Artificial_neural_network_using_Batch_Normalization.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNKtnXy+0v+PIfCwMx2BkAt",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BALAJIHARIDASAN/Deep-Learning/blob/main/Artificial_neural_network_using_Batch_Normalization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "VsFBHZjH0jtu"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "r5g2qdLU00s5"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "fashion = tf.keras.datasets.fashion_mnist"
      ],
      "metadata": {
        "id": "DXb3Fosc0_nI"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "(X_train,y_train),(X_test,y_test) = fashion.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VRnSi2oX1E73",
        "outputId": "d9ca6c44-3d76-42bf-b659-07433f77bccd"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "29515/29515 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26421880/26421880 [==============================] - 1s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "5148/5148 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4422102/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X_valid,X_train = X_train[:5000]/255,X_train[5000:]/255\n"
      ],
      "metadata": {
        "id": "EzhClZR71GZX"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "y_valid,y_train= y_train[:5000],y_train[5000:]\n",
        "     "
      ],
      "metadata": {
        "id": "bdiO9GsM1L44"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X_test = X_test/255"
      ],
      "metadata": {
        "id": "1T1jVFef1NTo"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "plt.imshow(X_train[5],cmap = 'binary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "aKMFmVco1OxY",
        "outputId": "78567f92-3bef-4ecc-9f80-c63549805ec4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import seaborn as sns\n",
        "plt.figure(figsize = (15,15))\n",
        "sns.heatmap(X_train[5],annot = True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        },
        "id": "ZcZXLOnI1QGI",
        "outputId": "74ce692e-0853-45ff-9b58-295651839897"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ],
            "image/png": 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9gpCw+FI9gcvlImnFXHr1voLk5DQWzJ/MoMFDSUpae1LqKMt8J9eufOUr37n5Tq5d+cpXvnPzg7n23OwUc1KKKGMHvnuubC/AT1D5ix4MyHkMmhGTDu3bsH79JjZu3ExOTg7jxo2nX9+ejsh3cu3KV77ynZvv5NqVr3zlOzffybVLcDvujokx5pOyKCQuPoYtyan528kpacTFxTgi38m1K1/5ynduvpNrV77yle/cfCfXLsHtmKtyGWMmFH4IOMcYUwXAWtvvKMfdBNwEYNyRuFwVTkKpIiIiIiL/A7QqV7FKWi44AVgJvA9YfB2TdsBLxzrIWjsKGAWlv8ckNSWdxIS4gieOjyU1Nb00h5ZKWeY7uXblK1/5zs13cu3KV77ynZvv5NoluJU0lasdsBj4P2CXtXYWcMBaO9taO/tkFrJw0VIaNKhLnTqJhIaGMnBgfyZOmu6IfCfXrnzlK9+5+U6uXfnKV75z851cuwS3Y46YWGu9wCvGmK/y/jejpGP+Lo/Hw53DRzD5hy9wu1x8PHosK1eucUS+k2tXvvKV79x8J9eufOUr37n5Tq5dgttxLRdsjOkDdLbWPlzaY0o7lUtERERE5EQ4Zrngr58K6uvj8peOCMh5PK7RD2vtD8APZVSLiIiIiIj8QwXN95iIiIiIiMg/V5ncLyIiIiIiIkfh1XLBxdGIiYiIiIiIBJw6JiIiIiIiEnCayiUiIiIiciodx6q4/yQaMRERERERkYBTx0RERERERAJOU7lERERERE4lrcpVLI2YiIiIiIhIwGnERERERMQhmkfVLrPsJ6hTZtkAA3fOLdN8cT51TERERERETiVN5SqWpnKJiIiIiEjAqWMiIiIiIiIBp46JiIiIiIgEnO4xERERERE5lazuMSmORkxERERERCTg1DEREREREZGA01QuEREREZFTScsFF0sjJiIiIiIiEnBB1THp2aMbK/6cw6qV87j/vtscle/k2pWvfOU7N9/JtStf+co/sfwzz+nI+HljmDh/HNcNG1xk/+mdWvPl9I9YnDyH8y88J//xxs0b8smkUXw7+zO++u8n9Ox/XrH5Nc9pxXnzXuS8+S/TcFjfo9YR26c9/dO/oMppdf0eLx9fjT7rP6T+rX0A6NG9G8v/mMXKFXO5996hRXLCwsL47NO3WbliLnPnTKB27QQAoqKqMG3aWHZsX8WrrzxZkF8+nO+/+5g/ls3k9yU/8tSTDx7jbIkTGGttmT5BSFh8qZ7A5XKRtGIuvXpfQXJyGgvmT2bQ4KEkJa09KXWUZb6Ta1e+8pXv3Hwn16585Sv/7+U3j6qdf/yEX8Zy88A7yUjbyhdTP+DBWx9lw5pN+W3jEmOoULECQ4Zeyaxp8/hx0kwAatdLxFrL5o3J1IiuzpjpH3LRWVdy3+7qRxRoOP+Xl/ll4LMcSNtB16lPsfjWN9mzJsWvnpAK4XT87D5cYSEsf/hjdi7bmL+v/ft3Yi1kLVnH+ncn03LeM/TucyXJyWn88vMkBl89jFWrCl7vzTddTcuWTRh2+8P861/96N+vF4MGDyUiojytW7egebPGNG/emOF3PQL4OiYdOrRh9uz5hIaGMnXql3Tp3KE3MOXv/Dc5lQ6MfrBsL8BPUPkhz5lAPG/QjJh0aN+G9es3sXHjZnJychg3bjz9+vZ0RL6Ta1e+8pXv3Hwn16585Sv/xPJbtGnGlo3JpGxOJTcnl6nf/0i3nmf5tUndks7apPV4C93P8NeGLWzemAzAtoztZG7Pomq1Kn5tqrZpwL6NGezfvBWb4yHl+/nE9GxbpI4mD/yLdW9NxHsox+/xmF7t2Ld5G3tWJ+fn+b3erybQt28Pv2P69u3Bp599DcC33/7AOed0BmD//gP88stCDh465Nf+wIGDzJ49H4CcnByW/r4cIKGkcyfBK2g6JnHxMWxJTs3fTk5JIy4uxhH5Tq5d+cpXvnPznVy78pWv/BPLrxlbg/TUjPztrWnbiI6tcdx1tGjTlNDQULZs8h8JCY+tyoHUHfnbB9IyCY+N8msT2bIO5eOqkfHjUr/H3RHlaDisL6tf/MYv78jXm5KSRnyh1xsXF0NyXhuPx8Pu3XuoVq1qqV5HZGRl+vQ5H+CnUh0gQem4OibGmC7GmLuNMT1Kbi0iIiIiwap6zWo8/cZIRg5/muOe2m8MLR4fxJ+Pf1ZkV5P7LmH9qMl49h8q5sCTz+128+knb/LWWx8BbDglT3qivN7g/ikFY0wvY8xqY8w6Y0yRG3yMMbWMMTONMb8bY/4wxvQuKfOYHRNjzG9H/H4j8CZQCXi0uAKOaHuTMWaRMWaR17uvpBoASE1JJzEhLn87IT6W1NT0Uh0b6Hwn16585SvfuflOrl35ylf+ieVvTdtGTFx0/nbN2BpkpG0r9fEVKkbw5mcv8sZzo1i+ZEWR/QfTsigfVy1/u3xsFAfTMvO3QyqGU6lxIl2+fYTuC1+j6ukN6Dj6XqqcVpeqbRrQ/JEr6b7wNerf2ItGd/Qnqm1Dv9cbHx9LSqHXm5qaTkJeG7fbTeXKldixI6vE1/L228+zbt1G3njzg1K/fjkxxhg38BZwAdAMuMIY06xQsxHAOGttG+By4O2ScksaMQk94vebgO7W2seBHsBVRzvIWjvKWtvOWtvO5apQUg0ALFy0lAYN6lKnTiKhoaEMHNifiZOml+rYQOc7uXblK1/5zs13cu3KV77yTyx/xdIkatVLIL5WLCGhIfQacD6zp88r1bEhoSG88tFzTPxqSv4N8YXtXLqeCvViiKhVAxPqJn7AGaRPX5y/P3fPAaY2v5kZ7e9kRvs7yVqyjl+HvMjOZRuZN+CJ/MfXvzeVNa+PZ+VTY2jQoE7B6/1XPyZNmuH3nJMmzWDwoEsBuPjiPsya9XOJr+Wxx+4jsnIl7rn3sVK9djlpOgDrrLUbrLXZwJdA/0JtLFA57/dIIJUSlPQFiy5jTFV8HRhjrd0GYK3dZ4zJPZ7qS+LxeLhz+Agm//AFbpeLj0ePZeXKNY7Id3Ltyle+8p2b7+Tala985Z9Yvsfj4dmHX+adMa/gcrv5fswk1q/eyND7b2DF0lXMnj6P5q2b8sqHz1K5SiW6du/C0Puu5+Kug+jZ7zxO79SayKqV6XeZb3bNyDufhhUFN7Bbj5c/Hv6YM8Y8iHG72DxmFntWp9Dk/kvZuXQD6dOXHNfrtR4vw4c/wqSJn+F2u/l49FiSktYwcuQ9LFn8B5N+mMFHH3/JRx++ysoVc8nM3MngqwuWUF69+hcqV6pEWFgoffv2pM+FV7Fnzx4eevAOVq1ay68L8hfiugF4/7iKC4Qg/4JFY8xN+AYlDhtlrR11xHY8sOWI7WSgY6GYx4DpxpjbgQrA+SU+77HmFBpjNgFewODr9XS21qYZYyoC86y1rUt6gtIuFywiIiIix3Z4ueCy8AR1yiwbYODOuWWaD3Do4JaALHN7vA58cG9QXx+Xv/7FY55HY8ylQC9r7Q1524OBjtbaYUe0uRtfX+MlY8wZwAdAC2vtUXtlxxwxsdbWOcouL3DRsY4VEREREZH/SSlA4hHbCXmPHel6oBeAtXa+MSYcqA5sPVro31ou2Fq731q7seSWIiIiIiLyP2Yh0NAYU9cYE4bv5vYJhdpsBs4DMMY0BcKBY67QUNI9JiIiIiIicjIdfTaTI1hrc40xw4BpgBv40Fq7whjzBLDIWjsBuAd4zxhzF75bQq6xJaxLrY6JiIiIiIgcF2vtZGByocdGHvH7SqDz8WQGzTe/i4iIiIjIP5dGTERERERETiHrDepFuQJGIyYiIiIiIhJw6piIiIiIiEjAaSqXiIiIiMipFOTf/B4oGjEREREREZGA04iJiEgQCg8JK7Psg7nZZZYNcEtclzLNf/Hbq8o0/+aLR5dp/uepC8o0XwLr1ehzyjS/T1RGmWUPyNhUZtkAV8V0LNN8cT51TERERERETiWHf8FiWdFULhERERERCTh1TEREREREJOA0lUtERERE5FTSFywWSyMmIiIiIiIScOqYiIiIiIhIwGkql4iIiIjIqaQvWCyWRkxERERERCTg1DEREREREZGAC6qOSc8e3Vjx5xxWrZzH/ffd5qh8J9eufOUrP7jye/boxpKlP7Fs+UzuvueWIvvDwsIY/ckbLFs+k5mzv6NWrXgAzjm3C3N/nsCvv01h7s8T6Nr1DAAqVqzALwt+yP9JT13OSy8+Xia1F9a062k88tMrPDrrNbrf2r/I/nOv78P/zXiJh6a8wO2fj6BqfHUAGp7RnAcnP5//88rqT2nVo12R439euop+d7/AhcOf44Px/y2yP3VbFjc+9S6X3v8S1z/xDhk7dgKwalMKg0e+wUX3vsil97/E1PlLi62/ZdfWPPfT67ww60363HpRkf09r+/LMzNe5akpL3P/549SLb6G3/7wiuV5Zf4oBj9+Q8knqxjB9rep/ONTq1srrpr1bwbNfYnTh/Ytsr/5oHO5YsazXDb1aS7+5hGqNowDoNGAM7ls6tP5P7f99QnVm9Uqcnz5M9sRP/5DEiZ+TOR1lxXZX7FfD2rN/Iq4sf8hbux/qHjRBfn73DE1iPnPc8R/9wHx375PSFy037FnntOR8fPGMHH+OK4bNrhI9umdWvPl9I9YnDyH8y8s+Lb7xs0b8smkUXw7+zO++u8n9Ox/XrHnpkXX1jzz02s8O+sNet86oMj+HtdfyFMzXuHxKS9x7+ePUi3v3waA99eP5bHJ/+axyf/m9vceKDZfnMlYW7bLlYWExZfqCVwuF0kr5tKr9xUkJ6exYP5kBg0eSlLS2pNSR1nmO7l25Stf+cGVf7h9/75Xk5KSzpy547n2mjtYtWpdfpsbbxpEixZNuPOOEVx66YX07deTIVffTqvTmrF163bS07bSrFkjvp8wmkYNzijyHLPnjefeex9j7rxfT2rth90S1wUA4zKMnPkqbw56mp3pO7hvwrN8fPtrpK9LyW/b8IzmbPp9LTkHs+kyqDsNOzXjo2Gv+eVFRFbg0dmvM6LTreQczObFb68CwOP10u+u53n34ZuIrhbJlf/3Os/dfhX1EwousO599VPObtOUfl3b8euf6xg/eyHP3HYFm9K2YYDasTXYmrmLK/7vNb578T4qVyjPzRePzqvfxQsz3+CFQU+Qmb6DxyY8zzu3v0LquuT8/CZntGDD72vIPpjNuYN60qRTc94e9nL+/qsevY5KUZXZt3Mvnz76PgCfpy445vk70fNfWsovm/xXo30X6cZlGDTnRcZf+Rx70zIZOOkJpg17i6y1qfltQyuWJ2fvAQDqdD+dllefz8TBL/jlVWuSQO/37+LTLvcA0Ccq43CBJEz4iPSbHyA3YztxX7zJtgefIWfD5vxjK/brQbnmjdjx7JtF6ox5/0V2vv8FBxcswZQPB2vp/1dm/muf8MtYbh54JxlpW/li6gc8eOujbFizKf/4uMQYKlSswJChVzJr2jx+nDQTgNr1ErHWsnljMjWiqzNm+odcdNaV7Nm9l7bhcXnnxsWzM1/npUFPkJmeycgJz/Hu7a8Wem81Z8Pva8k+mE23QT1o0qk5/xn2CgBvr/iUoc2LdpYAPtz0tTnmf6Agsf+1W4J6veCIO/8TkPMYNCMmHdq3Yf36TWzcuJmcnBzGjRtPv749HZHv5NqVr3zlB1f+4fabNm0hJyeHr7+eSJ8Lu/u16dOnO59/9g0A3303hW7dzgTgj2UrSU/bCsDKlWsIDw8nLCzM79gGDepSs0b1Ejslf6f2wuq0bsD2vzLYsWUrnhwPSyb+Qqse7f3arJ2/gpyD2QBs+n0tVWKqFclp07sTK2ctzW932J/rNpMYU52E6GqEhoTQ64zWzFq0wq/N+uQMOrRo4Hs9zesza7Fvf53YGtSO9Y1u1IyKJKpyRbJ27/U7tl7rBmT8lc62LRl4cnL5deI8Ti9U/6r5f5KdV9e639cQdUT9dVrUo3L1SP6cu6x0J6yQYPvbVP7x5Ue3rs+uTRns3rwNb46HtRMWUK9HW782hzslAKER5aCYD4sb9j+TtROKdmbLtWhMzpZUclPSITeXfVNnEZH3b0FJQuvVwoS4ObhgCQD2wEHswUP5+1u0acaWjcmkbE4lNyeXqcMD1dcAACAASURBVN//SLeeZ/llpG5JZ23SeryFbuL+a8MWNm/0dTC2ZWwnc3sWVatV8WtTr3UDtv6VzrYtW/PeWz/Tush7a0X+e2vD72upWsy/DfK/J2g6JnHxMWxJLvgUITkljbi4GEfkO7l25Stf+cGVX7h9Skp6kfZxcdEkp6QB4PF42LV7D9WqVfVrM2DABSxb+ifZ2f4X85f+60K++mpCmdReWGR0FFmpO/K3s9J2EBld9ajtzxh4DitnFZ1SdXrfM1k84ecij2/N2k3MERc8NatFkpG1y69N49qx/PTbcgB+Wvgn+w4cYueefX5tlq/bTE6uh8Ro/wufqtFRZKZuz9/OTMukavTRL466DjyPP2b5LvSMMVw+YghfPj36qO1LEmx/m8o/vvwKMVXZk5qZv703LZMKMUX//lsOOZ/B817izIcvZ87IT4rsb9i3I2vGzy/yuLtmdTzp2/K3PVu3ExJdvUi7iPO6EP/Vu9R88RHc0b7OeGjtBLx79lLz5UeJG/sOVe+6EVwFl4Q1Y2uQnpqRv701bRvRsTWKZJekRZumhIaGsmVTit/jVQq9t7LSdlA1OuqoOWcNPJfls37P3w4tF8bICc/zf989Q5tCHRpxtmN2TIwxHY0xlfN+L2+MedwYM9EY87wxJvLUlCgiIsejadOGPPHUA9xx+/8V2XfppX35cuz3Aajq2NoP6EKtVvX5aZR/p6lyjSrENa7Fyjl/b9Th7qsuZFHSBgY++AqLkzZQMyoS1xEXYNuydvN/b3/JE7cM9Hv8eJ054GzqtKrP5FHjAThvcC/+mLmErPTMEo6Uf7rlo3/k0y73MP/ZL2l/h/+9FtGt65N7IJvM1clHOfrY9s+ez5YLBpPyr5s5sGAJNZ66z7fD7Sa8TUsyX3qX1CtvIzQhlor9e5zoS/FTvWY1nn5jJCOHP82J3DbQacBZ1GlVn6l57y2A+zrfyhP9HmDUHa9yxchrqVEr+hgJQcra4P4JkJK+x+RD4LS8318D9gPPA+cBHwEXF3eQMeYm4CYA447E5apQYiGpKekkJsTlbyfEx5Kaml7icaVVlvlOrl35yld+cOUXbh8fH1OkfWpqhi8nJR23201k5Urs2JEF+D7l/eLLd7nphnvYuHGz33EtWjbFHRLCkt+Xl0nthe3KyKRqXMEIQ9XYauzKyCrSrnHnlvQcdjGvXvYYudm5fvtOv/AM/pj2G95cT5HjalatTHrezewAW3fsIrqq/2dmNaMieeXuIQDsP3iIH39bTuUK5QHYu/8gw174kNsv60WrhrWL5GdlZBIVV/AJdFRsFFkZO4q0a9a5FX2HXcIzlz2SX3/90xvRuH1Tzh3ci/CIcEJCQzi4/yBfPf9Z0RN1FMH2t6n848vfl55FpbiCUYCKsVHsSy/693/YmvEL6Pr0tX6PNezfqdjREvCNkLhjCkYx3DWrk5ux3a+Nd9ee/N/3fDuFqOE3+o7N2M6h1et908CA/TN/oVzLpvltt6ZtI+aIm+FrxtYgI61gdKYkFSpG8OZnL/LGc6NYvmRFkf07C723qsZWIyujaCe+WeeWXDjsEp6/bKTfvw0789pu27KVVQtWUKt5XbZtzihyvDhPSR8Puay1h/8S2llrh1tr51lrHwfqHe0ga+0oa207a2270nRKABYuWkqDBnWpUyeR0NBQBg7sz8RJ00v3KgKc7+Tala985QdX/uH2tWsnEBoayqWX9mXyDz/6tZk8+UeuGnQJABdddAGzZ/suXCIjK/HNNx/y6MjnWbBgcZHsf/2rL1+XchrX36m9sL+WradGnRiqJdTAHerm9L5n8seMRX5tEprX4fJnbuDdG15g747dRTLa9uvMoom/FJvfvH4im9O3k7w1k5zcXKbOX0rXts382mTt3pc/B/6D8f9lQDfftI+c3Fzuenk0fc9qS/eOrYrN37hsHdF1YqmeUBN3aAgd+3bh90L112pel2ufuZlXb3iOPUfU/+7w17i78y3c2+VWvnzmE37+dvZxdUog+P42lX98+RnLNhBZJ4ZKiTVwhbpp2K8TG2cs8WsTWafg4r/Oea3ZtemIjo8xNLiwI2snFN8xObRiNaG14gmJj4GQECr06sb+2f5t3dULOkYR3c4gO+/DikMrVuOqVAFXXkc+vENrsjf8ld92xdIkatVLIL5WLCGhIfQacD6zp88r1esOCQ3hlY+eY+JXU/JviC+s6HurM0tnLPRrU6t5Xa5+5mZeL/TeiqhcgZAw3+fqFatWomHbJqSt/XsjShJ8Shox+dMYc6219iNgmTGmnbV2kTGmEZBzMgvxeDzcOXwEk3/4ArfLxcejx7Jy5RpH5Du5duUrX/nBlX+4/fcTPsHtdvHpJ1+RlLSWEY/cxZIly5n8w4+M/ngs73/wCsuWzyQraxfXXH07ADffMoR69Wvz4EN38OBDdwDQv+/VbNvm+5T/4kv6cMlF1x71uU+09sK8Hi/jRn7IbZ88jHG7WDBuFulrk+lz17/YvHwDy39czICHBlEuIpzr374LgKyU7bx7478BiEqoQdXYaqxbsLLY/BC3m4euGcCtz76H1+tlQLcONEiM4a2vptG8bgLd2jVnUdJ6Xv9yCgBtm9bj4Wt9S/5Om7+MJas2sGvvPibM8V0QPXHLZTSpE+9X/6cj3+e+Tx7B5XYxZ9x/SVm7hYvuupxNy9fx+4+LuPyhqykXEc5tb/tWTMpM2c6rNz5X6nN0LMH2t6n848u3Hi9zHhlN/8/ux7hdrBw7m8w1KXS45xK2/rGRTTOW0OqaHiR0aY4318OhXfv48a5384+P79iEvamZ7N58lJEKj5cdz75JzDvPgsvFnu+nkbP+L6oMHUL2ijXsnz2fylcOIKLbGdhcD97de9j+iO+9hddL5sujiB31AhjDoZVr2fPNZL/X/uzDL/POmFdwud18P2YS61dvZOj9N7Bi6SpmT59H89ZNeeXDZ6lcpRJdu3dh6H3Xc3HXQfTsdx6nd2pNZNXK9LusNwAj73ya1SsKVjPzerx8NvJ97v5kBC63i3nj/kvq2mQG3HUZm5avZ+mPixj40GDKRYQzNO+9tSNlO2/c+DyxDRIY8sxNWGsxxjD5ne/8VvNyDH3ze7GOuVxw3n0krwFnAduB04EteT93WGtLnPRb2uWCRUSkQHhIWMmN/qaDudklNzoBh5cLLiuHlwsuK4eXCy4rpV0uWJzp8HLBZSV/ueAyMCBjX8mNTsDh5YLLkmOWC375xqC+Po64+72AnMdjjphYa3cB1+TdAF83r32ytVYT+URERERE5KQpaSoXANba3cDfWxJFREREREQKeIN6wCRgguZ7TERERERE5J9LHRMREREREQm4Uk3lEhERERGRk8RqVa7iaMREREREREQCTh0TEREREREJOHVMREREREQk4HSPiYiIiIjIqaTlgoulERMREREREQk4jZiIiAQht8u5nxs19IaW7RNkHyjT+P+8fkaZ5r/1c9l+UuqqXqVM8wkPL9v8sv7bd5kyjc9dtKpM870HPWWWPXx7vTLLBnhszx9lmi/Op46JiIiIiMgpZL1aLrg4zv1ITkRERERE/meoYyIiIiIiIgGnqVwiIiIiIqeSVuUqlkZMREREREQk4NQxERERERGRgNNULhERERGRU8lqVa7iaMREREREREQCTh0TEREREREJOE3lEhERERE5lbQqV7GCqmPSs0c3Xn75CdwuFx9+NIYX/v2WY/KdXLvyla/84Mrv2aMbr778JG63m09Gj+WVl9/12x8WFsa7771I69YtyMzM4tohd7B5cwqnt23Fa288DYAxhueeeZ1JE6cD8MeK2ezduw+Px0N2Tg6dzuhdJrUXVrtrK7o+NhjjdrHiy1ksenui3/6Wg86l1dXdsR4vOfsP8tODH5C5NhWA6k0SOffZ6wirVB7rtXzZdySeQzl+x//8x1qe/3wyXq/loq6nc/2FZ/vtT92+k0c/+I6s3fuJrFieZ26+hOioSABuffETlq9PpnXDWrx596Bi6/951RZemDDfl9+hMded29pvf1rWXh4ZO4s9B7Lxei139G7PWU1rkZK5h4v//RW1a/ieq1Xtmoy45Kwi+e5GbSjX7zowLnIW/kjOrO/89oddeC3u+i0AMKHlMBUj2ffYYADCr3sEd61GeDYlcfDjZ4qt31WnBWHnXQnGkPvHXHJ/m+y3P/Scy3HXauLbCAnDRFTmwBvDfM9XKYqwXtdgKkWBhUPfvILdvcP//Py1g3/PXYPXWgY0i+O6tnX89r84dw0LU7IAOJjjIfNADnNv6grAqz+vZe5fO7DW0jExivvPaoQxxj9/03b+PWe1L795PNe1q+ufP2c1C5Mzffm5XjL3ZzP3lnN8+fPWMHfTdqyFjrWiuP/sxsXnz16F12sZ0CKB69oXyp+9ioVb8urP9fjyh57ry5+7hrkbt/nya1fj/q5F890t2hF+xVCMcZE9dwrZU8YW/k9ESLuzKdf/arAW75YNHHjvWdyNTyP88lvz27hiEznw7tPk/v6L/7Gt2lN+8DBwucieNZlDE8cUyQ/t2JXwS4aABc/m9ex/y/dvROSnM/Bu2QiAd/tW9r08wu+4+G6t6PjEYIzLxZoxs1j+lv97t/Hgc2k6pDter5fcfQf5+f4P2LU2FVeomzOfv57qrepirZdfR35G+vwkALqe25lHn30At8vFl599yzuvfeiXGRYWystvP03L05qRlbWLYdffR/KWVAZc2pubhl2T365p80b0OecyVv65mn4XX8Btd92AtZaM9G0A1YHtRU6EOELQdExcLhevv/Y0vXpfQXJyGgvmT2bipOkkJa0N+nwn16585Ss/uPIPt7+4/zWkpKQzc853TJ78E6tXrctvc/WQf7Fz5y7anHYul1x6IY8/+QDXDrmDpJVr6HbWADweD9HRNfh5wQ9MmfwTHo8HgAt7X0Xmjiz2ZR8sk9oLMy5Dt6eG8N1Vz7E3LZPLJz7BhhmL8zseAKu/n8/yz/4LQN3up3PWI4MYf/ULGLeLnq/dyrTh/2F70mbCq1TEm5Prl+/xennmk0m8e/8QoqMqc+Vj79KtTRPqx9fMb/Pyl9Po27k1/bq04deVG3jtqx955uZLALjmgs4cyM7h65mLiq3f4/Xy7Hc/85+behMdWYGrXv+ers1rUz+6an6b9376nR6t6jHwzGasz8hi2AdTmdK0FgAJ1Soz7u5LjnWCKDfgRg68/zh21w7KD3uB3JULsVuT85tkT/oo//fQM3vjiiu4cM6Z/T05YeUI7djjKPmGsO6DODTuJeyeTMIHj8Szfil2R8H5z5n5JYe7eiFtzsMVXSt/X1jvG8hZMAnvXyshtBxY/094PV7Lc7NX807/NkRXLMdV4xbStW516kdVzG9z71mN8n8fs2wLq7fvAWBp2k6Wpu1i3OUdAbj2m0UsTtlJu4Sq/vmzVvHORacTXTGcq8b+Ste6Nahf7Yj8sxsfkb+Z1duOzN/JuCvP8OV/vZDFKVm0S4jyz5+ZxDsXt/Xlj1lA13qF8rs2KchfupnVW3f78lN3sjR1J+MGnenLH/cbi5OzaJdYkI9xUf6q29n30gPYrO1UeORNcpfOx5u2Ob+Jq2Y85fpcwb5nh8P+vZhKVXy1rV7Gvsdv8TWqUIlKz35M7orFfucf46L8NXey79n78GZuo9KT75Cz5Be8KX8V5EfHU67flex97A7s/r2YylUKjs/OZs/DN1Ec4zJ0enoI0654jv1pmfSd/ASbpy9m1xHv3Q3fzWf1p773bmL30+nw6CBmDHqBRlf6Oobfn/8Q4dUq0/2z+5jYeyTGwJMvPMxVl9xEemoGE34cw49TZ7F29Yb8zMsGXcyunbvp2v5C+l7UiwcfHc6wG+7n+68n8/3Xvk5146YNee/TV1n552rcbjePPvMA5585gKzMnTz06F10O6/zMOCxYl+YBL2gucekQ/s2rF+/iY0bN5OTk8O4cePp17enI/KdXLvyla/84Mo/3H7Tpi3k5OTw7deT6NPnfL82vfuczxeffwvA999NoWs338XXgQMH8zsh4eHlsPbEpgqc6LmJbl2fXZsy2L15G94cD2smLqBej7Z+bbL3Hsj/PbR8wcVv7bNbsj1pC9uTfBdxB3fuxRaa+vDnhmQSo6NIqBlFaEgIvTq2ZNaSVX5t1qdspUPTer7X07Su3/6OzetTIbzcUev/c/M2EqtXJqFaZUJD3PRsXZ9ZK/7ya2OAfYeyAdh7IJsalSNKc2oAcCU2wLsjDZuZAZ5ccpfNI6RZh6O2D2ndhdxl8/K3PeuXw6EDR23viq2HzdqK3bUNvB5yV/2Ku0Hro7Z3N+1IbtKvvtdVLQ5cbl+nBCDnEORm+7X/M2M3iZHlSYgsT6jbRc+G0czacPQPqqeuzaBXw2hfPoZsj5ccr5dsj5dcryUqIqxQ/i4Sq0SQEBmRlx/DrA3bjp6/Op1ejWLy8iE798h8b9H89F0kRh6R3yiGWeu3HiM/jV6NYwvyPR7/+iv457vrNca7NRW7PR08ueT8NouQNmf6tQk9+wKy/zsB9u8FwO7ZWeR5Q9ueRe7yhZB9yD+/fhO8GSl4t6WBJ5fsBf8ltK1/fti5fcieMR57OH930fziVG9Tnz2bMtib997dMH4BtXr6v3dzjnjvhkQUvHerNIon7ecVABzcsZvs3fupflpdqrepz6aNm9nyVwo5OblM/G4q3S84xy+z+wXd+ObLCQBMnjCDzmd3LFJbv0suYOJ3UwHfyLAxEBFRHoCKlSoApBY5KBh5vcH9EyDH7JgYY+4wxiSeikLi4mPYklzwt5SckkZcXIwj8p1cu/KVr/zgyi/cPiUlndi4aL82sXExpCSnAeDxeNi9aw9R1XyfNLdtdxoLFk7hl18nc9edj+R3VLCW78d/zOy547nh+qvKpPbCKsZUZU9qZv723rRMKh4x2nBYq6vPZ8jcl+jy8OXMfvQTAKrUi8FiGfDp/Vzxw1O0vaVPkeO2Zu0hJm9aFkDNqMpkZO32a9O4Vgw/LfZdXP+0OIl9Bw+xc+/+UtW/dfc+YqoUfHoeHVmBrbv2+bW5pUdbfliyjh5PfcGwD6fy4ICCC8OUzD1c9sq3XP/ORJZsSCuSbyKrYXcWTI2yu3ZgIqOKtAMwVWpgqkbjWbe8VLUDmIpVsHsKzr/dk4WpWPT8A5jK1XBFVse72TflxlU1Gg7tJ6z/bYRf/SihXf8FhaYpbd13kOhK4fnb0RXLsW2f/8XzYam7D5C6+wDt80YsTouNpF18Vbp/OI8eH83lzFrVqBdVwT9/7yGiKxZ0HI8vvwrtEqLo/v4cenwwhzNrVafeESM5xdZfKfzY+bsO0D5vROS0uLz8UbPp8d5szqxdrUi+qVIdb2ZBR8pmbcdVpbpfG1dMAq7oeCIefJWIh1/H3aJdkecO7dCNnF9nFnncFVUd746CjpQ3czuuqjX82rhjEnDFJlDx0dep+PibhLRqf0RwGBWffIeKj79JaNvOfsdFxFRl3xHv3f1pmVSIKfq302TI+Vzy80u0H3E5v470vXczV24mscfpGLeLiok1qNayDhXiqhERU5W0lIz8Y9NSM4iJremXFxMbTWqqr43H42HP7r1Ujari16bvgJ6M/2YKALm5uYy492mmzfuGhSt+omHj+gAfFClUHKOkEZMngV+NMXONMUONMTVKaA+AMeYmY8wiY8wir3dfyQeIiMhJsXjRMjq1v4Bzul7E3ffcQrlyvk9xe3a/jLO79OeSi6/j1luv4awuRT+JDJQ/PvmR0Wfdw8/Pfkn7OwYA4HK7iWvXiKl3vM1XlzxB/Z7tSOzc/Liz7768J4tWbWLgI2+zeNUmalatjKvQBfaJmPr7Ovq1a8T0EVfy5nW9GDFmFl6vpUblCKb+3xWMveti7unbiYe+mMneg9klBx5FyGldyF0+v8y++8DdpAO5axYVTNdyuXAlNCRn1jgOfvokpkoN3C26/O38aWszOK9+Tdwu37nfvHM/G7P2Me2azky7pgu/JWeyJDXr7+evSee8BtFF8687i2nXneXLTzmB/NXpnNewUH7mPqbdcDbTbjib37b8zXyXG1d0PPv/fQ8HRj1D+SF3QfmCDpqJjMKVUJfcFcVPNyyR240rOoG9T93F/jefIuKGezARvvzdd17B3kduZf+bT1N+8G24asYdd/yq0T/yTed7WPT0l5x2p++9u/bL2b7pX1OepOPjg9i2aC3Wc3L+blu3bcmBAwdZkze1NSQkhEHXDaR3t4G0b34eq1auAXjopDyZBERJHZMNQAK+DkpbYKUxZqoxZogxptLRDrLWjrLWtrPWtnO5KhytmZ/UlHQSEwreFAnxsaSmppfq2EDnO7l25Stf+cGVX7h9fHwMaakZfm3SUtOJT/BNKXG73VSOrETmDv+LojWr17Nv336aNfPNwU9L82Vs37aD8eOn0L790af0/N3aC9ubnkWluIIRgIqxUezNOPrF2+oJC6ifN9Vrb1omKb+t5mDWXnIPZrNp5jJqtKjj175m1UqkZ+7K396auZvoqpULtanMK3dcwbgnh3L7pecBULlC+VLVX7NyBdJ37s3fzti1j5qR/v+f9t3C1fQ4zTdV7LQ60RzK9bBz/0HCQtxUqeD7NL5ZQg0SqlXmr227/I61u3ZgqlTL3zaR1bC7MilOyGmdyV02t1R15+fv3em7cf1wfqWq2L3Fn/+QJh3w5E3jAt/oinfrFt80MOvFs/Z3XNG1/Y6pWSGcjD0F9ytl7D1EjQrFT42btjaDXo0KRv5mbthGy5hIIsJCiAgLoXPtavyR7j/aVbNiOTL2FoxgHDN/TQa9GheM5s1cv7WYfP/zX6T+PQePkZ+eP40LYOa6DFrGHpFfpzp/pPlPk7I7t+OKKvg811Stjnen/1Q3m7Wd3KULwOPBbk/Hm5GCKzo+f39o+67kLvkZDo98HsGbuR1XtYIRB1dUdbxZ2wq12Ubukl/A48G7LR1PWjKumIT85wbwbksjN2kp7joN8o/bn55FhSPeuxGxUexLP/p798ipXtbj5bfHPmdCj//jp+teISwygl0b0tifnkVsfMHfQGxcNOlp/lPn0tMyiMsbIXa73VSqXJGszILz2veiXkz4dkr+drOWvn/fNm/y3Zc16fvpAP7z2cRRSuqYWGut11o73Vp7PRAHvA30wtdpOWkWLlpKgwZ1qVMnkdDQUAYO7M/ESdMdke/k2pWvfOUHV/7h9rVrJxAaGsrFl17I5Mk/+bWZPPknrrzqYgAGXHQBc2bPB6B27QTcbjcAiYlxNGxUj782JxMRUZ6KFX0X1BER5el+fldWrFh90msvLGPZBqrUjaFyYg1coW4a9e3EhhlL/NpUqVNwoVL3vNbs3OTr+Pw15w+qN04kJDwM43YR36kJmWtT/I5tXjeezRmZJG/LIic3l6m/LqdrmyZ+bbL27MObN1/6g0lzGXB2m1LX3zyxBpu37yYlczc5uR6mLV1P12a1/NrEVqnIr3k3BG/IyCI710PVCuFk7j2AJ+95k3fsZvP2XSRU8/88z5u8Dle1WEzVmuAOIeS0LniSFhapw9SIx5SviPevkv+b+eWnbcRUjcZEVgeXm5AmHfGsW1o0PyoGwivgTV1fcGz6Rky5CCjvq9ldq6nfTfMAzaMrsXnXflJ2HyDH42Xa2gy61fWfqgSwMWsfuw/lclpMwbS7mErhLE7JItfrJcfjZUnqTupW9b8/p3l0ZTbv3E/KrsP56XSrV3TixsbMfew+lHPs/JSd1K3q36lsHnM4f78vf0063erXLBzvyz+Yw2mxR+aXZ3HykflZ1C00Fc2zcTWu6HhM9RhwhxDaoRu5S+f7tcn5/WfcjVsBYCpWxhUdj91WMO0vpMM5xU7jAvBsWIUrJh5XDV9+WKdzyVlcKH/Rz4Q0PS0/3x2bgHdrGiaiIoSEFjzeqAWeI26a3750A5XrxlAx771br38ntkz3f+9Wrlvw3k08vzW7N/reu+7wMELK+zp4cWe1wJvrZdfaVLYv3UDderVJrBVPaGgIfS/qxYwps/wyf5w6i0su7wdA737d+WXub/n7jDFcOKCHX8ckPW0rDRvVy5/Kela3TgBJxZ6wYOO1wf0TICWtyuU33m2tzQEmABOMMaW/w68UPB4Pdw4fweQfvsDtcvHx6LGs9A3JBX2+k2tXvvKVH1z5h9t/+/3HuN0uPvv0a1YlreXhEcP5fclypkz+iU9Hj2PU+y/x+7L/kpW1k+uuuROATme04657biYnJxfr9XLPXY+SuSOLOnUS+WzMOwCEhLj5Ysx3TJs+66TXXpj1eJn1yGgGfHo/xu1i5djZZK5JodPdl5CxfCMbZyyh1TU9qNWlOd4cDwd37WP63b6lkQ/t2s+S96dw+aQnsNayaeYyNv3X/6I6xO3mocF9uPXfn+D1ehlw9uk0SKjJW9/+RPM68XQ7vQmLVm3i9a9mAIa2jWvz8NUX5h9/zdPvsyltO/sPZtN9+Is8dn1/OrdseES+iwcHnMmt703B67X079CYBjFRvD1tEc0SatCteW3u7tuJJ76ay+dzffd+PD6wK8YYlmxI5+3piwhxuXC5DCMu6UJkRLhf/Xi9HBr/PuWvHwkuFzkLf8KbsYWw7pfjSV6f30kJPc3/pvfDyt/yFK4a8VAunIiH3+PQ12/hWXPEObJesn/8jHKX3g0uF7nL52F3pBLaeQDe9E141vvahjTpiGfVb/7h1pI9ayzhl90LGLwZm8hdNtv//LtcPHB2Y4aO/x2vhf7NYqlfrSJv/7qeZjUr062urxMxbU0GPRtG+y2le379mixMzmTgGN8ozZm1qtG1bo2i+d0aM3T8Et/5bx7ny1+wzpdfr2Zefjo9G8X45zeI9uV/vsCXX7saXesVk39OE4Z+twSvtfRvHu/Ln5+Xn9dJmbY6jZ6NC+U3jGbhlkwGfjofDJxZuzpd8/R4AAAAIABJREFU6xXq1Hi9HPz8TSLuehbjcpE9bxre1L8o138Ink1ryF02H8+fiwhp3pYKT77va//Ve9h9vpXFTLVoXFE18Kz5o8h/+8P5Bz5+gwoPPA8uN9mzp+BN2UT4JdeQu3ENuUt+IfePhYS0bEelFz70tf/iXeze3bgbNifi+rt8F6Auw6EJY/JW8/KNkFqPlwUjRtPji/sxLhdrx85m55oU2tx7CduXbWTLjCU0vaYHsWc1x5vrIXvXPuYO9713y1evTI8vHsB6vexPz2LOHe/kZ4584Bk++eod/p+9+w6Pqsr/OP6+M0kIIb1XWkJL6B0EEkADlgAqBhHBumoUG+ourqgrFuyKCwi6Aq7SEQQCoQgEkN4JJLRQQhrplbSZub8/bkyYzACiRMj+vq/nyQMzc+YzX87cDPfcc+4dvV7P4vk/c+pEMhMmPsuRQ4n8sjaeRT8u54uvP2DL3lgKCgoZ/+Tfa/65vfp2Iz3tIhfO1x6gyMrM5stPZrIkdg5VVQbSLmQAWL92tmgQlKtdtUVRlNaqqv6p/6Ft7ALkG2SEEOI6NbGzv3ajP+j3Xi74j/rMd+C1G/0JTy+Oqtd8NetCveYbt++4dqM/Qefpeu1Gf4Z9/W2bAOjq+YKhuht3jpE1hn3Hr93oTzCVWy7rulGWx1//eSbX41/lVxhk3UDnc4/U7xt8g5S+9eAtvX/cZPLCm9KPV50x+bODEiGEEEIIIUQd9XQhi4bulvkeEyGEEEIIIcT/XzIwEUIIIYQQQtx01zr5XQghhBBCCHEj3cQrX93KZMZECCGEEEIIcdPJwEQIIYQQQghx08lSLiGEEEIIIf5CqkmuymWNzJgIIYQQQgghbjoZmAghhBBCCCFuOlnKJYQQt6D6/nb2+jSqeWq95qvpyfWaj41dvcbbjnm6XvNVtf6+GRwAQ2W9xiu2jes135Rxql7zK5bV77ebO9xWf9/Ofmx7/W47adm59ZrfoMhVuaySGRMhhBBCCCHETScDEyGEEEIIIcRNJwMTIYQQQgghxE0n55gIIYQQQgjxV5JzTKySGRMhhBBCCCHETScDEyGEEEIIIcRNJ0u5hBBCCCGE+Cup8s3v1siMiRBCCCGEEOKmk4GJEEIIIYQQ4qaTpVxCCCGEEEL8leSqXFbdUgOTIZERfP75ZPQ6HbPnLODjT6Y3mPyGXLvkS77kN9z8hlS7Xc+eOL8wHnR6ylavpnTefIs29gMjcHzsUVRVxXA6mcJ337tq5vYTaXwcuweTSeXeHq14PKKD2eMZBSW8uWQ7xWWVmFSVF4Z0pX/bQLPH7/tiBc8M7sQjA9pb5h+/wMcrd2r5Pdvw+KDO5vn5Jby5KF7LN6m8cFcP+rdrSlpeMfd9soRmXi4AdGzmzaT7+1vk/3owkY/mLMNkMnHf4D48ce8dZo+nZ+fx1oz55BeV4OLowAcvjMXXw4307Dxe+uQ/qCYVg9HI6DsHEB3Zz7L+Q0l8NGe5Vv/gXjwx4naL/Le/Xlib//zD+Hi4kp6dx8ufzkY1qVQZjYwe2p/oyNss8w+f4KP/rtTyB/bgiWED6+Tn8/Y3S8gvKtXynx2Fj4crx8+l8/7s5ZSUlaPX6XhyxCCG9un01/dP4nk+XrZVq79PKI/f0d3s8Yy8Yt78cQPFZRXa9hPVl/5hzVm99wTfbzpQ0+5Ueg4LXnuQtoFeZs+37d4Tx2efR9HpKItbTdkiy22+0YCBOIx7FFQVw5lkiqe8C4DLBx9j0y6UqqMJFL35usXzAPQhnbAbOg50OgwHNlP160qzx+2GjEXXIhQAxbYRShNnLn34JIqLJ40enACKgqKzoWrPOgz7fjF7bpvwTgx/axw6vY7dizaz+Wvz7AFP3EWvBwdiNJgozSti8d9nkZ+WA8DdE0fTbmAXADb8exmHY3dZrf9q6vuzTdyabpmBiU6n46up7zP0rtGkpmawa+caVsWuJynp1C2f35Brl3zJl/yGm9+gatfpcH75RfInvIoxOxuPb2ZS/ut2jOfP1zTRBwbQZMwYcp8dj1pSgs7V9aqRRpOJKSt3MfOJSHycHRgzfTXh7YII9ql93rebjhDZoRnRvduSfLGA8XN/Ia7tyJrHP1u9j9taB1w5f/l2Zj51Fz4uTRjz1c+EhzUj2MetNn/jQSI7tiS6byjJF/MZ/91a4to1BSDQw5nFE+6/cv1GEx98t4Rv3nwOH3dXRr/+KRHd2xMc5Fdb339/Jiq8B8MjerE74SRfzVvFBy+Mw8vVmR/ffxk7W1sulVVw3ytTiOjeAW93F7P6P/juJ2ZNegYfD1ceev0LLT/Qt6bN5z+sJGpAd4ZF9GT30VNMnR/LB88/jJebMz+89xJ2tjZcKq/g/lc+IqJ7e8v8OT8z6/Un8fFw4aFJ04joGkpwoE9t/rzVRPXvxrAB3dh97DRTF63lg2cfxL6RLe/FjKKZnydZ+UWMfuMr+nZsjXOTxn9p/0xZEs/M50bg4+rImE8XEd6+JcF+7rXv7/q9RHZpRXT/DiRn5DF+1kriwh7l7h5tuLtHG0AblLz87WqLQQk6HU7Pv0TBP17BlJON27RZVO7cjjHlsm0+IIDGo8dQ8NJzqCUlKJdt85eWLERpZI/93VHWNyBFwe6uxyj/4QPUolzs//Y+hhP7UbPTappUrvuh5u82PYeg82sOgFqST/l/3gKjAewa0fjZTzCe2A/ka9E6hXsnP8Y3D39AYWYuL658n8QN+7l4ujY7LfEcX0a9QVV5JX0evp27X3+IH8d/RbuBXQgIa8Hnd03Exs6WmIVvcjz+MBUlZdb/HVbU92ebuHXdMueY9OzRheTkc5w9m0JVVRWLF69gWNSQBpHfkGuXfMmX/Iab35Bqt23XFmNaGsaMDDAYKN+4Cft+5kfgG99zD5eW/4xaUgKAqaDgqplHL+QQ5OFMoLsTtjZ6hnRqQXzSBbM2iqJQWlEFQEl5JV7ODjWPbTqWgr+bo9lAxiw/JZsgT2cCPZy1/M7BxB87b9ZGAUorKrX8MvP8azl6+jxNfb0I9PHE1taGobd1ZfO+BLM2Z1Iz6dW+NQA927eqedzW1gY7W1sAKg0GTFaWhRw9nUKQr6eWb2PD0L5diN971KxNcmomPdu30vLDQojfpz1ua2ODna127LKy6kr5Fwjy8SDQx0PL79OJ+P2J5vlpF+kZFqzlhwbXPN7cz4tmfp4AeLs54+7sSH5R6V/bP+cvEuTlSqCni/b+dm1NfMIZszYKUFpe/f6WV+Dl3MQiJ27/SYZ0a21xv02bdhjT0zBlVm/z8Zuw62s+a2N/ZxTlK5fXbPPqZdt81cEDqJcuWeT+RhcQgikvEzU/C4xGjEd3YtOm+xXb23ToiyFhh3bDaNQGJQB6W1AUs7ZNO4eQez6TvAtZGKuMHFq1k7BI8+zknYlUVffN+YOncfHVBnQ+rQI4sycJk9FEZVkF6cdTaBtuORt2NfX92XYrUE3qLf1zs1x1YKIoip2iKOMURbm9+vZDiqJMUxTlOUVRbG9kIf4BvlxITa+5nZqWgb+/71WecevkN+TaJV/yJb/h5jek2nWeXhizsmtuG7Oz0XmZH2G2CQpCHxSI+/R/4/71DOx69rxqZlbRJXxdancUfZwdyCo037l9ZnAnVh88Q+SUJYyfu5GJw3oBcKmiirlbjvLM4CvvMGUVleLr6lib79LEMj+yG6sPnCbyvfmMn72WiSP61jyWllfMqC+W8cTXqzhwJsMi/2JeAT4etYMiH3dXsnILzdq0bhbAL7sPA7BxzxFKyyooKNZqyMzJ5/5XPiTymbd4fMRgs9kAgKy8Anwvy/f2cOFinnl+m2YBbNxzpDo/wSJ/5KsfMyTmHR4bbiU/v9A8391avj8b92iDnY17j5nl/ybh9AWqDAaCfNzN7q/3/imo8/66OpJVWGLW5pk7e7F63wki35zN+JmrmDgynLrWHzjFnV0tByY6T0+M2Vk1t0052eg9Pc3a6AMD0QcE4frlNFy/moFt96tv85dTnN1Qi3JrbqtFuSjObtbbuniiuHphOls7MFWc3Wkc8xEOE6ZR9etK1OL8msdcfNwoSK/NLsjIxcXHejZAr+gIjsdr70N60nnahHfC1t4OBzcnQvqE4urn8bv/XVD/n23i1nWtpVxzqts4KIryCOAILAMGAz2BR6w9SVGUp4CnABS9Czqd5REGIYQQwoxej01gIHkvvITe2wv3f39FzqOP1xxN/iPWHj7LsG4hjOsfxuHzWUxavI2lLw5n5sZDjOkXikOjP3eMbe3B0wzr3ppx4R05fO4ikxbEs/SVkXg5O7D2jdG4NrEnMTWbl+du4KdXR+Job3dd+a+MG8GU75awMn43XduF4O3ugk6nHd329XTjp88mkpVXyEsff8sdvTvj4ep8XfkTxg5jyuyfWBG/l27tWlbn62ryl376dy3/k9nc0bsTHq5O15c/5m6mzP2ZFVv3061tC7zdnWvyAbLzi3jj64W890y02f2/V333z9r9JxnWqy3jBnXl8NkMJv2wnqWvj6l5jYRzmdjb2RLif3073jX0evQBgRS88iI6Ly9cP/s3+U89hlr6x7d5a2za98GYuAfU2iPhalEeZV//A8XJjUYPTsCQuAcovHLIFXQd0Y/Aji2ZMWoyACe3JRDUMZjxy96hNLeY8wdOYTLJd3aI3+daA5MOqqp2VBTFBkgD/FVVNSqK8iNw+EpPUlX1G+AbABu7gN81H5SelklQoH/N7cAAP9LTM3/PU3+X+sxvyLVLvuRLfsPNb0i1m3Ky0XvXzpDovbwwZWebt8nOpioxUVuWkpGJ4cIF9IEBGI6fsJrp7exA5mUzGBeLLuHtYn4gbPm+U8x4TDthulMzbyqqjBRcKifhQg4bEs7zZdw+issr0SkKjWz0PNi33WX5TcgsqN1BvFhYapm/9wQznrxTy2/uQ4VBy3d3bIydjR6A0EAvAj2cOZ9dSFhQbR/4uLtyMbd26c7FvAK8PcyP6nu7u/DFa08CcKmsgl92H8K5iYNFm5CmfuxPSiayT5fL7ncl87L8rNxCfNyt5L/6uJZfXsEvu4+YnedRkx/ky4HjydzRu/bkf283F/P8PCv5bs588fK42vy9CTX5JZfKGf/JHJ6PHkLHVs2oq977x7XO+1tQgreLo9lzl+9KZEbMMAA6tfDT3t/SMtydtNdYe+AUQ7u1sqgdwJSTg97Lu+a2ztMLY05OnTbZVB1PAqMRU2YmxrQL6AMCMZw8bjXzcmpRPopz7YBIcfZALcq32lbfvi+Va2ZbzynOx5SVir5ZG2APAIUX83G9bLDl6udB4UXL7Fa3tWfw+BF8PWoyxkpDzf0bp//Mxuk/A/DQ1PFkW5kxvJr6/my7JchVuay61uEJnaIodoAT4AD89onQCLihS7n27jtESEgLmjcPwtbWlujo4ayKXd8g8hty7ZIv+ZLfcPMbUu1Vx09oy1b8fMHGBvvBg6jYvsOsTfm2X7Hrou34Ki4u2AQFYUy/8g5NWKAnKTlFpOUVU2Uwsu7wWcLbBZq18XN1ZHeylnEmq4BKgxG3JvbMefpO4v4xkrh/jGTMbaE8EdHBbFACEBbkVZ1fpOUfSiY8tKll/iltycmZi/k1+XklZRirjxKn5haRklNIoIf5bENYSFPOZ2STejGXqioDa7cfIKK7+VXF8otKao42/2f5Bu4d2BuAzNx8yqvPbSkqucTB42do7u9jnh8cREpGNqlZuVQZDKzdcZDw7mFXzP9u+S+MGKgtdbuYW0B55WX5J87S3N+7Tn4gKZm5pGblafk7DxPerV2d/NLa/BWbGRHeA4Aqg4GXv/gvUf27ckevjlhT7/3T1IeU7ALScgu19/fAScI7tDBr4+fmyO6TqQCcycyjssqIm6M2sDKZVNYfPMVQK8u4AAwnjqMPCETnW73NRwyicud2szYV23/FrmP1Nu/sgj4gCGNGurU4C6b0ZHQeviiuXtrMS/s+GE7st2inePqjNG6C6ULtieOKszvYVO/G2TdB37QNppza37ULh5PxbO6Le6AXels9naP6cGyDebZ/WHPu/+BJ5jz5KSW5RbXZOgWH6iVyfm2b4t+2KSe3Hfld/6bf1Pdnm7h1XWvG5DvgOKAH3gCWKIpyBugNLLyRhRiNRl58aRJrVs9Hr9Mx9/tFJCaebBD5Dbl2yZd8yW+4+Q2qdqORoi+n4vbpJ6DTUbYmDsO5czg+/hhVJ05QsX0HlXv20KhHdzz/OxfVZKJ4xkzUoqIrRtrodUwc1ouY2b9gUk0M796KEB83Zmw4SGiABxGhTZlwV3cmL9/BvF8TQYF3Rt6GUudE36vmj+hLzLdxmEwqw3u2IcTXnRnr9hEa6EVEWDMmRPVm8pJtzNumnXT9TnQ4iqJw4EwmM9bvw0anQ6dTmHR/P1wc7Ovk6/nnEyOJeX8GRpOJEQN7ExLkx/SFqwkNbsrAHh3Ye+wUX82PRVGga7tg3njyAQDOpl7k0//+jKJoq3MeiRpE62b+FvmvP34/Me/PwmQyMWJgLy1/URxhwUFEdG/PvsTTfDV/NSgK3dq15J9PaFcsO5N2kc/+uwJFUVBVlUeiImjV1Er+o8OJ+fA7LT+iByGBvkxfsp6wloFEdAtlX1IyXy1cq+W3bcE/HxsBwLpdRzhw/CyFJZdYuVXb4Z38dDRtm/ub5ddv/+iYODKcmBkrMZlMDO8dSoifBzNW7yK0qTcRHVoyYUR/Ji/cxLzNB0FReGfM7TXbz/7kNHxdHQn0NJ/FqWEyUjLtS1ymfIqi01G+bg3G8+dweORxDCePU7lzB1X79mDXrQdu//keTCZKv/0atVjb5l0//zf6oKYojRvjPn8JxZ9/TNW+vZflm6hcMxf7sa+DosNwMB41OxXbgSMxpZ+tvsqWtozLcNT8IIDiGYD9kIdRVRVFUajaEYuadeGy0k0sf2suf/vv6yh6HXsXx3PxVCpDXh7JhYSzJP6yn3tef4hGDvaMnfEiAAVpucz526fobW14bsnbAJSXlDH/5emYjNe3lKu+P9vErUtR1atPJSmK4g+gqmq6oiiuwO1Aiqqqe37PC/zepVxCCCH+N6T2tr605UZxeSmyXvOxub7zQK6XrnmHazf6E1TVWK/5GCrrNV6xbXztRn+CKaN+Lzlb8umSes13uM3/2o3+oLfn1ls0AF+mb63fFwAMlWm/78jDTVb8wj239P6x01exN6Ufr/k9Jqqqpl/29wJgab1WJIQQQgghxP8yuSCAVbfM95gIIYQQQggh/v+SgYkQQgghhBDiprvmUi4hhBBCCCHEDSSXC7ZKZkyEEEIIIYQQN50MTIQQQgghhBA3nSzlEkIIIYQQ4q8kS7mskhkTIYQQQgghxE0nAxMhhBBCCCHETSdLuYQQ4g/Q6+r3uI6xAX/5lsvT/ev3BSoq6je/kUO9xpvOJdRrPuWX6jffwbFe41VH13rNJz2lXuMdevvUa76ufVi9ZX+VMbPesoU5VZWlXNbIjIkQQgghhBDippOBiRBCCCGEEOKmk6VcQgghhBBC/JXkqlxWyYyJEEIIIYQQ4qaTgYkQQgghhBDippOlXEIIIYQQQvyVZCmXVTJjIoQQQgghhLjpZGAihBBCCCGEuOlkYCKEEEIIIYS46eQcEyGEEEIIIf5CqpxjYtUtNWMyJDKCY0e3cjzxV/7+2nMNKr8h1y75ki/515c/JDKChCPxJB7bxquvPmvxuJ2dHT/+MIPEY9vYtnUlzZoFAuDu7sq6dYvIzTnOl1+8W9O+cWN7fl4+lyOHN3PwwC+89+7Eeqv9ev3Z/O2nMxg+PY6of69h9q9JFo9nFJby5PebGfXNeh6YuY5tpzIA2JmcyehvNzBy5jpGf7uBPWcvWs8/lcHwf68mamoss7clWuYXlPLk3E2MmrmWB2bEse1kem3+rHWMnBHH6Fnr2HPmCvlJKQyfMp+o9+cxe+MBy/z8Yp6cvoJRny3hgU8WsS3xfM1jJ9NzGTd1Gfd9tJCRHy+iospgmX/8AsM/XkzUh4uYvemQlfwSnpwZy6gvlvHAZz+xLSkFgLS8Ynq9Ppvoz38i+vOfeO+nbVfon3SGf7mSqC9WMHvrMev9M/sXRk1fwwPTVrPtZBoACak5RE9fo/1MW82mxAvW84+nMPzDhUR9sIDZGw9a758Zqxj12VIe+HSJef3/+A/Rny0l+rOlvLd0q/X8o2cY/ta3RE2axey1uyzz84p48rMFjHpvDg9Mns22hGQAqgxG3pq7mpHvfEf0u7PZeyLFar7Za525yPBvfiFq5i/M3nnS8rUKL/Hk/O2Mmh3PA99tZluy9W3mcvqQTjR+8Usav/QVtv2HWzxud+cj2D/7MfbPfkzjF7/E4Z9zAFBcPLGP+VC7//nPsOlxh/WaT6Yx/IsVRH32M7O3HLWsuaCUJ/+znlHTYnngq1VsO5Fm8fgrv1ygov/jNBr6LK+9avk7bmdnx7wfZ5CY+Cu/bltV83kG8PfXniMx8VeOJmzhjjvCAWjduiV796yr+cnJTuL5558AoFOnMLZvW8W+vevZtXMNPbp3vmLf1fdnm7g13TIzJjqdjq+mvs/Qu0aTmprBrp1rWBW7nqSkU7d8fkOuXfIlX/KvL/+39nffM4bU1Ax2bI8lNnYDx4/Xtn/s0QcpKCggNKw/DzwwjPff+ycPj32W8vIK3nnnU8JC2xAW1sYs94svZ7Fly05sbW1Zu3YhQ4cMZO26zTe09uv1Z/ONJhNT4g4w8+FwfJwbM+Y/vxDexp9gL5eaNt9uSyIyLIjo7iEkZxcyfv424l68BzeHRkx9sB/eTo05nVVIzLytbHg5yjJ/zT5mjh2o5X+7gfA2AQR7X5a/9ZiW36MVyVmFjJ+3hbjWw7T80QPwdm7M6YsFxPy4hQ2vDLfMX7aNmc9E4ePShDFf/ER4WHOCfd1r8zfsJ7JzMNG3tSc5M4/x364hLrQZBqOJN+b9wnsPDaZNgCcFpeXY6HWW+cu3M/Opu7T8r34mPKwZwT5utfkbDxLZsSXRfUNJvpjP+O/WEteuKQCBHs4snnD/1ft/1V5mPjoIH2cHxsxcS3jbQPP+2XKUyPZNie7ZWuufHzYT90oAId6uzH9mKDZ6HdnFZURPX82ANgFm/watf7Yz8+m7tfq/XFbdP5fV/8sBIju3JLpvGMmZ+Yz/zxriJo3R6vd0ZvErI69e/4INzHxpFD5uToyZ8j3hHUMI9veszV+9g8jubYkO70Jyeg7jpy0hrkMMP207DMDSt58gr6iU5/69hHmvP4JOp1zhtVSmrD/CzAf74uPUmDFztxDeypdgT+fa19pxksi2/kR3bUFyThHjF+8i7tnIK9aPomAX9QTlc99DLcrF/pkpGI7vQ82uHRxUxn1f83ebXkPR+bUAQC3Jp/ybSWA0gF0jGo//DOPxfajF+eb9s2oPMx+7XXt/v44jvF0gwd6utTVvPkJkh2ZE92pDclYB47/fRNxr99U8/vnaA7wzbQ6rZ33EfWE+jBo1gtjY9SRd/nn22IPkFxQSGtqP6AeG8cH7/2TMw8/Srm0roqOH07nzIPz9fYiLW0BY2ABOnjxDj55DAO0z5NzZfaxYsRaADz94g3ff+5y16zZz59BBfDjlDQbf8YBF19X3Z5u4dd0yMyY9e3QhOfkcZ8+mUFVVxeLFKxgWNaRB5Dfk2iVf8iX/+vIt2i9ZSVSU+c5JVFQkP/y4FIBly1YzcOBtAFy6VMaOHXspr6gwa19WVs6WLTsBqKqq4tDBBAIC/G547dfrz+YfTcsjyM2RQDdHbPV6hoQ1Jf5EulkbBSitqAKgpLwKL6fGALT1c8O7+u/BXs5UVBmpNBgt892dCHR3xNZGz5D2TYmvc0RYURRKK7SZipKKOvnO1fneLtbzU7II8nQh0MNZy+8SQvzRc3XqVygt/63+SrxcHADYeeICrfw8aBOg7US7NrFHr9PVyc8myNO5Nr9zMPHHztfJh9KKSi2/rBIvZwdrXW3V0dRcgjycCHR30vI7NCM+yXzmQwHz+qv7p7GdTc0gpNJgRMFyh/5oShZBHs7m/XPsav1TgZdzk99f/9kMgrxdCfRy1fK7tyP+sPmOqaJAaZn2+1RSVoGXiyMAZzJy6Nm2GQDuzk1wamzPsfMZV36tjHyC3JoQ6NoEW72OIaEBxJ/KtHytyt+2JQNeTvZXrV8XGIIpNxM1PwuMRowJO7Bp1+OK7W063oYh4VfthtGoDUoA9LagWO6uHU3Nrd7+q9/fjlbeX0Ux//26bPvZlJhC965dKM3Lojg3C1QTixevsP559sMSAH5atpqBA/vV3L948QoqKys5d+4Cycnn6NHDfAZk0KB+nDlznpQU7fdSVVWcnJ0AcHZxIj3D+qxTfX+23RJM6q39c5Ncc8ZEUZSWwH1AEGAETgLzVVUtupGF+Af4ciG19j+s1LQMevbo0iDyG3Ltki/5kn99+XXbp1lp7+/vS2p1G6PRSFFRMR4ebuTm5nMtLi7O3H337Uz9939ueO3X68/mZxWX4etSuyPk49yYhLQ8szbPhIcRM28rC/acpqzKwKyHwy1yfklKpZ2fK3Y2evP8ojJ8nevkp9bJj2hPzA/xLNh9UssfN9AyPzGVdn5ulvmFpfi61u5I+7g2IeF8lnn+0O7EzIxlwa8JlFVWMeuZYQCczy5AURRiZsWSX1LGkC4hPDbIvO+yikrxdXWszXdpQkJKnfzIbsR8u4YF2xO1/KfuqnksLa+YUV8sw9HelueGdKdrS/PBbFZRnf53cSAhNdc8f1BHYuZuZMHuE5RVGpn16KCaxxIu5PD28l1kFJby/v19LWZ8sgovXbv+Id2ImbWGBb8e1ep/+h7z+j9biqO9Hc/d2cOy/oJifN1qZyx83JxIOGs+uHgmqh8xXy5iweb9Wv5LDwLQOtCb+MOnGdojlIv5RSSmZHIxv5gOLbAqq7gc3+r9SJxGAAAgAElEQVRBGYCPU2MS0s1/X5/p15aYRTtYsP+M1lej+1oPq6Y4u6MW1va3WpiLLrCV9bYunihu3pjO1C7HUpw9sB87EcXdl8p1P5rNlgBkFV3C1+Wy7dO5CQkXcsxr/u393XmCskoDsx6/HYBLFVXM3XqM2W+9wLn82hrT0jLp0dN8Ow3w9yU1Vet3o9FIYVERHh5u+Af4sWd37fLGtNRMAvzN38PoB4axaPGKmtsTXn2bNbHz+fjDN9HpFPqHWy5vg/r/bBO3rqvOmCiK8gIwE7AHegCN0AYouxRFibjK855SFGWfoij7TKbSG1iuEEL879Lr9fzw32lMnz6Hs2evvSb+f8HaoykM69Sc9S9HMW10fyb9vAeTWnu07nRWIVM3HmHS3d3/WH7CeYZ1bsH6V4YzbUw4k5btwmSqk//LISZF/cH8A6cZ1rMN698ex7S/3c2k+RsxmVSMJpWDZzP4YMxg5jw/gs0JZ9l9MvX68w+eZlj31qyf9BDTHh/KpAXxmEwqXs4OrH1jNItevo9Xonrz+vzNlJRXXn/+kXMM6xrM+tfuY9rYCCb9tKOmfzoEebLshXuY9/RQvtt6jIoq4zXSrNWfzLAerVn/1sNMe/JOJi3YVFv/pDEsemUkrwzrw+s/bvxj9e9JZFjfDqz/6DmmjX+ASXNiMZlURtzWER83Jx764Hs+WbyRTsEBV1zG9btfKzGVYe2bsv65IUyL7s2kVfvNttU/w6bjbRiP7YLL8tSiXMqmv0bZly9g0yUcmrhcJeEKNf/2/v7jfqY9MohJS7ZjMqnM3HSEMbe1w85Wf+2QP8jW1pZ77onkp59ia+57+qlxvPLav2gR3INXXnuHb2d9Vm+vLxqmay3l+htwp6qq7wG3A2Gqqr4BDAW+uNKTVFX9RlXV7qqqdtfpft+0bXpaJkGB/jW3AwP8SE/PvMozrk995jfk2iVf8iX/+vLrtg8I8COtTvv09EwCq9vo9XqcnZ1+12zJjBkfcfr0Wf497bt6qf16/dl8b6fGZBZeqrl9saisZnnWb5YfOktkaBAAnYI8qTAYKbhUUd3+EhMWb+fd4b0IcnekLm/nxmQW1cl3rpN/8AyRYVfIL7zEhIW/8u69vQlyd7LMd2lCZkHtwbWLBaV4u5j/n7Z8dxKRnUK0/Oa+VFQZKCgtw8e1CV1b+uHm2JjGdrb0a9eUpNTsOvU3IbOgpDa/0Er+3hNEdmpZne9TXX85djZ6XJtoS4lCA70I9HDmfHahZf9c3v+Flyz7f38yke21c1Y6NfWiwmCq6Z/ftPR2wcHOhtNZBXX6x+Ha9e8+TmSn4Mv6x0hBaZ36g7wI9LRSv6sTmfm1izMu5hfj7Wq+HSzffoTIbm21/OAArf9LLmGj1/Fa9GAWv/kYXz57P8WXymnm7c6VeDvZk1lcVvtaxWV411mqtfxICpHtArTXCnCv7qsrD6bUojwUF4+a24qLB2pxntW2+g59MRzZbj2nOB/TxQvom7c1r9nZgczCy7bPolK8Xeq+v6eJbK8tadPeX237SbiQw5drD/Dyf2IpUez5bstRFu48TkCAL+lp5rNSaemZBAZqMyF6vR4XZ2dyc/NJT8uouR8gINCXtPTa5w4dOpCDhxLIyqqdxRk39gGWL18DwNKlqyyWfv2mvj/bbgmmW/znJvk955j8ttyrEeAIoKpqCmB7IwvZu+8QISEtaN48CFtbW6Kjh7Mqdn2DyG/ItUu+5Ev+9eVbtH9gGLGxG8zaxMZuYOzD2km99913N/Hx1nc4Lvevf72Gi7MTr7z6r3qr/Xr92fywAHdS8kpIyy+hymhk3bEUwlv7m7Xxc3Zgd/UVt85kF1FpMOLm0Iii8kqeX7CNFwd3pEtTT2vxhPm7k5JbrOUbjKw7mkJ4mwDzfJcm7D7zW36hlt+kEUVllTw/fysv3t6RLk29rOcHeZOSXUBabpGWf/A04e2bm+e7ObL7lDYTcuZivpbv2Ji+bZpyOiOPssoqDEYT+5PTaenrXiffi5ScItLyqvMPJRMe2tQ839WR3afSzfOb2JNXUobRpO09pOYWkZJTSKCH+eAqLMDDvH8SzhPeNrBOvgO7k7UdvjNZtf2Tll+CwajlpxeUcC6nCH9X80FHWJA3KTmF5v0T1sxK/6TV6R8r9Wdbqb+5HylZ+aTlFGj5+5IIrx4E1uS7O7P7uHZezpmMHCqrjLg5OVBWWUVZ9bk5OxPPYqPTmZ00X1eYnyspeaWkFZRSZTSxLjGN8BBf89dybszuc9rg8kxOMZVGI24OdlfMNKUlo/PwQ3H1Ar1eG3wc32fRTvH0R7FvgulC7ZXAFGd3sKnezbJvgr5ZG0w55udn1by/ecVa/xw5T3jbIPOaXZqw+0zd99eeOU8NIe61+/jsrla0ax3CS/cO5MG+oURHD7f+eTZWO0H9/ss+z2JjNxAdPRw7OzuaNw8iJKQFe/fWXlluVPRwFi1aYZaVnnGR8AF9ABg0sB+nTp+12nf1/dkmbl3XOsfkP8BeRVF2A/2BjwAURfECrA/7/yCj0ciLL01izer56HU65n6/iMREy8v13Yr5Dbl2yZd8yb++/N/ax676Eb1ez9zvF5GUdJK33nqFA/uPELt6A3PmLmTO7C9JPLaNvLwCxo6rvdTliRM7cHZyws7OlqioIdx9zxiKi4t5feILHD9+it274gCYPmMOs+csuKG1X68/m2+j0zHxzq7EzNuKSVUZ3rkFId4uzNh8lFB/NyLaBDAhshOTV+1j3u6TgMI7w3uiKAqL9pwmJa+EWVsTmbVVuwzwzIcH4N6k9ii2jV7HxLu6EfPDFkyqieFdWmr5mxII9Xcnom0AEyI7M3nVXubt0up+Z0Sv6vxTpOQVM2vLMWZt0S6jO3NsBO6OdfLv60/MN9ryoOE92xLi686MuD2EBnkR0b4FE4b1ZfLiLczbcgQUeGf0IBRFwdmhEWPDOzHmi59QFOjXrhkDQs132m30OiaO6EvMt3HV+W20/HX7CA30IiKsGROiejN5yTbmbUvQ6o8OR1EUDpzJZMb6fdjodOh0CpPu74eLg71l/j3difleWz41vGswIT6uzNh4mFB/DyLaBTJhaDcmr9jFvB3HQVF4574+KIrCwfNZzN6aiI1eh06B1+/pgVsTK/n39SPmmzXa+/tb/Wv3avW3b86EqD5MXrKFeVuPaPkPRlTXn8GMtfuq8xUmjexvvf4H7yBm6mKt/ts6EOLvxYyV2wht5ktEp1ZMGDmIyT+uZd7Gvdr28+hdKIpCXtElnv1qMTpFm3l57/F7uBobnY6JkR2JWbRT+7d0bEqIlzMztiYR6udKRCs/JgwKY3LcYebtTdbe67u7oihXWR5mMlEZOxv7R94AnQ7Dgc2oWanYDorGlJ6M8fh+7bU73IYhYYfZUxWvAOyHjkNFRUGhavsq1IvmJ7bb6HVMjOpJzNyNWs1dQ7T395dDhAZ4ENEuiAl3dWPy8l3M265dqvud+/ua16yqbF74HXfHvE4jB3uWfvwNiUknefutV9l/4DCxsRuYM2chc+dMJTHxV/LzCnh4rHaJ9MSkkyxduorDhzdhNBh58cVJmKoHmw4OjRk8eADPPmd+6fNnnnmNzz+fjI2NDRXl5cTE/N1q19X3Z5u4dSnqNdZHKooSBrQDjqqqevx6X8DGLkC+QUYI8T+n7hWWbrTfjiY3RMVzHq/fF6jnvsfZ7dpt/gyj5feZ3FDll67d5s9wsFxWd0M5ul67zZ9x9rp3Za6L6cy5es3XdepYb9kuY2bWWzZww87JuRpDZdqfO5noL1IwZtAtvX/sOm/TTenHa16VS1XVY4DltzIJIYQQQgghxA1yy3yPiRBCCCGEEOL/r1vmm9+FEEIIIYT4f+EmfonhrUxmTIQQQgghhBA3nQxMhBBCCCGEEDedDEyEEEIIIYQQN52cYyKEEEIIIcRfqeFeEb5eyYyJEEIIIYQQ4qaTgYkQQgghhBDippOlXEII8Qeof8E3GNeX7p6t6jVfadq6XvNNx/bVa77i5Vev+VRV1m9+I/v6zbexq9d4xdmzXvPVJvX7zfWKY5N6zedSab1F/xXfzC40qlwu2CqZMRFCCCGEEELcdDIwEUIIIYQQQtx0spRLCCGEEEKIv5JclcsqmTERQgghhBBC3HQyMBFCCCGEEELcdLKUSwghhBBCiL+QXJXLOpkxEUIIIYQQQtx0MjARQgghhBBC3HSylEsIIYQQQoi/klyVyyqZMRFCCCGEEELcdLfUjMmQyAg+/3wyep2O2XMW8PEn0xtMfkOuXfIlX/KvnV+3zaefzjB73M7Ojjmzv6RL147k5eYz5uEYzp9PBeDvrz3Ho4+NxmQ08vKEt9iwYQutW7dk3o9f1zy/RYumvDP5U/797+/o1DGUadM+pJF9IwwGA88//0/27jv0h2u/mt4RPXn53fHodHpWLljND9Pmmz3euVdHXp48nuB2wbwZM5nNq7fUPLb9wkaSj58F4GLaRV579A2L/O1Hk/lowTpMJpV7+3fmibtuM3s8I7eQSbNXUnypHJNJ5cX7B9G/YwhVBiOT/7uaxPMZ6BSFvz8YSY+2zS3zz2XzSXwSJhOMaB/I4z1bmj3+aXwSe1PzACivMpJXVsm2Z28HYOq2E2w7mw3A33oFM6SNn2X+sXN8vDQek8nEvbe15/HInub15xXx5n/XUVxWgcmk8sLwfvRv34LVe5L4/pf9Ne1OpWez4B9jaBvkbZ6fdJ6Pl/2KSTVxb+9QHr+9m3l+fjFvzttYmx/Vm/6hWj+cTM/hvUXxlFRUolMU5k14gEa25v+tb088z8fLtmr93yeUx+/oXqf+Yt78cYOWr6q8ENWX/mHNWb33BN9vOnBZ/TkseO1B2gZ61emfs3y8ZDMmVeXevu15fEgvy/75fi3FZdr7+8KI/vRv35Iqo5F3flzP8QtZGI0m7ukVyhNDzZ8LsP3QcT7670qt/wf25Inhg8weT8/O5+1Zi8kvKsHF0YEPnhuNj4crx8+l8f7sZZRcqkCvU3jy3sEM7dPZMv9UOh+v3qfV3y2ExweEmddfUMqby3ZSXFap9U9kZ/q3DiAhNYd3V+zRGqkqzwzqyKDQIIt8XYv22A1+CHQ6DIe3Yti9xuxx20EPom/arvqGHYqDM2VTnwOg8WvfoWZrnyGmolwql31lWX9yJh+vP6LV37k5j/dtY15/4SXeXLWP4vIqrf6B7ekf4kvBpQpeXbabY+n5DOvYjNeHWvbNtdwKn8vif88tMzDR6XR8NfV9ht41mtTUDHbtXMOq2PUkJZ265fMbcu2SL/mSf+18a21Wx24g6Xhtm8cee5D8gkJCQ/sR/cAwPnj/n4x5+FnatW1FdPRwOncehL+/D3FxCwgLG8DJk2fo0XNITf65s/tYsWItAB9MeYN33/uctes2c+fQQXw45Q0G3/HADe8bnU7Hqx+8yAsPvkpWRjZz1sxk27rtnDt1vqbNxbQs3n3pQx56ZpTF8yvKKxl3x5NXzDeaTHwwL45ZE8bg4+bMQ+99R0Tn1gT71+7cfrv6V4Z0DyV6YDeS07MZP3UhcR2f56etBwH46Z2nyS0q5bkvFzB/0hPodMpl+Sofbkrk6/t64ONkz5j5OwkP9ibYw7GmzasR7Wr+vuDgeU5kFwGw7UwWSVlFLHy4L1VGE08u2cNtzb1wbGRzWb6JKYs3MfP5+/BxdWLMx/MJ7xBMsJ9Hbf1rdxPZtTXRAzqRnJHL+Bk/E9f+Ce7u2Y67e2qvfSoth5e/WWkxKDGaTExZupWZMcPwcXVkzOdLCG/fgmBf99r89fuI7BxCdL/2JGfmMX5WLHFvN8dgNPHGD7/w3sO30ybAk4LScmz0Osv8JfHMfG6Elv/pIsLbtyTY7/L8vUR2aUV0/w4kZ+QxftZK4sIe5e4ebbi7h7aTeyo9h5e/XW0xKDGaTExZtJGZL4zU+uejeYR3DDHvn7hdRHZrTfSAzlr/TF9G3Hst2XDgJFUGI0snPUJZZRX3TZ7L0B5tCfBwMd9+5ixn1j+fwsfDhYfe+IqIbmEEB/rUtPl8XixR/bsxLLw7u4+eZurCOD54bjT2jex4L+ZBmvl5kZVXyOg3ptK3YxucmzQ2r3/VXmY+OggfZwfGzFxLeNtAgr1ra/h2y1Ei2zclumdrkrMKGf/DZuJeCSDE25X5zwzFRq8ju7iM6OmrGdAmwPw9UBTs7hhLxaJPUYvzsH/kLYynD6Hmptc0qdq0kKrqv9t0HYzOp1nt8w2VlM99mysxmlSmrD3MzIf64ePcmDGzNxPeyo9gL+fa+n89TmS7QKK7tSQ5u4jxi3YQN34ojWz0PBceyumsIk5X/05cj1vhc7mhU2Upl1VXXcqlKIqLoigfKopyXFGUPEVRchVFSaq+z/VGFtKzRxeSk89x9mwKVVVVLF68gmFRQxpEfkOuXfIlX/KvnW+tTVRUpFmbqKhIfvhhCQA/LVvNwIH9au5fvHgFlZWVnDt3geTkc/ToYX50ctCgfpw5c56UlDQAVFXFydkJAGcXJ9IzLv7h2q8mtEtbUs+lkZ6SgaHKwIYVmxgwpM6MRmomp5PO/KFLWx49m06QtzuBXm7Y2ugZ2jOM+EMnLdqVlFdof5ZV4OWq/bvPZGTTs11zADycm+DkYM+xc+lmzzuaWUCQqwOBrg7Y6nUMaeNLfLL1vgJYeyKDodWzImfySuka4IaNTkdjWxtaeTqx41y2ef65TIK8XAn0dMXWRs+Qbm2IP5Js1kZBobS8srZ+lyYWrxu37zhDurWxuP/o+SyCPF0I9HTR8ru0Ij7hbJ18LsuvrMnfeSKFVv4etAnwBMC1iT16na5O/sXq+qvzu7YmPuHMlfPLK/BytlL//pMM6dbasn5r/XP4tHm+Yr1/FKCsogqD0URFpQFbGz2O9nbm+adTCPL1JNDHA1sbG4b26Uz8vmNmbZJTL9KzfQgAPcOCid+vPd7cz4tmftpAytvdBXdnR/KLSszzU3MJ8nAi0N1Jq79DM+KTLljpn6rq/qnEy0kb2DS2s6kZhFQajCgo1KXza4lakIVamA0mI4akPehbdbFo9xt9aG8MSbuu+HhdR9PzCHJvQqBbE237Dw0k/mSGef0KlFZU119RhZejfU39XYI8sbPR/+7Xu9yt8Lks/jdd6xyTxUA+EKGqqruqqh7AwOr7Ft/IQvwDfLmQWvufTmpaBv7+vg0ivyHXLvmSL/nXzrfaJsB82U+Avy+pqdpOgdFopLCoCA8PN/wD/GruB0hLzSTA3/y50Q8MY9HiFTW3X331X3w0ZRJnk/fy8Ydv8sakKX+49qvx8vUiK712ZzwrIxsvP6+rPMOcXSM75sTN4j+rZjBgaD+Lx7Pyi/F1qz166+3mxMX8YrM2McMGsHpXAne8NpXnpi5k4mht56N1oA9bDp3CYDSRmp1P0vkMLuabH9nNKqnAx6n2CLiPoz3ZJRVWa00vKiO9sIweQdrR/NZeTuw4l0NZlZH8skr2Xcgjs6TcPL+gBF83p9p8V0eyCsx3bp+5uzer9yYR+ca3jJ/xMxOjB1q89voDJ7mzu+XAJKuwBF+32tkdH1dHsgpLzfOH9mT1/hNEvj2X8d/EMvH+/gCczypEUSDm65U8+Oki5mw8QF1ZBaX4utbNr1P/nb1Yve8EkW/OZvzMVUwcGW6l/lPc2dVyYGLRP25Olvl392H1niQi/zmL8dOXMXHUYABu79qaxo1sueP1mQyd9A3jbu+Oy2WzGQBZ+UX4etQeA/X2cOFifqFZmzbN/Ni4JwGAjXuPUlpWQUGxeR8mnE6hymAkyMfD7P6sojJ8XRxq63dxIKu4zLz+QR1ZffgskZ8sY/wP8Uy8u3YpXMKFHO77KpaR01YzaVhPixkrxckNtSiv5rZanIfi6IY1irMHOhdPTOeTau+0saXRuLdoNHaS1QFNVnE5vpdv/86NLevv347VRy8Q+dUaxi/awcQhnay+/vW6FT6Xxf+may3laq6q6keX36GqaibwkaIoj1/pSYqiPAU8BaDoXdDpLI/ACCGEAFtbW+65J5JJb35Yc99TT43jldf+xfLlaxg5MopvZ33GkDsfvIlVWndvz1FkZ+bg39SP6Uu+IDnpDGnn06/9xMvE7TnGsL6deGRIbw4np/LGdyv46Z2nGdGvM2czcnjove/w83ChU3AgOt0fv17LuhMZDG7tg756KVifZp4cyyzk0UW7cGtsR0d/V/SK5VHva1m77wTDeoUx7vZuHD6TzqTv17L0jXE1S84SzmZgb2dDiL/nH6p77YFTDOvZlnEDu3D4bCaTfvyFpf8YjdFk4uCZDOZNeAB7Oxuenr6C0CAverW2PM/hqvn7TzKsV1vGDerK4bMZTPphPUtfH1Nb/7lM7O1sCfH3uEbSFfL3HWdY7zDG3d5d65+5a1g66VGOnstEp1NYP+Vpii9V8NhnC+ndtimBnte3GGPCmHuYMvdnVmzZR7d2LfF2dzHbTrLzi3hjxkLeixn1h7aftUfOMaxrMONua8fhlGwm/bSDpePvQadT6BDkybIX7uFMViFvLtvJba38aWT7x2Yg9O16YTixD9Tamcnyr19FLSlAcfGi0ei/Y8pORS3IvkqKlfoTUxnWsRnjerficGouk1buY+lTt6P7A9u6EH+Fa/2WnlcU5e+KotQs6FQUxUdRlH8AF670JFVVv1FVtbuqqt1/76AkPS2ToED/mtuBAX6kp2f+rufe7PyGXLvkS77kXzvfaps08yUTaemZBAZqMyF6vR4XZ2dyc/NJT8uouR8gINCXtPTa5w4dOpCDhxLIysqpuW/swyNZvlw7SXbp0lUWS7+up/aryc7Mxvuy8z28/bzIzvj9Oz7ZmVrN6SkZHNhxiNbtW5k97u3mROZlsxxZ+cX4XHaEHWD5r4cY0kM7F6NTcCAVVQbySy5ho9fx2oORLH77b0wdH01xWQXNfNzN8x0bcfGyI8QXS8rxcmxktdZ1ly3j+s2TvYJZ9PBtzLy/B6oKTd3M/7/ydnUk87IZnosFJXhfNgMBsHzHUSKrlzl1aulPRZWBgtLamtbuP8HQbm2t1uTt4khmfu0Mw8WCErzrLAVbvjuRyM7aUqVOLXypMBgpKC3Dx9WRrsH+uDk2prGdLf1Cm5GUav7eebs2IbOgbn6d+nclEtmlVXW+X01+Tf0HTjG0m/n7esX+yS+2zN9xlMiul/ePlh+3N4nbQltgq9fj7uRA52B/jp03X4bn7eZMZm5Bze2s3EJ83FzM27i78MWER1j84cs8P2ooQM15JCWXyhn/8WyeHzWUjq2aUZe3c2MyCy/V1l94CW8n81mb5fuTiWzfVKu/qRcVBhMFl8xn5Vp6u+BgZ8PprAKz+9XifBTn2m1WcXJHLcm3qAPApl1PjEm7zZ9fouWphdmYUo6bn38CeDvZk3n59l9UZln/oXNEhgZo9Qd6aO/vJeuzitfjVvhcbvBMt/jPTXKtgckowAPYUn2OSR4QD7gDlmdi/gl79x0iJKQFzZsHYWtrS3T0cFbFrm8Q+Q25dsmXfMm/dr61NrGxG8zaxMZuYOxY7WPx/vvuJj5+e8390dHDsbOzo3nzIEJCWrB3b+0VtkZFD2fRohVmWRkZFwkf0AeAQQP7ceq0+XkH11P71SQdOkFQi0D8gnyxsbXhjuGD2LZ+x+96rpOLI7Z2tgC4uLvQsUd7zp48Z9YmrLk/KRfzSM3Op8pgZO2eY4R3Ml8S5Ofuwu4k7Xln0nOorDLg7uRAWUUVlyq0cxN2HjuDXqeYnTQPEObrQkr+JdIKL1FlNLHuRCYRLc1PMAc4m1dCUUUVnfxqj8YbTSoFZVr+yexiTuUU06eZ+axAWDNfUrLyScsppMpgZN3+E4R3ML/ql5+7M7uPp2j1Z+ZSaTDi5qjtHJpMKusPnGRod8tlUABhTb1JySkkLbdIyz94ivD2zc3zXZ3YfTK1Oj+PyioDbo6N6ds2iNMZuZRVaudp7E9Op2WdgVtYUx9SsgtIy62u/8BJwju0MM93c6yTX6f+g6cYamUZV23/FJj3T8fgOvlO7D5R3T8ZuVQatPr93J3ZU31/WUUVCWczaFG3/uAgUjJzSM3Ko8pgYO3OQ4R3CzVrk19Uismk7UV9t2ITIyJ6AFBlMPDy598T1b8bd/TqaL3+AA9ScotJyy/R6k84T3jbwDr978DuZG2H+ExWofb+NmlEWn4JBqP2uukFJZzLKcLf1XxQaco4i+LmjeLiCTq9Nvg4fdCiDsXdF+ybYEq77PycRg6gr17U0tgRXUArTDnms5Fh/m6k5JWQVlCqbf+JqYS3Nh98+zk7sLv6ynNncoqoNJhwc7A+eL8et8LnsvjfdNWlXKqq5gP/qP4xoyjKY8CcG1WI0WjkxZcmsWb1fPQ6HXO/X0RiouVJkrdifkOuXfIlX/KvnW+1TdJJ3n7rVfYfOExs7AbmzFnI3DlTSUz8lfy8Ah4e+ywAiUknWbp0FYcPb8JoMPLii5NqdqQcHBozePAAnn1uotnrPRPzdz777B1sbGyoKC8nJubv9dI3RqORT9+YytT5n6DT64hdGMfZk+f422uPcfzwCbat30G7Tm346Lv3cHJ1pN8dffjbq4/y0MDHaN6qGf/46BVUkwlFp+O/0+ebXc0LwEav4/WHhhLz5QJMJhMjbutMSIAX03+OJ6y5PxGdW/NK9O1M/n41P27YjaIoTH48CkVRyCsuJeaL+egUBW83J95/crhF/TY6Hf8YFMqzy7TLvQ4PCyTY04kZO04R6uNCRLA2SFl3IoMhrf1QLlu+YjCZeHyxdoTa0c6G94d2xKbOUh8bvY6J0YOImb4Mk0lleJ8wQvw9mRG7g9CmPkR0DGbCfQOYPH8D8zYfABTeGTuk5nX2n07F183pisuTbPQ6Jt7fn5iZK7X8Xu0I8fNgxprdhDb1JqJ9CyaMuI3JizYzb8thALndgg4AACAASURBVN55aDCKouDsYM/YiM6M+XwJCgr9QpsxIKy5Zf7IcGJmaJfbHd47VMtfvUvL79CSCSP6M3nhJuZtPgiKwjtjbq+tPzkNX1dHAj1d6pZemz9qEDHTftLy+7TX+mfVdkKb+RDRMYQJ90cwed565m06AAq8M3YoiqIwakBn3vphHfe9OxdUlWF92tO6zlW/bPR6Xn90BDFTvtW2n4iehAT5Mn3JOsJaBBLRPYx9Scl8tTAOgG7tWvLPx+7V3vOdhzlw/AyFJaWs3LoXgMnPjKJt8wDz+u/pTsz3m7T+7xpMiI8rMzYeJtTfg4h2gUwY2o3JK3Yxb8dxrX/u64OiKBw8n8XsrYnY6HXoFHj9nh64NbE37yDVROWGeTSKfgUUHYaEbag56dj2G4Ep8xzG09oBCpt2vSxmS3Se/tgNeUS7dJOio2r3arOreYG2/U8c0pmYBdu1+js1I+T/2Lvv8Ciq/Y/j79lNAoH0XoGQ0BJ6lZpICSBS1YAiNrwqV7BguSioF1RQ7AUEUYqIVBUkEAi9CYGETkJCEiCk90rKZnd+f2xMMtkUBBeS3z2v58kD2TnzmW/O7uxOOTPraMXSQ5H4utoQ0N6N2cO7sGDnadad1O/0zB/bq/L5Hf3tLopKNWi0Og7EJPPdo4bXidWlMbwvC/8/SbL89++0AiBJUoIsy60aamdi5n57CxAEQWjEjD1GW3eb7823ordD7UNz/ikHN043ar7uUrhR8yWfTg03uhOaMuPm38G1OLfExKzhNndAsjXuRc5ybO3fCfSP5V+t/QznP0VyNV7/WD77k9Gy75bysqQmcQFN5mj/Rr197BBy6J70Y71nTCRJOl/XJMC5jmmCIAiCIAiCIAh/S0N35XIGRqK/PXB1EnBrA5EFQRAEQRAEQRAa0NCOSTBgIcuywXlPSZIOGqUiQRAEQRAEQfj/THzze60auvi9zoHCsiw/9s+XIwiCIAiCIAjC/yIjXyEnCIIgCIIgCILQsIaGcgmCIAiCIAiC8A+SxVCuWokzJoIgCIIgCIIg3HNix0QQBEEQBEEQhHtODOUSBEEQBEEQhLtIDOWqndgxEQRBuA3G/GZ2Y3tfa9zvx5VLi4yaL1laGTUfyciDCYz9zew6I2/xaMuNm2/k1w+WNsbNb2Fu1Hipc3+jZVuabTZaNkBBWbFR84WmTwzlEgRBEARBEAThnhM7JoIgCIIgCIIg3HNix0QQBEEQBEEQ7iJZ17h/boUkSaMkSYqWJClWkqQ5dbQJkiQpUpKkS5Ik/dJQprjGRBAEQRAEQRCEWyZJkhpYAowAEoFTkiT9IctyZLU27YC3gIGyLOdIkuTUUK44YyIIgiAIgiAIwt/RF4iVZTleluUyYAMwvkabfwFLZFnOAZBlOb2hUHHGRBAEQRAEQRDuJlm61xXcKXfgRrXfE4F+Ndq0B5Ak6RigBv4ry/Ku+kLFjokgCIIgCIIgCJUkSXoOeK7aQ9/Lsvz934wxAdoBAYAHcFiSpC6yLOfWN4MgCIIgCIIgCAIAFTsh9e2IJAGe1X73qHisukQgTJZlDXBVkqQY9Dsqp+oKFdeYCIIgCIIgCMJddK/vuvUP3JXrFNBOkiQvSZLMgCnAHzXabEV/tgRJkhzQD+2Kry9U7JgIgiAIgiAIgnDLZFkuB2YCu4EoYJMsy5ckSVogSdK4ima7gSxJkiKBA8Absixn1ZfbqIZyjQwM4PPPF6BWqVi5aj2LP1nSZPKbcu0iX+SL/Kabf6fZ9vd3o8MHTyGpVSSt28+1b7bV2s5pTF+6rXyNsMC3yD+nP+Bl4duKTp/8CxMLc2RZ5uTIt9GVahTzHbt0jcVbDqLT6Zg4sDPPBPZVTE/Jzuedn3ZTUFyKTifz0vhBDO7sxY6TUazZG1HZ7kpyBuv/M5WOnsq7TR6LTWHx7rPodDITe3jxzKBOyvy8It7ZepKCUo0+f1hXBrdz5XhcKl/vv4BGq8NUreLV4V3p6+Vs8Hcfu3SVxZsPoJNlJg7ozDMjldd2pmTn886aXRQUl+jzJwxmcOe2aLRa5v8cyuUb6Wi1Oh7s58v0UTWvC4VjkddZ/Nthff39fXlmRO8a+QW88/Meff/IMi+NHcBgvzbsOBXNmv2nq/VPJuvfmEJHD0dlftR1Fv92FJ2sY+J9vjwzvJcyP6eAd9btq+r/sfcx2LcNADHJmXyw8SCFpWWoJIl1sx+hmalys+FY5DUW/3qoon4/ngnsY9g/f9Wv0/HSuIEM9vNix6nLrNlX/fnNZP2bjxnUr1jW+St8/Msu/WtpSE+mPzhYMT05M5f3ftxGTkER1i3NWfj8JJztrOvMuyv9cz2LT47EoJNlJvi68UyvNorpnx6J4VRSDgAlGi3ZxRqOPOcPwJfHrnDkehayLNPP0443B7dHkpQXTB87e5mPf/pD3yf392X6+KHKPsnI4b3lm8jJL8TaogULX3wUZ3sbLl9L4sOVv1F4sxS1SuLZicMY1b87w4YPYdHieajVatau2cSXny9X5JmZmfHdik/o3r0z2dk5PPPky9xISKJnr658+c0HAEiSxEcLv2bH9j0APP/vJ3nyqckgSaz44We+/uaHep+Tvxj7fVm4c7Is7wR21njs3Wr/l4HZFT+3pNHsmKhUKr7+6kNGPfAoiYkpnDi+k+3BoURFXWn0+U25dpEv8kV+082/42yVRMePnuF00IeUJGfRb/ciMnaHUxSjHCasbtmcVv96gNyIqlxJraLzkplcfHEJhZHXMbW1QKcpV8yn1elYtGk/y2ZNwtnGkqmLf8G/izfervaVbVbsCiOwZ3uChnQjLiWLmUu3EtJ5OmP6dmJMX/1OxpWkTF79/g+DnRKtTseikNMse9wfZytzpv6wF/8Obng7Vm2MrjgSRaCfJ0G9fYjLyGPmL0cIeflBbFs046spg3CyNCc2PY8Z6w6z59Wxhvkb97HspYf19X+8Dv+uPsr6Q04Q2Ks9QUO66+tf8hshH7Rlz+kYNOVatsx7kuIyDZMWrGZUn46421sr8zcfZNmLE3C2sWDqpxvx79wWb1e7qvzQUwT2aEfQ4C7EpWQzc/kfhPg9xZg+HRjTp4O+f5IzeXXFDoONeq1Ox6Ith1k2Y5w+//PN+Hf2wtulen44gd19CBrUmbjUbGYuDybkvTaUa3XMXbuXDx4fTgd3B3KLSjBRqwzzNx9k2YsT9fmfbMC/S1tl/+z+q/6u+v5Zto2Q+V6M6dORMX06Vqs/uN6dEq1Ox8K1O1n+xjSc7ax4bP4KAnp0wNu96jXx+YZQxg7sxrhB3QmLjOerzftY+PykejON2z8yHx2K5rvxPXC2aMbUTafw93LA286iss3rg9tX/n/9uRtEZxYAcDYll7MpeWyaot+ZffrXcCKScuntYavsk1W/s/zt53C2t+axuV8T0MsPb4+qHezP1wUzdnAvxvn3JuxiLF9tCGHhi4/SvJkZH8yYQmtXR9Kz83h07lcM6NqRTz7/LxPHPUlyUir7D/9GyM59RF+Orcyb9uQj5OXm0avbMCY9PIb/vv8m0598majIGO4fPBGtVouzsyNHTgSza+d+2ndoy5NPTWaY/yTKyjRs/O0HduzcS1zctTqfFzD++3JjIOua/F25jOK2h3JJkhTyTxbSt08P4uKucfVqAhqNhk2btjFu7Mgmkd+Uaxf5Il/kN938O8227unDzatpFF9PR9ZoSd36J46j+hi0854zmWvfbkNXUlb5mH1AVwojEyiMvA6AJqcQdLJivovXUvF0tMHDwQZTEzUje3Xg4Pk4RRsJiaKK3MLiUhytWxosPyT8MiN7dTB4/GJSNp62FnjYWmCqVjPSrxUHo5Nr5ENRxVmcwhINjpbmAHR0tcWp4v/ejlaUarSUlWsbrv9crDJfqr1+CSgu1VCu1VFaVo6piRqL5mbK/OtpFfnW+vye7Tl4QTn8WoKq/JJSHK1q6Z+IGEb2am/w+MXr6Xg6WFfl92jHwQtX684vLqus/3h0Au3c7Ong7gCATcvmqFWqGvlpyvxetdQvVa+/DEdrC2oKCY9mZE/D+hXLik/C09kODyc7TE1MGNWvMwfPRCvaxCVn0LeTFwB9O3lx8Mzl+jON3T9p+Xham+NhbY6pWsXIds4cjM+ss55dV9IY1c65YrkSZVodGp2OMq2Ocp2MXYsar5/YBDxdHPBwttf3Sf/uHAy/pOyTxDT6dvbR94mfNwcj9NPbuDrS2lW/I+hkZ42dlQXFKnPi469z/doNNBoNv23ZwQNjhivyRo8Zzvp1vwOw7fdd+Af0B6C4uAStVr/+NGveDP2BcmjfwYfwU+cqpx8+coKJE0bX2Qd/Mfb7stB41btjIklSzzp+egHd/8lC3NxduJFY9YGSmJSCm5tLk8hvyrWLfJEv8ptu/p1mN3OxozS5arhvaXIWzVxsFW0su3jR3M2ezL1nFI+38HZDlmV6bHibfns+ovWL46gpPbcQF1vLyt+dbSxIzy1UtHlhzH3sOBVF4NwVzFy6lTlB9xvkhJ6OYXRvwx2T9IJiXKxbVOVbmZNeUKzM9/djx4UEAr/Yzsz1R5gzqodBzt6oRDq52mBmoq6/fltL0vNq1t+fHSejCHx7OTOX/MacycMAGN6zPebNTBnx1jJGzfueJ4b3xrqleY38IlxsqjbUnW0sDPNH92NHeDSB76xk5rLtzHnYv5b+ucLoWjbs0/MKcbGtmV+kzB/Vlx0R0QS+t5qZ3wcz5yH98Kjr6XlIEsz47g+mfLqRVftOU9MtPb+j72PHqcsEvvMjM7/bVnv9Z64wupYdK8WycvJxsbOq/N3J1oq0nHxFmw6tnNkXEQXAvogoikrKyC28WXemsfunqARny+ZV+RbNyCgqrbWW5PxikvOL6eOhP1vTzdWa3u62jFh5lMBVRxjQyp62dsqd0vScfFzsbar6xN6atJw8ZZ+0dmXfyQv6Pjl1kaLiUnILlH/jhdgENOVaXFycSUpMqaopKRVXN+XwRje3qjZarZb8vELs7PXvGb16d+PPUyEcC9vB7JffQavVEhUZQ/8BvbG1s8HcvDmjRw3Fw8Ot1j5QLMfI78tC49XQUK5TwCH0Bw1qsqnlMUB572NJbY1KZXiERxAEQWjkJIn286dx6eXvDCepVdj260jYyLfRFpfSa8s7FJyPJ/vIxb+1iF3h0Yzr58cTw3txLj6ZeWt2sWXuE6hU+o+dC1dTaG5mgo+bw239CbsuJjCuWxue6N+Bczcymbf1JFtmjERVMVY/Nj2Pr/ad57uphhvMt1b/Zcbd58cTw3vr61+9ky3znuLitVRUKonQRc9TcLOUpz/bwH0dW+HhUOdHZ+35ETGM69eRJ4b25NzVFOatDWXLW1Or+udaKs3NTPFxs28gqY7801cY17cjT9zfg3NXU5n38162/OdRtDodZ+JTWDf7EZqbmfD8km34ejrSr71nw6GK+qMZ18+XJ4ZVr/9xZf2mt//8Vjd7ciCLft7JtqNn6dWhNU62lpXP8+0ydv/8ZfeVNIZ5O6Gu6JeE3JtczSli91MDAXhh2xlOJ9vR0822vhgDs6c+yKLVW9l2KJxendriZGeNqtqZnYycfOYu3cAHMyYbXL/yd0WEn2NAn9G07+DN0uWL2Rt6iJjoOL764nt+27aamzdvcvbcJbTaW7vl0/93t3jnq/85DQ3ligKel2X5/po/QJ3nI2VZ/l6W5d6yLPe+1Z2S5KRUPKvtRXu4u5KcnHpL897r/KZcu8gX+SK/6ebfaXZpajbNqm3QNnOzpzQ1p/J3E4vmWHT0pPdv7zLo1DdY92pH95/ewKpbW0pSssk5HoUmuwBdcRmZe89g2cVLke9kY0FqTkHl72m5hTjZKIfy/P7nRQIrjpZ3a+tGqaac3KKqsx67IqIZ1atjrfU7WZqTmld1RDwtv7hyeFZl/tmrBPrqNxa7eTpQWq4l92ZpRfubzN50jPfH98PTznCIkUH9OQU4WddSf8/q9WvJLSom5FQUA329MFWrsbNsQXdvNy5dT6uR35LUamcY0nILDfNPRBLYo50+38tVX3/1/jl9hVG92tXeP9YWpObUzFd+Jv8eFklgd5+KfJfKfGcbC3p6u2FrYY65mSmDfFsTlZhRf//U9vwev0Rgz2r11/r81n+2BPRnSFKzq86QpOfk42xrZdDmi1lT2LTgBWY9pL8I3KrGWSpFe2P3T8vmpBWUVOUXluLYslmttey+ksao9lVnJw7EZ9DFxZoWZia0MDNhYGt7zqcqzxA52VqRmlX1PXXpWXk42yov9neys+aL2U+y6aNXmTV5lKJPCm+WMHPxSmZNHkXXdq2RNSW4e7hWzuvm7kJKsvI1m5ycVtlGrVZjZW1BdlaOok1MdBxFRTfp5Kt/Xn/+aTP3D57AmJGPkZubx5Ur9d4tVr8cI78vC41XQzsm/62nzax/spBT4Wfx8fGiTRtPTE1NCQoaz/bg0CaR35RrF/kiX+Q33fw7zc4/E0eLti40b+WIZKrGZcIAMnaHV04vLyjmkO+/ONpnFkf7zCIv4gpnn/iE/HPxZB04h0WnVqjMzfRnTwb4UhSTqMj3a+1CQnoOSZl5aMq17I6Ixr9LW0UbVzsrwi4nABCfmkVZuRZbC/2Gk04nE3o6hlG9a99w9XO3IyG7kKScQjRaLbsvJeDfXjlMxNWqBWFX9RtX8Rn5+vwWzcgvKWPW+iO8PKwrPVrVfrReX3+usv6u3sp8W0vCoivqT8mirLwcWwtzXO2sOFnxeHGphgtXU/BytlPmt3ImISOXpKyK/NMx+NfYuXO1tSCsol/jU7Mp09TonzNXGFXH9Rl+rZxIyMwjKStfn3/mCv6d2yjzbSxr5OvrH9DRk9iULIrL9NfJRMQl07au+iv7J8bw+bW1JCz6Rv3113L9kMHf4uVGQloWiRk5aMrL2RV2Ef8eyvlyCorQ6fSHoX8MPsqEwYbD9u5q/zhbkpB3k6T8YjRaHbuvpBHgZfhau5pTRH5pOd1cqnYqXCybE5GUQ7lOh0ar43RyLl62LRTz+Xl7kpCaSWJ6tr5Pjp/Fv5evsk/yq/XJtv1MCNBfQ6YpL+fVz9cwdnAvRvTrCoBclIu3d2tatfbA1NSUSQ+PIWTnPkXerp37eHTqRADGTxzF4UMnAGjV2gO1Wj8U0tPTjXbt25KQoL+JhoOjvl88PFyZMGE06zf8Xudz8hdjvy8LjVe9Q7lkWd5Sz+S/dz6xAVqtlpdfmcfOHb+gVqlYvWYjkZExTSK/Kdcu8kW+yG+6+XeaLWt1RL+1kp4b3kZSq0hef5Ci6ES833yE/HPxZOyOqHPe8rwiri8Lpt+uhQBk7j1jcB2KiVrFnKChzFjyGzqdzPj+fvi4ObA0+E98WzkT0NWb2ZOGsOCXPaw7cBqQmD9tZOWQkojYRFxsLesc/mSiUjFndE9mrDuMTpYZ390LHydrlh64iK+bLQEd3Jkd2I0F28NZFxajzx/fF0mS2HgyloTsQpYfjmT54UgAlj0+BLuWVdcEmKhVzJk8lBnf/opOp2N8/876+rcfw7e1MwFdfZj9UAAL1oWybv9pkGD+tFFIksTkId15d+1uJr2/GmSZcf07077GXadM1CrmPOzPjKX6272Ov88XH1d7lu44gW8rJwK6tGX2hMEs2LCfdQfOgCQxf+rwqv6JS8LFxgIPh9pviWuiVjHnocHMWPaHvv/7ddLn7wzT53f2YvaEgSzYeIB1h84BMP+xYUiShFWL5kwL6M7UzzcjITHItzVD/NoY5j8SwIylW/X9X1n/cf3z26UtsycOZsH6fRX1w/zHRyjrt7Wss37lstS89fgDzPh0LTqdzITBPfBxd2LJb/vx83IjoEdHwi9f4+st+g3pXh1a8/a0MQ1kGrl/VCr+M6QD/952Bp0M431d8ba3YGlYHL5OVgR46V8Pu2PSGNnOWTGUari3E6cSswlaHwbAgFb2+HvVfP2oeeupCcxYtAKdTseEgL74eLqwZPNu/Lw8COjtR3hUHF9v0N+rqFentrz9tH6nYvfxc5y+HE9eYRF/HNZ/CfeCFyaz8IOt/Lp1FWq1mnVrN3M56gpvzXuZs6cvErJzH2vXbGLZD58RcW4fOTm5TH/qFQD69+/Ny689T7lGf1vu1199r/JMyk/rlmBrZ0u5RsNLL80lL0955qc2xn5fFhov6a87J/ztGSUpQZblVg21MzFzv70FCIIgCEYRYjvIqPmD1hlewP6PSktquM2dcLm96wRuWXlZw23uhM7Ig9fVxv2mAcnyHz3uaUDOzWi40Z3kx9Z/N7A7pRo4ymjZrkNu+esmbktBWXHDje5QeVlSk7gPb1L/oY16+9j9+P570o/1vrtIknS+rkmA4TdRCYIgCIIgCIIg3IaGDns4AyOBnBqPS8CfRqlIEARBEARBEIT/OQ3tmAQDFrIsn605QZKkg0apSBAEQRAEQRD+HxO3C65dQxe/T69n2mP/fDmCIAiCIAiCIPwvauh2wYIgCIIgCIIgCEZn3FtrCIIgCIIgCIKgIOuaxM3D7jpxxkQQBEEQBEEQhHtO7JgIgiAIgiAIgnDPiaFcgiAIgiAIgnAX3eb3m/+/J3ZMBKGJMvbo1Kb+nqmSjNtDuib8qTJosZdxF3Atxrj5NnbGzdeWGzff2N/Mbmyqpj3YQnJuY9T8ws9/NWq+hbW10bJbWTgZLRvgUvZ1o+YLTV/TfncRBEEQBEEQBOH/BXHGRBAEQRAEQRDuInFXrtqJMyaCIAiCIAiCINxzYsdEEARBEARBEIR7TgzlEgRBEARBEIS7SAzlqp04YyIIgiAIgiAIwj0ndkwEQRAEQRAEQbjnxI6JIAiCIAiCIAj3nLjGRBAEQRAEQRDuoib8Hb1GJc6YCIIgCIIgCIJwzzWqHZORgQFcuniYy5FHefONF5tUflOuXeQ3nfzq7d6opZ2ZmRnr1n1HVORRjh3dTuvWHpXT3nxzJlGRR7l48TAjRvgD0KxZM/48FkxE+B7Ont3Pu+++Vtn+/oCBnAzbxdkz+1j545eo1eo7rv923Up+Q23MzMxY9/NSIiOPcvRIjb5540UiI49y8cKhyr5p374tp07urvzJzIhi1qzpAHTr6suxI9sJPxXKieM76dO7+x3Xf7vuNPtYXBrjl+1h7HehrPwz2mB6St5Nnv35CJN/3M8jK/ZxJDYVgNybpTz78xH6f/IHi3afu/XlXU1n/A8HGbviACvDYg2Xl1/MsxuOM3nNER5ZdZgj8en158UkMf6LbYz9bCsrD100zMst4tkfQpn8bTCPfL2dI9FJBtP7z1/PmiOXas+PvMb499cwdv5qVoaeMszPzufZr39l8se/8Miinzly6SoAO05dJuijdZU/PV76isuJGYb5UdcZ/+E6xn6wlpV7Iwzzcwp49tutTP5kI498vIEjkdcqp8UkZ/LEF1uY9NEvPPzxeko15Xc//9I1xs9fzdj3VrIy9GTt/fPlZiYv+plHPlzLkYsV/XMyiqCFP1f+9Jj5BZdvNPBcn7/CuDnf8OCbX/Fj8BGD6cmZufzr4zU8PG8p0xetIi07r948gGNnLzNu9mIefOUjfty23zAzI4d/fbCch9/8jOkLviMtKxeAy9eSmPbuN0x8/VMefvMzdh0/W2u+ae++2K5ci93qdZhPfqzWNs2G3I/tD2uwXbEay7feqXzceuFi7H8Pxur9RXXXH5fK+O9CGbt0dz3r72Em/7CPR1bsrbH+Hqb/4m0s2lV77QPu78e2o+vZfnwTz8ycZjC9533d2RC6iojEwwx/8P7Kxzv4teOn4O/57dDPbN7/EyPHD6uz/voY+3NFaJwazVAulUrF1199yKgHHiUxMYUTx3eyPTiUqKgrjT6/Kdcu8ptOfs12Ycd3Elyj3TNPP0puTh6dfAcRFDSOhQvnMnXqDDp1asfkoPF06z4UNzdndoVswNdvMKWlpYwIDKKo6CYmJiYcOvg7u3cd4OSpM6z88UsCR03mypV4/vve6zwx7RFWrd7QKPuntjY7gvcQdbmqzdNPTyEnNw9f30EEPTKOhR++zdTH/02nju0IChpP94q+CQlZj5/fEGJi4unTd2Rl/rWr4WzbtguAhYvm8v4Hn7Nr9wFGjxrKR4vmMmzEI7ddvzH7pj5ancyi3edY9uhAnK3MmbrqAP7tXPF2tKpss+JYNIGd3Anq1Za4jHxmbjpOiI8LzUzUvOjfidiMAmIz8m99eXsusSyoH86WzZm69ij+3s54O1hWLe/4FQI7uBHUozVxmQXM/PUUIc8PrSNPx6LtJ1n29HCcrVow9bsQ/Dt54O1kU5V34DyBXVoT1K8Dcem5zFyzn5A3JlVO/2xnOAPbu9Wdv/kgy16ciLONBVM/2YB/l7Z4u9pX5e8+RWCPdgQN7kpcShYzl20jZL4XY/p0ZEyfjgBcSc7k1RXBdPRwNMzfcphlM8bp8z/fjH9nL7xd7KryQ8MJ7O5D0KDOxKVmM3N5MCHvtaFcq2Pu2r188PhwOrg7kFtUgoladffzN+1n2axJONtYMnXxL/h38Vb2z64wAnu2J2hIN33/LN1KSOfpjOnbiTF9O+n7JymTV7//g46eTrU+D38ta+HanSx/YxrOdlY8Nn8FAT064O1eNc/nG0IZO7Ab4wZ1Jywynq8272Ph85Pqz1z1O8vffg5ne2sem/s1Ab388PZwrspcF8zYwb0Y59+bsIuxfLUhhIUvPkrzZmZ8MGMKrV0dSc/O49G5XzGgawesWppXLUClwnLWK+T+5zV0mRnYfrucsuPH0CZcr2yidnfH/NGp5L7yInJhIZJN1Wv35uYNSM2a03zM2Drql1m06xzLHhukX39X1rL+Hr1MYCePqvV345+EzBxVsf76EpueX+v6q1KpeHvR6zwf9DJpKen8sutHDoYeIT7mWmWb1KRU3nn5A578t3KHq6S4hHmzFpBwNRFHZwfWh67kNOvqcAAAIABJREFUzwNhFOQX1vlc1LZ8Y36uNAbidsG1azRnTPr26UFc3DWuXk1Ao9GwadM2xo0d2STym3LtIr/p5Ndst3HTNsbWaDd2bCBr124G4NdfdzD0/kEVj49k46ZtlJWVce3aDeLirtG3Tw8AiopuAmBqaoKpqSmyLGNvb0tZWRlXrsQDsHfvYSZNfOCO6r9dt5JfW5uxYwPr7pvfdnB/Zd8EsqlG3/TpozwDMnToIOLjr5OQoD/aLssyllb6jWkra0uSU9LuqP7bdafZF5Oz8bRtiYdtS0zVKkb6enDwSoqijQQUlemPlBeWanC0aA6AuZkJPTwdMDO59Y+Riym5eNq2wMOmhX55Hd04GKvsOwmp2vLKcbRoVndeYhaedpZ42FliaqJmZNfWHIy6ocyTJIpKNfq8Eg2OVi0qp+2PTMDN1kKxI6PIv56Gp4M1Hg7W+vxe7Tl4Ib5GPhSVlFXkl+FobWGQExIezcie7WvJT1fm92jHwQtXa/RHtfziMhytWwJwPDqBdm72dHB3AMCmZXPUKtXdzb+WiqejDR4ONhX904GD5+Nq5EvV8ksr85X9c5mRvToYPK5YVnwSns52eDjZYWpiwqh+nTl4RnmGIC45g76dvADo28mLg2cu158Zm4CniwMezvb6zP7dORiuPHMWl5hG384++kw/bw5G6Ke3cXWktat+R9PJzho7Kwtyamx4m3TohDY5CV1qCpSXU3JwP2YDBinaNB89lpI/fkcu1M8r5+ZWTtOcOY1882bd9Sdn42lXY/2NqbH+SlS9/mtdf2s/E965hy83riaSlJBMuaacXVv3EjBysKJN8o1UrkTFodPpFI9fj79BwtVEADLSMsnOzMHWvvZ1rC7G/lwRGq96P1EkSbKSJGmRJElrJUl6rMa0pf9kIW7uLtxITK78PTEpBTc3lyaR35RrF/lNJ79mu6SkFNxrtKveRqvVkpeXj729Le5uLiTWmNfNXT+vSqUi/FQoyUnn2bvvMCdPnSEzMxsTExN69ewKwKRJY/DwrP2ocmPon1rbuLsq2uj7QP+hrdVqycvX942bu2vl4wBJiam4uynnDXpkHBs3bav8/fXX/8vHi+ZxNe4Uiz96h7nz6h5q0Zjfe9ILSnCxqjrC62xpTnpBiaLNC0M6sePiDQK/CWHmpuPMCex62/WmF5bgYll9ec1JL6yxvIHt2BGZROB3+5j560nmDOtcd17+TVyqbeg6W7UkPa9YmTe0KzvOXiXw41+ZuWY/cx7sA8DNUg2rD1/ihaF1/z3puYW42FadzXG2sSA9V7nx+cLo+9hx6jKB7/zIzO+2Medhf4Oc0DNXGN3LcMckPa8QF9uqHRlnGwvS84qU+aP6siMimsD3VjPz+2DmPKTfOLyenockwYzv/mDKpxtZte/03c+/lf4Zcx87TkUROHcFM5duZU7Q/TVjCD0dw+je9e+YpOfk42JXdSbAydaKtBzlkf4OrZzZFxEFwL6IKIpKysgtrHvDPj0nH5dqG8xO9tak5SiHf3Vo7cq+kxf0macuUlRcSm6Bsg8vxCagKdfi6WyveFzl4IA2o2p4mi4zA7WDg6KN2sMDtbsnNl9+i83XSzHt3bfOeg3qL6ixPlmZk15Q4/U/uGL9/XonMzf+yZyR3W4p28nVkdTkqoMG6SkZOLs61jNH7Tr36ISpqSk3riU13LgaY3+uCI1XQ4e6VqE/oPIrMEWSpF8lSfrr8NV9dc0kSdJzkiSFS5IUrtMV1dVMEIRGQKfT0btPIG28etOndw/8/PQbCFMf/zefffpfjh8LprCwCK1W10DS/0+mpqY8+GAgv/4aXPnYc889wWtv/Bcv7z689sZ8Viz/7B5WaFy7Lt1gXNdWhM4azbdB/Zn3RwQ6I95OZldUMuM6exA6YxjfPtSXeTvP3tHydp2/xrie3oT+5yG+fXIo8zYfQ6eTWbb/PFMHdqJFM9M7qzcimnH9fAl9fzrfzhjPvLWh6HRV9V64lkpzUxN83BzqSakn//QVxvXtSOj8p/j2uQeZ9/NedDoZrU7HmfgUFk4bwaqXJnHgfDxhMTcaDrzb+eHRjOvnR+iH/+Lbf09g3ppdyv65mkJzs9vvn+pmTw4kPPoaQe8uIyL6Ok62lqikOxsuM3vqg4RHxRM05wsiouJxsrNGVe3MUUZOPnOXbmDBC0GKx2+ZWo3a3YPc114mf+ECLF99A6ml4Vm327UrMpFxXVsT+tIDfDt5APP+CDfq+ludg5M9H37zLu++8iGyuAWVAVmWGvXPvdLQWuQty/IcWZa3yrI8DjgN7Jckyb6+mWRZ/l6W5d6yLPdWqQxP29YmOSkVT4+qI7Ie7q4kJ6fe0rz3Or8p1y7ym05+zXbu7q4k1WhXvY1arcba2oqsrBySklPxqDFvcpJy3ry8fA4eOkZgYAAAJ8IiCBg6if4DH+TIkROVw7put/7bdSv5tbZJUg5p0PeB/kyIWq3G2krfN8lJKZWPA7h7uJCUXDXvqFH3c+bsBdLTMysfm/b4w/z++04AtmzZbjD06+/Wf7vuNNvJsjmp+VVHWNMKinGybK5o8/u56wR2cgegm4c9pVotuTfLbqteJ4vmpBZUX14JThY1lnfhBoEd9M9HN3dbSsvrXp6TVQtSq50BSMsvwsnaXNHm94hYAju31ue1cqzIK+HCjUy+3HWa0Z/8xro/o/jx0EU2HFcO/XGysSA1p6AqP7cQJxvlRuPvxy8R2LOdPt/LlVJNOblFVX/jrohoRtVytgTAydqC1JyqMwxpuYU41Rjq9HtYJIHdfSryXfT1FxXjbGNBT283bC3MMTczZZBva6JqXFxv9Pxb6Z8/LxJY8fd3a+tWR/90rLV/FMuytSI1u+oMSXpOPs62VgZtvpg1hU0LXmDWQ/rrkhTXfNSWmVU1dCo9Kw9nW2tlGztrvpj9JJs+epVZk0cpMgtvljBz8UpmTR5F13atDfJ1mZmoHauugVE5OKLNzKzRJoPSE8dAq0WXmoo26QZqd4+aUbXXb1ljfcovxsmyxuv/7DUCfautv+Vacm+WNpidnpKBi1vVtTZOro6kpRjevKEuLS1a8O3Pn/LNR99z4XTtN5aoj7E/V4TGq6Edk2aSJFW2kWX5Q2AFcBiod+fk7zoVfhYfHy/atPHE1NSUoKDxbA8ObRL5Tbl2kd908mu2mxw0nuAa7YKDQ5k2TX8R9kMPjeHAwWOVj08OGo+ZmRlt2nji4+PFyVNncHCww9pa/+HevHlzhg8bQnS0foy4o6N+FTczM+ON11/k++/X3lH9t+tW8mtrExy8p0bf7Knqm0ljOFjZN3sIqtE3p05V3aVmctB4Nm7cpshKSUnDf0h/AIbeP4grscpx+3+3/tt1p9l+brYk5BSSlFuERqtjd2Qi/u2Uw9hcrVoQdk2/QRKfmU9ZuQ7bFma3Va+fqzUJOUUk5d7UL+9yMv4+zoo2rlbmhCXoN97iswrqXZ6fuz0JWQUkZRegKdey+/x1/Dt6KvOsWxIWr9+giU/Po6xci23L5qx6biQhb0wi5I1JTB3Qien+nZnSX7mB7NfKmYSMXJIy8/T5ETH4d2mrzLe1JCxafyYhPjWbMo0WWwv9xqFOJxN65gqj6rh+wq+VEwmZeSRl5evzz1zBv3MbZb6NJWExidXyy7G1MGdAR09iU7IoLtNQrtUREZdMW2e7u5vf2oWE9Jxq/RNt2D92VoRdTqjIz9L3f/X+OR3DqN6177gpluXlRkJaFokZOWjKy9kVdhH/Hsp+zSkoqrze4cfgo0wY3KP+TG9PElIzSUzP1mceP4t/L19lZn61zG37mRCgHwqoKS/n1c/XMHZwL0b0q304YHn0ZdTuHqhcXMDEhOYBQyk7fkzRpvTYUcy66g9sSFbWqN090aYk1xZnWL+bLQnZNdbf9rWsv1drrr91X7f1l0tno2jV1gP3Vq6YmJowasJwDoUevaW6TExN+GLVR2zfHMLe4AO3NE9Nxv5cERqvhu7KtR0YCuz96wFZlldLkpQKfPNPFqLVann5lXns3PELapWK1Ws2EhkZ0yTym3LtIr/p5NfV7r33Xici4hzBwXtYuWoDq1d/TVTkUXJycpn6+L8BiIyMYfOW7Zw/d4ByrZaXXp6LTqfD1dW54lbAKiSVii1btrNzp351f332DB4YMxyVSsXy5T9V7uQ0xv6ptU1UDO+9+zoRp/V9s2rVBlav+orIyKPkZOfy+LSKvomKYcuW7Zw7tx9tuZaXX55XuSHSooU5w4YN4d8vzlEs74UZb/LZZ/MxMTGhtKSEGTPevKP6jdk39TFRqZgT2I0ZG46h08H4bq3xcbRi6aFIfF1tCWjvyuxhnVkQcoZ1J2MBifkP9kSqGB4zesluiko1aLQ6DsQk892UgYo7AtW6vOGdmbHlJDqdzPguHvg4WLL0aDS+LjYE+DgzO6ATC3ZfYF34Vf3yRnerXJ5BnlrFnLF9mbF6HzpZZnxPH3ycbVi69yy+7vYEdPJk9gO9WPD7CdYd0197MP+hAXXm1Zr/SAAzlm7V59/ni4+rPUt3HMe3lTMBXdoye+JgFqzfx7oDZ0CC+Y+PqMyPiEvCxdYSDwfruvMfGsyMZX/o+6NfJ33+zjB8WzkR0NmL2RMGsmDjAdYd0t+Sef5jw5AkCasWzZkW0J2pn29GQmKQb2uG+LW5+/lBQ5mx5Dd9fn8/fNwcWBr8p75/unoze9IQFvyyh3UHTuufz2kjq/onNrGifxq+MNpEreatxx9gxqdr0elkJgzugY+7E0t+24+flxsBPToSfvkaX2/ZB0CvDq15e9qYhjOfmsCMRSvQ6XRMCOiLj6cLSzbvxs/Lg4DefoRHxfH1hhB9Zqe2vP30RAB2Hz/H6cvx5BUW8cdh/W2kF7wwmY5t3KsWoNNS+O2XWC/6FEmlomT3TrTXr9HiyWcoj7lM2fE/0YSfxKxXH2x/WAM6HUUrvkMu0J8Zsvn8G9SerZDMzbH7ZTMFny9GE151y2oTlYo5I7szY71+eKJy/bUhoL0bs4d3YcHO0xXrL8wf26tq/f12l3L9fbTqwnytVsuitz/nu/VfoFKr2bo+mLjoq/z7zWe5dPYyh0KP4te9E1+sXISVjSX+Iwbx7zemM8n/cUaOG0bP+7pjbWvFuMn6m6a8+/KHRF+69TtqGftzpTGQ/zdHRzdIut1xf5IkPS3L8qqG2pmYuYuBhYJgBMYeAdrUV9w7HVvekLs1TtsYClYYfifBP0pze0O9bpmNXcNt7oSVkfO1ht8H0qSY3t4Zs1sltax9R+4f0+zWhpjfroL/GPeaM4tpA4yWfd+bh4yWDXAp+3rDje5QeVlSk7gPb6zvyEb9IeITufue9OOd3C54/j9WhSAIgiAIgiAI/9PqHcolSdL5uiYBznVMEwRBEARBEAShDrp7eOerxqyha0ycgZFATo3HJeBPo1QkCIIgCIIgCML/nIZ2TIIBC1mWz9acIEnSQaNUJAiCIAiCIAjC/5x6d0xkWZ5ez7TH6pomCIIgCIIgCILwdzR0xkQQBEEQBEEQhH/Qvfx29cbsTu7KJQiCIAiCIAiC8I8QOyaCIAiCIAiCINxzYiiXIAiCIAiCINxFsk4M5arNbX/z+60S3/wuCILw9y1xut+o+U/t/ZdR88vXLTFatqpDO6NlA+Doatz8ciN/87uJkY85SsYdbCGZWxo339rJqPlfjV1r1PwH1LlGzV+vszJq/sfJxv12+abyze+X2z/QqLePO8bsbHLf/C4IgiA0QU15p0QQBONp6jslQtMnhnIJgiAIgiAIwl1k5AFLTZY4YyIIgiAIgiAIwj0ndkwEQRAEQRAEQbjnxFAuQRAEQRAEQbiLxF25aifOmAiCIAiCIAiCcM+JHRNBEARBEARBEO45MZRLEARBEARBEO4inSyGctVGnDERBEEQBEEQBOGeEzsmgiAIgiAIgiDcc41qKNfIwAA+/3wBapWKlavWs/iTf/bbg42Z35RrF/kiX+Q37nzPgK4MmD8NSa3i8vqDnF2yXTG90+ND8XtqBLJWh6aohMP/+ZHcK8moTNQM+eRZHLq0QaVWEbPlqMG8R09f5OMVG9HpdEwaMYjpD49WTE9Oz+Ldb9aQk1eAtWVLFr46HRcHWwC6T3yedq3dAXBxsOObeTMNalf7dMNszNMgqSiP2IfmyDbFdLPRT6Ly8gNAMjVDamnNzYVPI1k70Oyx10FSIanVaE7sovzUHoP8Y7GpLN59Fp0sM7GHF88M7KiYnpJ3k3e2naKgRINOlnlpaGcGt3PleHwaX++7gEarw1St4tXhXenr5WSYH3mdxb8dRqeTmdjfl2dG9FbmZxfwzs97KCgu1eePHcBgvzbsOBXNmv2nK9tdSc5k/RtT6OjhqMyPSmDx1qP6/Ps68cywnsr8nALe+WU/BSVl6HQ6XhpzH4N9WwMQk5zFB5sPUVhShkqSWPfqQzQzVX6sG73+yGss/vVQRb4fzwT2qZGfX5Wv0/HSuIEM9vNix6nLrNkXocx/8zHD/ItxfLx+tz5/cHemPzBQmZ+Vx7yVf1BwswSdTublh4YyuKsPmnItC37aQeT1FFSSxJtTAunTsQ01HT0TycerftO//of1Z/rEEYrpyRnZvLv0F3LyC7G2aMHCl6bhYm9LckY2r3zyA7JOplyr5dHRQwgKHGSQ7+XflWHv6dfd8xsOEvadcv3rPnUoPZ4YgU6rQ3OzhN1v/UjWlWSsPByYvm8x2XEp+r/zTCyhc1cZ5FsM6Ynru8+BSkXOplAyl21RTLd5aBguc55Bk5YFQPZPweRsCsXUzZFWy+aCSoVkoibrp2ByfglRzNvOvytj3n0ClVpF+MYDHK5Re9+pw+g3bQSyTkdpUSlb3/qBjNgkzG0seOy7l3Hv6s2ZLYfZ/t5qg7pvhbHfl+81WQzlqlWj2TFRqVR8/dWHjHrgURITUzhxfCfbg0OJirrS6PObcu0iX+SL/MadL6kkBn7wJDse+4iilGwm7VjAtdAIcq8kV7aJ3XqcqJ/3A9B6RE8GvPc4Ox9fTNsH+6I2M2HL8LcwaW5G0IGPid12vHI+rVbHwuW/8P38V3G2t+XR1xcS0Lcb3q3cKtt8tmozY++/j/FDBxB2/jJfr/2Nha9OB6CZmRmbv3y3nuIlzMZOp2T1B8j5WTR/YRHll8ORM5Iqm5SFrKn8v0m/UahcvQCQC3Mo+X4eaMvBrBnmMz9DezkcuSCnqn6dzKJdZ1g2dTDOVi2Y+sM+/Nu74e1oVdlmxZEoAn09COrtTVxGPjPXHyWknSu25mZ8NWUgTpbmxKbnMeOXI+x55UFF+VqdjkWbD7LsxQk421gw9dON+Hdui7erXVV+6CkCe7QjaHAX4lKymbn8D0L8nmJMnw6M6dMB0G90v7pih8FGt1anY9FvR1j2wlicrVsy9Ytf8fdrg7dLtfw9EQR29yZoYGfiUrOZuWInIb6tKdfqmLtuLx88NowO7g7kFpVgolYZ5hu7/s0HWfbiRH3+Jxvw79IWb1f7qvzdf+V3JS4li5nLthEy34sxfToypk/HavnBteYvXBfC8tlTcba14rEPfiSge3u83arardhxlJG9fQm6vxdxyRnM/GoDIV1n8evhMwD8Ov95svKLePHL9fwybzoqVdXGoFarY+GPm/n+nRdxtrPh0bc+JaB3Z7w9XSvbfPbTVsb692F8QD/CLsTw9brtLHzpCRxtrPj5w1cxMzXlZnEpk15bREDvLjjZWVfOK6kkhr//JJumfkRBajZP/LGA2L0RZFVbdyO3HefsOv266zO8J/fPe5wtTy4GIPd6GmsemEudVCrc5s/g6hPzKE/Nou3WLyjYG0Zp7A1Fs7wdR0j57zLFY+UZOcQ//DpyWTmqFs3x2bWEgr1hkFpeWfvYBU+z6vFF5KdmMeOPD4jac5qM2Kp199y2Pzm5bh8AHYf35IF3HmfNkx9TXqph72dbcO7ggXN7z7rrr4ex35eFxqvRDOXq26cHcXHXuHo1AY1Gw6ZN2xg3dmSTyG/KtYt8kS/yG3e+U3dv8q+lUZCQgU6jJXbbCdoE9lK00RQWV/7fpEUzZFkGQJbBtEUzJLUKdXMztJpyRduLV67SysUJDxdHTE1NGDW4DwdOnlNkx99IoV8X/QZk3y4dOBCmnF4flYcPuqxU5Jx00GrRXvgTk0596mxv0nUg5ReO6n/RavU7JQBqU5AMP64uJmfjaWuBh60FpmoVI/08ORidrGgjSVBUqgGgsFSDo2VzADq62uJkaQ6At6MVpRotZeVaZf71NDwdbfBwsMbURM3Inu05eCFemQ8UlZTp80tKcbRqaVBnSEQMI3u1N6w/IR1PB2s87K30+T18OHjxWo18iaKSivpLynC0bgHA8egbtHO1p4O7AwA2LZujVin7yOj1X0/T1/9Xfq9a8qXq+WU4WlsY5odHM7JnLflXk/F0ssPD0RZTEzWj+vpx8GyMQbvCklL9v8WlONpYAhCfkkHfTm0AsLdqiWWL5ly6pnxtXIy9TisXRzycHfSv/4E9ORB+QdEmPjGVfp31tfXt3K5yuqmpCWampgCUlZej08kGdbl29yb3Whp5N/TrbtT2E/iMUK67ZdXWR9MWzQDDnLqYd2tP6fUUNDfSkDXl5AUfxnLEfbc0r6wpRy6r2AkxMwWV8ui9R3cfsq+nkXMjHa1Gy/ntx+lU432ntFrtZi2a6d9wAE1xKdfDo9FUrHe3w9jvy0LjVe+OiSRJLpIkfSdJ0hJJkuwlSfqvJEkXJEnaJEmSa33z/l1u7i7cSKx600hMSsHNzaVJ5Dfl2kW+yBf5jTu/hasthSnZlb8XpWbT0tXWoJ3fk8OZcvQz7ps7hWPv/gTA1R0n0dwsZdrpb5l68kvOL99JaW5R5TxpWbk4O1QdPXe2tyE9K0eR297Lk70n9Eef9504Q1FxCbn5hQCUlWmYMvtDpr6xiP0VbaqTrOyQ87Iqf5fzspAs7QzaAUjWDki2TujiL1ab3x7zFz+hxevfoTmyTXG2BCA9vxgXK/Oq+q3MSS8oVrR5YYgvOy4kEPjlDmauP8qcUT0Mlr03KolOrraYmaiV+blFuNhUbUg721iQnleozB/djx3h0QS+s5KZy7Yz52F/g/zQ01cYXcuGd3peES42VTsCzjYtSc8rUrR5YVRvdkTEEDj/J2au2MGciYMBuJ6RiyRJzFgezJTPNrNqv2H/G73+3EJcbC2V+bk18+9jx6nLBL7zIzO/21Z7/pkrjK5lxyc9pwAX26qzX062lqTlFCjazBg3hB0nLjDija948asNzHlUv/Ha3sOZQ2evUK7VkZiRQ9T1FNJy8hXzpmXn4mxvU1W/nQ3pWXmKNu1bu7O3Ymd838nzFBWXklugf45SM3N46LWPCHzhXZ6ZMExxtgTAwsWWgmrrbkFKNpYuhutujyeG86/Dn+H/1hT2vfdT5ePWno48ufMDHt04F4+Ks1fVmbrYo0nJqPy9PCUTU2d7g3ZWowbgs/MbPJe8hamrQ9X8rg747PyGDsdWkbn8V8rTq2q1crYlL7lq3c1Pycba2XDd7TdtBLMPfcHIOY8R/N+fDKbfLmO/LwuNV0NnTFYDkcAN4ABQDDwAHAGW1T2bIAiCcDddWrOXDYNeI2zhBnq+NAEAx+5tkXU6fu41i1/6z6brcw9g2cqxgSSl1556mIiLMQS98j7hF2NwsrdBVXFkftcPi9jw+Vw+fu1ZFv+4iRsp6bddv0nXgWgvnag86gog52dRvOQNir98CZMe/tDSup6E2u26dINx3doQ+soYvn10EPO2nkRXbRmx6Xl8tf8C8x7oWU9KPfkRMYzr15HQ95/h2xfGMm9tqOLo+YVrqTQ3M8XHzXCD8ZbyT8cyrm8HQt97gm//NYZ5v+xDp5PR6mTOXE1h4dRhrJo1gQMXrhIWk9j46o+IZlw/X0Lfn863M8bXnm9qgo+bQz0pdQs5eYlxA7qx55OXWfLyFOb+uA2dTmbCoO4421ry2Ac/8snGPXTz9qh83f4drz0xgYjIWILe+JjwS7E42VlXDgdzcbDl18/mEPzNu/xx8CRZufkNpNXuzE97WTHkNQ59tIH+s/TrblF6Lsv6v8KaB+ax//11PPj1vzGzMG8gyVDBvpPEDHmG2AdmUXj0DO6fvFo5TZOSSewDs4i5/zlsJg1D7WBTT1Ltwtbu4XP/V9n90XoCKmoXbo0sN+6fe6WhtdRZluVvZFn+CLCRZfljWZZvyLL8DdC6rpkkSXpOkqRwSZLCdbqiupopJCel4ulRNa7Zw92V5OTUW5r3Xuc35dpFvsgX+Y07/2ZKDhbVrglo6WJHUUpOne1jt52gzUj9kIt2EwZw4+B5dOVaSrLyST0Vg2PXtpVtne1tSMusOkqalpWLk73yiK6TvQ1fvDWDTV++w0uP6zc8rCxaVMyvb+vh4kjvzu2JileObZfzs5GsqzZoJWt75IJsaqPuMoDy88dqnSYX5KBLu4G6jfLCdicrc1Lzq86QpOUXVw7P+svvZ64R6OsBQDcPe0rLdeTeLK1of5PZm4/z/vg+eNoZDjFysmlJarUzAGm5hTjVGIr0+4lIAnu00+d7uVJariW3qKqmXaevMKpXu1r/LifrlqRWP4OVW4STtXIo1e9hUQR289Hnt3GhVFNOblExzjYt6dnWFVsLc8zNTBnUqRVRiRnKfGPXb2NBarUzGGm5hTjZ1Mg/fonAntXyK+qvzI+IZlQtZ0tAf4YktdpZjvScApyrnaEB+P3oWUb26aTP9/agVFNOTuFNTNQq3pgSyKb3/sVXM4MoKC6ldY0j/s52NqRl5VbVn52Lk71y59fJzpov3niWTZ/8h5ce1V+DZNWyhUEbn1auRETFKR4vTM3Bstq6a+lqR0Fq3etu1B8naFcxXEpbVk5JxXOXdvEaudfTsfNSnjHQpGZh6lp1oMHE1aHyIve/aHNfr3pLAAAgAElEQVQLKods5WwMxbyLj8Fyy9OzKY25Tss+fpWP5aflYF1tZ9TK1Y68tNrXXYAL24/jW+PGCnfC2O/LQuPV0I5J9ek1z9GpqYMsy9/LstxbluXeKpXheNXanAo/i4+PF23aeGJqakpQ0Hi2B4fe0rz3Or8p1y7yRb7Ib9z56efisfZywdLTEZWpGp/x93F9z2lFGysv58r/tx7Wnfyr+g/wguQs3AfoNzZMzJvh3NOH3Liq4RF+7dpwPSWdxLRMNJpydh05RUDfborsnPwCdDodAD9sCWHiMP1dkfILiyjTaCrbnI2KU1w0DKBLikNl74pk4whqtX7n43K4wd8oObghNW+J7kbV9QOSlR2Y6Mfw07wl6tYd0GUqrxHwc7MlIbuQpJwiNFoduy/dwL+9sgZXa3PCrunP5MRn5FNWrsW2RTPyS8qYtf4YLw/tQg/P2o/W+7VyJiEjl6SsPDTlWnafjsG/i5cy39ai8kxFfGo2ZRotthVHtnU6mdAzVxhVyzAoAD9Pp4r8fH3+mVj8O7cxzL9SkZ+Wo6/fwpwBHVoRm5JNcZmGcq2OiLhk2rooN7yNXv9f+ZkV+REx+Hdpq2jjamtJWPSN+vN7GQ5TAvBr40ZCWjaJGTloyrXsOnkJ/27KWlztrAmLuqbPT86kTFOOnWULiks13CzVX9ty/FI8apWkuGgewM+nFddTMkhMy9K//o+dJqB3F0WbnPzCqtf/73uYeL/+Go7UrBxKKvLzC29y5nI8bdycFfOmnIvH1ssF64p1t9PY+4itse7atqmax3tod3Ku6dddcztLpIozM9aejth6OZOboDwjWXw+hmZt3DD1cEYyNcH6wSH6C9irMXGsOtBgObxf5YXxJi72SM3MAFBZtaRFb19K46vOuCWdi8O+jQu2Ho6oTdV0Hdufy3siFNn2bap2lDoM7UHWtX9ux8HY78tC49XQXbm2SZJkIctyoSzL8/56UJIkHyD6nyxEq9Xy8ivz2LnjF9QqFavXbCQy0vAit8aY35RrF/kiX+Q37nxZq+PoO2t4YN2bSCoV0RsPkROTRO/XHyLj3FWu7zlN56cCcR/kh65cS2leEQdeXQ7/x959h0dRLXwc/87sbnoP6aEmtITelRJACCgQkCoC0tQril7Bq4ICSlEUEUERQa8UAWmCCAQwgIJI7yA1BAikEdILKbs78/6xMclmAwFlDbz3fJ4nz5PsnPPbs2c2O3NmzswCZ5fuoOOcFxmw6yMkSeLi2t9IO19yVkOr0fDOi4MZ8/5cjIpCnyfaElzNny9X/kRIcHU6tW7CkTOX+Hz5j0gSNAupw7svDQbgyo0kpn21HFmSUVSFUf26m93NCwBFoXDLYuyGm25Lajj+K2pyHLrOA1ESYjBeMO3oaBu2xXBmv1lVySsAu+7PoaIiIaHftxn1pvkZGa0sM6F7E8Z8vxdFVenduAbB3q4s2H2WED93Otb1Z3zXxkzbcoyVB6NBgqkRLZAkiTVHYriensOivedYtPccAAuHtMfD0a5U/8hM6B/GmAWbUBSF3m1CCPbzZEHkQUKqedOxYS3G92nPtNW/sPLXEyBJTB3SBUky7VAei4nH182JwCrlT0HTamQm9G3PmK+3oCgqvVvVI9jXgwXbDhNS1YuODWoyPuJxpq3dw8o9p03tH9wZSZJwcbBlWFhjhny2HkmCdvWr0yGkumW+tds/oCNjFmw09X9x/gFCqvmY8p9uz7RVu4ryYerQrub57s53zZ/4bHfGzF2Foij0aduE4AAvvty4m9Aa/nRsUoc3BnZh2rJIVuw4hCRJTBvVC0mSSMvOZcxn3yNLEt7uznzwfO9y8jW8M7o/Yz5YYHr/d2pDcFU/vlwdSUhQNTq1bMiRs9F8/v0W0/u/fhDvPj8AgKtxN5n93UYkyTTtZXivztSpbv7+V40KO6csY8B3byFpZM6s3UNqdDztxvcj6fRVLu88TtPh4dRoF4pRb6QgK5fI8ab/3aqt69FufD+MeiOoKlHvLCG/zPVHGBUS3l9IjWXTkGSZ9HU7KIi+jvfrQ8g7E032rsN4jojA+YlWqEYFY0Y2cW/OBcA2uCp+74xGVU03KEj5ZgMFF2MB0zU9ilFh85SljPhuApJG5vja3SRHx/PEuP7En7nChZ3HaTM8nKC2DVAMBvIyc/nhja+Km/af3+dh62SPRqelfnhzlgz7yOyOXhWx9ufyw0B883v5JPUvTiSTJGmkqqqWN9UuQ2sTUIkz1QRBEB5NX3p3slr2iJ0vWC0bwLDSut83INctf2rRA+P1QO/tYslgsG6+1srfBFDOHdIeaLy9c8WF/k6+q+X31TxI83ott2r+U5qMigv9RasUl4oL/Q0fJ+yxaj6AoTD+kdjjP1k94qHeP24Su6lS+vHvfLpMfWCtEARBEARBEAThf9pdD6tIknT6TosAnzssEwRBEARBEAThDsQ3v5evovO9PkA3oOxtJCRgv2VxQRAEQRAEQRCE+1fRwGQL4KSq6smyCyRJ2m2VFgmCIAiCIAiC8D/nrgMTVVVH32XZsw++OYIgCIIgCILw/1tlfonhw8y6t9YQBEEQBEEQBEG4B2JgIgiCIAiCIAhCpbPyzc4FQRAEQRAEQShNfMFi+cQZE0EQBEEQBEEQKp0YmAiCIAiCIAiCUOnEVC5BECqFrVZn1fwCg96q+dZu/3MrOlst27hludWyAVCsfLsZTyt/v6+iWDdfa+VNr2TlY442dlaNVwvzrJqv8a1l1fye2rJf/fZgGRXrrd+3wm5aLRvg41VWjRf+HxADE0EQBEEQBEH4B4lvfi+fmMolCIIgCIIgCEKlEwMTQRAEQRAEQRAqnZjKJQiCIAiCIAj/IHG74PKJMyaCIAiCIAiCIFQ6MTARBEEQBEEQBKHSialcgiAIgiAIgvAPsvJN1R9Z4oyJIAiCIAiCIAiVTgxMBEEQBEEQBEGodA/VVK5u4R2ZM2caGllm8ZJVzPrky0cm/1Fuu8gX+ZWR37VrGLM+mYJGo2HZ0jV8+ulXZsttbGz45r9zaNq0AWlpGTw3bCzXr8fRuXM7pk1/GxudjkK9nnff+ZA9ew6Y1V277huq16hKk6ZPWKX93cI78tmcaVZre80a1eDWvuLH9v1xhVlrd6EoCk+3a8yo7m3M6iSmZTF5SSTZefkoisprT4fRvmEQeoOR6Su2cy42CVmWeHNgF1rWrWZWV64Rik3nwSDJGM7sxXB4m9lyXcdBaKrVNf2htUFycCFv/msASM4e2HQbjuTsAagUrJ+HmpVqVl9Tuwk2PUaCLGM4ugv9bxvN++qp4ci1GpjydDZIjq7cnjEC2a8GNhEvINnag6pQuHsDxjP7LdbFvvOxzNrwO4qq8HSbEEZ1aW7eN+nZTF65i+y8AlPf9GpD+5AaAFxKSGHGmt3kFBQiSxIrxw/AVqf9Z/PPxTJrw28oisrTj4UwqmsL8/y0bCav2GHKV1Ve6/U47UNrEHnkIst+OV5cLjohhVVvPkO9QK8y+deYtX5PUX4oo8JblsnPKslXFF6LaEv70JpEHrnAsl3HzPPfetYy34rvTYB9Z68ya92vKKrK0483YFS31pb5y7aX5PdpT/sGtdAbjUxdEcWFG8kYjQo9W4cwuntri/y7mTRzLr/tP4KHuysbv1twX3X/5NihOb6TX0TSyKSviSJ10Tqz5a79uuDz9igMN03/N2nLN5OxNgqdvxeBX01CkmXQakj/bjPpq7ZZ5Dt1aIb/ey+ALJO+Zge3Fv5gttyt3xP4TRyJvig/9btI0tdEYVe/JgEzXkZ2ckBVjNyav5bMyN/N6mobtsRuyCsgy+j3bKUgcrXF8+tahWHbZzigYrweQ97CDwGQPLyxH/0GsocXqJA7ZyJqyv19q7y1t1uVTdyVq3wPzcBElmU+n/cB3Z8aTFxcIgcPbGXzlijOn49+6PMf5baLfJFfGfmyLDPns2n06jmU+Pgk9u7dRGTkDi5cuFxcZviIgWRkZNKoYUf69+/F9BkTGP7cWFJT0+nffzRJicmEhNThp03fUTu4ZGcoonc3cnNuW639f5aP6DXsH2m7UVGYuWoHC18fhI+7M0NmLiOsUTBB/lWKy3wTuZ/wFvUYGNaUmIQUxs5fx7aGY1i/9xQAP7w3mrSsXF75Yh0rJw6neHMoSdh0GULBujmo2enYDZ2EMeYkampicbZ+9xr0Rb9rm3ZG9i7ZebR5ajT6g5EosedAZwtqmVnTkoxNr9HkL5mOmpWG3ZiZGM4fRb0VV1ykcOuy4t+1bboj+9cEQC0soOCHL1BTk5Cc3bF75WPyok9Cfkn/GBWFmT/8xsIxEfi4OTFkzjrCGtQkyNejpG+ijhLeJJiB7RoQk5TG2EVb2PZeDQxGhXeX72TG0C7UDahCRm4+Wo35JIJ/JH/dbha+0seUP3sNYQ1qEeRXOv8I4U1rM7B9Q2IS0xi7aBPbQkfQo2VderQ0DRijE1IY902kxaChJP9pU/4nqwlrWIsgP8+S/J//zG9ETGIqYxf+xLapNenRsh49WtYrlb+l/PwH/N6UZck8f80uFr7WHx83Z4Z8vNKUX7r92w4S3rwOAzs0MbX/yw1sm1GLHccvoTcY+WHScPIK9fSdtpTuLesR4OnKverzZBee7duTdz6Yc891zMgyfu+PIXb4JPRJKdT68TOydx2k8PINs2JZkb+RNHWh2WP6W+lcG/AGaqEBycGOoG0LyN51CENymlm+/7SXuDpsMoakVIJ+mkPWzkMUlMnPjNxLwnuLzB5T8gu48cYcCq8lovX2IHjzZ2T/dgIo+v+SZOyee43cWW+hpt3C6f0F6E8cQEmILXl6nwBsew4mZ8ZrcDsHydmteJnDi29TsPl7DGePga2d5WdDhV1n3e2W8PC676lckiR5W6MhrVo2JSbmGlevXkev17N27U9E9Or2SOQ/ym0X+SK/MvJbtWzKlZhYrl27gV6v54cfNtOzZ7hZmZ49wlm5Yj0AP/64lY4dHwfg1KmzJCUmA3Du3CXs7OywsbEBwNHRgVdffZ6PP/7Cau3/s/w/1fY/riZS1duNQC83dFoN3VrUZ/cp842zJEFuXgEAOXkFeLk6AXAlMYVW9aoD4OHiiLO9HWdjSwYdsm9N1PRk1MwUUIwYLhxGE9Tkjq9dU68VhguHTc/p6QeSbBqUAOgLwFBoVl4ODEZJS0JNTwajAePpfWjrtygbW0zbqB2GU6YzRWpqImpqkun37HTUnEwkRxfzvolNpmoVVwKruJr6pmltdp+5at43QG5+YVHfFOLl6gjAgYvXqe3vSd0A0060m6MdGln+h/NvUtXLrSS/WR12n7ly5/z8ArxcHC36bduxS3RrXsfi8T9ib5q3v3k5+VLp/MLi945Z/tGLdGtWTr4V35sAf1xLKuqfovzmddl96nKZfKlU/xcU978E5BXoMRgVCgoN6LQanOxsLF7D3bRo0gBXF+f7qlOafeM6FMYmoL+RBHoDmVt+w7lLm4orAugNqIUGAGQbHZJseXTdoXFtCmMT0d+4iao3kLn5N1y63ttZocKrCRReM/W3ITkNQ2omWs+S/y9NrXooN+NRbyWC0YD+0K/omj1ulmET1oOCXZvgdg4AanaGqb3+1UGjMQ1KAAryobDg3l53EWtvt4SH110HJpIkeZT58QQOS5LkLkmSx93q3i//AF9uxCUU/x0Xn4i/v+8jkf8ot13ki/zKyPcP8CUuvqROfHwifv4+5mX8fYrLGI1GsrKy8fR0NyvTp8+TnDr5B4WFph2TKVPe4PPP/8vt2/lWa3/Z8tZue3JGNr7uJTsMPu7OJGfkmJV5qVc7Ig+dJfztLxk7fx0TnukKQJ1Ab3afuozBqBCfksG560ncTM8uric5u6Nmpxf/reakIzmbt7O4rIsHsmsVlOvnAZDdfaDgNjYRL2M3bAq6sP6mvdAyddTMkqldalYakqsn5ZHcqiB5eKNc+cNimRwYjKTRoqaZTwVJzszB171kR9rHzYnkzFzzvuneishjFwl/byljv97ChH7tAYhNzkSSYMxXm3hm9hqW7DpOWVbPz8jF161sfpl1+2RrIo9eJHzyYsYu3MyE/mEWOVHHo3mynIFDckYOvu4lO9Y+bk6W750n2xB55ALhk79l7Fc/lZ9/Iponyxn4WPO9WW773Z0t+6fHY0QePk/4O4sY++UGJgwyTd/s0qwO9rY6uk5cSPdJX/Nclxa4OtpbvAZr0vp4ok9MKf7bkJSCzsfy/e/cvS21IucTOH8iWr+Ss01avyrUipxP7d+XkrLoB/OzJYDW1zxfn5SKztcy36X74wRv+5xqCyagK5X/J/vGtZF0Wgpjk4ofk9yroKbdKv5bSbuF5G5eV/YNROMTiOOkeThO/gJtw5bFj6u3c3F49X2cpi3EbtCLIN3fcXBrb7ceBqoqPdQ/laWid0oKcKzUz1EgADhe9LsgCEKlqF+/NtNnTODVV98BoFGjEGrWqsbmTT9Xcssq9qDbvv3wOSIeb0jUx68wf+wAJi3ZgqKo9GnbCB93Z579cBmfrN1F46AAs6ky90NTrxWGS8dKpmTIGuTA2uj3rCV/xQwkVy80oW3/UjaAtmFbjH8cBFUxe1xydsO2/6sUbFhw39NBALYfjyaiVT2ipo5g/os9mbRiJ4qiYlQUTlxJ5MNhXVnyWl9+PX2FQ5duVBz4T+cfu0RE63pETR/F/Jd6MWl5FIpS0g9nriVhZ6Mj2L/8AV/F+ReJaB1C1PTRzB/Tu/x8nZZgf8sd2nvKt/J7c/vRC0S0CSXqw38x/5W+TFq6FUVR+eOa6dqVqJn/Yuv0F1i+8yhxKRl/6TVYU86uQ1wOG8mVHmPJ2XeCgE/GFy8zJKZwpcdYLnd+Abe+T6DxdLtLUvmydx3mYvvRXH7yNXL2niRw9utmy7Ve7lSdM564N+fd//+XRoPsG0DuzPHc/uoD7EeOBwdHkDVo6zQgb/Uict5/GdnLD117cbZDuDcVDUzeBC4CEaqq1lRVtSYQV/R7rTtVkiTpRUmSjkqSdFRRcu9UzExCfBJVA/2L/w4M8CMhIekuNe6PNfMf5baLfJFfGfkJ8UkEBpTUCQjwIzHB/Gh4QsLN4jIajQYXF2dSU01H9/0DfFm1ehEvPD+eq1evA9CqdTOaNWvEufO/s3PXOurUrsWuHeYXmj6I9pctb422B9euiU2TJwHwdnMmKT2rOPtmejbebubTbX7cd5rw5qbrARoHBVCgN5CRcxutRubNgU+wdvJI5r7cj+zb+VT3LjnZrWabnyGRnMzPoJSmrdsKY9E0rj/rKsk3TNPAVAXj5RPIPuYXL5c9Q1L2DEppmkZtMZw2v/gWW3tsn5tI4Y5VKDcs55Z7uzqRlF5yBP1mRg7eruZTnX48dI7wJsGmvqnpS4HBSEZuHj5uTjQL8sfdyR57Gx3tQqpzPu7WP5vv5khSRtn8Muv24DnCm9Yuyvcrzv/T9uPRdG9e26JvTPlOJJU6C3EzI8fyvXPgLOHNSuXrDeb5xy7SvZyzJaZ86703y21/erZl/+z/g/Cis0WNa/lToDf1z7Yj52kbUhOdRoOHswNNgvw5G3t/F1//XYabqWZnKLS+VYovQv+TMSO7eMpWxpoo7BoEW+Ykp5F/KRaHlqHmjyeZ5+t8PdEn3Tk/bU0U9qXyZSd7aix+j6TZy8k7edGsnpqeguRRck2R7OGFmp5iVkZJu4X+xH4wGlFTklCS4tD4BKKm38J4PcY0DUxR0B/fh6Z6+e/RO7H2dkt4eN11YKKq6qfA88AUSZLmSJLkzD18J4yqql+rqtpCVdUWsmw5H7Y8R46eJDi4JjVqVEWn0zFwYG82b4m6p7qVnf8ot13ki/zKyD9y9CRBwTWoXj0QnU5H//69iIzcYVYmcusOhgztB8DTTz/Fnj2mOzK5urqwYf0Spkz5mIMHS+4a9N9vVhAc1JqQ+u3o8sQALkVf4YmuAx54+/8sb822X46+SuFJ0x14Qmv4cT05nfiUDPQGIz8fPU9YY/OdFz8PFw5dMF2UeiUxhUK9EXdnB/IK9eQVmKaKHTh3Fa0sm12YrCRdQ3L3QXKtYjrKWa8VxphTFq9Z8vAFOweUhJhSda8i2TqAvWlHUVOtvtlF8wBK/GVkTz8kd2/QaE2DjwuWJ9ulKv5I9o4o1y+VPKjRYjfkTQwn9mA8e7C8VUFoNW+up2QSn5pl6psT0YQ1qGHeN27OHLpkutj+SlIahXoD7k72PF6vKpcTU8krNF2HcCwmgVo+Hv9wvg/Xb2UQn5ppyj9+ibCGNc3z3Z3K5BtxdzJNSVIUlagT0XQvZxqXWX5KUf6xS4Q1ND+m6OfuzKGLN+6e37xu+flWfG8ChFb35Xpy6fZfJKxRUDntv16Un0qhwdT/fh4uHC56PK9Az5mridT0eaAz0CuUd/oSNjUC0AX6gE6La88O5Ow6ZFZG61VyYMC5S+viC9e1vp5ItqZrYmQXJxxahFJ4Jc6s7u3T0djW8EcX6IOk0+LaqwNZOw+blSmd79KlFQUxpnxJp6X6wndJ3/ALWdss73ZnvHoBjU8AUhVf0GjRte5kGoSUYji+D2090zVpkpMLsm8gSnIixisXkRyckJxNNxrQhjQ1u2j+Xlh7uyU8vCq8K5eqqnHAAEmSIoAdgIM1GmI0Gvn365PYGvk9Gllm6bI1nDt3qeKKD0H+o9x2kS/yKyPfaDTyxvgp/LTpOzQaDd99t5bz56OZNHkcx4+fYWvkTpYtXct/v53D6TO7SU/PYPhzrwLwr5eeo1ZQdSZO/DcTJ/4bgIhew7h1q/wj8Q+6/X+W/6fartXITHimK2PmrUVRVHq3bUiwvxcLNu0lpLovHRvXZnz/zkxbsZ2Vu44AElNHPIUkSaRl3eblz9ciS6aj2zNG9TQPVxUKd32Pbb/XTbfzPbMPNTUBXdveKEnXigcp2nqtMF44UqauSuGeddgN/A8Ays1YDKd/My+jKBRu/ha7Ee+abkd8/FfU5Dh0TwxCiY/BWDRI0TZqi+G0+U6PpsFjyDXqo3VwRtusEwCF679ESbxm3jf92jNm4SZT37SuT7CfJwu2HiKkmjcdG9RkfJ+2TFvzKyv3mF7L1GefQJIkXBzsGNaxCUPmrENCol1IdTqE1rDse2vn9w9jzIJNKIpC7zYhpvzIg6b8hrUY36c901b/wspfT4AkMXVIF6Sia3mOxcTj6+ZEYJXy7zSl1chMGNCRMQs2oqhqqfwDhFTzMeU/3Z5pq3YV5cPUoV3N892d755vrffmn/mDOjNm/npT/zzWgGD/KizYvI+Q6j50bBTM+H4dmbYyipW/HDe1f1h3JEliUIcmTFn+M32nLwVVJeKxBtQpc1exirz5/iyOnDhDRmYWT/QdzsujhtCvzI0u7sqokDT1K6otnY4ky2T8sIOC6Ot4vT6UvDPR5Ow6hMfwCJyeaA1GI8bMHBLe+gwA26Cq+LzzvGl6lSSR+t8NFFyKtchPeG8hNb+barpd8LqdFERfx3vcEPLORJO98zCeI3rh0qU1qtGIMSObuP/MA8C1RzscW4WicXfGvb/pupy4/8wFzpqyFYW85V/g+ObHptsF/7YNJT4W26dHYLx2EcOJAxjOHEHboAVOHy4GxUj+mq9Rc01n0PJXL8Lx7dmmZl6LpnB35H31vbW3Ww8DpeIi/5Mk9T7mFEqSZA8Eqar6hyRJI1VVXVJRHa1NwP1PChYE4f89W63OqvkFBn3Fhf4Ga7c/ZesUq2WrRy2PkD7Q/PRMq+bL7dpbNd/qZCt/t/F9Xmh832zsrJtvNFg1Xtuwk1Xzox97veJCf4NRsd76rdbh3m+1/ld4rrpg1XwAQ2H8I/EFIXt9+z/U+8ftk36olH68r3e3qqp5qqr+ecuUqVZojyAIgiAIgiAI/4PuOpVLkqTTd1oE+NxhmSAIgiAIgiAId6DySJzY+cdVdI2JD9ANKHubFgmw7lwAQRAEQRAEQRD+Z1Q0MNkCOKmqerLsAkmSdlulRYIgCIIgCIIg/M+568BEVdXRd1n27INvjiAIgiAIgiD8/6Y81Je+Vx4r37pDEARBEARBEAShYmJgIgiCIAiCIAhCpavwCxYFQRAEQRAEQXhwFHFXrnKJMyaCIAiCIAiCIFQ6MTARBEEQBEEQBKHSialcgiCUSytrrJpfYNBbNd/abn3Yzar56qnDVstWEpKtlg0g+3tbNZ/CfOvma22smy9b+ZigxsqbdlWxbn5ulnXzrfzZ5tsox6r5Zw94WS27Vh13q2UDaORLVs1/lIgvWCyfOGMiCIIgCIIgCEKlEwMTQRAEQRAEQRAqnZjKJQiCIAiCIAj/ICtPiHxkiTMmgiAIgiAIgiBUOjEwEQRBEARBEASh0omBiSAIgiAIgiAIlU5cYyIIgiAIgiAI/yBxu+DyiTMmgiAIgiAIgiBUOjEwEQRBEARBEASh0ompXIIgCIIgCILwDxK3Cy7fQzUw6RbekTlzpqGRZRYvWcWsT758ZPIf5baLfJFfnq5dw/j00/fRaDQsWbKa2bMXmC23sbHh228/o1mzhqSmpjNs2CvExsbh4eHGqlULad68McuXr2PcuCnFdXQ6HXPnTqdDhzYYFYXJUz7mxx+3WqX99+Pv5svVQ7EJGwiyjOGP3zEc/dlsua7DADRV65r+0NogOTiT99U45MA6pnpFJHdfCrd9gzHmVKnsEFMZScZwdl/52YF1zLMXjjdldxhQJvu/GK+c4m409Zph2+d5kDXoD0ah/2W92XKb3qPRBDc0ZepskZxdyX332Tv3TY1QbDoPNrX/zF4Mh7eZt7/jIDTVSveNC3nzXzPlO3tg0204krMHoFKwfh5qVqpZ/X0X45j100EUVeHpVnUZ1amx2fLE9Bwmr/mN7PwCFEXltSdb0r5+VeLTsuk7ez3VvVwBaFTNm0n92lq0f9/568za+DuKovJ0m/qMergKGZUAACAASURBVKJZmfxsJn//C9n5hSiKwms92tA+pDoAlxJSmbFuDzn5hciSxMpx/bDVmW92952LZdaG30z5j4UwqmsL8/y0bCav2EF2XgGKqvJar8dpH1qDyCMXWfbL8eJy0QkprHrzGeoFepnnn73KrHW/oqgqTz/egFHdWpfJz2Lysu1k5+Wb+qdPe9o3qIXeaGTqiigu3EjGaFTo2TqE0d3N6xbnr91lan/bRozqXk7+0q2m9isKr/UJo33DovzlP3Ph+k2MikLPNqGM7t7GMv/CDWZtOmDKb1WXUZ2blOn/HCav2U12XqGp/U+1pH39aqb1+8m6kvVb3ZtJ/dpb5N/NpA/n8Nu+w3i4u7FxxcL7qvsnXdNWOIx+FWSZgp2R5G/43qKMzeOdsH9mBKqqYrwWQ+5n00sW2jvg9vkyCg//zu1v5lnUde/UhFrTRyJpZJJW7iJu/sZy2+HZozUh377JiW5vk3MqBkmrofacMTg1rImk0XBz3R7ivvjRrI6mVkNswoea/ndP7kF/YIt5u7s8i1yjPgCS1hbJ0Znbn44BwGHiUpRbNwBQM1MpWDcXgPCuHYu3K4uXrCp3u7L427nF25Whw14utV1ZRIui7crr4yabusfejlXfL6RWreoYjUYiI3fecV0Ij4aHZmAiyzKfz/uA7k8NJi4ukYMHtrJ5SxTnz0c/9PmPcttFvsi/U51582bQo8cQ4uIS2bdvM1u27ODChZI6I0YMIiMjk9DQDgwY0IsZMyYybNgr5OcXMHXqp4SE1CU0tI5Z7oQJr3LrVgoNG3bEqCp4eLhZpf3342/nSxI2nQZTsGEuak46doMnYrxyGjUtsbiI/rd16It+1zbuhOxdFQAl7hL5K2eYFtg6YD9yBsbYc+bZHQdT8OM8U/YzFWV3RPYqlf39ByXZI6ZjvF4qu9zXImPb91/kLZyCmpmK/bhPMZw9jHrzRnGRwp++Lf5d164HckDQ3fumyxAK1s1BzU7HbugkjDEnUVNLtX/3mpL2N+2M7F2teJnNU6PRH4xEiT0HOltQVbN4o6Iw88f9LHyhOz6ujgz5YhNhIdUI8nEvLvPNrpOEN67JwMfqE3MznbGLo9hWfxAAgZ7OrB339B2bb1QUZm7Yy8KXepnyP1tPWGgNgnw9SvJ3HCO8SRAD2zYgJimNsd9sZVtIdQxGhXdX7mTGs09QN6AKGbn5aDWyZf663Sx8pQ8+bk4Mmb2GsAa1CPIrlR91hPCmtRnYviExiWmMXbSJbaEj6NGyLj1amgZ00QkpjPsm0mJQYlQUZq7ZxcLX+uPj5syQj1cS1iiYID/PkvxtBwlvXoeBHZoQk5jK2C83sG1GLXYcv4TeYOSHScPJK9TTd9pSuresR4Cnq3n+qh0s/PdAfNydGTJzOWGNggjyr1KSv/UA4c3rMjCsKTEJKYydv55tDf/FjmMXTflTRpry319M9xb1CahSJv/HfSx88SlT/3++kbDQ6mXW7wnCG9Vi4OMhpvX77Xa21a9WtH5dWDu+3x3Xb0X6PNWVZ/tF8M702X8tQJZxePF1st9/AyX1Fi6zFlF4eB9KXGxJEb8A7PoNIWviK6i5OUiu5p+JDs+ORn/u9B3zg2Y+zx8Dp1GQmEaT7R+RFnWU25fizIppHO0IeL4HWccuFT9WpddjyDY6jnd6A9nehua/zeXWxt+BXFMBScKm+3Pkfz8LNSsNu1FTMUQfR01JKM4o3FkyyNK26IrsW73kSQ2F5P93snl7JYl586bzVI9niYtLZP++LRbblZEjniEjI4OQ0PYMGBDBBzPeYeiwl4u2K7MJDalLaGhds9jP5i5iz54D6HQ6tm9fDfAkYH4ERHhk3PUaE0mSupf63VWSpG8lSTotSdL3kiT5PMiGtGrZlJiYa1y9eh29Xs/atT8R0avbI5H/KLdd5Iv8e6mzbt1mevUKNyvTq1c4K1b8AMCGDVvp1Ml0tPn27Tz27z9CQUG+Re7w4QOZNct0NkJVVVJT063S/vvxd/Nl35qomcmoWSmgGDFcOoomqPEdy2vqtsRw8Yjl47WbY7z2Bxj0xY/JPjXKZB9BU6vRnbPrtMRw6Wg52c0wXjtrll3ua6lWGyUlETXtJhgNGE7sRdvA8ij5n7RNO2A48dud83xroqYno2YWtf/CYTRBTe5YXlOvFYYLhwGQPP1Akk2DEgB9ARgKzcr/ceMWVau4EOjpgk6roVvjWuw+e92sjCRBbr6pXk5+IV4uDnftA7P868lUreJakt80mN1/XDPPRyI3X1+S72rKP3DxBrX9PKkbYNpJd3O0QyObb3L/iL1JVS83Aqu4mvKb1WH3mStl8ku3vwAvF0eLdm47doluzetYPP7HtaSifDdTfvO67D512Txfkkry8wrwcnUsft68Aj0Go0JBoQGdVoOTnU2Z/ESqersT6FWU37Ieu0+XzS/Tfjen4ue1yLcvk3+9zPptEsTus7Hm+UBuwZ/tv7/1W5EWTRri6uL8l+tra9dHSYxHuZkIBgOFv/+CTat2ZmVsu/aiYNuPqLk5AKiZGcXLNLXqILm6oz9p+XkB4Nw0mPyrSeRfT0bVG7i1cR8e3VpalKv+9jPc+HIjSkGp/39VRXawBY2MbGeDUmjAmJ1XvFj2D0JJS0bNuAWKEeO5g2jrNLPILn6toW0wnD1w1/6Q/YPMP2vXbSp3u7K8eLsSabFdyS8oMCufl5fPnj2m59Xr9Zw8cQYg8K4NeUgoD/lPZano4vcPS/3+KZAI9AKOAIseZEP8A3y5EVcyEo+LT8Tf3/eRyH+U2y7yRf6d6sSVqhMfn4i/v/mxCH//kjJGo5GsrGw8Pd25E1dXFwDee+8/HDgQyepVi/D2rnLH8n+n/ffj7+ZLjm6o2SUDLDU7Hcmx/DNBkrMHsmsVlBsXLJZp67awGLBITu7m2TkZSE7l9/Fds+u0wHCp/J0bswxXT9SMlJLny0hBcvUsv6y7F5KnD8boOxzNBSTnsu1PR3K+Q/tditp//TwAsrsPFNzGJuJl7IZNQRfW37SXW0py5m18XUt21H1cHUjOyjUr81LXZkSeiCH8g1WMXRzFhN6PFS+LT8th0NwfGf1VJMevJlm0KTkzF1+3UvlujiRnlsnv3oLIY5cIn/odY7+JZMLTpulCsbcykCSJMYu28Myn61jyywnL/IxcfIt21E35TiRn5pjnP9mayKMXCZ+8mLELNzOhf5hFTtTxaJ5sZjkwSc7Iwde9ZMfax93ZMr/HY0QePk/4O4sY++UGJgx6AoAuzepgb6uj68SFdJ/0Nc91aYGro715fnqZfDdnktPL5PdsS+Shc4RP+Iqx89db5r+9gO7vLOK5ri0t87PK9I9rOf0f3pzI45cJn/E9YxdvZ0Kfx4uXxadlM+izDYz+ajPHryTyT5M8qmBMSS7+W0m9hexp/pmn8Q9E9q+K84fzcfloAbqmrYoqSziMfJnby766Y76tnwcFCSX/r4WJqdiWOtsG4NiwJrb+VUjfedzs8ZQtB1FuF9Dm9De0OraQ+K82YcgoWXem/92SaZNqVtpd/nc9kdy8UK6VOiOr1WE3aip2I6agKRrQSM7uZp+18fGJBJT5rL3f7Upprq4u9OjRBWDXPVUQHkr3c1euFqqqTlJVNVZV1c+AGncqKEnSi5IkHZUk6aii5N6pmCAI/0O0Wg2Bgf4cPHiMxx7rwcGDx5j18ZSKK/4/oqnbEkP0cYspSTi4IHsGoMSe/evZdVpYLbs82qbtMZzaD+qDObamqdcKw6VjJe2XNciBtdHvWUv+ihlIrl5oQi2vAanI9pMxRDSvTdS7g5k/KpxJq/egKCpeLg5sf2cQa15/mjd6tWbi97vJyS+sMM8i//hlIlrVJeq955j/Qg8mfW+63sKoqJy4msiHQ55gyat9+PXMVQ6VmWJzT/nHLhHRuh5R00cx/6VeTFoehaKUrOMz15Kws9ER7F/+ALLC/KMXiGgTStSH/2L+K32ZtHQriqLyx7UkZFkiaua/2Dr9BZbvPEpcSkbFgWXzj5wn4rEGRH00hvlj+zFpSVH+1URkSSLq4zFsnfECy3ceIe7WX8g/cZmIFnWImvQs80d1Z9Kq3SXr993BrBnXlzd6tWHi97/+pfVrdRoNGr9Asif/m5w503B4+U0kBydsu/dBf+wQauqtv54tSdSaOoIrU5dZLHJuGoxqVDjU+EWOtHqZgJd6YVfN+y89jTa0DcbzR8w+e/Lmjyd/8XsUbPwKm65DkNz+Wva90mg0LP9uPl9+uQTgSkXlhYdXRQMTb0mSxkuS9AbgIklmh6vuWFdV1a9VVW2hqmoLWbY87VyehPgkqgb6F/8dGOBHQoLlEay/ypr5j3LbRb7Iv1OdwFJ1AgL8SEi4aV4moaSMRqPBxcX5rlOzUlPTyc29zcaNpqm/P6zfQtOmDazS/vvxd/PV3AyzI4mSsztqbvk7WNo6LTBePFz+4zEnQTHfyS97hkFyckPNKb+PtXVaYCznrMidsst9LZmpSG4lR3QltyqomanlltU2ufs0Lig6e2TWfvMzKGZ5dVthvFDSN2p2OkryDdM0MFXBePkEsk81szrerg4klTqCfjPzNt5lpjr9eOQS4Y1rAtC4ug8FBiMZt/Ox0Wpwc7QDICSwCoGezsTeyiyT70hSRqn8jFy8XcvkHzpPeONgU34NXwr0BjJy8/Bxc6RZLT/cneyxt9HRrn41zseZ72R6uzmSVOoo9c2MHLxdnczzD54jvGltU35NP1P7c0um3Gw/Hk335rUt+tOU70RSenZJfnq2Zf7+PwgvOtvSuJY/BXpT/rYj52kbUhOdRoOHswNNgvw5G2v+GeDtXiY/Ixtv9zL5+84Q3rxuUX4ABQYDGTm3TfmhRfkujjQJCuBsrPn/nbdLmf7JLKf/j1wkvHEtU36NO61fLwI9XSzWr7WpaSloqpTskMueXiipKWZllNRbFB7ZB0YjSnISSsINZP9AtHVDsX3qaVwXrcZhxBhsO3bDftiLZnULEtOwLXU9j42fJwWJacV/a5zscaxblUYbptLyyAJcmtUmZNnbODUOwqtve9J/PYFqMKJPySLryEWcmpRcL2b63y0Z7EouHnf839WEWE7j+rOsmnELY+wFZN/qqNnpZp+1AQF+xJf5rL3f7cqfFiz4mMuXr/LF/G8rLPuwUJEe6p/KUtHA5BvAGXAClgFVACRJ8gVOPsiGHDl6kuDgmtSoURWdTsfAgb3ZvCXqkch/lNsu8kX+vdQZMKAXW7bsMCuzZcsOhg7tD0Dfvk+xe/f+CtsSGbmTsDDTVJrOndrd0wXmD2P/lKYkXUNy80Zy8QRZUzQQsLzzleTuA3YOKImWB/NM151YDliUm7FlsltivGI5dcqU7Vh+9j1O4wJQbkQje/kjefiARou2aXuMfxyyfD7vACQHR5RrltPGzPKSriG5+yC5VjG1v16r8vvGw9fUNwkxpepeRbJ1AHvTjq6mWn2zi+YBQgO9uJ6SRXxaNnqDkZ9PXSEsxHzw4ufmxKHLpqkhV25mUKg34u5oR1pOHsaiwVpcahbXU7II9HQxz6/qzfVbGcSnZpnyT1wmrEEN83x3Jw5FxxXlp1NoMOLuZM/jdatxOTGNvELTdRTHYhKo5Ws+zSa0mk9RfqYp//glwhrWtMwvOtNyJSnN1H4n05QnRVGJOhFN93KmcQGEVvflenIG8SlF+ccuEtbI/GYFfu7OHLpoui7nSmIqhQYD7k72+Hm4cLjo8bwCPWeuJlLTp0z7q/txPTmd+JQMU/6RC4Q1CjbP93Dh0IVS+XoD7s4OZfILOXMlkZpl+6fqn+u3qP9PxpS/fqP/XL9F/V/u+s0k0POvXy/yVxiiLyD7BSJ7+4JWi027zuiP7DMroz/0O7oGpuuuJGdXZP+qKDcTyJ07g8wXB5L5r2e4vfQrCnb/TN7yr83qZp+8jF0tP2yreSPptHj1aUtaVMn/ujH7NgdDR3Gk5cscafkyWcejOTf8Y3JOxVAQn4JrO9OBIdnBFpfmtbkdXTLNSkm4guxR8r+rCWmD4ZLldETJ0w/JzgElvtS1RXYOoCm6t5K9E5qqtVFS4lESrhAcXKPks3ZARLnblWHF25Ue7N5t3l/lef/9N3F1ceaN/7xfYVnh4XfXu3Kpqjr1Do8nSZL064NsiNFo5N+vT2Jr5PdoZJmly9Zw7tyliis+BPmPcttFvsi/U53XX5/M5s3L0Wg0LFu2hvPnLzFlyniOHTtDZOQOli5dw+LFczl79jfS0jJ47rmxxfUvXtyHs7MzNjY6evXqRs+eQ7lwIZpJk2ayePFcPvnkPW7dSmX0C+Os0v778bfzVYXCX1dj+/S/i2/pq6YlomvTCyU5tnggoa3bEuNFywvTJRdPJGd3lLhyBmmqQuHuNdj2ec2UfW5/SfbNWIxXi7LrtCz3bInk7Ink7FF+dnkUhYINi7B/8X2QZfSHd6LcvIFN92cx3riM8axp8KRr2gHDib331je7vse23+umWymf2YeamoCubW+UpGvFgxRtvVYYL5Rpv6pSuGcddgP/Y2razVgMp83P0Gg1MhN6P8aY/25HUVR6t6xDsK87C34+RkhgFTqGVmd8z1ZM++F3Vu41TWWbOqg9kiRx/GoSC6KOo5VlZEliUt+2uDrYWub3bc+Yr7eY8lvVI9jXgwXbDhNS1YuODWoyPuJxpq3dw8o9p0GCqYM7I0kSLg62DAtrzJDP1iNJ0K5+dTqEVLfM7x/GmAWbUBSF3m1CCPbzZEHkQUKqedOxYS3G92nPtNW/sPLXEyBJTB3ShT8nLxyLicfXzYnAUneyssgf1Jkx89eb8h9rQLB/FRZs3kdIdR86NgpmfL+OTFsZxcpfjpvaP6w7kiQxqEMTpiz/mb7Tl4KqEvFYA+qUueuXKb8LYz7/wZT/eENT/qbfCanuS8fGRfkrfmblrqOm/OFPmvLDmjLlu230nboYVIh4vAF1Ar0t8/s8zphvthX1f11T//98lJBAL9P67dWGaev2snLvGdP6HRhmWr9XklgQddS0fmWJSf3a4epgd7d3q4U33/uIIydOk5GRxRN9hvLy6GH0u58bbyhGbn8zF+f3ZptuF7xrK8Yb17AfPArD5Qvoj+xHf+IwuiYtcf18GaqikLfsK9TsrHvLNyrEvPNfGqyahKSRubnqF25fjKP6W4PIPhlDWpTl582fEhZvp868V2i25zMkCZJW/8rt87HQpejmAapC4c/fYTf4LZAlDKd+Q02JR9ehL0riVYzRpkGKNqQNhnPmBy9kT39snxqJqqpIkoR+/5biu3m9/vpktmxegUajYWnxduUNjh87zZbIHSxZupoli+dy7uxe0tIyGPbcK8W5Fy/ux6XUdqVHzyFkZ2czccJrXLgQzaGDxTfieh747711ovCwkdSy85HvtaIkXVdVtVpF5bQ2AX/tCQRBqFRaWWPVfINitGq+tWXN6mndJ5CsdypdiU2ouNDfIPtbdz65VLv8MwQPjNam4jJ/K9/Kd+q3dvvl+7k89S/Ivv9rTe6H9vG+Vs3PfmGkVfPPHvCquNBf1OzVB3dXs/J4TH+gx7TLVZB/o/LmId2Hzb6DH+r9415JqyqlH+/66ShJ0p1utyIBD/R2wYIgCIIgCIIg/O+q6LCND9ANKHvlkQRUPKFcEARBEARBEAThHlQ0MNkCOKmqanGhuyRJu63SIkEQBEEQBEEQ/udUdPH76Lsse/bBN0cQBEEQBEEQ/n9TKvGWvA8zK1/BJgiCIAiCIAiCUDExMBEEQRAEQRAEodJZ+Z6FgiAIgiAIgiCU9lDfK7gSiTMmgiAIgiAIgiBUOjEwEQRBEARBEASh0ompXMJfZu37SchW/nZhycqvQLLiN3cDGK38zemP+jezZ4xtbtV8w/lrVs2/st3WatnBz+qslg0g+Vr5+3dv51o339HKx+xs7aybbzRYNV5y87dqvlyziVXzDfs3WDX/9H5vq+Z72OdbLVvyqmq1bIAIn2ZWzX+UKJXdgIeUOGMiCIIgCIIgCEKlEwMTQRAEQRAEQRAqnZjKJQiCIAiCIAj/IMXK070fVeKMiSAIgiAIgiAIlU4MTARBEARBEARBqHRiKpcgCIIgCIIg/IPEFyyWT5wxEQRBEARBEASh0omBiSAIgiAIgiAIlU5M5RIEQRAEQRCEf5D4gsXyiTMmgiAIgiAIgiBUuofqjEm38I7MmTMNjSyzeMkqZn3y5SOT/yi3/X7yy5b7pEw5GxsbliyZR7OmDUlLS+fZIWOIjY0D4K23xjJyxDMYFYVx4yazY8cebG1t+fWX9dja2qLRatiwIZJp0z4F4D9vjGHy5DeQJImz5y7Qrl0ERqPR7LkWfzuXZs0akpqaztBhLxMbG4eHhxurVi2iRfPGLF++jtfHTQbA3t6OVd8vpFat6hiNRs6evUTjxiFoNBqWLFnN7NkLLF7Lt99+Vpw/bNgrpfIX0rwof9y4KcV1dDodc+dOp0OHNiiKwvvvz2bjxm107RrG7NnvodFoWLp0NbNnf1XOc82haVG/DR06luvX4+jcuR3Tp0/AxkZHYaGe9esjGT58ABqNhsWLV/HJ7HL6f/FcmjZrRFpqOkOGlur/N19hxMjBKEYj48ZPYceOPdSpU4uVK0raUrNmNaZOm80XX3xL48ahLJj/EbZ2thgMBl599R2OHD35t94/f9WDzNfUa4Ztn+dB1qA/GIX+l/Vmy216j0YT3BAASWeL5OxK7rvP3j0ztAV2A19CkjUU/r6Nwp/XWpTRNu+Abc+hAChxV8j79iMAbPuORtuwNUgShvPHKVhj/t5wDmtGwHvPI2k0pK6OIvkr8/Z69O+M/zsj0SelAnDru0jSVu8AoNay93FsWoeco+e5Omp6+W2v0wTbnqNAltEf2YV+z4/m/dFjBJpaDUz9YWOL5OhK7rTnkP1qYNvnRbB1AEVB/+sPGM7st8jfdzmJWT+fRFFVnm5ak1Ft65ktT8y8zeSfjpCdr0dRVV7r3ID2tf04cOUmn+86g96ooNPIjOvSiFY1vS3zoxOZtf04iqLydLNajGofYp6fkcvkjYfIzi9EUVRe69KY9nX8ORCTxOc7T5Xkd21Cq1o+lvkXbjBr0wFTfqu6jOrcxDw/PYfJa3aTnVeU/1RL2tevRnxaNn0/WUd1L1cAGlX3ZlK/9pb5Z68x64fdKIrC020bMCq8lXl+WhaTv/uZ7LwCU37vdrRvUJPIw+dZtvNYcbnohFusensI9aqa99G+c9eYtX6Pqf2PhTIqvKVl/oodRfkKr0W0pX1oTSKPXGDZrtL5Kax661nqBXqZ55+6yMffbTLld2rJ6IhOZssTbqXz3tfrSM/KxdXJgQ9fHoSPpxsXriXwweIfycnLRyPLPN+nM90fa2zRP78fOcXHC5djNCr0fbIjzw+KMM+/eYspc74hLTMLV2cnZr41Bl8vTw6fPMusRSuKy129kcisd8byxOMtzNtv5fXr0akJwTNGImlkElfu4voXGy3KAFTp0ZoGi//DsfC3yT51Be9+7aj2cu/i5Y4h1TjW5W1yzl4zq+cU1oyAKS+ARiZtzQ5uffWD2XL3/k/gN3Ek+pumz4fUZZGkrYnCLqQmATNeRuPkgGo0kvzlWjK3/G7eN1dv8cmv51BUlT4NqjKqdZDZ8tm/nuPIDVNuvsFI2u1C9o4NB2DebxfYeyUZgBfaBNOtnr/Fa24S1pSR772ArJHZtXoHG8t8tvV8PoInngnHaDCSlZbJgje/ICX+FjVCavLCBy9h7+SAYlTYMH8d+8u0XXh03ffARJIkT1VVUx90Q2RZ5vN5H9D9qcHExSVy8MBWNm+J4vz56Ic+/1Fu+/3kly136MBWtpQpN2rkYDLSM6kf0o6BAyP48MN3GTJkDPXr12bQwN40btIZf38ftm9bTUhoewoKCugaPpDc3NtotVr27P6Rn7f/ytFjp5g69S169hzC7/uOEHP5EG+99QozZ35e/FwjRzxDRkYGIaHtGTAggg9mvMPQYS+Tn1/A1KmzCQ2pS2hoXbPX8NncRezZcwBbW1tuJv3Ba6+9y+rVG9m3bzNbtuzgwoWS1zJixCAyMjIJDe3AgAG9mDFjIsOGvVKU/ykhIXUJDa1jlj9hwqvcupVCw4YdkWUZDw83ZFlm7tzp9OgxhPj4JH7/fRNbtuy0eK709EwaNAhjwIBefPDBBIYNG0tqajr9+48iMTGZBg3qceBAJA0bdiyVE8X5UjkjRz5DekYmISHtGDgggg8/eIchQ1+mfr3aDBzYmyZF/b9t2ypCQztw6dIVWrbqVrx+r109yk8/bQfgow/fZfqMOWz/+Vee7N6Zj2a+yxNdB/zl989f9UDzJRnbvv8ib+EU1MxU7Md9iuHsYdSbN4qLFP70bfHvunY9kAOCyksyy7Qf/Aq5cyeipqfgOPELDKcPoiReL3kN3v7Ydh9E7ifj4XYOkrNpZ0ZTKwRNUCi5014CwOGtT9HUaYTx0uk/XzyB0/9FzJAp6JNSqbPpUzJ3HqYg+oZZE9K3/E78lEUWTUv+egOynS2eQ7rfuT8iXiDv22moWanYv/IxhvNHUJPjSvojcmlJfzz2JLJ/TQBUfQH5a79ATU1EcnbHfuwnGKJPQv7t4vJGRWXm9hMsHNIeHxcHhvx3F2F1/Anyciku883e84SHBDKwRRAxt7IYu+p3ttX2w93ehnnPtMXb2Z7LyZmM+X4vO17vadZ8o6Iwc+tRFg7rhI+LPUO+2UFY3QCCvF1L8n87S3hoVQa2rE1MciZjV+5hW50I3B1smTe4A94u9ly+mcGYFXvY8UZvy/wf97HwxafwcXVkyOcbCQutTpCPe0n+rhOEN6rFwMdDiLmZzthvt7OtfjUAAj1dWDu+X/l9/2f+2l9Y+GpffNycGTLre8IaBhHk51mSv/0Q4c3qMLBDY2ISUxm7YCPbGoymR6v69GhVjhlshwAAIABJREFUH4Do+BTGfb3JYlBiVBRmrtvNwleexsfNiSGfrCasYS3z/J+PEN60NgPbNzLlL/yJbVNr0qNlPXq0NA0ioxNSGPfNFotBiVFR+HDJRhZNfB4fT1eenTSfjs1CCAosGeDNWRlJr/bNiejQnENnLzNvzXY+fPkZ7Gx1zBgziOp+VUhOz2Lwu5/zeKM6uDjal+QbFT74cilfz5yIbxUPnnl1Mp3aNCOoemBxmdnffE+vLu3o3bUDh06eZd6SNcx862VaNQnlh69mApCZlcNTI8fzeLOG/+j6RZap/dFoTg2cTkFCGs1/nknKz0e5fSnOrJjG0Y7AF54i69il4seS1/9O8nrTzrZj/Wo0WPqmxaAEWSZg2ktcHToZfVIqwZvmkLXjEAWXzT8fMrbsJeE9888HJa+AG+PnUHgtEa23B7W3fEb2bydK9Y3KR7vO8lX/Vvg42zFk5T7Cgr0J8nQuLvOfTiUHAVYdv8bF5CwA9l5J5vzNTFY/1w69UeH5NYdoW9MLJ1tdqabLjJ7+L6YPeY+0pFRmbprN0Z2HiSv12Xb17FXe7jmewvxCwod2Z9jEEXw29hMK8gr4Ytxckq4l4u7twceRn3LytxPczsq987oQHhl3ncolSdJHkiRVKfq9hSRJV4BDkiTFSpIU9iAb0qplU2JirnH16nX0ej1r1/5ERK9uj0T+o9z2+8kvW27N2p/oVaZcr17hLF++DoD16yPp3Kld0ePdWLP2JwoLC7l27QYxMddo1bIpALm5ph0ZnU6LTqdDVVW6dOlAYaGeX3fvR6/Xs2nzzzw7uK/lc60wHR3asCGSTp3aAnD7dh779x8hv6DArHxeXj579hwAoEmTUG7dSkGn06LX61m3bjO9eoVb5K8ozt9qkV9QkG/RR8OHD2TWLNPRfFVVSU1Np2XLJsTEXOPatRvFz9WzZ1ezej17dmXlyvXFz9Wxo+m5Tp06S2Ki6aiTo6MDqqqSkHCzeD2V1+bi/t8QSafi/g9nbZn+b9nS/Mhg587tuHIlluvX44vb7+xi2gi5uDqTkHjT4vXCw/P+vBdytdooKYmoaTfBaMBwYi/aBq3vWF7btAOGE7/dNVNTsy5KcgJqShIYDeiP7kbb+DGzMrp2T1K4ezPczgFAzc4sWqIi6WxAqwWtDkmjRc1KL67n0KQ2BdcSKbxxE1VvIH3zXly73rm9ZeXsO42Sm3fH5XLVYJTUJNT0ov449Tva+i3vWF7buB2GU6adJTUlETU1sej1pKPmZiI5upqV/yMhjaruTgS6O6HTyHQLrcruiwlmZSQJcgv0pvYW6PFytgOg3v+xd97hVRT7/3/tOSeV9F6poSX0LjWgBK5IsRALVfSqSPGCqCBNUFGsqIAXFRCVKkoNUiVIJ6FDKAmhpPfez9n9/XFiTjYnEFou8v3N63nyPDk7n3nvZ2d3Z3dmPjPr7YyHvfEltZG7AyVlBkr1BrV+Qib+Lvb4udhhodPSr0Vdwi8lVNGXKCjRV9K3Mek7lOt7OFavfyMNfzcH/FwdjPptGhF+/rpaHygoKTXqF5Xi7mB70/Kryrlryfi7O+Hn5mTUb9+U8DNXquhLFBT/rV+Cu2MdM50/Ii/Sr31Ts+3nrqfg7+aIn5tjuX4Tws/GqvUlTPrFpbg72lWjf4l+7ZqYbT8XE4e/pyt+nq5Y6HT0f6Q14cejVDZXElLoFGRs3HcKbFSRXt/bnXrebgB4ODvg4mBHVpUXy7OXrlDXxxN/bw8sLHT8K7gLew8fV9nEXk+gc+sgo37rQLN0gJ0HjtG9Y2tsrK3U/tfy+XVoF0DR1WSKr6eilOlJ3XgQt/4dzOwaTH2OGws3IReXVavj8WQ3Ujeaj0batmlM6XVT/ZC95S8cQm6vfii9mkjpNeP9q0/NRJ+Rg87F1GFwLjkbfydb/JxsjfduU2/CY6p/BgBsv5hI/2beAMRm5NPOzwWdRoONhY7G7vYcupausg9o05jka8mkxqWgL9NzcMt+OvRVjxaeP3yW0vJr8/LJS7iUN6iTriaSXO57VmomOek5OFTy/WFBlv7Zfw+KmuaYDFAU5e+r6VPgWUVRAoC+wOf30xEfXy/i4k0PrPiEJHx8vB4K/YfZ9zvRr2qXkJCEbxW7yjYGg4GcnFxcXZ3x9fEivkpeH19jXo1GQ2TEThITzrB7z18cizhJnTq2GGSZdu1aAVC/nj+uri7qfVXSNBgM5Obm4erqzO0QENAQZ2cn9u49aPLHRx3Gcaf6jo7GinH27CkcPhzGypWL8fBwK9dJUpebb5Vyu419DRnyL9LSMigtLS3XScbH11tl41tpXwaDgZxcY/n7+HqrfYhPxtdHnTd06CDWrttU8XvylNnM/2gGV69E8MnHM5k+46Nqj/ufcn3eDpKjK0q26QGpZKcjObpWb+vsjuTqiSH6zK01nVyRs9JMmlnpaJzcVDYaTz80nr7YvvUFtu8sQBtkfDkxxF5Af+k09p+sxv7T1ejPH0dONvUYWni5UpZk8rcsKR0LL3N/nf71CE23f039b9/BwtvNLP2mvju4oORUKo/czJuXh5M7krMnhivnzNI0fgHGRlVmsmp7am4RXg6mHnBPBxtS89QNpdd6BhJ29gYhC8IYv/oAU/u3NdPffSGB5t7OWOq01eibXhQ9HWxIza2iH9yCsDPXCPl8E+NX7mPq4+3N9aPib6JfgJeT6UXd07EOqTnql+fXQtoTdiKGkA9WMX7ZdqYO6VqRlpCZx7Nf/s5L327hRGwSVUnNzsfL2dQD7elkR2p2vlp/QBfCIi4QMv17xi/eyNTQ3lVl2HniMv/qYN4wuS39f3UhLOIiITOXMv7bTUx9xrzPcefJaP7V3rxhkpqVg5erU8VvDxdHUjJzVDZN6/mw55jxmtkTcZ6CohKy86o0QGLiKNPr8fdU1/GpGZl4uZuuR083F1LSs1Q2TRrWZffBCKP+wUgKCovJzs1T2WwPP8zjwerOAqj982vl5UJJoinApCQxE6sq969dywZY+biSufuEWf6/8RjcldQN5qFKFp6ulCVWrh8ysPA0v38d/9WVxn98Td3FU6utH2xaN0ay0FF63XT/puYX41neSQDgaW9DWn6JWV6AxNwiEnOL6FjXqN2kvCFSVGYgq7CUyLgMkqvc9y5ermRUqtsykzJwraZu+5tHn+3LyXDzRmdA68boLHWkXE+uJpfgYaSmholOkqS/w71sFEWJAFAU5TJgdbNMkiS9IklSpCRJkbIshtYEt0aWZTp0DKF+gw507NC2IvxqX/ghPv10Ngf2b6G4uARFuT9rWGi1WiZOeImoqMtcvXqj5gy3iU6nxc/PhyNHjvPIIwM4evQEH300/b5oN2/emOeff5JDhyLvi15VLCwseOKJEH77bWvFtldfGcmbb71Hg0YdefOtOXy/5L72Rfzj0bXtgf70Ibgf151Gi8bDl8LP36Loh4+wGf4fsKmD5O6DxtufvKnDyHvnBbTNWqMNaHFH0jm7I4jq9jKX+k8kb/8p6n7xn3v3txp0rbqhP3fYrDwkeyesQydSvH4hKHf+ybDt5+MY1Lo+O/8zgIXPd2fGxmPIlXRiUnP46s+zzHi83V35vf3sdQa1acDONwezcFgvZvx+BFmuor/7FDMGmvdk35b+yRgGdWjCzhkvsHBMf2asDkeWFdwdbNk+/XnWTnqKNwd2YdqqveSX9/7ekX7kJQZ1DmLnh/9m4etDmLFiu8r/s1eTsLbUEeBz+w1Slf7xSwzqHMjO919i4djBzPh5p1r/WjLWFnevP3nYACIvxhI67SuOX4jFw8UBjcb06pGWlcv0b9cw99Whqu23y5RXhhF59gJDX3+XyLMX8HBzVutnZBF9LY6uHVrdlf+1en4liYA5o7jy3k83NbFvF4ChqJSCi3E3tbkVubuPcbH7S0T/ayL5B07h/7m6ftC5O1P3i8nEv/XVXd2/ADsuJvJoYy+0GmM3+yP13enewJ3Rqw8xLewkrbyd0Up33wXf48leNGwZwOYl6vlvTh7OTPhyEounfI1yl74L/nnUVAssBrZJktQH2C5J0leSJPWSJGkOUP0sWEBRlO8URemgKEoHjcZ82Lk6EhOS8fczTY7y8/UmMfH+tYBrU/9h9v1O9Kva+fp6k1DFrrKNVqvF0dGBjIwsEhKT8auSNzFBnTcnJ5fwfQcJCQkmMSEZKytLHn30abr3GEhGZlZFSFPFvipparVaHBzsychQ96ZVx+LF84mJuUpupV41X19vEhPVw9R3qp+RkUVBQSEbN/4BGMPL2rRpUa5jGp3w9fUmocqx32pfvr5erF37He+//wWOjvaVdLxITFD30iVU2pdWq8XRwVj+iQlJah/8vEhINOXt3783J0+dJTXV1IM1csRQNmzYBsD69VvMQr8qfP+HXJ+3g5KTgVRpNENyckPJqX7KnK5NzWFcAEp2BhpnU+y95OyGnK0OW1Cy0tGfOQKyASUjBTk1Ho2HLxZtu2KIvQglxVBSjP5cJNqGzSvylSVnqHo4LbzdKia5/40hOw+l1BiqlLFmF7YtapgTU9mv3Ewkx0rl4eBy8/Jo3a0ijKsCKxusR02nZOcq5DjzOT8eDjYkVxrBSMktqgjP+psNJ68REmicM9Daz5USvUx2YUm5fSGTfz3M+4M74u9iHmJk1DfNaUnJLaoIzzLpxxIS5G/U93ejRG8w6ecUMnnNAd5/sgv+LvZUxcOhDsmVRhhScgrwqBJKtSHiEiGtGxr163uW6xdjqdPiVMfY4xzo546fqwPX09SjCR5OdiRnmeqhlOx8PJzUx7nh0DlCykcrWjf0oaRMT3al8Lztxy/Rv716QYE70j98npB2jY36Dbxvom8+WgLg4exIckZ2xe/UzBw8XRyr2Djw5aSRrPvoDSY8awzB/HseSX5hMeM/Xc6E0H60alzPXN/VheQ00/WYkp6Jp5tzFRtnFsyaxK+L5zFxdKhR3850jnb8dZQ+XTtgoTOfUlvb57ckORMrH9MogJWPCyWV7l+tnQ11mvnT5vf36BKxCIf2jWnx0zvYl+8PwGNIt2pHSwDKUjKw8KlcP7hWTHL/m8r1Q+aandi0CKhI09jZ0GD5bJI/+5nCk5fUZWNnTUqeKVw5Ja8Id7vq+6N3XEyif5XJ7S93CWDtyB78d2hnFBTqOqvLNTM5A9dKdZuLtysZyeZ1T8turXlq/FDmv/wh+vLjALCxs2Ha8pms/uwXok9eNsv3MCAj/aP/HhS3bJgoivINMA94FRgM9AHeARKAF++nIxGRpwgIaED9+v5YWFgQGjqYLVt3PhT6D7Pvd6Jf1e7Z0MFsrWK3detORowwTpB++ukB7A0/WLH92dDBWFpaUr++PwEBDTgWcRI3N5eKEChra2see7Qnly5dISLyFE2aNKJ+fX/q1KnDM08/wTcLf6iyr12MGP4MAE89NYDw8n3divfeewtHB3tGjZ6oOpahQweydesuM/3hFfqPEx5uHuNblbCw3fTqZQwZCA7uxsWL0URGniYgoAH16pn2FRa2yyzfsGFPV+xr3z7jvhwdHfj99+XMnDmfH39cq9IJDR1crc8V5V+pTLZu3UVolfKPiDD1LTwbOpi1azeptBKTUujV03gsfXp3JzrmarXH/E+5Pm8HOS4ajbsPkosnaHXo2vbAcO6omZ3k4YtkWwf52sUaNQ3XLqHx8EVyNWpadAhGf/qIyqbs9CG0TYw9tlIdBzQefijpSciZaeiatAKNBjRadE1aIiebRvEKT0dj1cAHS39PJAsdzgN7kLtL7a/Ow/Si5ti3E8Ux6om1tyyP+Bg0bt5Izh7G8mjdHcMF81E5yd0XycYO+UallxetDuvhb6M/GY7h3BGzPABBPs7cyMwnIauAMoPMjvNx9GqiDiH0drTh6DVjp0NsWi6legPOtlbkFpcyYfVB3ujTkrb+1ffWB/m4cCMjj4SsfMr0Bnacu0Gvpr5V9OtwNDalXD/HqF/HityiUias+os3HmtF27ru1ckT5O/OjfRcEjJzjfqnrtArsK5a38mOo9HGUMPYlKxyfWsy84swyMbRpfiMXG6k5+Dnqm78BNXz4kZqFgnpOUb945fo1bKhWt/FgaMXjddEbHKGUd/O+GIvywo7T1ymf4fqGw5BdT25kZZdSf+yub6zPUcvxZXrZ1JaVkX/ZDT9q5m/AhDUyI8byRnEp2ZSptez/fBperVvrrLJyi1ALi+HpZv2MqSXcQ5TmV7PpC9/YmCPdvTtXP1oRoumDbmekEx8ciplZXr+CD9CcBd1KF5WTl6F/g9rNvNkSLAq/Y/wQ9WGcUHtn9+8kzHYNPTGuq4HkoUOjyHdSN9hur8MeYUcDHyJIx3HcaTjOHKPR3Nu5HzyTpfPA5IkPAZ1JXVj9c+2wtPRWNb3wcLPWD84DexJ7q5jKhudu6l+cOjbieIrxnMtWeiot2Q6Wb//Sc4f5s+2IC9HbmQXkJBTaLx3LyUR3Mh81bqrGfnklpTR2scU0meQFbKLyueGpOUSnZbHI/XV93DM6Wi8G3jj4e+BzkJHt4E9iKzie/2gBrzy0Vjmv/QhuRmmRp/OQsdb301j3297ObKt5uey4OGixlW5FEUJB8Krbpck6UVg+f1yxGAw8MZ/ZrAtbBVajYYfV6wlKur+tYJrU/9h9v1O9G9mN3v2FI4fP83WrbtYtnwNP/74NReiDpCVlc2w4a8DEBV1mV/Xb+HM6b3oDQYmvjEdWZbx9vZk2dIFaLUaJI2G9eu3sG3bbgBOnDjD2TPhgMSePX+xYsU6Zs16kxPHz7A1bBfLf1zD8mULiDq/n8zMbEaMHFfh66VLh3Cwt8fS0oKBA/sx4Ilh5OXlMW3qRC5ejObQwa0oisK+fRspKChkxYq1XLhwmVmzJnP8+FnCwnbx449rWbZsAefP/0VmZjYjR46vpH8Q+0r6TzwxnIsXo5kx4yOWLVvAp5/OJj09k1dfnYLBYGDSpFls2fITWq2WFSvWceFCNDNnTubEiTOEhe0u39eXnDu3j6ysbEaMMO7rtddG0ahRfaZNm8i0aRNRFIVt21YC8OOPa4i6cJnZs6Zw/ISx/JcvX8OPy78iKuoAWZnZDB9RXv4XLrN+/RZOn/4Tg97AG2/MqHiY29ra8OijPXl93FTV+X7ttbf44ou56HQ6SoqLGTv27Xu6fu6W+6ovy5T8vgSbV94zLo97bDdyShyW/V/AEBeD4bzxwWjRtif6k/tvW7N4zSJs35iHpNFQenAnctJ1rAaOxHD9MvozRzCcj0QX2I46s78DRab4t+9RCvLQH9+Prmlr6sxaAoqCPioS/ZlKDQ+DTPysJTT86T0krYbMdbspjo7Da/ILFJ6JIXf3MdxHD8ShbyfQG9Dn5HFjyoKK7AG/foR1Iz80dawJPLKMuLe/Ua28gyxTsvkHbMbMBElDWeSfyKlxWD72HIaEmIpGikWrbuhPq1+OdC27om0QiGRrj66dcd5DyfqFyEnXTDYaDVP7t2Hsqv3IisLg1vUJ8HBkcfh5Ar2dCW7qw+S+rZm79Tgrj0SDBHMGdUCSJNZGXOFGVj5L9kexZL9xwvR/h/XApY4p7l2n1TD18faM/XkfsiIzuG1Do/6fZwn0cSG4mS+TQ9owd0sEK48Yr5k5Qzob9Y9FcyMzjyX7zrNk33mj/ohgXOyq6A/pytjv/0CWFQZ3akqAlwuLd0QS6OdOcFA9Jg/swtxf97Ny/1mjfmgvJEniRGwyi3dGotNo0GgkZjzdHUdbk3aFfmgfxi763aj/SBABPm4s3nqIwLqeBLdqxOSnejJ31S5W7j0BSMwZ0Q+pPCzmeEw8Xs72+Lk5UR06rYapQ4MZu3ijsfy7BBLg7crisMNG/ZYNmfxkD+au3sPKvSeN5T+8r0n/SkK5vuNN9LVMGz2YsR8vRZZlhgR3JMDPi0W/7iSooR/B7QOJvHCFr9dsB0mifbMGvPviEAB2HDnDiYtXyckvZPNfxrkDc18NpVl9H5X+u+NG89q78zHIMk+G9CKgvh8LV6wnqEkDej/SnogzUXy1bC2SJNG+ZTOmjxtdkT8hOY3ktEw6tFI3lv5X51cxyERPW0qrNdONywWv3kvhpXjqv/0seaevkLHj1qG5To80pyQxneLrqdUbGGQSZ/2Xhj/NAa2GrHW7KYm+geekYRSdjSZ39zHcXhyIw2OdUQwGDNl5xE/5CgDHAd2x6xSEztke52ceBSCuUt2h02h4p08Qr/92DFmGwS38aORmz+KDlwn0dCQ4wNhI2XEpkX5NvSuuGQC9LDNmjbGzws5Kx4ePt0FXJUxPNsgsnfUd0396D41Ww951e4iPjuPZyS9w5UwMkbuPMeLdF7G2teHNxcZnT3piOvNf/pBHnuhG805B2DvZ0/uZPgAsmvI116Kq7zwTPFxIdxuXJ0nSDUVR6tZkp7P0FYF//0ep7YG+u4k3vhOkWj4C6R5iam8Hg2yo2egekB/ymN3s8eaTnO8nckH1E0HvF7HbbzqN754JeMGiZqN7QBtUfWjR/dtBLX+Cq455WNd9xdY8LO2+ItfuN6UlV/NvUtxPNK5+NRvdA4Yz4bWqf/Tf5qOw9xMXG/MVIe8XAe8G1Gx0D4z6MLZmo3vk1+ubHuCaUrfPLz7D/9EP2eGJvzyQcrxl7S5J0s2Wo5EA8zE9gUAgEAgEAoFA8H8eSZL6A18BWuAHRVE+vond08B6oKOiKLccKqyp28kT6AdUnfErASKwTyAQCAQCgUAg+P8MSZK0wCKMnxCJByIkSdqsKEpUFTt74A3gtoYSa2qYbAXsFEUxW4FLkqTw29mBQCAQCAQCgUAgMPEgP2J4n+gExCiKEgsgSdIajAtlRVWxex+YD7x1O6I1rcr1kqIo1a5TpyjKC7ezA4FAIBAIBAKBQPB/Cl+g8gd24su3VSBJUjvAX1GUsNsVrd3ZxQKBQCAQCAQCgeChovLH0sv/XrnD/BrgC+DNO8lXy0ubCAQCgUAgEAgEgsrU7tp5946iKN8B393CJAHwr/Tbr3zb39gDLYDw8lVKvYDNkiQNutUEeDFiIhAIBAKBQCAQCO6ECKCxJEkNJEmyBJ4DNv+dqChKjqIoboqi1FcUpT5wBLhlowREw0QgEAgEAoFAIBDcAYqi6IHxwA7gArBOUZTzkiTNlSRp0N3qilAugUAgEAgEAoFAcEcoirIN2FZl26yb2AbfjmatN0xqezW02v66dm3q1/aXx/W1/GXw2v5kqaGWv14suDW9PIJqVX/LO7X7hWH5ekLNRveAy/Jztar/gXfvWtN+o03jWtMGUJISa1Vf2++5WtVXCnNqVz8hplb1Je8Gtapf2/7rj++rVX0lLaNW9X1c8mpV33eoU61pazr3rTVtgMcMq2pV/2HiH/3Z9weICOUSCAQCgUAgEAgEDxzRMBEIBAKBQCAQCAQPHDHHRCAQCAQCgUAg+B/yf+DL77WCGDERCAQCgUAgEAgEDxzRMBEIBAKBQCAQCAQPHBHKJRAIBAKBQCAQ/A8R645WjxgxEQgEAoFAIBAIBA8c0TARCAQCgUAgEAgEDxwRyiUQCAQCgUAgEPwPEaFc1SNGTAQCgUAgEAgEAsEDpzZGTPoDXwFa4IeqiZaWlixf/hXt2rYkMzOLF4aN5fr1eADefns8L45+DoMsM2nSTHbt2oeVlRV7//wNKysrtDotp0+dp32H1mg1GsL3HaJ9u1ZYWlpw4sRZXnl1ClqtluXLFtC2XSsyM7IYNryS/lvjGP3i88gGA5Mmz2LXrn00adKQlb98W+FfgwZ1mTP3M775ZimvvTqS+fNnodFIpKVl8uxzrxAZeUp1LMuWLqBdu5ZkZGQxfMTrXL8ej4uLE6tXL6FD+9b8/POv/GfSTABsbKxZveq/NGxYD4PBwPnzl2ndOtDo8/I1fPbZYrOyWrr0ywr9ESPGVdL/L+3L9SdNmlWRx8LCggUL3qdnzy4YZJmZs+azYcO2Gk9av5BgvvhiLlqNhmXLV/PJp4tu+4TfDkL/4dbvGNyBcXPGotFq2LZ6O2sWrVWlt+zcknHvvUbD5g35YNw8/grbD0Cbrq0ZO/u1Cru6jfz5YNw8Du44pMp/8HoGn+6/jKwoDAn0YUz7+qr0z/ZfJiIhC4DiMgOZRWXsf6UXAAsORrP/egaKotDZ34W3ezRBktQLxGubtsVq8L9Bo6Hs6C7K9v6mSrcc9BLaRi0AkCytkOwcKZg5DADrl2ejrdcEw9ULFC/74I7K7W/utfwb9GrFY7NHoNFqOL0mnCPfblGltxnWh3Yj+6IYZEoLi9k+bSkZ0YkV6Q4+rry8ez4HFvzOse/U9cHB6EQ+2XYcWVF4sl0jxvQMUqUnZRcw8/fD5BWXISsKE/u2pkcTX87Gp/P+5mNGIwVe692SPoH+Zr5r6gVh2SsUNBr05w6gj9yhSrfoORStf1PjD50lkq09Rd9OQuPXxJivHMnZi9I/vsdw5bQq/4ET55n/wzpkWeapvt146en+qvTE1AxmffMTWbn5ONrZMm/SGLzcnI3l9tRYGtf1BcDL3YVvpr9u5v/B05eY/9NmZFnhyd4deWlQb7V+Whazv/uVrNwCo/7rz+Lp6sTFa4l8uGwD+UXFaDUaXh7Sh/6PtDbXvxjHJ5sPG/U7NWVMnzbq8s/KZ+bacPKKSpFlhYmPd6RH87okZObx1Ke/Us/dEYBW9TyY8XSPavy/zPyftyLLMk8Gd+SlQb3U/qdnMfu738jKK8Sxjg3zxobi6WrUHDt/OWevxNGmST0WThllpv0/8f9qGp/ujTLWDS38GdO5kSr9s71RRMRlAFCsN5BZWMr+8SEAfPXXRfbHpgLw7y4B9GvmY6avbdgSy5DhIGnQn9pH2eGtqnTLx15AU785AJLOCqmOPYWfjwXAdtqPyGlxACg5GZT8usBM37Z7ezzeHQsaDTnrt5P1wzpVusOQvri99RKYFcMyAAAgAElEQVT6FOMxZK/aQu767QA0PhdGyeVrAOiT0kgc9565/wGtsXx8lNH/E39Stn+z2v/+I9E0CDT6b2GFVMeBwo9eQnJ0w+r5N0GSkLRayo7sQB+5W5X34KkLzF++wXhuH+3MS0MeU6UnpmUy+9s1pntrwnA8XZ1ITMtk0mfLUGSFMoOB5/v3IDSkm5nv/sGt6DpnBJJWw8XV4ZxapK7Xmg/vQ9BoY71WVlDMX+8sJTs6EY1OS89PX8atZX00Wg2X1x8wyyt4eLnfDRMtsAjoC8QDEc2bN+bChegKgzEvPk92Vg7NA7sTGjqIefOmM2zYWJo3b8yzoYNp3aYPPj6ebP9jDYFBPSgpKaFvSCgFBYVYWlqSkX6RkSPHEbZtD1mZl3nmmTFs37GX2bOmMGLEUKysLMnKziEwsDuhQwcx78N3GTb8dZo3a0xo6GDalOv/8cdqgoJ6cvlyLB079QNAo9Fw7WokmzZtR6PR8PHHM5k48V1Wr9nI2TPhLFjwPt27D6w4lhdHP0d2djaBQT0YOnQQH37wLsNHvE5xcQlz5nxGUGBTgoKaqgroywVL2LfvMFZWVqQkn2PixOmsWbORgwe3sHXrLi5eNJXV6NHPkp2dQ1BQT4YOHcgHH0xjxIhx5fqfExjYlKCgJir9qVMnkJaWTsuWwRgUGRcXpxpPmkaj4euvPqT/488TH5/EkcPb2LJ1p+q83QtC/+HW12g0TPxgPG+/MJW0pHQWh33D4Z2HuR59o8ImNSGVTyZ/xtBXn1HlPXXoNK/2Mz7E7Z3s+enAciL3HVfZGGSFj/dd4tvBbfG0s2LYugh6NXCjkYtdhc2UHqbrfPXpOC6l5xn1k7I5lZTDuuc6A/Dib5EcT8img5+zaQeSBqsnX6Xou9koORnYvPEZ+qhjKClxFSalm5dW/G/RbQAa34YVv8vCN1BmaYVFl363VV5VudfylzQSIe+PYs2wj8lLzmT05rlE7z6uanhEbTrMqZV/AhDwWDsenTGcdaM+qUjvM3MYseGnzbQNssxHWyP576g+eDrYMGzJDno186ORh2OFzff7zhHSoh6hnRpzJTWH8b+E88dkXwI8nFj1an90Wg1peUWELt5Gz6a+6LSVBuIlCcvez1Py+wKU/Cysn5+GIfYMSmZShUnZX79SVv6/rnVvNB7Gxo0cf5nileUNQStbbF78AMP1KLX/Bpl5S1bz3Zw38HR15vm3PiK4Uysa+ZteQD//8TcG9u7C4D6PcPTMRb7+eSPzJr1olLW05NcFM25a9gZZZt7yjSyZ9jKero68MGMhwe0CaeTnWWHzxcowBvZoz6Ce7Tl6Poav1m5n3uvPYW1lwQdjn6WetxupWbk8P/1rurZqgkMdG3X5bzjIf195HE/HOgz7eiO9gurRyNN0/X6/5yQhrRoS2jWQKylZjF+6nT+a1wXAz9WBdZOfvrX/KzazZOoYPF0ceGHWYoLbN6ORbyX/V/3BwO7tGNSzHUfPX+GrdTuYN9bYIBw9oAdFpWWs//PYTfVr13+Fj/ec59tnOuFpb82wlQfpFeBBI1f7CpspvQMr/l994hqXUnMB2B+byoWUHNaM7E6ZQebltUfp1sAdOysL0w4kCcv+Iyle9QlKbibWY+agjz6Bkm66t0p3r6r4X9ehLxqveqb8+lKKf5h5U//RaPCYOY6El96lLCWdeuu+pmDvEUqv3FCZ5f/xF6kfLDbLrhSXcuOpcTfXlyQsnxhD8YoPUXIzsH51HvqLx1HSEkz+b//J5H/nfmi86xu187Mo/n4mGPRgaYXNuM8wXDLVzQZZZt7S31gy4zU8XZ14YdqXBHdoQSM/rwqbL37ezMCeHRgU3Imj56L5atVW5k0YjruzAz9/8B8sLXQUFpfw9JvzCe7QAg8XU70iaSS6fTCKsBc+piApk6fC5nJt53GyK9VrMRsPc+EXY71Wr287us4ezrbhn9DwiU5oLXWsf2waOmtLQvfOJ2bTYfLj029eVv9AFPGBxWq5ZSiXJEknJEmaIUlSo1vZVaITEAPEAqXAmoED1Q/zgQND+PnnXwH47bcw+vTuXr69H2vXbaK0tJRr1+K4cuUanTq2BaCgoBCAR7q0p7S0lISEZBwc7MjLy6dVK2OltHvPXzz55ONq/d/D6F2hH8K6KvodO6p7dvr06U5s7HVu3EigY8c2FBYWUVhUTFlZGRERp7C0tFDZDxwYws+/rAfg99/D6N3b2CNQWFjEoUMRFJeUqOyLiorZt+8wAG3aBJGWlo6FhY6ysjJ+/XULAweGmOn/UqG/zUy/pKTY7ASMGhXKJ58Ye2MVRSEjI8vMpiqdOrblypVrXL16g7KyMtat28SggXf3Eib0/+/pN2vTlIRriSTdSEZfpmfvpn10DemqskmJTyH2wlUUWbmpTs8BPTi2N5KSYvV9cS4lF39HG/wcbbDQaujX2JPw2Js/YLZHp9C/sfHFSkKi1CBTJsuUGmT0soKLraXKXlO3MXJGMkpmChj06E/tRxfU6ab6urY90Z/8q+K3IeYMlBTd1L4m7rX8vds0IutaCjlxachlBqK2HKFx3/Yqm9J8k38WtlYomM5D45D25MSlkX45gaqci8/A38UOPxc7LHRa+rWsR/jFeJWNJEkUlBibDvnFpbjbG1+sbSx1FY2QUr0BCfOnrMarAUpOKkpuOsgG9Jcj0TYyHzX4G23TjugvRZhvb9wew7VzoC9TbT8XfY263h74ebljYaGjf/eO7D16RmUTG5dE55bGDqJOLZuy95h5A+1mnIuJw9/TFT9PVyx0Ovo/0prw4+rG0ZWEFDoFGR+RnQIbVaTX93annrcbAB7ODrg42JGVW6DWv5GGv5sDfq4OxvJv04jw89dVNhJQUFIKQH5RKe4Otrfv/5V4o/8eLkb/u7Qi/PiFKv6n0imoYbn/DVXpnVsEUMfa6ub6te1/cjb+Trb4Odka64am3oTHpNzUfvvFRPo38wYgNiOfdn4u6DQabCx0NHa359A1db2i8WmEnJmKkp0GsgFD1BF0TdrdVF8X1AX9+cO37b91q6aU3UiiLD4ZyvTkbttHnT6P3Hb+mtD4BSBnJqNkpYLBgOHsIXTNOtzUXteyG/qz5aPVBoOxUQKgtYAqo8znYm7g7+WGn6eb8drp2pbwiHMqmyvxyXRq0RiATkEBhEca0y10OiwtjP3epWV65GqeCx5tGpF7LYW8G8Z6LWbTEeqHqOu1skr1ms7WCkUx6iiKsZ6TtBq01pYYyvQqW8HDTU1zTJwBJ2CvJEnHJEmaJEmS+VioCV8grtLveF8fL5WBj68XcfHGFrHBYCAnJxdXV2d8fbyIjze1lBMSkvDxNebVaDRERuxky5ZfuHYtjmMRJ0lPz0RRFFq3MYZfPPXUAPz9fMp1kkz6uUZ9H1/viu0ACfHJ+Pp4q3wLHTqItes2GQ/Ex5u//jrMRx9NJybmKI891pMzZ9QPJJ9KPhsMBnJz83B1deZ2CAhoiLOzE3v3HjQdr4+nyuZO9R0dHQCYPXsKhw+HsWb1Ejw83Gr0pfI5AYhPSMKnynm7F4T+w63v5u1GWlJaxe+05DTcvF3v2I/eg4LZu3Gv2fbUgmI87a0rfnvaWZFWUGJmB5CYW0RibhEd/VwAaO3tSAdfZ/ouO0DI8v10retKQ5c6qjySoytKtumFRMnOQHKs3n/J2R3JxQNDzNk7Pr6bca/lb+/lTF5SZsXvvKRM7L3M64F2Ix/j1b8+p/e059g929hLamFrRZexT3Bgwe/VaqfmFeHlaCovTwdbUnMLVTav9W5J2OmrhHy2gfG/hDN1gOnF52xcOk99E8Yzi7YxY2BH9WgJINVxQskzdY4oeVlIdaofxZXsXdA4uiHHXTRL0zXtUG2DJSUzC083U1l4ujqRmqnujGlS34/dR04CsOfIKQqKisnOzQegtLSM596cx7C35/PnkVNUJTUrBy9Xk78eLo6kZOaobJrW82HPMeML2Z6I8xQUlZCdp26AnI2Jo0yvx9/TRa2fW4CXk2lk0NOxDqk56ryvhbQn7EQMIR+sYvyy7UwdYuoUSMjM49kvf+elb7dwIjaJqqRm5eBVqZfaw8WRlKxctf91vdgTcd7of+R5CopLyM5TXwM3o9b9z69SN9jbkJZfQ91Q1/jMa1LeECkqM5BVWEpkXAbJeeqXV8neGSUvo+K3kpuJZF/9M1ZycEVycke+Vuk9QGeB9Zg5WI+ehbaaBo3OwxV9sqnu1KekY+FpXvfYhXSn3sZv8V4wHZ2X6ZktWVlS99ev8V/zJXUeNW/QSPYuKDlV/HdwMbMDkBzdkJzdkWNNjQvJwRWb1+dj++Yiyg5sVt2rqZnZ6mvftbpr35c9x4wdAXuOnVVd+8npWTwz5RP6jZ3Di4MfVY2WANh6O5NfqV4rSM6kjrd52QeNeoznDnxOl+nPcXCWsV67GnaMssISRpxYyLBjCzizZBsl2QVmeQUPJzU1TLIURZmiKEpd4E2gMXBCkqS9kiS9crNMkiS9IklS5Lhx4+YoStnNzG4bWZbp0DGE18e9g7u7a0V41H//+yPdunbk4IGt5OflYzAY7nofFhYWPPFECL/9ZoovbdosgLfemkNAQGfWrN1A72DzGMm7QavVMnHCS0RFXebq1Rs1Z7hNdDotfn4+HDlynEceGcCRI8f5ZP6smjMKBLWMi4cLDZrVJ2Jf5D3p7IhO4dFGHmg1xt69G9mFXM0qYMfobuwY3Z1j8ZmcSKx5lPBm6Nr0QH/mECgP33opJ37azZKebxL+8Rq6ThgCQPdJTxHxw3bKCqt/mbsdtp+5xqC2Ddk55UkWDg9mxm+HKnpAW/q78fuEAax8tR9L95+npOzu62Bt047oo08Yu0MrY+uAxtUX+fr5u9J988WnOX4+mtBJHxJ5/jIerk5oNMZH3/bvP2TN5+8yf/IYPlm6jrhKDfDbZfKwAURejCV02lccvxCLh4tDhT5AWlYu079dw9xXh6q23y7bT8YwqEMTds54gYVj+jNjdTiyrODuYMv26c+zdtJTvDmwC9NW7SW/uPTO/X/hcSIvXiV0+jccv3AVD2cHNJr7F2NS2/7/zY6LiTza2KuibnikvjvdG7gzevUhpoWdpJW3M1rp7o9LF9QFw4UI1fVZtHAyxctmU7LxWyz7DkNy8rhj3fzwI1x9dBTXh4yl8NBJvD6aUpF29dGR3Bg6keQp8/GY9hoW/t63UKrB/5ZdMZw/qvJfyc2gaPE7FH31H3RtekIdx1somDN5xCAio64Q+vZnHI+KwcPFseIa93JzZv1nb7Pl6+ls3hdBRnbeXfl9fsVu1nR/k6Pz1tBuorFec2/TEEWW+aX9BFY9MplWrzyOfV33u9IX/PO47VpSUZT9iqK8jnFUZD5Q3XhkAuCvKMp3iqJ0WLRo0feJSRkqg8SEZPz9jIMuWq0WR0cHMjKySEhMxs/PNBjj6+tNYkKyKm9M9FUKCgoJCQkGoKi4hMWLl9Ot+xPsP3CU6OjYch1vk76DUT8xIaliO4CvnxcJiaYemv79e3Py1FlSU409qwmJSTRp3IiNG/8w+p2YgpubuqcjsZLPWq0WBwf72wqdWrx4PjExV8nNNd2ovr7eJCaqh6jvVD8jI4uCgsIKn9f/tpW2bVvU6E/lcwLg5+tNYmLyLXLcGUL/4dZPT0rH3dtU6bt7uZNe5b6uieCBPTmw/RAGvfmLq0cda1LyTGGJKfkluNepPnxkR3QK/ZuYRhb3xqbR0ssRW0sdtpY6utVz5UyyukdYyclAcqrUC+nkquplrIyuTQ/0J/ff0bHVxL2Wf15yFvbepl5Qe28X8pJvXg9EbT5C4/KQCJ82AfSe9hxjD3xJhzH9eGTcINqN6lth62FvQ3KlHu6U3EI8qoTabDgRS0gL45yA1nXdKdEbyK7S0Gno7oitpQUxqdmq7UpBtqoHWrJ3RilQ2/yNrkkHDJfM5zLomnTAcOUUyOaNRU8XZ1LSTWWRkpGNh4u619XDxYkvp77Gui+nM3HYYAAc7IzH6Fk+Au3n5U6HFk24UKWjyMPZkeQMk7+pmTl4Vun59XB24MtJI1n30RtMeNYYovf3PJL8wmLGf7qcCaH9aNW4HlXxcKhDcna+yf+cAjwc1SN+GyIuEdLaGGrVur5nefkXY6nT4lTHOJoQ6OeOn6sD19PUPdoezo4kV+rlTs3MwdPZwdz//wxn3YcTmBAaovK/Jmrdf7sqdUNeEe52N6kbLibRv8rk9pe7BLB2ZA/+O7QzCgp1ndW+KXlZSPam57rk4KIaNaiMNtA8jOtvWyU7DcP1i+r5J4A+NQOdl6nu1Hm6UZairnvk7DyUMmMHbs767VgFNVblByiLT6bw2Bmsmquj6pW8TNXor+TggpKbSXVoWz5iCuOqgpKXhZwah7Zes4ptHi5O6ms/o5pr38WRL6eMYd0nU5jw/ADA/NrxcHEkwN+LExevqLYXJmVhV6leq+PlQkHSzeu1mE1HqN/PWK81HtKVuPAzyHoDxRm5JEdcxr1Vw5vm/aci/8P/HhQ1NUwuV92gKIpBUZTtiqK8WI19BMZRlQaAJfDc1q07VQZbt+5kxIihADz99AD2hh+s2P5s6GAsLS2pX9+fgIAGHIs4iZubS0WI0tlzF/Hz8yYrKwcLCwteeOEptm7dhaWlJVOmvM533//M1q27TPpPDSC8Qn8XoVX0IyJMQ/fPhg5m7dpNFb8jI0+jKAqhoYOwsLBgzIvPExt7rcqx7GLEcONk36cq7etWvPfeWzg62DNq9EQCAhpQv74/FhYWDB06kK1bd5npD6/Qf5zw8OorlcqEhe2mVy9jm7FP7+63NcE2IvKUypfQ0MFsqXLe7gWh/3DrXzx9Cd8Gvnj5e6Gz0NF7cC8O7br9OGuA3oN7s3eTeRgXQJCnPTdyCknILaLMILMjOoXgBuYhiFezCsgt0dPay/Rw9LK35nhCFnpZpswgcyIxmwbO6hdrOS4ajZs3kosHaHXo2vTAcN78BVhy90WyqYN83TyU6F641/JPOh2LSwMvHP3d0VhoCRzYhZhdJ1Q2zvVNjbWAPm3IumZs+Kwc+j7fdp/Et90nEblsB4cXbebEClM9E+Tryo3MPBKy8inTG9hx9jq9mvmqtL0dbTkaa+w0iU3LoVQv41zHioSsfPQG4+MrMbuAa+m5+DipX/zk5GtITh5IDq6g0ZY3MszneEjOnmBti5wUa5ZmnHdS/eTroMb1uJ6USnxKOmVlerYfiCC4UyuVTVZuPnJ5o+aH37bz5KPGUKLc/AJKy18Is3LzOXXxCo2q9EgHNfLjRnIG8amZlOn1bD98ml7tm1fRL6jQX7ppL0N6dQSgTK9n0pc/MbBHO/p2VvtUoe/vzo30XBIyc43lf+oKvQLrqmy8new4Wj4hODYli1K9Aec61mTmF2Eo3298Ri430nPwqzQpHCCooS83ktNN/h85Q692VfzPq+T/5n0M6aWO878Vte6/lyM3sgtIyCk01g2XkghupA55BriakU9uSRmtfUyhRwZZIbvIOAJzOS2X6LQ8HqmvrlfkxFg0Lp5Ijm6g0RobH5dPmulLrt5I1rbICTGmjda2oC1fP8jGDq1/Y+R09Tyu4rOXsKjng87XEyx0ODzei4K9R1Q2WnfTy7ldny6UxhobxxoHOyQL47xWjZMDNu0CzSbNywlX0Lh4ITm5g1aLtmVX9BfVi4sASG4+SNZ2yHGmVzrJwQV05fNmreugrdsMudKk/6BG/txISiM+NcN47Rw6Sa8O6hX7Kt9bSzfsZkhv4yIkKRnZFJcayz43v5CTl65S30c9mpR6OhbHBl7Yl9drAYO7cL1KvebQwHSu6z3ahtyrxnotLzED365GX3Q2Vni2CyD7SiKC/xvcclUuRVGeu1maJEkvKoqyvMpmPTAe2IFxha5lUVGXW82ePYXjx0+zdesuli1fw48/fs2FqANkZWUzbLhxecaoqMv8un4LZ07vRW8wMPGN6ciyjLe3J8uWLkCr1SBpNPz221beemscU9+ZQFJSCmvWLMHd3ZVNm7YTHn6Iw4eP8+Pyr4iKOkBWZjbDR5TrX7jM+vVbOH36Twx6A2+8MaPihrK1teHRR3vy+ripFQdiMBiYOetjvv/uC374/ktSU9N5YdhYZs16kxPHz7A1bBfLf1zD8mULiDq/n8zMbEaMNK2ecenSIRzs7bG0tGDgwH4MeGIYeXl5TJs6kYsXozl0cCuKorBv30YKCgpZsWItFy5cZtasyRw/fpawsF38+ONali1bwPnzf5GZmc3IkeMr6R/EvpL+E08M5+LFaGbM+Ihlyxbw6aezSUvL4KV/T6rxIjAYDLzxnxlsC1uFVqPhxxVriYoya5PeNUL/4daXDTLfzFzI/JXz0Gg0/LF2B9cvX2f0lJFcOn2Zw7uO0LR1E+b8MBs7R3se6duFUZNH8NKjxmhPTz9PPHzcOX34TLX6Oo2Gd3o25fVNJ5EVGBzoTSNXOxYfvUKghwPBDYw9jjsup9CvsadqKeDHGnkQEZ9J6OqjAHSt60qvBlWG9GWZkg3fYfPv90DSUBaxBzklDst+L2CIi8EQZXzptWjbA/2pA2b+2bw+D42HH1hZYztjKSXrFmKo5uXlZtxr+SsGmZ2zVvDsT28jaTWcWbeP9OgEekx+mqQzV4nZfYL2o0Ko1z0IucxAcW4BYZOX3Ja2Tqth6oAOjP1pL7KsMLhdQwI8nFi85wyBvi4EN/Njcv92zN10lJWHLoIEc57sgiRJnLyexrL9Uei0EhpJYtoTHXCuY63egSJTuncNVk++YVzO9PxBlMwkLLoMRE69jiHWeE3omnbEcMk8zE9ycEWyd0aOr76DRafV8u6/n2XsnK8xGGSGPNaVgLo+LFq1mcCAevTu1JqIc5f4+ueNSJJEu8DGTH/V+FiLjU9m7uKVaDQSsqww5qn+qtW8/tafNnowYz9eiizLDAnuSICfF4t+3UlQQz+C2wcSeeEKX6/ZDpJE+2YNePdFY7jJjiNnOHHxKjn5hWz+y/iyOPfVUJrV96mkr2HqkK6M/f4PY/l3akqAlwuLd0QS6OdOcFA9Jg/swtxf97Nyv3He05zQXkiSxInYZBbvjESn0aDRSMx4ujuOttbm/o8axNhPliPLCkN6tSfAz5NF63cR1MCP4PbNibwQy9drd4IE7Zs24N3Rgyryj567hGtJaRQWl9J3wse89++n6Naqyf/Of42Gd/oE8fpvx5BlGNzCj0Zu9iw+eJlAT0eCA4wvrjsuJdKvqbeqbtDLMmPWGBsBdlY6Pny8DbqqoXSKTOmOn7B+/m3QSOhP/4WSnoBFz6eQk65iiDbe57rALuijjqqyalx9sHr8RRRFQZIkyg5tVa3mBYBBJu2Dxfj98CFoNOT+vpPSmOu4ThhB8bloCvYewXn4YOr06QJ6A4acPJKnfQ6AZUN/POdMBFkBjUTm9+vMGibIMqVhy7Ee+a5xOe4Te1HS4rHoMxQ5IbZilS1dy67oz6k7NiV3X6z7DUfBuEBB2cGtKKmmKcI6rZZpY55m7IdLjNd+784E+HuzaO0fBDXyJ7hDCyKjYvh6VZjx2m/ekHdfMnakxiak8PlPm5AkCUVRGDUwmMZ11feWYpA5MHMFj698G0mj4dLafWRdTqDDlKdJO32V67tO0GJ0CL7dg5D1BkpyCtg7yVivnf9xF8FfvMLQPR8jSRKX1v1F5oU4BP83kJSq8by3m1GSbpTPPbklFpa+d7eD2/ejNuVrVb+6VWzuJ3r57uO9BYJeHkE1G90DW94JqFV9+br5KlT3E6ev722+TE184N27ZqO75I1PGtdsdA8oSbXbe6ntd9M+s/uCUphTs9G96Ffuea8FJO8GtaqvJF2tXf3k2r13lbQ7C0W9UxJ+Sa1Vfd+hNX8G4G7RPj2y1rQBVgxYVbPRPfJq/C8PxUK8C/2H1+r78b0yPu7BlOMtR0wkSaq+i9PYwDYfTxUIBAKBQCAQCASCu6CmDyx6Av2AqjOSJKDmCQ8CgUAgEAgEAoFAcBvU1DDZCtgpimK2wLskSeG14pFAIBAIBAKBQPB/mH90HNcDpKbJ7y/dIu2F+++OQCAQCAQCgUAg+P+RO//ak0AgEAgEAoFAIBDcZ2oK5RIIBAKBQCAQCAT3EfmhWDvsf48YMREIBAKBQCAQCAQPHNEwEQgEAoFAIBAIBA8cEcolEAgEAoFAIBD8D5EftAP/UB76hol8l1+uv21qW/8hprbDI11s7GtVv16d2v1G6Aitf63qj+pfu18XtnjumVrVj35pS63qt0s8Uav6G1x61qr+Y0u715544vXa0wa2zM+vVf2tX35Tq/rfdK366a77i9WjbWtVX8mr3S/X//JO7X75vZm+uFb1W/Wr3fPr0a523xtydqfUmrbN+U9qTRtAg1+t6gsefkQol0AgEAgEAoFAIHjgPPQjJgKBQCAQCAQCwcOECOWqHjFiIhAIBAKBQCAQCB44omEiEAgEAoFAIBAIHjiiYSIQCAQCgUAgEAgeOGKOiUAgEAgEAoFA8D9ErPlaPWLERCAQCAQCgUAgEDxwRMNEIBAIBAKBQCAQPHBEKJdAIBAIBAKBQPA/RK7tr1Q/pIgRE4FAIBAIBAKBQPDAqY0Rk/7AV4AW+KFqoqWlJcuXf0W7ti3JzMzihWFjuX49HoC33x7Pi6OfwyDLTJo0k1279mFlZcXeP3/DysoKrU7L77+HMXfu5wAEB3dj/vyZWFpacOLEWf79ypsYDIabOtYvJJgvvpiLVqNh2fLVfPLpovt20LWp/U/Sr2r3aRW7+3l+x//nZd6eNgFJI3EhKpp/9QlVnV9LSwsWLplPqzZBZGVm88qLk4m7kYB/XV/2HwvjSvRVAI5HnubtSe8B8PvWn/D0cqe4qBhLrY4Jz00hKyObLsGdePP9CWg0GjatDuOnhatUx9W2cysmzZ1AQPOGzEa5S0UAACAASURBVBg7lz/D9qnS69jZsiZ8Bft2HOCz6V+ZlVvd4Fb0fG8EklZD1Opwji/eokpvMbwPLUf1RTHIlBUU8+fUpWRFJwLg2syf3h+PwdLOBkVRWPfELAwlZar82qAOWD83FkmjoXT/dkq3rzXzQdehJ1YDRwAKclwsRT98jLZpa6yffa3CRuPlT9F389CfOqTKe/D8VT75dS+yovBk1xaM6ddZlZ6UmcvMFdvJKypGlhUmDulBjxYNKTMYmPPLTi7GpWIwyDzROZCX+qvzAtj1aofvrH+DVkPm2l2kfbtele78zKN4T3uRspQMADJWhJG5difWgQ3w/eB1tHa2KAYDqYvWkbP1gJl+Tdzr/eXeuzUt3h+JpNVwY+VeYhZurtbOe0AnOiydxF/9ppNzOrZiu42vK8F/fcalz9YT+22YWb6D56/xyfpwZFnmyW4tGBPSSZWelJnLzJ92kFdUYiz/wd3p0aIBYccusGL38Qq76MQ0Vr8zjGbaStpXkvlk5xnjuW1TnzFdm6q1cwqZuSWSvOIyZEVhYu8W9AjwIruwhCm/H+V8YhaDWtVjWv821R6zV+9WtJ1rvPZjV4VzceGWau38BnSk2w//YWf/GWSdvopnzxa0mv4cGgsdcpme03NXkXowyixfy15tGTF7DBqthvA1u9n67QZVev+XBxL83GMY9AbyMnP5/q1FZCSkVaRb29kwf/fXHN95lJ9mmT2y0LXuiM3I8aDRUro3jJLNq81sLLoEY/30KAAM169QuPADABxX7ka+YayH5IwUCj6bUe2x/83Bq6l8sifKeC5a+TOmc4AqPSm3iJnbTpFXojee517N6NHQ49aaMUl8suMUsqzwZNsGjOneXK2ZU8DMjcfIKykzaj7aih6NvTl8JZmv/zxLmUHGQqth0mOt6NTA00zfL7gVj8wxnt9Lq8M5vUh9fpsP70PgaFPdtv+dpWRHJyLptPT89GXcWtZH0mqIXn/ALC+Ac+82NHz/RSSthuSVe4hfuLHa43Qd0JnApW9xst875J++gqTT0viLsdi1bICk1ZLy6z7iv9lglk/XsiPWw8aBRkPZvm2UhK0xs7Ho1AurIaMABcONKxT9dx4AkosHNi+9icbFHRQo+GIaSnqKWr9VR2xGjAeNhtLwbZRsqeb66dzLeP0oYLhxhcJFHwLg+PMu5Ljy6yc9lYIvzK8fq84dcfzPeNBqKdwSRv7Pan2bx/vhMO415LR0AAp+20Dhlm0A2L/+CtZduwCQt/xnivfsVfvephO2Y4zXfsmeMEo2qJ+LABZdg7EJHW0sm2tXKFjwQaWd2+L41QpKjx2g6Afz52JtXzuCh5P73TDRAouAvkA8ENG8eWMuXIiuMBjz4vNkZ+XQPLA7oaGDmDdvOsOGjaV588Y8GzqY1m364OPjyfY/1hAY1IOSkhL6hoRSUFCITqdjX/gGdmzfy7GIkyxbuoCQ/s8S/f/YO+/oKqruYT8z99703isECEVC701AugXBBihNLIiKIkWKAioKiC+KolKsoCLqa0F6Cb0GCBA6AZIQ0stNb7fMfH9MzM3khmLJq/6+edZiLe7Mnj17ztmz55R9Ti4n8Ppr0xgz+hG+XGUfVABEUWTpB/MZdM+jpKSkc+TwZjZs3K6y7Y9Sl7r/SfprysUc3szGGnJ/Vf0ej41j1pyXGP7AU8QcjuXE+d28OOVplvxnRdW9HhvzMPn5hXRpO5ChD93DnDemMn7cFACuJSbT984Han3e555+mbiTZ6nvGlj1XNMXvMTEEVPJSs9m9eaV7N92kMTL16quyUjNYt5LCxk1YUStOp+Z/iSnYk7Xek4QBXq/NZZ1j71NcbqR4RvnkbAjtqrjAXBp3WHOfrMLgAb923Hn3FGsH/0Ogk5kwNJn2TFpBTkXknHyckMyW2reAOfHJlKyZCZyXg6ur36IJe4wUnqyre4CQnC8ewQliyZDaTGCuxcA1ktxlMx7VhFyccd9wZdYzseq1FsliYXf72TFiw8T6OXOyEVr6NUqkkbBvlUyn245woD2TRjWsw1X03OZ+PHPbHmrITtOxGO2WPlx9ljKTGYenLeKQR2bEerrabuBKBI6bwKJo+Zgzsglcv17FO6IoeLKdZUd+Rv3k/baStUxqayC61Pew5SUjj7Ah8Ybl1C07yRSYUmtdVEbf/r9EgVaLhzHkWELKEvP5c6t88nYHktxfKpKTOfqRIOnBpEXa6+3+Rujydp1qlb1Vkli4Q+7WPHCg0r5v/MtvVo2Upf/1hgGtGvCsJ6tlfJfto4tLZ7k3k53cG8npSF6OTWHyZ+sp1l4AKRdq9Qts3BrHCse60GghzMjv9hNr8bBNPL3sOk+cJEBd4QxrH1DrmYXMvH7Q2yZOAhHvY7nezXnSlYhV7ILa7VdEAXaL3icPcMXUpZupP+WN0nbfoLCGmWjd3Wi8VODyI29UnWswljE/jGLKc/Mx7NpGD3XzmBDuxdq6BcZ++bTLBr5BsaMXOatf4cT0cdIu5xSJXPtXCJz73sZU7mJvqMGMmLWGD6e+G7V+YenPsrFo+dqtR9BxHncJEoWvIyUm437/BWYYw8hpdpigxgUiuOQxyh+/QXkkmIEDy/b9SYTRbOerl13DaySzMId51gxrDOB7k6M/PoAvRoF0sjPvUrm08OXGdA0hGFt63M1p4iJPx1jyzN9bqJTYuGWE6wY1Uup38+i6dU0hEb+tvfv0/0XGBAVzrAOkVzNLmDit/vZMuk+vF0c+WBEDwLcnbmSVcCza/axY/LgGsUj0P2tsWx+7G1K0o0M3TSPa9tjya8W266sO8yFythWr387urw2iq2j3qHhfZ3QOej5qd8sdE4OPLJ7EVd/PUxxSo7tBqJIo4VPcXbYPCrSjbTZ+jbG7ccpjU9R2aFzdSL0qXspjI2vOuY3uCuig4ETd01FdHag/b73yV53gIrrtk4pgojTmBcpeWc6sjEbt9eXYT55GCmtWv0GhuJ436MUv/WiKnYCuIyfQcWGb7GciwVHJ5Br7LMkiDg/PomShS8jGbNxf3M55hM1/CcwFMf7H6P49ReRS2vxn1fG37B+EUU8p00id9LLWLOy8f98BeX7D2FJuqYSK9+5m4L3lqqOOXbrgkOTxmSPfQrB4IDvx0uoOBwDlFTpdnl6EsXzpim+v2gF5mMHkVKq2R4citMDIyl6daK97wPOjz6B5XxcrabXue/8C9D+8nvt3DSVSxCEDoIg7BYE4RtBEMIFQdghCEKBIAjHBEFoW8slnYArQAJgAr4bPHigSmDw4AF8/fV/Afjpp030uatH5fGBfP/Dr5hMJpKSrnP1ahKdOiq3KCkpBcBg0GMwGJBlGV9fb0wmE5cvK6OO0dH7ePCBe274LJ06tuXq1SQSE5Mxm8388MOv3F/Dtj9KXer+J+mvKff9D79SV/Xbr19PzCYzB/YdwWw2s3XTTh4efr/qXoPu6csP3yqjZxvWbaNHr65/6Pmj2t5BSlIqacnpWMwWtv+6i54De6hk0lMyuHIhAUmyDyXNWjbBx9+bI3uP1ao/sE0j8pMyKUzORjJbiV9/hIYD2qtkzMVlVf/XuzhWfeDq9WxJzoXr5FxQOhnl+cXIkvrjp2vQFCk7DTknA6wWzMf2om/TTSVjuPMeTLvXQ2kxAHJRvp2dhvZ3Yjl7HEwVquNnkzII9/cizM8Lg17HwPZN2RN3RSUjCAIl5SYAissq8Pd0VY4DZRVmLFaJCpMFg16Hm5OD6lqXNo0xXUvHdD0T2Wwhf8M+PAbYz6rUhikxDVNSOgCWLCOW3AL0Ph63uErNn32/vNtGUpKYQWlyFrLZStq6wwQN7GAn12zGMK58vMFutitoUAdKk7MoupRidw3coPxPX1XJCNRe/tXZcvwiA9urZ0POphkJ93ElzNsVg05kYPMw9sSnq3ULUFJpc3GFGX83JwCcHfS0DffDQa/jRvi0bURRUiYllb6f/OsRQge2t5NrOeNhLn60AWuFqepY/tlrlGcqflpwKQWdkwOig3osrVGbSDKT0sm+nonVbOHIhgO076+eTbpw+CymyrK5cjIen2oduogWDfH08+LsvtobT7rIZkgZaUhZ6WC1YDq8C0OH7ioZhz73Ydq+Drmk8t0qtH+3boez6fmEe7sQ5uWi1EWzEPZcUY++CwiUmJSBieIKC/5ujjfXmWok3NuNMG83DDodA6PqsedSmkpGoFr9lpvxd3cGoFmwNwGV/2/k70GF2YrJos5I8G/TiMKkTIoq6/fqr0eof5PYZqgW25CVWCfoRPRODkhmi0oWwL1tJOWJGZQnZyGbLWSvO4jPwI52z1l/xgiuf7wOqfq7JcuILo6gExGdHJBMFqxFav26hs2QMlORs5X6NcfsxtBOHTsdet1LxU772CmG1AedTumUAFSU28VOXSNFv1Sp33RkF4b2NfT3uRfTjl+RS3+//xiaN8OSkoY1LR0sFsqid+F0Z/dbXwjoI+pTceo0WCXk8nLMVxJw7GJ7dxTfT0XKVHSbD+zCoaNat2O/+6jYWrvv6xo2QfT0wRx3vNb717XvaPx7udUak2XAO8Am4BCwUpZlT2Bm5bmahALVhzlTQkOCVAIhoUFcT1ECo9VqpaCgEF9fb0JDgkhJsQXM1NR0QkKVa0VR5Pix7aSlniZ65z6OHjtJTo4RvV5P+3atAHjwwXsJCw+54YNUvy9ASmo6ITVs+6PUpe5/kv6acqmp6dRV/bq6umCVJFq3bQFAeL1QfHx8VPcKDg4gNTW96l5FhUX4+CgjNvXqhxG9/2d+2fQ1nbuqg90HHy9g5/5feOKlMQD4B/mRmZZVdT4rPRv/YL/bKToEQWDSa8+xdN7yG8q4BnlTnGas+l2cbsQtyNtOruXYfow58C7dXxnB3rlfAeDVMAhkmfu/mc7wzW/RbsK99jZ4+SEZbaOAcl42opevSkYMDEMMDMNlxhJcZn2ALsq+4Wzo1Bvz0d12x7Pyiwnyto3aBnq7k1VQrJKZcG9XNh29wIBXVjLx45+ZObwvAP3aNcHZ0UD/WSsYNPsTxvTrgKers/q+gb6Y02wjXeb0XAyBavsBPO/uRuMtS6m3bCaGWurHuXVjBIMe07UMu3M348++X07B3pSl5Vb9Lk/PxSlYXb+eLSNwDvEhK/qk6rjOxZFGEwcTv/inG+q3K38vN7Lya5Z/FzYdu8CAVz9l4rJ1zBx2l52e7SfiubuDumOSVVROkLutPgI9nMmq0XibcOcdbDp7nQFLNzPx+0PMHNj6hrbWxDnIh7JUW9mUphtxruH73i0jcA7xJX1n7TNGAGH3diLvTBKSST1b6B3kizHdpt+Ynot3kE/Ny6voNbwvp/ecAJR397HZj/Pt/NU3lBe9/ZBybbFBys1G9Fb7ni4oDDE4HLfXP8Rt3sfoW1drOBsccJu/Ard5H9t1aGqSVVyjLtydyCouV8lM6N6YTedTGbB8JxN/OsrMvi1urrOojCBPF5vO2uq3VxSbziQzYMkGJq7dz8xB9mOO0RdSuCPYy64T6hrsTXG6LbaVZBhxDbaPbc3H9mP4gXfp9OoIDlXGtoRNR7GUVjDyxEc8evR9Tq/cTEW+eqbTMdiHimqxwZSei2Owun5dWzbAMcSPvOgTquM5G48glVbQ5fSndIpdQery9VhqvDeCtx9ytdgpGbMRatSvGBSGLjAM19kf4DrnQ/QtO1Ydl0tLcHnhddzmrcBp+HgQ1E0q0aeG/xhzEL39VTKK/4Th9tpS3N74CH2rGv7z5nLc3vgIQ3t7/9H5+2HNtOm3Zmej87ePjU69e+L/1Wd4z38dMUC5v+XKVZy6dEJwdET09MCxXRt0gTbbRB9/pJwaZeOrtl0MCUcXEob7/A9xX7gMfZvKjo0g4Dz2OUpX3+S7WMe+o/Hv5VapXAZZlrcACIKwSJblHwFkWd4pCMLiG10kCMJ4YPxzzz3n07z57X/EboQkSXToOABPTw9+/O/nREU15dy5S4wa9RzvLn4dR0cHdkTvw2rVJsb+jdRWvwAH98Uwb8FMHB0dyMzIRpZvr34zM7JoF9WHvLx8WrWJYtWaj+jZ5T6Ki0p47ulpZKRn4ermyrffruCehwdSXlZ+a6U34OHHh3JoVwxZ6dm3Fr4FZ1ZHc2Z1NE2GdqXji0OJnrISUa8juGMTfrhvLpYyE0O/m0XWmSRSDt4g9eRG6ETEwFBKF09D8PbH9eV3KX59PJQpwVzw9EEMjcByrvbRrVux9fhF7u8SxZh+HYhLSGP2qs38OPtxziZlIIoC2xc+Q1FpBePe/Y4uzeoR5ud1a6XVKIw+Sv76vcgmCz6PDSL83ZdIeMyWb63396bee1O4Pu19+3SKvxtBoPkbozk1yf4j3fTlh0n4ZAvW0opaLrx9th6/xP2doxjTr71S/qu38uOrYxBFZduXM4npODnoiQy5vQ63Svf5FO5vVZ8xXRoTl5LL7PXH+XF8P0ThL9hSRhBo8/pIYiatvKGIR5NQWs8ewZ4Rb/+pW3V7oCcNWkYyf7jiN33HDCJu9wnyMnJvceUt0OkQg0IpfvMlRB9/3F77gKLpTyCXllD4wgjkvBzEgGDcZr+HNTkRKSvt1jpvwNYLadzfIowxHRsSl5rH7M2n+HFczz9VF1vPJnN/6wjGdG1K3PUcZq87yo/PDqzSeSWrgA92nmb5yF5/+B7nV0dzfnU0jYZ2pe2LQ9k7eSUBbRoiSxJr2r+Ao6crg3+eQ+r+sxQl/45YKgg0fONx4id9ZHfKvW0kslUipvV49F6utFr3Jvn7TlOenFWLoptQWb8lC6cgePvj9soSimY/BaIOfZMWFM2dgJybictzczDcORDzvi2/X39gGMVvTVb8Z877FM18UvGfSY8q/uMfjNur72K9/vv9p/zAYcp27AKzGZchg/GeM5PcF6ZScfQ4hjua4rfyI6T8fExnz8PvbUOJOsTgMIrmvoTo64/7m0spnPwEDr36Yz5xRNXp+6PUme/8A/iHfan+MdxqxqRcEIQBgiA8AsiCIAwFEAShF1DbKvNUIFyW5U9kWe7w8ccff5qWrg76aakZhIcpMxs6nQ5PTw9yc/NITcsgLMw24xEaGkxaqnrks6CgkD17DzJgQG8AjsTE0rvPg3Ttfh/79x+pSuuqjer3BQgLDSYt7feNrP4duv9J+mvKhYYGk1pD7q+q37TUDBwcHRhy9ygG9RmG0ZhPZqY66KSnZxEaGlx1L3cPd4zGfEwmM3l5ypTy6VPnSEq8TqPIBgBkpCsfpZLiErb9Ek3zts3IzsghMMS2gDQg2J/s9NvLVW3ZPopHxj3AupjvmDT3We55eCDP18gJLsnIwy3ENsrnFuxDcUbeDXXG/3qEhpXpLsXpRtJiLlGeV4yl3MS13XH4t4hQycv5Ocriy0oEb3+kfPV7J+flYDl1GKxW5JwMpMwUxMDQqvOGDj2xnDwEtWweEeDlRkZeUdXvzLwiAjzdVDK/HDrLgHZNAGjdMIQKs5X8kjK2HLtA9+YNMOh0+Li70KZRCOeuqdNTzJm5GKo1mA3BvlWL3H/Dml+EXDlabvxuO84tbIuCRTdnGnz5GhmLv6b05CU7+2/Fn32/ytPzcA6xzfA4BftSnm6rX72bEx5Nw+n281z6HluKd7tIOq2ehmfrhni1jaT5nMfoe2wpDZ++m8YvDiXiiQEq/Xbln19MgFct5d++evlbyC+xjYxvjb3EoPbN7GwPcHcio9oIemZhWVX6TpXuU0kMaK74SuswXyosVvJvsyNVlmHEOdRWNi7BPpRV832DmxOezcLp8/Ns7jv6Pr7tIrlz1VS8Wyvvq3OwDz2+mEzMiysouWbfoMzLyFWlZvkE+5KXYbSTi+reivsnPsySpxZiqfSjxu2a0m/s3bx3YAWPvjqWHg/2ZtiMUarrpLwcRF9bbBB9/ZHy1LFBMmZjiVXeHSk7A2t6CmJQGKC8dwBSVjqW86fQRagXs1cnwK1GXRSVE1CZNvcbv5y5zoCmSsxrHepdWRcmbkSAuzMZBaU2nbXWbyIDmocrOsP9VPWbWVjKlB8O8uaQzoT7qH0OoCQ9D7dqMxiuQT6UpN84tl399QgRlbGt0dBuXN9zGtlipTy3kMxj8fi3aqiSr0g34lgtNjgE+1JRbZRd5+aMa9NwWv38Bh2PLcOjXWOar56BW+tG+D94J3m7TyJbrJhzCik8dgm3No1U+uW8HAQf9SyBXEv9mitjo5yTgZSRgi4wDDkvG2vyVSUNTJIwnziIrn7jGtfW8B8fP6S8bDv9lhO38J/sdCwX7P3Hmp2DLtCmX+fvjzVbbb9cWAhmJcWtdMMmDE2bVJ0rXr2G7MefJvell0EQsFy3pZNKxmxEvxplk6u2Xc7NxnzsoGJ7VgbWtOuIwaHomjTH6e4H8Fj+Hc5jnsWx1wCcR9X4Ltax72j8e7lVx2QCMBV4AhgI3CUIQj5KGteLtcgfAxoDDQAHYMTGjdtVAhs3bmf06EcAeOihe9m952DV8eHDhuDg4EBERDiRkQ04euwkfn4+eHoqOeNOTk7069uTS5eU/Gp/f+WD5ODgwMvTnueTT76+4YMcO36KyMgGRESEYzAYGDZsCBtq2PZHqUvd/yT9NeWGDxtCXdXvseOniGzcgHr1Q3F1deb+BwbxyTJ1ysW2zbsY9thQAAYPHciBfUcA8PX1RhQV164fEUbDRvW5lnQdnU5Xleql1+vp0a8rCRcTOX/qIuENwggJD0Jv0DNgSB/2bz94W2U3d+Jb3N9xGEM7j+CDecvZ/OM2Pl7wiUomMy4Br4ggPML9EQ06mtzfhcQd6rQDzwjbbjcRfduQn6Q0jJP3nsa3WTh6JwcEnUho52bkXVYvHLYmXUIMCEXwCwKdHkPHXljiDqtkzCcPoWuqzF4Kbh6IgWHKB7USfae7ak3jAoiqH0RyVj6pOQWYLVa2xV6iVyv1Bz7Y252YS8o6mIT0XEwWC95uzgT7eHC08nhZhZkziek0CFSnYpTGXcYhIgRDWCCCQY/X4J4U7jiqktH726b4Pfp3ovyqkjEqGPTUX/kqeT/vomCLeiex2+XPvl/5p67i2jAI53r+CAYdIUO7krHdtoGApaiMbVHj2dnxRXZ2fJG8E1c4OnYxBXEJHBr6RtXxhE+3cHnpOpK+UN9bKf88dfm3VH+Eg308iLlYWf4ZuZgsVrzdlAaoJMlsPxHPoA5NqElUiDfJxmJS80swWyW2nU+hV5NgtW4PF2ISlQZJQk4hJouEt8vN1zb8hvFUAu4NgnCt9P16Q7qQus1WNuaiMtZFTWBjp5fY2Oklck9cYf/j75IXl4jBw4WeX08jbsF35ByLr1V/QtwVghoE4x8egM6gp8vgHpzYoV7rVT+qAeMWTmDJkwspzC2oOr580vtM7vYMU3pMYO381Rz4eQ8/LPpGda316kXEoFBEf+XdcujaB3Os2s/Mxw+gb67sSCa4e6ALDkPKSkdwdQO9wXa8SQusqepFydWJCvYkOa+E1PxSpS4uptErUr0LVrCHMzHJSsMzIbeosi4calOn6Az1Ueo3rxiz1cq2c8n0aqJOeVbqVxksSMguVHzHxZHCchMvrN3PpL6taFuv9pm27LgEPBoE4V5Zv42GdCG5RmzzqLaTV72+bShIVGJbSVouId2iANA7OxLQLpL8q+rZgKJTV3BqGIxjvQAEgx7/od0xbrfVr7WolCNRT3Cs43Mc6/gchScuc37sIorjrlKRmoNnDyXVTXRxxKN9Y0ovq/VbEy+iC6wWOzvfpXRCqmE5cRB9s8r6dfNADFLq15pwCcHFDcFd2UhA37ytatE8gDWhhv906YM5tkZsPn4Q/R222FzlPy7V/Metdv8xX7iIPiwUXXAQ6PU49+tD+QG1/aKvLd469eiGJalyUxRRRPBQvr36Rg0xRDak4mi1sr1yCTE4DDFA0W3o0QfTcbVu09ED6KN+831PdCHhSJnplH4wn4IJwyl8dgRlXy2nYu92yr5Rfxfr2nc0/r3cNJVLluU4lA7Jb0yq/IcgCONQ1p1UxwJMBLah7ND1xfnz8a1ee20asbFxbNy4gy++/I5Vq5Zy4fwB8vLyGTnqOQDOn4/nvz9u4HTcbixWKy9OehVJkggODuSLz99HpxMRRJEff9zA5s3RAEyd8iz33NsPURRZufKrqkZwbVitVia9NJvNm75FJ4qsWv0958/X/rH7vdSl7n+S/hvJ1VX9xp08y8HjWxAQ2Lv7IGu/+Znpr7xA3MmzbNuym2+//pGPPnmHIye3kZ9XwDNPKDtydenekemvvIDFbEGSJaZPfp38vAJcXJz57pfPMej1iDqRUwfjWLdmI5Ik8Z9X32fpt4sRdSIbvttMQnwS419+ggtxF9m//RB3tG7GO5+/iYeXO3f278b4aeMYcdfjt1W+slVi75zV3P/NdESdyPnv92KMT6Xz1IfIOp1I4o4TtHp8AOE9opAsVioKSoierKS2VBSUcurTLQzbOA+QSdoVR1LN3ZskifJvP8LlpQUIgojp4DaktGs43j8G67V4LHFHsJ47jj6qPa5vfKrI//gpcokyCi/4BiJ6+2ONr31XMb1OZObwPjz70U9IksSQri2IDPFj2YaDNK8fSO9WkUx5qDfz1mxnza4TIMAbowchCALDe7Zh7tfbePDNVcpama4taBKmzlPGKpE2dwUNv3oDdCJ5P0RTcTmZwMkjKTtzmcLoo/iNG4xHv87IVivW/CJSpilbT3re2wO3TlHovd3xflhZ13J92vuUn0+8rbqBP/9+yVaJs6+sosvaWQg6ketr91B8KYWm0x8m/1Qimdtjb63kJuh1IjOH9eHZj39GkmSGdI1Syn/jIZrXC6R3q0ZMebAn877dwZrdJwCBN0YPRKhMxYm9kkKQt3ut6XN6UWTmwDY8u/agort1fSL9PVi29zzNg73o3SSEKf1aMm/zCdYcVTY8eGNw+yrd7iKx2AAAIABJREFUd3+0lZIKM2arxO74NJY/2kO1o5dslTjxyip6rZ2hbBf83V4K41Np8fJDGOMSSdt+ws6m32j8xADcGgQSNflBoiY/CMDeEW9TkWvbAUyySnw19zNe/mouok5k3w87Sb18nQenjCDx9FVORh9jxCtjcHJx4oVl0wDITcthyVMLb6/wJYmyVUtxnfVO5XavW5BSknB6eByWxEtYYg9hiTuGvmVH3P/zpSK/ZgVycSG6xlG4PDVFSS0UBCrWr1XtxlRrXfRrwbM/HlXqomUYkX7uLDtwieZBXvSODGRK7zuYt+0Ma44nKvV8d+uqurihzrvb8eyafUiyzJA2DYgM8GTZ7rM0D/Gmd9NQpgxozbwNx1kTE6/oHNIJQRD4/ugVko3FrNx3npX7lG2aV4zqiY+rbRZHtkocmrOau9dMRxBFLn2/l7z4VNpPe4jsuESSd5wg6vEBhFaLbXsrY9u5VTvo9d54Ht75NggC8T/sw3hBvRMfVomrr3xGi7WzEXQimWt3UXophfrTh1N06irG7TdOPU37YitNPniednuXIAiQ8d1uSi/UKH9JouzrD3F9eZGyXfC+LUip13B84HGsSZewnDyM5cwx9C064LbgC5CslH//CXKJ4oPl363EdYaS1W5NuoxpzyZ7/as+xHXGImW76b1bkFKTcHrocSyJ8VhOHMJy+hj6lh1wf+cLRf7blTb/eXIySDKIN/Afq0TBe0vxXfIO6ERKN27BkpiE+1PjMF28RMWBQ7g+8iBOPborsxqFheTPr0yJ1OvwW67EUbmklLw35qtTuSQrpZ99gNuc/yi+v2sL0vUknEaMw3rlEubjh7CcOoqhTQc83l8FkkTpV4rv3w517jv/AiQtmatWBPkP5mMLgpAsy3K9W8kZHELrtOS1av37qOs/Wurj7H5roT/Bb9sF1xWjdeF1qn/soN+ZK/07MYx4uE71X36ybvedb5d240bvX8EvPj3rVH+/b/54Tv8tSbtxA/mvYP2M5FsL/Qk2OtTtDjwfdrtxSslfgWPf2ja1/Atxtt+V7a/kmxm33/H/IzSz/PF1f7dDq4F1W7+ypW5bJqWJdaffObhubf8xJqxO9QM8nfLNv+Jvqs+vP/If3YR99dqav6UcbzpjIghC7UOoSpu0blt1GhoaGhoaGhoaGhr/33CrXbkCUVK5ag4vCNincWloaGhoaGhoaGhoaPwhbtUx2Qi4ybJst8G8IAh76sQiDQ0NDQ0NDQ0Njf/DaH/gonZutfj9yZuce+yvN0dDQ0NDQ0NDQ0ND4/9HbrVdsIaGhoaGhoaGhoaGRp1zq1QuDQ0NDQ0NDQ0NDY2/kH/0llx/I9qMiYaGhoaGhoaGhobG347WMdHQ0NDQ0NDQ0NDQ+NvRUrk0NDQ0NDQ0NDQ0/odou3LVTp13TP7tOXSjQ7rUme6+Juc60w3Qzrlu/7ptUKviOtVvCHapU/2y2Vqn+ovPpdap/s+3hdap/le+mV+n+s1WS53qPxbUoU71N9/8RJ3qt3z/aZ3pFvx86kw3wP3vRdap/vuuXq1T/fr7n6lT/YJPSJ3ql4ty6lT/qP/sqVP9clZmneo3n8qvU/2Fl+pUPRevB9SZ7u796kw1AMdjK+r2BsDTdX4HjbpES+XS0NDQ0NDQ0NDQ0Pjb0VK5NDQ0NDQ0NDQ0NP6HSMLfbcE/E23GRENDQ0NDQ0NDQ0Pjb0frmGhoaGhoaGhoaGho/O1oqVwaGhoaGhoaGhoa/0Okf/32UHWDNmOioaGhoaGhoaGhofG3o3VMNDQ0NDQ0NDQ0NDT+drRULg0NDQ0NDQ0NDY3/IVoiV+1oMyYaGhoaGhoaGhoaGn87WsdEQ0NDQ0NDQ0NDQ+Nv5x+VyjVwQG/ee28eOlHkiy/X8s5/Pv7H6G/Rqw2PzX0CUSey7/udbF7+i+r8gCcH03NEXySLRJGxgC+mLyM3NRuAz6/+QMqlZAByU3NY+vTbt7xfcO9WdHxzNIIocmXtHs59tKFWufB7OtLrs0lsHjQH4+nEG+pz7dmeoDnjEXQied9vJ3flf1XnPR/qR+CMJ7Bk5gJg/HoD+T9sxxDiT9jy2QiiCHodeV9tIG/tFjv9hradcHnyBRBFKqI3Uf7zt3YyDt3uwnnE48iyjDXpKiVL3rSddHbBa+lqTEcPUPrpB3bX6pq3x+mRCSCImA9txbT9v3Yy+nZ34nDvKJBlpNQEyr98R7nv0CfQt+gIgGnLWiyx++z1R3XAacSzCKKIaf9WTFu/t9ffoSeOg0cDMtL1BMo+extd09Y4DZ9QJSMGhVP2yQIspw6prnXs3BHPlyaCTkfphk0Uf71Wdd75noF4PD8BKTsHgJKffqF0w2YA3J8bj1O3LgAUffk15Tt329lWv1crer8+GlEncva7PRxbpvaXVqP60HpMfySrhLm0nOiZn2O8nAaAX7Nw+i58Akd3Z2RJ5tvBc7FWmFXX9+/fi8WLX0On07Fq1XcsXrxcdd7BwYHPP3+Ptm1bYjTmMWrURJKTU+jTpwdvvjkTBwcDJpOZV15ZwN696rK5Hf5sbHDv1Zaw159G0InkfreDzGU/qc77PNyHkFcfx5yh+H/O6s3kfrcDgEZfvYZL2yaUHL9Awri3atV/4OR5Fn3xI5Ik8WDfbjz54ADV+bQsI3OXfUNeQTGe7i4smDSWIF/vqvPFpWUMnTSfPp1a8crTw+z06yJb43DvOBBELLE7Me//VXXe4e6xiA2iABAMDgiunpQuGIfg6YfjY9NAEBF0OsxHtmI5tkN1rVi/OQ69him6zx3Ecnyb6ryh5yPowpooP/QOCC7ulK2YghjWBIeej1TJCd5BmLZ8hjUhTnX9wcvpvLP1BJIk80C7hjxxZ3PV+fT8Euasi6Go3IQkybzYrzV3Ngnh8NUMlkbHYbZKGHQik/u3oVPDQPuyadgKh4GjFftP7cF8SO37Dv1HItZvXq1sPChd/Ew1AWecJyzCeuk4pm1f2ek/cPI8i778ubJuu/LkA/1V59Oyjcxd9i15hcV4urmw4MXRBPl6k5Zt5KX/fIYsyVisVh69uyfDBvSw13/0JIs+/hKrJPHgPX156tEH1Pozs5n7n48x5hfi6eHGwlmTCPL3BeC9lV+zLyYWSZbp2r4VM59/AkFQ/9W2AyfOseizHxT7+3fnyYcGqfVn5TL3w69s9k9+giA/xTfbPPgsjeuFAhDk78OHrz5nZ39d129d+6e+RUecHnsORBHzvi1UbP7OzgZDx144DhkDyFivJ1C2cgG6Zq1xfvRZm53B9Shd/haWk+r45tS1I97TngdRpGTdZgpXq/W73jcQr0njsWYpsb/oh18p+XUzju3b4D3Fpt8QUY+cV96ibO9B1fW+d7Wm2VtjEXQiKWt2kfThejv7AQLu7USbL6ZwZMArFMYl4BTuT/f971JyVfkOFMRe5sL0z1XX6CJb43DPWKXsT+zCvF+t22HQGMQGv71bjsq7tfBJJe48OhUEoTLubMNyPNrOpqhebRg2dxyiTuTA9zvZtnyd6ny/J++j+4i+SBYrxcZCVk9fhjFVKSfvED/GvD0B7xBfZBk+GreA3JTsWp9d49/FP6ZjIooiSz+Yz6B7HiUlJZ0jhzezYeN2Lly4/LfrF0SR0fOeZvGoeRgzcpm7fhGndhwj7UpKlUzy+UTmDZ6OqdzEXaMGMmzWaJZPfA8AU7mJ1+6Zdtu2CqJApwVj2TnibUrTjdy9eR4p22IpqGxI/obe1YlmTw0kO/bKrR6e4Nef5drY2Zgzcmj4yxKKdh7BdOW6Sqxw0z4y3lihOmbOziPpkanIJguCixONtiyjaGcMliyjSr/L+Jcoen0qUm42Hu+sxHT0IFLKNZtIcChOD42kcNbzyCXFCJ5eqvu4PPYk5vOnb1QgOA1/ntKlryDn5+Ay4wMsp2OQMpJtIv4hOAwcTuniqVBWjODmCYCuRUd04Y0oXfA86A24TH4Hy7njUF6q0u/82ERKlsxEzsvB9dUPscQdRkq36RcDQnC8ewQliyZDaTGCu2K/9VIcJfMqPx4u7rgv+BLL+Vi78vecNoncSS9jzcrG//MVlO8/hCXpmkqsfOduCt5bqjrm2K0LDk0akz32KQSDA74fL6HicAxyqc1+QRTo89ZYfh75NkXpRh7bMI+rO2KrOh4AF9cd5vQ3uwBo2L8dveaM4pcx7yDoRAZ98CxbX1pBzoVknLzckMyWGuaLvP/+m9x770hSUzM4cGA9GzdGc/Gi7d15/PHh5OUV0KJFLx55ZDDz589k9OiJ5Obm8fDDT5CenkXz5k3YsOFrGjXqXHs934A/HRtEkfC3nuHKyNcwp+fSdMNiCnYcpfyy2v/zNxwgZe4ndpdnrvwF0dkRv5EDa1VvtUos+PQHPpk7kUBfLx6d8R96d2xJo/DgKpl3v/qFwb06MeSuLsScucTSb9azYNLYqvMfrd1E++aNardfEHAY/CTlq95CLszFacJCLBePI2enVomYtqyu+r++8yDE4AYAyMV5lH8yG6wWcHDEeeK7WC8eV+vu/SgVv3yAXJyH04hZWBNOIxvTq0TM+/7Lb91UfeveiP7hAEgp8ZR/O1854eiC8+NvYk0+ry4bSWLh5uOsGH0XgR7OjPx0B72ahtIowLNK5tN95xgQFc6wjo25mlXAxDV72dLkfrxdHPng0Z4EeDhzJTOfZ7/Zy46pQ+zL5u6xlK95G7nQiNOT87DExyLn2HzftGONrWw69EcMilCpcOj9MFLyxVqL3mqVWPD5f/lkzvME+njx6KzF9O7QokbdrmNwr44M6d2ZmDPxLF2zgQUvjsHfy4Nv5k/GwWCgtKyCB6cupHeHlgT4eFbTb2X+0s/45J25BPn7MOK5mdzVtQONIsKrZBavWM3g/r0ZMrA3MSfP8MFna1g460VOnbvIyXMX+enTdwEYM2kOx+PO0bFNC7X9K9fyyRuTCPT15tGXF9K7UysahYfY7F/1E4Pv6sKQPl2JOX2RpV+vY8HkcUq1Ojjw3/dn11o2/7P6rUP/RBBxGv0CJYtnIBuzcZv7MeZTh5DSqsX+wFAc732U4gWT1LH/YhzFrymDUoKrO25vr8Zyzj72e894kaznp2PNzCboq2WU7juMJVEd+0t37CHvnQ9VxypiT5ExUulAix7uBP/yFeVHjqtkEAXuePsJYofNpzwtly7bFpC9LZaS+FSVmM7VifpP301+rDpmll3L5EjfmdSKIOBw3xOUr56vxJ1nFmC5GKuOO1ttHXl954GIwRFAZdz5dI4t7jy/GOulWOSiPJt6UeTReU/y/qg3ycswMmv9Qk7vOE56jXbV3sEzMJeb6DlqAA/NGs2nE5cAMO69iWz56GcuHDiNo4sTkiTV/hz/YP59Fv9vuGkqlyAIboIgzBME4ZwgCAWCIGQLgnBEEITH/2pDOnVsy9WrSSQmJmM2m/nhh1+5f3DtDYH/tf6GbSLJupZB9vVMrGYLRzccoO2AjiqZi4fPYio3AXD1ZDzeQb5/2Fbfto0oSsqkODkbyWwl6dcjhA1sbyfXevrDnP94I1KN0e2aOLdugulaGubrGWC2ULBxH+79utyeMWYLsklpqIoOBgRRsBPRN74DKT0VKTMdLBZMB3bh0Ek9MujYfzAVW35BLikGQC7Irzqna9gEwdMb86ljtZogRjRByk5Dzs0AqwVL7F70rdX2O/QYhHnvBiir1F9coFwbVA/rlbMgSWCqwJqaiL65uix1DZoq+nMU/eZje9G36aaSMdx5D6bd66G0Un9RPjUxtL8Ty9njYKpQH2/eDEtKGtY0pXzKonfhdGf3Wp+1JvqI+lScOg1WCbm8HPOVBBy7dFLJBLVpRH5SJgWV/nJpwxEaDVA/o6m4zGaPsyOyrCy7q9+zJTkXrpNzQfkQl+cXI0vqJXkdO7bh6tUkkpKuYzab+e9/N3DffepR4/vu68+aNcosxM8/b6Z3b+X54uLOkZ6eBcD58/E4OTnh4OBwW8/+G382Nri0aUxFUgam5Exks4W8DfvxHNDp1hdWUnzwNFK18qvJ2StJ1AvyIyzID4NBz6Ae7dh9TN3JTrieTueWTZXnadGE3cfOVJ07fzUZY0Eh3VrfUat+MSwSKTcDOS8LrFasZw6hv6NjrbIA+lbdsZw5oPywWpXGAYDOAII65IuBEcgFWciFOSBZscQfQ9ew1Q1165p0xBJ/3P5443ZYk86BRR2LzqYaCfdxJ8zHDYNex8AW9dhzSd1oEgSBkgrFxuIKM/7uzgA0C/YmwEP5f6MATyrMVkwWq9r+kEZIxkzk/GyQrFjPHUHfxD5WVpVNVFcs5w7brg+KQHD1wJpwplb5s1euUS/In7DAyrrt3o7dx9WyCSkZdG6hjNh3atG46rzBoMfBYADAZLEgSfZLXc9cvEK90CDCQwIxGAzcfVd3dh9Sx8GEayl0bqt0Njq1aVHtvECFyYzZYsFktmCxWvD1Vg/4nL2cRL3gAMKC/Ct9syO7Y27imy2bsvuoekbhZtR5/daxf+oaNkXKSkPOTldi/9E9GNqqY7NDz3uo2PXrTWO/vkNPLGeO2cV+h6hmWK6nYk1VYn/p9t249Opmd/2tcO7bk/JDR5Er1Po920VSmphB2bUsZLOVjHWHCBjUwe76yJnDSPxoPVL5zdsK1RHDIpGMNeJOM3vdv6Fv2R3LmcrZIru4Y99uaFDZrsq5noXVbOH4hoO0HqDWH3/4HObKdlXiyXi8gnwACI4MQ6fTceGA4ssVpeVVchr/fm61xmQNkAAMBN4AlgKjgbsEQVjwVxoSEhrE9RTbKFdKajohIUH/CP3egT4Y03KqfhvTjXgH3rjj0XNYX87sOVH12+DowNz1i5j9y0La3kaDyCXIm9I024xEaboRl2BvlYxPywhcQ3xI3Xnqlvr0gb6Y0232WzJyMNRiv/ug7jTc9BFhH81CH+xnuz7Yj4abPqLxgVXkrPxRPVsCCD5+WHOyqn5LudmIvn4qGV1IGGJIOO4LPsLj7WUY2laWgyDgMu45SlerU4OqI3r5IeXZpmilvBwET7X9QkAoYmAoLlMX4/LyEnSVnQ/pt45I5TSzvkkrBG9/9bVefkhGm345LxvRS61fDAxDDAzDZcYSXGZ9gC7KPkAbOvXGfNQ+zUrn74c101Y+1uxsdP5+dnJOvXvi/9VneM9/HTFAsdFy5SpOXTohODoienrg2K4NukC1/W5B3hRV85fidCNugWp/AWg9ph/j9r/Lna+MYM9rykiXd8MgQOaBr6fz2Ka36DDhXrvrQkKCSEmxjVCmpqYTGhpUi4zyflmtVgoLi/D1VdvwwAP3cOrUWUym3/cB+bOxwSHIF1O199eUnlur/3vd05Vm2z4gYsUMDMH29XMjMo0FBPrZnjXQx5us3AKVTJOIUKKPKO/qzpg4SsrKyS8qRpIkFq/+mSlj1ek71RE8fJALcqt+ywW5CO4+tct6+iF4ByAlnK12vS/Oz/8Hl2nLMe//VT1q6eat+i0X5yO42fsOgODug+jph3TdfnZB36QDlnj7gYWswjKCPFyqfgd6OJNVqO7kTejdgk2nkxjw7q9MXLOXmffYdyyiz6dwR7A3DnpdDZu8kQttvi8XGRHcb2C/py+CVwBS0rnfjuDQfySm6LW1ygNkGvMJ9LU19gN9vOzrtn4o0TFKY37n0dOUlFWQX1QCQEZOHg9NfZsBE+byxNC+qtkSgKwcI0HVYkGgvy+ZOer42qRRBNH7YxT9B2IoKS0jv6CINlFN6dQmij6PPE2fYU/TvUMbGtYPq2F/nto3fb3IMuap9UeEEX3kpKL/yCnFNwuVRrjJZGbE1AWMnL6IXUfsvzV1Xr917J+Ctx+ysdq3y5iN4F0j9geFoQsMw/WV93Gd/WFVWnB1HDr3xhyzy+64LsAPa6bt22LJykYXYB9bXPrcSdDaT/Fb9JpdfAdwHXAXJdvsvy1OQT6Up9liQ3maEccgdWxwbxmBU4gvOdEn7a53rudPl+iFdPhlLl6dm6nOCe414k6hEcHjZnHH3z7uPLcIl6kfYz6wXlWPAF6BPuRVsz0v3YjXTdpV3Yf15dwe5RkCGgZTWljChBXTeHXTOzw0S0l71/i/wa1SuSJkWV5V+f/3BEE4Jsvym4IgjAPOA6/UdpEgCOOB8QCCzhNRdP2r7P3H03VoTyJaNeLt4XOqjk3rPoH8TCP+4YFMX/s6KRevkZ2c+cdvIgi0f20kh15a+RdYrFC8M4bCDXuQTRa8Hh1E6H+mcG2UUr2W9BwS7p2IPsCH8BWzKdxyEGuu/ajRTdHp0AWHUTRnEqKvP+7zP6Rw0jgcevXHHBuDnPvnckMFUQf+oZQumYHg7YfLlP9Q8tazWC+cwFK/CS7T3kUuLsCacFGZPfm96ETEwFBKF09D8PbH9eV3KX59PJQpDRDB0wcxNEJJE/sDlB84TNmOXWA24zJkMN5zZpL7wlQqjh7HcEdT/FZ+hJSfj+nsebD+sQnguK+iifsqmqZDutL5xaFsm7ISUacjpEMTvh08F0uZiYfWziLzTBLXD567tcLfwR13NOatt2Zy332j/lK9fxUF0cfIW78P2WTBd+RA6r83iSuPzrn1hbfJ1LEPsPCz/7J+Twzt7ogkwMcLURT5fut+erSLUq03+TPoW3XHeu4IyLbRebkwl7KPX0Zw98bxsZexnDvyh3TrmnTAcvmESjcALh6IvqFI1/6Yz2w9c4372zRgTLdmxF3PYfbPR/jxubsRK2dnr2QV8EH0KZaP7v2H9P+GvnlXrBePVtmv79AP65VTyEXGW1x5c6aOGcrCz6vXrWeV7UF+3vz07kyyjAW89M6n9O/SBl8vj9+lf9ozY1jw4Wf8un037Vs2J8DPB1EnkpyaTkJyKtHfK9+Bp19+k9jT52nfqvktNNawf9xDLPzkO9bvOkK7qEgCfBXfBNj66XwCfb1JycjmqTlLaFw/lPBg+4bzzfhf1W9d+SeiDjEwlJJFUxG8/XGb9R5Fs59Wx/6wBsps+R+gbP9hSrYpsd/twfvwfX0GWc/aUr9FXx8MkQ0oP1x7RsFNEQSavjGGs5PsB/4qMvPY124i5rxi3Fs1oO2qaRzsOQ248ezwjdC37Ib1XIx93Fk2Q4k7j07Fci4GSgpuouXGdB56J/VbNeTd4a8BoNPpaNzxDt6692WMaTk8/dFkuj3cm4M/2HcO/8lof/m9dm7VMSkRBKGHLMsHBEG4HzACyLIsCTVX2FVDluVPgE8A9A6ht1XyaakZhIfZ8l7DQoNJS8u4nUtviz+jPy/TiE+IbZTDJ9iHvMxcO7nm3Vtx38SHeHv4HCwmW55+fqby4cu+nsnFI+eoH9Xgph2T0ow8XEJsIxMuwT6UpttGGwxuTng2C6P/T68C4OzvSe9VU9jz+Hu1LoC3ZOaqRoD1QX6Ya9hvzS+y2fv9dgJnPGGvJ8tIefw1XDpGUbTVtgBPNuag8wuo+i36+iPl5qiulXKzscRfAKsVKSsDKe06YkgY+qZR6Ju3wvHuIQhOzgh6A3J5GWVf23L9pfwcDNVmOURvP9VIzm8y1sRLIFmRczORMlMRA0KRrsVj2vodpq3KgkOncdORstSpBnJ+DqKPTb/g7Y+Ur9Yv5+UonRqrFTknAykzBTEwFCkpHgBDh57KokerOhUBwJqdgy7QVj46f3+s2erykQsLq/5fumETHs+Pr/pdvHoNxauVPHmv12djuZ6iurY4Iw/3av7iFuxDcaZ6dKo6l9Yfoe98JYe8KN1I6tFLlOcpI6RJu+MIaBGh6pikpWUQFmbLqQ8NDSY1Vf3uKDIhpKZmoNPp8PBwJzc3r1I+iO+//4SnnppCYmIyv5c/GxtMGbk4VHt/HYJ9b+r/uWt3EDprLLdLoI8nmTm28s405hHgqx4ZD/DxYsn0pwEoLasg+sgpPFxdiItP5MSFq/ywdT+l5RWYLVZcnBx5abQt114uNKpmCAVP3xs2pnUtu2Ha8Hmt5+SiPKTM6+gimiFlKrnmcnGeaoZBcPNCLq7dd/RNOmDaY78wWN+kA9arp2rt8Ad4OJNRaFsPlVlYVpW+8xu/nExg2aheALQO96PCYiW/tAIfNycyC0qZ8t0B3nygC+E+7rU+U/VRXMHdx25k9jd0UV0wbbWtxdGFRSKGN0Xfvh+CgxPo9MimCsy7bRtfBPp4kVltECbTmF9L3Xqy5OWngMq6jVHqtqZMZL1gYi9cZUDXtrbjfj5kVIsFmdm5BPqpR6UD/Hx4/43plfrL2LH/CB5urvy0KZpWdzTGxVkpzx6d2hJ3Pl7VMQn08Vb7Zm4+AT7qTnCAjxdLZk6o1F9O9OGTeLgp9gdWdpjDgvzp0KIJFxKTVR2TOq/fOvZPOS8Hwafat8vHHzmvxrclL1sd+zNS0AWFKd8bwNCpF5bYg7XH/qwc1QyIPsC/apF7lf4CW+wvXrcZrxefVp137d+bst0HatVfnmHEKcQWG5xCfKjIsMUGvZsTbs3C6PjzXAAcAjxp89U0To1ZTGFcAmaTEveLTidSmpSJa6NglCSZytnH6nHHw0c1O1kdXcuumDZ+Wes5uSgPKes6uvrNsJ6PqTqen2nEu5rt3sE+5NfSrmrWvSV3T3yQd4e/VtWuysvI5fqFJHKuK7Ndp7Yfo2Hbxhz8oVYTNP5l3GruawLKTEkeMB14AUAQBH/gL90y69jxU0RGNiAiIhyDwcCwYUPYsHH7P0J/YtwVAiKC8QsLQGfQ02lwD07uUI+O1ItqwNgFz7D0qbcpyrUFGhcPV/QOSv/Pzdudxu2bkXZZ3bCsSe6pBNwbBOEa7o9o0BExpAsp222pYeaiMn5s8SzrOk9mXefJ5Jy4esNOCUDZ6XgcIkIxhAWCQY/nfT0p3hmjktH724K/e7/OVFQujNcH+SI4KmsCRA83XDpEYUpQ22+5fBExOAwxIAj0ehx69MF8TL1ziDnmAIYWbQB1DVKzAAAgAElEQVQQ3D0RQ8KRMtMoef8tCsYPo+CZEZSuWk7Fnm2qTgmAdC0eMSAEwTcQdHr07XthOa0e9bXEHUbfRMk9Flw9lE5DTrqSU++qfPDE0AjE0AZYL6gXKFqTLiEGhCL4BYFOj6FjLyxxh1Uy5pOH0DVtreh380AMDFPykn8rv0531ZrGBWC+cBF9WCi6YKV8nPv1ofyAeucW0dfWGHHq0Q1LUmUDXhQRPJQRVn2jhhgiG1JxVD1ylhGXgHeDIDwq/aXp4C4k7DihkvGKsO1207BvG/KTlIb9tX2n8W0ajt7JAUEnEtalGcbL6o7b8eNxREY2oH595d155JHBbNqk3tlp06ZoRo58CIAHH7ynauctT08Pfv75S+bMWcThw39sRPHPxobSuMs4NgjGITwAwaDHe/CdFOw4qpLRB9j837N/J8qv3PwdrU5UZH2upWeTkpmD2Wxh64ET9O6gzoPPKyyuWpz52c/beKCPskbq7ZceZ/vKN9m6Yh5TxzzA4F6dVJ0SACn1KqJvMIKXvzLz2LIblov2ZSn4hSA4uSJdj7cd8/ABvbLOASdXdPWbIlVbGC5lXkPwCkDw8AVRh75JR6wJ9ptQCN6B4OSKlJ5gd053gzQZgKgQH5Jzi0jNK8ZssbLtbDK9moaqZII9XYlJUAZqErILMFmseLs6Ulhm4oVv9zGpXyva1qt9lF5KS0D0CVLKRtShi+qCJf6EnZzgG6yUTYpt8W/FuuWUffgSZR9NxhT9LZbT+1WdEoCoyHqVdZur1O3BE/Tu0FIlo6rbX3bwwF1K3Wbk5lFeoaQtFhaXcvJiAhEh6l2nWjSL5FpqOinpmZjNZrbsPkjvbupUobyCQpv+b3/hgUF9lHIL8OP46fNYrFbMFguxp8/RsJ46lSuqcX2upWdV881j9O50E9/8aSsP9O1WaXMJJrO5SubUxauqRf/wP6jfOvZPa+IldNVjf6femGvsqmU5cQh9s2qxPygMKcsW+w2d+2CqJY0LwHT+IobwUHQhSux3GXAXZftuHPude3bFXGPwxmVg7WlcAIUnr+LSMAjnev4IBh1BQ7uRtc32fbMUlbGn+Xj2d3yB/R1foCD2SlWnxODrDpWzVs71A3BpGETpNduAqZR61fZuVcWdWDsblLjjdvO4U6+ZKu4AJFW2q3wr21UdBncnrka7KjwqglELxrPsqUWqdlVS3FWcPVxw81G+jc26tSD9Fu0qjX8PN50xkWX5NGC3KEKW5WxBEIpqueQPY7VamfTSbDZv+hadKLJq9fecPx9/6wv/B/olq8SauZ8x9as5iDqR/T/sIu3ydYZOHkHSmSucij7OsFljcHRx4rllUwHbtsAhkWGMXfAMkiwjCgKblv+i2s2rNmSrxLFXV9P32+kIOpGr3+2lID6VVi8/hDEuUdVJub2Hl8h4Yzn1Vr2JIIrk/7iDisvJ+L80irIzlyneGYPP2Pv5f+ydd3gU1f7/XzO76b0nJKGFEhJqQhOEUKQooqBXULrAVRFExYqIBRAU9SoWBLGhIs2CFOkSRHqooYQSSiC9kV52d+b3x4RsJpuAbfXe3/e8nifPk91z5j1nPnPO5/Sz7n27aJvcCopJe1Y7+cIpIpygFyZqU7SSRO4n31NxVn+iCIqF0iXv4vHyW9pxwdt/wnLlEi4PjMd8PgnTwT2YjhzAoX0nvN5biqoolC39CLWosI7E1vUCFMpXfoTrlDkgGzDt3YKSnoLjnaOxXD6LJXE/llOHMLaKwXXmYlAsVHz/KZQUaSdxTXtL0ykvpfyLN21HzhSF8m8+wPWJuUiSTOXuzShpl3G6awyWy2cxH9uH5WQCxuhY3F5dosX/dglqiVYEJL8gZJ8ALGfrOVXMolDwn/fwe2c+GGRK12/EfPESHhMfpDLpDBW/7sHtvntwvrW7NqNUWMi116qOlDYa8P9IOz5ZLSkl/9XXbJZyqRaFn2cu5Z6vtPxycuVOcs+mcsu0e8lMvMiFrYdpP64/DW+NxmKyUFFQwuZp2vKPioJSDn+ykRHrZ6GqKpd2HOPiz/q15BaLhSeffIl1677EYDCwdOkqTp8+x8yZ0zh8+DgbNmzjiy9W8tln73DixE7y868xevQUAB55ZCwREY2ZPn0q06dPBWDw4NFkZ9uOjNXHn/YNFoWrMz8m4qtXtOOCV26n/OwVgqeNoDTxPIVbDxDw4J149esMZgvma8Vcfsp6ZHXzb+fiFBGGwc2Z6P2fkvLMBxT9Yl2vbTQYeGHiMCbN/hCLojKkT1eaNQzhw+XriWrWkN6d2nLw5Dne+3otkgQxUc2YUceRwPWiKFSu/wznsTNAljEf3oGadRWHPsNQ0pKxVDUWdJtPq5ACQnEeOAYVFQkJ0+51qJlXkK6PyqsKlfErcRoyVTsS9NQe1Lx0HLoORsm8jOWilqeNLTphqWuNvocfkoevrsFfE6NB5vk7Ypn01U4UVeHuDk1pFujFwp8TiWrgS6/IUKb1b8+sdQdZtk97p68O6YIkSaw8cI6UvCIW7zzJ4p3aDN6i0b3wdXe23kBVqNy0FOcHntVsc3Qnak4qDnH3oqRdxHJO85Xapvffv4TNaDDwwoR/Mem1hVgUhSG9u9IsPIQPV2wgKqIhvTu10d7tN+u1d9sqghkTtSNqL17N5K0v1yBJmvscO7gPLRo1sNV/bCKPPDcHi6Iw9PY+NGsczgefryC6ZQS9u3Xi4NGTLPh0GRISsW2jmDFVm53p17Mr+4+c4J6J05CQ6N6pPb26dbTV//dwJr36HhaLwpDbutGsYQM+/GYtUc0a0btzOw6eOMN7X61BkiRiopoz4+H7AW1T/6yFy5BlCUVRGX/PQN1pXn/b+7Vj/kRRKFv2Pm5Pva4dF7xrk+b7h4zFcuks5qN7MZ84iLF1LO5zPgVVoXzlx6glWt0l+QUh+wZgOVO/7897830C338DDDIlazdiunAZr4fHUXn6DGW/7MXj/qG49OxW5fuLyH1lfvXlhpAgDEGBVByu+0AC1aKQNP1zYla8gGSQSV2+g5IzV4l49j4Kj10ge7NtR+I6Pl1b0ezZ+1DMFlBUTj/7CeZrJTrbVG74HOcxL1j9TvZVHPrch5J6AcuZ636nG+YTdfidAaNQAQkw7V6PmqU/BVGxKKx46VMe/3IGskFm96odpJ+7yuAnh3M5MZnj2xK4d/ponFydeaiqXZWXmsPCf7+Bqih899pXPLnsJSRJ4vKJC+xasb3eZ/1vRSzkqhtJrb0e87deKEkpqqo2vFm837qU67+V0Q1+4+lVf4C+lS43j/QniHGpfznPX0Fw22K76juEuN480p9ANdlOjf+VFJ/87Seg/BFWXg29eaQ/wQvZu+yqb7KYbx7pT3AwuP4TZP4Kon6aYld988oldtOW/OvexPqX6Qfa/h7FX4mSnGxXfeNdo+2qL/k2uHmkP4FalHPzSH8C5Wi8XfXVrD+xB/M3YDp6kyP2/yQFifZt9iRd+X37fH4P3R++eZw/w7Qv7VvvAiy+tLrerQb/TTzb+IH/6vbx/EvL/xE73nDGRJKkeoYBkAD71jwCgUAgEAgEAoHg/ww32/wehHZUcO2hdwn4/T/fLBAIBAKBQCAQ/B9H/MBi3dysY7IecFdV1eYAc0mS4u2SIoFAIBAIBAKBQPB/jpttfp9wg7ARf31yBAKBQCAQCAQCwf9FbjZjIhAIBAKBQCAQCP5CxA8s1s3NfsdEIBAIBAKBQCAQCOyO6JgIBAKBQCAQCASCfxzRMREIBAKBQCAQCAT/OGKPiUAgEAgEAoFA8DcidpjUjd07Jj0Co+yqv368n131sdjxV0qd7JstzcnldtU/sdl+vz4L8J2zfbOni50nDI8pBXbV72VXdTjWuJVd9Rt+/Yhd9dX8DLvqK6ln7KpvGHSvXfXtiVqYa1d9WbZv2TWv/tSu+pVJWXbVN3g72lXfGNPSrvpyjzvsqu90S4Vd9X2+XmpX/UZb7Fe3SB4hdtMG2Ft+2q76gv99xFIugUAgEAgEAoFA8I8jlnIJBAKBQCAQCAR/I+KX3+tGzJgIBAKBQCAQCASCfxzRMREIBAKBQCAQCAT/OGIpl0AgEAgEAoFA8DeiinO56kTMmAgEAoFAIBAIBIJ/HNExEQgEAoFAIBAIBP84YimXQCAQCAQCgUDwNyJO5aobMWMiEAgEAoFAIBAI/nFEx0QgEAgEAoFAIBD84/ytS7k69erIlFcfxWCQ2bB8I8s/XKkLb9ulDZNfmUREq6bMmvwav2zYBUD7bu2Y/PKk6ngNI8KZNfk1dm/eo7ve0KwdjneMBUnGfPhnTLvW6sIdB45BbhIFgOTghOTmSem8CUhe/jg98BRIEpLBgGnfZswJ2/TazdvjOOhBkGXMCdsx/bJGr33HWOSmrau0HZHcvCidMw45pDGOd/0byckFVIXK+O+xJOrTDWCIaIvjgNGa/pF4TLvX6fX7j0JuHFVD35PS+Q/ViOCCy6PzsSQlULlpqY2+sU0nnEdOBlnGtPMnKjassInj0DkOpyFjARVLSjJli+Zq9/MNxGXCU8i+AaBCyX+mo+Zk6q717t2eprMfBINM5rLtpH6wxkYfwG9QFyI/fYZjA56j+FgyktFAs/9Mwq1NEySDgazVO0l9/web6yLj2jHkpbHIBpl9K3/m54/07zZuwh10ub8PitlCcV4RK59dRH5qDgB3Pj+CVr07ALD1/e85un6vjX7zuLYMemkMskEmYeUOfvlIb//OI/vSZXQ/VEWhoqSCNdM/Ift8Ki7e7oz46HFC20Zw5NtfWPfyF3U+d01i4mJ56JWHkA0yW1Zs4duFq3XhQyYOof8DA7CYLRTmFfDu0++SnZp9Q83GcW3p88poJINM4op4DizUp7/dqD60H9MP1aJQWVrO1uc/JfdcGp5h/jz483zyk9MBSDtynm0vfG6j73prLIEvTAJZpuDbTeR/skoX7jmkH/7PTMCcmQvAtW/WUfjtJs22JzZQcfYSAOb0bNImv2Kjv/vYGd74ci2KojK0dycm3NVbF56Wnc/LH68mv7AEL3dX5j46nCA/b5IupfHaZz9QXFaOQZaZOKQPA29pZ6t/8hLzv41HURSGdm/N+P6ddeHpeYXM/HIzRWUVKIrK1LtvpUfrJmw4cJql2w5VxzuXls3y50YSGR6o1z91mfnf/6Kl/5YoxvfrWEu/iJlfb9X0VZWpg7vRI7oxGw6eYenPh2vo57D8mfuJDAuwah8/xxvLftK042KYcGdPvW1yrvHypz+QX1iKl7sLcx++lyBfLwAmvfUliclXad+8IR9MG2Vjl79F/9Ql5n+3s8o20Yzv38nW9tdtoyhMvas7PaKbsOFgEku317R9DsufHaGzDcDuc2nM/+kQiqoyNCaC8T2j9frXSpj5/V6Kyk2a7fu1o0eLUBKv5jB77QEtkgqP9G5Dn6hwm/QbmrXTfL8kYz60HdOuH3XhjrePRW6i3bPa9899UKtXRjwNklxVr2zCfHCrjb6xfWdcH5wCsoGK7RuoWPONTRyHW3rhMmwcqCqWy8mULJhjDXRxxeudpVQe/JWyTxfYpj+6I873T0KSZSp3baJy00qbOMaOPXEaPBpQUa5coOyT1zG0bIfz8Eeq48jB4ZR9PBfzUX39tftSDm/uTEJRVIa0DmN8pya68Ld2JnHwSj4A5WYLeaWV7Hq0DwDv7jrLrovZqCp0aeTHs3EtkSRJr380qco3KAzt3ZkJd/fRhadl5/Py4lXkFxZrvmHyA1W+IZXXPvue4tIKDLLExKF9GXhLe5tn3338LG989ZOm3yuWCYPj9Po5+by85Afyi0rwcnNh7qT7rPl//lISk6/QvkUjPnhqtI02gKFVDM73PKTVvXu3ULntW1v7d7gVx9tHgKqipF6k/Mu3AHC8axzGKK28VG5egfnILptrXW+NJWjGI9W+OW+Jvj7xHHobAc9MxJyp1YfXlq2j4NvN1eGymyuNNyymePsesmZ/pLtWbtwax74jQJIwH9+F+cBPunCH3vdjaBhZ9RCOSK6elL0/BQDJwxfHgeOQPHxBhYrv3kEtzNVd3713V56b/QSywcD3y9by2Qdf6cJju7bn2VlP0DwqguceeYmt63cA0DK6OS++8QxuHm4oFoUlC75g84/b67D+fzeKOJWrTv62joksyzw+5zGeGfEc2ek5LNrwAXu27OXyuZTqOJmpWbwx7U2GP3yf7tqje47x7wGag/Tw9uDrX78gYechXRwkCcc7x1O+9DXUwlycH56LOekQanZqdZTKTV9W/2/sMgA5pDEAanE+5UtmgsUMjk64TH4Ly5lDqEX5VdoyjoMnUP75bNTCPJwnzcN8OgE1+6pV+ydrZ8DYdSByA805q5UVVHz7PmpuBpKHD86T36Ds3FEoL9Wn/fZxlH89T9OfOBvzmcOoOTXSvuVrq36n/sjBjXSP79j7XyiXk+o2viTjPGYqJfOfRc3Lxv2VhZiO7EVJu1wdRQ4KxenOByieMxVKi5E8vKvDXB96jop132A+eQicnEGtVZhkmabzJnJy2Cwq0/Not+l18rYkUHb2qi6awc2ZkImDKDp0tvo7v8G3IDk6cLT3U8gujnT45V1y1vxKxRVrQ1ySJe6ZNZ5Fo16jICOXJ9fO5eTWQ2Set9on9dQl3hn8AqbySrqN6sed00fy1ZQFtOrdgdDoxrx9x3MYHR14dMVLnI4/SkVxmU5/8KwH+XzUPAozcpm0dg6ntx4mu4b+sR/3cGCZ5vgib4vhjpmjWDr2DcwVJra9/S1BLcMIamHbqKmNLMtMmjOJF0e+SG56Du+se4f9W/dx5dyV6jjJJy/w5KAnqCiv4PZRd/DgC+OZP/mNejUlWeK2OWNZPfJ1itLzGLVuFslbD5F7Lq06zuk1ezn29c8ARPSLodfMUXw3Zj4ABZcz+fL2GTdKNIEzJ5M64QVMmTk0WvUeJTv2UZmcootWvPEXsuYstLlcLa8k5Z7J9cpbFIW5n69h8fSJBPl5MeLFD+gVE0VEWFB1nP8s28DgHrHc1TOW/SfPs2DlJuY+ej/OTg7MmTScRiH+ZOUX8sCM9+jWtgWebi46/XmrfmbRY/cQ5O3ByPnfENcmgogQv+o4Szbtp39MC4b1bEdyei5TFq5hY+sJDOrcikGdWwFwLjWHJz9ea9MpsSgK81bHs2jyEIK83Rn51kriWjclIsTXqr/lIP07NGdYjzYkp+cxZfFaNkaPY1Cnlgzq1FLTT8vhySUbdA1vi6Iw98v1LH52LEG+nox4ZTG9OkQSEWpNw39WbGZw9/bcdWsH9p+6wILV25j78L0AjLu9O2WVJr7dkVC/7e2sr9lmqGabN1cQ16ap3vabr9umrWb7RT+y8dUmDOoUyaBOkTVss96mU2JRFOatT2DR2D4EebowcvFm4iLDiAj0survPEH/1o0Y1rk5yVkFTPk6no3TQmkW6M03Dw/EaJDJLipj2MKf6NkyFKOhxkICSdJ8/xdztHrlkXmYkxL09crGGr6/y0DkkCrfX5xP+ccvWuuVKW9jSUqw1isAsozrhMcpnv00Sl42HvMWYUrYjXK1hm8ODsV56EiKXpyCWlKM5Gn1zQAu94/HfPpYnfZHknEZMYWSd55Hzc/Bbcb7mI/tRUm3ll05sAFOt99PyRtP6ny/5cwxSmZVDQi6euAx93PMp/T1rkVReX3HaT66J5Ygd2dGLt9HXNMAIvzcq+M8HRdZ/f/yoymcySoE4GjaNY6mXWPVqG4APLjqAIeu5tMx3LeGvsLcz39g8QsPab5hxnv0io2u5RvWa74hriP7T5xnwYqNzJ38AM5OjsyZdD+NQgLIyivggRkL6Na2pY1vmLt0HYufe1DL/y8toldMK33+/2YTg29tz109Yth/MpkFq7Yw9xGtjTJu0K2UVZj4dsfBeu3vfN8kSj98EfVaLq5Pv4P5xH6UDKu/lwIa4NjvPkrfeQbKSpDctbxriOqIISyC0vmPgdEB18e0dgfl1roLWSbopclcHV/lm1cvoPjn/Ta+uWjjTptOx3X8Hx9NWUJiHWmXcOw3iopVb6MW5eE8+iUsyUdRc631imnHCkxV/xs79EUOalgd5njHREz71qNcPgUOTjbtBlmWeWHeUzw07HEy07NYvukz4rfs4kLVIBZAemoGLz4+m3GPjtRdW15WzozHZpFy8SoBQf6s2PI5e3bsp6iwuM5nFPxvccOlXJIkeUmS9LokSUmSJOVJkpQrSdLpqu+8b3RtbSLbtyTtUhrpKRmYTWZ+/jGe7v276eJkXs3kwumLKEr9vci4QT04sOMgFeUV+gcJa4aSl4GanwUWC5bEPRgjO9ajAsY23TFfn7mwWLTKA8DgALVGbPTaZizHd2NsdQPttrdiPrYbADU3HTU3Q/u/KB+1uADJzVOvHxqBkp+Jei0bFAuWk/swtoytX7/1LZhPWkf95ZDGSG5eWC7U4VwAQ9NIlMxU1Ox0sJgx7d+BQ4ze9o5xg6jYvhZKi6vSek3TbtAIDAatUwJQUQ6Vett7dGhG+cUMKlKyUE1mstfsxneAflQUoOFz95P64RqUCpP1S1XF4OoEBhnZ2RG10oylqEx/Xftm5FzOIO9KFhaThSPr9tC6v97+5/eewlReCcDlI+fwDtYqt+DmoVw4kIRiUagsqyA9KYXIOP2Ielj7ZuRdziS/Sv/4ur206q+3f82OjKOr1cmayiq4nHAGU81nugEt2rcg/VIamVXl4Jd1v9C1f1ddnMS9x6vz95kjSfiH+N9QM7h9BPmXMilIyUYxWUhat4+IWumvrJF+BxfbSuJGOLdtiSklHdPVDDCZKfxpJ259bvnN19+ME+evEB7kR1iQHw5GIwNvaUf8oVO6OMmpmXSOjgCgc1REdXjjkAAaVdkn0McTX0938gtL9PqXMggP8CbM3xsHo4EBsS2JP56siyMhUVKVf4rLKgjwcrNJ58aEJAbEtrRN/+XMKn0vTT+mBfGJF2rpY9UvryDAsw79Q2cZENtCr33hKuFBvoQF+mq26dKG+MP6AYjk1Cw6t2qq2aZVE114l+gI3JydbO71t+lfziTc38tqm9g6bCPVtE0lAV7uNjobE84wIKaFzfcnruYS7utOmK+7pt+mEfFJ+gERSZIoqSqfxeWVBHhoDVMXR2N1J6TSbEFC7/ehyvfn1qpXWtn6tusY23bHnPir9sGmXrGtbg3NIlEyUlGy0sFsxrT7Zxw7dtfFcbrtTio2rUEtqfLNhdes1zdtgezli+lY3R1DQ5OWKNlpqDkZmu8/uBNje73vd+hxB5U7bH2/Lk5sD8wnEmx8/4mMAsK9XAnzcsXBIDOgRTDxyVn12mfTmXQGtgwBtDJRabFgUhQqLQpmRcXXzVGvfz6F8GD/Gr6hPfEJJ3Vxkq9m0rl1MwA6R0cQf0gL13yD1pEN9PWq8g36huuJ5Kua77me/7u2If7Qab1+Wjado6ryf1RT4g/Vyv8u+jTXRG7UAiU7HTU3EyxmzId/wdhG7+8dbxmAadcGKNP8llpcoF0b3BBL8klQFKiswJJ2CWMrvV93btsCU0patW8u+mkn7n31+jfCKboZBj8fSnYftgmTQ5qi5mehFmjtEnPSfgzNbGecrmNo1QXz6f0ASH4NQDZonRIAUwWYK3XxW3eIIuXiVVJT0jCbzGxas43eA2rN1l7J4NzpZBRFv0388oUrpFzUynl2Zg55Ofn4+P2uJqngv5ib7TFZBeQDvVRV9VVV1Q/oXfXdqhteWQv/EH+y0q2j4NkZOTdtcNVF77t6sX3NDpvvJQ9f1ALrNKFamIfk6WsTD0Dy8kfyCUC5cML6nacfLo++getTH2L6da1uVEvyrEPby4+6kLz9kXwDddrXkcOaIRmMqHn6ZVB1pt3Dp/60ewegXLzunCUc+42kcqvt9H/1NT7+qHlW2yt52Ug+etvLwWEYgsJwe3EBbjPfx9imU/X3amkJro+9gvusRTgPf8imgnUM8aUyLaf6c2V6Lk4hetu7tWmCYwN/8rfpHWDu+n1YSivofHwJHQ8tIvWjtZiv6SsPryBfrqVZ7XMtPQ+voLrfLUCXYb05HX8UgNTTWkfEwdkRNx8Pmt0ShXeI/t15BvlQUEO/sB79LqP7MW3nOwx4fgTrX/nSJvy34BfsR3YNW+Wk5+AXVHdeAug/vD+H6hmNvo5HsA9FaXnVn4vT8/AIss0/7cfcxsRdb9PzhfvZ/rI1/V7hAYz+aQ7DV80gtLNtw9sY6Ic5w5p/zJk5ONSRZvf+t9JozUeEvDsDY7A1f0lOjjRc/R7hK97Bra9thyYrv4DgGpVKoK8XmXkFujgtGzVg+wGtTG0/eJKSsgquFek7IInnr2Aymwmv9e6yrhUT7ONR/TnI252sWnnskUFd2XDwNP1nLGHKwjU8P0y/lAxgy+Gz3N7R1j5Z10oI9rY2poO83ckqqKV/exc2JJyh/8zPmLJoHc//K662DFsOn+P2Wo3vrPwign2to/+Bvp5k5hfq4rRsGMz2qo7a9kOnKSmv4FpxKb8Fu+v/Ftvf3pUNB5PoP/NTpnz0Y922OXKO22NtOyZZRWUE1+hEBnm6klWoT9sjvduw4dhF+r/1A1O+juf5QdZBjcQrOdzz/gb+9eFPvDi4k362hDp8f0GutjSlDrR6JdC2Xpn8Jq5Pf4Rp14/62RJA9g1Aya3lm/30s0JySDiGBmF4zH4fj9cWYmxftQxRknAZ8yilX9Y9Eg5afaTU8P1qfjayt77sykFhyEFhuD73Dq7TF2CIth10c+jcC9MB23o3q6ScIA/n6s9BHs5kl1TYxANIKywjraCMTlUzIu0aeNMxzJd+H++k/5KddGvkR1Nffac0K79Q7xv8vMjMr+0bQth+QBuU237wRD2+IQWT2UJ4Lb+VlV/42/J/QlX+Tzil5f+i35b/ZW8/lGs13u+1HJu2gxTYADkgFNcn5uM67UAr2H4AACAASURBVC0MrWK0uGkXMbaKgapl58bmbZG89XnDGOSPqUa7ypyRg7EO3+zR71Ya/7iQBgtq+GZJIvC5f5M9/5M60y65e6MWWesVtSgfyb2edomnH7KXP0qK1qmTfYKgohTHuyfjPOZlHOLusxnwDQoJIDPN2onNTM8iMET/fL+F1h2icHBw4Mql1JtHFvxPcLOOSWNVVd9QVTXj+heqqmaoqvoG0Ki+iyRJekiSpARJkhLSSq7WF+134xvoS9PIJhzceeOG2s0wtumG5eR+3aixWphL2cLnKFvwBMb2PcHN6wYKN9LujuXEPlD1PXzJwxunfz1GxfcLf9dotY1+dFcspw9Uaxg73Ybl/DGdA/lDGAzIwaGUzJtG6Uev4fLgNHB1A9mAsUVrylYspviVR5EDQnDoMeD3aUsSTV4dx6VXbfe+uHdoBhaFg+0e4lDnRwl9ZDBODQPrEPltxA65lfC2TdnxsbbH4uyu45zecYSp389i1HuPcenwOVTljx3St/+rrfwn7kk2v76cXo8N+cNp/K30GtqbZm2b893i7/4SvaNfbuOTHk/xy7wV3DJVS39J1jUWd32Cr+54kfjZyxj03qM4urvcRMmW4vh9XOw7lstDJlG65wjB856uDrvYdwwp900l4+k3CJz+CA7hIb9bf9rIQSQkXWDY9AUcOn2BQF9PZNnqvrLzC5nx0QpmPXyf7vvfyqaEM9zVJZotr/2bDx4dwotLN+lmbhMvpuPsaKRZg98/mAKw6dBZ7uoSyZbZ4/ngkcG8+NUWvf6lDJwdHWjWoP5Oan1Mu38ACUmXGDZzIYeSLhHo44ks2Y7+/1Hsrb/p0Bnu6hLFltkT+GDS3XXbxuFP2P74Je7q0JQtTw/lg1G9ePG7PdX6bcL9+f6xQSx7eACf7jpJhcnyh5/D2LY7lpP7bOuVD5+h7N2pGDvE/bF6xWBADgmj6JUnKFkwC7eHn0ZydcdpwBBMh/fpBp3+EAYZOSiU0reepmzJPFzGPAku1s6e5OWLHNoY88k/V+9uPpNB3+ZBGGQt76RcK+ViXgmbJ/Zk88SeHLiSx+HU/Juo2DJt5J0knL7AsOffqfINXra+YeEKZj0y7A/5hmkPDNTy/4sfWvO//Nflf0k2IAU0oPS96ZR98SbO9z8GLm5Yko5gPpWA65Nv4jz2GSyXkkD9/fmzeMd+LvQdx6W7H6Vkz2GCX38KAO8Rd1Ky82D13pM/gyGyM+azCda8L8vIYc0xxa+i/KvZSN4BGFrf+qfvUxv/QD/mvv8SLz0xB/VPtKv+KdT/8r9/ipvtMbksSdKzwFJVVTMBJEkKAsYBV+q7SFXVj4GPAXqH9VNBGxmu2RsOCPYnJ/33FYjeg+P4ddNuLGbbwqkW6WcxJE9f1MK6G+uGNrdQud52g6+mk4+SdQVDo0gsp7RpydozJLVH0XTabbtTua7WCISTC05jplO5dTnKlXO/Le1FdTtoQ/QtVG78wvo5rDlyw5YYO96G5OgMBiOqqRzTdusGRzU/B8nXanvZNwA1X297JS8by4XTYLGg5mSgZFzFEBSGmp+NJSVZWwYGmA7vxhARhYmN1ddWpufhWKPR4BjiR0W61fYGdxdcW4bT+vtXtfAAb1otfY7TY98g4J4e5O84gmq2YMoppPDgGdzbR1CRYh1JKcjMw7tGg807xJeCTNt327x7a26bMpQPh7+KpdJc/f22D9ew7UNtM/6oBY+RfSFdd11hZj5eNfQ969G/TuK6vdw9Zzx/pLuQm5FLQA1b+Yf4k5tpm5fa3dqe4VOG8/yw5zDXeJa6KMrIx6OBdRTXPcSXosz6K/iktfvo99qDAFgqzVgqtRHszMRLFFzOwqdpMJnHL1bHN2flYgy25h9jkD+mWmlWrhVV/1/w7Sb8n56gux7AdDWD0gPHcWoVgemK9R0E+niRkWtdPpKVV1C9udQax5N3nhwDQGl5BdsOJlavFS8uLWfKm5/z2LABtG1uO14S6O1ORr41fZnXign01o/M/rDnBAun3ANAu6YNqDCZuVZShq+HK6A1ngfGRlIXgd5uZNSYBci8VkxgreVIP+w7xcJJd2n6TUKoMFv0+ofPMTC2ua22jwcZNWaPsvIKCfLxrBXHk3emPmC1TcIp3Tr6G2F3/d9i+70nWfio1lFu1ySkHtvbzpYABHq4kFFgHR3PLCwl0NNVr3/4AgvH9NL0GwZoti+twNfdOtLfNMALV0cHzmddIzrU6gtsfL+XX72DQIY23ahc92mdYWpRPkrmFQyNI7VBsSqUvGxkv1q+OVff0VBzszGfOwUWC0pWBpb0K8ghoRhaROHQqi1OA4YgObsgGY1QXkbZso+t117L0Q4tuZ5+nwCUa/qyq+bnYLmQZPX9mVeRg0JRLml7AR069sR8ZI+2NK0WgW7OZBaVV3/OLConwK3upX2bz2bwfO9W1Z93nM+kTYgXro5aM6R7Y3+Op18jJtQ6Kh/o46n3DbkFBPnU8g2+XrwzbSxQlT8P1PIN8z/jseED6/YNPp6/Lf8/PsKqf/Dkb87/yrVcHGrMcsje/jZtB+VaLpbLZ0CxoOZlomSlIQc0QEk5R+WWVVRu0RanOI95GiUrTXetOTMHhxrtKmOwf/UBJFb9Gr559WYCqnyzS/tWuMRG4z3iTiRXZyQHB5SScijQDi1Ri6/pZgclDx/U4rrrFWNkZyq3WffBXm9HqQVaXracO4LcIAJLonXzfmZ6NkENrIOQQSGBulU1N8PN3ZUPv36b919fzPHDJ29+geB/hpsNHwwH/ICdkiTlS5KUB8QDvsCw33OjpGNnCG0SSnB4MEYHI33u7sWerbanI92IPnf3ZvuPttPJAEpqMrJvsDbVaTBgaNMNc9Ihm3iSfwMkZ3eUK9YN2JKnLxgdtA/ObhgaRqLkWB2Aknoe2S8EyScQDEYMbbtjTrIdPZL8GyC5uKGkWLUxGHEe+QzmIzu10bQ6037BmnbZgCG6K+azdaTdL0TTv2rt3FT8sJCyBY9T9t4TVG79BvOxXbpOCYDlYhKGoFAk/2AwGHHo0hvTEf3JKubDuzFGautHJXdP5OAwlKx0LBfOILm6I3lolYExqoNu0zxA0dHzuDQNwalhIJKDkYAh3cnbYt0MaCkq5UD0eA51epRDnR6l6PA5To99g+JjyVSk5uB1q3aamezqhEdsc8rO6Z3vlWPJBDQOxjcsAIODgQ6Du3Fiq94+odGNuW/uv/l04psU51qn4iVZwrWqIRQS2ZCQyIac2XVcd23qsWT8GgfjU6XfdvAtJNXS92scXP1/yz4dyL2UwR/h7LGzNGgSSlB4EEYHIz0H92T/1v26OE2jmzJl3hRmT5hFQW5BPUpWMo5dwKdJMF7hAcgOBiIHdyV5q37JnHdj62bRpn3bk1+VfhdfD6Sq0T+vhgF4Nwmi4LJ+jXh54hkcGjXAGBoEDkY874ijZIc+LxsCanSM+nSl8oK2+VL2dEdy0MqW7O2JS0yUzcbM6IgwUjJyuZqVh8lsZtPeY8TFttLFyS8sqV5n/OmPOxgSpy01NJnNPPnOlwzuEUO/Lm3rtE90o2BSsvJJzSnAZLaw+dAZ4to01cUJ8fVkf5KWrgsZuVSaLfhUzRwpisqWw2cZ2LHuxnF0wyBSsq+Rmlulf/gscW30JxOF+Lizv+owiAsZeVSaaukfOcfAOvZQRDcJJSUzj6vZ+Zpt9icS10HfQcovqmGb9bsY0rNDnemsM+321r9um2rbn7W1vY8H+89o41z12qaOvT0A0aF+pOQVkZpfrOknXiYuMlSv7+XK/gva8tkL2QVUmhV83JxIzS/GbNGeK+1aCZdyCmngrd/7o6Qma75fV6/U4/ud3W5crzRqqatXACznzyCHhCEHBoPRiEP3PlQm6H1z5cFfMUZX+WYPLwwh4SiZ6ZS+9xoFk4ZTOPl+yr76iIpftug6JQCWS2eQA2v4/k5xmI/p613TkT0YWmr77iR3T+SgsOqBKABj5951LuMCiA72JOVaKakFpZgsCpvPZtArwnbG+2JeCYXlJtqFWDsVwR4uHLqaj1lRMFkUDqfm08RXb//oiHBSMnJq+IajxMVG6eLofcPPDOlVwzf8ZymDe8TW7xuahup9z75E4mJukP/X/cKQuJg6tepCSTmLHNAAyTcIDEaMMT0xJ+r9vTlxL8ZmbQCQ3DyRAxug5GRoS6ZdtWWQcoPGyA2aYEnS+/XyxLM4NGqAQ5Vv9rgjjuKfa/tma0fPvU9XKpO1spb+zHwu9BnLhb7jyJ7/CYU/biPnP9YBWyX9IpJPEJKXv7ZyIrILlvNHbZ5R8g0GZzeUNOu+PSXjIpKTK7ho6Tc0bKXbNA9w8uhpGjUNJ7RhCEYHIwOH3Eb8FttTx+rC6GDk3c/fYN3qjdUndQn+/+GGMyaqquZLkvQ5sBXYp6pq9bCgJEkDgU2/9UaKReG9mR8wf9k8ZFlm48rNXDp7mQefHsuZY2fZs3UvLdu1YPYnr+Du5c4t/bry4LQxPNj33wAEhQUR0CCAY3uP13MDhcoNn+M85gXtyN3DO1Czr+LQ5z6U1AtYzmgNTWObbphP6B2/FBCK84BRqGgb8ky716NmXdFrr/sU53Ezqo4i3oGadRWHvsNRUpOxVFVUxrbdMR+vdYRx61uQG7fC6OqBMUZbt1753Yco6ZdqGFqhcuMXOI98TtM/uhM1OxWHXveipF3EclZzRrU3vf9mFIWyr97H7Zk3tCMLf9mIknoZp6HjsFw6g/nIXsyJBzG27oj73M9AsVC+8mPUEq2BX75iMW7PaccXWi6dozJ+g17fonDhhU+IXv4iGGSylv9M2ZmrNHx2OMVHk8nbUv8SgPTPNtF8wWQ67HwHJMhasYPS0/qOj2JR+P6lz3noyxeQDTIHVu0g89xVBj55H1cSL3By2yEGTx+Jk6sTYxc+AUB+ag6f/fstDA5Gpqx+BdA2sC978gMUi2Kjv+6lLxj35fNIBpnDq+LJOpdK3yf/RWriBZK2Habr2P5EdG+NYjZTVlDCt09Z13U//esCnNxdMDgYadU/ls9Hv6470av2vRbN/IhZX81GNshsXbmVlLMpjJw2inOJ5ziwdT/jZ0zA2dWZ5z+aDkB2WjazJ8yq14aqRWH7zKXc+9WzyAaZxJU7yT2bSvdp95KReJHkrYfpMK4/jW6NRjFZKC8oYeO0xQCEdYmk+1P3opgsqIrK1hc+p7xAvz4bi0L2nIWEffIayDKF32+h8vxl/B4bTfmJc5Ts2IfPqLtx69MVzBYsBUVkTH8bAMem4QS9OhUUFWSJvCWrbDomRoOB6ePuZtLrn6IoCkN6daJZWDAfrt5CdNMwesVGkXA6mfdWbAJJIjayCS88qI2wb953nMNJFykoLmXtL1oZn/XwMCIbN6ihL/P8sD5M+vB7FEXl7luiadbAn4Xr9xDVMIhebSOYdk9PZn2zlWU7DgMSr44eUH1s6aHzVwn28SDMv+7NlUaDzPP/imPSQu1I07u7RtEsxI+FG/YR1TCQXm2aMm1ID2at+JllO46AJPHqyNus+smpBHu7E+Zvu8zHaDAwffQgJr35pWabnjE0Cwvkw++3E904lF4xkSQkXeK91VsBidiWjXhhzJ3V14977RMupedQWl5Jvyfe4pUJd9O9TfO/UV/m+ft6MWnhGhRVrWGbvZrt2zRl2tAezFq+vco28Oqofnrb+HjUaZtq/UEdmfTlDu3dxjSlWaA3C7cfJyrUl16RYUwbGMOsH/ezbE+Spj+0K5IkceRyNp/tOoXRICFLEtPv7IiPm7P+BopC5frPcB47w1qvZF3Foc8wlLRkLEnX65Uah6lUIQWE4jxwDCoqEhKm3etQM2stNFAslH66APcZb4IsU7ljI8rVSzgPfxBL8hlMCXswHz2AQ7uOeL7zBSgKpV8tQi3W74OoF0Wh/JsPcH1iLpIkU7l7M0raZZzuGoPl8lnMx/ZhOZmAMToWt1eXaPG/XYJaoo2yS35ByD4BWM7WXe8aZZnnekfy6A+HtfcbHUqEnzsL954nKtCzupOy+Uw6A1oG644Cvq15EAev5DHsq70gQbdG/sQ11XdqNN8whEnzllT5hs40Cw/mw9WbiW4SRq+O0VW+QZvBj23VlBceHKrdc+8xDiddoKC4hLW/aANlsx4ZTmTjUL3+mDuZ9ObSqvwfS7OwID78bhvRTULpFdOKhNMXeW/VVpAgtmVjXhg7uPr6cbOXcCk9W8v/U+fzysShdG9bY+ZTUSj/dhGuj87S6t59W1EyUnC8YySWlHNYThzAcvowxsgYXF9YCIpCxY+fQ2mRdhLXE1WnMZaXUv7VW9pG+JpYFLJmf0TYp3NANlDw3RYqz6dU+eazlOzYj8/ou3Hv3RXVYkGp4ZtviqpQue1rnP41Tcv7ib+i5qbh0H0ISsYlLMlaJ8UY2QVL0oFa16pUxq/EefjTgISSeQnzsZ36pFsszH3hbT5a/i4Gg8ya5etJPnORR5/9N6eOniZ+y69Et2/Fu5+9jqe3B3H9bmXSMxO5J24kA+7qS0zX9nj5eHLX8DsAmPn4HM6ctF2R8t+MOC64bqQbrcuTJGkqMBk4DbQHHldV9ceqsMOqqt506OD6Ui57sX7871+T/buoY/r6L8Op/tM8/grMyX9sVP+3cmJz3Rvh/iq+c7bvadYudv590WPKzWc7/gy9sK/973L782uPb0TDrx+5eaQ/gZpv3/xffeKSnZC8fv9G0P8Wav9ewV9O/p/cV3ETlBP2XRpSmVT/yVV/BQZv+9Ytxpi6Z7D+KuQu/eyqj7nuDfp/FaavbfdT/pWkbbFfuyR8wu/fA/h76Pr26ZtH+pMcz9j7120CsiMPN77vv7pnsvjS6n/Ejjdr+f0biFVVtViSpMbAt5IkNVZVdQHUcbaiQCAQCAQCgUAgEPwBbtYxka8v31JV9ZIkSb3QOieNEB0TgUAgEAgEAoHgd/PHzgf9/5+brWXJlCSp+hd1qjopdwL+QBt7JkwgEAgEAoFAIBD83+FmHZMxgG6htqqqZlVVxwA9675EIBAIBAKBQCAQCH4fNzuVq95fR1RVdfdfnxyBQCAQCAQCgeD/b1RxKled2PdYIoFAIBAIBAKBQCD4DYiOiUAgEAgEAoFAIPjHse8PRQgEAoFAIBAIBAId4lSuuhEzJgKBQCAQCAQCgeAfx+4zJt2M9v71Yvv2OaWwYPvqe3jaTdsxtpPdtAFi7rXvL1/HlBTZVd/uOLvYV9/V3b76Tq52lbf7L7PbG7N987969qj9tHNy7KYNgMV+v0wNIDVsZFd9430T7KpvsPMvjyu7frKrvuTqZld99dR+u+pjMtlV3tCqiV31wxuW2VVfvm2I3bQtb822m7bg/w/EUq5/EHt2SgQCgUAgEAh+D/bslAj0iFO56kYs5RIIBAKBQCAQCAT/OKJjIhAIBAKBQCAQCP5xRMdEIBAIBAKBQCAQ/OOIPSYCgUAgEAgEAsHfiDguuG7EjIlAIBAIBAKBQCD4xxEdE4FAIBAIBAKBQPCPI5ZyCQQCgUAgEAgEfyOKKo4LrgsxYyIQCAQCgUAgEAj+cUTHRCAQCAQCgUAgEPzj/K1LuZrHtWXQS2OQDTIJK3fwy0frdOGdR/aly+h+qIpCRUkFa6Z/Qvb5VFy83Rnx0eOEto3gyLe/sO7lL+rUNzRrh+MdY0GSMR/+GdOutbpwx4FjkJtEASA5OCG5eVI6bwKSlz9ODzwFkoRkMGDatxlzwjYb/d2XcnhzZxKKojKkdRjjOzXRhb+1M4mDV/IBKDdbyCutZNejfQB4d9dZdl3MRlWhSyM/no1riVRT+3wG8zcfRVFVhnZowvjukTrt9IJSZv54kKJyE4qqMrVPa3o0D2HvhUze256IyaLgYJB58ra2dG4SaJv20ynMX/MriqIytGsrxveN0evnFzHzm58pKq9EURSmDupKj6hGAJxNy2XO6p0Ul1ciSxLLnrwXJwd91tmddIX5a/dq+p1bMr5P+1r6xcxcGU9RWSWKojL1jk70aNWQ1Lwi7nlzNY0CvABo2yiQF+/tYZv+c+nM33RY049pyvgeUXr9ayXMXLO/Kv0qU29rR48WDdibnMF7245Z7dOvPZ2bBv39+meuMn/dfu39dmrB+F5ta+kXM3PVLs0+qsrUgbH0iAzXhd/znx945Lb2jO3Zxlb/1GXmf/+Llv5bohjfr6NeP6+ImV9vpaisQtMf3I0e0Y3ZcPAMS38+XB3vXFoOy5+5n8iwAL3+iQvMX7UdRVEYems7xg/sWku/kJmfb6CorFyzz9A4erSJwGS2MPvrTZy6nIEsSzwz7DY6tWxom/6Tl5j/bbym37014/t3ttX/crOWfkVl6t230qN1EzYcOM3SbYdqpD+b5c+NJDI88O/Vt3f5Ss5g/pbjWv5p35jx3Vrq9QtKmbkuweoferemR7NgrpVW8PT3+zmZls9dbRsxfaC+XALIjaNx7HU/yDLmxF2YD27ShTvEDcMQXuWPHByRXDwoW/g4AJKHL479xiB5+AIqFT+8h1qYW0u/NY59R4AkYT6+C/OBn/T6ve/H0LBK3+iI5OpJ2ftTrPoDx2n6KlR8946N/u5zacz/6ZBmm5gIxveM1tvmWgkzv99rtU2/dvRoEUri1Rxmrz2gRVLhkd5t6BMVTm1+PXKKNz7/HkVRuKfvLUwY2k8Xnpadx0sLvyG/sBgvd1fmTh1NsJ8Padl5PPHmJ6iKitli4YHbezKs/602+ruPJvHGl2u1vNm7MxPu7lNLP5+XF6+y6k9+gCA/b5IupfLaZ99TXFqBQZaYOLQvA2+p4/02isIxbphWL57cjTlhs97+Pe/DENaihv09KFs0DTmsBY4976uOJ/kEU7nxEywXjunTn5zJ/K1VebNdo3ry5iGKKkxa2eodXSNvHuBkelXeHNDOJu1g/7pRd68LmczfloiiwNB2DRl/Swvbe204Yr1Xryh6RNj6e53mX91ukCTd9XLjaBz7PKC938RdmA9s1IU79BqOoWHVO7levj6YClSVrwFjreX3uwX6tB9N4o0v1mh5s08XJgzpqwtPy87j5UUryS8s0fLmlBEE+XmTlp3Hk299gaqqmCwWHhh4K8P6dbOxza29u/L8nGkYDDLfLVvLJ+9/qQuP7dqe52c/SYuoZjzz8Ey2rP+5Omzx8ndpG9uawweOMXnUUzd6Bf+1iIVcdfO3dUwkWWLwrAf5fNQ8CjNymbR2Dqe3Hib7fGp1nGM/7uHAsu0ARN4Wwx0zR7F07BuYK0xse/tbglqGEdTCtuLQbiDheOd4ype+hlqYi/PDczEnHULNtupXbrJmemOXAcghjQFQi/MpXzITLGZwdMJl8ltYzhxCLcqvjm9RVF7fcZqP7oklyN2Zkcv3Edc0gAg/9+o4T8dZHebyoymcySoE4GjaNY6mXWPVKK1gPrjqAIeu5tMpyqtae96mIywa2YMgT1dGfrKduBYNiAjwrNZbsus0/aPCGNYxguTsQqYs/5WNzUPwcXFkwf3dCfRw4XxWAZO+2cXWJ+7UmcaiKMz7fheLHhlMkJcbI9/5jrjoxkQE+1r1tx6if/sIhnVvTXJGHlOW/MTGqEaYLQozlm1jzoi+tAz151pJOUaDbKv/w24WPXSHpv/eGuKiGxER5GPV336E/m2bMqxbFMmZ+Uz5dBMbW2kN1DA/T1ZNu7fu93pd/6cEFo3uTZCnCyOXbCWuZSgRgV5W/V9O0j86nGGdmpOcVcCUZTvZ2OIufFydWPBATwI9XTifeY1JX+9k61N3//36P+5j0YQBBHm5MvKDdcS1akhEkLdV/+dj9G/bhGFdI0nOvMaUz7ey8XlrXn97/QG6twyr3z6r41k0eQhB3u6MfGslca2bEhFS4/1uOUj/Ds0Z1qMNyel5TFm8lo3R4xjUqSWDOmmV1rm0HJ5cssGmU2JRFOYt38qiJ4YT5OPByHlLiWvbjIgG/lb9DXvo3zGSYXEdSE7LYcoHq9nYZhLf7dIaMd++PIG8whImv7+aZdPHIsuSXn/Vzyx67B6CvD0YOf8b4tpEEBHiZ9XftJ/+MS0Y1rMdyem5TFm4ho2tJzCocysGdW6lpT81hyc/XmvTafhb9O1avlTmbTrGohG3avnzsx3ENQ/R+4dfk+jfKoxhsU01/7ByDxunDMTJaGByXBTnswo5n11om3kkCcc+I7QGf1E+ziNnYEk+hpqXXh3FtHMVpqr/je37IAda86XjwPGY9m9ASTkNDk5Qe820JOHYbxQVq95GLcrDefRLWJKPouamWfV3rLDqd+iLHGTtuDreMRHTvvUol0/VqW9RFOatT2DR2D6abRZvJi4yTF92d56gf+tGDOtcVXa/jmfjtFCaBXrzzcMDMRpksovKGLbwJ3q2DNXZ32JRmPvpaj6eOZkgX28emP4WvTq2JiI8pDrO21+uYXBcJ+7u1YX9iWd5b9k65k4dQ4C3J1+/9iSODg6UllVwz1Pz6NWxDYG+Xrr0z/38Bxa/8BBBfl6MmPEevWKjiQizNnb/s2w9g3vEcldcR/afOM+CFRuZO/kBnJ0cmTPpfhqFBJCVV8ADMxbQrW1LPN1c9Pbv9QAVPyxALc7H+f7pWC4c17/fX1Zb7d+uF3KA9n6Vq2cp/+Y1LcDJFZdxs7GknKplf5V5m4+x6IHumv0/ryNv7j5D/1ah1ry5ai8bmwVX5c1WnM8uqjtvYv+60eZeW46z6P5uBHm4MPKLncQ1DybCv8a99pylf2QDhsU0ITmnkCmr9rHx0f431Pyr2w0dw61+BUnC8baRVKz+j1Z+R71YVb5qvN/4lTXKVx/kwJrlawKmfRt05eu6Z7YoCnM/+57FMx7W8ub0d+nVMZqIsODq6//z1ToG9+zIXXGd2H/iHAuW/8TcKSMI8PHkqzlTcXQwUlpewb1Pv0mv2Ghd3pdlmRmvP8O/hz1GZloWKzd/RYbhJAAAIABJREFUwY7Nu0g+e7E6TnpqJjMen824SSNtbPvZwq9xcXHmvjFD67W/4H+TP7yUS5KkjTePZSWsfTPyLmeSfyULi8nC8XV7adU/Vhenoris+n9HV2slZCqr4HLCGUwVJupDDmuGkpeBmp8FFguWxD0YIzvWG9/YpjvmxD3aB4tF65QAGByg1ogEwImMAsK9XAnzcsXBIDOgRTDxyVn16m86k87AllrlJQGVFgsmRaHSomBWVHzdHK3aaXmE+7gT5uOuaUeHE38mTacnSVBS9fzFFSYCPJwBiAzxIdBDq4giAjypMFmoNFv0aU/JItzfizA/TxyMBgZ0aEb8iUt6fSRKyqv0yysJ8HIFYO+ZKzQP8aNlqNYI9XZzxiDLtfSzCff3tOq3jyD+5OVa+lBSUanpl1US4Olar+1qcyI1j3BfD8J83TX91g2JP5OqiyNJEiUV5hr2cbHax7PKPoFeddvH3vpXcgj38yDMz0PTb9eU+FMptvYpr7JPeSUBntbGxc8nL9PA14OIQG/q4sTlTMIDvAnz99L0Y1oQn3jhBvoVBHi62ehsPHSWAbEtbL4/cTGd8EBvwgK8Nf2OrYg/dq6WfaCkrELTL6sgwEureC+k59A5UpsZ8PV0w8PFmZOX0/X6lzKq0l+lH9uS+OPJtdIvWdNfVkGAVx3pT0hiQGxLm+/trm/v8pWWR7ivG2E+bpp/iAoj/qzehjb+wV3zDy6ORjqE++NoNNikG0AOboJ6LRu1IAcUC+akgxgibEfdr2OI7IQ5SZtlkHxDQJa1TgmAqQLMlXr9kKao+VmoBdlV+vsxNLuBfqsumE/v1/T9GoBs0BpN9eifuJpLuK+7tey2aUR80tVatpGstimvrC67Lo7G6k5IpdmCRB1+//xlGgYHEBbkj4ODkYHdY9iRkKiLc+FqBl1aa+Wmc+vm1eEODkYcHRyq9M0oiu346InzKYQH+xMW5IeD0cjAW9oTn3BSFyf5aiadWzfT9KMjiD+khTcOCaBRiDaIEOjrha+nO/mFxbpr5aDGqAVZqIVV7/fsQQxN9bO1NTG06IT5bILt981jsFw6CWZ9HazVXbXy5rlaeRMoqazhO23yZv3NEHvXjbp7pedrz+J9/VlCiT+XYXuv6mcxV9+rXk07thugqvzmZ9UovwduUn47W8uvXwhIcr3l68T5FMKD/Kx5s1sH4g/WypupmXSOvp43mxGfcAIAB6MRx6pZ30pT3Xm/TUwUVy5e5erlNEwmMz+t2UrvgT11cdKupHP21HlUxfYXP/bvSqCkuLTeZxX873LDGRNJkmLqCwLqz/114BnkQ0GadQq+MD2P8PbNbOJ1Gd2P7hPvwOBg5LMRr/1mfcnDF7XAqq8W5iGH2eoDSF7+SD4BKBdOWL/z9MN51LNIvsFUblmmmy0ByCopJ6iGEwrycOZERkGd+mmFZaQVlNGpamSjXQNvOob50u/jnQAMbxdOU1/riElWYRnBNRqiQZ4uJKbm6TQf6RnFpGW7WH4wmTKTmcWj9AUYYNvpVFqF+Ng0QrIKSgj2tja0grzdSLysd46PDOzIpEXrWf5rImWVJhY/chcAl7OvIUkSkxavJ7+4jAEdmvFgnw56/cISgr2tzxPk5UZiSi39/rFMWvITy3ef0vQfuqM6LDWviOHvfI+7swOTB3QkpmmI7lrNPtaOTJCnC4lXa9mnV2smfRXP8v1nNfuM6W1rn1NX67aP3fVLCa7R0A3yciXxSrZe/7YOTPp0M8v3nKas0sziif+PvfMOj6po//d9djeF9F5IoSS0BAgdpCWANJFieREFG4qvKKhYUbGhgoKiKKBYXiuiYEGQ3gnF0CFAIKQRCOm9Z8v5/XFCNie7IYis4O87N9deF9mZ85k5c2bmzDPzzOxwACqq9Xy9M55PHxrON7tOYI2cogbl7+FC/Dn1C/XRkb2ZuuR3lu86pug/Ps5CZ9Phs3w4xXJGMaeolABP86yhv6cr8anqwcejo/sz9cOfWL79kPJ8n5oAQNtgP3YcS2JEzwiyC0s4lZ5FdmEpnVrV1y8jwNNVnf+0Bvkf1Yepi35l+c6jVFbrWfqE5QrbpsOJfPjfMVbyb2N9W7ev0ioCXJvoHwZ0YOryPSw/mEyl3sjSeyxdhqwhuXggl5q15LJCNIGtrMd19ULj5oPp/GkANJ7+UF2J/eipaNx9MKYnoI/9RbWqYaFfWogmsLV1fTdvNO4+dYaOol+B/djHFf1zp9Dv+lmln1NaqW5bbk7EX8hTl82gTkz9ZhvL484odf8BsztK/Pk8XlsVR2ZxOW/ffpPFalV2QRH+3uYJAX8vD+LPqidd2rYIYkvcMSaNimHr/uOUV1ZTVFqOh6szWXmFPD53Keezcnn63rGqGWOAnMISAurp+3m7E5+knrRo1yKQrfvjmThyAFsPnFDp191HUjp6g5EQf2/VtZKLp+pdJpcVoQm4zPN1Nz/f+uja9kB/ZKvF9zmlVep3l2sz4i+q352PDmxQN+/uZzV9a9j63WhxL65N3Ev/9kz9aS/LD6VQWWNk6d2W7kkqTRuOGwAk14bP93Lty8t6+xrzWG37PYV+1y/mvBcUX0HdbK7UzVsGsnV/vEXdn/bul5zPymPGpFst6r5/gB+ZF7Pr/s6+mEPnbmo3zP/fMQlnLqs0tWJyAHgPeL/B5z3A+vTt3yTuu80siJ7BxneWEzPdcvB0LdB16ovxZJzqBSeX5FO55AUqFz6FrstAcHa/jMLl2XgmiyFt/NHWuqukF1WQWlDOxocHsvHhgew/X8DhjMImVNRsOHmeMVEt2fTUKBbd3Z9Zq/arjppLyilm4bZ4Zt3SmC3ZhP7hJMb0asem1+5j0ZRRzPphKyaTjNEkcyQ1kzkTh/DV9HFsj08lLvFC04IN9Y8kMaZHWzbNuodFk0cwa/kOTCYZXzcnNrx8Nz/NuJ1nRvfhxR+2U1ZV07RgQ/34c4zp0opNz4xl0cRoZv36p2qWJimnmIVbjjJrdOOraNdV/1gKY7q3YdNLd7HowaHMWqHsF/l0yxEm9o/EycHuqnTr9A8lMqZ3eza9OZlFj45m1nebVPmPT8vC0d6O8Obel1G5jP7+U4zp24lN7z7Oomn/YdZXfyg+1f064+/pyj1zvmH+iq1EhQWp3LiuWP/gGcb0jmTT21NY9Ng4Zn2zQZ3/1Ewc7XWE13Mvu6H0bd2+Tl1gTOcWbHriFhbd1ZdZqw9e86Mote17YTh72NxvajRogsLR71pJ1bK3kdx90EZe+aDTqn7iQbV+cBv0O1ZQ9d2bSB6+aDtemcFVnw3H0xjTtTWbnr2NRZNimPXL3rpn2ynEh1+nj2LZf4fzZexJqvWNz6g3xjP3jePQqSTGP/cuB08m4eflXlfHA3w8+eX9mfzx8aus3rGf/CLrLkuX4+mJt3IwIYXxMz/gUEJKrb751Z1bWMLLS35k9qPjVd//VbRte6if7yWc3NB4B2E6d9L6hU2w4eR5xnQOZdP0kSwafxOzVh+6pnXT1u9GVVqnLjCmYyibHh/OovF9mLXm2t2LLcYN9VHa16F67UurtK+dK6j6/i0kd9+/3H6fnjSag6dSGP/C+xZ1M8DHk5/nP8uahS+yeudB8otKrzrvgv9bNNWLJQD/lWV5UMMPkNfYRZIkPSJJ0kFJkg4eKU0CoCS7EPd6gx63QC+KswsakyB+zT4ihl75IE8uLUByN+tLbl7IJdb1tZ1uMrtxWegUYso5j7aFeoOdn7Mj2aVVdX9nl1bh6+xgVWNjYlbdcizA9qRsOgW642Svw8leR7+WPhzPLDJruzUjq8TsxpZdUlm3BH2J346kMSxC2WMQFexNtcFEUUV1bfwKnl65jzfH9iSkwYwKgJ+7M1lF5Wb9onL8Griq/BaXwLAoZYUpqmUA1XoDReWV+Hs40611IJ4uzWhmb0f/DqEkXFDP9vu5OZNVZHYhyC62on/gDMOiWtfq+1NtMFJUUYW9TouHszKjFBHsS7C3G+dy1TNKSvmYl2yzSyrr3KfM5ZPCsEjFNzoqxKdWv7Z8iit4+sfdvHlbH0K8XGmI7fWdyCquV/7FFfi5NSyfswzr3FLRb+FXVz7x5/P4cN1BRr6zkmV7TvHl9uP8uFft5+3n0aD8i8rwc1fXg9/+PMWwrm0U/VaBin65uc5tOHyWEd3bWORd0Xclq9A8oMouLMXPo4H+nuMM6660maiwIKX+lFWg02p4bvwQVrzyIB8+dgelFVW08PNqoO9CVqH5pZVdVGapv/cEw2rdzKJaN6+rn3X5P3SGEd3VbfYf07d1+3J1JKu0if7haBrDIoIU/WBvVf28HHJZUe3GVwVlhr3Ialxdu54Ya91ALl1ryj2vuJHIJoxJR1X+61b1XT2Ry6wPrnTte2GsdeMCc18sF+cq+mePoPFvobrGz7WZum2VVODXwE30t8MpDOuo5Csq1Ndq2bT2dcfJ3o6kHPW9+3t5kJ1v/i67oAg/b/WklZ+XOx889zAr5r/AE3crK45uzk4WccJDAzmUoHYh9PN0I6uefk5+Mf6eVvSfvp8V78xg+l0javWV519WUcW0ef9j+l0j6NxGXTagzKBLrua9fpKLR+Pl37YHxsQD1r9PPgpW3Gn8XB3V767SSvwauDf9duwcwzrUq5tGI0UVVzb5ZOt3o8W9lDZxL8fTzfcS5FWbVuP3YstxAyhtRP18PS28PS6ha9dL3X7r2tel9ntEtb/Lz8v9yurmsw+w4t1nmD5hJIB6j1NtnPCQAA6fVrsXZ2flENjcvJfKv7kf2Vnqvk/wf5OmDJPXLxNnemMXybL8mSzLPWRZ7tHVVXkZZxxLxrtlAJ7BvmjttHQefROnNx9SXefd0rypqt3gruQ3cLe4HKaMZDReAUgevqDVou3UF8PpQxbxJJ/mSI4umM4nmr9z8wJd7Yy0ozPa0PaY8tR+rJEBbqQXVZBRXIHeaGJjYhYxYZYnfKQWlFNSpScq0NyAA1ybcehCIQaTCb3RxOGMQlp5mQcukc09SS8oI6OwXNE+eZ7otmp3pkD3ZsSlKe4hKbkl1BiMeDo5UFJVw/Tle3hycCe6hlifzY0M8SM9t4iM/BL0BiMbjyQR3bGlWt/ThbizykxtSnahou/SjL7tQknKLKCyRo/BaOJQ8kVaB3g10PclPa+EjIJa/aPJREeoByiBHi7Enb2o1nd2pKCsEmPtC+9CfgnpecUEe6sH95HNvUjPLyWjsEzRP5FOdLugBuXjTFxKdm35FNfqO1BSWcP0H3bx5M2d6Rqq3tT9j+kH+5CeX0JGQamifyyF6Aan/wR6OBOXpLhHpeQUUaNXyuerR29h/cz/sH7mf5jYL4KHBnVmQl/1iWGRof61z7dY0T+cSHQntbtGoKdL3Ux8SlaBou+ivEBMJplNR84yopvl/hKAyJaBpOcUkpFXpOgfTCA6Su0mGejlRtxpxcUlJTNP0Xd1orJGT2Xt3qJ9p1LRaTSqTfMAkS0CavVr83/oDNGdWlvRT6/Nf35d/azL/+FERvRoJP+21rd1+7rUPxTV9g+nLlj2D25OxKUqL/WUvBJqDCY8nawPgOpjykpD8vBDcvMBjRZd+54Wpy6BciITDk6YMpPrXZuK5OAEzZQBnzakvWpTO4ApMxXJ0x/J/ZJ+b4xJRy31vQLA0RnTRWv6Sn+gDe1goR8Z5E16Qb22G3+O6PYN265Tg7ZrwtPZgYzCMgxGpe+5WFROWl4JzT3UBmVkeCjnMnO5kJ2PXm9gw57DxPRQn4pXWFKGqbYP++K3zdw2SDmxLiu/kKraul9SVsGR0ym0bK4+wSkyLIT0rDwu5BSgNxjYsO8o0d0jGuiX1+l/+fs2xsX0BEBvMDBjwTeMHtCdob2t7xsxZZ+rfb7eSvm37Ykx5bhFPMnTXyn/zBSLMG3bHhisGCxQWzcLG9TNNlbqZlrDumlvTc66vg3fjaq0Aj1ILyivdy8ZRIcHqOIEujWrdy+l1BiNl70XW44boLb9qtpXL4zJVtqvVwA4OjXSvmrbb2gH1aZ5c93MV+rm3iNE91C7WtWv+1+u2sq4Qcpph9n5RVTVKPt+SsoqOHImlZbN1fd94kgCoa1DCAoNxM5Oxy3jhrJ9465Gy/L/R+Qb/N/1QpKbWIaUJKk9EATEybJcVu/7EbIsb2j8SoWXW95Tl0DbmC6MevVeJK2Gwyt2sGPx7wyZcScZ8Smc3nKYUa/dR1i/jpgMBiqLy1nz6tfknFU2IT+7eyEOLs3Q2umoKinnq3vfITcpg5fuM8/iaNt0wX7k/cqxl4e3o9+1CrvB/8GUkYLxjGKk2A26E3R26Dcvr7tOE9YJh+GTkFE2z+jjNmE4pPjTSoHmwWZsai7v7TyDSZYZGxnEw71as2RfEhF+bnWdzaf7kqg2mniyv3kQYzTJzN2WoCzDStC3hQ/PRrdDcjX77ceezWT+pmOKdlRLpgzowJIdJ4kI9CSmXXOSc0uY/cchKmsMIMFTQzrRNyyAz2MT+HLPaULrzQZ9OnEAXs6O4Fkv76fOMf/3PZhMMmN7tWfK0O4sWb+fiBBfYjq2IjmrgNkrdlJZrVf0R99E33bK4HntwUS+3HoYSYL+HVowY/RNtTdmMOsnpDO/9rjgsb3aMWVIV5ZsPEhEsC8xkS1Izi5k9spYKms7q6dG9aZvu2C2HE9lyaaD6DQaNBqJqcO6E117jCrl5lnu2MSLzN9wBJNsYmzX1kwZGMmSbfFENPcipn0QyTnFzF5zQCkf4KmhUfQND+TznSf5cvcpQuutZHx6bwxeLuqZMJvoO5pnjmJPn2f+H/uV8unRhimDo1iy6TARwT7ERISSnF3E7F/31Ja/xFMje9C3rXqA9cnmIzg56MzHBTuZn3nsyTTm/xqLyWRibJ8IpgzvyZK1fxIR6kdMp9YkZxYw+8dtipEgSTw1ph99a09FO3D2Ah+t3st3z4xXpYeDedY3Nj6Z+SsU96Ox/Tox5Za+LFkdS0SLAGKi2pB8MY/Z32+oNUIknrojhr4RrcjIK+axj1agkZSVl9fuG0nzSzPO9TZaxp5IZf4vinvf2JsimTKiN0v+2EtEqD8xncNIzsxn9g+blfJB4qnbBtC3g1JPDiSe56Pfd/Pdc3fTGDbRrzbPhNqkfeWb/a9jk7KYv/m4oh/Vgin927Nk5ykiAj2IaVvbP6w7TGWN4or01JCO9K09tnrkog2UV+vRG024Otrxyd39aS2Zy17TqqNyXLAkYTixB8P+ddj1HYMp61ydkWJ302jQ2qHf/auqCDShHWqPogVTdjo1m78Fk1E5UKROv5NynKlGgyF+N4Y//8Cu3zhMWWnKTDxg13es0i/v+lmt3yIC+0F3ARKm7DRqNn4DJiNSqHl1IDYxg/nrlaO+x3ZrzZTojizZepyIIC9i2gcrbff3OHPfOawrfcMD+eNoKv+LPYVOK6GRJB6J6cjgDsoz0XQwH4cde/gk877+FaPJxLhBfXjkjuEs/nEtEWGhDOrZiU37jvDRD38gSdCtQxgvP/wf7O3s2HfsNO99uwpJUrxn7h4xgDuHKq4yssG8YhN7JIF5tccFj4vpxZTbhrB45UYiWwUT0yOSzXHH+ehH5ayZ7h1a89KDt2Fvp+OP2EO8tnSF6gSv2Y/eRfuWQZhizUcya1p2VI79lTQYTu3FcGA9dn1GY8o+hzFVMVLset8KOh36PatU5S+5euMw/jmqvnyR+oebSh5mT+7YpCzmbzmOyYRSN/u1q62bnsS0DVTq5vojtX2nxFODI811c/FGdd2c0E85cUtr3gtik3ej3vpBOrHJ2czfEq+k1TmUKX3bsWRXgtLO2gSSnFfC7PXHzGkNiqSvlSOI5Srzysu1HjcAUG81V9Oqk9JGNBoM8XswxK3Frt/Y2vZV2377jlHab6x5DwnUtq8Ypd83ZZ+jZtO3aAaPNuf9SALzvlmluOXG9GLK7TezeMUGIlsHE9OjI5v/PMZHy9eBBN3bt+alh+7A3k7HvuNneP+7NUi1tWbC8H7cebPSr3Uf/mad/oAhfZn55gw0Wg2/LV/DZx9+zbTnH+HksQS2b4ylY5cOLPxqHm4ertRU1ZCXk8/YaKUf/vb3pbQKb4GTczOKCkt4dcZb7NmhrLiezI776/7C14G7W4y7oTeZLD+36rqU42UNE0mSngAeR3Hp6gI8Kcvy77Vhh2VZbtJps75hYgvqGya2oL5hcs216xkmNsHTdnkHVIaJTSj/l/ukOjZrOs7fwenyrgl/G4crPzntqjD89b1ENxT1DBObUM8wudbIeY164l4bjH99r8Zfob5hYgvqGya2oL5hYgvqGya2oL5hYhO0jW9SvyY0YphcK+obJjah3Hb6mptts7f3EvUNE1shDJNrw/UyTJr6HZMpQHdZlsskSWoJ/CxJUktZlheClbMVBQKBQCAQCAQCgeAqaMow0Vxy35JlOU2SpBgU46QFwjARCAQCgUAgEAj+Mrb19/n30tTm92xJkup+r6TWSLkV8AE6NXqVQCAQCAQCgUAgEPwFmjJM7gNUR2PJsmyQZfk+wPJXjAQCgUAgEAgEAoHgKrisK5csy43+0pcsy3uufXYEAoFAIBAIBIL/vxG//G6dq/+ZWIFAIBAIBAKBQCC4RgjDRCAQCAQCgUAgEFx3mjqVSyAQCAQCgUAgEFxDruevq9/IiBUTgUAgEAgEAoFAcN0RholAIBAIBAKBQCC47tjclevdizttq/+OTeWZ0ryfzbSna1Nspg0QeqeDTfXlar1N9bWhATbVx8nJpvKSycY/n+TqYVv9fzslBTaVl1p3tqk+Hr6205bjbacN6Hcdsql+5peZNtXfUHHWpvr9TGU21Y86usCm+oa41TbV17SItK1+QJhN9Y2pR2yqL58/YzNtbXCEzbQBejQLtqn+vwnxA4vWESsmAoFAIBAIBAKB4LojDBOBQCAQCAQCgUBw3RGncgkEAoFAIBAIBP8gsixO5bKGWDERCAQCgUAgEAgE1x1hmAgEAoFAIBAIBILrjnDlEggEAoFAIBAI/kFM4gcWrSJWTAQCgUAgEAgEAsF1RxgmAoFAIBAIBAKB4LojDBOBQCAQCAQCgUDwl5AkaYQkSWckSUqSJGmmlfCnJUk6JUnScUmStkqS1KIpzRtqj8nwYTEsWDAbrUbD/75azrz5i28Y/YjoKMa/+iCSVsOen7ay6ZPfVeFDHhpFvwlDMBqMlBWU8N3zn1CQkUfbmyK585X76+IFhDXny+kLObbpgOp654HdCXjlESSthsKfNpG/dKUq3P2Om/F/YTKG7HwACr5bQ9GKTdg19yX4k1lIGg3otBR+u4bC5est8q9t2xWHMZNB0qA/sAX9jt9U4fa3Pog2rCMAkp0Dkos75a/fC4Dj5FfQhrbFmJZA1ddzmiwrbftuON4+RUnrz83UbP1ZFe4w7mG0bTqZ03J1p+zFuy+rqWkZiX3MBNBoMMTHYjiwQRVuFz0ebUj72j/skZq5UrnkSSUNVy/sh96H5OoFyFT/9hFySb7q+j1puczfkYDJBOM6BjO5V2tV+Hs7EjhwQfkl8Sq9kYLKGmIfuxmAhbFniE3NBWBK7zCGtwu0yP+epEzmbTyKySRzW9dWTO7fQRWeWVzOK6v2U1qtx2SSeWJIZwa0CWRfchYfbYtHbzRhp9Uw4+bO9Grlb6l/Mo15P+/AZDJxW7+OTB7WS61fUMIr326ktLJa0R/bnwEdW7F2fwLfbDH/SvfZi7ksf2Ei7UP81PonUpi3Yqui3z+KySP6WOp/tZbSyipF/7ZoBnQKQ28w8ub3Gzh1LguNRuK58TfTs13oP5//MxnM+2O/Uv492zA5ppNav6iMV1buobSyBpMs88TwbgxoH6wKv/2D33l0SBT3D+xomf+jp3n361VK/gf35qFxQ1ThF3MLeO3TnygsKcfdxYk50+7B39uDi7kFzHjva2RZRm80cveI/owf2tdCX5VWfBLv/rARk2zitgFdeWhUf3VaeUW89tVqCksrcHduxpwpt+Hv5da4XnI28zYfxyTL3BbVgsl926nLpriCV9YcMtfNQZEMCA+gqKKaZ3/dz8nMQsZ0bsGLw6Os6ms79sDx7seQJA01seupWf+TRRxdj4E4jL0PZBnT+RQqP5+Ltl0UjhOm1sXRBIZQufRtDEf2qq516t8d/5cfBY2G4p83UPC5uu90u+1mfJ97GEN2HgBFy9ZQ/PNGs66zEy3XLqVs615y3vzEIm8tojsT/fq9SFoNJ3/cwcEla1ThnSYNpvN9Q5GNJvQVVWyd+SUFZy8C4NM+hMFzJ2Pv2gzZJPPj6FcxVuutlhOAW0xXQt94GLQa8pZvJmvxr6pw7/8MJnjW/eizlL4o5+u15C3f0qheU8yas4Bde/bj5enBqu8/vSqNPQnpzFu1W2lbfToweUg3VXhmYSmv/LCN0qoaTCYTT4zqw4AIZWySeDGft1bupKyqBo0ksWzGHTjYqYclu4+c4t3//YzJZOL2IX156PZhqvCLOQW8uuR7CovLcHd1Ys6T9xPg7VkXXlZRybgn32Zwr868NGX8X7q3a1I+xxJ597s/lL4hpicPjYlW5z+vkNc++8XcXqeOx9/bHYCp735FfPJ5urRtwaJn77cmb/O+7XL83fLpFN2Fia9ORqPVsPOnraz9RD0uGf7QaKInDMFkMFFSUMyXzy8hPyO3LtzRpRlzNy/k8Kb9fPfaF385/evNv/2X3yVJ0gKLgaHABeCAJEmrZVk+VS/aEaCHLMsVkiRNBeYBd11O94YxTDQaDR8tfJsRt9zNhQuZ/LlvHWv+2ERCwtnrri9pJCbMfoiPJr1FYVY+M1fP5fjmg2QlZdTFOX8qjbmjZ6KvqmHgpKHc9uIkvpz2IYn7TjLnlucBcHJ3ZvbOjzm161jDzBH4+lTO3T8LfVYerX/7gNKtf1KTdF4VrWTtLrLeUDd+fW4haf+FRhdOAAAgAElEQVR5BrnGgOTkSNj6JZRujcOQU1D/BnAYN4XKL95ALs6n2bR5GE4dQM65UBel5o+v6v5v1/cWNM1bmdPYuQq9vQN2vdUvhEYKC8c7H6Xik1eQi/JxenoBhhNxmLLN91K9ytyB2A24FW1wa2tK9TQl7AffQ/UvHyCXFuI48WWMyceQCzLr5XEFl173ui6D0fiF1IXZj5iMPm4tpvQEsHOABmeHG00y72w7xSe398Tf1ZGJP+wjOsyPMG+XujjPxpgNieVHznEmtwSA2JQcEnJK+HFSX/RGEw+v3E+/lr64OOjq6ZuYu/4wn06Kxt+tGRO/2EJ0u+aE+brXxfk8NoFhkSGM7xFOcm4x036IZf2Tt+Lp5MDCCf3xc21GUk4xU5ftYvOM0Q3yb2Luim18Ov12/D1cmTjvB6I7hREW6G3W3xDHsG5tGT8wiuTMfKYtWcX6jg8xqlcHRvVS7u1sRh4zPlttMag3mkzMXb6ZT5+6C39PVybO/YbozuGENfcx66/dy7Ae7Rkf3ZXki3lMW7SS9Z2m8kusUtd/fu0hCkrKefzjlSx78X40Gumfzf/qP/n0oWH4uzkxcfFaojuEEObvYdbfdpxhnVowvk97krOLmPb1Fta3v7Mu/P21B+nXNghrGE0m5vzvV5a+/F/8vd2558UPiekRSVhwQF2cBd+tYfTAHoyJ7kncibMsXL6OOdPuwdfTje/eegJ7Ox0VVdXc8ex8YrpH4ufl3nha369n6TOT8Pdy457ZXxDTpR1hQb7mtFZsZnTfKMb0iyIuIZWFv2xlzpTbGtGTmbvxGJ/e3U+pm19tJ7pNIGG+ZkPm8z1nGNYhiPHdW5OcW8K0FftYHx6Ag07L49EdSMotJam2PVggaWg2cTrl77+AXJiH8yuLMBzdhykzvS6Kxi8Ih1F3Uz73KagoQ3JVnovxzDHK33hUieTsiuvcrzGcPKTW12jwf/VxLkx+CX12Hi1WLqRsWxw1yemqaKXrd1o1OgB8nryXyoPxjWRfIuat+/lt4juUZRYwYc1sUjYfqjM8AM6s2kf899sAaDW0GwNemcTv981D0moYvnAqG5/6lLyEdBw9XDDpDdbLqfZeQt/6L4n3vIY+M58Oa+dTtGk/VWcvqKIVrtlN+qzPG9f5C4y7ZSj33DGGl95876quN5pMzP01lk8fHY2/uzMTP/iF6MiWhAV41cX5fPMhhnUJY3y/jiRnFTDt83Wsj2iBwWji5WVbeOueIbQL8qGovAqdVu3EYTSamPP5Cj57dRr+3h7c/cJ8Ynp2IizEPPnz/re/MTq6F2MH9SEu/gwffb+aOU+aB/GLlq+le0TYVd3ftSifOd+sZunMyUp7fXUJMd3bExZknlxa8MN6RvfvxpiB3Yg7mczCFRuZM1UxoB4YNYDKGj0/b9vfqL4t+7am+DvlI2k03Dd7CvMmzaYgK5/XV7/Lkc0HuJhkru/nTqXy+ujnqamqYfCk4dz14r0smbagLvyOZ+7mzP5T1uQF/wy9gCRZllMAJEn6ERgL1D0UWZa314v/JzCpKdHLunJJkuQmSdJcSZK+kyTpngZhS/5C5pukV8+uJCenkZqajl6vZ8WK3xkzevgNod+ySzi557LIO5+DUW/k4Jq9RA3rqYqTuO8k+qoaAFKOnMWzXsd8iW639OHkjiN18S7RLKotNecuoj+fBXoDxX/swvXmPhbXW0VvQK5RXnYaezukegO+S2hCwjHlZyIXZIPRgOHYbnQRvSziXULXpT+GY7vr/jYmx0N15RVlR9OiDaa8TOT82rSO7ELXqXej8e26DUR/aNflNQNaIRflIhfngcmI4fQBtGFdGo2vbd8Tw2mlI5e8AkGjUYwSAH01GNTlfyKriBAPJ4I9nLDTahjeLoAdydmN6m84k8mI2lWRlIJyugV5otNoaGano42PK3vTclXxT2QUEOLpQrCnC3ZaLcMjQ9lx5qIqjgSU186kllXp8XVtBkD7QE/8av8f5utGtd5IjcGo1k/LIsTXg2AfD+x0WoZ3b8eO48kN9CXKa+tdWWU1vu7OFve1/uBphndvZ/H9idRMQvw8CPat1e/RgR3H1Aa9JEF5ZXU9fcWoS8nMo1d7ZXbUy80Z12aOnDyXqda3df7P5xHi7Uawl6uiH9WKHQlqo1+SpHrlX4Ovm1Nd2LaT6TT3dFG97FX6SemE+HsT7O+NnU7HiL5d2XHgpCpOckY2vSLDAegVGc6OgycAsNPpsK+dIa7RGzCZLn9Ky4mUDEL8PAn288ROp2VE70h2HD2jTutiHr06tFTSat+SHUfOWFGq1btYQIinM8Gezkrdjwhmx1n185GA8to+pqxaj6+LIwDN7HV0DfHBXtf4a0Tbuh2mnIvIeVlgNKDfvwNdV/WKkN3AkdRsWw0VZQDIpUUWOnbdB2CIPwA11arvHTu3RZ9+Ef0Fpe8sXbcTlyFX2HcCDpHhaL09Kd9z2Gq4f5cwitOyKUnPxaQ3krjmT1oP666KU1Nm7hvtmpknPloM7ERewnnyEhQjqaqoDPkyz9e5Sxuq0zKpSc9G1hso+H03HsMa7zuvBT26dMLdzfWqrz+RnkOIjzvB3m5K2+oazo4Taao4Stut17bclba178x52gR60y5ImeDwcHZEq1HXpRNJaYQG+BAc4IOdnY4R/bux/cBxVZyU85n07qS0+14d27L9gNnIPJWcTkFxCX2j1CvUV8rfLp/kC0rf4Oel9A19OrPjUIIqTnJGDr0ilcm5XhGtVeG9O4bj7OjQuL6N+7am+Dvl07pLONnnssg9n41RbyBuzW66NRhXnd53gprafj/pSCJeAebJqpYdW+Pm486J2AYTvYJrhiRJj0iSdLDe55EGUYKA+hXuQu13jfEQYOnS04Cm9ph8hfJe+gWYIEnSL5IkXWolV977XwHNgwI4f8E8WLuQkUnz5gGXueKf0/fw96Lwotn1pzAzHw9/S8PjEv3GD+bkjqMW3/cY3Y8Dq/dYfK/z90afmVf3tyErDzt/b4t4riP60XrtIoIXvYgu0DxbrQv0ofXaRbTZ/TV5S39Wr5YAkrs3cpE5/3JxPpK79fxLHr5Inv4Yk6zPIDaFxt0bU6H5XkxF+UjulvcCIHn6Inn5Yzx73Gp4XTwXD+RS8z3JZYV1s6oWcV290Lj5YDp/WsmPpz9UV2I/eiqOk17BbuCdyii6Hjll1fjXDv4B/F0cyS1TD4AucbGkkovFlfQMUe6pra8re9PyqNQbKays4eD5ArLKqtT6pZUEuJtfBv5uzcgpVRt6j0ZHsjY+nWEfrGHa8lhmjuhqkfaWhAt0CPTAXqdV6xeVEeBpfjn4e7iQU1Sm1h/Vh7UHEhj28udMW7KKmeMHWehvOpzIyB6WA/ucolICPM0z6P6erpb6o/uzNu4kw15YzLRFK5k5YahSPsF+7DiWhMFoIiOviFPpWWQXlv6z+S+pIKCeIePv5kROcblaf0gUa4+kMGzuSqZ9vZWZY5QBYUW1nq93nuDRIdbdlAByCooJ8DbXRz9vd7ILi1Vx2rVoztb9Spvauj+e8spqikqVPGTlFXLnc+8x/LE3eXDsoEZXS6D2WdQL9/N0syjPdiH+bD2k1P+th09TXlVDUVmFdb3SKgLc6tV912bklKrr76MDO7D2xHmGfbyeaSv2MXNY50bz1xDJwwdTgdlQlwvz0Hj4qOJoAoLR+AfhNPNDnF76CG3HHhY6dr1i0Mdtt/he5++DPtOsb8jKQ2et7xzan5a/L6H5wpfRBdSmL0n4vTCF3HmNu4C4BHhSetHc95RlFuDi72kRr/N9N3N/7Pv0f2kCO1/7FgCP1gHIyIz77nnuXvsW3R8d1Wg6APaBXtTUew/UZOVjH2jZT3uMvImIzR/Seunz2AX6WIT/k+QUlxPgUa9teThbtq0RPVh7KJFhb3zLtM/XMvO2AQCcyy1CkiSmLv2DCe+v5KttRyz0swuK8fcxl7e/lyc5+eq21bZlEFv+VN63W+OOUV5ZRVFpGSaTife++ZWn77e+WvhPkFNYrG6vXu5kF6pXF9uFBrC1diJj68GTlFdVU1Rqvb1a6Nu4b7Mlnv5eFFw01/eCzAI8rbTdS0SPH8LxHcoEgiRJTJh1Pz++/Y3N82lL5Bv9nyx/Jstyj3qfz672XiVJmgT0AOY3FbcpwyRMluWZsiyvkmV5DHAY2CZJUuO15/84vcYNoEXn1mz+bLXqezdfD5q3C7V047pCyrbGkRT9ICmjplG25whB85+uCzNk5pEyahpJg6fgcfsQtN5XN/sBoIvqjyF+H8i293606zYQw7E91zQtbfteGM4eNrtraTRogsLR71pJ1bK3kdx90Eb2u2r9jWcyGdLWH23tytRNLXzo38qXB376kxfXHaNzcw+0kuWqVVNsOJHOmKiWbJoxmkV3D2DWqv2Y6rmcJeUUs3DrcWaNshy0XZH+wTOM6R3JprensOixccz6ZoNqdj4+NRNHex3hza9uoLNh/ynG9O3EpncfZ9G0/zDrqz8wmWTG9euMv6cr98z5hvkrthIVFqRy47ph8n8slTHdw9n04n9Y9MAQZq2IxWSS+XTrUSb2j8DJwe6qdC/x9KTRHDyVwvgX3udQQgp+Xu5oameHA3w8+Xn+s6xZ+CKrdx4kv6i0CbUm0ho/lINnzjH+9c84dOYcfp6udWldDRtOnmdM51A2TR/JovE3MWv1IVXd/NtotGj8g6iY/wyVn82h2f0zoJl5sCW5e6EJboXh5MGrki/bHkfKkAdIG/sY5XsPE/DOMwB43HMr5TsP1O09+Tsc/3YL3wx4hj1zf6TnE+Nqb0tL8x5t2fDEElbeMZuw4T0I6Rf5t9Ip2nyA+Jse4dTQpyjZdZRWHz7xt/NuazYcTmJMr3Zseu0+Fk0ZxawftmIyyRhNMkdSM5kzcQhfTR/H9vhU4hIvNC3YgGfuv41Dp5IY/+w7HDyZhJ+XBxqNhp82xNK/W6Rqv8mNyNP33MLB06mMf/ljDiWk4ufpdlV9ZGPYum/7J+g7biAtO4ex7jNlb++Qe0dwfPthCrMKmrhSYGMygJB6fwfXfqdCkqSbgZeBMbIsW5/1rUdTe0wcJEnSyLIycpRl+W1JkjKAXYBLYxfVLvc8AiBp3dFoLN0uGnIxI4uQ4OZ1fwcHBXLxYlaT110pf0e/KLsAz+ZmW8wz0JuibMsG0b5fJ0ZMu40P7nodQ43al7j7rTdxdON+TA3ccAAM2fmqmS9dgA/6bPXmbGO9wUrRT5vwf2GypU5OAVWJ53DqGUnpBvPKjFycj+Rhzr/k7o1cbL1B66L6Uf371fsvm4rzsfM034vGwxu5ON9qXF3XAVT93PSGObmsqHbjuoLk4mnV3QNA164nNVt/UF1ryj2vuIEBxqSjaAJbU/8p+Lk4kF1vBSO7rApfF+vL5xvPZDJzcITqu4d7h/Fwb8WH+cV1xwj1VNd3P9dmZBWbZ8CySyrr3LMu8dvRVJbcMxCAqBAfqg1Giiqq8XJ2JLukgqdX7OHNsb0J8bJsdn4eLmTVmzXPLirDz0Md77e9J1gy7XZFv3VzqvUGisor8XJVVnI2HDrDiO7trd6zn4crWfVm+bILSy319xxnyROKX3RUWJCiX1aBl5szz403bwS/793vaOGnngW2ef7dnMiqN4uYXVKBXwNXsN8OnmXJg8oqT1QLP6r1Rooqqog/n8fm+HN8uP4gpbUbdB10Wib0NbuG+Hm5k5Vvro85+cX4e6pXPfy83Png2QcAqKiqZkvccdycm1nECQ8J4PDpFIb2sT6L6efhSlaBecY4p7AEf0+1K4WfpysfTBtfm1YNWw4l4ObkaF3P1ZGsknp1v7QSP1d13N+OnWPJBMX9KirYm2qjkaKKGrycG3cxuYRclIfGy7z/RfL0wVSkNgTkwjyMKafBaETOy8KUnYHGPwhTWiIAdj2jMRzeA0ZrfWcedoFmfV2AT90BIZcw1es7i1duxPfZhwBo1qUDzbpH4nHPrUhOjkh2dpjKq8hbYN5vV5ZViGtzc311CfSiLLuw0fs9s/pPBr39IJtRVlcy9p+hqlBZ/Uvbfgzfji05v+ek1WtrMguwr/cesA/wpiZT3U/Xfw/kLd9C8MvWN0T/U/i5O5NVVK9tFZVbtq24BJY8cisAUS0D6tquv4cz3VoH4umitIP+HUJJuJBL77bmjdn+Xu5k55nLO7ugED/vhm3Lgw+enwJARWU1W/48ipuzE8cSUzmckMyKDbFUVFWjNxhxcnTgqXvHXttCuAx+nu7q9lpQjL+nW4M4bnzwlOJ2X1FVzZYDJy36hkb1bdC3TRp4Vbf6lynMLsCr3kSSV6AXhdmWY4WIfp0ZPe0O5tz1St24KqxbW9r17MDge0fg6OSIzk5HVUUVK9/9/p/JvOASB4A2kiS1QjFIJgANt310BZYCI2RZzrkS0aam0dYAg+t/Icvy18AzQI21C2rj1C3/XIlRAnDg4FHCw1vRsmUIdnZ2jB8/ljV/bLqia22tf+5YMn4tA/EO9kVrp6XH6L4c36yevQuObMk9c6bwycPzKM233Ajac0w/Dq6xdOMCqDyeiH3LIOyC/cFOh/utAynbGqeKo/M1z/q43tyb6tqN8boAbyQHewA0bi449YikJkU962S6kITGOxDJ0w+0OnRR/TEmqE8FA5B8g5CauWA617hPelOY0s+i8WmO5OWvpNV1IIYTlhv3NH7BSE4umNJON62ZlYbk4Yfk5gMaLbr2PTGmWK48SZ4B4OCEKTO53rWpSA5O0EwZ6GpD2iPnq/d3RAa4k15YQUZxBXqjiY1nsohprd5ADZBaUEZJtZ6oQPOKlNEkU1SpNIXE3FLO5pVyUwv1gmJkkBfpBWVkFJahNxrZeDKd6LbNVXEC3ZyIS1X2taTkllBjMOLp5EBJVQ3Tl8fy5JDOdA21vhoQ2SKA9JxCMvKK0RuMbDx0huhO6gMFAr3ciDut+LqnZOUr+rUDApNJZtPhREb0aGtdv2VgrX6Ron8wgeiocCv65xT9zDxq9EY8XZ2orNFTWa2Uz75Tqeg0GtWm+X8k/8E+pOeVkFFQqugfSyW6Q7AqTqCHC3HJyt6KlJwiRd/Zka/+O5L1L9zJ+hfuZGK/CB6K6aQySgAiw0JIz8rjQk4+eoOBDXuPEN1DPTNeWKK4lgB8uWor4wYpe7yy84uoqlH8v0vKKjhyJpWWzS3rXl1arYJIzy7gQm4heoORDXEnie6ivu/C0oq61aQv1+5mXP/G92NFNvckvbCMjKJype6fukB0G/WpcoFuTsTV7ptKySuhxmDC08m+Uc36GFPPoPEPQvIJAK0Ou14xGI7uU8XRH9mDtp3iHia5uKHxD0LONe9z0fUaZNWNC6AqPhG7Fs2xC1L6Ttdboinb9qcqjrZe3+kyuA81yUrfmfncPFIG30/KkAfInfcFJb9vURklANnHUvBoFYBbiC8aOy1tR/chZbN6P4pHS/NG5lZDulCUpkx4ndt1HJ92Iegc7ZG0GoL6tKfgrMVkYh3lx87i2CoQ+xA/JDsdXmP7U7RZ3Xfa+ZnvxWNYT6qS/voKw7UkMsSP9NwiMvJLlLZ1JInoji1VcQI9XYir3cCfkl1Y13b7tgslKbOAyho9BqOJQ8kXad1gb2ZkeAvOZeZyITsPvd7Aht2HiemhdiWs37a++HUjtw1WvMzfeeoBNi19kw2fzuaZ+25jdHSvf9QoAYhsHVTbNxQofcOfx4nupu4/CkvLzX3D6p2Mi+5uTcq6vo37NluSeiwJ/5aB+AT7obXT0Xt0f440GFeFRrbiwTn/5cOH31GNq5Y+tZCn+z3Ks/2n8uOcb9nz685/pVFiQr6hP00hy7IBmAZsBBKAFbIsn5QkabYkSWNqo81HWchYKUnSUUmSVjciV8dlV0xkWX5ekqT2kiQNAeJkWS6r/X6DJEnXdA3ZaDTy5FOzWLf2B7QaDV9/8xOnTiXeEPomo4kfX/0f0799GY1Ww94V28k8e4FbZ4wnPT6Z41sOcceLk3BwcmTKEsXFqjAjj0+mzAPAK9gXz0Afzv7ZyOkRRhNZb3xC6NdvImk0FP28meqz6fg+NYnK+LOUbY3D6/4xuAzpDUYjxuIyLj7/AQAOYSH4v/Sw4rokSeR/8SvVieca3ICJ6t+/oNlDr4JGg/7AVkzZ57EfOgHjheQ6I8UuSr3p/RLNHn0LjW8QODji9NLnVP+8GGOi5R6aS2lV/fIpTo++oaQVtwVTVjr2IydiTD+L8aTyotV1G4D+cOwVlT+yiZrtP+Bwx1MgSRhO7EHOv4hd3zGYss7VGSm69j0xnmlgcMkyNTtX4njnMyCBKTsdQ7w6XZ1GwwuDI3js14OYZJmxkcGE+biyZO9ZIvzdiQlTBoobz2QyvG0gUj1XLYPJxOQVihHpYq/j7RGd0TVwm9FpNMwc2Y2py3Yp+l1aEe7nzpLtJ4ho7klMuyCeHhbF7DUHWRaXCEi8MbYXkiTx0/4k0gvKWLrrFEt3KfXn00kD8XI2z2rrtBpmjh/M1MW/YjLJjL0pkvDmPiz5Yy8Rof7EdA7j6dsHMvuHzSzbfljRv3d43X0cSrpAgKcrwT7WXQB1Wg0zJwxl6sIVin6/ToQ392XJ6lgiWgQQE9WGp+8czOzvN7Bs6wFF/4FbkCSJgpIKHvtoBRpJme1/a/Kt1vVtnf8xvZn6vy2YZBNje7Qh3N+TJZuPEBHkTUxEKE/f0oPZv+1l2e5TIMEbd/ZTPefLodNqeXHy7Uyd85nivhbTi/CQABav2EBk62BienTk4KlkPlq+DiTo3r41Lz10BwApGdm8/90aJEAG7r81hjahlsdN17+XFyeNZOqCZUpa/bsQHuTH4t+2E9myOTFd23HwTBof/bxNSattC16aNLJxPY2GmcOimPrjHkwmGBvVgnBfN5bsPEVEoCcxbQN5ekhHZq8/wrL9SUrZ39qtrmxGLt5IebUevdHE9sSLfDKhn+pEL0wmqpYtwmnGXCSNhprdGzFdPIfD2PsxpiViOLYP44mD6CK74/zmF0r8lZ8jlysrA5K3PxovX4yJjexDM5rIefMTgr98CzRain/ZRE1SOt7T76XqRCLl2+PwvHcsLoP6IBuNmIpLyXrx/St6rgCy0cSOV75h3HfPI2k1nPppJwWJGfR5+g6y41NJ3XyYzg8MI7R/JCa9karicjY9vRSA6uIKDn+xngl/zEaWZdK2HyNtWyP9Zu29pL/yOW2XvQYaLfk/baEq8TzNn72b8mNJFG8+gN/kUXgM7YVsNGIoKiNtxkdXfC/WeO61dzhw5DhFRSUMGTeJxx66lzv+wqEzOq2GmbcPYOpniuvm2F7tCQ/wYsn6/USE+BLTsRVPj+nL7BU7WbbzuNK27h6MJEm4OTlwb3QUEz/4BUmC/h1aMDCiRQN9LS89PJ6pby7GaJIZN7gP4aGBLF7+BxHhoQzq2ZkDJ8/y0ferkSToFhHOy3/xSGDblo+WF+8fw9R5XyntNbo74cH+LP55M5Gtgonp3oGDCSl89NMmpb22a8VLD4ypu/6B2UtJy8yloqqGodPf4fUpt9Ovc9t6+rbt22xZPiajie9e/YLnvn0FjVbDrhXbyDh7nttmTCAtPokjWw4y4cX7cHBy5PElivtlQUYeH05555rkXXBtkGV5HbCuwXev1vv/zX9VU5Iv4yssSdJ0FGsoAegCPCnL8u+1YYdlWe7W6MW16OyDrqEz8j/PlOZXvx+hKaZry5uO9DcIvbNpV4u/g3yZ8/ivBdrQa3f4gVWcnJqO8zeQXBv/7Yhrgv/VHfF4xeiubFb8qjE0uuh6bSixrf+x1PrKN4FfFeXFTce5SuSrPNziStHvOtR0pL9B5h7bnnS/ocK22yj7mcqajvQ3iDq6oOlIfwNDXJOTnn8LTYu/txenSf2Aqzs++Eoxplpu5L+WyOev3quhKXQDJ9hMG+DhHs/ZVB/gm7Rfrt0mHRtyS+gtN/T4eF36uutSjk317o8A3WVZLpMkqSXwsyRJLWVZXohyWpdAIBAIBAKBQCD4C1xuYeD/Mk0ZJpp67ltpkiTFoBgnLRCGiUAgEAgEAoFAILhGNLX5PVuSpLqdk7VGyq2AD9DJlhkTCAQCgUAgEAgE/3doasXkPkB17m3tLvz7JElaarNcCQQCgUAgEAgE/59i+1+L+3fS1KlcjZ5FKMuy9bNvBQKBQCAQCAQCgeAvcvU/BywQCAQCgUAgEAgE1whhmAgEAoFAIBAIBILrjm0PgxcIBAKBQCAQCAQq5Cv4dfX/i4gVE4FAIBAIBAKBQHDdEYaJQCAQCAQCgUAguO5Itv7lSZ19kFirElwVHo7ONtVv6exvU/0AOzeb6ntIDjbVtzWmf/kydpK+wKb6h/OSbKov+P+XnJHhNtXXF9v295XPnPa1qX4edjbVP+Fg2/JJlapspp1Qk28zbYCDeWdtqg9gqMn4V/wA+M0hw2/ol+CW8xuvSzmKFROBQCAQCAQCgUBw3RGGiUAgEAgEAoFAILjuiFO5BAKBQCAQCASCfxBbb6X4tyJWTAQCgUAgEAgEAsF1RxgmAoFAIBAIBAKB4LojXLkEAoFAIBAIBIJ/kH/7yZS2QqyYCAQCgUAgEAgEguuOMEwEAoFAIBAIBALBdUe4cgkEAoFAIBAIBP8gsnDlsopYMREIBAKBQCAQCATXnRvKMBk+LIaTJ3Zx+tRunn/u8X+V/r857zeq/uCbB/DnoQ3sP7qZJ2Y8YhFub2/HF199yP6jm9m4bSUhoUGq8KDgQNIuHuHx6ZMBCA9vxfbdv9d9diRu4O4p/wHgpkG9+CV2Gb/tXc790yZapNW1TxTfb/qSP89vZ8iomLrvA4L9+X7Tlyzb/D9+2vEtd9w3tsn76hbdnU+3L+WzXZ9z52P/sQgf9/A4lmz9hI83LuLt5W/jG+TbpGbn6K7M3/Yx7+9czOipt1mEj3x4NJ9sxpkAACAASURBVO9uWcicDQt48YfX8W6g2cylGR/9+Tn3zX74uum/t20RC3YuYfTU2y3Cb3l4DPO2fMQ7Gz7gpR/ewKdW3yfIl7fXvsecdQuYt3khQyYOvy76fWJ6sTL2O37Zs4z7pt1jEd61d2e+3fg5e9O3MnhUtEW4s4sTaw6u5Nm3n7SqfzluxLYr9G8cfbuuvXBf8h3uny7D8Q7Luglg328Q7ou+we3jr3F++hUANL7+uC34HLcPvsDt469xGDGmybTse/XC+9tv8V62DKd7rKflEBOD99df4/3VV7jNmtWkpuegLvTYvZCe+z4mZNq4RuP5jOrNwKyVuES1BsDv9v502zK/7jPg4k84R7a0uM5/UGeG7n6PYfsW0Hba6Eb1m4/qye1ZP+AR1UrJV9cwBm+Zo3y2zqX5yB5WrwuL7sxj2+Yzbef79Jtqqd994hD+u/EdHlk3hwd+fhWfNsp7rHlUax5ZN0f5rJ9Du+HW9TtGd2HO1o94Z8cibrHSNw97aDRvbf6Q2esX8Nyy11R985fJK3hj3Xu8se49nvh8psW1fWJ68VPst6zcs4x7rfRrXXp35puNn7E7fSuDGvRre85v5dvNX/Dt5i+Y//XbVvPeFLZuW4IbE+lyP/AiSVIA8BpgAl4FpgN3AAnAk7IsZzaVgM4+6IrWqjQaDQknYxlxy91cuJDJn/vWMenex0hIOHsll19X/X9z3m9UfY1Gw5mEPdw59kEuZmSxeccvPDJ5BolnkuviPPjwPURGtuPZGa9x2x2jGHXrUB5+8Km68P99+xGyLHP44DEWf/w/C/1TiXt4YNR/ybmYy697fuDxu2aQnZnLt+s/5+XH3iA1Ma0ufmBwAM6uztw7dQK7Nu5h69odAOjsdEiShL5GTzOnZvy04xsmj55KXnY+AXZuVu9r6c7PmDVxFvmZeXyw5gPmTZ/H+bPn6+J0uqkziUfOUF1VzchJt9Dppk7Me/xdCy0PyQEASaPhvR2LeGfiGxRk5TN79TwWP7GAi2cv1MXtcFNHko8kUlNVw5BJw+nQpyOLpr1fF37va5Nx9XajrKiMb1/9QpWOrfQvnUgiaTQs2LGYuRNfJz8rn7dWz2PREwvIqKcfcVNHkmr1b67V/3ja+2jtdEgSGGoMODg5Mm/TQl67fSZFOYWq/NtCP0lfUPdMf979PdMmPENOZi7frFvKrMdmk3r2XIP648SkRyewa9Metq3dqSrjp2dPx9Pbg+KiEt57eSEAh/OSLJ55Q27Etiv0r79+zsjwSwK4L/me0teewZSfi9t7Syl7fzam8+a6qQkMwuX5Nyid9RRyeRmSuwdycRHodIAEBj04NsP9o68omfk4ckE++mLJWmbx/u47ip59FmNuLl6ffkrxm29iPGdOSxsUhPvrr1M4YwZyWRmShwdyUZGF1JnTvnWaPfcuJH78m1RnFtB1w1xOT11IReIFVXytsyMdv38RyV5H0ktfUnYsRRXu1D6UyK+f40Cf6QDkYVerLzFs7wJ2j59LZWY+gza8xYGpiyhNzFBdr3N2pO/3z6Gx13H0pa8pOpaKtpk9phoDstGEo58Hg7fNZX3U48hGEycclPKRNBKP73if7yfOpSSrgIdXv8mvTywm76xZ396lGTVllQC0vbkbPe69mR/un4fO0R6jXtF38fPgv+vnsKDXNGSjiVSpqlZfwzvbP+a9SbMpyMrn1dXvsnT6B1xMMpdP+5s6klLbtw2aNJz2fSL5ZNoCAD45+T1TIyep7jWhJr+26DWs2P0dT0x4lpzMXL5a9ymvPPYmaVb6tXsevYvYTXvZXq9f23Z2PYPbjLR4tgfzrqxt/J22ZajJsFJBbzxigm++oX25dlzYcl3KsakVk6+BU8B5YDtQCdwCxAKfXsuM9OrZleTkNFJT09Hr9axY8TtjRlufnbzR9P/Neb9R9Xv17EpqyjnOpZ1Hr9fz2y9rGTnqZlWckaOG8OPy3wBYvWoDA2Juqhd2M+nnLnDmtPXB3cCYm8hIu0jWhWwiu3bgfFoGGemZGPQGNv2+lejh/VXxMy9kkZSQjMmk7kcMegP6Gj0A9g52aDSXb1Jtu7QlM+0i2elZGPQGdq3ZRZ9hfVRx4vcdp7qqGoAzR07jE+hzWc2wLuFkp2WSez4bo97An2t2031oL1WchH0nqKmqASDpSCJegd51YS07tsbNx4P4Xceui354lzZkp2WSU6u/z4r+qXr6Z+vpG/UGDDUGAOzs7ZA0lv2orfUju3bgQloGF+vqzzYGWq0/KZhMJovr23dqi5evJ3/uPGC1fC7Hjdh2hf6No69r0wFTVgam7EwwGKiJ3YZ9L3XddBg2mup1vyGXlwEoRgmAwaAYJYBkZwdN9G127dtjzMjAmKmkVbVtGw79+qniNLv1VipXrUIuq03LilFSH9eu4VSmZlGVnoOsN5C7ag/eVlYOWrwwgfOLf8dUrbeq43dbP3JX7bX43qtrOOWp2VSk5yDrjVxYtY/A4d0t4kW88B8SF6/BWE/fWFmDbFTas8bRDmvbBYK6hFGYls3/a+/O45yqzj+Of57MDPsi+6wCirsoWOBXqlIVF2oVtFrU1uLWUqsoasXaam3V1qVqW2vrQsWCtlgpdRcVrPuKiIPsmyDMBsOigCvMPL8/EmbBTDIyN5Nc+b5fr7xIJsn3PiQ5SU7Oued+uLqS6q1VzH/iTfY5pn7+9k4JQE6bljXnt31Wm5/dMod4vyHv0a8Paz+oqHlvnvnEq/Q/dmC92yyq8962/N0ldMrt8uWgOPbvv2+997UZjz3PkOPqP5/b39e8Ovjv16luW5mg2j2jT+mSrGPSw93vcPebgN3c/WZ3X+3udwA9gywkvyCX1SVlNZdLSsvJz88NRX6Ya8/U/PyCXMpKKmoul5VVkJffo95t8vJ6UFoSHbSrqqpi06bNdO7cibZt23DxpT/hlpv+2mD+yad8l2cffQ6A7rndWFO6tua6teWVdM9N3Bmoq0d+dx7830Seeue/TPrrv1i3Zn2Dt+2S24XKsnU1l9eVr6NLj4Y/KI497VjeeWFWwu13yu3ChvLabW4oX0+n3M4N3v7bpw1lzouzATAzfnj12Tz4+0lpzO/M+vLax2RD+Xo6J/jwPPK0o2vyATrndeGmZ/7EHW/+nSfufqTeaElz5HfL7cqasvqvn25JOpPbmRljf3MBf7nurkbdfkeZ2HaVnzn51qUrVetqX5vV6yuJdKn/2szKLySSX0T7m/5Khz/cSU7/2k57pGs3Otx+H7tN+A+fPTwZ39Dwe1ukWzeqKytrt1VZSVa3+lM6s4qKyCospNMdd9DpzjtpMWjQjjH1tMzrzOdltdv8vHwDLfLqt912fXvTMr8LG56bvePda3Qb8S3WPvrql/7eKq8Tn9bJ/7R8A63z6r+37da3F63zu1DxXPGX7t+p/54c/dIfOPqFmym+YkJNR2K79rmd+ajOe+em8g20z+30pZwBo45hzMt/5OhfnsEzv6l9ryzotyfnz7iZ85+9iaeuuu9L+Z16dGZDWd33tg10SvB5MmTkUObWeW/LadmCax6/masfuZH+x9Z/LrrldmNtWe3zGX1fSz6teLsWLVvwj6fv4d4n7mTIsMOS32EHqW5bkrmSdUzqXn//DtdlNXQnMxttZrPMbFZ19cc7XZzIzrjilxdx998m8vHHn8S9Picnh2HHD+W5J14IZHtrytZyxtCzOWnw6Zwwchidu375g2dnHHHykfQ5aC/+e89/A8kDOPTkIezRtw9P3fMoAEePGkbxC7PZUNHwF47Myv82vfvuyZOxfIh2NK4cdimXDvkZQ045kg5dO2Zs/o5OPfskXn/+LdaWVya/sUgqZGWRlV/I5qvGsuXW62gzZhzWth0A1esq2TT2XD48/we0PHIY1rFp722WlUVWYSEbL7mEj667jg6XX461a9eEQGOPa8/i/Wt3/HpSq33/PlR/+gWfLFrd4G0S5fe99kzmXvvPuFdvfHc5z337Cl4YdjV7XzyCSMucr74NYNb9M/jrkMv4303/5vCLavejKS1ezt3H/IJ7h/+awy4YTtZO5gMMPmkIvQ7ak6fHP1bzt8sPPZ/rhv+Cey7+Mz+45hy67d4jQcJXc/Kg0zjnOz/lmguv59Jrx1DQMz+wbPl6S7Zc8GNm1s7dt7h7zV5qZtYHWNzQndx9PDAeGr+PSVlpBUWFtS/cwoI8ysoqEtzjq0llfphrz9T8stIK8gtrfx3Jz8+lvGxNvduUl6+hoDCP8rI1ZGVl0aFDezZs2MghAw7mxBHH8ZvrxtGxYweqvZrPPv+CCeOjHy5HHzOE9+bMZ8O66C/faysq6VHQvSa3e1431las46tat2Y9yxetoP//HVyzD8qO1lesp1t+7S+WXfO6sj7OCMvBh/XjtDGnceXIX9RMJWrIxor19aZOdc7rwsaKDV+63QGHHsTwMafy+5G/rsnsc8g+7DNwP47+0TBatW1Fdk42n3/8GQ/d/M9mzN9AlzojDJ3zusTtyBx46EGcNOZUrh95ddzH5MO1G1m9ZBX7DtqfmdPeaLb8yop19Miv//qpLG/c66fvNw6g3/8dxClnjaBN29Zk5+Tw6cef8rcbxjfq/pnYdpWfOfm+fh1ZXWtfm5Eu3aheX/+1Wb2+km1LFkJVFdVrK6guXU0kr5CqZYtqczasp2rVCrIPOIitr9ffP6omp7KSSJ0Rkki3blRV1u9wV1VWsnXBgui2KirYtno1WQUFbFsc/+vE5+UbaJlf+97TMq8zX9QZgchq15q2+xRx8MO/BaBFt904YNIvmH/WzTX7mXQ76VDWPvLl0RKAz8o30rpOfuu8znxaXvvelt2uFR32KeLwh6MLArTq1pHBky7njbNu5cM5K2put3lpGds+/owO+xbW/3vFBjrWee/skNeZzRX1R1zrmvf4Gxz/u3OAe+r9fd2yMr745DO6711I+dza/I1rNtA5v+57W2c2xvk82f/QgzhhzCncdNqv6723fbgm+n+tXL2GRW/Op+cBvWHZgujfKirpnl/7fEbf1xr/A0pl7DO0bFU5s18vZu8D96L0g7Ik96qV6raVCTJ6B5M0Sjhi4u7XAIVmNtTM2tX5+zLg3obv+dW9PauYPn1606tXETk5OYwcOYInnpweivww156p+W/PKmaPPXqxe89CcnJyOPmU7/LMtP/Vu80z057n9DOiq5AMP2kYr7wU/bJ44rAfcEjfozik71Hcc9ck/nzr3TWdEoDvff8EHv7PkzWXFxQvoqh3IflFeWTnZHPsiKG8/Gz8D7Iddc/rRstWLQBo37EdBw86iJXLVzV4+yVzlpDfu4AeRT3IzslmyIlDeGvGW/Vus8cBezDmxjFcf951fLT+o6Q1vD9nGbm98+hW1J2snGy+eeJhzJ5Rf3+Fngf05twbz+eP593IpjqZd439M5d866dcetj5TP79JF55+MV6nYbmyF8+Z2m9/MEnHsY7cfLPu/Fn3HbeDfXyO+d2Iadl9PFv26Et+wzYj/Llpc2aX/v6yY29fo7ilemv0RjXjPkdwweO5KT/O53br7uLaVOfbXSnBDKz7So/c/K3LV1EJK+QSPdcyM6mxeFHsXVm/dfm1jdfJefAfgBY+45ECoqoXlOGdekGLaKvfWvbjuz9+lJd2vCow9bFi8kqLCSSG91Wq6OO4vPX6+/X8fmrr9KiX2xbHTuSXVQU3SelAZuLl9F6jzxa7d4dy8mm20mHsn567dTWqs2f8MYB5zFz4IXMHHghm2YvrdcpwYxuw79F5aPx2+PG4uW02yOXNrt3w3KyKDxpMOXT36l9/DZ/ylMH/JRnB47l2YFj2TB7WU2npM3u3bCs6Feo1oVdad8nn09W1+/0lc55n869c9mtqBuRnCwOOPGbLJnxTr3bdO5VO0qx91H92LAy+uV7t6La/I4FXem6Zz4fltTvGKyYs4zuvfLoWhh9bxt04mG8O6P+1N/dD+jNWTf8lL/8+CY2r99U8/c2HdqS3SL623S7Tu3Z6xv71lvQZGHxYop6F5IXe187ZsRRvDL9y/vpxNO+YztyWkRHdzp27shBAw+st5hMY6S6bUnmSjhiYmYXAWOIrsI1wczGuvv2ccAbgGeCKqSqqoqxl1zNtKcmkxWJMHHSQyxYsCSo+JTmh7n2TM2vqqriynHX8Z9HJhDJymLyA1NZvGgZV151McWz5/HM08/zr/v/w53jb2Fm8Qw+3PgRPznn0qS1tGnTmm8f+S0uG/trutC2Zlu3/OpP3PHgbWRlRXj830/x/pKV/HTceSycs4iXp7/G/gfvyy33/Z4Ou7Xn8GO+xehx53LaEaPovVdPLvnNGNwdM+Ofdz/I8kXvN7j96qpq7v71XVz3wPVEsiLMeGgGq5as4oeXncnSuUuZOeMtzr3qPFq1acWVd/0SgMqySq4/77qEmZOuuZcr7r+GSFaEl6b8j9KlqznlstNZ8d5yZj/3Nmf8ahSt2rTi4jsvB2B92Tr++OMbkz5ezZU/8Zq/c+X9vyGSFeHFWP6pl53B++8tY/Zzb/PDX50Vyx8Xy6/kth/fSH6fQs68+uyax/+p8Y+yevGqZs2vqqrilqv+zF8m30okK8IT/57G+0tWMnrcuSycs4hXpr/Ofgfvyx8mXF/7+rn8HE4/8uxGPT6JZGLbVX4G5VdX8cn4P9P+t7dCJMLn/5tG1eqVtP7BuWxbtoitM19n67szyek/kI5/nYRXVfPpxLvwzZvIPngAbc69ANzBjM8efYiqDxp+b6Oqis23306nW26BSITPnn6aqpUraXvOOWxbvJjPX3+dL2bOpMWAAXSZOBGvrmbz3XfjmzYlyKxm2a8mcOCDV2FZESoefIFPFpfQ84rT2Fy8nA3TE+9/13Hwfnxeto7PVq2Ne71XVVP8q4kc+uCVWFaEDx58kc2LS9nvilP5sPh9yqc3vN9Kl0H7sM9Fw6neug2qneIr/8EXGzZ/Kf/paybyw/t/gWVFKJ7yEpVLSznislMoe28FS56bzcCzjqX3YQdSvbWKzzZ9zGOXRdcVKhqwD6dfcCLVW6twr2ba1f/g041b6uVXV1Xzr2vu5ef3/5pIVoRXpjxP2dLVnHTp6aycu4zi52Yx8pejaNmmFRfc+XMA1peu4y8/uYn8PoWcdcNPqXYnYsZTdz1SbzWvqqoqbr3qdm6ffAuRrAhP/vtpVixZyU/GncOiOYtj72v7cPOE39F+t3YcdsxgfnL52fzgyHPotVdPfnHzz/HqaiwS4f6/Ta63mldjpLptSeZKtlzwXGCwu28xs17AVOABd7/dzN519/7JNtDYqVwiO9qtVduU5vdqG9x82njiLRccpO3LBYdVdcgHsrcvF5wqjVkuWCSemuWCUyTucsEBqlkuOEVqlgtOke3LBafK9uWCU2H7csGp0tjlgpsiLMsFH1pwVEZ/CL5W+nxaHsdk+5hE3H0LgLuvNLMjgKlm1hMIxRMvIiIiIiKZL9mqXGvMrN/2C7FOyglAV6BvKgsTEREREZFdR7IRk1FAveVp3H0bMMrM7ol/FxERERERaUjYpzOnSsKOibuXJLiuccvOiIiIiIiIJJFsKpeIiIiIiEjKJZvKJSIiIiIiAUq0Ku6uTCMmIiIiIiKSduqYiIiIiIhI2mkql4iIiIhIM9KqXPGpYyIZ68PPPk5pfvFn76c0X0QkHbo/vSzdJTRR6o8OLiKZSVO5REREREQk7dQxERERERGRtNNULhERERGRZuTaxyQujZiIiIiIiEjaqWMiIiIiIiJpp6lcIiIiIiLNSEd+j08jJiIiIiIiknbqmIiIiIiISNppKpeIiIiISDPSkd/j04iJiIiIiIik3VfumJhZ91QUAnDcsUcwf97LLFrwKleMuzBU+WGuXfnKV35488Ncu/KVr/zw5oe5dslclmhVADPrvOOfgHeA/rH7bki2gewWBY0aq4pEIiyc/wrDjj+DkpJy3nxjGmf+6AIWLlzamLunNT/MtStf+coPb36Ya1e+8pUf3vxMrn3bF6UWSBEp1j/30Iyey/VuxWtpeRyTjZisI9oR2X6aBRQAs2PnAzNoYH+WL1/JihWr2Lp1K1OmPMbwE48LRX6Ya1e+8pUf3vww16585Ss/vPlhrl0yW7KOyThgMTDc3Xu7e2+gJHZ+jyALyS/IZXVJWc3lktJy8vNzQ5Ef5tqVr3zlhzc/zLUrX/nKD29+mGuXzJZwVS53v83MHgL+ZGargd9A8mUEzGw0MBrAsjoSibQNolYRERERkdDTqlzxJd353d1L3P37wIvADKBNI+4z3t0HuPuAxnZKykorKCrMr7lcWJBHWVlFo+6b7vww16585Ss/vPlhrl35yld+ePPDXLtktqQdEzPb18yGAs8DRwJHx/4+LMhC3p5VTJ8+venVq4icnBxGjhzBE09OD0V+mGtXvvKVH978MNeufOUrP7z5Ya5dMlvCqVxmdjFwIbAQmACMdffHYlffADwTVCFVVVWMveRqpj01maxIhImTHmLBgiVBxac0P8y1K1/5yg9vfphrV77ylR/e/DDXLpkt2XLBc4HB7r7FzHoBU4EH3P12M3vX3fsn20BjlwsWEREREWmKsCwXfFDu4Iz+fvxexRtpeRwTjpgAEXffAuDuK83sCGCqmfUkekwTERERERGRJku2j8kaM+u3/UKsk3IC0BXom8rCRERERERk15FsxGQUsK3uH9x9GzDKzO5JWVUiIiIiIl9T1Ql2pdiVJTuOSUmC614LvhwREREREdkVJV0uWEREREREJNWSTeUSEREREZEAuY78HpdGTEREREREJO3UMRERERERkbTTVC4RERERkWakVbni04iJiIiIiIiknTomIiIiIiKSdprKJSIiIiLSjLQqV3waMRERERERkbRTx0RERERERNJOU7lERERERJqRVuWKTyMmIiIiIiKSduqYiIiIiIhI2qljIiIiIiIiaad9TEREREREmpGWC45PIyYiIiIiIpJ2GdUxOe7YI5g/72UWLXiVK8ZdGKr8MNeufOUrP7z5Ya5d+cpXfnjzw1y7ZC7zBMuVmdkwd38mdr4j8EdgIDAPuNTd1yTbQHaLgkaNVUUiERbOf4Vhx59BSUk5b74xjTN/dAELFy5t1H8knflhrl35yld+ePPDXLvyla/88OZncu3bvii1QIpIsT27HpLRc7mWr5udlscx2YjJDXXO3waUAycCbwP3BFnIoIH9Wb58JStWrGLr1q1MmfIYw088LhT5Ya5d+cpXfnjzw1y78pWv/PDmh7l2yWxfZSrXAHe/2t0/cPc/Ab2CLCS/IJfVJWU1l0tKy8nPzw1FfphrV77ylR/e/DDXrnzlKz+8+WGuXTJbslW5upvZZYABHczMvHbuV4OdGjMbDYwGsKyORCJtAylWRERERCTstCpXfMlGTP4OtAfaAZOArgBmlgsUN3Qndx/v7gPcfUBjOyVlpRUUFebXXC4syKOsrKJR9013fphrV77ylR/e/DDXrnzlKz+8+WGuXTJbwo6Ju18LPAS8Ctzm7pWxv1cAk4Ms5O1ZxfTp05tevYrIyclh5MgRPPHk9FDkh7l25Stf+eHND3Ptyle+8sObH+baJbMlnMplZhcBY4CFwAQzG+vuj8WuvgF4JqhCqqqqGHvJ1Ux7ajJZkQgTJz3EggVLgopPaX6Ya1e+8pUf3vww16585Ss/vPlhrj1TuFenu4SMlGy54LnAYHffYma9gKnAA+5+u5m96+79k22gscsFi4iIiIg0RViWC+7d5eCM/n68Yv2ctDyOyXZ+j7j7FgB3X2lmRwBTzawn0R3iRUREREREmizZzu9rzKzf9guxTsoJRHeC75vKwkREREREvo6q8Yw+pUuyjskooN4yCO6+zd1HAUNSVpWIiIiIiOxSEk7lcveSBNe9Fnw5IiIiIiKyK/oqR34XERERERFJiWQ7v4uIiIiISIASrYq7K9OIiYiIiIiIpJ06JiIiIiIiknaayiUiIiIi0ozSuSRvJtOIiYiIiIiIpJ06JiIiIiIiknaayiUiIiIi0oy0Kld8GjEREREREZG0U8dERERERETSTlO5RERERESaUbWmcsWlERMREREREUk7dUxERERERCTtNJVLRERERKQZuQ6wGJdGTEREREREJO3UMRERERERkbT7yh0TM+uSikIAjjv2CObPe5lFC17linEXhio/zLUrX/nKD29+mGtXvvKVH978MNcumcsSHXnSzG4CbnX3dWY2AJgCVAM5wCh3fynZBrJbFDRqEl0kEmHh/FcYdvwZlJSU8+Yb0zjzRxewcOHSxv1P0pgf5tqVr3zlhzc/zLUrX/nKD29+Jte+7YtSC6SIFOvRcd+M3slkzUeL0vI4Jhsx+a67r4udvwU4zd37AMcAtwVZyKCB/Vm+fCUrVqxi69atTJnyGMNPPC4U+WGuXfnKV35488Ncu/KVr/zw5oe5dslsyTom2Wa2feWu1u7+NoC7LwFaBllIfkEuq0vKai6XlJaTn58bivww16585Ss/vPlhrl35yld+ePPDXLtktmTLBd8JTItN6XrGzG4HHgaOAoobupOZjQZGA1hWRyKRtgGVKyIiIiISbtVaLjiuhB0Td7/DzOYCPwP2jt1+L+BR4HcJ7jceGA+N38ekrLSCosL8msuFBXmUlVU05q6Nksr8MNeufOUrP7z5Ya5d+cpXfnjzw1y7ZLbGrMpVQbSTcbi793X342Mdj6FBFvL2rGL69OlNr15F5OTkMHLkCJ54cnoo8sNcu/KVr/zw5oe5duUrX/nhzQ9z7ZLZEo6YmNnFwIXAQmCCmY1198diV98APBNUIVVVVYy95GqmPTWZrEiEiZMeYsGCJUHFpzQ/zLUrX/nKD29+mGtXvvKVH978MNeeKRKtirsrS7Zc8FxgsLtvMbNewFTgAXe/3czedff+yTbQ2KlcIiIiIiJNEZblgrt2F4R/ZgAADeRJREFU2Dujvx+v27QkLY9jsp3fI+6+BcDdV5rZEcBUM+sJhOKJFxERERGRzJesY7LGzPq5ezFAbOTkBOA+oG/KqxMRERER+Zqp1lSuuJLt/D6K6M7vNdx9m7uPAoakrCoREREREdmlJFsuuCTBda8FX46IiIiIiOyKkk3lEhERERGRAGlVrvgacxwTERERERGRlFLHRERERERE0k5TuUREREREmlE1msoVj0ZMREREREQk7dQxERERERGRtFPHRERERERE0k77mIiIiIiINCMtFxyfRkxERERERCTt1DEREREREZG001QuEREREZFmVK2pXHFpxERERERERNJOHRMREREREUk7TeUSEREREWlGriO/x6URExERERERSTt1TEREREREJO0yqmNy3LFHMH/eyyxa8CpXjLswVPlhrl35yld+ePPDXLvyla/88OaHufZMUO2e0ad0sURHnjSz2cDDwIPuvnxnNpDdoqBR/7tIJMLC+a8w7PgzKCkp5803pnHmjy5g4cKlO7PZZs0Pc+3KV77yw5sf5tqVr3zlhzc/k2vf9kWpBVJEirVu3TOjdzL59NMP0vI4Jhsx6QTsBrxgZjPN7FIzy09FIYMG9mf58pWsWLGKrVu3MmXKYww/8bhQ5Ie5duUrX/nhzQ9z7cpXvvLDmx/m2iWzJeuYbHT3y919d+DnwF7AbDN7wcxGB1lIfkEuq0vKai6XlJaTn58bivww16585Ss/vPlhrl35yld+ePPDXHumcPeMPqVLo/cxcfdX3P0CoAC4GRjc0G3NbLSZzTKzWdXVHwdQpoiIiIiIfJ0lO47Jkh3/4O5VwDOxU1zuPh4YD43fx6SstIKiwtpZYoUFeZSVVTTmro2Syvww16585Ss/vPlhrl35yld+ePPDXLtktoQjJu5+upnta2ZDzaxd3evMbFiQhbw9q5g+fXrTq1cROTk5jBw5gieenB6K/DDXrnzlKz+8+WGuXfnKV35488Ncu2S2hCMmZnYRMAZYCEwws7Hu/ljs6htIMGryVVVVVTH2kquZ9tRksiIRJk56iAULvjRgk5H5Ya5d+cpXfnjzw1y78pWv/PDmh7n2TKEjv8eXbLngucBgd99iZr2AqcAD7n67mb3r7v2TbaCxU7lERERERJoiLMsFt2xVlNHfjz//bHVaHsdk+5hE3H0LgLuvNLMjgKlm1hMIxRMvIiIiIiKZL9mqXGvMrN/2C7FOyglAV6BvKgsTEREREfk6SvdywGFdLngUUG8ZBHff5u6jgCEpq0pERERERHYpCadyuXtJguteC74cERERERHZFSXbx0RERERERAKUzulSmazRR34XERERERFJFXVMREREREQk7dQxERERERFpRp7hp8Yws2FmttjMlpnZlXGub2lmD8Wufyt2TMSE1DEREREREZFGM7Ms4G/Ad4D9gTPMbP8dbnYesNHd+wB/Am5OlquOiYiIiIiIfBWDgGXu/r67fwH8Gxixw21GAJNi56cCQ80s8QHa030AlzgHdBmtfOXvivlhrl35yld+ePPDXLvyla9T6h53YFad0+gdrj8VuLfO5R8Bf93hNvOAwjqXlwNdE203E0dMRitf+btofphrV77ylR/e/DDXrnzlSwq4+3h3H1DnNL45tpuJHRMREREREclcpUBRncuFsb/FvY2ZZQMdgfWJQtUxERERERGRr+JtYC8z621mLYDTgcd3uM3jwFmx86cCz3tsTldDMvHI76keKlK+8jM1P8y1K1/5yg9vfphrV77yJQ3cfZuZjQGeBbKA+9x9vpldB8xy98eBCcADZrYM2EC085KQJem4iIiIiIiIpJymcomIiIiISNqpYyIiIiIiImmXUR2TZIe2b2L2fWa21szmBZlbJ7/IzF4wswVmNt/Mxgac38rMZprZnFj+tUHmx7aRZWbvmtmTKcheaWZzzazYzGalIH83M5tqZovMbKGZDQ4we59Y3dtPm8zskqDyY9u4NPa8zjOzB82sVcD5Y2PZ84OoPV57MrPOZjbDzJbG/u0UcP73Y/VXm9mAFNR/S+z1856ZPWJmuwWcf30su9jMpptZfpD5da77uZm5mXUNuP7fmllpnXZwfND1m9lFsedgvpn9IcDaH6pT90ozKw6ydjPrZ2Zvbn9/M7NBAecfbGZvxN5DnzCzDk3Ij/tZFVT7TZAfSPtNkB9I+02QH0j7bSi/zvU73X4T1B5I201Ue0Btt6H6A2u/EgLpPoBLnYOuZBE98MoeQAtgDrB/gPlDgEOAeSmqPw84JHa+PbAk4PoNaBc7nwO8BXwz4P/DZcBk4MkUPD4rSXJQnSbmTwJ+HDvfAtgtRdvJAiqAngFmFgArgNaxy1OAswPMP5DoQY7aEF3w4jmgTxMzv9SegD8AV8bOXwncHHD+fsA+wIvAgBTUfyyQHTt/cwrq71Dn/MXA3UHmx/5eRHRHxA+a0t4aqP+3wOUBvSbj5R8Ze222jF3uHuRjU+f624BrAq59OvCd2PnjgRcDzn8b+Hbs/LnA9U3Ij/tZFVT7TZAfSPtNkB9I+02QH0j7bSg/drlJ7TdB7YG03QT5QbXdpN+jmtp+dcr8UyaNmDTm0PY7zd1fJroiQEq4e7m7z46d3wwsJPqFM6h8d/ctsYs5sVNgKxeYWSHwXeDeoDKbi5l1JPphPgHA3b9w9w9TtLmhwHJ3/yDg3GygtUXX+W4DlAWYvR/wlrt/4u7bgJeA7zUlsIH2NIJoB5HYvycFme/uC9198c5mNiJ/euzxAXiT6JrsQeZvqnOxLU1ovwnez/4EXNGU7CT5gWgg/2fATe7+eew2awPMBsDMDBgJPLgz2QnyHdg+itGRJrTfBvL3Bl6OnZ8BnNKE/IY+qwJpvw3lB9V+E+QH0n4T5AfSfpN8V2hS+22G7yEN5QfVdhPWH0T7lcyXSR2TAmB1ncslBNigmpOZ9QL6Ex3VCDI3KzaEuRaY4e5B5v+Z6BtidYCZdTkw3czeMbOgj+LaG6gE/mHRqWj3mlnbgLex3ekE/Kbo7qXArcAqoBz4yN2nB7iJecDhZtbFzNoQ/UW3KMl9dkYPdy+Pna8AeqRgG83lXODpoEPN7Pdmthr4IXBNwNkjgFJ3nxNk7g7GxKaz3LezU30S2Jvo6/QtM3vJzAYGnA9wOLDG3ZcGnHsJcEvsub0V+GXA+fOp/aHu+wTUfnf4rAq8/abqs7AR+YG03x3zg26/dfODbr9xHptA2+4O+YG33Qae21S1X8kgmdQx+Vows3bAf4FLdviFpcncvcrd+xH9JWiQmR0YRK6ZnQCsdfd3gshrwGHufgjwHeBCMxsSYHY20akPd7l7f+BjolMRAmXRAwgNB/4TcG4nol86egP5QFszOzOofHdfSHRqw3TgGaAYqAoqv4FtOgGO6DUnM7sK2Ab8K+hsd7/K3Yti2WOCyo11OH9FwJ2dHdwF7An0I9qBvi3g/GygM/BNYBwwJfYLaZDOIDW/tv4MuDT23F5KbPQ2QOcCF5jZO0SnuHzR1MBEn1VBtN9UfhYmyg+q/cbLD7L91s2P1RtY+41Te6BtN05+oG03wWsnVe1XMkgmdUwac2j7jGZmOUQb07/c/eFUbSc2TekFYFhAkYcCw81sJdEpdEeZ2T8DygZqRgW2D/E+QnTqXlBKgJI6I0hTiXZUgvYdYLa7rwk492hghbtXuvtW4GHgW0FuwN0nuPs33H0IsJHo3N2grTGzPIDYvzs1nJ9OZnY2cALww9iXs1T5F02YjhPHnkQ7tnNi7bgQmG1muUFtwN3XxH4cqQb+TrBtGKLt+OHYtNWZREdvd3oH/h3Fpkl+D3goqMw6ziLabiH6w0Wgj427L3L3Y939G0S/mC1vSl4Dn1WBtd9UfxY2lB9U+21E/U1qv3HyA2u/8WoPsu028NgE1nYTPLepbL+SQTKpY9KYQ9tnrNivAxOAhe7+xxTkd7PYKiNm1ho4BlgURLa7/9LdC929F9HH/Xl3D+wXezNra2btt58nupNiYKujuXsFsNrM9on9aSiwIKj8OlL1a80q4Jtm1ib2OhpKdG5tYMyse+zf3Ym+uU8OMj/mcaJf0Ij9+1gKtpEyZjaM6HTG4e7+SQry96pzcQQBtV8Ad5/r7t3dvVesHZcQ3Ym0IqhtbP/SGnMyAbbhmEeJ7kSLme1NdBGLdQHmHw0scveSADO3KwO+HTt/FBDoVJM67TcCXA3c3YSshj6rAmm/zfBZGDc/qPabID+Q9hsvP6j2m6D2QNpuguc2kLab5LWTyvYrmcQzYA/87Seic9+XEP016KqAsx8kOoS5lWijPy/g/MOIDn2/R3SqTDFwfID5BwHvxvLnkaJVKYAjCHhVLqIrrc2JneYH/dzGttEPmBV7fB4FOgWc3xZYD3RM0eN+LdEPunnAA8RWNwkw/xWinbU5wNAA8r7UnoAuwP+Ifil7DugccP7JsfOfA2uAZwPOX0Z0P7ft7bcpq2bFy/9v7Pl9D3iC6A61geXvcP1KmrYqV7z6HwDmxup/HMgLOL8F8M/YYzQbOCrIxwaYCJyfotf+YcA7sfb1FvCNgPPHEv1sXALcBFgT8uN+VgXVfhPkB9J+E+QH0n4T5AfSfhvK3+E2O9V+E9QeSNtNkB9U223wsQmq/eqU+SeLPeEiIiIiIiJpk0lTuUREREREZBeljomIiIiIiKSdOiYiIiIiIpJ26piIiIiIiEjaqWMiIiIiIiJpp46JiIiIiIiknTomIiIiIiKSdv8PQYN0+1vT044AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# simple Ann Model \n",
        "\n",
        "from tensorflow.keras.layers import Dense,Flatten\n",
        "from tensorflow.keras.models import Sequential"
      ],
      "metadata": {
        "id": "RLLBCYG41R_n"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Flatten(input_shape = [28,28]))\n",
        "model.add(Dense(300,activation = 'relu'))\n",
        "model.add(Dense(100,activation = 'relu'))\n",
        "model.add(Dense(10,activation = 'softmax'))\n",
        "model.summary()\n",
        "     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xAB-11mq1UW4",
        "outputId": "0ad6b77d-bb93-4d3a-e6d9-47be622f7fc9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten (Flatten)           (None, 784)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 300)               235500    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 100)               30100     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                1010      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 266,610\n",
            "Trainable params: 266,610\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "model.layers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gOb1wzNg1ZGI",
        "outputId": "2f72e867-7598-4d09-9a6e-561bc294ad31"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.layers.reshaping.flatten.Flatten at 0x7f9a719e4c40>,\n",
              " <keras.layers.core.dense.Dense at 0x7f9a719e4e50>,\n",
              " <keras.layers.core.dense.Dense at 0x7f9a71988d00>,\n",
              " <keras.layers.core.dense.Dense at 0x7f9a719e40a0>]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "LOSS_FUNCTION = 'sparse_categorical_crossentropy'\n",
        "OPTIMIZERS = 'SGD'\n",
        "METRICS = ['accuracy']\n",
        "     "
      ],
      "metadata": {
        "id": "JNebCxMu1a6I"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "from tensorflow._api.v2.config import optimizer\n",
        "model.compile(loss = LOSS_FUNCTION,optimizer= OPTIMIZERS,metrics = METRICS)\n"
      ],
      "metadata": {
        "id": "N4oG6VC41crI"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "EPOCHS = 20\n",
        "VALIDATION_SET= (X_valid,y_valid)\n",
        "\n",
        "history = model.fit(X_train,y_train,epochs = EPOCHS,validation_data = VALIDATION_SET)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ByOIIIEr1eUI",
        "outputId": "4ae440b4-3309-4b68-dff0-d7d2863ec77c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1719/1719 [==============================] - 7s 3ms/step - loss: 0.7137 - accuracy: 0.7668 - val_loss: 0.5058 - val_accuracy: 0.8286\n",
            "Epoch 2/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.4864 - accuracy: 0.8313 - val_loss: 0.4577 - val_accuracy: 0.8390\n",
            "Epoch 3/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.4425 - accuracy: 0.8451 - val_loss: 0.4233 - val_accuracy: 0.8556\n",
            "Epoch 4/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.4155 - accuracy: 0.8530 - val_loss: 0.4042 - val_accuracy: 0.8658\n",
            "Epoch 5/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3956 - accuracy: 0.8604 - val_loss: 0.4012 - val_accuracy: 0.8624\n",
            "Epoch 6/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3790 - accuracy: 0.8647 - val_loss: 0.3799 - val_accuracy: 0.8684\n",
            "Epoch 7/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3659 - accuracy: 0.8705 - val_loss: 0.3615 - val_accuracy: 0.8742\n",
            "Epoch 8/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3551 - accuracy: 0.8731 - val_loss: 0.3574 - val_accuracy: 0.8770\n",
            "Epoch 9/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3432 - accuracy: 0.8768 - val_loss: 0.3563 - val_accuracy: 0.8798\n",
            "Epoch 10/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3358 - accuracy: 0.8784 - val_loss: 0.3404 - val_accuracy: 0.8800\n",
            "Epoch 11/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3261 - accuracy: 0.8826 - val_loss: 0.3349 - val_accuracy: 0.8798\n",
            "Epoch 12/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3184 - accuracy: 0.8863 - val_loss: 0.3373 - val_accuracy: 0.8862\n",
            "Epoch 13/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3109 - accuracy: 0.8884 - val_loss: 0.3271 - val_accuracy: 0.8846\n",
            "Epoch 14/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3050 - accuracy: 0.8902 - val_loss: 0.3198 - val_accuracy: 0.8880\n",
            "Epoch 15/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.2982 - accuracy: 0.8927 - val_loss: 0.3231 - val_accuracy: 0.8846\n",
            "Epoch 16/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.2918 - accuracy: 0.8946 - val_loss: 0.3147 - val_accuracy: 0.8882\n",
            "Epoch 17/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.2861 - accuracy: 0.8965 - val_loss: 0.3149 - val_accuracy: 0.8904\n",
            "Epoch 18/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.2803 - accuracy: 0.8993 - val_loss: 0.3077 - val_accuracy: 0.8910\n",
            "Epoch 19/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.2740 - accuracy: 0.9013 - val_loss: 0.3233 - val_accuracy: 0.8854\n",
            "Epoch 20/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.2698 - accuracy: 0.9030 - val_loss: 0.3088 - val_accuracy: 0.8946\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y9VD8fv91sT3",
        "outputId": "1c76c57e-0eed-4113-8102-7becaef8cb2e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 4ms/step - loss: 0.3412 - accuracy: 0.8779\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3412156403064728, 0.8779000043869019]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(history.history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "hhXY38he2ZCX",
        "outputId": "2e4a4cac-3dce-47be-8f86-464a41f3b68f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        loss  accuracy  val_loss  val_accuracy\n",
              "0   0.713718  0.766800  0.505805        0.8286\n",
              "1   0.486358  0.831309  0.457718        0.8390\n",
              "2   0.442509  0.845145  0.423265        0.8556\n",
              "3   0.415537  0.852982  0.404183        0.8658\n",
              "4   0.395581  0.860400  0.401164        0.8624\n",
              "5   0.378980  0.864691  0.379921        0.8684\n",
              "6   0.365949  0.870509  0.361530        0.8742\n",
              "7   0.355068  0.873091  0.357424        0.8770\n",
              "8   0.343169  0.876800  0.356333        0.8798\n",
              "9   0.335825  0.878436  0.340368        0.8800\n",
              "10  0.326121  0.882564  0.334863        0.8798\n",
              "11  0.318397  0.886345  0.337297        0.8862\n",
              "12  0.310882  0.888382  0.327135        0.8846\n",
              "13  0.305023  0.890182  0.319821        0.8880\n",
              "14  0.298168  0.892655  0.323114        0.8846\n",
              "15  0.291804  0.894600  0.314689        0.8882\n",
              "16  0.286117  0.896455  0.314853        0.8904\n",
              "17  0.280313  0.899309  0.307701        0.8910\n",
              "18  0.274011  0.901327  0.323291        0.8854\n",
              "19  0.269762  0.902982  0.308796        0.8946"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4cce2108-57a7-434f-917c-4c6a76a126c1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>val_accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.713718</td>\n",
              "      <td>0.766800</td>\n",
              "      <td>0.505805</td>\n",
              "      <td>0.8286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.486358</td>\n",
              "      <td>0.831309</td>\n",
              "      <td>0.457718</td>\n",
              "      <td>0.8390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.442509</td>\n",
              "      <td>0.845145</td>\n",
              "      <td>0.423265</td>\n",
              "      <td>0.8556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.415537</td>\n",
              "      <td>0.852982</td>\n",
              "      <td>0.404183</td>\n",
              "      <td>0.8658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.395581</td>\n",
              "      <td>0.860400</td>\n",
              "      <td>0.401164</td>\n",
              "      <td>0.8624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.378980</td>\n",
              "      <td>0.864691</td>\n",
              "      <td>0.379921</td>\n",
              "      <td>0.8684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.365949</td>\n",
              "      <td>0.870509</td>\n",
              "      <td>0.361530</td>\n",
              "      <td>0.8742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.355068</td>\n",
              "      <td>0.873091</td>\n",
              "      <td>0.357424</td>\n",
              "      <td>0.8770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.343169</td>\n",
              "      <td>0.876800</td>\n",
              "      <td>0.356333</td>\n",
              "      <td>0.8798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.335825</td>\n",
              "      <td>0.878436</td>\n",
              "      <td>0.340368</td>\n",
              "      <td>0.8800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.326121</td>\n",
              "      <td>0.882564</td>\n",
              "      <td>0.334863</td>\n",
              "      <td>0.8798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.318397</td>\n",
              "      <td>0.886345</td>\n",
              "      <td>0.337297</td>\n",
              "      <td>0.8862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.310882</td>\n",
              "      <td>0.888382</td>\n",
              "      <td>0.327135</td>\n",
              "      <td>0.8846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.305023</td>\n",
              "      <td>0.890182</td>\n",
              "      <td>0.319821</td>\n",
              "      <td>0.8880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.298168</td>\n",
              "      <td>0.892655</td>\n",
              "      <td>0.323114</td>\n",
              "      <td>0.8846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.291804</td>\n",
              "      <td>0.894600</td>\n",
              "      <td>0.314689</td>\n",
              "      <td>0.8882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.286117</td>\n",
              "      <td>0.896455</td>\n",
              "      <td>0.314853</td>\n",
              "      <td>0.8904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.280313</td>\n",
              "      <td>0.899309</td>\n",
              "      <td>0.307701</td>\n",
              "      <td>0.8910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.274011</td>\n",
              "      <td>0.901327</td>\n",
              "      <td>0.323291</td>\n",
              "      <td>0.8854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.269762</td>\n",
              "      <td>0.902982</td>\n",
              "      <td>0.308796</td>\n",
              "      <td>0.8946</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4cce2108-57a7-434f-917c-4c6a76a126c1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4cce2108-57a7-434f-917c-4c6a76a126c1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4cce2108-57a7-434f-917c-4c6a76a126c1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
          "execution_count": 17
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      ]
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        "pd.DataFrame(history.history).plot(figsize=(8,5))\n",
        "plt.grid(True)\n",
        "plt.gca().set_ylim(0,1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "WeJchSTk2aZG",
        "outputId": "acb09e3d-ef10-4f84-c72e-3037e73dc045"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# approach 1\n"
      ],
      "metadata": {
        "id": "ZTGSN88v2cYG"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del model"
      ],
      "metadata": {
        "id": "DSwONxat2iOG"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from keras.layers import BatchNormalization"
      ],
      "metadata": {
        "id": "ca09SJCz3Ov0"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Flatten(input_shape = [28,28]))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(300,activation = 'relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(100,activation = 'relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(10,activation = 'softmax'))\n",
        "model.summary()\n",
        "     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gI65ER8W2i1G",
        "outputId": "61b2f10c-f3c3-48be-bd53-ac5a28532d87"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_4 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 784)              3136      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 300)               235500    \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 300)              1200      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 100)               30100     \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 100)              400       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 10)                1010      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 271,346\n",
            "Trainable params: 268,978\n",
            "Non-trainable params: 2,368\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BN_params = sum([784 * 4, 300 * 4, 100 * 4])\n",
        "BN_params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g3tjq5vH3GDe",
        "outputId": "6f194410-30f8-487a-9bd1-bc1988043848"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4736"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Trainable_BN_params = sum([784 * 4, 300 * 4, 100 * 4])/2\n",
        "Trainable_BN_params ## Gamma, Beta"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XoIi2MAe3kuV",
        "outputId": "6e79b209-3537-44a5-a15f-2ebe06478a74"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2368.0"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Non_Trainable_BN_params = BN_params - Trainable_BN_params\n",
        "Non_Trainable_BN_params ## Mean, std deviation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WWHFO00g3mQ1",
        "outputId": "a3514df6-ac4e-4868-be4d-05f15fe79f6c"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2368.0"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LOSS_FUNCTION = \"sparse_categorical_crossentropy\"\n",
        "OPTIMIZER = \"SGD\"\n",
        "METRICS = [\"accuracy\"]\n",
        "\n",
        "model.compile(loss=LOSS_FUNCTION,\n",
        "              optimizer=OPTIMIZER,\n",
        "              metrics=METRICS)\n"
      ],
      "metadata": {
        "id": "m8giOzx13nql"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCHS = 20\n",
        "VALIDATION_SET = (X_valid, y_valid)\n",
        "\n",
        "history = model.fit(X_train, y_train, \n",
        "                    epochs=EPOCHS, \n",
        "                    validation_data=VALIDATION_SET)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fW0BBwKZ3o8V",
        "outputId": "9812eca0-f155-4757-cc27-301dbdcb3e56"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1719/1719 [==============================] - 8s 4ms/step - loss: 0.5277 - accuracy: 0.8165 - val_loss: 0.3849 - val_accuracy: 0.8626\n",
            "Epoch 2/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3956 - accuracy: 0.8607 - val_loss: 0.3432 - val_accuracy: 0.8776\n",
            "Epoch 3/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3532 - accuracy: 0.8718 - val_loss: 0.3287 - val_accuracy: 0.8818\n",
            "Epoch 4/20\n",
            "1719/1719 [==============================] - 8s 4ms/step - loss: 0.3256 - accuracy: 0.8838 - val_loss: 0.3259 - val_accuracy: 0.8844\n",
            "Epoch 5/20\n",
            "1719/1719 [==============================] - 8s 5ms/step - loss: 0.3078 - accuracy: 0.8890 - val_loss: 0.3145 - val_accuracy: 0.8892\n",
            "Epoch 6/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2880 - accuracy: 0.8946 - val_loss: 0.3159 - val_accuracy: 0.8860\n",
            "Epoch 7/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2752 - accuracy: 0.8999 - val_loss: 0.3112 - val_accuracy: 0.8892\n",
            "Epoch 8/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2637 - accuracy: 0.9043 - val_loss: 0.3046 - val_accuracy: 0.8938\n",
            "Epoch 9/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2519 - accuracy: 0.9083 - val_loss: 0.3130 - val_accuracy: 0.8910\n",
            "Epoch 10/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2425 - accuracy: 0.9119 - val_loss: 0.3168 - val_accuracy: 0.8930\n",
            "Epoch 11/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2321 - accuracy: 0.9152 - val_loss: 0.3123 - val_accuracy: 0.8924\n",
            "Epoch 12/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2227 - accuracy: 0.9189 - val_loss: 0.3063 - val_accuracy: 0.8940\n",
            "Epoch 13/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2164 - accuracy: 0.9211 - val_loss: 0.3069 - val_accuracy: 0.8926\n",
            "Epoch 14/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2070 - accuracy: 0.9243 - val_loss: 0.3128 - val_accuracy: 0.8944\n",
            "Epoch 15/20\n",
            "1719/1719 [==============================] - 8s 4ms/step - loss: 0.1993 - accuracy: 0.9271 - val_loss: 0.3085 - val_accuracy: 0.8940\n",
            "Epoch 16/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.1941 - accuracy: 0.9287 - val_loss: 0.3098 - val_accuracy: 0.8956\n",
            "Epoch 17/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.1854 - accuracy: 0.9319 - val_loss: 0.3152 - val_accuracy: 0.8924\n",
            "Epoch 18/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.1778 - accuracy: 0.9357 - val_loss: 0.3175 - val_accuracy: 0.8954\n",
            "Epoch 19/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.1715 - accuracy: 0.9374 - val_loss: 0.3171 - val_accuracy: 0.8956\n",
            "Epoch 20/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.1696 - accuracy: 0.9386 - val_loss: 0.3206 - val_accuracy: 0.8956\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_GnjGnjH3rY1",
        "outputId": "fa0af209-c0b1-4d65-81e4-5356e47173ad"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3443 - accuracy: 0.8876\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.34433671832084656, 0.8876000046730042]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(history.history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "CsSNgao04XU0",
        "outputId": "02c7fb18-65a3-4a4b-f210-0227c8c929b9"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        loss  accuracy  val_loss  val_accuracy\n",
              "0   0.527676  0.816491  0.384874        0.8626\n",
              "1   0.395618  0.860673  0.343211        0.8776\n",
              "2   0.353196  0.871764  0.328695        0.8818\n",
              "3   0.325584  0.883836  0.325888        0.8844\n",
              "4   0.307825  0.889018  0.314500        0.8892\n",
              "5   0.288005  0.894582  0.315870        0.8860\n",
              "6   0.275168  0.899927  0.311154        0.8892\n",
              "7   0.263736  0.904327  0.304644        0.8938\n",
              "8   0.251946  0.908327  0.312961        0.8910\n",
              "9   0.242523  0.911855  0.316769        0.8930\n",
              "10  0.232134  0.915164  0.312266        0.8924\n",
              "11  0.222719  0.918927  0.306258        0.8940\n",
              "12  0.216426  0.921145  0.306896        0.8926\n",
              "13  0.207016  0.924327  0.312819        0.8944\n",
              "14  0.199291  0.927091  0.308536        0.8940\n",
              "15  0.194093  0.928727  0.309817        0.8956\n",
              "16  0.185409  0.931945  0.315214        0.8924\n",
              "17  0.177802  0.935745  0.317503        0.8954\n",
              "18  0.171455  0.937418  0.317103        0.8956\n",
              "19  0.169636  0.938618  0.320561        0.8956"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>val_accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.527676</td>\n",
              "      <td>0.816491</td>\n",
              "      <td>0.384874</td>\n",
              "      <td>0.8626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.395618</td>\n",
              "      <td>0.860673</td>\n",
              "      <td>0.343211</td>\n",
              "      <td>0.8776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.353196</td>\n",
              "      <td>0.871764</td>\n",
              "      <td>0.328695</td>\n",
              "      <td>0.8818</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.325584</td>\n",
              "      <td>0.883836</td>\n",
              "      <td>0.325888</td>\n",
              "      <td>0.8844</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.307825</td>\n",
              "      <td>0.889018</td>\n",
              "      <td>0.314500</td>\n",
              "      <td>0.8892</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.288005</td>\n",
              "      <td>0.894582</td>\n",
              "      <td>0.315870</td>\n",
              "      <td>0.8860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.275168</td>\n",
              "      <td>0.899927</td>\n",
              "      <td>0.311154</td>\n",
              "      <td>0.8892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.263736</td>\n",
              "      <td>0.904327</td>\n",
              "      <td>0.304644</td>\n",
              "      <td>0.8938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.251946</td>\n",
              "      <td>0.908327</td>\n",
              "      <td>0.312961</td>\n",
              "      <td>0.8910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.242523</td>\n",
              "      <td>0.911855</td>\n",
              "      <td>0.316769</td>\n",
              "      <td>0.8930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.232134</td>\n",
              "      <td>0.915164</td>\n",
              "      <td>0.312266</td>\n",
              "      <td>0.8924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.222719</td>\n",
              "      <td>0.918927</td>\n",
              "      <td>0.306258</td>\n",
              "      <td>0.8940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.216426</td>\n",
              "      <td>0.921145</td>\n",
              "      <td>0.306896</td>\n",
              "      <td>0.8926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.207016</td>\n",
              "      <td>0.924327</td>\n",
              "      <td>0.312819</td>\n",
              "      <td>0.8944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.199291</td>\n",
              "      <td>0.927091</td>\n",
              "      <td>0.308536</td>\n",
              "      <td>0.8940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.194093</td>\n",
              "      <td>0.928727</td>\n",
              "      <td>0.309817</td>\n",
              "      <td>0.8956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.185409</td>\n",
              "      <td>0.931945</td>\n",
              "      <td>0.315214</td>\n",
              "      <td>0.8924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.177802</td>\n",
              "      <td>0.935745</td>\n",
              "      <td>0.317503</td>\n",
              "      <td>0.8954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.171455</td>\n",
              "      <td>0.937418</td>\n",
              "      <td>0.317103</td>\n",
              "      <td>0.8956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.169636</td>\n",
              "      <td>0.938618</td>\n",
              "      <td>0.320561</td>\n",
              "      <td>0.8956</td>\n",
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          "metadata": {},
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        "pd.DataFrame(history.history).plot(figsize=(8,5))\n",
        "plt.grid(True)\n",
        "plt.gca().set_ylim(0,1)\n",
        "plt.show()"
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        "outputId": "400e32f9-8e8b-46c6-e87f-29e3a71f01ac"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# approach 2"
      ],
      "metadata": {
        "id": "xoQUL5QL4pur"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del model"
      ],
      "metadata": {
        "id": "pQpNLM7N4ocr"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "LAYER_BN_TWO = [\n",
        "          tf.keras.layers.Flatten(input_shape=[28, 28], name=\"inputLayer\"),\n",
        "          tf.keras.layers.BatchNormalization(),\n",
        "          tf.keras.layers.Dense(300, name=\"hiddenLayer1\"),\n",
        "          tf.keras.layers.BatchNormalization(),\n",
        "          tf.keras.layers.Activation(\"relu\"),\n",
        "          tf.keras.layers.Dense(100, name=\"hiddenLayer2\"),\n",
        "          tf.keras.layers.BatchNormalization(),\n",
        "          tf.keras.layers.Activation(\"relu\"),\n",
        "          tf.keras.layers.Dense(10, activation=\"softmax\", name=\"outputLayer\"),\n",
        "          \n",
        "]\n",
        "     "
      ],
      "metadata": {
        "id": "jptMLQKz4pOV"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential(LAYER_BN_TWO)"
      ],
      "metadata": {
        "id": "AZ7b5GHz5B60"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "BN_layer_1 = model.layers[1]\n",
        "BN_layer_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BJfrvYgi63uy",
        "outputId": "0273b067-a5d4-442d-ea2e-150b37deeb22"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.layers.normalization.batch_normalization.BatchNormalization at 0x7f99e2420a30>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for variable in BN_layer_1.variables:\n",
        "  print(variable.name, variable.trainable)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "958i5WA766ry",
        "outputId": "c9f3c4f0-f9ea-47ba-9f2c-58cf2ccd4a07"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "batch_normalization_14/gamma:0 True\n",
            "batch_normalization_14/beta:0 True\n",
            "batch_normalization_14/moving_mean:0 False\n",
            "batch_normalization_14/moving_variance:0 False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vlEwwu_K68PC",
        "outputId": "1d9a6faa-a5cb-4228-b11a-8110ea4a066a"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_10\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " inputLayer (Flatten)        (None, 784)               0         \n",
            "                                                                 \n",
            " batch_normalization_14 (Bat  (None, 784)              3136      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " hiddenLayer1 (Dense)        (None, 300)               235500    \n",
            "                                                                 \n",
            " batch_normalization_15 (Bat  (None, 300)              1200      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " activation (Activation)     (None, 300)               0         \n",
            "                                                                 \n",
            " hiddenLayer2 (Dense)        (None, 100)               30100     \n",
            "                                                                 \n",
            " batch_normalization_16 (Bat  (None, 100)              400       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 100)               0         \n",
            "                                                                 \n",
            " outputLayer (Dense)         (None, 10)                1010      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 271,346\n",
            "Trainable params: 268,978\n",
            "Non-trainable params: 2,368\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BN_params = sum([784 * 4, 300 * 4, 100 * 4])\n",
        "BN_params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVEY3Kd769fx",
        "outputId": "a4e848a8-889d-4c57-93ba-e309cd9d260b"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4736"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Trainable_BN_params = sum([784 * 4, 300 * 4, 100 * 4])/2\n",
        "Trainable_BN_params ## Gamma, Beta"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uTAW1t906-9i",
        "outputId": "c6250840-e806-4ab3-ea3e-84d28f3d623a"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2368.0"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Non_Trainable_BN_params = BN_params - Trainable_BN_params\n",
        "Non_Trainable_BN_params ## Mean, std deviation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZAr-BLWG7AFy",
        "outputId": "71c6f7ec-9088-4e56-b3c1-8bb243e7b4e1"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2368.0"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LOSS_FUNCTION = \"sparse_categorical_crossentropy\"\n",
        "OPTIMIZER = \"SGD\"\n",
        "METRICS = [\"accuracy\"]\n",
        "\n",
        "model.compile(loss=LOSS_FUNCTION,\n",
        "              optimizer=OPTIMIZER,\n",
        "              metrics=METRICS)\n"
      ],
      "metadata": {
        "id": "c1QPw71r7Bci"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCHS = 20\n",
        "VALIDATION_SET = (X_valid, y_valid)\n",
        "\n",
        "history = model.fit(X_train, y_train, \n",
        "                    epochs=EPOCHS, \n",
        "                    validation_data=VALIDATION_SET)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5pW4OS9b7C6y",
        "outputId": "a1ea5156-a48b-4972-e419-c5dafffd7800"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1719/1719 [==============================] - 8s 4ms/step - loss: 0.5900 - accuracy: 0.8016 - val_loss: 0.3899 - val_accuracy: 0.8650\n",
            "Epoch 2/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.4114 - accuracy: 0.8563 - val_loss: 0.3507 - val_accuracy: 0.8744\n",
            "Epoch 3/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3695 - accuracy: 0.8698 - val_loss: 0.3344 - val_accuracy: 0.8824\n",
            "Epoch 4/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3423 - accuracy: 0.8788 - val_loss: 0.3232 - val_accuracy: 0.8810\n",
            "Epoch 5/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3199 - accuracy: 0.8845 - val_loss: 0.3134 - val_accuracy: 0.8842\n",
            "Epoch 6/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.3047 - accuracy: 0.8911 - val_loss: 0.3099 - val_accuracy: 0.8892\n",
            "Epoch 7/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2885 - accuracy: 0.8970 - val_loss: 0.3008 - val_accuracy: 0.8888\n",
            "Epoch 8/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2761 - accuracy: 0.9015 - val_loss: 0.3032 - val_accuracy: 0.8826\n",
            "Epoch 9/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2650 - accuracy: 0.9037 - val_loss: 0.2982 - val_accuracy: 0.8896\n",
            "Epoch 10/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2520 - accuracy: 0.9076 - val_loss: 0.2936 - val_accuracy: 0.8890\n",
            "Epoch 11/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2413 - accuracy: 0.9133 - val_loss: 0.2974 - val_accuracy: 0.8900\n",
            "Epoch 12/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2311 - accuracy: 0.9156 - val_loss: 0.2905 - val_accuracy: 0.8946\n",
            "Epoch 13/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2242 - accuracy: 0.9189 - val_loss: 0.2935 - val_accuracy: 0.8930\n",
            "Epoch 14/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2161 - accuracy: 0.9222 - val_loss: 0.2909 - val_accuracy: 0.8944\n",
            "Epoch 15/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.2086 - accuracy: 0.9237 - val_loss: 0.2996 - val_accuracy: 0.8904\n",
            "Epoch 16/20\n",
            "1719/1719 [==============================] - 9s 5ms/step - loss: 0.1998 - accuracy: 0.9281 - val_loss: 0.2961 - val_accuracy: 0.8958\n",
            "Epoch 17/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.1957 - accuracy: 0.9302 - val_loss: 0.2934 - val_accuracy: 0.8952\n",
            "Epoch 18/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.1884 - accuracy: 0.9321 - val_loss: 0.3059 - val_accuracy: 0.8926\n",
            "Epoch 19/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.1796 - accuracy: 0.9351 - val_loss: 0.2990 - val_accuracy: 0.8928\n",
            "Epoch 20/20\n",
            "1719/1719 [==============================] - 7s 4ms/step - loss: 0.1750 - accuracy: 0.9369 - val_loss: 0.2983 - val_accuracy: 0.8948\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7yTdIZzo7E1i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf22d968-58fd-4a58-a9a4-c652a657ae80",
        "id": "9l_0owIz7IEW"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3346 - accuracy: 0.8883\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3345930278301239, 0.8883000016212463]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(history.history)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "outputId": "f3085dca-09b7-4f42-fa09-fd71a77ea544",
        "id": "vJMStVlx7IEX"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        loss  accuracy  val_loss  val_accuracy\n",
              "0   0.589978  0.801582  0.389916        0.8650\n",
              "1   0.411357  0.856327  0.350690        0.8744\n",
              "2   0.369473  0.869764  0.334434        0.8824\n",
              "3   0.342257  0.878764  0.323185        0.8810\n",
              "4   0.319950  0.884545  0.313427        0.8842\n",
              "5   0.304671  0.891091  0.309850        0.8892\n",
              "6   0.288528  0.897000  0.300813        0.8888\n",
              "7   0.276127  0.901455  0.303237        0.8826\n",
              "8   0.265010  0.903673  0.298155        0.8896\n",
              "9   0.251995  0.907582  0.293638        0.8890\n",
              "10  0.241275  0.913273  0.297372        0.8900\n",
              "11  0.231120  0.915582  0.290490        0.8946\n",
              "12  0.224182  0.918909  0.293550        0.8930\n",
              "13  0.216070  0.922200  0.290869        0.8944\n",
              "14  0.208566  0.923673  0.299634        0.8904\n",
              "15  0.199766  0.928127  0.296056        0.8958\n",
              "16  0.195660  0.930200  0.293379        0.8952\n",
              "17  0.188423  0.932055  0.305927        0.8926\n",
              "18  0.179636  0.935055  0.298993        0.8928\n",
              "19  0.175026  0.936945  0.298289        0.8948"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d82dd621-0dd2-445f-9eb4-5195c26b727a\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>val_accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.589978</td>\n",
              "      <td>0.801582</td>\n",
              "      <td>0.389916</td>\n",
              "      <td>0.8650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.411357</td>\n",
              "      <td>0.856327</td>\n",
              "      <td>0.350690</td>\n",
              "      <td>0.8744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.369473</td>\n",
              "      <td>0.869764</td>\n",
              "      <td>0.334434</td>\n",
              "      <td>0.8824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.342257</td>\n",
              "      <td>0.878764</td>\n",
              "      <td>0.323185</td>\n",
              "      <td>0.8810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.319950</td>\n",
              "      <td>0.884545</td>\n",
              "      <td>0.313427</td>\n",
              "      <td>0.8842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.304671</td>\n",
              "      <td>0.891091</td>\n",
              "      <td>0.309850</td>\n",
              "      <td>0.8892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.288528</td>\n",
              "      <td>0.897000</td>\n",
              "      <td>0.300813</td>\n",
              "      <td>0.8888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.276127</td>\n",
              "      <td>0.901455</td>\n",
              "      <td>0.303237</td>\n",
              "      <td>0.8826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.265010</td>\n",
              "      <td>0.903673</td>\n",
              "      <td>0.298155</td>\n",
              "      <td>0.8896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.251995</td>\n",
              "      <td>0.907582</td>\n",
              "      <td>0.293638</td>\n",
              "      <td>0.8890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.241275</td>\n",
              "      <td>0.913273</td>\n",
              "      <td>0.297372</td>\n",
              "      <td>0.8900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.231120</td>\n",
              "      <td>0.915582</td>\n",
              "      <td>0.290490</td>\n",
              "      <td>0.8946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.224182</td>\n",
              "      <td>0.918909</td>\n",
              "      <td>0.293550</td>\n",
              "      <td>0.8930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.216070</td>\n",
              "      <td>0.922200</td>\n",
              "      <td>0.290869</td>\n",
              "      <td>0.8944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.208566</td>\n",
              "      <td>0.923673</td>\n",
              "      <td>0.299634</td>\n",
              "      <td>0.8904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.199766</td>\n",
              "      <td>0.928127</td>\n",
              "      <td>0.296056</td>\n",
              "      <td>0.8958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.195660</td>\n",
              "      <td>0.930200</td>\n",
              "      <td>0.293379</td>\n",
              "      <td>0.8952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.188423</td>\n",
              "      <td>0.932055</td>\n",
              "      <td>0.305927</td>\n",
              "      <td>0.8926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.179636</td>\n",
              "      <td>0.935055</td>\n",
              "      <td>0.298993</td>\n",
              "      <td>0.8928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.175026</td>\n",
              "      <td>0.936945</td>\n",
              "      <td>0.298289</td>\n",
              "      <td>0.8948</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d82dd621-0dd2-445f-9eb4-5195c26b727a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
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              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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          "metadata": {},
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      "cell_type": "code",
      "source": [
        "pd.DataFrame(history.history).plot(figsize=(8,5))\n",
        "plt.grid(True)\n",
        "plt.gca().set_ylim(0,1)\n",
        "plt.show()"
      ],
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          "height": 324
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        "outputId": "cf030d33-43f9-41c4-a850-b4e05d094ed9",
        "id": "DVLx7JWv7IEX"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-B08wRiy7JmC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}