Google机器学习速成课程Code/multi_class_classification_of_handwritten_digits.ipynb

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      "name": "multi-class_classification_of_handwritten_digits.ipynb",
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      "source": [
        "#### Copyright 2017 Google LLC."
      ]
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      "source": [
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mPa95uXvcpcn",
        "colab_type": "text"
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      "source": [
        " # 使用神经网络对手写数字进行分类"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fdpn8b90u8Tp",
        "colab_type": "text"
      },
      "source": [
        " ![img](https://www.tensorflow.org/versions/r0.11/images/MNIST.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7HLCm66Cs2p",
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      "source": [
        " **学习目标：**\n",
        "  * 训练线性模型和神经网络，以对传统 [MNIST](http://yann.lecun.com/exdb/mnist/) 数据集中的手写数字进行分类\n",
        "  * 比较线性分类模型和神经网络分类模型的效果\n",
        "  * 可视化神经网络隐藏层的权重"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HSEh-gNdu8T0",
        "colab_type": "text"
      },
      "source": [
        " 我们的目标是将每个输入图片与正确的数字相对应。我们会创建一个包含几个隐藏层的神经网络，并在顶部放置一个归一化指数层，以选出最合适的类别。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2NMdE1b-7UIH",
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      "source": [
        " ## 设置\n",
        "\n",
        "首先，我们下载数据集、导入 TensorFlow 和其他实用工具，并将数据加载到 *Pandas* `DataFrame`。请注意，此数据是原始 MNIST 训练数据的样本；我们随机选择了 10000 行。"
      ]
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    {
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            "timeout": 600
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        "outputId": "b5839740-d46c-4456-9844-c967f83d9ff3"
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      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import glob\n",
        "import math\n",
        "import os\n",
        "\n",
        "from IPython import display\n",
        "from matplotlib import cm\n",
        "from matplotlib import gridspec\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn import metrics\n",
        "import tensorflow as tf\n",
        "from tensorflow.python.data import Dataset\n",
        "\n",
        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
        "pd.options.display.max_rows = 10\n",
        "pd.options.display.float_format = '{:.1f}'.format\n",
        "\n",
        "mnist_dataframe = pd.read_csv(\n",
        "  \"https://download.mlcc.google.cn/mledu-datasets/mnist_train_small.csv\",\n",
        "  sep=\",\",\n",
        "  header=None)\n",
        "\n",
        "# Use just the first 10,000 records for training/validation.\n",
        "mnist_dataframe = mnist_dataframe.head(10000)\n",
        "\n",
        "mnist_dataframe = mnist_dataframe.reindex(np.random.permutation(mnist_dataframe.index))\n",
        "mnist_dataframe.head()"
      ],
      "execution_count": 1,
      "outputs": [
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      0    1    2    3    4    5    6    ...  778  779  780  781  782  783  784\n",
              "9250    8    0    0    0    0    0    0  ...    0    0    0    0    0    0    0\n",
              "2328    4    0    0    0    0    0    0  ...    0    0    0    0    0    0    0\n",
              "7930    6    0    0    0    0    0    0  ...    0    0    0    0    0    0    0\n",
              "6343    2    0    0    0    0    0    0  ...    0    0    0    0    0    0    0\n",
              "7913    8    0    0    0    0    0    0  ...    0    0    0    0    0    0    0\n",
              "\n",
              "[5 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kg0-25p2mOi0",
        "colab_type": "text"
      },
      "source": [
        " 第一列中包含类别标签。其余列中包含特征值，每个像素对应一个特征值，有 `28×28=784` 个像素值，其中大部分像素值都为零；您也许需要花一分钟时间来确认它们不*全部*为零。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQ7vuOwRCsZ1",
        "colab_type": "text"
      },
      "source": [
        " ![img](https://www.tensorflow.org/versions/r0.11/images/MNIST-Matrix.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dghlqJPIu8UM",
        "colab_type": "text"
      },
      "source": [
        " 这些样本都是分辨率相对较低、对比度相对较高的手写数字图片。`0-9` 这十个数字中的每个可能出现的数字均由唯一的类别标签表示。因此，这是一个具有 10 个类别的多类别分类问题。\n",
        "\n",
        "现在，我们解析一下标签和特征，并查看几个样本。注意 `loc` 的使用，借助 `loc`，我们能够基于原来的位置抽出各列，因为此数据集中没有标题行。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JfFWWvMWDFrR",
        "colab_type": "code",
        "colab": {
          "test": {
            "output": "ignore",
            "timeout": 600
          }
        }
      },
      "source": [
        "def parse_labels_and_features(dataset):\n",
        "  \"\"\"Extracts labels and features.\n",
        "  \n",
        "  This is a good place to scale or transform the features if needed.\n",
        "  \n",
        "  Args:\n",
        "    dataset: A Pandas `Dataframe`, containing the label on the first column and\n",
        "      monochrome pixel values on the remaining columns, in row major order.\n",
        "  Returns:\n",
        "    A `tuple` `(labels, features)`:\n",
        "      labels: A Pandas `Series`.\n",
        "      features: A Pandas `DataFrame`.\n",
        "  \"\"\"\n",
        "  labels = dataset[0]\n",
        "\n",
        "  # DataFrame.loc index ranges are inclusive at both ends.\n",
        "  features = dataset.loc[:,1:784]\n",
        "  # Scale the data to [0, 1] by dividing out the max value, 255.\n",
        "  features = features / 255\n",
        "\n",
        "  return labels, features"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFY_-7vZu8UU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "a0e2672a-c55e-43f7-9399-aa641c0f9534"
      },
      "source": [
        "training_targets, training_examples = parse_labels_and_features(mnist_dataframe[:7500])\n",
        "training_examples.describe()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>784</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
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              "      <td>7500.0</td>\n",
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              "      <td>7500.0</td>\n",
              "      <td>7500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 784 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         1      2      3      4      5    ...    780    781    782    783    784\n",
              "count 7500.0 7500.0 7500.0 7500.0 7500.0  ... 7500.0 7500.0 7500.0 7500.0 7500.0\n",
              "mean     0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0    0.0    0.0\n",
              "std      0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0    0.0    0.0\n",
              "min      0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0    0.0    0.0\n",
              "25%      0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0    0.0    0.0\n",
              "50%      0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0    0.0    0.0\n",
              "75%      0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0    0.0    0.0\n",
              "max      0.0    0.0    0.0    0.0    0.0  ...    0.2    0.0    0.0    0.0    0.0\n",
              "\n",
              "[8 rows x 784 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4-Vgg-1zu8Ud",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "32a200f9-7bb3-4ebf-b2dc-081c2f788f9d"
      },
      "source": [
        "validation_targets, validation_examples = parse_labels_and_features(mnist_dataframe[7500:10000])\n",
        "validation_examples.describe()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
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              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
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              "      <th>745</th>\n",
              "      <th>746</th>\n",
              "      <th>747</th>\n",
              "      <th>748</th>\n",
              "      <th>749</th>\n",
              "      <th>750</th>\n",
              "      <th>751</th>\n",
              "      <th>752</th>\n",
              "      <th>753</th>\n",
              "      <th>754</th>\n",
              "      <th>755</th>\n",
              "      <th>756</th>\n",
              "      <th>757</th>\n",
              "      <th>758</th>\n",
              "      <th>759</th>\n",
              "      <th>760</th>\n",
              "      <th>761</th>\n",
              "      <th>762</th>\n",
              "      <th>763</th>\n",
              "      <th>764</th>\n",
              "      <th>765</th>\n",
              "      <th>766</th>\n",
              "      <th>767</th>\n",
              "      <th>768</th>\n",
              "      <th>769</th>\n",
              "      <th>770</th>\n",
              "      <th>771</th>\n",
              "      <th>772</th>\n",
              "      <th>773</th>\n",
              "      <th>774</th>\n",
              "      <th>775</th>\n",
              "      <th>776</th>\n",
              "      <th>777</th>\n",
              "      <th>778</th>\n",
              "      <th>779</th>\n",
              "      <th>780</th>\n",
              "      <th>781</th>\n",
              "      <th>782</th>\n",
              "      <th>783</th>\n",
              "      <th>784</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 784 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         1      2      3      4      5    ...    780    781    782    783    784\n",
              "count 2500.0 2500.0 2500.0 2500.0 2500.0  ... 2500.0 2500.0 2500.0 2500.0 2500.0\n",
              "mean     0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0    0.0    0.0\n",
              "std      0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0    0.0    0.0\n",
              "min      0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0    0.0    0.0\n",
              "25%      0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0    0.0    0.0\n",
              "50%      0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0    0.0    0.0\n",
              "75%      0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0    0.0    0.0\n",
              "max      0.0    0.0    0.0    0.0    0.0  ...    0.0    0.0    0.0    0.0    0.0\n",
              "\n",
              "[8 rows x 784 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wrnAI1v6u8Uh",
        "colab_type": "text"
      },
      "source": [
        " 显示一个随机样本及其对应的标签。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-euVJVtu8Ui",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "6b33bc56-1f18-4ddf-b207-fbc537e867ff"
      },
      "source": [
        "rand_example = np.random.choice(training_examples.index)\n",
        "_, ax = plt.subplots()\n",
        "ax.matshow(training_examples.loc[rand_example].values.reshape(28, 28))\n",
        "ax.set_title(\"Label: %i\" % training_targets.loc[rand_example])\n",
        "ax.grid(False)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZOuvPk/UfXntTsl5rKDDlnp3pobzju7Ym6x8avrvudQOcPX5KQ8vbgVkWS9gR2zSQeWuO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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ScmYX7xdZMXE",
        "colab_type": "text"
      },
      "source": [
        " ## 任务 1：为 MNIST 构建线性模型\n",
        "\n",
        "首先，我们创建一个基准模型，作为比较对象。`LinearClassifier` 可提供一组 *k* 类一对多分类器，每个类别（共 *k* 个）对应一个分类器。\n",
        "\n",
        "您会发现，除了报告准确率和绘制对数损失函数随时间变化情况的曲线图之外，我们还展示了一个[**混淆矩阵**](https://en.wikipedia.org/wiki/Confusion_matrix)。混淆矩阵会显示错误分类为其他类别的类别。哪些数字相互之间容易混淆？\n",
        "\n",
        "另请注意，我们会使用 `log_loss` 函数跟踪模型的错误。不应将此函数与用于训练的 `LinearClassifier` 内部损失函数相混淆。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cpoVC4TSdw5Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def construct_feature_columns():\n",
        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
        "\n",
        "  Returns:\n",
        "    A set of feature columns\n",
        "  \"\"\" \n",
        "  \n",
        "  # There are 784 pixels in each image. \n",
        "  return set([tf.feature_column.numeric_column('pixels', shape=784)])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kMmL89yGeTfz",
        "colab_type": "text"
      },
      "source": [
        " 在本次练习中，我们会对训练和预测使用单独的输入函数，并将这些函数分别嵌套在 `create_training_input_fn()` 和 `create_predict_input_fn()` 中，这样一来，我们就可以调用这些函数，以返回相应的 `_input_fn`，并将其传递到 `.train()` 和 `.predict()` 调用。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OeS47Bmn5Ms2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_training_input_fn(features, labels, batch_size, num_epochs=None, shuffle=True):\n",
        "  \"\"\"A custom input_fn for sending MNIST data to the estimator for training.\n",
        "\n",
        "  Args:\n",
        "    features: The training features.\n",
        "    labels: The training labels.\n",
        "    batch_size: Batch size to use during training.\n",
        "\n",
        "  Returns:\n",
        "    A function that returns batches of training features and labels during\n",
        "    training.\n",
        "  \"\"\"\n",
        "  def _input_fn(num_epochs=None, shuffle=True):\n",
        "    # Input pipelines are reset with each call to .train(). To ensure model\n",
        "    # gets a good sampling of data, even when number of steps is small, we \n",
        "    # shuffle all the data before creating the Dataset object\n",
        "    idx = np.random.permutation(features.index)\n",
        "    raw_features = {\"pixels\":features.reindex(idx)}\n",
        "    raw_targets = np.array(labels[idx])\n",
        "   \n",
        "    ds = Dataset.from_tensor_slices((raw_features,raw_targets)) # warning: 2GB limit\n",
        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
        "    \n",
        "    if shuffle:\n",
        "      ds = ds.shuffle(10000)\n",
        "    \n",
        "    # Return the next batch of data.\n",
        "    feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n",
        "    return feature_batch, label_batch\n",
        "\n",
        "  return _input_fn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zoGWAoohrwS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_predict_input_fn(features, labels, batch_size):\n",
        "  \"\"\"A custom input_fn for sending mnist data to the estimator for predictions.\n",
        "\n",
        "  Args:\n",
        "    features: The features to base predictions on.\n",
        "    labels: The labels of the prediction examples.\n",
        "\n",
        "  Returns:\n",
        "    A function that returns features and labels for predictions.\n",
        "  \"\"\"\n",
        "  def _input_fn():\n",
        "    raw_features = {\"pixels\": features.values}\n",
        "    raw_targets = np.array(labels)\n",
        "    \n",
        "    ds = Dataset.from_tensor_slices((raw_features, raw_targets)) # warning: 2GB limit\n",
        "    ds = ds.batch(batch_size)\n",
        "    \n",
        "        \n",
        "    # Return the next batch of data.\n",
        "    feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n",
        "    return feature_batch, label_batch\n",
        "\n",
        "  return _input_fn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G6DjSLZMu8Um",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_linear_classification_model(\n",
        "    learning_rate,\n",
        "    steps,\n",
        "    batch_size,\n",
        "    training_examples,\n",
        "    training_targets,\n",
        "    validation_examples,\n",
        "    validation_targets):\n",
        "  \"\"\"Trains a linear classification model for the MNIST digits dataset.\n",
        "  \n",
        "  In addition to training, this function also prints training progress information,\n",
        "  a plot of the training and validation loss over time, and a confusion\n",
        "  matrix.\n",
        "  \n",
        "  Args:\n",
        "    learning_rate: A `float`, the learning rate to use.\n",
        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
        "      consists of a forward and backward pass using a single batch.\n",
        "    batch_size: A non-zero `int`, the batch size.\n",
        "    training_examples: A `DataFrame` containing the training features.\n",
        "    training_targets: A `DataFrame` containing the training labels.\n",
        "    validation_examples: A `DataFrame` containing the validation features.\n",
        "    validation_targets: A `DataFrame` containing the validation labels.\n",
        "      \n",
        "  Returns:\n",
        "    The trained `LinearClassifier` object.\n",
        "  \"\"\"\n",
        "\n",
        "  periods = 10\n",
        "\n",
        "  steps_per_period = steps / periods  \n",
        "  # Create the input functions.\n",
        "  predict_training_input_fn = create_predict_input_fn(\n",
        "    training_examples, training_targets, batch_size)\n",
        "  predict_validation_input_fn = create_predict_input_fn(\n",
        "    validation_examples, validation_targets, batch_size)\n",
        "  training_input_fn = create_training_input_fn(\n",
        "    training_examples, training_targets, batch_size)\n",
        "  \n",
        "  # Create a LinearClassifier object.\n",
        "  my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
        "  classifier = tf.estimator.LinearClassifier(\n",
        "      feature_columns=construct_feature_columns(),\n",
        "      n_classes=10,\n",
        "      optimizer=my_optimizer,\n",
        "      config=tf.estimator.RunConfig(keep_checkpoint_max=1)\n",
        "  )\n",
        "\n",
        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
        "  # loss metrics.\n",
        "  print(\"Training model...\")\n",
        "  print(\"LogLoss error (on validation data):\")\n",
        "  training_errors = []\n",
        "  validation_errors = []\n",
        "  for period in range (0, periods):\n",
        "    # Train the model, starting from the prior state.\n",
        "    classifier.train(\n",
        "        input_fn=training_input_fn,\n",
        "        steps=steps_per_period\n",
        "    )\n",
        "  \n",
        "    # Take a break and compute probabilities.\n",
        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
        "        \n",
        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
        "    \n",
        "    # Compute training and validation errors.\n",
        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
        "    # Occasionally print the current loss.\n",
        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
        "    # Add the loss metrics from this period to our list.\n",
        "    training_errors.append(training_log_loss)\n",
        "    validation_errors.append(validation_log_loss)\n",
        "  print(\"Model training finished.\")\n",
        "  # Remove event files to save disk space.\n",
        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
        "  \n",
        "  # Calculate final predictions (not probabilities, as above).\n",
        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
        "  \n",
        "  \n",
        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
        "  print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n",
        "\n",
        "  # Output a graph of loss metrics over periods.\n",
        "  plt.ylabel(\"LogLoss\")\n",
        "  plt.xlabel(\"Periods\")\n",
        "  plt.title(\"LogLoss vs. Periods\")\n",
        "  plt.plot(training_errors, label=\"training\")\n",
        "  plt.plot(validation_errors, label=\"validation\")\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "  \n",
        "  # Output a plot of the confusion matrix.\n",
        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
        "  # in each class).\n",
        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
        "  ax.set_aspect(1)\n",
        "  plt.title(\"Confusion matrix\")\n",
        "  plt.ylabel(\"True label\")\n",
        "  plt.xlabel(\"Predicted label\")\n",
        "  plt.show()\n",
        "\n",
        "  return classifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ItHIUyv2u8Ur",
        "colab_type": "text"
      },
      "source": [
        " **花费 5 分钟的时间了解一下使用这种形式的线性模型时，准确率方面表现如何。在本次练习中，为自己设定限制，仅使用批量大小、学习速率和步数这三个超参数进行试验。**\n",
        "\n",
        "如果您从上述任何试验中得到的准确率约为 0.9，即可停止试验。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yaiIhIQqu8Uv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 951
        },
        "outputId": "adba7f27-6856-43df-d9d8-d74eac359b5c"
      },
      "source": [
        "classifier = train_linear_classification_model(\n",
        "             learning_rate=0.02,\n",
        "             steps=100,\n",
        "             batch_size=10,\n",
        "             training_examples=training_examples,\n",
        "             training_targets=training_targets,\n",
        "             validation_examples=validation_examples,\n",
        "             validation_targets=validation_targets)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "Training model...\n",
            "LogLoss error (on validation data):\n",
            "  period 00 : 18.06\n",
            "  period 01 : 12.90\n",
            "  period 02 : 10.15\n",
            "  period 03 : 8.10\n",
            "  period 04 : 10.04\n",
            "  period 05 : 7.87\n",
            "  period 06 : 6.62\n",
            "  period 07 : 6.22\n",
            "  period 08 : 5.95\n",
            "  period 09 : 5.76\n",
            "Model training finished.\n",
            "Final accuracy (on validation data): 0.83\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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zd+9ennvuOdasWUNYWBgTJ048p+NkK+lw1+fD5+4I4EzR+IWd9cgK7wSr34BKMPieMabs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\nYIwxPs4SgTHG+DhLBMYY4+P+P6/NfUay7NlxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EcEVE/Cin2GcC/5H+Z7cMeGceQVMyPgb4v3nEA4iIeZKuAW4DNgO3k89QrO9J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\nrNKc5Mys0pzkzKzS/j9pLgM0tBB+rgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "266KQvZoMxMv",
        "colab_type": "text"
      },
      "source": [
        " ### 解决方案\n",
        "\n",
        "点击下方即可查看一种可能的解决方案。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lRWcn24DM3qa",
        "colab_type": "text"
      },
      "source": [
        " 以下是一组使准确率应该约为 0.9 的参数。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGlBMrUoM1K_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825
        },
        "outputId": "26fe7249-c609-4c02-a519-8f2cd4050cf8"
      },
      "source": [
        "_ = train_linear_classification_model(\n",
        "    learning_rate=0.03,\n",
        "    steps=1000,\n",
        "    batch_size=30,\n",
        "    training_examples=training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=validation_examples,\n",
        "    validation_targets=validation_targets)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "LogLoss error (on validation data):\n",
            "  period 00 : 4.74\n",
            "  period 01 : 4.21\n",
            "  period 02 : 3.74\n",
            "  period 03 : 3.65\n",
            "  period 04 : 3.61\n",
            "  period 05 : 3.62\n",
            "  period 06 : 3.43\n",
            "  period 07 : 3.51\n",
            "  period 08 : 3.40\n",
            "  period 09 : 3.50\n",
            "Model training finished.\n",
            "Final accuracy (on validation data): 0.90\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rjPGOtwnjmIj0L3ohIv2AY/4JqWZx+jLasCPrGJ8sTQ98APFJcOnzNtTWGFOu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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W5Wq1WsvbcGnlYcCzaYD+XZLeRzZj5+Riq2VmZVCCW3ItJbmPABOBD5Hdm5sC/F2RlTKz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\ni1wYMJQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mk095OfpPdOx",
        "colab_type": "text"
      },
      "source": [
        " ## 任务 2：使用神经网络替换线性分类器\n",
        "\n",
        "**使用 [`DNNClassifier`](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier) 替换上面的 LinearClassifier，并查找可实现 0.95 或更高准确率的参数组合。**\n",
        "\n",
        "您可能希望尝试 Dropout 等其他正则化方法。这些额外的正则化方法已记录在 `DNNClassifier` 类的注释中。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rm8P_Ttwu8U4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#\n",
        "# YOUR CODE HERE: Replace the linear classifier with a neural network.\n",
        "#"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TOfmiSvqu8U9",
        "colab_type": "text"
      },
      "source": [
        " 获得出色的模型后，通过评估我们将在下面加载的测试数据进行仔细检查，确认您没有过拟合验证集。\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "evlB5ubzu8VJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist_test_dataframe = pd.read_csv(\n",
        "  \"https://download.mlcc.google.cn/mledu-datasets/mnist_test.csv\",\n",
        "  sep=\",\",\n",
        "  header=None)\n",
        "\n",
        "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n",
        "test_examples.describe()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PDuLd2Hcu8VL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#\n",
        "# YOUR CODE HERE: Calculate accuracy on the test set.\n",
        "#"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6sfw3LH0Oycm",
        "colab_type": "text"
      },
      "source": [
        " ### 解决方案\n",
        "\n",
        "点击下方即可查看可能的解决方案。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XatDGFKEO374",
        "colab_type": "text"
      },
      "source": [
        " 除了神经网络专用配置（例如隐藏单元的超参数）之外，以下代码与原始的 `LinearClassifer` 训练代码几乎完全相同。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kdNTx8jkPQUx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_nn_classification_model(\n",
        "    learning_rate,\n",
        "    steps,\n",
        "    batch_size,\n",
        "    hidden_units,\n",
        "    training_examples,\n",
        "    training_targets,\n",
        "    validation_examples,\n",
        "    validation_targets):\n",
        "  \"\"\"Trains a neural network classification model for the MNIST digits dataset.\n",
        "  \n",
        "  In addition to training, this function also prints training progress information,\n",
        "  a plot of the training and validation loss over time, as well as a confusion\n",
        "  matrix.\n",
        "  \n",
        "  Args:\n",
        "    learning_rate: A `float`, the learning rate to use.\n",
        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
        "      consists of a forward and backward pass using a single batch.\n",
        "    batch_size: A non-zero `int`, the batch size.\n",
        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
        "    training_examples: A `DataFrame` containing the training features.\n",
        "    training_targets: A `DataFrame` containing the training labels.\n",
        "    validation_examples: A `DataFrame` containing the validation features.\n",
        "    validation_targets: A `DataFrame` containing the validation labels.\n",
        "      \n",
        "  Returns:\n",
        "    The trained `DNNClassifier` object.\n",
        "  \"\"\"\n",
        "\n",
        "  periods = 10\n",
        "  # Caution: input pipelines are reset with each call to train. \n",
        "  # If the number of steps is small, your model may never see most of the data.  \n",
        "  # So with multiple `.train` calls like this you may want to control the length \n",
        "  # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n",
        "  # or since it's in-memory data, shuffle all the data in the `input_fn`.\n",
        "  steps_per_period = steps / periods  \n",
        " \n",
        "  # Create the input functions.\n",
        "  predict_training_input_fn = create_predict_input_fn(\n",
        "    training_examples, training_targets, batch_size)\n",
        "  predict_validation_input_fn = create_predict_input_fn(\n",
        "    validation_examples, validation_targets, batch_size)\n",
        "  training_input_fn = create_training_input_fn(\n",
        "    training_examples, training_targets, batch_size)\n",
        "  \n",
        "  # Create feature columns.\n",
        "  feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n",
        "\n",
        "  # Create a DNNClassifier object.\n",
        "  my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
        "  classifier = tf.estimator.DNNClassifier(\n",
        "      feature_columns=feature_columns,\n",
        "      n_classes=10,\n",
        "      hidden_units=hidden_units,\n",
        "      optimizer=my_optimizer,\n",
        "      config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n",
        "  )\n",
        "\n",
        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
        "  # loss metrics.\n",
        "  print(\"Training model...\")\n",
        "  print(\"LogLoss error (on validation data):\")\n",
        "  training_errors = []\n",
        "  validation_errors = []\n",
        "  for period in range (0, periods):\n",
        "    # Train the model, starting from the prior state.\n",
        "    classifier.train(\n",
        "        input_fn=training_input_fn,\n",
        "        steps=steps_per_period\n",
        "    )\n",
        "  \n",
        "    # Take a break and compute probabilities.\n",
        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
        "        \n",
        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
        "    \n",
        "    # Compute training and validation errors.\n",
        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
        "    # Occasionally print the current loss.\n",
        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
        "    # Add the loss metrics from this period to our list.\n",
        "    training_errors.append(training_log_loss)\n",
        "    validation_errors.append(validation_log_loss)\n",
        "  print(\"Model training finished.\")\n",
        "  # Remove event files to save disk space.\n",
        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
        "  \n",
        "  # Calculate final predictions (not probabilities, as above).\n",
        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
        "  \n",
        "  \n",
        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
        "  print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n",
        "\n",
        "  # Output a graph of loss metrics over periods.\n",
        "  plt.ylabel(\"LogLoss\")\n",
        "  plt.xlabel(\"Periods\")\n",
        "  plt.title(\"LogLoss vs. Periods\")\n",
        "  plt.plot(training_errors, label=\"training\")\n",
        "  plt.plot(validation_errors, label=\"validation\")\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "  \n",
        "  # Output a plot of the confusion matrix.\n",
        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
        "  # in each class).\n",
        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
        "  ax.set_aspect(1)\n",
        "  plt.title(\"Confusion matrix\")\n",
        "  plt.ylabel(\"True label\")\n",
        "  plt.xlabel(\"Predicted label\")\n",
        "  plt.show()\n",
        "\n",
        "  return classifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZfzsTYGPPU8I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825
        },
        "outputId": "e8bea68f-f499-49f4-9678-e5a8ba8c42eb"
      },
      "source": [
        "classifier = train_nn_classification_model(\n",
        "    learning_rate=0.05,\n",
        "    steps=1000,\n",
        "    batch_size=30,\n",
        "    hidden_units=[100, 100],\n",
        "    training_examples=training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=validation_examples,\n",
        "    validation_targets=validation_targets)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "LogLoss error (on validation data):\n",
            "  period 00 : 4.81\n",
            "  period 01 : 3.47\n",
            "  period 02 : 3.14\n",
            "  period 03 : 2.42\n",
            "  period 04 : 2.62\n",
            "  period 05 : 2.27\n",
            "  period 06 : 2.28\n",
            "  period 07 : 2.07\n",
            "  period 08 : 2.06\n",
            "  period 09 : 1.98\n",
            "Model training finished.\n",
            "Final accuracy (on validation data): 0.94\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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i03wvBvJHOWcBI0VEfI6+muvfJopWUWHez1nwRUgEXPMuRLWDN6+Abd97HZEx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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HgCnAR8juzU0D/rbISplZSZTgnlwrA/QXppdP8vzEmWZm5X66KukShumQGxF/U0iNzKw0\nSp3kgNNGrRYbkU3l3v0xAfoLuqla1CpNRaysteOOu+UeE+Dm2xY2P6kNE8eNKyTu+sh/PtmpEyc2\nP2mMlDrJRcQvR7MiZlY+tSpMmmlmNpRSt+TMzJopQY5rPclJmhARzxZZGTMrmRJkuVZmBp4t6Rbg\nrvT+1ZL+pfCamVnXq8qwrm8BBwIPA0TETWSLTZtZj1NNLW9jpZXL1VpE/LEhE68rqD5mViJjOSa1\nVa0kuXslzQZCUh9wDLC02GqZWRlU5enqh8guWbcDHgJ+kfa1TNJfArOBWyPiipFW0sy6UyWSXESs\nBA4dSVBJ10bE7PT6KODDwCXAZyTtERFfaqeyZtZd1P1Xqy3NDHwWGxnDGhHDLaVVP2ZmHvCmiFgl\n6Stka0RsNMnVr9Z1xhlneLUus25XhZYc2eXpoInA/wbubfKZmqQXkD29VUSsAoiIpyUNDPWh+tW6\noqiBpmaWm0o8eIiIDaY6l/QD4KomH5sGXEc2ZXpI2ioiHpA0Ne0zswqoxD25jZgBbDncCRGx/RCH\n1pO1BM2sAsqwkE0r9+Qe5fl7cjWyxaaPb6ewiFgN/L6dz5pZ9yl9S07ZN3g12boOAOt9r8zMBpUh\nyQ171zAltMsiYl3anODM7DlS61tr8TRX0p2Slkka8opR0v+RFJL2ahazlUcjN0ravbUqmlkvUV+t\n5a1prGxE1elkS57OAg6TNGsj520CHAu0NG30kCVLGryU3R1YlLLr9ZJukHR9K8HNrNpynoVkNrAs\nIpZHxBrgQuDgjZz3WeBU4M+tBB3unty1wB7AQa0EMrPeM5J7cvWd/ZP5qW/soG3YsA/uCuC1DTH2\nALaNiEslfaKVcodLcgKIiLtbCZS3Im5o9vf15R6zSC/edNNC4q5bn/9iK0vvKqZxX9SfWWG3l/P/\n0TJ5fPdO4D2Sf6f1nf3bLKsGfA04YiSfG+6nt4Wk44Y6GBFfG0lBZlY9OfeTuw/Ytu79dJ7v2QGw\nCbArcGVKri8BFkg6KCIWDxV0uCTXB3iEgpkNKecrrkXATEkzyJLbocDhgwcj4nFg87qyrwQ+PlyC\ng+GT3AMRcUonNTazaqvl2JKLiAFJRwOXkzWyzomIJZJOARZHxIJ24ja9J2dmNqSc751HxGXAZQ37\nPj3EuXNaiTlcktu35ZqZWU8q9djViHhkNCtiZuVThmFd3fts2sy6npOcmVVaJSbNNDMbShnWeCik\nipJeK2nT9HqSpJMl/bukUyVNK6JMMxt9OY9dLURRefgcYHV6/U2y6dBPTfvOLahMMxttec+1VICi\nklwtIgYXrNkrIj4aEVdFxMnADkN9SNI8SYslLZ4/v+0hbmY2SsrQkivqntytkt4fEecCN0naKyIW\nS9oZWDvUhxoG8HqCTrMu18tPVz8AfFPSicCfgKsl3Us2jcoHCirTzEZZrYXJMMdaIUkuDaQ9Ij18\nmJHKWRERDxVRnpmNjV5uyQEQEU8ANxVZhpmNnRLkOPeTM7MOlCDLOcmZWdtKPUDfzKwZD+sys0rr\n+QcPZlZtTnI94tmBgeYntWFCfzF/PH0luMQYVNSqWv394wuJOzCwJveYha0sRudJyvfkzKzSStCQ\nc5Izsw6UIMs5yZlZ2/x01cwqzffkzKzS/HTVzCrNSc7MKs1JzswqrQQ5zknOzNqnEkyaWdRqXR+R\ntG0Rsc2se5RhjYei0vBngYWSfivp7yRt0cqHvJCNWbmUIckVdbm6HNgT2A94J3CypOuAC4CfRsST\nG/uQF7IxK5daCW7KFdWSi4hYHxFXRMSRwNbAt4G5ZAnQzCqgl1tyG3yjiFgLLAAWSJpcUJlmNsr6\nenjEwzuHOhARqwsq08xGmejRJBcRS4uIa2bdpQz35NxPzszaVoYRD93fk8/MulbeDx4kzZV0p6Rl\nko7fyPHjJN0m6WZJv5T00mYxneTMrG01qeWtGUl9wOnAAcAs4DBJsxpOuwHYKyJeBVwM/HPTOo74\nW5mZJX21WstbC2YDyyJieUSsAS4EDq4/ISJ+Vffw8hpgerOgTnJm1jZpJNvzI5rSNq8h3DbAvXXv\nV6R9QzkS+I9mdeypBw9r1xWzqlZRqykVFbeIm8Xr1q/PPSYU9/SuiFW1ACZNmpp7zNWrNzpAqCuM\npAtJw4imzsqV3g3sBbyx2bk9leTMLF85/xK6D6if2GN62rcBSfsBJwBvjIhnmwV1kjOztuV8VbAI\nmClpBllyOxQ4vKG83YEzgbkRsbKVoE5yZta2PJNcRAxIOhq4HOgDzomIJZJOARZHxALgy8BU4Mep\n7Hsi4qDh4jrJmVnbWnxq2rKIuAy4rGHfp+te7zfSmE5yZta2Mox4cJIzs7aVYBISJzkza1/PzkJi\nZr2hZ2chkTSe7PHv/RHxC0mHA3sDtwPz0ySaZlZytZwfPBShqJbcuSn2ZEnvI3vk+1NgX7Lxae8r\nqFwzG0U925IDXhkRr5LUT9apb+uIWCfph8BNQ30ojWWbB3DmmWcyb17j0DYz6ya9/HS1li5ZpwCT\ngWnAI8AEYNxQH/JqXWbl0stJ7mzgDrJeyyeQ9U5eDryObPoUM6uAnu1CEhFfl/Sj9Pp+Sd8nW4P1\nrIi4togyzWz09XQXkoi4v+71Y2SzeJpZheQ9rKsI7idnZm3r5XtyZtYDerkLiZn1ALfkzKzSnOTM\nrNJ6tguJmfWGmvx0tav01/oKiTuur5hfZ0WtgLV2IP9VyyaOG3IgS1d6toCfAcAzzzyVe8wiVgAb\n1Gl9/eDBzCrN9+TMrNLckjOzSnNLzswqra8Ej1ed5MysbT09QN/Mqs+Xq2ZWaX7wYGaV1tMtOUk7\nAH8DbAusA5YC50fEE0WVaWajqwxJrpAxGZI+ApwBTAReQ7a2w7bANZLmFFGmmY2+vlqt5W2sFFXy\nUcABEfE5smnPXxERJwBzga8P9SFJ8yQtlrR4/vz5Q51mZl2ipta3sVLkPbl+ssvUCWTrrhIR90jy\nal1mFdHLXUi+CyyStBB4A3AqgKQtyJYmNLMKKMM9OUUU02CS9Arg5cCtEXFHGyFyr1iB37WQuIXN\nQrJuXe4xPQtJZkJ//u2Ggmch6egv71N//nPL/6imTpw4JhmxyNW6lgBLiopvZmOvDC0595Mzs7Z5\nSUIzqzS35Mys0kowCUlh/eTMrAdoBP+1FE+aK+lOScskHb+R4xMk/SgdXyhp+2YxneTMrG2SWt5a\niNUHnA4cAMwCDpM0q+G0I4FHI2InsoEFpzaL6yRnZm3LeVjXbGBZRCyPiDXAhcDBDeccDHwvvb4Y\n2FfNMmhElH4D5vV63DLVtWxxy1TXIuPmUS9gcd02r+H424Dv1r1/D3Bawzm3AtPr3t8NbD5cuVVp\nyc1z3FLVtWxxy1TXIuN2JCLmR8RedduoDFCvSpIzs/K7j2y2okHT076NniOpH5gGPDxcUCc5M+sW\ni4CZkmZIGg8cCixoOGcB8L70+m3Af0W6bh1KVfrJFdXsLVPcMtW1bHHLVNci4xYqIgYkHQ1cDvQB\n50TEEkmnAIsjYgFwNvADScvIJvs4tFncwgbom5l1A1+umlmlOcmZWaWVPsk1GwbSZsxzJK2UdGse\n8VLMbSX9StJtkpZIOjanuBMlXSvpphT35Dzipth9km6Q9LMcY/5B0i2SbpS0OMe4m0m6WNIdkm6X\n9Bc5xNwl1XNwe0LSR3Oq78fSn9etki6QNDGHmMemeEvyqmcljHUHwQ47F/aRdQbcARgP3ATMyiHu\nPsAeZBN+5lXXrYA90utNyFYvy6OuAqam1+OAhcDrcqrzccD5wM9y/Dn8gSadN9uM+z3gA+n1eGCz\nAv6uPQi8NIdY2wC/Byal9xcBR3QYc1eyjrKTyR4o/gLYKe+fcxm3srfkWhkGMmIR8RtynqY9Ih6I\niOvT6yeB28n+sncaNyLiqfR2XNo6fpokaTrwFrKp7LuapGlkv5jOBoiINRHxWM7F7AvcHRF/zCle\nPzAp9fWaDNzfYbyXAwsjYnVEDAC/JlsStOeVPcltA9xb934FOSSOoqWZE3Yna3XlEa9P0o3ASuA/\nIyKPuN8A/gHIew72AK6QdJ2kvHrmzwBWAeemy+vvSpqSU+xBhwIX5BEoIu4DvgLcAzwAPB4RV3QY\n9lbgDZJeJGky8GY27Fjbs8qe5EpH0lTgJ8BHI6eFtiNiXUTsRtZDfLakXTus44HAyoi4Lo/6NfjL\niNiDbKaJD0vaJ4eY/WS3F74TEbsDTwO53J8FSB1TDwJ+nFO8F5BdccwAtgamSHp3JzEj4nayGTmu\nAH4O3Ei2Wl7PK3uSa2UYSNdIyzH+BPjXiPhp3vHTJdqvyNa37cTrgYMk/YHsFsBfS/phhzGB51ox\nRMRK4BKyWw6dWgGsqGvBXkyW9PJyAHB9RDyUU7z9gN9HxKqIWAv8FNi706ARcXZE7BkR+wCPkt33\n7XllT3KtDAPpCmk6mLOB2yPiaznG3ULSZun1JOBNQDuroz0nIj4ZEdMjYnuyn+l/RURHLY1UvymS\nNhl8DexPdpnVkYh4ELhX0i5p177AbZ3GrXMYOV2qJvcAr5M0Of292JfsHm1HJL04/X87svtx53ca\nswpKPawrhhgG0mlcSRcAc4DNJa0APhMRZ3cY9vVkU8fcku6fAXwqIi7rMO5WwPfShIM14KKIyK3L\nR862BC5J03/1A+dHxM9zin0M8K/pl91y4P15BE3J+E3A/80jHkBELJR0MXA9MADcQD5DsX4i6UXA\nWuDDBTx8KSUP6zKzSiv75aqZ2bCc5Mys0pzkzKzSnOTMrNKc5Mys0pzkSkzSujQ7xq2SfpyG87Qb\na87gbCOSDhpuRpc048fftVHGSZI+3ur+hnPOk/S2EZS1fZ6zyFh5OcmV2zMRsVtE7AqsAT5Yf1CZ\nEf8ZR8SCiPjSMKdsBow4yZmNBSe56vgtsFNqwdwp6ftkowm2lbS/pKslXZ9afFPhubn47pB0PXUz\nVkg6QtJp6fWWki5J89XdJGlv4EvAjqkV+eV03ickLZJ0c/2cdpJOkLRU0lXALjQh6agU5yZJP2lo\nne4naXGKd2A6v0/Sl+vKzq3TrlWDk1wFpOl6DgBuSbtmAt+OiFeQDVY/EdgvDYxfDByXJmk8C3gr\nsCfwkiHCfwv4dUS8mmw86BKywe93p1bkJyTtn8qcDewG7ClpH0l7kg0L241sVozXtPB1fhoRr0nl\n3Q4cWXds+1TGW4Az0nc4kmwWj9ek+EdJmtFCOdYjSj2sy5hUN0Tst2RjY7cG/hgR16T9rwNmAb9L\nw6nGA1cDLyMbJH4XQBqAv7Gpj/4aeC9ks50Aj6dZNOrtn7Yb0vupZElvE+CSiFidymhlXPGukj5H\ndkk8lWzI3qCLImI9cJek5ek77A+8qu5+3bRUtgenG+AkV3bPpCmWnpMS2dP1u8jmmDus4bwNPtch\nAV+MiDMbymhnCu7zgEMi4iZJR5CNIR7UOAYxUtnHRER9Mhycs8/Ml6s94Brg9ZJ2gudmAtmZbKaS\n7SXtmM47bIjP/xL4UPpsn7JZeJ8ka6UNuhz427p7fdukGTF+AxwiaVKafeStLdR3E+CBNC3VuxqO\nvV1SLdV5B+DOVPaH0vlI2ln5T5hpJeaWXMVFxKrUIrpA0oS0+8SIWKpsZt5LJa0mu9zdZCMhjgXm\nSzqSbBLGD0XE1ZJ+l7po/Ee6L/dy4OrUknwKeHdEXC/pR2Rrb6wkmxqrmX8imzF5Vfp/fZ3uAa4F\nNgU+GBF/lvRdsnt116dpi1YBh7T207Fe4FlIzKzSfLlqZpXmJGdmleYkZ2aV5iRnZpXmJGdmleYk\nZ2aV5iRnZpX2P3e5+S/wD98SAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXvrOgtUR-zD",
        "colab_type": "text"
      },
      "source": [
        " 接下来，我们来验证测试集的准确率。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "scQNpDePSFjt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "ff92c979-32c6-4057-b8b0-7e2c0872dd1d"
      },
      "source": [
        "mnist_test_dataframe = pd.read_csv(\n",
        "  \"https://download.mlcc.google.cn/mledu-datasets/mnist_test.csv\",\n",
        "  sep=\",\",\n",
        "  header=None)\n",
        "\n",
        "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n",
        "test_examples.describe()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>...</th>\n",
              "      <th>745</th>\n",
              "      <th>746</th>\n",
              "      <th>747</th>\n",
              "      <th>748</th>\n",
              "      <th>749</th>\n",
              "      <th>750</th>\n",
              "      <th>751</th>\n",
              "      <th>752</th>\n",
              "      <th>753</th>\n",
              "      <th>754</th>\n",
              "      <th>755</th>\n",
              "      <th>756</th>\n",
              "      <th>757</th>\n",
              "      <th>758</th>\n",
              "      <th>759</th>\n",
              "      <th>760</th>\n",
              "      <th>761</th>\n",
              "      <th>762</th>\n",
              "      <th>763</th>\n",
              "      <th>764</th>\n",
              "      <th>765</th>\n",
              "      <th>766</th>\n",
              "      <th>767</th>\n",
              "      <th>768</th>\n",
              "      <th>769</th>\n",
              "      <th>770</th>\n",
              "      <th>771</th>\n",
              "      <th>772</th>\n",
              "      <th>773</th>\n",
              "      <th>774</th>\n",
              "      <th>775</th>\n",
              "      <th>776</th>\n",
              "      <th>777</th>\n",
              "      <th>778</th>\n",
              "      <th>779</th>\n",
              "      <th>780</th>\n",
              "      <th>781</th>\n",
              "      <th>782</th>\n",
              "      <th>783</th>\n",
              "      <th>784</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 784 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          1       2       3       4    ...     781     782     783     784\n",
              "count 10000.0 10000.0 10000.0 10000.0  ... 10000.0 10000.0 10000.0 10000.0\n",
              "mean      0.0     0.0     0.0     0.0  ...     0.0     0.0     0.0     0.0\n",
              "std       0.0     0.0     0.0     0.0  ...     0.0     0.0     0.0     0.0\n",
              "min       0.0     0.0     0.0     0.0  ...     0.0     0.0     0.0     0.0\n",
              "25%       0.0     0.0     0.0     0.0  ...     0.0     0.0     0.0     0.0\n",
              "50%       0.0     0.0     0.0     0.0  ...     0.0     0.0     0.0     0.0\n",
              "75%       0.0     0.0     0.0     0.0  ...     0.0     0.0     0.0     0.0\n",
              "max       0.0     0.0     0.0     0.0  ...     0.0     0.0     0.0     0.0\n",
              "\n",
              "[8 rows x 784 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EVaWpWKvSHmu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bfbdf6fe-7a96-4d2b-88dc-a91675928d03"
      },
      "source": [
        "predict_test_input_fn = create_predict_input_fn(\n",
        "    test_examples, test_targets, batch_size=100)\n",
        "\n",
        "test_predictions = classifier.predict(input_fn=predict_test_input_fn)\n",
        "test_predictions = np.array([item['class_ids'][0] for item in test_predictions])\n",
        "  \n",
        "accuracy = metrics.accuracy_score(test_targets, test_predictions)\n",
        "print(\"Accuracy on test data: %0.2f\" % accuracy)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on test data: 0.94\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WX2mQBAEcisO",
        "colab_type": "text"
      },
      "source": [
        " ## 任务 3：可视化第一个隐藏层的权重。\n",
        "\n",
        "我们来花几分钟时间看看模型的 `weights_` 属性，以深入探索我们的神经网络，并了解它学到了哪些规律。\n",
        "\n",
        "模型的输入层有 `784` 个权重，对应于 `28×28` 像素输入图片。第一个隐藏层将有 `784×N` 个权重，其中 `N` 指的是该层中的节点数。我们可以将这些权重重新变回 `28×28` 像素的图片，具体方法是将 `N` 个 `1×784` 权重数组*变形*为 `N` 个 `28×28` 大小数组。\n",
        "\n",
        "运行以下单元格，绘制权重曲线图。请注意，此单元格要求名为 \"classifier\" 的 `DNNClassifier` 已经过训练。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eUC0Z8nbafgG",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "test": {
            "output": "ignore",
            "timeout": 600
          },
          "base_uri": "https://localhost:8080/",
          "height": 1181
        },
        "outputId": "02c9b497-194c-452c-9c1e-a0d5a3ce60cc"
      },
      "source": [
        "print(classifier.get_variable_names())\n",
        "\n",
        "weights0 = classifier.get_variable_value(\"dnn/hiddenlayer_0/kernel\")\n",
        "\n",
        "print(\"weights0 shape:\", weights0.shape)\n",
        "\n",
        "num_nodes = weights0.shape[1]\n",
        "num_rows = int(math.ceil(num_nodes / 10.0))\n",
        "fig, axes = plt.subplots(num_rows, 10, figsize=(20, 2 * num_rows))\n",
        "for coef, ax in zip(weights0.T, axes.ravel()):\n",
        "    # Weights in coef is reshaped from 1x784 to 28x28.\n",
        "    ax.matshow(coef.reshape(28, 28), cmap=plt.cm.pink)\n",
        "    ax.set_xticks(())\n",
        "    ax.set_yticks(())\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['dnn/hiddenlayer_0/bias', 'dnn/hiddenlayer_0/bias/t_0/Adagrad', 'dnn/hiddenlayer_0/kernel', 'dnn/hiddenlayer_0/kernel/t_0/Adagrad', 'dnn/hiddenlayer_1/bias', 'dnn/hiddenlayer_1/bias/t_0/Adagrad', 'dnn/hiddenlayer_1/kernel', 'dnn/hiddenlayer_1/kernel/t_0/Adagrad', 'dnn/logits/bias', 'dnn/logits/bias/t_0/Adagrad', 'dnn/logits/kernel', 'dnn/logits/kernel/t_0/Adagrad', 'global_step']\n",
            "weights0 shape: (784, 100)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FEt+P4cEQdY7ss9rrpApUehriXMEl7DnD2FNylcz649gHFF0qSzLkr8D61/o2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\nsX8xVYuyJmMv0bgFMZ0rVhHRu57NEg1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N98pxGy3EmG7bAZpXyep5wq9xD+KSl1FfWrZJ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\nR0rPwxyLTWB8omM6V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fuPqH6Lf+syr917CPM8Oqm1KAaPJ/YCV+comZjREuDYIaK5/bFx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eM67qxU1hViY9/NAXiIjIrgQ3RWuqzfbwBSwOfugjIuo70Ga2nblYBDbFbvgMvsPGJnT+ignC\njh9Imp45a7bLbp8MG+W7+092mW1/0xDuJy9F2PExCPVikeYsLBF2vmZ8B+8XuckQpVVlm23PlX6z\n7b0yKOzGg6rP/f73lGgU52fT0z/+GhERufKlQ9nx8Htmuygry2xP2jxN2J186YTZ5jOsYlqpsMue\nU4Tv/i2+e+bMamG39+AZfEdentnOZU43f0W5uObwC0fN9qzVuD9XnlvY8flmYy/91BeAfP7xAKh3\nX6uwK90yyWyfewb9EFU2+vLZZUREdN93fkbXAsW52fTE975CRHJuEhEVrKo028PnsY7ybigTdkPn\nesw2d45Dp3qEXUFdhdnu2d1stjOn5wu74XP4Le4PPA0DuGaqvIY725796OtAy4iwy6/DuueHSme2\nXLMjl7CuAi3DZju5TG7g/iZ8Vn7XVLPd/Kezwi59mhFA3vu1H1GiUVyQQ3/6z68TEVEPm6dERBk1\nCCoi7EBctFaunZ59LWY7tRLBTOs7V4Rd+bqJZvuDXowQEfXuwffxIMo/gmCm/KZacU2gEy98+SaW\nUZsr7C49gfVScmOV2e58r0nYTdgC382D0pSiNGHnuYJ51bEb31F1h/RXPWPP9Omf/g9dC+Snp9OP\n7r2XiIisFrnB126cYrYvvIq5NXmTvMeWd+rNdsFc7C8pJXLe8gBipB7PH2iT62WoH2OSmYN+s2fC\nz/XU94prJiyD3+g+iPmYN1/ud417MLcmbcLaad/eIOzK2Dxpfu2i2U52St8bGsUYu9melLtI7if9\nB9uJiOhT12AcCzIy6H8/9SkiIirbMFF8xtfI5VfPfeB3lC3AHsUP203HWoRd6STsi/wFSldnv7Bb\n/A91Zpv/UaqX+cmms9JvpLqwtqu3YGy4nyUi6tnRZLZzlqKfVX/a/NJ5sz3pM/PMdv2jx4WduyLD\nbBevho/ivpqIqPNtY+5cizEkMuKbp35kxDfqywbu9/oPtZvtlPIMYecuw7/5YXf4vNwX7en8D0aI\nhILd8mCVXIQ5zeMTfsAerpdjPzqIF4T+ZuxVReuk/x86i3tKq+bx5YCwS61EPNe7C3t4+R1Thd3o\nCH43wJ4jVemj8djh3m/+hBKNksJcevHh7xCRfDFFJP+QJl6UKPP7jRf2Riq23gAAIABJREFUwo4F\n8stnyOd1V2PPvLwffm3QJ8dw8ea5ZttzCX3LzzChHhmHZc3HOh8534ffnCD7kr847duH9ZxakyXs\n+LxysnnUe7pT2BUvhh/isVzmTBl7bX1yJxER/c8779C1QHFeNj31w68SEdGVvTIeCbN4ef79N5jt\nQXY2IyLqvYj7H/ajf1f/yy3CruW1U2abx/JVa1cJuze+9Wuzvfa795vtjkM4T1x8Xfr4BQ8uN9tZ\nhbPNdl/TYWF35Hf78buLEN9Ublgm7Dw92Ce792At5swvFnb8xXvfsQ6zvfvlQ8Ju/Jz0zWeeoUSj\nKDuLHvvaQ0RE5CqS50Xx0oOtMe67iOQ5sGtHo9lWX9K5CjCnw0OIeVMqlPXCXgR5G3B2Dg/jmrTa\nbHGNv5nFR+zFnk19Wcb2DP4crny5LwY68JIuuZTFaDHph9In4zwb7MH6HTrZLezSJhpr/d6v/5g+\nDK7qxY3VZTU7RHmxRJ3soODIwobmb5cBJf9rUYxl1SSXy5dMReuxQSXnY0A7d8jgsJgdEEtuRqDI\nN8XBs3LDTWeHIj75+AZJRFTIDsADp9DRfXtkIFa8Cb8bYYHKX2T6sL+C+BrxsqdwdZWw69h2eewL\nKOEIeUJUv8s4JBQW5YjP1nzhRrPNJ5m/wyPsysqxAbS3IvhvPtcu7Pjby5WfWIrvU+bEyjUICM8e\nvPS+9931yinx7wV3zTfbu5+Cw0xPln/JdtnZXze7MGlr6mRw3nkAwXBLHzbZyny52fGJ72TZQUOj\nMkAdP3CpfzFPFKzJdsoYewnS+Xq9+CzQDafCnaNPXYvs7bfvFOZ+7mJ5YBq5jKAyfQoO42qmWNbs\nQrPN3y7zgDnYJwMiBwt+85cg4AhNkX/14OuU/yV//CBg3gPbJLJmYOxsSqZPG5vf3exlVMbMPGE3\nHiypf01LBKL+MA2fNHxTVHH4uWwT9zRhcxq+1CfssmYWmG1+WCxYIMeQj8cACwLUF+bebrnWx+Fi\nh+2QkpUU6se/k4vxkqD3oDxUZrNNzMNesGVOki942l6FDyjeUGO2e5SXqGnVCGyzp2Cs216RPqTs\nNuNFkO23SiZFguDMcNGkDcYLGn+rXGP80G9l2WH8ZT8RUTr7q17nUfSb65Sct+W34qWWyCizyxdG\nueUIXHIW4MVL51tYLzlF8q+Wnos4kFSyl1+Dp2SQUX0jfCd/WZNZLYOlYfZiuHz9+784JCJqeh6B\nMg/y1IO3u8q4X/UvcIlCfMy3q1kngVb4xqkfnWO21X2Mv1Tje33VYrm/dx/DPjnlk9j7XEc7hF3r\nq3jZxYN6/sJt/mcWiWuan0Nfir8SKs+UOQ++uvs9+D8lrKMhdoANsQxT/iKdSAbk3cn4PvcEOcfa\nG425NBqSQXuiEA1EyXPB8C35K+U98gwS/ldhZ64MysVft9kfHdQMHmcOYg1+yAp2yWyPQBf6cPAo\nDqYFqxFfqn/R5S9h+GdDZ+RazJmHte1pxD6Ru0C+bA2we+IvBJqfl4fU8jvwotnDDkX8u4mIcuYa\n85EfohKF4FCALrx0moiIajfJFy38BeSJt07jmrCcT3d9bYvZ5utU/Qs731MmrUAc37S/UdjxjKUT\n5+FDl27E+s2aVSiueedX75rt5fdgnTa+fVnYdQ9hL5i9As/ra5TnEX7ITKvCGEaUbJbxFzJEMmv9\nFmU93PN14+XH02eO0DVBEpkZa+0D8kVigMXLy3Kw/ryNcl8c9GLeutlL6ZQU6VOz5yAu4mN8/MdP\nCzve1w1b8cdkH/tD3opv3S6ucTgQW+z8Ll78LPyndcKO93XRCqztpCR5zM4oxB5+9iJe/qh/CD34\nM9zfIvYS/7a5t8r7SzFebKTtepMSjiScYzkLhogohcd67ExcskmerXi2rasQL39UP+lk53w7ixEC\nyvkzncWLIpGDbV6+FjmPHCwpYzyLnogoo1aegdu3YW2mT8ZndoV14r0Mf+hmL7V5zEMkX0RG2Uvn\nvKXyj+jjZ/0k+wdnw3PoGjcaGhoaGhoaGhoaGhoaGhoa1yn0ixsNDQ0NDQ0NDQ0NDQ0NDQ2N6xT6\nxY2GhoaGhoaGhoaGhoaGhobGdYqrqnETDURo5KzBJcxaUCQ+s7DCRZxPq/LichaAq80LL3bvbBJ2\nXEWIF+bLni1/t/EJ8FwzZ4Mj6GWcXLWKe/e74K/GWc2SmFKzI9ALbjJXmhhWCu71sAJTXNmlsK5S\n2PF6B7xGEC/4SkSUfcMYf/hxWcQxEbBaLJTlNriE/iFZTO2VX4AjuWI1irGdOyQ5uQvuAK+X13C5\nfFHWoEitAU/74NMoqDVr7XRhx4s486JwE1ih4p5hyfflPFZPAPMjJ1UW0JqwsMJscy7i9t/tFHYp\nTozvpHLww7v6JLd76CkUMWtjvN2aQslvHq8DEosmvjYKEVF4OGjWtinerNTreRMc7PLbwVn3KQVM\neeFwVwE4oMmFsghsiNWl4WskValfwGsI+FnBWl48mRd+JCIaaERhvTRWe8qp1sH4EwpY82fiNW2I\nJAd+4Di+u2B5hbCLeMA3LdkIbru/S/Jpx5W4rM6rcpUfCvYMFxVtMGp0BRS+b9ufUd8iymqZ8AJu\nRETdB7Hmqu/CuvJelMUuC1lRS15fS1VNq75nBvsX1ljbiyhS6syRNSGEchmreZA5WdYL8rbid9tZ\nrTJ7t+zblBLMvzArzDxwSRbTDXOlIlYHYrxPxzFeKyUp6RoUDSMii81KyWNFdSOKspItBT48mdXb\n8ihc/mxWH4r7TZ9i1/zyBXw3K4ScMUP2NVcd4fUpshey2kkX5Bxx5qNOB98/1ULhBcvBK+9ixbFV\nHjjnsPO+97VIX941iN+ybcfenDmnQNiNzzu1dlwiYHc7qHCsqOfpZ2Xh3cr5FWY7yOqHZU2T98fr\nTyWzQtrHn5I1JHLS8Fl0VMZHHKN92Nc4V376pxaY7aGzsuaJna0/7t+5byYiGjqB66o/Pstsn/z9\nQWG36O9Xmu2O7dhXitfWCDsvK5Sdv4jVKlMUWrLH9meb5dr8zdCe7qCCVRVEJGsrEEkRCDer8+Iu\nlgXAQ0z9jddTGzwh+zpzCtZcL/PDMVVVJRnz1VWM+ITHodnzZGHSodP4LRer/aDGv0OsYLKbxdN8\n/yAiKmO1sbysvlbZbVOEHY/FeC2qtApZKNc7VkNCVbNLNLxN0v+d3Id9qJbFaRnTpf+rfxbngiJW\nrPfAn48Ku0mlLNY7gtpiFYsqhN3gMVkAeBzJrGbHpedOi8+mVeJ39z2L+HfqZPnd59vwu4F2xB/x\nsIwds+dh7Nu2oY5bU6/cF5dPQ52c7EWYV+pYjdcbjceuTYxqTbZT9kxjX1uXWyc+4+c7XhCb17Qj\nIspninynt6J/d3/vN/K3PsCfTNg0Sfw7aTvspt51r9nuvLjDbO/43vPimnXf+6zZzmZqVud+uUvY\ndbDzgKcFvqarWdZLqtm00WxP/zwKM6uxZ/kczB+7S8bNHOGgsZ/GY4lfiza3w6yR51XqXHlZ/S+h\nVKrUkeL1/CIejGdKqTxn8DOdj/lqtb7kKPPPvF6NlxVkT1HOGVEfYhgXq3czcEIWw85mteR4Xcgh\npc5fHhPL4XM5c4aMCfh1vDbfuNCC+bvjcZlaZO4DoDNuNDQ0NDQ0NDQ0NDQ0NDQ0NK5T6Bc3Ghoa\nGhoaGhoaGhoaGhoaGtcprir/357pMiW3hUwiEY0y3fXcGyBFy2kbREQ+lvrIZRfVtCkuvchTrXLn\nS5nbrPlIHyxaBAnTy0/uNttcopaIqOQmUEsuP37CbDsVadwhlkblZvJ7XJqTSMqI25i0JKcQEBEF\n+0AZSWOpukMnpIQYJIgTn/oWj8cpNCadGFAkrKeWIG10/86TZptTloiI3nocKYJL65BmrSawH3oW\nUndZjMLkzJKS3W1HkDY2oxwpaJyeRjK7nvY8CQnwxbOQ7tvWJvvyvW24hzV3Q5K8NEfKwFWvR0rl\nwEHIstYslrQLnv5fVIZ+yZotU+Q6xuRRuRx3IuHISqYJHzHm+9AFmSrLJfK4lDSXbSYicpcitdrq\ngisIKJLQXJaSS/1175EUv9INoBxxemOMSeOqNAdOo+L3YK2S0sIFN1aY7RYmeVt0k0yt7d0L6oaF\n0ZsGFRlVTi/r2YvnCHvkmhiXP49/2BzGq0CSJYnsqcYcj+XIdNCSLZiPAZZC61fobumlSN3teAO+\ntnCtlMvkco0qJZSjg8khTvgIqFcR5ou4pDQRUd4ySBs2vI0UbpddSsWm5CDlv2gp5iinlRBJ+iWX\nhK5Ik9RRvn8MHkUqu0qJGpd4VKkTiULEN0q9+w2qhLrXBBndlj9z+54mYZfG6FH9R+B/VApA7MT7\np7VzGi4RkYWtszde2ov/Z32zYuEMcQ1fpzxlX/B6iWjgBPq6cBHGfui4XGPOQibvycbOc0XGDjUz\n0S+cPt1zQMrJZ07KGbvPxO+LFruFkguMPaqkUvryUA/GMI/FNs3PnhF2gwPosxmMzlSULWkm7mqs\n2SSW4p+3UEo4J6/H3tr+LmgCPbubzLZKncyYAbp4106k6KdWynvo7MUYxJ6SFA+OAKPJdpzDuKeU\nSXrRMNtnoq+AzpdcItdD1b0ziYjI+bKkWyYKYe8o9e411qIrX/5GjMVjyYx+NKhIuPI9nsvTj1Ow\nxsGlYy0OjGPmLDl/PJfQ15xCzP1UvzLXOT3APQFp/94mSVfgVK5AN8aKU6OI5DOmMwlcX6ukInGq\nFE/tDw5IWv14CQTV7yQCydkpNO2e2URE1PTSefHZ9DnY7zntonFPg7Dzh3Aemcgo3XWfWynsQuy5\nDj0O+eWyVTLuGxjCuC3bPN9sc/pIv0ehuqzEHpw7gBjqvUOnhB33yVyCePi0jOva3qg32650jHut\nW/qN7h7c06mnm8x2RopcD9OWGzFGbPTaUKVGB4PUOkaxVukuVffBtwX7MW853Y9IrpElf8dom2/V\nC7tgF75j2t+tMdsHfrhV2PWOIH5KffkZs83piEUF8mzQeRxniIyp2I8rl28UdiWnQbcKsrg2quxX\n+/7tYbPNJcQnKLTFqpuXm+3uE5gzGRNzhd22775KRETDncohKRGIx02KnUq3dbM9JXsuzsRq2RNO\nfednYjXW5meB1EnMRyl0cUc2zo/JeRg3TrVSw/XUasRX0SDixtbT0u/G2VboZPFrmkvSwLvYM7rY\nmd+uxKijA4g5rS6MNafMEoFSpZaW+SDojBsNDQ0NDQ0NDQ0NDQ0NDQ2N6xT6xY2GhoaGhoaGhoaG\nhoaGhobGdYqrokrFRqOmMo2aFp1SgXTO0UGkB6VMkNWdfQ1Ie7I4WDXxdZLy0MfoM7y6/fDlPmHH\nlUv6ziB1tebeRWZ7qEGmQw0yJYYqpqKiUkT69uG68TRqIqlERSTTo9IqkZLlaZQp4X178H2ltyGV\n1a2o84xTACyOxCvZ2Fx2yq01+ix9iqJEwtISJwRAe+naIaui3/SF1Wabp/hOr5OpfjFWFT+ZqY6F\nvVJ5JRxlakQsxTWL3d9EJeV6+1aoX+w/iXTaeVWSIsLTQ7f+8V2zvWyKTCXe/acDZnv15+rMdtfb\nMgWXp7j1XUT68bHHLwm7yFilfr9CR0sU4vE4RQLGPLErFL8Upgo10oi0wNQKOc86X0O6adX9SF31\nKwpHOYuQiptWBCpb2l0ThN3AZaY+xpZI4Y0Yk/bXpUJZ4Sp8FujB76pqXDlTkLrcswu0H662QkTk\nZBXjhcqNsmY5XSPUgxTNgjVy/jjHvsN6DdZi2BOi7l1NREQU7PSJz5LLkTJsseP9es58mRbNaStc\ndcKRLucET3Mt2Yy1PXBSqmXEWbfzdcqpBcXrpa8OMKWdijp8lqbQ3VJykVrbvgN0t2CffPbRAdDs\nuIJVWKE62dm+wCmz/Ydlxf685cactT+aeJU+IkN5IW+JQRny1Euf33YIc7VwOu7RnS7T1jnti+8n\nfA8iItp5EvScZAfsUs/KVN4KRm8tzkK/z14NOnFKqdybz7+AdGxfEH0dUVRH5iQjLXrwCOaPLV32\nryML98RV2TKUfccr6JxYi0M+OS/cI4Zf43SORCEWjpnzOFuhQvO5z+f6hLulOmLmRcQmnEISDMk9\nYPatN5nt4V6sg9RsqUIZjeL5azZuNtuH/+MR2Ch0bN7nf42yvvihFWbb6kJKuLf5g5VDcvM5xUvS\nEcuZMl9qOew8zTLNvW2roXY0OnRtaIsWu5VcY0o/QWUf41RcTwNTmCqX64DTMznFI9Ap40NO3+XK\npVwtSsX+S4gTynNBeahwSnpVQzv2Nd9b8IfJTunXu4bQvwUZeI6IEmNFWAp+xiSsPzW+qbiHzWkW\nD3Zul/NnnM6qKh8lAvyc0dQjaWyTGUXIM4DxVX1FlPmsA78DVXTuR+YJO3cJ+oyPx/A5SVOqWQPq\nctENODOMtINmXX5FznWuiMXPDJvuWSHsuLIjX8/2LOnTU9j48jPDjkfek3ZsjizZAHVYvq8SEZ3f\nY8zFoPfarEUiPPeFRqk4e/DrUD1bcyvOar3dchwdOaDF5M5D7HP2hJyPd/7k78z2G9/6rdlWaaqr\nvvuQ2d77r78y25ns3NbYJmPKsgKca4qqcfbxemXMz31F9Sr463hcUox8PsTd9U/i3KH68tFR3EfH\ndqzTJJvMt5izzLi/lHfeoEQjFo6aVP2IQm3y+uBDR4JYL6lVss+Hz+CzlDKcTSJKGQmuyBT2wH+5\nyyV9LmsK6MDcR7lysU+r1Lw+phjHqfhVS5R4n33HyZdQRmXAK/eSokysPy97b5CVLmlsnELrrsQ1\njkxZLiQ0XkYl/uFiG51xo6GhoaGhoaGhoaGhoaGhoXGdQr+40dDQ0NDQ0NDQ0NDQ0NDQ0LhOoV/c\naGhoaGhoaGhoaGhoaGhoaFynuKrCDRabhVxjNSS4nC4RUfd28HrTmPSWWpeASyq2bAXPsWe/5EDy\nehxBVvuCc9CIiGwp4GeXzodcXG/DIbOtyiBz+VlbMrogvUrKwCXno54J57ZziVciKWHK+dGqdG/G\nTM7tB5dt+IKs2zMuxRrxJb4+SsAXpLOHDY7lZEWmzuZGX47zxImISjbKseb3xesB7PnjPmE3dRo4\n+xHGWRxo7Bd2g4w/mJuOvgx24P/jSs2TbsbtvmUZOLIHzlwQdu+eQU2I1TNnmu2dZ84Ku7msNs65\n5yCFXja/XNh5L4LzX8bkHot8Uqb+4m6jlovDlvjaKEQGD9zfYcwTZ7bkS0aZrK+X1dxQpYpTGOcy\nxGoOpFVIjqqFcWqb38S6Sq+VfE5rMuaPlfF1+dhlzpRcfi5lns78xvizmd9nh0Ry+R3gHDc9KaVs\n05l8sucC5ln+SlmPx8648mH27Gp9lNKx2g1qTYdEIB6OmXPcmiqls/OYzHJ4BPfHub9Esj7DwcfB\nl176heXCLmt2odn2tUM2UpXi5t/P65KU3zbVbLe/KesUuVhdoZw58AcqhzkWw3NM2nKH2bZYJJd/\nsB9+xM/4w7yeGRFRdBT+q+1V7CWZTBKZiHPCEz+GRETRQJgGx+QcvfWyRkjNZsxVH6v3UbhG1jMZ\nPAE+e/0p1E1IVyRcqwuwfjLc2At5TRoiorypsCtKgw/LYzWSWl+RvpKD+817li2T93oMdW3SJmPN\nth2Re3h2BHtc4wHEB1mpUgrTwWp0JdkwRtPvmi3s+vYZ36/WmEsEwr5R6tpv1CNKL5W1wOLMZzW8\ninpqMeU+am9HfZCGP8EvZVbLuILPdxvzmX2X5Z4kalvNxhgs+/Y3zPbZlx8R19jcqDOUW4P7safK\n+kMjrHZN6SLsn1nzZA2QQAC1Aeqf3YUPlCEYOotaJONy3ESoL2Xe0yJjn7Q9Iv1dwhCPE43VN0mr\nlTW2Qn2IA3mdJYtV+oUYiykHjvEaYrL2Ea+TNnQGz585TfqprhPYu3gdmgkF8FMHz8t6GW5WpyQU\nwV66+4JcszfdgPE6ch5+eUGK7N+CVfA3XE7eXSHr+4xcxp45OoK9gMf0RESDJw1/p9aiSxTG99vi\nbPm7e0+gJtSm++rMdokiVZzM6vz17kWdsb79smYYr+u46EHsmSGlHkx6JWKd1771R7O9/KEbzTaP\np4iI4uyeJq5GjZwHv/xTYffvX/ik2T56CvPAq/j0m+6ADxgdwv3lZ8gx5H7p6Hb4oWlTKoRd75h8\neSQq+y5RCIRG6exlYy8LR2T9lo/95B6z3fAUaqvV3i/jluAIzkatL8P3LmE1TImI9v4bxmTplzEm\nba/LdTU6iu+b/9VbzbbbjTPO0Ld/LK7pZNLjwxMQr+7felTYrX1gldk+9egTZnvSveuEncWCtVmw\ngsWlSdIPHfvP3WZ7+mcWmO0Xv/+KsBuv5+QZ8VOikWSxmDU0vZdlbJO7DDFqgMXr6nm7dAvm/sBx\n+EK1Zo7FyeSyUxAjjFyQ9aa8bYhf08pxVunahRgjZ66sBcnjfSevm1Q5V9gN92IPnnkr6n76mqXU\netc57AtVa1HfjZ+9iKTEOa9N5GuR9bBiY2fxDxva6IwbDQ0NDQ0NDQ0NDQ0NDQ0NjesU+sWNhoaG\nhoaGhoaGhoaGhoaGxnWKq+JxJFkt5ByTsYopkpxcjtWRhVSk8IBM9+Op+BW3IY3cmSVTwnv2I73R\nxtJ8U8tkWmCA0ZYsVqTFpRYhvbmgRqbfjY4iHTQUQoq63S7TMsNupMKWTN5ktjsuvSbsMkqQKtX4\nOtKJowGZNhVj1KQ0lj6dUStTqccpS1y+MlFwOuxUVWLQJtKV3z30/BGzvfjjSEXs2dks7PwepMLF\nmOxitpICP9yJ9DI7owy1D0jJXJ7qyVP+H3sR8t13L10qrinJwb2/dgD3/cybb8rvLsK8PNeKFO6C\nTJnWmluOsXdXIv1u+zN7hV0Z+92TzyA1b+WdMnVz+s2GZGTyGzKtMVFIsiSRdYzmxylKREQ+lkqY\nMQ3p2J56SVGLMioLp7/1HZN0IS4jmb8UqZ12t0y/53LeBbNAcxhqBY0lY6KkV40O4rs5HSkWkem7\nPG2bUxonPbBQ2Hm4nO1fyTsMDSKFseQmrN+wV6ZvNvzRoM1xmeJEwZpsM6ldQUUGk9i6GmF0t4hC\nleqqR4r+tBWQuO87JMcwPIzretoxD6qWVwu7KPNR3lakc9pTkbpftnGSuIaPVe8hpKKXr5Zjk5WF\nf185glTinJqpws7XCYppdhV+K+CRae48rb+UyRE3PHFK2JXdNtYv14YpRdZkO2WNSVn2nesWn7W/\nBdnS4tWgVta/cEbYZRSAIlrfhT1p4xbp9y68CV83ZRmeOabI8mZNx7rnkred78FnnTkpJVVnzEO6\n+CeyV9EHwTuMuWpn81ZNh7emwOcXlGLdp0+S+47nEsYxaw78tU3xa2kTDR/NKXyJgi3FTnljND9V\nRpqv/Rmfu8FsNz4l51nna0ipr/00UrAdqTK28ftBaek9hD3Jc1HZFxm98+KuR8126TxQASasWSKu\nsVgYxSaEtPTCyrXCLux5Fe0w1nkkIundbneN2Z58L+gn5594VdoxeWLXHDzvqaePCbsFXzTmM5fP\nTiiSksyUdM9Fud8V3Ai6UIzRWDzN8pm5VHk3868q1fjiXoxjSQ7ih4aDUg68dhV8mHU//lZasLrC\nbK8slbFTYR18xXA96B3VN0raet9B+PlZEzBfbGlyb+b9PdiIeTbsl/vahKmgGOQuBP07EpC014I6\n497tD0t58kQgFolRsMfwK+fapM9PT8bZYug0xobTLIhkTJTOaHGX3jov7FwO9FP3/yLWm373HGHn\n78EcqSgtpPeD1SXvIcRiFh5fNdbXC7v7vvk9s/2Hb/+z2f7Kr34n7GpZLFtYgPmmSqGnsT6auwJ7\nK5cdJyKqu9mg3/x27066FnDZbDSx0OireV+9R3zW+BZ+s6sN8/vyt54SdrNXTzPbecsxvzMrZWmC\nnGmg7fz5m8+a7fVfXS/sLBaMd+cJlER44b9BX7vn67eIa5pexJz57z8hnv/JS/8s7DxNWFf5S0ER\nPf+IPC8WrkHMlTEBdu989zlht/5795ttmw3xQV7au8JuwceNPenRo/KskghE/GEaOGzsI7lLysRn\nnHZYsgH7hL9TljkYlxMnIipeA7vWbReFnSmJTUTeK4jj06fIM0OcxTqcTpxahTURDcq5bmf+kNMj\nAwF5tnWl4+znnoM5NpAhywOEWIzgygdlPUmhu1ls8AmpbI/suCJ9wHjf2h6VfvuDoDNuNDQ0NDQ0\nNDQ0NDQ0NDQ0NK5T6Bc3GhoaGhoaGhoaGhoaGhoaGtcprirnOOIdpe6xCu3hQVk5Oo3RboZZFeiC\nNVXCLjkPaUX9J1CZ2dMgK1b3nUXK+eT7kXbM6RhERHmTQMm48Mdt73vfOQtlumXeJFSLttlwP6FA\np7BzuJB6xSlVWWXThJ1nAGlUhcsrzLa3VVai5tXtefp197tNwq7iowbN5lqkhMdjcYqMVb5+6/Fd\n4rO194FS9uqv3zHbq2+VNKDiGvSLtxHjNq6GNY58ltr4B5YGeOvddcLOwVSRDjK61m03IC39Srek\nICycjJThJJYuN5GlkxIRzVwK+kj7KaQVn1dScDmlqHcXxmnBLEkL4UoVJazSePOuBmHnHKOGjSrU\nlkQhHoubVKeeM03is/SpSA3m84wraRARZdegD4MezG9Pk1yLLrZmucLUoEILyZ6OFOLBFqRBFtUi\ntb+3bY+4puLGOtxDEGOiqjoUTkdV/eFepK6mZEqVqmgQvofTR1KK04Wdl1GqBk8zumS6TP0uudmg\no9iflspHiUA8FjeVl6JKKvr5x0ExqLgJc7DlzS5hN+0e+L9Lz0FBIn+aTOdOrUKaJh/DkfOSTjDl\n8xirpCSkm/P5EQlIOpmH+QBnDmgS3r4WYdd5BKnJhfNmmG2rVVJJeMV9RwZSWf9CZY/92aH3ICgn\nRRsk/atrh0FdUBW5EoV4NEahAWOd5U2X/e67gmc5/TKev2pehbDoGdCeAAAgAElEQVTbt/2E2V41\nD+p3kRF5z+W5SBt++xWoiK3ZdIOwq38OVKwUN+auqxiUjMoCuXZSq5AmfKUe67ewVtrlLAKdIm0C\nrikKyX7n6mVxpjAV6JDUlDyWVs79Vd9FqbaYt9hIJ1ZpEYlAyBuiK/sM6ljtaoUKyOiDnDKYNUeO\ntb8dz+VIxZ420iyfgysQhXpAc4grdDdOBZxyx91m+9K2F8y2SpVqP4I5UTxvvtnuad0u7DIrK3Df\nA1jbvQekMliSDcopfadgV76hVthxlRt3Mah5pZPkfty9q4mI/lJRJFGIR2I0OqZqwhUviYiiTOmQ\n09C5gimR3DcijDrbuEtSC2tmI75JZ4p3lYpS38gVjOPsL0P5dNSLuV48W1IiQyHsrcVrN5vttst/\nFnYVdaA0eoahODVwSu4T3OePMkpjYZZ89tF+0MS4H7YqKlU9O5qI6C9VYxOBkREfvf2WoV65br2k\n2zoZLeG5R94y22vnzhJ2I+ex5p55/T2zvVlRTfvzIahk3rUUa2nolIxtdu+C7177EYxVsBfnkaqN\ndeIaTjvsOoKY5T8eeEDY8dj2Gw//wWx/4aabhF3NCsRrj/12q9leXCvXYnkN1tyZfSgfMWPlFGFn\nzzT2hSTrtfn7fTAcpgvtRswdZKpPRETBUawrrh5oU+ZZkPnHI48fNNtrvi3V6uJxzOnN/wqq03v/\n/pawKyvFGjEp1ES08U6cfc48dVxcUzwV/fmTB0GPOvCjHcJuydc3mO3G5/AdWXOlD/QyqiIvN7Di\nq6uFXesuzM0j2xAf3PXTfxJ2T37pB0RENNIrz18JQTxO8bGyKGpsXMCovCNMYS82KsscxGPY+2MR\n+FaFVURDJ7AOhDpsvaQQc9on91E2pk4ZU9SdcueD9mRl8cNIc4+wy6xEbJOUhD0iHlGo6Kw0jNWO\n7wsp70XS2Vl56Bx+Sy1TEhyjk8XDH07hTWfcaGhoaGhoaGhoaGhoaGhoaFyn0C9uNDQ0NDQ0NDQ0\nNDQ0NDQ0NK5T6Bc3GhoaGhoaGhoaGhoaGhoaGtcprqqIisVpJXe5UStiWOHej5wDp7T8DnApOSec\nSNbP8F5i8ml1E4Qdlwm+8AfUe5jx4GJhx+vaOHLAK89kNUtGFK68Mwu8T6sDXeBwS75vz0nw/B2Z\nqLXA+cJERF3vQv6x8m7UJ+h6U3Ki81dDjnLoNPh8tmQ5DD17jd9SpYkTAZvbQbkLDO730kr5vN3v\n4RknFYMffuIdKV27dMIysx1nsvC8/4mIRhn/uYBJfluU2j1cbrqIyXTnzwCPcOU6yfdtefWs2U6r\nBo9w1gMfFXbxODiDxasxnrOGJBeRczSrPgG+dPeeJmGXzmTcO/ehhkfZQsm53bPNqA0QDMvaJYlC\nNBih4TEeN68RRETkYzVHSll9FF+HrLkUGEFNp7ZX0Tc+RQ63fAu+o+MtyNhV3Cb54sNNkJ9NKUAt\nDV67JqtwhrhmqAdzi8v/xhUp79FRrGFnGubIYIOUXuVzLo/JmQa6Jf936Cz4plmzUKui/6CU0R6X\nlo1cg7UYC0XJ32iMiUWREi1bidpg3BfmKjVUBk+ilkHNrai9FfsrXFnOG6/YLOt5BD3oZy5lOMj4\nucPnesU1HsatzqrAWvS3yfn27huoXzWP1ZEq3Sw5+lwavdfb8r7/T0RUtA7Skq5c1D4YPi/vb1yq\n0vFI4usUERlzo3+/MW/yVsp9rOMk5lOK84Olc+tuR40aTz3W72if9FN9I6ibcOuD4NSre031HZgL\nVhfWFa8T5GuS4+Nrwb+zslETKmdBibDr2IaabnkzUdfG1yP54u5SrNOUXL7XKJKZTMLak4L+4vWS\niIg6x56RS9snClaLhTJSjN/zNQyJzzqa8Fzc1/I6KURE4QH4nku/P2y2uXw6EVHVzYiPRvPwjCqX\nv3BFhdluP42ac4EOrLfzj70hrokMoW+4zPqFk9JPclnlvDw2ThUZwo7Xe8lmdVy8Sh209FrUXvLw\n+mFN8pn8IeP+RgPXpsaN1WWj9MnGvfDaCkSyvmLnDtSk47URiIhsbqyX3jYWFyhS3O4yrBEPix/c\nJbKeWt7sive91+QM9Gc0Ktf5YD3Gy+PGmnAXyNoIrQdQp9DG6mWE+uScy5qKeHjCcuwtaj0KXxOb\n+6wIhbtEzgvHOsOX2p9MvBx4dmEm3fONW4mI6MDvpcTxlOXYrzavx1kg1C1lzbmE8F0hxKvqPr58\nKuSyzzRjr5mVViPt6lCHpW1vk9meePt0s91z7iS/hPKnIo7s34sYaGrdZGF35RmcBSaVImaZvVzW\npHnj2d1m+2++cofZ9lyWayylFDWWki5gDLkcMRFih6hSDyRRyC4roHv/6ytERPS7v/2B+CzTjbXI\n67xceea0sOscwnzk9d3adsi+vrQHe5LDBr88+w4p685rqUYCeO4/P7PTbN/5WSkhvuNPmIMrMhFD\nzLp/gbBrfR2x7PRPoiZZKCRrp4bD8MtOJ844h3/4vLDjtY+mVeJ8cfRHjwu7wFi9oJgSMycCtlQH\n5S03frvzDSlhXXQT1kiASYA7MmWclTkNNfL8TBo84pVno7RJiB35d6gy9hYb5nT6RPjD3gNYY07l\nLJpkxTUpafB/oaFzws7Xg9hxwlTUe4zWSP8Si2GvdzoRk4cLZeww1Nhkth2ZrO6d8k4ibew5kmwf\nLpdGZ9xoaGhoaGhoaGhoaGhoaGhoXKfQL240NDQ0NDQ0NDQ0NDQ0NDQ0rlNcFVUqHo3R6FiacslG\nmTY6xCTAwzwdu01Kf3KJy4JVFbBTpLP9bUip8gaRlhRUUkDfeBcpybfdBzk1nuIbHZVSXsF+mfY0\nDletlEsumgs5zb56pPBllU8VdgNZSIXrfA8puGra08BRUEmcLG03R5GtbH/VSPtT01gThrEU2AyW\nPqv+O9ANuoztgJT1FbK6FqSgqante5/Yb7ZnTARNTJVq6zyGVODyOqTeD59GinrjsyfENfP/9sv4\nvliItWUqrNOJ9MpIBGlsA6dkqmXBogqzPdKMOZqsSHtG/Ph+TwDpzSUs3ZOIaOpYymuyXcobJgr2\nVCflrzBoGX37pIRrxgyMY4TJTKvy8k1PY04HPFhjKTluYRdlKaWOLKQw9p2SEujeRpYmOBPpkc3P\nI50/tTqLX0JZM5Bm2L0XVD1nrpxLTa9infPU9rz5ksYRYDSvQC98hZoOnHsDUpKJZZjas2SaZ6jX\n8BUqdSsRSLJbyJFnpE/aUuX8GTrJ5GA3gUq0/ze7hZ2H+cYFbHzVlHDuSybcDv/Vuu2isJvA6FbR\nIOaOl6UYuxWKpZPRPf7n9y+Z7c4BmcL91VtvNdsONoYv/GybsOsYxG/duXiR2S6+WVKqOLp3Nplt\ndY6NyzDy50kkotEYDQ4Z+1WRImdaPBvzzHMRfkWVf3dxeUjmU/k1RESzpsA/cspuztJSYZdVhTTm\nwUbQqHjKcMa0XHHNHx973Wwvn4I0ffcFST0bZf3YfRS0Y1W6vLgOc8nhwPN1HD4s7DilxV2Wyf5f\nzuH+HsO/RCOJ3xdtLhtljVGBWo7L/W5iHWKd81uRDj/7EzJVvr8fsU5BBfo2VaHstL6OtH6rBWNY\ntE7KqV95FHuexQG79k6kWb9z6pS45oEH7zTb21+GNDj/HSKi7FRQWUeG4CfPbpfPPuTDZ3UrQTvI\nWyKpwQ4mFcv9biQmY6/aDca8cm67NrTFJJvFTLO3uSWNZ+Ak4jQuK12wWD4Lp0dXMloRpxkSEfkZ\nPcCegecZPCspg7lz4JeH6zF22VNAq7RYpN/IrC7DfV/EvuhpuCzs0irh63jcnVImqU1XHmfxExuS\nxi4pez3vVoyxl1E2kxn1mYioZ/e1o/P7+310+FFj7s7aImW+vVdwT8ePYO9aeptci43voJ/GJamJ\niDZ++kZhl8l8lv9NxEPbmBQzEdFtt6ww26XLKsx2RhWoLnl5a8Q17Y3YCzllfv/ze4RddQFipQtt\noHt0n5GS7llszXoZHT6lTFLzzr0D6XFOSj34xwPCbt4dc4noLymfiUJocIQuv2DIcd90X534rGQx\nzlaRCGK2qX8r474pUUzWM7+GHHjTjrPCbuJErJcDx/H8Db/bLuy4DHnXU+jDj27CvIgp+0s+K/PQ\nur/JbBf75R7Ozwqc2t95QPpoTmOfvBHzaurnFwo75yOQFC9YhfNTWNlnb73LiOceOSZphYlAPBqj\n0IARAxeurRKftb0IaXUuj22xS9r/wHGce90V8Fc8FiEiirGyG/zMkVoh480kHh8xamfBcvhTNd4f\naUAs2t0O2rEa1rvLMdZDxUfN9mhAxrL8bN9zHuOUUSXPI5yal1KKdaqOYd9e4wz3YUsy6IwbDQ0N\nDQ0NDQ0NDQ0NDQ0NjesU+sWNhoaGhoaGhoaGhoaGhoaGxnWKq8qRszrtlDGmHqBSoLiqR+9+UDfi\nEZmL5MhGSqmXqVgceV1SYapY+mD1UqQQD56S6YMzypHmylOvhKKAkq4bj+DfnFqRVSPTlHpOQQUl\njaV4DXdKegFX58hdgpT1gk/JiuZ9jBKUnM9Ud3ok/Su52PjMYk/8e7WRQS+9/ayRUleWIxUKSivR\n53lL0a9cKYZIpovzseEpbEREk2vxHbuP4JrbV20UdmlXkO7GKSMxNk52h0y/azmLNNTiSUhRvbD1\nT8LOnoF0aa5yoqJ9B1JrM6eAMnf29fPCbsDL0jpnIH1xXOHp/xfi0RiFxxSU8paWic8GjiElnM+t\n8FBQ2PHUR04fbNkhFWqiO5ESmrcCYzpyST5zsB19kzEZVIEwUzrpOSRVm7iCTsZU9PvwWUnP4CmE\nKYwS1PScTJn19eEecmex9E2FtphajrkwdBbp4vmLZF8Ojn1mdcr5lwjY3A7KXWikVvrbpT9NKUba\nrYeleU5eIulCfkZPS62CjwopdFAHS+XnNFJ3uVwTZ36xz2xzekWU+dDLZ5vFNTwNfFoZ+m/7bknr\n2jMZahrNvRjf+9fI9PWfvvhnsz3iBx0x9IIcaztTj8ieB8pdWrX0ay3PGtfx9NtEwp5sp+IZBt3V\n2yxVBVyMRtZxAmnwKVaZ3j7M6BVuRn9wFUjaIqezjqcwExEVzpf03XgcY8JpaVx5pmePpMXcPB/p\n6w1M0aJaoYGWMpp0kFFqJ2+5V9h5PKAetB8C9cDXKPuI01ZaX4K/VZUmJ99pKDa6tknFiETAnuY0\nVZyCnVJVr3kPFH64GtPZJ48Ju3AE84tTEy2KamRnB/p2wT2geKg+gP9ZzZmPeXTuMFOoKZP+6oc/\necJsTypB2vaqmVLNLxiET85hqh+D++Wzp7ngN/btxXguVfwpVwEZPoW5bEmSMcG4H7JYr83fDJOs\nSeTMMsZIjVG58hVXxRpW9rGcueg3bwtLxZ8mKe1D9RhH/jy5LC4gIgr0g+7oLkUq/sAFrL/0Stnv\nLa9gHeQtxhh76qX6VPIN+CzViphy1C/XGF/3nFo9c7JULjrwPGiMi27H3Oxjii1EoGNbkxNPs3Gl\nOWlynaEeNT6W4xgaRvw/9wbsJx175Z6UXYh+djJfdm6rVEidshF0Tl6S4aN/I1VMOW2fU2IyM1kf\n9e0S13iZqqKLUeaXz58u7Pi+9NDHbjHboR65hzd04dkzz8IfzJo6V9hlM8Wmii0Y334l9vKPrY9r\nVZLBmZVGNbcbe3v9i++Kzx558D/N9uYvIH4vmCGVSt/8P78z23x8VMWt4tWgBv/ro0+b7doSSV35\n1Gc2m+37vvhds/0Pv/ic2f7a/T8W13zpHozJPf/wLbP9s7//e2GXk4Z5kVyItchp70TSl3s8iGns\nKZI+6izEOHLqT9b0AmE3fMmIpfh5KVGIBqPkGVN/tivqe6W3Yf1xehRXdiWSPqLlNZydB3zy3Ftp\nw9k5ymhowR7pG4MdrIxHGvyahZ3/A4oy7u5dKI2x4eMrzbZfKdHClRiTLKC4pRTKkhlcOZH7qCRl\nv3Plv796mkMpyWAfoxp/WNqizrjR0NDQ0NDQ0NDQ0NDQ0NDQuE6hX9xoaGhoaGhoaGhoaGhoaGho\nXKfQL240NDQ0NDQ0NDQ0NDQ0NDQ0rlNcFUE1Fo1RaNDg2Ko8Nt9xcL6ifvDxxmVSx5E1DG6/hdWN\nqC2R/GF3DXj+vgZWAyVDkV3eAu42l3E78ChqNRy+Imt2nG8Fx/cHX/us2X7r288Lu5k34bsdbnAW\ney83Cbu0WtSAsbrAZW1/U0o3hrrB6UtZC85cj8IfLhyTeVblmxMBh81GJdnG/dYul5Lu1mTcO6+N\nEuqWXNtcxuUcOQt+eLoiL+tmNTdKGtBHLVtljaDCZahlEGFS401t4PR66+V8owPggE8sAvc+VZHv\nfuclSJIXZ+F+9l+U9/CRG5eb7Z4TkK/LK5DSwpyz392EZ8/Jk/KbvSMGf1iVQ00UYtEYhQYC4zcl\nPuNS122v4DnL75wm7K48Cd6nnc21I8p6+f0LL5jtX/7DP5jtIYWjuvgTi802l8urZ9zs8X4Zx+pq\ncMS9V1DLhfsQIqLcxXgmH6sjYnNLGdVUK8afc3LTq2Wdps7teEZe/6b1lQvCrnBMhtHiSPxajPrD\nNHjS6Jt0VleJiKhvD3wUr5s10i/rMXAeNJfsVhELwTf2su/u6ZXXVMzDWrxyGLU9xLwflrzgo2y+\nOFjdme99/vPCjtcHSWPtSy2Se39DLer4hFj9HKvCHy5kksS8hsOlJ2W9tLQ8w3er8pMJQ5woPiZl\nmVYl/QWvJxRgUqSODMkXtzE55YuvgvfOJWCJiKq3oB6AxYJn9vvlXpOaipo3aRWoqeBw4P76KpW6\nFdMwB7M8qBkU6pP+P4nV8/A1Yy60nXlD2NnYfpJehfWnyoVm1DCp8P2oVZGr1F4Yr+nF+zSRGK/R\nlnODrIuQ78Ka4H2h1nQbZRKffK9Pr5U1l/J6ERP99gfPmu3ZlbI2yrzbUCNv68OQtX30JdR3+48H\nHhDX/N29qMcQ8WLtZMyQ/iV7Osb34sOQPe1R/LMngJoqXLa484KsNZhbiFpZGbNhV6nUY2gfk0KP\n+KSUbqIQ8Yep74jhT3hNGyKiwTOoU5I5BZ+5cmQdqdERxBq5M1A7IzAoZb7zpmA/7b/Ea+HJeevM\nQk2LzvcazDaX23ZmypoHNXcvwe8Oox7Y5DtvFXbDfajDEHdgXrlSC4Vddn4F/hF/22x2b28Udrxe\n2aW38EwT5lcIu5SxOEtdy4mAdzhAB18zJHZv/OwK8VlrB/qi9wLmamm23N8vM+l3Xndi4koZ8w6f\nw/ctXg3p8SSlBhOvQ9O9F7WJQn2PmO2wR9bILFmPuXPgMvzzu6dPC7uv3Hyz2XZXYh35lDodK6dh\nvqWlYU61v1kv7I40YI7lt+FclT5VrofGt417CgeuzVoMDXupYZtR5+7kfhlX3bgZ0td8776yVcp3\nz70PdiMXMFZ95+RaLF6Dvv71M//HbO/4xQ5hl16DefLVj3zEbP/Xlx/G/3/pY+KavAXYD3as+KPZ\nVmu+ND2JcW1+7hzubUONsON7YdsuSEnnzS+VdpOwb6QUIA5oeemcsJt03yrjflISX/uNkhDLq/VX\neG0kXjMsTYm1T/0BtWJ5/F+eK+ejM5fV/rmEs0DeMlnHLZXVAOw/jLNaJqvVFg3K80PdOtRO6t+P\nuOdcm4yBpk1ATJlajd9R6wcNsTpuRWtQK9TXq9T9ZOdofs5QfYUje2zsPmSIqjNuNDQ0NDQ0NDQ0\nNDQ0NDQ0NK5T6Bc3GhoaGhoaGhoaGhoaGhoaGtcpri7/Pxan2JgsWcZEmf6bNQ0ypVxCcegNSUnh\nqfkT65C2qMp38VSnwT6kV5VNlBKhXCpsdBAprpye8fJbb4lrbly61GzvfgepahkpKcKOS/L62X1b\nFdoETw3jFDI1XSt3Wfn7fpa/WKaCIQ018e/VbFYrFWYa6ZjxqEz/8tYjPc3KKCi5y+X97X36gNnO\nYtKDJ7fKtNuVdy4y20s/DyqSv0PS5xq2XzLbxTORltjEJIPX1s0X1/D58svXXjPb62bPFnbvnAQd\naDqTjs/PlDLIP39pq9n+2t/eY7bbzkgaR1EV5nnzJaTpcbocEVGg0bgudq2oUqNR8rca64KnGBJJ\nil0hS+Nrelqm6FbcCTrF8Hn0tfOcpB99dNMms901hLU9vbZC2EX8SP/zXMZcKmWy8/5QSFyz/z2k\nes9g45NSKilvl7ciPTSVSdQmK3LJMZbSzGlUAUVSMHMG0ip7dyP12VUkqSnWZCONV6VFJALxWNxM\nwVYlOR05eEYvo6OoKZtNZ5HqWV4N+fPUGjkfuZx5xnTQJtrelrLrPA3VxeSr05lvzM+Sa4dLhXNK\nFffBRET3PgQpzvxDWDu+ESlxW/eFOrPN5bXbDkjJV18L+mVcVp2IyJ0uU4Zzxj6zKrLWiYLFZaP0\nSUbar1+RIDbpjEQUj8fxgZKK70jHeE+/GxSZiF+msXceg1wvl9lUabXWMoxdLIbv8PmQsp9eI/fw\ncRo0EdHgMYxdX4+UFs4fwXXZczDn4rG4sPM0Mvo0S8ePKqn5PIV4wjrEBKNDiqzo+DNeA8ZbqD9A\nDU8Yvii5RPqAgQu973cJld9UK/7NfZ6T+SVOASUiyq5C/80eAT1q7s1y7+rZBb+0oAbp9j966CGz\n/dVf/lJc853PQdaWr8W6WycLu0FGNai4A/vApV92CLsqRo964zhipY+tqxN2fJ/h0tGxsPRruYsM\nOoBKcU0U4uEYhfqMeRwplfPMySRYPYxW6sqT4+0uRAp//znQQG0p8p47G9Ef4zLnRESXHjkg7MJe\n7Iv3f//7ZvuJb/8L7qdRUlaT83FPjlT43lhM7p+uNFCigj7IDscdMvbsbdnzvvfDKbRERKs+vsxs\npxSjrEGwX9IlbSnXbl9MIiKbxfAJniuyX+Z+FHHg678GraaFSa4TEa35GJ5j59Mom5BWpdA43gYt\nde23N5rtY/+5W9hV34I1klGN9dt7BPGh6quP/wo0fU6TvXPxYmF3qgXrfOPqarM9fEHSLq6w/XRO\nJuy6BmUf8fho5AL6pWSz9Fflyw3f43hJUn4SBXuak4rHnuedV+WacJdibr336/fM9u0/elDYNW2H\nxPog649TzTIWmGNbbbZDw/C3S+9fIuzsqXjWLT/4gtle68P3tbxyXlzj70bs+HtGbX3oZ58SdmV3\nQqLclY0160yRlKALv4c0Ot9bYyG5ZktWgeJ84j9Bb/SPSpqN662DREQUGpGlCxIBW4qdsucae3xc\nbu+COjV8EvsJL5NARFQ2GxQw11n4qAGvjMltJzC/c+eD4sfjPCKiojrsmalliEWHLmGfTq2QlPXQ\nEGKbQDt+N9Mtzw+Zc7Hf8fcJviYZA3kY3Tm2DTFVwY0Vwo7H9UOMqktKrOTINGJWlaL5QdAZNxoa\nGhoaGhoaGhoaGhoaGhrXKfSLGw0NDQ0NDQ0NDQ0NDQ0NDY3rFFenKjUaJd8YPUNNV46w9MsspiSQ\nrajt5Cwqed9rVAUAD0svnvYJVISOK9STkUtIBeTpt4uZMon1jjvENVYL3ldlM9WOIoU+03cIaZB2\npgKipmGlT8DzBvqRUpU9vUjYde5E2i3vI2eWTO33dxppWNdCPSMciVDHgNG3WQ6p+ODpQpr/WVZt\nu+yMTKnPz8CYcnpZXnq6sOP3P3wRaY6th2SaI58jTcfw2QV2D/F3ZWpZbTFS6T69GmmS3qBMr//k\nqlVm+zz7vpUzpcJSVT4oUM+/iFTGez6xXtiFWJXw8hqMr5oeOH+LQXdI2f46XQskWZJMqoRa3X6k\nnq8J0ENKb5kk7AKdSBkcT9UjIlq1Zp6wc0/AuuB0iKyp+cKOf9b6Dksxt4KmM6uyQlzDFUi6DkLt\nyNIu0yj5uF7sQDr/5nVrhR1X5+D3o6aijzIKS+FqpF727msVdiZd8RrQM6LhKA23G2mgKeXSTw6w\nMay6DWnaqhpLUQ9S2LlqBFf7ISLigkwZjNY6fb1cBxdfAp2uoAZ2HZeQxuq0S8rADRNBb3luH9LS\nV0ydKuxGzsMHOHIw3/r65L1yGuSV3VDMqFEUQcJMxSfKUlJLb50i7FpfMlKfI0ol/0Qh4hs16SGu\nQkm7sDO1KL7XhIcl5cHL7v/SPjzz7DvnCLtRRmfKmY7+CA5LitZQA/xo8XTM78EOUBO5ChKR9BXZ\n8+HbMiNyn+Co34a08rxquYc3nMFamrIC+zFXaCEiOvDkQbM9dQEoQUP1kv7gdBjzLnoNFIlsqXbK\nW2pQglMnyDggezb6oms7FFtSFAVDnhb9+59CiY/TjYiIYiznfP3f3Gi2nYq60RtPgEJwmKnS3M2o\n3utXrhTXTKoF3bSlCWtWpbrw1O+hk0hfn798urAbuow1e/N80FSG+iXd2bYXY93diJR1lT43TuuN\nKtTQhCEJ6nFRhXrA4zZvK55/+LKkpIxcYAo47P45vZZI9mE3p20ukAqpZ94GzXflElA3/EGsvylL\npM8aDeD7snJBobNYZKwYCmGMbU58FonI1P6uXU1m25WHmC1tioztOD0xmSnZjA5JOuvwWSPtP6z4\nkETAYbNR2Ri9mlO4iYiCbG+YWw163sCInI+d7zWZbU7n3/7fUmVo6d03mO0GprJZeZOMlTyXWUzF\nKHP5C7Deei0ydug4hDIRyY4PpulOKwWVZMcToLQtXCbXYt0i2PUzqnG/QjnhdPR9Z9lcPiuVnZbP\nNb5f9ceJwuhQgFrHaEcFGTK+6dmJ/YkrDb3yjV8Ju/XfgeLWyFnMhTu+drOw8zK1MVcO5nfvQaka\nlDUR58+Wnehrrhoa6JD9+fTLmDO3L0X5h5f+bauw42O89C6oYUUDstxC4TrM21xGr3NmSGW5C78G\nTWzm38Pn+9plvHTksTGqlDfxazEeiVGozzjzqP3izEc/Z8xCrDhyRq7Z5jbsL+VFsMuplUqHDkZl\nDf+VkiMeVoqFU8zH6ZtEf3mmHjqDe0ibiH0gxyP9msWBs0WrFjgAACAASURBVAqnf3GVayKiFBfi\nOncl5nbrtkvCzsHuyZ6Ja5JL5Vl56LRxfx92LeqMGw0NDQ0NDQ0NDQ0NDQ0NDY3rFPrFjYaGhoaG\nhoaGhoaGhoaGhsZ1iquiSiXZksgxlu7Tt1umBeYzukHTM6jUbkuVafWuXKQt+lm6bJpaBboPlJSw\nBylgXW81CDufH6lOxxqhajRzAtSnHDb5mDxV2ccoGHmzJbXJ34b0yyBLE8ueJe0GziEdL4kpQWUU\nKVXc16KifdeRM2bbq9A4nGOpfqrqUyLgzkmlBfcb6X7d78i+dDmR1hWJYmy4OgURUUElUtwOHMRY\n3zBfpvumM7Wt9q1IIePfTUSUuxgpoN//4jNm+yMsJXzyFKkm1taI1Le8KqTo5yqp2c+/inTzLEZV\nSJ0o1QUunwBFZNN8UIVe/tO7wo7TQjwBzL0plTK9fnweJCVdA44NGbSGvDE1MpUqxauXZ+Zjvalq\nLlw1LcAq5ycrFAAOrpqjKjT0HoBPKGGV3/2MtsPXFBHR639COugiRm9sU1QiuobxHcuWzDTbjnT5\n7E5WzT8axPMWLC4XdmEfaDMdb4OakjFNpm8Onrm6FMargdVpo6wxhYqIT9J4ChdiTQR7QXNQx7CA\n+V0rS/NU00s5NWyQVe93K7QQnracPYwU0Gl3IV2/Z2eTuIYrk6ycBuqV6jfar2DNHm2A79kwR6rp\nBLswR3jVf4eSSpzK6GWcljlOjRrHOIV2nEKRaFjsVpMi5ciQ8zHIqJVh5vdUBYkIoxxMYOpg/Qdk\nmrVnAOs02M2+W6Es5NyA+TM6Cn/gyEAKsapkxqlSzS+A3lFYVyHsjr54zGy/dBA0p3lN1cIuGMZc\nLTyFecbViYiICrOw93P6dNl6SY0bON5JRNdmHK1Om+nPjv1ij/isiFGes+eBBnPpMfkcRSsrzPam\nedhD7DarsOMUGT5uKq0ok9GQH7gZijcRNnc+d99mcc3hPdiPZ88A7axvT4uw4+p0LqaAxWk0RESp\nI++/Fxw8KtfYhJUY+6oKjHXGJEmfG59ztuRroypFcSO9n0j6PCJJ1Sxej77peKte2BXeCCpDyzOI\n01TqFVd1K1wFP/zeE3uFHVf5+aevfgy3yoZ7uFmuc65QlJPPKARhqVDm78Mz5U2AWtHFV14UdjG2\nH4T6sJ/4miXFMpVRAlqegw8oWFsl7NLH1or9l4lXJPKHQmYsf+c/yvl9+in4nql3zDLbqS2SGsZp\nHb2MIh9VSi1YmBqfk8VKF7eeFXadTLlpZRn2HU6tzl8mYwxeOiA9GX5329Gjwu67v3zAbBe3sXNL\nptzveJyXOQNxSo4SK1Uy2v/ELdiPz7xwQtjtP2msYV9AlhdIFJKsFrKNxWcqVazqExi79GN4lgyl\nZMbeH0ARmFPus9vlvK1YCcp8IIDx3vOOpDPdvaaG3g8VK9aZ7Whwm/jsods/Yba5WtR31j4t7P77\nf//JbGcwGlDvYUnXyq8B5fTRB/7NbH/0p1KlqncASlwzXVD2vbTtiLCbM6a0lvK2fNZEIBaOUXCM\njs/PaUREURb3CWXWArmHlI2iLzgVqftCt7ArmY9ntLL9Yfiy9HkhVh5gxIP2gn9EWYyRRknX4qUs\nGvfjPQGfU8Z3I6ayMP/uOSfptMU346wycBS0xZxZkk4bYecGrsDnzJFUrvG9xOKU9/NB0Bk3Ghoa\nGhoaGhoaGhoaGhoaGtcp9IsbDQ0NDQ0NDQ0NDQ0NDQ0NjesU+sWNhoaGhoaGhoaGhoaGhoaGxnWK\nq6pxE/FHaOBY1/t+xrn9GYx/6S6TMnDJueA/W134+ZErivQnq4XDufihkKwFkVmC71toBX/RPwq7\nhTWS15i7EJJwvAZB74lOYdc2AG4d540OX5J8Ny5dxus9ZFRLbmPbG5AHDLSijkPOEskdHCc/qxLT\nicBIn4d2/GYnEUkOPRFRaTmecTmrI+LvkjJwe/ZBUnbTZyHFzesSERH5WlGXhM8J/wHJqY2wuh1f\n3LDBbHOZcG+3rI1SmAsu9tnjkJ6urSgRdi7Gra2bj2eyKHUSLjOJ6dVzYKfKGFZtmmy2Q0xide9L\nh+X9jUnLB4clzz5RSLImgfOs1EbgNSC4RF7nHsnl7+8EL7xgEriZGVNknZd0VsuG1xLpZJLfRMp8\nZVRyvn7dE6QM3uAxzC17Ou51oFnOuY2fAX+VywaqtVx6mKS4m0nujTRInmx6NWRQuUx0bFRy4L31\nxnVqfYNEIBIMU99lg4ubVSJrzURGcE9xVo+i8r6Zwi7MpJG7d4K7W7JB1gfxNIOjz6US43LqCD/X\n241r3C1YB/GovMjN6kXZj2KdeoJynVeVFZrtuXHUhEifLucbr7XizGP7QETWZPEwOd7cOag9krNI\n+oDxOki233ywJOv/CyKBMPWdMfZFr/LMeZnotwmMX89rMRERnf8T6g9YLVhH1VukpHoK4/ansPl9\n4cXTws7/OnzT8DnUuMmeg3otvhYpK8rrkfB76FJqGs24EbXMeI2b3zz3nLD75qc/jd+di7Gf2lcm\n7KorMHa9LYgDeO0VIqLOZmOthEcTvxZj4Rj5x2or8ZokREQOtl68zZhzZTfJNXbuReyLoQju8Wyr\nrAc4vxr1YKxMWvjg04eEHa/9c6Iea3vBPOxBvZckl7+2GH1pZzUyXn9tv7Djtdpy2J7pb5f7LJcw\n7b+A31rzsWXCLqUQtXB69+N5wyNyPaTVGH5X9SGJgj3dScXrjHUWVuqGcRlnXxuT255VKOw6Wc2b\n0TDGUV0vqayu3eBxxMVLtswXdgNHEVd6WL2GwCBig7QqWeMxk9XI8PkgBW+3y7pyvO7Xle2vmu2U\nErnPhlidNLWumbBj8bC7GnYRj6yh5fUbfRsNJ34tpqe7afVaow8vPyf9Wl4B+onXy7ClSt/e1IB4\nbv2n6nCNXcbUB5/Cmuv3YO5PLZUxOa+TyWsllW5CrYvWV6TcdjarqXilC/Pj8xvWC7ujj+Mepq6D\nvx8+J9f2aC/mS/8w9oHSHCnpXnILpMx5fDTnkwuF3Yyx2OHRo/voWsDisJrzMEM5awwxXxIPY1/v\nZrL1REQ3/eDrZvvkw4+Z7bAyH888hr1n4kdXmG1eh5KIqPmlc/R+OPrDR8x2+V1yzx25wtZsJ+LS\nX/7yH4Udj1s8jbimep0c7z88+H2zzWv/DDfJ82Ltaoxj45uoBanui+PnrKhSsy4RsLntlD3XiBnG\na8yNI20i5l3ectR36tneKOwGPOiz2qWIHZLOyLXIa7zxGoA8FiGSz8njreAA1u/Jp2UdqQirbcXr\n2k6fK98NjFzCuOWxmj4du5uEXT+Tmed7Wahfzjful5ysfpyvRdZocpcZ6+TD1kTVGTcaGhoaGhoa\nGhoaGhoaGhoa1yn0ixsNDQ0NDQ0NDQ0NDQ0NDQ2N6xRXRZVy5qRQzafmEBGRv1Om1Frs+Cqe7qNK\nXXvqkYqUw9K2Awodh0s5pk9GCnfFbTKNzc9SXrPnI014z2OQZOwdlimu6YNIN+VpZxkRSZMoWQUJ\nRO8VPIcqvXpiD6Qx566eYbbrHzsm7Ko+Bgm87r1NZju1VNJxfB0yjSqRsCYlUarTSH8unSCly3o7\nMDbZ6Uh9Tleks2/ZghS+9leRxlu6RcqfD19ASimn76QXKWm8fRhrlx3pzHZGvyucLNNB33sRKfqz\nKivMdkqF7Mu//dgnca9v4F5TFSlrTqkaGsZcnL9Yzrfdf0Ra6dSJkG7MVyhVRdVG39qd10b2NOIP\nU/8xIx3YqkjIcQoip/gUrpFyvQ5GDcydj3T58e8dx+BJpPmeOApZd5uSwlhdiJTzljNIJZx683Sz\n/eZj74lrJuQxqUA7nmM+owMQSWlMO0tJpVSZWphZDRqGtxP3nVom08NHGb2xhFEeQsMytd89lsKu\n9nEi4EhzUumyCiIi6tjbLD7LrsJ85+tAHZu0aszjAibbrEoLuxjlqO8Axia5KFXYlWxA6mgR84cD\nh/C7Pq9MBw2cQZ+VTsc8yp4tKQjE9gUnS7tVU/db/yxTzscx5JNUzAUPLjfboyx1OlWh517+g0FD\nGh28NrRFi9VCqVlG/yb7Zcr+EEsT9r4DCkbZ8kphZ2eylDHGXwso+2zOXEYrYum65QsnCDs/o3W4\nCjDGra+Armt3Sd/Eac2Cvqbw6S7shh/1h9Dvt6+XKeEtTKo49gokTPs88pnCjFaUxmRzk+xyzWWN\nScOrqdOJQDQQpqEzBqVMlcTOmYU45dDPkbJebpX3YWH3tfscUvJvnD5d2PWNYH/P2NFktkcjknYy\n4MXc4X6yvQEyqlxmmEj2DZccvecft8h7YDLGne/hHkrWyj3CwuivnL7eskPSZDmVpHAp0uY5FYwI\n0qkRv6QxJQqjg0Fqft7o+5LNkspmT4cfDTJJbO4biYgc2dhrfD0Yg/Zz0veWMnnXEPMtLq/8vpyF\nLC59EbSYJZsgGe9pkHEyh7sQfTgakjR9vo/x2Dpzpozt2q9gziQxupBKqbIzWiCXbE+vlvFSaMh4\nXos18WvR4rKaNIzwsIy1+XPt+h3W4g23zhN2PB7b/gfYBUblvFuxcrbZ5jHGd/7jUWH36dUoCXDh\nNKOCMN+YqvRRfxvGlP8u3xOIiCbfiHj6zad3m+05lXKPSCvHM5WUwKfnLZLUU04pbn8dvrpojZR0\nH5dwVmlwiUKSJYnsacZ+uOSfpdT101/+Me4rE/v/sn++S9hZrfDFlXeBJt59sEnY1d67xGxfeXGn\n2S7JlmOy7R1IbGe6sU7v/v4dZvt/v/SYuOaB/wblNzkVMc2u7z8r7EpnIvYpuwk+/9B/yO9b+9k6\ns/3Vh35utjf+y2b6IGz9+Ztme/4U6ddOHjZicv9I4uOb6GiUfC0GPdhdocTQQ/A9XOqalDIU+WWI\nZVu3Y984o1CIV9++2Gw3H2oy25M2yTPYiZdAK89Jwzl18DTi/WhMnuXLWBmQY6exJjyt8t1A/iLQ\no0KDeL78BZI6yalc7ccQhxXPLP5AO38r9n2V2ukbo2DHPiTdTWfcaGhoaGhoaGhoaGhoaGhoaFyn\n0C9uNDQ0NDQ0NDQ0NDQ0NDQ0NK5TXBVVKh6JUrDXSFc3FW3G0PL8WbNtTcbXcnoQEZnKDUREIZZq\npVZ752lZrlyky7kV9ZWuN5F6xVORJjHKREBR9vHUI4UxbwlSDm1umdYbZmozvIL98CWpgGVjae68\nX/p6hoRdQTvSsrg6T9dOWYV7XDUnrlC3EgFnsoNqZhhp9Y3nZBXzyqlIB+O/felwg7Cbyqgq7f3o\nC+demfrWcAnfv/CTSIM7vF0qBcyaDXpGFlNTaDmF7yuJyNTfy6xK/9yZoGipNLYrjyKtzlmIeXTl\n+TPCLshSWXkl+kqFdjEzhjTF1gtInS6fLlPp4tGx/vtwRcKvGharxZwn7lJFkciHPkjOx/wePNMt\n7Hiar4dRGrmyBBFRSxOuO9eGMf3cl24Xdie3QVWlvAipifWvg/qi0qvmL51mtnm6ZcZUqTTE1RE4\nDcjmki6s6yCoIDy9u3WrpN9w6p6VzeeYoh41XjE+Hkm8CkrEF6aBwwZlqHSlTGPmFCau9JKiUJuI\n9VnEzxSm3m0SZpmzsH4sTvQZp4ASEQWZ+ghPUx/sg2/deUaunbWzmI9ndKhAr5xHWZMxJ7i6UXKe\nfKaUYqS/Fq6sMNvNz5wVdlwZhj8733+M7zD8nf1ZuWclCha7hZyFRtp1oE3SgGq3YH7HmE8NMaoG\nEVF6ISgLUS+e5S+UBdncn3bnvWb75KMytT9jGvraz9KBy2+DqgNXSCIiCjF6Mu9DvlcREU1hqf1f\nZ2nuX3/8cWH3wMaNZrupFyoiddOnCbtL7fCjZRWYp1z9jIioZKPhe+3PS9WnRMCW6qC8GwwfzhVP\niIi8reinWfcvMNtn/iiVK7IyMI/LckHvfmr3bmE3tQyxyaxlGA97T4+wq50KylFaLdLNH/v5y2b7\n5oULxDVFG7GXPvGDF832+mWSSuJifuTEaVD4wq9L/1dzJ6jfh/4MmvDs5VOEXck67It9jM6pjmHu\nWB9zBZZEwppso4xpRt9bbJJqJxQRD7SY7RKFthhmin4OVgKgrFbu8Zw6xtep74pcVz2D+Dcfe8tf\noSxFmTppPA5/oKojppZj/aUzVTheaoCIqHMQfj69GWPSfkFRimHUuzRG4fdcljFv/ooxauaHVEG5\nKsRA8T55RlLy5rIYnSvyqCpDw374V67wtvFTNwq7nl2YB9/9nyfMNldnIyJ6fj9U2b74EVBakkuw\nV6VVSmWw3Gas2Z+/CsWvf77zTmF35PWTZnvlqjm4fqGcb3t+BcpXLqOI9FySfmPmZ6EelcHKTESY\nAiUR0bHLRlzvD14bqlRgwE+nx3xkYWWL+Cw/HfM9k/nNKy9KXzntXvjAkUbQBI+8ekLYcTXft9+C\nwus937xV2HX+20tme+kS+LbDv8DvblwiVeFaXgTt9dTprWZ7/kq5j1XdXGe2B1sQh2ayMSCSa/Yb\nn7nbbAu6ERHtewT+9o5v4zlUBeZ5Vcb3pWx/kxKNeCRmKiU5sqUvT6/B/OaKfakKpSrI4kAeV8yz\ny/N2w27sQ8Ew5mrve3LuXOnGeaSYUeEee3ib2X7xTdkXz/3qh2Z7yRrQI7NmSjq/T6FOjSPql2sn\nezbiV17iQVUH5HsOV8OKeCVlM2mM1szfM/w16IwbDQ0NDQ0NDQ0NDQ0NDQ0NjesU+sWNhoaGhoaG\nhoaGhoaGhoaGxnUK/eJGQ0NDQ0NDQ0NDQ0NDQ0ND4zrFVdW4SbJayJFh8MtDirRqziJIofkZz3/4\nspQvzJuOeiS+fvBrPecVDu1q8I5TmHx0evpcYecqghQ3lz0dPg3eZ3GdIr3K6tD421G7QeWABntY\nHQJWD8QblJLBHJzyO+X2meIzZw5qrFhbwHtWpXs9lw0+cjT44aTBrgYBf4jOHjd4wxkpUvaU8+iH\nTqCGzMxN8jkiAfTTLPbZ0a2Sd1pTiTkRYLWN0lyyRgHv51cOQy769jpI/KVMkLVm7lyGmjlCMlKR\nrnXk4Lf4/Og6IesKLZsCzv7EuZgvvftk3Z4drL7H5vW4h9RKyes8/uJxIiIKBa6N7Gk8GjOfu/34\nRfGZlfHAC2/Es2TPUOTfD2Mc+w+2m20u8Usk5XqzU9GHv/nZ88LurhVLzfYTOzCODlYDalp5ubiG\n8/xLN8A3OByyxs3oKOpO2O1MXnCHrDNRtAJythYLOLmFdZI/zPmm3azGFJcGJ4LMtCprm2ioHH0u\n6T4uqUlEZHFIl80lW7uON5ltl1ILh/OEi5lM7sgl6Z+bD0OWnMs1TiwCp/fW5YvENWfqcU1GCdZp\nRKk3xeubcd7zqCLBzuvfpORjrGu/uFDYBXrwfVzK1d8h68xEvMZ9mHWnEoxIIEy9Zw1/maTUfEjt\ngvTrqXfBlZ80Tcp385pGfK6pNUL8TB58wI26CwXL5fclMZneggWTzfbp/3rLbIfCcr8bjWJNcHny\noCKhWzoF9R8aGN/8K7fcIuzaB1CP46bN8JUNx5qF3YJNqOvAx9Gq1K+KBsb8UDzx9aYCfT46/ahR\nG6FwiuS98xp7vfuxJhZ8ZaWwCw5gH8tsgt3GuTJmmTIJY3V4J+q9LbvjBmG3+3lI107vxzVCXlz5\n0xv3p196+Jtmu233YWHXsQ9jUFOI5z1YXy/seh/FfKv7Yp3Z5tLTRERWK2K0jFqs++iosubGZFrV\ndZIoWBxWShmrzdL6sqxrlj4VtSZyJqJtdcq6AlEWB0aYtHKgVfqVOHu05EJWk6xR1klIZfFOzg2I\niWJhfDevRUdE1PUe9qTh89j7woPSVw6eZBK4rDZW6kRZa4HLxNuZdHtBSY6we+6dPWb77kLM79zF\nUnJ6/J547blEwT/kp1OvGPXylt8l9xou6T6T1R8cOCpr9ZRNho8qI7R7d8t4rnsYY7V0MvxklluR\ndK9FbDLYgdj9yDHMsTt/IGvX5DFp4cInMR5WpY7F4ruxr13chj0iptS4rGY1ljzdmIuTPirj88Fz\nrOYN85UprB4PEdGae4x47bf736NrAafbQRWLjfizesMG8dmkCM5dF59622zP+LiUDR/o3Wu2s2oQ\nyz6794fC7ngD6nB+49EHzXbPfqW2DpOJr7kb8eovN/+T2f7ibZvENY5srF++ZxatqhZ2Nhv6l8dz\nPH4jknvzocM4v3o6RoRd3VcgQb/jJ+ijm//9PmFX/6zRR/+XvfeOj+u6roU3MAVTMOi9ESAIEuy9\nil0i1Ztly5Ijy44T68VJ/JJ88eckTl7y4hS/PDuJ34uVxC22JduRZfVeKLFJ7L0DBAmA6L3OADOD\nmXl/zMzdax+Tifh58D4kv73+OuScuXPvPefss+/FXmtNR34TC0cplHg+S58vc/LuXaw/5QJdwrJt\nUq8xv4a1hCaX8Hic+eYHol/5PM779u3jZ0l8HiMi6jvM9+mFw4etdhXoyv3zl78svoM6kbmg92iu\nsQg826LW4uDxTtEPn9lRn8xMTVD7JzjA+cHAoQ7Rr/y+eHz5BU3DG0ArbhQKhUKhUCgUCoVCoVAo\nZij0xY1CoVAoFAqFQqFQKBQKxQzFzdmBR2M0FYiXTWPZD5Esa3bmcDmjaW813MolumijVfGxetHP\nBpa1NjuXqvn9khZSfjtSG7j8tmhNtdV2OGTZaOu7R6x22Vb+fvs7srS2AEodhTX4CXlNtVBGGZnk\nUqsJw1bUN4vpNDmLuDw5NCxpZzkJK2THj1NvXxuJRmnIH6cpjE7I380DC+F2sPmuy5DTpKWBy7yi\nUS41y3LLsv6cZVySduZVtor+wyefFP0evvNOq71yNpfZnbjAZduba+UYIgVp3me5FP3N//mW6Ld8\nHc8rpML1j8my59VzuOxx7CpYYxt0gkd/i8soJ3qY7mHakPcljj8VST3djYiIbGlkT5RgFm2RNAks\nl/cWcXnjcLMsz3OXcClg9iPcz7QbrIN+5S/zGnF4ZAnowACXHa+t43U1p4JLILPqZWm2F6wRe49w\nGXNGvjwHjDeeUi5VTlqiJ+Hv5s/S0vh8MkslTaz/HJei5yzltdj6rLSctmfH1+CUP/WUN0emk4o2\nx6ljMaPGMnsul322v84xz6RAlazj+zz3oZ1We3JSlnY2tXDMiwJNzJ4px3DRJ5m2UtvLx/Zf5ftq\nnsNKoAmU3crrd/SKpFOULebS+yvvsXWj37ClrrmXKZLBCS77xhJjIqIQUCQLF/K5BjqkXbkzQVH9\nqGWoNwu720GFC+Lza2pMzhO0h5xzjWkSznwZK3MX8fxsAdvzjALZDylVSKE4cUTuXevu4HEcdki7\n2CSyi6UFMd4fnCMvv/G+6Pe5el7PWHpesU7GoaafsX0tjh3SsIiIgr0cR1s/4JL3mq1zRL+eBE3J\njLWpgrUGjbXogDWCtrNub7Xo1/4mW5De9Udshe7JLRL9Lv4T38+tn95otTFuExHd9f/yvjgGNtBV\ns9kCNZmPJVFWv91q97ayxW3hamktjPHlhb96xWo/+geS7uar4njtcjFdpmC2pDoNtjLlawJokAHD\nXrVgVTlNJyKTUxb90ztb0pdxLfrRvtuIP309Q3RdGPlSf2u71W6DfGnDvHmiX9VtPI9xz0Waq0k5\n8kKuiNbpJqX2re/vttqV+TxWh/YcEP2QCo7z+WKDQVus5TwofzWPVXhUUrRiSYrBNNAWMzIcNKum\n5Lq/e+Etjo3VC3lOTwbkfem+yGN4qYPznq1LF4l+lRW8NtFCPMeQETgNzy0olfDYJ3ZY7WN/v098\nB6mi8yv4XJ/dKykivzrnXqu94r8wpRTzUCKiDKBselr4s9EGSXceb+LPkArSdFTKA5TmxddDxJCI\nSBUcPi9Vbl9JREQXn3lJfFb/Sb7mDNgLOxveEf1K6jbz8Ry818wqkjH1y//8Bavdd4zHG9cREdG2\n39hqtV//k59Y7W+8+Md8Phny2K9+5Qd8DrB3+Q1q09Wnfmq1A6McK4pWypg3CrF860NMBcSYTETk\nyub9efkdTIdrfvWw6Jekltungc6f7rSRuzIeswLtMpYXAj0bma9mLLODvEJaGt+/6vvni35Iv73r\nV7dZbX+r/N318LxdXsAxL3meRESOHPlc0I9UJ4hZJmM3fwVIvgC1P3NOnuiHMRRlQFCGxfytfqBH\n+Qwqq2kF/+9BK24UCoVCoVAoFAqFQqFQKGYo9MWNQqFQKBQKhUKhUCgUCsUMxU1RpSLBKRpNlDN5\nq6TLT9trjVbblcVlSrGILKWs2LbMageG2JFixCj3s7n51HxFXJKVmyudRbq7X7Pa49e4RLConkvF\nQyF5bCyZjcW4rMttUACwVNENjkRZ82VJG9IokP7lMBxBxqC8EV1tQsOyHDQ9UcaL5eqpgi/LQ1t2\nxssXTfeD402sEr5yLpfMXj4vy2nnr+XS3xC4HLy5VzpX2N/le5EBTgb/+vW/EP1aW9gZYQqoV5vv\n57F2FUqV/5JbeE70HeGSZXu6fBeJbg9Xdl+22hs3SSV+dJw6+Co7FU0Yjir29/ma8hYyvWH8qiy3\nvvuLcdrK947KsthUIRaK0kRCKX3wkKTFFO9gFfb297iEvWzbXNFvvJ3Lu/uhvDTY7Rf9psb5HlQ/\nzKXGWL5PRJTRy/N9Xh2XMKJrkOnc5injclAs9ZzoHRf9MI6Mt/C99lbKOIR0SaTq4NojInJCjIqG\nOAZkFMkS6ewFRb9wbqlCmiOdXEXxeR0el/Ns4BS7ZEwCJS93qXS8mRjk64rl8nWMd8ixWfAEU/zs\ndp7rZmxse++Y1Xbm8XgijQZjIVF8X0jClnHj+zTcd4KPBzWq0bBU9h/rYcqcr5jpGV1Hz4h++UuY\nstPxAX8WDcpS3VDCZSoSnB7aYmRyikYvx++33S5pQE3PMW0ru5TnamhQxvzuPS1WO7OWy7tjhuvB\n4GneM72VvHYyDae+wTPcL3ch0yALVrPDCjotEBGNb7+QxgAAIABJREFUtvG6QoepGqMs/cRhpmXt\n/OJtVnusSdIbNy1daLWD/UxDmHfPQtHPD+t57r382aQRA7IT+7Yt46bSlo8Eu9NOhQlaUGhAjg26\nlJVs59jaeUyWrGfN47wAy9w7D0jqXuZcLrv2lAJdtVTG50iE7xmWUiPFZtRw7fTXNF33OxmuUtGv\n8Z+YHnXv77Ljy9Xn5LnmzuW5k13P1LyIscYiAY7r6LiRWW1QnBNupNFpcnijaMxy43RkylgUHGD6\nAuYTJk3Vf4LH/5uv8H2aWyHpZq29TEH83XuZ+mHmDEPg0OnP45wLHZJcRkxFaiE6n46ckbRHk3aY\nRHm+pCR3Am2nsIzn39r7V4p+w3D84bMQQ4x9x1YTX4Pp07AWp8IR6u+M72uNxy+Iz9AJFeeZSftH\nmtJicLLMWSop0+8/x858q5fw+hvpk7HxtsXsjIM50Hv/8J7V3vAJ+WyyEJ3G2nkMTdfDxgO8ZlfW\n8HrJMugZ57/H+fUFoH9tv1f+buFmzo1PPMP7eU29nL/JvCL1ZLc4wv4AdR44TURE+avKxGcXfvSi\n1R5s4xwmZ4Hca974o69b7dv+/HNW+0t/8znRz1fAzysXD/N9Wvp720S/I19nd6YtX+TPuvcyRTc8\nJmnHuLfe+dWPWe3Ra3Itlt3FdG3co8ZazdyT1z1SXV/861dFv49/9UGrXbIBXJY90s1q71e/R0RE\nk4bTcyqQ7rSRJ/GsP9Yoc8ogUGKRcu/yyTU22MV5X2EFU4OHgm+Ifui+hWvbadCeahfDel7Cv4XP\nKRNdco1lwnMG7tPDF6ScSRieVXKAvo7P9UTSGQ7lYMxneXRg9s7ic0g6RyeRjP9mvncjaMWNQqFQ\nKBQKhUKhUCgUCsUMhb64USgUCoVCoVAoFAqFQqGYodAXNwqFQqFQKBQKhUKhUCgUMxQ3RVBNS2PL\nSlPzoWg9axGMg61X0bpK0a9t1wm6HvKXSw4kcqjRQmxoSPLKEb5ZzA8daGZtD3exT/RD/iFafuUD\nd46IyN/FnH1XAXOiO96+LPoVbeDvpdmYV27q+6CFWO/Ba1Y73SWHIdAW58NGw6nXZEizp1NGwh7X\n1AdZOMmWinYvj+/cpdWi3/gV1iRAW7PaYsltLKhkji5yINvOtot+s+fzHHHmMZ9xoo15wWGDvxka\n5nPNXsA8/LJcyam3wTz1OPkcms/Lc+jYz3O2upCPF4lKzmEecHAPvMNzeQvY+hERnflpXCdnYlDq\nxaQK6Rk28tXG7++EoVWBdnzFt1Rb7eHGbtEPeaCZwK22ZUg+J/LY21+4aLXz1kubw8p72Hrd7uQ1\nF4sxz7PvZIv4DupDoXVl9hypI+Xv4rmA/Nch4OETSWtJ8f+5Urum+x3mNDtgzpn22En9g2lwPaXI\nxJTFsc1fLjUoML5m1fI6ChrroHs3W3zOenABf2CccDTK3Nsr77BVbMWWpaJf4TqOZRke/l1vBc+d\nMcPmGy1q217j+TH7wfWi3zBYqqK+Q3Z9oejXd5jXZtoq/v+MPDmGV3/KujY1n2QNgrNPHhT9ChbF\n9Rlu1nLxo8LudVD+yvj4mbpk3ikeB8GfXii5/KiDgZbTU4b2UcU9rMMweIJ1kJbskLoxYbAlv3qY\n50j5bI7RI51S4wztcCtns6bFfbdL7ZWLr7Elb8vzrEGRv0LO4VMNrJm2YjHz/8++dFr0W/wAz8Fz\nL/JntRskl38woaEzZeirpAKhYJjarsTHoKpOXsc4aN11fchzuNDIWXAeY5xz5siYlLWc15XNyeu8\n7YNDol/NVrYajhZwDB3vYk2zvCXyXP39PCdwH2h+XWqtld7JOnVOsDDNqZG6GrUP3ML9nDx3Lv7s\nedGv/Rxrbsy9k21e0SqViCjQHv83xv1UwuZxUN6y+NzteqtJfDYE+i35a3jsWvddFf1qF3AM/G/u\nT1rt3Lws0W/PSc4xB8ZZj2nxghrRb2qMx67sYdDi+DZrcYSn5Jwu38A6JajNc/hio+iHucqhRv7s\ndEuL6PffHn3YamfW8l4/dFzmBEXbq/kfsM+SETr7D8Vj9HSMY0a2i+bcFZ9D85zSvhtzFrS9LsqR\nuezc7WzJ/u6zH1rtvGNSS2hF3WyrbYM8/PhVOSce/YP7rfZoA+tiFGXz7/Z90Ca+4y7j3/LNZc2h\n9vMdol+ul58trr7E8XTup+Te3NrPelabt7OGJ9rFExG1vcHzYMF2zsl8s+Xabns+vldPz64Y12lK\n6uR5S+XaaQRdm7qHeIwjxv65/LOs33P1jd1W27RdzljMcTAKuU/vETmO/iDnKuOt/ByD2ovV968Q\n3zm8n9d509NHrPaZ8/LYR5s43jy0jp8H1v7BfaJf31ken6oNbHe+/EP5TOL08vxpe5v33N1v/lj0\n++z/jluhe995hVKNWCxGsYQGod3QDPNU8NzHOD/QKJ+PcX9JT+djoG4iEVH+Mo7Jg2c5Lvlq5byd\n8HKsDY/yePqvcT6TZ+TTDlgjoVHOw8YvSV2+igd5veDzOurYEBGFIe6hfl9SszKJsUY+PmqpVT4w\nT/RLahd+VF1brbhRKBQKhUKhUCgUCoVCoZih0Bc3CoVCoVAoFAqFQqFQKBQzFDdHlbLbyFUUL9/q\n3iXLxNC+MBNs7DrfuyL65S4G+64JLg81LdPQrjEtjUuWuk6ckv0KuHze6WPKQ3CQy0uxlJ+IyzyJ\niGoeYVvo8XZpDVY0d63VvvLmm/w7ebL0GS2/goNo4Snfi4038zU60OLMoGEUb4qXyTqelCWQqcDk\n6CSdT5TdzV4kaWxYJh3s5euYHJT0jIq7uXR+6AyXtC3eWC/6DV/i0k7nKJeWFZdLq8r9H3J5/O2P\nbLLa4Qwet5Grcn5UP8jl2EgLcDnlPWt6g6kbbvjMtNFEy7/LXVxuPt+wAB0HS7zbv3Cr1Ua7RyKi\nsYQ9pUm1ShXSHbwWsxZKqgmW7k3kcVmhu0iWCeMam4TvlGyWpd4jYDlb/1ts5zfaKmlKSGEKjg1d\n9//Nct2SWTutdiDQYrX7m6T18xTYzU50yHuNCAC9ruIeLkdsfkrSM/KgVD5vMdNCWl+U9qNJ693p\noNmkO9LJVRiPX6NNkn40epFjka+O75nLGEN3OVPSLvwDUy3qvyAtQntOcqlt2Sa+L0NXmkW/3Foe\ne7eb44Ovhqk4Tt+H4jt2J5fM9hxnK820NLm9IP3r8s94fMs3Vot+aP/ozmNr7IwcGcddD3AcFmWo\nO+aIftEEZQRprKlEutNu0U4vPSvnbf3DsL+A7fXep+U9dDn43tRWw9xcKUt+u9/mfbdoG9MpQiPS\nhnKinUuX1/w2x9QAUA18dTIOl8M6Bbd2cmQaZelwrq4sHquRUzIeVAGNwwXWuIWGZeboRY4vFXN4\nLU4YtrlZiXJ7m+P6Fsi/DLyFmbT2C/HYNtIg84DhU0CxAaqst0rSM/qPcF6RCzalpn350EW+T0j/\ny1skqcYXn3nBatd9nONkZimXUyMFkojo2ssnrXbpbUzLKTQo6917gGJ5H89RjLNERN0nmCaQU8/n\nbdpAL3x4mdX2lvN9GTghaSEl26qJiMjxfTmnUoVIIEyDJ+P7d0aJjJWFQFdB2mLJArnGokDFq1zN\ntCnTDnfHNuZxdl8BG+0euT/NeYBjZ+N3j1ttp4PvYbZBB8A519TJ+Uh9uaQnY97ywBamptqM3LOz\nj8/d2clx01cvfxfpUUjVxz2ciKjygXj+5fyRtOpNBab8YRo8HJ83ptxA4eaq632Ffvb0u+LfmRf4\nvG7bzuOEEgBERPuA7r5mGeev5XnyvnS8wvSW8vt5/6wBCgVSR4jkM5IHqEKVi+QYhoZ4Lp671MLX\n8I58dmru4fW3YorP1WbIVowC5bUMaCrvf+t90W/ZxvgYpmekPp4SEdldDsqZE19bPYfltaz80m1W\ne/Aix83iJZIe9vIffsdqr7qP6WG5i6U9vcvFcRlzAU+5pGjVb+Znl+y5TMcvWcM085Fr18R3tj3C\ndFGkwpR0yHjwmdvYXtxbzXMhGJB0nDaQ2nj12zxvP/EnD4h+z3yJKVE7f22r1X78m78m+k1NxfOK\nWCz10hqxcJQmErbx6XaZP/XAHoJ7jb9dUrDx2chbzPlRTp2ki482cx5QtoGp70gtIyKatWmr1e69\nzHb3JauYctdz4jx+hdzwrIPP65UfXyD6oUxE5y6es6Yluc3Na278Ms8Df6u8dsx73KXc7jsgaZWh\n/vjzolKlFAqFQqFQKBQKhUKhUCj+g0Nf3CgUCoVCoVAoFAqFQqFQzFDcFFWKojGaCsRpKbmGanOw\nn91zHD6mpGTOyhH9Ot/gMrE0J783ylspHRqcUII92HLOas/Z9HHRb2SEKRAOB5enhcYuQi9Zbjnn\ncS6dnBzicuyc6tmi39QUlz05c7m81HRVKUB3CagmQ5VrIqIYuOZg6RUqYxMRjSaOHwmmvvTNneOm\nZQ/FSw4vvyZpIbkFXFbom8elosffPSv6xV7nducg34tFW+eLflX3czln8wtcujbUK52WsNwXy+av\ntnKJ8Jbf2Sa+I5xn4J775khXKW+Qr8kFpWrlblleitQ6VBA3xyZwlekOSMFzl0nnsqSy/TSYERER\nUVpaGtkTLghRw2WlYBWX4mKJc8xwGsLS/oUPP2a1p6bk+OSt43U12M/uJLWrHhP9xsYarLbHUw3n\nyvfz2gXpRoJrbLSPSyIzjNLEsSYuN/W34LqU/bDUsAtKHSsfknNzGKhIkRDfv8i4pApYFNBpYNlE\nJqdotCF+XZPd8p4Xb6u22sPgnIW0QCKirHlc7rvod7ZY7Y7dl0S/kk18vN4TXMKNrgZERJ2nmG6V\nNZvXNrqHOLLkPR+8xMdLg8XY+K97RD90f/N4+Rg5C2TJLNLacur5+sZaJF3S5uI1PALjaZZHJ/9t\nOgOkCuHRIHW/Fy8brlw/64b9XMUcf4qy5DleaOe12NzLNInxvdIh664V7HiBrgejjUY5dicfI7eR\nKTh4DtlzJFWq9QXeD0q2VvO5PSWdIEvBwQPno80j04ncAd7f0R2rYK2kCqQDzdrfBms7S9JpBi7G\nrykyDW6LUxNhGjwd32/wHhERlewE5xk3X2NOlcwXMoEq4fLyNSLlkIioN2Of1R6+yONkN9wly25j\nyh9SooaauMw6ZNCY5356E/RrsdqeIrkvZsO6yszk2OipkGXpeYt4rBu/wy5IFfdLV4zWn/PcqXyA\n9/2Rs5J2llUX/93pcnhLd9osClvQuDfO7OvTekx62Egz7/FIran51GLRLwS5QQicn0rvkFTNrtc5\n5y2/m93VWl/hGO2clHt47iq+74V7eG/Ye15SAGaDk6cd6N8LqySlqGYBU7494CbqKZd5C8ZRpFIP\nnewS/ZLui7Gp1Gc4gckgnbgU37u3PbxBfIZONOi29ZkvPSj64XU4YNx7T3WKfh0DHDcnQOZgyQY5\nv9F1qOtNdg8qv4/79R+S9IcIfKfjVc6NRgckBbT+MaYA9XTw+RRDDCYiWtPOufH+fbxH3log3RuL\nSjh33/8M7+dbHr9F9Gt7Kz4vpyZT79JHRBSLRSkSjset4rXSIfAH//VfrPb2nautdvESOZ/u+gt2\nQwuO8v6/928kNa5uJed6dUBPPvXUUdGvop7X1ZFv7bfayx7nc0g3qLj9B3lvnvsEPztOReQ+dLqJ\nqUO140zl8hjuvZNhnhePfeNRqz10QVKN7/7d2697TtfelLR/V3F8nU5NpN7hLRqO0ESC6pT8nSSy\n5vMeMtIIshhGnEUH0YHzfI9Mil9WDeeHGRn8TO2b3S/6oaRCQe1S+H8+doYhZ4JSLuhMh20iIn8r\n07q6h3kfqK6XOYu7lMcDH6tKb5UyE2Mg8YHP/yYlyl0Zj8MfNUfVihuFQqFQKBQKhUKhUCgUihkK\nfXGjUCgUCoVCoVAoFAqFQjFDoS9uFAqFQqFQKBQKhUKhUChmKG5K4yYyEaaR83HuaN4KqXGDlnbI\naw4bPMCK25n/O3iM+abnX5C8vdJZoIcAtpuHXvp70S/JDSMiqrmf7btHgXPnm2NY+70PWhrAhTOt\nB/MqmSvpAYvLkGFn2vYa81eLNjG32GtwG4dPMofRB9aUE4aVdCQQ55xGpoF7Oj7op/0/PkBEROs/\ntlp89uHzR6x2biffP7thnZ0L9tOuDuYzZhn2snv/ea/VXgpW4VkjUjfmyn62MLUBz7+mjPnbDq/U\nO0CNoABoCZ0+1ST61ZcxVxJ1Fvo7pV5GFIiK9fezrdyu5w6Ifhs38pzo2tNitYs3SE55kiPteSf1\ndplEcZ0Hf0f8uvOWSX2oofNgX7uU1+nAaclTL9/J1oiDfXyd7kxpgT4yyGvT5uTx6Wx7Wf7uWbaG\nL1nLNnvj3bzOcyrqxHdGR09Z7eAQc9ZdBkc1GmZ+aAzambMN7QbQfOk9xNzkiMEpxTmDXH57lrST\n7zsQt4bEuZMqxKailjaCzdC3QB2tpFUgEVHBWjk2bS+zTkLwFuboj12QvGAf3KeSVaxpcf4HL4p+\nzny2Jx65wPMIdT/S7fJ9/0Q3W86j5XX2fGlTH5ngeJa/jjnD49eGRb+sJax50/46x+r8NZJnHAUN\nsCyIpxm5cu4kNcOi06AZRhTXwchbHV+DqAVERDR0hmP+xDWO87NWSy2cLA/cdz9rWpTXy30W4173\nLuZ0m5zppfcz99sPmh0O+P7wJak/Uvco6x1d/inHbk+GjL05izkuo87L5Z/uEf1sXp7TqLU12TUu\n+qE+WHY9z5k0h5xnSfkkUwsmZUj8gMkzd8PcDw3zWmx4StrrZtawnp+vlnOEcVer6Nf5Nmhk3M0x\nOM2QfTn+D6wntuyJdVa77wO2q80o9IjvNPyQ9XOW/+bnrDZqiRERpdezvsBAJ+/7pq5eLMKxFrUK\nej+Qlrn+IO/pqGNYATp3RESRybi+g2kvnSpEwxEKJGzkUVOAiCgKWmaoWZZmxDNvLY/jkkefsNp2\nu9R46Gx9yWqjdg0Z+j3FO3iNZFbwsSNRPj9cU0REHrCRnQVjUNwpNVq2/xrrmqXZ+HdHG2T8j8Gt\nmBrnsRprkvo+GLMxluP+i8ePBOX3U4HsQh/d+Ztxu2jTqnrgCNvLnz3O80yqD0nNzIM/4Nxm9SdW\niX6f2Vhttc+9zfpBB0CThojozrtZa6d/lHVo0t/hteyrk88ZBz5gbchbP8n6MhW1Uj/n9b95w2pP\nwZzIf1mew5wtPMeKzvE8chuaXF7QeOuEPP6t7+0W/eaVJvaW2DQpMcZ4DT7/J/8qPsrL5HMuhH3d\nPyzjijuL9z93DudmD3z9j0S/p7/4Z1Z7GWg+Ln1MjrenmPcazAe+/99/ZrUf++K94juuIo6xba9z\nvrX5j+8T/ZpfYGvq0u285lt+IjVC53+S9+bgEOdsriI5js3PsrZrNuQ3lXcsFP32/OVrREQ0OSKf\nS1MBR7bLim2oP0ckn08x9pi6k9iv7TXO56JRGVPmfZb1+yIhec8QgR6w346Cth9soCHjGXO8lWMZ\n6t2Yz/yXu/gZKdfL8T48LI+Xkc85JupRdrwsNeIyQSsWLcQLN8n8b/xq4ppsH037TStuFAqFQqFQ\nKBQKhUKhUChmKPTFjUKhUCgUCoVCoVAoFArFDMVN1Rw7slxUelvc1s1uWHmNXeaSJd8CpivYjFJH\nLPWeBLvG8roS0S9/FZc6Dp/nku7clbJfcIBLzUY7mBqRASX/pt0j2sUG2qH8y6hSGr3CpYV9h7lE\ns+w2aQOKFKEA2IuNNUiL1vL7uCy6dx+XTxdtkWVTSWtXs0w0FfBme2jN3XH7wb4D0r5wfgWXLGKJ\n16UPL4t+WIJcsI6pGwe++4Hot/IOphUNnmAaTdQozfzE/3OP1R44zqXAWQt5HvUfbxffwbK40Q4e\nwy2flpaHw+eY7tF4nu/5wvWSstN1lkvk+vZxueasggLRLwwleCW3MD0qaljUNh1vISKiYCD1FBsi\nolg4YlFUJvsD4jNcmxEoG3UaFtvDDWA7DGXRdrcsV0X6TO58prGYpeg4XydHuUTXVcAlh5OBDvEd\nLCvHUkyTJhgAC/CS29la0qQ1jIBteP4KjiEdbxg2t1BKPdnH1JQKoC4QETX/OF6yGTNKxVMBu9dJ\nBesrrvuZr4apTVjO2fBDac08PsnlscV2KBUNy/s3DtSwMz95wWoPjkvaSkU+x7Kae5lShfaqZkyf\nbOeY5/QxneLKkaui36wFHF86LvF6q793kegXgLmYDrSYoVPdol/+aj5e20tcwly0WdIWkzRec76m\nCtGpKAUTa/DqQXnNSDOt3c4xx6xOx1Lo0gq2lBw4IOMeUurSXXzsQuOaHZk8Du//mOPy6jBTGFsb\n5FpcAvtf1YPcz7xvmblMd27dzdQctAclIprs5XWFNLnui3Ics7u4X9k9fI/6DDpO0op7Omg20WDE\nopRd/lDSbZEqVrmh2monc6Ek/ED5u/g0r9MKoGMQEc16iO9t126eL848SXta+Cm2CZ7o53vUdIXH\nbelsuXaQzjQyctxq9x6UdK3Le3lPz3DwfpFm8LVKt/C5F23mPAXzLiKivmaO90iPGjgm51jxxvgx\npssOPM2ebtHf3aXS6noC4jzSJPxXJW269jGmkPd27bLaaFtPRJSVz+Po8PD9dburRb/uC2zJjNSz\n0nW8ZnPmSlrpeBuf09zb2RI5b4nMf91Z/O9QiPc+0yYe4+joRR4rR7akQeI1RiY4by7dKed6kspr\nzpdUYGIwQGeeia+f2k3SWh3X/txy3sPbGyQNHKnN6z/LdtloIU4kqa24zgt8cu6MwxzpGuI2Uiu2\nVq0R39mwmfNfVxHnQG9+/S3Rb8dv3mq1+yEnb7sq42TfHqbaOu08nue+v1f02/FbfLyKWZyv5Xhk\nfDndGp+zE+HU092I4tT3lufi9LNP/u2vi8/2/eXzVvuNb/D9GJuUdB/cP7PdTE9ZvErm7xGQ5HAC\nzb75+fOi3xDQkNd9kanBv3U/5zreHDnXKe2w1RyBZ9GWF4+LbtUP8ngPnOV9u3tIUsEzmwbpepi1\nU0pX5K/g9ezwMU3s6Dd2iX6bv7Izfty9b173uL8UYkTRRP5prh1MYgJgo+2/Kq93pItzz4pt8tlZ\nHo6PN97Gx8itqxb93JlMnxu8yvsY5humTAnSB3sPcl7hb5MyJZs/u9Fqo615/2H5rIyU4hBIPOSt\nkbIV+MyFEisTnfJ3k88xHzWcasWNQqFQKBQKhUKhUCgUCsUMhb64USgUCoVCoVAoFAqFQqGYobgp\nqlQsFrMoIZFJWdNTsLbSaiNtpO3FS6LfOJRRZUAZUZqhphwCheycBVxGGh6X6s6oxD14issWI0CP\nKr1Vlmf1HeYyNqQ5oeozkSwV9YDzjEmL6XiVaRild3CZ3VSFLEG0Aa2j8Ba+X2Yp+mSCAjMd9Iyx\nYT/tfynuIrHpAVnaiU477hIuFTWdwfa9w2XgdaVctrbhiY2iXxAoPPmruYTMLHUfhdLBnIVc2tny\nJqvqt/RJB5TwFJfel+YyrWTEcEpBys2W39lmtS8/dUr0K5jF8+DgIVZz33rvje/R/ue4hPKW+6V6\nfVV13CXC6ZwmB5T0NIsS5TJcBTJyuFS0670rVjtrnqQyeCu4nHDoJJflhvqNeQf1e527mEYQHjVo\nYLCEAx1Mn+k8w+Xys3dIKhLSCaugXLXXKE3MXswxYLKX6T1m+SbOn+FzfE15K6U7j6+a5wxSHIYv\nyvnjnR2/R9NBWwyNBanj/ThVImeOdGTr3M2OQeU7OKbkGmNYA9fbAy5neYuKRL/8ZXz9vUc5/i14\nYIno1/Me/27vbj5eZh3fL9NhBO8Nls5nuiQ1D6kLtVA6bvKGirdWW+2BExzTQ32SntH0c3YeqLmP\n507YcP3z1cbPHalFqURaehrZvfFS5vo7F4jPmndxKW/3h1yiW7BczschI24lEfDLa0nr5r+1oBuO\nuYdgjH3gT9n9YgzcFTZ/TJ5r/3Fep80/OWO1Zz0sXSxO//1zVtuZD66CBlXKO4udT4JAU1nwK8tF\nv0GIPf3gBOcul3QFf4JCNx1UKZvLRr65cXrwugfni8+QYjMBVOiRRunc4wJK6bxH2TnEHJvBM3y9\n+SuZ7ufwSdqKHyjAmeBquegWdqVxGlSXUYinfUc5hvpmS8ebpRW8X43C3EN6KRHRmW8dtNpl65na\nM2E4g9XeKd2jkjAdm5LXjrlVqpEsubdlyL0X6VHjkHNUGs5XA2d5neKYoOshEVF/mNdLDlCI09Ml\nxcUDuVSgm+fPnJ13W223W1IdPR7OKXta2L3MZuQTwy1MtRs8zQ526LJHJOnA6eDWVrRe/m7HWxyv\nSiAOB42YasX8aaC8Od1OqloSz49NN0cPUCA6gB7V3Nsr+s27g9cwSiW88sP3RL8dQE9Bus3CykrR\n72Ajj8fmNexhlQ8uj/0fypzFCY5v3e/yOLmd0rlyvJmpV/3tPC+r5sm16Klkugc6SdmfP0c3wulz\nnP/d+vmt8nj74uf3vX2SapUqOHwZVJyQgfjBF/9RfLZ5G+8BZRGm+1XcIfPD7ALOT1o/5LHb98xB\n0W/NVh6Thg94Dt/+1c+Kfj2n2SE1u4jX/bl/ZnfNWOSM+M6HZy5a7XV1TNEqv1+6g0VCPFff+BeW\n2fj1f/wD0e/yC0x1QrfPwJCk+2XVcsz2lvJ6dhvuU8MX43M/6diXWsSs/QvnHxGRv5X3p7J7edyC\ngzJPQ0cmpB8NHpfXi86YuQvZZW/wkqSf4zOdA46HeTxKt8T7cRxPs3Me6DAkX9pAUsED+znShImI\nRi9f383KlJBBZ1Y77CXmXj98NjmGH835VCtuFAqFQqFQKBQKhUKhUChmKPTFjUKhUCgUCoVCoVAo\nFArFDIW+uFEoFAqFQqFQKBQKhUKhmKG4KQGO8FiQut+PayC4yyTPDrnRnW+zDka+YY+VXc9aFahJ\nY3Jy219mfRPUVCjcJLmndi9zytKH+T1ULthd0roRAAAgAElEQVRUTxl8am8V82TRfhrtZYmkzXLm\nHOYbZlbliH6jF5jrjnotIcOmeQp+NyOXdUj6jkrLzLLb4xaIju9IHlwq4PW6aPX6uLaBI1NybTOA\nkztygXnvC9dL3ilqliC3/cj3Je90+cdXWO0rey7wd7ZJK7/2k8wNLh5inYTqu5hDGnpF2hvPgd9F\nLZ3hc1IrYmCMOeW+Bh7D3pER0a9iJ9tO3rGQtXDCo1JTyV3BPM+afua1J625k5hK6L+glXNKEWOt\nJbtHjiNqzRQAB3vojOTeR0J8T/NAgwi5psnfSgL1ZcJDkvdeeiffw1HQfyiew/cpq1au8+w6Hu9r\nLzOXuGij5N4HQFPBlc9rx9RuGLvK6w/1Fcxr6geb2pItbL/cBdoyRBBfpoHL7/A5Lbtd5P4SERWC\n9kDb87x2/ONSZ2Gyk+9L0bZq/n/QNCAian6aedto/zveIq1w89ZyDBw6xvG56wTfr7zKXPEdO1hV\n2sGOOMvQrpmENYLaBVN+GZ/z1/E5FKzhdtO/Su551W3SKjYJd5nURmn81zivPWToNKQK6Q4beRK/\nGTY0GfLL+V55qjh2mPOxYgdfC1olm1ogqCGUNR+03/zyd6++ymspq4h/F7Wexlrl2HthX0Pu+Plv\nHxH9qnairTmPscMr4xCuRdR5GTrTI/qhht3Vl3iu5y6X1sfO3Pg5mTzyVMDucVJBQm8mEpR7zTjY\n03fAXjV7u9zH/O1s8Tn0DnPbs2bJfAEtSH2gY9C564ro56ngeZxWwePug1wk3dCQ6b7Cv1sB+3vn\nBWlxXnEP7+mofXbuR9LitmQRz5ce0AasukfqO+DYtz3HY1i8Q+oLBtrj+25smrZFm8tOOYkcM2Ds\nyagZgWuse4+M+XmgPxUe43XlyJT5WKCDx3HwDMfKwLUG0c+Rw99Dq+LBfL7XschR8Z2h87xGMD4W\nwn5ORBQE2+/Map5npmYkXm8xWLxfe/GC6Je3kvOAYdBnCI3IPCi5L05HfpOeYSNvIlY2vC7PryCP\nc+hJsLHeunOl6HfwOb6fmAM+/OX7RL/Lz/KesvgRzlejhi7VJx7kfHP4HI/NS99iK+u1dTIe4B6X\nu5rnlGmIjOM2F2yLp4yYfu411nSbvbzaak+GZD/UGFl3B2vJdLxxWfTLSmh6oeZRKhGZCNNIQjPw\n0f/xsPgsM5v3u8bn+B5iDkhEVHorzzvUtVmxRupSlcOeNHKZ951LP3pb9Jv3+Har/fpX/slq7/zz\nR6x2w/f3iO+sr+dYV7yDc8Wh0zKf7g/w3vCZ/8X255GIzMVm37/WavsH+RgRIyfAfa7pKd6DS3fK\nvKdsZfx4Do+XUo1YjNdCNCTXhANyvbEm1nwxc5vcVTz3Bw5zHonaN0REUdh3JyHfN2PZBOS8Jbfx\nasoAHdoJw+bbBZ9hzCy+Ta5GP2gAjl7kZ5iQ8RwYBL3FMMTGbMjJiIicoJPjKuA25hRERK6S+Pl9\n1LWoFTcKhUKhUCgUCoVCoVAoFDMU+uJGoVAoFAqFQqFQKBQKhWKGIi12EzWraWlpfUTUOn2nozAw\nKxaLFf773T46dAz/ryPlY0ik4/j/A3Qt/seHrsX/HNC1+B8fuhb/c0DX4n986Fr8zwFdi//x8ZHG\n8KZe3CgUCoVCoVAoFAqFQqFQKP7vQalSCoVCoVAoFAqFQqFQKBQzFPriRqFQKBQKhUKhUCgUCoVi\nhkJf3CgUCoVCoVAoFAqFQqFQzFDoixuFQqFQKBQKhUKhUCgUihkKfXGjUCgUCoVCoVAoFAqFQjFD\noS9uFAqFQqFQKBQKhUKhUChmKPTFjUKhUCgUCoVCoVAoFArFDIW+uFEoFAqFQqFQKBQKhUKhmKHQ\nFzcKhUKhUCgUCoVCoVAoFDMU+uJGoVAoFAqFQqFQKBQKhWKGQl/cKBQKhUKhUCgUCoVCoVDMUOiL\nG4VCoVAoFAqFQqFQKBSKGQp9caNQKBQKhUKhUCgUCoVCMUOhL24UCoVCoVAoFAqFQqFQKGYo9MWN\nQqFQKBQKhUKhUCgUCsUMhb64USgUCoVCoVAoFAqFQqGYodAXNwqFQqFQKBQKhUKhUCgUMxT2m+mc\n5XbHirKyiIjI4XKIz8LBKavtdPNnaQ75bigS4H6OrAyrHRqeFP1sLj612FT0ut8hIgqPwPfS0qym\n3efk3/SH5YXYuF8kwJ+lpaeJbmk2Pnf8XX/fuOjn8rmsdjpcbyxGN0TEH7rueRMRRcIRIiLqGRmh\nkUBAfvhLIjfbFysvzo+fXzQqPguN8znZ4NrtPnnPg8MTVjsjx80fROUFh0Z5bGxOHs/k9SWRfoNx\ni0X4eOkOm/jOVCB03c/SbDe+XTieZr/wSJDP1c3nOjUu504aTOcYXG+6U55fekb8GB3d/TQ0PJbS\nMSQiysvxxcrLComIKDwaFJ+lO/kko0G+12l2uRZxjeEcnDKO58jl+R0NybETx4MxxrmF9ykyMSW+\nE4td/x5GjX6ObJ6DOI6RoOyHx4iF+RyixpyzeyB+wbVHJuV4R0PxY3QNDtHwuH/a1mI0IteiE2JK\noGfMarsLvaLfOHyWVZZvtYOjMkbhidsz+V5OYRwiOb6ufB//TteQ1fYU+8R3goMBPu8cPm8y4h8e\ne2yQzy8zV16T3cMxIAwxxGHEIRz7CYhJmcb5jXWPEhFR39gYjU1MpHwt5vi8sbL8PCIismc6xWeR\nCZ5PEViLTlhTRESxKb5ZOCYYi4jk/J7y494lz8kGezDGADwfu7GX4j4rYOxPU7BPOCBeR8Py+1Fc\nm2k3vu0Y8/HYZkxN5g7dw8M07E/tWszxeGIlOTlERGTzyHsu7iVcY5pxTTFYwxjXzJiJsQfH0Bxr\nXD832rvM+IcLPRbi80nPkPfyRjFZrF+ScxH345gRr/B45hoQp5c4947ugenZF3OzYhWJfRFzBCLe\nk4nk3DTnGa4DHEcznt3oeDhfzM/E9+F30+xGfjMG+YgX8mkzV4T1HIXxsZtzCb6HYxWdvPH+KfZS\nY7yTuXJ7Zx8NDqV2HLPd7lhRdjYREWVkG/MR4gPmM2HjHjsgF8H1gmNGJPP/UJDbzgw5hngvgmPX\nf+ZweuR3cDHifY4Yebcd8tcw5Cm2dBnU7SIuXX88ieT8TXfBfmHkso6s+DqdtrUIOWpoaEJ+CPfN\nVeDhc5yQ5/hRc/sb5aXpRs6Lyzk6xd/BWGnmGZP9fquNsdvMp/H5MQ3GDn/H/B7GVPOBEY+B1x67\nwYNlxzSsxfz87FhlZfF1fxdjUXCIc0DznmPeg8+IznyP6CeeGWAOm8/lYs+E8cDv43sGInn/xBga\na0zsn3C94WHjmSiL9zico6ER+R5DxtAbPy/aEnGpvb2XBgZG/t0xvKkXN0VZWfT1T32KiIjK55WK\nz7ou91jtiqUVVttVLJPy4VPcr/T2Wqt97fmLol9WfYHVDsOiL9k+W/TrfKfJauNEKt5WbbUHjnaI\n72BiMXCGz8dpvIzCh8XS2/hcj33ngOhXt3Wu1faU8UND1EyEYdIOHOFzMjeT4bb4Q9IXf/QjSjXK\ni/Pp2Sf/mIiIwmMysbl2oNlq52RnWu3i7TWi3+WXz1vtuvsXWu2IEXRb371stXMrc632cPuw6Odx\n8X0u2lZttYMw7p7yLPGdweOdVtsN99wMurgQ8TOnkRB0vNFotbMXFlrtgcOdop/Ndf0HJ8+sbNHP\nNzt+vR/79T+n6UB5WSG99PRfEBFR9ztXxGfuSr5XgZYRq+0scIt+WfN4jWFi3/2WPF7lQ/Ottr9t\nhG6EzGoeY0z4w9AeOdcrvoMbrgfOe/R8v+hXdk+d1caXhSONsp+3ksdhoodfDkx0yxcZ+cs5fmHS\nPHxRnt9EW/yh/1f/9klKNXAthgZlYlO2rd5qn/y79632ot9YK/od+Ps9VnvHn3/Gare8/YHohw9+\nxRurrXbfkTbRz9/Ma3Pe49v5d772vNVe9ftbxHeuPH3Salfez+ctkhIiGm/jY+/9KcfQTQ/Jaypc\nzftH+1u8Lku314p+I5d57M++dJqP9+VbRb/dX3uHiIj+5Oc/p+lAWX4ePfVnv0dERMW3VInPBk93\nWW1/M6+dyo/NF/0w1uHegLGIiMhbwfO7/xj3sxkP5rmLS6z2eAu/dBuG/a7kNrmXBgf4HESyZCRO\n/Qd4zhRvrbbaE91jot84zKX0GyWrRFS8jfeXvgPXrLa3SsbUoUTu8OtPfptSjZKcHPruf3mCiIiy\nFst7nreE7+VEL8cR848JIXh5GIUHgYARM3OX8fEGDsNYL5K/iw9kToh5+DBrxj/8Q9lE+6jVzqzN\nE/3wZQ3OiYr76kW/fogP+OAXHpIJahheeBdtqKQbIZl7PfTE9OyLFWWF9MrP/pqIiAZOyL07uScT\nEY0385rwVuWIfpMwxvjQYT4g4/HGrvLxchbIccTfwgdWzGncRZn4Fere12K181eWWW27S+aKg2e6\nrTb+8TNnUZHoh+sPr2m0cUD085RzLpU5i++L+YfV5MPPfY98hVKNouxs+ubjjxMRUe2dcj72fcDx\nIaOQH/y6m+S+XVzDY2DP5LzeNydf9Bs6yfG5tZHnS1WtfL7xVnMsatrDea3NxjGgYlmF+A6O9XgD\n3+exSXkv80r42L0dg1Y72yMfbPNWcNzA+DwVkHl3sJcfon31fL19H8i9vnRHPP5P11osLyukl576\nKhERXXvpkvgMn9XmPrHKag+d7Rb9XMW8Lpz4hwbjBWagg2MdPnC7jJcD+EyGsRxjpZlnNH7/uNXO\ng9idYRwb/xCK5zrR5xf9MvI4lofhRWTM+MMHvpTAmD9l/HExifse/ePr/v8vg8rKYnrnnf8dP7+Y\n8QIqjc+v5YVTVjsjXz5njDdx/MN9sebTS0S/MLysli/L5B8CMD/KXcHrFF/U43sGIvnHofxVEE+N\nY4s4Cc8mHa81in7F26uttqeEY2b7G7JfZg3vEUHI8c3cJmdu/Fls587fo4+Cm3pxQ2lplJ54Q5Wz\nuFj+8JJi6AZvHs23pLD5db7BL13Mt9BYSRPq5wvu2iUfKrPmcmAaOsGL/kZv5YiIxi5yEC2DlxJX\n3moQ/aphUuAGWZQvN/rLe3iw8n08iL45uaKfD5KnEngRNHhSJhgVm+Pn5Hj+xn+5+v+KNFsaORJ/\ncR882iU+K6jg88us4WtsePGs6Ifpy/hV3miiU0Zik8Mv7XAM5z22XPTDRTpyqc9qOyBY9e5tEd/B\nZB8f/htfPCf6la/iJLLjXZ47DptMujGIDJ3jRY+bPpF8Szx+judEEM6biChvaTzA/1t/JfhlkGZL\ns14+pRlvbz0VnBDiX8rylslkpPOt67/0LNwsHz4nBzgRwI3GfBM+eJrvR6CVH1bwr03uClkNkQdr\nbBhe6uALPCKi3n2tVhtfvOYtl9c0AC/0ijfNstqYhBIRjV/jh0o3VGjgphr/LJ44mDEkFbB7nFS4\nLD6Pj3/jXfFZD8SEgvmchOMLGCKiOevw4fvGf4Xz1fL1d+3mdVC1c4Xo9+yL/2K1Q/Ag3zHI69z2\nd/vEd8rhRRBWXbmzZCJ76B+fstq3/84Oq+0tlS9lT/7dfqvd1M1zyntAxud7/+pRq70UHqJdXjkn\nNv3ONiIiytz7Fk0HbB4H5Sb2v2svyj9AFG3mOZi3FOb6eZlY4EsTfIE5dEomsviXS6zazF0o7zU+\ntGbDH0GyF/Nc6t51VXwns47j/2QXJ7WF6+WDOL54cUEFmN1YI/gHumA/xxAzbvTu57WN6yxnvnz4\nnEwkwFhRmCrYvA7KXVV63c964WUSxodfqNCFpA8rdEdah0Q/H9znjCLeT+xeud+PXeY8pWtPi9Wu\nvJv/UDR+RR67aBPH7rFL/H2b8cchUSEDFR0dRuKZA/PFmXvj6tpReIGE8dms4AlZla3Tsy+GRyet\nHBFfkBEReUo5znvLOXHugfElIsqFa06Hl/p2o5Jm8AznT3idjkz5x6PM2bCuejnXwfliVgfhyy9M\n+PsPyYdvVwk/2JZCTtQBezuRzM/913hvLtlcLfoF4OVrD+Rc+McxIiJ/Yn83//iXCqSnpZHLEb/X\nXe/JGFWwttxqD0H+WjZfjjXmabLCTZ4vvsTKcHC/fPgdIvnydf79i602/kGo5XCz+M6ihznPzYA/\nmrnbRkU/3NPLMvk6JnvkA78LXu7hfJkyqpMxZx2/zPu2q1C+aBhrin8WnbxxFfUvg1gkas2Psp3y\nZYiorsYq3yL5h378o7tvDq8jX418tsI/5OIfJFqePS/6ZS/gvRDjFMbr7n1yHAvW8FzIWcCxwdzD\nAx08JnhN+OKVCGOgXItm1RA+A+MfWf3X5B++Q33x+BA2Xq6mApFgmEab4882Q6dlLpI1n1+OBvt4\nfy9YK3ORbNjHPUXwsvuafNmKL+OwahFfihMR5a3m+4nV3/jSxdzv8KVa9zscU8rurhP9BIsCjld2\n1xzRrwf241kPcfFClfGHj96DvLf44QUW7rlERM0/iz9j/0Jl2g2gGjcKhUKhUCgUCoVCoVAoFDMU\n+uJGoVAoFAqFQqFQKBQKhWKGQl/cKBQKhUKhUCgUCoVCoVDMUNyUxo3T7aDKhABXz7uSezo+wfw6\n5Ir6qqW2BPIxC25hLlzEENhCfidygcvvlJw05IeiyFz3+3x+v8DhBN0RFP0rMXiyqHY+1sAcbhRE\nIiIqzmFuMWq+NB9vFf1KOpkPm7uSj5Fp8DUz8uK/i3oRqUKaLd26ZhRYSn6WxEQXc52LqgtEv+Kt\nfL3Nz7D+Td3npF5G35H2656D6VCAgpsZwKNHUU0UwSSS6t3IW17yhBQ6bfgXFhYrXAFc03/D8ssH\nnPSJLilqm27nuVMAopVF66UuTFIn599yYfqlECNLZ8AUtUQR5vyVzM811e2rHmSBVHQnMW8NfuYD\nAeIRQ9cnAAJvyAlFrYaiDbPEd1BrBsXAw+NSxV2IkIHOjikQNwlrDMVbQ4aYJuofoOCyv1XyhzMS\nMcB0pUoFgkMBuvrz+Pxc+aXbxWede5ibPXSGucCTnadFv/onWED4e7/5dav9qa89LPr58uZZ7XQn\nH2OsUwq3L1/G+hltV5nT/ODXHrHagX7JOX7+r1622p+95betdsNTUrendhXHDdR6+PA7Ukh58Vae\nl96LHA+qPrFA9Gt+4Sj/A/RGhs/uFv2KNsbn3I3cGH5ZxCJRKx6Zri99+5njnAX8elOXAPc4ISS9\nWa6XXhDnRIHZMdh3iGQMQIeG0Yu8j+Wvk1z0kQu8nvOAl9/1rtSVq3mUNR7QORB530RSEBD1N8av\nSbFe5M5PdQP/v0tqQSQ1uqZDbyriD9PQ0bhOQigsx8YFmiWYE/QdlPtbxX28xtBpLdcQRL2RRpCp\njeLM49/KBW2F3j2cV/hHA+I70d0t3Ab9DhR0JCKKgO4Yaht5K6TeVBfoEGaU8LkWb5GGBRMQdx2g\n94J5BBGR29KZmZ61mOawWbov5t6Luk+43kyhyJFLvEYwN+v9QOZzqC+DOYgpcI/aJCiej8dDMw4i\nqQFizgvRD0TJUQ8mYrhF+SGXInBSGWmQe/gkCKmi/lxGruEAk4ilpltZKhCJxWg8IeBbvkhqzQgH\nNBBBdZVIDZ7WvRyzypZDDmQKwIL2RVE5r4Nu4/mG4GvjAV73Jcs4Ts67S+5PqKUxNMbro84QXPa3\ncM7R2cCxMMst9fba32RR5PAUPEctkHqj6WCy0t/I4xsx9j9L/3GadBijwYglzG1qeqCel3AJNQxO\nUNsF96d+Q2g5eynHR1z3uFcRyecGfL5AvbLy2+eK79jtHBNDAdD6M/Yh1DuqvIfHGEV3iYg8Jfy9\nIOh7hox4hfqtWGJRtEE+awxfjN+XdNf0PC8mn8lMJ0x0lMS93rze7Dk8hj2HeV26DIdUND1wwHPg\nxDWZB2TCO4Uh0BlCd12fIcaf1HMiIqr5FdYzTTdcr/0gcj0J1+GrkcfzwjnYMyDXism4i/pm+Cxm\n6uONXojvOaZxw42gFTcKhUKhUCgUCoVCoVAoFDMU+uJGoVAoFAqFQqFQKBQKhWKG4qZqq2KRGIVH\n4yWhWDZLRFS6kilMaGk1dFxaTqPN3tV32Xqy/iHp6Y5UqSKg9Iy1yDL9pF0vkbQNK9nOlnA5FdLK\ny2bj75z7/vNWu/LeeaJf++t8fmN9XAZnlsj5m/l3CzdyGVtew4DoNwn0j+ZdXPY4+w75u/6OeCl5\nxCjZTgWmJsI0lLCxzF0iKV8BKE13QxmwaZGMtpiFUG7fvUfa6KGt7cAhLtVGq2giomKwZJ/s4fsc\nANvE4TOy/LhgPf8uUq0635Y2mIEQlzBjmXL7a9L2NBdoT0iLQetWIiI3lD0PgUX8yOV+0S95HTGj\nNDdViIYj5O+M3x+z7A6paFiOHeyW9pI5K/makUrkMcrlR6F0HC0l0xyyTLpsB68ztONzoM2tUa7r\nAEtGD4yjzbA4x9LvDKArhEclBQpLZp1ZXNqZu0iWE0/08jxzAkXLu1FSUyYS9qjpttS/454KTlFf\nc/zehr8r6UL1T2y22vte+4HVri6UtLjGp9mae9PGpVb73a9J6+uB8eesdiDIcWjpLHm9NVt5DBeD\nheyhv+HjRY0xXDmbY216Oo8nWlwTEfmg9Pfae7xOb/3DnaLf019+xmpvXsf7AtIoiYiOHGA62b1/\neLfVPv7tA6Jf3SPxe2lz3Jhy8MsiSZHKXSHptpNggy3sR7MNm+RhsPmGcnG0DiWSNJTqh3nPHb0q\n9xpcP91An0FbVpOCgaXeaMOaY9gq4x6MVru+ekkJwj28B+JQ3nJpj4qW05NgK2paTifL3E2qbSoQ\ni8UsipRJqQsBDaYHyrndxr7oBHra8Dner7ouShvVAhhTpDOlGTHmagtTe+Yt4z3yVAOXm7ud0kLc\nMcJ76+qPr7TaaGtNJGmu/WCl23dMUqrmPApl5TCnxg1KaSnMq0GwsDcp8NkJC1nzWlMFW4aNfLPj\n9KYp47eRzoVu5GZ5ev5yoOXCMVzFMufNnMXl8iGw4p0ckPQ1tO8dgn0W97GQYeWbA1a7mYW8XjJu\nl3ED8w6kN/a8J3OxiXamrGXOYfqXu1RSjPDfSCUxLXmjCfo00jBTBXeumxZ8PL6XIU2bSNpy91zj\na3cWSCpX7Z3Xp6qYVJzAVR6rvPVAqTJoKxOdfP+8Hs5TcB6b0gbFO3jN5sE5DB2X8SDNyccoLOWx\nmTTmBMpOII2mb59hZ7+a568vh+fs+Iiclx3H4rEs7E+9pTsRkd2XQSUJSuW4YWGNsQRpaaZ8A9Jo\nMyD3LNkmqZqYA0dCPGcik3K8Sxevt9qXW9+02kiJHGuRtOPyJUy3sts5/7ctkePtKeW8uf8Ex9Gs\nOZIGiWsdbchNeqMT9pfutzjmD9o6Rb+cJfH906RppwJp6WnWWJk5C1KTKsEGu/egpLGhxXbx2mqr\nHeiVFChEwSqOZe67JHVtcpBzpe63+b5kQHw242kU4gjORVPuofx2zqkiEAPM/Codnn069/CzvPks\nhnuJw8tjffn7x0W/7IXxOWJzf7RXMlpxo1AoFAqFQqFQKBQKhUIxQ6EvbhQKhUKhUCgUCoVCoVAo\nZihuiipl9zmpaFOcCnT+6RPisxwoq2/fzaVvbo8sx27cdclqV6/kMn2PUbKJFIpLPz5ptbOLJY0D\naTv5S7ksCUubxq4eEd8pu2URn8PHF1rtzvelkjyWPeXP53LumnvWiH4NT+212jYooar+xELR78rP\n2IFp1hYuLTapSKd+FHdLCY7Icq9UYCoQpp6T8VI7R5YsfWvfy9dfsY3pDwMfSvcMGyjEF4HrSVIZ\nOwlUIS/cxBQys3waaTXZoCKP5alho2S28c2LVnvObUw1CxguFnk5PK+StBciOb+IiNpe4nk52Mfl\nuFkeWYKbDnMiC9yc8HyIiHwJRwCz1DdViE5FLTqWv0VSz9AtCt0zikwXMfgsdyGv35BJP1rA14kl\nrr5Z0g2t9zCXSBatYSrbSCOvxVhUllZXblpntaPlXMrb9p6ML0IlHktCjepQpNN0vMV0nELDQScM\nzlRYzti1S8YAZ158DkenoyS8wEtLPx93QcsqluWgV15932o/9Jcfs9rNPz0j+pXeyuv0yHeZIrTj\nj+4Q/V796qtW+/FvPmG13/vqc6Jf8x6+Z0jD6Bjk8uGSHOkUWP9b7OSGqvom3a1s3dLrtsNhOX9/\n/Ul2prLbgd544KDoV1vMcxbpM2W1khb3C5MkxZjyh2ngUDxGFpnudzD3kX7qNyi/WIaMrgnjzbIf\n0jPGW/kzpNwQEfnAYRGdGNFZp3yHpBCXbeI5eOWnx6w2usIREY2BK1ThaqYXZBruPEiJLYD1N3xB\n0l7RNcIzi/f3jDwZe5NU3MhE6mNqeoadshLl8hjviKRbHo6hzXDx6IP4l1HAcah602zR79qHLVa7\n6pZqPgeDeroI7sXYJV5/fqA62tLl397mlksaWhK586U7D1LzkKKK84tIlpKPXeJ5kG6UdOeBSya6\njbgNelF4LB53P6p7xs0iMjFFw2fjTiNZ9XIci7dVW+1hcKjxzZF06FFwaMsA11GnMR9xb+/7kMc+\n3S7HBOkfQSjzL1nFFIyhZunchg5R2fM5b5kyaC1I/x6DWOGdI/dmBO6R7S9eEp9lL+PYWQAunO2v\nNYh+WYk1ku5MvavUVCBsUWTQJZKIKH8dz2PcnzpPyRy1MoPz0vErIGVwS6XoF4G4ic8tcz+1VPQL\nQIxC+ty8+26z2n2XThnH5vlh9/C5Ogtkvo/zDyke7btkTukJ8FpCV0ZHjnzGGj4B9CKIV1mG+2iS\n5prxsnwOSBVCQxPU+uw5IiIKGvO2CmQpMms55ph0nxJwr2v9OVOjUY6DiKgEnG6RsubIk3l+56kP\nrXblbeg4xfN4qKlFfKfp7desdg845ZbeImnml9/jNbL81zivHW+W1Ct08cJ50XhQxoANq7fQ9YCU\nMSKO39OxFqPhiPVM5TPcj/E+j3fwXoMxYtgAACAASURBVD/ZLdds+W2cZwxf5r3fZjjSeWt4HkyA\nZAbeIyKi9lc4ZlU8yBQtpDf+ApV3Oz9v9x3lMfTVSXo3xtAgUF5NimVGAa/FLDyGkWpivjXWwPPA\nHKuSDfEc7d9yEERoxY1CoVAoFAqFQqFQKBQKxQyFvrhRKBQKhUKhUCgUCoVCoZih0Bc3CoVCoVAo\nFAqFQqFQKBQzFDelcROdnKLRhriOSf3DkgM6BZoRdY/yZ9GQ5KPngG5JRj5z9S58W+rQIH+woYst\nxYv80tI4CnbL3X7mqIYjzHfLXVAkvtO++7TVdoGdOF4DkbRnK7uDdQI6D5wV/eoe22C1h68w19nf\nIe3OckCnA/mvpoVYVkIfJT099e/VorEYBafivx3sl/eyfBPzRDOB9z61RN6XMGjvoM6J3Su5iGhF\njXoqTsNWbuwKc+f7joEGw23MS8xbIa3LQ2BF1w/8cl+N5OjnAU8buedOw1oYdRzygWdry5TW79lg\n05nUtSAiys+W2ktpjvhvpU3DGBLF9RDcZfHfdJdIfSicW17QnTB1faZAK2LoXI/VNjWX8hawPkUk\nxGPv8kgtkew65oSONPH9zIJ5P2ToW/Q3nrPaaC9YuK5K9BsE6/VJ0Cqy+yQntPckz5+yjdVW29TW\nwfuClqZZhqWxpW8RS70mQ3gsSF0JXa3zzUfFZyu/tMNqd+5hrvv8z+8Q/d7+0x9b7YsdbEHZ+6cv\nin4b72Jr4Gd+//tW29RwevUon8c3XviK1U771iGrnW7w0C/8A2vPLP8S6/GgHgERkcfD6/mbn2Ed\nm9CU3CN+9X9+ymof+9YLfA7G79ptzBP2FnOM/+DgK6Jf0qoyFpU85VQhLT2N7AldD9OqOmsezydv\nOceICYMHjragA2DPbOqeZBTwPKxcxjpGQwOHRL/u/WwHjDoOYdBQGG6QVpgNr7CGAFpil9RIbYT8\npWwPjrHi/E9Pin7FdTwmZ59i+8sFn5C5A2rFoHXosGHV6amI3z/znqQC0VCE/AlNnrEWyY/PruP4\nhRolgWtSmwl1fPzwWbrB5V/w2HKr7cpjrvzAKWnzGp3ie+Gu5Bi/JZfvX2ezjKdF26qttuT5S8vg\nKGjMnHiD9cTq58m4awO794KNrA9SuFhqcqWl8TWO9fC+2H9Uao8k74upGZAqxGIxSz/HmSX3Bpxn\nqDHQu69V9PPO4r0h0Mk5nKm5hFpLrlLOI2NhqTeFGn6oWdV0dZ/VrjAsb+seYX2LST/vze5MGVPH\n+1usdtFaHp+pgMzZcK+PwvmZuae/ic8vbwmsc0NXI6mxgvl3qmDLsFNmwtI9e6HM3S+9xLk3atyU\nr7yxdo27nNdOz3stol9gAnLZPB5D1JAhIspdCrkO7EOdxw5bbdTSISJKh/mGW1fHebnOMxp4DV/u\n5jxn5eI60Q9jY1IriohotMPQOLyT59JYE+tqXNsvLeJ9rngePjU2PXbgzlwXzXo4rvPZ+a7UbxmF\nfNsOmpmm7uYA5HOYm0UNjSyMdahJNuve5STBY3TlWX7mzKpnbc1dP9onvjGvjJ8hcqr5eWfoWJfo\nV7mE5+B52O9KFpaIfjgZus+xpmCOkYud+mfOq7wuvi+zDCv0pqfjz7Om7k+qkJbQ5AkYOQs+Izoh\nDze13zAn6niTrbNNfbaax5ZYbdz7g0PSxj4HdLhc8A4hBM+loUF5L4596wOrvfCTy6x2do0cm5YX\nWaeq6BbeC9Ns8lw9BXwOPUdAi8rIUdFmPn8Fx+7216VmWFJTMDgor/VG0IobhUKhUCgUCoVCoVAo\nFIoZCn1xo1AoFAqFQqFQKBQKhUIxQ3FTVCmby0E5CdqRabeFpd5oNRk0Spa6wTLT4+Mya2+utI1s\n2culdV1gRbukSpbyRie5JHJwnEu5olDq7R2Q5UeTffzvqofAiq9X9stdzSVyXe/x+eSvkuWqF7+1\nmz/bwOXSfcc6RL8hoHmVFHAJVQ5YYBMxpcqecVPD85HgKfDS0s/F7cyvGNbCtY/y9SIzBCksRESz\n7uNS7dFWLuM1x9oL1qJoFdv9vlGyCSWQRWv43qJFvEmxwRL9rIVcyn/g1WOiX90VnjvVDy6w2ja3\npECdPcIlfAsWcyli0UZp+Rdo57JUB9idFxh2030H4vM8zT49VsTRcIQmEjZ9BcZ8HAOr4dFLbNEe\nM2xPEcEBHjuzfLrph1xSWvUQ38OG598X/fB+ILrf4/G2m/d9F9Mz5i3j+961p0X0c4IlYC6USmZW\nS4vCIKztCbCGN0sd0fa2820uV803bcNH4nN/Ouxro+EoTSTsTouXyzGMRLjs89BbXL55Ya8ssdzy\nZbYjXQ0UsswKSRlE6ssnt/MY/sYdfyD6/bcvf8Zq5+Susdq+Ki4HNe9luoP/fehrP7PaNXfXi379\nXfut9rLqaqu98PNrRL+h8xxTkB41a41ci++9wmXqeT/msuJH/urjot9IY3wNIPU1pUgjosSa6T8s\nY375HWyFiRaX+csk9XPgFJddF6zhOegy6BmBHh7jkREux54yqCdjF7kUPWcllwPnA3XUf03u4ceu\n8B43t5TPr/vkZdEP7ajnl/O8XfT4KtEPS7dLt4ElthFfhoGmmaR/EhE5siXVhellqV+Ldo+D8hP7\nvVnqjXEEadf5S+UYYty1gyV2br2ke3Tu4niTDcfb83Npdx+BNYv0RG8G35exyUnxnQvfesNq3/2x\njXysSUnfcQNFfPmti6y2WW6fC3QZdxHnaN1HLoh+SHdAu+mgQTnJWRKP3bZpyG2I4taxucvj49J7\nQNLDioB+H+rnfSJ/jYy97kK+NwMneD2PXZVUmChQkpF65QGqFZFpC833qWgD57LhMZljuX0cU0eA\n0uhcJo/tzedzj8X4d0Ij3aLfIMQXjAEld9SKfja4jmHIucx9NhnzzbWSCsRiMeuemfMxKSNARFS0\ngakpISNH9dXy+WLcNynyve/yvmYf4Wt3GjTIYsgDj/0TW0pjLFywQdLd/M0cX88381xcvmqe6Ld3\nP+/vy2FfnByWa3tgjONQSR5fn80mqZiDJ3isnZCT2QwaR9JGPM02PTnqVCBM/YlnoPxVZeKzKNDT\nu98GiQuDtnXuAOc7ax5ebbXNPHLgCK/TTMhzO/dLS3WMe5nVnCNlzebvfOJrMn+49gofo+0iU7eq\nV8h8xAOUvBpoDxyUOUFLD6+rKFD4fW45N0vK4bloS7XVNmNFUSJntf9MSiGkCsncN2BIfwwcBmv0\nnZznlGyfLfod/bu9VtsBc7V0taQ3+oGW6gFauceQgsiu5vyooGCr1e7tfdNqm9Ia+O/sGo6ZbrfM\n94s2coxHaY2JXkkT87c3Wu2sufz8bjfiIcal7r38HFS0Ub7HSFJZkZr8b0ErbhQKhUKhUCgUCoVC\noVAoZij0xY1CoVAoFAqFQqFQKBQKxQzFTdU5TvlD1JcoSTPLmC/v4pK2kmqmrrgrpNvOZXCImm/j\nMqWKO6WCuquZS9q2QXkVuvoQEY01gBtQFpdUYclRUqHe6gelsYe/d8BqP3dQlip/LnCr1faBqveI\nR9J2nEVczo5q9CMBSb0qr+RzdyENCErFiYh6Eirz4cnUOy9EJsJWCax5z7GkN+zn3/bVSorNcBOP\n4dBpLskN9cuy1vJ7uXS07yBT5Lpa5P1DR4ZAG5fL/eS7XPbdD2WiRES/8fi9Vvu9d5ke9cifPST6\nobsCUr6wnJSIaM0D7LrjAyVwdMMikhQrdCOKBmUpepImhiXUqURsKkrBRLm3OX98UPZJC7ncvv8D\n6fCRtZBL/LCs12dQqrw1XJ7d+hxTm/7h+VdFv1vmz7faKxZw6eS1fqZrzamRZeklOVyuevwQl6Se\nuybL3JfVMI2qcIDLmIurJM1wpIs/y6nkccxdJuMGOjTYwW1ryJgXJQkFf8e3U1+GGotGaSJRal2/\nTZas9x5ip5PCLI6h6KRBRLT/G0xXq13K5ZcmVerA17kfrqU/+5PPiX6zd9xutXf/2d9Z7ckQr6N1\nv79NfOcff/tfrPbnvvpJq52RI0t/pwIcU2o/ttBqB4dl3CiEElpcV+ZaRLqVA5zq0u1yzSUpl9Ph\nRkQUpwskXSnQnY6IqHs3l8eiu0levSyzTl8NZfo+3k/GrvWLfujiF/bzfev5UDrjFO/gcuWBo1yq\n7cjkfdvfJukAWKaf7WVajN1wf3BX8Xyc89Bm/p2mRtEP95PMQh7TwatNol8WUIVxXfa8Kym1eWsS\n5fZpqS/tT7OnU0ZB/L737ZX3Mn895ymNP2LnrBqgjRJJiilSNve/Liml827h2HjwKc45CrMNig04\nY55s5nuxYylTlZ/58EPxnff2Mx3xY5/fabVz5ku6FtJvMJdDVz4iIm8lnxPSYkynzok+LiV3FfDc\nqXpQ3qPmBD17yj89TjZp9jSLqmW6XKJDFJbzDxs0bMwT3JCn9RpOICXrOd4iVSzpTmYd/wTnSE5w\nZ+rcxdREe6bMp7HEHnPZsU5JgRoHel7JeqbgFM5aL/rlVXCsOP/D5622t0bmxkhNygP3uKBB20nS\nj0wXvVQgFo5SMJFLeiokTSJnEc87jC/n9l0S/erHeXxxXfY1yLGuWsRr213CeejJN6SMgKeSYx46\n/NRu4Rza3F9aj3McmVXI+cdYu5wfm1YzVXEC5AZGjeeHOogbSPlpfPW86FeMznBAt5y1Q+b7Z1+J\nX2MoND0U4mgwQoHW+Joz6fe4xkqBrvcLlEHId5BmmG7kArlAhUGJhbHmQdEPaTdD4OAzeJbX1fhl\n+Z0kvZOIqHSU4xbGeKK4M2ES/Zc5vg4Yzy59oxyHxiZ4vHeslQ5YQ73cLxfui814pghNI50/3WGj\nzMp4Lmm6zU7C3tADeU7Jdul6hQ7Pix7n5yyHV8a8sJ+vESni+YUbRT+/X1K3kygo4Of1oYafis/K\nlq3j857k56Dd//07ot/yL7BDNMqjZC+S+x3GyfPv8vnM+fhi0Q/zLaTAd70jc6AkfTzyEfdFrbhR\nKBQKhUKhUCgUCoVCoZih0Bc3CoVCoVAoFAqFQqFQKBQzFPriRqFQKBQKhUKhUCgUCoVihuKmNG7S\nM2zkS+jFhAxdgopFoF0BHPThU1J/YxHYeXeCzbdzd4vol7+Oj2e7wO+XchcXi35O4GejHXXnGeb1\nVz00n26EvEzmin71L54Qn7XvY95eI2jzDDRKLj9y+HJBGwDtyYmIFtzDXFa0b+09JLVHAgndC7Q0\nTxVC/hBdO9RCRER5hZJTHwszXzk4wVy7guXSWg11W8pvZ97syX88IPrlgw27Hezii0qkhsr+11mj\npr6MbQNPt7RY7QvnJY/3wbVrrfbG5ayX0XdQaqMUrOZ5lLWIdRbQ2pSIaLiBtSTQHnrKsCesfpTH\nsAM46mMvS9vBkg3S7i3VSHekkyvByUaNFiKithf5XFBXw1srdU9ccA/cZbwOTHvIt1/gcUXdpodv\nuUX0W3A/8ztHzjGXPNvDfFW0biciGt3HVpBLlzCHG3nARESV+WyNWLWT51zA0OlYcDdbRo428pia\ntqL4b5zDI01SU2QwoeFk2i2nAp6SHFr95XuIiOjk374pPpuCmDL/Hp7fphNyK/Bro7B+m354UvQr\nrWAdkc6zrIsw765HRL/RUf7ebLDzLlgImkXvnBDf2Q9r8/Sn/4fV/qNf/aToh9piE2AtWX/fp0S/\nrsZ3rXYE7vuet4+Lfvf9V9bjsYNdPOpaERGd+cHR+G8OSGviVGHKH6bBo3FtEFP7DeOeA9rRqNSM\nmAS+/fBFXjt5YMdMRNTyPN9r1PVBPQoiopwq1njIquW103eUtcaunJWxMjTFWgfvnmGNh3tXSZvv\n2WDt3f4Bj0nBcmn56gatk2u7jvDvjEgdg+x6npvDZ/nai7ZKHaCxprh+EO5TqUI0FKHxhM6XaXff\nf4D35xjsyWxPHgfagqJWT+3yatEvDNffPSwt2REbFnPecgr2wt4Rjnmb5svc5uH1rG1y7rWzVntZ\n5krRr3QV6yk0v/mB1c6vN+JzI2s2DYFWy5zPrRD90J4X7XNRN4SIKH9tPAbYn54e69rI5JS1fgrX\nSbtZzFlRb8vUy0CdwqlxjiVlm6V2QwQ0CH3VnNOMXpI6V5l1/Blq5GE86Hn3qvhO2wlem0WVvH7z\nVso1lg9rzutlrZCh3mOinzeHz73yXo7rA2ekptulg7yfzAP9mvQb2LdHp2EtxmIxioXj+9/QMXl+\ndh/PG0cOx7+FhhV3/3l+7ihZy/OgpVdq3Dy9l62Kv3DnHVbbY2jJpcEzTfmmaqudDfpckZC8F7Wb\nec8cv8x7bnu7PAcbaIg5QJ+tem216Id6NWGYlyX1MvY74B5h3mLaOc9ZG58vGa/JPStliMYoklhn\nrmIZB3D99bwP+ig7pdZfDjxPjZzn+5Zm6AlF4HgO0Jsavzok+nnL+ZkncI3vh7+LdWjqPiu1ZiZ6\nQL8L8uk0o+xhvIl/a3yS93e8BiKi6goer13H2ArenBfJ8SGS2nmVkJcREY0lr3E6nhdHg9T+Vvx5\n14yndjfHhHQXj8fZH8nYU72q2mpnlfMx7Hapf+ss4zg5PMB5xdCQfK7sBj2/9FtZd2dykmNm/wdG\nbgOxH3Vn7DY5j7r3cBx+8TXWi1vbIPWh8PlkxVZ+Jhy7IvWRbBl8/JJbeDwr7pNjmIyjju+56KNA\nK24UCoVCoVAoFAqFQqFQKGYo9MWNQqFQKBQKhUKhUCgUCsUMxU1RpWKRGAUTFIMre6UlV3Epl3OO\nDnDZWe4sSYtBW7hMoF1kLZIluqdfPm21HVDOtOfJ3aIf2tSuvW2Z1Z57H9MLbC6H/A6UzFdA2Xdo\nSJavD/m53ymw4/zsr9wl+mGZ3pULXKJ1sV1SoLDUuGAjl4wVLJOljoWJ0l3X66kvYXQXeGnp5+M0\nI9N6D6lmOfN4PIbOS7pbmoPf941e4RI+k9oVneTS+7ELTEGp/21p79b/N1wuXnErl5M9+fBXrPaZ\np2X5XX4F21j65vHcq964Q/RreuM1q41lo2ZJWwmUv146cdRqz//tdaIflszivPQtkLbUnYlyPixp\nTSUiwQj5EyWSaYZ9N0HFLlqJhvqlfWF/B6/TjAKw+jMqLrFk31fH933gtJwXk1BSmgkW8mO4Jt65\nIL6z5Y/vttpNTzGd4u77JQ3LBtau/fv4eJn1+aIfluBimX8kIC0v27vYrnEh0BpGzvWJfgUJK+Dp\nsJIOjfip9Y34vF74m2vFZxGwwf7gmxzz6lbPFv3OtTH1JW+IS3Xv/NO7Rb/u/S1W+/577rHarcde\nE/3qNnzaao+4fmK1YzE+n29/92XxnSf/15es9tf/+mmr/eGRc6Lfp8AaeKyJ199gn7Q07jvMcRNt\ndrffJ+8RWp47XTzfRtplmWz15nhMcb40PSXhzuwMKr8rXkprWgtnFHKZNFIrTaviXrCgzgY64chl\nSd3rvMx7yOxNXIqPVBUiora9XGpcsoHnTBBoIHUrJPWj9QzPpU8A5WbJ790h+qWn87l7NzL9LTgp\nr334DJ9rHlA6/IYdbg/QpHsHeC9w5kv70fxV8d+yeeW1pgJpaUQ2Z3xfMy2Oc5cxPbvrAx6n7g/l\nPPNf4+tyAUWo9bTsV1LMc3XNHB7DI03SIvTKNbbmnl3M5zAAFOxhv6T/oe3w6l/hfCi7olr0C4zy\nWOPe1/aatLz+P+ydd3icx3Xuz/YFdrHoHSAKO9i7SIpFElWoRsmSbEtuchw7thMnsZ3rx3Gub4pj\n58a+cRKXWO5O4iZbshrVJYoUq0RS7L0AIHpvu8D2vX/s4nvPGZWYyeJeOM/5/TUiZne//WbmzHyr\n8563iMmkkzHEgHMPHRT9uLi2YDnOM6aUZuBQ+l5M1b5oc9jJXZCeN11svRERVWzGfOdySn+9YYk9\ngnNg/gLYwLoMSXIyju821jrEXiPPsjH2fiefQ0xsmIu1s/2wlIHexCzfc2dA3lGxWErU8vMXW+2O\n5sdwrT65drxeWNF2t0Lq2v9ah+jH7ZfFWb1eyqwjA+mzoimrzgYOr5Py5qT39dNPnBB/m90EycLQ\nYXae3iBlHOGjOAcEL2Fslq+UEoXWPuz3Tx/EGXNDk7Sx5xOcn6m4JDKnWFqXjzoRu7l1+cwV9aIf\nt3G2MdmeKS86+ihkzPM2QBrmZRbzRETN22GN3nA7vq9ZZqL98fRa5+f0bOIuzqWGD6Tn8ViLPG/H\nQxgfTxmuP9wv41n5cuwbk3OOiCjWL6XvBSxGE5u3BQukjfOjX3nSaq9bjWfE8vUobTDRLe27yxZi\nLZINEmJu/01E9OQjr1ptbu1ddauU2bQ9CinpDUuwfgdGpJRtkD13uZx4VO87JJ8rY5nn1qmwA08l\nkpa0d6JXSoPHzg+81Uto6cfkM9Puf8b5lceLOXfcIfq1vvaC1a5cBmnveLBV9MtnkuTuo4ibyTjG\nwzw7fO7Pv221774G13eKnZ+JiDZHse5nsj130Qek1HjgEPbmImYX786XUic+n3sP4nvYjLDpbzCe\n4f4DNONGURRFURRFURRFURRlmqI/3CiKoiiKoiiKoiiKokxTrkoqRZSyUgNnbpLpX7FRyG4C7Ocg\n05VnUnpARFS6DumNw6elRGH+dUjxc7Pq8ScePyb6za5Dumn/SaROeln6Xd9BmVrG05S8ZUhHbHlZ\npiqfuIIU52vmIDXx4V+/JPrduZo52UwghW/9PJmWebEZaakdHfi+s9bKSuoO1+QFZj8NNTYaoc7n\n0t+zYIlMnUyx1N8JluJlOkNwB5eBI0gZ49XxiYhyqpA6OvtjSDU7/739ol/dariHdO9ssdpLPgO5\nx+IPyu/hr0K6XEHBaqsdCslUb17pn7uF8PRwIqKxVqTo5zGZT7hPpgfGQpjP9Q/ARenYD14T/QIZ\nJyWbmROXJVKJFMVG09eSW5ln/BEpk3x+v/C4rM5+x+/dYLUHmXtDx+njol/5DNzrPU8jNTGelGnw\ns5hscdIZjYiooRap83uOSalU05EWq111C2JKMirTd7kEKlaL78sr2xMRnfp3XF/DFrzfkz96WfTb\nsARpsmMXkMZbfbt0p0hl0i9Np5lsEJuIUdeJ9H1//RWZEn7zn95ktesacP/M6dTDXGnu/cI2q+3O\nkamXh3dAErWwF5K50vUyxfzkU9+12o03bLXa4TBSSp99RcpV371undVeXF+P7/C+jaJfbj7SkY/s\nftpqn9ovXfpu/itIufx+jNOrf/Md0a/h5k1W+/zDz1vtyhtkPJ10ieHShmySSuG9S9fI+8klIbnF\nkE0FW6SbUPl19VZ76Cj2sdwa6bxQUYu1OM7ew1MkU4O5tG/oDNa2rw6Sh3PPyLXYxaR2TXcitiUS\nUmI50oqUX1cAezN34yMiivbhdVwaZ64l7lJYvxx7AY/DRGTJtFNTMI52j5PyZqXlGcMn5FmEzxu+\nx+XPlTJN7hI0fARp7vy+EhG5Wdp7iMXJqkIp2VmyFWMwwWStI0yW0zNiuOoxaW9pKWTDkYghaw1B\n1hbqRIr+a/ule6PzNXzfdXfAXczhltJRnqbvq4W0Jx6S57+ZGemE5zE5X7NFIhSlwcPpM4m3XLq5\nhLnLJZPbDRyRzkUFTOrE5bZdJ6QUsGwj4tmzP9lptRvKpDxjJpMq8rHfvQ/7bPuAlB3kFuH8WsZi\nit8v96doFOvKG0BsyMtbIPoFg4ixXub25p8hnUWbD+E7NrFY4SmU4zWUcVs05SLZIDw8YUmkZjI5\nKBFRhMm9S5nrnDNXyifn3QWnl1A75nfr6y2i341MkuZjZ6XcGTLuctewQCnGIJHAObmwUEp5g9WQ\nGo+wmMJlQkRELWfxXFCej/HgLkVERBUFGA8u6eZOn0REjdexZzMmG4oMy1IQk8443BU3m0T6Q3T+\nx2lZnq9KnlEDcxA7uTwsalyjj8kY3WwO9uyS8pljT+K5cNYizAuv8ewyyu7VV374S6v92W04O/Hy\nG0RE/jpcg4tJnIevyH3i3o/gzNa2C+5EpUZJCk85eza9DDndjGXy7DB5NiQiqr4BMcRhOLwVZuRg\nrh9kXwpuI8ibTPe9qpuwNrn7Z2REjuGcFbh2vjcM9x4V/SqWYi3abNhfTAdrLoHl6/7I63j2m19b\nI17jz8Hc4c8m1xrP6MV5mKdvsPIox/9NluqYsRyxv/lhyF8rt8hSBtzRc5DtMw5DKh9sTsdxLql8\nJzTjRlEURVEURVEURVEUZZqiP9woiqIoiqIoiqIoiqJMU/SHG0VRFEVRFEVRFEVRlGnKVdW4sbsc\nlDupVTScx/IaoAN848eo91E7v0r0CzM70rEL0PW6i6Wl3bEXoBuLxKCf23lSWsw2VqD+wz0PXG+1\nuY700DNSS8ctMzsGoRF+7sgR0S/I6tUU+qAL5rp0IiJHHvRq3azmBG8TSY3qugdRF4LbcBMROTO2\nkzZn9n9XSyVTln35eLu0nytbC43lwDHUWXAHpHaybx+zIF6B8Z1p1Frh9uouVhen7j1Sf932JLSJ\nhYugD58YYda3Sx8Qr+nvR52Ns09DS1y6SupEG29DnQ2nE2M4cEXOCS+bf67luFbD4ZwGDzOdItNX\nLvrQStGv5VeyVkC2cXidlg263SPrDRQuRO2i7l0tVnvlTFn7g5dQKloJ21fXBTneF05BT8z11Ivr\n6kS/kjroOYeuYF39+4s7rfa2Vav4S2hwP/Td3jswPtEhuSb6mAW4bxZiTfPuy6Ift2jn1sfXLpwv\n+uWyOjlcO9357AXRr+SatFbWtAjOBjnFPlrwwXTtp0bDgrJvP77vidPQ2q6+cbHo90df+5DVPv5j\nWPQWFUuN/k2fhf66tA6x5+IL0to7txqvGxtF3R1uJ/7xd79bvMbN1jaPk7w2GRHRq3/7c6s9fxY0\nwsLKk4g6XsQYNN6O+LLkD9eKfld2wUb87FHco/JN9aLfyYPp95sISe11tkiMx2gwU5cmYdT0qLl9\nrtUOzEMNipGTsl5G/lxWn4LpN29eDwAAIABJREFU/4PNcg8JsppbvH7WjDpZHyUWxB7lq0BM3f/3\nz1jtf3rqKfGa9cwCN0+8n6yBMN6JuVpchnU08z1yfLr2Yq/mcTk8KGsy9B9EDIixGgfeInkm6MzU\nLklMgX1tdDhMbY+lbXRLr5V7yACzTC5egTjpr5MWyVcegw0v3/sXzpgh+sXiuP7qpdDi8/03DRvf\njevx3sdh59xo1PYoKcEZyOFAHJ+YkDUhxlldm5e+i720ukjWFeI1WUZPoabD6RZpo1oaQNzwMi3/\npJXsJOGG9D1LhGWdj2zhCnioMlMPYqJPWgvz/W74BGr+OA2b7x5WZ4+fbyY6ZYzuZXvSUlbbKxqX\n8/P0LpxvPv9t2NIuWwq7dl5rhYio9k7UXggUIObH4/IaxscR93JzUV9hZESeb5JJ7KdBVs/PP0uO\n94Io3oPbnwevGGfZTWlrddc3s19Xw5PnpdnXpevIhLtljcHCJTjv9+3HHMyplLVMPCWsjsgxzMfi\ngDyjcjtvJzvHFy2uFP0KK2DDPtCGfTavHGu7q13upQd/guegYy0tVnvUqEmztAE29TOX4P3MWjih\nHtyLfFb/a+yMPCuFWO2zrj1oV1wj48uczD32bpf7dLawOezkydTyqNoiz54X/w3z08fquJ0/3iL6\nVbK6X4Xz8dzmcMoz74p34/zd9jzOD1/57i9EP36v79+wwWrnzcH9LK+WZ6fOF1G7zcFqKr7whKzV\needHt1jtVZ+D1fVoh4yVOdVsz2T7fmxUnk9KZhSxvyGO2ovkd7/wr+l7yes/ZQt3QQ7N2JY+Ozf/\nQtZhHA4ghvJnoca7pB146RzEr97TGPfcAvnbwMlvoQ4jP6vN+7C04vaU4ow5cQU13tbcgng6fkXW\nfrt3Lc4mHranlVXI+MdrEvJaZeU1Rj27fMQ9/huC+zUZD5MxnJ3KWV3V2Lg8J7oztQIdXrmfvx2a\ncaMoiqIoiqIoiqIoijJN0R9uFEVRFEVRFEVRFEVRpilXZwdut1lW0DyVn4goh8lkJlPwiN4sNeG2\ngu2v4z3OtEvLbp56m8esvD7zh+8R/fJYqueZR2CvWN6AtDr+XkRE/WNIN53VADvxbS6ZpjR3PezO\nghdgzTevulr041+ygEkF8rwyBXEsjPSv5sdhxeo2Prf6jvT9mxIraRuRLWPlGe6RqcRDJ5H6FmCp\nmMEWaUtYwlK6uaSFy1mIiEo3Ie1z+CzSrHl6KhGRvxEp54HZSB10+TDu3PaSiCgQWGa1e31IZQy2\ny5TeQSa3an8Fspr6rXNFvwmWhlq+HhKgsVb53bkMo/M5pGSaNm6Fi9NpdqZddbZIJZKWLKP3VZkG\nzy06udzO65dpfIOvwcrdGUCacMdlaR276HpIKBpZGm7hsgrRL7cS6+zCt3BNfP197oc/FK/5g9tg\n+e47g7RYhyEBcBdjLoSZNe7sW6UEaoRZth7aBbnaurulRIvbwXKrYlN2NtlvKuzAJwZCdOrf0vbl\nV/qkteT1D0Lit/V6pK+3Py2ts+tuguxpMAjJw7JPrBP9Bk9gHfTuRfpw91k51tf+BSSJe74MaVN+\nAHHto9/+hHjN0a+/YLWbFiEV+dLjUi5YuwJxo/MI5Cezli4X/UYvQ0L7xOdhT775j68X/RpvuMVq\nFy9Baju3HSciuvf/pFN1v3lwL00JdiK7Kz0/ym6UKeFRlv5scyKeD/bJVF7/RXzngiak6NoN2+W6\n22ERfeUZpC4PnZLjyFN+d317p9U+eBGx8g9uvlm85lIP3qP/EPbj/PnS3phfU/MvseeWbZTSyfgE\nYmKoE9/XUyglUPlMQsbXJZ8HRGRJfMnYP7KBzW6zpCHDhu1z5c0Y0wjb7waPyXvuzMG1r7gV8WZy\nbvDPsv7mxv5QUC5lkG53CfsvvCa3AjGA71tERBd2sjU7l9laX5T3cuQ04o2HnT+2Hz4s+m1ZhPnW\nsAU2w4vzpLxouAvj6y1jZyDD0n3kVPreJqPZl54SEcXGotS9Iy0fMvcnzqTUh4io+9UW8Tcuj/Iw\nuadpLczlOENncD+L6uR3PrQD9rX//OlPv+X1LP6AlFoX18BaOpHgYyyvIZXCfZyYgCQjHpHzov0Z\nyLXCXTj3uQrlmSDIJLuJV1qsdm6tPEOPu9L9uBQga9ixD7+T3XiSxZeeI53ib7Puw7y1s3M0txAn\nknu/uwhnjIhhQTzihMQjxGSGo5fw7wcel5bB3OK9ikl+zFILXI5oZ1KJnHz5/NB6EZKvnsM4h81Z\n3iD6TXS+tZw2p0LKxLpeSM9LbuWcTWwOGzkzkhKz7EPRUqzNkhV4nuKxkYgotw720Se2Y79LGQ+W\n/k4mqWLyo7+olyUWrpzGuWPpBxCjB5hc13zvis24vwNHMQbbPn6T6Odh0t7uQ3i+q1q9VPQbvQip\nHZeTc8kYEVFOFf6bl6sw18Sc30ufnzxP/JKyTTwco8GT6bNj9e1zxN96XoFMk9g8Cw10iX4+pjLi\nz5Kx4HHRr3Qje15kUvLBk3KfjTMZeNWt2JP4Gd1llPeYyX534PuT2a+XyWRX3YFnzPFWeV7bx9b6\ntfesttrRUbm2+3cjJntL8LmdT8uSDP7Z6fkbNyRUb4dm3CiKoiiKoiiKoiiKokxT9IcbRVEURVEU\nRVEURVGUacpV6TgS4zEazKQk+uqlo8LYOaQFXjiL9KDhkJTjrFkLR6FwFGlBOW6ZettYAaeRC51I\nvSpdUyP6DbLUtRu/9Fmr3XrgWattpiqPsYrTLa14fYkhqWo+gFSw2sVI889PyFS6x3bss9r33IJK\n5T95/EXR74O33WC1yzchZXP0opQBpeKT6a/ZTwl3BTxUuSUjvTCUWDwFr4OlcuXOkPdlkLlG8JTH\nqjtkKp2/FnPk6D/uttqt/f2i3+ob4ahQNGOh1bbbkTZquin0X3kdr2EOAIf+YZfoN+cuvF/t9Uh5\ndxfKNNQrzyH9nEu5SldJWdz+b7xqtRuYA8B4h3To8mbSUm0uKXXIFna3g3Iy1e9DzVLOxR0punZA\nHsZTgYmkmwaXKCxaKau9x5nsooxJxUbPSnlPoBE5kfOvQQpj3WWk7POUYSKipUvQr/pGtHv2tYh+\nRatwTf3M1WyiS6aE8zTXpnqsWS5TISLKZdJOD0+RNhzeJsf1nVK2/ytMXu/7v/E58e+HvwrJQ82d\nkPXZjZTjfX/3K6t965futdqxsIy7OeVIsW+6/UGr/dT/+AvR7yM3/L7V/qeHv2C1xy4jRv3001Lu\ntoS5i335+5Bh/XjHt0W/v3ngS1b73rVwHnjta6+IfpXzEFPu+7q8Ps7+Lz9ktZd+ZqvVPv/Ll0Q/\nX8b9JzImY0i2cPk8lrxy6LSU2QQv4b7xNPCKeVLGUbgY/+3wYFv2lUvHrWAXJG+VLJ453HIrP/Ot\nA1b7Cou3hX7MA+5wQ0T01U99ymoHZmMtj7fLNGEXS+F3MHnQuUek68T8+5EizlO9K+qkRKvb9rzV\nHmOp1GPn5b5on7wvUyEhTqasOFe0RsZ8LkeaYDJNT7GMp9UfxD5mZ3Hf5SoR/boP4z4VL0ZcC4c7\nRL/RIaTbD5/FvHL6ELdNCWcyBukMT7suXSz35gkmiWlkcyw/V8rY5t4CKSqPIW5DxlFdgNidU4wY\nP3hGOqqUZ2QHru9k342IKH2+qd6avhZzLXKHIu4i6TfOsjxNP282ZE8pQxZUsAASQv4eMSNd/qbN\nkEFx6UdePe6TOY59rVi/iQikLIV1Uoo51gVJMp9zpvyLS1WON+NvkQtS4r2iEbJc7n7D5YxERM6M\npMc+BeebVDxJkb60Q05gvvzc8S7MW1cR5mDFXOn6cuJnkPwNsWeQ0j7pFuUtxTwouwbnuZFLUlp4\n6cdwnB0dZ26zZTgbN82WMqwv/eAHVvvmTZusdm2J/E7zl+GeR5kz0ECHPNc1LMCzTw6T7bmMtRgd\ngDy3jLm0xscNl6pg+nskp8AxkyjtlFOzNR13ho2zYj97huBOw/nMOYqIqPNlnF+X3cucvQ5JaVyk\nF/dt0gmUKO2+yim7FmNUWo8zSCqB54bEhFwT3TvxHFh1A9bfmOG01vUiJJEz7kLcjERkHEqysgpD\nzN2OxxMi6Z44yvYgU8pV2CTPCNnE4XVSYUa67c6Te4PTj70/xiXER6VUyrsZMY+7Z/bukaU1/I2I\nh3wecAdAIvnsxu95/X141vMukucr/qxjZ7E2bDgPvn4Wz715rZCLr9jQJPrVFCPecJer7r3yO5Wz\nsiI5rF/Z9fWi3+R+xOXW74Rm3CiKoiiKoiiKoiiKokxT9IcbRVEURVEURVEURVGUacrVSaXCcRo+\nn04hbFgk07O4o8xFJpW66fc2i37c7SJ4FGlwW//4RtEvGUf6nvco0gK5qwMRUUK4EyCVNTALqUwl\ni2YRZ+/fPWG17/4//9Nqn3/6UdEv1PrWTgkvPvea6PfBP7jdal/aBdeOdXOlc1F/F1If3ceR7mWm\n1o6eTqe2R0fkv2eDZDhOo+fT7580JF88bZS76+RUyWr0/hlIC+ZVwnmKMRFRSy9SvUvmIg2wco10\nfel8DellzlxIkXjFdS67IiLLAYSI6MJDrEq7U07pUBvGsGQlq15vZNsXMecUnl4ZC8n00qoqpPrx\n1D4uiSDC/LNPgRsRUXocxy6k16LpPtazB+nTRcuRGtxnpCYWsjX8Tu4Q8RDWLF9XNFem/FY33I1+\nJXCx6DmL8alMybXIU14TYdzrebfdL/rt+ZuvW+2xCcSAeLuU3XFp5vWfgjQxasQNLk/0MccMPq+I\nMO8cv2UK49XgryymjV/8EBERJRJGXEsirgmZyXXSQWJmOdJ9Q71Yf2YKqM2BOdLXDQln43qZev/d\nv9hmtb/1sW9Y7Udfxbq8c/168Zq95zDWP3jxa1b73E+kbPHu1ai+X34j0sMPPySlTVxCW7UF1ft7\nD0jZxcrPwWGwfR+kBQFjXpYvTbv1uP0+mgqio2FqfzYttUyGZZp1LUuZDrYitdp0L+MSF2cuYkYy\nKedFbjmkG6MtSCE207vbmKNJxyDm+qYmpPz+4mtfEq/h8mcux3Eaa4KvU+7cU3e3TCfmjlrefKQ+\n22wyRhdXYl50vgCJYLnhUtX5fHpvNVPFs4Hd7aDcunQc8M/IF3/jThHRIXyn/qPdol/xcsieOplE\ntWKj3EPKliEGOhzYW8d6m0W/vDJ8/0Q93qOTpYd3XJDXsPLjcJPzFGA/7z99UfTjsj0ea2Zvni36\n+ZizCU9L586S5vsFOyGLGO+U8sRJ6Wl8ipxsYqMR6ng2ne5uScIzcKkUX4um2w53KnUXQA5nntO4\npHrwDcgDTCe4me/FmETGsC7He3BvqhZtFK/pa8YZ08mchvrOnBX9eGo9PzOb55v4GGLq4pn1VvvZ\ng2+IfmXMCTSnDOdu83wwfCa91ySMeJcNHB6nJdUcOibnd8spyBcqChGvhg3n0wCT/NWtwjoypdWF\nTOofHoDc5vITp0U/p4PJ0IbwWTwWxeLyXvz0b/7Sar92BlL8OVVSis6/E3eiHQzKay0YxjzNrca6\nNGXcXZ04E1Wy42eXcVZquiMtLXE/LqVW2SKZTFp7QNFiKV3pYZKSiuuwTruZ8ysRUf292FN87BnT\nfNbg8qjoCNt3SqS8h2Oz4ebMWv0Bq51IyHU+vrwFn9sFmWvUcB5rfB+ksm43ng3sdimp9ZZiTucz\nF91Y8O2f97i7XdujZ8TffLXp/So1BZI3m91Orowb7bnvyOfe2nehJEPPrhar7TXiaWwcZ1EHi2Vz\nPyBdQu12fMeWl/dY7dnv2yD69RzG96/YiPMwd/Z15cqzHi/3kNeI+H74ZwdFv/WrIbd6+FmcX+de\nkmt21Z9dZ7V3fOkZqz1ziTyzDB/HPA1eRtzgsi4iov43Ot/0Hd4JzbhRFEVRFEVRFEVRFEWZpugP\nN4qiKIqiKIqiKIqiKNMU/eFGURRFURRFURRFURRlmnJVNW5ceR6qMmosTDJyBlquxnrowc4+eVL0\nq18PPeM170N9BrOWSD+rx1F5M+owcB0qEVHBAmjnue2aL4DrHOuX+u759yy22hMTLVa74abNol/H\nIWj6eA2BVTNlXYjXt8MqcN171+APhhSf62G9zBrM1Khe+s2pN/XPFjaHndyFac3l0BtSP1y6CjrR\n/j3Q3ZqWjy0/PW61Z/0+LPp8NbI2wDCbE9zu0qw3MmMz7ufAfnxuwXLoYkNXpCWtg9WI6BqEdnDJ\nPctEv9JF0GHu+QpqGJWUyZo59fcvstqdL0HL37NT1h3gcEtU05Jxsn5Qagos3Sc/z7I9Nezy8ljt\nHb6ufA2m7Slex+2ITd06tyd2+VBvJWjYIQaDsNLzeLAuCxqgm+8+ILXj8953q9VOJrG2XS5pG164\nEDWIalgNiiGjzsSixajbw+su2FzyN+o40xN7WB0DbjdKRNS7r/VN/bPFaGc/Pfs/v0dERI0LZc0I\nP6stEWJ2zHbD9tlThOt98u+fttqb7lpt9IPW250Dje/zj/5S9NvGann1j8Li/qmdsI7+2Lv/Wrym\nsQJzp/35U1Y7weoqEBEFmGXrBLvPTTU1ot+sd2MtDhxBDaT5d71X9ItGUUujn8WNmm2yttieL6e/\nY7BL1qHKFq48D1Ven97XegwbXl4Hw1+H9de7X9brabgVe6HdjrHi64iIKBhErYRJ7TkRUc/LMk5d\n6sa6uHYeYmCBj1lSGjVkJlgNkNxirKPhM9LKlWvEufUnvx4ioup5t1jtkRFoyZPJsOjXfnSH1eY2\noLw+GRFRxQ3pe+z6UfatpFPJlLXHm/a4vHYAv76SFVL3fvaHsCCuuRF1bIbPyftXcw3OQKERjFvK\nKFEwPgJ7cF7LJJfV5JrbIONkqANrNlCOa8ifKd88txJ1CFysfkLcOIeNXcaa4fOl99VW0a+rDfUz\nyiswP8YGZZ2OknmZeZWcmn2RbKgfNXRK1sHga5GfVTyFsgYFsXMXrxVmHsf4vli4DLXk+L5DRBSP\nY0zyilFDyF+Ec99I/1HxmoIa9Os7gzM0/0wiorLZiPMDV1CvZqJN7mN5TailMdaN67nv/htEv9Ez\nGEde44bXpiAi6wxpcxrFdLJAKpGy6pS0nZG2z4vvxflu8DD+5uiTZ5aaW3H/gs04p/S3yT1gqAN/\ni7IaNc29cu68eOyY1f7U1q1Wu3AG5vqxIxfEa+qW49xT3YN+JRXyHHb5Mr4Hr012/Z1rRL+xc/gb\nP0M782Q8LC/DZ/FiRzPXyppP1lnCLIiUJewOu3W2mqwBN8m8j6202vyMymuJEhENHccZNTKI71y2\nQdbJ5DWr+HoeNWpP8rqZgw7sSRW1WB+XDjwsXsOfNZzs/GvGMF6jZnwM8XH0grSWr7ketXAuP4La\nfPlNcq8vbsJzkdeLM5brQ3K8h8+n95dk/O3rVP5niY1FqDNTd6hoVaX42xirK8XP16PGeWGI1f/i\ndcIKl8u6VCUrUIM0wuLuaIc8K/EaYoko1mxRNZ5F7XZ5jyKzEA8jg3jOmLdB1nTjNbD++KsPWu1x\ntq8SEY1cwndc+i7EpIjx+wSvl1rM9ojhs/IeFcwrzfRXO3BFURRFURRFURRFUZTfafSHG0VRFEVR\nFEVRFEVRlGnKVUmlUsmUZQ1s2jz2nUJqduUqpP3nzS0S/UrZ38Z78R45JTJF7tFvwmLrXWuRSp8y\n0tNSzNI6ONhitTueQWqeM0/amRYsgOzC7UaKXDIpU/v9tSydNp9JCA60i36bPgorRy4JMtOmeJpY\nzRpYRF5+bofoNx6JZK4n+/Zu0dEwtb6YTuksaiwWf2t5GCm5wXGkJdYZ6Ys1zAZu8BRSGdsNK7+F\nH0eq55Fv77PaDVtketrZZyGfWXSflDpNEjMsNjt2t1jt/ecx1mtKpFVx12uw7ysqQor5UL9MfasZ\nx9gXr0BKG0/BJJJ2sDwFkqf5ERGFBzKpflOYEj4517h9KRHReDu+G7fyNm1Ka27GODqdSN/tOykt\nR7n1pPW9SErFiIhGByGT4QqxgWNIlaxav4g4oWHI0vKKIHEZGNgt+nH7x4ImrN/a26UsZvA44lC4\nH+svp9wv+pWuQRziki+ejktEFGpJyzVMOWM2sBGRK2MzWrFZSlDbnsQYcGvmigVSAjXUifm9fstS\nq127eZXo1/oiZJ+vPv4bq72kTsplosOY31/82Z9a7Te+jvG4bpEcw81rkfo7wtK5F/yxtLh95W+3\nW+1rPgGLR5tT/v+DiV6kq9bfsMlqtxzYLvpVLsN35MneDmOez7lzARERebZPje1pfDxK/YfSspYi\nQz7T+QJkujlVkKfkz5WxNy8P93RsDPHQ7ZZrO9SDtRRmc9WUcdhY+vuv9u612tuYJfssIzb5mR24\n3Y6UXV+tlMB6mexu/r33sr/I/SoaheyiuBj2mcGgjC88jgy8AdmAmTY/KRMxpSjZIH22yaTsG/dF\nnHXYjR47I1PgAzOYnTqzUA+dlnvIlV3YC90srT/cLc9UXhazeOwO9yAGFy+X6eveYn7PmMW8W8a/\n4998wWrPexAp5mb8i7N90R3AOHlKpc3uzBksrZ/JwEsdcqy6XkifERJTEE+JiOxOO3mK0/fUGZDp\n8lwRwlPVZ918m+g3cBT3ZvK9iIji41JGlleFPd9fiZT9oYsytT/YxqzH5+B7RyI4OxVXSMtbfhat\nXIT9d3TolOg31CUlVtbn1Mk1W3YN9rtIL+ZP3JCzOnzyrGx9zkkpx86flz438/NutrA5bOTKyH/y\nc+U8m+hkZ5thnKfzG6Vk0MXWX7gD68osPxBLYDz2nkVcihjW3oNMNnypB/diWTFi+pZPXidec+rn\nKKEwazH2WfMaZs3D2MxnUh6nMRadfZD9jLdjH1i6uUn0K1gCmSuX+YyelXbgE5n7kgjJOZAtosNh\natt+jojeQoK+H6Uw/Ex6y6VMRDIGxscw3vxsR0Q0cgznw/xFkByVr51Fb8fQOew1/TkvWW1njnws\n7nwGe3hgAZ4Xy9bKs9N4F5Op1uIcwKX4REQ9hzHPPMyuPNew0fb58JwUieBanblSTjMpoUuGsx9T\nHR6HJfHmewERUTKE/YWXWjCvL9yPeHPkN1gTjlPyPju8mKsxtrZr5t8q+vVcwVjlFOBZIJHAnGg/\nvEu8hktMuXyJ24kTSTtuLtPPmynPazzucYlc3y4pIc6ZwZ+dcH3mbwNFC9Lzxf5bxlPNuFEURVEU\nRVEURVEURZmm6A83iqIoiqIoiqIoiqIo05SrkkrZXXbKybgR8FRgIqJAH1J/EhGkbJkSklAnnCIC\ndUjpSyala8u2B1HtnqfPmalEPG269ddII82bg/S7UKt0p+DpwNEoUuyGmltEPydLqZroR6qft0Sm\nbyZjSBkrqEeaXlXTZtGv8/ROqz3QDGcmU8bhcmZSwqeg2rvD7aSC6nTqm5la18CclSJDGM+e3TL9\nK9LLUr5CGLc5DywV/Zp/DhnHyk9DNhFsl6njZcVIMQ80YNw6XkKKYtJwOiqoQ2rsHQ5UqO/de0X0\nK16F+Reuxn3OqZb33O5kLlUvQfJVdq2sXn9iF5xDCvPwHqMnZZXwUEbuFjYctLJFbDhM7Rk5jZkS\nbmcV3ouWIpVeptETRcdwba4ipAKaziJ+JgHg0qvR8zL1Nn8LUu5HWjFn8ucgvbT/lHReqFmB9GJe\nCd7tlhX2q2/BmEw6ThC92aFs5BTGgc9nd650chg8h+sbYSnERUsrRL/Jten6efadbNw5bpqxKC0D\ndeTI9FI/k78Nsqr8Q0efFP1WfPRTVnvgyPetdm6udL6LDu202tXzMCca77lG9OPOXh4P+hUx94yP\nfe528ZrT//Ky1V7yGcgOYjHp6LDqw/is0jq03YEjoh9PKX36Cw9Z7cV3LBb9nvrz71ht7pbkr5Ry\npfya9P7hyjHcY7KE3e0kf8bdx2mMY2wQc5U7EpkSg1QKsZjLlMbGpKyooAZp8c2ndlrtcFA6Nc1m\nTl8zSrD+bv/b+6x2aekW8ZrxccTOWAwxunS2jOsjvbimoZ5DVju3QO718Tgkb+Ew5COmVIBL48qv\nRfr55X8/JvpNpjhPhXuGK+ChyhvTriummxWXSXqKsfcXrpCxYvvPkZ5dfxL3snG+dIzjqeQ8fpmp\n9zG2t3KXL181ZDCJCRmrfQVwjuHrLxKUe26gEincrb+ARLryNilj5nsBl/2VbawX/bpehOS1/wji\nlSfHOCdm3I14uno2sTnslktWxJBTlK3FXs7nXM+FfaJfxer5Vnu0A7J4020x2I1U+jj7W06pPFv4\nivC5A5dwr/012HPbjj0jXsNdTN2FOP9Wzb1R9Gs7/hw+l5UbcOXL/Yo7bLmZ/GvwpHRPanwP9kwu\nL+AyfyKiiZ70/UvFsi/nj4/HaDAzhyaiUp7Rx1zsEqyUQOlMuXZ4CYWJEayxgnw5NlyyNxyCpKO6\n2JCyMsnWmXbMiZWrMFdMx0e7nZVNYGdmd4nch/gzzKVDcJmLJ2Sc87P9q6EJ5SP8hrNcz44Wq120\nEnu4f6aU3QabM64+UyA9JSJy5bmpPONEN3pRykr586OPxaKYcZbteBbnxcb7IcnuflWWZeBOkv2H\nsC4n+qRzEZflelksj7A4PN4lnfCqbkdMHGDvHeqQ+4R1P0lKbkzy2LOLizmCxQznUn4O6D2FuNH9\n/CXRr+y6eiIicuRkP6baHHbrnrkNydfYJYwpfyY++YODol8eKzNythP3b+YC+WzVuQNzv4i5yL7x\n3e+Ifvm81Eke3pvH4/bnpZN0KXsO5Nd64N8OiX5rPrvZaucw58WSelnCo+8yHPz8tXi2qLt/oeg3\nxEo39LJ1Wb5FSrQuZq6Dl6J4JzTjRlEURVEURVEURVEUZZqiP9woiqIoiqIoiqIoiqJMU/SHG0VR\nFEVRFEVRFEVRlGnKVYniEpEEjV1K66bNmgxXWqDl2vIh6OidTmlxduUxaHmPt6FeSN3KetGP29hx\na+GShhWiH7dUzKnBNXCW3IwvAAAgAElEQVTb5qYPS7vHjtdft9qhAWhmudaSiGi0BRpxP9OVV2+V\nOvCCygVWu+s43rts/S2iX8Oy91jtA//491bbWy5rj1TMT2vnXV55j7OB3eOw7KO79sjaNdwiM5dZ\n1/ack1aQS/4A9Sm4VnwgY4k7iX8WtJwTfejXv1/aqa/6H5+02lfeeNZq89oCBYvLxWt4rZXzx1tw\nbWtknQVeSyLciWuIGdae3G41yCwnW364X/RbfBd0tmPnofEsXSf1micN7WS2SSZSFM1Y5tmNegHc\nYpDXk+h9XdqUch19GSt1YvdIO+Uxpt3NYRa9pkV2y3bMfV5bJ78S+mOb/bx4zejwcXorikuvFf9d\nOBfjGo9Cj9xnfKeKLajx4M5lNpNuqVn316DmQyoOrbxZ3yeUsVbndbuyhbvAR413pW98ZEJqwC+/\nCo3u4g+ihpPDI2NC8+FHrLaXWfS+/tVvin5DI5j7jZthkXnm2ztEv0AT7pOvHrV/epoRG3JelWO2\n4Yt/ZbWf/LM/s9q1K+Wa8BRBI31l5EWrXbF0iejX8fxOq910PWoI9O6W9avW/f56q93N9NFjTEdN\nRHTge3uIiGikQ97jbGFz2Cz72bbHZU2anFrE0Y6XoE0vXSnr8Jw892OrXXEtt46Vn8UtR3OrsV89\n/aOnRb+XDiH+/OCvMCbdB1AzwHmt3O9ycjBel57CvJi0/p2kbundVrvjHCzaB/rl2g7UYS4Fu1AP\nINwrawj467FP8HoPuTPk9Q0cTI+ruUazRqamHLfoJpL222degFX7orvkvA2wGhRtA5hrxe3yDHT5\nDGJWzwjuy/JDUvc+MIa6evx8VM72mkC5PItwS9S+Y4ghNrvx/+jYvDrfgfXy1F/JfWvbRmwMnZ3M\n3n1NjeiXYPupx4e6DVNRA+WdSKVSlIqnv5xpGx8Zxr7hYGerZFTWo3A4UAclvwb3evScrOk21oI9\nJHgRZ8XYkKw3VbAc56cws+IOtuL1Zv2cPFaPhNemGx2VsZfX8RG1UnbLGg9ztsyz2s2vt+BzvNJ+\n2e7EPHHmog5J0TJpOx+btGaegvIoyViSwt3p+zTzlrnib/17cHZ0+FgNnhF5z5Mx7NddQzi/OB3y\nbJPjxnf8yH1brfbO3bLu2oxS1CcrCSAuediem0rKuV5zDeK4OMs2lYl+iTDWTk8L5ljNMlkbi9j7\nj7diH3AZtuH5zLK6cyf2xVyjxmHJ2reur5c1bDarTlLpKhkvRi9jvfTux3NIZEDWLOxtQxwd/wZq\nUdXeNkf081Vgr4nOZnPBqBPK4yCvpRcP4nmiYo2ccx07UV9m9r3XW+1USu5DBfV4j5EriPH+almD\nyOfDWmzdC2vrwibjGSeO+M/rTNax2o1ERNFMDScz3mWD2GiEunek6wnxZyQismqJERH17MHZrHyh\nrP0WG0HtnhuXoE5hfEzWrzrG6mKuYOv0Sr+MuzNYvajxduyfwQtY53kz8sVr7C68n53V7mrcJO3i\ng6xuUeFM7MfDPSdEv+p5N1vtM0/+zGqb9aZKV2MN87Ncwnh2svZJ47z3dmjGjaIoiqIoiqIoiqIo\nyjRFf7hRFEVRFEVRFEVRFEWZplyVVCo+EaOBE+m0z3IjVXbBFqS0D5xGSrgppxhn6WnXMOstM3Wo\noAwpVd1nDljtaFSmu7tcSCErZBZiIyytNTTaIl5Tvhypav0nkVKaK7PXKW8G0p4Chbge0z4ymUTK\nXPE8SDXaLz0q368E6X1VNyNFq+3RM6Jf5S1pK9+psHeLjkXoyq506pvLTBtlkq1eZgFu2pJHBpkd\nOEvV9RnpafmzkbLJU9Ua75f2soO9uJ+eQqSe1t6OcRo5J+22g81IM26cCRmNmRLOJUv1G2GRfPEV\nmdY/d+Mqq33kFdjKz5sl5R7HHjtqtdd8FFINl1+mq07e26kxWszIMzKWn4mgTNnk84an0eZWypT9\nnDKkhPuLmTwj8fbp7T1MXlewSKZ2Dh5Gyj23ZedytaiR0pzqRjroRBfawRnSvtYp0oGRT5hjfKfi\nmbBLjkRwPS07Top+eY3MGpMNkpmynlORXhPcYj1bREdC1PzUa0RElD9P2p9zK9GJTtyXY0/LVPn1\nn9yI/2Bplkv+5D7R7/JzkL7EWFrwwMio6Jc8gbHnkpEVf4i5fuKhA+I1hQsh19p1CmtnjWHlWjcH\nATbaj5To4CVpG16/DdKwSBDpr70HpcTy1199ympvXI304d/8/VOi3x2fvImIiHJfknKibJEIxWgg\nY9meWyvnY+gS5rGbyTO4zIlI2pEOnoC0omC+nBdijbAYeP1CaUP5/vtvsto8VdlbihhvWsYP9Oy2\n2oE5SD03ZYJHfgJ7Tm53bnPKaMdjD7fANq2gB7nFL7O3tjnl/lS8Oh3nnYY0IBvERiPU9Vz6LBBY\nKO95qA1rZOXvQToUbJFWs+u3Lrfara8jTu44IdOsNy2AtLq2Ap91+Ly0eV05H2cEfz3OOZ0v4sxS\ndZN4CfUzuTK3kT7/9GnRj1saF+dhzq6bK2UC48PY36uYHCFkfHduMV2xGSnmXJ5EhPGdinhKRJSM\nJGjsQvqMWHKNPKMOvoF5VncH7F29Xnnw6z0PCf+btIqMkeNYp6WbsH+aZ4GzP8OZoXMQsW7ZJsyD\niY4x8Rpuv+3KwziO9snzL5d58TPW3Jvmi36pJL5H9czyt/x3IqJwP6RcXUzaaabwuzNy7OQUSOFs\ndtub7uEkngqcD4tX4tw3cNCQ6c/E2X353RjrCcPqmZ9Neo/jvLBhuYynm/14j44LKMkQYdK3cJGU\nWBbMx/OIrxRn4WhI2kiPsvN01XzITKID0s6eW2i7S/FZb/zoNdFvxkLcl1AE86h6aaPo1/NyWkYV\nH5M21NkiNhqhzhfTc6jyBvnZ3AY7wM5iIxcM23DWz5mLfSPSJ22TW04fs9rlG7AWvYVyPx5j0itu\nDV48H9dnt8t9Z9ZWlNpIJNh4h3tFv2QSa7F+yb1WOxaTZ6zWg5AXc9vwnr0top/jOlxfiEmCTKvx\n/FnpucXXf7Zw5LqocHlaJukx7MCTcZwL3CxG9eyTkvbhdpxTimZhHTiMkgzLg9g3jjRD4jcckmM9\nfwXGaveLkDSe60AMeHqHLAHw7Avfs9rcBt7hkWeRyjmbrbbdju/kdMpn21AIz49c/sr3XCKiIJME\nVmyox7+3yxhQn5G/eX4h7/HboRk3iqIoiqIoiqIoiqIo0xT94UZRFEVRFEVRFEVRFGWaclVanFQq\nRbFEOj2qfZ90JKpaA0kJT3O0u+VH8HTxodNINZu1Wab2j4y8YbVL5yANnssfiIg6dqNfyUqkxoZZ\nKt3QaemKFGo5Z7Vjw0gT5BICIqJ1X/yM1W4/BbejsJGm1zl40Gp7S5CKbqauTYRarLaDVeiuuEmm\nEY5l0qumwsnG4XRQoCB9jcmoTHPlqYP+RqSaJuOyX+9OjL2vESnc9nyZJrb3669Y7QW3I/V09LSs\nEu4qQDpknMl+ilchhXm8U6Yb8grqYSYziL8mU2YXPIAUVz6+Kz+5XvRr/gUkKI3lSCXOrZOShhom\n6RthLlzRQZkSHo6lv0fyHVKt/yvYbDYr1bDyRil5CHXgXnHnCp6eSkTUs6fFaseW4d7klEj3gZGL\nbLzY95kwxiRvNlJeQ61IBeQp18EemRIeYG5tfJ55WDojkayqz9O5zVTHlhf2WO1i5oRhSqC464SN\nOWmYn9u5Pe3CY1bAzwapRMp639Hzck3Muwvr5YXvYx29758+Kfo9+edwI7rpi7da7fHxZtGvcAHS\ntofPYN4u/4O1ol/n85BhDLyOWDt6Ftd3ql1KliqPoXL+sgakuzoM2eKCB9+F9/jJb6y23RjDM9/C\n953/R5ut9uI/lWt2qf1GvN838Jptfyzd/J74xnNERDTSJ+drtrB7nRTIzH0uZSKSEgouD+GObkRE\nwYuQnnA3pfFu+X5RJnviTntc2kRENN6O75rPJMT5jWh3nHpOvCaZwNrm6yU+IaWYJauRiv/qv+yy\n2stvky5L0VHIqHp2tFjtlvZu0W/2AtwjH5MEmTKxSVnHVMhsUskUxTNj4g7IfaxwCe4zl3yZsYK7\ns4zshPy5tkS6crX2Yf3dcNdmq72+yi/68Zhz4QlIEBtuhJNU66NSAlW0AjEvyvbFhk1yj2h9BNcw\nGIR8pCxfpoQXMrkzdxGZaJdrKcGcvoQ8ytj+/HXpc4XpUJIt7F4H5c1Nr4W44RxZdSOkZ2MdmIOp\nKrkWx9n+yc9wVZvniX6DZYiDwi3qspSR5eVjP22qxP3lTlymk2MyjP2pZxdieX6TXBPla3F2nOjH\ndUf65RmVWCwOMJe4YIuUJI+cxrzga9G8Psu5y5F9MbjD6yT/nHQ87dkpnzO4SyaXFwfmyjXmzsdZ\nhzsOmi5D3BVq/ofgWDt6UUp2+l/HuXIVOzvyOGm+JjGB9RvsxrOOt0jGDR+TzQ4ewp4bMNz8mndi\nby4swmtq5knHL76f1q/Ffjx6Rp4xJp0m4+8gjf+v4PS5qCgjs7nyiIxT1XdCksnXGC9FQETUsADP\ndM0HMY4b/0I6BV9+GPLG/texLk0HQg+TdBYtwX3z+1HGouPkS+I1iQjue2Am9tnBE12iHz+Ltj/9\nDasd7pJrMX8xK+lxHPPicofcF7mbYU4Nxtt0j2rbnnayNMsQZAO7w27thzkl8lkoEcP8vvxTSNXq\n7lsg+sWGcV3crbj5Fel8V7cRsWwtK9tx4bSUXu3Zic/iMqoDZ7DnFlfKNTGwD+uXyyNzK6SUrq8N\nzw++YrxHOChLddidb71/FS+SpTUcDqz1/pNw9Bw+IX+TmJSjmSVj3g7NuFEURVEURVEURVEURZmm\n6A83iqIoiqIoiqIoiqIo0xT94UZRFEVRFEVRFEVRFGWaclU1btx+D9WsS+vRL+y8IP7GtWs55Wh3\nvCB1bNEwNIcnt8Mmc9zQTIe7oV2b/RHYbA6ektowrivvfAmf5a9HjZYrL8lrGGcWeUdaWqz2fR+W\n3ponfvkTq12xsd5qc6txIqLy9dDoh/ugF+8/Iuvx8Ho6SVa/purW2aJf++G2dP/x7NfV8BTmUP17\n0zWD2p+Vlti9+9usNrcCvnJZai8TCVx7bjfszprukTUO6heh9gW3YQz1SktGP6tZ4AqgPhLXp5as\nqBav4TpFB6s74PS5RL+hk9CQll+LcQoZdmxuZuU42oe6Er1HpY618V3Qb3pYTR9T37xkXVrrmPP8\n4zQVOHKclJ+pW2JqXrndHa/l0vXCZdGvdht0xt07W6w2t6onkjVg7KxGld2w9e1+Be+RW4F6DV6m\nVy1eKa1XO57D2qzdhhoCo2elpjSHaVFjIawLp3ENPA7xmkZFhnV5mNnYc3t7sybXpP2obQrqaoQn\nInT2ZAsREd38v6Rme9f/ftFqr1oFa9eJoFyLM2fgfl55Ahrf3FqpRy5mem5uxzzRI9fiWBfi8Iyt\n0H3z2gfBsNRS9x/BGll662J8Tq5ci4kEXle0Atdt6n0f2Q+78QXt0CYvamoQ/fg8Xfxp1LW5+Ou9\not/mm9K1C36471WaCuxOO+WUpee7Wf+Mr82hY/iegbmyJg230ub27z7DNtyZg7HjteT8TXJ+Fy3G\neI+1oubGyGXEQ7M+VC5bpxMsRkeM+l28btaa967GtRnj3f4U9pdzndgLFy+S9VZGmV2oi9Wm6N0r\n61tMztuEUbskG9iddqv+gTmG/gacJSbtbYmICljtICJZU2XWLNRm4FbvRESFi/A6XtfLrBcSGcDr\n6liNGl77wHzNsd+gRkRtDT7Ha9TPWc1iCo8V0WG5tjtOYNwGxjAvr31Q1pvq2406BLymwfAxubYn\na6XEhrNfj4GIyGa3Wzb0qYQssNPG4mPN7dj7bDYZ2x1sjZUvx56Uk1Mn+o3lYS31voq5GjPseouX\nYy2GmjHXx67gDDIRlWe9cmYLzc+eZg2EyAjWKZ+3ZetkrYUQiykJVrOqYIGcw3xv4LV+zFgxcim9\nHyR/y5oMV0N4LEwXdqZjR80cWauC133hdZK6jbPNaAh7euUSnB15rTYiIlceYugIqzMXHZLzc/aH\nUStxvBv3nNfbKlxUIV7D91Z+9y796xHRb/I8TkQUZusi5x1suvPmsHqCl2WdIh+rB8jncqRf2otX\nL0rvwe4cGbezhc1uJ3cgHc/LrqsXf4sF8d262dn5mk9sEP36D6JezZL7UYPozDd3i368FmXpWsz9\n2KgcR2fuW9vM91zG2cCsddr2PJ517axWVMqoX8nrWW4/dMhqN9XWin6b1mFv4O8xb4k83/Czg7cU\ne3MiLPe/yTlos2W/3lR8PEqDmedY1wZ577pexl4480OsluiYvOd8DvLnEbMG4qVXcJ9dTlanqUau\nq+BFvP+2+zZZ7ZUzsUd2DA6K11TfibNs8yOoF5dXK2u6Vd2EM+XFX+EcWbpOjmFBLerxzLkfZ69o\nVD63DJzB802oDfG++tY5ot9krPhth1AzbhRFURRFURRFURRFUaYp+sONoiiKoiiKoiiKoijKNOXq\n7MDjSSvdbv4dC8Xfjv8SttzX/AnSl0zbx/yZSPHLiyJNaeiCTGH0FSI1rONFpBuFu2VqfyKEtNTT\nVyD1WTKClKc3mqU17kO/+pXV/vT732+1//7v/k30+/htSL/f/Q87rPbCW+R35zKZwYNILS7dINNV\nR1iaJk9xTsZlumnD5vS1u5+Q9s3ZIBaMUPfuFiJ6c7o+t+Xjae+1M2Q6LbcRDzQhdTWnTNpIc0tK\nbjXb+J5Fol8bswrk11SyCKmDw83S5tvFrq/hFqRXXnzsFdEvwmw6h05AZuI2LBmbjyHVe+E2yD0m\nOqSEb4RZ2Oc34b7k1siUu8nvFBuampTwZCxhpTLzVEQiorELmI88zd/pN2VkSGPvZhKKkR/K1NuG\nbUirTzCZkqNaWuld6sH7zfchrZJbZg6dkqmELpaCfe7XsGQvnyvnHLeD5+nZ4yGZ1ppgEkSeEs6l\nf0REoUuQjxRwu1+/TAetviktY3R/S1oEZwNvjofmLawnIlgdT1JViji5cw/kD9ca9pa5tRgDVwDx\nYs5N94p+h/7pIas99yPXWu3zP5KyoqaPraa3gqfN3/PhG8XfSlch9XfXl1+w2hu/IKWnHa+9ZrW5\nBOillw+Jfp/77set9onvQDaVCMs4mWBpt207kH4e6ZYp4fM+uZGIiNz/KNd8toiPx6jvYDo+OQzb\n3MHD2A8qbkB6bfCKXGODl7FmG26FPMNM2+bSYC4XbX/mnOjHLWGrrsPnXvge7nVggbSb5TH/yuNn\nrbYpCQpeQBoyl/m2PyGvgdtMlwYw3r46w3J6KVKhU0w6ZK4JS4o0BSnhTr+bSten06G59S+RjKFc\nUtu3p030Gxlm33cm7m31VimF5unio5dwL0uvkenYXK7BpXQDTC5es0XKzvj9u9KKfrVGWj+3iI8M\nYL0ULpZp6Uf2QF7E7/r5x06KfrXr6tm1Yl6aElNLXmvP/hgSEaUSSUvuZVpYc5kNt85tf0ZKxn11\nkJqMsDNlqlbGHy6Nq9qK8+abLF3ZPOZS3p6XcC6t2CRlWNy6efgM9ma+5omIipdCSmRz4qzTxyyR\niaS8cKId89Q3s0D04+elmjuZdPm8PJ9XXp+OKa5/yf4Z1e1xUXVDek+ODRrnJzaPuWQzOC7liLO3\nQdLesh2xLJmU1teVzEacxxt/nbwvIbb/hZjEzV2Ac0H/fnnPy9iYDh3HWuRlEoiIxnsgY+NyxMRx\n2a9+A+I4l3T7ZhWKfly+NfAaYpm5HixZ6hTEUyKiZCJpyURN2eIAsz2f9QGUwujaJcta8EuLMelY\nwTIpDebnts7nILkZ6ZAlEbgk8WwH7k1+Ls4GM8vle59oQwz455/9zGpvuOYa0e/6RXiuWTkL8aBr\naEj045bnc9YgfpvS8kL2fNHBYlTZRhkrLEntVMRUm41smedC8yzCJWkj7PndWyTP2gG2xnKY5OvI\ncydEvzJ2Rmi4DbGHy/SJiFZuwPN3wQKMFY9x85fIsh382qvZnsnt4YmIfIX4TuWbsP4CFfJZfvAy\n5mn5XJyZYzFZMqNgDvZTHru7d8rfJCo2pZ91bW9jM26iGTeKoiiKoiiKoiiKoijTFP3hRlEURVEU\nRVEURVEUZZpyVVKp6ESU2k9lUtxOScekyiqkQ7U+iqrN3D2EiGjkFNI+ueShjKXbExG17kMq0XPP\nQ/6S45ZShttXoNL4V370I6v9tU99ymqfaZcpjDl+OCyMjCNNeO3cuaJf7wBSMVe8b5XVjhuuFvw7\nVWyBvGfwiHQk4nl/XFpjVuYfPZ1OO0tMSHeCbGBz2S2XH1dAyj9aH0dadNkajMeMu5tEv6HTSPvk\n6Wltj50V/XiqKE8Pn+geE/0qbkHqGn+/sU58jtOofF9Sj/EY6kHqYfWNMnX80k/wN+40dulhmaZX\n24iUtpbnkZZoN9JI4yzV9spRpFAu/fAa0a9wVTqF2fGLqarYb7MctMx5Eu5CujNP86+8UbpF8Yr7\n5cypxGNI3sK90mVlkojhvjKnEmnbD3zhL602X4stfTLtkTsULa2vt9oTnVISGWxBymvBwlKrbSgA\nhEsET591GRIoB5P4edn37dwu3fJCmTTkWOjtHR7+s4wFx2nH7rTE53pjnrkLsTY/9p0/w/UdeEP0\na98FN41Fn0Dq7m8++9ei3+1/94dWe9+X/9Vqr/n8e0S/3pPHrHZuJVJX59+Nfk987iviNTP7EEN9\nXlz3zz77C9Hv1g9fZ7XHWQyIJ2T8445vi/9oHa4nT0pJLj25y2of2IHrXnfLctEvmUxfXyol0+Sz\nRSqepGhGQhwfk+nEXiYn5Kn4w29Itx0eV4aOQtI594M3iH4uF9bzUBe+cyWTQxER9TE3jp4DkIF6\nKjDXffUyxZ67R1WxOBo3XCzOXUGK+eF/grPEdXdKmV1ZEdKLh47gO0UNlyoub7QzmU3ckEGWrEy7\nw5gON1khlbLS+VMxOU+4c90wcymc9eAy0W/wOL6jg0nEUwn5ft4SjAHf18zvVbSMuRExRwo+V/h7\nEREdPI69a3413HTyDblbgjlWBuZAntz9snTn2fggZJVcBhg39pzel3FeC7D47MyTcdeUTGSbZCRB\nwYwM1pQGcwkXH8ei5dK5KNQGWUz5aqyDaEjKGz1Mfusrx/0dPCvd0ELMMTV4Fqn0Jdcinplnyotn\ncbZYzF2/DBfAZuZw6mIul9XGXh8ZxprribRY7aKl8ruLGMUkedzJjAj7biKS/TMqEcYqf1Gp+PcR\nJrWOjWBPDhTIdTDwGuJf4UxWEsCQX7Yz19veEayx5e9eIfrx80Ns9K3PAp5yeQ0dzFXPUwopTsEK\nKUc89SucUfle2G444+ScwPiGghjP6mvrRT8uTfdW4lkn3CHP3cHz6XWSDE/NGNodNnJn5uSI4RLK\nJZ2Dp/CcxM+rRESdz2J8SlbjmeTSvx8T/bij0nAIc3XBfUtFv5EzuI49Z/C8c6EL15Aw5HQFTEb1\nd5/8pNVu7Tfkg4W49n3nIBveumGV6Medb30z8BzoMco3dDDJV/nmeqvd9riUJE/K/3hpgGzh8Dop\nMCst2zdlmv2vI0Zxx97Ry3LeVqzA8yN30WpaKZ/VZt1zvdUe68d+UjBXxoCxFkjPfBWQNFa9f4vV\n7muXDqIzluAc1d+LcyM/JxERpRJYs3ydVzTK81Xrpdet9ng7HGBLV8kz6sBR/E7CY2vRUsOBLuPi\nmYr/di59mnGjKIqiKIqiKIqiKIoyTdEfbhRFURRFURRFURRFUaYp+sONoiiKoiiKoiiKoijKNOWq\nxOI2m41czvRLvC6pH3YXQ3+ZNxua0gtPnRb9eE2LeRvmWO1jL0p7yRDrt4Lpy8ajUvf+1w8/bLV/\n8PnPW+1hVrtm7yHDbpZZgHP7vbU3SAuxCwehs+PWnB2vSisvfwl0pO1Mf2hqXrsuQTPsOA1NZVmD\n1PBNWqy6/zX7FsSpaJImMprrrl0t8nM3oz7PwD5o//LnSNvYILNStqzoiKhvVFpnOw5jjkT70M9V\nIC0k7V5o52tuwZw4/4PDVjuHaXWJiAaP4v6FmqE9L1wmtYP+2dCdnv05tMSBIvl+DqY7jTGdceU8\n+X55zM6e62VHz0kNb88baW2jaaGXLRLjcRo5ltbpm7a+3ip8N27xGx+X15JktRe8FXiNWZNhz2PQ\nczocGKtFTQ2i33gEmtDPP/ig1a6uxPXV1EmrRV5fYIjVhGq+JGtoVTD9cPshaGube3tFP27r2MDq\nDsx49wLRLzKI+MB11P55xaLfREYXnoxlvz5KaV0l/eH3v0hERE98/l/E32790v1Wu+cYbNL3PPKa\n6Pfer/+J1T7702etdpFfzu+L22HTveCjqEXicEg7RF4HoqiS6/zx7/VzZN2yGLPWnf8ANOXr6u4R\n/cIh1AC58iTqYd1653rR7/nvo6bZ/f/woNW+vF3qlk/uQ6x97z/8Pq4nKu03W36d3luiQ7K2Sraw\n2W1WLQ/T6pXXKrIxu86kscZmMDvlwkWIOX1Mh09EFGIWuOXroCvPCcgacTYb4ncOq+GU18isrY0a\nZyOsXkYus2t/9ms7jPd+63oCl/fJ+igFPnyuOw8x36xLZef1YFgtNLMk0WQNmfgUaPlTyZT1vnwN\nEBFNsFpeRUsQv6LDcj75alGvYKIL54pJ/fokPWzfLV0HTbxpEc/thWOslkjtRpyHeO0bIqLr3oU6\nV44cHO+CF+WaCMxHTOb1XvYfkfNtU7GsuzCJu+DtbaD5eDp9ssaNPzP/HDlTVPvNaSN3xuLVrJFU\nugo1fyZYrRhuh05EVNCE81jb8ziXmvU3OJ0v7rfaXuPcx+v6OAO4H7yWDv9MIqINf7TZanPrWF4b\nhEjWvuBn1OafHxf9CpZi3uazz+p5tUX04zVgqm6BjX13j1zbhQvSe6tZezAb2FkdxnGjLguvW5cI\nIQ74Zsqx4bVDBp6YszkAACAASURBVF5HTS5eX4SIqGgR7ovtJOJa744W0Y9bNU+MYN2HLuPMU71I\n7otBVoembxDrtHxE1sgpr8GZY6QHc8KsB8htv8tqEPt5rEn/N+Y2X+dmLc3Je2n3TkHNMCKKjkao\n8/n02SqH1XojIsqpwf6SYjVlWh+XdTKL2fmQr2ezvljLz1CzcumHcb4x67J4y3EueuADN1vt8/tR\nq62mUp6nC1fgGnY/fMBqb32XPLfwGibr2L9X3zpH9Ot8HrVrBg5gbrpL5FmM7w3c/r1so7SmHj0/\nkOnz21lJXw2J8ZhlZe+rLxB/C3djX+R7ek65HOuOvYhFLj/2jaLlcr2EhlEbrLpxm9Xu7XlW9ON7\nSjKOGJBKYY8sqV4nXjM8jGeYyfMgEVH9fQtFP75Xly2Gvfvl3U+IfnU3rsV7t2E8HS55psphz1Xd\nLyGO83pkRIg3sZHfrpamZtwoiqIoiqIoiqIoiqJMU/SHG0VRFEVRFEVRFEVRlGnKVeXI5RT7aMEH\n0+nzR38kU/brFs6z2n17YT9aWielB35mK9jzBtLE8rwydWgwiJSl0QlmU1pUJPp946t/arV5Smol\nS+F8KPcz4jW5AaSk+efi+sKdMuVw3kakuA0fh8wpZeR6583GNQ0fQ7+CRdKC01eH9E2etp83S96j\nzowNXJTZNWeVTKq7v0KmtLlZ+lbjhyB5ML8vt2wtXo10t6piaUHZ8QxSyLxFuOcXzrWJfg6Wel/A\nUle57TNPcSQiCjJ5lJu9tynzsTOL1bImvHfbMWkD17QB6YcRlgIYHZDp8OMepLwG2NwZOirtfec8\nkJbdeZ+U6Y/Zwua0kztjMWmmwRetwphEWepdxJCKcMtabiU4YFg3VhQgRdLJpFKHjp0X/WZVQOIx\nxqSOAWa97THsa8NMhsDlUV63TLHn5PuQvm9aNy6/DXLH3CrM75iRNj/RjfhSe/d8qz16QVo8Tkrv\nuOQsW0z0D9PJ7z1ORESLr28Sf7PZMG+feAgyp/d+8W7Rr+cE7MFza/B99+6XdvdcHpVkcpTBNplS\nzyUtvZeRFvyLL/3Gaj/wl1ICdeChPVb71QN4v01rpfR0qBNr1ufB5+w8f0T0u/X+jVb7ta8iTbZi\ngZQtLrkO96z/NORu0UEpfTh1PJ3mPzGRfUt3onSKcm4m9duUMgTPwxpTyJSK5H7nLkCc6DuA+Dhh\nSAVmvAtzdfg81ulQXEoGnSwl2e7CmuXSAzMtN8Fsv9sfQ8o6tzklkjLp9ZsxxgnDIprvd6EWjL3f\nSLnm9rUTzCq8YrOUYk7mY5tyh2wQG4tSTyZd2Vsm5UF8Txk5gXtetrZO9Gv5FdYclwZcfMWIk9cx\nifi/Q8adY8S8ssWI40OnMb4tOzHXzb35CrOoDTDZ6PxVcm8ePYt+50604Noq5Bobu4T56/LivnMJ\nIBFRYLE860xi2kgnM5KvqbIgdvk8VL4+PS6m1SuXNnOJN7ddJ5ISQi6PMveAwTewX+Uy6Uf15sWi\nX8uTB622rwFz//JuyDMmWqXkjcufq26EbW5kUO7hkzIJIqLhE5gjPEWfiMg/A5/LLbzzZsnztIPJ\nZobP4P14+QMiou5d6bT/2Fj2Y2psPEZ9mXvrsMv/t8ztsuvuwDPH8Al5/ho7g/vim4nvbspJvKVM\nzhlAzAxdkeMRH8X5IY/JsGrm4Yw6yKTeRESVyyHN4/eVnz2IiAKsFEHeONqOPfK7e9g5d/ANZl8d\nktLRojX43DE2P0wZryXdNSzSs4XL76bS9elztSlHDPciLnC7Z/Mcnc/nHXtO6DfWdv5iKTWcxJSp\nDh+BXLv2HpwfZrN9OzYmz4qDr2OdL12O2M2lQkREvkbMs4JKzBHzWotWIK7bHPhOngL5rDDeCdkc\nl1mPd8iSFGXXpu+x89tvf2b+z5JKpqx93YzZsz8Mm/Nz38VZsemTW0Q/XqKhZC7WrNOZL/qNDUGm\ne/ngL9DPkNvy++RwYf3GYlizA61HxWsqZm2y2gs/gme9iQkpAc2tRBwfH2Z258vni36XfrPTatfc\nOhfX/as3RD9PKfbgvLmItby8ABFRybVpqbvz57/dGGrGjaIoiqIoiqIoiqIoyjRFf7hRFEVRFEVR\nFEVRFEWZplyVVCqVTFIsmE6NNF2lCJlc5CpAqqlZTZynDg0w+dH8bYtEv9nMjYenqhUsk6403Mmh\n/wBS0rjkJq8iIF7DnYd42uKEkdbLq4Q3vh8p4W/88x7RL8zeo2ILHB/MtNYhlqaXYBXee052i365\nGRkBl35li2Q8SZG+dNoiT9slImp+HA5gjffAhSdhpMiVXotUM55S6imUKeZuluJWyKrDu1tkOmQl\nk+LwNOVQCu/dxiqxExFF4rgmLpep8ct5mT8fKZSJQqTYVRtpo06WylqzDalvoU6ZlhhnaZSRfqR/\nVt0sU9HP/yydqhcenBonm1Q8SdHMOKaMVFeeSslTvbmDAhFR54tI1eYuDPMeXC76TbC0Vp5O7Pq5\nTEfML8c6u+OmG6x29ystVjsal3Op+tp6tIuRFlt9+2zR7+QvIacpLsTnLKmTcgW+nnkatMdwR0my\ndPFOJumbdHSbZOhkeq5OhauUK89jxYun//F58bf71uN7feRbH7faA6elo12wGTK5GbciRX/hASlH\n5DIYrq448ZODot+6P7/Lal96ZJ/V5nI5M908kIN19V4mo+KyKyIiz25cO5f33f2praLfyUcwr6oa\nIMHoOilT0RtvQtpyAUs3TxjuGQU70+PrtE/N/6ewOWzkzux5/k314m/DTOLCZUpcWkEkU9+55C1v\nrpQodO1Aam/lDZBQdL5wUfTjac2VTGphc2KjNseRp2OXrIdLVVFMuj8MH8V+xWV3/L2JyLonRETj\nLtx7LtsgIoqwtOHgZcyLrpcuiX6TzoKmJCsbOHNdVLQicy6wye/BXVpq70XKdPeeFtGPO/wMHMKZ\npXZhteg3zvbMubciXb9jh0zb7joGKXnJDKRZd56GFD0SkzKJFcuwd7VeYnKKccOJi33FVfettNoT\nRho+sTUTPAfZBR9bIiIvk8AGWzCGCUN2ELgmPa+myskmRSnLsY1LZYlIjCuXp/S/bkiqmLSQO4z5\n6+S8LWMxmsvaEwkpb+TzNenDepmzFXPJZs45dha98oh0ZuW4mOSy9i7IELpfknOJu2h5S7AXcskc\nkTyfFzQh9pryjKKl6fOcYwokxO48D1Vfn94XubMaEZEjhvEYu4D5yF13iKSj2iCLweVrpPselxry\nsS5cKiWDvbvgeMPddfr2Yp/1Gs6nwUvM7XQF3i8ZlvvTmSchsZx3B1xuvLly/xx4DfEgl8tQe6Qc\nKDYK+Rq/D7ykAxFRz870dzL3y2wRG4tS76vpz6jaKs9VRcw5sYu5puXNkrJc7nRYzCSNJStkTOXO\nf23MmcrXKN9vxr2It0kuj2Ky4QLjnMzlopPPTkRvdozlc4m7ecUNKRt/ZuLj46+V8SUwE3s/32cr\nNkkJ8eT53zYF5xubA46Zg4fl+Ys7Y1ZtxfNP+w4pfQ8xGSh3Bsurl2Mzyu5z6bJ6qz3WZsjAPYhR\nl3+N82vRcsQK7r5JROTOg5Rr6Kx8P07+LIxb7z6s+aotMr7U3o7YPXQGz7Ola2V84TL1ERavCo1z\nXfuTaYdUc668HZpxoyiKoiiKoiiKoiiKMk3RH24URVEURVEURVEURVGmKfrDjaIoiqIoiqIoiqIo\nyjTlqoTGsWCU+vak9dXz3r9M/C3ONPXDF6HlKl4ptYj9bdDKzrgdeuzhU1J35vAwbdg4dIUOZq9K\nRDR6iml0mUa/i2l8zboaJR7o0Fx+1GGxGzpjboEYZnZ23JKQSFpGn3/spNU2ayrMug91fIaOQi/o\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+uEnUm2T7XJP4Qy3meaBpRFyTVc/OPqzf7F75R0j+h4bpcbwnaDso1/rqVXje7Dp89+gp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\nybAwL1X2fW0HcGZa9ccbjXIhO5sRyb0P1/+w5ch1lusBcS3H4mq5D+J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5SPU9yFEf/+o+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QpNV2e/GawyuHLT6Ga3IX4bOT0YToZ3c7rHZsJGK1Pew9REThwcz39o+N0Vg4bKMsojDPn64q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\ncaVeSxuXs/P1+o+gGTcaGhoaGho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zXHeNdNnrfBPtV1JZOwT+rEOE9iiftEbVjBsNDQ0N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cZ7cxrt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\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eK6E+3L/yO8Bp7q1utux5n1omjnnl6z+x7DX/iDWxffsh4Tdt03rLPvvKW5ZtSqF7WU+R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\nzmetP1KTZDxdxa4xEsV18OdrIqLS8TzR9cyHoxB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mesTIoWv21ll2XhbWZf8xSRfyMLqo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\nKT0Jbspy9XtwaogZ8orlfwSJw+BJ0Kg5HZqIKK8xI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5fqOyDDOPomqFb7npAUyxQVmlOO\n0wV7toMK1ySomaYTkt2NOchjjhTT16XrVS5zr+HUrrafnBH9Fn0dVMTR86CMuctkxe+Ro6A5etnv\nDh0HZbF0W624xlOIf0+NdFjt3jNHRb+JC6AbTo+Aslx7l5SSbP3zO6x237uo+j7VKul8mVm4p8pt\nkOP1vH9a9LM7klKpeaKhRvxBGng7QWXm0ijz37zCe5bhXsad27izS0am/Hsup3eXrgVNMxKRkoeS\nxm1WOxbD2vIXYR9d/h+HxTUzs6Aqh6Kg9eYargFleyCZsV3E8225R7qNcQ6+0wvnKNPJzMlorXwN\n82rxREShpNNVfB5cpeJxSNlMF6iiKlCLg4z66yuR1HFXESjD4x+DUmpKFPIZNbOUzWdwPCD6jV/E\nHs6pAQW09yj+/5G2EXHNi69AijPsB933H//wKdHvp//pP1lt7rpw+MkPRD83k81eOwHpR/M6KT29\nfAqSrXX3QsISNpxIcucSc23LnL//TpFaHfX75XrkMZy7m5z5/nHRr3IZZC3X38ZYr2+QzxxkstUx\nJun01MtnHny3A99dgLV0hckBypZLh5ryXdhjXCpbslHew9Kvrbfa3Jlw6ANJO85bjZygdgzrrOeS\ndNcam0ZcXrIIcX38jHSgy0zt03mIqY5cF5Xuqvvkz5i7Tv9xSPzKdslx4XM9chqy3JKNUoqz/JuQ\nSgy+h/yo97VW0S9vBXNv/BTJTs/Zd8Q1pqNRCu4SeeZmMse3OUZzHz8zYPTDnuGuVx03Loh+Ld+C\n5HJ2kEmADDn2fMyd/PoM657N2OZi+SGX6/UOSjk/lzxM3cSZaeYtXLLGHY5y66QsJIO7sYSwRnpe\nxXyv+zPpnMLz175zyGk8NdLhjQ9n1/OQeDU8KqU+s32Iy9MdkIsUbpSudcPvI7/xtOA5uHyciMjn\nS56t8zCdGbZMciVdHwvXSak6l/VFZuCYmb9GyjiCLN6ExhEbRzoMF8UluK5oFGdrljHX3IXSy1y5\nJi4ivzSdjra24CzwZrN8wy3ztQGWm/Azd/CaPGdLWA7Mpdvez0tpO891cuqwXrIMF8Gpa8m1PU9b\nMhqM0GRr4hnGTvaJz7h7Dnd9zDakccv7IVHpHIb8rba4WPR76+1TVntLM3L7ncvk2Py3n8F1NrYe\n51hdKWJtvbF3ul/GvuL5NHfNJSLKX4fzlLuazRlumPycLN1RZ7WdefI9la/bus9DEhSaCol+Kckg\nLyeQNmQQ2dyJc9csF8BLJWQVYm2NGrHC24Q4UnER746RqPw+Lt3etQLytDvWSLe27Hr0G2OS1+l2\nvKvl1EkZeNF6xLmpPpxxTraviX7buTQF8xzb9hhKMsx04Swx5zqLuVuVMGnZ8DEpu+1vTfwtZO4z\nyvmVcaNQKBQKhUKhUCgUCoVCsUChf7hRKBQKhUKhUCgUCoVCoViguCWplMvjsqom242K5zOdoF9y\nSmpjmaQwljWBksadpKIxKQv68z/4B6u9uh4U7s3NUuLCKfczvWg7HKBkZWZKCmPPzWetduevQH0e\nHZVVvbOYfIu7GF37sZTFuFi/qtWgRXsXSTnZW98DrXnvV3Za7cnL0s0quypJF5wHWnEsHKXZpBPB\ndLt027rJqmgXMGp24WZJp+WOKNw9qHCz5NGtugAAIABJREFUpIR//J13rXbjvZC+FS2Vzj2cZlez\nDJThK2P/arUdHklpG7oIqnbBYlDW3/rbN0S/NXtAlXRXgLbG3ciIJM2QU6IzHfJvm8Eh0MpHr13/\nxP+fiCiWpM3Pk3kGZbrsFg3WXSppvfxZht4FLbP8TlnxnKMmhrkLT0laYMtD91nt6SlUhXe6JF11\nauq81Q75QVXOKwPNs/GrckT4vY5dgHTSdN2ZZvLLlsdvt9p9x+Ve5O4enC7t8Mn1w/89zNynTHen\nRY8naJquFyTNOB0ITgTo+vOJ+FOxQlLC+y+Cblq/HevbbTh5dbyI+eCyvBxDGsGf15aFeGXSa7k7\n4AeHQD8uywPl+vh1KSnl8igenzd+85uiX3YV7r3vPOZzTYtclycuQTa2bEmd1Q4Oyj3WshyfxZhM\nxV0pxyg0kVzP8yTTsDls5C1LxOzrh6TDG5f8Odi4lzdLVwHuGrH7AKQ03aekw8VwP2i5K0uwJm+8\nL+Ue1UuwnvIYtX89i22cPkxElMncmgpXg/Y9fk3S3KeZfOQyk7KdvnlT9LMzadp9GyGbbdgm53vs\n9XNWO8OJa4o2yfOk57kEZX1e3DNCUZpNuiWOnZJU79LddejH6OKdz0gHlHwmDSvbhmvGL0tXEQ+T\nQxQw18KY8Vx+5n5ZuhL0/erbMDezE5JyXbQY+6/nPcTG2X7pdsZdLavugPzVdAnijjc3f4Z54k4r\nRETdr0FOwHOllCNJCnPJPWy6GaULkVCERnoS6zPXcLLhcqYQk5SV7JQy7J6XEN8KWAmA/BUyl51l\n7pi+Ouznvnel4+ozT71ttW9fhXl0MZp+7wnpUMbp97Ys7EuHQe3nEt46JpmZHZDznc2c0jKZNHiS\nOdcQEZXuY3JJ5tQTYnJdIrYH5yHBsec4LAlXynXRug82b+5KPBOXTRERRZn7z/B5nGkFFVJq1vox\nYtaK/VjTpsTdwdbOLHMtDDE3w2y3fM8o2Iy97b8C2VPnYSNW78T5ziXOmVxiR0TDR3EW5K/CWgwY\nc83z1xkmH/E0yv2QcrDKdMrfSRsyMqxnyKmX485dXUc/xvky0y3z8mwmOeK7NMfIeUdPYwz++lm8\n33151y7R79U3vs9uD3NcsQFSz5lJ6ZppOeER0RSLm64SmRNylzP+Ttf0xe2i39XvIx4MM9lrxe3y\nvajp0c1We/AkZJW+5iLRL+WMbLpupgPxWNySCcWNd3Qusbv5c+T+7UMypqzaCYetnCLM22KvlIZd\naO2w2itqICtKSbVSsLN/8zksWo98ITguJcN+ViaC7yvH7ygzkVOItdd2qkP027IV7+8T53C+m/m0\nje0tnl8VGBLQ0tsScTf79efps0AZNwqFQqFQKBQKhUKhUCgUCxT6hxuFQqFQKBQKhUKhUCgUigUK\n/cONQqFQKBQKhUKhUCgUCsUCxS3VuCGKWzrscy+cE5/kuqFXm577dEurwRvQv5U0oEZGbE5qaP/4\nAdTVCAagX800tKc9Y9CuzbDf9Y9Dc2d35YhruE3kO6dRK+X107Jexne/8ydWm2u/SzdVi35cx8at\n/rg2mYhozVroz7nFeW+rtOBcnLRPM/W96YDdnUUlKxM2hcUrpEC5NoZxHruM2ihD70mb1/y10L3H\nQpg3q5ZEEk0PQHPNreMme6Qu31dVZ7WnplA3IH8J6iHNjcr6FjlVqBNw+juvWu0tX9wk+vFxjkXw\nvINX5JjXeKFP9TaiNhG3YSUiGj4F3XeqJgKRtAkkIiremtBoup6ROs50IRaOUqAvoevl2loiaSOY\nyyw9Tavr4s1Yx8EJ7IngsBzr2Vnoa8eZ/d7gO1KXX8Fq6HD72cxMZqVZuFxcM9x20mr7mjHuZg0i\nbhk5O4n1w+sWEMkaFDnV0FWXb1gl+p3+uxesduFKzN10q7RMT9k6mlaI6YDT46LaHQl9e9cH7eKz\n5vsxTp2HUDOh/kFpb8mtwpd/eZ3VNmuQzQ6i1gSvYWTaF+YvQ0weuYL6OQ47Yln/+Li4hts69rHP\nmoz6ZtwWt2UT1krPOWkPXexF7QL/ELTrDfcvFf14PQCujbcbuuWxpNV9NJr+OSRK1HsIJ/ec1y33\n++QstNYrWQ0KU8t/6RLinr0fOvprfbK+DD9nnzt0xGpPzMg9+/YPf2i1f/Jf/yOuZ3vsxs/lGe5r\nwmeZdpw95h5zsZpkZ9uxbpdXy3PRyWq/dQxB89/QKmsqrL8Te5PXqhg4LGvmZCVrJGXMQ02GTKfN\nsuIt3iBr60ywWjOWhS4Rld8la/WMnsQ6HmRWsXVfkHuW2zY3PY7aP1PdstYdr+Fx4R9estot30bt\ng9xCWRdh+PpZ3PcZaO8X/d460e/Vv3zZal8/gZoOax5eK/r527AWHXmI4wHjjKi+C3VyQlNY8+ZZ\nkqq1NS/WtUTk8mVR092JenpmbYTuQ6hd42P1b2wu2S9/JfIObtHb9ZysX9XwCMa09x3MqRlTeRzM\nYnX2PA2ow5i3uERc47+KmijcItlXJfeYvxdnYe8rOKcr9st14W/F9+Wy/KbmviWi38hprGGeVyzd\ns0b0m07WtJyP+igZ9kzLkr3rmSvyM9YOM1tkszbTFLM/b+1Hzarta2VtiY9eQ72ZzdWsFkncKN7D\n/snt4ouZxe/Vly7yKyjjJO52mu0JX7GswTbD7Nl5XSZu5U1Els09EdG5X3xstVd/ab3ox99vvEtw\nng8eljlG1cHEe4DNyBXSidQ+zyqR72D8Pan6Htimtz91QfSrvhdxpfdV7N/O6/Jc/Py9u6x2+Qc4\n19YckHlfww5Wr3EatfScTuzFGWPqy/agBhF/p+P1aYiIho+gBlH9l/jvyi8s3ILzJcBqjw2flN/n\n9CLnLV5XZ7XHLvWIfqn6sjwfShfi0TiFJhP54pxRx7P9N8hZUrUgiYjcb8kaQeFJvI/YPZj3zsuy\nFs7GvRizQrZPRz6S48K3ZvOXEZf63sRe9jTJek5TLK/P4LmNkStePYRnWv4FfHfLol2i39BZZhG/\nCu8P9hzzXQx7sfMXWNtVDy4W/YaPJdZOZFrW6vo0KONGoVAoFAqFQqFQKBQKhWKBQv9wo1AoFAqF\nQqFQKBQKhUKxQHFLUqlMp51y6xOUspW5K8Vn3JYrg1H6Ol+T1rGcOn6EUdoq8vNFv7p6yHH6+0C/\nX3WnlFrkDIBGeuUMqNXRIGhj+UXyXofHIJO4zqjo//hf/oPox22WufRj9Iyk6cXZ83IuJ5fSEEnb\nycF3Oqz2KoPq+NGPjiWun5CWZulAJDBHg0mal2l5GGccNA+zVa79vKR6Dx0Fde39w5CXbV4p6V+R\nWtBXXR7Qc/03W0W/SmZh6vdD4tb2I9C+I2EppesaAfW3vgQ0Y24tTiRtHDnVz3ldUnwzmHVtbhls\ncj/6mxdFv5ZHQQnkVMaQX8oDU9aN4WkpH5sP9BqSAl8d9hK3mC027NrnmLUmtx816e3ROcxXcATU\nP1O2OHUT+3S2DxTQoeOwuHMattxBZqdZfQ/WT9nqJtkviD03eROSgohhXe5hEjqnB5Tk6VFJt6z/\nHNZ0kI3D5KUR0S8lyeNWfulCLBSl2aRkJr/IKz7jsrbCpVjf0+1SprToPjzH2DnI/2Y7pCwuqxxU\nZW7R61shKfojp2RsS6G6EPv31EVJCf/LJ56w2tw2trLAsONltNTujyC/rFgiJVX5Pbi//HX4bM5Y\nl3zPusvwfLGgpAwX+RJja7fNj+1phj2THAWJWFC4WEorMz/Auut+GrR/V6mkjq9+CLTc5//5dbSP\nHRP9GspxLq5thFTnwPaNol8ps293FiBOTbM9OjolbWQd7RifMJOVLf+jLaJf288Ql7/48D6rbVK1\n333vjNUuZ+f7yIhcmxkOzGPJbXXod1TuWYd3/mQ28WgMElODKj9+BlILZzHOFy7VICJy5ENC4WBy\n1bBBf+7phISpahxjMfCWjOONX8ZZw+XYgVHMm7tKUr091Rhn32pQuN/4q1dFv1XbEGtnO5CncGkQ\nEVHHr7HXuYVvbo3M17pfxdqeY/Gl+Rty7bT+6ESiMQ820kQJyUxqvkyb7+JNONfzmAzbtFPmcrhA\nP5OYTsqzpu1JyHxbWyFfsGfK/x66ag8knu++hGtKL+Cs2lS8VVzjYJbqY8cRk02L6LwWSGE8Ddjz\n8Yjci3xvZpfgrJkdkjlqpgvf330Fv+vvnBD9UjbNsaDMy9KB6GzYOstKd9eJz2Z7WJ7CcoeIIdPP\nZHPgy8aezTDGj9sOj56CTKxgTbno5/Qibxn6EJIYB/v/q5ZJGda545DilPgw1z1dUiLSvA25DpcQ\n8bhDRORkuW1lPfa2n1lUExF56lmsZTHU0yT3bEoCE4/Oz2a0OW3kqUw8txmzuX100VbI/3h8JZKS\nb26r7TlxSfSbZdL6e762x2pnGjLIsUGcp2XVd1vtcBjrqqB0s7im49grVpvLByNG/B8exh4p7sQ5\nm71c5t3cttuRi/UTmpDvEE37IOu68RbeQ4KGZKlwfSKu2XLSL3mzZdnI25LI/Tw1UrqX14JcJ8ri\ny0iXLDdQ0oQY5WXXFPfIuFvC3k9CTF4VYvuciMhVjLUUZtLqoRt4LzDP5rEBzE3VBux5syTD1j/f\na7WnunDNzIjciy521hc04CydmegS/UKsBAWXlbb+8rzoF0larYfnpOTz06CMG4VCoVAoFAqFQqFQ\nKBSKBQr9w41CoVAoFAqFQqFQKBQKxQLFLfH/49EYBccSsgJblqRlDTEXhYkp0Evrd0nJQ+hdVH7e\ndxCUtJFz0uXH0wha36oW0PTf/o2kjnMnqTJGD3d5QevtPPGyuGbkCOiDW1tQ0dx0AyjdAkrVCJNH\nVdwm3U0m2kCT5fKZWYOCm1OGZxrtBY28JFNSemtrEvIApzP91De7O4tKViWkSZweSEQUGMT9Dh6B\nlIG7FBER+ZaA7lZfDBqcq1TSHMdPgV7GXcN8jN5LRDQ9zSQELkgj+kdBuauqla5NjnHQzvwB0NGu\nPnlG9Fv+e5AQcBq5q8ikZIICeepvD1nt/Bop9+hjzg1cklH3JSnHK9yYoC+aVcbTBVuWnXxLE+NY\ntElSMa/+ChLE+n1wlxg5Kd17eKV/L3MzOHlMStma81FV38dch2YN2dPFD0ENrq/AfBVtxf2NnpBS\nnOZvIAaMXcY+ypdmFzR+GfICLunrPdIh+nEZX+lt2Feeaknz5MiuZPIqJishIhq7kIhL0cBnozDe\nCmKRGAWSMadsb734TFSXFzJUSTkuWAz6P5c3jmTLceYOdeV7ILGZ7pIUeO7AsWflCnzfBGLFL77/\nV+Iadzli7WwvYoin1if6nf01ZJUVJYjpJq11YAw04+svYM1y9yEiIpsb/92Bx/SeEUkdX7QusX7n\nwwGFiIgyIDHgDiFERLlL8Jzjl0DlzTGc4K68AMeBz/3pAavNJWpEROPMPaqhFHvs3BXp5HDv/aCV\nL7p3v9XuOfmB1V5aJeV5XNpbvgpxeOBD6SrI5RrBQdxPLBST/di6fY+5Zn3z4f2in5fJywZex3NU\nHJDOOD3PJiS+sUD65RlEiCujRpzkLopR9tvcMYJI0qftS5FaBQZlXtHIHEJm2JgHDdlnz2uIw9lV\n2GNNLQ9abVPufPknz1lt7rzyd08/Lfr9Z/fjVnvpQezz3kNS2u5jczPThrXd8bSUS7qZjGrR13Hm\ndr0iXWIs6e48SaXsXheV7UvENy6BJZJuaOe+d9xqmy6olUXYc7E5nPHxsFzfXM4UZhLRcz3S9YU7\n7XEJY3UVk8B2SAns8CXkw8u+DuexsfMyT+56HrlTEZOCRQyXJS4ZsduRB41flGd9gEkHVj4ChzHT\noavnhcRZH4+lfyIz7JnkzE+MbcyQfPHf41IVLgskkk42wx/jmc6+Ltfjli9grTpY7l6ySJYvGG6D\nixPPHacuQ1qdVSXdonh8drD3pdplUk7Lc8Te1/F+5DBkPllMXsslztyJj0i+g0RmsA64hIpo/iRS\nKUSDEZpMutJNnJXrNo/df14zckou2ScicmfXWe1IBJ+5yzyyH/u3pwq57OAx6aSVuxr55twccp2e\ns+9a7eIlMvn0X8cc8/hftkfmbGW78O+hY5DM2NxXRT/+rly4BXs2OCLjVf+196x25TZIqSMRGSs6\nX0ycrXyu04UMWyY5k+fakJEHuIqxHruehuNejku+F/CzgZcSKd4uHfKGjiNuxqOItTMjUhpWsIHF\nObb2F38B+eH4+UFxTT4rGcHPtKol94h+N48/a7WFk22/fJfn5SQy7Dgz3SUyBgwyR+amr2IOJ6/J\nkgyTyfu12T9bjqqMG4VCoVAoFAqFQqFQKBSKBQr9w41CoVAoFAqFQqFQKBQKxQKF/uFGoVAoFAqF\nQqFQKBQKhWKB4pZq3IQm56jzjYQmtmSltL7jlp51O1FD4dIbl0U/nxs6cHcZs+s90SH65TIbRlsW\nbnP/7+0R/bpegb7MW8vsEOPQxnJ7ciKihq/AZrN8GDV4CpubRb/eI6jJwOuB2O1SX+lrQD2A3sPQ\nHFewWhJERIMnoLdseQi68q5nr4h+3MI63YjHIhScTdSOsRtaxN6XMJbZ9RjLbEO7e/E11CvIY1aL\nResrRT9iloxFNeusdkaGrN0z2nPCatuzof0rL4RW9Yt/+h/FNV+5/36rvY3VKcozbEo7mfbSXYF5\n8zZLnfGNp1AXpuVxaBFNTXnxNugyJ1jNChOTVxPPEZ2bn3oM0UCEJi8mbCXHu6XmdeljuH9uoVy0\nUdbCOf7DD632sr2o23SmXeqCucWz4yNoMKeM2gBFuVgno+PQlWd+hFoL3qVy3EfPQwvMteOZmdKW\n1sP29sRl2GkWLJLf130BOllPJzTRZk2uwTdhvetbAY11qjZRCu0vJvZm2NCRpwOJOkWJ+zdrVfCa\nOqPnmH3wfln3Y/QSarsULmO2mnmyVk9qrRAR5TN7cdNi+44/gr1zcAw1PJrYOEcNC9iB9zustqvI\n/an9Kisxzj292DsVEblnC9k6ys9B3J1ulTaTRdtRg2x4EuuN15EgIspKWozyMyqdyLBlWHUK2k91\niM/WfBn1KXIbEc94zSEiecZxi8pKo8YNXydcS775jtWi39QV1PnhZ+HA29jbi78tLcQHWazyVGEM\nh97pEP16+hGjK1iMLtom40vpINZcLauFNmvUkuP1MwpZPSy/oQOfCSZqwERjstZIOpDpsFFOZaLm\nj7dB1jVzeTAWN5+BnbOP7SMioo6XUcvgYjf25fJqqeUvZDUdeG2mqrtkPcCh9xEbC1Yjx4hE/J/Y\nJiIq3oY98fk//N+s9h89+qjo13IH6jjw+l+ue2TcdRUgJgeWYN7Muhq5taxuyjWcmVlGLYpUjuD8\nuYxP6ULEH6SBt29+4m/z/cctYYPDsrYEr+M2dgpnFz8HiYjKWK2E+puoZ5LHYhYR0eELqKvyT7/4\nhdV+8Zn/YbXnBmUcrtmLtTDyMWoueRfJeMBr2Zz4Eeo/1lbJmi8RVpsjbzGez6xzEp1m5w6r9WQ3\nanLlJutOZmbd0ivEZ0I8FrdqiYQNi2Q+h0Os7l1Jvly30zdwVlSWYZ4iRq26OWPuU8hmtVWIiHLK\nkS/w/DzC6lKZNUbsDoxNdi3qifkvybiWVY71MhvE99WzvUxE1PMe7qGQncc5tfK8m2pnNZV24zkG\nXpN10EqMunrphs1lJ1/yPnl8ICKyM9vz6R7Uzmo8sEv0C8x0WG1e3yhg7BduG26zIX+o3CHr4mVk\nYE4cDqyl4iWwdPYPyFoulXci5xK5jk+uuclWzGugF7GS19YiIvK04Hdz6zAuWcUybuRW4nxpexa1\n6ar2y/dUZ7LWZKZhuZ4OxMMxmkvaj+ctlzFl4jJyuNI9dVbbZsSEIVbnhVu/974r3zMqtuM7wn7s\n+7oHZE1Zhwd5z+hpxOeZG1j3oZDcixWsHlEms2Pvb3td9ON1/txsPjpevSb6rfrDbVa75xV8Ntg6\nJPo57RgL/kxBo25Pdl2iHuRnrcOojBuFQqFQKBQKhUKhUCgUigUK/cONQqFQKBQKhUKhUCgUCsUC\nxS3xHG1OO+VVJmh5Do+UHvQNgCZWuBEyqu1/vFv0O/vPoHNyCnYgJKm304yyVMYogyMnpFVn1wh+\nd0kRqE1z47DvmjEsb6faQKNsuHcHrpmVFroFq2ADym1z+0+eE/3cpcyKjlHfjv3N26Jf3WbQtd77\n4ftWe/fvG2P00wQdOxJOv8wmHotTOGk13PFraemZxaREs+143sr7JDVvJaOQnXoaNolFp+TcFG8G\nLY7T9R0OSe2MMbruuX86arX/4O//3mqbVO/hSchgVnwbFn+zg9K2beQYpDPcdtiUo7nc+HfbU+et\ntrkuJ5k8avHvQ2rA6cxERDnVt0Z9u1U4fFlUeXdiXioMS87wNOi2ASZLMGUxjUuxr+aYNLGiQEoF\nOPX7fCdoj6+cOiX65TLZ3LUrkP997UHY1zb3lotrlt+xzGr78nH90Kkboh+X7XArPodXyv2abwfl\ndfQ45qRog5RAcSmgk1FZOYWXiKigIUEJt83DPGY6bZRdlVgnZ379sfjMYcPvlZViPiKzcj0WMslq\nRgbGIn+ppLUWLsfzu1yYg4w1kl473T3JrsH6sNtB9R64dl5cM8NiBTEZy8ANacudW4bv4PT1nAYZ\nD3js8TQhnsYiUiIzxWw6G7bAsv7qEWlpXJWfoDpn2Obrv1NkWJbtphVmgK1Vdwni6/CHXaJf4SZI\nhIaPYI+V7ZN09qocyELtjJL8w7/4heh3YB9iYusLoAN72LofPtktrslm9uATV0H5rX9spehnPwRq\ncM91WKoOHZLnrJ1JZf2zkCQ0bmwQ/XrO4D5amIR1/IKkHRcm96LdlX55Rmh8jrqeScSsoq1S8jU+\nDUkGjyM8JhERVWzBfuEU6YJVci/++F9fttr710DWyu2IiYiqH4CcyV2ItTM+jjNy+JS0ns5hc/ib\nf/kbq51pyMX5uRALIc/gZyQRUXAI65dbEA8fkWvH9/uQ33Ab1RnD5rpwVSJemfL1dMHhy6KKOxIy\noznDDtx/HfHI5kJ8HWqXkueSHbVWu5HJpgeOSGq/iz1n5zC+Y3JW/m51Edb07m2g2EeZ3Cy3WUqg\n4izWOXIRU2zG2ufzs/Er2PMjx+W6cDEr6Zf/6ytWe1mtlOO4ytAvh8WK6RvGPK5L7AO7IUFOB+Kh\nKAX6Enkcl60RSel5wz046wffknNzYwByvQk2H9s2Lxf9gkxyw3O1kRGZu2dnQ7qWl4e5iu88bLVj\nhl08tzSOhZH/OgukdIbiyN+amYztzK9kTrDsDkhGcpmcs+clKePIKkes4PKM8nukzLr/5UTZi4g/\nSPOBjEw7Od2J8zueJd9let/Gu0f+CshAR27IdxJ/K/Ysz9mn2+R6tLM8MKsEZxJ/fiKimjXINx0O\nSKpiMezFuWEp5eUSrVL2Lmq+axCTP19tR3y883OG1Ift54lriBuV67aIfjYb5rFiH35r8IMO0S8a\njP7WfaYNtgyy5yT2uCkz9LZgH3DpLF/rRERVB/D+yG21K7bVin5cMh0aw97x1srYODuIHJWXFHBX\nYbyWP7hLXDNwBu/srT9Hu3hthdGPyUNzENuKmmUc6nga6/TqFeRri6rl901NIfbwMzclrbfuI1nG\n4rOWSVHGjUKhUCgUCoVCoVAoFArFAoX+4UahUCgUCoVCoVAoFAqFYoHiljjHGfZMciUdOqauSYcP\ntxMUH07jMyUknD7d+DXQUIsMWj13QXn1yfes9qYWKdtZ3AzqGndUyCsHzXiuRlJXC1pA0eLV46NR\nSX0LE/5duBT9+o9eFf3yahhN/yegTtoy5d/FuDMVHy9TylW/KUGPd74gafdpQ5KaWbRZUsI9SdkG\nEVFkDhQ0k5575kcfWW3uJFSxT7pieLyg9TsckDzY7T7Rj8tQChtBK96/AzK2cx0d4pondkNedu0H\nkOxU3SPXB6fa5i8HJXPymqRHxxnNddkfgrJo0iFHP4acjlcTn+uXMqScxgTNOBaStMF0Ieyfo97X\nElTXfOY4QkTkYpKj/BWg6Xc/I93LfCtBfe9nsonN6yW1M7sW88WlIFMBKRVoH8KevXfvXqudyZxw\nFq2pE9d4mUtE39twTSjf9emOBx2/hEuHb5Ncw5wCz6nP4SlJBw4wxykuHzElBc6k01XmPDgSxYIR\nmk46QKy8V8pRuFsIl4O1vmI60OGz0ARiLXdDISIq3ASJR24dKJvT3TL2lK9ba7WdTlBUw2Hm0GXE\ng4J1WH9c9mi6/3BXp5ER/G7uIuk4MT2JeO3yYy07DOeQKHNKaTsKx4y1X1wv+sWSVGJOSU8nIoEw\njVxMUPPzKqTsq/+9DqtdzFz3rl+WUqkG5kjS8CU4YZjxY24UcWaqA2N4xxrpKmW6wFj3wOSr4xcH\nxWf9r0Ce2PAV3MP4JSlZ6rqGtVVejP3rKpESgPaLiCkr1oOmn7dEOsFNX8PZf+lZ0JjzDXeeqa7E\nGpyPmOrMz6KahxI5Q99rUqZZfRCSDO5Ox101iIjGr+DfV3uxFxdF5f0+vGe71XYUQG4z1SZzoNo9\nO622x4PxO/fL71ntyn3SubLjaTg+crnN//GDn4p+f3zvvVa78SHIB/oOtYp+XF7BXTamb0qpAnc7\nczPJesYaSR0PTyWkC/F5cAYjStD0ZwcSZ/bUdTmezgK36JfCssfWin5zzPGj9xBkl129cr8EXmdO\nlPXYV6euyDGcnMH37VyKs9V/HTm0q0Sude70xaUV5jnGweVR2dVe8VmYyWGayhCvK++V8hnuOjfb\njXIFoRF51qdk17FI+vdiPBanSFKyZ541HSznqr8XOX7J7jrRb+wlnHHcqcl0wZrrQX5XsI5JiDOk\nNNrjQW47NPSq1eb358iR13AZI88ruISKCLJ6IplfVpZIiUgWK8nAZe/c6dT8LLsS62BuUEqAMlL5\nUUb63YiIiMJTs9T7bkJWXb5Tvhu//yzCAAAgAElEQVRwedTwMZyFuYbba+/HOEOWfhFnXG69zBmy\nWMxp/QEkZpX3yveBQAC/Ndz7jtWORT9ZmkhE1Pcy9nPOI5grU1bP99htX8O7S8jYs4MfQFpTuA7x\ncej6adGvqAmyvhCTfJkuYjOdyTwgM/3zmEFwwBx+V7ptpd5xiOTa7Dosz08uM8ptwpqeuCjziugs\n8rkTl7DPm85K2Sd/76irwPsNL/Vx9K9/La5pfhBjmc/kiKb8ftW/32q1xTtikcxtgkPIUbMcWAcf\nXZWxf+tGnK2zfUzudk2eJflJx67PKndTxo1CoVAoFAqFQqFQKBQKxQKF/uFGoVAoFAqFQqFQKBQK\nhWKBQv9wo1AoFAqFQqFQKBQKhUKxQHFLNW5icxGaujL6iZ8tfgg1GrgN48xNWUOhjln4pbTIREQF\ny6RlZh7TxYXHoO/7LU0uswEcOwMbOD+zii3bKetl+LuhI+26DJ2yqZ0v3oiaBIEw9KGT56U2L7eh\nw2pzS7NswxqWW0mveQi66gvPS3vxqtrEWJi2aunA3OgstT55loiIyrdLO7ZZVoNi8DDG1W3opZv3\nonZN4WpoNM16GbMO1J7JqcB35BdsFf34cw7dwBitrKuz2o2lhr3xevxuEdPRx406Fn5WO6nvTegP\nZzv9ol8oAn1laBIayu6nL4t+vtVMU8k0/2b5jJQlaoZtfvTDDq+LKu9M6IajIanT7H8DGlNHHtag\nd5nUD8+wGhkp22siokun20S/xVHUjspvwXc8vvmA6Mc1vr/+5Vu4vhL7aMyoZVVzL7Nn9GBfBSel\npp7XcnHkoy5Ex4uy5ksZq+HB9aYlfVJTumI39PHjZ2EdWm7UjIgma83Mh9ViptNGOTUJzbSp5ec6\n6wDTppc2SFtCHrMyWX2f0603Rb+sDmjFvW7Ueqitk/WRQpPHrHbPcWiaqzZgXMOGfWjvZcTdohJW\nL8io8eVdjLUTnsB3mDrjgib043WKJs/KOeQa69IiaN6vPyctRVseXEFE0GqnGzanjfJqE78/1Cbr\nnix5BLp8Xttgy1el9af/Gs6r1u8hbpoW5rxOUMXdqBtQtW2D7JcB3XUohHGb7sf9FRpWmHxeeY0l\ns55AZSWzfi7EXuQ1h4iIGtd/cp0q0wq6eCfiy/ir2M8RozZMfjInMPdKOhCamKOeFxK6+qr7WsRn\nflZ7hte4MWu68XoSodcwFpVG/vHB0yes9qY7UEvIXJ9To4hfdjv0+7wm0Cyz6CYiqvsc4ulHf/uu\n1f7cFrneSlajnge33J2YlrXaqlhMGT2DvKnmoKyD1vEM9hyPSQ6ftDivPZDIEzNtsh5IuhALRmkm\nWTdspkee8VnMEnu6CzW7QmPSMtiRh3uOs2epLpOxt38YNWr4Pti+a5Xo13MJ41bowxrpH8X1VQVy\nzeWUYF55fcDD/+3Hol/tGuyd7rOI8cEb/aJfNqupWLsN6zE4IZ+96gDuY6oddYzm+mV9lJT973yc\ni3aPk4qSdVuCYzIPqNvP7q8N4xcckOu2fivqTtpPYK2Z5Vxi7P65XW9+i1yf09PYi/E44vh0D9ZR\nPCrHYvoG7q+A1TLJLs8V/SauIO/hdanM+ixhZrlM7Dn4GUkka+bwdzEzvpTuqSMiIsfPPpsF8a3C\n5nZYtTsyM+W7kLsIZ7ctG2vVrDG48c/2WO3Ri9hHN4xaf0UV2CNldyKHGz7WLfr52Zrh9RUn2fnL\nx4+IqILX+crDNYNtsp5J/hLs2Y5fIh5Gg/JcbPnWJnwWwjk7fkXmN+PdqKU6N4J5zDPqAKXy13gk\n/XXD+ByGp0LiMzu3Nb+ANex0yNo/0TnE0A+ePGq1+TsXEdGyauSYO3Yghl45K3PZLV/GWRZgdWNs\nrH5VbamRx8/ht7zNhZ94DRHRZCvWAa8t+dK/Hhb97vkyaqxOjCNuNK2U79S57LcG2Dt13W3y/kaS\nta143vW7oIwbhUKhUCgUCoVCoVAoFIoFCv3DjUKhUCgUCoVCoVAoFArFAsUtcY7D0Sj1TyTkFYs2\nfToVafom6GjchpaIKKcCNMHsfND02549Rp+GyoOgRw4clrQpuw80v8FW0LUabgfNkEuyTMwxeqTP\nsCkd+hDWcZw+G5qTdKaz/wbqc2E+qLCmVeep34ACv4lZD1bXSRmQZUFsT//f1TIzMsjlTFDZBj6U\nlrTVd2PMnIWQU5RsrRH9vEWYD6cT9OGBD34pf4zrh1g7O2dAdOP2m/lFGD9vDizYeoZHxDX1daCE\nn/h72MWv/cZm0Y9bsPWcg63css9L+1xOZxw9C+pm6V5Jc+fUUz4/KcmL9btJmja3GU8n4pEYBZK2\nj9ymlUhKZrxM2mT28zN75fAkKNOLauWe9S7BHI9/jLEJdMt9NT2FsXnif/2c1eZyMS5LIiIaPoU1\nyK06M+2S/htj48glN+2XpVXgG987ZLU/d+A2qx01KIjcUjHQhxgw8pH8vpStqnk/6UBkNkxjyfEc\n6JHru2ELqN5dH0GyVFQuZStv/Qhr35eN/XLbASmd+fjN81abSzh7uqTs891XYZF5+0rIX/2XcH+/\n+fCouCbCrH2/9e/ut9qFRXIdTTJb6elJ7PnBE2OiX/NuxKGb70O2V5AnKeaXT0AS6LRj7VTXSPnI\nyScT8XlmVNLp04VYOGatocY7peRh8jKemdv/eupkvMhbgTPgw7fOWu2wIRfavBpS4ws/g+1p3WZD\ngrhvo9XOzMTZxW2b+9+Skkg3O5u5zGTIOCcK2dl17BAsTN1OSbnf86e3W+2u50Ft57RvIqJAP+II\nPz8LNkopV0oixeNbupCRmUG27MT3d79wVXzGJRB5Kxkd/tcXRL/SXTgrcn2ItW/94gPRL8wo4uOM\nYp5jSJI99aCmD05gz5XdxfMKKX/ofRN7on4XpFxLjNjP7WW59fTcoNwjHb+GvXg+k2UNHpXWsCXb\nkCMMvANKOM8piLhkYp7+m6Etw6Lwl+2qEx/dfAPW3nn5kJ75R6XEu3HbCqvNZWlmPlZVhfH4+DjW\n96olMu9b9VXsRW4vPjQJmU2mQ35312uIATy3WPnoOtGPS9B5frN4pxz3q+/jd0fYGe4wZIeTPpwN\nngacNTUPLhH9okl5e6Yj/XsxHotb53XIkEoFelnOwXLKvDUyh3ax3KZ4CGt64KqUo3DZMJdwXviH\nt0W/wtV4V3EzqVOQ5YNuQwKVxeLp8IeQ7OStkvcamoRElZ8Ds71S6sfjs5PJVEZP94l+ESZZtbFY\nad5fSlpjyvzThchs2MqluztlTM1h51/BKpwn051yLwZGkZtVb9xptbn9NxHR0BHEIx6/y/c2iH59\n7P2xfDs+4zHL21Qgrul+Dt9X9wWMZ/EaacPe8zr6LXoCcqjOQ/Kc6HgW/85lv2XmqNMdkCpGA/hs\n4qqUY6feTU1ZdToQj8YoOJHYg2U7pQyoh8UyLtNa9NU1ol/Xb3CGLFuBMfctl3naFJPsjrczGWlh\noejnysOedeQg5+B7ftIoycA/49Lgt56Tf3coz8NZuOoA5Fo7N68U/biEs3JdldUePCP34gR7jvLt\ndVbbmSclxCMneolI/h3ld0EZNwqFQqFQKBQKhUKhUCgUCxT6hxuFQqFQKBQKhUKhUCgUigWKW5JK\n2W02KvUlKG4dpzrEZ4sYvX2GOfZEZiT9a/QUqESOXNDWTDcgLvHofw30XzIqozsZBaqZOdTwivPH\nnj4prlm1AffKqc8mTXiKOQ94GY3SZrilcDp7zUNwWwhPS/eVVftBwR05yqiTKyV1MsV+NqvFpwOu\nomxq/FqCytb59CXxGadmlu0G7TsalHT9aBT0xbk5UK7LDfeMbA9oceEw5uPiD54X/XzLIcWpfxRj\ndPofP7TaK9jYEREFmMtB8x2g8Z79t49Ev8JCUDIbd6M6fP8bUibQ8i1IrDpfhfxkydckNZk7YHlY\n9fmhY1JO4ErK3UwHlXQhHo1TeCqxvobe6RCf2Vk19LlhJklhDmxERA1fgVys/x3sRdMJq/tdjFVe\nJaiEBWvLRb8CRvUsWAzqfO87WGd1Dy0T13Q+D9cuLuPINGifXe/h/s604zk2NEpa+ucfhAsBd6Mr\n2CBlF9PMUUs4NBjUcXuKijkPjkQ2l51yFyWosqn//SRULAFN21xP3G1tLozxv/yepCbXs35vnAUN\nf9vixaLfI9u2We3sWtDwr3yM8Y8ZsfrO1WwdnQJdPyWrTWHjg9hL/DmiN6VMjDvRNNwGuUfMcGfI\n6IfMpGU79nbvKekksf7RhFQh+/ArNB9weF1Ufkci1pkOWdnc4aMfe9E8Fy/9CnNy+1ch8XvlXyVl\nP+yHfKZpH2RZrgK36Hfy/0aMXfmHcPHjziTcmY9IOpmNnYOksX98XPS7+DzGN5UPEBFt+vYO0Y9L\nlHNqsJauHZLnTuVixJGS3XVW24xrKZeV+Yipdo+TircnYhaXNRARVe7H2ho6ijjf8IikhI8x9yBi\nbjX7Htkm+nHJw+v/8KbV3m3Q+r2VoGD7e7GvgqM4p0uWynNxmlGz/Zexr6az5Bwu+crdVnv46nn6\nNJQyenzBCsShsQtS8uougfSo6h7kV6EpmQN1v584n0MT8yNbzMjMsNbx+Gl5j3mFGPf8NXgWn+Em\nGhrHucFdJLnLHhHRzUHIbtYuRZwaGZJxb1E5csx4CJKqHQchoXL55P7lsgnu9sal30RSZlizAXPV\nfULeK5dOTTHJZskuKX+YuIBn4nE4ZDgJpnKP+XCyyXTYyF2WWE/ciY+IKINJlrOKIA023YNyF0Fe\nkVWGtVnfKKXGHYfxblGUDTec5ifWin6BIeSbU0yGERyGVIo7yhLBWZSIiBqQNzk8UlKaYUduwR3Z\nzDEfZ66K3C20aFOV6BeagIxj/GPsgdxGmWP0vZx49ojxO+lCRiakWny/Ecl1O8VKa2RXSDlXSd12\nqx0KIZ4NfSDXd8UdyAMdORibTLuck+H3eZ6Ocee57NARmcs3f2u91Z64BpmSOT/le3APE22QI+Yt\nk250XLaTchMiIspbKvsFRzGPxczV03QbtrsS3+f0SvlNWpCRYUmUw0bOYvdgv+Sz8et4Up4nJazc\nBH9najf6VT+AXDSnBvvFY5Sh4OslbyliK7+/13/0rrjGdPpL4e6v7Rb/PvzTI1b79EtMsm44YG26\nH7nsVCvup+lheR5PXMSe5fucu4gSEU0FEnMdi322eKqMG4VCoVAoFAqFQqFQKBSKBQr9w41CoVAo\nFAqFQqFQKBQKxQKF/uFGoVAoFAqFQqFQKBQKhWKB4pZq3DhynVS2J6FXc57uF5/xWjNRptMa6JDW\nZdyGueMF6H09pVLbSKyMQgazShwdkPphZz50fby2wfg0NKmFHo+45sSHF6128D1o19Y1SI35+U7o\nKJeyOjaLviitwfKuQ/N6jtVYMe1r3dXs36xmxsR5aVFoy0loBz+rNditIp7U35s2gD5ms2xzYmnM\njUpbwkgR9KmT7VgHMUMrPu3AXA28iRoZlfdJq0qOGz86Y7U3/S/Q4fe8LS31eD0Grh3kdVISN4WH\n5HWUFv87WXdg8CPcXzGzJxy/JO2SuT0lrwlUe4/URIeDic/sOQ6aD8TjcYpHEs/jLM4WnzlyoaHO\nqYI+VGiuiWj4JOomeJg9Nm8TEdUw3XF2AbTKc365t50e9Jtow3d7md6cWxMTERVvgXaXr7meQ9dE\nP28e7B/Xs7o2hctkfag4m29PMzTdYaPWQuFqzPGNn0LL6jY01im7T1Nrnw7EQlGa7U6sk9xmqT+f\n6UR9rd7LTAedI20wXQ6sLydrB0Ih0a97BJpafwBreJrVqCIiGpnC3h64hjlYVo15+pPvfF1cw604\nc+pRQ6B4RNax4NaxDmaHWFgl6w70vomaStmFWNsxo9bWyjuWW21ez6JijbQhv/zMOSIimhuXNtTp\nQjwctTT7ZszuPA29fEkZ5jh/jawPlVWKeW17FfWJdh/YKPpNXEQ8ylsCTXxwwrDNZfPf9SLO2fI9\nOOOiRs2g0CSziGbz2Dwtz0X+jFFmPRsYkPU3xlmdnMq7UCfGZVhTuwowx4PMltW0A+95MbEewxNy\nzaYD8WiMQsnvdRnxlMeU6jux5vjZZ6LhcWjqs3JljLr03det9qY9yCX4eBERla7Gb3UyS9UrPYit\nm4yxmGnDmVvH6sVFArI+Qf9pWMmPsVyu8UurZT9W34zXtclfKp+p+2XEirqDOAtjMbkuK/YmYrfj\n7100H8i0Z1pWq5X3tojPel/CPfJcwLSX53UwKg8iV1m/TdaDWcds4q/8PyestlkDceAYfrdwK+qR\njB7rtdq5DTIG8lwqwupSmTVAeL2LCKuLU7+7SfQ7/twpPMd+zHGGUbttphs5DbcX9rfLGknlhtV6\nOhGemKO+Q61ERFR5v5xDXtuQr2mzzkvvK61W21WId5OBTpmzTLHzr5qNs/+mtBMOM8tuP6sRxN91\nJt66Ia4pqMacBlj9zMi0PJv5nE6yfNNjrImQF3mdh9Xq4XU0iGT9NF4LJ2z8bsnuxHq2/0LW3EkX\n4jHUx8xbKnNKXzPeNWZ6kOsMGHUY50YOWe3sCtRJG2iTeflwO/Kb+n04a+xumX/zdzCnk9WeiiHH\nyl8t6/H0vo61VHsv4rrXK2ucDXagHl3fK1gLZr3Goh3IpfhZZta9c5fhXnvZ2opHZB5Udlt98hnS\nn6MGxmbp4s9PExGRy6gX1HAQ9Vz5e0Z36LroN3Udc1O2EmdD7lJpnR1lsWzyAuY3u9Ir+t14G9/f\nzOrAxsJ4/h375NwUrEEu4Wc1qsZOyTN8YhY54o57N1jtrBKZs0yw++M1yCYvy3XJa3JxG/OpazK+\nNB9MnPWuQ7LW2adBGTcKhUKhUCgUCoVCoVAoFAsU+ocbhUKhUCgUCoVCoVAoFIoFiluSSsUjMQom\n6e/RKUm74zaC5bvrrHZpSNK3OA315hBoRbEBSRO+fSuTUGThNsdnJP0+dBl006adoMg1MTp2ZFbe\nq/1NUK1K6kE3NymM65fg+9yVTG5lSIwmz+M5GpjltElr7XgG1sc1jMZ78VdnRL+SigSlnlO004Ww\nP0j9bydkQYu+LGn4kSBozTd/BvmIp6VQ9MsqAB2b24A6c6QkbegUaI9ld0LeMvS+tPKbGwI9rfZh\n0O9uPg3ZGZcSJJ4DFMPBM6DcVa+vEf247KedSQZKN0nrck6F43aNdsO6kUulItOgNs6OSAruYNKu\nMDQ5P1aLRGRp3XxLpI3gTDeT2bwAmnbeOkkB5Zaj/jFQ90Za5bM03gu79bkRSMpO/Vxary/ehrVf\nvAn7t+tprPvM/fJvxb2HQEO9fBOxoSJf0oSH/aBwF+ViX/Wd6xX9CopB2fTUYe5NOeIEk9Zkl+P7\nTKvhidOJ66IGjTUdyLBlkCNJf+bSKCIiH7OG5Gvz4hvSSrmuAZKbbPa8xQ4pF/rgWcxVUzmuqa2R\na4Lbpl96AXaNy74oqacc5bdjbweGmP38UWmryW12uUTOni3pzL7loDD3vAmKcJZL7kUuJ8ipl1Rs\njrpNdURE5HxhfuQZkUCExpNnQM2DS8RnvWx9xhmVd9iwHM1ne7NyHWj/OYZskUuTrnz/pNXOrZSW\nmQ07MSd8nDzFdVa7/6Q8d3LrsOd8ZbDmnL4pLckL12GN+KoRb6/+8B3Rz5aNc7v9KUhdQ2G5l0rW\n4vvyVkGCY66L8cmETCIyD7LFTKedPEkLUn6mEUl7U1c59mLdA1JWNNWFPMDuxlqb6pdzzaVYPE7y\nPIeIqP3lo1a7dHed1a5wQQbD54yIaLIatPTrP8H8Vt0ppTM8Xythlt9zozK/4tIU/yV89/gZma9x\nSTKXBplS4blkfDBlG+lCLByjuaR1M5cYEREVMZkSMUl124uXZb9GyDimbkAWM25K2pnEasX/tM9q\nh+ektHz4JCT8TiYRbfo6kw3kLRbXuIuwzrJyEMuv/exN0S+X5Wa5TN54/CfHRL+1eyGb4zbak5cN\nuTOToHtbMA7jbZLab8ntzWQ4DbDlOCg/eQ51PXNFfGZ3YY+4SvAc+avkOZa/EmcIn+sWw5qZy8Ei\nTE7NYzWRLMnAxy/G7NBLmuRezCpFHOGlAkxb7vD0J+eIDkPaXsDi7sgJyCXJeE/gecDMTUjcQuOG\nnDa5t/mZkk7Ysx2WJD3TLuWIc6PI+adZ7sPLHhAR+a8i5nBZX65bSkqy2L7KYZIqbh1NRFR3H+Qv\no21YWxVrN1ntiX4ZD0ZP4AzveA4xNbv6pujH39d4LJ+cmBb9sll+zs93873Iwyzk53ohQy5n71JE\nRLNJmbZpE54OOLMcVLEksbfyV0t5N7edHz2NMcrOk1LjyjshNx28cNpqFxp7dpJJmBY9gfkoKtor\n+sW/jD3HJYzOPIx51bYN4ppIBHMQHMPaC7jlmfvlv/q81Z4bxlnIZf5ERIPtiJst9y2z2jNdMo/P\nZ7lsNPTp85M6j6OBz/aeoYwbhUKhUCgUCoVCoVAoFIoFCv3DjUKhUCgUCoVCoVAoFArFAsUtSaXC\ns2EaPpOowuw1KNy+xaAgcrlBxxuSdlbcAupQUR8kCuUVRaLf4DsdVrv6IGikPTckRbdmKWik/kug\nL007QZHLNaQ+Sxntv/9VUPFNF4u2N0D59TAa5KUnT4t+tTvgusHvIeU0koKD0Tw5Dat5v6TXX381\nQeGLhNJPYYxFYhQcTlDFxi73iM+mGFWtaBso3K58SUucuIZnHGE0wkVPSDkFp5fefBYSj8aHlol+\nUeYWM8wooNlVoDzmNkh6aW4ZKPql20FRb//5edFv+iqeif9uzxtXRT/+W+PMZaNsr3RUGW8FdTO/\nGWvepLmnKOKma0O6EJ4KUvd7Carm4selo1WQufnkrYH0wHTc4jR27vwU6JJUb1chaNujpyFLa9my\nSPTjVf95v8lR7IM4c3sgInL4QM32ZYNiWdIkKc2VXtDcOb3ZVSDXpp9Va+e0xQpDKhBileA55XPG\nePbaLyTWjPM3n63a+61gbiZI15NuZrWNkoY6chT0+ryVmMOVd68Q/fgzckmMw5D4rVqO5+9kbjhF\n26tFP067Xv4QpCBcJmDPlmu940SH1a5eje8r3SK/O6cGZwaXz/E1QETU04nfWnI7YuPUdUl7zqmG\nPMjfinkPdMu4OzSQuC70KZT0/79w+rKo6r6E9LX7ORlXqjcgTnF3hAzDaWKCuYkUb8a4jZw0pIBr\nQC8u3QGJixlnOP2+/w04A41cw7gHxyR1nssmwrsgMylaL2V3F78P2V3DQfQzKdzXf3HOai/+yjqr\nffPn50Q/Thef7oAMd+qKlGdULEucz86s9Dv1RaaCNPBOYi/WHZQSYkcBxo/H+cHjbaLfbA9ix/CH\n2L+V90gXxeAAkyOxabNnSWlEgDn8cBk3l7BMXJEuFtztaZLlIvmLJS3dy87TER6rDTlF02MYi1gM\nUke/IUHIa0J87ngOjlWlO+tEv1QuYZ6X6UJ0NkwTZxNj4iyU48klYXyuavbIdZvJ8teetzDHtQek\nnCk6B1r7yEXIJnxNMt8s2YQY0H0I8YHT77Nz5bhPM6ediVnkHO5yKePzVGPv9L2BXHbr720X/Xi+\n2fkqSgVU7qgT/Yq34F65C1fRcrl+YilnufSrFikWjlm5s80pJQre5cgL+PiNfmw41DAZO3eX5HuC\nSLpR9bK59hn5ZjF3rYLZG/Wz9xRqlZIl7tpadjvySNN9le9tXjag85A8S9y5yEHy2Tkw0yWddnm+\nPjyG84M7ORIReZMusvO2F4MRmkzmy5nGPOYvxnvgrAdr3dskSx1w5zA3K3+RZ5QHIOao5C7BeyXP\n84iIhs9h7fuaEEfb34DM1+GV+Uh2Lc5tLofi+QcRUU4p7inKynPkkpTQlW6rs9oT17EeS26TrnXe\neqzB2FY8X/dLcl2k3KjmQ34aC8dobjCxJs1SIjPsrC5hjnteI/71vo4xD43iDMmqkLGMIx7Hu29f\n5wvyd1nOW7QWuQn/HadXrnUuI+MOWF2vy/eR4M8h6fYtw/pwFUn516onIMXq/BXebV1lsqTHtadQ\nciS/FvM53CVzm6ykI+zvklNxKONGoVAoFAqFQqFQKBQKhWKBQv9wo1AoFAqFQqFQKBQKhUKxQKF/\nuFEoFAqFQqFQKBQKhUKhWKC4JXGj0+uiqtsTtRK4TSsRkbsMerUIs5crWyP18f9ve3fzE1cVxnH8\nGWCGGWF46QhVJAxQLUopJr4FrUmNaWy0CxKtxlWjaWLizn9D165duHBloo1WTbRqotbE0mhSCK3S\nOC0KxfIO0hnexsWFeZ7nxERMxjqQ72d1Ey7DvXPuOefeG87vsWsTb4xqXs3SrC+ZVmXyOOx6/c2i\nX0daa0rl2jKbhRldMxyW5bYlkm0Z1rD8aOdRU1I1r+e0tu6zZ1Km7N/I55oh0FXX7vazuQZ2nV7T\noVa3X8+JqCR28mz5czVq6hPSspVf09Dt1yKumzXb9lhXgqyempR+T4k6zdIozOfdfvPDuv7+LlP2\ntLnbl+K+8NZHpe3O57RM+pLJrQjLaiee0jWMcyOaiZG+35/T3JDmeTRkNcNo7P1ht19dTtvj4GuP\nl7bXb624/dJ363rXtQU938mvfnX7dQ8+ER3nm37NY7kkGpLScSzqi2tLwTreC7re+55nTBn2L3Nu\nv3ZTuvimKd3c/KjPW1m+riUla+/UazKd9Wt3bYlHq2GfGRuCstrZFzV3yK6hbTvmcwcWr2qmgi3z\nGmYf2fXJuQ+13OP0iC/leuCkLlSvNtdzuvvvz8mW/SyXVGNK+o5H5x+WEYxn9HteNiU9bW6USFDS\n3ax1r034sWzDlFDuP9Ff2i4G5zXzg66Jn76hn9ec1jacXfTjQdtBHUPt8dlSniIityb092aXdLsj\nKKPaldax4vIXup472+vnErv+3a43D0sqPjAQ5QIlPyn/eCqyVdZ9axzsfLnP/Sxv8qbsdzs57r+b\nh04PlLbH3tMMmK4gD6xg+jTEEfIAAANcSURBVNjU17nSdv0B/x3aktZ/mhLPy2e0T6wU/LiRadB5\n0o69TUG+RTKhY77NA7Pr10X8WG7HkLCP2YrCdi69ZrKTREQ6G6N7h7BkbDnEG5PS/mx0vJPf+bkh\n85hedwuj2m5hqWubjZI9+WBp+/Lb591+fW8cL23nPh0qbduxVUSkKmVLH+s80nifrr3fKPhr/ZrJ\nklub1/bNfXDJ7Wfzzrpf0Gtv8bcg9+53ncNtJpLtyyIiTffqnGE/b33dZ4YlmqJzDMurlktiX0o6\nXor6zM/v+izC5BVtu4khPc/eVx5x+9nv1M738bTPvkhm9H5z/IyOUyvjfiyfGtb7XPu3tjOVRESW\nrvrMoJmf9Hfs/WbbEZ+DMXNRx5QWkyk2a/qliL/H7DmlmXgTJu9GRGTV3NNkBvS6D3MO57YyuYqb\n5S8HHouJVMWjvtTypM88WRjVTJBVkwfT+nSn/wyT+TV1Llfajjf7Npz+XrOo0ib7ws6rIiL5af1b\n0+f12tl/RI9v7qL/zt1xmwyzpn4/nuandHyuMX0+GeRl2GOymRthG9g2TKVNGfP9/vOmv4nOfX2p\n/NkoIlFJ9e08TZcRJCLjH+szWMeg3oeOn/X5LfZ+zuY5xRt8ht+mKd9eNM+Is0H2UfZ5nU/nr2ib\nLP9i+l+QF9dz+mhpOxbTMX5tdc7tN/mtzq3Nh7WNC0FuWKxaP79o7ss28v650j732vya7b6xbTu/\nMfGOvzcsh1i8Smpbo2vt+md+rMgc0jw1e6zhc4AtN2+v4aY+/9xr7znmx7Rt1oJnv/wf2l9sdk3C\njMdzl3wWbvUder2ku/T+o77Z94n2Qb1nmTyn47N9RhARGTVzy+HXdb6z84CISO+rOt7P/KjXYt+p\nh91+26Xgq3c4L/IfNwAAAAAAABWKFzcAAAAAAAAVKlYs7vxfHWOx2E0RufbfHQ4C2WKx2PLPu+0c\nbXjblb0NRWjH/wF9cfejL+4N9MXdj764N9AXdz/64t5AX9z9dtSG/+rFDQAAAAAAAG4flkoBAAAA\nAABUKF7cAAAAAAAAVChe3AAAAAAAAFQoXtwAAAAAAABUKF7cAAAAAAAAVChe3AAAAAAAAFQoXtwA\nAAAAAABUKF7cAAAAAAAAVChe3AAAAAAAAFSovwDKrv5tS/qRzQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x1440 with 100 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kL8MEhNgrx9N",
        "colab_type": "text"
      },
      "source": [
        " 神经网络的第一个隐藏层应该会对一些级别特别低的特征进行建模，因此可视化权重可能只显示一些模糊的区域，也可能只显示数字的某几个部分。此外，您可能还会看到一些基本上是噪点（这些噪点要么不收敛，要么被更高的层忽略）的神经元。\n",
        "\n",
        "在迭代不同的次数后停止训练并查看效果，可能会发现有趣的结果。\n",
        "\n",
        "**分别用 10、100 和 1000 步训练分类器。然后重新运行此可视化。**\n",
        "\n",
        "您看到不同级别的收敛之间有哪些直观上的差异？"
      ]
    }
  ]
}