Copy_of_S+P_Week_2_Exercise_Question.ipynb

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  "metadata": {
    "colab": {
      "name": "Copy of S+P_Week_2_Exercise_Question.ipynb",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RhuthuHegde/Tensorflow-coursera/blob/master/Copy_of_S%2BP_Week_2_Exercise_Question.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zX4Kg8DUTKWO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "both",
        "colab_type": "code",
        "id": "D1J15Vh_1Jih",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c9849b08-3dd5-4915-e4af-f8b5baac0cef"
      },
      "source": [
        "!pip install tensorflow==2.0.0\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/46/0f/7bd55361168bb32796b360ad15a25de6966c9c1beb58a8e30c01c8279862/tensorflow-2.0.0-cp36-cp36m-manylinux2010_x86_64.whl (86.3MB)\n",
            "\u001b[K     |████████████████████████████████| 86.3MB 66kB/s \n",
            "\u001b[?25hRequirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (3.3.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.8.1)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (3.12.4)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.15.0)\n",
            "Collecting tensorflow-estimator<2.1.0,>=2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fc/08/8b927337b7019c374719145d1dceba21a8bb909b93b1ad6f8fb7d22c1ca1/tensorflow_estimator-2.0.1-py2.py3-none-any.whl (449kB)\n",
            "\u001b[K     |████████████████████████████████| 450kB 43.3MB/s \n",
            "\u001b[?25hCollecting keras-applications>=1.0.8\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/e3/19762fdfc62877ae9102edf6342d71b28fbfd9dea3d2f96a882ce099b03f/Keras_Applications-1.0.8-py3-none-any.whl (50kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 4.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.1.2)\n",
            "Collecting tensorboard<2.1.0,>=2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/76/54/99b9d5d52d5cb732f099baaaf7740403e83fe6b0cedde940fabd2b13d75a/tensorboard-2.0.2-py3-none-any.whl (3.8MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8MB 42.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.12.1)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.31.0)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.18.5)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.9.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.1.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.2.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.34.2)\n",
            "Collecting gast==0.2.2\n",
            "  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow==2.0.0) (49.2.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow==2.0.0) (2.10.0)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.17.2)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.0.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (0.4.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.2.2)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (2.23.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (4.6)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (4.1.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (0.2.8)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.7.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (2020.6.20)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.24.3)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3\"->google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.1.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.1.0)\n",
            "Building wheels for collected packages: gast\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7540 sha256=8ed0fe107d3b29677d247550b933df3904a30250c6ea29c36496531dfdfa6759\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
            "Successfully built gast\n",
            "\u001b[31mERROR: tensorflow-probability 0.11.0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "Installing collected packages: tensorflow-estimator, keras-applications, tensorboard, gast, tensorflow\n",
            "  Found existing installation: tensorflow-estimator 2.3.0\n",
            "    Uninstalling tensorflow-estimator-2.3.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.3.0\n",
            "  Found existing installation: tensorboard 2.3.0\n",
            "    Uninstalling tensorboard-2.3.0:\n",
            "      Successfully uninstalled tensorboard-2.3.0\n",
            "  Found existing installation: gast 0.3.3\n",
            "    Uninstalling gast-0.3.3:\n",
            "      Successfully uninstalled gast-0.3.3\n",
            "  Found existing installation: tensorflow 2.3.0\n",
            "    Uninstalling tensorflow-2.3.0:\n",
            "      Successfully uninstalled tensorflow-2.3.0\n",
            "Successfully installed gast-0.2.2 keras-applications-1.0.8 tensorboard-2.0.2 tensorflow-2.0.0 tensorflow-estimator-2.0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BOjujz601HcS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b62b06e6-82bc-4ed7-c3d3-f1b2cfed0e65"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Zswl7jRtGzkk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "466317c1-ad90-4033-a663-85698d6edf25"
      },
      "source": [
        "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
        "    plt.plot(time[start:end], series[start:end], format)\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Value\")\n",
        "    plt.grid(False)\n",
        "\n",
        "def trend(time, slope=0):\n",
        "    return slope * time\n",
        "\n",
        "def seasonal_pattern(season_time):\n",
        "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
        "    return np.where(season_time < 0.1,\n",
        "                    np.cos(season_time * 6 * np.pi),\n",
        "                    3 / np.exp(9 * season_time))\n",
        "\n",
        "def seasonality(time, period, amplitude=1, phase=0):\n",
        "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
        "    season_time = ((time + phase) % period) / period\n",
        "    return amplitude * seasonal_pattern(season_time)\n",
        "\n",
        "def noise(time, noise_level=1, seed=None):\n",
        "    rnd = np.random.RandomState(seed)\n",
        "    return rnd.randn(len(time)) * noise_level\n",
        "\n",
        "time = np.arange(10 * 365 + 1, dtype=\"float32\")\n",
        "baseline = 10\n",
        "series = trend(time, 0.2)  \n",
        "baseline = 10\n",
        "amplitude = 40\n",
        "slope = 0.005\n",
        "noise_level = 3\n",
        "\n",
        "# Create the series\n",
        "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
        "# Update with noise\n",
        "series += noise(time, noise_level, seed=51)\n",
        "\n",
        "split_time = 3000\n",
        "time_train = time[:split_time]\n",
        "x_train = series[:split_time]\n",
        "time_valid = time[split_time:]\n",
        "x_valid = series[split_time:]\n",
        "\n",
        "window_size = 20\n",
        "batch_size = 32\n",
        "shuffle_buffer_size = 1000\n",
        "\n",
        "plot_series(time, series)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GfUTNqti_lNC"
      },
      "source": [
        "Desired output -- a chart that looks like this:\n",
        "\n",
        "![Chart showing upward trend and seasonailty](http://www.laurencemoroney.com/wp-content/uploads/2019/07/plot1.png)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4sTTIOCbyShY",
        "colab": {}
      },
      "source": [
        "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
        "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
        "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
        "  dataset = dataset.batch(batch_size).prefetch(1)\n",
        "  return dataset"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TW-vT7eLYAdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "db37cc61-31d9-42be-be51-a66b42ca1eae"
      },
      "source": [
        "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
        "\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Dense(100,input_shape=[window_size],activation='relu'),\n",
        "    tf.keras.layers.Dense(10, activation=\"relu\"), \n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "\n",
        "model.compile(loss='mse', optimizer=tf.keras.optimizers.SGD(lr=2e-6,momentum=0.9))\n",
        "model.fit(dataset,epochs=100,verbose=0)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f43ddf46780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "efhco2rYyIFF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "d5c5c9fc-2c3d-4140-ba90-0a49d7255db2"
      },
      "source": [
        "forecast = []\n",
        "for time in range(len(series) - window_size):\n",
        "  forecast.append(model.predict(series[time:time + window_size][np.newaxis]))\n",
        "\n",
        "forecast = forecast[split_time-window_size:]\n",
        "results = np.array(forecast)[:, 0, 0]\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, results)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fDn07/NnlOIrrh+IpOPw9kIrDmgcZdKYak5YEXkJMRempxsoeR8kCL6VUC/B24CYArXVca90DvBe4xdnsFuD8/K8ghNiTuYNgOuNlB16dtGBM25eAMx/QwTT0gD/9s9yQa6waj7qQyXA8xZaGI9mtWln1+F8ZdjNeEngJMSXVQuf6A4AO4PdKqZeUUjcqpRqAWVpr9/R1JzAr35OVUhcrpZYopZZ0dHSU8DCFEJXg1VtYdiYpc6oxYChCTsarQ7eAT4EXjCPjlUhx8zObWJbcj0DPegadwCuRkhovIaYi75RpT814AQHgOODXWutjgUFyphW13c0s7+mj1voGrfUCrfWCtra2Eh6mEKISvMArI+PlBlumqbzMUoeehkoMQWzAl50WU1xfHwqQsjS7+mPs1K3spTvtmi8k4yXElOVe0LMH13htBbZqrZ93bt+JHYi1K6VmAzj/SlMcIWqQV1yfShfXf+2M+QBEAqbXtqFDt9iP+5D1Sue6xrqq0bT33Rdju55Bs+4jGRsCpMZLiKnKWzJoT814aa13AluUUoc6d50OvAbcA1zk3HcRcHepjkEIUb285TsyMl5fOuVgNl59DqGAQV/Uvr+DaQD85dEXfNin205i9O3qQ3bg1d4fZaduBaA5YZc8SMZLiKmpFmq8AP4duFUptRw4BrgKuBo4Uym1FjjDuS2EqDHe2WfKWYfRyG4n8bXT53Pk3GY6tB14Pbd8tQ/71M7Z7tjF9QDtfVF2MAOAloSdnE9a2gsahRBTR3qtxsqGXoFSvrjWehmwIM9Dp5dyv0KI6pevxivTXs0Rrv3QMXz0Wnudxn1C/b7ss9gaL4BowmKHsjNes+mkLmgynEhhaTArfdoshBgX72SvokchneuFEBXjtJNI5W+gCtAQDtBFE1Ed5HDDn4WyjSKvanTt0HbGa2/VRXOdHZBJ93ohph5vqrHCkVdJM15CCFGINwhqt51EaMQ2DeEAFgYPWm/i9ORTkIhCMDLxfbpfFNHHyxUjRFybNKooxwY3EzLWkUqdBQFzlFcQQlSbdMZrz72qUQghCnIHQbPAWo0AjWH73PDvqbfSqAdg8zOT26cef8YLIEqIMAm+Er+ZX4R+ifHsLyZ1HEKI8tNVUl0vgZcQoiJGLpI9MuNlOt3r1+nZ9h39Oye1T7db/lgZr4ZQIONrkxhBwsS941E7X5nUcQghKqfSU40SeAkhKiLdub5w4OXq0s32F4OTXMWiyLUaZ7ekpzNbG0PECBFRCRqxe3kR96GZqxCirKok4SWBlxCiMrx6C8tpJ5FnqtE1SIS4CsHg7knv01BjX9UYMNNDY2tDmJi2M14N1qB9Z3xwUschhCi/9CLZUuMlhKhBXk+d1Mi1GkdS9BnTJh142a+U/v9o5k6rA2BGg5PxIkGd5WS6EkOTPg4hRHl5WXaZahRC1DJVoI+X68ZP2q0A+4yWSU816iLXagSYP6sRsBfsjhIkQoxIyg68lGS8hJhyLG+qUTJeQoga5NVbuH28jPwZrzPeMIsTDmil15jmQ+BVfMbrxx84ik+/ZX9OPmgGMR1ipup16sOwF+0WQkwp6anGyh6HBF5CiIrQaGbRResL19h3GIWHo5Bp0KNaYKhzkvt0FDHy7tUU4fL3HEF9KECUIG2qF4BBHcZISMZLiKmmWlb6ksBLCFERWsPHA48UtW3AVHQrZ6pxEqOnLvKqxkzBgCJGiJmqD4CdulUyXkJMYZLxEkLUJK1hi24ratugadgtJZJRlq/fxtbuiQU+Gm2HXOMYeUOm3cfLtVO3YlhxcKdIhRBTgndBj9R4CSFqkQY3DBpTyDTopQGAz//uUT70m2cnvF+lxpnxMhVRnS7834m9aPauzi6efn3yV1kKIcqjWtZqlMBLCFER3hWGAF97ddRtg6aiV9cD0KwG6RyMT3CfFH1VoysUMLIyXju0HXj9+y1PceGNz5NMyYLZQkwF6bUaK0sCLyFERWTlnYpoaNpj2X21zjOf4YK5vRPe73gH3ZBpECUj4+UEXj29PQDs6I1O+FiEEOWTznjJVKMQogZprTFwskVq9KEoaBp0W3bG68uBe/hh++cnvM/JZLwsFWCXngbAnHr72Ld0SaG9EFOB106iwschgZcQoiLSPbVgrKEwZCp6nYzXpPbJBK5qNA1i2gm8Qk0MYB/HhcZDgGbLBAv9hRDlJTVeQoialg6CKCrj1ZXyIfByg71xZrzcqUYr2MCwDgNwRvQh3qRWs6VreNLHJYQoPa/GS6YahRC1KN1Ti6JqvLp9CLxgghkvd6oxWM8gEe+xBhVls0w1CjE1VEkHVQm8hBAVYffUKi7jFTIVg6nJD1cT6eMVzsh46WB9VqF9K32090lxvRBTgaby04wggZcQokK0BqPIC7yDpj9D1UQ614cC6RovgvVs1zN4InUUAK2qn2gi5cuxCSFKK7uutHIk8BJCVER2jdcYgVfAp8DL3ec4TnuDpkGCgP38UANJAlyU+BZxbTJX7UbHZapRiKlAozGqIOUVqPQBCCFqVGYD1bECr3wZLysFhjneXTpnvOPLeIVwlgcKNjj3Krpo5tOBBzm3/wVgw7iOQwhRftb4zrlKRjJeQoiK0IxnqjHP4/GBCe1VqfFmvBQNKmbfCDV493frJgDadNcEjkMIUW72iVflIy8JvIQQFWFfYFR8OwmAF62D03fGJhJ4TSDjZRqssvYBIHHgGd79EWLpjarkaikhRGGa6ijyksBLCFERduf68U01fjB+OZdaX7LvnEDGS2vnf+PIeCml+OMV/07qG+vRh53r3b+36k5vNNQ57mMRQpRZdcRdEngJISpjfA1U7eEyhUkyZC/ZQ6x//PvU2FON4xx+G8MBzMYZmEb6eZ00e18nu7cAsKs/Sl80Me7jEkKUnrSTEELUtIm2k9ARJ+CZSOA1gT5emQJG+jgujH+bP6nzAEg4gdcJP1jIGdc8MaHXFkKUlr1Wa+UjLwm8hBAVYWmdHgLHcVWjGbGL2ic61TjePl6ZMjNem/Te/LPh/QAku7d69+/qj414nhCi8sZZZVAyEngJISpmvFONAMF6O+Olo33j3t9E+nhlChjZz1MNbcR0EN2zeUKvJ4Qon4mfcvlLAi8hREVkrdU4xnAYysh4BetbAUgN9Uxov5MZeI2cwKulIcwmvRcvLVvK67smdpWlEKI87IxX5UMvCbyEEBUxnrUaAxmBV6hpOgltYg20j3+fenIZr1zNdUE26NnMSW7jx7+9iePV6jGf09Ef49FV4z92IcTkeDWeFSaBlxCiIrKK68cIhOZMi2AaiiPnNrNXcx2dNKMHdo1/n16w58/wGw4YbNB7M9/Yxg2p/+aW0I/GfM6FNz7HZ/6whGTK8uUYhBDF0VUy1yhLBgkhKiKrncQYo+G86fWsufJdmIbiT89tYrduYfpAx4R2OpmrGnMFTYMNerZ3u1FFMRl90ew17faUZNLSBMa34pEQYpKqIO6SjJcQojLSPbUoKhByrygMGordugU1OJGM1+SuaswVNBXb9Mys+2bSW9RzE5LxEqKstNZS4yWEqF3ZNV7jWTvRYDctGEPjz3hpPbk+XpneNn8m7zl6Dk9bR/DNxMV8Kf4fAMxWxa3dmEzJMkNClFNNNFBVSm1USr2ilFqmlFri3NeqlHpYKbXW+Xd6KY9BCFGd3EJ3PUZhfa6AaWe8zKHOCa6R6E/A86fPnsjMxjAagztSp7BJ7w3AfwTugiLqzxKWZLyEKCddQ0sGnaq1PkZrvcC5fSmwUGs9H1jo3BZC1CBjAtN+IdOgQzejrDhEx9dSwq+pxrcebE8v1gXTRVo7tN3m4jRzGdZD3xvzNSTjJUR5aTRGFaS8KlFc/17gFOfrW4DHgW9V4DiEEBVkT/vpMVtJ5AqaBrt1i31jYBfUFZ801z4U16+76t1e2BbJCLy6aPK+TmGOeVYrgZcQ5WXVSOd6DTyklFqqlLrYuW+W1nqH8/VOYFaJj0EIUYW8dhLjHAkDpuJ1Pc++sWP5+PbJ5DNepqG8RqrhQOYQmn7NVGLsZYNkqlGI8rIrEyofeZU68Hqr1vo44F3Al5VSb898UGutKVBwoZS6WCm1RCm1pKNjApeNCyGqWjr8Gf9U4yq9D6lAA2x5fnz79LJs/gy+hqG46KT9+NNnTwDg1Ng1bLbaiqrxkoyXEOWm9/yMl9Z6m/PvLuDvwAlAu1JqNoDzb94RSmt9g9Z6gdZ6QVtbWykPUwhRAZZ73jXeqcaAQQqTvpnHwJbnxvXciQZ7o/mf9x7J2+bbY9QGPZuVej/UoN2Z/iM3PMu9y7fnfZ60kxCivPb44nqlVINSqsn9GjgLeBW4B7jI2ewi4O5SHYMQonpNeKrRmebrmXk87HwVBjvHu+eSFnrYV1zupr0vynPru/jKX17Ku13SkoyXEOWka6DGaxbwlFLqZWAxcJ/W+gHgauBMpdRa4AznthCixrj1VmoCxfUAO2e9zX6VdQvHtVM/G6jm00ELZrSL5Zt2A3DorKa828mSQUKUl907sPKRV8muatRarweOznN/J3B6qfYrhJgitJ5YOwmnoH1nw2FYdTMx1j4ER32ouF3iXwPVQnbrFhSadZs2AXBgW0Pe7RJS4yVEWdVCxksIIQryrjAcbwNVZ6rxkr+9wpMDc6Dz9eL3WY6Ml54GwNYtG4DCA31SrmoUoqyqZI1sCbyEEJWRrvEa3/PcqUaATprRg7vHtU8/r2rMx22k2rndDrxiifwBllzVKER52RmvyodeEngJISrC7iI98alGgC7dBEPFF9d7U40lOO+95ytv4b/PfQM79AwAZlp2QBhNpvJuL1c1ClFe2qflwiZLAi8hREWku8iPbxhqqQt6X3fpZlRiCOJDRe+TCWTZinHUvGm895g57KaZOAHmqk6OntfiZbx+t2g9d724FYCzjcXM2nCX/wchhCisSmq8ii6uV0rVa62LG92mmJc2d7Oxc5D3HTuv0ociRM3QgIE17pEwEjSZVh+kZyhBJ832nUO7IbRvUc8vZY1XXchEY7DDamUfs5OZjWHa+6MA/OBfK73tfhu6DpYA536pJMchhBhJUx2B15inmkqpk5VSrwGrnNtHK6WuL/mRldE9L2/nv/+xotKHIURNmWjGC2BWUwRwphqh6OlGL+Qq0egbCdhrN27XM5mjuggHjYI1XgADsWRJjkMIMZK9ckXlI69iRrxrgbOBTgCt9cvA20d9xhQTNA1ZN02IMptojRfAjMYQAJ3ayXgV2US11Fc1GoYiFDDYTTPHsoqP7f5fYolEwZ5d1z9W/BWZQojJmTIZLwCt9Zacu/JXi05RQVNJTx0hym0SVxi6i1N34Wa8ir2y0V2mqHSjbyRgsNw6EIC3dv+dC2N/oy+aP7PV2T/2YtpCCH9MpSWDtiilTga0UiqolPoGsHKsJ00lAcMgZWnsNbuFEOVgaW0PQBOYanRbSnhTjUW2lEgPvKUbfutCJr9PvZOPTruV5dPP4rPWHQxufdV7XJHOfqUSe2TZrBBVSQNGFaS8ihnxvgB8GZgLbAOOcW7vMdzL0yXrJUT5aM2EpxqDznu2jwYsFSg645Vu2lrCjFfQJEmAeLiVp/b9AgEsrM3Pe483kQ62moa2uJdaCiFKzKqSlNeYgZfWerfW+kKt9Syt9V5a6487y/7sMdxO2NJXR4jymWjneoB3HrG385UiGpw2rl5epe5cXxc0vX/j9bOxtMLqsdtIvNt4jgPUTm/by7f+Gyz9Q8mORQiRoTrirrHbSSilfg8ju45prT9TkiOqAHfaQjpJC1E+6c714x8K33P0HE46aAYLrnyEoeA06osuri/9Wo1hJ/CKBA1C4RAdtHDAil/y++CTnGq+TK+uz37CukdhwadLdjxCCJtGV0Xn+mL6eN2b8XUEeB+wvTSHUxlB0/5FxCXjJUTZ2F3kJz7tN6MhRMBQDJrTmDmOqcZSr9gWcaZBw0GTcMBkh25llurhVPNlAFpUTl1XKP8i2kIIf1XJTOPYgZfW+v8ybyul/go8VbIjqgAv4yUtJYQom8nUeIG95lp9yKTfbIHBjUXvs9Q1XnWh9FRjJGjQrluB9YWfEIiU7FiEEGm6SjrXT2TJoPnAXn4fSCUFnMArkZSpRo1f7RIAACAASURBVCHKabJBUEM4QK9qGWdxPZQ245Weagw7X4/KkiaqQpSDnWWvfORVTI1XP+nxSgM7gW+V+LjKyp1qlCaqQpRPut5q4kvG1oVMelQzRHshlQAzOOr29j7Lm/HKvIoxr2hPyY5FCJE2ZTJeWusmrXVzxr+H5E4/TnXuVKNc1ShE+dhrNU6u3qohFOCVbifYGuoax55L2U7CcP61a7y+n/wEj6SOzdrG0hn7j/aO+ZrtfVHiSRmfhJiMapnTKhh4KaWOG+2/ch5kqbntJOSqRiHKJ31V48QzXvvNqGdztM6+UeR0Yzn6eLn/hgMGK/V+fC7xTfjcQvjwnyHUyMcTl6WfMDx6xktrzYlXLeSS25eV7JiFqAV2xqvyKa/RphqvGeUxDZzm87FUTDAgGS8hys2+tHtyQdB1Hz6Gj7+60L5RRPf6cnSuzwy83K+n1Qdh3gJ7g8PO5ZnL/sXViY/wicDD7D3cw9bOQfabkf/qRrex832v7OBXJTtqIWqBroIKr1ECL631qeU8kEoKGnbgtXxrLwfv1UhTZPQ6ESHE5GkNhp7ctF/ANNCRaWBRVK2ULsNajZkNVC2nK/2+rRm9u5x9/yZ1HnurLi7ofZp3/ORxNl59Tt7Xk6uthfBHtdR4FdPHC6XUkcAbsPt4AaC1/mOpDqrc3OL6y+9ZwX2v7OBvnz+pwkckxJ5P4wyCk5hqBIibDXbgFR8ce58aVImX6EnXeBns1RQG4MIT9827bS8NNOohFJZd+J/nUyFpSQmEEH7wxpwKK+aqxsuBU7ADr38B78Lu47XHBF5uOwmAxRuKLdAVQkyG1nrCneszpYL1kABiA0Xsk5J3rs+cajywrZHlV5xFc4Esep+ux1CaSwJ3En+pn/BxHxuxjdSeCuEP+6rmykdexZxqfgA4Hdiptf40cDTQUtKjKrOQObkzbiHE+KWbmU7u/Zc0ndqo+NiBF5R+rcZIxlQjUDDoAujRTQD8R+AfhO/5Yt5tklJ7KoQvpkzGC4hqrS2lVFIp1QzsAvYp8XGVVcBUfNx8mGEdZrrqh8TpEJRu0kKUkndV4ySDIBUIk8LALCLwKkeNlxt4hYNjB5Sv6APG3CYhU41C+KLqr2pUSv0K+CuwWCk1DfgdsBQYAJ4tz+GVR9DQXBn8ffqOp+bBqd+u3AEJUQMmu1ajKxw0iao6Goqt8QJKmfFqCtvDamN47PPatXpu1u3OgRgzGsNZ90nGSwh/WLo6rmoc7ZRsDfAT4Fzg28DzwJnARc6U4x6jvnt19h39OypzIELUEG+txkkGXqGAwbCqK2qq0c6vlTbj9db5M7nuw8fwxrljV2TonCH48j8+OGIbKa4Xwj9VkPAqHHhprX+utT4JeDvQCdwMPAC8Tyk1v0zHVxb1O57LviMZq8yBCFFDvCBokueg4YBpB15FFNenW1eXbvQNmgbnHzu36CmNd8V+yDLrQAB+uesT8PojWY9Lcb0Q/khnvCurmCWDNmmtf6S1Phb4KHA+sKrkR1ZGgWTOgJ0Y9r5csb2XPzy9ocxHJMSeL32F4eSK60MBg2HCxbWTKEON13it1PtxS/Ls9B2d67Iel8bOQvjDbtpc+ff+mCOeUiqglHqPUupW4H5gNXBByY+sjBInf4OrEh9N35HRiPGcXzzFFf98rQJHJcSeza8ar1DAYJDiphqh9Fc1FmPpd8/g/754sne7i+b0g6HsDvYy1SiEP6ol4zVacf2Z2BmudwOLgduAi7XWY59WTjHBgEE3Tek7+kbWeBVqbiiEmCCf2kmEAwaDOlJcjVcZ+ngVY0ZjGNNIH0Onzhh/on1Z20pxvRD+qJbO9aONeJcBzwCHa63P01r/ZU8MusBeJLtXZ5xldq6Fx3+UtU1cBj8hfKXxp51EOGAwoCNFTjW6/6/86NuQcdVjl87IeMVyAi/JeAnhCzvLXvn3/mjF9adprW/UWneX84AqIWgadGeecQI8flXWzVhSAi8h/GR3kZ58xitkGgzocJGd6/3plu+HoGkQDtjfe2fmVOOIjJcEXkL4YZJLw/pGWrYDpqHophGAB1JvInHIufaHQSrpbROXwEsIX3kZLx/6ePVbxRbXu6pg9CW9asZJh87lPP0z+oxmiPZmbZOQRbKF8EWVxF0SeLl6tR14degWBue9HbQFgx3e4xJ4CeEvb8mgSQ6FIdOgzwpDYhDGCFK0O21XBRkvsFfNAHjb/Dam7fdGutV0iGUHXpLxEsIn1ZHslsDL1UMjcW3SoaexJWmn/aNdW73HCwVeX/jTUj79+8VlOUYh9iReF2kfiusHtLPEV2L0rJfycl5VMPoCASfjFQka1AUNBqgfkfGS4noh/FEtNV7FrNVYExIEuDD+HdbquTz6cAf3hmGwcysQAgoX1z+wYmcZj1KIPYcGDGX50k6iD+fimKEuCDeNsnV1ZbzcqcZwwKQuaNJH/cgar4zierm6WoiJmwpXNdacF/Rh9NBEu54OQKxrm/dYLGGxamcfl9y+TM5AhfCDT60dQgGDnbrVvjHWcl+62jJe9nFEggaRoEmfVTcy45UxfSpXVwsxcZoaCbyUUqZS6iWl1L3O7QOUUs8rpV5XSt2ulAqV+hjGo60pTCctaGWQ6NkOwAfNx2lc9Tf+468v8feXtrFh9x7ZVUOIsrLT/hZ+LBm0Xc+wb/RuHX1jL+M1qV36xu3lFQmYRIIm3bp+RDuJREaNl9SaCjFx9pXUlX/zlyPj9VVgZcbtHwHXaq0PBrqBz5bhGIp228VvxsIgGpqBds6efxK8gQOe+ob3C+sdThBNpCp5mEJMeX41M83MeP1j0Qv85ol1hTeusoxX0HCmGoMGdSGT3lQdDHVCfMjbJimBlxC+qImMl1JqHnAOcKNzWwGnAXc6m9yCvfZj1dirKQzAUKAFhrJbmLm/sE/ctJir79+jlqsUouy8qxp9KK7vpx4r2Ej3jg2jvjd1ldV4BQPuVKNJJGCy1bLLHPhzelW2EVONieGsVjdCiOLYNV6Vf++XOuN1HfBfgDtyzAB6tNbuqLEVmJvviUqpi5VSS5RSSzo6OvJtUhKN4QB1QZMB1YiK9WCQHvTcX9hwIkV7X3TM11qxvZelm/b4/rNCTIhG+9K5PuQ0IY037M1s1TXqtorqas0QcDJehoK6kMGtqTNIHnQmbHvRC64yM16xhAU3nQmPfr8ixyvEVKbdK6krrGSBl1LqXGCX1nrpRJ6vtb5Ba71Aa72gra3N56MrTCnFrOYwvTQQjPfRSn/6sYztiulkf84vnuL9v36mBEcpxNTnV8YrHXjNZm/VOfZOgaqZanSK6xMpTV3QxMJgaP55kIpBlz1lmpnxSg71wM5XoHtDRY5XiKmsWqYaS9lO4i3AeUqpdwMRoBn4OTBNKRVwsl7zgG2jvEbZXPauw6gPmQDs1RShq7eeuck+DoiklyEJqHRdVywpNV5CTIad6/Khc70TeMUie7GXWjHGTqtrqvHUw/bihY3d7N0cYXOXXdc1NP0wewGh9hXQdmhGcb3G6HjN/jLWn+/lhBCj8OpKK6xkGS+t9WVa63la6/2BjwCPaq0vBB4DPuBsdhFwd6mOYTw+/46D+MRJ+wMwsynE7mQddakBDm1MF7k26GHvaylyFWJy/KzxAogFmmhieNRtq23JoC++4yCevew09p/ZQF3QPvEbaDoQlAntK+gajPPqtl4OUVvYGLmQbff/zH6iBF5CjJumOvrgVaKB6reA25RSVwIvATdV4BhGVRcM0K0bqGeY+eFecJJejQwCdYA91Sj9vISYDH9qvCJOwDJs1DNPDWfVZY7cpfNYFQy+YJc2zG6xxxQ38BqyAlgz57P65Wd59yMPozV80LSnHd+WdEoXcpqsCiHGVi0Zr7IEXlrrx4HHna/XAyeUY78TFQ4adKbqAZhvpGdCGxnCC7wSli/NDF/f1c8BMxu9fj5C1Ip0xmtyf/v7z2hAKdg+HGI+0DhK1qvalgzK5AaQQ/EUu+oOomnX897MaJ+uz95YMl5CjJt0rq9i4YDB9pi99tvJHbd7958Yf847m46nrElPN3b0xzjr2kU8JMsOiRpkr9U4+anGhnCAA2Y28Hq/Hbg0MVRwW2/MrYbRN8fc6fZJ3abOQXZGDmae2u19L3XEszeWwEuIcbPPYyr/3pfAK49wwEyv/QZ8Pf4FAD4yeCsfNR8FIJZIZQVeWo//MvXe4TiWhs7B+NgbC7GH8YrrffCG2c2sdDpJNKnCGS9dZVc1ZtqvtZ7GcIAV2/tYZ+wLwOcC9wHQmPE97aIV4v1gFXfid+/y7ex/6X30DiX8P2ghphB7rdNKH4UEXnmFAwa9Oh14vWIc6n29wFjNCWolsaSV1VIicyHbXIWCMvf5UqgvalG6c/3kh6HDZzezI2avPjZaxstrKVgNo28Ow1AcPruJB17dyV+3zgTgq4G/c4DaQT3pvoFLOcz+Il5c1svt5L+pS5Y6E6Ia3vkSeOURCZr0O7VcyUADMbPRe+x95tP8Lfx94hmB1zT6SSViBV+vUC2YF3hJkb6oQRrsqXsfgqAZDSH6nTqoJjVK4OWdA1XD8DvSYXs3s6s/xpLdQb4Y/yoAe9FDg7IDr97Q3ixOzrc3dqYbF63pYPdA4fHH66BRpd+zEOUiNV5VLBww2Krb2GK1sfot17EzNnId71gyXeO1LPJ5And8kiUbu7ht8eYR2xbKaLn3xxISeInqt7a9f0JT6oX4Oe0XDtrLBgF8OXA39O0otFdnl1Uw+ubxiZP241Mn788Fx85lg54NwFHGOg5S2yHUxJ/efC/tVou9cayf3z6xjk/evJjfjrI+pVXd37IQZaOpnUWyp5xw0CBKmLfFf070wDOIE2SDNStrG5WKEk2mCGIv6xFY9xAf+M2zXHrXKyNer1CX+3TGS5qxiuq2aE0HZ167iDuWbvX1de2M1+SHoUjA9DJexxtr4b6v59/QbSdRBYNvPofMauKK847gZx8+hvkH7AfAd4J/4VzzeQg1UB8KMOBk44n1869X7QtzhhMjx5BXt/Xy8pYeL8BNSGZd1DjJeFWxcMAc8fWp8Wt50TrYu38aAwxEkzQzdt3EWBkvqfES1e71XXYzu9e2+9c/yq92EuBmvOrSdxhm/g29JFsVjL5jMOpas+8IN1IfMhnQzvcZ7WMwZp/4RfNkzc/936d476+e9qYai1nmTIg9WbUsGSSBVx5uJ2yAulD+AbxV9dM/nKA5o57k5uCPCTHyyqFCgZW77JAMiKIW2Wl/fMl4hQMmMTJKAqbtV3CvtioYfcdQ31BPXGeMP6EG6kKmN6VKrI8hJ/AaiicLvk7UGWfkBE/UOnuR7Mq/9yXwyiMz8HKbGgLetCLA/eHLmLXmz7RkZLxOM5cxW3Witc6qhSkUWEnGS0wVpThLtDNeFr7UeAWKG8pUla3VOJqmSJBQxvqwBCI0hALpZqo9mxnwAq/0dk+u7fDuB9jUaZ8cygmeqHUaquKcSwKvPDKDrbqMrwNk11EcvOFWmlX2VGMjw8SSVsbCtqNlvCTwElOLv8X1/rWTyCwPACAxWkuJqaE5krOwSGKI+pBJO9Ppazse/dz1WPEhTFIs6HkQElG2dA3xiZsWc9ldr9AUzn6+jDN7hhufXM/Tr++u9GFMSVqDUQUnXRJ45ZE11Rg0uemiBQA8YR2TtV0SMyvjBXbzxljSonso3RTVnVLM5V3VKAOiqHKlGKo0GqX9aScRCdrv2aOiv2O3bkYnCjRRrbK1GkfTXBfMviM+5JQ+KDYe9jnUQDtH6HV8xfwHX+n9KSz7Mzv77LYTa9v7mTOtLuvphcYhMbVced9KLrzx+UofxpRkTzVWngReeYSDmcX1BqcfPou3H9LGT5If4tTYNd5jKU1WjRfYGa8HV+zkxKsWAvDNwG3Ub36c3QMxYskUJ171CA84VyJJjZeoZekrjPyYarTfs3000KMb0fH8gZeeQjVerQ05bWzigzQ4WayOBruX10HGdi4KPGg/vmM5W7vt8SgcMLByspOS8RK1Torrq1hmxstwFq+OBAxSmF5vHYBZw+v4QfDmrOc2McQTazq8218O3MMbHv00C658hEdX7qK9L8Z1j6wBMmq85DJvMUX4N9HoDIJ68ms1gn1VoytKqGDGq5rXasx11hv2ZtMhn07fER/0Sh86zb2wAhFOMFbSquwrTntWP8nmTvv7DppG1moanzbv56QlXy3fwQtRhbzyhgqTwCuPfIW6mVmw0ZxivsxewxsAMHNqwhau2gXAkXPtBogxr4GqTAGI2qO9RbIn/1qZ71k78CpQ41XFazXmCgUM9vvYdXzrsIftO1oPoN65ynowbtFbvx/vMJYDsNSaz7TB9Tyx8F5m0EtfNJHVt+stxqvM3f1UxvcvRO3RaFQVnHRJ4JVHJE+QFckY2P8t/nVuSZ6Z97nvNZ/h8i2fAaAhY301gKfW2gWRzRG7dkMyXqKWpft4+VtcP6xDvLxhJ6t35lvLcOrUeLl+9JET4GN/gwvv9KYar314DU93T2e6k+36a+o0AO4KX8Hd4e9xTN9jWBmNmeeoLgJWHIY6y/8NCFElJONVxfJlvDKDsYetBXw/+QlvLbVCcgMvt/A1t7ZrIrUXXYNxrvrXyrwFs9FEyterz4QoBY0TePncTiJKiAhxXtjYlX+n4Ms+y+qQs6FpFuGAgVLQF03ympXuVXZ/6gTv63lqNz/W1/K2+JPefXsrJ+Dq2+b7oW3cPcj+l97H8+slqBOV8/z6Tu5eNvrft/ZnuJk0CbzyyDetmBuMJQlwv3XiqK/ToPLXmcRyrmacSHH9f/5tGTcsWs8LG7qz7h+MJTnsew9wzUNrxv2aQhRSivR8eqpx8sOQW4sJ6cArYOQ75qnTxysfpRT1zvj0WMZV1oPU8UDgtKxtP2jdD0CYuFcHRm/6g+mWZzaybEvPpI/pmXV2wPX3l/wP6kQ2y5IT6kI+fMNzfPW2ZWNuJw1Uq1T+Gq/8P6oVH3qaHyc+lPexzIzXycarnGEsBTIDrol3lH5stV3Ab+Z8uPRF7c75d/q8pp4Q4G+JkMZdq9HfgTCqQ0RUfMR7I71XqIrT3gmqC9nTjSv1vln3/7rlErhsG+1zz2JR6o0sMNZgYLG3Smf+HnthGRt32y1wLr9nBef/6ulJH4/765Mke+klJfCaFK11VZxzSeCVR74z5YCR/0elW/ZhvZ4zcnuSNKh04PWX0FXcGLqGjZGPcUrHrUA64NrZF2XVzuLXwBvM6EqdO9XoRvPa1+vPRK2b6GClteaWZzZm/c2mH/NefcLHlc8wYeaqTo5c++sRj6kptFZjIWcfMcv5SvHx+GU8/KYbAagPhyDcyJpTrucJ6yjAbm8zR6WnAFeuXsmnfr/Y1wWz3eEyt32FmLyfPbyGt/7oUe920tpz6oF/88Q6lm7qHnvDcRqOF75YrUpmGiXwyifftIobjB01r4VLzjgEsAfAoGlgMvLNMI1BGsk/1fj+rt8B2TVe77xuEQP3fge2vTjm8Q1mrMuWuziuG3DJiZGoBo+v7uDye1Zw5X0rRzzm1Xj5MNWYKeqs2Xj46l+BlTsIu++Xahh+J+bK84/k1s/ZZQ5PWW9kcM7JAF7hfdA0vPUc7w1/h0sDfwUghcFs1UnvcIL+aOG1HcfLHS9zx5yn1u7m0VXtvu1nqkmmLJKTDHB/sXAtW7vTnyN7Qsbrnpe38+6fP8nV96/i/b9+xvfX7xyMFXws3TuwsgJjbyIgXUNy8kEz+eoZ83nr/BkcMaeFrd3D9nRJjlbVN6K4PlfmFGMbPTQu+SWsugO+MXp9VjJjOaLcjJf7mJx8ilIYbyZ12GmV0j0YH/mgV+Pl81QjGR3f44MQac7cqf1PNYy+E6SU4i0Hz/Ruux/GTZGMwMtZz3FftYt91S76dD3rggcz39pG91CCnzy4iiBJvh24ld/eN53bV8Z59Bun0B9N0BQJjtzpKNwlWHL/Nj5+k91dfd1V7y4w7Tt1aa3Z0jXMvjPqC27z5h8uJGlplv33Wb7tN3Psn6ouuX0ZqRIGkN2DCeZNz/+YRhbJnlLcgcNNpx+/XyuRoEnQVDxkLeDu1MlZ27eq/qypxhEsi1gyRT1Rfhn8BY+Ev2nfX8TZf+YfbSwn45U+I5r6b1BRPSY6VI02DVWyjJcOp2/k9vOaqlc15nHnF07iq6fP5x2HtDG7JcLn33EgYNeoDpC9XNDT1hGssA5kvtpKgCR/XbyF040X+XTgQdqe+wHrdw+yamcfb7ziIX63aH1R+3/g1R1c/Mcl3k+y0Mle3qtLp7g/PLORt//kMV7d1ltwm90DcXqGEhN6/UTKyltIvydMNbq96EplKmS8JPAq4PeffhMPXfJ277bp/LZyI/WAaRAjxFcTX+GT8W/xf6m3AdBKX9ZUY0rn/LZ7NhFPWpxjPse55nO0uEsP1bWOeWyZ6ebcjFfKeWNKxktUBzcbMpLW2Gs1+hwEuVONgJ3xyjL1+ngVsmD/Vi458xDamsI8e9npHLa3ndnLzHi5nrSO4uXEXMIqyV2hy2lmkBZl/2xS2v4Y2NJlj1c/+NdKokU0df7Cn1/kodfavZKJxRu6si7qmb9Xo73vtR15nz+VPetcyeku0eS3+d+5ny/8eemI+zMzXlO1ZVBjxuLtpciEZq6TnEuWDKpypx66F4fMavJuu1ONuYFXMOMPZ5F1NFcnPgrAXLWbNxrpM8eNem/iOiPS37CIWNJiFjnFhXrsM5pUxllPbo1Xwp1qzHlOLJmid3hiZ19CuMY71o92xZud9sf3kTCeWUGRG3hNoc71ExU0Ff0ZGa+/JE/jjtQ7WJ7cB4CjjA2caKykGftn42bHhjJqRzd3DbGzN8ql/7eceNLK+yEfcq7+3uIEH9t6hvnGHS972wZN+/Hh+NTP0uRyL05wv8dSeOi1dH2c+7mT+fmTKNO044U3Psd/3fmyb6+XmfGK5OkgMFENzut2DowSeGmohve+BF5FMgtMmeRG7F00kdKK7wT/wjnmYu/+AerYqVt5kcPZpObC8tuJJ+1Lvbt0Y/oFBnaOeSyJUWq83Ddm7kD5mT+8wNH/89CYry2EnwwvqMoz1ehj5/pM9Zm1lTmB11Raq3GiRmS8jvskh82dwTo9h5ctezpynupglrJP+iznY6Arow5vfccgV9yzgtte2MIh372f9/zyqRH7mV5v14Jt7szO+uzqt6d63Gmx1BSdHktZuuDV5u4YHChB4JUvyHV/lplXo+Zrnl0KT7/eyd+W+NeeqCEj45VvlZiJcvtvjpbxAk01lBtK4FUks0DGK/eNl8KknZGVfUM6wv+m3sc/6t7Hg5wMm55GJYaYpXpo1628M3UdtyVPgeFuSBaeo849hmjC4q4Xt/L6LrtBovvGzC0PePp1OzU+2atsRGmc/MOFvO/6yfdUKhX372m859juGJevltYLvHw2TWUEW4ncqcY9P+MVDhhZGa+PveMo2prCJAnw3vj3GdARvhH4G58L2A1Wm7ADp8zAa2PnYFax/Kvb+kYEBNPrQ962mda020s1udNiuVfirdzRx/8uXDup77EcfvXY67zzuifz1nG542z+Jr2Tk28JOfdnmVXfO4H+j9WgITT5wKsvmuA3T6zLqoNzL1brynchj8OSGq+pxV0LLveNFjRH/hZ36Bkj7vtN6j3ckTqFlS1vY7NuA6Ax2cUs1UW7no7VeiDL9MH2xoOj10Tk1nh9/W8vc8bPngAKZ7xcnaP8UVa7vmiCR17bMy9P394b5aXNk+8iXioTvQopPdU48vl29tj/jNdi67D0jZyMl3cc1TD6lkjQNBgm4wKDuumEvBNExRbdRoNKn9w1OfWl7tgwvT7Ixt2DI7L5m7uyM1vTnIzXppyM19p25yTQydK090W5YdE672d/wfXPcM3Da8p2EtgfTXD/KzvG/bxXnIArs52Dyw28StHeIZpnatbdT3KcgdePHljF4g0Tv7ihP+p/eUpDOB1sFWpMnum8Xz7FF/6UXe929f2ruPr+VTyyMv154GYAR7ugwV4to/LvfQm8inT+sXO5+O0H8vWzDs26P19xYGbgpQP1cEUvT1hHAzCtPsTOlF07Zgde3bTr6Rw5p4UO3WI/qX/04CIzdT+cUwRbqMbL/ZzZ1Td6Nq2afe22ZXzuj0vY1pO/P5qwsw07ev3/+Uy0Oab7OVHo88nPzvU/+9DRnH7YXjxuHcPZsavtO+PZQUEpMmzVJhgwyMroRaZlfcDl9h30Ml4DceqCJge2NfL6roGMaWLbi5uz61HdYHwgpznuWif77mZpHlm5i6v+tcpbq9Yds/Jldkrhny/v4Iu3vsiuvtHb++RyT6rzXUnojrOJEmSdcsd0SM9UZLUSGuMCCMvS/PrxdXzot89O6Dj+9coO3njF5MpT+qOJEQF2XUbGq66IjNfyrb08sCK7BMe9+KPHqVu2LO39TvpGCRaluH6KCQUMvv3uw2mpy+5xE8zT0X67E3j9Pnk2fV95Neux1voQO5N24NWc6KRN9XHCUUdw9pF7s1M7VzSOsZBtZo3XQE4jxFSBOSH3uHf1j2/wqSTL0qze2e/d3uRMaQzl6YKe6c/PbaJ9nINstRmMJXnHTx4b99nqWdcu4qQfPjr2huM00cDLu8o2z2NuwsuvjNcFx83ju+e+AYBO9yQmPpBnp1TH6FsiI7LwhpH1ARd3+pz9PHkB2/QMmlV6qrEhbPLeeYNENy/l5a3ZGdh1u7Kzh/kCBIC29qcg1j+i+DuRHP022P3eimm6+vjqXVkXA+Tzp+c28YenN3gfxLkB4ljc1Ury9c5KZ7yyg4r9L72PTzj9yyYqb+DlZbwya7xGD/oGxvj5jOXRVbsm9XytNW+84iG+9X+vZN2fOZZM9G3o/j1H8wTxmReRdQ7Esmrh7PKGypPAa5KMvBkvO4Bao+cRrG/JemxaQ5DdzofCfDZhYHHAAQfSHAmyTpft+gAAIABJREFUxZmCpGfTqPt0g6sZ9DK7e0nWY4kCH3TNETfwmjoZr5uf3sDZ1y3yzrTd7OJo6f0dvcN89x+vcvEflxTcZiroHIizqXOI17YX7hNUThNNTri/q0VrOjj9msezHtNop/mwf0OhWwow6E615fbx8lTD8FsaoTwF33s1pace/z3x7/x34iKuTX6AJdahXsZr92CMhnCATy79APeGv+u1l3DlTjvlW5rleLWar7dfCo/+gKRl8SXzbo5TdkPo3CAllhr5/Pdd/zSf+cOSUbMWq3b28anfv8AHfv0sNyxaV3C77/3jVa7452veclWFAsVCAk4Am295Jfe+eJ6g7Mm1u8e1n1z5fq4TmWp0VyeYaBnavOl1Y280io4B+7Pm7mXZiYTMDNhE1imGdODl/qwy+1lmBl7HX/kIX/pzejUYe63Gyr/3JfAqge3a7iq9Tc8cscbj9PoQXdj9do5VToHptP1prgvQRyOJYBN0Fw68LEt7b77PBO7nP9u/RZh03VbK61yfPSC4Xa2n0lTj8q120LHFqS0xnZ/laPVG7ll016hXtlQ/9wxucJR1x0aTt1P8JEw845V+3rqO3HorwOfO9W5w7vXyGnFV456f8cr3wbJ3S/pD1Jh5MIvbPgBAn66nSQ1zb+jbXNp/NQ0ZmbE3qVX2mrMMEyFGX0Z2/cXN3Xnb03ws4GRb+7eTTFn8V/B27gpfAdgZlMxC9dyM2HA8xcbOIU4yVtC39K6C39+OXjub/dqOPn760OirfAAMxuz3UG7rnbG4sxn52jZ4gVCBM5LJ9NjKFyBedd9KXt3Wm5V9GytocQNltz55vDIL3zNLamLJFD+8f+WYJR9ubVxbRtAP2RnEo2MvQqr4OrL7lu/gxifXU+e0jnB/Vm5Wa7o5zL1DH4e1D3vPWZiRuauWQgMJvHyQm9p/wjqK3xof5m1nvs/rdeNqDAeIEyQZauY4wwm8Wg/wpgIH6uahuzey4MqH+eWj2Vf+PLRiJwd++18sc4qw56hOAqTYT9mpea21d1aZ+wfmfv4VM9V4/ys7uOmpDWNuV2rue9398HZP5EfLeLnff259ylTjnlHnW1y6GCt3FL/oejEs76KN8T1vtCVO7M71+Fpc7y2ZhUFURfL08Zr6azUW6/zY/2PxabcBMLsl4t2/8D9P4YGvvZ2ffvBo+qmnmUGONDZyFs+yTzBdx3VH+P/xo+DvWBT+GneHvkds0B53rntkDRdc/wy7M/olHai2c3ngFt5uLAcg0b2VYCrzg1lz5X0rOfd/020pcgMHt8P9F817mP7c1QW/r8x1Jgv1GMvkTkkW0xQ2k5vxytcOw63tKvT3PdGLUbZ0DXFznrH3vld28NBr7TlTjaN/P+7PqZgC9nwyf14LQlsgZpd9XP/YOn77xHpuX7x51OfnBl6Wpbn87ldZ5ZSPnGSs4CfRK2DRTwu+Ru7v9st/eZEr71vpnVykAy/753JK/WZaGEQ/8eP8vwO5qnHPsfYH7+aDx88D7Eu5o4R5Zt7n+LdTDx+xbdgJxOLhGTSqKJYyYdq+NDuBV094NoPt69g9EOfaR9ayYnsvf3jafiMu22IPfNc+Yp/lzTHs2wep7bzfWER/X7cXlOSORe6btKOIqca7XtrGrc+NPt1ZDkbO4rvu6gGjnem5Z7VTPfByB/RCgddwPMWNT67Pu6wIpC/p90tqgmfwudNLmb87rf2fapw7vY5zj5qNoSCWL/ByTfG/j2Is0wczuNfxAOydEXi56oIm/bqekEp/wB5G9vv+/eaTzFD9HGps5bROe7HtPz47cmz4WfB6e/khZWe0hrevJJRM19cdpLaPeE7u+9i9anKaGsCIFb7CN3fKc6wifbe2K98U3mjc5qj5Ml4J531XaN+Z948n+/XJmxdzn3MF5hx2k3kKHUuksqcax8jguT+nfFPPxXDH0jBxbue/4I5PAfAXJ+Bqrht9TU93pmJmox14be4a4pZnN7GtZ5i9myOcOssJzLs3FnyNQtOpa3b2c4TayOFb73C2s3+3R4Tsn10yPC3vFLF9slf5974EXj5x+3m1NthTHLkdjX/2oaO54Ni5Xvp2KGTXgUUb5oIZpDEUQCnoDM4hPLAFhcU7DmnjnF88xRX/fA0YmcVyGyC+13yGa0K/IfyXC7wP7FadfQWS+yYdLKLgMppIebVilWTkrI/pprtHO9OLOo/50V5nZ2+US25fNu4zZT+4A/dALMWu/ii7B7ID5p8+tJor71vpDdIuN/s63kLisaQzXuMLwHKzk5kF0enWDv4NQ6ah+OXHjmPBfq1EVV2etRr3/D5emdyszex8gVfIoI/spYUOtkau0/iTxIdYoo7gA4O3wZ2fxUqlf4cKi5uCP+GYjFU6Vlr70KyGONBI/20epraMeN3cD8at3cMEDMV0NUAo0VswvZrbLmCsWqchJ+AarcYrZekRJzFuveBoNV6FphozLxwYT8sJ933eRg9PhC/hdCNdnxRLWtlXNRZZ4xUOGrT3RQs2gy0kmkgRCRpcdKxTp7zJvjrSvaArc//3v7KDj9/4fNbPw814pU+g08e+34x65jY4N4Ij/zYzjyGT253+hY1d3Bf+NudvuwYsywsS36DsJEUyEcvKeO10pqftGq8ivvkSk8DLJyFngJvmNBUM50wxXnDcPH724WO8+zvq7A7SZsp+oxmGoikcYJcxk6BO0Ep/ztUYesSZXpuz3NBJxgp7n+0vYQ53cJbxAk8HvgDrn/C2dd8k7iD0+q5+lm/Nf1Y5HE/lveKo3Nzgyf2AduvlCp3ppSztDQp+ZLx+8K+V/P2lbTy4YuzVBPziDhbuwD4UT3LCDxay4MpHsrZzP3xyByb3bG6itWGFuGPYeKdQcrfPPC5VwisMQwGnl9WIjNeeX+OVyX3P5F6NDXYNzy49Leu++bHXoO0wdoX25fHU0TyVOoJbU6ezvO5Ee4NX76Qpmb7S9k1qNaebL2W9xkPWAgDONZ7z7mtRIzOP37xzOXcs2cJfF28mmbLY0j3EvOl1tKoBTJ3yprZy5Z6EjJX5cbPGo51Afe6WF/iff67Iui/gZbwKNzTNzIZl/q1nZrxGm27P5Z6wt6kegirFbJX+WUcTqaxpz6KnGgMmJ161kHde92TRx+HurzkSZG7ICVoMO+hxs99uBnFXf5Qv3voiT72+O6u21l3H0j3OzMArYCqaLefzx8xYWzXHUM44Nm+6faKQ1Y9yqNP7fDsgaZ8AqL5tWT/3N/9wIbv6o+nyhgqTwMsn7hvGXUZDFyjjc5c1eGz2vwHQc/D7vMea64JeL69W1Z/1RxdLWlm1DQ0M0+Aswu0tsA2EBndwnPG6fWPbkozn26815BSanvGzRZz3y6dJpqwRLQuGE6m8g025ucGTeyhuxms4keJHD6zyUtmui25ezCdvXpy17WS4pXsTrdeYCPfDIeFlvPJnrty/r8xCaq21N+CP1XJjvNzBdrxTjrkfOtnH5X/GyxUOGAznnWqszYyX+3eSOe1UFzRZo+dlbX/YwPMwbwG/fuNtfCrxLT6e+A49NLGm7Wxvm+ZU+oTtgtDI1gn3p05knTWbCwMLvftONl7lXUb2tit39PHNO5dz2V2vcOldr7C1e5j9pwe9cY3h/K1UctfiGysAGcyp8cp3xeSmriE25jSCdd9a+Yry3fdZ5sxA5piZGXj98dmN7H/pfUVd8OJm2Rqdn0EDUb4RuJ1Pmg8SS1peoPdOYzGJ4dHLCdKB18RrvCJBkybt7McwsSztTRG7GcQXN6X/HjKnc92xyw2KMoPUgGHQknSu/iwQYGfuw5V3andgp/c3MCNuZ1kjPa8TePbarM16hxJ2O4kqeOuXLPBSSv1/9s473Iry+v6fd8rptxfaBS4dESmCAnbFhjX23mI3GqNGY+wxxhKNGksSNcXeYtTYYkdjRUQQFQQB6fX2dtqU3x/Tzzm3YAD9fWU9j4+XOTNzZubMvLPetfdeOyKE+EQI8bkQ4ishxG/s5YOEEDOEEIuEEE8JITqnu/8fQXGJl3U6neUhOU1BV6SjjEg9SPPOV7iflURV1mtW38bBYg3nNd7GEGGV4mb0IPGqFtbN3mIGQwWhZB2a87P6qkWcmz831Hjnm99w9H0fMdtnjpjM6lvM3LArdBZqnL+mhT+/s5hzHwu6Gb+/yCvj3hSKl1NFuTncqTuD83LoLserEK/3E8RNr3hZ++4sp6wzdKV4bc5E95Ai0UEkz8frx1DV6Ie/08a7l+7B+5fv6f47GpJZYVbnb9RvIkWRoEIWqxzA4enrAKgUXmXiJGle3uarzXJeNiYFlh0kz+DPoT92epzPzFrJyoYOhhX57o9kY8F169tzFK/uQo32ZLM5meXVL9cw5rrXOeju97jwSU+pS2eNPEXMuXe7MjT1Rwb8xMtvrPrAe1b4y6nGdHDw3e/zSE4urfN7xUXK/n+S85V/c736kK14mfShnr+E7mS3mT/r8rydCMnG2mg4SGUNIqpE3LCIkSnkwLV+5Ys1XPX8Fyze4D1jTgUpeO/ATDY/NKlIguKs1aHF7OjcqzA3L69gnl7rWtKaQYIOQkYH9ablkxl/73f0F54nnGaYmPzft5NIA3uZpjkWGAfsL4SYDNwC3GGa5lCgETh9Mx7DFoMTaiyLWwNWZ4NBia2IrWlOkSZENOQNcMURlbWaFfi+L3QH+2TfZppkKTjprBEINTr5XTP87VGAULqOrGlZR5g28TJNb5bSkdEDeTqzV1j78RuOpjI6mm6yaH3b99rb0Xln5BIvZwaaK0MHtt0Ed7YjDmws2fhf4Nw3rnLVDYHyDyF+gtidueTGwrkGGxE1AcjLFSyseP0PB9YJQorVKHr1unUBHyHhHv/3P/huCfhzTQdWxKku8vJpoqrsNsgOoP8kiiNKYFHf0gj1tg1OBRbxUtHob+a34mkhXpjQdYP69gy1MR856eSFXNeao3h1F2q0n4XbXl/IOban05erWvj3HC/hP63ppHLGbGfyk0vIMprhht6d4pHVTcmAouOQsLPkFxmSXeh+h4NkRueLVc1c/XzQYNuZwDuKVxUeye3bPo+sphMX1md9mmbxwpyVbjukdxasDxA5Z6K+ZEMnBSbdIKVZipdDvBBygMStaU7x6MfLWbzeI17JrPd8pzWDIWIVz9cdBAtfDzjtK7IgkbEmymYnBLs9rfGo73yaO7Ikszp9qSOBp04aLWvIaIb7TpxnDHQ/21fyoj5tac3q1bhRV2HzYLMRL9OC84uo9n8msBfwjL38IeAnm+sYtiTUHMWrM+JVGrU+X217oPg7tRdHFVZlEoH1HVfpXMWrFOvSfmYMB+BLoxaAUKoODSuc+c9PvmXH370ZOJb2tOYa24E3iPn5VTKrk8zq7H37u/z+tQXdnvvmgpRTxegQLydU0BUh2jSKlx3q/B88eTYWXYUakxmdevu3K3REfpXSP/PcFNANK5fwl0vPAq3nHmG63pXiZf9/M4QaQ7JEsxlDzrRw4ZNzfJ/8uBSvrkLujgllq5ljlFm9DQnfuBRWJCoSIepNm3gJK0l7kFiDgs5io09g83d+uafXhQNLASsECYOLlH9ShrW/Kho5YdbR3grOC3n2YzD/RXdxQ0eG/mIdD6k3EyfZbagxt7tHmAwDRNAdP5018lrwOPlUuYqRX3XJ6AYzltSz081v869ZK739aQZg8ivlSfY33mVHMZ/YV4+7nzv5Tw6em72Sdxasd38vR/HaVlrqrnP1mvPpv+IFoj7fxrufeplzH7PI5Kn/mBkgctZE3WR7sRCZjR8PUlmdiCIT063fxxRywTy5RRvaCCsSxbShNfmvgc5EyfZZm/fvwHuon76SkpS1rtlRmHjd+Mp8npzpFWWMvf51mpNZHgv9jt+oD7nLm9YvJ60Z9Lbz4Z6JHM5tlb8lUz6SPaTPOV1+mSgpjvrLR9a75Afw6G/WHC8hhCyEmAOsB94AFgNNpmk6T8JKoN/mPIYtBWemUtod8XIUrybrwYqFPJO64ojKilRwEKwSTQwQ69Drvw0QryjWC/gr02L3S81epOU4kXQ9YWE9mO3tbRS3LSbTWg/AuZHXOcB4N5Ab5SSqap30f5zxbQOH3vsBp/7jk55diE0Ihzylc4hXk53A2ZUQtUmJ1yZWvP763hJqL3/ZJVd+AumoeYV8vK547gsm3PAmTR0ZV7X0n6Y/n2qTK16myc3KAwxML4Tm/Aq1ztB1VaNzn236kTCsSjSZcYrJrWr88fh4QYH2QT5E7LFnavo2Ts9c4n0gBAlb8QopEqfsVEtxRKWNKLoUokK0sK1Y6np2zTGHAPCgti9zf7qYaEgOEC//39+ET+JQyfLy2kuazYXKc1ylPmYdR06SvrliBjx5Avz7PHjqRG5/YyHJjE5zMsuvlCfZXZ7LXtLsbkONuWH30+RX+U/ociuNI2WRipSm55EKxzIiV1FzxkeBgZHNukbPM3y5shndIEYaWZgkzA6eDv+WETO8tJIVNvEqsgnun99ZzKMfL3PJn6N4jRZLA9+daF3ijv0AfUR93vmuaU6SsXOC71Hv4tnwdewhzclbD+CjxfWdRjVSWYOwKhF1iJeZH44FWLC2lTE1JVynPsy2b57qLk9nDa8vqBCB3+mwuvswhMqnxnBEqhFWzIQNwUl+Z82u+4oG9pe891H5jFuJ1n1Jb7vYbPsx47hn5RBaov3YVf6Sq9XHmBU+l5uV+61D+QE8+5uVeJmmqZumOQ6oAXYERnaziQshxFlCiE+FEJ9u2LBhsx3jpoIzwJXbocZOc7xUmbAi0ZrWkEQw8bE4qtKYDm5XTRO3qvdR8falgVBjTFgP3zdGDa1mlOVmL1qVciLpOuL2g1ks2nkzfBnRJyxR8Vc8yB2hP7NwnScN17dnCJHFbLcGDcMwA8mkAvh8RRPvLPj+fgNnMHJ8vBrtB7IrN/We5tavbkry62e/KDhrdr6vkI9PV6hvS1PXlmbGkvxBEeCutyxjXGdg8Stqjh2G853+l8Zzs62Q2d/e/5aZS61BJki8chSvF34Oz50T+G7DMHn1y7UbbQthmKarpHrkpXvkktZAscBmVbxkmowoUWHd33n4kSheuZ0z/HAUr/WUMd0Yz3xjAA373wtAxHY732VoJVccsI2d8yXoCJUzRizhxdCVXKU+ho7MAqO/9V3oqGqIkCwFyFarLw9VFTpXqpby45ALxVZjMmYwvCk+uR++fsn9911vfcMdby4koxlI9s1jIHHs/R+7z1Rn2Fn6glPlVwEYJNYSF2neCl8KDx+KbjdYzk2i1zsJNTpjxS3KA1z52a4F82GTGd1txVTsK37CtuJwrBYcL6ysbh1Dq/18JIRjxRB8fnSEO/aDl+vrf56n3PQ2o697jQ0NjRwkWwUNfUV93i3/5apmjnvgY276z9d5x++cd0SViWgW8ZIyrYFJuYxOMe2kNYP+5TFGiWXEW5e4jenTmuEVSggpMMYW6000lI5mhjESkWqCv+0N9+7IhtY0b86z1Mh4ON9xP0yGsMgSF8E8v+EL76OXrXjtPH47AFamvLB6TKSZJn/C9mIhYaNrx/0tgS1S1WiaZhMwHZgClAohnCesBijYEdo0zftN05xomubEqqqqLXGY/xMch3qnbLsr+dtRveIhJZDoVxJVAzk9Xxq1VIsm+ov1KC3LaUtrbgjAmfW0EeXwzG/4O4fSLJcRTdcTw1LThtmJ+eqGYJn0upVWye1wsYJyWvirehs/eWMXwHvxO/g+qxud7047ypA9uDjVQV2FGnuaQHnrawt44pPlvDkvvyGsk1y/MT5ezcksE254k4k3vMkx939cMBnU8XJr7HDCvH7FKxhqLETg7357kduuwz97y+bmeC18DdYGG9Q+/ekKznl0Fk/N7Llq5Ryjmw9k9FxNy1O8fCFQyTcb3tQIKRIbNEs99qte3vvpx0G8ugo1qrLkJnMbSEzL3EzxDscDXjWk8/s5406bXMZO8jwkYXKndjiP1FzLS/oU0qbCE/pUZElY+XV4yr3ICYzL6DwVup47Q38CIG1a42Gpz3IiYxZuc7Oq0bnvrX0Ol1YwRKzi9je6bh30WOgmrlMfBsygSrT6M7a/+jl60ZA39mmdJNc7z+bRimXXo2Wssdh/G7emsi55KvKrrh3WdztRB8dVPqMZZHXDnZjEKdxhxDAh4gs1VmMRr8YcdSijGRg+BalSNGOaQYLmEMbX5xW2y0lrRpB4ZdsZ8uTuLuF6NnQt74cvZLBYTXlUZpBYY/0u9d/Y2+teIYaeCSiHEaMdXU3QZCYQvvHk3EdnccbDn9KczBIL5RBxvHCig9nqeADC7avpIzViRkoZ0q+K8niIxW3BApES0cGz4evY99tbCp7vlsTmrGqsEkKU2n9HgX2A+VgE7Eh7tVOAf2+uY9iScG6SEjuHq6uETyfPKxoKDi65Ca2fG0PoLRqopgm1fQ2GaboGrTGbeHUQ5huzhlC8jCZRSiyzwc0PGCN9W/D7y9d/hMDg9fCv+GfoN+wm2y/nTEceUXBmZt8HnNDZ0zNX8OLnq12C4hKWLlSbnhYFROyBL9cbyI+NcbzOrUJc0ZjfpNn53Rva84mXc99kcwhXER3d5mk420RUiUi6DtrWQjpomugM0EvqNi7h1jDxqmW1nvf79P8OAoP2tC8/bDOamYYUya34Lfa90H9sVY25Rs65iKrBMchJmVDcvqjW7+eoD03CsrvRJZWWCT9nwC7HsppKRqQf5iuzlqKIYqv43vVVc+5bGYNJkqeyOH01S4SlxH808S6e1vcIbJNWrPwy5zl1FK8Llecs5aqHKKM17+X9lHwNMyLnMyE7O5DQ76RfpLI6C9a2Unv5y3y6tCEvtGm0W2TKGScipGlJZl3CXy58dgntVuTAGVed/DPNMGhOZt1HwlWKcqBmmwOhxipb8fIXRzkY6usWcKHyHC+GrkBLecfiTPJym6H7P4+qEuGsl+AfafmWClo4LDqbsdISikUHlytPMFCuIyysc5nz2QxaUlnSmuERL5/XFkDYSGKoCeps+yQH6+3OKrk5cAAXK8/wbvhi69hssv5en9N4XD6UyrYF7Bv6AlE1AiEEO9SW8W1bYWf9qo6u1dEtgc2pePUBpgsh5gIzgTdM03wJ+BVwsRBiEVAB/G0zHsMWw4Hb9eGR03dk277WAHHO7oM7XdepbPQn1oMnOzs31WqzgiKRRBEGkmGZqtZGkwwUa4mJNIakotvhn/JEiPnyCMpTK5gkzQ/s11Ci+NOx+7R8wQjVqigZ4nOXpnVN3uyuUCPc7wrTNPn7+9+yvD7/oSoEZ2bZ2JHlgidmkzWsRNEpKcsIsDPBa5hYyeN1R3XZbNxBsV02X6iHZUbv3vE6F7mHtLQAwXHCOA7x0gooXv5lAoMvImdwi/pA3r4CoUb7RVESVanN2INLOminkLBfoK2pjcsBMwzTvdfIegP1c7NX8vzsgqJ13nl8Ej6PQ7843/23uRl9vEKycF3Zg3leP04fr84QCRVWlnYYVMZBY/pww0+ssI0zVs0NjQNANrJcc9h49/mRJcEz50yhT0nUJW8rjCrWUIkigvdarpqzr/wpd6l3U0oberiE5tp9uUr7KUelr2HH1L18bI5Gsfs+Os9M7mnlCnudqeFjpSUBU1KAkZKl/v5NvhHzqRPc5c69m8oazPjWIlf/+mxVXupB/YY1HCW/wzVrzqOKRr6OnEbtgr+6itcw4SWc024p644BaCplrZPVTVfJP3x8P0aWeZvopndy4UyzG2psF3Gq7Uq+9QVawQ2VVqEhscjoC8B20lJ4x+uB6Z9QFpqoWsn1ErH2lYHlZaKV4cp6MqbMdH0so6RlnPSJVyP33kcfcNGTczBNqLYLMcz2ukAUKGx0YIbifGkOCuzb6eu4oqEjkA+6hzSbn8meRvOiPgUDQbxqIDOS/VDQ6aWvhSmWxcaQqgRNBAvV3HOVowWXb0lszqrGuaZpjjdNc4xpmqNN07zeXr7ENM0dTdMcaprmUaZp9nz6/ANGNCSz67AqIqrM0psP5KQptZ2uW2oTrFw3aWcQ2zV9J8dH/sR6gq7SvUUD17T9lnfDF1Mj1mMoXu5EeTzMi8q+pOQ4vUTQkV6XI4FZUm3qa8YpBUhJy6pOw2pdJen60ZbWOt3HqqYk1780jwufml3w81xkcwZP3TB4NnwdfwrdBXTevmaYWEmcDqhf1O13OKXmS+vyyaDfgqOnyFWqljfk79d52a1vSXH181+yrN4iZ6PFErSkNVD580bK7ArWI+X/5u3LfwmcF0JJVGWYvthamGyA60pg6QeAp64VMmbtyGhc/fyXeR0SwMnxchQvj3g9+OGyPC8iP3TD6sfYjw1UiRYGtcx0P9uczvWqLNFiWtYsAdf0LVih+kOA0k2yo1/xOnHyAPfvsCJzz/HbM6jSuoZxW9F/Rp7GYqMP6yZYyoMTNg/JEhNrg9WLu2fu4KjwfXmKlyqC/+4tGjlE/oiBYj1mpMyOHghmmiOJVvTjv9ooZDNLmIxLvIqV4D16gDITdI1XvljDnBVNro2JFc72fvMHQ7+nSHSh4q+a7Y5fum6yrfiWqR3/ccfqpo5MXvg/vfxTblXvZ7i+iAmSNeEZvvJfbohR9uVprX38PL6YO4u2lMYQsYq58oloX71AVjNcNXr3EVX0i3nXaJHp1Z+FM41E7LF8ndzHzfHqTPFaK/djtVnhLhPrvUl5R4H8UT+SWZ0abTmRthV8qI9yl5eLVmrFWlaY1aw3y6gRnn9ivVTJNmKZq6hXS37iZVBMO1crjxDV2zDDRSw2+wa+s8rX19HJby2mnQdDtwby3R7R9+GGQY9Q1m8oHxqjmWsMomn8OTDyYMBSvJvNOIWQkWIFl29JbHWu/x7gVDJuP6AssNxRvDZQRltiIIuN4E3ZT9QxNG0ZFh4kz8BUvRuoOKLw+Qadb6Shed+nZJrdWX+LGWOtSDGgAAAgAElEQVSQsZTtpQI5EQ8fimS/oHO1m9x4O8BLc1dzWk614+hrX2PfO/7LioaOgH8SWNUv0HU1oh+5s7CG9uBgW657D7yfhDnJrNoTx8MTxxfc99drW/hseSPNSdvrpoAy5QywPc3xSmX1gFUHwNL6QoqX9di9v6iORz5exlkPz0JF46XwVez4sTVj8xsz+g0rc+FXlLK6gYzObclrOVd5MbjizL8CXrJ+n6bP4Js3Aqs89vFyHvl4GX95d3He9+imX/HyBvnWVLbg9WlOZpm7sgnNMDlZfp0PIhd6H7qKmZPjtemHIVX2+hA+FLrF7TO3Ob3DfohQehhqvPXIMa66VQgRVUISsL7DZGrmNlonXeIuh8KVvwYSkbDKHdqReZ8Vwh7y5xArC0QCRvYuotX+HYtIukpRmRSc0Nwj3wEz/8p5j33GT+79wE1TWBI5kdvU+3r0/QC6nmXk1a+S1qyG1C+Hr+SS9L1uiLqpI5uX97q3r5/iSMlqIC3paVfx8qO3voaSNy6mPaOxq2SleJgf/5ntjS9cZb0ooqDq3vl94yNekWwTMbtqfZXUm96SNTZsyFO8TEaI5TTGB1utsxx0eGOm832ViTD3TA9OUk3TKjYY1WJFFz4yPOJVRis15hqWmb1ooMhdfqK4kc/kMYyTFmPYxNcJhYqOOtJZg/OV5zld+Y+1QSgR9JGTQ8j2BH95Q4ebtpFr/QHQTJx1Sj8GVsTZQCmHZH5HySE3uwaOqizRTGHi5bQ++j6xlXh9D1hmqyA7DgrOEP3eOSVRlTlmkETtmVsS7CNeRRGFVNbg66QVM28wE3ykj2KWMQxhGvSzZyUfGtuioHO0/nL+gZkG/d61ZrJLIyfwB/VP7keFigXOf3w20xdsyCNIyxs62PX307nwyTm0prKc88gsrnvhK75YZQ0SvYvDefsqhKxuECVFP6y8CH/Y7hh5Oq+b58D0m+zj847BmWkqegoWFDhPYP873+PwP33ohlJXFcgpcFSnnuZ4HXrPBxz1l48Cyx79eDlfrgoSJ4csOcaGa1tSriFgrwZLEfIP7n7iVUqwvYZuGNz/38V8uaqZrG5STgtjMrMDlU+A2w/NUboOaHgYXr+64HkUapFihRrzFa/WVGGF88S/znBbUjlFHi42WPk9YjPmeKmycBUvwK2O877px8G8ulO8HPW1u1wwIQTxkEJDWwYQLuEK22FzzSicUxlRJd41xnJI+rc9Ol4pWhaoZhveq8itiizyVQeW0Ja3La1e2sTyhg63eKOQUtzp9xvWvby8viN4Tq3WGNSczOYpXpN97v3bCkv9lY1MMKnej0w77WmN4bJ1vOqKD3lI9q5PLKSgat5Y51eFIlozCZFBQ2GtWUEljVyiPE3xBo/8TRRfszRyAoOkdXSUb+seR4sZRXR4hQXOuLbL0ArqcoibM572Si4iUzyQv+vTmKFYPTjLRSu9tNUsNXu73m4Aq0OD+VQbTKVooZexHhmdErMFzZQQ2Q6MTLtbyQpA2CJtFxi/tP6tZ8ikrUnd8oakW4hTW4h4mXF0w2RghfcO9BdUhRWJJrNwqDGsFbh3tjC2Eq/vASN7WzfcxNqg4lXkS64vjYUwkFhjl2VryBwkWy/1bwxrBiSFvZvOIW2rqATgXWMsx2Wv4iFtXwD6Cyu34A19grvN5yVTAXha291dlor1dsvvj5Dfd5enNaPT0F5HF4rQZ8ubePWrtTz44VIem7GcMlo4Yd1tkCzcoNuPrG7yYOj3rlriz7W6WnkEAPO/vwc9GyBHgfLtgvv1Bs4Wm3i15zj6gy/U2EPFa8G6wj3HHPdl0zS5d/oi5q225He/yuYURLjHaBgMEOt4TP0dp8ivu8uPkt9lqmS1Suov1lG+9GVufOVrTv3HJ2i6QVR4yesLDK8PX1PGavHhzCLjWqOb6OvACScXyvcwTNDNfMWrLaUVJGoOyU5mdVb5Qh2AW2m5OXO88ma8cacyevNVUv4Q0X2o0VMIukM8rLh2B06I0fl/Zyq2k88YUF26gBQrc8OaAEOrE26FpP+l7fYP9MFo8yqTp/3xvbxcsrf08XDs4/y96lecmrmMszIXkc6xsJCEyR7SbFatWIJumG5+ldxm5Tk1J7NkbGXZQYnoYInRG8MUbCNZz3pUbwkUN/m/J5GpozWlsYOSqyxbFzEsC8JpjyCt8T0/0WwTMSlDRoqwziwlSoYLlOc5ad6ZlKrWWLaDL5qRLB/pXvt5Zi1SR70bbndCjdXFEdozOoZhsqKhg8dmLHOLfErTq9BKamknygltPwdgpFhOxEyyzOwVIF5SOMYHqVoARuvzqKQZGcMVECLtq71KZkDYxOvFzPZcmz3FvlAWKVrZ2EF7RmPqyGqu2ik/J6uVGJphUhEPcfj2/Xjk9B0Dn4cUqdMcr7DWdY/LLYGtxOt7wDUHbcvrF+1GZSI4GPkVLycP7ID0jfwkfT2z1XEUiyT1pWNYYDe2lULejeW8K1eZFvFyQotOm48BNvFaTYXrVD275niOSl/DFZrXtckwDGpE8IV8qfIkz6jXoq2aW/B8OrpwSV/kayexoTXNb9SH2K3tP2z49Dm+Wl04hPbYjGXc+eZCsrpX/RQmE3hoEyLFl0YtwjSgdW2gFDxvpplDqL5e4z14TT5Lh9xqpYzj4/Md+x5OG92bsTUlTF+wnjMf/pSl9R3c+tqCAIEcLZbwWugy+udc86xmMkmaz87yV+wne20vrlQf52+hPzBBLODN0KXs89XlgFWAcOMr8wO5fJ8bQ9y/569PcfHTc1yFr8Roxkw2kMpkeWu+NaN0wlKZAr5lVqjRfolrTkKwQTKbbzzpR0dGD5DBdaISvnoO2Pw5Xml8OZS2C/qPTfHqrlm8E2rsLgkfgr5KDuHKrczOhbNeLvHyTwoCkJRAqLFPSdRVvEZKyzlN/g8xFaJ6vmqh1wXDZYmcysDBh10JIw/ky8oDeMcYx+vGDrxjjMvbz4OhWxn9zplouum+vNU2q0Kw0c7xKiaYQtBQvA11lLj5ThImh8ofup9no5W8PexKFiUmUm42cre4lWHGksA+5obP4GDpQwbMvIFQ0iORbWaU2tTj3JQ9DtXMUCma0eQIa/RgReCuipW/lcX7TdrKtuGK7Onckj2Wd/WxCD3lNo53xqEKu1K+I6tz3AMfc+VzX7LOLjYqTq7CKLFy/zQUWolZIWEsu6MGPOIVC8nMNwfyrdGLE7Tn3CjLdN26xhVtC1FEPvFyzhGAjDU2r21OkUylOarlQXp/+ntyoSOjGwZCCG4/ehy7DgtaTlmdK6yJV0PVjtyRPcL9zLHH+D6xlXh9D4iGZIb3Kspb7h9wyuzKx0aKmWMO5WXD8tlqqD3AlVBFyFO8nARth3iV2SXM9Xa57r72y7vdjHBy5nKmx6eRrBzLTHMkGgpnZi5mg1lCuGMtA33S7vZiIftLM5kgfYMx57GC55PbeNsPfwNVgcEekvXQvv3ZfC55+nM+X9HEzje/HaievPK5L7nzzW8CTuyHyB+yJHJiYN/vG6OtP1pWd614ZYMD8JwVXouKFT67jFwriIymU04LfZPBKtGeYmzmcw5QZ7GuJc0b89bx6ufLXfJ4ivwa1yoPcbHyDCOklRwkfextaFiePmV0PjP7V/g3bvm2goZumHy+stm1GfmbNo1/Gzt552IIUlmDr1a3ACZltCJMgz+9MpPTH/qUmUsbXMWrkHdboapGpxTeGcA/XlLP058G/cE6Mpp7TGfH7+QpfU9Y/DY0LkNQODy1KaDalgYXZOwqyqRdybYZyd4PCdcePIqymNqtn51DnEI9VLwcOHmKEaXr7VziZYYCy78xg8TLUfZZPy/YzSOq0GYrXreq93Ot+gjHh94PbHtJ5hw+kCYgGiwiU0EzEobb09DBoAFWBV3CF1m43DiXq7Kn5R13edtCQlqr+/IOtVvEq8N2zg8UbAB1pWM7bY0EECntzV4nXMbn2/yStKmyrzyLBrUXHxvbeOcqklynPkRi9Qdo4VLu0w60r5UV4XDG9qGsRJMirNSKA99RKVvjnjP2038yeqIv6yjnz/oh1DkkqcOxv9CIqrJ7PdrTmmtzsWh9G0V0EM42Y5bVut/RLIqpEXVoKHxpDqLOp3hFVRkdmXu0wxhiLmdv2Qp/vm+MxpBCVLUvCIxpaszb1vmNFbupfUdGZ7vW99m/4dFOr2lXvtaWl1wMwxSIPmP4o34Eo1N/5R/aflsVr60IIuQbxPqUBuXVp5MTuEU7lrZtj6fRSWhUvXWO3sFyj95gWpWQTi/HhWYNc6M7WqXEQFaOMdscxhO9fklJzJuFvmFM5J/67kRSGxgsvFyJZ8PXuZYTRvMa5q+xZgt+otOV4rV4fRuxkMxvDxjEGTWrXVIUa1/O6qYkt7+xkFVNSWYt85zzHfgr+25V78/b90xjhPVHyyr35d+belfdc5HThHVNsxeC0A2TXnbOWW5/w4xm8GjoJu5svgiMjVe9zll+EWevuYZKmlHQOObDg7lV/QthMvxGfYjTlNfYS7by9ib4wgNm21qyukFvtWdeW35Dxaid2zXpgJO9WSS45fiL1rdRTLtbWZZutq7VsvoOVzUtHGo0vZm0HWp0LClSWStMe+z9H3PZM0FVNJnRiZJivVlKR/koXspaeSKsmOEjQZvHTgLgRWMnFhg1mK4/04/DTuK0nQcx+5p9u10vsjGKlx0CVCTh+X3Z/z9+0oCC2zi5YLmKl/+FDfCxGMtsZSzse0Ogm0dJVHWLJBxcpf+JDtUjOc8bOzNLjEZJ1nGd8iCzIufyXOga/hm6PngwCatxtz+yUF5eyaP63oHVFksDkTAZlPnanWxEOjxPrI+W1FOSo3gtrDmC1TYxKgQpYSky8YHj2Tb9N6amb+WtnR7NszYQmISaltAw/Bhu1o7j9OL7WWBa13aZ2QuAYSxHU6Ks1IKKV4noYGb4XH6mvGCF1k9/jbCvarXBtN8bH94Numap0SHZvR5+i5lF69vc9BRR5jWdbjCsSf/KyFDShGjw/Y4OMf/CtoiYKFkGrivNKpKlw9il/p+MlzwPrXDcO34nnCxlW93weK2xHBNB095/YI7h2TMlI9bvqHeSVwjWu9RA4krtp7SPtibsbcTYYJaimJm8yfiWxlbi9QNFeTzEVQd6s6EOQ+Ex5XDUWClNTtKwjwzst21v7jpuPEvN3sw3+nOddgolURUDib8rx7jriYj18EVDsptTNnVkNRfsNZQ1ZjmSqRWueASWLlvCtD++R31bmrW+8uWuFa92epdEOKnhXq6suwywZr8VmTW0pDSXwDkzc7/9gmYYGGbnL4TZxjDrj5bVbp7Rx5ELvMasDnKIV1OON1mfkmjB88joBqPsnA1aVrOxyEhWy4pHQzfyS+VpyvUNHCG/z4LIqXnrOl5CAEfd9AQtyQxVcjtrzbK8dXMxTKxiaeR4xopFbqgxFC1yZ5GAWyUVJsM1qjeL7K1YBN0qk7dd8wsk7Oj+3rJ2qNFtVm523lap3Q41dphhqosiLDH7YMphWDvXaz20GdQnf6ucJhJeI94fieLVUzihxp7leFnrhnNUroU3TOOGQ0cX3MYhdo5JqoNm4tSmHudN3XIe75CLubHyZhi8R0ClK46ogZZDDmYNOosTM7/m79r+6Mg8be5NY6+dOFWx8iHHSksoE75wZKgIIhZJOGhMXy7ZZziKJBhUmcBPwu/IHsHDg2/HQDA2PcudyMSTayxjYhmmz1/jKl6/yJzH2/u/RTxRxJeGRTh0U/CC2CtwvNLOVn5Uv9IoGgqLzX5EK2pI73Ozp/YB5aINYWRoS9RiItEa8wjtMrPa/duQo+4k20HM7KDKKcSJWXlhsu85cEnSzAfgswdJZnSqlQ6Gr3iGOEkWr/H8zSziZaU/yOW17nLTfma/rbDygp2qRiNU5CqVjjI3USxEFyoNFNFUtQOqmaXc95v4iZczSVSy7a6FyQBpHa2hXmjjTuJF3VLvb8oexyeHWyHcrnroOvfzE/pUzMoR7nI377MHOcabE1uJ1w8UYUXijF0Hs/jGA9wQUFFERZUlmpyZixZMHi0KK2RRmJa5hcjIfbnzWCu2PrPVS85UotbDF7V7RjoY0buIdfZLfk87HJiLaMp6EFtSGmuavRmDY3SXn3xvUteWprZIwLzn3aXTjXHsxBzmhs9g1/qnAWvo03SDG172KoSymkmGfBsLBw0Uockxm3jp5NuX2kgFH7Jm2y/nzdAvuUB+lr6lEY6U30Vd+FJgvYxm0OhUxjQu7fQ4OkNSLUeTowwWqzlHeSnv85WdzJCfCV/PSUsvp5wWmkQxr+sTeFzb083Ny4VTtXWO8iJT7AqrUDQRULxCNvE6Vp4eqPKqkq2BsKE9Y+e4mZiZfE8g0zRRsYlpjuL1mPo7tM+fKnwNMjox0iQJU10cRkOho3QYrP3Su182R3K9795uMhM/OsWrp/CIV09yvKxnMZLjdh9SJKROcsmc/Rs5r5qX9CkAZOw8PE2JBUKZtx01lhfP34VYSCZZwHepuWQU7xvbcb12MgD12RALB52Yt56Lol7un6P6FnPB1GEMrU6w4yBvYrPI6Msf9SNIVNXwqrEjhyWfc3O2opkGymMhno3fxPPiUiqwlP+55mDk8oGUxVQ3bCgLk58nz+BsruSkzOXUph6HQbsB0KvEU/4SYQWjtJbzMxfkHW55rUVkT9/VMxhtIUGLQ3SUKC3ESJkqLWaUlAgTM3xEM2oXZdnqtSIJN98XgM8eIZnV+ZnxONvMuoavIqcz8q1T3Y8Xb2hzq6nVUq+q8trsqVyvXsiUU27k6oNGkSbE9dmTaDn+ZZcwtROlwUwgCZNkpBoTibuV0/infGDgHKN+4mVPEkNaG4Mq43wQvoAj5PdpjdUQUiQe0ffhdu1Ifn3dHW6EwiLNheGPHimy4LEzJjG4Mu55e6W2Eq+tKADnxpEl4eZfFEUUVFnQ6FRraGnuO2kCfzpheyCYu3D/yRMZUG4NWKtS3mxTjVjbRlTZfTlldIPqoohbthwTadc93w/LrM/kkqfncN+7XmLoqsYkU256i8+We8rS4dJ/WRw+kQQdHGy8BXbsPhmudAlBsejg7MxDVo8vIZi7qpk353thwpSmd0m8QNAR6QUtK0lm9Dz530We4pVhWCkMlVZzifoM0+ILuU29j6HTzwXdImXbXfcaC9e1ufK82bAE0zTJaAbrWlKu2tMVInorG4YdzVuG9fvckj028Pld2mEAzDM8Kf8r++8p2kym6J/SJoo5K3sJ14uzAwqWH4Ps0PA0eSZn2B454Wg8sH7Ibgyb2zB6v4XX0Jt6N2n45/Jz/GnpNDcB14Fump4Rpq14taayhMiys/wVLPdy1ALNujMaUdJ0EKbadqVuLBqBvmYube413Dx2Eg4azYSX4+Vgq+IFeDlePVG8HC+/XOLVFZxQYwDXNbs5Xk7rnx2GD+DMXb1w0pETatiupgQhBIlohH3Tt3B71vMDay8bGdhlStP5NpGfKO8ilP+SfvUXu7nfOSL1IAdkLGuaPiVRrswE877iWiN9lFZGZb5guLSKWmkthilYaVZRFFEoj4eYawa7lZSM3o/3jDGBZZXxIPEK27lIuSit2ZalNx/Iftv2DixfISwCaXk4Ctabpawzy2kXCRKm75mNWoTSsa6JqLI7sdbDZbBmDmaqJaAKDmyZxfZiITeqf2Px+hbXskOJlzPjCqsCfo45lHciexEJKRw10foN/65PQ+2zLaP6eMRupWmFVqWSfkwb3ZsnZ63ijx37BM5Fiha7Kpmjaqp6OyPKoJ/dTzMZryGsSGRQ+bDf6aBGGdm7mId/uiPXHjyKzhD23c+yJNh5aCWn7lxLIwmScgKyPeuesrmwlXj9QOFXo5w4fbGteLmsXU+z37a9OWC7PgCBMmzI78MGUGTndUVUmaFV1mC0z6heVBeFA34xbxgTAtstNXoRE2mKSPLZ8ibeXbiBGCl+o/yD6hk3saY5yW9fmg+YXKQ8w+Xqk8jC5NfKExyy7h4Yshf8fDYf7PUsf9EP5vfZY9g59UdCQmd36XN0w3BzxfYYYT20rSnNnRHnoq3CGtBawr2hcSkpTc/rweYil3h1ZJlS7JVrHzznHO/DRW+63w3QYkvT2bolPPDeEoZf9R8m3fgWB971XuHvsiEwCGlt9K6uJrH7BXxlDuJxfS+2S/2VuXZI4pYrr+TrU7/ifs2bCV6cPZcHtX3pMMNImLTJ1qwwEVYCCpYfw6SVecui8WI6fHk1YcP2CRPBa6EaaY6R32FDa5qMbnC08o71QXNwn7phoop8xcshu2a7dz1bfLkiHWmdmEiTNEP0KrZCr2sjg5GT9V4Pu83RJFv2hxqLkFJNYJq+hs1biRf4cryk7l8FTrup7ioZC+2/MzjFJqMG9mHnoYUV4OKIwkKzP3fphzPLTi+Qw0FzTNOEy19aygPaAYW/SM0nN+ClOKQJuWNNn5JInhVBkdHMPuYH7r8nS/NZSzkZVIojCpUJS809P3MBr+30OADT7HE5cL4+ZTDuEK+cUKoZSkCscKL+PKMWsBQ4sIoUFpg1tIk4JaavStywJjWOF1lElUgTojb1GFfpVhV7UXIVvc1gPuyz4es4Xn6LgfpySkQ7hhwBNUqv4ojbS7jIVib9z1hYkdjGR7ycgprUkP259/jtOWXKQFaa1Wyf+guv6Lb1gxpzybyTx1dKK/1NL8c4JEuEFZlnzpnC307dwV2+2/CqLu8tv+Kl2vd2SJb4wNiOa7d5BfpN6GzTLYKtxOsHCseYEAgoXiFFIuW8UHOaFRflNNn235jvlxwMsQpXzo+qMv3LY8y9bl9OmjzQfikK/qpNA+BNffvAvhxSdqd6LzcqVs/AM+RXOEV5g/2anuQY+R2+rWunmiYuVJ51W1mcoLyFbOqwz2+hfDDxyhqWmH35k34oq6ik3QxzpfIYo9850zVpdZSRlmQW2Vf55uZ7HfcU0bMt1/W1kcGw/mtSqXReDzYXdjz/5v98zcMfLaWpI8tIJZizdX7mAlKhcpj9aCBk6lghaBsW8+RMLw+rs8ayDhKkEJiISCm77n0oB6Z/RzMJth1cwymZX/HOxHsRsTKUWCnr8MIdC8wBXKedyoe2U3SHYhGveFhxE1BzUSXyy6Mj8SJM3+MdMZIcGf20oJlkL9HA6q8/4aPF9SRN+95qClYnmqZVPQm4s8W2tOY2NvaTW8eiAyw1NSGlCcWK2HWY9WJdK1mz+J3K7ePeTD5e7vGYcYSehmzHj65JdndwJmchpfvr0e4abnaeRJ6LnhKvQoqUg2Jfa7VjM1fz2Unz3Imp44no4HfaiTyn75y/k1Bh4lUIvUsimEhuJWaDaalJYwyvunmS9DXLDEt9SoRVym1LhpeMKey374G8/6s9mTyoIn/nPiTCCmFVziseoLhvp/fn7elDAdAiFjG7IHsBl2bPptWMUWn6JlW2Yu10RznGLr4Cwdx2a1l5egX9teWkhh3Indrhge95LXw5ZysvY0S8PDKHcDu/RzgQzpPcCAvAfLsgIDv6aCRJcOQE6/sbKObC7Pm8sM87IIS7jzQh0qFS+op6qjPe2NN7RysqMLG2PK/FXlfIDTX6l3WWj7olsZV4/cDgVHSEAoqXP9QoeQnXw4LSbSLcueL15pBfw2VLXAk2GrL+XxyxSs6jIZmQLPE77QQmZ/7EcrNXYF8O8Zoqz+Z4ZTrDxEqGSqtYZlTTLEoYKxbTnMwWbJMBQLWV/+A3jRVCsNysRhU61WvfJW1XJpbGQpTQhmykKZI8cukSj6JeyKEIRWGF5aEhoKdRmxYHFK/HtT3ZM/0HDKGw4uuZJN/9I395dxHX/PsrmpNZBurew62XDOQlYwqL+hwEC18l2bTW/azY/n5lzaeUSj2vhBnquLVHLOJ05zHjOHpiDcN7FdFIMd+WWS+HkCy5IQA/5hiW6aBTNdaV4lUI4bC17sTUn3lG342omeQ283ZKRTtrS8dzSPq3TEvfRIcZ5nhlOq+Er+CbZStod0h98/LA/nTDJOSGGh3FK+tWzzo5VGPFItrXWn5KlylPcrHyNKVKlh2H1VAUUQkrEmts4nWgPt3e+2ZIrveFGh31wuxo8Ii8+P7bhvwQ4LxMe6J4Ob6DnVUwFty/bwyalr6J1afMAODQcdZ44v4eocLtXSDY0zaLQlVZGVOGVHDCpAG8cP4u3HhYsNVRnVmSu4tABXh3x9vXLrZJ2gUBK+yk9rHp2axNeAVPTrK7E2r0o6Ys1m3eXCKsEFEl2okElovivp1sAWuo4IKKB1gw5RYAOoiQJEIrMarwEy/ruRxYEWfpzQeyxwgvMd8Z27dLf0bYTCGG7cMjWvBd4sD0ES9HnXJ6CudalUiS4O7jxjNhYBm/zp7B1PStqMXWd43s4xHkLAqpcKW9jbd9a7gP/UQdFekVgIBfryQ0+pBOr0VXCBAvXxshIK/zwPeBrcTrBwbn5vDPJhzFK2HneNVTwg6pe2GvYMuXeA7x8u/DGRic/ReaicbCMiYSqUhVwIQP4L/GGP6u7c+l2bPQTIk3wpdxiPwRq6imnhL6inomifmucStYBnsuJC+X5NHTJ3Hw2L70Lo64gxrAC59Y5cclEYXPI2fxcOhmFNPLSXIro0LWQ1wcVVksWWG7SP08+ogGTAT/GPc012qn8a3Zh9WJUfRf8QLR6dcwSFiEamL2U3Ze51X2OUZ+X5ftCYZGZpnXfzJOkpSpEmpfw7PNxzJRfJ133XIxVZrFc+FrrX/YxOsn4/vx+yPHui8i5+EPKYWJl5MDU24n8XaleBWCsAebOkpopohyvDBEZUmcueYQ5psD+drs7y4/Sn6XrJNTN/8lq+zcJlS6afoULyfHS6PUru6SUg2Ayb/D1zDq+f0Ak/OUF/i58jxhM+UqDsVRlRVYv/m41AznYHt8Xj2FPwzSblovNjPdTsg5B6VnTrZRzrEAACAASURBVOr/17Hb8CpOnDyAmrLu763z9hjCmxfvXtCDsDP4x5n55kBEuZXD+Mdjx3PJPsM94qWECm0OeC/6qqIwVx80ipqyKJWJML87bDtCiuROIh0UJF4TT89floOTpwzkn+dMoTSmElEl1wJjhZ2vFDfbWFWxCxm7g4NDYGIhueB42pl5rTMuW6FGmbyJR7wqfyMfGqIDkOOemqbKgmYzRrWfeB10Z2AbfweDVmKY0TL2zb4NQKh2Mo2ihFMzl3J9+BLe6eO7VlFvbHLOsbgL5engsX2ZPLicNmIsNvu556rKEhftPdwd/5yoguwjb01qL/qKekqTy6C0v9tS6LvAr3jnKl65RtnfB7YSrx8YVDlf8XK61pfFQu4NtYEyl8w48G8DwXyCMod42SGFQt1/4r4ZzQJzAK/qXky9zizheu1k/qnvweXame7ytVI1dUaCPeTPeSr8WyZJnhw/xxjC49peLNs76MG1y7BK7j5uPMURNZCH9NViq8VGX8nKF3Jc6wkleKXiFLdM2Rmki6Mqi4y+oEQob5xLjdSASFSzJjTAJRBPVpzv7v8c+UX2k2ZyjvIiabUEDrdCpmLYPiiSoE5Yg0y22XORj5lJXjKmuP++Uf0bFyn/zL94PowQvvyoaLDk+9w9hnDw2L4cu6OlGoQUifYChGq6MY63Yvvzai/rWsdDcsGy+p4gKYL7V6q9WftaXyn7mcrLXrh28Vvw+lXwhXWugapGW/Fq8eV4qekmV+WT9ZTrAQQQNtKgWopGUUShPpNTMNGSn6P2v8I/8DqKgplpJ+wUF2wlXoBlb3DDT7brtpk22Hmh1Z2HBAtvE9yvX1lTZMkX+u38+4uj1v0yqDLO6bsMylNanLZEQ6ri3HT4dnnE64qx78OQPTvd//YDrGf0+kNHM7qfldBfHgu5YXf/5LC1fFt+nT2Tt/VxjNv7OHYcVN6pSW1ny1+8YBeuPGAbQoqUZ80BdKn+gXW+/u3iYYUmI4Yk7Gt51EPQL5gqkls8kU4MIIRGm1KKqBpJPKzwjjGez4qn8mnt2fxLtwy7RcwjXjE31NhVwVNQ5fQf54V7D+OQsZaa5zhBSL5rtF6qop+oo7h9KVQE+xRvLPwTr9woUqaAT+GWxlbi9QNDyB5E/DOBujYr3HXUhP49qj7yw0l43Lav9X9n+0Lu5I5PT3HUsqU4J3uR+5m/Qu4ZfXc+tw3t6pTebDC8wXhn6Uv373pKuEI7A6UTuTgRUTB9s70yO2w1sGNecMX9buTt3qdzduYi3h58KZRapKU4otCYNmHQ7gxpfJ9h8hqoGMrxOw5wB4lZmYEcmraMFI9R3uG+0B1MluazZOgpMOZoOHM6Yq+riIVk6gxrhqXbPd9UNFSyfGv05s0xtwMwXFrFhcpzjBWLAj0L/ajzl21Hgi+B0liIu48b74ZPCoUjQnYVz83KeSSLagkpEqosdVrV2B1aDR/J+OnrsI9nLOlUjc4u2pNeosktn3dRb/WTs5Lr7VBj1qtqLLVzvFQjxU9lq6JSFwoTheelFjaTruJVFFEDRo3IYRgc9DzaFPDbSXQ4oZxMm1fVuZV4bVb0KbGueS6h878QVVn0KPRbKKfID0eJKY+HiKgSn5nDmG94Sm5lvHM1DeCJsybz+bVBs9ki36TQT7xaek3iX8Zu/DR7GfvuuSdPnz2FrnD8pAH8w5cUDlbj7zN3Gxw4p3rTp+6oXROvsCoRkr3rlQgrNOi+cGWBQoJc9W223ae3tWgoCOGmqZTFVKIhmXX2hEzyTRydXXSXa+VX/3J/f4d3OxN/vziwVlSSEClKGr/6n4mX/15xCHDYDTV+txZwmxJbidcPDJftb5m9+eXce44fzx+PHceAili3vddy8fIFu7DkxgPcJMuuWL8TqnSkfT9acvKLGu2BolmtCrgX7yZ/4VvHImQVnQx8ibDCLdljXQuF29U/8zP5ebb/5KLgiqE4qizYQBlf9fPMYIujqtXkepuDKM+uYVvzG6gaQW1lnE+vstyo17em8owGAZpGHGX90W97kBUSYYUmTQU1jtFmkY+43eutQ0SYE9+FhfJwd/t/h6+B6TcUPC83nAV5xCtvXfv3uCBzPn8faPUkc5JUN7SlmTS4gr1GVKP6qp+e13divtHzPJsme7u62BAYMAlCMQZWxOxztMjj1+V7uC1PVpkV3JY9irrECLDbsBgmKE6OV9NyuHUYtY0fUa16oeXjFStnKymijJE8uxEZw30ZFEcUNrSmOTVzGa+PvAGuXg81m77CyE9o3VBjpo2wsImXvJV4bU48/7OdefC0HfKy9/y5d6os8WfdnpT13o7O4IxHnREvp9lzWSxERJH51uzDtMwtaKaEieCM3QYX3M5BWJHzyEQiorihxgYfKQolujc09uPGw7Zjz5HVnX7ukJQJ6fu42bGbiXX9HRFFDkQ3EmGFFr8aXiCfLXeCd97iyfxKP5f4EfcAXmFWWTxEPCS7ecSS6b0nnB6uhd4Pfji5YIXbUFnHYRQINS7XfcUI/6viVeBecd99W0ONW5GLoyf2Z+nNBwZmDQeN6cuh4/r1aPvrD92WB06e6P5bkkRgVuEkjhaatTihxtzqSLDaLfjhtIXIqKWBRqmF0FllUyKisJYKLshaBoJDpdVcqj6dv2Io4YYo/DOo4ohNvEYcgIFkveBtl2LnoV/fmnbdlR2clrmUYUNHBJbFwgqN7RnMeAXFK6ezqzSXhLBIiSbHuWf6IkQ2xydsw4KC5+VvVN0t8bKP80VjJ74umgxAfzvfpqkjyyFj+/KXkyagSoI6StBMiWuyp/GE3nnoJBcv6ZP5VfZMnpvwsLvstV/sxtSR1Tyt7wFAXek4d2b/D21/7tEPY26ywke8rFDjGios88H29UxueZVearDg4CltDxJGK2NCq4IH4RIvlVWNHbxjjGNd7XdLnO0JVMmveNkkK9PuU7wiBbbaik2FXsUR9hhRnWdp7Cdeiix4zxjDr8e836l9AniT0EIvU/DGsgkDywJjzdj0Ayw6fX63RKEQiiIK7+pjAauwaJYxjJeHXEM01HmY7cbDtuOsbkheLvxk8hF9H/6u7Q+Tzu16G7VAqNFvf1FQ8Qpeu8akxsqBP6G4xko72GmInewuBLGQ4jPp9p7vrE1YelpdOHlIflWn8ypycrz80djZuu/aVQzp0Xd0hi6J19ZQ41Zsapw8pZZ9RvXq9PMTJw/kD0eN5dgd8hUTJ9QY8w0u/9Qsx+Vc5+m7tMP5R69fM79op6BM7oPTzLszOL4wjTnrafE+/DzzM29BKO4qff4k0ZKoajXXjlcyT7HzlqosVUqRJRRJ0JrSPPsN4Er5IibsfYxboeVg277FvPX1etbrRSRal/BH9R7+oP4ZgLqspdg9oOd6BBVWH2N+4hXumpQWygOpKcsfOFVZ4l/6rhyRuY4W4nkVjn/IHkk6x/TW7UFJlKf0PQPeRxFVpiIR4g1jIifUvIZR1M/N9zp2Z8ucsjFcA03LQLeacKvovMWOMMZSHRNaI1Wyp3jNNwa4hHysr/Qe8IUaFdfnq7gAwd9U8IcaHcWLTIcvx6vr8NNWbBrkdrPwE2LPX6lrFd950XfWyHuXYZU8ddZkztx1sFsBDtZ9L0e+W4J2IqzwZ/1gLuvzD74xazgi8xsW9z2EeBceZsdPGsAVB2zT6eeFoMjB471eOxnCwfFwcFWcKYMrXLIVVqQ8xasxEKrMV7yUApESp1UawOHbWxN7TTeIhWW3otPf09BJT+kquR48MnXQmAI+ZsJRvKx/+yM436R8kYlNmOPlLnOS67NbiddWbGHIkuCICTUFQ5bxAs7Ul2ln8dDUmYDV09FBFoV5ldMoiYeCD72Dw+7n38ZOXR6Lk1fQnGNWaCZ6BZK+nVAjgOmbQw+siNGe0fm2rp3Xxc5oKFC9rft5IaXthhOncv5ew/KW33rkWISAL5usAadctLnJ/cP6WwPI0/qe1KYesxL6AbOTNkJOfzfOfDuvAKIrOInLY/tbA9DgKo8oKbLl3/a5aQ1IawjOJp83dmZE+qHAsvcu24tbj/Scs8M5ic5OEmxYkYmHZbc8fmipxGHj+7EwWwWGBs0rMEwr5y1jKpiH3Yc54TQG6d9SLrWz0qzEMAWrJ19DvR12VtBds0sgkFzvoLsB/H9BINTo5MZl2nxVjVsVr+8DfvXdUb+6S+x3CLrf2zAXkwZXIEki75nviU1GIRRFFEwkWmJeV4lYSN4o89jvgkKZJG9fsgdPnDXZJV4RNT/UWN8d8SpAbp08PIAxNaXcfdx4rjxwFPGQwsfGKCudYc8r3XUc4uVXvG4+fDtuP3psYL9HTqjhH6fuwFETavK+0xnbnLHO/x5q6Mjws8zPyfSbAiX987bdGBR6vzmT7c6MerckNt+Ucyv+v4PfXNWBiYQSCvPJFVMpianc9toCHnjPqj5MRBTCqkSTrfw0mAmvCerYY5gxONVlI9NE2HqAc9U0UdTLq2AEKB+MIltWEH7zuz1HVHMtX/H21+t5ODsVMWofLvL1ZAsrEm1Bj1lEUf4sDHArjHJb6gBcdOD2LHpP4ZUv1nLeHkN5KPVPaj69kbPrXoZXLoMDfh+Y2cdIW2XYG+mOfMB2fZg0qIIxNSXsmGMYmKsKfGSMYpf0nbwf/gUA7X4FzCYVIUWiNOYpO7kvrkjIa3ocCyncoR1MbVzjkO1PoqppNV8mK60RomExhhFCQSdrShx7/8cclC7iJNoo6fiUZ41duDh7Hnf1G0+9uczd/2fGcCZI31j/KLK8u4p8YZ/vEgLqKQqFGs1MO2GRwUQgpK1D3/cNh3AVIgV+dBdq9CO3W0d3++4Mjuof8xGteFgJRAM2B6QujH0jqkxLSiOiSjmhRjmY7lEg1FiIgPYuCU4+DrYrDqMhmVZiTMvczNK+XhumrJvj5V0DpzrbD1WWOs1rO2L7fowfUMoQu2uK/3xTWYOXmcxNJ11DaCMmrIVQKJJQmQjz/q/2dDtofJ/YqnhthQtnkMn1xZGFoLo4QliRufLAUfznwl0Bq3VEWSzkekH9STs0sF11USQgZ+ci0UmoSSrqzSqqOC1zKf/a4y2IlqLaMxjNR7wGVMQYWp3g3YUbaEnrGKUDA/spmIxb1HkYNqzIeS11AESkhOoi62EtjakMrIix3vHe+uQ+FqxYEyCEMZHutjKpEBRJYmz/UoQQDKiIURLziEm+KiBY6au2cioed0vfAb/wChz8oZHc0n6vQbJEPCzTTpR7ImdBpISqRJiFmr3/hm/RDQNV6GRMhRnfNvDgGm9G+rE5mjN2GYSa04j3Q+HrnVdl5dT5Fa+SbsrS/xf4lRUdGU0KIzKWj5cmhbY61/8A4DzTajeqVMlGEK/ckFqhEFtP4H82HMRCcpehxk2Brm5LR7EO5yTXx8NKoACgJ8n1AL07ISC5reccOLlRuX6RGwMhhEu6AC7eZzgRVQpMMgu1uttUsIxtv3/a8/0fwVZsNP517k6888s9Nvl+nYc5d3aUOwtzXp6JiEJJVGWRWcO47IP8TZ+2Ud/nfwnPMLymt1KxpY5MN8ZT2dciU05yqNN7zMGI3kXMW92CYeYnfYYLPcBdmPKFFYlndYtUvqhPhkPuhnPeh+qRbmVmfXuGaEjmfWO0u91fnns7cFxRPMPQjUFXFavdDRZOn7l+g0ZBwiNk/tBIruLlH+CcwdYhttXFYTZQiqFEoWEJkm1kmzKsbRabXrHH7y+9gKsOGoXic+Ff0Xsflsu+cIFtClm8hRQvP0KyREaKgu3jZUhb87t+CFB7qnhFek688pP5v9srznluNJ9iHw8pmz3U2Jn3F3h+ZRFVCuQwFYUVmvFN9AoQr0JjS67i5SAWLnyOfzx2HLsMrczLj/1fsNvwKr7+7TQqE47Bt/hBEKPNjf/7Z/h/EBMGllFbufGKSndwCJYJvHTBLuxkV6XkjgW9iiPsM6oXkwdXUGaHsjpExO0NmOoVNO/rDH4Z/5jMNVycsZtVJzxVatIgK9fLGZy1nNBl35KI63OW+yL3K15Hpa9h8cRruzyekCLxF/1gRqQe5ILsz2H7k90y98Mn1FBVFOaoCTVEVZkF5gBWH/mSdWwtK8hq1nEdKH3MbtIXnTbl7QpdE6/uZ+6Hj+/HE2dNDizzz05zFUDnJWKYprte1iaQVYkwIEgWDbSIl2HlRvk7GhyZvoavhp4DJVYuhyIL2oixU+ouPpt0h9tgHHBvoi2V4+VHWJFI+4iXJm21kvghwHmmu3vRFkcVZEnktUQrhAHlsYCS811Djc6kRPdNqGJhebOHGrs6WkfxiqhygKDFw0qgLyty/nNV6Bp3Fo2IdUIudxpSyaNnTNpoS6OewPltu+vt+X8FW4nXVrhwK04Mk9H9StwBLHcWpsoSD5w8kTE1pZTF7b5d9mcjUg+y7ojnevR9uYOB2yzazgeSfcmyihtqDCpevX2Dhz805z8fgJnmSNZtc3KXx2MRE0GafEWkX2mUmVfuzdDqIndQ/snjluN6TXoh6bRlPXFv6C6KRLJb9+lC+F8UL6DgqO1vBZNLvCJu+w6votWveAE0R/pD/WIk0yJemi8t9FNzJMvH/sI7RluVXE0lYVVlQyr/mEf1LWZYdYJfTxu5xQbZsGoRL9G8nHLRiiFtGcK3FcEOGbmTB+ee7m5SEVZkHv7pjhxXIJ8oFxFV5uMrprr//s6hxk4Ur81BOvzoKtToV7z86Cxlww//cf/20G0piiiUxQo/B5ubXBaCMwnrjPT9X8PWDNOtcOG82x1zO+f/XY01TvK2MzylCaGGeqYo5A64nxjbcKc4iV8MmcqbF2uBxrPOwFFI8XIwLKedybw1Vp/DX+0/kv8u3MD4/l0bEzqhuD4lEV67aLdO13OS0tfbTvcXyv+i7vUY4Au1fgfFq6uXRE9m7qIA8/IPorlEx5vV+xQvm3iVx63f8OXGGs5MvcGR8guA53QfUiQymhEIvfiP0Xo5CH6W+TlnHHUI4+3lNWUx3rh4927PZVMirMikRBR5+QfsL0OjVLtFv//HjHF2FdvfTpnI1G2C+ZWKaxHT/aTiu1aifdeqRufZ8OeUxjsJwW1KdJdcD/kpAz0J2fvHlpOm1HLSlNpO1/0+yI9HvH4clGSr4rUVLhxlS7cJl+ku73ybUvuB8bsB9zRGnzuAZFF4UBwCaoSh1YkA8XLUmtwek/48hYEVQZXJIWVn7zaYJ86a3G1+hiPll0TVLgczLzfKuzCV8x/xehnCJs/xkrtJBp86spqL9x3e5Tq5dhLO72SYppfjZYdWnMqlm5qmMscYwqHaa9bndqhxZzsMbfh+ED+Rdn7bl43JlA8Y1eVxbW6EFen/tXfv4XHVdR7H39+ZpJOmF5K26Y1eUmgBW6AFwlWgYrmtyIpSUVwFXfG2sqi7j/dnV7zgsuris6I+LG5VcHkEllXhUVB5kPWyy5alUm4i2mpZwBZRbgIlbZLv/nF+J3MymZkkk5w5k8nn9Tx9enLmnMwv38w5+Z7fldxAcbSqarzqp3veDHZcesawpAuK/a9G04xeq1prvOIkJ/mgFycFZxy6iE+edXDZ88aralPj4HQSQ6/jjgo1V0O+7xgGk2TRxyrunztVmhqnRnopozJ/VlTL0RU6T5ZbyLRUZ/vwZrlKEx2WOma/ubzthBWD01NA+cW7AV7bs5Rf7HyW92wYOgfX4o5iU1pp4nLjhcfTP+BDRrhVk5ygsJpkp/RP7z2Xs/L/zercw2zI/bx4UA3zRFX7IxH/CvbrmsHBi/fhpnt+B8DpvZdy8ZkvYdNxR1Y8d8HsAo8/2zvs+8fxGvBih9r4Cb8ln2NWWwt/erGPuwdWsq4lWrMxTrwuO2cd/77lEdYfUOzIn6xdSCZ5lTrx1su0lhwLny9Oc6HO9Y2hdZTzeNXCLLqXjPbaLxXPcRUv3wXFAShfesPo+rDWYjQ1Xm0lD6xx0vKy3n/islcuZaJKt7iO1238oDu9dWrUBSnxkkFnH76Elrxx5qHRfC7vP/VAnn5hT9mn1Vi5DtKtLaO72eVzxkfPWD0k8RqokHm1teb5h9ccOmx/PMKm3NDorllj60QdL1BebaJGGDpS8Mr+M7mhfz0/b3snPbnEEkJ7ni9zZnWj6T/y8gPnc+baxYOJ1y99Gb3z1lQ956INq/jot+8fbD6MJZfviP+oxGuFQvQk/acX+9juiwf37fEWchat6fb2E4cu69EypMYrkYSNEM+0FVpyTPPihG4D6lzfEAZHNabQb+qW95zAHdv/WPP5a5d2cPVfHsVRK+bw1f+K7k9pj2iEEaaTiB8MS5KTOPHa4Yt4Yf7ErHv6w/edOKGjF0cyO0wtUy3xHKtopGRjThujxEsG5XLGqw8rzja8bG4733jr0VXPyeeM9568iuNXzmPjFXcAtfergMo1XtXe/5oLjh6cCXk8Kt3YSpU+cT7JLF6wdg7JFRNIep8d8/tXaw6I+285w29OIzXV/MXRyzn3yGXDnv6Ly3c4+Zyx49IzhrzeMX0aj7B7SOLVR35ILWNSsqYz62QrqdCS54sd7+fCpz8LwEBeNV6NYLAGJ4XmpYMWzuaghdWX6xrJiQd0Dfl6NNNZjFe1e0ChQh+vZPwmqvP/AQtqW2qpVnHyuHcC11Hc/JGTJ+x7TTQlXjJu7z15aN+i8fTZqFTjVc1ELQEx2qbG4Z1sjUdtEYdYIvF6ceyJVzXJ+3HpvXk0TbvlmlziPnSlfeNicd+R3wwUZ/s/q6ebD64vn4wnm4zaWnPc+ZENg/0Fs1RozfGjgZN48xHPM3PLlxnIq8arEXTPbedzr13LyVVq1KeC09Ys4AcPPD64XUlyyaCkZI1hmv3l0hQ3Ne7pn7j7RdojUMdDiZdMuLF05IRozrBb7t/Jl27fXlPiNVEKo2xqnDuzwHf/+nheefnPBvdt65vHAbntxYPGueRFJeXCU2sfmbVLO/jaW44cnK+tVPwU+jidg8tBnbpuf6iQqCX/ABRa8mNu6k1LoSXHk88P0N8a1YqO9fMp6TAzNpZZz2+queKNR2Bm7HrmRebOrFwbW6whHHq9J1sYGjnZqCa+15ROF9SspkZPNmloB++7D+eF4c1ZVpAUl+QY+bI4aOHQqvjfDiSeVNe9Ec7eNKFlizk+rMZrPE+5Jx04v2KiWRwtZRzb+0WuXfkZ6K48zUZyNNRIzbX1VGjJ09s3wN7W6HeWHxi+HqdIVuIHgYX7tFUdUdiWWDIoKdm3cjzdPLI0O4WmxkY2OX9L0nSSk3lmZbR9vGBoLdOcGdPYPlDsB8Vpn4I5K0b9vmMZDu4+fNDAaEeRjlXH9Ojpe0nndHqZxvbOE6DKjb1S5/qs3PP3p3L3350SzVzf109fSLxafE/GJRMZu5mFFsyGz7OVrOWqdab+rBX7eGXfNaEe1NQoDSF+mqvXMjLlTGsp/0Q5kuVz29nySKKfW2FsnXp/+L4T2fXMi1WPSd5O589q486PbuCoS24D0pt3J04I1y7t4NGndo84ND+52HFayeBYxCsZFFpz9O4dYG9oaswPKPGSyefsw5ewYt6MYYtUJ6//aiNE33HifhX7c2YtnqJnzxSp8VLiJRNmxrQ8z+/pr+ncQkuei89czfoD5498cEqKfbzGljR0z53B3f+XaGocY/+u+bPamD9rbHPmJI9P6yk3fgpdt6SD7927c8RJXJPlaKR+VHFTY19LVOOVU+IlY9CzvHPIZM5Z6ZwxrezUPslcq1ofrw+/4iVpFGtCdM0qcMrqBbzthP2yLkpdKPGSCfPjD5zEM7tr7z/z5peOvnkuDXHCNdZh48vntgPGUz6TTnsuhZJVn98nrdql41bO48y1izl59QIuufnBEefYadRmjnh5oz0tcY1X7whniBTd8K7jsi5CVWZGPmf0D3gms85PhHzO+Mp5PVkXo24m529JGtK8mQX27xr/fFpZGe10EqXWhvXo1vdexiPnbZ7wcgEcsTxaZ3J9ydxCkF5T474d07n83MNYPqed1Ytms2pB9d9ta4N27I37eO3JR/OP5frVuV6aS1u4Z03WUY1TTWo1Xma2FLgaWEA07+OV7v7PZjYHuA7oBnYA57j7U2mVQ2S0Kk1QOJJ4iaVnmYl1LpvwcgEcuqSDhz51etmytabckT2XM25+zwmjOq4RFVpyDDjs9rCguyZQlSbwmY2HcuPWx4BocNLze/qVeE0Sad6x+4C/dffVwDHAu81sNfAh4DZ3XwXcFr4WyVwhP/pRjUltrTmO3S+aCyvN2a0rJYRpLLnSTOK4PZWfx2f3nsOvN/xrxiUSGb9zepZyzQXHAMVa+qkyHcNkl9pfCXff6e4/D9t/Ah4E9gVeBVwVDrsKOCutMoiMxVjm8RpyXkueTW/u4Svn9Yy5k/xEmKz9Ouol/r2+sLefL/WfRV9Htn0JRSba51+3jqNXzGFBmTVrpfHU5Y5tZt3AYcBmYIG77wwv7SJqiix3ztvN7C4zu+uJJ56oRzFliivUOJ1EoTVH+7QWTlmdzdInal6oLp4j7tndfYBqCKX5HL3fXK57x7F6CJskUv8tmdlM4D+A97r7kAXs3N2J+n8N4+5XunuPu/d0dQ3vUCwy0WqdTiKNRX5H41/edETVtd0kEvfB2xnmSmvU0ZciMjWkOp2EmbUSJV3XuPu3wu7HzWyRu+80s0XA79Msg8hojWXm+qS2MdaQTZTT1izktDULM3nvySSe6X/XM7uBybusiog0h9TuQBbNoLgJeNDdL0u8dBNwftg+H7gxrTKIjMXSOe20teZYNqd9TOeNZ61ESd/8kHj9LtR4qWlWRLKUZo3XS4E3AfeZ2daw7yPApcD1ZvZW4GHgnBTLIDJqS+e088tP/tmoj//eRcdzx/Y/NtQs7VnrntvO4cs6sy7GEHNmTMOMwWWZ1MdLRLKUWuLl7j9j6BJzSRvSel+RelmzeB/WLN4n62I0lP98UTG97AAACRtJREFU/0lZF2GYlnyOuTMK7AxNjarxEpEsqbODiDS9rlkF9vZH43g08ktEsqQ7kIg0vbifF6jGS0SypcRLRJrevJnFxEt9vEQkS0q8RKTpzSgUp/zIaxSqiGRIiZeINL3kpLiq8RKRLCnxEpGml1wGShOoikiWdAcSkaanGi8RaRRKvESk6SWXgcop8RKRDCnxEpGmV8hoPU0RkVJKvESk6bWNceFzEZG06G4kIk1PNV4i0iiUeIlI00t2rhcRyZLuRiLS9ApqahSRBqG7kYg0PTU1ikijUOIlIk1PTY0i0ih0NxKRptfWqhovEWkMSrxEpOmpxktEGoXuRiLS9NTHS0QahRIvEWl6GtUoIo1CdyMRaXpqahSRRqG7kYg0PTU1ikijUOIlIk1PNV4i0ih0NxKRppfLWdZFEBEBlHiJiIiI1I0SLxEREZE6UeIlIiIiUidKvERERETqpCXrAoiI1MOnX30I82cVsi6GiExxSrxEZEp4w9HLsi6CiIiaGkVERETqRYmXiIiISJ0o8RIRERGpEyVeIiIiInWixEtERESkTpR4iYiIiNSJEi8RERGROkkt8TKzr5rZ783s/sS+OWZ2q5n9Ovzfmdb7i4iIiDSaNGu8vg6cXrLvQ8Bt7r4KuC18LSIiIjIlpJZ4uftPgCdLdr8KuCpsXwWcldb7i4iIiDSaevfxWuDuO8P2LmBBnd9fREREJDOZda53dwe80utm9nYzu8vM7nriiSfqWDIRERGRdNQ78XrczBYBhP9/X+lAd7/S3Xvcvaerq6tuBRQRERFJS0ud3+8m4Hzg0vD/jaM5acuWLX8ws4fTLBgwD/hDyu/RjBS32ihutVHcaqO41UZxq43iBssrvWBRi9/EM7NvAi8j+gU8DnwM+A5wPbAMeBg4x91LO+BnwszucveerMsx2ShutVHcaqO41UZxq43iVhvFrbrUarzc/dwKL21I6z1FREREGplmrhcRERGpEyVeRVdmXYBJSnGrjeJWG8WtNopbbRS32ihuVaTWx0tEREREhlKNl4iIiEidNG3iZWZtZnanmd1jZg+Y2cfD/hVmttnMtpnZdWY2LewvhK+3hde7E9/rw2H/Q2Z2WjY/UX1UiduFIQZuZvMSx5uZfSG8dq+ZHZ547fywIPqvzez8LH6eeqkSt2vC5+b+sHB8a9ivuAVVYrcp7LvXzG4ws5lhv65VKsct8foXzOy5xNeKG1U/b183s9+a2dbwb13Yr2uVqnEzM7vEzH5lZg+a2UWJ/VM+bmW5e1P+AwyYGbZbgc3AMUTTWbw+7L8CeFfY/ivgirD9euC6sL0auAcoACuA7UA+658vg7gdBnQDO4B5ieNfAdwSzjsG2Bz2zwF+E/7vDNudWf98GcTtFeE1A76Z+LwpbiPHbnbimMuAD4VtXatV4ha+7gG+ATyXOF5xq/55+zqwsczxularx+0twNVALrw2X3Gr/q9pa7w8Ej/ttYZ/DrwcuCHsTy7UnVzA+wZgg5lZ2H+tu/e6+2+BbcBRdfgRMlEpbu5+t7vvKHPKq4Crw3n/A3RYtCrBacCt7v6kuz8F3AqcXocfIRNV4nZzeM2BO4El4RjFLagSu2chenIGplNcYkzXKpXjZmZ54LPAB0pOUdyo+rehEl2rVI3bu4BPuPtAOC5ekUZxq6BpEy8AM8ub2VaipYluJXqSe9rd+8IhjwL7hu19gUcAwuvPAHOT+8uc05RK4+bum6scXik+ilsibqGJ8U3A98MuxS2hUuzM7GvALuAg4PJwuK7VoELcLgRucvedJYcrbkGVa/WS0Cz2eTMrhH26VoMKcdsfeJ1FayvfYmarwuGKWwVNnXi5e7+7ryOqZTiK6OYtIyiNm5kdnHWZJoMR4vZl4Cfu/tNsStfYKsXO3d8CLAYeBF6XYREbUpm4nQi8lmKSKmVU+Lx9mOhvxJFEzWAfzLCIDalC3ArAix7NVP8V4KtZlnEyaOrEK+buTwO3A8cSVXfGM/YvAR4L248BSwHC6/sAf0zuL3NOU0vErVo1cKX4KG4hbmb2MaAL+JvEYYpbGeU+c+7eD1wLnB126VotkYjbScBKYJuZ7QDazWxbOExxK5H8vLn7ztAs1gt8jWJzq67VEiXX6aPAt8JL3wYODduKWwVNm3iZWZeZdYTt6cApRE/NtwMbw2HJhbrjBbwJr/8o9Mu5CXh9GBG0AlhF1FenKVWI2y+rnHITcF4YwXIM8Exo4vgBcKqZdZpZJ3Bq2NeUKsXNzC4g6tNwbtwHIlDcggqxe8jMVoZ9Bvw5xc+hrlUqxm2Luy9092537wZecPeV4RTFjarX6qKwz4j6/t4fTtG1StW/Dd8hSvgB1gO/CtuKWwWprdXYABYBV4WOpjngenf/rpn9ArjWzD4F3A1sCsdvAr4Rng6fJBr1g7s/YGbXA78A+oB3hyfwZlUpbhcRddZdCNxrZje7+wXAzUSjV7YBLxCNcMHdnzSzTwL/G77vJ7xBFkRPSaW49REtCH9HdD/nW+7+CRS3pGGxA74H/NTMZhONirqHqBMv6FqNlf3MVTlecYtUulZ/ZGZdRJ+3rcA7w/G6ViOV4vYz4Bozex/wHHBBOF5xq0Az14uIiIjUSdM2NYqIiIg0GiVeIiIiInWixEtERESkTpR4iYiIiNSJEi8RERGROlHiJSJNw8zmmtnW8G+XmT0Wtp8zsy9nXT4REU0nISJNycwuBp5z989lXRYRkZhqvESk6ZnZy8zsu2H7YjO7ysx+amYPm9lrzOwzZnafmX3fogXNMbMjzOzHZrbFzH4Qz2wuIjIeSrxEZCraH3g50VJE/wbc7u6HALuBM0LydTmw0d2PIFr495KsCisizaOZlwwSEankFnffa2b3AXng+2H/fUA3cCBwMHBrWOopD+zMoJwi0mSUeInIVNQL4O4DZrbXi51dB4juiwY84O7HZlVAEWlOamoUERnuIaDLzI4FMLNWM1uTcZlEpAko8RIRKeHue4CNwD+a2T3AVuC4bEslIs1A00mIiIiI1IlqvERERETqRImXiIiISJ0o8RIRERGpEyVeIiIiInWixEtERESkTpR4iYiIiNSJEi8RERGROlHiJSIiIlIn/w/fV6dlys4ZtgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-kT6j186YO6K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0c1431de-92d5-46fe-a546-e11554e8f38b"
      },
      "source": [
        "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()\n",
        "# EXPECTED OUTPUT\n",
        "# A Value less than 3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.971245"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NkLTpTjW2CKp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}