhzxYoVKz7D0GF4MF+4zQbefQPJGX+YcINVaWVIyBQfpkQ6txBDSXFjsufnh1RJ9aSTquIpX2tK6O0d/uaS9n7DvvjdPQiVoeyfkuInI6VUxNq8iFa4z9YKRs5GQG03yHZTFEClA8TJfL6SFPUOCd7W/Got1RTiyE0J0cb2Nb/2m8s1rvhcYCWEvsB47We/yf6j9zj/t284PNoQDy3+Q9h/yZQp2ijSC9kvZIcbhNRZTDvJc/F+YnjiiE2G3pjm6jekAXKJSaykSWUWXIlPp/TAKuB6R6bhHnh/94jGJxzKtpkYc+CxO/JGe8dF0/N8OCersA1wP3UcpoZhatAsxF0mdYKbhObWyA8BYhDyRudiMAL53uQx8Uw5vOUQ7wgv91x+p+Ppr14gV4o2mXSWiJPHj8WItCieZCqsfbBYeg0Qd0ruFKYi//T2HDeZaslc+ZdjNCLEc1MPpccZPzrCAcK9neR4YVH3OcD0zoBKRzjC9tvXyONH6POXVm0Oj7j58ZbDO0q8ipCFIbg5Maf7oLG2tMkkpuOlXXfcKfkystuNHJ7vOP9G4L3/6SXST8jdviZtrlixYsXnCum3vol/f4P/I1+dPRcAmyO++V3S38uw+vQ4r14hXYe7OJ9TwqRpIHjy179pPg7MgtkVK1asWPEjhNz38Etf/57HPz2ehy+/R358Ds7hD0YCSczIvp9bz8T7h2SQfVFMnz3SNhAjcnPP+bcaxrPzuSWtetJR9k0o+GPGDxkXs5E3zWY+roSiRGob8tUZuWuQKSHDZCbWbWkRSBkZi8n0doNME+m3v2XtcLsd45/4aVLnGB55xnPhTBzo6gz0ecJKCH2BET9+Snt7xwd/5x/l/Baae4sHrCbRUMgab0Swloh6BKTJqIPNi4w/bkxZ0qillbkTUqj4D50aMIuWgeyEmEFKOxeO7IL5CbUTrU+ctyatzyokHOd+4KXscIWlGlJgSt7IbK9oa4762giSHeFOLIr9JElNSzuXjA4N9pq4MzZcpkjYJ5q7wHShMxt/Gu9YSS6LnTRvoGqSbcocV/qITSkl0VLWXPEZmr0qiveQHgUVJZ1ZApmL4Hs1n5+ttbYB+DYzPspMZzIz/OQM3qNnG/rXhXiRoM1w9PP5IdBemzldvYbpUs30tFXCNpKSw+0920+U/LVfX9n/FStWfO6RhwH3ze9+7+PHvz+TZx1H8vUNSGGWnCAv/EwGrVixYsWKzzfSJ89w/UB+703cNz6wSPucre3s6hJxDrqO/OLlg9eFN1+3li7njJQZLYo+PL/j8tstcefnhDP1Vqg3Wwoxz9fsEA24sy0aTNGjIjjvZvVSvNoyXQR8nwn3Hi9COu9m0+zceja//Qn56TObt6pi9nik+4VvgghnTQMhEP+/9u48xrLzvO/893nPcpdaemE3u5vsFilT9CIrHpqjWBrY41HixJH0D21gYNgBLCXwQAFCD+KBBrFsDMb+ayBgJjbswBbGhgVJQGxHM44ixVEsxxo7nklMWrRWLpJIce1ms/eu7W7nnPeZP95zb1X1QopL1231/X2AQlWde+vWe9461af76WeJCgbdahQQWnBxMOCuX/kvAOTH7+Tij56gWW6wXpOCPFsBz207I6YNhuRlQ683pDjnlOtdhpOA92t8UKbMn+CEyXaPoFl9VSQFVsJ2T6KpMAaLhmeBwaBD3QRWemP6+YRJzBk2BRt1l4PFFpk50Y1JzBhUBVWdYQZ5WVNVAZo0IrI6kEanWQP5Vjsi0oCMdj2WAkKZp+bYsB0ImfYHIjWntiZlBIXpn4MOTQeartN0Uw2v1Wki2erTTt2DanlHQ+hxyhKalc+1QSVroFxPfSuanjPZH7EmQDSycZpAhqVgUz3KsH0Vo9s6xH19wjMvplrgXpfJgR7j2yLebzBLwa4wbiefTYxyw5mspKCQ51CtNpin1+32JmyeX6J/LtA/U93Yi05E5Gbh/vKj61/F61wZ/FFIXURkcfh4TFxfJ1tbollb33VPCG0wBY9wRUDFqypllRY5jCcpkBQMi5Hy4hDP+6m33dqEppvjefo3TqicaiUjm0TyC0Nsa5gykHodmv09rGmwSYUNRuRrJaEqsXFDmNTU+3pkW+NZuZkXGb6+uatHXlqc01wRwJJbjwJCsq3ImawYB46tc3zfGtGNx7kD1jqUG6lp8vpbnDDIqDsZt9++iXdW6Z+LjM5m7HvHBc6tHUpTw8K0TCql1cTcZz2DZhk2DYSm7avTtlSIDTiBxjpMDsKkqLkwWqKT1dRZoIoZm01qilbHjPVxl/VBl6rKiDHg0Qhlk4IwwcnzhrH3yDYy8mFaU9MOIqNOE8isse2eost9qiOrXH5LyeY9FTRGthUoNgLZaLvUre6lsi9rm7oV59KUmpgDITVrzgekgM7QZj2G3CB2INuErJ1GMFk1ig1PZWQWGHzXhHqcyrtiYRSbbRCrB1QBDw31srP+XUvs+9pW+h/uLKO4OCAbdIidDC8ioZ1KFqqUITU81AaD2mCYueFlJPRqjq5ukP+q449/a3t6joiIiIiIfFt8PKZ++tmrjseNjauf3GouXCQvyxTIefpFoO1btLJCWNvCDvYoNyvsv3zlqn+4N+/52+SXx8RHvz4rvMiPHqE5ukyYFPhgRHPuHJxKr+uA9/uM/v7bWP7PJ2nOX9h+rddx3vKdTQEhmYlnz3P7f+5x6YGGw51NxjGHzZxqNRKqQGfoxF4kDAI+zhjXOfHuZSBluOQhpr5DtaXMlLGlkqSMVEZG6k8G6XOrbNZDaNZzqEjj7HGI0RhXOZe9x1JnQjAnmFN7YNzk1G36ToyBps7wOuCNYZljWQQ3qkm6xL1wmrZM02EiAAAgAElEQVR/jrEzO8dmja5Tc+vA+LaSwVGDwrFxSI2x6+2ADrQ9kdrpYdYY5lD30vj4pptSgDqXUhle70LEoqcxlB1jsi9lKtW5tU2n0+t1Npz++cjgTSE19Y5QbBqx/Q8Ga0iLqANuMN5nhP374PIaNA3hwjrl+kHqJcPLdq1tWRrAZP+0Vi/tP0s1WdnQbBSM/+Uxime+TrxG01QREREREbkx6tMvwUvbE33jaASjEZw38udOXvfrOp/7Ir5zqhipJUh+9jwONNco74qDAf1//0UalTRLSwEhmYlbW9jXn6KbH+dIZ51BLPHgxEM1k7pMk7HaIAqNsTUpiEczuhcj+cAZ13maQmY7SsV2jp8PO3oK2Y5ysTDtxZP671i0Nv0+zDJ/8iwyCKmmq87bQFAbnXEHj4a363IH94AFx5u20XXmxE7KSEoH2qU1qf+PWSr1AqiWApMDsQ2jt+fsO/ogWeoDFNvpjdPO/7FoewmVzuRAKvsKFfTPRYrNmrqbEVYyJqtG02kDYNk048jobESWXhhQXlimXnKavpNNjNjGaCymvaI9h6Zj0O9hoxE+qfDBkGLDCYeNWEQIO8ZaGjS9iLXTCojpNYI5cRzo/duH9T8DIiIiIiLzcK3sfPeXb+B8vX4+r9DnR/3tZKfwyk+RRXOot8l3917i/qVnOXzXJX7ke54kvHmL8T7onslTlk1lbGz22DruNB2j2HIurS2BtY2K8+2pYtm4nfTVBoWmzdBmgZTcqVYibmmsejZM3ai9Npoqo6kDTTQGVcGlUY/NqsNm1WHc5LOsIzNPk8+KmAImowwf5FCnyJMXTuxEmnI7ywfaLKXc8TKmErCqpuoZfqDCRqnGzQM7+gWlwFA+LbFt/+xOmUKQjYx8PcNDCgqNDqayr/LsFt3zI4rNhljC+KAzPuCzxs5bJ5ytoxlhfcjRv2oo1gKx19B0fNbzZ9rPiGmwDfAix7pdrNOBLLTZSFD0qpT5NA3ItX2TvEjnU2wZ4XxBtV6SDfXHgLw+ZvZRMztrZo9e47EPmpmb2aH2czOz3zSzp8zsq2Z2/96vWERE9pLuEyIiNyf9S1B28bpm+A+7/G9feg//be9ZfvDwSTJzlvsjRnc0aYpWIJVjjXLq5RTIKbaceL6D1SGVUNWQjQGDuutM9rXZPzXkIyPfTOVkTSc1c26WG5qe05Te9rchNXxuM1nyLFJkDZk5g6pkc9JJJWtumHl6XkyBJOs1hOWKsFxh3QaKNlCUp8CQxbYxtLfvQwom5cO0B03X6CxNsIlhMZWZFVupefRsUloN+da0T1AqH4sFs+BRGAdiDvWKs3V7oN7fY7KvZLw/Y3AszhpRN/1I04Xqtpq17214/idvp39yk+XnnfJ8Tr0at0vTYtsoehLa8rG03rg1IG5upayqkDKamiojG7Vr6DnVkuN5xEM6f6tSpteJ/2Dc+79/c4+vMrkFfQx495UHzewE8OPA8zsOvwe4t337APCRPVifiIjM18fQfUJE5KajgJBcpX7hJIf+bY8f++MPcm60zJnhSnpgpaIpmU0Iowr4UkPdS3Vg3XNteVZIz5mOZre4ozxsOoo+bgdQPKRpX7Qj7puSXeNZYgyMq5wmptefNBl1DNQx4G7ENhA0+xoHb0IqF5v20KlTOdlVY1+mZWHejnH0NoOmqFMvo0jqITQNwEwzgnJmU8hiO7ksG0M2NPKBtcGwFPQZ3WbUvYymm1H1Db9tgsWUORVGITV/HmQQYXQo0vQLyk2nvJQmgMXct8vVfPebtSMtQ6+LHz/C8FBIWVhbRZpmljuxdGLXU2CsbXxdLzlHH4qsfPXsroZyIq+Fu/8lcK0xFL8O/HN2/+Y9AHzCk4eA/WZ2bA+WKSIic6L7hIjIzUkBIbmmlX/9EN/762f5ynPHWRt3KbJIp1cRu94GcMCqQN6tqZehKY3e2XasfNsLCJj1rJkGVFIDaW8DQSlggUEYpswiz5ym5+2o+tQbKNbGpMqp2n5AkzqjasLsLUbbLrud9hOqDa8CXgeoQ8pcmoTtgM7O3kZtoKjpAjGVXBngZdxeR7b7byqx2O7/M52elg2NYpY1lLKLYhmZHIzUSxl1z6h7Rm95nMbTjyAfpult+ZYRJkazHJkcKMlHke7F1ATai7bZdmDWIDrtLVDVWJZhK8sMjy8zPtC2axpMeyeBl07sRkLREAaBYiP1S1r+s8evOQlB5I1gZg8Ap9z9K1c8dCfwwo7PT7bHRERkgeg+ISIyf2oqLdfVPPUMb/lZ2PqT7+L7Dp7hCY5wptfFxiEFNLYCfgCGh50wCRx8YsLG3UUasT5rtNyWirXNm4MbEbDMZ5PFAJaeDzQdGB8EOzjCz3ewcZoa5mVMzaXLkBJj2jKxSZ0zGpbEaaNoSMGd2I6Rj5YaOw9TiZUBTdlOPWujOxYgjAJeOMOjER+NKTecC4MO+UqFb2bEwhkfMGbfxdpgUN4GXCyNd8/G2+dtDcTCqJYDzWrD5rF0ok0PhhtdOiMjjNs9Kpn1CsKcjTtz9j1bsXwqsnZvQb0a2zK1NmjUj7CVUW44vjXA9q3SHD3A4HCORciHhk/afkeF40XEykiWR+769Jjwn76Uvu+NuGhEADPrA79MKgN4Pa/zAVK5AF36b8DKRETkZqD7hIjIzUEZQvKKVn6xwxc+9bfoFRUrRzYpjw6wI2Pq2yqa9ZJmpWFw1PHcWH4BysvtWPX+jobIecqgCRMIlbUBk5RtFEbGbY9XqVGzOSGbRmuAMrJ0cMjq8pBOURPdmNRp8lhs+wtZaANMgbb0zCFPfYOmI+WB1OB6kvobhTpl2DRlagQdRkZ2dAj7VsiHkXiuS7c3SRPVSAGblMWz3dAZUrlbLFLvo+nkMItp3Hz3vNG5ELBhYOtOGB1O08WysyXE7eyiMGnX40Du1D0jGzaUa5M2iyriRSR2I7Hn0I1kY2P/1zfBI/HAMpMDnVkZWaggTNKkN2/7I3k0YgzQXGOCgcgb7x7gzcBXzOxZ4DjwRTM7CpwCTux47vH22FXc/Xfc/e3u/vaCzg1esoiI7CHdJ0REbgIKCMkril95gmMPDXnx4Tu478gp7j50kcMH1+nvH2ITg+A0S5HB4Zxy3Sm2UnCj6bYv4MzKxVJwyFPPnk4kVEa5ZnTOD6m7ELtObEvHvHCsjHSKKq3DDW9r0eoYUoCjZeZYcMi8HRXffsP2KebMStemb2Fisz5BVhtFWRNXumQTp3MhECyVbE37H1nNrG/Q7G06xb7NGJoFwNospFBDNgi7yr5CNV10+7Vhe00Ep+lBLAJEJ1SQX8qhMazb4EUkrOV0zzn56UvQ6RC7BU0npNK1tsQN357mNi2NW364R3F2442/QESu4O5fc/fb3f1ud7+blO5/v7u/BHwGeF87ReadwJq7n57nekVEZG/pPiEicnNQQEi+LeE/fYm3fOQ57l99nruWL3Ln8hp3Hbw0CzZ4Gdm4yyiGTrHphLHRLKX6qe0R6anBcVOCZw6dSDY0+i85YTBhfFuE5Zo4zPHSsW5DXtYAjCYFVZ0R3QjBaWa9g3b0DwqOFTHVQjVtE+ngs2BQqFIpmbXlV9moXQfpHGIMVPu7hInTO+dEt1nQxtqgVszT+Hkv0tdaG/TBUjlaU6bH637KBgIoNi1N9Yq23eR6Oq0spteydkqaZU7dd5pewLNAsWmsPAf5ekberSBzlp8P7H+6ojl7DlaWaPoFTcdoyvbcpmvtRKw2mARskHH0t/+a5pvfusFXiiwiM/sD4K+A7zGzk2b2cy/z9M8CTwNPAb8L/NM9WKKIiMyR7hMiIjcn9RCSb1t96kU+97fvgBjZfM893Pk/P8nS8Q02zy9hg4zx4YbxaooxFlswPhGpC0/ZLeMU3IhZygKqj45hlNM/bex/csD6Ww8QD08IueMXC/I7B0DK/BlNCuoqw92omoymCTRNKoHyton0jLUTyyCNky8iVrdlYXUK6ODp42ILqkFIWT2FM9rosHlHQWe9oXfBWR+WNKsNTdew2lKPn1lAabuRdKhSb6DJqtP0Ik1l9F9sJ5MF2p5KlkbcRyAYTbH9WCyc0GZa9ZfHDO6CyWMl/acH3P5ITuelLZZPr7J2Zpn9F52DX10jW9vCT9xB3L9E081oCqPpGXUvlbel0jXje37rPPHpNMnV63qPrhRZNO7+M6/w+N07PnbgwRu9JhERuXnoPiEicnNSQEhelThIgZrVv36BU//HvfT+h4s0B4aMyhLc2DrWIR+1AZONHO/EdnR7+/XdNPGKSca+rxYc+MaYfHPC+R/oEXIn1ql5cwg+ywDKsghlTWj7AblDnkdidJo6o5lm3kxlqW8OBkRLY9frFCDJh0bMU5lW3U/9e2KvzRSqjMmqUW4ZxVZDvNiBXoMTyAYhxZraJtnBjGyrnSq25XAplcjVS1B3Ip5nmLdj6dvfMrfU7yjm7eSwLDW4jgXUvUhcbgghwmZBsRWx9S164wrfGrA8qei92MezQOwXxN4+LDpuRt0PVG1GUt13DjwBBx/dwIPhJ0/j1WTPrg8RERERERH5zqCAkLwm9akXWfp3Zzn9I28n3Dlg3/4Bg1HJ+FCBXw7kW5APjNraxs9NajRNTCPRy/WcA9+c0Dk/JHZzJm+apADKJCM41FUGQMhSvdk0GBSjUdcZWRZTdhDsDgZBCgZljhn4JMx6+9h0EljbzLkJtE2A2heIRt2HmBvlWqR/smBwzGbBpZin51llZANL4+VHTj5yQjM9R8c7kZhnWLMjINT28nFr+wy1pWw0aT2xk157a7NL96WMcmOIb23BxibeNNhwRHapA/uWqe7cT1NkbYAqMF4JVCupZKz/krH/m1v4I4+mU7oRP3wRERERERH5jqeAkLxmXtfc88GHeObD/w33vesZvnn5MGfqjAkdwiRQbBhuAcL22PnycqB/xrn94cuEc5eJh/ezcVefI0fO8dKLB7BBhjVGvVZi/Rozn00US6VigWazoMm3uz2bsaPzMykYFFJzaatCCsLkYKWnPkK0gZoyNW22uB3Mqfvt5LFhzbG/ilz4vi7DI4Hx4QZWKvIXO3TPGf2zEQ82a5gd8zbzJ0+NsGPR9gdqG2nP+gYZNL3UYykbQTZJPYeqGPBhIJzJOfzlmuLUZXw0TmVeFiBrwJ2QZ4ThMjEvqfsZm8cyRrcZTS9lG935L79Oc+nS3l8MIiIiIiIi8h1FASF53d7y4cd54XP3kn3oEiFrqJYbRtEoNoz+GSMfOPkAOuuR/qlNsnNrNC+ewb/7zVx62yoX32Z0Rh1skJGN07j0bBBoykAsInUdyPNIltW4GyM3mnEGVUiNo2vDC4c8YrljbUDIY1sKFpxIwKLRdNsx7xWziWEwnTqWMpvqnrFxd49iEFl9oWbpTCrJwkqKrUjTMdbeHBgfbANSMY2j9xzyjQy7lJGN0+tOR8ubk3oItf2HPHOw1H8o33J6Z9MasonTf2Yd2xpCWWJLfXw0xoocihJCIEwaYm4MDqVg0J1/sUX+2DMANBuaIiYiIiIiIiKvTAEhed2ay2vk62P63SGT1Yy1zBmVJdaU9M9A92Kkc6mm++IGNBFf6sEP3MvF71th7bsC1W0VcViSDcIskAJGHGU0BtaOr3c3PBoegSpNEbPaCJWlKfMEnAjtyPfUtMeh9PRpFbDZnPiUFQRpFLxnqYyrXE9j6CcrxvBQzsrJhnyroVxzsnHD+t1dNk8EBvdOYBSwKqSG0AbZKE0yszplBzVl2zQ6c4JbWk5M4+5j6W3vIAhFCgyFiWPRsPEEn0xSqViRY90O5DlWlvhyj8nBHuP9OdWKUS87oY406+t7/FMXERERERGR72QKCMkbwsYNT507xGSS04xyGAfq1Qa3jKxy8kGNXVrHD6xSHV5i40SHy99tjA/XhF5Ns1HQHbSZNm1wJoyMJgRiYZilcjGv25Qet3aMezuuvQIc3AO449Om0oDlEa9t9rpklirMvB33HmLKMGqg3HBiYYyWjdFhZ+klI5tEsq2KMK4Z7+8zOBY5dPs650/ub8vNINRGGKfm2bPso6wtFwspE8nb0rlQtX2EMk/j4jvbr+HT38jo0DRp/d0u5BleFjT7ekz25zSlpf5Fm4ZVzVVtlERERERERERejgJC8oaIj36dE//99uf50SM89RtH2LxriaaXM17NWFo+TsyM8f6MtbcExndMIBp+saR3JqPYBCxlzXjWjmofB2LIoV8T61QiZnlqGu3mkBsxGPlmKgmLOTS9NsBjbW+gIen75M7kQBpLn42NfCv1AIodx4tIsZHTuVynqV1LAWuMcr3Gamd8uMt4X8Z4f0o6unB+heJSlnoQNakf0LSN0bRMbBaAoh0tXxk0qXQs1G2zbU+TwWKZMpVGtXH40ArFYJT6B+U5vtTDuwXeKZjsKxmvBJZPTSj+7G/Sa+/hz1lERERERERuDQoIyQ3RnL/APf/LEja6SPWmQ5z9r/tMlkuaHlRLMD7cELoNcZylbJo8jYGPRQqQGKSJZJOUahOz7UlgPjGypZpYGz7MU+BoknoPeXCsTtlDPm02bZZKxRzCOOBFyiDyYNRLEc8cG2YsPwfl2oRiEAhNQcxzLDphXNN9aYLbEqHNUApFJBaevnfVNqnO2DFJzFPWUGXYKC292IB85ExWjaY7DVil5tax9FkfpMEdXVY2VglmxP0reK+g6edUSznD23IOPXQOf/GMAkEiIiIiIiLymikgJDeE1zXNk08DkB1coen2ARgddOrVBus3eJPKvfA2oFKkCVzNcqS8kMbOe5567TAtFbN2BHyT+glBO8kra8d9WVt6FTyVamWpp8/0ieakHkRuxMJpliJWGeWa0bvgZFuT9G2qyGQ5YHUaD291xKJjkbTemKabeZami2XtiPnpJLFZw+q2cm3avJo47TU07S+UgkdeOBSRUDbUvZy4XGJVj+pwn8lqzmQlUHeMA98cwemzRDWPFhERERERkddBASG58Tw1am66UN9W092fUmZGG53ZOHYPaWx63fc03v2FLDVm7hrVagraeOapYXTu+CTMgkmQgiuWp+bR1pAey1KgyGrbDtQwbSadSsWsXxPOdigvG53LFbY1wqoa28rprZaEJqZm0NaWeEWwOk05y5gGslI20PT1idtBKc8Md2hKJ5YGI8gHTjZMj8due05tMChkTt0x6qUCYo9qOWd4W8boYBvs+n+/RLMnPzQRERERERG5lSkgJDdc/OrXOf5Un2/9r/8VvQNDThy4TBYiaytdhpOCwahkXPQgjxRLFSvLQ+Jml3yYyquGb4oQHCaph1C+UlGvl9gklWSFSWoYHdumzdnQIKQgTLMUyYZZyjQq0xSvadaRFxEGOdkkBXpiYVhVw3iCNZHu6U2sjimgBRQbNf0zGRAYUOB5yh6aTRa74rzrPmBtCVs7UQyM7gWnc3m6FmiyiBURy5wYjaZnrN9V0j+X0f3jL9AFsICIiIiIiIjIG0UBIdkTcTjknt+/RLPS5cL3nuCO9z1D1WRkwVnqjSmP1rgbo2HJxZP7edNzNRadbJwxOpPj9wyovMC2cppzXULD9iSvtmTMGiMfpWBQytIBQmriXJfg3UicGM1yxCZG96V8VtrVdMCD4f1uCuzUDTaq4PxFfDSGECjOFCxnd+PWoSmN4R0R2t5F1pa9pUbS7Tl3UvZQ0/dZ0MgDVP0UCAo1FGtGLAJNyIil4Y1x7M8vYFtDGI2pfdqpWnlBIiIiIiIi8sZRQEj2hjvxq1/HgNsv3stjb7ubfND2AJr23olQbhorlxxramKZev50Lhqbb8rShLHpePbMU7mYt8EYaJtGQ7XiZJO2u3Pmqbk0QDTygdG5mFOuQ+9castcd42scjrnx9hwnAJAdY0BzfomXk1mp5GvHaO7kuNZRrUaUj+jMvUv8h3fK9QpUATQLEV8GNpxYDYLGFm7dmuM/rNFmlgWIT71LD4e37ifhYiIiIiIiCw8BYRkzzVPPMlb/qcnr/u4dTpc+If3Uy2noEr/bGR4utN2Z26bMHecMDLyrUCYQLUvBVOyMYxu9xQ4agzymHoFOWQbGcvPw5E/eY765Klrfu/6mke3hUlNeWlC90xDzJfZPGFMbmuwymZTw8LECIO2lGwJJstO7EW8SOVg2ThlC4UqBbjc4K5PX6B57BvArC2SiIiIiIiIyA2jgJDcdCzLWLs3NZpeft7Y/399iQOfyiEEiBFC4Fu/9Daq2ytiN+N7f+kJnnvwbYxvi5RrcPwTT+LD0ey1vvXPv59QGavPOAc/+SXq15F90zzxFOWJOzj/3x3nwDdGdNZKBoczCDBZTUGpYhO6FyN11+hdbFj+f9pAz7T8K+4YGO8OWUazNXjNaxIRERERERF5tV4xIGRmJ4BPAEdIyQu/4+6/YWYHgX8N3A08C/yUu1+yNI7pN4D3AgPgH7n7F2/M8uVW5JMJb/rTMRgUl0bE8fiqEqq7/sOIaiXHmoZmY4Pjf75J3S8oz27RXLwMcbvnzps+N8Kik1/cohmNXt/iYkM8f5GDX1xieGKFuhcggDWeMoMasDqVju3/5pDy1CXq9fXX9z1FhA0unf8z/7+3gPPzXstN5BDajytpT3bTfux2q+7HXfNewM1A94lrulWv+ddK+3E17clut+p+XPc+YbOshes9wewYcMzdv2hmK8DfAD8B/CPgort/2Mw+BBxw9180s/cC/yMpIPQO4Dfc/R0v9z1W7aC/w37s1ZyQyNxVP/52Rgdymo6RjZ2mY1jjZBXkw8jyI89Tn35p3suU73AP++dZ94tXDrFbSGb2iLu/fd7ruFloP66mPdlN+7Gb9uPWp5/xbtqP3bQfV9Oe7LaI+/GKGULufho43X68YWZPAHcCDwDvap/2ceAvgF9sj3/CU6TpITPbb2bH2tcRuWUUf/oIxcs8/kr9iERERERERETmJbyaJ5vZ3cAPAg8DR3YEeV4ilZRBCha9sOPLTrbHRERERERERETkJvBtB4TMbBn4I+AX3H1XU5Q2G+hVDUcysw+Y2SNm9kiFRmyLiMgr+p15L+Amo/24mvZkN+3HbtqPW59+xrtpP3bTflxNe7Lbwu3HK/YQAjCzAvhj4HPu/mvtsW8A73L3022fob9w9+8xs/+z/fgPrnze9V5fPYRERK5NPYRERERERORGeMUMoXZq2O8BT0yDQa3PAO9vP34/8Okdx99nyTuBNfUPEhERERERERG5eXw7JWM/DPws8HfN7Mvt23uBDwN/38yeBP5e+znAZ4GngaeA3wX+6Ru/bBERWRRm9m4z+4aZPdVOtVxIZvasmX2tvQ8/0h47aGb/0cyebN8fmPc6bxQz+6iZnTWzR3ccu+b5t/8p9ZvtNfNVM7t/fiu/ca6zJ79qZqeu+Dvb9LFfavfkG2b2D+az6hvHzE6Y2Z+b2eNm9piZ/bP2+EJfJ4tA94lE9wndJ3bSPWI33SOu7RUDQu7+/7m7ufsPuPt97dtn3f2Cu/+Yu9/r7n/P3S+2z3d3f9Dd73H3v+Xuj9z40xARkVuRmWXAbwHvAd4K/IyZvXW+q5qrv9Peh6cjUT8EfN7d7wU+335+q/oY8O4rjl3v/N8D3Nu+fQD4yB6tca99jKv3BODXd/6dDaD9vflp4Pvbr/nt9vfrVlIDH3T3twLvBB5sz3vRr5Nbmu4TV9F9YrdF/v3/GLpH7KR7xDW8qiljIiIie+yHgKfc/Wl3nwB/CDww5zXdTB4APt5+/HHgJ+a4lhvK3f8SuHjF4eud/wPAJ9r/pHoI2G+p3+Et5Tp7cj0PAH/o7mN3f4aUyf1DN2xxc+Dup939i+3HG8ATpEm3C32dLADdJ16e7hML+vuve8RuukdcmwJCIiJyM7sTeGHH5yfbY4vIgT81s78xsw+0x47s6NP3EnBkPkubm+ud/6JfNz/fprd/dEd5yELtiZndDfwg8DC6Tm51+jlu033iavr9v5ruEbpHzCggJCIi8p3hR9z9flIK84Nm9qM7H/Q0NvSVR4feohb9/Hf4CHAPcB9wGvgX813O3jOzZeCPgF9w9/Wdj+k6kVuc7hMvY9HPv6V7hO4RuyggJCIiN7NTwIkdnx9vjy0cdz/Vvj8LfIqUyn1mmr7cvj87vxXOxfXOf2GvG3c/4+6Nu0fScI9pyv9C7ImZFaS/6P8rd/837WFdJ7c2/Rxbuk9ck37/d9A9QveIKykgJCIiN7MvAPea2ZvNrCQ1PPzMnNe058xsycxWph8DPw48StqL97dPez/w6fmscG6ud/6fAd7XTgh5J7C2Ix38lnZFf4OfJF0nkPbkp82sY2ZvJjXJ/Ou9Xt+NZGYG/B7whLv/2o6HdJ3c2nSfQPeJl6Hf/x10j9A94kr5vBcgIiJyPe5em9nPA58DMuCj7v7YnJc1D0eAT6W/y5ADv+/uf2JmXwA+aWY/BzwH/NQc13hDmdkfAO8CDpnZSeBXgA9z7fP/LPBeUlPMAfCP93zBe+A6e/IuM7uPlPL+LPBPANz9MTP7JPA4adLKg+7ezGPdN9APAz8LfM3Mvtwe+2UW/Dq51ek+MaP7hO4Tu+gecRXdI67BUpncfK3aQX+H/di8lyEictN52D/Pul+0ea9DRERERERuLSoZExERERERERFZMAoIiYiIiIiIiIgsGAWEREREREREREQWjAJCIiIiIiIiIiILRgEhEREREREREZEFo4CQiIiIiIiIiMiCUUBIRERERERERGTBKCAkIiIiIiIiIrJgFBASEREREREREVkwCgiJiIiIiIiIiCwYBYRERERERERERBaMAkIiIiIiIiIiIgtGASERERERERERkQWjgJCIiIiIiIiIyIJRQEhEREREREREZMEoICQiIiIiIiIismAUEBIRERERERERWTAKCImIiIiIiIiILBgFhEREREREREREFowCQiIiIiIiIiIiC0YBIRERERERERGRBaOAkIiIiMX7648AAAU0SURBVIiIiIjIglFASERERERERERkwSggJCIiIiIiIiKyYBQQEhERERERERFZMAoIiYiIiIiIiIgsGAWEREREREREREQWjAJCIiIiIiIiIiILRgEhEREREREREZEFo4CQiIiIiIiIiMiCUUBIRERERERERGTBKCAkIiIiIiIiIrJgFBASEREREREREVkwCgiJiIiIiIiIiCwYBYRERERERERERBaMAkIiIiIiIiIiIgtGASERERERERERkQWjgJCIiIiIiIiIyIJRQEhEREREREREZMEoICQiIiIiIiIismAUEBIRERERERERWTAKCImIiIiIiIiILBhz93mvATM7B2wB5+e9lpvIIbQfV9Ke7Kb92O1W3Y+73P3wvBchIiIiIiK3lpsiIARgZo+4+9vnvY6bhfbjatqT3bQfu2k/REREREREvn0qGRMRERERERERWTAKCImIiIiIiIiILJibKSD0O/NewE1G+3E17clu2o/dtB8iIiIiIiLfppumh5CIiIiIiIiIiOyNmylDSERERERERERE9sDcA0Jm9m4z+4aZPWVmH5r3eubFzJ41s6+Z2ZfN7JH22EEz+49m9mT7/sC813mjmNlHzeysmT2649g1z9+S32yvma+a2f3zW/mNc509+VUzO9VeJ182s/fueOyX2j35hpn9g/ms+sYxsxNm9udm9riZPWZm/6w9vtDXiYiIiIiIyGsx14CQmWXAbwHvAd4K/IyZvXWea5qzv+Pu9+0Ynf0h4PPufi/w+fbzW9XHgHdfcex65/8e4N727QPAR/ZojXvtY1y9JwC/3l4n97n7ZwHa35ufBr6//Zrfbn+/biU18EF3fyvwTuDB9rwX/ToRERERERF51eadIfRDwFPu/rS7T4A/BB6Y85puJg8AH28//jjwE3Ncyw3l7n8JXLzi8PXO/wHgE548BOw3s2N7s9K9c509uZ4HgD9097G7PwM8Rfr9umW4+2l3/2L78QbwBHAnC36diIiIiIiIvBbzDgjdCbyw4/OT7bFF5MCfmtnfmNkH2mNH3P10+/FLwJH5LG1urnf+i37d/HxbAvXRHWWEC7UnZnY38IPAw+g6ERERERERedXmHRCSbT/i7veTylweNLMf3fmgp3FwCzsSbtHPf4ePAPcA9wGngX8x3+XsPTNbBv4I+AV3X9/5mK4TERERERGRb8+8A0KngBM7Pj/eHls47n6qfX8W+BSp3OfMtMSlfX92fiuci+ud/8JeN+5+xt0bd4/A77JdFrYQe2JmBSkY9K/c/d+0h3WdiIiIiIiIvErzDgh9AbjXzN5sZiWpKe5n5rymPWdmS2a2Mv0Y+HHgUdJevL992vuBT89nhXNzvfP/DPC+dorUO4G1HSVDt7QreuD8JOk6gbQnP21mHTN7M6mR8l/v9fpuJDMz4PeAJ9z913Y8pOtERERERETkVcrn+c3dvTaznwc+B2TAR939sXmuaU6OAJ9K/94lB37f3f/EzL4AfNLMfg54DvipOa7xhjKzPwDeBRwys5PArwAf5trn/1ngvaTGyQPgH+/5gvfAdfbkXWZ2H6ks6lngnwC4+2Nm9kngcdI0rgfdvZnHum+gHwZ+FviamX25PfbLLPh1IiIiIiIi8lpYarkhIiIiIiIiIiKLYt4lYyIiIiIiIiIisscUEBIRERERERERWTAKCImIiIiIiIiILBgFhEREREREREREFowCQiIiIiIiIiIiC0YBIRERERERERGRBaOAkIiIiIiIiIjIglFASERERERERERkwfz/SYxFo0zwVYwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x1080 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtbdfBGU6Y5-"
      },
      "source": [
        "## Visualize Segmented Image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "_59MdO2C6Y5-",
        "outputId": "d24bb385-b6b1-4159-f45e-33abd338e046"
      },
      "source": [
        "files[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/drive/MyDrive/BraTS/subset/HGG/BraTS19_CBICA_ANP_1/BraTS19_CBICA_ANP_1_flair.nii.gz'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BrqyFNR86Y5_",
        "outputId": "92774649-2338-471b-cfcb-260e62447c39"
      },
      "source": [
        "seg_img = io.imread('/content/drive/MyDrive/BraTS/MICCAI_BraTS_2019_Data_Training/HGG/BraTS19_2013_11_1/BraTS19_2013_11_1_seg.nii.gz', plugin=\"simpleitk\")\n",
        "\n",
        "print(f\"shape: {seg_img.shape}\")\n",
        "print(f\"dtype: {seg_img.dtype}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "shape: (155, 240, 240)\n",
            "dtype: int16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "NtiiRaI16Y5_",
        "outputId": "34c56907-2bfd-48f0-f195-dee9b99632db"
      },
      "source": [
        "fig = plt.figure(figsize=(20,15))\n",
        "\n",
        "ax1 = fig.add_subplot(1,3,1)\n",
        "ax1.imshow(seg_img[100,:,:])\n",
        "plt.title(\"segmentation for axial (transverse) plane\", fontsize=15)\n",
        "\n",
        "ax2 = fig.add_subplot(1,3,2)\n",
        "ax2.imshow(seg_img[:,100,:])\n",
        "plt.title(\"segmentation for coronal plane\", fontsize=15)\n",
        "\n",
        "ax3 = fig.add_subplot(1,3,3)\n",
        "ax3.imshow(seg_img[:,:,100])\n",
        "plt.title(\"segmentation for sagittal plane\", fontsize=15);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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I5KjdmoyZ2RcI/W7EgVvc/Yp22ZGIiIiIiIiIiOSkXRJC0RDd0wnDOs8n9J/yVXf/IO87ExERERERERGRnLRXH0JjgZnuPsvd64BJhBF2RERERERERESkwNqrD6FBwLyU6fnAfqkFzOwM4AyAOPHRVXRvp1BERLZc61lLnddaoeMQEREREZGtS8E6lXb3icBEgO7W2/ezQwsViohIp/WyTy50CCIiIiIishVqryZjC4AhKdODo3kiIiIiIiIiIlJg7ZUQehUYaWbDzKwMOBl4pJ32JSIiIiIiIiIiOWiXJmPu3mBm3wOeJAw7f5u7v98e+xIRERERERERkdy0Wx9C7v448Hh7bV9ERERERERERFqnvZqMiYiIiIiIiIhIJ6WEkIiIiIiIiIhIkVFCSERERERERESkyCghJCIiIiIiIiJSZJQQEhEREREREREpMkoIiYiIiIiIiIgUGSWERERERERERESKjBJCIiIiIiIiIiJFRgkhEREREREREZEio4SQiIiIiIiIiEiRUUJIRERERERERKTIKCEkIiIiIiIiIlJklBASERERERERESkySgiJiIiIiIiIiBQZJYRERERERERERIqMEkIiIiIiIiIiIkVGCSERERERERERkSKjhJCIiIiIiIiISJFRQkhEREREREREpMgoISQiIiIiIiIiUmSUEBIRERERERERKTJKCImIiIiIiIiIFBklhEREREREREREiowSQiIiIiIiIiIiRUYJIRERERERERGRIqOEkIiIiIiIiIhIkVFCSERERERERESkyCghJCIiIiIiIiJSZJQQEhEREREREREpMkoIiYiIiIiIiIgUGSWERERERERERESKjBJCIiIiIiIiIiJFRgkhEREREREREZEio4SQiIiIiIiIiEiRUUJIRERE8sbMxprZJa1cd5SZXWJmPdPmTzAzN7OueQmyZbGcbmazzazBzKZ01H63VPl8jcxsipndl4+4RKRz0TWi8zGzodHxOypl3oVmdkhaubLo+O/Vyv3MMbNr2hguZnZIFO9ubd2WKCEkIiIi+TUW+GUr1x0Vrdszbf5jwDhgXRviajEzGwD8GXgYOBg4qyP2KyJSBHSN6HwWEo7f8ynzLgQOSStXRjj+rUoISedUUugARERERJri7kuBpR24yxFAHLjN3d9py4bMrNLda/ITVov3WeHu6ztynyIihaJrRNu4ey3wUiFjkMJRDSEREZF2ZGa7mtkTZrbczNaa2YdmdnZamfFm9pqZrTezRWZ2lZmVppU5wcxmmFmNmT1rZntHVaYnpJSZY2bXmNnFZrbQzFaa2bUWfMnM3jez1Wb2kJn1Stt+bzObaGaLozheMLP90sq4mZ1jZr8xs6VmtsTMbjSz8mj5BOCPKWU9WZXezHYys0lmNs/M1kWxnGtmsWj5IcCj0a5mR+vOSW43vTmAmfU1szvMbFm0vSlmNiYt3uTxOM/M5ptZdRRD+q/LqetcAvwnmnw79RjnsM9rzeznZjYfWJVtX1H5083s3eiYLzaz+8ysR8ryE6PltdGxu9zMSlKWJ4/N2CieGuBH0bLPm9nLKdv+U9oxTFa7P8TM7jWzNWY2y8zOSotxnJk9Ep1Ta83sLTM7pannleW5JmPd18z+E53L083sK82s1+S5k8tzicp+1syei7a1zMz+Ymbdcn0+IvlgukZMiaZ1jdh8X0eb2evReVFt4fP84JTl55vZq9HruNjMHjWzEWnbMDP7dfRarDKz28zs5CjuoVGZTZqMRce1D/DLlNfpEGB1tNnbU+Ynt3GFhWvVmuhY3mWhJlVOUl6Tn0fn+ppoWz2aWa8lx2KKhWvs18xsZnQ8/mVmg9PKVVh4j82zcO1928y+lOtz2VKohpCIiEj7ehT4EPg6UAvsCHRPLjSzE4F/ADcDPwF2AH5L+NHmgqjMGGAScB/wfWBn4O4s+zsZeAX4JjAauCza1kHAz4FK4IZoH2dG2y8H/k2ohv8jYAnwXeDfZjbS3RelbP984Jno+ewRbWcucBWh2v61UZlxUfnkze4gYBpwF+Gmci/g0iie3wJvRM/3GuBYQhX22izPEeAhwq+0FwCfRnE/a2Z7u/vMlHInAu8AZwCDgd8BvyF7Ff9boud/I3AKMAv4KMd9fg14P9pH1nstM/sZ8CvgT9G2qoAjga7ASjM7gvA63xkt3wP4NeFG/cy0zf0j2s6lwAoz2xV4AngaOA4YAlwBDAe+kLbuX4A7gInAV4Ebzew1d38lWr498F/gJmA9cADhC0HC3f+R7fk14e4o1t8A3wbuNbPR7v52lvLNnTstfi5mdgDhXH8IOJ5wLK8AekXTIh1N14hA14gUZrYD4fW8PtpOBeH16p1SbDDhtZpLOGfOBF6IXpOVUZlzCefN5YQmYeMJr0VTvgI8G+3/lmjeB8DnCa/tZYTXEsLrANCPcNw+AbYhOg/MbDd3TzSzv3RfBWYCpwMDo3hvAU5oYp2WHAuA/YBto/gqCcd3IpCa8LmPjU0bPyKcI4+Y2Rh3fyvH59LpKSEkIiLSTsysLzAMGO/u70azJ6csN+Bq4E53Pytlfi3hi+xv3X0ZcBHhC8PJ7u7AExZ+Hb4yw27XAye4e2NUbjzhC8JId58dbX9P4FQ2JhW+DuwG7OruM6Iy/ybcnJ9PVOMkMsfdJ0T/Pxl9wT4WuMrdl0a/LOLum1Q/d/fJyecePe/nCQmQ04HfuvsqM5sWFX/T3edkOayY2RcISYlD3P25aN4zwJwo1u+kFK8HjnH3hqjcLoQvRBlv9t19vpl9EE2+4+7vtWKfAEc11Wwr+gX6J8Dv3f2HKYseSPn/V8AUdz81mn4iHDp+a2aXufv8lLJ/cPfrU7Y/iXBjfHR0LmBmy4G7zWycu7+Ysu4/3P2yqMwU4MuE1/SV6JhMStmuAf9HuPk+nfBFNVe3uPs10faeJHzR+DHhddlMc+dOWvEmnwsh+fOCu5+U8pwWAJOjLy7vteL5iLSKrhEb6Rqxmb2B1e6eemwfT4vlvJTnHCf8ALCEkPS5M5p3IXCTu/8iKvqUmQ0j/EiQkbu/aWYNwPzU18nMXo3+/SjD6/ettFheBOYDBxKuGbmoBI509zXR9tYCfzOznd39wywxN3ksUop2j7ZdHZUdAFxnUdM9MzuU8MPMhteRcMxGAT+l6aTUFklNxkRERNrPcmAecJOZnWRm/dKWjwK2A+4xs5Lkg/ALXAXhBhxgX+DR6EY/6ZEs+5ySTABEZhJu0GenzdvGzMqi6cOA1wnV8JMxADwHbFLdHXgqbfoDQnKgSVEV7EvNbCbhV916wi+Ww1L211JjgSUpN2u4+1rgn4Sbz1TPJm/0U+LtZ2nNLfK8z8kt6MNnHOGm9/ZMC6Mb2n2Ae9MW3U24fxuXNv+xtOmxwINp58L9QEOGeDe8pu5eD8wg5TU1s15m9gczm0t43eoJv6aPyvbkmvFgyv4ShI5Zx2YrnOO5k/W5mFkV4bilv9+ej7Y5upXPR6S1dI2I6BqxmXeBHhaaoB1hZl3SC5jZ/mb2tJktI3y2ryPUME1+Ng8BBrD5uZDt3Gg1M/uihWaEK6NYkj9YtOY68XQyGRR5EDDCeZ5t/80di6RXk8mgSDK5Nyj6exiwCPhv2ntuMpuf61sFJYRERETaSfRl9wjCzcVtwCILfafsHRXpG/19nI1ftOuB5I158he8AWzeYWa2DjRXpE3XZZlnhBFDknHsnxZDPaFJQfqviJm2VZElllRXEqrRJ6tm70uodk4L1081kPDLX7rFbFqdHrI/9/J23OfiFmyvT/R3YZblfYHSDNtKTje3z4Hp86IvgcsyrNvca/pX4CRCTYUjCK/dbeT+uiWlH8clUbzZ5HLuNPVcehE6gv0Tm57ntYRjnfUXc5H2oGvEJnSNSOHu0wi1W4YTXv9PzezvZrYNgJltR0i+GaH20QGEY7aEjccr2YdPS8+NVjGzfQlJpvnANwiJ9/2jxa25TmxyHN19HbCGLNeJFh6LpEyvd2qcfQnHLf1cv4St9BqhJmMiIiLtyN2nAsdFvzZ+lnDT+5iFTgyXR8XOAN7MsHrypn8RoU1+qvTptlgOvEboEyJdU3005OIE4I/uvqHvAjM7spXbWkjoryBdfzYe03zLZZ+eoVy6ZdHfgYS+JtJ9SrgJTd9n/+hvc/vcLN6o1lGfDOtmZWYVwFHA2e5+U8r8tvyo2I+Nzz85nS0xBvk7d1YQjtMlpDW9iHzSim2KtImuERvoGpFeyP0xwrnQg9CM6feETrlPJvQFV0VobrgWIKrJkpp8Svbt1J7nBoQ+h5YCJyVrqZnZ9m3YXvq1q4pQ2yfbdaIlx6KllgMLgGNase4WSTWEREREOoC717v7M4QOKwcSOuecRrjxGOrur2V4JL80vwp8OepXIenoPIY3mdAR5scZYni3uZXT1MGGREKqSlK+OETJifQ+Y9J/qcvmZUKV/oNStpfskPn5HONtqXzv80WghtBPx2ai2jyvs3l/BScCiWj95uL9SnSck44l/BiYS7zlhPvF1NeuG207/zaMKhYllsazsY+fTFpy7jQr+qLwErBjlvebEkJSMLpG6BqRjbuvdPe/E5pO7RLNriRcC1Kbu53IphU+5hGSQuPTNtmScyNTza5sx78SqE9rspjzSJQpDreUEeMI1wwnJCUzacmxaKnJhBpCazK951qxvU5PNYRERETaiZntQRgR5W7CSCS9CJ1/vu3uy6My5xM6S+wO/ItwwzWc8OvU8VFV6SsJN5uTzOx2wggyp0e7yXX0jkzuJHQeOsXMroli7UPoE2GRu1+Xw7amRn/PiTrUXBVVfX8aODvqH2I5cDabV8lPdhj6HQudIq/L9GXD3Z80sxcIHSRfTKhtcgHhpvDqHGJtsXzv091XmNmvgcujfjoeJxyPI4FL3X0BYYSTJ6PXfBKwO2GUsb/4ph1KZ3IZoUbBQ2b2Z0IfHlcCT/qmHUo3F+dKCx2J/sLMVhHOt4uBlaSMhJSjb5tZHfAeYZSxEYRRZbJpybnTUhcSOpBOEEaSWU3oo+VI4KfuPr2V2xXJma4RukZkY2bfITS9eoJQe3Ek4QeCZAfJzxCawN5uZrcCu0b729Akyt0bzexq4GozW0oYLfJowrUEmj43pgJHmtkThOZa09x9tZnNBk40s/cIHZS/Q3jtzjWz3xNGzfsMoSPy1qoh1Iy6mpAcvZrQJ94HWco3eyxy8DTwJPC0mV1JGA2uO2HUuwp3/3ErttmpqYaQiIhI+1lE6Cvgp4Qb+T8RRoLZ8Oucu99N+PVuL0IHwg8QRjd5g+jXuOhXqa8SOr19iDCMeLLqfnLI3laLOrf8HOFG6FJCW/zrCTegTdXcyOQ/hJu3cwhfUG6O5n8/WnYjoa+M90gbIcrd5xJu4o4l3Lg+2sR+joni/T3huBnwed90aN98y+s+3f23hNfxMELHyjcTagWsjpY/RfiFfAzhWJxLGLL5ey3Y9vvAFwlV7x8gJIj+QeuGVv8a4QvgnYTz4n42HbUlVycTfvF9CNiT0MwgU3OYpGbPnZZy9+cJw2tvA/yNcFwvJPyS3pK+n0TySdcIXSOyeYfwOfU7wvH+GfAXQsKQKBE2gTCM+j8Jn9MnEJL1qa4jHMezCJ/dvQjDw0PT58aPgLWEAQteZWOn+2cS+tn5dzR/W3d/PIrrOEJfQgcTmhq31iTCsPe3Eo7lv4DTshXO4Vg0K6rldCzhHDyXkBy6mZCca6/aZQVlm9bsKozu1tv3s0MLHYaISKfzsk9mlS+35ktKsTGzrxO+0A5PGx1GpFMyswmEUdW6pY0gIyJ5pmuEZGNmtwCHu3tb+vlpF2Y2B7jP3S8odCzFQk3GREREtgBRs5+ngWrCcOQ/Ax7Tjb6IiOgaIZmY2W6EUSJfIDQR+yJhdLiLChmXdB5KCImIiGwZ+hCaE/Qh9E1wN6Gpi4iIiK4Rksla4EBCU+MuwFxCMujaQgYlnYeajImIdGJba5MxM/sCof+BOHCLu19R4JBERKQT0XVCRKT9qVNpERHpUNFQsjcSqi3vAnzVzHZpei0RESkWuk6IiHQMJYRERKSjjQVmuvssd68jjCYxvsAxiYhI56HrhIhIB1AfQiIi0tEGEYZ4TppPGNNakmwAACAASURBVCp0E2Z2BnAGQJz46Cq6d0x0IiJbkPWspc5rt7amxbpOiIjkSVPXCSWERESkU3L3icBEUF9zIiLZvOyTCx1Cweg6ISLSvKauE2oyJiIiHW0BMCRlenA0T0REBHSdEBHpEEoIiYhIR3sVGGlmw8ysDDgZeKTAMYmISOeh64SISAdQkzEREelQ7t5gZt8DniQMJ3ybu79f4LBERKST0HVCRKRjKCEkIiIdzt0fBx4vdBwiItI56TohItL+1GRMRERERERERKTItKmGkJnNAVYDjUCDu48xs97A3cBQYA5wortXty1MERERERERERHJl3zUEPqcu+/l7mOi6YuBye4+EpgcTYuIiIiIiIiISCfRHk3GxgN3RP/fARzTDvsQEREREREREZFWamtCyIGnzOx1Mzsjmtff3RdG/y8C+rdxHyIiIiIiIiIikkdtHWXsQHdfYGb9gKfNbGrqQnd3M/NMK0YJpDMAKqhqYxgiIiIiIiIiItJSbaoh5O4Lor9LgAeBscBiMxsIEP1dkmXdie4+xt3HlFLeljBERERERERERCQHrU4ImVkXM+uW/B84AngPeAQ4NSp2KvBwW4MUEREREREREZH8aUuTsf7Ag2aW3M7f3f0JM3sVuMfMTgPmAie2PUwREREREREREcmXVieE3H0WsGeG+cuAQ9sSlIiIiIiIiIiItJ/2GHZeREREREREREQ6MSWERERERERERESKjBJCIiIiIiIiIiJFRgkhEREREREREZEio4SQiIiIiIiIiEiRUUJIRERERERERKTIKCEkIiIiIiIiIlJklBASERERERERESkySgiJiEi7MLMhZvasmX1gZu+b2TnR/N5m9rSZzYj+9ip0rCIi0vF0nRARKSwlhEREpL00AOe7+y7A/sDZZrYLcDEw2d1HApOjaRERKT66ToiIFJASQiIi0i7cfaG7vxH9vxr4EBgEjAfuiIrdARxTmAhFRKSQdJ0QESksJYRERKTdmdlQYG/gZaC/uy+MFi0C+hcoLBER6SR0nRAR6XhKCImISLsys67A/cC57r4qdZm7O+BZ1jvDzF4zs9fqqe2ASEVEpBB0nRARKQwlhEREpN2YWSnhJv8ud38gmr3YzAZGywcCSzKt6+4T3X2Mu48ppbxjAhYRkQ6l64SISOEoISQiIu3CzAy4FfjQ3X+XsugR4NTo/1OBhzs6NhERKTxdJ0RECquk0AGIiMhW6wDgG8C7ZvZWNO8nwBXAPWZ2GjAXOLFA8YmISGHpOiEiUkBKCImISLtw9+cBy7L40I6MRUREOh9dJ0RECktNxkREREREREREiowSQiIiIiIiIiIiRUYJIRERERERERGRIqOEkIiIiIiIiIhIkVFCSERERERERESkyCghJCIiIiIiIiJSZJQQEhEREREREREpMkoIiYiIiIiIiIgUGSWERERERERERESKjBJCIiIiIiIiIiJFRgkhEREREREREZEio4SQiIiIiIiIiEiRUUJIRERERERERKTIKCEkIiIiIiIiIlJklBASERERERERESkySgiJiIiIiIiIiBQZJYRERERERERERIqMEkIiIiIiIiIiIkWmpNABiBQ7KykBazo3642NkGjsoIhERERERERka6eEkEgBxXv2YOaFuzRbV2/g841U/POVjglKJM/MLA68Bixw96PMbBgwCegDvA58w93rChmjiIgUjq4TIiKFoSZjIh3ED9iLmmPGbpiO7bkzS47bhcYKp7G86cey3UpYc8J+BYxepE3OAT5Mmb4SuM7dRwDVwGkFiUpERDoLXSdERApACSGRDlAybHuW7VrJp7uXEB8xjPiIYazYpQfVuzlY8+vX9E/w6Z4x4iOHb1i/ZPhQsBasLFJAZjYYOBK4JZo24PPAfVGRO4BjChOdiIgUmq4TIiKFoyZjIu3MSkqYcfq2NFY64Ew7u3+rttPQxZl2Vr8N07FaY4dfLsRra/MUqUi7+D1wIdAtmu4DrHD3hmh6PjAo04pmdgZwBkAFVe0cpoiIFIiuEyIiBaKEkEg7iu+6I7OP60OiPJH3bSfKnLkXjwaHLgucPre+mPd9iLSFmR0FLHH3183skFzXd/eJwESA7tbb8xyeiIgUmK4TIiKFpYSQSDvxcXuydOcq6nrlPxkEgEFdz2jbFqPLlzf2T1SyrpGSya+3z35FWu4A4Ggz+xJQAXQHrgd6mllJ9OvvYGBBAWMUEZHC0XVCRKSA1IeQSL6ZEe/bh8Vju7B89475saquZ4L5n49teHxyYDnxvn02Pvr07pA4RFK5+4/dfbC7DwVOBp5x91OAZ4Hjo2KnAg8XKEQRESkgXSdERApLNYRE8izWtSvTLxqFx9upZlAL1PVIMO0nIzdMx9cbw3/xKt7Q0MRaIh3mImCSmV0GvAncWuB4RESkc9F1QkSkAyghJJJHtu/uzD+4W0gGFXIAsLR9J8qcT84Zy+DHl9L44YzCxCRFzd2nAFOi/2cBY5sqLyIixUXXCRGRjqcmYyJ5EttrF5bv2pV1gwqcDMrA47B2cIJEl/JChyIiIiIiIiKdgGoIieTJvC/0pKZ/4ZqJtUSiNE6stAyvryt0KCIiIiIiIlJAqiEkUkRmHVvFkm+PLnQYIiIiIiIiUmCqISTSRvFevVhy7E7U9eiYEcXawkuctdsasdPGbZhXvipB13tfLmBUIiIiIiIi0tGUEBJpg3j/fjSM2JbqXb3T9RuUTUNXp3q3jdNlK+L02HkkiVkf47W1hQtMREREREREOoyajIm0wcqDh/PR8RVbTDIok7qeCaad0Yd4v20KHYqIiOTCDCsta75YSQlWWrbhQSzeAcGJiIhIZ6caQiIiIiJboJJh2zP9jIGMvHYmjUuXZi23/Ov7Ur3TxulBzzVQ/q9XOyBCERER6cyUEBIRERHZAnk8RqLMWXTcCOJ1I7KWWzPESJRvHAXz0z1LqRy4sS+5nh+tJ/bcm+0aq4iIiHQ+SgiJtFJ8m21oqDCg83cmLSIiWx+rqaXLghgNXWDdQKjvnshSctPrVE3/BDX9N043llcw4JPhNM6Y1X7BioiISKejhJBIa5jx0Q9G0NBFySARESmMhvkLGHjtAgBqjhnLgoNb1zXkylHOmu36M/wX8/H6unyGKCIiIp2YEkIiOYqPGMbHxw2ksTKxRXcmLSIiktRY7sz52RgAus11et/2YoEjEhERkfamhJBIDmK77cSKXXtSMyBbtXwREZGO13VaNYMbewGwZHQJdT1yvE7ZxiZn9V00CK2IiEgxUEJIpIViVVUs/mwvVo7aupqJWQJi9Qa+dT0vEZFi0vjhDCqmxSnp15dlu+wAPVq/rUQplAzoT8PiJbo2iIiIbMX0E5BIC8QqKvjo53uyasTWd2NcvizG8J+9SsP8BYUORURE2iDepzdTLxxGTf+21WJdOzjBtAuGEe/WLU+RiYiISGekGkIiLRGL4XHwHFOosf7r+cGez3LdlC9gdZ20wyEHb2godBQiItJGiepqdrph0YZpLy9lxjf7kijN8ccMC9e7uWfvxqDn1mIvvJ3nSEVERKQzaDYhZGa3AUcBS9x9t2heb+BuYCgwBzjR3avNzIDrgS8B64AJ7v5G+4Qu0rlZ//Xsu/1czuw5i78NXU5NXWmT5WvWleNLyjsoOhER2dp4QwMNs+ZsmLaSEvq83YeVO8So65V7n0Lr+yVYvmsVfRp2h1fezW+wIiIiUnAtqSH0V+AG4M6UeRcDk939CjO7OJq+CPgiMDJ67Af8OforsmWLx3Mrb/CzfR5nQvclQJxX9r632VVuXDGE3z11ZJjoyJZpBpj6EBIR2dp4QwM97nqJhtPHUderddtYORLqundj4Cv5jU1EREQKr9kGMO7+f8DytNnjgTui/+8AjkmZf6cHLwE9zWxgvoIVKQT/zJ7Mung3EiUtS5gkKhL8+yvX8NVuufXJ8+0es3ju2Gt47thrGLDzktaE2iq1fRLM+fX+lAzatsP2KSIiW4ZBzzYw8LqXCx2GiIiItIPW9iHU390XRv8vAvpH/w8C5qWUmx/NW0gaMzsDOAOggqpWhiHS/rw0RkNVy2vPWIPx/dkn8IvtH2FsDi3Ayq2UwSWhWdnS6m5ULYixblD7D2/vMWjo4hBTH/OSf2bWE7gF2I1Q9+1bwDQyNDsuUIgiW501J+zHum021mytGRBdwxyGP1xLfE0d9T0qmHNUKds91Uj50nVZtxWbv5TGRGN7hyxFTNcJEZHCaXOn0u7uZpZzWxN3nwhMBOhuvdVWRTqleP9+rO/edN8/6azR+ODjgXwyqBeUr2nVfi3mYFCyzmis8Jw7sxbpRK4HnnD3482sDKgCfkLmZsciRaVk+FAS3TP/KOYffoTX1ja5fqyqCkYN3Wx+9ag4tX2z/KDQ6MQXfEpsTj3dd9mJqrfn0LBwUeaygFJB0gF0nRARKZDWJoQWm9lAd18YNQlLtm9ZAAxJKTc4mieyRZr3PyNYNzC3WjqJigTTD/0LpZZjv0Mppn32Tm7afRB3/fQoFo+N0VihnKlsecysB3AQMAHA3euAOjMbDxwSFbsDmIJu9KXYxOJ88sVtWTUiwzXGYafr+9Ew75MwnVpDJ7bx2mJDtmXaqd0zbDzLdctg1nEVDH5meyoee51+N7yAxpiUQtJ1QkSksFqbEHoEOBW4Ivr7cMr875nZJEJn0itTmpaJbJlyGC2+305LuWGnf1BqZe0Xj8iWYxiwFLjdzPYEXgfOIXuz402oabFsrWJdujDr4j1orMyeuJlx5mDwwZSsNYb85kVwx0bvyqzjUhJAMWjNKASfHBinYuf92PaqF1oVv0ge6TohIlJALRl2/h+EDH1fM5sP/JKQCLrHzE4D5gInRsUfJww5P5Mw7Pw32yFmkXYXq6pi/Wd3oaFLC8rWGxVLjbKDPuVr273G6PL8JIN2r5jHgmPqYUUZ1v5dCbFyv0F071ZF4wfT239nUixKgH2A77v7y2Z2PaHa/wZNNTtW02LZGsV3GcWn+/ahsarp5sDJmqEeh5Wn7AcO63vHsieRcpAocxrLc/i1Q6T96DohIlJAzSaE3P2rWRYdmqGsA2e3NSiRQrNuXfn4/5XQkkyMJaByqXPBjk9yYteVeYvhgIoYsw6/jZFTJtC4vByrb9+b94UHGImS3nT7oF13I8VlPjDf3ZNDFN1HuNHP1uxYZKtWMmhbVu7Si2V7tfx7a6LMWTp6w1TeYvE4lAzbnsb5C/H6urxtVyRHuk6IiBSQuqoVaaOGgbX85/I/5DUZlOqDg2/lwNEftsu2RdqTuy8C5pnZjtGsQ4EP2NjsGDZtdiyyVZv7jaEsPKBz1Myp65Fg6g8GEh+ybaFDkSKm64SISGG1eZQxEQlDxreXUotz0cAnueuQFTx1wwGsHg4NVfmvFT300Xoqpi9SB6OSb98H7opGjplFaEocI3OzYxERKT66ToiIFIgSQiJbgF3LKjm6x5v8p3ocaxritKYT0eZ4iUFclQYlv9z9LWBMhkWbNTsW2VrFunWj5qCdqO/S+bo4Wb7fAHp2qyTxtmqiSmHoOiEiUjj69ieSB7Ven/M69d7IukRu/TZ4O7Y0mPvFEqrHDWq/HYiIFKFYVRU2ZCDzDo3T0LXzJYSWjIVPR/ck3qtXoUMRERGRDqaEkEgbWXUZOz1yNi+tb8xpvcPfP45xV59Lvbdsvd7x9SzaP0Z9tw4YckxERPJi1ZG7M+3bvaFzdB2U0fLdnJk/2gkrUcVxERGRYqIrv0hbOcTWx6gj3mSxr83+HC9+MGLDdNXsUnosTjDqsTPBYMTwRTy986NZ11+XKKHbHGPt4I3DEYuISCdlxtLv7M+6gdaiESsLysJoZp/8YCzpg3sP+O9qeOXdwsQlIiIi7UoJIZEOMnVZP7q/V0r3j5M1gsLfwf8KFfXm7T2YOwf15avdFlNqmyeXYubUdwVXvT4RkS3C2kHQ0LWTJ4MiHoe1QzaPdfmKrvQq2ROA+FszSKxb19GhiYiISDvRV0uRDvLGmLu58Ky7sy7f5s0EN/3yeD5trMm4fI+yCt4750/Eh61pjz6lRUQkX8yIlZcXOoq8WL6H89EJlXx0QiWxPr0LHY6IiIjkkRJCIgUy//85x132ZO79SkztSrc5euuKiHRW8Z1HMvPSvWnohKOKiYiIiCTpW6VIBr5mLQNfcEpXt18voJULSvjzewdtMi/W6Bz26neYtDr7aC+DPrOAVaMaqVoQ6/TdUoiIFJu6/zeGBYf3JVHmLUr4l6wxek4Nj7Lqzn1b9snR27Hs2+NY8Y1xYJ24l2wRERFpkc595yFSIIl16+j+7AxK1jV/w+sxSHRtpMKaH3q+S6yWmr4xavrGqFrkdPt3F2r6xDbMW98rRuUT3bl1/oF8WJe5n4bJuzzC58a8T9lq/fIsItLZrNq+lDVDm87WewzKBq+lfMga6nskqFoSHiXrOyjIVlo1IkH1rk71LhDfZRSxLl0KHZKIiIi0gTqVFskg3m8bpl84HG9JFZyedcw87BbiVtps0WO6rOHLl9zYbLkRj57J8ZMv4P3v/Snj8lu3e57qnz3F6IfPw+r1K62ISKfQklozBl6e4J3P/JVSi3P+wn14Zcq+oW+45KOTf6w3ljvTv9WLkXcYvDO10OGIiIhIKykhJJKFGy26KY8tLWO3id/j+m/8hSOqmq8lFLfmK+bdcNidLG3o3vR+81xdf/hDtZRN+4SGvG5VRKQ4xCoqmHPhPjR0zV5781dfupfPVMwlblBqXQG4tP8LvHHNG/zol9+lx+xGKpbFWL57564BWlJj7PD7mSSqqwsdioiIiLSBEkIimdSsp9cHxqrh0FjZ9I25NRhVC511Xg40nxBqiSOr1gPZ2w58WLeOB1ftDW3sQ6hycYyKZeH5lb7/MQ2fLmvbBkVEilUsRkNXJ1G6+TUjUZngiH3e5eDKuQwu6brJsufW9+ScV06mX51jjVC2xumaMnCAx2Ht4ETnqjXk0LhsOSQaCx2JiIiItIESQiIZNK5aRZ9bXqT2R59hXTMJoUS5s2JnqLB6ar2e8hY0HWurf67ZnVueOwRrbNs3hG3erKPsydcA0G29iEjrxKqqiPXfJtQsTXKI1xoN3RJ0G7Camwe/CIRkUL038mxNBQli/PHjQxl4b3lYAYjVO90/3viJnCg1avvYptsGEqXgJZ27JpGIiIh0bkoIieTJWU9MYI/d5/DQyCfbfV/n9prOSePf5pCHz8caOtPPxiIixadh3x2ZNb4cbNMETd+3nPpvVPPqPvdsMv/jhhou/fFZxFrQRjdW7/R7bfPET/WOcWr6KyEkIiIiraeEkEieWL1Rl4hnXX7szMN5693hG6b332s6fx/2bNbyU2pifOuJ03niqN8xqnTTkVxKLU6PWLzVTQgsAdv9q57KqYvUZ5CISBss/9Y4qg+r4fb9b9ps2SdH9mLP8gVA1Sbztyup5KzL7uOyu06i77utq5/ZfU6CshXGyh07PinUWOHMv2g/tr9/MY3TP+rw/YuIiEh+KCEkkifWr5YR3ZZmXf7Jmh5UfBISRj1nJnipYUcm9p7GGT0+yVh+au1ABv0bFn2xC6Py3QrNoeKVGTSsWpXnDYuIbP3ifXpDrx4AJErAl1Twy4/GA/Cz4f/k0MqQ5Dlm3lhurqvYsN4to+5ih9KulFqcU7ot47aD5jNr+36QMLZ9Ok6sfmNyZ8HnwLtkTxZ1f6eMeJ0zcs95TP9wMFbXcbVFLWF0m5fA1mXv605EREQ6PyWERJqSwxDAt427nYMqsi9/aa/7YK/w/55XnsWg5xqZ+O54Tr30Dx3S79AGamEgItI2A7Zh9Y49qe0Wo3J5gqrnoOG5AQBcd84RDB12L3UeY8Edw6mo3tj7/92XjuakHq9vmJ448u9UjYLe8XL2f/scSldv/IC+6Uu3NDly5bDYaZTNL+OJnR5jxKIJNK4qA4fY+uZHsmyrWB30+PurNKhTaRERkS2aEkIiTRhy64esGzeCeYdnbwrWGnecex1rvZQZdQPY//JzOPXsxzm315y87iObrvNiDPrfGTSuXt0h+xMR2Rqt7R/j+Z9ez743nkvvqRsTIyv+uB3/EzsfgIqGTYeCfPxXh/CYHbLJvCVjYsz4xp956cfX0+gbE0JVsbIm9z/9iIkkSAClfHjwrWFefR1HP3he65+UiIiIFJX2/xlJZAvWWF1N6dqW9bJz+eyj+Ouqfi0qu1d5OQdUxDiqy2waDl/BPpVzNiuzb+Vsqr+xhqElazJuo8JKOHzsO7BNbYv2mWQN0Lh0KbiqComItMa8I/tw7HefpSpWxulff5wFX9lYk8cSEGsgY4fR1rhxWfLRcyqM/Nt3Wd5YS1WsjNfqytjrf8/hldrstYMg9CWXrF066tHvcsrsIxhWEufnX3yA0lUxer1n9PrAsESTm8lZj+nG8HuqNeS8iIjIVkA1hETyZOY7g7knlmBC98dbVH5+wxrmN1Ty7n5/z7h8x9IEf9rrLvrHyzMuL7dSbh78IuOWbcuSpdu0Om6R9mRm5wHfJjRWfBf4JjAQmAT0AV4HvuHudQULUiRXDvUeao6e22sO1Xu+zN8XHgRAjxlQuazlWZiKFQn6vwLnHDCefuVreGvZIPq/nODHY45j5x6LW7SNvq/Eea12JL/puoTL+r3LZSPXsbKiEhzKVsSo7+IkyvP4I4BpdEvJH10nREQKRwkhkQI59t1vknigLy/+6gZKbfMmaf+7agfu+tlRXH7NzU32TSTSWZnZIOAHwC7uXmNm9wAnA18CrnP3SWZ2E3Aa8OcChiqSkyGPL+PRNQfxo5++TowYF/V5k4tOeROAPe7+AZXP57Y9S8DCa0ewkI1d1tXdOJC3Gdii9StIMGgK/Ov9A7n4F68y7eDbiFuMTxvXcuRPLmDNEKO2j+PZ6oUbeCwkjKyx6WTPylHO2sE9GTa1HK/NrYaqSDpdJ0RECksJIZEO9NS6Us679XQAKpY7lSsT7HXD9/nh/zzAaT0WbVL2+G7TabzscfYpWw/kJyM05OlGurz9sYaal45UAlSaWT1h7O2FwOeBr0XL7wAuQTf6soXpsjjBoT/ZvL+e/jV5bqOVg6pPExz64/P45o8f4cyeC+gb78K9l1/N5+6/gF7vG9W7Zq4ltN2uC7l3p1Bb9dDXv82a2T2a3E9DpTPrkn0Y8b/LaXx/Wt6fhxQdXSdERApECSGRPJq7vBe/Wz6cc3rNJG6b/hT7x+rtuXnagZt0PgrQe2oj8+t6A5smhPrGu3B2z3k0lwz63IAZPFZb1uwNPEDFkhoaFmQe5l4k39x9gZldA3wM1ABPEar+r3D3ZF5yPjCoQCGK5Cy2207U9usSmmOtLlzyB2DtgBh9jp8PwJzXBjPgpQRlaxJc+8+jubLXxtR/z5kxYg3Zm4zNXdiHU2InAbBqWZfmO5g0aKxwvERdUUrb6DohIlJYSgiJ5NH6j7vxx6WH8f0vzyC9Edj1b32ebf5ZTqZx35fVd2Fhw6adR5ea0Tfepcn9LWxYww/7vsioykX8evax2Qs6xOoMGtWRtHQcM+sFjAeGASuAe4Ev5LD+GcAZABVUtUeIIjlr7FFBY3nnSIQ0VhinDHoZgEtn94foyjPgxQQQA4MVO8QpWxU++0vWGI1Vmzcds2VlzFw2GNBoI9KxdJ0QESksJYREOsj7h0zk9r2HctdPj9ps2etXjeY4G73JvOodY3x45p+ybu/TxrWM//mPWD3UqO3TSFO9PsRrjR1+/TaJmprWhi/SGocBs919KYCZPQAcAPQ0s5Lo19/BwIJMK7v7RGAiQHfrrWymdAr237eo2nVH1ozsgRe4c+Xucxq59cdfAcIbKV1DhfHsD65mv/vPZ9vnnH5vwKL9YnhTHUwnF6nfaOkYuk6IiBSQfggSaUbp1AUMf6AWa2jb3XG5lXJU12mMuOgDavps+taL1Tvxuk0fsaZHHKZbrIw9z3qHupE1TXYC2m12jOH3rw7JIA01Lx3rY2B/M6syMwMOBT4AngWOj8qcCjxcoPhEWiUxfTZd/zsbK8BnaqLUOPOK+7jkmlvY6eL3Nsyff3QDl1xzC5dccwsrRoaaQiXrncN/+yP6vdKC7ZYnuOeYPxBrMHrMUDZIOoyuEyIiBaSEkEgzGhcvoeT1aVge7vsHl3Tl9u3+w7J9GlkzaPORxZJWbRdn3YjMo6veunIAD63tSrmV8pch/6Wyqo6SNdlv3surHX/tPSWDpMO5+8vAfcAbhKGEY4Rfci8CfmhmMwlDCt9asCBFWiE2cihr9x/W4ftdtV2c6hPXML7LAg6tbGR0tzkblpV/XM55757Iee+eSNlKZ+WwOAuPr6XLkkZKajZ+/nf5xChdtentX8kao+ucEk5985tULTDizQweFqszBv7X+f/t3Xu0nHV97/H3d/ZOdm4EcoEQAiExBJWLIqWIl1bUqmC1EevxwDqKenThBVtdi9ZiLwdtj6t0udAWWrF45IBVoZxaC1UUEaVUEcKlXANIgJALuUHuCbnMzPf8MUMyO3vvXPbOnmeyn/drrb32zO95Zub7/HiG35PPfn7PU1m19kBunkrIcUKSiuWUMWlf1WmcSr8PfzjdkbV+byX/kmfecxVnzHof+c0pO9si2Xmq/rHvfZqb5v643/e97NvvZev0Gr97TmM62YuLD2HKU/DCq/t+TtQp5C/Y0ksy8xLgkt2anwZOL6Ac6YDYcuyhrH5VN1MW1Pa+8gG0eWadha//NjCGZ3ZsYsGWo3YuO/yBGjwwsfmszvI3Bn9/+nV8/s6PMvb5Ol3bGmPBIUtqZHRR6wnqoxMCurcEE5bU2b5uIqO21Nk+Yc8DXdRg4n8+Q23NumHaUpWJ44QkFSeyA/6xODEm52vjrUWXIe1R16RJLD/vlWyYu/e7ytTH1/jrN/0L5x4y8F9Pn69t5rnartDoc0//PpuvaFwFt5KYuAAAHzdJREFUYtJnn+0TCN23bTsf/9Jn6FlfJyux84B99KakOjZ44dV9v8vHX7MennyW+pYt+7SN6jx3521syDWln7/hOKFOUxk/ns1vO7Gt1xFa/sZg4XlfB2D2TRdw9C17/uwd44IfXHrZzmsItaqODVadRuOPHAn1sXUeffff88b7P8j6hZP2/MeP5lsdf8066g89PvgN0gHhONHgOCFJ/dvTOOEZQtI+qq1dy7T5Gzh00VgIWPZb3dQHuDBnZXMXn//l7/Ptly3jB8f/qN91pnaNZ2rLSUSfnflT/uKj8wD4wPRf9Vr3z1edzHfuOYOjX6iz9N01YmM3M25vBFNbjqiwdUrziH73Oja+SNUwSJIOqO7Zx7L29Omc8xe30kXy9UfeyLR/Hjvsnzv5oeCEtZ8CYOqypHHq6h7q3Jq86co/ZspzLaegApuO6mLLkS1tAbG9wmt++TG2rx1DZQ/RwqQFwRF3rAYgly4fwtZIkqSiGQhJ+yHvfYQegAjGvvx1VMcF2QXbD633+WvqYfeN5rF1x8Lx+/beZ43bxlmn3tDvsl+smsOhD49i4zHwxdd/j7s2HsedT50KwPZDoTq++DP9JKk0dlSJevLHk58C4Pbpx7OWY4f9Y2tjYOuRjWlqPWu7Gjfp3oOow+THek9re3FqhW2ToDoh+6xbfW7cHi8uOfHJCpMXbKH2xMLBlC9JkjqMgZA0GJlM/8qdAHTPOIrHL5rZZ5UNc+pMnHNgrq9w+0n/Biften7+xOfh4rsAmHPDJ5jyQPR7DSFJ0oFXXbqMiY8d0tbPrPUEa0+u8/R7/xGA2TdewITn9m+WUHbB+jmNO4oNxlH/vpjqkqWDeq0kSeo83mVMGqLaylW84vLn6Hlht69TwLplE5nzs49w0uWf4gOLzhz0Z/z5qpM57vYPc9ztH+aXW3sfyE+YtZ51+3gWkiTp4PS6P57PI++5YtCv33ZohRVnNC8kLUmShGcISUOW1SrVRYs5/IEjWTe3m00zdwU2lW0VWNVD9xaYv3gmfzj6N3l47VFc+/LvMLN7wj69/9+8MJcfPnsiuXIMAJctfQc3HbKaMZUd/PnUhxjXs51NYz3Al6R2qmzczBkPvI+Viycz7tluJjM8dxzbPqHCnE88zsem/IJxlXE72//kTT/kzlPm9Puah757Eocs3VXPphldbJ0C2bXvY0XUYdYPtlPZuut96i+sGcQWSJKkTmUgJB0gY34wnym/8xtsmjmq94KAjbPrsHwctzx1GlMfqPOzv3oZ75uwmAmVMf2+16b6VrZk4yD86/PfRGXDrq/qg/fN4UHmkKOS8999N9XawLe3lyQNj9y6jdWPz+CIB4LRG4cnDNpyeIX1L0/unHUbXTGu17JPHLaMTxy2rN/XnTDxpF7Pt0+E7YfteZpY1GD8ssqu60zXYdT8J6hv3rxzncFNNJMkSZ3KQEhqo+rYZOUZwV/e8l5u/o0nueFlt/W73jlP/DeeemQGAJUBjsBjR/C2f/sjwiN0SSpMtlzGJ5szh6P3Tb0GJ2D9m7ay8M3/l/2Z4b8jBxFOJVS2B9OvmE9WqzubHV4kSRrZDISkA2jMg4uZu/aInc83z5zAc7/d8q+FaPzjIepwz5OzOHXNf+/3fdauOmTAIKhVf2HQ5IeCKQ9tAKC+YtV+1S9J2j9rT4Qxq7qY8Fydv/6rq5jctYWvrngbC//mhCG97+/95U/5wMQHgX2bXgxw37btfPxLnyGPhhWv3RUiZfeeB5TJjwSH/+sCai1hkCRJGvkMhKQDqLZ6NaxevfP5IetnM3HGdDbOqpO7zeyqrBvF+nWT+n2foVztfdzqGnnfo8DQ/0AtSdqz+qhk0yt3MOltL/C6MdvoiTF8/Ijb+cOPHdl33e9PZewLew5nXpxSoXLO87zvkAeZvo/XmgO4fuMkvv7sm9h2WFAdk9R79m0EmHY3HLpgLbV16/f5syRJ0shgICQNo9rCZzjia4vZ8sXTqY3LndMJhkU2zhiKujGQJLXT1GkbuPz46+mJHgDOGNPF/Nf8vz7rHbfgk+xY2kXU6XXR51bVccFfHv9DpnWN3uvnrq+/yH+8OIUju9fz7eVnsOTRI2HG/k30mjR/BdWnF+3XayRJ0shgICQNt3qNl136EC+871W8cMrwhjUvv2IZtedWemaQJLXRmicnc87iP+DR3/t7xlUGDnIW/o8rAXhqxybO/6OLqPQzQ+uQJTW+/LkP0vXla/ndcVv3+LnXbTiO7/zZu1h5eoXaGP/PL0mS9s9wnq8gqam+eTNT73mB6b8cngP2njUVjr15B7UVq8gd24flMyRJA6tsr3Dyzz/O9Rv7nwrcamb3WD7zpeu58NIb6PrkykF93gcWncnf/OpsVv1Ghfro/R9bRq+v8Iq/XUZtyXOD+nxJknTw8wwhqU1qC37NoVtmsvwNRx3Q9x29vsKExcmon97nmUGSVJQEnu/hG0t+i3VH3TfgLeEBtuR2vrbozdQzWLJ4Kke3LNs2scLqM7czq3stMLbPa2tZ55LVr2b+4plUNnRTHT+4//NHDarPLhnUayVJ0shgICS1W+uxewy41j6/1zG3bCZ+9eAQ30iSdCAsevgo/m7tocx77ZUc0TWOruh7MvaaWo1NN0ynUoXDgW2H7lq28djgmXd8k/7CIIBtWeU7d76OyjZP8pYkSUNjICS1UW3pc7zissZFI9a9/hhWvH7wiVBle3D815+jvnwl+3cJUUnScNq+dDxvWH4Rt7/nMmb2c6ew2aMm8MsvXt7vaytUgK5+l0mSJB1IBkJSG2W1SnVZ43oNozbNYLAH/WNWV5j0RI3qs0uh3v+daiRJw6e+Zh3HX7ueZ981iW1T+8byUQvOmv8JPvnK/+QPJj3bZ3lPjGpHmf2a8kAwdf5qWkePypgxrPjoqf3eDbNnXXLYP/2qbfVJkqT2MBCSCtK1vU73psF9BScuqjP+e3cf4IokSfsqd2wnH3yMrre/fsB1ti2ZwPcmvoZRset2YiePWcIbxgxuutczOzZx46aTiNrQ5htnF9QnjCF+8+SdbdWeLjbOrpP9/J1ix6oKhw3pEyVJUicyEJIK0n3bfcz62SAP6tPLR0vSwWDJo0fy5UffDUB2JSe9+llumvvjQb3XzZtfyRU/OWvIl59bc3Ky5uTx+/6CgOjuJqvVva8rSZIOGl6RUCpS5uB+pA4SEVdHxKqIeKSlbXJE3BoRTzZ/T2q2R0RcHhELI+KhiDi1uMql9vrbs/+J6+fcVHQZ+23blDpP/+/fpPvoGUWXooOU44QkdSYDIUnSUF0DnLVb28XAbZk5F7it+RzgbGBu8+cC4Mo21SgNixk/W8+UB/ftnJ0vPv4urlh74qA/6y3jnuD8t9zB+W+5g1EzNg/6ffZX9+Zg5o+3UV+7rm2fqRHnGhwnJKnjGAhJkoYkM+8A1uzWPA+4tvn4WuA9Le3fyoa7gMMiYnp7KpUOvLzvUQ59Zus+rbtu4WS+t/gUbn9xcIdfrxw9jksOX8Alhy9g+qQNg3qPwYhqMHrZOnL7jrZ9pkYWxwlJ6kwGQpKk4TAtM5c3H68ApjUfzwCWtKy3tNnWR0RcEBH3RsS9O9g2fJVKB0I2f/bihV9P4SO3foxteZCEKwnbD6vzxCePoOvII4quRiOL44QkFcyLSkuShlVmZkTs98WvMvMq4CqAiTHZi2epY3Xf/RivWHQ4T3z6aLJ777tqZWuFV9zyyT2u84O3XMGJo8f2avvWhqlccmfjJIp4sWvIF5feF0f9Z3LInc8AUF39Qhs+UWXkOCFJxTAQkiQNh5URMT0zlzdP9V/VbF8GHNOy3tHNNumgVd+6lVy2nKN+MZ3nT+pm29T6nl+QUFm/50OwP1n0Xg4fs6lX2+Nrj9jr6w607i01aitX7X1Faf85TkhSwQyEJEnD4SbgQ8Clzd83trR/OiKuB14LrG+ZMiAdtLJaZeyN8xk77XVsmzr093vsv47lsaG/zaBUdgTdWxrnH3VvrRVUhUrAcUKSCmYgJEkakoi4DjgTmBoRS4FLaBzg3xARHwWeBd7fXP1m4J3AQmAL8JG2Fyxpj8YvCaZdcWfRZWgEcZyQpM5kICRJGpLMPG+ARW/tZ90ELhzeiqTiHPnvz3Do0zN49ndHFV3KoBxza43xDy6mWnQhGlEcJySpM3mXMUmSpAOkunwFYx9fweSHgsqOdlz2+cAas+pFqsueK7oMSZLUBnsNhCLi6ohYFRGPtLR9ISKWRcQDzZ93tiz7fEQsjIgnIuIdw1W4JElSJ6ouXcbka+5i1IYgqgdfKCRJksphX84QugY4q5/2r2bmKc2fmwEi4gTgXODE5mu+FhFdB6pYSZKkg0ImMy+dzxH3eSdsSZLUmfYaCGXmHcCafXy/ecD1mbktM5+hcTG404dQnyRJ0kEpq1ViL3eglyRJKspQriH06Yh4qDmlbFKzbQawpGWdpc22PiLigoi4NyLu3cG2IZQhSZLUmcau3s74pRXo8BOFogYTFlWobHix6FIkSVKbDDYQuhKYA5wCLAcu2983yMyrMvO0zDxtFD2DLEOSJKlzdf38fo65flHRZexVZUcw/R/mU/v1U0WXIkmS2mRQgVBmrszMWmbWgW+wa1rYMuCYllWPbrZJkiRJkiSpQ3QP5kURMT0zlzefngO8dAeym4DvRsRXgKOAucD8IVcpSZJ0EIrTTmLVyYfQqXPGpt0FPetrVHbUyVqt6HIkSVIb7TUQiojrgDOBqRGxFLgEODMiTqFxdLMI+DhAZj4aETcAC4AqcGFmenQhSZJKaevhY9l8dNCpgdCke1ZQfXpR0WVIkqQC7DUQyszz+mn+5h7W/xLwpaEUJUmSNBL0/OgeZj90FI9fNLPoUiRJknoZyl3GJEmStBe1lat4xVeX0vN85xx2jV5f4RVfWUptyXNFlyJJkgrSOUcmkiRJI1BWq1SXLOWI+3cwYVFnHHpFDapLlpI7thddiiRJKsigLiotSZKk/dPzo3s4Ystr2Hr4GABqPZDd7bm2UNSga2vsfN71Yls+VpIkdTADIUmSpDap/Md/MeuORjCz6lOvY8Nx7QmExi+rcOTf/qotnyVJkg4OBkKSJEntlI0Q6KgfLuXICeN2Nq/8rUmsP/7ABUSVHcGc6zYQO2pUtmylmp15pzNJklQMAyFJkqQCVBct7vV88uGnEvWeXm317mDDnDoEjNoYjF/W8vqxwaZj6wD0PF9h7PO9A5+oAg8/Qb1apT4sWyBJkg5mBkKSJEkdoOvn9zP157u1HXYomy4+ASowYTFM+T+7pn11HTebJy84EoCpj1QZe+P8Pu/pOUGSJGkgBkKSJEkdqrZuPcd94b8AyFq9V8BTe2oRx/2v5Y1l1arhjyRJ2i+dce9TSdJBKyKujohVEfFIS9uXI+LxiHgoIr4fEYe1LPt8RCyMiCci4h3FVC0dPOpbt1LfurXvLeIzdy2rVospTtoHjhOS1JkMhCRJQ3UNcNZubbcCJ2Xmq4BfA58HiIgTgHOBE5uv+VpEdLWvVElSAa7BcUKSOo6BkCRpSDLzDmDNbm0/ycyXTlm4Czi6+XgecH1mbsvMZ4CFwOltK1aS1HaOE5LUmQyEJEnD7X8CP2o+ngEsaVm2tNnWR0RcEBH3RsS9O9g2zCVKkgrkOCFJBTAQkiQNm4j4M6AKfGd/X5uZV2XmaZl52ih69v4CSdJBx3FCkorjXcYkScMiIj4MvAt4a2a+dAOkZcAxLasd3WyTJJWM44QkFcszhCRJB1xEnAV8Dvi9zNzSsugm4NyI6ImI2cBcYH4RNUqSiuM4IUnF8wwhSdKQRMR1wJnA1IhYClxC424xPcCtEQFwV2Z+IjMfjYgbgAU0pghcmJm1YiqXJLWD44QkdSYDIUnSkGTmef00f3MP638J+NLwVSRJ6iSOE5LUmZwyJkmSJEmSVDIGQpIkSZIkSSVjICRJkiRJklQyBkKSJEmSJEklYyAkSZIkSZJUMgZCkiRJkiRJJWMgJEmSJEmSVDIGQpIkSZIkSSVjICRJkiRJklQyBkKSJEmSJEklYyAkSZIkSZJUMgZCkiRJkiRJJWMgJEmSJEmSVDIGQpIkSZIkSSVjICRJkiRJklQyBkKSJEmSJEklYyAkSZIkSZJUMgZCkiRJkiRJJWMgJEkakoi4OiJWRcQj/Sy7KCIyIqY2n0dEXB4RCyPioYg4tf0VS5LayXFCkjqTgZAkaaiuAc7avTEijgHeDixuaT4bmNv8uQC4sg31SZKKdQ2OE5LUcQyEJElDkpl3AGv6WfRV4HNAtrTNA76VDXcBh0XE9DaUKUkqiOOEJHUmAyFJ0gEXEfOAZZn54G6LZgBLWp4vbbZJkkrEcUKSitdddAGSpJElIsYBf0pjGsBQ3ucCGtMFGMO4A1CZJKkTOE5IUmfwDCFJ0oE2B5gNPBgRi4Cjgfsj4khgGXBMy7pHN9v6yMyrMvO0zDxtFD3DXLIkqY0cJySpAxgISZIOqMx8ODOPyMxZmTmLxun+p2bmCuAm4PzmXWTOANZn5vIi65UktZfjhCR1BgMhSdKQRMR1wK+Al0fE0oj46B5Wvxl4GlgIfAP4VBtKlCQVyHFCkjqT1xCSJA1JZp63l+WzWh4ncOFw1yRJ6hyOE5LUmTxDSJIkSZIkqWQMhCRJkiRJkkrGQEiSJEmSJKlkDIQkSZIkSZJKxkBIkiRJkiSpZAyEJEmSJEmSSsZASJIkSZIkqWQMhCRJkiRJkkrGQEiSJEmSJKlkDIQkSZIkSZJKxkBIkiRJkiSpZAyEJEmSJEmSSsZASJIkSZIkqWQMhCRJkiRJkkrGQEiSJEmSJKlkuve2QkQcA3wLmAYkcFVm/l1ETAb+GZgFLALen5lrIyKAvwPeCWwBPpyZ9w9P+ZKkMtjI2ud/mv+yGXi+6Fo6yFTsj93ZJ73ZH72N1P44tugCOoHjRL9G6j4/WPZHX/ZJbyO1PwYcJ/YaCAFV4KLMvD8iDgHui4hbgQ8Dt2XmpRFxMXAx8CfA2cDc5s9rgSubvyVJGpTMPDwi7s3M04qupVPYH33ZJ73ZH73ZHyOb40Rf9kdv9kdf9klvZeyPvU4Zy8zlL53hk5kbgceAGcA84NrmatcC72k+ngd8KxvuAg6LiOkHvHJJkiRJkiQNyn5dQygiZgGvAe4GpmXm8uaiFTSmlEEjLFrS8rKlzTZJkiRJkiR1gH0OhCJiAvA94LOZuaF1WWYmjesL7bOIuCAi7o2Ie3ewbX9eKkkqp6uKLqDD2B992Se92R+92R8jn/+Ne7M/erM/+rJPeitdf0Qjy9nLShGjgB8At2TmV5ptTwBnZuby5pSw2zPz5RHxj83H1+2+3kDvPzEm52vjrQdgcyRpZLk7b2NDromi65AkSZI0suz1DKHmXcO+CTz2UhjUdBPwoebjDwE3trSfHw1nAOv3FAZJkiRJkiSpvfZlytgbgA8Cb4mIB5o/7wQuBd4WEU8Cv9N8DnAz8DSwEPgG8KkDX7YkqSwi4qyIeCIiFjbvallKEbEoIh5ujsP3NtsmR8StEfFk8/ekouscLhFxdUSsiohHWtr63f7mH6Uub+4zD0XEqcVVPnwG6JMvRMSy3Y7ZXlr2+WafPBER7yim6uETEcdExM8jYkFEPBoRn2m2l3o/KQPHiQbHCceJVo4RvTlG9G9f7jL2i8yMzHxVZp7S/Lk5M1/IzLdm5tzM/J3MXNNcPzPzwsyck5knZ+a9w78ZkqSRKCK6gH8AzgZOAM6LiBOKrapQb26Owy/dEvVi4LbMnAvc1nw+Ul0DnLVb20DbfzYwt/lzAXBlm2pst2vo2ycAX209ZgNofm/OBU5svuZrze/XSFIFLsrME4AzgAub2132/WREc5zow3GitzJ//6/BMaKVY0Q/9usuY5IktdnpwMLMfDoztwPXA/MKrqmTzAOubT6+FnhPgbUMq8y8A1izW/NA2z8P+Fbzj1R3AYdF43qHI8oAfTKQecD1mbktM5+hcSb36cNWXAEyc3lm3t98vBF4jMadbku9n5SA48SeOU6U9PvvGNGbY0T/DIQkSZ1sBrCk5fnSZlsZJfCTiLgvIi5otk1ruU7fCmBaMaUVZqDtL/t+8+nm6e1Xt0wPKVWfRMQs4DXA3bifjHT+d9zFcaIvv/99OUY4RuxkICRJ0sHhjZl5Ko1TmC+MiN9uXZiN24bu/dahI1TZt7/FlcAc4BRgOXBZseW0X0RMAL4HfDYzN7Qucz/RCOc4sQdl3/4mxwjHiF4MhCRJnWwZcEzL86ObbaWTmcuav1cB36dxKvfKl05fbv5eVVyFhRho+0u732TmysysZWadxs09XjrlvxR9EhGjaBzofycz/7XZ7H4ysvnfsclxol9+/1s4RjhG7M5ASJLUye4B5kbE7IgYTeOChzcVXFPbRcT4iDjkpcfA24FHaPTFh5qrfQi4sZgKCzPQ9t8EnN+8Q8gZwPqW08FHtN2ub3AOjf0EGn1ybkT0RMRsGhfJnN/u+oZTRATwTeCxzPxKyyL3k5HNcQLHiT3w+9/CMcIxYnfdRRcgSdJAMrMaEZ8GbgG6gKsz89GCyyrCNOD7jWMZuoHvZuaPI+Ie4IaI+CjwLPD+AmscVhFxHXAmMDUilgKXAJfS//bfDLyTxkUxtwAfaXvBbTBAn5wZEafQOOV9EfBxgMx8NCJuABbQuNPKhZlZK6LuYfQG4IPAwxHxQLPtTyn5fjLSOU7s5DjhONGLY0QfjhH9iMY0uWJNjMn52nhr0WVIUse5O29jQ66JouuQJEmSNLI4ZUySJEmSJKlkDIQkSZIkSZJKxkBIkiRJkiSpZAyEJEmSJEmSSsZASJIkSZIkqWQMhCRJkiRJkkrGQEiSJEmSJKlkDIQkSZIkSZJKxkBIkiRJkiSpZAyEJEmSJEmSSsZASJIkSZIkqWQMhCRJkiRJkkrGQEiSJEmSJKlkDIQkSZIkSZJKxkBIkiRJkiSpZAyEJEmSJEmSSsZASJIkSZIkqWQMhCRJkiRJkkrGQEiSJEmSJKlkDIQkSZIkSZJKxkBIkiRJkiSpZAyEJEmSJEmSSsZASJIkSZIkqWQMhCRJkiRJkkrGQEiSJEmSJKlkDIQkSZIkSZJKxkBIkiRJkiSpZAyEJEmSJEmSSsZASJIkSZIkqWQMhCRJkiRJkkrGQEiSJEmSJKlkDIQkSZIkSZJKxkBIkiRJkiSpZAyEJEmSJEmSSsZASJIkSZIkqWQMhCRJkiRJkkrGQEiSJEmSJKlkDIQkSZIkSZJKxkBIkiRJkiSpZAyEJEmSJEmSSsZASJIkSZIkqWQMhCRJkiRJkkomMrPoGoiI1cBm4Pmia+kgU7E/dmef9GZ/9DZS++PYzDy86CIkSZIkjSwdEQgBRMS9mXla0XV0CvujL/ukN/ujN/tDkiRJkvadU8YkSZIkSZJKxkBIkiRJkiSpZDopELqq6AI6jP3Rl33Sm/3Rm/0hSZIkSfuoY64hJEmSJEmSpPbopDOEJEmSJEmS1AaFB0IRcVZEPBERCyPi4qLrKUpELIqIhyPigYi4t9k2OSJujYgnm78nFV3ncImIqyNiVUQ80tLW7/ZHw+XNfeahiDi1uMqHzwB98oWIWNbcTx6IiHe2LPt8s0+eiIh3FFP18ImIYyLi5xGxICIejYjPNNtLvZ9IkiRJ0mAUGghFRBfwD8DZwAnAeRFxQpE1FezNmXlKy62zLwZuy8y5wG3N5yPVNcBZu7UNtP1nA3ObPxcAV7apxna7hr59AvDV5n5ySmbeDND83pwLnNh8zdea36+RpApclJknAGcAFza3u+z7iSRJkiTtt6LPEDodWJiZT2fmduB6YF7BNXWSecC1zcfXAu8psJZhlZl3AGt2ax5o++cB38qGu4DDImJ6eyptnwH6ZCDzgOszc1tmPgMspPH9GjEyc3lm3t98vBF4DJhByfcTSZIkSRqMogOhGcCSludLm21llMBPIuK+iLig2TYtM5c3H68AphVTWmEG2v6y7zefbk6BurplGmGp+iQiZgGvAe7G/USSJEmS9lvRgZB2eWNmnkpjmsuFEfHbrQuzcTu40t4Sruzb3+JKYA5wCrAcuKzYctovIiYA3wM+m5kbWpe5n0iSJEnSvik6EFoGHNPy/OhmW+lk5rLm71XA92lM91n50hSX5u9VxVVYiIG2v7T7TWauzMxaZtaBb7BrWlgp+iQiRtEIg76Tmf/abHY/kSRJkqT9VHQgdA8wNyJmR8RoGhfFvangmtouIsZHxCEvPQbeDjxCoy8+1FztQ8CNxVRYmIG2/ybg/OZdpM4A1rdMGRrRdrsGzjk09hNo9Mm5EdETEbNpXEh5frvrG04REcA3gccy8ysti9xPJEmSJGk/dRf54ZlZjYhPA7cAXcDVmflokTUVZBrw/ca/d+kGvpuZP46Ie4AbIuKjwLPA+wuscVhFxHXAmcDUiFgKXAJcSv/bfzPwThoXTt4CfKTtBbfBAH1yZkScQmNa1CLg4wCZ+WhE3AAsoHE3rgszs1ZE3cPoDcAHgYcj4oFm259S8v1EkiRJkgYjGpfckCRJkiRJUlkUPWVMkiRJkiRJbWYgJEmSJEmSVDIGQpIkSZIkSSVjICRJkiRJklQyBkKSJEmSJEklYyAkSZIkSZJUMgZCkiRJkiRJJWMgJEmSJEmSVDL/H3YsAeJQ1ewrAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x1080 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jqS76KtA6Y6A"
      },
      "source": [
        "# Convert to Array "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vuxhbFB26Y6A"
      },
      "source": [
        "def to_array(path, end):\n",
        "    \n",
        "    # get locations\n",
        "    files = glob(path+end, recursive=True)\n",
        "    \n",
        "    img_list = []\n",
        "    \n",
        "    r.seed(42)\n",
        "    r.shuffle(files)\n",
        "    \n",
        "    for file in files:\n",
        "        img = io.imread(file, plugin=\"simpleitk\")\n",
        "\n",
        "        # standardization\n",
        "        img = (img-img.mean())/img.std()\n",
        "        img.astype(\"float32\")\n",
        "        \n",
        "        for slice in range(60, 130):\n",
        "            img_s = img[slice,:,:]\n",
        "            \n",
        "            # resize\n",
        "            img_s = cv2.resize(img_s, (128,128))\n",
        "            \n",
        "            img_s = np.expand_dims(img_s, axis=0)\n",
        "            img_list.append(img_s)\n",
        "            \n",
        "    return np.array(img_list,np.float32)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GzmMgML46Y6B"
      },
      "source": [
        "### np.expand_dims()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3fXfyo616Y6B",
        "outputId": "084b5c78-79df-41c7-b919-b34cb8bf4f14"
      },
      "source": [
        "first_img.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(155, 240, 240)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_zxcmvXY6Y6B",
        "outputId": "77917108-eea8-4e1e-ae00-92cc1dd9db3e"
      },
      "source": [
        "exp = first_img[60,:,:]\n",
        "\n",
        "exp.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(240, 240)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXoW1Tz36Y6C",
        "outputId": "0b7ddf5a-9b15-45ef-e23a-b2d19a383fc7"
      },
      "source": [
        "exp = np.expand_dims(exp, axis=0)\n",
        "\n",
        "exp.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 240, 240)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37xHSJSY6Y6C"
      },
      "source": [
        "# Applying the Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Unejo0xV6Y6D"
      },
      "source": [
        "train = to_array(path=path, end=\"**/*flair.nii.gz\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_TRWIQKx6Y6D",
        "outputId": "9f1e957f-40cd-4a98-a81c-a56b89d6e525"
      },
      "source": [
        "train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2520, 1, 128, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "sHyHfZGh6Y6D",
        "outputId": "731deeee-9b93-4542-ded8-d258f50fe163"
      },
      "source": [
        "plt.imshow(train[550].reshape(128,128))\n",
        "plt.title(\"train: 550\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cO_Yh0TWTEoJ"
      },
      "source": [
        "t1ce = to_array(path=path, end='**/*t1ce.nii.gz')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "S0yS0omuTQH5",
        "outputId": "829e09e8-09b2-46b0-a891-0070a6de6483"
      },
      "source": [
        "plt.imshow(t1ce[550].reshape(128,128))\r\n",
        "plt.title(\"t1ce: 550\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HlgqRK0X6Y6D"
      },
      "source": [
        "# Segmented Images\n",
        "\n",
        "1 - Non-enhancing Tumor\n",
        "\n",
        "2 - Edema \n",
        "\n",
        "4 - Enhancing Tumor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "46vMWJ2d6Y6E",
        "outputId": "8d1f38ef-bc8e-49d2-d270-74fdbd6fd3e7"
      },
      "source": [
        "seg_img.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(155, 240, 240)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "6d5JsL5A6Y6E",
        "outputId": "eba43de2-a3e5-48c8-cde8-d02ed7cd5abf"
      },
      "source": [
        "plt.figure(figsize=(7,7))\n",
        "plt.imshow(seg_img[100,:,:]);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "GJw24Kih6Y6E",
        "outputId": "cc9d9f4d-d9b9-4db2-f644-d61bf1eaac96"
      },
      "source": [
        "seg_all = seg_img.copy()\n",
        "\n",
        "seg_all[seg_all != 0] = 1 # show all tumor area\n",
        "\n",
        "plt.figure(figsize=(7,7))\n",
        "plt.imshow(seg_all[100,:,:])\n",
        "plt.title(\"All area of Tumor\", fontsize=15);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "6tltwklD6Y6E",
        "outputId": "f5a3d2f9-fda5-4fce-ec5a-7a289fb0fa09"
      },
      "source": [
        "seg_non = seg_img.copy()\n",
        "\n",
        "seg_non[seg_non != 1] = 0 # show non-enhancing tumor\n",
        "\n",
        "plt.figure(figsize=(7,7))\n",
        "plt.imshow(seg_non[100,:,:])\n",
        "plt.title(\"1 - Non-enhancing Tumor\", fontsize=15);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "fFT-AlvG6Y6F",
        "outputId": "9715e9ee-e9de-418f-e88d-247bc3237001"
      },
      "source": [
        "seg_edema = seg_img.copy()\n",
        "\n",
        "seg_edema[seg_edema == 1] = 0 # cover Non-enhancing Tumor\n",
        "seg_edema[seg_edema == 4] = 0 # cover Enhancing Tumor\n",
        "seg_edema[seg_edema != 0] = 1 # show Edema\n",
        "\n",
        "plt.figure(figsize=(7,7))\n",
        "plt.imshow(seg_edema[100,:,:])\n",
        "plt.title(\"2 - Edema\", fontsize=15);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "tIoNVwFu6Y6F",
        "outputId": "45bd84c8-5cbf-47c1-8a3b-c7ac76400e54"
      },
      "source": [
        "seg_enhancing = seg_img.copy()\n",
        "\n",
        "seg_enhancing[seg_enhancing != 4] = 0 # show Enhancing Tumor\n",
        "\n",
        "plt.figure(figsize=(7,7))\n",
        "plt.imshow(seg_enhancing[100,:,:])\n",
        "plt.title(\"Enhancing Tumor\", fontsize=15);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ZwaRR686Y6F"
      },
      "source": [
        "# To Array for Segmented Images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kX-yK5lO6Y6F"
      },
      "source": [
        "def seg_to_array(path, end, label):\n",
        "    \n",
        "    # get locations\n",
        "    files = glob(path+end, recursive=True)\n",
        "    \n",
        "    img_list = []\n",
        "    \n",
        "    r.seed(42)\n",
        "    r.shuffle(files)\n",
        "    \n",
        "    for file in files:\n",
        "        img = io.imread(file, plugin=\"simpleitk\")\n",
        "        \n",
        "        # all tumor\n",
        "        if label == 1:\n",
        "            img[img != 0] = 1\n",
        "        \n",
        "        # Non-enhancing Tumor\n",
        "        if label == 2:\n",
        "            img[img != 1] = 0\n",
        "        \n",
        "        # Without Edema\n",
        "        if label == 3:\n",
        "            img[img == 2] = 0\n",
        "            img[img != 0] = 1\n",
        "        \n",
        "        # Enhancing Tumor\n",
        "        if label == 4:\n",
        "            img[img != 4] = 0\n",
        "            img[img == 4] = 1\n",
        "            \n",
        "\n",
        "        img.astype(\"float32\")\n",
        "        \n",
        "        for slice in range(60, 130):\n",
        "            img_s = img[slice,:,:]\n",
        "            \n",
        "            # resize\n",
        "            img_s = cv2.resize(img_s, (128,128))\n",
        "            \n",
        "            img_s = np.expand_dims(img_s, axis=0)\n",
        "            img_list.append(img_s)\n",
        "            \n",
        "    return np.array(img_list,np.float32)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnUyydP26Y6G"
      },
      "source": [
        "seg_all_tumor = seg_to_array(path=path, end=\"**/*seg.nii.gz\", label=1)\n",
        "\n",
        "seg_non_enhancing = seg_to_array(path=path, end=\"**/*seg.nii.gz\", label=2)\n",
        "\n",
        "seg_out_edema = seg_to_array(path=path, end=\"**/*seg.nii.gz\", label=3) \n",
        "\n",
        "seg_enhancing = seg_to_array(path=path, end=\"**/*seg.nii.gz\", label=4) \n",
        "\n",
        "seg_original = seg_to_array(path=path, end=\"**/*seg.nii.gz\", label=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I0nz_3YB6Y6G",
        "outputId": "106b946c-fe35-4a4a-b411-9da9299da45f"
      },
      "source": [
        "seg_all_tumor.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2520, 1, 128, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LMIejb4e6Y6H",
        "outputId": "2b65ceac-cbe6-4efd-94f3-5f3a3974377f"
      },
      "source": [
        "seg_non_enhancing.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2520, 1, 128, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "NT7UkKRP6Y6H",
        "outputId": "08979b6c-ab70-40ff-c9c1-2b522a0b0ac7"
      },
      "source": [
        "idx = 1000\n",
        "\n",
        "plt.figure(figsize=(20,15))\n",
        "\n",
        "plt.subplot(3,4,1)\n",
        "plt.imshow(seg_all_tumor[idx].reshape(128,128))\n",
        "plt.title(\"seg_all_tumor\")\n",
        "\n",
        "plt.subplot(3,4,2)\n",
        "plt.imshow(seg_non_enhancing[idx].reshape(128,128))\n",
        "plt.title(\"seg_non_enhancing\")\n",
        "\n",
        "plt.subplot(3,4,3)\n",
        "plt.imshow(seg_out_edema[idx].reshape(128,128))\n",
        "plt.title(\"seg_out_edema\")\n",
        "\n",
        "plt.subplot(3,4,4)\n",
        "plt.imshow(seg_enhancing[idx].reshape(128,128))\n",
        "plt.title(\"seg_enhancing\")\n",
        "\n",
        "plt.subplot(3,4,5)\n",
        "plt.imshow(seg_original[idx].reshape(128,128))\n",
        "plt.title(\"seg_original\")\n",
        "\n",
        "plt.subplot(3,4,6)\n",
        "plt.imshow(train[idx].reshape(128,128))\n",
        "plt.title(\"train\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jmYpksuI6Y6H"
      },
      "source": [
        "# U-NET MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPgsyRXm6Y6H"
      },
      "source": [
        "flair = to_array(path=path, end=\"**/*flair.nii.gz\")\n",
        "t2 = to_array(path=path, end=\"**/*t2.nii.gz\")\n",
        "seg = seg_to_array(path=path, end=\"**/*seg.nii.gz\", label=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sd8MU1BR6Y6I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "447502de-5c0f-44ca-9938-d7d813531ee5"
      },
      "source": [
        "flair.shape, t2.shape, seg.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2520, 1, 128, 128), (2520, 1, 128, 128), (2520, 1, 128, 128))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NEN7ylxw6Y6I"
      },
      "source": [
        "## Concatenate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjsU4ga86Y6I"
      },
      "source": [
        "X_train = np.concatenate((flair, t2), axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6U0ysV16Y6I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec1a9b29-0d8b-49e3-f81d-3523a6a3f321"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2520, 2, 128, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-F4FfcjFWQq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "689b92a8-e58f-4e09-ec29-7b39c5e69192"
      },
      "source": [
        "X_train.dtype"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('float32')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ahcbYCR_6Y6I"
      },
      "source": [
        "# U-NET MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9Q_ScRC6Y6J",
        "scrolled": false
      },
      "source": [
        "def dice_coef(y_true, y_pred):\n",
        "    smooth = 0.005 \n",
        "    y_true_f = K.flatten(y_true)\n",
        "    y_pred_f = K.flatten(y_pred)\n",
        "    intersection = K.sum(y_true_f * y_pred_f)\n",
        "    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n",
        "\n",
        "\n",
        "def dice_coef_loss(y_true, y_pred):\n",
        "    return 1-dice_coef(y_true, y_pred)\n",
        "    \n",
        "\n",
        "K.set_image_data_format('channels_first')\n",
        "\n",
        "\n",
        "def unet():\n",
        "    \n",
        "    inputs = Input((2, 128 , 128))\n",
        "    \n",
        "    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (inputs)\n",
        "    batch1 = BatchNormalization(axis=1)(conv1)\n",
        "    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch1)\n",
        "    batch1 = BatchNormalization(axis=1)(conv1)\n",
        "    pool1 = MaxPooling2D((2, 2))(batch1)\n",
        "    \n",
        "    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (pool1)\n",
        "    batch2 = BatchNormalization(axis=1)(conv2)\n",
        "    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch2)\n",
        "    batch2 = BatchNormalization(axis=1)(conv2)\n",
        "    pool2 = MaxPooling2D((2, 2))(batch2)\n",
        "    \n",
        "    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (pool2)\n",
        "    batch3 = BatchNormalization(axis=1)(conv3)\n",
        "    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch3)\n",
        "    batch3 = BatchNormalization(axis=1)(conv3)\n",
        "    pool3 = MaxPooling2D((2, 2))(batch3)\n",
        "    \n",
        "    conv4 = Conv2D(512, (3, 3), activation='relu', padding='same') (pool3)\n",
        "    batch4 = BatchNormalization(axis=1)(conv4)\n",
        "    conv4 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch4)\n",
        "    batch4 = BatchNormalization(axis=1)(conv4)\n",
        "    pool4 = MaxPooling2D(pool_size=(2, 2))(batch4)\n",
        "    \n",
        "    conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (pool4)\n",
        "    batch5 = BatchNormalization(axis=1)(conv5)\n",
        "    conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (batch5)\n",
        "    batch5 = BatchNormalization(axis=1)(conv5)\n",
        "    \n",
        "    up6 = Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same') (batch5)\n",
        "    up6 = concatenate([up6, conv4], axis=1)\n",
        "    conv6 = Conv2D(512, (3, 3), activation='relu', padding='same') (up6)\n",
        "    batch6 = BatchNormalization(axis=1)(conv6)\n",
        "    conv6 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch6)\n",
        "    batch6 = BatchNormalization(axis=1)(conv6)\n",
        "    \n",
        "    up7 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same') (batch6)\n",
        "    up7 = concatenate([up7, conv3], axis=1)\n",
        "    conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (up7)\n",
        "    batch7 = BatchNormalization(axis=1)(conv7)\n",
        "    conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch7)\n",
        "    batch7 = BatchNormalization(axis=1)(conv7)\n",
        "    \n",
        "    up8 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (batch7)\n",
        "    up8 = concatenate([up8, conv2], axis=1)\n",
        "    conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (up8)\n",
        "    batch8 = BatchNormalization(axis=1)(conv8)\n",
        "    conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch8)\n",
        "    batch8 = BatchNormalization(axis=1)(conv8)\n",
        "    \n",
        "    up9 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (batch8)\n",
        "    up9 = concatenate([up9, conv1], axis=1)\n",
        "    conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (up9)\n",
        "    batch9 = BatchNormalization(axis=1)(conv9)\n",
        "    conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch9)\n",
        "    batch9 = BatchNormalization(axis=1)(conv9)\n",
        "\n",
        "    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(batch9)\n",
        "\n",
        "    model = Model(inputs=[inputs], outputs=[conv10])\n",
        "\n",
        "    model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss, metrics=[dice_coef])\n",
        "\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P8zaEq-AJ63q"
      },
      "source": [
        "model = unet()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tPLqMQQF6Y6J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd2aa30a-fc66-433b-faa2-66776bb1b847"
      },
      "source": [
        "model.fit(X_train, seg, validation_split=0.25, batch_size=32, epochs=50, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "60/60 [==============================] - 38s 447ms/step - loss: 0.8896 - dice_coef: 0.1105 - val_loss: 0.9252 - val_dice_coef: 0.0736\n",
            "Epoch 2/50\n",
            "60/60 [==============================] - 24s 402ms/step - loss: 0.7895 - dice_coef: 0.2105 - val_loss: 0.9094 - val_dice_coef: 0.0892\n",
            "Epoch 3/50\n",
            "60/60 [==============================] - 24s 407ms/step - loss: 0.7416 - dice_coef: 0.2584 - val_loss: 0.9018 - val_dice_coef: 0.0967\n",
            "Epoch 4/50\n",
            "60/60 [==============================] - 25s 413ms/step - loss: 0.7129 - dice_coef: 0.2872 - val_loss: 0.9018 - val_dice_coef: 0.0966\n",
            "Epoch 5/50\n",
            "60/60 [==============================] - 25s 418ms/step - loss: 0.6821 - dice_coef: 0.3180 - val_loss: 0.8937 - val_dice_coef: 0.1046\n",
            "Epoch 6/50\n",
            "60/60 [==============================] - 25s 422ms/step - loss: 0.6498 - dice_coef: 0.3502 - val_loss: 0.8825 - val_dice_coef: 0.1156\n",
            "Epoch 7/50\n",
            "60/60 [==============================] - 26s 427ms/step - loss: 0.6096 - dice_coef: 0.3902 - val_loss: 0.8442 - val_dice_coef: 0.1533\n",
            "Epoch 8/50\n",
            "60/60 [==============================] - 26s 432ms/step - loss: 0.5862 - dice_coef: 0.4138 - val_loss: 0.7287 - val_dice_coef: 0.2671\n",
            "Epoch 9/50\n",
            "60/60 [==============================] - 26s 435ms/step - loss: 0.5480 - dice_coef: 0.4519 - val_loss: 0.6592 - val_dice_coef: 0.3354\n",
            "Epoch 10/50\n",
            "60/60 [==============================] - 26s 432ms/step - loss: 0.4937 - dice_coef: 0.5065 - val_loss: 0.5595 - val_dice_coef: 0.4336\n",
            "Epoch 11/50\n",
            "60/60 [==============================] - 26s 432ms/step - loss: 0.4524 - dice_coef: 0.5478 - val_loss: 0.4897 - val_dice_coef: 0.5023\n",
            "Epoch 12/50\n",
            "60/60 [==============================] - 26s 432ms/step - loss: 0.4265 - dice_coef: 0.5734 - val_loss: 0.5338 - val_dice_coef: 0.4589\n",
            "Epoch 13/50\n",
            "60/60 [==============================] - 26s 434ms/step - loss: 0.3744 - dice_coef: 0.6254 - val_loss: 0.4753 - val_dice_coef: 0.5165\n",
            "Epoch 14/50\n",
            "60/60 [==============================] - 26s 434ms/step - loss: 0.3417 - dice_coef: 0.6584 - val_loss: 0.3874 - val_dice_coef: 0.6031\n",
            "Epoch 15/50\n",
            "60/60 [==============================] - 26s 434ms/step - loss: 0.3162 - dice_coef: 0.6835 - val_loss: 0.3064 - val_dice_coef: 0.6828\n",
            "Epoch 16/50\n",
            "60/60 [==============================] - 26s 434ms/step - loss: 0.2976 - dice_coef: 0.7025 - val_loss: 0.3916 - val_dice_coef: 0.5989\n",
            "Epoch 17/50\n",
            "60/60 [==============================] - 26s 434ms/step - loss: 0.2436 - dice_coef: 0.7560 - val_loss: 0.2939 - val_dice_coef: 0.6951\n",
            "Epoch 18/50\n",
            "60/60 [==============================] - 26s 434ms/step - loss: 0.2243 - dice_coef: 0.7754 - val_loss: 0.2905 - val_dice_coef: 0.6984\n",
            "Epoch 19/50\n",
            "60/60 [==============================] - 26s 434ms/step - loss: 0.2036 - dice_coef: 0.7965 - val_loss: 0.2917 - val_dice_coef: 0.6972\n",
            "Epoch 20/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.1780 - dice_coef: 0.8216 - val_loss: 0.2966 - val_dice_coef: 0.6924\n",
            "Epoch 21/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.1703 - dice_coef: 0.8298 - val_loss: 0.3262 - val_dice_coef: 0.6633\n",
            "Epoch 22/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.1577 - dice_coef: 0.8424 - val_loss: 0.2892 - val_dice_coef: 0.6997\n",
            "Epoch 23/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.1454 - dice_coef: 0.8544 - val_loss: 0.2805 - val_dice_coef: 0.7083\n",
            "Epoch 24/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.1282 - dice_coef: 0.8717 - val_loss: 0.2904 - val_dice_coef: 0.6986\n",
            "Epoch 25/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.1205 - dice_coef: 0.8794 - val_loss: 0.2958 - val_dice_coef: 0.6932\n",
            "Epoch 26/50\n",
            "60/60 [==============================] - 26s 432ms/step - loss: 0.1167 - dice_coef: 0.8832 - val_loss: 0.2898 - val_dice_coef: 0.6991\n",
            "Epoch 27/50\n",
            "60/60 [==============================] - 26s 432ms/step - loss: 0.1093 - dice_coef: 0.8903 - val_loss: 0.2753 - val_dice_coef: 0.7134\n",
            "Epoch 28/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.1022 - dice_coef: 0.8977 - val_loss: 0.2815 - val_dice_coef: 0.7073\n",
            "Epoch 29/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.1326 - dice_coef: 0.8675 - val_loss: 0.2849 - val_dice_coef: 0.7039\n",
            "Epoch 30/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.1037 - dice_coef: 0.8959 - val_loss: 0.2779 - val_dice_coef: 0.7108\n",
            "Epoch 31/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.0946 - dice_coef: 0.9054 - val_loss: 0.2690 - val_dice_coef: 0.7195\n",
            "Epoch 32/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.1030 - dice_coef: 0.8970 - val_loss: 0.2832 - val_dice_coef: 0.7056\n",
            "Epoch 33/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.0896 - dice_coef: 0.9105 - val_loss: 0.2751 - val_dice_coef: 0.7136\n",
            "Epoch 34/50\n",
            "60/60 [==============================] - 26s 432ms/step - loss: 0.0846 - dice_coef: 0.9153 - val_loss: 0.2730 - val_dice_coef: 0.7156\n",
            "Epoch 35/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.0822 - dice_coef: 0.9173 - val_loss: 0.2840 - val_dice_coef: 0.7048\n",
            "Epoch 36/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.0774 - dice_coef: 0.9221 - val_loss: 0.2774 - val_dice_coef: 0.7113\n",
            "Epoch 37/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.0751 - dice_coef: 0.9249 - val_loss: 0.2834 - val_dice_coef: 0.7054\n",
            "Epoch 38/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.0879 - dice_coef: 0.9121 - val_loss: 0.2732 - val_dice_coef: 0.7155\n",
            "Epoch 39/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.0791 - dice_coef: 0.9209 - val_loss: 0.2753 - val_dice_coef: 0.7134\n",
            "Epoch 40/50\n",
            "60/60 [==============================] - 26s 432ms/step - loss: 0.0679 - dice_coef: 0.9316 - val_loss: 0.2832 - val_dice_coef: 0.7056\n",
            "Epoch 41/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.0700 - dice_coef: 0.9300 - val_loss: 0.2797 - val_dice_coef: 0.7090\n",
            "Epoch 42/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.0765 - dice_coef: 0.9235 - val_loss: 0.2780 - val_dice_coef: 0.7108\n",
            "Epoch 43/50\n",
            "60/60 [==============================] - 26s 432ms/step - loss: 0.0674 - dice_coef: 0.9326 - val_loss: 0.2661 - val_dice_coef: 0.7225\n",
            "Epoch 44/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.0686 - dice_coef: 0.9314 - val_loss: 0.2703 - val_dice_coef: 0.7183\n",
            "Epoch 45/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.0642 - dice_coef: 0.9358 - val_loss: 0.2674 - val_dice_coef: 0.7211\n",
            "Epoch 46/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.0628 - dice_coef: 0.9372 - val_loss: 0.2725 - val_dice_coef: 0.7162\n",
            "Epoch 47/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.0614 - dice_coef: 0.9386 - val_loss: 0.2746 - val_dice_coef: 0.7141\n",
            "Epoch 48/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.0627 - dice_coef: 0.9373 - val_loss: 0.2668 - val_dice_coef: 0.7218\n",
            "Epoch 49/50\n",
            "60/60 [==============================] - 26s 434ms/step - loss: 0.0616 - dice_coef: 0.9382 - val_loss: 0.2776 - val_dice_coef: 0.7111\n",
            "Epoch 50/50\n",
            "60/60 [==============================] - 26s 433ms/step - loss: 0.0646 - dice_coef: 0.9351 - val_loss: 0.2723 - val_dice_coef: 0.7163\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f372dcf8978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1SNAfvpAjNg"
      },
      "source": [
        "model.save_weights(\"/content/drive/MyDrive/BraTS/subset_model.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GOBy6GyrldPu"
      },
      "source": [
        "# PREDICTION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FybJt0CDlcpk",
        "outputId": "39ed064f-a090-43f0-ed42-b9b9a61e910d"
      },
      "source": [
        "plt.imshow(X_train[1000][0]);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4RUfy7ih7B2",
        "outputId": "0558081f-d8bc-4d90-de83-f5365c9b7869"
      },
      "source": [
        "plt.imshow(seg[1000][0]);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpy__Qhpm566"
      },
      "source": [
        "expand_img = np.expand_dims(X_train[1000], axis=0)\r\n",
        "\r\n",
        "pred = model.predict(expand_img)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gfiAdwb1n7xK"
      },
      "source": [
        "## Visualize Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ibbcUkunrOK",
        "outputId": "ddfb12c7-c0bb-480a-eff3-ab5269dbbd7f"
      },
      "source": [
        "plt.imshow(pred[0][0]);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V87qK9FqoCNp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "c078ad36-cae1-4fd3-88cb-4ba8cd57de75"
      },
      "source": [
        "x = 333\r\n",
        "\r\n",
        "sample = np.expand_dims(X_train[x], axis=0)\r\n",
        "pred = model.predict(sample)\r\n",
        "\r\n",
        "\r\n",
        "fig = plt.figure(figsize=(17, 15))\r\n",
        "\r\n",
        "plt.subplot(1,4,1)\r\n",
        "plt.title(\"Input: Flair + T2\")\r\n",
        "plt.imshow(X_train[x][0], cmap=\"inferno\")\r\n",
        "\r\n",
        "plt.subplot(1,4,2)\r\n",
        "plt.title(\"Segmentation\")\r\n",
        "plt.imshow(seg[x][0], cmap=\"inferno\")\r\n",
        "\r\n",
        "plt.subplot(1,4,3)\r\n",
        "plt.title(\"Prediction\")\r\n",
        "plt.imshow(pred[0][0], cmap=\"inferno\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1224x1080 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "chVyq47tu4jm"
      },
      "source": [
        "# Cropping Images\r\n",
        "\r\n",
        " * First Step: \r\n",
        "  * Get coordinates of Tumor Center\r\n",
        " * Step Two:\r\n",
        "  * Threshold\r\n",
        " * Step Three\r\n",
        "  * Get coordinates that are equals 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "fGeHDgyljBCe",
        "outputId": "03a8e597-a64d-4bc6-90e3-eee7e69825e6"
      },
      "source": [
        "tmp = seg_out_edema[666,0,:,:]\r\n",
        "\r\n",
        "tmp.shape\r\n",
        "\r\n",
        "plt.imshow(tmp);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8jvW-CWvvbT"
      },
      "source": [
        "# threshold\r\n",
        "tmp[tmp > 0.2] = 1\r\n",
        "tmp[tmp != 1] = 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iw47trGWxOHy"
      },
      "source": [
        "index_xy = np.where(tmp==1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ghTpvb1bIy5z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1665c869-6848-42ef-8536-329805ba147f"
      },
      "source": [
        "index_xy[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([83, 83, 83, 83, 84, 84, 84, 84, 84, 84, 84, 84, 84, 85, 85, 85, 85,\n",
              "       85, 85, 85, 85, 85, 85, 85, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86,\n",
              "       86, 86, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 88, 88, 88,\n",
              "       88, 88, 88, 88, 88, 88, 88, 88, 88, 89, 89, 89, 89, 89, 89, 89, 89,\n",
              "       89, 89, 89, 89, 89, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90,\n",
              "       91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 92, 92, 92, 92, 92,\n",
              "       92, 92, 92, 92, 92, 92, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93,\n",
              "       94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 95, 95, 95, 95, 95, 95, 95,\n",
              "       95, 95, 95, 96, 96, 96, 96, 96, 96, 96, 96, 97, 97, 97, 97, 97, 97,\n",
              "       97, 97, 98, 98, 98, 98, 98, 98, 98, 99, 99])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ep-IKnYnL0a7",
        "outputId": "aa25bb57-76c0-4c19-e1f0-65deded4c683"
      },
      "source": [
        "index_xy[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([84, 85, 86, 87, 82, 83, 84, 85, 86, 87, 88, 89, 90, 81, 82, 83, 84,\n",
              "       85, 86, 87, 88, 89, 90, 91, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
              "       90, 91, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 80, 81, 82,\n",
              "       83, 84, 85, 86, 87, 88, 89, 90, 91, 80, 81, 82, 83, 84, 85, 86, 87,\n",
              "       88, 89, 90, 91, 92, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,\n",
              "       81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 82, 83, 84, 85, 86,\n",
              "       87, 88, 89, 90, 91, 92, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,\n",
              "       82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 82, 83, 84, 85, 86, 87, 88,\n",
              "       89, 90, 91, 83, 84, 85, 86, 87, 88, 89, 90, 83, 84, 85, 86, 87, 88,\n",
              "       89, 90, 83, 84, 85, 86, 87, 88, 89, 87, 88])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7aSNhuaSL2Cf",
        "outputId": "c40232fe-4de8-4022-af2c-dacb2a9cdaf0"
      },
      "source": [
        "center_y = (max(index_xy[0]) + min(index_xy[0])) / 2\r\n",
        "\r\n",
        "center_y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "91.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRZ4yixrP-LE",
        "outputId": "0bf5440e-3713-48ab-e64a-3d16b4d5c8b5"
      },
      "source": [
        "center_x = (max(index_xy[1]) + min(index_xy[1])) / 2\r\n",
        "\r\n",
        "center_x"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "86.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YK11zqdxQB4b",
        "outputId": "6637863a-82c4-4533-e6ba-4790f74203a9"
      },
      "source": [
        "img_x = np.zeros((64,64), np.float32)\r\n",
        "\r\n",
        "img_x.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(64, 64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I0ST_zlEYK3N",
        "outputId": "f2ba2c70-6e69-4439-e9e5-94764a8208f1"
      },
      "source": [
        "x = t1ce[666,0,:,:]\r\n",
        "x.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vw7HFVMeYZBR",
        "outputId": "d321706c-be9b-4207-e225-0444b7cf6d08"
      },
      "source": [
        "img_x[:,:] = x[int(center_y - 64/2): int(center_y + 64/2), int(center_x - 64/2): int(center_x + 64/2)]\r\n",
        "\r\n",
        "\r\n",
        "plt.imshow(img_x);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oRsw4uu1Y91E",
        "outputId": "eb7d8cd0-8cf7-47c8-f457-340eef6245f7"
      },
      "source": [
        "seg_ = seg_out_edema[666,0,:,:]\r\n",
        "\r\n",
        "seg_.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wMh8QdWXh7jY",
        "outputId": "e37cc9e2-3c65-40ed-aeb2-52ecb1e50125"
      },
      "source": [
        "img_x[:,:] = seg_[int(center_y - 64/2): int(center_y + 64/2), int(center_x - 64/2): int(center_x + 64/2)]\r\n",
        "\r\n",
        "\r\n",
        "plt.imshow(img_x);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD7CAYAAACscuKmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANJUlEQVR4nO3dXawc5X3H8e+vfg3kxTihloNRocIK4qKY6IgXgaIEl8SlUeACoZAosiJLvqEVUVOl0EpVIrVSuAnhokKyCo0vaIC8UCMUhbguUVWpMhyCSQwOwaEg7BqctqCkkerY5N+LHUcH6xivz87unvj5fqSjnXlm1vOXZ39nnpmd80yqCklnvt+ZdgGSJsOwS40w7FIjDLvUCMMuNcKwS40YKexJNiV5Psn+JLf3VZSk/mWh37MnWQL8BLgOOAA8CdxSVc/1V56kviwd4b2XA/ur6kWAJA8ANwAnDfvyrKiVnD3CJiW9nf/jl/yqjmS+ZaOE/TzglTnzB4Ar3u4NKzmbK7JxhE1Keju7a9dJl40S9qEk2QpsBVjJWePenKSTGOUC3UHg/Dnz67q2t6iqbVU1U1Uzy1gxwuYkjWKUsD8JrE9yYZLlwCeBR/opS1LfFtyNr6pjSf4EeAxYAtxXVc/2VpmkXo10zl5V3wG+01MtksbIO+ikRhh2qRGGXWqEYZcaYdilRhh2qRGGXWqEYZcaYdilRhh2qRGGXWqEYZcaYdilRhh2qRGGXWqEYZcaYdilRhh2qRGGXWqEYZcaYdilRhh2qRGGXWqEYZcaYdilRpwy7EnuS3I4yd45bauT7EzyQvd6znjLlDSqYY7sXwM2ndB2O7CrqtYDu7p5SYvYKcNeVf8K/M8JzTcA27vp7cCNPdclqWcLPWdfU1WHuulXgTU91SNpTEa+QFdVBdTJlifZmmQ2yexRjoy6OUkLtNCwv5ZkLUD3evhkK1bVtqqaqaqZZaxY4OYkjWqhYX8E2NxNbwZ29FOOpHEZ5qu3rwP/DnwgyYEkW4AvA9cleQH4w25e0iK29FQrVNUtJ1m0sedaJI2Rd9BJjTDsUiMMu9QIwy41wrBLjTDsUiMMu9QIwy41wrBLjTDsUiMMu9QIwy41wrBLjTDsUiMMu9QIwy41wrBLjTDsUiMMu9QIwy41wrBLjTDsUiMMu9QIwy41wrBLjRjm8U/nJ3k8yXNJnk1yW9e+OsnOJC90r+eMv1xJCzXMkf0Y8PmqugS4Erg1ySXA7cCuqloP7OrmJS1Spwx7VR2qqh90078A9gHnATcA27vVtgM3jqtISaM7rXP2JBcAlwG7gTVVdahb9CqwptfKJPVq6LAneSfwLeBzVfXzucuqqoA6yfu2JplNMnuUIyMVK2nhhgp7kmUMgn5/VX27a34tydpu+Vrg8HzvraptVTVTVTPLWNFHzZIWYJir8QHuBfZV1VfmLHoE2NxNbwZ29F+epL4sHWKdq4HPAD9Ksqdr+0vgy8BDSbYALwM3j6dESX04Zdir6t+AnGTxxn7LkTQu3kEnNcKwS40w7FIjDLvUCMMuNcKwS40w7FIjhrmpRme4x/5zz6lXmsfH3r+h50o0Th7ZpUYYdqkRduMbsdCueh//pt39xcEju9QIwy41wrBLjfCc/Qw2jvP0hZhbh+fv0+ORXWqEYZcaYTf+DLNYuu5afDyyS40w7FIjDLvUCMMuNcKwS40w7FIjDLvUiGGe9bYyyRNJnknybJIvde0XJtmdZH+SB5MsH3+5khZqmCP7EeDaqroU2ABsSnIlcCdwV1VdBLwObBlfmZJGNcyz3gr43252WfdTwLXAp7r27cAXgXv6L1FnkhPv8PMPYyZn2OezL+me4HoY2An8FHijqo51qxwAzhtPiZL6MFTYq+rNqtoArAMuBy4edgNJtiaZTTJ7lCMLLFPSqE7ranxVvQE8DlwFrEpy/DRgHXDwJO/ZVlUzVTWzjBUjFStp4Ya5Gn9uklXd9DuA64B9DEJ/U7faZmDHuIrU8D72/g2/+ZHmGuZPXNcC25MsYfDL4aGqejTJc8ADSf4GeBq4d4x1ShrRMFfjfwhcNk/7iwzO3yX9FvAOOqkRhl1qhGGXGuEYdGewuVfkHZtOHtmlRhh2qRGGXWqEYZcaYdilRhh2qRF+9aaJ8g90pscju9QIwy41wrBLjTDsUiMMu9QIwy41wq/eGnHiV16T/Cs4v25bHDyyS40w7FIjDLvUCMMuNcKwS40w7FIj/OpNY+HXbYvP0Ef27rHNTyd5tJu/MMnuJPuTPJhk+fjKlDSq0+nG38bggY7H3QncVVUXAa8DW/osTFK/hurGJ1kH/DHwt8CfJQlwLfCpbpXtwBeBe8ZQo8ZgId3st7vrzm774jfskf2rwBeAX3fz7wXeqKpj3fwB4Lyea5PUo2Gez/5x4HBVPbWQDSTZmmQ2yexRjizkn5DUg2G68VcDn0hyPbASeDdwN7AqydLu6L4OODjfm6tqG7AN4N1ZXb1ULem0DfN89juAOwCSfBj486r6dJJvADcBDwCbgR1jrFOLgOflv91GuanmLxhcrNvP4Bz+3n5KkjQOp3VTTVV9H/h+N/0icHn/JUkaB2+XlRph2KVGGHapEYZdaoRhlxph2KVGGHapEYZdaoRhlxph2KVGGHapEYZdaoRhlxph2KVGGHapEYZdaoRhlxph2KVGGHapEYZdaoRhlxph2KVGGHapEYZdaoRhlxox7PPZXwJ+AbwJHKuqmSSrgQeBC4CXgJur6vXxlClpVKdzZP9IVW2oqplu/nZgV1WtB3Z185IWqVG68TcA27vp7cCNo5cjaVyGDXsB30vyVJKtXduaqjrUTb8KrOm9Okm9GfYprtdU1cEkvwvsTPLjuQurqpLUfG/sfjlsBVjJWSMVK2nhhjqyV9XB7vUw8DCDRzW/lmQtQPd6+CTv3VZVM1U1s4wV/VQt6bSdMuxJzk7yruPTwEeBvcAjwOZutc3AjnEVKWl0w3Tj1wAPJzm+/j9W1XeTPAk8lGQL8DJw8/jKlDSqU4a9ql4ELp2n/b+BjeMoSlL/vINOaoRhlxph2KVGGHapEYZdaoRhlxph2KVGGHapEYZdaoRhlxph2KVGGHapEYZdaoRhlxph2KVGGHapEYZdaoRhlxph2KVGGHapEYZdaoRhlxph2KVGGHapEYZdasRQYU+yKsk3k/w4yb4kVyVZnWRnkhe613PGXaykhRv2yH438N2qupjBo6D2AbcDu6pqPbCrm5e0SA3zFNf3AB8C7gWoql9V1RvADcD2brXtwI3jKlLS6IY5sl8I/Az4hyRPJ/n77tHNa6rqULfOqwye9ippkRom7EuBDwL3VNVlwC85octeVQXUfG9OsjXJbJLZoxwZtV5JCzRM2A8AB6pqdzf/TQbhfy3JWoDu9fB8b66qbVU1U1Uzy1jRR82SFuCUYa+qV4FXknyga9oIPAc8Amzu2jYDO8ZSoaReLB1yvT8F7k+yHHgR+CyDXxQPJdkCvAzcPJ4SJfVhqLBX1R5gZp5FG/stR9K4eAed1AjDLjXCsEuNMOxSIwy71AjDLjXCsEuNyOC29gltLPkZgxtw3gf818Q2PL/FUANYx4ms461Ot47fq6pz51sw0bD/ZqPJbFXNd5NOUzVYh3VMsg678VIjDLvUiGmFfduUtjvXYqgBrONE1vFWvdUxlXN2SZNnN15qxETDnmRTkueT7E8ysdFok9yX5HCSvXPaJj4UdpLzkzye5Lkkzya5bRq1JFmZ5Ikkz3R1fKlrvzDJ7m7/PNiNXzB2SZZ04xs+Oq06kryU5EdJ9iSZ7dqm8RkZ27DtEwt7kiXA3wF/BFwC3JLkkglt/mvAphPapjEU9jHg81V1CXAlcGv3fzDpWo4A11bVpcAGYFOSK4E7gbuq6iLgdWDLmOs47jYGw5MfN606PlJVG+Z81TWNz8j4hm2vqon8AFcBj82ZvwO4Y4LbvwDYO2f+eWBtN70WeH5StcypYQdw3TRrAc4CfgBcweDmjaXz7a8xbn9d9wG+FngUyJTqeAl43wltE90vwHuA/6C7ltZ3HZPsxp8HvDJn/kDXNi1THQo7yQXAZcDuadTSdZ33MBgodCfwU+CNqjrWrTKp/fNV4AvAr7v5906pjgK+l+SpJFu7tknvl7EO2+4FOt5+KOxxSPJO4FvA56rq59OoparerKoNDI6slwMXj3ubJ0ryceBwVT016W3P45qq+iCD08xbk3xo7sIJ7ZeRhm0/lUmG/SBw/pz5dV3btAw1FHbfkixjEPT7q+rb06wFoAZP93mcQXd5VZLj4xJOYv9cDXwiyUvAAwy68ndPoQ6q6mD3ehh4mMEvwEnvl5GGbT+VSYb9SWB9d6V1OfBJBsNRT8vEh8JOEgaP0dpXVV+ZVi1Jzk2yqpt+B4PrBvsYhP6mSdVRVXdU1bqquoDB5+FfqurTk64jydlJ3nV8GvgosJcJ75ca97Dt477wccKFhuuBnzA4P/yrCW7368Ah4CiD355bGJwb7gJeAP4ZWD2BOq5h0AX7IbCn+7l+0rUAfwA83dWxF/jrrv33gSeA/cA3gBUT3EcfBh6dRh3d9p7pfp49/tmc0mdkAzDb7Zt/As7pqw7voJMa4QU6qRGGXWqEYZcaYdilRhh2qRGGXWqEYZcaYdilRvw/1OlF8AQPA1wAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n9X6uyXtiBxL"
      },
      "source": [
        "def crop_tumor(mr, seg):\r\n",
        "  \r\n",
        "  mr = mr[0]\r\n",
        "  list_ = []\r\n",
        "\r\n",
        "  # thresholding\r\n",
        "  tmp = seg[0,:,:]\r\n",
        "  tmp[tmp > 0.2] = 1\r\n",
        "  tmp[tmp != 1] = 0\r\n",
        "  index_xy = np.where(tmp==1)\r\n",
        "\r\n",
        "  # some images contain no tumor !\r\n",
        "  if index_xy[0] != []:\r\n",
        "\r\n",
        "    center_y = (max(index_xy[0]) + min(index_xy[0])) / 2\r\n",
        "    center_x = (max(index_xy[1]) + min(index_xy[1])) / 2\r\n",
        "\r\n",
        "    img_x = np.zeros((64,64), np.float32)\r\n",
        "\r\n",
        "    img_x[:,:] = mr[int(center_y - 64/2):int(center_y + 64/2), int(center_x - 64/2):int(center_x + 64/2)]\r\n",
        "\r\n",
        "    list_.append(img_x)\r\n",
        "  \r\n",
        "  return np.array(list_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "U9iqaQHps_RR",
        "outputId": "c69dc587-e149-4ac2-a1e8-a07a6f6332e8"
      },
      "source": [
        "img_666 = crop_tumor(mr=t1ce[666], seg=seg_out_edema[666])\r\n",
        "non_666 = crop_tumor(mr=seg_non_enhancing[666], seg=seg_out_edema[666])\r\n",
        "enhancing_666 = crop_tumor(mr=seg_enhancing[666], seg=seg_out_edema[666])\r\n",
        "without_edema_666 = crop_tumor(mr=seg_out_edema[666], seg=seg_out_edema[666])\r\n",
        "\r\n",
        "\r\n",
        "plt.figure(figsize=(20,15))\r\n",
        "\r\n",
        "plt.subplot(3,4,1)\r\n",
        "plt.imshow(img_666[0])\r\n",
        "plt.title(\"All Tumor\")\r\n",
        "\r\n",
        "plt.subplot(3,4,2)\r\n",
        "plt.imshow(non_666[0])\r\n",
        "plt.title(\"Non-Enhancing Tumor\")\r\n",
        "\r\n",
        "plt.subplot(3,4,3)\r\n",
        "plt.imshow(enhancing_666[0])\r\n",
        "plt.title(\"Enhancing Tumor\")\r\n",
        "\r\n",
        "plt.subplot(3,4,4)\r\n",
        "plt.imshow(without_edema_666[0])\r\n",
        "plt.title(\"Witout Edema\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7GFntrzwu-jR"
      },
      "source": [
        "def tumor_to_array(tumor, segmentation):\r\n",
        "\r\n",
        "  im_list = []\r\n",
        "\r\n",
        "  for i in range(len(segmentation)):\r\n",
        "    img = crop_tumor(tumor[i], seg_out_edema[i])\r\n",
        "\r\n",
        "    if len(img.shape) > 2:\r\n",
        "      im_list.append(img)\r\n",
        "\r\n",
        "  return np.array(im_list)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8ZSmFKlHWyw",
        "outputId": "5673e992-4f52-4a79-bce1-a7d338b56c64"
      },
      "source": [
        "without_edema = tumor_to_array(seg_out_edema, seg_out_edema)\r\n",
        "enhancing = tumor_to_array(seg_enhancing, seg_out_edema)\r\n",
        "t1ce_crop = tumor_to_array(t1ce, seg_out_edema)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
            "  del sys.path[0]\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
            "  del sys.path[0]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iQBrfvl0d06F",
        "outputId": "eb5e4a87-c90b-4f9f-e877-a9989a75b8cb"
      },
      "source": [
        "without_edema.shape, enhancing.shape, t1ce_crop.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1148, 1, 64, 64), (1148, 1, 64, 64), (1148, 1, 64, 64))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCds-FPMNOTL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3bf632c8-706d-4c52-bf86-b03cbde40171"
      },
      "source": [
        "plt.figure(figsize=(20, 15))\r\n",
        "\r\n",
        "plt.subplot(1,3,1)\r\n",
        "plt.imshow(t1ce_crop[450,0,:,:])\r\n",
        "plt.title(\"T1CE\")\r\n",
        "\r\n",
        "plt.subplot(1,3,2)\r\n",
        "plt.imshow(without_edema[450,0,:,:])\r\n",
        "plt.title(\"Without Edema\")\r\n",
        "\r\n",
        "plt.subplot(1,3,3)\r\n",
        "plt.imshow(enhancing[450,0,:,:])\r\n",
        "plt.title(\"Enhancing Tumor\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x1080 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BJuBVX3bfMS4"
      },
      "source": [
        "# UNET MODEL FOR SEGMENTATION IMAGES"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZ3rjaVQeqIl"
      },
      "source": [
        "def unet():\r\n",
        "    \r\n",
        "    inputs = Input((1, 64 , 64))\r\n",
        "    \r\n",
        "    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (inputs)\r\n",
        "    batch1 = BatchNormalization(axis=1)(conv1)\r\n",
        "    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch1)\r\n",
        "    batch1 = BatchNormalization(axis=1)(conv1)\r\n",
        "    pool1 = MaxPooling2D((2, 2))(batch1)\r\n",
        "    \r\n",
        "    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (pool1)\r\n",
        "    batch2 = BatchNormalization(axis=1)(conv2)\r\n",
        "    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch2)\r\n",
        "    batch2 = BatchNormalization(axis=1)(conv2)\r\n",
        "    pool2 = MaxPooling2D((2, 2))(batch2)\r\n",
        "    \r\n",
        "    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (pool2)\r\n",
        "    batch3 = BatchNormalization(axis=1)(conv3)\r\n",
        "    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch3)\r\n",
        "    batch3 = BatchNormalization(axis=1)(conv3)\r\n",
        "    pool3 = MaxPooling2D((2, 2))(batch3)\r\n",
        "    \r\n",
        "    conv4 = Conv2D(512, (3, 3), activation='relu', padding='same') (pool3)\r\n",
        "    batch4 = BatchNormalization(axis=1)(conv4)\r\n",
        "    conv4 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch4)\r\n",
        "    batch4 = BatchNormalization(axis=1)(conv4)\r\n",
        "    pool4 = MaxPooling2D(pool_size=(2, 2))(batch4)\r\n",
        "    \r\n",
        "    conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (pool4)\r\n",
        "    batch5 = BatchNormalization(axis=1)(conv5)\r\n",
        "    conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (batch5)\r\n",
        "    batch5 = BatchNormalization(axis=1)(conv5)\r\n",
        "    \r\n",
        "    up6 = Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same') (batch5)\r\n",
        "    up6 = concatenate([up6, conv4], axis=1)\r\n",
        "    conv6 = Conv2D(512, (3, 3), activation='relu', padding='same') (up6)\r\n",
        "    batch6 = BatchNormalization(axis=1)(conv6)\r\n",
        "    conv6 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch6)\r\n",
        "    batch6 = BatchNormalization(axis=1)(conv6)\r\n",
        "    \r\n",
        "    up7 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same') (batch6)\r\n",
        "    up7 = concatenate([up7, conv3], axis=1)\r\n",
        "    conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (up7)\r\n",
        "    batch7 = BatchNormalization(axis=1)(conv7)\r\n",
        "    conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch7)\r\n",
        "    batch7 = BatchNormalization(axis=1)(conv7)\r\n",
        "    \r\n",
        "    up8 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (batch7)\r\n",
        "    up8 = concatenate([up8, conv2], axis=1)\r\n",
        "    conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (up8)\r\n",
        "    batch8 = BatchNormalization(axis=1)(conv8)\r\n",
        "    conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch8)\r\n",
        "    batch8 = BatchNormalization(axis=1)(conv8)\r\n",
        "    \r\n",
        "    up9 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (batch8)\r\n",
        "    up9 = concatenate([up9, conv1], axis=1)\r\n",
        "    conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (up9)\r\n",
        "    batch9 = BatchNormalization(axis=1)(conv9)\r\n",
        "    conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch9)\r\n",
        "    batch9 = BatchNormalization(axis=1)(conv9)\r\n",
        "\r\n",
        "    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(batch9)\r\n",
        "\r\n",
        "    model = Model(inputs=[inputs], outputs=[conv10])\r\n",
        "\r\n",
        "    model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss, metrics=[dice_coef])\r\n",
        "\r\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBw1HCW9gNJq"
      },
      "source": [
        "# T1CE and Edema"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "atmmiF1hc_cR",
        "outputId": "23608865-94d3-4d42-8bf9-9ec6428a982c"
      },
      "source": [
        "model_without_edema = unet()\r\n",
        "\r\n",
        "history = model_without_edema.fit(t1ce_crop, \r\n",
        "                                  without_edema, \r\n",
        "                                  validation_split=0.20, \r\n",
        "                                  batch_size=16, \r\n",
        "                                  epochs=50, \r\n",
        "                                  shuffle=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "58/58 [==============================] - 9s 111ms/step - loss: 0.7840 - dice_coef: 0.2160 - val_loss: 0.9319 - val_dice_coef: 0.0664\n",
            "Epoch 2/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.6038 - dice_coef: 0.3963 - val_loss: 0.9648 - val_dice_coef: 0.0343\n",
            "Epoch 3/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.5245 - dice_coef: 0.4755 - val_loss: 0.6029 - val_dice_coef: 0.3824\n",
            "Epoch 4/50\n",
            "58/58 [==============================] - 5s 88ms/step - loss: 0.4266 - dice_coef: 0.5734 - val_loss: 0.4936 - val_dice_coef: 0.4933\n",
            "Epoch 5/50\n",
            "58/58 [==============================] - 5s 88ms/step - loss: 0.3767 - dice_coef: 0.6232 - val_loss: 0.4602 - val_dice_coef: 0.5274\n",
            "Epoch 6/50\n",
            "58/58 [==============================] - 5s 88ms/step - loss: 0.3369 - dice_coef: 0.6631 - val_loss: 0.4863 - val_dice_coef: 0.4939\n",
            "Epoch 7/50\n",
            "58/58 [==============================] - 5s 88ms/step - loss: 0.2972 - dice_coef: 0.7028 - val_loss: 0.3807 - val_dice_coef: 0.6021\n",
            "Epoch 8/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.2835 - dice_coef: 0.7165 - val_loss: 0.3827 - val_dice_coef: 0.5918\n",
            "Epoch 9/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.2160 - dice_coef: 0.7840 - val_loss: 0.3320 - val_dice_coef: 0.6451\n",
            "Epoch 10/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.2024 - dice_coef: 0.7975 - val_loss: 0.3059 - val_dice_coef: 0.6652\n",
            "Epoch 11/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1794 - dice_coef: 0.8206 - val_loss: 0.3016 - val_dice_coef: 0.6694\n",
            "Epoch 12/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1674 - dice_coef: 0.8326 - val_loss: 0.2817 - val_dice_coef: 0.6949\n",
            "Epoch 13/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1529 - dice_coef: 0.8472 - val_loss: 0.3110 - val_dice_coef: 0.6604\n",
            "Epoch 14/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1443 - dice_coef: 0.8557 - val_loss: 0.2790 - val_dice_coef: 0.6910\n",
            "Epoch 15/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1372 - dice_coef: 0.8628 - val_loss: 0.2854 - val_dice_coef: 0.6849\n",
            "Epoch 16/50\n",
            "58/58 [==============================] - 5s 89ms/step - loss: 0.1287 - dice_coef: 0.8713 - val_loss: 0.2753 - val_dice_coef: 0.6945\n",
            "Epoch 17/50\n",
            "58/58 [==============================] - 5s 88ms/step - loss: 0.1138 - dice_coef: 0.8862 - val_loss: 0.2875 - val_dice_coef: 0.6828\n",
            "Epoch 18/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1068 - dice_coef: 0.8932 - val_loss: 0.2785 - val_dice_coef: 0.6916\n",
            "Epoch 19/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1022 - dice_coef: 0.8978 - val_loss: 0.2892 - val_dice_coef: 0.6819\n",
            "Epoch 20/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1070 - dice_coef: 0.8930 - val_loss: 0.2833 - val_dice_coef: 0.6869\n",
            "Epoch 21/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0925 - dice_coef: 0.9075 - val_loss: 0.2827 - val_dice_coef: 0.6875\n",
            "Epoch 22/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0846 - dice_coef: 0.9154 - val_loss: 0.2990 - val_dice_coef: 0.6719\n",
            "Epoch 23/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0847 - dice_coef: 0.9153 - val_loss: 0.2988 - val_dice_coef: 0.6721\n",
            "Epoch 24/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0717 - dice_coef: 0.9283 - val_loss: 0.2768 - val_dice_coef: 0.6932\n",
            "Epoch 25/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0732 - dice_coef: 0.9268 - val_loss: 0.2885 - val_dice_coef: 0.6819\n",
            "Epoch 26/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0742 - dice_coef: 0.9258 - val_loss: 0.2818 - val_dice_coef: 0.6884\n",
            "Epoch 27/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0705 - dice_coef: 0.9294 - val_loss: 0.2948 - val_dice_coef: 0.6759\n",
            "Epoch 28/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0647 - dice_coef: 0.9353 - val_loss: 0.2722 - val_dice_coef: 0.6975\n",
            "Epoch 29/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0606 - dice_coef: 0.9394 - val_loss: 0.2908 - val_dice_coef: 0.6808\n",
            "Epoch 30/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0595 - dice_coef: 0.9403 - val_loss: 0.2619 - val_dice_coef: 0.7074\n",
            "Epoch 31/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0843 - dice_coef: 0.9157 - val_loss: 0.2731 - val_dice_coef: 0.6967\n",
            "Epoch 32/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0605 - dice_coef: 0.9395 - val_loss: 0.2768 - val_dice_coef: 0.6931\n",
            "Epoch 33/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0608 - dice_coef: 0.9392 - val_loss: 0.2450 - val_dice_coef: 0.7236\n",
            "Epoch 34/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0527 - dice_coef: 0.9473 - val_loss: 0.2700 - val_dice_coef: 0.6996\n",
            "Epoch 35/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0562 - dice_coef: 0.9438 - val_loss: 0.2727 - val_dice_coef: 0.6985\n",
            "Epoch 36/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0544 - dice_coef: 0.9456 - val_loss: 0.2619 - val_dice_coef: 0.7074\n",
            "Epoch 37/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0498 - dice_coef: 0.9502 - val_loss: 0.2714 - val_dice_coef: 0.6984\n",
            "Epoch 38/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0498 - dice_coef: 0.9502 - val_loss: 0.2735 - val_dice_coef: 0.6963\n",
            "Epoch 39/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0501 - dice_coef: 0.9499 - val_loss: 0.2725 - val_dice_coef: 0.6972\n",
            "Epoch 40/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0472 - dice_coef: 0.9528 - val_loss: 0.2779 - val_dice_coef: 0.6939\n",
            "Epoch 41/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0493 - dice_coef: 0.9507 - val_loss: 0.2399 - val_dice_coef: 0.7319\n",
            "Epoch 42/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0453 - dice_coef: 0.9547 - val_loss: 0.2759 - val_dice_coef: 0.6974\n",
            "Epoch 43/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0498 - dice_coef: 0.9502 - val_loss: 0.2529 - val_dice_coef: 0.7160\n",
            "Epoch 44/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0421 - dice_coef: 0.9579 - val_loss: 0.2638 - val_dice_coef: 0.7057\n",
            "Epoch 45/50\n",
            "58/58 [==============================] - 5s 88ms/step - loss: 0.0463 - dice_coef: 0.9537 - val_loss: 0.2797 - val_dice_coef: 0.6905\n",
            "Epoch 46/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0409 - dice_coef: 0.9591 - val_loss: 0.2680 - val_dice_coef: 0.7022\n",
            "Epoch 47/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0436 - dice_coef: 0.9564 - val_loss: 0.2797 - val_dice_coef: 0.6934\n",
            "Epoch 48/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0491 - dice_coef: 0.9509 - val_loss: 0.2561 - val_dice_coef: 0.7167\n",
            "Epoch 49/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0422 - dice_coef: 0.9578 - val_loss: 0.2379 - val_dice_coef: 0.7317\n",
            "Epoch 50/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0412 - dice_coef: 0.9588 - val_loss: 0.2593 - val_dice_coef: 0.7112\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTY6ecLOgQvy"
      },
      "source": [
        "# T1CE and ENHANCING TUMOR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bHJEOIwjgcn6",
        "outputId": "b0528ec6-7f23-4a70-a6a5-006ef22111ef"
      },
      "source": [
        "model_enhancing = unet()\r\n",
        "\r\n",
        "history = model_enhancing.fit(t1ce_crop, \r\n",
        "                              enhancing, \r\n",
        "                              validation_split=0.20, \r\n",
        "                              batch_size=16, \r\n",
        "                              epochs=50, \r\n",
        "                              shuffle=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "58/58 [==============================] - 7s 93ms/step - loss: 0.8711 - dice_coef: 0.1290 - val_loss: 0.9552 - val_dice_coef: 0.0431\n",
            "Epoch 2/50\n",
            "58/58 [==============================] - 5s 88ms/step - loss: 0.7498 - dice_coef: 0.2503 - val_loss: 0.9394 - val_dice_coef: 0.0583\n",
            "Epoch 3/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.6871 - dice_coef: 0.3128 - val_loss: 0.8199 - val_dice_coef: 0.1727\n",
            "Epoch 4/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.6464 - dice_coef: 0.3536 - val_loss: 0.7009 - val_dice_coef: 0.2869\n",
            "Epoch 5/50\n",
            "58/58 [==============================] - 5s 89ms/step - loss: 0.6035 - dice_coef: 0.3966 - val_loss: 0.7983 - val_dice_coef: 0.1933\n",
            "Epoch 6/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.5808 - dice_coef: 0.4192 - val_loss: 0.5719 - val_dice_coef: 0.4104\n",
            "Epoch 7/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.5513 - dice_coef: 0.4487 - val_loss: 0.4402 - val_dice_coef: 0.5398\n",
            "Epoch 8/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.5225 - dice_coef: 0.4775 - val_loss: 0.4051 - val_dice_coef: 0.5761\n",
            "Epoch 9/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.4991 - dice_coef: 0.5009 - val_loss: 0.4340 - val_dice_coef: 0.5483\n",
            "Epoch 10/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.4492 - dice_coef: 0.5508 - val_loss: 0.3717 - val_dice_coef: 0.6046\n",
            "Epoch 11/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.4271 - dice_coef: 0.5729 - val_loss: 0.3499 - val_dice_coef: 0.6279\n",
            "Epoch 12/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.4025 - dice_coef: 0.5975 - val_loss: 0.3547 - val_dice_coef: 0.6196\n",
            "Epoch 13/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.3689 - dice_coef: 0.6312 - val_loss: 0.3078 - val_dice_coef: 0.6734\n",
            "Epoch 14/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.3661 - dice_coef: 0.6339 - val_loss: 0.2994 - val_dice_coef: 0.6838\n",
            "Epoch 15/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.3235 - dice_coef: 0.6765 - val_loss: 0.3127 - val_dice_coef: 0.6696\n",
            "Epoch 16/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.2860 - dice_coef: 0.7140 - val_loss: 0.3152 - val_dice_coef: 0.6634\n",
            "Epoch 17/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.2946 - dice_coef: 0.7054 - val_loss: 0.3295 - val_dice_coef: 0.6507\n",
            "Epoch 18/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.2680 - dice_coef: 0.7320 - val_loss: 0.2804 - val_dice_coef: 0.7001\n",
            "Epoch 19/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.2466 - dice_coef: 0.7534 - val_loss: 0.3175 - val_dice_coef: 0.6544\n",
            "Epoch 20/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.2456 - dice_coef: 0.7544 - val_loss: 0.3128 - val_dice_coef: 0.6611\n",
            "Epoch 21/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.2228 - dice_coef: 0.7772 - val_loss: 0.3171 - val_dice_coef: 0.6595\n",
            "Epoch 22/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.2164 - dice_coef: 0.7836 - val_loss: 0.2770 - val_dice_coef: 0.7054\n",
            "Epoch 23/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1949 - dice_coef: 0.8051 - val_loss: 0.2989 - val_dice_coef: 0.6781\n",
            "Epoch 24/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1914 - dice_coef: 0.8086 - val_loss: 0.2718 - val_dice_coef: 0.7069\n",
            "Epoch 25/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1885 - dice_coef: 0.8115 - val_loss: 0.2969 - val_dice_coef: 0.6760\n",
            "Epoch 26/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1685 - dice_coef: 0.8315 - val_loss: 0.2714 - val_dice_coef: 0.7078\n",
            "Epoch 27/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1593 - dice_coef: 0.8407 - val_loss: 0.2589 - val_dice_coef: 0.7196\n",
            "Epoch 28/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1634 - dice_coef: 0.8365 - val_loss: 0.2815 - val_dice_coef: 0.6971\n",
            "Epoch 29/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1447 - dice_coef: 0.8553 - val_loss: 0.2840 - val_dice_coef: 0.6946\n",
            "Epoch 30/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1454 - dice_coef: 0.8547 - val_loss: 0.2793 - val_dice_coef: 0.7000\n",
            "Epoch 31/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1344 - dice_coef: 0.8656 - val_loss: 0.2727 - val_dice_coef: 0.7057\n",
            "Epoch 32/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1434 - dice_coef: 0.8566 - val_loss: 0.3004 - val_dice_coef: 0.6783\n",
            "Epoch 33/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1319 - dice_coef: 0.8681 - val_loss: 0.2901 - val_dice_coef: 0.6896\n",
            "Epoch 34/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1246 - dice_coef: 0.8753 - val_loss: 0.2846 - val_dice_coef: 0.6971\n",
            "Epoch 35/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1260 - dice_coef: 0.8740 - val_loss: 0.2670 - val_dice_coef: 0.7154\n",
            "Epoch 36/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1208 - dice_coef: 0.8792 - val_loss: 0.2898 - val_dice_coef: 0.6843\n",
            "Epoch 37/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1219 - dice_coef: 0.8781 - val_loss: 0.2786 - val_dice_coef: 0.7016\n",
            "Epoch 38/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1174 - dice_coef: 0.8826 - val_loss: 0.2704 - val_dice_coef: 0.7106\n",
            "Epoch 39/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1120 - dice_coef: 0.8879 - val_loss: 0.2741 - val_dice_coef: 0.7081\n",
            "Epoch 40/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1131 - dice_coef: 0.8869 - val_loss: 0.2742 - val_dice_coef: 0.7042\n",
            "Epoch 41/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1189 - dice_coef: 0.8811 - val_loss: 0.2762 - val_dice_coef: 0.6984\n",
            "Epoch 42/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1078 - dice_coef: 0.8922 - val_loss: 0.2995 - val_dice_coef: 0.6760\n",
            "Epoch 43/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1082 - dice_coef: 0.8918 - val_loss: 0.2886 - val_dice_coef: 0.6873\n",
            "Epoch 44/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1081 - dice_coef: 0.8919 - val_loss: 0.2711 - val_dice_coef: 0.7022\n",
            "Epoch 45/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1058 - dice_coef: 0.8942 - val_loss: 0.2389 - val_dice_coef: 0.7460\n",
            "Epoch 46/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.1057 - dice_coef: 0.8943 - val_loss: 0.2884 - val_dice_coef: 0.6855\n",
            "Epoch 47/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0978 - dice_coef: 0.9022 - val_loss: 0.2728 - val_dice_coef: 0.7052\n",
            "Epoch 48/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0992 - dice_coef: 0.9008 - val_loss: 0.2718 - val_dice_coef: 0.7052\n",
            "Epoch 49/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0983 - dice_coef: 0.9017 - val_loss: 0.2612 - val_dice_coef: 0.7169\n",
            "Epoch 50/50\n",
            "58/58 [==============================] - 5s 87ms/step - loss: 0.0996 - dice_coef: 0.9004 - val_loss: 0.2911 - val_dice_coef: 0.6860\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "aSQ4nbFcgt_j",
        "outputId": "2584de07-b96f-421f-d44d-2b96a48a899f"
      },
      "source": [
        "idx = 450\r\n",
        "\r\n",
        "plt.figure(figsize=(20,15))\r\n",
        "\r\n",
        "plt.subplot(1,5,1)\r\n",
        "plt.imshow(t1ce_crop[idx,0,:,:])\r\n",
        "plt.title(\"T1CE\")\r\n",
        "\r\n",
        "pred_enhancing = model_enhancing.predict(t1ce_crop[idx:idx+1,:,:,:])\r\n",
        "plt.subplot(1,5,2)\r\n",
        "plt.imshow(pred_enhancing[0,0,:,:])\r\n",
        "plt.title(\"Enhancing Tumor Prediction\")\r\n",
        "\r\n",
        "plt.subplot(1,5,3)\r\n",
        "plt.imshow(enhancing[idx,0,:,:])\r\n",
        "plt.title(\"Real Enhancing Tumor\")\r\n",
        "\r\n",
        "pred_edema = model_without_edema.predict(t1ce_crop[idx:idx+1,:,:,:])\r\n",
        "plt.subplot(1,5,4)\r\n",
        "plt.imshow(pred_edema[0,0,:,:])\r\n",
        "plt.title(\"Non-Enhancing + Enhancing Prediction\")\r\n",
        "\r\n",
        "plt.subplot(1,5,5)\r\n",
        "plt.imshow(without_edema[idx,0,:,:])\r\n",
        "plt.title(\"Real Non-Enhancing + Enhancing\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yeTXTVejQtbW"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}