Fashion_MNIST_Image_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Fashion_MNIST_Image_Classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1Ztd0xxzWJW95NPWXjHmyCcxTDFOj0sQh",
      "authorship_tag": "ABX9TyM4JpjdRyXJiDNyqlAEMQ4b",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Suhanip/Federated-Learning/blob/master/Fashion_MNIST_Image_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nebRCLun9ki7"
      },
      "source": [
        "#Fashion MNIST Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZwxzHYjlZ3fJ"
      },
      "source": [
        "##Approach1: Manually creating the workers and dividing the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNKDZb-d9nE5"
      },
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Btu2f6V5hRke"
      },
      "source": [
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels),(test_images,test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4gXH6u3m-X-E",
        "outputId": "1b310603-47db-49c8-9972-d4897e59a588",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_images.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "obbuJPJB4dcp",
        "outputId": "85f3ecb3-e28e-4414-a92a-c7d285eb15ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_images.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0L0Box4tAxq",
        "outputId": "bc7c7ea4-fc0e-4337-da78-d49c22559349",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(36):\n",
        "    plt.subplot(6,6,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[train_labels[i]])\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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er2M8Hh9ogUHHiSFkMnBelkqlgvfffx+FQgFPnjxBIBBAIpFw9JqimNYn4GQ5Of4Url/dTtBZZZ833of7Q+brUPT9MxwO8eTJE1QqFVy9ehU3btxQ1aVnkefmSdDDTPRarYbNzU34/X5Eo1FkMhnjgOlG1guABwDK5TL+5V/+BY8fP0aj0UCtVsN4PFa9R0whK8DJwphEByT6fUzXTxtTvh9fq18/GAxQr9cxGAxQLBZRrVaRTCYBwAjeZlUIYAhE/H4/8vm8SvRjPhYVTTAYdCQb6lURKysrSCQSuH37Nl599VXVoqDT6aiqI+mJeU1arRa++uorbGxsOEAesN9QTTIMspGdZHyoaGUeFPOvJOiU+4ChY12kR83rGJZkqJh5W4VCAYlEAplMBul02nOgJxAIIBwOYzQaORhiGSLnvLTbbfR6PViWhbt37+IHP/gBkskk3n//fVSrVdV6g84X70HmGIACQ+fRI+ZlS71ex2effYYnT56oggUCHuaQEWxQJOjRc3pkDo8EH9RVMswuK7wkKyrfy5TjJvNFt7e3sbW1pXQ/73cWFaYzCXp0cCLjuZ1OR1VAMBfC7/erfAg5mXqTJC9k8pMWpvfDWDlDIRJlm8aRwEcCEDcgw9ceZjBN99A9Cn0x68CH92CoLpFIOOhWL4hkALjJaWwBIJvNYnV1VYVAGOZjXhZ7+YRCIeRyOSwsLKjkT125eGnc3CQQCCAejyOVSqlOs6T1ZS4C4DQAkqrXw7zSIZBKXt9Lci6l6IaFQuBKMMsqJuYzeK2dgAQ9TDjWDTB/6w6VBLAMI7IXjCkvS6593cH1mnBNsmqKCfcEJlL0MSdAMYEhYF8ny5wpPiadL9mN/Ci6xhSCJOtKXcdcPIaETyMzB3pMjAyTtgaDAba3t1WVEiuMmITF2DlRajweV560Hss87L0pbkb/ZW2s4XCIcrmMTqejOvEyxKdTvnLRyxwaljPLhlP696EB0NkZHagA+zknuuEg4IxEIqqPj5wHCXz4eK1Ww6effopCoYDf+73fw8rKiidK2GnsCFCYvxMKhZBKpWDbNr7zne/g7t27jiRFWaURjUaRTqdV8zz23olEIqqvjEz29LLyB54nwDPp9dmzZ3j48KFqjskQB3OcuO5leAtw5q9Rd9Co9Ho9APsJ0DKRXwIrk0hvWQIydg5m8v69e/fUkRVeEp/Ph0QioSrYOAcyfMFcEj4PHGQL2FiT+optBDhvknkYDAaqTNqL6348HqvSf7YkabVaaj3LUnQ5xlL3S9GBjw7+5TqnrbDt520ZmIfD15ruA+wnQMt7yc/DfkKWZWFpaQl37ty5fKDHJKSFu90uSqUSHj9+jFarpVBhOBxW3Tjl5Nu27cjzmcZYTDMSFwn0kO1ij5tOp4Nut6sqH4jkdQROZM2EWeb+MD6rfx8qJz0spo+FpC35Oi5u2cAqHA4bvVl94/T7fezu7qLX6+Hq1aue6S8jaWPZuE426mLF2ng8Rr1eVwqOAJ8GgJVB0WgUtm2ryiCpmLyo9HWJRCJYWlpCPB5X+R79fl8BbrINoVBIech6tZUUOT96jgPg1AOHhQz1smCZT8R8tWw2i6WlJU+GHi1rP6en0+koR0wPK0onVReGbGKxmGqtwdQGU+iev72iM3SRYJzl//1+X4VPpaMq17ocF6nPdQaTIF6PlvBa6iwCTL6O18pebfrj8j3k+/d6PVQqFcVgm0LGx5WZAz1SYXODsGMkm+1VKhW0Wi1HGTDbx0sPrNVqYTweq42TSqWUYnOjpk2fR/b7MFF15yk8b6ZSqaDRaDgOm9RDV3Lzcyx9Ph/S6TTy+bwRoXNcaCSkgZCsjP6Z9J4oUhHxMzKfhaJ/XgDq/Kh+v49ms+kpBaYrA2BfmUhFI/MWZKhFJmjSwPPvywJ0pAQCASSTSQQCAZXYTdDCNvlMcJVKX46VZGQkINK9TQIoE1MpjYtk2jjXMv8nl8upHB4ek+BFIbiLx+MqsV6Ou2QVpIEGoI75kBWokimQDJseGeCce3Fcub4IOgaDwQEdoF/P39IZkmDHFLY1/S1zeugoA86cT0YX+FoyRAy/cW5kft1oNFKhrUwmc8COnERmDvQAcAwe8Pxcna2tLRSLRTx+/BiPHj1Cq9VCLBZTuR8ERsB+k6RIJIJUKoVwOIzr16/jtddeU11v9b4AbgadxwjooRhO4HlLv9/Hzs4Onj17hu3tbaUYuBn07yFBA5XF6uoqXnvtNXVAH5usMfufbFK/31fJs3rPC6n89Q7BpC1LpRK63S6ePn2Kzz//XFHTpiQ33rPRaODRo0cIBoN47bXXzgT5XwSRdLEEewxf6evO1GdEjhU9Pam8TIycF5U/JRKJYHl5WTkC29vbqNfr2Nrawt7enlK0MueJ4IfKVbKRvJ5GmHpI796s544QWJGJYJhdBz2BQAA3b97EW2+9hXg8rnoJeVH8fj9SqRSWlpZUCoJulAnsmejMsa/Vaip0T0aBDpnekgHYTxanEywrxLwkDIuz1xkP0uV6I4DkGme4W+oCOllSZBrDNDE5DSb2X3fu+DfZHADKEW6329jc3ESj0UAsFjvArp5EZg706AgU2O/H0Ww21Q/LPn2+5232SYEy5si+G+z/kEwm0ev1lDHRqzHcPovMVNcrmV6GyLLXTqfjyKLXUbtbflI8Hsfq6iqy2awCjvSaE4mEMiK9Xg+hUMiRVCw9NckKydAZXx8Oh9Fut1GtVtV10zaXZVnKe/H7/eh0Op5jeuRvwNnzgsJ1Jo0xjadMINTXpWm+vSzMF7Pt5x1lE4mEGkt2Hydo1oGKrrxlOEoCVAIheq16KEWyRARSNDSytxLfh51vef6UV8Wy9hs0MqcSOFggQT3CUBd1CHsvSc9fZ5x5PzmHnCsvrn257wmuZR4fsK+LaQc5fhwTydiYbJnOGMt5k3NhCovpDrYeSZBJyvwMtO2WZanmlaeVmQM9chApw+EQ9XpdlTPzBGMObDAYVMcayAMCbdtWxy9sbW2ppM9UKoVMJqMauzHfhOXAsiEWM8uB5wqLfUBSqZTqOHyeMhwOsbe3h6dPn6JYLColT6MnfwP7G0XG0JPJJG7evKm8MCJ/NktjAuHCwoJKmqvX6478E5nzIENrVGI8/I9l1NL7olGQXgkVFk/8tSxLnaXEECa9hFkVPWwrFb+8hiFBCcxpRKXhdRPJ8l2GsJdlWchkMnjllVdQq9Wwt7enFClz3igyjwSAOkJCVgsBTodBT+yUjI8M/0q2gvfggbtkVbPZrKNrrldFhrfI7kojq4daqH8nkwk++ugjdDodVKtVPH78GPV6XTWyi8fjqrEn70Mh2+PFRo+AM7WB1Z2SLdZLy2WLBmAfaDIRnMwYGTSpoyXo15kbAAeqxZhkTf1dr9cdYXsCLobjZFEBAdxhTvFRZeZAj0kRsH/L9vY2CoUCKpWKo/pIApx4PI5sNouFhQW0220UCgX0+33U63U8fvwYgUAAS0tLWFpaUi380+m0al0fi8VQr9exvb2NXq+HUqmE3d1dAM/DQktLS4hGo7hy5YpSgOcpvV4Pz549w2effaZAip6wppfKDodDR4J3JpPBG2+8gWvXrmF3dxcbGxsqh6bT6SCRSODKlSu4evUqisUitra2UCqVkEqlkM/nFZWvx4kJIkOhELLZLBYXFxEMBlWzQVkuyc0qE6AJetjHpFaroVwuq8/sBdDjxr5RKfA6CUb1e+h/m9agblS8DnyWlpaQyWTQbDbxxRdfqHywdDqtQIYM01KBy3OEeJ0uMmwoPWad7pcVi/Jw2EwmgytXriCRSKjzhrxqmClk4VKpFBKJhOPsMp0tm0wmKsw4HA7x85//HP/wD//guB8dVt4bOFh9xFYAnE+vCR1I2kDqTwkgpE7gDwE5x5COO3tEkeVPJpNqDcuzGekkyA7kempDv9/H3t6eOrSXh5ZKp0A6bYwIkJU9qyRmYAZBjxSZCMueNLKDJyedBkIOHpGuZCS4AHhY4GAwUIpwPB6rsmrZ2p79fwCoPCJO8ssojWTGe6vVcpQNyu8nxWQkCU7YvI5xYvlduSCZ38PTkBOJhAJSeq8GmfvAMWazLD25UN+UJi+Qc8cNYvp+syL6PBy2bs4CqBzGCHlJSJ3bto1IJKL0gAQocp3pP24erc6eygoXivRQ5T04h2ROyUB7Nfyii0xe1avZOF4y6ZvhSJ5wTwaClaDSGLuJV5OYdeGY6K0OpJ4kYJFgPBKJKBCaTCaRSqVUIcBRQI9MJpdsUr/fx2AwUMeuME0CcBa1mACvvM+lZHoow+FQ0XjPnj3D7u6uOp2bE8PrbPv5sfYsax8MBtjc3ASwT00zREIKm6iyUqkoKnxraws+n0+BCl6TTqcV87C3t6cWmsxiPy8Zj593oi4WiwfCI7qRk4ZTomyZB/To0SP89Kc/RaPRUCAymUyi1Wrh9u3b2N7exnvvvYednR0sLS3h5s2bCIfDKJfL6lBTLtZQKISFhQXE43HcuXMH2WzW4Z3REHDMdIPMeeKhj+PxGIVCQYUIph0zctGF406vjOMmk2enxdPlb7f7S6ZI9k7yMvDRgXMwGMS3v/1t/OQnP0Gn00G5XEaz2XS8RuZLScPAOZLeqXwPaaB5H92zlsJxTyaTuHHjBpLJJHK5nKfDWhQaWJkSYCr80PNBLMtCKpVSOlaGDmV1HUNc3EucTy+v9XA4jKtXryKVSiGXyyEej6NWq6FaraJUKmEwGCjHNRwO48aNG1haWlLpGAz9McdKOqRsAgkcPDNNF11HcR9cu3YNtm2j3W7jnXfeQbfbxVdffYUPP/xQ2VP2q+I8DYdDVWXNjtunlZkFPaPRCMViEcViERsbG9je3sbu7q7q7inbkJPt4YFrBASJRAJra2uK0SCqJY06Go1U/ojsggvsJzbm83ksLy/Dtm08fPgQ6+vrSKVSqlT2vEEPq9QYMkqn02osaER1VE3hd9NBz89+9jMUi0WlmNLpNLrdrqoQ+6d/+ifs7OxgbW0Nr7/+OsLhMNbX1/H111+r3BN62Kurq0in03j33Xfx7rvvqj4kcnPIckcKn/f7/YjFYipEsLe3h+FwiMXFxZlnemQCou7hSNpfZ71Mv/V7AwerK2RCrRdFX98EPffv38e1a9dQq9Xwi1/8Ah9//LHjdRLsmEJW0ria9pE0wvKz6L/5N0FPOp2+NCwPw4WJREKBHr3cnNcBzl4uNMSmaiFeS9BDlt40F14Tgp7JZIKbN2/i7t276PV6WF9fx6effqpCS+VyGYlEAn/wB3+AN954A9FoFIuLi+pIHz3HR6YayKov/k0Hisy+rBQmK8SUBvYO4z3/7u/+ToWbpY2W5eutVkv1jru0JevAc2aCk1iv11V3WkklcwL4NyeTCoq9DJgvwjOJSNfJ+KQMl8lyVU4Gwzw8IRnAucaO5SI0sQFSTHFdXiuvp2JKp9OOztaJRELlHYTDYWSzWYxGI+TzeZWQmcvlVJ8gGoVQKIRkMqmSw6mIpJE4LGwj54aVapFI5NzB5YsQ+d1N46AbztOsLZOx8JpIpS2FHY6Hw6EjaZjrVObiuVVk6qEu/TdweENTMnh0tLx21MRholdlmQyaro8ky0a9LB0AyW6b+sLo9/SSUB8ytUDm8rCKkWEmgk3q4U6nM7XZpowEyL5vEvTI5oScS70XFgtd5B7LZDLKfhJ4MeoiC4hISpxWZhb0dDodfPLJJ/jwww9RrVaxs7OjkhNlaAAwnyLL3JytrS3V/p8MArCfOJtOp5UnwmuAfc+jUqng2bNnGAwG2NnZUcxDMpnErVu3zqXsdDKZoNlsot1uo1KpAIAqM5ceqwSB8n/2CCG9KZXKt7/9bfzFX/wFut2uo4qFwOeVV17Bd7/7XYxGI8RiMdUArt1uo9VqObwxZvAPh0PcunVLHRrKkFUqlVLVdBQ9n0JW1rTbbWxsbKDVauHOnTszbcBlyGka+DH9Pe1at3woPSQzy2M3TUzghNWYTCLO5XKKWu/3+45EYmmY5f24F2S1nGxqKo0AXyfniWyEz+dTh/9eJmEPnng8rhzOarWqDJze6FGKKcShz7HeooP38nLJerPZxMcff4ydnR3H+mOhB9d6v99XDij7uo90DikAACAASURBVH388ccoFAqO+8l5kInKcp3LNc15kdW6MmpAYoL5Qkws//GPf+yIQDD0yXwt/l5ZWUEmkzn1OM0M6NFDF/1+H0+ePMFHH32kWlWz3I0iPQdJVVOJSQPLwaVQOa6uriKXy6kTluU5XbZto9Vq4dGjR+h2u+roBzbxW1lZMVZ7nLUQJVerVXXWCpGy2waXFCONrV6C6PP5cOvWLcTjcQWMqOiZTBiLxbC8vHygmaNOVdu2rUJi1WoV+XxeAUii+Wg0qjoJu3naVFzA8xL6crmsejnMuuHWmQET0zMtfOUGkHQWQgc+sz5u08S09qnM2YOKPbpYCME1Lh0g3WGSnZqp4OXZWzwPjUpcT9JlpYzMobhMwtAHmbbBYKB0Fx02yeroxtWNPTPpO8nYexXwAM8rdx8/fowHDx441urKygpufpNrKfUoGfJSqYRf/vKXePDggeN+nB/qZ4YV2R5Ezg9wMMdQJjD3+30UCgW0Wi1Eo1EsLCwgEong3XffxZ/+6Z8ik8k4Do8lCyVDzfI9TiMzA3pMC9nUIl5ea9oYMj9C94BJBfIx9jfgYaZ+v191x+U9arWayvWxLAuxWEx5K+e1uSaTCer1OnZ3d1WX48OMGZ+XAEd+L1ZrybChbHBFA+Hz+YwJZtI75udgEjkZn3q9rpSdPC4DcJ62Kz0+SYMzz4pK00silZYJBJ0UqLgZhssukraXbCiwf+yHG2tmMqqmsIwUKnMq+cs4H2QP9PwnKSYmweQMyLkyMaCAM4zuxfEma5hMJh3OI1uyUE/SlmUyGSQSCQwGA2SzWZVoLNc/1yZTGwh69L5K04R2enFxEclk0tGPKhAIqBJ2gh6fz6dAj+xinkwmsbi4eOCA8OPKzIAeXTgRbFiln6IOOHN6TJQbFQ8Ni06HjkYj1fOAJYCBQMCx+dgMCnjeC2RlZQVra2uKxTgPGQwG+Oqrr/Cb3/wGpVIJxWJR5biQvZKLRMZcOV6yKVuv10OhUHCEqWTpP0vSSVW2Wi1HjwadApUtBFhFxyZVfr8f6+vrqsMzc3/4HuwhxJAAY8sA0Gg0MJlMVEXCrDMW0ohKet/E5LjlYUkxPe51xX9ckWHF4XColC+wHyYxdaiVuYNc5zI0rIdbdCVNpkl6zZdJaKAZ4pL5THKdm7r88m85ZvI5qeslCAqHwwfyCb0k4XAYV65cgWVZ6tgVRgB44G6hUMDu7i7C4TBeffVV1U/unXfewTvvvKMSkmWOqh5ylyy+tIX8MbUe4Hpnn6RYLKZIhA8++ECxn2R1WDnWarWwvr6Oer2ON998E3/+53+O5eXlU/VbmmnQI7ukSk+LBkO2mKfIJCwmwknjLCeLhle+p+55kxHhe6bTadXn4LxkPB6jXC7j6dOn6uBVt8RAiqTkdS+W4SsCFJ7QzWaHHD/Sl+ysKT1dvaU4f3q9nkq0Y2ixVqup+wDO5mIyzis3Hz0YbhYZEptl0cMhuvJwC30dZfNzzwDmJp+XXeQ6lSJZTKns5R6SbI9JTEwPcxq8ehbUYULHSH5/N2BjYq718K1bjps01l5m1vx+P5LJJBYWFmBZFvb29mBZz5v7MVF5fX0d6+vr6kzFcDiMVCqF27dvI5PJqH524/EYjUZDNTkcDAZKP+s2kr/5t3R4qcPC4TCWl5dVVTPzdNbX17G7u4tOp+NwGvjZqtUqPv30U1U9/KMf/QgLCwunSvqfWdAjPTQdiVKkAtIpVBp3k0KT1+l0qXwP3aOTwOA8y4B9Pp/qNM2ktX6/f4DylWNGipNtxqWRLZVK+M1vfoNUKqUACgEOgSTHnMBTxoqBg+EtborBYOA4QBAAtra2VImjvoGkUAkS4SeTSZWgl0qlZl6RTcvl4W837/ao96Vx1pnPyygEHpFIxLH+THrEBGj0ULkcU74OwAEjQGBLploHPboxvwziNsaHiR5G1H/L56lzvFol1+/3sbGxgSdPnqDVamF3d1edTEC9z+7KZFkKhQKq1SpqtZraByw956GlsjgIOFjVRZHrVrehoVAIu7u7jjw2v9+ParWqytXJ3rA5Lpshsp3DlStXpvZ0OqrMNOjRD1YzlZvSsMrSVCZkDQYDVVYNTC/vJutjSg7l3zxThCXe5yV+vx+5XA43btxArVZDq9VCIBBAr9dDo9FQeTQELxwzxnWplImy19fX8fnnn6vvYDLG08ZqmtKW3gDRvkyYk4nVsgqP1TGkSQOBAK5evYrbt28jnU5jaWlpptkLuc70BHApkmmUzx8lfwvYNy5k2ghEvdy0jWLau9FoVDUX1c/FIjiRDQolYGSys1znEkBS90gPFtif43g8jlQq5ajeIujXDbiXRbL2x9nDptwpAI55ApzOkqwK8po0m0188MEH+Ld/+zeV2mHbtgrrcQwWFhYAANVqVZ1UX6vV0Ov1HOyY1CmmnDYpMoQrHVa9l5Jt28rRYBUwj7qQhS2pVAorKyvI5/NYXFzEYDDA3bt3kcvl1MkIJ90bMwt6ACeDoD8mlQwNOkMukUhEhZ9oaPVEXnkv3cPWRTI9pAFlzP9FC+lDVlnxRGmfz6dOptXHSHq2unS7XWxtbTkq2/g+UhmbFriMpVOohORGYkKcz+dDOp1GLpdz9GCQgFV6aARIzAXIZrOqN9Csy2FMjxz7464tuYbletXZzsskNLam4wtMjI98nekak6Hg9RIcEdToLSV4D/6+DICHItmBowA+t+em5aodFoKcdWFj2lKp5BjPyWTiAO6RSER1OO52u+h0Ouo0AwAOu6nnaOo2gKIz+3ourWT5ec5dIBBAPp9XgIxsD50CkhSMyrC9wWkAD3DBQY+JMZAD2O12HcdBcOB1BcYJkIwGKTImMOonKMtJk4ZafgYAjjJ2ThgXH085f9ESCASwuroKy3peKruysoJ6va6y9nk2GY+SKBQKKBQKKrmYn5GLmw0J2bdI5ieZ4ug6qj9M6FVzUfNA2PF4rDyxSCSCmzdvYnl5WSVYyzwIslsrKyuIRCJYXFycSWUmDwWUB7S6Gb2jgh23MBj3CIE5558A6DIlONMAJJNJDAYDB4iXzdf4nK7YCeQ5dtJI8F7SYOi6QAdDczk6s2XaH9P2P0Gml3N6QqEQVlZWcOvWLfT7faXveWSTZVmIx+OIx+Pw+XwqN6bX62FlZQWdTke1UtDXpiQE3Jh/NwaI18pcWt6bLBTwPLdzZ2cH4XAYw+FQ2bByuYxut4s333wTa2tryk7E4/ETzeOFBT2m/BkASkn3+320Wi20Wi0HKDE14+MgUymxNI7okv19+JjMKZGVXqRFZTkge31YlqWMR7/fR6VSwfb29rmEuYLBIG7cuKFakNN7Z1tvVk1Vq1V0u1188skn+Oyzz9BqtbC1tYVisajuxaqKhYUFjMdjRUVyXKRRNjEEh4VZKAQwPp9PbczJZKLeO5/P48/+7M/w/e9//0Auls7gmYDuLIht2ypnSiaIS1bsJDk8FB2YUpFxj0hmkiAI2Af3XhcZ3ur3+yq8RbAj/5f5bwDUHLGtxWg0UvkIkuoH9oGm3tBNAqTLLG7sgX4NYG7bYApx6bqGNsDLoIfHULTbbdRqNVUVy/8ty8L169dV2fjS0pLK22Rblmg0ilQqpY7xkLpe7g1JCugiwRL3jARRMtWCxEWv18OzZ8/w2Wefwe/3o16vY2dnx1G91Wg0cP/+fQDAwsICotHoiXTVhQU9FKn4qWTYOVU/0PM4XoL+o4ex3GKb8jkqQ7bW5uciQDjPU9blgXAUHsRJSpHhrlwup8752dvbM4JLLlAJEGVlCz3d44g+bjQGkiFi+DEajSKTyahKBK+KG7h/WZ/lMonMJTEZQj3MLV9n0hV87qj0+2UNK56FnDT85+U1Lp0aPRTFtebzPW+9Ig/XpkMP7DckJODXw1TSeXJzfiXjKbs6c7/p92SurN/vd7REYWNDOoXyHMfTzOOFBj061T8ej7G3t4ft7W1sbW1hb28P7XZbTSS9V9NmIE3t9/vVAWZs4sdJklVgfr/fkUQL7IdvGC6ybRurq6u4ffu2QqetVgvZbFadNfIyNxlDRwQ84XBYLaJwOIxKpYJqtYqtra0DC1cuSOmhTlPU+neVG03eUx9jglcmtq2trSGfz6uTlL0sZACk8dUVlptMe97kEfNv7iep8JjXctlYB5nrJ/P6dNEBjjyHiM8zNMwiCZnwzDC63tdEz7e7jOIWMpl2jX4dHU2ZF6Qb6MFggE6no1hVr8loNEKz2USpVFIHdxLEMBSUy+VUb57BYIBCoaBCSL1eTzmdHDPd9gHu86UDf/1vyfhwvyUSCSwuLmIymWB7exsrKysAnjM5mUwG8XgcwWAQ3W4Xd+7cwfLysjpB/qQO4syAHuD5wBeLRTx+/Bg7Ozsol8uqvl82GWTsXIa5JC0nOwrzOt5fTqJeBcDkX6JQhreuXbuGcDiMvb09lEolpNNpBXpephD0AM+pT3bq9Pl8SCaTKBQK+Oijj4xMA8eLm0OCQl7n5hmb/qZSYuiNnoScn8lkgng8rhb2ZTiTSIIembt00u8t58fNgDDXhGCYlP9lCWtRTEyPCfTJ+ZC6SC/jZQiMDUz1fSOZX675y5xIbpLDnESdXdDXrNTh+j5iHqhXQc94PEaz2US1WlURELI5TBzO5XJYXl7GZDLBxsaGAkiFQgHtdvvAWje1XtETzily7iS4l8I8Hvayu3XrFnK5HPx+P5aWlrC4uAjg+SGkrKrMZDIYj8e4desW8vm8OrLipA7ahQU9bpS/biRM1+ulikzc5L14D+mlyYnWJ12nUqVy5LlP9DJisZg6ioK5MBdF5Hd3827Pk5kyhQ/pBcxijs5J5Ty/p1vI9rKMtS4yHCX1iszLMQFRXg8cNALUF36/X42zPOLmMIfhsoopjcC0Lo9SUSvvRweA+XPnmXZwnkJgzTw9eXyQtHUE5br95BqVzP600LvJLprGlU6WTkDwf7nndKcCcDYU5t+SsT6uXDjQIwdar2wgQ7G0tKRO9abh5oDok8TXswcBJ53PEQnL95UDy9CLTGQMBoNIpVKw7ecHjj548ADRaBTXrl3Dq6++ikwmg1u3buHatWuOQ0zPW6RHZPJi6e2zbxEXHcfpOErlJJ/NZHyZI8Uwgf5eJ32/iyryu3C+XgTjwrnVwzKXSdzCVgQ20WgUsVhMVRByzzN0LsEP/+Z8sfGb7C3FviM8dsW2baVrLmsXZjc5KsNz1DCYZHIIPjudjupj5sX1z076lUrF0Y6CRwlx7SUSCUwmE9W6RYbVZTGQzppJEM817raGeZ1MepZ5c8x/ZTd/VvByn7TbbZX+wPzdSqWCZrOJZDKpwl6eAj1uRjoSiSCdTqPZbCplJOOP+kRx8lihQqAj+3PI0lP5m6+TybsECkwa7vV62NraUqBneXkZ2WwWi4uLWFhYcPSeOU/RN78bba83BTOBTj1OzutM7NhxP6MuBKE6fakjey8ZDB34nLUXqs+bBPWXXWSOAR0A2aVctreQe1kaBgAqbExjIJ0ptgbg45etNcBpxcQASTExELqTZNvPj63hIcpeDG9NJhP0ej1V0Uz9z/xV5pSFw2EFvCWrzvUuz7QygR597ExMJz+PntPDqAuTl9nqgY4eH+92u+o8TXaVZl+hXq93KjLhhVjkacr0NBt9NBphd3cXDx48QKFQQKPRcCgnPYdHGm0OKv+XdJvcJG7eoOk5SSdyAt0qQc5b9M8sPw+T1STS5zUS8Oiv0+W4YMdE7+tUpaw+uAxJtdNAzrS8HP063uuor+Hr5D64jEKqnR4pO5WT6qejxNw2rktWuMjyW7muCdypwNkVnd4tK1Ko2CkvW2+8TDGxOG5/668zjZuu03mulEwo95rotkzvtcP1K6MaOusl9Yi8l9uRE4cVt/B+8jppZ/QiCkk46KXyLMQ5TYjyTEGPnuFNkV/wMGM6zdANBgO8//77+Ju/+Ru0221Uq1VlIHVKTt8I0kuTn4fKTgIVTriMb8qQmARK7BUUi8XQ7/cRDodVuOhli0Tn8ruxjTe7N0ciEccmkK/V50k3kMcFPnIN0LDIzcBzWWRPFL6PDsi8INzQJu9U95xMooPaw+ZDfw+d4fOy0dWNoG3bqiiB3jCrMoPBoKMfF1/HfcETwpmwzPA6HSB5xpNkjmUj0Hq9jtFohFwu5whDe3kO3MS0xk025ChgRbIRHE+GfrrdLprNpifDW4BzfKi/JTFAh5drUkYy5L7QdZJt77co0ds3mFgh+XnkPOqH+sp0Br1/GO05jyYaDAbqIGxps44rZwZ69AVqAj+6N3RcYTfhBw8eYDAYOMqxJehhkpQUTr5pMvnbjeXhj0TNEiSwPFDvynoRxMT4SKaH4EJXAvQG3MbkOAuO95nmjcl7y7JteY2XjbIORo4jx7neNG96eM2rY2wSghV5jp9M/jT129I7s8sz7Rg6oU7ShbqHTE8gEPBsYu3LFNN+IlMnz1D0skiCQf7ouTkSIAJmpoePuzHTJj2u21kTE21Z++kicr9IsCNDyLKR6ksBPTqDQNFzCOTj0xTqaDRCq9VSfXBqtZr6m8coPHv2TA0Uy0PJHpjuLyeaaFeiT9mHQH4vigyRyUUhB52fodlsYnNzE71eDzdu3Dj+gL4AMQEGWQEnw0gS8ct8BQBGNsLtffTH5HqQ9zaVNerhL6+LNK66B2USExA8TNwAle7ZndYpmTWhHmEr/FQqhU6no7o0E5BIdkbXJ7yHbduqTw+9YipxObeyZxYB02U85d62bRXqa7fbCnDqoHuaXjGxowBciwEkuPVqeAtwghZ9v8uGhAAUsND1iAnEyN+6I6vreB1guc2Z3ixRhpZ1O2Tb+4VFp2HqTgx6TAr6tAqz3++rXJ1CoYAvv/wSzWYTxWIR29vb6PV62NnZcVRbyHOb5ERQ8VAxMSGRn50TLZOXTWfj6D+Ak/6j0vP5fCgWi3jw4AFWV1fx2muvnXgczlJMlKMsCyflKZNbJcjj2Mh7UfS5dmP75AaUxkKWIZrCLl43vjLkoVPM/Funl6cZhGkAyLQO9M9BptKrYnKIeHDtYDBAPp+HbdsK9DDfhu0u6CRMJhOVp8NQ1mQycSTgy/AWq0/8fj8SiQRCoZBiheR9LpNMJhOVGlCtVhVDZmIdTEJ9IgGOiaWUDh07/Ho1kRlwD1PTfgUCAUSjUQwGAwA4AOp5D/k6k7jpKf4v0yokGNXZomAwqFIs2EtIrwzj/ekkcD+dK9Nz3Dc7Clq3bRu9Xg/NZhP1eh3VahXFYhH1eh2FQgHb29sq+14aRYZhOCg68NJBizTqEi26hWzcwI78Dny+3++j2WwilUpdaO9NHz/KUcfkuKIzgm4baZphnvb4LMs0yvg497gIn2MWRXqULE9nXp4s5QXg8ESlh8wfVn9J0MPx5GO8t2VZqirlMlbS0Wsn23PW3183uAAcTtZlYDZNYyoZfj0EddbjcJiul2yQbA6q22v9dafVUycCPSaKXP9gsvsxO2AyWZCIjfG5crmMRqOBdruNzc1NNJtNNJtN7O3tqRI8HpwJ7Ccl64eBMvlQZqdTdCbHsixH/D4ajaqj63nf8XiMRqOBTqdzACBIIMTHW60WCoUCQqEQer3eSYb2XMXEYsnnpjEIJsZBF30zTfMgprFIXlVKlKNs4hc5BpcV8IzHY1SrVcUiBwIBpFIpxGIxxfRIz5l5B5PJBJ1OR+XyEAAxKVOGj23bVkcfyKovJk/3ej10Oh11wjXDD14X9o+pVqtot9sIBoNIJpOIRqOqRYAbw0znUybhA+bQr2QMeAxFo9HA3t4ednd3EYvFkEqlPFUcARwcI2Cf2YzFYmpcyPQcxqjx9XJs5XhPC2HxtSYQQ7vKCICJDdJtzWlZuhOHt6YpYdu2Fagh+zEcDtFoNFCpVNTp3+12G51OBw8fPsTm5ia63a46T0unxQhYGKYisGJCM5NyJd0pkT1pMdlkTJbFpVIpLC4uIhgMKjDW6/VQrVbRaDQc35l5AARXXEDNZlN5fN1u96RDe6ZyGIMl2TJ9YR5maKcxQSZWz3Q/Ocd6xcW0670iJoXO34eFq04CUqbN12UDPWx4trGxoQwCz/thq3uZ90bHiqw0DUav11OhQfYPIdPDMA6LHaQBkYCHoIdGHzhdcvtFl8lkos6Jajab6tw9NiXVWWeKNIjT1ix1icxZ7Pf7aLVaqNVq2N3dRTqdVmf8eQ30AAer2LiG+X1ZvezGNOoAxwQq5bXycfm+3Af8X4pkekx92QBnQraM6pxUThzekiVvjEnzZzweq2ZIvV5PJaoR9IxGI3Q6HXS7XXQ6HXVsfLfbVQ2IpLek5w/JwSHIIQiSg8TPKkUOPpWWzCuRfQFMxtrEjvBvslq850WRwwCqG8X+oo3gYZ9LV/peNcoX6XtdpM/yokTqKeZ4yLYX3P96cYPUDVKoP6gDaEz4HjJniyIPN2axhmxo6DWQo4usdmN+iX6sB3C0PDW350xjSHvSbrfRaDRUd2Kviq5DJavC593Ao8kBPs26PIoO19ukuLF8p5ETgZ5Op4NKpYJ+v48nT57g0aNHqmkQM6sJemT8m3SuVACj0Qi1Wg3dblcl+7GMWsYd1QcWcfZ4PI5wOKwYom63q6hpv9+vFA+TdvlZ2u22QykBQK1WU3F8fhcObiqVgmU5j6+gsHmZbduKsiazddGFCZns4Kn3MjItMCqTkxpH+TpS2PpZL5eJdZDjfJzvfJTwon69yRuTBt6ruSW6AaQeohNWLBYVwxsOhxUrLPN3LMtS7e+51wmaGo0GBoOBYoDJIkvHUNcHdBRDoRCazSYePnyIWCyGW7duIR6PX4gGpy9SxuMxisUinjx5gp2dHQczzlwb+T9wEAwdNj5yvQcCASSTSQUqHz58iHq9jsFgoA6MvgzCdiUMfekH3k5Ld5Adx03MjQkYyTwdRkV0HUTygeFfE5MEOHv8nDvT0+v1UC6X0Wq18Lvf/Q6/+tWvFEXLnB1WJcgvLat0mNDHgaGikG2wTYdOMrwVCoWQz+eRSqVQKpWwsbGBarUKAMhms44QGKk8AqFer3cg54bxZbbJ5meXMVDZ00Z6b9xcBD0EVRdd+JlbrZbKR+Dp59PAh1Q4xzWSeqyWc6I/f1mAz4v8riYvTYId+aMzG14RnaK37eehd55HVK/XUavVVChLOmcMQXW7XViWhWw2q05+Zok1W2uwNT5zegic3OaVaz8QCKDdbmN9fR2RSASZTMa15NpLwtDi1taWcqBNOuUoa1I30iaG3u/3IxaLqbzP9fV1VKtV5PP5mXBQjyMm0MIxoX2ls27a9zLNQeoOacenMT8yGkOQpN9Lfi7pWHB+3ICNfO6kOvPETE+hUEC9Xke5XFYhKdlunbky+heUyoeDpydbUSHIwdepZp/Ph0gkglQqhcFggHQ6jeFwiFAopCoCZKa+nHR2TJbHU7D8ne/HSZC0txsClguEynPWvTQ3lues7k0xjdM0utWLYhqP43zXaXlbbu8jRTI9XgQ+Ugh6Wq0W2u02bNtWTpRO53P8pJJnIrOsOOJeob6Qni11lcxdo66gM0YQRbAkwz1ezDUB9ueBZ2HpQGWaI8DxnjY2OlvAMSewZUhzGjD1upjY/KOMqUmOwv6TIdLnWOZfkelhztxhn+kkcmzQM5lMsLm5iZ///OcoFArY2dnB1taWI2kYgGNju52GShZGKhjT2VUSRJEqzuVyWFxcxGuvvYZqtYpgMIhKpYJSqYSnT5+i3+8rz0tKKBTClStXEAgEEAqFVN+MwWCgWCp5oBlBDL+THAe5UQnCbNtWjMlFEjcjqgNMYL/6zkQhy+9soiHdPC5pQKiwZAxfegaXtYxXB9W6InH7m9eaxI0qls8zF41zcpTQwayI/j0mkwlKpRIePXqEfr8PAFheXgawn1NC5evz+dRZTXRoksmk0kOyvw4Z6GQyqcaw3W4DgAJVZJEYxqfzxaqifr+PWq2GcrmMcDiMVCqFaDR6XkN1rjIej1USebfbVeFEqSd0wEIxGWtd+FoaTgl6eIZjMBhEuVz27JEUh4m0q25jbPp/Gns57W/93C/aGf5EIhEsLCyg0+mgXC4rtlW/32ntwrEts23bqNfr+PLLL7G1taUaTNGj0Vtfy9CSFD1xmK+Rv/l+kgIj3RyPxxGPx7GysoJwOIxms4lYLKYSp5mkxvuxaRgVUywWQywWQy6XQygUclSWyfeWE2VaHIAzu5xxyYvioU0zXm6LVA+5nNYATluwblTsZQM8FEkjU0xA8zj3mwZ4AOehm5eB6aFSJaPCZFb5/WVOIZ0zAhWCFn1/MF9C6jsZsqeR4WuYa6jnQrbbbUwmE8RisfMennMTWbIuD2zV9eZJ9IBunCWokSkOlmWh0+l4fs27iR7WlsnNbukLprE6il6axmDTyQ0EAojFYo7w7lnZICnHAj2DwQBbW1solUqKlgTgOOZdR4fSc5ciWSGGkqgICDRkG3duCDI0tm1ja2sLn3zyCQAgEolgZWUF4/FYAR92SOW9JfNEpdVsNtXip7KRScwy/0E2tXJjN/g8q0IuuvE2AVLg6GCJMo0dMHkRXOS6XDYDrAN/+dyLYltkjF0qvsvArtm2rYoNRqORAh7AvtLXnZxpc2QC6BK0yiagBD3Um/KMQFmmTYM8C3mBJxWG9OSp59LQHgbWD2N65LVS6DwT8LoxHbMq8rRywJ2hkftegkJTJ/yzGhcd+Oh7ik6DdCrcHOLT6KljgZ5Op4MPP/wQDx8+RLFYRK1WQzweV9VN3NSStmK4yIQYyfYwD4gZ9jxlm4yJPAWdwGc8HuP999/Hb3/7W1y/fh1/9Ed/hOvXr+POnTt4++23VRkokTwTjweDgSMPiZ2e5Wd3M+rydFg9tCW/03A4VP0gLrrikk3UAGdJP0UHeKaQAZk+ubClEpN5EfL+OuDivPEkYK+LDjikMjcZHEJQRAAAIABJREFUWcpJFRHnhUaG97lMQJNNRMfjMRYWFhwVVxwTedgomR45/m4hWOlgyTXOpqdkoVl5ymvY+JDVrMPhENls9vwG5pxFMj0AFPjUGQbTOud+Ocoe0EMpBFutVgsAHOd+zXoOFW0jq7No89yAu7R5Othx+9v0vw6quH90kcyp6fWhUAjpdFoREzJvS88DOjfQw4z7RqPhaB/OJEDGuDng/JBuTA9BAundUCikFh8ZGlOLalZYVSoV9Ho9lZWfSqWQSCSwsLCguinX63X1Hqy2aLfbKoFNVpxxICVa1kMAJjHlXvA9L7rnzPHUE8X5nEmpy79N38/tcTdFpgMsgstp7+0l0VmFFyluY3iZmB55GroEOqZ9IK9xY3p00T1a3TGSh2vKcBrfn/lCXgagBCF6w1g+d1iOmpSj6gU9VYKfQeYZzrrIY1MOk6OwZdPCUvq95N+m63VngNdKuxsKhVTkyLS/zkJHHgv0MFmX+TC9Xk8BECoFApNoNOpYVBJxAnB4UPLcm2QyqRKBeU+GvaSCYha+z+dDv9/Hb3/7W2xvb6uBsywLjUYDtVpNJWoyCZp9gXgAnX7Yo5wY+fsohpgLie3OLzJbYVLKbnKYInKjUaddLz+DrHgxlSzKteAlcWN25FieRU6Vfg+Zf3cZhAaWYIdFB7Lzr3SwAGcxhr42pfAa3VuWjh+vo+4CoBwverYyvCWPYfCSyKOJ+EO7QjbNLZzolk9iYqX134FAQLF67KNGZ5s92hKJBBKJxMzpGLcxk7k6TKaXuTKmfB4pEuhPY6BNn0feQ7+n/jg/P7tFj8djxxEw8vqzcM6OBXp4SnA8Hkc6nXY03+JmJmpnbg6VjWSAGNLS49mhUAjZbNZBz8lSUFk+zmSnSCSCdruNf/zHf4Rt26q1eCAQUJ2embjGag0OmFQ2UrlJz49/k4HST6Tl/eTmGw6HaDabKJfLFz68ZTKIuhxlgR1GN09jjaTRkKFRr7MOgDm8xb1ylLk5iriFJGUOC3B2LQkuovT7fdTrdRVqTyQSqnvycDhU7DJDvVLhcm1KlkYPA+uhXWlQeC/Lshzl8WzgGolE1PEL4/EYnU5HdXX2mtAeyOM36NlLwybHfhrYkeNucrpoo5jzmcvl1EHWBF+FQgEAsLKygng8PnOgR+oQwGybSCrooF4Cfq5bvYUM34P/82eavpBgSicTTLqIoCeTyajPo5MR/Mzyu55Ejl29xVbp8ogIHdnJBSlzcPi4Pkm8H8+2YWybX1IX3bMaj8cKvTM3KBAIoNVqqcfZ7pysERUQz7nRQY4s4+ZrqCRlnFEibDmhEux5XY76Hd2Ajz6eZxG3nQWRSvq8v6ubAvLqmNOJkjkMUuT+nTYfhz2uJ5Hqr5GhHAJ83TuXIRcvsZvS8SXjQ2cSgIN1k2ICPkeZH/01jALIzvoEPrIvzKyKWyhIEhJu6/4473FaJ1niA6nr6XhI50OCLVlgcBo5EdNDKhJ4nnxkGkgqGH5gWUUF7Ie3KAQWfE4mOstD0QieyDBxAIhiAaBer6tGXxxMelOWZamTjiXQkS3kZQ6RpLZNHrksN7VtG+FwGOFwGK1WC3t7ezOxkfTQ1EkWlRwTN0pTXqs/Lw2O7k17WaZVTrlRwacRt5Am2Q6vjjmTV9lAlSLXOw0udY+e88Nr5FzJHBy9mZpk7ai3YrGYMrYMtbGHj9Sb8hR2yUjPstj28yTycrmMUql0oCu+BDeSKdAZMwInMvBu4yKb4fG9LctS7D+LbNjnLZfLzTToN4ER295vvsmedNOYMb1vnpvI1+ghKPnao7D/1PdkeviZX5QzeOycHgl6GJJiAy1+eG7c4XCosslJsTE8JXM2TLFI2ceHikWPXcrXESGORiPU63UHcqTSYMhNnshORRUOhxGLxRyIWGd/+LjMeu90Omi1Wg5jEQqF0Ol0UCwWL3SLc7moTInMlGkL1w0kme7htoClJ3IZw1umRH+dEgbOloWRYRnJgnDfeYVdoBD0dDodxfjq1L38TaMow7YmZm4y2W9PwTA/jTBBlKTpY7EYwuGwCu0QLPG9yBAHAgF1XI5s4zHLYtvPGzYWi0WUy2V0u12HwdSBuP5a/pAlYtRAAh/d8JIxGI/HaLVaqsCFDjOPEQkEAlhbW5tJnaODFD18FQgEVF87Rjb4vNzr0vk86v4/jq0wvQdtOwuZaJf16q2z6N9EOTboYZfjaDSKaDR6IOFOfiEJIAh2ZOzQNEB8nIbAlDwsRS5uAA7qWnZ3Jvhh6EvGGqflo0jgIzPjaewJrGS+kgnxXmQxIX7T/4fJWVT/SOXmVdZBFzeP5kWvH+lFM8zg1TF3c5aAfZ2lrz1pFKaV9ErnTD6vj6/UidSbEmhK1k8erqiXy8+qkBkn4ON30xPIdZFhL31uJBCSQB44CIBMHYhlUrUXEsdNrDDHV54uoF9zksfk4xJomZh9+bx+L6552mYJ8F8E23Ms0BMMBrG2toZisYh79+6hVCqhUChge3sb4/EY4XBYMS5MTvP5fI5+FHLB6uErPq8rJ9vePwGcCkGKDJ/Ztq06MUvGRp6JI38kkmSsma+Tz/F7MN+IyophOZbD87TgSCSiTkueBTnK4jpK/ocbgOLrpcGR76kzGl5Q8oeJpOr1vDC3+Ptp3ouiv8dgMECtVlN9Mrw49iYwI9lgwHmYoWTfaJilMZVAiedlyaIL6eAxty8YDCKTyageVDycmOcFSsDj8/lU9/nJZHLgOJ1ZlMlkgnK5jK+++grFYhG9Xk/1K6IjqjPsFIZB9OMpdHsB7Bt5vaKObByT2Tk3pVJJhb+8tPal7Uwmk6oHnq5nTQBeOgHAwWOXpokJdFqWpc6Xk3MoQSert7rdrqrW5r1MKQAnnatj5/TwzKurV68iEomg0Wig3W4rAEBGhQBIbn72rtEBjmRJZGm7XkpK0T0uhq/0M6RMIIfvq3twsncD0SYVFV/LEJlkkngPGfICngPEaDTqCUr6ONdOC2FNW7CXEfQATmMsxRTWmhYiPOw9+HopMpzYbrcdh116UUwNBcn+SnZF6iEqXr0KRf5miEvm/8hwLcP+PNQ4mUyqjtDdbhexWMzR9oM6iC013CqYZk1s20aj0cDW1pY6foJ2QhbGyLUvx9g0fzLXk/pblmbLe3Fts4qXhrher8Pv989EB/2jiA5imGISiUQUwDc5nqZctMMcVPk+FBk50QGstOsAHH36fD6f+pymhrmm9zqJnOhUzFgshqWlJQSDQdRqNdRqNZU0zA9EGpFVWXLjEggxWUl2PJULOxAIOMAJX6t/aQmsdDGhWZm3IEGPTJiTr+ckkQJlHFK2NOdEMzconU5jYWHhQjM90uOXHvBp7ge4G1mTmK7VN9ushQuPKlz7Mq/jMDkp8Jl2Pxp+L+SNuIlkUfQEZVZmsmeXBD3AwZ5GkpHjvXXdxddRV1AXSudJhnUk8JXst9ccACZpk3Vht32y57JyTR+bwWCAYDCIxcVFRKNRhMNhZDIZhEIhlMtlddC0BKm8DwCHEeX4Mh+L7QO8KCQFZMm6zvayHYwkDNzAhpsenuZc8XHOpwSl0rlgBfe09zjtnjgR6FleXsb3v/999Pt93LhxA7dv30ar1cKXX36J9fV1jMdjtYiY6MxNzi/EcJWJ3ZFVFPqX0xGnfJy/ZSWEHCSTEjFRfZZlqfCcNEasNpDvxXO+ACCZTCKfz2NlZQX37t3Dd77znQt/aKBc9Ec1usDBjH8dMLotWi5y/Xl6xxQTE2R63SyLbdvodrtoNpsHcgrcNvY0alq/t/4a+Zw02szTk2FprwlZFZ5mzhymcDiMeDyuQk0MM1F30BjI5oHSeQPgOEOKRhuAYpA6nY5idlgMEo/HEYvFHFS+XrUqQ15eEK73SqWCarWqErknk4liGqWTadu2CscMBgO0221ks1m8++67eOONN5BOp3Hz5k0kk0n84he/wF/91V+pyqx+v6/0uM5uMHF2NBqh1WqhWCwqtnPWx9rEEDNsnU6nHWFSST7EYjFVaCR715n0i+k9THZaF+4dto9hdEgWPSWTSfR6PYcekk6HzkCdRE4EepgJLr2lWq2Gvb09laAn49ySsZHsCg2ZLE+THo4eBuOXliKRoomSk8DH7R7yMelJ01OQnpxu1Hu9HjqdDizLQiqVQjwed4AfmS1/EUXOyVGv15mw47ye95gmZ7GwZ0G4PhnGMHlWeiiXj5mYMNP95Wt1kV4dc/H0XAovCNcTlat0suQJ6DLERT0kWTDeS9cBemhMDxcweTcajSpDLHuSkb0m8zAajRAMBh3MkRf2Atc7cx8JPoH90JPU03o/IwKZq1ev4v79+8jlcnjttdeQyWRUN37OwzTWUrbEIJgKhUKeZnrC4bDjpAPA2e+OTFAgEFBnXrrpAjkvelhLF6mnZAW2tItc5/ycjAyZ9tpZ7IUTgR6KZT0vwVxcXEQ8Hsf9+/cVUiuVSuh0Ouj1eqjX62rRcmFJBkb+SMUxLXmJNLFMdLJt+0ACsgl0TBs4hqj009mB/bBYOBxGIpFwNGf0+/1YXl7G0tISFhYWZvawwKNSmPJvqagkIp92H8q0jXIZxBTzNo2x6f9pY+vmpfE5OWfA/qGYXg5v0RGTVZb0dKmQGYrSgZGsOuVjephKX/smkRWkOtCUbCedx36/75mqOsuyEI/HsbS0BL/fj+3tbYfD5OaM6jaA46SnNPBvWbSir2f5WgBKl+tJvrMkbjpD6mRZtSyvI0CU+bV6mFZ3rCToke8j31v/fFLIapIcYbhTb/7L18rPIj/nSeXYoEcaM8uysLCwgGQyiclkgjt37iia+OnTp6hUKnj27Bl+/etfo1gsqi9H6lZnYXQjKul+LmLpRbHnhRSeDcbwGSeLSkYCLF3ktfqRGsB+v42FhQVcv34dsVgMyWQS6XQaoVAIV65cwcrKCkKhEFKp1HGH9qWKaYGb8kfkdUdB3SYmQhp4/Tm9usbLQgVMD5VKSMbA5Tjoil8+r4+VG5Di6wgCACgP0AsN8NxkOByi0+k4wigAVMUIe0NRP3GvM+wndYDMy+n1eso7lUdYmIROGI8EYNImPWuG/al3BoMBms2met9ZF5/Ph8XFRXzrW99CsVjE5uYmnj17ZmQU6OnruaKSlZC5HxLM6JV2FMlscJzj8TiWl5eRzWZn8ggKXUy6grkyMsEb2AcQZL5ISMiqTlnpJsfPlOagfwb+zbwq6hjuNd6/1WqpvC7On9yDkkmVFZYnlVMxPcB+U0DgeU4LALTbbYxGI3Uulkyg4pchypOMDuA0pib2Ri5+XicXPhWS7KpMSlnmEU0DPXyN3tuAr4nH48hkMkgkEshkMsjn84hEIgr0zNrGOc/qEBOQ0mUaC+c10cuhTSBG//uo43CYYtIZOlOZvFeEekeGzwHn95ZMM0P3HG/d6dINgK6jdLAqdZXUL/JIBHlfAlN5IvusC1n0VCqFXq93oEu/iS3Wx1KOtV5RpzPzOtsp17a0FdFoFLFY7MKnIhwm08D2tHCVzqzwt2R95B7h/4CZqdc/C+eP9ld/nKyP3B+m73NWzvCJQM9hSjcYDGJhYcERQ6zX66pdO7tjMnGMG5uKwLIs5XWx/w8TsDgogUAA6XRadYPmZwoGg6pjtFz8kto7bNB0ull/XSKRwOLioqrUisfjCAQCqsX8rAiVUCwWcxynweeAg4qCAEkaBT4nx1v/rTMT08IveujFq+L3+5FKpWDbNprNpuokzvYHLOPUx4mOgIn5kf/rIkurpYFnci2Z01law0cVAh6ZZGzKP6RIxtGUSEnw1O/3lc4hY0c9x0OO2VmZSev1el313zGxnjKc7qUjWSTTk0qlkEgkHAdP6y1OJNB0Y4vlvWknpBNNHS5DljK3K5lM4lvf+haWlpawvLw8k6Bf6ksdVLvZukAggMXFRdy+fVuRAmSEaD/JxnAP6KTAND2tv698Dxb3DIdDLC0tYWlpCalUymE/TfbhrPTSsUHPUd44FApheXkZk8kE165dw5tvvulQIpPJRGXwD4dDldTm9/tVpRcVxGg0QiKRQCqVcoScgsEgstmsakQoP5/pMx53wKZdb9qYpvjxRRef73lfhEQi4SjblCDF5E0R+JDqNykm+beMvTNxUS/ll/N2WcJbfr8fmUwG8XgclUoF29vbCowwaVPvUQU4mTnTHLmJrJzgtfR2c7kcYrGYOjLGayKNHeAEPTyPSxZOyAOPOQ98jq9hU7VgMKgcHwIVFjjwd7vdRiQSQb1eR7VaRbPZdHQRls4Df9PoyP5fsyx+vx+rq6tYXFxELpdDKpVy9IWaTCaOMAzXtsmA6nqevWii0agaf14rE8oJdlgZl8vl8NZbb2F1dRW5XO5CtxhxE6kvZUqGXoElx5HHbty7d8/RP4/hXpnnA8Bo30x62gSCdDa00+kgFAphMBhgbW0Na2trKq9KZzz5oydPn8Y+nDq85Say9bXeTVROzmg0UmWkfr9feUyMlR8Gei56SfgsiFQe0xaxRPiyko2AVt9g0mPVFywT6OR76l60FxT9YULKV+Z6HMZ0uSWNm0QfW4rMa2Do18uJzKTRyShLsO6WGGkKMbqFWfSQjASsMrQgQwZua1zqucOOaJg14TqLRqNKr+tJtFIHmcZIpj6YxBRWlPf2+XyqcWwikVAtBGa5XYMeAuRvt5ArGZdMJqOKjMbjMUKhkDqDUt8TJt3gVmhEMYVtJcOaTCbVkVayskz//PI7vJSS9dOKZVmqfJPhLb2jMyeC6JO0u1Q2sx6DfdlCQ8AMenpCnAdgv7wTcAIgmYMl47FSdDqUr2N8lxVw9I79fj8ajQZqtRqCweCB7sCm95hlkeHcZDKJO3fuYHl5Ge12G5VKRYWCOf6yh5VufHWmx00JMtE/FoupXDQmcZJ+9qL0+33UajW0Wi1HGESGvUx5CLKEXYKYXC6HQCCAer2OZrOJdrvtUMipVArhcFg1y/P7/UgmkwiFQg42r9frOUL3ZNtisZjygvmYlySRSOCHP/whEokEdnd38d5772Fzc9NxtA/HTd8D/X4fnU5HzSEA5Ty3220HoyfnkJV7q6urePvtt7G4uIjf//3fx/LyspqvWdMv/H6yqaOel0NbKlsEhMNh3L17F5lMRl1j285Dut0AjP7+8rdJdN0kU1fYxJenwLuNP0Gx/j1OIi8F9ABANBo9dCPrSlvKrC3Oiyik/CXgGQwGDopXnv/D1+gbSzcYusgkOP6w1bgMCZD6bDQaiEQiDtAj389Lc8+xSSQSuH79OsbjMcrlMsbjsQqPkPmSp89LRsGU92NK1uVpy+FwGLlcDrdu3UIymVS9Obw0rrr0+300m000Gg1l4CQD4AZ6OO40xJPJROUTJpNJ+Hw+bGxsHNgXiUQCCwsLjjJ5JsvK+3K/8fBmJvqy6/3Vq1fV416SWCyG73//+3jllVfw+eef4+HDh3jy5IliYugEycaNwPN5IeiJx+PKCWDIkQeZyp470mCORiNkMhn88Ic/xO3bt3H9+nUF/mdVpG7Qq18lwJYNUEOhEG7evIkbN24AOHorjLMSadtN4UqTcF5l5fdJ5KWBHuDwwfWyEr4IQkVO75Psm/RoWZorNxPFFDYxiV7iaNvPex3R85VJu7JbrZ7t72XhuDPcxPJZhgNknxkqMpkIO80xkKHmRCKBcDiszoGSjfe8LJFIBLlcTp19lUwmEQwGkUwmFZ3P55lnOJlMkMlkkE6n1T5hGJIVP8lkErlcDgBU+woZqhwOh1hYWFBnb6XTaSQSCQyHQ2Vso9GoygniZyO7I3MzvCTMJ0wmk8hms7h27Rqq1eoB48doQCgUQrvdVgn3kUjEwUwGg0GkUimk02lHYUwikVDFLpSbN28in88jk8kgFovNPLupRz+CwSAmk4lqhSBbI8i9flG/N0NvdCq47+S+0kNhx5HLY1XmckD8fj+y2SzW1taUEh6NRkrBM8yoJ1LqoZPDcg5M3VGZv0Wvjom7q6urWFtbQzqdRjabPVKCrleECimdTitaX/alkO3bZfKtXt3DSgkqOQIcmTNHOlkvmfaiWJaFGzdu4I//+I8xHA4dXV8J6hke4djyOINUKoVMJqP65vD8p4WFBSQSCeTzeSwtLaHf76tcFZl8OZlMcPfuXXQ6HRXSZdjrtddeU+33CW6o3MkmhUIhT86P3+9X1bfRaBR/+Zd/iXK5jHa7jXK57EgAHwwG2N3dRbFYxOLiIu7evYsbN24o1tKynveLe+utt7CysqKq5YLBIK5fv47V1VVEIhGsrq4qkHX79m0FiGbZueJeJ2DOZrNYXFzEcDhEOp3GZDLB1atXce3aNQXKL/paCgaDuHbtGt566y1lG8bjMRYWFrCysqIckZOCttmd7bmcWvRkNtLArISQp09LkaCHbfzlAXK8hiKrXySVSbDE3Aafz4eVlRWsrKwoxueyiDRsNARSbNt2tO5vNpsqLs4QmLxPLBZTOVP0jmXy8mWTfD6vuqS7JR5z/JlTNRwOVS8uAGg0Gmi1WggEAioPCgBWVlZewjeabWFeJ9mva9euAQDK5TKePHmCTqeDarWKUqmEbrerAMrCwgKWl5eRz+cd52olk0kFhBjmCofDeOONN3D79m2k02lVmu41IZPLc+QSiQQmk4nSIQsLC8jlcmoMddAzLY3kZUggEEA+n8f169fR7XZRrVbVGshkMshms8bvcVS5fNpvLqeWo3iex1mQJkp7LgflOGMuGbL5eD4Xt9yBaePjti7nY/rixLR2jzLe89zP08lFr5Y9q7m0jvNFLcsqAnh6Ju98eeSGbduLp73JfOxPLPPxf7kyH/+XJ/Oxf7kyH/+XK8bxPxbomctc5jKXucxlLnOZVZmHt+Yyl7nMZS5zmculkDnomctc5jKXucxlLpdCzgX0WJb1X1mWZVuWde+I169blpU3PN465vse6/op9/mPlmWtncW9zlssy1qwLOujb352LcvaEv+79l23LOumZVmfujz3v1mW9e9dnjswVpZl/QfLsv4ny7J+ZFnWD073jWZH5mM/W2JZ1vibufnMsqzfWZb1P1iWNXcMTyjz9X8xRazzTy3L+r8ty5p6lpNlWb+wLOu73/xttM2zJOe1of8bAL/65vcsyn8EMJOgx7btsm3bb9m2/RaA/wPAf+L/tm0PDnu9yz3/Z9u2/0F/3LIsP8xj9V8C+HsAPwJwaRTPfOxnTrrfzM0bAP4Iz8fuf9Evsixr3urjCDJf/xdWuM7vAxgA+O9e9gcCAOu5vHBM8sLfwLKsBID/AsBfAPgP4vEffYMg/9qyrC8sy/rPllaTZllW1LKsv7Ms67813Pd/tCzr/7Ms62PLsv7XKe//n77x3H5uWdbiN4+9ZVnWv37z2v/Hsqys2+OWZf3XAL4L4D9/g46jbu81q2JZ1huWZb3/zff72LKsV755ym9Z1v/5zfj9v/zulmX9X9+MC5H//25Z1od4DmodY/XNnL4FoILnm+u//+a5P/zGo/vH/5+9Nw2OLMvOw76X+45MJPZCodC19XRXL9Pr9PQMZ6bZ9AxNi5RFjUekZYUlh23KYTOscFiWw3YoHGGHN4UdtiwxJsK0RJGyrJFpKswhac04hpwmp3uml5qq6upaUIWqAlCJRCL3fc98/gF8FycvXiaAKuzAiahIVObLl+/dd+9ZvvOdc9d/8weGYcyI83/bMIxPDMO4ZxjGn9vvMdkvOR37wymmaSYB/LsA/oN1ZfxXDcP4fcMw/hjADwzD8BuG8Q/Wn901wzD+PGD9PNeP/UNjDT36zDCMv3SgN3eI5HT+H7j8GYCLxpo9/gO+aRjG3zMM468O+qJhGP/R+nz+zDCMv7H+3n9nGMa/L475Lw3D+I/X/95ks9efw5xhGL8N4DMAZ3f/FntlP5CePw/gX5imeQ9AxjCM18RnrwD4GwCeB3AewJfEZwEA3wXwf5qm+b/JExqG8XUAlwC8ibVJ/ZphGF+x+G0/gE/WI7f3sBG1/TaAv2Wa5ksAbg563zTN3wXwCYC/vO4d155kEA65/HUA/8t6RPY6gNj6+5cA/P318csD+It9vp8xTfNV0zT/MTaP1SsAbpim+Qi90d6fAfhfAfyj9fH+PwD8XXHOWaw9338FwLcNwzi6m+MMltOxP6RimuZDAHYA7Gj3KoBvmqb5VQD/OYA/Nk3zTQDvAPg7hmH4Yf08fx5A3DTNl9ej63+xz7dymOV0/h+QGGuI5b+MNVu30+++BuCvAfgCgLcA/DuGYbwC4DsAviUO/RaA72xhsy8B+A3TNK+Yprnnpfn74fT8KoB/uv73P0Vviusj0zRjpml2AVzH2mSj/D8A/qFpmr9tcc6vr/+7BuCnAD6HtYHTpYu1hwAA/xjAlw3DGAIQNk3zvfX3/xGAr/R7f9t3ebTlxwD+M8Mw/hbWehvQsXtkmub19b+vovf5SPlOn/eBNYX///b57IsA/sn637+DNUSQ8s9M0+yapnkfwEOsPePjKKdjf3Tk/zNNM7v+99cB/KeGYVwH8EMAHgAzsH6eNwH8S+uoxM+Yplk4gGs/rHI6//dfvOvz9hMASwD+9yc4x5cB/HPTNCumaZYB/B6AnzFN8xqAMcMwpgzDeBlAzjTNxxhssxdN0/zJ093S9mVPnR7DMIYB/CyA3zQMYwHA3wTwrXXYEQAa4vAOerfFeB/Az4tje04N4L8V+eGLpmlu58GdNiUCYBjGXzA2CIWvm6b5TwD8EoAagD8yDONn1w8d9HykVAb83NcBfP8JLlN/Vsfi2Z2O/dERwzDOY23sk+tvybE2APxFoYNmTNO8Y/U811HuV7Hm/PzXhmH87X28jUMlp/P/UEhNzNtfX+dXtdHrDzwNuvV/AfgmgL+EDad0kM0e9Ax3XfYa6fkmgN8xTfOcaZqzpmmeBfAIwM9s47t/G0AOwN+3+Ox7AP4tY40vBMMwzhgB7tkuAAAgAElEQVSGYbWpim39GgDgXwfwo/UoK2cYBq/hrwB4r9/763+XAAS3cc1HQkzT/Odi8n2yrtwfmqb5d7GGsL30FKdXY7WOnjlM08zon63LB9jgef1lrOWXKf+aYRg2wzAuYC31OfcU13Ro5HTsj4YYa/y/bwP4e6Zp2cH1ewB+nUHZOrQPq+dprFUUVddTMH8Haw7QiZTT+X9oZRHA84ZhuA3DCAN4d4vj/wzAv2oYhm89rfsXsDGG38Ha2H4Taw4QsH2bveey11UIvwrgv9fe+7/X3x8ES1L+QwD/wDCM/8E0zf+Eb5qm+X3DMJ4D8ON1nVMG8G9gIyKjVAC8aRjGf7H+GQmE/ybWcrU+rMGXf22L939r/f0agC8eQ17PtwD8FcMwWgASAP4bAKEnPNdvYWOs/kcAstLiuwB+11gjff76+r9/aBjG3wSQwsZ4A2uw60fr1/HXTdOsP+H1HHY5HfvDI4T9nViLfH8HwP/U59j/CsD/DOBTY63i5BGAPwfr5/kG1jg/XQAtAP/ent7F0ZLT+X8IxDTNx4Zh/DOskYkfYS0NNej4nxqG8VtYGycA+M311BZM07xlGEYQwLJpmivr7/Wz2Z1NJ99jOd2G4lT2TAzD+E2sLYYd5WvXF9MfmGsk8lN5Ajkd+1M5yXI6/0+ln5z2mziVPRPTNP/tg76GkyqnY38qJ1lO5/+p9JNTpOdUTuVUTuVUTuVUToTsCOkZGRkxZ2dn9+hSjqcsLCwgnU5bVaDtSA5q7FutFprNJkzThN1uh91uh2EYsNvtsNn68+A7nQ46nQ663S5arRba7Tbsdju8Xi+cTue+Xf/Vq1fTpmmOPu15Tuf+k8np+B+cHEbdY5omTNNEp9NBsVhEtVpFt9tFs9lEp9OBy+WCz+dTusLv98O6gHdn0ul0UKvVlD6rVqtot9twOp3weDyw2+3w+Xzw+Xy78nvA8Z/73W4X7XZb6fhGo4FutwvDMGCz2dQ48v8ulwsOh0PZj90a537Sb/x35PTMzs7ik08+2b2rOgHy+uuv78p5nmbsJZq33YlG5bSysoLFxUV0Oh1EIhGEQiE4nU4MDQ3B67VuTm2aJorFIvL5PBqNBlZWVpBKpRAKhfDiiy9icnJyzyc8xTCMXWl2dTr3n0xOx//g5DDoHimmaaLVaqHVaiGfz+MHP/gBrl+/jnK5jMXFRRQKBZw9exavvvoqIpEIrly5gtdff72vntmJFAoF3Lx5EysrK4jFYvj444+RzWYxNTWFy5cvIxQK4dVXX8Vrr722a0HZcZ/7tVoNqVQK1WoVKysruH//Pmq1GjweDzweD2w2GxwOh3Ioz507h0gkAo/Hg6GhoT0PfvuN/ymn55jLIIeHSqjT6aDRaCCTyaBWq6FcLiOZTKr3kskkut0u/H4/vF4vHA4HvF4vXC7rPQNN00StVkO1WlUKrlQqwev1YmFhAZFIBMFgEJOTk/D5fBgaGkI0GoXDcTodT+VUjpuk02ksLy+j0WigVCqhVCqhXC7j9u3biMfjaDQaaLVaMAwD5XIZ9+7dg9frRaFQQCqVgsfjgcPhgNPphM1mg9vt3oQYdLtdhSzXajXUajV0u110u10AQLVaxcLCArLZLAqFAvL5POr1OnK5HB49egSv14tut6v01PT0NEZGRmC32+FyuWC32w94FA+flMtl3LlzB6lUCqurq3jw4AHq9TqcTiecTicMw+hBeUZGRhAIBBAKhTA9PQ2fz4dms4larQbTNNXzstvt8Pv98Hg88Pl8OHPmDAKBgDof7Zhpmk8UPJ9amRMsdE5qtRqy2Sxu3ryJZDKJR48e4erVq8jlcqjX6wqCZkpLn3z9zt3tdtVrp9OBw+GAy+WCy+XCM888g69+9auYmJjApUuXEAwGBzo9T4JWncqpnMrBimmaWFxcxA9+8APkcjmsrKxgZWUFrVYL1WoVjUYDhmEoRyadTmNhYQGdTgeBQADhcBgOhwOBQAB+vx9OpxORSASBQKDHIWm1WqjX62i321hdXcXq6qpKvTDVXiwW0Ww2Ybfb4XA4YLPZUKlUEIvFYLPZMDc3hw8++ADRaBQ/93M/h1deeQVutxvhcPjU6bGQTCaDH/3oR5ibm0M6ncbjx49Rr69V91Nf0xEl6mOz2TA5OYnPf/7ziEQiyrElBaLT6cDpdCqnc3JyEj/7sz+L2dlZdQ6en/+2skW6nDo9J1A6nY6aYNVqFdVqFaVSSaE6iUQCi4uLyGazKlf7NIR36fHTabLb7VhdXYXD4cDo6Chqtdomh2or3tCp7J/0UzCcF6eO6O4LDQaAHg7EURlrcneq1SoymQzS6TRWV1exsrKCdrut5pTT6YTL5YJhGGi32yiVSgoBKJfLsNvtCIVC8Pv9cLvdymFyOByKj0OeTrPZRCKRQDweVzqO19FsNtFqteB2uxEKhWC329X7vF6iToVCAdVqVX33VDZLq9VCLpdDKpVCNptFJpNR3Cw+WzqeUjqdDsbHx9FoNJDL5bC6uopGo4FOp6Oej81mU5yrSqWCZrOpUmVPO/9PnZ4TIpworVYLt27dwmeffaYmXblcRqlUwuPHj1EsFpHNZlGv12EYBpxOp4py5OIn1MxjbDabytczuqJBpIfOhQAAxWIR169fx6NHj/DgwQPcuXMHXq8XoVBIKbhnn30W09PT6vpPKw03ZD+Rr1arhWKxiEajAY/Hg1AoBIfDgUajgVptrU8n8/insjtSr9cxNzeHx48fw+/34+zZswgGg/B4PArlkNGuJI4eBqnX6yqFtLq6inw+rxyYaDSqyK8kvpID4vV6EYlE0G631bkMw1Ak2VqtBsMwUK/X4Xa7EYlE4HK5UK1Wkcvl0Gw2le6y2+0qzQJsOI4MwCh+v78HRWg0Grh37x6azSZGR0fx5ptvwufz7e8AHhIZFNR0u100Gg1FYyDdQaL8brdbHUsHqNFoYHFxEclkEtVqFYVCocc5dTqd6HQ6WF1dRTqdxsjICJLJJMbHx3H58mX4/f4eZ3SnjtCp03MMZZBBbLVauHbtGr7zne8oB6dQKKjJyIlE5cBcOgA1aamkCC17vV7Y7XbU63WUSqWeii0A6hymaapKsEKhoMh5rNJwu904e/YspqenMTY2pvK5eoR70lNd0nmk7OU4NJtNpFIpFItFRCIR+Hw+OBwOxYkwTRORSARut/tEPo+9kGq1imvXruEnP/kJRkdH8cUvfhHT09MIhUIK3WAkDWATAnfQUqvVEI/HUSqVsLKyooIrl8uF0dHRHlQFQI9j4na7Fc+Q3Jx6vY5KpQKHw4FOp4Nyuax0hmEYqFQqSKVSCs2R56KeCgQCcDqdaLfbKJfL6Ha7cLlc8Hg8MAwDzWYTjUYD9Xodt2/fxsOHD3H+/HlcuHABk5OTBzaWh1VYEVepVNBut+FyuWCz2SwdEj7DdruNer2OR48eAUBPoCyR/WQyCdM0MT4+Do/Hg4WFBVy5cgXT09PK6aFjtdNswKnTc0KECqRcLiObzSKbzaJUKqFQKKBcLvdAkYyQKDI6kiWJVsJJz1QW3yNSw3PISI8TuFarIRAIqJLVXC6HfD6vykpPic698qREvkEiUTo+m2q1ikqlgnK5DIfDgVwuB7fbjWKxiFwuB8Mw4PP5VO7+MBnfoyByrOv1OhqNhkoXZDIZ2Gw25PN5xXvT0wU8x2ESGjmmnKxQX5m+k2k8zmu2uQB6nTqZnm+32+qfNLbUP2yzQT6JTK9TH/E3eBywZozJeaRhlseeFBmE9HC8+SyAtXHvdDqbjpc2ROd7SlSPwufJ52Dl1D/pnD+1IsdQrCbHvXv38P777yOdTuPq1atYWVlRysjj8aDb7SplJHk19NL1c0sOTrVaVb8jJzOPZR8OqYikApHKjFB4MBiE1+vF0tISxsbG8NZbb2Fqasry/k5ld6Tb7SpHkyktVuGtrKygUqn0PGNGxR6PB1/+8pcRDod70p4nSbbrgMq1xKCgVquhVCqhXq/j008/xc2bN5HP5/Hpp5/i0aNHGB4ehs1mw8TEBJ599lmMj4+rMu7DyvOpVquIxWJIJpMoFAqKhCwDIOl8AFABFx0PcjikE070me+Te0MCLYM1coTcbrd6j/OVSClRCF4LuUW8HhrdZDKp0ozDw8P72mfsIEU6FXJ+c/03m02USiXk8/me+Ser5ui8AFDPjv9sNpviTTFgksF0u91GKBTC888/jxdffBFTU1M97QuedM6fOj0nQFhB8b3vfQ+JRAKJRAKpVAoA4PP54PF4ekoGdaKq5PJQSTC64jEA1GSWRGQAPUqLEKiMmPi7nU4H6XQa9XpdoT2xWAyXLl3C5cuXldNzKr2yWwbPNE2Uy2WsrKygXq8jmUwim82i0WggnU4rwnsikVBGBgCCwSDOnTuHz3/+8z28iVPZLJIQTmk0Gsjn8ygWi/j444/x3e9+V6VrCoWCqmCKx+NwOp14++23D/AOtieNRgPJZBLxeFw5xi6XS6HJdHBoQCXqoqMyOhoknZJSqdSDQNNhov5xuVyKI1Iul9FqtRSSraPVrCyVqTeSdROJBKLRqOpTdlLECumhLiehnAgwx5TPl7aj0+nAZrPB6XSq46jfASgOFp+fdEp9Ph+eeeYZvPjiiyqdSTl1ek5lk3Q6HdTrdRW1F4tFtfC54KXzIZVJvwklFQyjNCqj7UxCKz6KFCo9khXz+TwKhYK6dpa9n/Sqrq0ifEa1AJSykZ1vqbDa7bYyMJ1OB8vLy4jFYmg2m4p82m63VYWLy+XC8PAwOp2OelY+nw/BYLCn+u4kiVw30kgMguP5LLrdrirTLhaLSKfTPVwXdiiORqOYmJjYVD59WMd6qy69QO8YyUIFXafIlBTPrRtjiRjpolceykrSrdJVTLtXKhX4/X7L1OJxFn1MmbaUXa3p1Ojf05EiiegxBSadXjkHGEC73W71jBhs67pvp2vg1Ok5xsJOp8ViEffv38fS0hLS6TRsNptq9iQNXr9JRZEcHdkvga/9DJ58TxIv5Tmp1LxeL7xeLwzDQCaTQaFQAADcu3dP9cw4c+bMia4U2mqxd7td1XYAAEZGRjA0NIRGo4HV1VWUy2Wsrq7i7t27KBQKyrACQCKRwOrqKkzTVFGyy+VCIBCA2+3G5OQkpqenVaVdOByG2+3G1NSUKjU9rIb4aaWfw25lrMkl0Y/hfGcD0Gq1iqtXr+L73/8+8vk8VldXUalU0O12EQgEEAgEMDs7i6997Wt47rnnMDY2hmAwCGDDIeDfh0na7bbigUnHRXI+9K0KaPToJEmSKo0gsIEc68JzseEqj5XPhOfiXNXnq0zNkNycSCQUt+TcuXN7MVyHUuR8p35vtVpIJBLI5XKIxWKqvQBTiXqxie740L7I7SvIC2LK0m63IxwOq0o+VgZLMrvL5VK/J52l7ayDU6fnGAs7KmezWayurqpKrVAopBwLOSmpKAYpFL4SjennzVOkAtEXEV/p8PCVqESlUkG9Xkc4HEYymUQymYRhGJiYmNitITp0shvkZML+8Xi8Z3xrtRoSiQSy2SyWlpbw4x//GOl0WiE1AJBKpZRjPDw83NMfhe3jL168iHA4jJGREYyPjx8rgrmcv/2egz7HOa+l0yMjX6tztttthWA+fPgQn3zyCTKZTE8DUK/XC7fbjWg0iosXL+KFF15Q7/Fch83ZoRDJqtfrPU6GjvTQ0Ml7kXqC35FVpLrekefj9624hZIPJCuFBqHG7BtkGAZGRkZOTM+efg5+p9NBqVRCOp1GoVBAvV7vQd70c1idk86O3W7v6aVEh4hVd9JO0SlihTHtBYnRO5Hjo61OZZOUSiXMzc0hHo8rA0iCn15SyMkmmfS6ASbZTH6+lTAdZiVWClsuDJIL6/U65ufn0Ww2cfnyZUxOTqr+QXo0fViNwF6JTFkBGxysfD6PXC6nOmEz3UWeSLPZRDQahcvlQigUwvDwMOx2O6anp1VajCRQdsRldEWDwZJVGqTj4PxsZ/70QzP7OUzS4WfFYiaTwe3bt5FMJrG0tNSTcgbWUpITExMYHR3F7OwswuEwPB5PT9+Zwyw0YjRuwIaTw1QUt57gXJLfBXpRF6mbrNJb+m/3C7AoTOvqKIFMtwAbFAGuoZ0a2KMqMhXI/wNrY1+tVtVmseRNcTNROrFE2DieMj1JHSU/BzYyCC6XC0NDQxgbG0MoFEKtVkMmk1FbhVDX+P1+db070f1HX0udYNEjIl3i8Tj+8A//UDUiBKDY741GQzk8bCpFAymVjf57so8PZbv8Gn1S6iku3g+VGxdSqVTC97//fdjtdnz1q1/FhQsXVH8N7oosHaXDHAHvtpBoyaoUloDGYjE8fPgQrVYL8XgcHo8H7XYbxWIR9XodLpcL58+fh8PhwNjYGKanp+FyuRAOhxEKhdDpdJDNZlEul1UJdb1ex9DQkHKc2dySHXOJFh1X2WpekXzJY/Xvsjsxm9/93u/9Hubn51Gv11Gv13s4ch6PBy+//DJeeeUVjI6OYmZmBqFQaFOFy2ET6Sw0Gg00Go0ecjENo9wrS/++RMz4yo68UgcB1oiz7AisO1NMmdHpkRxCKfL3C4UCarUazpw5Y1lefVxFTz8Ca85iJpNBLBZDJpPpCYiIyrDfkXy2si0Au2Dr5ercs8vn8+Hs2bO4ePEifD4fcrkcWq0WQqEQJiYmVJ+qoaGhTUH4aXrrBIl84FQALBtdWFiA2+2Gz+dTcCCdCwkZ9yNf9oumpPc/SBHzM70yTH5mJYwOms0mstmsQnrYAZQND3US5FF2eHZ67VQg0ulptVqKfCkND5EZKhByRsbGxjA5OQmv14vx8XGMjY2h3W4jHo8jk8moNvCmaaoKDUbndJ5paI7y2D+tWK0dKZLcWygU8PjxYzx8+BBOp1PxE/gdu92OSCSCqakpDA8Pq5LvoyB6eklveaGnlfo5HDpaI5EBzjXqHnmc7jjpyI/kDMnr4DyW10VESr6eJLF6No1GQ+kWBs10WPgdOpxW3E1+Jp1emW1gccTQ0BAcDgdarRZKpZJqgMt015M+i1On5wiLDqEDQKVSwf3795FOp3Hjxg0V1XNiMkKnsbLqwSORFyuYfrdELgT5O/xdKkhZwtpsNpFOpxEKhTAyMqIcOauxOG5ChU+SKDucEnZnOtBms2FkZAQXLlxQm8myUzaVVDgcxqVLl9TmjcFgUKWzqHx8Pp/Ks/N9lrKzyy3Lf91uN4aGhg56iPZF+AyotGVjPH2nb72RHpv1xWIxdDodlTJkV2Cej/yp0dFRBIPBI+HwyK671Wq1p1UF74trGdggFFsRj61SUzKFa5UCoxDNoXHUP7NK21C/cJ5LPddsNi3Ri5MoTJ2vrKyodhYAlB4gCdntdisHiWlMbioLrJWpc/77/X71rNhRm0Rnp9OJ8fFxtTv7+Pg43G63qhal7ETnnzo9R1z0h53P5/Hee+/hxo0bSCQSqFarqkcGCZAy+rdqctZvAskI6UkcC4n46A6P7vQAvRUbTME1m00sLy8rpTY6Omo5DsdRaDjq9Tri8Tjy+bx6hlQ6HKfp6Wn4fD6Uy2X89Kc/RTweV1V7LpcL4+PjeOWVVzA1NYVWq6VK0ml8bTYbgsGgag/AlBY3qAU22gtQCU1OTh7p57AdEjOwYdwbjYZ6HlTY/DudTiOZTKrWAETiqMzp9Ph8PrUNi2EYCgnyer0YGRlRVXGyP8lhFfbCIcpIZIS8MxpFSVZlikkiQ3QegY20iJVjw/et+FREf6wCNcnhkXpP36sL2KhC43M86U4PmzUuLCyo1DewptsZNBGVZPPIcrkMYGO86Qy3Wi34/X6MjY3BMIyedDoDBbfbjZmZGUxMTMDv96u91iSfc6c659TpOWbCqpBkMolisajgWh0NkRC0Tubr59TIHj1PI0/DSSCyobe3B46/4yONAseB0RCViSR7AhsGgIgZUQW/36/SW7JLrZwnso0/32ePH84R8iNIpj7qfCqruS/5IRxjwvvtdhu1Wq1nXrbbbeTzeeTzeTSbTeUISIPJjVrl+MqSXTr8RGgPM4+HwjFiUz+rz6XwnjnveP+UQXNJR2p0HSaRYqvf5Wd0drhGrMa5X2rtOEu/e6QDW6vVepAvmUKUc1oiezKNKFF8WVHH46mrSMvw+/3wer27sh3RqdNzzIREs+XlZdWYEOiFh62qHwYtYqv8925EPIMUGn9TRn7kpKRSKdjtdkxMTByryGuQAycNCh0b2YofgOqO2mw2sbS0hKWlJZimiWg0ihdeeAE+nw9TU1MqNUjiMdEicnb4e9yvrdlswm63w+Px9EDP2WwWqVRKlVVPTEzA7Xarnj5HTWTahOMvx+DBgwdYXFxEq9Xq4TnxO+VyGYVCQSFn7Fqey+VQLBZVWtHr9aJSqcDtdvdwdWQTP7lRY7+S4EH3cRBCBIxjA6DHYdZT0F6vFzabracMmQZQfw46/4OvWyHUDodjU4pLji8JuNIB4phTX8pKUc4HOqTHUawoD/KzarWKfD6v0E6Sktmvh2MqGxkahqEI4y6XS7UdIdew2+1ieHgYIyMjiEQi+MIXvoDXX38dgUAAU1NTKsW7G87/qdNzzKTVaiGbzWJlZUURJ4HNOXEp/aIlq2P4/aeZfIP4N/L/XHx0eAgxp1IpGIaBYrF4bJ0eYPPYWDk93P+m2Wyq8udyuYxbt27hxo0b8Hq9+Na3voW3335b9dgZHR3t6UYr04gUGdGxcoYNIWmMM5kM7t69C4fDgXPnzuHChQvw+/09qdSjIjrZldJoNFQ3808++QQffPCBKv9nmTnTNMViUaW0GJ1ya5VsNgu/34/Z2VlEo1E0Gg24XC4Eg0GV9mJ0LauJdurwyPvZb9FJrgB6+HhSbDYbPB6PiuZZTcWUhlzXTIXQeNJhAXpRHStHUTo9Et3hfGeTTR3FlFwVt9utEAw6PUepfcBORF8DVk5PuVxGPp9XqV1ZINHtdnsazDKVJfdTI1/N5XKhUqmoPf1GRkYwOjqKsbExvP766/jyl7/c46Dulpw6PcdQ+inw7YpVjlye+2kIzdtBluTnetRBkmSpVDpRfTOkyKiW0RS5JOyb4ff7EY1G4ff7EQ6HEQwGFUS83QiVHCJJLiWpGYBl3x69KuOoyCDUUcLtXq9XcafYg0Z292VvGjZ8ZMqPJE7DMJQjKR1Y/j45VESAZBO9oyCy2ZzeBJDOC1tjkD9Gh4XjQ/K8jrzRSbcqpZaIEkW2sOAxfJUpLdklWN8gk8LrYSpXVo6eBOG6lnwbonPA5j0aZeWW3INR0if4OR1NOWe4xvZifI+c07NVNAz08hm2GrR+yMZWIo3AYVJKVA70jvstYiuRE5ivOoRs5WToTpHV31s5O/IaGRUwYuN7pmmiWq1ibm4OwWAQFy5cOFZ9MwYhYABUdYlUKLVaDfF4XO1kfeHCBTidTrzyyisA1hoMnj9/HmfOnFHO0HbENE2F9DB/32g04HQ6MTo6quY9FdPIyMimLrxHzSBYrWOPx6OckDfeeANTU1PodrsKwZDoAVsFMLJ1Op0K/SAS98d//Me4d++eep9kZhLMx8bGMDY2hmg0qlKRkvvST/S1dhBjz+dPXpPX61WOHx2hYrGIUqkEt9uNZ555RqWW6EzKPeGY3gY2dBPH26oQgvcs23Iw9SKfk9PpVOR/rh/JcWTlIhEeGvRms6n6Upmmue21dJRE8qBkYJXNZpHP5xGLxZDL5VAul+F0OtW85brnc6YDxF5rdHhZyUXifrVaRS6Xg2maaqscIkZ7JUfO6QF6DaTubW8Fzw06506O1eHXw6Tg6Skzr07p1xdDj6ZkzxWpTPrdJxeKXkkx6FgpMkKQCk0+R75fr9exvLwMt9uNTCZzrNrCW81n+ZkkFvNYlvBnMhmcO3dObUo5NTWFs2fP9nTc3okwqqWzQ2SCpelEe2iMQqGQWheyGuwoidUYyYZply9fxszMDGw2G3w+3444HaZp4u7du/jwww+RTqcVIZrPm7t3Dw8P49y5cxgeHkY0GlVpgn5diK0Q3YNyemjYZDmy1+vtqWorFotIJBLweDwYHR1VOsPtdsPlcimeCIBNaSn+bYV+SaeG48EiDt0xIjrncDhQLpdV8zt+TlSPjVpJESApnRVK3W53yw1Lj6LoASpTWtyiplQqqWaaRI+JfjHlRfsonX+mwGQDXKZD5eenTo+FSMPQz0D0Mx4sq2TfhWKxiFarheHhYUxMTGy7H8Z20zT7JYTKSRyTFTbABg+DIh0ZvlpFSvw/AEsHQyokK8dnkAwizMlqGR53mBC1vZJB80lWYXm9XpVKYXn65OSkSmXJzfiedI7y/ESW9H2LvF6v6s3jcrl6nB4aruMksqTZqhpy0Dhz/LxeLwKBgFL2kp/FrRYqlYoijcvv6+tZd4L4/4NKLXLNMk0kUyLlclkRwNnUrl6vo1qtKgRTcnL06q/tBGBshSEdRBKSZRWX7BAt/3Gu2+12VKtVlcKS6AX5PEfRqd+J6HO7Uqkgk8kgl8uptS17JNGZ1B10pi3Zo4mOov48DcOAx+NR+qtcLiMWi6kgazc5gkfO6bEy0lI4kP0+r1aruHv3LlZXV7GysoJr166hUCjga1/7Gr71rW8hEolseQ165HHQwgqeYrGI1dVVFAoFVKtVpWg5JrLkkvA8eRsSNpYOko4UyG0orMagX3WX7rD0c3ikUtONp15aehjGfj+Fz4x7z4yMjODMmTOYnZ1Fq9WCx+NBMBhUxpkOo75H2XaEXKFisai4QiwZZd8n9pLh8TQO5XJZdU49LmIYhoLqgd7AQDofgxxzl8uFyclJXLx4EZlMBo8ePerh/LhcLpTLZSwuLqodyuXv6+Opv8drYYpgv1FQWb3F7Ta4VQmrSUulEorFIrxeLxKJhOq+y+0FarWa4qjpm4zq9yrf5/9lWoxopWwQKY00nXimsYrFIrLZrPaWPrcAACAASURBVHI+S6WSQvkcDgeq1SqSySQajYbaquUoNI18WmF39uvXryOVSql0lEz9kZgOQPEK+bfceoTEf7lfF7C2nsbHx/HCCy/A4/FgaWkJ8XgcY2NjeOuttzA1NbVr93PknJ7tSj+F22q11N4hDx48UHDz1NTUjqLTw6TQqejK5bKKpFhxwwhKL32VyoJGTTo4dIh4LEWiQfr7OlokZSv0RzpQXExEGnTn7WkRjKMqjKbo1FDGxsY2HStJmXqOfjsiSZs8h2EYyuHi3GIExrlH7sNx4lpR9NSrDMC2I3a7HcFgEJFIRFUi6UiP5E7tFC3j2pFVSPspcs4QeSY6whJn8sOIHhSLRfVdOvU8B+ebjqpbOZpyvy3qOjri/JvXJKsQJQGalZA0zHRGWSpNvhHL7Pd7fA9K6BCurKyoff504VhSZE8eOb6y75ROSGejQpvNhng8rvb7e+mll3b1fo6c02NlgKXUajUUCgW1cMg2L5VKqurn/v37SKVSSKVSANby9vl8Hjdv3kQikcD4+DjGx8cHRm1cYIVCAdlsFgAwPDyMcDh8YPl0/Z/+OY1gPwNIT1xGSla/QyRId1T4uTwfsBEVW52L35ev+mdycTCaJBJ0KpulnzLeKW+NnB5gs8GRvyHTGhLmPu5ilXKSos9vp9OJkZERTE9PK86L/qyor/x+v2r42K9pHrlT7JJLjhvXLhsg7pc4nU7VXXd4eBhutxvVahUOh0M5PaFQCKFQSKX6WLLearVUE0cAm1Igg+a03opD6gx9qx3qjWKxiGazCb/fjxdffBHdbhcPHz5U/arYeoFtHiKRCIaGhjAxMaEQzuPI57EScnoymQwKhYKau2yZoQsDLmCjJ5IMhux2u9LdMnMwNDSEM2fOKMeXa+v+/fvIZrOIRCI4e/asclqfVI6k0zNIoeZyOczNzaFSqSCbzSKTyaBWq2F+fh7Ly8sKPZA5Z5/Ph+XlZfz+7/8+hoaG8NWvfhXRaLQvUVHyTZaWlnD9+nWYpolXX30VoVDoQBaDjHBk1EOhUZIVUXIsJRdHd1zkMfwtmQKTClkeI9/bio+jn4O/K7t38j7YJ0XvcnuSRI929WenOyVWJNitzk/eCVNqOl9CRtYyOj9Jqcd+KS2OC7CBinq9Xly4cEHB+1evXu0JMDqdjgrMyCsh8dNK77HypVwu4/3338dHH33UY7DZtHK/xOfz4dKlS5idnUW1WkUmk0G9Xkc4HEahUECj0cCbb76JN954A51OB/Pz84jH4woNYr8pYIMAK8vYt5rv8nNWWEmjSnSmWq1idXUVDocDr732Gt599114vV7cuHEDN27cUPql2WzizJkz+PrXv47z58/3VH0RATouIoNhfZ6x19T9+/dVuwWfzwcAqrqTSJpMeQHoCYDkM2JzTiLJdrsdk5OTePnllxWy73A4UKlU8Kd/+qcolUp4+eWX8Uu/9Esnz+mhWEWbjG7y+TyKxaLa/6ZSqWBhYQELCwsAoMpsZfv3arWK5eVllXMeBF1SUXU6HVQqFaTTabWYDkqk08P/6/cgu4wC1sTgfpG81XHSGZLpsCeVrVJgMh3HhTUIuTqpstXz2q5IIjnXi0wXWDlAwJM31DtK8iTzzW63IxAIYHh4GIFAwDI4kg0oGZhJvp10krjtRblcxurqqtJvw8PDarPY/RSW9gMbpf71el3dLzvxXrhwAa1WC7lcDvl8XjX8I8oFbCYuM4Wli5WTL5E3mUaRxpecIfKsAoEAVldXEY/HFZetVqupTS6np6f3pFHeYRE5jlaIJbla3HpCUiYY9Oh6GYBCduQ59dQjf9PlcqkNpH0+H3w+H6rVKtLpNBKJhNon8GnlSDk9XOjkrGQyGVWJxSghFovh9u3bqhU6ITVubsbqFxI92ceB3y8UCigUCgMNeKVSwerqKmq1GpaWlpBIJGCz2VCtVg9E2RN+TKfTyOVyyvPW70FydGQlEMlkulIZtLityMvbcXx0p8w0N7o76+k5Lia5F5Fpmgp1YLk288GMPk6K6GmVrV53Ihx/kskLhYIyYNVqFT6fD9FoFB6Pp+f5SKL1SRd93Jn+8Xg8iMfjCAaDyjmgMiePxWZb6/Acj8cVwuBwOJBIJHDv3j2USiXUajWVErp16xbS6TSCwSBmZmZw8eJF/PCHPzyAu14TEpRdLhcuXryId955B51OB5cvX1b9WMLhMIaHh1EsFpFKpVAsFpVe2olzoTs+Ep2WnJJarbYphc5eUz6fT7UjYNl9o9FANBpFNBo99sUTeqoWgKquy+VyqlCm3W6rNg5Ar00husb1z1Qit7Hh99jMU+qnVquFGzdu4Dvf+Y5qqDoxMYFms4lsNovFxUXlLD+tHCnNRDSFO7feuXMHKysrKJVKiMViKJfLiMfjmJ+fR61WQyAQ6KlmCQQCPZv4sXcE88lU5tlsdqDzUiwWcf/+fRQKBczPzyMWi8HpdKJcLh+I08PqrUQigVQqpfpkDDI8UrlwgjL9JSek3vvCytnh+1QiW5WtW6EDUiTcrxM9TdNUXWtrtRoSiQTa7bbKtR9XpaRLv/uUyp7ypAgc10ahUMDdu3eRTqcxPj6uImBgjUStd1CVSvGki0QYXC4XpqamMDExgXg8jlAo1OP08DlxfmezWSwsLMDlciEUCsHlcuHatWv43d/9XcTj8R60rVAooFKpwG6349lnn8U777yD3/iN3ziw+3Y4HAgEAgAAv9+PixcvqrXrcrlQrVYRjUZVhVqj0UA+n4fb7VY628oQ66IHSlJkmoR/S7SHiAU34L18+TLOnz/fcz6msk4Kf0cKUZZMJoNMJoN8Pg/TNBEOh5Xul2lH2hSPx6O4UUTyTNNUmx3TKZJ2ptFo4Cc/+QkePnyI8fFx/Oqv/iqef/55lbGZm5vDs88+uyutAg6900NjzH/FYlHlsXO5HLLZLEqlEnK5HEqlEkqlkkJ/2OqcHVTp9VMpywHUoVD9GmRapVKpIJ/PKyY7HQdgoyHWfkcFsixTVwD6dWxVbk9FY9UkUIrk6mzl7OlOkJWissrT66Xt8prprJ6EvhlPI1s5oFvNU859FgZww0WJTvBZ6amEkypW90/DC2x0qqUhZqBAJ0aSR7k3lcvlUgYonU73nJvPgq0F2Mn5IIW/T7KyFDrK5MbIe9fnj64HdiIyTUadpvMO+T4dspMq+ph3Oh3VPoBOuc6rokibQvunj7MsX7eyB9xX0W63o1KpqDQvMzZMsVUqFRVkPYnsutNjpUTlZN7uQiS0TrLw/Pw8qtUqFhcXsby8rPo/sEyWFT02mw1jY2M9+7tQuKkdt6sHgFKphHa7jampKUSjUYRCIczMzChUgdUR1WpVoUr37t3De++9h2w2i2eeeQZvvfUWgsEghoeHkUwm4Xa7EQqF4PV6d29gtyH9HAmJ6sgtDPqdQ8LLkpSmHwegh3OwUy4HnUgJQTPPqztwhE8lGTuRSODatWuKdD45OXkiI7KtRFdmVn/rc0Lm2FutloL4XS6XWndnzpxRqeJms4l8Pq+U0UlLNVK2O//ZzZZcFyp4pgMajQY++OAD3L59uwdtYNUpK+W49qhvpqenEYlE+nKG9lP68W24loPBIEZHR1WPKekESkSGrzKVMih1Kx1wCnUauwZzu5B+pf39nuNxdeat7qtQKGBxcRGZTAbFYrHHeZQNB/VgtdvtKtoI5zS7XAcCgR7eD58T6QqmaSKTyeDmzZtwu91YXFxEPp8HACQSCfzwhz/E/Pw8Ll68iCtXrjxR08JddXqsyFCSeLeTyIMD12q1sLS0hA8++AC5XA53797F/fv30e12Ve6bbcEJCw8PD6vflvwQOfgk+tHzHBsbw4svvohwOIzJyUlFGqSHmc/ncf/+fSSTSXz22WfK6RkdHcVrr72GSCSiegAxstlvp4dipWxk2et2InE6Ibp3b8Xbkc94O6JHtLpDLGF7PbKgY2SaJlZXV/HZZ58hGo0qWPpUrEWSzrd7PDcU9fv9iEQiqo8Mq4Kq1aqK3prNJorFItxuNyKRyInk9FjNv37rjCXbcpf1brerOIudTgexWEw1eeR5nE6n4lHReTCMtXLf0dFR1ZWbhNCDEh2xlSkQrvVAIIBut4tqtao2wrXas03qCivdZYXMW31Op4pkcRrlQfegy3F1enQxzbUmo8vLy2rrCenwkA/KgFjqfwbCzM5Qn3PvP7k/mwyaWTVnmibm5ubQbrdVmTwApFIp/PjHP0Y0GkWn08GlS5cO3ukBdl4lItNXJBYTuSkUCmg2m1hZWUE+n1d7flCkIeS59ImqG2xgo78FHxgNOeGzdDqNhYUFxRshwZkpNACYmJhAIBDA6OgofD6fQpTo3B3E4tgqtaBDvfyOlK0chyeFmaXSk0RDq9/Tuz4PSsMxkjippes7lUGpLmCjYqvZbKr8vGmairTM5myM0LjPjoS/5Y7ZJ0V2aiA9Hg8mJydV9VUul+uphDFNU1WwyN+wcgiAtXRZIBBQzsNRGHuiL/oO6fLa9fQ2MFhHWel/3QY8afr1KIzpbolprlVs5XI55HI5dDqdHgdD0ilknyTaP6vAm04nHaF+hTOkkORyOVVJTd4pU1tshsruzzsJsvYkHJOLk9FIP2eo3W4jmUwqns7jx48Vm//hw4col8sol8vI5/MKAmbre0lu5e8SIpPogUzXdLtdxUY3zQ1iXaVSwZ07d2C323H79u2eHXTlwrPZbBgdHcWv/dqvIRgM4syZMxgfH1f5adkJeb+E5FHZ7ZIiYWG54HVFIxGxfimQnTq0/a6VfBCJ6PC8soxR3+uJ1yIjDNle/hTp2Vr6cU2AtefLlg2tVgtOpxMTExOw2Wz43Oc+10OurdfrCAQCWFpags1mQz6fV8aae3KdJCMB7MwozszM4Fd+5VdQKBTw8ccf43vf+x6y2azaD5C7ul+6dEk5951OB4lEArdv30a5XO5xMiORCC5cuICxsTFFID5IoZ7h2paOB1/lxp46J4RrXEeJrIwpRT8W2KBJcJxcLpfaX2srfbYVonTUxSq9LbMiy8vL+Pjjj1WwPzY2pjg2xWJROeg6Asemm/L8HHufz6ccHlZW0w5IcvPjx4+RTqfVHA+HwwCAeDyOXC6H6elpzM3NIRKJYGJiAqOjo9t+Prvm9FihLJRBnnW320WhUEAymUQ2m8WtW7eQTCYRi8Vw48YNBZmzZJMVWdKhYedYDiAdH+l4SGizVCr1wG0OhwONRgPZbBbdbhfFYlGVn3MbB6/Xq9Cdc+fO4d1331WdVSU3RRrq/RQqin6/KxEe6fD043sMkp06F1YcI4qVsyLToVY8MN4DsIFMnDo825dByBkRViqhoaEheL1ejI2Nwe/3q/YM5XIZnU4HmUxGBRt0RLlf0UmSnRrEaDSKL37xi6ohIcvLiXJ7vV7Mzs7irbfeUs+l0+ngzp07ePTokaoUZcre5/NhbGwM0Wh0VzdnfFrpx73hGpY8Hr7PNa9z+qwQ/UEoP9+XOkYWtFjpFnl9uxHkHXaxGkc6JblcDg8fPkQ+n4fP58PQ0BBarRbS6bRCeGVfJUnK15F3mS7nd+n0SEeVm7xms1m0Wi24XC5EIhEFQrAHH/fObDQaCAQCGBkZ2X+nRw5avx9nt1HuJFypVFCv1/H48WPVRDCZTKJYLKLRaMDlcqkNDlnbD0DtxyQXBx2efhOfwoElPC/z6RT+lsPhwOTkpFIk0WgUXq8Xo6Ojqqy9VCohn8/3cFA8Hg/Onj2LaDS6L8aYE/Tx48dYXV1VKcB+ziYdBYmi6Ez73Ra9FF4XGcHp/YSogGRrcwm/Ms1yKk8m3AOJnLRsNgvDMBAKhVRkxpJotqE3DEORTwGoKg/JrTuV/pLP57GwsIBSqaQ6yJumiTNnzuDSpUsIh8O4ePEixsbGepxKbtIp14bD4UAwGMTIyAgikcihcnoo/ZwUq4og6ZDQ8ZFOka7nBxk7/o5uAzh3j0oqcC9F3n+tVkMmk1Hl6mwa6Xa71bMgQsdCI+prFgrJdimDaAfboWFQ1+toElscAEA6nYbf71fc3q3m/66mtwYhOqa5Rjy9deuW2v9qfn5eVWGRKyMnezAYVL11mNMjCcrq/PLVCsGQ0Kk0pPLVMAyEw2F4PB5EIhF84xvfwKuvvqqY/w6HA9lsVimqhw8f4vbt22i1WvD5fPB6vYhGo/j617+OK1eu7EtX1Gazibm5OfzJn/yJKumXioPpJI4riXwSFZKTV0ZXuyWcuBx7ub0EIwvDMHp2h7ZqgkVEzTRN1UgyEokglUqd8nr6iFW0LD9bWVnBp59+qgIRtgF4/vnnEQ6HUa/XEY/HVU8VonCElm22tUZ6bC1/GCqHDkKkgR2EIgDA/fv38e1vfxv3799HsVhEJpOB0+nEz//8z+MXf/EXEQwGVTUpsIF+1mo1BINBpNNphfI4HA5MTU3hhRdeQCAQwNDQ0H7d8rZEjosMfPT0tkSi5THye9TlkhcoRQ+upF7j86DDYxgGAoGA5XOS9uS4OkVW+iCVSuGjjz5COp3Gp59+ilQqpVpU0Jnwer1qQ1y5KTTPSYdnUAAt14cVcieJ+rTPks6Sy+Vw//59BINB1UDR7/fj0qVLGB8fH3jf+1ZiQRLS6uqqumDuc0KCMku9WZ3AMk3p8bGiy0qsJqc+aTmQLH9mKkxHeliCfvHiRbz66qs955ibm8ONGzeQTCZx7949XL9+Hc1mE8FgEKFQCJOTk6pJ4H4Y4k6ng3w+j+XlZVSrVTQaDYXgWAmdDDmZ9lOkgtMjP6tUGJ+Prrzq9XpPc7PTFNdgsVLgXJd0aiRiQzJzt9tFPp9HPp9XeXm2gxgaGurZHJK8suNqKKT0G8/tfJ7L5XDz5k1cv35d8Qq54eJbb73VNz0YDod7AgcGEn6/XyHRhxnpsXpfrms6LNTPOk9HL3XW0zIUOkgSJeA5ACjbclLm6naFDV8TiYTa5Vxyb3T7KZ1LipXNG8SP0tE+YKO5aj/HiLyiTqfT0/ZhO/3ansjpaTabqNVqKh9NUh23h5A3IvvtxGIxzM/Po1KpqE313G43DMNQnB3Zoto0e3vEWE1sSWTWxcqwcrHIDdH0AWf+vNVq4YMPPlAlc7ynRCKB+fl5FItFlMtl1W6dBK5ms4lkMokHDx6oXar3UkxzbXuOcrms9kahApEQMlEnmUfnou8HNe/V9VJkihHo3WxUil4SCWz0OiG0eaq8+gvTApzb5XIZiUQC1WoVjx8/Rjab7dm+pNVqIZvNIhaLqdSKrJIgn4TNOQftBn6YZKvo82lFohH6+crlMu7du4dkMolbt27BbrerfZ0uXbqESCSCz33uc5aVKFbpIBZMsHqUG5ke9mcAbBSdVKtVFXjKNhmyKghAT1UbgB6HB9jcTkMvPukn5FDVarVN26ccZ5Snn1SrVcRiMcRiMeRyOZVhYWsBicQZhqHSSdyuhvOUfgALa1jdLJ0nmY3QHSX5/EnFcDqdKn17/vx5vPLKK2ovO7Zp2A6X8ImcnlqthmQyqWDvWCymiMB0gCSpuFwuo9VqoVar9bC+GZGwe6gsfQU2Jq7k68hyZlmpJJWBbrilA6WXTHOSy4dbKpWwuroKu92OeDyOP/iDP+hx3sgL4rmDwaC6hm63q3Z1r1arqFQqTzLEOxLTNBXER8UgSWV6WbfX61U9hAj/WjUgBHa/YkEqEpYvAlCKT26FIZ+j9PR5Dip8piNPmoLarshUAXl0i4uLeP/995FMJtVu6uSjeTweNBoNxGIxFAoFOJ1OBINBNWe4lur1OkqlkioQ0CsCD5Po88dKpKP/JPegG2Fdstks/uiP/ggff/yxash34cIFfOUrX8Ev//Ivq3SWXvkpkU6u7263C6/Xq+Z/KBQ6Unwq6kmmRen0ANiE8AAbG7HqBGR9nHUDqgdv1C0U7ttos9lUAKVTIw7jfH4a6eeUA2tcs9u3b+PBgweqJNxmW9smhePkcrnU++FwWHVQZoU1g3CCGSQwA1ApMYkYSZEBeLPZVPzbbrcLv9+PmZkZhEIhvPnmm/jGN76BYDDYQ07fTpfmHTs9TAlVKhXUajUUCgVkMhk0Gg3VSEiWCbKZGQ0vja8sT+NFy7SLfDhWKASjShpHqwfYD1q2euhS2XGBsW8PFyBRFI/Hg1AopBqMcaHw3jqdDmq1GkqlUl9nYjeFypCEblmZJdNG8r63QzTbiew0taSjcNLRlU6pjJytUgeyrfmp9BeuIe7MXSqVkMlkkEqllBNjGIYqISZ6Q+UlKytkXw6ucz7Lw+r07Kfo98+oleTQeDyuUuhutxujo6OYmZlRTVWtxMpp4zMhAjcopX0YhTZB8imBDadarnepH7az3gcZdnmMrCLSdeFxdHj6Ce+dIAULFoANHUxnRdoXZg0IQEg7zvkoN7WW2ZpB2QQrW839M4eGhhCJRDAyMvJE7Rl25PRUq1V8+umnSCaTePjwIarVKrLZLNLptKrIYkdFXizREQ4gERWpJGUqTA6GHFiejwOqE5L1JklWg0iHhr+hK3DpXMkHReHO7FLJyNQMF4/D4dgXLg9zmJVKRaUWTdNUHrRhGGqiSvhYOkTAxoTfqg+G7ojIY+T59OP5t66Q+Qxl9CqvXzazktfE81JhklR3mGQ7Sne3fkP+X+dHEAVk8HHr1i0kEgnV+JP7lpHYPjMzg8997nNqZ29C/kwhkkdF57pSqcBmsyEYDMLn86kWD4dN9Lm41Vx9mt+RxrLRaODu3btYXFxU1TAjIyMYHx/Hiy++iEgkgsuXL/dsl2MlOtrDvbii0SgikciBdX9/UuEmySsrK8jlcj0ojtRLkpKgO0VAb5pLR3QA9DiCegaBvWK4n5wcQ6kHj6PzI++HgEWtVsPy8jLy+bwqFpJBKYNRpskZBHG9k0AvdTntI3URbTcRmXq9vql9irw2u92OQCAAn8+H2dlZfOlLX8L4+DjOnz//xL3wduz0XL16FbFYDHfu3FFpK0kG5oQikkPUhzejOxIcFD11wYHRDSV/RxpviXRIkT10pJGW/BFem6wkkN0n+yFIcjFJUjCbE+6H00OvnARURiuEH+U9yYmlR0q6A6O/z2Pls9Ajse0aER1xIqIgnVZJZuSC0p87sOH0OZ3OvnvoHITo97iXv6FHw3wujLyY0ioUCkgkEvjwww9x9+5dRULmhrxEX2dnZ/G1r31NpQv1Z18sFrGysqL4GMViETabTSkmnRdxmGS3HJud/E69Xse1a9fw3nvvqfk9OjqKZ599Fr/wC7+AmZkZVTpN0eeONCLUae12Gx6PB6OjoxgeHt6Uljlsol9bu91GLpdTBHpZTaqjvkCvE09EXQ/irAI5qetkgE1ks16vo1qtKhSB+u64OjvA5mfRaDSwvLyMTCaDx48fq07ITEsRLJB2k8AAKwjZK4e2j7aoUCgo/q8ENWjba7VaDwBiRT8JBAIIhUJ45pln8M477+DcuXNP1QB4x9qJF8dIXOdfSLHi2UheDd+3gm7pzEiRaAyPkU6PPCcXjHxPV3ryGBk9WN2zFD0i0e+dCIbsLbTbQieLCI+8dj4jq2iln0ctvzvIOFhFydsRq3EYFHFLZaU7xDyORl7Os8MkB6Uw5dzm5oqlUkkhgnQwpfIKhUKq3Nzn8/WtAJLom86fOCrpFelESJ4egwVge89u0DF0yBmUFItF2O12hMNheL3eHh7OTq5bXr9Mbx1GdG0rof7St5EZlM7WddpWMgix5jmIGO9HoHoYpd1uo1QqqV0RJPBA8EKuGbnuJdlYIjrSeZHjzGyI7KQvswD9ninTWx6P56lRzR05PV6vFy+99BLOnDmDiYkJ1Go15HI51T2xWCwqr47b0fPGdHJaP0MlnR7+I/xu5QTxlb8jz6vzgICN/V50Qpw8Rh4r35PVBVbwKc/t8XhUk7G9Kh81TRPFYhFLS0tIpVKqyzSvlddNA0evmN60rFrToynd8elHGnwakWlLLigdgSIHhePN6ExGBtIJPiwyyKnci9/gGMr5ysKBWq2Gubk5zM/Pq7YQs7Ozyhmq1+s4f/48XnjhBQSDQczOzg5EauTckBC2zN0fZseH6T6m4peWlpBOpxEKhXDx4kUMDw+rgMXq+Q1CYYhOdrtdLC4uYn5+HoVCAQ8fPkSlUsHw8DDeeOMNXLhwAdFotG8/HatAQ447x9zlciEcDmNoaKjv9R4GsQqiiEBy6w06cXogKnUTReoMSVfQ7186/tKIy5Q/bVin00EoFOp5vocxkNoLKZVKuHHjBu7evYt4PK6ccyIshmH0BExyXGXQ3Wg0YLPZFMFepsRarRZWV1c30VlY5aXv1i5tEzMyu9XzbkdOj8fjweXLl1EoFDA6Oop6vY6VlRUsLy+jXq8jmUz2MLhpcOlF08PTJ6we1UtFAkApbx2tkTCojFytyNAURnTk5BBmoxHWnRtgY0ERgraKQCQC5nK5MDIygpmZmW2xyZ9ETHOtv0oqlUIymexp2CidOTnBZBQrU3C8H6vc6lbIT7/3t1LAEo5mxMqIW84FGRXwPSsDcNgU1H4YIB0Jo9E1TVOV4RaLRSwuLuLmzZtwOp0YGRnBmTNnUCgUUC6XFcfky1/+MoaHhy2Nx1a/LZ2ew04q59gUCgWUSiXcvXsXDx48wPj4uCp7Za8hHT3pN8eobyQ8v7Kygp/+9KcoFouIx+OoVqsYHx/Hc889h7fffhvA4FJqXazmPKvqiNIdZrFySGSxhwxopM6SiKIe5MrUdz+R6AQDVKbUAahgHYDio/a75uMqlUoFc3NzqqrQMNa6sQ8NDSlyvdvtVpXZBCDIhWo0GqrfnmEYqqKL+t0wDIUmkXvJZ8dnIm2urs9l9fZu6PknSm8RZqJHSIiSi7DVaiEYDCq0p1KpKL4La/mlA6Q7OrpT4Xa74Xa7N6U3pPSL9q3QG7fbrTgPjJCkwrZS/HLR8LoAqH3B+GDbx+0C8QAAIABJREFU7Tai0ahilu8l7Gw1CSQqoxtF+T3J/9CdSXncoN/UHZBBwkhOnlc6izTYOoJj5QTL+zwVa2RJbulSr9fVZ4zO7HY7xsbG0O12EY1Gn7jUmc0trdKneyWDUhbcW4/ocLVa7ekhZJqm2r+HvYrS6TQAYHFxEa1WC8PDwzh79mzfctp+v029xt/N5/Mol8vwer1qY2K/32+5Dra6X5mS5HfYsoFcqq3G6DAJ70lWvPL97XzXKtixCoj7iZwvNN5W9Ib9mM8HIdIGV6tV9U+CB+zHR9vGsSaBGYDqrwdAOa52ux3NZrPne4ZhqIaQnMu67Ze6X475ILv8JPJEjEMS6EzTxOjoKC5evKigKqa06AgxqiLxkeXrbHlPj5/OkN6Uiu9JaIsDy/JaOi2MNrmfitx1nMQnpp90pIccJTm4urG1GnDmGGUJvdvtxvj4OIaGhvYM6QE2p+rk+/ouxRIVY2pDeu3S4OkcLSvnSSocHXrWRUKVOooEQKUETNPsySFzDun3IKODU9msmDudDhYWFvDhhx+iVquhWq0iFAqh213bTDefz2Nqagpvv/02RkZGVCffnf4e55HNZlOR914iPbph1J09wzBQKpVw7do1xGIxZDIZ3Lt3D6VSSTkIhNqbzSaazaYicPp8Pty7dw+hUAivvvoqvvnNb2JyctLy3nWhc0Qdx7TZZ599BgD4whe+gCtXriAcDqtz0uiY5ka1IkW/P+pW/uM6GhoawszMjOrv0y/IOSix0ptcy6RAsCWI1Lm66HwfPUug6y+pm2RQJc9FHdNsNpHNZtWz264jetREv69Op4NsNotisYjl5WUsLy8jkUgou8k1zYaEwIZdIeev2+0iEon02Gh+p1gs9iBsDocDo6OjcDqdqnUDbT7nswx6+Zu05x6PZ9f2Sdux00PDRIRE7o8kj6Hzwpxpo9FAsVhUE6xYLKpNDkulEqrVqjLCempKn8QcGDbj4sDQEfL5fLDZbKpLKd/nMfRO2eVROkX6oFo5YRTDWNt0kb/HsZDn2KtKFqnode7RIKdATlCrSMlqUkmY0gp10Y/pJ1bIFH9Twp1WKF6/6zxuymk3xDTXdil+8OAB6vU6AoEAAoGAWmu1Wg0zMzN49tlnMTMzs+PzS8XZbDZ7OjLv1fOQTq98T58HzWYTsVgMc3NziMVi+Oijj5DNZuH1ejchr+12G9lsFpVKBQ6HA4lEQiG3tVptW9ck56KE/PP5PFZWVuB2uzE8PIyXXnoJXq9X7aUl15Gc//2CBiJIssLR4/EgHA5bNuc8LI4P0PucZADDLr5Ab7Uv0BtoWelkSZLdSqRDJc9lGEaPYde3MDjOuqXb7SoQgqneQqGg0qQOh6Nnax82LTVNU21FA2zYHz5L6gRuwcRdCtj2IhgMwm63I5fLAegtsrEKZgho0Hbvu9NDJ0eKbCstnRRJSKNzQXITuzH7fD60222Ew2FV9t7PybAaEKapeF18pacqmybJ5l2SA6STePXf1a9Fh1XdbreKdlmuy+tgFLJX4vV6EYlE0Gq1+ub09Uli9X+9nL3feWS6ic9YHr8dJcSozGryygUg8/o7SaMdVpH3IuFdrh+73a72tHpSKZfLajuJTCaj5h67zjqdTpw5cwYejwezs7N9e8NYOab659Jw7Uf1HOepvD4rx5eBFjv91ut1RbDUU3iyaot6gRF/JpNR+wD2KwfX38vn85ibm0OxWFQb4bKRKaNjyauTlS6DxDQ3Kl9kjzK2HSCR+TCK7mhwzjMVIuc737M6B7CBzlDP6HOun8OoI83SpvB5EwGUc+swOY47ka2CT2DNbicSCTx69AhLS0s9pePSFtD+dbtd1RbFbrerpqUShOBxBBZ0vc2xbrfbKjtCZ15H++R56T/sFoCwo7MwuqDjIlnVVugMX5lm8vl8GBkZ6VnEg76nIzzyc52ES9FTMHzV+SGyqgzYKLnTqwb0SE5HSshvkqkmh8OBYDAIj8fTd3PUpxXC2+fOnVOKVVee0qHR0R85tnxfVt70UyB6ykyen5/L39fPxWOsDKlMf+nkdHmdcrEdFSGnjelcKhA2DvT5fJienlZbmjyJJBIJfPTRR8jn80ilUupZkOMyPj6ON954A8899xw8Hg8ikcimc1ghKlKpARsVFdy+gv069tpI6PvsWT3/druNTCaD5eVlpFIpRdjmvJXbzXDN+v1+5fB0u12k02lV7TY1NYWZmZlt9QRZXFzEd7/7XSSTSdhsNpw7dw5DQ0OYmppCNBpVgRevfbtKnGgEnTgGWOFwGGfPnkU0Gj2Ue89ZzQdZpu50OuH3+9Fut3sMKu9FVmwCmws0ZMAl56z+tzSgssQagCqQIfF/q/l1WEW3m7p+1O+lVqvh008/xfvvv490Oo18Pq8CdZnOklQDImH5fL7HMTKMtT24hoaGVPqL20dww1IAStfZbDYMDQ3BMAyUy2Xk8/lNSLFsTkv0dbe2WNmx66QjAXQcrDxvYLPh5YSWFTlWsJV0evRIgf/kOaSzQgUnSx8lcZrf0yvLZHm0zh/RI3XCsk6ns6ckkg9Lb7S122IYa7lXv9+vOufqnw/622pxy4WyHcRGvuqR0VYKQ1+k+mc6orTd8x5WkQgP4V9y25hK6dcnarvCPfGYsuHv0qgAUFWFg66Tr3pKQn/GfDb72eNkq+dP50U6lnpqSM4tosHUDSz1LxQKCAQCCIfDW64FnqtUKuHx48dIJBIYGxvD1NSU6sVj1bNru3OZc4eoGnUp0wzb2WTxsAj1s0R6qK+l4bPSQzrX0Ep36PyffoEXX2mY7Xb7wJLo7aAnByVWulQHC/Tj2RwyFoup5pA6TUK+SjvPKjdmNGTwIHmtBEV4Dhl4eL1ehRhJZ1QKbepub7HyRHgRJ6tcgPpg6yJhXB3KslJG0sno5/TIaoZ+qI4VWiSdEn5GpSKdJfkdeaycAHLHcumdDg0NPXW6YpCY5lqfnmQyidXVVVVOKInN0uGksZWpQIoeFfRzOOXxkoMjx0aeQ/+/PBfHmNcnUSg5N/rxh46akMfWarVQLpcViV9u0yJh/+0ucBr5TmetrX86nVYkQbaKn52dxfj4uCItDxIJL/P/8lUKKzPtdvueIZryt1ZXVxViZpqmKquV1R2SV8MgBtiIHKmkZXsJudWJy+VCrVbDrVu3EI/HYRgGzp8/37cgIZ/PY35+Hvl8HtevX1cVc5cuXcKVK1cQiUQQjUafas7SSMn0lkQ9D7NI3UunkpVCRA/kGpeIr9Tv+jllgMv10u9Yfc8oXg+wuSq3n83h62EZbyu7pl+bleOSTqeRTCaRTqdVj6pWq6WCZwZIdFZo25kG7na7PdtCSYemXq8rO8pnR1TP6/Xi3LlziEaj6hgGI+l0Wo27fI58ZSr3wJAeAJsW26BISJKTdSPGc8jujHq/Havz6Q6NTHdYRQL9UiSSyMxz89Xqb31h8B54PhKZ2eGWVWJ7Id1uF7lcDvPz86o/Eq9HRlByfzD28vH7/T0bvup9knS0S040qSz0iKwfubCfEyUnulX1ivxbR6OOWnqrXq8jk8moncnL5TKAjQ7HrHxkxLXddAVTH81mE5lMBrFYDMlkUimWcDiMK1eu4N1331U8uq1kECwux52OnGEYPaTHvZBms4mFhQXF1zFNE88880zP/mB0elg9UqvVlDMjixnYAoPjTO4CtzMpl8v40Y9+BJfLhUAggC996Ut9OyenUil8//vfx/z8PJaXl7G4uIhOp4NIJIIvfelLimj8tE4P231wb7/D3oW5H4Jbr9dVEQvnLREAaUD5SqdGfk47IY+lU6uL7KGkV7pK55fv68EYrxs4PMGWDPqtAsZ+0ul0sLS0hBs3biCVSuHu3bt4/PgxvF4vJicnEQqFkM/nEY/HVYAsbQBJyfo18HlUq1XYbDbVV09mWnw+H55//nk899xzKJVKqncVWwUw3UmRDi2LhQ6seosySDHqIveB2sn3gK3TLIMiUquoVU4SWbIuf0safFkpIB0r/pPGn8qXEeVe59m52SjJYXJxWjkHcgKz+k5Hw6xQNqsIahACIB0fXfnpkR+vS4/CpMLRHS0rsVKyh0msEEhgI9LcTqM1K2GERUVTr9dVXx46v8FgENFodMdGctAzBjZQJkl43CshF6pcLiOXy6HbXSuXLZVKCq1xOBzKkDKKlPciEZJ+SIlhGMrxcTgcKJVKipcgjQtTeuVyGel0umcTV66vUCiEYDD41G0rJKqhB3lHTSQPVBL6pehZg34OlBWaL0XqPV2fWAVRUvdZod/ynAct+hgNOoZzhxXU6XRaFTxw/FkAJJFQXSSaKsdPp38QlQSgggqv14uhoSFEo1HYbDZVxa3rPavxlZSRA3V6titEEjgwVgaWsJnVRB6UNpF/DzKG8rd4LB+WvDb5nX7f6/fbulLd600XTdNEoVDAwsICMpkMCoWC+ky/Dk5EYG0Snj9/Hi+99BKazSbu3LmDWCy2KaUnUwbyHqWzoqM8VqKPmUTQZLTGhTQ0NIRgMKh6wNDbHwRtmuYGbM75dNiiYJ/Ph4mJiZ7W7cBGqS73vaKjvN3Fncvl8OGHHyKRSGB+fh4rKyuoVqu4dOkSnn/+eYTDYUxPT2/7fNs5jtfXaDSQz+dVdLeXwvTdwsICrl+/jkqlgomJCUxNTfX04MpkMqqCilwNzgfOCXaQBdDD9ZMbJ3MDzPn5ebz//vsYHh7GxMQExsbGUKvVMD8/j9XVVTx+/Bh37tzB8vIyDMPA+Pi46mPm8/l2Be2VDh8NhexRdlTENNe2AMlms4pgLkvWaQckzUAi6bLiVuoO6iPZZ00G2uRCSRK5NLaS98WqpL3cN/FpRY4J/0/hWEhyeCqVwvLyMsrlMj7++GN88sknKkgaGRmBy+VCp9NRPMDR0VGlf9lYWGZedDsg35O6y+Px4MUXX8SFCxcwNjaGt956C+fPn8fi4iIWFxdVGk0/j06DcTqdPZXaTyv75vQMkqeJ0PWUyXZ/Y6+VxV6f3zTXOssyL6tvFCedOqa5+CwuXLiAd955B5VKRVWqyMo1KlV9Eg/aV2mQA6o7RzrZkNA9Wf2hUAitVgvZbFa1oQc2txWQwnJIHncYnR42AOwXlT5J9J7P5/Hxxx/j7t27yOVySCQSAIDx8XF85StfUdVDO1Hg272GRqOBXC6nCIl7ibR1Oh3k83k8ePAAP/zhD5HNZlXFCNPUTqdTNZsjZ4opLOn40OkhoiPnDY0r18GDBw/wwQcfIBKJ4KWXXoLdbkc+n8f777+PW7duIZfLYW5uDvl8HmNjY5idnVURLZ2e3bh3NvJrNpvKQTisu9n3Ezo9uVwOhUJBoXKSWyU5bsDmLYpkyoQ8OF0vyKBNEsA5Xno6C9iorqQeInJ4WKWfruAaZE+uZrOJhw8f4pNPPkE2m8XVq1dx9epVGIaByclJjI2NqaCxUqmoLZQMw0A2m1WkZTbGJHqsj7eV/vJ4PHj55Zfx7rvvIhKJ4MqVK5iYmFBri2tQnke+UiRt5Eg4PduRp7mR3Yxgj5JQAUhnpV/6Rzd4hBqdTqdqXMcFL9n2/J3tQKn6tQ36TD8fDZPNZlPl941GoyfHO8ghMM21kkr229gNQ7PbIqOjer2uUCnd0ZSdwgellfisCoUC8vk88vm8Ii4TTg4EAvD7/dsqt34SkQZor6u3bDZbz3YLjOArlYqaPyQky+hRGkRpKLluJFJAJ4nHEmGh851IJBAMBlEoFBS6ysqXbrerysgjkYhqWLpbIosn9EjfSg6rvpPIimx/AfSmo6wqubY6r1XqiufjMYO+KyvLrDIMh2lMOc85R+mc8B+5U41GQ221ks/nUfr/2XvzIEmu/Dzse3Vl3WdXH9PdMz0XMBgcg9kTQADLXZO7pOllmAqtJVKUREqWTdkOhmwFJTtsBU3KEZLlsGwGRUuyRPEUGRRJaS2K4eXK5B5cEQcBDIYzGAADzNHou7u6676v9B9V3+tf5WR19/T0PflFdHR3VVZW5svM9773/b7f75VKuo+XGY0S8jkg8ZOZbdZrZg0LspBhPB5HKpVCIpFALBYbWAnBup9hIIG1ptPz89zmYXAkSI+D3UHOfORsSWb0AIOEwePxYGRkBJcuXUKj0cDs7Cy63a5emDKfzw/cjHy4+Hl5w+7U6yNfs/NaBAIBpNNpGIaBZ555Bs8884xegXlhYWFg/1Rx5Dl1u11kMhncvn0b8XgcTzzxxJFN4221Wrh+/TreeustnWHVaDQQj8dx6dIljIyM6LWfhi0NUavV8N5772FpaQl3797FrVu3cP/+fSSTSczMzCASieDcuXMYHx/XWRl7CRkCon9or1ZAHoZgMIirV6+iWCzi7bffhs/n0/4lkkaCx8LZOj0BfB6Ylg5seg6kfM9zBIC1tTW88cYb8Pl8uHnzJuLxuM44Ybip0+nA4/Hg1KlTePHFFzE2Nobz588PKAWP4gnh88yKtz6fTxu47fZ3lAZnCRIeDtL09siQFABt6peFZoFBr6Xcpxx0gc1JFIkt15qTxSFlDRpe+0ajobNgDcM4kpMnoHfO6+vrWF5eRqlUwrVr13D79u2BBBQW6eTEaGNjA41GA4VCQStd1WoVa2tr8Pl8OvGGVcqpoMViMa2yyn5b+p6YcUUf3DPPPIMrV64gmUzipZdewlNPPaXD9zx+aw22YWB6ezgc1gUNZTsQD3PPO6TnGEOSHul74WzFbsailNI+j3a7jQsXLqBYLCKTyWg5VNYs4t/cPyHj6hKcncmZkVQ5OMuT2/p8PsTjcUQiEUxPT+PSpUsoFAp48803B457mIza7XZRKBSwsLCAer2OqampPW3nvUS73ca9e/fwrW99C5VKRWcZ0e9z5swZtFotjI+PDyUrjUYDc3NzeO+99zA3N4ePP/4Yi4uLCIVCGB0dxcjICMbHx5FIJPaF8MiBgt6k/TYyG4aB8+fP4/79+0in02g0GtjY2NCZXBKyM2bhUOlr42ALbBIjno88j263i3w+j7W1tYH9s6S+fB48Hg+SySSeeuopTE5OYmRkZNfmdCsYfmB6916X5T9IMIxEYiP7EfYRzGK0m9xYE2Ks9yOwOTFi/9hsNnWxPDvPKPfJSchBGPMfBfRzzs/PY319Hd/85jfxR3/0RwMp/nw2JUmkV4n3LScNwWBQk55SqaTVoHA4jFgsppUjhozl/SzDhBwzJiYm8PLLL+vnYWpqylb13InKQ78ja13J675bOKTnhIAP/U5DUYzNJpNJTE1N6bWZ0um07ijYAZH0BAIBvf6KLAwp92mn8HBmIauxym0pg7KWQzKZBIAHSuvbnRsHlnq9jnw+D5/Pty81YziTkeclCZg1Fda6nR0YK6dJNZPJ6LRypifLGSkHi0KhgEwmg9XVVT1zi0QiSCQSmJycRDqdRiwWOzAj5kH5p6TUzUGf972stSLb21pBnL+tbWOV64HNYnhUT6VXTl5rDrK8dnarnj8MpGLB45GhOKlgDTuHowJrWIj+JPYv0rjK7aVqs11mo0xJBwZNvta2sy6ZJBVtSRask8X99Ko9DGR4Np/P68VyS6XSQHgPeLCWHl+z1rQCNsPlDNVKQ7gsNyMVN2ulbLfbrRfYjsfjSCaTiMfjA0TFiq1USnk9eQ/sNNS5HRzSc0JAcuJ2uzUx2e5h9fv9eO6553D27Fk0m02dTWE3CyL4sFARsmIY6ZHFDK1kQA5iLDi3uLiIeDyu9yUJDx84aapjeKtYLOLKlSu7acIt0Wg0cPfu3YGBTxa7k+meMr2Sv6W5WnqYuOJws9nEO++8g/v376NSqeCJJ56Az+fTKwwzhLe2toaVlRW8+eabuHbtGgAgGo0iGo3i+eefx3d/93djfHwcyWRy37w8soNl+x8U8WGnSkMsZ7NcDkYen2maul6I1YfA0JMcYO2ID68xgIGOl+dN+Z0TiNHRUYyNjQ3U/5L72w5W5UM+b1zEkdVs5YAiZ/lHNauL1yOfz+sBlmSVbUXjcbPZHFivTPpt5P6kaszf7G/YV9DrJv1evJby+jPjyev12io9h01+GJZlePvrX/+6rqsDbCaaMNxkHQNkv0sSQ8KzurqqvYDxeBxer1cvM8O2NwxjIKuWk2KgV+l9enoa4XAYzz77LC5fvoxIJGK7bh37RTsDunVsYCQgEAg8QKAcT89jCHmTsAN4mA7W4/FgdHQUo6OjO9peFpG0Ou+tMzrrLEDOhneSFVGv1x9YORp40JTI90ke/H6/XnJhL8Gy7XJmLz0HXExX1qyQg561bUie2ImYponl5WUUCgVMTk4OZLbwvJnuy3j+3NwcwuGw9u6Mj4/j3LlzmJiY2DLT7VEhSc+wejf7Bd5Dfr9/YDBUanMxZFkwTS6IynuG2SAA9D1sJTxWJYdEx0ruJOH1+/3aPG7FTttHGkLlc8wZPrBpLpXkxvq5o0B6rMosB0n62GQ4Sn7GukQR29zqA5Eqjkwxl88Xt5F91zAvifTD2B37YbepJL+ZTAZ3797VpRl4bNtl2Mofue4kSwcwBMZ1I2XxSDm5sZrQSfoTiQTS6TRGR0eH+irlJHBYPSBgk8BKZdNReh5juFwujI6O4tlnn9WlxWdnZwckUCnp86F9FIlQdiZyJms3m5B/y4FjpwMxB5NQKKSPXQ5MslMzTVOvOE9Jda9B0mE3E5cDolRxZPFL/t9sNnUYjgvwulwuBAIBjI6OIhwOIxKJoFQqYXV1FcDmYDE3N4elpSVks1kAQCqVQjKZxKVLl7SJWRJFaTbcK0i5ORQK6eM/KON4JBLBzMwMQqEQNjY2tBLCwVSGeWWnLEMecmBgxpcMh9ippNZBkM8VjyeRSGB6evqRCxFav49/SyWH5uthIbqjDPnMyLCF9bmSSgAVCUJmrVJxphoqiY4dubH2VcOu97DPHCZqtZr2XubzeT3xZF857Fm3kh3ZLnYT50KhoIkprwXVRU4w3G63tiuYpolUKoXnnnsOY2NjmJ6efiBDy3psdqTH+mzx+I5dnR4H+wO3240nn3wSqVQKq6urmJ2dxdtvvz10hkum/iizf3mTyvVXtoNUfXZ607pcLoRCISQSCT2g0eMiZ39cmZkrzo+Ojg5dMuBR0Ol0dN2KUqmkH3jOWNnmfEj5m2maclDN5XIIhULw+/0IhUJoNBq6FHwoFEI0GtVLi2SzWaytrenU07W1NT0AnDlzBtPT0/ie7/keTE9PI51OIxKJDCggAIbOqHYDkgafz4dUKoXz58/D5XIhkUgcyEx4dHQUn/3sZ1EoFLC2tobXX39dV6SmqZr3O2eJvO9JQGX4gwRaqgbymZHnJJdE4Aw4FArhhRdewIULF3DmzJkB8rdb+d36vTw2qlLBYBCmaQ4QXDlwH5VB2g5ywsLwoCSucukcOVmSfQfDVFRJ2+32wESHJNHqzZGheus+hw3Ocp9298RBolgs4s6dO8jlclhaWkK5XNZG5EAgMDDh5THzt1Q0rYRHnk+tVsPKysrA6uYej0cvp0ILBcNfJJ4zMzP4wR/8QZw5cwbhcFiTf2k8l6F+qZLLa8DrJj1dVFCZlPCocEjPMYVSaqCCbzQaHVgbxc7H8DB1L7b6Xvl7vyAZPgC9mJ0kENyOkixDC/tRVEwOPEy3rdVqWv2R4T521lbSQzAdl+pQIBDQtZN4TTnTymazWFlZ0ZJ2LpcD0Bv4IpEI4vG4rkws7wce816D7S39LHLw2m8YhqH9SjxfnrO8962GVakyyvuHhEeeH/Cg0mJVBfhD8jcxMYF4PP7I3qZh18w6YO9n+PKgYEc6+DoAW8LDbWXIalhdna0UHGtI1rrdVu8dFmSVZLlQKycikixIWAmx9X63Env2c1SgAWhvIftaubaj1+tFJBLBxMQEJicnt/xuO/WfGBZ2lBXXHaXnMQfDJ7FYDC+88IKup/Dhhx9qRYBhrnA4rA2vyWRyVx2mdSDYClvdnDu5cd1ut65XUyqVdDopH8hut6tVkVAohEuXLuHKlSuIxWIDBui9gt/vx5NPPqkrP0vDpfQhWEMRbCc5g+Eq4Hyds1qllH4vm83qpReKxSLa7TZCoZBO3eQgOzo6ivHxccRisQFfg9UguFeQoYdcLof33nsPSilcunQJ3W73kQf97UAjs9/vx8svvwy3261T/7kO3dzcHHK5nDb3y7We5CBqR2Ks8r+E2+3W5luabEdHR3Hq1CmcOnVKq2yPArtJSavV0tW2ZYhuv2sj7Qd4/8gQowxJyUGVZFaGh6leWpMrmNosa+vwu2Q40I5cEVIFtIbpd6va7SUikQjOnj2LVCqFubk5jI2NDfRFVF2sJNDqvyHkxJEp/fL1ZrOJUqmkrxMne7lcDqVSCbFYDJcvX0YsFsMnPvEJ2xC3fN6sbWpNgLASeV5ru2zFYWRqJ3BIzzEGb4RkMokvfvGL+MQnPoHZ2Vn8yq/8CjY2NgBs3hzxeBwXL17UA+XDkB67WcJWnQdh97r1xt/q3EZHR3H27Fm9oKNcMdvr9er1kBKJBK5evYqXXnpJx373GsFgEFeuXBnaBpLcsChYo9HQNS9oQOx0OiiXy7oIGj/LsvGcyW1sbOiCdLVaDS6XC+Pj4xgbG0M0GsUzzzyDqakpXdiR0q/syPaDgHDAdbvdyGQyuHbtGrrdLl566aUDmRH7/X6taH7pS1/C5z73OTQaDdy/fx9LS0tYW1vDt7/9bdy5cwfFYhHLy8va2L6dwX2nx+/3+zE2NoZkMolTp07h9OnTmJmZ2bMsNutz0Ww2kclkMD8/r6V+wzA04ZafO8xBeTtYCYy8R6WpmeoCM7d4XjIsKRdYlqqq3+8fCDXLDEu7vkf+pmJpt+1RaNtEIgG/349arYaPP/4YN27cQLFY1GnrNCQPq7IMPHgu/C3blj/se5RSqNVqyOVyeo2uVquFyclJfOlLX8KTTz6JqakpRCKRB753mLpqF3XxAb4KAAAgAElEQVSQaqzsWzm5t5JRK0nbKRzSc8zBziASicDlcqFYLA6YKaW8L2XCR8F+DG5WAqWU0kbmcrn8AMvvdrvw+XyIRCJa7XnU+ihbQamdLW/BEFyr1dKDM4uj0fxnVRaAzSq0SvUyumSokteY6fzyN0neQdbKkYMWO9iDCgHIgVIuoSKXg0ilUshms/D5fHp5EtnWMvRrTYHeyhfDTppLpdA47/f79+y+s4Zt+bcskWAYhi4yZxduPuzBeRikkiZrJvE9+Zt/Sx8VPVfys3J/1v9lxpb8frvrbGcDOGpg5iLQU32SyeSA+sVwk1SfgeHLcEgFi+9bf2QVc4LZiuFwGKlUCiMjI4hGo0P7IGtbWtU+OzOzJGeyqroT3nIAYHNAZkYNsx1kFVcWwKMBzRp2kQOZ3f6tf+9kkNupAmQXZ+YgPzo6qo2KfDj4MKdSKVy5cgWpVArj4+NHwuNAwywHKK5gLIujybAYwVkaK9ZWq9WB1Y2VUohEInqWL03LB7kwIuP3hmHg9OnTuHr1KkzTxNjY2KENFG63GyMjIzAMA2NjY4jFYlopIxmi/6rT6eiqs+12G6VSSfuyKN8zBGm9J/1+PwzDQCKRwEsvvYQLFy5sWfJhN+3BTl5+PhQK4fLly5rYJ5NJGIaBCxcuaLLFZ+YoKBLDYJqbC45Wq1VUKhWdKi2fbV4DucyJTBZgFXM+K51OB8FgEPl8HrVaTScY0PyfzWZ1O0UikYF+kc8XB1S78NZRAe8NpRSeeeYZvRQLw02VSgUff/wxcrmcLnZK5bhYLA6Euaxk2c66IJMhOEaEw2FcuXIF09PTOH36NJ5++mlMT08jGAzuuB/yer3aZhEMBvUxMDkE6KmpgUBAkypmtlrDYfIcdgqH9JwAsEP2+/36JpKzHRbdKpfL8Pl8A1kuHIx3Um9lP2eTdibGSCSCkZERVKtVW9ITj8fx1FNPYWRkRK8MfNigQkXYGSztYDfjstu3bJ/DOF+Px4NwOIxOp4PJyUlcvnwZpmkinU4fGulkiDeRSAAALl68+EAIkmGAZrOJ1dVVnRG3urqKfD6PZrOpa56QJMmqs7wfg8EgxsbG8MUvfhFXr17ds5AWIY3VkvRcvHgRqVQKoVBIr1MXiUT0YL7bAeCgUa/X9Qrr1Wp1IC1aKTWQqclMIQADE4dqtapJD8sUlEolFAoFGIahCW6z2cT6+jrW19cRDAYH1m+Si9JKReOg6049DCQpu3TpEi5evIhOp6P9bLlcDu+88w4WFhawvr6O27dvo1AooNvtatIzzOy8Hdg+fr8fV69exQsvvIBkMoknnnhCZ27utM08Hg8ikQhisdjAelqc+HGZF7/fj1gshkQigWQyqUNcbAs7lXNH3/9QWzs48vB4PIjFYkilUmi324hEIuh0OhgbG8PIyAji8fgAuwaOpheADziznKwdkbVw1VFQeexgJ+2eBCilEAwGkU6nYZrmoS/waiWEVnAQpU9Kri9Hf0wwGNTrPtE0T7AWEWsTWdfe2utzkaDPhaQrEAjoEIPd547KPcbwiTwe+jNkdWSZ/ScXTbYWJmTIxuVy6eJ5rLjNJA22C0PJ9NEFAgFEo1GEw+GBKtpUX6VZ1q5q/FFpU0J6o1iBv9VqIZlM6qKmlUpFZ4QyDCzb3a7Yo9UTKMs9+Hw+pNNpjI2N6bUSZb22nYJKT6PRwNjYmE4CkGnsXAtsbGxMq0hO9pYDW4TDYXzyk5/UZkB2JiRCwWAQ58+fH6iZABw+8bHGl6Wnh5kysgAXU5X3Ig3fwcPD5XLh9OnTusM9TKVnJ2CqOwfIiYkJHdJiqINhFSqkVpCAG4bxQGrufsLv9+s11ejpkaZb4PCfXzvYEX62fb1e10oPSYz0sQE974hUeAHokCSTBWiqTSaTmJychGEYA2HiXC6n16cjUeU1VEqhXC4jn89rdSESiegV7K3ncdTaF9gkxDRy+3w+HeIrFos6oYLkr1QqoVwuo91uo1KpaNLIMKFc3oTqGJUZKmWnT5/GyMiIXh7iYcHEEy4OfebMGV2BnzaNYDAIv9+PRCKBc+fOIRgMPpCo4YS3HADodZBnz57VpIAzGs4UfT7fgP/lKMniVvWJHTxlTd70HHyO6hpDjwuSyaReHPaoQy49YZdlcpTh8Xh06O64QKo8cqBi5fRGo6EJCkOmsngkB3M52LlcvUWLGYZkSLLZbOpMOsMwdBiMJTy4UDCPgQoy+xJJvOjbOqjEgL2A7OtjsdjQ7brdLrLZrC7nkM1mNQFiuNDr9SIYDGpPZSKR0Jmy8Xh8T/rbYDCImZkZmKapS4y0Wi2tYnK8YpIGxy0rdnssR3dq5mBPIF3wOzUWO3DgwMFBYbvkCev/O/EWSqL1sErNSe4PrePAsDCetd32o022ui77eQ3Uw6SaKqUyAD7et6M5mThjmmb6UXfitP2u4bT/4cJp/8OD0/aHC6f9Dxe27f9QpMeBAwcOHDhw4OC4wglvOXDgwIEDBw4eCzikx4EDBw4cOHDwWOBQSY9S6n9SSt1SSt1QSl1XSn12D/f9eaXU7+3V/k4alFKdfpu/q5T6baXUloVWlFLfUkp9qv/3rFJq5GCO9ORjP54Deb0eZZvHAU777w+UUql+e15XSq0opRbF/0OLHCmlZpRS7w557+8qpb5nyHs/ppQ6ZXnth/rX9/NKqZce7YyOF5RSP6iUMpVSl3a4vW2/rpQq222/xX4eavst9vPA9dwLHFrKulLqRQBfBvAJ0zQb/cben2pfDwmllMc0zeO3hPHDoWaa5vMAoJT6dQB/HcD/cbiHBKiebV+ZpvnwZUOPIY7yc/A4wGn//YNpmhsA2Mf8NICyaZr/+yPu86fsXldKuQH8GIB3ASyJt/5jAD8H4AcAlAG8+ijff8zwwwD+Q//3/3zIx7Ib/BgevJ6PjMNUeiYArJum2QAA0zTXTdNc6rPNn1FKXVNK3SRLVUqFlFK/qJT6E6XUO0qp/7T/+oxS6jv97a/ZsXml1Kf7nzmvlPqkUurbSqm3lVJfV0pN9Lf5llLqZ5VSbwH4GwfXDEcC3wFwwaqOKaV+Xin1Y1t9UCn1N/tq0btKqf+2/9r/qpT6b8Q2P62U+sn+339LKfVmf1b9M/3XZpRSt5VSv4reTT6996d4ZDHsOfipfju9q5T6Z30yyPv0H/Sfgw+VUq/0Xw8opX5TKfW+UuqrAHTVMKXUP1FKvdVXM37mME7yCMNp/0OEUurpflte7/cJF/tvuZVS/7zfZv9eKRXob//LSqmv9P+e7V+La+gN7J8C8Ov9fQX61+x5AFn0JnX/Xf+9V/p9zjf63/mHSqnTYv//tH+9PlRKffmg22QvoJQKA3gZwH8O4IfE65/v38O/o5T6QCn167y3xTYBpdTXlFL/hc1+H+i/h3z//9m/dn+olEr3X3teKfV6/7NfVUolhr3ev8YD13NPGgawX1n1IH4AhAFcB/AhgH8M4Lv6r88C+In+3/81gF/o//33APzF/t/x/udCAIIA/P3XLwJ4q//35wH8HoCXALwN4DQAL3pMP93f5s8D+MX+398C8I8Pqz0Oof3L/d8eAP8WwH/FNhPb/DyAHxPt8ylxjUYAfBLAzf51CAO4BeBq/+fbYj/voUdkvgTgnwFQ6BHu3wPwOQAzALoAXjjsdjmE6zDsOUiKbX4NwA+I6/AP+39/P4A/6P/9N8W9/ByAtrheyf5vd//zz1mv6eP647T/gbXzTwP4SZvX/xGAH+n/7UOPLM702+/5/uu/hc2+/5cBfKX/9yyAvy32NdCeAD4B4Fftvh/AvwPwo/2//yqA/0fs//f7/dNFAAvojy/H6QfAjwD4F/2/XwXwyf7fnwdQADDVP8fXALws2nMGwB8A+MtiXxwrbPtvm+82xTX9KQA/3//7hni+/i6An93m9X15Pg5N6TFNs4zeoPlfAsgA+FdCVfg3/d9vo3cRgF6D/w9KqevoNYYfm0TmnyulbgL4bQCXxdc8hd5F+gHTNOcAPAngGQD/X38/fwe9i0/8q707wyOPQL8N3gIwB+Bf7GIfLwP4qmmalf71/DcAXjFN8x0Ao0qpU0qpKwBypmnOo3cNvwTgHQDXAFxCr2MBgI9N03z90U7p+GGL5+ALSqk3+vf1fwTgafExu+fjcwD+ZX+fN9DrSIg/158Nv9Pfj3xGHms47X/oeA3A/6iU+u/Rq6tS679+3zTN6/2/ZTtbsVWf/X0AvjbkvRcB/Eb/719Dry8jfss0za5pmh8BuIdeP3Xc8MMAfrP/92/2/yf+xDTNBbNnIbiOwbb9twB+yTTNX7XZ51b9t0QXm9flXwJ4WSkVAxA3TfPb/dd/BcDnhr2+47PcBQ51GQrTNDvoEZhv9TuXH+2/1ej/7mDzGBWAP2ua5m25D9WLFa8CuIIe+6yLt5fRI0dX0YsLKgC3TNN8ccghVR7hdI4btKeHUEq1MRjy9D/C/n8bwFcAjGPzAVAA/r5pmv+35Xtn8Hi1/QBsnoMfR08t+JRpmvP9e1xeC7vnwxZKqbMAfhLAp03TzCmlfhmPdl1PHJz2Pzgopf4MNv0lf800zd9QSr0B4D8B8P8qpX4cPaLREB/rQIQLLdiq3/gSgD+7i8O0Fq87VsXslFJJ9Ij6s0opEz2F0VRK/a3+Jta2lffwHwP4PqXUb5h9uUXuGjb99w5wpNrv0JQepdSTIn4L9GKvW1Wd/DqAnxCx9av912MAlvus9S+hd4GJPHoP099XSn0ewG0AadUzL0Ip5VVKyRnc446PAVxWShlKqTiA795m++8A+EGlVFApFQLwZ/qvAT2i80PoEZ/f7r/2dQB/tR9vhlJqUik1utcncZww5DkgsV/vt9VXdrCrPwLwF/r7fAa9QRsAougNDAWl1Bh6xk4HfTjtf7AwTfOrpmk+3/95Syl1DsA90zR/Dj2V4bltdrEVSgAiANBXEDxmz0w98F4fr2LT6/Ij2Oy3AOA/U0q5lFLnAZzD5v1wXPAVAL9mmuYZ0zRnTNOcBnAfwCs7+OxPAcgB+L9s3ttp/+3C5jPzFwD8B9M0CwByqu+BQ2+s/vaw1/t/W6/ZnuAwlZ4wgH/UH1zbAO6gJzEPM479LwB+FsANpZQLvYv4ZfTi8P9aKfWX0YvFDjB/0zRX+2a0r6EXu/0KgJ/jQ9Hf5609Prdjif6s9rfQMxPfR0/G3Gr7a/2Z65/0X/qFfmgLpmneUkpFACyaprncf+3fK6WeAvBan7uWAfxF9GYbjyuGPQd59K7DCoA3d7CffwLgl5RS7wN4H72QAEzT/FOl1DsAPgAwj95MzsEmnPY/XPw5AH9JKdVCr63/HnpEcTf4ZQD/VClVA/AP0fOmEP8OwO+oXgLMT/R/fqmvfmQA/BWx7Rx6fVoUwF83TVNGD44DfhjAP7C89q/7r+/EwvE3APyiUup/M03zb/PFLfrvNcvnKwA+o5T6O/33/nz/9R9F7/oE0VPz/so2r/8yNq/niyL0+UhwlqFw4MCBAwcnCkqpX0BvEvZQPsH+JO73TNP8nX05MAeHjkP19Dhw4MCBAwd7DdM0/9phH4ODowlH6XHgwIEDBw4cPBZw1t5y4MCBAwcOHDwWeKjw1sjIiDkzM7NPh3IyMTs7i/X1dbX9llvDafvd4e233143TTP9qPtx2n93cNr/8HBc+p5ut4tms4lOp4Nms4lqtYputwulFFyu3rzc5XJBqQdPpdN5MAfC7Xbrz7VaLbTbbZimiW63q393u71VbmKxGFKpFDyevXd6HPd73zRN3b6y/e2uw1HEsPZ/qCs9MzODt956a++O6jHApz61N+sJOm2/OyiltiqDsGM47b87OO1/eDgufU+hUMD777+P9fV1LC4u4ubNmygWi/B4PPD7eyWNSFb4t2maaLfbqFQqaLfbUErB6/XC5XIhEAggEonANE2USiVUq1WYpolWq4Vut4tGo4FCoYBut4svf/nL+PEf/3Gk04/MTR7Acbn3W60WSqUSGo0GarUa8vk8Wq0WCoUCcrkcOp0OlFIPkJ1AIIBUKgW/349ms4larfbANWo0Gmg2m3C5XPD7/fB4PIjFYpienkYoFILP54Pf739g39J2s1uSNaz9HSOzAwcOHDg4NDQaDSwvL2NhYQHLy8tYWlpCpVLR6gLJS6VSQafT0epNq9VCtVpFq9XSgycHVw6kJEJutxs+nw9erxe1Wg3FYhHtdlurSo8z6vU6FhYWkM/nsbKygtu3b6NUKmFlZQXz8/NoNBqo1+uo1+uafLbbbYyOjuLq1atIp9MoFApYXV1Fs9nUhKXdbiOXy2kCOzIygmAwiIsXL+L7vu/7MDk5iVgsBq/X+4DStp9qkkN6HDhw4MDBoYHhrVqthkajoQkNYZomKpUKisUiut0u6vW6Jj8kPYZhaEWBgzRVH6/XqwdWKkTtdhvNZlOHvEzTPDZhm92C4Sp5zgA0Cczn88hms1hbW0OxWNQEtNFooFqtolqtotPpoNPpoN1uo1arYWJiAqZpIpfL6W2lEpfNZlEoFODz+VCtVhGJRBCNRlEoFBCLxeDxeBAOh+H1enU4U/7eDzikx4EDBw4cHBo4GLfbbbhcLoRCIbjdbv1ap9NBIBDQKoPf79dqQzQaRafT0WqOUgoejwdutxtutxuGYWiFx+/3w+v1ajWC4ZdSqYRgMAjDMGAYxmE3x56j1WrpMNOdO3dw9+5dHerrdruoVqtYWVlBuVxGsVjEysoK6vU6qtUq3G63Vs3cbrfeXimFbreLTCajQ2Fra2sDSg/Q81elUim43W54vV60221kMhm8+uqruHXrFgKBAEKhEDweD9LpNNLpNAKBAKanpzEyMrIv7eGQHgcOHDhwcGigKtBqteByuRCJROD3+3VIheqE2+0eMNfyswA0CZJQSiEQCMDn82l/kM/n02pEp9NBvV5HsVhEIBBANBqFz+c7cYpPs9lEsVhEpVLBG2+8ga997Wuo1+s6TNjpdLRixnYxTXOABJI00hROjxRVoVKphGw2O3ANvF4vRkdHkUwmYZqmVtaWl5eRyWSglNLX1uv14umnn8bly5eRTCZhGIZDehw4cODAwcmDVHrkoMr3pIkZgPb5AL2sIpfLpYmQzNJiBpf1u+S+2+02Go2GDqudRDSbTRQKBZRKJeTzeW3ulqGqRqNha1jm31TRAMDn86HdbsPn8w2QJKm2UXFjaJGqEsOWknh2Oh243W4Ui0UUi0W43W4davN4PAgGg3uaXeeQHgcOHDhwcGhot9soFovY2NhAvV5HqVQaICOmaWplotvtDig/HHjl4CvT0kl8SGg8Hs+AylEqlbCwsIBGowGXy4VYLAa3273V4R4rmKaJjz/+GN/+9reRzWZx+/ZtlMtlHRL0eDyanLBMgCQ69NrwxzRNhMNhnTFHeL1exONxmKapr4tSCoZhwOv1wjRN/R2SxPJ/pRRqtRru3r0Lv9+P9fV1vPrqqxgfH8d3fdd3YXp6es/axCE9Dhw4cODg0NDpdFCpVJDP57VpluGuRqMBAFqJYViFGVsul2uA+HBApWpBjwmNsVLV6Ha7qFQqWFtbQ7fbRSqVGvCjnASYponl5WW89tprWFtb0yqPDP3JbSXBATaVHmbBkci43W5tJKfqEwgEdPacYRg6pMWwFsnPsDZuNBpYWlqCaZq4e/cuAODSpUu4fPmyQ3ocOHDgwMHJgAxzyB9rMUGSHgADaowkPPyfSgIN0cBmIUO5HxlWO0mp60zHb7VaKBaLqNfrugDksMwoK+GRkETFWkPH+hleL2shSDvCIz/LEJYMUfI8isUivF4vDMOwDVs+DBzS48CBAwcODg3dbhe1Wg2VSgWtVgv1el37P6gSNJtNNBoNKKXg8/ng8/k08bHz6bRarQGVguTG7XajXq9rktNut1Gv11Gr1U6Up6dQKODDDz9EPp/HRx99hHw+j0qlAqUUwuEwgMFaOMOqLVP9ITF1uVza8MwQI4kLjei8XgC06mb9Dvm3x+OBUkpnicmyAqZpYm5uDi6XC8lkEmfOnEEgEHiktnFIjwMHDhw4ODRwkGs2m5qwMIxFxYeDIGf5HChdLpdttWa5fIL0j1BFkmEwmcV0UsJbtVpNZ0llMhnUajUdEpSKyk4qH8vt6JMiQWQqO99jm/I1mWknlxWR//OHZJYeLm6bzWZhGAaUUpicnHzktnFIjwMHDhzsEizhT39DoVBAp9PRWStutxuBQACGYehCbNJH4QAPzOw58Hk8Hq3IyJR0ekWYwk51QBbdswujyMJ3BMMnVJlOChqNBlZXV7G0tIRsNqtN4HakblhIi+9ZMYwYWkNc1tekKsRrwPfldZPeok6ng2w2C9M0EQgE9kSNc0iPAwcOHOwSlUoFd+/eRT6fx/z8PN59913UajXEYjHE43EYhoHJyUmk02lEIhGcPXsWqVTqsA/7SIGhrFqtBp/Ph2AwODAw0pRM1Ueak5ldRFjr+BBUJEikSJRqtRrW19e1qfmkKD35fB43b97EnTt3UC6XBwgds7SsKo8MF1qxFTGS2/Dz0ntlJZssHEmVzvojv69Wq+HOnTta2bt69eojtEoPDulx4MCBg12Cac+5XA6rq6u4f/8+yuUykskkUqmUXgbB4/EMDNjA7tcXGjYoHWeQ0ADQ5ISDJDN/ZPo5t6XPhJCp69Y2kQMwwWwuprGfFLRaLeTzeWQyGR1yGqaCAcNXsSd2ssK6vF5W8sLP2S0zYT0eqRLx+TLN3vprdoT2YXHiSI9cU4WpjSxt/qgGKAcOHDiQ4OCyvr6uU66bzSZKpZLu3IvFIm7fvo14PI5CoaAXWjx9+jSCweCOvoeDVqFQwPLy8sD6Ux6PB1NTU5iYmNjXNYv2GtJ/0+l00Gw2dV0XejhkbRip9DAUJrO8ZOiDBIdLUtAzQlMzTbNKqQEj80lReiSsRm8SRd5TrNUzLKPL+vd220kflRV2ZFUSIqpAhPR68TrVajVdW2g3OHGkhyl6rVYLlUoF5XIZHo8Hk5OTDulx4MDBnqLZbCKTyWBhYQGZTAbVahWNRgOtVgvlclmvNZTP55FMJjE/P4+ZmRmcP38e8Xh8R6SHfhaW8H/11Vd1Ib9arQa/348vfOELGBkZGainctQhjcTMzgqFQgOrpHs8Hh3e8vv9eukI1vBh6EZ6grxery6OZ/VWcaCs1Wr69Wq1Co/HozOOTiKkEVkajkkY2U5WhWwY0bGSHOtrJC52ig6/V5IcuWaa3FYqcaVSSf8EAoGhRG07nAjSI01s9Xod5XIZzWZTG9S4yNxWNQoe5btlSp5krQ4cODjZ6HQ6aDQaOjtG1ifhYF4ul5HP56GUwvr6OoLBIFKplFakt/NLcKDnGkobGxtYX1/Xs95AIKAJFvu340J6hhlYOSDyfKyDY7fbfWDQk/vgZ/nDzzNsJpdMkETgOCs9VLuomtnVx5EKizXTChj05ezkPrIjPFsdn9xWVswetq0MV1Llazab2uz+2JKeWq2G1dVVVCoVLC4u4tatW6hWqwgGgwgGg5oVUhLbywyKbDaL1dVVdLtdBAIB+P1+eDweRKNRR1ly4OAEYCsPTrPZxNraGhYWFlCv13UJfnbUbrcb8Xhc9wuLi4vaOHv16lV4vV4Eg0FEIpEHBgCmbGcyGbzxxhtYXFzE6uqqNqdymYZgMIiPP/4Yi4uLCAaDSCaTCIVCB9I2uwVT0jmIuVwuHdoKBAIIBoMDq6xTHeCATnUH2FyLi/8bhqFX9pamWWbQtdttrSCxzY8z2SHK5TJu3bqFlZUV3LlzRy8ASpuHrFgNbKowpmlq8ziAB5QZK6zZV9t5gbZ7n+RTlihgmJjHSuFidXUVpmlifHwc4XB4V+LCiSE98/PzWF9fx61bt/CNb3wD+XweZ86cwczMDCKRCKLRqCZBrAfwqDBNE9lsFh999BFarRYSiQRisZhezdchPQ4cHG9sV8uk0Wjo8BYNyz6fb8BPmEgk4PF4UKlUMD8/j3w+D6/Xi8XFRYTDYaRSKQSDwQcMufQzrKys4A/+4A/wp3/6p1rJZjin1WohGAxibm4OS0tLerJ1XEgPQ1UMRfn9fgQCAQQCAf2e9P2QBHGAtFZhNk0Tfr8fyWRSqzpSfXe5XGg2m9rbcxwUsZ2iVCrh2rVruHnzJnK5HHK5nL4PGR5lqEiG/oBB8zAVNLY5YY1iWE3KdiBBkhlj1vAZ98drylXfOYngcbZaLayurqLVasHv9+PUqVO7Woj02JEeWdq6Vquh2Wwim81iY2MDuVwOpVJJr61C2dnlculVW5vNJuLx+I6+h/Ug5Mq/1oesVCppExy3PUlFruxgmqZeC0cydWuaqTXsN2zb3YI+AAADUraMDTtwsB+QJlwqFgAGOmGZtsvQCjv+er2OtbU1RCIRbUamD4Um02KxiEqlguXlZRQKBV1LhgPYdhk5RxmyH5fKAzDYX1gzgSQ8Hg9GRkaglNImcuuiozLcZ80ckmnaJwEkLFyTzC5kR+LIc5fFGvf6HpLKkSQ+wGbNHvbZBImPJLUej0dPAmq1GqrV6kAW5MPiSJMe683OzqLRaKBYLOL69euYm5tDuVzG0tISyuUyyuWylpJN09QmwkqlgnfffRenTp3C93//9yOZTA79Xt4YjUYDs7OzWFtb0yRKyqzdbhfValVnahiGgWAwqGPOJxWsnbC6ugrDMJBIJHSb+/1+AL0y6LlcDqZpatmaK/H6/X4tY++20zFNE0tLS/jwww/RbrcRiUS0CfLUqVM7IrYOHOwGcjLEhTJzuRxCoZAOo3PyA2wu1sjQOtB7Pr761a8iHA5jcnISFy5c0AqH3+9HvV7HBx98gPn5eVQqFSwtLemVwKkgM9QTCoW0is0QzlEHlR5OXAHoCYsMu5BYcpCWhtypqSl84QtfwOTkJN5880387u/+LjY2NtBqtVAoFOD1enWfAGyST5/Pp1+jOsdB+ECUvk8AACAASURBVDgjGAziySefRCQSwUcffYT79+8PtC3bVJIc9r+NRkPbP6SXys6jalVqtgqLWffFH04ASNJM00S5XEYul9OKFMOQAODz+VAsFrG4uIhisYh0Or3rMfboPx3YNFUB0JVPs9ksbty4gRs3bqBer6NQKGiDE1d57Xa7KBQKME0TKysr6HQ6OHfuHF588cUtv4+kp1arYXFxEXfv3tXZYGSgnG3xovl8Pr2my0lNfSSazSYWFxdx584dhEIhTE5O6s4lHA5DKYWVlRUsLS2h2+3q9wKBgH7wut2uXq13NzBNExsbG/jggw/QaDQwMjKCZDKJSCSCRCLhkB4HewK7TBYZeqrX6zphwu12DxS+42DNGa3H49Fhp3K5jNdeew2NRgMzMzNYWFhAKBRCNBpFJBJBpVLBH//xH+PWrVswDAPxeFyviu33+/VSAEr11qIyDAOGYej3jgPYhqyPI1Vg2U/Iya/04CSTSbz88st49tlnYZomvvGNb2gfS7Va1VlcVtJD1Y2EZ6f+lKMOwzAwNTWFSCSiz5/rjckwH9ubJma3241ms6nvHanIbGVUtns2+LfVlG41l7OoJEkohQQuksoxlmUGlFK6kGStVkOpVDq5pEcSHj4EUonh0vbA5k3NmRXZIl3fTCNlqMvtduuBlx1Yp9NBuVxGpVJBpVLBysoKstnsQKYBZxA0xzEVMpVKIRKJaOP0ccNW3gUA2lxWq9W0wkMTJlNvG42GDj1Wq9WBGhp8L5fL6Y5c+iBknFmmMMp6HNwPZ3NU/rhgIQeexx0y9GfNWDkug+JRRafTQalUQrlc1p2vVaaXkGs+8b6WdUa63S6KxaK+ZtVqVXf+hmHojp+kQPaD8vliH3ccVGap4sj2s7ahtRaPTHWWNXhkXR7uh76URqMx8L51AcyTMkF1uVzw+/1ot9tIp9O4dOkSwuEwKpWKFgVY4waAvgeptFAV4v/AYIbVVu01LNVdvidJJYmVDHPxuZCVmv1+P0ZHRxEKhRCLxTAxMYFgMIhEIrHrfuxIj8x2jctUdEphmUxGS75UeEKhEJRSOl2Uqk2j0YBhGPjggw9gGAYikQgmJiYQCASQyWQwPz+vTdELCwtoNpvI5XI6ZJZOp3Xjnzt3Ts/MaFTk97MmxHGDNW1UotPpYH19HZlMBqZpIpVKIZ1OwzAMxGIxGIaBjY0NLCws6MXulpeX0e12dafdbrd11kkgEEAikYBhGIhGoxgZGYHP50MikUAikYDP50MsFkMoFNKDTLPZRD6fx9LS0oB5naGESqWi03gfZ3S7XZ1VaJq9wm7BYBBerxfRaBSGYRz2IR4r2JmXZ2dnsbi4iPv376Pdbg/UgLGG5aUPh8Reene63S5mZ2dtv3tsbAxKqYGQPcksiY7b7dYDm2maGBkZ2aeW2FtIPwknqzINXRIe1uUBoAmg3+/XSjH/Zx/MwZOTIvbJcj0vq//wuKesezweJJNJrRZOTk6iWq3i+vXr+Na3voV8Pg/TNFGtVqGU0qZ33lPsl0mECK4lxwmoXTvJ0i2ywKDMFuM9T4JlmuZA6YBgMIhYLAYAemHUVCqFK1euYHR0FPF4HFNTUwNjx67aaVefOiSwAelIp6zM9VqsqgEAvS0LeZXLZWQyGSwvL6PRaCAWi0EppSudlkol3LlzB3fu3NGf7XQ6iEQiiMViCAaDmgDFYjEkk0mk0+kTY4gbVvuAD0s2m4XX68X4+DhisZj2KXg8HpRKJZ1dwh9KlG63W5OhUqmkU2sNw8DIyIiWVwHoLA6GAqjU1Wo1FItFrK6uolwuY2NjA7VaTc+gGTI7yUXGdgLT7K0pxIX6eE07nc6Rz+o5Duh0Orq/oAosl0nggCCXTZCvyzAOfYGFQkGTIxqb2cfItGvrApxWUiDTfI865AAqVRpCGnGthmdpDOdgyv5fJjLQbsB9UyEDBuvFHEczuBXS7xWJRHDq1Cndd968eVNPhthugUAA4XBYF/RtNptaCRtWP0qGHiXszOPyPX6WRF8+E9ye/T4AvUhvKpXCzMwMpqenkUgkMDU1pbfZLY406bE2Ls3C0WhUD5ycNTGURVMfY4WxWAytVkuXNmeqaLfbRSQS0cXCCoUCNjY20Gw2EQgEcPbsWe0c5zIWk5OTCIfD2j9C4+CwC3wcwZuyVqvpRfjK5TLq9TqWl5exsrICj8eDWq2myQ6vw8bGBvL5POr1uiYgZOy8sePxuI6zR6NR/T4760wmg3q9Dq/Xi0KhgEgkohUiyvfhcBh+v18rP0xrrVQqCIVCx6bT3y04U+J1ymQyqNVqerZsmibm5+fx8ccfA+ipBalUCqFQSKs+e4FisYh8Po9ut6vLNHBGfZJXEm82m1haWsJHH32EXC6nlUWrSRTYTAWWg4I1q4YqBRMg6LVgf2UNK1jDBDRU53I5fXzHCRwM7dbGIkiKaHLlb0lg2Oezv+H/JFIkPfJayEH3uPbZW0EphXQ6jeeffx6FQgEzMzPIZDKa6NRqNe1L433DtpF+HAl5f1vfk8Rfjt/We1eSq3q9Do/Hg/HxcZw/f15fNya+nDp1ColEAqFQaE/EhQMnPdv5RiSsN6LL5dL+kVarpeW5druNQqGAdruNRCKBSCQCpRRCoRAMw9CDA43GN27cwJtvvqnrORiGgXA4jHA4DMMwcObMGTz99NPw+/1IpVL6dWuWknxI5YM07GY5SrC7aXnDUiVoNBqYn5/H3bt3Ua/Xkc/nUSgU4HK5dL0jtgVvYlkEKxKJANh08Hc6HRiGodUfys30UZmmibW1NV3fhP4omZabSqVw9uxZ+P1+mKaJfD6vjW2lUumxUHrYXlTOXnvtNaysrAwUuVtYWMDc3BzcbjfOnTuHqakppFIppFIpJBKJRz4GJge8++67aLfber/MnjvJpKdWq+HmzZv45je/qUkKlbRms/mACVT2eVRq5MBhVy+FRIj9lyRKVkNvp9NBLpfD/Pw86vU6Ll68eGBtsRfgIMgQigTbiUSm3W7rVP5yuaxJD/sh2hzYP1OZZyIKJwv8sWYWnTQopXD+/Hmk0+mBwo75fB5/+Id/iOvXr6NUKqFYLKLRaOj7mGqjVN5IGoEHqykPiw7Ie1Z6eADolPlyuQzDMPDpT38ar7zyysC6a3JSTeL7qDhUpWdYKGUrUL6kimAYhk5/pMG11WrpxrWuKUIpOZ/P69RQwzB0MSuqSSMjIwgGgzqW6PV6EYlEhnbmshM76jLpsOPj63JxN5a9r9frurOhskaJmaRHkj2ZBiqNhYFAAJ1OR19Dt9utZ2wMWVarVV1EzDCMAZMjPSnszFipk2ZOuQLzSYK8t9h5cT2atbU1LC0tIRgMIh6Pw+12I5PJYGNjQ8fF2W4kpbvp4CX5pIeKKcJUmag8nGRw5edMJjOgyFgnPdZ2lu0nn0E7A68MaQEYek/zO+l15HIYxwVsn+2IBwdMkhWGAuXkTRqbAeiQF/uQWq02oMDJNj2JhIcIhUIPhLU3NjZw/fp1GIahiwAOU3R22jZ2YS+5H7kdf3PS4PF4EIlEMD09ve+ew0MjPY96kwUCATzxxBO6Yulbb72ls7JkhoRUXGhuZuxwfHwcTz/9NBKJBEZGRjA5OalnquPj4/D5fFptsK7+anc+7Oj24vz2GtsRMYaKarUa1tbWcPv2bZ19FY1GddiQYS+Go6TSRW+BNKjxu63fL1MWOeOSvghgs9Oih4pGN6kQMcuORnVKteVyee8b8QDBNmM2T6fTQT6f17H3fD6vPVaVSgXA5orflIenpqbg8XgQCAR0TZSNjQ09G45EIjtaqZjhxVarhUwmg3v37qFSqeD+/fu4ffs2Op0OTp8+rWXoZDJ5bMy0OwVDilxBvVKpoFqtDiiWwGZWivQ+WM2fciCxy4iRCpAcwOUEgs8av4NeN04WjgusYY9hHhKSlGGkh97CaDSq73UuYMprw2QK7tfuu49av72fsJ6zDLfK+5lKGzHM/yT3ZxfWtYoCcgJ1kJ6qfSM9w8JYe3VTBYNBPPfccxgfH8d7772HmzdvolKpoF6vI5fL6QtHnwHDYp1OR5OZqakpvPLKK5icnMTExATOnDmjnf9SpdjpA3HUHhq7jhSwP85Go4HFxUXkcjncu3cPr732GorFIi5evIhnn31Wzwgoj3KwlfuiQkS5XxoHuRgiySgVMxl/Z4fOB4DqQTQaxfnz5zEyMjLgW2FWF6VuZm6Vy2UUi8UjdS0eBtL7IQ3b9+7dw9zcHJrNpk7Xp58DgDaRA8Do6KgmPXJFaq4TJ4tEbgde71KphFu3buH3f//3sbq6qhMC3G43Lly4gLNnz2JiYgIXL17EuXPn9rWNDho0h5dKJeTzeR1OZV0qLmvAgdhqyOVkQKoL1m34Wy4CKU261kFCkp5SqYTFxUWtZh9HbKXycNCUFbAl6aF/s16vI5vN6oQLWQ/M5/MNFI3c7ntPOqzkXKqU0l9mVXCkWibvUztYyYxUOmXIdqtVDKyvP+r1OhRPD0+SNx9j1rJuznagryQWiyESiQxUIpUpiAB0h2SNiXPwZSyY+9nq2O3+tp4bcPgP0las2XqcjOMy7ZXKglRgSFKoeAWDwQf8BQxxyY6d4Q65jZTspb/BOiOwkiReG3n/8MG03lfHMcTCc5DkkbVgZFYcsxHr9ToADHQ8vGbWbBhZcJMKBZdosaoOBK8FlTNZm4ahToYiSTxl3azjDLvnuNFo6HOWISSpwlg/b/17u+8cFsayCx3I76S6wfvmuEJOoqSqBWydZcV+igqy7BekQradYv84YZgdQ/YJVPPt7suHDX8dFewb6bFrCMnQV1ZWsLq6ikajgfX1dRQKBaTTabzwwguYnJzcdv+sSRAMBlEsFvHEE0/omXEmk9FhLBmrlPId0JsZz87OasWCSo+EZLXWdFF2Uny4mDLKmZ/b7T6U+PowZsyblJ0jzd5+vx+lUgk3b97ERx99hHA4jKtXrw7UzWGNEN7kZ8+eHShapZSyXXeM7cJrn81m9cDI46A/he3abrd10cd4PK5r9fBzvIdovuO14LWm/2i/23cvH3aa7flsfPDBB5rskITKNWdo1pR1LuSAwbWdrN8xPz+PjY0NhMNhZDIZbfhkmQcOECTBJFdra2s6nEaPGwCdvcUsskAgoOupHCdIwm2n7LZaLdy/fx+3bt3C8vIyisUigsGgTq2lQV/uwyrjW79Pvi8NzFJdtk4GZB8EbK71VSgUUCwW4XK5jqXSw/OVC0xywgNAhxZZhdq6dITP50M8HtcqKPsEbks/IfusUqmkv/ugwipHDbLQqx1RlhNJYHANLU6u7O7hYeRfQkZRXC6XjsgM23YvcaBKjxzo5ubmdMd+584dLC0t4YknnsCFCxd2RHrcbjdisRhisRgKhQLOnTuHbreL5eVlLfEPi1mysZmdVCgUMDIyMnSGygvKh44DLy8+q6CykjMrY1JOPUzYeQeYicUOxu/3o1Kp4Pbt27h27RquXLmCV155BePj4wOqCTsawzCQTqd1dpadL4Hg9zG8cufOHWQyGa0K0CPBNVdktVpZ+JCkgPvh5xjbl8fBGfl+wG4GvxcPpcwwnJubw6uvvopMJjMwiHJ9MaaFc2BgmEpWV52dncXy8rLOmOMqxTQt+v1+rKys6HbmgEAS1Wq1sLGxoSu55nI5XUogGo0iGo3qrAvWUMpms4hGo8fKUyIhqwPL6sck9PPz89o7WKlUtDeK6bXAZnaWvCeGzajlhIoDiyy7z/esvgeGyoBNnwpVwFAodOyKc8r7TqrwVGXkeXMSyX6C7cREk0ajoftdthEVTxJ8qTwDww3mJxkk0HIZELttpJJjJeHWybQd8bGSe+6Hv2X4drcVlh8WD016HlbOYqogvQecnbNDZbYO5ftsNotMJqMrK29nHgagixiVy2V0u11t8pRsXzaqnDXxM+vr61hZWdFluqVvhAoEVQXeLHyfHR3Phebcw84kkiqUNAIWCgWdeUIpuF6vIxwOI51O61o6VE1YyTMWiyEajeoZ2U7SB03THBgQwuGwHnhlBVC+L1U0DtL0OEjy1Gw29X1llbH5vQeB7Z4Fa4cqZ+3yp9FoaGWHig/VK14n67PAcBbJEgkMAN2xc6bLgVQqOXIBXe6PAxBDbOwU7dRDr9c7UBPLmm103GRvO0MriwUy9JvL5VAsFnXGmrX2i9yX3d/D3rcOBJL0SGXH+j0yjVf2U0xCOMqwnreEaZoDWZ8ABgZU63IqvAetC65aw9/8rsc9zCWjFCTcbGtpMdhuH3K7rUKzVvC5OYySAbtSeoaFT+yQyWTw9ttv68J1a2treq0kzvSz2azOGvrOd76De/fu4cyZM/j0pz+ty1JvhVQqhVdeeQVXr17F/Pw8zp49q9WDjY2NAcLCC8O003w+DwC6OCEH/nQ6rf0MDL1wH7J8umwTDiAejwfxeBzBYPBQpH6peGxsbOjjpkT84Ycf4sMPP4Tb7cb09LTOtPnMZz6Dz3zmMwgEAgiFQmi1Wrh9+zZef/11mKaJz372szrsJSEHTeu9IEN9Ho8H586dw+TkpFb9WGOJRQnn5uYwNzeHbrdXqJDrabGTY5o2194qFAraD2YNZ+5n224Hdiq852gmZiiIlaSpHvJa1Wo15HI5LdeHw2FdNZwpyXL/pVJJq5tU7ajkpFKpB2bRnPUyXCaJFfcrZ9YkX1QgpNwdjUZx7ty5AYIfDAahlDpWadOENbQEALlcDisrK8jlcrh58yauXbumSaCsT8U+AsDQGav1+ZCDsLXUA9/nd0lPolSsadaVRJWFE49bmNGubWiolSoDCw7KwoN+vx8jIyNwuVx6GSKZyg8MKnkyNHxQCsNRgmmaWqHls8qJkTXUKz8jlUfre7xmMhxm5xPke3KdNHnf7zf2JLy1FQkqlUq4ffs2FhYWsL6+jsXFRbRaLd2JMizEWOyHH36oV0R/9tlnd0R6wuEwLl26BNM0dYZPoVDA4uIifD4fGo2GTveVs9lqtYr19XW97kitVkMwGMTMzAzOnDkDoFd1lp4Kxj8BaNIja2zwAfL5fGg2mwiHw4ca3iKhILmkInD37l3cuHEDbrcb5XIZY2NjSKfTuHr1Kk6dOoVqtarDGUtLS3j77bd1WvKVK1cAPBjmYVvYkQIOql6v13ZNslwuh9XVVU0wl5aWNCmtVCpalQOgawdxkOUDxk5s2DEcJKSiQ5IsF8ctFosolUp6NWgqWuvr6/p/FuVKJBIYGxsDAKytrQ2QHoYO+WxxlW23240zZ84gnU4PzHo5wyV5lOEC2WGxQ5MqE0MJJGoc9MfGxvQzXKvV4Pf7dTjoOMGug+akZ2VlBRsbG5ibm9NrZLFoKWesMsRq9fMM27fdBEEOwrKInt3smxMJkgKSaxK140g8gcGwilQ42Qb83xreYn/LvoBknEX3uG9OxOyUvccJ0h/JcVES7mHYKhRoHRekuiyvqyzFIJXog8BDkx75QMvXhoGzTnoGGKKQ7J3hDbfbrWfxDFHJInhbHRM7H0rulPc50MuS5eyYac5tNptIJpNIJBJ6PRJuQwMcO3sZLrKSHt2oHo+ubXPQEirJAhURDqRS4eLxAZu1XXw+38CK5aVSSRfzIvFkzSK50B/w6J6WarWK1dVVrbwxFVteV7Y7wz+8lrwGMp5MqXu/wdAHSRmPUWaVyZL5PG4qPTKmLlfH5jPD54zVqmmSZ+dEHw+9PHI2rJQa8JoQvKf5nMhUarvYuzX8wBmZ3Famtcpiocd1IKnVatrLdO/ePdy+fVuHtbjuj7z/rYO0DHnJbaywehyGQRpG+Tnrd8mBhJ62ox7esvo95GRBqmbWhAm7kIjH49HqtFx7cStjuQyvyP0/DuD5c0yzZnvyGkiR4GEsA3Z9iGxz9tvA5jU+qAnrrpSehzkwn8+HVCqFdruNtbU1TWZisZjuvAOBAGKxGBqNBhYWFtBoNBCPx7G+vo5QKKSJyFbfywYLhUIYHR1FMBjEvXv39KBP0kP/TyQS0QOQafaWN5iYmNBhgVQqNRAyYUcuY+v8Xl5EDmCsC0ST415hJ51ktVrFBx98gJWVFV0xl+uJxeNxAL0KnTMzM2g2m9jY2MDKygrK5TLOnTuHYDCIbDaLhYUFTZbOnTsHr9eL6elpjI6ODqSP2x3jw9wfpmlidXVVG0TX19eRy+UGsgkk4WRqrixxIGcLQK9wJUndfoCdJusB0RtGUkNFkLNMWVtEznRIlmTWGokDJwmlUgkLCwsDtY/YwQcCAe3FktlvQO+549pmhIzhe71eXaVVHhsnGMOIHJ8JWU6g2+1idXVV10qhQnKQ2I5gPMyAtr6+jnfeeQfZbBY3btzAa6+9pidPqVRK709+H9tGhlclMbc7XquXyjqDlq9LAmMlopzA8VoXCgUsLS0dG0M570veb1yGhl4+tq0kPAAG7u1gMIiJiQm9MLR1/8BgwoXVy/K4wTRNPRGT9y7bneREKmIkkNYwq91zJfsr6/MgJ6W8b7fK3tpr7Hv2FmetwWAQHo9HV82lesKG9fv9Wu5nKIrZOnIZia3Ah5/7VkrpWTUHBNbliUQi6Ha7uuNPJBLawMs1uzi4cNY9zNBJUsTjZVyZA/JeQc5YhrVFu93W8natVkM+n0ez2UQ8HteVY5mBQ+k+n88jHA4PVDVmJhoAvYQBlR67NXIe5Zyo9GxsbOgQlzV+TPJA30K32x0wLcpQlxwA9hpylshlM7h4X6VS0aSMpIdGbKpCAPRDzuOWShaw+cy43W4Ui0W9Wj1VNknyuP4Zs7N4jFR6rKSHA6Hs0JrNph5c2XbsFKUniYU9eQxM1WaRSEnyDzK0sh3hka9v14fIkNba2hpmZ2dx584d1Ot1XRMMwMD1suJhfF/bbc/37NQj+b4kdcfFyCwhnys7QijP07q8DQAdDjZN8wHVcyvS+bgoO1awX+VES65YL2t9SQWRn+PvYfeXVVmzfqfcnzWMfhB4aNJjvSllHNAOgUAAU1NTiEajWFlZQSqVgtfrxcjICNLpNAKBAGZmZjA6Oqo9C5x1smT+6OioXkiRgwkJEhUA/i6Xy3rl40ajgWg0qhfuY4fNdGs5M47H44jH43oQLZVK+vt8Pt9Ax89BRsqupmnqwmUcDAKBwJ5eSOsMzw4cjLggqN/v1wZTtiGJXaPR0OGrRCIBl8ulPTRcZ4x+DdnJWGVIuzDIdmg2m3pJBS5qmsvlBpQ11j0CBguTWbOSqE5IT5Wdd+hR0Gw2MTs7i1wuh+XlZTSbzQFVRGY5ybRlWW+EShTJCIl0u93WDz6N8OwgSFBN09QrpJPYU1mZmZlBKpXSs2SlFMbHx3VHZu38JWkDMLBIrAzJ0ffD1+V+GKqmfyebzaJYLB5KbSrZwe6EQBCdTkd79qrVKpaWlrSy9s477yCfz+taRrK6u/w8CagcHGSIZtigK7e3PjvcXpIcqSjLc5WZi9Kkzmf7qMJKcqyvy/AL+3sA2m7ARaJ5PUjit1IM5P3voAd5L1G15Y+s/yW332piMWxCzn3ScC89PCQ+RzJlXcqCMqtjq1hcLBbD008/rUvZv/322wgGg5icnMTU1BTi8ThefPFFXL58GR999BFWVlZQKpVgmiYWFhZ0xzo+Pq7Tllk0bXl5Gfl8XhuVZRE8psinUikopTA2NqYXFaVaIWfY4XAY8XgcLpcL2WwW6+vrME1T10BhhgaNcly3SA4WVEcYXtlruW4nhMLl6lWqjkajuoaLNSOF6Ha7GBsb0ynkHo9Hkz3K+J1OBxsbG/qmtHPZ74bYVSoVzM3NoVgs4tatW7h27Rry+TwmJycxOTkJ0zR1BWCGsUgaJHlgBW8OPKwWvdfhrWq1iuvXr+P999/Hd77zHZRKJZw7dw4XL17UJJfPgswio9onOxd2zszkotGSA5skHn6/H6Ojo3C5XEgkEpq4crBg2JKDgl0GkXUg4TacXMiOjJMHkl0ql9JPwUrOVAWpLhYKBU1WDxq7uQfb7TaWl5f1zze/+U3Mzc0hn8/rMHsoFNKhdaufCYAmsySqclJI8isnhlZiw9ckYZKkx0qs+DohvRckzsyMPOpKj7z3rPcnCTzvL/bTHo9HK26ySCb7CPo67RQwqzWBeNzVHvafHo9nwKfIib0sbTJMPbMLzcrXSdA5qZMZXnx+jiTpATBwQ/Jk7EiP/J8DFmenHKyoRMRiMSSTyYH1gNjper1eHTagFM/OtlKpaNNusVgc8DXwBucsLRKJaCLCkIBMPacaRHAQkUUISaZYh4eQUmGr1dpxOG4/IAdPWT/FKhnzJmbIi50NK1lbY7kP04HaPRBWMDTEIoWs/MsZBoABGVseE89BViXmNeXvvZZLu91eHaZCoYCVlRUUCgXEYjFMTExor5c8dtM0B2ZKdp0w92uVk6kW0YzPaykN/XKwk8oR21r6dGQVX6qVfE5kJ0XlST7jVG1ke/Ic+QxyQsAsSLsCfYcBO4UL2FRQaNjP5XJYX1/H8vIy5ufntVrcbDb1ICtJh/V6Wvs/ORHi99kZ/2XoytpWdoOINIAOU4/4fUed8BBbKQdSdZB9l8fj0WOKVMtkyYGtvk+2jTTTHvb9etCQz4P1dTm+2X3G+jf/32rckyZm63h0kO3/UKSHhe2YFUTTrjWzSp4EO2zTNLG0tKTVGNaPyWazGBkZQafTwdzcnF7LZ2NjA6+//joMw0A2m9UDNAfHdrutM32AnpGNC4qyo04mk/qYODvmrIlZJuwApSwfCoUwMTExYEpldWA5EAODseFgMKj9SbKs/0GC7bK6ujpwHWQnKk2zcrC2EqROp4O1tTWsrKzA5/MNNafy5rdWqubgytCP7MCKxaL28IyMjOALX/gCGo0GJiYmMDY2pgkuSSY7Odn2JM+ynWOxmFZG9hI+nw8zMzPav1MulxEOh3Voi4RNEhSr/8BKghheqdVqA+EJEhbZXlSNAo/8gwAAIABJREFUOJu1hk+kCisJ+LDZtAxZDcuckIONXUhTmk+pynk8HmQyGaytrR3KICKJG2sesVwDi1nm83nk83nUajXMzs7qzEGquz6fD8lkUitpMmWa3wEMEsHtQiYktnZeB157eQ58Xd7bMrNGbiufb94rx8GcK/sfGcJikU2ZWSgnqAxtsTAmsJn6LItkynaxmsqt4frHlfSwz6LiI6M4HBcZdud1kCFJqWzK+4+wGwMl4TkMcv5QpIcy9urqKu7evftAuXPZQcoy4iQAlIybzSYymQxWV1e1UbhYLCKXy6FUKsHtdiOTyeD69euaIHW7XYTDYd3wwObMhhlXfr9fXzSgV7+HWVo0ysqHQ4KdJdAjPcw4y2QyyOfzMAwDY2NjOlOmVCrpG4DnzfOUKcQHDYYRl5eXB4y08jxZ5I5yMQfUeDwOwzB0dWAOENlsFsFgUKdOW8EHhEtDUBUj2aGaQwJEZYBpwel0Gt/7vd8Lt3tzaRGXy6VVP0nIJJGQRfXYaXq9Xu1P2ksYhoFz585pM7GsHs5zYbE/O9VTenqkkpLP57VnSsrI0h/ETkje21aywzaQaicHfzk4y8HAavyWoTmqZ3ZqFSH3RT+bUkoT5cO4/yXJzmazuHfvnlZz6BmcnZ3F7OwsarWaLjzo9Xr1kieGYejK49Z9U3FmG9mZPK3g6xxYrMRHmsqtVdzl5FGadnk8cgBifyTvpaMMOZBK0kM/JO9lnhcnn36/H9Fo9AHSw5CxVKn5PdY2l+HagzbSHhVI8mENLcoMLgoGdvWirITHzqgsybr09VgjJtx+v7GrZSiskqOdgU+euKzZYWXwMtuj2+3q5SeYtWV3UTiwWGfQPBYO5NJ/Itk8P2edMcnvkh29nAXIc7aeJ8Fj2suBl6RMznisMjsAbT7ldnIxOZ4jQ4QABm5SqmYMVTC8we9jCvawm56EVlZ/lmESDsbWdFGfz4dwOKwzMDjYMmzDQcE6K5NeCWbr8Rrt9QyCYSSWV5A+L6oyW0GG59gGXq/3gTR2zrpkxyPbTM7CrD8Mh0myxGPnbzno8hqQpLOd+fxYZ81W8D2qI36/X4eo9yv9lB0rnwepZvE9JjiQsDNclc1m9bIqhUJBZzmx/hHw4ISN14DYSafM55IzZODBUg5yMBi2T/Yx1s/ZQYaJSGKPyyBuJe/yuZbvE9b7VMLab2/1nfIzJxXDwk1WZXBYmNEubDsMdgrksGOy7pOvbaeY7gUeivSwIwyFQkgmk2g0GjqsZJqmrlVC5YC+GQ54o6OjePrpp7UZmNtwoPN4PHjmmWfQaDRQLBZx4cIFtFotnD9/Xq+ALpUehgP4wFQqlYGUWipKnCXQ3wNgILwjO0ygd/Fk2jQLkq2trekHjhkEslPjoM9tQqHQnhGfZrOpTZbMHpLhHc7QK5WK9kIBm/4LydDZIfN1yeBpZiO5cblciEajCAQCKJVKuHfvnvYA2R0jlSVJUKWPRHpUSGqlMsbiioSV3PE1nhuvP4laIBDAhQsXMDo6uqedGTvRRCKhV9RmyITK1lZZS7LNJYFntqJ1RmSd8fN1GZqyhlvkPSxfl99tp0xYiaQdqbQ7H/7Ia2wYBqamprQRfq/B2lP1el2nk9PwSj9RpVIZKArJvoHqZblc1hOxVCqlDbH0t8n7jfeYVFCAQVWFs1Z5Da3hKmtHb0d6rAOxJJx2xnT5WU40TNNEPB7H1NTUA4UpjyrY31YqFcTjcd03yHCL9ImFw2Ekk0lEIhHborXS1MwwrFX9sioaBzHYHjR2EnKVEyuqzLzn5DIR3J/1vrOSFmBTWZN+Kb5uvcfZdx9k1udDkx52DtFoFO12G/F4HMlkEt1uV6+vxdCEx+PRIY92u41kMqkJBOvidLtdvWpxMBhEOp2G2+3W9Xra7TbS6TQmJiYGzMfyorCEP6s384EplUooFot6MJfpjJwtygGaA4Y0aVNx4Myx2WwiFotpU7ZkzFRHOAthgbm9QKvVwurqKhYWFvD++++jWq1qwsm6Lj6fD51Or+CiNf0ZwMDMX2avccbMTB15c/I8uBL74uLigNwpB0k585bqglTYpALGtHI5yMiCWTINXKZMSy8KPUiVSgW1Wg3RaFRnOe31DI4EkGv8WDuV7ToZu8FNkhe2AzshnqPVCzKMFPEY+VsawrdTFf5/9t48RtL0Pg973rrvqq4+5ujp2d6dHc5yzSUpHhKXkBiJCgjFlhQFZmTLki3FcWwjgWAkiJ3LUGwHsGHDQgQ5keUjEm3ZgixLUQIr0REzJENpSfMWRWpnd3abMzvT01d1d9131Zc/qp63n3r7q+6e2T6r6wEKXV311Xe8x+/9/Z7f8R52z4dBLTWm4Z8EuEdfpVLBV7/6Vbzyyit2M1Bu88GsM7qpWMOIijxlRCAQsC4tHYNkyvie/aD/U2awv4DRdlaF3E33d1lqF/xeA3W1fV3miWODjGMmk8GVK1cujNLDe2espmZjUhbpc3P9GVfxnv1Lea5uRVV6GJOq15hEjGNdVIbzL8e0jlE/pcc9j35HxUmVex2/Ogco5zWj66TxxEoPG4CLpQZx0rVBn51S+crO6AN7nmcVKdXG6UpgZ6ibhUJILV2dKKrQuAFsfK+Cy6XX3A7noqOCTBcmPRdx3MFxdAMx601dGaqpUyGkwGNKt7riyIIpnaxQBkzjZqrVKjY2Nvb5v90xoROH37tt6roqeQ616jR9Wo9nO2shPcaUBIPBkVTs44JrKR5Gy46jlA86/7ixogokML42lgorv9dR8LTtNm5RPi5QEalWq7agJt1VrnuRBpEyJSp81X0F7LlGlYnTv+77JwHPy2uo/NJsRL2m68L0awt9Xs4LuuZisdhIDZuLADVotBabtgOfmYHOuuGowjWuVF4cd9jBRYaumX7wc20dVT64x4/7zZOe9zjwxEoPXRvVatVSypVKBf1+3wbIBoNBWwuHqcnc84l7WjFzJRgc1FXJ5/O24GCv17MsBjCw/plZoTn9dIn1+33rbvI8zwbN0nIA9ja91AXdVVzY6AxmVHieZy3CQCBgt7bQ82kKsCvU3i5CoRCuXLli241p/CzeR5aF7cJnVQVQlQkNKNZMIT6Py9j0+32srKxgZWUFgUAAqVTKtrcqJgdZ+CpsmFnDcUH3lvbTOOWU98XaSgCs+2J+fh7vete7RmJ8jgN02bL92N+kZP2sb9cFxLHh3pf7ubajxqi5xxxFSLi/02sexRDQ8ewH/R37hfPyOEGZ8+abb+K3f/u3sbW1hVKphFKpZA2jeDwOz/OQSCTQ749umUGXOp+FZSW4uPoxK+OEsRoLfu3qKpl+SqpusqhtSAOS7IfLkFIp8JNfkUgEiUQCV69exfLy8qnsP/d2oMYO5RfLCAD74xPVvTU3N2drpbnn5L5ybBe6T9yMWj+j7LIgEBjsGkBvizKXWvNM5Y16Q1zjT2WGygrNpnbljV7XJQ1OEk8smbgg6bb09J9zMVULikGzFExUWBg3EgqFkMvlkMlkLFPUbDatNm+Mwc7Ojo2d0NosFBjq6tKNHWkxuK4IpaX9FlMKRWCvc6gV87c8t9tZvI5akseBYDBoaw0xkJbbE2hcDl1dVNo4mV2lh33CrUHUZUKFgQoT26xQKKBYLCIUCmFmZsYuLlT+dPC7cK3narWKtbU1NJvNkSKDtODGuRsUMzMzts+YMh0Oh0fu57jAMaZp+WxHfXZX2VH2QJUbV8Dq/+wDMm26kLrCxg/j2Ce/7933GiivgvAwpYe/o7Jz3AsIx1mhUMDdu3extrY2kkFFY0jB+2Y/cJ6qW1SNBfbxQffgnl/htrcurioL+D+ZCo0vUQac96oslfYP57QmNrCC9+zs7LErnscNdzzzeZjE4gaps/2Zsj4ufIDKLmU44x/HKaSXERwr8Xjctrkq8+rWUsXHhTvm/RQbVx7xPKpIHSazjhNPxfS4MRpajZQLLwUSKz2ykWlNqXbXarXsAq47mVNR8jzP7n9FK9KNc+D1gb0gQsai8DPek1p/umM3n5GCx124mEKtwlQHB2NQdOuB4xL+bL9UKoWFhQVbr4hp0mxLWrxsA7aPWohcrNn2dEm5mVquwJmZmUGtVrPXIPOhQYbqQnOpe1Uw6/U68vm89edqeQO2sU4YpfB5bsbv0F3XbrcxMzOD2dlZqzAfN5Tt5P8uI3AUpsfv3lS4uJks7rm0XV0cJDhcocS/2s4a0O+3UPidT+OsOE+OE51OB2+99RYKhcIIo6rKhMu2uYqhPoMKWj+G7TDoOHfjrty+VqPLFfh8NiovGg9FeUUZy3lCg0bnHo1EzZ67iMwF5Y2GJrigUjNOvrKNKM+00CxDJVxD5ThDEc4L3PHtfufH3vgZOOMYT7/zucce9Du9zmkoO8QTKT2BQGDEl8oFQF1BbDi1vFg/gRuPkkUABjEcpVLJ7vnkFs2jojMzM7PPMlBLVIUzmQPWdAAwUuiOm4Fq7RiloHl/LlXPe9d7SyQSdtPSYrGISqViFYLjTBvlYrKwsIBMJrMvzoVtom2kWrTWbfErFtjr7W3qqQNfr6FWJoWtDnaWG6AC42aOUUiTAWSxOJ2c6t7SdHs+EwUaxyJZP23zq1ev7ttp+bjgxvUcRslq/x8khFz4LdaHMQtPg8MsNcVh11em9LgXkGq1is9//vN4/fXXLeurCx8XN72uy/RQmdaxr8/lLrQuu+YqslR2OCZcdxOhCiDngpaDUOOFSj/lKuWPyhM3lsXz9jY7XlxcRDabvVAp6woaX35lIDi+WMfHb0Nnfs86S8ViEfV63SqNlD16/kl0b7mGkd/cZVuo4qdt4Wc48Ld+n/nJcpWRBylVR3XXHweeyr2ltJefda+WqTIhrtUK7EXuUxtXS4iNqHVEVFipZqnn5H24G0963l4WVyQSGaG1VUCqBaDPSYaJExOATZHne13wj1v487xuvJELto+6tFTp0S0K1FJnujn7iTEE7kLAY9ViAgbMWiqVGtkElPfDNqWgZ5qxxgMBoxsr6rhwlWyen6xdJpOxwZusjH1S0El7kYJFLzK63S4KhQLK5bJvxhvhKm2UDX7ZOyqgXQWWn6ms47kA7Fsc3GvqtV02mvfPFPtmszmyV5a60TkXlT1mXKTKKzLYyWRyJDbpokGNWb+FkM990FYzKhsAjBjY6g502bhJw2FKhDtOdb1To/eojIyrDLnr89vBcZyDeKrsLboh6BphILP6pcmqaAyEMcZa5jrwaCW5YKPR50hqksqQDmBXiAFAuVy2vlyyG4x5CYVC6HQ61mpU5Uz3HOI9ajySdm4sFrOZZ1rzwbU6TxMqmOl6ZL8pFe4KY27joRq/G7PEz9yihwCsj9j1CevkYhtpptc4dwD7V9uRgl+tbLJgtPwmUYBNsad4cFxQkVfFAvCPn6I7F4AvS6nHusqMO6ZcS1iNMIV+ThnIew8Gg0ilUggEAtY4CwaDttaXMcYmg9DQUbc+jS8+K2Mrj1Io87yCz0LlRPuZiy+9CGTE/OY6FcNOpzMS18S1iEZfr9cb2Y7oMsBVyv0YZddYd5VPv3No+41jbVxlCtiLET5NPBXTo3ujVKtVuyu6DkC+JyvAh6TCoVSYnxanGj9ZAWCQ9eOWJydN5y7Cbqe4Kdpuyh47oF6vW2WoUqnYXaV192z+VQaChc54z2et9Cgrx2wVwF9bHzcBxg1IP4rTtZjGuUPG/fW7tt/YcN094xarKSYHOra0ppRmmhBULlyWhjJknEDW7Qvc2EX+zs8q5uKs51IlXxdwsjvZbNZua7O4uIjbt28jHA7j0aNHePTokVX21dDU2CBVEHheysbTqndynHDbi4anGqCUwXRv+Rk47MdUKjUSWK+GGmM5GXLhGl6TDj9FxU+WAv4707OfDnN/ucaI9pfGrZ02nljp4SRkULIbTKaam1KULhPkt+AdRFNrcKUbMOVabH4aqAoCN3BLY3n0XnhdMkS8PoWPasZ8vvO26LrPNMUUFxHG7FV5V2NFZY3LwqrL9KgxA66r+6D54+cWA/YvKqqwcePcZDKJdDqNdDqNmZkZzM/PIxwOo1Qq2SKjeh0aWeNcCBrUfNEXb20/l4lTFnkco0sl0XV/ueedhLZ6UrgMzziDUxX9o6wb4xSfceB81fF7WniqnMZ0Oo3l5WWbBcR6OKRXybyQTuT3oVDIbq6oA87VONnI9Mnq4GaAsgoChXaaukL8YmzUUuRv6ApjUC+ZHrX+3OtxMmYyGbsRHuvHTDHFFG8fqVQKL7/8MtLpNEqlEnZ3d1Gv10eSEVTJIShr3Aw4VSAo2FXR8WOhXYaHUJZCa+h43iCG8MaNG5ifn0c8Hsf169dthhW3NMnlcpifn0ev17OV5dvttlWQeA1CXQRkzXnMSVXCPmm4LLEqcnS5a5C3G6OlYAhDq9UaSURR+c/+Yv9e1Gy3JwWNc9fTAowqlS676Z6D7afhB37MPo9TMsRtb2VC/XCQR+hp8NRKDzOWNIZld3cX5XLZur04eVlIr9FooFqt2rgSxptosCwbgwGrrpuF2y2wACKzFBjcqsKLmVWcBCzWNU54EZrRVKvVLC1OxUmDrPV6jCs5SrDxFFNMcXQkk0l86EMfQiQSwdraGra2trC6uoqHDx9a9zcTE8gIAaMZjboRMVkhxpUB+2N33EXBZX8Ym8hjKOOU4QmFQlhaWsKLL76Iubk5vPzyy1heXt53vkAggEajgW9+85u2YKi627iA6D0yRk+Z9IvqpvFTRtUlxThSTRYZB64dLJ2gTL4yZqr0nETG4XmEukRdpsutYA5gZHzrOdyX33WAUQVJ46s0CF8zGU8DT6X0qEuIGhw1cdaI4EDlg7ouIkbXuxqjxqAwMFU7R6+hg1rjbcYpISpEDoJLg6qQoeWoE4YvXmMaTDvFFMcLY/Y2O+ZmpgxKbbVadu8tHktwLjMuhPNX2RI3CPMgGeFawGR3KL+YLUrZk0gkMDc3h3w+b/eEY6V5F1r4lXuH+THLlG8aL6SsyEVx2+iCeVDyg7IxR1FOyOD7MfN6beIgA/iiw2VHxikrR33242ojN27osDF7XCwP8JRKjwtOxHQ6PVKwjhOQlC8D1NRicTtArSjXj82Br8HEnARKl6llp4GOR1VE9B60IKEr8Fzq209TnmKKKY4P169fx/d8z/eg2WzaJIpGo4HXX38dDx48QL1ex1tvvYVisbivtIIW8nNL7SsT5Moe1xXf7/dtNXpgT95cvXoVzz//PNLpNJ599lncvn0bsVjMKj10aY2D53l2fztu78NyGCp7eA809DzPQ7FYRLVatQz7eQ9mViaH7kc3RlS/Z403ZrmNA41dhhdoIVFVmNy14jK4t6ik01hQw12P4V839ofzQavEAxhRtA+L43HdyABGyqmcBo6tTjkVBE7Siw52ynnfv2aKKS4TZmdnkc/nAezFJ9RqNczMzCCZTGJnZ8cWCdU4w3a7PVIAUCuAc45rKQStHK8V4smulEolNJtNy/AaY7C0tITl5WXMzc3hgx/8IF5++eV9SstB8DzP7knH2j1032s2LJW3eDxumSVmblHhuQhsjwYUa5sDozE96klg3x0EMoKsju8uptoXT8IgXTT4BSuzTTWmx+93LtmgUJesqyRRgeXfcfek/3NendaYPd+bs0wxxRRTOHDZ1XA4jGw2i6tXryKRSKBarSKXy43EE9brdZRKJV/B6sYNqpKhsQf8nAVO+/0+otGoZXGee+45LC0tIZfLIZvNPjF7wGSIxcVFtFqtke08XNc6sFcjDNizlq9du4ZcLnchFvBxLhZl15SlOaggoZ7jSdxb510xPG5oFvVhbXDQ9+OClsedx43Vcn93mrFoU6VniimmuFBQ5YTujDt37mBxcRGdTgcf/vCH922iWygUsLq6imaziWKxaBMuuAVOt9tFtVodqRju1ijRWMHr169jZmYGV65cwbd927dhfn4eyWQSMzMzdtNPvw0/D4qjCAaDuH37Nj72sY+h0+mM7EOn8Yp8T2Y9FAohk8nYeKGbN28e675/JwWttQaM1nYLBAK274C9jCzGcx4ElgQg0+O6stzAb43LnHSQ/WS9KDdEA9gfhMzPdN65yqfG5Wg7M7ZXS7ooeMxBzNNxY6r0TDHFFBcWjMXJ5/PW7eWHtbU1vPnmm2g2m1hfX8fm5iZarRY2NjZQLBbRbrcBwMYA+QloMg3JZBKLi4tYXFzEjRs38OEPfxiLi4tHvudxQZmBQAD5fB63bt1Ct9sdiXHRGCQmZzCpIxwO4+rVq1hYWLgwC7eyOXRfud/pokv25ihJImybg7bjcGNQLkq7vR2oy1BLK/i1kRvLoziorejWUkZHlZnDMr1OA1OlZ4oppriQeJKFigHFrVbLMjHtdhsLCwvWBVar1Ubqi/m5wbjFwdLSEvL5PObm5kb293s79xwIBJDNZnHjxg2b4uuyO4x9McaMFIi9qLGUbvFBPyaAn7kV9MfBr82APaaOCpTneVaR1erdkwRXaVF2zU/J8MtkO8z15H6vbiw3GNpNAjoLN+NU6ZliiikuFJ5mYUqn03bLAd2zT99rnRs/Qa9ZnSxPEQqFbM2yt3v/wWAQ169fx+zsrK/bge/9XhfBneWCbc6sHjfDVvvAL7trHDSmR+v0qNLDcgBkhA6r/TMp8Mve0rpS/KvKIrCfGSP85ou6uciYKvvjskCnHXQ/VXqmmGKKiQcXwfMOVpy/DHDjmw6Kq/FLddbzuIqhVhbWQHT3e1WELprS+DTwcx0q3PY9jI3x+8ytg3XQbzQG6LRw/qXAFFNMMcUUEwsyVayk7efOohvKbz8t/lXWi8qjlh5QZUevzfNehoKymrKuu9f7QdmecS4uN+bK3VJKa+W5TJK6aLm7wmlgqvRMMcUUU0xxZqC7iYUH1R1FpYWuLde95ReIHAqFEI/H0Ww27e/9GAwqPH7nnUTQnchtH8j4KJujYFwUf+un+LB8AmOkNAPMja1SqNITiUSQSCSmSs8UU0wxxRSTi4OCXf0WyYPcWzxGjz2IuTnt2jDnBX7urXFp6bonJtPK/c7nxuVoTI8qVnxpHJfGdelYOEkFdKr0TDHFFFNMcaro9/u2SjZftVrN7scYCATQ6XTQ7Xat8uJXnNBvkVSlh8Hqmi2kVa2ZqXea2yCcJdju+rz9ft+WamAdH8/zrOuJG+v6ZWn5FTmkEmTMoFq5y+BEIhFkMhmEw2H7+3g8jt3dXTQaDcsAnVRg+VTpmWKKKaaY4lTheR5arRYajcbIizEm3LSaTIPG3ug5/N67Sg8ZhG63a89LZsHNZpp06Ca1uvUDFSDu+dbpdGxcFJVFP+XGLfDIz3kMs+OUCWJ5hWg0asdALBZDsVhEq9Wy9ZimSs8UU0wxxRQTAY3nYDxPMplEOBxGLBazLAELMbIaMytQHwQumIzXIaPAYGUG3QKwQc8nySycFfxKHrB9U6kU5ufnsbi4OLK1SbPZRDQaRa/XGykR4Kamq5vMvZ4yPZFIZKS/GL+Vy+UQjUZtfBHvadw+aMfp7poqPVNMMcUUU5wqgsEgstksrly5gnA4jOeffx6ZTGYkY6hcLiORSCAYDOKZZ57BrVu3kM1mkUqlAIymV+vfSCSCVCqFXq+HXC6H2dnZEXaDsSb8fn5+HtlsFslkcmKCmdmO6voLBAKYnZ1FLBZDvV5HJBLBe97zHnQ6HbsFS6vVQrVatUoQ2RkyYf1+f1+NH3cbEY3p0TpI8XjcKqGJRMLuYReLxRCNRvHSSy8hlUrtU0DdkgNvF1OlZ4oppphiilNFIBBAPB5HNpsFACwsLOxbNBm/EwqFMDc3h/n5eaRSKd86Rm72ViwWQ6vVQiqVQjqdRrvdRqPRQKfTAQDLZsTjcbtvGV05kwI/hTCVSiGVSqHb7SKRSOC5555Dq9Wy8TSMs6KCyNgnMjJ0B3LbFnVvERq8TCYvHA7bNuY2IcFgEOl0GjMzM4hEIpifn0csFjvx0gGTX5hgiimmmGKKcweXqTnKYjdJSslp4LD2cgPAj/pet+0Yl44+rk/9ygcc5V6PC+ZJUvaMMVsAHpzc7UwknvE8b/7tnmTa9k+NafufLabtf3aYtv3ZYtr+Zwvf9n8ipWeKKaaYYoopppjiomLq3ppiiimmmGKKKS4FpkrPFFNMMcUUU0xxKXBulB5jzP9gjPmmMebrxpivGWO+4xjO+WljzAfe7jGXAdP2P12cRHvLub/bGPObx3W+iwxjzA8ZYzxjzAtHPP6+MWbO5/PqE173iY4/4Dw/YYy5fhznOk8wxswOx/3XjDHrxphV+f90NmGaYDxt+xpjlo0x3xjz3d8yxvz7Y77bN06NMX96KOe+2xjz4bf3RMeHc5Gybox5GcD3A3if53mtodCZDvxTwrT9Txfnub2NMSHP8/ZvsnNx8SMAfm/4938843t5GvwEgG8AeHzG93Gs8DxvG8B7AcAY8zcAVD3P+/v8/rTHoTEm6HnexJRkPqx9n/KcP+X3uTEmCP9x+h8A+FkAPwCgCuCVt3P948J5YXquASh4ntcCAM/zCp7nPTbG/JQx5ovGmG8YY/6xGea0DdmBv2uM+YIx5nVjzHcNP48bY37FGPOqMeY3AMR5AWPMPzTGfGloXf/Ns3jIc4xp+58uxrX3fWPM3zTGfMUY84dkJ4wxSWPMLwzb+6vGmP9w+PmyMeazw+O/4mdNGWM+OPzNLWPM+40xnzHGfNkY8zvGmGvDYz5tjPkZY8yXAPyV02uGk4UxJgXgOwH8pwD+tHz+3cNn/jVjzF1jzL/k2JZj4saY3zLG/Gc+5/2rw3nx9YPGsjHmfx6O908aY+aHn73XGPP54W9/wxgzM+5zY8zHAXwAwL8cWujxcdeaBBhjPmGM+XljzL8D8PcOaCvLDhtj5owx94fv/9hwjnxt+Jvbw89/TD7/R8NFGsaYqjHmp40xfwDg5TN56DPEuPbqtvEkAAAgAElEQVQCEDTG/JPh2P1djrth/3x8+P7+cA34CgYGxcg4Hc6n9wLYAfCXAfyXw+++ayi3/t/hNT9pjLkp5//54TrxujHm+0/kwf3KS5/2C0AKwNcAvA7g5wD8e8PP83LMLwH4geH7TwP46eH7Pw7g3w7f/1cAfmH4/t0AugA+oOcCEBz+/t1yrg+cdRtM2//s2+EctPd9AD85fP+fA/inw/d/G8CPDd/nhr9LAkgAiA0/vw3gS8P33w3gNwF8GMCXAdwEEMbA0pofHvOnpK8+DeDnzrpdTqCdfxTA/zZ8/wqA90v7lADcwMDw+xyA75Q+WAbwbwH8OTlXdfj3YwD+MQAz/O1vAviIz7U9AD86fP9TAP6X4fuvS3//LQA/c8jnEz8/APwNAP81gE8M2zN41DYBMAfg/vD9P5A2j2BgdL0TwL8BEB5+/nPs12Ef/fBZP/9pta/P537ttYyB3H7v8PNfxZ7s+QSAjw/f3wfw1+RcI+MUwPsA/HO/6w/748eH7/88gP9Dzv/bw3l1G8AjDOXbcb7OBdPjeV4VwPsB/EUAWwD+lTHmJwB8jzHm3xlj/hDARwH8MfnZ/z78+2UMOgoAPgLgXwzP+XUMJg3xw0Ot9KvD87x4Ig9zATFt/9PFAe0N+LfrxwD8t8aYr2EgXGLYU2T+ybB//jVG2/SdGCzOP+B53lsA7gB4F4D/Z3iev47Bok/8q+N7wnODHwHwK8P3vzL8n/iC53mPPM/rY6CALst3/yeAX/Q875/7nPNjw9dXAXwFwAsYCGgXfey16b8A8J3GmCyAnOd5nxl+/s8AfGTc50d+ysnCv/Y8r/eUbfI5AP+9Mea/waBGSwPA92Iw1744HPffC+C54fE9AL9+7E9wceDXXgDwLc/zvjZ8r3LIxUEy4/sA/NaY714G8MvD97+EARtL/KrneX3P8+4BWMFgfh0rzkVMDwB4A3/qpwF8eijE/xIGbMEHPM97aAZ+yZj8pDX828Mhz2GMeRYDS+KDnuftGmM+4Zzr0mPa/qcLn/b+8eFXfu1qAPxJz/Ne03MM+2QDwHswsI6a8vUaBm38bRj42Q2Ab3qeN47Gr72Nxzl3MMbkMVDUXzLGeBgwjJ4x5q8OD2nJ4e4Y/n0A32eM+WVvaILqqQH8Hc/z/tET3tK0INrRcJRx2MVeaIaVI57n/fLQNfYnAPzfxpi/hEF//TPP8/47n/M0vQmK4zkMxpj/CHtxbX9hTHutYP/cGOdWPaivPgbgTz7Fbbrz5Njnzblgeowxd8SfCAx8gRTwhaFv/uNHONX/B+DPDM/5LgwWbQDIYNBBJWPMFQwCrKYYYtr+p4sx7X1QxdXfAfCTQz85jDHfNvw8C2BtyFb8WQwWdqKIgTD7O8aY78agP+fNIIgaxpiwMUaZu0nDxwH8kud5z3iet+x53hKAbwH4riP89qcA7AL4X32++x0Af344J2CMWTTGLPgcF8DenPkzAH7P87wSgF0zjIHDoM8+M+7z4fsKgPQR7nmicEib3MeAvQFELhljngOw4nnez2LA1r0bwCcBfJx9ZIzJG2OeOfknOH/wPO83PM977/D1pTHt9bSw43TI0oW8QTD1yHdDvIK9GLsfBfBZ+e4/NsYEjDG3MGDkRgy948B5YXpSAP6BMSaHgRb/BgbUfxGDiPB1AF88wnn+IYBfNMa8CuBVDKg5eJ73B8aYrwK4C+AhBpbcFHuYtv/pYlx7jwvc+58A/AyArxtjAhgs3t+PQXzCrxtj/hwGvvARy8vzvI1hMOBvYeA7/ziAn6VQGp7zm8f8bOcFPwLg7zqf/frw86O48v4KgF8wxvw9z/P+Gj/0PO93jTHvBPC5oQ5aBfBjADad39cAfLsx5q8Pv/tTw89/HMDPG2MSGFjV/8khn39i+HkDwMvigrgMGNcmfx/Arxpj/iKA/0uO/2EAf9YY08FAZv1tz/N2hn3wu8O50wHwX2C6rQPg014YGKhPg09gb5z+NAYxccS/AfBrZpCA8ZPD1y8OWdct7PUrALwF4AvD+/jLnucpe30smG5DMcUUU0wxxRRTHAuMMf8UgySMzz/h7z4B4Dc9z/u1E7mxIc4L0zPFFFNMMcUUU1xweJ73F876Hg7ClOmZYooppphiiikuBc5FIPMUU0wxxRRTTDHFSeOJ3Ftzc3Pe8vLysd8E2SbP89DtduF5Hnq9HrrdLvr9PsLhMMLhMIwxCAaDCASeXFfjufr9PrrdLnq9HkKhEGKxGILB4OEneErcv38fhULBHH7kwTipth+Hfr8PYNBubDu+DwQCCIfDtt2GAZ22fT3PQyAQsK9QKIRQ6Gw8qV/+8pcLnufNv93znHT793o9tFot9Ho929ae5yEejyMejz/VmHfR7XZRq9XQbrdtH/JvJBKx/XicOO/tr0z3STz/WeKiyh4/cF70ej00m010Oh2/InwAgEgkgkgkgkAggEgkgnA4fCb3fN7H/qRjXPs/0Uq0vLyML33pSwcew8GnAsT9rN/vY21tDevr6+j1eiOvRqOBTqeDWq2GQqGAbreLdDqNbDaLUCiEaDSKaDQ6ct5QKIR4PI5QKATP8+yCzWv2ej3s7OygUqmg3W6jVquh1Wohl8theXkZyWTSLtjGGKRSKaRSKYTDYWSzWSQSiQOf7yB84APHs5fmUdr+SeDXJ81m0yqbrVYL/X4fOzs7KBQKaLVa2NzcRKFQQDQaxfXr1zEzM4NgMGgFTLPZRLVaRbfbRTweRzQaRSQSwfz8PHK5nFUyT1MIGWOOJUvjONufArzZbOLBgwfY3NxEvV7H48ePUa1W0Wq1UKvV0O/3cevWLbz44ouIRqM4zBXNOeR5nu1XGhLdbheVSgWvvvoqNjY2EIvFMDMzg0gkglwuh7m5OSQSCSwvL+OZZ545NkPgPLa/gn0BAIFAAMYY9Pt9u6iGQqEDx6u78PJcfE8DrtfrwRiDUCg0YgzwmiehcJ1X2QMcTdms1WpWZq+trWFjYwPNZhObm5soFosAYNuY7W2MQTabRS6XQywWw82bN3Ht2jVEo1Hk8/kRWX7SOO9jf9Ixrv1PzPw+aFB3Oh3cvXsXv/d7vzdi3fJYYwwajQYqlYplZXSRDgaDdpD3+30kEglcuXIF8XjcWsn9ft+yQp1OB4VCwU6UYDBolZu7d+8iFouh0+mg1WohEAjgmWeewdLSEtLpNG7fvj0yUXQx8Xu2iwK/PimVSmg2m1bp6fV62N3dRaFQsMJma2sL0WgUsVjM9gUVzlarhUajYS2yVqtlF89Go4FYLIaFhYUzs7zOC9i+Ozs7+NSnPoUvfOELaLfbqFQq6HQ6I4vwo0ePcP/+fYTD4RGLlvNEP2u322g2m1b4A4NFoV6vo9FooN1uY3t7G/V6HcFg0C66kUgE0WgUyWQSP/RDP4TFxcUTZT/PEygjVJ7QMOr1eojH40ilUvYYFypvlKGj3Or3+7btA4GAnSuxWAyZTOZYGLxJhOd5KBaL+Na3voVarYY//MM/xGuvvWYZeiqObE/+xvM8lEol3Lt3D5FIBJubm7h+/TpyuRxeeumlU1V6pjifOFWfA62eTqeDSqViGQQKjUAgYIVQq9VCpVJBv99Hq9UaWQx4Lv5NpVKIRCJot9vodrtoNBp2cgSDQXS7XRQKBezu7sIYg1gshlAohF6vh0AggGg0ahcMKkO5XA4ArBJgjLFWmftMF1XxISjwqfh1u110Oh3bV+12237XarVgjLHf68JLq4sLQK83KHbabretQukqsJcNXBBbrRbq9Tq2t7exurpq27fT6VhGwBiDeDyORCJhlR6XlVBwsXbnSaVSQb1eR7fbRbVaRbvdHmEXOOdSqRSKxSI6nc4ICzHp4BimotJsNq3SA8C2xTilh7KJLKmya1Q6aQB0u11EIhEAQCqVOtXnPE9gm/uB8oPMcaVSsYyz53lIJBLWbUWWWRk29l8oFEKpVEIymUQwGES73b7UsmeKAU5c6dHBtbGxgfv376NareL+/ftWQAeDQUSj0ZFjw+EwUqkU+v0+IpEIWq0WPM+zik0oFMLMzAzS6TTy+TxeeOEF5HI5fOtb38JXvvIVVCoV69vlQOfgp4uFMQ2cMLQYSqUS3nzzTcscbW9vI5VK4caNG0inB4UlJ2HyeJ5nrdBWq4VqtTrCApBRazQa9jgqLZFIxAofKp31eh3AgMmga4uKbLfbRbPZxO7uLhqNBqLRKNLp9JnF+pwVer0eHjx4gHv37mF7exsrKyvY3d0FMBjznAdUslutFra2tux4VeWn3+8jFoshn88jGo2iWq1apoeKDNkGKqnBYNDGx3Hssm/6/T5WV1dx9+5dpNNpXLlyxSr/k45KpYI/+qM/QqFQQKlUwsbGBlqtllU6XaWHCiEVHPaHq/hz8abSz7a/desWPvrRj+LKlStn+NTnA2o4Mqyh2WxiZWUFr776KqrVqg11IDPG46mYatvy/0AggHq9jkePHqHRaODmzZtWZiWTyUsne6YY4Fh7/SCXlud5WFtbw+c+9zkUi0Vsb2+j0WhYZiUWi1kBAuwNYmDgemG8CbC3qC4tLeHatWu4du0avv3bvx0LCwv41Kc+hc9+9rPY2NhAMpm0AisUCiGVSiEUCtmBD8BaaLSuPc/D9vY21tfXEQ6HUa1W8ejRIywsLCCTyVilZxJAK5RxJHRvxeNxG+DteR5qtRoajcYIKxcOh5FMJhGNRjEzM2OVHFL8sVgM8fhgyxYyRLSkg8Eg0um0ZdwuE7rdLlZWVvCpT30KxWLRKj/hcBj5fN4GFHNRpcuQTEwymRxR/nO5HBKJBLLZLABYpobKJgB7PACr0CroWms0Gnjw4AG+8Y1vjMT7XAaUSiV88YtfxN27d1EoFHDv3j3U63UbQ6hKD/snGAyOxJS48YQArLuMMUKUix/+8Ifx3ve+d6r0OKBBTBfV17/+9RGDi1CFvdfrIRqNWkWf7sN+v49yuYzNzU1Uq1UsLy8jHo8jnU4jGo2OuMX0nFNMNo59xfFz99DiIZvA4DRi3GCj0O73+5bu58Cm0NH/aQW4AohCiuyF+vGV6dF4Hbq+Go2GXfQ56SalthHbiIKDIAugiy8DwumGqdfrKJVKiMfjiEQi6PV6aLfbI+4X9l8oFLJxJhrcOSnt+KSg67ZWq1kln+D8cQORA4GAdTsCe8JeX3SpaF+S4fET7Hyv1yMbR5bpsqDX69nAWfZNvV5Hu9227kBgtH+CwaBtW3csaztT4Ve4i/gUAzDDsFqtolarodls2hADbUM3tpKyifKK8p2uZCYHVCoV22/6+ykuD05E6QH2BiUzsRhMvLW1hVKpZIPQeKwGMyuMMdZNpdRxOBxGs9lEqVRCMBjEG2+8gUKhgLW1Net+iUajSCQSNsOI7iw3SNMNJqT7ixlJm5ubiEaj1sWmz+f3zBcFnudZFq3f71vrJxqN2uePxWJIp9MwxqBer+Phw4cwxmB9fd0GYz7zzDOWuUmn0wiHw4jFYrbtuSgzqFPjHlxWYpLBsVuv17Gzs4NarYZAIIBsNgtjjBX4dHNRIaeSyEVAlZVKpYLV1VUUi0UUi0Wsr6/boFkyl1SuVJnVhToYDCKRSMAYg7W1NXz+85/HwsIClpeXcefOnQs3rp8GnU4HxWLRZtIp40bGmayw53k2u1EzvSKRiGVI6Ub0PG9kkdX4qcvGcrrwG1fVahUPHjzA1tYW1tbWUKlUrNJCt7i2HdeMZrNpY37YD2R6KpUKer0evvnNb2JzcxM3b97EzMyMTcbQOXHQvU0xwEVvpxNxb1GgqiBvNpsol8vY3d1FuVxGOp22iymw55t1LVFaVFR6SBUzK6tarQIAHj58OBLsxtgdulAY9Kb3p3ADOzkJms0mKpUKstmsFW4A9rFKep6LhG63ay1ZtlE4HLbMGv3fLCfAWAe6u/L5PO7cuYPZ2VksLCzgueeesyUA1H1ljLF9x3akMgTgUig9wF5pgFKpNOK2YrYb24bxCVQW6aIiYxaJRGzbFgoFlMtllMvlkdgHPxez+wJgA6bp2t3c3MS1a9fwvd/7vafePmeFXq+HSqWC3d1d9Hq9kTmgYF+Q8STD0O12kUqlbDkNKj18r/3G9p5mbu1Ho9HA2toaVldXUS6XbQkTxhuqi1yDz2u1Gra3t9Hr9ZBIJJDJZPYF8a+srGBzcxP9fh/vec97bPLMtB+eHBd5zTtWpYeKgioQXCxZG4eLHIWHCxXE+r0WvdMgW2YXsT5Mq9WyTIMrtFylzO867rPwxawwZgXQEr+I4ELKuBC2LdkAxjjRHcXiglQCqbyoO4XvqdTQBUbXFq9XLBYt28N+isfjNsh8UoVQvV5HpVKx7l22ubI2wH7WkTS96xLUbCEqQ27BNlcY+SlBbk0rgqxcqVSy/TTJiikVcLoPAezrG45PzhO2Mz/nX9ctTJAhAjDCGk+xBxpfZJspZ8k6x2KxkRhQ9RBQlrGgJwDbP9p36hIGRg2ui8Lac+4T7nqlCQtTjOLYlR7XwqRLZHd312rZwF5wpcbp8HMqNwQHNzCID2EwMRUqxgoxlXpmZmakBgYXbxVoAEYEFK/D4zUuiNdZW1tDJBJBJpPB1atX9xVJPO8DjM9Ur9ety5EsnKYz000VDAbR6XQsc9PtdlEsFkdidxibQIWzWq2i0+lYK9cYYwOltU5MPB63FPPVq1fx3HPPWQWIAmtS4HkeHj16hK985Ss2eJkGgGYBcRxqHRJgbzxqSjSVS4050ZpWqvjQSOC9aCyXO3Z5LF1d3/jGN6wLk8HSkwjOi3K5PJIKzTpG2o4c58wijcfjlo2OxWK+Fcv5OxZJff7550fiGqcYgIkRTO2nsZVIJGw7MxDZld1UgLie0JXMJBhWI6esajab9hpujJCf0XBeQJd1qVSybBVlBMsnJBIJzM7O2jXqOOEq8xcNx+5UdhuDvvJCoYBqtWoHE5UJvwBkCmUNoFW3EtkDCnzP81Aul+F5HmKxGJLJpHV/8Xs3sFlZDJft0QGvLEWlUsH29jaMMZiff9vVxc8EVDbr9foIRd9uty2dTKWDQp51jChE2u32SO0S3TqBlmy1WrUxDaVSyTJ9VHpisZgtKxAIBGwWC39zkSeVH4rFIt544w1sb29jY2NjpEqvMgrKEOj/nA8auMm/ZB44VgllXTX7iHNB2Sb3HgCgXC7bGKFr166dWludBWgUcZxTLqiL3S0pwD5hhpcWfFTWh6CyVCwWbZzJFKNgdi1d6lQM5+bmkM/nR/qErBDbm32mMYQsF8CtKxiLxXlDpWoc439eoXXsuIYqe+953qXJvHxSnHgkHWN6uOhpiqeWzHfZFhcuTczf0c1FqDuAvxtH83OwuAvGOHdCo9FAtVq19YP87u28gm4QslZkd7QopFZZbjabllKm1apxUXQFMFYLgI27YvwOP9eCe5lMBrFYzMYKkRHSasSkZ89y35zjBBXNcrlsywJoXJPrChk3nnS8k9XRsaxzi33mBmlS4OuxwJ4Sxf8bjQY2Nzfx8OFDdDod3L59+0Tb6DyAbaMKDzDKNPNzJle4RRz9jCtVnCjnmM3abDZH4n8uO7rdrmWiWdSRbim2K1nOcDhsKzLTtU6lhwu/uuOBPZnPY1zF87wqPrr+dDodrK+vY21tzcZOcpsaylBWxqdbmmParSVFhlmLPGrtIz+XtpsNOk5eqUHs5wUiDvpMDTX3r8ouMnyZTOZQT8GJKz3tdtu6tsrlsm0wrXLsKi58ABXo44QKgzkpNNhx7FCyQm6cg6a9azqwsk36m06ng52dHcsmuULwvIN1eDqdjl18gT3mzBiDXC5nhU6xWES320U+n7f7kLEkPwNxKbBXV1exs7ODUCiEZ599FoFAANVqFdvb2zZLLxqNIh6P2z24dCIGAgE8fvwYgUAAMzMzyOVyds+ui74YcAxVKhU8evTIZi+yRlUsFrPH0up0hYOynJFIxDJqrntEBQHb3TUo/Fxg6uai0Gy327h79y62t7dx69YtvPTSS1haWjrRtjpLqIBW9ksVU2AvQFm3p/BTLvUcjCmhggQMFvdyuYxisWhrx0xiLNuTgi7Azc1NW1k5EAggk8lYBmdnZwfVatUW5mR8j8YmUkliyQauN1QCaLCRWSbOo3uLRtPW1hbeeOMNm+G2srICz/MwOztrM0B5PIvHkjnjWKV3RFkh1uPidkwsz0KZ7cb6MeFC109gVFFhf1DpDAQCNjtY13A/RpTgfPIrc8KxQMW21WohmUzine98J65fv35ge5640sNGYoEpgoOTSgk/G5e27qf0EMrYuN9T6PsFTvM7N37I1YT5ebvdts/hd5/nGaSKSeEzCI5ByhTmjD1Q1xewpxyxJg8ZNU4S9jOPZTl/jYlgarTrj2eQOLC3Q7IbqHeRwbHebDZtNoq2oQpYV4nWMeuOV78xSKtKj3fvRV/6O455jhXGyc3Pz09MXxwEd86ri9ttU84XVXr84LopVTniHHMX3ssKLmxcxFjfSItA6noC7K9XxXalAavsMbCXmauMp17fvZ/zoPhouzD7eWtrCxsbG/YYDQcBBs9Zq9UQDAaRSqWsksJ21WxRKo2JRMLWM+r1BvvOkWkjeB/K3h+k9HDfPxaMdEsNqNE97tkpe5RxVRdmvV5HvV5HNpvFzZs398XuujhxpafT6dgAQcbXqOVJVoYP5lL8akXxcz+h78Y+uOfyc2lxcfZTiFy6n5qxuiYuEmj1cBBzoAP7s9m4MWi/30cul0Mmk0Gn08GLL76ITqeD3d1d/MEf/AEePHhgMwTIgC0sLCCfz9tr8XzMwlCGh3+18Jgxg81mqWRyso6zBs47OM6Wlpbw0Y9+FLu7u3jzzTexsrJiM+C0ZICfwsP+4XhVdwjnDt2UpKk1u9BPyQH2xrgWPWQ17lQqhZdeegnPPvssrl27hnw+f+JtdRagDGI/6Ca545hmul+VseQYVeVGt03gb5n5SRdwo9EY2SrnMoJbrdTrdWxubiIUCiGZTNpaO8paMoaK7DHlmioxLN9AxkPrIXneIAh4bW0NzWYT165dQzabHWEsFOdB8Wm323j48CHW1tawvb2NWq2GdDqNd73rXQD2Eh8Yd0oGXoPxZ2dnEQ6HLUOsbqdut2vjLtmOnBd+SRJUOBjvqcwZ28tVbpgo47anbgfF/mXQOY1p3q8mDdAAVwWWew8yrGIcTiWmh4XTCLWgeIxLBeui6LfgcRFQDZfHjvM3qgLFhVq1Uv5GLT51AbAwHIv5XSSohh4IBGxGFhULfZ5UKoW5uTkb58MaLi+//DJefPFFPH782Fod2pbJZBJLS0u4evWq7z2Q7qT1poOWSg+VZLI/7XZ75NiLiEAggBdeeAFLS0uo1+v45Cc/iX6/j2q1ivX1dWuR6RYRqmxr39DqpbBRVxWzh7TyuBoVOi903FNgGWOQz+exuLiI+fl5fN/3fR8+9KEP2firSQQFOIP7q9WqdberIOaCqGnOVF6UyaTCRDZPY0Y8zxuJQ2k0GqhUKjYeY1LhspUuarUa7t27h42NDZTLZZuhS4XFdaEwQ46yg+n/XPx2dnawuroKz/OQz+dt1iH7sVQqYWVlxY7ppaWlkT507/ms0Ww28dprr6FQKNjNiZ9//nncuXMHALC+vm4ThVZXV7G9vT2ilMfjcVt2olwu2/WLbciQB9anomzRMBGy1OrSCofD1oVWq9Wws7ODTqeDaDRq+0gr9jOLl+wOjTQ1iIPBoFXyWHOM7Hgmk8H8/Dyi0ShyuZydfxwDwWDQ7hJwEE5c6VGlYZzyQqGsQoM46kLnskEHTTRlefQeCXeh4e/pcrloCo8ucBovNS6GgAOaBdp4HGN7ms2mHdTAnl+WFKY76FxGTwPbdGFRH7zSl25/XkSwxkgymbSC2BhjNxsF9sYdFXfXl679pQq+KvKack6oUq/nVdca+zmZTCKdTiOXyyGfz2N+fv7CKpuHwTV6/NxbruHE3xF6jM4pxgkC2HdOzTLSOlmXATr++NwsncH934A9tzsXNm0fd/84v1AEraUE7PcQUMlnyRO69s9jLSrP86xbiq67YDBolTbNQNYAbWVlXDbfNf7JcuqeZJo9x+QXVfYpr9SLwOOpQHIN4bmUSaWi466njFmk0at13RgS41ezzF0zxuHElB5tZFZEVp+cChNq6tqRbFT3WGVgVJAzk8Wl8v0ElN6H3i8bTC1fDgL6SNXned7hp2xwgCh1z+AydfVxgoXDYRt/Qv+w7jodiUSQzWYRjUYxOzuLdDqNZDJpd2Z3lUllHNxFRycnBRPT2rXWzEVGKBTCO97xDgQCAezu7uKVV16xjFupVEKr1UIikbCWrrJgqgCqwqJVtNWlpXEN7njVooazs7NYXFxEIpHAO9/5TrzwwgvIZDJYWlqaWIUH2GsjCmtVAGmB+skKCnQ3FkEXVvaTKvc0CjhvGFdxWeKlgFEDkgHJ29vbWFtbw8bGhh2X2nbuGNS4HMo1VVjm5+dt32SzWSQSCQCwDAezUzudjnUzJxIJPPvss3j22WcPjNE6CwQCg6rt1WrVjqVarYaNjQ14nof19XWsr6/bWB3Ky0gkYpmT9fV1+//c3BwAWPc6N2Xd2dlBNpu1NeiazSbq9bptZ557bm7OpsOzXzUZiLI7GAza/ux0OnaT8Xg8jnw+bxVbGtBcW8kqqUdFY0pZe4j1lxjflMlkcOfOHaRSqQPb88SUHtW8ebMUslRwKBgY4c2duv3ic8bR/fzMb5DqscooAPtT4lQhoEbK2Bf6juljpO/zvEM1fX3RLQjsxThpf/FZOZDpv81kMnanb2rbgUAAuVwOqVQK+XzeKj0aMOdew+/lxlBxXFQqFcTjcSQSCZsuf5ERCoVw+/ZtPPvssygUCrZuT6VSwfr6OorFInq9nt1NXWMV3IBCthVjgVRYq4KpMVO6YFAZmpmZwXvf+17k83m8733vw8lbM1wAACAASURBVPvf//59sRCTCho4rtJDit51/RI0AtzPNE4NwMjirTEJNCZ065FJhqv0dLtd7OzsYGNjAzs7O1hfX8fm5uaIm8Nlgwkeo6npGscWCoWQy+VgjLGJE91u18a7UL55nmczoViu4+bNm+fOuKKxqen5LCnR7/exsbGB9fV1u4bRPUVXd7fbxfr6OsLhMK5fv27DD8isUel59OgRFhYW7LY4tVoNxWJxH/mQyWRw7do1G6fDkAktY0K5zz5qtVrY3d1FtVpFNptFNpu1HgW2PXc8UKWH19XkAcbA0e1FdyZL4hzmij8RqaYMg5+SwmPc45QFco89yjWPem8KvZYbEOfnInCzAc4zlL1x79l9tnHuLj63UsW0PPL5PJLJJGZmZpBMJu2mlarcuHCpVTe+RJUetvekBXpSqCcSCczMzODKlSuIxWKoVqtW8Qewb8HV94fFSYxjO1X5mZubQyAwKAw5OztrK5lrlsUkw5U/wP494A6z+I/CCHBukWVQo06V18sEulS0YjKAfXKB8kuZXmX4NSlFXbzAnhKq1eH5V40wMhTqavRbg86K/VHDhS9u86Pp+K47lr/TRAk3uN51vQJ7dW+U4VS2UoP23QB+tiv7SNuWBrQbOqIEiBYcVmOBCpK68MjUMttvnIHi4kQkGzVpRlOTOXH9uRxkvGk2AqO5gf0p6q5vUq/pB2Uv1GLgXzY4tVqN2aHfnfdKGpydct7BtiWFz7ZTK5UDlK48P2HM9/R9h8NhvO9970Mmk7Fl42OxGK5du4Z+v28D1nhujTPhJNPJwvt0BQ7HBO/hosMdt4lEAt/xHd+B5eVl1Ot1rK2toVqtYmVlBa+88gq2t7dtpoobtMzzqTLq9pleE9jLoGi1WnjhhRfwgz/4g7hx4wZmZ2dx/fp166I8b5buSYFClrKKApiBzK6ABvbmi18f8L27OCoDpLWY6D64iCUwngRcvHQs9no9bG9v4+HDhzbrh24OKoa6oTTdVAyI5RYSWuBU5xf7MRqN2lRsynMaHOqSUfag3++PMJ3j1pzTQrfbRaFQwM7ODsrlslUUuTtAMBi0G2Jzfqv7ms8QDocxMzNj/2exw3a7bWMN4/G4jcthWzHsJBaLWeaMbcGYnFgsZsMceA+9Xs/G46hyoiEO4XDYMjNk/3j9VCplawjREGRBT1Y2J9PDosFUfg7CiSk9vDkuui7TQEVCU0VJ06uVqZ3G/3UAum4qfu73mav4aEAc6U8GSoXD4X1slbtL+HkH71k3FgX2Bisw6pOlRu3nGuFn3ILi9u3bmJ+ftxOCLgHNEtPrsd3VVcP+43hR9wKwx3SwLyYN0WgUL7zwAu7cuWPp30ajgc985jP4/d//fZRKJaTT6ZECYS57o3NBDQR1HRIsPEkB8ZGPfATvec977HgfF9g+qVBLmNaya3Bp+/m16UHgOFcjQ7dZ4UJwmJCeBLjKYK832NWeGTpclDU2hwqpMQbpdNoqN8zwpKGWSCRGFnkNJtfYLGCv8C1jNbnAUhllIT03UJp/z0Lp6fV6KJfLqFQqNpW7VqsBGKxhCwsLmJmZsTXTANhsY3U5RSIR1Go1+zyaGRWJRJBOpxEKhUbWN64JjDN0WWCOb2YlGmOsLHfrJlGeq9LDvuD9aBYZ92GcnZ1FMpm0GWKtVguFQgFra2sjAd6cS4cZyCfm3tKgJLVQ1ZXhfq6L5DhogOBRqOeD3FnuZyrsNDCa96VWGov8ndeIf2C/C1HfK52s96/uLH6vL1ps9AUzOI5CxKU83XvxuzeNN+L5lN6eNBeAKzzZB4zzoPWkwefjXI+qDLnj3Z1X+hmwp/SPq1FyWeAq1H7t5saVaDtyvmhfjRuvrhzkZ5cRHON+GUeMB+HcYNArgJHxyn5yDWou3I1GwxZcdV3FmrhBA5GxPrrlDq9zVu4tldFUYlS+0guh7jnXYGWbqYFZqVSwu7trGXW2MRUVTY7odDqoVCpot9s2MBzYIyHoqmQ7s8aSKjsaQsEsLhYWpBEA7CUY0BBhfR9uTULFhgayG/N42Hw6EaWHkdqFQgGFQmGksi8Hng50ZYL8AmuBUQVGFZ4n8blro/i5uXhtvbd2u21TsLmwG2PsgGGJ77OaEAdBBz4HHxdV9Zeqr9Rtcy3URtdWrVbD6uoq3njjDSSTSdy4ccPG+MzOziISidi0Sl1A9K9mkDFgud/v23Lo/JxWyCQwPaqgUAiows0S+czsYX+NWyhdyt0V/Af9jgLDLYh4WeEqi51OZ2SRdV3p7B+XBSBboeMdGN0bzVX0J02pPwrYxtzyQNcInRc0gsg0MOGFyrq7fgB7TBAwWFyLxSI8z7MZucroUNYDgw1219bWkEgkbA0a3utZ9g+ZFtZ2qtfrNtAegK1rpsqe1jkjC8+No3d3d9Fut7GysoIHDx4gFAohlUohm81axQ8YsJKZTAYAsLOzg93dXYTDYezu7iKVSiEajSKbzSIcDmNnZwePHz9Gq9Wy9XT6/b6tvQQM+pVtWigUEAqFRqr4l0ol2x/0qlAx0gBuHqPp9bxfehMOkmknGtNTqVRGOoRQC96NUwD8g2zdz/3+Pwzj2CE3s4sTku95D5pVwOrM53lXcGV29KXtTuGhlo+rVGqQGpWgarWKzc1NZLNZ5HI5m57oZl/ovbhWGY8na8bvaG2wH1wFeFLgKvEagKlMj9/vDmIS9Bj3OP3fZeOm2ANllBY0db93x7QyFW58mjsX+HcSlPmngSp/GuiqoJHFF+Nu3KBm152hLhRdAJUNpexRRbTVallWQeW//vYsQGXaGGMVAjVW1KXjkgr0RpDFYfwki8tubW1Zw51ZymxTY4xtcyqPNGTr9TqSyaQt6kjDlXtgsfgtU+Zp1FG2UymNRqMol8v2OJX73MaI81ANDV2XqBBpyMZB68WJKT0MnnXjXzRdmkKCN65FkYiDBprfg41TjFSx4We8D7XGOLAYWOtnhZHqi0aj53rfHE569oEucJz0bmbUYS4p9k0qlcLCwoItZscJ4Fq37iLsLrJu25Nx0wVlUpUeF5zI41wp7iI5biH2gyrvfr+9rBinHKo7VxUfHceM/9E4IGXYdG8u99x+7ycNLhMJjFbA1j0Adc89Khxa8bfT6djAVcbhkLFh3J/Wb+Fc0XVFFVTdMoEvLtyq9JyH+UIlzRiDeDyOXq+HXC5na+Uwvds1KrUSM0t+MOam1+thdnYWnufZ9HaN22R8jcrrXC6HcDiMhYUFZDKZkeriiUQC8/PzaLfbiMVidu2vVquoVqs2YJnbhzB+iJ+RBUqn01aJYx9QIVbDgv3NNYybWWug9TicmNLDCGvSUcBefQUuarRkNSUNwMhgdOG3EPspOu5nrvDhOdQSoEbc7XZtIcLZ2dl9v2k0Gtja2rIUOLdsOG+g9cJBrm4sat8sbAfAavGqaOhiqf7kK1euwPMGGRezs7O2ErNSrLQsVHHkgq4DGIAVTMo8cYxo4NskwWXU/CplK4tAoQTsVWEFjsbusA3VQprkBfeoUOYT2K/s0N3ImB0ex+yeWCyGmZkZa7Fubm6ObLOiSpNreCmDOonwY+9pwTNrjW1Fo4lsS6/XQywWQy6XQyAQQL1eR6lUsgoJ+4PxIOl02u6hxQwsyiBdBMkyMMOIgdAAUCqVEAqFUK/Xsby8vO95zmq+ULkLBAa7zUciEVy9etVmy7711lu2dpzub8mq73SFUWbUajWEQiE8++yzuHPnDur1OtbX11GtVq0LiRWRd3d3EQqFMDs7ixs3biAej2NpaQn5fN4mrXS7XbvbPRUdbnexvb2Nzc1NpNNpXL9+Hfl8HjMzM1heXrbZVgymzufzCIfDNuOLe265GV/AIPOVHgYaigyvOCzc5MQCmRlH4hcnQqiLRa1btRCUGfK7Dv8eJbbHL/bBvZ4u7pxY7vmp1LGzziuUQgb2mBcV8u4iqW3k1078LBqN2pRCMl4aw8Nz+bW5WsyqDKlrjcf4KV+TAL/ncJlIQp/fZWvGjWW/z1zFc4r9gbBuH6hyoscptU6Fn9Y2WVRVeMbhMvWDMs9cG+gCYQFbBjHTGNYNjRmAq+dTtl7bW/tTN7/k8ZqIwnnFRZxZqMRZ9xGfk24uBgozhZ8uI3XVkURgGyobybUtkUhYxqZQKNj20Zo6zGYmy5RIJKz7Slk3sj00tHkeenxYjoCsTDabRSaTscHRHAd8Ps0k1n5WQoQsHtcexmIdVmPsxJge7qzOOj2u4kIXkrINutuqngvYb826OEiR4Xl0sQX27+Dut8jqOYh2u22Ds2hVnCe4qYK6Hwondr1et5YstWXVmt1BxvcaKe8OMNcN4y7WCo0h4ljg+Xk9FZAsgcCJcdaC6O3AT1lR699Nc+bYBfaK542bIyr0/OJINGPrIrfhcUAXYY47P+bFrx1d5ZTKjsbLKbOj/arXUIE+idD28jwPxWLR1qNqtVp2Q8pMJmNdMJRdTGDggquZRRo/xWvo7uFktTnWXQXJrVLOazUaDVvEz/U4nNV84X1x/yk+6+7u7kjtLZWzbCc+K+sUkeWJx+OYmZmx1ZfZTizd0mg0cO3aNSwtLVk2n1Wud3Z2bOo4t87JZDLI5/N2WyIGIK+trdl4rHQ6bQvZAnubkT948MCSCNz2giwc70k9FGS+NESDipgx5lAi4kSUHhaQ2t3dRblctsLAjSnhVges86KMzzhh4KeM8H8/Yc/jVHN3qWy1Dg5brDmY6F/WugfnAcrkUFnglhqc/I1GA6VSCYFAwFYApjbvxi+4rILSxvo7vzZzs1WA/WUBtLoogJEBq7FhnBR6zYsOPxcssD+VXBVBl4lg+7rGAS0nfqYMBZXVSWjDtwNlpF23ocv08Hi2sxoM/I6yod1uW5eY67Z0Ge1JVXjGGTrb29t44403rAslkUhYF2EqlbIZScq0Mwg2m82i3+/b0ANVWAFYOadjXwN4qSBp7IrneZap5rpFpYc4a1cwn5mKoDGDuJyNjQ30ej3LlgCj4QOaocZn51qczWbxjne8A9lsFt1u17pi2+02isUi6vU6bt26hZdeegnJZNK2Ub1ex71797C+vm73C2y327h586Z1cUWjUeTzeVSrVbz55pvWxZhOp20oBJWT7e1t3Lt3z4bCkH1jMUrOTwDWs6Jzlgort0IKBAIjRW39cGLuLWpl1Lw5ucctkE9DueuifFThMU5Bca08P4GkNKibUnye4MeKKQvASa+xM0d9Bj3OzwLW/8cpjvxeSwO42QcueMykBDUfNtZ1uwLAP87Npfpd+H3mFyh9meHn3jrseD8FVBcbYHQc83s3y+hJ5t0kgEohK/OSxVGZO24ca6ahn1HLBZ2KErA/eUVloP52HNt/nuDGhqncpmKtst41gpTtUgWIjIm6GllyhC40xjzxtzRAaYwyKJ0ymoomq8kzuFjrjzUaDVsckvcwrs1dosM1NFxD251nLk7UvaXFhFzFRxdeYLTglNthioMYIP7Wpe7d925qMD8ntcrPXdZHt83gTuuun/k8wE8oK92pFbCVAvYLqNSJpQJfs8LIRGg7kKVhO1LA0XLTXcUrlQoqlQoAWAuO/UCXJ4tdep5nt2WYVLD+BXci5tjTVF0dl37xWX7ZRMBehdXLWIHZD5RDykS6bilX0WRshWbGxONxa8HyOM4LlTHAnotSlf5Jg8odBWvz6O7yZC2CwaBldvv9vo3/IFPP37DorV6HbLGfocf3lHucS7FYzGaNua4zvnSRPSuEw2FcvXoVhULBViyOxWKIxWKWqcrlctblRRcR5ejCwgKWlpYQDodtunowGMTW1hY+//nP2wrH9Xod6XQaH/zgBxEMBnHz5k3rqnr06BHW19fRbDaxtrZmN0ZmHzYaDayvryORSGBubs7u63fr1i0rc9rtNt56660RxbLZbCKdTiOVStmMrH6/bwOhm82mfc+6QcFg0CbN0B3Hcy0uLtqtNsbhRLehoNJDTU5jMdhgqpUz0NYVEi4OUzIOoiNVkKnLxhhjy5WTDqXgUkqUE4abvZ33fbg0CFMLLpIOBfazCi782B1OKgokNw5IlSLXItHBykyKarVqhZcuQBwzrPipFs2kgpM7l8tZ9x+h7T+OyeNxOob5ORV+xgZcZihrq6yDunjdGB+2My3iaDRqN9tlCi2P07g1QmWTysBJHtOqCGrRPFr3jJHkAsrjdQsWZdc1JpGuWs36oiLDoFh191C+sP+o8ITD4REjjdc7D8xoOBy26eCPHz+2MUuZTAbGGBtUXKvVUKvVbBYWy67E43E899xziMfjNm6m2Wzi4cOHeO211wDsrb/z8/O4desWkskkUqkUMpmM3d/q7t279jqMx2XbcNd3TY+PRqO4efMmMpkMGo0GHj9+bLOeeY+5XA7z8/N2D8dcLodWq4V79+5ZZader6PRaCCVSmF2dtbuEQkM5hDdnRrfdBBObCtlLmzjWBsKB63P4kff+zEIh8G9ljtglSrTYw/KtlDNmcKKBZPOM5QOdRkvjTNwKWY/a8k9r9+LTI7f8QqXatXx4LfIqHUwyQvEUaELiatY6vd8737uKvx+brPL4gI7aEy5beCOb8ouTQLw+924614WhYegS0VjUNR1q8ylxuuo8sk2IzvPMiO6qStZUa2Q7collxXVz3WfQU0qOCtoPGa9XkckErEV3BmbydgcxufonmasyEyWzd08m/2kFZI1oYLKPX9HV6JmMfLaxhj7Pe8d2FPyNTuM8p9kAzeCZT0fem408ysWi9m4Hg3XYA2jw3SEE2V6VKP3q4XBRqOm7Rdc6Vqw41xeCg2a1vO5Vpir8NAycBddLfGtUf0swnSehJYqEwDsxAdG0zopGKiZk9p1q6LqwqhtRktJrS0KMLbRQTFDmpnFtmRWAQtlcfK422hMOuiC1D1rCI49HaOc9OOystSdy+9INzNb5aJnxD0tlI0Zp9z7ZczpHGLsAoM9OS+Oct2jCOmLClVogMHYZSV3WvlauZfxIpVKxcpwtjPdOcouRCIRzM7OIpFIoFar2YWfi2QoFEImk0E6nbbn5RYLfp4GNdy4uSVdR2ep9HDBr9fruH//PnZ3d21Nmmg0ihs3bmBhYcFuKErWipnFc3Nzdqfy1dVVfOtb37IVlmu1GrrdrvVcGGNw8+bNkcKDoVDIusd4D4VCYeQeZ2ZmcPXqVZuNVyqVAOxtkUH3JOv6sN17vZ7d7ywWi+Hq1auWzQsGgyiVStaVmc/nLQu1traGBw8ejFRxJrt1GE60Tg8FNxdBPwaGA3vc1gUA9gmkoyo+7uKg1q072PmelgHPqUqXRpLzvOdxx3VVEtmu1LyV3aHCQmFB6nccM6Z9yPOqVeUyZ8oyqfAj1CdMi43WAj/XxWGSApkPgroHAX+WzG1bdbtoTBqwN655Lv5W61FpYdDLBD8X1FHgGmxUdhgrdVi8lMqhSR7TrjznYgvAxnGQ6aVhSRe8yhcq5cpKhkIhpNNpW91X5wwAW2mYeyfSMAMwkmDjnpfxIVS+0un0qbTVQWDQ79bWFra2tqw7J5lM4vr160gkEggGg0ilUrYdacSnUilb2brdbmNzc9O6+hhDReUil8vZ4GRlWahgVSoVm92l60EymUQul7NsCxUujZEis6RzjQYeZVQ2m0Wv10M+n7eKz+bmJhqNBjKZDBYWFpBOp23WGFP5yXIdZT0+MaXHZRz0IakU6aKnCs/TurNcel+1d/e8en6l4ciGuKnz6trSsubnUVi5wtS9Rw0idxUJP2vfFVx6vDI7tOz4GzfOR/vHjT1RSpsTg7Sp1quZVKuYMU4MRlSFR5kG13JWuK5GVwHlb/r9PqrVKlZXVxGJRJDP53Ht2rWRQnCXAa4h5bp2gf0Bufq9yhZlkd1zj3OrXAb3FrD3nFoWgPKUngAu4kw717gcxljyGA3cV5ngV4SQ59TfurGHZB94DOOOqtUqAOxbw04b/X7fBifH43Ekk0nr2jLGWFaHLh5g8Nx0D5EBojxm9etYLDYSl8qCs2QtmVLueR7K5TIqlYrdtDWdTo+sL8FgENVqdd9eia6bTN8DsCSDMcbuB8Z+pQI1Pz9vyxoAe25Pv7WNStxBrPWJxfS4m0jyQejLZQZUJBIBAOsLBPYzOy4OU45cN5m70AJ71Kv7O2CPuuYxWuRJFaDzKKyohKg7yG9gUBOn0PFTJlxXiiv8AVjakp/51fyhsNE2U58w7ycYDNpAPC4otFBUUT6P7f6kcJXMWq2G1157DYVCAffu3bNCTt2+qsgq3U4FXWPStI10R2pmU66uruKzn/0sXnvtNbz73e+2JeD1ni4DdOHUmAM/hVIzejSWR927PA/fu7GLXGg1c2tSFXmCrD8Da8nkttttG3i6vb2NRqOBWCyGTCZj92ViwblGo4GdnR3LHrDPdJHTisAMO6jX6/Z/JtXo77mlEBUr9sXW1haCwSDy+bzda+os269QKKDb7WJubg6xWGzEjVqr1bC5uYlwOIxcLme3CKJc7/f7WF9ft2Nufn4enudZhaPZbKJQKKBWq2FxcdFu5VAsFnH//n1riBWLRRtUffPmzZG+6HQ6tm6Qxunm83nLxGkBYs0i5fzZ3d3F66+/bl3G2WzW1vbROVOtVtFsNveVw6F8K5VKp6/0uJaMfu66i/ziDY66qLnC3c+yPeq98h4Oqu3guhKe5F5PC7w3ZXAOY3r8jjlKW7JvddDpufwCkt17Yhtr3BRdLhqUyOuct/Z+Gvg9Q6fTwe7uLjY2NlAsFkdixzSz0c9NCIzfwgLYnxlJH/7a2ppN83RjuS4bXNZg3DHaB8oquwabH2PknmfSXVsE5zfHGMejKi3M4AQG8SFkeWgUU3lxZa+2nxpHBK9LZccNnmUciCbSKNNDNuQsQeWM7IeOP2V6gL1yFzq2yuXyCHucSCRsHCX3umLQNndbj0QiaLfb2NnZQaPRwPb2NorFIhKJhHUx0Z1IlpoFFNm/zDBTY0y/U/clmR4qVrqHFs9RrVZRKBRGQmYIzi8yVwfhWJUed2HjZ+rTU1orm81anytr5KiwP0xxUQHlurH8BMm4eCFglN1RtoK0pyug/ATdebCQyUzpJHcVNo2jcql3XVwVSuOPS+FU6xfA2IJTbCtWQaXLkPejiz3PxawB+qwvMvxcSKr0lEqlfbFV7pyi+49/3S0rdHHWRZhWVq/Xsxs4klW6jHANMb+xr25aDZRlnR7KMN3Q141BcY2ky6T46HOShfY8D8lkEplMBrVaDZVKxZY1YduwHgvHOfuBDBlT1YFRg4sZTgAsk+BmCwF7tdm4qPI+GeZAZeCs50YwGEQ6nbYlVTi+qDDE43Gk02kEAgFsb29jd3fXJpowdi8ajSIUCqFYLGJ9fR3GGGSzWaRSKZsWzjimzc1NRKNR7Ozs2HRwdfe5G4AGg4NNXldXV23sTSaTged5I9X02S9sX12vjTGYm5sbKWJIZodbWfHZA4GA3YyWdXwqlYqtWH1YDNaxKj1KE+vkphWvGhh3iqVmqmloVI7GpbG7gsN1wbgWm5tOqswEBQ4FGAeICjBOoIOUHtZ+OGvFh/Su1kdyWR8qHFqgjgunWjXus+oO6H4KJM+hdY5cBVY/YzYGsFd23nXbcIFmFsAkKD1+aLVaePz4Md58803s7OxYi2lcjA37yU/pAUYVTnWzqDW8vb1tt4uZxDY9ClxlZxxLo64ruh25hUIqlbIWMrAnJ8YV/OR1L4NrCxjNKCWzw60GFhcXUS6XbdE9d2FPpVI2eyuTydi6LFwEyRBpogMX2EAggHQ6jUQiMWII6tyIx+OYm5uzhfuoRNGNQkbjLMFdzre2tqzSR/nN9XNubg71eh0rKyvY3t5GIpGwWz4wyLjXG2yz8frrr8MYg8XFReTzeXQ6HRuvw34Kh8O2bVlItlQq2fYlO8Y1r1wu4+7duyiXy7h58yaWl5et3GZMUaVSsdsfsZAnXZyUdel0esSVXK1WsbKygmKxiFQqhatXr1pF9erVq1ahqtVqdhuTubm5g9vzODtnnLWkVg0tWE0rdJUS/uZJMU7hcF0p7nf6vQZacRJpzYFx5z/I9XDa0PijcRblQUGvB+EobezH0injQCHiZu65x+n/GgMxCVax2z79fn+kvPth0GBMPedhY1AVJBoh4xi5y4SjPr/LlupO1pr1Cfi7tPzONeltT3mk87ff79vYDTLrapRqiRNgf2IEv+PY1RhGrTrMgGjGnuh5tcYM07z97uGs+4dGqu5NybWT6yfHHvdV7Pf7Nh4qHo/b39F1B8AqeSQb6AZkULQGJetaqJ+556WxzTbTzFuen8xbIBAY2Uha21qV5Gq1apWlTqdj24H7tKmhTKP7VGJ6aD26aWnuX3cHVFUuXJrX9Zu71zsMbq0eVwHQYwCMZAaon5G1TBj1zkJRdNnxe5Y1P2vooORLK5ZSoye9S0vIjadxU5/H0f56vKZMUwgFAgGbDcBqnvwdrRVmHZCeprYPwNZt0GJakwYyoRq7AOxfLEnD6wLA44D9yuy4z3iuo6RXXwZo+6j7VtuMQpixJqxam81mkc1m7Rh23e4HKeyXQekpl8t48OABarUayuXySAkFXdwYSrC9vQ0A2N7exuPHj23cCl+s1JtKpXDjxg0kk0lUKhWsr6/bUAQa1/F4HPF4fITNiMViSKfTVpYzBojJNerKPw+uR65Z0WgUCwsLdoNuLvC1Wg2rq6uoVCpYW1uzVY/p8qIXAhgwW0wL5+ai9A7o+meMQSqVwszMjHVHdjqdkZT0QCAwUgqFW1bMz8/jypUrVq6rIkk5R+OM5yZ7mk6nrQJaKpVQKBTw8OFDrK2tIZ/Po9fr2VpuyWQS8Xgc169fBwCbjMH1YxyOVenRzeRcN4kuwgo2nOuScoW01lfwYxHGYVwwLc+jQZ7a6XRvlctlNBoNO3noElIlr16vW0vvrJUefT59NRoNGyCrtQxYaZO+7bd2swAAIABJREFUUQa06XP6TXpXMPA9FR/P8yyTwPo7wIBdqFQqtsqnXxZdvV4fYXwYeMvaEpPoEiDTo9S9uk/HMWjqulR3sCqkah0Ce3NL49j85pBrgEw61GIG9lev5qJM4yadTtvtQmZmZuzGjLpwAHv70GmWJPvsPCyqJwnP81AqlfDmm2+iUqlgd3fXumaAPSaAcqlSqaBQKNjUdYIKD10tjUYD169ft+nbxWIRb7zxBmq12ojbhWwIA5a73S5mZmaQyWRs/Ajlfr1eR7lcHqlBdh6YHgB2sb927dqIG6rfH+xTtbGxgWq1isePH2N7e9tmejFuku2RTCaRzWZtjR6yPpS1dNEaMwggnp2dteO3Wq3a2Nt6vW7ZJo5zzoErV67YEhi8R7JBVJLUoKX7nTuxM2i52Wxic3MTKysrePjwIWZnZ9Fut5FIJPDMM89gYWHBrtOMa2IRx1NReoD9hb7cBVMnuEZz87unhUvpu/8fxhKx0fXeVAAqpa2sEf+6cUznDa6yx/bxy1RzlU+/8xCuW2wcO6HXolXH4/gdlSJq//obdQOoQqfj56KDi6pmubjfH0Xx8FOMDrvuZWAbxsFvrI5zz+pv1L1A14MrL9zrKCZl3B4FXDS1/pYxe/sBcryzjXmsgoY0U9+pwLAvaHRrUUOVaVqOQEt5UN6xPzQO6yBZeJpQo5DjzSUP9F79DCOm9buyxWWR2VeU1ewruiLVzca2ImNPBkr3oNPAc/aLXlfXUx5Lo5nfK2lC9xgDzjWhgGVvDpNnx+7eYk0Ev4HFwQfA3ii1bVWW/AI43cHHScNJ5PeQGnDLRuJv3WN0AU4mk1hYWBgJkuJ9uenDbk2c8wC/+6SywcHH4lSkP/U5/NpTz0GlhYNT3WkUTtre0WgUsVjMWhbFYtGWODfG2F2qAaBYLNo6CwxC1AWfNSN6vR4SiYRNa7zooNuRG/G5wlyFsiqALtxFm8epUs/+bbfbtk95zkloy6NCBa2b1aNxE9p2bHO6SFKplK0nw1gFtV41XlEFNeWXMnWTClduMKuI9WUY08G9nYD98l6TROj+IFvMBc+trcZxzr7l3263i3K5bO+HwdJkjYC9QOZGo3Hmcr3f79vCpWTItM5ZNBq142h2dhahUMhu8RGJRFAul/Hqq6+i1+thbW0N5XLZznUqCqqwb2xs4P9n702DZM3S86Dn5L4vVZW13Lpbd0/fmZ7WTM/0tILxCDcyFojACMLYgX4YhwaCEKsijGxAGIMJQygAGYcBByH8w4GMZGNboJCFTcjMSBrJ0syo95nby3Tfpe5We+77+vGj6jn15Kkv697bXUtmdb0RFVWV+eWXmWd5l+d93vcAsJ2SaS9WV1dtn550Oj0WMPG4DtqXZrNpU1n8SSaTWF5ets4LMxAsFKpUKrh37569F1Nry8vLY80WO52OtWXa04cl+I/TY8eK9NDpYdWQ2/SOCgbYUyz0HF0HxnV6Jjk16tH5GemjoinXI6S3yjyw53n25Fi9hp4q30OjFSqvszQcLi9BDSR/8wRcLnh1SEkw8xPl6QAHfCw+5xpQKnsuTPJVGo0GPM+zbeh5ho4xBo1GA/V63TqfhJjVUNfrdTvObL8+66LpLUXg1Ci68/I4hIJjpMpJ0TGmMic1p/w0iAZkwOGjcbh+aWQ45tFoFIlEwjrsPDtOdYGbUuS8agPJaUaIj0v4PbWIBdgj3WoglkwmbVm0i1poEKdNIbVzsyJIahdYscjH2Fem3+/bqmF1oIbDIUqlki9V4yxkNBrZsVJdp44LAyWW37OSKRwOW24MdS+dDDZ+JVpijEGz2bTpsWAwiEQiYW1FoVBAOBzGwsLC2HEXnuchm83i0qVLGI1G2NnZwdbWlqUl0JFlN2lWbGm1ned5lpOUSCSQzWaRyWTQ7XYxPz+PTqczhvYoYkVuFgEUjs0kOZE+PS50xUWoUWckErFGT43nJOfGL0XlF8Xq9Rwk5ZnoZ/FzlLgp6T0D4+e06GZ0UaxpUV6ukXMf52m2rsNAxfw46H1SutAPwue1VO5a8cLnFI7WfLzf+zFSU/7VrIi7L1xRxMytQHjca/3kcdfqHqLT+mkjNqtOcMUl8HPtqfOoDdY4bo/TA24gMi1646TE1ZMUrcBiOflRZye5OuHjBpcarAIHKSPVOy5V46xF1xZ1BJ1r9jOi4wjAIlbUu3TeeVI5cIC4qTPOVgwsi+d7anpLHUu1vWp3FADQz65pOgCW7KzOKYnV5HHyvbXSm/w5Dda5BxWY8JNjTW9xITHKIUmMi1rTW/l8Hp/5zGesd0lv8EkVNRWQ5mTdihYqJT+jqWgFH9dKI5KiQqGQzRWTSKuGQTfsNGwQfg9FZPSz0iu/cuUKotGojQzo7fM7+xkDzikXu5tC0Wt13Jm6GQz2Dsibn5+3jg8A22CLkQU7hnKha2kkIVFj9kh558VgcO90Op0xBcyx1YhX/6ccZTz99pTOGXkS3W53rAvueRdFINxyaj9FznEi7M+mhEyzkgSq+sVvr7glvOcd7SEiQDK3ppuAg5Q1iym434HDDqQiZUfZiqMQd+oQOljpdBrxeHzMMZsmva7Vr/xhWigQCCCbzVoEJZ1O25Qcy8EB2NPsU6mUXeNsvsgsRyAQsAe4auprNNorf5+bm7P6h8cG0XFiERORGI6j9nZjkQZtDZE1NjJUCsPOzo7da6xay+VyuHTpku3xQ3Sc35OO06k2J3QRFJcQxs1OT41NhFjK7EK/+lvfQ1M1LjFOvUmNjhXtUUa5IkV8HRUZP6tLgONk8m9+r7PeHMA4sdpFpfjDw9uY5uI1ykHgvVynZ1IaxY1a1cNXZcJSX3UcifzxN6MJ5T/oex11rtgsC+eMUYsaSmA8dfk44Wv1Nw2wIpiKnLnw+adFjkJpXZTCTdMwemawwNdMEj6ne3Qa9MZJijp6iorze3e7XTSbTTSbTZvq8NP9FE2RuXvERfx5jT5Ow8zHaHy1JYbrCJ+lkFZBh4N7lU0e9XTyUChkOyyTWwvABjJE0hVlU7SHyAuvUQ4uHSY6S/xswWDQOrTaDJd2MhAIjPEHleyspGf3zDQG6eRbzc/P4/r160gmk6hUKjYNR6eHHFXatElyrE6Pn0EE/HuNaI6W12h6ZVJ6w1X4CpdxcftVv6gQyXAJXNoFNx6PW/KUNh6jk8TPqkbjrDcHMG4Y6cQAB1UJGnk+CTzsOpFqRJU3pPOgG8ZVQuwLpK/hxqXDy8fVIEQiEes0Kcx5lvyp4xRGVsxL6zo/aq7cQOOoYMHvdVRQevDsUVHyeRQdO5f0quOmTk84HLanUbu8HT0rzg2G/FIA513oZNBIu5F4s9lEqVQacwj9EB4/mYRw6hr2C9z4uBZwtFote34UDbuiU08acJyEuHSKdruNWq1m9QbXJVNCPFKi1+vZIF6bQbL0X6vd6MCTS6kpwHa7jfX19THUjqkwHgfSarWss8jGiDymhU1XWW7PsnIeRcI9QzSf2R/NWlQqFWxvbyMej6NaraJcLlu7y5TYpOpXlWN3etQh8HteoUmiJcBBFRXzq8DhCJfX8TV6WKlWROhAuKkAQmuu4Ve4cHFxEfPz85Z3pE0HNZWm/Wg44NMgbrn9YDAYS1spSZPXHBXpKndGeTfa40Lz47FYzJ7mrY6L53m2QkI3CTlGvKe2gieBlC3/SRpVR/Q8CPPxjNhc7ogGFGo4XadSSd+PE84LlSSbbDLn/2kQdXhcfp5rfJkyGI32Dn7M5/P2BGkAlguo/V+Awz15/H7Os7DHS7PZxMLCAubn58d0drFYxMbGxhgaRARCdfRRToe7D3ReNfOgj1O/8Oy53d1dix7QeaDxZUBMFPq0RakC/X4flUoFDx48wGg0QrFYtKl+1ceNRgOj0QgrKytYWVlBPB63R6bwwOF6vW4DUZadFwoFy2nl+z18+BCbm5tjmRXV29wbALC4uGibEy4uLiKfz6NcLqNer9tecKxSvX79OpaXl5FIJJDL5ZDNZtFut/H222/j9u3bdu3wN3visUsz05N01J6ko/2xn7L+OASBz/v16eFjWparr3HRGX0v3QychEnQqB+aBIxDfcpv0VSMGzVotDxNyku/pzag80tRPQ6O50Ln+Ls/Ghnre6kB4XsQpSFMChycsaWpTWC8bJUOG9NgLldr1kUdfkY7uv71tyt+kP5RKRN33pUMeN5TLZPEz1AqGgkcNNLjOicKrEiPEvGfRCecd6eHukHbYWi7imAwaIPNTzoWfkEAcLhDNt+L+0z5VeT6KMKjKc2zFkU1GDQy8AbGTz4gz4UIOSsOmaYyxliODXCAuBOdUTpKt9tFqVQas9NsHcBWDeQHzc/P2/di887hcDjGEaIj6Xne2PlghULBOmI6L3RkKpWK7UJdrVYBwO5Fnauj5NibEyqJTxER5c1w4anh4ubQBlYumVM3kCI2flEURT1kDlyn0zmkoEi0Zd6U9+Xi4b20WycXyzRxelR0PAiDep43dkKvRgfA4UopYNyhdKNhvUYdFp0TnX9dkFz4zPcGAgF7AKDneWMtzEmGYzdXvyqvaRe/dcnHdR7cTeumrvy+M9fspPedtDa5xnlIIyPGWRrX4xS/9JPC/FzL7NOja1SDJeqRfr9vD9H8NAn3+iTCttqKer1uD7F8WuLw0zhKGmDTiSHaQ5RjeXkZ/X4fpVLJHoTJz+qXNj4NoV5sNptWb9OhYKpVdfloNEIsFrPHQly6dAnLy8tjh3wOh0Pbjy4Wi2F+fh7JZNJSD1yHj2iQctsAjI0j/ybfyBhju+83m01Eo1Hk83m7Z3q9nuVSsR8eU2EkVdNG0MlySfC0yZqeY+XaJDlWp0c9en4gRU5cxaFtvpUUTAMdj8fHUlasAqJ3yh4CyhxXRIOkWABjJGTei6mYYDA4lnPWygs2jKPQ6LpRuR7ONg0GQ5W3jikAm9fld9VSUdd5cVEhdXr0+6rzqqkCAJblr5sSwNhZU+wLkU6nsbq6esiR5HswfaDE0VmQo9KHHC/OhYs6+BkBP5Rt0ntoipd7Uu9JKJyK9NMqSsDkmLq8HOqydDqNfD4/1n4hGAzaFCyVOnv3nHdExxXyxGgANbjhmiVxuVqtjjUC/DjjpKjapNdzzygFgp+RDe6WlpZsCrPT6VjiP1GMs5jDQGCvZxlPLOe5WjwiwiXZA3vVWjymY3V1Fc888wzC4TBKpRJKpRL6/T4ymQwuX75se/BoWpv2Up2eXC53qPcO9bmiYUShWJBBgng8Hsfi4iK63a7lzPIIJAa0JEVHIhHk83lr15VjxEBNHR4iWeQvHSUnUr31cV+r91CDqvfVfK9OOD12DgLFjagVygcOyrg5eQpVK0pExMIlmfqhS9MkfuiCom9PUqHwuJSlylEkRD8HSh0kjjGhSj1ny83tnxfR9TNpLR23I+3eyzX0n2bxG3t3XLh/NCULHESdfly5xxnk8yYayLpEbv6vFbEusnaS46Q6R4NtrfxR+gPtBdGUsxDaQdXXdKj1e6mO5Vrkb82K0NYxiPcrLFK0DjjoBu9yrTQLQyeRNlpThZop0QpdteF+bQK0IOdxGZUnscXH2qeHX5xlZCQVMZJXGF979yhnRFEW5fe4KRbP8+x9yUTvdrs2N0jvj16fezQGcHDYqVZeERFJJpPo9/tIJpO2d0Eul7MnvdJ7VQRpWhSaRvN+iA8jUmOMJXBPavanaUaNdPx4UQplqwPpkst5Dz1Hptls2o2Sy+XQ7/dRLBZtrpobl5Ej4dlZkUlRokZJj1s/n+R5Xd/u45y3WWz4+EnFz1EBxqsdOTZc08q/0tcS0VQ+hGsYNPDi+58nJ57CoIUlyEybDId7VTgALGo/HA4RjUaRSqVsR3+Oz5MSh1UXTRpTDXCbzSY8b+9A6VKpZFF+tirhHNG2FItFZDIZW610mjIc7nWIXl9fx0cffYR6vT6WctXiIaZSB4MB1tbWEAgELIlY06wkaVerVVs8wmqsubk5SwquVCq2C36r1bKvZ9aAa5+OCnV5pVKxiHw0GkW327WfQ+2lMcYWpnS7Xezu7to+PSSVK8ihwvdWTtjm5iZSqdSR43nsnB5+cZancRETjqLCoJevCsC9B78YJ5TC59gosNVqoVgsWuKWOj2E7Pje6rlycbgeMp0lOj0kf9HpaTabFnbT3Oc0cHqOSkep08MumMPhcGwxu+KWTutidY0FlQTfV/uWMOXJ54ADp8fzPOv0xGIx5HI59Ho9u+E0l84Nqo7UrIifw6FOj6ap9HupA+n3fd1rH1fVpqlICtfyNDnvJy2uQw8crE2NLhmpqkPEtavGlkYnGo3afi/q7Po5uOfR4QEOjlVh6sLP6VHngpWyXPt63pw7Rqrj/H78XqPIE23TYLB3Ble5XLa6XtEpXlur1VAsFgHA9pY7TRkMBoecHjZx5ZrjWiSfhSeu0zbWajXEYjHMzc3Z08objQYqlQqMMajVaggEAigUCpibm7NptHK5bNNV5NoQqVFR4jedJRKUU6mUTWOWSiUEAgFLr6DTEwqFUK/X0Wg00O12sb29jVKpZJ8nIqS6kXPcbrft/tzZ2bHNbSfJsae3/DgfqkQV7WEUwLSGC4WqMvGLhBUq1dcclQahA0U+i8JyCiEyCnmcI6OR8rQbC3ds/D7vpMVylOI56v00uqWoI+tWeikEChwm8Z4HedLvoaia3+N6P3ePfdzPdV7G+EnFz3lXvTUcDq1uAg4QIL+x0lSClqwDkxsgntcxV4feDbompQNd7pTei+I3jo9LafB5zpumV1zjzc/lfo9pCGjJg2Va1S2mUXvJ78vAU9FK9wc40K9MR6l91PcH/NcsQQbP88aCAdpT9/0IPOi6cPlzfu/D/zWtTAdKkddTcXo00ie6wx8dWA5CtVrFo0eP7JfleR88nZWOkJumAXAo6lVvkH1cotGonURFdDhI4XDYkpTZJZKTVSqVcPfuXQvTacqO/Wd0sTWbTUvAmlbhBuCmB8aZ74ow6I8LG3M+dEGqU6MdP/m4QpTcqEwF8DVc8Jp6Y2rT/R7uhp9lcZ0/iuso+kWufsreLxAA4GtMqJRUmT5OYZw3UW4OhUgDFamS/LV60B0rdo7NZDKo1WoWeSafQQNBvvd5dXpYdMKGf7VaDa1Wyx7QyqNnqAO63a5tlgfgUNDjJ7Q5rqOqwmwCK55omPP5PACgUCjYghitglLnVd/vLMQYYyufrl+/bu0NdbEeIqp7mKgU7SH1qXusEnWQMcYe+hmPx23fLtIK6MSr40GbkkqlUCgUrCOprUkajYbNjAAHqDIAm1Wh/aTTow1sXUoKszF0drimMpkMlpeXH4vGHXv1Fg0cB0k3uUZPjUYDxWLRNmVjZ1NNtWjEpF6iaxw4CDSkVODAQR6Xg6lIAj9jp9Oxi2I0GqFarWJjYwPtdtueBkuuCjvXqgPQ6XTsSbfTKn5QPnDgDLmeuCoRvd7Nm9Nwut65Pq6QvjpeTG0xMlHDojwv93vQSZh1A+3C8m7lnHsdcLTi1eCCr3vSz3AexvNpRVFfNW6KTPB5rkcGTH7tErR6i20u6PRr4QWF9z6Pjs9oNLJGlbxBNpZjdVssFkMqlbJnMjHgBQ63wAAmo9N+AQCvVQOr7017k06nrcNDY61B3ZPsu5MWOhKpVMpWP6mtbbVadr3RSYlEIshms/Z/PcaC+pg2Tfd+q9VCpVJBq9UaCwiY7nLXL5/nyeiahgL2xo3cXm3WSd5WLBazpfhqR9hCgN9Pq7VGo5HlgPEIjkwmY88Hy2azR47nsSM96lnrUe+aSlIoijCjKmouNB08v0jXheZ08nSxuvdWb9hNybm8CkVG2E9ASWRKSJxW4fdXlj6/L7+Tjh83vft6HUtXwSi06sc/cQ27/g8cGBqOuQuD67Xu55plcceEMilyPUqeFPnyc6r8KhN57TSv7eMQjj1Jl9oRXnWDtvH3GxO3Gob3cN9L3/O8OppEuBh0qm6h7mQncLfFyaR1/zQpXNdB4kGUuVzO9lgiIs0iF34Wv5Pez1rXKLFbOX7AuGNN+6bHpBAE4BrnvAAHpxpQNFDVI4M0PeamoICDta/34+s0eFDdrlVlnHeKtlTReeLxFTwUnORrgh20aUfJsTo99NZ7vR4SiQTm5+fHOkNyQdHD5gnfeg/1LomocND0EEYXmaCi8juhmsKB5gCRAEWyLJ0bThQVHSeyXq+PoTmBQOCxA3yWopFOOBxGJpOx40vPu1wuY2try/ZFcA+6dB0dP86Wpsm0ZYAqD5cL4TqpSjD3Qx1cJOS8EG75Xd2yUpWPgwQ87np9D7YJ0CDFvfY8GmaVUCiETCZjD+LlmjTG2D3OAw1zuZzvoYbs39Nut22FzSTHX7kZjyOez6KQ0MoCBZ6dx3QHDRkpBESB3RQgMI5cTkrruuIGVLFYDM8//zyuX7+OXC6Ha9euIZFI4K233sJv//Zv2x446XR6rCDD1XlnoXNIqPY8z6YG6XzQtrKLMQtv0um0Pd6BBT2e56FSqdhqNQBj1V60F0xF5fN5rK6uIhgMYnt72/baI9lYuZkkolOH0DljLyZg3DYTXGAHaPbsY1qZKS/gIGVXLpfx4MEDdDodzM3N4ZlnnrGIHZ09VokdJcea3tK0FnOo7J2jnii9Qj2W3oXNgHFSoZ/iV/Kr3hvARKdH4WwSpLX6SsvpeG8acS48NdjTzOPRDatQJL+3HrKnPCoqexfV8XOCAH8UBzhAK3RuNNLV3+5c+8kk9GmWRSMljtEnJU0e5aC4USKvP088qY8jTHnwhGkAYzqAY8To2c9RYUBFveaizKo7VBedR4eShpRVbNS5RCWoY0gvoNOjqA/gv14ftz84Xy7SMzc3h5WVFRQKBXzuc59DKpXCgwcPbGURq3LdNOSTOlonKXRKGJTSVmmTRXWo6cDxrEMiNjy6guOvDhS/o/bU4WGgTE8SxOD7qR4nUso9oHxOvSdtwmg0GquEBA4aqWoDXTpF9Cv4WD6fHzt+gs4UnaxJcuzVWzp46pRwQtw+IIqwKEqgka/mZ9VIctKZHz4qanKdIiogdnTWM7Z4H43EmIZz4TklBU+D8tJx0QWqRL3RaGTzrMy3cyMdlRZ0Rb18jgedKbeaQKMurg1uAG5abj59T96D68oYM1ayfl7ETeFN4ikcJW66l+LewzUaaqzPqxH2E8/b6/XVbDYtL4IBmKZaqLB5JtGkXi1Uxoyu+R7Ko1AuRbvdth1rZ915d0XTLOxdpMYaOGh2x8fctJIbxPI3ycgMprUbP3mCnENNVW5tbSEUCtly6Fgshlu3bgGA7eTLsnUifO5J62chzIxoZkEdiLm5Oayurlq0MplM2kId6kmOx2g0sk65MXunnHe7XVQqFdurh3MGYKyLNm0dD/ek3ggGg0ilUvb+iorx1ANj9ojVBEJIE0mn02N6R+23Agqc92Qyae0u7QALp0KhkHWEjpJjTW+p96leKL1PParBjaBoMOnEkDBII83JV2VDohwjLxp2V+G7+UN9nLCfG31xY0WjUetQ6TUuPD0tETLTiHRoarUaut0ucrkcMpmMhSir1So6nQ4qlQrK5bLdHK7To4rH73ES7IbDoW0wpYcLUvlp5QDRNI4bIxA30lPkgwtcldt5MhY6TuoguoiYnzzNGChCxjFm/p8nLft9tvMohN/L5TIqlQoajQYajYZV+C7akM1mMT8/j1wuZ69RYXqLQYZGzlzHNKREFx7XJ2tWhTpcD5NkSp37X4tdWFHkNw66B2hEtQKXJ3fzkEq9L434aDTCBx98gLt3744FFAxoyfdZWFiwqR+eR8d+Q097LthxSTgcxuLiIiqVij03kroxGo3i2rVryOVyAA7AgtFoZE8hj8fj1mkaDveaunJ+2B/n1q1btqFgKpWyIIA2/ovFYmO/WYGntpE2nJ+RafPRaGTPCiMNhsgNg2bdb0TdPM+zhOV4PI65uTl7bEiz2QQA29+HJ7WfanNCXUwuUuBH2NPn9TqX0+FHClQ0yYWN3ftP+qy8N71LTc+4/BMXeXCvnRanBxgfG4Uu6aDR+aByUEXk3mPSnFFcPoo6tcBh58VFcwCMPeamOV0i/Fnn189C3Dk4SvHqnD1pqsxN+X5ahKioRvIuV43BANMKhN39xokGVCNpVx/q/tD3PG9rWfUiUXPt00NHx93TfK2Kq4c49oFAYKwDvJ8N0HmgU8sDdofDIebm5rC0tDR2nqJf3xhXP56mEGWkk8LAj2ObTCYPrTXqd0UXiaaQB8PUFwAkEgm02227vrV8n6CABkTqgBJd05J2BTQ4XwwEWDGmzpHOHXlFvBd1E4nVvV5vDNHjGtDKtaPk2JwefjglIZOURCVANAA46NipxlAVAgdGFTgNI9EeRVs46Bo5qOPFz0IhtK0oEqXf76Ner9vNxInjoqFio6ebTqctieusJRjcO7TTGGNhdh5QR6Kylu8vLi5aMpmWJaoS9lPwriPK38oh0tdpnyRVWnwN503Z/gsLC/bzci640QjlnhcjTTSL5HoqqydxRKjwVNTx9Lte9xb37aeN0xMIBGx02Gq1bHEF0ctgMGijfK63ubm5iRA6D2UEgEwmY3uF0ZBo+TRP9M7lcsjn8+eOzEyHUlPtRHPoZCoqzGocXkMd5Kaw+ZjneRaF4XEXegoA0RrVQ2pgSZRluxRFwAeDATKZDK5du4Z8Pm/TRWeV+iUfialTdRY8z7MUBo6POvNEu5hi0nFgZVe73cbi4qItHadTRJSODorbp4cOhvLXNLjmNS4QwUosdYL1c9E3YMoqlUohEomg2+3aFjLazZ/rIBqN4tKlS3YPThzP45wczfFRibMxEj1o9QpJONIDybSEnaXUHDxuBDpOTEHxOW4ycnTc/DDRAi4G94wovm+320W9Xh+rNnO5PlRkVJTZbHZsYZ2VBINBZLNZuznm5+cxHA4tSdMYYxUDHbfFxcWTHGr9AAAgAElEQVQxKN6Pz6NpL50TzfVSkXFM9WR1dXo0CgAwNs40+KFQCMvLy1heXh7bJIosaWnwrAsJeI1G45DTqU6K62S64qKnk9AeNxLXpnuPi7TPiwQCAaRSKczPz6Pb7WJubs6266fTU6/XrY7JZrNYXFzE/Py8r9MTi8WwsLBgnR9yf0jwVAJvJpPBlStXUCgUUCgUfDlCsywMYLQRHh1I8kS0RDwSiSCTyVinX1/vImXU9bFYzOoychP5Hiw6UUSeaF08Hkc2m7U6Uc9+Yu84OqOJRMKSec9qjsLhMJaWljAajXDlypWxPe5mVVRcHeLuYXUyNBB2My9+93Pv4b6Xe437+kmZBPd5YBytU12m99Wio8cFECeC9HBxudAuS9FGo5FdaADGIloqXgCHFLAxBx2WORjk17iEZx0UFTet5Wc49X/NV7ppGz6vHS/PWjgP3PAALHmN401nBTjofOqHjrmL1O9vXXhHbb4neV4RP6I6dKCNMZYbwfk4b6RbTX88LjWr1+l8uQrRz4Hyk/M0jk8qxhhbzkvjRhSHTg/XWCAQQCaTsRUxk4jMNKSpVArZbNZyTej0hEIhdDodZLNZe5AxEaHzKsrfIfKj65I9dFgZFwgErLOkRxXxNQxaSSzn+DJV49IQlCTLqmLOM5tJMp3DDIMGAKprzmqejirSuZCnk2N1elKpFBYWFjAYDGw/geFwaA8ZW1xcxI0bN8Y8bGVfsyTU7c7Idub9fh/b29toNBoA9jxgbhLtOFmtVu3zXKzcOJFIBHNzc9b54gbi3wCQSqXsKevaY4joj+d5Fr1SmJpppbMUomV0fIisuXyeWq02xvcB/D15HRc/0dSUKjJ1ohQa1ffgfTUnT2KiMvVdD16RufMinDcedKvKnqK8JkbBinBqN1nAPy2p++RJHCx+tvMooVAIS0tLSCQSWF5exvz8PGq12lhKnqkSY4w9jDEWi2F+fv7Q/ZLJJFZXV62jc/XqVYtu03mn8c9kMrh06ZJNr/kRyGdZlIvBAyRZJcofOpzpdBrPPvss8vm8dXaGwyHq9bole7fb7bG02HA4tCmoTCaDUqmEZ5991pKltQszg9tUKmUdXKJ1DNKHwyFu376NO3fujHEdFxYWLHn9vO6DT5scm9NDYtTc3Bza7bZFcnia63A4xNWrV/GFL3zBcjGofFutls3fsYKECmI4HKJWq6FUKqHT6WBnZ8dunnw+b9uKc0G2Wi00Gg0MBgMbCQDjEFg6nbbnc9CxIvwK7JUvxuPxMZY5m2g1Gg0EAnslhJ7nWYXJFtxnLXQQJgnTfxwjCh1Hd2NPIlnyOvJt3PysGlRGeC5apFUcTF0xb8sSaqYMzwP8f5TSZCWGnkWkTTkBjDmOSlRUdIx7wY9cz//pHPkhRJ8mCYVC1qh5nocXXnjhUHoQGEeHj3IoGch5noeVlRV8+ctftvdyOXCasjxLBOGkhN+PTk+xWESj0bA6mQFuPB7HpUuX8KM/+qN49tln7etHoxFKpRK2trasvqIuZrsNBpzxeBy1Wg07OzuH0Goi3NFoFEtLS0ilUmNOD69lpdZbb72FZrOJRqNhuSIvvvji1BWrXMjHl2NFesh14Tkcc3Nz1pHo9/tIpVKW/8JNQfSFTo92nCSCwBSTHhBKwi6PkafSaLfbtqsmYU/gwOlJJBI2YtPNwegagIU9Ca8GAgHU63VbGhmPx5FMJi0JTlNgsyZ+qInmdF1j6F7HNBrzs6rcXQXPxwGMGWjtg3SeDcFRwnEg4RUYr6pSTgLTlRx/OpV6Le+pvxXq5//qIPmt4fM+B7rOnjZo8XOQdO1OWw+v0xSm+tgN3v3+1N/5fN4i727VDdOE5PsRkaFu5vN0NInQKF+EaSumLJPJ5BiPh9cSKVXOIHmJTIExq3Ahsy3HivRkMhm7mL7yla+gUCjYU1vpMb/wwgv2OAQuIG2exIXn5nCJJtDjB2CJuCpEHjSlAxwoKLLBlYioCx84yJ8OBgPUajU0m01sbGwgkUjg3r17mJubw5UrV5BMJrGysjJWojqtonlxKmQ1dsABOjOJL6PfkfcjPKwlowAseRGAVRY00C6xliRaY4w98ZiEePc7TPMYfxJhJQn3hjbc4ti450IBB4f8Ksn7qDHSMmnel9E34f5Pkyji9TR72CV+uo76JEdHgwGtHD1vxjQej9tU36NHjyzpmGnqZDKJl156CZ///OdthsAVprcZANOhoQ5hJWcwuHd49KVLlw7pFtVxrPrUVDnnic9ns1nr6PR6PaysrOCZZ57B888/P4bEXsjsyrEiPVSexhhcv34diUQClUoF4XAYrVYLly9fxurqqk1vTUM66ChRvtH9+/extraGdrtt25mnUinkcrmZIdRqBERl4jYOnOT0KCoAjPfW4es1ulVODw23EpL5OtcB087RfimG8+r4MKqMx+Po9/s2CtVTktnoS6NR7bOkaI1LXjbmoEW7VhGpM+UXkZ930UDgaV/DMdTCgcfdR8nnaqDPm9MTDoeRz+cxGo1s9ZMi4tFoFFevXsVLL700ccyY4j4NISrECleWsOfzeRQKBSwtLZ3K57iQk5cT2Wl+i3gWYXO/9MCnWY57zp72fueddzJpPE5i3c3C/ruQ8yXTvOam+bNdyPGKeRpDYozZAXDv5D7OuZRrnucVPulNLsb+Y8vF+J+tXIz/2cnF2J+tXIz/2Yrv+D+V03MhF3IhF3IhF3IhFzKr8unO11zIhVzIhVzIhVzIp0YunJ4LuZALuZALuZAL+VTIVDs9xpj/whjzrjHme8aYt40x/4wxZs0Ys+Bz7b9qjPm5Cff5UWPM107+E58vMcYsG2P+T2PMbWPMG8aYf2yMufGU98gZY/6Dk/qM502MMcP9tf6uMeYdY8yfN8ZM9T6dRTHGzO+P89vGmE1jzCP5/6Iu+YzlQvecvYguescY8+Z5saFTy+kxxvwRAH8NwI96ntfdd3QiAP4AwCue5+0+4X1CAP4SgIbneX/1xD7wOROzV87wBwB+yfO8X9x/7CUAGc/zfu8p7nMdwP/jed4PncTnPG9ijGl4npfa/3sRwN8B8Pue5/1l57qQ53kDv3tcyNOJMea/hqMfTnt8jTFBz/OGp/V+0ywXumc6xNFFPw7gL3qe98+d8cf6xDLNEeQKgF3P87oA4Hnerud56/vP/cy+5/l9Y8znAMAY83VjzN/Y//t/N8b8ojHmuwD+PoB/D8B/vO+1/tEz+C6zKH8MQJ9KBwA8z3sHwD81xvyCMebm/vj/JAAYY1LGmG/KvPxr+y/77wA8tz/2v3D6X2N2xfO8bQA/DeA/MnvydWPMPzTG/BaAbxpjksaYv2WM+UNjzFscc2PMi/uPvb2Pkj6/f+0/2o/abnLeLuRAHL3xPxhjvmSM+c7+GP6aMSa/f93vGGNe2f97wRiztv/3oXHff/zflMf/N2NMcP/xhjHmfzTGvAPgj5zJl55OudA90ycZAGXgyPGGMea/NMb8wBjzT40xf9cY8xfO7BNPkGluv/pPAPxXxpgPAXwDwN/zPO9b+8/tep738j50+RcA/Ds+r78M4Gue5w39IrkLeaz8EIA3fB7/1wF8CcBLABYAvGaM+V0AOwD+pOd5tX1U7jvGmH8I4OcA/JDneV86pc99rsTzvDv7RnJx/6GXAXzR87ySMebnAfyW53n/tjEmB+APjTHfwJ6T/z95nvcrZi9VEwTwLwNY9zzvTwCAMSZ7+t9mJkT1xvcA/Izned8yxvwVAH8ZwJ874rWHxt0Y8wKAnwTwI57n9Y0x/yuAPwPgbwNIAviu53l//kS/0ezJhe6ZDokbY94GEMMeCPHP7z/egf94vwLgT2FvfsIA3oT/PJ6pTC3S43leA8BXsBfp7gD4e8aYr+8//X/v/34DwPUJt/gHF3Dxicg/C+Dvep439DxvC8C3APwwAAPg5/cNxTcArAK4aGN6/PL/eZ5X2v/7XwTwc/uK6Xewp5yuAvg2gL9ojPnPsNerog3g+wD+BWPMf2+M+aOe51XP4LPPgvyDfYcnCyAngdYvAXj1Ma/1G/c/jj099tr+PP1xADxZcwjg/zr2b3B+5UL3nK60Pc/7kud5nwPwLwH428YYg8nj/SMAft3zvI7neXUAv3FWH/womWakB/tOy+8A+B1jzPcB/NT+U93930NM/g7Nk/10517eBfCnn+L6PwOgAOAr+xHtGvaM8IV8AjHGPIu9db69/5CuawPgT3me9wPnZe/vp2j+BIB/bIz5dz3P+y1jzMvYQ3z+W2PMNz3P+ysn/flnUJ5EbwxwEDDaNe553t9xxx17c/RLnuf95z736VwEZr5yoXumTDzP+/Y+qlPAng6Z2fGeWqTHGPNZ5sT35Uv4+F0p6wDSn/xTfarktwBEjTE/zQeMMV8EUAHwk8aYoDGmgL3o9w8BZAFs72+CPwbg2v7LLsb+Y8r++P4igL/h+Vcc/Cb2+G1m//ov7/9+FsAdz/P+ZwC/DuCLxphLAFqe5/0ygF/AXprsQibIPhJWNgccwD+LPWQBANawh94AYpz9xh3ANwH8abNHSocxZs4Yw71xIf5yoXumTMwedzYIoIjJ4/37AH7CGBMzxqQA/Ctn82mPlmlGelIA/pd9rsIAwC3spbo+zkD+BoBf3Sdc/czTVAB8WsXzPM8Y8ycB/PV9uL6DPWX/57A3N+8A8AD8p57nbRpjfgXAb+wjcq8D+GD/PkVjzO8bY24C+H89z/tPzuDrzJIwjx7G3rr/P7BXxegn/w2Avw7ge2avrP0u9vbHvwHgzxpj+gA2Afw89tIAv2CMGQHoA/j3T/RbnA/5KQC/aIxJALgD4N/af/yvAvj7+0b5H8n1h8Z9n3v1lwD8k/056gP4D3FxrMBEudA9UyPURcAeYvlT+6nfSeP92j6353sAtrCXUp+6NPrUlqxfyIVcyIVcyIVcyOyIMSbleV5jP1D4XQA/7Xnem2f9uVSmGem5kAu5kAu5kAu5kNmRv2mM+Tz2OD6/NG0OD3CB9FzIhVzIhVzIhVzIp0Smlsh8IRdyIRdyIRdyIRdynPJU6a2FhQXv+vXrJ/RRxqXX66HdbmM4HKLX66HX62E0GtkfACBK5Xme/QkEAggEAjDGIBwOIxKJIBgMIpVKIR6Pn8pnV1lbW8Pu7q75pPc5zbFX4bgCgDEG+4VCj5V+v49+v2/nIRQ6m0zqG2+8set5XuGT3uckxt/zPPR6PQwGAwyHQ3S7Xfu71WphOBwiHo8jmUwiGAzacTTG2DWua380GmE4HGI0GqHT6aDT6WA4HKLf72MwGCAUCiGZTCIajSIUCiGRSCAUCiEQCJzY/Ezz+Ktw3AeDAbrdLprNJobDoR1PAAgGgwgGgwBgH3OFe2U4HGIw2DvFIhQK2Z90Oo1oNIpgMIhoNIpA4OTizlnXPbMus7L2Hyee51l9DsDaALUFaidCoRDC4fBT2YuTkEnj/1Sa7vr163j99deP71NNEM/z8PDhQ7zzzjuoVCp49OgR1tbWrDFot9vwPM8qFSqq4XCISCSCZDKJcDiM5eVlXLlyBdlsFl/72tfwhS98wSqt05JXXnnlWO5zWmOvwsXe7e61RYpEIohEIhMXsjqhW1tb2NragjEGly5dwsLCwplsAGPMsVTJnMT4d7td3L9/Hzs7O6jX67hz5w4qlQru37+P119/HfV6HS+++CJ++Id/GKlUCpcuXcLi4iJCoRBisRhCoRA8z7Nrv9VqoVKpoNPp4Ac/+AHeffddNJtNbGxsYHd3F3Nzc/jqV7+K69evY3l5GS+99BLy+TzS6TRyudyJOD7TPP4Uz/NQq9Xw0UcfoVgsYm1tDd/97ndRqVTQaDRQq9UAALlcDun0XgV0r9dDv98fc1roCI1GI1QqFZTLZRhjsLCwgFwuh0KhgFdffRXPPfcc8vk8nnnmGSSTSQA4kb0xy7rnNEWDuuOUWVj7TyL9fh8PHz7ExsYGAFggQQGGwWCATqcDACgUClhaWrLOz2nbXMqk8Z8qInO5XMb3v/997OzsYHd3F3fu3EGz2US1WkWxWLQRMb3KwWBgHR8iQIPBAL1eD4FAAL1eD7VaDfF4HM1mEx988AHy+Ty++MUvYmVl5Yy/7XRJq9XC+vo6Go0GOp0OarUaer2eRQqAPQ+e0W4ymUQkEsFwOESn07FzQQNcr9dRq9UQDAZRKBSQz+cRjUZRKBQs6pbP5xGJnN8DrblGiegQuWy1Wmi1Wuj1etja2kKlUkG327VKg+OUSCTQ7Xbx7rvvIhwO486dO8hkMmOoD9f8aDSyCMVgMEC1WkW320UgEMDly5dx+fJlJBIJZDIZDIdDNJtN3Lt3D7u7u0ilUtbpSSaTSCQSCAaDiMVi52Z+qBeGwyE2NzextbWFfr+PZrOJXq+HZrOJ9fV11Ot17O7uYn19Ha1WC51OB61WC8Cek1qpVADsGQI/tIcGtNPpoNvtwhiDer1uEevXXnsNa2trSKfTWF5eRiKRQCQSQSwWQzgcxsrKClZWVqzBOCuE9DSEerzX66Fer6Pb7aLb7aJer9v56na78DzPGlkAdr0DB45KIBCwxpWoZyAQQCKRQDQaRSQSQTabRTweRygUQjweP4SyKb/1LBGKaRDP83D//n189NFHaLVa2Nraws7ODowxdq0Ce+gnnR4Gx9lsFrlcDolEAi+88AKuX78+VeN56jvqKK/60aNH+OVf/mW89tpr6HQ6dvETJgsGg4jH44jFYmOpARriYDBoB58LfzQawRiDb37zm4hGo3j++efxsz/7sxdOjyPVahWvvfYa7t27h+3tbdy9e9dC/K5yj0ajuHTpEnK5HNrtNorFItrtNprNJsrl8ticBQIBGyHn83m88soruHLlChYXF/Hiiy+eG6PqJ5oyqVar2NnZQafTwcbGBjY3N9Hr9dBoNNButxEIBKxCTiQSuHr1KjqdDu7du4c333wT/X5/TPEzwgIOlDyNCNEFOpif+9zncPXqVXieh2aziX6/j1KphGKxCABIJpPIZrOIRCJYXV3FpUuXrON1XuaHAVCr1cJ3v/td/MEf/AEajQY2NjZsQEWHUYVOJbDnyDAAOCq9ZYwZQ0U7nY51Xm7evAlgbw+l02mEw2Fks1ksLCwglUrh1Vdfxauvvmod1PPs9FC3NJtNrK2toVKpWKSt1WqhVquhVCphNBohEokgGo1iNBqh3W6j1+uNpU/4fCAQsAFYKBTC8vIy5ufnkclkcOPGDSwuLiKZTCIUCiEajQLA2D66cHz2ZDgc4u2338av/MqvoFgsolKp2CA2mUxaqgjngCl0nau5uTl8/etfx9WrVw+hPWc5zlOzo+jEbG5u4u7du9ZgeJ5nFyjhNA5gv9+3GycQCFhUgo93u130+314nmcRolgsNuZMnRX0Ni1Cx7DT6aBYLNq01MOHD9FoNMYMKn/HYnsdx9vtNtrtNra2tiw6VCqVrMIhtNlqtZBIJNBut7Gzs4NkMolYLGaj5bPO/R630CD2+3202230+32LfHU6Havce70eOp0Oer0ewuGwHS/yPcjZoWPE8Z8kHEM6TgBstJvL5WxAQEes1WphNBrZ/RKNRpFKpZBOp9Hv95FKpRCLxew+8cvlT7somtBsNtFsNlEsFrGxsYFarYZHjx5hZ2dnbBzC4TBisRiCweAYX6rf71vUZ1LwRqcHOOAAjUYjG6ARPYpEIpZTlcvl0Ol0kMlkUCwW0Wg0AADxePxc7g8AdkyIttVqNZsS3NnZQavVsn97nodoNGqdHjruAKz+dp2efr8/lloZDAao1+tIJpMwxlgUVO3JhRwEUAzUHj58iGKxiFqthkajYfmxHGvX6SEqFwwGLTLK9a5Bm/uep7m+T93poSKneJ5nPXc6IwAQDoftAqUQ6aGXyU2gg8+FPhwOLSJEo87Uy4cffohsNmvz6qlU6nQHYUqEnJKtrS08evQIb775Ju7cuYN2u41Go4FerzcW/XAs2+02tre3bYqmVquNRVeMdEmSHQwGaLfb2N3dxZtvvolbt27hxo0byOfz6HQ6SCaTNm0za+Iav16vh93dXTQaDWxvb+PmzZuoVCrWGR+NRja9pSTk4XDvCKZQKGT/DgaDmJubw2c/+1lL5u/1egDgqzyU60OkJxqNotvt4tGjRxgOh2g0GlZBcT90Oh2USiUEAgGsr6/j5s2biEQiKBQKyGazyGQylodCYz1tc+UXOdLR6fV6+Oijj/DGG2+gWq3i9u3b2N3dtWnwbDY75tSr06np3WQyaa+hgg8EAtYA0ICPRiNrpPl8JBKxaBJRCu4VY4w15G+88Qbq9TpyuRy+/OUv47nnnkM0GkUmk7HIxKyIIo/U+3RIGo0G3n77bdy+fdsGTkTamIbkPuEaZUpFnX86hdQxfF9gbz9Uq1UEAgE0Gg00Gg1bGLC8vIxYLIarV6/ixo0b1rmnnDcnk/K4wpRKpYIHDx6g0Wjg3r17NmhjIEX0XvcKRe9FPff6669jNBphfn4eL7/8MlZXV+21rrPzcYtmnlbOBOlxB6fZbKLRaKBer1tkJhqNYn5+HuFw2CI2wF70QyfFb8CYBqAzxAmqVqvWIyXB89lnn7UpAFdOitw2TdLtdvGDH/wA3//+97G+vo5vf/vbWFtbQywWsxVDwMFYcG6MMdje3kaxWBwzAMBBpYryEWg4Go0GHj58iMFggM3NTTz33HMYDocWcp42Q/o4oaEEDtJN3W4XDx8+xPb2Nt5//338+q//Oh48eGDTFQoPK2/GjzAeCoWwtLSEbDZrHRY6S5reotB5jEQiiMfjSCQS1km9d+/emFIhx2Q4HNrUJLk+TLcx5XLlyhX8+I//OJ577jkb5U3bXOl349j0ej2USiU0m028+eab+NVf/VW7ZofDoXVIaPD4OuoN8gI5L3ReyJ8aDAaIRCJIp9OIRCIW6aSRCAaDCIVCyGQySCaTY0gP9w0rxGq1GjzPQ7FYxOuvv475+fkx9I1O1KyIOpG6Vnu9HlqtFra3t/HNb34T3/jGN8YqF7PZLFZWVhAOh62jQlSec8LqQ74P9T7HVavlyuWyRenIc0ulUlhcXEQ8HseP/MiP4OrVqxa9Ps/6HoBd+7reVcrlMt555x3s7u7i9u3baLValkJC9Jj3AWDtLHB47BqNBr797W/j5s2beOaZZ1AoFKzT43e9nz49CTmz9JYuVuZvG42GnRD19Nyolt49DbCmr1xPkcpZoTVWufA9W63WWJndp0XojVerVZt66fV61ml0DZuWSCvXx025uPNHoXKj4qtWq6hUKkin00embWZJlLTM8ez1epY4yTSWli2TN8P1yeep5EkYJ4GZ17rjG4/Hkc1mbauGaDQ6Fl0DOLSnOI8sseZnBw7aRtBQd7tde89ZEKY0arWaXeOE6HX8aEB13fKxSSXl6hjxfkwBunOj/Da+ljqMe03TCu12G5FIxKZ7tFJ11oUpX64rplhdp1UDJtX3RNEofC3XL1FSjrEKnR/li7bbbctFOY9pxCcRZkKY0uIPCytcfa4IjQILmv7meu90OmPVjOVyGcFgcCzdddoB1Jk4PZ7nodPp2DTKO++8gzt37tjqIS7qZrNpyclapcXJ4L24mIEDpIEDys3EqBoAtra2bGomk8lgaWkJq6urFkoGxslt+v95ksFggO3tbdy+fRulUsn2hdHUFHAwBuFw+IkIaFz0WmGhLH/CzW+++SYePnyIV155BdeuXZupSBbAmCHjWNDQkiT8mc98BktLSygUCrhy5Yp1dtx+O+49+bzem8iMPqZ/+/XHYGDB/UFx55HzVa1WLRmdJdvGGJTLZTx69AiDwQALCwtTN1fc68DBeBSLRXznO9/B+vo6fvCDH9jUnqt0KdqPh4qdRpIBgjsnjIIjkYh1DokicR7D4bDlHCoZWj97OBweq8Rrt9u4efMmGo0Grl69ikKhgLm5uRMfx+MSrmM1kKPRCDs7O7hz545FxPL5PMLhsEUoE4kEstmsNZgbGxtjDjud/nA4jH6/b7lS5AWNRiNks9mxXlTkszHVS6Su2+2iWq2iXq9bHte0retPIn62S4EArv1yuYy33noLm5ub2NjYwPvvv291GIMvt4pXA7JAIIBut2uDJUVPGehub2/jN3/zN/Hee++hUCjghRdeQC6XQyaTwfz8vNV3p+EAnRnSw9LEcrmMjz76CG+99RZqtRra7bZd2HRulPNALoI2KqSXPxqNbLQLjOd+A4GAnYhSqYRKpYLBYIB0Oo3NzU0Mh0NcvXp14qI/bbLVachwOESxWMSDBw8sUZZIhDa/exyB1a/0043cyLOiUW61Wnj//ffx4MEDFAoFy1WZNXHHhMaxVqvBGIOrV69iOBzi8uXLuHHjhuWhcT0zkvVDywAcQiD95kAdHOWcMB2p4s4J9wXz9aVSCeVy2bZ4YIPJWq1mDQM/+7SJOzbVahXf+973LIeHa5zGU/uMKFrspt9pWOk8KmI3Go1s6wASc5mOIT+LQQR1Fx1/fX8iG0qCv337NjY3N1GtVvHqq6+e3kAek7jIyWg0QqlUwtraGmq1Gvr9vi0jX11dRSqVsg6k53mWxKyIDccrGAxap4foEdEbTVcyVQ/sIaFsp7G+vm55pM1mE7FYzBrx86bnXR3ufr96vY533nkHH3zwASqVCh4+fIhOp2P3SSgUGquYY9CmBUbAgb0Oh8OWd9tsNtHtdrG7u4tvf/vbeOutt/Dcc88hEolgZWUFo9EI+XzefrbTkFN1emgMWDJbKpVQq9VQLBYt8gLAKhFd7O7m4WOKIGjqhRNxVC8Nsss9z8Pm5ibu37+PTCaDdDqNTCZz7ha/KzSOTDkxtajPP+l9HjdWfnAom0zy96wK0RSiPKVSCVtbWwAOHMJer4dqtWpTVeqc+KUJ1elx0y8q7nzxvqxE0rlRZ1TTMdFo1CopNv9kmpPGoN1uo1qtWq7JNLsrwggAACAASURBVAt1B6vjSIblODJSdZG2SWufQZjOlTZn43vqvNKRAg73kCHhXz8D78GGk0TeyGdUh2va+FSPE84BA11+JxLviX5Sn3M8yE0zxowhZFyb6oyqTYjFYsjlcra9CceUY64NbD3Ps2Xx1IFc95z38yREuIg69no9PHz4ENVq1ZKWmUZXfQIc7A8iktQvRHo00KWozuJckdPF+zOdr2vhJHtUnZrT43ke7ty5g+9+97uo1Wp4+PAh1tfX0e12bUkuI6BEImHzrZqj1XsZYxCPxxGNRm0+UvPiigQBsPCZRs6NRgPvvvsuQqEQ7t+/j9u3byOTyeCrX/0qvvrVrx7i+Jy3VBdRCaJeasyUoHaUqHJ3xXWGOP7G7JFMNzY2YIxBsVicekN6lBAxK5fLePDgAb7zne/g7bffxtzcHK5fv45UKmV78wAYW5sksrriF5lNIvcpqZD/+/HbyDdy50U5Lslk0hL72edkOBxiY2MDDx48QDgcxssvv3xcQ3fsQmeaxNXd3V1sb2/DGGPRLKI8LK1lGoaKnrA8sAfVh8NhDAYDlEolW7Ku1aWuk0k0iby4WCyGTCaDTqdjjQvTL1p5yvtq8EaSOXlJNNiz4vh4noeNjQ1bPLKzs4NyuYx+v49EIoHl5eUxI0v9HwwGMT8/j6tXr6LRaGB9fd12uCavhzaCAQcRiNXVVXz5y18ec6aAg7ElYklb8dprryEajWJxcRGLi4tIJBK4du0alpaWznj0Ppm4uoL7+OHDh2g2m7h79y62trbQaDTw4MEDmx6kXWWzUyUXk5pCe8s1zzlkta7qNM4Br9/a2sK3vvUt2wvs0qVLtjfZtWvXEIvFsLS0ZBGg45ZTRXrK5TLee+897O7uYm1tzVaUJBIJy7chO98vrUIlzufYyRSALXdkJMU8JKFmepRaDtztdi1/gaTabDZrUxJ+nv55cny06kFTLu41Ku73dqPao15PhIfQdLVatdV7fojcrIjnebb/y/b2tnWgW62W7bqrlSgca63gcUUdFfcxv/fnb795UHRNeUEUfh5Wiy0vLyMcDiOVSiGZTKLZbGJ7exu1Wg3Xrl2bageV/D5yDFgVGovFbPpEEQX+7Ua1iuhEo1EMh0NUKhUbDIRCIZuGUSeWP8pn4T2UdOsGc5xbBgUs2SZaRQ7kUXttWqVWq2FtbQ31et0S4ul8UMdqKp1RvnYK397etlV0bA9AJEzHk/yg1dVVRKNRi1wCB1wtpl/o9Ny/f9+imaQ8LC4uns1gHYNMQt49z7OpvWq1ips3b+LOnTtWL1A3K49H04vk1zKtSLSI7WFSqZSdT822KL+LvEFmWObm5rCzs2NTj4lEws77ScmJOz0s02RKq1wuo1qtWtIsgLESRJefQAeGz/FxrWShgmaFDO+lPBFliuvBgSzrDYVC6HQ6CAaD2Nrawt27d5FMJjE3NzdW0j7J8MyiaHqLG+Vpv5+L5CgE6ir0oz4DlSGjtVkTKtJUKoVCoYCVlRUsLCxYaJ0OJqNZcgdcJAeYPF5PMjdqEDW9wr1Dp0ujNG0hD+zl+EkkDYfDY/2Bpl36/T52dnZQrVZt75dut4tQKGTHX9PnjEzdVJdWWgEHJFryc0jQpL4JBoNjx7EoWZ3FFxxDF2HivLpoNnVdp9PBzs4OHj16hLm5OdvUcJpFUyCaotOgVikJ+l3ZW6fX69lea7FYzKIzdHY0aGCKRBuikrNDZ5EpK+VQEfXh/qhUKmMOp6aBZ0Xcz1qpVLCzs4N2u43bt2/jwYMHaDabFrWkPiJi41fNqJWJ2qOKlAj3nC3OJ+ebIAX3i4IH1D2bm5vW2WVD11gshoWFhWM9LPzEd06r1bLdfe/evYu1tTWUy2WEw2Hk8/lDip6KRztw+nVy1AiL56oousPUTbPZtIqFC1i7d/J9ifrQAw6FQvbYhOeff95+xvMkhCqbzaYl/bnVPxpZTuKUuOkXKnpGCJom1J4+quhqtRqSyaT9maWxJiSfz+fR6/Xw+c9/3nJxqLRZ/TAcDpHL5WxpOXv2aJQP+Ke33PH3u5aiEZrfWGpzwlarZUmg1WoVGxsbY8ifNj2cdmm1Wvjggw9w9+5dfPjhh9je3rY6QNMo/M0UOhuf0hl1U9s0mplMBsDe+NVqNcRiMczPzyMajVouFJ0sGgJWCZEUzrFUdMPPsDKSrlQqeO+999BqtWy/k+M0AichHB9y2dgaJJlM2hYV7XbbrkHuDTbKZAqLRGdyLdlwUit4gb3gdWFhwXLQeE5UuVy2FcFEMBXNIG/I8zyUy2Xcv38fqVQK169ft/2CZmXt+4nneVhbW8Pv/u7volKpYHNzE5ubm2PgAe0inREGRer483ntukw0h5VxWplKHc+x03sA4zqp0Wig2WyiUqng5s2biEajuHHjBlZWVrC8vIyvfe1ruHz58rGNyakgPcxJ1+t1u/gJnXPja8SjCoAD7HZ+5Kbh8zy3SJU5Iwt1etT5oXMFwCqrQCCAcrmMzc1NWxUwCS6cdeECJ3kNOIzcHAWl67Xq8LiRK+dUezC5n4FoAtOVsyT8flSQLMN0Uyb8rf156OSxIsh17t0xneT0+KWC/VKGvL/2KCFy0e/3bb8mPdKFSKw2hJtWGQ6HqFar2N7eRqlUsggLAyhFYeiM8hgQJSHrmCpixlQXUR2OiRZUaCTMtKKiqUSGVPzSmUTi+v2+PbqkUChMdXqRomlGcm64/4kq6NEFwAHqqw3x6BxyDfI+2reI+y8Wi40RnwHYo0fUiVfEPxqNIpFIWKen2Wza1xMBnOb1fpRQB9TrdXucRLlcRqlUAgDbGJW6h+k/pgsV+eT+51irQ8R0pB/nUIM5RYJ4rWYamMZlxR33kKLQxyEn7vS02208fPgQOzs72NnZOVRZ5To9NI7AQQ7WNZ5EEpje0r4BanDz+TwKhQKAg3O6gPGeHmqIGeUxSolEIjMD6z+tKKzsiqvs/ZCEx6WsgIMUlxpgt8oF2Kvu2N3dtcYgnU7PlJNJvgcNbalUQrVatVUkTH0xlcqjVIwxtgKF69d1PieN+ePSX+7zOidq7InI0QlbWVlBLpezwYSmZ7h3WYVJxHSa5opp9I2NDVQqlTGDqVwcOkH8XjTSvD6RSFhlTmeU3ZjJaeDY0cCqcnaJnW7Xcuoo1VsAxoy69jIrl8v2oN9ZcHp6vR4qlYpFZbgHlMrAE+YZGGv6l3NFJzMej9sDWtmyRA1wIpGw1VrkhQAH1Yiaftb0MucH2JsXngvYarWwublp18GsVXJVq1Xcv38f9Xod77//viUsM/2uOp7Hp7ATO9d8PB5HLpcbC57o6GuqmEgpX0udYczBMS4ubUH1FFFX9lVi4UClUkE8HsejR4/sc/l8/hOjbifu9NTrdXz44Ye4d+8eNjc3xxwdd/MehRQo6ZBdTOmwaPmoXre8vIyVlRUEAgHU63XLLSqXyzbPS0UHwCrwbreLYrE4FjGcJ9HKITd15eep+4nOD0XvpXl6dXz8yLv1et02vkskEjNXNTEcDrGzs4Nbt26hUqlga2sLpVIJ8/PztgGXGj41mso50BLoJ3EkXOeUj7lQMpUWHR0qeb5OT/ReWVmxnJF0Oo14PI5KpYL3338fu7u7CAaDtmcPG8pNm9PDQ4sbjQYCgYA1toxqmdLgOGivF5Ip5+bmEIlEkEql7Hfk72AwaBFgplu63a4tjXd5W+SrMbWlfbBcZJsVW+xaXq/XEQwGsb29jdFohCtXrhxqbjiN0u12sbOzYxtcMnWoKXSmonq9nuVUep6HWq025vR4nodMJmPP6uPe0Ua06XQa6XQasVgM2WwWc3Nz8DzPIj081Z493FTXcV+Gw2FLnK7Varh37x5yuRxyuZzt9TMrsrOzg9/7vd/D+vo61tfXce/ePYxGI6yuruKll16yNpGtKG7dumXTgBoksC+X9qlqtVpWT2namPtLieW0BaSV+PEWuRfI26KO3N7eBgDcunULrVYLS0tLx8JnO5X0FtuOs1yWQqfFLdtU0ef5vz43Ce4H9jx3RtRu3xI35QAcQP9Mt2j/h/Mk6oD4IT2uMwM8PtXl3t/vb/7vokyE/9m4bdbgZG5SHj2hfY80NUICLRETt62Cil+6w32e7+3nbOo9ONeTSuTdPaBHZSQSCeuU8fVUfvr9pkWYwtK+Lm56HDgcYPG1vV7P9lJSfqBfapzjzfd5EgTGReP8EDltuaHpX36naRtzP1FEjOtdU4eaYuL48n/uEyW9ai8p7firJFkabJ5dp5wVOkfuOlBdxM9A9Id7eRZtAFPVpVIJjUbDOt0MZvgdNWNCxBMY7wyvQatmRiat5UnBMGWSzda0M21vr9dDo9FAtVpFKpU6FpTzVNhZbjknNza/KCMxF/3xc4Jcg03kiO+jQlIc2fxuKavmjDXFRtZ/PB6fiajqaYTRKQnM6mS4i/moVIofZ8Rd7MrkpxHha9w8PvuRzGI6cTTa6w1Tq9VQq9XscQTFYhHvv//+WJdvjWgCgQDS6bTtR6ER/9OkryY5ma5jpA6RphhbrRbW19cP9dbI5XJIpVKWSLu7uwsAeOGFF2zUPo0GmEgijR4VqSp+ps45D4PBwKZOjDF2DpWfo2gRD18kEZznltEokKvA15E3onC/n9NDbiFbCKTTaUuSZrpoFtJb6qi5gYxLsNfvw6KU4XCI+fl55PN563Bns1m0221ks1kUi0WLBAWDQczNzWF1ddVy6nK5HIbDoT16REnqnAeX6wkc6DVF5mZhvF2h01Mul9Hr9WzaKJvNWrSLB7Aq50rtKgMI5SLyRzMsAGygRERHHSk6vQz0FCVSO6LXJpNJZLNZGGPw4Ycf4tatW/jiF7+I559/3hYTfFw5FaeHi0wjF80JstycG+Wow/UUHdCyU02jUFj5QMiNi5l5Xs27q8HhRk0mk+fO6QFgvWfXyXB5UUelLfyMnUbE+lrdTH7oAMmLdEyn0ZAeJZ7nWaeHaVTyMQjRauRUKBSwtLSEWCyGubk5zM3NHelo+j2mDqY6ny6qo86lkqWZ6hoOh7h//z42NjZsOoUBQT6fRyqVQq1Ww61bt1AsFpFIJFAul23X8mmbK9UvLlqgTg/XeiwWs1VBgUDARvfsqUREKxQK2XLxYDBof3MfNZtN+xmojzqdjk2RpdNpWx2nnefdz870FtMtTDWwhHpWelppcQL1uetY8Drl99EwA0A+n0c+n0cwGEQqlcLq6iparRZisRi2trYszyMUCmF+ft725qEBZtkzD8plWk0RTz9SOdcKbcAsOz3ktZE/xRQfeYTqZLvABJFOdQ61vxUw3vSXP+yzo+lFTd2r48P3M8bY9wuHw0in00gmk6jVavjwww9RLBYRDofxYz/2Y594bE7c6VGHx61kUXb8J1Gek15LT5ULXRn8fF4/G71T5Z2Qi+ESDmdV+N2okJ5k3CelWR73Wr80Iis3uJGAAyhcyeazJuq0kCyrzr1GRqo46Hz4OTnu/49zRDVV5qJqdHpYFeN+NhJGeeo170Glx6jarz39NIlGqU+zllSpa6UKcJCGd6uNFP73g/fVcCgnghwWjX799CCdVD7n6tBpl0mf1w+NVFGHXtcu54Uoo6IKRBm0+68i2K5MGkMNzgD4BmnTKpoa1XPgtGScjuBwOLTkZW1YqgGTzp/uBV3/k7IDAHz3A8UP8deqPHWKFbk9jrk4caeH5Z2M4il6zgl7lQDjbG6FQXVAFap3ibI0LnwPjTIIOxNy5tlbbInO87b4Pozei8XiGKlxloXe99bWlj2E0RWOAY0jH+Pr+Vs3A8UP7aGBDYfDKBQKSCaTtqU/U46VSmWMzDhLQk4B1/DS0pKNEIk40JgybZTL5WDMXh8RPYdm0nhPEhci1nlx94vel9UZdMBu3LiBwWCABw8e2NdmMhlbzRKJRNDtdnH16lVbOjyNiAMRFqaBlFBJB0JRXc6P53mWEzIYDCzRmeuT0XCz2bRID9N7bDlABU3hvUnmHQwGaDQaFlWiY6VQvlau8vOQ4zBLQQHHgjrYT2/QENOQKkoJ7I0fyejqjLCKh2RxIvYMcAFYJ5WIDXWZGnE3raWfgbYqk8nMzJj3+30Ui0Wr32u1GhqNhq1sM8bg3r17aDQaGA6H9hqSk4GDFjNaRa0gAB0TbVaoAR0BBtoVbYaqqWK108DefLFknaRoPkbdyoBZucAfR07c6aHXydwhALu5+QUZCekgKldHv6AagUkIETcJ7wscdGTmfZm/bzQatkKLUCkniWeP1Go1W2Y868JUTKVSsTldFRdV4Jjp8+p0qjGdhFZQAUYiEWQyGSwsLMDzPDx48AAALGxPR3TWhOgOuTv5fN52jyWapqklpjt6vZ4tcVcHScU1Fu56V36bPq/VW24U53ke4vG4bapXKBRw+fJlq2SKxSIAWMNOnoTneSgUCtaAfFKE9iSEBsutunQr49Qhp04gX8fzPKtg6bBwfhqNhh0TomPagJJcIUWXNX2gwR8/k+d5Ywdj8nE6PppmmxWiv+rfSc6x6hi+Rq8l10+DqNFor70CS8+VqKzoJXUO95S7XlU/8TFNCWs36VlxeobDoeXxsEK52+1a0jcAbG1tWf6eNnlU5JknrPNxjit1mRvYuqAEx41/U9xu5aqrOOYMjrV0nRWYCoZ8XIcHOKX0lsJlfo+rc6IN2vzSI/q3DvYkReA6ThoxTFLcahwYrbB52XkQjV5dPgi9aD8EwRWdB3ccFQVSBIM9NBKJxCFC2ywocz9hZMOuu+SEMXXleZ51FKi4W62WdUCBg1SGi9K44iptd+w4zspV8MvXB4PBsbL5UqkEY4xNb3G/MOWr64TI7ZOmR09T6PQwZehXqDAJpdR78MftLwIcVBvy5HAXOeD7aiM29jMh6kfh9ZwLrY7heylqMmvpFtXzAMYKWFTX63dX8UP5aTNokLXi6yhboPOqdoOOpCJOimrMynjTOXGrR/XzsyrNJd7rfqATAmDMieTzXMtuQKaBBR/X1BTXgF/ajNdzDykFg85Qr9dDu91Gq9UaO1HhaeVUkB4tR1ZHg45Ou922SoKdlVWJ6MTpb7d01GWfAwcVRIwIOAEcSDpcLteH1RdEejzPw/z8/EkP14kLDW2xWESxWLQeOcePi1zhTYoiP0+aX1UPPhgMolAo4JlnnsFwOBxLFU4rcvAkMhgMsLm5iQ8++ACe5/mShQHYx5rNJur1uiXH8iBM8hSUWK+l0k9KMud78RoXmh6N9g55LZVKNrW4sbEBALb6jNwJHhmgc767u4t6vY75+fmpCwRIitcTyRlV0uAyPeeWn1P0+zL9Tj1GrgSPrOH1wEHHZgD2jC7OGwMuNaRaMVOr1QCMt+jQCJuHdSrnappFvyv3vwaRXF/ayVrP3nMRZ9XzpB24jpMiGn6ijhXtAlMq3Kvcr0TWZqmwgtVq5XLZdlZ3q7IajQZKpdIh1It6h7qfJGcGZVrwwzQx23RwPl10WW0/MH7aOsn8Ol/cM0SciRjxu5AKwCOs5ufnD5HQn0ROBelh9KVGlAPOxcbzaggxu2jPpIXnR3pzeRG8TiE8jaC0fJGiuV1ChLOgbB4n/E6MUpU4RqWgEL0rfo9pBO0+rggE+QsshdYI2p27WZLRaIRGo4Hd3V0YY5BKpWzqg2iaGtKdnR1bcsvqIaYySMhU8iHXraIHk6Bld37cNAMjJ+47HjzoGmXuF6Z5aKg9z7Ml2tPKv+KeBWBT0gqLc//7pc95rSIC4XDYGkA+r0fgMOpUp1SNg3sQo+oVcpD4t9vlWgMwUgSmccxd0f2saSM3OFUHT/lMRzkvmu6jY8r95r7WRYkU5eHj3BPaQJT7YNaQNToqTMu5fCrqfo6brlvOh86TpmJdnaT7RPm0Oud0cnVf8N4UdWjVCVbfoNvtotPpoNFoWJ7Sx52XUyEysxxZnQsODAdPRQfR3RCu8H4K0/k976Zp/CJmF01i2oJRxVGl9LMkNFiMBICD82i0a6aOFa/hbz9D+zg0goudPzpXs4ryAAcRPlvfM2LpdrsWTZmfn8fKyootBedJ5qlUylafsP07MF71yHGnM8LnXXHXL//W9KI+TkW2tLSEK1euIBAIYG1tDWtra/YAXtdI8GgW9o6Zxjlj8MSx1H3rGl8qcuAg0AHGGxpy7Mi7Ie+B92VVkVZbcWyBw9VcHHv2+gEwdrSC6h83IPs4cP5ZiDrbmrZSbpI6Looi0ElUPqFroP30ixpjzqWSnWkngAOuiRsgq2PkVvFNu6gzrSCDBj0k4QOwvDS+TtcZ/yZyqUUtwEEQS5I0K0M1PeumsEjGV4SVlXiK9gHjaV++v5+t/jhy4k5Pr9ezsJR20nS9PYoqbioVXcSuF8/nVVnob1Uk3EQK/bsGXIV9Nba2tsYguVkWz/Ns/yIy9wHY9ApPHHaFY0SlopGxGzEfhRBpRYc6ompwptGQHiWBwF6TwUKhMGZgO50O7t27h2q1iq985Sv47Gc/i0wmY8eo1+thc3MTpVJprCRcCZiuA8PfmiqmYXR5aq6Dz1QN+Wk8IPXFF1/ET/zETyASieDXfu3XcOfOHXS7XTx48AAbGxuWWKjlwpFIBJVKZepInkyR62HGqqw1iOJa5nWMijXVSFSLBiAUClmdxnJgojOsMnENqHavpmEgOZzOLg0C55GIFD+vVnvNghBV7HQ6YzqBxSM8QZ1Vjgy4tBydhpBGUn807au6icaXjj7XrstvU+dHkQauFT1v7ZOQZk9TGKTzUG+uOyK6dCgXFhbG+nAxOGMFF8eIfaJctJgSjUaxsLCAcDiMVquFUqmEfr9v5xEYBx0UzWTaOZ1OY2FhAcYYbG1tHQq0uIe0EeIntQ+nwunhhnbTJkexsPWLaaQ6Ke3i9zpV/grHKYp0lFBhES6cxdSLn7iRFnBQ0qv9ZfxEnUt3/DQCm7QwOf7TZiw/qVBpK7wLwPJ3ANjuy0Qh6Hz6iR8/Tf9X6F0VuYtk0Nmh0aTh5j4KBoPIZDK4fPmyPSCVe4WwsqZnAIw5CdMmNH4khQOHEUt9TIVjq4ZV04lEIoCDyJOP84fG0g+lcZ1R7je+hq9zUc9J+22axUUYFWmhI6NOujotHEtNC/vp/Un6h+9Pva9OkqJo/N8vWDsqgJtW4Z5XJ1t1Bp1L7g8GB8Fg0LZU0IIj5aJNAhUYOCl/aBLfkzpDCf50rPheivxpMD1pD30cORGnRyNREv9YiqaLiMrbj9ysSJAuQE2LTFqQOinqaZKc6xJxXcdLDXe9Xsfu7i6i0ei56M6sjhzJYsAe1Pncc89hcXER5XIZDx48sGcuuQrYz7FxlYSLpOn8uSiG5n9nybFUB0TJmlTa5OAYY5BOp3H58mUUCgW7J3q9Hra2tvDee+/ZAzzZmVz5DS6xHzh8Zg2vU0eIzo1WkTG/zuoj5sz5uRcXF/HCCy9YAn+r1UI8Hre9hzR1k81mpyYKdkuMqTj5+Ziq4N9qBOkstttt9Pt92wGZY6vHiHDugIO54eneRBmAcf3F9+Raoej60aaFnG91rNSJmgWhLqfep7Ooa1j3PIMGN7Xl6vJJ7+WKi9gAB+kvfR2RQc6DOlmzWL3VbrdRr9ftIbicg06nY9Pn0WjUcsloE4ne6nlpROVVH2mQFQgELMGe/XS0MzPXcCwWs87OYDCwjlIwuNcskYEfeb3qvCm3V6u3Pgm37cScHioHnorb6/VsaktJkwB8Fxn/Bg4iIvVa/TaBmzbT5/Wwx0mL2XWiRqMRqtUq1tfXZ7aHjJ/0+31LYKUCT6fTeOGFF3Djxg3cvn0bOzs7lrOhMCPFjzU/Ka2lxl+rJChc4LPUfA3AmMPD78Q0iNslNpvN4tlnn8XS0pLtzdNoNPDw4UO88cYbCIfDWFxctPlxKuhYLGZTS0r0J6qk+XFXYSsCxL3EcScMToIgo+LV1VW8/PLLaDQauH//Pra3t5HJZPDcc88hl8tZFNAYg7m5uY9VPXHcQn3D70JDquRLJQgrCqZOoZag8/lcLoeFhQUEAoExxJd8BJ6rlUgkLJ9Cgz4iqHRIdY2rPqKzRoMAjAcOOrezIFwnPH9MqxEpShymjtBqN0b+wOEI/0myBdQ9TLW4iCmdHqZwOp2OHWMaVRr9WRBWb1UqFdTrdVudTLQ2ENg704oH6jLoDQaDtvEuHYvBYGCPpFHekzrqHCfqOO4vTcVq1TTvzzXOx4vFot0LdMja7bbdG5xfkrDZX2jqnB5lvx+1WdWRUc6N+zzg36VWr3dTAX7vNUn8DDaV13k7cd2FngHYhZ/NZsd66PB6v/FRAqiK3zxp6stVfrx2liJZ4MDpYXREp5iHIOrapVLQU6D9SnMV3ufrqLBoPJX0qa/TwgA1mC4XwiWEUmjIU6mUNUT6HaiklA8xKfVwmqLwvTp7foiwpjT4Wteh171BPcaI2I+E7xcsqegca8DG9/EL4h6Xwj/rMX+c+OkY4HAT00lz5N7rad+bv5Xc7IpfCotrXD//rOgk3QeKDLo8P/5oJkXn4Sjqh65lHSM/jpXqIP7WAJjIqNsLyA2yGYBwL35S9O1EnB6t2NKTvN2BpWju1k8ha072SSJLPxRoEuv/qNytwoXsyjrr4i58SjQaxfLyMq5fv45yuTwWFWuOVzkOFIUg+R76fgqPxuNxZDKZMXIho8JZSm953l7pdqVSQbFYxNraGt5//31LFEwmk9jd3R1rXkdoOZfLWSj/S1/6EtrtNmKxGJb/f/bePEi2LD8P+k7ue+VS+1u63uvXy/Qya/fMaMJy2CMsIdsRSEhgW4ZAloQxiyMMWGAggsUQwg6xGIcJDYEBGVkGyQ4UjIyMrNV4kO3xLD2aHk03/aa73157Ve77vfyR9Z367qmbVfWWepVZL38RFVWVefPmveee8/t9v++3nOVl5HK5gKLVluwartFn6OY+6Jj7vo+5uTmrUBiSicfjFswzTGWMQTqdSagKtwAAIABJREFURrlcBgDU63V8+OGHyOVyGA6HmJubQ7PZxPb2tqXEX3vttQAgO69nwXWqu9wzbGLM4b5ZfA7uLtDGjFoNMLGz2Wxaz5LsmGsEmeypVWxkF1zhHKfiZnPKSCSCRqMR8IoJhpX1ppCVYijgvMZ8nHDOaZM8svSu4+MCEnWGXDDkOgcnAT7qEc57Ff08wzya8wLAMrfT0iKA4oI1BTacU+y43Ov1rD4gA0NHHxiNDVu18IdspAIT/iijFwZs6fApKaJ7QKrt5+c434fDoQ3bMXQ3UUyPggUtVR8nLspjEhsXhJaVhuU16Hn0fBR3IPXzOtDuNXJBGGMs3XYRJAz4JBIJLCws4MqVK7hz504g+167n6qEKaSw7+GCMcYglUohl8vZfZ8o05jT0263sbu7i+3tbdy5cwcffPCB3d6kUCjYfd00zMFEYtLMr7zyCgaD0V5Pq6urdjduKoKwMfU8z8brVSmocuKxwGHeD3BYqssGff1+37b0J+gpFovodruo1+u4d++e3Ycqn89jb28Pt2/fRr/fx40bN+y2DOOM/dMQ3/ft9TYaDZuvBsBuEeF2j9b5TXCUz+cDO5oz5NFsNgN6iM+SOQmk2pmXonqM64fzgJ4qABtaZrhQQ8EU91kyT3JcKe95iq513nO32z2yfY86mtTxZNJ4njCdMk7PuGyM66iFCYGl9kui4eU5GXKfJp2kjKer48lidbvdwFhGo1Gb36OOre8fbsdCwK52mHrIzY8NAz1kkQm8iAt0Lmv+ld4PKygbjYZNy3icbvBnpqXUiLl0u6LscWVoHNRHXdBqpJV+Aw4fdNjDCWOJLqK498gx0fwQHucyOfz7tOOk5+JnXZp0HAM1LZJMJu3+MAxduWW2rrdKhoKtA9j5l8rAVdw6n7XsX6s11HBw/WhlENcjgYEaYQC2YSLzktxnpUwEKWet+DgPIbBww9Cqd3iMS+W75+A9KeugoS4gqFu0EaLqLDUeCkj5va5HrnM+TCdRNEdr0lge4CjTwNfCjnPZybDzuGAm7DzHXctx+kSvVefwSdc2qcL1Py6lhOCex7phpDBGzB3/MDutnw2bx8q4jquSc9cqP3cWY39m4S12T6THFXbD7oCHxVddmlI/EzZQKqr46U0QcTKxkEgWCO4Iy+8P876mXXRRq6hBY4+NMIDoApZxosbdze9yF1NYrsSkC8crnU7jueeewyc+8QkAh03sjDHY3NwEAOvlAIfzst1u4/d+7/fwW7/1W0gmk7ZCSsOBYWsGQGCswoyL5t7o5wmQCKwAWLYpEhn1G1peXgYAFItFu9Gu7pdGJTU3N2dDQQwXnYf4vm/7hFSr1UCLewVnzWYTAI70gQFG47W/v2/nprI5ClJ0/LlOWGWi36f5VwSQ/CyZbALbcTkKYUabyapsg3DS1gtPU8gI8Ed7uyhzz3mpOoY/bj6V5nMcB4SU5dT1o+w8x5K5JPzN8JZuPUEGQjuYKxCeRGEFIkO8DM2588idL0z018aaPJ9+VtNUlL3hOXi8m7jO9cNnTL3Jz7mhZgWhtNu+7wccycd5DmdavcXKLeCw2mdcvE9FjWlYUrMbujru8zyWioZ/a18Vfch6Dp4/jLK7aKKMAHDI1KlXP+5zx51TQY+b26WiC2xahIAwkUhgfn4ea2trAXq53W7bVgcKmnmfvV4P9+7dsyXrm5uboaGA4yrlXEXM16m8AQSeqVuNwpwSDT8Co9Add1lnU1GCOSpHrVji8zuPNeL7vgVfpMJVCG60skoVMOc69xHS3iHu/aiDRAmr6iToicVitiElEGTt3J/TsKc00swDm6T1QkBDwBPGOLhGVDv0uqy/Oy6nZYxcliksREh7oAU36vSq4dXrmGTQw3XAKkOXIBjHJtKxV8fFBSoueOJ7ymhqVCeMmKAuIwB2Q7MM2Yddq9qnx7XFZ1q9pV7lSfS3ezOnUQCnmYBhAMtFoO6i1M/oIp4kBfO44ioCAAHQQ5CiiP004x1m+NxJqz9h+QvTIFQwtVoNjUbDxpjpuSvT4hpPHuN5HmKxGDKZjN3GIpvN2vPzs+Py2dwfFQU9/Kzm/9BLi0QiR4yylsez4sxt2EdFybDSeea7hSlkirsRoisu8+jOdz4XzfEATq4wYrUex5PjyzGkF06jry0cXK/c1WE6fyZNFDSMe18TtNURDTNkBBvKALj2RBlUl/XUkKKKC4qUIXIZaZepmlTnl3lp5XLZ5t+oLWZzUtexomPrpoEAwa1QKAp6xkVutC8TfytbNC7Uxe8MW8tcg9or61HkzBKZ2fyMCcBhyA8IKm8FPrpwXGUf5tm6okwNhd+t1WA0BOxE7Bp2vj9tCW2nERdNa8dZepQupXjcgnefMWlJGkieyy29Vg9vWoCl7/vY39/H7du3bW8MAkUyBhoeVJDNse33+8jn81haWkI6ncbKysoR0KP7Oqmno4DRVfpUGm54i+uy3+/bbRASiQTW1tYsTc2QTiaTQS6Xs5VmqVTKVj0xPJRKpexmpefduNPNywAO9+BKp9N2OwQXnOk4UfmT2SKLNzc3h+FwiFqtZndDHxf2UsZMN5Ll3E+n00gkErZ3iuYE8fkpcxTm2VLxT5oj5jq7Ybk9TPyORqO2uot5cK7uVWCnzier1xhGVCZTwyxhrIQybcpAM8HX3WVdWaBJzKFSSSaTuHLlCvr9Pt5//33cvHnT3ku73bYNUIvFYkB36H0CCOyirs0JdTw131ZBKfUd148bEubntS0HcLhuFDQrc0S2ttFoIJ1OT171FoCABxiG2lRUmR8np2F/3OMprvfkTv5xnqpOiElSMGchaljDaN1H8XBcNkJ7xUxS5cmjSK/XQ61WQ7PZtMrB9RbDxs5lerLZLFKpFLLZLAqFQmC9aENCTYgeB3p0vbnlqp7n2eTlVCqFSqWCZDIZYHr4jOgVuknZbF9PADQJTA8ljBbXaz/uc0AQYChA5zF0Aqi06b2qDuE4uhtn8pkRQAKHYTHml7jAifeg98PvOe/k8TBxQXfY9am37x4Xpgtc8KJrbBzod+1NWGhKc0WVdXLDXK4Bngamp1QqWcCtzCAAC+o1T5P36+oL9/7HiTv+nOfUOXpufdbjxtFl4YBDpogExOPY4zPdhoLsCCtCdBsKekD8zU60bsgp7MbCGJnTXBMnLBcB4+1U4kpDA4eJVe4GmRddXA9NjbYCR1cUlIYpIi4+dywVEE2qQnHF9w+TZ7llA2PptVrNJvKzvFK9clWs3Kg0lUrZ/j7AoTImAHHDWOMMhBpilx0dDod2zXEXeM5vd515nod6vY6trS3LSDCvp9VqWTDgeZ5tannp0qVze370aJmv4QIOHTuXkdTW95yrjUYDQDAPAYB9Pjz/YDCwPX3YdJJKn8+i3W5bPUhD1Ol0AqW3ZBHIhqhT4BqISQU9wOF618RjBcSq97VjeVjYlhvjAgiwnWHfp2BEw5l6XrcZqIJXrg81qMqQKqCddHHnjIYUW60W6vW6tcmcpzo2YQyZsvaqK2jrgUM7QUbZteUcZ5cdVaeMbJ6mlCh41Wf0qHJm4S0m2/X7faTTaeTzeVtpwAlNIMSmYEB4VQpF/w8DPi6zQ8bCRY5KZy8tLVlFo7sc02jw4V2kDUeBo3kmwNGYuDsp3ck+DviEiQIeTmjgUEGpkZoGoWG8d++e7Q/DcO7W1pbdk6ZarcL3/QAdSyp9OByiVCphbW0NqVQKi4uLgeotpXaBYL5EGAh3FYS+zz4oZGdojAGEtnQfDofY3t7G7du3LRvFsE+xWEQ8HsfW1hbu3buHubk5LC8v4yMf+ci5PD+uWc4r0utssBnmVXJstYSX4IfPjQwMP5/P5+2GrDQYZPn6/T6SySTK5bLNdSK4rVaraDabNseH4c39/X27PQ93Z2f4kSEyNzzKZ68s+iSJMp1c69SnNK5sTMqqNn0GaoD1+bgspxpo10HjuVVHaShdQ6Gap0a7xXXE7+PcUjZuEoVj5gJ9PotIZLRXFsF5uVy2oIfbP3ieF8hP5Pk4Hu5r+r+yO9y7ziU5EomELSogW6cMJrfGUBtB0cq6xwntnhnTo2h/XE6IUvbcG+skKm3cjbpK7aRQGAdcvSlNPtXvGkfVXgQ5zjN3aeeTxlTFBVA8l6ukeN4nlZn/NMWdF+rJ8P7orWrekipdTRzWFu3qabkeWNh36zUBwR4X7nOj4dWE0jAHg9+hCbzRaNT+zbwlKrLzFDcHQENKYaJjq86RKmAF5jS+kUjEhq50Y0V9jyyDhlB6vZ4ddwUECl41N4XX4bJ17r1OmoQ5TjqXdK2HsXAqalx5Pvd32Hfo593zhTGmLoDidYa9Psmidld1t+ssaYdxCgGmO8ZkwMY9I5c11YRlXYPUdWHnUaYHOGQyNdymc/9xwf6Z5fRwgDUnRh8Ey0IZ42bXV6Wuxhk/VQZhjIMbAnAnOh8ivQ4qLt1s0H34k6pkHkXc/BDKafKXXEN6EmjSxcPPuwaZbF8+n7cJtZMukUgE165dw+c//3nbG6PZbKLdbmNjYwO1Wi1A4X/uc59DJpOxFPPOzg52dnZw8+ZNvP3220gmk1hYWEA2mw2UlruGQec9JexvNQAaNtZ1yOfXbDaPKPREIoGrV6/iYx/7GIDDbqnZbBYLCwt2O41yuYxsNovl5eVzA6tUsmRJlMXhM1AjS/pd1wEANJtNO/e5/xhwqC80SVarrjiHmf9Ej5ffzbAniyXoqZJZAhDob+OuPZ6LespNPJ0UccGH5smQVQGCzLGbPOt5nmUgafjIlHLcWq1W6Bze2dkJVB0CwdCUJj0rIFWjS5aD4JXsxmAwCPRamkTp9Xq4f/8+bt68iQcPHtgd7rWSk2NOHeP2SdK+SmoLFWzoc1MHg60glFFTIEWiQdcnHQfP8wIVX/wOl+nWTYUnjulxwY5LxyvdlU6nkclk0O12bfmvm4Co4IP/0yi4HjFFUaaLQj3Ps+WoyWQSw+Go6Zd6e+73TfKEP61wHFSJUpSa1vtVJUXRcToOnLrncNk8BT2FQmGimq0dJ9FoFDdu3MBzzz2HwWCA3d1dm8uzvr6Oer2ObDZrK7MIDnzfR7PZxNbWFjY2NvDOO+/ga1/7GhKJBBYXF5HJZKwB5/ymomZfKT477Y8BhIeDXQeAyoyUdjweHwt6rl27hjfffNOuYd8fNSRcXV21OUjLy8t237bzCk1yTXc6ncBeZy6TTEXL8VUdQ2q90+kEQnisFOI5CUrcTtbUZRxTKvLhcGhzd2hAldqnTmKDPP4ow8E5wLwWN+dhkkRZETWeCsLVKDKUB4zuk9VdGiIbDoeoVquo1WpH9A2NL0PGmswLwI41wy+a66PsE8Ekk8vdCMA0FLN0u13cuXMH77zzDqrVKobDoQXWuv4JglhByHWhQNS1A/oc3XFQ8K3PMoz1U4abxANDa+qEE5QZYwL7dRH0TFzJuisuawIc7h6tCcRK61MxP4q43jA9NPd69Dq0HNEFPO5nLqKosjrtvZ7E9FBctK4KhN91mpL4SRN6KZoMnEgkbJVPNptFqVRCOp1GNpsNGDIaZTIEuvu6Kh/NY1Dv0026D1tjOv/D1paWw7vjHolEkMvlUKlU7DPzfR+FQgFzc3O25J1Ow3l2ZAaCLANwtDrkOHreNX5q8PQY/Vu79FJcYx/2XfyM60yFsTbKLrnXM2ksj0qYgzhOr5xWx497fvq+hlPcdaA/YaEtHuc65mFzYFJFgQGBHgH9aa5dwY2+dtL/49YV5znZHRc8hV2TAiNKGMP9OM/jTECPUleK+FSRk2VJJBK2KRvfpwFhlr/eoAtgXK9onKJzJy8nBVvs12q1AIWnMX6KPoxpSbgNE3cCAoeNn4ii3UmnylfPwb95nP6m6FxoNBrY3t7G/v6+7Wejnvk0gR4Kwz5MEs5ms5ZazmazNlGb3i1DRIlEAp/5zGdQKpUQj8dRqVRsmFe9eI6JdkMOAzJAuFIIWwe9Xg+NRgODwQALCwtHepCkUim88sorWFxcDMyVZDKJbDZre84wKXVSGDreK9enUvdkTBhaJyVP9oZ6iN79cDjqqq0MsMuYAUFwWa1W7XPSkv52u21ZGs1fHPfcjjPM1EOTmGPihrTJRpIVU9bdZSwB2LlEoM1nk0wmUalUbGI4Q2AcH7JzZPTC5r+CIq0+DGMXlFkjyJ3E8VZRxzUSiSCTySAej9vdyTVfSRsV+r5vdT5ZLY3O6JxzgQbXmeZp8Xxk8ZXl528NefK61P7o+3QGyVo97rM4M6ZHs+WBQy+IYCOZTCKfz9vQRjKZtDQyu0cqalcQ4ipXjRdTNPTiPij17FKplO2IywF1jYgb7poE5f6oooDHZbI0M37cpAoLc7nvu6JKptVq2S7G2gxMjdO0CecR+6+QWud77rHcyTyZTOL111/H4uIi4vE4isViQOG7Y+smOgNHQ1v6uoYQXcXfaDTw4MEDdDodlMvlI+uH4a21tbXQ+x13f5MiYXOJrzHvw/d9GxKLxWLI5XK2PJ/5Pe122+aQhLVUcPUEFT2rsTSUoJVDLlujTlQY4FGQ6+ZXTIq4c1KvmeEIzQVRZ0iNJ+ciAQgAW41HFoNGld+Ry+VQLpcRj8dt5bDLdCqTp6FCl4XW0AsN7cOy4OclnBvUSclk0oZOOWdc8K+5tPwJC1G6OgVAAEhyfdB+cAz12vQaXQDPsLM6fW7hQFiqzMPKUwtvuZNFY+2uh+96O5QwAHOcKPBRhoiD7ftHK8tcGk2vgZNjGkMxFCrlbDaLXq+HYrGIxcVFyzacdmGPU7g65hSdqFQ66XQalUoFsVgMlUoF2WzWeibTOK4qJ11/v99Hq9VCs9nE7u4utra2kEgkMBwOQ0EPf2ufF30vLKdHX1cjSsXUaDSwtbWFbreL/f19NBqNQPsAHj9NzyKM1VU6nZJMJm2XZSpa4NDguo6PjpvbM4kGWL1X9uxxPWsqcBp1rfJytwyZNEBzWtHxVkPogjwFiY8zx8KYPX6vOqguk6xA67hz854mlVk7TsJCowrsXKdXQQmfD22d9uLhXOexfF2/12Uuw4gJV8LCXi7r9iTkTEGPTnZFZ1QC7NOjEzZM2WgJJ3A48GHAJOwa9FzKaPj+KAeDTA9Lb90wFpUbWQqyVCc9xEmUSCSCcrmMa9euYXFxEcViEXt7e1hZWUGhUDhStg8cNaYaIgSCJYXuhAUQqKxJJBLIZrO4dOkScrkcBoMBVlZWsLa2hlwuh1KpNFWGNkzCDJZSvLu7u/jggw+wt7eH3/md38E777xj+/RkMhlrSF0l4FZhuSHK0wiVGKtduA6uXr2KhYUFVCoVVCqVqXoGnH+uIVMwQU82Go1iaWkJr7/+Ovr9Pt555x202+1AvxaudzINZPC0RJ1bS3Q6HWxvb1tDwjCWUv0MNbBalBWKHGNuY0IjTTZ8nIQZh0kQzktWHjJfEkAgZMH7UxAJBCupXOeTa8Ldu0mZBu2irWuGwrWiUYiwcXbPzXua9ERmIFi8o795L/1+H/V6HalUyrIqbpoDfxhaAmDH1PM8W2ShkREAgWepz5SsnNsihse5wFfnAZ8re1wpE/WocmY5PS5zo795M2EdOcPO5f7/qGyPXg8fGhNJNVdCrxU4RK6MObqJctMkxox2xya7kkwmMT8/j1KpZFmGMIXqTrIw0BkGQqkItVsu2xQwrEK2SZu0TZOEhU+PY8Ha7bZleD788EN8+9vfRiqVwt7eHvL5fKCb+bjFHZYMzu84TqjseQ3D4RDXr1/H7u6uNcphLOs0SJjeoYeqAH1ubg4rKyvo9Xq4c+fOkbCH63yRkSHNHolEbOiACpr6geehY8X/uRdROp0ONE0EECgn5jVOIqg5jWjoQsMbCtKB8brCnXdhxpifc1kbZXkItmhf1FjrsWH2hb9pC56EoX1aomBBf7sOKMGcC6A1jBW2HggAyf7weXOM9Xt5Pr7/qPeiPcw0JPao6+PMmR7XINovPoiru4ls7mfDkL+iWZeF4GfHGQLXU+YCYWI1cLjFPYBAWbBuTjeNCgk4VPqXLl1Cr9dDpVJBp9NBOp22oMelPd1FcZJCVuVFdoPHcyFls1mUy2U77sxxyWQyU2dwT3O9On81dp1KpVAqlew4MJFZW/CHhQI0zh7GKlHcdUCjzQqz4XBoE695TdM6t8PEVZJkborFos3lISAiaCETwRwCnfs0GNQjnudZQMPvUsMAHO2Ozdc1yZPn4v8K0nTuqFGatOek9+/7fsBY6fiftImx6hjXUVZjGsZAu2Oja0jzhnRMxwEqzUl9XEP7tIQgxLVRvB8AR9YDcHTMw5xeZencVjL8W1MYVHSOu59xc4n0mbvA7VHYbVfONJHZnVyKLBOJhA0RhRkNvWGNP4bRZy7zokZCzwcc0qwMH7BPUKFQwPz8vN2RmdeqpbiswlCDNG0SjUZx6dIlW4rMidbr9bC3txe4d/6w4kSFk8+lh8eBTX4Pw1vLy8v4xCc+gVKpZIEnwwZPKnZ7XuLOPVe63a7dpqJYLOL5559HLBazO3CTEnbnseaDAAgY1eNElQYNUa/XQ71eh+d5WFhYQLvdRrVaDezwPW3iKms6KgpsjDF26w9uDUGA0mw2bTGFuy2K5432IqvX6zbvh8ewaqher2N3d9eGYbQVv4YKCKi4BQBDB9r3h/MgjPp3m7hOivAeGN4io6thRu5zqP2M+Fn+DgP0Gh7R47T6h0nJCmB0bVDPhNklDScDsNfO5n6TuKu9K2QbGbLWsWNCNo/h/0oeqD1wgYX2pYpEIoEu7C4JwWorIJgfxLGm8FlxvoRVrWrCNVNdJnLvLco45c/BptIIQ6X6+bDzKONz0jW44jIPyvZw7x73wfOBTWIn1IcRhrcymUzg9UajYY2ga0TdBXQadk2/j58ngNVE5oWFhSd9ixMtqmSHw6HNDzNmVNXFuaedSznP6SG7DgXPGybuWqKh1XnOeX+SYlfPcNLF9eT1dbbJIBvB9zWZmIaC46Y5DMPhMFBdSgNOwOgCGN2pHUBgHYzLKzmOTdUQ0qSJ6k3OWe0yzXE9bg4r4HFZY3V2uUb4mTCmwmUNwmxKWEhfn4/e16SLO9+BoNMzjsnhZ8P+ds/L8xH8a/6aficlzDl2z6u5Re460XnyJFjOM8vpGbeYtf6eoYxOpxPar0UTnN3zh50XCGaOh31Ow1XKHrGiiYtSlQonyyR3Qn1YGTdG9C65IexJnpj7jF2KmF4zEz/d7QJOup6LKOwezM14aQS0L5V6SErLu4mApxWeS/umUNl0u13cvXvXMnAnKcRJE2UYuK+Qq3+4rpmXwx96vpzz/E1wSOExLrNMR4hCJkMVtVZ3tVotALDrgSCM4TbqGDJyFDpnvu/brrS6ie0kCA0WdQDXvRozze3Rua3zXe2Hy8ro+V39wffC8g0VuPL7jDE2T5ObY/NYMnNasj5JYx0mLtADjrYPoMMViUTQ7XZtCwCOh3s+zVmjjdUqaDeXSp0FPY8WI9HpiMViaLVatmO5u9Epr5vgmc9wYnN6OAjqLVE5kdrN5/MAgpn97gCqhP3vonp9SGHgKSwmzLBKoVCwtKZreHidNFaTvgCOkzDUzonJyckdtcP6LCgVqkrF9SI4ScnssCy71WoFdq13PY+LDHx830ev10Oz2US/30cul7M5IRq+CvNuT8PshH2fPm/+JjgYDAZ2W4xkMolr164d69FOIttD4OIaLjWYBDyc1+l02oIG5jcp+CF7o2ETzmdXyJABsAwqn6Xv+9jf37fPm2HNbDZrmb10Oo3FxUXbH6jRaAQAKpU+n3mr1UK9XrdGYpJE9QKb34Xt56TbgriMjla96RiMAzz8HPNNwvJC+D4/SxaHTTq1USoA6/QxTDMtOT1hgEeZYm6rYoxBu91Gs9m0x7tOlYJITXlQ4oLjqI4Cv09tCsUYg2w2a1NJNjc3sbe3Z/en0/48Cnjcfeceh3U7M9BzXGhLqU8eG2aIOXhhN8jPhIVVwl4/6Xqo7HQvGPe4J4EyJ0FOc+3ueOkzCKOPw86tNKX265j28TuNhIEDzT3QElid7y7AOY7VeRjgw99hRmM4HNoGZsftlj6JgIeiChrAEaeJ69vtyaWG2KXWXYZBGQj3Pb5PI0kQSwOvuSguCNBr6/V6RxKcef2aVOuWGk+KuGMYFsLS8Ro3n8LWzmnudZxect/X8zJkqdEC/XHvaxokbJ2ruPcz7rgwva9rRkOMrg7TzymgJdh1d2xwxdWBCnwf51mcacm6S1HqhGcnZtLF2tshbDNFPbcOsiojnstdZJ7n2QHm+fkdrgHSz7moudvtHvEIplHCgCInopssyN/jwKV+Xl93x871OlyDNM3jeRrpdDrWi7937x4++OAD9Pt9lMtlWzUYJuPm5Dgl4Y6rfk7ZA+CwjJjJj+McjGkQnWNka4HDcmNWBmYyGaTT6QBwIDPBz0WjURuCZSiJ41csFu14cqsR6hU3LKYOHp8x30skEpaVYl4XcNh5mOBIE8vp6dbrdbvH26SuG1c3EIR6nodOpwPgMC9H5xyfI5+LVtGGgVoNtVO3a6Krznl1MjQ/juOsQMzN6Zk20KOhWQIUNtZlryi2TxgOR53HNW0jLEri6hPP89BqtexcVQZN0yL4bMieciNfAHZtAoch37C54xbSPI6cKdOjHhVFq3jy+bxVOETbJ4W2FDWGIciTrkkHkd97msz84XAYAD3TahwoYePLsKMaRjWy4/4e5/mquN7ySddz0aTT6did2O/fv29BT61WQz6ft0bU7XSqjMA472xcKMzNaWHVBec+DTZ/nkSS4HkJFTsdnHQ6HQibJ5NJFItFG1YiGNLEZDZM1QZ73HsLALLZLIrFIjzPQ6PRQKfTsWPOrSdyuZxlbGhks9msvU7XoA8GA6TTaVvFyCapvV7P7k8HHCp/hocnEfSEAXLOO7LoHHd67RrO5fzmuGmDD+/NAAAgAElEQVTFEPMBFfTwWH4nja0Wm7jVXsPh0LJqBKj9fj8UKLmJzNMGfKgLPM8LNOdkPy5uncO8Hs5nlw3VcwHBsdQ8NH6WbCe/m2uSQItNPfmc2LdKGwSHJU2rE/E49uJMq7dOmiAKjJR6D0uCfhwv1L2OMNr1uM+ExTgvohxHN7vyMMZRlYV6UuOu4SJIGMVLha85TmQfPM+zG1TyeCDIYBxH7bpOxrgwGZOl1Ruj8JmoAzKtz0NBuCbn8/5dJc7fNLga7uA40WCw8k2Ti7XXyDgmVUG/fo+eW/d7cj+vemgSDXDYnHd1JV9zncyT7inMydI14p4n7HPjzqufCYtK6L1Ny3pww6PqdLJVgFvV5Sbpu+yO/qa4Y6/fRXFtvLKsZIG0uMAYY52GcWDzceb+mYAeVwm4g+Gicr4HHC0VVHGT1PRz4wy2skGUcWEcdwLws7o53TT0a3gUUTDiTnQVfYZhCmGc4nIVyzQb1EcR9YpWVlbw6U9/GsBoc1KWT4eVIYeN5zjQ7Y5rGAPkrivf99FsNrG/v2+NO/s1Mcl60iXMcPF1epTlchmVSgX5fN6yMTpWDF8zZEIwEo/HUS6XAyEPMmRkjFwGWIs3gGBlqeoY3dyXidbpdBrAqJcTN+alDtJk9zDHcBJE70+rXTlnWXWm64GhFzefTJ0jdTj1dWVElblzASvPoYaX46/sE8Gny7xO6nirKDB2AQvvOZfLIZ/PI51OW7ZHx4NsMMGRMiya1qFMneug6aattLMEMvqMuYbm5uYAwM6BTqeDvb091Gq1Iz3j+D2PI2fG9IQpcZcp0ex8ivvA9PXjRN8fB374HunWsO9QRaWeGZUhF8JFAz1AuDcZJmFeKKl3931F6qqwniXAAwQTJsvlMl566SUYYwKgR4G2G5MHxudNKbhxq7/4txp3VYKe52F7eztwrY1Gw9LO0wB6KC47SyDD6qt8Po+5uTmbt+MCfO2zQx3FTtmc36q7eP5Go2EBj1abkLZ3E5ZdRo3GQ5ue8llyLmgX43E6clJEdajb7I75MwwrqXPM5xTGWAJHt77RseV8Zu4PQUyYKMOmAMk19Bomm+TxVnGv0133DG2RaaZDT/3EvlUcOxd0ch6rk+yGEN38WraGYB6Pjj+BPsvX2+02EokEqtXqkYR91648qpwZ06Ox67D3dEsHIBwAhZ33ca+LopPBLYFTozMuEfsiyrhqC5UwEAuEVwuEjdM4hXbRpd1uY2trC81mE9vb29jb2wMAW8YMHO1VQhkHelxxwdK48eUcpsGtVquo1WqIRqO2SeVwOEShUHj8G3+Kctx8oqOjJessSdbOyNqXBUDA03Upfo53IpGwwMWVk5hoGgXug6dGRo2J6lT9f9IkbG2HOT1uQ1iGM8KMKBDcQ8sF+fwcx47nJkOmYJLPmQaf88HzvEBIRe9Hf0+6aBhLhXqXeWvMn9HcJbV5ah95XgXkfI78jMtuUnTPLA1pMYlf8QGfG8PHlDCn7XHkzECPTmQONieWMaMeAfV63T4krcXnOdxz8rerfFROMzl1ELm1hMvqaJtu/pCGvYhMDxUIJ2kYyHNDW27JImWckuZ4jttv7aKK7/tYX1/Hl7/8Zezt7WF3d9du9qn5Jadl1oDwmD1w1AM6jpJXj5w5Rfl8HoVCwf6Uy+WHv+GnLGpI3SRM3mMymcTc3BxKpRLm5+extLSESCSCYrEYugM6z0s9wJC2q/jj8TgKhUKgJb8airCQA58V10ChUMDly5eRyWRQrVZtXx+CKQA2ZEBncRK3oQCC6Qn8zfEjk8P+PZxfZMmU1TLGBLa/UYdMdYeG/bQQg5/TZ8ox42bH8XgctVoN8/Pzdqf7/f19q9OUVZsGCQPlAKxtjUQiKBQKWFhYQDweRyaTsXk0Oo7aXJNzXjfQ5fYc7jwPAz4sKmA4U0ObnudZx4rzgltTaV6bFlpoNOFRbfCZhbd4ge4D0Lif0l3j8njcc46TMEpv3KDowtHFyB+d7K5RmtbqlpNEDcdxtDB/hzE67mthx7v09bMgvj/Km7l//z62trZQq9Wwv78foPYpahSB8ERBAAFgqsrnNHOT485z0FgkEgnUajXLQnF9ToOcxB7Sw02lUshms8jlcmi1WtbrJYDhlhEuSHEdANUP9JrHNU5zDbW+TqaoUCggm83a3ArXoVAnQsNik6SLlBVxn4eOi4b/UqmUHR/+hHX0VcPn6noX9CoLoft/AaM5z3nAaiI2UHRZPj3/tMg4B4l6na0buN7dcec4cgw55/hD26AAVUNZru2lbtFuy7o/GJ0Jno/MnyZUAwishce1wWdavQUEy/9837flge7Nuol6boWFPoRx33Oa17gQmDxIJULKk0aAg64hA1U8F1HGgR6lSxX4KaPjKme37F29AXoW06RMHkd435lMxubvAAgYLteoAuGVEi5LwNfDjnevAQj231DlxmvU6i73+Uzy89L5pzqGhozrm/k8BDPMMeFxrrF1f/OYcYyzmyfkHkNjPBgMbG+U4XAY2OBS1yDBD3Ukj2O39El8Jjq/eI3UCwQZ7Ii9s7ODbreLvb09tFqtQHhL+/QwHOkmRqszTbDY6/VsLyD2hlHdTeYhkUig1Wod2XIHwBF9NmkAc5y4TjxfU72sW6Uoe+KCVSUu1DFzwb+Gx1xbyfVFYOT2seKWTwxpsUfV/Pw8hsOhnSuaU+cCooeVM01kpoLgQm21WoFYHm+AYIfoUxOsgGAylTEmlNbVxR+W16DnonJhT4BOp2N3/yb9yYWi5+WEuohsT1h4Czga0jruJ8yIU6iktL37JCrssxL2iQFGCpy9edQAc48mIBzAuImcKuM8PAABI6pAhwwHWddIJIK5uTlreF2nw10Pkyacg2wEyZAdG6HOzc0hn8/bZmi+P0pe5m7rWm4eBmj0e6jUXV1D547jquyEGv9Op4P9/X30ej27Hcnc3ByazWYgD4KfpV7MZDI2BEmWZBKF86vb7VqDNRgMkM1msbKygmg0ina7jQ8//BDdbhfb29u2JxGFrEAsFkOlUsH8/Hxg7z6yc8aM9tDirvYMIfN5EkQRkKbTaezs7Nh9wajvd3d3Qx0PN2oxqcLrZEI+QZwCoXQ6jWKxiGg0asOk6txriJiMDO9d5zrHVkOOfBY8L+e56n52KWfoVndrj0ajtmL0+vXrWFpaCkRbXObpUeWpMD1uMnAY/ase6EmUoioSVxGHAR6XclMApAOodL/7WUrY9V8EUZo4bOzHgZiTFEEYXaw9IsKOn1RF/jiiCZSqQKlk2DTtOOXqPgM3FOaKAn7tNQMg0D241+uFzv1x55t0Uc9UlbJ2YlfhvNdE5nGgR3VJWKWcXkPY51TP8TgNrblrQ51HDUNMKluq4Q81WBwPAjdjjGVkWPJP0KOgkgxAoVAI9G1RNllZMQAWyNIw83p4LVxr3PKDCeRqO1zHNsyRm0ShPVNywNUZDGuF6XCX6dQx19fDAH+YrWQerOZqAQg4BwRrmnuUTqftvBnXyuNR5amAHi5SnYDc4NP3R0metVoNnU7HbujJQSUw4U2OQ3knhbf0AZHuY7npYDCwPQtIueln3bjmNEz+hxUuBgDWGLrAxFUgytRxUrPVuS4CZRnoqWYymcAzGcdgXBQh/U6qV8eGP2QgjhN3nMbNQ1cJqaF1r0uBZq/XQ61Ws97zaa7hvIVzN5vNot1u2xAdcLibNkN32pwwk8ngM5/5jN1OgpsgHsfyqDwsQFengtV8g8EAH//4x3Ht2jWk02k0Gg1sbm4GmG79jlQqhYWFBbt9yUl5kE9TFDy6OWc0wPl8HpcuXcJgMMCHH36Ira0t9Ho9VKtVy1C4TDOfXzqdDhTDaDoE9ZAyacqw8VzU+wQ8KysruHr1KiKRCLa3twN5Lu76OckZP29h+CiTyQRAmjL4uVwOhULBhkq5/QYZUk0oZ+K3W2lN9k5Fc4K0j50mozOkxtAuvyuXy9n8rnK5jG63i/X1ddy5c8eGvJhrx1DX47CcTwX00KvkhQOjxVssFi2S3N/ftxQl+zhoPFhR62lBj/s+DbUqeoKtSqVie5JQMfLhafjGLWm8KMKwH40DQaqrVF1Wjs+J4UAXDOlzi0ajtpV/NpsNlKZedOn3+xbUA0EgyDHUvIJxIP6ksNY4oM85q8mwrKRQBpRhFxqHMMM+zjM8LyFtn8/n0e12bXKy5oRwfJnPAAC5XA6f//zn8eqrrwbKxo+T087Vcc+Pa6fX69nWAJVKBaurq/B93xperaBR9juVSmFpaQkLCwsoFosTBXoAHGHT1Ek0ZtSXam1tDZ1OB++//z4ePHhgd5cnI0CnSw2p7tXIfMxo9HArCQIrGuhOpxPIh/I8z853DePMz8/j0qVLSCaTuHfvXsDhA6YD7FDIkLDDO9cvnwnneKVSQTQaRbPZtKFCzxttuaIMKfOjfP+w0SQA+1tZGE2IBmBtgKZL8PdgMLDh51gsZsNtDH2y0OPdd99FMpnEwsKC7Vw/Nzdn83wmFvQA4dQgUboaTk5InWguHXZc06njRJMz1VNVGjYs7KKARwHTRRMFMZqYBhxN5AxLZFYa1D1OQaJS85OmsB9Xxnn+HAOWbOpcpKInXX8ce+CGPfT8FGXj+OMCd62edOc1Qw7cg2paxFWwwFEAqOW4wEgnaG4VS3jdz1Iedt2753BBTyqVwmAwQKFQsPklzN2h0+GuN94HN2ecJGPs6uwwxoxeOoBAQrbv+zZ/Rxsx8pkpE89cKWWVtJ0Iq/N0uxDmd7kdlnkNOp4uMzRNMi4S4c4TdXKbzWagbF3D4RwzOrVA8LnqWuOc1e/SNak6n8VErKbk2uO1E7gCsKDsSZENZwZ6lG5sNpuo1WpotVp2ANk3w/d9XL58GZ432rH1wYMHaDabdkLrwgeOZ3pchR/2vmb5Z7NZLC8vI5VKYXV1FZVKBb7vW2VEb5h0dLvdtkzUNLM940JWnJQs5cxkMnby6fhzUhL993q9I/sFUakoSxeNRjE3N4eFhQVks9lQVmOSlPjDiCoaFzR7noe9vT3cvHkT29vbKBQKyOfz1rsiszkuz2nc94X9ze8P81AV/PAZArB9UzRhcH5+3jZNnHShwuWu0QRvnHtkgpaXl7G4uIhCoWDnZj6fP9J2/6yvVdlv3/ctu+D7Pubm5nD58mUkEgmbCDwYDOx9sdCCIYFJXC/U2QznaroC2YZcLofv+q7vwksvvWQTljWlwXXCyuWy3eyVFVx6jKYf1Ot1VKvVQKiG8911eCuVChYWFuwY0yj3+310u11rnKcB/JBB2dvbsyFf2jzqoXa7jb29PeTzeaytrWF5eRn379/H/v5+oGEjx0ydJdVxYTrHTVfgNdCu6L5ytAlra2v4yEc+gng8jgcPHuDu3bvY29vD/v7+EaDT7XZRrVZtocKjPpMzBT2cjAQ9WqrOXdYjkQiWl5fheZ5t567l6+qpAuMTylyGKEw4AbgfT7FYxMrKCnK5HJaWllAqlQKZ5QQ9AGxootPpBPYgmWZxwQZBCL2fVCplKVANj1Ch8TVXkasycinQXC6HcrkcyLvgOS7CmKr3Q0XjeR5qtRpu3bqFzc1NLC4uWrZlZ2cHtVrNsmXHsSvuvB+ngIAgKzQu1EKvjk3itKKj0+nY3cUnXTj3WBUIHLYDoOFjLszS0pLNBzDGWC+T53ma4oJkPovV1VUAIzBKfUjHw/M822tF0wUmTQhOdH8xgh6GJ1ZWVgLNIMOcBoqCl5PkOIbGfZ1J1GwGqQCKlXVuVeqkCgEfAQzXhQKXTqeDarWKeDyO1dVVfOxjH0OlUsH7779vgSQ/yx+1rUAwud59T6/FGGMZNIYiNVwZjUbxwgsv4BOf+AQikQh+9Vd/FV/72tews7Njt8JR0DMYDFCv19FutwOO+MPKmYAeJmVSobDHAgeIVCVvPJ/Po1KpIJlMot1u2+x+Dr474dzfLtg5Lmzi+771ngqFAiqVCrLZrC0BHQwGdlGSAmTJfSaTQTabtfcyzXKcgmdnXuYMcPIx+Q0Ihv2oGDT7XsukqUTK5bLNqXATBU+6pmkUnZeZTAaLi4uIRCJYXFzEwsKCpdxpdLX/SFjIyl3kxylgF9C6a0MVVi6Xs3F1GqlyuTw1+24RsBOoM/4PHIJQgoSwPlTnNe/CvpfGgflH7FDLhnLUpZp3Nynihrq1kohhbU065mvnKQw7a2UcHQ9NwD7OgZgUYZVbqVSyupqAn/tbccNRMoUMN5bLZdulnGG+cakmYaAHQOj7GlLWHnwEQtpCgqGuXq9n2UANP9JRo4PzqM/iTEBPMpnE5cuXkc1mYYzBe++9Z2lCVkqtrKzYG85kMrh+/bql55id7+bgAKdT9McNhjIPpFpZGZDNZtHtdi3l1ul07CJdWFjAyy+/jEuXLmF1dfVUlTbTKsViEa+99hoqlQo2Nzdx//59AMDKygoWFxcRj8dRLBaRTqdRr9dx69YtGwrh2FOhMbkumUxiZWUFzz33HLLZbCBn6CLIcexiNBrFSy+9hB/5kR9Bs9lEsVhEsVi0oV8mHIbJOHbnNAs+LNwbFkLTJnIMW2YyGaytrY31uk97DU9DIpHRdhIsvX/hhRcC5czRaBTXrl1DPp+3zQkp48KST0PCjEcmk7Fh9hdffBFbW1vW+YpGo7hx4wYuX75sWZJJWkNk0ZvNpu3JxuqdUqmERCKBXC53pA/beQrD+Ux3KBaLtgdQt9u1u96HgeVJk1wuh0996lPIZDJotVrY2NhAp9NBqVTCysoK4vE4Pvaxj+HNN99EPp/H/Pw8jDEoFov43Oc+h9dff/0IwBm31t18nnHihitd9iiXy9ktLVZWVvDRj34U1WoVkUjEgrdsNmttdalUQiqVwtWrVx8ZMJ8J6InH49ZTrFarWFhYsCEh5swUi0WLQMvlcoDmPE/pdDqYn5/HyspKIKyzuLiI1dVVXL582T6MiyrZbBZXrlyxXiW9neeffx7Xrl2zoLVUKmFzcxP5fB7b29uBWC1jw8zOz+fzKJfLmJ+fv7CAcRxAMMbg0qVLiMViGAwGyOVyyOVyAGANgx6r5zoun+C04EPPq6XEPC/DagzpkrWrVCqnvs/zlEgkgkwmg1KpBM/zsLy8bPOktEqH7LLKeYYsXOaO7HYul0Ov18Py8jKuXLliwwTRaBRLS0sol8uYm5s7l2s+TpiMzBQAMpe6rxnvI4zpPQ/RXBOyhHS6mfsJhOeJTpqkUilcu3YNyWQSm5ubGAwGFkCQ0XnuueesDqcOymQyeOGFF8756kfO9tWrV1Gv1wP5hAScxWIRly5dQjabxfz8/CPn3505dD3OEBx3zHmKGpNJR/dnKWHhJ9fQusxBmCF2vYdnVVw62H3vuLE7zbHjvvOk8457VtP6vGZzb3Jl9hzOTo5jm8P+n5Zn8aSxgnkYL8cYswXg1mN/67Mlz/m+v/C4J5mN/SPLbPzPV2bjf34yG/vzldn4n6+Ejv9DgZ6ZzGQmM5nJTGYyk2mVZzd2M5OZzGQmM5nJTJ4pmYGemcxkJjOZyUxm8kzIxIMeY8yyMeZ/N8Z8xxjzVWPMrxhjXnzIcxSNMf/GWV3jRRJjzNAY85Yx5lvGmG8YY/5dY8zEz5NJFGNM5WAs3zLGrBtj7sn/Y8sVjTFrxpi3x7z3F40x/8yY937UGLPqvPbHjTH/kTHmDxhjPvd4d3QxROb428aYv22MyZxw/G8bY944+PtDY8z807nSZ1dmev985SKP/0QbMzNK1f4lAL/t+/7zvu9/CsB/AGDpIU9VBDBxgz+h0vZ9/+O+778K4A8B+H4A/4l7kDHm4tbsPyHxfX/nYCw/DuALAP5b/u/7fu8Rz/kf+77/6+7rxpgogB8FsOq89f0A/m8AfwDADPSMhHP8NQA9AH/mvC8IGOm7mYMx0/vnLRd9/Cd9gf1BAH3f97/AF3zf/waALxljfvrAU/umMeaPAYAxJmeM+Q1jzNcOXv/nDj72lwA8f+Dd/fTTv43pFN/3NwH8aQD/1oFC/lFjzBeNMb8J4DeMMVljzP9sjPmyMebrHG9jzKsHr71ljPldY8wLB8f+Xwfs0dt8Zs+6hI3VwVtRY8z/eMC4/X1jTPrg+J81xvzwwd8fGmP+sjHmawD+BIA3APz8wbnSB8rr4wB2MTLs//bBe999wCb95sF3/oYx5qqc/wvGmK8YY/4/Y8wffdpj8pTlHwK4ccCE/V2+aIz5a8aYHz3ug8aYf+dgLr9tjPlzB6/9JWPMvynH/KfGmD9/8PdPGmP+6cGY/2cHr60ZY941xvyvAN4GcOXJ3+LUyUzvn69c6PGfdG/9NQBfDXn9n8dImX8MwDyAf2qM+X8AbAH4Qd/3a2ZEQf9jY8wXAfwFAK8deNwzeQjxff/9AxZh8eClTwL4qO/7u8aYnwLwm77v/5gxpgjgy8aYX8fIwP53vu//vBmFcaIA/jCA+77v/xEAMMZMXne185GwsVoC8AKAP+H7/r9qjPlFAD8E4G+GfH7H9/1PAoAx5icA/Hnf979y8P8nAXzD9/0PjDFfANDwff+/OnjvlwH8Dd/3/4Yx5scA/FUAP3BwzjUAnwbwPIDfMsbc8H2/cyZ3f45iRmwlmbCH/eynAPwpAJ8BYAD8E2PMPwDwCwD+CoD//uDQfxHA9xljvhejZ/rpg+O/aIz5/QBuH7z+r/i+/48f744ujMz0/vnKhR7/SWd6xsnvA/C/+b4/9H1/A8A/APAmRsrkp4wxvwvg1wFcwsNTcjM5Xn7N9/3dg7+/F8BfMMa8BeC3AaQAXAXwjwD8h8aYfx+jXgltAN8E8IcOmInv9n2/eg7XPokSNlYA8IHv+28d/P1VjIBImPzCMef+ZwH8vTHvfReAv3Xw989htKYov+j7vuf7/nsA3gfw8vG3MHWSPpizX8EIdPxPj3CO3wfgl3zfb/q+3wDwfwD4bt/3vw5g0Rizaoz5GIA93/fvYLRWvhfA1wF8DaMxJat3awZ4TiUzvX++ciHGf9KZnm8B+OGHOP5PAlgA8Cnf9/vGmA8xMsQzeUQxxlwHMASwefBSU98G8EO+77/rfOzbxph/AuCPAPgVY8y/5vv+bx4wD38YwH9hjPkN3/f/4llf/6SJMeYHcZgj9RO+7/8td6wwAhpd+dgQQHrMKZtjXgdGRvaHHuEy3eZdF62ZV9v1Po0xAwSdwMfRG38bI721jENQagD8l77v/w/O967h+Gf4LMpM75+vXOjxn3Sm5zcBJI0xf5ovGGM+CmAfwB8zxkSNMQsAfj+ALwOYA7B5MPB/EMBzBx+rA8g/3UuffjkY2y8A+Gt+eBfLXwXwZw9yR2CM+cTB7+sA3vd9/68C+D8BfNSMqopavu//TQA/jVGY7JkT3/d/SZKZvxI2Vo9xejvPD8KHMd/3d9z3DuR3APzxg7//JEa5LZR/wRgTMcY8D+A6ABfUXkS5BeAVY0zyIFT7PScc/w8B/IAxJmOMyQL4QRyO4S9gNLY/jBEAAkZr5ceMMTkAMMZcMsYsYiZhMtP75ysXevwnmunxfd8/8Iz/ygH93wHwIYA/ByAH4BsYeaH/nu/768aYnwfwy8aYb2JEXb9zcJ4dY8z/a0ZlwH/P9/2fPIfbmRYh9R8HMMAo9PHfjDn2P8cof+F3zajq5AMAfxSjPIZ/2RjTB7AO4KcwokF/2hjjAegD+NfP9C6mR8LGqvCI5/pZAF8wxrQB/NcYUc2UXwbwdw6SDP/swc//Yoz5SYxi8n9Kjr2NkTIrAPgzFzGfxxXf9+8c5E69jdE8/voJx3/NGPOzGI0TAPz1g9AWfN//ljEmD+Ce7/sPDl77+8aYjwD4Rwc+QgPAv4QRizcTkZneP1+56OM/24ZiJjO5gGKM+esYGeKHyhU5MOR/1/f9v3MmFzaTmcxkJucoE830zGQmM3k08X3/J877GmYyk5nMZNJkxvTMZCYzmclMZjKTZ0IeiumZn5/319bWzuhSLqZ8+OGH2N7eNo97nqc59u12G/v7++j3+0in08hms4hEIojFYojFTjdl+v0+BoMBhsMhWq0WOp0O4vE4isUi0ulxhUhPXr761a9u+76/8LjnOa+53+/30ev14Ps+hsMhPM+D7/v2dyQSQSQSgTHGHgPAHgsA0WjU/mQyGSQSY3fAeOIy7eMPAL7vYzAYYGtrCzs7o7zwaDSKSCQSOGY4HGIwGACAXTexWAz5fB7ZbPapX/e06B7f99Hv9+F5Hvr9PrrdLjzPC+ieR5HhcIhqtYp6vY5YLIa5uTmkUikYY+yaOUu5CHPfFa4Fz/PQ6XSwu7uLbreLfD6PhYUFxOPxMx/X08q48X8o0LO2toavfOUrT+6qngF54403nsh5nubYf+Mb38AXv/hFbG5u4rXXXsObb76JXC6HSqWCcrl84qT2PA9bW1vY2NhArVbDW2+9hXfffRdLS0v4gR/4Abz22mtP5T4AwBhz60mc53HG32VTT6sUfN/H+vo67t69i06ng2q1imazieFwiE6ng8FggHg8jnw+j0QigU6ng2azicFggHq9jnq9jkgkglKphHw+j1KphE9+8pO4fPnyI92D7/swxjyUUpuE8X8Y4T1SCGT29/fxMz/zM/i5n/s5eJ5nwTvHxfd9VKtVbG5uwvd9fPzjH8cbb7yBSqWCz3/+83jjjTce2Xg/qkyL7ul0OtjY2ECz2cTW1hZu3ryJdruN1157DZ/+9Ketk/Qw8873fdRqNXzxi1/El770JVQqFXz/938/Xn75ZSQSCQtIz1Kmbe67QucKgAWJ/X4f29vbaDQaePfdd/GLv/iLuHnzJr7ne74HP/7jP47V1VXrYJ23jBv/WU7PTI6IMcZO3N3dXXzrW99CMplEoVBALpdDLBZDMplELBazCh8ABoMBut0uhsMh6vU6qtUqut0uarUa4vE4EonEU1f8kyiuYQ0Tz/MwHA6xvr6Or3/964Y9nb8AACAASURBVGi1Wpa98TwPrVYL/X4f8XgcuVwO8XgcjUYD1WrVMmyDwQDRaBStVgupVAr1eh2rq6tIpVJW8Z+knBSwTYoHd5bi3qPneRgMBta7pSjbxmeSSqWwtLSEaDSKUqmEZDKJeDw+EQbgPGUwGFi2kmNJtiUSiWAwGCASiSCVSmEwGODBgweo1WqIRCLwPC/ATCrTwHO40m630Wq10G63cevWLcTjcaTTaSSTSSSTSQBAq9UKnBM4fPaRSMQ+u2ddOCbU871eD9VqFbu7uxgOh3jxxRdRqVSwuLiIBw8eoNVqoVKpYGFhYWJ1/Qz0zMSKLn4q63v37uG9996D7/tIpVJWGczNzSGTGW1OPRwO4fs+Wq0W9vf3rdEdDoeIRCLIZDJIpVJIpVLPvAGguIpWhYa23+/jvffew6/8yq+gVquhWCwin8/D8zw0Gg0bMsxkMojH45Zp6PV6yOVyyOVyNiwZiUSwsLCAhYUFGGNQKBRw+fLlsaFGXp97nc8C8FEZDofo9Xo25EJDPBwO0e/3A6A/l8vhypUrSCQSWFlZQTabRTqdfqbnvO/76Ha7qNfrGAwGFowAQCqVQjweRyQSQSKRQDweR7fbxfvvv48HDx7g9u3b+OY3v4loNGoB53A4RLvdRr/ft3pKjavnedjc3MSDBw8QjUbxwgsv4MqVK8hkMshkMkin0+h0OjZ8T+CqoeJYLIZSqYRYLPbMzXcg6OhwbDWktbm5ibt37yKZTOKzn/0sEokEarWatROvvvoqSqUSEonERDpNM9Azk4BoCCMajaLX62F/fx+e5yEej9ufwWCATqdjvVzf91Gv160y0XPQME8q8j9r0cV+msIBz/Osoa3X69jd3UWtVguco9FooNvtIhaLwfM8RKNRy64Nh8MAcO12R82dU6kUGo0Gms0mEomEBavuNT7MtV40ce9ZQQ3ns5tXxfdpvNPpNBKJhDXk7rw/bswvknB8CBDJ+HS7XTuWACy44G8CzWazafWIsm31eh29Xs+ON0MvBKQbGxtYX19HIpHAc889ZxmeWCyGaDQKY4x1LKi/ANjv0vy5cWzSsybK0nW7XXS7XSQSCczNzSGfz2MwGOD27dvo9XpotVoTrTtmoGcmAeEij8fjiMViyGQymJubs14uldVwOLTeryo3AAHPLRaLIR6P22OedVG6eJzRW19fx1tvvYX9/X3cvHkTqdSoo3ssFkO/3weAI/Q7Ez+Z8JhKpZBOp+F5nk3m3N/fx3e+8x10u12srKxY1ogGWpX7RTfIFDV8/FvncjQaRTKZRDabxcrKCl588UU0Gg2sr69ja2srcK5erwcAyGQyuHr1Kp5//nkUCgW7frhmGM4hA8e/HzZfapKl1+uh0WjYRPx2u23BeT6ft+GsZDJpw0mxWAxra2v4vu/7PgvegVEy/61bt3Dv3j30ej3s7u5if3/fhmgTiQQymQyKxSLi8TguXbqEQqGAVCqF559/HpcuXUI+n7f5iPF4HIVCIRAG5vPhd9brdcukMmfuWRXP87C/v4+9vT3LstGRLRaLmJubw927d3Hr1i3U63VcvXo1ACRPE85/mjIDPTOxwglK0ENjq5Q+lYIaC3q7NMhUaKy8oHHWnIhnXY5TAvfv38ev/dqv4c6dOwBgaXlWtkSjUZujQM9rMBggn8+jUqkgkUhYA9rv9y27MxwOcfPmTWxvb6NWq+Hy5cuIxWLIZrOh+QvuNV400Ervtd/v27AJ5zXncqFQsJVXly9fxiuvvGLDJw8ePAhUz7XbbQyHQ2SzWSSTSbz88sv28wRVDEvGYjEb7mXFi55r2oXgpN1uW1bMGGPnslYSRiIRWw1348YNXL161eYF7u3t2bn74MED9Pt97Ozs4MGDB0ilUiiVSkilUtZBy+VyeOONN/DZz34WmUzGjj/1kDEm4DAQlBGQdjod+929Xg+ZTAbJZPKZBj3D4RC7u7u4deuWZe3S6bQFPeVyGZ7n4f3338fOzg4++tGPWjsBTJ4DNQM9MxkrBECxWMyGTNwJTEDE8BdBEL3YaDQaSPa8aIbzUYTgcVwZOpmZZrOJaDRqQQyNsRtyocHksfF43J5XaXpgVCnTaDRs0nMqlbLnjEajgRAA/6ZMmvJ6WNE8Jf5wXip4d+cpx5eMD0Eix4lJ/xrKClsr7vdpZQznA9ccX582CRtX4HA8dLwU5HGstC0GQSkAy5gZY1CpVNDr9ZBMJlEul5FMJlEqlVAsFpHL5VAsFm11HQGoMqy8HgD2GjSsz7XGH23/MI3P5FHEZWeUraNu4A91BB0IMmg8dtL0xgz0zCQgXPCMvQOwRpcKQo9V0eROCs/V7/eRy+WsEntWxfM87OzsYHNzE/1+H3t7e2g0GrYqotvt4u7du9ja2kK327WG1PM81Go1NBqNQF4JQ4nGGKRSKWtE9vb2sL6+buPwuVwOxhjU63UbduDr2WzWVoDNz89jfn4e6XQaly5dwvz8/HkP2RMRDWP0ej1bTQQcznk3mdUYE5j7pVIJV65cQSqVwuXLl1Gv121eQyKRsKCRFUL3799HPp/H8vKyTerkMcxTIRvXbrcDxoRs6TRVEDHRlfl+ZFcU5HCcCDSUOQYQAI/pdBrGGORyOXz2s5/F2tqaZZAajYZlyyKRCLLZLAqFAuLxOJaXl1EsFu338DvUWaAQbHLsk8mkNdh8VmTwmK91kRPT1SGgfh8Oh9jb27OVcJcvX0apVMLc3JwFqDp3e70earUaPM+zTJme/7xB0Az0zOSIkFFg7JaKiyyCondN9NNy0m63a8MF/X4frVbLlrM/y8L4+N27d9FqtXDnzh1sb2+j2WxifX3dAiD226Ey8TzPNo2ksufzoHFMJBLWoFarVXz44YfwfR+lUgmFQsGGupggvbOzYz/PUMP169dx7do1FAoFFAqFCwN6OKfZLLPZbAKANcIUZc00uTYSiSCfz2NpaQmRSASLi4vY3t5GJpPB8vIyMpmMBf083/b2NjqdDgqFAkqlklX4ZD+V7eO60OfIfLhpEa57hls5fvF43LItBEAMpbssJ/UNAJtrBgClUgmvvvoqgPF9r/S3m5AcxuC559I8K2UpOp0Out2uNeAXGfQAQUaU41ir1bC+vo50Oo0rV66gWCwGWl5oiJJhXOZPEfS45z0vmYGemRwRTmANd+j/KkoT0zvixOexVGRUKM+iEAT2ej1sb29jc3PTgph6vW4NBdmGVCoFz/OQTCYDf8diMSQSCVuRwnGnsdXKuUwmA9/3LZvDECXPowqcSem1Wg17e3vo9/vY2Niw4GrakzkVXHCMFeDwGOCwERsVPul65lIVCgWsrq6i1+shlUpZZoznN8Zgfn7ejh1ZDSp7/k/D6jIQGlKZprAK52Gn07H3w7HV8PhJ9+KGxIBHv3/XQVNx2zK4f+tz0nmgc+ciipt8zHtnYrc6XRyDeDyObDZrK0qZt5bL5QLnPm/AA8xAz0xChMqdIQ9Obk0AVA9KFZzSnazyikajSKVSmJubmyrP9UnK1tYW3nvvPdTrdbzzzjv49re/bROTFaiQvSGgIcAhIPI8D7FYDAsLCygWizYs1uv1bIdg9ka6fv26DcswGdetVPE8D81mE/v7+2i1WhgMBtjZ2UEqlcLGxgYWFhawuLiIN998E6urq+c8io8uVNo0vgydaE4C57MaSjKe7FO1srKC+fl5rK6uBhKStVGn2/YhmUzaxOiw0A6ZPF5nr9dDNBpFp9OxxnWSG3ty3NjeolqtIplMIp/PIxaLYTAY2IRmBevqBKkh1LE6bVUb14Z7Lj4DPU6PVXDJEKgyclpxBxw6cIlEwm5pcZHEZcuA0ZjRQQNGhRVLS0vWAQOAubk5vPTSS9jf30cmk8Hm5iYajYat/p2kcZqBnpkcESpZGmCGt7jYXc9IjYVS18Ch10YgddGp4TBhX527d+9if38f77//Pm7evGnZMBqAQqFglSkNhoKeWq1mu1szD4dhsE6nAwDWE9NW+4VCAfl8/khiYqvVsvlW/GE8Ph6Po9PpYGtrC41GA6+88sp5Dd8TEVZPDYdDO6cJemh4tW8RhTlRTM7nc8nlcjZkE9aHp1aroVqt2s8RbGniLI8FEAh58ToYGiagmmThtbfbbTQaDfi+j0KhYEEPc6i0Wg0IMsW8R4JB1SN67HHXwN+alHwcw6PvucUWZFEViGqTyosqYeNFfcEk/nw+HzgmlUphcXHRMkD1et32W5o0mYGeZ1TcKgaVMNpfPSL3s24lBGVc9cazKN1uF9Vq1bIyLk3MDRE1hOWGFAk6mehJZmh+fh69Xs961ABs1QoBlT5LTdhVZomMHJNDk8mkTfyt1WrY2dmxPVGmAby6OSNA+DYT4z7L4wlINOwVptD1WACW1QzbnDQMxHDdkd3Q6qVJBT0Kzth/iE6SMifKhLETMueZnkt/a/n+afSGy/AAh8ymOmSnOZ9WPwJHw3Tq6F0Uce+HJfyNRsP2AXNz4CjJZBLFYtHqKrKUk1i4MgM9MzkiVFwsQVe6OayEWUtsNanZ3en7OO/oOBA27eL7o27Vd+/exe7uLjqdDnK5nN2jid5TsVi01SP0jDUfiuwO83GMMbY6KB6P49atW/jmN7+JVquFy5cvo1wu20RSMnQ0AgQ4nuchm83aRoWVSgXz8/PwfR/NZtNuYnrnzh0MBgOUSiVcvXp1KkAPAZvufu7S9uM68mqYSjsGE9T0ej3bkZzz3N1fjtu0uPk6/D4FXLwmZZ6YGEyDM4ni+6NtJrgXXDQatQmuTBxnQisAm0iue8aNk9OGtTRXymWZO52OrSRjGFKTbjVhWZOrGXamcL7TuZjm/LYwURBH1o3Jy81mE/1+37YCCLv3fD6PtbU1tFotbGxs4MGDBzapnaJg/7Ss3WmOfViZgZ5nUE7rNWoPHiBI/4dNXldJuazQs8j0qHfbbDbRaDRsWItMTy6XQyKRsL/pCROcUOFqOS+VE6lm5uCw/JkK+7hxJ0jleWhcCYC63a699kajgVqthlQqNTUVeBou0l5F7jEaDlHA4xpTsm48H5+T7uGkRQDKrmn+mxsedteQhtuYEzepTA9w2A6ASb4EiZzDCuT0uJMM4LjXXRDpGms9TkNTyqCNC0vyN8NampulDM84tm5aRec7heu+1WrB88I7t1MYco9Go9jZ2UG/3w/0n1JH47xtwAz0PIPixsrD3qei4kIHxtPy+p56WWGG4Lwn/NMUtuLvdrvY3NzExsYG9vb2AqEl7QDMRNuwUAzBR6/XgzGjMloaVSoltuCfn59HoVCwipvPYTgcotPpoNfrBXqc+P6oISJ7KTFxc39/H9vb2+j1erZUm0zUtIgLYmjQdMuUsLCIazyBYBdydQC0Dw3P7zJKYV6um3ui3zUtRpVMT6PRAHC4Qznnno439QrzQfb29rC7u4ter2c7IGsCNEE+v4c/ZO/YIkB76BDAJxIJm6TPpnoEXDq27OJMYMl1wlw3MqJMNue8maY18DCi84/sNPXL/Pw8SqVSoO8OhVt7MDxOwOnOYXeen2QPzsJezEDPMyrHTSb1aqlU1FvVuLj7uoIerU5hiOY4r24alPzDSKfTwcbGBhqNBu7cuYPbt2/b6gY2aiRLAIzAi9scDwh6nmxiR4qZibk0GC+99JKl9VkxQ2PN/hnNZhO5XM6WoQ+HQ2xtbaHf7yOfz6NUKqHX62FzcxP3799Hs9m0lWLpdHoi4/RhouErF/QQmGgH6uPOwTwevqagRyuMeLx7DhU3eTcscXRa2ATP89BqtVCtVm1eWiqVsqCBho/OT6lUQrlcRrfbxc2bN3H//n3U63XcunUL1WrVNoBMp9N2qwM6XmSUuJUH2VOC9UqlYsNmBFbtdtv22dnY2LC7vRMIXb58GS+++KLdB4xgiaGZZDKJXC6HVCplX1NAdxHEZTYpu7u7ePfdd9HtdrG4uGj3NGPYVoW9wgaDAW7dunUk/xMI2hx17MLyus7SOZ6BnpkcEbfyIczrPS5UoEKDoS3nj/veiyQEH61WyypeghMqBc0J0URWZQGAw2eiCZWk7ZVRY+6OlsLT+LBCi9+nZcM05PwuUtNkl5jDoiBtWiSMYTmNuEybAno15OMUd9i6GfcdLsMzbQZVr1nXuQs89X3uG8c10mg0kEwm7f5yGorSxOJ2u20BSK1Ws32B2CBSQ4tkNrn+tGM0v58VcvycAledOw87f6ZZyCxTdwEI7JvmCh0IAIH31cHgcec9fjPQM5MjQiVOyp6K4DSgxJ3Q2pp/knuNnIWw5HtnZwfNZtPues7ux7FYDK1WC77v2zJojpXm7vA5rK6uYm5uDu12G3fu3MH6+jrm5+dx9epVzM/PY2dnB/fv30en08He3h62t7cRiUSst6uhAWOM3RS2XC7j6tWr6Pf7WFtbw/Xr19HpdLCzs4N6vY50Oo1Wq2W3zJgW0ENDRVCn4I5joIZOFbKbH8Lz6W+KhrYeBrRwLWgvID3/JOQ/nCSRSAS5XC4QLtS+NmxUSoayXq/bvKhcLofLly+j2+2iVCqh0+lgbm7ObtuxtbWF+/fvW6A+HA6RTCaxsLCAQqGA7e1tbGxsYHd31/aDyWQyljkisGfI68aNGzYnjeCd+6kxf5HrRAGtltyTYeX2JBdBwvKbANgu8f1+Hy+//DLW1tYsS32csHs/dc/e3p7dlZ3jd5ITcJYyAz0zCRUqC1Xo48RV9G7+AkuinzXQw1yezc1NNJtNu2s9497ASLHwPWOMLQdX48tnsLKygkQigd3dXXznO9/B7du37W7VCwsLdmuJarWKjY0NrK+vWyaIHjDL2Al6MpkMyuUyLl26BM/zsLa2hmvXrqHVauHevXvY2dlBNBq1+w81Go2pymdQxlLHgqBHaX3Xq9f39XxuKTWfkYZ0gfGK3c3l8X3fVoJpuG0ajGokErHVhww9MQTE95lLRnDPZ5HL5VAoFAAAa2trAEZsQqlUQiQSwVtvvYW3334brVbLMj2FQgEvvfQSrl27ZsPvOzs7dr+tfD4f2BE8l8vZcz7//PO4cuVKQF9Vq1Xs7u5aRpPVRmF7pOn60V5DF0HCkrqbzSY2Nzct2FxbWztxx3mOE4/p9XrY29uzjtwkVL3NQM9MjpUwQAOE98TQ993/p0WJP0lhgjKNABMAdSsP0r/Mgeh2u9ZQaLdaUvy6WSYNJre4oIftlmCTznfbx/P7gFFfHz6rVqtl84F0w83zpqUfR8YBmbAKQyA8ofi4kO7jhKTCQkDTJMqWaWjJrXbScC6AAMBTIE6Wk8nJWkLOkCwriVKpFNLptO1vxb3kuO1KNpu1uUE02JqHqOEsFhMACCT5a5Ujw8EX1XkjE6q7pWsS+mmKUVRfsJoUGN8T62nLDPTM5IhoCEANI5G6S/OHldRqxUZYP5JnQfr9PqrVKra3t20PHFL9ZE4Y2qDXyioIrRqhol5fX8fW1pbdLqJUKsEYg/fee88maRKosFEYgRJ3/F5aWsLi4iKA0S7gwKjHxrVr1xCJRLC1tYUvfelL1vMtl8sADhXWccnokyZqaIHDXBIqY+Awx0C9eDf5meJulMlk5l6vF+hVpd/P71BRYOX7fiB84vbCCvv8JAmNIYE6MAr9EKRQjzDxl2yKlvUz+VgbchpjsLCwgNdff932ftna2kI+n7dAplwu44UXXkA+n8fVq1dRLBaRy+Vw48YNLC0t2bVAsF8qlWzTRD5jAp1Op4Pt7W1sb28jGo3aCkgWDbC7eSqVsizQRQQ+mifFpG/gcDPWsPt2w7AM0bOLfK/XQ6FQsEyc6wA/jeRllRnomUmoaBiACsst9wSOesfqRY3rjfKsCOn+RqOBSCRiy8g9z7NhIiZkGmOs8fQ8D3NzcwBg83w8z8Pe3h6+853v2OdBpfzgwQNsbGwEFFM2m7U9f/b29rC3twdgZJBKpRLq9To2NzfR7XZx/fp1LC8vIxqN4vbt2/jWt74FAFhaWkKxWAzkAk2bouc8pHfOuUsDrR2PCegJ1F2w7iYmu8BIw1KnUeTKNuleduNCa5MqCmAYwqJowj6rntgyQRP4ydiozM3N4bnnnrOhv1arZVkdVlWtrq7aLRDy+bzdEFYr6sg86PPl8+P1sGP6xsaGBTfsQEz2SMviL6r0+33UajW0Wi0bVqSEMVzKiHKuM/zHEBnBkzYq1DXytHPXZqBnJkeExpjVOmH0v2sU9H23SosTvtfrTQzF+TRElaruMq9MGUNN6j2ybJagicmgzWYzQLdzvKngdTsJfq/nebZLLhMQm82m3UKBiac7OzsWeDEMp71LpvW5aegpzLsMC00dBzZOCne5x1LCKn/GVTgd51RMshOh+U1kcGg0u92uBc2cnwRHzGlSkMLXyXTm83ksLCwgk8nY/DeyosChc6DdrHWdkVEjy8fqLc3XSafTliFlLpAWHVxksEPhprGNRgO9Xs/mGD5M4jZBozEGjUYD7XY7tKnpec3lGeiZyRHpdrvY3d3F1tZWIKFWFRLzR1SxxONxu3u1NmpjX41IJDKRG9CdlQyHQ9TrdVSrVeRyObvbsIZC2OyLipfvEyRub29bT5djF4vFbI4Cz2OMQS6XQ6lUsps8smSduQ5MRL5z544FWel0Gru7u9je3rYGd2lpCQACoYBpYR1UNAwFBEtp3TCVskAK9FQxh5Uxu9/n5r4BCLBM2qGWz1t38mY7A80jUeZJ58+kCXUAWy6wD9T+/j52d3fR7XaRzWbRbrdtcjzzbIbDoS2HJoiPRCI26f/GjRtYW1uzCdCc788//zwGg8GRxGOGC91ri0Qi6HQ62N/fDzQgTCaTWF1dxeLiIqLRKMrlMvL5PJLJJAqFwtjNZS+a7O/v49vf/rYtiFhZWbFjcJqQlDEG2WwW8/PzaLVatqJUN0UO+1yYhH3HkwiFzUDPTI4Ie2E0m80jcVwaC+ajAId5DG5SInC4Q2+v17N5LM+KcIw6nY71fBQ40rvMZrOBcSNgGQ6H2N/fx87ODjxvtEUEz5NMJo+EA9LpNObm5mxYixtAFgoFZLNZ1Ot13Lx5E9VqFalUynq1tVrNAlw2jzPmsL/Pk0jWPQ9RBkWTa8eBFRe0jFO6GoLia/q+/v3/t/ftMZJld3nfqequrndVP2Z6pmfHu95Z8Bob72DW2JAlxiEhxEAQgRASFOFICIgUpERJFBQUkkBEFBHyABIsgoIJjwSCgrDzJLFl5YEDOLC2F/ap2d3Zmd6ZfkzX43Y9urvq5I+q7/RXp8+tfs3sVHXfT2p1961bt26de87vfOf7PY66qUL3A+wrghonpJ+h6uqkT7q8Pz9Th/VeVA0C9nex397edu1C9ZNuLKbFszoz23B2dtbFnKmyyf/1WWpQMveTo1tZF2wkbaVSCfl83sWnqMvuLKPb7WJ9fd1lbRWLRWd3iJBySrAtmRjBhV8+nz9WUdPD7MxpVM/z8SQTHAtq8Omf9YM4WfCL0FWQGjauZKdVLTgt1PBy0qLxnZmZweLiIi5evOhcWbpFBFWydrvt3AHMKqH7gHK9tRbtdtsFYhJUc2iMSqWSi9VqNpvu99bWFqy1QePGXdb5e5qIayg+Rl2zfuq6T3Z84gEcXG2qchcqwKnvV8WUY0IDa/U9+nmhgOxJBt20qVQKi4uLuHbtmnNx8XtohhAJPNVFVd54jP1OVS/+aJYkFw0+eC5dW71ez7nPVFXiYoSB1ZPe1vcTDOheW1vDxYsXcfXqVacgq6o/DnNzc07VttYiiiJnd44KP4Yu9PpJkZCeBEHQ+JP06A7Te3t7aLVaaDab7lxg0BE5ODgpa9qjlu4/LwhNtACckX3729+OJ5980qkwLOTFSWBtbQ31et3F2lDaZ3AlV7hMDb179y6stU4J4l5GVIcYnLyxsYEXXngB9Xrd1SqhNH358uWRVN52u43bt2+jVqvh6tWrU+OiHKfoaFq/Bu3rpMzXNetL+zqPK5EZR5ToagT2N+lkBpHu6xVSlqhcHDVt+GGDcWQk0isrK9jd3cWtW7dw69Yt7O7uotlsungPunQZl0ZFhiSEqg+AoB2hohqKNST4Pt3nLpPJOEWnXC47N44GpU+6unY/EUURbty4gVu3bmFlZQUf+MAHUK1WsbS0NNIOcf3PGOM2QOYeXJubm8jlciOBzEdBnJJ0WkwM6XlQrC7B6cHieMBovIOqNxqvABxUe84b2VH4E622EV1S1lq34mQMDgBX9E4ze/heXfFz8uTmgLoS1mq5Gjjd7XZdTR5uYOq7BUgIWq2WcwlMa1Az4cfd6PcJBRGHVB5FiOjEub/0dbZvyHUYIj7TVMeHxAHYj0HjJraq3DBjkcQPGC3IyeP+FhF+H2Tph1ByBbC/47oSW1WSqPgwdmfacJJ+oW3DduXmsc1m0y1i5+fnHYHx3xsCnx1LbXBRdhS7cdj3OBOkh6mIlBx9KZ+KgaYb0uCfl4j6BwFfoqdRoDxMqX5ubs49B2sHhfCstc7Xq6tQ7n3DIEIGDnJncH7OgwpQmxQoCeRPp9NBvV4HMAgW3Nrawu7uLu7evYvbt2+72iXlctmtthhk+b73vQ8AnLFOp9PY3d1FFEUwxhwo1KZF4BhPxUDZra0ttNttRFHkap0UCgUsLi66wGrGFUVR5MZmJpNBuVx2CpSSqkmHTzB4LHSeH9Dsu5J4rRCp4fnAaPyNH6PD4/5GpepSjlOMphlUuvL5vLMvXChR4WEMjcb9AHBJE+MWUEyDV7AtORaU+PCZzM7OOls1beD8yWKiSmD0O/pZo+rCazQaeOWVV7C5uYlnn30Wt2/fxtbWFhqNhlPiGMrghzuQ4Khrlypmq9VCOp128VFUjf0FM8mQklkte0LCxHpJjPs66VYgD5309Ho91Ot1bG1todvtYmNjA61Wy5UOZ1BUqVQa2QuKD20aO+okgnE6DCwkU9faFKoIVCoV5HK5kX1sKB3zfw4Ojf0J5hsdjgAAIABJREFUrWjHrXKnETpgNSiZKsm9e/cc+bh9+7bb+4e7VG9tbeGFF15As9nEU089hWeeeQaZTAYvv/wyXnvtNfT7fbeR4tzcHKrVKubm5lwbMgC6VqsBgNvQkTtN1+t1WDvIrikWi25hoUrQzs4OoijC1taWi7nQWiqafjzJ0L7kx8WElLNx+8zFTbhKatQ9pcqeurdU/dTP1HvzyVZIDZpGcOJie2tBQ6ZH+wREF1b+BE6QvDBzTI8TJD466XISn9Zg5X6/79zTfkA87Y2fEUdbs7S0hFKphDfffBOf/OQn8dJLL+HWrVt4/fXX0W63UavVsL29jdnZWbTbbUdiCoWCK43BuCcqZcYYt9BiIky1WnUVslmfjHME71mVOHU3kzzt7e2hVCrh4sWLyGazLrB94kkPfanaKXd3d7G9vY1Op+NWoNvb226vINYsIatU4tPr9dwXV/+6+mOnfQJ9q8BOxwma0IHCc1Rpo/H243ZU3tbPUCVp0ifM00L7ue+i4A/HhJbxZ/uzDXUDUj/oVY2YHxNCdwGVHqbBU0GiUsoMvbj4BVU3VL2YNowj1r56c5hLNs7lFbI3SnrGXSPODXZWoPbZj0tStcCPoQopauM+g+f49t8nwL4C558zLWBcn6rsfp0u3T4jlUqNxJLt7Oyg2Ww6kqMB9qoUc26g612zdjVGju9hMDnnbNZr88kZz+OCW0kPYxVZrZ626zTJFMcmPSeVta21ePHFF/GZz3wGURQB2Dfq8/PzKBaLbpPDZrM5QnC4s6tvkNUFU6lU3O/l5WUXyJnP52MN0TR28AcF+ttbrRZqtZpzh6jEz1oLytRVNmantta6IFp/kNVqNWxsbLjaDxxAZwkkiOy/xhgX2EfDT5ettRaNRgN7e3toNpvodrvIZDJ4z3ve4/r2jRs30Ov18Oqrr2J1dXVEur5w4QIuXbqEfD6PRqPhqizThcY+3mw2R2rSMHBzZmYG7XbbbWHBzSO5QuP2GLVazRWr5IosRGwnEboAYn9mn+R34Xl0kfgBzXot4GBQtLWjQcYhUsWJBMCI2qDuSJ3QlQCcFVvFAHxr7ci2E3GZbz5UEfMx7r3s85lMxk3KoXo+0wa6jOr1ultAaTgBMLp5qlbPnpmZcRuCkljQxU3y4c/HdF/xeYXcgmxPXpOlC9bX1935/K3kjHOIjgnNHN3b23NzOsWQk3h6jk16TuqGsNbixo0b+PjHP461tTU3kPP5PK5fv44nnngC29vbuHnzJhqNxogqpLVMtG4IH0yxWMSVK1fcb9Z1AODSdUPf4SzFkRwX/nfmPin1et11MhphJT30yWq2CRUi1uKhQaP6AOy7z5rNJu7du4disegm2LMGPx6DcTfMDFGVrN/vu1Ltt27dQqPRwLVr13D9+nVUq1W8/vrrePHFF9Fut7G6uurqZ3Ac0BAVCgUnc0dRhHq9jlqt5tShWq2GTCaDSqXiSupzvyOqrcxmoauAVWlZYJGrN5IC30U0iQit9FVlJGnn86B7JDS5hggPiYzG88QRHj9mwXeDarC6Kqz+daYVbF/GAtL9rYuDo/SlcaQnTrHRZ6v2SgP3pxHWDqpZc87kwknrHZFk+KSHbux6ve7IEl3sbJO7d+8iiiKUy2U3p3JBoOOI9p3PlYsl7ivY7/exubmJXq/nMkpnZ2ediuSXGmBf5wKOIRKVSgW9Xg+lUunEz+2Bu7doVJhZQpmsUqm4omnz8/MolUqubkkmkzkgz+lW9b6RoaG21rqJlS4UDizuoZIgDCoxdIGE4Bt5PeYHz/nkWF0u3W53JAblrMKX77m6oaGgwQLgAvmBfQPCscJz1XgB+8GdHAe6mmUmSjqddiqTrsoYF8RClFT26AbjJERFSifgacoiIkKToZL2UBr0YS6u45w3Tv3xx1LcvZ8FcOJVUhenxPN8/g65FP3zQu4wvYa6zvyMyGmFfi8lzGzn0G72/nmMpeJ8S+WSPzwHONhPqZqqS5Ln0WYp4aSayvf52ak+8b/fz+fYpCduNROHKIpw8+ZNRFHkylHPzc3h/e9/P5555hlHeorFogtaUteJtRbNZtMxWWaUADiwl1EqlUIURXjuueeQSqWwsLCApaUlZLNZXL16FcvLy0eWUU+LaVuVUenZ2Nhwezxpdhw7LNU3JTfqf2UBMtaG4aBikGAURa4mDCf5syTf+2Ab6C7P7Xbbkfd79+65tF2qL7u7u7hx4way2Sza7fbI+7l7MePgqID2ej1ks1lcunQJu7u7mJ+fR71eRzqdxoULF1CtVl2mBxcgW1tb7lq1Ws09S5LS5eVlVCoVpFIp5HI5tNvtkTozk5y67hMKYJSc0MiyOi/bUCdKdYnxmIL/68p5nEKk1+Q9+MHunDx0ctLvwglpWkFSTrWREyJwkExrDIpOujpJAgcJkBIZbUeqz3TBx2U3TRP4vVhriGSCCUCq7ABwtpqKG5MZyuWyU1/YtpVKBUtLS27zYgoPjOFRAqtlTQi657lDPQDX1zWOR0UMjc3lb258SgFDsx5PgmOTnuN+UKfTwd27d91Oz8zquXbtGr72a7/2QCl9BQf63bt3cevWLSd1NRoNGGNcgCcjxanyrK+vo9frodFoIIoiFAoF9wD5HR5kBw8Z3EkHJ8RmszmSnuivjDVY2Zft/aj8kDuBdSAKhcJET5r3E1RMaIQo93KPs0ajgVwuh0uXLmFubg69Xg9ra2vuXNbt4V5A3FOLRgWAG1ckN9zTKJ1OY3l5GQsLC+h2u1hbW3Mq6sbGhsvI02cOAPl8HpcuXUKhUMDe3t5I/MWk9++QggKEqxyTqHNDRJ/0xCGkQChB8d1qPuHRe+DY0WDQuHMntc2PCg2c97e4ocsklMVGG+K3r/+MidAz0CQYX+mZ5oUXiSQAF+vCrFt1a9ETQvKsZS40M5OkMJ/Po1QquexSdWeHyLmvoGlmnJIu3ehV5xJVp9TWqNp0P2LcjkV6+v2+qw2iE9q4YKJarYYbN27g7t27aLVaeOyxxzA7O3ugwmMI/AzusssS+dxQsdvtumhzHmcqGx9aLpfDzMwMoijCnTt3RiYPja24n5imwUNjQcJC365fwEsNdCgIk4bbLwAGjE4M/Jxp96WPg6+AabwGwYHNQPtsNut2QqdLiuoPA54BjGyuqLs/0+Dw8wCMVNGm35wkCYBTOFinBAAKhQIKhYL7fP+56kptUlN8x7mLfLLCdoyT7vU9ejxEpPxx79s3f3INLRriXGyTTjSPCt+t5Qdr63dku6gbBDi6ih7XZjqpHieIetrAvqX9TfuajmvOhaz6DmAkzMRX2kJkk5+jc6q11qmYjC/kc9fyAqFnpQQ35JY8KY5ltXZ2dnD79u0RRqayVwgvv/wyPvGJT+CVV17BBz7wAXz4wx/G4uIiHnvsMWc09eGEDEW5XHYrMRZLi6IIzz//PO7eveuITC6Xw/LyMi5evIiZmRm0Wi1XF2B1dRWvvfYa8vk8Hn/8cSwsLLiAqgdhvKdl5aBR8nQjajaWpnaSsLDzhgKZW62Wm2R1F3ZOwIwXYcn4swa2k8blMOtJV5ZcaS0vLzvVcmFhYWSXepKhfD4Pay02Njac8eB5JPWcGFjcM5VKOdcX3cKpVMplzEVR5Goo8VkDA9LD7AgGOev32N7exvb2trseP2OSECLo/koV2N8KhO0IwO1jFqcOhSbRkM1SdZPv9eMUlBgzfpGTwllUevidNHvIX8GzTfQ9vp04SjvogovnauwbFQgNpp60fnwakGxoP+Yx2vVOpzOSBk5bQ0GiVCq5GjlsJ537fZWT/Z2LKC60WN253W67Rdjy8jLy+byL8/XHlpI1Kt3GjG7GfNKxcCKlRxUS9Yv6jQ4AjUYDr732Gl566SU89dRTePTRR7GysoJyuTwSLxLyxxJsRGutS3nk/bRaLWSzWVe8sFAouJT1zc1NpwgxlqRUKuHChQvOWJ/Fifc4oDElodF0TmC0noWv8ugErxkoqvT4cuR5UHrUXeHX1VEDzMmWrqhisTiixJDckPRopdo4pYdGQXe5pqxNIsPrsTIu7xMYuLVYJE5X2LoqpNJzGsPzoBBybfmrUbUvDOCMiysJXT8EXw0KLXpCREY/M6SMhr7XpLX5caCTpcbsKNTd4as++noc1KarWqDEM6TynDXSo30KwMhY5tztZ2LNzs66+CASIkLbMOSlYfvxOrTxvAaDpFWt9hXWODVVucJbrvTcvHnTpSb3+31Uq1VUq9WRCHBg36h3Oh1cvnwZe3t7WFhYcOX4u92uY4DaoX25mF+SEyqzi5iWS5a4tbWFKIpcQ8/OzuLevXvY3NzEzs4O7t27h3q9jiiKkM/nEUURisWiK7Mdt7pTtSM06FQW1WPMliF5mESwQ+o2BXHSZei9ISOtVTW1vfi6EoFpNt5x0D7K7Cglh/4ErCte3XNLXbvMdCNpYSAhxxzdvI1GA7VaDbu7u84drMadhKdQKDjfPld53HNIs8M47rRfaIbXpBNXneSAfXuiygqwH4jMH+27ei29hk4eOu5DE2icOqTVbHmeLihCis9ZwFG+k7Yzz1dbelhohMK30f7Ezcl/GmpOhaALzW63i3a7PWInCLVDvo3WRRKTJzifz87OulIXoUUsr81r6SKa7wX2nzvnxU6n42ylX/eLizk/0FkLLZ4UxyI97XYbX/jCF7C5uYk33ngDu7u7uHLlCh555BEXpc2GoRFvtVp4xzvegZWVFaysrKDZbB5Ia9bGVzma4IPkhMKVJosqtdttNBoNAINdqemCY/A068tw195ms4lSqYT5+Xm8/e1vR7FYHPGp03hRAiSxChEgNVq890wm40p8K1OeNNAdoptJKmk5bDWl5yjp0QqhPrSY4SRPmCdFr9dzGYZRFLnJVZUy4GBMCXeR9veYU/cWVRgaaKZ/sgjY5uYm7ty547LB5ufnnZ+e165UKigUCiN9mru6q2rESabf748oc0xxj6IIpVLpIbf20eETc9aT0nRc383hr5Z5DVU2lVBpYK5+Hs9X11Y6nXb1aqju6eedxbEBjA/qVkLCYzqpck7wJ/MQlIhqEG3oONXRSY1ROwzsiwwviKLIqcL0kPiqIrBP9rigyufzIzV/qOay0K+/15Wv6tOdxnFgrUU2m8Xy8vII4cpms27Hde4bps+Z44NKn25BQnJ2Gnfksd1bjKfhXllMeyN70/TbdDqNnZ2dkQBkflmdWDXoNdSZuUWF1vwhU1TpjCtXGqN6vY56ve5Wp5zQ6/W6i41oNBruHnzSY611PkdVm9QwaQl/JT35fN6l/00ydDU/TmL03+MbKX8FHPd+Gv9Jb5eTgoNUiZ1PerRNfHXA/1ECrn/rSlXLyavP3lcg1CfP/s3noFKzTjp+DRv2Fx0vk4hxLi1102p5/nHKik7GcZ+jOIqSwXN8khTnvlGCdRYR+s7qmmK/j3sW+h49R9U4IkRK/fdNG9T+qpKj/Slkj4B9lxRFBdoTVndWm6PXo63XMaXzATNMdf5kXI/eY6j9/flIbeJpntGxSE+xWMQzzzyDmzdvYm5uDq1WC8YYbG5uAhhUb1Q/KUnP9va2Y9PchEwbhuSFwZ3+pnG6iaVmw/ABaQOShAADZYrZXVpLJpUa1PNpNpsuJkgna324ofiWkQYU0sMfxmFMOunp9/tudaCuGOBgnRL/e7PNfRdCaNIl2L46QWs0/1lAr9dzdW+Y/s8aSHfu3EE6nXYl49lvdF8tGhkObI4xqnJsNyqX1lqn2qTTabdbOlPNAYxUSG00Gm41R+Kvz1pVDPYLBjMC+/FBWkxxUqHjWAkjyRxXlAxo1clVCWHcdTWgE9h3uXBsqGH2FwaqKjF1mP9zIaefoUG40wwl8vr9gH2Co9/RJy/Hdffpc2Rf8F2Z7N+sXTNNoP3gfWezWbf1gxYF1D7Nduh2u66/VatVZLNZ3LlzB2+++aar1UbSQkVYbYQSKbVZFB/Y1gwf4XyhxRLZ5jpHA/vuLd8VzT5zGlfksUhPuVzGhz70Ibz88svOffTGG2/g9ddfd4ZQq/nSqPALrq+v49VXX3UDnQ+EW9hzx2g2ksaEECqD6b45IXeJKhh6T+1223UWusK0Y8QNqtBK3K9YmU6nUSqVUK1W3cOcVLDjb29vu6wflfL1PB/qF1ZfLIllSO3hhEnCyucTKmw1rej1emg2m9jc3Bwp795oNLC6uuoWAkqwdR8a9W0bYxzJt9a6WlTMxiLx2dzcRLfbxZUrV/COd7zDucBI2GkAKUEbY9y2E/7KieNqd3cX9+7dczFCVHB7vZ5TXjU7ZBKh/VLJHb8j3fDZbPaAWuuvkH3oZB2KGVFosC5f52dTFSfpSaVSI+X8lSTwO00zfNKjqjrJoD4nfd9xCQ9wMDNY40Jow+jap7txmtqYc2k+nwcw2HaJBI4ZV/48yr5NsSGVGhTyZTYnt6YgScnn8+6atFN6Pf6mgkP7Qdvmu2/V5tD2kSjxfRwLujMDsJ/UdBq151ikhyoGi/1Za1Gv11EqldDtdkfS1FQx8LeAD92sz+79zyX4EHldEic2uJ4fVytGBxsbUKU+vZ/QKkMfmqbTafS7xmdMMjQrZ9wEFlJ6fOnRPx5HlvhDZn8UyXpa4ZPHuMlUDYIvR7Od+Ky0Vg4NECcLVTJYHoAqKbC/aNDUbO37+jw5QfiTht7vJMI3xiGEbIF/Db9vh96vdsU/L9SnQ/eksRehlXToc6cVcS4LxUnIzVHg228NZOYcMK04iloe6ov+eAfgFq20MbQzXKCGElVCLrS4DC+dK/S+QnOsf99xY+04OFHk1uLiIq5fv45Op4N3vetdqNVqIwGPKqNTKieTI2tTuY1uKrq3fOkqpDqECAd/k5FqkLHWgoiLoQg1sq489P26a626t2jAuLUG45kmERqjxUBxP9bDhxJKqnra2TXeg4RGBwfJDt2eURS5INppN+jAfuYei/wVi0X3w8qmVGh0haNxNlR4gP12tnawHQuVl1qt5rI0rly54hYAW1tb7rVWq4V0Oo2rV6+6cUCyw6wJLelA1YErxOXlZSwuLqLVamF1dRVRFLnPmWR1TlVItTN+QKSqLL676ihEXI133PmhCUHvkXJ/oVDA3NycK7zKc/Szpn180D5ocojaZd8W+zboNCSbtpoJACSZAEZ2+Z426MKFRIXCgx/EzL9J8jSYmxmZulkxtxRiJfc7d+64xAs+H1X1OQY0zke9B7wHjaFTt5eOHw1T4QaqWt/pNLbnRKSnWq2O7MSqA5kT6ZtvvukK3dH/zwmWDcmOHycnx0m6vqGgX55KC40YN1okSVH/OK+pgVxsZB18OiiVWKn07AeA6t/jCjc+bNDdxAh6dsg4lg7sr4ypOviThCoT/nPlaxwsjBdJp9NOnp12cCLN5XIjPyzzboxxRAPAiLHV1GWCBoExNswIi6II29vbmJ+fx9LSEiqVChqNBtbX1507LYoizMzMoFwuu/FK95gaLI5DPluOGSYoMCFgZ2dnpJjbJMJXr9S9oQSc9oBlAOIWPfp/SN3yiY9/H/6qNqSusb4YV9K+++AsQZNR/AmQ0MWlHzvC38clgGqruVimPeI4CGWbTgO0P6lribFh2re1/1E4oA3o9Xouk7fZbLr+yUXWxsaGqyfGOFittqwLYj6rEJFUoUBjadVNxjmWxDhUV+mkOHGOXmjlwU5KFw8fQiaTQa/Xc5vMseNzBasde5yLiWBDcG8RZZ9M0WUAFuuPaCqpskz+aOyRfreQQqRKT0ganyaE2LiSGKoPIXcMyZEa9qNm9XAi17iss4KQbOyvULQqKl9n3EyoT2klUg0gZ0xKPp/H7u6u858zZk5dy1SVNPuRz5Bp8LqaUuOksWvTBvZlJRo6dn3XepzrLo7AEDoWjqrMqDrM/+PcW9Ou9ACj2X9AOE6Sx09K+kLtpJOrumvVVk2yyzYOvhcCOFjIkcf4v/99/f+1n2vyEON/dCHL9tNA48PgexJChJf37BNev5+cBPe1MAEbf25uDouLi251yYZRgx1ieP7fh0nLuuLUgEUlJyEiE+cuC70eOj5uVThN4KTLHbupoHHQhNqJ71N5nm3R7+9vveCrQHoNVS6azabbJG8aJ1Qf6krRLB3uaWOMwdbWFprNJqy1LpsR2HelKnFXmVezwQC4zIdLly5heXkZ9+7dg7XWlZcnaSERYtCm7rROQ8Y+XSgUUCqVkMlkcOHCBWfkstksisWiU6smGT7pJJHQrEIulrQacyiFNkRkVEFS+8bFl78Y8o03/+Y57BscEyzPoWUkiGl2c1FpZEB+v7+fbasuLv37pAgRACofumCgWqHn+kqCkoFJbHv2O909nn1dST7tLsc8lcXQgoCxslyIMRN6b29vRLwggeVcq0QqNJ8CB9VtzZzUc+h+V2Kqi8eTPov7Xo2JZKRQKNzvSyd4AFDVDdhP64wjPP4qQDs2OzMNWojw8G9+7jRU9j0u/EmTUi0nRGv3NwBtt9sj7zVmPwWVZIlxYeoWAPYDYFlokzWoGBdC46bFM1lNnS4yv0wB/+e+dMVi0SmhrDI+DeTUJz00+OpS0rgGJTK+S1ah11PSw/6uREldCqGVNq+l7vlQ7bGzBA2QpZ1RdUCJz/387v5zYrvTbvEc//nocT02SWAfVgLjExnCFx58Twuvp4qjxgqpOqaxmyFS6M8jelwXzH4f0HsPZV8/NPdWgumHvyoADtbn4Xm+AQqpQcBo0UleT68L7BMrxvTQzXkWwJUjfeSMG6GiQKLClZLGmdGVxOrmJBjcZV2zTVgMdGZmBo1GA5lMBq1Wy7m7WHuGMTokUaVSCcYYVydIjRqAA8GPwGgApG5bEhfsPgkIqbI6CehE4UvyIXfLuM/g+8cZY53EdXzpxO/f51FV72mCTz40TCD0Hf1jJ20HfZ589qp2ABiJbdGEmjjFYhKgtkN/4hYmSuziYmn1XI0T4m7rh5Fx7dcaHxpyVSnp0n6gISja7rRXp7E9Cek5x7DWukJ3dDVq6j8w6gv336sF2NjBdV+mkFuQ9WKsHdRn2tvbQ7lcnugJ9Djo9/tuH6xOp4NMJjOSyQUMNvVkMTFuOKpF8vL5PKrV6siGoXt7e9ja2nLVTRmMnMlksLq6ivX1dVdKgsacQeKlUgkLCwsuvo7PhwUpST5p4HxJWQlPp9NBs9l0qsQkktUQwWZfV1eU7nem/RXYHwdxEzFJSkh2V4Ot90DohEBoEDtVEFWP9LOnGVp2gaTHz+BSZUzbzm9DIL78iU9YdZHGxAlO4J1OB8BASW02m5idnXWuXGJSXVu0qdls1u1+oIUuQ4sXVbbUnRtSbLgYontL+6d/H6HnF1LSfBWUXga6ufiMNIHIzwA/StxoHBLSc84RYs5xkmRoAPmsPSSXKlS65iQ6jamiceD3JynRVSVXXxqnw31tGJ+TTqdRLBZRqVScGkTSwwKejBWiEWcKvAYfa2q5blTKndWpMvE3PwPACMnlc/SDFyd1l3XFuInPjx3xV6N8/1E+Q13Cce+JUyx0/PA6jJGYZJfKSRCyD0chEzp5a7sdRYXT/5UIc5xwjLAvq2sx7lqTBnXTakJOCGq3Qz/6vfm/BjLHVdsPfcZhx5Qg+c9YFw/67A6bX46ChPScYyjxAPZXq6GVFTDqb+dgIJPXCTE0MFTt4XlUmCbdVXIckDzo9iUkGfV6HQBc6jcAl2HoGyy2rw58ViPVSZEKESftjY0NGGOws7ODfD4/ktkIwH0uVR6mt3JVy01KU6lB6j1X5X6cy6SSHe2jdAnyXkNZQ6G+ftiEoXK8nj/OFeN/Dq/D56vlMnTV7ZODSZ+ADwPjytS95bsZD5tQgX07Mu5Zhdqf7av74rG/qC2aloxSa61TX6MochWVfSLux5exHdgWDC7nXpN+jCc/w1qLdrs9EjTN37ThPqkNZXSFsve4kONx/qaN87fUOCkS0nOOYe3+NhSse0SpX6VPNb5UKzhYNIVRFYBxJMaYQa2aKIqQTqddjaCzAMq16uJLp9Mj+8QxtZxkgxlRWuSShpdKTb/fd1W+lXgwkJn1dFZXV91eOvPz8yN1q5gpRLmYhHdubg7lchmzs7NYWFjA4uIier0e1tfXXZEynaynhfj4aorWMCHi1CqdJFTtUqmen3XcbCN/Zc04L14jrjih/p5WaKIDFUhVQ4Fw4HBcfwsRH40j8aFuGwbhahmJVqvlFNZpAN3pW1tb2N7edpm4msBA1TfkamJCSb1eR6vVQq1Wc3GWmv3cbrdRq9XQ6/UQRRFKpRKA/bFGZdt/bn42tXoECCVmIbcyawJxwXZa25OQnnMMJTbKvDWgkucp4gyRPzkctmKjgnCWsrd8pUcnN35fKmRa1Xvc5Bla5ftSPa+h+82pSy00SYcCe+fm5txeO7yepqby/k8bTPhWQmX0UH89Lu5XX/UNvz/p8+esEB5g1D0Rp17FufW0LQ5zb+l1CF/58e8DOJrtmiToYlN//JIq/I4hRYbueF2w+sqkJjz4hWmpBmnF87jn6i9EgP0YLNoVf8Hij9lxwddHQUJ6ziFCHdmXi/10Rt8QkST5KyqupuM6Kq/BgcZVwrQYmcOwu7uLzc1N3L59G1EUYWNj48AO9lRfWEOHKxhmWzFrS2OfWC01iiLn1lJDxo39lpaWXEYKgzIBOFWORQt5jFtgMGuLq+R+v4979+7hjTfeQLfbRa1WQ6fTgTEGS0tLzj05icTHd1v4x31yGTLOvrJFxLmsSDJDr/vQMUbSq9k2GmenNVB01XxWEFLk9DVFyNUX196cPLW92K/5Ou3d3NzcxKuXcVDXEzc1XltbcwkR6j7UhQ7j++bm5tz2E9x0mtmjSrq1HdvtNhqNBgCMLJD9NHT2bSVFurjm+9XFy/P9wqr8DN4XY69OgoT0nFOowsMfYDSmQIu1ESpP+q4BEhm/yjInG76f16D//KySHspy2STbAAAbMElEQVTOzHLiBDY/P49CoeDibTSVXUmP73dnMUcAzi2g2Umso9Pv9xFFERqNhrvGzs4OMpkMqtWqI0KUtmlw+Fm85tbWFm7duoVut+sKI87OzuLy5csAgFarNXHPLaTo+Kt4JUNxq1GfzIWUBV08sE+HXo+7T/2tap+OL46j0xZkm0SMIzw85r/uE5/Qe/zrabwUiQ/dMVpQMrSlwqSDdjSKItRqNayuruLWrVvI5XJurPu1cEhGOp0Ocrmcq9nF+l2sC0bFWgkKALedlL844JjRBZnuzN5ut91Cl9fyA6/5GmN41MVmjEGlUgGAU80ZCek5h/BJjXZY4igrn9A5oUlHoaRHy8L7Uv60goNWXUsc9LpLse/C8FUWzTIKSb0qUfNz+Vk8Tr98XMmB0PNl8UKqODRKTJ9nFljcpDMpiCMpPH5YlosPVULj+n3ctcapP+NcV/7nTHqbH4bTEuTD7IOvvB3VFRbn9poWqJ3wFXrgIJEPEWuOCZJAXRwYY1xpDRLEuNAH/xivE3ffvvoaej/hz1knRUJ6zhm4MiBbZ+yJrob03JC8ryRJOyWPh1baWsBNY0o0FZKkIC4IcRqQTqdRLpdx4cIFV6tne3t7RFnjJrw68WrsjdYxYbCnot/vj+xSbcwgs4urH7qearWaq45ORUnbl22v9YA2NjZc/SRjDC5fvjzSDxYWFrC0tDTRW1LEkRIazXw+j2Kx6AI8fWKhv/Vvf1XLVauSEa2PEnKhqStMY7oIXQCoq5jB6JPY3oeB7eXHgfG4tlEcyT/qAkz/VpLKdub11QXDRYG6+jXeZ5JB91yhUEC320U+n0cmk3EFSTXxgedrQDHjcHQssPI7bXUqlXKbjOuiVduU1wbCzyqdTjviFCKZAJy7n/aM2VxaV8zPJj0JEtJzDkGpkXLjuNVN3HFfpeCxEBNXY6akh5OQFsRTgjSNIMmoVCro9wd7VulmoTQqTFtnTI2fcaKBgnRHETRWvV7PpZrzGnyezMqjckQfuU6cOhHRkNXrdRe7Mzs7i/n5eRfzozWEdDPfSYNOpmqk2S/9LTXi1Bf+rUZdV818RkroQ4ql3gPvj+fzJ+47MBZLrzOJbT4OSjh9IuoTlbi4qMNIj3/NkLqh19Z21Dif0H1OMjhO8/k8Wq3WyJ5yWvndDy/QOBsAbuHT7/dRLpfdYojZntVqFaVSycUWdrvdkQWU/q1uWb1PqkTAaPYY43P4eSRTqpCTiGlCxUmRkJ5zCKoN/vYTwNGNzHFl4JCCxL95P91u1/mBpxnqly6VSs43TeLDoGEOeBpkrXBN6H5bURQ51QjYV3zq9bpzZ3E1FEWR22qCx5mhMTs761JTGadDI8Z7VBcQsF/DSVWJaZgYxrmkiKO4pXgtvQYn0VD2XYig8H+er64FrrI1aPmw+54WxGX8aVo1EVLd+LdPDseRVf8cdaOEoJ85baRS+xUTGkh+aHt80gOMJqEc1sc0UFnfy8WFkhw/acWvN6Z9WvuD79anjfSfoR9nelxM9+yS4NiwdhDtX6/X0Wg0RuqWqLQ+bmWmio4qO3ruOOWIv/neTqeDra0t9Pt9t/3CtBkeghPY7Oysk4T7/T7W19dx+/btkRo+VLlY3j1EOHmtXq+HtbU1V3wwl8s5pYh7brXbbbRarZGVFgkOr0F1jxkYXD0B+2qRTg7WWrd3GNUR/x4nFX57qqJIxKknITeXr4xpvFOI+ISK7dGlxcB0qnVcebMyN69Bl69OCNPgdlFov+Oqniv/XC7nFC3aDH0+bC++h2Cb+koOj6md0Yk6VP1dlSFVhKahjXnPtBPFYhHVahVzc3POhavwCQmP6f6H2nfZXlw48Xz2Qy7IGKvogwHjWime44jXUvujGb18XV2gWvz2pEhIzzkDjX+n03Gb6/mvh1xX416PmwBDKzYe10A43s/c3NypUhEnBTQa3C2dExorpgI4UPBPV8PAqDGnQanX64iiaMQw7+7uYnt7G8D+/jh7e3vOp8/P4nEWMaO61u/3R3Z1J1RxoFFTd9AkB3uO67dK0PWco0xwusrkMw4pYCEiRSLJH41l40TA92qhwnH3PC3gxKbERttOJ0Fg/1n5barwCa2vXoQWEUchi9NEeAgl2XQRUe0h6VGC4xMGKjGqsgCjY4Zkg8cJteVqs5Sk8JnzWftKj28H9bP9z0oCmROcCFrMigbIHxRxCg6hUqOqP6GO6itA2mk1WI2umWlbySqstS4DisZkZmZmxOhnMhlUKhX0ej1XNl63k1AXCNUErpY4WSwsLKBQKDiliG3farVgjEGxWMTi4iIymQyWlpZQrVZdwHqpVMLu7i6iKEKv10OxWEShUDiwWtNJR7+f7tE1iZOwH7+hK33d7sN31Y1zS+nrnCAOc5WEVqO6iuY4VPXJX9lO2wQcgk6cwP6kSDKkz8efeA/77qHnNc5e+df0J+Ber+fGW9yibdLAAGCNy/GJQYhYqLKm+2qF1DW142xvPyidCLmsrB3EGfok1ye8/vUYp9jv91GpVNzGzaeJJ0xIzzmEKj1qYH3CoyszGn7/PK0y7LN3f/Cpi4GDi++NoggAUK1WH06j3Cf0ej1sb2+jXq+7gTkzM+MIHbOHGOC8vr7uggYpS7MqMtUiKgGVSgWlUgmZTAYrKyuoVqvY3t7GnTt3EEURjDGo1WpIpVJYWFjAE088gWw2i4WFBZRKJedKpDq0sbGBbreLXC7nSI9WdFZSQ0OnfWNSVTkljYTGj+hGkz5xGefu0kBlDV4GRo2+PyGoaqOkh65OuikBuMknFCDqf860gBMX+xXJJu2QupUUIXKix0PEcByx8ckrX6ON0m1KNN5qkrPmjNnP3mJbajYucJDY8ZgSblX9WadH1R1g360V1x5q5wkl9Gov/DkCgNvrT+ca9pFer4elpSVUKhUsLi6iVColpCfB0eC7UnxVxZcV46TE0EAKvR53TI0PB+A07Nx9GHTlRBKhZIEyby6Xc/EawL5bY25uDtls1lVTJenZ29tDPp93aZ/FYhHlchnGGEegVM6mIczlcsjn88jn8+6zeU/b29vOaDKOyldH/BgI/xlOIsYZw5Cac5zvERdTo8f8Pq5/+2NNjb+6s/wJfVIn3aPAtyk+4VCyM07lCbV7XNv47awTady9+WrIpPZvBQm+FjTV7xFnp9VVpEVoVSX1n1foWen1Qgh5C/wFFX+HEjl4f/RKMB1f1ajjIiE95wzWDnbJZWxHt9s9cA7dAFxxaufXFF8OIg3o5OSqAWtUO3g9v3N3Oh1sbGyg3W7jwoULU2Fs4qAuEKo+qVTKubuMMbhw4QKuXbvmygZsbW25c7nq0u0hWCys1Wo5EsLYHma8ZTIZXLp0CUtLSwDgAkR5LXUj6Mo6lUq5FHpjBltMXL58Gf1+H7VaDVEUuWfXbrdd8GmoftCkgAZcs6BU5WK7s23jVAHCnzBDE+i4iddXOvk/a6rkcjm3qSLdoxx3PkmbVvKjbjvNCPQz3gCMfHcfvvLIPqwKhv/s9NwQofI/n4sUup0nXenR2D0uenZ3d1Gv152imc1mDxAiXXCpW1Gfg9ZvI1nha6qqxT0r3UDUtz/APqlR1zOw7zpXopbJZFAul1GtVp0SfhIkpOecwVqL7e1trK+vo91uu8Ba37Bq8bTDSA+PM0Wbk7WmqOqGmL7vvtVq4e7du8jlcnjkkUemmvQAoxvoNZtN9Pt9F0g8MzODlZUVPP3002g2m7h16xZeeeUVl83T6XQwMzODVqvl3FskF81m05UaqNfrjrBSrVlcXMTVq1cxOzuLu3fv4vbt2854cyL1jZsxxhWqTKfTuHr1Kp544gn0ej3cvHnTkQASJxYZo+I0aTWVVMnUYGw+g52dnZH7z+fzI+/1V6XjFKFx/TREpLQInjGDuCsAbvXK11m4kt/B/5nUCTgO2q5cAAHxpAc4OJHGubeYku27YuL6eqi/kmSxv3DsMgaFnzGJSKVSKBaLyOfz2NnZcW27s7PjEhyKxeKBGDa+l22n6iLbOhT3Buyrvz6BDPVLn2j6Ll4uToD9xTPHiiZ2sATI/Pw8lpaWTjUOEtJzTuHLuTpZaCyOxuroj9bdiJODeR4Njv8Zel4oq2AawQyKQqEwEoTHdmMgMSc8uqBCbUqSyHZhzAFjhPxVrbYfJwFrBwGEOzs7I/dJBUj9+iRZxWIR/X4fxWLR7a/FZ0t3GdWeSUVIUle3Lgmof844112cK+QoJD3kYtGgdU7SSo5CBGwaoeM7TsFRxLmiDnsPPyvkAjvK+32COy6RY5LAPjQzM+OyNjVcwC/T4EMXr3HfN+74OMLD94X+Dr3HT4wBMEK8tKDoaZCQnnMGYwzy+TwWFxexvb09shUFAKcy1Go1NxHm83mnyNDFsr29jVarBQCo1+vY29tDrVZzbrNarYZareZSoZmmy60ZeC9c8ZbLZRfgO63GHRisqq5fv46rV6+OGE7dvO9tb3sbFhYWkMvl8NRTTyGfz2NjYwOf+9zncOfOnZGJVSsf7+7uunoad+7ccaskblGwurqK559/3rm92u02jDFYW1tzxEozKRqNBnZ2dlAqlVxmxJUrV/DEE09gZmYGV65cObCpKF2YMzMzWFxcPFAHZBIQMtAkoGxDFoVUUq9qgUr8/NsP8maMQ8j9pYG5vnuL1w0V4eTmriS2vM9Jn3jHQev0xMUR+oHf/D3u73F2IuR6UUIZgv+cGNQ8LW2fz+fx5JNPolKpYGNjAy+++KKrxbW1teXIkW55EyKJuoDVTC9VKkPQ66iS40O339F4HX0/iyuWSiU8/vjjmJ+fx8rKCgqFwkmbxyEhPecM9AFXq1XMzMxgdXXVTcYMcG2324iiCHt7e061SKVS6HQ6zhBHUeTSo+nCaTQaaDQabjdwxqiwA+/s7LjXOVlzhVIsFl0q4jQjn8/jne98Z+xqCdiXlXnu0tISbty4geeeew61Wm0kY4GBzbpv0M7ODra2tlxRwkqlgkwmg3a7jWaz6TLEcrncSDA03VQ06szeu3r1KpaXl1Eul7G8vIxr165hbm7u0NXdpLpaQm4qzTjz3X2+CgTgAJnhhEnSyffRJaKIW/2qckrD72dm0S0xMzPjiliqIjWJ7X0YSPipNob6lLan7+byf/sEyEecmuSr0eOeE0nyNBHObDaLa9eu4fLly3j11Vdx+/Zt51anq4u2GBglPdxuQmOj/EWbT/jj4LsTNXbID5fgeNRxqDGilUoFX/qlX4pHH33UuaVPi4T0nDMYMwh6LZVKLrW53W6PrLY4KPb29pxSw/dpJeCZmRkUCgUUCgXk83ksLCzgkUceQbvdxsLCgtvrSQNGs9ksrB1E4bNiKCsX5/P5id3P6Tg4qvyaSqWQzWZdFdWVlRW3fQQVFrrLCBIXktfZ2VkUCoWRNt7b23MuNBqPUAorjdiVK1dw+fJllMtllMvlYCr3tEANrX4HthP7bDabHYlJUnXGN9pxagMnjHGqg38+sB/gqXE6PEe3LqGcz5iSaR0XfpAqcFANCwXSxhEaVSG0sF6ca9InLvoZoeerdZymaRzQVtA1vbi46AL42+32SHA/gJE4Pe3rOhdo22g/JeLierR9lfT41yL0+czMzGB+fh7FYtHVI+NYuB9jICE95wypVApLS0vI5XLY2dnBlStX0Gw2sbu769wdnU4HzWbTKQ4kRaVSyWVlcRXKOjDZbBZf/MVfjA9+8IOwdhAszZToUqmEfD6PXq+H+fl5dLtdVKtVXL16FYVCAeVyGUtLSyNk7DxAiwxWq1VUq1XUajVXR4fFC7ldgVbRprtAjVWn00Gj0YC1FpVKBZVKxQUez83NYWZmxmUMsQozj7H+z6VLl6Z67zMS60wmM0JiFhcX8e53v9tt+0D1jK5BthN3pQYOppz7MWycRLgNhU4WSp78gF2+7k826XQaV65ccSRncXHRkVAtXDltYJxYqPwBf/s1oQgSGip1+jrdIz6hVPLEdlXVgokWfAacTNVlyT7kV9ueZKTTgw2BdTHUbrdx79493Llzx6ltdDM2Gg2nAGm6O9tZ66+pTWBf1GrkoVR34GANJT4HvwgtCRsX1teuXcPKygry+TwuXrzovA33Y26YXuuW4EQgCSmVSrDW4tKlS25CXVtbc2nsVBzW19fx5ptvot8f7L5bKpUA7HdgEhUW11teXkYqlcKLL76IP/zDP0Sv10O5XEaxWIS11lURXlpawpNPPuliec4T2SFSqZRTyhYWFvC2t70NAFz7M05qc3MTu7u7zmVIo6GTgbWDPdW4j1e1WsX8/LzbgZm1eObn513w9Pz8/NS7E30YE96wtlQqoVAoOPXM72u6StYJEth3d9DIc5LmZ2nWCc/3J11OKsyIDK12qbwyho4T7zQSHQVVKz8WxK8VNs5l4j8TYLQIZFzMj595RdJDUszaWLwWFScSWb/I5SSDSiEwcLMvLS25+L9isei2qWm1WiOJDdqmqpz5hBLYt1lsH7rFODb0mv7zVHvV6XTQ7/ed+z2dTrt6YsViEe985zvx+OOPP5C2P1+zTIIDGCfNj3vPYf8f5ZzQ8QQHywcc1kYh94se19d0AjiPbX+S/p7g9JiENj/MJvHYJNzr/UDIHtzP7xayT75K5y8wjvr5D/I5mOMEaRlj1gG8/kDu5OziUWvthdNeJGn7EyNp/4eLpP0fHpK2f7hI2v/hItj+xyI9CRIkSJAgQYIE04rEvZUgQYIECRIkOBdISE+CBAkSJEiQ4FxgYkiPMeYHjTF/YIz5vDHmWWPM++/DNT9tjHn6tOecJxhjesP2/wNjzOeMMX/dGDMx/eQ8ITQmjDGvGWOWAuf+aWPMD8Rc52uMMV/14O/47CBp+9PDGLM4bLtnjTF3jDG35f/YPUyMMY8ZY56Lee2HjTF/POa1jxhjVrxj3zF8luf2OZwUMhd8zhjze2el/SYiZd0Y85UAvhHAe6213aFhmdyNfc422tba6wBgjLkI4JcBlAH8XT3JGDNjrd0LvD/BfcBxx4S19uMAPh64zgyArwEQAfitB3O3ZwtJ298fWGs3AdCW/D0AkbX2H5/ymj8UOm6MSQP4CIDnAKzKS38KwE8A+Cac0+dwCuhc8CcB/EMAH3y4t3R6TMoK/jKADWttFwCstRvW2lVjzA8ZY37XGPOcMeZnzDCHbajO/CNjzO8YY14yxnz18HjOGPPvjDHPG2N+HYCrWW2M+WljzGeHq7e//zC+5LTBWrsG4HsA/BUzwEeMMR83xnwKwCeNMQVjzL8ePoffN8Z8MwAYY941PPbscKX8RcNz/9Nw1fCcMebPPdQvN/kIjonha98/XHl9wRjzJOBWuT81/PtjxpiPGmN+G8CvAvg+AH9t+Dy++iF8l2lD0vZvEUK2YvhS2hjzr4b2+jeNMbnh+R8zxnzb8O/XhvPA7wH48wCeBvBLw2vlhvPFdQD34D2HoZr0qeFnftIY8za5/keHc8VLxphvfKvbZEJRBrAFAMaY4rDNOA6+mScZY/6OMeZFY8z/Nsb8W2PM33hodxyDSSE9vwng6rCT/UtjDNnkT1lr32etfTcGBEY74Iy19isA/FXsqxB/GUDLWvvO4bEvl/N/0Fr7NID3APigMeY9D/ILnRVYa28ASAO4ODz0XgDfZq39IIAfBPCp4XP4EIAfM8YUMDAw/3y4SngawC0AXw9g1Vr71PB5/te3+KtMG+LGBDCYkN8L4KcBxBmVRwB8lbX2zwD4KIB/aq29bq39Xw/2ts8EkrZ/6xCyFQDwRQD+hbX2XQBqAL415v2b1tr3Wmt/EcBnAXznsK3bAL4MwOesta/i4HP4SQA/b619D4BfwkANIh4D8BUAvgHAR40x2fv4facJuSFJfAHAzwL4keHxDoBvGY6DDwH48eGi+H0YPKenMFDYJjJsZCJIj7U2woCgfA+AdQC/Yoz5CIAPGWN+2xjzBQB/DMC75G3/Yfj7/2HQSQHgjwL4xeE1Pw/g83L+tw9XBL8/vM6XPJAvc/bx362194Z/fx2AHzDGPAvg0wCyAN4G4DMA/rYx5m9hUCuhDeALAP7EcGX21dba+kO496nBmDEBhPu+j39vrY3fUjpBLJK2f0sRshUA8Kq19tnh3+Pa+lfGXPvrAfyXmNe+EgPXPQD8AoBn5LVftdb2rbUvA7gB4MnxX+HMoj0kiU9i0Jb/ZqieGQA/aoz5PID/AeAKgGUAfwTAb1hrO9baJoBPPKwbH4eJiOkBgKGR+DSATw9JzvdioMo8ba19wwx8wsq4u8PfPRzyPYwxb8dgVfY+a+2WMeZj3rUSxMAY8zgGbbw2PLStLwP4Vmvti97bnh/K+98A4D8bY77XWvspY8x7AXwYwD8wxnzSWvvDD/r+pxmBMfFdw5eO0ve3Y44nOAKStn8wMMZ8C/aV+e+21v6ybyswIBpdeVsPEqrgYVxbfx3iFaJx8IvXnftidtbaz5hBbNsFDGz4BQBfbq3dNca8himaTydC6THGvEN8ucDAD8uJdMMYUwTwbUe41P8E8BeG13w3BqQJGPgjtwHUjTHLGEhvCQ6BMeYCBrLwT9lwFcv/hkGMA2Otvmz4+3EAN6y1PwHgNwC8xwyyKlpDGfrHMHCTJYhBzJg4aVXWJoDS6e/qfCBp+wcHa+2vD9WD69baz4ZsxSku79raGFPBIARi039tiN8C8B3Dv78TgLoe/6wxJmWMuQbgcezPRecWw/i1NIBNABUAa0PC8yEAjw5P+z8AvskYkx3O2RMZDzUpSk8RwE8aY6oA9gC8goG0XMMgGv8OgN89wnV+GsDPGWOeB/A8BrIorLWfM8b8PoAXALyBwcNJEEZu6K6axeBZ/AKAfxJz7o8A+GcAPm8Gae2vYtDRvx3AXzTG7GLw7H4UwPswiPnpA9jFIP4qQTzixsRJDMknAPzaMODw+5PYkkORtP1bh5CtKJ/wWh/DIAanDeDHMXC9ECPPYfjzc8aYv4mBC/Mvybk3AfzO8D6+z1rbOeH9TDs4FwADVf+7rLU9Y8wvAfjEUAH9LAbzKqy1v2uM+TgGYSV3MQhpmLgwhmQbigQJEiRIcKZgjPlZAD9rrf2/x3zfxwD8R2vtrz2QGzvjMMYUrbWRMSaPgefle6y1v/ew70sxKUpPggQJEiRIcF9grf3uh30P5xQ/Y4z5EgxifH5+0ggPkCg9CRIkSJAgQYJzgokIZE6QIEGCBAkSJHjQSEhPggQJEiRIkOBcICE9CRIkSJAgQYJzgYT0JEiQIEGCBAnOBRLSkyBBggQJEiQ4F0hIT4IECRIkSJDgXOD/A0UF2NKI3vfDAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 36 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GQestzI5GquI"
      },
      "source": [
        "#splitting of data into 4 workers(15000 training each)\n",
        "train1 = train_images[:15000]\n",
        "train2 = train_images[15000:30000]\n",
        "train3 = train_images[30000:45000]\n",
        "train4 = train_images[45000:60000]"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3BCMl_0fVLCK"
      },
      "source": [
        "len1 = len(train1)\n",
        "len2 = len(train2)\n",
        "len3 = len(train3)\n",
        "len4 = len(train4)\n",
        "len_test = len(test_images)"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRXOo7qyil8Y"
      },
      "source": [
        "y1 = train_labels[:15000]\n",
        "y2 = train_labels[15000:30000]\n",
        "y3 = train_labels[30000:45000]\n",
        "y4 = train_labels[45000:60000]"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnNHhsbzWXUz",
        "outputId": "c60d1819-0625-4bec-f2da-912bb3a2cdda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "df = pd.Series(data=[len1, len2, len3, len4], index=['Worker1', 'Worker2', 'Worker3', 'Worker4'], name=\"DATA\").rename_axis(\"Data set length\", axis=0)\n",
        "\n",
        "x = df.index\n",
        "y = df.values\n",
        "\n",
        "plt.bar(x, y)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 4 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        },
        {
          "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": "avrv0wp7bTyN"
      },
      "source": [
        "# normalizing the input\n",
        "train1 = train1/255.0\n",
        "train2 = train2/255.0\n",
        "train3 = train3/255.0\n",
        "train4 = train4/255.0"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zfv45K2mbho0"
      },
      "source": [
        "# reshaping images in 3 dimensions\n",
        "train1 = train1.reshape(-1,28,28,1)\n",
        "train2 = train2.reshape(-1,28,28,1)\n",
        "train3 = train3.reshape(-1,28,28,1)\n",
        "train4 = train4.reshape(-1,28,28,1)"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J9R-pEoucR6k"
      },
      "source": [
        "def MODEL1():\n",
        "    model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dense(10)\n",
        "    ])\n",
        "    model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])\n",
        "    return model"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5YAP21u2c_EU"
      },
      "source": [
        "#4 models for 4 workers\n",
        "model1 = MODEL1()\n",
        "model2 = MODEL1()\n",
        "model3 = MODEL1()\n",
        "model4 = MODEL1()"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tYpTKQ5WdJuc",
        "outputId": "4a124820-bc75-49e1-c7bb-83c27bb52db5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#training 4 models for 4 workers\n",
        "for i in range(1):\n",
        "    h1 = model1.fit(train1, y1, validation_split=0.2, epochs=2, verbose=1)\n",
        "    h2 = model2.fit(train2, y2, validation_split=0.2, epochs=2, verbose=1)\n",
        "    h3 = model3.fit(train3, y3, validation_split=0.2, epochs=2, verbose=1)\n",
        "    h4 = model4.fit(train4, y4, validation_split=0.2, epochs=2, verbose=1)\n",
        "    \n",
        "    plt.plot(h1.history['accuracy'])\n",
        "    plt.plot(h1.history['val_accuracy'])\n",
        "    plt.title('Model Accuracy')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend(['train', 'test'], loc='upper left')\n",
        "    plt.show()\n",
        "    model1.evaluate(test_images,  test_labels, verbose=2)\n",
        "    \n",
        "    plt.plot(h2.history['accuracy'])\n",
        "    plt.plot(h2.history['val_accuracy'])\n",
        "    plt.title('Model Accuracy')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend(['train', 'test'], loc='upper left')\n",
        "    plt.show()\n",
        "    model2.evaluate(test_images,  test_labels, verbose=2)\n",
        "    \n",
        "    plt.plot(h3.history['accuracy'])\n",
        "    plt.plot(h3.history['val_accuracy'])\n",
        "    plt.title('Model Accuracy')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend(['train', 'test'], loc='upper left')\n",
        "    plt.show()\n",
        "    model3.evaluate(test_images,  test_labels, verbose=2)\n",
        "\n",
        "    plt.plot(h4.history['accuracy'])\n",
        "    plt.plot(h4.history['val_accuracy'])\n",
        "    plt.title('Model Accuracy')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend(['train', 'test'], loc='upper left')\n",
        "    plt.show()\n",
        "    model4.evaluate(test_images,  test_labels, verbose=2)"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.6834 - accuracy: 0.7662 - val_loss: 0.5144 - val_accuracy: 0.8087\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4748 - accuracy: 0.8355 - val_loss: 0.4558 - val_accuracy: 0.8323\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.6841 - accuracy: 0.7645 - val_loss: 0.5836 - val_accuracy: 0.7943\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4793 - accuracy: 0.8313 - val_loss: 0.4965 - val_accuracy: 0.8380\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.6620 - accuracy: 0.7749 - val_loss: 0.5195 - val_accuracy: 0.8240\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4708 - accuracy: 0.8382 - val_loss: 0.4770 - val_accuracy: 0.8343\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.6957 - accuracy: 0.7604 - val_loss: 0.6044 - val_accuracy: 0.7820\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.5018 - accuracy: 0.8213 - val_loss: 0.4451 - val_accuracy: 0.8403\n"
          ],
          "name": "stdout"
        },
        {
          "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 70.0893 - accuracy: 0.8202\n"
          ],
          "name": "stdout"
        },
        {
          "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 78.6791 - accuracy: 0.8142\n"
          ],
          "name": "stdout"
        },
        {
          "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 74.2723 - accuracy: 0.8146\n"
          ],
          "name": "stdout"
        },
        {
          "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 70.5172 - accuracy: 0.8125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkqiFhZnq6J-",
        "outputId": "092b23c0-49c7-4000-baa9-e8d43fd523ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model1 = MODEL1()\n",
        "model2 = MODEL1()\n",
        "model3 = MODEL1()\n",
        "model4 = MODEL1()\n",
        "\n",
        "for i in range(50):\n",
        "  h1 = model1.fit(train1, y1,validation_split = 0.2, epochs=2, verbose=1)\n",
        "  h2 = model2.fit(train2, y2,validation_split = 0.2, epochs=2, verbose=1)\n",
        "  h3 = model3.fit(train3, y3,validation_split = 0.2, epochs=2, verbose=1)\n",
        "  h4 = model4.fit(train4, y4,validation_split = 0.2, epochs=2, verbose=1)\n",
        "\n",
        "  w1 = model1.get_weights()\n",
        "  w2 = model2.get_weights()\n",
        "  w3 = model3.get_weights()\n",
        "  w4 = model4.get_weights()\n",
        "\n",
        "  a = np.array(w1)\n",
        "  b = np.array(w2)\n",
        "  c = np.array(w3)\n",
        "  d = np.array(w4)\n",
        "\n",
        "  mean = (a+b+c+d)/4\n",
        "\n",
        "  model1.set_weights(mean)\n",
        "  model2.set_weights(mean)\n",
        "  model3.set_weights(mean)\n",
        "  model4.set_weights(mean)\n",
        "\n",
        "model1.evaluate(test_images, test_labels, verbose=2)\n",
        "model2.evaluate(test_images, test_labels, verbose=2)\n",
        "model3.evaluate(test_images, test_labels, verbose=2)\n",
        "model4.evaluate(test_images, test_labels, verbose=2)"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.6723 - accuracy: 0.7703 - val_loss: 0.5363 - val_accuracy: 0.8057\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4745 - accuracy: 0.8322 - val_loss: 0.4585 - val_accuracy: 0.8313\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.6735 - accuracy: 0.7657 - val_loss: 0.5331 - val_accuracy: 0.8090\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4818 - accuracy: 0.8306 - val_loss: 0.4643 - val_accuracy: 0.8397\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.6593 - accuracy: 0.7719 - val_loss: 0.5368 - val_accuracy: 0.8180\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4783 - accuracy: 0.8328 - val_loss: 0.5036 - val_accuracy: 0.8303\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.6881 - accuracy: 0.7622 - val_loss: 0.5263 - val_accuracy: 0.8133\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4925 - accuracy: 0.8250 - val_loss: 0.4471 - val_accuracy: 0.8387\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.5599 - accuracy: 0.8082 - val_loss: 0.4879 - val_accuracy: 0.8197\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4454 - accuracy: 0.8457 - val_loss: 0.4517 - val_accuracy: 0.8337\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.5700 - accuracy: 0.8025 - val_loss: 0.4992 - val_accuracy: 0.8227\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4571 - accuracy: 0.8366 - val_loss: 0.4806 - val_accuracy: 0.8333\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.5598 - accuracy: 0.8133 - val_loss: 0.5358 - val_accuracy: 0.8160\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4485 - accuracy: 0.8411 - val_loss: 0.4541 - val_accuracy: 0.8363\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.5722 - accuracy: 0.7993 - val_loss: 0.5271 - val_accuracy: 0.8083\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4536 - accuracy: 0.8368 - val_loss: 0.4430 - val_accuracy: 0.8380\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4271 - accuracy: 0.8482 - val_loss: 0.4522 - val_accuracy: 0.8347\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3955 - accuracy: 0.8606 - val_loss: 0.4007 - val_accuracy: 0.8457\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4372 - accuracy: 0.8417 - val_loss: 0.4319 - val_accuracy: 0.8457\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4054 - accuracy: 0.8543 - val_loss: 0.4105 - val_accuracy: 0.8520\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4286 - accuracy: 0.8494 - val_loss: 0.4812 - val_accuracy: 0.8260\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3991 - accuracy: 0.8583 - val_loss: 0.4029 - val_accuracy: 0.8490\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4436 - accuracy: 0.8443 - val_loss: 0.4605 - val_accuracy: 0.8277\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4037 - accuracy: 0.8553 - val_loss: 0.3989 - val_accuracy: 0.8467\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3877 - accuracy: 0.8605 - val_loss: 0.4049 - val_accuracy: 0.8513\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3584 - accuracy: 0.8693 - val_loss: 0.3893 - val_accuracy: 0.8520\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3971 - accuracy: 0.8553 - val_loss: 0.4400 - val_accuracy: 0.8417\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3742 - accuracy: 0.8639 - val_loss: 0.3917 - val_accuracy: 0.8587\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3894 - accuracy: 0.8613 - val_loss: 0.4330 - val_accuracy: 0.8410\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3677 - accuracy: 0.8667 - val_loss: 0.3923 - val_accuracy: 0.8570\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4013 - accuracy: 0.8533 - val_loss: 0.4169 - val_accuracy: 0.8460\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3779 - accuracy: 0.8629 - val_loss: 0.3742 - val_accuracy: 0.8620\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3596 - accuracy: 0.8685 - val_loss: 0.3830 - val_accuracy: 0.8513\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3349 - accuracy: 0.8791 - val_loss: 0.3840 - val_accuracy: 0.8497\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3683 - accuracy: 0.8666 - val_loss: 0.4081 - val_accuracy: 0.8503\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3466 - accuracy: 0.8737 - val_loss: 0.3926 - val_accuracy: 0.8507\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3699 - accuracy: 0.8672 - val_loss: 0.3814 - val_accuracy: 0.8617\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3422 - accuracy: 0.8774 - val_loss: 0.4065 - val_accuracy: 0.8550\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3772 - accuracy: 0.8632 - val_loss: 0.3910 - val_accuracy: 0.8573\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3533 - accuracy: 0.8727 - val_loss: 0.3855 - val_accuracy: 0.8590\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3463 - accuracy: 0.8750 - val_loss: 0.3738 - val_accuracy: 0.8553\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3211 - accuracy: 0.8802 - val_loss: 0.3918 - val_accuracy: 0.8510\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3499 - accuracy: 0.8729 - val_loss: 0.3676 - val_accuracy: 0.8713\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3267 - accuracy: 0.8812 - val_loss: 0.3899 - val_accuracy: 0.8577\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3494 - accuracy: 0.8741 - val_loss: 0.3674 - val_accuracy: 0.8647\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3253 - accuracy: 0.8807 - val_loss: 0.3472 - val_accuracy: 0.8733\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3569 - accuracy: 0.8718 - val_loss: 0.3798 - val_accuracy: 0.8623\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3349 - accuracy: 0.8777 - val_loss: 0.3767 - val_accuracy: 0.8637\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3237 - accuracy: 0.8851 - val_loss: 0.3832 - val_accuracy: 0.8557\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3030 - accuracy: 0.8865 - val_loss: 0.3505 - val_accuracy: 0.8683\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3338 - accuracy: 0.8774 - val_loss: 0.3626 - val_accuracy: 0.8683\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3121 - accuracy: 0.8847 - val_loss: 0.3688 - val_accuracy: 0.8693\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3333 - accuracy: 0.8813 - val_loss: 0.3624 - val_accuracy: 0.8690\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3088 - accuracy: 0.8891 - val_loss: 0.3433 - val_accuracy: 0.8697\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3370 - accuracy: 0.8792 - val_loss: 0.3451 - val_accuracy: 0.8723\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3172 - accuracy: 0.8855 - val_loss: 0.3609 - val_accuracy: 0.8653\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3102 - accuracy: 0.8848 - val_loss: 0.3654 - val_accuracy: 0.8593\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2944 - accuracy: 0.8919 - val_loss: 0.3741 - val_accuracy: 0.8623\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3157 - accuracy: 0.8825 - val_loss: 0.3767 - val_accuracy: 0.8593\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2971 - accuracy: 0.8902 - val_loss: 0.3389 - val_accuracy: 0.8743\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3168 - accuracy: 0.8839 - val_loss: 0.3366 - val_accuracy: 0.8783\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2954 - accuracy: 0.8933 - val_loss: 0.3363 - val_accuracy: 0.8780\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3255 - accuracy: 0.8816 - val_loss: 0.3444 - val_accuracy: 0.8780\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3054 - accuracy: 0.8902 - val_loss: 0.3597 - val_accuracy: 0.8677\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3009 - accuracy: 0.8867 - val_loss: 0.3611 - val_accuracy: 0.8653\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2808 - accuracy: 0.8974 - val_loss: 0.3417 - val_accuracy: 0.8727\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3051 - accuracy: 0.8867 - val_loss: 0.3481 - val_accuracy: 0.8720\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2869 - accuracy: 0.8943 - val_loss: 0.3331 - val_accuracy: 0.8770\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3069 - accuracy: 0.8883 - val_loss: 0.3388 - val_accuracy: 0.8830\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2859 - accuracy: 0.8967 - val_loss: 0.3327 - val_accuracy: 0.8793\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3110 - accuracy: 0.8859 - val_loss: 0.3842 - val_accuracy: 0.8667\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2944 - accuracy: 0.8921 - val_loss: 0.3326 - val_accuracy: 0.8797\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2872 - accuracy: 0.8917 - val_loss: 0.3704 - val_accuracy: 0.8637\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2753 - accuracy: 0.8964 - val_loss: 0.3438 - val_accuracy: 0.8740\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2979 - accuracy: 0.8885 - val_loss: 0.3274 - val_accuracy: 0.8780\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2775 - accuracy: 0.8954 - val_loss: 0.3437 - val_accuracy: 0.8710\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2955 - accuracy: 0.8922 - val_loss: 0.3448 - val_accuracy: 0.8707\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2794 - accuracy: 0.8986 - val_loss: 0.3358 - val_accuracy: 0.8810\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3041 - accuracy: 0.8887 - val_loss: 0.3223 - val_accuracy: 0.8807\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2787 - accuracy: 0.8971 - val_loss: 0.3424 - val_accuracy: 0.8760\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2784 - accuracy: 0.8976 - val_loss: 0.3448 - val_accuracy: 0.8697\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2595 - accuracy: 0.9036 - val_loss: 0.3535 - val_accuracy: 0.8710\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2827 - accuracy: 0.8957 - val_loss: 0.3241 - val_accuracy: 0.8797\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2703 - accuracy: 0.9020 - val_loss: 0.3342 - val_accuracy: 0.8763\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2898 - accuracy: 0.8957 - val_loss: 0.3374 - val_accuracy: 0.8780\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2677 - accuracy: 0.9027 - val_loss: 0.3209 - val_accuracy: 0.8870\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2906 - accuracy: 0.8941 - val_loss: 0.3455 - val_accuracy: 0.8797\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2794 - accuracy: 0.8968 - val_loss: 0.3395 - val_accuracy: 0.8807\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2720 - accuracy: 0.8997 - val_loss: 0.3768 - val_accuracy: 0.8567\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2539 - accuracy: 0.9068 - val_loss: 0.3264 - val_accuracy: 0.8793\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2773 - accuracy: 0.8982 - val_loss: 0.3208 - val_accuracy: 0.8827\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2580 - accuracy: 0.9048 - val_loss: 0.3403 - val_accuracy: 0.8767\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2761 - accuracy: 0.8978 - val_loss: 0.3407 - val_accuracy: 0.8790\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2631 - accuracy: 0.9028 - val_loss: 0.3192 - val_accuracy: 0.8837\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2890 - accuracy: 0.8937 - val_loss: 0.3238 - val_accuracy: 0.8857\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2688 - accuracy: 0.9009 - val_loss: 0.3720 - val_accuracy: 0.8710\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2665 - accuracy: 0.8986 - val_loss: 0.3469 - val_accuracy: 0.8667\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2431 - accuracy: 0.9099 - val_loss: 0.3505 - val_accuracy: 0.8697\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2685 - accuracy: 0.9014 - val_loss: 0.3373 - val_accuracy: 0.8770\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2537 - accuracy: 0.9060 - val_loss: 0.3327 - val_accuracy: 0.8790\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2699 - accuracy: 0.9012 - val_loss: 0.3343 - val_accuracy: 0.8797\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2490 - accuracy: 0.9072 - val_loss: 0.3325 - val_accuracy: 0.8863\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2771 - accuracy: 0.8971 - val_loss: 0.3451 - val_accuracy: 0.8753\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2579 - accuracy: 0.9059 - val_loss: 0.3413 - val_accuracy: 0.8790\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2570 - accuracy: 0.9063 - val_loss: 0.3328 - val_accuracy: 0.8727\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2424 - accuracy: 0.9090 - val_loss: 0.3331 - val_accuracy: 0.8733\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2623 - accuracy: 0.9023 - val_loss: 0.3274 - val_accuracy: 0.8827\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2430 - accuracy: 0.9093 - val_loss: 0.3139 - val_accuracy: 0.8877\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2607 - accuracy: 0.9040 - val_loss: 0.3300 - val_accuracy: 0.8847\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2458 - accuracy: 0.9108 - val_loss: 0.3154 - val_accuracy: 0.8887\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2686 - accuracy: 0.8992 - val_loss: 0.3466 - val_accuracy: 0.8760\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2578 - accuracy: 0.9043 - val_loss: 0.3171 - val_accuracy: 0.8840\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2486 - accuracy: 0.9076 - val_loss: 0.3418 - val_accuracy: 0.8743\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2330 - accuracy: 0.9135 - val_loss: 0.3332 - val_accuracy: 0.8773\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2607 - accuracy: 0.9034 - val_loss: 0.3342 - val_accuracy: 0.8767\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2411 - accuracy: 0.9104 - val_loss: 0.3284 - val_accuracy: 0.8800\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2604 - accuracy: 0.9029 - val_loss: 0.3394 - val_accuracy: 0.8787\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2421 - accuracy: 0.9119 - val_loss: 0.3192 - val_accuracy: 0.8897\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2634 - accuracy: 0.9028 - val_loss: 0.3498 - val_accuracy: 0.8797\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2449 - accuracy: 0.9107 - val_loss: 0.3182 - val_accuracy: 0.8860\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2421 - accuracy: 0.9077 - val_loss: 0.3378 - val_accuracy: 0.8773\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2306 - accuracy: 0.9104 - val_loss: 0.3598 - val_accuracy: 0.8697\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2493 - accuracy: 0.9078 - val_loss: 0.3008 - val_accuracy: 0.8833\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2344 - accuracy: 0.9143 - val_loss: 0.3181 - val_accuracy: 0.8750\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2498 - accuracy: 0.9095 - val_loss: 0.3271 - val_accuracy: 0.8793\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2347 - accuracy: 0.9136 - val_loss: 0.3162 - val_accuracy: 0.8887\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2622 - accuracy: 0.9048 - val_loss: 0.3526 - val_accuracy: 0.8770\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2416 - accuracy: 0.9103 - val_loss: 0.3365 - val_accuracy: 0.8860\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2359 - accuracy: 0.9087 - val_loss: 0.3235 - val_accuracy: 0.8817\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2174 - accuracy: 0.9165 - val_loss: 0.3338 - val_accuracy: 0.8807\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2427 - accuracy: 0.9090 - val_loss: 0.3200 - val_accuracy: 0.8837\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2257 - accuracy: 0.9151 - val_loss: 0.3174 - val_accuracy: 0.8870\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2453 - accuracy: 0.9097 - val_loss: 0.3126 - val_accuracy: 0.8903\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2238 - accuracy: 0.9187 - val_loss: 0.3226 - val_accuracy: 0.8923\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2532 - accuracy: 0.9055 - val_loss: 0.3359 - val_accuracy: 0.8790\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2382 - accuracy: 0.9096 - val_loss: 0.3266 - val_accuracy: 0.8870\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2315 - accuracy: 0.9103 - val_loss: 0.3467 - val_accuracy: 0.8750\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2124 - accuracy: 0.9197 - val_loss: 0.3222 - val_accuracy: 0.8820\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2404 - accuracy: 0.9106 - val_loss: 0.3192 - val_accuracy: 0.8827\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2215 - accuracy: 0.9160 - val_loss: 0.3129 - val_accuracy: 0.8887\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2393 - accuracy: 0.9144 - val_loss: 0.3250 - val_accuracy: 0.8923\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2227 - accuracy: 0.9194 - val_loss: 0.3525 - val_accuracy: 0.8737\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2486 - accuracy: 0.9053 - val_loss: 0.3363 - val_accuracy: 0.8783\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2321 - accuracy: 0.9138 - val_loss: 0.3166 - val_accuracy: 0.8820\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2257 - accuracy: 0.9154 - val_loss: 0.3333 - val_accuracy: 0.8777\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2110 - accuracy: 0.9206 - val_loss: 0.3662 - val_accuracy: 0.8700\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2334 - accuracy: 0.9133 - val_loss: 0.3461 - val_accuracy: 0.8770\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2188 - accuracy: 0.9174 - val_loss: 0.3132 - val_accuracy: 0.8887\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2353 - accuracy: 0.9147 - val_loss: 0.3065 - val_accuracy: 0.8953\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2192 - accuracy: 0.9197 - val_loss: 0.3098 - val_accuracy: 0.8980\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2409 - accuracy: 0.9097 - val_loss: 0.3407 - val_accuracy: 0.8837\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2236 - accuracy: 0.9159 - val_loss: 0.3302 - val_accuracy: 0.8833\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2203 - accuracy: 0.9169 - val_loss: 0.3228 - val_accuracy: 0.8863\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2050 - accuracy: 0.9238 - val_loss: 0.3309 - val_accuracy: 0.8813\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2293 - accuracy: 0.9135 - val_loss: 0.3090 - val_accuracy: 0.8930\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2117 - accuracy: 0.9223 - val_loss: 0.3105 - val_accuracy: 0.8907\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2275 - accuracy: 0.9167 - val_loss: 0.3114 - val_accuracy: 0.8913\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2124 - accuracy: 0.9227 - val_loss: 0.3149 - val_accuracy: 0.8933\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2386 - accuracy: 0.9095 - val_loss: 0.3248 - val_accuracy: 0.8867\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2199 - accuracy: 0.9168 - val_loss: 0.3247 - val_accuracy: 0.8817\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2149 - accuracy: 0.9182 - val_loss: 0.3171 - val_accuracy: 0.8823\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1992 - accuracy: 0.9226 - val_loss: 0.3382 - val_accuracy: 0.8733\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2230 - accuracy: 0.9151 - val_loss: 0.3109 - val_accuracy: 0.8843\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2052 - accuracy: 0.9240 - val_loss: 0.3160 - val_accuracy: 0.8870\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2277 - accuracy: 0.9174 - val_loss: 0.3549 - val_accuracy: 0.8817\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2080 - accuracy: 0.9227 - val_loss: 0.3078 - val_accuracy: 0.8940\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2307 - accuracy: 0.9144 - val_loss: 0.3206 - val_accuracy: 0.8820\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2104 - accuracy: 0.9198 - val_loss: 0.3390 - val_accuracy: 0.8810\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2145 - accuracy: 0.9182 - val_loss: 0.3333 - val_accuracy: 0.8757\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1904 - accuracy: 0.9288 - val_loss: 0.3266 - val_accuracy: 0.8840\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2209 - accuracy: 0.9169 - val_loss: 0.3226 - val_accuracy: 0.8807\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2045 - accuracy: 0.9238 - val_loss: 0.3117 - val_accuracy: 0.8860\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2196 - accuracy: 0.9195 - val_loss: 0.3490 - val_accuracy: 0.8807\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2051 - accuracy: 0.9239 - val_loss: 0.3631 - val_accuracy: 0.8790\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2246 - accuracy: 0.9164 - val_loss: 0.3494 - val_accuracy: 0.8843\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2121 - accuracy: 0.9217 - val_loss: 0.3425 - val_accuracy: 0.8823\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2083 - accuracy: 0.9230 - val_loss: 0.3344 - val_accuracy: 0.8800\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1930 - accuracy: 0.9273 - val_loss: 0.3443 - val_accuracy: 0.8800\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2163 - accuracy: 0.9182 - val_loss: 0.3241 - val_accuracy: 0.8920\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1981 - accuracy: 0.9250 - val_loss: 0.3204 - val_accuracy: 0.8900\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2184 - accuracy: 0.9191 - val_loss: 0.3363 - val_accuracy: 0.8897\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1981 - accuracy: 0.9293 - val_loss: 0.3252 - val_accuracy: 0.8930\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2223 - accuracy: 0.9172 - val_loss: 0.3491 - val_accuracy: 0.8803\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2066 - accuracy: 0.9250 - val_loss: 0.3287 - val_accuracy: 0.8840\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2083 - accuracy: 0.9220 - val_loss: 0.3399 - val_accuracy: 0.8860\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1867 - accuracy: 0.9299 - val_loss: 0.3425 - val_accuracy: 0.8833\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2081 - accuracy: 0.9216 - val_loss: 0.3172 - val_accuracy: 0.8883\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1881 - accuracy: 0.9318 - val_loss: 0.3081 - val_accuracy: 0.8960\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2103 - accuracy: 0.9225 - val_loss: 0.3646 - val_accuracy: 0.8820\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1975 - accuracy: 0.9262 - val_loss: 0.3258 - val_accuracy: 0.8863\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2168 - accuracy: 0.9182 - val_loss: 0.3467 - val_accuracy: 0.8797\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1994 - accuracy: 0.9275 - val_loss: 0.3825 - val_accuracy: 0.8730\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2000 - accuracy: 0.9243 - val_loss: 0.3355 - val_accuracy: 0.8823\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1822 - accuracy: 0.9325 - val_loss: 0.3322 - val_accuracy: 0.8840\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2052 - accuracy: 0.9240 - val_loss: 0.3185 - val_accuracy: 0.8887\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1897 - accuracy: 0.9294 - val_loss: 0.3238 - val_accuracy: 0.8863\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2034 - accuracy: 0.9243 - val_loss: 0.3466 - val_accuracy: 0.8857\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1880 - accuracy: 0.9296 - val_loss: 0.3319 - val_accuracy: 0.8953\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2182 - accuracy: 0.9186 - val_loss: 0.3369 - val_accuracy: 0.8807\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2008 - accuracy: 0.9251 - val_loss: 0.3485 - val_accuracy: 0.8803\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1958 - accuracy: 0.9249 - val_loss: 0.3263 - val_accuracy: 0.8877\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1788 - accuracy: 0.9313 - val_loss: 0.3204 - val_accuracy: 0.8857\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1995 - accuracy: 0.9247 - val_loss: 0.3127 - val_accuracy: 0.8910\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1872 - accuracy: 0.9303 - val_loss: 0.3299 - val_accuracy: 0.8890\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2030 - accuracy: 0.9243 - val_loss: 0.3303 - val_accuracy: 0.8900\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1866 - accuracy: 0.9319 - val_loss: 0.3268 - val_accuracy: 0.8910\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2113 - accuracy: 0.9207 - val_loss: 0.3373 - val_accuracy: 0.8833\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1897 - accuracy: 0.9303 - val_loss: 0.3605 - val_accuracy: 0.8780\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1932 - accuracy: 0.9244 - val_loss: 0.3264 - val_accuracy: 0.8887\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1772 - accuracy: 0.9327 - val_loss: 0.3550 - val_accuracy: 0.8803\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1937 - accuracy: 0.9270 - val_loss: 0.2940 - val_accuracy: 0.8983\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1813 - accuracy: 0.9331 - val_loss: 0.3324 - val_accuracy: 0.8843\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1979 - accuracy: 0.9289 - val_loss: 0.3380 - val_accuracy: 0.8853\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1840 - accuracy: 0.9323 - val_loss: 0.3169 - val_accuracy: 0.8930\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2029 - accuracy: 0.9233 - val_loss: 0.3412 - val_accuracy: 0.8870\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1892 - accuracy: 0.9290 - val_loss: 0.3426 - val_accuracy: 0.8817\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1856 - accuracy: 0.9303 - val_loss: 0.3879 - val_accuracy: 0.8707\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1724 - accuracy: 0.9335 - val_loss: 0.3474 - val_accuracy: 0.8803\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1939 - accuracy: 0.9262 - val_loss: 0.3322 - val_accuracy: 0.8853\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1827 - accuracy: 0.9331 - val_loss: 0.3176 - val_accuracy: 0.8947\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1933 - accuracy: 0.9287 - val_loss: 0.3320 - val_accuracy: 0.8967\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1810 - accuracy: 0.9336 - val_loss: 0.3415 - val_accuracy: 0.8907\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2020 - accuracy: 0.9272 - val_loss: 0.3387 - val_accuracy: 0.8873\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1862 - accuracy: 0.9297 - val_loss: 0.3676 - val_accuracy: 0.8830\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1829 - accuracy: 0.9306 - val_loss: 0.3383 - val_accuracy: 0.8827\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1704 - accuracy: 0.9353 - val_loss: 0.3339 - val_accuracy: 0.8863\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1902 - accuracy: 0.9302 - val_loss: 0.3201 - val_accuracy: 0.8857\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1738 - accuracy: 0.9362 - val_loss: 0.3144 - val_accuracy: 0.8893\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1916 - accuracy: 0.9274 - val_loss: 0.3372 - val_accuracy: 0.8927\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1757 - accuracy: 0.9358 - val_loss: 0.3433 - val_accuracy: 0.8910\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1990 - accuracy: 0.9262 - val_loss: 0.3359 - val_accuracy: 0.8863\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1820 - accuracy: 0.9315 - val_loss: 0.3628 - val_accuracy: 0.8810\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1817 - accuracy: 0.9302 - val_loss: 0.3429 - val_accuracy: 0.8813\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1637 - accuracy: 0.9363 - val_loss: 0.3521 - val_accuracy: 0.8800\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1842 - accuracy: 0.9334 - val_loss: 0.3301 - val_accuracy: 0.8867\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1725 - accuracy: 0.9336 - val_loss: 0.3582 - val_accuracy: 0.8790\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1901 - accuracy: 0.9311 - val_loss: 0.3253 - val_accuracy: 0.8937\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1755 - accuracy: 0.9362 - val_loss: 0.3438 - val_accuracy: 0.8910\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1909 - accuracy: 0.9282 - val_loss: 0.3402 - val_accuracy: 0.8853\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1791 - accuracy: 0.9326 - val_loss: 0.3649 - val_accuracy: 0.8807\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1774 - accuracy: 0.9332 - val_loss: 0.3300 - val_accuracy: 0.8887\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1591 - accuracy: 0.9399 - val_loss: 0.3414 - val_accuracy: 0.8897\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1842 - accuracy: 0.9314 - val_loss: 0.3261 - val_accuracy: 0.8887\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1743 - accuracy: 0.9349 - val_loss: 0.3528 - val_accuracy: 0.8803\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1850 - accuracy: 0.9305 - val_loss: 0.3238 - val_accuracy: 0.8970\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1695 - accuracy: 0.9383 - val_loss: 0.3418 - val_accuracy: 0.8897\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1902 - accuracy: 0.9302 - val_loss: 0.3866 - val_accuracy: 0.8807\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1742 - accuracy: 0.9341 - val_loss: 0.3456 - val_accuracy: 0.8870\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1736 - accuracy: 0.9347 - val_loss: 0.3433 - val_accuracy: 0.8827\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1575 - accuracy: 0.9410 - val_loss: 0.3327 - val_accuracy: 0.8873\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1832 - accuracy: 0.9327 - val_loss: 0.3121 - val_accuracy: 0.8957\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1677 - accuracy: 0.9365 - val_loss: 0.3414 - val_accuracy: 0.8833\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1817 - accuracy: 0.9338 - val_loss: 0.3432 - val_accuracy: 0.8877\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1649 - accuracy: 0.9417 - val_loss: 0.3444 - val_accuracy: 0.8850\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1853 - accuracy: 0.9303 - val_loss: 0.3279 - val_accuracy: 0.8893\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1723 - accuracy: 0.9362 - val_loss: 0.3563 - val_accuracy: 0.8803\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1714 - accuracy: 0.9339 - val_loss: 0.3587 - val_accuracy: 0.8817\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1541 - accuracy: 0.9410 - val_loss: 0.3845 - val_accuracy: 0.8763\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1769 - accuracy: 0.9342 - val_loss: 0.3137 - val_accuracy: 0.8933\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1610 - accuracy: 0.9403 - val_loss: 0.3422 - val_accuracy: 0.8860\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1837 - accuracy: 0.9332 - val_loss: 0.3450 - val_accuracy: 0.8937\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1660 - accuracy: 0.9375 - val_loss: 0.3378 - val_accuracy: 0.8970\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1849 - accuracy: 0.9298 - val_loss: 0.3696 - val_accuracy: 0.8777\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1680 - accuracy: 0.9388 - val_loss: 0.3544 - val_accuracy: 0.8800\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1672 - accuracy: 0.9358 - val_loss: 0.3628 - val_accuracy: 0.8870\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1524 - accuracy: 0.9403 - val_loss: 0.3748 - val_accuracy: 0.8823\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1712 - accuracy: 0.9352 - val_loss: 0.3175 - val_accuracy: 0.8873\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1616 - accuracy: 0.9392 - val_loss: 0.3195 - val_accuracy: 0.8900\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1703 - accuracy: 0.9382 - val_loss: 0.3454 - val_accuracy: 0.8913\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1659 - accuracy: 0.9359 - val_loss: 0.3886 - val_accuracy: 0.8743\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1778 - accuracy: 0.9327 - val_loss: 0.3740 - val_accuracy: 0.8810\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1664 - accuracy: 0.9389 - val_loss: 0.3819 - val_accuracy: 0.8793\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1675 - accuracy: 0.9358 - val_loss: 0.3623 - val_accuracy: 0.8873\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1562 - accuracy: 0.9405 - val_loss: 0.3482 - val_accuracy: 0.8893\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1726 - accuracy: 0.9352 - val_loss: 0.3553 - val_accuracy: 0.8790\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1561 - accuracy: 0.9431 - val_loss: 0.3227 - val_accuracy: 0.8910\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1740 - accuracy: 0.9364 - val_loss: 0.3623 - val_accuracy: 0.8893\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1548 - accuracy: 0.9442 - val_loss: 0.3464 - val_accuracy: 0.8943\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1735 - accuracy: 0.9371 - val_loss: 0.3517 - val_accuracy: 0.8823\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1605 - accuracy: 0.9401 - val_loss: 0.3876 - val_accuracy: 0.8780\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1624 - accuracy: 0.9366 - val_loss: 0.3513 - val_accuracy: 0.8883\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1497 - accuracy: 0.9432 - val_loss: 0.3540 - val_accuracy: 0.8873\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1790 - accuracy: 0.9326 - val_loss: 0.3528 - val_accuracy: 0.8813\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1556 - accuracy: 0.9435 - val_loss: 0.3173 - val_accuracy: 0.8983\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1697 - accuracy: 0.9378 - val_loss: 0.3539 - val_accuracy: 0.8943\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1561 - accuracy: 0.9433 - val_loss: 0.3884 - val_accuracy: 0.8840\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1685 - accuracy: 0.9371 - val_loss: 0.3576 - val_accuracy: 0.8843\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1628 - accuracy: 0.9407 - val_loss: 0.3652 - val_accuracy: 0.8860\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1597 - accuracy: 0.9388 - val_loss: 0.3656 - val_accuracy: 0.8870\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1446 - accuracy: 0.9438 - val_loss: 0.3983 - val_accuracy: 0.8747\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1698 - accuracy: 0.9351 - val_loss: 0.3353 - val_accuracy: 0.8837\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1479 - accuracy: 0.9451 - val_loss: 0.3365 - val_accuracy: 0.8913\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1691 - accuracy: 0.9371 - val_loss: 0.3687 - val_accuracy: 0.8860\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1494 - accuracy: 0.9450 - val_loss: 0.3501 - val_accuracy: 0.8943\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1735 - accuracy: 0.9360 - val_loss: 0.3929 - val_accuracy: 0.8760\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1511 - accuracy: 0.9452 - val_loss: 0.3698 - val_accuracy: 0.8790\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1622 - accuracy: 0.9377 - val_loss: 0.3626 - val_accuracy: 0.8837\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1455 - accuracy: 0.9452 - val_loss: 0.3700 - val_accuracy: 0.8800\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1587 - accuracy: 0.9433 - val_loss: 0.3476 - val_accuracy: 0.8853\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1535 - accuracy: 0.9425 - val_loss: 0.3267 - val_accuracy: 0.8893\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1558 - accuracy: 0.9424 - val_loss: 0.3363 - val_accuracy: 0.8917\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1492 - accuracy: 0.9476 - val_loss: 0.3493 - val_accuracy: 0.8880\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1744 - accuracy: 0.9351 - val_loss: 0.3682 - val_accuracy: 0.8800\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1544 - accuracy: 0.9425 - val_loss: 0.3935 - val_accuracy: 0.8793\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1563 - accuracy: 0.9414 - val_loss: 0.3615 - val_accuracy: 0.8813\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1379 - accuracy: 0.9474 - val_loss: 0.3526 - val_accuracy: 0.8900\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1606 - accuracy: 0.9396 - val_loss: 0.3510 - val_accuracy: 0.8863\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1471 - accuracy: 0.9454 - val_loss: 0.3511 - val_accuracy: 0.8883\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1617 - accuracy: 0.9395 - val_loss: 0.3508 - val_accuracy: 0.8920\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1484 - accuracy: 0.9465 - val_loss: 0.3808 - val_accuracy: 0.8910\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1682 - accuracy: 0.9376 - val_loss: 0.3970 - val_accuracy: 0.8837\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1538 - accuracy: 0.9430 - val_loss: 0.4096 - val_accuracy: 0.8820\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1528 - accuracy: 0.9410 - val_loss: 0.3826 - val_accuracy: 0.8820\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1385 - accuracy: 0.9474 - val_loss: 0.3864 - val_accuracy: 0.8810\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1605 - accuracy: 0.9395 - val_loss: 0.3582 - val_accuracy: 0.8840\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1455 - accuracy: 0.9469 - val_loss: 0.3394 - val_accuracy: 0.8903\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1560 - accuracy: 0.9449 - val_loss: 0.3819 - val_accuracy: 0.8943\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1471 - accuracy: 0.9464 - val_loss: 0.3547 - val_accuracy: 0.8933\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1616 - accuracy: 0.9393 - val_loss: 0.3691 - val_accuracy: 0.8830\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1488 - accuracy: 0.9451 - val_loss: 0.3851 - val_accuracy: 0.8823\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1503 - accuracy: 0.9432 - val_loss: 0.3580 - val_accuracy: 0.8870\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1380 - accuracy: 0.9486 - val_loss: 0.3899 - val_accuracy: 0.8853\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1508 - accuracy: 0.9435 - val_loss: 0.3448 - val_accuracy: 0.8873\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1465 - accuracy: 0.9452 - val_loss: 0.3511 - val_accuracy: 0.8877\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1542 - accuracy: 0.9438 - val_loss: 0.3665 - val_accuracy: 0.8937\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1377 - accuracy: 0.9497 - val_loss: 0.3567 - val_accuracy: 0.8923\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1621 - accuracy: 0.9375 - val_loss: 0.3598 - val_accuracy: 0.8873\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1470 - accuracy: 0.9436 - val_loss: 0.3968 - val_accuracy: 0.8853\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1459 - accuracy: 0.9433 - val_loss: 0.4070 - val_accuracy: 0.8743\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1360 - accuracy: 0.9488 - val_loss: 0.3889 - val_accuracy: 0.8897\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1537 - accuracy: 0.9423 - val_loss: 0.3508 - val_accuracy: 0.8900\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1396 - accuracy: 0.9467 - val_loss: 0.3919 - val_accuracy: 0.8813\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1517 - accuracy: 0.9441 - val_loss: 0.3834 - val_accuracy: 0.8890\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1443 - accuracy: 0.9473 - val_loss: 0.3712 - val_accuracy: 0.8967\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1615 - accuracy: 0.9392 - val_loss: 0.3876 - val_accuracy: 0.8843\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1435 - accuracy: 0.9455 - val_loss: 0.3839 - val_accuracy: 0.8853\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1506 - accuracy: 0.9427 - val_loss: 0.3716 - val_accuracy: 0.8850\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1329 - accuracy: 0.9498 - val_loss: 0.3867 - val_accuracy: 0.8863\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1563 - accuracy: 0.9421 - val_loss: 0.3591 - val_accuracy: 0.8833\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1409 - accuracy: 0.9476 - val_loss: 0.3505 - val_accuracy: 0.8883\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1459 - accuracy: 0.9467 - val_loss: 0.3722 - val_accuracy: 0.8937\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1410 - accuracy: 0.9482 - val_loss: 0.3688 - val_accuracy: 0.8890\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1539 - accuracy: 0.9436 - val_loss: 0.3670 - val_accuracy: 0.8783\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1376 - accuracy: 0.9496 - val_loss: 0.3764 - val_accuracy: 0.8810\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1416 - accuracy: 0.9461 - val_loss: 0.3969 - val_accuracy: 0.8883\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1325 - accuracy: 0.9488 - val_loss: 0.4183 - val_accuracy: 0.8757\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1505 - accuracy: 0.9452 - val_loss: 0.3493 - val_accuracy: 0.8870\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1433 - accuracy: 0.9477 - val_loss: 0.3863 - val_accuracy: 0.8807\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1497 - accuracy: 0.9445 - val_loss: 0.3697 - val_accuracy: 0.8883\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1364 - accuracy: 0.9517 - val_loss: 0.3906 - val_accuracy: 0.8903\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1548 - accuracy: 0.9398 - val_loss: 0.3986 - val_accuracy: 0.8807\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1409 - accuracy: 0.9452 - val_loss: 0.3706 - val_accuracy: 0.8810\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1438 - accuracy: 0.9437 - val_loss: 0.4123 - val_accuracy: 0.8850\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1248 - accuracy: 0.9530 - val_loss: 0.4240 - val_accuracy: 0.8780\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1465 - accuracy: 0.9451 - val_loss: 0.3413 - val_accuracy: 0.8863\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1386 - accuracy: 0.9485 - val_loss: 0.3578 - val_accuracy: 0.8917\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1495 - accuracy: 0.9467 - val_loss: 0.3968 - val_accuracy: 0.8903\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1350 - accuracy: 0.9511 - val_loss: 0.3721 - val_accuracy: 0.8907\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1525 - accuracy: 0.9451 - val_loss: 0.4168 - val_accuracy: 0.8803\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1359 - accuracy: 0.9492 - val_loss: 0.3747 - val_accuracy: 0.8827\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1328 - accuracy: 0.9499 - val_loss: 0.3934 - val_accuracy: 0.8837\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1250 - accuracy: 0.9506 - val_loss: 0.3792 - val_accuracy: 0.8860\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1434 - accuracy: 0.9455 - val_loss: 0.3701 - val_accuracy: 0.8873\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1332 - accuracy: 0.9495 - val_loss: 0.3560 - val_accuracy: 0.8953\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1455 - accuracy: 0.9479 - val_loss: 0.4107 - val_accuracy: 0.8847\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1334 - accuracy: 0.9518 - val_loss: 0.3938 - val_accuracy: 0.8890\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1487 - accuracy: 0.9434 - val_loss: 0.3807 - val_accuracy: 0.8850\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1372 - accuracy: 0.9468 - val_loss: 0.3829 - val_accuracy: 0.8847\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1373 - accuracy: 0.9459 - val_loss: 0.3715 - val_accuracy: 0.8857\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1220 - accuracy: 0.9548 - val_loss: 0.3956 - val_accuracy: 0.8897\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1372 - accuracy: 0.9485 - val_loss: 0.3676 - val_accuracy: 0.8923\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1250 - accuracy: 0.9542 - val_loss: 0.3519 - val_accuracy: 0.8920\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1424 - accuracy: 0.9467 - val_loss: 0.3816 - val_accuracy: 0.8877\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1273 - accuracy: 0.9543 - val_loss: 0.3729 - val_accuracy: 0.8883\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1446 - accuracy: 0.9473 - val_loss: 0.4027 - val_accuracy: 0.8730\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1308 - accuracy: 0.9535 - val_loss: 0.4077 - val_accuracy: 0.8810\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1341 - accuracy: 0.9495 - val_loss: 0.3769 - val_accuracy: 0.8827\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1208 - accuracy: 0.9542 - val_loss: 0.4151 - val_accuracy: 0.8870\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1431 - accuracy: 0.9451 - val_loss: 0.3551 - val_accuracy: 0.8970\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 3ms/step - loss: 0.1313 - accuracy: 0.9513 - val_loss: 0.4019 - val_accuracy: 0.8847\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 3ms/step - loss: 0.1412 - accuracy: 0.9485 - val_loss: 0.3925 - val_accuracy: 0.8850\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1249 - accuracy: 0.9528 - val_loss: 0.3882 - val_accuracy: 0.8870\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 3ms/step - loss: 0.1461 - accuracy: 0.9443 - val_loss: 0.3838 - val_accuracy: 0.8833\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1308 - accuracy: 0.9522 - val_loss: 0.4144 - val_accuracy: 0.8877\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1364 - accuracy: 0.9475 - val_loss: 0.4100 - val_accuracy: 0.8743\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1237 - accuracy: 0.9540 - val_loss: 0.3871 - val_accuracy: 0.8877\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1365 - accuracy: 0.9473 - val_loss: 0.3664 - val_accuracy: 0.8880\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1266 - accuracy: 0.9514 - val_loss: 0.3753 - val_accuracy: 0.8927\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1373 - accuracy: 0.9488 - val_loss: 0.4047 - val_accuracy: 0.8847\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1230 - accuracy: 0.9546 - val_loss: 0.3713 - val_accuracy: 0.8937\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1452 - accuracy: 0.9446 - val_loss: 0.3846 - val_accuracy: 0.8823\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1307 - accuracy: 0.9514 - val_loss: 0.3952 - val_accuracy: 0.8817\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1234 - accuracy: 0.9540 - val_loss: 0.4106 - val_accuracy: 0.8840\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1197 - accuracy: 0.9554 - val_loss: 0.4023 - val_accuracy: 0.8847\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 3ms/step - loss: 0.1415 - accuracy: 0.9459 - val_loss: 0.3689 - val_accuracy: 0.8943\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1234 - accuracy: 0.9531 - val_loss: 0.3584 - val_accuracy: 0.8930\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 3ms/step - loss: 0.1331 - accuracy: 0.9503 - val_loss: 0.4084 - val_accuracy: 0.8867\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 3ms/step - loss: 0.1215 - accuracy: 0.9564 - val_loss: 0.4032 - val_accuracy: 0.8933\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 3ms/step - loss: 0.1386 - accuracy: 0.9473 - val_loss: 0.3906 - val_accuracy: 0.8850\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 3ms/step - loss: 0.1235 - accuracy: 0.9534 - val_loss: 0.4138 - val_accuracy: 0.8847\n",
            "313/313 - 0s - loss: 85.0301 - accuracy: 0.8690\n",
            "313/313 - 0s - loss: 85.0301 - accuracy: 0.8690\n",
            "313/313 - 0s - loss: 85.0301 - accuracy: 0.8690\n",
            "313/313 - 0s - loss: 85.0301 - accuracy: 0.8690\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[85.03009033203125, 0.8690000176429749]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9iBhIKsvYBF",
        "outputId": "3c816d7a-0be7-4346-b4a5-dec119d39a5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model1 = MODEL1()\n",
        "model2 = MODEL1()\n",
        "model3 = MODEL1()\n",
        "model4 = MODEL1()\n",
        "\n",
        "for i in range(50):\n",
        "  h1 = model1.fit(train1, y1,validation_split = 0.2, epochs=2, verbose=1)\n",
        "  h2 = model2.fit(train2, y2,validation_split = 0.2, epochs=2, verbose=1)\n",
        "  h3 = model3.fit(train3, y3,validation_split = 0.2, epochs=2, verbose=1)\n",
        "  h4 = model4.fit(train4, y4,validation_split = 0.2, epochs=2, verbose=1)\n",
        "\n",
        "model1.evaluate(test_images, test_labels, verbose=2)\n",
        "model2.evaluate(test_images, test_labels, verbose=2)\n",
        "model3.evaluate(test_images, test_labels, verbose=2)\n",
        "model4.evaluate(test_images, test_labels, verbose=2)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.6758 - accuracy: 0.7674 - val_loss: 0.4938 - val_accuracy: 0.8187\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4724 - accuracy: 0.8313 - val_loss: 0.4510 - val_accuracy: 0.8323\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.6964 - accuracy: 0.7603 - val_loss: 0.5649 - val_accuracy: 0.7997\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4846 - accuracy: 0.8269 - val_loss: 0.4717 - val_accuracy: 0.8403\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.6624 - accuracy: 0.7749 - val_loss: 0.5079 - val_accuracy: 0.8277\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4717 - accuracy: 0.8372 - val_loss: 0.4883 - val_accuracy: 0.8303\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.6766 - accuracy: 0.7646 - val_loss: 0.5274 - val_accuracy: 0.8087\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4788 - accuracy: 0.8328 - val_loss: 0.4493 - val_accuracy: 0.8430\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4162 - accuracy: 0.8555 - val_loss: 0.4244 - val_accuracy: 0.8503\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3829 - accuracy: 0.8643 - val_loss: 0.4442 - val_accuracy: 0.8430\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4331 - accuracy: 0.8453 - val_loss: 0.4457 - val_accuracy: 0.8423\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3974 - accuracy: 0.8567 - val_loss: 0.4578 - val_accuracy: 0.8387\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4249 - accuracy: 0.8507 - val_loss: 0.4697 - val_accuracy: 0.8250\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3891 - accuracy: 0.8662 - val_loss: 0.4237 - val_accuracy: 0.8500\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.4325 - accuracy: 0.8455 - val_loss: 0.4591 - val_accuracy: 0.8330\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3940 - accuracy: 0.8556 - val_loss: 0.4031 - val_accuracy: 0.8590\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3579 - accuracy: 0.8730 - val_loss: 0.4137 - val_accuracy: 0.8507\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3295 - accuracy: 0.8821 - val_loss: 0.3924 - val_accuracy: 0.8563\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3653 - accuracy: 0.8676 - val_loss: 0.4289 - val_accuracy: 0.8517\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3387 - accuracy: 0.8767 - val_loss: 0.3962 - val_accuracy: 0.8593\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3596 - accuracy: 0.8714 - val_loss: 0.4013 - val_accuracy: 0.8530\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3424 - accuracy: 0.8775 - val_loss: 0.3961 - val_accuracy: 0.8650\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3701 - accuracy: 0.8682 - val_loss: 0.4250 - val_accuracy: 0.8480\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3472 - accuracy: 0.8748 - val_loss: 0.4218 - val_accuracy: 0.8507\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3113 - accuracy: 0.8875 - val_loss: 0.4037 - val_accuracy: 0.8553\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2945 - accuracy: 0.8939 - val_loss: 0.3767 - val_accuracy: 0.8587\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3221 - accuracy: 0.8847 - val_loss: 0.4243 - val_accuracy: 0.8450\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3102 - accuracy: 0.8898 - val_loss: 0.4006 - val_accuracy: 0.8543\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3251 - accuracy: 0.8866 - val_loss: 0.4258 - val_accuracy: 0.8483\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3050 - accuracy: 0.8919 - val_loss: 0.3911 - val_accuracy: 0.8587\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3266 - accuracy: 0.8826 - val_loss: 0.3848 - val_accuracy: 0.8643\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.3121 - accuracy: 0.8867 - val_loss: 0.4051 - val_accuracy: 0.8553\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2869 - accuracy: 0.8972 - val_loss: 0.4763 - val_accuracy: 0.8343\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2704 - accuracy: 0.9003 - val_loss: 0.3756 - val_accuracy: 0.8610\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2870 - accuracy: 0.8953 - val_loss: 0.4042 - val_accuracy: 0.8623\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2746 - accuracy: 0.8973 - val_loss: 0.4052 - val_accuracy: 0.8647\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2859 - accuracy: 0.8980 - val_loss: 0.3980 - val_accuracy: 0.8593\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2723 - accuracy: 0.9038 - val_loss: 0.3840 - val_accuracy: 0.8643\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2970 - accuracy: 0.8947 - val_loss: 0.4110 - val_accuracy: 0.8543\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2856 - accuracy: 0.8959 - val_loss: 0.3990 - val_accuracy: 0.8620\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2579 - accuracy: 0.9062 - val_loss: 0.4715 - val_accuracy: 0.8363\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2529 - accuracy: 0.9076 - val_loss: 0.3861 - val_accuracy: 0.8620\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2568 - accuracy: 0.9082 - val_loss: 0.3752 - val_accuracy: 0.8667\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2483 - accuracy: 0.9112 - val_loss: 0.3822 - val_accuracy: 0.8687\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2641 - accuracy: 0.9035 - val_loss: 0.3769 - val_accuracy: 0.8700\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2540 - accuracy: 0.9069 - val_loss: 0.3979 - val_accuracy: 0.8550\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2725 - accuracy: 0.9030 - val_loss: 0.4377 - val_accuracy: 0.8517\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2566 - accuracy: 0.9061 - val_loss: 0.3958 - val_accuracy: 0.8643\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2370 - accuracy: 0.9137 - val_loss: 0.4184 - val_accuracy: 0.8480\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2289 - accuracy: 0.9165 - val_loss: 0.3948 - val_accuracy: 0.8567\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2423 - accuracy: 0.9136 - val_loss: 0.3967 - val_accuracy: 0.8640\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2266 - accuracy: 0.9177 - val_loss: 0.3886 - val_accuracy: 0.8683\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2417 - accuracy: 0.9104 - val_loss: 0.3653 - val_accuracy: 0.8713\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2312 - accuracy: 0.9158 - val_loss: 0.3707 - val_accuracy: 0.8750\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2440 - accuracy: 0.9092 - val_loss: 0.3829 - val_accuracy: 0.8643\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2354 - accuracy: 0.9145 - val_loss: 0.4156 - val_accuracy: 0.8590\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2237 - accuracy: 0.9188 - val_loss: 0.3950 - val_accuracy: 0.8587\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2108 - accuracy: 0.9227 - val_loss: 0.3788 - val_accuracy: 0.8677\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2243 - accuracy: 0.9189 - val_loss: 0.4022 - val_accuracy: 0.8653\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2100 - accuracy: 0.9246 - val_loss: 0.4246 - val_accuracy: 0.8503\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2211 - accuracy: 0.9200 - val_loss: 0.3787 - val_accuracy: 0.8730\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2145 - accuracy: 0.9211 - val_loss: 0.3857 - val_accuracy: 0.8683\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2267 - accuracy: 0.9156 - val_loss: 0.3919 - val_accuracy: 0.8653\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2195 - accuracy: 0.9195 - val_loss: 0.4016 - val_accuracy: 0.8577\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2025 - accuracy: 0.9245 - val_loss: 0.3933 - val_accuracy: 0.8653\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1992 - accuracy: 0.9258 - val_loss: 0.3787 - val_accuracy: 0.8670\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2077 - accuracy: 0.9258 - val_loss: 0.4065 - val_accuracy: 0.8623\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2015 - accuracy: 0.9286 - val_loss: 0.4500 - val_accuracy: 0.8473\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2053 - accuracy: 0.9259 - val_loss: 0.4026 - val_accuracy: 0.8690\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1903 - accuracy: 0.9317 - val_loss: 0.3879 - val_accuracy: 0.8697\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.2085 - accuracy: 0.9242 - val_loss: 0.4407 - val_accuracy: 0.8630\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1993 - accuracy: 0.9272 - val_loss: 0.4179 - val_accuracy: 0.8547\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1837 - accuracy: 0.9341 - val_loss: 0.3926 - val_accuracy: 0.8620\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1844 - accuracy: 0.9338 - val_loss: 0.3924 - val_accuracy: 0.8707\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1928 - accuracy: 0.9300 - val_loss: 0.4380 - val_accuracy: 0.8623\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1835 - accuracy: 0.9333 - val_loss: 0.4099 - val_accuracy: 0.8703\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1869 - accuracy: 0.9343 - val_loss: 0.3816 - val_accuracy: 0.8770\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1787 - accuracy: 0.9361 - val_loss: 0.3814 - val_accuracy: 0.8783\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1940 - accuracy: 0.9301 - val_loss: 0.3909 - val_accuracy: 0.8680\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1852 - accuracy: 0.9324 - val_loss: 0.4039 - val_accuracy: 0.8627\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1698 - accuracy: 0.9372 - val_loss: 0.4010 - val_accuracy: 0.8703\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1613 - accuracy: 0.9438 - val_loss: 0.4112 - val_accuracy: 0.8710\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1788 - accuracy: 0.9346 - val_loss: 0.4443 - val_accuracy: 0.8630\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1728 - accuracy: 0.9377 - val_loss: 0.3999 - val_accuracy: 0.8747\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1718 - accuracy: 0.9370 - val_loss: 0.4024 - val_accuracy: 0.8707\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1657 - accuracy: 0.9415 - val_loss: 0.3859 - val_accuracy: 0.8807\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1775 - accuracy: 0.9345 - val_loss: 0.4285 - val_accuracy: 0.8600\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1684 - accuracy: 0.9375 - val_loss: 0.4492 - val_accuracy: 0.8530\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1572 - accuracy: 0.9423 - val_loss: 0.4384 - val_accuracy: 0.8660\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1612 - accuracy: 0.9412 - val_loss: 0.4042 - val_accuracy: 0.8727\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1699 - accuracy: 0.9379 - val_loss: 0.3890 - val_accuracy: 0.8763\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1566 - accuracy: 0.9420 - val_loss: 0.4418 - val_accuracy: 0.8620\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1642 - accuracy: 0.9414 - val_loss: 0.4297 - val_accuracy: 0.8660\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1522 - accuracy: 0.9469 - val_loss: 0.3882 - val_accuracy: 0.8803\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1660 - accuracy: 0.9398 - val_loss: 0.4142 - val_accuracy: 0.8660\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1573 - accuracy: 0.9451 - val_loss: 0.4319 - val_accuracy: 0.8643\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1483 - accuracy: 0.9461 - val_loss: 0.4334 - val_accuracy: 0.8630\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1429 - accuracy: 0.9480 - val_loss: 0.4090 - val_accuracy: 0.8747\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1545 - accuracy: 0.9439 - val_loss: 0.4302 - val_accuracy: 0.8713\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1523 - accuracy: 0.9447 - val_loss: 0.3986 - val_accuracy: 0.8740\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1521 - accuracy: 0.9457 - val_loss: 0.4142 - val_accuracy: 0.8697\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1434 - accuracy: 0.9496 - val_loss: 0.4077 - val_accuracy: 0.8797\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1501 - accuracy: 0.9462 - val_loss: 0.4417 - val_accuracy: 0.8673\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1526 - accuracy: 0.9451 - val_loss: 0.4480 - val_accuracy: 0.8643\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1369 - accuracy: 0.9504 - val_loss: 0.4291 - val_accuracy: 0.8677\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1276 - accuracy: 0.9554 - val_loss: 0.4415 - val_accuracy: 0.8700\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1451 - accuracy: 0.9473 - val_loss: 0.4370 - val_accuracy: 0.8700\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1415 - accuracy: 0.9491 - val_loss: 0.4190 - val_accuracy: 0.8773\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1384 - accuracy: 0.9514 - val_loss: 0.4323 - val_accuracy: 0.8637\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1381 - accuracy: 0.9509 - val_loss: 0.4308 - val_accuracy: 0.8670\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1452 - accuracy: 0.9478 - val_loss: 0.4691 - val_accuracy: 0.8597\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1379 - accuracy: 0.9500 - val_loss: 0.4776 - val_accuracy: 0.8583\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1253 - accuracy: 0.9558 - val_loss: 0.4223 - val_accuracy: 0.8727\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1222 - accuracy: 0.9566 - val_loss: 0.4885 - val_accuracy: 0.8620\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1304 - accuracy: 0.9527 - val_loss: 0.4385 - val_accuracy: 0.8727\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1305 - accuracy: 0.9521 - val_loss: 0.4393 - val_accuracy: 0.8683\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1348 - accuracy: 0.9508 - val_loss: 0.4197 - val_accuracy: 0.8730\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1207 - accuracy: 0.9570 - val_loss: 0.4490 - val_accuracy: 0.8680\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1346 - accuracy: 0.9509 - val_loss: 0.4789 - val_accuracy: 0.8643\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1260 - accuracy: 0.9557 - val_loss: 0.4525 - val_accuracy: 0.8660\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1226 - accuracy: 0.9567 - val_loss: 0.4538 - val_accuracy: 0.8697\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1148 - accuracy: 0.9575 - val_loss: 0.4490 - val_accuracy: 0.8723\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1257 - accuracy: 0.9553 - val_loss: 0.4376 - val_accuracy: 0.8740\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1227 - accuracy: 0.9567 - val_loss: 0.4519 - val_accuracy: 0.8743\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1181 - accuracy: 0.9579 - val_loss: 0.4639 - val_accuracy: 0.8680\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1138 - accuracy: 0.9589 - val_loss: 0.4856 - val_accuracy: 0.8663\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1243 - accuracy: 0.9538 - val_loss: 0.4899 - val_accuracy: 0.8670\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1170 - accuracy: 0.9585 - val_loss: 0.4958 - val_accuracy: 0.8620\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1114 - accuracy: 0.9588 - val_loss: 0.4474 - val_accuracy: 0.8693\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1004 - accuracy: 0.9651 - val_loss: 0.4693 - val_accuracy: 0.8590\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1245 - accuracy: 0.9535 - val_loss: 0.4596 - val_accuracy: 0.8683\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1109 - accuracy: 0.9603 - val_loss: 0.4469 - val_accuracy: 0.8733\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1090 - accuracy: 0.9627 - val_loss: 0.4577 - val_accuracy: 0.8740\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1128 - accuracy: 0.9604 - val_loss: 0.6281 - val_accuracy: 0.8557\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1202 - accuracy: 0.9568 - val_loss: 0.5381 - val_accuracy: 0.8490\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1141 - accuracy: 0.9597 - val_loss: 0.5093 - val_accuracy: 0.8597\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1074 - accuracy: 0.9626 - val_loss: 0.4721 - val_accuracy: 0.8680\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1011 - accuracy: 0.9644 - val_loss: 0.4837 - val_accuracy: 0.8680\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1138 - accuracy: 0.9600 - val_loss: 0.4512 - val_accuracy: 0.8727\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1051 - accuracy: 0.9614 - val_loss: 0.4714 - val_accuracy: 0.8763\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1067 - accuracy: 0.9639 - val_loss: 0.4584 - val_accuracy: 0.8800\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1014 - accuracy: 0.9626 - val_loss: 0.4705 - val_accuracy: 0.8763\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1122 - accuracy: 0.9595 - val_loss: 0.5133 - val_accuracy: 0.8593\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0987 - accuracy: 0.9657 - val_loss: 0.5126 - val_accuracy: 0.8583\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1012 - accuracy: 0.9651 - val_loss: 0.5506 - val_accuracy: 0.8567\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0945 - accuracy: 0.9656 - val_loss: 0.4793 - val_accuracy: 0.8740\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1005 - accuracy: 0.9650 - val_loss: 0.4949 - val_accuracy: 0.8710\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0996 - accuracy: 0.9651 - val_loss: 0.5868 - val_accuracy: 0.8580\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0996 - accuracy: 0.9622 - val_loss: 0.4953 - val_accuracy: 0.8737\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0984 - accuracy: 0.9656 - val_loss: 0.4788 - val_accuracy: 0.8640\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1054 - accuracy: 0.9632 - val_loss: 0.5231 - val_accuracy: 0.8613\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.1002 - accuracy: 0.9639 - val_loss: 0.5130 - val_accuracy: 0.8660\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0830 - accuracy: 0.9706 - val_loss: 0.4910 - val_accuracy: 0.8700\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0813 - accuracy: 0.9734 - val_loss: 0.4896 - val_accuracy: 0.8707\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0931 - accuracy: 0.9673 - val_loss: 0.4919 - val_accuracy: 0.8680\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0930 - accuracy: 0.9657 - val_loss: 0.5057 - val_accuracy: 0.8720\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0924 - accuracy: 0.9660 - val_loss: 0.4926 - val_accuracy: 0.8733\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0867 - accuracy: 0.9689 - val_loss: 0.4708 - val_accuracy: 0.8800\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0910 - accuracy: 0.9676 - val_loss: 0.5389 - val_accuracy: 0.8567\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0973 - accuracy: 0.9660 - val_loss: 0.5518 - val_accuracy: 0.8580\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0794 - accuracy: 0.9727 - val_loss: 0.5591 - val_accuracy: 0.8553\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0790 - accuracy: 0.9718 - val_loss: 0.5494 - val_accuracy: 0.8583\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0922 - accuracy: 0.9672 - val_loss: 0.5324 - val_accuracy: 0.8727\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0843 - accuracy: 0.9703 - val_loss: 0.5017 - val_accuracy: 0.8707\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0930 - accuracy: 0.9653 - val_loss: 0.4889 - val_accuracy: 0.8767\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0809 - accuracy: 0.9709 - val_loss: 0.5098 - val_accuracy: 0.8757\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0920 - accuracy: 0.9671 - val_loss: 0.5758 - val_accuracy: 0.8623\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0812 - accuracy: 0.9722 - val_loss: 0.5443 - val_accuracy: 0.8617\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0819 - accuracy: 0.9704 - val_loss: 0.5251 - val_accuracy: 0.8703\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0740 - accuracy: 0.9742 - val_loss: 0.6228 - val_accuracy: 0.8480\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0850 - accuracy: 0.9707 - val_loss: 0.5647 - val_accuracy: 0.8623\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0798 - accuracy: 0.9717 - val_loss: 0.5279 - val_accuracy: 0.8727\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0848 - accuracy: 0.9697 - val_loss: 0.5095 - val_accuracy: 0.8760\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0826 - accuracy: 0.9713 - val_loss: 0.5366 - val_accuracy: 0.8713\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0862 - accuracy: 0.9678 - val_loss: 0.5288 - val_accuracy: 0.8640\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0792 - accuracy: 0.9707 - val_loss: 0.5773 - val_accuracy: 0.8583\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0737 - accuracy: 0.9747 - val_loss: 0.5471 - val_accuracy: 0.8683\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0737 - accuracy: 0.9742 - val_loss: 0.5718 - val_accuracy: 0.8660\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0886 - accuracy: 0.9687 - val_loss: 0.5308 - val_accuracy: 0.8730\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0725 - accuracy: 0.9747 - val_loss: 0.4990 - val_accuracy: 0.8763\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0830 - accuracy: 0.9709 - val_loss: 0.5599 - val_accuracy: 0.8543\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0867 - accuracy: 0.9685 - val_loss: 0.5290 - val_accuracy: 0.8783\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0761 - accuracy: 0.9728 - val_loss: 0.6061 - val_accuracy: 0.8573\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0759 - accuracy: 0.9743 - val_loss: 0.5865 - val_accuracy: 0.8610\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0645 - accuracy: 0.9767 - val_loss: 0.5587 - val_accuracy: 0.8697\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0626 - accuracy: 0.9774 - val_loss: 0.5705 - val_accuracy: 0.8677\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0787 - accuracy: 0.9720 - val_loss: 0.5834 - val_accuracy: 0.8627\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0712 - accuracy: 0.9753 - val_loss: 0.5620 - val_accuracy: 0.8623\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0710 - accuracy: 0.9755 - val_loss: 0.5137 - val_accuracy: 0.8767\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0693 - accuracy: 0.9771 - val_loss: 0.5582 - val_accuracy: 0.8687\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0844 - accuracy: 0.9704 - val_loss: 0.6162 - val_accuracy: 0.8487\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0681 - accuracy: 0.9774 - val_loss: 0.5752 - val_accuracy: 0.8650\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0588 - accuracy: 0.9804 - val_loss: 0.5872 - val_accuracy: 0.8640\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0635 - accuracy: 0.9773 - val_loss: 0.5776 - val_accuracy: 0.8673\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0778 - accuracy: 0.9713 - val_loss: 0.5535 - val_accuracy: 0.8697\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0643 - accuracy: 0.9780 - val_loss: 0.5610 - val_accuracy: 0.8730\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0744 - accuracy: 0.9731 - val_loss: 0.5347 - val_accuracy: 0.8757\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0746 - accuracy: 0.9718 - val_loss: 0.5074 - val_accuracy: 0.8843\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0668 - accuracy: 0.9765 - val_loss: 0.5909 - val_accuracy: 0.8647\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0722 - accuracy: 0.9740 - val_loss: 0.5892 - val_accuracy: 0.8700\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0559 - accuracy: 0.9810 - val_loss: 0.6042 - val_accuracy: 0.8693\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0602 - accuracy: 0.9784 - val_loss: 0.6119 - val_accuracy: 0.8603\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0598 - accuracy: 0.9801 - val_loss: 0.5860 - val_accuracy: 0.8667\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0676 - accuracy: 0.9770 - val_loss: 0.5699 - val_accuracy: 0.8750\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0657 - accuracy: 0.9772 - val_loss: 0.6113 - val_accuracy: 0.8707\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0587 - accuracy: 0.9812 - val_loss: 0.5337 - val_accuracy: 0.8790\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0560 - accuracy: 0.9812 - val_loss: 0.6532 - val_accuracy: 0.8603\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0637 - accuracy: 0.9768 - val_loss: 0.6260 - val_accuracy: 0.8600\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0640 - accuracy: 0.9765 - val_loss: 0.6193 - val_accuracy: 0.8603\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0508 - accuracy: 0.9820 - val_loss: 0.6165 - val_accuracy: 0.8710\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0615 - accuracy: 0.9787 - val_loss: 0.5935 - val_accuracy: 0.8777\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0615 - accuracy: 0.9802 - val_loss: 0.6131 - val_accuracy: 0.8607\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0661 - accuracy: 0.9770 - val_loss: 0.5817 - val_accuracy: 0.8737\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0682 - accuracy: 0.9766 - val_loss: 0.5587 - val_accuracy: 0.8720\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0559 - accuracy: 0.9810 - val_loss: 0.7109 - val_accuracy: 0.8457\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0621 - accuracy: 0.9788 - val_loss: 0.6291 - val_accuracy: 0.8593\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0584 - accuracy: 0.9790 - val_loss: 0.5873 - val_accuracy: 0.8717\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0518 - accuracy: 0.9827 - val_loss: 0.5895 - val_accuracy: 0.8680\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0570 - accuracy: 0.9804 - val_loss: 0.6111 - val_accuracy: 0.8713\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0582 - accuracy: 0.9806 - val_loss: 0.6391 - val_accuracy: 0.8673\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0616 - accuracy: 0.9783 - val_loss: 0.5340 - val_accuracy: 0.8807\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0547 - accuracy: 0.9802 - val_loss: 0.5720 - val_accuracy: 0.8710\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0593 - accuracy: 0.9789 - val_loss: 0.6971 - val_accuracy: 0.8527\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0594 - accuracy: 0.9787 - val_loss: 0.6412 - val_accuracy: 0.8620\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0424 - accuracy: 0.9862 - val_loss: 0.6453 - val_accuracy: 0.8660\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0529 - accuracy: 0.9818 - val_loss: 0.6180 - val_accuracy: 0.8647\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0537 - accuracy: 0.9817 - val_loss: 0.6487 - val_accuracy: 0.8653\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0642 - accuracy: 0.9783 - val_loss: 0.5845 - val_accuracy: 0.8753\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0638 - accuracy: 0.9770 - val_loss: 0.5836 - val_accuracy: 0.8763\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0553 - accuracy: 0.9810 - val_loss: 0.5702 - val_accuracy: 0.8823\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0519 - accuracy: 0.9829 - val_loss: 0.6358 - val_accuracy: 0.8663\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0567 - accuracy: 0.9812 - val_loss: 0.6688 - val_accuracy: 0.8577\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0450 - accuracy: 0.9842 - val_loss: 0.6731 - val_accuracy: 0.8660\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0540 - accuracy: 0.9811 - val_loss: 0.6475 - val_accuracy: 0.8637\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0528 - accuracy: 0.9835 - val_loss: 0.6032 - val_accuracy: 0.8687\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.9831 - val_loss: 0.6034 - val_accuracy: 0.8757\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0465 - accuracy: 0.9841 - val_loss: 0.6022 - val_accuracy: 0.8830\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0521 - accuracy: 0.9821 - val_loss: 0.6018 - val_accuracy: 0.8743\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0541 - accuracy: 0.9806 - val_loss: 0.6919 - val_accuracy: 0.8523\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0455 - accuracy: 0.9849 - val_loss: 0.6648 - val_accuracy: 0.8593\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0402 - accuracy: 0.9877 - val_loss: 0.6650 - val_accuracy: 0.8687\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0571 - accuracy: 0.9799 - val_loss: 0.6686 - val_accuracy: 0.8527\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0498 - accuracy: 0.9825 - val_loss: 0.6547 - val_accuracy: 0.8640\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0423 - accuracy: 0.9862 - val_loss: 0.6509 - val_accuracy: 0.8740\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0621 - accuracy: 0.9768 - val_loss: 0.6077 - val_accuracy: 0.8707\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0502 - accuracy: 0.9815 - val_loss: 0.6044 - val_accuracy: 0.8757\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.9819 - val_loss: 0.6962 - val_accuracy: 0.8527\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0407 - accuracy: 0.9861 - val_loss: 0.6920 - val_accuracy: 0.8583\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0454 - accuracy: 0.9855 - val_loss: 0.6171 - val_accuracy: 0.8770\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0308 - accuracy: 0.9902 - val_loss: 0.7055 - val_accuracy: 0.8627\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 3ms/step - loss: 0.0428 - accuracy: 0.9852 - val_loss: 0.6605 - val_accuracy: 0.8707\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0458 - accuracy: 0.9838 - val_loss: 0.6361 - val_accuracy: 0.8697\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0422 - accuracy: 0.9852 - val_loss: 0.5826 - val_accuracy: 0.8860\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0418 - accuracy: 0.9868 - val_loss: 0.6373 - val_accuracy: 0.8693\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0500 - accuracy: 0.9822 - val_loss: 0.7153 - val_accuracy: 0.8553\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0503 - accuracy: 0.9818 - val_loss: 0.7036 - val_accuracy: 0.8577\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0390 - accuracy: 0.9869 - val_loss: 0.6829 - val_accuracy: 0.8563\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0621 - accuracy: 0.9778 - val_loss: 0.6392 - val_accuracy: 0.8687\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0546 - accuracy: 0.9812 - val_loss: 0.6940 - val_accuracy: 0.8657\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0429 - accuracy: 0.9848 - val_loss: 0.6758 - val_accuracy: 0.8763\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0528 - accuracy: 0.9803 - val_loss: 0.6458 - val_accuracy: 0.8720\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0490 - accuracy: 0.9823 - val_loss: 0.6372 - val_accuracy: 0.8690\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0441 - accuracy: 0.9844 - val_loss: 0.7400 - val_accuracy: 0.8523\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0524 - accuracy: 0.9817 - val_loss: 0.6667 - val_accuracy: 0.8620\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0281 - accuracy: 0.9915 - val_loss: 0.7280 - val_accuracy: 0.8667\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0489 - accuracy: 0.9824 - val_loss: 0.6851 - val_accuracy: 0.8703\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0492 - accuracy: 0.9830 - val_loss: 0.7310 - val_accuracy: 0.8620\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0491 - accuracy: 0.9819 - val_loss: 0.6987 - val_accuracy: 0.8683\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0508 - accuracy: 0.9829 - val_loss: 0.6373 - val_accuracy: 0.8777\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0401 - accuracy: 0.9857 - val_loss: 0.6408 - val_accuracy: 0.8770\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0415 - accuracy: 0.9859 - val_loss: 0.7785 - val_accuracy: 0.8520\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0360 - accuracy: 0.9876 - val_loss: 0.7660 - val_accuracy: 0.8557\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0280 - accuracy: 0.9917 - val_loss: 0.6868 - val_accuracy: 0.8673\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0285 - accuracy: 0.9917 - val_loss: 0.6627 - val_accuracy: 0.8750\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0350 - accuracy: 0.9885 - val_loss: 0.6971 - val_accuracy: 0.8727\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0338 - accuracy: 0.9883 - val_loss: 0.6676 - val_accuracy: 0.8717\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.9818 - val_loss: 0.6424 - val_accuracy: 0.8813\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0448 - accuracy: 0.9843 - val_loss: 0.6409 - val_accuracy: 0.8767\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0477 - accuracy: 0.9843 - val_loss: 0.7804 - val_accuracy: 0.8503\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0407 - accuracy: 0.9868 - val_loss: 0.7399 - val_accuracy: 0.8583\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0385 - accuracy: 0.9852 - val_loss: 0.6705 - val_accuracy: 0.8700\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0349 - accuracy: 0.9893 - val_loss: 0.7608 - val_accuracy: 0.8593\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0480 - accuracy: 0.9843 - val_loss: 0.7256 - val_accuracy: 0.8723\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0389 - accuracy: 0.9861 - val_loss: 0.6996 - val_accuracy: 0.8730\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0381 - accuracy: 0.9875 - val_loss: 0.6766 - val_accuracy: 0.8687\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0457 - accuracy: 0.9840 - val_loss: 0.7134 - val_accuracy: 0.8650\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0270 - accuracy: 0.9915 - val_loss: 0.7717 - val_accuracy: 0.8520\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0427 - accuracy: 0.9860 - val_loss: 0.7677 - val_accuracy: 0.8557\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0336 - accuracy: 0.9889 - val_loss: 0.7518 - val_accuracy: 0.8620\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0373 - accuracy: 0.9879 - val_loss: 0.8008 - val_accuracy: 0.8597\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0500 - accuracy: 0.9832 - val_loss: 0.6701 - val_accuracy: 0.8770\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0381 - accuracy: 0.9879 - val_loss: 0.7365 - val_accuracy: 0.8700\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0383 - accuracy: 0.9877 - val_loss: 0.6773 - val_accuracy: 0.8747\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0405 - accuracy: 0.9847 - val_loss: 0.6660 - val_accuracy: 0.8830\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0416 - accuracy: 0.9858 - val_loss: 0.8012 - val_accuracy: 0.8583\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0396 - accuracy: 0.9862 - val_loss: 0.7527 - val_accuracy: 0.8570\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0290 - accuracy: 0.9904 - val_loss: 0.7192 - val_accuracy: 0.8663\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0313 - accuracy: 0.9887 - val_loss: 0.7392 - val_accuracy: 0.8647\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0352 - accuracy: 0.9883 - val_loss: 0.7063 - val_accuracy: 0.8787\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0314 - accuracy: 0.9902 - val_loss: 0.7212 - val_accuracy: 0.8733\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0461 - accuracy: 0.9837 - val_loss: 0.6713 - val_accuracy: 0.8710\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0381 - accuracy: 0.9872 - val_loss: 0.6924 - val_accuracy: 0.8823\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0547 - accuracy: 0.9815 - val_loss: 0.7961 - val_accuracy: 0.8620\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0348 - accuracy: 0.9895 - val_loss: 0.7128 - val_accuracy: 0.8630\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0318 - accuracy: 0.9889 - val_loss: 0.7939 - val_accuracy: 0.8567\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0528 - accuracy: 0.9814 - val_loss: 0.7444 - val_accuracy: 0.8650\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0274 - accuracy: 0.9912 - val_loss: 0.7291 - val_accuracy: 0.8770\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0360 - accuracy: 0.9887 - val_loss: 0.7236 - val_accuracy: 0.8777\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0483 - accuracy: 0.9828 - val_loss: 0.6572 - val_accuracy: 0.8750\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0321 - accuracy: 0.9895 - val_loss: 0.6733 - val_accuracy: 0.8740\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0354 - accuracy: 0.9886 - val_loss: 0.7684 - val_accuracy: 0.8610\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0255 - accuracy: 0.9919 - val_loss: 0.8330 - val_accuracy: 0.8463\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0272 - accuracy: 0.9909 - val_loss: 0.7348 - val_accuracy: 0.8697\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0220 - accuracy: 0.9930 - val_loss: 0.7367 - val_accuracy: 0.8700\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0382 - accuracy: 0.9863 - val_loss: 0.7592 - val_accuracy: 0.8717\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0315 - accuracy: 0.9894 - val_loss: 0.7403 - val_accuracy: 0.8723\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0298 - accuracy: 0.9898 - val_loss: 0.6965 - val_accuracy: 0.8827\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0522 - accuracy: 0.9812 - val_loss: 0.6882 - val_accuracy: 0.8800\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0321 - accuracy: 0.9885 - val_loss: 0.8611 - val_accuracy: 0.8573\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0411 - accuracy: 0.9854 - val_loss: 0.8524 - val_accuracy: 0.8573\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0224 - accuracy: 0.9918 - val_loss: 0.7748 - val_accuracy: 0.8653\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0379 - accuracy: 0.9872 - val_loss: 0.7542 - val_accuracy: 0.8707\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0359 - accuracy: 0.9868 - val_loss: 0.7560 - val_accuracy: 0.8747\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0358 - accuracy: 0.9876 - val_loss: 0.7147 - val_accuracy: 0.8747\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0305 - accuracy: 0.9895 - val_loss: 0.6757 - val_accuracy: 0.8793\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0266 - accuracy: 0.9904 - val_loss: 0.8245 - val_accuracy: 0.8653\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0264 - accuracy: 0.9918 - val_loss: 0.8207 - val_accuracy: 0.8593\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0259 - accuracy: 0.9908 - val_loss: 0.8194 - val_accuracy: 0.8597\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0195 - accuracy: 0.9942 - val_loss: 0.8142 - val_accuracy: 0.8683\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0311 - accuracy: 0.9889 - val_loss: 0.7806 - val_accuracy: 0.8647\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0251 - accuracy: 0.9918 - val_loss: 0.8282 - val_accuracy: 0.8613\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0342 - accuracy: 0.9881 - val_loss: 0.8137 - val_accuracy: 0.8697\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0344 - accuracy: 0.9872 - val_loss: 0.7088 - val_accuracy: 0.8797\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0381 - accuracy: 0.9874 - val_loss: 0.7350 - val_accuracy: 0.8710\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0204 - accuracy: 0.9934 - val_loss: 0.8184 - val_accuracy: 0.8577\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0449 - accuracy: 0.9843 - val_loss: 0.8328 - val_accuracy: 0.8593\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0255 - accuracy: 0.9917 - val_loss: 0.8765 - val_accuracy: 0.8530\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0453 - accuracy: 0.9840 - val_loss: 0.8271 - val_accuracy: 0.8610\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0271 - accuracy: 0.9902 - val_loss: 0.7691 - val_accuracy: 0.8673\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0381 - accuracy: 0.9858 - val_loss: 0.8105 - val_accuracy: 0.8687\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0274 - accuracy: 0.9912 - val_loss: 0.7083 - val_accuracy: 0.8730\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0396 - accuracy: 0.9865 - val_loss: 0.7105 - val_accuracy: 0.8773\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0346 - accuracy: 0.9887 - val_loss: 0.8371 - val_accuracy: 0.8627\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0291 - accuracy: 0.9904 - val_loss: 0.8237 - val_accuracy: 0.8600\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0209 - accuracy: 0.9936 - val_loss: 0.7951 - val_accuracy: 0.8727\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0252 - accuracy: 0.9920 - val_loss: 0.8783 - val_accuracy: 0.8607\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0229 - accuracy: 0.9931 - val_loss: 0.7594 - val_accuracy: 0.8803\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0219 - accuracy: 0.9929 - val_loss: 0.7752 - val_accuracy: 0.8733\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0317 - accuracy: 0.9899 - val_loss: 0.7588 - val_accuracy: 0.8747\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0316 - accuracy: 0.9893 - val_loss: 0.7353 - val_accuracy: 0.8760\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0152 - accuracy: 0.9952 - val_loss: 0.8318 - val_accuracy: 0.8633\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0287 - accuracy: 0.9906 - val_loss: 0.8633 - val_accuracy: 0.8620\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0365 - accuracy: 0.9868 - val_loss: 0.9360 - val_accuracy: 0.8433\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0230 - accuracy: 0.9926 - val_loss: 0.7923 - val_accuracy: 0.8733\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0417 - accuracy: 0.9869 - val_loss: 0.8295 - val_accuracy: 0.8690\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0233 - accuracy: 0.9925 - val_loss: 0.7889 - val_accuracy: 0.8727\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0248 - accuracy: 0.9921 - val_loss: 0.7647 - val_accuracy: 0.8760\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0338 - accuracy: 0.9873 - val_loss: 0.7870 - val_accuracy: 0.8683\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0365 - accuracy: 0.9879 - val_loss: 0.9139 - val_accuracy: 0.8533\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0335 - accuracy: 0.9887 - val_loss: 0.8702 - val_accuracy: 0.8577\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0115 - accuracy: 0.9973 - val_loss: 0.8554 - val_accuracy: 0.8573\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0517 - accuracy: 0.9827 - val_loss: 0.8716 - val_accuracy: 0.8557\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.0326 - accuracy: 0.9887 - val_loss: 0.8442 - val_accuracy: 0.8717\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 2s 4ms/step - loss: 0.0164 - accuracy: 0.9948 - val_loss: 0.8052 - val_accuracy: 0.8720\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 2s 4ms/step - loss: 0.0289 - accuracy: 0.9901 - val_loss: 0.7454 - val_accuracy: 0.8687\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 4ms/step - loss: 0.0493 - accuracy: 0.9826 - val_loss: 0.7489 - val_accuracy: 0.8730\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 3ms/step - loss: 0.0248 - accuracy: 0.9919 - val_loss: 0.8582 - val_accuracy: 0.8537\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0261 - accuracy: 0.9918 - val_loss: 0.8930 - val_accuracy: 0.8587\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0252 - accuracy: 0.9912 - val_loss: 0.8551 - val_accuracy: 0.8687\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0142 - accuracy: 0.9956 - val_loss: 0.7983 - val_accuracy: 0.8727\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0405 - accuracy: 0.9854 - val_loss: 0.9004 - val_accuracy: 0.8570\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0325 - accuracy: 0.9890 - val_loss: 0.8440 - val_accuracy: 0.8710\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0302 - accuracy: 0.9894 - val_loss: 0.7419 - val_accuracy: 0.8797\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0150 - accuracy: 0.9958 - val_loss: 0.7646 - val_accuracy: 0.8770\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0336 - accuracy: 0.9897 - val_loss: 0.8753 - val_accuracy: 0.8580\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0365 - accuracy: 0.9870 - val_loss: 0.8896 - val_accuracy: 0.8567\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0232 - accuracy: 0.9923 - val_loss: 0.8800 - val_accuracy: 0.8640\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0251 - accuracy: 0.9915 - val_loss: 0.8906 - val_accuracy: 0.8607\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0165 - accuracy: 0.9945 - val_loss: 0.8142 - val_accuracy: 0.8733\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0244 - accuracy: 0.9915 - val_loss: 0.9529 - val_accuracy: 0.8627\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0363 - accuracy: 0.9887 - val_loss: 0.7211 - val_accuracy: 0.8787\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0400 - accuracy: 0.9848 - val_loss: 0.7375 - val_accuracy: 0.8730\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0199 - accuracy: 0.9937 - val_loss: 0.8634 - val_accuracy: 0.8620\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0232 - accuracy: 0.9923 - val_loss: 0.9029 - val_accuracy: 0.8583\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0175 - accuracy: 0.9949 - val_loss: 0.8842 - val_accuracy: 0.8687\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0482 - accuracy: 0.9840 - val_loss: 0.8706 - val_accuracy: 0.8593\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0375 - accuracy: 0.9861 - val_loss: 0.8480 - val_accuracy: 0.8713\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0196 - accuracy: 0.9935 - val_loss: 0.8745 - val_accuracy: 0.8723\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0288 - accuracy: 0.9892 - val_loss: 0.8023 - val_accuracy: 0.8807\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0261 - accuracy: 0.9909 - val_loss: 0.7634 - val_accuracy: 0.8747\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0276 - accuracy: 0.9896 - val_loss: 0.8922 - val_accuracy: 0.8590\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0222 - accuracy: 0.9929 - val_loss: 0.9060 - val_accuracy: 0.8547\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0181 - accuracy: 0.9943 - val_loss: 0.8212 - val_accuracy: 0.8687\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0074 - accuracy: 0.9984 - val_loss: 0.8288 - val_accuracy: 0.8730\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0149 - accuracy: 0.9951 - val_loss: 0.8649 - val_accuracy: 0.8683\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0193 - accuracy: 0.9943 - val_loss: 0.8474 - val_accuracy: 0.8657\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0393 - accuracy: 0.9868 - val_loss: 0.7822 - val_accuracy: 0.8757\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0272 - accuracy: 0.9923 - val_loss: 0.7831 - val_accuracy: 0.8810\n",
            "Epoch 1/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0248 - accuracy: 0.9913 - val_loss: 0.9592 - val_accuracy: 0.8503\n",
            "Epoch 2/2\n",
            "375/375 [==============================] - 1s 2ms/step - loss: 0.0318 - accuracy: 0.9887 - val_loss: 0.9578 - val_accuracy: 0.8483\n",
            "313/313 - 0s - loss: 240.1384 - accuracy: 0.8458\n",
            "313/313 - 0s - loss: 232.0466 - accuracy: 0.8426\n",
            "313/313 - 0s - loss: 244.6367 - accuracy: 0.8439\n",
            "313/313 - 0s - loss: 242.1173 - accuracy: 0.8469\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[242.11729431152344, 0.8468999862670898]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-wVvO7nFWJw"
      },
      "source": [
        "##Approach2: Federated learning using Pytorch and Pysyft"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KzaVJpEEWrrv"
      },
      "source": [
        "###Importing libraries and creating workers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHs4qWI2_WRs"
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "import logging\n",
        "\n",
        "# import Pysyft to help us to simulate federated leraning\n",
        "!pip install syft\n",
        "import syft as sy\n",
        "\n",
        "# hook PyTorch to PySyft i.e. add extra functionalities to support Federated Learning\n",
        "# and other private AI tools\n",
        "hook = sy.TorchHook(torch) \n",
        "\n",
        "# we create two imaginary schools\n",
        "westside_school = sy.VirtualWorker(hook, id=\"westside\")\n",
        "grapevine_high = sy.VirtualWorker(hook, id=\"grapevine\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "klf_t-q5W6J_"
      },
      "source": [
        "###Defining the hyper_parameters such as batch_size,learning_rate etc"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "geaLbeViAHSf"
      },
      "source": [
        "\n",
        "# define the args\n",
        "args = {\n",
        "    'use_cuda' : True,\n",
        "    'batch_size' : 64,\n",
        "    'test_batch_size' : 1000,\n",
        "    'lr' : 0.01,\n",
        "    'log_interval' : 100,\n",
        "    'epochs' : 10\n",
        "}\n",
        "\n",
        "# check to use GPU or not\n",
        "use_cuda = args['use_cuda'] and torch.cuda.is_available()\n",
        "device = torch.device(\"cuda\" if use_cuda else \"cpu\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5KpGEl6LaO6f"
      },
      "source": [
        "###Simple CNN Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TkdZQyLPA67K"
      },
      "source": [
        "class Net(nn.Module):\n",
        "    \n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        \n",
        "        self.conv = nn.Sequential(\n",
        "            nn.Conv2d(in_channels = 1, out_channels = 32, kernel_size = 3, stride = 1),\n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(in_channels=32,out_channels = 64, kernel_size = 3, stride = 1),\n",
        "            nn.ReLU()\n",
        "        )\n",
        "        \n",
        "        self.fc = nn.Sequential(\n",
        "            nn.Linear(in_features=64*12*12, out_features=128),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(in_features=128, out_features=10),\n",
        "        )\n",
        "    \n",
        "    def forward(self, x):\n",
        "        x = self.conv(x)\n",
        "        x = F.max_pool2d(x,2)\n",
        "        x = x.view(-1, 64*12*12)\n",
        "        x = self.fc(x)\n",
        "        x = F.log_softmax(x, dim=1)\n",
        "        return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6QhGR9FxXYW4"
      },
      "source": [
        "###SENDING DATA TO REMOTE WORKERS\n",
        "\n",
        "Load the data first and then transform the data into a federated dataset using .federate() method. \n",
        "It does a couple of things:\n",
        "\n",
        "*   It splits the dataset in two parts (which was also done by the torch Data Loader as well)\n",
        "*   But the extra thing it does is it also sends this data across two remote workers, in this case the two schools.\n",
        "\n",
        "\n",
        "\n",
        "then used this newly created federated dataset to iterate over remote batches during the training loop."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5ZiQOWgBGP4"
      },
      "source": [
        "federated_train_loader = sy.FederatedDataLoader(\n",
        "    datasets.FashionMNIST('../data', train=True, download=True,\n",
        "                   transform=transforms.Compose([\n",
        "                       transforms.ToTensor(),\n",
        "                       transforms.Normalize((0.1307,), (0.3081,))\n",
        "                   ]))\n",
        "    .federate((grapevine_high, westside_school)),\n",
        "    batch_size=args['batch_size'], shuffle=True)\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "        datasets.FashionMNIST('../data', train=False, transform=transforms.Compose([\n",
        "                           transforms.ToTensor(),\n",
        "                           transforms.Normalize((0.1307,), (0.3081,))\n",
        "                       ])),\n",
        "        batch_size=args['test_batch_size'], shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KPwwmu4BLgw",
        "outputId": "f3910fad-2e33-4e3c-cd08-484886c77c4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "for images,labels in federated_train_loader:\n",
        "    print(images) # batch of images pointers\n",
        "    print(labels) # batch of image labels pointers\n",
        "    \n",
        "    print(len(images)) # len function works on pointers as well\n",
        "    print(len(labels))\n",
        "    break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(Wrapper)>[PointerTensor | me:91718234201 -> grapevine:21636052621]\n",
            "(Wrapper)>[PointerTensor | me:73961953267 -> grapevine:42421860582]\n",
            "64\n",
            "64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uDL1XxM8YXuf"
      },
      "source": [
        "###Training and validating the model\n",
        "\n",
        "Now each time train the model, that is needed to be sent to the right location for each batch by using .send() function\n",
        "\n",
        "Then, all the operations are performed remotely with the same syntax like done in local PyTorch. When we're done, we get back the updated model using the .get() method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jsy-E4_cBazn"
      },
      "source": [
        "def train(args, model, device, train_loader, optimizer, epoch):\n",
        "    model.train()\n",
        "\n",
        "    # iterate over federated data\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "\n",
        "        # send the model to the remote location \n",
        "        model = model.send(data.location)\n",
        "\n",
        "        # the same torch code that we are use to\n",
        "        data, target = data.to(device), target.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        output = model(data)\n",
        "\n",
        "        # this loss is a ptr to the tensor loss \n",
        "        # at the remote location\n",
        "        loss = F.nll_loss(output, target)\n",
        "\n",
        "        # call backward() on the loss ptr,\n",
        "        # that will send the command to call\n",
        "        # backward on the actual loss tensor\n",
        "        # present on the remote machine\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer.step()\n",
        "\n",
        "        # get back the updated model\n",
        "        model.get()\n",
        "\n",
        "        if batch_idx % args['log_interval'] == 0:\n",
        "\n",
        "            # a thing to note is the variable loss was\n",
        "            # also created at remote worker, so we need to\n",
        "            # explicitly get it back\n",
        "            loss = loss.get()\n",
        "\n",
        "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "                    epoch, \n",
        "                    batch_idx * args['batch_size'], # no of images done\n",
        "                    len(train_loader) * args['batch_size'], # total images left\n",
        "                    100. * batch_idx / len(train_loader), \n",
        "                    loss.item()\n",
        "                )\n",
        "            )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cl7uTqgABgeY"
      },
      "source": [
        "def test(model, device, test_loader):\n",
        "    model.eval()\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "    with torch.no_grad():\n",
        "        for data, target in test_loader:\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            output = model(data)\n",
        "\n",
        "            # add losses together\n",
        "            test_loss += F.nll_loss(output, target, reduction='sum').item() \n",
        "\n",
        "            # get the index of the max probability class\n",
        "            pred = output.argmax(dim=1, keepdim=True)  \n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "    test_loss /= len(test_loader.dataset)\n",
        "\n",
        "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
        "        test_loss, correct, len(test_loader.dataset),\n",
        "        100. * correct / len(test_loader.dataset)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTkIlX4MBkzX",
        "outputId": "3e204fde-a71b-4ba1-cee8-a180f8efba10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "model = Net().to(device)\n",
        "optimizer = optim.SGD(model.parameters(), lr=args['lr'])\n",
        "\n",
        "logging.info(\"Starting training !!\")\n",
        "\n",
        "for epoch in range(1, args['epochs'] + 1):\n",
        "        train(args, model, device, federated_train_loader, optimizer, epoch)\n",
        "        test(model, device, test_loader)\n",
        "    \n",
        "# thats all we need to do XD"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Epoch: 1 [0/60032 (0%)]\tLoss: 2.296969\n",
            "Train Epoch: 1 [6400/60032 (11%)]\tLoss: 0.665887\n",
            "Train Epoch: 1 [12800/60032 (21%)]\tLoss: 0.776979\n",
            "Train Epoch: 1 [19200/60032 (32%)]\tLoss: 0.545363\n",
            "Train Epoch: 1 [25600/60032 (43%)]\tLoss: 0.636503\n",
            "Train Epoch: 1 [32000/60032 (53%)]\tLoss: 0.677906\n",
            "Train Epoch: 1 [38400/60032 (64%)]\tLoss: 0.638186\n",
            "Train Epoch: 1 [44800/60032 (75%)]\tLoss: 0.666836\n",
            "Train Epoch: 1 [51200/60032 (85%)]\tLoss: 0.465158\n",
            "Train Epoch: 1 [57600/60032 (96%)]\tLoss: 0.567458\n",
            "\n",
            "Test set: Average loss: 0.5214, Accuracy: 8108/10000 (81%)\n",
            "\n",
            "Train Epoch: 2 [0/60032 (0%)]\tLoss: 0.571922\n",
            "Train Epoch: 2 [6400/60032 (11%)]\tLoss: 0.516779\n",
            "Train Epoch: 2 [12800/60032 (21%)]\tLoss: 0.592080\n",
            "Train Epoch: 2 [19200/60032 (32%)]\tLoss: 0.461282\n",
            "Train Epoch: 2 [25600/60032 (43%)]\tLoss: 0.464961\n",
            "Train Epoch: 2 [32000/60032 (53%)]\tLoss: 0.379698\n",
            "Train Epoch: 2 [38400/60032 (64%)]\tLoss: 0.496116\n",
            "Train Epoch: 2 [44800/60032 (75%)]\tLoss: 0.329194\n",
            "Train Epoch: 2 [51200/60032 (85%)]\tLoss: 0.374751\n",
            "Train Epoch: 2 [57600/60032 (96%)]\tLoss: 0.477268\n",
            "\n",
            "Test set: Average loss: 0.4358, Accuracy: 8447/10000 (84%)\n",
            "\n",
            "Train Epoch: 3 [0/60032 (0%)]\tLoss: 0.580441\n",
            "Train Epoch: 3 [6400/60032 (11%)]\tLoss: 0.396779\n",
            "Train Epoch: 3 [12800/60032 (21%)]\tLoss: 0.339162\n",
            "Train Epoch: 3 [19200/60032 (32%)]\tLoss: 0.443020\n",
            "Train Epoch: 3 [25600/60032 (43%)]\tLoss: 0.354685\n",
            "Train Epoch: 3 [32000/60032 (53%)]\tLoss: 0.342905\n",
            "Train Epoch: 3 [38400/60032 (64%)]\tLoss: 0.293804\n",
            "Train Epoch: 3 [44800/60032 (75%)]\tLoss: 0.421124\n",
            "Train Epoch: 3 [51200/60032 (85%)]\tLoss: 0.294556\n",
            "Train Epoch: 3 [57600/60032 (96%)]\tLoss: 0.341806\n",
            "\n",
            "Test set: Average loss: 0.3761, Accuracy: 8650/10000 (86%)\n",
            "\n",
            "Train Epoch: 4 [0/60032 (0%)]\tLoss: 0.317725\n",
            "Train Epoch: 4 [6400/60032 (11%)]\tLoss: 0.308133\n",
            "Train Epoch: 4 [12800/60032 (21%)]\tLoss: 0.484548\n",
            "Train Epoch: 4 [19200/60032 (32%)]\tLoss: 0.542802\n",
            "Train Epoch: 4 [25600/60032 (43%)]\tLoss: 0.397401\n",
            "Train Epoch: 4 [32000/60032 (53%)]\tLoss: 0.342689\n",
            "Train Epoch: 4 [38400/60032 (64%)]\tLoss: 0.286493\n",
            "Train Epoch: 4 [44800/60032 (75%)]\tLoss: 0.449426\n",
            "Train Epoch: 4 [51200/60032 (85%)]\tLoss: 0.235932\n",
            "Train Epoch: 4 [57600/60032 (96%)]\tLoss: 0.507365\n",
            "\n",
            "Test set: Average loss: 0.3510, Accuracy: 8751/10000 (88%)\n",
            "\n",
            "Train Epoch: 5 [0/60032 (0%)]\tLoss: 0.308155\n",
            "Train Epoch: 5 [6400/60032 (11%)]\tLoss: 0.449045\n",
            "Train Epoch: 5 [12800/60032 (21%)]\tLoss: 0.148288\n",
            "Train Epoch: 5 [19200/60032 (32%)]\tLoss: 0.550347\n",
            "Train Epoch: 5 [25600/60032 (43%)]\tLoss: 0.260887\n",
            "Train Epoch: 5 [32000/60032 (53%)]\tLoss: 0.232994\n",
            "Train Epoch: 5 [38400/60032 (64%)]\tLoss: 0.315804\n",
            "Train Epoch: 5 [44800/60032 (75%)]\tLoss: 0.227293\n",
            "Train Epoch: 5 [51200/60032 (85%)]\tLoss: 0.221031\n",
            "Train Epoch: 5 [57600/60032 (96%)]\tLoss: 0.187468\n",
            "\n",
            "Test set: Average loss: 0.3325, Accuracy: 8800/10000 (88%)\n",
            "\n",
            "Train Epoch: 6 [0/60032 (0%)]\tLoss: 0.419015\n",
            "Train Epoch: 6 [6400/60032 (11%)]\tLoss: 0.398374\n",
            "Train Epoch: 6 [12800/60032 (21%)]\tLoss: 0.398613\n",
            "Train Epoch: 6 [19200/60032 (32%)]\tLoss: 0.261806\n",
            "Train Epoch: 6 [25600/60032 (43%)]\tLoss: 0.181555\n",
            "Train Epoch: 6 [32000/60032 (53%)]\tLoss: 0.270664\n",
            "Train Epoch: 6 [38400/60032 (64%)]\tLoss: 0.508069\n",
            "Train Epoch: 6 [44800/60032 (75%)]\tLoss: 0.217101\n",
            "Train Epoch: 6 [51200/60032 (85%)]\tLoss: 0.262959\n",
            "Train Epoch: 6 [57600/60032 (96%)]\tLoss: 0.404721\n",
            "\n",
            "Test set: Average loss: 0.3690, Accuracy: 8561/10000 (86%)\n",
            "\n",
            "Train Epoch: 7 [0/60032 (0%)]\tLoss: 0.282973\n",
            "Train Epoch: 7 [6400/60032 (11%)]\tLoss: 0.346462\n",
            "Train Epoch: 7 [12800/60032 (21%)]\tLoss: 0.379018\n",
            "Train Epoch: 7 [19200/60032 (32%)]\tLoss: 0.199540\n",
            "Train Epoch: 7 [25600/60032 (43%)]\tLoss: 0.238826\n",
            "Train Epoch: 7 [32000/60032 (53%)]\tLoss: 0.157182\n",
            "Train Epoch: 7 [38400/60032 (64%)]\tLoss: 0.183207\n",
            "Train Epoch: 7 [44800/60032 (75%)]\tLoss: 0.248853\n",
            "Train Epoch: 7 [51200/60032 (85%)]\tLoss: 0.308873\n",
            "Train Epoch: 7 [57600/60032 (96%)]\tLoss: 0.328363\n",
            "\n",
            "Test set: Average loss: 0.3069, Accuracy: 8854/10000 (89%)\n",
            "\n",
            "Train Epoch: 8 [0/60032 (0%)]\tLoss: 0.264864\n",
            "Train Epoch: 8 [6400/60032 (11%)]\tLoss: 0.481614\n",
            "Train Epoch: 8 [12800/60032 (21%)]\tLoss: 0.203496\n",
            "Train Epoch: 8 [19200/60032 (32%)]\tLoss: 0.394938\n",
            "Train Epoch: 8 [25600/60032 (43%)]\tLoss: 0.192925\n",
            "Train Epoch: 8 [32000/60032 (53%)]\tLoss: 0.117413\n",
            "Train Epoch: 8 [38400/60032 (64%)]\tLoss: 0.204722\n",
            "Train Epoch: 8 [44800/60032 (75%)]\tLoss: 0.166204\n",
            "Train Epoch: 8 [51200/60032 (85%)]\tLoss: 0.138194\n",
            "Train Epoch: 8 [57600/60032 (96%)]\tLoss: 0.122171\n",
            "\n",
            "Test set: Average loss: 0.2967, Accuracy: 8916/10000 (89%)\n",
            "\n",
            "Train Epoch: 9 [0/60032 (0%)]\tLoss: 0.218487\n",
            "Train Epoch: 9 [6400/60032 (11%)]\tLoss: 0.319672\n",
            "Train Epoch: 9 [12800/60032 (21%)]\tLoss: 0.231824\n",
            "Train Epoch: 9 [19200/60032 (32%)]\tLoss: 0.311967\n",
            "Train Epoch: 9 [25600/60032 (43%)]\tLoss: 0.218264\n",
            "Train Epoch: 9 [32000/60032 (53%)]\tLoss: 0.141311\n",
            "Train Epoch: 9 [38400/60032 (64%)]\tLoss: 0.290313\n",
            "Train Epoch: 9 [44800/60032 (75%)]\tLoss: 0.270897\n",
            "Train Epoch: 9 [51200/60032 (85%)]\tLoss: 0.341308\n",
            "Train Epoch: 9 [57600/60032 (96%)]\tLoss: 0.296660\n",
            "\n",
            "Test set: Average loss: 0.2839, Accuracy: 8975/10000 (90%)\n",
            "\n",
            "Train Epoch: 10 [0/60032 (0%)]\tLoss: 0.134946\n",
            "Train Epoch: 10 [6400/60032 (11%)]\tLoss: 0.311904\n",
            "Train Epoch: 10 [12800/60032 (21%)]\tLoss: 0.207448\n",
            "Train Epoch: 10 [19200/60032 (32%)]\tLoss: 0.228013\n",
            "Train Epoch: 10 [25600/60032 (43%)]\tLoss: 0.245735\n",
            "Train Epoch: 10 [32000/60032 (53%)]\tLoss: 0.268299\n",
            "Train Epoch: 10 [38400/60032 (64%)]\tLoss: 0.216537\n",
            "Train Epoch: 10 [44800/60032 (75%)]\tLoss: 0.089234\n",
            "Train Epoch: 10 [51200/60032 (85%)]\tLoss: 0.329030\n",
            "Train Epoch: 10 [57600/60032 (96%)]\tLoss: 0.160582\n",
            "\n",
            "Test set: Average loss: 0.3162, Accuracy: 8869/10000 (89%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlzKnfJrBoz7"
      },
      "source": [
        "#Accuracy for this model: 89%"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}