01_Train_first_Neural_Network_Basic_Classification.ipynb

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  "metadata": {
    "colab": {
      "name": "01_Train first Neural Network Basic Classification.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/redcican/Machine-Learning-Basic/blob/master/01_Train_first_Neural_Network_Basic_Classification.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "Yv8cuw5kThaz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "This guide trains a neural network model to classify images of clothing, like sneakers and shirts.\n",
        "\n",
        "This guide uses tf.keras, a high-level API to build and train model in TensorFlow"
      ]
    },
    {
      "metadata": {
        "id": "Drv2Fj3vTTuS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "37111a6c-a5f6-46f5-a46f-9c076abdc2b7"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.9.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "QJSEyYV3UCdN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 1. Import the Fashion MNIST dataset"
      ]
    },
    {
      "metadata": {
        "id": "F4-VrC5tUHc7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "This guide uses the Fashion MNIST dataset which contains 70,000 grayscale images in 10 categorical. The images show ! The images show individual articles of clothing at low resolution ($28 \\times 28$)"
      ]
    },
    {
      "metadata": {
        "id": "CFZPP5HxUk45",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![Fashion MNIST](https://tensorflow.org/images/fashion-mnist-sprite.png)"
      ]
    },
    {
      "metadata": {
        "id": "lKGexM7SU9I7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "This guide uses Fashion MNIST for variety, and because it is a slightly more challenging problem than regular MNIST. Both datasets are relatively small and are used to verify an algorithm works as expected. They are good starting points to test and debug code. \n",
        "\n",
        "We will use 60,000 images to train the network and 10,000 images to evaluate how accurately the network learned to classify images."
      ]
    },
    {
      "metadata": {
        "id": "2dO-im6PUA24",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "9960b1a9-0d2b-4d90-ce6c-2688da968618"
      },
      "cell_type": "code",
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels),(test_images,test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 1s 0us/step\n",
            "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "R49i610SVijs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Loading the dataset returns 4 NumPy arrays:\n",
        "\n",
        "\n",
        "*   The train_images and train_labels arrays are the training set -- the data the model uses to learn\n",
        "*   The model is tested agaist the test set, the test_images, and test_labels arrays.\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "HoUmA77uV5Cf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Each image is mapped to a single label. Since the class names are not included with the dataset, store them here to use later when plotting the images."
      ]
    },
    {
      "metadata": {
        "id": "QIgNXtAAVfw6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class_names = ['T-shirt/top','Trouser','Pullover','Dress','Coat',\n",
        "              'Sandal','Shirt','Sneaker','Bag','Ankle boot']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WwVlg6mQWOR9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 2. Explore the data"
      ]
    },
    {
      "metadata": {
        "id": "NC_EWlEuWLkZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d209b18d-faaa-4005-9daf-6cbc122b7620"
      },
      "cell_type": "code",
      "source": [
        "print('training images shape {}'.format(train_images.shape))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training images shape (60000, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "h3mAdYNAWbmN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "586a0f1c-1e61-4385-f7be-c48ba62029fa"
      },
      "cell_type": "code",
      "source": [
        "print('training label length {}'.format(len(train_labels)))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training label length 60000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "El9eWWM7Wn5R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f5f910f0-d9a7-458b-e3cb-e7be7b8c48d5"
      },
      "cell_type": "code",
      "source": [
        "train_labels"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "GY6ob_zxW12_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8ea10331-a241-4d20-e6ea-61fefca04b40"
      },
      "cell_type": "code",
      "source": [
        "print('testing images shape {}'.format(test_images.shape))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "testing images shape (10000, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "tzHjrDG6W88S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "afc31101-e5f7-4f1e-aa32-fe372238834a"
      },
      "cell_type": "code",
      "source": [
        "print('testing label length {}'.format(len(test_labels)))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "testing label length 10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GBtgecIBXBOB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 3. Preprocessing the data"
      ]
    },
    {
      "metadata": {
        "id": "xEiOKZRfXEJr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The data must be preprocessed before training the network."
      ]
    },
    {
      "metadata": {
        "id": "DEaJ7y39XAQZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "db86c81a-d7e2-4503-ea4e-185dc2c97bb0"
      },
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[1])\n",
        "plt.colorbar()\n",
        "plt.gca().grid(False)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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56SCiFOtL1tatWxfT5wdwb7KzDdBw6CAAwMOCwaDa29uH/vv8+fPKysoakdwU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\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f12567287f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fN6INnCBXif9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We scale these values to a range of 0 to 1 before feeding to the neural network model. For this, cast the datatype of the image components from an integer to a float, and divide by 255. It's import that the training set and testing set are preprocessed in the same way."
      ]
    },
    {
      "metadata": {
        "id": "Y3CcOc6EXP-u",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_images = train_images / 255.0\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MtDhsPWUX4Ph",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Display the first 25 images from the training set and display the class name below each image. Verify that the data is in the correct format."
      ]
    },
    {
      "metadata": {
        "id": "_w0Vmoh0X2u_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "a3cac318-6729-4feb-daf7-bbaaa62b2488"
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid('off')\n",
        "    plt.imshow(train_images[i],cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[train_labels[i]])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Av0ThXzx6wCkT59wWAM4E0Nd7vyeAkQBaFzd3A3AqCg+TOwLoWWIXH3nv+3vv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\nndcGHBPBndaBOEaJ7zl77LFHrR+XZc6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KU+w657RxWxmZJSqujGxTwzmtnasc//vf/47G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\nhBBCCJFLal2uysJmMJRbmTUlG7G8kZKreB8c1W8j+fl9vD/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AgVG7WhtX5bwShBBCCNHq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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f12541846d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "73BDrv9xYoKV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 4. Build the Model"
      ]
    },
    {
      "metadata": {
        "id": "dK1TvF6uYsDw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Build the neural network requires configuring the layers of the model, then compiling the model."
      ]
    },
    {
      "metadata": {
        "id": "wXk9UNWiaL4s",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 4.1 Setup the layers"
      ]
    },
    {
      "metadata": {
        "id": "51EYfy_6Ybf5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28,28)),\n",
        "    keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "    keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aUn2O80CZNm9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The first layer in this network,` tf.keras.layers.Flatten` transforms the format of the images from a 2d-array ( of $28 \\times 28$ pixels), to a 1d-array of $28 \\times 28 = 784$ pixels. Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data."
      ]
    },
    {
      "metadata": {
        "id": "A4leaVDQZrBP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Afer the pixels are flattend, the network consists of a sequence of two `tf.keras.layers.Dense` layers has 128 nodes (or neurons). The last layer is 10-node *softmax* layer - this returns an array of 10 probability scores that sum to 1. Each node contains a score that indicates the probability that the current image belongs to one of the 10 classes."
      ]
    },
    {
      "metadata": {
        "id": "779tj607aOi3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 4.2 Compile the model"
      ]
    },
    {
      "metadata": {
        "id": "UD78Ga6paSkT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Before the model is ready for training, it needs a few more settings. These are added during the model's compile step: \n",
        "\n",
        "\n",
        "*   Loss function - This measures how accurate the model is during training. We want to minimize this function to \"steer\" the model in the right directioin.\n",
        "*   Optimizer - This is how the model is updated based on the data it sees and its loss function\n",
        "*   Metrics - Used to monitor the training and testing steps. \n"
      ]
    },
    {
      "metadata": {
        "id": "FkYHKlMxZIQd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=tf.train.AdadeltaOptimizer(),\n",
        "             loss='sparse_categorical_crossentropy',\n",
        "             metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hGES8zv3a5_y",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 4.3 Train the model"
      ]
    },
    {
      "metadata": {
        "id": "WRQJqACba9Rb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Training the neural network model requires the following steps:\n",
        "\n",
        "\n",
        "\n",
        "1.   Feed the training data to the model. in this example, the train_images and train_labels arrays\n",
        "2.   The model learns to associate images and labels\n",
        "3.    We ask the model to make predictions about a test set-in this example, the test-images array. We verify that the predictions match the labels from the test_labels array\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "PmwB_NRca4av",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "582f5df7-ed72-49f7-f893-1b350ea3fd1c"
      },
      "cell_type": "code",
      "source": [
        "model.fit(train_images,train_labels, epochs=10)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 6s 108us/step - loss: 2.3297 - acc: 0.1424\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 6s 94us/step - loss: 2.1292 - acc: 0.2120\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 6s 93us/step - loss: 1.9873 - acc: 0.2832\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 6s 93us/step - loss: 1.8752 - acc: 0.3854\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 6s 92us/step - loss: 1.7812 - acc: 0.4665\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 6s 92us/step - loss: 1.6991 - acc: 0.5192\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 6s 92us/step - loss: 1.6253 - acc: 0.5593\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 6s 93us/step - loss: 1.5583 - acc: 0.5871\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 6s 92us/step - loss: 1.4975 - acc: 0.6082\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 6s 92us/step - loss: 1.4422 - acc: 0.6248\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f12533cad68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "vNeCjehrcAHV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 0.62 on the training data."
      ]
    },
    {
      "metadata": {
        "id": "hMtOGjVXcI_J",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 4.4 Evaluate accuracy"
      ]
    },
    {
      "metadata": {
        "id": "dmU3C-LLcL5d",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Next, compare how the model performs on the test dataset"
      ]
    },
    {
      "metadata": {
        "id": "3e2BtxwVbeUA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "c7d4c3f2-3f47-4f7f-a223-1f0f9700032b"
      },
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(test_images,test_labels)\n",
        "print('Test accuracy:',test_acc)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 0s 47us/step\n",
            "Test accuracy: 0.6284\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "k_aJLb0ccffL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 4.5 Make predictions"
      ]
    },
    {
      "metadata": {
        "id": "fA8TMUu5ciRp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "With the model trained, we can use it to make predictions about some images"
      ]
    },
    {
      "metadata": {
        "id": "B_erwXOBcZO7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "predictions = model.predict(test_images)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BUcA97qicq4B",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Here, the model has predicted the label for each image in the testing set. "
      ]
    },
    {
      "metadata": {
        "id": "vDRxlIywcpMC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "c7676109-eb4f-4337-c808-63104be713e6"
      },
      "cell_type": "code",
      "source": [
        "predictions[0]"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.04073562, 0.02026923, 0.07504299, 0.03317018, 0.03961883,\n",
              "       0.18327093, 0.05011577, 0.15945728, 0.15697908, 0.24134007],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "FILQt7bic1Yb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "A prediction is an array of 10 numbers. These describe the \"confidence\" of the model that the image corresponds to each of the 10 different articles of clothing. We can see which label has the highest confidence value."
      ]
    },
    {
      "metadata": {
        "id": "tq-maDeMcwBI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8f8fdf6c-a5ee-4d3e-a06b-e947e3de7c16"
      },
      "cell_type": "code",
      "source": [
        "np.argmax(predictions[0])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "JnhDARdcdDjr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "3469aa5d-cc35-4fea-8577-a31e3f600f96"
      },
      "cell_type": "code",
      "source": [
        "# Plot the first 25 test images, their predicted label, and the true label\n",
        "# Color correct predictions in green. incorrect predictions in red\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid('off')\n",
        "    plt.imshow(test_images[i],cmap=plt.cm.binary)\n",
        "    predicted_label = np.argmax(predictions[i])\n",
        "    true_label = test_labels[i]\n",
        "    \n",
        "    if predicted_label == true_label:\n",
        "        color = 'green'\n",
        "    else:\n",
        "        color = 'red'\n",
        "    plt.xlabel(\"{} ({})\".format(class_names[predicted_label],\n",
        "                               class_names[true_label]),\n",
        "                               color=color)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SZO67777ouDWa5Fhda+F7Y+/b0UcfHeR77rknOmfvXR32eahVbc/3l00C1iTHauSDDjoo\nyHkZwKvBZlmZ5FLysutvttlmNX1+3333jY4vvPDCIMt007TYscjHbNKx/cAbJDM2NQqPQTa7KEt7\nPFdZEyebpfl9YsPve/XqVbWchU2q1sTHY5b7pSi1j3XByGv7oEGDqrbBlrPfn11eWC5yzVgRpGES\nQgghhChBCyYhhBBCiBKaxSSXpy63qjxWFbLnfX2yu/LmhGPHjg2y9bzfeOONg8yqPGueYRNd3oaT\nwPJqxDps21ndbM+xmprrf+yxx6rW3ZqwpkyG7323bt2icxxtYeH7XZQ9vdbnIy8LvK2Pn4Htt98+\ntz6+rs1oLBPR8uSZDwYMGFDT57fYYovomCO1iqJPW2Nm/LaGdXXge8rPuu2Hrl27Vq2P518gniPy\nMkV3VNi8X+TGwePLmuS4X2wfsemNx5Q1a7EZlvvIvlt5jufnxradz7HJsGg+t5s28/fnaHc28TUm\n0jAJIYQQQpSgBZMQQgghRAlaMAkhhBBClNBoPkxFGbYb4mPC2CzSnEV55syZ0bm33noryBzSyNlj\ngTgM/v333w+yDVtkfxb+XtwGILYLc3ZoG1aZ58cBxPbZouzVzz//fJCHDRuG1oBNK8A+PeyHYP1J\nZsyYkVsn29bzMgYDtWd55vtdlFWev0utqS9sX7LfQUfFZubmsGSeE9h/oYiiTMbyYWpe+H5z6gDb\nD3k+ipx5GgDmzp0bZJ6Prc9KR4TnI3t/eZ7lcn369InKderUKcg8DoF41wOuvyidD8/p1oeJyxXN\n4Zxuhv2IuT22Pn4vAvG8u2DBgiDLh0kIIYQQooXQgkkIIYQQooRGM8kVbXT7zjvvBHnOnDlBtqpB\nPmY136xZs6JyRdlIWfXIKkW7SR/Xz3XY9ACsEuaUABx+CcSbU7JK2dbHKmq7MS+rttkMx+GStlxr\nodYw+k022SQ6fu2113LLsjmM6y9KR1FEXqZvu6Ej12fTIDBFJrlaNyNuz9h79/rrrweZ7z9n3S+i\nKGtwkbkuL+WHaDicRoRTBNhs/T/4wQ+qfn7rrbeOjqdMmRJk3g1A6Tniecu6Z/Dcxe4pQ4YMicrx\n54rGSlFWbW4HX9eaTdlcV5S+hd9jbDYfPnx4VI5Nd9bEy3Xa92lTIA2TEEIIIUQJWjAJIYQQQpTQ\nJJm+x40bFx1z9m1WB1qzRV6kXZHZzarh2HzFZhKbwZtVe6z2tfVxm1htaFWjHBlXqznGqhc5cohN\nhtb8V6RSbSms+javjdYk9/DDD+fWmRf5ZM1f3H9FEZn8OZaLzMkc6WWjvooi4ZpDPdza2W677aJj\njohklX59NjXOw45vxppcxYrD4/bVV18NsjXJXX/99VU/b6N72Tzzxz/+Mcg2u/s222xT/8a2cdj8\naedVfk+w24m9b/xOYpcRIDZr8TxuxxRn+uZ22DmX28Rzs80+zvPuG2+8EWS7Wfnjjz9etW4gNj3a\n79UUSMMkhBBCCFGCFkxCCCGEECVowSSEEEIIUUKjOcPcf//9Qb766qujc2xn5PB79kUCYnsnhxAX\nZfe0dbC/D9tWOTTR1sF2URv6yNdm/yhOlQAAL774YtU22LYz1g+K0ypwBldbrijUvaWwoaV5fkHW\n3v3SSy8F2dq4i+5dLdjP52X3LvIJY/+MHj16ROf4ebBtVyg7MGLEiOj4mmuuCTKP72effbZB9fOz\nVJTpu9adBkQ+1m+QxxaPEU4xAMTzGGPHHPvfcIqBon7tKDzzzDNBtj48fMzvJOsf+9RTTwWZfZGA\neHywbOdPHrPcL7YcH7P/oPUl5Gdg2rRpQba7cvC7xX5/nmf5Ox588MFoCjSTCCGEEEKUoAWTEEII\nIUQJjWaS4xDiJ554Ijo3ffr0IE+cODG3DjZrsKlt/fXXj8rx8TrrrBOdY3MYq5E5NBOIs6KyWq8o\n5JLVhptvvnlUrl+/fkF+4IEHgmxDM4vMA6yi5A1JrYrSmhdbA1bFnmdOs+kHOJzYqoobkuW31o14\n2WRYpPa//fbbg8x9DMSqctuvixcvrqkd7ZmddtopOmbzDN//hpqYeVwUZXtXtugVx44rnmfZTFJr\nCgebKoXnBTbPFW263VHgdDacRRsA5s+fH2R+L9i0Avzu4hQ4QL77gB1T/C7LSwEExC4kbMaz5fiZ\n4s3sDzjggKjccccdF+Rvf/vb0Tl+Z7C7T1MhDZMQQgghRAlaMAkhhBBClNBoJjlW85133nm55TgD\n8uTJk6NzbCbj7J6srgOA5557Lsh2A19WI7LKz5pM2KzHm/3tvvvuUbl99903yHkRHxZWKXIGUwDY\nYIMNgmxNbWyGZBOXVXMPHjy4pnY0J/b+WtVxHRwVB8RqXvs9WR3P6twi80teNm8g31xXZLLhZ8+a\nYW+55ZbcumVKAPr27Rsd8/PO/W6fFd6kd8CAAbn1swm/6H6vaLSlWB42tbAbA5uPirBRpTzfcV/a\nyNSOyDHHHJN7jt+nPG5stuyxY8cG2UbQcR08F1rTHe9swCZVO37ZxYFl+45gUzy78dgNmzlLuY0Y\nr/Wd3FhIwySEEEIIUYIWTEIIIYQQJWjBJIQQQghRQrNve882yNGjR0fn+Pikk05qtjY1NnfccUdL\nN6HZsf5HeX5BNtye7d+2jlqzhfNxXjZve1zk68SpKiZNmhTkIt8xey2bkVbEfkvs22BDzGv1YeIw\nYvY1sz4a8mFqfDj7Mo/hWn1K2AcKiMdg3o4PYnn4fco+ljb1DKfVsWl68nzGbLoBroP7y/YRz8c8\n5ov6kq81derU6Bz7Ebc00jAJIYQQQpSgBZMQQgghRAnNbpIT7RMbJswZWDls9cwzz4zKjRs3LsjW\njFXrpql5Zrhasz/b63Cm4VGjRgV5//33j8pVKpUgW/OhzfDeUchL6wEABx54YJBvuOGGIFvzLe8G\nYNN8MDYzfLU2AMub6MSKwxtPs8mz1qzqNjw8b9NXu6l3R8c+23y/eQ6yO2oUbTDO44jr402VgXzz\nOD8Ltg4219qUE9y3G264YZAfeeSRqByb5GpNFdNUSMMkhBBCCFGCFkxCCCGEECVowSSEEEIIUYJ8\nmESjYLeoYXt60RYWXbt2DfIrr7wSnWObeWPsOJ/nX2P9rzj1Aafv79KlS27d1g9qzpw5DW5nW6bI\nh+kb3/hGkP/6178G2YYb33rrrUH+xS9+kXst9nUpSidh01WIFad79+5BXrBgQZDzUoFYrF9ZXig6\njz+x/LOdd795mzEg3ubE+leyfxN/rn///lE59kGaP39+bn08V7NfqvU/4nHPsvWJYuz3L5pvmgJp\nmIQQQgghStCCSQghhBCiBJnkRKOw8847R8ecIZuz/9ps2S+//HLTNqwR4QzUANCpU6cgW7X0dttt\n1yxtam0UpWvYZ599gswmGXvvak0nMWzYsCBPnz49yDbb9FtvvVVTfaJ2uC+feuqpINdqkuOxA8TZ\n9TkUvW/fvg1tYoeAzdJ8761LAI8xOwfz54YMGRJkmxH8xRdfDDKbv6ybBZv4uJ+5j4E4wz+3z2YY\n53PWvC6TnBBCCCFEK0MLJiGEEEKIEmSSE42CNUFxdARHQNRqbmmNWNUzq4rtBrI2q21HoVaTDJta\nnnjiiegcq+Qff/zxIO+0005ROTZHsBnH9sW7775bU5tE7bDZk+99rf1v4fmC+3+jjTZqUH0dhTwz\n1K9+9avo+Le//W2Q77nnnujckiVLgsyRcTY7OPcRRy/aDdXff//9quds9BtH7nEE8sknnxyVK4py\nbe73Sdt9ewkhhBBCNBNaMAkhhBBClKAFkxBCCCFECfJhEo0C7zYNAFtttVWQ2d+hyLfniy++iI7Z\nH8JmiW1K+FrchkGDBkXl9ttvvyCzHwAA7Ljjjk3UutZNraG9J5xwQpA5lBkADjvssCBbvyXmyCOP\nDPLSpUuDvPbaa0flvva1r9XUJlE7Rx11VJAnTpwYZE43UB8OOOCAqn8fPnx4g+rrKOT58KyxxhrR\n8XnnnZdbxxtvvBFkTh3wzjvvROXYN6lo5wXeOYHlPn36ROU4FY0ds60VaZiEEEIIIUrQgkkIIYQQ\nogTXnKYOIYQQQoi2iDRMQgghhBAlaMEkhBBCCFGCFkxCCCGEECXUlFbAVVxPAHMBnOMTf0EN5R8C\n8Euf+HH0t1HZ33ap8ZrXApjoE39VLeVL6vquT/zfcs6NAXC3T/wt2fEhAP4JYEef+CeqfcZ83gNY\nxSf+C/rbLwCs7BN/To3tmw1gd5/4V2spX1DPmgD29okf6yruFgB/8Ym/fwXq6wdgJoBJ2Z9WATAH\nwEk+8UtyPhb6H8AXqEefryiu4lYCcCeA/wGwF4BjAMwC4ACsAeAan/jLCz7fD+kzt1FjPn9VrrMu\ngLsBHOITP7+x6y+59oUAtgOwOoCtsKxvr/aJv74521JG3dgE8H0AqwLoAWA9ADOyIqf4xE9voeah\nMcZYI7RB/dlItIb+zMW5fsiZi+Hz52K4eC6Gb565GC6aiycD+DWArwH4DEBnANfA+0vh0nXBcu1y\nrgeAS+H9IVXq3gnA2wC+BPA3APvA+w+a7LsQteZh+h6AFwEcDaB0wdSayF6i5yG9sfbcIQDWqFss\nZRwHYDrSl23pgqmVsRWAgwCMBfADAJNdxW3pE//hCtS50Cd+VN2Bq7jfAjgHwI9XpKFNxJkApvnE\nT3IVtxeA6+sWra7i1gYwzVXcxJaclAHAJ35Jtqi+CkDDEtc0/No/BaLF4ajmvH6tmLFZ92PmaKQ/\nLL7bkm0jGmuMNRj1Z6PS4v1ZwkJ46l/X+udieD8Jzn0HwCYAdob3Hs6tC+ABOHdX7qe9fxvA8oul\nlGMA3ATvx8G56wBcCODExm1+dWpdMB2LtEHXuorbySf+cSBoRn6PdNLvD+CHPvHj+YOu4q5B+iv/\nEfpbHwCXAVgTwNoAzmZtFLGdq7iDAWyEVDtwkau4tQD8BUBvpKvs63ziL88WRpcA2AaABzDBJ/5c\nAGMA9HUVd79P/J6m/nOy71bXrt4AdgIwCsAEV3Gn+8R/kk1GdwC4D8D2ADoB2M8n/k36bGcA4wGc\nbb7/rgASpFqOzwGc4BM/q8p3Pd5V3FcBdAdwsk/8Q67iBgP4M1LT6coAfuYTP9FVXHcAV2f3bjWk\nD8y92d/WcxV3oU/8T13F3QXg+Oy+NBaPIJ1YIs1YmQax2ncBsBjAWJ/4TbIyvZEuUvsA+BaAU5De\nt4UAjveJX+Qq7v3se67kE38q1b8ygJ8AGFbt+j7xH7qKmwpgE1dxG3Bb67RJAKo9g3AVdyyAHwL4\nGMA7AE4AcC6AxT7xv8rKnIP0uTgXwJ8ADMqOb8ye26MB7I/0F/XFPvH/dhV3YTY5T6123eYmW8T1\nB9AXwI8AfIDqz9+1IO1bnZYV6S/IC5Dep9UBnOoT/2TeGMien5sADPDJcr8ko7FZ0OaHAExF+mNh\nNwB7I/2B9HH27/s+8fPznlVXcacB+C6V/272nJ0C4NvZ934JwElIx+adSH9QPe8T/6smGmONgvqz\nffVnFcJcDJfeD3j/aq7Wpg6XPxfDp3MxXPFcDO8XwS2bi+GXzcVwy83F6yN9168E4ItMI/bVrGxf\nACvBucuR9vmnAPYD0AXARHi/EZy7Nvv7JgCuRLqQ2g7OnQHgGgC/gHPnwfuF9b+F9aPUh8lV3Aik\nN3UCgOuQru6YT7KFyC8BnGo+WwHwoU/8+eYzlwO4yCd+NwAHALgqe+FZeiFdjO0C4GxXcetn11ji\nEz8C6YA6y1XcAKSDoT+AnQGMALCnq7iRSAf2QrtYysyMPQE8Q38+BukL/BkALyDV1tQxFMC12XWn\nAjiUzq0C4GYAv/WJf4CusSbSB/Mgn/iRAC4F8Lsq3xMAFvnEjwZwGpW5FMDl2a/GE5HefwA4H8DD\n2d+/gfR+rox0cnug7lcngAeQTjiNQrYoPQjAow34+HLfxSf+BQCfuIrbPCvzbQA3Iu33nyOdEHcB\n8BCWLUTXRmpCjZ41pANwjk/8gpy29wKwLYAn69PobHFfATA6a/tcAGcA+DuAg6nooQCuR9p/b/rE\n74p0cX0Yfb8tAezrE//v7LhR+6eR6A9gV5/4p5H//OVxOtLF4K5ItdE9axgDr9iXa87YLOLDrO7V\nkGrtvpW14R6k81IR5wPYP/v8JQB6uYrbDsCBAEb4xO8IYAnSlygAbAqgUrdQRuvsQ0b92b76M8Wt\n+FycaavS58CnczFc/lycLcC3E75XAAAgAElEQVQegpmLo8VSylcBzIEPc/F1SPtyHpz7G5w7Gs5x\nau9NAfwC3u+AdAG+V5U2rwXvR8H7vyN9//4I3k+A958DeAzA6Abch3pTi9P3cUgXCh7pau7b2aCp\n46Hs/zlIV5J1HI10pXhalTp3BVDJfk38A+lN6lal3DifeJ/5y7wKYGOkL6EHAMAn/hMATwHYOvt7\nXfn/In2QvlrwvXoDmJd9L7iKc0gXTGOy82MQLw7fzV7w1b7rlQBe9In/p7nGMKQTxdjsu/4YQNec\n9tQttB4HsFkm83edDqCzq7gu5u8LAMxDuvq2zAHQL+d6tdLVVdxDWfsfBPAmgP9rQD1534UXHoci\nNZ3uiPS+3Zdd97DsGEh/5TxWpf7eSBczzJFZ2x8G8FekGtA59Wz31gCe9kmwkT8E4KuZVmg1V3ED\nXMUNBfCFT/zzSJ/tA7N2j0f6y7xuT5VnfOI/pbobo38amyfqxgTy+yyPGwD8ylXcRQC6+8TfgfIx\n8HiVeqKxWQN1dQwG8I5P/Lzs+CEUzwFA+gv5XldxPwcwK/ueo5D22YNZm3fJ2gQA7/nEz6TPt8Y+\nZNSf7ac/u8K5hzK/pEaZi+HT5wCufC7Orlv/udj7pfB+JIDdkb6vvwPglUy7BAAvwfu6fVjmAVi3\nSp3Vnqs6mq3PCk1ymZnpWwDecBVXp21ZKftbnUMhbwDGG0mthtSxbzcsb+r4FOkvlHdL2scb1jik\npjY76Mr+XiujkT4If3AVB6T3ZmNXCZ36hSnP3/VNAIe4ivuNT/zb9PdPAbxRo19B3Xd1JDfVd60P\nCwvaz9dctaSevDbfiHSCuwbA6j7xU7N7PsUnfv+cuj4ruVYdwYeppC1FbS+61zcgnWDWwjIfuU8B\nnO9jv7g6f41a292ScBtLnz9XceHe+cTf5CruPgB7AjjPVdwUpCaaojHQGPekro6ivqr6rPrEn5k9\nb/sCuM1V3I+Q9uEdPvEnR5Wlpvm20IeM+rP99GfswxTTaHMxXDoXw/up2aJmCvwKzMXOrQLAw/vn\nATwP4BI493ek64hnUPxurf06zUCZhulwpKafoT7xW/rEb4k0wsGa5apxBdKV5F9cxVmtykSkKj+4\niuviKi7PXrxbVmY9AAMAvIzUrrpX9ve1kPosPZ39fQ9XcS4z743M/vYlUpOZZS5S36g6jgNwbt33\n9IkfhlQr8b0avuvZAH4F4LpMU1XHywC6uIoblrV3hKu47+fUUadS3BnpQwXzXbdCarZbZP7eC+lC\nb2aV79oXwOwa2t9Q3seyX2q7lZSt+l2yX4/vIrV51y06nkTqv9YjK3+Iq7hvlNQ/l9pSS7s3zJ6V\nNZH+4srjaQDbuIrrlB3vjmXBADcA+Hr274bsb/xsf8VV3MWZKbkaTd0/K0re88f9PhrZBJyZ4FfK\nNK2nIf11Wp8xUIcdm7XyMoBumRkViPtquWfVVdx6mZ/PXJ9GT/4JacTZYwD2cWmgAFzFneQqLm83\n5dbeh4z6E+2qP5kGzcVw6XMA7xfB58/FWdQa4NwhcPWei69DatZDVscq2fmGRoU393suULZgOg6p\nfwxzC4Ch2eq8kEwdejGAaxGvGk9FarZ4FGmY6YScKt50FXcbUvPa+Zlp7lIAnVzFPZJ97nyf+NlI\nfYheRfrCmgjgNp/4x5Bqf952Ffd0tsCqa9tbAN5yFbd19kLbB6nJkbkcqWmxdAt2n/grkT60P6W/\nfYLUAfHqzCz0PwAezqli/czh8GIsi3o4BcAJruIezL533fbsCYBdMvXyWKSOkB8CmAJghEvDd4F0\ngrm3rO0rwEVIv9u9AD4qKZv3XYBUFXw8skWHT53pTwNwV9bPx6E8YvFJAH2qLM6rMQ3Ac0h/3fwV\nBerebEF3LoBxWVu6InMI9anzvkeqhXsr+8ifAHzoKm5S1uYlPvHv5VTf1P2zouT12RikvlkPIvXL\nWpr9/RUAD7iKG4/0PvyinmMAQDw269PY7FrHAbgpGxujkTobA1WeVZ/4xUgd8590FTcOqVP+lT7x\nT2Xtf8hV3ESkJp1pOZdt7X3IqD/bV38yFwG4Gq72uRiufC6GXzYXw9VvLoYLc/H/A7AxnJsM5yYg\nfZ/fBe/vqPXLGR4AcAWcOyhzMN8JqftDk9Oh95JzaajrgT7xR7R0Wxobl0aCTQawFfnftGtcxf0E\nwHo+8WeXFm5hXMXtAeBMn/hmTSvQVmgLY7MjjrGGov7sYLh0LoZv4rnYuRMAbA3vmyWtQIfO9O0T\nfzOA/7g0dUF74wqkCSY70sC/GMCWBer2VoFLE1dWsCxSRxjayNjsiGOsQag/OxwXA9gSrgnn4jSZ\n59Egq05T06E1TEIIIYQQtdChNUxCCCGEELWgBZMQQgghRAlaMAkhhBBClKAFkxBCCCFECbVuvlsL\nrcJ7fNSoUUFeaaWVgrzaaqtF5f7zn/8EuV+/flX/DgDvvPNOkDt16hTk//73v1E5Pr777rvr1+gV\nozRHVANotr7koAPn8r/K4sWLg7zeeutF51577bUgv/vusuTx3P9A/AwMHz68/o1tepqiL4Fm7M8v\nv1yWnN8GlNj+qOO66+ItzSZNmhTkL75YlgTY9vuQIUOCfOyx+fu61vqM5X2mPp8ztOmxKSLUl+2H\nBvelNExCCCGEECU0ZlqBFlktv//++9HxwIEDg9y1a37S508++STI/Kt49dVXj8p95SvL1pRrrrls\nz+HPPou3tuFrTZiQl7i8SWhTv3ysZo61DvZZZI3Q559/HuQ11lgjKsd9ue6661b9DACsssqybPon\nnHBCkC+88MKa2t4MtHkNU60899xzQd5iiy2iczvuuCx1C48/7j8AmDhxYpBZM5ynyQIapm1aAdrU\n2BSFqC/bD9IwCSGEEEI0FVowCSGEEEKUoAWTEEIIIUQJjRkl1yLYqDb2S2B/mVVXXTUqx8ccfWN9\nbNhHiv0pOLIOWN6vRlSnyL/kpptuio7PO++8ILPPy8033xyV+8lPfhLkZ599Nsjjxo2Lyu2+++5B\nPumkk4LMkVgAsPLKy4ZFM/u8tCteeuml6JgjTrt16xbkyZMnR+WSJAny0qVLg2zH2FVXXRXkRx55\nJMjs2wQAZ511VpDtPCCEELUiDZMQQgghRAlaMAkhhBBClNDmTXK33nprdLxo0aIgb7TRRkG2pjZO\nJcDh6/x3IA5ZZ9MNmwoA4M033wzy008/HeRtttmm+AuIAJvCAKBXr15BPuecc4K87777RuXuvffe\nIM+aNSu3/ssuuyzI1qSah8xwxfCzDgC33XZbkHlMAMDOO+8c5CVLlgR5/fXXj8ptsskmQV6wYEGQ\nrUmO0xFwmo/OnTtH5ThtxMiRI4O86aabRuW6dOkCIYTIQxomIYQQQogStGASQgghhCihzWf6tiYv\njsQZNmxY1b8DcXTdp59+GmTO5g3EUV18zpoHXnnllSCfffbZQT7xxBOLv8CK0yoy0BbtvcXmkmee\neSYqx6YZ3gcOAF588cUgH3HEEUHmfgXi7N4cffXyyy/ntnfmzJlB5v4HYlMgZwvv3r17VI6jJhuJ\nNpHpm6PORo8eHZ2bMWNGkDfYYIPoHPfb7Nmzg2z3XuQxzaZ0208ffPBBkPfaa68gW3P5E088UbW+\ntddeOyr3zW9+M8gbb7wxGoFWMTZFo6C+bD8o07cQQgghRFOhBZMQQgghRAlaMAkhhBBClNDm0wqw\nLwoAbLvttkHmlAB253pOH8C+DOxvA8SpBNZZZ52qMhD7s9hw6o5AUfg9+yI9+eST0TkOIR88eHB0\nbssttwzy/Pnzg/zhhx9G5TiUfauttgqy9Yni52GttdYKMqeiAGJ/NM4Mvcoqq0TlOlIY+vTp04N8\nxx13BPk3v/lNVI7TNdg0EQMGDKhabvHixVG5Y445Jsivv/56kD/++OOo3NSpU4O8/fbb55Zjn7QN\nN9yw6ucB4OKLLw7y5ZdfDiGEYKRhEkIIIYQoQQsmIYQQQogS2qRJ7q233gqyzeDNYeWcJdiGgLOp\nZe7cuUFeffXVo3JsrmOznt2wlT/HmcNFbHIZNGhQdI7Na127do3O8cbHHKJuTWFPPfVUkKdMmRJk\nm35g4cKFQeaQdN582V6Lnxs26XU0OKM3Z1YfM2ZMVO72228Psk0rwJm1eWPeO++8MyrH/c7pB2xq\nEDadcsoHa6Znsx5nFR86dGhUbr/99oMQQuQhDZMQQgghRAlaMAkhhBBClNAmTXJvv/12kG1mboaz\nT9vM3BxBxZF1zz//fFSOTUZsnrOmQM4Ibs16HRG+b2z+6tmzZ1SOI66GDx8eneNs7IzN0MyRjWw2\ns1Ft3Gcc1WefIT7miCsbfdWRmDBhQpD79+8fZI5kBOLoUdtPbCKdM2dOkO0zsdtuuwX5tddeC7KN\ndOXIPTbTWtMdm+tsHcy8efOCbCMsO1JEpBCiOtIwCSGEEEKUoAWTEEIIIUQJWjAJIYQQQpTQJn2Y\neBd666fCGZwZm4maUxOwnwRnigbiEOW+ffsGmdMSAHFWY6UVAJYsWRJk3mW+R48eUTn2N+GwfyDu\nyyIfsc6dOweZnwf2YQPiMHf2teGs7/aY/aOs/wt/r/be5xzqz2k42P8PiP2RrA/auuuuG2RONWHH\n8MYbbxzkpUuXBtn6mvHYZD85vg4QPy8jR44M8q233hqV4zQFNvu7fJhEe4XnSZZtKp6G8MgjjwR5\nxIgRK1xfrXz00UfRcd66oL5IwySEEEIIUYIWTEIIIYQQJbRJkxxnCbZqelbFsRmHTUTA8lml69hh\nhx2iY96gk816bI6x56y5riPC95vvhzWXcJZte0/5XJGqmE0/nD7Cmu64HKcfsM8Qmwk5o7ttO5up\n8p6n9kKeOe3uu++OyvF9sJnR2RzLGbxZtsc81jlLNxBn8D7++OODbDe/5jH88MMPB/nxxx+PyvFz\nYJ9FIdor/O4q2kSdOfXUU4P8xhtvROe+9rWvBXn8+PFB5nQkANC7d++arsVzsN3Qm/ntb38b5Jtv\nvjk6x2lRbLqT+iANkxBCCCFECVowCSGEEEKUoAWTEEIIIUQJbdKH6dVXXw0yh4cDcRg4hytbv4aj\njz66at3HHntsdPznP/85yDb8nGF/KZY7Kuy/wj5M9t5wObsdBfvDsN9SkZ2dbdx2+xruP04DYO3i\n7PtUZDPvSFvgbLPNNkH+3ve+F2TrB8R+Re+99150jlN5sB8Ub6MDxP5vnC7A9ieH/vO2JpweAIi3\ntMnbEgmI/bSsv5QQbQn7rmqInxKPZQD46le/GuQjjjgiyFtvvXVUjud4TuVyyimnROVuu+22mtpR\nNAdff/31Qf7HP/4RZDunsC+kHff1QRomIYQQQogStGASQgghhCihTZrkOJybw8iBWN3ImZltlubT\nTz+9at2sdrT1sZrTmpbYxCOTXBzCX5Slm0P42UwDxCYSNr+w2RWIVbZ8LZvegc163JecKRyIQ885\n87tVZdtM4u2J6dOnR8c33nhjkA8//PAgW9U/hwBbczmH8/I5259F2dWZvMztVoXP/c7PxN577x2V\ne/vtt4P84IMPRueOPPLI3Ha0F2x2czbJsFkTiEPJhw0bFuS//OUvUTm+b7169QqyfTY4hQhjn69a\ns0/z2KzVBNVaKfoueeeK7pMdb/zc83xn35FnnXVWkDfffPMg27QgnNpn0003DfK4ceOictznZ599\ndpC/+c1vRuXYtWbixInRucsuu6xquS222CIqt+GGG6IxkIZJCCGEEKIELZiEEEIIIUpokyY5Vrnb\nrJ2sluQILLvp64ABA2q6Fm+6yephG0XD6myb4bgjwmpfzqBszVhsXrV9xNFSRepmfh64j6z6mlW2\nbDqy3HLLLUEePHhwkNmkACy/uWx7wm5eyWr7a6+9Nsg203eSJEHmewcA3bt3DzKb2ubPnx+V23HH\nHYPMfd2tW7eoHI9B3rDXlmNT74EHHhjkGTNmROWmTZsWZBv501pMckVm4DzTk40uZLM1Z0C+9NJL\no3K8Kbl9Hti0OXDgwCCziR2INzv+4x//GGRrnrnjjjuCzLst1Gpasub3tm6GY4q+S965Rx99NPcz\nPEaB2Fx19dVXB9maQzkSdcqUKbn18/uPn9f99tsvKsdm2csvvzzIY8aMicp16tQpyDaSuk+fPkHm\neWPy5MlROX7P8Abh9UUaJiGEEEKIErRgEkIIIYQoQQsmIYQQQogS2qQPE4cTF/micLZPG0JcK+xX\nw7Z/9m0C4qzGRRnBOwq82zuHj1ofDM7AalMOcKoGtotbnwyGz1n/B+6Xoh2r//WvfwX5Rz/6UZCt\nn4TNJtueGDp0aHT861//Osh77rlnkDkbOwDceuutQbah4xtttFGQuW9uuOGGqBz7F7IfDWcKB2I/\nDX7G5s6dG5XjbOHMvvvuGx3vuuuuQbbfvzVSa8i9TXPyzDPPBPmSSy4J8iabbBKVO/TQQ4PMmd6B\nOOUH+7FNmjQpKnfllVcGmX1R2J8NiH3LeFf7n/3sZ1G5Aw44IMh2PHZEeNcL9vnkNCBAPM+ee+65\n0Tn2T2NfReu3xu9anquLdlRgP09+JwDAIYccEmTu15kzZ0bleA7o3bt3dG733XcPMs83N910U1Su\nsZ4VaZiEEEIIIUrQgkkIIYQQooQ2aZJjc4rNDs1qQ1ZXXnTRRbn1sQrRqrVZPcxhldYUwWpJLtdR\n4XBXzqRt1bKcJZZV9rYsq3Y5PQAQ9xnLRRnXi1IdsBmWQ945uy3Qvk2vdgPbl19+Och8XxcsWBCV\n4/FnVfVsVuU6rAnthRdeCDKbEuyzw/3GaQo4CzUQm8s322yzIFuzEH/n5557Ljpn+76l4HHV0B0F\n2LzG6VAauuEwb8bMsmXWrFlB/uUvfxmdmzp1apDZ1M2mYFsHh4fbjZ752SgyGeU9QwCw2267Bdmm\nmWgM+HnjjWOBODUGz33WvMxt5v5j8zIQ72BhUwLwnMlztX2++D7y/WYznm0jj3k7fvkcv9OtaXiX\nXXYJss0Iz+3gzXzZbQeI5xR+p9cXaZiEEEIIIUrQgkkIIYQQooQ2aZLj6CmbbZnVuaxuLYp6KYqs\nYhU+q4Ot+WjhwoVBzttIsj1j+4HvKfcXZ1y12A0+11prrSBzNm9rksvboNVGULI5g6MmbKbpN998\nM8hF5tWOZJLjqBi+3//85z+jchdccEGQeewAcWQV3zs2jwLAEUccEeRnn322ahuAeDzus88+QeaM\nv0Cstj/jjDOq1g3Ez599xjgCib9Hc8PPI5tJgfg+8qbkdk7jTVXZTPL4449H5fg72/HNzwCPdWvu\nYXMNm3uGDBkSldtjjz2CzFnbObISiM0uHCVp52Puv6INovkcf19g+Y3YGxvObs1Z5oF4zmTsxtIc\nGcbvoKVLl0bl2MTH8yoQj6Pnn38+yHbu4/vD5rSiuZ+x34mf0W233TbITz75ZFSOM8QXvZ+LdoMY\nNGhQ1TbVF2mYhBBCCCFK0IJJCCGEEKIELZiEEEIIIUpokz5Mw4cPD7LdlZjtqWwL51BxS9GO2JwN\n+A9/+EOQrb8N2+obGp7blinKvs2+C9bvgmGbNhDbvLl+m2Gbw1+LUkTwOfZj4N26gTjc3PryMGwz\nL9oRvi3y9NNPR8f8THMous3Kyz4WEyZMiM5xuDD34cMPPxyV22qrrYLMz4v1MeF2jBgxIsg22zT7\nq/Hu5taHiZ8Duys6+4e0pA8Th19b3xFOp8D+J3Yc8PzJu9Nb2L/JZkpmPxj2j/n2t78dleMQ7hXZ\nJb6OH/zgB0Fmf0jr21O0AwT7ttqUIkxT9/PBBx8cZPvO4FQbnDrHphVgf0v2Z+J0LfYc+ywBcUZv\nvo/2ueE62F+OnycgTmnAY3Ts2LFRufvvvx+1wN/ZvncZfiabalcGaZiEEEIIIUrQgkkIIYQQooQ2\naZLjTSGvueaa6ByrFFlla80DvIFokVqWw1954z+rruQ68jb77Ejw/WH1LW/8abEmubzM0DbkO8/8\nZdX03KaiPmezhzU5MXkmvqI2tRVsaP4OO+wQZA495iy8QJxSY/r06dG5zz77LMh52ZaB2LzJzw6b\nxWw5vv82zQSr57lvbYoLNi3YMcxmp5aEzUR28+COBpv62yIcEt+3b9/oXJ750pr+2Sz7+uuvB9lm\n4L/nnnuCfPTRR0fnOIs9Z8hu7M2Nv/71r0fH9957b5C32GKLINt3K8+ldtN0njvYtGg36mZT3oq4\nzEjDJIQQQghRghZMQgghhBAltEmTXJF5hr3hudz1118flWOTnDXdMF26dAkyR8LNmTMn97o2I3FH\nwJpB2OTC0WR2k0zGZutlFSubc6xamlXHRdEx/Dywuc8+Q6yWzssiDtRu4muL8GaoQJwpl8/ZCENW\nhdsM6mxmYJOX3SyXI7w4osdmHmY1+zvvvJNbH/fn4MGDg8zPFBBnlbbjmyOVOFpIiIbCz5E1D48f\nPz7IPKfZuYpNtMOGDQuyjSY7+eSTgzxgwIDoHI8Djg4tci3h+ZNlIJ4L+d1q5wqOguas7WyeA+I5\nuGhDYP7ONirObsbbUKRhEkIIIYQoQQsmIYQQQogStGASQgghhCihTfowMdZWyfZU9iWyu2g3BA7h\ntJmQ2c5qbbodAesPwj5MbMcuujfWN4R9xthfyPY5ZyTmcuw7BcR9xO2z4cncDusPw/D3sjuit3Xu\nuuuu6Jj9En7/+98Hea+99orKbbPNNkG24cFbb711kDmT8XbbbReV43Brvq/2+WDfDvZ7sH5ynOqA\nUxOceeaZUTlOIWH9r84+++wg9+vXD0I0JpyyptpxHa+++mp0zPMp70pgs+LzWLSpUnj+5LnQ+kvx\nfMrh/TZMn+dZnjd4PgeArl27Vr2WnUu5DvYltLAPrG3TwIEDcz9XH6RhEkIIIYQoQQsmIYQQQogS\n2rxJbuedd46Ob7jhhiCzWq4xspayKt6qBlmtWbQRbXvFhtVbc1gdNgMrb5BsP8MhpGz+smkg+Jjv\nfVGof9GGy5tuummQX3rppdxy7dkk97vf/S465szfbBK1qm42BdiUDGwi53BouzE2hx/zfeWNRoE4\nFJufHWvOYFM6m46PP/74qBxnLbf9aTOaC9EScHqPIuyGuKJxkIZJCCGEEKIELZiEEEIIIUpo8yY5\nzmAKALfcckuQ2exiowZ4o0Kb+TQP9sK3WVBZhc9ROR2FWk1yNuqMsyvbOvLMnDbSLi+ruDWr8Lmi\naD2OAOHrWlMrmwyLMoy3RXh8ALE5je/DJptsEpXjDMVjx46NzvHGy2xeu/baa6NybO7maLoZM2ZE\n5djUxvXZLOWLFi0KMmf4t5v5crZwG/XJ8wdH9wghOg7SMAkhhBBClKAFkxBCCCFECVowCSGEEEKU\n0OZ9mOwOyByuzOHP1ieBM3/X6sPEqQmszwr729hrdURsSHkd1neIw2TtTtS8mzX7H9lyfK7o3tvP\n5bHWWmtVba/dAZzTCrS3Pv/oo4+iY/b3YXnbbbeNynE2b04ZAcSh+dOmTQsy+0cBwGGHHRbkF154\noWrdQOxLdcQRR+S2iTN/77333lXrBuL5wn7/jpi9XwgRIw2TEEIIIUQJWjAJIYQQQpTQJk1yHH5u\nw9f32GOPIN96661Btpm+b7/99iCzCaAIDje3WYe5Te0t63MtcIg3kG/+mj17dnS80047BXnWrFnR\nOc4KvsYaawTZpm1g8x+baazZlMvlmQzttZYuXVq1bmD5jOPtCZs2g8P7eQPQNddcMyp33333Bdne\nL+4P3ohz6NChue3g+m32Yk59wKb4bt26ReU4XQA/UzyegTjlhf3+9vkWQnQ8pGESQgghhChBCyYh\nhBBCiBK0YBJCCCGEKKFNOmGwj5D1ldl3332DzNuksF8KAMybN6/e111nnXWCbMPI2a+Gw5g7CtZf\nKG8rDesLwiHgdmsU9jvjOuz9ZV8UfjZsaDj7pfC2ObZNHL7eo0ePILMfDxBvC1LkE9UWsf5CO+yw\nQ5BffvnlIK+yyipRuffffz/I1m+Q/cEmTZoU5C5dukTlxo0bF2QO9bfpPyZPnhxk9l20/cR+c4MH\nDw7yyJEjo3IvvvhikDt37hydGzhwIIQQHRtpmIQQQgghStCCSQghhBCihDZpkmNzioWzCXMWcN5t\nHIjDmjnr8BZbbJFbN6vpbdZnNk1wiHNHwaZ34GNOwWBNmQcffHDTNozYYIMNairHZkI2CU2YMCEq\nx2Yra/5r6/Tp0yc6Hj9+fJA5/N6Oxeeeey7IvXr1is7xmGEz2frrr5/bDjb12mzbfMzmVjs22UTH\nZl/OJA/E6QfsDgI2lYUQouMhDZMQQgghRAlaMAkhhBBClNAmTXLW/JMHmxWmTp0anWMT2gMPPBDk\nIpMcq/2LNuNk1X5HYc6cOdExR0SxOfTcc89ttjY1BqeddlqQ+/fvH51js67N7t7WTTg2Su7SSy8N\nMm9cbTnqqKOC/MQTT0TnOKKVTZ3WVPraa68FmcepNbXxMZsGiyJYhwwZEmQ2H9rjfv36RedqnXOE\nEO0XaZiEEEIIIUrQgkkIIYQQogQtmIQQQgghSmiTPky18vOf/zzInLEZiH0jbMbfPA499NAgd+/e\nPTrHqQRGjx5dr3a2B+zO75z5mtMxjBo1quY6OQS8pXxIvvWtbwXZZq7m7OPtjZVXjqeGgw46KMh2\nLDHDhg2rKluOPfbYIG+zzTbROX52ODWB9Svq2bNnkIcOHZpb7utf/3rVNtjrsh9U7969o3PyYRJC\nSMMkhBBCCFGCFkxCCCGEECU4u+GpEEIIIYSIkYZJCCGEEKIELZiEEEIIIUrQgkkIIYQQooQmSSvg\nKu5CANsBWB3AVgAmZaeu9om/vimu2VBcxY0BcDeA7wNYFUAPAOsBmJEVOcUnfnoLNQ+u4m4B8Bef\n+Ptbqg0riqu4ngB+C2A4gLr9ZX7hEz+ugfUdAeAfPvFfVjmXAPjIJ/53ruIGZ9ftA+BjAJ8A+KlP\n/DMNvO53feL/5irudPZcANMAACAASURBVAAb+MS3rX1eynCuH4CZWDZeVwEwB8BJ8H5JzqcA5x4C\n8EsAXwD4JbzfpSmbSdddCcCdAP4HwGQAvwbwNQCfAegM4Bp4fymcG1W1Xc71AHApvD+kSt07AXgb\nwJcA/gZgH3j/wXLlWhmu4vYB8P8B+C+AtQDMAvADnxT0X231rgzgc5/43PwKruKOBrC7T/x3q5z7\nHoBtfeJPaQ1tBPAHAOcD2N8n/osVue6K0lT3w1zjFwBW9ok/p5HqGwPgbp/4W1zF/QjAtwB8CmBd\nAHcBOM8njecg7SrpHNOQd0ZjvkObRMPkE/9Tn/hRAA4DsNAnflT2r7Utlg4BsIZP/C0+8Xtmbb4A\nwAPU5hZbLGX8AMBlruLWLi3ZCnEV5wDcBmCST/wWPvG7ADgRwN9cxQ1sYLUVVHl2XcVtB2CPbLG0\nBoB7kS7St/KJ3xnpS/1uV3HrNOB7rATgPADwib8EwK6u4nZoYPtbMwvh/ajs384A5gNolEm2CTgT\nwDR4PwnA4QA2AbAzvB8FYDcAR8G5/rmf9v7tqoullGMADID3swFcB+DCRmx3k+AqblWki7tDfeJ3\n9YnfDsBsAMe1cLt6I10Q/Ki1tNEnfgqAZwD8qDmva2kt96M+8HvTVdzOSMfeCJ/4XQHsAGB7ADu1\nZBsNjfYObfbEldlKtz+Avkgf1g8A/BnpC3BlAD/ziZ/oKu5aABN94q/KPueR/uL9GtJFzcdINVin\n+sQ/6SpuVwAJAAfgcwAn+MTPchU3G8BNAAb4ZLnJ8RwAx6KEbHU7Fam2bDcAeyN9eX6c/fu+T/z8\n7Fq7+8S/6irpr1qf+F1cxZ0G4LtU/rs+8YtcxZ0C4NvZ934JwEkAuiP91TwdwPM+8b9yFXcXgOMB\nXFJ6g1sfowF4n/g/1f3BJ366q7hNfeIXZwuRSwBsA8ADmOATf66ruK8gfS6GAFgNwGSf+FNdxVUA\nDAIw3lXcgT7x79G1fg7g/zL5OwCm+MTfQdd9KLvuUldxawH4C4DeSJ+r63ziL8/+fh2A9QF0AnCz\nT/xvAIwB0NdV3P0+8XsC+B2AnwH4ZuPerlbHI0gnHMClzze8fzVXa1OHc4NhxjWAxQDGwvtNsjK9\nATyBVAP4LQCnIB2/CwEcD+8Xwbn3AVwNYCV4fyrVvzKAnwCoy465PoA1AawE4ItMI/bVrGxfACvB\nucuRjuFPAewHoAuAifB+Izh3bfb3TQBcCeAQANvBuTMAXAPgF3DuPHi/sP63sNlYA6mGYq26P/jE\nn1Unu4o7EMBPAfwHaZ8c6RM/O5vfxiF9yQ0GkPjE/91V3CZIX+YfA3iQ6ukO4PqsjnUA/N4n/rqC\ndv0YwJU+8Z9lP1ZaSxsvBvCCq7iLWlDLVNZnswH8HsA+SN+bP/SJH+8qrg+Ay5A+82sDONsnfpyr\nuCEArkCq7e0M4Byf+Pv4gpmW7TAAXwewC1bsvbk+0vl5NQBf+MR/CmDP7Dr9ANwB4D6ki6hOAPbz\niX+z4H1dtf9N+68BMMsn/vzmfoe2lA9TfwC7+sQ/DeBSAJdn2p0Tkb6sijgdwMXZavZoAD1dxa2J\ndHI+yCd+ZFbn7+gzr9hOz8xEPZH+yqiFD7O6VwNwFYBvZW24B6nmoog61e9IpB3WK9OGHIh0Zb4j\ngCVIOxQANgVQ8Yn/VXb8ANJFWltkMwBP2j/6xC/OxG8jfR52BjACwJ6u4kYiNYs+5xM/wid+++zv\nw3zik+xzo3mxlC28dgNQp3Ytu+6pAJb4xI/IPneWq7gBALoBuC3r250BnO0qrjPSwb0wWywBwHgA\no7Prtk9Sk9dBAB5twKcvBXB5pu1Jx7X3LwD4BM5tnpX5NoAbAfRCutjdPVuAPQTg7KzM2gDujhZL\nKV8FMAfeL8iOr0M6NufBub/BuaPhol+UmwL4BbzfAekEvVeVNq+Vadb+jvQH0o/g/QR4/zmAx5Au\n/lstPvFLkT6nU13FjXMV9/NsQVHHusg0GUjdEE6mc2v7xO+LVLPx0+xvCYAx2bz1HJXtBeCPPvG7\nAdgf6cKjiL2RantbVRt94t8F8AaAbUva32TUcD8A4JNs3vkl0nkLAC4HcFH2/Q4AcFVmkuwB4Fyf\n+NFZ2f/lilzF7YH0/n0L6Q/FFX1v3ovUjD/PVdwtruJOdBXXhT4yFMC12Tw7FcChJe/rov5H9oP5\nw2yx1Ozv0JZaMD1B9s3tkX4ZZOavzuaGW24A8CtXcRcB6J5pEIYh7cSx2S+RHwPoSp95vEo9vQHM\nq4edta6OwQDe8Ymflx0/hLpfsvlcDeBeV3E/R7oyng5gFFJNyYNZm3fJ2gQA7/nEz6TPzwHQr8Z2\ntjb+i/RXfx7bAxjnE+994v+L9OX8VaQPf29XcZOy+9MTqUYgjw2Q+i/U+ZnUct265+4TAE8B2BrA\nAgBfcxX3ONJfRqsj/RUVkV3nU8TPWXugK5x7KPNLehDAm1imtasP4f7Cp+MaznUB8HcAB2dlDkWq\nHdgRaf/el133sOwYSH+BPlal/t4A5oYj75fC+5FI/VOeQqphfCXTLgHAS/D+nUyeh3RitlSbJ+po\nE2Mw04b2RTrn9AUw2VXcidnpdwD81VXcw0h/bPJ4eij7fw6WPe/DAUzM5AlU9k0Ah7uKmwjgH0jH\nXhFRX7WyNrZ4v5bcD6D6994VQCWbG/+B9EdANwBvAfixq7hHkf445/s3HNn484n/CI3w3vSJ/9wn\n/mCki85xAPYA8KqruLp34rs+8S+Y9hddt6j/j0aqGT4tOx6FZn6HttRecp+RbBcsLvtb+Htm500L\nJ/4mV3H3IVX7necqbgpS1eEbmZaq7Hor2ua89tpz3OYzXcX1BbAvgNsyJ7lPAdzhE29X0P0aqb2t\nhelYtuoPuIobDuB15N/Pw5AunL7mE/+Fq7inGnDdg+wfXcVtg/SXaN51T0eqqdjZJ967inu3ntdt\n6yzMtELVqPp811AWWHZ/bwRwL5y7BsDq8H5qtqiZAu/3z6mrfDw4twoAD++fB/A8gEvg3N+R/pJ+\nBqmJwran/tdp5biKW9MnfhHS+3yjq7ibAVzkKu4qpPPk1j7xr7iKOxmxZoXvj6P/6wIr+MfHL5Fq\nHw7P/ELq5QzfFtrYnOTdD6RaJKD69/4UqYYmmp9cxT0A4Eaf+DGu4oYhdcCuYxDSxdfJAM7N6lih\n92amYV/ZJ/41AK8B+LOruP8FcCRSrV61cVf1uq7iVkFx/6+GdN7ZDenirNnfoa0hrcATyNTjruK2\nArAoe3jex7LV4mhkE3CmklvJJ/6fSFeaOwJ4GUCX7AGBq7gRruK+X3LduQA2akB7XwbQLbMhA+kv\n2icymdu8W9aW9VzqtzXXJ/5yAH9CGkH4GIB9ssEMV3EnuYrbMeeafZE6ArY5fOIfBvCBq7if1f3N\nVdxmSG3bGyG9d3u4inOZSnlk9rfuAGZmi6VtkA721eqqRapOZhYBWNVVXKfs+EYAm7k0oq7uuiMB\n3ILUp4Gfu7WQ+lA9nV33xWyxdABSH4HVkE7Kq1Bda2d/b80+LY3Ncs93AeH+wqXjGt4vgvfzALyL\n1P/ob1nZJ5H6C/XIyh8C575RUv9caguQmuSW7badLqB6A3i1pJ48ov5GGxiDruL2AjCJxgAADEB6\nDzoh/U6zXcWtDuAbWDae8ngR6fwKpPNcHd0B1GkNjgDwpau4orpCX7XCNrZov5bcjyImIjVpw1Vc\nF1dxdb45/L0PRXz//oU0mOFb2VzYGO/NXwL4k0t9TuuCfPqXtD/vumX9fwVSzfFfXMV1RQu8Q1vD\ngukUACe4insQqS3zyOzvYwAclv19SwBLs7+/AuABV3HjkS4+fpGZVL4L4OpMlfc/AB4uuqhP/FsA\n3nIVt3V9Gptd6zgAN2VqwNFYFkV0UdaGewF8lJVfjPRBeNJV3Dik9vQrfeKfytr/UKY2HgVgWs5l\nd0fmA9BG2Q/AIFdxz2f9czFSO/VMADcjHVwTs3+3+cQ/lv19x6z8t5DauP/gKm49pPfiKUdRdpk5\nbzxSlTB84j9DqqI9yFXctKyeMwDslf0quxRAJ1dxjyBV5Z+fOReOAXC0q7gJSAf+37N/bwJ421Xc\n09kCazSA8dl1OwoXAbgabtnzXcApAE6AW25cA+n9PB6peR3w/k2kP37ugnOPIB1fT6CYJwH0gXN1\nqvz/B2BjODcZzk1Aatq9C36Z0389eQDAFXDuoMzBfCekz1erJXPuvQppQMRD2TM/GsD/y/z9bkB6\n325Cmm5jN5dGPOVxPoCTMo3+JlimLfgjgPMzbcYHSO/LDQX13Its8dya2ugqbgOkAQf11V43GkX3\no+SjpwI4MDO93Y1l5siLAFyX3Y+JAN5zqftK3fU+QvquHIPU4XxF35vnI3XQnpy9qx9HavK+rKCO\nqu/rWvo/c2e5GMC1SH/gNus7tEPvJZd1xIE+8UeUFm4hskE9GcBW5J8jquBSJ8CLfdL0eYCyAfpj\nn/iyF7toKpz7CYD14P3ZpWVX7DonANga3p9YWlYsR6aNvw/AFtkPmVZBZjp6P/MhEjXSFt6bTGO+\nQ1uDhqnF8Im/GcB/XMUdXFq45bgCwElaLJXj09wqD7iK+3FTXseliSsf1GKpxbkYwJZwuWr4FSdN\n5nk0lkVliXriE/8GgN8g1X60CrIfV9ugFbWprdBG3ptMo71DO7SGSQghhBCiFjq0hkkIIYQQoha0\nYBJCCCGEKEELJiGEEEKIErRgEkIIIYQooTEzfbc67/HTTjstyNOnT4/OHXnksrQwH374YZBXXjm+\nJWPHjq1a3/775yUljvnyyy+j4698pdHXqNUyFq8ora4vOwhN0ZeA+rOlaLdj87//XZZ+bPbs2UEe\nOHBgldLFnweAlVZalqSb5+phw4ZF5ZxrqiFSSrvtyzyuvPLK6HjJkiVB/uKLZQm811577ajcRhst\ny2t54IEHNlHrVogG96U0TEIIIYQQJWjBJIQQQghRQmPmYWoR9eJDDz0UHV922bKM7KuttmwbGmuS\ne+2114LM6uA111wzKrfDDjsEmVWPq6++elTuggsuCPL66y+3uX1T0uFUxe0YmeTaF+12bP7nP/8J\n8hNPLMvfOmrUqNzP8LumyLR23333BXmvvfZqYAsbnVbRl/Z9nXcfbTk2oa2ySrwNJ5tH2SWF359F\n17J//+STT4K89957B/mee+6p+nnbPusW0wTIJCeEEEII0VRowSSEEEIIUYIWTEIIIYQQJTS5sbAx\nmDlzZnT8m98s21z65Zdfjs5tvvnmQZ4xY0aQ11hjjahcly5dgvzuu+8G2YaxLl68OMhF9t3TTz89\nyIMGDQryD3/4w6hct27dIIQQbRn24bzqqquCvO6660blttxyyyAX+S3dfvvtQf79738f5Fbkw9Qq\nKPJh4hQ2Nn2N9VtiTj755CDze61nz55RuVVXXTXI7MP22WefReU6deoU5KlTp+Zel+F3a1HKiZZG\nGiYhhBBCiBK0YBJCCCGEKKFF0woUqd4uv/zyIHPYKgCstdZaQbbh/Rz6/8ADDwT5pZdeisqxiY7l\nfv36ReUmT54c5OOOOy7I6623XlTu/fffDzKHVb799ttRuT//+c9B7t69e3SuSKVaQKsIdxWNgtIK\ntC/a7dj8/PPPg8wuCHY+ZheJY445Jsjnn39+VI5NPOwW8be//W3FG9s4tMq+5H4oMrvdfffdQf7d\n734XneMUO1yf7Ut+N7755ptB5p0ygPg9VrS++MlPfhJkdmlpgt0wLEorIIQQQgjRVGjBJIQQQghR\nQqvN9H3SSScFmb3ugdh0Z7OCclRbr169gsyZuIHYhHbRRRcF2aqKd9tttyCzSpIjBmw7WH7rrbei\nckOGDAnyGWecgUagVaqKRYOQSa590SHG5vbbbx9kztgMxBFUbLqx5h52ceD6/u///q/R2rmCtIq+\nrHUz98MPPzw6/uc//xlku1ku727BUXe2LzfYYIMg87ph4cKFUTl2cWG3m08//TQqx64rHF156aWX\nRuUOPvjg3DY1MCu4THJCCCGEEE2FFkxCCCGEECVowSSEEEIIUUKryvTN/kccZtq1a9fcctamyf5O\nbFu1u2i/8847QWb7bv/+/aNy7HP00UcfBdlmN+VwTLbh9ujRIyo3b968ILfmjKZCCFELPN/Nnz8/\nOsfzMfup2J0SeL7ntDGidh588MEg33bbbdG5vn37BpnfVcDy79A67Dtu9uzZQR46dGiQrW/SkiVL\ngsy+atZvjfuZ23TsscdG5ThbPKewAGJfqqJM8o2FNExCCCGEECVowSSEEEIIUUKrMsnNmjWr6t9Z\nXQvEKkBrxuKQyTfeeCPInEYAiDcWZDOczczNakhWL9ss3awOZFPbBx98EJXj77J06dLo3Prrrw8h\nhGhLsHmGs0YD+dmn7d95Xtxwww1zr9XcJpjWRlEW7CuuuCLI9r3IZjfrCsL3lNMW2D7iY870bc2r\neX1kUxhxm7hu+x05/c6dd94ZnWvuZ0AaJiGEEEKIErRgEkIIIYQooVWZ5DjCglW01kzGkWfW1DZj\nxowgs7e+zbjNkR1c7tlnn43KdenSJcgcMTd37tyoHKs5OaOtjZJj7IbAO+20U25ZIYRoKaw5hU0h\nHO1kMy/nmWesS8OiRYtyryXy4Xs1ceLEIHP2biCOQrNmLK6Dy1lTG5v52HTH0eNA/G7luov6lc1z\nnTt3js498sgjQZ4+fXp0bvjw4bl1NgXSMAkhhBBClKAFkxBCCCFECVowCSGEEEKU0Gp9mNh+am2k\nbO/kHZQBYM6cOUHmjOA2yyjX361btyBvuummUTkOd+Q6bKqDwYMHB3ncuHFBtjtDs0/UCy+8EJ2T\nD1PjYm3m7MfWq1evINvn6+KLLw7yySefHGSbgXjVVVfNvTb7tCmDu2jrFIVvv/rqq0EuCnvndDA2\n3QrP4zyH16cdHZGbbropyO+9916QrR8Q+xzZe7jOOusE+eOPPw6yzQjOcxrPhVw3EPczvzOLfKeK\n/s7HF110UXTu2muvrVpHUyENkxBCCCFECVowCSGEEEKU0KpMcmzm4tB8VvkCwCeffBLkfv36RedY\ntcvmNA5bBWJzHashrap4wIABVeuzZhbO2j1p0qQgDxs2LCq35557Btl+L1EbRSHOr7/+epBPP/30\nqNwPf/jDID/zzDNBPu2006JyrOb+97//HeQbbrghKrf//vsH2aat4LDe73//+0G2JuSOmrn4sssu\nC/Lzzz+fe66IjnrvWhu86WufPn2iczxnWtMNw/1n062IfB5//PEg8zvJmtMY60rA71P+nM30za4w\nvJGyhcclm/GsuTZv/NrNgPl7Pfroo7nXbQ6kYRJCCCGEKEELJiGEEEKIErRgEkIIIYQooVX5MPE2\nJxzqzf5MANC/f//ccwMHDgwypw6YMmVKVG7hwoVB5t22bX1s02Vbr009z226+uqrg/zzn/88Ksf+\nUjacXdRGkb8K+5zdcccdueXGjh0b5D322CM6x+keOES2d+/eUbmHH344yDZtBWO3i2hJ+BkG4m0M\nisoVpVBgilIo3HXXXUHm3c45rQcAHHXUUUH+3//93yDb+5/3HNjd2Gttn6idV155Jchdu3YNst1O\ng+Hwddt3fGz9AUU+7IvJPkLWz5PHr7337DvM85j1JcrrI1tf3lzx2Wef5Zbja9m28zNl37vNjTRM\nQgghhBAlaMEkhBBCCFFC67EVAJg9e3aQWQ1n1ejf+c53gnzBBRdE5zgUklWUbO4D4jQDCxYsCPK0\nadOicptvvnmQ81SIQJyOgFMdWBUim/y0K3fjM2HChCC/9tpr0TkOeeYMsTa7+2233RZkzmhrVc+s\nlt5ll12ic3ztO++8M8jf/e53o3JsPmoO0x2bu4A4k/nIkSODnGeqWxE4XcB2220XZKvC32ijjYLM\nKR6s6e7AAw8McqdOnYJs5wu+xw0dc0pbEMOmIDa12PuUF6Zud0rguXrevHmN1s72Ds8z/Nzb55xT\nOtjwfp53ikxjXI7rsCkM7Lsx77p55axJna9rXWaaG2mYhBBCCCFK+P/ZO/N4u6bz/39WqanmKRIi\nxog5EUGCDGY11UxpDdUWRanpp6XbRunEt61SWkPNiqKGEIJoREQMIcQsiSlChJjVsH5/rH3X/awn\nd+9zcpN777nJ5/165ZVn373O3uvstffa66zP8zxLAyYhhBBCiBo0lCTHEgcvUvvBBx8k5TiCZ801\n10z28TQfZ4y1HvocscFSIEfvAMDmm2/e4mfsApEsCXC2aZs5nKMQ7PQiR9B1ZDRAlWzBU+5czmbx\nrTcaidvLTu2WHcNKOOecc060+drbyLUVVlgh2hdffHG0+/Xrl5Tja7/VVltFe+mll07KPfTQQ9Hm\nqEsglfn+/e9/R9tKcu0hw3E0ppU7OJKQ7z+boZ6/O18f2+6vvfZatC+//PJkH19/fr5ZsgSA3Xbb\nLdr87A8dOjQpx883R0faqMcePXpgVrGyQJmkMa9G3Y0ZMybafD3sdeM+oiqKi/uVrl27RtuuhrDG\nGmu0ssZzJ1OnTo02P1P1ymRA2i7cDlZe5WNULebLx+NyNnM417FeyZvf1UDqamMXHG4LNMMkhBBC\nCFEDDZiEEEIIIWqgAZMQQgghRA061IfJ+hXxNuug1p+Ht60fEPs8sO+C9YPiVAJ8jD59+iTlOPyV\ny1m/CNZSF1100Whbv5dp06ZFm306AODtt9+ONvtktDf16slV5cr8oOxn2IenXn8eTgkApLr2+uuv\nH22bSX2ZZZaJNvtJWL+eI488MtrsI9CrV6+k3DbbbBNtq58feuih0eZ77eqrr07KWZ+mtuCZZ54p\n3cfX6Jprrok2p9MAUr8xtq2Pyfjx46Ntn+8tt9wy2hyWvv322yfl+Pnmc+2www5JOb6uL774YrRH\njx6dlGN/snXXXTfaG2+8cVKOM1Zb36R51VepDM6Gz3219S/kPrNqRXrex74tnP4FkA+ThftZvkft\ns8fX1/qZVfktMeyPxP5S7Ptot7lO1t+R4TpVlbO88MIL0ba+qG2BZpiEEEIIIWqgAZMQQgghRA06\nVJKz0/mc3ZunZWfMmJGUYznFyjg81cvZiu0xeKp3yJAh0eapfSCV0BgrE/LUI9eB0w3YbTvdbFMQ\ndBStyYbcWsmCr9VFF12U7HvyySejzVPABx98cFKOQ/+vvfbaaE+YMCEpx/fUgAEDSut0wQUXRPu4\n445rsT5AKsNx+gkgzSrO9mOPPVZ63raC5ejp06cn+/j54WfklltuScottdRS0ebraO/v/v37R7tn\nz57JPpZuOG2BfcZ4Sp8lbVt3fgbLrjeQyuUjR46M9tixY0uPt+SSSyb7WILnjONWpq1afHZugmXw\nqhUQeJvvNSsZlX2GF/kFgE033XSW6zo38eabb5buK0v5MqcoSyNj25z7B5vduwz+jJV1q77LxIkT\noy1JTgghhBCiAdCASQghhBCiBh0qydnItTJJzkbscHSZjXDiCDWO0LDn4ulLPp6dAuZ68NSgXcyX\npyU52sZOSXKWcq4rMLNs2FG0ZqFRG3nBEh0vZmvbi2UuG/F40EEHRfvBBx+Mtl0sl7N7c8Ztu8Cn\nXby1DP7+HIllvyNLRzZyb7vttos2t7OVizgztt03p+AFhK3kfMghh0SbF4228hdfS5arbDZ1Lvf0\n00+X1omviZW3Wa7hyFE7Vc+SKH/GPpss59qo1bK6c7sD6QoAXN+zzjorKcdRj5yxfG6D79u11lor\n2jbDNMPPlXWlKMukzlGXIo0Kq8JGmtUrjVXBfTq7k1h3DO4TuB5VdeL3p33Oq6LmeHWQ9kAzTEII\nIYQQNdCASQghhBCiBhowCSGEEELUoEN9mKyfQFlKAJsRm3Vy66fSpUuXaH/xxRfRtpo5l7v//vuj\nbUPROeM2h1bb83J9WdO1emxZRlsg9W9qFKpCOllbrkorMG7cuGjb68YrWJ944onJPs66zn44zz33\nXFKOfcbYJ8rWnbNsH3744aX1ZbiNJk+enOzjsHnrZ8dh+T/4wQ+i3bt376Qc+2i0lQ/TbbfdFm1O\nyQGkvjns+2MzzXPd+DnlzwPpM2d9vhj2KbS+e3xPsN9ZlQ8TY32i+FnnOtnQdva9sL5ZfG34O1t/\nv/POOy/ac5MPk21Lfo7ZF6UqXUDVCvd833Bfwj5sIvXXrMK+77gvtNee26+qHMP+xvbe4Hau97xs\n23JVPkzss9oeaIZJCCGEEKIGGjAJIYQQQtSgQyU5m+GXswazFLLqqqsm5ViSsaHoPLXLUt7rr7+e\nlGP5i0ONWVoDUimI99nspmUZfq3sxuWsZGTlqo6C61Ulq/C0rw3lfuWVV6LNUhYvjgukEujJJ5+c\n7LvhhhtaPH737t2Tcryo7gMPPBBtm/mVJWCWYTlTuGXDDTeMNi/ECwD77rtvtHfZZZdk34477hjt\n73//+9G22dzbo805o/7qq6+e7OOM27xIr03/UBZWXzVdbvexXMO2nYLnZ4Sn3O0zxs8jy3gW7me4\nTrYtWCa0+zgNAreZTUNi05fMLVg5muH7wS54ze1SJtXYbZZDOX2BmNmNpQz7THE/3tpVGcoyfdu2\nLFvM175L+HlmSc6+M6uebZv+pK3RDJMQQgghRA00YBJCCCGEqEGHSnI2KoynYnkfZ+oF0mnJJZZY\nItnHU+c8PW6jBnjqmKW7qgzHHLFhF+csWzi33sUogeoIk/akKiNvGXZa9j//+U+0OTutlaA4go4l\nISDNJsvSDEd9AcCxxx4b7REjRkQ7z/OkHLffmWeeGW0ryXHUVlV28KoIHq4TYxfwtdF1bQFLJo88\n8kiyj+85fv7svchZzTk6zT6bLJFXybksC9h7jLd5qt5GyTE8bW8z6HN/wd/Dysh8b1pZgOvEz7C9\nn+09N7fw/PPPl+6rklO4zbicvTe4/+BrXbXY7LwIuzpY+Ppa2Y3fp1USVxUsw3Xr1i3adhF5fiaq\n3iXcx3AEuj0eJkyLHAAAIABJREFU19ceQ1FyQgghhBANhgZMQgghhBA10IBJCCGEEKIGHerDZGH9\nm/0ErB/Qs88+G227CjNvsw+TDbNkzZTPa/VdDl3mMEirEZetsG4zJjNWj2X/io5k9OjR0b7ooouS\nfeyjUuZrYvdxuggbFsu+YHblafa3ueuuu6LNqSMs7I9mM0gzrJNvuummyT7W+7fddtto8z0DANdf\nf320f/7znyf71lxzzWhvtNFG0bbh2X/+859L6zinOP/886Nts4lzSg0Ov7c+TOyLwvep9fnjtrbP\nLd8jfH/Y9AN8/fm5tT5MXMeyY1ft4+8OpPeizfTNZddaa61o8/0xN1OvL5Ftc6YqFJ37YG7zMt/Q\neRWbiofvZ76G9pniclWrN/A+W46fI+6rq1KLVD2X3D8PGTIk2nfeeWdSju8p+362/k5tjWaYhBBC\nCCFqoAGTEEIIIUQNOlSSszJZWWiwXWx1wIAB0e7Vq1eyj+Uwls1s+CFPD3KIqw135WlOnkK005Us\nF/AUpQ2z5TrZaf+q6ez2hEPdDzvssGQfX8f3338/2jZFBG9zmKktx9fj1FNPTfbxdeRQds7sDaSh\n+iyrHH/88Uk5XqS3Srr7zW9+E23OeG0XruV7wO5jKZezxdv7tT3anKXEs88+u83PJ+Y+rDRWrxTE\n93fV4rsMyziN0ic2CrYd+J3JUnmPHj2SciyVjxkzJtm34oorRpvdHaraqGofw21u3TbsKh1NWNcH\nlt2srFeVuqQt0AyTEEIIIUQNNGASQgghhKhBh0pyVtbi6Tae8rPRLEcccUS0X3311WTfE088EW2W\nYMaPH5+U40Vf+fh2io+lG57+5MVIAeCHP/xhtDfbbLNoW7nH1oOxU5YdBUtIW265ZQfWpGPgiDwh\nxMxRcmWL6tr+s0y6qYpu5uPZiNiqiOZ5ASvJsfTPEau9e/dOyvG71Wb75/dwldTG5aqk0rLFd+2x\n+Xgsw/Xs2TMpN3z48GjblQWqIvTagsZ4QwshhBBCNDAaMAkhhBBC1EADJiGEEEKIGjRUWgGGtfAt\nttiitJzNpF2WWXvQoEGlx6jSzFkjbg3sRwVUf+f21mOFEKIePvzww2SbVz2oyhzNaUOqfGXYh6nK\nl5P9dLp06VJR47kT6yNW5sfFmbOBdHUMS72+s9xmnKbArlDRmlQQnCrG+imxD5Ota9W91xZohkkI\nIYQQogYaMAkhhBBC1KBDJTme1gXK5aqq8FE7RVnvFDDD03ytleDKzsVTl7Z+VoKzC54KIUQjYLMy\n1xvSX7aYq128vOx4dqUEXlB9XpTk7DuzLNP1brvtlmyPGzeu9Jhlmdqt/FW2eLJ9b/HnuFzVoum8\nUsbAgQOTfeecc0607XucV4BoDzTDJIQQQghRAw2YhBBCCCFqoAGTEEIIIUQNOtSHadq0ack2a6ms\ncdsViuuF9U4bfljvasv1wvou1936MLGOa/fNi6n+hRCNz+eff55s8/JJ7EdjfWq4X+Rwc+uLw30m\n+8SsuuqqlfWY12BfH8uiiy4abRua/8knn0Tbvvu4jepNbcNLtFhfJ74HqpZGYdgXyd4bfN/Y+pX5\ncLUVmmESQgghhKiBBkxCCCGEEDXoUEmuKmspT8N17dp1ts9VrwRXJd1VpSkok+RsmgKWHW1GVCvR\nCSFEIzBq1Khku6yvsv0db7OcZN0PyvpZK8G98MIL0d5www1rVXuug6VQIE33UCVX8vW2kldZlnWb\n+oHbiN939l3I23w861qz0EILRZszydus8oytO2cIbw80wySEEEIIUQMNmIQQQgghatChkpz1rmfP\ne87oWuUJb73m7TTirFIl3bUmss5OQ/J3sdPSdrpVCCEagcMPPzzZ5uzLHNXGfTgATJkyJdpLL710\ntG0Gb5brqhZ2XWqppWal2nMdQ4cOTbY50vyzzz4r/dzLL79c1/GrIh5ZKuX3mn0v8juY3U6qFsp9\n+umno33aaaeVnrej0QyTEEIIIUQNNGASQgghhKiBBkxCCCGEEDXoUB+mQw45JNl+/PHHo80+TH37\n9i09RmuzgM9prD9WEzYlAm/bui+55JJzvmJCCDGbnHHGGcn2+uuvH+0JEyZE2/rR9OzZM9q9e/eO\ntvVNWmSRRaLNqQP233//VtZ43sBm9C6DfcQ4nB9IfWnZtn5m7EvEx6jydWJsOfZV69WrV2ndGwnN\nMAkhhBBC1EADJiGEEEKIGrhGCtkTQgghhGhENMMkhBBCCFEDDZiEEEIIIWqgAZMQQgghRA3aPybf\nuVUAvABgdPGXbwOYDOBIeP9ByacA50YAOAvAVwDOgvdbtGU16bzzAbgdwJnwfjSc2wHAaQjXbj4A\nzwE4Dt5PqzhK2bEXAbADvL8Zzt0E4O/w/p45V/m2weVuRwCnAPgawHcATATwU59VtF99x50fwJc+\n86Vr0LjcHQxgG5/5A1vYdxCAjX3mj26EOgL4C4AzAOzsM/9VWfn2oK2uhznH6QDm95k/dQ4d7zIA\nQ33mb3K5Ox7AngC+ALAkgDsA/Npnc84J0+Whj/GZH96Kz94E4O8+a7zn1+VuFczc544EcIbP/Kcl\nH5vTdegK4N8Adin+XwrA+wg/2qcjtOXT5UeYrXMfC2AZn/nTahbuxBTX+A8A1gfQtEbN6a25n4vj\nfR/A9T7z37SwLwPwic/8H13uehbnXRnApwA+A3CSz/wTrTzvgT7zVzdiu3XUDNO78H5w8W9zAG8C\nmCOdbBvwCwBPFYOlDQD8DcCP4P2mAPoBmADghlYeuw+APQr7pwAuhHOLzm6F2xKXuwUAXA1gX5/5\nIT7zmwCYBOBHHVyv7ggDguMbpY4+848CeALA8e15XkujXI9ZweVubwALF4OlzQHsD2Cgz/wQAJsB\n2BTAgI6so+GnAC50ecM+v+/6zA/2mR8MYGsAiwG4th3PfwmA3Gf+vWL7+KI+AwFcCGCYy12Ptjix\nz/yfAAxxudusLY7fCLjcOQC3AhjtM7+hz/wWAI4AcLXL3eqtPGyOFsYILnebANi2GCwtDOBuAJf6\nzPfxmd8cYWJjqMvdEq34HvMB+DXQmO3WGFkfgf8idDiAc5MAbAPvX4Zzg1E1m+RcTwAXITTq/AD+\nH8Kvlpvh/VpFme4AHkEY/e4J4GgADsC7AA6D9+/BuQ8BXApgPnh/DB1/fgAnAliv+MtJAH4H758H\nEDJ0Ofc7hAe+5fp4/xCc6wXgYoTZscURBof/Lc65FJz7Pbw/Cc7dAeAwAH+a9UvYbiyMMEMRVwr2\nmT+5yXa52x3hOn2OcA1+4DM/qfj1PhzhJdcTQOYzf43L3VoIL/NPATxAx+kC4KriGEsA+LPP/JUV\n9ToBwD985v9XPKiNUsfzADzrcnduB84y1WqzSQD+DGBHAKsCONxn/j6Xu5UR7u1FACwK4Jc+88Nd\nPvP97DM/jE9YzLLthzCjsAWADOG5+xLAj33mJxbn/ReA1Xzm9zZ1PhXAoYW9NIAFi39f+cx/AWC7\n4jyrALgNwDCEQdRiAHbymX/L5W5IyXlbbH9T/8sBTPSZP8Pl7mgA+xRlnwdwJIAuCDPP4wE84zN/\ntss7xfMLn/nPi1/vL7ncrQNgEwA7I8z6nAfgYYR+bDmE+/pcn/lri+v5W4TnYCEAxwB4EmEwtBYA\nD+BJn/mf8flc7voAWNneI1Sfe4rZxJ8BOMnlzf2xz/wxJdd/PoQB31IIM2a3+8z/xuVuX4S+4BOE\ndj/EZ/5VAH9EeD98b/auXsOyNQDvM39B0x985se73K3tM/9+MRD5E4C+CO10v8/8aS5330Jo614I\nz9eY4prnANYAcJ/L3e4+89PpXL8C8H+FfQCAR33mb6PzjijOO8Pl7jsA/g6gO0I7Xekz/7fi71ci\nPNuLAbjRZ/53AC4D0MPl7h6f+e3QYO3W8T5MQfLaA2GKeFY5H8Df4P1ghNH0lfD+WQCfFbNBQHjQ\nrgPQDaGhtykGYCMA/LIosyiAoclgKdAPwGR4/06xvS6AsUkJ77+B9zNK6xNYAcBp8H5rhE7mN/D+\nM4TO5154f1JR7l4AO7TiOrQbPvMzEF5C41zuhrvc/aoYUDSxJIqZDABDARxF+xb1mf8uwsxG03fO\nAFzmMz8IAE/JdwPwV5/5rRA68/NqVG0HhF86DVVHn/lpAF4DsHGN+rcZdVwPAPis6KDOQrhHgTCb\nem7x/XYFcEkhSa4A4DSf0f1MuNxti3D99kToJC8CsEdx/c5H6ASbeMkOlgppoSvC7BwQ2vUFAG+4\n3N3kcneEyx2nOF4HwD+L2YpxAPZ1uVuk4rxV7Y/iZfFxMVjaBMDuCLNb/QF8gDAoAoC1EWZNzi62\nG/75bcJn/ksAjyHINwDQG8B3febvRLgH7i7afSCAM1zulgNwLIDziut2MEIbrQ9gU5/5/j7zAxDu\nMTuzEJ/NCkZTXRZFkGKPqbj+2wL4ts/8lgg/cD4uXv6/BHBUMZN2EoAVi2PeB2DrYuAwNzLzuwmA\nz/z7hbkPwo+hzRHadDuXu0EIA86nfeYH+sxvWvx9PZ/5rPjc1jxYKq7fVgCapOda5z0GwAfFs7kV\ngJNd7lYDsDyAW4t7aXMAv3S5Wxyhn3q36IuABmu3jhowLQfnRhR+SQ8AeAvNI9ZZYVOETgrwfjyA\nxeHcsgCuAbBXUWZfhNmB/ggP+LDivPsV20D4JTKqheN3B/A6bX+N8MtmVuszBcAJcG4kwii/LJ/9\nZACrVBy/ISh+CfRA+BXYA8AYl7sjit1TAVzhcvcgQqfK33VE8f9khF8WQOgkHyrs+6nsWwD2d7l7\nCMD1AJapUa2krRqsjh3erjWuB9Dy9x4CIC9m3q5HmKVZHsX97PIW7+f1UTx/PvOfIMzOdgVwc3Gc\nExBmLpp4uIXqdgfwRpN/ks/8lz7zeyEMOocjvCxfdrnrV5Sf5jP/rKl/1Xmr2v9gADsB+HmxPRjh\nl/YDxXG2KOoHANN95l+gz3Z4O88iSyD0aQDwRDFzB4R2P6L4vncitPuqCDM6Z7vcnQugSzGr8ByA\naS53Q4v76eZigM7YfrRWXbg/HoyWr/8oACu53N0A4IcALil8bf4J4J8ud2ch+BqOBACf+Y8Q/N/4\n3pubqOfdNNxn3vvMf40wQdEPYQDa3eVudHF9u6L8/QSEPu7L4nrWe957AcBn/jOEQfpGAN4BsKXL\n3cMIs8MLobnfiTRau3WUJPduMQvTEuzEuUBJmZbKAuFB8wgzSnfDucsBLATvx8G5HgAehfc7lxzr\nfzXOBYTp980BPJqe1W0G7x+pqM9fAVwH7y+Dc+shOKx2WlzuFil8Ea4DcJ3L3Y0AznW5uwRBYtnI\nZ/4ll7ujkM6ssCTl6P8mp0J+8M5CmH3Yv/AL+QizQGeoY3tSdj0QZpGAlr/3FwgzNElAg8vdvQCu\n85m/zOUz3c9rIAy+jkIIjvgCwGvFL/6WqPncFb8u5/eZfwXAKwAucrn7DYAfIMzqWanTlZ3X5e7b\nqG7/BRH6na0QBmdfALjNZ97OQq1ST90blWIGrjfCLN5ApN/lCwBH+sw/Zj72qMvdMAQ59Ncud4/6\nzP8S4cW3EcIs61iXu8195qfMYpU2B/A4bTfVp8XrX3yHDRF+CO8G4DGXu4185v/P5e5ahFmti13u\nLvGZv3gW69IZGY/mmc+Iy936AF5F+btpP4SB05Y+81+53Nk2r+e8e9g/utz1RZiNLzvvsQjP2uY+\n897lbtaDpjqAjpfkZuZDNP+C26pG2UcAbA8AcK4PgPfg/Xvw/g0A0xD8j64uyo4FsAmcW6Eovzec\n263G8V+nugDA7wEcD+c2jH9x7gSEF2d5fYK/Q9Mv4H0RbhQgvISbVzsMv/wn1ahTh+Jytz2A0S53\ni9GfVwPwMoIW/Q2ASS53CyF0ZAvOfJSECQidHhAiy5rga/Z9AN+43FUdK7ZVA9axQ9u1xvWo4iGE\nqXy43C3rctfkm1N2PwPALQAOAbBnMeX/IoBli4EVXO4Gutz9pMZ5XwewEm2fBeCCQnJpcnBdtUb9\ny85bq/0vRvDL+HshQ40CsGMxIIbL3ZEud/3RMg3//AJx0PgXAPcW/j0WbveFXe4udLmbv5Aq5/OZ\nvwFhBq6/y93GLncH+cw/4TN/BsKgp6c5nu1HbX12QJDdLmphd4vX3+VuOwRftVE+8ycB+BjA8i53\nvwUww2f+CgCnIwQIoPj8ggi+q3MdPvMPAvjI5e7/Nf3N5W5dBP++lRDeTdu63LlCVh9U/K0LgBeK\nwVJfhB88Tc+DR/p+AoD3ACxAfcl1ANZ1IaKu6byDANyEMGsY34ku+C31RbhHugCYUAyWdkXwk1wQ\n5p3YaO3WiAOmcwFcCufuRnDcq+JoAD+Gcw8g+Cj8gPZdgzDiDpEg3r+F8JDfAef+i+Bj8UiN448F\nsDKcW644xnMIo+kL4NxYOPcwQsfd5JBWVp9zAVwJ54YhdEbT4dy5CDNVA+HcZUW5bVBb6+9QCsfN\nSxCcAUcUssbWAH5WaN3XIly3fyGEmm7lQsRTGWcAOLL45boWmmcL/orgO3EvwszNfaiO6rkbxYPZ\nSHV0uVsGIeBgVn+5zTGqrkeNjx4DYPdCehuKZjnyXABXFtfjIQDTC5mm6XyfADgQwYFz4cK+tDjv\nmQAerFHfKQCmFLMWQLj+nyPIiA8gyHhvoCnYouVjfNbSeetpf5/58QgzV/9E6NwvADCikF4HA3iq\n5LSN/PwuV7T9SARH7Q/R7FRvOR3AmsX3/S+CI/dXAF4CcK/L3X0I1+R0hBm/vVzuHna5ux9B4rHu\nDfHZJM4t6vMkgOMAbNfSrFQxy9XS9X8BISJ2ZCEl3eMzPxnhh/LDRR1/gWa/ta0B3FfIUXMrOwFY\nw+XumeKePw/BV+8FADci/MB4qPh3q8/8qOLv/YvyeyJcr7+43C2F0G6POYqyK67ffQiyOHzm/4cg\nk+7hcvdUcZzjAGxfzEyfD2Axl7v/IvQfZ/gQYHEZgIOLe2ZVhPf1NQhuDm+73D1eDLAaqt20llwt\nnDsRwFLw/pc1y87eeZYBMAZAH3jfsNJOo+JCRNcwABsWD3FDUEhHHxY+RKJOigHM7j7z369ZuAEo\nBsZjAPQh/w5R4HJ3J0IUaYfkqSoGWyf4zNf6kSxq4IIj/nk+a/tciI3Wbo04w9RonAegN1zpNPyc\n4mKE5J3qbFuBz/xrAH6HMPvREBQdS180UJ06Cz7zNwL43OVur5qFG4OLEfx+9Py2zGEATi8Glu2K\nCykUHmiUl25nx4f8cve63J3QludpxHbTDJMQQgghRA00wySEEEIIUQMNmIQQQgghaqABkxBCCCFE\nDTRgEkIIIYSowZzM9N1pvcenT29eV3CRRRZJ9i200ELRZgf5r75Kkwt/+9s2v1e74WoXmWU6pC1n\nzEhXVLjxxhujPWnSpGh3757mwPvyyy9btF988cWkHG8vtlhzDkfb5iec0Bz8sdFGG6EdaYu2BBr8\n2bz11luT7U022STajz7anFT/f/9Ls0V8/vnn0V5wwebck/vuu++crmJrmWueTTH3tOVjj6Up4bi/\n22WXXaK93377JeW6devW4vFee+21ZPtPf2pee/rll5tzy55//vlJuVVWWaW+Cs95Wt2WmmESQggh\nhKjBnEwr0CGj5d///vfJ9u9+15wfsGvXrtGePHlyUm7RRReNNv9S/eCDD5Jyiy++eIs2z2QAwODB\ng6N9/fXX11P1OUWn/uXz1FPNSZN/8pN0xYzXX29er7NLly7RPuCAA5JyDz7YnDj6jjualzU75JBD\nknIvvfRStOebr3lJOPtLh2ezDjuseXmm/fffPynHx5hDNOwME/cT33zzTbSdS6v8rW+V/wbbYovm\nPHcrr7xytB966KGk3PzzN09883mnTp2alPvss8+iveOOO0bbzvaeccYZ0d5ww+ZVjewzzO1Z9T1m\ngU79bIqEhm9Lvp//+te/Jvvuvrs5Ab19F/LM0fPPPx9tfi8CwFJLLRVtfkanTEkTtK+99trR5tl7\n7lcBYLPNNov29773vWi3wwyxZpiEEEIIIdoKDZiEEEIIIWqgAZMQQgghRA06vQ/Teeedl2yPHTs2\n2uwDY6PaOCKLtVrWZgFgxRVXjPamm24a7YkTJyblNt5442ifdNJJddV9DtHw2vptt90W7REjRiT7\n2DfpjTfeSPa98sor0f7000+jbaPk1ltvvWgvsMAC0eZIOCCNuPr444+jvcIKKyTlOOKK67TwwguX\nnveYY47BHKBhfZhawyeffJJsL7300tEeOHBgtFdfffWk3KhRzYvd8/Vfc801k3LcTvvss0+0rU/U\n97/fvH7vbrvtVlfd5xAN/2yKumnItnziiSeifeaZZ0b7vffeS8rxe82+49gPkf0TuY8EUn8k9hNk\nX0IgjSzn9y73zXYfR8C+//77SbmRI0dGe4kllsAcQD5MQgghhBBthQZMQgghhBA16PSS3GmnnZZs\nv/vuu9HmhFos6QDAW2+9Fe2VVlop2jZ5Ik9fckK95ZdfPinHIZdHHXVUXXWfQzTkVDEnneRp4yWX\nXDIp953vfCfaNgEaS2DLLbdctG2469dff91iHezfOZUEh43b83JqCU458MUXXyTl+F7Zaaedkn3b\nbrtti3WqQYdKcjwdXxVWz2HEEyZMSPZxe1533XXJvgEDBkSbQ5m5bYE0keUpp5wS7f79+yfl+F5i\n+fb+++9PynH7cmqJDTbYICnXu3dvzGEa8tkUraIh25KfCZbJuK8D0r6r6p3P8pztP/kYXM6mV+Ft\nLmf7FK4vy+sfffRRUo6/C7t3zAaS5IQQQggh2goNmIQQQgghajAn15LrEGymYY4OqMpIzN72LLW9\n8847STlef4yn/W0UQs+ePWel2nM9HOnE8qWNlOCICLtWGJf98MMPo73MMssk5Vi648/YqWKeUmZZ\nj7NOA2l0HR/DSkcsCd17773JvlZKch1KlQx37LHHRpvbiWU2IM0obNuJpQCOTLTRb/ysskzGmfYB\n4K677mrxeHZKn6N4DjrooGjbKMohQ4ZE+4orrkj2WSlZiI5g3LhxyTbLWuw+UuW2UO8KBbYcR78x\n9t3K56rqU/j9zO8Bu7YnR+tZF4B11lmn9PhtgWaYhBBCCCFqoAGTEEIIIUQNNGASQgghhKhBp/dh\nqgr1ZjjsH0g1Xl6h2fo1WL+JJmxopvW/mdew2V7Zl4g1besjxjq5TdXAPjDsc2a19TL/Ek4PAKSa\nOWPbmLe5vtYvwPpjMXwfzqHstO3Kf/7zn2T71VdfjTZnNT/xxBOTcpy5fd111032sR8a+y3Ze+KA\nAw6I9jnnnBPtqVOnJuWOOOKIaPfp0yfaa6yxRlKOfS+uueaaaD/44INJOV7tfccdd0z23XHHHdG2\nvllzCxdeeGGyfeSRR3ZQTdoW7rut/02jY1cU4OeSU6DYvon7TPvuKnvH8fNgt6vSBbAvFfe59l3N\n70z2xbLpDLi+N910U7Lv17/+dYt1bys0wySEEEIIUQMNmIQQQgghatDpJTnOLAykIYg81WjlGJbe\nWE6yklxZKKVdIHCzzTars8ZzJ88991yyzdOtPB1sF3TkBVnt1HCZDGcXj+SpXm5LOwVclhHcyqll\nEpqV+FiasTLh+PHjo73FFlu0eLxGxn7X6dOnR/t3v/tdtLt27ZqU4wWJ7QLVfE222mqraN9+++1J\nueOOOy7anC7Ayics//Hiu1bi4wV8Od3Dk08+mZS76qqrom3ThrBEeeihh6IzYRce5+fngQceiDa3\nsd1n72+WVPkZ5ozNjUpnk+EYzrIPpAtc82oWVe4CNgs4uw/wgvVV5bhvtWkAuD9ddtllo83PK1Ce\ncdy6d/Dx7eLtkuSEEEIIIRoMDZiEEEIIIWrQ6SU5ng4G0um8Hj16RNtO/7FEx1OPdiHWMinISku8\ngO+8yOTJk5Ntlmo4E7eVOjg6oiqbMstp9trz8bm9rFxblhV32rRppeflOtm6c51shJ+tY2fDSm0M\nt7VdsJblL3tdORv6nnvuGe377rsvKceLV/PxbXuefvrp0eZ+wEqvN9xwQ7RHjx6NMgYNGhTt1Vdf\nPdn38ssvl36uEeH+zUrYvO/KK6+MtpWqnnnmmWjba88RiywFWRmH4T7Y9sdlMpn9O38XtjkC0x5/\nxRVXTPZxHVmu7Nu3b1LuRz/6UYt1am94MWkrlbMbA18Pe934ejz77LPJPpZR+Z3ZvXv3pBxHCbPs\nbV0YuH9gF5cxY8Yk5fjZ5r7ZulJwFJ69v5566qlob7jhhmhrNMMkhBBCCFEDDZiEEEIIIWqgAZMQ\nQgghRA06vQ8Ta59Aqrv27Nkz2lYzf+yxx1q0OQwSSLMGs1Zvs5vO66uZ29Bl9iNZbbXVoj127Nik\n3Lvvvhvtbt26Jfs4PJV9VGwYK5+LV6q3obWs/3NKANt27H/Ex3jhhReScoMHD462DYWt8ovqDLzy\nyivJNvsVsM+Y9flj34k333wz2cf+EezPdOeddybl+Jlm/wh7f/DzfeaZZ0ab7ynLHnvsEe1+/fol\n+374wx9Ge6eddkr27brrrqXHbESqVok/6KCDos1+efY54Lbk5wpInwsuZ/1PykLHq7I5MzadAcP9\ng00bw/24vQ85YzWnjXnrrbeSco3iw8TpHexqA9wv8rWy7c9+S9ZXi6+BfZ8yfN34XrGraPD9wOl2\nrK/TQw89FO3+/ftH2/rccTtb39Dhw4dHWz5MQgghhBANgAZMQgghhBA16PSSnJ2m52yiPNVop4BZ\nVth5552jPXLkyKQcLyDKU5686KE977yInYrna8VtZMNCH3300WhbiYRDhVkCqJqmZ2wGb/4cy6sW\nlvw4fNZmIrfSIGOzJnc27HPAz893v/vdaP/73/9OyvFzxVmIgbTtb7755mjbdme4DTnDOJC2B8tw\nQ4YMScrxIrJ77bVXtH//+98n5VjWGzduXLLvnnvuKa1jR1EmYwGpLDJp0qRkH2fpZpnaSiEsM1vJ\nmZ8ltrmWOgXBAAAgAElEQVT9bR1Z0rHnKvtMVVZuvidtn8DPt02JwPclvyOsrMfPwJZbbllaj7aG\n70v7nvnDH/4Q7UceeSTanEkfSF0QrOzGUtall14abXvty6QxWyfuqzk9yYQJE5JynMWf29Iuss3H\ns8/s9ttvj/ZEM0xCCCGEEDXQgEkIIYQQogYaMAkhhBBC1KDT+zDZUEXW0Dnk0urTrN0feOCB0eY0\n9ECqn7IWzinfgZnTEcxr2FBu1szZD8heN24Xu9I5hzKzD4X1k2BfItbT7XIJXI59KKw/Ey/vscoq\nq0TbhrSy7m79mfg7d0auu+66ZHu33XaL9o9//ONosy8SALz44ovRXnXVVZN93IZ8f/zqV79Kyo0f\nPz7a7Jdh/Wh4yYu999472r/4xS+Schw6f/3110fb+ilxyPYGG2yQ7FtuueXQaFgfJg4l52fH+jDx\nM8L9oE3DwT4rNkyd/ZGqfAo53UiVzxH7y7Btz8vb3F9Yfxuun/VfZfg72tQoNpVCI8DLBtlt9j/6\n7W9/m5TjZ5HT6ACprxb3rXZJGd5X1X/OmDEj2kOHDo229XNlX7opU6ZE+7DDDkvKHX/88WgUNMMk\nhBBCCFEDDZiEEEIIIWrQ6SU5O43MU4U8ZWslOZ7O5tBHO7XLx+BpXouVieY1WPYA0tXeWeIaOHBg\nUo6nva1EwqtZ87S6le742vO0r21zPgbLa7btuBzvs+X4XFaSqzf1QWdhk002iTZnbrchwHwdevXq\nlewbNWpUtFkGZ3kOSDMAczvtuOOOSTnODsz3H8uCAPD4449HmyU+W47TEay88srJvtGjR7d43o7E\n3t8sV3G6Dl6tAAAefPDBaHM/aKUrvr9tGgDersr0zXXiz9jng8vVK6eVfR5In1Xbp/M7gzNWP/30\n00k5K9F1FNxGVTIsZya3khz3s1Yq52eMpWebloXvN5bHbX/Mday6hyZOnBjt5ZdfPtpVEpy9H9q7\nn9UMkxBCCCFEDTRgEkIIIYSowVwnyfF0IE/L2qglnka0U5RMWXbojp4abDTs9eCsznzt11lnnaTc\nsGHDol1vVIrNVMttyZFxHCUJpFPMVVP99p5qwspuXF8b/Vd2jM4CL5QLpAsN8wLKts04aufJJ58s\nPSbLRNdee21SjiUvjj4dM2ZMUo6zqXO0rG2nyy+/PNosJdhyLN3YKEGOoGsUSa4q0zdHHW299dbJ\nvrKM21bi4yzYVp7i/o6vm5VdWDKqyuBddYyycvw9bN2rFqLlbT5elQTVkXAdq9qcqZITbd/HzwH3\nn7YduB58fLt6A0decv9QtUhvvXT0e1YzTEIIIYQQNdCASQghhBCiBhowCSGEEELUoNP7MNkM2+xr\nwD5MNiUA+5hw5lvre8JhkWUrdAOt02PnJqwuXuYjZPVuTulg/WHK/ICsbwH7lvE+66PCsAZv27Is\nRYTNfPv8889H29ad/SHY18b6VTUSHF5sVzvnFc1/9rOfRZvDgYF0BXIbbrzDDjtEm68Jrx4PpKkn\nbrvttmhzGDIAvPTSS9Hed999o/3yyy8n5d5///1oH3zwwdHmFANA6svIqRMA4OKLL472iSeeGG1O\nn9HetLbPKfNvsb4+VX4wvM9+juHnke0qPyX206k6dpkvkj1G1T72abOZ5DvaX6Yl6m1z23dWfY7b\nsspfistx+9nnl/vJqnuI+wf7XijD1qm937uaYRJCCCGEqIEGTEIIIYQQNej0kpwNMWd5hqUVu0Ag\nZ3hlFl988WSbpTyWeKzcwAvzzotUhR2zTFolk9k2YlmPj1ElofG0upUF+V7hfTacmOEQ2e7duyf7\nqtIKcJ34GI0sybHEZa8x399XXHFFtPfff/+k3LRp06LNC+ICqXzFstn222+flOMUAdxmAwYMSMrd\nddddLdbXphDhtjjuuOOizZmRAeC5556L9jHHHJPs46zgfF67GGqjwP2bdUdgGZVdDmxfWubeAJRn\nn66SSFjGsf0Fy3VzQmbhOln5r0watAu52z6+EZgTklRVugQ+fpWcVpbNG0j7irXWWqv0vNwH15su\noaNdXzTDJIQQQghRAw2YhBBCCCFq0OklOTuNWhYBYacNrfTWhJ2WZqoW850X4agqC0dpcMZgGw3B\n07IcrWgpW/jRwpEzNnKNZTOWcKqOZ+8bhutu5T++V+pdQLSjYSmL5akq9thjj2Sb5UdudyCNjOOp\ndfssspzEETgcqQek9xJHW/LivUCaYZjlKVv322+/PdpWIthggw2izd+xvak3gmzQoEHRvvHGG5N9\n/BxwO1RJJlWL6lbJJFyuLOu33eZn08ppZfJf1aK0tk/n573svC2duzNh7416JS+m3necbf8ymde6\nY3BW8XqlNkXJCSGEEEI0OBowCSGEEELUQAMmIYQQQogadF6RtsDqzhwKyRqsDRGtdzX5Mj8VDrmd\nV3nvvfeiXaX3V2XMtSHgZZ9jTb6q7arqwf4w9da3alXuqrDjKt+nRmXllVeOtvVfePzxx6Pdp0+f\naO+yyy5JuVtvvTXaU6dOTfZxaD5fS+uHyHCIss2+zX5LPXv2jPajjz6alOPvcs0110T7yCOPTMpx\nqgObaoJD8a0vVXvCPhvWf4P7J/ZT6tWrV1LugAMOiDZnN7e+dmV+nkD6DNbrw8TYVAd8vau+Y2uw\nvnT83HK6BPs8N2Km73ppbd35Wanye+J2qfKX4nbtzNezCc0wCSGEEELUQAMmIYQQQogadHpJzk4H\nLrPMMtG2U7FMmSRjpQgux9PIjZgFtiOx0618HatSNbz++uvRXmWVVZJ9czocvzVhwiy9lmWHB2ZO\nYcASjs1g3qi8++670R49enSyb8yYMdHma2KfgxkzZkT75ptvTvaxTMlSCGcDBoARI0ZEm6/59773\nvaQch/e/8sor0e7du3dSjuU/lpFt5vY333wz2jZlBmcjt/dpe1KVVbtM8thoo42S7Z133jnaLF/a\nzM5839pM9ryvXtmM62efbe4vuE9vbfoWloLs88f7WFp8++23S+vb2aj3+rYWvg9tG5WlnKiS+DpL\nmh7NMAkhhBBC1EADJiGEEEKIGnR6Sc7KPSy7sCRno5bKppGtbMPT+TzVWLVg67yInb7mafAq+ZKz\n7tootHqZE1PMZfDUto3OW2mllaI9fvz40mN0lnuF7317TS+99NJoT5kypfQY/F056swen7Nvs5wG\npNF0LLvZBa6XW265aLOcZqPCWKK74447SuvOkvudd96Z7Pvzn/8c7WWXXbb0GG3NnIga44z6nG2Z\ns4MDwMSJE6P9xhtvJPv42ldlzS/LeG8lmHqjs5iqaLqqSFqub1VW8aqVDBodew2rJLmya29lvdmV\nKG2b8/E6Sx+pGSYhhBBCiBpowCSEEEIIUQMNmIQQQgghatDpfZgsrF1zmPTkyZOTct26dWvx81an\nffXVV6PNodCs/c+rsL+CvW68XeXDxKHMVX5QrKfXq3fb85alKbB+F6zxs1+L9d2pSpfAdJas3/zs\nrLjiism+J598Mto2DQDz/vvvR9teb860vsEGG0T74YcfTsrxde7fv3+0rV8GP4Psb2KfbX5u+Xm2\n8Hlt9nH2Udt1111Lj9Ge1OtjwqkUgNRXh+9hzuYOAP369Yu2TcHw9NNPR5vbxfqZcR25nPWj4TpV\nhayXfccq/xp7ncr6I5suokePHqXHbHTss1Ll78XXmPfZa1rWllXHq0oRwZ/rLKlXNMMkhBBCCFED\nDZiEEEIIIWow10lyvJjrbbfdFm2WA4Dy8Fy7KCRPPfNULodFz6uwnFa1cGVVxm6WPmy5sjQOdsFe\nrgeHO1vJjNu2Kgs8w/cNZ7EG6s8c3llCZjmD99JLL53sY6llwIABpcfgNADTpk1L9nEaBr7+diFr\nDmfn+8M+m5x+4KWXXor2mmuumZS76qqros1pBS688MKkHD/TVsZi2XCrrbZq8TPtTZUMxVmreZFi\nIF18l1MM2Pa65ZZbom0XHF5jjTWiPWnSpGjb68YZwvl5rJLkeN+sSEtMlWRUhq1TvQu0tyf1plyw\n1CtZ1ps6oN5rXy82y3yjohkmIYQQQogaaMAkhBBCCFGDuU6S44gnjpyxU35lU4BWquEpZp6utFFE\n8yI2uozhqIeqaV6OTLELfDI8PV51PG6/emU3G73B5+L2t5nIue5PPfVUso+nuef0IsJtBUsSHFkG\npFLnYYcdVnqMcePGRZtlGwDo2bNntPn5sVP4vOAuy3OvvfZaUm7YsGHR5uzbVhZiaX6RRRYprXuf\nPn2ifeuttyb7OJN4o8gHNmrz7rvvjjbLwDaT+sknnxztqVOnRttGuLGEas/F15jlW3ttWBqsksnK\nJLnWZjavksu5/+CISo4EBRozcqve62Hla6a1MmdZu1RlDq+C69GI8mdLaIZJCCGEEKIGGjAJIYQQ\nQtRAAyYhhBBCiBrMdT5MTFkGU6Dcr8Tqu5ylmXXxKo14XuGjjz6KttWg+fpUZcRmnxLrQ7HyyitH\nm/0JbJg+a+h8DOuHw3Us85mwdSr7PJD62lT5xnQWHyb2A2LfEwA45JBDos0ZoC3sN2jTD6y//vrR\n5rYdOXJkUm6zzTaLNvvfDB8+PCnH/jecEfzSSy9NyvEzXeWX0qtXr2hbPyhOZTFhwoRoDxo0qPR4\nbQH72YwZMybZt8MOO0TbZq1mDjrooGi/8cYb0barF3DKBPYlA4Dnn38+2uzf1rdv36TcUUcdFW1+\nHq3/I/fP3F6tDaOvOgb7MHE/Ze+NVVddtVXnbgSq/Ijsvqq+kKm3H6s6Rhn1rprQ0WiGSQghhBCi\nBhowCSGEEELUYK6T5Fga4Wl1O01YNjVvZReehuRs1p1lCrEtYZnCXrey7NsWlkF69+6d7FtvvfXq\nqgfLChwKPSfYaKONom2npPn72wV2uWyjhKHXgrNqr7DCCsk+bsOqtA78OStpcFvz/WLvnXXWWSfa\nvFjuM888k5TjjNtvvfVWtJ977rmkHLdFVSoMzmZtZRyuBy/q3d5w6gO2Z4Wdd955TlWn1Vi5XMw+\nvCqBffaqnoEyCc26sdSbBbyMqn6wtekj2hvNMAkhhBBC1EADJiGEEEKIGmjAJIQQQghRg7nOh4lD\nJFlztT4JZanzbWg7a78cjlnvSvXzClYHZ58eDr+3cPjz9OnTS8vxPquls88K+7BV+aNV1Y8/xyu2\nsz8TkPpLWf+m2dX7OwIO4V988cWTfewPWOVvULbsBJD6AD7wwAPRfvnll5Ny22yzTbT5uV1ttdWS\nctxuf/3rX6Ntw+PZt6PKL47b0/YD/J3Zl2qvvfYqPZ4QHYVNHcDbtq9ujT8uH68q9UNZughLIy5D\n0xKaYRJCCCGEqIEGTEIIIYQQNZjrdKUyScZKaGVZS23oI09Xsl0VnjyvwGH19nrwlG1VhthNNtkk\n2ocffniyjyWYqnQBLM2wJLTooouWfqaKM888M9o333xztO1q5ryCO2edBoCuXbtGu97VuzuaF198\nMdrrrrtuabnXX3892t27d0/28TNy1113Jfv4mrz22mvR5ozSQJrCgDOHW0mUJUTORL3QQgsl5Xgf\n37MWlhBt2Du3L6c9EKJRYPnLym7cB9vng2HZzL4LuW9lu6p/53rYOvHxrftEo6IZJiGEEEKIGmjA\nJIQQQghRg7lOknv22WejzdP5ljKZZNq0acm2jbhpgjP/zqtw1JO91jxNu/zyy5ceg6dieaFOADj1\n1FOjfdhhh0XbyquTJk2K9gcffNBi/YA0G/TkyZOjbeU0Pr6V4RheBNhm1uVz2yzgjQpHIvLiqkD6\nfQ499NDSY1xxxRXRPuOMM5J9TzzxRLT5etmFYh988MEW68QZ3YFUGmRZduutt07K7bbbbtH+8Y9/\nXFp3xkYOsQzHGdGFaE+qIs143+eff57s477KHoPv9arI8ip5jWGpreq8XI777UZGM0xCCCGEEDXQ\ngEkIIYQQogYaMAkhhBBC1MBVaaKzyBw70Ozw0ksvRXvYsGHRtv4sBx10ULTZj8auiP6vf/0r2uyX\ns+eeeybl+vbt28oazzZtscxzXW15xx13RPuRRx5J9q2++urRPuSQQ2a7Quy/YlMYsO7OoeY23PWj\njz5qcd/aa6+dlONV66uYMWNGtC+44IJkH4els4/O7rvvXnXItlqyuyGezXmQDns2xRynIdrS+t6W\n+RLZfubLL7+Mtk23wulRqtLlcDoCHjdYH82yc9l0Bosttli0OaUHZ+232PFK1aoDFbS6LTXDJIQQ\nQghRAw2YhBBCCCFqMCclOSGEEEKIuRLNMAkhhBBC1EADJiGEEEKIGmjAJIQQQghRgzZZGsXl7vcA\nNgGwEIA+AEYXuy71mb+qLc7ZWlzuLgMwFMBPACwAYAUASwF4rihytM/8+A6qHlzubgLwd5/5ezrg\n3KsAeAHN7fdtAJMBHOkzX5rL3uVuBICzAHwF4Cyf+S3atKLN550PwO0AzgSwPYBDAExECCNdGMDl\nPvN/q/j8KgAe8plfyeXun4V9SRvUc0mEe25vn/k35/Tx2xKXu64A/gBgfQBNeRpO95kf3srjfR/A\n9T7zM61V5HKXAfjEZ/6PLnc9i/OuDOBTAJ8BOMln/gn7uTrPe6DP/NUud8cCWMZn/rTWHGduwTzr\nHsCiAO4DcIrPZt/Rtbhv/g1gl+L/pQBMBzAfgC8A/Mhn/rVWHLdh229234MudwcD2MZn/kDz994I\n1+voFj7zXQCP+MxPL7b/D8D9AN4D8LbP/Gyv6UVteS6Apjr0Rng3vA/gTZ/5A+o8lgfwbZ/5r8zf\nrwdwvO0fXe66AejlM39/sb0hgF8D+AGAHXzmb27F96n7HmqTGSaf+ZN85gcD2A/Auz7zg4t/jTZY\n2hvAwj7zN/nMb1fU+bcA7qU6d9hgqeCnAC50uVu0Zsm2gdtvcwBvAji11oc6iF8AeMpnvqljuqqo\n9yAAWwE4weVu/Y6rXqAYbJ4OYI4PxtoSlzsH4FYAo33mNywGwkcAuNrlbvXqT5eSo4V+yOVuEwDb\nFoOlhQHcjfCi6VPch2cBGOpyN8sLuxUD618DgM/8nwAMcbnbrJX1n5toetaHAOgPYE8A9SUlq80l\nAHKf+feK7eN95of4zA8EMArh2Z1lGrn92uo96DM/rqXBUsFxAJam7SEAHkD48bja7JyXaGrLfzd9\nJwDjENp0cL2DpSp85vcr+TE5BKEvb2J7APcgDEj3aOW56r6H2n3xXZe70wGsCqAHgOMRfqVehNBp\nzg/g//nMP2R/4TeNRAFsiTCo+RRh5H6Mz/xYl7shADKE2YQvAfzYZ36iy90kAP8CsJrP/N6mOqcC\nKF9JtLnOIxBuiD4IjbUDQof7afHvJz7zbxbn2sZn/mWXu8EoZldc7n4O4EAqf6DP/Hsud0cD2Kf4\n3s8DOBJAF4RZkvEAnvGZP9vl7g4AhwH4U80L3Pb8F2EQh7Lv29KHihmCpJ0Rfo3c7DO/VlGmO4BH\nEGYR9kT49eIAvAvgsOKafQjgUgDz+cwfQ8efH8CJANZr6fw+8x+73I0DsJbL3TJc16Z7DUCLsyQu\nd4cCOByh7aYC+DGA0wC87zN/dlHmVACLFX+/AMAaxfZ1PvPnFr8Wd0b4ZX2ez/ydLne/d7nr7TM/\nrqXzNiBbA/A+8zFLp8/8eJe7tX3m3y8GIn8C0BdhluJ+n/nTXO6+hdD2vQAsCGCMz/wxLnc5wnW6\nz+Vu96ZfxQW/AvB/hX0AgEd95m+j844ozjvD5e47AP4OoDtCH3Glz/zfir9fifACWQzAjT7zvwNw\nGYAeLnf3+MxvB+CPCPfj9+bs5erULI1wLacCgMvd7gBOAvA5wvP7A5/5SS53fRGu/ccIs6Y5gEV5\nxsDlrg+AlX3mh8FQ3BsrAXi22O4C4KriHEsA+LPP/JUudwsBuALAKgDeQJi9vrd4P3T69nO5+y3C\nu+ULhB+lTZmVF3e5uxrAOggzOHsAGITmd8sINL+bbkB4P17jcncIgA8BfABgWwB7A9jE5e44hOtX\n9s79DGFg1RXAP33mzzP1LG3LGt9vCFp4bxe7j3G52wXh3befz/zTTe8WAFugud+8GaGPdy5304u6\nbYfwnrgFwFLFrN4paLkfGozwQ2sywhjkg+J8H6LOe6ijfJhWBTDEZ/5xAOcD+FsxSj0CoYOr4liE\nF84QAAcD6OpytwjCDbBHMZtwPsIFaOIlO1gqphW7Aqh3Sv/j4tgLIoyw9yzqcBdCI1RxBoCdi8//\nCUC34hf07gAG+sz3R2i8w4ryayOM4M8utu9FGKR1KMULcQ8AI1vx8Zna2Wf+WQCfudxtUJTZB8B1\nALohvDC3KQY1IwD8siizKIChPFgq6Adgss/8OyV17wZgYwBjW9pfhsvdyggvga2Lur+O8CvuGgB7\nUdF9ETr6nwN4q7g3NgWwH32/3gC+6zN/Z7HdEO06C6yLFq6fz/z7hbkPwrO9OYCBALZzuRuE0Nk9\n7TM/0Gd+0+Lv6/nMZ8XntubBUnGfbYXwy7Ge8x4D4INitmIrACe73K0GYHkAtxZtsTmAX7rcLY7w\nw+rdYrAEBOlp6+K88zLLudyNcLn7L4JLwj985pvSQC8JYN/iWg4FcFTx9z8j9FWDEPqwBe1BEe7x\nu83fzi1e9s8j/Mj5Z/H3bgD+6jO/FcKLsumFfSCCdLMpgJ8hvCib6NTt53K3FMJ36u8zvyXCwKBL\nsXtdBHeRvgjXaaMWDvGxz/yg4ofM2wAO8JmfgDD7cq/P/C1ongG6H9Xv3BV95rdHeH5PLX5cMi21\nZT3M9N6mfROKv1+L8GPU0tRvXoBwn1zlM39e8d7v4jP/HJqVoZNQ3g8B4Tqe5DM/AEGmPLj4e133\nUEcNmB7xzbr4pggvDvggfy3ucrdsxWevBXC2y925CBfrNoQbqSuAm4uH8AQAy9FnHm7hON0BvOHr\n1+ebjtETwFSf+aa1OkYgvKyruBTA3S53vwIwsfiegxF+XT9Q1HmLok4AMN1n/gX6/GSEX1YdQVMn\nOgJhavctNP/ynxXK2pkHHvsCuBpBDugKYFhx3v3Q/IA5hCl8S3eEwQzzg6LuDyL8Oj3cZ37yLNZ7\nIwCP+8w3+euMANCvmBVa0OVuNZe7dQB85TP/DMKU8e5Fve9D+DW1RvHZJ3zmeR2BjmzX1vA1gs9J\nGZsCGO4z733mv0YYWPdDeJF2d7kbXVyXrgCqnvFlAHxJ17ye8zbdW58BeAyh3d4BsKXL3cMAhiG0\nxdL2w8V5vkDaZ8yLNMlGAxHaaG2Xu6aB0VQAVxTP0sFobr/eCM8EANxUctyWns0m+aYnwg/KpsHx\nWwD2d7l7CMD1CPdCch6f+bcRZoRRbHfq9isG/sMAPOhydzyAh32zP9dYn/lPi/fUmwgDV0tL7zcg\nDCpb8n2teufeU/z9AwAvAljTfLaltqyHlt7bTYwo/n8DLX8/2282MQhB8bCU9UMA8KxvlvpGIczc\n1X0PtbskV/A/su2AxRV/i393uYuLvfnM/8vlbhjCzfBrl7tHESS314oRc63zzW6dy+pr93Gdf+Fy\n1wPAdwHcWjwUXwC4zWf+KPpMk/PlnKjvnOLdiuva4vetoyzQfN2uQxhMXg5gIZ/5ccW1etRnfueS\nY9V7fa7ymW/J38rWparuVe19LcJg7zsIAz0gtOsZPvPJy6OQ5BqpXVvDeDTPgkYKv7BXUX6t9kPo\nsLb0mf/K5e6xVpx3Jv+EQg56uuK8xyLMeGzuM+9d7qbN4nnnWXzm/+dydyOAw1zuLkboYzfymX+p\nGERtXBT9FoAmh/2vWzhUPeca6nJ3TZNUjqAI7O+C32bToJnP0+pzNQoud+cjBE7M8JnfzWd+L5e7\nXgB2Qhg4NS1W+pX9aAuHm6lfKaTOPgAeb6F8VZ/2rZK/zxIudzshyGdACByY6b3tM9+kGvB3rOv7\nFTT5L1na7Ps1QlqBRxC+eJM++p4PjoEfonnGZWsUX6zwe5jPZ/4GBPmjP8JIeFmXu/WKMgNd7n5S\n47yvI2jns8qLAJYvpBog6KxNK89ynbcq6rKUC35br/sQoXUBQuTEKAA7Fp0CXO6OdLnrX3LOHgAm\ntaKubc1M37eCFtu5mKmbhvBwNQ06xiLo7SsU5fd2udutxvFfp7rUU+8VXe5cMa27aUXZxwH0dblr\nWimS2/tahKifXQobCL989ynq/S2Xu/Nc7maa1Sho1HZtEZ/5BwF85HL3/5r+5nK3LoDbEJ6lRwBs\nW1zX+RF+AT6CIC+8UAyW+iLMuDVJN02+icx7ABaga34dgHVdiKhrOu8ghBmNJZDeW99BmHZ/vDjv\nhGKwtCuARYrzfsPnLJ7BBRF85UQzAwE8g+D/9Q2ASYUv0W5obr/nAQwo7DKn28pns5CsPyv6/S4o\n/JkAfB/ANy53C/J5XO6WR5iRb/p8p2s/n/mjixm23YpZ6uN85p/3mT8XQZJrrbN90729MYAnfXP0\nKd/zZe9cIMyQN8mEayBETjJ19bM+83f6Zif30SXv7dn5fk11vb+Fv5f1QwDQq3DHAcI99DRQ/z3U\nCAOmowH82OXuAQRt9QfF3y9D8P94AGE6tmlp+JcA3Otydx/C4OP0Yhr+QACXFlPGZwJ4sOqkhTY/\nxeWuJU246nOfAfgRgH8V8sLWaI4aO7eow90APinKv4/Q4Yx1uRuOoMv/w2f+saL+I4rp58EAnio5\n7TZonW7c1sz0fSsoa2cgyHKHoRh0+My/hfBQ3VH4U/wIzTd8GWMBrOxyV8+0/FMID8oTCFJd2ZQ2\nigHdaQCGF3VZDoXzvc/8RIQX/rvk63EBgI9d7kYXdf7Ap87MTKO2axU7AVjD5e6Z4lk7D8G35QUA\nNwJ4GWHQ+BCC/9Co4u/9i/J7IvgX/qXolO8G8JijKLtiGv0+BGdV+Mz/D6Fz28Pl7qniOMcB2N5n\nfhrC/bRY0T73I8zwTULoQw52ubsfwafhmuLfWwDedrl7vBhgbQ3gvuK88zJRfi9kzC4IzsXTEZ7N\nsebWAE0AACAASURBVAgzTX8AsJULUcYnAPhT0bctgfA82BQRd6N4QRPnktR/IZpl+b8COMPl7l6E\n2aX7inP/E+FH8WiE528kmmcmOnv7vQGgj8vdo8V7bVWEsP3WMAwhaGhHpLMv9wK42OVuD1T3xe+7\n3N2K8P7M/MzpY1pqy3qY6b3dimMAod0PKRSJGSTbPwpgoAtpgsr6ISAMxs8p3rmLodl/q657aJ5e\nS6544Hf3mf9+zcIdRDFNPQZAH7o5RAu43J0IYCma6m1YXO62BfALn/kdO7oujYgLQRHn+XbI4VV0\nnif4zNcalAuDC9FP033mnyp+fF7ni6hXU+5OhIi3VuWTc7lbEcAAn/kbC7npCQBHFLMXar85gKsz\n99zstmVH4Soiueu9hxphhqnD8Jm/EcDnLnd71SzccVyMkChSg6XanAegd4W02RC4kLgyRwv+QCLg\nM/8owi/SE9ryPC4krXtAL9tW8yWAS1zuRgL4G4qUIy1wGIDT3cxRV/XyAYLi8ChCAsi7isGS2q/9\nmd22bChm5R6ap2eYhBBCCCHqYZ6eYRJCCCGEqAcNmIQQQgghaqABkxBCCCFEDTRgEkIIIYSowZzM\n9N1u3uNvv/12tI888shk35NPPtlcIXJoX2qppZJyK664YrTHjBkT7S22SCMOP/3002iPG1e+Ruqe\ne+4Z7QsvvLC0XBvQUmbU2UWRAB1DW7QloPbsKPRszj3MtW153HHHRfvDDz+M9vTpafq4SZMmRZvf\np927p3ksP/7442jPN1/zikY33HDDbNd1DtHqttQMkxBCCCFEDTRgEkIIIYSoQbsvvssymXPlM2Pv\nvPNOtP/whz8k+y6++OJos7QGAK+99lq0u3TpEu2vvkrXMNxmm22ifccdd0T7scfSdUH/9a9/RfvZ\nZ5+N9rRp6Tqe/Ln1118/2v369UvKnXvuudG2MqEQQgjRlsyYMSPZfvPNN6O92GKLRXuJJZZIyq22\n2mrRnjJlSrQ/+CBdPeXzzz+P9iOPNOeC/OSTdPWs73znO7NS7YZAM0xCCCGEEDXQgEkIIYQQogYa\nMAkhhBBC1KBdfJjq9Vvi8Mbrr7++9DOspS6yyCLJPtZFF1hggWhbH6azzz472kOHDo32vffem5Rb\nbrnlWjw2a71AGj7J3Hrrrcn2yJEjo33jjTcm+3r37t3iMYQQQog5Afv5AsDUqVOjze/TBRdcMCn3\n7W9/O9r8vvvyyy+Tcvy+Xn755aP9/PPPJ+X69u07K9VuCDTDJIQQQghRAw2YhBBCCCFq0C6SXJkM\n98tf/jLZvuuuu6K96KKLRtvKX4zd161bt2gvvvji0V5yySWTcquvvnq033vvvWgPGjQoKTdx4sRo\nc/hk//79k3K8jzOd2tQBLOsdc8wxyb5rrrkm2pw9lSVNoFrWFEIIIcrg9x2QynBLL710tHmVCwD4\n4osvos3v3ffffz8px+9ddoUZNWpUUk6SnBBCCCHEXIgGTEIIIYQQNWgTSe7rr79Ottmj/q233or2\nLbfckpRbeeWVo82Zvq0k9c0330TbTgeyV/5HH30U7c8++ywpx5IfTyHaaDeWvwYOHNhi/QBg/vmb\nL+UKK6wQbZsFlSVDvhYA8KMf/Sja99xzT4t1EEIIIVqLfZ+yvMb7OGM3AHzrW83zK/wOtvD7n9/H\n9nidEc0wCSGEEELUQAMmIYQQQogaaMAkhBBCCFGDNvFhslm12S/okksuaT75/Onp2VeHQxgt/DkO\n4bef4xB+q9s+8cQT0WafI7uiMvtVcYoB9o8C0u/MmU/ZPwpI9V2bcoCPyd/DZlwVQgghWsOMGTOS\nbX438rvLvoMXXnjhaE+aNCnaCy20UFJuiSWWiDb7Pdl3a2dEM0xCCCGEEDXQgEkIIYQQogZtIslV\nSUijR4+ONi/mB6TSHUtZLK0BwLvvvhttniYEUmmMUw7YjOA8bcgS3zLLLJOU4/BJ/gxnRAXSqcfX\nX3892jadAX9ne504A+t5550X7VNOOQVCCCHE7GLD++17uAkryT333HPRnjJlSrS33nrrpBy/k617\nTmdHM0xCCCGEEDXQgEkIIYQQogbtsvjuuHHjos3TgXZBXJbGXn311Wj36tUrKceymZ3yY+mN99ns\n45zp22b3LoOPYeU0jvBbaaWVom0jAXn600YNLLvsstEeOXJktCXJCSE6kqrVG1rDPvvsk2yz/LP3\n3ntHu3fv3km5VVZZJdos/VTVh10kgPR99OKLL0b7+OOPr1HruQN7rdjthNvh+eefT8r169cv2htu\nuGG0bcR4mUvO7N4zjYBmmIQQQgghaqABkxBCCCFEDTRgEkIIIYSoQbv4MN18883RZt15gQUWSMqx\nftq9e/doL7LIIkm5//3vf6XHYJ2UfZhs6CRnO7XHZzjzKWdE/fTTT5NyfHw+r9VteZ/N9M3fi88r\nWsfw4cOT7S222CLanJ3WZoFnf7R6mdM+Hp0Vvpb2unLqjbLPAOn1Z/+Kss8DwH333RftAQMGJPu4\nz7HnKquHvQd4X1U95laqvjPf+1X3PffpNi3LqquuGu1zzz239BjcDvwM2/Qt/F6w4fHsV8r+TRtv\nvHFSbtCgQaX16MzYNvrwww+jze8dzuYNpD5e/Fz+/e9/T8qV+RjzZzor896TL4QQQggxi2jAJIQQ\nQghRg3aR5N54441oc1oBu2gfy2Q8Rfvmm28m5Xha1i5u+9Zbb0WbUwfYcnZKuKxOnC2cp57t51mi\n4ylPK9Xwts24ygsJf/DBB9G2YZs2a/m8xlVXXZVscwoGzgI/atSopNwVV1wR7R133DHarZHgLFVS\nRJXUM7fB369K1qqSxvhzLAU98MADSblzzjkn2vxcWUnuzDPPjDbLAvW2ma0HZ+E/+uijk3JlWZM7\nO7Yt65Xh9txzz2jvvvvu0bZuEEOHDo0293e2P2a3Beadd95JtrlOVk5kiZYlKLso7dxKlbzKUqa9\nl3nFDU4rwM8DUP7+s+/gzohmmIQQQgghaqABkxBCCCFEDTRgEkIIIYSoQbv4MDGsi7J/EJD6BbGO\nvcQSSyTlqkKNWWflcNL33nsvKVeWVt+G83PIZZcuXaI9ffr0pBzXkfV0qwN37do12jYUlpdU4e/x\nwgsvJOVs+Ou8xoUXXphsv/3229HeZJNNon3++ecn5dj36amnnor2kUcemZRrjdZul19g/xr2Tbvy\nyitn+didlSo/pSo/ijFjxkR7v/32i7ZNw8HttPzyy0fbLunAVPnbVPlf3XLLLdHO87zF8wLAgQce\nWHr8uYmy63jooYcm2yeccEK0+/fvX3q8Y445Jtrjx4+P9jPPPJOU433s57n22msn5dj3yfp8brfd\ndtFmv7inn346KbfrrruW1rczY1PxMPy+sqlzVlxxxWjz9bbpAvidyWkFqtL3dBY0wySEEEIIUQMN\nmIQQQgghatAuktxLL70U7eWWWy7aVq7ilADLLrtstG2mVpay7NQ5T8WyrGVD+DmElOvBUh0A9OjR\nI9oc0mrrzuHsZWHRADBlypRor7XWWsk+/s6cZXXatGloT1hKsWkRGJ6Wt/ILb7c26/XYsWOj/be/\n/S3adgXzTz75JNpTp06Ntr1v+Bg77LBDtIcNG5aUY6lnq622irad2v/Pf/4TbdvOLNHy/WBD44cM\nGYK5laoUChxGfuyxx5aWq1oZgPsBvscmTJiQlLv88sujfcghh0SbpQMgvd9sO7Esvu6660bbSqxz\nkyRXbzqMX/ziF9G+6667kn2XXXZZXefq1q1bi/b2229f1+dbC/ez11xzTbLv1FNPbdNzdxRWQuN3\nY1Vm7vXXX7/F49l3Jn+O7yFJckIIIYQQ8wAaMAkhhBBC1KBdJDmWvzhKbMEFF0zKcZQYR6HZCLeq\nqT2WYXga2U7ns7zCx7PSXdmCuzx1aY/B57UyzgorrBBt+71YQmRpsb0z0HL9eaHKej8zKzz88MPR\nHjFiRLLvV7/6VbQ33XTTaO+///5Jueeeey7a3A42So4lnDXXXDPaLIUC6SKhLLvZbMK8mK+N5OR2\nZ8nw9ttvT8q1hyRX7wK2rVmYdlba/S9/+Uu0WYZbb731knL8HHC0rH0OOGqVJTP+PAD84x//iDYv\nFPrKK68k5bjvsLIvP7fcl3BEH5BGbPJn2gOWz9muylpelZm8qm2zLIs2RyXy6gr11rWlOjZh5fx6\n7zdejPnqq69O9l1//fXRXnLJJaNtozD5HVS2MkRnpKrNua+wkhxfK8b2fdy2/AzMDYtWd/5vIIQQ\nQgjRxmjAJIQQQghRAw2YhBBCCCFq0C4+TKxrs1bNvh1AGsI/ePDgaLMPApD6Oq2yyirJPs6qzMe3\n/lKsjfNnrFbNx1hjjTWibX17+BivvfZatPv165eU47QKN998c7JvmWWWiTb7Uk2cOBEdhc1+y34e\n/N2sPxb7dnBaBA77B9Ks3QcddFCy7ze/+U1dx2BtnP1abLjrFVdcEe2yzOx2+6ijjor2s88+m5Rj\nHxh733B92eZ7qL3gZ876JZT5jlh/gzL/g3HjxiXbvHI5PwcAMGrUqGhvvvnm0bb9AD+b3J6vvvpq\nUu7NN9+MNvtlVH1H9lezWam5vnYVAn4O2L/QrgzA90h7+zDxdWttKg+Gr/cpp5yS7OP0KJwBmn24\ngPQZ5H3W76U1cBqIPffcM9nH/fO2226b7ONUBfwMW/8o9s0aMGDA7FW2gbD3rH03NsH3eRXWP9j6\n9zZR5SPZWdAMkxBCCCFEDTRgEkIIIYSoQZtIcnbKj6fseOrchgnzNDhnWLZhkFWL5fKCjBxebKfY\neWqep29ZWrPwPntePgZPSdoFDFm6qQrv5Lpbaamt4anT008/PdnH35vDcG02cq4zL6BqZRpeCJND\nyIE0DQCHqlppjKVMvjfs1PDqq68ebZZzNtxww6Qc1/H444+Ptg2ZXm211aJtZT2ezuaUCCeffDLa\nmzkhz3BG6wsuuCDaVqrj589O6W+55ZYt1sk+cyy51hvOzfeKXdSaU5mwzDR69OikHJ/LphVgSYYX\nhLX9gF2EuT3hPo6fEV6hAEif25EjR0b7xRdfTMpxxnSW3QCgb9++0Z48eXK07bPEx+Sw9J/+9KdJ\nuYsuugj1wNee+w57r3H/edNNNyX7OOUHZ/Xn1RqAmeXFuQV7P7D7Bz+XVYv0MtY9pSz1Q2tTzzQS\nmmESQgghhKiBBkxCCCGEEDVoE0nOyjMcEcEe+XbKb/nll482Txtb6YqlFs7GCqRRXHxennoF0mlI\nnlK0ETZ8DK6vjezh78VSoz0efxeONAHSiD/+/vY7tjWPPPJItG3mc562LluYGEhlkeHDh7f4eQDo\n1atXtG+99dZkH0e+8FS8jbbgaL0bbrgh2jvuuGNS7o033og2Syf2fuVFWXfbbbdob7TRRkk5jt60\nWYIfffTRaHMkINcBSKVLlhbbCpbMgHThYf7e999/f1KOMyfzM7H22msn5ThLur2uLL3xvWMlOb7n\nWGayGbxZuuNoOvsdOcqUn2Guq62T7Zv4erAMZ2XfsmzIbYHNYP3HP/4x2iwh2tUGeDF0rj9LzADw\ns5/9LNq84DCQ3jd8PHvdOCqU+/dbbrklKccLJPOit1bC/u53vxttlnjsdef+hyOTgbSvZinPvmfm\npsg4xsrofO34utUbyWjvLytTN2HfhZ0RzTAJIYQQQtRAAyYhhBBCiBpowCSEEEIIUYM28WGy/j2s\naf7/9s4+2q6iOuC/LUEwSCrfEggEiAlNE+RLQoAGAkGMDRUEFCisUpFgKEELSpEPDweUFhSKtYgo\nxCgKRZBaggJCIEiIUYorEoUCugglhvCtCMYIrN0/Zu59+8y755z7Hve+D7N/a2VlzjtzZ+acfWbO\nnL337LHpKt8cu0w7XZpfteTe+ipZe2xal7Vj2/JSP5IyX6cUaxe25aW7cttrSX0yrD3d+n9UhTro\nBta3avz48YVz1l5tbdIjR44s5LPys34ukydPLuS79957m+lHHnmkcM7eK+uXssMOOxTyWT+jJUuW\nNNPpfbP+FYceemgzbZedA0yYMKGZtj4YqY+DfVZWrFhRODdjxoxm2i5rTpedH3bYYc301KlT6Tan\nn3564dgus580aVIznUZut5G57ZL7dDm3XWKe+jLYYxtCIvUxsb5Jtv9YH6u0PPu8peEfbL+19aZ9\n0/o6pX5Qdrm17QPpmJD6AXWTW2+9tXBsxyB7LVW+WtZPJ/UvtCEB0pADNsq2fVbSsd/eR+t7mPr8\n2bAFt9xySzP93e9+t5DPjttVPn/2uUyXs9vnxvpepm234+BAR23vJqkPqL0/9n6k42IZ6b2xY0BZ\nPcMV1zA5juM4juPU4BMmx3Ecx3GcGrpikkuXD1r1qDVDpSo/q5pPwwBYrEo8NbtURc+2WPW1VUPa\nsARQHlU8XZppy7DtS6Msb7fdds20jTydlmkjkafq625jl+5ecsklhXN2abA1Q6URmSdOnNhMW3Oo\n3ewyzZcuNbbPijUdpEu5bRgEa/K77bbbCvmsKto+a2nbbaRvu2Q6NfVYlb29Digu1bUq6jTS+YMP\nPthMd8skZ6M5p2EiyqKfp+YZGw7BXo99TqEos/S5tX2rbMNP6L1Muew3tr/YdLq03crdmn5SE4G9\nN3aD77QMmy/drcD2725w+eWXN9OPPvpo4ZxdBm773KpVqwr57Fi1ePHiZrpq0+J0nLXHdnxPy7Au\nEzadhiGx99HKMjXrVr0XLNZUmkZ+t6ZYe8/S5y7tK38upO84i5VLu2bINKSDHTPtM+Sb7zqO4ziO\n46wD+ITJcRzHcRynhq6Y5Ko2i7Xn0lVi9pxV86YmGLvSJVW3WpWiVbemJjSr6rWq/lQFbNW3VoWf\nqmutmtOqw9P2WVNQqhotu/52zYydwtZtzV0A8+fPb6ZtBGurhoXiPbVmuFRlv2DBgmY6Xc1jzVzW\n1GajfkMx6rCVS2pysebR0aNHN9PpaiPLnDlzmmlrPoNiNO/999+/cM6uGrSmk3RV1VlnnVVad6dY\ntmxZM53eO3sf7P1KzY/22K4mSleuWVNNutLMrnq0sqiKPGzPpeOA7Zv2uUr7pr0umy99Puy5tK6y\nFT6pyb1dk1F/sasvrVyhuJLNtj+9v2Vja9o37TWnz60dW+34lN4nO8bZ9qXuCNYcbVdhHnvssYV8\ns2fPbqbtuJKaa+04nrbdttemU5NcuuHwnwvpO87KzKbb3fi6aieOsvfncMU1TI7jOI7jODX4hMlx\nHMdxHKcGnzA5juM4juPU0BUfptSmaf057DnriwTlNvM0oqs9Z3cph/KIvKm/kLXrl/lCQNHvwrYj\n9bexbbf5Ul8Q6z+QLkm2tmWbr2z3525hl3+mducTTjihZTrFRhqeN29eM50uJ7bPQOp7Yv2/bMTx\n1Ldgzz33bKaPOeaYZjqNPm7Lt344aZRuG3Hcyjn1uTv44IOb6fS6bMiBk08+uWUa2t8R/M0wd+7c\nZjrtczaqsg0XkPof2efR9p20v9h+lvrO2H5r+1zqY1IWLiDF9vV2d0JPo3uXkY5hto3WRyMdf6ra\n2wlsNHbrTwjFqO02RIf1tQN4+OGHm2m7o0CVr2h6P8p80NJdGWx/vP/++5vpqijdVdgo8KtXr26m\nU/mnvnUW295ddtmlmU79++wY1u1wEUMFe2+qfJEt7fomVe2UMVxwDZPjOI7jOE4NPmFyHMdxHMep\noSs6sjSip11mb5cxpxt3WnWgVQHb6NJQXJpftTTYmmCqzDOWVGVvIxnb67KqYSiqL63qMV3uatXe\nqUnOmmesSWegTXKdWP5pN8i98MIL33R53cSaOQBmzZrVMt/RRx89EM3pKmeffXbpsTWBPvTQQ4V8\ntr/YdBo2wz476bNv+6DtI6nqv2xz23QJvx0vbL1pvrIIw1WRrVNTug0XMFSXR9uNf88444w+/96a\n56AYlT69ZjtW2XuTRn7vNFmWNdN209+99967kM8+e+mG19YcaPOlz3I6Lvy5UPX8VoXnaJcys3dq\n8h2OuIbJcRzHcRynBp8wOY7jOI7j1NAVk1zVprpVEVitGcpG+01XuNkVTql6sUzFmqrfbTvs6qd0\nJZRV+1ozYZUZwZadbjZrf2fNVmkbbV39XVHiOH3BPptTpkwZxJY4g0FqTuu2ea0/lJnLqxg7dmzn\nGzKMSd+FZSvG03dhGe2uDE03xR6OuIbJcRzHcRynBp8wOY7jOI7j1OATJsdxHMdxnBoGJPSmjRhr\nI+OmfkA2zEDVck+75Dc9Z+2ktox0abGt20Y1Tu2s1h/JLldOI9parI+VXeoLRd+sNKyA3endLsFM\no0g7juM4Tn9IwwVYvyX7nmw30ncasqcM6+c7XHENk+M4juM4Tg0+YXIcx3Ecx6lhQExy1tRmQwRU\nbcZnl4JaExcUo2ynmyLa0AR2aX4afdSes+1IN2K16sY0crHFhk6wm1tOmDChkM+qPNMNTss2Hy6L\nVOw4juM4fSGNhG/fjdb9w7qPV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xI64z/W/PI04HBE7iP4itwd/34pYSC8gzC4v4jIpaa+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\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f1238e4f908>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "dCkWe-Sld3Sm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}