TPU_Keras_Fashion_MNIST.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Keras Fashion MNIST",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "Ot79jiI7GiHR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Fashion MNIST with Keras and TPUs\n",
        "\n",
        "Let's try out using `tf.keras` and Cloud TPUs to train a model on the fashion MNIST dataset.\n",
        "\n",
        "First, let's grab our dataset using `tf.keras.datasets`."
      ]
    },
    {
      "metadata": {
        "id": "Zo-Yk6LFGfSf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n",
        "\n",
        "# add empty color dimension\n",
        "x_train = np.expand_dims(x_train, -1)\n",
        "x_test = np.expand_dims(x_test, -1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Hgc2FZKVMx15",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Defining our model\n",
        "\n",
        "We will use a standard conv-net for this example.  We have 3 layers with drop-out and batch normalization between each layer."
      ]
    },
    {
      "metadata": {
        "id": "W7gMbs70GxA7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748
        },
        "outputId": "cbad524c-859e-4a7a-8e9d-8f7f171b30a0"
      },
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.BatchNormalization(input_shape=x_train.shape[1:]))\n",
        "model.add(tf.keras.layers.Conv2D(64, (5, 5), padding='same', activation='elu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2,2)))\n",
        "model.add(tf.keras.layers.Dropout(0.25))\n",
        "\n",
        "model.add(tf.keras.layers.BatchNormalization(input_shape=x_train.shape[1:]))\n",
        "model.add(tf.keras.layers.Conv2D(128, (5, 5), padding='same', activation='elu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(tf.keras.layers.Dropout(0.25))\n",
        "\n",
        "model.add(tf.keras.layers.BatchNormalization(input_shape=x_train.shape[1:]))\n",
        "model.add(tf.keras.layers.Conv2D(256, (5, 5), padding='same', activation='elu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2,2)))\n",
        "model.add(tf.keras.layers.Dropout(0.25))\n",
        "\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "model.add(tf.keras.layers.Dense(256))\n",
        "model.add(tf.keras.layers.Activation('elu'))\n",
        "model.add(tf.keras.layers.Dropout(0.5))\n",
        "model.add(tf.keras.layers.Dense(10))\n",
        "model.add(tf.keras.layers.Activation('softmax'))\n",
        "model.summary()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "batch_normalization (BatchNo (None, 28, 28, 1)         4         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 28, 28, 64)        1664      \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 14, 14, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 14, 14, 128)       204928    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 7, 7, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 7, 7, 256)         819456    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 3, 3, 256)         0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 3, 3, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 2304)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 256)               590080    \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                2570      \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 1,619,470\n",
            "Trainable params: 1,619,084\n",
            "Non-trainable params: 386\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xLeZATVaNAnE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Training on the TPU\n",
        "\n",
        "We're ready to train!   We first construct our model on the TPU, and compile it.\n",
        "\n",
        "Here we demonstrate that we can use a generator function and `fit_generator` to train the model.  You can also pass in `x_train` and `y_train` to `tpu_model.fit()` instead."
      ]
    },
    {
      "metadata": {
        "id": "pWEYmd_hIWg8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 972
        },
        "outputId": "27fede51-6f57-4e2f-aa37-2093b814824e"
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "tpu_model = tf.contrib.tpu.keras_to_tpu_model(\n",
        "    model,\n",
        "    strategy=tf.contrib.tpu.TPUDistributionStrategy(\n",
        "        tf.contrib.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])\n",
        "    )\n",
        ")\n",
        "tpu_model.compile(\n",
        "    optimizer=tf.train.AdamOptimizer(learning_rate=1e-3, ),\n",
        "    loss=tf.keras.losses.sparse_categorical_crossentropy,\n",
        "    metrics=['sparse_categorical_accuracy']\n",
        ")\n",
        "\n",
        "def train_gen(batch_size):\n",
        "  while True:\n",
        "    offset = np.random.randint(0, x_train.shape[0] - batch_size)\n",
        "    yield x_train[offset:offset+batch_size], y_train[offset:offset + batch_size]\n",
        "    \n",
        "\n",
        "history = tpu_model.fit_generator(\n",
        "    train_gen(1024),\n",
        "    epochs=10,\n",
        "    steps_per_epoch=100,\n",
        "    validation_data=(x_test, y_test),\n",
        ")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Querying Tensorflow master (b'grpc://10.35.169.186:8470') for TPU system metadata.\n",
            "INFO:tensorflow:Found TPU system:\n",
            "INFO:tensorflow:*** Num TPU Cores: 8\n",
            "INFO:tensorflow:*** Num TPU Workers: 1\n",
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, -1, 6141478845085583005)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 13222358604219301175)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_GPU:0, XLA_GPU, 17179869184, 16366472774922891846)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 17179869184, 13332117330396033526)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 17179869184, 4415038029975259010)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 17179869184, 12754675319092046402)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 17179869184, 17530955879784512251)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 17179869184, 8153113216849723858)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 17179869184, 5633517780969906582)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 17179869184, 9047928533386558867)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 17179869184, 6001714098511624895)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 17179869184, 4562730664270928627)\n",
            "WARNING:tensorflow:tpu_model (from tensorflow.contrib.tpu.python.tpu.keras_support) is experimental and may change or be removed at any time, and without warning.\n",
            "INFO:tensorflow:Connecting to: b'grpc://10.35.169.186:8470'\n",
            "Epoch 1/10\n",
            "INFO:tensorflow:New input shapes; (re-)compiling: mode=train, [TensorSpec(shape=(128, 28, 28, 1), dtype=tf.float32, name='batch_normalization_input0'), TensorSpec(shape=(128, 1), dtype=tf.float32, name='activation_1_target0')]\n",
            "INFO:tensorflow:Overriding default placeholder.\n",
            "INFO:tensorflow:Remapping placeholder for batch_normalization_input\n",
            "INFO:tensorflow:Started compiling\n",
            "INFO:tensorflow:Finished compiling. Time elapsed: 2.9434518814086914 secs\n",
            "INFO:tensorflow:Setting weights on TPU model.\n",
            " 99/100 [============================>.] - ETA: 0s - loss: 0.8651 - sparse_categorical_accuracy: 0.7379INFO:tensorflow:New input shapes; (re-)compiling: mode=eval, [TensorSpec(shape=(128, 28, 28, 1), dtype=tf.float32, name='batch_normalization_input0'), TensorSpec(shape=(128, 1), dtype=tf.float32, name='activation_1_target0')]\n",
            "INFO:tensorflow:Overriding default placeholder.\n",
            "INFO:tensorflow:Remapping placeholder for batch_normalization_input\n",
            "INFO:tensorflow:Started compiling\n",
            "INFO:tensorflow:Finished compiling. Time elapsed: 1.9441907405853271 secs\n",
            "INFO:tensorflow:New input shapes; (re-)compiling: mode=eval, [TensorSpec(shape=(98, 28, 28, 1), dtype=tf.float32, name='batch_normalization_input0'), TensorSpec(shape=(98, 1), dtype=tf.float32, name='activation_1_target0')]\n",
            "INFO:tensorflow:Overriding default placeholder.\n",
            "INFO:tensorflow:Remapping placeholder for batch_normalization_input\n",
            "INFO:tensorflow:Started compiling\n",
            "INFO:tensorflow:Finished compiling. Time elapsed: 1.5719544887542725 secs\n",
            "100/100 [==============================] - 18s 178ms/step - loss: 0.8602 - sparse_categorical_accuracy: 0.7391 - val_loss: 1.2460 - val_sparse_categorical_accuracy: 0.5752\n",
            "Epoch 2/10\n",
            "100/100 [==============================] - 7s 75ms/step - loss: 0.4377 - sparse_categorical_accuracy: 0.8453 - val_loss: 0.9144 - val_sparse_categorical_accuracy: 0.7072\n",
            "Epoch 3/10\n",
            "100/100 [==============================] - 8s 75ms/step - loss: 0.3605 - sparse_categorical_accuracy: 0.8750 - val_loss: 0.5550 - val_sparse_categorical_accuracy: 0.7968\n",
            "Epoch 4/10\n",
            "100/100 [==============================] - 8s 80ms/step - loss: 0.3005 - sparse_categorical_accuracy: 0.8945 - val_loss: 0.4599 - val_sparse_categorical_accuracy: 0.8288\n",
            "Epoch 5/10\n",
            "100/100 [==============================] - 8s 76ms/step - loss: 0.2544 - sparse_categorical_accuracy: 0.9075 - val_loss: 0.2596 - val_sparse_categorical_accuracy: 0.8976\n",
            "Epoch 6/10\n",
            "100/100 [==============================] - 8s 76ms/step - loss: 0.2276 - sparse_categorical_accuracy: 0.9149 - val_loss: 0.2456 - val_sparse_categorical_accuracy: 0.9080\n",
            "Epoch 7/10\n",
            "100/100 [==============================] - 8s 75ms/step - loss: 0.2179 - sparse_categorical_accuracy: 0.9220 - val_loss: 0.2225 - val_sparse_categorical_accuracy: 0.9192\n",
            "Epoch 8/10\n",
            "100/100 [==============================] - 8s 76ms/step - loss: 0.1866 - sparse_categorical_accuracy: 0.9338 - val_loss: 0.2371 - val_sparse_categorical_accuracy: 0.9152\n",
            "Epoch 9/10\n",
            "100/100 [==============================] - 7s 74ms/step - loss: 0.1738 - sparse_categorical_accuracy: 0.9377 - val_loss: 0.2409 - val_sparse_categorical_accuracy: 0.9168\n",
            "Epoch 10/10\n",
            "100/100 [==============================] - 7s 74ms/step - loss: 0.1523 - sparse_categorical_accuracy: 0.9434 - val_loss: 0.2394 - val_sparse_categorical_accuracy: 0.9216\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "y1IpwKWeqDSF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "3e04218c-7c88-4401-81d6-3017b4b73950"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Get training and test accuracy histories\n",
        "training_accuracy = history.history['sparse_categorical_accuracy']\n",
        "test_accuracy = history.history['val_sparse_categorical_accuracy']\n",
        "\n",
        "# Create count of the number of epochs\n",
        "epoch_count = range(1, len(training_accuracy) + 1)\n",
        "\n",
        "# Visualize accuracy history\n",
        "plt.plot(epoch_count, training_accuracy, 'r--')\n",
        "plt.plot(epoch_count, test_accuracy, 'b-')\n",
        "plt.legend(['Training Accuracy', 'Test Accuracy'])\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy Score')\n",
        "plt.show();"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8++yzKIpC//79CQkJISQkJN8xQniUyUTgE4+g37UTHA58AFWnw9bwTq6em5Ue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\nGoxHlQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcf1ab94400>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ESL6ltQTMm05",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Checking our results (inference)\n",
        "\n",
        "Now that we're done training, let's see how well we can predict fashion categories!  Keras/TPU prediction isn't working due to a small bug (fixed in TF 1.12!), but we can predict on the CPU to see how our results look."
      ]
    },
    {
      "metadata": {
        "id": "SaYPv_aKId2d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "2728a268-e9e8-45e5-adc6-aac55d75886c"
      },
      "cell_type": "code",
      "source": [
        "LABEL_NAMES = ['t_shirt', 'trouser', 'pullover', 'dress', 'coat', 'sandal', 'shirt', 'sneaker', 'bag', 'ankle_boots']\n",
        "\n",
        "\n",
        "cpu_model = tpu_model.sync_to_cpu()\n",
        "\n",
        "from matplotlib import pyplot\n",
        "%matplotlib inline\n",
        "\n",
        "def plot_predictions(images, predictions):\n",
        "  n = images.shape[0]\n",
        "  nc = int(np.ceil(n / 4))\n",
        "  f, axes = pyplot.subplots(nc, 4)\n",
        "  for i in range(nc * 4):\n",
        "    y = i // 4\n",
        "    x = i % 4\n",
        "    axes[x, y].axis('off')\n",
        "    \n",
        "    label = LABEL_NAMES[np.argmax(predictions[i])]\n",
        "    confidence = np.max(predictions[i])\n",
        "    if i > n:\n",
        "      continue\n",
        "    axes[x, y].imshow(images[i])\n",
        "    axes[x, y].text(0.5, 0.5, label + '\\n%.3f' % confidence, fontsize=14)\n",
        "\n",
        "  pyplot.gcf().set_size_inches(8, 8)  \n",
        "\n",
        "plot_predictions(np.squeeze(x_test[:16]), \n",
        "                 cpu_model.predict(x_test[:16]))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Copying TPU weights to the CPU\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sPtxUKRVlSIbvx0tWQNz3AcnC7356P+mbksv7ff+ZTPkm98mvYiPbZLUbiPpl\npE/D90VJv4AfMiR9d9JXKfcPor4KGSLhfy/dPlwf6aOTITJ+2IJMDef7cH2dJPD9Q1IHqYrHS59N\nKp+zXG/g60PuU1ZWVqRN+ntl2jjfX+z7luNEXrv+/ksuvvjiOHYn7fjhT4WRBQsWhLJfOUeGrXz+\n+eeRNumbldXS/Pudfw+VSD+3HIcyhAei41Kecz/kUrb5+5EsDM+/L8p98qu0yfEsx6u/PkDOPf49\ny9/nnNA3XEVRFEWJAZ1wFUVRFCUGYs80JYsWA6xcuTKUfRObbJOmPd+EIM15fmUZuexcZi/xl/BL\nU4E0+/lmE2mK8E2Y0vwgTaf+MnYZtpOK3JoffTNoQSqe7ZtupO6kOd2vWCLDPnxTq+wrzbz+Un95\nXqRufJO0NGv5Jn5phpKmbd9NIkPJzj777EjbJ598EsryWOrVqxfp57tDFOVwkK6SiRMnRtrk/c43\n0co2ef+sWLFipJ8co/61myx0yh838n4g5VRVu3xkmxzn/n1Wjj0/rE8es5T935Xmcf9eMXjw4KT7\nmEDfcBVFURQlBnTCVRRFUZQYiMWkPG/evFB+6aWXIm3nnHNOKPvZUKR5WJp2/RWEss03FcttSFOB\nb3aVZkq5Dd9sIE0dvtlEroqT5gx5/P5+pFrpJzNI+aZZaQbyM035ZvV08uWXX0Y+S7OOlH3zjzTD\ny1XoEDUbSX34WcvkuZWrcH3Ts+znuwnk95IVlgCw1oaynyVKroqWx+yb4aZNmxbKjRs3RlEOh2ee\neSaUq1atGmmT5mB/7EmzqTSp+vdC6Ubx70FyHMkx6o8beQ+VYyOVSdnPaiX7SneRf4+Xv+0XuJf3\nGzle/YIHxphQlnNXbtE3XEVRFEWJAZ1wFUVRFCUGdMJVFEVRlBiIxYcrK074GU9mz54dyp9++mnS\nbUj7u1/EWC5X923z0p8mfae+r1dWGfrmm29C2fezyWxYvp9h+vTpoSx9cH6YiFyi72fNSlY9yQ9l\nkT4Zf7m+9IXEXXXGx/ftyFAdmcXGDwuSFUt8H648Xj8kSiL9Q7Kf76eVvh6/2Lj028qqOb7upQ+3\nbdu2kbaWLVuGcrdu3ULZz2Ljr2FQlMNBrj/o0aNHGvdESaBvuIqiKIoSAzrhKoqiKEoMxGJSlqaN\nm2++OWk/f7n3okWLQlkm4p4yZUqknyyK/cUXX0TakoX7+KZbaVY86aSTQrl+/fqRfr/97W9DuUGD\nBpG2ZEm0feR++OZSWehAms5906z8Lb+Ytb+UPZ1ccsklST9LffuFK6QpXJprASpVqhTKMhOO7yaQ\npmcZ+uCb8eVnP2RIfpZm5BOaNVcDAAAgAElEQVROOCHS79///nco//GPf4y0yXAKGd6Q2+tFUZTM\nQN9wFUVRFCUGdMJVFEVRlBjQCVdRFEVRYqBAOZH8ygw1a9bMUb7yyitj26f84PXXX0/3LhQIpL5P\nPvnkSJssVO0XdpY+aukHPvXUUyP9ZEiXrDzl+3pl6I8f7iP9u76vPNlvybUHkLqgu6IoRQd9w1UU\nRVGUGNAJV1EURVFioECZlJXMR5pzpeybcr/99ttQ9rNsSWS2Jr96k8wYVqNGjaTbkBmv/G3IrF3y\nt2RFEYhmifr4448jbdKknCo0TVGUzEbfcBVFURQlBnTCVRRFUZQYUJOyEivSjJrKpCpNyn4BBrmC\nWWYImzt3bqSfLIAgM3UtXbo00i9VUQuZJUq2yZXNEF1xvXz5cpIhj9n/LTUxK0pmo2+4iqIoihID\nOuEqiqIoSgzohKsoiqIoMaA+XCVtyBAcPyxo4cKFoexXkapVq1Yoy/CcunXrRvpJ3+9XX32V9Lfk\n9qVPGKBcuXI5bs+vOCT9ytLvC9GC91ohSFGKLvqGqyiKoigxoBOuoiiKosRAMT80IZ+I5UeUHMmv\nWJPD1mmqrEuy8Ptrr70Wafv0009DWRau97NJSfPttm3bQtkvHr9u3bpQLlOmTKRt7dq1oSzDffxQ\npUqVKoXyrbfeGmmT4Ul5RH7oVMdo+iiwY1Q5ZHLUqb7hKoqiKEoM6ISrKIqiKDFQKCfcgQMH0qFD\nBwBmzJiBMWa/laGKohQ+7rvvPm6//fZ070aRYcyYMZx33nnp3o0ig8YoKHnGjBkzKFWq1H7hOclI\nlcpQVgi6+eabI23y84YNG0L5+++/j/ST/t3169eHsl8RSCJTNPqfZYF7Y0zK7ymKovgUyjdcpWAy\nbNgw5syZk+7dUBRFKZCkbcL98ccfMcYwadIk2rRpQ506dejUqRMrV67M0czRpUsXnnnmmQNud+XK\nlXTv3p3GjRuTnZ1Nt27d+Pnnn9mzZw9NmjThnXfeifS/66676NmzJwDWWq677jrOPfdczjvvPB5+\n+OEwocGYMWNo3bo1zz33HPXr12fZsmV5dCYyg65duzJ16lR69+7NNddcgzGGYcOG0bRpUwYOHAjA\n7Nmz6dSpE9nZ2Zx//vk88cQTYdIJ6SZI0KJFC0aMGAG4xBWJ7zZq1Ig777yTjRs3Au6NdciQIXTv\n3p3OnTtz9913RwoZPPDAA0yZMoW+ffsyevToOE5HRjBkyBBatGhBvXr1uPjiixk+fDhAOG6vvvpq\nsrKyaNu2Ldba8HszZ84MddWkSRP69+8fWhX27t1L//79ad68OfXr16dNmzZMnTo16T688MILtGrV\nKrRQjBw5kssvv5x69erRqlUrxo8fH/ZN3CPatWvHtddemx+npNAzZ84crrzySrKysrj22mtZvXo1\nsO9+/Oabb3LeeecxZswYACZNmkS7du3IysqiRYsWvPHGG+G2Uo3JVG1FmbjCgvbDGFMVWAL8E7gG\n2AS8DewB3gKes9ZWFP0/AT631v6fMeZRoI21tqEx5iJgKnCstXazMWYmsBi4CTgSGAEcY61tZowZ\nCFS11l4RbPMoYDXwR2Ay8B0wCHgGOBEYA0y21t5vjLkOGAC8DDwM7LbW6rJ7gTFmKU5vLxhj9gLT\ngf8BVgIVgaXAg8BLQDXgA2C4tfZhqdMk21sAjAIeB44F3gTmWWvvMcbcCXQHfovT/fVAP+B0a+26\nYDvbgCuB71RvB8YYcz7wMdDIWjvXGHMuMAm4EJgDzAS6AD/h9LjOWtvBGHMq8A1wB/AGUAMYD/S2\n1g42xnTB6eZc4AegG268nWqt/cUY8zpuvHY0xlwFPA/8xlq72BjTDngNuAz4Ivh/NJBlrf0muEdU\nB34H/Ef1HMUYcyTunvt34M9Abdz5KwM0DNrexY2fjUA27v7cEfgQaARMAK6y1k46wJhM2hbP0RZM\nCoIPd5C1dhmAMeY53OB8/1A2ZIyphxvIHay1G4K/PQrMMMachJvQPzTGlLHWbgEuwU3wE3A346Os\ntY8Hm/vBGPMU8CJwf/C344BnrLX7gkSVVLxtrf0ZwBjzB2C5tbZ/0DbfGPMy8L+4B5gDUQ7YZq3d\nDaw3xrSx1iacsV2BAXbfa9YrxpjuuBvv4OBvE621C/LgmIoKiZyWmwGstbOMMRWttXsC//XIxPk0\nxozH3aQBrnbd7dDg83xjzPPAdThdjATeF+NzFPACUBP4LPHjxphGuAezy621i4M/dwWGWWtnBp/H\nGWMm4R6YE2N0lrU23I4SoSFwKvCEtXY78Lkx5h2cbhL8VejmBmCCtXZC0PaZMeaNoP8kUo/JVG1F\nloIw4Vohf497K92WpO+BOBPYZK39UfwtkZS3KvBvYB3QGvdk1wF411q70xhTDahgjIkmyYUjjTGJ\n4qcbrbW/HOK+FUXkKqYzcW8+koU4veSGPwPPG2P+iBvsI4FZQVs1oF/wwJbgCOC0JPuiHJiPgY8A\nG7w5TgJeBxKZQJaIvluBRFLrakB9bxwVw1k5wL1N9TPGXA4cL/rIAsMnA2OB1621M8TfqwEtg4ep\nBEcAG8Rn1XNyTsXdw9aJv1mvjzx/1YBLctBl4oEn1ZhM1VZkKQgTrswWn1i2mpMp6Mgc/uZzdIq2\nvdbavcaYvwPtjDHvAW2BzkH7NtyTec2cvhw81e/OqU1JijxfyXSTyuwX6txa+6ox5l2czq7EPW3f\naa19Aae7m621f8vlvigHwFq7A7gisBq1xb3B3muMSSyuSPa2sg340Fp7WZL2F3GmyouABTirkf8Q\nex4wHPiTMeYla+0ise0HrbWpFnOonpNzNPvf8/11PPL8bQOGWGu75bSxVGPyAOO1yFIQVilXE3IV\nnMK3A6UTfzTGFAPOyMW2FgHHGmNOEX87G3dTTwzat4HLcQN+LzAl+PtCoKox5jjxu8cbY8oezMEo\nSVmE04XkbPZZIHydl8b50ROfK1pr11prh1lr2+F8QzcFzQuBSCxSsEZAOUSMMcWNMeWstV8FbpYs\n3JtkhwN8dSFQ2xgT3luMMZWNMYk34EbACOvYCzTIYRvvW2uvw/kT3wh8j4lt+3o+Xf6WkpLlQBlj\njMxLWitZZ3I+36cYY0oEctIxeYDxWmQpCG+4NxljPsXdcHvizA/fAaWMMb/FOevvJPXba4LPgXlA\nH2PMn3A38MeA8dba1UGf6TgT2CPAO9baX4O/TwJWAP2NMT2Bo9hnQvvj4R5kEWEbUC3JQ8rfgMeM\nMbfjFp7VBG7BLUQDp/OzjDFZwLfAEwT+w2AhziJjTCecf780bsFHYrJ+GXjOGDMB5wf8LTDKGJMt\n/LrKwXE3cJUxpp21dilggArsO+fJGAk8BTxqjHkat1huDG5txsO4RW0NgwWLtYFbgR2AfEhOjMnb\ngbnAPUBvnJ4nGGPexi3UagSMw/nqPz6cgy0izMC51O4zxjwE1Afap+g/BOgR3Etfxy1I+wB43Bjz\nIUnGZC7Ga5GlIDwZDsetEF4BHAN0tdZ+AfTHrTBeAZTArUROSfDE3A7nG1oKzA7+7+z1+TvQFLca\nOvH33TjTx5nBb84DVuFWvyq54xXcU+w0v8Fa+wNON9fgHmJGAwNxK1bB+ezewa2KXIQ7/98F3/0R\ntyL2cdxq9sQimkSVgGHAX3CT+ibcQ9bVOtkeFv1wepxhjNmKu3E+Y60dm+pL1tr1ODPiZTg9f4bT\naWIx4r3AWTgz8vPAfbjVzEMCv67c1i/ADcAjxpgsa+0U3MN3f5yehwJ3W2t1ss0F1tptuDF4GbAe\n9xDTJ0X/BcBVuHO+EffQNMRaOzTVmMzFeC2yFISwoDrW2nlp2QlFURRFiYmC8IarKIqiKBmPTriK\noiiKEgNpMykriqIoSlFC33AVRVEUJQZ0wlUURVGUGEhrHK4xpg4uwfUx1tqqKfp1xCW9r4ZbYt7L\nWjsmaCsGPIoLN6kA/Be4zVr7ddBeDpeTtTnuAeMj4JZEvlAl71B9Zh6q08xC9Zle0vaGa4z5PTCR\nINYyRb+6uHjcR4FKwEPAm8aY2kGXW3Bp59rhguenAR8YY0oG7UNwF0UWUCeQEwntlTxC9Zl5qE4z\nC9Vn+kmnSfkY4DccOEPMn3C5Wd+z1m631r4ffOfGoL0brlLM3KAC0GNAWaC1MeYEXCq6+621K621\nq3BPbR2NMRX3/ynlMFB9Zh6q08xC9Zlm0jbhBtlKfshF1wY4k4Xkv8C5QX7WWrLdutJ5c3Fl+rJw\n+ZK/Et/9Clckof6h773io/rMPFSnmYXqM/0UhkVTFXBpyCTrcDlaj8cpMll7BVy5vkRu1sTFsSlo\nV+JH9Zl5qE4zC9VnPlEYJlzYV7bvUNoP9F0lflSfmYfqNLNQfeYDca1STppd46GHHmLo0KFJ+9Sv\nX5/GjRv3xyUsB6Bbt27MnDmTYcOG/ZSVlcXQoUNny+80aNCARo0ace655/7pxhtvZNeuXXtLlCjB\njh072LVrF0cccQSPPPLIyG+++WZk4js1a+ZYBjcly5Yti3wuVapUKFesWGAe5vLr4s9RX3HqE6Bd\nu3YAnH766SN///vfj5TfO/rofQWmtm/fV0P7jDOilR5l288//xxpO+aYY0L511/Dh3b27ImWgx0+\nfHhOh5tf5IdOC8QYBcIxOnTo0JG46kOZTkaP0R07dnDEEUfw6quvjly3bl1En+XLy0qByZHj7Ygj\nou+JK1asCOUTTwwrelKsWFrn/Rx/vMC/4dauXZt586K1DebOnUu9evU4+uijOeuss5g7d27YtnPn\nTr799luysrKoWbMmxYoVY/78+WH7/PnzOeKII6hWrRpK/OS1Prdt2wZEH3aUeMlrnc6bN48jjzyS\nWrVSlWpV8gvVZ/5RICfc1q1bM2PGDAA6derEjBkzmDx5Mjt37mTChAl8/vnndOrUCYDOnTszfPhw\nFixYwNatW+nfvz+VK1fmggsuoHz58lx22WUMGDCAtWvXsmbNGp5//nmuuOKKyJuLkr/klz5Xr17N\nqlWrKFeuHEceeWSqXVDymPzUab9+/Wjbti1ly+ZUVlnJD/JTnwMGDOCKK65QfRJfLuX9fqRVq1Ys\nX76cPXv2sHv3bo466igAJk6cSIsWLRg0aBDNmzcH4KOPPqJv374sW7aMqlWrcs8999CsWbNwWwMH\nDuStt95i06ZNnHTSSbRq1So0VQwaNIgtW7awa9cu9uzZw969e/n111/3Mx2WKVMmlNesWZOrg/Lf\nqkqXLh3KxYvvs9a3bds20q97930lduvWrZur3zoMYjFX5aU+X3zxRUaNGsXGjRvJzs6mV69eVKlS\nBYDNmzfzyCOP8Mknn1CsWDHWrl0bvuWeeuqpSXd248aNoSxNwxDVvdQbELlJbN26NZRPP/30SL93\n3nkn6W/nA7GYlOPUaa9evZgyZQrFihXj0ksv5eGHHy5KVouMG6NSn5dccgkPPPAApUqVYtGiRZEd\nNMbkvOPevJTKPCzftuvUqXPAkxITOe5w2ibcvGT37t2hPGLEiEjb4MH74q1XrlwZyjrhHjZpq3qx\nY8eOUE4MctAJ9zDRKibpI+PGqETen4v6hFsgTcqKoiiKkmnohKsoiqIoMVAoTcrPPfdc5HPv3r1D\necOGaH5saeaVpsOE/yLBli1bQlmaLP3wDxlq4i+8kqZKGWoiTZEQNbFcfvnlkbaxY8eSx2ScuUrq\nqnr16qFcqVKlSD9p8k/4eSEaOgBRffjuBBneddppp4WyvK4AXn755Vztex6hJuXMIuPGaDLuuOOO\nyOd77rknlE855ZRcbePzzz+PfH7mmWdCOWbXTirUpKwoiqIo6UInXEVRFEWJgbTWwz0Yxo8fH8p3\n3313pE2aIo499thIm1zdJs3nO3fujPSTJkIp+6vj5Oddu3Yl3V9pzjzuuOMibTJm9IMPPoi0XXvt\ntaH817/+Nen2izIff7yv2Mnq1atDuUaNGpF+UsfS3O+bg2Xbpk2bkm7jl19+CeUffojmgJdZx6Tp\nWVGKOnJ8jRs3LtI2derUUP7Nb34Tyt26dYv0k6Zn6VICaNiwYZ7sZxzoG66iKIqixIBOuIqiKIoS\nAzrhKoqiKEoMFBof7o033hjK5cqVi7RJn6gM8QD46aefctxehQoVIp9lBYpElQvY36cnw0v8MBQZ\nQiT3SYYIQdSX7C+Ff//990NZhhP5fseiTP/+YRGTSHYp/1z++OOPoSzDwKy1kX4yvMvPNCXb5HWx\ndOnSSL/Zs/cVT1EfrqLsQ94LzzzzzEibvF9/+eWXofz73/8+0k+GdJ500kmRtsqVK+fJfsaBvuEq\niqIoSgzohKsoiqIoMVBoTMrr168P5ZIlS0ba5LJz34T88MMPh/JNN90Uyn7yeWmalOZCv6TUWWed\nFcrSZAnRLFQyTEQm2IeoeVgeF0SXvMuQF38bRRmZaeaSSy4JZT+jlzT/y2vE16k0+fuhXtJcJb/n\nF8H+/vvvc7XvilKU8bPzLViwIJRlVjffTCzvmX5YkB8KWpDRN1xFURRFiQGdcBVFURQlBgqNSVma\n/fw6tKkKMNx///2hLE3Rfk1Uaabo0KFDKI8ePTrptrOzsyOf5UpVub/Dhw+P9OvatWso+4n05Urn\n6dOnh3JRNilv3rw58lnqTp4/v8axXB0pr5nvvvsu0k+2+eZmWchCrqiUqyZhfzeHoij749f/liuT\nfVOxRLrr/H5Vq1bN8TsHU1M3LvQNV1EURVFiQCdcRVEURYkBnXAVRVEUJQYKtA/X97Mm8EMy/ExO\nEll1YtiwYUn7ycLj0m/7wgsvRPodf/zxoexXvti4cWMoy3Cf9u3bR/pJH65f4F76HWfOnBnKV199\nddJ9z3RklR5IHgbg+2xkPxkuJkOJAKZNmxbKfmYxGWokdSUzV4FmAlMUiRyL0nfqr32Q2dvk+PLv\n8TJsc8WKFUl/q6Cjb7iKoiiKEgM64SqKoihKDBRok7Jv3ksgza6Qejm5n2Q+GZ999lmOf/dNudIE\n4psyTj755FCWoSzyOwfDN998c0jfyzT8LE6ywIBvopJIU5YsGD9//vxIvyZNmoSyDFMAOPvss0NZ\n6tEvcqBhQXlDKvNgXoR1yJAwmTUuv9m5c2fks++SyDSS6covHCLHkdS9nzVOuuv84gV+mN+B9iGd\n6BuuoiiKosSATriKoiiKEgM64SqKoihKDBRoH+6GDRty1U/6R3x/qbTvp/IP+SnHEkj/HsC3334b\nyn5FCxnGc84554Ry06ZNI/1kuIof+iT3f/HixUn3tygxZ86cyOfjjjsulKX/3tf9qlWrQvmEE05I\nuv3mzZuHskynCdH1AjLNo+8fkqnnlEPnUPxujz32WOSzXLfhh4CNHz8+lF966aVQltfUgUgVviKR\nKV1ff/31SNuECRNCOdP9uZJ333038rlGjRqhLFOsrl27Nuk2/HP+9ddf59He5T/6hqsoiqIoMaAT\nrqIoiqLEQIE2KUuToMTPziTxCxzLDEPSXOWbl2Wx90cffTSUU4Xm1K9fP/JZmoDlvr/xxhuRfhMn\nTgxlWXQZoqbJ3IY0ZTp+KEGlSpVCWZp5/VAdef569uyZdPs33nhjKD/xxBORNlkhSOKbAf3fVqLk\nRbiPzN4mw7VuueWWSL9GjRqFsh9CKMfbnXfeGcpDhw7N1T5AcjPyJ598Evkst+mbSGVlq9NPPz3X\nv10Ykcfuh/Qky9AmxzhEz7l/vciQv4KOvuEqiqIoSgzohKsoiqIoMVCg7WCyoIDENxPJlb67du2K\ntMkVwQMGDEja77333gtluVJVFpWHqHnEz0p07bXXhrI0L3/++ec5HMX++w5R04m/j0UVf7W6dBtI\n85Jv/pWr12+77bak269SpUoo++ZCqZ9kBe2h6JiUUxX1TpawPqfPEnmOZYY2uYIV4PHHHw/lPn36\nhHJWVlakn8xM5merq1OnTijLlcK+a6d///6hfPnll0fa5HUgV9A//fTTkX5y1fwFF1wQaStfvjxF\nBRnZ4ReakdeFdA/5q/5lNII/1hYsWJAn+xkH+oarKIqiKDGgE66iKIqixIBOuIqiKIoSAwXa8eRX\niUng+9lkmJDvx5MF43v06JH0t2Q/WfVnxowZSb9zyimnRD7LwsipKgSl8jsmyzrjh0KlynCTafjn\nUmYFkufS99fJotVSv6nws4dJv2SFChVC2Q9ZS1WxKpNI5YtN1ZasogvAn//851CWY2rcuHGRfnIM\nLFu2LJT97GASv+qM1GeHDh1CuWzZspF+Tz31VChLfy5Er6Xly5eHsgxVgmiGOb8a2bZt20LZD2XM\nNP71r3+Fsu9/lTqVuvHX6aTK7nXmmWeG8rp160K5IPrJi85dW1EURVHSiE64iqIoihIDBdqkvHLl\nylz1k2bYjh07RtrGjh0bylWrVg1l32QhQ0hkOE65cuWS/q5fVFqaw+Tyd99cJbOoTJs2LdLmmzQT\n/PLLL5HPBdFckl9IUy4kD5eSmYgArrrqqoP+LRkiBFHzlSyA4GcO8sO7iiLSnCfDewBGjBgRyvPn\nz4+0yXAfaWr1x790GchrwM9WJN00vitGbl/KMqQP4Oqrrw7lr776KtImw1CqVasWym3atIn0k/eO\nIUOGRNpSuZwyDXn+/PuuDP+R5n/fbCzvtf42ZDiRLFRfEO+R+oarKIqiKDGgE66iKIqixECBNin7\nJsIE0mwA0VVq3bp1i7RJU1aq1YDJVsulyvbkr8qUfaVJ2V+Zd9ddd4Wyb1JOhn/MBdFckl/4Jnlp\nupTmw3nz5kX6vfbaazluL1W2pJo1a0baZAEEae73r4slS5bk+FuFBWkiHz16dKRNrtqXZliIujqk\n2c93jbRu3TqU/frQMhJAmhL9leXSDCv1LguPQHTlsL9yXe6/NEX6xyxXwvvXhKyxKzNU+SvXZSYr\nP2m/vF78IiiZhqxXW7JkyUhbMleeb3KXuvLHr9yGjGyRLsSCgr7hKoqiKEoM6ISrKIqiKDGgE66i\nKIqixECB9uFKv5L0s/lZfU477bRQ9v19Ehk+5PvgclsEO9V3ZGhIsioYAOeff36utinDHfzwhqKE\nn31L+sdl+Il/jpIV9vb7yTCDunXrRtq++eabUJZ+cz9kxa82U9h45ZVXQtmvkOVXbpHIcyfDYPzz\nI8eyn6FNjllrbdL9+PHHH0NZ+m19P628P/ihexI5vvz1HY0bNw5lP5PVo48+Gsry+Bs2bBjpJ8ey\n75OUWdAynUWLFoWyX9nJvzcm8MOC5DoYPwRP6mDWrFmhfOGFFx78zuYz+oarKIqiKDGgE66iKIqi\nxEChMSnL5eR+aII0ZUkToI80S6QyNeXWvJwqvETKMozlQNuX25SmTz/TVFHCN9XJRPAyO41vDk4W\nBpbq/Ldt2zby+eGHHw5lGZolC1VANHtYYURm5fLN4zI0yr+W5TmR4Th+pqnFixeHsp+9TWZu2rBh\nQyj7Zlj5We6jr+fs7OxQluFIAGvWrAnlN998M5Tfeustcos0g/rFR5Ltb6rMdpmIvHfJMeqHSPrF\nJRL4ZmM5Zv0i9tLlJK/Bgoi+4SqKoihKDOiEqyiKoigxoBOuoiiKosRAgfbhSv9IKr+bTL2Wyocr\nl5r7oSFy+75vNhn+Psn9lT5nP0VlqmLoySqd+NVpihJNmjSJfH7xxRdDWfqEpH8OolVpateunavf\n8lPwVa9ePZSlH86/fgq7j12mb+zUqVOkLbcpUeW161+vcgy8//77kbabbroplM8444xQLlWqVKSf\n7wc9XC666KJQnjNnTqRNpov1x3mqcB+JDD30Q5ekH9g/zkxA+uIlxx57bOSzvC5k+Jk/vuQ92ffv\nlilTJpRlaseCiL7hKoqiKEoM6ISrKIqiKDFQoE3K0iSTypwkzYUffvhhrrbnI00WUs5tCA9EzSCp\nTE3SpOxXtPArjiRIZqIpCvhZZ+S5lTr1M1K9+uqroTxgwICk25P4xcyXLVsWygsXLgxl30RY2M2C\n0gXih6xI07wfBiN1IU3P0kQN0VCOm2++OdImx4M0F/ohI344SLL9TZa9CKL3EZk5zL92pIn5rLPO\nirTJfUzlppL74YcyympEmUgy065/H5fjV46hVGPUP89SdzLErCCib7iKoiiKEgM64SqKoihKDBRo\nk7JcfeZnKJFI84Of8FyavPKiAEAyc5L/OZUJXCZ2l6syIZqgXa7a8zP3FGWkOVfK8noBmDJlymH/\nljQFykLpvnkzt6ugCwO+ufOcc85J2ldmnpImVL8ovNSTP25++OGHUJbmYX9FqxxTcht+UXP5PX9F\nq3QDyZXlfvYred/wXUfyc6pxKU2k/rGkilTIBGQmtlRuuWTmf/9eLT+nKjxT0F1v+oarKIqiKDGg\nE66iKIqixIBOuIqiKIoSAwXahyv9KKmqcki/j1/FRYYqpNpGMg6mML30Mfm+I8m7774byn7IwT//\n+c8cf9vPolSUkYWlZdYpv7B5XvjJatSoEcry2vJ9uHmxPqAwIkNrUuFn8FIyG+kfl/dFP4QrmW/W\nv1enChNKtf2Chr7hKoqiKEoM6ISrKIqiKDFQoE3KMhTIz9QikVlh/GXmMmRAmil8E0WyggWpQgJS\nkcp8LYtxN2jQINL2yiuvhLI0KafKklXU6Nq1ayi/9tproeyHYsnwK5kk/WBMzdJkKkNgfH1UqFAh\n19tUlExHhnrJ8Eb/vihNyrk1G/tuPum+k64e362X18UvDgV9w1UURVGUGNAJV1EURVFiQCdcRVEU\nRYmBAu3DlVUgUvlOpW/NrzAi/QepfATJ2lIVn/bbklUc8n2GkyZNCuWaNWvmap/84yrKyDR5J5xw\nQij7ReClP+frr78OZb+gfSpkej4ZcuCHHySrZKMoRZHcFpaXn6WP1b/fy3ut74tN5pv1x6Sf+jUd\n6BuuoiiKosSATriKoiiKEgMF2qQsM03JwuB+8e977703lEePHh1pk6bY3C4Ll+YL39ScKqOQXIYu\nf8s3dbZr1y6U27RpE5P0xy4AACAASURBVGm79dZbc9xGqqLamU4q81Lbtm1DeejQoZF+MiTsrbfe\nCuWDMSnLyjlS937IQVHNNKUoOSGrKEnXYCrkGPLHk7wXSjePjwzX891walJWFEVRlCKCTriKoiiK\nEgMF2qQsTQIy65Q0NUPUvOcnSZ83b14oyxXBqYoLSFKtjk5l9pBmFL/wgFxZ6xf7lshjXrp06QH3\nNVNJZVK+4oorQllmnYJo4Yrvv//+kH5bFiaX2c6kDkGLSyiKRI4VGVXg3zPlfViOcz8jVSo3n3S3\nyXt8QczOp2+4iqIoihIDOuEqiqIoSgzohKsoiqIoMVCgfbjNmjUL5fHjx4eyDBGCqN927ty5+b9j\neYjMyAJQtmzZUJY+7MaNG8e2TwUNP6OXpFq1aqEsi8UDrF69OpRlhably5dH+p188slJty997Fu2\nbAllP9Qht4XYFaUoIO/XqapzyXu59PXKkD7/c6pQTTlG/XUvqcZ5XOgbrqIoiqLEgE64iqIoihID\nBdqkfMEFF4RyKnNeKpNjQcdf/i6X08sE+anChzKd3Oq3SpUqkc+ffvppKMtQsqlTp0b6de7cOek2\nZVazVAUkVqxYkat9VJSiwAMPPBDKPXr0CGV5fwPYsGFDKC9btiyUK1WqFOkn75OyGAJE3XAbN27M\n8e8FBX3DVRRFUZQY0AlXURRFUWIgbRPujBkzmDNnTrp+XilEjBkzhrVr16Z7N5Q8QvWZeahOc0fa\nfLjDhg2jSZMm1K1bN2kfaceXIUJ+1Qc/1aNELhlPl6/XT00ol7VXqFAh0talS5dQlv4N6c8uSvz6\n66/07t2b2rVr73eufP785z9HPsswAOn3P5hqQddee20on3jiiaHshwG1bNky19ssyhyMPpXCQU46\n7d69e9jeoEGDUP7yyy8j35XrIuRccPbZZ0f6yfUs/hoeGf7TtGnTQziC+EjLG27Xrl2ZOnUqvXv3\n5pprrsEYw7Bhw2jatCkDBw4EYPbs2dx2221cccUV/M///A8bN24MJ65169bx448/RrbZqlUrRo4c\nCcBXX31Fp06dyM7OpnHjxvTo0SN0pu/Zs4cXXniBli1bkpWVRfv27fnss8/C7bRo0YKXXnqJVq1a\n7XcDV1KzbNkybrzxRurXr8+FF17IK6+8AsDKlSvp3r07jRs3Jjs7m27duvHzzz+H35s+fTodO3Yk\nOzubJk2a8MQTT4Q5Vhs0aMDGjRvp0KEDAwYMSMtxFVVUn5mH6jS9pGXCHTJkCKeccgr3338/I0aM\nAGDSpEmMGTOG7t27s27dOq6//nouvPBCRo8eTd++fdmxY0ekxmIq7rnnHs4//3xmzpzJpEmT2LJl\nC4MHDwZg+PDhvP/++wwaNIhZs2bRqVMnbr311kjN2nHjxjFo0CCefPLJvD/4DKZ79+5UqVKF6dOn\n8+qrr/Lqq68yceJEbr31VkqUKMHkyZOZMmUKu3fv5q677gJg+/bt3HrrrbRv354vvviCkSNHMm7c\nuLCu8bhx4wBnsrrzzjvTdmxFEdVn5qE6TS/FUlXDyU+MMUuB56y1Lxhj9gJ3Wmv/ErTdAXS31p4l\n+t8D/K+11hhjHgXaWGsbJtneKqCftfbpoO0Ia+2eQP4aeNla+4L47hzgRWvt4GA771pr961lVw6I\nMaY+8F+gsrV2dfC35sA64EvgNGvtj8HfGwEzgJOttSuMMWWBzdbaX4P294EV1tqbjDFVgSVAHWvt\nPP93lfxB9Zl5qE7TT0FapSzrp50JfOO1LwSq5nJbfwYeNsbMN8b0BxqItmpAP2PM9sQ/4GzgtCT7\nouSOasCWxEAGsNZOxelyU2IgBywM/q8a/P87YI4xZkugj98C0WA7JW5Un5mH6jTNFKQJV2aASKbI\nVK/jYTFaa+2ruAm0D3AG8JkxJuHF3wZ0sdaWFP+OstY+mGRflNyxh5yvp1SDcq8x5mJgEPAkcLy1\ntiTwfj7sn3JwqD4zD9VpmilIE65kEe6tU3I2+566tgNh1mtjTGngRPG5orV2rbV2mLW2HfA4cFPQ\nvBCILI0OTCLK4bEYKGWMCS0FxpjLgSrAscaYU0Tfs3EPT4uARsAia+1Ia+1OY8yRQL0Y91vJGdVn\n5qE6TTPpnHC3AdUC34DP34DTjDG3G2NKGGPqArcArwft3wFnGWOyjDElgSeAzQDGmFOBn4wx7Y0x\nRxpjjgVqs2+yfhnoZoxpErS3Bb42xpj8OtCigLX2S2A28KQx5lhjzNnAUGApMA/oY4w5xhhTGXgM\nGB+YthYDJxljqhpjKgIvAL8AiZieRC64GoEulRhQfWYeqtP0k84J9xXcW+c0v8Fa+wPQDrgGWAuM\nBgYC/YIuY4F3gH/insDm4SZhAj9EF9xb7SbcxQJwa/D/MOAvuEl9E+7Cutpaa/P06IombYATgJXA\nJGCAtfZvOF0ejxvYs4P/EwmMRwP/AOYCXwAzgXuBRsaYEdbalUGfUcAzcR2IAqg+MxHVaRpJ2ypl\nRVEURSlKFFQfrqIoiqJkFDrhKoqiKEoM6ISrKIqiKDGgE66iKIqixIBOuIqiKIoSA2krzwdgjKmD\nW0p+jLW2aop+HYEHcanJFgO9rLVjgrZiwKO4EKIKuFyht1lrvw7aywEvAc1xDxgfAbdYazeg5Cmq\nz8wiSJDwEvAbXKzlWKCntXZnDn1vw4XenQ58i9PJf4K2Y3FZitoD5YGPgT9Za38O2qsDzwFNgGLA\ncOCuRN5eJe84SJ3eCtwOnAqswMXs9rbW7jXGlAKexYUTlcOFZT5mrX0vh+3cAQwAzrDWLs2P4yos\npO0N1xjze2AiQfxsin51gRG4m3Al4CHgTWNM7aDLLcD1OMWfgovr/SBIiAEwBHfjzgLqBPLgvDwW\nRfWZoYwB1gDVcZPh+bi49QjGmC7AU7i4+uOB54FxwcMRuJvthcBFuIxwy4E3g+8WB8bhEtdUx+m1\nMS4fupL35Fanv8VNqNcBxwIdgJ7ADUGXx3A6vQA34T4DvOMnEDLGnAzclQ/HUShJp0n5GNxT1scH\n6Pcn4ENr7XvW2u3W2veD79wYtHfDBW/PtdZuwV0IZYHWxpgTcBfK/dbaldbaVbg3q45BxhQl71B9\nZhDGmIZANnC3tfYXa+33uEn1T8YY/77RFhhtrf3UWrvDWvs6MB/oKNr7W2sXWWs3AT2AJsGbrQn+\nPRj8zjLcZHsTSp5ykDptBMyz1n5mrd1jrZ0D/Af3QJRon2Ct/cFa+2uQPGMHLquf5C+4PMwKaZxw\nrbVDg4xSB6IBzqwo+S9wbmDWqCXbrbW7cBlRzsVdHHuBr8R3v8KZreof+t4rPqrPjKMBsMxau0b8\n7b+4N9hqOfT3M+isZ9/N2W/fjjNnJmtfD5yiD1F5zsHodAJQyxjT3BhTPLBMNcJlnAJXvKCtMeas\nIEVuJ2AXLvsfAMaYy3BWqOfy53AKH4Vh0VQF3ACUrAMq4i6UYinaK+DKToW+oOAGviloV+JH9Vk4\nSKYn2P9cj8NZGZoZY44yxrQBmgbbSLTfZYw5I3ioegg4Kmi3ODfEU8aYsoEV4x6xD0rekWudBv73\nHsCHwE5cvdyB1toPg/a+uDfeBbiJ9hXgD6LObilczuVuOfmHiyqFYcIFdxM+1PYDfVeJH9Vn4SC3\n5/oNnA/vr7gcve1xi+d2Be09cfl5Z+Am1+24/Oe7rLW7g/4nAj/g3At/D76X+L6Sd+RKp8YVpn8G\naI2rzNYMuC1Yq4Ex5kGcefpsnDvpDuBtY8yZwSYeBKYH9XaVgLSuUs4lq9n/SbcCsAr3dLYnSfvc\n4LvHGmNKBG9CGGNK4BYBrMrPnVaSovosHCTTE3jn2lq7F1ex64nE34wx77CvoMh64Fr5HWPM/wE/\nBu1fAxeLtnNw18GKPDgOZR+51iluLcVYa21iTca/jTFv4hZRvY1bvfx/oujLMGPM7ThLx/u4xVVa\nws8jrgk3aYWEhx56iKFDhybt06VLF5YuXXo+7okJgCZNmlCjRg3uvffebW3btqVNmzYfJdp27NhB\nmTJl6Nu3b5N69erRpEkTRo0atRPgm2++4emnn+bBBx8kOzv7o+uvvz78nYYNG4by8uXLQ/nss6Nl\neTdu3BjKa9eujbQdddRRofzdd/sW67722mvJDj8O8uuNMEd95bU+d+7cmVSfAG+99RZdunRh2rRp\nH23fvj3yWyVLlgzldevWhbLUIcARR+wz9JQoUSLSdtJJJ+V0GOkmP3Qa0de7775Lhw4dWL169d5K\nlSoB0LdvX5566immTZu2UPZdsmQJixYt4pJLLgGczsqXL8/TTz8NcP+sWbMoUaIEWVnOZTt//nxK\nlCjB9OnTJwF88MEHZGdnh+f6/vvvZ9y4cdSrV2/r448/Hv7OV1/tc93ffPPNobxhQzQiTOrwzTff\njLTdd999oVy/fu7c/n6Bl2LF8vz0xzJGD0anLVu2pHTp0uAiBgC45ppr+OGHHwD2litXjnvuueev\nOKsGADVq1KBt27ZZO3bseGbw4MGULl165XnnnRdus2zZskuGDBlC165dw79NmDAhlNes2eda3rUr\natwoW3ZfFdfTTjst0taoUaMDnog0kKNOC6RJuXXr1syYMQOATp06MWPGDCZPnszOnTuZMGECn3/+\nOZ06dQKgc+fODB8+nAULFrB161b69+9P5cqVueCCCzj++OO57LLLGDBgAGvXrmX9+vWMGDGCCy+8\nkOLFC8PLfWaQl/ps3bp1qM/Vq1fTr18/2rZtGxmQyuFTq1YtsrKy6NOnD5s2bWLZsmW8/PLLdO7c\nmWLFikV0umrVKnr27Mns2bPZuXMnzz77LBUrVqRJkyYA/Oc//+G+++5j1apVrFu3jscff5yOHTty\n3HHHAe6h6dlnn2X79u18++23DB48mBtuuCHpvimHxsHotEWLFnz44YfMmjWL3bt3M3fuXMaPH8+l\nl14atv/1r39l2bJl7Nq1i7Fjx7JkyRKaN2/Oddddx+TJkxk7dmz4D+CVV17h6quvTtvxFwTiKs+3\n34+0atWK5cuXs2fPHnbv3h2+HU6cOJEWLVowaNAgmjdvDsBHH31E3759WbZsGVWrVuWee+6hWbNm\n4bZefPFFRo0axcaNG8nOzubRRx+lSpUqAGzZsoVevXoxZcoUtm7dyjHHHEOlSpUiT1apKFeuXOTz\n5s2bQ3n37t2RtjJlyoTyli1bQvm//40uys3tk3UeEcvTc17rc+TIkWzatIn69evz2GOPhfrcvHlz\nqM9ixYqxfPly1q5dy969e/d7Kpb62Lp1ayiXL18+0m/Hjh2hLK0UEH0juvvuuw/i9Px/e2ceZkVx\nvf93cEFc4gICQSGIQAMi2yggbohCAFHBDaNR81M0LsQvGpNoDBrEJF8JigJqjBGiAn7jFmOioqDi\nFkUElL3YjRsDgggIBgV/f/RM8dZxur2MMz0zd97P8/A851J1+/bt6uqa+54651QoFf4LFwCKiopw\n00034c0338Qee+yBAQMG4Nprr8Uuu+yCKIqCMR03bhzGjRuHTZs2obCwEDfffDMOOuggAPEv3htv\nvBFTp05FrVq10Lt3b9xwww2oXbs2AODll1/G6NGjvSrUpEkTNG7c2LeXMGPGDG/PnTvX2/aP5332\n2VEDvVevXkHb3nvv7W1WQG688cagX506dRIvVAWQmQqVy5h2794dAPDggw/i4YcfRlFREerXr48z\nzjgDl1xyCQoKCrBp0ybcdtttePHFF7FhwwY0bdoUgwcP9u/dZZdd/Gdu374drVu3xtSpU9GkSZPg\nfHgMWGmy6xK38bMVAM4991xvW0WD2b59e6nHqyBKHdNKW3Ar7INS5J9+/fp5++mnn87peFpwE6nQ\nMU2bHDzG/FDWgvudqLTC2DvcgMAdd9zhbS2435kyjSnPrzT5nPvxfAW+ueCW9v9AzVtwq6SkLIQQ\nQuQbWnCFEEKIDMg7STmNkk0awDflKt6NyrtY02RjKz+yZLFgwQJvT5gwIeh33nnn7cxpf1eqlFxV\nHrz22mveXrhwobetdH/vvTtSLN96663efvvtt4N+jzzyiLfPPvvsxGPw/ZMmc1fALlZLtZOU16xZ\n4+0RI0YEbTyGhYWF3p43b17Qj6XE5cuXe3vVqlVBv759+3rbuhnq16/vbd5ox24FAOCUwOeff37Q\nxs+AcqJKzdGke7msu7WHDh3q7ZEjw6RT7NPlZ7KVjXl8rHzNc/GDDz7I6ZzSjlFOSFIWQgghKgst\nuEIIIUQG1Khg1I0bN3q7OKjbw4kRWG7gXXRAKDGnJVpgWP4S3x3eldq0aVNvc0A9EI73448/7m2W\n+y1333138NruUk8iAxm5SpK08/Opp54K+k2bNs3bvFMYgI/XtW1WVuRdytzv4IMPTjyn559/Pmj7\n5S9/6e26dXckXbLJMz788ENvX3XVVUHb6NGjvc3ycgYJMjKBz3vbth0lie0OY+add94JXt9yi086\nhtmzZ3ubJX0gvGdYxufEQfY87HXm1yxRsxsJgI/1t5+bJfqFK4QQQmSAFlwhhBAiA7TgCiGEEBmQ\n9z5c6wcqwYb0sN+CfUDsOwBC/6H1A/Br9v0VFRXtxBnnN0khB/Y6FydJBxD6YoE4VWAJ8+fP9/bh\nhx8e9ONwLM4uVZImsgTOWmThcBYOMbG+Xb5nbNhIdfXl5UKSL8yGXjVq1Mjb1hfIxQa4IIgN0SrJ\n8wuE98ekSZOCfldeeaW32W8HhPN3y5Yt3rZjxiFDNoTkn//8p7f5+PkyzjxH0/y2gwcP9vaf/vSn\noI2zffG1tcdbv369t5ctW5b4WTxWNgsYny/3GzRoUNCPM8U99thjQVvXrl29XZEZqfQLVwghhMgA\nLbhCCCFEBuS9pMzSE2PrnnJye5Y9rATCcoPdns6SI4cIsSxZ00mS3Wy2IM4axeECQCgJt2zZ0tv2\nOrO89MYbb5T6fiAce5tZjLPfsLRtz5fvJytD2RC0fIXD5GzIXIMGDbxt60izO4GlyNWrw5ro/fv3\n9zaH7YwfPz7o16ZNm1JtICw+wthxZ9mS5XAAeO+997ydcUL8SmXRokXB6+K61wCAFi1aBG08/jbb\nF8MZpLiIha1xy3OUQziBcH6xbcPP2BXVu3fvoI1DN9n9VN6hXvl9hwghhBBVBC24QgghRAbkvaTM\nGVB4Z7Ldlci7mVkO4V10AHDggQd628oLLD/wMSog2XneYXciN2/e3Nt2pznvEGapiXeWAmGxAZbD\nrHzIErPNOMSyNEtULH0C4b1gj19TYEnfSnF8HW2hAHbb8JjZDG1cVKRx48betnLmRx99lHgMPj4X\nLLFzmWVue28mfZeMa+hWGEmy6ZgxY4LXPG52TJNcb1Ze5mcy2zbTFLvo7K7xpGgHG/nAx+cxBICr\nr77a2w888ECpxy4P9AtXCCGEyAAtuEIIIUQGaMEVQgghMiDvfbivv/66t9nnYP0ynIGGt5137949\n6Pfqq696m6uNAKGfgcMPbBiKiOFt+tZPW69ePW/byjPnnntuqcewsM+G+9mQFQ7nSPMP8fjasDL2\nTVl/Vk2Bfbj2GrNf1YbZsN+cs37Z/ROciaht27betmFGPEdtJaHDDjvM2ytXrvS29ffx/GVfr+Xj\njz/2drNmzRL7VVfY//r0008Hbbynwe5bSCpcb324HHbHz2C7B4DHw4bZpVUSYtIyxfEzhsc0bezL\ngn7hCiGEEBmgBVcIIYTIgLyXlLn4MUuMNitMUsiBLWCdVOTAvmaJheVRsQOWHa00vP/++3ub5Ugg\nlCBZ5rVjyhIVh/Hw/1s4bMQen+UqK12lhSPwvZD22dUdzv7EUiEQugzs9eFQuxUrVnjbSsUcXsfH\nb9iwYdCvsLDQ27ZICYfucGiIzUg1efJkb9uMRVxEnbMj5aOkzC65Tz75JGjjMLw0SZldb2mFSNgV\nY8Pu+PjWZZOUyS1NXk4rYj9y5Ehv33bbbYnHKAv6hSuEEEJkgBZcIYQQIgO04AohhBAZkL8OpWI4\nVIH9Z7YKEPsWbOHiJNKK0zM1NUzk2+C0jDbMhn10NvyK/T7sX0ur7MFjwH5ZIL06FIcS8DlaXxTf\nCzaVJ59vPvtweTzt9Vm4cGGp/YDQ98n7Hew9wWPDoXs2pIw/m1OEAqFPl4/36aefBv2eeeYZb3fp\n0iVoO+2007w9f/58b/fp0wf5xosvvuhtu0ciLYUpjx2H9Ni9GjwePNfYTw6ElaPSqrTxOaY9D+z+\nG56Xzz77rLflwxVCCCGqIVpwhRBCiAzIX32rGA4pYfkxrQrET37yk8S2tMLyXGSbsZKXiGGZ18qH\nLA1ZCZLlP84Yk2tlDysbp4X7cF8+JxtuklZ83H5evsLjYseMQ3xOOumkoI0lx5YtW3rbSv8colJU\nVOTtjh07Jh7PSph8Xvw8iKIo6Mey4qWXXpp4DCud5xtvvfWWt+19zLKsnQ/cl8OvbEUldsXw/OXw\nMHs8G3LG8DPF9ksL3eO+NiSpPNEvXCGEECIDtOAKIYQQGZD3kjLLuZxFyCbRZmzWGaZv377e5p2M\nQPKuPc6kI3bAcp+Vq3jX4KpVq4I2li45GT1LiUAoGyVJw7bNytIsMbPsxLtTgXA3bHkXra4ucLF3\nKzGyy+CCCy4I2oYPH15qPztOLN+yq4gzXAHAjBkzvM1Zp+x58f3H2cuAsOCIzTTFfdOyGeUDc+bM\n8bbdYc/f3e76ZfiaWymXX6eNfVqREobfZz8r6XOB8LvZohnliX7hCiGEEBmgBVcIIYTIAC24Qggh\nRAbkvQ83CesTYA0/LRvQIYcc4m3rs03y59hMSSKGr58NAeFraf1rHH5lw08Y9qWyfyjN72Z9R0m+\nqQkTJgSvr7/+em/bikNpvqR8gn2pdt8Ch9DZzGutWrXyNt8T1k/O4SWckYoL0wPAkiVLvH3CCScE\nbVyFin3OtjIRh6VwVisgDG3hfQN2X4gNdauO8Jja7E/8DE3LOsVzyu5v4Nd8/ey8435pGa+SfPSl\nfXbSOS5evNjb9l5NC0nKBf3CFUIIITJAC64QQgiRATVKUk6SL4AwvCQNDhdIkz3Et8NyjQ29YN5+\n++3gNcs6LNtZ+SdJOrYSL4+bfY8Nbylh3Lhxwetrr73W22kFEPINvpZJSeSBUGa37gOWZTnki903\nto2T2dtwMJaRWYa2fblogg0F4fP9/ve/H7SxPN6oUaPE82D5urrCz7i0DG1p7+P5lZatiu2DDjoo\n6MfjYzP35eou4nvV9ktyHdmQRH7+lwX9whVCCCEyQAuuEEIIkQE1SlJOq2fauXPnnI5x8skne5tl\nRKDm7EYtL1hqsjVkeech7xoEgD333NPbLGNa6TapoID9/7TCA0lyVePGjYN+STKjfV++8cknn3ib\n5Xe7a5V3+lqplWVGHkN7T7CsyLtne/bsGfRbsGBBqedn4fvI7ijm87dRBiw387hb+bq6SspJBRl2\nRlLOFZ57fM3t/cOv0+Z5WqEQ3rVsdxsnfRebxUySshBCCFEN0IIrhBBCZIAWXCGEECIDapQPl7d+\nW82eq72kwb47m8kkqaJFRRY0rm6wL4bHwGb3SqsOwuE/HGKSVgUoLRNOUgiDPQb7tlauXBn04+o1\n7du3D9ry2YfLIRrsF7MhWsccc4y3rW/2/fff9zaHh9kMY+zD5XGy/vSFCxd6295HPBZ8PLv/gv22\n9r7i7GY8t224SnXF+i2T4GuWtg8iza+aFHZkw3G4jX3vQLJ/154Tt1kfblJYUK7XIlf0C1cIIYTI\nAC24QgghRAbkvaTctGlTb3MmGCtzNGnSJKfjpUknSWFBVkKrySRJNzajU1FRUeIxkjJN2WMnFbhP\nyxCWloGG5UMrY86bN8/bffr0STx3Pl7avVRd4KT/LO1Z2Y+zNdnrz+E0DRs29LaVpXkMeb4+/fTT\nQb9Zs2Z5u3Xr1kEbS8X8ufZ8eWys/Mj3C99jX3zxBfIB6y5JoiwFQdKyvDE2G1layBB/Nh/Puqn4\n/rGfm/Rc+vjjj0v9/7JS/We8EEIIUQ3QgiuEEEJkgBZcIYQQIgNqlA93zpw53rZ+AFvsOom0Le5J\nbWlF0kWMTa03d+5cb1t/S1KR6bSUjWn+0rQC2Um+qX333Td4zekELXyMfPbh8jWx/lcOu7MhHxxu\nxe+zKTLfe+89b3MITlrqRVuFin21SYXLLTaVIPt0eWztd66u5BoKw/evvUZ2PucCX0s775L8tPY1\n+4jt8zjX8+XjpaUGLQvVf8YLIYQQ1QAtuEIIIUQG5L2kzBluZsyY4W277dwWOS8LSWEB9rNqMknb\n761MxAXGrbzEsiBf87RwAZYM06o62fPgY7JEZQtkp2WySgp9yAeSMk3ZrEsNGjTwNlcOAoD99tvP\n2ywBc8F5IAzz4CxRdixY5rYVfDgjGFfz2bBhA5Kw2aqSZEsrU1ZXbHWuEtKysFm3mQ2lSiKpWpCt\nyvPBBx94244pnxfPcyvxH3jggd62YYcsKfP3SgtPLAv6hSuEEEJkgBZcIYQQIgPyXlLu1auXt0eO\nHOltuytx2rRpO31suwsuSVbMZ0lxZ2E5Li35OUtU9vqxRM+yo+3HUiDLS2m7g63knZTw3O5SXr58\neeIx+fPyuZABXys7F7iNs3IBybvJ7Q5RHt+0jFQsD9oMUjyGLFFb2ZgLy7do0SJo4zHkz7JSZ3WF\n3Tlp14ivg30WJrmO7P3P9wmP/bJly3I6nn0fn4d1EwwYMMDbEyZMCNr4WcT3iHV/fFf0C1cIIYTI\nAC24QgghRAZowRVCCCEyIO99uO3atfN2nTp1vG0zoXA1k1yxfrwk/1w+++2+C2m+bQ4DOOyww3I6\nRlpmGfZFpYUF5XqObdu2DV7Pnj078Rj57MPla85ziisrAWHmsB49egRtPL4ctmN97dzGezCsf49f\n21AdDidKChsDwnvEHoPvJfYR50tYEGf+Yn8mV1sDgCiKvG2zfU2ZMsXbXNnJhk4mza+d2feSlL2N\nv4fFnm+S37q8IeXS6AAAIABJREFUx1S/cIUQQogM0IIrhBBCZEDeS8os+3JGGyttcBgKh5pwNhqL\nlaGS5IedkTDzHZbx0sJzOFE9S1cWvuZpIQcsO+VaoMAeg+EE+fY8rMTJ3zMtvKE6wvc2S3H2GrOE\nd+qppwZtPN+cc94+4IADgn5cIIIlfCsP8pjZAgg8Z9lt8ZOf/CTod8ghh3jbhobMnDkTpZFW2KQ6\nwd93r7328jbLrgDQrVs3b9ti788995y30woPMNxvZzK38Wu27Vzj++SII44I2h555BFvc1a08n52\n6xeuEEIIkQFacIUQQogM0IIrhBBCZEDe+3AZ9ttabZ7bPv74Y2+n+XC5uD0ALFq0yNv16tVL/CwR\nY1PFMexTtz5c9tMkFY62r9lO2+qfa1F468PlkAl7/DS/UnUnySdn7/mTTjop8Rg8x7p27ZrYr2XL\nlt7u37+/t9N892UphG6xIYNJYV75Ms+T/PKWvn37envWrFmJ/XKdU3wv2ecup2lM8++mwc/kY489\nNmhjH25FhvHpF64QQgiRAVpwhRBCiAzIO0nZSgAsU1x00UXeHjt2bOL7uHJQmzZtEj+Lw4zSzsNm\npKrJJBV0t3BmouOOOy5o4yoyXNWFqwgBYTHzpCpFQCjzprUxHC4BhKEt9h7MVVKrjnD2trTvmSZN\nJmUKSpvLaf9fFhk57bPsWPM58v1iqxZVVzh7VppMzrLsm2++mdiPXUe5hszZ8WA3TVpYX5qbirNm\nde/ePWhLCmlLcymWhfx9EgghhBBVCC24QgghRAbknaScxllnneXtUaNGBW2cgeaBBx7w9hVXXJF4\nPCtfJO2KtRmpajIsG7HEY6UmLuZ99NFHB20sIfExrAzFyctZ+rTSM8tVacnVWQ63xQsaN27sbZaX\nAaB+/frezrddyryjn6+jdRekScpJpEmHO5PcPhfSJGV2TQChdMxupXyZ5zynGFuQgqVnW9ggV6k4\naT7YOcTjYWXupOeuheVh63ZIykpX1h3RSegXrhBCCJEBWnCFEEKIDNCCK4QQQmRA3vlw03wx7du3\n9zZnHQFC312uYRydOnUKXr/xxhveZv9GUVFRTserafDYWB8Q+8nSsg9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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcf3c40d828>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}