220079842_assignment1_solution (1).ipynb

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      "source": [
        "# SIT744 Assignment 1: Image Classification with Deep Feedforward Neural Network\n",
        "\n",
        "<div class=\"alert-info\">\n",
        "    <p>Due: <strong>8:00 pm (AEST) 19 April 2021</strong>  (Monday)</p>\n",
        "\n",
        "\n",
        "This is an <strong>individual</strong> assignment. It contributes <strong>30%</strong> to your final mark. Read the assignment instruction carefully.\n",
        "\n",
        "<h2> What to submit </h2>\n",
        "\n",
        "<p>\n",
        "This assignment is to be completed individually and submitted to CloudDeakin. <strong>By the due date, you are required to submit the following files to the corresponding Assignment (Dropbox) in CloudDeakin</strong>:\n",
        "\n",
        "<ol>\n",
        "<li>\t<strong>[YourID]_assignment1_solution.ipynp</strong>:  This is your Python notebook solution source file. </li>\n",
        "<li>\t<strong>[YourID]_assingment1_output.html</strong>: This is the output of your Python notebook solution <emph>exported</emph> in HTML format.</li>\n",
        "<li>\tExtra files needed to complete your assignment, if any (e.g., images used in your answers).</li>\n",
        "</ol>\n",
        "</p>\n",
        "\n",
        "<p>\n",
        "For example, if your student ID is: 123456, you will then need to submit the following files:\n",
        "<ul>\n",
        "<li> 123456_assignment1_solution.ipynp </li>\n",
        "<li> 123456_assignment1_output.html</li>\n",
        "</ul>\n",
        "</p>\n",
        "\n",
        "<h2> Marking criteria </h2>\n",
        "\n",
        "<p>\n",
        "Your submission will be marked using the following criteria.\n",
        "\n",
        "<ul>\n",
        "<li> Showing good effort through completed tasks.</li>\n",
        "<li> Applying deep learning theory to design suitable deep learning solutions for the tasks.</li>\n",
        "<li> Critically evaluating and reflecting on the pros and cons of various design decisions.</li>\n",
        "<li> Demonstrating creativity and resourcefulness in providing unique individual solutions. (<em>Warning: Highly similar solutions will be investigated for collusion.</em>)</li>\n",
        "<li> Showing attention to details through a good quality assignment report.</li>\n",
        "</ul>\n",
        "</p>\n",
        "\n",
        "<p>\n",
        "Indicative weights of various tasks are provided below, but the assignment will be marked by the overall quality per the above criteria. \n",
        "</p>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5QKV13ZpxFel"
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      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
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      "metadata": {
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      "source": [
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JOr2bP8kAzKE"
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      "source": [
        "## Assignment objective\n",
        "\n",
        "\n",
        "\n",
        "This assignment is for you to demonstrate the knowledge in deep learning that you have acquired from the lectures and practical lab materials. Most tasks in this assignment are straightforward applications of the practical materials in weeks 1-5. Going through these materials before attempting this assignment is highly recommended.\n",
        "\n",
        "In this assignment, you are going to work with the Fashion-MNIST dataset for image recognition. The dataset contains 10 classes of 28x28 grayscale images. You will see some examples in the visualization task below. \n",
        "\n",
        "This assignment consists of five tasks.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cDqvFgbRAzKG"
      },
      "source": [
        "## Task 1 Load the data\n",
        "\n",
        "*(weight ~5%)*\n",
        "\n",
        "Load the Fashion MNIST dataset (https://github.com/zalandoresearch/fashion-mnist). You may get the data via Keras (keras.datasets) or Tensorflow Datasets (tfds). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Krg_ro9M-t_k"
      },
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_datasets\n",
        "import math\n",
        "import numpy as np\n",
        "\n",
        "# Loading the data using tensorflow\n",
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n",
        "\n",
        "# class names\n",
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nFbty6i5AzKm"
      },
      "source": [
        "## Task 2 Understand the data\n",
        "\n",
        "*(weight ~15%)*\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "Vg6WnS54Cl_G",
        "outputId": "540eebc7-27e3-4fe5-c5ef-3d216e89c5d2"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 25 images from the train set in the form of 5x5 matrix\n",
        "print(\"25 images from the train set in the form of 5x5 matrix\")\n",
        "plt.figure(figsize=(5,5))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(train_images[i])\n",
        "plt.show()\n",
        "\n",
        "# training data\n",
        "print(\"Data type of training data: \", train_images.dtype)\n",
        "print(\"Shape of training data: \", train_images.shape)\n",
        "print(\"Data type of training labels: \", train_labels.dtype)\n",
        "print(\"Shape of training labels: \", train_labels.shape)\n",
        "print(\"Length of training data: \", len(train_labels))\n",
        "\n",
        "# test data\n",
        "print(\"Data type of test data: \", test_images.dtype)\n",
        "print(\"Shape of test data: \", test_images.shape)\n",
        "print(\"Data type of test labels: \", test_labels.dtype)\n",
        "print(\"Shape of test labels: \", test_labels.shape)\n",
        "print(\"Length of test data: \", len(test_images))\n",
        "\n",
        "# unique labels in the data\n",
        "print(\"Labels  in the data: \", np.unique(test_labels))\n",
        "\n",
        "# size of each image\n",
        "print(\"Size of each image\", train_images[0].shape)\n",
        "\n",
        "# memory required for training data\n",
        "print(\"Memory required for training data: \", train_images.size * train_images.itemsize, \"bytes\")\n",
        "\n",
        "\n",
        "# plotting the numeric range of input\n",
        "print(\"\\nPlotting the numeric range of input: [0-255]\")\n",
        "plt.figure()\n",
        "plt.imshow(train_images[100])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# The numeric range of input data is in the range [0,255], these values need to be normalized to the range [0,1]. \n",
        "# Normalisation ensures that each input parameter (pixel, in this case) has a similar data distribution. \n",
        "# This makes convergence faster while training the network.\n",
        "\n",
        "# normalisation\n",
        "train_images = train_images / 255.0\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "25 images from the train set in the form of 5x5 matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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V7q5mfVKrGM74+OsNuLoHB+EOYZVTuFspQNjt+NHo963CD2LMcBKFOysFB4KehECvrVO7GvMbr/0VciORyrgF28JV09uuLOCsqBEPFxuTuYtHGa94zAqQFq8naF5Msd3egdB2jSDjHH9gC9mLhxzbYN+E1GTC48ESsVV0TcCarTMwIQMTUjMJWjoZxS0SznuCfScn2W6x9dFjdN6T8dNHX0EJy6apMun1aKkBj1Zv8oOT51jOmvy/Fz7A1mYVsRYggKxu2XoEdMViGwZ0hkgUzXOKwYKl/ZFllsUc7dYUnHvXpbwjsqrkZ5pneTltcDGbRmEJZVaSFMC03yWMnE9+9Ycsr/07/4BVPeTfDE7yP8z8OJ996s/5uwtf5Rff80lS4/Ebx/+QqvTxUPxmZ5H/68LHEP8sdC6s8WnI2P1tq8hQZMbjXLLA5XSKzYcV/qBB+OK97euOISR5JNCRswyEdj+jeFIZR5K3DniPyNj4YJXcf3Iak6Z433qNI//1HJd/8gg849T4wghkUgS8jVu3MBaZiJKITeA2LUfxxPFNCaguWYJ/9c19z87thuwMiFYrbGZVYutimBpJZhWx8VnP63w3XsAXmlBmKAyx9bmRtgCIpFPQ36+DuO/kxOwk137Y8L5HrvBIdIOmjJEYmjIuUvUesfFRvuETi6+zPN1gZbFOphVx7rngZSphqFxKV0D3w0Nmprp8euEcf9CZpfHy6t2vTyq8YwukTcGl3GfTuNSrFAasRInc6XpkxlLWIs/dVXrkS5JH819CJgKVCJrLgj+5+AzvrT1N9YYzIz46+xhWOldAR5ZwXdJOr6BSy/nhHMeidRd4tx7GSgyCgQkBGJzI6S95zip8wLBSbBOP2PmzwzAYd+mMI67ydZKD0RPDmjL5a9MUsbxGtDbP2noFEWmXrEg9RI77KSQCMi8MBgEycxvVYeHWFfsUBsSWhz84ONbSDgxjvH7OqcoqZ4KbfGt4srTQAYyVpSTGWIFh+zmNILWK1HhFgG3vse/klLciPv3kyzzTuMRRb6N8XGFJiy9DYohkxg80z9Gvh/RNSGYVG3mNP+S9rG3W0cPQZVFCwyfPnOeR2hJPVi7zr3rfj37tjbten1CKbHGKtAk3dZOOjsoDNNIoRTIjEplL/Rsna5h4/gb1S21kmmMCj2Q6pHJziLq+Vn62Hcn9pWDw9AmsMi6or2EpaTIbdImEe40urvRRAL4x3yWenLjrfd0LrNi2lEqInXGl8VhTSV527P2iiF3tN8bY1OY5utMh6FpUT0Ijw3qA8BCA1EV2rgiMC0ERWnDfh61RZmHB/fb6Am8P6832EjZNUf2EOX+LYyrhW7CjqmHkFYy0eqOb5EhiYKwkN3KH1GIvse/kJHLDtUGbhXCLo/46XV1BI2nIoUtXipwp1QOgaypIaYhERmYVbTXg509+jYEJ6eoIJQxVmfKJ2jm+Ex/nH1z9QcLNe3OIZa3C+Z+OOPLYTWZUl74J6ZqKIygBDTkozeCBCYiiDPPhx0kaPlldOm2PByaAwXQd7+EaUjvXQCXWxW+UIJ6QWAXJj7+fjSdsWYzZNREL/iax8emKiKpMqauYHz5+jn82/+xeHII7hygsiKSQQxRBfVvElUQRqykx9m+ZHwx90zuiIN8oStFaktnQib59i4rdJo1XfAepU8rbUcLKUggvnYUVbgi83sEkJ72xhZKqdOmksERkpW4PtisYNJJQZEjrCGsUON+MK8iB/AsoJbgFUqswSBd4Kx4bBeoCod3zVpbq6UmvxyS98v2RyJhRKTWZkBvpMkr3AuF0Kha4kk0RG59IZNRkgi80U6rHmq7TNyHGCqZqA65//wTGd2a+rtgi+Gtd3KJIPwsrEDnlhaArFistMhVUFruldkph8UWOFk4lPlKQXxpMugtlHzAil1Ft3A4raoygdpOQFTjLQ+DaqciDyVJCW2QqEQKk3HX+jPRZyunzSlcVSqmB1AKNi0N5A4tK9r9M55YwGvKczCo0BREJV4q1u7IBXKXC7gqHNFelW7vX2HdyskrS9GMaKsZHo4QlM5I1U2cjr7GcNrg6aCOF5WMTb5ZiRKAoD7FUZUJbDYrAsYe2MKl6PDlxja9UFu8pLmPTlMZ5xQ07wz9tfJCnmld4NLrBMW+dhsyYkYLnE1jKWiTG43NHXuIX//PfBsBYi8ZigNRaFOALQSQUcnTAMWTWlMrx61rwSjrPN3oPUfVTwsJlNEKiMLwZz/BGb4azX3uI6Zf3KZaxSyJQxlcYe3yX2zcKHkttMUqQNSCvqv0/AUcYK30KuoZwQ2GMcORUWIoyc1k7CitK5K4PkvGLMrrU3XCEBhqAhcqawesm7lLf57rQW8IaBjpkUBy4URmVz7a2bvSYsbKMSWkkEsMwCe7bTfLBnxtjwkERhuQVj5PVNeoqZmBDYuOXaXRtXRFtPwvxlWbS65Faj428Rl3FhXnptFKbukqj0AN9eXiSrqlQlSmDaUnzkdPw3btbrk0S5r7ep/1GyPmXH+Xl9mPkNUsyrQmmYv7awy8zH27RUkM0kvW8xvNxk4EN6eio1IPANpk6q2j7zmSQ+CInsx7LeZOBCairhHP9eS50plj+8gIydSe9SkAllqOXMipXuw80AyTCEFmvORemyLqNa5re9n1jVpTIQeIa0ZlAIPwAm79zF80HDa+XEW54DKxACIP1LKYQ5LvMHajYCUx16ApoReHOiUL3BZSkfT9cnr2EQaB3mbkadzNUpWbCkKHK2Kd7nyTPFH7GPXXDfDs8WHIqauCs1mAtslolrypORqvUZELfhKX/Gwmn6k6NR2oUQlgmVY9NXQVqJTE1VUzfhGzpkLZyjuDXu6fxpGbW75JMCJJj7bsnpzxHfO0FIiCi6JpQr5E+fITNMzX+wLyPTzz0Op9qn8VYwWZW5ZuDh1jLamxmVSpqp9xgN0a+/aijQTePaHgx036Xi91JLl2c4bG/99Itu2A+6EiGCAJEpVLGl8bxdnGkMv5UBMCFccJN41uMJ1zLX32Lesl9hOpnhJ2InhFI4dxtEzhr0Mdl6qxxcacyDsVYWc8YF432e5Chi0zw3cCYwpq8D3jwlpNSYJyp3PnUI6w8JflwdIGODbmZt2irAdoKDJKqSpgOegStnFDmZRq9ruIiqxASFfqLlupz0l9l01T4/AvvQ4aaY3NO0Gi8vTM7Ta+HHQ7xen1mbkwTdmb5s7/xED8y8RLgrKOGijG4EzuUtzbh1ZjgTVtJQ8VoKxlqn6pMaakhF69OU3/dh2xvm3jdNbIMm6TIzAWBb1mOsjtEM56tY9vd8/oCb6Axw+GBspoAROYU/EbL7V5bY1m4kQgV6cjXG7jnsoZTyeO5fQoB2hfgHQTNxNvDFxq568ApDKq4qY7cuPGuGsZKUquo1hKSZoQQYs+rkx48ORVBVCEF2ndCvlBoAusk8QqLFi7w1lJDVGBpeUMktgyWj6wQhavv0dhSPJZZD+EbTKq4sjzB1KbF7+zhxV0UGdtuFxX4VG62SAZ+KS8AR1CjervRWp3p7A7siJgktrxjSWGKH+f2BSJHDDy8HgcG1lqE0bdfI1fqCnY+VuqezMHohrkbwhhEDkYL8ly55EWRzChhR3ICUWifHGEbz9Ubllk7IQ6GZOJdoN6mx5jCFQSPguKj5NRIg1iPEgaR/d7uSiA8D2tsWU9nUTQuxwzmq2yZEI2gKhKCwmFPreKhYJm2TNk0AV0TcS5ZKOp68rKh2by3xbqus5lVOZceQVvJ33rqK/zTC09hvzDJ9NdX0a+e39N9oJQrJjZFT6JUcTN3qlkprLPqxg7oCEqYkqDeDqO7mLYSORT4fev8eSFK33+/L+iRdODO3gQCu132ctAw/p1qg9AW0/WJc4nMBCoWqKEoNU7BAHRQBMMlYCHctOQVQVYDlYjCujx45Lsb41b8qLfYKC4qi9YZoxiUQbqid5wm6kRzg6WpFvejqdO9k1Nx0YixE24UUxrH7rYlwve48LkK9cfXiK1PbF2jq0imToBpFeu6zsBkpEX5Rk0mrvf22JURW3+b2a2T1H+i/ipfqD3K1nAC8reu5V5gjUUU0R6bJHgrXWR/htXM9QFXGBKzbUkZBBK7w0oCZzWpQiSkgazonunq9ZwrKHOBSu22takUdh+7RwohypNw1PIE3hpvKh+/5YdQZr9cLGq3rHz/YUMPHUkINV6oyQPlOkdUimxcLvD7wsXNgm1ykilY32I8i4lciUvSktSqvjvyY2r0gwY1drRKjdPYb2UNhpH4uPBwrORYZYNXGvP3ZU33Rk6jJm9CghTu5MVdwHCLO7yQCOXITNZrfOKTL/KfzP4JN3XTiRt1hQbCbbwo1UitIigk9JHICtW4W7YZc+WMFQSFQPODoeZItUM3O7r3mRKjGfWKs2mKuLmC159lM6viSY0Urq/Tu1lIu5EUexoVEgOIDEdOUH7H+wopXcxwhHGNz26MKjneEjgvZOSjwyLkwQiGj0kJrFLoQCBDjR/k5MrHhgY84+p5M4kJPKxvsb5FVHOEtOSZ3PFx2lMkbY+85uEXn33gIOSOgt+RSyffJoo/Cj+As5yOh+tM1/sHyK2TRYP9omcS4Hzr0fO7D4JU8MHHGSxWWXlSkcxnNOZ6/GTTEdP5ZB6FdQRkMwymnNoxMGERu3GuXCA0NelcQ20la7pObEYZvoxIpqzrhMfrN/jK9z1M64028vW72uWtMXKvjMZqjRkMkJlgqH1qwqVbY+OjipKbtwQaR+LKXRm8gXH9q1tq6O5MwiCM2FkJv88QnoeIwlJCUEoERilztktTxoPgI1ghENZiS12BGzdlkv13Vcf/vuzHRGsR6kpE6odU1yTWc/EkmRWi2arF5gIbg1xXyNwV/hrfFQL7HXfs8qrraLF/rQHfHt78HPrYLDPeK4RiuxPGuJsXCI2xsmhGJ9GW0njQ1udksMqRaofNA+nWQWlBCc8D30dEEUJJ8H1svYKpR6x8oM7giEA+0eGZuZt8qH2JthoQm6AoagWsIhb+jtjMqPAQthXim7qKFJZA5EyqHoHSZX2bjya2rkPA3MImeW2CYE82eQuMOnAayKws6o606/kt7Q5iKgPfu8hKFmmg8QksckwHVSrcrSndu32D3Hbr3oLdeqd3OVdLZfXI8j4I1tMIvoeOFF5PgBSEGxarBFY5jZlRgrQlyu6e3oCCnMCErrlesAUIyJoWfUD1XLZeJZmKnJ7QUggrLT7O4HDpp1Hcie3HxoTQbTlgOuyx1ZpDpikmjvdsfXdHTrumfoggQE20sVNt8naFzsmIrC5IJgTDJ4Y8eeIqvzTzEg013GEJdU2ExPBodJ03kzkuJ5NsqQq+0FRlypy/xVFvg45xdXNTss+X+o/xx8uP0QqGLFS2+OW5LxKW6mpFbBUDqzgdLPGzJ5/nH7c+u/fkNNYDHOu6H+bGBcFd9ba7MsfvQJnxd9yVZGEpus8YKw9AFHcq+9Yatf2GKPLjo84Cb5ONeyeUbt3o3/fBHbhXDE42WXuvR9Bx5Sf1mzkit8jM4vWc1Z43AvKKQkcCb2hc2UpqyJqKpKEQ1pJVBVnbEE9K2vOz6OUVl0i5j4M47gT5bJPOcY8LyQxTXo+BCVybnrFjGAjtej3tyOBZMgSJ8TnhdXi28Sbf/tDTNM5VYQ9nKt4xOclGAzE/Q+f9M/TnFTpyWYu85oKDOrTQTpGeRSrNwzPrLFY3UcKU7U9ifKSw9HREV0d8pXOGE9E6z9Qu8vJwkYEO8H035SG2Pse9Daoypyosj0Y3uNZqMxt0mfD6XMldQDy2funeKdWnIWOeCK+hg/sfp9Gh5Whlk8R4SGHx5Fj2owiEq9v0z5ymxNU22Vu4RvsNq96FTN4hDiWsddNXinpCBAjlkin2ALivwvOQrSaDaY942hAKifUEfeuVGcrKmuduRhVB2hBkNYE3lGUBtA7dmHJwCnJyQdIWdD54lObXMvKbS/tOSiPEsyG941CV6Y4YqRK2VIzfKvbkqhkU2kou5S5evPIBiUzbRGf3bn13Tk6tJv1Hprj2I4aPP/FdztSWmfT6HAvWyqLcSGb0TcjVdNK1EUHQ1W4G26h7AMBWXuFq3Oab147TPj3kY5UrvBovMNQBsURlXEIAACAASURBVPLLLN6ilzMh3fsf9ZeJGz4L/gYKwxvZDF1dYUu7BnWj3kptGXMy6JZlB/cTJoST0SoX4+myaHLkmspbCNxuhVGGxE1ccSeKlXanRma/r+B3cuvGsYugRrEmYDsWJZyYEW+fq+t2jSdjepJ4SmCmU7IswISCvCLIqxYdunpAmUNegbRtyVoar+cyxXlt1/Eppq+kbcvq+xWNc024ufTg9/g2GMwo9KmYlucqK1wIZWembgRT6px2Pv5aOs+WrlB7ao2tlWki9g53fGb0n1yg/59u8RNHXufJ2mWXZiwC2F1dceUnhdc3YuTEemwV5KStU3631YCnKhf5Ak8gv9Hkd1Y+zNceOsXfOf1HHGtt8mY2jS5UqL+5+QGW0wYvby7QCp07dzx0I5S3dBXYFocpnM5oXVdZ14WSeS8x0hzteIxSOiCFpa6SUnRZklRZU7ctKVBFUbBBEMmMzCgGJtgWdEqwu3vH7+dddyQlKESH4wFxK7clBMAOV09YW5avlE8L0IFENBuIXn//imHHA+EzUyx93wzGB/9qUGqavIELbJvIEHQkMoO8WuzFQLDp/p03XO2digV5xe3Z60tMaEknDMPjLarxCfKLlw+E9TSYE3zsoTdcL7KiL34g8kJ4KcshBrut/pHOECAxPpHI+fETL/KP5j65p+u7I3ISvk/viMdPHH+BhWCDmkx3FAKOQ2GoqaQsaB1ddMmYnqcpY1cwG4LXUVy5MMM35x+iX7nGhWSW2PgkxuPiYIpOFrHSrwEwHfbL7NYow+DEi9tNsTKcitVKEH4A6S2XeXfYfWKNkRNQ6pdulWpTwmDewjjO/fOlxmhHahpZlEkcML+uIKdb4bb7NI1i/BJs6O+UJ+wjbCVkMO8U315fYEJKJbiz9CwyK0ZelRaX2FHKI7RApgJbd+nM7ZIXS1ZXmFbtwCQA8prlsdqSa8mzq03KCL7Ib+naudKsrHiN5r3BGnnV7qlu7Y7IKZ0IWHs25xP1V3l+8DAvJYvMh1tUZcqM12He3yw7N45amczIhJoUfDWe41o2wZV4glDmVGXKufQID0c3+Ye/8Pf5ma/8bRb+Rcjnv/YJfr8Cw1k3qMD4oE71ODq5xS8+/NWyj9Kof1JLDYoBf9vs4+4CbmJE0hbIk4uwd3G6t0Bq2Mqrpck7MMEOpbfBBbr9oqRl1HbCJ0cKQ1hkPzKryAt1ObjWvWlN7L++qYDwPIzvlf3Bd5DRrYLjt/oM43w+YcfI6YAExU09YvhQir/sE24IbOasQRMUtXT+yALcqZIv70GjMpYUaGXIQKN7FbCg+pKkBd6RGtHLByPGljcMn6i/ysVshtj45fk7qmxQxfl6q8oGVQzsa8ohbTXgyWAN09DIMMQkyZ4Q1J1ZThZEIrmSTaER1L3EqaGNV0b6R1qkkcR9yUrQcD6ZYyOruX5NVtEr2t1WZcqU6vHk8at8+4dOIgYKlCWcG1ALU+phypnWCnNhh7YalCbn6Evc/tLGrDgr8UXh4tVdVuJ+khOWHVKAEXYfVDUmYNttKo/KBIBCBS+KYZtjrtB+SwmE2B5cMJ6xu51l2XFZBGW3SCsE8gDUnqmJCbKqh4hVGdyWerTG4kXWFfbuJqOy64Ky5VdhtftydMW6PlA5RcxKMj6vcV8gFTIKsZFmQQ24mLlrKXgbK2lkyZdvL+QEPvBc7zS+0JyZ+jIyyhEnFpFXrmMGg7d8zp3izshJg9eVvDxcpKGcS7alKwx0wHpaYzLoU1cJVZXQkDHSN1wzE3R1xAudY+RWMh30XftZHdHJozKz9bcX/pQTx3+f3976ELHx+SuNcxxVW8ypjIGFrvG5mE+V/Z5GP4nxQTkXbtQh02DwrRslnTUsgyP3fwzAqCnX7oDhbqhblLKMRk05S0u4tr82KMYMOeW9LeI2+wopsUrtsB5uV+qwUxZhkVrwLl/Vg4NUMDNJVvPwuhKhHQl5MY6cFAWhCifGtGMlPOPDQseHNuQCowW2rqGv8GM3sSar7r/SXyiFbDbwqjkLXliec6PeaNqKMiDuNIhyW6DJtuYQ4Lmlk2gj+ZsTzxNWMganJ6iub8GDJieZW/yu+2IvxVMMtU9FZVRkyunqCj0dspFXuTycRArLeW+ufK8vNSE5FZXSUDF1FTPp9Uvld9+EvJlP8kz1Qvmey/kEF3PJtWyCgQlQWNbzGitpnaH2yY0iNdvxikjlVFTGqcoqkczwhSafyNk8fZ9TdsL53e9GHq6vU74dk7oFjHUkl1kFnpNnHBh4CuvLHZbEiHRut1pn9HqhHcGZqo/c55iTkIJstkHSUqWlVLZFoeg0UBDUuDtnfNA1A0VdJ8o660kKvA0PHSvkZILJBFYq8ghXFDw5gdnq7Il1cTeQrQbxE4tMtDpIpIvRWlUS0ij+FMmUlkzo2yENGZUZO3CF+QCPTCyjhKUqcqYbfTrHG9TOVmDl3td5h5aTxe/C5eFEeRFJLEFRqDowAd0sYiuLkMKSGEUo3eDJQOb4wg2eHBUOVmVSXKyWftF5YMHbQAnDSqGf6JqIG2mb3EgqKmMzq7KVVYhzn9xKhrlPphWpVlT8jJqfUism8FZVCp6lSBTed4wLLOH2mniZopDZF5pRYnHU/gVpMQcjVuwgRKlzEnanNXSrnuFvi8LisAryisJ7N+3U/YaQ6KpXauLKwuViWUXZYzHpdxtWWQgKJf+Yntb64MVuYKgINKnvObLznQ6KSoSIkz2xLu4GolKhtxDQimISm+2YuDLSN/lC46MJijIrZynpIpTi+q1pK5kLu4TSXcPNMGZtQmCDvTEG7kxK0B8y+60BXzn1OE9+8A0+PX2WF/vHWE3qvNaZLYmiHQ2peil1L6UiUyoqoypTfJkTiZzYenSLmNNICt/VET0d8h17HGMFuVVoK8iNwivaqKxnNQKZMxt26auQofYZ5j5x7tHpRyShR98LSLTHXKXLk80riIEivMdpv7eL8Vq5cW1TKSUoHhsvyYHt0hYlDH6x19F0lwMx262A9RQ2GF2x21kseHdiKuNUhQRB5E5B3T0eUDlfhdW1d/6A+wwri3IUASIXqNSJi60qSEhYRCZKN05YZ1H5lQwI3HeRC0xkSHxL/U2FNxBMPNVlCch7qkjgCvRkHZVmsLHxzou6T9CzLZY/rnl/fZ3vZpItXd3uDV6QVENuFTMkXc3qqE+/sZJr+QSZcTfQ09EyVZmQWcnx2gavnFrENPZG7XTHOif/+gaz3zjCC+I050/O8MTsTSoq40ilw0ZaoZ+F5EYyyAOWrSBSrotlRWV4UhPKvJjiIMp408iNycfMBE9oPAEVlZUX/cjvDWXOUDt/p+IV6UylCZQmkJqF2hYSy2v9eWqXFZPn9lJHcAsUUoKItxb6jjeYG7V2GQ3kfDsZBmy3UHGDAUaEsM8pnjAgq3nb/Zh2Lf9tradbBM2FcfVoKrWg9z91paNC2S2LdQ2d0NIEttSaicyp28VoP9LiB7l7vmilYisa0dDIrIJIYJj5WOsITlcsQkusJ29PzHqfYEIPvx2zEG1SFTlz/hap9WjImK6JiI3PrOpxTbf47974Ca6uTJBvBfzyD/wLPlS5CEDHRAxMiBSGmkyoCs1CuElteoAJvD0Jj94xOeUXLtG8cIn61afYeLTF0t/o8572Eo9Ub3IxnmY1qbM0bBCnPhum4kSa0hCqHE8aAul+S7FdGGsQpds3IrG6Ssq40c6WDu49S0kTg6DuJ0yGfUKpC8LTvL9+lfPDOZ5bOsn0Sxn+v/7mHnxVbw+hBYnxaNzCBdsWXDpsizUNekzvNCIqid2uzxPcdiznQUDXApK2Kq05K8aElyNV+C2IyAqBwO4UZhpXRBv0DOz3NBLpAtV5RWA8g8xck7/hnCtNsoFFxs6aKgWn2hFOFGSkHk52kAtkRTM/tcUgj/D70BmGGK2wnoXQkINzjffRlTWB4ujUGqfCFWaU5aFg2XXSEG7ceNdWWPRyvh5P0vutBU5/pwNnX+XrXz7Fj9XfYFJ2WDcD1gsBdFUmTCrFI9ENHptZYjM6zl5EI+66dsB/9TJzV+vEF6b59uwRvviwxHtmgx9YfJ3H6jepypRJr4csWum69iGm1EGZYrTMwIZcTKfLz31zOOPcNe2TGo+h9vHGUvAj37iTRSS5Rz8LXGtbabh4aQa14fPlzaeorFgmzicEZy89sEEAIxX4uHxAjrlyqnDtdElYdkfiVo+ydsUVL+QByNCNQWiDzGwZFN6hEB8ZU2bMhQOwLpFStuct3mNC4SbJxAa7z5aTEK5OLq8Cwk24CXouGWGqBpSFWCET4eYPGvD7FjWQxKnvuHrXcVIJeENDMvCxuURYgc0kIhckkwFqq7oPO3Xw1wdc+vYCX2o8yg9W39zxXCQyWqqPwnkz/tAiMo3FFbfH1tI1kmwUoyrqWrdMwivDRV68epRTw7252dw5ORUKUL26BqtreBcuMTk/R7h1nCtTLV6pH6FfD2n7g8IFyzDkxNrJ45HsKNLtmgqv9o+UAfY3OtMMMh9rBUmuSFMPKS1iLO9srUBridESnary9l19I6CybGleSohu9NCvfPeBEJOwbkrMCLczyWL3cMLd/x998A43aZ9LHkSi8QcGUDum/Loni9/mrcaTGBlNY+JF4zvdkxfrt3S52A/oQJR1mM7ldG1SUBY8t2c3rVhgC+W3yAVZ6hGyvS8pLL7SxCPrMpNgipIfXfR4qkhstH81hbIX07gAl5924+zHa0FVUbIiiz7+QluEcU32EuPGZbkW1DvPcW3h6nACfbOCHGzuycCZO/+GbnGB5EvLRP9mg0f/LIIw5IascqM5y7dPPU3aUOhQEHQNxhcMpyThliXoaGRqUMMc/8LNcu5VVXepvsPfegvGhIk2TcFabJaj9YM74b0+vLo5x8xsl6pIi+JdF1/KjZstP2rXO1KIgyOxzLj+zJHMSovL2GIYwgELiHPhCrW1Bp3jp9CRszZkNjanrbCKhLFl7d1upK2iWt+CN7T4565hOvs8xUFKdATWs6jEkVTSlFhpQVrUKCOnAUmxd0Basq0QX4GpAO2UqXaPhxprfG3+KDpSZZzQSosomDyrCky4N3GZu0F+8Qpz/3CZ1z72KEffUwUGbBqP8+lsOWkFnFXkEgDuQN4YNHkzm+SYt0lmFX0bMK86NGTGca/Kc9dOcOYf9xAXr+/JOveGvq0bXKCz7cCz7PaoaENUCTGBhxwk4Ckq7QqqGyN7MWgNSeraSHwPw0oIlSvJqcq0VL6HMisFlpHI8EVeTIvZTtsaJKn1iERKZj0uMENVOi1YpZaQ1cJ9DZ6OwyYJpidpXsxJ2op4Um4LMhVj1pPYQU7j1p9MXSFtZdVQvR5je/239Jd/4BDCVRJUHTlZWWicRu61cJIOK504U+YQrVvXWK4Jo06gcjlk1WtwOUycmtxzUgKbS0QmkQOJ3xNUlzK8jcEDnztYwrgOrtWXKnz26Of41VO/x6TKmFK9sie/QjidYCSwvruZ1vyUSdWjJTUS11RuUqasm4Cfvfgp9Mst1I2L6CTZk2XeN9vSDAaYNy+W/x8bgsquuYPf8zA+TEd9Jrw+NZkgheGYv8a8t0VAMe4JSyQ04S6eySxkCCJh6Y6sqGJY6GJ7i9cmqtvZulF75H2CzXNst0vlT16idmSO9Y8cIau7liI63HZtdltN5QAADdGqJdowNL74mjtH9uhEvhcIpUinNLaWI5eCtxRcC+HElSYAm4AaWJoXUvJKSHLEZfNUDO3vQjeOuMA0laKdb1DJSGMfhoqgI4hWLLVvX8Z03jok9UHj+O9eZ/DCEV779Vl+LFhF2w6xlSRW4QvlevY3BCZyAxrmoi5nvIwJVWfLDKmZAXMq5IXBBK//H49x8pUO+bW9sZpgP+bW/QVEuA7fuHKc1zZmsFbQG4b4fk7o6TJWpqQh14p8bFCjLJp6GSOxFoyRJEMf5RnCKCN9rUn7itgeqnkAYjMAJs1geZXJ55z2Cd8jb1UwkSJteW4eYXFxy9zi9wz+IEf1M2RniBgm6INgMRUwgwGnfzvB+hKv10MkGSI3zHy7Ql7zGMxVqKxpwrUBed1Hpgb/zZss3miRfb2C1+sjMg1pxnSzQt4M8VfWEdaSvFBHaItMY9QgQwxT9MYmNtvnDCVgllaoDmP+7v/4M/xP05L+giVvakQ958OnL3J2ZY65V2O8pU20sfzxt5/gr/cmWaxt8uc3F+lfbFG/KKmsGKafvwkbW3tqDR6S0x7AG1rSzZCVgQ9aIPoeibJ05Zj5IEGkrp3GdmrLOtXxyNKwEORO/DeoRLSuCOo39L5ns94CozH9PuZCv3zIm55C1Guo2Ra66pNXFMK61rXhUg+x1cOsb6AP4IRfm+eo5152cxWNLtuhCGsJazXC08eQK5vo9Q2C40cR2pDfuAk3biJ5qxegxh7zXtl+/IAdRXcM+32av3WTiZkZ+s+eoj+vGM54fDtcJF2PWLyxhu30wBoq1zxe9+e43m6SnW8y+6Jl8k/eJL9x8764qMLewYkihFgBLt2HdTwInLDWzrzbi/4y7BEO9/k9gr8M+3zbPd4ROR3iEIc4xIPCQUpUH+IQhzhEiUNyOsQhDnEgcUhOhzjEIQ4kDsnpEIc4xIHEITkd4hCHOJA4JKdDHOIQBxKH5HSIQxziQOKQnA5xiEMcSNxR+UogQhtRu6s/JHwP63vkNYWuWmqVhEwrciMxmZt6MWp9irK0ophB7rt+OasWMUyw5u4LALpsrN6O2vZe9rjfuN09wt7tU1QiTCCRqQGtsenO+e9CCAgDTKDQgcDfSrHZvc2I34997gf+Muwzpk9qk1u23bgjcoqo8az41Lu+ToQhaqJN9tA8aTsgqyuShiBrCPqLhsrpDh8+cplQ5fTygMvdSTIj0UZSC1IafsJT7Su81Fng1eU51FdaVJcM/tDi9TV+J8N7/Tqm08HeZlX7H9vfvS1p/+3u8SDidvcIt7nPXaOlRRgiKxHm9CLpRETnRMBgXpBMG4IN6TpIdna24zW+IG1B2jbolqb+XZ/qsmXi1R7q5gb5lav7v88Dir8M+3zefuFtn9v7wl8hUBNt4scXufBjPrNnVvn43JvUvQRtZTlBpa4Sfrz15yyolCNevXx7YjPWdcJX4wUaKmY+6tI+PcBYwcXBFC8uLRC/2eDUPz+Kf86giwZzh9hj3KKHlGy3YHqCS59toR/v8Ycf/TXmlKQl3332lraGHE38mZwvDaf4b37nP2Tu6xUqtyKnfW4Nc4iDgT0lJzU1CbNTnPuPphBzCdPtNSYrA27ELQxu4kqs/XL6yotbR92op6jnWodYST8PSI1ikAflQITNrFL2ED8+scH64wmv1ycJlx/hyFdzKpe30Gfv57zxv2TYbTE99QSv/UIdOZ0w0Rzwo0ee40xlCYVlSRuWdJ+qsGQWVkzIpq4ysCEnvTUaMqMhBX1jGVhFVWiOexv85I9+hW985ASv/9RTeJcjqtcF87/5LUwcO2IakeNBvfHc4/rU9BTm2DzywlVMr49aXMD2h+iVPZhGeQCgpqe48vOPUrtpmPzWOvbKDUy3i/AD1yrnNm4+e2s5TbYZnmjz7LPf5dH6Euf7s8Tao5eHJLlHbp3rlmhFrhVb3QrWCsIoK/sZ6dzF6D1fU4tSJisDYqXxhCHyMqajHkerWxxvbnCt12J9Y54p08Q/u6c7OQQ4K3hygs1HGvzHn/wCH6y+yRl/i5ZUGGtZN25MfN8GtGVMZiXX8wmW8yYbeQ2/kjNl+2hiBsanY0O0jGnIjL8z8zxbU1/l+qkKv/bQX+X5CydZ+PwsrKxh+v13X9t+QggQ8o5HdQnPA6Xc79kp+qfqNOJZVKdPemIKf3UAq6sHl5DfDUK4UeetJubkEfj4JisXmoSbbeobHUdOxdQZ+6DJ6fpn5om/r8scgld781zvtVDS4Enj+mIbSWYk1gqEsIRRhjGCLFMYLTFaIJVFeZpKmOIpTaK9cuR4NwvLkVK+0kxEQ6LPXuVGfZGTzzUxgwF2v8cM/UVAcXGoVpPXfv04nz7zHX6h/R1uasXFvE5NpCgsKT4KS1MkGCvwheHp8CaENwHoGkVmJV3j4wvDlBjSNQFdAla0Lcaza3712B9w/UiFv/W//hzeFxeZ+7Wvfu9eoO8A+/R7GB6psHXSI52AeC6n+u8HNKuGUG1w9asLnPzV0DXzO2hu7S5runzYK+Yr5jne3Cz58Vle+y89fuyxF/iN6T/l9x95L//nyY/j9xZQN4pZAbdJ6ntDTlIho5CkDcemNsmNLKeRWCvIjXSz6lTRGVJprBUoaZwllXloI9FaEvg5gadpV4aosTFJ4Br/22L6w+gzj1Q7XJw02IcWkW9eRXc6e7Klv+xQMzOYxVk+cvIin26/TGotmZXbk4ihHHUFrhm+xLl2ElBv0/Z8NFB0N1oy4TMnz/J7J5/l6IljmKUV5+J9j0CEIfLEImIQY/sDmGpj6xX6J+sI7SYcrz3hk0y6bpPWNxAYhr2QYTfCDhST161rLLjfw1N3o7AUhRJYY3cQpzUWGfiomWkGTx5j+Smfj5w6yyea5wA36m1wpYHX67kzxVj3GbeBPSEnGYXI+VnStuF4bYOluEFuJKGXkxeWUsXL3BTfsYGaozhUqr2ScOp+QqQyZoJeMb1EkRgPY93gykHu080iEu2hrSGUOWIu5ubH2hzpx3BITnuC7PFFVt5f4b+d+394b9Dlm8kUshghFAntiIjtaTEAsVV0TbBj7PoIo0GhDZGBKCbPWElsPc6lbkTRfzX9Zb75vuOsf+wok18Fc+nKA971bcJasDstGzU5wfVPz1G7qalf6LH6dJPuSfjpz/4ZnTziYn+Kn539DlNej8+vP8nZjTmuX51k5t8GtM/3EX/+EjZLd88jfXDYHUOTClFOdpb/P3tvHixZltf3fc45d8s9375VdVXX1l29dw/TMz3Ts4/EYAZD2AKBAAskJBBIYdmKkGwF4bAikAgZGYyEDRiwJYwDE2I8Ax5AAzNMD7P3MtN7ddfWtVe9elu+XG/ee885/uPczJev9uXVdA28b8SLt+XNvCdv3t/5Ld/f9wdSIDwPYS2m2904xmhEUKLzxE5Ofo/gk9/5S8TWo2MD/qx7gP/vG49x8FcuYs8tOrZQeuPTt7fEOAnPw1QKmLLm3uIyfePyS55wxsOTGiUsntAUVD5U85K51YPfQ5nhS01RJmgr3Yys/Htq3Dz3vvG42K9grKCgUqbGWizeHzLz7Ns3qPDbGldI7p75UIF3/PXXqciYdWMZV203qtr6xFbhC3PZgFCFRV1pHtQIRg2aRiDzOWkAS8bjh3Y8x6d/6hHW+ruoNNvoRsOd111YwVNjY4h6FaskyVyd5j5DZ0Gytr9Gb9Zgi5r/57XvwCyHFM9L/m1vDwBpGUwAfmDxewaMRS3MYltt9Mrq27OYS0M246IbB+0MlNaXeT1qYpzswE7W/36L791xlNg6k3I2HePnn/k4E88rWFy+YcrPKLYmrPM9dDlEFjPm/Abn/DqpUZS8PjWvR1EmdE2AxFJUffrGH85uG4W2crjbplYNjVIoMnyZodRoiCfpG4+CSpgptmnMFdElf5vyfqsQEqxGeB6yXIIHW/yfuz7HodTQMj51mbhR1aYwvHZqxDMaht/5t8uGLg5+v8L/fZGhkTRMxEeKh/m7+87w+O5/SLVehfUmYBDShfR3RS5KCITnI2oVsilnnOLJADueoKUlM4KomJAmHv6hIqWzlvqxHv6rJ7G9mOwd99HeEdLcLd1Yc1+hp2ooKeHtMk5XgrUbyf8cQilEFLpflILJcdq7Cvz6I79JJDSruogvMhazGpPPKsYPdW451bI1nlOtytp9BWrVVSKZcrB4HnAfOjcWydAyEWtZiW+u38PJ1hgrzRL9lQKyL5F9gdBuHpjsu5lnUjOcFdbbkRFO9Pjxg18DoKUjJoI2vtBM+016OuB0VMNKfyuW81cPI2GKfuphjv4Dy48eeJZTWY9z2QR12eWgJ+maPquUSVBcFn9YNuUHr4WBYXIDHEUeBhqWdJUATU32aO8yNN45R/X8IiaOXfn5LjBMslhEjtXpPLKA6ruhsKrTp3TGMPvHJZYfEdQeWaHRLGLPR9z770+58w58TC/GxH2CI+coBjto7QpZO6Bo7CsiExg7GhGOjFP7luEqtAjhB6jpSWwxwhZDsmpEWvVY2+eTFSErWYwH2UTGuWyMpo54qz/NXNDgjfYcE6+2Uacvcqslqq3xnPoJhVXNuSNj/C/yI+ysrlELYqbDFoeas5xvVVlvR2R9D1o+XlvitQT1dZCpRWQbs85kZtzo5nx6LAKCpkdaqfC/J0+zc3qN90wdZ9zrUZR9N+PN6zFe7KH9Kpf7Y9u4GaRVjw/sOcR90XmSkcR1KHygP5xKDJd7RzeSMBkcM3qsxk1HlhikMCgE1BO6UxHVwcy+u8AwQV6dCgN0QWI9gQkk8XRIUpas75fogmF5sUp41qd4DvSFiwglEZXKxjgo4caTyxSyCExoSasWL/Yojo1h2p2bys3c/qIG77EedgHo+3fRrwX0x/JRXz7EE4Ks6KqMspJSrsR0uyGlKOViVqVrApcXNgEdHSD66W21Km2JccrOnqNw9hz3fW0GMz3Gmx/fT29WU9rZQn2+zuxXmky+fhTT6206ThaLiCBAFAsQ+Fjfw5Qi8CQ68pCpRvZSOH4G02qBEFz86afgJ46zK1imrjosZVXmgnWeGD/N18I5tn2n20O/qvjJmc8T5SFbUfYpyhRwXk5qFcZKENp9Jzc04sbzQRoxPNb9LjFWEomUikipyogds2uc3zU7LFW/LZD5VmfN0DiKYgEb+sjE0q9J+jVJ45GM2V1LvPDw7/KBF38E//8aZ+wv3iI7fyFPAgN55VH4AXa8hvEFfsuiA4GO4MNPvcJnyweZePkee71S7wAAIABJREFUvCNn0Msr37JlCuXGplvjEvt6xxTH/pHi/oWz7Cs0aWUh7TTk49MvU5R9vtHZzWOlU7yn8BY/f/5jLPYqvNpZoKBSIpnSNz5x5rthsPIa7oJUXGum1JZcee/eXSx9YB4duEmpQRO8niK9WKe0YkirIef+6WOkJXeBpQaRgUzzsdUjfp/1NsZbW+mmqRq/jrDgr7vd9lOfeJpPek+DBa8DKgHVt8wcujPzs24LV3KZr8UuvgqfZIjBxb5DyWFhoGUiAtkF4UKvNDckgypckuecIpGSoIaGxozQCjQSlU9qUyMuVXKJb6swxMbHIPFFRoqkbft89/yrfCLzIHgbt5tBSX/ketg0hdwD6k1KOjsNxVMerbdm+I4v/2Nqxwz1F5cx61fOswglyaqRa4CPBNGqJWgKPvvKQaJTATbQbv5fksL6HV+hW1O24d2YZgt12jL1R/dyvrKbC9rtO8LAr1XvxUo3p/GZ6El+oQT7P3aM75p+jZ3BCmfTMU7Ek3zm7EEWT43zwNIZR6i9QivUjZBYt8Zzmqqy/E6DFRahBeMvScKGJVoW+D1LUvc4+OEjPDV+HIVlXRdYzwqc6ozRSiNWuwUyrYY8JnAjoCM/oxz2ef/0UYoy4Ysr+zj8td3s/oMuqpsg4hRW1kC7jvhhifNuwjCZOLL7KneDjhJGhee5r0IB2+9fdS1CugSlHdnNtwzScZhi618esg0eIgxpnnPyRYaxksQqpDBoXKFC5xVWNajo5a1Ho/8DCIRG5c+XWo9IpBgraJmMj5ZfY32+wEve3Nau8WZwpfc3zRBphjAWXQA9kVJ+OaD6Voz88stg9LU3SCnRRY8sdKFSYcni9Q1ZISBsWIyS2EKIqFa+ZcZpdJ2DQZtjf+o+m9fy4ITnsf7hBd5VPMqsctW4Q515Fs/VKZ70MGsNFy2NGiIhhxQFe539dWs8p6Umk8+Wad0riKczVp7MCOsxH9t7iE+/+RDqeIHmJ/Zz3O4nK0BSs2Rlg/UtLiIQWOl+ZjAlVwvawDJw7mvzeF2B3wY1AUf+dojIIkQm8FpTJLMZB/edpfPvDlL5whH06trdk6MYXIhsZPe9lMUuBO3vewdrBxQPf/cbvPwnj7Hz575yxee7Uwx44XmIB/fT3CV5wF+mZT3WTUhJJERC0zYxvrDUZddV7IQhsJrEKlIUKv/wuQrrgC4g0bgc0miuSuXGyhkq5xlFImWP77yNvoXYevTN3TeQWrdaiG6XwmqDXd8oQiHCNltOKiYKsWl2zXyR8Dx6kz46FARNS9AyBK2UuUWL8SVZQZHMVJDjJXgbaV56tZGf8ICAqS5blzWWE8dm+NXKh/nFhc+QWMWZbp1d9yxzKhh3IfmlnDCrh3ZKVauIztWv8ZZcfVsI6U0L+mMGVUvx/IzpWpv9hUV2Ti1wMlFYGSAM6BB0RSPLKb7vGOODdhYp3ReA1nkPjoW+LaCLkqwoSCY11dkWcd8nSxQ6jRChpuglNAWg767A7kpsWG92BgoRplJAl0PSasDyI5Jkd8z3TL7E1/fsgScfRh0+hW5csn0KgQgCx0bu9MjOntuyczUFHxNASQpa2hmPkkjxhUHnBkcJMzQu14O8xgDuAUdqYLQGJs2w8bpVL75ySPB2wlpslmFbLZcHHUAqZCECKa4dmitFVnBeExaEsYjMoloxlEJsxcMEEivf5nUP0gbDFMSVr2V03uPFuQVYcL9nVjJfWmetWgDf2/xeCIEsl5GT4yQ7xskCiXk2uOopbIlxau+v87Ef+iqpVSTG4/W1WQD+bOkBDtYX+Wszb/ATH/kGYzICILWaFM260aSWYVXIIIjzfIafM5EjYZmUbgFHM8MXOvfxueX7ObVeZz0tUjkpsKci3nz5ADtfX7r8Zn67cWluSCrWPnAv7R2S1t6M3fsX+cl7/oKGLhJbn3Npnffef5RX/vkcM//TbsRXXtp0uPBcq8CxH56hdsxS/+0tMk5CYpXEKvDz8r5GMq5SIiHoW4NGobBEYiNHoYTBWLmRVxIGhdkU1g3+Puo9SWHQVhFbn0BoAuE+C2nOHN/nWx4tnuJZuXtr1rfVkGpzqJKH6kIIbM4ZuyKUpF8TIFye1ErhqtJSYgNJWpQIY+8uvp7Rm2yT8Dy36VrDzHMpF7Nx9BOWQGiksDxadTI468USot0ZEjBFEGAP3MOZ99d48G8c4q3mONnPXH0D2xLjFKxn/NHxB7lnfI2F4jqPTZxhNSny6tIcb16cRmeSL83uxZOGpW5pmFcyVjgelDQuohMWPfI/m0urSGGHHlaSKVKt+NDOI4Q7Mj6RPUFU7rNncoWVc7sY7/XJTp29a9jEamYaxmu07h8nHpP0xwTZU01mai3mpGZHqcHx/jQX0woA94SrPFo9zf7SRZ6pvIfL9pWH9tO8t0yyq0/cCLfsPIWSdBYi+uOaUHgMyiiDmySxlm6uQBCJ9DLPZ4OvxGV0g9GE+MBgBehh+DegEJRyaZVVE9AwPRKrSO6dJjCG7PyFLVvrlmBTzs9gM7Px92tBSEwA8aQlm07o10NK5xUTLyaIxOD1DTK1Tln0bsAVPMCBYcJaioeXqVdmOJN5JFZRD7p5gUPQOzhLOFFFNbuc+6552jstWV0zvrDMR8YP8S9f+F5E8+rVvK3JObX6pMernFWGyajDB0oneEPO8+XmXsSFkKAhOXyugMgE0YrbKazMv5TjebjdY7B6clKmQCauUmAF6AiyklNUfPrBw7wnOkvjYJF3VE7w47UTPL73v6Z6tIY4c+5t750cJLjt3CSdXWXOv0ciFnrcN7/Iz97zaaZUjz/r3Mf5tM7JeJzFXhVPanZHy+wJltnprfLZ4vs2jJNUCKVoHqjQOCCZmFyjXQmvX927UShFd1JCPcYXCkmWe0QOLg/k0zEhdc8l6xPjjOMgfBtNohskWDaFgAZJgEYKkzcAb7x8gKYoFLHI6NiAluljrKR9T0StMwF3nXGyl/1s0+QyRvWVro/xIJtK+fDBN/lzcz/GD5j4pkVmG4ZJJnfH5npFjNxc+uhbVOslzuoaqfUYD7rDXOHavoDCmEfYKBJ9fJH/fu9nAajKmFnVpHJMsbZ+dR7UlhgnXQ4Q93QoR30aSYHXejt4fuUeotcLxFOGeH/M/h0XCb2Mfua5/FL+dSku/ZvEkllJX3sst0usn6lROeTzOwee4ujYW3zj4gKvrMzxyfBxxl/XqLcuoLc67zRa+s+NxNWSnsLzkJUKje+8j4vvEDz05HEeqhyi5vVo6YhOFvLp5mOuSpVLwdT9Hi8tL6CN5IlayK+e+iCHj8xz34k2VirU1ATp/nmWHyky9/0neF/9HF/4N+9mbimBdz6EOnZ2a3qyhKuS+sKdl0bQteBfhV3ZtSGRSIlEOjRMvtC5V+U8pIFIoLYbzymBukxIraJrQup+l4pMCEUAZMTWJxKGB8LzrHxPj3i8xvQ3b3953xIMEsCbjNSIl2U0wTqkiz5fLO6hWO/RFSDiFF0MaO3wqB0zeOt3ofTPaP5vVIwwMzzb2cuY12EhXGOnv8qT5eMk//gFYuMT24AFf5WScC1Qr8cL/Oba+6ie1tckaW6JcTKeZLzaoho4RQFfaFKtCNahPw4q0EwV2lT9mNS4svOgQXQjjJPDEA7ItX4MoUxJraKnA0KV0ThfxW9bmv2Itg6Hod46EUFm70w4dx1OhiyVEMUiTNbRlYjWjiLLjwpq96/wsalXmffXaOgii1kNgJ728zVuGOgoV3BY1wV6mQ8GGg9UKE49Tqei6MwqWvcaKknIs8u7qB3rInspuhRsWdJYGIs1m5/L5QMNSU4DgI1k9nD9ef7osvdlxGsaGKxB/6Q/wpkK0PjC0Lcp6Ygd9DHMT6yzVP32E+6/EmSxiCgUCFoWc0HQT0t0ZzPQAl0rkFZ9skggtUV27jK5mKsYJgDRifndQ+/ggblFPjj5JlNek5JIOJzMAFCSfXw0fk5oXE7LvL44y9x6dk2vf2t4TgXFE1NnCGRGUSY8WTrG64VZ0lVDb1qSJIpYe0gRsNovDW/IRLsPtCcHPBgxVCeQwhKplKKXDsXm7imt8VqwgBcrIi9lIVxj3/jyUGLlC9NTlCfHYW39+iSKG8XgogySnEZfpuJnD+ymub/C+Q9YJnev8qsP/BZLusLFrMJLnXt4obWbvlHU/R5VL2YucmXataxEVwd0dMjT08cwVnC0M8V99Yt86L2HefCjZ6jKmIYu8nJ3J19f2Y3+pRmiz7+G8NYgDPGLEabbu/Ssbx7WomKwyaCp16kM9K2iaz1i6xFbf8MbGqmywWai5bUQW2d4fW9DUqUoUyJhOaf1UHIltpK+ddf5LmQUXB9XkFVh3z0ktQLVt3pMPN/BvnWa3ocfpjOrWH6sjMnbRLxGH3vq7Ntz3lfDNYyIPnKcPT8a8NbPfAfv/4kj7PfXOZ0V+YXf+gFk5lp0nvzeV/jx6S8ypTqc6E4gvlElWF675ktuzWUX4AmNsYKuCTA4sTmvn3tB3sbCBlpOUhhMXi71hMGwYZQ2PVYYPOE8Kk9oyAR+d2MHngw7QymWtOR2ICHF1uWcrGtK3bRcz0O/92E6syGN/ZJ4TuON99g12WChtM4r/R00dJG2jih7fQrKkQtDmRHJjWTyUIHBKFZT5x1UvD4FlSCxvNbbwVvdCb7+uQfxW44XE1UsyX/+CCsPu9ydSgQLz0wRvnkObqNwJ8KQ5XdqHj5w2lVTc3KlRhAJTV26MLZBbohukE4wikFlL8254wpLJFP8PDXeNR4aQUX2SJFIYfnB+ef4F7MLt76wtwOX5JnUvnvp3D9FZ05hBUy+3CGrFzFPPUBaVqi+pXoiRUeSpKpQ6x2y5PbGZ90RXKWzwdu5g8M/s5Oxh5fYFSzz4S/9Q7yjBXY+20NkFhNIjnxginjSJxYJbzXHmXgtQ65cW61gy/YkKSyJ8dBGOGKeVqjYfQSV525CYyUSiyd1boSy4bFOjEwOQ4HMKlfJExalMifNKzQYgd827vkxjPsdVC73mhUgrQYEYgsKsQPy2ajg1mCtpQLn3lGgdTDlv33Pn7A/vMCUavGV7n5WsxLPt+4dJgUPls5TlK6UOughuxTGClYTp0U1HbaGmlcvNXbw6tk5DvzyUUyziRCCC3/3CRqPpPz8B3+fxCpe6tzD51bezWxr4jaNU8BTjx7hB6e/Tt+mxDm5EhytY1IpYtsntd5lYd2NYNTb0lairTNOJdknEoZACJo2JEBTEimplfjC8MOV8/zCzF2uKT6KYa7JfY6FUvT2TnD+aUVWNqiuZPJFSzIVsrbfx29bwqYlOnQWChHBXB3bbN011eZRDDsbRhQihB+Q7J7iUz/4i0RCcyKrMf97AYU/+OrwOAUs/dQjuRaYx/JahQPPn8FcJ0+6NSRM6XqkJI7lbaykl/lE6ynC+AThhsCcJ/XwZ/fdXUiTazfJ/G/9zMMTGp1rUw8MmuhLwuU2F+OQdV3MG1EFUlpMALqgHBHuNqAmxjH3ztO4r0xvWtKdsVjfYnyLLWn8YsqemZOMh13OJGOciCdIraKcU/hLXp8J2XY6VEKTWo91XXAGVmbDm3ugixSqLBfVE7QylzT3pOaDk2/ykck3+Nzv348UVcp+nx3yJXypeaGzGyksocxYfzyhN1OF529n0YrvqJ3k/mCJlrEofOqyS0U6Q/WF3jiB0EMZHGzOBr/FjaCbbz6zqklFCvyR5wlzj9Ig+Gpf0V2//uipuwa5p+3Nz5HtnOTY95XIxjL8ahcbe2RSce6DFVQP/JZFWNAB6NkJhLWuSncnuxvy3kwxco/YQQHpOq9rdS46N6KWeezn3kH1gRV+demD/PGzj7HnP6aUXzm60cKTk4aVcsb6K9396JUQfXF5U0/flbClnhO46prGDTIodlPXnCuvHAKYS0h5l2pLuyS5HIYQUljXhBi75LHOZX5VLvtqpWs8vh2IwMfsWWDtYJn1fdCfzqjMtgg9TeBlzBRbTIRd6r4rp/e0TycL6RsPP9QbwxysxAgX5moraekIX2iiXGvbjHgentB4QiERw1BWCUtddZn11tm/cIGi7FOXMa/0F1hMaxztTRPIjDGvS7HWo5vcurcoPNdgO+OvU5fQMXnOD2dIUytZ1WXqqjtMal6KAZ9JYVwb0rBZeINdDhs5qoGaZlFkKATGWlKr8n67DeG6K3madx1Gwh3heYgwJNk3Q3NXhL2nRzFMCTxN80KBoCGRffA7lsKqJotyb7IaIHsZMk7vui6HTcjzJWpmGjs3idzdYe/YCv/p8AOMf1OinvnG5t7CnKA6ME5Hu9OojrwhSZgtTTUOvCJtBXHiM35xDZnVKAbpZbkkYHiDesP/DSa1WIRwp5YYhS83lisMiH6CtRFqEOrh8jYIeyXt/JtCMh5w+L8q8sRjR3jIjymoFE9qMqPoaX/4ezOLhscUVErFd9WVnvZZTUpDzexAupvZWElBpRSUuyi+0JS9Pr7QGCHwvR5SWGpej1CmRCIjNj4nkknSXPpUCjNUEa17XYoqYdpvDi/8rUJOjJPO1Zn1GoRCcs5IijKjIjK6VhFbb0is9PPcoskZ4LChWoowRFgUepOEyqAhOBmp6LVMxLjqUpcGjSC2loYuoZQlEj1S65LyT4YxUeXmJV5vbOHqEjLlzR07aMIWgQ+5traamiTbOcXRH1XMzC+hL9RJpaFS6HPvHyZ4X3ltaHyssciHD5BMFunMhhSWJOHJRUzvDlbqzOC1b+HYkfdp+WN7sd+/QpDC8y/t4/6fPYxptVxZZNRY59rjkZ8RCM2Xz+yhcPHGHIgtM06XaoJrK7Ct9mWe0+jjMpPPqBMbIV428qYNhh6kRrmQkXxD9j2yTLGabS4xW+U8JyHELQvFqxjGXpG8mOzDTCYUyn1may1KfkI96FIShqJMqHmuQubnYv/gEr7aSsb8LkWZUFbuQzaQDinKPpFMHc0fQ0k68bYURceEeaPsCLs6z8P5VufSJd4mGWOnp+7TulChfPzWL6WdnaC1q0BJOMOZIKlgiQR0LaTWo2tCJmijMCRs0Ao0Ajlo9rVwaSpqoN10aY4qtj5d49MVrpycWElF9qjIGB+Ih7wp5Sb23AncjrKDNVgjEdJgkxShJGpsjPjgAsuPhpBmLK1W8IsJ5myR/l+UmThxluxSLW1tHYVjcNn7/TvuOYkwRI2Pke6ZJZ4KqHz9FGa9eXVVj9wQ2yxDjY3ReXo/jfvhQLnF8c/ey9xh4wzToCn9kvfUWtfhoRF0GgXG2zf2nm+NTG9uUIbtKMK6AZmtFlKDrzZyCIMc0+Dx7vtGbx157mnw/wxFZnXe72VAgg080lTRSItUvY0yujNOt7cW1e4z88xFaifGWDsQ0Z0NObE7oFyO8cY1Va+PLzWTXhtfZBSlSxJrJIHI8gkllp3+CguqTWIlSlj8/Gb3hSASCokckh0NhkW9TMdIFnV5yMSO8cE6NvXAGA0KBj0duN+xFM56jB+6ddJePFemvVMSiWwYXkkgEhutKYPw1OXQXE/cAIbL3dVrJcwNkthKOjbAN3powOrK5bh8IdBG5DlIcQeN0208b55bskaCSUCGiKlx1u4L6D3ZQZ0pIlqKwoEG4qRk+n/7yhXlaoXd2HSFtZhevJED2ipcUtyRlTLZrmkuvLtIe49m/4VpvJNsNk6j3o9SCD/vp5uoc+YjksI9TepBj4VnusgvvXhtZ8CYvDVNIhsewbfSOF0LRkHJczuyM15mSD7cNJMuzx8NdfBH5t3F2QZp0XoWE3mYtZAXVxb44OwRlDB09dW7m28WotmmcKhP4aiH9RS2GGICj2ZpJ4uTPklF0p0RZCVLOmaglOGFGZ6vkdLtEp40eMpgLKRa0e2GmExiM4noKkQqULFAJq5Fx++CTEDFFpmBSu2wvWOQRxt8kIWBcE07sl5q2H36PDRufSSWMHYYhWksLVPAqC5SuPxYSSRD5dEA44wmLn80COfc5BVXNY2tR2q9y0iYSrh+Oo2hY0I6efvLwNDt9BpEwhIJD4MdNoHftRjhMqn5WQ7/nWl0wWLXQsRUAk2PmV8I8c+cvrqOtrVYIUiqgmxNoZLk1oymcJUz95zufbdZhnrgAKe/e5LOQzG75ldYapUBKAQxT88c5pHSaT5x/xO88fJu9v2TpY0q4ajAXpZitcabnqS7d4L3v/s1nnnpfo79x/sZP/oWmRDIMHRqDVm22bCFIXJ8DE8aGrpI/Q1B5WTuUORyxVfDlhmnK01TAbcjBOrmdvVLQ8TMymEI6NQxJbIrWe9Fl8t33DZZ2mL7CcQxNu67IYeAVBIVBATTE5hakeJ8iaQi6U0q0pJCFwIyn6Hu+fA8ckpQ0BZugEPqkqHOIFlUalB9g9fViNSg+hoygxjsnkJgfbXxswBhQa20oJ9g49hpCd3Gbqu6GV7HCcwZGPKbRlGULj82qDJuau61Ej9vBt48ZPNy+CKDPIdmkC6stR4SM+Q7DY6/WbrCbSEPXYTnge8jhEC3WjdkKLzZGZId45gdMWQSYgUdj3BZoV54g+x6w0EF6FBg/Nvpk8zfKyk2Sd9mtQLt/Snf8+Ar/O2JL/NCvJtuTnTdH15g3ltn9p51/lX8XaiJcScFc+n55l5iuneO5i6fyaCN1/AYe+EippnLxki5uadwcFa+hy1GSKFpmQKlixpvtXNDirVbE9Zpy4W4Ss2PqXo9fJENXXHrQc2P6WQB2aD6htlkgEYJmI6gaYcETWPFsAdNCVcJsgKChqC9UkTusTm5b9BQfHsfaJtpTLuDLBWQE+MbMrGD3SDTqOUmpVMXKSsJUeiUOEc0a1Cj5VbpjEoU5MYlNzZKYD0nU2J8iQ4ltqCIw9DpSocCowDhptIMpIt14Nbod8pI7Tye4sUU1c3gyvp014X/xmmmknkapsBO0txouApafzAhJSdQxni0TCE3SBubTiA0Ua79RJ6nGjVUMDIhOD9uMKTTJ8NHU5IGBcRWk1r/W1qpU2M1RLGInqyRjkXoSFL44hubNZuuBKk4/bf20rnHAH1KtR7lmT7lf1VFPfs6ZjTHNDKocjA510p3/ZMqpEXJLZMmrL1iBSyt+hzYe56P1l7jkUChOZkz/Q0ruszr/Tn2B4s8OX2K57/zccZebsDLb1z2PMLzOfpTgvt2nOT/fe0xJg45Zvjgc35V5dZSiWS2AjQ4EU9Sef7sDWuQ3b5xyqVdR41NQ5fIssFuD37OUZKX+HCDsvsVT+yS/216nBJ4PZDtjQSxRpKnrJxBuA1YrbFx/qHqX/IW5dNP3dBBg+iLoQFCSafL4w2E8a37n5JYLz+n0R4lY0G5cE3lEhleT2OUm+phldhcfRQuxHPMcDvsYA+WOsj2rVd47OwU7d0l18BrLYlV9K0iJWNSpRRtNpTuHShXqpFhmAMvJ8HpjQ+S+hv0gpweMaAVWDk8Nrb+SL8dKCGGgnOwoUt+RyHd/LWsViCeLjgPRjhiquh5V1UflVGEqFTozlmChQ61Uo/FU+OIw3XGT5+5PPmdYyg5MvhdQFa2ZIVb31hFGKD2HcCUQuSpRczKqguzPMF8aZ266iIRtExEQ5domYjVrOzmSQpD3e+y+AGN361RfPmS5/6Oh1h5pMLju49Q9BLOPbeb2rHejXl5niIruIndPR1Adu1+uk2H3sL7sAnC94beyiCftK4LpGnOJpWWQGabeEyDHdETGgQkxrtMjcDL8xNZ3hA84A6Re0dBy5I0xTCsy4zCSotRWxAKGI2J9XBixqb1hqGbGJN7VNYYROBjA99NkPFGDBFglTNmVgiEtQide5ROwCp/PdykmdRsMjJW5R6Wr1z8ri1WCWfgtEUmGaLXxywukXVunUXdvbfK2gFJRSZooGvCvJcuZUEVads+F/RGInzo8Qw8ICuHYV4HP+8E2Lj5BoZJ5TSDRDhZ3tj6xMb16zl+k3s/EmtHKnx33jgJKUjmqvTHfbpTEhWD37PDa3014yTKJZgeR+7o8vj8GUpeQvsvppn7xSsnv4FNWkiQ5xElZLWMtHTrwxx0wWPtsXF6k5JZQHR72E4XHUr2Fpeoij4GjxVd5kwywan+eM7PcyH6pN/i59//+/zcsR+ieIl65eK7qkz/F6f48bkv8Udrj7Hw6XPY5dUbGyaiFFnBeeIdHWA3DfqQ10zD3L7nlMvQOjKkC8neaM+hmy45Z8VGg+eA+zPEoHl0hMA5+H82UnYbCM+lVoG0ZEVFtGrQwUb1ynyL8hM2SbBphuhtSGKI3GNCOC9KjnpH1wszhzPD8iRmOvKxloP3Z+M5Rp/Nao21FhPfHg+oscdDP9ymLjMMEBufpomomASjLJ9s7+Jf/uF/ybuePsS/WPg0oYDUwrrxh15TnIdhA05WkOenBtC5CG9JpBRFSl3GlERGRQp+r/kALR3xdLROag0tu7k9JvQz1Mw0prF+S2OtrwerNcErJwh9n+ogTM8y9Ora5utx6fv2kf2c/6jmew98k+V+mef/w6PMv3SNIRtXakmxTg2zPtskPjt+y2swvuDiO+HHP/LnfOn79nKuuYNe7LNz8jxPlY5QlBmLOqWlCxRln3eW32LBW2NKdfCFwcdSkoL/+ellzqdPMffFFrrkc+LjAbsePcM/uOcZPtN4mM8eO8C+5RPo9o1thqYY0RuXeFaw0i+xqU/VmmvuPVuTEB+kV3KG8Eq/hOgPwphB79zVQ61BI7B7LrnJ0AyqeiLvv0NadKjwega/MyIPO8CdtlF5hWaUxGbvwh7Nm0FahrmxJhKGkiUGORwJdbg3y9QLlhf3LXB6psp9fpNACuKRrvvUqg1ly5FrOQqNHMovS8AXTl/w1c48a0kROf6K69HcxPfSFIMUOzOO7PfRd8A4YS16ZdVJmpSLEAYgBdIYbLd3Wd5J+AFqcpz1vZIPPvwac8E6hxqzTL3YwT+1fNMTbq2CyXKHk9HYbawBTMGJ9BGzAAAgAElEQVTww7Xn+VD5dVZ0maWsSkX1mFIdtBWs59cjkik7/RV2e21mVIi2FiUEHooPzh/hk+8ostwukxUEB544wXdNv8Z9/kX+zfJfx5wpOpLojfb+eU77XxhJJw0IzY0raGwhCXOjafdkY4xgfdAwu0FCBIhUOsw/bfTZXdLGkv9tENKNPlb4hqTsU11KiIKRcDLv69vGzcMq19+3qAN8YZj114cCcgbDG60Zan/wIkn1cf5O68f4J+/8Mx6OTjMlu0wpS01GfDPRaCRVsWE8tBBDDpPLRynGpaYiPVJreK5f45nWQT7/ucdQsaD9E3+Ij/OuN6q/mkcmzvHljzzBwp9auIMa8emT99P5Z+vsrKxR8hKe+9TDjL+RUfiD5zblSeSuBY7+2CwPvv8Iv7Dwn/jPXv4xGi9PsvfFl8h6NydfY6XEKMFMocXx6NZDWG+ly+wXJL/yzvfzgeob7PeX2e8vu55NBCezMRq6SEX1qMsuu702sRW8mWrqMkPiGnT/2dSX+OdTX6bztKFrBRd0CYVhUZfpfHqWvc+3N3ribkCF1UQeaRlMpljrFpi1Ix7XdY7dOuM0YhnanYigl5+4gb7xaCRORK2fbbzkIJzTVgyP3sR9smJIOg69jNnI7WDaBxVn+K0No2bYkP/dxq2hKDICYS6bmmKsxPT7jB3qgS3wS0vfjS4Z8Cwi0AhlMT13XUWYbyJiRLjOCvezFohAM0wltX28dcn0NwxpURJbiy82KnsAfZsyHzZo7dFkteim9x8RBng7doMxkGnIMke/0AbS1H23xoXIUlAN+jxYOc++aJEvPryPFVFg159XHDkyS/F27aR9/xTVx1a4t7TCl+IZ1r85yeQreXh9s1QAybCgdFtcU2vxe4Zj7SneVz1MXRrO6WAYHjsqiGtEj0RKx2xEKHqE2a/yr0AIUuu03psm4lw2RnFR451dJbvOGoVSIFz/nJUCE4BJPRLrzhMhnJqs1t8antMosmZAOfeEhRH0TMBKr0inH9DrOvXKjWGqAkxO3hFssIHFRmFLeZooTGkUXaFVhwLZjvHNiJZxTuDcNk63BoNgQllkThuQl/LHAPmlF5n4Ekxc4XgZRa5QUK1srkgOPshZhk1TzHrziglm+dgDxFZQk4O2lQyNpGUy9oWL7H3gHMnYLDc70kEXfFoPTyNTi9fTyL7GW+kgkxTb7SGS1OURezHCWnqZa4B+X+EkP/3oF/jN8D2IiTHkyhq6rekcnGHpUY//44Hf5bneHn77/FPs+pMe8tnXLxMhvBnE2kPo23P9VWw52RgjnvWpyYBXkhIpikikTKnO0EPqW7igiy7/JzeuhQEaBmIraZlgmD+8kNU41JundCYmO31m4wWvYqSE57mqdZpgfYkuWEzioRPpctRCIsIQe50ewi2gEuShVc5P0khEXyKTvCqlLCXVdzK0WpEFGaGfEQUp2ki0EWRaDfNKwwUKm48As8Sph6c0db9HVEroj7mmW5FqQulkgUOZYT3rKmV326yzuxy7/qTF6skdnPgfA+7zMyZkh0hkbl6dVWQ3YPFNv4/IMkRyOddmWKEx9uqtGZnhK717eSw8zbhKWdJumkdFepxMJjl6ZI79jet3sl92Xj40d3kkFTChh/HAqqLbCzNQPYFKXZ9yUgWzWuXf66f4ZPg4h0/O4l/0WXuyTDw+T39M0LsvplZv8VtL7+ez33yQya8rpo+/RaY1wg/c+m7CSIlU47cN33z9Xmqnb+9zWzi2QvtTM3zmJx/i+8srPBw4pUklBIm19C00TEBsPRqmSCoSYptSlCnKWqK8cOXjmP6+0EzIPj975PtY+8Isu8/lTPfrhXNy4x60UmB8i+4pRNc1WgvpCkfiEsLopdgyJcxh+wnuQg/aIWQqWE1KpFqRaemGZfr5GCicbbuSYRrFgDPVNx7GCGcPjSutuxK1ozAg8xL9Nm4K4o0TTHbm+MP1J/ho5TXXfJvn8c7rhGY/uj45cDBs8hYnEgutea27wJTXZEYtAy4p3zAZhzszFE96qFbzGmM6r3ZeOYHVh6wAJjLYQh5aGoFIJCLNbyRlsa2AxZ7PorAE53xUT9DaKejOG+Rsj70zK0gsXzmzm+ohj6mvL2PWGq7yJD2EvUkV1sxRSETiIdPbieuA1QYTrxT52und/NHEC1SlR1322OFlKFwE6aqrcsg9G3jIEjcrT+J6Y6PcaiRWcm6xzq5vJNhme+O1bnDqj/ElupAL72U3d3PePs8pV8frZT49HVzGV6ofMTxTfwgTGYQWeE1JR5Zoi42GYSwMmtqHSYVBXiJv/0gV/Hk7wj8eMX5II5pt7HgtJwc6WWDYDutuBabVQhzp89zff4xPffh9/PLf+3VX4UHw3xz7fs6/OMseTlz9CfIcwpCgCkOKCbCZcHgJz2f4FN2YT7zyOI37i3xo4fPMqDbndIX/7szHefYvDrL33750S1Ii/mqP+d94EZRCCJFP5JWIMMDWKmQTJdKKT1JVLu81oYbpgf6EJZ7TFObaTBX6FPyUY2/MUzir2PHZFt75U2Rnz2/IkNxCJVEkKcaX/NOPfpp/Xf4upn7tpp9iCL26hlhvcu9PVfn16l9j6f3zrD0IP/KdX+Dj1Rd5LPC4BzCOzQaAHGaZLseJrMu/W/4gpVcjwj9/Hn0dcbgBrNYbdmHS48EHTnKuWWXNVK/Y4nI13J5xEq4XyaoNOoDCYAPrWLZA5a0eRhXRoUIY10827D+zDJOAg79d6vmI3DgZBf3TBYoXDeWTHWynC+O1TY+1yroxz9u4adgsRZ1cJFopO/lckeELiFSGVRvKhwPpjMuONxbQiCu4rvYqhmrwnBgNSYpcCji9s47BUJIGnUm+cnQP9ZNgbpFkao25YmuF8DxkHOPHfbxiRFgKMaFHspSTa6WgX3cTeOOzNZoerCsYP20pLmd4Z1dcX9ltyunaTpdgNeZPlx9Ardzmhzf3XvXKKmK9yfjrVby4xG977+c/TLybYqVPrdijEvRZKK4zEzaZ9NtMek0imVKVG8b/Ylbha619/Okzj7NwKLu8NeZaXpPWw2sermleO7qAt+xTWRIuv2csIk03fy6ugNsP6woR2neyFgMSpg0NOszlQL/6EvWvXuc5bhLuNgBlZ3N537w1JtToyLvt9pW/krAWvXgRv72HouxTk4Ki8NlbXuKl8i4AZOAjguDyhtiR7vybCmmEcMbOCmyaUjwnObOnTtdqikKQohj7UsjEa1swXeYS2Cxzo+tHqAkCNiXco8uO2sBWTZXTyysoIXnlK/uoH9miJ8Wtj+deofIcVH4v/6NUqIP76M+O8c0D99CZh2RSE032KEZ9pkpuA9BWcnqljjlaZt//8Nzlm9H15HxHHl88usL8Z6aonGijVlpkvdhVR7Psuob99oyTtZjGOtFKxvHFSc41qyhhKR73KZ+781Kjohvza6+8j3q1SyXsE5wNKJ+xcI1Bfdu4Nione/zNz/60K/trQfG0Yu4tM9yV7wisBW0IWpa1bkDDQGolF9K6C/3vpKb2XQDTbLL3E21k48a69W/9hTScPk+0UmD2TAVTDjGRj44CjBcSexuRyEJs8Ndbt60tZS8sUX82xbY7jmoxCIFv4LN0256T7fdRvYy0HZAlCqxgbNUSro8s6iojZW4b/QRztsByotDjgqApCNf15v6dbdwUvLUu1ddK+B2L17MUlhLCiz2X/jP2pho3bwRD194aVB9Mooitomt8Wib6VrTWve2w/T4898qdNUw5dLMJzSZcWASct3g1I7AVb71pta6v7HAViJu5kYUQS8DJW3qltx+7rLVT13vQX4U1wvY6v03wV2GdV13jTRmnbWxjG9v4VmE7c7yNbWzjrsS2cdrGNrZxV2LbOG1jG9u4K7FtnLaxjW3cldg2TtvYxjbuSmwbp21sYxt3JbaN0za2sY27EjfFEA9EaCNKN/ZgAUJK0vECumQpRzGJ8UhbvhtH3LqGEPwlMPUSOoKgmhCnHiKWhGvZxvimG0CLteUbIbTd1BpvEsL3sb5HWpFYCVIzVF7wegaRZm5A5i3iRtcId3addxrb67wc367rjOmQ2P4VtVRuyjhFlHiX+Mg1HyM8D/Ouh+iPBXSnFCuPGyb3rvLwxHnW04gXT+0keKNA9bjBiy0qsXgdjdQGtMUEChNI4rEN6YrVhwTpTMJ77zvGieY45xbr1L8aUj2ZUXzueN63c205jc/a378h9uyNrPFWYd79GEuPF1l/LMEvJWR9z8nXGkF41idcE4y/nmB8SVaUGOUMWLSSEl5oY948lkub2ivq6dzoGuHOrvNOY3udl+PbdZ1ft5+76v+2XKZXFAoc/Zsh0/tW+LHdXx+OhXq1s8COwho/+54/4uy7apxOJniueS9nOnWOnp/CxAoSiayklCtd/tbe5ymrmJLsM++tYZB8vbOXJ6qnqO3q8sIDu/na+V0EjR34p5cxZ85u9VK2HBffWeTRH3iVn575PLu8HpGQFKVPKJxUxqGky9944e8xXW3z9ORJJv0Wa2mJ33/jcYpfnWDu1DknFWN1rotzuS7SNrbxlwVbapy8Pbvp7xrHm+5R8FM+s/QgZa9PQaW0spBGWuDT4lFCmRKJjPvL55kJmyhpOLU2RmetwMJ0g9lSk9QqLqZVujrgi8kBDIJAZmRmnI52M/GmSh2O/sAYU8/vpPZ/34XG6RLvJotgb3GZhinia01RZBidARnjUhMJ+EcHn2HWX2e3t4IUltgq7n/8HL9S/RDn1MMs/PFF9JtHkYGP1eaKI6i3sY2/DNhS45TO1GjuCqmU2njScGa9RjlMKPqJu9G0z+tmjrlondlwnXHVoSgTmuUCzX5E3AuYLTWZL6yTWsVaWmQ1KXGmXcdYwY5yg2YasRYXGIt6lPw+9zx0notLC9T9wI2suVs8CZEP3RyZ7WY9mAsaJFbRMAVSkQxHJklalKTh4+U3qUhFTRbomgSwPBkuc2bny/zGgQ8w++VCPvpc3T1r3cY27gC21Dit7y+y8phFtop04xClDLEyKGkIVUYv81ntFWkkBc4HteEsOoD58jqThQ6B1KwmRS7GFdpZSCsJmS01CWRGNwuGk4XPtypYK6hGffoThvT9DxO+fAq9tLSVS7p1CCfgbg1OVC0ISKqWjxQPs2JCjJVEIqMm+kTCsG58lrIiJ5JJ9gYXeSjo0R0R4/rU6UfY/zsJ6thZtLWg9WYp3G1s4y8ZttQ4ZQUw1QyMQGcSKQ3WCrSR2Fxb2gLaSLpZMJzqEchsOFiznYZIYfCkwViBJ53QvrGSWPukRrmx5sYN3DRWoIuG9kJAdKwAd4ltAjbGlQcBamEOXdGMS0nLGJJ8sEMgjJt8K+wmAR0fhS8MsbW8lWmanYiJTgKehywW88T4Tcv9b2Mb3zbY2rCuJCjWesTdAGPcjamNIDUS30qksESeU8CLtUcjLmCsIFB604BNJSyThTaBzPCEIdYemVU04gKWfABhLgsMoGoJawcLjL9c3srl3Bbc+Bs3fE/Wa6y9c5b6XIMxVeS0jjFG4ot0SDTzMVRFn2mvxbiMKcoivlUYE/PH7Yfor0foYoI3XkMGAdmZsy6sG2hwb2Mbf8mwdcZJuMmexTAl7gUYLejHAUKApzbGzyjpvCIpLKUgwVhBqDJ0PnpcDbylXI9cCo0nDMZmUIDMSPranbbOvacoSulMe+iCf9dMJLeDsMtaRCGiuVtSDxOOpW1OZ5MoLPv8GF84rfVWHqLt9BpM5AMF1k3M8TTid44+iegqVh4uUjkTEC5FiPMX7pxs7ja2cRdgi+bWCYTnY5QbGy4EWCPQWqADic3DLwR40uDl87JC5W6uQGoyK0m1Qkg7NEyeMHhS4w9mawlLnBumfuaBNGgrCP0UUxPY4O4xTptE/wOfeNpQDWPO6jJLWZVIpCjWUPkZp/n3KWUIhaRrErrWclFXaJ6r4PcEvSmB31aoXoAvvg2Gh+ZFAZd7s9se3jZuCltinGS5jNgxS1a2zvj4Gan1sD2Pfs9HZxIlDTKwTAVtstxLKgpXBpfC4llHFRiEd7H2iVSKN6KsPMgxGSvopR6pdl5HMUiZmVilF80TbMWCthimVmTnI+d51/gJjJWcScZZTst8fv1+dkWrPFA4S2x8KqrHg0GH15KML3b38/Hya9zjrYGyFBYFs19t4528iFlvYgYUAnv33vDejgVstUR/pkx4eg195PjbfUrb+DbClhgn4XnoSoSVbEz9zefS2b4izSTdgo8QlkZSGBoYT24kdAfHGetGBRkEWR62SWExVhBrn04a0EkC+qmP1hKlXOJcG4lVws1gv4XhhluKS/hNxpccqF1kR7BKJFJ2BKuUVczpeJxIbrSrxCbgUNrgVDZObD1eSaY52p+leMKnckrjnV/DrDWuy4Z/u6HGxhBjNdaenCUpu+tat3WCpRq62b5hD0p4HmrHPGiDjfuYZvPtv7bb+JZha4xTFNKfjLCeJc7cyHBrAQGyo5B9QceP6Pd9mt3IefvCUgoThLBkWuXpGTH0hjxp8D2Nr6Kh8UkzRbsTkbV8RKSRnkUVEvqpx5IuUYokanyM7MLi28oBEkpttJkAJlC8t3qEA8EiM6rH4xVHBzha2jDO57IaF7Iav3zho8xF6xyIzvNrZz7IoePzHPxfX0U3m1s2K+1OQ9+3k6XHSuz7kcPsKq7yiZefIK1EzPZ2oV4/4SaA3ABkucTiRxdQCZTOJRRePeOu7Tb+SmBrck6eR1JRiEyw3i4gpaVQjtm1c41XT87DhRDT9kkCBeUEKS1KGZLMGaLMuLyUtQKt8ypfXoZX0pBpRaYlrU5EGKZMjrXw83zTSrOEVe5xcV1Smp9ALC2/rcni4bgjIVD797C2O2RvcJGmiTiczLA3uEhJpEgBXePTNBF12UV6hpLXZ0ewyrsLJ/ls9ADCv4vpAiMeohobw+6c4exHx+nOG5h3gzCXkzIzMw2W3lnh6K4i9/3GHLy22TiJMERWynTftYesIMlCgfEExofunCBYh2hNgbfl3Va3BDU5Qfu9ewkbKd5aj/+fvTeNsSzNy/x+73K2u8e+5L5UVlXW3l1FVXVXd9NNAxqYAYPHGhgh0BhGGku2PB8t2ZItY3vkMdZ8QMbDWCCDrDED2ECDaMRAN0M33TTdRXVtXZVVWblvkbHf9Wzv+/rDe+JGZG1dWRWRmbjjkVIRcfPcc897zznP+a/PX/ZGuECTHu6gCoscleilDVy3j1lf/677k/U6/R96GFk4ovUcfeYyZmX1Dqzk3sYukZOiqAmEgyLV1JoZs80+f3/2JS6uTzBYDpEjibMCVxM44XDOkxJAUfjDcA6s3XILBUZXLl61rRlqdC3j0alrAAzKyJNT5RLmTUE2nRDdxjz2PcGW2yIV6eEOgwXBohryomnx+miR1IVMqT4ngmW6NmbZtJhVfaQaEsmS+WCTU0Gdhs4R0kESI0bpvdeqslUBLwSi1WBwuEX9B5d4oLnBYrLJpcEEq1mdE+1VHp68QXCf4cwXHiI4o71lKSQi0MhOGzczyc0nAsqGo0wcVBleNZCokcCpu7zWLUgFnRarpzW1JUX9uibcjDCxZvXhCJU6wr6jpSU6jpB5Xk23ddu1aW774SWUQnbaLD8hkZmgfl0xs9SCe5ichNb+Aez2trdzV8jJhQF5R1DWDXE956dOPs+p+DqPR9d45eAB/gMnGbzZgQIm2wNGeUBe6DGpSGmxlfWklENKi1KWOCiphzn1IKeXR/SXGuSFZj2v8eMz32ZK9WkFKdeGbZaGDVYWHSoPmPuKviduZKEU15+LCB5fJxYQiJJAGq7mE9wULVZNg8wGpE5Tlxm5U6zldQY2wrguf/Kd08Rvxpz9L08w/ZKj+Vt/fbeX9E5UGUPbrjOaUnx2/i02ioRz/WmudluE2vDMgfMcDFeZ1T3++fc/wdTUx+k8v0R2ZJK1+yPWHzfUZgYc6lzi6mab8kqDqeclzcs5ydll3CiFUYoZfnCZnV2BrBhxx8PGfuIRykhx+IsbyI0+brPH4Ln76C8qescNwghkIbj5gwohm8jrM0Srgvp1R+vciGC5j3nzPKpRh7lpuo/OMFhQZIs5lJKiqZh6+d6p13s7VKfN6JlTBN0cvTrAXb6G3aPzsjsxpywnWbaUiWLk6mweT3gjXeCLa49yvjvlg9WBw4a+lQW2CHfLStpmXylt5S1sB8hDWVIPBLJRkA1CvvnGMSbCISdrNxmZgMvdNuvnJ2jeEMTrFuy94QoJJUnnSx6aWmboQOGY0H4efSAMLTkiFzmRCwmEd0MTVYx/19cjGpcda484BnOSxrOPod+4jFldu2trei+IUY7KHYEwJKqgFaRccW3yUrFa1IllQSgM5YmU5SDGRPOkE5LhgoPIUBSKt5amscsx9cuS1qWM6PIG5aWrnhzeRSLmjqxLKWSjjmi3sO0G6ycSnIIWoEOFbNQYTSnylhhL/Djl0KFBB4ZRI0AYhTACYWOiiZC6VthaSD4RUcZVOYgVoC1lx8co74kiESGQtRpydhoz1UTkJWUzZvV0QLyqaTQC4pU1+IDkJLRGHToA1uIGQ+xmD97HhtgVciovXKJ98TJTBxYpFyf5g9lHKFcTHvjf1uk9N8XoQYebygmjklEeUJSKslRobRACrJUI4dDajq/Bsty246fiAZPRgKeOXeTbf34/J/7NJf7qZ57gT4+WxFMj9Dea3P+rL+NGI5wx2LsVDH/Hk1Zy/6mr/PTc3/BWMUFNZjyTvMWMGhEJqAtJ6ixpdbg9pzmeLDOl+gDM/K2j/edvYKL72TxlyX90k/JfHiP4sztITu83Sn5H1s28eY7mdJMLwylON67zVOM8VwdtVgc1/uzy/TTjjOlkwC993+8w/+wmXxme4oXNw7yyPE/yFxO0Lmqa31mFjWuYm8vg3HYRiVQ+yXAnGrt3kqA1yEYde/8Rlj/WYPOUw05nCOVYGWhEVkNmEmFAOEe4phAWhAUzqmGA2sAXJ6fTjv5JC9KhNybBgnCw8FeG5sWUwWJCMWmozw4ok5hgb1f5gSCTBHH0IFd+aIrs2R75zTYusDz98Ov87aVD9F6vc+x8Bz7gw1I2m1z+iQPokaN1saT+4lW48t7b716E0Tns+gbaGKZ//yjBwMLSCrXlCdJpBYcz6nHOKA+wVrzjySCEG2fx/E/flzfMQlITEEqDxCFKgV3fYOGvZsleDyhqdZoXh56Y7oWK6arfTU1NwvwMp9tvcl94kwvFJKEzBDIndYqBE5w1NSbVkBlZctlErJkGRRVcUUKy+g8HrJ1+gNlvlXRPKP7Tw1/l37T/472/cHfeoLdBBnq5yze//CBLzzT5H4+/wo8vvshK0aRvInKrGZmArok5l83yK1/5AZIrmtZFS+vcAL06gJuruNHonZ/prC/n2iti2lnMuiMepE6fYnikxfVPatRIkCwJ8lGEDcHVLTgB0mEi55VfS4GTgHRgBbIEWQqcdIx73KVDGIhXBI0rlvjmCKSgbFhUs6CZpOTtBvHMDNzcm+W+J6Ty122nyeD+abK2ZDQr6R+xxMqiJjOcE3zz3BHsIMAljvTYJDFgzp5/1/OjOm2YmeLmZ+boHYPF77vK9fUWgzcaHFuauEPkBNjBADsY0PytGwAYIF7OiGcSgiSjE4+4NJoYu2ywLeoInpi2ijARrrKwfOW4Dbx7J4z/HPmVF0iAZGs/u7mQD4udjbgTbUaHWtxfu8EhZXk9D4idr2ny5BTyanaAh6KrHFJ91kyDq8UEhVMYJwHDbz716/zbE8/ynT99CKjxs60VfrktadzpWq6qAwApEEJgs+zdiWJljYNfmuStQzNMncz48cYrFAiulU3eyOd5czTHmmnw7d5B5r8iab+2iXvhVQDetfLp7RXwYytu99blf+5MoPggr1CK3qkO66cUR5+5xFt/e4iplyHoQlkTDBckwvnNiR1OOVzgQDlEaHCZwpQCUQhvTe1YoMwFybKj87eefcxUA1c31OoprTBjvSFhsn1HyUlsNZTPTZEeaHDzSU02ZRCdEUr7mHCrOWSUhZgzTawGkzh6BwKwE0SXr73TOBASMTnB8Pgk3c8P+OkHn+dnO9/gz4en+D9qz5F9pfW+x7S7udl3aVcIbmzSCRVXerVbii5FFfQWAozZKiXw53pnUy9OMChCIl1yqLmOfXsJuNyRxrnb7RE7btjRiSmWngyY1V0GztIzCQqHEnasDnogWGdGDWjIiDezea7kExyOVollwdDmnA4sPzXxDX76nzzBI6fO+x3/5CoXjn6M47/06geuF/rQ6xACNTuDPTzH9U806R+zuImcB/+7Vcrz71SQNd0+0TffpPXQw/z8/M/wT498haPBMqkLxsW0fROTqIIbn7I40aT9wo4dSIWMI0QtQSQJBBoXhz4+04nQgxJZGPjm7+zuWquMo2o24eA8LK3g0oyrP16CLOj/6kEWM4PKHKK0qNQQXl0fv9/p7WvQxRG2EbL6cEQ6LRguWFyjJG5lhFaQDwNqNxx6ZClnmuTtkHRSoZMULS2jMiBvCbIDbXj9NtfybhbgB3iPPrBIemqe8z+pcYGFwEFWIkqBWIogF5AL0qyJTRzuviHOSlwuWX1Cs3F/SO2Rj6FGDp16InYKBguCvO0op0pkofh/zz3Gpx99nSnV53MH3uCvmk+/76HteeGIGGXofo41CuPEjlKBHdtURLTTihq/LhylleRG+Zta3BM20vtDCIazAaPjOakNWDG+NaUus6qnzmEQxKIgEJZAKDZNQq+MqScZYWVHBEL5os37L/Bk5xLrZsjTcxf501MxLMyiYM8ISh9YxE61GB5s0l/Q9E4YJo6tc6yzxsrpY9SFoDx34dY3WYPpdmldLLny0jy/Ez3J/a0l5oIuPROTWU0gAhJVMH90lY2lOTpPPozsjhClwSURNtaUtRCTKF/rFArKWJI3BJKmvNcAACAASURBVNGmRqe7e/5lrYaIIggDmJ5g4+EJGp0EvTHCpQqZSupXRgwXY/oLiok3c3Q3fVdyBl+zpRp1Wq3j6JEGJFknILUCVS/8wzgWZB2JUwlpR5B3BHGSo5WlsH74hQ13uRxGKoQUCK0hCBBRCEohtKY4PE3/QEgwM8AaiSk98UDllioo6g4twVYxBecAK3Chw0jLUEpkDir1lqJTkM4ZXM0QNTLyNGA0CnlxdARZPaC3ZJTeC7tLTtVTaKd347IMNchxroaxElMqpLJobcbWkpQ7yckh8Fm6LcsqKzQ9GTG0IcK+bUF321qCbdXLKqsko4i1R+CXnvttXksXuVG2+ZHGq9SEIxaCnnWkTpK6wBOVs1xOJ1hOG8xPbNKWGUoErJkMJeDfnfgTrpQjvpLO8vnOq0w92OePvv/TTL7aQH712x/tuLfwtqEJN370CGvPFHz8vvMc1jkbeY2DtQ06esjLP7uIfHWRw7948V2f0LU//janvhxz7Sce4o3DJ7CP9JhuDTjaWkNFlgPROr9x+j/wx4cf4nef+Rg3Xp4j6AmyGYMLHCI2iK1kifEDIJwRBDcDdF/AFz78km+Bc4hjh0gPNMlbis2jiqkfusab52dILtV54F9vIEY5+UKLq593/Pwnvswf/i+fZXJlOP7+fKB+251xWYbJMoJ/v05bSNqAPrRIfniaC/8gQSymdB/N/CWjLJ3OgJk4ox7kDIqQzVGM+LDJ5veyloRA1muIOIbpDqYZM1pIKOqSIhEMDgrKmqNcjYmXNI1V6B63uNDhBCT3b/BjR1/h0miCy/0JLr+4gLQC6SorSUI5XYwHzQltEcLhCgUWss0YEfn79NfOPEs9zplv9Cr3+b1JeG8sJ6m2C7SE73lzzms7WSuQyhNSWVbsLE0Vh9oOlFfdL4S6xDlBXmpe35xDvz1reZdSzLfAbQUfQM/NsvHpY8T3bfJIdJ1rVRzpzWKKo3qdpoaOdAycfUegxTqJqb4BicQAxkHmCiwQCkPuoKFSusfByYRp9zj6jasfTgH0HYHn7b+DgUNuah5oLtFUKa/2F+gWMRt5wlOHLvG19Dh6bhbb7b2jzsWVBXYE9aWSohnQmegyV+sxGQ54rTvPK3aRi6NpXlpb5OqVSWprgqAP4abCaTCh3qoy8U9h4SWOozXQw90512pulvSRQ6wfDMk6wu9XwqVrU4jEMDruyKcSVBqyeSxClJY/uXaa+lKBXO1yC3+82zW4Q5WiWJzkxjMJZj6l3fSV81pZkqBgsbFJXeWMjHd9+zJCpo5g80PW6QmBmppE1GvYdh0XakysKWoaE0nypsRqgQnBhD6TaLUP0kcrChs4BgfAaYcoBHooGA0j1oo6iSp8W1lXjgnUJwBArHhVEhe4rVsBKf25QzgYKpyFzMYMm4ZsVtMALzf9HtgTcvIxJ189LJTEKgXOlwy4yvKRVZW4EKCETxuPizIrV04rQ6gNeanIS8XVzfY7yQnuIYICOz3B9U85fuzwGxzTMW01YKVscS6boyOHHCEnlhpsMY497YSpFEElAouXUunZcuwGpi7wJvHBlK6MgYT5lTbssjxxvGGIVgIOR6tMqT4X0yne6k6zmcX8Fye+zEpax85OIMpyu85ly4KsEK2MCOY0J1srdIIhgTBcXJtgNIh8TdNaRHJDEa/6qup4zXiXJqhcfwvCOH9TNSTxhkFlH8KseLfrY3qCpaci8gmHSQz1i/4pH14KKU+kzM+uMZqeReWawQGBzARXL0zz4NVNzPLKeDfuu0klC8HgUEL+ZJ+DnR6NMCOUhprO6YQj5sKu/17SSVKj0cpgU9Brg9tfZhAiAg2zU+STNYaLMUUiKBrCq9QGYGL/veoRWIV/CEiQhSBehcFBh5nLoBcgRwI9gHQQcGXYYSbqU1pJ0N8O8JvYE1TQqwgvFqiqL90kDqvBaU9yMvefm00IenFCXQrPYO+BPY85uXaTdL4GlF6JIDCoqjUhCksc220sOxEov/q8VLdk98oE9KGDmJvLPmMl5N2Vq33bhZ/P1vnHn/oazzbe5Eo54oHoOsNgjVXT4K18llezkE8mb9GWlqO6z4bVfCXVKOFohSOGNqLYMYhZ4ZhWCZgRsSj47ZtP8c2LRzjxywa1uoQYptgPU5RZFdiJKMIOh+/I/tW+dpYjF+f4t5/5PlpRystvHGL665qJ14f85i8+S+kk65+YYPZbGlZW0QvzuMk23Qc6ZC1J3hKUdShajr++doT+RoJeDgk2BTXjb5SgC8mqZbAgGc0IhrPS30ARhJugMp+iL2uCvA3qNUnQ+xDlIu/y4BKjjGTZ0bjq4yqyMOPvJbsc008S4sCCEHTOWjZOSNLYQV7g8nx7v+8jWSObTbKnT3H9M47f+r5f4/9c+RQrWZ1nOucZ2pD1osbQhpRWsjRq0SsiSqNIugZ37fYanM1knUv/+ZMc/uxFXj/TIb6uUZknEVmAToER0POEr0dQ1AQ2gqAvcMITTbAp0IOY2nWHygEcehRw/o3jnFMVseVbxabVTwlFHe/WCUfZ8MRXdqrvxoFJKpnuxBC1Mk5Nr7FaO/y+a9pzcrLNmGxCgfBEJKRDyu2SAS9C58YEtCXDGyqLseIWawqgDMC16oi1dVyW3WKl3W2oiQn60wGfb75KR45InaQjc+qiRApLUUywZkJumAYDlxILQ8+GdG2MxBEIy8BGpFWtk3FgEEgEBli1dZ6/cgj9eg356suYXu9DH6uQEjk5gZ1qIYcZoii3b2JTueRFySAP/YNCOHTq0Bsj1vOIqWTI+YcsUbfB5OoRivkOeSdkMK8o65WefBWzGFxrEnQl4YZ334R1WO1dKZWDLD1ZlQljd84psFpUP8FJh3D+xtoNOCWx2n+2E1AmsnJB/N+i9L2awoLKvBXg4q2b7dZjEEHoYydKectFa//QbDcYLIS4sORcPssrawusDWoMy5BRGbA+THDOSwMNejGulFAITm4Uvt7rdiCgrDse6VzjwtQkWVFDpRJR+iC1LBmXNAgrsBqKBpQ1h8p9fVZZ9+dLOG/dyBJMuP2dOFUFuqeq0onKjXPKga408B2+nEI7oponcWu89JEAmo0RM/UBp1o3+cvmEdydjjntNHUHh+qsPSiQ0nnd7MCgpA+Y5aX/+CQsyMutbJ4fjBDqkrQSlJPCy/vWwoK1uiOfqRPeiGAwqNyIu0hM1YUqtKb/6ftYfkLwbJyxaUs2LExKCAScFAXL+jJLZokvdJ9gvajRUBkn4pvcF90gkgUjE3Apn2I+2KCkR4GgqNy8K2XClzZPM/tbCfU/+ta22NyHPewopPfkAVYeqZquqxtTWP+kVZl/Ih5unOdkc5mnn7zA/x0/yfoDUxyPL/KJyXP81k/8Hj9x+j/h7IMHcdWVJKwjWhM0Ljls6PdVX7IM5iSDBVCpJzmEw0lBXhe0LhgQsHFCEW04mpdLRlOKMvHkpDKINiDom3e1gj4MbLvG5gMOG1uogrUqtLRbAwqjKApNupyAgKCTMT/ZZTIektUmt3dS1X+pA/O4JMI0Y4pORNbRlLHARDCaETTOBPzL53+K+k3LbN9QbEwT5ob5UQ7GIIyFdA20L51wN5axt1lQrHs5R/9wxL+/+izmkMNNFXSOr6PVjtFkTtAbRQgBQVBwsrPGkdoaIxsicdR1RlOlNFRKR3k3PBZFVf5Sckhv0BQlM0pjcFjnSCuvJRaSnrMsm5BVU2foIm4UbQqnx3FU6yRDGzKtezwQXeePTnwcE99pt26Hm1XUBUXH64Y75wOBWhmUdORlVdMkLUoKbzXtcPGUdATK+Czf1sCE2DGaCYjCe0vzUmjN0vcpkgc3OFcUnCunuJxPcV90g6ZMaYqCWMAhXfDpxussly0u5tPjPrpPNM9iELRkyqLeJHUOhYOxRHGlvV66XWlqFs6hRpZwE/TAoQrIOgIc6NRhFdhQ8OrZA3wnWMCNNMG61+Z67fWDvNme5aUjBzj75gLtZf8kFs5bHMJCmQj0yFdGjyY1JvQkg6gsIrn9JM5avqBR5j5I2zuoUfk2QVoNJhLI3KJ7t1d8KqIIHn+EweEaKnXIwqIyy2AhvKUwUlQN584JAmUIlKHsKK87VsuwTrCW1hCHm9TcA+NYiROCdCLGhhIbCEwkMAHgPDGHG6Byhx7577RoKMpaAlWmS1RkKwuHCSVFXdJ5SaGuCti8jYVah17aZOYFaF6JKOqarDVNufPedxAX3lW2SnCmPs134irjJsAF3kLdSkA46QPjW1EVF/ikgVM+6C2s8MHvqlJeGOHPf+kVSmTh/29n9Y8wYAOHCWHuJcda/t4Pmz0ip+0PLGoCMZGNs9ZaGYJqlt1WfZMnJ4mx2zVPW69L4RjmcuzemcQynNF04qj6rHukyTcMWXjqOv/RgW/znXyeF4ZHODuYodeOmVQDOmrI6fAGh2XMDySGdXOVP3SKusyxTvK55AYNGWGxDK3/J4FAOJSQhBTUZI7brfIX6wi6BcmypnEtQ/VzuiebAIR9Q9pRlDVofidEpdC8VjKcgqIJyQsKG9T49psPMHHdUb9psNqfH5VbRpOKdFIQ9vzNN5iXSFPFPfBkYwNw0ltGpjqVKnOUdcFwHpoXIOpanBTj2IjKLHLz9gLFNlasPNFg7eMlqqtQqSLoV2Ra+IyUUxKUwVZZ4SQsiHRJPOlryJRwjIqAYRYQHtFk7Q5OCoT1rkwZCx9vqZ4l3vp0yNITvbCAcxR1iQl9DG2LmG2VrJLGu1BF0xFttKkN09sjJxxuaQV59jy1D/gOobXPlhkDSiGjCFeWOGMQwgerRRJvl5xsSb9UMjA7BRU/DGSziRi998Nm98lpK0Bc1YCUdUG7NRwHvXcGt/3m2+OijBWYUo9rnfxrcrxdXipEzTA4qHC12O9vS1fmTmNHQ6w+epjs2DQ/tvgX/EjjVZZNwmebA55raLo2ZsPU+UrvFH8tThDJkkk9YFp3+VRygdRJhk7zO/2TAPzj5jkCIZFCMDSGwgkKZ+i6iGtZG1nuUlbSWZwSjKYlRS1GljFZRyCNt3qKhs++6JG/uYYzirDrqC8Z0gmFyxxh18ctNo8p1AhU4dAj3wgbrzqKupco3irck4UnJvBWmasyRUHfIUp8bENBMBCYBEaBHBOYjwNZL59yG5BpSefNnLwVVRkrR9FwyFIQbgroKpxQ2EhjAxglMYPEICKL0BYpHWFUVKdawA+u0TeKwXqCUA6hfafDO4qDnSAISx6cXSK3ml4eYYoAZyWJ9A/nYCu8YRTXrk+gIsNke0D64hRJcHu3pjMGl+eoVgtRSyAIYCueYyqlDmu3U/fGbGfKtl4PAx97LArcjnDFGJF/ioit9+ysmpbVwA0hfGW/klUtgbuVwIQAJXFKIQcjuHyn3Lq3jeAWWmMVhHK7DmiLnFQVBBfVlBUx3kVl5gqH2dIjh3E7lQwNZcvigupLvgcsp2Jhgo2TESejG8wpyYY11Kvq7xumgXWSSJZkVjMqQjK77Ycr4VDOsV7WMU6ybEqaUhCLraC4qKypGr0iht1arvMBZhOBcAJrfIyIKghqq0yMrDxIn21xY3IUzrsrhfNWiFT+ejWBJzhZOMpEgvCkJAsfzJZVKGXchlQFaWVFTip3uKEYF7pJ47AIhMRn1PKC24IxhDcHNK4EZG2BicU4EB70tm8aE3nlzbImMLHERA4bOox2DLdCCNIRzfUIdclQWy8TrY2/9yol162uBqksWllm4z4jExDKklXq4xjqVhx1S4KaQmKcYFMlzGTOk8RtnU/8KDIpPNFskcNOVMSAddsk5augt3/f2m6cHDG3vh/89jv3vUVK1ZzGMTFp9a7k5KQEvfX+O+3WAUIHyE4bWcJmL2Gy7c3xfhoRBSWRLiutcTl234DxSZNVf52Tlq3WpUBZ2s0hWVxsz6i7E/VNb+8ZfJsC4NXvr/PQj57hRLBKzxpAkzrNwAkeCbvUogE/0bjJ0BYMnOWPB6dYKZr8bu8xFoMNjgbLPJZcomtjfrf3GKfjqzwdrZI7SYFkaAtulk2uDdrIYpfYqTTIYeGzUanPmjnpA9oqB4TDFoJ402ICb0mNpiXppPDWjPTWT9BzNC/bKqXs4y1G+niEHjlU4euXTCwpY0G8WoIQdI/ocdzFCW9RhQPniz9Lhwl9pqx2LaVoBfQXA2R3hNm8vXYdVxrsq2dov662a7CqtgmhFCIMIAgRjRouDLC1CJRfY9n0pCRLi1USpwXQwCpB0vCWghOQLBeotERmBUhfdGziABNFvNh6bPx9bRF7Wquahh0EQ0uUO44PDLqfo1d62OXzlP3+bZ9SV+SYjRw2NreJ5AO98W19Y7dzT32E8WQWcPa9SXiXLaetGxhEGOAmWpgYdLDNvrIaqhnI7dd0ZeZuDd8U1bQVKRxhFRC3rhqiaaX/7iqWfle237X1bFmC1rtB9taaKjU9RfboUYanMj43+TrPp4doqZTjwQoWi0EwsA4jynHBZSgE83qDlhwxo7t05IiOzPnl65/nSq/DdDIgni75TLzBlpkkhSB1vrVhardS6ThEYVCZd+PKWtU3VVlCJvIuVrkptkfJS8YX41ag2gaCMvI1Mr4Xbrv2BTxJZZ3t+qXRlL/hi0YVQLdQxlVwXDNWknRV5rB3sI5wVTYx0B/OUnbuXeV0nBCIokSoDAo/6l31qwCYEKikcmNK462BHZaDS7YTMrI7hKL0MZnKvdGBxmlFnIR+eyEQlQiiC/X4uEReIgoDRYlIc1y39+6yMR9izXu6/Ud93wfA3llOcUQx3aCoO+phMXbXAmWIdElYkZMQEKuCXCkKpTA7eucC5af9pqXGWenrfqohCE6A1IHPXL1Lj9OurKGSCRkXKLpbTVy3OMOVHwj51AOv8sP1M/xPN36Yus54ZPovMMKXAWzagE0gEJZYGCIBh/U6gbA8FCZASOYE33jpJMlVzdLpIUcbqzQmLgJDrBMESDIbMBhGTO+W5eRA5AV65BjNCMq6Qxbb7pSJfR1Lubb9Glu/Vtejk1UwWwjylic3G3j30IYOJ31myGn/k+pvLMihqgLFYCZKglrBoZl1jJUMi4Cs0N7lcYLBekJ0JcSGendvBuczn64APsK4rQ96Rt7tyO9yX8M9jb0jp1qN7tGYYsLSTlK6aYQU0IozTzhG+yks1QTgms5xTpCVGmMFuVFEuqQdjciMJhWavFQo4QjDkrIRE7Qat0rW7rL15Mq3xTe2xLgm21z7oVk2Hyz5bz77e6Qu4Hd7jzET9iic4ldWPs1j9Us8Gl2lLQuUgNQJJP5CzpHjq/KfXv4kX/7LRznxxxlQcuNjBRNVj45FYBAoIRjakGIYIMxtxlzeb31XrjP75zkuCnFxQLrQwCS+VSRvCEwiSKuyni0ZDO/6+b/10BOVkxCt7chOGZ992tou3DSozCCzqtDTgtgRy3BRgIkU6ewCOEgKR1y1VggDc0NDsLaJvHDtbla07eMOY+8qxANN3hS42BCpEmsTnHAkuqieiHIsLieFnxQcKFNZWILCyKowLCdQhsJKQFUBdIeJhNf8uUUwbHcvXZkkiFqCm58BLavsVkI2odn8WMYTJy7xs62rfHHY5G8GJwikd0GXsiYrUYuNYI1JVRAAxY5nZCwMqVP8xUjyF2/dx8LXLOF3rkCnRaD0LYM2t5DaADLpC/ZgV4jYDofYi8NxMWGSHcY2Y3QnImhpikSQTvqgtsrcuGLbB7B9C4QTnpzCvkXljrBbInOLzA2UFpmXsLKOG46w/f74mG85ciFQYUhrZtr/vzE+2ySFH6iZ5bjBAJPuD9T8XsKekZMLNEULVOKbHEsrCZRhIemymtXZyBJPTDsE6LSwhMorFAzSEFEfcCDeYD2rURhFLt2Y0LK2oj7dhms39ixjVzz9AKsPxfzcf/bHPB5fIhAlHZnRlIaa8I25l0rHA2HK0WCNX199DoXlH838DeeyOb7YfZSf7nyDaVUQCcgcpFZyUku+OJziv/+Vn+HIiyn6qy9gygLVaaGVvaUh2CAwzrGcN4lWFDL1CgWejHdpNE/l3pg3zwMQSkEo5Fj50m/ixr9vv23HZ291BVTnYqtLwGy99n7H6RwuyyivXnvfbfbxvYU9qHOq6pkCTd7yqdZySxdb+hL55azBsAiQwhFUQfBQ+vgS1H29k5FI4WjrIbEuiMqAVPrDFcJRBmDjveHWLcnSG4/G9J8c8Xh8iUO6WykDeE2mwjkKvOTuVjzp8folBjbiQj5D4RTTQY/UaYauZEb69xbO8pdpk99ZfpLplzOii2uUOyq+jRXjMgODwFaSvSMTeCGvKiC+Jz2FlTbWLXpcvPvve4J9AtrHDuzq3S12KNu5SFFMGppRQW62yMkyEQw5a2cYpCFRUI5HRSWqIFE5V/odn5UzEi0tM7pHU2ekOqCbR+P92xBMrNFbN+l3UdW7rXVEEWJ6ksHTQ/7VU7/NjBowcJo1U0PuUAJTeCunTklNGP5+/TznypD/felznG5c43R8lYELCazhiBZo/Pfwf918lm+8coL7v/oS5U41AOd8X1dVqWidJyqLrcgJKPejLvv43sDeqhJYP+IpM5pAGUJlGJqQzGifcdO+U7kTjJgJe7T1kJflIgBKG3Tl3mwRgrG+JmpLX1zseNIKIXZP935xjpvPzXFy/hIH9AZvFVMA1KVv9pLC0hKZt/yqXE1RWTvzKuNnZr7GhqmzXLb4evcEiSr4yYlvcVz3mZQhL/+705z6es9Lb7xt7FGWafpVP0ckzNiK6pcR0bpD3G4R4j728XcUe0dOVWOgtb4CVitfWDkwkZ+mYsW4AjxROc2qE9qPHxdVwakjr9rdZTUqyv9eyTO8PdQkdsnNCTRlXfgaKwRDG3lCIkUKW1lM/t+WHriv5PYu3/3BJueEYZCHbBYJIxPQszFnCy/NO/NiCn/zsj9kJRBK4krAOaxRO8ZD+epxiddQ16nbHhh6t9UY9rGPPcbelRK4asackRRVMNw6wVvdaXpphLOSotCUYUHNq1qRWq9xMxpG6KBkWIbcLFpY5+NPxgpU5b7JEmRWjgOvzu1ej5196yILqxu8euI4v5Z8mr838TIzqsu82pbhDITXW7JAUcnrrhpv1QXAA8GAR4Ihj0ZXkcJxUkue/dbPEf8/HaZee2tMK24rpb6lpFmKMTmlTpE6TSR8djMY2rFbt6W97O4FDfV97GMPsIfZOoVtlNSTnFrgywe0tNSDjE5NEWjv5k0mQ6Z1n0ndpy4zJmpeZGuiNuJU6yYPJ1fYLBMGZRWjqtpbfPe325Mgrctz7MYm0y8IvpQ9zp8dfIDJzoBn589zunaNo+EKp8NVLzMK1JzBAFtqyIHwvXGRCJhUI17PJ/gfrnya/NsTLLzWxfVv7azfIlhRGugHrOW+r7wuSqwQXCoLlnoN5lZzXFUsuJtkvI993IvYM3IysWZitsdMfUA7GlVTe0sWk03mkx6FVdR1xoQecjxaYkoOaMuM0xM3WKvXeLBxg+caZ/iBxHAu22SjqNEKffDYUg0qLO3eZJCq1HbnN79ORyr0kYMMHpjli599kr9+aIXPLr7J/ZNfpSEDavL9daVqQvA3wxNc+tf3cfTldeyLr733MRcl0bLi+rANwKSUSAq+mR2gt9Tg0PmrmC1iM+a761fvYx9/h7Gr5OSsQ1TZN9XP2bjQYb3RRMUlpheAFTwvQI4kMqvU8ULHb0w9Q1QrqMUZG2cnUangm/EpfmPiWdoTA7q9BJspxEhBs6DZHtHeMMj1HnbLejB75N5Yg725Qj0vOLEyRfalDn/VeJovNZ+lrAnSKSgbDlOzXsbVCUQq0QOJ7vvJrvGaZer5G7i19ff/qNU1jvzBBOvnDnLi8X9G0BeoVBBuwtGzOXZ1baxf/VG1dPaxj3sduzy3rmqOBWReEm5IykJgY0W4LpGF8GLrQz/iRxjf2Z6uxhTNiPV6jdZ5iR55VcC8FdFvhyhAWa+FnFnIkgI1srjh2/qh9uhm3RqzztVrhEAI1AHVamFPHCKdr5FNKPJmgLAQ9hzJck64MoQz57Fpygfp+rNpCi+8ytTgBGFvmngtQw0K1Fof1+1hdvZ/7RPTPv5/DuFu4yIXQiwDF/fucPYUR5xzM99to++FNcL+Ov+O4Hthne+5xtsip33sYx/7uFPY5YHs+9jHPvaxO9gnp33sYx/3JPbJaR/72Mc9iX1y2sc+9nFPYp+c9rGPfdyT2CenfexjH/ck9slpH/vYxz2J26oQD0XkYup7dSx7ih7rKx+koO0jrbGe+PlszUquwIrxfDKqQQDjx4HzygpWA4GXK1bSEinDYDMmvH57Y7fhg68Rdu9ciq0BiqXB4e7IOJE9X6cAISSEAU5LTCRRuUNkJa4o3rc6X2gFWmNi5UdfdT/8mKc7fT5FNfXXBhITVUNfUzfWTbPVIExZWkRhcFn+nvv6oEgZkLvsXZUib4ucYuo8LX7gIx/Q3cCfud/9QNWzH3aNQmuGP/QxNo5r7HOb5LnCFMrLLlW650mS04wzslKTFpr+Wo2gVtBujkiCglqQ80jnGn/4h89y5L/92m0fwwddI3yEcykE+sAirt0gXWgwmg1IOxKxowk5WbXEqyV5W3vyFX60eRkLgoEj7Fkaz1/Cdnu+LWjHvj/IjfyR17ljlPzO39X0FNljR7GBxCnI2oq8IRguCsINCDcdUc8rYjgBemTRIzOe64eDbFIzmvLDR2UBM9/OEM7P9ItvDJDdIeWFS9uf/T7r3dPzueOzVacNUYSo18gPTTCci9g4IREWJs+UyMyhcstoOvCTdjYN8dIQeWUZu7bhx7N9SHzD/fl7/t/eKmF+L0Ep1k9q+idKPjl/dfzypyfeYF5vcEBvcEgXzKo6bxV9Vm3E5WKK5bLJUtHmwmgKieOpxjl+r/70XVzI+0PogP4TB9g8pkmf6/HJw2f4B1Pf5pHwBm0paMuYuk+mmwAAIABJREFU/+rGU/z+a4/x1LE3mY+7BJW++ifii/xB/2H+4NpjjP7nA8QX1uDs+e2d7+WA1J3YIgapxrrpAOV9B7n48watc5SyFLlGB4b5do9mmNHQGRaBFpZEFby4ssjyzRYq8nql1gimJtd4dOoGy2mDjTThwiMdZGCJ4wL3zQna59u0rlz3N/TbyPGOYud3fWAe04wo6wGD+YDRjGR00EApiNcUeujQqR8XhgQTakzUIK6FBC9nfsrwHmCfnHYRfmIuvLE+Q62yhM4M57muO5xRGTWZE4sCg8Q6r7C5UjRYzptcH7aIVen/v27RBw9glle2B3reDQiBnpsle/AA538sxAUOpx0Ti5vM1Qc8OnGVth5xOZ8id4op1eexsMsj9Sssn2jwI5MvM683AbhpmvxB/2H6Juahzg3+9BfalPkELpum9UpA84qh8YUXtm/aPV4XMCYmEUVs/MMn6B6T1GvrpFlAOgoRAopccGOjxUZYUIty5mo9lvMG596ao/VawNHXC679k4JjM6usDusoaTnfnWJ1UCPPtR8i6gR5rihO5qRzGhN8nPbZIeLrL1bHs8uDKj4g1MQEotMinWvgJAT9AqtD8ha40CKMJOxCtFESbuYIl2AVhD2vBFLWFOrEQVR3CnP2/K6ft31y2iUIITw5SeiPvAZ4oAxrRZ2RDdHCYJ3EImjqlEAYlLB0KyG9tPTTaEJhIDaYmQ6y28PcVXKSmAPTrD0Q8ZOf+Wsm9JCayjgUrBGIksJpbpRtVoomQxvS0wnzqs+k6vNU6yIPhDdoy4ICwYVimlf7B5gJe8xHm/yzh79KJAtiUfAvan+PvJPQ/tYcdm0D2+vdwTUKZBKz9oigXEipa8MoDbCFRGqLQ5AVAaaUFEbRjlJ6WUR8NWDiTEHyjbOoXzjAkcYamdGMCq/mOhxEOCMQ0vk5okbRnBpgOpL1UQuVJzS/znha0d2AqNcwnQZlTSGMQ468Pr2NHCIyOCOwgfBzDUuLyixSCoLNHBspTKzIJ2J0qJHnd3/i9j457RakJF0omTywwYFWl1aQjgc31FTGpBqwVLS5WTQJhCGWBTWZI0NHJAu0NCSqoClHzMx2ufn0NAvrHeh279qSVKvBmX8e8tiRs9yXLLFSNLmed1gpmgAUTlFaSeEUIxPyql3giysPMxGOaOkR39j8YYZlwMqoQaRLIlWymtXGg1S38Pjhy5QHFRufTFj+8iMc/Be3H2+7HQilvFCfM+j5OcyBaTgyZKo5Yr1bAyeQQaUT5sBZQdEPKboh15XBOEE2a1h+PKA+9wBJtMpy2uBmt0GWBdhcjT/LbSVDHPQ3aj7+eGTE8FqNplTbM/12/n6H4GoxZTsi3MiRuUHkJVHXEq0qHnzuKk90LjP5qQG/f/0xzpyfo3EmoH7DUn+tO44x5osdkAJZq2FH6UeKP70d++S0WxACIksnSemEQ1o6o6VHTOoBgSgxVZpOYVHCInHUZFaNfvK64Q2VoXDUw5yVhsBp9V0+dO+gF+Ypj8xyaG6d+5rLfhy6U1gnyJyuxlZ599Q6/7O0itQEDEpDIA2p0aQm8MMfnEAKV23rP6N0ktJKQuUn7RxsbHBlcgF9/Cj2xk3scPj+B/kh4YnJk4+d6jA8UCOMBmhlKi/MIRAgHM5W1o904ASjLPD7SAzZtMCGElUEXB+0KEvlI+Xg57TDdvbSiWpfEASGogH6wAJ2eQWbpnszh/A9ILRGNuq4WoSJJK6uEKUmlALhHHroWKxt8kTtAgf0Bmfa81zrtCnrgR9TP9nAaYkNFYPFECeh5Y6hl3u4a0vYUXpLLO/DYp+cdhFhPWcu6TET9mnplLYeshisYxCcz2bJnPZj1yvLaVL3CYxB4WiotIpJldSDnBt1ILh7p6f/5GGWH9N8fvI1JvWAs8M5tDQEwowHMEA1CQdLpLxJb6tRVt0ipqZzYlXSCPwYLS3smJBK662utAxY6jWxTjBRG2FaJUufW2DuS2DPXdibxe24cYbHWqw+pImDwrvVUUFZKj/UVVqfaUQipJ9EnQ1ChHSEjRzdGSGlY7CZMNhICJLCT6QOTUVGYEvpQ1zC+fiTcGhtSCccvY8v0vymxb7fpOPdhhCemI4skk8mlDVF75ACB7WbGlk6kjXHqdoNnopukjqo64wgKBm2LMJK1h5ukDcFeRvShRKUY+10jc6ZGtNfA3n1xq48WO4ZclInj5EdmeTmxyJMDNEq1FYstesZwcvnMBubd/sQ3x9SEkcFiSq4OJwc34zBhKGjhiwE6wxtxNBGBMIQyQKFQ1Uz+fomrqb7wlzc49W5EhcFd205a/dr6s+sMBv2kMISye15eaVVWAQSNyajrTFfkm2XrbSexLSwWASp0Z6UnGRUBtUIMF/jZY0nq9r0kNWnaky90oJze7/OtKMYzVla2hBISxwWjJyfGrQTzglftyY9yRgjMVvbCIeQ/rUtInKVO0gpcdoitUNqN7aoTN3SO6BpvBr7/d8J2WUh0IsLuHaDbK6OVQKVW+JVgYkEwzlJvGoJho7L6SRLtYCTgeOTjTfIDmm+cOnjOAW9IwJhQBioXdYI69VtEY7+Q9PUWgl6fUB5/uJHWtM9Q07lXJu1ByPiz6ww1+jx+tV5+ldiGhcTFm7OIPPiu7KxjGNQ1ZDKPPeyt3cIQgiS0MeOVkYNykqu+L5GC4WfXKyEGwfCZTV0z1RjpYYmHP8+GQ7QnRwb3j23bjTn+EeHX6KtRhROjeNE1gkKJ6txXdvTX7aIdWcsyeLduVCasbWUW4WxkqzU1Tj6bSvGOMFUY8h8u0feniXc65ICoGgAnRwlHEI44qAkLzWltNsVDTviRkKCEA5nvJu25fYh/WtIV7lowrt4RoDy28hqn+CzYdmkwkXVgIw7EWsSEtdpUrZjirpC5RZhIOxbCicZzgmiDYHKDGd6c7zemOehcIUHwmWK5ht8gY+DhaJj0QNBmAqiNYfKQRiHDQSDWYUo60ShRl76aEHye4achgsRmw8aPtZe42Btg5kTfU4/do2nkvP8/Cd+juSVxzn4r55/19S60BqRJCz/1MN0j0PZsky8JJn59efHk3T3EkJrSGLaUUqiCpZ7dYyRWCv4sjhFI8xYSLocTNY5HK4yqfpYJDeKNpumxlpZH1tbL8cHsU4wN9nFRu273l8UiBIpLDWVU5M5gTB0y5jSSXBybDGBJ6iyIilbxZkAUuNjVBY/HkxLixCOwiqyUpMVAWXpZxPW6kOON1d5eXqR2uIC5bXre3r+so5gcW4DLf3Q1kBa4sDfUHmu/ZDXUoIVnqQqzhHS3WIZCeEtpXHIyFTfS2DH26rAoZRfu4gMeUfhojv3ABJSULZiXCCJlzNMrLChL7aMNg0LX8/RvQw5zOn9r4f4xYePMfMLv4oiQAlL+3XF5JnMr78i7LKucBJUanFS4JSgTARuNqLRbuEGww9tJNwz5JQ3JNFsn1D64ZuTwYBJNWBSDXn2+Hm+Zo+jZmewa+u3VBWLIEQeP0z34SnWHrU0DnWZa/a52DvI/PwsdnVtzwKr42PQGhH6J2BuNVkVNJXCkRtFYZUfvOkkBkkoDLmD89kMvTKmX4ZsZglKWjZMjcIpIl3elc5HNTVJef8hzFRBJAtq0j8M4rBACYupyKew77yptshoJzFt/5TjoDhsW1jGCZS0yNDSjDMS7WccZm2JmZ9ALN3c9RT1GEKAYEyWW4h0iZIWY6QnIIB3abAQwgfPt19w2wHx8WtU1pSPNWlpyUuFVA5Tt7jgDpGTEKAUTgmskqiirGY/Vm1VFmRhfduVENQu9ahPdSicZsNGXMyniTcswVqKrVVJAQEilj67WTqcqoZwC4nVIJoNPxXp7xQ5CeHt4x2BydGs4EeOv8pS1mItrzMXdXm+f5SvbZ7kv178/9h70xi9svS+73e2u7xbraxikc2l2ev0bJpNM6OJZMuKbDlOpCSKERuBYSswYEB2EMRGAAdwEsBBgiSfkg/ZvCQKkABSYsQRJFkRLEsjayYzo1l7ZnqmN/bCbu61v9tdzpIP595bVWx2T5PNIqmYD1Bgsd73rbrnLs95lv/z//8TvrVyhr/38V+k/3IKL1+MH5IKubjAlT+zxr/1V3+PJ9PrLKsJK2rK36j+IuNPnmL4zXD8zmnQxy8MmNuSG8UAt58gcovOazJtyXXNcjIjk/HBG8o51+wi//zaE1in8AG8l2RJzZ7tUXp9JD26n1Z/5ByXftnxp869yLKasqInLMoZF/SMba+46fp8W5yLIFKbkCgb07ZDEZP1skvzWmsL4Vr6A8fkJdYplvsz+qbiwmCTuUvYqXKmjwW2pyNWXtDH45yEQGiDcDAuE5YbMdfSapbzGQNd8qJdYx4EVrZ5XZO2tddGBoQ5uE5ShVibChCQXaeudUwLeYGWnrc3F5HKka+XuCzjfrgnkSTIXo86UXgjkE4inUcUAbIY/ZRLCSpX6LnBvL1FujciEzXfKB7n9288Te9ajdqbUpyM437Cx1QO4qlpI6cgIQiBPbkYHcxd1osffOQkFWrQpxoFPt6/xB/5J5g7QyotpTfMneGleo2+LKn++haX3lxh+Mo6ZhxPzP7n5lw49RZrZp8tN2DX9SjMDuN5xvrYwXHtuodMZBlukOBCReU1hPaG9NQ+PpQKz//8rS8w/E7Kn/vLX+LHB69xbrTDzfmA3XlG7RSiDmzVfcZ1/L95AIK+5tqY7CtrvLiwxr+y/F3qEG+RBZlgRE0m9lk0M0qvKVx8zTZRERyKlDiInqQIEQ/lFUVTCK+cYl4Zqlrz+OIWZ/IdTqZ77NkeNkjqjYrJPGXFmLveed/LRJKgVldwGV0ap4RnkJSspFOGusD6kzgrm0Hg6KDaWcn4NB7dQIQgCrK2KaCPNSfdrzizsouRjtJppPQkiWOUFwSV3/O13c5kr4dYGBJk0zisPEEJhAI9tdGpqKa5kSp0LyPZs/zS7/+7yLEm3ZacdnP8qBfPhwdZx4kBiP/3Cuq+7NK+epSg9rO7PuYH55wanIlQCrEwwg4CTyQ3eEE/hkeQyoPW9GvlGk+m1/jix36VX3t8g3/w+E9ydWuBNKv52mf+HtsevlU8xpvVKoU3GOEoC4Mq7PGlBIfNaFyuu4eurU1I6SMeqCmO919IOfU/fovv/MJj/PjgNU5nuxTWsF9keC+onWJss6arJXkgvbqtHda+MeC1Ty+we66HEZahnNOTEhUEULGgZ+zbDCnig3U4jYv/f2c+GprzUFhN7SSV1dS1wtaaBVNwJttmKAtKb6i9IhtUVIsmsh4cg8k0JSwOcVkg0/EekSIwMCULZs6SnnUdOdHAAETjpIQIbfbTLO4A29ThoyBuUk6gteP8YJu9OouprYyNgJ6psVoc/ywhINKE0MuiQwqRWcALSRACWVqQAm8U3ki8EYTUoGc1q1/uY+YBM7UIG3A904zkBISP2pPRGQW8jvUmVcW11H1Fkt/9XfxgnNOhC6HWT3DlF85iTo25WK1hvUQSqINipOf0dUkdFG9WJ/j1YFjTY/7rp/4R2dMWh+B3Z4+x73P2XM5AFazqMSfNLqPhjNmpJRYu92Bz63iXYzQukRSVwTSKx0IGUu1YSAsS5Xh9uoKeQwghDoRWq+zWPVbSKSvplMuzBVyI0YUUgdzUD6Tm5LZ30d+a8+x/ssavbfwcl/9mzU+ffZULa/+M71VrvFmd4Kn0Oif0mN362a6WNHFxZCeRB5tBpuou/YM4zjOtEkIQjPKCXZ9TThSv7q+yU+VszgdcemWdE1+VnPvhGHX9beze8SDkxdIC2x9bol6KgFEpApmqWUpmDFQZU3AfUzOhYqQU2oJ4EO+sLUGHberqVAA+puylVywlMzJV87ZepLKK6+MBw5Gid+4M9tLlewJcfFczBp8aXBZvKmETVGFR8zp2hYVA+BBTPS8QsxK3lLH5E5bkmmbwtiK76ZHzGjFKYvCoBEE1KVymqIaS+apgdCmgZ55yUeIzc7ty3fuyB57WhWGP3ecsT67sUgd9EDE1xVMjaqTwOARjl9OXJT1ZsygtRRBcswuU3uCCRMkCI+Lw7Cgr2V+RjNpW7XGbiJFP3URJgqbl3NzEmbJN4TFQVIY922PuTJf+aOlJhDvSnn8g5h1+NsO/9gbqeh//3Y/zW3sf5dXzqyynM5aTGeeSm2SiJpUOG2LX7XDkJIXHN/ADiA+zEAFFwAe6SFIpj0wcb7yxxhtWoKaShdclSy+OkRcvY3feW779g1gwmmoowPgjRfpcRTxX4Q/t+LdzRIdrgodeP+K4BI0zi5ivQTIhlba7L5yT1H2JWxki3hZtMnE8pmNnsC2A+0SiyttEbD5urJ05gXQC4QL4iKcQ1oM8SAMBkLHmhIiwAuFiJBW0+OPrnMqNEf/tz/5v1EGz5QasmgmFN8x80gEU22l+JTzX6kXeqlZwxJmuq9UimawZqCKOUyDJRM2To01+75kNVp+/T+R4IVDXilIfnFIpAntlxgIFf3LlZZ7vfwiA6Tzh7WKJ7bLHzWmf8TTjzOouS+mMgY51K+sl+kH7qemUs3/3KxEfA7zw73+W5Gc2+cTgTTJZ09cl82Y8pTUtfUxtOag/CREihED4Lr2bV4ZUO7KlCQv/0wD55e/GXxA8IQSOfYgjMZRLApXFqGlaJyTSsaRnTFzKTTuIKZyMVChdatd8XBxyvIFDeCgvCFZ0aPCg4vtmNuF8toURlj8QTxKanHC6IdDzIQvHVfhvLOSREkVWnqAFdV8iK4WoXeds/CFYQ8gMsnQsPZ+SjD3pbuOQEo0qfUz/EolwcZ0+iXU2VYKqAtIFXBIJ6u42AXigMBq1ukKxonnKbNKXJROXIYUnk9ERGeEYqIMJ/ipoHBEP05clmaixXjJzCROXMXEZY5fjkJzKdknPTrD9+1e5CYEuUoL4cCrpSbVlWU9opz7q/ZTLswXODbbJjMWWmmmVMLMJkoNO1kNhcaQevEM4cF50m8bcGXyQpE1aBEcR4u28XQvgPBwVChFQ0kcQ5qG/cYTj6BjNp4ZyOZBmFbo5Li0dG8ludFY27a6lkKEBXx7UnLp1NDAEIUNM/2QA1XyvAsILQhBI4THCYoTrTitAPQrMVxo20eMwIRBpis9jSheUwKWSYklSjxQ+bzZTHxr21uZLxgWrMjK2QkzfEIc6crIJEiV4I3Ap2B7dfe6NwOYKtbgQsYB3aPf/CWhvPCHgxDLFsuSMjtHO2MXKfnsTG+HIhEU1aOp28DRpUrdM1tRBUXrNzCWMXdYNqG6YXT5y8ir14D7iSA4VhuNuKlAikEjLUBYxrQseNVZsz3ucy7bJtCVUsXNVOt2t3d8ulXgILATRodunNtaZ0kN1psOMAy1FzOHX2odbtg863N4ZHbOD8pnGLlp6aU3SzAUm0nFS72KEY2YN3h84p9jiOvQFsQDerKNzwKp1VBF8SUs60LyuhI/9kuZzduipFgB1PPepUAqRJPhExyFfJXCJoFwUVH2Jux0INIQuZVNVQHgOUtQjkAHR/EzgjMBlApvTMYN6Ay4ViMHg4XZOMssQadr9X2jD63/+BLN/ecKrteBitcb1asTEZZTeNDtw3G3anbp1SJmo2XU9tu2AVFpyVdNTsYs0UAU37YgLyQ3+zmO/RbF4n5xTCHgvsU4iS4kro8MZJCVDU3LTDrsdKLspubE5YllP0MIjKslknHFjPIhjIcQ06GEzl8Bib446FDkYGecE245drmp0w7qgpSORtknpjiZqzgvKWjMtE4S7Za0hHHv3yqeKfGXOSm9KX1ddlLSmIpfUzCbIBtPUPJdNHRGkDEjl47/yIEr0bdGcOPAbrCToeF9cm44AmvJE7NYNshKxUFEtBsQxOePQUECIECKiOxPUucD24oJk5Y+Uz5AQGq7wIMCmAmc4wDMpie2pGIXp6IhcKpickcw2AtWqw2bRcakiOjm3vojIUu7U7ptzCoduNjkcok6fpHp6zufOvMGuzylCdEiuQVIftlt/5pAUwVD4iIfKZE0qbBdl7fscJTxPakm1IFAn3hdH/D1YY1sQBXwseLa1lrHPIjjPB8wEwkTHdFV6gorYGGsVU5d0gMaHzYKGvol8PXHe7p0OVIqDAnM7qnKrY4J4nqyXVFYd0IrcRwtK0M8qero60mGUwlMH1SHgxaFo7+CL7qu11nnFX37LC0Ewr3XT5IlpoFaeTFuSzOJ6sZ5zHCakQIhY0JY2psxB0hSuQZauSc1aMKU44ETnUKDYhXuHF9osV4LNYqQURHSCwofY0ZPgM3NXkeF9K4gfnokLz57n+scG/N3P/B/8bO8S/3R2Fh8kJ9O9mKY1wD8fJHXQ1CGmO4p444xDzp7t4REs62nHqAjRcV0qV1hUU3rSsf+ko//TT8Cv3a+VEl2+E9S16h7Mt4plVAUEz+CKo1yK9bNRUqBGVXe9L02W7uOB3pm5FE73dpn6NI7YSNfBPtq0JZWWuWjGd5r6kxUHfE+qiUZqF2lJbK0Q7v5T1HojOdGfsJTMj2DqimAY26yBPDRvPpy+HepkdbUn3ZTNvACv4jCw9qBadgKYFSlFMCgCiXYkynEinxCC4K1CH1taFwmkNDRMli6J3TdZg5l59O4Mu9zvHFKQscOGlIgAugioMqBK37xHIGyABkYgmmlol8UU1uwr0n2Lnlq8SXBJA+qUD8o53RqSvktIrk6cYPrZx7n2WUXvYzsoPC/WsZsmmwJ44SOCOGJPfLc7K+LrDolsCNt8UPHBCNFxSRG6ulQdNDNfwcgyOXXnIeXdmJBxsDPo2NkJTnTDsGOb0QYQ+dWCbK0HRDoReUuaFII4wpn0sJhPAieSCTOfUHpDX5dYH2t+cLQYftjaDp4NEqNcTOm8xDmBqxTC2/sePAUBPV2hm2J+qixDXXJaTZAE5lV0sG1b/XB37vD/hYhO6TC26UgrvhlpsbWKANOgSJQj1XHsR0n/jkjknpoUiCwDfYDcDhJsHnCJINziFEUItK3SFhXhjcAi0fPomKQLeAleSZyKaZ/wTY0pCVQDhawDqojRmk/kXYFpP1j+0MW38pYvcfQ9UiFMQji1ypWfUnzqT73Ir3/iH+CQ/KA4HYc/8Qch9aEHU3HgeMyh4nhrLsjmM7pxTLKDGUxCTW9UMF+7H3QUAtmkMS2kP3gRRzG8Yq/KYs0pBMyVbXo34zqk8EcegEzFodOH0byGdbPPpGk89GRM8eauHXT2TWf1gOdJCt85YN9M/SfaNZGGJFTy2OtLtzUZnZNpIvJc14z0nHUVo/Taqtt25w4fqmimAIIXR1Hyh7t5DZTAWcnMJ1TNUHeqLKm0seZ4jM6p06LT8lB0BD5t6kj6kAuQdClZaAtsxOtuc4FviuTCNnU2Bc5EnnHhieWJzFH3BTaX6CIiyH2Lgr9D+2CRU3ul3oNaVH7sWcZPjrj+iyUfPn2V/+b0P2bscv6f6dPs2AMM0rYdsFX3u9RgSUfmgSI0UIAAtWidj8CImFIMVUERNNu2T9YQorUPzZaLHEuz4TE/7ErGmaXmZkUGZG5Js5pEWvbrjB9ePsnKbjyOMJlipgfHFJpZvFuLqw+diUihMnYZCs+qiVFU5fVti/iROdNTHuJ6SlUEIc6UpwwCUUmEC/c1chIm6Qq8uapY0HPO9reRIvC/7D3Dd7dPU0wTesMSKT3WqohnChwUyVtwpVXx2oUWAqIITnYcT0p7bKlgrrhWjlgz+zzW3+3Ojw0yPkZav0Oq6p6sNU1xK0NcTxO0wCsggKzoIvnYkDhwXgQQtUeo5n5tak2q9hETlTRAY9/ABgKYSfxsLQ5qiKqKTZTpumKQ3/mM3T1L64RSyEE/hpC9nJAa3DDj5kcH7D8BP//Md/nJ0ct8Mtnk6+Uar5VrHZtiHTRlE/mkTXfONGolDtHNavmmvlEHFRHhbdoXYoG2DarUoUKtOASEOzZriohSxhY5XiAEmKa2UFQGOzaosomoihJZ+Y5/++jMVsTFuNsMlj5oC+IomVxLp2K9PBIhSeKXEu9M85T0aAS6JZnz3Pd1iiztogAjIvXwSBf4INi0AwqrCT5Gwu+2RbQo7xb9/o7c7HDEFQQiCCof7/G1dMzcmQ6KIQSIJEEYTSjvcf1NCnyi8Frgdeys+SQ6FmlDVO7NzTuZXsKB0m9cA+ACwvkYCQHShRhR6ejsZCmQSTwV3sTCuzMC2787Pvx74pxEkqCWl5h+4gz7ZzX7TwCn5/yF577Jnx59j4+aGb83P8nleon/bvp453wWVKSpKIJ+R4cOms4PUB/6WeENO7bHQMUHI3ZWNKXXTEK82G3k1G8IxER9vN2vIOOwZGJiVKDGCp85Eu3oq4rdkGO2NGbSFF6nU/SsZubj8bYEZC3/U+k0lVXcpnzzQC2oBvwqa4yI9MMKz8wmLCZz+rrqUvAWRqBEaMCaonFi0XFlxjKWnvuewAqBXBhh8zbtDA1vVbxnHIJMW+QheH5Lx/ujNDC9a2hSxAElb2Q1CITUMXeGmUv5s6Pv8p3iLP9866l4rozFrS4gqwp3/ca9Xa9UBCOjxHgimG5IXAZBhhi9X98krJ7Fa4maWzASf6g+JAK0ihTSeoQPUa+u9CR7lslGiu0J9DTOH0onsFmEHAQBtici2+hd8OHf8SfUaASn19n7yDKzdUXdj12ceuRxA48cFJxZ2+FUf491s8+Veold1+PVcr0ppMYWumv0utqdK/Jqx4e38HH2TB0qiAPMvD7o5DWKJTu23812tfUoh4zOTyp29vr0rt6H1ryAYVaipWe3BhdAK4eWDhck6Y7AzA5a1sIF9lyODRKlGmIz4gyeC/K4Ost3ZrdOy0ua2cb4IFchFnld63SEbxD8vgEcRkdkpANMsy4XI8QgkMoTUk84LnT0bdckoyRSKkgbwQZFYKjmTH3K1XIpcpu33ExNhy6eBtE5qDZaOoiamkjpltqKVHGeMFjJW+N6qUCJAAAgAElEQVRFEmn5if4rABRtrU4GfKZR5himGYKPNyNNral54tU8DvoKrXFGErSkiRXie40iqDie0uKZvFERmuBj907NLbaXUQ/i7zPTQLYDxbLA9gTpTmjGW6BeyjHra7gbN993pHzHzkksL7L33DJX/5zlp599gc8uvMZps82zZpMXqjWu1EucMjt4JDfsiO/PH+N6GQFospFxbod6Sx8HfdfNfof2nrvY0dgJvei0mhsIOIQgj4urg2K37nW/O5W2e80Iy4LM8TsJwzfvz/68mMWrK+smZZDxmKyXZFsBNa0PaishsFP3OgI2axXeSxJlqbw6UoR9IHabAmaQgZ6IzskhKEJC3XQjpfBHwJldenco3bNediotIYgYMebuaFH2uJclBSFPcKkgV1W3AQ5lQeENV8sFah83jHembUfX19KmtCnereUiAQjp8U0d5ubuINYS1+LrLZZKyhDn2o5DbaehNoFm3KTJrsysKWxrjU/kEWwTEFNBIxuGS4GX4HKFrASyDsjKI2cVNod66El3JGYn0L9aMTuZUQ8C+U06SpVqKUGfXEFsbb/vGcI7OhvCaLY/f4rRX32bnx1ucTLd541ilcvVEt+XZ9izOaXXXK8X8AgmNiVXNY/nm+zYXueUpAhdlKMaxoFb0zofBLOQ4BsuZhsUm2UsmCfS8nhviwU9i9xNTS6/oOf0ZEVPloxkwY6b0X9Dsfy145XeEc0NMDAlfVXxgxVH0qtJtWWgSmqnGF6qUFtj2ssirOft2SIAy/0ZV+tRNy7hm4fiYTM9kfzR7IkuctpzeSTXA4zwjapxPG4tPFo6FJ5SxC5mrutu81DSR1YCFW479H+cFowCCXMXO44n9D6Lasq3Z+f44vPPYkYV/V6Ja66DlBFEHBouJik9qbE4L7BOdd3VWnmqSkdNA+2QMuCciJS8xlPvZNyoFcsfmnFS77GUzqicJtEWm/dIkmOInKSKjk80BWwNegqrz5ek18bgHKrwTY1IdAPJ+Mg+IEIDKWicmggBVXiQYEcZ6S5IG9NZVQXM9gyznwICVXucAxHioLFdzFB3oHB8R84pJIbJGclf3vh2I3OUMAsJpdVMSLtW/lYdV2KDYqBjjaKnqoOd4vAO2xS5W+qQuOPGLciF+Dvi9wItoj5aX1UMVEEmLGUD+Cu9ZuyyrmB+ww55RU1IdwP+5vHyObVhqm4iw5B6jHEd7icAZr+CojzymZ2yx7Adb9HulqHh4z3k92XiqMijcLBnc9IkVgEjVc1B7aZzPO2/RKekfJTJSmT8XW2ZOXarH8BCxcEmIIWPIyXE2pje0djcoZXH1rrr0oVuwzjAO3kvj3QZZZMCuna20DdCmSJEBeEdg6fB8QnX8V0JESLw8ThEVGWD+A7QnH6khWR7jpjH+7EljTuMAI/jLs20g6Cj+8HF2UGvJCjQs4NmgLQBMSsjZKZJVoQHYRu8lJJ3REl8R86pXJLUnx4zVAXbdsCezaNKbUN87xHUXnG9HKGlY9HM40gKggU1R2rfoL7Vka+dut993xJ9GeEYu4z9Zl5rqAoeX7jJGbPFBT3hpXqBy/USF4sTbFc9bsyH7JUZVdP2zZOapWxO/5rDj8d3sswPZFo6ZG47ySMlPLVTqN0ZYX6Q1IvKcnlrgXMndvjw4lXm1lB5RV9X7FeRpveBduuEbCSODn4UDKyaCbXX+EbOygfZpG0HeLTWchWv41zEMaNKRW06DokdCHl8oxvvatYfdHYb91IEw9wnSBsxiFo5ynEeCefa8AFwTZQ0b5xVCDFKUipgzEG6Uk2TqMASQA1qFhemzF7OETuSN+wKu65Hrmp2qx7WKeq+xH8A1sh3NeeQlaelXpKljDCCuvVUt2QsKqoY63FA4FGVp+4rXCLQc4csaopRrxv6zbcdbiKZrUr03BOubxLkSVwWO3Wygt71QLrvYsH9Dkir7jjJ1dqzqKZsq0FXnAbIZI1rEMDr6X6U+ZEVRlpU46DqBiF7eHbJIbsuTkuNArFQvqynzdcEFyQvzE7zu+VzvD1d5LH+Lom0XJ+PmNqEcZlS2aiJplUUSByYkv37DLQ2wqG0w+iIAnYhDgOL6RRfHeo7Ok89S5jX8Ybsm5I8SBbMnD1997zLx2ZSHYkSWiUZKXxM0Q5FS22tqTUjDihVbIPgz7SNHcnb+aVjpq0NRoKEOkiKoDssXe0V0h7UC7O8wlqFrRXIOOibp/EazkuDUjE1bZfgmzqalJ6QWLwX2FqTZTW5sbQyGyfVHlVTX61cPB9eHwzcHqdJG9Mv2nsxMQ1sQHSQbOHpIq4jqR7QUqYIH2JE5MCrEJ1RgwTPdmKTQzZgTWyUjpLVnfFV3VUFblHOWFDTOOfmMiShq0MArOrxEU4fh2TPRsmjfZs1Dkl1KrKlN+Sqoqeadm5oum2N+sdJtc+W6/OrVz7Dq69ssPh9zaufXGe4PEW0f8PJrh6QGcsoLVhLx1xW929XbhHSxkR+6EFDMVxZTRhP8IfTOu8RU8WsGZNYSAokgTUzZlMPjhSXH7g1ODbEwWDsYQGDNl0DurEj1dCDqFvGczr2Al0zVw+AJV3IWOxVUcZr5qIKs5KB0itE8/yk2rLcm1NYzc64hzGORFtODsc4L7kahgyzkmFSUlhD7SV7s5xEW1JjGSXxWm/OemQmSsxvNVnSOT1n1xfMbEJhTWyGaBG7Y8exZkmMTkVDBlcSU7rEHKhKhwi2bKED7QDw4VpTixoPCmQNem5xRiJSie1FFDla07taoucJtteCNQOqcMh5jbuDesUdOadsy5H/xoj/IP23+fG1SzzTu8aH8svUIdZ7HFESaNMOcUF23TgjHGWDZdLSI0OkjGjn4KDBmwjLqWSHrKHm/dVrP863Xz7HytcMvRuOdLfmmf0pam/K7kfWODGYcmM86AqVLSF9paPC7ECV3M8Bf+ubWT8ZyHTN0BTs1jlFYfBleQT9K6xDTySzImHuIpdTqiw9VSKF7xSDHwoTEpGlBBWpduugKLyh9BrVYJpaayEhpWwgEYTuZ4l0zIj1mtLHQnKW1gSZHOt42a0WjMSncK63zbl0k1M60gGPdInLIycTQKpip9U7iZOBWii09PR0hR8KLgw3eaZ3nd++/mF2Zjmz/YwicaRpLPwnyjHKyqazJzCTCFZ8vlrhK9Mn+f7VDVZHU3pZiTeDD0Rp+14mbCBkcYzEGyCA39xCnD1NvdI/imtqKHbbA5F1aByTxPV0xImV0VHZXDcOKzo8bwTh9BpmZ46a1+w8N4obmoV6oBEue0ep4L3szgri84LlH065+MMVvlgmvL2yyOeXX2uUYBuWP+EYICh95MeOcIAY4rblw7a7UXgTB3qloydjkXvX9Zi5lIuzE3zn9TOMvpew/k/fxr5xKR4DYIUAeYL1fMy4TLt2r23Acom2ZKpunNN9uO1FDH1tU09raWlzVbNd9fC1fGeF23nUXFDXuiNlc02hFJoJ94ckeBJSIBJDkBxJ11rk/q1jKy3G6VZr8U9CBLyPD2JqLEGmRx/KWwrx99q8imnUsp4ylAV9WVIHTa4qbD8ydJZWM0jKrqHRdlLbMZ2+rlg0c1b1fqewE6zAS4nTkqJuJLXSKGtVexXFABTs+4ydukdd6tgsUZ7SRLHLe74ltelxU+wOTbHbFwVKiJjiHjIRQoPab0/WwfdeCWSj3hJk/H+LIm+SIFwvwVzfQ1Y1QY2i8woH5HR38jTeeVr3Ry/w1PcyRJ5Rj4b8yl/6Weqn5/zyx/+AZ7IrnNG7XNDgCGw7x7ZPGPuMfZ9RBMOu65M0jmwo56yoCZ9ILGWwbHvPz335b5B8v8e5f/gqz85fJVQV9jYS5Msbe/w761/hlcWTXZdwQcWs/mKxxlqyz0ezt/m/e5GmlHsvfdZZMFGocL/KGOp4rD1dsaRnvLi3DhPzzkJgXdO7Fth+LD4UY5sejOkEGWe2HmBBXMhmoBuiovGg3w00t6alQ3vf0dy2A9cAWriusdGyFrQ/t0JSuxiFpNqCGtynVQFS4HKFzQPrZo++LFEEjCx5Nr/KyWdvMClSNsd9BitN1FNH3JOW/pA4qGO3znmRU+zMcqpKoXKHVA6lPNN5Su0UH1m+ytSm7FY5k6crEHBS77GWjOkPCwREqaxFqIfq2DmMVHXgSERtkYUjaHnAbOlBEDonJXyMooKK4yrCeoJQ+ETgEomZOGQVMNOAtBASCbN5RIW1NawQ2QlkfWd4wzs/F95FOfB5gZwXrH1rjcm1jP/+6p/G53Eq+fTpbRazOSeyCUNdMFAlS2baoXFroVB4Cm94szrB/7WzwldvnufK5WWWvm4YXapxN7fecwhy99Vl/qvkz7Izy/E+tnUh7nDVfho7JR6evFhESeRjtCDiQGUIAk+jpCLjwzmrE0R1G0CjtZhZQNRH1XFv5Zh+KEwpQpbS9oFbTFrLJHE4mmrR+m2Do42W2jrUYciBEBE9fj/ZnISIE/NIeLVYZ2xyFtSUkSrYtEPmlen429u1dMwCh0oQiYyzn/s2bzqQohsI9l5ia40QMNIFSgQKpxFJPE8znzJQBadG+2TKsitz5k16dFwWGjiAngZ04ZFZhlvoU66kJHt1HOg1snFEguBEizCNg8AhpofCR9hDaKSk4muhGyL2SkZiyUOZwt0KCt2do26mp/10SvYbf0QGrLYHYhLKn/k41zc0r1yA6qSlvzLj2RPXWUmnnEr3OujBxKX8cP8kL331PKf/0PL0b3399n/v1oGmEDj5Fdh/fYOkoWWQDvQ8oOpAsmdJr01wL7wU335Xi7wDawjfbZARj6UiMjyVNdPKIMvb3HXOk4w9sjiQ8UZAJqpmmFmAfzioU4RS+EFK0L5znnCQvrVjKsAtdUTfdWCNcA0w06N9KyjgSZRjdh8EDQ4OWlIN4vX67t5pVtJFFsyc0+kOr81X2R/3UNqhtYuEeEGgdBy5aWfstPAdl9VuncfRo2YcB+IG6UoFIrCg50gRmOgEqSO9yq7rMZQFn1x6i9Jr3lJL3DgKQL/nFlSkPMn2PGbiEIM+xVrOdEORjGtEFQiaDucUpIj3tWgck4uRj/CRB0qEBtfkAwHROSBvRCOW0JJCNeDPxukdb1oH7xnRBFuTf/tNei9mrH6zh+8l+DRhKzvPphK8eLh7FkBVnidv7CGvbfGujcbbhBGL/+9bLORppHtoOaedB+8JVU04Bgnrdz082Ux8+9iFhObBJVBZjbzdwoJHlQFRi6alHC/F1XopYl9qhQj+wZad2nqdUpFqVR8g+2+ldWnR4PFjEUiriOdgoIqmQWKYCxOdlopOSxKONWK4nXkdvzJV4xq2gJ6sutlOZxXeScZJgvcSZxUOcDLOBKbKcird6+S92qhdiBCL540TIwguziJFdOV1dFhWcs0udHi/uU8onEG4u48w3tO0xvY1xZKiHsDwrehgwmPrzNYN81XB4qsCGUKsCbXjLs2GIXzAzDzCSVyuInFcI0XeEsl5LVBlwCsolzS9xBBmNnI5JVCOBMJrRIDkuBDi78tCeMdktQTeS9qyVaO5E7NvX77DTxyjNW1a6yWVPwBWOSLEQbijOBGasDfuRBHd7olT+zOfUDkVw+qHJbdTjUaZOkqB4pBHNOvgQAxViohtk01T5DCNzWGxzco/AA5xAcjQILRlR5ZnmqFkaByNP7q+VnPPBdFxh9XNHOStSHfRME2M6xTdyNK3QM5Zo47sg6B0mrk1yJqOxO2eL9eFDvGt5xGU6XJDe6uGpqETcTkceRhFAFkGjPORZqbFODVfreqvtCEyZqYN0t05zCx2nb1pald3OPbwwEU1/39jAXZneYcMn9qEzXpIVRqSiqiC0bKGBhdrTtsFepZSNbUNTxRyKJ2GUnXT5A/clML2FNJYMhGxaG3kVHnN1CYdShyaOTvp2LO9jnFiYlP2bdp9znnJdpkyrwynqvvLIa4KEE6wke3z0v46u2XORrpH6Q0rixNyU5Mqy6RKsV5iFhzey86ZbJd9biRDbJD0dUWW1FQtNa/2hBC7kKk5UK/2QTBYmnUUzG3UdL0Ycn08oH/Vk27d+2jf7+6RvSDIXs8JRsP2HkJJwtKIle9all42MfJPVeTrCpGzCdGg30uHbBxX3dcgwExcpGBJGxJBG5B1oBypKA2VJviyZPQbzyOyFJHnhLqGqsbV1Y844gN75JzugXkdOXLypKZvKmqnSFUkzfNOHAnXO5yH9x2xv24m93XDwJBIB9o/YMnTAxNS4hKJbJDgC3rGgoYFNWfH9rsxJtOkRYdpcBRRJHWoCgZNJ7Mde7Fp7HxVgw3SLMPfh1Q8OEe259DTyAG2kk5JlOW3Lz3HdJ5Q72aIzCGNx001tNzgqUMlno3RPj1dcbVYYGYT5tawkBc4Lylsw6UuAtv7PfZ9znieRQS99Ez3MxBwcbbKcjLjRDLm2nyIc5L+1Rq9OX730sbdrre2+PEEUdcR5T+dQr+PH2R4IztBgxYFHqSIhXHooqm2bNGJZTbfeyO6oe3uPVLg+xlyOMBtbSOsRdQ2MhE8ipzuv7mephoKzvQnrGdjaqe64eTgRDdweSvHurDRa/V1Fbt8wtNTJX1TIjN3fCqw79fam0kpbCZQOs7PnTK7rKgJn0y32XKCbZ91hXJFwAiPwVM33jUTjrE37Po8iqf6HB8ES2bGhtnlHy7/62SDPpTl8aeyztF7e0Z2esS4zniidxMlPD/4jWcYbcXmiu1rXArJbpQ3cikUK5pyxbN4fs5yMuP5rVNdV+/Dy9dIleXSdCmSxwnHjddWMLuSSuZ4Az736HGkc35h6SQfXr3G5wYXuTRfxntJ9oO3sTc27/162+76dNr9SA0HFGtZgwiPUU97rb2RncNp0eI+ldhMIptI3mWyY9aMb4xzdC6NHcF6KSMtlmF7l2DtXcusP3JO98DSGzNGbype/MEZXhzUBCe5sjRipb9B7+WUhdfcAc6pod8N1iJu7pBtrfD85in6SUWua16drXFxexX5doacbd/XNvthC5HrAoho4uWvKFS1wX/+tb9ItRhwWcAtHLxHpg6pAklaI2UEKroQuzNaOcraUJWaemqgkph9hSoEag5nv7eF3xsf6sYeX5cyOIe6eJkTqebLax/j905X6J7l3NcKkutjqG3kVZLyYP5MSkJm8Lnhyhef5LISmLknaQ735XwlIqGrg+L+k1sFsrS0FM5BS2QVMUWzry7x9Y+vcuOnhrz0+gbZpYRQXLrn/OG3Namijp0NqIbZsmMtOIRDaoVOgxRIG2lShIvvtX2FLnwseJsYRek5cTi4EJhxhZgVB9fxLmclHzmne2BiVpJt56Q3UuxUElRgInLqWjG4Gci2DgZ+WwXW4BxhOkXPYH+adUXivTpnNksxYwH2Qbkmmg5og1YvCvwbl1hwnsEbC0zPDqh7gnLBRCFFHUUVg4Eqj5zth8eGggJZCVQJw22BKqIeWrbryG6UcOUG4Q5qER90XW5nB/P2gMWXM/ZcQj3SpG9cwV2+9iOPI7/Nz96t2XP4ceyQAlIxXF7E5k/x1kcXMdcN+Y1AODwUfowm5AG9ibQeYQMuVQ1WyXcHLWsHQuC1RPj4vXA+Kv72FcKBKhxBqEiL0nQbhQdR1geOPf7Ru0L8i3AHHk0IcRN4847/ysNh50IIP1L691+ENcKjdf4xsX8R1vmua7wj5/TIHtkje2T3yx6SftAje2SP7JEdtUfO6ZE9skf2UNoj5/TIHtkjeyjtkXN6ZI/skT2U9sg5PbJH9sgeSnvknB7ZI3tkD6U9ck6P7JE9sofS7gghnog0ZPTv/K8ICMNeR7Buph4m8x/9ucbcSh+vQZVRoz3M73xAdMzO5vsBtN3VGoVACEG4F+RwH4B07P2uET7AtXwXE4khGE09jPNjHV+1J1K4lo5QvJNu+W7sQa7zftr9XKdIDPVCgktBJB6tXMP3TqeiUzqN9Qo/V+hpQO7OfvQv/hFWMKUKt2NjvFP1Ffp8VvzM+3uzEIgkgY89zeRcj52nFfUwYEeehRcUKz8o3kEyFoToZJFFiNPPLhVc+5zEDR3pdU22Bf1rnsU/fAN79dr7PvbfDf/ofaFn72iN7VK1jlS2tUUohcwzfFkSqltGIYREKIVQEqSMnzMatIaqjkOS83mca7sLcOz7XSPc3TpvNyOlVpZheZG9T6wxXxGMz3MwviJB1ILeVcHoTcfwpT14/a04iPoB7NjX+ZDY/Vin0Jrxv/lpxmck44+ULK+OObewQ6Zsp66dyhotPVObsl31+N6NDeYXRyy+JFj/7UsfiFvta+GfvetrxzZbJ7RBLi5w8RcGfPZnXuBsvoMSkTf8S09c4OJnlhCyGZT0LRdooD+cY1SU5l7szTnT3+PnBtcYqII6KEpv2LM539z9FOYOnNNxWrAWrEWkKbLXg7UV5PYefidKDnVCAYmJ/DZZBjrycvthhh0kmJ05cjzHvv7mw0My9z7MXzjN9nMDJv/amNXhlGraYz5N8LPI/YPxqKenXHl7geHZZR77x7PbO6dbqZgf2fHYLedZDvr8xN/+Gv/e6h+yoXJ2fMGWE3y9OEsdNCfNLruuz9hlXBjeYFHNOHOmxHxaUIfAX7j6N8mOifjx2JyTfPIcb/ziCZY+eYPPLrzOG8Uqc5dQes1Gf5/e2ZrNST+yChrL5s0R8kZCnUXNL+uiCvByMmXsMmYuoQ6KtWSfT/Wv808+9XlOuk9h/uD5u6ZkuJcmTMKNX/ok0zMgnppQzUeE4nx8MUSxBSSgAmgf7xEZ0Ikly+ZYqyiurPPM376Bn33wcPnYrIme1NIS5ScucPMTKeNnak4PZijpo+STCojUEwoFhWJ3cwCpY/9Zz/RD6/SlPHDCbTT2yCndHzt0ntXKMmFjjYG6ybYzfLVYJhEOIywXy3VmLuFisdboUQrqoEllzffwnDY7nFT7XPkpxerwcyz8n984eA7v0UZzbM6pXumjPrXLF9Zf44zZ4rX5CUqvmTvDUjLnZDbGh3Wcl4zSgs2tIaopJWnlsE5ipGOgSmY+adgEoxLwSb3H7PGa7XHKyS/rh8Q5aXY+7nj66Sv8rXO/w7YbsOt6UVooCGY+JW2kklqrg2JRzVhUUxSBX9n4Avv6jwdRhBj02X0qYfyE5cKF6wxNSeF01CcUgaA9QTSqJIVCjCpGozmTU0uY8SLq0tsxfX1kD8akguVFiseGZLJmGgw/nJ9m1YxZVDMmNmXiolyZkQ4tHNu2jxKePZtTB0WW1uRP77LlFln63SX8/j7hNjJud2vH8yQIgR0YPnfqFRySL0+e5mS6x5KZ8fpslVRaclXx2ZU3Oony6jHFjcUBP7HxBivJhJlLMMLRU1UjXy7RqmbP9vjW/Dx/5bNf5vfPP43433O4j2IG72pKsXR6j48tXeb54ix1wws+UAWLasZz2WUWZUFfWL5enOGmHXG1WuCGHNGTFXs255XtVdbC1Qe9kne3QzuhXxmx8/mKk+u7nB9s8+HBFa5WC7x6dS1KPiWWC6evI0Xgrd1FlvszNnr7fONnE/af6vPk9/v4yTRuLH8cUzqpIl/RH6djbkz2+4jHNnjr59eoPj1hVY+Z+pRP91+jL6JA7uLCFBckfVlG4VFh+U75GLuuRypr6qB5uTrJz5//Pt9fOsVLxVOsf6Mm/a2vHxXyhLs+R/feOUmF+MSz7D2uGenYkbP+qDabJ/IoD1RJr+Gk3uhFDa8TyZihKjDC4YKk8KYjzW9J5QtvWNAz1npjxuc2UFrjbt6850u5ExNCsNSbs5bsU3tNGTS1V83xS34sfQsfBLshYd/nzHxMceugqH1MV5d777+D+cAtBIIVzMqEq/MRO1XOdtHHjaNYgJWB18UKUnrmk5Sy0kyrBGMc84GPdbd5AV0qcLwqv9A8lFkaWR+lAK0Je/v3hR74YTChNeGTH2K2nrN3XjN5tuLD6zepg2LscxbVNOoLEuiLCicERTAsMmcoHX1Z4oJgKOcd3/1QFTzW2+UHz0654fucrD9N9q3XcZtbH/h477lzkv0eF//8CHlhwkCXnXMpvKH0unkgJUZoVs2EVNasJTWrZoLrSTJZRyFGAjOv2bc5qaxJpWWgCnzjsPZsTJmufWGRlRcy1BcfrHMCON3f5UJyk1fLdWqn2Lc514oha9mEf3X4PV6rl3mx3GDH9im8YdKocCgR+EtLX8WvS35XnXnAq3h/Joqa5Jphvx4ynWbISxmqEOQehI06grIyEKBvwKUZe70B9mxB0IEw7COLAleWsYvZcqsf5zGfPoldHSBrH3nfe5rspWv4uyno3g/Wyntsstfjh38l58LT1/gvz/8Ol+oVbtQjNu2QOmgW1TTqDiIxwlL4nOdn53D5WyzKGyzKGUM5Z1HOGfuMXd/jer3AiWTMf/rJ3+Q7z5zl6z95jvo/O4v8w62jEdTDwIQptMY9VvCRk9dYM/ts1kP2bdY5JogPI8DYZQBsmN3u80ZY6qC5UY2og0IKz0CXpMI2stE1A1WQiRozdHzjJ84hfMaJL97rldyhSUEqbXP8CikCuaqovGa/znilXmHLDuJrRP03HyQ2SGqvqIn1NNZPoHzA7e8/4AW9t4XMUK1ZzKikn1eMdQZ5oF6yqLHCTCShD4iAS8AngZBEeSJRCUIvjTLxHJU+v9cmhECfP8v0uXW2njOUq4H8emxQBAXp2bOk48dIdhvNuixKbXsF+WYNAWxfoQqPLBuHpAQ211EeyYUDXJoAl0ZVElX4A/23Ro77yPlrlXUbWTFvIie3zQRL399DXNmEowprH8j0+bOU51c5fWGTDy1c50uTp7tn0gbFphww80mnDCMJeAQuSL45e5xvzh7HCMdAFTybXqUKMSvYczljm3G9GpFIy6dWLvHbf/IUK6ufpf+b347Mog9FWicESEGSWpaSeafAIUXoUpjWpAjMncGIGC62skKKwLTRcnPITtY6a4rJqeL4zKcAACAASURBVKzJREVfxnTwyY0bvLV07p4u424tlZZEuG6dRrgYNTrNtXqRmU+p/dFTXntF4WI0KYXHLfXQ+z14iJ2TSFNcL8EMK/p5RWos+yrgBaRLBWXIkVbgdVSR9T0Xu5QyCgYIL3CDFJOlzS9sHNNd7rDvaUpRn1pi6znD5NmKwcqMcW+IsAIkzNcFspL0LydR/mgocEmkHu5dlRCgGgn0LCpKR7luqPsCaRutuUMKJC6LlMV6HqlskZHCVtYH64rqu/H3tGDVoKKQQj0MpHtD+vPqnjqnemOJvcdTPr/6FmeybZ7fP9NJxM+dQQtP1TSsShfrpYm0rKdjrpdDNosB6/mYBTMnE3XX6BnbjN0qZ7fKOT/Y5scGl/j1D83Yosfgd/QHol++t84pBNzOHhf+wwEvffrDfO+XNvjEibf5SP8Kb1dL1E41lX+PkQ7bFMNfLddZ0lNW1IRXyzX2XM7cJ+SyIlc1kijQuCBLVvSEk2qPv3/jT/ClNy9w4b+oOXfllQcmBAAxWhR5zpO9N7lgtvlSo+ZqvUQ3TvdqvcjMJcx8wtSmSOFZNDNSmTCXhmtuxGY9ZL6eMdgbwkOC4bq1qClMwv6/8QkmpyR2y7F/LUfWAmWj8ka5lSOcwOYBdIC+5QtPX+TydIGrOyO8F9hFweU/0ePEdwzpm28d3MBC3HMHZRdSrn2uz+RJy9I3Df1rQ3hKMV/3LD25TWkVxTxBvd4DYLYROvHL/SeAEMn762Fcnx1EgKm0ATUXqLmg2HAEHVATGbnSC8HstCeoCEJVFai5aAQBwD47I++VjLKSy1eXkLsmOkIZVVqmJxXCL8NL9+w0sPnxHnv/UsGSmeGDpK9LlAi4IHBNJ3y3yjtx18IZpEgwwjOzSYzuveLV8Ql+/VufQPUs/X7BUys3GZqSUVJQe8XFYo315X2unE5AqR99YO9h974g7h32jUuM+jkXv73K1z8mOXF2wqoZM5MpY5t1rUmIEVQLrtwXOROXNg+1I5WWnqwifJ7AUM15q1rhy+VT/N4PnqX3SgKvfAf3gHFBIkkgS8lELNi3taTDVnjTOCwVI8ZGo64UHo9gyw7YsT1cKgnmg13Ue2pCdioaajRCLC8yOS0pVhpn5WKNqY0eZCkJMsRIyUGoJNfnQ7amPcpJimhEJ8ulwP5ZzdpnPop85RJudy/+gmPofgUNGA8opA3YHHwWOsiKkIFqKJA16Gl0QkFBda6KUKzrCS4JhDQQcgdWoG5oZN3ouuUOmTqcVviZAiFxCxahA+wYQh3VTXwCNg8sjmaMsoKFZM5VvUCQUR7d5gG5XOJNfs+lyV0u6A+KLpsZ6YJ9mzF3pmtWeQ4k5tsm1GHrJOg9eCuwTpIpy4KZHwQcXrKQFlzr14h2Y7tLOzZQjXvhJc7/x4Irf+vzfPnPXODvXPhNXJBcmi+TSMtAlQfS1QT2XM5mPaD0GikCK3pKT8U2ZhEMmag5qff4lctf4LU/PMeHfnUL94OX71jG/DhM9HL8QpxrGnvDlfkCQ12Sq+isfBDMncE1c9aF01GGO6WTw75YrnNlvkCdC0JqHthabrVYqI6dNP/EGcZPDRk/YRF9SyhVvLEblL8IoIqoX+a1R08VYip5fe8xVAn5XFCueELmcUs1O5+Bnc+kPPM/nIFv7B2LY5I2YMYBnGB+QlD3NdWTc5LUsj/LKIsEVyrqC550S7L6fUexIKkWBR958g0AvjF7ErFQsTCc44JgvNVn8WWoe4F6IBDa0+uXZIuWvUlGvZ9y8nScDrixfQJVCJIxTE8F7KLjRH+Clp7CGXyh0aWgHgbcRsnPPfUiX/nyJ0lv3tsNN0gwKpZFBqpgI4Hr5ZDL00WW0hlaHn2SEunQ0pEqi3a+k5Pv6YqljX2ECCTa8UT/Jmtmnxv1iMIbrJecH2xzfXEQO6IfwI4X8RcCp740Ye/aBn/9qb9GebbkP/rcb7Nj+2zb/oEqrPAQYn0pbVRj22hJisCPpZf4XnGGv/a//jKDS4EzrxVw/RgECO/SRJZRDxLqoCmCJpGxziYbFd/DO5A8tCXGqFFS2DiSUzlN3Re4TD80dBGHgZLleo/98zJqnc01wgqCCbjMY7birVQvOORckuzE6C+IGF2pUmD+v/be5Fey7L7v/JzpTjG+ObMyqzJrIqtUpFiWmjRo2JYhyVoYlmHAgNGbBroBL7xtoP+B3vXCMNBoL7oXhrwzZAs2YMMLqy1ZssRJpsgyWWQVq8isyjlfviFejHc853hxbsSLzCpKyqyXWc/2+wIJJjOjXsaNuOd3f8P3+/3NodoMP0vOdJjaaY+LFKrTOZW1SHVm0zBrBMWWIBqUFNpDJWGhqawk6xekWYnseNiESdSDH4bgWnfh7myAkQ6fNUjpKSpDMY1RJzrsebMgG2BqWEhPZ6siTcLevn5UhgcQIGswU4/tOaKNguM8Y5rHLMYpCE+zXbOxPeWlwQnXkiP+OAEXne3RtBHspIEykYiKS/EJd8shx2UniHm9QEuH84Jm+b7bxEG257ByYdBzdTBeZVHLiuDrnZ/yoBnw02KPVNUM0wKuXEIJiV3KuJ4Qz4yECYQn4bd/wOA7gs1Xr/PwVy7x9t+4yQ+LFzmsu2FNc0sboC1v1LI8wK8O8mtmwjuF4JV/ehN7cIgvy8+1x/QJGI2LVTvp0BhpkSJM40KQCsFJ4T4xlWrahnhuDZVT2ARcJM9NcFrPZsqhIt8J0ynRCEQt8LFDZQ1iP2jpRNYgFhF6FprD6Lb8a0BWbTtHhAzL6VDquFhhet2VbOcsaQVeQd33bHRzZG9BbSWjjzfwXkAfuklJPyrZSuZ8tzAgUmwqqLue8SLFKItJGrwX1LVCzBQqD+996bogC4GNNc4L0qgmNg0dUyKaKDTEPegylISDbs6iMuTTBPPQUO/WdIY5v3b1A3ajCXt63Dbkz/YOcBFsxAsUnkhYrugTNsyCRNVUTuO8QvrQ5HaIcCYJD9PlPbysdC6n41XzfMlh/FJ0hBENd6pNjLAMopzp9hbRrAvnKjgtb+h1Fu3RCWaxxyVV8rEKZEMjLBKPkQ2102HE3garRNaoduQeLfe45/mZ0uPPDOJ0Z7wUjo4K68WX1AktHH1drLLEcZ1ivVhJWayXKOFJVB2WVKrPlg4/KzSxwHZtCEDShwNuBXauSU/C9KvaDk9dF4OXoXlc9y1NR1DsClwcok6Thr6UV57JtZg+V9CHR+2mYXlmjXFhw6+X+iMezPuUtSbaW4TBsnRspQsupxNuz4fgBdOriskvlrz18j0+uL9L6Q0bgzmHRz0YG3ZfO0IIz4MXNlAjjRkLklempFHNwc0Nkp2cty7dZ1bHlFYzfPWY416fJo2IOhWJbrg+OGYxjBhdShnEBZmu2C97lE7jYhkcOcxZByfPbjJrg40jEw0vxwfUfcXP5jsUrR2KFI5InsrBTuqUxksi2aBlCFTTOqHxkspqUlWzqWcMpSYRNdZLBipnK16wvxejT7pPvU3vuQm5fFUhLRgCXWA9MC2hhHtEeyZbHxnrfehtnFMtlpfikYAihaO0EaU7bWyXTjNxCbmLkIR13UtC6rIJufp55yU2LQNEa3/jDKA93srA3TEOWypEIdugCviwntomPmRHog1C7TUJJ0LQStvv0kM5EBSbhq6Q8CxyYg+LJmI0TykWEWmnQsngWdQ4ybhOOClSrJXUHaCR3J/2sI1EKk8vLpllMXkTvqumUVDKQEcAqlKHj8kLItNwNTvhnaOrjBYps2mCKBRee5pKMS1irnYdfROCUqJqlPAcFF0WTZjkqgJkdbafg43ghfhkdb4S4UhETSyadnIuV6XdMtNfZU94ImXXsim/qmxCMuFYz/WlcMSyoewHysjT3s7PPji1Tz9f1SsimhENsWzotUrfsU1Xte16cDLChmgMj3Ckzh2kxGuBbWU5jVdMm5hZHdM1ofF/pxjys/E2B6Mev/LKT9mJphxUPWZ1jGvHuY1frnY+B3otIVaSEhFFqJ1t6o5ARBZ5EIUJ01aFmiiiE0l+JYzTAVzicAmYmxpZCqpLHlFL9FziBdjUMbgyYVFEVLOI/JIHoegpiT9rDXfLM7pxsIX7aZd0LPBfq8jiiszUPJx1uVluUs4jfKGoBp7uBwb/vS3c2w3J9oKXuiOud48pneYbP3kVsx+x966n6gaKgfhxhzrz6OsLvrJ3l98cfp//ePt1pjcH7P5J4ESVGwJ/N2E8iTjqzthK5ryYhnJnZmNujjYoS41tFJfuOczB7Ew/BrtR8xu9H/JOcS3IU6TAiKb9iDxaOLQOQUVLu5rgNV6i1OmXIvHEqqG0GonBekntPxlGOrpkeg3S45jsKd/zM26InzYOfFMjrGfRnjuzRlaE06xpGZyWfyeFY+HFik1+LmE0NpbUTq++qGXDULdp9LLpCOGLy1RF6UIgs06u/j6YtJ2D1GlNUqI2hkx/+QpNBn6uiY+C22WhImQdMiVzIlcZk1ceVOD0ANBIRC0QDXgDCEiimrw0UEps7GkyAVK2mr36zCZ3XgYqQb2IoOtwsWA7LUl0OHCRtkBFZBo80Gwr3KRPMvKIRqKV45d6t/jt27/M3Y+3SR5ohIWjL0PTt8huTfSzFDMVVLcyPhzuMN1OmZ5kRCcSrzzFtiD/YkGnXzAwDfM6wihLR1fsxZNA3tUN+SLGH0dh4JBGZ3L9SIXsZMjY0hN1eBAiyIQKKgvZ0DMFqVOtoFdROk2qqlUGtbxvpQhtFi0dkdTEKlB9jGhwj83NE1lTXWoohvq8Bif/yO9l45l7jSNo6Gyb8C1TxMAmby/SKxRhYrdom2+4c5BRfAq8ltgoNBILb1pSm8A6uWLhFl5iXXgaZbIik1WQrzhJ7eRpeSfOUVnXwve7jF7XNJlHLSTRJBx6mwX+jpeeaBL6btUwBCUXeVzs22mdQDQisMaNBwlxaygoK4mPPE3mn4k7gZftr0JB6nB9R2pqoraUSXTopcQ6OD/2o4J3kh46d+AESjpeifd5OOox/KFGNp5yQ9B9Y8Reb8puOuXbt9/CTASdqeDgap+HTR+mGjMPrO9ix/H2y7fZSWY4L3nn4ArzOiKPzEr5EGmLd6BnEnC45GyOpjAa2e+hI0tPuhWdJRam9W6yZLICCQOdr+g8sWxW6oxlcDLSrvrCRhjKVrkRLXunyJYrJclkRX9nRjXYeOr3/lzNg1Th+DeTtxmonK4qGDUdrJcY2ZCIhljWRKIJqaJQKOFIZMVD22W/7MO5YDV9Et6oMLJ2ZuVC0DhF4yVmSbIsOpxMMprjhJmN2Y0mdHTJpA4ZoWuDU5jUnIPo5F3gNwEYTTXwocmtYPyGBRnseNVUYeaCJgkBqd6wCCvAQnW5Do3zUrbZoMB2HD61jPOEOjfoQtD0gsxFJDGiLM/Un0sVkD4UTHccaqRRhebWyWX8sOaNa/cZpjkOwff/4IvIUlD3HXav4d7fb0iTisk043//nf+N9L4gHjv2f7Vhe3fCm1sPVt+tv54z6yRsvSOI3035v0a/idioaC41pFlBVhl+cOcK7jCQc196Y5/rvWP+p/7HfHdynf28hxSefj9n/rJjcTvDzD5J5H0ayG6H5sVtulmxGiwtSzYjGhJRr6RlpdPMbEzlNPOWSGy9WGlh9VrLxfkQiCwCi6TwoRRcJhqxrHl964Af9f4bCU74IPbNZEVMS1BcZk/CrQKTRaLa5lvtNTWawurA8l3nwDwLLdZTIDTEw++XroHrbFuAstE0tUIWYnUzqLaxCKeps1fh550neKNoeh5cGJ07E5rcIrK4SuCq0Bz2CtAOKoVeSOrMhizQBdqBbAetQoaxtHdB0gHtWL7bQRRnG5yE8wgHnWHOvOogrMKMJZUwjMtkVbokBwLhoO6D6Vdc2T5BCs89N8AcCKKJR1WE6aQL30/pApl2eQd6DaqE6FjCbsNOf8ZLvREfTTaZTxOSA4VoYPRSym4WoXB8PN3kYNplpzcjynI6ccUk7XBmLVatqbuGSAcO2TIwScSKR2iRNF61D8jw99afBrLgMBMY9hCCVBAFC0pnmLuYwgeJ2dLWCCBRzWfa7/Rcg5OXsGcmxLKmdKcs6FkbsHqy4F69Qe0VRlgKZzjyXQpvmNUxamCQVY2bTk9LgHMAr2TYDrNGsGycpLLhy7ReUFmFzxV6LjgqO+xE2eoGWEIKjzMedy4yJ7/yV7KdiOzahNn9LtGRQtaKJnPEw4ISqIwmPmiFrl4QjRSdu55FHmGTwIuKxoLkyFNuCWwH0qhmKkPAw4csrLq+TaQU7sZnW4CwDuGg6sH/8ebv8Xt7b/KT4x2a399G55J7yRYPOg1CwM6+o9iSXP3FBwzjnI14wV/u3+A/d1/mm+/+ItEJmLll69uafG+LH33dY1QgLcqPU+KpYLG3LGlhkJVspzOcF2SmptMriPZj0iPHw8GQd65FXEom3Hz/EuZEcuXX7nI5GdNVJb/duXRmDygRRRRbhlhZ5s6vMh3VTkbXH6AOEYjDgGnvZbdGIA70F0dXla2jgeFh1aP2iuM0hJKNNgiWzoSE4jMUO882OC0zG6nga28xeiNeNd0WNl41wes2ai+nXbVXK7e9hYvZ1hO+0H/IH/2tr7L9ww3kH37/XGRMK4gQeOM2TV4ijGYtOGisDBlEI1a1vBLBlsK3r4ukbTOnz/FaHoPMMupIUtcKlKfpePxWhVCecmHwc42aK5qOx2lA+kAVUKGkElZgU48zUA4FLnVI44JbZlpTbSmIHVZKpi/GDMo+3Di79x/WkXm+OX6Ng6KLc5JywyNLQXrL4GKNV5BvC+oe3D0cck8MQHhGlzNujYdEEyg3BfmuIb9s8ZFncX8Qskfl8RuWph/6hbIVA1eNYl7HjMqMWRnTNIrmsqDpKPQU6psZ/3b2NnohsQmk6tTCuck85eCMbgITVAdDU2Eei3fhvOnVtNgIt+oDrxMxH/k82zMKoKUlt6GV0RENU1GjcKu/H5hiNcF9GjyXzEkoxeGXO4xfD9yKhYuY2ZgNE6KsXC/h2uCkcJTeMLUJ16JDvtS5y+/+9Tc4rrts/+HzeNdPABGmbImsSeTplANYyXEaJxE22GwsXT2X121dIGFGqjldq3QeIAQiS7GRDNwe7XGZ48reCZVVHNzaQE8VeiEoLtlWXNvaf0QCVYbMxSZhimcT8IlF6yDpyZIKvxGCcyMV8xc0yXHM2XRbArwO7+cHhy9gXVDcV0NHfKzofexXotvFC9AkHnscI3OJtPCuu4ydGbamnskr4K4V/MKVB4yKlKM/2QsMdwPqpTlJXFM3ivwgw0w1jZXkjWGcJzRWYq2gvtxQ9yWd25J4LBA3NYtLgmprbRAE2DgIkc/m+hVNJsh0xeOV4lJu5R674Vbi37Z0exzL82mEZe4DFSYWoNaC2dJ1w30GmejzCU5GM3rTs/HKMYU3K1kHhAsa6AVSOKY2WdEJ1n9NXYLC8deu3eAbO195Hm/5ieBlMA2LZY1pSYTOh0MXBM6SxTzBjCXxKFx3yAxDf0qsBTOvz1fPSRiDiyRpWlHcSUkfCPb7fbQJHk06FyQHoAqNi4IfkVfBesRFHm889Gv8QqNmChlboih8RpNpirybYDthG0018NRddabBSVahpOxEFceLlHwRk95X2NRz9OsF/jjCTCXxETQp/NpX3+V7D68wGnW5vDmh6GmOvjrADEp2BnPeHt7hbjHkj5O94NNk4R+89Q2uRYf8k49+lXuloSolX9wM3KiHRZeBKdiOZ/zr979CUyW4CBaXPOL6nOZhiiwEf3TrVd7Y3ed/vfwN/vnsV+jfOqO+m9FUPZjVMT+sQstk7mJ+Vs/4YfEWP569QCSb0P9sNa6rz661KgLWAlgoZZUIdKBUVeQ24v89/npozaiCWNZkouLF5BgXPX3m9Gyf0Ws2nW7QsNc9JZapVnsGn4y4608RgMoH47mdaPqZIvEzhVhOMNa80r1oR7IO2/abzCz8ucKvBJZKupW4cqk9OxcQErczpOxLhPDBUmThcVYgpcP0S8oNR7kF1dBTDR3NTkUzsNjUw05JtLdgY3NGtFlgN2v6vZxBllNbhaslsia4UopQ+rkzflwK51EFjPOEIo+whQq+TB3P6y88RG5VNFm4NuFgL56w05nT7ecM4oJhWmD6FUp5FmW04q7ZOJSxXsKOnrKlZswrE9RaxtOPCjbNnN1kxtV0xKvJQxA+GO3F0Gw1fO2lW8itCps58nHCvdmAe/UGshLBxO4M4JXAtZSphQ/bVGqvOHIxh02XURlYSMssXrU6uvX/v471pjmwKkU/mO1yrxyuSrpIBA7UZ2nsP5+GuJTEvZIr2Xi13ilu7Wytl0jlMDgSWTNtwmjdEj7Esq2JITBpP+Fzc142d3govKbwIXpaL/FesKnnzGyMPjKk+57+rYLKhv1fDkGiagZxEXpTllbqcT6ik4wMD/7qJvOrHvKo1XwJTNzw4sYJv7H7HnwpvHbPjLmkT/h6nPMPb/86f/T9N/ifv/SnvJXd5cRmLFzEwsZciw85aHr81vtfD5Ov1CM2KrR2NHWKO2tdoYBo4hn/aCP0wbSnfCPn8s6Y/+XKt/n/6r/O7dk2shHomeA7R9cZRDm/fOkOEs9B2QWgvJ/hTyTfzF4hVg2+X2MbCQ6+NXmVb/jXGf9kM7RZjSdVNdtmyqaerygy9TgmnoQS+MtfuM1vXfs9/p/+6/zB0Rd471sv83C2zT+e/BpxAeXGGR1NKXGRp2tKejJoWhcu4na9xUeLLe7N+rzUOSZVNSd1Gv6TtQTBtdHl8cZ57RU46OoS20h+crjLYhjxQnxCVxVIHB1ZBm+vp8Rzm9b1spKdaIptJwKZKpnZJDgRcKrcP82m3GrRQaAWuODi9xkabM8KXgmWDH7rZWB+O9kuBg1psJ4L4qlDTSoi1dCRJbUL5nO+takIDHF/fso6KXFRS7isFTL1lJuCutLMqphMlo84Ltj2u6ucRpaBEmJEw1AtSERN0g4MerJgtz/jbqVxM42bGpwEEouNz/aWdErgDOhcYKNAYbCVYlLEfG92jdEiBSeYvOqxg4atJPRB501EZcN7ee3SAR/6XWqXBDdPK9EHEXhAwH/44A2k9NiOA+OQieXefID1gqHJ2Y2mJKams7Ng7jvoseL2yZBvleHgb8Xz0EyvBPU4Jq3PkIjrfXDgbIXmRlgWLuL7i2vcnQ+ZFxGpqslkxaELgTg4EMi18xeGOM0azUAiseJUbjZfxMw7EUY2RKI5zbgET22B83x6TkKwmS64HI0pvG4p75aZTULm1EZp6+XqBodTgfAS23oW5A9n6PdzFniU5xQCTW1DgFpOI6MpmKlFThdo4ejJnKolajY+MMQbF6xaz01ZRytBkcHR0mYusL5bAWto6jsckrmLw43vT5hUYRNLbg2113RkiRHNajqbyZJX+4eM84SxjDHjlnJxtcCesdGeV+Ai0dq1iOBfUkrm04TvHb/IfJogrKD/hRG73RmXkgkPix6TOmFWxfTjgl/feZ/aKW7YbarjBDVTZA/Cd+40yMM0kFC/kJOkFb205OGsy+Ei40o/eMFv6ylv7OzzM7VNeXuTk8Muvzv5cujT6CIEpwaYaORZ6gu9XwmUI4LZ3GHd5d3JCzycdinyKCgWWnqAFKGKgbVEQfhHxBnOS1xroWKEDQ+mqSEfGhLRIHHhzwj3szAaXz35jr9n7+fUYhDnDNScohm0NijNJ8S8S56Qa7kY6804JRwDNQ9q940Bbjw5F5t+ITC6naH9ok6V21o6LukT7poNVO6JxhU8PALUyiQelk81F0hrscWeF5teEa7Lxh5hXPBmEp4reydc7Z2cEmbXau2i9QRaBtiwaOiUwAcQCcvL2SHf9S8SH6rA7YohSppVf+TMLsGGJQQ2EdQ9hzPQualxWnPr4DI+c5BZJrOU6Szlxv423U5BN67Ia8OsjPhnJ3+Zqgp+Vcm+RpbQZMFETpXQdML4XylHXStGTcb2YEakLA/nXY7yjHfEVZR0ZHHF5I2cJGn4T/uvcfvGDmasgmlfFtwa1AeG9OiM7m3X+klJR9IKfRcu4mDRCfQQTo0dK6eIJCtvtSXb2z12Tp0X1E4FyYtaMFUhYE8WCVObsKMnZLJk6lJ85FAvXMLtH6z8uv6ieG5lXeNCs3i9+f1oltT6VD82MVhiOX732oeFjLP56ULGzxm+5TmppR9V21QUwtOXBbFo0DnIvMGVJY0P6bMWa/YUy/9G+fOVOUWEPljR3qDKM0xytqNTouQySFnEinPnWV5Ta/Hafu3Lsn6g8jAIaMJBt6kjM01oiJ8h818QDqfTbdkVO7yK2kxF4LTDpDX1wkAtEaVkcdkxSItABygN5UkSPNFFYMd72QajInCa6p7Hpg5qha8k1BLbWxBHJbUNWeZ8EaOUQylHt1cQaRukIQuJWgjqqw0itmjj8MKcWUMcCK6dwq+IlY1TzMsI27Ti+jWVwlLdYNsHzPIkrk/yHEFTaddKRVkJqkpzVHd4JYZE1ExJQXlcJ0XoJw81zyU4ee+5Od7gvd4L7JlJ0OKsbfJdmrBJgiZnqTNbYplB9WWBTyxuq4+YTM+P8VxrNhdGqME7XLWezDtqjhKO7v0adTLDAqMy48D26ZuCwmqkiEI6LW3Qop0XnpOUlBsOrzy9nxhkE8qk8uWgWk9kjfWnRL7H+TKrB07rvljgVq4NsazpxhXTgUe9PGO3P2crXfBBb4jq9XCLxdlkxu0yFWdg8+oJb20/4OMrWyvN2HY6QwrPO99+nfhYkB54jjcMXxg85BvTVygnMdlHpi0PPZe/fo8rnTGF1RwVHSZFzF5SMs4TFu9skt2H7n3L3b/XY/vFOX/z8vv8q4++QvReRu+mxxrI/v4DXuqNeLVzwA+zKfM65u3NO9wv+rx/vEfRy6h7Z5M9i7We09KSKLeG6XEHRPDkWja4q9a/COm72wAAELdJREFUXy97oMKvotNSauVosyag9pKeyslUiZ4JioOE/9R/ja+8dostNefA9pHG0mykmAdPXq4/o7LudGMHhJ6Tko/2jx63Y4DwASj86umr1szolq8TkcP2EvRTROJnjWjZV5E2bJhRgXG7cBHRUY5fhGnJrIo4sRmprEhU84ktF+cBQmtEluJjF/bNuaCwtwlsJXM29M9P0aXwa2Xd6bUtVxEBDNWczFR47SmPU+4XhuxKy65P21XlZ3EdzqOLwEeazhPupkMqq1DSYZTl5niD2SKhc1cEmoQBv9C8e3yZ+WGGnKlTZwMJszJmEiVkuiLVNT4WdExFZRXTKHg3FQOFm8OtkyGJqilKA4mnzgABx/OMVNcMTU6iGiJlV5q0ulGokuCKcBbw4btbnp9EVNReImYKnzpEfGpdtNquwml2VDu1ciOQIkiRTje0qNat1q+WW4wmGbXXJCJkhkJ5bKoxT7Em6tmd8NaoDCFAKRLdrGxSFI5EnC7bCxmUW5lfrUMRJgdWSCQObSzVMEJH54/wJFvWrBY2eN6ooF0aNynqwQg3noDzzMuIUdOhrwtO6qy1oThfAUqkKSJLW7F1CDZNBtXAcSU5YdtM/9yf8WlBt/YaIxq21CwY8RlP8kDjlGK2E7X/dhKaqJ9hIePqOqxH58GbqRrH3GaIMZbYNCS64Xi/T3TfsPFhjTOCk1c1eqK4d3uL+H6bLZpQFjoN41mgulwbHBNJC6Yi0xW5Ntiuo3QSrwRqLpk+6PFupalzAz1HuRHMBOcnKfeEJ9U1/SgnVcHetnKaojJ0Fx49rf+cK/sLos2clpy6pK1MzFhSGY+UjtoprJBrQSfYttjlJN2HRR1GWmqnVrvtlpBt2a5KqEYxhTetY4NESkeTyXNU1q1nTVGESOLwpGobxut9lkcyp7YBJ9uGeHiNe6RcUNrRJBIhz0vtA6IJvRPVmnHNm5hE1Qxbf7zcGvx8jqtqhApLHB+UA4YmZB9KuhDQhAq9mXMQp2S3gx12kVONS9yK8e0Sx83FJqmq+avdDzixGYU/7WLbtSdr7VUrNBWr/uJQLejJnC9HE746vMl7V/aoij7CBo+nuu/JX9shyQs4g32ENpKMvii5/FfuUllF1WiKWlM1mofTLtevHZC+WvP+5ovoqcRMoNlo2NibMOvFlNOY7IahGnjEbsnucEaimxV5EWA7mbGTzrh/aU4uM1Sh4XLBsFME5f40JppKym2HzRyvXdvHSEvlVDvsCbsbN6MFb+494N3XXsOrDL75mS8fnEfa4GjpEHRk2AqTHAnqoSCOG2Y2XlU1p9lTu4G6Ldcbr1ZZ77JHVTnN1CWUzqz0k8DKhw3AGEvZV/TMk4ea53PCpVwLSI+mq09V0pyjhjHQWuue9lignY6omoqwl85X9SpoN5XipE5XPbdILte2O7wP+98+d8QRLjNBVS48NnNBxOnDU7j2KnhHt9/nsvn9abDttHWZGTskhQ9lfiepVkQ9IYLcpe7rp3rSfhqCKZ7nSmdMohtEO0WV0mGtZBAVvNY7IL00o95qcAZk0rCR5ewOZ0T9MvSbYkechAUFWjhqqygbTdloZnXMoonQ2gb9YexJ04phlpOYJgw5AJs5ZLdmEOVhmUV72K0Pa71LG3o+LvLU2bO5yU3rRKBzDy54hjftUttVq+UxycpSGLzEektm7mIKF3pyp0s+PJL2e1cOGxNcTp8Qz75x4zy+abDtjHgZUZcyD7U2sbKItQi+Jm1ZF0VagSo93p0f4zlZu+D1A3REzWY0R9ZhB/2B7XFSZXg7am1IHEwMd2ZDrmdHdHSJi4PMJXcRvpFnvu31aeA7KeVmhN+uMFFDc5BiTiSqElz/2hFvpXeYupTa65UP1zqNwstTaoVD0pM5PVnwh/M3eFAO+Jd1xq35BtNFgioFshbUVp1abJwRS144SO8Lvvn9Lwb7YOO4/tIBkbTkjeGj0Sbv7+9Sl8HewyaeKAlkzMpqtHTc+oJGeMhnMZO4JtbB0reoQxl299YWNO1oy0O1aenqhkhatvojrBMcN/2wARj48GiHLK7YSHIKG7zKPjrZZDpLqUcxGz8VdB+c0SRaheUTur2plq6VsmrXWkm3CopAa5TokTJUOEvnjKrVFdm2BbOcLs9swqRJTkmjLvRejYCpC6VDnQm8Pk89pxbeWmgaGic/Qbh8EiyzEucksnKnm1gea75/bnBws9qmp/KVIBLgdrXFwaLDwB2vXqoWknGetPyS9vNAhilII85FcHKJps4k3gbfYJ9amjrotFQ7dbOtYaDEnTbAxaPZsF36yRAeNP9h/00+OtjE3cnweyWvX3nIB70MtWjvh9OXnw1a3qWaSrzxuFgwiEKz/XCRMT3JYKJRC4kUIThp4SmsCdMtZYnTmuIoRU8UI+1J0opuUuKcDPfjQqGKYJznDNjE0VjVlm0C5yQ0YdblvWQ6TpnJhAPV4+r2CbFqOL4/QOQKswgi8qpzRh+CCDv2ZGuHkoianilpsqBGqO2pV//yf39elfPzHAos8lTz6oMWtvasPNv8kh7yhHgOmZPFV8uRs3qk9PnUl3/qSHpNtW8Fet6APQcn+DG8P78UeB/SkhJIlh/mexyNOwzWXmdmguk8aUs5v2owlk6BFedi+4pNDXVHQCVxkcR0KxrtaWzoBy5cTEeeUjkkyzVBn0TwVQ88qA8+eIHhjzSX/+MRH/29Lf7uL7/DP7q/S6PNasrnlDizzMnLUG6YeVjf1dSevXTCvImZLRLUfkRyIIhPPFVfMH0jZCwnRcowyQMNJC2pZx26HwsmJmY+lMQm3NPeg54JzLTdaNyDaiipak1lFYU1FJVB5hKXAg3ImURWAp0LHn6lYdDJ6dwwyDowzl0MxdbZBCevJTY+3S49kCXb0YyqDyhPVelVGbeOx9stdu1crgIZvvViE7jMhSUWLkynF15RtMHJGUCdx7KuRdUEj+LVIsnHbuMljSDUq8ttuac0eCk8NQpXKsxoiq8++yTnrFANDMUOvJ495JX4IR9X260xvOTfH/wC9ShZZXfeeaIx5OOwvy5TQb6wG02JZUOynVP1up/zFYEqGqKZx4w0TSmxS2ve2LJtZgzUo26VtddMbMKPq42g2t+oyW3Efj1koOYc2y4nLuNX/9KP2X+zx+zvxHwx/hk/mL+IfxgTzQTbX5pxp7NJnUXwFGXAp15H6ckeOmaxDGTEUvC7/+VLwSGgDEsPFlcENg0bZfSJpjnu8YAe0zdHGG0ZT1MQUA0I22YmhuNiiCwkqhSodgONjQNrPD4WNGWXh1GXe0OLKCW6FKhShdVYXYdwYRvN4jCjWETofmgo20FD7yeG5OhsH1CLJuKB7fNL0WGggYiwtbkuNaMyI9GhB9a0gl4pPNILlsXlaR9KAKolVQvuFUPGdYKPLaLURGPJYd1j6iIcAgGnxNonxHMlC4UaNghEH18k+Tgs4hNPYetDeizyMpSL5wReB+awkc0qsCpR01Elh4sOevroQdNzj5qplol7+jQy0gZvp/PQ8LceYT2ylEgVFHS+dX6EpbVGy/wWEghiz4WPiVSDysJtvfSVdu1+s18Zvk8kLC+YEd/Lr/PN0avhgFehr+F9IHqeWc+pcSTHDVXfrJXLpl3BFTYPex0GGrIKXB0zB1l7jjd6YDxyoommElWCGQuMDLWnKoPbp9e0o3S/2jDMcTiU5Tj0q/Dhz72CEomZCaIJ2H2NjRU6FzTCY53AzDzJ6Azvb+EprWZqU4wQGNmEz6IVLi8Xap4uy1y3MPIrAqdrBcDL12oCobOyGplYmGh0HtxDltZ2SjoqyVM5bTw34e+L/TFvpvfCqN3HK2uRJawXWCGIRBO0O6hH/JFqYOFiRC1gkePPUVmXHFR0b6X80dHr3My2+XCyw6v9Q67GI/ZvbTK4I/BL5aR3DD6usGnMzxY7lFYzqYPR/r18gPjTPoOPzojj8hngY4WNg6WJ7VlkpyZOGtK44v3ZJaY24WudG2zpCR1RrZrfA1nzrc4RH3Z2eC3b5xfiu7xqRlReUiNZOMOJy/h347e5mw85yLvUWw02Vrz/p9fIDmUgIJ7VwycviH//B+yuj7LDLvL2t+1iifV/r5XO7P22CYHXWrz34NyjUyfXiln/LNueTzmUQqnw86wFpRBC4L0P70UKfFWf+cN3URseNAMcdzise3TvOBYveF7aHXG9c4yWlnkTrwiXsawfCVKPJxPL5bGprBgaw/RSzI3JJeKRYFonK2skrVy7EOOcBicgTCWcYUvPVoEpkxVGNvRlHixuW//tRNSPBK+iHXMua13/eXs3PQb9cMIwVnzwB6/wXuLRc8GNzovYxLP1rqB755RGgPfE96cMteCP/+BLYWJSC97PrqNK2HuvIbkz/dyXYC37Xs1Gg+lWSOVwTjCbJ3zrp28gPPyL3lfR3Zpup6ATV6SmZiNe8M7tq/jbGb/lv852d85GvKByYez+YNojzyPc3RRZCVQp4HID2yVIqKskPNXPakehB19XT0fo/Ow0q88fPhjcZabmBTMiFjLspTtxxEeaO4dD+nFBompmdRwyIhFIl4+rONb7wcvtQl1TUjSG24dDomNFfOKovSQRNRt6Ti8uGcXgzw2V4LHg4b1nXCYcNj2umFGrUoeeKshkyaaa0ZEliWjIZEnt1SPB6cR2sD5MGj5R652DSZ398Ab6Q7j2+3/B1//4A6Ifwyv/vwrv/7HP6/O/IsA5hIPty2O2sjkH8w7jSQd3HPHm/32X5uZtAPQr16le3GCxO2CSSg77gu0DR/awZPzekON0g2MJsvKoEnZ/lmOO5tj3fwCA0IYP/tEv8cUv7vPl4T1+x/0l5Pfjz9888L8XtBy8rWTO2/E9MpEwb2LSBwv6N3pMbcZP1C5JHGQ2UgbBulznnnmBc+GXX8ugvIduVlLWGvFhh97H0LlXUjnNQJa8aI7YS6fc6Fm8OS/B6XE4x/Gkw08Xu1yNjjm2XUZNh4OqR+0UfyJeJlU1XVWSW7Nqli8jdleF9d1jlaPmEj+dnaueE1KF1d0/T6gqBCKKAufL2lMvqnVPKiHCCnClHn3N5wR54x7DfIcbi5jU1DRW0fluyt535riDo9Xr3P4BZjpn+HECSuFjE3qCecHO7Q4oFRZqWhfKoPEMXxSr4OOtxUwFt0YbTMoEcyNl+J3buMOjn/fWLvAEkEcn7P5pj/987WX+SfI36OuCP7zxGq8fnbD13ZLNH2iqb2V4FQUnhGWF+lgVtqrwHntmOBMhrCcaTZCTHE6mfOe9V/g/+dtUVvH9D6+x/ScKfTDhSZlbz62sq0vNqEqZuzjstHKG4ypj0UTkjSHVNZkOAsrlpG65VfRyOl6JE2UNrig/98O7DiGDfvDPsnARSgXWm3efnuwJiZACoeTPf81zhB2NUN7R1K+s1qv37zrEN//LI5mdm89h/nP2zO3/Bf4h71CloMgjjp0gHrHKyi7w2eHnC9JbU8zhBj86uYwWjuYoCKv9/X1cUZxJEPAEl2mEwBy8xo+391DCYw4MvdvVSvT+JBBP0r8RQhwAN5/4XzkfuOa93/nzXvQ/wjXCxXX+N4L/Ea7z517jEwWnC1zgAhd4Xjg/0v4LXOACF1jDRXC6wAUucC5xEZwucIELnEtcBKcLXOAC5xIXwekCF7jAucRFcLrABS5wLnERnC5wgQucS1wEpwtc4ALnEhfB6QIXuMC5xH8FX1tb2tdbwOgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Data type of training data:  uint8\n",
            "Shape of training data:  (60000, 28, 28)\n",
            "Data type of training labels:  uint8\n",
            "Shape of training labels:  (60000,)\n",
            "Length of training data:  60000\n",
            "Data type of test data:  uint8\n",
            "Shape of test data:  (10000, 28, 28)\n",
            "Data type of test labels:  uint8\n",
            "Shape of test labels:  (10000,)\n",
            "Length of test data:  10000\n",
            "Labels  in the data:  [0 1 2 3 4 5 6 7 8 9]\n",
            "Size of each image (28, 28)\n",
            "Memory required for training data:  47040000 bytes\n",
            "\n",
            "Plotting the numeric range of input: [0-255]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLYAtx-ZAzKo"
      },
      "source": [
        "Display 25 images from the train set in the form of 5x5 matrix.\n",
        "\n",
        "Answer the following questions:\n",
        "\n",
        "1. What are the data types and shapes of the features and the label? \n",
        "2. What are the unique labels in this dataset?\n",
        "3. How many training images and how many test images?\n",
        "4. What is the size of each image? How much memory is required for holding the whole training data.\n",
        "5. Find out the numeric range of the input. Do we need to rescale the input? Why?\n",
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-tkl-PwsrXip"
      },
      "source": [
        "## Task 3 Construct an input pipeline\n",
        "\n",
        "*(weight ~15%)*\n",
        "\n",
        "Creat train/validate/test data splits and construct tf.data pipelines. Make sure that the training data is batched. \n",
        "\n",
        "- How do you determine the batch size?\n",
        "- Do we need to shuffle the training data? If yes, how do you determine the buffer size?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hVmNO1PzOU6E"
      },
      "source": [
        "# train, test and validation splits\n",
        "\n",
        "# defining batch size\n",
        "BATCH_SIZE = 512\n",
        "# training dataset\n",
        "fmnist_train_ds = tf.data.Dataset.from_tensor_slices((train_images, train_labels))\n",
        "fmnist_train_ds = fmnist_train_ds.shuffle(5000).batch(BATCH_SIZE)\n",
        "\n",
        "\n",
        "# test dataset\n",
        "fmnist_test_ds = tf.data.Dataset.from_tensor_slices((test_images, test_labels))\n",
        "fmnist_test_ds = fmnist_test_ds.shuffle(5000).batch(BATCH_SIZE)\n",
        "\n",
        "# picking out the validation set from the training dataset\n",
        "fmnist_train = fmnist_train_ds.take(round(len(train_labels)*0.7))\n",
        "fmnist_val = fmnist_train_ds.skip(round(len(train_labels)*0.7))\n",
        "\n",
        "\n",
        "# batch size should be in powers of 2 and the larger the better, provided that the batch fits into the (GPU) memory;\n",
        "# small batches can offer a regularizing effect, perhaps due to the noise they add to the learning process.\n",
        "# larger batches provide a more accurate estimate of the gradient.\n",
        "# the batch size here is 32.\n",
        "# train-data shuffling ensures that the model gets to see all permutauions of input in every batch.\n",
        "\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CawIS4uLiDE5"
      },
      "source": [
        ""
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lnE7ZFS_AzLg"
      },
      "source": [
        "## Task 4 Construct a deep forward neural network\n",
        "\n",
        "*(weight ~35%)*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ChlhkwhkAzLi"
      },
      "source": [
        "### Task 4.1 Setting up a model for training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yuxhQM6jAzLl"
      },
      "source": [
        "Construct a deep feedforward neural network. You need to decide and report the following configurations:\n",
        "\n",
        "- Output layer: \n",
        "    - How many output nodes?\n",
        "    - Which activation function?\n",
        "- Hidden layers:\n",
        "    - How many hidden layers?\n",
        "    - How many nodes in each layer?\n",
        "    - Which activation function for each layer?\n",
        "- Input layer\n",
        "    - What is the input size?\n",
        "- The loss function\n",
        "- The metrics for model evaluation (which may be different from the loss function)\n",
        "- The optimiser\n",
        "\n",
        "Justify your model design decisions.\n",
        "\n",
        "Plot the model structure `using keras.utils.plot_model` or similar tools.\n",
        "\n",
        "What is the number of trainable parameters in the model. Explain how the total number can be estimated from the model configurations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dpH3urepb_6v"
      },
      "source": [
        "# Different layers are appropriate for different tensor formats and different types of data processing. \n",
        "# Simple vector data, stored in 2D tensors of shape (samples, features), is often processed by densely connected layers.\n",
        "\n",
        "# single-label, multiclass classification problem\n",
        "\n",
        "# Layer-1: Flattening: Input Image: 28*28 = 784 pixels, gets converted into a 1D array;\n",
        "# Layer-2: Dense layer: 128 units, relu allows the model to solve nonlinear problems;\n",
        "# Layer-3: Output layer: 10 units, softmax provides probabilities for each possible output class, as this is a case of single-label, multiclass classification problem;\n",
        "\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),\n",
        "    tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "    tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jVZawM4BcKJr"
      },
      "source": [
        "# Categorical crossentropy loss function is almost always suited for multiclass classification.\n",
        "# It minimizes the distance between the probability distributions output by the network and the true distribution of the targets.\n",
        "# Since, the labels here are encoded as integers, SparseCategoricalCrossentropy() is used.\n",
        "\n",
        "# Accuracy works well to evaluate a classification model as the outputs are discreate labels.\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "2yELI3vhBLTq",
        "outputId": "8f47a514-17d2-4220-f406-c3b2b444191a"
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model, show_shapes=True)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MEaLJ9bZE446"
      },
      "source": [
        "# saving the initial weights for later experiments\n",
        "init_weights = model.get_weights()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZ2BVK5tAzMM"
      },
      "source": [
        "### Task 4.2 Fitting the model\n",
        "\n",
        "Before you fit the model. Think about what initialisation method have you chosen? If you did not specify the initialisation method, find out what is the default one. Choose a layer and visualise its initial weights. (Hint: You may use UMAP to visualise a collection of high-dimension vectors.)\n",
        "\n",
        "Decide and report the following training setting:\n",
        "\n",
        "1. The training batch size\n",
        "2. The number of training epochs (at least 1,000 epochs recommended)\n",
        "3. The learning rate. If you used momentum or a learning rate schedule, please report the configuration as well.\n",
        "\n",
        "Now fit the model. Show how the training loss changes. How did you decide when to stop training?\n",
        "\n",
        "After fitting the model, visualise the model weights again. How did the weights change? Why?\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-F2RvaPdcRdz",
        "outputId": "05c01f56-da36-4080-902b-5ff527db9ec5"
      },
      "source": [
        "tf.config.run_functions_eagerly(True)\n",
        "\n",
        "model.fit(fmnist_train_ds, epochs=50, steps_per_epoch=math.ceil(len(train_labels)/BATCH_SIZE))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.9942 - accuracy: 0.6700\n",
            "Epoch 2/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.4711 - accuracy: 0.8413\n",
            "Epoch 3/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.4256 - accuracy: 0.8536\n",
            "Epoch 4/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.3897 - accuracy: 0.8665\n",
            "Epoch 5/50\n",
            "118/118 [==============================] - 2s 13ms/step - loss: 0.3739 - accuracy: 0.8700\n",
            "Epoch 6/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.3526 - accuracy: 0.8758\n",
            "Epoch 7/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.3417 - accuracy: 0.8785\n",
            "Epoch 8/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.3326 - accuracy: 0.8803\n",
            "Epoch 9/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.3185 - accuracy: 0.8868\n",
            "Epoch 10/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.3131 - accuracy: 0.8874\n",
            "Epoch 11/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.3017 - accuracy: 0.8914\n",
            "Epoch 12/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2959 - accuracy: 0.8927\n",
            "Epoch 13/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2916 - accuracy: 0.8941\n",
            "Epoch 14/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2784 - accuracy: 0.8989\n",
            "Epoch 15/50\n",
            "118/118 [==============================] - 2s 13ms/step - loss: 0.2770 - accuracy: 0.8989\n",
            "Epoch 16/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2680 - accuracy: 0.9030\n",
            "Epoch 17/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2607 - accuracy: 0.9061\n",
            "Epoch 18/50\n",
            "118/118 [==============================] - 2s 13ms/step - loss: 0.2585 - accuracy: 0.9058\n",
            "Epoch 19/50\n",
            "118/118 [==============================] - 2s 13ms/step - loss: 0.2543 - accuracy: 0.9083\n",
            "Epoch 20/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2482 - accuracy: 0.9087\n",
            "Epoch 21/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2496 - accuracy: 0.9093\n",
            "Epoch 22/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2418 - accuracy: 0.9127\n",
            "Epoch 23/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2357 - accuracy: 0.9166\n",
            "Epoch 24/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2343 - accuracy: 0.9159\n",
            "Epoch 25/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2271 - accuracy: 0.9173\n",
            "Epoch 26/50\n",
            "118/118 [==============================] - 2s 13ms/step - loss: 0.2257 - accuracy: 0.9186\n",
            "Epoch 27/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2188 - accuracy: 0.9221\n",
            "Epoch 28/50\n",
            "118/118 [==============================] - 2s 15ms/step - loss: 0.2217 - accuracy: 0.9178\n",
            "Epoch 29/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2231 - accuracy: 0.9181\n",
            "Epoch 30/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2140 - accuracy: 0.9233\n",
            "Epoch 31/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2107 - accuracy: 0.9224\n",
            "Epoch 32/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2083 - accuracy: 0.9251\n",
            "Epoch 33/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2032 - accuracy: 0.9268\n",
            "Epoch 34/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2028 - accuracy: 0.9259\n",
            "Epoch 35/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.1975 - accuracy: 0.9287\n",
            "Epoch 36/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.2000 - accuracy: 0.9268\n",
            "Epoch 37/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.1905 - accuracy: 0.9316\n",
            "Epoch 38/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.1904 - accuracy: 0.9310\n",
            "Epoch 39/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.1917 - accuracy: 0.9295\n",
            "Epoch 40/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.1821 - accuracy: 0.9345\n",
            "Epoch 41/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.1803 - accuracy: 0.9357\n",
            "Epoch 42/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.1808 - accuracy: 0.9350\n",
            "Epoch 43/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.1767 - accuracy: 0.9366\n",
            "Epoch 44/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.1705 - accuracy: 0.9395\n",
            "Epoch 45/50\n",
            "118/118 [==============================] - 2s 13ms/step - loss: 0.1695 - accuracy: 0.9395\n",
            "Epoch 46/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.1714 - accuracy: 0.9388\n",
            "Epoch 47/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.1698 - accuracy: 0.9383\n",
            "Epoch 48/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.1695 - accuracy: 0.9383\n",
            "Epoch 49/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.1646 - accuracy: 0.9401\n",
            "Epoch 50/50\n",
            "118/118 [==============================] - 2s 14ms/step - loss: 0.1583 - accuracy: 0.9443\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f2a984f5a50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EsBPCoXMin7k",
        "outputId": "db2a7d56-6914-4125-c0d4-8a700e0649ab"
      },
      "source": [
        "history = model.fit(\n",
        "    fmnist_train,\n",
        "    validation_data=fmnist_val,\n",
        "    epochs=1000, batch_size=128) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "\r  1/118 [..............................] - ETA: 4s - loss: 0.1644 - accuracy: 0.9355"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZydRZpuI9zp9"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot training accuracy values\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.show()\n",
        "\n",
        "# Plot training loss values\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edSDDugWr5Bj"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "pd.DataFrame(history.history).plot(figsize=(12, 7))\n",
        "plt.grid(True)\n",
        "plt.gca().set_ylim(0, 1) # set the vertical range to [0-1]\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9C2VXCM-4Nw9"
      },
      "source": [
        "### Task 4.3 Check the training using TensorBoard\n",
        "\n",
        "Use TensorBoard to visualise the training process. Show screenshots of your TensorBoard output.\n",
        "\n",
        "**Optional task:** Record the gradients during training and use TensorBoard to visualise the gradients."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gHQVKiwwFYSi"
      },
      "source": [
        "!rm -rf ./logs/\n",
        "\n",
        "from datetime import datetime\n",
        "import os\n",
        "\n",
        "root_logdir = \"logs\"\n",
        "run_id = datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
        "logdir = os.path.join(root_logdir, run_id)\n",
        "\n",
        "callbacks = [\n",
        "    tf.keras.callbacks.TensorBoard(\n",
        "        log_dir=logdir,\n",
        "        histogram_freq=1\n",
        "    )\n",
        "]\n",
        "\n",
        "\n",
        "# reset the training\n",
        "model.set_weights(init_weights)\n",
        "\n",
        "history = model.fit(train_images, \n",
        "                      train_labels, \n",
        "                      epochs=20, \n",
        "                      batch_size=128,\n",
        "                      callbacks=callbacks)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X5L0t5dKFfMT"
      },
      "source": [
        "%reload_ext tensorboard\n",
        "%tensorboard --logdir=logs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oxltxSp5jt5T"
      },
      "source": [
        "test_loss, test_accuracy = model.evaluate(fmnist_test_ds, steps=math.ceil(len(test_labels)/32))\n",
        "print('Accuracy on test dataset:', test_accuracy)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4EgXZCBNAzMs"
      },
      "source": [
        "## Task 5 Overfitting and regularisation\n",
        "\n",
        "*(weight ~30%)*\n",
        "\n",
        "Go back to the previous task. Plot the training and validation loss and accuracy. Answer the following questions:\n",
        "\n",
        "1. Do you see overfitting or underfitting? Why?\n",
        "2. If you see overfitting, at which epoch did it happen?\n",
        "\n",
        "Now retrain the model with only 200 training examples. (Make sure that you  reinitialise the weights before retraining.) Do you see overfitting? How did the validation loss and accuracy change?\n",
        "\n",
        "Neural networks are overparametrised models, meaning there can be more parameters than the training examples. Some form of regularisation is almost always necessary to obtain a useful model. Below are some options:\n",
        "\n",
        "1. Add dropout\n",
        "2. Add Batch Normalisation\n",
        "3. Add layer-specific weight regularizations\n",
        "4. Change the learning rate\n",
        "\n",
        "In addition, you may also try changing the weight initialisation method.\n",
        "\n",
        "Apply different regularisation techniques to the model training. You may also try other techniques for improving training such as learning rate scheduling (see https://www.tensorflow.org/guide/keras/train_and_evaluate#using_learning_rate_schedules).\n",
        "\n",
        "Run **five or more** experiments of different training configurations and record the validation accuracy achieved in the Markdown table below. You may modify the table heading to match your experiment design.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d1LRIBrEgu92"
      },
      "source": [
        "\n",
        "|Dropout (rate)   | Batch Normalisation (Y/N)  | Optimiser  | Learning Rate  | Number of Epochs   |  Validation Accuracy |\n",
        "|---|---|---|---|---|---|\n",
        "|   |   |   |   |   |   |\n",
        "|   |   |   |   |   |   |\n",
        "|   |   |   |   |   |   |\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dLjJA98Qgxg5"
      },
      "source": [
        "\n",
        "Answer the following questions:\n",
        "\n",
        "1. Which configuration achieved the best validation accuracy? Report the test accuracy of your final model.\n",
        "2. Which setting had the most impact and which one had the least impact?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UgzZh0jN5ULb"
      },
      "source": [
        "## Task 6 Generate HTML output\n",
        "\n",
        "Use *nbconvert* to convert your completed notebook into an HTML file and name it **[YourID]_assingment1_output.html**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cWbr7jjEAzNb"
      },
      "source": [
        "---\n",
        "**END OF ASSIGNMENT ONE**\n",
        "\n"
      ]
    }
  ]
}