Transfer_Learning_CIFAR_10.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FStp_vbUkRz5"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "# Transfer Learning\n",
        "In this notebook, you will perform transfer learning to train CIFAR-10 dataset on ResNet50 model available in Keras.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpiJj8ym0v0-"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AoilhmYe1b5t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "084aba9f-f2b4-48e3-84dd-a010394a5319"
      },
      "source": [
        "import os, re, time, json\n",
        "import PIL.Image, PIL.ImageFont, PIL.ImageDraw\n",
        "import numpy as np\n",
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications.resnet50 import ResNet50\n",
        "from matplotlib import pyplot as plt\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "print(\"Tensorflow version \" + tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n",
            "Tensorflow version 2.12.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HuG_q_1jkaZ6"
      },
      "source": [
        "## Parameters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v4ocPhg6J_xw"
      },
      "source": [
        "- Define the batch size\n",
        "- Define the class (category) names"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cCpkS9C_H7Tl"
      },
      "source": [
        "BATCH_SIZE = 32 \n",
        "classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O-o96NnyJ_xx"
      },
      "source": [
        "Define some functions that will help you to create some visualizations. (These will be used later)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CfFqJxrzoj5Q"
      },
      "source": [
        "#@title Visualization Utilities[RUN ME]\n",
        "#Matplotlib config\n",
        "plt.rc('image', cmap='gray')\n",
        "plt.rc('grid', linewidth=0)\n",
        "plt.rc('xtick', top=False, bottom=False, labelsize='large')\n",
        "plt.rc('ytick', left=False, right=False, labelsize='large')\n",
        "plt.rc('axes', facecolor='F8F8F8', titlesize=\"large\", edgecolor='white')\n",
        "plt.rc('text', color='a8151a')\n",
        "plt.rc('figure', facecolor='F0F0F0')# Matplotlib fonts\n",
        "MATPLOTLIB_FONT_DIR = os.path.join(os.path.dirname(plt.__file__), \"mpl-data/fonts/ttf\")\n",
        "# utility to display a row of digits with their predictions\n",
        "def display_images(digits, predictions, labels, title):\n",
        "\n",
        "  n = 10\n",
        "\n",
        "  indexes = np.random.choice(len(predictions), size=n)\n",
        "  n_digits = digits[indexes]\n",
        "  n_predictions = predictions[indexes]\n",
        "  n_predictions = n_predictions.reshape((n,))\n",
        "  n_labels = labels[indexes]\n",
        " \n",
        "  fig = plt.figure(figsize=(20, 4))\n",
        "  plt.title(title)\n",
        "  plt.yticks([])\n",
        "  plt.xticks([])\n",
        "\n",
        "  for i in range(10):\n",
        "    ax = fig.add_subplot(1, 10, i+1)\n",
        "    class_index = n_predictions[i]\n",
        "    \n",
        "    plt.xlabel(classes[class_index])\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.imshow(n_digits[i])\n",
        "\n",
        "# utility to display training and validation curves\n",
        "def plot_metrics(metric_name, title, ylim=5):\n",
        "  plt.title(title)\n",
        "  plt.ylim(0,ylim)\n",
        "  plt.plot(history.history[metric_name],color='blue',label=metric_name)\n",
        "  plt.plot(history.history['val_' + metric_name],color='green',label='val_' + metric_name)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPq4Sw5akosT"
      },
      "source": [
        "## Loading and Preprocessing Data\n",
        "[CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) dataset has 32 x 32 RGB images belonging to 10 classes. You will load the dataset from Keras."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E103YDdQ8NNq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80399a49-c586-4ee8-dd4d-f0993b8beb9b"
      },
      "source": [
        "(training_images, training_labels) , (validation_images, validation_labels) = tf.keras.datasets.cifar10.load_data()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170498071/170498071 [==============================] - 6s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "prd944ThNavt"
      },
      "source": [
        "### Visualize Dataset\n",
        "\n",
        "Use the `display_image` to view some of the images and their class labels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UiokWTuKo88c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "2593bb48-b0f7-4fb4-8fb1-64f399533bc5"
      },
      "source": [
        "display_images(training_images, training_labels, training_labels, \"Training Data\" )"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x400 with 11 Axes>"
            ],
            "image/png": 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EEEIIIYQQQgghhBBCCCHGhh5ECCGEEEIIIYQQQgghhBBibOhBhBBCCCGEEEIIIYQQQgghxoYeRAghhBBCCCGEEEIIIYQQYmzoQYQQQgghhBBCCCGEEEIIIcaGHkQIIYQQQgghhBBCCCGEEGJs6EGEEEIIIYQQQgghhBBCCCHGhh5ECCGEEEIIIYQQQgghhBBibOhBhBBCCCGEEEIIIYQQQgghxoYeRAghhBBCCCGEEEIIIYQQYmzoQYQQQgghhBBCCCGEEEIIIcaGHkQIIYQQQgghhBBCCCGEEGJs6EGEEEIIIYQQQgghhBBCCCHGhh5ECCGEEEIIIYQQQgghhBBibOhBhBBCCCGEEEIIIYQQQgghxoYeRAghhBBCCCGEEEIIIYQQYmzoQYQQQgghhBBCCCGEEEIIIcaGHkQIIYQQQgghhBBCCCGEEGJs6EGEEEIIIYQQQgghhBBCCCHGhh5ECCGEEEIIIYQQQgghhBBibOhBhBBCCCGEEEIIIYQQQgghxoYeRAghhBBCCCGEEEIIIYQQYmzoQYQQQgghhBBCCCGEEEIIIcaGHkQIIYQQQgghhBBCCCGEEGJs6EGEEEIIIYQQQgghhBBCCCHGhh5ECCGEEEIIIYQQQgghhBBibOhBhBBCCCGEEEIIIYQQQgghxoYeRAghhBBCCCGEEEIIIYQQYmzoQYQQQgghhBBCCCGEEEIIIcaGHkQIIYQQQgghhBBCCCGEEGJs6EGEEEIIIYQQQgghhBBCCCHGhh5ECCGEEEIIIYQQQgghhBBibOhBhBBCCCGEEEIIIYQQQgghxoYeRAghhBBCCCGEEEIIIYQQYmzoQYQQQgghhBBCCCGEEEIIIcaGHkQIIYQQQgghhBBCCCGEEGJs6EGEEEIIIYQQQgghhBBCCCHGhh5ECCGEEEIIIYQQQgghhBBibOhBhBBCCCGEEEIIIYQQQgghxoYeRAghhBBCCCGEEEIIIYQQYmzoQYQQQgghhBBCCCGEEEIIIcaGHkQIIYQQQgghhBBCCCGEEGJshAd5c7FYHFc7xO8zer3eoexHY07s5jDGncacYDTuxGGjNVbcD5TrxGGjXCfuB8p14n6gcScOG62x4muVAz2IWL27YpZlZmYWx4MD7SjzvZy/5v3ttV/NDrTH/eG0gP6Q0U49z6MYX08z/JFJlo3+0UnGOyESL8X3G8Y+huantE9uE23PzMwP8EAy6ogo2hoqnmc2OzO1Z3vvNT/1g3/cBt22mZkNEnwt83EIxxm2OywUnO15IfYHhRYMcGy31tYh5t7rDvAvpXIF25gzckM/gDgqYjsHvRZ+IOtCWKD55Bdxn3FQhrjfz1mEEmzXIMa4M+hDPDFVh7gY4DF4NI771I9547xH75msTUAcelv7iIpl+96//TPO58fFn//z32OdTsfMzCq0sIYBDhjPsB/8nCnv+3js9Rqer6PzMxCnKY6pT//uZyEeNK5C/K53PQrxdH3aaUPcxjHU72K8SeN8anoS4koV29zr4Pho0sSYPnbaaUOr1YF4EOJxJxHmlqQfQ+xlmAAGNH66PTymJMHYzKxQiCD2PT5/r+aUQrFo/80P/S1nG+Je4q5HyEFX/r3rivz37Oczf3D5mX/y0ztzideKTgfnkEe5rFwefeHBuT+OcR73+phLEnq9WMJ9cB3W77RHtsGn9cqBhkBAa3QhwjacPfMAfjxn86trKxDXaQ2t1moQX7x4EeL1dczJgz72i+9jLivkXATWJqsQVyqYx6NwaxthGNp3fui7nM+Pi+/+Cx+yzvC8cbt96nufirRK1T3OLMW1IqYxxWOo3cXXNzeaEEcR1lBpSvWS514DTUzi+eRawPNwGxmtX/0uzr0CtSGj+jbIHdMHu64KQhxDHWqDH2GbixHWqp0W9ruZ7ZzXbUqlUm4ryqWy/dxP/Is923uv+c5v/y7rtLfqEM4jnKe4Dsur5TlP+D5fb+H7R612DLchDz6Og76fj3vU63lXtd6Ia2Wn/nf6CWPfMNcFVPstHll02jA1uwDxV7/6PL5huK6UK2X717/0r53Pj4tLly7tHH8c43zZz/llRvVtn3LfqM8zvD2us/Pe0+1y7Y3nK4owz/A2DzoG8+B98GfcOsTNXbvhc8PHlPc3/szufBCGoX3Lt3zLnvu8l/yTf/eyDeKt9tTpRlGxtwzx81/6AsSV+VPO9hbOPgZxkvG1MJ6zJE32fJ3nfELX0lnO3AgpL4TONnAfg5TPGeUyWkMDWucDz812mfF9PhonVCPHI8Yh7yHi+5A503XUNpPh/IpC3/5v3/G4u4Ex8swzX9iZB2FI94loXvN6ORi4czJOeBxw/9O6zX1D59Tj+2fUv3HOuOM28HvodpozRozGkUfHzeMsyMnRIY0rbrdHYzujNmcJ37/e+1zk4ebh/JwaBIG9691vH7k9swM+iLAs27kzv59FgT/6RhnHg4iR+zzgTkctfPv5jPM6Dy6OeQK4f9jz87nbGPH6YTLotq3/Oh9EpKmb1DxKjCldywVUxPU7+FCAU1SPHkTwVM7r75QWvyzDdg56dGMlxRu4Rg+OKD9ZHHBhOvpBRH+AcY8eRBTpYYkXYN+7DyLw87kPIvp0IR7iNtO8uzuHQKfT2bmY9mjBiUY9iMhpMj+I6IV0I62PRTwXs502jsFBaxM/38XxMujxhb9Z3MMxxA8ieJwPynguBrRaDHo0T2huDvruQ4A+tWFADwqSDNud9PZ+EMEPu3r7eBCR0Tb2ehAhxB8GBoOBDYb5mm9i8NrBDyKCYPRNsFEPIngf/DpfOHAB3ctb34j8m7a7N0rv50ROtQXfxOAbcWbuzRb+TJLQuj/Yu+/7lGQD/hZKzsXLYEBfcsi5qXQ/6HTa1h6usXzDwnkQEdGaG7gXiu6DCOy7lCo3vuHeppvnEX0xYz8PIsLC3l9K8PhLRbx+dbBNScK1Pj+IyFurDvogAvutQ3WBT21I6cZbp+v2A/clf3nqfj747bQ71m4Pa7uDPojIqWE5r+hBxP628UYfRHQ7dE1kZkWe0226jordG8mHQZZlO8fP53McDyJGbfOgDyLytjfqJj3HPC/49XvxIMKZe6Nu1uY8WNgNH2Peg4uDPIg4bAZxuvMgIqZaIeAajK7nwr57rNvb2oZvvroPItI9X+fcmPAN5pxxx2vJqG0MnG1QLnPuGdHYz5kr7j24e/sgwvlC9Ot5EBEfPK/cK9I0tXRY06Xp3rV73mfdv42ap/wggsYIP4jIeBzu/QDNzCyhm/o8tt0HETw5uA20A+e7cW4/8T02HhfugwjKsc6DCM6Xzi4dRj2IGJVT85BHhBBCCCGEEEIIIYQQQgghxsbBvvrp+zuPTDx/75/bOA978p6CHVB7aZSMksN+nu44TzbpSSV/E8/f+6do/IiKv8GU34a9/5D6/BSMvtGf8bfr9/4mn5/z1T3+9nqfvpFfL9S3/uc+fJGpEpYsCreePm5k9C17+ilmp03fsGeJIzOrTqL8T43kmwLcpFVrKFHjR/St/S7uI6Fv7ng538IpkxRBsYo/6V/p4rd9MsNvivO4q4YkH1TA9xfKeMxmZqGPP/vnr2q1Omv0Mva989MykokoBvQT/q77Dab1zQZuk35dUJ+cGu77cAfe8s3L1m5tSTU43/Tib2TQ/IoKblqdqeP5PVE9C3E9xfn23MuvQHz1lcsQL0yRPApJHxRy8m27hd90vH71CsTNBp7v1RUcUzOzKKN09OgJiDdv3oK4u46/2jAzK0+hTEhxAeO1Nn5mfQ37JfDp20f0jeJ4gN+Ia7YbThtSd3XCaFd+LZVpjoyZ3d8GGfXtrnvBQb9J+fraMOpXgpQfRzSJc59be+R8i8r5yR/37d7bPOi3ePL66SAyASyfM27ieLDzTflWC9czbmdE38LOk4EY9esD51tmIUso4joe0DofO78gc9dY9yfh/A76htKor6GxnGIJc1ez1XDaUKBfERZIHujFl16CePkOSjllKfZ1jaQL6/UpiIslV4qS+65MOW07z4Th4f4SrDY5YcHwFwT8K41Jkjjy6BttE2XsezOzforjsMtyo3w6Kb+WK7iGRiRBRE2wgCVQLW/MYV4o0DaDIp6bEv/qlOoh/kUMS5GYud8OdCTGijxG+DjoF6A0LAKaq0mOukmSskRK/rfk+JfAh4HneTv5/KC/DMj7dvOotYLzzr6ujQ/IqHWZ233QfTrfUt3H+ub0HX/7kptA39hOKCcsLM5B/Kf/zJ9x2vDVC5cgfuaZZyCOhmPdyfVjZq/+HjUG876FP+p8cz4dNY5fzxjkz3Au4n3w+sKv8xjj7e/nVxmj2siMqrG5jsnrd97HXt8Ivhdz/SD4QbDzjf90sAGvXfj8b0P84ldQxuyBd7rSZxX6xXm1iPWIU/WN+LUs1918nc/3GfM2yWeE5dFTZ6zTB+gPjtQea3eb+214S/eeX05lxbmSf6Ux4hjNzHz+q7PNrY2EOe0fN81eb2ceBAHmL/ce6z5yEf0awUv5mmOEqgzPWzpf/Gs8Vl4wMwteo3+38Uf8qoJ/NcNtTj0aJXm3zA/4iwg+846VALXxoJYLeWz/yvsg9wr0iwghhBBCCCGEEEIIIYQQQowNPYgQQgghhBBCCCGEEEIIIcTY0IMIIYQQQgghhBBCCCGEEEKMjQOJwibJqzJkbMjO2lT7cX1/o4zS23M1mXPftec2U274CE1CR/PudbiQO74VpDMbc5NI38wnjdeAdMBCFqAzs7u3b0D88ouoF/jwo49ubdv3bW7mKefz42QQD2ww1C5jPbluHzXNItbCDNwh3t1sQtwnG4kp0tvvtdDboECvRwXUfeYxNch6xvRI9zMk/biggBrhEcUl0jP2jbYXYD/VplDf38xsEKOmZ6+L+o/zVey7ZnOVPs+adqgdXK1hGzPP1TNOUtzmSgM9AorFreNOWD9vzExNVm3b6iGLUY+QdfVYkzlHRthi0lG/ffsmvt5Af4WvPncR4oDO77ve9TaIzzzwGMQrd3B7ZmbPfvkqxHeXGhAXy9jGCdLuZw3EKMBx3yX90EvX3TY89viDEC/4OLdW1+9gG2+ib0VA3iqsse2Rbrej5WmWIxKK5y9JXu2HmMf4mDmIlvD9YJSecf5nWAN0b48ORzuT9DqbLczfm5uYM7qUx8xcneVej7yE+vj6xjrq6bJvwuQk+gax7n6edjFrB/N7nn32WTPb0lr+3u/9Xufz4yRJkp328Tke6fcwoibKw6MaJPSw/33yiPBone9vYF7wElfbNOU6icYZtzsgMfxCCX0KKrQGG63r/Zw2VKfqEF+/fRvi9TZ64FQmUHO5UsBxNVXHcVet4OtFqk3MzEoV8uFZw74rDD2yDtsjolQpWDb0/Enb5LFCQ67MNVZOYe1kEX9v3eZKhfwagr1973idD43rPrOI9hmzXviI1Fcgf6kBeUIUyd+BfSzM3DzDcRThcfZobqUp5UryliqUONfl+QXs7ZmzPfeSnDkzbtI03dl/nufDXuStwa91bLs+hO/na+MD+i7tpw37+cwb2WeOzaAzlkf6LNF1rePtRN6Ib3kb1rwzC+gZYWZ283c+CbHj3TQ83/febevewWMyb4yOGnOj+n6UX9WoOI8CeS2O8s0aNfe4zex7YWbW6eD1A9dlo9pw8HtIbj+M0rjfnX8Pu4YP04Flwz4YrKDv4PVb6BE4dfw0xKcefNLdYETX9NS/fL8sY39T7j7n3heT95e975clI+6nOXUC2wdQHmJdfjOzAd+vcRpJ/pm018DxxqCPj/B/2NoG50/6yHAX9+OysRvHO7WP78xBfO9+/Pi4Mg3Svc9pjhEIhk537t1G9x1554zHvr/36+yHso9ck3h7j22+teva4u19LcfkXdvtd22RR4QQQgghhBBCCCGEEEIIIb4m0IMIIYQQQgghhBBCCCGEEEKMDT2IEEIIIYQQQgghhBBCCCHE2DiQKGxmr0prsfyTowbFumu5opLEPrTC9t4pvbwviapR2mF7a7U5umyOft1ogTZHD25EEzzn+REfA2opFjLUfg8GrmfBylX0hLj6wuch9rMtndgwjOyppw7XIyIOzAZDaTOSxTN2HfAT8koouL4E3QG+Z408I0Lq724LdZxZhbkcoJZ0QFrL1Zm60waP9OOSGM9ZdQI/U5ucxzbQ660W6j5311Cfv9p3ddM7pOm/sYk66NOTqIlNVgkWkwg3e5lEJI8cFVz96iPHjmEbGg2I+8Nz5QW0sTEzNTNlhfLW2Bn0UIe0RGOqQDreeb4kbfIZubuJ52ttDcfg3SbGD51Aj49zC3WIl643ID5z/mGnDc3uZyEOaKC/42n0mfBITnD5xhLE7CnSIX+GV5bwmM3Mjp+gMXbnMsSPHDsLMftSXF5G7f4sxXORkuZ0kOOH42o1vrbe5P13Zfj9RUz518wspnzbp1zU7eI42djEc7y6sgLx3ZW7EDcpf+fpWhbIW6RAucinwf7SSy/tGbPu+qlTpyBmrWIzs7k51LRm34lPf/rTZmZWKpUO3SMiCIIdjfxRdRdrIudqmSaks0ufCVOcp3EbvYK6lB+bHTzHSRdjP3PHHesX83GFEeaOPvkqdSLM65NVfP/66jVsU89dY1dWLmO8jGO3ROMyKqGfQ2UCa4vaBL6/VsXXjx074bRhgnwqtr1Ittn2VDlsj4iwWLBw6ENUpBy8ST4w/QD7Pu8bVF5I/gw0JgrkMxHQPsMQcwB7KQyoRmP7BzOzjPwVAm4o+UvFVNBGIR5nqYxtThOem3kavux5hG1qUd9yvVKk+mbQx/rXaHt51zjcBs4Z/f7WNUgcH75HRJa9ehk3SgN+P5ruI3XkqQZhS41RGvOj9P/3s41RjDpux9okZ/Mea14fuA14nFP1aYjf9vS7IJ6ou753y3eWaZvuFTr+ezj4vr/TllGeS/z6fjwi+uRBx3C9wueG97Ef3ydeL0aNoVF1w6iYvcDMzHo9vJdRYv9GWuP3o0e/G+63vHM30iPmPlIKMguG7fM6y/Qqtrs7wDqcvd3MzELyeXRu6zkL3mvNv+Hn+eMjPr21TxpHPnvy0LpOCTeg43L9a2mMpG4/sJ6/P2KboXF+pg1S7mM/M8c/x/LOD43DYcxeaYdBt5+86hERjLhZTO3ja34zswG9yaeDyrvKfyNkOfered12zsgI/wYeE46Xb8aenDl+NCM8IZxy0/GMGOGtsY813PGF4X1uew06n3xt9IsIIYQQQgghhBBCCCGEEEKMDT2IEEIIIYQQQgghhBBCCCHE2NCDCCGEEEIIIYQQQgghhBBCjI0DicIOknRHDy9ODqaD5+qw5eDo9x1oF/eE0ZpZIz4xynJiPztlDVHuPNbIo/eXjTRc19Ev4MYV9IMwM2teexHio1U8v2G8pWfNGoGHwXq3b73ulgZmsYS6zdMVjDdX1yFOY1c7mjUCa+S30Keh3aP+DkindyJEXcogxDbFOVOlWMBtRD5OxQHpErZICzMuoJ5jVEM/B+ujRnnfc6d6abKA2yRNukYT99EfkM436XGyd4lHesfJwO2IIukTT02iLmy8rW9J+s7j5pHH3/OqjnEf+75YxOMKQ2xbknPCWTv/ygWcg1/47CcgXmuQBj3J4X7qd3G+9igfz0+5/TVTw7899XWoufvmt78d4sbKHYgvh89BfO3qZYiDYArizZab/TZWGxB3Snic3Qi3scBzs0Ca6XXU5mddRdZ4NTPr9tp7vme3lm2x6Pqa3C9cvePRCySvV64OL/WPsxbh+zsdzAnLd1Hz/vo11M03M7t1C9efbgfn08Y6ekKs3EVPiNnZWYjLZcyvd5ZxnLL3gpnZmTNnIH7o4UWIA9KHP3UKtfanpjCffulLqLP/7LNfhrhSQa1/M7N3ksZ1qYxjqzT0B2Cd48MgyTJLhueaNeV96htXdtvNd4HhuEpJw7pDnhC9xg18fxfPoZ/i570Y/Ri8HG8SRxebfSrI7cnLyBMgwTxx5zrmv5W7V3F/TXfc9TcbEBdo7QiKuAYPijhu+l0cp9XSQ7g98p+anV9w2lCpYG3gk7Bsb+htkSaHW9v1Bh3r9bfySUzeCQH5PTTJVybJ0eEuG+uBk54u1YJhhLVH4Ohbsz44vpqyaLCZFel88nEMyJ+NvVQ4/3KbOJ+nOT4VrFHuel/gcSfsQUfXG3PkMbJBawB7Z5i5Y6xMuc4fNqlM9fxh4PveTvtGacTn+d8cFPZO4As83scoD4E8z4CRbRjhO8Fwm1i328+pPdK8wbhHGwKq/SdqExA//uSbID5x6gGIN8gbautv6CPgeF0Mj+tenNeDEIbhzj5fq02vFed594waM/yZweBgXiz78ffgMTRqXPI245i9w/b2uWC/BzO3FhzllTLqvL8ejxjuB453b/Owx10hfPXbxlVKtXMlbOe6h/2/snTJ2d6Js2+GmNftjNZE1tofdfQ80vOyFG8zofUqdYTzR41LnBusw5/lXEOGbKBI4yShljva/Dm1A+yT3p9X73A/cH277d2U3YcbqXGaWTwsLHyeUyN8CPI8Lbgm4djxHqFrEvb04NqQt5bk9NmIU2bm8Tnie8N7e1OyR1Ka58/A9d/ee3TXXKppeR3fT74c6e0zzAnZAcoU/SJCCCGEEEIIIYQQQgghhBBjQw8ihBBCCCGEEEIIIYQQQggxNvQgQgghhBBCCCGEEEIIIYQQY0MPIoQQQgghhBBCCCGEEEIIMTYOZFa922zDsc0caYhycJOeN+rrsx+jIcd30dlntmfoGI85jRi1/RyzD9pKRM+L+OlR4JPh6+oSxLee+zTEazdectrQXLmNcQeNixrtrTi6D+atX7502zrtLWOyoIiGu0+eOQ5xRB0eFdz2hgEZRvbRrOjMSdxmZw5NK9fJcDeqoMlasYjxrRvXnTZ0u2jIeub0GYjZYNIiNEHsk+FuKcR9VkI0fQ581wAvKOH097toHhaV8fVCBfutR0a3Hhkvxuw6FLhmY80NNPgsT6ApbFTe6ofCIY+7+fmTOwZvjgEajbE+Gac6BsDmGkldegWNwG4u4/nsbOC5+NzLaApcKuP7v+5BNHn+/Gc/57Th4s0GxG995zsgfuElNIxtraLJ8NkTaJzqpWgMeGMDz3+16p6zbguNLj1Dc9bWBprYVkpoTn104TTEU0cexffTvPF8N+EmKRqGpslrm+7lGQWOk73M7EYZ3eWudt7eZlRsGHhnGdeBy5cvQ/zyyy9DfOUKvr5OxtNmrqFg6GOfXr+K425qEsfyqZNoSH7tKpoEX3j5AsSPPIJjwszs+NFjEDvmZAmuAWGE+ZLNIM+dOwfx2loD4pW7OI7NzF56idv5MMTbZox5pozjJvMCy7YN+MjsLyVLtIwNzizHpLSHx9+nXBI3MZ95fcwlQYz1RyEkI74Ux223hTnYLKeuItPeQYbbSOm4/BDPQxJj3uh7VDNt4OtmZiVat2sFMtekbWYx5vW4h4asyz7mz5n5OYirk5gvzcxamzgnkxTHejY8v1muNeT4SAaxJcM11jVoxXNVoDHJ66mZ6xXo0xjxyFjTpw94jjE01ZJkZl5kU0wz8wr4HjbzjAcYR2RuzbUGXwtw8V8IsB42y8nxnNvIqDHgeobMOWNqQrdL4zznXFTInLpQwHZum5oWaH4cBp7n7fTRvTCrdkx6+TM0TtggcnISa95qFWuipSW8nttPmxzTXX7DAQ1M3X5y2xCMMIXlWuPE0RMQf9MHPwjx0WMnIW62cE1YbeC1g5nZJplVO206XK/gHXb33yhD5YTmX9755s9wzHVrka6bRxksd7u4nuaZXXM7uUbidvP555iPoVTCHMJ1pJlZq4VjgD8zyqS90+ns+TofU57J+17m1LyNUUb09xo/HZg/bJ9vNH9uYE02oPsYn/rtf+ds78PHsLb2qVZNMq4VETbb5fWtQGusl7PGDtjImRbFjPMtnZ8S1abBgOLuOm5v4NaWAx+vUeIJzGVBpY4foDZ4HuUl9jWmjstbpxLHcPlw67e96PZTG8Rb+YFT8KilJ81xhWYTdI/viXq8xmb0Ot1TdXIw7S9nofCpPnTmvXOOR5hyO2bVbKh9cJPxketCxv1ycLPqUftMh59hg/C90C8ihBBCCCGEEEIIIYQQQggxNvQgQgghhBBCCCGEEEIIIYQQY0MPIoQQQgghhBBCCCGEEEIIMTYOJICdxIMdzagsQX0/R2RrX/pWewujOXpXzsf31llz95ajPUba+ZnHfgyku0YaeKwX72q7cSPcZz+ushvrGqIWY5ihZl3nzisQX/nSJyBev4m63r2Oq625voI6ec02akIWwlkzM8tYMPYQWGl2rT3UgB6sYdtPLR6F+FgNNeJZ59nMLIhQLzMmPep6Gc9RrYh+C90u9U0VtRWTBPtoql532jAZo5ZlWET9y4T14nqoyxsaHlexgtrQa13UlR10XI3PWoJ91e/iPktF3GaxhG3ssGZ2G/U2Y9LM8ws5esbk4eGFqBkcDs9VGLmfHScbG5vW72+NC580J1nDne03osid40GAxzU7g34L5RKei3qZfExIz/rsCdQGXzg6C/F//NRXnDZcW8PzW/utz2ObJjHPNDdQrzwMHof49LknIG5dR63/qHTHacPmANvQH7DeNGr8ThjNNfJ32CRvGzPs17DgLnGUwm1Aut3xrnF92Hr9u3MRawmnrD1Lr/N8MzMbdDG3NZuoOX+D/GteeulFiK+SH0O73d4zdvSyzWx+fh7i1ibm8MkJ8gE5SucwxPlXKOI5ffrpt0P89rdjbObqbt9ZWYb47ip6FkzXZyBOJrDvWcf0xAnUiG3neBZsbKBWP2u3nzixtZYVCoevm77ZbFpvmO9Cj7yBHH8hnIPtpjvPmyuYC/w21hcl0isOAxrbpHGdDPD9vR6tNR3Xn4G9LAqkW+/HVMv1STc7o9wzhbnA49yQkysmpnBsl4ukgUz74HIxZf+vdRy3gyb2q5+56/zaGr6H9b63/RiC8HD1qyPPt3joS5LyGlvCORAkeP49c9vaa1GNRLmIbF+sR/4bfP4CujwKyEuoVHLXlk6f2klzJ6W1ZkD1dIl00FnDN+Xrj5yvkgUe7tOPcB9F9qAjn4lmTDrBdNxhRP4PvrvucLuTHh53tbiVj0tFV/d93Oz2iBjpu8Ta03neJCN0lR3/kwjHzbvf/W6Ip6fxeuPnf/7nIU6THA8yb2/N64PiHLcjop3TbyNuAfDYPUq+TY899AjEd5ZwXfnsxc9CXJlCbw0zsz75l/g0//xhu/PO4ziJ43hnHPC+2TdglK9B3md4jLF/Q4+uH9mfYVSdux+vNN7nKEZ5ZYx63cw9bu6rUX2Z17e7eT1+HXuNrdej+/5GKIWJxcM7TDwGLr1yhd5ch/Art59xtnf+qa+D+M3v+GaIB+Rxxf44VOZZ6OG4yzxc92MP11MzsyTF/qXbLRb18ZqwsonXMBNxA+KCYS25fhs/v76O16RmZuVJvN7uL2FfFk+i/2Iwh9c0kZscIaS7q6/hv4Of6bFX2zBkX47DYKPZtP4wx+T5ee1m1HppZpbySaaDCsm/gS/yW3Tt9cpFvCc6oPsQ586eddrAHjW8Tjv3hei4R+YzJxXl+FTwtRivufwZ2sfAuX+9d/2T9yrfD3PuUwxzZniAXKdfRAghhBBCCCGEEEIIIYQQYmzoQYQQQgghhBBCCCGEEEIIIcaGHkQIIYQQQgghhBBCCCGEEGJsHMgjwrLBjjZkRlq0rJnMMl95clGsP5WRXp+jT0V/YM1s/rzHhg05bgwZ6xpmGCfkhZGwxmC2t15dQvvkNubtg99TJb3cSoZ6ZndeQq33zSXUP+u31iBe23Q9IlLqq6mZKWxTeUubLCoero6wmdnbH33Aep0t7cFOB7W3FybRn4H1xPNEdEvlKv0F+7vZxP4plLAvyuQJEZOG7uYm6tGVAlfzOyziNhpN1ELMUhwTRdKVHRTwfPnrqHF+/A7qHN4uuf3Q5r5Jse9Y/z2mNgWkvx+RtnmpgJp6YcXVA477nEfw9UJpq5+i4uF6RGxubu5oagakP8h6qUFAPjGj5Ryt1cRxfGwRNSff9aYjEN++iprrJ4+g5uTFFTxXL6zkaKZT3165gWNkZgL9Vp6/gdtc/Z1LEP+pP/Y0xBNVbNNm5zmnDTagfErpt0UeBuFd1ESvRTh3kwC9NTZbOAazlrvEhaSzuLmJ+9ytC3vYev3dXtu2Bwuvqazry+Pyzh3sKzOzly9cgPjmrVsQs24s68evreHaMTmJusxHj+KYYS8GM1dbs7GKmvWc28qk5d/ro4brY4+jlvSZM2cgnprCfG3m6o5evoa+SpubqAN76tRpiFst8iSgc/HYY49BvLFO+vM5beCa6MhwTh+2L4mZWViILBmOu34bx0RAczY0HBN+G9ceM7MK6e5GBfK5orHtk1ZwsUxeQdRXjds41uPA1afmWo31iUs+jruMvKE2qB9aA9zexASu4QuzOG7NzKKAfAVozfSo3ky5xqXtNckrY/XmNYhvXsUcbWbWpNqC6+bicG3djw74vSRN0h092YjmPOeZRmMF4n7P1Y7OqPOiAnl6kN8Gjw9HBt+5HiEfk5q7NpQS8u0hr5jFKnqGVGg9m6xRrUk104A8KFY3Xf3qu+11iNlnpMw1c4D91O3ifOc1odfHeVGcxDXYzCyk77j1yRtjO8dFOR5u4ybLsle9Dp2c7O0Zv9b29voMeyP4VC/Oz6Pf1/Q01oI1ut7YXG84bfCda929j8PR5ebtsfj03psfbpPnC/tM4EYK5DlXJe3p7A7WKo2buO5cv+16E6V0Lcb7zOjf+8F+NNF3k+c5wPUH13GtFl7D8hzmz3O+ZW8E3p6ZW6ew3wJvg9cXHpP8/j7lujwPik6ns+d7SiVcV0b1G7eRj2k/HhHM7s+MOtf3mmohtWRYg9RPHofXHn/oDMSf/hLWEt1NnG9mZv/+l38G4mNnse6dmlmA2BuQV5pPnhDkL9Rr45xv38HrXjOzYoDnaKqIY7vYQ0+IYIC1eBbivYT1Dt6vWb6LtUYWoReAmdkc+doFLfzM8qXfgXim/AGI/Snsp5Qykk+eWLn+XeSVwR6BybavpLM2jJ9Wq2H9Yc4ZtabuZ431qJ7wsr2PfY08/668gnXxnSUcZ1xX3SLvRDOzhNaWhx56COLTp/GakY+LfX3Y74H9xPLyfpHug42s2Z378FSr0C5cHwu3DewX5eTxYY6NEvde92uhX0QIIYQQQgghhBBCCCGEEGJs6EGEEEIIIYQQQgghhBBCCCHGhh5ECCGEEEIIIYQQQgghhBBibBxIFPZ3P/UbO1qDvT5r2qMeVOx4L7g6ZaxVy/p+rKfHen2Ofh95LViM2nFZirGZWa2Az2Kmy6jT1elhm1Y3UG+u3cN9dqjNPdLN9HLaQE2wmUnUjT0+gxqiNQ/bNFhHrfeki9rufK6W1xpOG3p93GZtEnW/YtvSZC4UXZ3/cVP2U/OHWoLTddRLrZBeoB+SZ0eOmKkfobZvp4mfWb6F2ognTqB+ZpbhNgdt7N+IdNaiiqsdHafYBo90uD3ygIiqqA233EPtxasfQ5+Qb7i8BPHc0487bVg5h+1KS7jPQQ91QRsNHGfFCuokVit13AFpcCe+m278EuoLsz55GG21MQwP1yOi013f0Q/1fR5DHLPOd46WaQvn5PUbqGM5t4Bz/uyZExD3SaN1nfT3yvOo/zkxiT4xZma1EupzJjTnT5w6iW0u4Vz7/Ge/APGll2Ygjsr4/rUNN9e1fNK4bmO+7bXxOG+uYD+d8bCfJk6hpvagj22ICq5fAOsgTk2RPu6udeWw9fpbrc2ddW9tDb0UeAllf4aJCVer++gx9BqZqmN/sOfOMvlMsCYl67CzTn6eR0STfD+mZ1Bz1ZledKD1eh3i06dxnC4u4vqYp5u5QVrqy8uoLx1T/q2RlvvS0rMQX758BeK3ve3t1Cb0SzEzu0B+Hewt88ijDw7/fvg+TN/0TR/YGfcrpL196yU89tuXX4K4Grp6/YUq5f426etnOK9KVZqn5EVV5jU1I33VAHXxzcxa5P3C44o9WKyCbZpdQB3fjPLbzDEch8XMzXdeF9tQYE18x3MM29SPsW/LtO5vUo64cvmi04ZCbR5irtWDYY477HEHWv30Gtf2DPs/mJmVaN4XI9IRTlBPPKTcXqTcVS5ybYKfTwO3tqySH9hUhPXyo8dRR/jELK7zUYg1WYnq7STGnrp+96bThs98FefrrQbWs70OjqnmANfcZotrMNKCp/0VAvdcFFhrnbxNdsxQeA4eArvH3SjuhUeEs02OaQHkNZTX2A2a81vb2PtSfvRRIE7/OMeYo5Xv760dHdMsL5EvTEReURl7kVAt8uKVy04b3HayZ+Br/H3MhGG406d5Xge74dfzcmGj0YCY/fx4G5UK5rJuF9cR9vFiTwh+v5nrK8H+C6OOk4+LPSH4dfbxMjNbX8d1n/3BuH5nTwhuI6+Bo9ahPPYaW4c97srZwJLh9Whjo4FtoXsnM3NYl0crqKNvZnb5+d+D+Nd/6Z9C/KHv/MsQ1yqYy/wU/Rjs1pchzDbxvsUkrdlmZpHhOJtIcRyGHr5OpacNEspLGfmRFTEvVabx+sLMrFDDfaZGuWoD1+XeVbw/Uz33Voi9Yh23TzcF83wnPToOjvvDD0XB4X/fPLK+ZTash+l+mXsv2LkAdDeY8TFgnFHuqZB914Nn8f7Ig2fw2tmjNvQSd57GlM5CyhXtDuZgrrOdXOKcVPYidesq18MBX+frc/anYr8Q168Dt+/e+xrtN5UMO6qQU6O/FvpFhBBCCCGEEEIIIYQQQgghxoYeRAghhBBCCCGEEEIIIYQQYmzoQYQQQgghhBBCCCGEEEIIIcbGgTwifudj/9663S3Ntm2viDcC6+Gyppar70jaVCR/FZL+VZG02ydKrs78sRnUsCsWcaPdPj6rWbuLGnct6oeUdfFJe6wUuPpntRpq0tVLqJXYX0KN16UmtqFMZzGgfmp1UL/uzirqP5qZJR6KqrUz/EyvuaXBVirj3w+DVi+2Xm+rn2tTs/BaXEDtS48UbP0cTcYkIZ20EPUxPRomGZ3Dag332W6gDnRE2m6VCdQbNzMLSBOwv4n9mvo4dpuG+7i6vIKvL6OW4m9cfQXies3Vj3/7yQchbvg4LtbaDYg75D1SKOHcYX05Iz27TtfNGayDN4ixDZtDfflC8Y3nm4Nw7tz5nRzHecj3+LhYT9XVtYz7pGFYwvkWpah/miXkVUN+DuusK1zA7c1W3NR+fLEO8QblgdU1bMMH3/kUxKdCfP/5BZwov3cRdTGbHbcfwgjb/aUrmMti8tIoFXF8NH0c1w8WcQzOnkU99NBRtDarVNjnhjRDd+lNHrZHRLvV2dF1fOEF1OKfIW+FiQnUKWWvCzOzxUX0iFhdRX3puRn0+ejT2L1KOsznzp2H+Mgiam2+/LKrUX/jxg2I3/nOpyGOaM187rmvQMw66SXScOV83mq5WsJXr6DXyOY6zTcaAw3qpx7pf966cR3iO8ePQRz4rj5yiXRfF+ZwLSsXtuYT64AeBhvNjR295LBAa6ihDnMWYxwW3fZGIa27IeazUojrUXkSx2FKHhEx9X/qkcfEpLvGZqThn1IO9umcc63GflhTi3iOq9PoIdFvutrtPtWkhQLVGlQOJqy3Sh4TtQjHflymNTh0fR78gHVl87XcD1u/ulQqWjbMzxmtqXx90SO9eD/n0qVYwr7haRRkrP/NnU++SxGub1GZ1v2BW0fXCviZ05OYs+fJ66pAnh9F0kKuhLiul6jm8uu4fTOz5skzEA9iym2dBsRJivOCtYx90u6fmcW56lyImVmJ+i6m655weHLC+5DrPM/bGet8rK9nDjjzaYQjQ0Ya2HyNwjUH6/vvx99ivx4Yr/fzWc455/Kf+4X192fnUHvdp1qvTv5Wb38S/VR+7+UXctrF3hb54Rvtn4Pi+/7OPjm3sf/CfjwilpZQS79EuY+9Evh4ucbgc8NjMM/768oV9Mlij4cHH8TrS4b9GkZ5grJHSN42uN2j+pL9xEb5lOblh9FzJcv9/8Mg7TYsHV7PLN3F+0hkbWnlIuWhnKaWDMfJ53/730Jcof45f/4cxKeqeL5mkwbE01RTlXmtMbO4i9eMfE56Ma6ZsY9jgvNUoYQ1WXWqDvHEjHvvpEy+Pb0O1gIe3SZL7qI/XDfCcVg9+SaMF9DToNtx77t5Hs1ZmsOFYW11P9ZYS7KdemqUpwDnaMfXYGsrGLF3ARXSpTLlCrrfMsr7peLnXfdzP+6dr1yPHPK1yzP+2L23HB+mLCMvWe5bf0QuYl9lx8cCPx8P3H4a5X0RDz0isgEZdeyBfhEhhBBCCCGEEEIIIYQQQoixoQcRQgghhBBCCCGEEEIIIYQYG3oQIYQQQgghhBBCCCGEEEKIsXEgj4hjRyes19v6COt8jdL3Yx0wM1fny9kmb4M00QIfdcCOzNQhDvuod13K0dyqeOTHsIHa+2trqE3s91Cj68Q87jMe4OtZF4+hUnS1x8oeaolFG6g1zF4aaYx6cUlK2rekK9sibf5u5j5/mj92Cj9DAoI3b27pv5crJCx4CFRKkQW2dUxZhMc2RXq4aRfb12uhLqKZWSHAbQTkJRLROGk17kA8OYM6hkXSo2u3Sc964I67QYw6h902jtXJadS8vn0HNcmvbixDnA1QZ/QY6a6niasxuHzrMsRRHXVAwxJqI1Z91CE1w7HM45R9C/o91+eB27XeuAtxr7OlkV0ssa7/eJmanH/VKyAjLXHSkg4CbFu5hDnAzCwgjcOjpDc+aN2G+JUvfwriS1dRE7Z+9jGIM/ax8N02FFgrPMAxcvkyjvM/9h7U8q+eQE37qUnc3n/2zV8H8XPrrkbg7z3zZYhfuI568xHp/R87gtvI7mJ+fpDGz/QEjtFO4mqmj9Kk3K27eNi66ZVqdWcdPHHiJLx28SL6L4SU50+exPebmRVoDrJ/TZbgOJkjH4rJGuahxgquTZNV9KVIY1dTst3E3HbjKnpG1CYwz6w3MDde9/H9s7OoLc36xXnnjPU4H3n4EYhXVnBcLd/G+ThFPj9nT2NftzbX94y39on+GuUinpu7d7ZyehAcqCy7J/S6TYuHY6Gziv29dhN9WWplbHexzOuCmdfHNdAjv4aA8lNUwHmakHZzn9bkQYrz3ovcPpugsZyRNvdghEZ1iXyyirTPzRVcgwthjldGgPOtUCC9YprDA9KVLZLma1TEcZiS19TqMq6fZma1PvY9e6ltSwsHh/y1pCAIX9XoDtm/AfNIgeq+LHPnOOe2NMaayGibEa3J3SbqPDeobiuSvwN725iZzVdRmz3cwHp0afkyxL0ZzJ/HT2IdPmhTm+m4y5nrRXN8Csf9rSk6rg7m13q9DnGFdIETOjcBxZ1N15OnSFrt/iR5XQz13otFN3eMm90eEaPW9/1oxDseEXRd66XkVZNy/Yj9ybr1fH7ydOYPWqeM0qpnDwHn/Tn61ixFvttry8zsyOIixA898jDEFTrOp971ToivrGP+7ZB/jpljI+n8IRm2ydXvHi/9fn+nD1st0pP39h4feX6c6+tYX/DxsK8Ij6lRY5BrKvZ/MDO7desWxLOzeH3A7eZ9cMxtZv8HHk957eRxy5/h4+D6mPuRP7+v+X/I1wx70d+8Y0m8dR7uLGG90kvw2OePn4V44QpeD5qZDdYaEDdbOA6f+R30jEiuoa9LcLQO8cRDD0HsT+L5CVPXn8F8vN4OyPuV10ifrgHZ8yhOcJyGdI8up7S0zOP6BD+ztob3nRotzFXJK+ivcuwpbMNbT6BHRNJ3x1RKdRx7D2VDvX+u9w6DTqezM9dGed45946THF8COgZvxPp04PvT7GfkufcNRtm78HGO8qFwaou9N5+7Dzf38D5oAyPvy3O/5bVib4+r1+P/pV9ECCGEEEIIIYQQQgghhBBibOhBhBBCCCGEEEIIIYQQQgghxoYeRAghhBBCCCGEEEIIIYQQYmwcSIx4oupZMdrW1iRN3xS19ZIYdaRYozDvb6yplZL+abGIeoAnjj0IcZW0OVevobbxZM5jl4Q8BTbIA2LpFmpHJ6T3uTCPGq9+jHrYRprnhdTVTU+7eJwDFvoiHcMgQk081tdNPOzXLrWhXHd1WaePobZtsI79Et+8tvWvHa62pplZwQ/M/K1jXL2NGuXzi9ieyRqOket30VvBzKxQJE149k8g3fSYzkeng9rDxTJqFoaG/dvvufqaJEvvjJtGA+PlVdRinJysQ3xjGtt0IcI2vf+JJ5w2BO9AnfTuGvZDpYv9EJRwXBVrqN+YpXicnTZqoS4cQd1DM7MNknmd3MDjLA8FGguHrCV86dJLOxqHUeT6uuyGtVDzNBFZL69UxL4ssK9LjPu8vUJ5pYQalN0CdqTfcfUJ+22cK50U88LKHdQEXadx/sCjT0Lc7KHW9MmHn4L4gx/EnGJm9qWvfBVizk0DUkpsZ9i3xydRI32hjmNwdgb3udLM8RYhfWPPSBN7l07sKJ3He81uzdtz587Ba8vLqPHKGr2sJW1mNjExAfHGJp6zO7SNiNaSyUlc35aXcT3kNlx65ZLThg2a05evXIb42LEjEPP8Yf+GGzfQw4B1gotFWh/NrTVq5H3xlre8BeIrV1DDlfU8+dzw+8PQzRmPPvooxKxPvK37nFcrjZv6RGmnhmtcRp+BrIt+D1EFj82pR8zMUlfXejd+Rmti0qHXycuE2uAZfj6KcJyamQXkz9CjueyTR4f1cL0qhZgve50GxMs91MY/fQo9QMzMClQvpSnrXJMIK+lmF8o4TsMy6v8HJTzu1XU8BjOzdoBz3nc8SLbbeLja1kma7vgtsb5xRLrPrvZtju8Wa6mT1rBH/jV1yo1BB+sfP8adTlXx/VM1d30r0z7DAfuPYd9PkKdRSGOuT2OSh0to7vpUIu+KwHBM1SfJx6JIOvo0d3s0Zr1shH+AmUWONxG2oTusLbxDHnOjGOWdsB9YZ55rCC/k/sbXuQnT5HXDXmxbnzmYJvYoremR3hi5p42udWkMvOlNb4L4xHG8Hgjo/XyUrS7WEdNUC5qZtZYx5w/6e69Dh0W/3985Bzw+eL3n1/P8Gfh88mdGnc+DkudT8cgjeP34wAMPQHz3LtYRvI1RY4z7Je+6qlTC/HnQccw+FaO8Q/JeP8hx3Iv8chDamw2LB1vj5/pNvGZcfNsHIK4fPQPx7Zt4r8XMrD14GeKwg/330JGjED99Huf4wgKuPROUA/r03WiuC8zMCmW85iuQtwjn05Q8VOOY6jryOCpweZST6wY9rEdb69i3X7nwPMQ3buK1Nfu4Pj2Pc+nttGaXC+59wxZ5bqYJ9l049CwLw8NfYzvtjvX6veH+sUOdtYk9GnM63PETptd5XvI+XM8jHiPkm+bvfc8nb58HvW7rkJcst7Fcdu9d8FrgrOOcI+m42CNiVO7Ky1dOrUGvx/2D+zDpFxFCCCGEEEIIIYQQQgghhBgbehAhhBBCCCGEEEIIIYQQQoixoQcRQgghhBBCCCGEEEIIIYQYGwcStSqUvB2tLkdzkqRKoyjYMzYz80mPajAgfVXSWX7wQdTgTfr4+hLpNJdI699tgVlMOlabXWzDgHTXfPLGSAfkJ9BHXXX2iOjGruZWQrqy/I5aGTWwyxXyJKADa3ZQO449IxZPoOaomVmlTpqTpJ34yJNnzMysWMrRXB8zlXLVwqGu+1oD9Y6/9OnPQHz05BzECenYm5k176AW8IDOSVTB/vZKqEXaJd3R2w3SXlxYwPf3yIPCbOd4tslI8/ryrdvYZtIaLgT4+SdOn4K4NzULceVR1CA0Mwv6uM1KiXwKNlG3O51C3eY+Sfk1G6jheuc2HsNTdWyTmVm5Mg/xsQU8F+urWzqVfna4HhH9QXtH4/DipcvwWkQikhO1OsSdjnu+i6TxWKR82CH98X5AWaCCn7946QLEf+xN6N/wDe97n9OGm2uo2dq4jP4pt1qolfnK0hLE73zXH4F4mjQRByFqaD/+pKvbvrCA8/PG9ZsQ18mT4MHzJyE+O4v7rNAKViQvkXTD1dButzEn7KXRy1qS46a0q/2s1/jUk+jBcWcZdUfZz8HMrNNh75kGxKvkv1Cvo4aroxnpeGbg67Wqq9uc0hpYojWkQhqvDz/8MMR8DKxR2SatzW6X1mBztTRbLdRej2Ncx++QXwqPEfbj6FGO3yTd2bzPLNH8unjxopm52tqHwWZj1eJhvXTnNuaFqEy5i7RLvdBtb7GA42iQ4DlKO+hp47GWt4drkz8gTwiqwyzK6TN6T1jEccZKtDF5RfWpTc0Ex0xlEX1Cwglcy8zMQp/yeB+PO6NGRCGusTHNnTb59vAykXpuvrpLc9ynsRw0t+roKK8Px0gcxzYYzrt+H9tdIr+pIOM6O0d/toB9V8uwjpssYv1xcu4YxJGH1xPNNvU1bb9UcGuSuIFzOprCfFirkQ4+XxfRgCgU8PUgwjwWmatl3OxgHdboNvAzNGgGCfZ1RmPWp65mDecscL/P1upizg7pUtMb+v952f51hO8Vnue5ngdDRnoj7OMzozSwffLYiEk7n7WVq3Q9wtfNZgf3gBh1XKP6getZM7PZuRmI2Ufp3e95D8TlMs4f3udqk9Zo6pe3PPG404YqjcUbN9HDan1Y/xy2D9Pu88G+BNy3fP75/WauDxbXUBxzfcK+d1z/8D65BjMzW11FD4HFxUWI+XxyGzjmNu8HHucH9c4Y9X5mX95EX0N87quvWG9YDwdH8Prh5Nu+CWKP7hMdPfuQs71N8pgLi+jxcP445oD6DOaJmZN4PVeZxprJJ4/WvJokYP8orj/pHPqUEwYtuia6g/XR7Dz5WMy79y3u3sF1/spLL0J89do1iDdaWP+WF/B+zfFzeM1TJF+H/sC9pom7mB/ThLxkt68lucg8BOJ4sLOujfKGGRWb5VyH0iFx/hr1eX4/7zMM3HHn+kwc/Dh2w/3Ca2reGj0qT/O65qz71CTnGmhEv+T9zfF2Hl6z5K1br4V+ESGEEEIIIYQQQgghhBBCiLGhBxFCCCGEEEIIIYQQQgghhBgbehAhhBBCCCGEEEIIIYQQQoixoQcRQgghhBBCCCGEEEIIIYQYGwdybBoMAusPtj7CBhWuGRY+4+j3cwxIyLSr4KHZ5nwVTX9bd9CE5vbKVYjjARpEhimagWw0XcOXbh+N2thAifzrzA/IYJuMcbIMDToSMsxO+66pSkr21BwHZPwWFclsMMI4ozb6IZqCRTnmcm0yqjUfjVPmF+pmZlYoHL5ZdXVq1gqlrXNX72Hbb17DMbB0Hc2UFufQQNfMrNfFY2WTpnKpDnGfnHGymAyuyNSyT2OqXHHbwOOmQWNzLcU4CcmUa4Dj9JFJNCB/6Bwau6+WXVPD1l00k54qoVlYjYwW77KBKJmst9vYploVt5cNXBNnn4xv5ms4viaKW+ZW4SEbuJ574FGL463zuv3vNpNTaB744Dk0Abt5C42qtraBY6JP5m9rK1cg9gz3efLUUYi/snwR4m986mmIHz1+1mnDLTKh7bd+A+K7dxsQf+7Lz0P80PkHID7/5JshjsjEdq7umhW9773vhPgXfvFXID5zFnP+E4+jiVchRoOuHpm4W4rjKTHXvLXfR+Mw9sndvbYl6eGOu3BXLm+20Nj2y1/5MsSzs2iidpvM4c3Mrl/Hc765wUbdextKzc7gPqbJcD6K0DiummNWfeHCy/QZzEWnT5+GuERmdC+/jJ/foGPgNTvPiPLIkSMQT02hIR3nbDabZiPGmRk05ZuYwBx/9y4aw5u5ptpra2j2uG2UOMowcRy8cvFl6w/zcxrj2lOZxHxXoXPczalpzCdT+Sk0qU/JYNCjtWTQwf7P+timAo27NMfUsEPtSgP8DJuppVTz9GKqwyi/1WfRaDGouGbxIdVanCMDqpubPTz3ieExNNcbEPebaEy8tIY5w8ysRHV0uYrnszCsFzP/cL+XlCaJpcOx3u/i/Cv5mCMqRe43d45MhPie09NopHlqDtfQiRrO2biNbSikOAZjn0xm3aXFErru6ZIZ8wzlnaSH+4xTyse0j9IEzr2469ZUt9aWIV5rohmnRw0PanyRQ2OWjrvdpfUzxyiySeacRdpGbXg+A//wjTSzLNsxXBxl2swGla+1vd2wmfcoRhnoVmt4zktl9xpsQOsXb3OUeTUfQ5lruTnM32fO4pptZvbYY49A/PgTT0A8P4/58sYSjtMvf+lLELeoRvYKmLcWZ3ENNjObeOtb8T2LNyH+4he/aGbu8Y2bNE2d87oNm5byupRnxMo1D59fZ217jX2/1utcqywv47kyM5uexmvOVgtrc/4M10Q8pkolvE/Bx811Y147eVzzZ7i2GmXyzvOC92dm1u1ibcJG4ru3McrA9l7TqT5ovXArNxx/CK+9Mh/7m/yR7eijb3a2t7F0AeJiEWum2VlcU6dmcb2bnEWz6qCIuS0L8JwX6VrAzMwL2eydzhnVbV3KIyvLeJ20fPsGxMfP4LVCGLlrwPVLeE1y48orEPt0UTk9RSbe59AI/IHzeL2etbGNgwZex5mZ2QbWfpvrOA7D6tY1y9Y15Tn382MkSVNLRuScg24PoLHK83aUib1P9UhGr2c5a3hG48yj2iUK8JqnS/dtA6dNuGa3qGbq5tR2BboPllI+iel+M9ciIR230ed5Lcqrj0bVK+nwHkyYyKxaCCGEEEIIIYQQQgghhBBfA+hBhBBCCCGEEEIIIYQQQgghxoYeRAghhBBCCCGEEEIIIYQQYmwcyCPiyuUl6w71QT3Su2JdZp/0s1gL18wsJAOGo/PHIL5+/Q7EA9KRNZIF7seomeZnqJdVSVzNss4A39NlzVbSeA0CbHOfthmQJldAumF52mOssRWzXmOHNLAL7BGBepce6YAlJEvY6bk6hZmH2mCDlI572JfF4r3TfdsvnV5i/d7W/ss11DU8dhw1y5tt1KlsrKI+rpnZUdJWX5xDTcDiFGqZXrjVgHiqjrqHxQjPaY80Cf0CeiWYmcU0P1YGOO7apC3dTfD8FNo4biemMJ6qoN7cdToGM7NeCTVXp2dx/nmk8z1LevwD1i4l3fR+C7Xcl2+7OoetVfRGWKxjm848vqVrGUb715u7FxSLxR2d1be8BbU1fcplYYBtLpdcnfw0Ix09SmVhgMms20G9x+NHUN968xTmhBtLaxA/uoB6vWZmx8uoc/+WI+gj8utfegHiL7yA2pnpL/4axN/awnH+9LvfC7Gf85z7HU+/GeJmF3N2r4k6sn3yFaFpYJvkS5LEOEZZr9XMLKuhljuvXbt1XPN0acdJsms9Yd8C1thlrcY8bwHWtGUd3o1GA+KA/HLmTuOYuXIFPXnIzsjRDTYzO+voSZOGK2lpdjrY5vV1HCOsk8lawnn9wH/jmPWMud+aTdTeZz1kjlk32Mzs0qVLEK+u4nEVh+sEH99hMOi3bdDfmktZSnPKcMykXOPk+DP4pFEdFlhfFeONO6jl3W02II7II8kCalOO9PKASpWgiJ+xAuaG0jTWAWmC/TCgWq5E3lxp39WP7lFfFcrkbWGYXwY+juWgQvlnDQ90fQN9DCan0Q/CzGzx6CmII8qJxaFeehgc6HLgDZPGiaXbc5fytjegMRXjeJotuXnm9BHyhJjH3FUrkS48a/6SFnSpSOfGuYZxa+HNPo6Z9QHmgWOn8FzMkHfNrcuoNf38c1+F+D1f/80Q++SDZ2b2CvmmZXQdRIdtHc5VtAawJneSsI7wwb/Ptu0NkuZck42b3R4RjOMJ4e0Z5m+f1jePdhXwuKM6KaNkNlnD641KzfWiWVnB9SugnfpUfxbIo2eaavfTZ1Gz/Mknn4T4/MOocW5mtnAEc0+J5tsrr+DY/sVf/GWIP/Gxj0FM1nw2Qf5U50/g/DYzq02w9xOO1WzYD1l2uOMuSZKd63yuN0d5QuW9ztsY5SvB9cuoNvzmb/4mxOxJYWb28MPo38Y1D9ena2t4jdKg2rNSwetkrr3zanH+Gx8398soTxjuFz6mDl3bm7l9xzX27jp+lFfHveboU++z/tDTshhhuzK6xxNT38yewzlvZlZ77lMQJ5u3IJ6Zq0M8tYDXreUJfL3Vo7WJvW0K7vUbw3PZ8fVo4H2I1SX0X2jR9cUEeWjdue7et3j+81/AbdA+eBw9QfnzgaffBnEtQb+4zhL5U3Vx+2ZmCfm6hj0cq3689Rn/PlxPtNpt6w7bw3OUZyBfC+StzY4PAZ1zxwsh4PxGHhG0BhfIR7fFFw9mdvcunqM25dQe5YoeXUNOkY/g1BSu4+UKrpeTOes831dv0RhIaE6zB2SPDsvJR+x3leORxeeHt7H9enaAslC/iBBCCCGEEEIIIYQQQgghxNjQgwghhBBCCCGEEEIIIYQQQowNPYgQQgghhBBCCCGEEEIIIcTYOJAo7MZmx7pDjTzWAA5IS4q1NsMcramZOmpiJaT7tbSMusHVMmq39foxxaiX5ZOWaRa6WmkDkiPrkmZdt4+aWz75ATRbqJ1YK5A2MvXwIEd7rDfAbfYH2FdJF/eRUV+WSqij1yNTiB5pGHqZq7sXx6TBFmOboqFuacCi4IdAJfAsGnom3FxGnXSf+juIUGfyzh3UTjUzMzqnvXXU956eQR1I1o8LE3y9VMKTHPexjzZ7qCVnZrbSwX2udzHuxXiOWx06p6xZ3icN0MoJiIsld/6FVdSFTX32M0Ei6oc0RU28UgW31+3jmBokOD/NzF65ij4Et66T78TElm9FtA+tyHHBGpQxafh2O6TnyEnFXO0+9qIJSfO8WCSvA9Jh/8AfQa3oOyuoR/jMjRedNiyS/ubVNdKlpLzRbOK5+OILFyFemEbtx8Uj6K2y+ABqyJqZ1euoe/j2tzwF8bNfQA3S9U3s2yDFYygV0Y+jQ7my38sZ9yGOJZ88deJdvkFZerjeJLtPEXsFzM9j//LrrLFr5q7DrHFbJh3ntbUGxKdOob9DRnqQV66gx8vGBn7ezKzZxNzmaLi2MXdNTuIYYQ3exUXUhh6l62tmduIE5sPBAGuDmzex1mDdyzt30K/q6lXUYWdvjFOn2RfD7KWXLuA+b+A2g6FPDGsMHwZZPLAs3uqTHp2PtR6ev04Hc1d9es7ZXlAiLy3yvGHjqpi12EP6A/kxZKw7m6P9nFKe9kk3Nqbyt1DEcVcMcAxwDjbS5y+Tv4OZWbuB+auQoYZ5N8Hj6FJdTZYSlrJnGdW0T7357U4b+lRzbjSxTd5Qd9bz96OAf+8oR5F5yVb7Peq6AvVLJcDcdu7IGWd7x6ax/iiRbnDSxzFYYM8ISvWlIhXvtIazLvHW37Cve+RBF5Nw7vQRzEudLo6pxU26vphDze2pqpsryi99CeL0Ds7nCdIqbm2i/vTUFPZLm+Z/wfF7oX40M5/yZ8HDzo2Gue6wPZjMbKuw3U4NdAp5bfKoCs7Y8MHcOpnnEX/Ep75wPCIoN5ZKOPaPnkCfETOzZfKI4MvthRlcn06feQDihx95FOJHnngM4uOnTkJcLLu1Bl+Pf+V5rEF/9T/8KsQf/63fgnj1Dmq3p3Qu7iwtYXwNPZfMzKo1rAfZvyQdzuFe323/OOl2u6/pEcFjjv0Y8jwianScozwi2POK98F13IULWKvw9szcGoprrLPkMzI3h3UC11R8DNxm3p+ZW6eN8lHjfXCtxbXjxgbmRq5NzUZ7W+yuufncj5ssjXZqdpaDp8sex5vGinVne2ff/kGIr33ulyBeOI6eR9NH0HeSdePjBGvJMuWVvLqOff8G5KXXTfAcsj9tQr4xBbq30KV7K1/+/O85bVi5hbmqQu2enMVa5JEn8Vr47AmsA/0Oem1sbqBvxVrDvXfC92BLdBzh8P5MeMi+JGZb52373Dn+DiPaw3PUzM2RbCPBvkx9ym88Bti7t0deC+xnZGZ28yaeo0oZ6565WcxvBfIcS+i6qhvQuKT8dnTR9Xtjzwe2zhuQp+b6egNi9n3layRnvu3Db5HP5/Y28s7ja6FfRAghhBBCCCGEEEIIIYQQYmzoQYQQQgghhBBCCCGEEEIIIcaGHkQIIYQQQgghhBBCCCGEEGJsHEiwzg8K5odDrUDSkvJIc4s10UuBqwW6MI9az6wVzTrB3QT1rzaaqCGYeaixtck6YTmSVTEdR4v8A5bJP6DUw9f7E6gNF9ZRu5GljhtNV7+62cJ290ljPvVRg4t9EWZjfP/yyhrEay3UdyxV3HPB+p6Bx/q4W/vw/cP3iOhsrFi/u6WzOFlBnxC/SpqgAWqgHzvuanUP2qS/R7rpvRbqQkZV0nJbI+8RQ22+JMLzs9RGHwQzsyuN6xC3uriPQR+1n3ubuM9TNdxnYYAad60VPIbKBPabmdkgxLEcd/AzHp3rHvuGlFDncGZxFuISaRF3ug2nDQ95eJyDDdT4DMLa8N/D9YjodDo7mqQ+a/56/PwW+7Fccdva6ZDXjI8xa4/2+riPTfJKqFNimT6H+qyrpJtp5uayCz6+Jy3juQhJC/7UAp7Po0ewzQPSx7YQ56aZWTJAX4r1Ow2I4y7uMyN95Abpm7PO6d01HMN+xdXQrtDfPMPjTgqvHsdha7rulrqMQszTrNO7vIy60KzBa+bqqZZJ1/LIQ49AvHQLdTCZc+fOQVyv1/dso5nZ9euY61if+ORJ1J/mbR4/fhxi1p7s01jnz5u5/cCfmZrCXMbnfX0d14yjR1Gr/b3vex/E7Odh5vpr3Lr5axDv6BMfrlS/mZn5mW/+UMA33sA8kfTx2LukHexnboPjFulg0zlnf5M0wv4fkH6qR/q3xQnMPVnXHXeW4T7jkPXCsY0hpa+AdH+LdWxjSlrQaeJq37KnTXudPG8izEXzs6jlHpMP0wZ9fvEE6nCfIV1uM7Mb11HPuEE50kuGHhGHPPAiz7NkWH9HRcxLVcO148g0zqdjs1hrmJkVSTh40MHrg4TWioA0fD36vO/h+cxIz7pUcuvo2Zk6xP2E6tWQ/BcoJ0wfw7zy9qOosT1/FP0BeuRRZ2Z29CTm6Iu3MP/6Ac6lMw88CPHCHI7Brzz/RYgL5Em3seFqtxdJi5iHVqu9lWOSnDkzbjzf2/FxYO1p572smZwzRfgtvM2E4gJ5HXqk+9xPqXajHZw/417TrCzhNcbJY3ht/dijuM4//NSTEJ8gT6P6dJ2agHNlZQXrODOz5597AeJP/e7vYvwJ9P+6xb5MpG/N/lZegeZ37M4/9sDiWmF7XR913u81uzXT87wOdsPHwLXL9vYOEnPN9YUvfAHiF17Ac3fmzBmIm6Sbb+bWglzHca3JMddIfK64n1ZX3THHng7cdzyGuEbmvmXfipWVFYi5H/Pgvt/tncHtGTdRFJoN1zHf8bfBdmac3HL04WfPYN4IeliPVOZwTk7M4jneaDRwn+R5lZDvlpczx8vshUfN5muQmPwD/BLWnu1NXINuLGO9VMi5hjxzDj127izhuDlyCtftmZM01ql2yMgjcmA435ZuutdlC4tYKxQmcH5dvrzlY1fIyR/jJk0SS4fjh88Hewx4NCeKOb4gMdUJMW0jS+geJ12/VSvYN50W9u+ll9DPaGMF70OZmS3WcN0+soi5ZHKyDnFC92V5HU/IMGVAa9KNy5edNgSUPh56+DzECwu4jl+9hl6GS8vkC9rHfMbnIm+ddM4fHde2Z0Sakz9eC/0iQgghhBBCCCGEEEIIIYQQY0MPIoQQQgghhBBCCCGEEEIIMTb0IEIIIYQQQgghhBBCCCGEEGPjQALY6fA/s1d1oLbJSCc9IF31mRnUHTUzC0gbc5100D3S4O10Uc+qE6OeXBCgnlU/wbjZd7UZ26Rftkoa5Qlp+C6to7bYZg81CY10iEN61rOyhseY97c4wc+UKqhpV6Z4lT6/uoaazoMINfWizH3+RBJrlrLe+/b5Dg5ZM93MoqLtaKezT0GljLr1nodjph+6xzp5FPVTN7uoiR37pF9Nev1pDzurOIPvv9pEjcGXN1EL1czsTg99PLIO7iMg7cuFCOfX+x9HT4AHItQQXG/iOb+1jpraZmaFCdRZ3mhjP7z4wrMQnzx+BuJH34S66CXSUuScYL6r/Tf/GOqKRgPUdb7b2OonL8Q+Hjdra2s7OvKsiZen2bqbPB3QXh/PZ0Bif6Uy+Rb4OMYaa6hVenfpMsQz5/F8ewVXe/lqG8fc3TbmjYUZ1FGcqWHO/vp3PQXxW9/xJtxBUIfw2lXXG+ULX0Rt2q888xmI5+ZRh7E2OQNxp4v5dnUVj+nKZdREfPBN6C9gZhZGmMMCmu9JfB89InZNEaedpKH75S9/GeLpaXeNXVhYgPjI4hGINwqoF886vnz8o/Qh+fNmZu94xzsgLpVwrE+Qlwx7Sty+jfmU98l+D3k+FVyvsPYvaw3XaqRRfwT77d3vec+erzdIC9fM1UPn87WysvWZ8D6ssZm9ak/S7WBfhH3y4hpg/2/mrLH9Ah5DuYjnuFjF9SiKsC+yDN/PXglGnkG9iZw+S3EceFT3FEiKvUc1T4Fqv8lKHeIy+Rr0O259ybWcT5qr5SrlngE2qtvFvi+XcFxOz2It4/uupnKxwJ44pOk6zHdOzTdmSlFxp66MO3iuogzP5xR5sVXLruFblGLfdsjnbEAecwNak2nJtSyj/iA96zCnr/k6p0A+I5PTqCu8soH5d7JKtQW1YZPa/PGPow6/mVmTrnsCH7e5tobXMAtTuMYeI/+bq7cuYBs2cYx22nQNZGY+5fgownG+naPjMMfb5WsIVyM5x1uA1iOf4lOnzkD8xJvfCnGR9Ku7MZ7jmHSc63XXe+uD3/QBiB96ELWjT53FNkySvrVPHo6tTRwjly9dhvjlF19y2nDhAo6T3/vcZyG+eQVrs0EPx5E3Iv9w7bEfvf4ocueomVnMF7yHyN27qD9erZJnyGtobu+Gayj2XGq3ac2mcTxLHjvskcU1GddHZu51Dvsr8DZG+TNcu3YN4pvkIZJ3XcXbYH8w7gceQy+//DLEr7zyCsTc99xGM7fenJnBfLrbC4P3P27Cgm/Z8Lo7pXa61l7kY5jjh5OSV0z5AbwGnDiK/TWFQ8DJK0Uat+wL4oVuvo1o7MfkR1so4jb5uqlUrUNcjfHzi+fRM+mxN7/ZacOFT3wS4t/73DMQH3vwIYhrVL8kVKsklPuqFawjikU359dn2XeC7lUub90HLJZcj4txE6eJ4+Pwmu+lcz7I8bcc8HUnXSN5NAaM6mhvgDXKxgr6K56Yx/5epPNlZhaSZ2O5ioO7T/eXBzGeU5/vK9J886jW8zK3/zzyzlula+MbVzB/NVvkcUy1XkjrI8+/PCslXks4L2/nw4PkOv0iQgghhBBCCCGEEEIIIYQQY0MPIoQQQgghhBBCCCGEEEIIMTb0IEIIIYQQQgghhBBCCCGEEGPjQGLESRZbnG3pP6Wk/xSn5AlBWn2loqvpevMmakayB0ShjNqJvRi1qWLS70s9jnGfrY6rPXabfCnWBtiGo6cfgLhKWokbK6iLeHW5gTsgKcpWjp5qkzSZWVusMEAdr8gjvcca6efWyDehhP1o5or/BaT9l5AW6mCoVxbk6JaNmyQbWJJtnbu1xiq8lpL/RYX0NpOB6y3QGeCx9fqk21xC3cnaZB3fTxqujQg9OS41L0O82nPHXa9JGsg91HJ76ASej69/Ao/z4RnUUZ80POdXNlCDO77FY8CsHOI2j59BvenGOmqZsh5nt4NzZ+km9kuxiNqEc3OoPWxmNlGrYztbqCFZXdzywggOWas/y7IdLTzWuuOYNepZ49XMLKV5E5H+f2ioE1yg13vTmDeSDmtLY66bm0ZvADOz3jrqCXZPoe7kh7/1wxBXSb9zcho1KWvTeD6vXLkC8e984pedNty+S34pAWoS1qbR0yEqYO6rT+G47nXx9bV11H5cvoPeKWZmPuuclrGvd2vkH7pHxK5UxPt+5OFHIGY92zwNXdZzvHoN19xLpOsc+rjP2gSOkaOkH84azHltYG3g1NGYxPlSovw7P8/awphXVlYwT929i/4PZmbr5Nmwvo45e5Qu8yzp/kYhjxmcn1cuX3bawPrCfH63tWxZ0/YwGGSJDYa1XZ/W/ibVPFNVPOeba1gDmZn1SD/Vi1CDulYkLf0CjrOQ4m4Tz/HGegPbmLn+DOl19I85QlO5solr7loJc3Q7IP8GD7dX9FF/uj9wtbyTPhaAtQjHdhxg3yY0jsoRjoVKBfuxR74U/b5bn/kjxtO2/jdr4I6bWq1q0fD4+oZrqN8lDWU6vwkX1mZWIJ17P8UTng6wr/sx1vpBhv3ENXAYYW7LkdC2KvkqzZAnj5Fe862buGaWqliDhQXcy0YP6992gmPYzKxL+sgBeZlsdrDGevFFXAPOnztO8VmIP/2ZZyDOcjTs2x3sW9aB9rZrqOTwc93u2s71gOCziq/nlHaWkPb6I48/BvG3feg7IK7PYm126RXUqe/1yBeGvNeOnUR/ODOzU0fwnPF65ZFHB1+HXr6IY+D6KzguV25jjn/mS+j1ZWZ28fIliK9cxm3EPZx/ExW8/ggLOBb43OR5JTCs18/r+nZ9wn5N46bX6+1cNywvY43K/gy8/rP3gpnZ5CTWwdw3S0tLEF+6dGnP19lLodnEHMG1vRl6H+ynTXz9yF4Z7NvFXmF5/mMnTuBc4G1cvHgRYq5PeXxwPcsea+x7kbdN7pfdnzn02i7Ntv4zM85ljt8bzbckJ9lxukxC8oWk67XU8B4B+7pEVHN025gjSnXXj3Fypg5xn+7ZcQ7o0X1FPqxSFWvNiHxHZ+dpDTezS+RDsXjqFMS1SdxGcx37oUD3rQoVHBcx1XGZ515XlUo4H5rknzjobq25vnf4a2yr2drxc+T5wXmBr4PcNdkcvwmf3lKga8gK5Zoydd/kEbyOPUbzPG99uH4D7130yCvWp/rRN6onE2xjRr4g7EmW1w8D8jP58jO4Dl+7hnl6k/L43HEcp4+/6c0Qez62uR+711WcI/vsPTNsN1/n74V+ESGEEEIIIYQQQgghhBBCiLGhBxFCCCGEEEIIIYQQQgghhBgbehAhhBBCCCGEEEIIIYQQQoixcSABbD8w25G4GyGtWS6jRtfqmqvb3GigznmhhFptMXk6dLqoV5VyG0jWyyO96zhxtfo3WqirFtSxDX4ZNfBq5EFQmSCdtlU8pvYGat6FgavDO0XaiiwHF3p4HH3ytfBIAy6lk5OxRtvA1dfNUvL8MIyLQ+0/v3D4enPddtv63S0Ny2oN+29lBbUu43gWYvbLMDNbXUOtZ+P+o7hPY/mlu6jxeesWan9vrKPeZkI6bWZmJ2kcvO8J1Kh78jE85wslbHNIcyMuoDZxt3gS4voR1CE1M+t1UV84jXAcPvamp/H1Lu6zXCR9/gbOcT8mX4oJbKOZ2dLGZYgjH+fbN3zg280sX5t3nFQqlR0tPNbqY81D1szrdFwfmGIBj6tOeSMbYF91uziGOh3SIyc9wSjA8TJZdz0i3vI2PJ+PPvkoxIVCHfdB2poXLrwI8Y1bqF3bIf3P1Ts3nDZEIeaeBx59COJjx1CPOsiwryuk0z2gvJUE2K+1qqsry1rojkdBOdr13sP1iEh2HQ+PuxOkDf2hD30I4o9//OPO9p577jmIea2JaI3skM5oQv3LOr3nz5+HuFrD9dPMPY6Y1p8ujZuUvh+RURyEqM86t4Aarn7o+gIZaV+u5+Rk2AfN6dukZ3xiE9f5iTrm1weoX8zMVsmnYnX1UxAXhvm0UDxcrX4zs+tL1607PPcdWhdi0pz3ezhGPD/HEydFDVyPtUap/6emMD9GdL54jS5jWWW3PvZlpw0Ta6RRXcYcOpvgOKnPYpsHM6j13D6ObeyTdnt7BceEmdnKDcyBkyHWEl6VjpvWhWny/+qSzjrPtjwdbbIUc3SqdzyO9i/pek/wPN+8YbHr8wIfYt5p9nC8dHO0a3kbffLncPTjI3x/QPrWnU0c5yGt+6zXa2ZWIA+PyiTWowktJ0mMbbizjN41E9Ok7b+Bnkdveieu4WZmL72IevAvXsbjnl3EcX2dxujtO5jr3vYOXKO/+PmvQNzruD4VIflSsB5ycegNkoWH/124vT0iKPb2DHO3cewY+jWcpPj2bbx+qJF/wzytZ/V59HuYoe2ZmU1QXumRB8Qrl1+C+MYrqL9/7SL6Td24itc0t0kf+4VXcHtmZteXcBxx7VSvY+1RomvrIKTrMNISZ037PA1t/hu3Ybt255pv3DSbzZ32s6fcxgauG3ycl3P8ps6cOQMxe0DwcbM/1Ve+gnN4QB4u7Blx9Sp6i5m5/gzsn8DbZL8GbjN7ibVamFfYM8LM7Ktf/eqen+HxwOtjle7nLC6SRw/1I/tcmJlNT+N1bZ38UQu7vPYO2yMiThKLh3r3acL3ffb2w8nze+N7UUbXZ80++bxU8P3syTEg/8yNZbyHwOuImVlUehD3QeeY/U1u3MS8tLmBa+zkDK7Rm3R/JzvqeltyrdCm46izXy35h4Xkg+f75LtGHpAdqvvMzILA3zM+e2ZrnYhyxuy48bxXzzXn8TwPzd1wnWZmlmTs2YDxZKUO8Tx51Pkx3y/Bcdpp4xja6LjXh5ttuudG9+AKtAaHdE8mTem4HL8abJNnbn2ZUs3ZprGc9nHdP3MMa73Uw5zcW8d7OOcewnry5l30JDMzu7uOY9Njb7nt2uoA9070iwghhBBCCCGEEEIIIYQQQowNPYgQQgghhBBCCCGEEEIIIcTY0IMIIYQQQgghhBBCCCGEEEKMjYMJYMdm2VDmyiNtOC9D3a+1lQZ+NnH1rpIEP+MlqOPVbaH+VZ81WekxSoU0taqkD94qu23okwb2FGnyJjFqsyXURiPtsvIkfr5E28vTCMxIxJcl1Dzqp/ZV3Ge/i9pjKynqgPUzPM2skW5mFpFWa0QeBslgq41JcMhCwmY2MTFlg6Em32oDxwS3c510uDc2Xa23chWVlYszqAt5nST+L124AvHdFdTLjPqk2xziuDtSdfWM3/UY7vPhI3hOgw7uc3ODNQLRA2JQexvEfQ91Dwdd9BUxM1u6hXrDL3/20xC//cmHIX7L278O4osXXsA2kkdEtIj9cPScq5t+7RrqfhbKeG6iHM35w2Bzc3NHy5U1DhmWqo1z9KuP1I9B7OihkodORnN+Zha1/jZC3Gk/RX3COEP9XTOziSnUQ/VpTHTIE6JQwPeXyqhlvNHEubh06zbEtQl3eXngIRxTx0+T3qeH7fZT7IcK6TDGpLu40iQdx4GrR9nrJRSjDqK3y6QnL1eOk936mbxWsHYwa9E++eSTzvZYA5d7o0h+Ci3SV2W9zvl5HIfdLualGzdcX5BSCc/pydNnIA7JP+X5L3wJ4gbl9Keeegpi1u29fNXVEr55EzWuWa96kXTTM9LLjTPS5iTN1jZ5a+Rp9U9Oo3784hHcZxRtzZfCfdB03ey2rDP0peE5lZKW6XrMXl1ufgyaDYgnIxyHE/O4jSTDcTfooUZ1RPvcvIzeUFOXXH+G4wXs37iB+S6okD40+VLcXWpA3D9Lev9lzA39rquVn5BmazrAcVSZwnESFLCvu33cZp/8Ojo9fJ2tNczcPME5YdvTyGdzsjEzOzdt/f7WmvPy8svw2mMPPgJxt4n92Gm6fe2T/1CT9MIHpI+bUKGd0VrTI++aNuXCo0dxTTczm5pFPf+Icl+phGtirYZjaqWB+tRnHzwF8Vc+gbnxxHtPO22oTeKYqk9jrmuS3xTbInTamNsGfey3I0fwuF98AXWGzcwqIR53QoZ+yXCspfHhX08cBEdFPceXgLXxWQN76Trq89cr+P6z57AGYq+EjU0cx0nfzbdXXkb9/aUreI2y2cX8evkVfP2ZLz4D8e3bWMvdIH3+RtvNt6ybPjlFfikVHJesQc/9xnV3nmY9M8rPbXsf+9nWveTFF1/cuZ64S35Ta+RZyPUS131mZi+9hB4d3Hcc8/nc3HSvB/d6nb0TzNza78KFCxDX6NqNa0n2nVhfx+sJ9trL89476Bjh13m8sH8H+1zknQtug+NFtOtcjLqWvNekcWLJUB/fua9EMbcsyXHECalG4Hest7A/e1P4/kIR17/GCl733rhyGeI4p57ptt8EcatF4/BF9DK8TONydg7XQz/Femj5Ls5Hv0xel2aWkN/G3bt4HOefwPqlUsLcx94jIfkENck35vZtd43t0b3IEtWOtcrW2YkKh2ywaWZpmu2M9VGeEPvxkOBxxx4/d8jXo0D3oxPyN71F14MTE5irmhuuN0Kb7neUC1S707W0Rz4g7FC8uUF+Nn2cO0FOt4U+vSfFfpkq49gu+timAl3zpB3Mb/NTWL/Oz7s17qc//wWI2z2+HtxqYxq4Xh+vhX4RIYQQQgghhBBCCCGEEEKIsaEHEUIIIYQQQgghhBBCCCGEGBt6ECGEEEIIIYQQQgghhBBCiLGhBxFCCCGEEEIIIYQQQgghhBgbBzKr9j3fgm2TMTIoZA+vbgeN3thcZOtv+Byk18fPDGK09+B9BBk62RTJ0CQjY0A2vTQzi8hgq1jE2PPwOAsRHwe2Ic/MDDfovu7RZ2IyRMooLpXIQJVNvqiv+2Ssk+NBZAkZxgQBDo1seL59L8c9aMxM1Gcs7m2Zfl+4iGaoU2Q8VKig0RcbyJiZrW2gcdullVcgfmkT+/v6MhoFTfXQzOjEPJnPHUODl1qIhuVmZsUC9jeb7YQZzoWIjDeX28cxXsLtHTmKZmNFzzXJOncaDa8jQ9OncIDxTTK3TskofGoG2xiUsV+mT51z2jBz4gGIE3JO7A2Nv7aG7IHS1Rtiba2xY1aZkVEtmyslCZr1zM2g4Y+Z2eLiUYjrdeybAZmaxmQdFhUxTmM0Orp2Fc0KgxyT5cefeCvECZ3fzCNDdDKwP07jpbKOxkcrq2gge/Y8jlEzs3PnH4W4WsO5sr6J/dBqkQE6jeMamTP16Jg4l5qZdcmElHN8ussELIr2b7h0Lwh2uc3yOOv3cD1c30DzrLlZNEA3M5uhsbhCRomW4HxrkHHi6iqadrF5XxThOGODQjOzdhvNUT//+c/jG8hhl43cz59Hk/tCAXP6lStXIF4hAzwzs2oVc/DRozgf2Yi9RUa3bPDLhpJVMgFjw1Ezs8ceewzi+WnMly++sGVAyUZ2h8GxYyet19+aOysZnq/WGp5zv4Dt65ORtJnZgGqOdBPHUbR0CeIy1TRxE836og3MC73LuBad91xz8OKAjJ9TzAXLCW4zWMfj6KK/p9kq5pp2C+dSMXNz7nEyQS/2sQ3VOs6XtIbjkHN0l0zSl25h7XL7Fs4FM7NHH8G8PzuLBsnLw9qDDdrHzeRUzQaDrT47cw5NmcsTOL8W5rAfN267c7w8heOyk+A4jj3s+34D+/LIIpnp0hy+u4r7nDvuGvndWMZBU6ccfuTYEYhPHD8DcRChwWFIps9vfcs7cH+30AjZzKzVxblz7Bgazd5awjbeWcLaky+01hq4JnAunZrEeWHmLCs2O4Njrt3cqk+9+/BduMxe9ed2jTGx4by+HT/hnvPHHsWa5smnnoJ4kQzMJ6huTslIc7OFeWmJ1uybt/F8mJlly/i3/kYD4pVNHLvPPIOGk1/86nMQN8gslfN5teYauLJpd7mMY9endZ6ve5kgoPfThevrylcZ/XtIdLvdHSNkroeaTbweZUNkNj/Oew/XXfx6pYJz9tQpzLd8L2aS5nSxiHnJzL3XwWbSbGbN55NrKq4tuf7lz5uNrpV4G6OMpdmsmuNcI90Rbdh9XIdtVu15r94e8tnQnO4TZWTwm+bMzyzFv3F/tLr4+vI6vj4zjblwndaW0iTmkGrFreuuvow1z90G1pZ3VvAeUT/G+VapLUAcTdSxDfNnIW713WvI1TXMyRubOIdLVbovVcT51aIcUJ/A929s4P2bmSNnnDYkZezLzvLLuI3hdVBE1yqHQZqmr3us582xmK4xMrr/nAxwHq/3sX9PHMV1++ptNKv+3Bd+D+KZnPvV87S+VQLMiT7dR+h3MP/1B5hL0h62OaLt5Vl8899q1SmIC3S/c4PuERQzfL1UxVqk18Wxfuoo1o5mrqH11Rs439Lh/bIs5xr4tdAvIoQQQgghhBBCCCGEEEIIMTb0IEIIIYQQQgghhBBCCCGEEGNDDyKEEEIIIYQQQgghhBBCCDE2DiRGHATBjk4fez6wXqDvo/YUaxbmwdsslViXEF8PKJ4grT7W3sxSV3VrkrTZyGbCsoy0junZDWuVsQ4m6wfm6aaxFJ/P0nwktTVF+vIF0jGNSMevSFpdQY4GGz+T8sjLIgy2Xo/Cw392dWNp1frdLc23iWnU99vs4DmeKqOmYOSMIbN4HfVPkxbG58qoXXn2LOr6zkSoVzw9ifssUBv8BM+XmVnSQQ3ADdIxjAqo4VmsPowbmCCN1jXcHuuoh4E77pIBauVPVbHdy7dR878wiX3fj1B3dHEedfgqddR6v7PqattOktYs6w/fL5Zu39jROHXkAimulHBOnFrEc2dmNjeDGq6FEh5nrV6H2Kd5luCpMt8jfUHSvy7XXG+UiVk8f9Vp3OfGKnqhdJoNiL2Q8grnTtJIjEmD3czM9yoUY35kTeb1DdTi7LVQz/rsGZwXrH9+O8cXiP2LyqSfG0Wv9l12yJquu8eWH+AYKFIum8gwr+z2ttiGtZzZR4K1NVdJj5rXr1deQX1W1hY+ffq00wZHk/6ZL0G8RHqd9fo0xKzTe/36dYhffPFFiDn3mZk9/PAjEB87jh4RvS7mX9Yj5vrlwoULe7aRvTnMzAoRtot9Ko4edT1VDovZ6ePWH3p0VSjhDajeIPsoazSx78xcfy/2Zem2cC0IUqo/Bqj7G7JnDsneFnM0XYs0tqeoLmq1sN13EmxDRL5L/Ruox9+ew7wxd/whpw2lCao/2uRLUcBxVqD5Gvi4Jne72C/tTezHW1fQK8jM7PzZxyGeJK3bbWnoLK8sHCPVasnieKvPa1VcM59/7nmI3/3Wd0Oc0HgyM4t9nKOFOs63MvnB3b5FnljkL5b0cfx06VpgebPhtOHOBo6padJNn5rH3FYhLem5OdbQxtq0NoGawOstHA9mZkGRLyCwX86cRK+nxhr5sRRwHszO4j7b5GFw4gTWx2ZmG5vosVOp4LmIu1tJpHAf/HBiP9kZKx6tmRWq3d/xtrdB/M53Pu1s7+FHcG3hNXb5FfRtWb3TgHijg+fn1g1cDwsZ5tK5CVc3vU0+A+UavqcS4Ti7dgd9QtiHokTa7FNT+PmJqusRUSJ/kzytb3wDxXQhzNfFrp1HjtEDv4m3+ao7yN5tu8fU6/UdrwD23eLaYT/a6lyXcV9zHc3+HQzXO/x5jvPayW3gNvI++J4Re0SMHD+W75+x1zY45r5fX0dNdfbKKJfducd/45p4dxv3c0z3FM/b2WeSYl8F5PfpeEikOfOL/BJ5GsUZHvvyJvZNaQLXv4UHMHceP4N+O3euoZeYmdmFi/g3r4zr0+nz6NFz4jw28iitV0XyjCjUcA3OzK01mm2sHZo9XBM98mgMIuzr5ZuY4ytUF5x5+Ay+PoNemmZm/YBqwwHVkqWtew5Rjr/LuEnSZOeaief5qLzB88fM9SYZkN9CQvu4fAt9C5rkrfbIm56E+MyZMxBPF9y6ZIHu0UR0j7tE93Q8ug5tkgdyWKJcQsfdabv3T9bJw7FUxJp1oor3mXpd8l2m+7oD6tfpOfLyovxoZnZnBWuFzRbee4yH5/cgHiH6RYQQQgghhBBCCCGEEEIIIcaGHkQIIYQQQgghhBBCCCGEEGJs6EGEEEIIIYQQQgghhBBCCCHGxoEEOtMstnSok8qaXawm57HvAfkcmLl+CqznyFphHHsxalAVSQM27aOeXa/v6gmWJlGbLSQdbtfbgnUs944D0vlO87wynOMmrT467irpDg+6qH+WUbcGpDefxa52lyutiW2Kk+1/R3t93GuSxCxJthpYLKIOW2kC44T0VG/fRi1UM7N+D7UobYBabPOkr+rROV+ooyZhnTR0vSLqpzbXUUPNzCxrkZ6mh/rSqYd+C3EBdQ031vG4pmgcl0qoHdfbQO1LM7P1xgruM8HjnF/ENrD+3O0uvv+Va7cgfu9Z1O8/fsLVQO+TljjPv21d0UNW1rTBoGP9/ta4yEhjOQiwNWeOomeIdVFDz8ysvYHna3rmCYj7/TrEMWkaWoBjqjaJ8QR5hhw94WpKzsyjJn1IOpaTpN958fkvQtztNLAN06gn+MDDb4b47hLqYJqZ9bqYezLDnJwkeNy1Go7jUkia2kdQj7lQxvjWbdTyNzPrkW53SvPfwlf3mec3ME720pDl9a9C3hatFupym9mOz8nOZ0jPtkNa6xcvof7qs19CPwfOp+02aqJ/+ctfdtowN4fnZG5hEeKzD5zds83XrqE2/+XLlyHe3MT8yvszc/umR/sIR2gqd7o4Rlib+LnnvgJxjrqulUkT9L3vfh/E8wuu1vphERRLFgzHV6GLeb5cRc3cOnkiHTviFnceeTqsbzQgbiyj18uA/GiK9BWZ2Sqej/IZzFWNNfQN2fojtYmmB2smd2k9G6zh6/0G9oNP3ie1SXfcxV3chk+6zAlV4M0+toHna6GC+a/QxVpmbQV9fszMXnwRx2ZUIJ+eodYt+7qNm9RiS4f5f2EB+67/FcxLd9ZwTT1+CmsTM7PLlzHXV2bwOAPK5XXDPBTRmlqm65NSB3X4vZK7Nqwso9/CrbuoBz+3iLXC0QU83+UK7nNzBfNUm65ppmdxHpiZpSF56lzFuTFVw3r11AmsC6amccyFBfKGKuEa9eDDri9QmzS0r1/BHD4xsbXGFknj+DAIkq3/zMwKPp7Db/yG90P87R/6dogXj2FfmZkVydclpuvMBnlqLF3HOnl9gMluba0B8clJnJezNfecT05jzt4kPf4pH+fX01/3Xojvkm9eQEZA7E9V8Nyc73yrMW8R3OPljKp89l90JafdWslz/sb641sxe5ONm90eC6ydzXGeHwPD1+fsx8D3Ldifgeta597KCG8Fs9Fa78yo1517DjFfG7j3HthXgtvE23S8E6nf1kiDfXER14j5ebrWM7cO71FNvbvv+TyMmzRJLRn64CQJ9qczhuheVZTju8WeqX3qf76mbA2wv59fxn2cOYr+DGdnsH8uvYReUWZm/SLWXUePnof45DG8nghpTZ2s8/0drtFwf8WCe9+QvTD4vlib/GmPLmDtOLuIa/DiSczpRzIcU51uThtC7NuXyOfHhm0o3Ifvm8dx/Jr+LaN8WvI8IgLnb3SS6J7M5iauuZe/9AzEFy68DPGJRbz2qk66HkhX21j/1emaZIL8vqp0DRk5XjJ035fuM3XM7b/L5Kd4ZIFqtwWcT3xdm6S4jy55S714GT0gb9xArw0zs2tLWL9wXkmHeYU9LvdCv4gQQgghhBBCCCGEEEIIIcTY0IMIIYQQQgghhBBCCCGEEEKMDT2IEEIIIYQQQgghhBBCCCHE2DiQUGIYBhaGW3prrNc3SlMwTx/Q/RN/Bl9lLSqLUZ+u3cE29Umbs5ejMThBus0F0iyNB6RTSBpblu3tmcBNthxtzZh09piQxDTbHdRfbTVR37NdxNOapaM9IjLWeyRNtmiopbitN3iYzNanbdDbOi+s7Xn9NurisZRcyXeHeKmK5zyjcxyR3tzaMnoplMqol7pJ3gqFGvZRmLjnd76MGoCTpP3s1VGL8uY6ejykNAaCOmma0+tpgrqVZmazpHEdFEhntoX672XSwHvTI6jF+KmP/xbEX37m87i/U+ecNpRn6hAnpAvrH7Km5jbnHjhvvf5WnyU0x5MY88r0FI4xn3wvzMxuXXwW4tkZ1CScn0P/jAH5FnTZMoK0TY/Q9o4eO+O0IYj21oWtTKKO5cJx1H4uVHHcLx7HfbD2uBe4+p53bqI2Y0ZzJ/Lx/E/PYptmZh7BNhw9BXGvQ+MncteduI1i8QUWK96VL7NDTnferu8GOGsmi5dSuytlV9eyQ34Y3Q7mgSJ5yZw4cRLilbuY+5pN7LuNDfRnWF1FjV0zs0YDc1eXPHoeOPcgxO0m5p2LFy5CfO066o17Ho7j1qbrlbFG7Xr+q1+FuFwmLxKKWQeYtftZ1znPS6nv4XHfuY16/v6w/vE8z2ZI63PcZH5o2fbc82l9NBxXfZpjpbI7xwIauwuklzpPa+jaKvbfzVtXIF5q4xg6Usd8138K1zIzs9ZlzMOFZfa9whzaK+JxVRewH0rHcf2LSUc2zfleT49q2ukZ1AL2CviZ9R75VmTYL5mP/VapYL+2ac02M3vxpecgnphAvfjtms73DjfZzc3NWDKsjaapDnjPe78O4rVl1LKdOerOj+t3r0I8fZTrG+y7yWk8NxPknbAQUJ1Ha/J6C+tuM7NujPmVde9v3MTctbaCerunTuOam5Imd2MN91mjfjMz65KXRX+AxUNC1xvHj+NcKkziPGk0sMYulnDeUCrc2gb50i0FuM1abWsuFQqH7xFR6AUWd7fqyvMP49rz7d/2YYjPPIBeWyvrDWd7L11Bb5KbV1FXOW3gZ9apPz/3HH6+RH33tm/7IMTdKZzzZmYbLTzHm208x3zte/4c+re99724jn+V1kfW8WZ9eTN3HR7N3j6T7vbfuFPcdruzkXsbH+xTMMrPIQ/2QmD4/Izyc+BrgVE+FmZuu0d5Oox6nWPeZ57mPHtEjPKM4HHM54LruAcfxPwwNYVrRN4++PzdT4+IZrdt/cH2+o45omC4vkU0JZIcD5b+AM9Ri/JOFOHaEBexP5sxfr5QxXH34BG8B9HNud9UIb+aqUncRq2AfeyHOG7msIyzsID7SNj/luoAM9fLcpLWsfVNXIOtgFr+nCl9ukeUhXgNU6266yRZkRpZRlhv2NXJ4Vp/mdnWPU2ei9vwHOBcxR4rZq/6DuxA18JN8gBcurUEcZc8N9cbWEctr+B17lQFx6HZq/dAd5rg3K/Gs7p9r3wnplxToDFUrtC96Bx/yrt30Fvvc19CH09uE9+vHYy4z8ueXTyfzXJyGK0l2/d0DnLvRL+IEEIIIYQQQgghhBBCCCHE2NCDCCGEEEIIIYQQQgghhBBCjA09iBBCCCGEEEIIIYQQQgghxNg4kEeE77+qwZ9lqAvlagiSbtQ+NCX5PXmagLshKU1LSdIuJa3/OHT1+TZIM7u8yW3aW2OQtRZZ59BIFzpP92swoH30SMfQw34JqG/bXdzH7U3U+yxVcPuFHM079r7wjPQZC8N9ent7YoyDXnPD+t0tfV4/RMG7Dun0lop4bJ7nDvHpOuo8lkirbdBHPbmjx1G7jR0A+m0cQ93BKsTtxm2nDXMnFyGOPBzr/Q6Ns03cx4kaaudvksbg6irq0IaZq9c3dxL19aMqbtOv4Lleo3H10JFjED/15rdAzPryxYIrVhjxfHBywtY43Bp9h/fcdH7+2I7GYUja0glpRZcyPL+V0BXHa/dQo/7GRfTPOB3hGFtYRI+QdhsFIQd9PJ8zs6j7Xa64OsKB07f4eko5fW4RPUBmF/B8B3Q++wP8fJW0yM3MikXUT45JDzKKMEeXC6jzPb+AGtrVKr7e2cTtT9frThum5nDuOTl5l68Ma8aOn93HTxqUNP5Z55g1Kc3MSiXcxiZpl/qUF3zyMPLJY6dHGvYx+Q3lWCOY71O7MoxX7mC+vHbtOsTLS5jLIh/PVxhgG9McXdl0gH1VLuB8mijjfGmQFvjSNfRz4GqGxxB7SpiZJVTPXHsFNe1Pnz5hZmZhGNmHv+tPOJ8fJ34QmT/Uxk3Ih8Avoa9Bs4U6pWlKBjZmVibB4SIbXaU47oplPIenzp+HeOkW5tgv30KtfT9Hjnahi/uskydEHOF86h3F16Oj2A+FOo7bJCR96iSnH2qoNVuYwHERkIdN7FMd0MeYvTfYF6abuPVOp00eAQn5TXlbOS49ZEOc06dOWTb0QEho7TldpLVnFtfciVlXw/f8Y6jnPchIv5rWq8IkrrkB5Tq+pqlN4efv5NR18wuoBd3fxIQ4Sfn4ziqO40GG9ez0DNYBrTbWYFlKgtdm1u3gNhbmyCuDdIEnp3AMtRP8fJbh+6fIS2r9rusL5JEv3cQUnq/isI4vHPr6alaYmbSktLXf2hzmts8+g5rLX/gq+qu0N1z/oRlaS9rXMD8+99wXIH554ybEV5awTu6Q59+NJfSQeMvXv99pwyOPPAEx+9V02jh/+Nr6zJkzEPP6xZ4R6yu4ZpuZGdUfjg8DLwH0cb7+z7tn8EbZntOjPBbuNbvryCJdo446zry2jmo/ez6EpG8+ap88PrpsUpfzHvYjO6inB2vK8z7z2sB68nx/ZtQ9JH7/iRMnID57FtchPndmbrv38roYx5jei1Zzw3r9rfYUI/LkoPscBfJzSMy9nkipP4t0j63bxTEwIJ/WPvktdqvYH50Wxo4vrJnNTeM5mJ8lP1Mf98GXH8Uynq84wTWVjUaTHM/HuSN4bXv+DK77g80GxJsbOE59H4/z6qVLEE/NY/6tz7rjptvGa92ve+878fWhTyj7SR4GSZy8er1DeWDUfMm9V8zmr7S+jfKf8ehamvNjs43eam3KZWZmZfITrpCfQoXuE/F9Wt/jcUl5gq6lY3MvagZ0nD3KX5zPOD/G5MlZqeI44/sdWU4O4PPnrNvDwzjIGqtfRAghhBBCCCGEEEIIIYQQYmzoQYQQQgghhBBCCCGEEEIIIcaGHkQIIYQQQgghhBBCCCGEEGJsHEg8rNPp7GjAsU6U76MeFmtNhaGrBRoGpNPVJP1TRysMY480KaMq6pCyNq4XuXpXfdYWa6I2GOtcsfZYEOA24wFraGPsB6O1Fvt91PUq0eOi6RnUaC2QVlncRh3Tbht1w0o1V786Je0w9owY9Lb6JUsO/9lVqzOwfnfrGCbqqMU9OYP6rAn5bVSrrlZ+IeJhj+cwLZBuGsXtBmri9nnc0jiNCm6fLa+gvnDSwzb0M9T+zTKcP1GB9eFx7NcmUVu4HLneJCvreBxHJlEvtzpVx21M4zb9CMfd8XOPY/wg5oRi1dV1TmLuK3ydPVoOi063v6O3F7dQP9DLcD7Vpth7wZ3jlSKe38kS5s/NBmpEzlQfhXi6jr4GgwF5iJDmIecQM7Mg4HZi37PvhJFfAOsLlkgfdEB65s1N7Dczs14Pz2c6wHayBuvsPPqYTE8fhzgjXcWsh/rn8zM4ps3MqvMPQBzQOuLt8iDg/P61BGvw5mlr8rpbJB3LTfJCuHVrCeJXXrkM8eoqakP3aczk6UKyFxTrWDYa2Ib1dTyHfJwRHZOzzxwZ3iTG9axAiYZjngsb65jjm0302mB9/TwvqDjGbU5M4tpVLm/lU8c35xAoRkXzh+epSR5SCVuoxHjs/Rg1zc3MykXyYyD/hG4DddHTGPNAZQZ17Y8fQe3mlZsNiFsXXJ36wgauN9UY5/KmR7q9XRpHOAytkaL2e/M09hPJ+W+1gcpen/yDUtJALlG6iXs47nzygvJp3JaL6H9lZtZNsBGbm1gfZsN1gefAuIkib8d/wEtp0nrY5pjq7jhzdZtnSO//2rXL+BlaM6tzqPnbJr+xlEzn5ufqEPf7R5w2LE6jj9Ld6zjOvRSvL6bIp2J2AWt71l3vdHE8FAvuJdwCeSBN1+i6icZDsYLbKLPXnpHON/XjqdM4N83MVpdxriwazmeLt857FLqa6+Pm7GTVusNrwfc/gF407Su4/mVdHHeNhuuN8Ill9Hy4vorHfnsVa/3bDfQ8apLvUkrrxKc/h59/+fotpw1/7s9/L8T1OvZ3nzzoShXs92qGa9HRo6h5zp4R16+ht4mZ2SsXsYZlTySXg3lC7KfeOWwN/v2y26OB6x32Phh1z8HMraFG6azzPYZRmuqcd16PRwTfI+Lj4s/z+0fFZm67896zG9aG5/fPzuK84ftY/Pm8v7Efx+42Hvb1RCnydvTxM7rWTjKquSbwOAqBu7YEtC63uzyO+JoQ+3eSFpdijLXIc597CeJy0b1vODmJf6uWuaZibyiqRftY2K1vYL72fNx+4LteJyfI49Po+sArUT916F6k0Zyn+4brWDZYseDmgGIJ/3b0OF4bLy9t1cR+znkcN91+z7pDfwKeD5zHeU7kecukfK+CPSD4GpHm7ShfCqaXua93ybup2cLroEqEa2qd/Nus4t7/2k1I130J18Rm1mEvC/JkabZwPrGHhD/CW4P9FScnXQ+y0ev01j4GOf4ur4V+ESGEEEIIIYQQQgghhBBCiLGhBxFCCCGEEEIIIYQQQgghhBgbehAhhBBCCCGEEEIIIYQQQoixcSDxsMRSi4faWYmRRhdr3wWoI8UaymZmLAVWRsl5Cz1sXkb6VfwUJU1RDyumuJwjRxqznntKjSA9rMAnvVXSEovYM6LP2mSuhmFAfRVSXCmSDjBrupawzac91AnuZfj5ao7uHsnhWky9u+3HUSqjpu1hEJQqFgx1DuOMfEEKeOwFnzw5crTe2k3Uk26TtiUbFdR8jIvUf0mPNCJJf7VWcH0q/BjHZjdBvcawUod46Tbqyg5Id6+yOAdxSjrqg8zVptxo4tivtjCeWUS949o06hezpp1H83N2gUSzc/Q1WQ3QJz159p45LGbmZ20w1OrtkOdKTHqpXopjjr0YzMxqs6jfOHMUvQ+6pGF459LzEJdqqEc9MYvnO6Ge7K+7/gyeV8c/ZKTdT546FtI2yc8hIa3o1RXU2tzYQL1lM1e7MSqhJmuJdBVLVdT9HvSwDY1blyFuN1CvOQ0XnDZ0u9huPl+e9+o4DsPfPx4R+3lPrUa+BOTJEZCeKGsD8/auXr0K8dqaq9W/sdGAmHUpS0XUn2b941Ha0Px6no4vezawbmmlim3gfWySD9AoLeI8Hc1RvlrxUMPX9w9f53qzsb6j/Z6wtjO9t0hrU8zeCmbW6qN+athHfdWkjf1ZpnU8oNK0Qv5ex4qoXRp3XT3SY0a5hF5POphLonXS5u7h2O/N0z5O4Jha33DHfhSRB1LI3lvkU0E52YsxjwcDbNOAPHK8CbekP3MOdfBjqkGXV+4O2+bWheOk1+1Ymm31aVRkIxJ6b4fGk+eOuQL5kpRoTBl9Ztv3bBuu7WtVHD81uoCo52j+1sr4twqtyxvr6DHA68uxo1hzrZKefLGK9Xez7fqzTJLX13L7DsS9Ln6mUEa962NHcLy02tiGlWU8F0dOnXTaEPrYt60+iV4Pts5VFB6+fvWt1Rs7Wssf+81fhdeSBh5bj9aqu6nrTXK514D4OvVXj/JMKcVjrtHaU4jweqFENdP3fOjDThu+7m1vhvhXfvM3IP73v/HrED/5CPq5vf0tb4N4hn0Iaf0sFd2LaX7PhQsXIO5RrsvzctoLXlPz/aj2rom2X99P7XQvmZyc3Kl7bt1Cj49nn30W4lH65Waj/Re4BuKYazDHL25EfWPm1vL78fDYDddgHPN4yvPdYn15blOZ7lfwNtj75PHHcV7w9rjfzNy+36sezatNx0kyiC3pb42nlO5FTUxiO6fp1k6dJO7NzArkC3mnQfmQLpd6VO/MTOE5ni5hTrjw3EWIH3kzng8zs4kqbiMgvwXH62mAbfZ9bNPaXfKISPEgqlW3Lpo7ivlxcwmvY5fJe68yxT6jeC7KZfJRoAKo13TXncijWoCuUSzz8N9DxPO8nXkwyleX84SzTpjZgL0M+H7lCD8inqMccxvyrvozquXYA2mD5hdfOzep3lyYwXs4U3TftpPjy7O5ieeYr0s7dE+GT32B7q/xueE2c042c3MY91023Id/AD8c/SJCCCGEEEIIIYQQQgghhBBjQw8ihBBCCCGEEEIIIYQQQggxNvQgQgghhBBCCCGEEEIIIYQQY+NgHhHp1n9mZhk9wxjEqDXV66FeVpC5zzyCSdRuKxbIX6FHOoYkDJaSZle7i9piA1I3DlyJQYsHrL1GOrOkhZnleF3splhALbhyEY8xTV3txSzD44wTbEO1QJ4E9H6ScrcamW34CelD9l3tsYJPel4k75hs6+Cne2uxjYOgWLJgqEPWJ28F1m0LPDwO1jwzMytQf9YmUNc3Ix3E9U3UHKwEOAYqddSrHpA22iBHDLVaQAHGQoiDs0M6heEk6aJX8POdPmneke/F9Cx6DJiZzRbRpyAoYD8sr61DXJtHvf1ihUQlqV9Zny7JmTsh+XE4phH3ibn5BYuH+nmdJup091rYt91V9O+YIT1IM7PaJGo/l2sYe13s61uXvgTxnRsvQnzs7Fsh9kt47ryCq2tZpFzW28S50Vi+ATHZBVini8c9OTlDr6O+cn0ix5SH2tXt4brRJ633zirqDDfvXoO4dRfbbEWaa7PuuRhE5PlBWo9x/GqOZo3Y+8l+9Y8PQkAakIuLqBc+N4c6lm9+y1sgXlpCH5CLL7/s7OPll1H3dXUVddLbLVy305T0ikl7M6Y1yKOk4eX4yrAWcInW5Q7ly40NV3t9NwGtl4MY25inrcl/q9VQC3xyom5m92fMTU1N7OhCe4a5iDVb2XOqWMY8YGY26JCmK+miD1LuH9JiTknjms55NMC8MZ+4/T2R4Djq8HGkpF0b4j5LU3h+/FPo89OeQy3+vK/1dEmP349xnEWkr0pNtl4Lz0W/SefGx7xfrLj64lEZ+/b08dMQP5Ce22qbd7jfS+oPepYOzyvZwVlC579Yopq46nqVxTQHPbrmKJI+OKeJiUmsqdi/odPG8bMwQx5YZuZRbV4hPetBjOeiPodjqtPBWqNMvhRz5AXWJn8CMzOPOnN6BuvTNulNV8nTrML6/yn2iz+L/ZgMSJvazKIinotqleq8wdb5C8Oci7Ixc6OzYa2h79fNJfTPyMg/qpng+dz03PnVHLjXGLuZKmP/16uoBV0qYX8n5LX2treif8Of/uhHnX3cXcM19fIrr0D8Cq3LS1exbnr2i1hvPvrooxC/9a1Yby5QnWBmVq1hLpqZRd30ixexDrh1A9uQpnt7I3B983rqnfvlEdHv93fqzOPHcc6vr2NOZ/3yPG8Ebv8obwTW9eZ6Z5SOe6nEDktbvhe74esL3ie3meshPgZ+Pa8N/JkajUF+ndvE7+eY25CnRc8a6aP8Nw6TXjOxXn+rPSUqKxfo+mxmAs/5TD3nXhfdvxpQd5QqNM4qNC5pnV+ga+XBObwnMTnr1palAu6UfbYyyiMZnY/NPq5X/T7Ov7CF47TZcmvLch3HydoGbrNYxbHKuTEY4L2UIKDrB7q+aNM9BjOztI/vKZIVaTz0HQ1stOfMvSaO4515wPOec81+vH982objw8keD1TLBjTv04Q9Iuhece6UpeOgfWTkMTYgf9qVTRxnjgcEXfcGkTvumk3ysCI/RaeoHeHbk+fdvJs8r6BR3j7ZcJ8H8XfVLyKEEEIIIYQQQgghhBBCCDE29CBCCCGEEEIIIYQQQgghhBBjQw8ihBBCCCGEEEIIIYQQQggxNvQgQgghhBBCCCGEEEIIIYQQY+NgZtWxZ0m8ZUAxiNHkIs3I1IKMN8psSmtmGbnZdsgoLBngPrpkgBinbMTBxhtoFhJFZMhsZj6ZMjmGWT5uc5T9hu/h+4u0Sy90jSgHdJylqEwxbqQXozlPkwy36dRYZ8DO0+5R+EXcZ/waBkxhfPjGN82NhvWGRrnHF9AgsE8GhRGZQLfaaO5iZhZFZDJDpjOpj2PVC9BAcHkVDVofOPMAxAGZXXfIRN3MNSSfmERjt2YL2714Ck2c4g5us0+meqePoRlSRIZ5ZmZtGhfnHnoC4turaMCUkvlM5rNZD5nJkfmnn2PgyqZonnf/jL2Qgm3njyBgc1w0eoszyiERjkEzszRAJ6niBJ6f2hya/8VkcH79pWchXrnxAsRJhOMnDtxcN+jiOL575zrEC7PYxrmFo7iBFI3DyiU8zjjFMdbLmXvLN9FsOqVx3llDU664eRPi/gbOve7mMsThDLZ5cgKNIc3MAjJZ5yGX7DLGzTMd/n0NGz7yVxEo7/P6OD2DY2CaDCkfIZNLM7OuYwSNeaXRQEPf9Qaaem1u4vvZ3PH6NRxTFy9dctrQJhPY5RUcN7dv38Y2rmObHMNBWkJLZcz5eYZrbKJdIHPHVnurjVGO0fy4ybJs5xhHGTo2N2leZ64pfVDBfJYGOOdSD3NNJ8H1rIVDxsIE1/mghiaH4ZRrIhnewb9VPezXZgHPR43Ger2IueMqmYu3KTdMmXveOq0WxIGP45ANJBMyvs363C/Y930yMp0ou4aeLWrDzRuYU7dNR33fXTPGSRSWLR2aO7N56vJKA+LFuSMQV0rumPM9XI8GXaxVy9Q3vT7uk9cBNqQvk8F9rYpj0Mxsg9aj1MdxO38U58XCAhrXbrSxTY3NNYjjDdxeqep+l6zfxXE9O1eHeIrmSqmIgzD1aY32GhAnPubf1ZZb3/o+nouE8mGtvJVTwuDwc12/nVm/vXWy213szw1aJxJ6PQrc69hJqq2nKmSAW8XYp0nv08CL6bo1pGuzjz+DtaCZ2Sd/5+MQf+4zX8R99HFM9GLMMzduYC3YaDQgfvEFrDfPnT/vtOHJJ/H6YW4OjdUjMt9sUh2wtrIC8TgMpbfXuMM2EC4UCjvXOu973/vgtXe+850Qc9vy+oHfw9dRHHM90iOD3z6ZnvI+2fzazDXR5piNotnMmsmrmXbDJrdmbj+woTUfB++Dax33ehQ/z+83c82p2eB192cO2yQ9iwc7Zs1TZE599hjWMyfm8djZxNnMrN3F+qNE91IKPs7xaoBxMcTzNVPHNXTqyUcgbuXcb/INc1e3iTH3cObh+WltNCBOeni+CgNss5+656zfxPlSLOJnjpw6BvFEFdfDqmE9kyVUixiO01YDc6WZ2YDW+QFvI+gO/z38NTYeDHbmAecB5705BvAMz5u4j+esR8bPHuWFkI2leXuvYzlgA+2U87Zx7qD7uJRzbyzjNWg5p77s0f1OHuzOSKU28bKXGb9O/ZZz/4Pfw2vJdg5NcoyuXwv9IkIIIYQQQgghhBBCCCGEEGNDDyKEEEIIIYQQQgghhBBCCDE29CBCCCGEEEIIIYQQQgghhBBj40AC2J7nmbettUVaU75Humq05SjHGyEh/bcu6X4lJNy1voH6nQNHUnBv34pazdU5dHULcZ+dXnuPV13dwm6GerxTRdT5qpRd3S/fUDusQH4aPunq9anfBhT3MorJPyAwVwu4SxrMrBm5rVvNPgCHQRiYpcMmh+TBwQqCHunzT85NGNPtkPcBeYukpAk4MYVa0b02apnevHUL4iMnz0G8OI96gWZm68uov9gnmbza1CS9ThrZpL1Y8kmfboDjNvVcbdtz55+EuE4+BbU51C/uxLjNTg91+QqkzxmQvlyePib/hTU6WdP+sBj0BxYP51VMOt1JhsddnULfEr+MmsBmZgPSFi5N4pgqlTAvzHmYl9bv4Hh56cufhrgbrELcbLu6zUGKg2yWfEQefvJNEHshtsGL8dywtnGWkYdPj4TeLccXqISeEOkGzqXrt1/GDaTY91bCfh0U0MOgO3CXuLPzeL4GA2z37tznjMf7yH60gw+Msw3ykOBz7LYK3x+4320o16p7xovHUC91FDGdr2YTdWuvX0e9azOzVy5fhvjuMmq516awTaurOJ82m7hmcO3iekjk6DqTPnEWkA/MrS1t9lFayuNgY33D+kOvKe5P1l326fsrceqOCi/AHOiRbnpQIM8iWlviNsb9pAFxNIu5q/OYu8432uiFML+Jc3mOfJpqpGVbiDAnt7p4nN1NrJE6BXfsDzJ8T2+A4ygr4DhKyCujSfq5sY9jozg5DXGYo+Xt6OvSNrf9GdgTYdxUypOWDWvVpI1zY5KOi/V4mxuufvXsDI6JmTptgz0fCthXHdK/jskfoEKavXHmrrEWkb5/heou0mvu9FhrGt8/HdUh3mjhcceJq6+cxLiPLGPfO2w36wC3e/j+Vov0qcnnJIhoTTaz0Mf5mJBx3ezi1rkK+ELxEPCzwPyhr5fvY19FIea66gSuC7Wy6/9VJf3viGpz9gZyfJpozeXzdYM8kP7FP/tnThteeP553AatkUcXcY3tDbA261It32vjdezygLxqmuihZGZ2+SLWarUJtw6GfXZy5s8ueE3dj6/DqPdsvz7Kj+BeUyqVdu4VsGY652P2Icir80Z5HXA/8H0K9nzYy9cg7/1mbp3CHhHcbm4TH8MoPwe+J2Hm1uecyw7qETHq/Xnja9R7dr9+2B4Rng3MG16XLdK9kCPTdJ8pxbze3nB9CRLyUI0C0sGn+wxZAcdIrczni/w16Hoj6bseEQWusxLcZkb3AT3ypgno/YUU81R9Hu+D5I395TUci5N1vJ6vT6F3nkfX3iy9H1CtmVKNffQMemeYmWVUW3p0v7Q47Hs/OPw1dute8dbYYA8BnnM8f/K8YPhvfA3I43JULmrT9cV+1hpu5xtdQ5xcQ/chNzbdNTZNaJ8j7seOvuO2d5vy+oHPxWvdlzhI/+gXEUIIIYQQQgghhBBCCCGEGBt6ECGEEEIIIYQQQgghhBBCiLGhBxFCCCGEEEIIIYQQQgghhBgbBxIPS9PU0qEWV0ba0FGIumuBo1noalP1eqhL2CWh/HYbX+/0SYuP9KxYFywlzbRBkqOYhbuwQR+1M1k/3NHbJY3WIu0iSHEHYdHV4U1Ye2xAulykRx2TBl5M/hse6dGHHj1vyjkXCe0yJe2xaKgHGRVdr4FxkwwyS4bnfkAaZ1EZNV17MWqdep7rTRIVUNPV80mfmHRg19ZRq23u5AMQN26jzn1IOrMDV/LOphZRh3C1gdq/U5N1bCNpC3se6RzSGLh9Y5led/VYzz76NMRpiOPEp74ueHhc/BjTI604P+Uc4T735LHpeXu34bC4ee2lHd3UlPQH+33Ukp6skh56SPrnZjY9i+ebc1WvjXkno1zVy7Dvr9y+g22Yxr5++RJ5K5jZQw/huD06exTiVot1LGl56JOOO3lnBLQGRJG7vNQm0MOhQKd3cxm1MjvkXZOQB8ziAziGq7MPQZxGrifPKJ3E3Rq+h+0RsZeG7KHoyzq7YD3r+09I46o+Xd8zNjN74sknIOZ1u09a7eyTsLGBawDrno7SHjZztaBZa3N7vuV9dtxEUbRT03E7uS9Y1z1X5528QkKaR6lPdQR5HGWko23kjdAt4zqf1tw2dNaxDYOvov/MdIf0jWu4j7XjdYiTAuaSMCW9f3NrDZ/WgmYXx02vhVrsGRViYQW1hydm0bepMoNxseDmO15TmenpLS8Ff8T77jWFQtmyoS7uNGlJT5D/QkreGetrrn5ujXTsC0U8H2mCc5x1hgOuT6ieIZsD6wzcmmq1gXXXxASeP76mWVpbgnh2Gn0uKmUa9x2sAwo5fjJTZRxzQYi1xd11rB0KtM6nA9S3bpN3BnumBTleGeUQt1EMsfMmhv4Bvnf4HkxhFFo4zC8TtJZMeXhsnIvzcjP/LXE0k+n9xtrQ9H7a3qVLFyFep2sFM7OJCdR/r03N0DtwH4UB9nuR5gqvb33yb+x2sBY0M8uoTu6R7wTXg7xu8DUNs5/6Z78eEfvxm7iX+L7/mvseNcbyalB+D/tOjKpbR2mi8+fz+ovbwHUDM0pj/aCeEnntPCgHrbXyjpG9K7iuO2xfiN0UomCnH2fIRmv1zlWIi+QdVA7dtWVjE+uVYgXncDEkLX66n5ZQHdcP8JyuN/H95aJ7Lc3DIKSxz/df2AeoWkWPwEqEOb8yQWtXle57mFnYwXs+M8dxm5NTeE8gJp+GsEJrdECekMu4/Shyx6lH/rMd8tMoFbbaPSqvjoNBf2CD4bzg9Y3vffAc7nZdzyn2sOF8Vyxh/416P/s6cW7hz+cxypdnFOwdxfsc5OQarg34gtxpAace9nbmtSYcnfdHsZ3/Evaz2AP9IkIIIYQQQgghhBBCCCGEEGNDDyKEEEIIIYQQQgghhBBCCDE2DiTNVC69+lOgMMafukTR3tJMYZb3zIN+/uL8zI5lkTB2pZnwcAbUxnIpR6Ynwp8JRSHuYzDY+2d2o6SZyiX8WVep7P50PolZ1oH6wdtbmimgvk1Jeimkn4XlSTN5zmfw3JSH7S6V8qROxkuhVN75/6iI/Rnwz8pTPJ8eSzyYWcByQCQLEZFMQIH2mWbY/8Vd7ctrY1hwf+IYRXvvg9vgOz/3JBkc6oeIfrIf5fzM0if5DN/5CSlJV9BPyfhnX/x5nit5P1Dde4/3j90/5UtTzkuUMyiH8Jg028dPgEf0HW+zWMQxVyhQnsmZpyFLuFGbnPPF42HU+R31eXNlQjzKdUFIfUs/P/Vp3oQj+t7PORf8U1D+WebunyMetjST+Npg1FgeNZ/3I+HxWhIN90uaaZtiznq1m/1IM4VUi4X0k9804N8IY5hkNO9I3pBU/6yQ5uTcCuaOqIY/xw5JFiCkn2uHZcqxXAfQGlvI6Tc/wIb7vM5z7knwuMMC1RYRr+skw5mT77j+CyjelmQ6bGkmz/ydBd+tA7AfuN7xc8Ycv2fUNjxvbwkdz/aWjvE896fnLAPhe9TX1MUB1Z4sVTTquHn7+e3kbbDMJ2+TZFqcYxrxe38z80kSNSS52O3jvB/STJVdeYFbPirPv5687mzT2/v9fH3BkyOJXa1XlpqoVLn+o2ucAV0vkGwSyx8W+nwt7jTBCiTbW6IalbcZ+HuPU2Y/Ejd58j272e7rSuVwr2P3qiNHSTXtR5qJ33PQGmJUG/YjzTSqVj7o9cLrkWY6aBsO2k95Y3CUjNXuzxz29cTue2Jcgzl1gE+1Ct83MrOAriH5PW5se77O13/u593+4vUqo3WYP8NrDO8jo1p0VBvN3OvMIB3VLzw/eRzS63wdG7jj1At4/tE2h/vM68Nxszu/utJMXH+MzusxyRaxHFdE43IQ4/s5dyQke8RzlveXt41R8najoDLLqYEHibvO8/0Tvq3uZEi+R5fSfV4adxW6RmLJd7O8fnDeYmZuTbIXXqPR2FfvTZEuqPjDDWuIjotijgau+MPLYYw7jTnBaNyJw0ZrrLgfKNeJw0a5TtwPlOvE/UDjThw2WmPF/WB93fW0Yg70IOKwBrIQQgghhBBCCCGEEEIIIb62KRaL+3oQIY8IIYQQQgghhBBCCCGEEEKMDT2IEEIIIYQQQgghhBBCCCHE2NCDCCGEEEIIIYQQQgghhBBCjA09iBBCCCGEEEIIIYQQQgghxNj4Q/Mg4oMf/KD94A/+4P1uhhAjx+JDDz1k/+Af/IMDb/dHfuRH7Omnn34jTRN/CFFuFEIIIe49Wl/FG+Xy5ctWKpXsS1/60hve1l/4C3/Bvuu7vusetEoIZD9j6/Ve3wrBaG0V9xPlu3tDeL8bIIRAPvnJT1q1Wr3fzRBCiK8JfuRHfsR++Zd/2T772c/e76aIP0Ro3Akh7jcnT560y5cv29zc3P1uihBvCF3fCiH+sKB8Nxo9iHid9Pt9KxQK97sZ4g8g8/Pze74+GAwsiqJDao0QQgghhBDisAmCwI4cOfKar2dZZkmSWBjqkl58bTPq+laIrxV0n0+8UZTvRvOHRprJzCxNU/uhH/ohO3r0qJ0+fdp+5Ed+ZOe1q1ev2nd+53fa7Oyszc/P20c+8hG7ffv2zuvbsjc/+7M/aw8//LBNTU2Zmdm/+Tf/xt72trdZvV63Y8eO2bd+67daq9Xa+dzP/uzP2pve9Cabmpqyp556yn7yJ3/y8A5YfM0Sx7H9wA/8gC0sLNjx48fth3/4hy3LMjNzf8pVKpXsp37qp+w7vuM7bGZmxn70R3/UzMx+7Md+zE6dOmVzc3P2fd/3fdbr9e7LsYjfP7RaLfvoRz9qs7OzdubMGft7f+/vwetra2v20Y9+1I4cOWLT09P27d/+7fbyyy/De37mZ37Gzp07Z9PT0/bd3/3d9vf//t+3xcXFQzwK8fuRNE3t7/7dv2uPPfaYTU5O2vnz53dy2d/8m3/TnnjiCZuenrZHHnnEfviHf9gGg4GZmf3cz/2c/a2/9bfs2WeftVKpZKVSyX7u537ufh6K+H2Exp04LLS+itfLr//6r9v73/9+W1xctGPHjtmHP/xhu3jxopm50kwf+9jHrFQq2a/92q/Zu9/9bpucnLRPfvKTO9epP/3TP70zhj7ykY/Y+vr669rv7n3/wi/8gn3Lt3yLTU9P2zve8Q779Kc/Ddv55Cc/aR/4wAesXq/buXPn7K/+1b8K18LiDw6j7nv8+I//uJ05c8aOHTtm3//937+zpv7/27v3IC+rgg/gX8AARV9UBgeRIATX1VxBQBlQCQN0NEWUMZ0QRVwalbiYjbEpcTOFIi9U0qTmBDSOrFmZZYhJCCHXdh0nuRkw0NQMIELSFI7g+wfD5qJcQh/k8vnM7B/7+z23s3PmnD3n+zznSfY8vu3du3dOPPHElJaW5tlnnz2o5eHQt6++ddu2bRkxYkROP/30nHzyybn44osze/bsWtvsq40qKSnJ/fffn4EDB6Zp06a54447DkbROMRp74p1VAUR06ZNy3HHHZc5c+bkO9/5Tu6///689NJL2bFjR6677rps2rQpM2fOzG9/+9usXr06N954Y639//rXv+ZXv/pVnn766SxcuDD/+Mc/ctNNN+Xmm29OdXV1XnzxxfTp06dmQvmpp57KuHHjMmbMmFRXV2fs2LEZM2ZMpk6d+mkUn0PItGnTcswxx2Tu3LmZOHFiJk2alJ/+9Kd73P6+++5L7969s3jx4tx888155plnct9992XMmDGZN29emjVrJuRinyoqKjJnzpxUVlbm+eefzyuvvJLq6uqa7wcNGpQ///nPeeaZZzJ79uy8//77ufrqq2s61nnz5mXIkCH52te+loULF6ZHjx6ZMGHCp1QaDicjR47MxIkTU1FRkaqqqvzsZz/LKaeckiQ5/vjj89hjj6Wqqirf//738+STT2bSpElJkuuuuy7Dhw/P2WefnTVr1mTNmjXWuGa/qXccLPpXDtS//vWvDBs2LPPmzcsLL7yQunXr5vrrr8+OHTv2uM+9996bcePGpbq6OmVlZUl2jlN/8Ytf5Nlnn81zzz2X6urqDB069GOfd9SoUbnzzjuzcOHCnHHGGbnpppvy3nvv1Zyzd+/eueaaa7J48eJMmzYt8+bNy/Dhwz/+H4ZDyr7mPWbPnp1Vq1ZlxowZefzxxzN16tR9BvhjxoxJnz59smjRotxwww3p379/li1bdjCKw2FiX33r8OHDM3/+/EyZMiWLFi3KtddeWyvo39826uGHH865556b+fPnp6Ki4iCWkEOR9q54dTZv3vz+/mzYuHHjw/qO6169emX79u15+eWXaz676KKL0r1793Tv3j1XX311li1bls9+9rNJkqVLl+a8887L3Llz06lTp4wbNy7f/e53s2rVqppHbaqqqtKlS5csX748rVq1+tA5zz777IwaNSrXX399zWcPPPBAZsyYkT/+8Y/FFphDVq9evbJhw4ZUVVWlTp06SXYOKJ5//vlUV1enpKQkQ4YMyZAhQ5LsTFCHDBmS733vezXH6N69e9q1a5dHHnmk5rNu3brlP//5j/Ws+Uhbt25N8+bN8+STT6Zv375Jkk2bNqVNmza59dZbc9ttt+Wcc87JrFmz0qVLlyTJW2+9lbZt2+bxxx9P3759079//2zdujW//OUva447YMCAvPDCC7WeIIMPeuedd9KiRYs89NBDGThw4D63f+ihh1JZWZl58+YlsVY/B0a942DRv/JJ2rhxY1q0aJElS5akUaNGKS0tzYIFC9KuXbvMnj07l112WSorK3PVVVfV7DNu3LiMHz8+K1asyGmnnZYkNRMnq1atSrNmzVJeXp4tW7aksrJyn+f9/Oc/nzVr1qS0tDSTJ0/OLbfckuS/4+PXXnstZ555Zm677bbUq1cvP/rRj2qO86c//Sm9evXKpk2b0rBhwwL/UhxMe5v3KC8vzyuvvJKlS5emXr16SZJ+/fqlbt26NTdgftT4dtCgQbXuGu7WrVvat29fc1MAR7d99a1Dhw7NWWedlZUrV6Z58+Y1+11++eU183f700aVlJSkffv2mT59+kEvI4cm7d2Ba9CgwV6fxtzlqHoiYtcdI7s0a9Ys69evz/Lly9OiRYuaECJJzjrrrJx44om1UqqWLVvWWu/r3HPPzSWXXJJOnTrlK1/5Sp544om8/fbbSXbeZbJq1arcdtttadKkSc3P+PHjs2rVqoJLyqHuggsuqAkhkqRz58558803s3379o/cvkOHDrV+X7ZsWc4///xan3Xu3PmTv1COGKtWrcq7775bq96cfPLJKSkpSbKzTh1zzDG54IILar5v0qRJSkpKatrBFStWpFOnTrWOu3s9hN0tW7Ys27ZtyyWXXPKR31dWVqZ79+5p1apVmjRpktGjR2fdunUH+So50qh3HCz6Vz6ON998M/37909paWmaNm2aM888M0n22h7tPi5Idr7YelcIkewcF+zYsSMrVqz4WOf94Ph51/sq1q9fnyR5/fXXM3Xq1Fpj3auuuio7duzImjVr9qP0HC72Nu+R7LwBc9ekXPLfeZa92X3s2rlz56P6DmFq21ff+pe//CXbt29PWVlZrTZozpw5Wb16dZL9b6M+qk3l6KW9K95R9War3V/wW6dOnb0+9rq73d98Xq9evfzud7/Lq6++mpdeeimTJ0/O6NGj88orr+S4445Lkjz66KO1Bh5JUrfuUZX/8AnYve4BHC6OPfbYPX43f/78DBgwICNHjkyvXr3SuHHjTJ8+vdbTXnAg1DvgcHDttdemZcuWefTRR9O8efPs2LEjHTp0yLvvvrvHfT6JccH+nveD4+ddN1HtGj9v3bo15eXlGTx48IeO/8Eb/Dj87W3eI/n48yzwv9q6dWvq1auXV199tdakcPLfNnJ/2yhzLXyQ9q54ZsSTnHnmmfnb3/5W6w6QpUuXZvPmzTnrrLP2um+dOnXStWvXfPvb386CBQtSv379PPfcczUv/lq9enXatGlT66d169ZFF4lD3KJFi2r9vmDBgrRt2/ZDneielJaWfugYlo9gb04//fR85jOfqVVv3n777axcuTLJzjr13nvv1apHb731VlasWFHTDpaUlGTJkiW1jrt48eKDcPUcztq2bZtjjz02s2bN+tB38+fPT8uWLTNixIh07Ngxbdu2zdq1a2ttU79+/T0+LQZ7ot5xsOhfOVC76sGIESPyxS9+MaWlpbXuuvxfrFu3Ln//+99rfl+4cGHq1q1bc/dwEedt3759li5d+qGxbps2bVK/fv0DKgeHrj3Nexyo3ceuCxcuTGlp6ce9TI4Q++pb27dvn+3bt2f9+vUfan92Pb2ljeJAae+KdVQ9EbEnPXr0yDnnnJMBAwZk4sSJee+99zJs2LBcfPHF6dix4x73W7hwYWbNmpWePXumadOmWbRoUTZs2FBToe69997cddddady4cS699NJs27YtS5YsyebNmzNs2LCDVTwOQevWrcvdd9+d8vLyVFVVZfLkyRk/fvx+7z948OAMGjQoHTp0SNeuXfPUU0/ljTfeEHKxR8cff3wGDBiQioqKnHzyyTnllFMyatSomie02rZtm6uuuip33HFHfvjDH+aEE07Ivffem+bNm9esQ3z77benZ8+eeeSRR/KlL30ps2bNyosvvlhrmTHYXcOGDXPXXXflnnvuSf369dOlS5ds3Lgxb7zxRtq2bZt169Zl+vTp6dixY37/+99/6J+8Vq1aZc2aNXnttddy2mmn5YQTTkiDBg0+pdJwuFDvOFj0rxyok046KU2aNMkTTzyRU089NWvXrs3IkSMP6FgNGzZMeXl5xo8fn3/+85/5+te/nr59+9ZMyBVx3m984xvp1q1bhg8fnltuuSWNGjXK0qVL84c//CEPP/zwAZWDQ9Pe5j1ef/31Azrms88+mw4dOuTCCy/MU089lUWLFuXHP/7xJ3zlHK721beeccYZueGGG3LrrbdmwoQJadeuXTZu3JhZs2alrKwsl19+uTaKA6K9K54nIrIz7aqsrMxJJ52Unj175oorrkjr1q0zbdq0ve73f//3f5k7d2769OmTsrKyjB49OhMmTMhll12WJBk4cGAmT56cKVOmpGPHjunVq1emTp2az33ucwehVBzK+vXrl3//+9+56KKLMnz48AwePDjl5eX7vf91112XioqK3HPPPenSpUvWrl2br371qwVeMUeCBx54IBdeeGH69u2bK664Il27ds15551X8/1PfvKTnHfeebn22mvzhS98Ie+//35+/etf1zx+2LVr1/zgBz/IpEmTcv7552fmzJkZMmSIlxGyT9/61rcybNiwjB07Nu3bt8+NN96YDRs25Morr8zQoUNz5513pnPnzpk/f34qKipq7XvNNdfk0ksvzWWXXZYWLVrk6aef/pRKweFGveNg0b9yIOrWrZspU6akqqoqHTp0yN13353777//gI7Vpk2b9OnTJ3369MmVV16ZsrKyPb4E85M6b1lZWWbOnJmVK1emR48e6dy5c8aOHZtTTz31gMrAoWtf8x4HYuTIkamsrEynTp3y85//PFOmTNnnahQcXfbVtz722GPp169fvvnNb+bcc8/Nl7/85SxZsqRm2SVtFAdCe1e8Ops3b35/fzZs3Lhxtm3bVvT1AHAYuf3227N8+fK8/PLLn/alAMARQ//K/ho3blx+85vfWKaVw0bDhg0zffr09O7d+9O+FIBCHU3tXYMGDbJly5Z9bmdpJgD220MPPZQePXqkUaNGmTFjRqZNm+YFrwDwMelfAQA40gkiANhvixcvzoMPPph33nknrVu3zoMPPpiBAwd+2pcFAIc1/SsAAEc6SzMBAAAAAAD/s/1dmsnLqgEAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIIIgAAAAAAgMIc879s3KBBg6KuAwAAAAAAOALtdxCxZcuWIq8DAAAAAAA4AlmaCQAAAAAAKIwgAgAAAAAAKIwgAgAAAAAAKIwgAgAAAAAAKIwgAgAAAAAAKIwgAgAAAAAAKIwgAgAAAAAAKIwgAgAAAAAAKMz/AweDSBa6H9tyAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-q35q41KNfxH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "4bcbd070-3b7b-452e-feec-ece9070ec0ba"
      },
      "source": [
        "display_images(validation_images, validation_labels, validation_labels, \"Training Data\" )"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x400 with 11 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "validation_images[0].astype('float32').shape"
      ],
      "metadata": {
        "id": "6iIw-0ousl1C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6dcba54d-ce9d-4255-cd7f-6ea3d6489aa0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltKfwrCVNuIu"
      },
      "source": [
        "### Preprocess Dataset\n",
        "Here, you'll perform normalization on images in training and validation set. \n",
        "- You'll use the function [preprocess_input](https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnet50.py) from the ResNet50 model in Keras."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIxdiJVKArC6"
      },
      "source": [
        "def preprocess_image_input(input_images):\n",
        "  input_images = input_images.astype('float32')\n",
        "  output_ims = tf.keras.applications.resnet50.preprocess_input(input_images)\n",
        "  return output_ims\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QOqjKzgAEU-Z"
      },
      "source": [
        "train_X = preprocess_image_input(training_images)\n",
        "valid_X = preprocess_image_input(validation_images)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fooPL9Gkuox"
      },
      "source": [
        "## Define the Network\n",
        "You will be performing transfer learning on **ResNet50** available in Keras.\n",
        "- You'll load pre-trained **imagenet weights** to the model.\n",
        "- You'll choose to retain all layers of **ResNet50** along with the final classification layers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56y8UNFQIVwj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0e9a66b-4e64-40ed-fde0-535ec22ef73e"
      },
      "source": [
        "'''\n",
        "Feature Extraction is performed by ResNet50 pretrained on imagenet weights. \n",
        "Input size is 224 x 224.\n",
        "'''\n",
        "def feature_extractor(inputs):\n",
        "\n",
        "  feature_extractor = tf.keras.applications.resnet.ResNet50(input_shape=(224, 224, 3),\n",
        "                                               include_top=False,\n",
        "                                               weights='imagenet')(inputs)\n",
        "  return feature_extractor\n",
        "\n",
        "\n",
        "'''\n",
        "Defines final dense layers and subsequent softmax layer for classification.\n",
        "'''\n",
        "def classifier(inputs):\n",
        "    x = tf.keras.layers.GlobalAveragePooling2D()(inputs)\n",
        "    x = tf.keras.layers.Flatten()(x)\n",
        "    x = tf.keras.layers.Dense(1024, activation=\"relu\")(x)\n",
        "    x = tf.keras.layers.Dense(512, activation=\"relu\")(x)\n",
        "    x = tf.keras.layers.Dense(10, activation=\"softmax\", name=\"classification\")(x)\n",
        "    return x\n",
        "\n",
        "'''\n",
        "Since input image size is (32 x 32), first upsample the image by factor of (7x7) to transform it to (224 x 224)\n",
        "Connect the feature extraction and \"classifier\" layers to build the model.\n",
        "'''\n",
        "def final_model(inputs):\n",
        "\n",
        "    resize = tf.keras.layers.UpSampling2D(size=(7,7))(inputs)\n",
        "\n",
        "    resnet_feature_extractor = feature_extractor(resize)\n",
        "    classification_output = classifier(resnet_feature_extractor)\n",
        "\n",
        "    return classification_output\n",
        "\n",
        "'''\n",
        "Define the model and compile it. \n",
        "Use Stochastic Gradient Descent as the optimizer.\n",
        "Use Sparse Categorical CrossEntropy as the loss function.\n",
        "'''\n",
        "def define_compile_model():\n",
        "  inputs = tf.keras.layers.Input(shape=(32,32,3))\n",
        "  \n",
        "  classification_output = final_model(inputs) \n",
        "  model = tf.keras.Model(inputs=inputs, outputs = classification_output)\n",
        " \n",
        "  model.compile(optimizer='SGD', \n",
        "                loss='sparse_categorical_crossentropy',\n",
        "                metrics = ['accuracy'])\n",
        "  \n",
        "  return model\n",
        "\n",
        "\n",
        "model = define_compile_model()\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94765736/94765736 [==============================] - 3s 0us/step\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
            "                                                                 \n",
            " up_sampling2d (UpSampling2D  (None, 224, 224, 3)      0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " resnet50 (Functional)       (None, 7, 7, 2048)        23587712  \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 2048)             0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 2048)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1024)              2098176   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 512)               524800    \n",
            "                                                                 \n",
            " classification (Dense)      (None, 10)                5130      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 26,215,818\n",
            "Trainable params: 26,162,698\n",
            "Non-trainable params: 53,120\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CuhDh8ao8VyB"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2K6RNDqtJ_xx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26f30d27-e466-49a4-ab7a-6460300ece2c"
      },
      "source": [
        "# this will take around 20 minutes to complete\n",
        "EPOCHS = 4\n",
        "history = model.fit(train_X, training_labels, epochs=EPOCHS, validation_data = (valid_X, validation_labels), batch_size=64)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "782/782 [==============================] - 569s 687ms/step - loss: 0.4010 - accuracy: 0.8686 - val_loss: 0.2355 - val_accuracy: 0.9176\n",
            "Epoch 2/4\n",
            "782/782 [==============================] - 535s 684ms/step - loss: 0.1025 - accuracy: 0.9664 - val_loss: 0.1789 - val_accuracy: 0.9402\n",
            "Epoch 3/4\n",
            "782/782 [==============================] - 534s 683ms/step - loss: 0.0353 - accuracy: 0.9895 - val_loss: 0.1833 - val_accuracy: 0.9450\n",
            "Epoch 4/4\n",
            "782/782 [==============================] - 534s 684ms/step - loss: 0.0146 - accuracy: 0.9967 - val_loss: 0.1736 - val_accuracy: 0.9509\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYb5sAEmk4ut"
      },
      "source": [
        "## Evaluate the Model\n",
        "\n",
        "Calculate the loss and accuracy metrics using the model's `.evaluate` function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "io7Fuu-w3PZi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b696b88f-f25e-4727-99ba-1e54255d8111"
      },
      "source": [
        "loss, accuracy = model.evaluate(valid_X, validation_labels, batch_size=64)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "157/157 [==============================] - 28s 177ms/step - loss: 0.1736 - accuracy: 0.9509\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yml-phRfPeOj"
      },
      "source": [
        "### Plot Loss and Accuracy Curves\n",
        "\n",
        "Plot the loss (in blue) and validation loss (in green)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1ZMMJ6T921A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "2fdacb0b-02a0-46f0-d374-fffd1b3d4443"
      },
      "source": [
        "plot_metrics(\"loss\", \"Loss\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QbnWIbeJJ_xx"
      },
      "source": [
        "Plot the training accuracy (blue) as well as the validation accuracy (green)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0YpFs3J99eO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "c52a836b-ae95-4815-8fce-2b637bb5073f"
      },
      "source": [
        "plot_metrics(\"accuracy\", \"Accuracy\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9jFVovcUUVs1"
      },
      "source": [
        "### Visualize predictions\n",
        "You can take a look at the predictions on the validation set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIQAqkMV9adq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "2f43ac20-76e3-4af6-dbce-566b4d60b365"
      },
      "source": [
        "probabilities = model.predict(valid_X, batch_size=64)\n",
        "probabilities = np.argmax(probabilities, axis = 1)\n",
        "\n",
        "display_images(validation_images, probabilities, validation_labels, \"Bad predictions indicated in red.\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "157/157 [==============================] - 29s 175ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x400 with 11 Axes>"
            ],
            "image/png": 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98axlqy0v28J999u+n/hXtu/Hf3Tj74XTp90vP0vdp3ZsNwtDS01MtPwFysa9rD+T02ds4I7bN/7erNWsODll3Ve1b4tbRWrHdlu478vWd/NNIF3FPJ/nI4QQQgghxLMhjwghhBBCCHHZNGs1m7/3S+ZFo9a1d6+ZmXkR3zzPs7DZ2PhecXLKZj/9GTh26KUvMTOzsx/6P/D30x/80GWVYepjf2/1fGEjnvnEJ60yO2eDa+ffjNHXv9YqMzM2+Rd/5XzWKJetXiw+77IOvvgui6TTdur3P2CNSgU+u9hYOZJKbng2bEZieMi6r77Kpj72t1ZbeWZDOHfsuM3fe58Nvfylbc/BVLNZ5289V19lZmbNau2yz3cpDN55h3nRwM5+6CNQD6f/tH2d9lxzyJITE3b6Tz8EdWD2TJ0+8ysQ/OXAmQ9+2DlfJJk0M7M6nWvkm19tXiRiJ377d6GM69epLmXNzKz38DUW6++3c3/2FyBFNvU3f+uc84Vm9HWvtbDRsJO/+3vOZ816faM+L+f5NGs1y588ZeXZuc4VXAghhBBC/D+BfhEhhBBCCCHaMvf5L1rh1CkzM6ssLNqFf/i4FU+fsd3vebcF3V1mZjb08pfZ6T/+U3voB37Yxt70BqsuLNrZj/yZpXbusNxTRzfO1XPoaht74xvs7Ef+zGq5nPXddKMt3He/Fc+evawyRTO99sA7vtu2vfVbrbIwb2c++GFL7dxh27/j29oeO/6WN9v0P33CnviP/9kW7v+K9d18o4WNphVOnbLpuz9ht/zxB6z32sPPq6xBd5dd/fP/3h7/uffZl9/67Tb2pjdYtKfHck8dtUa5bNf9j/9mZma91xyy6Y/fbUd++Ves99rDFqRSNvzKV7Q858F/92/s4Xe/1+5/+ztt4u1vtUa5Ymc//BGLdnfZvh//scuqPzOzk7/ze7b04EM29PKXWWLbuFUXFuzsR/7cEqOj1nfLTZd9vkshNtBvu3/w++3U73/AHvmhH7HBl7/Uck8esbkvfNGifX2bHuv5vh36xf9oj7znR+1Lb36rTbztWy0+NGT5U09b/vgJu/VPPmBBd5f13XqLPf2BP7ZmrW6JkRGb/9KXrHRu0jlf7+FrzMzs+K//po2+8fXmB4ENfdPLLbVzh+3/qZ+wY7/6v6w0dd5GXvVNFkmnrTQ5ZTP3fNq2f8fbbfe7f8D8aNT2/9RP2BPv+wX7yrt+wMZe/1orTk7Z1Ec/Zsnt253rvZD033arbf/Ob7dTv/8BWznylA3edZd50cCKp8/Y9Cc+aVf//M/a6Otec1nPpzwza/e+9o02/q1vsev+xy+/QHcmhBBCCCH+JaAXEUIIIYQQoi0nfvO3N/5/Px639J7ddug//0fb/o7v2Pj7wB232+Ff/i926g//yJ76r//dkhMTduDf/rSVpqbgRYSZ2eH//ksW6++z8//wjzb76X+2/ttvt5v/8Pfscy/9pksu0973/rDljh61U3/wAasXCjZwx+126D+/b+NfuW+G5/t24+/9tp3+kw/Z+b/9O5u959MWSSYsuX277XzX91h6164rUtaJt7/NYv39duoP/8hO/u7vmx8Elt6z23Z9//dufGf7d73DVo48ZVMf/Zid+ZM/tcS28Wd9ETF41512y//+Azv+W79rx3/zd8wPAut70a128N/+9HMyRx5+5SusNDVlkx/9G6suLlmsv8/6b73V9v3kj1m0u/uyz3ep7P+pnzQ/Frdzf/4XtvDAVyxz/XV2y598wB7+oR9pe+zQS15sL/rwB+3Eb7/fnv7fHzQLm5bascMmvv2ZF1DX//r/tCO/+F/t7Ef+zCwMbfDFd9rN//sP7XN3vQzO1Xvdtbb///sJO/tnf2FzX7zXrNm0l372HgtSKdvznh+y1K5dduaDf2onfuf9ZmaWGB2zwRffZcOvfObZb//Ob7ew0bSn/+iP7eiv/Kp1HTxgN/3+79jx3/ht+0bjmv/yC9Zz+Bo79+d/acd+/TfMi0QsObHNxt/8Jsvc/MyLp+fzfIQQQgghhGiFl81mt841TgghhBBCiOfJwgNfsQe/+/vsht/6Xzb6ute80MURQgghhBBCCNEGeUQIIYQQQgghhBBCCCGEEKJj6EWEEEIIIYQQQgghhBBCCCE6hl5ECCGEEEIIIYQQQgghhBCiY8gjQgghhBBCCCGEEEIIIYQQHUO/iBBCCCGEEEIIIYQQQgghRMfQiwghhBBCCCGEEEIIIYQQQnQMvYgQQgghhBBCCCGEEEIIIUTHCC71i729vZ0shxBCCCGEEEIIIYQQQggh/oWxvLzc9juX/CLCzKxSqTznwgghhBBCCCGEEEIIIYQQ4v8d4vH4JX1P0kxCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGHoRIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGHoRIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGHoRIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGHoRIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGHoRIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGHoRIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGHoRIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGHoRIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGHoRIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGHoRIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGHoRIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGHoRIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGHoRIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGHoRIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOEVzOl+PxeKfKIf6FUalUtuQ6anPiYrai3anNCUbtTmw1GmPFC4FyndhqlOvEC4FynXghULsTW43GWPGNymW9iNi9e7fl83kzM2s0GvCZ53mbxs1m0znf0NAQxK973euc613MiRMnIF4vyzqzs7MQV6tViBOJhFMG38cfhUSj0U3LeP3110M8PDwMcSQSgZjrga/X6jthGELMddcu5mfD52sFf4fLtH7OIAjs7W9/e9vzXUne/PqXWbFYMDOzehPvLZmKYRzFuhjqTTnnmxjDZ7aYzUI8PTcPcUCJ1ffxGdermOB9+qGR77vdrJgrQJxKdEGcy2HbvbCQgzibx2s2uV15+DwDapdmZmGIddls1CHu6qK6ozYRBHjNnTu2QZxM4X3X6yWnDEEEy1AqY72Uy6tlSibT9ud/cZ9zfKf4kw993Gq11WuHUcwbp6YXIC4tUDx13Dnf1HHMXZ7h8ylTm9p1cD9+3lyB+OGvPg7x9LnTeP4A69XMLEm5LVbHMtz+0ldBPLp9H8QP3P8wxA3DNroyj/k30+32vVS6H6/5hm+DOF+jdtrEa+yZwHxcW16EeH4en0XQYoTr7cUydGcwH1yc+6LRwH7wXa92T9IhfvwnftrK5bKZmU2ePYvlMsxtIeXCctntXw1qZxGqEB6varUaHk9jiTXxfOko5sZUKumUwY/jNRMJzNkRGnu60mn8fhL738IiPvOlLPaNujvVMI9yMI+ZPtWt8XyFcl8yhW07GsHzV6pltwx4CksEWA/x2GqcSCbt/X/8J87xneR1d77GioWimZnddtON8NmrX/YSiA/v3AVxahDHLjOzehnvv5QrQlxewfErP4+fn89i/Vdr+H2vivU9cUOPU4bMELYbv4K5ZOYMtqPTT2L/qTawXYaGfSNXxjImGvSAzaxaw7/NZbEMCzQ1q1BcLtP4GNJcj/p3k/urmdEwb6GH43Ztre0nUzH76D/8mHN8p3jTe3/cimvtJB3DvrBjZy/EoeGzqTfcTr5IeSGVxFxUKOLzKxcx942OZCAeHMTjl+bw+FrBXdjfcvUYxH0ZHHPn89jOm018FsUKPqxcAXPbzgmsl2jULcPEIObPgSSes1DBMXJ6fg7inu4+PCFNLX0fc9/kFJ7PzKywgvPVqw4egDiVXB03IpGY3X7Hv3GO7yTNi/pMvV7f5JtmvHJqtlhLOX9pt97itfLm374keFXptV/ybUoY0pqUPue8Y2bWpBvhb4TtbrRNvTWdEzy/muuLR9t/6QrxKx/4v1Zdm1sNDeIcdmRkBOKVFezzK3nsS2ZmlRqOIzzv8jzMj6fPPo3nXM5iGfoHIA5pHOF9EDOz7u5u529QJp7H0Trao+edpM/jERwTVpaXnWsEAT7DSBTnBR6tUY3muz7FqTTOGSYnz0F86tQppwyRCF6jQvOMi/cHYtGY/cx73+uco1P89ce/bPX66rNs0Pqe9zH8gPaVWvRxC/FePZpLhPR5SOsF3mdqt+/E8aUc4/J8hV/c8/t8363qir4BEe+9tNgf3bwEZj5dskFzw+ZavUSDiL3jzXe1Kd+V5e5/+MpGuxse3w6f1eiFyOc//UmIt+0cd873ojtvh7hA86KRkUGIY3Fs2wHVYKOCfSGexFzz8ENfdcrw5BNPQPwtb30TxKPbsAwN2l+pVumaccw1dUwbVmgxv6yH+Dfe/yzXsV3VaP0RNrDdnjk9CfHKMu6pr6xgbGZ27Bjtw+dwrZdMrs4P4/Gove993+Mc34rLehGRz+ctl1sdFK/Ei4gkLRTKtHjlFwn8eamEi5NiESf5fHyrMvCLgRgtiPiavFHDE9l2SfJf6ouIdhP2TlIsFqxYWN2grjWpvg0nLyG9iCjH3Huv0IZduYTtplTEzfCgufkA/lxeRJSK2ME9msUXi9h2C/TSLd+RFxHYtnky2+5FRInq0Tx+EUGfW/sXEaUSlmmrqNXqz7yIMHz+lSptaNDEs1xyNyHXN/nWafciokTnKDexzebzWE/reXnj/C1eRNTbvIjgfFouYxsr0D3wiwh+MRzlmZKZmeHg69RllTeE8fNqDZ9FjY7nRUCLfTmrVjc/Z/uJbecol8sbYw6PZ+1eRDj9z9q/iAgo5jGz3YsIL8rjgvvM/QZeg/MOv4gInFyGYbtxfyteRDB1WhCXK+5LIZ83aqjuWm0ibxXFQtEKazmlQrmnQf2DNymcujKzkDaK+ZgmTZibdI065YE691Gao/P5Wl2TY76vWoU2mik/8ouIKr2IiLR6EVHFv1VoPCtTf+IXEaXLfBHh9FczC6mLhn7rFxFbTbFctuJaX/aoDZUrOE6Ehm2y1YuIEs3rPOpwpTLmtlKJxtwKj01YT+UKP393nK/SBmGtRotReqHGLyIq1QbF+P0aHe90BDOrNzAX8T8waTSoLukfiNTr9DKZX0Q4L69b1EOVXxxhOV/AVCfEllOt1TbmulVaS3Muq9FYxvPTVn8L6jyHxXNWeC+FNgP5cx4rqzV3HVZrsydQp3NwzC8iGlQPDboHPn71JM5bUvzYeXdFey38Dy6bm1+zVT1EKIfzd2pUt1tJvd7YaE88N+BpNs+BW7+I4H2iNi8aviFeRDzPt7ItX0Tw3PAb90XEC0G93rjoBRj3c5rzVnjO5OaVZnPzXOG2KzzeeSne5guNFusJ3je4lH3V50Or0zv32eaYdjHXY915keHWQ4XmwWWaV/v+5b/gl0eEEEIIIYQQQgghhBBCCCE6xmX9IsLzvGd9+9jureSl/CtTftvT7l/2s6wE/+tOPr7VW0c+hmO+RrtfKzDtpJrMWv9KYrNj+Pvt6uVSfiHR7k31C0k8ErFGsHpP/ST30gw3/1eNi8stdPFClF5y/vVWDeu36WF98k8xffpX3n29+JPUA3vwp2lmZnMkIzY5hT+Pjyc2lzPxcljmGr1VTqSxnuJxVzKF/+VGpYrPfH4R/6Ub/+uGsRH8OS//S57lGbzHaNR9u9rbjXWVTGC5Y2s/F00m8Xud5rGT5zZ+6dCVQukRn+qyTv/icH7J/QlxogflgOI9eD99dH9BiuQVKC/tJnmUmw+ijF1fzM0pmV6UdeA80kjgz68HRvGnht/+VpRu+uz9+FPF7cPYzof7XcmWgCTI4iTtk6N/DcH/+vLseWxT+ydQCmP7th0Q9/e4epX04xPzg2fXtIwE7i+JOonvhRv/2qaQx5+fVsr4L/9D+qVWpeL+y9Q8/6vdNv/iiMcK/gVFbxc+v0HKM7llV6ajRP9iNko/n0/RrxD9QWx32UW8zwj/HJ/+JVCh4P4aYXmZfmlF9eLRPy+Kx7FMXC+sg8r1WKu5/wrO57kDtfX+vtUcwZIGW8ETUyc2flX1xDmUcfjU/Q9C/LJb74T4pS9+mXO+b3oD/pR64KpRiCtlrK9EFp9Z6nwW4ubxKYhzi9gXzn39vFOGp0rYzuJRamceyUUV8Zr1Bj6HOZJL5PFuKeLOmfgXXhWaLgb0B+dfF9Hcg3856fwapcW/GvWM5rA0NKznkTC6tf8uqZbPWXXtFxGNEvbP8xdw3pZK09xjvIVUSA+OBZkMynXNTKN00+TT+CvCdAyfd5NSZ5R+fVtvMTQcPzON51zE5xVJ49zBD/EkRZI06iI12at24H1345BuZmaxAOsuUse6rflYhq6uDMTJGM1vqInl8lQxdbfdTwyivEJXFOv29JnTZmYWJdnLrcC/6N+Xem3WXg6hu97jFR1/hf+1LC8Br8RKy+n1z/Mfw/Jc30k7raSZ+ByXe03+xSedwGNpprZaT8/OVv9b4Zn5xY31xArNT85OXYA4S1LBtRY5vUp/S5J8ZRDgHc4v4PqyUsGcsFzCvBNSizqXnXHKwIoWvBfCv8JP0PjL8mFJmgfy90tFd14Xi+FcsEBqBiGt1WNUT9U6roEzGUyo/CyWSdLKzCxK5SzTPDxyUYdneapOE48GG9evU6N3fn0Q4S+0OCFLL/Fkgg4KaV7t/ELCvQBEEV6s2eXvwT1/3P7n+5vnKsZVPeE9ps1VUVrVFPefkOeKa6eMsDzZFjA4OLTxr+35V0b9Ayj9yPJCvK41M0tQPz+fXcIvhNiPJyZw/hEh6dwuWsd29beXGG7Srwd6elCajveOy7xeN157U9v26BfyrX5pTs88iGC5V+ZxzyCf4/UEtoVCAestGuA8rVZ1pZkqJfrVRAnnmwtra+0EyV1thn4RIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI5xxTwiWn33Ylp5DrT7Tr2+uWYWa3KlUqhX3U7D0MysVELdQdaCZvgc7McQjUYvK251jnZ+DVwGji/3+Es553oZuc63gqHBpJVKq+XJ9OEzrdZYAx01zirlFprJPrarZArbYaWBz2Mxi7prrJPY2416c2XSTJufQ117M1envm8ItSlLs3jNuuF9xlNYhoShHluKdPcSSVeLdymL+nBl0tljPbmhMdT227MXtdyTKWxn6R68ZqvMUcqjhl2zwX4n62XaWq3+ofFtVqmutpMm6XwXKWcszaLnSLHmahxG2vhM8CHxJuaJjI91mUpi3V//olsgrs+hJqyZWSMkz5w49uWzk1mIz80fhfjOmw9CPNCHZYhRbujrxXxsZtaMkkZ2BO/T91hnlsYIagczWdQwXFlCj4KdE9c6ZYiT3mSdNEAbF2lBsh9Mpxnoy1ilspo/9uxCv4unT52AOCTh5u3jqJFuZnZuErX1Wb+WxwZHe5jGq2v274f4hquvhvj8Bbyemdn0POoLN5t4DR5RGlUqYwOfAesh921D/5ViyfXKyC6jb0s2h5rIyzlsR+whwWN0uzE7HnO1gD3ykomTH86N111vZmaJxNbrplvENlJsGMGcfHrhFMQzn8A+9rWHTzqnO0Z/e+sPvgLivTcchnikC/VW6xOo7x9ePQyxV8YcPJF3c25hDp/pkQeehHjhDLbDoV4swyL5UASk5x+Q/nGjhVa+38A+2kXtpuThXMGn3JStUV9h3XT6t0SRFlrCDdYSJq3bDZ8J/nuHGRwILVVeLVy6C8eF4XGca6SSmCV6elz/oekZ9GdYzmI7jkWx7jO9eI5alfyJaLzM5fBZFaqu/1iN+vgyeZZt756AOE5l6k1gG9y7DeeFE8Ok4dt0/agqVdRJ9328jxLNT/t7MX8OZHBcX6E52uLCOYgzvRmnDNwun3oa88H04uqYEIu5c4ROc3Ft1MnQwfF7oLiVN4Kz3mrnjtAB/70WatKXdbzz7ZDX5vhxo1Wesc3XnXySdnsJfDz74zieEZdwjo1jzcxsC/X6PX/1PzMrV7D/8VyjXMX+Vm+691Bt8LwN+7xH82gemmrUYGp1LFOc9L2bCXfd30xg7srncUwmyXNr0HjpU28rFbAMIc1F6+RpaGbWTZ6RNcOLVimn18qYL9lTYiGP2vNlWuuxH6CZWbyB7Yh91WKJZz6PtJgXdpR6Y/U/M/PaeCnwvJzvw6xFfnS6OHs80Odt/HFYh78VHl3D5zmR44O3uS+F52j38/lalIG9L5w9NvcqCPmM8sd8Dy12T3ic4ZXqeroMnJN3nlqttrGO9mgfqVjCva2AJrXxuLvXEyXPm16aq8djtA9LvhhNyiVnz+IcZpeP/pa8TjYz6x/AeVKa/BG5L/i0yec7foyYS1aWaQ1acvNdph/nf9zKlhbxHDMzWYh7umi+RXuZpRLtx7XojuzXWlrBHFpfG7/q7DmzCfpFhBBCCCGEEEIIIYQQQgghOoZeRAghhBBCCCGEEEIIIYQQomPoRYQQQgghhBBCCCGEEEIIITrGc/aIuFSviM14Nv3Gi6+3WZzJZCCenUUt/lOnUNt4bMzV0O7vR92v7u7uTWPWb06nUS+Ly9hOW/rZ/nYx7Twf2NeiXb0+F4+I9bhdWTtCaBtiaIuL5NdAWn0J8k4IWmhbpuiZDfSTHvV51DSvhqjlloihnnEqQF3nNOlrVhuu1ltxBbXcPKrXkQHUL+7rxTJemEUtywXSyq9UsMyzs3hPZmZF0gpukD5jIoF6ll3dWJflGmrD1XJY93Gqp650Cz3gJmpwRiLkEeBH18qytVrCgRdaY01bskL9qVbFMp85cQziRsPVjo6QCURXGb+TSpNGuo9taGoGn2/D8PuffgD9HCLNVt4omJuapJ0ZCfF512vYJj9zL2qsZ7PYBvce2AtxlHTfzcxy1BUaVCY/snnOZ6nMfBk1DReyOAbc++X7nTJUy+SNUsdzVC7S6I3HY/biF9/knKNTFPN5K69pBpcK2L9Gh1Anv5CnHNIi7+/fjZrkDdLZjfjYp+t1/LxUxjIMZbBPbx8fh/jAgQNOGY6cxP7xxBNP4DXoPqrUN2p0W7Pk95CivFKt4fM0c/2mxrdtg7i3iJr0PHeo0fHlMvoHhKQJG7TyFqFxOpXAugzX+mPobT5+d4amrSuNs04v603nQ/SfeXzW7WPTH8d89Pjpr0H8Xd/+Vohf88a7IA6GsF2WfGwTkRh+PrBr1CnDyG48ZnA/5qPcFLbtWA6f8dmj6MkydgrbXT2P+XKlhTdQbgW1ZrMVbJuhh3GePHB88rMKKAE2KZ/6YYt2R2LdDW6raxq+wRWY018O+w4OW2XND6Y7jfOdwMf7KJOGeaWCOcPMrEn1v7yCdRsJ8HmVKLEUSR932zi2l2Qv1k+56M6jU93Ypwd7cN62axzXG/Ua3tcQ5bJr9o1AnIjT2FVsoVlO3ngrizgmpgP8vLcrgycgT546ySWPDuOYUmGfNjMrkfZ6KoHzme0jq2NZNLr1fjjVi1wiajRPcrS4HU1zNzc323gh8ElC8qVw12vtnCpacYX7LntE8Mct64H6A3s8cDXR8Y5npON9iO2ylW4682xr561WTY94oUXWKoAzdJm8ZhyfnxYa7+z7E6f+1WxiXggprtH8pEpjfq3G/jjYn81c/wX222RNdF6SxMh/jLX+G1XMdakWPodGPga8fggolzVoLR7Qfo4fxXqsFfCeimV3Le/TuMJ1h+Pv1u6f+Baav9ae2g3vPC/wWvk1kN4/+05wbuOmzPNk7og87oct1rEtCkUReSe0WUO2ywWXNi2iL7E3Rpt8+lxgjxyy0NnwDeI541aQz+etvuZNsmPvEHzW34f9xQ+wzVQruLYyMxsawvVZVxf2U253MVoflIu0pqR1cJE+P3d20ilDX18G4ij5uYX+5vtn3DXYU4d9DKOBm+/icfKYoXwXi2HDGx3FMqcSuPe5vIx5vVjEfcJai7V0PI51Pzg4APHiWodJ0Ji0GfpFhBBCCCGEEEIIIYQQQgghOoZeRAghhBBCCCGEEEIIIYQQomPoRYQQQgghhBBCCCGEEEIIITrGc/aIaPXZ5cRmrn4j+xKw9wHrPK+soF/A+fPnIc7lchCnUq7O/MGDByHeuXMnxENDqG/GvhR8zdFR1CqOkg7ic/HWuJS6vJh2nhLPhXVviBfCI2J6bsaKhVVN4ATpRHaTBm+ljPdaqbjals06NvvZ6QsQ16ndBRHUL86RH8Ncbh7i3btQQ7c3g8ebmdWqqL0WpXqtN+iZU7V3p1Ar7umn8R6Wl1G3t9FspR2N9UDyj5Ym7eDFBazbE8dRTy4Ww3obHsL+dtuLrnGKkIzhObmP16rrz3trPSLCas3CNY3SgMRLzxx/DOJYlNpLEtukmVlvP+rl9VPd5ipY+UukjxrJDEKcoueZZh3ZS9CZD1tpgF5EmfRUK1XsS5EKahouzpyDuLs3414zihqF9YDzCY0JdBsRRw8U64H7/8MPHXHKUKvhfUdj2LYa9sznySRpMnaYUqls5TWt3aWlLHxWLOB4FlDOqNVcb5LlkJ4hadSzFnScxqvRUdQ0v/lm9Mu45vBhLEPojk2PPvUUxCdOnsYvUDvknM4+IstF1NacpXrieYOZO7eYmVuEmOuSvx8N2OuJ2h35jNSarnZ7gjRFh4ZQW7NvbW4Rj2+9bnq49n+rsKb55sdWyOfHzOw8eWh85mufgfjJp78G8eNPfgvEb/iWb4Z4YhjrJLG0gGXowfxoZpYjvecu8kOJpPGZNsiX6dAgtu2V3qcxnsIyXJhz9VRTdRrXyVegGVKZ2EvISCe7gW0oT82s0EJTuc7eQDS3CNfmAWHzspYDz5v+gV6r1lafa4KeTTWHuQzv2iw09z67UjS2lLHuK5T3wwg27AxpAEcS9Czi2Odj/fh9M7ME6QRvJ43e3WMY53KYh8bIg6cnRXN50lNuFNzOWSavi+7uXoiTNHfILuI4HqTQG6O3G/WZV0jbuFJy59hjVJc5mo8U18yighfg38Jd7IXGed5xZ3A8Ilzae0Twxx1Yn/G86HmekvWsHa+MlmVuUw+by8M7sOcVzwWbLc7A+vBcyvXnvcV2OBa1ujVsNZ/EYzguxX3SGud6C1yt7RKlv2a4+d5IvYrrwYBqplbFCqlX8QJN310rNCkX8dotpEoue7huZj+AaATHgIAWpKkEjwJm9SrlFfYXo3lYg++bfIJqFcxt5RLdozvEO2uOGn2pedHjC1sc30mi/jPrescalPeVKBd7vCFgZj7/jT0dnHPQfIcSE+9l+Xw+/zmMD+xNQ2Xg5Ngu/ba0qeBc1m6PzjHZ2Xzt3c73wsyMuyR7YayPXQEnzi0gk+nbGGfZK2BgAOcj8QTO0x55+EHnfK9+9avwHIO4LnU8cWgN2N2Fc8NgO3oEnjmDexePP/64U4ZXveqVeA1qOHXynQgimK+qVKYirWPXPTXW4TWnmVkuhzm0pw/3LrbtyNA58fgSLRhyebzm0BA9m7ibc7u68ZpzM7gOKq9dtNWxz4Z+ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGB1zp3supsztTLyKRTRAPHcODUbYoImNb6pV1ymoVELDkHQaTU34HH/7t38LMRtiv+Md74A4Hm9vdsqGaQzXZTuT78v93Mw1+LwSBmpXigMHt1t5zfyyUkFDGDZejMfQ7G+l5joNNcpo3BUxNHApkcF1Ko0mlukhNO/zBzCOkAnizIUppwyD/Wi2E4+hGeeF2SWIC2W8j7l5bHchmWIGHrY7r4V7XYPNdsjZpkQmhP0DWOZdu/ZAPDGBRjddaTx/o+aaGnK7K+ay+Lm32me9CPbLTlO3iNXXDKIWzmOemZ88A3GEvbBauFulAyx/cRlNKuvRDMQTO3ZCXKbU1aB23WxuHrcibGPUmGqSiV4MvzE/j23s8SOPQhyk3Gd23Q23Q1zm3NTG8NA1Q8MyRcmgtqcH26SZWanEps54n426a/q8VdQadas2VvvhYhZzQKmI/ae3B01FQ3PHkSqZeHE6XMpm8Q9s8pXB+ush49KeDMZPHnHNwZ98FI2/to+NQ7ySRwOu7AqapxbLmPONDPMiPj7zsIVRtOfzmIj36dFMKBbDP0TJxLuLxoRaHctYoHmBmdnwABoqX7X/AJ5zrb/ELmHO8I2E77mmaqFPeT3EudmZLPaxD/75/4X4S5/9EsTf/4Nvgfh13/EiiL0mjulmZsHkPMQrs9jOmkt4TEhlqpHBXaoP5xax24Ygjj4645RhLotGbj4ZfE6T0WVIzz5Xxe9XOT2yEXULU8MImRWyyWFjbW4Q32JTw3q9brW1OUeCDAuTZHTXrGChi8UWxuCUBxpJ7MNNmvdlevEaPb1UTzQfKq7g+sOi7vJpx74RiPfuwvzZ24330U3G9KO9PRCHNWyT1Qa24Vo+65Qh8LEN9aYzEOcXcFzxophPi3VcE6WqWK/Tp3E+myLDRDOzUgHz39IC9sVadfW+gmj7ecqV5qGHv74x7xweHYXPto9hjvbJBLjeymLZMSLd3DCZz+D8a0DH43lz0+fVc5BBq3PKzc/hnJJduumM/qWYVVM+uVwDbTYB5+PDFs+C79NZx67HW+xW3ZOMWnxtXRiPYd4Joth/ggjmwv4RnC+ZmfUMYZ5ZXx+vs7SE/e0CrUEXFnBcWlrAuWWF5lyOSbGZRWnezLtJRcpdtFS3OJlwR308gU/Pt1l1n1mTTYIbWHcN3i+g9lEp4TgSOu70eN/JqLumifuYwz2aDyUi8Zb//1bgrf2fmbXNU1zfrfqr75hNX54DPecpYyPpsP0+lJNPqb75GJ/O6Rrat83IbhkuM3/wNX3ncC4T5z73enwONgLfOORyE+8VoFTMW6O++mxXspgnKkNkdjw3B/GXv3y/c77/9Wu/AfHLv+mlEL/yVS+HuIvm7lXKZw8+gIbY//jxuyF+rIVZ9dAwzg1e+oo7IQ7ivA7a3Ig9ncJ6WAlw3rWcxXWwmVmd8luZ9tQGqIzFIs5fpmm9v7yM40CT2hkbU5uZ7dh5COLz53GsSXatHhONuuvCZ0O/iBBCCCGEEEIIIYQQQgghRMfQiwghhBBCCCGEEEIIIYQQQnQMvYgQQgghhBBCCCGEEEIIIUTHuCyPCM/zNjS5L9cDotX3WcuNv8OeEIuLqKu+vIwaWnnSmq7VUBcsCNzbPX78OMSsk59Moi7w0hLqqw4PD0PMPhR8zVZlaAd7PLTzlGDdfa6HVrB+GceRNd3KSOTSdb+uFIV8ecPLo1rFe2Htt0oJ9eYC363vRBz/1t2DzzjeR/rfpCceJ/3iUp68FEhXfaAPtdzNzMpF1INjCc6pKdSbXilg3yhUMC7XyY/B0Qt0ddN7e1Cv8vrrD+M1Ctjfuun7e/ejTilr/S3OoibzSg77p5mZH8Ebr5PWeiSyqiMa8UmPtNN43oaObI20pbsS7EuCeoRB1C1rlHIb+5DEMtsh7u0bgLg6j3mnSn28SXqE0Rjqr5qZNVkrvI1HSJxyVT3kPIL3GSNPiKdPPOmUYagbNbD7D9/mfOdiHL+bkD0lsP0kEqThWnTbXIPqwdUb957l/+88TQs3dGzrdK9V6hvsIdHd7erXdnWTjwSNBV5AnhukTbpv1y6Ih8kTYpn8HO6++xNOGbqTqDN50623QPz1Jx6DeHLmAsTFEva/Shm1hyPcTuturuPxLKC805VGnV8e5yoVvGYsjv0rFsOcn0+4WsB7du6GuCeNz8Zba5feFmv1O/Bcjedp/P1WWsI1rD/PJ6110iLNx/EZT05j2/7Qh/8R4r07UAv16jsmnDLED2CuSe7EZ1xbwWdaPp+FuDmF88/SORwPgxk8X1fZ9amIdmF/66M+HZD5z8w8XrMQwb5Tonqs0fkiLbSE0/SAYhQ31rxlcBbUeSL+6n9mZpGAGxHWG/sZRVv4kiRJkzbSjX0wliatYrpkhPw7ynkaD0Ps8wN9bh8f78fvdMfw+UbJqyZCc4kIe9nQvK5M7SURuHONSJPm7iTOTpLZFtB8NkdjZm4Gc3yUJqujtAYyM5udehrigSH0U4mn1uZ1W6yZbmb25BNHrLo2Do4t472ODmLOSEVonhW2+Ld7bTz8HFl0Otxvk+4vya/P29zvy/Gt4HPyPfC/UWyjL292Cf+q8TLv02sTs6Z9y3M8y+fPxcPy+RDx6hZZW4M1qE+zp1WVl/e+6z2U7sJxoZ/i0cwOiIe7sJ/N92E7f7SI+yBZWmfXa64nT0Ai9ZlMBss0iGXozeA1E3EacWjsqlexXgq0H2RmxtsRo+TDlVvAMXuBvDOCbeTRM4HzCF4zBS18gXhPh71NkhetxYJga/dPmmHDmmsJv8leCeyvQcd6LTznQppv8FTVa5MFQuMysMEihq08r9jHI+LkLqKxeW5sUpnZtShskaCd+6C+4HqqbO4U1M4ft1W24hzNT6u+dg7H92QLiFlojbV77qd1aYz6S1cP7nVEA9eX4PGvPwHxuUn07Vwkz83tO7ZBPDt1HuLP33MvxEtZzC2JmFuGOvvo0h6b77fzKsHnUCN/mnoF42TSndsN9OH6m5tZnPZkZubRA2LqLNaDTwnU44TaIl1Fyc9tZCQDcXVtbysILv13DvpFhBBCCCGEEEIIIYQQQgghOoZeRAghhBBCCCGEEEIIIYQQomPoRYQQQgghhBBCCCGEEEIIITrG5RsWXCKXosHIGlrs8fDUU09BfO4c6oJFSYudPSX4/PPzqA9o5vpOPProoxCPjKAO/uHDqKPPZTh16hTEPT2oi8ieEmaufjXDng9ct+305dhTopXPA//t2TwjXhCPiFLZSsVV/eUIaQMnYqh9menF+o60qNqAdHU96gYLC1k6gjQJ6XlESEht2zi2mYlRV7v9xHFsJxEf7+Paw/sgvkBlOnYiB3GD2kSD9I5b6Y7zo3zFS14EcdhAne7JqSMQj/ZhPT71CPYl1ia8/sYDThmmF6chPn8Brzk3s2JmZqn05r4oV5p41DdvTQ84mkId0VNnjkJcKqA2eF8veoyYmW0b3wXx1MwkxIe23wpxgfQCm+RT4jVIu5Sef6yFdjRr6zsCn3XyXwg21xBtkr5nV28/xHNnsZ7MzM4cfRzi1G7Mp55Heqt0vKuXzLrBpJtZZ8VPsxL5s9SrlD8jz9wpa5N3nLC5+p+598reB6k06lgOkn6kmdnZSWxnJfKZYJ3RvjTmqh2joxCPD6Ce5ySNn8m4601y+83oCVEgnfOnjp6EeCGHmuQBtcMGPVP2cUq0KINHrTeVwrrrIV8f9iLpcsYV6hvUl7YNuOP8+BDWJetsb3iAtBq0Os1Fnjjc4oPnoDFLsvQWoX7t01WyJeyTeZuC+NzTOE78ws/+FsR33oLjpZnZnqtwHH7ZrddAnB7G/hLbhs84vgM9PKp5bHdVGoOrUy08kDL4LHuoHe7rxzy9RLmpRv418SqWIUN+ALmm6wdG9lJWJL+l2lpeida3dm6XSiUtqK3NK2ksqdJ4V6lgvcQTOCabmYUheTqQF5jPaxIaYwPymMhRnkqR/u5VOzEXmpntG8c8ko5imcIq3keRPHvyHufjDMReFcfwVMyth+wc5uS4YT4M6D4qZVw31QoY+9R3x8Ywt4UNVz9+oDcDccOwHmaWVnXvg8Atf6eJxXzz1ur5/AVcU84v7oR4xwjmCL/eSrMcY/ZZcqAEG7b5/iV5RDjeWW3OwWtGnt21S/mX4GPU5pItvt/Gx4Lu0WtxPl7rOlrr6+P2FntElCrFDZ+prjSOM11duF5o0Nx+fm7WOd/UeZzXJRPYx/t68Jw+tQi++9FBnLunEzinqlRc/6NIBPPCwEAG4uFhPCf7AcRi7E9GHj8pLEM/zcHMXI/BTBzzSXUWNdIDyvlj23COcPM1h7AMtI5q5c/Zbq/r4rmn729tuwvDqoVr3hAhmwPRWOO4GLTqr06f5nZFOvmOFwKvx2hviub6rZJGEMFnks+vQMweLLksrifY92N0HH1BGsZz+xZrQGrLPnkHsX8A+4Zwzm+f+9x2w35gXFPrHg2cN7eCer2xsU6rk28uP9PbbrsD4i995j7nfMuLuC+0tITP9P3v/32IozHMTV4N5x9j/WMQN2mOlIi78xLe+3W2T+rcJvALtRKWIbtA3lu059rV43rLcrliMZrLl/EavDfZR/vRAR0fof3sSt1dT+RpfV4p4pzUW/NPuZxMp19ECCGEEEIIIYQQQgghhBCiY+hFhBBCCCGEEEIIIYQQQgghOoZeRAghhBBCCCGEEEIIIYQQomNclkeE53nPqofXzreglQ8C/+3rX/86xGfPnt30GqytyLrPhUIB4lrN1bviMiRIe5aPeeKJJyCenUX9xoUF1CSskj7agQOuTv7evXs3LVM72tV1LIb6Z618Hrhun+15Xor3x5XGawbmNYO1cuC9RUknnf006g3UTDMzq9IzrZPuMuvKsy8FawtHfNZlw3iFvEvMzGYXUCu4uws9IrbtHIS4EcNnfO4Ctu3oImnrk05itIXueJG8DY4exf73nh9+JcSJ6jjE992H2u7pAew7AwOo8Xnz1a6m8hc+g14Z412oK9odW21vySQ+506zcPqoldc0ogsrqImXz6E2+PIS6hdaA5+NmVlAmpFVanPpbtQD9BqknUl6kKw5yR4SjbKrC8nJnmUnWdu4zn4CdI0m9a2uNLbZef+4U4ZSBeuG86NFsFAB6ZjyffI9VEiZsNlCS7ybdFqXi1imxkUn9Vr4DXQSz5rmreumkgalT3Vx+OqrIX7FS+50znf3p+6B+NEj6LtUrWIOGJlAvdSRvgzErHG+fQj761vf+FqnDLEY9t3P3vsAxPML2H9K7OvBeqw0BtWq+PyKvqtnHCU943IB2x37AoWU8nnUS1O7GB8cgnh0AmMzsyRp22b6MR+em75gZi+MD9NoIm1dtdUGxz4iNda75VzTSkM32DxfhUb9mHSx6yE+Q5/G4LvnjkH8xb93/Wh2fx713VfuRy3h1xxEzddqP80Fh7HNBAPYjmPUTLvIH8DMzNuGObJSxLEjk8Nr3HUG6+3AMfz+0jyer7tE89WKO87PRbB/eCHONZ6srNZ9LLq1WsLVcm1jLtZoYJtrkN4uC1bXKQ+ZmcWiWBcRnzsxniPp4zUDHz8f70O/nP4kxrtGXA3fXpqneeRtUS1iu44GWMYSjUUJH6+ZiNA8KHTnt1EPc9PiLObXCvmQhFHW1ceGPdyP3jZRyn2RmLuMjASoRfzUiSchDlLe2vecQztOLHgmn5+dnIHPpi5gPNafgTjSQjed9fcd/wEn9/HHm+uDe5fgEdFOc7zVEXCNNmVyjr4E7yzW0GZt9nY49eJc012L8nqd14O8vt8qqtXqM3NdsgwcGuK5AuZw1u02M2vS86vRPK5CsUdjdoT2CJw9BKraVr5bedpfefoUrgdPn8W1Xd8gjsd7dqOvU7WKz7dOuu71htvmeC3fS+vETAZz9Eia5oF1nAfO0H7O4BD64VzK7gfPdS7uv5e7t/N8aTYb1myu1mODfQbbdMfn4vPq+BE4Pgbk8+LTOpdWqYPkO2JmNjSEvh5Z8gs4ceI0xLPz+Ex5eVGmvNTdg9fsy7jjPN835xn2EuGW22jnI+SYcbjfiNAixfFwXDvI8cbaAkLfs3CtrS8t4xw2mMZ8NjKM84s3vvENzvnOnj4D8dGTOP+fP4r7rpUq5oWkzz5XuJe1aw/mop17dztlePFL7oI4HsV5NK/bIuwtG8fPMz24XmBP1VjSHavYj2F66gLEjz+O+9PFEo4DBdrz82hf8PD110I86oxNZmWqW/bCaKx5pEYuw+tQv4gQQgghhBBCCCGEEEIIIUTH0IsIIYQQQgghhBBCCCGEEEJ0DL2IEEIIIYQQQgghhBBCCCFEx3jOHhHt9OPaeS+YmZVKqFU6NzcHMeussS4bf97by5pb7XUzo6S/yL4THNfrqFs4NTUF8fT0NMQXLqCG18wMapCamQ0OorZ6Tw/qq/J9Mo6mKD2bIGj/mNtqgq593l579MrjNyLmN1b11YrUZgp5jPlem6R3a2aWiGN97tqBmoM+ae9FSJs94ugVo+5aQLqHtTLp4JtZYRnL3ZMireccnnPhNPpMpEl/7pbD2IayOdSSO30GNfTMzLwA2/ajj6LG5+OP7YH4dXcegnjfNuyvJ09jvY2kMD4YcXVGI9fvh/jrM6g7+uWHV31i6i38XTpJf8SsstaUFpfm4bOgxv4b2OYiEVfTNUn6gHv2ob5/rYltZPZp1ECMBui3kU6j0GyD6mdlBfXQzVw91VQXal8m4tgGG+THUuf7DvB5OudLkBiumeVIX7BWwXzqk0dEjMYNn3S/66xDzP2/H3OpmVmihn1jII/tuH6RxnZii9/VRyKRDa3JaAzbEY+hHA8NunqOd912K8TLWdQLf/Ioekbs270D4tFR9DEIG9hOvQo+j9E+9IwwM7v3wQchvuczd0PcMDxnNI7PMGyy5jaFpEtbrbq66QMjGYhvvOFGiI8eRz+T7EoW4oD0Lhtl8iIqYbv23GHHRvoxR/fRuL+UXb1mLI7ao1vBQCRmibX+3Aiw3UVo+tEkPVzWx21FhVRyy6RtXyCdevad4DlNdw2fR4V9nMzsqQKOsf/zq5+F+MQZ1J3d34sa1vt6USfWT+I1wgS222SvO8ftH8Mx1k9TW09hDu0dJn+vBHmRePRsViifLrvj5M4QzxGSz0T13OoxgZuuO0opX9vQ8k1H2AsI4yDAMkdbdLBYjM5BXl0pGhuGyZepfwDHiv4Ufj7clYE4nnC9aJpVHHe9GnmlFGnu6GF7aJJnRIX8bgZoPVLMu2Vgf6jsHGpoBzSGDvVizs6VcQ4WCVB3fZH8dAZHXe+vpXIe4v4h/M760OVHtj7XZTK9G+u4qQcehc/OnsX12VW7cA7cl2q1lmIvg8v0a3B01jdf57bCc7y0aO3srKXpeNaubzPt4XtsdQ0uNR/CczmfjAnY78HxnGhRrU2qB5rCWq257nXoHttJFubnrVRe7atzs7ieOHXqNMQk5W+VFmufOs15KhVcH44OYn8bHsQ+Ho/imFCj+Uw6ieuNRguvxXQSr8FrkuFxXFfH03jOAuWuWg3vYWCA8gp7/pirtc9eiHHSbW8WyAujSn2tjfb/pejtb+bZudUem/V6w+prpgjOnIq+6+wjXZKfRRt/G+O9KDxn/wC2y917dkI8OOCuJ1LUNjk9dnfjGJmlNc88jV8L85jz2Vc0mXRzPq8HXD8bale0ZxS28dR1EnQLdxI+h+MltFaG5gvgERFNpsxf83RZzOL8o84JjkaKgwcPOue75aabIL73/i9DHNCaad9+3Fc69tjjEF91Fe6/vOktb8bzJdy9qvk53EObIz+ZRALL0JPG+WNImxULc9gul5dx7jhP+05mZseO4b7QyZPow3OG1jTlMuVY8uSsU+e5/qYbIP6Od77DKcOefein4ZOPy/LK6rqLx/PN0C8ihBBCCCGEEEIIIYQQQgjRMfQiQgghhBBCCCGEEEIIIYQQHUMvIoQQQgghhBBCCCGEEEII0TEuyyNiM1j7juNiEfX/zFpoQF6CFubFVCqo9b2wgBpem2n1rcOeD7lcbtNrxkmLrK8PdYVTKdRT5XtkHwwzs9OnT0M8OjoKMftUxGKk+dvmPi/F1+FS/Ti2WuPQzKw77Vt07Z4GB9AHhDUizccmXaq4+pop0nru6cP6DUjz36Nm6VFdlYtYJxcmUdutL+Xq4O4ZIS8S0p4tLOM5etPYf1KkLb1SxuNzedTlK9ddTWW6TQs91F58+EHUTd83gXqN+3ajJv1giNp+kRrW0yJpD5uZnZhBHe9//gJq1p85u6o13EX+A50mHomaF1ltW90J7NOZFD67QhFzRitvhCj9rWzYBuMR9nlBLb8F0rUMIuMQk2SlFQqoP2hmll3Cv3nn8aC9+1BX0UuhZna9QTru1DGiCdQvn9iGep9mZnHy5CHpTAsovbDMYMhamaxPz2NI1dXQ7i1h3+g7dwLi5Yv05uMp7BOdxvO8DR1a1lf1Sf94aXER4hnyJzIzu2o/9skk6VbuehQ1sl//ZtTKPHAV6kGGRezDzQY+zyYnFTM7Q75IuSqO21092DfyBcx1rNvL4xtro7JWtZnZ/ALqeTabOC68/CV3QfzJT30C4koF29GuiQmIrz2A9bxzHD83M9uxA/tDnco9vDbuR2OuPmmnadYr1lz3TqFOV+Uh32ctYfd8SdL4537cJPHuKLX1pm0+n4wlsF92t9DQ7SOvpggV4ovdGOd2oifEsa5hiFcK2PZLFcw9/afdXJM6g+Nbdzc+26EAP782xP6TMWyn8W68by+J95AadHW0yZbH6jHsf9fnV5+Fn9raf5e0NJ+z8lq/ivVl4DOf/IeaTaz7eNK9zy7SIM+VMI+ENBbs3I59dGwCy1DLo2ZvL83tg4Q7NuSrWE7fQ9+QJuU+zmV1mqel+vD4Ovme5BZxLDMzW1nEctd4DkxVF/fxGlUalOdns3j+HJ4/kW5RDyH7puE5i2vrrEjg+qp0mt6u3g3t95Us1udXHnwM4v0T6JnUf8jN603SFG+yRwSvUXgN1UYvnPXIW63BGg3Symc9fWpnjhY+tbvQGUJ5IuaOsZw9fJoLNOia2eUsnpLOGZCPwflZnO8sLaMPiVmrqsU/rK/3gyBiO7e5z7JT1Ov1i669+fo9TnklkXbXE5Uarg/S5DF4yy23QDw4gPsU3D56e9GPISCfu1nSQzczW6L1BO+FjG7DNUqR9Mq/cvwhiLNZXEcNHcb1ZhBzc0WFPSCp2UeTuPYOyKtvwMMy9/bg/oJrptJ+/8OZn150zJZ7RDTM1re4OEcwzh5ei+24dntJHnshkBfY0DD6hlx3/WGIY1F8Prll7PNmZmefxnaXy+E4P0da/ufPPg1xdhnbWbobn/niHPatUsH1Wxynth0nf0V+zO224FzvERoTeAwxszr5ZPm8FbY2DrVaD3UaLxYzb729lfH5xMnbsFHH+Ukk4vaR0VFsN/tpr+LsJHrx7tl9AOJafvO94umZ8xB/iTwozMz+/uO4JoyS51hvN+bpoQFcP1BXsGoR21mB1xfV9nvmvH/dDDf3gfEaeHyD5qOf/+d7IO7pdud2P/sffhbLST5o63vkl5Pq9IsIIYQQQgghhBBCCCGEEEJ0DL2IEEIIIYQQQgghhBBCCCFEx9CLCCGEEEIIIYQQQgghhBBCdIzn7BHBenKsHVetov5Vg7W8r8A1mXYeE60+fzYvhHUiEdSs4/vg7+/ciTrQu3btgvjhhx92ynD33XdDfPAg6k3v379/0zggrbJ2On6X4hnB97l+zKUce6XpH0hYpbxaz5Eo3mu5iuWcW0T9v2rVLW8QRa3fbA7baqYHr8G+FBfOox7dShHLUMji+YIWVRaL4zvArx9B75CzF1Avbm4F42IF22WZtOOiCewrfcOoAWpm1tc/CPGOUYybpBt7/jTq6A3uRE28lRx+/5GTeE9fOom6fGZmX30KvTCW5lGzrtFYvc9qfWvfmS6vLFulvFqWgDQ/e8mvYiFL9+W5aXXHnqshrjZRJzERw+cZJlDbNNJDn5OuYq2JWoGRFprpXUl8Xo0aNszcMupNR+n5R33Sr46jjmwihvc9vMv1iCiSbnduGfU+SxXUbB4bQx3fSID1whqJNYojU1NOGSbOn8bvrKCHQfUiLdVYxdXn7SSe75u31t5Y77HRxPo/O4n39vFPfNI53+tf93qIb7ntTojf+s7vhnh8HP2J5qfxGv/0hS9B/NSTJyEeGhtzylAgbdo6mQqUSvjMy0WMG6QDHJDPSIRNClroV2cyqAVcLqK+dHcC+9fL7noRxIsLmKcOHbwKz086zkOD6J9jZhbrwu8UVlB7trymNd5obP2/D6mu/Wfm6tSXIlSfNAfwWKfZzHINzAU+6TvHuvB5xChd1cj/K5fH57XsYf7zSKfdzKyfvHwOHrgJ4tG9GC9nsd7nz+FcIkL9r1nHephqocHcn8S5Q7OEZbrQQF+XvzmLutmHl1Fndm8MtYmDCJapJ+q2nUyT5yOkxV5Z1SyPdG2tlnDUzNavmCPvg2SKyujh803GXb1wkpe27jhpzB9DL6Al0rMeT2FeWbyAuW9gH84bI75bX9Uyttt6AdtpSG0kRf2ilCcvjAiOd7lsFuLsvDunatSwrrI57DtBgG1koIy5qkxdif2p+vrQOypsuD5sYR2f58wSaswP9az1gxa5utPUw4itPwaf5hOz81mIP/Lnfwnx6I++yznf6AjOm2sNbAPukgnvuVLlOS8mw2XKffUWa+lYBNt6kXyW2E8xQV6HaZrTBjH8nP0AMpmMUwb2yjh6Avtbmeasj30d/anKNA84sB91vhfJQyBJ2u5mZgkaY7mummuGOVGaQ3Sa66+/waprvg7dXdh/EnEsczyOutyRFp5RZLFkTarbwf4MxB6vD2gvpYu90EL8fGzEnc8YjYdLNEfyqeEnyWtveQbXh/kVbLOzZ3G92dWN9WZmFuvDduuR90WE9kbqnKvI9479xlr5ijJt91su47tXmrD5TIrlVMslcf34XNj/xlWh53yO7W45i3lkfg7HhePH0SNy+vykU4YV8njgbT32wqiUyJuygfOhCJlhLC1iu5whjzszs+UVzMmHDl3tfOdiuO6j5J8TjfE+I5aR17VmZnXyQ/Woz26ccettXa1SD62x1rdKFbyX8+dxznvjdegTUiu73j/LtDeRSmHOPHsW28nXHz0C8Qj511w4i/6nX7jvMxDPzLtevsUytqtu8lssLuN6bu485sN4FOd66Rjeg09eUBH2eTKzCHncNAMa39hviCbFdfKdiEXJe4/O/8SjX3PK8OADD0B83a13QLy8strHfd8zs0vzdtUvIoQQQgghhBBCCCGEEEII0TH0IkIIIYQQQgghhBBCCCGEEB1DLyKEEEIIIYQQQgghhBBCCNExnrNHBNPOO6EVndbLY0+JVh4T7XwnWFuz3fEnSBeTvTL6SKvMzGxhAXVev/rVr0J89uxZiM+cOQPxHXegRhdfg+u5Vb3z39rVy1YyO7dkpTWdv0YTy5UkDeZGDbU/l7Oo22ZmRo/Eijl8xjOGuoUDt1+HMemjHnvqSYijTdL/G3S1LU+fRQ26B57AZ1r1UT+uWMH+FJI+p0dab7v2oD7/zTff6JRheBz1pseHMhA3ls9B3JfGipuaXIT43sfxnr7wNazHE+dd7b8CeXgESdSmja/dVrLL1YTuJI88/tiGzu7IIOp0G+mOptMp+tjVdE12o8bq7Ams22YadSj7+lGr3wvLEFcpLyUTqPGairneBj753VRJCz5BvhQDGazz40dQ07dRQO1NjzQTz82hf4CZWcVQo3VP/26IIz5es0namE0Wxw037xeRZVdDu3fhaYiLEWyDF3sSNH1Xj7mz+GYbngfkrUD3tlImjd5Tp52zzf3FX0N8mrQ03/Dab4Z4hHJbkMRn+sAR1Na8/wtfhPhNr0dPCjOzbSPYlnePYd7p7sH8OED6xgHpztbYD4D0VhMx0js2sxjpqeYWUavWq+I5b78e/QOapCW8ksNxZYU0TM9PY240M5uaQ3+NyfOoQV8oruabVCplP/ATP+Ec30liYcRq4Wp+SFB9xkPss+UQ80i0q985X88A5rvBEXzm6R48JiCd3loNx5pcDnWBY5TLurrQe8HMLJnCvLxtDMfERhGfeb+H/SmLh9tKEcu0nEe91UYBc7SZWVjCv3nkbVGpZvEc5OdQ3oFzuek4erDMT2I7jhYwh5uZxevkL1TCsSO2lu/izbihg0xn2bdz2Gq11bLkKvgsMr3Yh2M+zusaNdSTNzML6W8+eXjsmdgGcWUFn81n/unLEN90yzUQs+9ScTHrlKGax3PGQix3bxfOV1mCvB6jfkD+DmnSGR5sodWfzWM9ZFdOQzxMXmB50tCukUfBynIWrzmA+TqdcPX28/Pk+UGa/Ova65eiwX6lWanWN7wDzszinPXwQfQlSJJ30AOPuXOarjM4l6tUMC84uuqk179AZajUMf/OkEdHue6uSeNUj2XyGmnQ+BUj3wEeg5NpzKeVCrbr4QE35xdoTDzxNNZVvoj1Mk/3HdJcb/o8fj7eg7lw7zVuzg9iWA/sdxSsKacHbCjTYXbu2rnhZ5ZO4ZwqSn3a96hskRZljZAePPndBLR8L+bx2bCWeIO91ch3izXtzcwmaP2YJN+RhVnUSF8s4nqwuIhzpmoJx77HH3oE4lQLj4gb7kQvr1gU2wRvdfB9VWgNU6B5Rpr6VTRw64G3Snjn5OIct9X5rtmsWqO57j1GaxkuKLU7r4W5AM+DXc8I2kei9VM+j/VbKWP953JZiNnjw8xscBjnlj2UuzK9GYiLORyLzp3FefcKjbEhrSnzFdefIaQcnqd2k0zSOpb2rYx8LLwm53Q8f6v9uEaF/Uxonrd2iMfmH1tAtVS2en21Hs+dxv3LrjSOPfn8doj9Fr5R7GnUS7lgbBjXmIGH1/CpXRbIhzBXxPUaj5dmZl00JqaTuEBIk89OgnyWouTjFPUxjgTU/3x3n5b9pEq0menTs/ap3fjGMeajGOX5Gvl7mJnVq+R7Vse6Kqys9oVI5NJznX4RIYQQQgghhBBCCCGEEEKIjqEXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYl2VW7Xnes5oYX4oh8lbDZWhlFBQlEzU+hg1+2ply8/Gzs2jQFI+7ZoLpNBrLsvFiQIY9X/4ymuqxWc+LX/ziTct0KUbU3wjPb51Eqs/MW623waEB+KxUQgO0WBzjRNI17eVn1qzhvZbz+PnXHkQz6vFhNBu7fg8a5URq2Ebivmt80yBz1FgKn7kfoBFOLI5mcckAyxxJofHN0hIavf3DP3zKKUPoYdtPBmjgc8NevE+75RCEF6bRoOmhE2g+lg/QFLF3zDW4m0hi3VRLaBrk2WpdplLuc+wkyXRqw4UsmcC6jZEhXjSKfXpiN5qbm5llBtEE0SJouLXrqv0Q9wxiOy+X8NnU6hh3deGzSkRdw96ooRnSTBaf19lT2M6/8s9/B/H5s2jKGCOTSj+CbbrRIofMzpyHePdVZDRMpttNelfuOd5o+HkkxNxWj7i5bilCBoZVbIORi/qW71xw63DydJu87bUwFJwmo8tPfxbNpfuovmNk7pjpQ4PIV994M8QNGt8iNddI8+DEBMR3/qsfhzi7hGXMUVwtoJncCsVnZ9DofXHWNSjPk3lnIY/nePzIUYhDNt0jky/+PEJzi1oLs7MmPb8KGY0F63ORVuaUHSbq+xZdu4cGjY+xCLaR7j40TM6M73HOF6TxmDK1q0oODc/SScyx6RTmv4kBbEM9ZBzXlXbHB57b9XfjMSMDGTon5vGVMo5v81k0hi6QETW3YzOzE0+huXshxHPEqNhjdRwjB28YgXg8TQbc1+AJTj6F+dXMbHYSyxlJYZ6or5lsJxJbm+sOH5qwRmO1HbAdpNfE9kFDsM3NTDvnY1PK5SU0aN29ezfEvQEaR3/87z+PZfBxTE1Rm24UWxj55bGdlyjvGBnTVqo4X10k48zlWZwPrSxgrotGqWLMLOjGNlQgA+1SGXP04iKaygY0L0yRF7VPhupLs3SPZpaMYhtLJrEuF1ZW7ysI8DlvBffef6+VK6t1MD2H7chr4vN7+R23QzyXd8e3R57CedNKbom+gWNDSPm1RHO70DAP8bheqbtG7UYG1jzt4TViroLP7NxpHMe9Bpa5m/JOqeg+80oZ2zKvdYtkih6L0tyN+kaUb4LGy/IK9m8zs1gvXrPsYV036qvniHmuwXoniQVx873154j35UzrPJ57kMmwmTVpHyJkY1Pa6vCM2gcZ2/JcMyTjYm6zZmY+7UsMDg1DHKW5+X2f+RzE85NoGhyL4fPP57E95Ra4X5lNj+Hae3jfToiTPTjmN+pYTzMXcJ08P4PxgUMHIR4dwfG4FbzPFLloPsdmsp2mXq9tmKTz3hU/86bRM24xHeBj+H7cfSOMOSekKK8cOnQ1fd9dx6ZSeEwyid8pFrHddNHeyq234X0u0zp4YQHXD7NzOAabmS3RXG9pCdtmko2HySQ9DHHGw6bCURo/F5fcXFdvYg7juUC1tprrwtjW5jozs+XpBaut7YP10X5mnOaZR48+CvHEKJpXm5mtNLA+azWcN2wfxzVJlcbUEpnSBwG2w3gMyxhpuAblTs+luYIXct9obR6+UUYyKA8a+MzjnrtXHPXxO3mqhybl/SYZRvs07gU0blSrWG+xFvPLvh7cE1hZwLZZK1RbXnsz9IsIIYQQQgghhBBCCCGEEEJ0DL2IEEIIIYQQQgghhBBCCCFEx9CLCCGEEEIIIYQQQgghhBBCdIzL8oiIRCIbenftPCFeCB+CdmVodT3+G/sxuJp4m7+7yWazEC8vo/5cIoE6iGZmY2OobxaLoS7X9ddfD/Fb3vKWTcvA+mmOFmALvcd2z3M9fiG8I0rlmpXKq7pjuRxqvfHjqFRQdy2su1pv7K+waw9q0gUR1LM9fx61LLk+u7vJS6FKepwNV9P1mqvxmulh9Aw4MYmarUsrqI8aJQ3CCt1muYzfX8m5esYB6eKNjqH221AG48dPoH7mY6ezEG/ffxjP15WB+MTJp5wypEkvsBbD/rcuw5dMba1ueiyVtMbaY45Tf0ySzuXwKOqUXnXNtc75Eik8pi+agbhRQ13LwhI+UNa7rtexDWYNNSqDwNX2Y46feAziz92DnhDZLOpRD/WjBmw8loE4mkJt6plz2F7MzObmUH/TJy1aLnfD0dZ0TgmwTmOu7ua62Tpec3eI35nwn9Fdj/pb603i+97GGMN5JkLeARyXK65+dWg4FnT1YP2Wyzg+feqf/hZi1r1PkTb/gVFsE9ZwNb+nT6JOvk/58dx51Ok+fwHjLGnzN+i+HztxDI+fd9udkV5nMo7jcKNCfg00D2jQONIMN9e+rVXdevCpLUfoGuX66jHhFusIm5kVw7oV13Rr46S5m9mOeT2ZRr3xesPVsK6ShnhXTwbiRArPkYyjdmmE6oCz/9LyIsWufnSvo4uOng9nZi7gAeQVFY1iGXaPoKfAyw6iHnVvP87TzMy8N78W4sUlLEM+i/cxl8tCPNCL/a2/LwPxIM0dOSeYmX34l/4K4oe/fAriQrD6vFnXuNN0JRvWXBvgV/JZ+CxOfSURwbw1MuDm5VIcj+lL4zH9fdjnwyLphU9jG7rv3kcgrtV2QLw0hWU2M1ui+X8tj3O/Ppr/UwqwWpPn3Tg2LV04A3G55uYZrwfnkivknRFfxjZYq2JfjdOkeucunKvGY1imPPlamJnFm9h3Tp54Gj/PrOp8B9Gt94hohlVrhqv5/tabsc92J7GPN2l+0oi4etvX3HgLxJUqe8dg/VyYQ08Oj7wPfMNrJMiLKwjcOU29hudg/6EU5fQ4fd5Fnkb1MuV00sPOsxi1mXVlcC6QTmFbn9iLPmm8lg6pDIkY+T2QDnuk29WPD2nO5NVw3E6tdbgod7wO4/sRe0aOG8vI2v1BQGNfi72UKP0bUp/9MEnjvE5+RhFa0/ghPW+aZ9er7vqRzxHQ2FMjnfZyAdc43IIKOcxL5SLOE+sNd/L/yP0PQjyyhOuLifw+iPPLuI468ziuSSdGcA1z7WH0LEi38Phst9fVvCiHb7VHxMrKilXX5qK8L8Rz1liUYzfX8X5LvoD1WaW5fZTW84OD6PU1MID1vW0brqXZf9GsvY8r55WAtOqTlDd6evAaI+QBum83zrHMXI+cIrVtrmseEzxa59ZDLMNjj6JnXbmIx5uZxZJYtxcuTEKcW+tP8YRbh51m8tw5q1ZW6+DgVThP7h9k/1M8lj1AzMyeevIExOyrOzSAnnJ3f/zjELMPE/fDSItx3YU9VDbfM221z7rZ99mHsOG12Kel2NnjpnG6QeNA4GGbCR3nC8zh8Xgrj13sTzMXcK9yvS8El7Ge0C8ihBBCCCGEEEIIIYQQQgjRMfQiQgghhBBCCCGEEEIIIYQQHUMvIoQQQgghhBBCCCGEEEII0TEuSyix2Wxu6F6xLtsL4R0QJQ071sd1/AIuwSOC4XOyfhl/Pj+P+p9Mf3+/8zc+ZzstvxtvvBHiYhH16RYWUCexSvqOre7Z0et8Fr2zdrpnnSD0ahZ6q3WyuEj6qvQurVHDdtmsu/fa24vN/oaDqP1bqOIxxRJq5XdnUEf26LGzEHfFUdd55yhqVZuZ1XOod7qtF/VU81lsExfOZzEmzU8LSR+ZynjwwKBThkIJdQ4HqAzNMp5ziWRCkyPjEA+NoraiH6IuX08c26mZWSqN91lPoiZddl0XNrK17a57oM+i5VUtyUaR9OMj2B/7e9FL48hTX3XOd/YM+i80DdvpnbfdDvFVBw9CvDh5DuIwxDbM3jPJlKvtd+Rp1Or/0j/fDXFgqCd4YD/qo+7fiVqPB7bvh9gLUHuzUUL9czOz5UXSZW+Tf1nBkL/t+gLh57WIO8QVu7BvVEnn9OJqaLgWMx0lbIYWruVYvrca+RRw1QUt5Gdffv0hiN/w8pdAnKa8P3UOdUYDGjviSWxnu0dHIOaxxszs/DSe8+wF9NypkB7802eorVPX78pkIF5cZp1a96EFEdbfxDG23sBy12qYX3muwRqj1WqJYlf7nK0U2Fshsabj7bXQ+e80YaNp4Vp5MuO74bM0ecNwHytm0WfEzKw7hX2sN4050ouRJiudMySNeW7rUR//EIm6/TxXQM1psoiwbirDcA+ORdddhfrSg/2Y337tD38H4mXyrzIz+6Vf+HmID1yHc42GoZ5/EGdxburz1A5r1SzE56dx7mdmVu4+CfFU7CGIi2u+OukWerCdJB0PLVzzRIiSFn+aNHs90nluplrow5P2c7OGiaOL5hb5CPa/wTH8/NOf/SzEDz6C4/7OMewnZmZJms+kSYuey5DuwjLXSjhHOnca/TzSPj3/mjunOn0EPXOqceyLY7tIhzuJfW1kCPWWB8bw+Bj1tbHt5BNkZsePPQlxdxrv01vTD74cHeErxe6J7VZdW2Pt3onPo7sL81Q6hXP5SNL13kokyFfQz0CcpHnv/DLmiaUCjj110lnPlzDeNo45w8wsXyDd9Dq1kzrWf6mMz5z9pRLkexfQPKG7113T7N1NnjndWLfstcYDSaHI/n94zSj5yVnD9cQqLuKcs3Ya12aFNc8r9mHsPJ6tD3Ks683a4DzvKxfdOVW9hLkrTv1omudcT2Me6aa8MzyAOt/sOdLKB2GEvPHqNFbN0FgUSWBu7B9B7f2FeVwTF2kdznM0M7Ml8oRYehLb0GMnj+AB5METkLnitmHs/+z/2KrdtNtDuthP7FK8U68ky8vLVlnzPyuXySeE5plxEuvv7sbcZ2YWT0Q2jQcGcezgNrJjO3pE8LqVPZF4D8+svW9rD3mDGc0VlxbQQ65ewfw8NJCBONbCFzZG85UYjcuF/OZepcsF/PzxE+iB8OST6Knkt9j+8ALsL7Va6/VhC0uhjhOLRjcW6ys5XJ/t3IP9fmIC15ALM67fG7cDbgPj47gXNb5tG8RPHUEvmGQS27rrDez26WaT6pd2Izj/tfModvbQaX3oNd18R1aHzrq0QvmM92trzrqXLkoXSHe543wkwLor0vxlPY9HW6zJng39IkIIIYQQQgghhBBCCCGEEB1DLyKEEEIIIYQQQgghhBBCCNEx9CJCCCGEEEIIIYQQQgghhBAd47I8Inzf39Bja6d191y08J6vz0QyibqH7K1QKqGOs5mr08W6eVwmjvn77AHB8fCwq6fKf5uYQB29m266CWK+z5UV1GDje+K4lc8Da6Q9m57ZVmscmpltGx+wyppef7WMGmceeSOwnmal7OqsjY5l8BzkP5BK4DlH+lEnskR6dXt3ouZdlT5nvXEzsyi1myL7NfSgFmUmhc8nm8d68On59aVRO25bkrTgzKwrg/qMXhL15Y5OZyE+eQ7L2D2A1+jtwjaTiOI9pFOuT8XSAnp+lEjI24usnoM1bztNaM/4EXDfSCawPczlshAfO4/6yGZmJFHv9OFHv3Y/xE89/jDEjQY9bx+fJ5dxaMSt6yL5imwbQf2/sUHKTTXyX5k5D/HJGdTfnVvOY5kGUR/UzKzRJJFDJ7/SAfSqPCSNV46bnOta6HtOU1ys4LhQusi/IxGr2Q87Z+gczygJm3Ga9ozGJvpC3HHUMLtm5y6It2dQw3WZNJWHenG86iU/jSZpEacHsJ2Nk3+DmVnxKOpzPnGMNMyb+Ix80qmMklbtSfKxKBRQ49VvUQ/NOraLEutz2uZjZJW+79M46IypoVsGn55fQGPA+ilfgCHWuhJJ89bqaCCNbWSwB3WT+fkEgzj+mZlVPMxHJTJbqTepPiPcT7E+G3XuDNjv475baR6VYbAL8/ZVuzE/3XLdHohHtmG7+83f+wDEn3/oa3hB3x1jf/M3fhnif/Put0IcjeIcNeqTJ0scx+jaNNZjdg7Hz7/9/JecMpyawmN2b78O4pXi6pibbOG70ElS0ciGJnSS5gpdEbzvKrWfRos+EiP9/hrPFT3yZEnjSa69cRfEX3/sMYjrNSzT3Ir7vCM0j+ulOq3VMN+mstiXSjQWLS3ifGjbAOru+xF3XhTQ3xZz2KYGBtFj4ODV2H+TMeyLXoTaZArruVJuYaQUw/u6+ZabIT5+elUTOxJsbZszM3vg8/dvrAVTlBP6B1Gvun8I62pkmzunyZOANw8FvmEfT1Oui4Wc6zC3zS1hm4lGXZ16nzpEIoZtdX6GvAtpnpVI4fcDyq9jIzgm7Nq53SlDN+cPmpvxNRt0300a+KLkR1UhD7OpSZwHmJlV51D/PXsWPcnya/rwrE2/lfCeAet88xr7S/c96Jzja4+g9wF7AhZW0Lcpv4x5JEaGYlGPfUpwrdCqvtJdOD5WKuxBwMfgeFwlPXMj/4VomjyLom6u83zSPKfcFYvTFhctMHxamMVpDOmiuWcrLyr29GzQXLNaf+Zz9hbrNHv37N3wDsguZ+Gz6WlcCRXJo6VQJO88M7uKfLNe8U0vhriLfGFi5DvBfii8N8V9oZXnHPt08D5fk30Dqc598g30uT/SWrrZwpvE6JhGgO2uSjk+SfO4k+ew7h95DD0iqjUqExvlmVnN2c/E+1zfE/BaTZY6TCQwi6z5vLGHbZ681GpVvje3vHv2oh9XdgnzG+fU6669FuK5WfTA4XZVLpO/W4tFmOvtg1zKPuvF1GlOy3tETc89ntsy94VaDfcey5STua94tJ6P0F3Vm+7+yfkLOJcIQzzH6adX94Vi8Uvfs9MvIoQQQgghhBBCCCGEEEII0TH0IkIIIYQQQgghhBBCCCGEEB1DLyKEEEIIIYQQQgghhBBCCNExLssjIh6Pb+jhscYW+y88X7+HVvgt9L4vhjW6xsfHIU6z5qCZzZGm5CJpZvN9dJEu4itf+UqIDx06BHE2m4WYNfDMzMbGUKN1zx7UKt6+HfU4CwXUM+NnwXrxrFXWSrusXke9MkcPbS1+ITwizI+a+ataaCHphJLEmfVlUKc0FUftTDOzNOlIzs+h3pyRDlt3N2rp+xFq66SrlhrCa1ZyqM9pZjZzjjTu6JlctR01WXuSOyE+emIJYpZjGx3Ge+zvcnVlF+ZQ23Ipj3pyE2PY1uNdKYirTaqndAZi7lvnJ1Gv1czVtOvtxeeX6Vq9sWRyaz0irN5Y/c/MogFrUmIZy9kZiKNRN/eRDYlFjL0OSFeUdBNjMdaRJf+GBn4/u4jtw8ysnEetxqUsfmfuPOpWNuqYV/rieM0mvccuk57g7n7XDyeVxr5Uq+LzD5LYxtgDwlVmJI8I6kf1FrlunrQZn1hawDNGnvk83cTydZrQwo0xh0e7oEk5mrxnDux2dZt3jaC+f3YOfT4unMe22yTPHY80KGdoPPv8g1+FeIg0tc3MUqT1HASoU+lTnCXt4KNHj0K8uITtlsekZgs9Vdan5nHd4/5I3w+ov7GGaIP6r2t2YubTOVI0H/HWdE5jLTTAO00xXP3PzKxRwXtJ+Vge9reJ8CBsZpUY+eqEeM4aPaM66SzXyCOiSv0+bJLWaYtpyVAv1u9Ne3FedcN1OKYODeF9HJ3EdvfxT38a4uuvvgbihUXXC+rUkSzEx+45BbFPXiKRCNZDpYznDKv4ebmBZZ6abuGD5mM9lEvkleB3rf3v1ur1R5pm682gVse+UXP0cTfX+jYzqzSxbpqU56MJbMcheUaMjaM/zo3XHoD45DGavzRd7ehKBZ9nlcbt5QKWKV/E+65UcG6fo7mjT34EQcP1Z+jpRt+DAIdcy3RjGUdovjozQ/OZBN5Ds4lxNovjp5lZ08fndW4R5xaVtboPPNdno9PEvcCa3uo91PLoazVL2vhLS1ju2QvYf83MPJ/GEg/7pG84/sXi2B+rS1mIL5A329FT6MV1YWrKKcN1V++HePIkjvNz86jrPDyEXhiDQ9j2ozSX66G1wMrUcacMy9wWQ9brx7ldhPpGhPpnzGjtViVvIpzOmplZuhfnH5nDeI5SaVX3PtZiHb5V8ByV9y1Y97tcd/PMw0cfhzidxDaVZG1+Sqcx0smP0xDO3ovNoju2BTl83qw/noxhPo6Sn0Ojgf2G54Vx8i3hPGRmFo/yjdH8NU45n4+nvZKde3ZBXKZ5SVjAfGHm7p1w3Ljoebfbw7rSXH3NoY32xntR6S5sMytZ3JOoVt0Otn//XogHB3F90aQ1SoRyI/vbRHyeO5KfQyJuDHs6eD7r3OM1a7Q27s1grgtCHC+bVd7fcee3TdbSJw+zCu3JrZBP0/Qc+W32b4N4bi4Lca3u9r86zR25ba+nlRdgiLVms27NtflYPIZjRyGP9XvuHI5nmW53z258HPdI0ylsu4u038F7vzt34lz/ySefgLhCvq4tbCosGsW+y/XNexWOdwzl+VodP0/GcP7dyiOiWSUfClpXOd6+vPdLpfZoHIhGsL/1D+A8wcwsX8C64j2E6ponjXcZuU6/iBBCCCGEEEIIIYQQQgghRMfQiwghhBBCCCGEEEIIIYQQQnQMvYgQQgghhBBCCCGEEEIIIUTHuCyPiFgstqFfyH4LrIfFOnmXAms9s9cBx6ylODExAfG2bai7NjzsapbzfUxOTkLMunrsAfHOd74T4oMHD0I8PY0ao628M1hzmf02+BzFImr3OXrXHusOo8Yde0q0Ogcf80J6RCwvVaxcWtXIC+jVWUCakH4E42rFvddmBdvqxBjqisZI63Ilx7qQeI0IaaGt5FBDLeq53WzPQWyLqQDrdWEG20CDtCkPH0AdvYlR1OELV1BbuDDjegYMZzAOBlBv7gLpGg70ok5fMYYankaahT7pQ/YNuRrUlQrWXeCjDmi1vFqmiL+1QodXHzhg1TU97qljqAvs9GHSU2W9czOzkNpAhHQr/YD6H/WzehXzKWuNBhQ3qm7+zZO2/sIC6gYHpNl6sVeCmVlA2sZhiDli+wTqMNaqrl55bw9pipKAZb1BdUd5KGQPAyed8vmcIlgxhqLZ52t03xdpIHaHW6vX70d889c096Okvb+TdDLvuvE6iF9554uc8w2QX0mB2gBXUKGMzzwkTdd8DT8fH8NcGfju+BAlb4QD+1DP+v7HHoP4q48/ikVsYzfFmsutxlh3jKTP6fs+3Qfrez4nDyy6JuuSRqKrOaJW33qPiLlGzfJrfW8XPa9cGftxJcQ20NNDIvRm1h8ljeoo+VbROO5T5RRI7z8Xbq4vPZBy6+zQbtSJvWHfKB7Tj/XvR9Ef7Ov3P4DXLGIZXnHT1RB/4t6HnDLky9hupsnDoVjGMdaLYpm6yZOsdxD1jGcmUWvYTw06ZSiR1nOZ6tJf0ze+8q5um7My39jwcWiQDrOjiNzAekok3efNa5AIzakS1AajlDd2jeP64Tve/maIv/i5+yA+fvSMU4aTZ3GuXo/SXLwbn2eN2keT8jFJ+Nr0LOrxxny3HvYfRB3vt7zupRCnevGk+cUsxLkl1AqP92Cb6+8fodjN+ekMzhXyZZxrhKXVa4bBVrc6s8xQnyXWcm+ZcluT10G05my1+vGarBdO/cvbfG4XpzlQtIF9eqQLy5AI3TVNivJpk7zXUqSVn07GN/18vB/XF5OnTkC8RF6KZmbRCK4HmjRmBtwfyQMgQetg9lDK9GBuGxpw/ahGJtAnq0768fd8+pNmZhaPbf0Yuw6vrdv5Buza6Xp/bR9HD8HcMq4P2efJp7l63d/c34H1vVv5SvqUs+O0HkzE8XnHyCgvSp4QQTSx+ecRt558uo8Y+YykKHelyBdhYBDb1MGrcEyvsXdf0fVN4HbOz7dxkR9WZIvTXRipWbimNR9GMG+M78D+MzSM3gnssWRmNjyM9dVkDw0apz3yvAoilEEbm2vae6380kiLv5jD2QL7NRjlnQblxgbPb8mXaWUl6xShUsbv1Bt4DvbFW6S9lAuLWIZkGvNtV4G8wnLunkKN5siOJv/6fky49Xt24xOjVq+teWzSvGuF9qZOPY37K8MDmNvMzALK4+y1y/mL959vuukmiC9cQA+l+YVZiGstfHnSNF7FyDOCfQJ5n5XXeyVqd0YWxmELG6Mq+UKyx2qC8h/nohr16YDqcWgY53b79l/VogzU/4q8X706R00kXX+XZ0O/iBBCCCGEEEIIIYQQQgghRMfQiwghhBBCCCGEEEIIIYQQQnQMvYgQQgghhBBCCCGEEEIIIUTHuCyPiEgksqE5xf4MHLNGVytvAf4b635xzFqKIyOoZ3XXXXdBvGfPHohbeSMkEqj7deedd0LM2mPsM7FzJ+qis+fEvn37IC6XHfVbO3MGtWbPn0f9Mq6ndj4NXPesb30p/h2sgb0evxAeEfVy0+rlVd0/1sWrBagHWCAdxL6Mq1/dl0HdyHQa20BA+tbZZdS0q5DvRBiSjiz5HCyXUPPVzGxxEXXV+ruxTKfOLkA8O7+C15zBdpSdnIP4mp14T6ND7jvHPGleLxfwmHg/ag0nQmzbo0PY/+Zm8D7PFlB3z3zylDCzShXLcGEOtWe9NT3IdBq1lTvN6eOnnumr5LcQobzUIM3K0JVTtVpIerkRjFnfmsWhm83NxUWbpKHotfBGCEk7MxaQtmkM2/22CdTyZy3AXAn1Us/PYt5qNt1CDG0/AHG1iu042ZXBMnus1U8aoqTa3KDKd3QzzSxOus/xGGlmV56p6/pz8QJ4HjSb4YYe7/AIekJ8010vgfiqnTsgrjbdvF4PsK0W6qj/vZRnfVXyQqA4SiY9KdJAH+5DDxAzs+1jeB8B6fQ+duIIxM1w8/zKSt08JrXyb3DHUP4GHsOayBxHHL3i9mVwxmFqy/G1tsptfCtYKeUsV1wd5566gM/DJ+3u/j7UEl6aOeec74HJ0xAHMcxPA4N4jtEeHEuSvdiOusmHIk3tbnDAHVuGAtRPzZ87CnH2FPaFWhb197/0uQexTGnUrj12LgtxLOr2v3gX+XNlMD50NY6xddKbnzqP2t9PzeC84PQ0xpZKOWWosQcO9a91D5ZGi/zRSY4+ccbqaxq3HuXkXhrvB3uxDdaL7jw6cAzEMC6WcX5Sq5KOOpUhlcS6fPFLboY40WJMPj85BXFuGecz5SLNJQv4LALyZUqnmvQ5XrMWus9sMYdzxYEB7Fuh4TUXF7DdLy/i8TtpzZNM47NgPx0zs0QDx51yJQtxem3dFQkuXUf4SjE9O7vhw9dw1kJ4LzyH6Uq7muWZXtT3zuexz/Kaj/WuC0XMU1bDZ753J3rb7N2PPoVmZsPj6IczSFruZ888TWXAvjHch/k1Qve9PIceH8UC++aZRWjtxa0iID+QMq2LfPbOoLX3fID+jUELn4fMKfKxI4OpU0dXx4Bkwh0vOkmj2dzQv+d9CN474X2PAzvQu8bM7FU3oT/YIw9+FeJynp5Fg/wayH8s8MnXhPz9ol2uYHkqhX9LklFJECdPzwR+vy+D4+nwMLbznp4MXdHNM42QNdLxuQ4M4jXYq6pCmulV0obnMaHWYu/EmX/S9K1+0byv2cYP5EpTrxU35q4RHwuWSuLzCGjuECN9eTOzCPlL1UqYB3hanKP1RbmIfZ7bPntENMxdQ3rkTbKygucsFmhNQ82mStr8Hunsh7QmbTZdf4ZEGusu041jQCKJ+fTzD3wF4guzWYijKbzPOHljxOn8ZmZWwLkEt82NNUsLf5dOc/zosQ1/1qsPXwOf9ZEHRO9ABuJW+wYnjh6DePs4+v8WF7ENTNM87OpD6P0yPIxlePwYtol6090r9subz48jtBdp5EVrtCZ0/KmM1ph+1rlGncaOVILXZjhXKxSxfxar5EtIvp+Hr78e4omdWM9mZlNT6IM2sh3nl5G1cSEWa2Fy8SzoFxFCCCGEEEIIIYQQQgghhOgYehEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGJflEdFsNjd0x1jHkL0RWAexlS8Bez6w1h5rKg8Oou7lwYMHIWa/hkOHUEuTjzczW1hAjd0LFy5AHCGdPL5PjgPSFGR90BMnTjhl4GuyvvTl+jKw3jzrW7eCv8NluJRzdIpM35BVkqt6avki6tJHovh8EjFsU+le11sg1Y3HzC6gJm40hvp+DR+1SuM9qNc3MoY6zyTdZymUcTMzs5m5UxA/fRy1hI+d21y7tlrEMqcGUFOw4mNbX867msqTc6hRt0J6qvtR2s+GBvBGSitLEM+fPonnm0OPiPmyq7VYIN8B1qAcHVt9FqkW2ted5KsPPmSFwqru4OGrMI+wzp4f4Tzmni9Cng+c+wLKM3yKy7VmaVTd/jrejxqsqSjmrgM3ocdOkbxQnjr6OMR+DdtoqYQNPxZ1NUazWcy3fF9OHnL0/+mEdHyDtP7T5EdgZhavkj4wXTPwwpb//1bQbDY3PAJYe/sfP/5xiD9Nbeqd3/5253zpLtRvXCqTT0g3avE3yffDIliGIdL2j5J/QDru6kLWSZu9WcRxfXQI9Tp7+jC/Ls5hrvM97DtNx5TFfWb8F7cZ4Tm5nXHcJC1/flasd736N/p3H17rc7TSXO80Xhiat3aTk9OoJx4lz6PREnqTDA2gPrmZWZVu4egx1HitPIE5tJbA8enWHTi3O5TBa9YGcIzueRB17s3MygGe80t0Hz0DQxAnaN50dgo9JG5/0ash9qvYKG497M4vH3gItbtnF3C8a5xADdevP4Vj6PHzOIYul9ALqtnAezy4D319zMzyJfY3wbwRruW/WnRr53i1WsHqtdXyV0o4lozR800lyLdrgSZZZkbS+5Yi7y/z8AtR0pqOkS5z07A9JEiS/poDu50yPPEU+pBUfdIYJ0+sAnk87NuLc8laFdtgqYC5dOc+7CdmZp/54hch/vwX74X4zjtuh7inF9vtzgSWeds20qinMbFRJ48DM4sHWFmFLI4r695gvF7aCurlmtXX5qK8xuSxJJ3E+9gx4vbx0RHUZa5SHpmfR3+FFZoD1UuYE+ayWYgbNDb5UfffDw5xuTwsw2fvwblbJY/XmD6F43pXEudNlTL2t3IVy2xmFkSwjwbUnyLkS+DTnJfX2jxH9qmvtPJhujCFfkUFWnPMLayuWbZ6PRGaZ+HaZLVKvlsN8uxhhfRow9VM37Ud+2SRfAzrFTymxLr51EZ9Dz/3Sa88iLrzuij5LbDXYi/NFfuGcLydoLwyRF40FuIkotTCX7NYIw+CEsYFmntaDc9Zp/5eovyc7sLPed/LzF2r8So3vGi22dzif/sb9z1rrpWwSfNK3ieKJ/F5JqItPDlqWJ9eE++nSr5Lc+SFwL4vnBPyOWzHYQu/APZLXKG2PzeP+ZU9eGKUP8eHca4x2E97fCnXxyigdU48jWuWE0/jfs5jj+O8Ll8m/1uaoyVi2JcScXec9GnC0yRfg/Wxjce4reDc2bNWLq3W+zbKVXsOoG9uN3m2njyFe2NmZrv24zFdVD/LszjGZrrwmTbJryFOfjUh+WU2WnhE8BZopYLtKqBc4jfYX4r2IsjIcz6L98BeXmZmSbrva8l/47pD10J88gS2uyMncB0W0k310do7Grg5oLsH+8PhW9A7bWVtT8/1UXx29IsIIYQQQgghhBBCCCGEEEJ0DL2IEEIIIYQQQgghhBBCCCFEx9CLCCGEEEIIIYQQQgghhBBCdAy9iBBCCCGEEEIIIYQQQgghRMe4LKewIAhamvWYmSUSZFTUi6YXuZxrLtfORIVNmvnao6Novjo+jqaJXIaRETTuNDM7cOAAxMfIVPGRRx6BmM2mM5kMxEky71xeRrO5pSU0+DVrbwTNRt9s0tXO9PtS4HPwNdaf1XM59/Plrd/+Nms0VuugQmZSy1mszziZq/T3uGa1PpnELGbRDHV4bA/EZTLlPTs1RZ+jadDeG9BAMBF323n2y2j6VFxGg56uCB5TJ4OzVA+2s4AMlbIlfH61qmscXG7iMct5NBQ88ugTELNpU1hHM53hHswBfXsxfuQ4mkiZmZ2YwXooNLAdTq8Z9qS7XEPETtLT32fBukEmm8eSwVaMcl+rPsJ9nI2OPOp/1qCcQAZdbOwXIVPhMTIdMjPbkUTj9vHDN0HcfdWtEM8VsF+Ua2hQOH1hEuL5WTSKq1Zdc/JKGftSKoX90ycjVTaT87zNcx3bF/oxchg1s0iAfcePYrvujT0Td8Vco7JOUm80rL6Wa8+fxzyTSeHz6yczwOOn0WTYzGx+AU18wxr22TjVd3cajcPi1OdzZBgYo0fstZgelCmvDHRnIM6Q0VsswP4UxLGvJBP4/BpkaFdv0e7aQu2I5yY8BnO7u5Rx0WdbQ5/P4cP/biWe523cA+eik5NPQjy7iHl8aABN1szM9mwfg/i2W++kb2BDaZRxnpQKaGyK4DO/MI0mz4mmO5UtD+Mx80X8TnEe21W5jOfMV+k5lHCsGsqguebkaZwbmpnV6pinT53D/vipBx+GeK6YhXixRjm4iv0vTgbMrUxYUwksZ4MMPtOx1ecetdZz+05x7bW7rdlYzUdFGg97U/jsuiiP16otzGZpXpfk+QoNqck4XiPGbtQ0ptYDzCvhsNvnR7ahIes3fTManCcT2KZyWWxzfRk0WTx5DM2vv/jZ+yB+0S03OmWohvh8v/LgAxCzWeSBgxgnmli3ZXo20QaOsuUV7LtmZstlHGciIT6Lkb6dZmbmR7Z2fDUz8xqheWv34NHYMdSPY9H+fbsgHhvE8dHMnR6a4fjV34P1WasMQNygueFTJ89DPLWA4+fZM66ZJxu1Zxcxz8yex7nBXsrPYQXX58t5zHXDI30QZ3rcedXyEuaq6akLeA0aV9ioPEZ9nNekEbJxzvS6c9xoiN8Z7MbnNdq3w8zc/YqO40c2Jqe8FmAD+/lFNLpdXnLXTeU81nXXGOadBq0futjYm9YTI2QYm0pgvyyVWhhFl7Bdcp329OA5k5TTGx6WcXoe2ywbF7MJvJmZF8M2xPN/3sfgdRObcEfj+HnIZ2wxzavTXJHngu32dzrJ4uz8xt4Jmxs7dUHzznrNXXMnUthHFxdw7pelHPD0acxlC9S2Fyk+c/oslil0x4c4rdd4/s9T8Z5eXGOOjWH+7aMtIn8Q23Eq5e6dxGmfb4bu+6N/848Qnzs3B3EY4Jjg+7i2LiWwUEGsRRnIwPrZ9vD471tBOpWyyFp7i1EfO3rkCMRxygsLi1hXZmY3Xn8dxMkA22E1j/OoXppHFYqYv646cBjiJk197/vSF5wy5BfxGhPbt0NcoXm1V8cxdHQY5+ELy2T8XsCG292DY66Z2c5tOyC+6abbIB4fxj3w/j7c856dxbrlud3cOex/mZS7f5oIsLKKRRwH8oXV+wpkVi2EEEIIIYQQQgghhBBCiG8E9CJCCCGEEEIIIYQQQgghhBAdQy8ihBBCCCGEEEIIIYQQQgjRMS7LI+JiWHeM/Ru6SZuxlSYj6y6zv0I6jfpUQ0Oog8h6uKy7Xq2iTinrBZqZxeOoN3fjjai5ytc8c+YMxCdPnoR4enoaYr7HctnVWmTaabrVSCvxcj0jWtUDH8Ma2evHtDq20/R3py1c0wHsHkDNs2gKteBipPsbibhN3Cfdx0IBtdrKddR0zOWxXR0/h3qA/QOo3b5nH+qvPnCfqzf3pc+hNnS0RD4VfaxViXpxMWr7zQa2iYVlbPvlmqtTOZvHtrmUw2P8ENtNH+kU9nehTt9gL7ahsTEs4x3X3uyU4e/vRR38R89gXQep1byRSrtadZ0kmoxt6M0WSau0UEFNvALptXZ3YZs0M4vHks7fLmaY9ANz5C3TIB3ZWoO8Fkjvc6DL1dDeN5jBeC+2qVMV1PucnkFt2lNnjkNcWkbNxFIO6yXdi9czM9t98FqI6zXS9WY/FRpXaqRPHfK7dNI5bbTIV5mxnRBvX7oKr3H68Y3/P53Y/LldacJm08I1TdlyBceK04voh3N+GjWYv/7k48Y0SdQ2Fsc+26AxcufENoj37t4FcU8ac92OMfz+WAsfpqCGzyBO+p4DaewvAZWpUkH91Cbl74ByfCN0hXz9kPRTjfKhx14keI7L92Fy2x17PzSpbYZrIvYhi9lvAc1mc0PL2JlP0PNaLmBeyBVd36vp89gWR4ZQ23T3zqvxGuR9UI1jHTRIKniQdGVnYm6+yzZwLFkuYx6vNzGnzuZRr7gYkjZ+iDm5N41zw8dKeLyZ2UwZ6+bMFGq0zleyEMeoLfcncB4dTeA1IxHyTuCKMjOfddOT2HZ7gtWxJMGGL1vAeknSNPdv0By3FJIeeZerUe9RH/Zp6lcnre6Q6sGLkkZ2heqDckiW5o1mZrv2o7/Y2DiOsWGdtIy78fNoBMe77dsnIO7L4PNfybpt7g2vfQXEiSTpKVO7Xy5j3eZKWYgbTeybAylcy0VoLmJm5lN+zXShLndy7b69yNb6kpiZWbO6+p+ZjQ2jz9K27Th+JWKYCxcXXT+ckNZKnNd9yvu+h59H2YeK9PvnL6DOeq3qes5NNjFXzVzAefVgD46xt9yI/mD3f+nLEF+gPHXzLajRPTTs+jPkcti2H3sMvYXOTWKZauQjMjo8CHGC1uYJmrvs27fXKUM0it9h34l1XXz+e6dpNhvWXFvDsjY/j/e8V1JoobVdKtHcu1RyvnMxvD+TiOM1EmkcPxOUj4MW+zcp8oBo5/HBa5bsCq6bGPZWCOItcj75jLBhC9ct98UYtbE43SffQyu/B/Y6ce77orFsq/X6n3zqhNXW5tO8v8ZtwlkqhW6eGRnC8apew7FjOZuFeH4eY/ZIzeVxTlYp49iUK7hzkiBKPnW0LxEEVMe0F+J5mCsbDfIGI5+u0VEcc83cfaXT53BcyLF3KQ9zUfaAxLrmZp3ocvtf0GaNsr4fGuGTbQHFUs7KpdXrc67KXsB59Mgojrkvvet253wR6tfZeazfEu3JlKqYD4MI5rfD19wA8aFr0de1J+HuN81N41zrZS9/OcTNBrbLrz7wRYiXyA/lRbegJ2cQx2sOD+GekJlZlNZiF85ju+vtxrrszeCYup3W69Pnz0G8PI1j9HyLvazhXbshdvr02h53ELi+Js+GfhEhhBBCCCGEEEIIIYQQQoiOoRcRQgghhBBCCCGEEEIIIYToGHoRIYQQQgghhBBCCCGEEEKIjnFZ4mFhGG7oB7NWHusesm5eK48I1jRjv4Ye0rVkvwYuA2v950jbPUv6da2uwd4WO3agtjFrQx89ehTiuTnU1uTvt9IYZFijmY9p59PQTt+6VRn4b88WvxAeEQ8/NbfhtRHEUV9ubAz9GEaHUYs2nXI1aNNJ1GKLUVwjvwaPtKK3D6FOesrPQly8gNpwxemvOmUoZlHbbXAA9Ru9COoa5hZQAy+o4OcN0pUtkb5xvurq+C6V8ZhKBXUKuwOMiyXsTwP9GYwHUDd2IIXn7+5z080rXozafN3bSU83vqrhy9qdnaZSqW5oLHoh3ved1+2DeDKLupkPHjnmnK+rG/NMo4F99OqD10A8O0u6wKSTv8i6fEuYd9Ixt65f8zr0v+nKYLkTx9Bz4It3fwnipalTEI/uRG+F/kHMz0vLWG9mZv1DqKFdKuB3kqQ/H/SgxuEQ6Q4WAmwXKyF7RLha/qUUadv2Ys7oDp45JhW08wK4slzsEcFa07Um9uEK9fF6ze3j9QaN06QL2yTd3kxfBuK9u7Gtv+iGGyBOJFCzMgzdMjRI29fPYy67uh+fx7//yR+B+IEnjkD8pfsegHjqAvoysZa7mVmTNF1Z47VJ9cQeSTyGsv5ljeu+xTAZIa3nCOsFh/S/W0gk8C2ypq3bpLHEI58RP8KeVK6WcIXa6plpzB1LBdLQTWI/Xqb8libN1v0T6PMy77vjfHeV5lHkAZCjrn2uSHrF9Ey9KOrMLlN+86Nuzp08i746rHWb6MK2P0gar7Em6cdTuwsi2G67k27b76FyJagdNtb8pRqXMDe9klTyNWvWV69druEcyyOPga40zo9SSXc+EPJ6IIeavR7dd4V8KHq6MxD7hnXt0xqHPerMzA5s3w5xaTmLcRXbjOfkAGqjJcyVPUM4x0q20I5OpbDN3Hor+gGUyLNnbnEW4sUS9r1e8gUqk68T+0GYmdV9aqc+Pj8/stqfvRb9ttPs2T1hlTUd4/4B7H+RAO+lWkF962YLG5Ua+QA2G24+vJhGgxM8zu1On0IfwhNHcPzbd8AdIII69p/KShbi4X6cR01P4/pjfhHXVYUiri9OPY1lWlp2236DdM57yENg7y7M2TzGjo3jWo49H82LbB6b68nIOWHdIqDRwteko4SNZ3TgadyJUU6Px3Csqxfduq53UT6MsjcCfp/3Z9gfoFbD541ZxyyIuHXNezwBndOnfQjPx3lglI73aGzk599qWhSwLwWV03c8I/B49kyLBJvno1beYO38wy6ua2fO12Fy+cqGV+rSMj1Vqotiifube775Rdwb2bGNPHVSuKYcGcF2un075gD2O+V26bd46Ozv5tMcKEZr32aN1014n01as0TJ0GFuierFzBrk9eSRIdWLbkZPnRNncY2SpfxqDezjjSiO4c24226CENs6jzvrni2x2NaPsalkYqP/Ly/imj7Ti3OYCu0BZOfx+2ZuO5mfpX1W6ufj29ELoVjAupmbxTlPhbzc9u3Y5ZTh8FXob5kk39Kd29GzqCeGz+xzn/0MxC955avxAhGcy12YonWtuWsO9sSdX8xCvJIj79kR9J0orZDnVRVzxIXJs04Z4n24X7JtHM85NLaaE3itsxn6RYQQQgghhBBCCCGEEEIIITqGXkQIIYQQQgghhBBCCCGEEKJj6EWEEEIIIYQQQgghhBBCCCE6xmV5RFwM6+BxzJqErKNn5urBseagoxfXxuuA/QtYg7IVrCnJ5+RrTkxMQMy+FCdOnICYtc1a+TNcru8Cl4mPZw08rofn4lOxXk+tNBI7zUotYrU1XdueKLUrKk9IWqlezW0DYRzbGetIsk9IPIbabT0xPOf0E+gJcOIrqAv92c886pThzCy2m+ksPpPhfmw3h3eiTq+R/vHJKdSCK1eoXbfQyo943P3xO/kq6hgmSvj91DKWMVfBMo16qAXI+nZmZqMD2MfDp1C7757Pr+rBp0mnuNNE475FG6ttrZ/0V9/x7tdB/PnPPInxVx5yzre0vEx/wT5899wMxOUyaVCG2D5SVB+s9RcfQ+1OM7Pxa1DLr3cUPR7iIXqZvGwPttHHTmE/uPrqWyCenTmN8VzWKUO5hG2qUsZyHz31dYhHhrDMh0f78fgkem/UY+hTEWm62rbJHNVtCfUmhy/yKkqSb9ELCY8SzrjRIjcH7I1gmLv6elEj97brDkCcJpHWCtVdXz/qOjdCt76rVcwLQRPLlEljDjhw8GqI77r+MMRveskdEH/4bz4G8ee/8ohThlqI10imsP806pjLymUsM+sZ89ykSDrfdcqdZu64y4/LX3tWjm78FtBshNZ0tMtXcTR5vWfxtrj4T9wUqVkss0dECbVO46R9GlJlnSeN17jfQsPaa+2FsE6Z5knzNdRHTSax7y/kSct2Ae/hyEnXGyhFfjTdXaivGpDHSpzuM0FlZt+mLtIzjrnVYNUq9tkwhvcVX9O6jbfwPOgk8zML1ljzhvBojB0cwTzPc7BSwdVt5rm4M/+nZuvRXLJRxzLwsysVsR6Xc1mnDD49rxXyKbE4XrOvB+dIDZqvNiJYpqEJHA/jpBVvZlYk76AmddB6DXNbkfw5llcWIQ74FoyMEkJWlDdrGLbrvm7Sh9/Idc95CfqcGRvut2p19Z7rDcrT5I/jU6Opt/DDYH82J6Z2WCO/tgb5EyWTWFc7tuNcLh5x1zQ18h6JRjkp430uki/IxPZxvOY2WleROUYrv0Vel0bIw4F93iKUs6vke1cq4pg6v4Da4V1dqNFtZhYjPxwehoK1z+vxFmYfHcQ3M3+tHzbqeJ/NNmvqeAu/t75enPcarbXcvRJsMx49m4DyK3/O/latrhElvwb23WLvqTo10ZC/b5t7L5iZheSpQ8sk7s7OvpRHExNnryTEZ9XKF4jZbE+p2cJro5PUGhGr1levWau22QfycV5QKmH/MzM7dnIS4rOTuG5NJXE+0pfBMXScNOoHyZNuaBDH/UjMnVzm8riWDsmPLEPeNJzD2QtjgXT1yzR+rhTdesiv4DmqVTzG93i9QXOJBp6z2cTvs+dBs8W4w2uQuvG4sHqOILL1e3a9mW5LVFbb08gw+hPlV3Cs6iPPiEWa25uZHTuJ+6rsWbN3L/oz9PbjOc9P4/HHnsA9m3wW/RhufRHubZiZ7dyBY+T8Eu65ZRdw3pSmXNFNezbVMrahdDeOZ9WSO6+q01wtCKhtV/CY5Tzmr2Hqb9t3opdGmtYTjbjrQdaVwXPkad7tr52j1ZjxbOgXEUIIIYQQQgghhBBCCCGE6Bh6ESGEEEIIIYQQQgghhBBCiI6hFxFCCCGEEEIIIYQQQgghhOgYlyXQGYbhhq4c6/W18zmIkX6gmatxxjFTJd1l9l/I51HDlfX+LqUM7e6LfSyGhlCTfHISNfS4zJfiscDX5Ji1/dr5XPDxl+LXwawfw/e/FQwP9Fm9vvosfdLKm51DXbZz585B3JV2NR0H+lCLra8HdXbTpB+eTKJOWiGLmvKTTz0OcbSGGoZ7hty6TUdRNy/0UJ9x5gJqL87MoKbg9lH8/mgN72k5izrAuaKrWV6pYbvpSuF9+iH2n1IN281KCeOZBdL+I63bvoxbDz1pbE+7tm+HePvc6n0lU64WcieJ1+rWXMsvCQ/rbjSDbSpJWtMZ1m81szzlqlQKn1dvbwbidDdeI6A+u65xvM7iFLaHwRZliPehbmK9D7WH+3achnh0AsvcSGOZPR+fyRNPPgZxGLramo8/fg/EQYBt4vzpIxAfm0S/lUeojWb6UFN0ZHA3xLsnrnfKMFDC+/rqkScgvm7omXMmYq5GYidphqE11/J1u3GgXWxmZqT1m/KwT7/ljlshftmN10B8/DTqdT5+hHRmYzie9fe57a5So3G7iu0iHcG2PHciiycgHeDdA5g73/vtb8MyVVwN7S98FdtVvUZ6xHWMA9Iub9DnTdIW98gUwfFRsFbjMsbRNd3Ty/WMuiLUvdX/zFxh7efiWeFt7s/F8yDWaq+RBwDPWZapjK1qzPExIw8MLmGTbrOeQ73Vrz/2MJ6PBKgXSCvVzKy3D+eHqQjm0ChpvCZIf78/if03GcM2U6riNQuNFmMseV0kSbt2cWX1HMktbnfdvV3WrK+WN+NoQ5N+fBHbQ6mMcxczMyNd5hjl7lwBNX2j3K4bNMYW8VmwZn3Vc8sQI/8x9r7IDKNHSJzyTDmPba47g2N2LIFj7uISe0+Z1ejfl0Vi1CaiODdskCZ2iub4jQq2sVqAzyIWb+VNhGVI0bhdXdMy9iKbrzs6QbNR3cjfYVsfQfbjc7W64+QjxbXBvYrXpZynBgb7IL7mMHp5tRofnDGWvQ7pGgF7KTiFxONZW79VGfhvHtUdVzWvW7leIjQ3DALS12+4Pg8NYx8mvO9GY+2aWzzErra51WuzVwIXJkK+Xn4Lre1oEscRHh85dtscfZ+uyY2Yn79Ze49P7io1nnOxbwVdNKC81UpznO+rXt+83XJdh+Sl6AU0j4vwustdR1/OHtJWz+2KpZpV1vI7e9Gwl6jrI+Ker0kecAtLODbMk9dllsYzo3YXpz4doziMufm5UsLxq0pxMYf3NTaG6wX2qkl14Rh7YfYsxEePn3bKUKvynhr1WfLfTKTJdzSBbaZE/jhNatfs9WdmFobYdoNo6z7v+e09Ya80jUZj4/phg/oYtbMI9ftcC/+hchHb2SDtuw6O4Lxqfh79hE6eRo+IUgnngrvJK6E75e4V18hDrkneJDQEm0fPMKQxuUF7QnXyUrSmO77lVtCXrkJ9uiuDc4cqex/Wqf/RHh17k/YOoS+GmTNNtgp5yXQ3V8vAvn6boV9ECCGEEEIIIYQQQgghhBCiY+hFhBBCCCGEEEIIIYQQQgghOoZeRAghhBBCCCGEEEIIIYQQomNcluC/53nP6nHAmoSsF8g6mq2+k2DtNtKy5XOwNh9r3pVIu4pjM1djkO+jnaYfl4ljPt9z8YhguMwct6OVziHXTbGIemiLi6teDK18NjpNpVjc0BOtsOdGBOuzh7T2vRblXS5h/eYL6G0QsSzEJOdnR7/2RYgXj5+E+MZdWIY33rrHKUPvILaT7hHsCw8+gvr9f/cPqJX/+AnUBh5M4fFp0oCt+K7eHGusNpuozxglvcxYQHVJ0oP5HOrL5VewDNvH0f/BzGx0dBTivRXUQD6XX638eHxrtfrP52etsKb5HZKO3h9+4GMQH38a9QiPnXzaOV+ljrnJo7yQIM+IOOt6k15qnfw9xshDY2Bsh1OGZp70T2fOQLx4Fs+xnEcdxp4M5oTlFbzvUgnbpGdumytUT0M8NLQTv0Carj4Jt1dWsB5n81MQT5O3imeut8juq66FuMo6s41n6qnevHSdwytNu3GAx5JWGrrcx68aRy3M2/dcDXGiQGMJlWG2kIW4izx5vIarKZn0sZxV0iQvkc5ldwY9eur0efbceYj7kuhL8Z0vfblThnwBx7eHT5yCuFYn/4A6eWGwz1ILD4iLYT1QMzOPn6czFwjpf7eOmBezmBdrefXacyoP3//m54hENvfB8qkNsYZuwxE5f8byYp0Y6fbWqf6jDfYJwZw7dWEe4iR55vR2o8+BmVmKfAR6I5gjMwH5DdE4UKY8P1+mMpLGfk/C1fJu0lgzuYTjdK62ekwqsrW5bmCoz8I1rf58GfVxS3nM8w3yfeFxwcz1kfJ9rIsEuYL41Oa6umgMjuCcozuJz2ol79Y1+y3weMa5itcsadKS5n5QI2+onh7UtzYzK5bxO1GjeiDftP5h1NCOkW9JLoeeaIU8jvvlsrueSHdhDu+iZ5MtrWkdt+i3naaru8tq1dVcx/XLcYW0u/3A9bS4FP+Ey6Gdh0SrNWScdLg5+zrrWtarpvHO6HNea7fyGXTuO2StfPrY2o25WIZYgGsFx4SgVRmMx5XVONZiP6KT+PbMv/pkrwtuP6zlXwtb5OUo5iaPzuHYUPibe1369DnXPedSM7MI+4zwfZAnhMf3TXmJ+57TJlt0K/YRidDz57p192PIK4PWuNzCuIyt/nYpezxbRaFQtPLaeMDF4jzDHhE8HpqZdXfj+FSmcZvzeW8vjjUjI2MQR2LYhpZyuBeT6nbXb13dqIO/XMtCPDOD87TTp3GNUqfnlS9gji+S/1SB/KlWofU7rc+r1HYrK+hJwPOCGvX5Kq1HSi3GWI/We7x/uk6jeXn7g1eCWCRqtja/Ok0eqimaR/k0z7YW89B0AvP1jTccgviqQwcgPnMG9wXi1JYzaTxfooLtePL0aacMuRzO3ZvkbeFTW66RJ12T+kaM/MMqeTx/0CLhpeNYVwkqw0oW2/7wEM7tIjX02iiR/1dI66wob36aWdTDtsrXGFxbv/OabTP0iwghhBBCCCGEEEIIIYQQQnQMvYgQQgghhBBCCCGEEEIIIUTH0IsIIYQQQgghhBBCCCGEEEJ0jMvyiDB7Rv+OtfbaeSGsa/zDxUmnkHUou0hntK+vb9N4cBC1qvj8K6zTZmZzc6h/ypqCadIBdrWLN9dpY0+FVtq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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "F_eVM4GIS0JE"
      },
      "execution_count": 15,
      "outputs": []
    }
  ]
}