[ { "path": ".gitignore", "content": "*.ipynb_checkpoints\n*.png\n*.jpg\n" }, { "path": "001-keras_overview.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-Keras概述\\n\",\n \"\\n\",\n \"Keras 是一个用于构建和训练深度学习模型的高阶 API。它可用于快速设计原型、高级研究和生产。\\n\",\n \"\\n\",\n \"Keras的3个优点：\\n\",\n \"方便用户使用、模块化和可组合、易于扩展\\n\",\n \"\\n\",\n \"\\n\",\n \"## 1 导入tf.keras\\n\",\n \"TensorFlow2推荐使用tf.keras构建网络，常见的神经网络都包含在tf.keras.layer中(最新的tf.keras的版本可能和keras不同)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/doit/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n \" from ._conv import register_converters as _register_converters\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0\\n\",\n \"2.2.4-tf\\n\"\n ]\n }\n ],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"from tensorflow.keras import layers\\n\",\n \"print(tf.__version__)\\n\",\n \"print(tf.keras.__version__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2 构建简单模型\\n\",\n \"### 2.1 模型堆叠\\n\",\n \"最常见的模型类型是层的堆叠：tf.keras.Sequential 模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model = tf.keras.Sequential()\\n\",\n \"model.add(layers.Dense(32, activation='relu'))\\n\",\n \"model.add(layers.Dense(32, activation='relu'))\\n\",\n \"model.add(layers.Dense(10, activation='softmax'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 网络配置\\n\",\n \"\\n\",\n \"tf.keras.layers中主要的网络配置参数如下：\\n\",\n \"\\n\",\n \"activation：设置层的激活函数。此参数可以是函数名称字符串，也可以是函数对象。默认情况下，系统不会应用任何激活函数。\\n\",\n \"\\n\",\n \"kernel_initializer 和 bias_initializer：创建层权重（核和偏置）的初始化方案。此参数是一个名称或可调用的函数对象，默认为 \\\"Glorot uniform\\\" 初始化器。\\n\",\n \"\\n\",\n \"kernel_regularizer 和 bias_regularizer：应用层权重（核和偏置）的正则化方案，例如 L1 或 L2 正则化。默认情况下，系统不会应用正则化函数。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"layers.Dense(32, activation='sigmoid')\\n\",\n \"layers.Dense(32, activation=tf.sigmoid)\\n\",\n \"layers.Dense(32, kernel_initializer='orthogonal')\\n\",\n \"layers.Dense(32, kernel_initializer=tf.keras.initializers.glorot_normal)\\n\",\n \"layers.Dense(32, kernel_regularizer=tf.keras.regularizers.l2(0.01))\\n\",\n \"layers.Dense(32, kernel_regularizer=tf.keras.regularizers.l1(0.01))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3 训练和评估\\n\",\n \"### 3.1 设置训练流程\\n\",\n \"构建好模型后，通过调用 compile 方法配置该模型的学习流程：\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model = tf.keras.Sequential()\\n\",\n \"model.add(layers.Dense(32, activation='relu'))\\n\",\n \"model.add(layers.Dense(32, activation='relu'))\\n\",\n \"model.add(layers.Dense(10, activation='softmax'))\\n\",\n \"model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n \" loss=tf.keras.losses.categorical_crossentropy,\\n\",\n \" metrics=[tf.keras.metrics.categorical_accuracy])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.2 输入Numpy数据\\n\",\n \"对于小型数据集，可以使用Numpy构建输入数据。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 1000 samples, validate on 200 samples\\n\",\n \"Epoch 1/10\\n\",\n \"1000/1000 [==============================] - 1s 503us/sample - loss: 11.8979 - categorical_accuracy: 0.0920 - val_loss: 12.1516 - val_categorical_accuracy: 0.1550\\n\",\n \"Epoch 2/10\\n\",\n \"1000/1000 [==============================] - 0s 21us/sample - loss: 12.2874 - categorical_accuracy: 0.0910 - val_loss: 12.9158 - val_categorical_accuracy: 0.1150\\n\",\n \"Epoch 3/10\\n\",\n \"1000/1000 [==============================] - 0s 31us/sample - loss: 13.3758 - categorical_accuracy: 0.0940 - val_loss: 14.4959 - val_categorical_accuracy: 0.0900\\n\",\n \"Epoch 4/10\\n\",\n \"1000/1000 [==============================] - 0s 28us/sample - loss: 15.4028 - categorical_accuracy: 0.0920 - val_loss: 17.2651 - val_categorical_accuracy: 0.1000\\n\",\n \"Epoch 5/10\\n\",\n \"1000/1000 [==============================] - 0s 24us/sample - loss: 18.6433 - categorical_accuracy: 0.0930 - val_loss: 21.0552 - val_categorical_accuracy: 0.1000\\n\",\n \"Epoch 6/10\\n\",\n \"1000/1000 [==============================] - 0s 31us/sample - loss: 22.0638 - categorical_accuracy: 0.0930 - val_loss: 23.7690 - val_categorical_accuracy: 0.1000\\n\",\n \"Epoch 7/10\\n\",\n \"1000/1000 [==============================] - 0s 25us/sample - loss: 24.8034 - categorical_accuracy: 0.0930 - val_loss: 27.7582 - val_categorical_accuracy: 0.1000\\n\",\n \"Epoch 8/10\\n\",\n \"1000/1000 [==============================] - 0s 24us/sample - loss: 30.0158 - categorical_accuracy: 0.0920 - val_loss: 34.5013 - val_categorical_accuracy: 0.1000\\n\",\n \"Epoch 9/10\\n\",\n \"1000/1000 [==============================] - 0s 27us/sample - loss: 37.2771 - categorical_accuracy: 0.0920 - val_loss: 42.4729 - val_categorical_accuracy: 0.1000\\n\",\n \"Epoch 10/10\\n\",\n \"1000/1000 [==============================] - 0s 30us/sample - loss: 45.5502 - categorical_accuracy: 0.0930 - val_loss: 51.9351 - val_categorical_accuracy: 0.1000\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"train_x = np.random.random((1000, 72))\\n\",\n \"train_y = np.random.random((1000, 10))\\n\",\n \"\\n\",\n \"val_x = np.random.random((200, 72))\\n\",\n \"val_y = np.random.random((200, 10))\\n\",\n \"\\n\",\n \"model.fit(train_x, train_y, epochs=10, batch_size=100,\\n\",\n \" validation_data=(val_x, val_y))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.3 tf.data输入数据\\n\",\n \"对于大型数据集可以使用tf.data构建训练输入。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train for 30 steps, validate for 3 steps\\n\",\n \"Epoch 1/10\\n\",\n \"30/30 [==============================] - 0s 12ms/step - loss: 67.4564 - categorical_accuracy: 0.0948 - val_loss: 87.7801 - val_categorical_accuracy: 0.0938\\n\",\n \"Epoch 2/10\\n\",\n \"30/30 [==============================] - 0s 2ms/step - loss: 110.4207 - categorical_accuracy: 0.0983 - val_loss: 137.2176 - val_categorical_accuracy: 0.0729\\n\",\n \"Epoch 3/10\\n\",\n \"30/30 [==============================] - 0s 2ms/step - loss: 166.3288 - categorical_accuracy: 0.1026 - val_loss: 200.0635 - val_categorical_accuracy: 0.0729\\n\",\n \"Epoch 4/10\\n\",\n \"30/30 [==============================] - 0s 2ms/step - loss: 234.6779 - categorical_accuracy: 0.0929 - val_loss: 276.1790 - val_categorical_accuracy: 0.0938\\n\",\n \"Epoch 5/10\\n\",\n \"30/30 [==============================] - 0s 1ms/step - loss: 316.2306 - categorical_accuracy: 0.0855 - val_loss: 362.6940 - val_categorical_accuracy: 0.0938\\n\",\n \"Epoch 6/10\\n\",\n \"30/30 [==============================] - 0s 2ms/step - loss: 405.1462 - categorical_accuracy: 0.0962 - val_loss: 454.0105 - val_categorical_accuracy: 0.0938\\n\",\n \"Epoch 7/10\\n\",\n \"30/30 [==============================] - 0s 3ms/step - loss: 495.8588 - categorical_accuracy: 0.0897 - val_loss: 542.2636 - val_categorical_accuracy: 0.1042\\n\",\n \"Epoch 8/10\\n\",\n \"30/30 [==============================] - 0s 2ms/step - loss: 589.8635 - categorical_accuracy: 0.1132 - val_loss: 637.4122 - val_categorical_accuracy: 0.0833\\n\",\n \"Epoch 9/10\\n\",\n \"30/30 [==============================] - 0s 2ms/step - loss: 679.3736 - categorical_accuracy: 0.1079 - val_loss: 724.9229 - val_categorical_accuracy: 0.1146\\n\",\n \"Epoch 10/10\\n\",\n \"30/30 [==============================] - 0s 1ms/step - loss: 757.8416 - categorical_accuracy: 0.1004 - val_loss: 787.8435 - val_categorical_accuracy: 0.0938\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dataset = tf.data.Dataset.from_tensor_slices((train_x, train_y))\\n\",\n \"dataset = dataset.batch(32)\\n\",\n \"dataset = dataset.repeat()\\n\",\n \"val_dataset = tf.data.Dataset.from_tensor_slices((val_x, val_y))\\n\",\n \"val_dataset = val_dataset.batch(32)\\n\",\n \"val_dataset = val_dataset.repeat()\\n\",\n \"\\n\",\n \"model.fit(dataset, epochs=10, steps_per_epoch=30,\\n\",\n \" validation_data=val_dataset, validation_steps=3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.4 评估与预测\\n\",\n \"评估和预测函数：tf.keras.Model.evaluate和tf.keras.Model.predict方法，都可以可以使用NumPy和tf.data.Dataset构造的输入数据进行评估和预测\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\r\",\n \"1000/1 [=======================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================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=] - 0s 26us/sample - loss: 820.5671 - categorical_accuracy: 0.1000\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"30/30 [==============================] - 0s 1ms/step - loss: 786.1435 - categorical_accuracy: 0.0969\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[786.1434997558594, 0.096875]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 模型评估\\n\",\n \"test_x = np.random.random((1000, 72))\\n\",\n \"test_y = np.random.random((1000, 10))\\n\",\n \"model.evaluate(test_x, test_y, batch_size=32)\\n\",\n \"test_data = tf.data.Dataset.from_tensor_slices((test_x, test_y))\\n\",\n \"test_data = test_data.batch(32).repeat()\\n\",\n \"model.evaluate(test_data, steps=30)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.245288 0.00419576 0. ... 0.11711721 0. 0. ]\\n\",\n \" [0.29617038 0.00480588 0. ... 0.14704514 0. 0. ]\\n\",\n \" [0.15192655 0.00397816 0. ... 0.1538676 0. 0. ]\\n\",\n \" ...\\n\",\n \" [0.23659316 0.00576886 0. ... 0.13454369 0. 0. ]\\n\",\n \" [0.31788164 0.0062953 0. ... 0.14174958 0. 0. ]\\n\",\n \" [0.40483308 0.01813794 0. ... 0.15039128 0. 0. ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 模型预测\\n\",\n \"result = model.predict(test_x, batch_size=32)\\n\",\n \"print(result)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4 构建复杂模型\\n\",\n \"\\n\",\n \"### 4.1 函数式API\\n\",\n \"tf.keras.Sequential 模型是层的简单堆叠，无法表示任意模型。使用 Keras的函数式API可以构建复杂的模型拓扑，例如：\\n\",\n \"\\n\",\n \"- 多输入模型，\\n\",\n \"\\n\",\n \"- 多输出模型，\\n\",\n \"\\n\",\n \"- 具有共享层的模型（同一层被调用多次），\\n\",\n \"\\n\",\n \"- 具有非序列数据流的模型（例如，残差连接）。\\n\",\n \"\\n\",\n \"**使用函数式 API 构建的模型具有以下特征：**\\n\",\n \"\\n\",\n \"- 层实例可调用并返回张量。\\n\",\n \"- 输入张量和输出张量用于定义 tf.keras.Model 实例。\\n\",\n \"- 此模型的训练方式和 Sequential 模型一样。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 1000 samples\\n\",\n \"Epoch 1/5\\n\",\n \"1000/1000 [==============================] - 0s 351us/sample - loss: 13.1064 - accuracy: 0.1080\\n\",\n \"Epoch 2/5\\n\",\n \"1000/1000 [==============================] - 0s 59us/sample - loss: 21.9265 - accuracy: 0.1180\\n\",\n \"Epoch 3/5\\n\",\n \"1000/1000 [==============================] - 0s 68us/sample - loss: 33.9123 - accuracy: 0.1210\\n\",\n \"Epoch 4/5\\n\",\n \"1000/1000 [==============================] - 0s 52us/sample - loss: 52.5335 - accuracy: 0.1190\\n\",\n \"Epoch 5/5\\n\",\n \"1000/1000 [==============================] - 0s 40us/sample - loss: 85.4629 - accuracy: 0.1180\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"input_x = tf.keras.Input(shape=(72,))\\n\",\n \"hidden1 = layers.Dense(32, activation='relu')(input_x)\\n\",\n \"hidden2 = layers.Dense(16, activation='relu')(hidden1)\\n\",\n \"pred = layers.Dense(10, activation='softmax')(hidden2)\\n\",\n \"# 构建tf.keras.Model实例\\n\",\n \"model = tf.keras.Model(inputs=input_x, outputs=pred)\\n\",\n \"model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n \" loss=tf.keras.losses.categorical_crossentropy,\\n\",\n \" metrics=['accuracy'])\\n\",\n \"model.fit(train_x, train_y, batch_size=32, epochs=5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.2 模型子类化\\n\",\n \"可以通过对 tf.keras.Model 进行子类化并定义自己的前向传播来构建完全可自定义的模型。\\n\",\n \"- 在\\\\__init\\\\__ 方法中创建层并将它们设置为类实例的属性。\\n\",\n \"- 在\\\\__call\\\\__方法中定义前向传播\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 1000 samples\\n\",\n \"Epoch 1/5\\n\",\n \"1000/1000 [==============================] - 1s 1ms/sample - loss: 15.5352 - accuracy: 0.1130\\n\",\n \"Epoch 2/5\\n\",\n \"1000/1000 [==============================] - 0s 84us/sample - loss: 23.1448 - accuracy: 0.1150\\n\",\n \"Epoch 3/5\\n\",\n \"1000/1000 [==============================] - 0s 75us/sample - loss: 31.5394 - accuracy: 0.1000\\n\",\n \"Epoch 4/5\\n\",\n \"1000/1000 [==============================] - 0s 70us/sample - loss: 39.0555 - accuracy: 0.1040\\n\",\n \"Epoch 5/5\\n\",\n \"1000/1000 [==============================] - 0s 77us/sample - loss: 45.8000 - accuracy: 0.1010\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"class MyModel(tf.keras.Model):\\n\",\n \" def __init__(self, num_classes=10):\\n\",\n \" super(MyModel, self).__init__(name='my_model')\\n\",\n \" self.num_classes = num_classes\\n\",\n \" # 定义网络层\\n\",\n \" self.layer1 = layers.Dense(32, activation='relu')\\n\",\n \" self.layer2 = layers.Dense(num_classes, activation='softmax')\\n\",\n \" def call(self, inputs):\\n\",\n \" # 定义前向传播\\n\",\n \" h1 = self.layer1(inputs)\\n\",\n \" out = self.layer2(h1)\\n\",\n \" return out\\n\",\n \" \\n\",\n \" def compute_output_shape(self, input_shape):\\n\",\n \" # 计算输出shape\\n\",\n \" shape = tf.TensorShape(input_shape).as_list()\\n\",\n \" shape[-1] = self.num_classes\\n\",\n \" return tf.TensorShape(shape)\\n\",\n \"# 实例化模型类，并训练\\n\",\n \"model = MyModel(num_classes=10)\\n\",\n \"model.compile(optimizer=tf.keras.optimizers.RMSprop(0.001),\\n\",\n \" loss=tf.keras.losses.categorical_crossentropy,\\n\",\n \" metrics=['accuracy'])\\n\",\n \"\\n\",\n \"model.fit(train_x, train_y, batch_size=16, epochs=5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.3 自定义层\\n\",\n \"通过对 tf.keras.layers.Layer 进行子类化并实现以下方法来创建自定义层：\\n\",\n \"\\n\",\n \"- \\\\__init\\\\__: (可选)定义该层要使用的子层\\n\",\n \"- build：创建层的权重。使用 add_weight 方法添加权重。\\n\",\n \"\\n\",\n \"- call：定义前向传播。\\n\",\n \"\\n\",\n \"- compute_output_shape：指定在给定输入形状的情况下如何计算层的输出形状。\\n\",\n \"- 可选，可以通过实现 get_config 方法和 from_config 类方法序列化层。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 1000 samples\\n\",\n \"Epoch 1/5\\n\",\n \"1000/1000 [==============================] - 0s 258us/sample - loss: 11.5199 - accuracy: 0.0860\\n\",\n \"Epoch 2/5\\n\",\n \"1000/1000 [==============================] - 0s 67us/sample - loss: 11.5205 - accuracy: 0.0870\\n\",\n \"Epoch 3/5\\n\",\n \"1000/1000 [==============================] - 0s 69us/sample - loss: 11.5205 - accuracy: 0.0850\\n\",\n \"Epoch 4/5\\n\",\n \"1000/1000 [==============================] - 0s 72us/sample - loss: 11.5201 - accuracy: 0.0830\\n\",\n \"Epoch 5/5\\n\",\n \"1000/1000 [==============================] - 0s 67us/sample - loss: 11.5201 - accuracy: 0.0810\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"class MyLayer(layers.Layer):\\n\",\n \" def __init__(self, output_dim, **kwargs):\\n\",\n \" self.output_dim = output_dim\\n\",\n \" super(MyLayer, self).__init__(**kwargs)\\n\",\n \" \\n\",\n \" def build(self, input_shape):\\n\",\n \" shape = tf.TensorShape((input_shape[1], self.output_dim))\\n\",\n \" self.kernel = self.add_weight(name='kernel1', shape=shape,\\n\",\n \" initializer='uniform', trainable=True)\\n\",\n \" super(MyLayer, self).build(input_shape)\\n\",\n \" \\n\",\n \" def call(self, inputs):\\n\",\n \" return tf.matmul(inputs, self.kernel)\\n\",\n \"\\n\",\n \" def compute_output_shape(self, input_shape):\\n\",\n \" shape = tf.TensorShape(input_shape).as_list()\\n\",\n \" shape[-1] = self.output_dim\\n\",\n \" return tf.TensorShape(shape)\\n\",\n \"\\n\",\n \" def get_config(self):\\n\",\n \" base_config = super(MyLayer, self).get_config()\\n\",\n \" base_config['output_dim'] = self.output_dim\\n\",\n \" return base_config\\n\",\n \"\\n\",\n \" @classmethod\\n\",\n \" def from_config(cls, config):\\n\",\n \" return cls(**config)\\n\",\n \"\\n\",\n \"# 使用自定义网络层构建模型\\n\",\n \"model = tf.keras.Sequential(\\n\",\n \"[\\n\",\n \" MyLayer(10),\\n\",\n \" layers.Activation('softmax')\\n\",\n \"])\\n\",\n \"\\n\",\n \"\\n\",\n \"model.compile(optimizer=tf.keras.optimizers.RMSprop(0.001),\\n\",\n \" loss=tf.keras.losses.categorical_crossentropy,\\n\",\n \" metrics=['accuracy'])\\n\",\n \"\\n\",\n \"model.fit(train_x, train_y, batch_size=16, epochs=5)\\n\",\n \"\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.3 回调\\n\",\n \"回调是传递给模型以自定义和扩展其在训练期间的行为的对象。我们可以编写自己的自定义回调，或使用tf.keras.callbacks中的内置函数，常用内置回调函数如下：\\n\",\n \"\\n\",\n \"- tf.keras.callbacks.ModelCheckpoint：定期保存模型的检查点。\\n\",\n \"- tf.keras.callbacks.LearningRateScheduler：动态更改学习率。\\n\",\n \"- tf.keras.callbacks.EarlyStopping：验证性能停止提高时进行中断培训。\\n\",\n \"- tf.keras.callbacks.TensorBoard：使用TensorBoard监视模型的行为 。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 1000 samples, validate on 200 samples\\n\",\n \"Epoch 1/5\\n\",\n \"1000/1000 [==============================] - 0s 142us/sample - loss: 11.5200 - accuracy: 0.0870 - val_loss: 11.7025 - val_accuracy: 0.0750\\n\",\n \"Epoch 2/5\\n\",\n \"1000/1000 [==============================] - 0s 88us/sample - loss: 11.5206 - accuracy: 0.0900 - val_loss: 11.7015 - val_accuracy: 0.0950\\n\",\n \"Epoch 3/5\\n\",\n \"1000/1000 [==============================] - 0s 88us/sample - loss: 11.5197 - accuracy: 0.0810 - val_loss: 11.7022 - val_accuracy: 0.1100\\n\",\n \"Epoch 4/5\\n\",\n \"1000/1000 [==============================] - 0s 91us/sample - loss: 11.5198 - accuracy: 0.0810 - val_loss: 11.7029 - val_accuracy: 0.1250\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"callbacks = [\\n\",\n \" tf.keras.callbacks.EarlyStopping(patience=2, monitor='val_loss'),\\n\",\n \" tf.keras.callbacks.TensorBoard(log_dir='./logs')\\n\",\n \"]\\n\",\n \"model.fit(train_x, train_y, batch_size=16, epochs=5,\\n\",\n \" callbacks=callbacks, validation_data=(val_x, val_y))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5 模型保存与恢复\\n\",\n \"### 5.1 权重保存\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model = tf.keras.Sequential([\\n\",\n \"layers.Dense(64, activation='relu', input_shape=(32,)), # 需要有input_shape\\n\",\n \"layers.Dense(10, activation='softmax')])\\n\",\n \"\\n\",\n \"model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n \" loss='categorical_crossentropy',\\n\",\n \" metrics=['accuracy'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 权重保存与重载\\n\",\n \"model.save_weights('./weights/model')\\n\",\n \"model.load_weights('./weights/model')\\n\",\n \"# 保存为h5格式\\n\",\n \"model.save_weights('./model.h5', save_format='h5')\\n\",\n \"model.load_weights('./model.h5')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5.2 保存网络结构\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'backend': 'tensorflow',\\n\",\n \" 'class_name': 'Sequential',\\n\",\n \" 'config': {'layers': [{'class_name': 'Dense',\\n\",\n \" 'config': {'activation': 'relu',\\n\",\n \" 'activity_regularizer': None,\\n\",\n \" 'batch_input_shape': [None, 32],\\n\",\n \" 'bias_constraint': None,\\n\",\n \" 'bias_initializer': {'class_name': 'Zeros',\\n\",\n \" 'config': {}},\\n\",\n \" 'bias_regularizer': None,\\n\",\n \" 'dtype': 'float32',\\n\",\n \" 'kernel_constraint': None,\\n\",\n \" 'kernel_initializer': {'class_name': 'GlorotUniform',\\n\",\n \" 'config': {'seed': None}},\\n\",\n \" 'kernel_regularizer': None,\\n\",\n \" 'name': 'dense_23',\\n\",\n \" 'trainable': True,\\n\",\n \" 'units': 64,\\n\",\n \" 'use_bias': True}},\\n\",\n \" {'class_name': 'Dense',\\n\",\n \" 'config': {'activation': 'softmax',\\n\",\n \" 'activity_regularizer': None,\\n\",\n \" 'bias_constraint': None,\\n\",\n \" 'bias_initializer': {'class_name': 'Zeros',\\n\",\n \" 'config': {}},\\n\",\n \" 'bias_regularizer': None,\\n\",\n \" 'dtype': 'float32',\\n\",\n \" 'kernel_constraint': None,\\n\",\n \" 'kernel_initializer': {'class_name': 'GlorotUniform',\\n\",\n \" 'config': {'seed': None}},\\n\",\n \" 'kernel_regularizer': None,\\n\",\n \" 'name': 'dense_24',\\n\",\n \" 'trainable': True,\\n\",\n \" 'units': 10,\\n\",\n \" 'use_bias': True}}],\\n\",\n \" 'name': 'sequential_6'},\\n\",\n \" 'keras_version': '2.2.4-tf'}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 序列化成json\\n\",\n \"import json\\n\",\n \"import pprint\\n\",\n \"json_str = model.to_json()\\n\",\n \"pprint.pprint(json.loads(json_str))\\n\",\n \"# 从json中重建模型\\n\",\n \"fresh_model = tf.keras.models.model_from_json(json_str)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"backend: tensorflow\\n\",\n \"class_name: Sequential\\n\",\n \"config:\\n\",\n \" layers:\\n\",\n \" - class_name: Dense\\n\",\n \" config:\\n\",\n \" activation: relu\\n\",\n \" activity_regularizer: null\\n\",\n \" batch_input_shape: !!python/tuple [null, 32]\\n\",\n \" bias_constraint: null\\n\",\n \" bias_initializer:\\n\",\n \" class_name: Zeros\\n\",\n \" config: {}\\n\",\n \" bias_regularizer: null\\n\",\n \" dtype: float32\\n\",\n \" kernel_constraint: null\\n\",\n \" kernel_initializer:\\n\",\n \" class_name: GlorotUniform\\n\",\n \" config: {seed: null}\\n\",\n \" kernel_regularizer: null\\n\",\n \" name: dense_23\\n\",\n \" trainable: true\\n\",\n \" units: 64\\n\",\n \" use_bias: true\\n\",\n \" - class_name: Dense\\n\",\n \" config:\\n\",\n \" activation: softmax\\n\",\n \" activity_regularizer: null\\n\",\n \" bias_constraint: null\\n\",\n \" bias_initializer:\\n\",\n \" class_name: Zeros\\n\",\n \" config: {}\\n\",\n \" bias_regularizer: null\\n\",\n \" dtype: float32\\n\",\n \" kernel_constraint: null\\n\",\n \" kernel_initializer:\\n\",\n \" class_name: GlorotUniform\\n\",\n \" config: {seed: null}\\n\",\n \" kernel_regularizer: null\\n\",\n \" name: dense_24\\n\",\n \" trainable: true\\n\",\n \" units: 10\\n\",\n \" use_bias: true\\n\",\n \" name: sequential_6\\n\",\n \"keras_version: 2.2.4-tf\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 保持为yaml格式 #需要提前安装pyyaml\\n\",\n \"\\n\",\n \"yaml_str = model.to_yaml()\\n\",\n \"print(yaml_str)\\n\",\n \"# 从yaml数据中重新构建模型\\n\",\n \"fresh_model = tf.keras.models.model_from_yaml(yaml_str)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**注意：子类模型不可序列化，因为其体系结构由call方法主体中的Python代码定义。**\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5.3 保存整个模型\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 1000 samples\\n\",\n \"Epoch 1/5\\n\",\n \"1000/1000 [==============================] - 0s 380us/sample - loss: 11.5446 - accuracy: 0.1030\\n\",\n \"Epoch 2/5\\n\",\n \"1000/1000 [==============================] - 0s 43us/sample - loss: 11.5470 - accuracy: 0.1090\\n\",\n \"Epoch 3/5\\n\",\n \"1000/1000 [==============================] - 0s 46us/sample - loss: 11.5488 - accuracy: 0.1090\\n\",\n \"Epoch 4/5\\n\",\n \"1000/1000 [==============================] - 0s 44us/sample - loss: 11.5616 - accuracy: 0.1090\\n\",\n \"Epoch 5/5\\n\",\n \"1000/1000 [==============================] - 0s 43us/sample - loss: 11.5849 - accuracy: 0.1090\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = tf.keras.Sequential([\\n\",\n \" layers.Dense(10, activation='softmax', input_shape=(72,)),\\n\",\n \" layers.Dense(10, activation='softmax')\\n\",\n \"])\\n\",\n \"model.compile(optimizer='rmsprop',\\n\",\n \" loss='categorical_crossentropy',\\n\",\n \" metrics=['accuracy'])\\n\",\n \"model.fit(train_x, train_y, batch_size=32, epochs=5)\\n\",\n \"# 保存整个模型\\n\",\n \"model.save('all_model.h5')\\n\",\n \"# 导入整个模型\\n\",\n \"model = tf.keras.models.load_model('all_model.h5')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6 将keras用于Estimator\\n\",\n \"Estimator API 用于针对分布式环境训练模型。它适用于一些行业使用场景，例如用大型数据集进行分布式训练并导出模型以用于生产\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING: Logging before flag parsing goes to stderr.\\n\",\n \"W1003 15:52:41.191763 139697242609472 estimator.py:1821] Using temporary folder as model directory: /tmp/tmpliq4yvi6\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = tf.keras.Sequential([layers.Dense(10,activation='softmax'),\\n\",\n \" layers.Dense(10,activation='softmax')])\\n\",\n \"\\n\",\n \"model.compile(optimizer=tf.keras.optimizers.RMSprop(0.001),\\n\",\n \" loss='categorical_crossentropy',\\n\",\n \" metrics=['accuracy'])\\n\",\n \"\\n\",\n \"estimator = tf.keras.estimator.model_to_estimator(model)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 7 Eager execution\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Eager execution是一个动态执行的编程环境，它可以立即评估操作。Keras不需要此功能，但它受tf.keras程序支持和对检查程序和调试有用。\\n\",\n \"\\n\",\n \"所有的tf.keras模型构建API都与Eager execution兼容。尽管可以使用Sequential和函数API，但Eager execution有利于模型子类化和构建自定义层：其要求以代码形式编写前向传递的API（而不是通过组装现有层来创建模型的API）。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 8 多GPU上运行\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"tf.keras模型可使用tf.distribute.Strategy在多个GPU上运行 。该API在多个GPU上提供了分布式培训，几乎无需更改现有代码。\\n\",\n \"\\n\",\n \"当前tf.distribute.MirroredStrategy是唯一受支持的分发策略。MirroredStrategy在单台计算机上使用全缩减进行同步训练来进行图内复制。要使用 distribute.Strategys，请将优化器实例化以及模型构建和编译嵌套在Strategys中.scope()，然后训练模型。\\n\",\n \"\\n\",\n \"以下示例tf.keras.Model在单个计算机上的多GPU分配。\\n\",\n \"\\n\",\n \"首先，在分布式策略范围内定义一个模型：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"W1003 15:52:43.881619 139697242609472 cross_device_ops.py:1209] There is non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_9\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_29 (Dense) (None, 16) 176 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_30 (Dense) (None, 1) 17 \\n\",\n \"=================================================================\\n\",\n \"Total params: 193\\n\",\n \"Trainable params: 193\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"strategy = tf.distribute.MirroredStrategy()\\n\",\n \"with strategy.scope():\\n\",\n \" model = tf.keras.Sequential()\\n\",\n \" model.add(layers.Dense(16, activation='relu', input_shape=(10,)))\\n\",\n \" model.add(layers.Dense(1, activation='sigmoid'))\\n\",\n \" optimizer = tf.keras.optimizers.SGD(0.2)\\n\",\n \" model.compile(loss='binary_crossentropy', optimizer=optimizer)\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"然后像单gpu一样在数据上训练模型即可\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"32/32 [==============================] - 1s 42ms/step - loss: 0.7060\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = np.random.random((1024, 10))\\n\",\n \"y = np.random.randint(2, size=(1024, 1))\\n\",\n \"x = tf.cast(x, tf.float32)\\n\",\n \"dataset = tf.data.Dataset.from_tensor_slices((x, y))\\n\",\n \"dataset = dataset.shuffle(buffer_size=1024).batch(32)\\n\",\n \"model.fit(dataset, epochs=1)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "002-keras_function.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-Keras函数式API\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"函数API是一种创建模型的方式，该方法比Sequential以下方法更加灵活：它可以处理具有非线性拓扑的模型，具有共享层的模型以及具有多个输入或输出的模型。\\n\",\n \"\\n\",\n \"它基于以下思想：深度学习模型通常是层的有向无环图（DAG）。Functional API是一组用于构建层图的工具。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# !pip install pydot\\n\",\n \"#!sudo apt-get install graphvizf\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/doit/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n \" from ._conv import register_converters as _register_converters\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"import tensorflow as tf\\n\",\n \"import tensorflow.keras as keras\\n\",\n \"import tensorflow.keras.layers as layers\\n\",\n \"tf.keras.backend.clear_session()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1 构建简单的网络\\n\",\n \"### 1.1 创建网络\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"mnist_model\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"img (InputLayer) [(None, 784)] 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense (Dense) (None, 32) 25120 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_1 (Dense) (None, 32) 1056 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_2 (Dense) (None, 10) 330 \\n\",\n \"=================================================================\\n\",\n \"Total params: 26,506\\n\",\n \"Trainable params: 26,506\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\n\",\n \"inputs = tf.keras.Input(shape=(784,), name='img')\\n\",\n \"# 以上一层的输出作为下一层的输入\\n\",\n \"h1 = layers.Dense(32, activation='relu')(inputs)\\n\",\n \"h2 = layers.Dense(32, activation='relu')(h1)\\n\",\n \"outputs = layers.Dense(10, activation='softmax')(h2)\\n\",\n \"model = tf.keras.Model(inputs=inputs, outputs=outputs, name='mnist_model') # 名字字符串中不能有空格\\n\",\n \"\\n\",\n \"model.summary()\\n\",\n \"keras.utils.plot_model(model, 'mnist_model.png')\\n\",\n \"keras.utils.plot_model(model, 'model_info.png', show_shapes=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"“层图”是用于深度学习模型的非常直观的结构图，而函数式API是构建结构图对应模型的方法。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.2 训练、验证及测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 48000 samples, validate on 12000 samples\\n\",\n \"Epoch 1/5\\n\",\n \"48000/48000 [==============================] - 3s 53us/sample - loss: 0.4210 - accuracy: 0.8833 - val_loss: 0.2289 - val_accuracy: 0.9322\\n\",\n \"Epoch 2/5\\n\",\n \"48000/48000 [==============================] - 1s 27us/sample - loss: 0.2056 - accuracy: 0.9399 - val_loss: 0.1690 - val_accuracy: 0.9501\\n\",\n \"Epoch 3/5\\n\",\n \"48000/48000 [==============================] - 1s 27us/sample - loss: 0.1633 - accuracy: 0.9516 - val_loss: 0.1466 - val_accuracy: 0.9568\\n\",\n \"Epoch 4/5\\n\",\n \"48000/48000 [==============================] - 1s 27us/sample - loss: 0.1403 - accuracy: 0.9585 - val_loss: 0.1343 - val_accuracy: 0.9594\\n\",\n \"Epoch 5/5\\n\",\n \"48000/48000 [==============================] - 1s 27us/sample - loss: 0.1235 - accuracy: 0.9638 - val_loss: 0.1311 - val_accuracy: 0.9579\\n\",\n \"test loss: 0.13712323768194765\\n\",\n \"test acc: 0.959\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 模型的训练、验证和测试与训练模型完全相同\\n\",\n \"# 下面使用mnist数据集进行展示\\n\",\n \"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\",\n \"# 将数值归到0-1之间\\n\",\n \"x_train = x_train.reshape(60000, 784).astype('float32') /255\\n\",\n \"x_test = x_test.reshape(10000, 784).astype('float32') /255\\n\",\n \"model.compile(optimizer=keras.optimizers.RMSprop(),\\n\",\n \" loss='sparse_categorical_crossentropy', # 直接填api，后面会报错\\n\",\n \" metrics=['accuracy'])\\n\",\n \"history = model.fit(x_train, y_train, batch_size=64, epochs=5, validation_split=0.2)\\n\",\n \"test_scores = model.evaluate(x_test, y_test, verbose=0)\\n\",\n \"print('test loss:', test_scores[0])\\n\",\n \"print('test acc:', test_scores[1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.3 模型保存和序列化\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 模型的保存与序列化与Sequential模型完全一样\\n\",\n \"model.save('model_save.h5')\\n\",\n \"del model\\n\",\n \"model = keras.models.load_model('model_save.h5')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2 使用共享网络创建多个模型\\n\",\n \"在函数式API中，通过在图层网络中指定其输入和输出来创建模型。 这意味着可以使用单个图层图来生成多个模型。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"encoder\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"img (InputLayer) [(None, 28, 28, 1)] 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d (Conv2D) (None, 26, 26, 16) 160 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_1 (Conv2D) (None, 24, 24, 32) 4640 \\n\",\n \"_________________________________________________________________\\n\",\n \"max_pooling2d (MaxPooling2D) (None, 8, 8, 32) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_2 (Conv2D) (None, 6, 6, 32) 9248 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_3 (Conv2D) (None, 4, 4, 16) 4624 \\n\",\n \"_________________________________________________________________\\n\",\n \"global_max_pooling2d (Global (None, 16) 0 \\n\",\n \"=================================================================\\n\",\n \"Total params: 18,672\\n\",\n \"Trainable params: 18,672\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\",\n \"Model: \\\"autoencoder\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"img (InputLayer) [(None, 28, 28, 1)] 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d (Conv2D) (None, 26, 26, 16) 160 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_1 (Conv2D) (None, 24, 24, 32) 4640 \\n\",\n \"_________________________________________________________________\\n\",\n \"max_pooling2d (MaxPooling2D) (None, 8, 8, 32) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_2 (Conv2D) (None, 6, 6, 32) 9248 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_3 (Conv2D) (None, 4, 4, 16) 4624 \\n\",\n \"_________________________________________________________________\\n\",\n \"global_max_pooling2d (Global (None, 16) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"reshape (Reshape) (None, 4, 4, 1) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_transpose (Conv2DTran (None, 6, 6, 16) 160 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_transpose_1 (Conv2DTr (None, 8, 8, 32) 4640 \\n\",\n \"_________________________________________________________________\\n\",\n \"up_sampling2d (UpSampling2D) (None, 24, 24, 32) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_transpose_2 (Conv2DTr (None, 26, 26, 16) 4624 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_transpose_3 (Conv2DTr (None, 28, 28, 1) 145 \\n\",\n \"=================================================================\\n\",\n \"Total params: 28,241\\n\",\n \"Trainable params: 28,241\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 自编码器网络结构\\n\",\n \"# 编码器\\n\",\n \"encode_input = keras.Input(shape=(28,28,1), name='img')\\n\",\n \"h1 = layers.Conv2D(16, 3, activation='relu')(encode_input)\\n\",\n \"h1 = layers.Conv2D(32, 3, activation='relu')(h1)\\n\",\n \"h1 = layers.MaxPool2D(3)(h1)\\n\",\n \"h1 = layers.Conv2D(32, 3, activation='relu')(h1)\\n\",\n \"h1 = layers.Conv2D(16, 3, activation='relu')(h1)\\n\",\n \"encode_output = layers.GlobalMaxPool2D()(h1)\\n\",\n \"\\n\",\n \"encode_model = keras.Model(inputs=encode_input, outputs=encode_output, name='encoder')\\n\",\n \"encode_model.summary()\\n\",\n \"# 解码器\\n\",\n \"h2 = layers.Reshape((4, 4, 1))(encode_output)\\n\",\n \"h2 = layers.Conv2DTranspose(16, 3, activation='relu')(h2)\\n\",\n \"h2 = layers.Conv2DTranspose(32, 3, activation='relu')(h2)\\n\",\n \"h2 = layers.UpSampling2D(3)(h2)\\n\",\n \"h2 = layers.Conv2DTranspose(16, 3, activation='relu')(h2)\\n\",\n \"decode_output = layers.Conv2DTranspose(1, 3, activation='relu')(h2)\\n\",\n \"\\n\",\n \"autoencoder = keras.Model(inputs=encode_input, outputs=decode_output, name='autoencoder')\\n\",\n \"autoencoder.summary()\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"请注意，我们使解码架构与编码架构严格对称，因此我们得到的输出形状与输入形状相同(28, 28, 1)。Conv2D一层的反面是Conv2DTranspose一层，MaxPooling2D一层的反面是UpSampling2D一层。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**可以把整个模型，当作一层网络使用**\\n\",\n \"我们可以通过在另一层的Input或Output上调用任何模型，将其视为层。请注意，通过调用模型，我们不仅可以重用模型的体系结构，还可以重用其权重。\\n\",\n \"\\n\",\n \"下面是对自动编码器示例的另一种处理方式，该示例创建一个编码器模型，一个解码器模型，并将它们链接到两个调用中以获得自编码器模型：\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"encoder\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"src_img (InputLayer) [(None, 28, 28, 1)] 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_4 (Conv2D) (None, 26, 26, 16) 160 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_5 (Conv2D) (None, 24, 24, 32) 4640 \\n\",\n \"_________________________________________________________________\\n\",\n \"max_pooling2d_1 (MaxPooling2 (None, 8, 8, 32) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_6 (Conv2D) (None, 6, 6, 32) 9248 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_7 (Conv2D) (None, 4, 4, 16) 4624 \\n\",\n \"_________________________________________________________________\\n\",\n \"global_max_pooling2d_1 (Glob (None, 16) 0 \\n\",\n \"=================================================================\\n\",\n \"Total params: 18,672\\n\",\n \"Trainable params: 18,672\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\",\n \"Model: \\\"decoder\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"encoded_img (InputLayer) [(None, 16)] 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"reshape_1 (Reshape) (None, 4, 4, 1) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_transpose_4 (Conv2DTr (None, 6, 6, 16) 160 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_transpose_5 (Conv2DTr (None, 8, 8, 32) 4640 \\n\",\n \"_________________________________________________________________\\n\",\n \"up_sampling2d_1 (UpSampling2 (None, 24, 24, 32) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_transpose_6 (Conv2DTr (None, 26, 26, 16) 4624 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_transpose_7 (Conv2DTr (None, 28, 28, 1) 145 \\n\",\n \"=================================================================\\n\",\n \"Total params: 9,569\\n\",\n \"Trainable params: 9,569\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\",\n \"Model: \\\"autoencoder\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"img (InputLayer) [(None, 28, 28, 1)] 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"encoder (Model) (None, 16) 18672 \\n\",\n \"_________________________________________________________________\\n\",\n \"decoder (Model) (None, 28, 28, 1) 9569 \\n\",\n \"=================================================================\\n\",\n \"Total params: 28,241\\n\",\n \"Trainable params: 28,241\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"encode_input = keras.Input(shape=(28,28,1), name='src_img')\\n\",\n \"h1 = layers.Conv2D(16, 3, activation='relu')(encode_input)\\n\",\n \"h1 = layers.Conv2D(32, 3, activation='relu')(h1)\\n\",\n \"h1 = layers.MaxPool2D(3)(h1)\\n\",\n \"h1 = layers.Conv2D(32, 3, activation='relu')(h1)\\n\",\n \"h1 = layers.Conv2D(16, 3, activation='relu')(h1)\\n\",\n \"encode_output = layers.GlobalMaxPool2D()(h1)\\n\",\n \"\\n\",\n \"encode_model = keras.Model(inputs=encode_input, outputs=encode_output, name='encoder')\\n\",\n \"encode_model.summary()\\n\",\n \"\\n\",\n \"decode_input = keras.Input(shape=(16,), name='encoded_img')\\n\",\n \"h2 = layers.Reshape((4, 4, 1))(decode_input)\\n\",\n \"h2 = layers.Conv2DTranspose(16, 3, activation='relu')(h2)\\n\",\n \"h2 = layers.Conv2DTranspose(32, 3, activation='relu')(h2)\\n\",\n \"h2 = layers.UpSampling2D(3)(h2)\\n\",\n \"h2 = layers.Conv2DTranspose(16, 3, activation='relu')(h2)\\n\",\n \"decode_output = layers.Conv2DTranspose(1, 3, activation='relu')(h2)\\n\",\n \"decode_model = keras.Model(inputs=decode_input, outputs=decode_output, name='decoder')\\n\",\n \"decode_model.summary()\\n\",\n \"\\n\",\n \"autoencoder_input = keras.Input(shape=(28,28,1), name='img')\\n\",\n \"h3 = encode_model(autoencoder_input)\\n\",\n \"autoencoder_output = decode_model(h3)\\n\",\n \"autoencoder = keras.Model(inputs=autoencoder_input, outputs=autoencoder_output,\\n\",\n \" name='autoencoder')\\n\",\n \"autoencoder.summary()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"模型可以嵌套：模型可以包含子模型（因为模型就像一层一样）。\\n\",\n \"\\n\",\n \"**模型集成**\\n\",\n \"\\n\",\n \"模型嵌套的另一种常见模式是集成。以下是将一组模型整合为一个平均其预测值的模型的方法：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_model():\\n\",\n \" inputs = keras.Input(shape=(128,))\\n\",\n \" outputs = keras.layers.Dense(1, activation='sigmoid')(inputs)\\n\",\n \" return keras.Model(inputs, outputs)\\n\",\n \"\\n\",\n \"model1 = get_model()\\n\",\n \"model2 = get_model()\\n\",\n \"model3 = get_model()\\n\",\n \"inputs = keras.Input(shape=(128,))\\n\",\n \"y1 = model1(inputs)\\n\",\n \"y2 = model2(inputs)\\n\",\n \"y3 = model3(inputs)\\n\",\n \"outputs = layers.average([y1, y2, y3])\\n\",\n \"ensemble_model = keras.Model(inputs, outputs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3 复杂网络结构构建\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.1 多输入与多输出网络\\n\",\n \"假设我们正在建立一个系统，用于按优先级对定制票进行排序并将其分配到正确的部门。\\n\",\n \"\\n\",\n \"模型将具有3个输入：\\n\",\n \"\\n\",\n \"- 票证标题（文本输入）\\n\",\n \"- 票证的文本正文（文本输入）\\n\",\n \"- 用户添加的所有标签（分类输入）\\n\",\n \"它将有两个输出：\\n\",\n \"\\n\",\n \"- 优先级得分，介于0和1之间（标量S型输出）\\n\",\n \"- 应该处理票证的部门（softmax输出）\\n\",\n \"仅使用几行Functional API构建该模型。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"model_4\\\"\\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # Connected to \\n\",\n \"==================================================================================================\\n\",\n \"title (InputLayer) [(None, None)] 0 \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"body (InputLayer) [(None, None)] 0 \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"embedding_1 (Embedding) (None, None, 64) 128000 title[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"embedding (Embedding) (None, None, 64) 128000 body[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"lstm_1 (LSTM) (None, 128) 98816 embedding_1[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"lstm (LSTM) (None, 32) 12416 embedding[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"tag (InputLayer) [(None, 12)] 0 \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"concatenate (Concatenate) (None, 172) 0 lstm_1[0][0] \\n\",\n \" lstm[0][0] \\n\",\n \" tag[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"priority (Dense) (None, 1) 173 concatenate[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"department (Dense) (None, 4) 692 concatenate[0][0] \\n\",\n \"==================================================================================================\\n\",\n \"Total params: 368,097\\n\",\n \"Trainable params: 368,097\\n\",\n \"Non-trainable params: 0\\n\",\n \"__________________________________________________________________________________________________\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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ciIiKCg4MXLFhARKmpqRYWFqdPn25tp7W1tdJ/pQVKVVdXE5HsRVF6hT579uzw4cMDAgKIiF3yN2zYcPv27bi4uOfPnxNRWloaG/8qW+3S0lJWLCczVrWqqiogIMDExEQgEJSWlhYXF585c+bAgQNsdosff/xR+lad0NDQhoaG33//nT2WJ//Y2bFIJxUBAAWxXjK7u09E27ZtmzZt2vz581euXOni4hIfH19WVsZWJSQkjBkz5v3331+xYoWhoeGaNWvS0tLq6urOnz9fUlJCRIWFhdJim7dfQoxCjAIAaAcd7vd7e3ubm5tfuXKFiGprazdu3EhEJSUl27Ztq66ull179+7dVatWEVFhYWFcXFxdXd25c+c8PT2//fbb0NDQ8vLy/fv3szG4xsbG5ubmLb7k64cffli9ejWbNejdd99NTk7+7LPP2EU6MjKyqqoqLi6O/XYfEREhvdbGxsZWVFQ8fPiwtLT04sWLbMqOqKgoNze3mJiYpUuXuru7Ozs7z5s3j03xIVvt8+fPs8nyiouL//rXv77++uuvv/66k5NTz549d+7cOWnSJCLauHGjubn5Bx98YG9vv3btWktLy40bN0p/H7exsZk0adI777wjPQpjY+Pmxy4UCj/++GN2LMuXL7927VpHfF4AfLV9+/apU6fOmDEjICBg2bJlzs7ObEJMU1PTzMxMX19ff3//sLCw8PBwa2vrc+fOGRoavvTSS+fOnXNycvL29nZxcfnxxx9ffvnl9957r6CgQHpTvHn7JcQoxCgAgHYQcFo56YFAIEhKStLpwYtDhgxhU9FpsA61tbUvvfTS9evXO+Jtmjz4jFrj7e1NfBxpytfj6lAa/J53aPslxChdxte2zNfjAtAeOny/H9qUkJAQGBjYQZ0GAOhQ+tB+9eEYAQC0hz7O59M52CDUmpoaMzOzTt715cuX33333draWrFYzKYLBABd0WntFzEKAEDf4H6/+tXU1Kxdu5Y99xYUFJSVldXJFTAzM6uqqjIwMDhw4MBf/vKXTt67XhEIBEKhMDw8PCoqSnbi8/z8/OjoaCISiUQxMTGhoaG+vr7jxo375ptvFCm2xVxisXjVqlVsfLZ0L1FRUUFBQQKBoMk07aC7OqH9IkbpD8QoAPgTzb0yTB7i7/vVeaNDP6P79+9rsBDF3xVPRIMHD26SeOHCBV9f34aGBo7jIiIirl+/ztLj4+OJaOvWrW0W21qux48fz5gxo6CgoMn27PVPbRar+HGBFGKR7kKM4hCjAODPcL8ftE5hYaGvr682FKIINv+J1M2bN/38/OLj442MjIgoMTGxvLycrfLz8yPFHllrLZelpeVHH33k6enZZCpDExMTNRwJACgGMQoxCkBHod8P2qW4uNjDw+Phw4caL0QFHMfNnTt3wYIFVlZWLEUikRw/fpz9/ejRI1Lsfahycg0dOtTe3n7FihVqrzwAKAIxSn4uxCgAbYZ+P3Sgqqqq8PDw1atXh4aGTp48OTQ0lL24Z9euXQYGBmy4Z3V1dUxMjHRxz549N27cKCsrY/OCZ2VlhYWF2dnZPXjwwMvLy9ra2tXVlU2OrnghRHT+/HlbW9v09PQOPd7k5OTs7Gz2alImLS1t9erV0rWGhoYRERFtliM/1+TJk3ft2lVQUKDWugPoI8QoxCgA/aLpgUYtI4yp1XptfkbV1dWOjo7r1q1ji+Xl5Y6OjoMGDaqsrOQ4jr2eU7qx7CIROTk5cRwnFotTUlLYHH+BgYHp6ekHDhxgb1jLyMhQsBDmxIkTXbp0OXnypCKHptTYWdm9zJ49WyAQNDY2Nt+yoaFh8ODB+/btU6RY+blycnKIaNOmTdIU9nbVNkvD2FkVIBbpLsSo5ntBjALQc7jfDx1l8+bNt27dCggIYIsvvPDCBx98UFBQwN6szIaWSjVZZAwMDNzd3dnPx5s3b3711Vdnz5798ccfExF7kkyRQhhPT8+qqioPD4/2HpVcmZmZ3bt3bzKalvnyyy+XLl06Z84cpQpsMZeNjQ0Rff/99+2pKgAgRslCjALQB+j3Q0fJyMggInbrixk3bhwRXbp0SalyDAwMiKhLly5s0dPTk4hkJ6RTkFAoVDaLssrKyiwtLVtcdefOneDgYGULbDGXhYUFET148ECFGgKAFGKULMQoAH2Afj90FHYtLCwslKawm0Ddu3dvT7F9+vQhxZ4863xCoVAsFjdPr6urGzZsmLKltZYL02ADqAVilBRiFICeQL8fOgq7c5aamipNYe8JmjhxIv1xYWhoaCAijuOePn0q3UwgEIhEotaKraioUK2QFq926tW7d2/2UGATpqams2fPVra01nI9efKEiHr16qVCDQFACjFKCjEKQE+g3w8dZeXKlS4uLvHx8WVlZSwlISFhzJgx77//PhGxx7w2bNhw+/btuLi458+fE1FaWppEIrG3ty8tLWUXYCnpFfHs2bPDhw9nQ3IVLyQ1NdXCwuL06dMdesjjx4+vrq5+9uxZk/SgoCB3d3fZlOjoaGdn50OHDskprXkuhs2aN3bs2HbXF0CvIUZJIUYB6An0+6GjmJqaZmZm+vr6+vv7h4WFhYeHW1tbnzt3jj1SFhUV5ebmFhMTs3TpUnd3d2dn53nz5lVWVopEIm9vb3Nz8ytXrsiWFhsbW1FR8fDhw9LS0osXLypbiLGxsbm5ubGxcYcesp+fH8dxmZmZTdLr6+vr6+tlUwoKCnJzc8PCwuSU1jwXk5GRIRQKfXx82l9hAH2GGCWFGAWgJwQcx2m6Di0QCARJSUmIGtqs0z6jIUOG5ObmduYX1dvbmxR7aaVAIHBycrp586Y0xd3d3dHRcdu2bW3mvXXrlp+fX1ZWlrLV8/T07NWr186dO6UpCp4ixY8LpBCLdBdiFCFGAcCf4X4/QLuwH+6lEhMTT5061eZEFrW1tfHx8bt371Z2d5cvX75161Z0dLRsopyhxgCg5xCjAECqhUl8AbRKTU0N+9fMzEzTdWnB3bt3ly1b1qdPnxkzZjg4OPTs2fPo0aMhISG7d++WzuvXHJsjXHYCQUWUlpZGRkaePXuWZczPzz927Njjx4/v3LnT3sMAAFUhRkkhRgFoOfT7QXvV1NRs3LiRPfcWFBS0aNGikSNHarpSf9Liz9YuLi6RkZEJCQkrVqxoLaOLi4uy+xKJRHv37t2/f7/0Suzg4BAeHk5EUVFRypYGAO2HGCULMQpA+6HfD9rLzMwsMjIyMjJS0xVRmp2dnZwLqmoMDQ3ZFRQAtARilCzEKADth/H9AAAAAAD8h34/AAAAAAD/od8PAAAAAMB/6PcDAAAAAPCf9j7X2/yFgqBt+PoZFRUVEdHhw4c1XRE1Kyoq6tevn6ZroXv4+j3XB3z97BCjAEA12vu+Xk1XAYCHvLy88C5MpSAWAXQmxCiADqWl/X7QFYcPH545cya+RQCgPRCXAABahPH9AAAAAAD8h34/AAAAAAD/od8PAAAAAMB/6PcDAAAAAPAf+v0AAAAAAPyHfj8AAAAAAP+h3w8AAAAAwH/o9wMAAAAA8B/6/QAAAAAA/Id+PwAAAAAA/6HfDwAAAADAf+j3AwAAAADwH/r9AAAAAAD8h34/AAAAAAD/od8PAAAAAMB/6PcDAAAAAPAf+v0AAAAAAPyHfj8AAAAAAP+h3w8AAAAAwH/o9wMAAAAA8B/6/QAAAAAA/Id+PwAAAAAA/6HfDwAAAADAf+j3AwAAAADwH/r9AAAAAAD8h34/AAAAAAD/od8PAAAAAMB/6PcDAAAAAPAf+v0AAAAAAPyHfj8AAAAAAP+h3w8AAAAAwH/o9wMAAAAA8B/6/QAAAAAA/Geo6QqAjikqKvL39xeLxWzxyZMn3bp1mzBhgnSDF1988fPPP9dM5QBALyEuAQAoAv1+UE6/fv3u3bt3584d2cSLFy9K/x43blynVwoA9BriEgCAIjDOB5Tm5+dnZGTU2tpZs2Z1ZmUAAAhxCQBAAQKO4zRdB9Axd+7ccXBwaPGb4+zs/Ouvv3Z+lQBAzyEuAQC0Cff7QWn29vZDhw4VCARN0o2MjPz9/TVSJQDQc4hLAABtQr8fVOHn5ycUCpskikQib29vjdQHAABxCQBAPozzAVWUlpb269dPIpFIUwwMDNzc3C5duqTBWgGAPkNcAgCQD/f7QRW9e/ceM2aMgcH/f38MDAz8/Pw0WCUA0HOISwAA8qHfDyqaN2+e7CLHcTNmzNBUZQAACHEJAEAu9PtBRV5eXtKhtEKhcOLEiT179tRslQBAzyEuAQDIgX4/qMjS0nLSpEnsEstx3Ny5czVdIwDQd4hLAAByoN8Pqps7dy57hM7IyOitt97SdHUAABCXAABahX4/qM7T09PY2JiIpk6d2rVrV01XBwAAcQkAoFXo94PqzMzM2O00/JgOAFoCcQkAoDV/mr//8OHDM2fO1GBtAEBPqOXNId7e3t988037ywEA/ZGUlOTj46PpWgBohmHzpKSkpM6vB+gosViclJTk6+urrgK3bdtGRCEhIeoqUHtkZmbGxsaifbHzoK7SRo4cyctvC7SHanGJ3y105syZwcHBo0aN0nRFNAw3N0HPtdDvx/+DQSnTp083MTFRV2lHjhwh/n4JY2Nj+XpoSlFjv79fv344pdCcanGJxy105syZo0aN4uvRKQ79ftBzGN8P7aXGTj8AgFogLgEANId+PwAAAAAA/6HfDwAAAADAf+j3AwAAAADwH/r9AAAAAAD8h34/AAAAAAD/od8PfDBy5MiVK1dquhYak5+fHx0dTUQikSgmJiY0NNTX13fcuHEKvtOqxVxisXjVqlXFxcUdW3UA3afP8QfBB0C3oN8PfGBnZ9eh0/YVFRV1XOHtdPHixXXr1gUFBRHR+vXrJ02aFB0dfeDAAR8fH29vb3ZJlq/FXEKhMDw8PCgo6O7dux1/EAA6TG/jD4IPgM5Bvx/44ODBg+vXr++gwgsLC9X4QmL1unnzpp+fX3x8vJGRERElJiaWl5ezVX5+fvTHe9Dkay2XpaXlRx995OnpWVNT00H1B+AB/Yw/CD4Augj9fgB5iouLPTw8Hj58qOmKtIDjuLlz5y5YsMDKyoqlSCSS48ePs78fPXpERLa2tm2WIyfX0KFD7e3tV6xYofbKA0CbtDb+IPgA6Cj0+0G3SSSSI0eOzJ8/f/z48USUnJwcEBBga2tbWVk5f/78Hj16uLq6Xr16lYiysrLCwsLs7OwePHjg5eVlbW3t6up67NgxItq1a5eBgYFAICCi6urqmJgY6eKePXtu3LhRVla2ePFitsfz58/b2tqmp6dr7Jj/kJycnJ2d/eabb0pT0tLSVq9eLV1raGgYERHRZjnyc02ePHnXrl0FBQVqrTsAH+ht/EHwAdBVnIykpKQmKQCdzMvLy8vLS6ksv//+OxE5OTlxHFdUVNS1a1ciioyMvHfv3r59+4jIzc1NLBanpKSYmpoSUWBgYHp6+oEDB7p160ZEGRkZHMfZ29vLfvllF6WFMydOnOjSpcvJkyeVPTS1t6/Zs2cLBILGxsbmqxoaGgYPHrxv3z6lCmwxV05ODhFt2rSpXXWVocbzoMK3BaA1qn0zdSX+EFFSUpKyuVqjo8GHU/d5ANA5uN8POk/21+S+ffv27duXiNasWdO/f/85c+bY2Nhcu3bNwMDA3d2dbbl58+ZXX3119uzZH3/8MRHFx8cTERuiKtVkUZanp2dVVZWHh0cHHY7iMjMzu3fvbmho2HzVl19+uXTp0jlz5ihVYIu5bGxsiOj7779vT1UB+Eo/4w+CD4COQr8f+Ib9Pi5laWn5/Plz9reBgQERdenShS16enoSUX5+vrK7EAqF7a2lOpSVlVlaWra46s6dO8HBwcoW2GIuCwsLInrw4IEKNQTQN3oSfxB8AHQU+v2gv/r06UOKPXymnYRCoVgsbp5eV1c3bNgwZUtrLVeTfgwAqIVOxx8EHwAdhX4/6K+KigoimjhxIv1xgWloaCAijuOePn0q3UwgEBM7qPEAACAASURBVIhEItmMLV7wOl/v3r0rKyubp5uams6ePVvZ0lrL9eTJEyLq1auXCjUEgNbodPxB8AHQUej3g8579uwZEVVVVbHF+vp62bXV1dVEJHvhlF41z549O3z48ICAACJycnIiog0bNty+fTsuLo79NJ+WliaRSOzt7UtLS+/fv89ypaamWlhYnD59uqOPq03jx4+vrq5mhy8rKCjI3d1dNiU6OtrZ2fnQoUNySmuei2GT640dO7bd9QXgIf2MPwg+ADoK/X7QbbW1tRs3biSikpKSbdu2RUVFFRYWElFkZGRVVVVcXBx72XtERIT0ehwbG1tRUfHw4cPS0tKLFy+yR9OioqLc3NxiYmKWLl3q7u7u7Ow8b968yspKkUjk7e1tbm5+5coVlt3Y2Njc3NzY2FgjxyvLz8+P47jMzMwm6fX19U06HwUFBbm5uWFhYXJKa56LycjIEAqFPj4+7a8wAM/obfxB8AHQUQKO46QLhw8fnjlzpmwKQCfz9vYmxV70qIIhQ4bk5uZq6hveEe3L3d3d0dFx27ZtbW5569YtPz+/rKwsZXfh6enZq1evnTt3qlTBFqjxPHTotwX0TUdfATUbfwQCQVJSkhr70LoYfKgDzgOAbsH9fgAdlpiYeOrUqTbnu6itrY2Pj9+9e7ey5V++fPnWrVvR0dGqVhAA+AnBB0AXabLfLx0QqTLZh58UX8VXOJ9tqqmpkf7LDz179jx69GhISEhtba2czQoKCjZu3Oji4qJU4aWlpZGRkWfPnmUvGAI0sdbgzCiCZ/EHwQdAF2mg3y8Wi6Oiol599VVra2vVSnj+/PnGjRtHjx7dvIQWV40cOXLlypWq17gtJSUliYmJM2fOHD16tIJZtm7damlpKRAIDA0NJ0+ePHXqVA8Pj4kTJw4YMEAgEEgf4VIE/85nR6ipqVm7di07sUFBQSr84qy1XFxcIiMjExIS5G+j7OVTJBLt3bt3//79/fr1a18FdR7PmhiCT+fja/xB8AHQPbIv71XtLeUqqKurs7Kyas++5JTQfNWsWbMiIiJU3pciZF/VrqCSkhIicnBwkE2USCQeHh537txRau98Op9eXl5eXl4dVLhmdVr70nJqPA+d9m3hUxPjEHxawe8WSkRJSUmaroXm4TyAnmvhJdudwMTEpGfPno8fP+6IEpqvOnjwoMo7UpAK717p3bs3NXvzokAgWL16ddeuXZUqin/nE0Cr8KyJIfgAAOgnzfT7oTW5ubnDhg0zNTXVdEUAQL8g+AAA8J4q4/vr6+s/+eSThQsX/u1vf5s0adKvv/5KRLW1tfv37/f19R0zZkxWVtYrr7wycODAjIyMW7duTZ8+/YUXXhgyZMjVq1ebFHX79m1PT08rK6sRI0ZcuHBBTvlEVFdXFxoaGhAQEBERsWbNGtmno1pbJZFIjhw5Mn/+/PHjxxNRcnJyQECAra1tZWXl/Pnze/To4erqKlurTz/9dN68eUuWLDExMRH8QYVTxJw/f97W1jY9PV2RjTmOKy8vDwwMZE/I4XwCqIsehiwEHwQfAIAWyA76UXB046JFi9gkxBzHvfHGGzY2NlVVVRKJ5Pbt20TUvXv31NTU3377jYgGDhy4ZcuWp0+f5uTkENGECROkhbDXEwYHB585c+bzzz83MzMTCoXXr19vrXyRSOTm5rZo0SKWfufOHfa6E47j5Kzi/jzyvqioiP2KHRkZee/evX379hGRm5sb2zI+Pl4oFFZUVHAct2nTJiIKDQ1VfMgUNRvff+LEiS5dupw8eVJOlubKyso4jtPb84nx/bzX+eP79TBkIfioEHz43UIJ49o5jsN5AL2n9Hu7fvzxRzc3tyaJKSkp7CXbAoHAycnp5s2bRNSvX7/i4mJpaTY2Ng0NDU+ePGGL7A0mVVVV7En/7du3L1u2zN/ff8mSJS2WX1hY+P7779+8eZNdY4joxRdfvHXrFsdxCQkJra1ii7K1cnJyysvLk67q1atXZWUle1PgtGnTUlJS6uvrjYyMbty44eLiMnLkyObvI2yN7F6kxGJxk0G0rWXhOK68vNzb2/vIkSM2NjbNN9CT8+nt7V1UVBQSEiJ/M12UmZkZGxvL+hb6jJ0H+XFGQYq8t0tvQxaCj7LBh10B+dpCZ86cGRwcPGrUKE1XRMPYR4z3doHeUnp8/5UrV1xcXH755Zc2t2wydZeVlVVubm5r27z11lvLli377bffWit/2rRpRDRw4EBpioHB/wYp/fe//21tVXNNfuq1tLSUvnZk0qRJycnJqampb731lomJCRG9/vrrbRxkW+Rcd5tXzMbGJiQkxMjIqMUN9Od8ZmVlzZw5U5EtdRGPD0076W3IQvBRLZjzuIXGxsbGxsZquhYAoElK9/srKioKCgpqa2u7dOkiTZRIJHKisyLYTab+/fu3Vn5xcTHbe9++fZvklbNKKe+//76pqek777yTkZGRn5+/fv36NWvWtKdAFUyfPp2Inj171qVLl/acUp0+n15eXvLv4OooRX5P0wfsPHTa7hCyFITgw/C1hQoEAtznpmb/XQTQN0oHdycnp9ra2qioKGnKzZs3P/3003bWg73QxMPDo7Xy2c++qampLVaptVVKEYvFv/76a1ZW1pYtW7799tuIiAjFb5jJKVOFXHPmzGlnbOLr+QRQlt6GLAQfBB8AgKZkB/sr8lRTfX39oEGDiOjtt9/ev3//Bx988MYbb1RVVXEcV1dXR0Qvvvgi29Le3p6Iqqur2SL76VYsFrPFIUOGENHjx4/Z4pIlS6ZNmyan/GvXrhkaGlpbW58+fbq2tvbcuXPm5uZEdPfuXTmrOI6rrq4moj59+shWQ3o47K5SY2Mjx3Hr16+3t7f/4osvTp8+fenSpVu3bolEIoWekuA49qLyJu/BSUlJ6dq163/+858Ws5SVlRGRnZ1dk9MbEhLi4+Ojt+cTz/XyXic/16ufIQvBR4Xgw+8WSnieleM4nAfQe6rM51NYWMjma+vVq9e777778OFDjuMePHiwfPlyIjI2Nj579mxaWhqbhyEoKKiioiI+Pp7dQ/rkk08ePXrEcdyZM2emTp06YcKEd999NygoKCEhQXoVabF8juPS09PHjBnTrVu3QYMGbd68edy4ce+99953330nFotbW1VdXb169Wr2P5yYmJjNmzezvzds2PD06VPpSMdVq1bV1dWdOXNG+kwb88ILLxw9erTNE3L+/Pl3332XiIyMjD755JNr166x9DNnzvTp0+fcuXMtZmG/qgsEgiFDhkyePNnd3X3s2LFsjOzOnTv19nyi3897nT+fjx6GLAQfFYIPv1so+rsMzgPoOaXn8+GxxMTER48erVixgogkEklJScn58+fDwsKkz4qBUlQ7n4rM0KKj9Lx9SanxPPD426IIhKzWqHZm+N1CMb6fwXkAPYf39f5PVFTUqlWrKioq2KKBgUG/fv3Gjh3bt29fOaNdc3NzX3zxxc6qoy6Rcz41WzEAfkATaw3ODABAa9o1owWf/PDDD0S0Y8cO6dUiOzt71apV+/btk/NzCTr9rZFzPjVaLwCeQBNrDc4MAEBr0O//n6+++iowMPCLL77o16/fmDFjfHx8srOz9+3b99e//lXTVdNJOJ88cPfu3e3bt2/ZsoW9vRW0CppYa3BmeADBB6CDYJzP/1hZWW3fvn379u2arghP4Hy2X35+fnJycmhoqEgk2r59e3FxcWlpaVFRUVBQkJeXV5vZVcvFVFdXr1mz5j//+c/u3bsnTJjQfIP4+PigoCA2ElosFq9duzYwMBDjKDoTmlhrcGbaD8EHgLdkR63wezYD0AkdPZ/P/fv3NVWIUu3rwoULvr6+DQ0NHMdFRERcv36dpcfHxxPR1q1b2yxBtVwcx5WXl7/yyiuOjo7S2VeauHLlCnsTkzTl8ePHM2bMKCgoUKT8zp/PB0ARHX0F1GDw4ZSZx4bHwYfDfD6g9zDOB/RIYWGhr6+vNhQi382bN/38/OLj442MjIgoMTGxvLycrfLz8yPFZrBRLRcRzZ8//+eff967d2+PHj2ar62srPz2229tbW1lEy0tLT/66CNPT8+amhpFdgGgbxB8FNk7gg9AR0O/H/RFcXGxh4fHw4cPNV6IfBzHzZ07d8GCBVZWVixFIpEcP36c/f3o0SMianLla5FquVJSUk6dOjV58mQ3N7cWN9iwYcPKlSubT3I1dOhQe3t7NnMiAMhC8EHwAdAS6PeDTqqqqgoPD1+9enVoaOjkyZNDQ0MrKyuJaNeuXQYGBuzCUF1dHRMTI13cs2fPjRs3ysrKFi9eTERZWVlhYWF2dnYPHjzw8vKytrZ2dXU9duyYUoUQ0fnz521tbdPT09V1aMnJydnZ2W+++aY0JS0tTfrGouTkZENDw4iIiDbLUS3XV199RUT9+/cfP358t27dhg8fnpqaKl0bHx/v4+PDXqHa3OTJk3ft2lVQUNDmXgB0F4JPm+Ug+ABoL9lBPxjfDxqnyIjt6upqR0fHdevWscXy8nJHR8dBgwZVVlZyHGdvby/7NZZdJCInJyeO48RicUpKiqmpKREFBgamp6cfOHCAva80IyNDwUKYEydOdOnS5eTJk20emoLta/bs2QKBoLGxsfmqhoaGwYMHy59btp25Bg4cSETR0dGlpaVZWVm2trYCgeDHH3/kOC4zMzMmJoZt5uTk1PxYcnJyiGjTpk3yd4Hx/aCdFPlm6mjw4RQb18774MNhfD/oPfT7Qbso0pNbu3YtEZWWlkpT9u7dS0QrV67kml0VZBebXDUdHR2JqKamhi3GxsYS0axZs5QqhOM4kUikyKEp2L4GDhxoYWHR4qodO3Zs27ZNkX2pnMvExKR3797SRTbl+dy5cysqKt5++22JRMLSW7z0lpSUENGUKVPk7wL9ftBOinwzdTT4cIr1d3kffDj0+0HvYZwP6J6MjAwiYnfImHHjxhHRpUuXlCrHwMCAiNjsEETk6elJRPn5+crWRygUKptFjrKyMktLyxZX3blzJzg4WNkClcrVq1cv9jwf89prrxFRXl7e4sWL586de+vWrby8vLy8vOfPn7N02R/WLSwsiOjBgwfK1hBAVyD4KAXBB0DbYP5+0D3skllYWOjs7MxSbGxsiKh79+7tKbZPnz6k2PNnHUooFIrF4ubpdXV1w4YNU7Y0ZXM5ODh8//330kU2q4aVlVVycvLhw4ebbOzk5DR48GBpZ6X583YAPIPgozgEHwAthPv9oHvYDTbZR77u379PRBMnTqQ/LgANDQ1ExHHc06dPpZsJBAKRSNRasRUVFaoV0uKVUmW9e/dmjwk2YWpqOnv2bGVLUzaXr69vfX39tWvX2CKbi2PEiBF1dXWyPxRKf2qXvUP55MkTIurVq5eylQTQFQg+ikPwAdBC6PeD7lm5cqWLi0t8fHxZWRlLSUhIGDNmzPvvv09E7KqwYcOG27dvx8XFsR+F09LSJBKJvb19aWkpu05LSS+cZ8+eHT58eEBAgFKFpKamWlhYnD59Wl1HN378+Orq6mfPnjVJDwoKcnd3l02Jjo52dnY+dOiQnNKUzTVv3jwXF5ctW7awxePHj/fq1Wv58uWK1Jxdp8eOHavIxgC6CMGHQfAB0FHo94PuMTU1zczM9PX19ff3DwsLCw8Pt7a2PnfunKGhIRFFRUW5ubnFxMQsXbrU3d3d2dl53rx5lZWVIpHI29vb3Nz8ypUrsqXFxsZWVFQ8fPiwtLT04sWLyhZibGxsbm5ubGysrqPz8/PjOC4zM7NJen19fX19vWxKQUFBbm5uWFiYnNKUzSUUCr///nsTExN/f/+IiIisrKyffvqJjZ1tU0ZGhlAo9PHxUWRjAF2E4MMg+ADoKtmfzzCfD2hcZ87Q0uK8EB1H8fY1ZcqU4OBgRbbMy8tzc3NTtiaq5WrT1KlTFy1a1OZmmM8HtFNnXgE7OfhwCs9jw+/gw2E+H9B7uN8PoHUSExNPnTrV5twUtbW18fHxu3fvVqpw1XK16fLly7du3YqOjlZvsQDQmRB8APgN/X7QXzU1NdJ/tUrPnj2PHj0aEhJSW1srZ7OCgoKNGze6uLgoVbhqueQrLS2NjIw8e/as7PyGANAaBB91QfABUAr6/aCPampq1q5dyx6PCwoKysrK0nSNmnJxcYmMjExISJC/jQqXOtVyySESifbu3bt///5+/fqpsVgAXkLwaUfVmkLwAVAW5u8HfWRmZhYZGRkZGanpishjZ2e3YsUKTdeibYaGhuHh4ZquBYBuQPBRIwQfAGXhfj8AAAAAAP+h3w8AAAAAwH/o9wMAAAAA8B/6/QAAAAAA/NfCc73e3t6dXw/gN47jBAKBIluy2S14+SUsKioinh6aUth5UJesrCycUlAL3rfQbdu2HTlyRNO1AABNEnAcJ13IzMyMiYnRYG2Al27cuFFVVTVq1ChNVwS0iFr6HzExMZmZme0vB3RaUVFRXl7e66+/ruDNBdBzy5cvx/UI9Naf+v0AHWHv3r0LFy4sLi5+4YUXNF0XAOCb0NDQ8+fPZ2dna7oiAADaDuP7ocP985//NDExOXjwoKYrAgA8lJOTM2zYME3XAgBAB6DfDx3OzMzMy8vrq6++0nRFAIBvOI77+eef0e8HAFAE+v3QGfz9/bOzs3/++WdNVwQAeKWwsPDx48fo9wMAKAL9fugM48aNs7e337t3r6YrAgC8kpOTY2Bg8NJLL2m6IgAAOgD9fugMAoFg7ty5+/bta2xs1HRdAIA/cnJyHBwcunbtqumKAADoAPT7oZPMnz//0aNHaWlpmq4IAPBHdnb2K6+8oulaAADoBvT7oZMMHDjw1VdfxdO9AKBGmMwHAEBx6PdD5/H3909OTn706JGmKwIAfPDgwYPS0lL0+wEAFIR+P3QeHx+fv/zlL0lJSZquCADwAXtX18svv6zpigAA6Ab0+6HzmJmZzZgxA0N9AEAtsrOz+/fv36NHD01XBABAN6DfD53K39//ypUrv/zyi6YrAgA6LycnBw/1AgAoDv1+6FSvvfaanZ3d119/remKAIDOw0O9AABKQb8fOhWbyH/v3r0ikUjTdQEAHfb06dO7d++i3w8AoDj0+6GzLViwoLy8/MyZM5quCADosJycHI7j0O8HAFAc+v3Q2ezs7MaMGYOnewGgPXJycnr06NGvXz9NVwQAQGeg3w8a4O/vf+LEiSdPnmi6IgCgq/BQLwCAstDvBw3w8fExMDDARP4AoLLs7Gz0+wEAlIJ+P2iAubn59OnTMdQHAFRTV1eXl5eHwf0AAEpBvx80w9/fPysrKzc3V9MVAQDdc/36dZFIhH4/AIBS0O8Hzfj73/9ua2u7d+9eTVcEAHRPTk6Oubm5vb29pisCAKBL0O8HzTAwMJg3b97evXvFYrGm6wIAOiYnJ+ell14yMMAlDABACQiaoDHz588vKSn57rvvNF0RANAxeFMvAIAK0O8HjXFwcBg5ciSe7gUApYhEol9++QX9fgAAZaHfD5rk7+9/7NixyspKTVcEAHTGzZs36+vrMYknAICy0O8HTZo1a5ZAIDhy5IimKwIAOiM7O9vY2HjIkCGarggAgI5Bvx80qXv37tOmTcNQHwBQXE5Ojqurq5GRkaYrAgCgY9DvBw3z9/fPyMjIy8vTdEUAQDfgoV4AANWg3w8a9sYbb/Tr12/fvn2arggA6ACO437++Wf0+wEAVIB+P2iYgYHBnDlz9uzZg4n8AaBNd+7cefr0KR7qBQBQAfr9oHlvv/12UVHRhQsXNF0RANB22dnZQqHQ1dVV0xUBANA96PeD5jk6Oo4YMQJP9wJAm3JycpycnLp06aLpigAA6B70+0Er+Pv7Hz16tLq6WjaxsbFRU/UBAO2Eh3oBAFSGfj9ohVmzZonFYjaRf2Vl5eeffz5q1KiEhARN1wsANOydd95ZsmTJrl27rl69+vz582vXrqHfDwCgGkNNVwCAiMjKymrq1Knbtm3773//e/z4cbFYzHHcjBkzNF0vANCwp0+fHjt2zMDAQCwWC4VCS0vLixcvGhoaDhs27OWXX+7WrZumKwgAoDPQ7wfNy83NPXToUHp6enl5eW5urkgkIiJjY2OM8wEAR0fHv/zlL8+fPycisVj86NGjU6dOnTp1SiQSCQSCESNG/PDDD4aGuJYBALQNsRI0huO4HTt27N69Ozs7+y9/+UtDQwMRsU4/w1IAQJ85OjrKhgWSiRIcx82bNw+dfgAABSFcgsYIBILy8vLs7GxqpYuP+/0A4ODg0OLLPYRCob29fUBAQOdXCQBAR+G5XtCkDz/8cObMma3drkO/HwAcHBxaTBeLxZ9++ilu9gMAKA79ftAkgUCQmJg4dOhQIyOjJqs4jkO/HwB69uzZ/OFdIyOj6dOnT5o0SSNVAgDQUej3g4aZmpqmpKRYWVk1uW+Hfj8AMPb29k1SBAJBTEyMRioDAKC70O8Hzevdu/epU6eEQqFAIJAmchyH53oBgIicnZ2FQqF00dDQcNWqVQMHDtRcjQAAdBL6/aAVXnnllf3798um4H4/ADAODg7S3wMFAoG1tfXKlSs1WyUAAF2Efj9oi3/+858REREGBv/7TqLfDwCMo6Oj7K9/cXFxZmZmGqwPAICOQr8ftMi6deu8vLzYjT2JRIJ+PwAQkaOjI8dxRGRoaDhixAgfHx9N1wgAQCeh3w9aRCAQ7NmzRzq9D3tDJwDoOelUnhKJ5N///rfsg0AAAKA49PtBu5iamiYnJ1tYWBBRfX29pqsDAJpnbm5uZWVFRO+8886wYcM0XR0AAF2FN578z+HDhzVdBfh/ISEhH374YVFRET4XXho9enS/fv00XQu0el3So0eP2traESNG4FPTBu0falVUVHTp0iW1VAYA5LC1tR01atT/L3PAcRzHae4TAdA7SUlJmm7xHIdWD6Cq9re+pKQkTR8EgF7w8vKSbXq43///kpKS8LiYZh0+fHjmzJnSDtmpU6emTJmi2SqpkUAgwHeMiLRqcDY+EfXy9vYmoiNHjqi95JSUlMmTJzd/sXenaRKd9BY7D+oqDedTq3Rc+9U4vW2/7DOVhX4/aC8+dfoBoD08PDw0XQUAAJ2H53oBAAAAAPgP/X4AAAAAAP5Dvx8AAAAAgP/Q7wcAAAAA4D/0+wEAAAAA+A/z+QBAC+7evXvy5Mnnz59Pnz598ODBmq4OACgHTRgAmsP9fuCDkSNHrly5UtO1UKf8/Pzo6GgiEolEMTExoaGhvr6+48aN++abbxTJrlouprq6OjAwcNKkSUOHDl2xYkXzHkN8fLx0Dn6xWLxq1ari4mLFywdogn/tl9CEQW/wrP0KBAKhUBgeHh4VFZWfny9Nb2eLJqKSkpLExMSZM2eOHj26yaovvvhi2LBh3bp1e/nllxMTE1li87aZn58fFRUVFBQkEAhUfxNO+9+6xw+kNe8Q1WfsDY4qZJw1a1ZERITa6yN1//799hei+HfswoULvr6+DQ0NHMdFRERcv36dpcfHxxPR1q1b2yxBtVwcx5WXl7/yyiuOjo4PHz5scYMrV6506dJF9mN6/PjxjBkzCgoKFCmf06a2pj014Q0vL68m74ZUhE60X6WiE4+bsMpRuoPKATVC++U4jogGDx7cJLH9LZr5/ffficjJyUk2cdWqVXPnzk1ISFi2bJmpqSkRxcfHs1Wttc2BAwcqeDjNP1O0uv9BD0AbaOeV4O7du6+++mr7y1HwO/bbb7/179+/oqKCLfbr1+/s2bPs76dPnxKRm5tbm4WolovjuClTpgiFwqysrBbXPnnyZO3atS+++GKTj+nnn392cXF59uyZIrvQnramPTXhDdX6DR1KXe1X8ejE7yaMfj+Pof1yHNe8X66WFt1a+ffv358zZ450MS0trcl/PFpsm05OTir3+zHOB0Ce4uJiDw+Phw8fds7uOI6bO3fuggULrKysWIpEIjl+/Dj7+9GjR0Rka2vbZjmq5UpJSTl16tTkyZPd3Nxa3GDDhg0rV65s/vPi0KFD7e3tV6xY0eYuADpTJ7dfQhMGUJ/Ob7/NqatFt+bevXts+BDzxhtvvPDCC+Xl5dIUtbdN9PtBt0kkkiNHjsyfP3/8+PFElJycHBAQYGtrW1lZOX/+/B49eri6ul69epWIsrKywsLC7OzsHjx44OXlZW1t7erqeuzYMSLatWuXgYEBuxZWV1fHxMRIF/fs2XPjxo2ysrLFixezPZ4/f97W1jY9Pb0jDic5OTk7O/vNN9+UpqSlpa1evVq61tDQMCIios1yVMv11VdfEVH//v3Hjx/frVu34cOHp6amStfGx8f7+PiYm5u3mHfy5Mm7du0qKChocy8AUjxrv4QmDPqEf+23OXW16NaMGTPGxsZGNqWhoeHVV1+VTVFz21T8twl+I/zirwVU++VXdsBcUVFR165diSgyMvLevXv79u0jIjc3N7FYnJKSwkbOBQYGpqenHzhwoFu3bkSUkZHBcZy9vb3srmUX6c+/yp04caJLly4nT55Utp6KfMdmz54tEAgaGxubr2poaBg8ePC+ffuU2qlSudiQwejo6NLS0qysLFtbW4FA8OOPP3Icl5mZGRMTwzZr8RfGnJwcItq0aVObe9GetqY9NeENFcYJ6Er7VTA68b4JY5wPj6H9Nt+j2ls0NRtHJCsjI8PU1DQ7O1s2sXnbbM84H7S6/0EPQBuofCWQbUhNhq7a2NgYGxuzvx0dHYmopqaGLcbGxhLRrFmzuGatSHaxeSsViUSqVbLN79jAgQMtLCxaXLVjx45t27Ypu1OlcpmYmPTu3Vu6yKL23LlzKyoq3n77bYlEwtJbjDglJSVENGXKlDb3oj1tTXtqwhuqjQ/WifarYHTifRNGv5/H0H6b71HtLVpOv18kEo0fP/7gwYNN0pu3TYzvB/h/TYauWlpaPn/+nP1tYGBARGwuCyLy9PQkItmJuhQkoxXW9QAAIABJREFUFArbW8tWlJWVWVpatrjqzp07wcHByhaoVK5evXoZGRlJF1977TUiysvLW7x48dy5c2/dupWXl5eXl8fOZ15enuzPjhYWFkT04MEDZWsIIEun2y+hCYN+0/X225zaW7Qc//rXv/7+97/PmjWrSbp62ybe2wX6q0+fPtS+J3LUTigUisXi5ul1dXXDhg1TtjRlczk4OHz//ffSxR49ehCRlZVVcnLy4cOHm2zs5OQ0ePBgadRWfS5hAJVoYfslNGEAxWhn+21OvS1ajpSUFDMzs/Dw8Oar1Ns2cb8f9FdFRQURTZw4kf5oVw0NDUTE/TE5FyMQCEQikWzGFqOAWvTu3buysrJ5uqmp6ezZs5UtTdlcvr6+9fX1165dY4tspoIRI0bU1dXJ/koo/YVR9lbNkydPiKhXr17KVhJANVrYfglNGEAx2tl+m1Nvi27NmTNnioqKZDv9mZmZ0r/V2zbR7wed9+zZMyKqqqpii/X19bJrq6uriUg2cEijxtmzZ4cPHx4QEEBE7EK4YcOG27dvx8XFsZ8m09LSJBKJvb19aWnp/fv3Wa7U1FQLC4vTp093xLGMHz++urqaHZGsoKAgd3d32ZTo6GhnZ+dDhw7JKU3ZXPPmzXNxcdmyZQtbPH78eK9evZYvX65IzVkPY+zYsYpsDCDFp/ZLaMKgZ3jWfptTb4uuq6ujZv91+e677zZv3iwWixMSEhISEj799NPly5efOnVKuoF62yb6/aDbamtrN27cSEQlJSXbtm2LiooqLCwkosjIyKqqqri4OPaO64iICGk8io2NraioePjwYWlp6cWLFw0NDYkoKirKzc0tJiZm6dKl7u7uzs7O8+bNq6ysFIlE3t7e5ubmV65cYdmNjY3Nzc2NjY074nD8/Pw4jpP9jz5TX1/fJJ4WFBTk5uaGhYXJKU3ZXEKh8PvvvzcxMfH394+IiMjKyvrpp5/YyMI2ZWRkCIVCHx8fRTYGYHjWfglNGPQJ/9pvc2ps0RcuXGDPAxQWFm7ZsuXnn38moszMTE9Pz3Pnzr3/h8DAwNjY2AULFkgzqrltKvI4sD4gzOyhBTp6hgfFH4HvCAp+x6ZMmRIcHKxIgXl5eUq9JrA9udo0derURYsWKbKl9rQ17akJb3To+z41234Vj078bsKYz4fH0H65lubb6egW3abmbRPz+QDwR2Ji4qlTp9p8cr+2tjY+Pn737t1KFa5arjZdvnz51q1bsi8dBNBbaMIAOk06BxHToS26TS22zSbPPCgF/X4llJeXHzlyhP2qBbqopqZG+q/W6tmz59GjR0NCQmpra+VsVlBQsHHjRhcXF6UKVy2XfKWlpZGRkWfPnmXvYeEZtHrtoRPtl9CEAVqiK+2XiO7evbts2bKoqCj24HuHtmj5mrTN/Pz8qKio8PDwO3fuqFwm+v2Kys3NXb9+vY+Pz9dff62RCpSUlCQmJs6cOXP06NEKZrlw4YKPj49AIBAIBO+9996lS5da3OzLL790cXF5+eWX+/Xrxza+cOECEZ0/f14gEHTv3v2ll14aOXKkQCAwNTUdOXKkq6urqampQCDYsWOHtPyLFy82L/nSpUtsrZeXFytTU2pqatauXcueDQoKCsrKytJgZdrk4uISGRmZkJAgfxsVLtKq5ZJDJBLt3bt3//79/fr1U2OxWkKzrR7tV0q32i+hCeuIH374YfXq1exL7u/vn5yc3NF7VLBR84xutV82GCYuLi48PNzBwYEldlyLlqN523RwcAgPD4+KimLv4FOxXDWNPtJ5pMBIX/YMh5wXLDP3799XX73+RPaF2Api/z0dMGBAaxt8+eWXRHTo0CG2ePz48e7du3/99dccx6Wmpr722mvSt+vJ7rqiosLBwaGgoED6319PT8/mhc+ePZu9pKOsrEyR2vJ7xKci3zF9oD3nQftbvW61X66DxwdrFr+jk+I6f3x/x11SmQEDBhBRbW1tx+1C9hDabNQahPbLPxjf3y6KPEJeWFjo6+vbQRVQ4Q0Xpqam0n9btHfvXiL6xz/+wRbfeuutnTt3FhUVEVFdXd3KlSulb9eTZWVltXjx4rq6OlbymDFjUlJSbt++LbtNWVnZ48eP+/fvT0Q2NjbK1hxAG2i21aP9gp7r0Esq02Yra6cmh9DRuwOQD/1+dSouLvbw8Hj48KGmK6IEiURCRNu2bZOm/POf/2SPik+ZMmXSpEmtZVyyZIn0J7Dg4GCJRBIXFye7wc6dOxcvXtwhlQbQGppt9Wi/wGO6eEltggeHADyDfr/qfvrpp5EjR77//vsffvihkZFRTU3Nnj17bty4UVZWxq6XtbW1+/fv9/X1HTNmTFZW1iuvvDJw4MCMjIxbt25Nnz79hRdeGDJkyNWrV9tZjfPnz9va2qanp6uWPTAwkIjWrVs3bdo09ri6UCh86623iMjU1FQoFLaW0djY2MjIiP09ffr0AQMGJCYmSl9r19jYmJaWNnXqVNVqBaCd1N7q0X4BWtOkcRFRfn6+t7f3qlWr/Pz8xo0b98svv0g3/vTTT+fNm7dkyRITExPBH0jJJpacnBwQEGBra1tZWTl//vwePXq4urqyBpuVlRUWFmZnZ/fgwQMvLy9ra2tXV9djx44R0a5duwwMDNjuqqurY2JipIvND6FNLR7j/v37zczMBAJBVFQUe+vTgQMHjI2Nv/rqKyKqr6//5JNPFi5c+Le//W3SpEm//vqrRCK5ePFiSEiInZ1dSUnJhAkTBgwY0OJ7Z0HvaGrIkbYhxcYck8wYWUdHRysrK/b3zJkzy8vLm2wgkUjYT+fdu3dPTU397bffiGjgwIFbtmx5+vRpTk4OEU2YMEHZejYZanzixIkuXbqcPHlS8SxNfP311+zFLlZWVjt27BCLxUqVw75FW7duJaJPPvmEJR46dGjr1q2cklP28nsEnoLfMd7TnvOgDa2eT+2Xw/hgPdDJ4/ubfG8dHBzs7e05jmtsbLSwsHBxcWHp8fHxQqGwoqKC47hNmzYRUWhoKFvVZhOT/ZIXFRV17dqViCIjI+/du7dv3z4icnNzE4vFKSkpbHBOYGBgenr6gQMH2EOcGRkZHMfZ29vLHo7sYvOmJ79Rt3aMH3zwARHduHGDLf7+++/Tp09nfy9atCg3N5f9/cYbb9jY2Dx69OjSpUtsmN+mTZvOnj27cOHCZ8+etX6mOQ7tl4+af6aGHfKfCf3w5MmTx48fb9++PTAwMCIiwsTEpMkGAoGANf7evXtPmTKFiPr27VtYWMhe5/byyy/37Nnz2rVr7ayGp6dnVVWVnBt7bZo7d+4//vGPDz/88PPPP3/vvfdSUlIOHTpkZmamVCELFy5ct25dfHx8SEiIoaHhl19+Kf9t1XJ4e3urllH7bdu27ciRI5quBahO7a2ef+03KyuLl02YPTXBy0NTCjsPmrJ48eLevXsTkVAotLa2zsvLY+lnzpzhOI51xKdOnbp69eqMjAy2Sqkm1rdv3759++bl5a1Zs4aI5syZExoaeu3aNQMDA3d3d1tb21u3bm3evJn1p8vLy4ODg+Pj40ePHi399YxpsqiWYwwJCYmLi4uNjd25cycR7d+//5133iGiH3/8cdeuXbt27ZItJCsri1U4Ly8vICDA0tLy73//uyJ7R/vlmaysrJEjR8qmYJyP6v79739369Zt2bJlI0aMePbsmSLzNzXZxsrKSi2/u7Wn08BYW1snJCRcvXq1f//+KSkpK1euVLaE7t27L1iw4P79+0ePHv35558HDRpkaWnZzloBaJuOaPVovwAKCgkJmTp16meffRYZGfn8+fPGxkaWPmnSJIlEkpqaSkTsf+Ovv/66NJdSTYyNz5GytLSUvsXJwMCAiKTPynt6ehIRm+JdjVo7Risrq8DAwK+++qqkpISIvvvuuzfffJOIrly5Iv1NQMrd3V16LGjL8Ced92ODdiPlf/HnOO7OnTuTJ08mIiMjoz179jTfoElKkx/NVXhtdfPyVc5SXl7+3XffZWdnyyYWFBQIBALpSAZFypEeQn5+voGBgZub28KFC6W/RWKcj5SC3zHe057zoBOtXofaL4dxAnpAs+N8fvzxRzs7u//85z9csy/n7t27rayswsLCpk2btn79epFIpGBN5LdQ2cUmq9gkv2+99Zb8XPLjg6zy8vLGxkY5x/jo0aOuXbuuWLHip59+WrNmDUv817/+1aVLF+mMvQwb74f2K6W37RfzeKrTRx99NGjQoNOnTx88eLCxsZGNvRMIBO15f7Jq2FM+ylqyZImFhcXy5cvZlCCMnZ2djY1Nz549FSyE5WX/Dh482MPD4/Lly8XFxX/961/ZBpzKr5YA0D4d0erRfgFa06Rx+fn5NTY2svvcst98sVj866+/ZmVlbdmy5dtvv42IiJC9x69aE2tTRUUFEU2cOJH+uLPe0NBARBzHPX36tLVDkGPJkiVCobC1YyQia2vrxYsX79ixY/v27W+//TZLdHJyqq2tjYqKkm528+bNTz/9tJ1HB3yFfr8S6urqiIj9F5+Itm7dyn6v9/Ly6t69e9++fYnI3t6+tLSUvZdOurH02sl+sHv2f+zdfVxM+f8//tc0XWxilVwrSoRVVtpdFykXkZWUq1TSCEvLGyGktbztrqzWVX3U+2tZWyZKRlmpVkTYTbVW2otCKem6SOn6YmbO74/z3vnNu9LlNGeaedz/cJt5nTOv8zxjns3znHmd86quFl/aLLHbDaDZn7Do6GhNTc0bN260+pKioiJCSFVVlfj3d2VlpZub23vvvWdoaHj37t3169eLQoqKiiouLm45ToCeW7vlJNWlpaWEEPpGIoSQHTt2EEI2b97c7IWiNw2gd+nprEf+ArShWXIVFRUVFBTcunUrJCSEzsTffvstPz//8OHD169f/+WXX2JjYxMTEzMzM0VflG2nGPknL0TZ0ezTXlVVRQgRL9xFPcfFxZmamrq5uRFC6DPrhw4dev78uZ+fHz00KDY2VigUttwF8u6kZrFY79pHek0PD4/Gxsbc3Fz6OiJCiJ2d3ejRo7/++uv169eHhITs379/+/bta9euFe0LncUA/yXtnxxkFWnvF//s7Oxt27bRb5qvr295eTkhZMqUKUeOHHF2draxsXnx4gVFUV5eXsOGDQsPD6coqqSkZOfOnYQQNTW1uLi42NhYZWVlQsi2bdvKyspOnTpFnyH47rvvXr9+3W6E8fHxGzduJISoqKh89913qampdPutW7eGDx9+586dli+5c+eOnZ0dHfP48ePnzJkzZ86ccePG0VMRnT9/nqIo+vohbW3t+fPnz58/f8aMGVevXm3WT2xsLP1HhBDy+eef3717l26/du0afac/Gxub27dv043Lly+nf2FMT0/ft28f/aqVK1fGx8e3u4/y/Utcu58xBSE774MsZL085S+FcQIKQMrjfMSTi6KogICA/v37f/LJJ0lJSX5+flpaWnZ2dmVlZbdu3Wo2u9ygQYPoV7WRYr/88svevXvp9Z2dna9duxYQEEA/PXTo0Nu3b319femne/furauro4v7Y8eOvX79urS09MiRI6I75GRkZEydOlVDQ8PKyiojI8Pc3NzFxeXSpUsNDQ3iu9CRpH7XPorCtrGxoefkFsnJybG1tR0wYMDQoUM3btz46tWrmpqar7/+mt7Qxo0bHz9+3JH/FOSv/Gn5f6qI70KrZKcWUWTynZn4jNFk532QnUjkBuoGuSflur+DfvzxR9FdaAUCQV5eHpfLHTx4sKT6p3XhkjyJq6mpGTNmTG1tbU90jvyVP7iPp+xqdg8BcU+fPh03bpw0gwEAAOgVfHx89u7dS4+2J4QoKSnp6OjMnDmTHoYnZwICArZu3UrPJADQBaj7ZQWF6+cAAAA66ddffyWEnD592s3NTVtbmxCSkpLi4+NDz7olQfRA+Zqams7Oj9F9ycnJGzdurK2tFQgET58+lfLWQZ7gul6AXiAzM/P48eOEED6ff+LECQ8Pj1WrVllYWFy5cqWDPRQWFgYGBjo4OMyYMaPZonPnzpmYmPTr12/y5MmBgYHii4KDg21tbb28vObOnbt582b6IjOBQLB3796CggJJ7BmAwul+Oqenpy9ZsmTgwIGDBg1ycnKiLxWltZrO8p2z58+f37p167lz53R0dMzMzFauXJmSknLhwgXRbam6r6amZt++ffS1udu2bUtKSpJUzx2koaFRWVmppKQUEhKiqqoq5a0rFBaLxWazPT09fXx8xCdnkP63cMu0zczM9PHx2bZtG4vFamOQSDsYGnEkcwhG+sqAnh6Bl5eXx2AnXf6M3b17d9WqVY2NjRRF7d+//88//6TbT506RQg5duxYB/vJzc0lLe4bvXfv3tWrVwcEBLi7u9O/HZ86dYpedPr0aUJITEwMRVFpaWnknztVUxT15s2bZcuWZWdnd2F3ZCfXZCcSudHT44MZTGFJ/XXqfjqnp6cvXbr06tWrjx8/dnFxIYRYWlrSi9pI5+7krDjZHN8PEoH8pSiKEDJmzJhmjUx9C78rbfX09Dq4O7iu951QAciCHv0mePHihbm5OYOddO0zlp6ePnLkSNHNHHR0dOLi4ujH9C2ip06d2qkYxP/i5OXlOTs7i57GxsaK/8mjz0m8evWKfjp48OB+/fqJVv7jjz+MjIxEt7PoVAwykmuyE4nc6NG6gdkUlshfJ4mks5+fn+iyzqamJk1Nzb59+1LtpTPVjZwVh7pfjiF/qdZmVWPwW5h6R9p2/BJzzNsFCqqgoMDGxubVq1eMd9IpFEWtXr167dq1AwYMoFuEQuHVq1fpx69fvyaE6Orqdrn/ly9f0j9c0qysrAYNGkTf050QQm/07t27hJCampqysrK5c+eKVp40aZKBgcHu3bu7vHWAjuulKSxOUum8bds28cs6+Xz++vXrSXvpTJCzwJzem7/MfguTHkhb1P3Q+1RWVnp6enp5eXl4eCxYsMDDw4Med3727FklJSV60FtVVdWJEydET4OCgtLS0oqLizdt2kQISUpK2rVrl76+fklJyYoVK7S1tY2NjSMiIjrVCSEkPj5eV1f3/v37PbSnkZGRKSkp9MSNtNjYWC8vL9FSZWXl/fv3d7l/MzOzZje9bmxsNDc3px+fPHnSwMBg+/btubm5/v7+u3fvDgkJEV95wYIFZ8+ezc7O7nIAoJgUJ4XF9UQ6HzhwwNfXl77NfNvpTEPOQvcpVP4y+y1Mk3Dadvy3CflG8Iu/DOjIL3FVVVWGhoYHDx6kn5aWlhoaGo4ePbqiooKiKHoKQ9HK4k/JPz+uCQSCqKgo+oTZ1q1b79+/HxIS0q9fP0JIQkJCBzuhXbt2rU+fPtevX+/I3nXhM+bk5MRisZqamlouamxsHDNmzIULFzrVIWnxC6a4hIQEdXX1lJQUUcurV6/MzMx0dHR27tzZcv3Hjx8TQr799tvOxiAjuSY7kciNjowT6KUp3P1xKZJN56tXr1pYWBBC9PX1f/jhh5YrtExnqqs5Kw7jfOQY8rflJhj/FqZaS9vujPNB1v0XKgBZ0JHMpOcQLSoqErVwuVxCyJ49e6gWySD+tFmyGRoaEkJqamrop/QJM0dHx051QlEUn8/v4N514TOmp6enqanZ6qLTp0+fPHmyU71Rbf7F4fP5s2bNCg0NFW98+fKljY3NwoULCSG7d+8WCoXiSwsLCwkh1tbWnY1BRnJNdiKRGx2pG3ppCne/TpVsOpeXl6enp/v7+/fp04cQEhQUJL601XSmupqz4lD3yzHkb8tNMP4tTLWWthjfDwokISGBEEKfG6DRJ70ePHjQqX6UlJQIIfRXJiHE1taWECJ+064OYrPZnX1JxxUXF2tpabW6KCsra/v27RLc1ldffWVpaeno6Chq+e2330xNTdesWfPTTz+ZmZkdPXr0wIED4i/R1NQkhJSUlEgwDJB7CpXC4iSbzpqamhMmTPjXv/71/fffE0Lo2kukZTqLXkWQs9ANipa/zH4L0ySbtqj7oZeh/1jk5OSIWuixcf379+9Ot8OHDyfduzqnJ7DZbIFA0LK9rq7OxMREghuKiorS0NBoNkjRy8vr9evXs2fPVlVVvXTpEiHkzJkz4it0/f7BoMAUKoXF9VA629nZEULE7+neajrTkLPQTYqWv8x+C9Mkm7ao+6GXoU8tREdHi1rouVTmzZtH/kmPxsZGQgj1zz22aCwWi8/nv6tbeo73LnTS6l8ESRk2bBh9vVQz6urqTk5OktrKrVu38vPzPT09RS2JiYnkn3eArid0dHSGDBnS7K9PeXk5IWTo0KGSigQUgUKlsLgeSmd60i5ra2v66bvSmYachW5StPxl9luYJtm0Rd0PvcyePXuMjIxOnTpVXFxMtwQEBJiZmW3ZsoUQQg96O3To0PPnz/38/BoaGgghsbGxQqHQwMCgqKiI/gslIvqTERcXZ2pq6ubm1qlOoqOjNTU1b9y40UM7O2vWrKqqqurq6mbt27ZtW7RokXjL8ePHJ06cSJ+Vf5e6ujrS4q/k7du3jxw5IhAIAgICAgIC/P39d+7cGRMTQwhZtWoVIYR+nJubW1JS0uz3R/oWZjNnzuzyDoICUqgUFiepdD558uSPP/5IF0MNDQ2enp4ODg70u9dGOtOQs9BNipa/zH4L0ySbtsoS6QVAatTV1RMTE7/55ps1a9YYGxuz2Wxtbe07d+4oKysTQnx8fAoLC0+cOJGcnOzv7x8REaGnp1dRUcHn8+3t7YOCgh4+fCj+S6Kvr6+rq6tQKCwqKrp3715nO1FTU3v//ffV1NR6aGc5HM65c+cSExPnz58v3l5fX19fXy/ekp2d/fTp0127drUcGki7e/duaGgoISQnJ+fo0aNWVlYffvhhYmKira1tbW3tnTt3RGuyWKznz58TQjZt2kRR1MmTJ3///ffs7OwDBw588cUX4n0mJCSw2eyVK1dKan9BEShUCouTVDpXVlb+5z//oZeqqqpu2bLF0tKSENJ2OtOQs9BNipa/zH4L0yScth29AlneEdzZQwZI8w4PHb8cXlK69hmztrbevn17R9Z89uxZp2YN7L7Fixdv2LChs6+SnVyTnUjkRo/O99mMlFNYIn+dGE/nruWsONzPR44hf6nW7rcjg2mL+/kAyK3AwMCYmJh2L+Svra09derUDz/8IJ2oCCHJyckZGRniEw0CQNuYTWfkLEBH0COLRGQwbdu4UqJdqPtBQdXU1Ij+lWWDBw8ODw/fsWNHbW1tG6tlZ2cfPnzYyMhIOlEVFRV5e3vHxcWJ380NQJp6SwqLYzCdkbMgU2Q5f1+8eOHu7u7j40PfV1R20jYzM9PHx8fT0zMrK6vLfaLuB4VTU1Ozb98++sKgbdu2JSUlMR1RO4yMjLy9vQMCAtpeR2pf53w+n8vlXrx4UUdHRzpbBBDX61JYHCPpjJwF2SHj+UsPhvHz8/P09Bw7dizdKCNpO3bsWE9PTx8fH3oOza51i+t6QeFoaGh4e3t7e3szHUgn6Ovr7969m+ko/ktZWVn8dmMAUtYbU1ic9NMZOQuyo5fmr9ykLc73AwAAAADIP9T9AAAAAADyD3U/AAAAAID8Q90PAAAAACD/UPcDAAAAACgACc0m1usx/f8AoEBkZJZcpt8GgN6q+9lHz58KAD2t2Xy9uI/nf+FvEMiB6urq8PDw5OTksrKyESNGTJ8+ffr06TJ4x+4ZM2YwHQIhyHoZdvLkSULIjh07mA4EesqMGTN6RQLm5eX98ssvv/76a1lZmb6+vpWV1dy5c5kOCqATdHV1xZ+ycNILQP48evSIy+VeuXKlsLDwgw8+sLe3d3BwmDBhAtNxAXTIypUrCSGXL19mOhBQUIWFhTwej8fjJSQk6OjoLFu2zNXV1cTEhOm4ALoLdT+A3BIKhQ8ePODxeOIHAI6OjuPHj2c6NIC2oO4HRtTV1UVFRXG53Bs3bmhoaNja2trb21tbW7PZbKZDA5AM1P0A8k90AMDj8YqKiugDACcnp3HjxjEdGkArUPeDNAkEgvj4eC6Xe/Xq1bq6ujlz5ri4uCxfvlxDQ4Pp0AAkDHU/gAIRHQBcvny5uLiYPgBYtWqVoaEh06EB/P9Q94N0pKWlBQcHnz9/vri42NTU1MXFxcnJafDgwUzHBdBTUPcDKCLRAUBYWFhJSQl9AODs7Dx27FimQwNA3Q89Ky8vLyIiIigoKDU1ddSoUY6OjuvXr8dfP1AEqPsBFJpAIEhMTOTxeJcuXSotLaUPAFavXj1mzBimQwPFhbofekJFRUVkZGRwcPDt27c1NTXt7e1dXFzMzMxYLBbToQFICep+ACDkHQcALi4uBgYGTIcGCgd1P0hQY2NjbGwsj8cLDw8XCoXz5s3jcDh2dnaqqqpMhwYgbaj7AeB/iA4AQkNDX716RR8AcDic0aNHMx0aKArU/SAR9B2NQ0NDy8rKpk+fTv+Yqa2tzXRcAIxB3Q8AraMPAIKDg8PCwqqqquhvTXt7++HDhzMdGsg51P3QHS9fvrx06dIPP/zw/PlznLkAEIe6HwDa0dDQcPPmTR6Pd+3aterqahwAQE9D3Q9d8ObNmytXrnC53AcPHgwbNmzFihX29vYzZ85kOi4AGYK6HwA6qtUDgJUrVw4bNozp0ECuoO6Hjquvr79161ZwcPBPP/2krKxsY2Pj4uKycOFCZWVlpkMDkDmo+wGg0+gvWh6P99NPP9XU1NAHAA4ODkOHDmU6NJAHqPuhXfTNiIODgy9dulRTU0NPtrVs2bK+ffsyHRqA7ELdDwBdJzoAoOe5nDZtmr29vaOj45AhQ5gODXox1P3QhrS0NB6Px+VyX7x48cEHH3A4nDVr1uCkA0BHoO4HAAmoq6uLi4trdgCAmS+ha1D3Q0uFhYU8Hi84OPjRo0e6urpLly5du3bt5MmTmY4LoDdB3Q8AkiQ6AIiIiKivr6cPAFatWjVo0CAQeB8VAAAgAElEQVSmQ4NeA3U/iNTV1UVFRXG53Bs3bvTt23fx4sUcDsfS0hKTbQF0Aep+AOgROACALkPdDwKBID4+nsvlXr16tbGx0crKyt7efsWKFX369GE6NIBeDHU/APSs2tra27dvBwcHR0ZG8vl8+vI7Ozu7/v37Mx0ayCjU/YosLS0tODg4KCiopKTE1NTUxcUF5wsAJAV1PwBIydu3b69du8bj8W7evMlisebPn29vb79kyZL333+f6dBAtqDuV0B5eXkhISGBgYHPnj0bNWqUo6PjZ599NmbMGKbjApArqPsBQNoqKioiIyPpAwAlJaV58+bhAADEoe5XHPRfg+Dg4Nu3b2tpaa1YscLFxcXMzAzD9wF6Aup+AGBMeXn59evXeTxebGwsm82mDwCWLl3ar18/pkMDJqHul3uiSQCvXLlCUdS8efM4HM6SJUtUVFSYDg1AnqHuBwDmtXoAgCl4FBbqfjn26NEjLpcbEhLy5s2b6dOnczgcR0dH/NYHIB2o+wFAhrx58yYqKoo+AFBWVra0tMQBgAJC3S9/nj59eunSpQsXLmRlZX3wwQf29vZr1qzR19dnOi4AxYK6HwBkUVlZWXR0NI/Hu3HjhoqKCn0AsHz5cg0NDaZDgx6Hul9uvHnz5sqVK1wuNyEhYfjw4StWrOBwOKampkzHBaCgUPcDgEwTPwBQVVWdO3cuDgDkHur+3q6+vv769etcLpf+4c7GxsbFxWXhwoXKyspMhwag0FD3A0Dv8Pr165iYGB6P9/PPP6upqc2dO5fD4dja2qqpqTEdGkgY6v5eSigUPnjwIDg4ODQ0tLa2lp6sA+P0AGQH6n4A6GUKCgquXLnC4/EePHjQv3//xYsX29vbL1iwQFVVlenQQDJQ9/c6aWlpPB7v/PnzOTk5H3zwAYfDcXV1HTJkCNNxAcD/QN0PAL1Vfn5+eHg4DgDkD+r+3oI+COdyuSkpKbq6uqtWrVq7du24ceOYjgsAWoe6HwB6vby8vIiICPoAQFNT08bGxt7e/tNPP8W9wHsp1P0yTjT39s8//9yvX7/FixdzOBxLS0tMtgUg41D3A4D8yM3NvXr1Kg4AejvU/bJJIBDEx8dzudyIiAg+nz9//nx7e3t7e3t1dXWmQwOADkHdDwBySPwAQEtLa9GiRTgA6EVQ98saerKtS5culZaWmpqauri4ODs7Dxw4kOm4AKBzUPcDgDx7+fLlTz/9xOPxEhIStLW1ra2t7e3tcT9BGYe6X0bk5uaGhob++OOPGRkZ48ePd3BwcHFxMTAwYDouAOgi1P0AoBBycnLoEck4AJB9qPuZVV5efv369eDg4Nu3b2tpaa1YscLFxWXmzJlMxwUA3YW6HwAUy4sXLyIjI+kDgIEDBy5cuNDe3t7a2prNZjMdGvwX6n5GNDQ03Lx5Mzg4+Nq1a0pKSosXL3ZxccHoOAB5grofABRUdnY2j8fjcrnp6ekjRoxYvny5vb29mZkZ7kkifcnJyX/88Yfo6ZkzZwghGzduFLV8+OGHU6dOZSAyBUBPtsXj8UJCQt68eTN9+nQOh+Pk5NSvXz+mQwMACUPdDwCKjp5y6PLly0+ePNHR0Vm2bBkOAKQsKipq8eLFbDZbSUmJEEJ/MdHvv1AoFAgE169ft7GxYThKufPkyZOwsLDg4ODs7OwPPvjA3t7e1dVVT0+P6bgAoKeg7gcA+C/6ACAsLOzp06e6urpLly7FAYB0NDU1DRw4sLKystWl77///qtXrzAdm6SUlZWFh4dzudyEhAT6l641a9ZMmTKF6bgAoMeh7gcAaI4+ALh06dKzZ89Gjhy5ZMkSHAD0tM8//zwwMLCxsbFZu4qKyrp1606fPs1IVPKkrq4uKiqKy+XeuHFDQ0PD1tYWV7YAKBrU/QAA70QfAISGhmZkZIwaNcrOzg4HAD3k3r17s2fPftciCwsL6YYj64RCIT0mqiNrPnjwIDg4ODQ0tLa2ds6cOS4uLsuXL9fQ0OjpIAFA1qDuBwBoH30AEBISkpmZKToA6MidDUtKSs6ePbtv3z4cKrRNKBQOHz68pKSkWfugQYOKi4s7WOMqiOvXr0dGRp49e7bt1dLS0oKDg7lcblFRET3ZlpOT0+DBg6UTJADIIPwlBQBo38SJEw8ePJiRkfH333+7urrGxMSYm5vr6+u7u7v/+uuvbbwwPDx8//791tbW5eXlUou2N1JSUnJxcWk2iF9VVdXV1RVFv4hQKNy/f7+dnR2Xy3379m2r6+Tn5/v5+ZmYmBgZGYWGhnI4nGfPnv3+++/u7u4o+gEUHM73AwB0Bf0LwIULF7KysvT19RcvXtzqLwDm5uYJCQlsNnvo0KGRkZEmJiaMRNsrpKSkmJqatmzEm0arqqpydnaOjo4WCoVsNvv7779fv369aOnbt2+vXbsWHBx8586d/v3729jYcDgcS0tL/NAEACKo+wEAuoU+ABC/GeLKlSs/+OADQkhxcfGIESOEQiEhRFlZmcViBQQEbNiwgemQZdfYsWOfP38uejp69OisrCwG45EdWVlZixYtysrK4vP5hBAlJaUZM2b88ssvAoEgPj6ey+WGh4cLBIL58+dzOBw7Ozvc/ggAWkLdDwAgAfTVk5cvX75y5UpRUZGRkdHKlSv5fP7hw4fpQo3GYrFWrVp19uxZdXV1BqOVWV999ZW3t3dTUxMhRFVVdd++fQcOHGA6KObdv3/fzs6upqaGfmdoLBbL2dn5559/Li8vNzc3d3FxWbFiRf/+/RmMEwBkHOp+AABJEs1+yuPx1NTUcnNz6fP9IsrKyuPHj4+MjNTX12cqSJn1/PnzsWPHip4+e/bM0NCQwXhkwZkzZzZv3kxRVMsP0qBBg7Zu3ers7Dxy5EimwgOAXgR1PwBAj8jPzx85cmSrf2NVVFT69Olz6dKlTz/9VPqBybjJkyf/+eefhJBJkyalpqYyHQ6TGhoa3NzcuFzuu76px44dm5GRIeWoAKD3wk0SAAB6RHh4+LtmRGpqaqqqqrK2tt67d2+zk7jA4XDYbDabzeZwOEzHwqTCwkIzM7MLFy60cXouMzPz8ePH0owKAHo11P0AAD0iJCREIBC8a6lQKKQo6ujRowsXLnzz5o00A5Nxjo6OQqFQIBA4ODgwHQtjUlJSPvrooz///LONjxAhRFVV9cKFC1KLCgB6O4zzAejF7O3tmQ4BWldXVxcdHd32OvQNFimK6tOnz4wZMzQ1NaUSWi9w9+5dQsi7pu+Vezk5OSkpKUKhkMVitX0XToqiVFVVbWxscLPOXoTH4zEdAigu1P0AvRiLxZo2bZqOjg7TgUBzBQUFT548UVFREW9UVVUVr8/o0Sz0YyUlpfHjx7/33ntSjbLD8vPzk5KSVqxYIZ3NZWdns1gsqV33fOXKFdnJo4qKiuzsbNHTZh8hWrN7dOrp6ampqfV4ZNBtdB6h7gIGoe4H6MVYLFZYWNjKlSuZDgTk3OXLlx0cHKT2fUEPfBowYIB0Noc8AumQch4BtKTMdAAAAAD/Q2oVPwCAQsF1vQAAAAAA8g91PwAAAACA/EPdDwAAAAAg/1D3AwAAAADIP9T9AAAAAADyD3U/AAD0lGnTpu3Zs4fpKCSDxWKx2WxPT08fH5/MzExRe2Zm5vHjxwkhfD7/xIkTHh4eq1atsrCwuHLlSgd7LiwsDAwMdHBwmDFjRrNF586dMzEx6dev3+TJkwMDA8UXBQcH29raenl5zZ07d/PmzRUVFYQQgUCwd+/egoKCLuxg93ckPT19yZIlAwcOHDRokJOTU1FRUds7wmy0nX3bW0abmZnp4+Ozbdu2dmdYA5AVFAD0WoSQsLAwpqMA+RcWFta17wtHR8f9+/dLPB6RvLy87nfSwTwihIwZM6ZZ4927d1etWtXY2EhR1P79+//880+6/dSpU4SQY8eOdTCG3NxcQsj48ePFG/fu3bt69eqAgAB3d3d1dXVCyKlTp+hFp0+fJoTExMRQFJWWlkYIWbJkCb3ozZs3y5Yty87O7uCmJbUj6enpS5cuvXr16uPHj11cXAghlpaW7e4IU9HSOvu2vytaPT29jiRIl/MIQFLw+QPoxVD3g3TIZr3y4sULc3Pz7vfT8bq/WYGYnp4+cuTIsrIy+qmOjk5cXBz9+O3bt4SQqVOndioM8f7z8vKcnZ1FT2NjY8UPPOhT1K9evaKfDh48uF+/fqKV//jjDyMjo+rq6g5uWiI74ufnV1tbSz9uamrS1NTs27dvuzvCVLQinXrb3xXt+PHjUfdDr4BxPgAA0PsUFBTY2Ni8evWKqQAoilq9evXatWtFs4wJhcKrV6/Sj1+/fk0I0dXV7XL/L1++pMex0KysrAYNGlRaWko/pTd69+5dQkhNTU1ZWdncuXNFK0+aNMnAwGD37t3S3JFt27bRZ8dpfD5//fr17e4IU9G+i2SjBZA1qPsBAEDyhEIhj8dzdXWdNWsWISQyMtLNzU1XV7eiosLV1XXgwIHGxsaPHj0ihCQlJe3atUtfX7+kpGTFihXa2trGxsYRERGEkLNnzyopKdEjp6uqqk6cOCF6GhQUlJaWVlxcvGnTJnqL8fHxurq69+/fl84ORkZGpqSkfPrpp6KW2NhYLy8v0VJlZeX9+/d3uX8zM7MhQ4aItzQ2Npqbm9OPT548aWBgsH379tzcXH9//927d4eEhIivvGDBgrNnz2ZnZzOyIwcOHPD19fX19W13R2QhWnGSjRZA5jD8ewMAdAPBOB+Qiq6NTxAfPJ2fn9+3b19CiLe398uXLy9cuEAImTp1qkAgiIqKos8Tb9269f79+yEhIf369SOEJCQkUBRlYGAgvmnxp+R/R2hcu3atT58+169f72ycHcyjZptzcnJisVhNTU0t12xsbBwzZsyFCxc6G0azcUTiEhIS1NXVU1JSRC2vXr0yMzPT0dHZuXNny/UfP35MCPn222/b3a5kd+Tq1asWFhaEEH19/R9++KEjO8JgtFTn3/ZWo8U4H+gt8PkD6MVQ94N0dLleES+qxo0bJ97JkCFD1NTU6MeGhoaEkJqaGvopfZ7Y0dGRalFRiT9tWbHx+fyuBdmFul9PT09TU7PVNU+fPn3y5MkuhPGuApTP58+aNSs0NFS88eXLlzY2NgsXLiSE7N69WygUii8tLCwkhFhbW7e7XcnuSHl5eXp6ur+/f58+fQghQUFB7e4Ig9FSnX/bW40WdT/0FhjnAwAA0tDsRodaWloNDQ30YyUlJUIIXSkSQmxtbQkh4vfK7CA2m93dKDusuLhYS0ur1UVZWVnbt2+X4La++uorS0tLR0dHUctvv/1mamq6Zs2an376yczM7OjRowcOHBB/iaamJiGkpKSk3c4luyOampoTJkz417/+9f333xNCuFxu2zvCbLRt6360ALIGdT8AAMiW4cOHk+5dnSkFbDZbIBC0bK+rqzMxMZHghqKiojQ0NJqNWffy8nr9+vXs2bNVVVUvXbpECDlz5oz4Ch2/nXwP7YidnR0hRFVVVdTS6o7QGI+2JYlECyBrUPcDAIBsKSsrI4TMmzeP/FNjNTY2EkKof27USGOxWHw+X/yFrVaEPWTYsGH0VFnNqKurOzk5SWort27dys/P9/T0FLUkJiaSf94QuqrW0dEZMmRIs2K0vLycEDJ06NB2N9FDO0JP2mVtbd32jshItM1IKloAWYO6HwAAekR1dTUhpLKykn5aX18vvrSqqooQIl64i6r2uLg4U1NTNzc3Qgg9cvrQoUPPnz/38/OjhwbFxsYKhUIDA4OioqK8vDz6VdHR0Zqamjdu3Ojp/aLNmjWrqqqK3kdx27ZtW7RokXjL8ePHJ06cSJ+Vf5e6ujrS4rjl9u3bR44cEQgEAQEBAQEB/v7+O3fujImJIYSsWrWKEEI/zs3NLSkpaTYchb6j5cyZM9sNQFI7cvLkyR9//JE+MGtoaPD09HRwcNiyZUvbO8JUtLTOvu0towXoZZi+wAAAuo7gul6Qii5cj1hTUyO6u+KJEyeOHDlCPz506NDbt2/pK3cJIXv37q2rq6OL+2PHjr1+/bq0tPTIkSOieZEyMjKmTp2qoaFhZWWVkZFhbm7u4uJy6dKlhoYGLy+vYcOGhYeH02veunVr+PDhd+7c6ezedTCPyP9eAHrv3j1CyM2bN5uttmHDhrlz54q3bN68WUlJacSIEe/qOT4+fuPGjYQQFRWV7777LjU1laKoBw8eiC54EGGxWFlZWfSrAgICPvnkEw8Pj6VLlx44cKC+vl68z//3//4fm82mV247AEntyMGDB8eMGaOlpbVp0yZ3d3fRXFrt7ggj0VJdfdubRUvDdb3QW+DzB9CLoe4H6ejpeqWDZVMP6VrdT1GUtbX19u3bO7KJZ8+edWoS2e5bvHjxhg0bOhgA4zvSq6OlUPdD74FxPgAAAB0iugERLTAwMCYmpt37utTW1p46deqHH37oydD+R3JyckZGhmje2XYDYHZHenW0tGbXmQDILNT9AADAsJqaGtG/suzFixfu7u4+Pj70PUYHDx4cHh6+Y8eO2traNl6VnZ19+PBhIyMj6QRZVFTk7e0dFxdHT3/WkQAY3JFeHW1mZqaPj4+np2dWVpYEtwLQg5j+wQEAuo5gnA9IRc+NT6iurv7iiy/o76N169YlJib2xFba1s08ys7O/u677yQYT3c0NTUdOXKksrKyC6+V/o4oTrQ0jPMBxrEoimLuoAMAuoXFYoWFha1cuZLpQHo3Ho/H5XILCgoGDRr03nvv6erq6urqvn79+ujRo0yHJisuX77s4OAgr98XyCOQDvnOI+gVlJkOAADkXH5+vo6Ojmz2/Pr165UrV+bl5V28ePGTTz4hhFAUFRIS4u7uvmTJEgmF2VGy/EYBAIAcwPh+AOhBOTk59I3GZbBniqKWLFnyxx9/JCcn00U/IYTFYjk7O4eHh0t5rLksv1EAACAfcL4fAHpKQUGBjY1NT0yhKpGeIyIiEhISvvvuuwEDBjRbNGvWLHrKWOmQ8TcKAADkA873A8i5mpqaQ4cOubi4uLu7z54928/Pj26vrKz09PT08vLy8PBYsGCBh4dHRUUFISQyMtLNzU1XV7eiosLV1XXgwIHGxsaPHj1qu7fMzEx7e/u9e/dyOBwLC4u//vqLEBIUFJSWllZcXLxp0yZ6tfr6+u++++6zzz77+OOP58+f//fff7e7xe70TAiJj4/X1dW9f/9+y3cmIiKCEGJpadnq+7Zs2TKFeqMAAED+MXpVMQB0C2nvPiRNTU2zZ892cXERCoUURQUGBhJCrl+/XlVVZWhoePDgQXq10tJSQ0PD0aNHV1RU5Ofn9+3blxDi7e398uXLCxcuEELouW/e1RtFUWPHjjUwMKDX0dTUNDIyEkUoPtXRhg0bnj59Sj+2srIaMmRIZWVlG1vsZs8URV27dq1Pnz50kM18/PHHhJC3b9+28QYqzhvVNvm+D0m7eQQgEfKdR9Ar4PMH0Iu1W6+cOHGCEPLs2TP6KZ/PDwwMLC8v37dvHyGkqKhItCaXyyWE7Nmzh6KocePGiX85DRkyRE1NrY3e6EWhoaEURQmFQgMDAxUVFVGEoqIzOTm55amHqKioNrbY/Z7pOFt9c6ZNm9bsTWhJod6oNtD1CgB0X7vpBtBzML4fQJ7dvXuXECK6lwubzXZ1dSWEJCQkEEJEE+UQQiwsLAghDx48IISwWCzxTrS0tOipMd/VGyFkx44dNTU1//nPf968edPQ0NDU1NQymIcPHxoZGdHjT5p51xa73zMdZ6vtH3zwQVJS0pMnT4YOHdrqCkTB3qh2yWv17+DgsH379unTpzMdCMi5xMREX19fpqMAhYa6H0Ce0UVhZmbmhx9+KN6upKRECMnJyZk4cSLdMmTIEEJI//79u9AbIeThw4cODg7/+c9/Nm/efPHixVZfXlZWlp2dXVtb26dPH1GjUCikg3mXnut51qxZP/74Y1JS0pw5c961Dt4ocfJ6h3sHB4fp06fL696BTEHdD8zCdb0A8oyuO729val/Zop5+fLlzz//TJ+0jo6OFq2Zl5dHCJk3b14XeiOEcDicpqamTz/9lBAiFApFL2GxWHw+n348fvz42tpaHx8f0dInT574+/u3vQvd7/ldd7NZvXq1qampn59fUVFRs0UNDQ30eB6FeqMAAEDOMTzOCAC6gbQ3vj87O1tDQ4MQMnfu3ICAgP3797u5uQmFwtraWiMjIx0dHdHIdXd3dzMzs6amJoqi9PT0xP84jBgxghDS1NT0rt4oiurfvz+Lxbp58+bFixcHDx5MCElOTs7LyxszZoyGhkZubi5FUfX19aNHjyaErFu37uLFi19++aWVlRV9Uem7ttj9nqOiovr27fvzzz+3+v48efJk1KhRo0ePjoiIoC8DqK2tvXPnjqWlZVJSEv1UQd6otsn39Yjt5hGARMh3HkGvgM8fQC/WkXrlr7/+WrBggZaW1ogRI7Zv3y66fU1VVdWePXusrKw8PDz27Nnz9ddfNzQ0UBQVEBBAnxQ4dOjQ27dvRb9K7927t66u7l29BQQE9O/f/5NPPklKSvLz89PS0rKzsysrK/Py8ho2bFh4eDi9Wk5Ojq2t7YABA4YOHbpx48ZXr161u8Xu9ExR1K1bt4YPH37nzp13vT9VVVU+Pj6LFi3S19c3MjKaPHnyvn37ysrKxFdQhDeqbfJdr6DuB+mQ7zyCXoFF/fMzNAD0OiwWKywsDOOSoaddvnzZwcFBXr8vkEcgHfKdR9ArYHw/AAAAAID8Q90PAAAAACD/UPcDAAAoiszMzOPHjxNC+Hz+iRMnPDw8Vq1aZWFhceXKlQ72UFhYGBgY6ODgMGPGDPH22bNns1rIysqil547d87ExKRfv36TJ0+mZ7AmhAgEgr179xYUFEhu/wCgLbh/PwAAMCw/P180zRmznci3e/funTlzJigoiBDy9ddf29vbGxsbE0L8/f3t7e2PHTvm4eHRbifDhw+fN2/eunXrxo8fL2p88uRJZWXlsWPHBg4cSLckJycnJCQYGBgQQry8vPLz8zds2JCRkXHmzJl169bV1NRs2bKFzWZ7enp+9tlnx44d09fX74ldBgBxON8PAABMysnJWbVqlSx0It+ePHnC4XBOnTqloqJCCAkMDCwtLaUXcTgcQgiPx+tgV7q6us1a/vzzz1u3bnl4eKz5R319vb29PSEkPz8/Ly8vODh48+bNvr6+P/30EyHEz8+PfqGWlta///1vW1vbmpoaSewlALQFdT8AADCmoKDAxsbm1atXjHci3yiKWr169dq1awcMGEC3CIXCq1ev0o9fv35NWqvmO87BwUFbW1v0tLGx8erVqytWrCCEvHz5kh5ZRLOysho0aJDokIMQMmnSJAMDg927d3d56wDQQaj7AQBAMiorKz09Pb28vDw8PBYsWODh4VFRUUEIOXv2rJKSEovFIoRUVVWdOHFC9DQoKCgtLa24uHjTpk2EkKSkpF27dunr65eUlKxYsUJbW9vY2DgiIqJTnRBC4uPjdXV179+/z9A7IXMiIyNTUlLo6ZxpsbGxXl5eoqXKysr79++X1OZiY2N1dHTogUBmZmZDhgwRX9rY2Ghubi7esmDBgrNnz2ZnZ0sqAABoHbPTBwBAdxDMNwRS0ZH5hqqqqgwNDQ8ePEg/LS0tNTQ0HD16dEVFBUVR9Dhv0criTwkh48ePpyhKIBBERUWpq6sTQrZu3Xr//v2QkJB+/foRQhISEjrYCe3atWt9+vS5fv16R/ZOEfLIycmJxWLRUzs309jYOGbMmAsXLnSqw2ZveDPOzs5fffVVq4sSEhLU1dVTUlLEGx8/fkwI+fbbbzsVQ6+DebuAcTjfDwAAEnDkyJGMjAw3Nzf66aBBg7788svs7OzDhw8TQugx5SLNntKUlJQWLVpEjzY5cuSIubm5k5PTN998Qwg5depUBzuh2draVlZW2tjYdHev5EViYmL//v2VlVu5mcePP/74r3/9y9nZWVLbqq+vj4yMpAf3NyMQCL744osff/zRxMREvJ3+QeCXX36RVAwA0CrU/QAAIAEJCQmEEPr0PM3CwoIQ8uDBg071o6SkRAjp06cP/dTW1pYQkpmZ2dl42Gx2Z18ix4qLi7W0tFpdlJWVtX37dgluKzo6euTIkRMmTGi56KuvvrK0tHR0dGzWrqmpSQgpKSmRYBgA0BLqfgAAkAC6Xs/JyRG10Cdx+/fv351uhw8fTrp3ySkQQthstkAgaNleV1fX7NR794WFhdFX9DYTFRWloaHR6lUE9HUaANDTUPcDAIAE0Gf3o6OjRS15eXmEkHnz5pF/CrvGxkZCCEVRb9++Fa3GYrH4fP67ui0rK+taJ62WuQpr2LBh9DXWzairqzs5OUlwQzU1NdHR0S0H+dy6dSs/P9/T01PUkpiYKHpcXl5OCBk6dKgEIwGAllD3AwCABOzZs8fIyOjUqVPFxcV0S0BAgJmZ2ZYtWwgh9K1dDh069Pz5cz8/v4aGBkJIbGysUCg0MDAoKiqiDxJERFV7XFycqakpfdlAxzuJjo7W1NS8ceOGdPZd9s2aNauqqqq6urpZ+7Zt2xYtWiTecvz48YkTJ166dKmN3urq6sg7jqwiIyNHjRo1ceJE8cbbt28fOXJEIBAEBAQEBAT4+/vv3LkzJiZGtAJ9I9GZM2d2crcAoHMwXy8AAEiAurp6YmLiN998s2bNGmNjYzabra2tfefOHfpaUh8fn8LCwhMnTiQnJ/v7+0dEROjp6VVUVPD5fHt7+6CgoIcPH4oP5vH19XV1dRUKhUVFRffu3etsJ2pqau+//76amhpT74as4XA4586dS0xMnD9/vnh7fX19fX29eEt2dvbTp0937drVchQ+7e7du6GhoYSQnJyco0ePWllZffjhh6KlYWFhzU72JyYm2tra1tbW3rlzR9TIYrGeP38uepqQkG3homsAACAASURBVMBms1euXNmNXQSA9rEoimI6BgDoIhaLFRYWhi9L6GmXL192cHCQzvfFhAkTnj59Ks3vJgXJo0WLFhkaGp48ebLdNTMyMjgcTlJSkhSiotna2g4dOvTMmTNS2yIjpJlHAK3COB8AAAD5FxgYGBMT0+49c2pra0+dOvXDDz9IJypCSHJyckZGhvicvgDQQ1D3AwCADKmpqRH9CxI0ePDg8PDwHTt21NbWtrEaPeWCkZGRdKIqKiry9vaOi4sTvwMsAPQQ1P0AACATampq9u3bR1+bu23bNmmOM1EQRkZG3t7eAQEBba8jtRKcz+dzudyLFy/q6OhIZ4sACg7X9QIAgEzQ0NDw9vb29vZmOhB5pq+vv3v3bqaj+C9lZWXxO3sCQE/D+X4AAAAAAPmHuh8AAAAAQP6h7gcAAAAAkH+o+wEAAAAA5B+u6wXo3RITE5kOAeQKRVEsFqtZI/0xu3z5MhMRSUOredTqWwHQZfhzDYzDfL0AvRiKEgCA3gV1FzAIdT8AAJCYmJj169erqqoGBgbOnTuX6XBkwp07d9auXdvY2Hju3Dlra2umwwEA6C6M7wcAUGh1dXXu7u42Njbm5uaPHz9G0S8yd+7cv/76y9bWdtGiRRwOp7q6mumIAAC6Bef7AQAUV3JyMofDKS0t9ff3d3Z2ZjocGRUeHv7555/37dv3/PnzFhYWTIcDANBFON8PAKCI+Hz+wYMHzczM9PT0/v77bxT9bVi+fPnff/89adKkOXPmuLu7NzY2Mh0RAEBX4Hw/AIDCSU9Pd3FxefLkybfffrtt2zZcIN5BXC73X//6l56eXnBw8OTJk5kOBwCgc3C+HwBAgVAUdebMmY8//lhFRSU1NdXd3R1Ff8dxOJy//vprwIAB06dP9/HxEQqFTEcEANAJON8PAKAocnNzXV1df/nll3379u3fv5/NZjMdUa8kFApPnTq1Z8+ejz76iMvlGhgYMB0RAECH4Hw/AIBC4PF4kydPLikpSU5OPnjwIIr+LlNSUnJ3d3/06FFtbe2UKVPOnDnDdEQAAB2Cuh8AQM69evVq2bJlDg4O9vb2Dx8+nDJlCtMRyQMjI6PExMRNmzZt2rRp0aJFRUVFTEcEANAOjPMBAJBnsbGx69atU1ZWDgoKmjNnDtPhyKEHDx5wOJyqqqozZ87Y2dkxHQ4AwDvhfD8AgHyqra11d3dfuHChmZlZamoqiv4eMmPGjJSUlCVLlixZsoQ+AGA6IgCA1uF8PwCAHEpKSuJwOBUVFadPn162bBnT4SiEmJiYzz77TE1NLSgoaNasWUyHAwDQHM73AwDIlaampoMHD86cOXP06NGpqako+qXG2to6NTX1ww8/nDt3rru7e0NDA9MRAQD8D5zvBwCQH2lpaRwO5+nTp4cPH8aEXEzhcrlbtmwZNWoUpvcCAJmC8/0AAPKAoig/Pz9TU1NVVVVMyMUsDofz559/Dhgw4JNPPjl48KBAIGA6IgAAQnC+HwBADrx8+dLV1TUhIeGLL77AhFwygp7ey9PTc8qUKVwud8yYMUxHBACKDuf7AQB6Nx6PZ2Ji8urVq6SkJEzIJTvo6b1+//33uro6U1NTTO8FAIxD3Q8A0FuJJuRycXF59OgRJuSSQUZGRr/99tuOHTs2b95sbW2N6b0AgEEY5wMA0CvduHFj3bp1qqqqQUFBs2fPZjocaEdiYiKHw3n79u2ZM2eWLFnCdDgAoIhwvh8AoJepqqpyc3NbuHDhzJkzHz9+jKK/V5g+ffqjR4+WLl26dOnSlStXVlRUMB0RACgcnO8HAOhNRKeNv//++6VLlzIdDnTazz//vH79evxQAwDSh/P9AAC9Az0hl7m5+ZgxY1JTU1H091ILFy5MTU01MTHB9F4AIGU43w8A0AukpaW5uLg8f/782LFjGzduZDockAAej7dx48YRI0YEBwebmJgwHQ4AyD+c7wcAkGmiCbnee++9lJQUFP1yw97ePjU1deDAgVOnTsX0XgAgBTjfDwAgu16+fLlmzZoHDx5gQi55RVHU//3f/2F6LwCQApzvBwCQUVwu19jY+PXr18nJyZiQS16xWCx3d/dHjx7V19d/+OGHfn5+TEcEAHILdT8AgMwpLS1dunSpq6vr2rVrHz16hMHfcm/ixInJycm7d+/28PBYuHBhYWEh0xEBgBzCOB8AANkius/j+fPnZ82axXQ4IFW4TysA9Byc7wcAkBWVlZVubm7W1tYzZ85MTU1F0a+Apk+fnpKSsnTp0mXLlq1cubK8vJzpiABAfuB8PwCATHjw4MGaNWsqKyu///77JUuWMB0OMOzGjRvr169XUVEJDAycM2cO0+EAgDzA+X4AAIY1NDTs3bvXwsJi7NixqampKPqBEPLpp5+mpqZOmTLF0tLSzc2ttraW6YgAoNfD+X4AACb9/fffLi4u2dnZR48exb35oSUej+fm5jZs2LDg4OApU6YwHQ4A9GI43w8AwAyhUOjn5/fRRx+pq6s/evQIRT+0yt7e/vHjx4MHD542bRqm9wKA7sD5fgAABuTk5KxZsyYxMRETckFHiKb3MjEx4XK5Y8eOZToiAOh9cL4fAEDauFzupEmT3rx589tvv2FCLugI0fRejY2NkydP9vPzw2k7AOgs1P0AAJL39u3bVttLS0uXLFmydu3atWvX/v7775MnT5ZyYNCrTZw4MSkpqd3pvd718QMABYe6HwBAwrhc7pIlS4RCYbP2iIiIiRMn/vHHH/Hx8X5+fmpqaoyEB72aiorKwYMHf/311+zs7IkTJ4aEhDRbQSgULlmyhMvlMhIeAMgy1P0AAJKUmZn5+eef371718/PT9RIT8i1fPnyhQsX/vnnnxYWFgxGCHJg2rRpqampHA5n9erVzab38vPzu3v37ueff56ZmclghAAgg3BdLwCAxDQ1NU2bNu2vv/5qampSUVF59OiRsbHxgwcPOBxOVVXVmTNn7OzsmI4R5EpsbOy6deuUlZWDgoLmzJnz5MmTyZMnNzY2qqioTJgw4eHDh6qqqkzHCACyAuf7AQAkxsvL648//mhqaqKfOjk57d6929zcfNy4campqSj6QeIWLFiQmpr60UcfWVpabtiwwcnJiT6d19TUlJ6evm/fPqYDBAAZgvP9AACSERsbu3DhQvE/qmw2u3///t9++y3uzQ89jcfjbdq0qaKiQvwG/ywWKzIy0sbGhsHAAEB24Hw/AIAElJaWOjs7s1gs8UaBQFBeXj5hwgSmogLFoaOjU15e3mxWLxaLxeFwioqKmIoKAGQK6n4AgO6iKGrNmjWVlZUt7+GjpKTk7OxcVVXFSGCgIGpqapydnZWUmn+nC4XC6urq1atXt/xkAoACQt0PANBdx48fv3nzpmhYvziBQFBUVLR7927pRwWKw8PDIz8/n8/nt1zU1NR09+7dEydOSD8qAJA1GN8PANAtv//++/Tp01stuWjKysoCgeDnn39esGCBNAMDBUFfWMJms9v+ECYmJn700UfSDAwAZA3qfgCArquurp40aVJeXl6zkktFRUUgEFAUZWRkZG1tPW/ePAsLC9xREXqCQCBITU2Ni4uLiYlJSEgQCoXKysrNfn1is9kjRoz466+/3n//fabiBADGoe4HAOg6Z2dnHo9H11gsFos+5zpgwIC5c+cuXrzYxsZmwIABTMcICqS6ujo+Pj4qKio6OrqgoIDNZlMURQ/uV1FRsbOz4/F4TMcIAIxB3Q8A0EXBwcEcDofFYlEUpaamZmFhsWjRIisrK9zAB2TBkydPbt68GRMTc+/evYaGBvqDyuVyXVxcmA4NAJiBuh/gfzS7DyMAdFP3v2WQlQCyD/Vkr6DMdAAAMmf79u3Tp09nOgqFlpiY6OvrGxYWxnQgbcnJydHS0urfv39nX+jg4KAgnzH6/1EiXSnIOyYFb9++LS8v19PTYzoQQnpJpncZMh1kEOp+gOamT5++cuVKpqNQdL6+vvL6v+Dg4KA4nzFJVQOK844pGmS6fEDd31vg/v0AAAAAAPIPdT8AAAAAgPxD3Q8AAAAAIP9Q9wMAAAAAyD/U/QAAAAAA8g91PwAAAACA/EPdDwDyY9q0aXv27GE6CknKzMw8fvw4IYTP5584ccLDw2PVqlUWFhZXrlzpYA+FhYWBgYEODg4zZswQb589ezarhaysLHrpuXPnTExM+vXrN3ny5MDAQLpRIBDs3bu3oKBAcvsH0BXI9JaQ6dARuH8/AMgPfX399957r+f6z8/P19HR6bn+m7l3796ZM2eCgoIIIV9//bW9vb2xsTEhxN/f397e/tixYx4eHu12Mnz48Hnz5q1bt278+PGixidPnlRWVh47dmzgwIF0S3JyckJCgoGBASHEy8srPz9/w4YNGRkZZ86cWbduXU1NzZYtW9hstqen52effXbs2DF9ff2e2GWAjkCmt4RMhw6hAEAMISQsLIzpKBQdPX8n01E09+LFC3Nz8+7308HPWHp6+siRI8vKyuinOjo6cXFx9OO3b98SQqZOndqpjY4fP1709NKlS69fvxZfYe3atd988w1FUXl5ec7OzqL22NhYQsiYMWNELX/88YeRkVF1dXW7G5XU/yOyUl4h0ylkOkgXxvkAALSvoKDAxsbm1atX0tkcRVGrV69eu3btgAED6BahUHj16lX68evXrwkhurq6Xe7fwcFBW1tb9LSxsfHq1asrVqwghLx8+ZIeb0CzsrIaNGhQaWmpqGXSpEkGBga7d+/u8tYBZBYyXdSCTJdLqPsBQB4IhUIej+fq6jpr1ixCSGRkpJubm66ubkVFhaur68CBA42NjR89ekQISUpK2rVrl76+fklJyYoVK7S1tY2NjSMiIgghZ8+eVVJSYrFYhJCqqqoTJ06IngYFBaWlpRUXF2/atIneYnx8vK6u7v3793tidyIjI1NSUj799FNRS2xsrJeXl2ipsrLy/v37JbW52NhYHR0deniAmZnZkCFDxJc2Njaam5uLtyxYsODs2bPZ2dmSCgCgg5Dp3YFMB/wuA/A/CEYUyICu/Wqcm5tL/vmNOz8/v2/fvoQQb2/vly9fXrhwgRAydepUgUAQFRWlrq5OCNm6dev9+/dDQkL69etHCElISKAoih72KupT/Cn53x/Qr1271qdPn+vXr3c2zo58xpycnFgsVlNTU8tFjY2NY8aMuXDhQmc3Kh58M87Ozl999VWrixISEtTV1VNSUsQbHz9+TAj59ttv294oxvlA25DpyHSQMvw/AfwPVBiyoMvfIuLfeePGjRPvZMiQIWpqavRjQ0NDQkhNTQ391NfXlxDi6OhIURR9Jkz0KvGnLb9Q+Xx+14Js9zOmp6enqanZ6qLTp0+fPHmyCxt9VzVQV1fXr1+/9PT0lov4fP6sWbNCQ0ObtRcWFhJCrK2t294o6n5oGzIdmQ5ShnE+ACCf6F/tRbS0tBoaGujHSkpKhJA+ffrQT21tbQkhmZmZnd0Em83ubpTvUFxcrKWl1eqirKys7du3S3Bb0dHRI0eOnDBhQstFX331laWlpaOjY7N2TU1NQkhJSYkEwwDoGmR6ByHTgeA+ngAAw4cPJ927eE7i2Gy2QCBo2V5XV2diYiLZbYWFhdHX+TUTFRWloaHh6enZclGzSgugV0CmI9MB5/sBQNGVlZURQubNm0f++Z5rbGwkhFD/3EePxmKx+Hy++Atb/cKWiGHDhlVUVLRsV1dXd3JykuCGampqoqOj7e3tm7XfunUrPz9fvBRITEwUPS4vLyeEDB06VIKRAPQ0ZDoyHVD3A4CcqK6uJoRUVlbST+vr68WXVlVVEULEv85F3+VxcXGmpqZubm6EEHqY76FDh54/f+7n50cPGIiNjRUKhQYGBkVFRXl5efSroqOjNTU1b9y40RP7MmvWrKqqKnqPxG3btm3RokXiLcePH584ceKlS5fa6K2uro68o3aJjIwcNWrUxIkTxRtv37595MgRgUAQEBAQEBDg7++/c+fOmJgY0Qr07QVnzpzZyd0CkABk+rsg06FdGOcDAPKgtrb28OHDhJDCwsKTJ082Njbm5OQQQry9vbdu3RoYGEjPOb9///5///vf9Et8fX1dXV2FQmFRUdG9e/eUlZUJIT4+PoWFhSdOnEhOTvb394+IiNDT06uoqODz+fb29kFBQQ8fPqTHCaipqb3//vtqamo9sTscDufcuXOJiYnz588Xb6+vr29W5WRnZz99+nTXrl0tx+bS7t69GxoaSgjJyck5evSolZXVhx9+KFoaFhbW7BRgYmKira1tbW3tnTt3RI0sFuv58+eipwkJCWw2e+XKld3YRYCuQKYj06FbmL6wGEC2ENw5RAb09N0hmt3KQ8o6+Bmztrbevn17Rzp89uxZp2b07L7Fixdv2LCh3dVwPx9oGzKdQqaDdGGcDwCALAoMDIyJiWn3Thq1tbWnTp364YcfpBMVISQ5OTkjI0N8pk8A6DJkOkgT6n4AUDg1NTWif2XW4MGDw8PDd+zYUVtb28Zq2dnZhw8fNjIykk5URUVF3t7ecXFx9BRIskz8Sk1QTMj0LutFmQ6dgrofoMdNmzZtz5490n9tq+Li4qytrVksFovFmjt37ty5cz/++GM7O7tz587Rt7aQbzU1Nfv27aOv2Nu2bVtSUhLTEbXFyMjI29s7ICCg7XWk9sXM5/O5XO7Fixd1dHSks8UuaGhoOHz48IwZM7S1tZmOhUnIdGR6l/WKTIcuYnqgEYBsIT0wktjR0XH//v0SeW1eXl7346Gve9PX16efCoXC69evGxgYjB07Ni0trfv9d598jxbtic+YbGJqfH9dXd2AAQOk9hGSSFb2xOaQ6cxCpoMMwvl+gB4XGhr69ddfd/+1OTk5q1at6n489OQ1ottTsFgsGxubX375pbq62tbWttlNJAB6nffee2/w4MHS2ZaksrInNodMB4BmUPcD9A4FBQU2NjavXr3qof6HDRv2zTffZGVl4SougA7q6azsic0h0wEUGep+gM5JSkratWuXvr5+SUnJihUrtLW1jY2NIyIihELhvXv3duzYoa+vX1hYOHv27FGjRr1584bH47m6us6aNYt+eWVlpaenp5eXl4eHx4IFCzw8PCoqKjry2qCgoLS0tOLi4k2bNhFCLl68qKGhwWKxfHx86FlaQkJC1NTUzp8/TwiJj4/X1dW9f/9+p3ZtxYoVbDb75s2b9NP6+vrvvvvus88++/jjj+fPn//3338TQiIjI93c3HR1dSsqKlxdXQcOHGhsbPzo0SP6Jb///vu0adO2bNly4MABFRUV+nK6VvsBkKy6ujoPDw83N7f9+/d/8cUX4pdytvoJfFci0y/JzMy0t7ffu3cvh8OxsLD466+/Wk1SX19f8aysra29ePHiqlWrzMzMkpKSpkyZoqenl5CQkJGRsXTp0kGDBk2YMEGULO8KrI0Ua/ZHgCDTAaCzmB5oBCBbSJsjMgUCQVRUlLq6OiFk69at9+/fDwkJoa+1io+Pf/DgQZ8+fQgh3377bVxc3GeffVZdXZ2bm0sIGT9+PEVRVVVVhoaGBw8epHsrLS01NDQcPXp0aWlpu6+lYxM9pijqyy+/JISIxunm5uYuXbqUfnzt2rU+ffpcv369jd0U70pk2LBh2tra9OMNGzY8ffqUfmxlZTVkyJDKysr8/Py+ffsSQry9vV++fHnhwgVCiOiW0oaGhgMGDKAfOzg4lJaWvqufd/4HUBQl76NF2/6MyROpje/n8/lTp04V3Wg8KyuLnpuJftryE1hRUfGuRE5ISKAoauzYsQYGBhRFNTU1aWpqGhkZNTQ0tJqk4qkkFArpOY/69+8fHR2dnp5OCNHT0zt69Ojbt28fP35MCJk9e7Yo7C6kWLPMRabLMmQ6yCAWRVE9fmwB0HuwWKywsLC2pyccN25cRkZGTU0NXQT4+flt377d0dExNDR0/Pjxz549e/PmjZaWlnif48ePf/LkyZdffunt7V1UVDR06FB6UXBwMIfD2bNnj4+PT9uvbfaYEPLmzRs9PT1HR8czZ84QQo4cOWJsbCya2l0gELDZ7DZ2U7wrkZEjRwoEgoKCgt9++23q1KnNlkZFRS1atIiOU/SnY+jQoRUVFfRY4cGDB7969crPz2/r1q3p6ekjR4588uTJu/pp4x2+fPmyg4MD/V0ifxwcHLZv3z59+nSmA+lxiYmJvr6+3f+WaTcrAwICtmzZ8uTJE3qeJvJPklIU1cYnuY1EPnny5LBhwxwdHSmKGjt2bG5uLn0PnJZJ2jKVxFt0dHQKCgpE78CQIUMaGxvLy8sJIV1LsZabQ6bLLGQ6yCLGjjgAZBLpwBmaZnNAZmdnE0JMTU1bLhL1SZ9ymz17NiGkurpatIieYX7mzJntvpZq7dTdF198oaqqShcW8+bN4/P5Hd/NlmcBGxsbVVVVra2tKYry9/c3MjLqyO6LP71y5Qp90vSjjz5KSkpqu582yGsdoJg6+7/fEmkvK21tbQkhdXV1ohbRx7Ljn2TxRKYoqrq6OiAg4JtvvqFvZdjqS6jWUkm8pY1k6VqKtdxc21pdH5kOPaGz//vACIzvB+gu+qYZurq67a6ppKRECKFrfdqQIUMIIf379+/apnfu3Kmqqurr6/vo0aNPPvmkjdN+HXHnzp3GxkZLS0tCSFlZWXZ2drN5ZIRCYds9LF++PDU1dcGCBb///ru5ufn58+e71g+N6T+PPYUo2K//UkDfsLKsrKzloo5/AsUT+eHDh8bGxqNHj/7yyy/p4S4S153U6CZkunQQZDrIHtT9AN1FVxvz5s1rd00LCwtCSHR0tKiFnlamI68lhLBYLD6fL96ira29adOm06dP/9///d+6devEF9EX+3ZcY2PjF198YWJism3b/9fe3cY0df1xAP8VcETqikg2J2oilLgauqnxBUYni5kPRBx7YzEwxW1mc8ZR3UCraY1mWpSYoqbiGzbZloHGh805H9A4FA0r6OKcy9Sig06dRRiKdFwYbb3/Fyf/phasfaJP9/t5Ybjntr97Svv1Hm7vPVdJRDKZjOO4srIyxwNu3LixZ88e90U2bdqUlpZWW1u7f/9+q9Wq0Wh8qwPgFXYo2jlZzqs8/AQ6B7mwsNBqtWZnZ9PzBq8DU+l5n32IxsDNIekA4IVQ/5UIEF7I4/N8HCfVfP3119OmTbNarTzPT5gwgZ4+k8disRBRSkoKz/Mcx8nl8nHjxpnNZrZ29erVM2fO9OS5PM+np6eLxeI7d+44d6atrS0+Pt75YkGe548fPz5ixIhTp04N2n92TG7ChAmOlitXrmRlZaWmpl6/fp219PX1paWlEdEHH3xQXV2t0WjmzZvHrtJj/XQ8d+zYsUTEXkJCQsKjR494nrdarYmJiZmZmW7quBHdV4l58hmLDkG7rvfq1atxcXHJycm1tbUcx9XV1UkkEiJqbW118wl0E+TExESRSHTmzJnq6mp2K4Cmpqa7d+8ODKlLKnt7e4no1VdfZYtSqZSILBYLW2RPt9vtvK8Rc9kckh7OkHQIQzjeD+CjXbt2dXZ2dnR0mM3m+vr6/v7+LVu2sHN4Pvvss6tXrxIRx3GlpaVEdP/+/Z07d9psNoPBUFBQsGzZspKSEpVKlZyczL5zf+5zLRaLQqGQSCSXL1927sbo0aPnzp27fPly58b4+HiJROK4X4+zhoaGoqIiIjKZTLNnz87Ozn7nnXe0Wu3ixYt///33SZMmOSrU1dXl5uYePXq0uLi4vb29urr6xRdf3Lt3L+unVqvt7u7evXs3O8Vi48aNfX19HMe99dZbZWVl77333qxZsw4cOPCsOoF9L0DgJk+eXFdXJ5PJFAqFXC6/dOnSlClTPv7445aWlmHDhrn/BLoEmU0EVFpaKpFINBqNVCpVq9VJSUkajUan07mElIicU9ne3q5Wq4nIZDL99NNPZ86c+euvv4hIrVY/fPhwz549bFGn03V2dvoWMZf/BJB0APAK5vMBeIon8/lMmjSJTVcXtF65wXHc5MmTr127xiYljA5slo8w+Q0HnCefsegQqPdxiH5jYRVkYULSo0N0v49RBsf7ASJbRUVFUVFRNA36AQAAYCjEhboDAJGH3Zyyp6dHLBaHqg9NTU0fffQRx3F2u/3mzZuh6gZA5AqHIAMABBOO9wN4oaenR61Ws0l4lEplY2NjqHoiFou7u7tjYmJqampeeOGFUHUDIBKFT5ABAIIJ434AL4jFYq1Wyy6K//LLL6dPnx6qnsjl8tbWVqPRGMI+QGjdunVLp9MRkc1mKy8vLy4uLigoyMrKOnz4sIcV7t+/X1VVtXjx4hkzZjga7Xb7+vXr2UWc0Sp8ggzwXP4n3UGv14tEIvazEJIOA2HcDwCCc+/evTAp4rP6+vrNmzezKdg///zzuXPn6nS6mpqavLw8hULBRgnPlZKSMmfOnIMHDz569MjRGBsbq1KplEpla2vrUPUeICiQdGe//PLL+vXrHYtIujBh3A8AwmIymQoKCsKhiM9u3LhRWFio1+uHDRtGRFVVVe3t7WxVYWEhER06dMjDUoPeZzopKWnTpk25ubnsDHiASISkO+vq6jp69KhL3pF0AcK4HwAE5O+//164cGFHR0fIi/iM5/klS5a8//77o0aNYi1Pnjz5/vvv2c///PMPPWM075XXX39dKpWuXbvWzzoAIYGku9i6deu6descJ/k4IOlCg3E/AESq7u5ulUq1YcOG4uLi+fPnFxcXd3V1EVFlZWVMTAzbw1kslvLycsfiV1999ccff7S1ta1cuZKIGhsbS0pKUlNTHzx4sGjRouTk5Ndee+27777zqggRnTt3bvz48RcuXAjCqz527NiVK1eys7MdLadPn96wYYNjbVxc3MaNG/3f0Pz58ysrK1taWvwvBeAPJJ3xOel6vT4vL4/dxHogJF1Ygn2DYIDwRoK5s3o48+Su7xaLZeLEL+PrdgAABUVJREFUiZs3b2aL7e3tEydOTEtL6+rq4nleKpU6V3BeJCKZTMbzvN1uP378OLv1QVFR0YULF2pqatgdRhsaGjwswvzwww8JCQk//vijJ6/Oz89Yfn6+SCSyWq0DV/X396enp3/77bdeFXR5LQ6//vorEW3bts3Hjnr2PnoCqYxWSLobgUq6wWAoLy9nP8tksoG/8PBJOgQBjvcDQETavn17c3PzihUr2OJLL72k0WhaWlpKS0uJiJ0O6+CyyMTExOTk5LAvyrdv3z5r1qz8/PwtW7YQkV6v97AIk5ub293dvXDhQn9flQcMBkNiYmJc3CB3X9m3b9+qVavefffdgGxo9OjRRHTx4sWAVAPwDZI+cJXnSX/48GFlZeWaNWvcPAZJFxSM+wEgIjU0NBARO2jHZGVlEdHPP//sVZ2YmBgiSkhIYIu5ublEdOvWLW/7Exsb6+1TfNPW1paUlDToqj///NP9Dt4rI0eOJKIHDx4EqiCAD5D0gTxP+sqVK5csWdLc3Gw0Go1G43///UdERqPR+aweJF1QcL9eAIhIbC9uMpkyMjJYCztqlZiY6E/ZlJQUCsR1sUMnNjbWbrcPbO/t7Z06dWoANzTwEkCA4EPSXXiV9GPHjh08eNClUSaTpaenO/7mQdIFBcf7ASAisWN+J06ccLSw26/OmTOH/r8n6+/vJyKe5x8/fux4mEgkstlszyrb2dnpW5FB99BDYcyYMeyiRhfDhw/Pz88P4IbYpP6vvPJKAGsCeAtJd+FV0nt7e53P7Xac3+/8RQeSLigY9wNARFq3bp1cLtfr9W1tbayloqJi5syZn3zyCRGx3dvWrVtv3769e/du9u326dOnnzx5IpVKzWYzGzo4OPblZ8+enTZtGjuZ2PMiJ06cGDlyZG1tbRBe+JtvvmmxWP7991+XdqVSmZOT49yi0+kyMjIOHDjgplpvby89YyjDJgp84403/O0xgB+QdJd235LuBpIuKBj3A0BEGj58uMFgKCgoWLZsWUlJiUqlSk5OrqurY5fBlZWVZWZmlpeXr1q1KicnJyMjY+nSpV1dXTabTaFQSCSSy5cvO1fbtWtXZ2dnR0eH2Wyur6/3tkh8fLxEIomPjw/CCy8sLOR53mAwuLT39fX19fU5t7S0tNy8ebOkpORZpc6fP8/OEjaZTDt27Pjtt9+c1zY0NMTGxubl5QWu7wBeQ9Jd2n1IuntIurAEef4ggDBHmDEwDARzVrhBJ7YbUv5/xhYsWLBmzRpPHmk0GjMzM33byttvv/3hhx/69lwG83iCe0i6e0JLOgQBjvcDAESYqqqqkydPPnf+DY7j9Hr9F1984cMmmpqampubdTqdTx0EgABA0iHgMO4HAEHr6elx/BspXn755SNHjnz66accx7l5GJvjXC6Xe1vfbDZrtdqzZ886T54IENGQ9IGQdAHCuB8ABKqnp0etVrMr9pRKZWNjY6h75AW5XK7VaisqKtw/xofduc1m++abb6qrq8eNG+dHBwHCBZI+KCRdmDB/PwAIlFgs1mq1Wq021B3xUWpq6tq1awNeNi4uTqVSBbwsQKgg6YNC0oUJx/sBAAAAAKIfxv0AAAAAANEP434AAAAAgOiHcT8AAAAAQPQT8Twf6j4AhBGRSDR9+nTMbxBa9+7da2xsXLRoUag7MiQOHz4skM8Yex/938sgldEKSY8OgUo6BAHG/QBPUSgUoe4CQFQ5dOiQnxWQSoDw53/SIQgw7gcAAAAAiH44vx8AAAAAIPph3A8AAAAAEP0w7gcAAAAAiH4Y9wMAAAAARL//AWmsHBpS5XrjAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 构建一个根据定制票标题、内容和标签，预测票证优先级和执行部门的网络\\n\",\n \"# 超参\\n\",\n \"num_words = 2000\\n\",\n \"num_tags = 12\\n\",\n \"num_departments = 4\\n\",\n \"\\n\",\n \"# 输入\\n\",\n \"body_input = keras.Input(shape=(None,), name='body')\\n\",\n \"title_input = keras.Input(shape=(None,), name='title')\\n\",\n \"tag_input = keras.Input(shape=(num_tags,), name='tag')\\n\",\n \"\\n\",\n \"# 嵌入层\\n\",\n \"body_feat = layers.Embedding(num_words, 64)(body_input)\\n\",\n \"title_feat = layers.Embedding(num_words, 64)(title_input)\\n\",\n \"\\n\",\n \"# 特征提取层\\n\",\n \"body_feat = layers.LSTM(32)(body_feat)\\n\",\n \"title_feat = layers.LSTM(128)(title_feat)\\n\",\n \"features = layers.concatenate([title_feat,body_feat, tag_input])\\n\",\n \"\\n\",\n \"# 分类层\\n\",\n \"priority_pred = layers.Dense(1, activation='sigmoid', name='priority')(features)\\n\",\n \"department_pred = layers.Dense(num_departments, activation='softmax', name='department')(features)\\n\",\n \"\\n\",\n \"\\n\",\n \"# 构建模型\\n\",\n \"model = keras.Model(inputs=[body_input, title_input, tag_input],\\n\",\n \" outputs=[priority_pred, department_pred])\\n\",\n \"model.summary()\\n\",\n \"keras.utils.plot_model(model, 'multi_model.png', show_shapes=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"编译此模型时，我们可以为每个输出分配不同的loss。甚至可以为每个loss分配不同的权重，以调整它们对总训练loss的贡献。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model.compile(optimizer=keras.optimizers.RMSprop(1e-3),\\n\",\n \" loss={'priority': 'binary_crossentropy',\\n\",\n \" 'department': 'categorical_crossentropy'},\\n\",\n \" loss_weights=[1., 0.2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 1280 samples\\n\",\n \"Epoch 1/5\\n\",\n \"1280/1280 [==============================] - 6s 5ms/sample - loss: 1.2873 - priority_loss: 0.7099 - department_loss: 2.8868\\n\",\n \"Epoch 2/5\\n\",\n \"1280/1280 [==============================] - 1s 1ms/sample - loss: 1.2777 - priority_loss: 0.6978 - department_loss: 2.8995\\n\",\n \"Epoch 3/5\\n\",\n \"1280/1280 [==============================] - 1s 1ms/sample - loss: 1.2747 - priority_loss: 0.7001 - department_loss: 2.8730\\n\",\n \"Epoch 4/5\\n\",\n \"1280/1280 [==============================] - 1s 1ms/sample - loss: 1.2660 - priority_loss: 0.6968 - department_loss: 2.8459\\n\",\n \"Epoch 5/5\\n\",\n \"1280/1280 [==============================] - 1s 1ms/sample - loss: 1.2594 - priority_loss: 0.6972 - department_loss: 2.8109\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 构造数据并训练\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"# 载入输入数据\\n\",\n \"title_data = np.random.randint(num_words, size=(1280, 10))\\n\",\n \"body_data = np.random.randint(num_words, size=(1280, 100))\\n\",\n \"tag_data = np.random.randint(2, size=(1280, num_tags)).astype('float32')\\n\",\n \"# 标签\\n\",\n \"priority_label = np.random.random(size=(1280, 1))\\n\",\n \"department_label = np.random.randint(2, size=(1280, num_departments))\\n\",\n \"# 训练\\n\",\n \"history = model.fit(\\n\",\n \" {'title': title_data, 'body':body_data, 'tag':tag_data},\\n\",\n \" {'priority':priority_label, 'department':department_label},\\n\",\n \" batch_size=32,\\n\",\n \" epochs=5\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"如果使用Dataset构建输入，它应产生一个列表元组（如）([title_data, body_data, tags_data], [priority_targets, dept_targets]) 或一个字典元组（如） ({'title': title_data, 'body': body_data, 'tags': tags_data}, {'priority': priority_targets, 'department': dept_targets})。\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.2 小型残差网络\\n\",\n \"Functional API还可以使操作非线性连接拓扑变得容易，也就是说，模型中的层不是顺序连接。\\n\",\n \"\\n\",\n \"常见的用例是残余连接。\\n\",\n \"\\n\",\n \"下面，我们为CIFAR10建立一个玩具ResNet模型来演示这一点。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"small_resnet\\\"\\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # Connected to \\n\",\n \"==================================================================================================\\n\",\n \"img (InputLayer) [(None, 32, 32, 3)] 0 \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"conv2d_8 (Conv2D) (None, 30, 30, 32) 896 img[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"conv2d_9 (Conv2D) (None, 28, 28, 64) 18496 conv2d_8[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"max_pooling2d_2 (MaxPooling2D) (None, 9, 9, 64) 0 conv2d_9[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"conv2d_10 (Conv2D) (None, 9, 9, 64) 36928 max_pooling2d_2[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"conv2d_11 (Conv2D) (None, 9, 9, 64) 36928 conv2d_10[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"add (Add) (None, 9, 9, 64) 0 conv2d_11[0][0] \\n\",\n \" max_pooling2d_2[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"conv2d_12 (Conv2D) (None, 9, 9, 64) 36928 add[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"conv2d_13 (Conv2D) (None, 9, 9, 64) 36928 conv2d_12[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"add_1 (Add) (None, 9, 9, 64) 0 conv2d_13[0][0] \\n\",\n \" add[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"conv2d_14 (Conv2D) (None, 7, 7, 64) 36928 add_1[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"global_max_pooling2d_2 (GlobalM (None, 64) 0 conv2d_14[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"dense_6 (Dense) (None, 256) 16640 global_max_pooling2d_2[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"dropout (Dropout) (None, 256) 0 dense_6[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"dense_7 (Dense) (None, 10) 2570 dropout[0][0] \\n\",\n \"==================================================================================================\\n\",\n \"Total params: 223,242\\n\",\n \"Trainable params: 223,242\\n\",\n \"Non-trainable params: 0\\n\",\n \"__________________________________________________________________________________________________\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"inputs = keras.Input(shape=(32,32,3), name='img')\\n\",\n \"h1 = layers.Conv2D(32, 3, activation='relu')(inputs)\\n\",\n \"h1 = layers.Conv2D(64, 3, activation='relu')(h1)\\n\",\n \"block1_out = layers.MaxPooling2D(3)(h1)\\n\",\n \"\\n\",\n \"h2 = layers.Conv2D(64, 3, activation='relu', padding='same')(block1_out)\\n\",\n \"h2 = layers.Conv2D(64, 3, activation='relu', padding='same')(h2)\\n\",\n \"block2_out = layers.add([h2, block1_out]) # 残差连接\\n\",\n \"\\n\",\n \"h3 = layers.Conv2D(64, 3, activation='relu', padding='same')(block2_out)\\n\",\n \"h3 = layers.Conv2D(64, 3, activation='relu', padding='same')(h3)\\n\",\n \"block3_out = layers.add([h3, block2_out])\\n\",\n \"\\n\",\n \"h4 = layers.Conv2D(64, 3, activation='relu')(block3_out)\\n\",\n \"h4 = layers.GlobalMaxPool2D()(h4)\\n\",\n \"h4 = layers.Dense(256, activation='relu')(h4)\\n\",\n \"h4 = layers.Dropout(0.5)(h4)\\n\",\n \"outputs = layers.Dense(10, activation='softmax')(h4)\\n\",\n \"\\n\",\n \"model = keras.Model(inputs, outputs, name='small_resnet') # 网络名不能有空格\\n\",\n \"model.summary()\\n\",\n \"keras.utils.plot_model(model, 'small_resnet_model.png', show_shapes=True)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"训练残差网络\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 40000 samples, validate on 10000 samples\\n\",\n \"40000/40000 [==============================] - 91s 2ms/sample - loss: 1.8759 - acc: 0.3005 - val_loss: 1.5772 - val_acc: 0.4288\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\\n\",\n \"x_train = x_train.astype('float32') / 255\\n\",\n \"x_test = y_train.astype('float32') / 255\\n\",\n \"y_train = keras.utils.to_categorical(y_train, 10)\\n\",\n \"y_test = keras.utils.to_categorical(y_test, 10)\\n\",\n \"\\n\",\n \"model.compile(optimizer=keras.optimizers.RMSprop(1e-3),\\n\",\n \" loss='categorical_crossentropy',\\n\",\n \" metrics=['acc'])\\n\",\n \"model.fit(x_train, y_train,\\n\",\n \" batch_size=64,\\n\",\n \" epochs=1,\\n\",\n \" validation_split=0.2)\\n\",\n \"\\n\",\n \"#model.predict(x_test, batch_size=32)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4 共享网络层\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"函数式API的另一个优点是：可以使用共享层的模型。共享层是在同一模型中多次重用的层实例：它们学习与层图中的多个路径相对应的要素。\\n\",\n \"\\n\",\n \"共享层通常用于编码来自相似空间的输入（例如，两个具有相似词汇的不同文本），因为它们可以在这些不同输入之间共享信息，并且可以在更少的空间上训练这种模型数据。\\n\",\n \"\\n\",\n \"要在函数式API中共享图层，只需多次调用同一图层实例即可。例如，这是一个跨两个不同文本输入的共享Embedding图层：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"share_embedding = layers.Embedding(1000, 64)\\n\",\n \"\\n\",\n \"input1 = keras.Input(shape=(None,), dtype='int32')\\n\",\n \"input2 = keras.Input(shape=(None,), dtype='int32')\\n\",\n \"\\n\",\n \"feat1 = share_embedding(input1)\\n\",\n \"feat2 = share_embedding(input2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5 模型复用\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"由于函数式API中要处理的层图是静态数据结构，因此可以对其进行访问和检查。\\n\",\n \"\\n\",\n \"这也意味着我们可以访问中间层（图中的“节点”）的输出，并在其他地方重用它们。这对于特征提取非常有用！\\n\",\n \"\\n\",\n \"下面的一个例子是VGG16模型，其权重在ImageNet上进行了预训练：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from tensorflow.keras.applications import VGG16\\n\",\n \"vgg16=VGG16()\\n\",\n \"# 获取中间结构输出\\n\",\n \"feature_list = [layer.output for layer in vgg16.layers]\\n\",\n \"# 将其作为新模型输出\\n\",\n \"feat_ext_model = keras.Model(inputs=vgg16.input, outputs=feature_list)\\n\",\n \"\\n\",\n \"img = np.random.random((1, 224, 224, 3)).astype('float32')\\n\",\n \"# 用于提取特征\\n\",\n \"ext_features = feat_ext_model(img)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6 自定义网络层\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"tf.keras具有广泛的内置层。这里有一些例子：\\n\",\n \"\\n\",\n \"- 卷积层：Conv1D，Conv2D，Conv3D，Conv2DTranspose，等。\\n\",\n \"- 池层：MaxPooling1D，MaxPooling2D，MaxPooling3D，AveragePooling1D，等。\\n\",\n \"- RNN层：GRU，LSTM，ConvLSTM2D，等。\\n\",\n \"- BatchNormalization，Dropout，Embedding，等。\\n\",\n \"如果找不到所需的内容，则可以通过创建自己的图层来扩展API。\\n\",\n \"\\n\",\n \"所有层都对该Layer类进行子类化并实现：\\n\",\n \"- 一个call方法，指定由该层完成的计算。\\n\",\n \"- 一种build创建图层权重的方法（请注意，这只是一种样式约定；也可以在__init__函数中创建权重）。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"model_6\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"input_8 (InputLayer) [(None, 4)] 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"my_dense_1 (MyDense) (None, 10) 50 \\n\",\n \"=================================================================\\n\",\n \"Total params: 50\\n\",\n \"Trainable params: 50\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# import tensorflow as tf\\n\",\n \"# import tensorflow.keras as keras\\n\",\n \"class MyDense(layers.Layer):\\n\",\n \" def __init__(self, units=32):\\n\",\n \" super(MyDense, self).__init__()\\n\",\n \" self.units = units\\n\",\n \" def build(self, input_shape):\\n\",\n \" # 构建权重\\n\",\n \" self.w = self.add_weight(shape=(input_shape[-1], self.units),\\n\",\n \" initializer='random_normal',\\n\",\n \" trainable=True)\\n\",\n \" self.b = self.add_weight(shape=(self.units,),\\n\",\n \" initializer='random_normal',\\n\",\n \" trainable=True)\\n\",\n \" def call(self, inputs):\\n\",\n \" # 正向传播\\n\",\n \" return tf.matmul(inputs, self.w) + self.b\\n\",\n \" \\n\",\n \" def get_config(self):\\n\",\n \" # 支持序列化\\n\",\n \" return {'units': self.units}\\n\",\n \"# 构建模型\\n\",\n \"inputs = keras.Input((4,))\\n\",\n \"outputs = MyDense(10)(inputs)\\n\",\n \"model = keras.Model(inputs, outputs)\\n\",\n \"\\n\",\n \"\\n\",\n \"# 模型序列化\\n\",\n \"config = model.get_config()\\n\",\n \"new_model = keras.Model.from_config(\\n\",\n \"config, custom_objects={'MyDense':MyDense}\\n\",\n \")\\n\",\n \"new_model.summary()\\n\",\n \"keras.utils.plot_model(new_model, 'myDense.png', show_shapes=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**在自定义网络层调用其他网络层**\\n\",\n \"\\n\",\n \"构建一个rnn网络\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(32, 8, 32)\\n\",\n \"(32, 8, 32)\\n\",\n \"Model: \\\"model_7\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"input_9 (InputLayer) [(32, 10, 5)] 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv1d (Conv1D) (32, 8, 32) 512 \\n\",\n \"_________________________________________________________________\\n\",\n \"my_rnn (MyRnn) (32, 8, 1) 2145 \\n\",\n \"=================================================================\\n\",\n \"Total params: 2,657\\n\",\n \"Trainable params: 2,657\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 超参\\n\",\n \"time_step = 10\\n\",\n \"batch_size = 32\\n\",\n \"hidden_dim = 32\\n\",\n \"inputs_dim = 5\\n\",\n \"\\n\",\n \"# 网络\\n\",\n \"class MyRnn(layers.Layer):\\n\",\n \" def __init__(self):\\n\",\n \" super(MyRnn, self).__init__()\\n\",\n \" self.hidden_dim = hidden_dim\\n\",\n \" self.projection1 = layers.Dense(units=hidden_dim, activation='tanh')\\n\",\n \" self.projection2 = layers.Dense(units=hidden_dim, activation='tanh')\\n\",\n \" self.classifier = layers.Dense(1, activation='sigmoid')\\n\",\n \" def call(self, inputs):\\n\",\n \" outs = []\\n\",\n \" states = tf.zeros(shape=[inputs.shape[0], self.hidden_dim])\\n\",\n \" for t in range(inputs.shape[1]):\\n\",\n \" x = inputs[:,t,:]\\n\",\n \" h = self.projection1(x)\\n\",\n \" y = h + self.projection2(states)\\n\",\n \" states = y\\n\",\n \" outs.append(y)\\n\",\n \" # print(outs)\\n\",\n \" features = tf.stack(outs, axis=1)\\n\",\n \" print(features.shape)\\n\",\n \" return self.classifier(features)\\n\",\n \"\\n\",\n \"# 构建网络\\n\",\n \"inputs = keras.Input(batch_shape=(batch_size, time_step, inputs_dim))\\n\",\n \"x = layers.Conv1D(32, 3)(inputs)\\n\",\n \"print(x.shape)\\n\",\n \"outputs = MyRnn()(x)\\n\",\n \"model = keras.Model(inputs, outputs)\\n\",\n \"model.summary()\\n\",\n \"keras.utils.plot_model(model, 'myRnn.png', show_shapes=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1, 10, 32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"rnn_model = MyRnn()\\n\",\n \"_ = rnn_model(tf.zeros((1, 10, 5)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 7 何时使用函数式API\\n\",\n \"如何决定是使用函数式API创建新模型，还是Model直接对类进行子类化？\\n\",\n \"\\n\",\n \"通常，函数式API是更高级别的，更易于使用和更安全的构建方法，并且具有许多子类化模型不支持的功能。\\n\",\n \"\\n\",\n \"但是，在创建不容易表示为有向无环图的层的模型时，模型子类化为您提供了更大的灵活性（例如，您无法使用Functional API实现Tree-RNN，您必须Model直接子类化）。\\n\",\n \"\\n\",\n \"**功能性API的优点如下：**\\n\",\n \"\\n\",\n \"下面列出的属性对于顺序模型（也是数据结构）也都是正确的，但对于子类模型（它们是Python字节码，不是数据结构）则不是正确的。\\n\",\n \"\\n\",\n \"- 它不那么冗长。不需要\\\\__init\\\\__函数和call函数。\\n\",\n \"- 在定义模型时，它将验证模型。\\n\",\n \" - 在Functional API中，输入规范（shape和dtype）是预先创建的（通过Input），并且每次调用图层时，该图层都会检查传递给它的规范是否符合其假设。\\n\",\n \" - 这样可以保证使用Functional API构建的任何模型都可以运行。所有调试（与收敛相关的调试除外）将在模型构建过程中静态发生，而不是在执行时发生。这类似于在编译器中进行类型检查。\\n\",\n \"- 函数式模型是可绘制且可检查的。\\n\",\n \" - 可以将模型绘制为图形，并且可以轻松访问该图形中的中间节点-例如，以提取和重用中间层的输出。\\n\",\n \"- 函数式模型模型可以序列化或克隆\\n\",\n \" - 因为函数式模型是数据结构而不是一段代码，所以它可以安全地序列化，并且可以保存为单个文件，可以重新创建完全相同的模型，而无需访问任何原始代码。\\n\",\n \"\\n\",\n \"**功能性API的缺点如下：**\\n\",\n \"- 它不支持动态架构。\\n\",\n \" - Functional API将模型视为层的DAG。对于大多数深度学习架构（但不是全部），这是正确的：但是，递归网络或Tree RNN不遵循此假设，并且无法在Functional API中实现。\\n\",\n \"\\n\",\n \"- 有时，需要从头开始编写所有内容。\\n\",\n \" - 在编写高级体系结构时，可能想做“定义层的DAG”范围之外的事情：例如，可能需要在模型实例上公开多个自定义训练和推理方法。这需要子类化。\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "003-keras_training_and_evaluation.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-使用keras训练模型\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"本指南包含了TensorFlow 2.0中在以下两种情况下的训练，评估和预测（推理）模型：\\n\",\n \"\\n\",\n \"- 使用内置的训练和评估API（例如model.fit()，model.evaluate()，model.predict()）。\\n\",\n \"- 使用eager execution 和GradientTape对象从头开始编写自定义循环。\\n\",\n \"\\n\",\n \"无论是使用内置循环还是编写自己的循环，模型和评估训练在每种Keras模型中严格按照相同的方式工作，无论是Sequential 模型, 函数式 API, 还是模型子类化。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# !pip install -q pydot\\n\",\n \"# !apt-get install graphviz\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/doit/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n \" from ._conv import register_converters as _register_converters\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"import tensorflow as tf\\n\",\n \"tf.keras.backend.clear_session()\\n\",\n \"import tensorflow.keras as keras\\n\",\n \"import tensorflow.keras.layers as layers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1 一般的模型构造、训练、测试流程\\n\",\n \"使用内置的训练和评估API对模型进行训练和验证。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 模型构造\\n\",\n \"inputs = keras.Input(shape=(784,), name='mnist_input')\\n\",\n \"h1 = layers.Dense(64, activation='relu')(inputs)\\n\",\n \"h1 = layers.Dense(64, activation='relu')(h1)\\n\",\n \"outputs = layers.Dense(10, activation='softmax')(h1)\\n\",\n \"model = keras.Model(inputs, outputs)\\n\",\n \"# keras.utils.plot_model(model, 'net001.png', show_shapes=True)\\n\",\n \"\\n\",\n \"model.compile(optimizer=keras.optimizers.RMSprop(),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=[keras.metrics.SparseCategoricalAccuracy()])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"端到端的模型训练。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 50000 samples, validate on 10000 samples\\n\",\n \"Epoch 1/3\\n\",\n \"50000/50000 [==============================] - 2s 40us/sample - loss: 0.3406 - sparse_categorical_accuracy: 0.9038 - val_loss: 0.1747 - val_sparse_categorical_accuracy: 0.9478\\n\",\n \"Epoch 2/3\\n\",\n \"50000/50000 [==============================] - 2s 32us/sample - loss: 0.1562 - sparse_categorical_accuracy: 0.9521 - val_loss: 0.1353 - val_sparse_categorical_accuracy: 0.9605\\n\",\n \"Epoch 3/3\\n\",\n \"50000/50000 [==============================] - 2s 34us/sample - loss: 0.1142 - sparse_categorical_accuracy: 0.9647 - val_loss: 0.1093 - val_sparse_categorical_accuracy: 0.9670\\n\",\n \"history:\\n\",\n \"{'loss': [0.3406039907693863, 0.1561644163942337, 0.11421714420557022], 'sparse_categorical_accuracy': [0.90378, 0.95214, 0.96466], 'val_loss': [0.17468911408782006, 0.13526522952914238, 0.10934125750362873], 'val_sparse_categorical_accuracy': [0.9478, 0.9605, 0.967]}\\n\",\n \"10000/1 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=] - 0s 8us/sample - loss: 0.0680 - sparse_categorical_accuracy: 0.9643\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"evaluate:\\n\",\n \"[0.11812290600538254, 0.9643]\\n\",\n \"predict:\\n\",\n \"[[5.96028303e-06 1.22943675e-06 5.60810586e-05 4.95154876e-03\\n\",\n \" 3.37098766e-10 1.87345813e-05 1.95063844e-11 9.94938374e-01\\n\",\n \" 3.62650326e-06 2.44577204e-05]\\n\",\n \" [9.77753075e-07 1.13667054e-04 9.99821007e-01 5.17880726e-05\\n\",\n \" 1.29938716e-11 9.91447632e-06 3.67613637e-07 2.29428121e-08\\n\",\n \" 2.27706096e-06 1.34117118e-11]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 载入数据\\n\",\n \"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\",\n \"x_train = x_train.reshape(60000, 784).astype('float32') /255\\n\",\n \"x_test = x_test.reshape(10000, 784).astype('float32') /255\\n\",\n \"\\n\",\n \"# 保证还是float 32？ 否则后面会出现：TypeError: Input 'y' of 'Sub' Op has type float32 that does not match type uint8 of argument 'x'.\\n\",\n \"y_train = y_train.astype('float32')\\n\",\n \"y_test = y_test.astype('float32')\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# 取验证数据\\n\",\n \"x_val = x_train[-10000:]\\n\",\n \"y_val = y_train[-10000:]\\n\",\n \"\\n\",\n \"x_train = x_train[:-10000]\\n\",\n \"y_train = y_train[:-10000]\\n\",\n \"\\n\",\n \"# 训练模型\\n\",\n \"history = model.fit(x_train, y_train, batch_size=64, epochs=3,\\n\",\n \" validation_data=(x_val, y_val))\\n\",\n \"print('history:')\\n\",\n \"print(history.history)\\n\",\n \"\\n\",\n \"result = model.evaluate(x_test, y_test, batch_size=128)\\n\",\n \"print('evaluate:')\\n\",\n \"print(result)\\n\",\n \"pred = model.predict(x_test[:2])\\n\",\n \"print('predict:')\\n\",\n \"print(pred)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2 自定义指标和损失\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 配置网络\\n\",\n \"在对模型训练之前，我们需要指定损失函数，优化器以及可选的一些要监控的指标。我们将这些配置参数作为compile()方法的参数传递给模型，对模型进行配置。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model.compile(optimizer=keras.optimizers.RMSprop(learning_rate=1e-3),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=[keras.metrics.SparseCategoricalAccuracy()])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"TensorFlow2提供许多内置的优化器，损失和指标\\n\",\n \"常见的内置参数如下：\\n\",\n \"\\n\",\n \"- 优化器： - SGD()（有或没有动量） - RMSprop() - Adam() -等等。\\n\",\n \"\\n\",\n \"- 损失： - MeanSquaredError() - KLDivergence() - CosineSimilarity() -等等。\\n\",\n \"\\n\",\n \"- 指标： - AUC() - Precision() - Recall() -等等。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 自定义损失\\n\",\n \"用Keras提供两种方式来提供自定义损失。\\n\",\n \"- 一、例创建一个接受输入y_true和的函数y_pred。\\n\",\n \"- 二、构建一个继承keras.losser.Loss的子类\\n\",\n \"下面示例显示了一个损失函数，该函数计算实际数据与预测之间的平均距离：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 50000 samples\\n\",\n \"Epoch 1/3\\n\",\n \"50000/50000 [==============================] - 1s 28us/sample - loss: 4.3488\\n\",\n \"Epoch 2/3\\n\",\n \"50000/50000 [==============================] - 1s 24us/sample - loss: 4.3488\\n\",\n \"Epoch 3/3\\n\",\n \"50000/50000 [==============================] - 1s 25us/sample - loss: 4.3488\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"def get_uncompiled_model():\\n\",\n \" inputs = keras.Input(shape=(784,), name='digits')\\n\",\n \" x = layers.Dense(64, activation='relu', name='dense_1')(inputs)\\n\",\n \" x = layers.Dense(64, activation='relu', name='dense_2')(x)\\n\",\n \" outputs = layers.Dense(10, activation='softmax', name='predictions')(x)\\n\",\n \" model = keras.Model(inputs=inputs, outputs=outputs)\\n\",\n \" return model\\n\",\n \"model = get_uncompiled_model()\\n\",\n \"\\n\",\n \"def basic_loss_function(y_true, y_pred):\\n\",\n \" return tf.math.reduce_mean(y_true - y_pred)\\n\",\n \"model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=basic_loss_function)\\n\",\n \"model.fit(x_train, y_train, batch_size=64, epochs=3)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"如果需要试下带参数的损失函数，可以子类化tf.keras.losses.Loss。并子类化以下方法：\\n\",\n \"- \\\\__init\\\\__(self) 接收相关参数，初始化loss之类。\\n\",\n \"- call(self, y_true, y_pred) 使用 y_true和y_pred，计算模型损失。\\n\",\n \"\\n\",\n \"下面例子，展示了WeightedCrossEntropy计算二分损失的损失函数，某个类或整个函数的损失可以通过标量修改。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 50000 samples\\n\",\n \"Epoch 1/3\\n\",\n \"50000/50000 [==============================] - 2s 35us/sample - loss: 9.5178\\n\",\n \"Epoch 2/3\\n\",\n \"50000/50000 [==============================] - 1s 27us/sample - loss: 9.5171\\n\",\n \"Epoch 3/3\\n\",\n \"50000/50000 [==============================] - 1s 27us/sample - loss: 9.5171\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"class WeightBinaryCrossEntropy(keras.losses.Loss):\\n\",\n \" def __init__(self, pos_weight, weight, from_logits=False,\\n\",\n \" reduction=keras.losses.Reduction.AUTO,\\n\",\n \" name='weight_binary_crossentropy'):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" pos_weight: 正类标签权重\\n\",\n \" weight: 整体损失权重\\n\",\n \" from_logits: 是否使用logits来计算loss，（或使用probability）\\n\",\n \" reduction: reduction类型\\n\",\n \" name: 名字\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" super(WeightBinaryCrossEntropy, self).__init__(reduction=reduction,\\n\",\n \" name=name)\\n\",\n \" self.pos_weight = pos_weight\\n\",\n \" self.weight = weight\\n\",\n \" self.from_logits = from_logits\\n\",\n \" \\n\",\n \" def call(self, y_true, y_pred):\\n\",\n \" if not self.from_logits:\\n\",\n \" x_1 = y_true * self.pos_weight * -tf.math.log(y_pred + 1e-6)\\n\",\n \" \\n\",\n \" x_2 = (1-y_true) * -tf.math.log(1-y_pred + 1e-6)\\n\",\n \" \\n\",\n \" return tf.add(x_1, x_2) * self.weight\\n\",\n \" \\n\",\n \" return tf.nn.weighted_cross_entropy_with_logits(y_true, y_pred, self.pos_weight) *self.weight\\n\",\n \" \\n\",\n \"\\n\",\n \"model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=WeightBinaryCrossEntropy(0.5, 2))\\n\",\n \"model.fit(x_train, y_train, batch_size=64, epochs=3)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"使用网络层的方法构建loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 50000 samples\\n\",\n \"50000/50000 [==============================] - 2s 36us/sample - loss: 2.4702\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"class ActivityRegularizationLayer(layers.Layer):\\n\",\n \" def call(self, inputs):\\n\",\n \" self.add_loss(tf.reduce_sum(inputs)* 0.1)\\n\",\n \" return inputs\\n\",\n \"inputs = keras.Input(shape=(784,), name='digits')\\n\",\n \"x = layers.Dense(64, activation='relu', name='dense_1')(inputs)\\n\",\n \"\\n\",\n \"# 添加计算正则化损失的层\\n\",\n \"x = ActivityRegularizationLayer()(x)\\n\",\n \"\\n\",\n \"x = layers.Dense(64, activation='relu', name='dense_2')(x)\\n\",\n \"outputs = layers.Dense(10, activation='softmax', name='predictions')(x)\\n\",\n \"\\n\",\n \"model = keras.Model(inputs=inputs, outputs=outputs)\\n\",\n \"model.compile(optimizer=keras.optimizers.RMSprop(learning_rate=1e-3),\\n\",\n \" loss='sparse_categorical_crossentropy')\\n\",\n \"\\n\",\n \"# 训练\\n\",\n \"model.fit(x_train, y_train,\\n\",\n \" batch_size=64,\\n\",\n \" epochs=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.3 自定义指标\\n\",\n \"\\n\",\n \"自定义指标只需继承Metric类， 并重写以下函数：\\n\",\n \"\\n\",\n \"- \\\\__init\\\\__(self)，初始化。\\n\",\n \"\\n\",\n \"- update_state(self，y_true，y_pred，sample_weight = None)，它使用目标y_true和模型预测y_pred来更新状态变量。\\n\",\n \"\\n\",\n \"- result(self)，它使用状态变量来计算最终结果。\\n\",\n \"\\n\",\n \"- reset_states(self)，重新初始化度量的状态。\\n\",\n \"\\n\",\n \"状态更新和结果计算保持分开（分别在update_state()和result()中），因为在某些情况下，结果计算可能非常昂贵，并且只能定期进行。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1/3\\n\",\n \"50000/50000 [==============================] - 1s 27us/sample - loss: 0.0590 - binary_true_postives: 9186.0000\\n\",\n \"Epoch 2/3\\n\",\n \"50000/50000 [==============================] - 1s 24us/sample - loss: 0.0499 - binary_true_postives: 9957.0000\\n\",\n \"Epoch 3/3\\n\",\n \"50000/50000 [==============================] - 1s 25us/sample - loss: 0.0441 - binary_true_postives: 10939.0000\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 下面是一个简单的示例，显示如何实现CatgoricalTruePositives指标，该指标计算正确分类为属于给定类的样本数量\\n\",\n \"\\n\",\n \"class CatgoricalTruePostives(keras.metrics.Metric):\\n\",\n \" def __init__(self, name='binary_true_postives', **kwargs):\\n\",\n \" super(CatgoricalTruePostives, self).__init__(name=name, **kwargs)\\n\",\n \" # 会更新的类变量\\n\",\n \" self.true_postives = self.add_weight(name='tp', initializer='zeros')\\n\",\n \" \\n\",\n \" def update_state(self, y_true, y_pred, sample_weight=None):\\n\",\n \" # 获取结果id\\n\",\n \" y_pred = tf.argmax(y_pred)\\n\",\n \" # 正确的结果\\n\",\n \" y_true = tf.equal(tf.cast(y_pred, tf.int32), tf.cast(y_true, tf.int32))\\n\",\n \" y_true = tf.cast(y_true, tf.float32)\\n\",\n \" \\n\",\n \" if sample_weight is not None:\\n\",\n \" # 对正确结果加权重\\n\",\n \" sample_weight = tf.cast(sample_weight, tf.float32)\\n\",\n \" y_true = tf.multiply(sample_weight, y_true)\\n\",\n \" # 修改正确样本总量\\n\",\n \" return self.true_postives.assign_add(tf.reduce_sum(y_true))\\n\",\n \" \\n\",\n \" def result(self):\\n\",\n \" # 返回相应tensor\\n\",\n \" return tf.identity(self.true_postives)\\n\",\n \" \\n\",\n \" def reset_states(self):\\n\",\n \" # 重置为0\\n\",\n \" self.true_postives.assign(0.)\\n\",\n \" \\n\",\n \"\\n\",\n \"model.compile(optimizer=keras.optimizers.RMSprop(1e-3),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=[CatgoricalTruePostives()])\\n\",\n \"\\n\",\n \"model.fit(x_train, y_train,\\n\",\n \" batch_size=64, epochs=3)\\n\",\n \" \\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"使用自定义层的方式获取相关指标\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 50000 samples\\n\",\n \"50000/50000 [==============================] - 2s 41us/sample - loss: 0.2940 - sparse_categorical_accuracy: 0.9151 - std_of_activation: 0.9423\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 也可以以定义网络层的方式添加要统计的metric\\n\",\n \"class MetricLoggingLayer(layers.Layer):\\n\",\n \" def call(self, inputs):\\n\",\n \" # 该层的作用就是添加指标\\n\",\n \" self.add_metric(keras.backend.std(inputs),\\n\",\n \" name='std_of_activation',\\n\",\n \" aggregation='mean')\\n\",\n \" # 直接把输入进行输出\\n\",\n \" return inputs\\n\",\n \"\\n\",\n \"inputs = keras.Input(shape=(784,), name='mnist_input')\\n\",\n \"h1 = layers.Dense(64, activation='relu')(inputs)\\n\",\n \"# 直接套在对应的网络层中\\n\",\n \"h1 = MetricLoggingLayer()(h1)\\n\",\n \"h1 = layers.Dense(64, activation='relu')(h1)\\n\",\n \"outputs = layers.Dense(10, activation='softmax')(h1)\\n\",\n \"model = keras.Model(inputs, outputs)\\n\",\n \"# keras.utils.plot_model(model, 'net001.png', show_shapes=True)\\n\",\n \"# 配置并训练网络\\n\",\n \"model.compile(optimizer=keras.optimizers.RMSprop(),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=[keras.metrics.SparseCategoricalAccuracy()])\\n\",\n \"model.fit(x_train, y_train, batch_size=32, epochs=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"我们也可以在构建好模型后直接使用，model.add_loss和model.add_metric添加损失和指标。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 50000 samples\\n\",\n \"50000/50000 [==============================] - 4s 81us/sample - loss: 2.3469 - sparse_categorical_accuracy: 0.1130 - std_of_activation: 0.0010\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"inputs = keras.Input(shape=(784,), name='mnist_input')\\n\",\n \"h1 = layers.Dense(64, activation='relu')(inputs)\\n\",\n \"h2 = layers.Dense(64, activation='relu')(h1)\\n\",\n \"outputs = layers.Dense(10, activation='softmax')(h2)\\n\",\n \"model = keras.Model(inputs, outputs)\\n\",\n \"# 直接把计算loss或metric用到的输入(h1)带人\\n\",\n \"model.add_metric(keras.backend.std(h1),\\n\",\n \" name='std_of_activation',\\n\",\n \" aggregation='mean')\\n\",\n \"\\n\",\n \"model.add_loss(tf.reduce_sum(h1)*0.1)\\n\",\n \"\\n\",\n \"# keras.utils.plot_model(model, 'net001.png', show_shapes=True)\\n\",\n \"\\n\",\n \"model.compile(optimizer=keras.optimizers.RMSprop(),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=[keras.metrics.SparseCategoricalAccuracy()])\\n\",\n \"model.fit(x_train, y_train, batch_size=32, epochs=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"处理使用validation_data传入测试数据，还可以使用validation_split划分验证数据\\n\",\n \"\\n\",\n \"ps:validation_split只能在用numpy数据训练的情况下使用\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 40000 samples, validate on 10000 samples\\n\",\n \"40000/40000 [==============================] - 3s 79us/sample - loss: 2.3014 - sparse_categorical_accuracy: 0.1141 - std_of_activation: 3.6019e-06 - val_loss: 2.3013 - val_sparse_categorical_accuracy: 0.1115 - val_std_of_activation: 1.2214e-06\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"model.fit(x_train, y_train, batch_size=32, epochs=1, validation_split=0.2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3 使用tf.data构造数据\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"到现在我们已经了解了如何使用使用numpy作为输入数据进行训练和验证。下面，我们将介绍如何使用tf.data作为输入数据进行。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train for 100 steps, validate for 3 steps\\n\",\n \"Epoch 1/3\\n\",\n \"100/100 [==============================] - 1s 15ms/step - loss: 0.8740 - sparse_categorical_accuracy: 0.7706 - val_loss: 0.4562 - val_sparse_categorical_accuracy: 0.8802\\n\",\n \"Epoch 2/3\\n\",\n \"100/100 [==============================] - 0s 3ms/step - loss: 0.3867 - sparse_categorical_accuracy: 0.8891 - val_loss: 0.3228 - val_sparse_categorical_accuracy: 0.8958\\n\",\n \"Epoch 3/3\\n\",\n \"100/100 [==============================] - 0s 3ms/step - loss: 0.3206 - sparse_categorical_accuracy: 0.9095 - val_loss: 0.2346 - val_sparse_categorical_accuracy: 0.9323\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"def get_compiled_model():\\n\",\n \" inputs = keras.Input(shape=(784,), name='mnist_input')\\n\",\n \" h1 = layers.Dense(64, activation='relu')(inputs)\\n\",\n \" h2 = layers.Dense(64, activation='relu')(h1)\\n\",\n \" outputs = layers.Dense(10, activation='softmax')(h2)\\n\",\n \" model = keras.Model(inputs, outputs)\\n\",\n \" model.compile(optimizer=keras.optimizers.RMSprop(),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=[keras.metrics.SparseCategoricalAccuracy()])\\n\",\n \" return model\\n\",\n \"model = get_compiled_model()\\n\",\n \"# 构建dataset实例\\n\",\n \"train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))\\n\",\n \"# 打乱\\n\",\n \"train_dataset = train_dataset.shuffle(buffer_size=1024).batch(64)\\n\",\n \"# 获得验证数据\\n\",\n \"val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val))\\n\",\n \"val_dataset = val_dataset.batch(64)\\n\",\n \"\\n\",\n \"# model.fit(train_dataset, epochs=3)\\n\",\n \"# steps_per_epoch 每个epoch只训练几步\\n\",\n \"# validation_steps 每次验证，验证几步\\n\",\n \"model.fit(train_dataset, epochs=3, steps_per_epoch=100,\\n\",\n \" validation_data=val_dataset, validation_steps=3)\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"如果只想对该数据集中的特定批次进行训练，则可以传递steps_per_epoch参数，即批训练多少步。同样我们可以使用train_dataset.take(), 来获取每批次中训练的数据，其和steps_per_epoc等价。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1/3\\n\",\n \"100/100 [==============================] - 2s 17ms/step - loss: 0.7795 - sparse_categorical_accuracy: 0.7961\\n\",\n \"Epoch 2/3\\n\",\n \"100/100 [==============================] - 1s 5ms/step - loss: 0.3308 - sparse_categorical_accuracy: 0.9080\\n\",\n \"Epoch 3/3\\n\",\n \"100/100 [==============================] - 0s 3ms/step - loss: 0.2465 - sparse_categorical_accuracy: 0.9308\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model = get_compiled_model()\\n\",\n \"\\n\",\n \"model.fit(train_dataset.take(100), epochs=3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"# Evaluate\\n\",\n \"157/157 [==============================] - 0s 1ms/step - loss: 0.2669 - sparse_categorical_accuracy: 0.9243\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[0.2669110751835404, 0.9243]\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 模型测试\\n\",\n \"test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test))\\n\",\n \"test_dataset = test_dataset.batch(64)\\n\",\n \"\\n\",\n \"print('\\\\n# Evaluate')\\n\",\n \"model.evaluate(test_dataset)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**其他格式输入数据支持**\\n\",\n \"\\n\",\n \"除了numpy数值和TensorFlow Dataset,还可以用Pandas和python迭代器作为数据输入。\\n\",\n \"通常小数据推荐使用numpy， 大数据推荐使用TensorFlow Dataset。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4 样本权重和类权重\\n\",\n \"在模型训练时可以，可以人工设定样本权重和类权重。\\n\",\n \"\\n\",\n \"“样本权重”数组是一个数字数组，用于指定批处理中每个样本在计算总损失时应具有多少权重。 它通常用于不平衡的分类问题（这个想法是为了给予很少见的类更多的权重）。 当使用的权重是1和0时，该数组可以用作损失函数的掩码（完全丢弃某些样本对总损失的贡献）。\\n\",\n \"\\n\",\n \"“类权重”dict是同一概念的更具体的实例：它将类索引映射到应该用于属于该类的样本的样本权重。 例如，如果类“0”比数据中的类“1”少两倍，则可以使用class_weight = {0：1.，1：0.5}。\\n\",\n \"\\n\",\n \"添加方法：\\n\",\n \"- 使用Numpy数据时： 通过sample_weight和class_weight参数传递。\\n\",\n \"- 使用Dataset数据时： 通过使数据集返回(input_batch, target_batch, sample_weight_batch)。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"下面是一个Numpy数据中加大第5类的权重的例子。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 2.0, 6: 1.0, 7: 1.0, 8: 1.0, 9: 1.0}\\n\",\n \"Train on 50000 samples\\n\",\n \"Epoch 1/4\\n\",\n \"50000/50000 [==============================] - 2s 49us/sample - loss: 0.3598 - sparse_categorical_accuracy: 0.9047\\n\",\n \"Epoch 2/4\\n\",\n \"50000/50000 [==============================] - 2s 32us/sample - loss: 0.1603 - sparse_categorical_accuracy: 0.9545\\n\",\n \"Epoch 3/4\\n\",\n \"50000/50000 [==============================] - 2s 32us/sample - loss: 0.1172 - sparse_categorical_accuracy: 0.9666\\n\",\n \"Epoch 4/4\\n\",\n \"50000/50000 [==============================] - 2s 32us/sample - loss: 0.0940 - sparse_categorical_accuracy: 0.9738\\n\",\n \"Train on 50000 samples\\n\",\n \"Epoch 1/4\\n\",\n \"50000/50000 [==============================] - 2s 42us/sample - loss: 0.3718 - sparse_categorical_accuracy: 0.9024\\n\",\n \"Epoch 2/4\\n\",\n \"50000/50000 [==============================] - 2s 32us/sample - loss: 0.1749 - sparse_categorical_accuracy: 0.9506\\n\",\n \"Epoch 3/4\\n\",\n \"50000/50000 [==============================] - 2s 32us/sample - loss: 0.1298 - sparse_categorical_accuracy: 0.9630\\n\",\n \"Epoch 4/4\\n\",\n \"50000/50000 [==============================] - 2s 31us/sample - loss: 0.1031 - sparse_categorical_accuracy: 0.9702\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 增加第5类的权重\\n\",\n \"import numpy as np\\n\",\n \"# 类权重\\n\",\n \"model = get_compiled_model()\\n\",\n \"class_weight = {i:1.0 for i in range(10)}\\n\",\n \"# 第5类的权重为2\\n\",\n \"class_weight[5] = 2.0\\n\",\n \"print(class_weight)\\n\",\n \"model.fit(x_train, y_train,\\n\",\n \" class_weight=class_weight,\\n\",\n \" batch_size=64,\\n\",\n \" epochs=4)\\n\",\n \"# 样本权重\\n\",\n \"model = get_compiled_model()\\n\",\n \"sample_weight = np.ones(shape=(len(y_train),))\\n\",\n \"sample_weight[y_train == 5] = 2.0\\n\",\n \"model.fit(x_train, y_train,\\n\",\n \" sample_weight=sample_weight,\\n\",\n \" batch_size=64,\\n\",\n \" epochs=4)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Dataset数据中加大第5类的权重的例子。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1/3\\n\",\n \"782/782 [==============================] - 4s 5ms/step - loss: 0.3777 - sparse_categorical_accuracy: 0.9011\\n\",\n \"Epoch 2/3\\n\",\n \"782/782 [==============================] - 2s 3ms/step - loss: 0.1792 - sparse_categorical_accuracy: 0.9496\\n\",\n \"Epoch 3/3\\n\",\n \"782/782 [==============================] - 2s 3ms/step - loss: 0.1295 - sparse_categorical_accuracy: 0.9629\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# tf.data数据\\n\",\n \"model = get_compiled_model()\\n\",\n \"\\n\",\n \"sample_weight = np.ones(shape=(len(y_train),))\\n\",\n \"sample_weight[y_train == 5] = 2.0\\n\",\n \"# 在构造dataset时传入sample_weight\\n\",\n \"train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train,\\n\",\n \" sample_weight))\\n\",\n \"train_dataset = train_dataset.shuffle(buffer_size=1024).batch(64)\\n\",\n \"\\n\",\n \"val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val))\\n\",\n \"val_dataset = val_dataset.batch(64)\\n\",\n \"\\n\",\n \"model.fit(train_dataset, epochs=3, )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5 多输入多输出模型\\n\",\n \"有些模型是是多输入或多输出的，此时可以为他们设置不同的权重的loss和metric。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"image_input = keras.Input(shape=(32, 32, 3), name='img_input')\\n\",\n \"timeseries_input = keras.Input(shape=(None, 10), name='ts_input')\\n\",\n \"\\n\",\n \"x1 = layers.Conv2D(3, 3)(image_input)\\n\",\n \"x1 = layers.GlobalMaxPooling2D()(x1)\\n\",\n \"\\n\",\n \"x2 = layers.Conv1D(3, 3)(timeseries_input)\\n\",\n \"x2 = layers.GlobalMaxPooling1D()(x2)\\n\",\n \"\\n\",\n \"x = layers.concatenate([x1, x2])\\n\",\n \"\\n\",\n \"score_output = layers.Dense(1, name='score_output')(x)\\n\",\n \"class_output = layers.Dense(5, activation='softmax', name='class_output')(x)\\n\",\n \"\\n\",\n \"model = keras.Model(inputs=[image_input, timeseries_input],\\n\",\n \" outputs=[score_output, class_output])\\n\",\n \"keras.utils.plot_model(model, 'multi_input_output_model.png'\\n\",\n \" , show_shapes=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"设置不同的loss和metric\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING: Logging before flag parsing goes to stderr.\\n\",\n \"W1011 16:45:08.059540 139768509077312 training_utils.py:1348] Output score_output missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to score_output.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 可以为模型指定不同的loss和metrics\\n\",\n \"model.compile(\\n\",\n \" optimizer=keras.optimizers.RMSprop(1e-3),\\n\",\n \" loss=[keras.losses.MeanSquaredError(),\\n\",\n \" keras.losses.CategoricalCrossentropy()])\\n\",\n \"\\n\",\n \"# 还可以指定loss的权重\\n\",\n \"model.compile(\\n\",\n \" optimizer=keras.optimizers.RMSprop(1e-3),\\n\",\n \" loss={'score_output': keras.losses.MeanSquaredError(),\\n\",\n \" 'class_output': keras.losses.CategoricalCrossentropy()},\\n\",\n \" metrics={'score_output': [keras.metrics.MeanAbsolutePercentageError(),\\n\",\n \" keras.metrics.MeanAbsoluteError()],\\n\",\n \" 'class_output': [keras.metrics.CategoricalAccuracy()]},\\n\",\n \" loss_weight={'score_output': 2., 'class_output': 1.})\\n\",\n \"\\n\",\n \"# 可以把不需要传播的loss置0\\n\",\n \"model.compile(\\n\",\n \" optimizer=keras.optimizers.RMSprop(1e-3),\\n\",\n \" loss=[None, keras.losses.CategoricalCrossentropy()])\\n\",\n \"\\n\",\n \"# 可以使用字典的方式设置\\n\",\n \"model.compile(\\n\",\n \" optimizer=keras.optimizers.RMSprop(1e-3),\\n\",\n \" loss={'class_output': keras.losses.CategoricalCrossentropy()})\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"为多输入和多输出模型构造numpy数据，并训练。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 100 samples\\n\",\n \"Epoch 1/3\\n\",\n \"100/100 [==============================] - 1s 9ms/sample - loss: 9.0288 - score_output_loss: 2.9604 - class_output_loss: 5.3522\\n\",\n \"Epoch 2/3\\n\",\n \"100/100 [==============================] - 0s 249us/sample - loss: 7.4229 - score_output_loss: 2.1365 - class_output_loss: 5.3362\\n\",\n \"Epoch 3/3\\n\",\n \"100/100 [==============================] - 0s 201us/sample - loss: 6.5389 - score_output_loss: 1.3290 - class_output_loss: 4.9687\\n\",\n \"Train on 100 samples\\n\",\n \"Epoch 1/3\\n\",\n \"100/100 [==============================] - 0s 289us/sample - loss: 5.9722 - score_output_loss: 0.9410 - class_output_loss: 5.0048\\n\",\n \"Epoch 2/3\\n\",\n \"100/100 [==============================] - 0s 214us/sample - loss: 5.5640 - score_output_loss: 0.6367 - class_output_loss: 4.8433\\n\",\n \"Epoch 3/3\\n\",\n \"100/100 [==============================] - 0s 312us/sample - loss: 5.2626 - score_output_loss: 0.4297 - class_output_loss: 4.8506\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.compile(\\n\",\n \" optimizer=keras.optimizers.RMSprop(1e-3),\\n\",\n \" loss=[keras.losses.MeanSquaredError(),\\n\",\n \" keras.losses.CategoricalCrossentropy()])\\n\",\n \"\\n\",\n \"# 生成数据\\n\",\n \"img_data = np.random.random_sample(size=(100, 32, 32, 3))\\n\",\n \"ts_data = np.random.random_sample(size=(100, 20, 10))\\n\",\n \"score_targets = np.random.random_sample(size=(100, 1))\\n\",\n \"class_targets = np.random.random_sample(size=(100, 5))\\n\",\n \"\\n\",\n \"# 训练\\n\",\n \"model.fit([img_data, ts_data], [score_targets, class_targets],\\n\",\n \" batch_size=32,\\n\",\n \" epochs=3)\\n\",\n \"\\n\",\n \"# 可以使用字典匹配输入输出数据\\n\",\n \"model.fit({'img_input': img_data, 'ts_input': ts_data},\\n\",\n \" {'score_output': score_targets, 'class_output': class_targets},\\n\",\n \" batch_size=32,\\n\",\n \" epochs=3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"使用Dataset构造数据。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1/3\\n\",\n \"2/2 [==============================] - 0s 177ms/step - loss: 5.0879 - score_output_loss: 0.3531 - class_output_loss: 4.7348\\n\",\n \"Epoch 2/3\\n\",\n \"2/2 [==============================] - 0s 11ms/step - loss: 4.9957 - score_output_loss: 0.2925 - class_output_loss: 4.6843\\n\",\n \"Epoch 3/3\\n\",\n \"2/2 [==============================] - 0s 13ms/step - loss: 4.8919 - score_output_loss: 0.2547 - class_output_loss: 4.6686\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train_dataset = tf.data.Dataset.from_tensor_slices(\\n\",\n \" ({'img_input': img_data, 'ts_input': ts_data},\\n\",\n \" {'score_output': score_targets, 'class_output': class_targets}))\\n\",\n \"train_dataset = train_dataset.shuffle(buffer_size=1024).batch(64)\\n\",\n \"\\n\",\n \"model.fit(train_dataset, epochs=3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6 使用回调\\n\",\n \"Keras中的回调是在训练期间（在epoch开始时，batch结束时，epoch结束时等）与不同时间点调用的对象，可用于实现以下行为：\\n\",\n \"\\n\",\n \"- 在训练期间的不同时间点进行验证（除了内置的按时间段验证）\\n\",\n \"- 定期检查模型是否超过某个精度阈值\\n\",\n \"- 在训练似乎停滞不前时，改变模型的学习率\\n\",\n \"- 在训练似乎停滞不前时，对顶层进行微调\\n\",\n \"- 在训练结束或超出某个性能阈值时发送电子邮件或即时消息通知等等。\\n\",\n \"\\n\",\n \"**可使用的内置回调有**\\n\",\n \"\\n\",\n \"- ModelCheckpoint：定期保存模型。\\n\",\n \"- EarlyStopping：当训练不再改进验证指标时停止培训。\\n\",\n \"- TensorBoard：定期编写可在TensorBoard中显示的模型日志（更多细节见“可视化”）。\\n\",\n \"- CSVLogger：将丢失和指标数据流式传输到CSV文件。\\n\",\n \"- 等等\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 6.1 回调使用\\n\",\n \"下面是几个回调使用的简单例子\\n\",\n \"\\n\",\n \"1）提前终止\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 40000 samples, validate on 10000 samples\\n\",\n \"Epoch 1/20\\n\",\n \"40000/40000 [==============================] - 2s 51us/sample - loss: 0.3631 - sparse_categorical_accuracy: 0.8978 - val_loss: 0.2154 - val_sparse_categorical_accuracy: 0.9335\\n\",\n \"Epoch 2/20\\n\",\n \"40000/40000 [==============================] - 1s 35us/sample - loss: 0.1695 - sparse_categorical_accuracy: 0.9501 - val_loss: 0.1844 - val_sparse_categorical_accuracy: 0.9447\\n\",\n \"Epoch 3/20\\n\",\n \"40000/40000 [==============================] - 1s 35us/sample - loss: 0.1267 - sparse_categorical_accuracy: 0.9632 - val_loss: 0.1592 - val_sparse_categorical_accuracy: 0.9519\\n\",\n \"Epoch 4/20\\n\",\n \"40000/40000 [==============================] - 1s 35us/sample - loss: 0.1013 - sparse_categorical_accuracy: 0.9694 - val_loss: 0.1440 - val_sparse_categorical_accuracy: 0.9594\\n\",\n \"Epoch 5/20\\n\",\n \"40000/40000 [==============================] - 1s 35us/sample - loss: 0.0837 - sparse_categorical_accuracy: 0.9754 - val_loss: 0.1453 - val_sparse_categorical_accuracy: 0.9582\\n\",\n \"Epoch 6/20\\n\",\n \"40000/40000 [==============================] - 1s 35us/sample - loss: 0.0715 - sparse_categorical_accuracy: 0.9783 - val_loss: 0.1446 - val_sparse_categorical_accuracy: 0.9599\\n\",\n \"Epoch 00006: early stopping\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model = get_compiled_model()\\n\",\n \"\\n\",\n \"callbacks = [\\n\",\n \" keras.callbacks.EarlyStopping(\\n\",\n \" # 不再提升的关注指标\\n\",\n \" monitor='val_loss',\\n\",\n \" # 不再提升的阈值\\n\",\n \" min_delta=1e-2,\\n\",\n \" # 不再提升的轮次\\n\",\n \" patience=2,\\n\",\n \" verbose=1)\\n\",\n \"]\\n\",\n \"model.fit(x_train, y_train,\\n\",\n \" epochs=20,\\n\",\n \" batch_size=64,\\n\",\n \" callbacks=callbacks,\\n\",\n \" validation_split=0.2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"2）模型保存\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 40000 samples, validate on 10000 samples\\n\",\n \"Epoch 1/3\\n\",\n \"38656/40000 [===========================>..] - ETA: 0s - loss: 0.3797 - sparse_categorical_accuracy: 0.8946\\n\",\n \"Epoch 00001: val_loss improved from inf to 0.24097, saving model to mymodel_1.h5\\n\",\n \"40000/40000 [==============================] - 2s 47us/sample - loss: 0.3731 - sparse_categorical_accuracy: 0.8963 - val_loss: 0.2410 - val_sparse_categorical_accuracy: 0.9263\\n\",\n \"Epoch 2/3\\n\",\n \"39040/40000 [============================>.] - ETA: 0s - loss: 0.1717 - sparse_categorical_accuracy: 0.9493\\n\",\n \"Epoch 00002: val_loss improved from 0.24097 to 0.18096, saving model to mymodel_2.h5\\n\",\n \"40000/40000 [==============================] - 1s 35us/sample - loss: 0.1713 - sparse_categorical_accuracy: 0.9495 - val_loss: 0.1810 - val_sparse_categorical_accuracy: 0.9459\\n\",\n \"Epoch 3/3\\n\",\n \"39680/40000 [============================>.] - ETA: 0s - loss: 0.1260 - sparse_categorical_accuracy: 0.9630\\n\",\n \"Epoch 00003: val_loss improved from 0.18096 to 0.15665, saving model to mymodel_3.h5\\n\",\n \"40000/40000 [==============================] - 1s 37us/sample - loss: 0.1260 - sparse_categorical_accuracy: 0.9630 - val_loss: 0.1567 - val_sparse_categorical_accuracy: 0.9521\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# checkpoint模型回调\\n\",\n \"model = get_compiled_model()\\n\",\n \"check_callback = keras.callbacks.ModelCheckpoint(\\n\",\n \" # 模型路径\\n\",\n \" filepath='mymodel_{epoch}.h5',\\n\",\n \" # 是否保存最佳\\n\",\n \" save_best_only=True,\\n\",\n \" # 监控指标\\n\",\n \" monitor='val_loss',\\n\",\n \" # 进度条类型\\n\",\n \" verbose=1\\n\",\n \")\\n\",\n \"\\n\",\n \"model.fit(x_train, y_train,\\n\",\n \" epochs=3,\\n\",\n \" batch_size=64,\\n\",\n \" callbacks=[check_callback],\\n\",\n \" validation_split=0.2)\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"3）学习率调整\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 40000 samples, validate on 10000 samples\\n\",\n \"Epoch 1/3\\n\",\n \"39680/40000 [============================>.] - ETA: 0s - loss: 4.9481 - sparse_categorical_accuracy: 0.2915\\n\",\n \"Epoch 00001: val_loss did not improve from 0.15665\\n\",\n \"40000/40000 [==============================] - 2s 42us/sample - loss: 4.9245 - sparse_categorical_accuracy: 0.2911 - val_loss: 1.8496 - val_sparse_categorical_accuracy: 0.2757\\n\",\n \"Epoch 2/3\\n\",\n \"39680/40000 [============================>.] - ETA: 0s - loss: 1.7370 - sparse_categorical_accuracy: 0.3520\\n\",\n \"Epoch 00002: val_loss did not improve from 0.15665\\n\",\n \"40000/40000 [==============================] - 1s 35us/sample - loss: 1.7349 - sparse_categorical_accuracy: 0.3529 - val_loss: 1.9447 - val_sparse_categorical_accuracy: 0.3140\\n\",\n \"Epoch 3/3\\n\",\n \"38720/40000 [============================>.] - ETA: 0s - loss: 1.4804 - sparse_categorical_accuracy: 0.4632\\n\",\n \"Epoch 00003: val_loss did not improve from 0.15665\\n\",\n \"40000/40000 [==============================] - 1s 34us/sample - loss: 1.4759 - sparse_categorical_accuracy: 0.4642 - val_loss: 1.2941 - val_sparse_categorical_accuracy: 0.5334\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 动态调整学习率\\n\",\n \"initial_learning_rate = 0.1\\n\",\n \"lr_schedule = keras.optimizers.schedules.ExponentialDecay(\\n\",\n \" # 初始学习率\\n\",\n \" initial_learning_rate,\\n\",\n \" # 延迟步数\\n\",\n \" decay_steps=10000,\\n\",\n \" # 调整百分比\\n\",\n \" decay_rate=0.96,\\n\",\n \" staircase=True\\n\",\n \")\\n\",\n \"optimizer = keras.optimizers.RMSprop(learning_rate=lr_schedule)\\n\",\n \"model.compile(\\n\",\n \" optimizer=optimizer,\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=[keras.metrics.SparseCategoricalAccuracy()])\\n\",\n \"model.fit(x_train, y_train,\\n\",\n \" epochs=3,\\n\",\n \" batch_size=64,\\n\",\n \" callbacks=[check_callback],\\n\",\n \" validation_split=0.2)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"4）训练可视化\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 40000 samples, validate on 10000 samples\\n\",\n \"Epoch 1/5\\n\",\n \"40000/40000 [==============================] - 2s 46us/sample - loss: 0.3759 - sparse_categorical_accuracy: 0.8946 - val_loss: 0.2314 - val_sparse_categorical_accuracy: 0.9332\\n\",\n \"Epoch 2/5\\n\",\n \"40000/40000 [==============================] - 1s 36us/sample - loss: 0.1778 - sparse_categorical_accuracy: 0.9472 - val_loss: 0.1941 - val_sparse_categorical_accuracy: 0.9389\\n\",\n \"Epoch 3/5\\n\",\n \"40000/40000 [==============================] - 1s 35us/sample - loss: 0.1310 - sparse_categorical_accuracy: 0.9617 - val_loss: 0.1643 - val_sparse_categorical_accuracy: 0.9503\\n\",\n \"Epoch 4/5\\n\",\n \"40000/40000 [==============================] - 1s 37us/sample - loss: 0.1026 - sparse_categorical_accuracy: 0.9696 - val_loss: 0.1788 - val_sparse_categorical_accuracy: 0.9465\\n\",\n \"Epoch 5/5\\n\",\n \"40000/40000 [==============================] - 1s 36us/sample - loss: 0.0841 - sparse_categorical_accuracy: 0.9754 - val_loss: 0.1470 - val_sparse_categorical_accuracy: 0.9581\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 使用tensorboard\\n\",\n \"tensorboard_cbk = keras.callbacks.TensorBoard(log_dir='./full_path_to_your_logs')\\n\",\n \"model = get_compiled_model()\\n\",\n \"model.fit(x_train, y_train,\\n\",\n \" epochs=5,\\n\",\n \" batch_size=64,\\n\",\n \" callbacks=[tensorboard_cbk],\\n\",\n \" validation_split=0.2)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 6.2 创建自己的回调方法\\n\",\n \"构建一个可以记录每个epoch的loss的回调\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 40000 samples, validate on 10000 samples\\n\",\n \"Epoch 1/3\\n\",\n \"38528/40000 [===========================>..] - ETA: 0s - loss: 0.3842 - sparse_categorical_accuracy: 0.8918\\n\",\n \"loss: 0.37819265995025636\\n\",\n \"40000/40000 [==============================] - 2s 42us/sample - loss: 0.3782 - sparse_categorical_accuracy: 0.8935 - val_loss: 0.2499 - val_sparse_categorical_accuracy: 0.9256\\n\",\n \"Epoch 2/3\\n\",\n \"38720/40000 [============================>.] - ETA: 0s - loss: 0.1810 - sparse_categorical_accuracy: 0.9465\\n\",\n \"loss: 0.18032952436208724\\n\",\n \"40000/40000 [==============================] - 1s 35us/sample - loss: 0.1803 - sparse_categorical_accuracy: 0.9467 - val_loss: 0.1945 - val_sparse_categorical_accuracy: 0.9423\\n\",\n \"Epoch 3/3\\n\",\n \"38720/40000 [============================>.] - ETA: 0s - loss: 0.1307 - sparse_categorical_accuracy: 0.9608\\n\",\n \"loss: 0.13051214462518693\\n\",\n \"40000/40000 [==============================] - 1s 35us/sample - loss: 0.1305 - sparse_categorical_accuracy: 0.9610 - val_loss: 0.1679 - val_sparse_categorical_accuracy: 0.9508\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"class LossHistory(keras.callbacks.Callback):\\n\",\n \" def on_train_begin(self, logs):\\n\",\n \" self.losses = []\\n\",\n \" def on_epoch_end(self, batch, logs):\\n\",\n \" self.losses.append(logs.get('loss'))\\n\",\n \" print('\\\\nloss:',self.losses[-1])\\n\",\n \" \\n\",\n \"model = get_compiled_model()\\n\",\n \"\\n\",\n \"callbacks = [\\n\",\n \" LossHistory()\\n\",\n \"]\\n\",\n \"model.fit(x_train, y_train,\\n\",\n \" epochs=3,\\n\",\n \" batch_size=64,\\n\",\n \" callbacks=callbacks,\\n\",\n \" validation_split=0.2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 7 自己构造训练和验证循环\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"我们可以自定义循环和验证方法，而不是简单调用fit和evaluate()。\\n\",\n \"\\n\",\n \"### 7.1使用GradientTape构建训练\\n\",\n \"GradientTape可以计算变量梯度，并实现反向传播的功能。我们下面使用GradientTape来构建自定义训练循环。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"epoch: 0\\n\",\n \"Training loss (for one batch) at step 0: 2.3862709999084473\\n\",\n \"Seen so far: 64 samples\\n\",\n \"Training loss (for one batch) at step 200: 2.245845317840576\\n\",\n \"Seen so far: 12864 samples\\n\",\n \"Training loss (for one batch) at step 400: 2.180447578430176\\n\",\n \"Seen so far: 25664 samples\\n\",\n \"Training loss (for one batch) at step 600: 2.0862836837768555\\n\",\n \"Seen so far: 38464 samples\\n\",\n \"epoch: 1\\n\",\n \"Training loss (for one batch) at step 0: 2.0792737007141113\\n\",\n \"Seen so far: 64 samples\\n\",\n \"Training loss (for one batch) at step 200: 1.997469425201416\\n\",\n \"Seen so far: 12864 samples\\n\",\n \"Training loss (for one batch) at step 400: 1.7244919538497925\\n\",\n \"Seen so far: 25664 samples\\n\",\n \"Training loss (for one batch) at step 600: 1.7059353590011597\\n\",\n \"Seen so far: 38464 samples\\n\",\n \"epoch: 2\\n\",\n \"Training loss (for one batch) at step 0: 1.6949570178985596\\n\",\n \"Seen so far: 64 samples\\n\",\n \"Training loss (for one batch) at step 200: 1.5404092073440552\\n\",\n \"Seen so far: 12864 samples\\n\",\n \"Training loss (for one batch) at step 400: 1.4513380527496338\\n\",\n \"Seen so far: 25664 samples\\n\",\n \"Training loss (for one batch) at step 600: 1.3362979888916016\\n\",\n \"Seen so far: 38464 samples\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 构建模型\\n\",\n \"inputs = keras.Input(shape=(784,), name='digits')\\n\",\n \"x = layers.Dense(64, activation='relu', name='dense_1')(inputs)\\n\",\n \"x = layers.Dense(64, activation='relu', name='dense_2')(x)\\n\",\n \"outputs = layers.Dense(10, activation='softmax', name='predictions')(x)\\n\",\n \"model = keras.Model(inputs=inputs, outputs=outputs)\\n\",\n \"\\n\",\n \"# 优化器和损失函数\\n\",\n \"optimizer = keras.optimizers.SGD(learning_rate=1e-3)\\n\",\n \"loss_fn = keras.losses.SparseCategoricalCrossentropy()\\n\",\n \"\\n\",\n \"# 准备数据\\n\",\n \"batch_size = 64\\n\",\n \"train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))\\n\",\n \"train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)\\n\",\n \"\\n\",\n \"# 自己构造循环\\n\",\n \"for epoch in range(3):\\n\",\n \" print('epoch: ', epoch)\\n\",\n \" for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):\\n\",\n \" # 开一个gradient tape, 计算梯度\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" logits = model(x_batch_train)\\n\",\n \" # 获取loss\\n\",\n \" loss_value = loss_fn(y_batch_train, logits)\\n\",\n \" # 由loss计算各变量梯度\\n\",\n \" grads = tape.gradient(loss_value, model.trainable_variables)\\n\",\n \" # 使用优化器计算反向传播\\n\",\n \" optimizer.apply_gradients(zip(grads, model.trainable_variables))\\n\",\n \" \\n\",\n \" if step % 200 == 0:\\n\",\n \" print('Training loss (for one batch) at step %s: %s' % (step, float(loss_value)))\\n\",\n \" print('Seen so far: %s samples' % ((step + 1) * 64))\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 7.1 在自定义训练中设定指标\\n\",\n \"我们可以在自定义训练循环中随时使用内置指标（或编写的自定义指标）。流程如下：\\n\",\n \"\\n\",\n \"- 在循环开始时实例化指标\\n\",\n \"- 每一批数据训练之后，调用metric.update_state()\\n\",\n \"- 需要获得指标时，调用metric.result()\\n\",\n \"- 需要清除指标时，调用metric.reset_states()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Start of epoch 0\\n\",\n \"Training loss (for one batch) at step 0: 2.3234963417053223\\n\",\n \"Seen so far: 64 samples\\n\",\n \"Training loss (for one batch) at step 200: 2.1431407928466797\\n\",\n \"Seen so far: 12864 samples\\n\",\n \"Training loss (for one batch) at step 400: 2.11155366897583\\n\",\n \"Seen so far: 25664 samples\\n\",\n \"Training loss (for one batch) at step 600: 1.9793719053268433\\n\",\n \"Seen so far: 38464 samples\\n\",\n \"Training acc over epoch: 0.3366200029850006\\n\",\n \"Validation acc: 0.5264999866485596\\n\",\n \"Start of epoch 1\\n\",\n \"Training loss (for one batch) at step 0: 2.0552053451538086\\n\",\n \"Seen so far: 64 samples\\n\",\n \"Training loss (for one batch) at step 200: 1.9160033464431763\\n\",\n \"Seen so far: 12864 samples\\n\",\n \"Training loss (for one batch) at step 400: 1.797502040863037\\n\",\n \"Seen so far: 25664 samples\\n\",\n \"Training loss (for one batch) at step 600: 1.626814842224121\\n\",\n \"Seen so far: 38464 samples\\n\",\n \"Training acc over epoch: 0.589959979057312\\n\",\n \"Validation acc: 0.6836000084877014\\n\",\n \"Start of epoch 2\\n\",\n \"Training loss (for one batch) at step 0: 1.6038627624511719\\n\",\n \"Seen so far: 64 samples\\n\",\n \"Training loss (for one batch) at step 200: 1.4073700904846191\\n\",\n \"Seen so far: 12864 samples\\n\",\n \"Training loss (for one batch) at step 400: 1.3236889839172363\\n\",\n \"Seen so far: 25664 samples\\n\",\n \"Training loss (for one batch) at step 600: 1.2352226972579956\\n\",\n \"Seen so far: 38464 samples\\n\",\n \"Training acc over epoch: 0.7089800238609314\\n\",\n \"Validation acc: 0.7746000289916992\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 训练并验证\\n\",\n \"\\n\",\n \"inputs = keras.Input(shape=(784,), name='digits')\\n\",\n \"x = layers.Dense(64, activation='relu', name='dense_1')(inputs)\\n\",\n \"x = layers.Dense(64, activation='relu', name='dense_2')(x)\\n\",\n \"outputs = layers.Dense(10, activation='softmax', name='predictions')(x)\\n\",\n \"model = keras.Model(inputs=inputs, outputs=outputs)\\n\",\n \"\\n\",\n \"optimizer = keras.optimizers.SGD(learning_rate=1e-3)\\n\",\n \"loss_fn = keras.losses.SparseCategoricalCrossentropy()\\n\",\n \"\\n\",\n \"# 实例化指标类.\\n\",\n \"train_acc_metric = keras.metrics.SparseCategoricalAccuracy() \\n\",\n \"val_acc_metric = keras.metrics.SparseCategoricalAccuracy()\\n\",\n \"\\n\",\n \"# 准备数据\\n\",\n \"batch_size = 64\\n\",\n \"train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))\\n\",\n \"train_dataset = train_dataset.shuffle(buffer_size=1024).batch(batch_size)\\n\",\n \"\\n\",\n \"val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val))\\n\",\n \"val_dataset = val_dataset.batch(64)\\n\",\n \"\\n\",\n \"\\n\",\n \"# 自定义训练迭代\\n\",\n \"for epoch in range(3):\\n\",\n \" print('Start of epoch %d' % (epoch,))\\n\",\n \" \\n\",\n \" # 获取每一批数据。\\n\",\n \" for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" logits = model(x_batch_train)\\n\",\n \" loss_value = loss_fn(y_batch_train, logits)\\n\",\n \" # 计算梯度， 反向传播\\n\",\n \" grads = tape.gradient(loss_value, model.trainable_variables)\\n\",\n \" optimizer.apply_gradients(zip(grads, model.trainable_variables))\\n\",\n \"\\n\",\n \" # 更新指标\\n\",\n \" train_acc_metric(y_batch_train, logits)\\n\",\n \"\\n\",\n \" # 输出\\n\",\n \" if step % 200 == 0:\\n\",\n \" print('Training loss (for one batch) at step %s: %s' % (step, float(loss_value)))\\n\",\n \" print('Seen so far: %s samples' % ((step + 1) * 64))\\n\",\n \"\\n\",\n \" # 每一批次结束，获取一次指标\\n\",\n \" train_acc = train_acc_metric.result()\\n\",\n \" print('Training acc over epoch: %s' % (float(train_acc),))\\n\",\n \" # 重置指标\\n\",\n \" train_acc_metric.reset_states()\\n\",\n \"\\n\",\n \" # 迭代验证\\n\",\n \" for x_batch_val, y_batch_val in val_dataset:\\n\",\n \" val_logits = model(x_batch_val)\\n\",\n \" # Update val metrics\\n\",\n \" val_acc_metric(y_batch_val, val_logits)\\n\",\n \" # 获取验证指标\\n\",\n \" val_acc = val_acc_metric.result()\\n\",\n \" val_acc_metric.reset_states()\\n\",\n \" print('Validation acc: %s' % (float(val_acc),))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 7.2 在自定义训练中添加loss\\n\",\n \"\\n\",\n \"我们可以在自定义训练中添加自定义损失。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\",\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"##　添加自己构造的loss, 每次只能看到最新一次训练增加的loss\\n\",\n \"class ActivityRegularizationLayer(layers.Layer):\\n\",\n \" \\n\",\n \" def call(self, inputs):\\n\",\n \" self.add_loss(1e-2 * tf.reduce_sum(inputs))\\n\",\n \" return inputs\\n\",\n \" \\n\",\n \"inputs = keras.Input(shape=(784,), name='digits')\\n\",\n \"x = layers.Dense(64, activation='relu', name='dense_1')(inputs)\\n\",\n \"# 添加正则化损失层\\n\",\n \"x = ActivityRegularizationLayer()(x)\\n\",\n \"x = layers.Dense(64, activation='relu', name='dense_2')(x)\\n\",\n \"outputs = layers.Dense(10, activation='softmax', name='predictions')(x)\\n\",\n \"\\n\",\n \"model = keras.Model(inputs=inputs, outputs=outputs)\\n\",\n \"logits = model(x_train[:64])\\n\",\n \"print(model.losses)\\n\",\n \"logits = model(x_train[:64])\\n\",\n \"logits = model(x_train[64: 128])\\n\",\n \"logits = model(x_train[128: 192])\\n\",\n \"print(model.losses)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Start of epoch 0\\n\",\n \"Training loss (for one batch) at step 0: 2.320131778717041\\n\",\n \"Seen so far: 64 samples\\n\",\n \"Training loss (for one batch) at step 200: 2.3046109676361084\\n\",\n \"Seen so far: 12864 samples\\n\",\n \"Training loss (for one batch) at step 400: 2.309241771697998\\n\",\n \"Seen so far: 25664 samples\\n\",\n \"Training loss (for one batch) at step 600: 2.3098361492156982\\n\",\n \"Seen so far: 38464 samples\\n\",\n \"Start of epoch 1\\n\",\n \"Training loss (for one batch) at step 0: 2.3076140880584717\\n\",\n \"Seen so far: 64 samples\\n\",\n \"Training loss (for one batch) at step 200: 2.307394504547119\\n\",\n \"Seen so far: 12864 samples\\n\",\n \"Training loss (for one batch) at step 400: 2.3062784671783447\\n\",\n \"Seen so far: 25664 samples\\n\",\n \"Training loss (for one batch) at step 600: 2.3098881244659424\\n\",\n \"Seen so far: 38464 samples\\n\",\n \"Start of epoch 2\\n\",\n \"Training loss (for one batch) at step 0: 2.3012585639953613\\n\",\n \"Seen so far: 64 samples\\n\",\n \"Training loss (for one batch) at step 200: 2.3015058040618896\\n\",\n \"Seen so far: 12864 samples\\n\",\n \"Training loss (for one batch) at step 400: 2.3131601810455322\\n\",\n \"Seen so far: 25664 samples\\n\",\n \"Training loss (for one batch) at step 600: 2.311933755874634\\n\",\n \"Seen so far: 38464 samples\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 将loss添加进求导中\\n\",\n \"optimizer = keras.optimizers.SGD(learning_rate=1e-3)\\n\",\n \"\\n\",\n \"for epoch in range(3):\\n\",\n \" print('Start of epoch %d' % (epoch,))\\n\",\n \"\\n\",\n \" for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" logits = model(x_batch_train)\\n\",\n \" loss_value = loss_fn(y_batch_train, logits)\\n\",\n \"\\n\",\n \" # 添加额外的损失\\n\",\n \" loss_value += sum(model.losses)\\n\",\n \" # 求梯度，反向传播\\n\",\n \" grads = tape.gradient(loss_value, model.trainable_variables)\\n\",\n \" optimizer.apply_gradients(zip(grads, model.trainable_variables))\\n\",\n \"\\n\",\n \" # 输出\\n\",\n \" if step % 200 == 0:\\n\",\n \" print('Training loss (for one batch) at step %s: %s' % (step, float(loss_value)))\\n\",\n \" print('Seen so far: %s samples' % ((step + 1) * 64))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "004-keras_write_layers_from_scratch.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-用keras构建自己的网络层\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1 构建一个简单的网络层\\n\",\n \"我们可以通过继承tf.keras.layer.Layer,实现一个自定义的网络层。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/doit/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n \" from ._conv import register_converters as _register_converters\\n\"\n ]\n }\n ],\n \"source\": [\n \"\\n\",\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"import tensorflow as tf\\n\",\n \"tf.keras.backend.clear_session()\\n\",\n \"import tensorflow.keras as keras\\n\",\n \"import tensorflow.keras.layers as layers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[-0.07703826 0.02132557 0.06587847 0.11276232]\\n\",\n \" [-0.07703826 0.02132557 0.06587847 0.11276232]\\n\",\n \" [-0.07703826 0.02132557 0.06587847 0.11276232]], shape=(3, 4), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 定义网络层就是：设置网络权重和输出到输入的计算过程\\n\",\n \"class MyLayer(layers.Layer):\\n\",\n \" def __init__(self, input_dim=32, unit=32):\\n\",\n \" super(MyLayer, self).__init__()\\n\",\n \" \\n\",\n \" w_init = tf.random_normal_initializer()\\n\",\n \" # 权重变量\\n\",\n \" self.weight = tf.Variable(initial_value=w_init(\\n\",\n \" shape=(input_dim, unit), dtype=tf.float32), trainable=True)\\n\",\n \" \\n\",\n \" b_init = tf.zeros_initializer()\\n\",\n \" # 偏置变量\\n\",\n \" self.bias = tf.Variable(initial_value=b_init(\\n\",\n \" shape=(unit,), dtype=tf.float32), trainable=True)\\n\",\n \" \\n\",\n \" def call(self, inputs):\\n\",\n \" # 全连接网络\\n\",\n \" return tf.matmul(inputs, self.weight) + self.bias\\n\",\n \" \\n\",\n \"x = tf.ones((3,5))\\n\",\n \"my_layer = MyLayer(5, 4)\\n\",\n \"out = my_layer(x)\\n\",\n \"print(out)\\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"按上面构建网络层，图层会自动跟踪权重w和b，当然我们也可以直接用add_weight的方法构建权重\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[ 0.05495948 -0.07037501 0.20973718 -0.08516831]\\n\",\n \" [ 0.05495948 -0.07037501 0.20973718 -0.08516831]\\n\",\n \" [ 0.05495948 -0.07037501 0.20973718 -0.08516831]], shape=(3, 4), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"class MyLayer(layers.Layer):\\n\",\n \" def __init__(self, input_dim=32, unit=32):\\n\",\n \" super(MyLayer, self).__init__()\\n\",\n \" # 使用add_weight添加网络变量，使其可追踪\\n\",\n \" self.weight = self.add_weight(shape=(input_dim, unit),\\n\",\n \" initializer=keras.initializers.RandomNormal(),\\n\",\n \" trainable=True)\\n\",\n \" self.bias = self.add_weight(shape=(unit,),\\n\",\n \" initializer=keras.initializers.Zeros(),\\n\",\n \" trainable=True)\\n\",\n \" \\n\",\n \" def call(self, inputs):\\n\",\n \" return tf.matmul(inputs, self.weight) + self.bias\\n\",\n \" \\n\",\n \"x = tf.ones((3,5))\\n\",\n \"my_layer = MyLayer(5, 4)\\n\",\n \"out = my_layer(x)\\n\",\n \"print(out)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"也可以设置不可训练的权重\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[3. 3. 3.]\\n\",\n \"[6. 6. 6.]\\n\",\n \"weight: []\\n\",\n \"non-trainable weight: []\\n\",\n \"trainable weight: []\\n\"\n ]\n }\n ],\n \"source\": [\n \"class AddLayer(layers.Layer):\\n\",\n \" def __init__(self, input_dim=32):\\n\",\n \" super(AddLayer, self).__init__()\\n\",\n \" # 只存储，不训练的变量\\n\",\n \" self.sum = self.add_weight(shape=(input_dim,),\\n\",\n \" initializer=keras.initializers.Zeros(),\\n\",\n \" trainable=False)\\n\",\n \" \\n\",\n \" \\n\",\n \" def call(self, inputs):\\n\",\n \" self.sum.assign_add(tf.reduce_sum(inputs, axis=0))\\n\",\n \" return self.sum\\n\",\n \" \\n\",\n \"x = tf.ones((3,3))\\n\",\n \"my_layer = AddLayer(3)\\n\",\n \"out = my_layer(x)\\n\",\n \"print(out.numpy())\\n\",\n \"out = my_layer(x)\\n\",\n \"print(out.numpy())\\n\",\n \"print('weight:', my_layer.weights)\\n\",\n \"print('non-trainable weight:', my_layer.non_trainable_weights)\\n\",\n \"print('trainable weight:', my_layer.trainable_weights)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"当定义网络时不知道网络的维度是可以重写build()函数，用获得的shape构建网络\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[-0.25201735 0.09862914 0.06587204]\\n\",\n \" [-0.25201735 0.09862914 0.06587204]\\n\",\n \" [-0.25201735 0.09862914 0.06587204]], shape=(3, 3), dtype=float32)\\n\",\n \"tf.Tensor(\\n\",\n \"[[-0.0270178 -0.03847811 -0.09622537]\\n\",\n \" [-0.0270178 -0.03847811 -0.09622537]], shape=(2, 3), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"class MyLayer(layers.Layer):\\n\",\n \" def __init__(self, unit=32):\\n\",\n \" super(MyLayer, self).__init__()\\n\",\n \" self.unit = unit\\n\",\n \" \\n\",\n \" def build(self, input_shape):\\n\",\n \" # 在build时获取input_shape\\n\",\n \" self.weight = self.add_weight(shape=(input_shape[-1], self.unit),\\n\",\n \" initializer=keras.initializers.RandomNormal(),\\n\",\n \" trainable=True)\\n\",\n \" self.bias = self.add_weight(shape=(self.unit,),\\n\",\n \" initializer=keras.initializers.Zeros(),\\n\",\n \" trainable=True)\\n\",\n \" \\n\",\n \" def call(self, inputs):\\n\",\n \" return tf.matmul(inputs, self.weight) + self.bias\\n\",\n \" \\n\",\n \"\\n\",\n \"my_layer = MyLayer(3)\\n\",\n \"x = tf.ones((3,5))\\n\",\n \"out = my_layer(x)\\n\",\n \"print(out)\\n\",\n \"my_layer = MyLayer(3)\\n\",\n \"\\n\",\n \"x = tf.ones((2,2))\\n\",\n \"out = my_layer(x)\\n\",\n \"print(out)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2 使用子层递归构建网络层\\n\",\n \"可以在自定义网络层中调用其他自定义网络层\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"trainable weights: 0\\n\",\n \"trainable weights: 6\\n\"\n ]\n }\n ],\n \"source\": [\n \"class MyBlock(layers.Layer):\\n\",\n \" def __init__(self):\\n\",\n \" super(MyBlock, self).__init__()\\n\",\n \" # 其他自定义网络层\\n\",\n \" self.layer1 = MyLayer(32)\\n\",\n \" self.layer2 = MyLayer(16)\\n\",\n \" self.layer3 = MyLayer(2)\\n\",\n \" def call(self, inputs):\\n\",\n \" h1 = self.layer1(inputs)\\n\",\n \" h1 = tf.nn.relu(h1)\\n\",\n \" h2 = self.layer2(h1)\\n\",\n \" h2 = tf.nn.relu(h2)\\n\",\n \" return self.layer3(h2)\\n\",\n \" \\n\",\n \"my_block = MyBlock()\\n\",\n \"print('trainable weights:', len(my_block.trainable_weights))\\n\",\n \"y = my_block(tf.ones(shape=(3, 64)))\\n\",\n \"# 构建网络在build()里面，所以执行了才有网络\\n\",\n \"print('trainable weights:', len(my_block.trainable_weights)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"可以通过构建网络层的方法来收集loss，并可以递归调用。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\",\n \"1\\n\",\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"class LossLayer(layers.Layer):\\n\",\n \" \\n\",\n \" def __init__(self, rate=1e-2):\\n\",\n \" super(LossLayer, self).__init__()\\n\",\n \" self.rate = rate\\n\",\n \" \\n\",\n \" def call(self, inputs):\\n\",\n \" # 添加loss\\n\",\n \" self.add_loss(self.rate * tf.reduce_sum(inputs))\\n\",\n \" return inputs\\n\",\n \"\\n\",\n \"class OutLayer(layers.Layer):\\n\",\n \" def __init__(self):\\n\",\n \" super(OutLayer, self).__init__()\\n\",\n \" self.loss_fun=LossLayer(1e-2)\\n\",\n \" def call(self, inputs):\\n\",\n \" # 就一个loss层\\n\",\n \" return self.loss_fun(inputs)\\n\",\n \" \\n\",\n \"my_layer = OutLayer()\\n\",\n \"print(len(my_layer.losses)) # 还未call\\n\",\n \"y = my_layer(tf.zeros(1,1))\\n\",\n \"print(len(my_layer.losses)) # 执行call之后\\n\",\n \"y = my_layer(tf.zeros(1,1))\\n\",\n \"print(len(my_layer.losses)) # call之前会重新置0\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"如果中间调用了keras网络层，里面的正则化loss也会被加入进来\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\",\n \"[, ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"class OuterLayer(layers.Layer):\\n\",\n \"\\n\",\n \" def __init__(self):\\n\",\n \" super(OuterLayer, self).__init__()\\n\",\n \" # 子层中正则化loss也会添加到总的loss中\\n\",\n \" self.dense = layers.Dense(32, kernel_regularizer=tf.keras.regularizers.l2(1e-3))\\n\",\n \" \\n\",\n \" def call(self, inputs):\\n\",\n \" return self.dense(inputs)\\n\",\n \"\\n\",\n \"\\n\",\n \"my_layer = OuterLayer()\\n\",\n \"y = my_layer(tf.zeros((1,1)))\\n\",\n \"print(my_layer.losses) \\n\",\n \"print(my_layer.weights) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 3 其他网络层配置\\n\",\n \"## 3.1 使自己的网络层可以序列化\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'name': 'linear', 'trainable': True, 'dtype': 'float32', 'units': 125}\\n\"\n ]\n }\n ],\n \"source\": [\n \"class Linear(layers.Layer):\\n\",\n \"\\n\",\n \" def __init__(self, units=32, **kwargs):\\n\",\n \" super(Linear, self).__init__(**kwargs)\\n\",\n \" self.units = units\\n\",\n \"\\n\",\n \" def build(self, input_shape):\\n\",\n \" self.w = self.add_weight(shape=(input_shape[-1], self.units),\\n\",\n \" initializer='random_normal',\\n\",\n \" trainable=True)\\n\",\n \" self.b = self.add_weight(shape=(self.units,),\\n\",\n \" initializer='random_normal',\\n\",\n \" trainable=True)\\n\",\n \" def call(self, inputs):\\n\",\n \" return tf.matmul(inputs, self.w) + self.b\\n\",\n \" \\n\",\n \" def get_config(self):\\n\",\n \" # 获取网络配置，用于实现序列化\\n\",\n \" config = super(Linear, self).get_config()\\n\",\n \" config.update({'units':self.units})\\n\",\n \" return config\\n\",\n \" \\n\",\n \" \\n\",\n \"layer = Linear(125)\\n\",\n \"config = layer.get_config()\\n\",\n \"print(config)\\n\",\n \"# 从配置中构建网络，（已知网络结构，不知超参的情况）\\n\",\n \"new_layer = Linear.from_config(config)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"如果在反序列化中(从配置中构建网络)需要更大的灵活性，可以重写from_config方法。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def from_config(cls, config):\\n\",\n \" return cls(**config)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.2 配置训练时特有参数\\n\",\n \"有一些网络层， 如BatchNormalization层和Dropout层，在训练和推理中具有不同的行为，对于此类层，则需要在方法中使用train等参数进行控制。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class MyDropout(layers.Layer):\\n\",\n \" def __init__(self, rate, **kwargs):\\n\",\n \" super(MyDropout, self).__init__(**kwargs)\\n\",\n \" self.rate = rate\\n\",\n \" def call(self, inputs, training=None):\\n\",\n \" return tf.cond(training, \\n\",\n \" lambda: tf.nn.dropout(inputs, rate=self.rate),\\n\",\n \" lambda: inputs)\\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 4 构建自己的模型\\n\",\n \"通常，我们使用Layer类来定义内部计算块，并使用Model类来定义外部模型 - 即要训练的对象。\\n\",\n \"\\n\",\n \"Model类与Layer的区别：\\n\",\n \"- 它对外开放内置的训练，评估和预测函数（model.fit(),model.evaluate(),model.predict()）。 \\n\",\n \"- 它通过model.layers属性开放其内部网络层列表。 \\n\",\n \"- 它对外开放保存和序列化API。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.1 自定义模型\\n\",\n \"下面通过构建一个变分自编码器（VAE），来介绍如何构建自己的网络， 并使用内置的函数进行训练。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 采样网络\\n\",\n \"class Sampling(layers.Layer):\\n\",\n \" def call(self, inputs):\\n\",\n \" z_mean, z_log_var = inputs\\n\",\n \" batch = tf.shape(z_mean)[0]\\n\",\n \" dim = tf.shape(z_mean)[1]\\n\",\n \" epsilon = tf.keras.backend.random_normal(shape=(batch, dim))\\n\",\n \" return z_mean + tf.exp(0.5*z_log_var) * epsilon\\n\",\n \"# 编码器\\n\",\n \"class Encoder(layers.Layer):\\n\",\n \" def __init__(self, latent_dim=32, \\n\",\n \" intermediate_dim=64, name='encoder', **kwargs):\\n\",\n \" super(Encoder, self).__init__(name=name, **kwargs)\\n\",\n \" self.dense_proj = layers.Dense(intermediate_dim, activation='relu')\\n\",\n \" self.dense_mean = layers.Dense(latent_dim)\\n\",\n \" self.dense_log_var = layers.Dense(latent_dim)\\n\",\n \" self.sampling = Sampling()\\n\",\n \" \\n\",\n \" def call(self, inputs):\\n\",\n \" h1 = self.dense_proj(inputs)\\n\",\n \" # 获取z_mean和z_log_var\\n\",\n \" z_mean = self.dense_mean(h1)\\n\",\n \" z_log_var = self.dense_log_var(h1)\\n\",\n \" # 进行采样\\n\",\n \" z = self.sampling((z_mean, z_log_var))\\n\",\n \" return z_mean, z_log_var, z\\n\",\n \" \\n\",\n \"# 解码器\\n\",\n \"class Decoder(layers.Layer):\\n\",\n \" def __init__(self, original_dim, \\n\",\n \" intermediate_dim=64, name='decoder', **kwargs):\\n\",\n \" super(Decoder, self).__init__(name=name, **kwargs)\\n\",\n \" self.dense_proj = layers.Dense(intermediate_dim, activation='relu')\\n\",\n \" self.dense_output = layers.Dense(original_dim, activation='sigmoid')\\n\",\n \" def call(self, inputs):\\n\",\n \" # 两层全连接网络\\n\",\n \" h1 = self.dense_proj(inputs)\\n\",\n \" return self.dense_output(h1)\\n\",\n \" \\n\",\n \"# 变分自编码器\\n\",\n \"class VAE(tf.keras.Model):\\n\",\n \" def __init__(self, original_dim, latent_dim=32, \\n\",\n \" intermediate_dim=64, name='encoder', **kwargs):\\n\",\n \" super(VAE, self).__init__(name=name, **kwargs)\\n\",\n \" \\n\",\n \" self.original_dim = original_dim\\n\",\n \" self.encoder = Encoder(latent_dim=latent_dim,\\n\",\n \" intermediate_dim=intermediate_dim)\\n\",\n \" self.decoder = Decoder(original_dim=original_dim,\\n\",\n \" intermediate_dim=intermediate_dim)\\n\",\n \" def call(self, inputs):\\n\",\n \" # 编码\\n\",\n \" z_mean, z_log_var, z = self.encoder(inputs)\\n\",\n \" # 解码\\n\",\n \" reconstructed = self.decoder(z)\\n\",\n \" # 获取损失函数\\n\",\n \" kl_loss = -0.5*tf.reduce_sum(\\n\",\n \" z_log_var-tf.square(z_mean)-tf.exp(z_log_var)+1)\\n\",\n \" self.add_loss(kl_loss)\\n\",\n \" return reconstructed\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"训练VAE\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 60000 samples\\n\",\n \"Epoch 1/3\\n\",\n \"60000/60000 [==============================] - 4s 58us/sample - loss: 1.0678\\n\",\n \"Epoch 2/3\\n\",\n \"60000/60000 [==============================] - 2s 32us/sample - loss: 0.0695\\n\",\n \"Epoch 3/3\\n\",\n \"60000/60000 [==============================] - 2s 32us/sample - loss: 0.0680\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\n\",\n \"(x_train, _), _ = tf.keras.datasets.mnist.load_data()\\n\",\n \"x_train = x_train.reshape(60000, 784).astype('float32') / 255\\n\",\n \"vae = VAE(784,32,64)\\n\",\n \"optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)\\n\",\n \"\\n\",\n \"vae.compile(optimizer, loss=tf.keras.losses.MeanSquaredError())\\n\",\n \"vae.fit(x_train, x_train, epochs=3, batch_size=64)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"自己编写训练方法\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Start of epoch 0\\n\",\n \"step 0: mean loss = tf.Tensor(192.1773, shape=(), dtype=float32)\\n\",\n \"step 100: mean loss = tf.Tensor(6.5825667, shape=(), dtype=float32)\\n\",\n \"step 200: mean loss = tf.Tensor(3.3554409, shape=(), dtype=float32)\\n\",\n \"step 300: mean loss = tf.Tensor(2.2692108, shape=(), dtype=float32)\\n\",\n \"step 400: mean loss = tf.Tensor(1.7223562, shape=(), dtype=float32)\\n\",\n \"step 500: mean loss = tf.Tensor(1.3939205, shape=(), dtype=float32)\\n\",\n \"step 600: mean loss = tf.Tensor(1.1746095, shape=(), dtype=float32)\\n\",\n \"step 700: mean loss = tf.Tensor(1.0176468, shape=(), dtype=float32)\\n\",\n \"step 800: mean loss = tf.Tensor(0.8996144, shape=(), dtype=float32)\\n\",\n \"step 900: mean loss = tf.Tensor(0.8074596, shape=(), dtype=float32)\\n\",\n \"Start of epoch 1\\n\",\n \"step 0: mean loss = tf.Tensor(0.77757883, shape=(), dtype=float32)\\n\",\n \"step 100: mean loss = tf.Tensor(0.70945406, shape=(), dtype=float32)\\n\",\n \"step 200: mean loss = tf.Tensor(0.6533016, shape=(), dtype=float32)\\n\",\n \"step 300: mean loss = tf.Tensor(0.60621214, shape=(), dtype=float32)\\n\",\n \"step 400: mean loss = tf.Tensor(0.5661074, shape=(), dtype=float32)\\n\",\n \"step 500: mean loss = tf.Tensor(0.5315464, shape=(), dtype=float32)\\n\",\n \"step 600: mean loss = tf.Tensor(0.50146633, shape=(), dtype=float32)\\n\",\n \"step 700: mean loss = tf.Tensor(0.4750789, shape=(), dtype=float32)\\n\",\n \"step 800: mean loss = tf.Tensor(0.4516706, shape=(), dtype=float32)\\n\",\n \"step 900: mean loss = tf.Tensor(0.43074456, shape=(), dtype=float32)\\n\",\n \"Start of epoch 2\\n\",\n \"step 0: mean loss = tf.Tensor(0.42340422, shape=(), dtype=float32)\\n\",\n \"step 100: mean loss = tf.Tensor(0.40543476, shape=(), dtype=float32)\\n\",\n \"step 200: mean loss = tf.Tensor(0.38920242, shape=(), dtype=float32)\\n\",\n \"step 300: mean loss = tf.Tensor(0.3744474, shape=(), dtype=float32)\\n\",\n \"step 400: mean loss = tf.Tensor(0.3610152, shape=(), dtype=float32)\\n\",\n \"step 500: mean loss = tf.Tensor(0.34867495, shape=(), dtype=float32)\\n\",\n \"step 600: mean loss = tf.Tensor(0.33732668, shape=(), dtype=float32)\\n\",\n \"step 700: mean loss = tf.Tensor(0.326859, shape=(), dtype=float32)\\n\",\n \"step 800: mean loss = tf.Tensor(0.3171746, shape=(), dtype=float32)\\n\",\n \"step 900: mean loss = tf.Tensor(0.30814958, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"train_dataset = tf.data.Dataset.from_tensor_slices(x_train)\\n\",\n \"train_dataset = train_dataset.shuffle(buffer_size=1024).batch(64)\\n\",\n \"\\n\",\n \"original_dim = 784\\n\",\n \"vae = VAE(original_dim, 64, 32)\\n\",\n \"\\n\",\n \"# 优化器\\n\",\n \"optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)\\n\",\n \"# 损失函数\\n\",\n \"mse_loss_fn = tf.keras.losses.MeanSquaredError()\\n\",\n \"# 评价指标\\n\",\n \"loss_metric = tf.keras.metrics.Mean()\\n\",\n \"\\n\",\n \"# 训练循环\\n\",\n \"for epoch in range(3):\\n\",\n \" print('Start of epoch %d' % (epoch,))\\n\",\n \"\\n\",\n \" # 每批次训练\\n\",\n \" for step, x_batch_train in enumerate(train_dataset):\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" # 前向传播\\n\",\n \" reconstructed = vae(x_batch_train)\\n\",\n \" # 计算loss\\n\",\n \" loss = mse_loss_fn(x_batch_train, reconstructed)\\n\",\n \" loss += sum(vae.losses) # Add KLD regularization loss\\n\",\n \" # 计算梯度\\n\",\n \" grads = tape.gradient(loss, vae.trainable_variables)\\n\",\n \" # 反向传播\\n\",\n \" optimizer.apply_gradients(zip(grads, vae.trainable_variables))\\n\",\n \" # 统计指标\\n\",\n \" loss_metric(loss)\\n\",\n \" # 输出\\n\",\n \" if step % 100 == 0:\\n\",\n \" print('step %s: mean loss = %s' % (step, loss_metric.result()))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "005-keras_saving_and_serializing_model.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-keras模型保存和序列化\\n\",\n \"\\n\",\n \"## 1 保存序列模型或函数式模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/doit/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n \" from ._conv import register_converters as _register_converters\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"3_layer_mlp\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"digits (InputLayer) [(None, 784)] 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_1 (Dense) (None, 64) 50240 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_2 (Dense) (None, 64) 4160 \\n\",\n \"_________________________________________________________________\\n\",\n \"predictions (Dense) (None, 10) 650 \\n\",\n \"=================================================================\\n\",\n \"Total params: 55,050\\n\",\n \"Trainable params: 55,050\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\",\n \"Train on 60000 samples\\n\",\n \"60000/60000 [==============================] - 2s 28us/sample - loss: 0.3133\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 构建一个简单的模型并训练\\n\",\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"import tensorflow as tf\\n\",\n \"tf.keras.backend.clear_session()\\n\",\n \"from tensorflow import keras\\n\",\n \"from tensorflow.keras import layers\\n\",\n \"\\n\",\n \"inputs = keras.Input(shape=(784,), name='digits')\\n\",\n \"x = layers.Dense(64, activation='relu', name='dense_1')(inputs)\\n\",\n \"x = layers.Dense(64, activation='relu', name='dense_2')(x)\\n\",\n \"outputs = layers.Dense(10, activation='softmax', name='predictions')(x)\\n\",\n \"\\n\",\n \"model = keras.Model(inputs=inputs, outputs=outputs, name='3_layer_mlp')\\n\",\n \"model.summary()\\n\",\n \"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\",\n \"x_train = x_train.reshape(60000, 784).astype('float32') / 255\\n\",\n \"x_test = x_test.reshape(10000, 784).astype('float32') / 255\\n\",\n \"\\n\",\n \"model.compile(loss='sparse_categorical_crossentropy',\\n\",\n \" optimizer=keras.optimizers.RMSprop())\\n\",\n \"history = model.fit(x_train, y_train,\\n\",\n \" batch_size=64,\\n\",\n \" epochs=1)\\n\",\n \"\\n\",\n \"predictions = model.predict(x_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.1 保存整个模型\\n\",\n \"可以对整个模型进行保存，其保持的内容包括：\\n\",\n \"- 该模型的架构\\n\",\n \"- 模型的权重（在训练期间学到的）\\n\",\n \"- 模型的训练配置（传递给编译的）\\n\",\n \"- 优化器及其状态（这使您可以从中断的地方重新启动训练）\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"# 模型保存\\n\",\n \"model.save('the_save_model.h5')\\n\",\n \"# 导入模型\\n\",\n \"new_model = keras.models.load_model('the_save_model.h5')\\n\",\n \"new_prediction = new_model.predict(x_test)\\n\",\n \"np.testing.assert_allclose(predictions, new_prediction, atol=1e-6) # 预测结果一样\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.2 导出为SavedModel文件\\n\",\n \"SavedModel是Tensorflow对象的独立序列化格式，支持使用Tensorflow Serving server来部署模型，支持其他语言读取。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"W1013 16:41:11.934549 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer.decay\\n\",\n \"W1013 16:41:11.935179 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer.learning_rate\\n\",\n \"W1013 16:41:11.935569 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer.momentum\\n\",\n \"W1013 16:41:11.935910 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer.rho\\n\",\n \"W1013 16:41:11.936488 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-0.kernel\\n\",\n \"W1013 16:41:11.937078 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-0.bias\\n\",\n \"W1013 16:41:11.937702 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-1.kernel\\n\",\n \"W1013 16:41:11.938094 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-1.bias\\n\",\n \"W1013 16:41:11.938518 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-2.kernel\\n\",\n \"W1013 16:41:11.938826 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-2.bias\\n\",\n \"W1013 16:41:11.939161 140085562951488 util.py:152] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/alpha/guide/checkpoints#loading_mechanics for details.\\n\",\n \"W1013 16:41:11.940948 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer\\n\",\n \"W1013 16:41:11.941336 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer.iter\\n\",\n \"W1013 16:41:11.941909 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer.decay\\n\",\n \"W1013 16:41:11.944620 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer.learning_rate\\n\",\n \"W1013 16:41:11.945564 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer.momentum\\n\",\n \"W1013 16:41:11.946080 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer.rho\\n\",\n \"W1013 16:41:11.946562 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-0.kernel\\n\",\n \"W1013 16:41:11.946892 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-0.bias\\n\",\n \"W1013 16:41:11.947290 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-1.kernel\\n\",\n \"W1013 16:41:11.947617 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-1.bias\\n\",\n \"W1013 16:41:11.947977 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-2.kernel\\n\",\n \"W1013 16:41:11.948273 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-2.bias\\n\",\n \"W1013 16:41:11.948586 140085562951488 util.py:152] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/alpha/guide/checkpoints#loading_mechanics for details.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 导出为tf的SavedModel文件\\n\",\n \"model.save('save_model', save_format='tf')\\n\",\n \"# 从SavedModel文件中导入模型\\n\",\n \"new_model = keras.models.load_model('save_model')\\n\",\n \"\\n\",\n \"new_prediction = new_model.predict(x_test)\\n\",\n \"np.testing.assert_allclose(predictions, new_prediction, atol=1e-6) # 预测结果一样\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"SaveModel创建的文件包含：\\n\",\n \"- 权重\\n\",\n \"- 网络图\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.3 仅保存网络结构\\n\",\n \"仅保持网络结构，这样导出的模型并未包含训练好的参数\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 获取网络结构配置\\n\",\n \"config = model.get_config()\\n\",\n \"reinitialized_model = keras.Model.from_config(config)\\n\",\n \"\\n\",\n \"new_prediction = reinitialized_model.predict(x_test)\\n\",\n \"assert abs(np.sum(predictions-new_prediction)) >0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"也可以使用json保存网络结构\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 将网络结构导出为json格式\\n\",\n \"json_config = model.to_json()\\n\",\n \"reinitialized_model = keras.models.model_from_json(json_config)\\n\",\n \"\\n\",\n \"new_prediction = reinitialized_model.predict(x_test)\\n\",\n \"assert abs(np.sum(predictions-new_prediction)) >0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.4 仅保存网络权重参数\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 获取网络权重\\n\",\n \"weights = model.get_weights()\\n\",\n \"# 对网络权重进行赋值\\n\",\n \"model.set_weights(weights)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"可以把结构和参数保存结合起来\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"config = model.get_config()\\n\",\n \"weights = model.get_weights()\\n\",\n \"\\n\",\n \"new_model = keras.Model.from_config(config) # config只能用keras.Model的这个api\\n\",\n \"new_model.set_weights(weights)\\n\",\n \"\\n\",\n \"new_predictions = new_model.predict(x_test)\\n\",\n \"np.testing.assert_allclose(predictions, new_predictions, atol=1e-6)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.5 完整的模型保存方法\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 导出网络结构和权重\\n\",\n \"json_config = model.to_json()\\n\",\n \"with open('model_config.json', 'w') as json_file:\\n\",\n \" json_file.write(json_config)\\n\",\n \"model.save_weights('path_to_my_weights.h5')\\n\",\n \"# 载入网络结构和权重\\n\",\n \"with open('model_config.json') as json_file:\\n\",\n \" json_config = json_file.read()\\n\",\n \"new_model = keras.models.model_from_json(json_config)\\n\",\n \"new_model.load_weights('path_to_my_weights.h5')\\n\",\n \"\\n\",\n \"new_predictions = new_model.predict(x_test)\\n\",\n \"np.testing.assert_allclose(predictions, new_predictions, atol=1e-6)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"W1013 16:49:21.690537 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer\\n\",\n \"W1013 16:49:21.691003 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer.iter\\n\",\n \"W1013 16:49:21.691583 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer.decay\\n\",\n \"W1013 16:49:21.691886 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer.learning_rate\\n\",\n \"W1013 16:49:21.692354 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer.momentum\\n\",\n \"W1013 16:49:21.692843 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer.rho\\n\",\n \"W1013 16:49:21.693265 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-0.kernel\\n\",\n \"W1013 16:49:21.693663 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-0.bias\\n\",\n \"W1013 16:49:21.693997 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-1.kernel\\n\",\n \"W1013 16:49:21.694370 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-1.bias\\n\",\n \"W1013 16:49:21.694779 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-2.kernel\\n\",\n \"W1013 16:49:21.695171 140085562951488 util.py:144] Unresolved object in checkpoint: (root).optimizer's state 'rms' for (root).layer_with_weights-2.bias\\n\",\n \"W1013 16:49:21.695466 140085562951488 util.py:152] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/alpha/guide/checkpoints#loading_mechanics for details.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 当然也可以一步到位\\n\",\n \"model.save('path_to_my_model.h5')\\n\",\n \"del model\\n\",\n \"model = keras.models.load_model('path_to_my_model.h5')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.6 权重保存格式\\n\",\n \"有.h5或.keras后缀时保存为keras HDF5格式文件，否则默认为TensorFlow Checkpoint格式文件。可以使用save_format显式确定。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model.save_weights('weight_tf_savedmodel')\\n\",\n \"model.save_weights('weight_tf_savedmodel.h5')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model.save_weights('weight_tf_savedmodel_tf', save_format='tf')\\n\",\n \"model.save_weights('weight_tf_savedmodel_h5', save_format='h5')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.7 子类模型权重保存\\n\",\n \"子类模型的结构无法保存和序列化，只能保持参数\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 构建模型\\n\",\n \"class ThreeLayerMLP(keras.Model):\\n\",\n \" \\n\",\n \" def __init__(self, name=None):\\n\",\n \" super(ThreeLayerMLP, self).__init__(name=name)\\n\",\n \" self.dense_1 = layers.Dense(64, activation='relu', name='dense_1')\\n\",\n \" self.dense_2 = layers.Dense(64, activation='relu', name='dense_2')\\n\",\n \" self.pred_layer = layers.Dense(10, activation='softmax', name='predictions')\\n\",\n \"\\n\",\n \" def call(self, inputs):\\n\",\n \" x = self.dense_1(inputs)\\n\",\n \" x = self.dense_2(x)\\n\",\n \" return self.pred_layer(x)\\n\",\n \"\\n\",\n \"def get_model():\\n\",\n \" return ThreeLayerMLP(name='3_layer_mlp')\\n\",\n \"\\n\",\n \"model = get_model()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"首先，无法保存从未使用过的子类模型。\\n\",\n \"\\n\",\n \"这是因为需要在某些数据上调用子类模型才能创建其权重。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 60000 samples\\n\",\n \"60000/60000 [==============================] - 2s 26us/sample - loss: 0.3128\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 训练模型\\n\",\n \"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\",\n \"x_train = x_train.reshape(60000, 784).astype('float32') / 255\\n\",\n \"x_test = x_test.reshape(10000, 784).astype('float32') / 255\\n\",\n \"\\n\",\n \"model.compile(loss='sparse_categorical_crossentropy',\\n\",\n \" optimizer=keras.optimizers.RMSprop())\\n\",\n \"history = model.fit(x_train, y_train,\\n\",\n \" batch_size=64,\\n\",\n \" epochs=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"推荐的保存子类模型的方法是使用save_weights创建TensorFlow SavedModel检查点。\\n\",\n \"\\n\",\n \"该检查点将包含与模型关联的所有变量的值：\\n\",\n \"- 图层的权重\\n\",\n \"- 优化器的状态 \\n\",\n \"- 与有状态模型指标关联的任何变量\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 保持权重参数\\n\",\n \"model.save_weights('my_model_weights', save_format='tf')\\n\",\n \"\\n\",\n \"# 输出结果，供后面对比\\n\",\n \"\\n\",\n \"predictions = model.predict(x_test)\\n\",\n \"first_batch_loss = model.train_on_batch(x_train[:64], y_train[:64])\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"要还原模型，将需要访问创建模型对象的代码。\\n\",\n \"\\n\",\n \"请注意，为了恢复优化器状态和任何有状态度量的状态，应该先编译模型（使用与以前完全相同的参数）。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 读取保存的模型参数\\n\",\n \"new_model = get_model()\\n\",\n \"new_model.compile(loss='sparse_categorical_crossentropy',\\n\",\n \" optimizer=keras.optimizers.RMSprop())\\n\",\n \"\\n\",\n \"#new_model.train_on_batch(x_train[:1], y_train[:1])\\n\",\n \"\\n\",\n \"new_model.load_weights('my_model_weights')\\n\",\n \"\\n\",\n \"new_predictions = new_model.predict(x_test)\\n\",\n \"np.testing.assert_allclose(predictions, new_predictions, atol=1e-6)\\n\",\n \"\\n\",\n \"\\n\",\n \"new_first_batch_loss = new_model.train_on_batch(x_train[:64], y_train[:64])\\n\",\n \"assert first_batch_loss == new_first_batch_loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "006-3-callback.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-自定义回调\\n\",\n \"\\n\",\n \"自定义回调是一个很好用的工具，可以在训练，评估和推理期间自定义模型的行为，包括读取/更改keras模型等。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/doit/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n \" from ._conv import register_converters as _register_converters\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division,print_function, unicode_literals\\n\",\n \"import tensorflow as tf\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1 Keras回调简介\\n\",\n \"在Kreas中，Callback是一个python类，旨在被子类化以提供特定功能，并在训练的各阶段（包括每个batch/epoch的开始和结束），以及测试中调用一组方法。\\n\",\n \"\\n\",\n \"我们可以通过回调列表，传递回调方法，在训练/评估/推断的不同阶段调用回调方法。\\n\",\n \"\\n\",\n \"构建一个模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_model():\\n\",\n \" model = tf.keras.Sequential()\\n\",\n \" model.add(tf.keras.layers.Dense(1, activation = 'linear', input_dim = 784))\\n\",\n \" model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=0.1), loss='mean_squared_error', metrics=['mae'])\\n\",\n \" return model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"导入数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\\n\",\n \"x_train = x_train.reshape(60000, 784).astype('float32') / 255\\n\",\n \"x_test = x_test.reshape(10000, 784).astype('float32') / 255\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"定义一个简单的自定义回调，以跟踪每批数据的开始和结束。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import datetime\\n\",\n \"\\n\",\n \"class MyCustomCallback(tf.keras.callbacks.Callback):\\n\",\n \"\\n\",\n \" def on_train_batch_begin(self, batch, logs=None):\\n\",\n \" print('Training: batch {} begins at {}'.format(batch, datetime.datetime.now().time()))\\n\",\n \"\\n\",\n \" def on_train_batch_end(self, batch, logs=None):\\n\",\n \" print('Training: batch {} ends at {}'.format(batch, datetime.datetime.now().time()))\\n\",\n \"\\n\",\n \" def on_test_batch_begin(self, batch, logs=None):\\n\",\n \" print('Evaluating: batch {} begins at {}'.format(batch, datetime.datetime.now().time()))\\n\",\n \"\\n\",\n \" def on_test_batch_end(self, batch, logs=None):\\n\",\n \" print('Evaluating: batch {} ends at {}'.format(batch, datetime.datetime.now().time()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"在训练时传入回调函数\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: batch 0 begins at 14:47:16.854006\\n\",\n \"Training: batch 0 ends at 14:47:19.215169\\n\",\n \"Training: batch 1 begins at 14:47:19.215812\\n\",\n \"Training: batch 1 ends at 14:47:19.219226\\n\",\n \"Training: batch 2 begins at 14:47:19.219737\\n\",\n \"Training: batch 2 ends at 14:47:19.222817\\n\",\n \"Training: batch 3 begins at 14:47:19.223307\\n\",\n \"Training: batch 3 ends at 14:47:19.226198\\n\",\n \"Training: batch 4 begins at 14:47:19.226991\\n\",\n \"Training: batch 4 ends at 14:47:19.229930\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = get_model()\\n\",\n \"_ = model.fit(x_train, y_train,\\n\",\n \" batch_size=64,\\n\",\n \" epochs=1,\\n\",\n \" steps_per_epoch=5,\\n\",\n \" verbose=0,\\n\",\n \" callbacks=[MyCustomCallback()])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.1 以下方法会调用回调函数\\n\",\n \"fit(), fit_generator()\\n\",\n \"训练或使用迭代数据进行训练。\\n\",\n \"\\n\",\n \"evaluate(), evaluate_generator()\\n\",\n \"评估或使用迭代数据进行评估。\\n\",\n \"\\n\",\n \"predict(), predict_generator()\\n\",\n \"预测或使用迭代数据进行预测。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Evaluating: batch 0 begins at 14:52:23.452381\\n\",\n \"Evaluating: batch 0 ends at 14:52:23.492404\\n\",\n \"Evaluating: batch 1 begins at 14:52:23.492663\\n\",\n \"Evaluating: batch 1 ends at 14:52:23.493493\\n\",\n \"Evaluating: batch 2 begins at 14:52:23.493606\\n\",\n \"Evaluating: batch 2 ends at 14:52:23.494279\\n\",\n \"Evaluating: batch 3 begins at 14:52:23.494388\\n\",\n \"Evaluating: batch 3 ends at 14:52:23.495022\\n\",\n \"Evaluating: batch 4 begins at 14:52:23.495128\\n\",\n \"Evaluating: batch 4 ends at 14:52:23.495710\\n\"\n ]\n }\n ],\n \"source\": [\n \"_ = model.evaluate(x_test, y_test, batch_size=128, verbose=0, steps=5,\\n\",\n \" callbacks=[MyCustomCallback()])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2 回调方法概述\\n\",\n \"### 2.1 训练/测试/预测的常用方法\\n\",\n \"为了进行训练，测试和预测，提供了以下方法来替代。\\n\",\n \"\\n\",\n \"on_(train|test|predict)_begin(self, logs=None)\\n\",\n \"在fit/ evaluate/ predict开始时调用。\\n\",\n \"\\n\",\n \"on_(train|test|predict)_end(self, logs=None)\\n\",\n \"在fit/ evaluate/ predict结束时调用。\\n\",\n \"\\n\",\n \"on_(train|test|predict)_batch_begin(self, batch, logs=None)\\n\",\n \"在培训/测试/预测期间处理批次之前立即调用。在此方法中，logs是带有batch和size可用键的字典，代表当前批次号和批次大小。\\n\",\n \"\\n\",\n \"on_(train|test|predict)_batch_end(self, batch, logs=None)\\n\",\n \"在培训/测试/预测批次结束时调用。在此方法中，logs是一个包含状态指标结果的字典。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 训练时特定方法\\n\",\n \"另外，为了进行培训，提供以下内容。\\n\",\n \"\\n\",\n \"on_epoch_begin（self，epoch，logs = None）\\n\",\n \"在训练期间的开始时调用。\\n\",\n \"\\n\",\n \"on_epoch_end（self，epoch，logs = None）\\n\",\n \"在训练期间的末尾调用。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.3 logsdict的用法\\n\",\n \"该logs字典包含损loss，已经每个batch和epoch的结束时的所有指标。示例包括loss和平均绝对误差。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"For batch 0, loss is 31.99.\\n\",\n \"For batch 1, loss is 888.84.\\n\",\n \"For batch 2, loss is 20.06.\\n\",\n \"For batch 3, loss is 8.30.\\n\",\n \"For batch 4, loss is 10.34.\\n\",\n \"The average loss for epoch 0 is 191.91 and mean absolute error is 8.17.\\n\",\n \"For batch 0, loss is 6.29.\\n\",\n \"For batch 1, loss is 7.78.\\n\",\n \"For batch 2, loss is 5.60.\\n\",\n \"For batch 3, loss is 6.66.\\n\",\n \"For batch 4, loss is 5.95.\\n\",\n \"The average loss for epoch 1 is 6.45 and mean absolute error is 2.06.\\n\",\n \"For batch 0, loss is 6.19.\\n\",\n \"For batch 1, loss is 5.54.\\n\",\n \"For batch 2, loss is 7.04.\\n\",\n \"For batch 3, loss is 10.74.\\n\",\n \"For batch 4, loss is 15.87.\\n\",\n \"The average loss for epoch 2 is 9.07 and mean absolute error is 2.46.\\n\"\n ]\n }\n ],\n \"source\": [\n \"class LossAndErrorPrintingCallback(tf.keras.callbacks.Callback):\\n\",\n \"\\n\",\n \" def on_train_batch_end(self, batch, logs=None):\\n\",\n \" print('For batch {}, loss is {:7.2f}.'.format(batch, logs['loss']))\\n\",\n \"\\n\",\n \" def on_test_batch_end(self, batch, logs=None):\\n\",\n \" print('For batch {}, loss is {:7.2f}.'.format(batch, logs['loss']))\\n\",\n \"\\n\",\n \" def on_epoch_end(self, epoch, logs=None):\\n\",\n \" print('The average loss for epoch {} is {:7.2f} and mean absolute error is {:7.2f}.'.format(epoch, logs['loss'], logs['mae']))\\n\",\n \"\\n\",\n \"model = get_model()\\n\",\n \"_ = model.fit(x_train, y_train,\\n\",\n \" batch_size=64,\\n\",\n \" steps_per_epoch=5,\\n\",\n \" epochs=3,\\n\",\n \" verbose=0,\\n\",\n \" callbacks=[LossAndErrorPrintingCallback()])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"同样，可以在evaluate时调用回调。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"For batch 0, loss is 23.48.\\n\",\n \"For batch 1, loss is 20.85.\\n\",\n \"For batch 2, loss is 22.11.\\n\",\n \"For batch 3, loss is 24.52.\\n\",\n \"For batch 4, loss is 26.07.\\n\",\n \"For batch 5, loss is 23.15.\\n\",\n \"For batch 6, loss is 23.08.\\n\",\n \"For batch 7, loss is 21.37.\\n\",\n \"For batch 8, loss is 22.78.\\n\",\n \"For batch 9, loss is 22.38.\\n\",\n \"For batch 10, loss is 24.63.\\n\",\n \"For batch 11, loss is 26.51.\\n\",\n \"For batch 12, loss is 23.29.\\n\",\n \"For batch 13, loss is 25.12.\\n\",\n \"For batch 14, loss is 25.20.\\n\",\n \"For batch 15, loss is 22.27.\\n\",\n \"For batch 16, loss is 26.29.\\n\",\n \"For batch 17, loss is 25.76.\\n\",\n \"For batch 18, loss is 24.94.\\n\",\n \"For batch 19, loss is 23.94.\\n\"\n ]\n }\n ],\n \"source\": [\n \"_ = model.evaluate(x_test, y_test, batch_size=128, verbose=0, steps=20,\\n\",\n \" callbacks=[LossAndErrorPrintingCallback()])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3 keras回调示例\\n\",\n \"## 3.1 以最小的损失尽早停止\\n\",\n \"第一个示例展示了Callback通过达到最小损失时更改属性model.stop_training（布尔值），停止Keras训练。用户可以提供一个参数patience来指定训练最终停止之前应该等待多少个时期。\\n\",\n \"\\n\",\n \"注：tf.keras.callbacks.EarlyStopping 提供了更完整，更通用的实现。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"class EarlyStoppingAtMinLoss(tf.keras.callbacks.Callback):\\n\",\n \" def __init__(self, patience=0):\\n\",\n \" super(EarlyStoppingAtMinLoss, self).__init__()\\n\",\n \" self.patience = patience\\n\",\n \" self.best_weights = None # loss最低时的权重\\n\",\n \" def on_train_begin(self, logs=None):\\n\",\n \" # loss不再下降时等待的轮数\\n\",\n \" self.wait = 0\\n\",\n \" # 停止时的轮数\\n\",\n \" self.stopped_epoch = 0\\n\",\n \" # 开始时的最优loss\\n\",\n \" self.best = np.Inf\\n\",\n \" \\n\",\n \" def on_epoch_end(self, epoch, logs=None):\\n\",\n \" current = logs.get('loss')\\n\",\n \" if np.less(current, self.best):\\n\",\n \" self.best = current\\n\",\n \" self.wait = 0\\n\",\n \" # 最佳权重\\n\",\n \" self.best_weights = self.model.get_weights()\\n\",\n \" else:\\n\",\n \" self.wait += 1\\n\",\n \" if self.wait >= self.patience:\\n\",\n \" self.stopped_epoch = epoch\\n\",\n \" self.model.stop_training = True\\n\",\n \" print('导入当前最佳模型')\\n\",\n \" self.model.set_weights(self.best_weights)\\n\",\n \" def on_train_end(self, logs=None):\\n\",\n \" if self.stopped_epoch > 0:\\n\",\n \" print('在%05d: 提前停止训练'% (self.stopped_epoch+1))\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"For batch 0, loss is 23.36.\\n\",\n \"For batch 1, loss is 1043.09.\\n\",\n \"For batch 2, loss is 25.19.\\n\",\n \"For batch 3, loss is 8.33.\\n\",\n \"For batch 4, loss is 6.51.\\n\",\n \"The average loss for epoch 0 is 221.30 and mean absolute error is 8.41.\\n\",\n \"For batch 0, loss is 10.12.\\n\",\n \"For batch 1, loss is 6.01.\\n\",\n \"For batch 2, loss is 7.86.\\n\",\n \"For batch 3, loss is 6.52.\\n\",\n \"For batch 4, loss is 5.53.\\n\",\n \"The average loss for epoch 1 is 7.21 and mean absolute error is 2.26.\\n\",\n \"For batch 0, loss is 4.40.\\n\",\n \"For batch 1, loss is 4.59.\\n\",\n \"For batch 2, loss is 5.08.\\n\",\n \"For batch 3, loss is 4.74.\\n\",\n \"For batch 4, loss is 6.71.\\n\",\n \"The average loss for epoch 2 is 5.10 and mean absolute error is 1.89.\\n\",\n \"For batch 0, loss is 5.96.\\n\",\n \"For batch 1, loss is 4.13.\\n\",\n \"For batch 2, loss is 5.94.\\n\",\n \"For batch 3, loss is 7.71.\\n\",\n \"For batch 4, loss is 16.43.\\n\",\n \"The average loss for epoch 3 is 8.03 and mean absolute error is 2.30.\\n\",\n \"导入当前最佳模型\\n\",\n \"在00004: 提前停止训练\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = get_model()\\n\",\n \"_ = model.fit(x_train, y_train,\\n\",\n \" batch_size=64,\\n\",\n \" steps_per_epoch=5,\\n\",\n \" epochs=30,\\n\",\n \" verbose=0,\\n\",\n \" callbacks=[LossAndErrorPrintingCallback(), EarlyStoppingAtMinLoss()])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 自定义学习率\\n\",\n \"在模型训练中通常要做的一件事是随着训练轮次改变学习率。Keras后端公开了可用于设置变量的get_value API。在此示例中，我们展示了如何使用自定义的回调来动态更改学习率。\\n\",\n \"\\n\",\n \"注：这只是示例实现，请参见callbacks.LearningRateScheduler和keras.optimizers.schedules有关更一般的实现。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class LearningRateScheduler(tf.keras.callbacks.Callback):\\n\",\n \" def __init__(self, schedule):\\n\",\n \" super(LearningRateScheduler, self).__init__()\\n\",\n \" self.schedule = schedule\\n\",\n \" \\n\",\n \" def on_epoch_begin(self, epoch, logs=None):\\n\",\n \" if not hasattr(self.model.optimizer, 'lr'):\\n\",\n \" raise ValueError('Optimizer没有lr参数。')\\n\",\n \" # 获取当前lr\\n\",\n \" lr = float(tf.keras.backend.get_value(self.model.optimizer.lr))\\n\",\n \" # 调整lr\\n\",\n \" scheduled_lr = self.schedule(epoch, lr)\\n\",\n \" tf.keras.backend.set_value(self.model.optimizer.lr, scheduled_lr)\\n\",\n \" print('Epoch %05d: 学习率为%6.4f.'%(epoch, scheduled_lr))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"按轮次调整学习率\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 00000: 学习率为0.1000.\\n\",\n \"For batch 0, loss is 24.55.\\n\",\n \"For batch 1, loss is 1134.79.\\n\",\n \"For batch 2, loss is 26.95.\\n\",\n \"For batch 3, loss is 8.14.\\n\",\n \"For batch 4, loss is 6.28.\\n\",\n \"The average loss for epoch 0 is 240.14 and mean absolute error is 8.91.\\n\",\n \"Epoch 00001: 学习率为0.1000.\\n\",\n \"For batch 0, loss is 7.12.\\n\",\n \"For batch 1, loss is 7.65.\\n\",\n \"For batch 2, loss is 5.11.\\n\",\n \"For batch 3, loss is 5.36.\\n\",\n \"For batch 4, loss is 5.43.\\n\",\n \"The average loss for epoch 1 is 6.13 and mean absolute error is 2.06.\\n\",\n \"Epoch 00002: 学习率为0.1000.\\n\",\n \"For batch 0, loss is 5.18.\\n\",\n \"For batch 1, loss is 5.11.\\n\",\n \"For batch 2, loss is 5.08.\\n\",\n \"For batch 3, loss is 4.34.\\n\",\n \"For batch 4, loss is 4.21.\\n\",\n \"The average loss for epoch 2 is 4.78 and mean absolute error is 1.77.\\n\",\n \"Epoch 00003: 学习率为0.0500.\\n\",\n \"For batch 0, loss is 5.30.\\n\",\n \"For batch 1, loss is 3.95.\\n\",\n \"For batch 2, loss is 4.07.\\n\",\n \"For batch 3, loss is 3.63.\\n\",\n \"For batch 4, loss is 4.44.\\n\",\n \"The average loss for epoch 3 is 4.28 and mean absolute error is 1.69.\\n\",\n \"Epoch 00004: 学习率为0.0500.\\n\",\n \"For batch 0, loss is 3.40.\\n\",\n \"For batch 1, loss is 7.80.\\n\",\n \"For batch 2, loss is 4.98.\\n\",\n \"For batch 3, loss is 3.08.\\n\",\n \"For batch 4, loss is 4.66.\\n\",\n \"The average loss for epoch 4 is 4.78 and mean absolute error is 1.75.\\n\",\n \"Epoch 00005: 学习率为0.0500.\\n\",\n \"For batch 0, loss is 3.86.\\n\",\n \"For batch 1, loss is 6.12.\\n\",\n \"For batch 2, loss is 4.59.\\n\",\n \"For batch 3, loss is 3.81.\\n\",\n \"For batch 4, loss is 5.60.\\n\",\n \"The average loss for epoch 5 is 4.80 and mean absolute error is 1.70.\\n\",\n \"Epoch 00006: 学习率为0.0100.\\n\",\n \"For batch 0, loss is 5.00.\\n\",\n \"For batch 1, loss is 4.14.\\n\",\n \"For batch 2, loss is 3.43.\\n\",\n \"For batch 3, loss is 3.61.\\n\",\n \"For batch 4, loss is 3.96.\\n\",\n \"The average loss for epoch 6 is 4.03 and mean absolute error is 1.58.\\n\",\n \"Epoch 00007: 学习率为0.0100.\\n\",\n \"For batch 0, loss is 5.50.\\n\",\n \"For batch 1, loss is 4.07.\\n\",\n \"For batch 2, loss is 4.17.\\n\",\n \"For batch 3, loss is 3.99.\\n\",\n \"For batch 4, loss is 3.56.\\n\",\n \"The average loss for epoch 7 is 4.26 and mean absolute error is 1.60.\\n\",\n \"Epoch 00008: 学习率为0.0100.\\n\",\n \"For batch 0, loss is 3.93.\\n\",\n \"For batch 1, loss is 3.56.\\n\",\n \"For batch 2, loss is 3.22.\\n\",\n \"For batch 3, loss is 3.66.\\n\",\n \"For batch 4, loss is 4.47.\\n\",\n \"The average loss for epoch 8 is 3.77 and mean absolute error is 1.52.\\n\",\n \"Epoch 00009: 学习率为0.0050.\\n\",\n \"For batch 0, loss is 4.83.\\n\",\n \"For batch 1, loss is 4.33.\\n\",\n \"For batch 2, loss is 3.32.\\n\",\n \"For batch 3, loss is 4.38.\\n\",\n \"For batch 4, loss is 3.41.\\n\",\n \"The average loss for epoch 9 is 4.05 and mean absolute error is 1.63.\\n\",\n \"Epoch 00010: 学习率为0.0050.\\n\",\n \"For batch 0, loss is 3.49.\\n\",\n \"For batch 1, loss is 3.63.\\n\",\n \"For batch 2, loss is 3.74.\\n\",\n \"For batch 3, loss is 3.56.\\n\",\n \"For batch 4, loss is 4.65.\\n\",\n \"The average loss for epoch 10 is 3.81 and mean absolute error is 1.54.\\n\",\n \"Epoch 00011: 学习率为0.0050.\\n\",\n \"For batch 0, loss is 3.46.\\n\",\n \"For batch 1, loss is 4.41.\\n\",\n \"For batch 2, loss is 2.83.\\n\",\n \"For batch 3, loss is 5.34.\\n\",\n \"For batch 4, loss is 3.91.\\n\",\n \"The average loss for epoch 11 is 3.99 and mean absolute error is 1.53.\\n\",\n \"Epoch 00012: 学习率为0.0010.\\n\",\n \"For batch 0, loss is 4.89.\\n\",\n \"For batch 1, loss is 3.13.\\n\",\n \"For batch 2, loss is 3.65.\\n\",\n \"For batch 3, loss is 3.14.\\n\",\n \"For batch 4, loss is 2.93.\\n\",\n \"The average loss for epoch 12 is 3.55 and mean absolute error is 1.53.\\n\",\n \"Epoch 00013: 学习率为0.0010.\\n\",\n \"For batch 0, loss is 3.93.\\n\",\n \"For batch 1, loss is 3.33.\\n\",\n \"For batch 2, loss is 3.01.\\n\",\n \"For batch 3, loss is 5.31.\\n\",\n \"For batch 4, loss is 3.33.\\n\",\n \"The average loss for epoch 13 is 3.78 and mean absolute error is 1.56.\\n\",\n \"Epoch 00014: 学习率为0.0010.\\n\",\n \"For batch 0, loss is 4.76.\\n\",\n \"For batch 1, loss is 3.42.\\n\",\n \"For batch 2, loss is 3.05.\\n\",\n \"For batch 3, loss is 4.45.\\n\",\n \"For batch 4, loss is 4.50.\\n\",\n \"The average loss for epoch 14 is 4.04 and mean absolute error is 1.57.\\n\"\n ]\n }\n ],\n \"source\": [\n \"LR_SCHEDULE = [\\n\",\n \" # (epoch to start, learning rate) tuples\\n\",\n \" (3, 0.05), (6, 0.01), (9, 0.005), (12, 0.001)\\n\",\n \"]\\n\",\n \"\\n\",\n \"def lr_schedule(epoch, lr):\\n\",\n \" if epoch < LR_SCHEDULE[0][0] or epoch > LR_SCHEDULE[-1][0]:\\n\",\n \" return lr\\n\",\n \" for i in range(len(LR_SCHEDULE)):\\n\",\n \" if epoch == LR_SCHEDULE[i][0]:\\n\",\n \" return LR_SCHEDULE[i][1]\\n\",\n \" return lr\\n\",\n \"\\n\",\n \"model = get_model()\\n\",\n \"_ = model.fit(x_train, y_train,\\n\",\n \" batch_size=64,\\n\",\n \" steps_per_epoch=5,\\n\",\n \" epochs=15,\\n\",\n \" verbose=0,\\n\",\n \" callbacks=[LossAndErrorPrintingCallback(), LearningRateScheduler(lr_schedule)])\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "006-eager_execution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-Eager Execution\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"> 最全TensorFlow 2.0 入门教程持续更新：https://zhuanlan.zhihu.com/p/59507137\\n\",\n \"> \\n\",\n \"> 完整TensorFlow2.0教程代码请看https://github.com/czy36mengfei/tensorflow2_tutorials_chinese (欢迎star)\\n\",\n \"> \\n\",\n \"> 最新TensorFlow 2教程和相关资源，请关注微信公众号：DoitNLP， 后面我会在DoitNLP上，持续更新深度学习、NLP、Tensorflow的相关教程和前沿资讯，它将成为我们一起学习TensorFlow2的大本营。\\n\",\n \">\\n\",\n \"> 本教程主要由tensorflow2.0官方教程的个人学习复现笔记整理而来，中文讲解，方便喜欢阅读中文教程的朋友，tensorflow官方教程：https://www.tensorflow.org\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"TensorFlow 的 Eager Execution 是一种命令式编程环境，可立即评估操作，无需构建图：操作会返回具体的值，而不是构建以后再运行的计算图。这样可以轻松地使用 TensorFlow 和调试模型，并且还减少了样板代码。\\n\",\n \"\\n\",\n \"Eager Execution 是一个灵活的机器学习平台，用于研究和实验，可提供：\\n\",\n \"\\n\",\n \"- 直观的界面 - 自然地组织代码结构并使用 Python 数据结构。快速迭代小模型和小型数据集。\\n\",\n \"- 更轻松的调试功能 - 直接调用操作以检查正在运行的模型并测试更改。使用标准 Python 调试工具进行即时错误报告。\\n\",\n \"- 自然控制流程 - 使用 Python 控制流程而不是图控制流程，简化了动态模型的规范。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-beta1\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"print(tf.__version__)\\n\",\n \"# 在tensorflow2中默认使用Eager Execution\\n\",\n \"tf.executing_eagerly()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.Eager Execution下运算\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"在Eager Execution下可以直接进行运算，结果会立即返回\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor([[9.]], shape=(1, 1), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = [[3.]]\\n\",\n \"m = tf.matmul(x, x)\\n\",\n \"print(m)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"启用Eager Execution会改变TensorFlow操作的行为 - 现在他们会立即评估并将其值返回给Python。tf.Tensor对象引用具体值，而不再是指向计算图中节点的符号句柄。由于在会话中没有构建和运行的计算图，因此使用print()或调试程序很容易检查结果。评估，打印和检查张量值不会破坏计算梯度的流程。\\n\",\n \"\\n\",\n \"Eager Execution可以与NumPy很好地协作。NumPy操作接受tf.Tensor参数。TensorFlow 数学运算将Python对象和NumPy数组转换为tf.Tensor对象。tf.Tensor.numpy方法将对象的值作为NumPy的ndarray类型返回。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[1 9]\\n\",\n \" [3 6]], shape=(2, 2), dtype=int32)\\n\",\n \"tf.Tensor(\\n\",\n \"[[ 3 11]\\n\",\n \" [ 5 8]], shape=(2, 2), dtype=int32)\\n\",\n \"tf.Tensor(\\n\",\n \"[[ 3 99]\\n\",\n \" [15 48]], shape=(2, 2), dtype=int32)\\n\",\n \"[[ 3 99]\\n\",\n \" [15 48]]\\n\",\n \"[[1 9]\\n\",\n \" [3 6]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# tf.Tensor对象引用具体值\\n\",\n \"a = tf.constant([[1,9],[3,6]])\\n\",\n \"print(a)\\n\",\n \"\\n\",\n \"# 支持broadcasting（广播：不同shape的数据进行数学运算）\\n\",\n \"b = tf.add(a, 2)\\n\",\n \"print(b)\\n\",\n \"\\n\",\n \"# 支持运算符重载\\n\",\n \"print(a*b)\\n\",\n \"\\n\",\n \"# 可以当做numpy数据使用\\n\",\n \"import numpy as np\\n\",\n \"s = np.multiply(a,b)\\n\",\n \"print(s)\\n\",\n \"\\n\",\n \"# 转换为numpy类型\\n\",\n \"print(a.numpy())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.动态控制流\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Eager Execution的一个主要好处是在执行模型时可以使用宿主语言(Python)的所有功能。所以，例如，写fizzbuzz很容易：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"Fizz\\n\",\n \"4\\n\",\n \"Buzz\\n\",\n \"Fizz\\n\",\n \"7\\n\",\n \"8\\n\",\n \"Fizz\\n\",\n \"Buzz\\n\",\n \"11\\n\",\n \"Fizz\\n\",\n \"13\\n\",\n \"14\\n\",\n \"FizzBuzz\\n\",\n \"16\\n\"\n ]\n }\n ],\n \"source\": [\n \"def fizzbuzz(max_num):\\n\",\n \" counter = tf.constant(0)\\n\",\n \" max_num = tf.convert_to_tensor(max_num)\\n\",\n \" # 使用range遍历\\n\",\n \" for num in range(1, max_num.numpy()+1):\\n\",\n \" # 重新转为tensor类型\\n\",\n \" num = tf.constant(num)\\n\",\n \" # 使用if-elif 做判断\\n\",\n \" if int(num % 3) == 0 and int(num % 5) == 0:\\n\",\n \" print('FizzBuzz')\\n\",\n \" elif int(num % 3) == 0:\\n\",\n \" print('Fizz')\\n\",\n \" elif int(num % 5) == 0:\\n\",\n \" print('Buzz')\\n\",\n \" else:\\n\",\n \" print(num.numpy())\\n\",\n \" counter += 1 # 自加运算\\n\",\n \"fizzbuzz(16)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.在Eager Execution下训练\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 计算梯度\\n\",\n \"自动微分对于实现机器学习算法（例如用于训练神经网络的反向传播）来说是很有用的。在 Eager Execution中，使用 tf.GradientTape 来跟踪操作以便稍后计算梯度。\\n\",\n \"\\n\",\n \"可以用tf.GradientTape来训练和/或计算梯度。它对复杂的训练循环特别有用。\\n\",\n \"\\n\",\n \"由于在每次调用期间可能发生不同的操作，所有前向传递操作都被记录到“磁带”中。要计算梯度，请向反向播放磁带，然后丢弃。特定的tf.GradientTape只能计算一个梯度; 后续调用会引发运行时错误。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor([[2.]], shape=(1, 1), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"w = tf.Variable([[1.0]])\\n\",\n \"# 用tf.GradientTape()记录梯度\\n\",\n \"with tf.GradientTape() as tape:\\n\",\n \" loss = w*w\\n\",\n \"grad = tape.gradient(loss, w) # 计算梯度\\n\",\n \"print(grad)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 训练模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Logits: [[-0.0330125 0.00557926 0.01437856 -0.07978292 -0.05083258 -0.02875553\\n\",\n \" -0.02977117 -0.02533271 0.0575003 0.03532691]]\\n\",\n \"........................................\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0, 0.5, 'Loss [entropy]')\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 导入mnist数据\\n\",\n \"(mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data()\\n\",\n \"# 数据转换\\n\",\n \"dataset = tf.data.Dataset.from_tensor_slices(\\n\",\n \" (tf.cast(mnist_images[...,tf.newaxis]/255, tf.float32),\\n\",\n \" tf.cast(mnist_labels,tf.int64)))\\n\",\n \"# 数据打乱与分批次\\n\",\n \"dataset = dataset.shuffle(1000).batch(32)\\n\",\n \"# 使用Sequential构建一个卷积网络\\n\",\n \"mnist_model = tf.keras.Sequential([\\n\",\n \" tf.keras.layers.Conv2D(16,[3,3], activation='relu', \\n\",\n \" input_shape=(None, None, 1)),\\n\",\n \" tf.keras.layers.Conv2D(16,[3,3], activation='relu'),\\n\",\n \" tf.keras.layers.GlobalAveragePooling2D(),\\n\",\n \" tf.keras.layers.Dense(10)\\n\",\n \"])\\n\",\n \"# 展示数据\\n\",\n \"# 即使没有经过培训，也可以调用模型并在Eager Execution中检查输出\\n\",\n \"for images,labels in dataset.take(1):\\n\",\n \" print(\\\"Logits: \\\", mnist_model(images[0:1]).numpy())\\n\",\n \" \\n\",\n \"# 优化器与损失函数\\n\",\n \"optimizer = tf.keras.optimizers.Adam()\\n\",\n \"loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\\n\",\n \"\\n\",\n \"# 按批次训练\\n\",\n \"# 虽然 keras 模型具有内置训练循环（fit 方法），但有时需要更多自定义设置。下面是一个用 eager 实现的训练循环示例：\\n\",\n \"loss_history = []\\n\",\n \"for (batch, (images, labels)) in enumerate(dataset.take(400)):\\n\",\n \" if batch % 10 == 0:\\n\",\n \" print('.', end='')\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" # 获取预测结果\\n\",\n \" logits = mnist_model(images, training=True)\\n\",\n \" # 获取损失\\n\",\n \" loss_value = loss_object(labels, logits)\\n\",\n \"\\n\",\n \" loss_history.append(loss_value.numpy().mean())\\n\",\n \" # 获取本批数据梯度\\n\",\n \" grads = tape.gradient(loss_value, mnist_model.trainable_variables)\\n\",\n \" # 反向传播优化\\n\",\n \" optimizer.apply_gradients(zip(grads, mnist_model.trainable_variables))\\n\",\n \" \\n\",\n \"# 绘图展示loss变化\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.plot(loss_history)\\n\",\n \"plt.xlabel('Batch #')\\n\",\n \"plt.ylabel('Loss [entropy]')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.变量求导优化\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"tf.Variable对象存储在训练期间访问的可变tf.Tensor值，以使自动微分更容易。模型的参数可以作为变量封装在类中。\\n\",\n \"\\n\",\n \"将tf.Variable 和tf.GradientTape 结合，可以更好地封装模型参数。例如，可以重写上面的自动微分示例为：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Initial loss: 69.425\\n\",\n \"Loss at step 000: 66.713\\n\",\n \"Loss at step 020: 30.274\\n\",\n \"Loss at step 040: 14.048\\n\",\n \"Loss at step 060: 6.822\\n\",\n \"Loss at step 080: 3.604\\n\",\n \"Loss at step 100: 2.171\\n\",\n \"Loss at step 120: 1.533\\n\",\n \"Loss at step 140: 1.249\\n\",\n \"Loss at step 160: 1.122\\n\",\n \"Loss at step 180: 1.066\\n\",\n \"Loss at step 200: 1.040\\n\",\n \"Loss at step 220: 1.029\\n\",\n \"Loss at step 240: 1.024\\n\",\n \"Loss at step 260: 1.022\\n\",\n \"Loss at step 280: 1.021\\n\",\n \"Final loss: 1.021\\n\",\n \"W = 2.9795801639556885, B = 2.0041041374206543\\n\"\n ]\n }\n ],\n \"source\": [\n \"class MyModel(tf.keras.Model):\\n\",\n \" def __init__(self):\\n\",\n \" super(MyModel, self).__init__()\\n\",\n \" self.W = tf.Variable(5., name='weight')\\n\",\n \" self.B = tf.Variable(10., name='bias')\\n\",\n \" def call(self, inputs):\\n\",\n \" return inputs * self.W + self.B\\n\",\n \"\\n\",\n \"# 满足函数3 * x + 2的数据\\n\",\n \"NUM_EXAMPLES = 2000\\n\",\n \"training_inputs = tf.random.normal([NUM_EXAMPLES])\\n\",\n \"noise = tf.random.normal([NUM_EXAMPLES])\\n\",\n \"training_outputs = training_inputs * 3 + 2 + noise\\n\",\n \"\\n\",\n \"# 损失函数\\n\",\n \"def loss(model, inputs, targets):\\n\",\n \" error = model(inputs) - targets\\n\",\n \" return tf.reduce_mean(tf.square(error))\\n\",\n \"\\n\",\n \"# 梯度函数\\n\",\n \"def grad(model, inputs, targets):\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" loss_value = loss(model, inputs, targets)\\n\",\n \" return tape.gradient(loss_value, [model.W, model.B])\\n\",\n \"\\n\",\n \"# 模型与优化器\\n\",\n \"model = MyModel()\\n\",\n \"optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)\\n\",\n \"\\n\",\n \"print(\\\"Initial loss: {:.3f}\\\".format(loss(model, training_inputs, training_outputs)))\\n\",\n \"\\n\",\n \"# 训练循环， 反向传播优化\\n\",\n \"for i in range(300):\\n\",\n \" grads = grad(model, training_inputs, training_outputs)\\n\",\n \" optimizer.apply_gradients(zip(grads, [model.W, model.B]))\\n\",\n \" if i % 20 == 0:\\n\",\n \" print(\\\"Loss at step {:03d}: {:.3f}\\\".format(i, loss(model, training_inputs, training_outputs)))\\n\",\n \"\\n\",\n \"print(\\\"Final loss: {:.3f}\\\".format(loss(model, training_inputs, training_outputs)))\\n\",\n \"print(\\\"W = {}, B = {}\\\".format(model.W.numpy(), model.B.numpy()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5.Eager Execution中的对象\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"使用 Graph Execution 时，程序状态（如变量）存储在全局集合中，它们的生命周期由 tf.Session 对象管理。相反，在 Eager Execution 期间，状态对象的生命周期由其对应的 Python 对象的生命周期决定。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 变量对象\\n\",\n \"变量将持续存在，直到删除对象的最后一个引用，然后变量被删除。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"if tf.test.is_gpu_available():\\n\",\n \" with tf.device(\\\"gpu:0\\\"):\\n\",\n \" v = tf.Variable(tf.random.normal([1000, 1000]))\\n\",\n \" v = None # v no longer takes up GPU memory\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 基于对象的保存\\n\",\n \"tf.train.Checkpoint 可以将 tf.Variable 保存到检查点并从中恢复：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 使用检测点保存变量\\n\",\n \"x = tf.Variable(6.0)\\n\",\n \"checkpoint = tf.train.Checkpoint(x=x)\\n\",\n \"# 变量的改变会同步到检测点\\n\",\n \"x.assign(1.0)\\n\",\n \"checkpoint.save('./ckpt/')\\n\",\n \"# 检测点保存后，变量的改变对检测点无影响\\n\",\n \"x.assign(8.0)\\n\",\n \"checkpoint.restore(tf.train.latest_checkpoint('./ckpt/'))\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"要保存和加载模型，tf.train.Checkpoint 会存储对象的内部状态，而不需要隐藏变量。要记录 model、optimizer 和全局步的状态，可以将它们传递到 tf.train.Checkpoint：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 模型保持\\n\",\n \"import os\\n\",\n \"model = tf.keras.Sequential([\\n\",\n \" tf.keras.layers.Conv2D(16,[3,3], activation='relu'),\\n\",\n \" tf.keras.layers.GlobalAveragePooling2D(),\\n\",\n \" tf.keras.layers.Dense(10)\\n\",\n \"])\\n\",\n \"optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\\n\",\n \"checkpoint_dir = './ck_model_dir'\\n\",\n \"if not os.path.exists(checkpoint_dir):\\n\",\n \" os.makedirs(checkpoint_dir)\\n\",\n \"checkpoint_prefix = os.path.join(checkpoint_dir, \\\"ckpt\\\")\\n\",\n \"# 将优化器和模型记录至检测点\\n\",\n \"root = tf.train.Checkpoint(optimizer=optimizer,\\n\",\n \" model=model)\\n\",\n \"# 保存检测点\\n\",\n \"root.save(checkpoint_prefix)\\n\",\n \"# 读取检测点\\n\",\n \"root.restore(tf.train.latest_checkpoint(checkpoint_dir))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 面向对象的指标\\n\",\n \"tf.keras.metrics存储为对象。通过将新数据传递给callable来更新度量标准，并使用tf.keras.metrics.result方法检索结果，例如：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(2.5, shape=(), dtype=float32)\\n\",\n \"tf.Tensor(5.5, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"m = tf.keras.metrics.Mean('loss')\\n\",\n \"m(0)\\n\",\n \"m(5)\\n\",\n \"print(m.result()) # => 2.5\\n\",\n \"m([8, 9])\\n\",\n \"print(m.result()) # => 5.5\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6.自动微分高级内容\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 动态模型\\n\",\n \"tf.GradientTape 也可用于动态模型。这个回溯线搜索算法示例看起来像普通的 NumPy 代码，除了存在梯度并且可微分，尽管控制流比较复杂：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def line_search_step(fn, init_x, rate=1.0):\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" # 变量会自动记录，但需要手动观察张量\\n\",\n \" tape.watch(init_x)\\n\",\n \" value = fn(init_x)\\n\",\n \" grad = tape.gradient(value, init_x)\\n\",\n \" grad_norm = tf.reduce_sum(grad * grad)\\n\",\n \" init_value = value\\n\",\n \" while value > init_value - rate * grad_norm:\\n\",\n \" x = init_x - rate * grad\\n\",\n \" value = fn(x)\\n\",\n \" rate /= 2.0\\n\",\n \" return x, value\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 自定义梯度\\n\",\n \"自定义梯度是在 Eager Execution 和 Graph Execution 中覆盖梯度的一种简单方式。在正向函数中，定义相对于输入、输出或中间结果的梯度。例如，下面是在反向传播中截断梯度范数的一种简单方式：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"@tf.custom_gradient\\n\",\n \"def clip_gradient_by_norm(x, norm):\\n\",\n \" y = tf.identity(x)\\n\",\n \" def grad_fn(dresult):\\n\",\n \" return [tf.clip_by_norm(dresult, norm), None]\\n\",\n \" return y, grad_fn\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"自定义梯度可以提供数值稳定的梯度\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.5\\n\",\n \"nan\\n\"\n ]\n }\n ],\n \"source\": [\n \"def log1pexp(x):\\n\",\n \" return tf.math.log(1 + tf.exp(x))\\n\",\n \"\\n\",\n \"def grad_log1pexp(x):\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" tape.watch(x)\\n\",\n \" value = log1pexp(x)\\n\",\n \" return tape.gradient(value, x)\\n\",\n \"# 梯度计算在x = 0时工作正常。\\n\",\n \"print(grad_log1pexp(tf.constant(0.)).numpy())\\n\",\n \"# 但是，由于数值不稳定，x = 100失败。\\n\",\n \"print(grad_log1pexp(tf.constant(100.)).numpy())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"这里，log1pexp函数可以使用自定义梯度求导进行分析简化。 下面的实现重用了在前向传递期间计算的tf.exp（x）的值 - 通过消除冗余计算使其更有效：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.5\\n\",\n \"1.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"@tf.custom_gradient\\n\",\n \"def log1pexp(x):\\n\",\n \" e = tf.exp(x)\\n\",\n \" def grad(dy):\\n\",\n \" return dy * (1 - 1 / (1 + e))\\n\",\n \" return tf.math.log(1 + e), grad\\n\",\n \"\\n\",\n \"def grad_log1pexp(x):\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" tape.watch(x)\\n\",\n \" value = log1pexp(x)\\n\",\n \" return tape.gradient(value, x)\\n\",\n \"# 和以前一样，梯度计算在x = 0时工作正常。\\n\",\n \"print(grad_log1pexp(tf.constant(0.)).numpy())\\n\",\n \"# 并且梯度计算也适用于x = 100\\n\",\n \"print(grad_log1pexp(tf.constant(100.)).numpy())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 7.使用gpu提升性能\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"在 Eager Execution 期间，计算会自动分流到 GPU。如果要控制计算运行的位置，可以将其放在 tf.device('/gpu:0') 块（或 CPU 等效块）中：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Time to multiply a (1000, 1000) matrix by itself 200 times:\\n\",\n \"CPU: 1.698425054550171 secs\\n\",\n \"GPU: 0.13264727592468262 secs\\n\"\n ]\n }\n ],\n \"source\": [\n \"import time\\n\",\n \"\\n\",\n \"def measure(x, steps):\\n\",\n \" # TensorFlow在第一次使用时初始化GPU，其不计入时间。\\n\",\n \" tf.matmul(x, x)\\n\",\n \" start = time.time()\\n\",\n \" for i in range(steps):\\n\",\n \" x = tf.matmul(x, x)\\n\",\n \" # tf.matmul可以在完成矩阵乘法之前返回（例如，\\n\",\n \" # 可以在对CUDA流进行操作之后返回）。 \\n\",\n \" # 下面的x.numpy（）调用将确保所有已排队\\n\",\n \" # 的操作都已完成（并且还将结果复制到主机内存，\\n\",\n \" # 因此我们只包括一些matmul操作时间）\\n\",\n \" _ = x.numpy()\\n\",\n \" end = time.time()\\n\",\n \" return end - start\\n\",\n \"\\n\",\n \"shape = (1000, 1000)\\n\",\n \"steps = 200\\n\",\n \"print(\\\"Time to multiply a {} matrix by itself {} times:\\\".format(shape, steps))\\n\",\n \"\\n\",\n \"# 在CPU上运行:\\n\",\n \"with tf.device(\\\"/cpu:0\\\"):\\n\",\n \" print(\\\"CPU: {} secs\\\".format(measure(tf.random.normal(shape), steps)))\\n\",\n \"\\n\",\n \"# 在GPU上运行,如果可以的话:\\n\",\n \"if tf.test.is_gpu_available():\\n\",\n \" with tf.device(\\\"/gpu:0\\\"):\\n\",\n \" print(\\\"GPU: {} secs\\\".format(measure(tf.random.normal(shape), steps)))\\n\",\n \"else:\\n\",\n \" print(\\\"GPU: not found\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"tf.Tensor对象可以被复制到不同的设备来执行其操作\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"if tf.test.is_gpu_available():\\n\",\n \" x = tf.random.normal([10, 10])\\n\",\n \" # 将tensor对象复制到gpu上\\n\",\n \" x_gpu0 = x.gpu()\\n\",\n \" x_cpu = x.cpu()\\n\",\n \"\\n\",\n \" _ = tf.matmul(x_cpu, x_cpu) # Runs on CPU\\n\",\n \" _ = tf.matmul(x_gpu0, x_gpu0) # Runs on GPU:0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "006-keras_and_rnn.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow教程-keras构建RNN\\n\",\n \"循环神经网络是一种在时间维度上进行迭代的神经网络，在建模序列数据方面有着优越的性能。\\n\",\n \"TensorFlow2中包含了主流的rnn网络实现，以Keras RNN API的方式提供调用。其特性如下：\\n\",\n \"- 易用性： 内置tf.keras.layers.RNN，tf.keras.layers.LSTM，tf.keras.layers.GRU， 可以快速构建RNN模块。\\n\",\n \"- 易于定制：可以使用自定义操作循环来构建RNN模块，并通过tf.keras.layers.RNN调用。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/doit/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n \" from ._conv import register_converters as _register_converters\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function, unicode_literals\\n\",\n \"\\n\",\n \"import collections\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"print(tf.__version__)\\n\",\n \"from tensorflow.keras import layers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1 构建一个简单模型\\n\",\n \"keras中内置了三个RNN层\\n\",\n \"- tf.keras.layers.SimpleRNN，普通RNN网络。\\n\",\n \"- tf.keras.layers.GRU，门控循环神经网络。\\n\",\n \"- tf.keras.layers.LSTM，长短期记忆神经网络。\\n\",\n \"下面是一个循环神经网络的例子，它使用LSTM层来处理输入的词嵌入，LSTM迭代的次数与词个个数一致\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"embedding (Embedding) (None, None, 64) 64000 \\n\",\n \"_________________________________________________________________\\n\",\n \"lstm (LSTM) (None, 128) 98816 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense (Dense) (None, 10) 1290 \\n\",\n \"=================================================================\\n\",\n \"Total params: 164,106\\n\",\n \"Trainable params: 164,106\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = tf.keras.Sequential()\\n\",\n \"# input_dim是词典大小， output_dim是词嵌入维度\\n\",\n \"model.add(layers.Embedding(input_dim=1000, output_dim=64))\\n\",\n \"# 添加lstm层，其会输出最后一个时间步的输出\\n\",\n \"model.add(layers.LSTM(128))\\n\",\n \"model.add(layers.Dense(10, activation='softmax'))\\n\",\n \"\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2 输出和状态\\n\",\n \"默认情况下RNN层输出最后一个时间步的输出，输出的形状为(batch_size, units), 其中unit是传给层的构造参数。\\n\",\n \"如果要返回rnn每个时间步的序列，需要置return_sequence=True, 此时输出的形状为(batch_size, time_steps, units)。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_1\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"embedding_1 (Embedding) (None, None, 64) 64000 \\n\",\n \"_________________________________________________________________\\n\",\n \"gru (GRU) (None, None, 128) 74496 \\n\",\n \"_________________________________________________________________\\n\",\n \"simple_rnn (SimpleRNN) (None, 128) 32896 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_1 (Dense) (None, 10) 1290 \\n\",\n \"=================================================================\\n\",\n \"Total params: 172,682\\n\",\n \"Trainable params: 172,682\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = tf.keras.Sequential()\\n\",\n \"model.add(layers.Embedding(input_dim=1000, output_dim=64))\\n\",\n \"# 返回整个rnn序列的输出\\n\",\n \"model.add(layers.GRU(128, return_sequences=True))\\n\",\n \"model.add(layers.SimpleRNN(128))\\n\",\n \"model.add(layers.Dense(10, activation='softmax'))\\n\",\n \"model.summary()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"RNN层可以返回最后一个时间步的状态。\\n\",\n \"\\n\",\n \"返回的状态可以用于恢复RNN或初始化另一个RNN。此设置通常在seq2seq模型中用到，其将编码器的最终状态作为解码器的初始状态。\\n\",\n \"\\n\",\n \"要使RNN层返回最终的状态需要在创建图层时置return_state=True。请注意，LSTM有两个状态向量，而GRU只有一个。\\n\",\n \"\\n\",\n \"要配置图层的初始状态， 需要网络构建中传入initial_state。其中状态的维度必须与图层的unit大小一样。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"model\\\"\\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # Connected to \\n\",\n \"==================================================================================================\\n\",\n \"input_1 (InputLayer) [(None, None)] 0 \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"input_2 (InputLayer) [(None, None)] 0 \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"embedding_2 (Embedding) (None, None, 64) 64000 input_1[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"embedding_3 (Embedding) (None, None, 64) 64000 input_2[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"encoder (LSTM) [(None, 64), (None, 33024 embedding_2[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"decoder (LSTM) (None, 64) 33024 embedding_3[0][0] \\n\",\n \" encoder[0][1] \\n\",\n \" encoder[0][2] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"dense_2 (Dense) (None, 10) 650 decoder[0][0] \\n\",\n \"==================================================================================================\\n\",\n \"Total params: 194,698\\n\",\n \"Trainable params: 194,698\\n\",\n \"Non-trainable params: 0\\n\",\n \"__________________________________________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"encoder_vocab = 1000\\n\",\n \"decoder_vocab = 2000\\n\",\n \"\\n\",\n \"\\n\",\n \"# 编码层\\n\",\n \"encode_input = layers.Input(shape=(None, ))\\n\",\n \"encode_emb = layers.Embedding(input_dim=encoder_vocab, output_dim=64)(encode_input)\\n\",\n \"# 同时返回状态\\n\",\n \"encode_out, state_h, state_c = layers.LSTM(64, return_state=True, name='encoder')(encode_emb)\\n\",\n \"encode_state = [state_h, state_c]\\n\",\n \"\\n\",\n \"# 解码层\\n\",\n \"decode_input =layers.Input(shape=(None, ))\\n\",\n \"decode_emb = layers.Embedding(input_dim=encoder_vocab, output_dim=64)(decode_input)\\n\",\n \"# 编码器的最终状态， 作为解码器的初始状态\\n\",\n \"decode_out = layers.LSTM(64, name='decoder')(decode_emb, initial_state=encode_state)\\n\",\n \"output = layers.Dense(10, activation='softmax')(decode_out)\\n\",\n \"\\n\",\n \"model = tf.keras.Model([encode_input, decode_input], output)\\n\",\n \"model.summary()\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3 RNN层和RNN Cell\\n\",\n \"除了内置的RNN层以外， RNN API还提供单元级API。与可以处理整批次的输入序列不同， RNN Cell仅能处理单个时间步的数据。\\n\",\n \"如果想处理整批次的输入数据，需要将RNN Cell包含在tf.keras.layers.RNN中， 如：RNN(LSTMCell(10)\\n\",\n \"\\n\",\n \"同效果上说， RNN(LSTMCell(10))等价于LSTM(10)。但内置的GRU和LSTM层可以使用CuDNN，以提高计算性能。\\n\",\n \"\\n\",\n \"下面是三个内置的RNN单元， 以及其对应的RNN层。\\n\",\n \"- tf.keras.layers.SimpleRNNCell对应于SimpleRNN图层。\\n\",\n \"- tf.keras.layers.GRUCell对应于GRU图层。\\n\",\n \"- tf.keras.layers.LSTMCell对应于LSTM图层。\\n\",\n \"\\n\",\n \"注： tf.keras.layers.RNN类可以使为研究实现自定义RNN体系结构变得非常容易。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_2\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"embedding_4 (Embedding) (None, None, 64) 64000 \\n\",\n \"_________________________________________________________________\\n\",\n \"rnn (RNN) (None, 64) 33024 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_3 (Dense) (None, 10) 650 \\n\",\n \"=================================================================\\n\",\n \"Total params: 97,674\\n\",\n \"Trainable params: 97,674\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = tf.keras.Sequential()\\n\",\n \"# input_dim是词典大小， output_dim是词嵌入维度\\n\",\n \"model.add(layers.Embedding(input_dim=1000, output_dim=64))\\n\",\n \"# 添加lstm层，其会输出最后一个时间步的输出\\n\",\n \"model.add(layers.RNN(layers.LSTMCell(64)))\\n\",\n \"model.add(layers.Dense(10, activation='softmax'))\\n\",\n \"\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4 跨批次状态\\n\",\n \"在处理超长序列（有可能无限长）时，可能需要使用到跨批次状态的模式。\\n\",\n \"\\n\",\n \"通常，每次看到新的批次时， 都会重置RNN层的内部状态（state，非权重。即该层看到的每个样本都独立于过去）。\\n\",\n \"\\n\",\n \"但，当序列过长，需要分不同批次输入时，则无需重置RNN的状态(上一批的结束状态，为下一批的初始状态)。这样，即使一次只看到一个子序列，网络层也可以保留整个序列的信息。\\n\",\n \"\\n\",\n \"我们可以置state_ful=True来设置跨批次状态。\\n\",\n \"\\n\",\n \"如果有序列s = [t0, t1, ... t1546, t1547]， 可以将其分为以下批次数据：\\n\",\n \"\\n\",\n \"\\n\",\n \"s1 = [t0, t1, ... t100]\\n\",\n \"\\n\",\n \"s2 = [t101, ... t201]\\n\",\n \"\\n\",\n \"...\\n\",\n \"\\n\",\n \"16 = [t1501, ... t1547]\\n\",\n \"\\n\",\n \"具体调用方法如下：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sub_sequence = []\\n\",\n \"lstm_layer = layers.LSTM(64, stateful=True)\\n\",\n \"for s in sub_sequence:\\n\",\n \" output = lstm_layer(s)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"要清除状态时，可以使用layer.reset_states()。\\n\",\n \"\\n\",\n \"注意：在此设置中，i假定给定批次中的样品i是上一个批次中样品的延续。这意味着所有批次应包含相同数量的样本（批次大小）。例如，如果一个批次包含[sequence_A_from_t0_to_t100, sequence_B_from_t0_to_t100]，则下一个批次应包含[sequence_A_from_t101_to_t200, sequence_B_from_t101_to_t200]。\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"跨批次状态例子\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"para1 = np.random.random((20, 10, 50)).astype(np.float32)\\n\",\n \"para2 = np.random.random((20, 10, 50)).astype(np.float32)\\n\",\n \"para3 = np.random.random((20, 10, 50)).astype(np.float32)\\n\",\n \"lstm_layer = layers.LSTM(64, stateful=True)\\n\",\n \"output = lstm_layer(para1)\\n\",\n \"output = lstm_layer(para2)\\n\",\n \"output = lstm_layer(para3)\\n\",\n \"lstm_layer.reset_states()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5 双向LSTM\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"对于时间序列以外的序列，比如文本，RNN不仅可以正向处理序列，也可以反向处理序列。例如要预测句子的某个单纯，上下文对单词都有用。\\n\",\n \"\\n\",\n \"Keras提供了tf.keras.layers.Bidirectional的API，构建双向RNN。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_3\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"bidirectional (Bidirectional (None, 5, 128) 38400 \\n\",\n \"_________________________________________________________________\\n\",\n \"bidirectional_1 (Bidirection (None, 64) 41216 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_4 (Dense) (None, 10) 650 \\n\",\n \"=================================================================\\n\",\n \"Total params: 80,266\\n\",\n \"Trainable params: 80,266\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = tf.keras.Sequential()\\n\",\n \"model.add(layers.Bidirectional(layers.LSTM(64, return_sequences=True),\\n\",\n \" input_shape=(5, 10)))\\n\",\n \"model.add(layers.Bidirectional(layers.LSTM(32)))\\n\",\n \"model.add(layers.Dense(10, activation='softmax'))\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"在内部， Bidirectional将复制传入的RNN，并将逆序输入新复制的RNN，并输出前向输出和后向输出的叠加。如果想要其他合并行为（如串联），可以修改merge_mode等参数。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6 TensorFlow2.0中的性能优化和CuDNN内核\\n\",\n \"\\n\",\n \"在TensorFlow2.0中，内置的LSTM和GRU层已更新，已在有GPU时默认使用CuDNN内核。通过此更改，先前的keras.layers.CuDNNLSTM/CuDNNGRU层已经弃用，可以简易的构建模型而不必担心其运行的硬件。\\n\",\n \"\\n\",\n \"由于CuDNN内核是根据某些假设构建的，因此如果改变内置LSTM或GRU的默认设置，则该层无法使用CuDNN内核，例如:\\n\",\n \"\\n\",\n \"- 将activation从tanh改为其他激活函数。\\n\",\n \"- 将recurrent_activation由sigmoid改为其他激活函数。\\n\",\n \"- 使recurrent_dropout> 0。\\n\",\n \"- 设置unroll为True，将强制LSTM / GRU将内部分解tf.while_loop为展开的for循环。\\n\",\n \"- 设置use_bias为False。\\n\",\n \"- 当输入数据未严格右填充时使用掩蔽（如果掩码对应于严格右填充数据，则仍可以使用CuDNN。这是最常见的情况）。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 6.1 在可用时使用CuDNN内核\\n\",\n \"我们构建一个简单的LSTM网络，来实现MNIST数字识别。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"batch_size = 64\\n\",\n \"input_dim = 28\\n\",\n \"units = 64\\n\",\n \"output_size = 10\\n\",\n \"\\n\",\n \"def build_model(allow_cudnn_kernel=True):\\n\",\n \" if allow_cudnn_kernel:\\n\",\n \" # LSTM默认cudnn加速\\n\",\n \" lstm_layer = tf.keras.layers.LSTM(units, input_shape=(None, input_dim))\\n\",\n \" else:\\n\",\n \" # LSTMCell内核没有使用\\n\",\n \" lstm_layer = tf.keras.layers.RNN(tf.keras.layers.LSTMCell(units),\\n\",\n \" input_shape=(None, input_dim))\\n\",\n \" model = tf.keras.models.Sequential([\\n\",\n \" lstm_layer,\\n\",\n \" tf.keras.layers.BatchNormalization(),\\n\",\n \" tf.keras.layers.Dense(output_size, activation='softmax')\\n\",\n \" ])\\n\",\n \" return model\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"加载MNIST数据集\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mnits = tf.keras.datasets.mnist\\n\",\n \"\\n\",\n \"(x_train, y_train), (x_test, y_test) = mnits.load_data()\\n\",\n \"x_train, x_test = x_train / 255.0, x_test / 255.0\\n\",\n \"sample, sample_label = x_train[0], y_train[0]\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"创建模型实例并进行编译\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model = build_model(allow_cudnn_kernel=True)\\n\",\n \"model.compile(loss='sparse_categorical_crossentropy',\\n\",\n \" optimizer='sgd',\\n\",\n \" metrics=['accuracy'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 60000 samples, validate on 10000 samples\\n\",\n \"Epoch 1/2\\n\",\n \"60000/60000 [==============================] - 10s 169us/sample - loss: 0.1457 - accuracy: 0.9560 - val_loss: 0.1290 - val_accuracy: 0.9566\\n\",\n \"Epoch 2/2\\n\",\n \"60000/60000 [==============================] - 10s 170us/sample - loss: 0.1284 - accuracy: 0.9614 - val_loss: 0.1394 - val_accuracy: 0.9543\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 使用cudnn的训练\\n\",\n \"model.fit(x_train, y_train,\\n\",\n \" validation_data=(x_test, y_test),\\n\",\n \" batch_size=batch_size,\\n\",\n \" epochs=2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"在没有CuDNN内核的情况下构建新模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 60000 samples, validate on 10000 samples\\n\",\n \"Epoch 1/2\\n\",\n \"60000/60000 [==============================] - 11s 185us/sample - loss: 0.9310 - accuracy: 0.7030 - val_loss: 0.5106 - val_accuracy: 0.8419\\n\",\n \"Epoch 2/2\\n\",\n \"60000/60000 [==============================] - 10s 172us/sample - loss: 0.4209 - accuracy: 0.8715 - val_loss: 0.4863 - val_accuracy: 0.8416\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"slow_model = build_model(allow_cudnn_kernel=False)\\n\",\n \"#slow_model.set_weights(model.get_weights())\\n\",\n \"slow_model.compile(loss='sparse_categorical_crossentropy',\\n\",\n \" optimizer='sgd',\\n\",\n \" metrics=['accuracy'])\\n\",\n \"slow_model.fit(x_train, y_train,\\n\",\n \" validation_data=(x_test, y_test),\\n\",\n \" batch_size=batch_size,\\n\",\n \" epochs=2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"使用cudnn的模型也可以在cpu环境中进行推理\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"预测结果: [5], 正确标签: 5\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"with tf.device('CPU:0'):\\n\",\n \" cpu_model = build_model(allow_cudnn_kernel=True)\\n\",\n \" cpu_model.set_weights(model.get_weights())\\n\",\n \" result = tf.argmax(cpu_model.predict_on_batch(tf.expand_dims(sample, 0)), axis=1)\\n\",\n \" print('预测结果: %s, 正确标签: %s' % (result.numpy(), sample_label))\\n\",\n \" plt.imshow(sample, cmap=plt.get_cmap('gray'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 7.具有列表/字典输入或嵌套输入的RNN\\n\",\n \"嵌套结构可以在单个时间步内包含更多信息。例如，一个视频帧可以同时具有音频和视频输入。比如以下的数据格式：\\n\",\n \"\\n\",\n \"[batch, timestep, {\\\"video\\\": [height, width, channel], \\\"audio\\\": [frequency]}]\\n\",\n \"\\n\",\n \"另一个例子，如笔迹数据。可以有当前的位置坐标x，y和压力信息。格式如下：\\n\",\n \"\\n\",\n \"[batch, timestep, {\\\"location\\\": [x, y], \\\"pressure\\\": [force]}]\\n\",\n \"\\n\",\n \"### 7.1 定义一个支持嵌套输入/输出的自定义单元格\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"NestedInput = collections.namedtuple('NestedInput', ['feature1', 'feature2'])\\n\",\n \"NestedState = collections.namedtuple('NestedState', ['state1', 'state2'])\\n\",\n \"\\n\",\n \"class NestedCell(tf.keras.layers.Layer):\\n\",\n \" # 初始化，获取相关参数\\n\",\n \" def __init__(self,unit1, unit2, unit3, **kwargs):\\n\",\n \" self.unit1 = unit1\\n\",\n \" self.unit2 = unit2\\n\",\n \" self.unit3 = unit3\\n\",\n \" self.state_size = NestedState(state1=unit1,\\n\",\n \" state2=tf.TensorShape([unit2, unit3]))\\n\",\n \" \\n\",\n \" self.output_size = (unit1, tf.TensorShape([unit2, unit3]))\\n\",\n \" super(NestedCell, self).__init__(**kwargs)\\n\",\n \" # 构建权重、网络\\n\",\n \" def build(self, input_shapes):\\n\",\n \" # input_shape包含2个特征项 [(batch, i1), (batch, i2, i3)]\\n\",\n \" input1 = input_shapes.feature1[1]\\n\",\n \" input2, input3 = input_shapes.feature2[1:]\\n\",\n \" \\n\",\n \" self.kernel_1 = self.add_weight(\\n\",\n \" shape=(input1, self.unit1), initializer='uniform', name='kernel_1'\\n\",\n \" )\\n\",\n \" self.kernel_2_3 = self.add_weight(\\n\",\n \" shape=(input2, input3, self.unit2, self.unit3),\\n\",\n \" initializer='uniform',\\n\",\n \" name='kernel_2_3'\\n\",\n \" )\\n\",\n \" # 前向连接网络\\n\",\n \" def call(self, inputs, states):\\n\",\n \" # inputs: [(batch, input_1), (batch, input_2, input_3)]\\n\",\n \" # state: [(batch, unit_1), (batch, unit_2, unit_3)]\\n\",\n \" input1, input2 = tf.nest.flatten(inputs)\\n\",\n \" s1, s2 = states\\n\",\n \" \\n\",\n \" output_1 = tf.matmul(input1, self.kernel_1)\\n\",\n \" output_2_3 = tf.einsum('bij,ijkl->bkl', input2, self.kernel_2_3)\\n\",\n \" \\n\",\n \" state_1 = s1 + output_1\\n\",\n \" state_2_3 = s2 + output_2_3\\n\",\n \" \\n\",\n \" output = [output_1, output_2_3]\\n\",\n \" new_states = NestedState(state1=state_1, state2=state_2_3)\\n\",\n \" return output, new_states\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 7.2 使用嵌套的输入输出构建RNN\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"unit_1 = 10\\n\",\n \"unit_2 = 20\\n\",\n \"unit_3 = 30\\n\",\n \"\\n\",\n \"input_1 = 32\\n\",\n \"input_2 = 64\\n\",\n \"input_3 = 32\\n\",\n \"batch_size = 64\\n\",\n \"num_batch = 100\\n\",\n \"timestep = 50\\n\",\n \"cell = NestedCell(unit_1, unit_2, unit_3)\\n\",\n \"rnn = tf.keras.layers.RNN(cell)\\n\",\n \"input1 = tf.keras.Input((None, input_1))\\n\",\n \"input2 = tf.keras.Input((None, input_2, input_3))\\n\",\n \"outputs = rnn(NestedInput(feature1=input1, feature2=input2))\\n\",\n \"model = tf.keras.models.Model([input1, input2], outputs)\\n\",\n \"model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"构造数据训练\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 6400 samples\\n\",\n \"6400/6400 [==============================] - 191s 30ms/sample - loss: 0.3808 - rnn_7_loss: 0.1197 - rnn_7_1_loss: 0.2611 - rnn_7_accuracy: 0.1003 - rnn_7_1_accuracy: 0.0333\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"input_1_data = np.random.random((batch_size * num_batch, timestep, input_1))\\n\",\n \"input_2_data = np.random.random((batch_size * num_batch, timestep, input_2, input_3))\\n\",\n \"target_1_data = np.random.random((batch_size * num_batch, unit_1))\\n\",\n \"target_2_data = np.random.random((batch_size * num_batch, unit_2, unit_3))\\n\",\n \"input_data = [input_1_data, input_2_data]\\n\",\n \"target_data = [target_1_data, target_2_data]\\n\",\n \"\\n\",\n \"model.fit(input_data, target_data, batch_size=batch_size)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "006-marking and padding.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-掩码和填充\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"在构建深度学习模型，特别是进行序列的相关任务时，经常会用到掩码和填充技术\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/doit/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n \" from ._conv import register_converters as _register_converters\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function, unicode_literals\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"from tensorflow.keras import layers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1 填充序列数据\\n\",\n \"处理序列数据时，常常会遇到不同长度的文本，例如处理某些句子时：\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[['The', 'weather', 'will', 'be', 'nice', 'tomorrow'],\\n\",\n \" ['How', 'are', 'you', 'doing', 'today'],\\n\",\n \" ['Hello', 'world', '!']]\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"[\\n\",\n \" [\\\"The\\\", \\\"weather\\\", \\\"will\\\", \\\"be\\\", \\\"nice\\\", \\\"tomorrow\\\"],\\n\",\n \" [\\\"How\\\", \\\"are\\\", \\\"you\\\", \\\"doing\\\", \\\"today\\\"],\\n\",\n \" [\\\"Hello\\\", \\\"world\\\", \\\"!\\\"]\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"计算机无法理解字符，我们一般将词语转换为对应的id\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[[83, 91, 1, 645, 1253, 927], [73, 8, 3215, 55, 927], [71, 1331, 4231]]\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"[\\n\",\n \" [83, 91, 1, 645, 1253, 927],\\n\",\n \" [73, 8, 3215, 55, 927],\\n\",\n \" [71, 1331, 4231]\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"由于深度信息模型的输入一般是固定的张量，所以我们需要对短文本进行填充（或对长文本进行截断），使每个样本的长度相同。\\n\",\n \"Keras提供了一个API，可以通过填充和截断，获取等长的数据：\\n\",\n \"tf.keras.preprocessing.sequence.pad_sequences\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 83 91 1 645 1253 927]\\n\",\n \" [ 0 73 8 3215 55 927]\\n\",\n \" [ 0 0 0 711 632 71]]\\n\",\n \"[[ 83 91 1 645 1253 927]\\n\",\n \" [ 73 8 3215 55 927 0]\\n\",\n \" [ 711 632 71 0 0 0]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"raw_inputs = [\\n\",\n \" [83, 91, 1, 645, 1253, 927],\\n\",\n \" [73, 8, 3215, 55, 927],\\n\",\n \" [711, 632, 71]\\n\",\n \"]\\n\",\n \"# 默认左填充\\n\",\n \"padded_inputs = tf.keras.preprocessing.sequence.pad_sequences(raw_inputs)\\n\",\n \"print(padded_inputs)\\n\",\n \"# 右填充需要设 padding='post'\\n\",\n \"padded_inputs = tf.keras.preprocessing.sequence.pad_sequences(raw_inputs,\\n\",\n \" padding='post')\\n\",\n \"print(padded_inputs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2 掩码\\n\",\n \"现在所有样本都获得了同样的长度，在求损失函数或输出的时候往往需要知道那些部分是填充的，需要忽略。这种忽略机制就是掩码。\\n\",\n \"\\n\",\n \"keras中三种添加掩码的方法：\\n\",\n \"- 添加一个keras.layer.Masking的网络层\\n\",\n \"- 配置keras.layers.Embedding层的mask_zero=True\\n\",\n \"- 在支持mark的网络层中，手动传递参数\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 掩码生成层：Embedding和Masking\\n\",\n \"Embedding和Masking会创建一个掩码张量，附加到返回的输出中。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[ True True True True True True]\\n\",\n \" [ True True True True True False]\\n\",\n \" [ True True True False False False]], shape=(3, 6), dtype=bool)\\n\"\n ]\n }\n ],\n \"source\": [\n \"embedding = layers.Embedding(input_dim=5000, output_dim=16, mask_zero=True)\\n\",\n \"mask_output= embedding(padded_inputs)\\n\",\n \"print(mask_output._keras_mask)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[ True True True True True True]\\n\",\n \" [ True True True True True False]\\n\",\n \" [ True True True False False False]], shape=(3, 6), dtype=bool)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 使用掩码层\\n\",\n \"masking_layer = layers.Masking()\\n\",\n \"unmasked_embedding = tf.cast(\\n\",\n \"tf.tile(tf.expand_dims(padded_inputs, axis=-1), [1, 1, 16]),\\n\",\n \"tf.float32)\\n\",\n \"masked_embedding = masking_layer(unmasked_embedding)\\n\",\n \"print(masked_embedding._keras_mask)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"mark是带有2D布尔张量(batch_size, sequence_length)，其中每个单独的False指示在处理过程中应忽略相应的时间步数据。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 函数API和序列API中使用掩码\\n\",\n \"\\n\",\n \"使用函数式API或序列API时，由Embedding或Masking层生成的掩码将通过网络传播到能够使用它们的任何层（例如RNN层）。Keras将自动获取与输入相对应的掩码，并将其传递给知道如何使用该掩码的任何层。\\n\",\n \"\\n\",\n \"请注意，在子类化模型或图层的call方法中，掩码不会自动传播，因此将需要手动将mask参数传递给需要的图层。\\n\",\n \"\\n\",\n \"下面展示了LSTM层怎么自动接受掩码\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 序列式API\\n\",\n \"model = tf.keras.Sequential([\\n\",\n \" layers.Embedding(input_dim=5000, output_dim=16, mask_zero=True),\\n\",\n \" layers.LSTM(32),\\n\",\n \"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 函数式API\\n\",\n \"inputs = tf.keras.Input(shape=(None,), dtype='int32')\\n\",\n \"x = layers.Embedding(input_dim=5000, output_dim=16, mask_zero=True)(inputs)\\n\",\n \"outputs = layers.LSTM(32)(x)\\n\",\n \"\\n\",\n \"model = tf.keras.Model(inputs, outputs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 也可以直接在层间传递掩码参数\\n\",\n \"\\n\",\n \"可以处理掩码的图层（例如LSTM图层）在其图层中的__call__方法中有一个mask参数。\\n\",\n \"\\n\",\n \"同时，产生掩码的图层（例如Embedding）会公开compute_mask(input, previous_mask)，以获取掩码。\\n\",\n \"\\n\",\n \"因此，可以执行以下操作：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"class MyLayer(layers.Layer):\\n\",\n \" \\n\",\n \" def __init__(self, **kwargs):\\n\",\n \" super(MyLayer, self).__init__(**kwargs)\\n\",\n \" self.embedding = layers.Embedding(input_dim=5000, output_dim=16, mask_zero=True)\\n\",\n \" self.lstm = layers.LSTM(32)\\n\",\n \"\\n\",\n \" def call(self, inputs):\\n\",\n \" x = self.embedding(inputs)\\n\",\n \" # 也可手动构造掩码\\n\",\n \" mask = self.embedding.compute_mask(inputs)\\n\",\n \" output = self.lstm(x, mask=mask) # The layer will ignore the masked values\\n\",\n \" return output\\n\",\n \"\\n\",\n \"layer = MyLayer()\\n\",\n \"x = np.random.random((32, 10)) * 100\\n\",\n \"x = x.astype('int32')\\n\",\n \"layer(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 在自定义网络层中支持掩码\\n\",\n \"有时，可能需要编写生成掩码的图层（例如Embedding），或需要修改当前掩码的图层。\\n\",\n \"\\n\",\n \"例如，产生张量的时间维度与其输入不同的任何层，都需要修改当前掩码，以便下游层能够适当考虑掩码的时间步长。\\n\",\n \"\\n\",\n \"为此，自建网络层应实现layer.compute_mask()方法，该方法将根据输入和当前掩码生成一个新的掩码。\\n\",\n \"\\n\",\n \"大多数图层都不会修改时间维度，因此无需担心掩盖。compute_mask()在这种情况下，默认行为是仅通过当前蒙版。\\n\",\n \"\\n\",\n \"#### TemporalSplit修改当前蒙版的图层的示例。\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[ True True True]\\n\",\n \" [ True True True]\\n\",\n \" [ True True True]], shape=(3, 3), dtype=bool)\\n\",\n \"tf.Tensor(\\n\",\n \"[[ True True True]\\n\",\n \" [ True True False]\\n\",\n \" [False False False]], shape=(3, 3), dtype=bool)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 数据拆分时的掩码\\n\",\n \"class TemporalSplit(tf.keras.layers.Layer):\\n\",\n \"\\n\",\n \" def call(self, inputs):\\n\",\n \" # 将数据沿时间维度一分为二\\n\",\n \" return tf.split(inputs, 2, axis=1)\\n\",\n \"\\n\",\n \" def compute_mask(self, inputs, mask=None):\\n\",\n \" # 将掩码也一分为二\\n\",\n \" if mask is None:\\n\",\n \" return None\\n\",\n \" return tf.split(mask, 2, axis=1)\\n\",\n \"\\n\",\n \"first_half, second_half = TemporalSplit()(masked_embedding)\\n\",\n \"print(first_half._keras_mask)\\n\",\n \"print(second_half._keras_mask)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"自己构建掩码，把为０的掩掉\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[ True True True True True True True True False True]\\n\",\n \" [ True True True True True True True False False False]\\n\",\n \" [False True True True True True True True True True]], shape=(3, 10), dtype=bool)\\n\"\n ]\n }\n ],\n \"source\": [\n \"class CustomEmbedding(tf.keras.layers.Layer):\\n\",\n \" \\n\",\n \" def __init__(self, input_dim, output_dim, mask_zero=False, **kwargs):\\n\",\n \" super(CustomEmbedding, self).__init__(**kwargs)\\n\",\n \" self.input_dim = input_dim\\n\",\n \" self.output_dim = output_dim\\n\",\n \" self.mask_zero = mask_zero\\n\",\n \"\\n\",\n \" def build(self, input_shape):\\n\",\n \" self.embeddings = self.add_weight(\\n\",\n \" shape=(self.input_dim, self.output_dim),\\n\",\n \" initializer='random_normal',\\n\",\n \" dtype='float32')\\n\",\n \"\\n\",\n \" def call(self, inputs):\\n\",\n \" return tf.nn.embedding_lookup(self.embeddings, inputs)\\n\",\n \"\\n\",\n \" def compute_mask(self, inputs, mask=None):\\n\",\n \" if not self.mask_zero:\\n\",\n \" return None\\n\",\n \" return tf.not_equal(inputs, 0)\\n\",\n \"\\n\",\n \"layer = CustomEmbedding(10, 32, mask_zero=True)\\n\",\n \"x = np.random.random((3, 10)) * 9\\n\",\n \"x = x.astype('int32')\\n\",\n \"\\n\",\n \"y = layer(x)\\n\",\n \"mask = layer.compute_mask(x)\\n\",\n \"\\n\",\n \"print(mask)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"直接用网络层实现掩码功能：call函数中接受一个mask参数，并用它来确定是否跳过某些时间步。\\n\",\n \"\\n\",\n \"要编写这样的层，只需在call函数中添加一个mask=None参数即可。只要有可用的输入，与输入关联的掩码将被传递到图层。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class MaskConsumer(tf.keras.layers.Layer):\\n\",\n \" \\n\",\n \" def call(self, inputs, mask=None):\\n\",\n \" pass\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "007-Ragged tensors.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-不规则向量\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/doit/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n \" from ._conv import register_converters as _register_converters\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function, unicode_literals\\n\",\n \"\\n\",\n \"import math\\n\",\n \"import tensorflow as tf\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"数据有多种形式。张量也应该如此。 不规则的张量是嵌套可变长度列表的TensorFlow等效项。它们使存储和处理形状不均匀的数据变得容易，包括：\\n\",\n \"\\n\",\n \"- 可变长度功能，例如电影中的一组演员。\\n\",\n \"- 成批的可变长度顺序输入，例如句子或视频剪辑。\\n\",\n \"- 分层输入，例如细分为小节，段落，句子和单词的文本文档。\\n\",\n \"- 结构化输入中的各个字段，例如协议缓冲区。\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1 不规则张量的作用\\n\",\n \"不规则张量受一百多个TensorFlow操作的支持，其中包括数学操作（如tf.add和tf.reduce_mean），数组操作（如tf.concat和tf.tile），字符串操作op（如 tf.substr）以及许多其他功能：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"tf.Tensor([2.25 nan 5.33333333 6. nan], shape=(5,), dtype=float64)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"digits = tf.ragged.constant([[3,1,4,1], [], [5,9,2], [6],[]])\\n\",\n \"words = tf.ragged.constant([[\\\"So\\\", \\\"long\\\"], [\\\"thanks\\\", \\\"for\\\", \\\"all\\\", \\\"the\\\", \\\"fish\\\"]])\\n\",\n \"print(tf.add(digits, 3))\\n\",\n \"print(tf.reduce_mean(digits, axis=1))\\n\",\n \"print(tf.concat([digits, [[5, 3]]], axis=0))\\n\",\n \"print(tf.tile(digits, [1, 2]))\\n\",\n \"print(tf.strings.substr(words, 0, 2))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "007-Tensor.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-张量\\n\",\n \"\\n\",\n \"由命名可知，TensorFlow是一个定义和运算涉及张量的计算的框架。TensorFlow将张量表示为基本数据类型的n维数组。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"tf.Tensor具有以下属性：\\n\",\n \"- 数据类型（float32, int32, string等）\\n\",\n \"- 形状\\n\",\n \"\\n\",\n \"一个张量中的每个元素都是一样的数据类型，且数据类型已知。形状可能已知也可能部分已知。某些情况下，只有执行图时才知道张量的形状。\\n\",\n \"\\n\",\n \"一些特殊的张量：\\n\",\n \"- tf.Variable\\n\",\n \"- tf.constant\\n\",\n \"- tf.placeholder\\n\",\n \"- tf.SparseTensor\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1 Rank\\n\",\n \"rank是tf.Tensor对象的尺寸的数量。类似于order,degree或ｎ维。TensorFlow中的rank和数据中矩阵的秩不同。如下表所示，TensorFlow每个rank对应一个不同的数学实体。\\n\",\n \"\\n\",\n \"| 秩\\t| 数学实体 |\\n\",\n \"| ---- | ---- |\\n\",\n \"| 0\\t| 标量（仅幅度） |\\n\",\n \"| 1\\t| 向量（大小和方向） |\\n\",\n \"| 2\\t| 矩阵（数字表） |\\n\",\n \"| 3\\t| 3维张量（数字的立方） |\\n\",\n \"| ñ\\t| n维张量 |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.1 rank 0\\n\",\n \"rank0张量创建\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"mammal = tf.Variable('Hongmeng', tf.string)\\n\",\n \"ignition = tf.Variable(360, tf.int16)\\n\",\n \"floating = tf.Variable(3.141592653, tf.float64)\\n\",\n \"its_complicated = tf.Variable(12.3 - 4.56j, tf.complex64)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"注：在TensorFlow中，字符串被视为单个对象，而不是字符序列。可能有标量字符串，字符串向量等。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.2 rank 1\\n\",\n \"要创建rank 1的tf.Tensor对象，一般传递列表作为初始值\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mystr = tf.Variable([\\\"Hello\\\"], tf.string)\\n\",\n \"cool_numbers = tf.Variable([3.14159, 2.71828], tf.float32)\\n\",\n \"first_primes = tf.Variable([2, 3, 5, 7, 11], tf.int32)\\n\",\n \"its_very_complicated = tf.Variable([12.3 - 4.85j, 7.5 - 6.23j], tf.complex64)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.3 rank 2\\n\",\n \"rank 2包含行跟列\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mymat = tf.Variable([[7],[11]], tf.int16)\\n\",\n \"myxor = tf.Variable([[False, True],[True, False]], tf.bool)\\n\",\n \"linear_squares = tf.Variable([[4], [9], [16], [25]], tf.int32)\\n\",\n \"squarish_squares = tf.Variable([ [4, 9], [16, 25] ], tf.int32)\\n\",\n \"rank_of_squares = tf.rank(squarish_squares)\\n\",\n \"mymatC = tf.Variable([[7],[11]], tf.int32)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.4 高阶张量\\n\",\n \"高阶张量由n维数组组成。例如，在图像处理期间，使用了许多等级4的张量，其尺寸对应于批中示例，图像高度，图像宽度和颜色通道。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"my_image = tf.zeros([10, 299, 299, 3]) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.5 获取tf.Tensor对象的rank\\n\",\n \"要确定tf.Tensor对象的rank，可以调用tf.rank方法。例如，以下方法以编程方式确定\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(4, shape=(), dtype=int32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"r = tf.rank(my_image)\\n\",\n \"print(r)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.6 tf.Tensor切片\\n\",\n \"\\n\",\n \"由于 tf.Tensor是n维数组，因此要访问其中的某个元素，需要指定n个索引。\\n\",\n \"\\n\",\n \"对于等级0的张量（标量），不需要索引，因为它已经是一个数字。\\n\",\n \"\\n\",\n \"对于秩为1的张量（向量），传递单个索引可访问数字：\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"my_scalar = first_primes[2]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"对于2级或更高的张量，情况更加有趣。对于 tf.Tensor的rank为2的，传递两个数字将按预期返回标量：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"my_scalar = mymat[1, 0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"但是，传递单个数字将返回矩阵的子向量\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"my_row_vector = mymat[1]\\n\",\n \"my_column_vector = mymat[:, 0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2 形状\\n\",\n \"张量的形状是每个维度中元素的数量。TensorFlow在图形构建过程中自动推断形状。这些推断的形状可能具有已知或未知的等级。如果rank是已知的，则每个维度的大小可能是已知的或未知的。\\n\",\n \"\\n\",\n \"TensorFlow文档使用三种符号约定来描述张量维数：rank，形状和维数。下表显示了它们之间的相互关系：\\n\",\n \"\\n\",\n \"|rank\\t|形状\\t|维数\\t|例子|\\n\",\n \"|---- |---- |---- |---- |\\n\",\n \"|0\\t|[]\\t|0维\\t|0维张量。标量。|\\n\",\n \"|1\\t|[D0]\\t|一维\\t|形状为[5]的一维张量。|\\n\",\n \"|2\\t|[D0，D1]\\t|2维\\t|形状为[3，4]的二维张量。|\\n\",\n \"|3\\t|[D0，D1，D2]\\t|3维\\t|形状为[1、4、3]的3-D张量。|\\n\",\n \"|ñ\\t|[D0，D1，... Dn-1]\\t|D维\\t|形状为[D0，D1，... Dn-1]的张量。|\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 获取tf.Tensor对象的形状\\n\",\n \"有两种访问形状tf.Tensor的方法。在构建图形时，询问关于张量形状的已知知识通常很有用。\\n\",\n \"- 1、可以通过读取对象的shape属性来获取tf.Tensor。此方法返回一个TensorShape对象，这是表示部分指定的形状的便捷方式（因为在构建图形时，并非所有形状都将是完全已知的）。\\n\",\n \"\\n\",\n \"- 2、也有可能在运行时获得一个代表某个张量的形状的tf.Tensor。这是通过调用tf.shape 操作来完成的。这样，可以通过构建依赖于输入的动态形状的其他张量来完成网络构建。\\n\",\n \"\\n\",\n \"例如，下面是如何制作一个零向量，其大小与给定矩阵中的列数相同：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"zeros = tf.zeros(mymat.shape[1])\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 改变tf.Tensor的形状\\n\",\n \"可以通过tf.reshape来改变张量的形状，只需确保所有形状的尺寸的乘积相同\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rank_three_tensor = tf.ones([3, 4, 5])\\n\",\n \"matrix = tf.reshape(rank_three_tensor, [6, 10]) \\n\",\n \"matrixB = tf.reshape(matrix, [3, -1]) .\\n\",\n \"matrixAlt = tf.reshape(matrixB, [4, 3, -1]) \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3 数据类型\\n\",\n \"除维数外，张量还具有数据类型。\\n\",\n \"\\n\",\n \"具有一个以上的数据类型是不可能的。但是，可以将任意数据结构序列化为strings并将其存储在tf.Tensors中。\\n\",\n \"\\n\",\n \"可以使用tf.cast命令将tf.Tensors从一种数据类型转换为另一种数据类型：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"float_tensor = tf.cast(tf.constant([1, 2, 3]), dtype=tf.float32)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"要检查tf.Tensor的数据类型，请使用Tensor.dtype属性。\\n\",\n \"\\n\",\n \"tf.Tensor从python对象创建时，可以选择指定数据类型。如果不设置，TensorFlow会选择一种可以代表数据的数据类型。TensorFlow将Python整数转换为tf.int32，并将python浮点数转换为tf.float32。而在将numpy转换为数组时使用的相同规则。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4 打印张量\\n\",\n \"\\n\",\n \"出于调试目的，可能需要打印tf.Tensor的值。TensorFlow2中可以直接用print打印\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(7, shape=(), dtype=int32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = tf.add(3, 4)\\n\",\n \"print(t)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "007-Variables.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-Variables\\n\",\n \"\\n\",\n \"TensorFlow 变量是表示程序所控制的共享持久状态的最佳方法。\\n\",\n \"\\n\",\n \"变量通过tf.Variable类进行操作。tf.Variable 表示张量，其值可以通过在其上运行ops进行更改。特定操作允许您读取和修改此张量的值。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1 创建一个变量\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/doit/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n \" from ._conv import register_converters as _register_converters\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"no gpu\\n\"\n ]\n }\n ],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"# 要创建变量，只需提供初始值\\n\",\n \"my_var = tf.Variable(tf.ones([2,3]))\\n\",\n \"print(my_var)\\n\",\n \"#如果tf.device作用域处于活动状态，则变量将被放置在该设备上；否则，变量将被放置在与其dtype兼容的“最快”设备上\\n\",\n \"try:\\n\",\n \" with tf.device(\\\"/device:GPU:0\\\"):\\n\",\n \" v = tf.Variable(tf.zeros([10, 10]))\\n\",\n \" print(v)\\n\",\n \"except:\\n\",\n \" print('no gpu')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2 使用变量\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"tf.Tensor(9.0, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 变量相加\\n\",\n \"a = tf.Variable(1.0)\\n\",\n \"b = (a+2) *3\\n\",\n \"print(a)\\n\",\n \"print(b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"tf.Tensor(9.0, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 更改某个向量\\n\",\n \"a = tf.Variable(1.0)\\n\",\n \"b = (a.assign_add(2)) *3\\n\",\n \"print(a)\\n\",\n \"print(b)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 3 变量追踪\\n\",\n \"TensorFlow中的变量是一个Python对象。在构建图层，模型，优化器和其他相关工具时，您可能希望获取（假设）模型中所有变量的列表。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(, , , , , , , , , , , )\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"12\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"class MyModuleOne(tf.Module):\\n\",\n \" def __init__(self):\\n\",\n \" self.v0 = tf.Variable(1.0)\\n\",\n \" self.vs = [tf.Variable(x) for x in range(10)]\\n\",\n \" \\n\",\n \"class MyOtherModule(tf.Module):\\n\",\n \" def __init__(self):\\n\",\n \" self.m = MyModuleOne()\\n\",\n \" self.v = tf.Variable(10.0)\\n\",\n \" \\n\",\n \"m = MyOtherModule()\\n\",\n \"print(m.variables) # 模型中的所有变量\\n\",\n \"len(m.variables) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "008-AutoGraph.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2.0教程-AutoGraph\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"tf.function的一个很酷的新功能是AutoGraph，它允许使用自然的Python语法编写图形代码。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"import numpy as np\\n\",\n \"import tensorflow as tf\\n\",\n \"from tensorflow.python.ops import control_flow_util\\n\",\n \"control_flow_util.ENABLE_CONTROL_FLOW_V2 = True\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.tf.function装饰器\\n\",\n \"当使用tf.function注释函数时，可以像调用任何其他函数一样调用它。 \\n\",\n \"它将被编译成图，这意味着可以获得更快执行，更好地在GPU或TPU上运行或导出到SavedModel。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"@tf.function\\n\",\n \"def simple_nn_layer(x, y):\\n\",\n \" return tf.nn.relu(tf.matmul(x, y))\\n\",\n \"\\n\",\n \"\\n\",\n \"x = tf.random.uniform((3, 3))\\n\",\n \"y = tf.random.uniform((3, 3))\\n\",\n \"\\n\",\n \"simple_nn_layer(x, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"如果我们检查注释的结果，我们可以看到它是一个特殊的可调用函数，它处理与TensorFlow运行时的所有交互。\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"simple_nn_layer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"如果代码使用多个函数，则无需对它们进行全部注释 \\n\",\n \"- 从带注释函数调用的任何函数也将以图形模式运行。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"def linear_layer(x):\\n\",\n \" return 2 * x + 1\\n\",\n \"\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def deep_net(x):\\n\",\n \" return tf.nn.relu(linear_layer(x))\\n\",\n \"\\n\",\n \"\\n\",\n \"deep_net(tf.constant((1, 2, 3)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.使用Python控制流程\\n\",\n \"在tf.function中使用依赖于数据的控制流时，可以使用Python控制流语句，AutoGraph会将它们转换为适当的TensorFlow操作。 例如，如果语句依赖于Tensor，则语句将转换为tf.cond（）。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"square_if_positive(2) = 4\\n\",\n \"square_if_positive(-2) = 0\\n\"\n ]\n }\n ],\n \"source\": [\n \"@tf.function\\n\",\n \"def square_if_positive(x):\\n\",\n \" if x > 0:\\n\",\n \" x = x * x\\n\",\n \" else:\\n\",\n \" x = 0\\n\",\n \" return x\\n\",\n \"\\n\",\n \"\\n\",\n \"print('square_if_positive(2) = {}'.format(square_if_positive(tf.constant(2))))\\n\",\n \"print('square_if_positive(-2) = {}'.format(square_if_positive(tf.constant(-2))))\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"AutoGraph支持常见的Python语句，例如while，if，break，continue和return，支持嵌套。 这意味着可以在while和if语句的条件下使用Tensor表达式，或者在for循环中迭代Tensor。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"@tf.function\\n\",\n \"def sum_even(items):\\n\",\n \" s = 0\\n\",\n \" for c in items:\\n\",\n \" if c % 2 > 0:\\n\",\n \" continue\\n\",\n \" s += c\\n\",\n \" return s\\n\",\n \"\\n\",\n \"\\n\",\n \"sum_even(tf.constant([10, 12, 15, 20]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"AutoGraph还为高级用户提供了低级API。 例如，我们可以使用它来查看生成的代码。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"from __future__ import print_function\\n\",\n \"\\n\",\n \"def tf__sum_even(items):\\n\",\n \" do_return = False\\n\",\n \" retval_ = None\\n\",\n \" s = 0\\n\",\n \"\\n\",\n \" def loop_body(loop_vars, s_2):\\n\",\n \" c = loop_vars\\n\",\n \" continue_ = False\\n\",\n \" cond = c % 2 > 0\\n\",\n \"\\n\",\n \" def if_true():\\n\",\n \" continue_ = True\\n\",\n \" return continue_\\n\",\n \"\\n\",\n \" def if_false():\\n\",\n \" return continue_\\n\",\n \" continue_ = ag__.if_stmt(cond, if_true, if_false)\\n\",\n \" cond_1 = ag__.not_(continue_)\\n\",\n \"\\n\",\n \" def if_true_1():\\n\",\n \" s_1, = s_2,\\n\",\n \" s_1 += c\\n\",\n \" return s_1\\n\",\n \"\\n\",\n \" def if_false_1():\\n\",\n \" return s_2\\n\",\n \" s_2 = ag__.if_stmt(cond_1, if_true_1, if_false_1)\\n\",\n \" return s_2,\\n\",\n \" s, = ag__.for_stmt(items, None, loop_body, (s,))\\n\",\n \" do_return = True\\n\",\n \" retval_ = s\\n\",\n \" return retval_\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"tf__sum_even.autograph_info__ = {}\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(tf.autograph.to_code(sum_even.python_function, experimental_optional_features=None))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"一个更复杂的控制流程的例子：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Fizz\\n\",\n \"1\\n\",\n \"2\\n\",\n \"Fizz\\n\",\n \"4\\n\",\n \"Buzz\\n\",\n \"Fizz\\n\",\n \"7\\n\",\n \"8\\n\",\n \"Fizz\\n\",\n \"Buzz\\n\",\n \"11\\n\",\n \"Fizz\\n\",\n \"13\\n\",\n \"14\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"@tf.function\\n\",\n \"def fizzbuzz(n):\\n\",\n \" msg = tf.constant('')\\n\",\n \" for i in tf.range(n):\\n\",\n \" if tf.equal(i % 3, 0):\\n\",\n \" msg += 'Fizz'\\n\",\n \" elif tf.equal(i % 5, 0):\\n\",\n \" msg += 'Buzz'\\n\",\n \" else:\\n\",\n \" msg += tf.as_string(i)\\n\",\n \" msg += '\\\\n'\\n\",\n \" return msg\\n\",\n \"\\n\",\n \"\\n\",\n \"print(fizzbuzz(tf.constant(15)).numpy().decode())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.Keras和AutoGraph\\n\",\n \"也可以将tf.function与对象方法一起使用。 例如，可以通过注释模型的调用函数来装饰自定义Keras模型。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"class CustomModel(tf.keras.models.Model):\\n\",\n \"\\n\",\n \" @tf.function\\n\",\n \" def call(self, input_data):\\n\",\n \" if tf.reduce_mean(input_data) > 0:\\n\",\n \" return input_data\\n\",\n \" else:\\n\",\n \" return input_data // 2\\n\",\n \"\\n\",\n \"\\n\",\n \"model = CustomModel()\\n\",\n \"\\n\",\n \"model(tf.constant([-2, -4]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"副作用\\n\",\n \"就像在eager模式下一样，你可以使用带有副作用的操作，比如通常在tf.function中的tf.assign或tf.print，它会插入必要的控件依赖项以确保它们按顺序执行。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"v = tf.Variable(5)\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def find_next_odd():\\n\",\n \" v.assign(v + 1)\\n\",\n \" if tf.equal(v % 2, 0):\\n\",\n \" v.assign(v + 1)\\n\",\n \"\\n\",\n \"\\n\",\n \"find_next_odd()\\n\",\n \"v\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.用AutoGraph训练一个简单模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Step 10 : loss 1.85892391 ; accuracy 0.37\\n\",\n \"Step 20 : loss 1.25817835 ; accuracy 0.5125\\n\",\n \"Step 30 : loss 0.871798933 ; accuracy 0.605666637\\n\",\n \"Step 40 : loss 0.669722676 ; accuracy 0.66\\n\",\n \"Step 50 : loss 0.413253099 ; accuracy 0.6976\\n\",\n \"Step 60 : loss 0.340167761 ; accuracy 0.728\\n\",\n \"Step 70 : loss 0.438654482 ; accuracy 0.748428583\\n\",\n \"Step 80 : loss 0.346158206 ; accuracy 0.766875\\n\",\n \"Step 90 : loss 0.241747677 ; accuracy 0.780555546\\n\",\n \"Step 100 : loss 0.229299903 ; accuracy 0.7935\\n\",\n \"Step 110 : loss 0.300931275 ; accuracy 0.803727269\\n\",\n \"Step 120 : loss 0.369899929 ; accuracy 0.812583327\\n\",\n \"Step 130 : loss 0.305111647 ; accuracy 0.82\\n\",\n \"Step 140 : loss 0.396656752 ; accuracy 0.825857162\\n\",\n \"Step 150 : loss 0.308267832 ; accuracy 0.831266642\\n\",\n \"Step 160 : loss 0.323994666 ; accuracy 0.836312473\\n\",\n \"Step 170 : loss 0.264144391 ; accuracy 0.840941191\\n\",\n \"Step 180 : loss 0.450227708 ; accuracy 0.844333351\\n\",\n \"Step 190 : loss 0.213473886 ; accuracy 0.848105252\\n\",\n \"Step 200 : loss 0.224886 ; accuracy 0.85145\\n\",\n \"Final step tf.Tensor(200, shape=(), dtype=int32) : loss tf.Tensor(0.224886, shape=(), dtype=float32) ; accuracy tf.Tensor(0.85145, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"def prepare_mnist_features_and_labels(x, y):\\n\",\n \" x = tf.cast(x, tf.float32) / 255.0\\n\",\n \" y = tf.cast(y, tf.int64)\\n\",\n \" return x, y\\n\",\n \"\\n\",\n \"def mnist_dataset():\\n\",\n \" (x, y), _ = tf.keras.datasets.mnist.load_data()\\n\",\n \" ds = tf.data.Dataset.from_tensor_slices((x, y))\\n\",\n \" ds = ds.map(prepare_mnist_features_and_labels)\\n\",\n \" ds = ds.take(20000).shuffle(20000).batch(100)\\n\",\n \" return ds\\n\",\n \"\\n\",\n \"train_dataset = mnist_dataset()\\n\",\n \"model = tf.keras.Sequential((\\n\",\n \" tf.keras.layers.Reshape(target_shape=(28 * 28,), input_shape=(28, 28)),\\n\",\n \" tf.keras.layers.Dense(100, activation='relu'),\\n\",\n \" tf.keras.layers.Dense(100, activation='relu'),\\n\",\n \" tf.keras.layers.Dense(10)))\\n\",\n \"model.build()\\n\",\n \"optimizer = tf.keras.optimizers.Adam()\\n\",\n \"compute_loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\\n\",\n \"\\n\",\n \"compute_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()\\n\",\n \"\\n\",\n \"\\n\",\n \"def train_one_step(model, optimizer, x, y):\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" logits = model(x)\\n\",\n \" loss = compute_loss(y, logits)\\n\",\n \"\\n\",\n \" grads = tape.gradient(loss, model.trainable_variables)\\n\",\n \" optimizer.apply_gradients(zip(grads, model.trainable_variables))\\n\",\n \"\\n\",\n \" compute_accuracy(y, logits)\\n\",\n \" return loss\\n\",\n \"\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def train(model, optimizer):\\n\",\n \" train_ds = mnist_dataset()\\n\",\n \" step = 0\\n\",\n \" loss = 0.0\\n\",\n \" accuracy = 0.0\\n\",\n \" for x, y in train_ds:\\n\",\n \" step += 1\\n\",\n \" loss = train_one_step(model, optimizer, x, y)\\n\",\n \" if tf.equal(step % 10, 0):\\n\",\n \" tf.print('Step', step, ': loss', loss, '; accuracy', compute_accuracy.result())\\n\",\n \" return step, loss, accuracy\\n\",\n \"\\n\",\n \"step, loss, accuracy = train(model, optimizer)\\n\",\n \"print('Final step', step, ': loss', loss, '; accuracy', compute_accuracy.result())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5.关于批处理的说明\\n\",\n \"在实际应用中，批处理对性能至关重要。 转换为AutoGraph的最佳代码是在批处理级别决定控制流的代码。 如果在单个示例级别做出决策，请尝试使用批处理API来维护性能。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[-5, -4, -3, -2, -1, 0, 1, 4, 9, 16]\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"def square_if_positive(x):\\n\",\n \" return [i ** 2 if i > 0 else i for i in x]\\n\",\n \"\\n\",\n \"\\n\",\n \"square_if_positive(range(-5, 5))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 在tensorflow中上面的代码应该改成下面所示\\n\",\n \"@tf.function\\n\",\n \"def square_if_positive_naive(x):\\n\",\n \" result = tf.TensorArray(tf.int32, size=x.shape[0])\\n\",\n \" for i in tf.range(x.shape[0]):\\n\",\n \" if x[i] > 0:\\n\",\n \" result = result.write(i, x[i] ** 2)\\n\",\n \" else:\\n\",\n \" result = result.write(i, x[i])\\n\",\n \" return result.stack()\\n\",\n \"\\n\",\n \"\\n\",\n \"square_if_positive_naive(tf.range(-5, 5))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 也可以怎么写\\n\",\n \"def square_if_positive_vectorized(x):\\n\",\n \" return tf.where(x > 0, x ** 2, x)\\n\",\n \"\\n\",\n \"\\n\",\n \"square_if_positive_vectorized(tf.range(-5, 5))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "020-Eager/001-Tensor_and_operations.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2.0教程-张量极其操作\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 导入TensorFlow\\n\",\n \"运行tensorflow程序，需要导入tensorflow模块。\\n\",\n \"从TensorFlow 2.0开始，默认情况下会启用急切执行。 这为TensorFlow提供了一个更加互动的前端节。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"import tensorflow as tf\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1 Tensors\\n\",\n \"张量是一个多维数组。 与NumPy ndarray对象类似，tf.Tensor对象具有数据类型和形状。 此外，tf.Tensors可以驻留在加速器内存中（如GPU）。 TensorFlow提供了丰富的操作库（tf.add，tf.matmul，tf.linalg.inv等），它们使用和生成tf.Tensors。 这些操作会自动转换原生Python类型，例如：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(3, shape=(), dtype=int32)\\n\",\n \"tf.Tensor([ 5 13], shape=(2,), dtype=int32)\\n\",\n \"tf.Tensor(36, shape=(), dtype=int32)\\n\",\n \"tf.Tensor(24, shape=(), dtype=int32)\\n\",\n \"tf.Tensor(25, shape=(), dtype=int32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(tf.add(1,2))\\n\",\n \"print(tf.add([3,8], [2,5]))\\n\",\n \"print(tf.square(6))\\n\",\n \"print(tf.reduce_sum([7,8,9]))\\n\",\n \"print(tf.square(3)+tf.square(4))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"每个Tensor都有形状和类型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[ 6]\\n\",\n \" [12]], shape=(2, 1), dtype=int32)\\n\",\n \"(2, 1)\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = tf.matmul([[3], [6]], [[2]])\\n\",\n \"print(x)\\n\",\n \"print(x.shape)\\n\",\n \"print(x.dtype)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"NumPy数组和tf.Tensors之间最明显的区别是：\\n\",\n \"\\n\",\n \"张量可以由加速器内存（如GPU，TPU）支持。\\n\",\n \"张量是不可变的。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"NumPy兼容性\\n\",\n \"在TensorFlow tf.Tensors和NumPy ndarray之间转换很容易：\\n\",\n \"\\n\",\n \"TensorFlow操作自动将NumPy ndarrays转换为Tensors。\\n\",\n \"NumPy操作自动将Tensors转换为NumPy ndarrays。\\n\",\n \"使用.numpy（）方法将张量显式转换为NumPy ndarrays。 这些转换通常很容易的，因为如果可能，array和tf.Tensor共享底层内存表示。 但是，共享底层表示并不总是可行的，因为tf.Tensor可以托管在GPU内存中，而NumPy阵列总是由主机内存支持，并且转换涉及从GPU到主机内存的复制。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[36. 36.]\\n\",\n \" [36. 36.]], shape=(2, 2), dtype=float64)\\n\",\n \"[[37. 37.]\\n\",\n \" [37. 37.]]\\n\",\n \"[[36. 36.]\\n\",\n \" [36. 36.]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"ndarray = np.ones([2,2])\\n\",\n \"tensor = tf.multiply(ndarray, 36)\\n\",\n \"print(tensor)\\n\",\n \"# 用np.add对tensorflow进行加运算\\n\",\n \"print(np.add(tensor, 1))\\n\",\n \"# 转换为numpy类型\\n\",\n \"print(tensor.numpy())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2 GPU加速\\n\",\n \"使用GPU进行计算可以加速许多TensorFlow操作。 如果没有任何注释，TensorFlow会自动决定是使用GPU还是CPU进行操作 - 如有必要，可以复制CPU和GPU内存之间的张量。 由操作产生的张量通常由执行操作的设备的存储器支持，例如：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Is GPU availabel:\\n\",\n \"False\\n\",\n \"Is the Tensor on gpu #0:\\n\",\n \"False\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = tf.random.uniform([3, 3])\\n\",\n \"print('Is GPU availabel:')\\n\",\n \"print(tf.test.is_gpu_available())\\n\",\n \"print('Is the Tensor on gpu #0:')\\n\",\n \"print(x.device.endswith('GPU:0'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**设备名称**\\n\",\n \"\\n\",\n \"Tensor.device属性提供托管张量内容的设备的完全限定字符串名称。 此名称编码许多详细信息，例如正在执行此程序的主机的网络地址的标识符以及该主机中的设备。 这是分布式执行TensorFlow程序所必需的。 如果张量位于主机上的第N个GPU上，则字符串以GPU结尾：。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**显式设备放置(Placement)**\\n\",\n \"\\n\",\n \"在TensorFlow中，放置指的是如何分配（放置）设备以执行各个操作。 如上所述，如果没有提供明确的指导，TensorFlow会自动决定执行操作的设备，并在需要时将张量复制到该设备。 但是，可以使用tf.device上下文管理器将TensorFlow操作显式放置在特定设备上，例如：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"On CPU:\\n\",\n \"10 loops: 1.2e+02ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"import time\\n\",\n \"def time_matmul(x):\\n\",\n \" start = time.time()\\n\",\n \" for loop in range(10):\\n\",\n \" tf.matmul(x, x)\\n\",\n \" result = time.time() - start\\n\",\n \" print('10 loops: {:0.2}ms'.format(1000*result))\\n\",\n \" \\n\",\n \"# 强制使用CPU\\n\",\n \"print('On CPU:')\\n\",\n \"with tf.device('CPU:0'):\\n\",\n \" x = tf.random.uniform([1000, 1000])\\n\",\n \" # 使用断言验证当前是否为CPU0\\n\",\n \" assert x.device.endswith('CPU:0')\\n\",\n \" time_matmul(x) \\n\",\n \"\\n\",\n \"# 如果存在GPU,强制使用GPU\\n\",\n \"if tf.test.is_gpu_available():\\n\",\n \" print('On GPU:')\\n\",\n \" with tf.device.endswith('GPU:0'):\\n\",\n \" x = tf.random.uniform([1000, 1000])\\n\",\n \" # 使用断言验证当前是否为GPU0\\n\",\n \" assert x.device.endswith('GPU:0')\\n\",\n \" time_matmul(x) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3 数据集\\n\",\n \"本节使用tf.data.Dataset API构建管道，以便为模型提供数据。 tf.data.Dataset API用于从简单，可重复使用的部分构建高性能，复杂的输入管道，这些部分将为模型的培训或评估循环提供支持。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**创建源数据集**\\n\",\n \"使用其中一个工厂函数（如Dataset.from_tensors，Dataset.from_tensor_slices）或使用从TextLineDataset或TFRecordDataset等文件读取的对象创建源数据集。 有关详细信息，请参阅TensorFlow数据集指南。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/tmp/tmpvl0kyn0w\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 从列表中获取tensor\\n\",\n \"ds_tensors = tf.data.Dataset.from_tensor_slices([6,5,4,3,2,1])\\n\",\n \"# 创建csv文件\\n\",\n \"import tempfile\\n\",\n \"_, filename = tempfile.mkstemp()\\n\",\n \"print(filename)\\n\",\n \"\\n\",\n \"with open(filename, 'w') as f:\\n\",\n \" f.write(\\\"\\\"\\\"Line 1\\n\",\n \"Line 2\\n\",\n \"Line 3\\\"\\\"\\\")\\n\",\n \"# 获取TextLineDataset数据集实例\\n\",\n \"ds_file = tf.data.TextLineDataset(filename)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**应用转换**\\n\",\n \"\\n\",\n \"使用map，batch和shuffle等转换函数将转换应用于数据集记录。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_tensors = ds_tensors.map(tf.square).shuffle(2).batch(2)\\n\",\n \"ds_file = ds_file.batch(2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**迭代**\\n\",\n \"\\n\",\n \"tf.data.Dataset对象支持迭代循环记录：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ds_tensors中的元素：\\n\",\n \"tf.Tensor([36 25], shape=(2,), dtype=int32)\\n\",\n \"tf.Tensor([16 9], shape=(2,), dtype=int32)\\n\",\n \"tf.Tensor([4 1], shape=(2,), dtype=int32)\\n\",\n \"\\n\",\n \"ds_file中的元素：\\n\",\n \"tf.Tensor([b'Line 1' b'Line 2'], shape=(2,), dtype=string)\\n\",\n \"tf.Tensor([b'Line 3'], shape=(1,), dtype=string)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('ds_tensors中的元素：')\\n\",\n \"for x in ds_tensors:\\n\",\n \" print(x)\\n\",\n \"# 从文件中读取的对象创建的数据源\\n\",\n \"print('\\\\nds_file中的元素：')\\n\",\n \"for x in ds_file:\\n\",\n \" print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "020-Eager/002-custom_layers.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"sFaB2n259LyH\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"7z5MNErn9hLk\"\n },\n \"source\": [\n \"# Tensorflow2.0教程-自定义层\\n\",\n \"\\n\",\n \"tensorflow2.0建议使用tf.keras作为构建神经网络的高级API。 也就是说，大多数TensorFlow API都可用于eager执行模式。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"3ef8wxGvJzY3\"\n },\n \"outputs\": [],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function, unicode_literals\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 107\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 45625,\n \"status\": \"ok\",\n \"timestamp\": 1559141517811,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"-jisbMBWKMsO\",\n \"outputId\": \"98edad8c-80fc-42c2-d42d-bdcae392d98a\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001b[K |████████████████████████████████| 79.9MB 1.4MB/s \\n\",\n \"\\u001b[K |████████████████████████████████| 61kB 20.4MB/s \\n\",\n \"\\u001b[K |████████████████████████████████| 3.0MB 38.8MB/s \\n\",\n \"\\u001b[K |████████████████████████████████| 419kB 46.6MB/s \\n\",\n \"\\u001b[?25h2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"!pip install -q tensorflow==2.0.0-alpha0\\n\",\n \"import tensorflow as tf\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"T85cnaZtKdPF\"\n },\n \"source\": [\n \"## 一、网络层layer的常见操作\\n\",\n \"通常机器学习模型可以表示为简单网络层的堆叠与组合，而tensorflow就提供了常见的网络层，为我们编写神经网络程序提供了便利。\\n\",\n \"TensorFlow2推荐使用tf.keras来构建网络层，tf.keras来自原生keras，用其来构建网络具有更好的可读性和易用性。\\n\",\n \"\\n\",\n \"如，我们要构造一个简单的全连接网络，只需要指定网络的神经元个数\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"-w9d8-2wKYHy\"\n },\n \"outputs\": [],\n \"source\": [\n \"layer = tf.keras.layers.Dense(100)\\n\",\n \"# 也可以添加输入维度限制\\n\",\n \"layer = tf.keras.layers.Dense(100, input_shape=(None, 20))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"szlH18xYNwgM\"\n },\n \"source\": [\n \"可以在[文档](https://www.tensorflow.org/api_docs/python/tf/keras/layers)中查看预先存在的图层的完整列表。 它包括Dense，Conv2D，LSTM，BatchNormalization，Dropout等等。\\n\",\n \"\\n\",\n \"每个层都可以当作一个函数，然后以输入的数据作为函数的输入\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"S0QZ2EN9NfGy\"\n },\n \"outputs\": [],\n \"source\": [\n \"layer(tf.ones([6, 6]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"MNSBfM3EPFFa\"\n },\n \"source\": [\n \"同时我们也可以得到网络的变量、权重矩阵、偏置等 \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 2184\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1695,\n \"status\": \"ok\",\n \"timestamp\": 1558271994963,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"m67fg_IeO-Rj\",\n \"outputId\": \"afc51859-8048-4a92-babb-c8831dfc9e09\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[, ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(layer.variables) # 包含了权重和偏置\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 2184\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1120,\n \"status\": \"ok\",\n \"timestamp\": 1558272093654,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"bf2DjO6CP6TF\",\n \"outputId\": \"ec4ed75e-e813-4226-f85a-0baab3ff4354\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" \\n\"\n ]\n }\n ],\n \"source\": [\n \"print(layer.kernel, layer.bias) # 也可以分别取出权重和偏置\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"a8gZBodZQrmJ\"\n },\n \"source\": [\n \"## 二、实现自定义网络层\\n\",\n \"实现自己的层的最佳方法是扩展tf.keras.Layer类并实现：\\n\",\n \"\\n\",\n \"- \\\\__init__()函数，你可以在其中执行所有与输入无关的初始化\\n\",\n \"\\n\",\n \"- build()函数，可以获得输入张量的形状，并可以进行其余的初始化\\n\",\n \"\\n\",\n \"- call()函数，构建网络结构，进行前向传播\\n\",\n \"\\n\",\n \"实际上，你不必等到调用build()来创建网络结构，您也可以在\\\\__init__()中创建它们。 但是，在build()中创建它们的优点是它可以根据图层将要操作的输入的形状启用后期的网络构建。 另一方面，在\\\\__init__中创建变量意味着需要明确指定创建变量所需的形状。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 437\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1064,\n \"status\": \"ok\",\n \"timestamp\": 1558273172643,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"SjQtJxARQLHY\",\n \"outputId\": \"237aa853-a405-418d-ad07-53705e1aa261\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[ 1.0200843 -0.42590106 -0.92992705 0.46160045 0.7518406 0.32543844\\n\",\n \" 0.34020287 0.08215448 0.22044104 -0.5337319 ]\\n\",\n \" [ 1.0200843 -0.42590106 -0.92992705 0.46160045 0.7518406 0.32543844\\n\",\n \" 0.34020287 0.08215448 0.22044104 -0.5337319 ]\\n\",\n \" [ 1.0200843 -0.42590106 -0.92992705 0.46160045 0.7518406 0.32543844\\n\",\n \" 0.34020287 0.08215448 0.22044104 -0.5337319 ]\\n\",\n \" [ 1.0200843 -0.42590106 -0.92992705 0.46160045 0.7518406 0.32543844\\n\",\n \" 0.34020287 0.08215448 0.22044104 -0.5337319 ]\\n\",\n \" [ 1.0200843 -0.42590106 -0.92992705 0.46160045 0.7518406 0.32543844\\n\",\n \" 0.34020287 0.08215448 0.22044104 -0.5337319 ]\\n\",\n \" [ 1.0200843 -0.42590106 -0.92992705 0.46160045 0.7518406 0.32543844\\n\",\n \" 0.34020287 0.08215448 0.22044104 -0.5337319 ]], shape=(6, 10), dtype=float32)\\n\",\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"class MyDense(tf.keras.layers.Layer):\\n\",\n \" def __init__(self, n_outputs):\\n\",\n \" super(MyDense, self).__init__()\\n\",\n \" self.n_outputs = n_outputs\\n\",\n \" \\n\",\n \" def build(self, input_shape):\\n\",\n \" self.kernel = self.add_variable('kernel',\\n\",\n \" shape=[int(input_shape[-1]),\\n\",\n \" self.n_outputs])\\n\",\n \" def call(self, input):\\n\",\n \" return tf.matmul(input, self.kernel)\\n\",\n \"layer = MyDense(10)\\n\",\n \"print(layer(tf.ones([6, 5])))\\n\",\n \"print(layer.trainable_variables)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"SOijVhx1EvdZ\"\n },\n \"source\": [\n \"## 三、网络层组合\\n\",\n \"机器学习模型中有很多是通过叠加不同的结构层组合而成的，如resnet的每个残差块就是“卷积+批标准化+残差连接”的组合。\\n\",\n \"\\n\",\n \"在tensorflow2中要创建一个包含多个网络层的的结构，一般继承与tf.keras.Model类。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 1081\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 855,\n \"status\": \"ok\",\n \"timestamp\": 1559143734067,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"xBDo-EYSUMkp\",\n \"outputId\": \"2cc12988-ea98-47b7-d6cf-2078930dcd8f\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[[[0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.83203167 0.9436392 1.0989372 1.2588525 0.8683256 1.1279813\\n\",\n \" 0.7571581 0.47963202 0.88908756]]\\n\",\n \"\\n\",\n \" [[0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.79764616 1.0550306 0.9386751 1.1079601 0.9402881 0.99479383\\n\",\n \" 0.9072118 0.5618475 0.9134829 ]\\n\",\n \" [0.83203167 0.9436392 1.0989372 1.2588525 0.8683256 1.1279813\\n\",\n \" 0.7571581 0.47963202 0.88908756]]\\n\",\n \"\\n\",\n \" [[1.0775117 1.1620466 0.72680396 1.0019443 1.2767658 1.1365149\\n\",\n \" 1.1792164 1.0868194 1.0623009 ]\\n\",\n \" [1.0775117 1.1620466 0.72680396 1.0019443 1.2767658 1.1365149\\n\",\n \" 1.1792164 1.0868194 1.0623009 ]\\n\",\n \" [1.0775117 1.1620466 0.72680396 1.0019443 1.2767658 1.1365149\\n\",\n \" 1.1792164 1.0868194 1.0623009 ]\\n\",\n \" [1.0775117 1.1620466 0.72680396 1.0019443 1.2767658 1.1365149\\n\",\n \" 1.1792164 1.0868194 1.0623009 ]\\n\",\n \" [1.0775117 1.1620466 0.72680396 1.0019443 1.2767658 1.1365149\\n\",\n \" 1.1792164 1.0868194 1.0623009 ]\\n\",\n \" [1.0775117 1.1620466 0.72680396 1.0019443 1.2767658 1.1365149\\n\",\n \" 1.1792164 1.0868194 1.0623009 ]\\n\",\n \" [1.0775117 1.1620466 0.7268039 1.0019443 1.2767658 1.1365149\\n\",\n \" 1.1792164 1.0868194 1.0623009 ]\\n\",\n \" [1.0775117 1.1620466 0.7268039 1.0019443 1.2767658 1.1365149\\n\",\n \" 1.1792164 1.0868194 1.0623009 ]\\n\",\n \" [0.87889266 0.9541194 0.8929231 0.96703756 1.0905087 1.0646607\\n\",\n \" 0.9235744 0.9829142 1.1302696 ]]]], shape=(1, 3, 9, 9), dtype=float32)\\n\",\n \"['resnet_block/conv2d_12/kernel:0', 'resnet_block/conv2d_12/bias:0', 'resnet_block/batch_normalization_v2_12/gamma:0', 'resnet_block/batch_normalization_v2_12/beta:0', 'resnet_block/conv2d_13/kernel:0', 'resnet_block/conv2d_13/bias:0', 'resnet_block/batch_normalization_v2_13/gamma:0', 'resnet_block/batch_normalization_v2_13/beta:0', 'resnet_block/conv2d_14/kernel:0', 'resnet_block/conv2d_14/bias:0', 'resnet_block/batch_normalization_v2_14/gamma:0', 'resnet_block/batch_normalization_v2_14/beta:0']\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 残差块\\n\",\n \"class ResnetBlock(tf.keras.Model):\\n\",\n \" def __init__(self, kernel_size, filters):\\n\",\n \" super(ResnetBlock, self).__init__(name='resnet_block')\\n\",\n \" \\n\",\n \" # 每个子层卷积核数\\n\",\n \" filter1, filter2, filter3 = filters\\n\",\n \" \\n\",\n \" # 三个子层，每层1个卷积加一个批正则化\\n\",\n \" # 第一个子层， 1*1的卷积\\n\",\n \" self.conv1 = tf.keras.layers.Conv2D(filter1, (1,1))\\n\",\n \" self.bn1 = tf.keras.layers.BatchNormalization()\\n\",\n \" # 第二个子层， 使用特点的kernel_size\\n\",\n \" self.conv2 = tf.keras.layers.Conv2D(filter2, kernel_size, padding='same')\\n\",\n \" self.bn2 = tf.keras.layers.BatchNormalization()\\n\",\n \" # 第三个子层，1*1卷积\\n\",\n \" self.conv3 = tf.keras.layers.Conv2D(filter3, (1,1))\\n\",\n \" self.bn3 = tf.keras.layers.BatchNormalization()\\n\",\n \" \\n\",\n \" def call(self, inputs, training=False):\\n\",\n \" \\n\",\n \" # 堆叠每个子层\\n\",\n \" x = self.conv1(inputs)\\n\",\n \" x = self.bn1(x, training=training)\\n\",\n \" \\n\",\n \" x = self.conv2(x)\\n\",\n \" x = self.bn2(x, training=training)\\n\",\n \" \\n\",\n \" x = self.conv3(x)\\n\",\n \" x = self.bn3(x, training=training)\\n\",\n \" \\n\",\n \" # 残差连接\\n\",\n \" x += inputs\\n\",\n \" outputs = tf.nn.relu(x)\\n\",\n \" \\n\",\n \" return outputs\\n\",\n \"\\n\",\n \"resnetBlock = ResnetBlock(2, [6,4,9])\\n\",\n \"# 数据测试\\n\",\n \"print(resnetBlock(tf.ones([1,3,9,9])))\\n\",\n \"# 查看网络中的变量名\\n\",\n \"print([x.name for x in resnetBlock.trainable_variables])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"jZqlHOMhOGXr\"\n },\n \"source\": [\n \"如果模型是线性的，可以直接用tf.keras.Sequential来构造。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 161\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 769,\n \"status\": \"ok\",\n \"timestamp\": 1559144265421,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"6vJ_tjNcMfof\",\n \"outputId\": \"d66e1c37-f911-475e-c08f-6500e90d791a\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"seq_model = tf.keras.Sequential(\\n\",\n \"[\\n\",\n \" tf.keras.layers.Conv2D(1, 1, input_shape=(None, None, 3)),\\n\",\n \" tf.keras.layers.BatchNormalization(),\\n\",\n \" tf.keras.layers.Conv2D(2, 1, padding='same'),\\n\",\n \" tf.keras.layers.BatchNormalization(),\\n\",\n \" tf.keras.layers.Conv2D(3, 1),\\n\",\n \" tf.keras.layers.BatchNormalization(),\\n\",\n \" \\n\",\n \"])\\n\",\n \"seq_model(tf.ones([1,2,3,3]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"N26giRCjPXOR\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"collapsed_sections\": [],\n \"name\": \"002-custom_layers.ipynb\",\n \"provenance\": [],\n \"version\": \"0.3.2\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "020-Eager/003-automatic_differentiation.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"K5de6EUfWWUb\"\n },\n \"source\": [\n \"# TensorFlow2.0教程-自动求导\\n\",\n \"\\n\",\n \"这节我们会介绍使用tensorflow2自动求导的方法。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 2148,\n \"status\": \"ok\",\n \"timestamp\": 1559219648589,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"49BpOoOXWa8C\",\n \"outputId\": \"6aa76be9-e27c-4b1c-e751-7217f394efbc\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function, unicode_literals\\n\",\n \"# !pip uninstall tensorflow\\n\",\n \"#!pip install tensorflow==2.0.0-alpha\\n\",\n \"import tensorflow as tf\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"qpi0MAurTzfy\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"YqfVrbeTQupu\"\n },\n \"source\": [\n \"## 一、Gradient tapes\\n\",\n \"tensorflow 提供tf.GradientTape api来实现自动求导功能。只要在tf.GradientTape()上下文中执行的操作，都会被记录与“tape”中，然后tensorflow使用反向自动微分来计算相关操作的梯度。\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 71\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 760,\n \"status\": \"ok\",\n \"timestamp\": 1559219653881,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"S2dV3uiKQZsE\",\n \"outputId\": \"1dcd5f41-ee73-4eed-837d-b45638fc63e3\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[8. 8.]\\n\",\n \" [8. 8.]], shape=(2, 2), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = tf.ones((2,2))\\n\",\n \"\\n\",\n \"# 需要计算梯度的操作\\n\",\n \"with tf.GradientTape() as t:\\n\",\n \" t.watch(x)\\n\",\n \" y = tf.reduce_sum(x)\\n\",\n \" z = tf.multiply(y,y)\\n\",\n \"# 计算z关于x的梯度\\n\",\n \"dz_dx = t.gradient(z, x)\\n\",\n \"print(dz_dx)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"fPn28EA3Uqp6\"\n },\n \"source\": [\n \"也可以输出对中间变量的导数\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 942,\n \"status\": \"ok\",\n \"timestamp\": 1559219655992,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"C-D2Lf06TLgc\",\n \"outputId\": \"6b6e57d1-3540-4839-d877-0818733da407\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(8.0, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 梯度求导只能每个tape一次\\n\",\n \"with tf.GradientTape() as t:\\n\",\n \" t.watch(x)\\n\",\n \" y = tf.reduce_sum(x)\\n\",\n \" z = tf.multiply(y,y)\\n\",\n \" \\n\",\n \"dz_dy = t.gradient(z, y)\\n\",\n \"print(dz_dy)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"5Ntrsuv1tewy\"\n },\n \"source\": [\n \"默认情况下GradientTape的资源会在执行tf.GradientTape()后被释放。如果想多次计算梯度，需要创建一个持久的GradientTape。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 89\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 766,\n \"status\": \"ok\",\n \"timestamp\": 1559219848611,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"5kZoUqbrVAO5\",\n \"outputId\": \"65806f79-3d13-492f-fa1b-29e9c86a1566\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[8. 8.]\\n\",\n \" [8. 8.]], shape=(2, 2), dtype=float32)\\n\",\n \"tf.Tensor(8.0, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"with tf.GradientTape(persistent=True) as t:\\n\",\n \" t.watch(x)\\n\",\n \" y = tf.reduce_sum(x)\\n\",\n \" z = tf.multiply(y, y)\\n\",\n \" \\n\",\n \"dz_dx = t.gradient(z,x)\\n\",\n \"print(dz_dx)\\n\",\n \"dz_dy = t.gradient(z, y)\\n\",\n \"print(dz_dy)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"meFCHe37wBc8\"\n },\n \"source\": [\n \"## 二、记录控制流\\n\",\n \"因为tapes记录了整个操作，所以即使过程中存在python控制流（如if， while），梯度求导也能正常处理。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 71\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1517,\n \"status\": \"ok\",\n \"timestamp\": 1559220443136,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"OP5XoSJovsJs\",\n \"outputId\": \"b20ae483-321c-4a15-9e76-7a4773137ed3\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(12.0, shape=(), dtype=float32)\\n\",\n \"tf.Tensor(12.0, shape=(), dtype=float32)\\n\",\n \"tf.Tensor(4.0, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"def f(x, y):\\n\",\n \" output = 1.0\\n\",\n \" # 根据y的循环\\n\",\n \" for i in range(y):\\n\",\n \" # 根据每一项进行判断\\n\",\n \" if i> 1 and i<5:\\n\",\n \" output = tf.multiply(output, x)\\n\",\n \" return output\\n\",\n \"\\n\",\n \"def grad(x, y):\\n\",\n \" with tf.GradientTape() as t:\\n\",\n \" t.watch(x)\\n\",\n \" out = f(x, y)\\n\",\n \" # 返回梯度\\n\",\n \" return t.gradient(out, x)\\n\",\n \"# x为固定值\\n\",\n \"x = tf.convert_to_tensor(2.0)\\n\",\n \"\\n\",\n \"print(grad(x, 6))\\n\",\n \"print(grad(x, 5))\\n\",\n \"print(grad(x, 4))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"EihwzFwIyXKX\"\n },\n \"source\": [\n \"## 三、高阶梯度\\n\",\n \"GradientTape上下文管理器在计算梯度的同时也会保持梯度，所以GradientTape也可以实现高阶梯度计算，\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 53\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1144,\n \"status\": \"ok\",\n \"timestamp\": 1559221001653,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"CCgxtUbNx5s_\",\n \"outputId\": \"3b68826e-dfa1-40f5-c78d-fee9cb862257\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(3.0, shape=(), dtype=float32)\\n\",\n \"tf.Tensor(6.0, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = tf.Variable(1.0)\\n\",\n \"\\n\",\n \"with tf.GradientTape() as t1:\\n\",\n \" with tf.GradientTape() as t2:\\n\",\n \" y = x * x * x\\n\",\n \" dy_dx = t2.gradient(y, x)\\n\",\n \" print(dy_dx)\\n\",\n \"d2y_d2x = t1.gradient(dy_dx, x)\\n\",\n \"print(d2y_d2x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"qBxk9ly4z60f\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"name\": \"003-automatic_differentiation.ipynb\",\n \"provenance\": [],\n \"version\": \"0.3.2\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "020-Eager/004-custom_training_base.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"ZlboV5Zt1buQ\"\n },\n \"source\": [\n \"# TensorFlow2.0教程-自定义训练（基础）\\n\",\n \"tensorflow2.0推荐使用tf.keras这样的高级api来构建网络，但tensorflow仍然保持了灵活的构造网络的低级api，我们这节就来介绍怎么通过使用这些低级api构建一个神经网络并训练。\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 10273,\n \"status\": \"ok\",\n \"timestamp\": 1559221761435,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"NuAeh0Fn0ffq\",\n \"outputId\": \"87efd080-b991-4b97-e2fc-1c88a09d13f3\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, print_function, division, unicode_literals\\n\",\n \"!pip install -q tensorflow==2.0.0-alpha0\\n\",\n \"import tensorflow as tf\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"KblizbY-5Jtj\"\n },\n \"source\": [\n \"## 一、Variables\\n\",\n \"TensorFlow的张量是不可变的无状态对象。当我们有要改变的张量时，可以使用python的特性，把计算得到的值赋给这个python变量。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 143\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 803,\n \"status\": \"ok\",\n \"timestamp\": 1559222931093,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"UnUuvOYn280u\",\n \"outputId\": \"cb538471-592a-4376-d5bf-3052321b19e0\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[4. 4. 4. 4. 4. 4.]\\n\",\n \" [4. 4. 4. 4. 4. 4.]\\n\",\n \" [4. 4. 4. 4. 4. 4.]\\n\",\n \" [4. 4. 4. 4. 4. 4.]\\n\",\n \" [4. 4. 4. 4. 4. 4.]\\n\",\n \" [4. 4. 4. 4. 4. 4.]], shape=(6, 6), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = tf.ones([6,6])\\n\",\n \"x = x + 3 # x+3后得到了一个新的张量，并把这个张量赋给x\\n\",\n \"print(x)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"0b-EsceM8B-Q\"\n },\n \"source\": [\n \"然而机器学习中间需要变化的状态（每个参数朝损失变小的方向改变，所以TensorFlow也要内置有状态的操作，这就是Variables，它可以表示模型中的参数，而且方便高效。\\n\",\n \"\\n\",\n \"Variables是一个存在值的对象，当其被使用是，它被隐式地被从存储中读取，而当有诸如tf.assign_sub, tf.scatter_update这样的操作时，得到的新值会储存到原对象中。\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 53\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 741,\n \"status\": \"ok\",\n \"timestamp\": 1559223575052,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"ipYeAlhP7csm\",\n \"outputId\": \"efaaba7d-1517-45da-9429-41ae9a45db49\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"v = tf.Variable(2)\\n\",\n \"v.assign(6)\\n\",\n \"print(v)\\n\",\n \"v.assign_add(tf.square(3))\\n\",\n \"print(v)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"rUzj7ieg-EzI\"\n },\n \"source\": [\n \"注：梯度计算时会自动跟踪变量的计算（不用watch），对表示嵌入的变量，TensorFlow会默认使用稀疏更新，这样可以提高计算和存储效率。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"44Iuq8NA-wC_\"\n },\n \"source\": [\n \"## 二、示例：拟合线性模型\\n\",\n \"\\n\",\n \"使用Tensor， Variable和GradientTape这些简单的要是，就可以构建一个简单的模型。步骤如下：\\n\",\n \"\\n\",\n \"- 定义模型\\n\",\n \"- 定义损失函数\\n\",\n \"- 获取训练数据\\n\",\n \"- 模型训练，使用优化器调整变量\\n\",\n \"\\n\",\n \"在下面我们会构造一个简单的线性模型：f(x) = W + b, 它有2个变量W和b，同时我们会使用W=3.0，b=2.0来构造数据，用于学习。\\n\",\n \"\\n\",\n \"###１、定义模型\\n\",\n \"我们把模型定义为一个简单的类，里面封装了变量和计算\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1468,\n \"status\": \"ok\",\n \"timestamp\": 1559225737005,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"rvOw-KVK957v\",\n \"outputId\": \"8e7dd070-caf5-479b-e816-55929410f5b2\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(10.0, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"class Model(object):\\n\",\n \" def __init__(self):\\n\",\n \" # 初始化变量\\n\",\n \" self.W = tf.Variable(5.0)\\n\",\n \" self.b = tf.Variable(0.0)\\n\",\n \" \\n\",\n \" def __call__(self, x):\\n\",\n \" return self.W * x + self.b\\n\",\n \"# 测试\\n\",\n \"model = Model()\\n\",\n \"print(model(2))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"m6EKTFkSDTuE\"\n },\n \"source\": [\n \"##２．定义损失函数\\n\",\n \"损失函数测量给定输入的模型输出与期望输出的匹配程度。 这里使用标准的L2损失。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"LFAFmxyhDOKi\"\n },\n \"outputs\": [],\n \"source\": [\n \"def loss(predicted_y, true_y):\\n\",\n \" return tf.reduce_mean(tf.square(predicted_y - true_y))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"OpQvjudfD0xP\"\n },\n \"source\": [\n \"## 3.获取训练数据\\n\",\n \"生成带有噪音的数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"jo81SIryC7Y_\"\n },\n \"outputs\": [],\n \"source\": [\n \"TRUE_W = 3.0\\n\",\n \"TRUE_b = 2.0\\n\",\n \"num = 1000\\n\",\n \"\\n\",\n \"# 随机输入\\n\",\n \"inputs = tf.random.normal(shape=[num])\\n\",\n \"# 随机噪音\\n\",\n \"noise = tf.random.normal(shape=[num])\\n\",\n \"\\n\",\n \"# 构造数据\\n\",\n \"outputs = TRUE_W * inputs + TRUE_b + noise\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"lZfyvhbTE9A6\"\n },\n \"source\": [\n \"在我们训练模型之前，让我们可以看到模型现在所处的位置。 我们将用红色绘制模型的预测，用蓝色绘制训练数据。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 305\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1897,\n \"status\": \"ok\",\n \"timestamp\": 1559225738636,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"ZRcHF6ewEvVf\",\n \"outputId\": \"7953f827-62f9-4f1a-d991-db603620662c\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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oCPP07qCt0tYxQWZjACDVxXrzBtEW7EtURy9TavVgfVKlshpC3tfvif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\\nwYBPRDQk/j/LDvF33JKjzQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Init Loss:\\n\",\n \"tf.Tensor(8.763554, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"plt.scatter(inputs, outputs, c='b')\\n\",\n \"plt.scatter(inputs, model(inputs), c='r')\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# 当前loss\\n\",\n \"print('Init Loss:')\\n\",\n \"print(loss(model(inputs), outputs))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"cl3fhKTSGpXY\"\n },\n \"source\": [\n \"## 4.定义训练循环\\n\",\n \"我们现在已经有了模型和训练数据。 我们准备开始训练，即使用训练数据来更新模型的变量（W和b），以便使用梯度下降来减少损失。 在tf.train.Optimizer中实现了许多梯度下降方案的变体。 强烈建议大家使用这些实现，但本着从第一原则构建的精神，在这个特定的例子中，我们将自己实现基本的优化器。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"MB5j9idIFlLg\"\n },\n \"outputs\": [],\n \"source\": [\n \"def train(model, inputs, outputs, learning_rate):\\n\",\n \" # 记录loss计算过程\\n\",\n \" with tf.GradientTape() as t:\\n\",\n \" current_loss = loss(model(inputs), outputs)\\n\",\n \" # 对W，b求导\\n\",\n \" dW, db = t.gradient(current_loss, [model.W, model.b])\\n\",\n \" # 减去梯度×学习率\\n\",\n \" model.W.assign_sub(dW*learning_rate)\\n\",\n \" model.b.assign_sub(db*learning_rate)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"4Zl7MUDkIPxU\"\n },\n \"source\": [\n \"我们反复训练模型，并观察W和b的变化\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 449\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 767,\n \"status\": \"ok\",\n \"timestamp\": 1559226636263,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"v3xP2b8uII0w\",\n \"outputId\": \"70e06a49-9838-4c43-e838-ac5529522f51\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 0: W=5.00 b=0.00, loss=8.76355\\n\",\n \"Epoch 1: W=4.61 b=0.40, loss=5.97410\\n\",\n \"Epoch 2: W=4.30 b=0.72, loss=4.18118\\n\",\n \"Epoch 3: W=4.05 b=0.98, loss=3.02875\\n\",\n \"Epoch 4: W=3.85 b=1.18, loss=2.28800\\n\",\n \"Epoch 5: W=3.69 b=1.35, loss=1.81184\\n\",\n \"Epoch 6: W=3.56 b=1.48, loss=1.50577\\n\",\n \"Epoch 7: W=3.46 b=1.58, loss=1.30901\\n\",\n \"Epoch 8: W=3.38 b=1.67, loss=1.18253\\n\",\n \"Epoch 9: W=3.31 b=1.73, loss=1.10123\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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igqsnHn0igaeVXXmPW4MzMzycrK4sQTT2TFihW89NJLDB8+nKVLlwK2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\\nrkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"model= Model()\\n\",\n \"\\n\",\n \"# 收集W，b画图\\n\",\n \"Ws, bs = [], []\\n\",\n \"for epoch in range(10):\\n\",\n \" Ws.append(model.W.numpy())\\n\",\n \" bs.append(model.b.numpy())\\n\",\n \" # 计算loss\\n\",\n \" current_loss = loss(model(inputs), outputs)\\n\",\n \" train(model, inputs, outputs, learning_rate=0.1)\\n\",\n \" print('Epoch %2d: W=%1.2f b=%1.2f, loss=%2.5f' %\\n\",\n \" (epoch, Ws[-1], bs[-1], current_loss))\\n\",\n \"# 画图\\n\",\n \"# Let's plot it all\\n\",\n \"epochs = range(10)\\n\",\n \"plt.plot(epochs, Ws, 'r',\\n\",\n \" epochs, bs, 'b')\\n\",\n \"plt.plot([TRUE_W] * len(epochs), 'r--',\\n\",\n \" [TRUE_b] * len(epochs), 'b--')\\n\",\n \"plt.legend(['W', 'b', 'true W', 'true_b'])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"0dzWsRsEIcQ7\"\n },\n \"outputs\": [],\n \"source\": [\n \" \"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"name\": \"custom_training_base.ipynb\",\n \"provenance\": [],\n \"version\": \"0.3.2\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "020-Eager/005-custom_training_walkthrough.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"Kk2cahdPEsX-\"\n },\n \"source\": [\n \"# TensorFlow2教程-自定义训练实战\\n\",\n \"\\n\",\n \"本教程我们将使用TensorFlow来实现鸢尾花分类。整个过程包括：构建模型、模型训练、模型预测。\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"VEjExcBmPWNy\"\n },\n \"source\": [\n \"## 导入相关库\\n\",\n \"导入TensorFlow和其他所需的Python模块。 默认情况下，TensorFlow2使用急切执行来程序，会立即返回结果。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"CDuwOUXPEkJs\"\n },\n \"outputs\": [],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function, unicode_literals\\n\",\n \"import os\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 71\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 31258,\n \"status\": \"ok\",\n \"timestamp\": 1564752585232,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"iIbJspJUQXtN\",\n \"outputId\": \"ffc0ced4-b615-4ed2-cda1-be2c2d3bf164\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001b[K |████████████████████████████████| 79.9MB 429kB/s \\n\",\n \"\\u001b[K |████████████████████████████████| 3.0MB 34.9MB/s \\n\",\n \"\\u001b[K |████████████████████████████████| 419kB 55.7MB/s \\n\",\n \"\\u001b[?25h\"\n ]\n }\n ],\n \"source\": [\n \"!pip install -q tensorflow==2.0.0-alpha0\\n\",\n \"import tensorflow as tf\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 53\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 962,\n \"status\": \"ok\",\n \"timestamp\": 1564752800388,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"p7YBIBwCQii3\",\n \"outputId\": \"b7c7223f-09c6-40d1-c2fc-7eafaa1e1b33\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf version: 2.0.0-alpha0\\n\",\n \"eager execution: True\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('tf version:', tf.__version__)\\n\",\n \"print('eager execution:', tf.executing_eagerly())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 323\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 2381,\n \"status\": \"ok\",\n \"timestamp\": 1564752879806,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"KN00YNtIh4-I\",\n \"outputId\": \"b526fcab-9223-49d0-b029-1b36b84f1d8d\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Fri Aug 2 13:34:38 2019 \\n\",\n \"+-----------------------------------------------------------------------------+\\n\",\n \"| NVIDIA-SMI 418.67 Driver Version: 410.79 CUDA Version: 10.0 |\\n\",\n \"|-------------------------------+----------------------+----------------------+\\n\",\n \"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\\n\",\n \"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\\n\",\n \"|===============================+======================+======================|\\n\",\n \"| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\\n\",\n \"| N/A 47C P8 15W / 70W | 0MiB / 15079MiB | 0% Default |\\n\",\n \"+-------------------------------+----------------------+----------------------+\\n\",\n \" \\n\",\n \"+-----------------------------------------------------------------------------+\\n\",\n \"| Processes: GPU Memory |\\n\",\n \"| GPU PID Type Process name Usage |\\n\",\n \"|=============================================================================|\\n\",\n \"| No running processes found |\\n\",\n \"+-----------------------------------------------------------------------------+\\n\"\n ]\n }\n ],\n \"source\": [\n \"!nvidia-smi\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"PasatZ2jR441\"\n },\n \"source\": [\n \"## 鸢尾花分类问题\\n\",\n \"\\n\",\n \"想象一下，你是一名植物学家，正在寻找一种自动化的方法来对你找到的每种鸢尾花进行分类。 机器学习提供了许多算法来对花进行统计分类。 例如，复杂的机器学习程序可以基于照片对花进行分类。而这里，我们将根据萼片和花瓣的长度和宽度测量来对鸢尾花进行分类。\\n\",\n \"\\n\",\n \"鸢尾花有300多种类别，但我们的这里主要对以下三种进行分类：\\n\",\n \"- Iris setosa\\n\",\n \"- Iris virginica\\n\",\n \"- Iris versicolor\\n\",\n \"# ![](https://www.tensorflow.org/images/iris_three_species.jpg)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"Y7yJD48rTLtp\"\n },\n \"source\": [\n \"幸运的是，有人已经用萼片和花瓣测量创建了120个鸢尾花的数据集。 这是一个流行的初学者机器学习分类问题的经典数据集。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"XoBuC7GGTO1f\"\n },\n \"source\": [\n \"下载数据集\\n\",\n \"使用tf.keras.utils.get_file函数下载训练数据集文件。 这将返回下载文件的文件路径。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 6480,\n \"status\": \"ok\",\n \"timestamp\": 1559464545246,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"BpGa5eF7RDUF\",\n \"outputId\": \"222762a0-754f-494a-eab7-624e28a60f3e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"下载数据至： /root/.keras/datasets/iris_training.csv\\n\"\n ]\n }\n ],\n \"source\": [\n \"train_dataset_url = \\\"https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv\\\"\\n\",\n \"train_dataset_fp = tf.keras.utils.get_file(fname=os.path.basename(train_dataset_url),\\n\",\n \" origin=train_dataset_url)\\n\",\n \"print('下载数据至：', train_dataset_fp)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"xOH2I3MKUWa8\"\n },\n \"source\": [\n \"## 检查数据\\n\",\n \"此数据集iris_training.csv是一个纯文本文件，用于存储格式为逗号分隔值（CSV）的表格数据。 使用head -n5命令在前五个条目中取一个峰值：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 130\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 9637,\n \"status\": \"ok\",\n \"timestamp\": 1559464548420,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"Zccn4y_mUKt3\",\n \"outputId\": \"75d514b3-1426-4184-e899-64b0db98988e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"120,4,setosa,versicolor,virginica\\n\",\n \"6.4,2.8,5.6,2.2,2\\n\",\n \"5.0,2.3,3.3,1.0,1\\n\",\n \"4.9,2.5,4.5,1.7,2\\n\",\n \"4.9,3.1,1.5,0.1,0\\n\"\n ]\n }\n ],\n \"source\": [\n \"!head -n5 {train_dataset_fp}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"DX1RYV49X3xk\"\n },\n \"source\": [\n \"从数据集的此视图中，请注意以下内容：\\n\",\n \"\\n\",\n \"第一行是包含有关数据集的信息的标题：\\n\",\n \"总共有120个例子。 每个示例都有四个特征和三个可能的标签名称之一。\\n\",\n \"后续行是数据记录，每行一个示例，其中：\\n\",\n \"前四个字段是特征：这些是示例的特征。 这里，字段包含代表花卉测量值的浮点数。\\n\",\n \"最后一列是标签：这是我们想要预测的值。 对于此数据集，它是与花名称对应的整数值0,1或2。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"TfXADTDqUpEC\"\n },\n \"outputs\": [],\n \"source\": [\n \"column_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']\\n\",\n \"# 获取特征和标签名\\n\",\n \"feature_name = column_names[:-1]\\n\",\n \"label_name = column_names[-1]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"2h2J2is4ZUOH\"\n },\n \"source\": [\n \"每个标签都与字符串名称相关联（例如，“setosa”），但机器学习通常依赖于数值。使用标签数字来映射类别，例如：\\n\",\n \"\\n\",\n \"- 0：Iris setosa:\\n\",\n \"- 1：Iris versicolor\\n\",\n \"- 2：Iris virginica\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"ugETL6NbZFGW\"\n },\n \"outputs\": [],\n \"source\": [\n \"class_names = ['Iris setosa', 'Iris versicolor', 'Iris virginica']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"qpv35o3lZ587\"\n },\n \"source\": [\n \"## 创建一个 tf.data.Dataset\\n\",\n \"TensorFlow的数据集API处理许多将数据加载到模型中的常见情况。这是一个高级API，用于读取数据并将其转换为用于训练的数据类型。\\n\",\n \"\\n\",\n \"由于数据集是CSV格式的文本文件，因此需要使用make_csv_dataset函数将数据解析为合适的格式。由于此函数为训练模型生成数据，因此默认行为是对数据（shuffle=True, shuffle_buffer_size=10000）进行混洗，并永远重复数据集（num_epochs=None）。同时还需要设置batch_size参数。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"N3ZHbT89Z22R\"\n },\n \"outputs\": [],\n \"source\": [\n \"batch_size=32\\n\",\n \"train_dataset = tf.data.experimental.make_csv_dataset(\\n\",\n \" train_dataset_fp,\\n\",\n \" batch_size,\\n\",\n \" column_names=column_names,\\n\",\n \" label_name=label_name,\\n\",\n \" num_epochs=1\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"cFO6ovo3bOHO\"\n },\n \"source\": [\n \"该make_csv_dataset函数返回tf.data.Dataset的(features, label)对，其中features是一个字典：{'feature_name': value}\\n\",\n \"\\n\",\n \"而这些Dataset对象是可迭代的。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 266\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 9571,\n \"status\": \"ok\",\n \"timestamp\": 1559464548444,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"H9NpQ-U1bEqC\",\n \"outputId\": \"356b3b5e-941c-486d-fab5-b0ed9a2088d1\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"OrderedDict([('sepal_length', ), ('sepal_width', ), ('petal_length', ), ('petal_width', )])\\n\"\n ]\n }\n ],\n \"source\": [\n \"features, labels = next(iter(train_dataset))\\n\",\n \"print(features)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"_TuigCMIb1xh\"\n },\n \"source\": [\n \"相同的特征被放在同一个数组中，数组维度为batch_size大小。\\n\",\n \"可以可视化如图：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 283\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 9551,\n \"status\": \"ok\",\n \"timestamp\": 1559464548445,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"cvCg8fVubdx8\",\n \"outputId\": \"87d9beb3-65e6-49b6-bbce-bedec47f9725\"\n },\n \"outputs\": [\n {\n \"data\": {\n 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KziFCySe+W17so9CfGZeO0jYDGs7DispHc0H8ynKPwceM3MngGMdOuK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\\nqSiIiEiWioKIiGSpKIiISNb/B2VWaEgufc9bAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(features['petal_length'],\\n\",\n \" features['sepal_length'],\\n\",\n \" c=labels,\\n\",\n \" cmap='viridis')\\n\",\n \"\\n\",\n \"plt.xlabel(\\\"Petal length\\\")\\n\",\n \"plt.ylabel(\\\"Sepal length\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"OCfiG8Tz712X\"\n },\n \"source\": [\n \"一般我们会把同一个数据的不同feature放在同一个数组中，我们使用tf.pack()來将features重构为(batch_size, num_features)形状。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"yXhTfd8PcGI4\"\n },\n \"outputs\": [],\n \"source\": [\n \"def pack_features_vector(features, labels):\\n\",\n \" features = tf.stack(list(features.values()), axis=1)\\n\",\n \" return features, labels\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 118\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 9528,\n \"status\": \"ok\",\n \"timestamp\": 1559464548458,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"5iz9qWly9hfO\",\n \"outputId\": \"facd97d3-5d88-43fd-ea48-b37eb55c6b6e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[7.6 3. 6.6 2.1]\\n\",\n \" [6.9 3.2 5.7 2.3]\\n\",\n \" [7.2 3.6 6.1 2.5]\\n\",\n \" [5. 2.3 3.3 1. ]\\n\",\n \" [6.7 3. 5.2 2.3]], shape=(5, 4), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 使用tf.data.Dataset.map将重构函数运用到每条数据中。\\n\",\n \"train_dataset = train_dataset.map(pack_features_vector)\\n\",\n \"# 查看前5个数据\\n\",\n \"features, labels = next(iter(train_dataset))\\n\",\n \"print(features[:5])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"MHiac-2m_jMP\"\n },\n \"source\": [\n \"## 选择模型\\n\",\n \"\\n\",\n \"一个模型是功能和标签之间的关系。对于鸢尾花分类问题，该模型定义了萼片和花瓣测量与预测的鸢尾花物种之间的关系。一些简单的模型可以用几行代数来描述，但是复杂的机器学习模型具有很多难以概括的参数。\\n\",\n \"\\n\",\n \"我们能否在不使用机器学习的情况下确定四种特征与虹膜物种之间的关系？也就是说，可以使用传统的编程技术（例如，很多条件语句）来创建模型吗？也许 - 如果你对数据集进行了足够长的分析，以确定特定物种的花瓣和萼片测量值之间的关系。这对于更复杂的数据集来说变得困难 - 可能是不可能的。良好的机器学习方法可以为我们确定模型。如果我们将足够的代表性示例提供给正确的机器学习模型类型，程序将为我们找出关系。\\n\",\n \"\\n\",\n \"选择具体模型\\n\",\n \"\\n\",\n \"我们需要选择要训练的模型。有很多类型的模型和挑选一个好的经验。本教程使用神经网络来解决鸢尾花I分类问题。神经网络可以找到特征和标签之间的复杂关系。它是一个高度结构化的图，组织成一个或多个隐藏层。每个隐藏层由一个或多个神经元组成。有几类神经网络，这个程序使用密集或完全连接的神经网络：一层中的神经元接收来自每个神经网络的输入连接上一层的神经元。例如，图2说明了一个由输入层，两个隐藏层和一个输出层组成的密集神经网络：\\n\",\n \"\\n\",\n \"![](https://www.tensorflow.org/images/custom_estimators/full_network.png)\\n\",\n \"图2.具有特征，隐藏层和预测的神经网络。\\n\",\n \" \\n\",\n \"当对来自图2的模型进行训练并喂入未标记的示例时，它产生三个预测：该花是给定的鸢尾花物种的可能性。这种预测称为推理。对于此示例，输出预测的总和为1.0。在图2中，该预测分解为：0.02对山鸢尾，0.95对于变色鸢尾，并0.03为锦葵鸢尾。这意味着模型以95％的概率预测 - 未标记的示例花是变色鸢尾。\\n\",\n \"\\n\",\n \"##使用Keras创建模型\\n\",\n \"\\n\",\n \"TensorFlow tf.keras API是创建模型和图层的首选方式。这使得构建模型和实验变得容易，而Keras处理将所有内容连接在一起的复杂性。\\n\",\n \"\\n\",\n \"该tf.keras.Sequential模型是层的线性堆栈。它的构造函数采用一个层实例列表，在这种情况下，两个Dense层各有10个节点，一个输出层有3个节点代表我们的标签预测。第一层的input_shape参数对应于数据集中的要素数，并且是必需的。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"bfEEzsDK-7qS\"\n },\n \"outputs\": [],\n \"source\": [\n \"# 构建线性模型\\n\",\n \"model = tf.keras.Sequential([\\n\",\n \" tf.keras.layers.Dense(10, activation='relu',input_shape=(4,)),\\n\",\n \" tf.keras.layers.Dense(10, activation='relu'),\\n\",\n \" tf.keras.layers.Dense(3)\\n\",\n \"])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"OQQUlt7nN9k4\"\n },\n \"source\": [\n \"激活函数确定在层中的每个节点的输出形状。这些非线性很重要 - 没有它们，模型将等同于单个层。有许多可用的激活，但ReLU对于隐藏层是常见的。\\n\",\n \"\\n\",\n \"隐藏层和神经元的理想数量取决于问题和数据集。像机器学习的许多方面一样，选择神经网络的最佳形状需要知识和实验经验。根据经验，增加隐藏层和神经元的数量通常会创建一个更强大的模型，这需要更多的数据来有效地训练。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"d8JrLagJOggc\"\n },\n \"source\": [\n \"## 测试模型结构\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 118\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 10134,\n \"status\": \"ok\",\n \"timestamp\": 1559464549092,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"p3lKRc3ZN0s6\",\n \"outputId\": \"7a1b8c8a-0b24-471a-dc90-5f9d757436a8\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"prediction = model(features)\\n\",\n \"prediction[:5]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"-1DoLLc7Os-p\"\n },\n \"source\": [\n \"多分类任务需要使用softmax进行归一化\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 118\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 10116,\n \"status\": \"ok\",\n \"timestamp\": 1559464549095,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"R3TrHwRSOqaN\",\n \"outputId\": \"017c168e-6971-4864-b666-219bb048de72\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"tf.nn.softmax(prediction)[:5]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"NNK0BI0VPMY_\"\n },\n \"source\": [\n \"使用tf.argmax获取概率最大的类标签\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 82\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 10095,\n \"status\": \"ok\",\n \"timestamp\": 1559464549097,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"AdhaPPeaO5zZ\",\n \"outputId\": \"a2a16c38-8f75-4bb0-c94b-785327eff249\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"prediction: tf.Tensor([0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0], shape=(32,), dtype=int64)\\n\",\n \"label: tf.Tensor([2 2 2 1 2 0 0 0 2 2 1 2 1 2 2 1 0 0 2 0 1 2 0 2 0 1 1 1 2 1 0 2], shape=(32,), dtype=int32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('prediction:', tf.argmax(prediction, axis=1))\\n\",\n \"print('label:', labels)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"IalhdqdgCo2m\"\n },\n \"source\": [\n \"# 训练模型\\n\",\n \"训练是机器学习中模型从数据集中学习知识并优化自身能力的过程。\\n\",\n \"\\n\",\n \"Iris分类问题是一个典型的监督学习问题，其通过包含标签的数据集进行学习。而无监督学习则是仅从特征中去寻找相应的模式。\\n\",\n \"\\n\",\n \"训练和评估的过程都需要计算模型的损失，它可以衡量预测与正确标签的差距，训练过程都是要最小化损失。\\n\",\n \"\\n\",\n \"我们后面将直接使用tf.keras里面包装好的损失函数来计算损失。\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"_CPDyoTLP1sb\"\n },\n \"outputs\": [],\n \"source\": [\n \"# 损失函数\\n\",\n \"loss_object=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 10063,\n \"status\": \"ok\",\n \"timestamp\": 1559464549101,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"WpYuT9YKEiQn\",\n \"outputId\": \"c1bf906e-7f47-449a-a4ff-f8c1ee00af3f\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(1.3738844, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 获取损失\\n\",\n \"def loss(model, x, y):\\n\",\n \" y_ = model(x)\\n\",\n \" return loss_object(y_true=y, y_pred=y_)\\n\",\n \"l = loss(model, features, labels)\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"Xfccc9znFC2h\"\n },\n \"source\": [\n \"使用tf.GradientTape计算loss对所有变量的梯度。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"oa6VTHxjE885\"\n },\n \"outputs\": [],\n \"source\": [\n \"def grad(model, inputs, targets):\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" loss_value = loss(model, inputs, targets)\\n\",\n \" return loss_value, tape.gradient(loss_value, model.trainable_variables)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"DFQv_sk3OlHV\"\n },\n \"source\": [\n \"## 创建优化器\\n\",\n \"\\n\",\n \"优化程序将计算出的梯度应用于模型的变量，以最大限度地减少损失函数。 您可以将损失函数视为曲面（参见图3），我们希望通过四处走动找到它的最低点。 渐变指向最陡的上升方向 - 所以我们将以相反的方向行进并向下移动。 通过迭代计算每批的损失和梯度，我们将在训练期间调整模型。 逐渐地，该模型将找到权重和偏差的最佳组合，以最小化损失。 损失越低，模型的预测越好。\\n\",\n \"\\n\",\n \"![](https://cs231n.github.io/assets/nn3/opt1.gif)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"dr_qJLJcPM5k\"\n },\n \"source\": [\n \"TensorFlow有许多可用于训练的优化算法。 该模型使用tf.train.GradientDescentOptimizer实现随机梯度下降（SGD）算法。 learning_rate设置每次迭代下一步的步长。 这是一个超参数，您通常会调整以获得更好的结果。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"rALMMe6lGKso\"\n },\n \"outputs\": [],\n \"source\": [\n \"optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"qzEiEMNgP49Q\"\n },\n \"source\": [\n \"优化器使用如下\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 50\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 10030,\n \"status\": \"ok\",\n \"timestamp\": 1559464549111,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"3MiDlfWhPxVI\",\n \"outputId\": \"22ba84b7-e7f5-4b1a-81aa-05f392c46c3e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"步数：0, 初始loss值：1.3738844394683838\\n\",\n \"步数：1, loss值：1.1648454666137695\\n\"\n ]\n }\n ],\n \"source\": [\n \"loss_value, grads = grad(model, features, labels)\\n\",\n \"print('步数：{}, 初始loss值：{}'.format(optimizer.iterations.numpy(),\\n\",\n \" loss_value.numpy()))\\n\",\n \"optimizer.apply_gradients(zip(grads, model.trainable_variables))\\n\",\n \"print('步数：{}, loss值：{}'.format(optimizer.iterations.numpy(),\\n\",\n \" loss(model,features, labels).numpy()))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"SRxS5eX9R7DN\"\n },\n \"source\": [\n \"## 训练循环\\n\",\n \"每个epoch数据将会被训练一次。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 101\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 18329,\n \"status\": \"ok\",\n \"timestamp\": 1559464557422,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"yvgUj4tzRO2r\",\n \"outputId\": \"bfc1c5ca-cc43-4bb2-dc71-418cbfca8fe3\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 000: Loss: 1.048, Accuracy: 70.000%\\n\",\n \"Epoch 050: Loss: 0.074, Accuracy: 99.167%\\n\",\n \"Epoch 100: Loss: 0.059, Accuracy: 99.167%\\n\",\n \"Epoch 150: Loss: 0.054, Accuracy: 99.167%\\n\",\n \"Epoch 200: Loss: 0.051, Accuracy: 99.167%\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 保存loss和acc\\n\",\n \"train_loss_results=[]\\n\",\n \"train_accuracy_results=[]\\n\",\n \"\\n\",\n \"num_epochs =201\\n\",\n \"for epoch in range(num_epochs):\\n\",\n \" # 用于记录loss和acc的类\\n\",\n \" epoch_loss_avg = tf.keras.metrics.Mean()\\n\",\n \" epoch_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()\\n\",\n \" \\n\",\n \" # 训练循环\\n\",\n \" for x, y in train_dataset:\\n\",\n \" # 获取loss和梯度\\n\",\n \" loss_value, grads = grad(model, x, y)\\n\",\n \" # 梯度优化\\n\",\n \" optimizer.apply_gradients(zip(grads, model.trainable_variables))\\n\",\n \" \\n\",\n \" # 记录loss均值\\n\",\n \" epoch_loss_avg(loss_value)\\n\",\n \" # 记录准确率\\n\",\n \" epoch_accuracy(y, model(x))\\n\",\n \"\\n\",\n \" # 保存每个epoch的loss和acc\\n\",\n \" train_loss_results.append(epoch_loss_avg.result())\\n\",\n \" train_accuracy_results.append(epoch_accuracy.result())\\n\",\n \"\\n\",\n \" if epoch % 50 == 0:\\n\",\n \" print(\\\"Epoch {:03d}: Loss: {:.3f}, Accuracy: {:.3%}\\\".format(epoch,\\n\",\n \" epoch_loss_avg.result(),\\n\",\n \" epoch_accuracy.result()))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"o_g5s0hOVTDj\"\n },\n \"source\": [\n \"## 可视化训练过程\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 582\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1122,\n \"status\": \"ok\",\n \"timestamp\": 1559464652806,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"UggcRc8OUqqA\",\n \"outputId\": \"f2ffee13-3df4-475e-808e-597256ebe9ca\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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EsiYt2kPmuBtwHnppTOBH6rGXW30rrlg9y+7XFSSq0uRZIkSTlo1oj3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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, axes = plt.subplots(2, sharex=True, figsize=(12, 8))\\n\",\n \"fig.suptitle('Training Metrics')\\n\",\n \"\\n\",\n \"axes[0].set_ylabel(\\\"Loss\\\", fontsize=14)\\n\",\n \"axes[0].plot(train_loss_results)\\n\",\n \"\\n\",\n \"axes[1].set_ylabel(\\\"Accuracy\\\", fontsize=14)\\n\",\n \"axes[1].set_xlabel(\\\"Epoch\\\", fontsize=14)\\n\",\n \"axes[1].plot(train_accuracy_results)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"u_QsWTX5V0qV\"\n },\n \"source\": [\n \"## 评估模型\\n\",\n \"评估模型类似于训练模型。 最大的区别是示例来自单独的测试集而不是训练集。 为了公平地评估模型的有效性，用于评估模型的示例必须与用于训练模型的示例不同。\\n\",\n \"\\n\",\n \"测试数据集的设置类似于训练数据集的设置。 下载CSV文本文件并解析该值，然后将其打乱：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 74\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1700,\n \"status\": \"ok\",\n \"timestamp\": 1559464851296,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"ZJPLpvmTVir7\",\n \"outputId\": \"e63593c8-2978-4d2d-af88-501d09ed62fe\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv\\n\",\n \"\\r\",\n \"8192/573 [============================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 0us/step\\n\"\n ]\n }\n ],\n \"source\": [\n \"test_url = \\\"https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv\\\"\\n\",\n \"\\n\",\n \"test_fp = tf.keras.utils.get_file(fname=os.path.basename(test_url),\\n\",\n \" origin=test_url)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"FuZT638JWTA3\"\n },\n \"outputs\": [],\n \"source\": [\n \"test_dataset = tf.data.experimental.make_csv_dataset(\\n\",\n \" test_fp,\\n\",\n \" batch_size,\\n\",\n \" column_names=column_names,\\n\",\n \" label_name='species',\\n\",\n \" num_epochs=1,\\n\",\n \" shuffle=False)\\n\",\n \"\\n\",\n \"test_dataset = test_dataset.map(pack_features_vector)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"EZh0SnauWkbg\"\n },\n \"source\": [\n \"评估测试数据集上的模型\\n\",\n \"\\n\",\n \"与训练阶段不同，该模型仅评估测试数据的单个时期。 在下面的代码单元格中，我们迭代测试集中的每个示例，并将模型的预测与实际标签进行比较。 这用于测量整个测试集中模型的准确性。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1700,\n \"status\": \"ok\",\n \"timestamp\": 1559465738675,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"g_FNdjbiWVuV\",\n \"outputId\": \"4f9f564c-d7e4-405f-bf30-64a35d0fc6f4\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"测试集准确率： tf.Tensor(0.96666664, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 准确率统计类\\n\",\n \"test_accuracy = tf.keras.metrics.Accuracy()\\n\",\n \"\\n\",\n \"for (x,y) in test_dataset:\\n\",\n \" logits = model(x)\\n\",\n \" prediction = tf.argmax(logits, axis=1, output_type=tf.int32)\\n\",\n \" test_accuracy(prediction, y) \\n\",\n \"\\n\",\n \"print('测试集准确率：', test_accuracy.result())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"b0DUmLjEZyXr\"\n },\n \"source\": [\n \"结果对比\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 538\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1719,\n \"status\": \"ok\",\n \"timestamp\": 1559465856252,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"wsfZ1uZ0XNj4\",\n \"outputId\": \"d1e85280-095f-43ca-e0f9-8bcd6e4d27e3\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"tf.stack([y, prediction], axis=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"lQHhEk-CaTnq\"\n },\n \"source\": [\n \"##使用训练的模型进行预测\\n\",\n \"\\n\",\n \"我们已经训练了一个模型并且“证明”它对Iris物种进行分类是好的 - 但不是完美的。 现在让我们使用训练有素的模型对未标记的例子做出一些预测; 也就是说，包含特征但不包含标签的示例。\\n\",\n \"\\n\",\n \"在现实生活中，未标记的示例可能来自许多不同的来源，包括应用程序，CSV文件和数据源。 目前，我们将手动提供三个未标记的示例来预测其标签。 回想一下，标签号被映射到命名表示，如下所示：\\n\",\n \"- 0: Iris setosa\\n\",\n \"- 1: Iris versicolor\\n\",\n \"- 2: Iris virginica\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 67\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 974,\n \"status\": \"ok\",\n \"timestamp\": 1559465983463,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"dT_bl8RKaIWx\",\n \"outputId\": \"2c4f15c2-e7f5-4e3d-be1e-9c68131d1479\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Example 0 prediction: Iris setosa (99.9%)\\n\",\n \"Example 1 prediction: Iris versicolor (99.9%)\\n\",\n \"Example 2 prediction: Iris virginica (99.1%)\\n\"\n ]\n }\n ],\n \"source\": [\n \"predict_dataset = tf.convert_to_tensor([\\n\",\n \" [5.1, 3.3, 1.7, 0.5,],\\n\",\n \" [5.9, 3.0, 4.2, 1.5,],\\n\",\n \" [6.9, 3.1, 5.4, 2.1]\\n\",\n \"])\\n\",\n \"\\n\",\n \"predictions = model(predict_dataset)\\n\",\n \"\\n\",\n \"for i, logits in enumerate(predictions):\\n\",\n \" class_idx = tf.argmax(logits).numpy()\\n\",\n \" p = tf.nn.softmax(logits)[class_idx]\\n\",\n \" name = class_names[class_idx]\\n\",\n \" print(\\\"Example {} prediction: {} ({:4.1f}%)\\\".format(i, name, 100*p))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"c8_o9k3Ganll\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"accelerator\": \"GPU\",\n \"colab\": {\n \"collapsed_sections\": [],\n \"name\": \"005-custom_training_walkthrough.ipynb\",\n \"provenance\": [],\n \"version\": \"0.3.2\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "020-Eager/006-tf_function_and_autograph.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"Z-saUB9-PKrO\"\n },\n \"source\": [\n \"# TensorFlow2教程-TF fuction和AutoGraph\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"WSVHzfadSCcU\"\n },\n \"source\": [\n \"在TensorFlow 2.0中，默认情况下启用了急切执行。 对于用户而言直观且灵活（运行一次性操作更容易，更快），但这可能会牺牲性能和可部署性。\\n\",\n \"\\n\",\n \"要获得最佳性能并使模型可在任何地方部署，请使用tf.function从程序中构建图。 因为有AutoGraph，可以使用tf.function构建高效性能的Python代码，但仍有一些陷阱需要警惕。\\n\",\n \"\\n\",\n \"主要的要点和建议是：\\n\",\n \"\\n\",\n \"不要依赖Python副作用，如对象变异或列表追加。\\n\",\n \"tf.function最适合TensorFlow操作，而不是NumPy操作或Python原语。\\n\",\n \"如有疑问，请使用for x in y idiom。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 2519,\n \"status\": \"ok\",\n \"timestamp\": 1560061694048,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"0lo-ivSzO6wo\",\n \"outputId\": \"956c066f-b985-4c15-f0ed-1c79168530a0\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-beta0\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function, unicode_literals\\n\",\n \"#!pip uninstall tensorflow\\n\",\n \"\\n\",\n \"#!pip install tensorflow==2.0.0-beta0\\n\",\n \"import tensorflow as tf\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"rF5kXAQusuTS\"\n },\n \"source\": [\n \"下面的辅助程序代码，用于演示可能遇到的各种错误。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"_2IK6UeITExX\"\n },\n \"outputs\": [],\n \"source\": [\n \"import contextlib\\n\",\n \"\\n\",\n \"# 构建包含上下文管理器的函数，使其可以在with中使用\\n\",\n \"@contextlib.contextmanager\\n\",\n \"def assert_raises(error_class):\\n\",\n \" try:\\n\",\n \" yield\\n\",\n \" except error_class as e:\\n\",\n \" print('Caught expected exception \\\\n {}: {}'.format(error_class, e))\\n\",\n \" except Exception as e:\\n\",\n \" print('Got unexpected exception \\\\n {}: {}'.format(type(e), e))\\n\",\n \" else:\\n\",\n \" raise Exception('Expected {} to be raised but no error was raised!'.format(\\n\",\n \" error_class))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"-s2D0hxBvBKm\"\n },\n \"source\": [\n \"一个tf.function定义就像是一个核心TensorFlow操作：可以急切地执行它; 也可以在图表中使用它; 它有梯度; 等等。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 67\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 2501,\n \"status\": \"ok\",\n \"timestamp\": 1560061694052,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"m0ctt0w6vHKk\",\n \"outputId\": \"4ca9475c-44a3-4710-b7bf-a0da0b6ae0dd\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 类似一个tensorflow操作\\n\",\n \"@tf.function\\n\",\n \"def add(a, b):\\n\",\n \" return a+b\\n\",\n \"\\n\",\n \"add(tf.ones([2,2]), tf.ones([2,2]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 2488,\n \"status\": \"ok\",\n \"timestamp\": 1560061694053,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"tv6YXLJwvtZZ\",\n \"outputId\": \"8a93042f-1398-4dbe-c03a-2ec0e859b196\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# tf.function操作可以计算梯度\\n\",\n \"@tf.function\\n\",\n \"def add(a, b):\\n\",\n \" return a+b\\n\",\n \"v = tf.Variable(2.0)\\n\",\n \"with tf.GradientTape() as tape:\\n\",\n \" res = add(v, 1.0)\\n\",\n \"\\n\",\n \"tape.gradient(res, v) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 84\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 2782,\n \"status\": \"ok\",\n \"timestamp\": 1560061694357,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"0YWC-My4vWkz\",\n \"outputId\": \"44585da6-79eb-44df-8937-a11ddcba4e15\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 可以内嵌调用tf.function\\n\",\n \"@tf.function\\n\",\n \"def dense_layer(x, w, b):\\n\",\n \" return add(tf.matmul(x, w), b)\\n\",\n \"\\n\",\n \"dense_layer(tf.ones([3, 2]), tf.ones([2, 2]), tf.ones([2]))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"Fp-q1QwrzcWI\"\n },\n \"source\": [\n \"## 跟踪和多态\\n\",\n \"Python的动态类型意味着您可以使用各种参数类型调用函数，Python将在每个场景中执行不同的操作。\\n\",\n \"\\n\",\n \"另一方面，TensorFlow图需要静态dtypes和形状尺寸。tf.function通过在必要时回溯函数来生成正确的图形来弥补这一差距。大多数使用的微妙tf.function源于这种回归行为。\\n\",\n \"\\n\",\n \"您可以使用不同类型的参数调用函数来查看正在发生的事情。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 168\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 2771,\n \"status\": \"ok\",\n \"timestamp\": 1560061694358,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"83W6rdiTyUJG\",\n \"outputId\": \"ac5569a7-6f56-473a-b575-7313c060757f\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"追踪变量： Tensor(\\\"a:0\\\", shape=(), dtype=int32)\\n\",\n \"结果: tf.Tensor(2, shape=(), dtype=int32)\\n\",\n \"\\n\",\n \"追踪变量： Tensor(\\\"a:0\\\", shape=(), dtype=float32)\\n\",\n \"结果: tf.Tensor(2.2, shape=(), dtype=float32)\\n\",\n \"\\n\",\n \"追踪变量： Tensor(\\\"a:0\\\", shape=(), dtype=string)\\n\",\n \"结果: tf.Tensor(b'cc', shape=(), dtype=string)\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 函数的多态\\n\",\n \"@tf.function\\n\",\n \"def double(a):\\n\",\n \" print('追踪变量：',a)\\n\",\n \" return a + a\\n\",\n \"\\n\",\n \"print('结果:',double(tf.constant(1)))\\n\",\n \"print()\\n\",\n \"print('结果:',double(tf.constant(1.1)))\\n\",\n \"print()\\n\",\n \"print('结果:',double(tf.constant('c')))\\n\",\n \"print()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"UI5P2YJ_1kZ8\"\n },\n \"source\": [\n \"控制参数类型：\\n\",\n \"创建一个新的tf.function。tf.function确保单独的对象不共享跟踪。\\n\",\n \"使用该get_concrete_function方法获取特定追踪\\n\",\n \"指定input_signature何时调用tf.function以确保仅构建一个功能图。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 171\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 2763,\n \"status\": \"ok\",\n \"timestamp\": 1560061694359,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"YSm_G3eQ0H2y\",\n \"outputId\": \"b7c1a01e-105c-423b-c673-97f96f8554e2\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"构建许可的追踪\\n\",\n \"追踪变量： Tensor(\\\"a:0\\\", dtype=string)\\n\",\n \"执行追踪函数\\n\",\n \"tf.Tensor(b'aa', shape=(), dtype=string)\\n\",\n \"tf.Tensor(b'bb', shape=(), dtype=string)\\n\",\n \"使用不合法参数\\n\",\n \"Caught expected exception \\n\",\n \" : cannot compute __inference_double_98 as input #0(zero-based) was expected to be a string tensor but is a int32 tensor [Op:__inference_double_98]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('构建许可的追踪')\\n\",\n \"double_strings = double.get_concrete_function(tf.TensorSpec(shape=None, dtype=tf.string))\\n\",\n \"print(\\\"执行追踪函数\\\")\\n\",\n \"print(double_strings(tf.constant(\\\"a\\\")))\\n\",\n \"print(double_strings(a=tf.constant(\\\"b\\\")))\\n\",\n \"print(\\\"使用不合法参数\\\")\\n\",\n \"with assert_raises(tf.errors.InvalidArgumentError):\\n\",\n \" double_strings(tf.constant(1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 138\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 3066,\n \"status\": \"ok\",\n \"timestamp\": 1560061694671,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"S1IzrEGm2foY\",\n \"outputId\": \"5a49a349-6b9e-469a-f10a-ab4287960fee\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tracing with Tensor(\\\"x:0\\\", shape=(None,), dtype=int32)\\n\",\n \"tf.Tensor([4 1], shape=(2,), dtype=int32)\\n\",\n \"Caught expected exception \\n\",\n \" : Python inputs incompatible with input_signature: inputs ((,)), input_signature ((TensorSpec(shape=(None,), dtype=tf.int32, name=None),))\\n\"\n ]\n }\n ],\n \"source\": [\n \"@tf.function(input_signature=(tf.TensorSpec(shape=[None], dtype=tf.int32),))\\n\",\n \"def next_collatz(x):\\n\",\n \" print(\\\"Tracing with\\\", x)\\n\",\n \" return tf.where(tf.equal(x % 2, 0), x // 2, 3 * x + 1)\\n\",\n \"\\n\",\n \"print(next_collatz(tf.constant([1, 2])))\\n\",\n \"# 只能输入1维向量\\n\",\n \"with assert_raises(ValueError):\\n\",\n \" next_collatz(tf.constant([[1, 2], [3, 4]]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"-g6JfGdh4lDU\"\n },\n \"source\": [\n \"## 什么时候回溯？\\n\",\n \"多态tf.function通过跟踪生成具体函数的缓存。缓存键实际上是从函数args和kwargs生成的键的元组。为tf.Tensor参数生成的关键是其形状和类型。为Python原语生成的密钥是它的值。对于所有其他Python类型，键都基于对象，id()以便为每个类的实例独立跟踪方法。将来，TensorFlow可以为Python对象添加更复杂的缓存，可以安全地转换为张量。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"sBW4e19c50j-\"\n },\n \"source\": [\n \"## 使用Python参数还是Tensors参数？\\n\",\n \"通常，Python的参数被用来控制超参数和图形的结构-例如，num_layers=10或training=True或nonlinearity='relu'。因此，如果Python参数发生变化，那么必须回溯图。\\n\",\n \"\\n\",\n \"但是，Python参数可能不会用于控制图构造。在这些情况下，Python值的变化可能会触发不必要的回溯。举例来说，这个训练循环，AutoGraph将动态展开。尽管存在多条迹线，但生成的图实际上是相同的，因此这有点低效。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 50\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 3054,\n \"status\": \"ok\",\n \"timestamp\": 1560061694671,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"2Vy9Zrkr4LBi\",\n \"outputId\": \"ed6cc253-339c-4c15-d57a-6f456ca8da56\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"追踪： num_steps = 10\\n\",\n \"追踪： num_steps = 20\\n\"\n ]\n }\n ],\n \"source\": [\n \"def train_one_step():\\n\",\n \" pass\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def train(num_steps):\\n\",\n \" print(\\\"追踪： num_steps = {}\\\".format(num_steps))\\n\",\n \" for _ in tf.range(num_steps):\\n\",\n \" train_one_step()\\n\",\n \"\\n\",\n \"train(num_steps=10)\\n\",\n \"train(num_steps=20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 3374,\n \"status\": \"ok\",\n \"timestamp\": 1560061694999,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"yZIGmt776ORD\",\n \"outputId\": \"453e97ff-1d67-45fa-f397-d5535fba0da2\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"追踪： num_steps = Tensor(\\\"num_steps:0\\\", shape=(), dtype=int32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 使用tensor，同类型不会重复追踪\\n\",\n \"train(num_steps=tf.constant(10))\\n\",\n \"train(num_steps=tf.constant(20))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 3368,\n \"status\": \"ok\",\n \"timestamp\": 1560061695000,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"Bq1MGQQI6ZEk\",\n \"outputId\": \"a9a618d9-8203-4e48-dba9-480717f34aa1\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"追踪： num_steps = Tensor(\\\"num_steps:0\\\", shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 使用tensor，类型不同才会有新的追踪，（前一个单元格已追踪int型，所以该处不追踪）\\n\",\n \"train(num_steps=tf.constant(10, dtype=tf.int32))\\n\",\n \"train(num_steps=tf.constant(20.6))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"o5KQqFTK79aP\"\n },\n \"source\": [\n \"## 副作用 tf.function\\n\",\n \"通常，Python副作用（如打印或变异对象）仅在跟踪期间发生。你怎么能可靠地触发副作用tf.function呢？\\n\",\n \"\\n\",\n \"一般的经验法则是仅使用Python副作用来调试跟踪。但是，TensorFlow操作类似于tf.Variable.assign，tf.print并且tf.summary是确保TensorFlow运行时在每次调用时跟踪和执行代码的最佳方法。通常使用功能样式将产生最佳结果。\\n\",\n \"\\n\",\n \"tf.function函数中的print()被用于跟踪，所以要调试输出每次调用(副作用),就需要tf.function()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"nNE9tUEU6fzd\"\n },\n \"outputs\": [],\n \"source\": [\n \"@tf.function\\n\",\n \"def f(x):\\n\",\n \" print(\\\"追踪：\\\", x)\\n\",\n \" tf.print('执行：', x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 101\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 730,\n \"status\": \"ok\",\n \"timestamp\": 1560062207799,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"AIbz4shg8-TM\",\n \"outputId\": \"1e0f69aa-58c1-4160-b341-847959f204a7\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"追踪： 1\\n\",\n \"执行： 1\\n\",\n \"执行： 1\\n\",\n \"追踪： 2\\n\",\n \"执行： 2\\n\"\n ]\n }\n ],\n \"source\": [\n \"f(1)\\n\",\n \"f(1)\\n\",\n \"f(2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"lhGssQgi9dZ8\"\n },\n \"source\": [\n \"如果想在每次调用期间执行Python代码tf.function，可以使用tf.py_function。tf.py_function缺点是它不便携和高效，也不能在分布式（多GPU，TPU）设置中很好地工作。此外，由于tf.py_function必须连接到图，它将所有输入/输出转换为张量。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 171\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 732,\n \"status\": \"ok\",\n \"timestamp\": 1560062465050,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"E64cIU0H9CQu\",\n \"outputId\": \"7d5414cc-807e-4fc4-d84d-684af63fb851\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING: Logging before flag parsing goes to stderr.\\n\",\n \"W0609 06:41:05.048375 139792217777920 backprop.py:842] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.int32\\n\",\n \"W0609 06:41:05.053524 139792217777920 backprop.py:842] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.int32\\n\",\n \"W0609 06:41:05.056409 139792226170624 backprop.py:842] The dtype of the watched tensor must be floating (e.g. tf.float32), got tf.int32\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Python side effect\\n\",\n \"Python side effect\\n\",\n \"Python side effect\\n\",\n \"[, , ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"external_list = []\\n\",\n \"\\n\",\n \"def side_effect(x):\\n\",\n \" print('Python side effect')\\n\",\n \" external_list.append(x)\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def f(x):\\n\",\n \" tf.py_function(side_effect, inp=[x], Tout=[])\\n\",\n \"\\n\",\n \"f(1)\\n\",\n \"f(1)\\n\",\n \"f(1)\\n\",\n \"print(external_list)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"qEvUyInkCWJN\"\n },\n \"source\": [\n \"## 谨防Python状态\\n\",\n \"许多Python功能（如生成器和迭代器）依赖于Python运行时来跟踪状态。 通常，虽然这些构造在Eager模式下按预期工作，但由于跟踪行为，tf.function内部可能会发生许多意外情况。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 67\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 904,\n \"status\": \"ok\",\n \"timestamp\": 1560063871419,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"APe8jOwi-BES\",\n \"outputId\": \"4ed69333-d8b7-4e8e-c23d-155e47fd811d\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"external_var: 0\\n\",\n \"external_var: 0\\n\",\n \"external_var: 0\\n\"\n ]\n }\n ],\n \"source\": [\n \"external_var = tf.Variable(0)\\n\",\n \"@tf.function\\n\",\n \"def buggy_consume_next(iterator):\\n\",\n \" external_var.assign_add(next(iterator))\\n\",\n \" tf.print('external_var:', external_var)\\n\",\n \" \\n\",\n \"iterator = iter([0,1,2,3])\\n\",\n \"buggy_consume_next(iterator)\\n\",\n \"# 后面没有正常迭代，输出的都是第一个\\n\",\n \"buggy_consume_next(iterator)\\n\",\n \"buggy_consume_next(iterator)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"eBOKO-DZDlcU\"\n },\n \"source\": [\n \"如果在tf.function中生成并完全使用了迭代器，那么它应该可以正常工作。但是，整个迭代器可能正在被跟踪，这可能导致一个巨大的图。如果正在训练一个表示为Python列表的大型内存数据集，那么这会生成一个非常大的图，并且tf.function不太可能产生加速。\\n\",\n \"\\n\",\n \"如果要迭代Python数据，最安全的方法是将其包装在tf.data.Dataset中并使用该for x in y惯用法。AutoGraph特别支持for在y张量或tf.data.Dataset 时安全地转换循环。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 104\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 954,\n \"status\": \"ok\",\n \"timestamp\": 1560064507338,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"A5yosSxIDMMj\",\n \"outputId\": \"39b311d3-c6b6-4139-9b61-ccb3c803e107\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"train([(1, 1), (1, 1)]) 的图中包含了 8 个节点\\n\",\n \"train([(1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1)]) 的图中包含了 32 个节点\\n\",\n \"train(, ), types: (tf.int32, tf.int32)>) 的图中包含了 4 个节点\\n\",\n \"train(, ), types: (tf.int32, tf.int32)>) 的图中包含了 4 个节点\\n\"\n ]\n }\n ],\n \"source\": [\n \"def measure_graph_size(f, *args):\\n\",\n \" g = f.get_concrete_function(*args).graph\\n\",\n \" print(\\\"{}({}) 的图中包含了 {} 个节点\\\".format(\\n\",\n \" f.__name__, ', '.join(map(str, args)), len(g.as_graph_def().node)))\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def train(dataset):\\n\",\n \" loss = tf.constant(0)\\n\",\n \" for x, y in dataset:\\n\",\n \" loss += tf.abs(y - x) # Some dummy computation.\\n\",\n \" return loss\\n\",\n \"\\n\",\n \"small_data = [(1, 1)] * 2\\n\",\n \"big_data = [(1, 1)] * 10\\n\",\n \"measure_graph_size(train, small_data)\\n\",\n \"measure_graph_size(train, big_data)\\n\",\n \"\\n\",\n \"measure_graph_size(train, tf.data.Dataset.from_generator(\\n\",\n \" lambda: small_data, (tf.int32, tf.int32)))\\n\",\n \"measure_graph_size(train, tf.data.Dataset.from_generator(\\n\",\n \" lambda: big_data, (tf.int32, tf.int32)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"u_c0lfVhF-MU\"\n },\n \"source\": [\n \"在数据集中包装Python / Numpy数据时，请注意tf.data.Dataset.from_generator与tf.data.Dataset.from_tensors。前者将数据保存在Python中并通过tf.py_function它获取性能影响，而后者将数据的副本捆绑为图中的一个大tf.constant()节点，这可能会对内存产生影响。\\n\",\n \"\\n\",\n \"通过TFRecordDataset / CsvDataset / etc从文件中读取数据。是最有效的数据处理方式，因为TensorFlow本身可以管理数据的异步加载和预取，而不必涉及Python。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"TEMIwcR9GZNS\"\n },\n \"source\": [\n \"## 自动控制依赖项\\n\",\n \"在一般数据流图上，作为编程模型的函数的一个非常吸引人的特性是函数可以为运行时提供有关代码的预期行为的更多信息。\\n\",\n \"\\n\",\n \"例如，当编写具有多个读取和写入相同变量的代码时，数据流图可能不会自然地编码最初预期的操作顺序。在tf.function，我们通过引用原始Python代码中的语句的执行顺序来解决执行顺序中的歧义。这样，有序状态操作的排序tf.function复制了Eager模式的语义。\\n\",\n \"\\n\",\n \"这意味着不需要添加手动控制依赖项; tf.function足够聪明，可以为代码添加最小的必要和充分的控制依赖关系，以便正确运行。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 817,\n \"status\": \"ok\",\n \"timestamp\": 1560064847261,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"1hZ5UACIFOwG\",\n \"outputId\": \"f0ed92ab-024a-4c06-b9ea-53a280adf9cd\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 按顺序自动执行\\n\",\n \"a = tf.Variable(1.0)\\n\",\n \"b = tf.Variable(2.0)\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def f(x, y):\\n\",\n \" a.assign(y * b)\\n\",\n \" b.assign_add(x * a)\\n\",\n \" return a + b\\n\",\n \"\\n\",\n \"f(1.0, 2.0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"sCyejiCzHg9s\"\n },\n \"source\": [\n \"变量\\n\",\n \"我们可以使用相同的想法来利用代码的预期执行顺序，使变量创建和利用变得非常容易tf.function。但是有一个非常重要的警告，即使用变量，可以编写在急切模式和图形模式下表现不同的代码。\\n\",\n \"\\n\",\n \"具体来说，每次调用创建一个新变量时都会发生这种情况。由于跟踪语义，tf.function每次调用都会重用相同的变量，但是eager模式会在每次调用时创建一个新变量。为防止出现此错误，tf.function如果检测到危险变量创建行为，则会引发错误。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 306\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1011,\n \"status\": \"ok\",\n \"timestamp\": 1560065070996,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"MvEHlVfOHsY2\",\n \"outputId\": \"78e52b1a-9160-4dba-f12a-27117d314cf7\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Caught expected exception \\n\",\n \" : in converted code:\\n\",\n \"\\n\",\n \" :4 f *\\n\",\n \" v = tf.Variable(1.0)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py:262 __call__\\n\",\n \" return cls._variable_v2_call(*args, **kwargs)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py:256 _variable_v2_call\\n\",\n \" shape=shape)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py:60 getter\\n\",\n \" return captured_getter(captured_previous, **kwargs)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py:364 invalid_creator_scope\\n\",\n \" \\\"tf.function-decorated function tried to create \\\"\\n\",\n \"\\n\",\n \" ValueError: tf.function-decorated function tried to create variables on non-first call.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"@tf.function\\n\",\n \"def f(x):\\n\",\n \" # tf.function会重复调用相同变量，而eager每次都会创建新的变量\\n\",\n \" v = tf.Variable(1.0)\\n\",\n \" v.assign_add(x)\\n\",\n \" return v\\n\",\n \"\\n\",\n \"with assert_raises(ValueError):\\n\",\n \" f(1.0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"UB6kOLf2Hqim\"\n },\n \"source\": [\n \"不会报错的方法是\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 50\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 827,\n \"status\": \"ok\",\n \"timestamp\": 1560065172070,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"-HPVyn4nINGe\",\n \"outputId\": \"16c68f4c-11fb-4829-e3fa-ff4e539af814\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(2.0, shape=(), dtype=float32)\\n\",\n \"tf.Tensor(4.0, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"v = tf.Variable(1.0) # 把变量拿到tf.function外面\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def f(x):\\n\",\n \" return v.assign_add(x)\\n\",\n \"\\n\",\n \"print(f(1.0)) # 2.0\\n\",\n \"print(f(2.0)) # 4.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"X8lVcCU4IJk7\"\n },\n \"source\": [\n \"也可以在tf.function中创建变量，只要可以保证这些变量仅在第一次执行函数时创建。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 50\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 839,\n \"status\": \"ok\",\n \"timestamp\": 1560065271907,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"Os5GPslSG6_3\",\n \"outputId\": \"56d6f91d-4a8d-4712-e0a6-8a54bc5e3372\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(2.0, shape=(), dtype=float32)\\n\",\n \"tf.Tensor(4.0, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"class C: pass\\n\",\n \"obj = C(); obj.v = None\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def g(x):\\n\",\n \" if obj.v is None:\\n\",\n \" obj.v = tf.Variable(1.0)\\n\",\n \" return obj.v.assign_add(x)\\n\",\n \"\\n\",\n \"print(g(1.0)) # 2.0\\n\",\n \"print(g(2.0)) # 4.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"enPov9K2IvkW\"\n },\n \"source\": [\n \"变量初始值设定项可以依赖于函数参数和其他变量的值。 我们可以使用与生成控制依赖关系相同的方法找出正确的初始化顺序。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 50\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1182,\n \"status\": \"ok\",\n \"timestamp\": 1560065512639,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"NlpF8mUYJl86\",\n \"outputId\": \"3caac82d-395f-4d42-a28e-b5057b40d66f\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(12.0, shape=(), dtype=float32)\\n\",\n \"tf.Tensor(36.0, shape=(), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"state = []\\n\",\n \"@tf.function\\n\",\n \"def fn(x):\\n\",\n \" if not state:\\n\",\n \" state.append(tf.Variable(2.0 * x))\\n\",\n \" state.append(tf.Variable(state[0] * 3.0))\\n\",\n \" return state[0] * x * state[1]\\n\",\n \"\\n\",\n \"print(fn(tf.constant(1.0)))\\n\",\n \"print(fn(tf.constant(3.0)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"t6i29GwEJlAk\"\n },\n \"source\": [\n \"## 使用AutoGraph\\n\",\n \"该签名库完全集成tf.function，它将改写条件和循环依赖于张量在图形动态运行。\\n\",\n \"\\n\",\n \"tf.cond并且tf.while_loop继续使用tf.function，但是当以命令式样式编写时，具有控制流的代码通常更容易编写和理解。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 655\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 938,\n \"status\": \"ok\",\n \"timestamp\": 1560065826332,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"lKn39wWTKMQB\",\n \"outputId\": \"76e9991c-90f8-4d43-ca9b-8a8a74ca9960\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0.829342961 0.858322263 0.900950909 0.851897 0.530384183]\\n\",\n \"[0.680123031 0.695392191 0.716760576 0.692059278 0.485674709]\\n\",\n \"[0.591599405 0.601434886 0.614898741 0.599303305 0.450776756]\\n\",\n \"[0.53104496 0.538069844 0.547566235 0.536553681 0.422537297]\\n\",\n \"[0.486179501 0.491525501 0.498693913 0.490374774 0.399065822]\\n\",\n \"[0.451178908 0.455426365 0.461089343 0.454513818 0.379149348]\\n\",\n \"[0.422867566 0.426349223 0.430971652 0.425602287 0.361968517]\\n\",\n \"[0.399343461 0.402265817 0.406133026 0.401639521 0.346946776]\\n\",\n \"[0.379387051 0.381885976 0.385184318 0.381350905 0.333665]\\n\",\n \"[0.362175018 0.36434418 0.367201209 0.363880038 0.321810097]\\n\",\n \"[0.347128421 0.349034756 0.351541221 0.348627061 0.311142713]\\n\",\n \"[0.333826423 0.335519224 0.337741673 0.335157365 0.30147627]\\n\",\n \"[0.321954757 0.323471278 0.325459719 0.323147237 0.292663]\\n\",\n \"[0.311273336 0.312642276 0.314435244 0.312349856 0.284584]\\n\",\n \"[0.301595032 0.302838922 0.304466605 0.302573323 0.277142316]\\n\",\n \"[0.292771578 0.293908447 0.295394808 0.293665737 0.270258158]\\n\",\n \"[0.284683794 0.285728157 0.287092626 0.285505235 0.263865024]\\n\",\n \"[0.277234435 0.278198302 0.279456645 0.277992576 0.257907033]\\n\",\n \"[0.270343572 0.271236718 0.272402078 0.271046132 0.25233686]\\n\",\n \"[0.263944477 0.264775217 0.265858531 0.264597982 0.247114092]\\n\",\n \"[0.257981181 0.258756459 0.259766966 0.258591145 0.242203966]\\n\",\n \"[0.252406299 0.253132015 0.254077554 0.252977312 0.237576365]\\n\",\n \"[0.24717927 0.247860536 0.248747766 0.247715324 0.233205199]\\n\",\n \"[0.242265314 0.242906466 0.24374117 0.242769822 0.229067564]\\n\",\n \"[0.237634286 0.238239139 0.239026278 0.238110229 0.225143358]\\n\",\n \"[0.233259991 0.233831868 0.234575793 0.233709976 0.221414775]\\n\",\n \"[0.229119495 0.229661271 0.230365857 0.229545817 0.217866093]\\n\",\n \"[0.225192651 0.22570689 0.22637549 0.225597292 0.214483246]\\n\",\n \"[0.221461684 0.221950635 0.222586185 0.221846417 0.211253688]\\n\",\n \"[0.217910782 0.218376443 0.218981609 0.218277216 0.208166167]\\n\",\n \"[0.214525893 0.214970052 0.215547174 0.214875415 0.205210552]\\n\",\n \"[0.211294428 0.211718708 0.212269917 0.211628318 0.202377662]\\n\",\n \"[0.208205134 0.208611 0.209138155 0.20852454 0.199659243]\\n\",\n \"[0.205247864 0.205636591 0.206141427 0.2055538 0.197047815]\\n\",\n \"[0.20241344 0.202786222 0.203270242 0.202706844 0.194536477]\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 简单的循环\\n\",\n \"@tf.function\\n\",\n \"def f(x):\\n\",\n \" # 直接用python中的while写循环\\n\",\n \" while tf.reduce_sum(x) > 1:\\n\",\n \" tf.print(x)\\n\",\n \" x = tf.tanh(x)\\n\",\n \" return x\\n\",\n \"f(tf.random.uniform([5]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1101,\n \"status\": \"ok\",\n \"timestamp\": 1560065918037,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"Pf9vcB5qIuTj\",\n \"outputId\": \"3b0c472c-ce7b-4d18-b6c2-0721e33264ab\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"x6m7Js_-LXqM\"\n },\n \"source\": [\n \"可以检查代码签名生成。 但感觉就像阅读汇编语言一样。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 558\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1260,\n \"status\": \"ok\",\n \"timestamp\": 1560065998816,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"MTgjr-RHLcy8\",\n \"outputId\": \"9cc417bc-e624-4528-96d2-be1d78a3f6b2\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"def tf__f(x):\\n\",\n \" do_return = False\\n\",\n \" retval_ = ag__.UndefinedReturnValue()\\n\",\n \"\\n\",\n \" def loop_test(x_1):\\n\",\n \" return ag__.converted_call('reduce_sum', tf, ag__.ConversionOptions(recursive=True, force_conversion=False, optional_features=(), internal_convert_user_code=True), (x_1,), None) > 1\\n\",\n \"\\n\",\n \" def loop_body(x_1):\\n\",\n \" ag__.converted_call('print', tf, ag__.ConversionOptions(recursive=True, force_conversion=False, optional_features=(), internal_convert_user_code=True), (x_1,), None)\\n\",\n \" x_1 = ag__.converted_call('tanh', tf, ag__.ConversionOptions(recursive=True, force_conversion=False, optional_features=(), internal_convert_user_code=True), (x_1,), None)\\n\",\n \" return x_1,\\n\",\n \" x, = ag__.while_stmt(loop_test, loop_body, (x,))\\n\",\n \" do_return = True\\n\",\n \" retval_ = x\\n\",\n \" cond = ag__.is_undefined_return(retval_)\\n\",\n \"\\n\",\n \" def get_state():\\n\",\n \" return ()\\n\",\n \"\\n\",\n \" def set_state(_):\\n\",\n \" pass\\n\",\n \"\\n\",\n \" def if_true():\\n\",\n \" retval_ = None\\n\",\n \" return retval_\\n\",\n \"\\n\",\n \" def if_false():\\n\",\n \" return retval_\\n\",\n \" retval_ = ag__.if_stmt(cond, if_true, if_false, get_state, set_state)\\n\",\n \" return retval_\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"def f(x):\\n\",\n \" while tf.reduce_sum(x) > 1:\\n\",\n \" tf.print(x)\\n\",\n \" x = tf.tanh(x)\\n\",\n \" return x\\n\",\n \"\\n\",\n \"print(tf.autograph.to_code(f))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"GNaylIavLzFd\"\n },\n \"source\": [\n \"AutoGraph：条件\\n\",\n \"AutoGraph会将if语句转换为等效的tf.cond调用。\\n\",\n \"\\n\",\n \"如果条件是Tensor，则进行此替换。否则，在跟踪期间执行条件。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"FbkERy0-L4JI\"\n },\n \"outputs\": [],\n \"source\": [\n \"# 测试\\n\",\n \"def test_tf_cond(f, *args):\\n\",\n \" # 获取图\\n\",\n \" g = f.get_concrete_function(*args).graph\\n\",\n \" if any(node.name=='cond' for node in g.as_graph_def().node):\\n\",\n \" print(\\\"{}({}) 使用 tf.cond.\\\".format(\\n\",\n \" f.__name__, ', '.join(map(str, args))))\\n\",\n \" else:\\n\",\n \" print(\\\"{}({}) 正常执行.\\\".format(\\n\",\n \" f.__name__, ', '.join(map(str, args))))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"PtVaFUIfM-Zq\"\n },\n \"source\": [\n \"只有条件为tensor，才会使用tf.cond\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 67\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1043,\n \"status\": \"ok\",\n \"timestamp\": 1560066497194,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"8uJQ8aVENPNs\",\n \"outputId\": \"57df931f-0a41-41c1-e6dc-cd32cc6a40fd\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"hyperparam_cond(tf.Tensor([1.], shape=(1,), dtype=float32)) 正常执行.\\n\",\n \"maybe_tensor_cond(tf.Tensor(-1, shape=(), dtype=int32)) 使用 tf.cond.\\n\",\n \"maybe_tensor_cond(-1) 正常执行.\\n\"\n ]\n }\n ],\n \"source\": [\n \"@tf.function\\n\",\n \"def hyperparam_cond(x, training=True):\\n\",\n \" if training:\\n\",\n \" x = tf.nn.dropout(x, rate=0.5)\\n\",\n \" return x\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def maybe_tensor_cond(x):\\n\",\n \" if x < 0:\\n\",\n \" x = -x\\n\",\n \" return x\\n\",\n \"\\n\",\n \"test_tf_cond(hyperparam_cond, tf.ones([1], dtype=tf.float32))\\n\",\n \"test_tf_cond(maybe_tensor_cond, tf.constant(-1)) # 条件为tensor\\n\",\n \"test_tf_cond(maybe_tensor_cond, -1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"fT9Eeg80NrN5\"\n },\n \"source\": [\n \"tf.cond有一些细微之处。 - 它的工作原理是跟踪条件的两边，然后根据条件在运行时选择适当的分支。跟踪双方可能导致意外执行Python代码 - 它要求如果一个分支创建下游使用的张量，另一个分支也必须创建该张量。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 84\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 911,\n \"status\": \"ok\",\n \"timestamp\": 1560066757390,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"BIOripk2Nqbv\",\n \"outputId\": \"23be71ec-0b08-4028-f65c-e1b50da23f65\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Tracing `then` branch\\n\",\n \"Tracing `else` branch\\n\",\n \"执行，x： 1\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"@tf.function\\n\",\n \"def f():\\n\",\n \" x = tf.constant(0)\\n\",\n \" if tf.constant(True): \\n\",\n \" x = x + 1\\n\",\n \" tf.print('执行，x：', x)\\n\",\n \" print(\\\"Tracing `then` branch\\\")\\n\",\n \" else:\\n\",\n \" x = x - 1\\n\",\n \" tf.print('执行，x：', x) # 没有执行\\n\",\n \" print(\\\"Tracing `else` branch\\\") # 该分支虽然不执行但也被追踪\\n\",\n \" return x\\n\",\n \"\\n\",\n \"f()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"GrqHlCpmLb-M\"\n },\n \"source\": [\n \"两个分支必须都定义x\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 440\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 847,\n \"status\": \"ok\",\n \"timestamp\": 1560066896672,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"SKnZT9UyOqtS\",\n \"outputId\": \"75b09b9f-dad1-4cb3-fb78-459db7d791f7\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Caught expected exception \\n\",\n \" : in converted code:\\n\",\n \"\\n\",\n \" :3 f *\\n\",\n \" if tf.constant(True):\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/operators/control_flow.py:439 if_stmt\\n\",\n \" return tf_if_stmt(cond, body, orelse, get_state, set_state)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/operators/control_flow.py:456 tf_if_stmt\\n\",\n \" outputs, final_state = control_flow_ops.cond(cond, body, orelse)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/deprecation.py:507 new_func\\n\",\n \" return func(*args, **kwargs)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/control_flow_ops.py:1147 cond\\n\",\n \" return cond_v2.cond_v2(pred, true_fn, false_fn, name)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/cond_v2.py:86 cond_v2\\n\",\n \" op_return_value=pred)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py:716 func_graph_from_py_func\\n\",\n \" func_outputs = python_func(*func_args, **func_kwargs)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/operators/control_flow.py:486 wrapper\\n\",\n \" outputs = func()\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/operators/control_flow.py:512 wrapper\\n\",\n \" tuple(s.symbol_name for s in undefined)))\\n\",\n \"\\n\",\n \" ValueError: The following symbols must also be initialized in the else branch: ('x',). Alternatively, you may initialize them before the if statement.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"@tf.function\\n\",\n \"def f():\\n\",\n \" if tf.constant(True):\\n\",\n \" x = tf.ones([3, 3])\\n\",\n \" return x\\n\",\n \"\\n\",\n \"# 两个分支必须都定义x， 否则会抛出异常\\n\",\n \"with assert_raises(ValueError):\\n\",\n \" f()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"aDnjKM7EPNBK\"\n },\n \"source\": [\n \"AutoGraph和循环\\n\",\n \"AutoGraph有一些简单的转换循环规则。\\n\",\n \"\\n\",\n \"- for：如果iterable是张量，则转换\\n\",\n \"- while：如果while条件取决于张量，则转换\\n\",\n \"\\n\",\n \"\\n\",\n \"如果循环被转换，它将被动态展开tf.while_loop，或者在a的特殊情况下for x in tf.data.Dataset转换为tf.data.Dataset.reduce。\\n\",\n \"\\n\",\n \"如果未转换循环，则将静态展开\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"P5X7vKnTPcDa\"\n },\n \"outputs\": [],\n \"source\": [\n \"# 测试\\n\",\n \"def test_dynamically_unrolled(f, *args):\\n\",\n \" g = f.get_concrete_function(*args).graph\\n\",\n \" if any(node.name == 'while' for node in g.as_graph_def().node):\\n\",\n \" print(\\\"{}({}) uses tf.while_loop.\\\".format(\\n\",\n \" f.__name__, ', '.join(map(str, args))))\\n\",\n \" elif any(node.name == 'ReduceDataset' for node in g.as_graph_def().node):\\n\",\n \" print(\\\"{}({}) uses tf.data.Dataset.reduce.\\\".format(\\n\",\n \" f.__name__, ', '.join(map(str, args))))\\n\",\n \" else:\\n\",\n \" print(\\\"{}({}) gets unrolled.\\\".format(\\n\",\n \" f.__name__, ', '.join(map(str, args))))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 67\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1083,\n \"status\": \"ok\",\n \"timestamp\": 1560067150414,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"XP8vb1SvPvJE\",\n \"outputId\": \"9e9c3366-ff40-44b2-d7ef-6f797e251633\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"for_in_range() gets unrolled.\\n\",\n \"for_in_tfrange() uses tf.while_loop.\\n\",\n \"for_in_tfdataset() uses tf.data.Dataset.reduce.\\n\"\n ]\n }\n ],\n \"source\": [\n \"@tf.function\\n\",\n \"def for_in_range():\\n\",\n \" x = 0\\n\",\n \" for i in range(5):\\n\",\n \" x += i\\n\",\n \" return x\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def for_in_tfrange():\\n\",\n \" x = tf.constant(0, dtype=tf.int32)\\n\",\n \" for i in tf.range(5): # 生成迭代的张量\\n\",\n \" x += i\\n\",\n \" return x\\n\",\n \"\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def for_in_tfdataset():\\n\",\n \" x = tf.constant(0, dtype=tf.int64)\\n\",\n \" for i in tf.data.Dataset.range(5):\\n\",\n \" x += i\\n\",\n \" return x\\n\",\n \"\\n\",\n \"test_dynamically_unrolled(for_in_range)\\n\",\n \"test_dynamically_unrolled(for_in_tfrange)\\n\",\n \"test_dynamically_unrolled(for_in_tfdataset)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 50\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1310,\n \"status\": \"ok\",\n \"timestamp\": 1560067295790,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"oPAm3JkDQTVY\",\n \"outputId\": \"fd39137d-88e5-4bd1-e27a-1b30e1822b8e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"while_py_cond() gets unrolled.\\n\",\n \"while_tf_cond() uses tf.while_loop.\\n\"\n ]\n }\n ],\n \"source\": [\n \"@tf.function\\n\",\n \"def while_py_cond():\\n\",\n \" x = 5\\n\",\n \" while x > 0:\\n\",\n \" x -= 1\\n\",\n \" return x\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def while_tf_cond():\\n\",\n \" x = tf.constant(5)\\n\",\n \" while x > 0: # while中的x为张量\\n\",\n \" x -= 1\\n\",\n \" return x\\n\",\n \"\\n\",\n \"test_dynamically_unrolled(while_py_cond)\\n\",\n \"test_dynamically_unrolled(while_tf_cond)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"_yYDbvEoPaIs\"\n },\n \"source\": [\n \"如果有一个break或早期的return子句依赖于张量，那么顶级条件或者iterable也应该是一个张量。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 289\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1250,\n \"status\": \"ok\",\n \"timestamp\": 1560067489145,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"xM9fdNvRQym6\",\n \"outputId\": \"210c62b9-7f4a-4c65-8a7f-b37b6bea7946\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Caught expected exception \\n\",\n \" : in converted code:\\n\",\n \"\\n\",\n \" :3 buggy_while_py_true_tf_break *\\n\",\n \" while True:\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/operators/control_flow.py:313 while_stmt\\n\",\n \" return _py_while_stmt(test, body, init_state, opts)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/operators/control_flow.py:401 _py_while_stmt\\n\",\n \" while test(*state):\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:698 __bool__\\n\",\n \" raise TypeError(\\\"Using a `tf.Tensor` as a Python `bool` is not allowed. \\\"\\n\",\n \"\\n\",\n \" TypeError: Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor.\\n\",\n \"\\n\",\n \"while_tf_true_tf_break(5) uses tf.while_loop.\\n\"\n ]\n }\n ],\n \"source\": [\n \"@tf.function\\n\",\n \"def buggy_while_py_true_tf_break(x):\\n\",\n \" while True:\\n\",\n \" if tf.equal(x, 0):\\n\",\n \" break\\n\",\n \" x -= 1\\n\",\n \" return x\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def while_tf_true_tf_break(x):\\n\",\n \" while tf.constant(True): # 有break，顶级条件必须为张量\\n\",\n \" if tf.equal(x, 0):\\n\",\n \" break\\n\",\n \" x -= 1\\n\",\n \" return x\\n\",\n \"\\n\",\n \"with assert_raises(TypeError):\\n\",\n \" test_dynamically_unrolled(buggy_while_py_true_tf_break, 5)\\n\",\n \"test_dynamically_unrolled(while_tf_true_tf_break, 5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 289\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1223,\n \"status\": \"ok\",\n \"timestamp\": 1560067582158,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"0hkyGy13RU1Q\",\n \"outputId\": \"cbfe0309-f66c-472c-f5ec-1be88afbf255\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Caught expected exception \\n\",\n \" : in converted code:\\n\",\n \"\\n\",\n \" :4 buggy_py_for_tf_break *\\n\",\n \" for i in range(5):\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/operators/control_flow.py:110 for_stmt\\n\",\n \" return _py_for_stmt(iter_, extra_test, body, init_state)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/operators/control_flow.py:117 _py_for_stmt\\n\",\n \" if extra_test is not None and not extra_test(*state):\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:698 __bool__\\n\",\n \" raise TypeError(\\\"Using a `tf.Tensor` as a Python `bool` is not allowed. \\\"\\n\",\n \"\\n\",\n \" TypeError: Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor.\\n\",\n \"\\n\",\n \"tf_for_tf_break() uses tf.while_loop.\\n\"\n ]\n }\n ],\n \"source\": [\n \"@tf.function\\n\",\n \"def buggy_py_for_tf_break():\\n\",\n \" x = 0\\n\",\n \" for i in range(5):\\n\",\n \" if tf.equal(i, 3):\\n\",\n \" break\\n\",\n \" x += i\\n\",\n \" return x\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def tf_for_tf_break():\\n\",\n \" x = 0\\n\",\n \" for i in tf.range(5): # 有break，顶级迭代器必须为张量\\n\",\n \" if tf.equal(i, 3):\\n\",\n \" break\\n\",\n \" x += i\\n\",\n \" return x\\n\",\n \"\\n\",\n \"with assert_raises(TypeError):\\n\",\n \" test_dynamically_unrolled(buggy_py_for_tf_break)\\n\",\n \"test_dynamically_unrolled(tf_for_tf_break)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"pD_vDEaGR4BW\"\n },\n \"source\": [\n \"为了累积动态展开循环的结果，需要使用tf.TensorArray。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 2167\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 897,\n \"status\": \"ok\",\n \"timestamp\": 1560069110088,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"B488EpdZROn5\",\n \"outputId\": \"860a02e0-a31c-4e97-b9bd-20f3e432c7df\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 实现一个动态rnn\\n\",\n \"batch_size = 32\\n\",\n \"seq_len = 3\\n\",\n \"feature_size=4\\n\",\n \"# rnn步，输入与状态叠加\\n\",\n \"def rnn_step(inputs, state):\\n\",\n \" return inputs + state\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def dynamic_rnn(rnn_step, input_data, initial_state):\\n\",\n \" # [batch, time, features] -> [time, batch, features]\\n\",\n \" input_data = tf.transpose(input_data, [1, 0, 2]) # 每个时间维度，都是整个batch数据喂入\\n\",\n \" max_seq_len = input_data.shape[0]\\n\",\n \" \\n\",\n \" # 保存循环中的状态，必须使用tf.TensorArray\\n\",\n \" states = tf.TensorArray(tf.float32, size=max_seq_len)\\n\",\n \" state = initial_state\\n\",\n \" # 迭代时间步\\n\",\n \" for i in tf.range(max_seq_len):\\n\",\n \" state = rnn_step(input_data[i], state)\\n\",\n \" states = states.write(i, state)\\n\",\n \" # 把 batch_size重新换到前面\\n\",\n \" return tf.transpose(states.stack(), [1, 0, 2])\\n\",\n \" \\n\",\n \" \\n\",\n \"dynamic_rnn(rnn_step,\\n\",\n \" tf.random.uniform([batch_size, seq_len, feature_size]),\\n\",\n \" tf.zeros([batch_size, feature_size]))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"FIq22kd-QjdU\"\n },\n \"source\": [\n \"与此同时tf.cond，tf.while_loop还带有一些细微之处。 - 由于循环可以执行0次，因此必须在循环上方初始化在while_loop下游使用的所有张量 - 所有循环变量的形状/ dtypes必须与每次迭代保持一致\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 289\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1137,\n \"status\": \"ok\",\n \"timestamp\": 1560069233709,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"OpvijyewXioF\",\n \"outputId\": \"3cb3ba34-30a2-4207-8333-bfb65e4f2d1c\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Caught expected exception \\n\",\n \" : in converted code:\\n\",\n \"\\n\",\n \" :3 buggy_loop_var_uninitialized *\\n\",\n \" for i in tf.range(3):\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/operators/control_flow.py:95 for_stmt\\n\",\n \" return _known_len_tf_for_stmt(iter_, extra_test, body, init_state)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/operators/control_flow.py:125 _known_len_tf_for_stmt\\n\",\n \" _disallow_undefs_into_loop(*init_state)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/operators/control_flow.py:50 _disallow_undefs_into_loop\\n\",\n \" tuple(s.symbol_name for s in undefined)))\\n\",\n \"\\n\",\n \" ValueError: TensorFlow requires that the following symbols must be defined before the loop: ('x',)\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"@tf.function\\n\",\n \"def buggy_loop_var_uninitialized():\\n\",\n \" for i in tf.range(3):\\n\",\n \" x = i # 必须在循环上方初始化好x\\n\",\n \" return x\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def f():\\n\",\n \" x = tf.constant(0)\\n\",\n \" for i in tf.range(3):\\n\",\n \" x = i\\n\",\n \" return x\\n\",\n \"\\n\",\n \"with assert_raises(ValueError):\\n\",\n \" buggy_loop_var_uninitialized()\\n\",\n \"f()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"OGkKNLOAX90B\"\n },\n \"source\": [\n \"循环时 变量的类型不能改变\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 70\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1083,\n \"status\": \"ok\",\n \"timestamp\": 1560069367284,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"z5RAJ-OpX7fr\",\n \"outputId\": \"3bc7eccc-0b42-40bc-a147-cf01336aaba0\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Caught expected exception \\n\",\n \" : Input 1 of node while/merge/_10 was passed int32 from while/next_iteration/_28:0 incompatible with expected float. [Op:__inference_buggy_loop_type_changes_2119]\\n\"\n ]\n }\n ],\n \"source\": [\n \"@tf.function\\n\",\n \"def buggy_loop_type_changes():\\n\",\n \" x = tf.constant(0, dtype=tf.float32)\\n\",\n \" for i in tf.range(3): # Yields tensors of type tf.int32...\\n\",\n \" x = i\\n\",\n \" return x\\n\",\n \"\\n\",\n \"with assert_raises(tf.errors.InvalidArgumentError):\\n\",\n \" buggy_loop_type_changes()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"L-mfJKvNPBGN\"\n },\n \"source\": [\n \"循环时变量形状也不能改变\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 474\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 1343,\n \"status\": \"ok\",\n \"timestamp\": 1560069506649,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"7q_6LL4-YwkE\",\n \"outputId\": \"a88ed062-24b3-4a69-c349-2aa7bbee361a\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Caught expected exception \\n\",\n \" : in converted code:\\n\",\n \"\\n\",\n \" :4 buggy_concat *\\n\",\n \" for i in tf.range(5):\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/operators/control_flow.py:95 for_stmt\\n\",\n \" return _known_len_tf_for_stmt(iter_, extra_test, body, init_state)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/operators/control_flow.py:156 _known_len_tf_for_stmt\\n\",\n \" opts=dict(maximum_iterations=n))\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/operators/control_flow.py:327 _tf_while_stmt\\n\",\n \" retval = control_flow_ops.while_loop(test, body, init_state, **opts)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/control_flow_ops.py:2646 while_loop\\n\",\n \" return_same_structure=return_same_structure)\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/while_v2.py:213 while_loop\\n\",\n \" len_orig_loop_vars], expand_composites=True))\\n\",\n \" /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/while_v2.py:869 _check_shapes_compat\\n\",\n \" \\\"specify a less-specific shape.\\\" % (input_t.name, shape, t.shape))\\n\",\n \"\\n\",\n \" ValueError: Input tensor 'ones:0' enters the loop with shape (0, 10), but has shape (1, 10) after one iteration. To allow the shape to vary across iterations, use the `shape_invariants` argument of tf.while_loop to specify a less-specific shape.\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"@tf.function\\n\",\n \"def buggy_concat():\\n\",\n \" x = tf.ones([0, 10])\\n\",\n \" for i in tf.range(5):\\n\",\n \" x = tf.concat([x, tf.ones([1, 10])], axis=0) # 循环时变量形状不能改变\\n\",\n \" return x\\n\",\n \"\\n\",\n \"with assert_raises(ValueError):\\n\",\n \" buggy_concat()\\n\",\n \" \\n\",\n \"@tf.function\\n\",\n \"def concat_with_padding():\\n\",\n \" x = tf.zeros([5, 10])\\n\",\n \" for i in tf.range(5):\\n\",\n \" x = tf.concat([x[:i], tf.ones([1, 10]), tf.zeros([4-i, 10])], axis=0)\\n\",\n \" x.set_shape([5, 10])\\n\",\n \" return x\\n\",\n \"\\n\",\n \"concat_with_padding()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"HM0DYSlOLL_P\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"collapsed_sections\": [],\n \"name\": \"006-tf_function_and_autograph\",\n \"provenance\": [],\n \"version\": \"0.3.2\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "021-MLP/001-MLP.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# tensorflow2教程-基础MLP网络\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"import tensorflow.keras as keras\\n\",\n \"import tensorflow.keras.layers as layers\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.回归任务\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(404, 13) (404,)\\n\",\n \"(102, 13) (102,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 导入数据\\n\",\n \"(x_train, y_train), (x_test, y_test) = keras.datasets.boston_housing.load_data()\\n\",\n \"print(x_train.shape, ' ', y_train.shape)\\n\",\n \"print(x_test.shape, ' ', y_test.shape)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_10\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_33 (Dense) (None, 32) 448 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_34 (Dense) (None, 32) 1056 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_35 (Dense) (None, 32) 1056 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_36 (Dense) (None, 1) 33 \\n\",\n \"=================================================================\\n\",\n \"Total params: 2,593\\n\",\n \"Trainable params: 2,593\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 构建模型\\n\",\n \"\\n\",\n \"model = keras.Sequential([\\n\",\n \" layers.Dense(32, activation='sigmoid', input_shape=(13,)),\\n\",\n \" layers.Dense(32, activation='sigmoid'),\\n\",\n \" layers.Dense(32, activation='sigmoid'),\\n\",\n \" layers.Dense(1)\\n\",\n \"])\\n\",\n \"\\n\",\n \"# 配置模型\\n\",\n \"model.compile(optimizer=keras.optimizers.SGD(0.1),\\n\",\n \" loss='mean_squared_error', # keras.losses.mean_squared_error\\n\",\n \" metrics=['mse'])\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 363 samples, validate on 41 samples\\n\",\n \"Epoch 1/50\\n\",\n \"363/363 [==============================] - 0s 430us/sample - loss: 371.0176 - mse: 371.0175 - val_loss: 50.0381 - val_mse: 50.0381\\n\",\n \"Epoch 2/50\\n\",\n \"363/363 [==============================] - 0s 28us/sample - loss: 96.3428 - mse: 96.3428 - val_loss: 51.2528 - val_mse: 51.2528\\n\",\n \"Epoch 3/50\\n\",\n \"363/363 [==============================] - 0s 44us/sample - loss: 93.5432 - mse: 93.5432 - val_loss: 60.6331 - val_mse: 60.6331\\n\",\n \"Epoch 4/50\\n\",\n \"363/363 [==============================] - 0s 45us/sample - loss: 96.2177 - mse: 96.2177 - val_loss: 88.8679 - val_mse: 88.8679\\n\",\n \"Epoch 5/50\\n\",\n \"363/363 [==============================] - 0s 42us/sample - loss: 93.1511 - mse: 93.1511 - val_loss: 100.1161 - val_mse: 100.1161\\n\",\n \"Epoch 6/50\\n\",\n \"363/363 [==============================] - 0s 35us/sample - loss: 94.3889 - mse: 94.3889 - val_loss: 43.3363 - val_mse: 43.3363\\n\",\n \"Epoch 7/50\\n\",\n \"363/363 [==============================] - 0s 38us/sample - loss: 90.5968 - mse: 90.5968 - val_loss: 75.2889 - val_mse: 75.2889\\n\",\n \"Epoch 8/50\\n\",\n \"363/363 [==============================] - 0s 34us/sample - loss: 96.9396 - mse: 96.9396 - val_loss: 42.4718 - val_mse: 42.4718\\n\",\n \"Epoch 9/50\\n\",\n \"363/363 [==============================] - 0s 44us/sample - loss: 90.4116 - mse: 90.4116 - val_loss: 46.8739 - val_mse: 46.8739\\n\",\n \"Epoch 10/50\\n\",\n \"363/363 [==============================] - 0s 32us/sample - loss: 89.8437 - mse: 89.8437 - val_loss: 68.0937 - val_mse: 68.0937\\n\",\n \"Epoch 11/50\\n\",\n \"363/363 [==============================] - 0s 46us/sample - loss: 92.9359 - mse: 92.9359 - val_loss: 42.6696 - val_mse: 42.6696\\n\",\n \"Epoch 12/50\\n\",\n \"363/363 [==============================] - 0s 35us/sample - loss: 89.6814 - mse: 89.6814 - val_loss: 46.7261 - val_mse: 46.7261\\n\",\n \"Epoch 13/50\\n\",\n \"363/363 [==============================] - 0s 34us/sample - loss: 90.5774 - mse: 90.5774 - val_loss: 41.6673 - val_mse: 41.6673\\n\",\n \"Epoch 14/50\\n\",\n \"363/363 [==============================] - 0s 44us/sample - loss: 88.5909 - mse: 88.5909 - val_loss: 55.3597 - val_mse: 55.3597\\n\",\n \"Epoch 15/50\\n\",\n \"363/363 [==============================] - 0s 435us/sample - loss: 90.5146 - mse: 90.5146 - val_loss: 51.8985 - val_mse: 51.8985\\n\",\n \"Epoch 16/50\\n\",\n \"363/363 [==============================] - 0s 43us/sample - loss: 91.7516 - mse: 91.7516 - val_loss: 43.2147 - val_mse: 43.2147\\n\",\n \"Epoch 17/50\\n\",\n \"363/363 [==============================] - 0s 32us/sample - loss: 91.9017 - mse: 91.9017 - val_loss: 58.9149 - val_mse: 58.9149\\n\",\n \"Epoch 18/50\\n\",\n \"363/363 [==============================] - 0s 36us/sample - loss: 96.3520 - mse: 96.3520 - val_loss: 39.5248 - val_mse: 39.5248\\n\",\n \"Epoch 19/50\\n\",\n \"363/363 [==============================] - 0s 39us/sample - loss: 84.0867 - mse: 84.0867 - val_loss: 54.0607 - val_mse: 54.0607\\n\",\n \"Epoch 20/50\\n\",\n \"363/363 [==============================] - 0s 38us/sample - loss: 83.8994 - mse: 83.8994 - val_loss: 100.1479 - val_mse: 100.1479\\n\",\n \"Epoch 21/50\\n\",\n \"363/363 [==============================] - 0s 45us/sample - loss: 101.9472 - mse: 101.9472 - val_loss: 42.8121 - val_mse: 42.8121\\n\",\n \"Epoch 22/50\\n\",\n \"363/363 [==============================] - 0s 41us/sample - loss: 90.9167 - mse: 90.9167 - val_loss: 53.8228 - val_mse: 53.8228\\n\",\n \"Epoch 23/50\\n\",\n \"363/363 [==============================] - 0s 48us/sample - loss: 91.6642 - mse: 91.6642 - val_loss: 42.9756 - val_mse: 42.9756\\n\",\n \"Epoch 24/50\\n\",\n \"363/363 [==============================] - 0s 45us/sample - loss: 92.6146 - mse: 92.6146 - val_loss: 76.5047 - val_mse: 76.5047\\n\",\n \"Epoch 25/50\\n\",\n \"363/363 [==============================] - 0s 38us/sample - loss: 94.8085 - mse: 94.8085 - val_loss: 51.9597 - val_mse: 51.9597\\n\",\n \"Epoch 26/50\\n\",\n \"363/363 [==============================] - 0s 33us/sample - loss: 92.2917 - mse: 92.2917 - val_loss: 42.8546 - val_mse: 42.8546\\n\",\n \"Epoch 27/50\\n\",\n \"363/363 [==============================] - 0s 38us/sample - loss: 90.6413 - mse: 90.6413 - val_loss: 42.8628 - val_mse: 42.8628\\n\",\n \"Epoch 28/50\\n\",\n \"363/363 [==============================] - 0s 38us/sample - loss: 91.5466 - mse: 91.5466 - val_loss: 42.7437 - val_mse: 42.7437\\n\",\n \"Epoch 29/50\\n\",\n \"363/363 [==============================] - 0s 32us/sample - loss: 93.5539 - mse: 93.5539 - val_loss: 48.5895 - val_mse: 48.5895\\n\",\n \"Epoch 30/50\\n\",\n \"363/363 [==============================] - 0s 33us/sample - loss: 95.7540 - mse: 95.7540 - val_loss: 43.6151 - val_mse: 43.6151\\n\",\n \"Epoch 31/50\\n\",\n \"363/363 [==============================] - 0s 32us/sample - loss: 89.4952 - mse: 89.4952 - val_loss: 71.6649 - val_mse: 71.6649\\n\",\n \"Epoch 32/50\\n\",\n \"363/363 [==============================] - 0s 48us/sample - loss: 89.5014 - mse: 89.5014 - val_loss: 44.1873 - val_mse: 44.1873\\n\",\n \"Epoch 33/50\\n\",\n \"363/363 [==============================] - 0s 30us/sample - loss: 93.1029 - mse: 93.1029 - val_loss: 93.6874 - val_mse: 93.6874\\n\",\n \"Epoch 34/50\\n\",\n \"363/363 [==============================] - 0s 53us/sample - loss: 95.2734 - mse: 95.2734 - val_loss: 45.0203 - val_mse: 45.0203\\n\",\n \"Epoch 35/50\\n\",\n \"363/363 [==============================] - 0s 45us/sample - loss: 90.2837 - mse: 90.2837 - val_loss: 98.1514 - val_mse: 98.1514\\n\",\n \"Epoch 36/50\\n\",\n \"363/363 [==============================] - 0s 40us/sample - loss: 95.9373 - mse: 95.9373 - val_loss: 44.1146 - val_mse: 44.1146\\n\",\n \"Epoch 37/50\\n\",\n \"363/363 [==============================] - 0s 39us/sample - loss: 91.6945 - mse: 91.6945 - val_loss: 42.3970 - val_mse: 42.3970\\n\",\n \"Epoch 38/50\\n\",\n \"363/363 [==============================] - 0s 45us/sample - loss: 88.5509 - mse: 88.5509 - val_loss: 45.7873 - val_mse: 45.7873\\n\",\n \"Epoch 39/50\\n\",\n \"363/363 [==============================] - 0s 27us/sample - loss: 93.6409 - mse: 93.6409 - val_loss: 74.0321 - val_mse: 74.0321\\n\",\n \"Epoch 40/50\\n\",\n \"363/363 [==============================] - 0s 23us/sample - loss: 87.0454 - mse: 87.0454 - val_loss: 37.7646 - val_mse: 37.7646\\n\",\n \"Epoch 41/50\\n\",\n \"363/363 [==============================] - 0s 26us/sample - loss: 92.6228 - mse: 92.6228 - val_loss: 44.1021 - val_mse: 44.1021\\n\",\n \"Epoch 42/50\\n\",\n \"363/363 [==============================] - 0s 40us/sample - loss: 88.6639 - mse: 88.6639 - val_loss: 32.3741 - val_mse: 32.3741\\n\",\n \"Epoch 43/50\\n\",\n \"363/363 [==============================] - 0s 42us/sample - loss: 92.7604 - mse: 92.7604 - val_loss: 42.6321 - val_mse: 42.6321\\n\",\n \"Epoch 44/50\\n\",\n \"363/363 [==============================] - 0s 43us/sample - loss: 93.3373 - mse: 93.3373 - val_loss: 60.7727 - val_mse: 60.7727\\n\",\n \"Epoch 45/50\\n\",\n \"363/363 [==============================] - 0s 26us/sample - loss: 94.0312 - mse: 94.0312 - val_loss: 43.1645 - val_mse: 43.1645\\n\",\n \"Epoch 46/50\\n\",\n \"363/363 [==============================] - 0s 31us/sample - loss: 89.4547 - mse: 89.4548 - val_loss: 42.7366 - val_mse: 42.7366\\n\",\n \"Epoch 47/50\\n\",\n \"363/363 [==============================] - 0s 31us/sample - loss: 89.8723 - mse: 89.8723 - val_loss: 44.8266 - val_mse: 44.8266\\n\",\n \"Epoch 48/50\\n\",\n \"363/363 [==============================] - 0s 34us/sample - loss: 90.3171 - mse: 90.3171 - val_loss: 47.8453 - val_mse: 47.8453\\n\",\n \"Epoch 49/50\\n\",\n \"363/363 [==============================] - 0s 31us/sample - loss: 86.2326 - mse: 86.2326 - val_loss: 29.5082 - val_mse: 29.5082\\n\",\n \"Epoch 50/50\\n\",\n \"363/363 [==============================] - 0s 28us/sample - loss: 80.1490 - mse: 80.1490 - val_loss: 30.6706 - val_mse: 30.6706\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 训练\\n\",\n \"model.fit(x_train, y_train, batch_size=50, epochs=50, validation_split=0.1, verbose=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"102/102 [==============================] - 0s 116us/sample - loss: 75.0492 - mse: 75.0492\\n\"\n ]\n }\n ],\n \"source\": [\n \"result = model.evaluate(x_test, y_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['loss', 'mse']\\n\",\n \"[75.04923741957721, 75.04924]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(model.metrics_names)\\n\",\n \"print(result)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.分类任务\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(398, 30) (398,)\\n\",\n \"(171, 30) (171,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.datasets import load_breast_cancer\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"\\n\",\n \"whole_data = load_breast_cancer()\\n\",\n \"x_data = whole_data.data\\n\",\n \"y_data = whole_data.target\\n\",\n \"\\n\",\n \"x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.3, random_state=7)\\n\",\n \"\\n\",\n \"print(x_train.shape, ' ', y_train.shape)\\n\",\n \"print(x_test.shape, ' ', y_test.shape)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_14\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_46 (Dense) (None, 32) 992 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_47 (Dense) (None, 32) 1056 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_48 (Dense) (None, 1) 33 \\n\",\n \"=================================================================\\n\",\n \"Total params: 2,081\\n\",\n \"Trainable params: 2,081\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 构建模型\\n\",\n \"model = keras.Sequential([\\n\",\n \" layers.Dense(32, activation='relu', input_shape=(30,)),\\n\",\n \" layers.Dense(32, activation='relu'),\\n\",\n \" layers.Dense(1, activation='sigmoid')\\n\",\n \"])\\n\",\n \"\\n\",\n \"model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.binary_crossentropy,\\n\",\n \" metrics=['accuracy'])\\n\",\n \"model.summary()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1/10\\n\",\n \"398/398 [==============================] - 0s 44us/sample - loss: 0.1982 - accuracy: 0.9196\\n\",\n \"Epoch 2/10\\n\",\n \"398/398 [==============================] - 0s 28us/sample - loss: 0.2094 - accuracy: 0.9221\\n\",\n \"Epoch 3/10\\n\",\n \"398/398 [==============================] - 0s 40us/sample - loss: 0.2128 - accuracy: 0.9246\\n\",\n \"Epoch 4/10\\n\",\n \"398/398 [==============================] - 0s 28us/sample - loss: 0.2101 - accuracy: 0.9271\\n\",\n \"Epoch 5/10\\n\",\n \"398/398 [==============================] - 0s 21us/sample - loss: 0.2175 - accuracy: 0.9146\\n\",\n \"Epoch 6/10\\n\",\n \"398/398 [==============================] - 0s 31us/sample - loss: 0.2925 - accuracy: 0.8945\\n\",\n \"Epoch 7/10\\n\",\n \"398/398 [==============================] - 0s 37us/sample - loss: 0.4531 - accuracy: 0.8618\\n\",\n \"Epoch 8/10\\n\",\n \"398/398 [==============================] - 0s 26us/sample - loss: 0.3105 - accuracy: 0.8920\\n\",\n \"Epoch 9/10\\n\",\n \"398/398 [==============================] - 0s 34us/sample - loss: 0.2934 - accuracy: 0.8794\\n\",\n \"Epoch 10/10\\n\",\n \"398/398 [==============================] - 0s 27us/sample - loss: 0.2597 - accuracy: 0.9045\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.fit(x_train, y_train, batch_size=64, epochs=10, verbose=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"171/171 [==============================] - 0s 463us/sample - loss: 0.3877 - accuracy: 0.8772\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[0.38765583248340596, 0.877193]\"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.evaluate(x_test, y_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['loss', 'accuracy']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(model.metrics_names)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "021-MLP/002-MLP2.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-mlp及深度学习常见技巧\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"我们将以mlp对为，基础模型，然后介绍一些深度学习常见技巧， 如：\\n\",\n \"权重初始化， 激活函数， 优化器， 批规范化， dropout，模型集成 \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"from tensorflow import keras\\n\",\n \"from tensorflow.keras import layers\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.导入数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(60000, 784) (60000,)\\n\",\n \"(10000, 784) (10000,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\",\n \"x_train = x_train.reshape([x_train.shape[0], -1])\\n\",\n \"x_test = x_test.reshape([x_test.shape[0], -1])\\n\",\n \"print(x_train.shape, ' ', y_train.shape)\\n\",\n \"print(x_test.shape, ' ', y_test.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.基础模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_3\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_11 (Dense) (None, 64) 50240 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_12 (Dense) (None, 64) 4160 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_13 (Dense) (None, 64) 4160 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_14 (Dense) (None, 10) 650 \\n\",\n \"=================================================================\\n\",\n \"Total params: 59,210\\n\",\n \"Trainable params: 59,210\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = keras.Sequential([\\n\",\n \" layers.Dense(64, activation='relu', input_shape=(784,)),\\n\",\n \" layers.Dense(64, activation='relu'),\\n\",\n \" layers.Dense(64, activation='relu'),\\n\",\n \" layers.Dense(10, activation='softmax')\\n\",\n \"])\\n\",\n \"model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"history = model.fit(x_train, y_train, batch_size=256, epochs=100, validation_split=0.3, verbose=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'validation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000/10000 [==============================] - 0s 25us/sample - loss: 0.4429 - accuracy: 0.9632\\n\"\n ]\n }\n ],\n \"source\": [\n \"result = model.evaluate(x_test, y_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.权重初始化\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_5\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_19 (Dense) (None, 64) 50240 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_20 (Dense) (None, 64) 4160 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_21 (Dense) (None, 64) 4160 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_22 (Dense) (None, 10) 650 \\n\",\n \"=================================================================\\n\",\n \"Total params: 59,210\\n\",\n \"Trainable params: 59,210\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = keras.Sequential([\\n\",\n \" layers.Dense(64, activation='relu', kernel_initializer='he_normal', input_shape=(784,)),\\n\",\n \" layers.Dense(64, activation='relu', kernel_initializer='he_normal'),\\n\",\n \" layers.Dense(64, activation='relu', kernel_initializer='he_normal'),\\n\",\n \" layers.Dense(10, activation='softmax')\\n\",\n \"])\\n\",\n \"model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"history = model.fit(x_train, y_train, batch_size=256, epochs=100, validation_split=0.3, verbose=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'validation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000/10000 [==============================] - 0s 21us/sample - loss: 0.4355 - accuracy: 0.9587\\n\"\n ]\n }\n ],\n \"source\": [\n \"result = model.evaluate(x_test, y_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.激活函数\\n\",\n \"\\n\",\n \"relu和sigmoid对比\\n\",\n \"![image.png](https://camo.githubusercontent.com/4ebe11b2d35be8367317d7b2540969035a3684d4/687474703a2f2f63733233316e2e6769746875622e696f2f6173736574732f6e6e312f72656c752e6a706567)\\n\",\n \"![image.png](https://camo.githubusercontent.com/e41af2fedc3c479e87ee43f4954572257545469c/687474703a2f2f63733233316e2e6769746875622e696f2f6173736574732f6e6e312f7369676d6f69642e6a706567)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_6\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_23 (Dense) (None, 64) 50240 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_24 (Dense) (None, 64) 4160 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_25 (Dense) (None, 64) 4160 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_26 (Dense) (None, 10) 650 \\n\",\n \"=================================================================\\n\",\n \"Total params: 59,210\\n\",\n \"Trainable params: 59,210\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = keras.Sequential([\\n\",\n \" layers.Dense(64, activation='sigmoid', input_shape=(784,)),\\n\",\n \" layers.Dense(64, activation='sigmoid'),\\n\",\n \" layers.Dense(64, activation='sigmoid'),\\n\",\n \" layers.Dense(10, activation='softmax')\\n\",\n \"])\\n\",\n \"model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"history = model.fit(x_train, y_train, batch_size=256, epochs=100, validation_split=0.3, verbose=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'validation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000/10000 [==============================] - 0s 29us/sample - loss: 0.1526 - accuracy: 0.9529\\n\"\n ]\n }\n ],\n \"source\": [\n \"result = model.evaluate(x_test, y_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5.优化器\\n\",\n \"![](https://camo.githubusercontent.com/246c076552c81303a7fcf65bda596179dc9b8418/687474703a2f2f63733233316e2e6769746875622e696f2f6173736574732f6e6e332f6f7074322e676966)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_7\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_27 (Dense) (None, 64) 50240 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_28 (Dense) (None, 64) 4160 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_29 (Dense) (None, 64) 4160 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_30 (Dense) (None, 10) 650 \\n\",\n \"=================================================================\\n\",\n \"Total params: 59,210\\n\",\n \"Trainable params: 59,210\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = keras.Sequential([\\n\",\n \" layers.Dense(64, activation='sigmoid', input_shape=(784,)),\\n\",\n \" layers.Dense(64, activation='sigmoid'),\\n\",\n \" layers.Dense(64, activation='sigmoid'),\\n\",\n \" layers.Dense(10, activation='softmax')\\n\",\n \"])\\n\",\n \"model.compile(optimizer=keras.optimizers.SGD(),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"history = model.fit(x_train, y_train, batch_size=256, epochs=100, validation_split=0.3, verbose=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'validation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000/10000 [==============================] - 0s 44us/sample - loss: 2.1199 - accuracy: 0.4749\\n\"\n ]\n }\n ],\n \"source\": [\n \"result = model.evaluate(x_test, y_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6.批正则化\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_8\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_31 (Dense) (None, 64) 50240 \\n\",\n \"_________________________________________________________________\\n\",\n \"batch_normalization_v2 (Batc (None, 64) 256 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_32 (Dense) (None, 64) 4160 \\n\",\n \"_________________________________________________________________\\n\",\n \"batch_normalization_v2_1 (Ba (None, 64) 256 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_33 (Dense) (None, 64) 4160 \\n\",\n \"_________________________________________________________________\\n\",\n \"batch_normalization_v2_2 (Ba (None, 64) 256 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_34 (Dense) (None, 10) 650 \\n\",\n \"=================================================================\\n\",\n \"Total params: 59,978\\n\",\n \"Trainable params: 59,594\\n\",\n \"Non-trainable params: 384\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = keras.Sequential([\\n\",\n \" layers.Dense(64, activation='relu', input_shape=(784,)),\\n\",\n \" layers.BatchNormalization(),\\n\",\n \" layers.Dense(64, activation='relu'),\\n\",\n \" layers.BatchNormalization(),\\n\",\n \" layers.Dense(64, activation='relu'),\\n\",\n \" layers.BatchNormalization(),\\n\",\n \" layers.Dense(10, activation='softmax')\\n\",\n \"])\\n\",\n \"model.compile(optimizer=keras.optimizers.SGD(),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"history = model.fit(x_train, y_train, batch_size=256, epochs=100, validation_split=0.3, verbose=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'validation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000/10000 [==============================] - 0s 25us/sample - loss: 0.1863 - accuracy: 0.9447\\n\"\n ]\n }\n ],\n \"source\": [\n \"result = model.evaluate(x_test, y_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 7.dropout\\n\",\n \"![](https://camo.githubusercontent.com/9acd196479c9c42db52a9b7ecefaaa301abb07fa/68747470733a2f2f696d6167652e736c696465736861726563646e2e636f6d2f6c65637475726532392d636f6e766f6c7574696f6e616c6e657572616c6e6574776f726b732d766973696f6e737072696e67323031352d3135303530343131343134302d636f6e76657273696f6e2d6761746530322f39352f6c6563747572652d32392d636f6e766f6c7574696f6e616c2d6e657572616c2d6e6574776f726b732d636f6d70757465722d766973696f6e2d737072696e67323031352d36322d3633382e6a70673f63623d31343330373430303036)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_9\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_35 (Dense) (None, 64) 50240 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout (Dropout) (None, 64) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_36 (Dense) (None, 64) 4160 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout_1 (Dropout) (None, 64) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_37 (Dense) (None, 64) 4160 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout_2 (Dropout) (None, 64) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_38 (Dense) (None, 10) 650 \\n\",\n \"=================================================================\\n\",\n \"Total params: 59,210\\n\",\n \"Trainable params: 59,210\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = keras.Sequential([\\n\",\n \" layers.Dense(64, activation='relu', input_shape=(784,)),\\n\",\n \" layers.Dropout(0.2),\\n\",\n \" layers.Dense(64, activation='relu'),\\n\",\n \" layers.Dropout(0.2),\\n\",\n \" layers.Dense(64, activation='relu'),\\n\",\n \" layers.Dropout(0.2),\\n\",\n \" layers.Dense(10, activation='softmax')\\n\",\n \"])\\n\",\n \"model.compile(optimizer=keras.optimizers.SGD(),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"history = model.fit(x_train, y_train, batch_size=256, epochs=100, validation_split=0.3, verbose=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'validation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000/10000 [==============================] - 0s 27us/sample - loss: 0.6157 - accuracy: 0.8132\\n\"\n ]\n }\n ],\n \"source\": [\n \"result = model.evaluate(x_test, y_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 8.模型集成\\n\",\n \"下面是使用投票的方法进行模型集成\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![](https://camo.githubusercontent.com/c5d820739f5ab9bef5ecbd8b8db585d11d858ecf/68747470733a2f2f656e637279707465642d74626e302e677374617469632e636f6d2f696d616765733f713d74626e3a414e643947635273314342534574707035796a36534a354b5f6e486431464e6679455961394b4c6a57666f4d595f763741525471337464705677)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\\n\",\n \"from sklearn.ensemble import VotingClassifier\\n\",\n \"from sklearn.metrics import accuracy_score\\n\",\n \"\\n\",\n \"def mlp_model():\\n\",\n \" model = keras.Sequential([\\n\",\n \" layers.Dense(64, activation='relu', input_shape=(784,)),\\n\",\n \" layers.Dropout(0.2),\\n\",\n \" layers.Dense(64, activation='relu'),\\n\",\n \" layers.Dropout(0.2),\\n\",\n \" layers.Dense(64, activation='relu'),\\n\",\n \" layers.Dropout(0.2),\\n\",\n \" layers.Dense(10, activation='softmax')\\n\",\n \" ])\\n\",\n \" model.compile(optimizer=keras.optimizers.SGD(),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \" return model\\n\",\n \"model1 = KerasClassifier(build_fn=mlp_model, epochs=100, verbose=0)\\n\",\n \"model2 = KerasClassifier(build_fn=mlp_model, epochs=100, verbose=0)\\n\",\n \"model3 = KerasClassifier(build_fn=mlp_model, epochs=100, verbose=0)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ensemble_clf = VotingClassifier(estimators=[\\n\",\n \" ('model1', model1), ('model2', model2), ('model3', model3)\\n\",\n \"], voting='soft')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"VotingClassifier(estimators=[('model1', ), ('model2', ), ('model3', )],\\n\",\n \" flatten_transform=None, n_jobs=None, voting='soft', weights=None)\"\n ]\n },\n \"execution_count\": 56,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ensemble_clf.fit(x_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"acc: 0.9504\\n\"\n ]\n }\n ],\n \"source\": [\n \"y_pred = ensemble_clf.predict(x_test)\\n\",\n \"print('acc: ', accuracy_score(y_pred, y_test))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 9.全部使用\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from tensorflow.keras import layers\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\\n\",\n \"from sklearn.ensemble import VotingClassifier\\n\",\n \"from sklearn.metrics import accuracy_score\\n\",\n \"\\n\",\n \"def mlp_model():\\n\",\n \" model = keras.Sequential([\\n\",\n \" layers.Dense(64, activation='relu', kernel_initializer='he_normal', input_shape=(784,)),\\n\",\n \" layers.BatchNormalization(),\\n\",\n \" layers.Dropout(0.2),\\n\",\n \" layers.Dense(64, activation='relu', kernel_initializer='he_normal'),\\n\",\n \" layers.BatchNormalization(),\\n\",\n \" layers.Dropout(0.2),\\n\",\n \" layers.Dense(64, activation='relu', kernel_initializer='he_normal'),\\n\",\n \" layers.BatchNormalization(),\\n\",\n \" layers.Dropout(0.2),\\n\",\n \" layers.Dense(10, activation='softmax')\\n\",\n \" ])\\n\",\n \" model.compile(optimizer=keras.optimizers.SGD(),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \" return model\\n\",\n \"model1 = KerasClassifier(build_fn=mlp_model, epochs=100, verbose=0)\\n\",\n \"model2 = KerasClassifier(build_fn=mlp_model, epochs=100, verbose=0)\\n\",\n \"model3 = KerasClassifier(build_fn=mlp_model, epochs=100, verbose=0)\\n\",\n \"model4 = KerasClassifier(build_fn=mlp_model, epochs=100, verbose=0)\\n\",\n \"ensemble_clf = VotingClassifier(estimators=[\\n\",\n \" ('model1', model1), ('model2', model2), ('model3', model3),('model4', model4)])\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"VotingClassifier(estimators=[('model1', ), ('model2', ), ('model3', ), ('model4', )],\\n\",\n \" flatten_transform=None, n_jobs=None, voting='hard', weights=None)\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ensemble_clf.fit(x_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y_predict = ensemble_clf.predict(x_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'y_pred' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'acc: '\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0maccuracy_score\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0my_pred\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0my_test\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'y_pred' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"print('acc: ', accuracy_scoreecuracy_scorecuracy_score(y_pred, y_test))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "022-CNN/001-cnn.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# tensorflow2-基础CNN网络\\n\",\n \"![](https://adeshpande3.github.io/assets/Cover.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"from tensorflow import keras\\n\",\n \"from tensorflow.keras import layers\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.构造数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(60000, 28, 28) (60000,)\\n\",\n \"(10000, 28, 28) (10000,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"\\n\",\n \"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\",\n \"print(x_train.shape, ' ', y_train.shape)\\n\",\n \"print(x_test.shape, ' ', y_test.shape)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.imshow(x_train[0])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x_train = x_train.reshape((-1,28,28,1))\\n\",\n \"x_test = x_test.reshape((-1,28,28,1))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.构造网络\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model = keras.Sequential()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 卷积层\\n\",\n \"![](http://cs231n.github.io/assets/cnn/depthcol.jpeg)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model.add(layers.Conv2D(input_shape=(x_train.shape[1], x_train.shape[2], x_train.shape[3]),\\n\",\n \" filters=32, kernel_size=(3,3), strides=(1,1), padding='valid',\\n\",\n \" activation='relu'))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 池化层\\n\",\n \"![](http://cs231n.github.io/assets/cnn/maxpool.jpeg)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model.add(layers.MaxPool2D(pool_size=(2,2)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 全连接层\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model.add(layers.Flatten())\\n\",\n \"model.add(layers.Dense(32, activation='relu'))\\n\",\n \"# 分类层\\n\",\n \"model.add(layers.Dense(10, activation='softmax'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.模型配置\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"conv2d (Conv2D) (None, 26, 26, 32) 320 \\n\",\n \"_________________________________________________________________\\n\",\n \"max_pooling2d (MaxPooling2D) (None, 13, 13, 32) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"flatten (Flatten) (None, 5408) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense (Dense) (None, 32) 173088 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_1 (Dense) (None, 10) 330 \\n\",\n \"=================================================================\\n\",\n \"Total params: 173,738\\n\",\n \"Trainable params: 173,738\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" # loss=keras.losses.CategoricalCrossentropy(), # 需要使用to_categorical\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.模型训练\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 54000 samples, validate on 6000 samples\\n\",\n \"Epoch 1/5\\n\",\n \"54000/54000 [==============================] - 8s 148us/sample - loss: 1.5187 - accuracy: 0.5294 - val_loss: 0.7187 - val_accuracy: 0.7482\\n\",\n \"Epoch 2/5\\n\",\n \"54000/54000 [==============================] - 8s 150us/sample - loss: 0.4631 - accuracy: 0.8745 - val_loss: 0.2158 - val_accuracy: 0.9463\\n\",\n \"Epoch 3/5\\n\",\n \"54000/54000 [==============================] - 8s 154us/sample - loss: 0.1684 - accuracy: 0.9540 - val_loss: 0.1314 - val_accuracy: 0.9642\\n\",\n \"Epoch 4/5\\n\",\n \"54000/54000 [==============================] - 8s 150us/sample - loss: 0.1067 - accuracy: 0.9699 - val_loss: 0.1097 - val_accuracy: 0.9722\\n\",\n \"Epoch 5/5\\n\",\n \"54000/54000 [==============================] - 8s 149us/sample - loss: 0.0799 - accuracy: 0.9768 - val_loss: 0.1175 - val_accuracy: 0.9712\\n\"\n ]\n }\n ],\n \"source\": [\n \"history = model.fit(x_train, y_train, batch_size=64, epochs=5, validation_split=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'valivation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000/10000 [==============================] - 1s 62us/sample - loss: 0.1191 - accuracy: 0.9667\\n\"\n ]\n }\n ],\n \"source\": [\n \"res = model.evaluate(x_test, y_test)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "022-CNN/002-cnn_variants.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# tensorflow2教程-CNN变体网络\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"from tensorflow import keras\\n\",\n \"from tensorflow.keras import layers\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.载入数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(60000, 28, 28, 1) (60000,)\\n\",\n \"(10000, 28, 28, 1) (10000,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\",\n \"x_train = x_train.reshape((-1,28,28,1))\\n\",\n \"x_test = x_test.reshape((-1,28,28,1))\\n\",\n \"print(x_train.shape, ' ', y_train.shape)\\n\",\n \"print(x_test.shape, ' ', y_test.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.简单的深度网络\\n\",\n \"如AlexNet,VggNet\\n\",\n \"![](http://www.hirokatsukataoka.net/research/cnnfeatureevaluation/cnnarchitecture.jpg)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x_shape = x_train.shape\\n\",\n \"deep_model = keras.Sequential(\\n\",\n \"[\\n\",\n \" layers.Conv2D(input_shape=((x_shape[1], x_shape[2], x_shape[3])),\\n\",\n \" filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),\\n\",\n \" layers.Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),\\n\",\n \" layers.MaxPool2D(pool_size=(2,2)),\\n\",\n \" layers.Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),\\n\",\n \" layers.Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),\\n\",\n \" layers.MaxPool2D(pool_size=(2,2)),\\n\",\n \" layers.Flatten(),\\n\",\n \" layers.Dense(32, activation='relu'),\\n\",\n \" layers.Dense(10, activation='softmax')\\n\",\n \" \\n\",\n \"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"conv2d (Conv2D) (None, 28, 28, 32) 320 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_1 (Conv2D) (None, 28, 28, 32) 9248 \\n\",\n \"_________________________________________________________________\\n\",\n \"max_pooling2d (MaxPooling2D) (None, 14, 14, 32) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_2 (Conv2D) (None, 14, 14, 32) 9248 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_3 (Conv2D) (None, 14, 14, 32) 9248 \\n\",\n \"_________________________________________________________________\\n\",\n \"max_pooling2d_1 (MaxPooling2 (None, 7, 7, 32) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"flatten (Flatten) (None, 1568) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense (Dense) (None, 32) 50208 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_1 (Dense) (None, 10) 330 \\n\",\n \"=================================================================\\n\",\n \"Total params: 78,602\\n\",\n \"Trainable params: 78,602\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"deep_model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \"deep_model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 54000 samples, validate on 6000 samples\\n\",\n \"Epoch 1/5\\n\",\n \"54000/54000 [==============================] - 72s 1ms/sample - loss: 0.2774 - accuracy: 0.9280 - val_loss: 0.0612 - val_accuracy: 0.9822\\n\",\n \"Epoch 2/5\\n\",\n \"54000/54000 [==============================] - 73s 1ms/sample - loss: 0.0646 - accuracy: 0.9802 - val_loss: 0.0516 - val_accuracy: 0.9850\\n\",\n \"Epoch 3/5\\n\",\n \"54000/54000 [==============================] - 69s 1ms/sample - loss: 0.0471 - accuracy: 0.9856 - val_loss: 0.0466 - val_accuracy: 0.9883\\n\",\n \"Epoch 4/5\\n\",\n \"54000/54000 [==============================] - 70s 1ms/sample - loss: 0.0385 - accuracy: 0.9879 - val_loss: 0.0614 - val_accuracy: 0.9843\\n\",\n \"Epoch 5/5\\n\",\n \"54000/54000 [==============================] - 69s 1ms/sample - loss: 0.0317 - accuracy: 0.9897 - val_loss: 0.0463 - val_accuracy: 0.9867\\n\"\n ]\n }\n ],\n \"source\": [\n \"history = deep_model.fit(x_train, y_train, batch_size=64, epochs=5, validation_split=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000/10000 [==============================] - 2s 219us/sample - loss: 0.0445 - accuracy: 0.9863\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[0.04454196666887728, 0.9863]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"deep_model.evaluate(x_test, y_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'valivation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000/10000 [==============================] - 2s 219us/sample - loss: 0.0445 - accuracy: 0.9863\\n\"\n ]\n }\n ],\n \"source\": [\n \"result = deep_model.evaluate(x_test, y_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.添加了其它功能层的深度卷积\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x_shape = x_train.shape\\n\",\n \"deep_model = keras.Sequential(\\n\",\n \"[\\n\",\n \" layers.Conv2D(input_shape=((x_shape[1], x_shape[2], x_shape[3])),\\n\",\n \" filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),\\n\",\n \" layers.BatchNormalization(),\\n\",\n \" layers.Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),\\n\",\n \" layers.BatchNormalization(),\\n\",\n \" layers.MaxPool2D(pool_size=(2,2)),\\n\",\n \" layers.Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),\\n\",\n \" layers.BatchNormalization(),\\n\",\n \" layers.BatchNormalization(),\\n\",\n \" layers.Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),\\n\",\n \" layers.MaxPool2D(pool_size=(2,2)),\\n\",\n \" layers.Flatten(),\\n\",\n \" layers.Dense(32, activation='relu'),\\n\",\n \" layers.Dropout(0.2),\\n\",\n \" layers.Dense(10, activation='softmax')\\n\",\n \" \\n\",\n \"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_1\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"conv2d_4 (Conv2D) (None, 28, 28, 32) 320 \\n\",\n \"_________________________________________________________________\\n\",\n \"batch_normalization_v2 (Batc (None, 28, 28, 32) 128 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_5 (Conv2D) (None, 28, 28, 32) 9248 \\n\",\n \"_________________________________________________________________\\n\",\n \"batch_normalization_v2_1 (Ba (None, 28, 28, 32) 128 \\n\",\n \"_________________________________________________________________\\n\",\n \"max_pooling2d_2 (MaxPooling2 (None, 14, 14, 32) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_6 (Conv2D) (None, 14, 14, 32) 9248 \\n\",\n \"_________________________________________________________________\\n\",\n \"batch_normalization_v2_2 (Ba (None, 14, 14, 32) 128 \\n\",\n \"_________________________________________________________________\\n\",\n \"batch_normalization_v2_3 (Ba (None, 14, 14, 32) 128 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_7 (Conv2D) (None, 14, 14, 32) 9248 \\n\",\n \"_________________________________________________________________\\n\",\n \"max_pooling2d_3 (MaxPooling2 (None, 7, 7, 32) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"flatten_1 (Flatten) (None, 1568) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_2 (Dense) (None, 32) 50208 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout (Dropout) (None, 32) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_3 (Dense) (None, 10) 330 \\n\",\n \"=================================================================\\n\",\n \"Total params: 79,114\\n\",\n \"Trainable params: 78,858\\n\",\n \"Non-trainable params: 256\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"deep_model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \"deep_model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 54000 samples, validate on 6000 samples\\n\",\n \"Epoch 1/5\\n\",\n \"54000/54000 [==============================] - 120s 2ms/sample - loss: 0.2683 - accuracy: 0.9163 - val_loss: 0.0470 - val_accuracy: 0.9880\\n\",\n \"Epoch 2/5\\n\",\n \"54000/54000 [==============================] - 114s 2ms/sample - loss: 0.0979 - accuracy: 0.9697 - val_loss: 0.0444 - val_accuracy: 0.9853\\n\",\n \"Epoch 3/5\\n\",\n \"54000/54000 [==============================] - 118s 2ms/sample - loss: 0.0718 - accuracy: 0.9780 - val_loss: 0.0358 - val_accuracy: 0.9903\\n\",\n \"Epoch 4/5\\n\",\n \"54000/54000 [==============================] - 115s 2ms/sample - loss: 0.0559 - accuracy: 0.9825 - val_loss: 0.0463 - val_accuracy: 0.9887\\n\",\n \"Epoch 5/5\\n\",\n \"54000/54000 [==============================] - 115s 2ms/sample - loss: 0.0504 - accuracy: 0.9839 - val_loss: 0.0315 - val_accuracy: 0.9922\\n\"\n ]\n }\n ],\n \"source\": [\n \"history = deep_model.fit(x_train, y_train, batch_size=64, epochs=5, validation_split=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'valivation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000/10000 [==============================] - 4s 365us/sample - loss: 0.0288 - accuracy: 0.9909\\n\"\n ]\n }\n ],\n \"source\": [\n \"result = deep_model.evaluate(x_test, y_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.NIN网络\\n\",\n \"Min等人在 2013年（https://arxiv.org/abs/1312.4400）提出了减少模型中参数数量的方法之一\\n\",\n \"即“网络中的网络（NIN）”或“1X1卷积”\\n\",\n \"方法很简单 - 在其他卷积层之后添加卷积层\\n\",\n \"具有降低图像空间的维度（深度）的效果，有效地减少了参数的数量\\n\",\n \"![](https://raw.githubusercontent.com/iamaaditya/iamaaditya.github.io/master/images/conv_arithmetic/full_padding_no_strides_transposed_small.gif)\\n\",\n \"GoogleNet 中就用到了NIN结构\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x_shape = x_train.shape\\n\",\n \"deep_model = keras.Sequential(\\n\",\n \"[\\n\",\n \" layers.Conv2D(input_shape=((x_shape[1], x_shape[2], x_shape[3])),\\n\",\n \" filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),\\n\",\n \" layers.BatchNormalization(),\\n\",\n \" layers.Conv2D(filters=16, kernel_size=(1,1), strides=(1,1), padding='valid', activation='relu'),\\n\",\n \" layers.BatchNormalization(),\\n\",\n \" layers.MaxPool2D(pool_size=(2,2)),\\n\",\n \" layers.Conv2D(filters=32, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu'),\\n\",\n \" layers.BatchNormalization(),\\n\",\n \" layers.Conv2D(filters=16, kernel_size=(1,1), strides=(1,1), padding='valid', activation='relu'),\\n\",\n \" layers.BatchNormalization(),\\n\",\n \" layers.MaxPool2D(pool_size=(2,2)),\\n\",\n \" layers.Flatten(),\\n\",\n \" layers.Dense(32, activation='relu'),\\n\",\n \" layers.Dropout(0.2),\\n\",\n \" layers.Dense(10, activation='softmax')\\n\",\n \" \\n\",\n \"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_2\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"conv2d_8 (Conv2D) (None, 28, 28, 32) 320 \\n\",\n \"_________________________________________________________________\\n\",\n \"batch_normalization_v2_4 (Ba (None, 28, 28, 32) 128 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_9 (Conv2D) (None, 28, 28, 16) 528 \\n\",\n \"_________________________________________________________________\\n\",\n \"batch_normalization_v2_5 (Ba (None, 28, 28, 16) 64 \\n\",\n \"_________________________________________________________________\\n\",\n \"max_pooling2d_4 (MaxPooling2 (None, 14, 14, 16) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_10 (Conv2D) (None, 14, 14, 32) 4640 \\n\",\n \"_________________________________________________________________\\n\",\n \"batch_normalization_v2_6 (Ba (None, 14, 14, 32) 128 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv2d_11 (Conv2D) (None, 14, 14, 16) 528 \\n\",\n \"_________________________________________________________________\\n\",\n \"batch_normalization_v2_7 (Ba (None, 14, 14, 16) 64 \\n\",\n \"_________________________________________________________________\\n\",\n \"max_pooling2d_5 (MaxPooling2 (None, 7, 7, 16) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"flatten_2 (Flatten) (None, 784) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_4 (Dense) (None, 32) 25120 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout_1 (Dropout) (None, 32) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_5 (Dense) (None, 10) 330 \\n\",\n \"=================================================================\\n\",\n \"Total params: 31,850\\n\",\n \"Trainable params: 31,658\\n\",\n \"Non-trainable params: 192\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"deep_model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \"deep_model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 54000 samples, validate on 6000 samples\\n\",\n \"Epoch 1/5\\n\",\n \"54000/54000 [==============================] - 62s 1ms/sample - loss: 0.2729 - accuracy: 0.9147 - val_loss: 0.0657 - val_accuracy: 0.9818\\n\",\n \"Epoch 2/5\\n\",\n \"54000/54000 [==============================] - 63s 1ms/sample - loss: 0.0872 - accuracy: 0.9739 - val_loss: 0.0437 - val_accuracy: 0.9865\\n\",\n \"Epoch 3/5\\n\",\n \"54000/54000 [==============================] - 59s 1ms/sample - loss: 0.0657 - accuracy: 0.9800 - val_loss: 0.0404 - val_accuracy: 0.9890\\n\",\n \"Epoch 4/5\\n\",\n \"54000/54000 [==============================] - 49s 913us/sample - loss: 0.0535 - accuracy: 0.9834 - val_loss: 0.0622 - val_accuracy: 0.9830\\n\",\n \"Epoch 5/5\\n\",\n \"54000/54000 [==============================] - 49s 913us/sample - loss: 0.0441 - accuracy: 0.9860 - val_loss: 0.0435 - val_accuracy: 0.9892\\n\"\n ]\n }\n ],\n \"source\": [\n \"history = deep_model.fit(x_train, y_train, batch_size=64, epochs=5, validation_split=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'valivation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000/10000 [==============================] - 2s 196us/sample - loss: 0.0335 - accuracy: 0.9887\\n\"\n ]\n }\n ],\n \"source\": [\n \"result = deep_model.evaluate(x_test, y_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "022-CNN/003-text_cnn.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# tensorflow2文本卷积\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.基本文本卷积\\n\",\n \"- For more information, refer to:\\n\",\n \" - Kim 2014 (http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf)\\n\",\n \" - Zhang et al 2015 (https://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf)\\n\",\n \" \\n\",\n \"
\\n\",\n \"- 使用卷积进行句子分类(Kim 2014)\\n\",\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"
\\n\",\n \"- 多个卷积核\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from tensorflow import keras\\n\",\n \"from tensorflow.keras import layers\\n\",\n \"from tensorflow.keras.preprocessing.sequence import pad_sequences\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"num_features = 3000\\n\",\n \"sequence_length = 300\\n\",\n \"embedding_dimension = 100\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(25000,)\\n\",\n \"(25000,)\\n\",\n \"(25000,)\\n\",\n \"(25000,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"(x_train, y_train), (x_test, y_test) = keras.datasets.imdb.load_data(num_words=num_features)\\n\",\n \"print(x_train.shape)\\n\",\n \"print(x_test.shape)\\n\",\n \"print(y_train.shape)\\n\",\n \"print(y_test.shape)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(25000, 300)\\n\",\n \"(25000, 300)\\n\",\n \"(25000,)\\n\",\n \"(25000,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"x_train = pad_sequences(x_train, maxlen=sequence_length)\\n\",\n \"x_test = pad_sequences(x_test, maxlen=sequence_length)\\n\",\n \"print(x_train.shape)\\n\",\n \"print(x_test.shape)\\n\",\n \"print(y_train.shape)\\n\",\n \"print(y_test.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.构造基本句子分类器\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def imdb_cnn():\\n\",\n \" model = keras.Sequential([\\n\",\n \" layers.Embedding(input_dim=num_features, output_dim=embedding_dimension,input_length=sequence_length),\\n\",\n \" layers.Conv1D(filters=50, kernel_size=5, strides=1, padding='valid'),\\n\",\n \" layers.MaxPool1D(2, padding='valid'),\\n\",\n \" layers.Flatten(),\\n\",\n \" layers.Dense(10, activation='relu'),\\n\",\n \" layers.Dense(1, activation='sigmoid')\\n\",\n \" ])\\n\",\n \" model.compile(optimizer=keras.optimizers.Adam(1e-3),\\n\",\n \" loss=keras.losses.BinaryCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \" \\n\",\n \" return model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_3\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"embedding_3 (Embedding) (None, 300, 100) 300000 \\n\",\n \"_________________________________________________________________\\n\",\n \"conv1d_3 (Conv1D) (None, 296, 50) 25050 \\n\",\n \"_________________________________________________________________\\n\",\n \"max_pooling1d_3 (MaxPooling1 (None, 148, 50) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"flatten_3 (Flatten) (None, 7400) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_6 (Dense) (None, 10) 74010 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_7 (Dense) (None, 1) 11 \\n\",\n \"=================================================================\\n\",\n \"Total params: 399,071\\n\",\n \"Trainable params: 399,071\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = imdb_cnn()\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 22500 samples, validate on 2500 samples\\n\",\n \"Epoch 1/5\\n\",\n \"22500/22500 [==============================] - 23s 1ms/sample - loss: 0.4255 - accuracy: 0.7789 - val_loss: 0.3002 - val_accuracy: 0.8732\\n\",\n \"Epoch 2/5\\n\",\n \"22500/22500 [==============================] - 22s 960us/sample - loss: 0.2367 - accuracy: 0.9060 - val_loss: 0.2985 - val_accuracy: 0.8752\\n\",\n \"Epoch 3/5\\n\",\n \"22500/22500 [==============================] - 23s 1ms/sample - loss: 0.1171 - accuracy: 0.9603 - val_loss: 0.3549 - val_accuracy: 0.8680\\n\",\n \"Epoch 4/5\\n\",\n \"22500/22500 [==============================] - 22s 969us/sample - loss: 0.0286 - accuracy: 0.9937 - val_loss: 0.4745 - val_accuracy: 0.8656\\n\",\n \"Epoch 5/5\\n\",\n \"22500/22500 [==============================] - 22s 964us/sample - loss: 0.0047 - accuracy: 0.9996 - val_loss: 0.5525 - val_accuracy: 0.8684\\n\",\n \"CPU times: user 4min 33s, sys: 4.77 s, total: 4min 38s\\n\",\n \"Wall time: 1min 50s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"history = model.fit(x_train, y_train, batch_size=64, epochs=5, validation_split=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'valiation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.多核卷积网络\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_4\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"embedding_5 (Embedding) (None, 300, 100) 300000 \\n\",\n \"_________________________________________________________________\\n\",\n \"reshape_1 (Reshape) (None, 300, 100, 1) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"model (Model) (None, 1, 1, 192) 76992 \\n\",\n \"_________________________________________________________________\\n\",\n \"flatten_4 (Flatten) (None, 192) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_8 (Dense) (None, 10) 1930 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout (Dropout) (None, 10) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_9 (Dense) (None, 1) 11 \\n\",\n \"=================================================================\\n\",\n \"Total params: 378,933\\n\",\n \"Trainable params: 378,933\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"filter_sizes=[3,4,5]\\n\",\n \"def convolution():\\n\",\n \" inn = layers.Input(shape=(sequence_length, embedding_dimension, 1))\\n\",\n \" cnns = []\\n\",\n \" for size in filter_sizes:\\n\",\n \" conv = layers.Conv2D(filters=64, kernel_size=(size, embedding_dimension),\\n\",\n \" strides=1, padding='valid', activation='relu')(inn)\\n\",\n \" pool = layers.MaxPool2D(pool_size=(sequence_length-size+1, 1), padding='valid')(conv)\\n\",\n \" cnns.append(pool)\\n\",\n \" outt = layers.concatenate(cnns)\\n\",\n \" \\n\",\n \" model = keras.Model(inputs=inn, outputs=outt)\\n\",\n \" return model\\n\",\n \"\\n\",\n \"def cnn_mulfilter():\\n\",\n \" model = keras.Sequential([\\n\",\n \" layers.Embedding(input_dim=num_features, output_dim=embedding_dimension,\\n\",\n \" input_length=sequence_length),\\n\",\n \" layers.Reshape((sequence_length, embedding_dimension, 1)),\\n\",\n \" convolution(),\\n\",\n \" layers.Flatten(),\\n\",\n \" layers.Dense(10, activation='relu'),\\n\",\n \" layers.Dropout(0.2),\\n\",\n \" layers.Dense(1, activation='sigmoid')\\n\",\n \" \\n\",\n \" ])\\n\",\n \" model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.BinaryCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \" return model\\n\",\n \"\\n\",\n \"model = cnn_mulfilter()\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 22500 samples, validate on 2500 samples\\n\",\n \"Epoch 1/5\\n\",\n \"22500/22500 [==============================] - 40s 2ms/sample - loss: 0.4740 - accuracy: 0.7557 - val_loss: 0.3136 - val_accuracy: 0.8724\\n\",\n \"Epoch 2/5\\n\",\n \"22500/22500 [==============================] - 40s 2ms/sample - loss: 0.2623 - accuracy: 0.8968 - val_loss: 0.2967 - val_accuracy: 0.8796\\n\",\n \"Epoch 3/5\\n\",\n \"22500/22500 [==============================] - 43s 2ms/sample - loss: 0.1840 - accuracy: 0.9350 - val_loss: 0.2893 - val_accuracy: 0.8868\\n\",\n \"Epoch 4/5\\n\",\n \"22500/22500 [==============================] - 40s 2ms/sample - loss: 0.1215 - accuracy: 0.9599 - val_loss: 0.3308 - val_accuracy: 0.8772\\n\",\n \"Epoch 5/5\\n\",\n \"22500/22500 [==============================] - 45s 2ms/sample - loss: 0.0694 - accuracy: 0.9806 - val_loss: 0.3699 - val_accuracy: 0.8848\\n\",\n \"CPU times: user 10min 52s, sys: 4.88 s, total: 10min 56s\\n\",\n \"Wall time: 3min 28s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"history = model.fit(x_train, y_train, batch_size=64, epochs=5, validation_split=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'valiation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "022-CNN/004-pretrained_cnn.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-使用预训练模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" % Total % Received % Xferd Average Speed Time Time Time Current\\n\",\n \" Dload Upload Total Spent Left Speed\\n\",\n \"100 39238 100 39238 0 0 28682 0 0:00:01 0:00:01 --:--:-- 28661\\n\",\n \"001-cnn.ipynb\\t\\t003-text_cnn.ipynb\\t dog.jpg\\n\",\n \"002-cnn_variants.ipynb\\t004-pretrained_cnn.ipynb\\n\"\n ]\n }\n ],\n \"source\": [\n \"#! pip install Pillow\\n\",\n \"! curl 'https://gfp-2a3tnpzj.stackpathdns.com/wp-content/uploads/2016/07/Dachshund-600x600.jpg' --output dog.jpg\\n\",\n \"! ls\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"from tensorflow.keras.preprocessing import image\\n\",\n \"from tensorflow.keras.applications import resnet50\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(600, 600, 3)\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"img = image.load_img('dog.jpg')\\n\",\n \"print(image.img_to_array(img).shape)\\n\",\n \"img\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.导入模型\\n\",\n \"目前看使用模型：\\n\",\n \"### Import model\\n\",\n \"- Currently, seven models are supported\\n\",\n \" - Xception\\n\",\n \" - VGG16\\n\",\n \" - VGG19\\n\",\n \" - ResNet50\\n\",\n \" - InceptionV3\\n\",\n \" - InceptionResNetV2\\n\",\n \" - MobileNet\\n\",\n \" - MobileNetV2\\n\",\n \" - DenseNet\\n\",\n \" - nasnet\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model = resnet50.ResNet50(weights='imagenet')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1, 224, 224, 3)\\n\"\n ]\n }\n ],\n \"source\": [\n \"img = image.load_img('dog.jpg', target_size=(224, 224))\\n\",\n \"img = image.img_to_array(img)\\n\",\n \"img = np.expand_dims(img, axis=0)\\n\",\n \"print(img.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.模型预测\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pred_class = model.predict(img)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"('n02099849', 'Chesapeake_Bay_retriever', 0.51448697)\\n\",\n \"('n02099712', 'Labrador_retriever', 0.1818683)\\n\",\n \"('n02088364', 'beagle', 0.05007153)\\n\",\n \"('n02105412', 'kelpie', 0.03155613)\\n\",\n \"('n02087394', 'Rhodesian_ridgeback', 0.020672312)\\n\",\n \"('n02090379', 'redbone', 0.018476445)\\n\",\n \"('n02100236', 'German_short-haired_pointer', 0.01802308)\\n\",\n \"('n04409515', 'tennis_ball', 0.011181626)\\n\",\n \"('n02107142', 'Doberman', 0.009305544)\\n\",\n \"('n02101388', 'Brittany_spaniel', 0.00871648)\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = 10\\n\",\n \"top_n = resnet50.decode_predictions(pred_class, top=n)\\n\",\n \"for c in top_n[0]:\\n\",\n \" print(c)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1, 224, 224, 3)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# img = image.load_img('dog.jpg')\\n\",\n \"# img = image.img_to_array(img)\\n\",\n \"# print(img.shape)\\n\",\n \"img = resnet50.preprocess_input(img)\\n\",\n \"print(img.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"('n02106550', 'Rottweiler', 0.6937907)\\n\",\n \"('n02107142', 'Doberman', 0.10025302)\\n\",\n \"('n02107312', 'miniature_pinscher', 0.057240624)\\n\",\n \"('n02107908', 'Appenzeller', 0.041135676)\\n\",\n \"('n02101006', 'Gordon_setter', 0.036703203)\\n\",\n \"('n02112706', 'Brabancon_griffon', 0.014862828)\\n\",\n \"('n02089078', 'black-and-tan_coonhound', 0.014462694)\\n\",\n \"('n02108000', 'EntleBucher', 0.0043801107)\\n\",\n \"('n02093754', 'Border_terrier', 0.002769812)\\n\",\n \"('n02099712', 'Labrador_retriever', 0.002542331)\\n\"\n ]\n }\n ],\n \"source\": [\n \"pred_class = model.predict(img)\\n\",\n \"n = 10\\n\",\n \"top_n = resnet50.decode_predictions(pred_class, top=n)\\n\",\n \"for c in top_n[0]:\\n\",\n \" print(c)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "023-RNN/002-rnn_variance.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Tensorflow2教程-rnn变体\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from tensorflow import keras\\n\",\n \"from tensorflow.keras import layers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.导入数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"num_words = 30000\\n\",\n \"maxlen = 200\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(x_train, y_train), (x_test, y_test) = keras.datasets.imdb.load_data(num_words=num_words)\\n\",\n \"print(x_train.shape, ' ', y_train.shape)\\n\",\n \"print(x_test.shape, ' ', y_test.shape)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x_train = keras.preprocessing.sequence.pad_sequences(x_train, maxlen, padding='post')\\n\",\n \"x_test = keras.preprocessing.sequence.pad_sequences(x_test, maxlen, padding='post')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"print(x_train.shape, ' ', y_train.shape)\\n\",\n \"print(x_test.shape, ' ', y_test.shape)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.LSTM\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_2\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"embedding_2 (Embedding) (None, 200, 32) 960000 \\n\",\n \"_________________________________________________________________\\n\",\n \"unified_lstm_4 (UnifiedLSTM) (None, 200, 32) 8320 \\n\",\n \"_________________________________________________________________\\n\",\n \"unified_lstm_5 (UnifiedLSTM) (None, 1) 136 \\n\",\n \"=================================================================\\n\",\n \"Total params: 968,456\\n\",\n \"Trainable params: 968,456\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"def lstm_model():\\n\",\n \" model = keras.Sequential([\\n\",\n \" layers.Embedding(input_dim=30000, output_dim=32, input_length=maxlen),\\n\",\n \" layers.LSTM(32, return_sequences=True),\\n\",\n \" layers.LSTM(1, activation='sigmoid', return_sequences=False)\\n\",\n \" ])\\n\",\n \" model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.BinaryCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \" return model\\n\",\n \"model = lstm_model()\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 22500 samples, validate on 2500 samples\\n\",\n \"Epoch 1/5\\n\",\n \"22500/22500 [==============================] - 100s 4ms/sample - loss: 0.6589 - accuracy: 0.6056 - val_loss: 0.6090 - val_accuracy: 0.6544\\n\",\n \"Epoch 2/5\\n\",\n \"22500/22500 [==============================] - 100s 4ms/sample - loss: 0.6418 - accuracy: 0.6076 - val_loss: 0.6356 - val_accuracy: 0.6068\\n\",\n \"Epoch 3/5\\n\",\n \"22500/22500 [==============================] - 100s 4ms/sample - loss: 0.5763 - accuracy: 0.6980 - val_loss: 0.5984 - val_accuracy: 0.6724\\n\",\n \"Epoch 4/5\\n\",\n \"22500/22500 [==============================] - 102s 5ms/sample - loss: 0.5749 - accuracy: 0.7150 - val_loss: 0.5044 - val_accuracy: 0.7900\\n\",\n \"Epoch 5/5\\n\",\n \"22500/22500 [==============================] - 102s 5ms/sample - loss: 0.4893 - accuracy: 0.7993 - val_loss: 0.7230 - val_accuracy: 0.6756\\n\",\n \"CPU times: user 14min 29s, sys: 42.5 s, total: 15min 12s\\n\",\n \"Wall time: 8min 24s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"history = model.fit(x_train, y_train, batch_size=64, epochs=5,validation_split=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'valivation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.GRU\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_3\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"embedding_3 (Embedding) (None, 200, 32) 960000 \\n\",\n \"_________________________________________________________________\\n\",\n \"unified_gru (UnifiedGRU) (None, 200, 32) 6336 \\n\",\n \"_________________________________________________________________\\n\",\n \"unified_gru_1 (UnifiedGRU) (None, 1) 105 \\n\",\n \"=================================================================\\n\",\n \"Total params: 966,441\\n\",\n \"Trainable params: 966,441\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"def lstm_model():\\n\",\n \" model = keras.Sequential([\\n\",\n \" layers.Embedding(input_dim=30000, output_dim=32, input_length=maxlen),\\n\",\n \" layers.GRU(32, return_sequences=True),\\n\",\n \" layers.GRU(1, activation='sigmoid', return_sequences=False)\\n\",\n \" ])\\n\",\n \" model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.BinaryCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \" return model\\n\",\n \"model = lstm_model()\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 22500 samples, validate on 2500 samples\\n\",\n \"Epoch 1/5\\n\",\n \"22500/22500 [==============================] - 94s 4ms/sample - loss: 0.6498 - accuracy: 0.5847 - val_loss: 0.4840 - val_accuracy: 0.7664\\n\",\n \"Epoch 2/5\\n\",\n \"22500/22500 [==============================] - 98s 4ms/sample - loss: 0.4172 - accuracy: 0.8255 - val_loss: 0.4279 - val_accuracy: 0.8300\\n\",\n \"Epoch 3/5\\n\",\n \"22500/22500 [==============================] - 101s 5ms/sample - loss: 0.3179 - accuracy: 0.8825 - val_loss: 0.3918 - val_accuracy: 0.8472\\n\",\n \"Epoch 4/5\\n\",\n \"22500/22500 [==============================] - 99s 4ms/sample - loss: 0.3306 - accuracy: 0.8870 - val_loss: 0.3959 - val_accuracy: 0.8468\\n\",\n \"Epoch 5/5\\n\",\n \"22500/22500 [==============================] - 96s 4ms/sample - loss: 0.2607 - accuracy: 0.9120 - val_loss: 0.3849 - val_accuracy: 0.8532\\n\",\n \"CPU times: user 14min 25s, sys: 38.9 s, total: 15min 4s\\n\",\n \"Wall time: 8min 10s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"history = model.fit(x_train, y_train, batch_size=64, epochs=5,validation_split=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'valivation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.CuDNN LSTM\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"module 'tensorflow.python.keras.api._v2.keras.layers' has no attribute 'CuDNNLSTM'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m\\u001b[0m\\n\\u001b[1;32m 9\\u001b[0m metrics=['accuracy'])\\n\\u001b[1;32m 10\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mmodel\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 11\\u001b[0;31m \\u001b[0mmodel\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mlstm_model\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 12\\u001b[0m \\u001b[0mmodel\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0msummary\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36mlstm_model\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 2\\u001b[0m model = keras.Sequential([\\n\\u001b[1;32m 3\\u001b[0m \\u001b[0mlayers\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mEmbedding\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0minput_dim\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m30000\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0moutput_dim\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m32\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0minput_length\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mmaxlen\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 4\\u001b[0;31m \\u001b[0mlayers\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mCuDNNLSTM\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m32\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mreturn_sequences\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 5\\u001b[0m \\u001b[0mlayers\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mCuDNNLSTM\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mactivation\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'sigmoid'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mreturn_sequences\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 6\\u001b[0m ])\\n\",\n \"\\u001b[0;31mAttributeError\\u001b[0m: module 'tensorflow.python.keras.api._v2.keras.layers' has no attribute 'CuDNNLSTM'\"\n ]\n }\n ],\n \"source\": [\n \"def lstm_model():\\n\",\n \" model = keras.Sequential([\\n\",\n \" layers.Embedding(input_dim=30000, output_dim=32, input_length=maxlen),\\n\",\n \" layers.CuDNNLSTM(32, return_sequences=True),\\n\",\n \" layers.CuDNNLSTM(1, activation='sigmoid', return_sequences=False)\\n\",\n \" ])\\n\",\n \" model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.BinaryCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \" return model\\n\",\n \"model = lstm_model()\\n\",\n \"model.summary()\\n\",\n \"# tf2版没有\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%time\\n\",\n \"history = model.fit(x_train, y_train, batch_size=64, epochs=5,validation_split=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'valivation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5.CuDNN GRU\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def lstm_model():\\n\",\n \" model = keras.Sequential([\\n\",\n \" layers.Embedding(input_dim=30000, output_dim=32, input_length=maxlen),\\n\",\n \" layers.CuDNNGRU(32, return_sequences=True),\\n\",\n \" layers.CuDNNGRU(1, activation='sigmoid', return_sequences=False)\\n\",\n \" ])\\n\",\n \" model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.BinaryCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \" return model\\n\",\n \"model = lstm_model()\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%time\\n\",\n \"history = model.fit(x_train, y_train, batch_size=64, epochs=5,validation_split=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'valivation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"layers.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "023-RNN/003-cnn_rnn.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-CNN-RNN结构用于图像处理\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.导入数据\\n\",\n \"使用cifar-10数据集\\n\",\n \"![](https://image.slidesharecdn.com/pycon2015-150913033231-lva1-app6892/95/pycon-2015-48-638.jpg?cb=1442115225)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"from sklearn.metrics import accuracy_score\\n\",\n \"from tensorflow import keras\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y_train = keras.utils.to_categorical(y_train)\\n\",\n \"y_test = keras.utils.to_categorical(y_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(50000, 32, 32, 3) (50000, 10)\\n\",\n \"(10000, 32, 32, 3) (10000, 10)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(x_train.shape, ' ', y_train.shape)\\n\",\n \"print(x_test.shape, ' ', y_test.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.简单的cnn-rnn结构\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from tensorflow.keras import layers\\n\",\n \"model = keras.Sequential()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x_shape = x_train.shape\\n\",\n \"model.add(layers.Conv2D(input_shape=(x_shape[1], x_shape[2], x_shape[3]),\\n\",\n \" filters=32, kernel_size=(3,3), strides=(1,1), \\n\",\n \" padding='same', activation='relu'))\\n\",\n \"model.add(layers.MaxPool2D(pool_size=(2,2)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(None, 16, 16, 32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(model.output_shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model.add(layers.Reshape(target_shape=(16*16, 32)))\\n\",\n \"model.add(layers.LSTM(50, return_sequences=False))\\n\",\n \"\\n\",\n \"model.add(layers.Dense(10, activation='softmax'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"conv2d (Conv2D) (None, 32, 32, 32) 896 \\n\",\n \"_________________________________________________________________\\n\",\n \"max_pooling2d (MaxPooling2D) (None, 16, 16, 32) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"reshape (Reshape) (None, 256, 32) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"unified_lstm (UnifiedLSTM) (None, 50) 16600 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense (Dense) (None, 10) 510 \\n\",\n \"=================================================================\\n\",\n \"Total params: 18,006\\n\",\n \"Trainable params: 18,006\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.CategoricalCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 45000 samples, validate on 5000 samples\\n\",\n \"Epoch 1/5\\n\",\n \"45000/45000 [==============================] - 139s 3ms/sample - loss: 2.1328 - accuracy: 0.2132 - val_loss: 2.0399 - val_accuracy: 0.2386\\n\",\n \"Epoch 2/5\\n\",\n \"45000/45000 [==============================] - 145s 3ms/sample - loss: 1.9657 - accuracy: 0.2684 - val_loss: 1.8785 - val_accuracy: 0.3004\\n\",\n \"Epoch 3/5\\n\",\n \"45000/45000 [==============================] - 146s 3ms/sample - loss: 1.8475 - accuracy: 0.3048 - val_loss: 1.7853 - val_accuracy: 0.3290\\n\",\n \"Epoch 4/5\\n\",\n \"45000/45000 [==============================] - 135s 3ms/sample - loss: 1.7433 - accuracy: 0.3447 - val_loss: 1.7078 - val_accuracy: 0.3520\\n\",\n \"Epoch 5/5\\n\",\n \"45000/45000 [==============================] - 137s 3ms/sample - loss: 1.6751 - accuracy: 0.3693 - val_loss: 1.6920 - val_accuracy: 0.3664\\n\",\n \"CPU times: user 28min 48s, sys: 1min 28s, total: 30min 16s\\n\",\n \"Wall time: 11min 41s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"history = model.fit(x_train, y_train, batch_size=32,epochs=5, validation_split=0.1)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.plot(history.history['accuracy'])\\n\",\n \"plt.plot(history.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'valivation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000/10000 [==============================] - 6s 603us/sample - loss: 1.7132 - accuracy: 0.3590\\n\"\n ]\n }\n ],\n \"source\": [\n \"res = model.evaluate(x_test,y_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## CNN和LSTM结果合并\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x_shape = x_train.shape\\n\",\n \"inn = layers.Input(shape=(x_shape[1], x_shape[2], x_shape[3]))\\n\",\n \"conv = layers.Conv2D(filters=32,kernel_size=(3,3), strides=(1,1),\\n\",\n \" padding='same', activation='relu')(inn)\\n\",\n \"pool = layers.MaxPool2D(pool_size=(2,2), padding='same')(conv)\\n\",\n \"flat = layers.Flatten()(pool)\\n\",\n \"dense1 = layers.Dense(64)(flat)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"reshape = layers.Reshape(target_shape=(x_shape[1]*x_shape[2], x_shape[3]))(inn)\\n\",\n \"lstm_layer = layers.LSTM(32, return_sequences=False)(reshape)\\n\",\n \"dense2 = layers.Dense(64)(lstm_layer)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"model_1\\\"\\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # Connected to \\n\",\n \"==================================================================================================\\n\",\n \"input_1 (InputLayer) [(None, 32, 32, 3)] 0 \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"conv2d_1 (Conv2D) (None, 32, 32, 32) 896 input_1[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"max_pooling2d_1 (MaxPooling2D) (None, 16, 16, 32) 0 conv2d_1[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"reshape_1 (Reshape) (None, 1024, 3) 0 input_1[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"flatten (Flatten) (None, 8192) 0 max_pooling2d_1[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"unified_lstm_1 (UnifiedLSTM) (None, 32) 4608 reshape_1[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"dense_1 (Dense) (None, 64) 524352 flatten[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"dense_2 (Dense) (None, 64) 2112 unified_lstm_1[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"concatenate_1 (Concatenate) (None, 128) 0 dense_1[0][0] \\n\",\n \" dense_2[0][0] \\n\",\n \"__________________________________________________________________________________________________\\n\",\n \"dense_4 (Dense) (None, 10) 1290 concatenate_1[0][0] \\n\",\n \"==================================================================================================\\n\",\n \"Total params: 533,258\\n\",\n \"Trainable params: 533,258\\n\",\n \"Non-trainable params: 0\\n\",\n \"__________________________________________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"merged_layer = layers.concatenate([dense1, dense2])\\n\",\n \"outt = layers.Dense(10,activation='softmax')(merged_layer)\\n\",\n \"model = keras.Model(inputs=inn, outputs=outt)\\n\",\n \"model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.CategoricalCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 45000 samples, validate on 5000 samples\\n\",\n \"Epoch 1/5\\n\",\n \"45000/45000 [==============================] - 679s 15ms/sample - loss: 11.2904 - accuracy: 0.3047 - val_loss: 1.8392 - val_accuracy: 0.3776\\n\",\n \"Epoch 2/5\\n\",\n \"45000/45000 [==============================] - 642s 14ms/sample - loss: 1.7014 - accuracy: 0.4163 - val_loss: 1.7302 - val_accuracy: 0.4252\\n\",\n \"Epoch 3/5\\n\",\n \"45000/45000 [==============================] - 574s 13ms/sample - loss: 1.5295 - accuracy: 0.4751 - val_loss: 1.6444 - val_accuracy: 0.4626\\n\",\n \"Epoch 4/5\\n\",\n \"45000/45000 [==============================] - 623s 14ms/sample - loss: 1.4177 - accuracy: 0.5120 - val_loss: 1.6575 - val_accuracy: 0.4666\\n\",\n \"Epoch 5/5\\n\",\n \"45000/45000 [==============================] - 611s 14ms/sample - loss: 1.3566 - accuracy: 0.5345 - val_loss: 1.6687 - val_accuracy: 0.4760\\n\",\n \"CPU times: user 1h 42min 35s, sys: 4min 12s, total: 1h 46min 47s\\n\",\n \"Wall time: 52min 10s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"history2 = model.fit(x_train, y_train, batch_size=64,epochs=5, validation_split=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(history2.history['accuracy'])\\n\",\n \"plt.plot(history2.history['val_accuracy'])\\n\",\n \"plt.legend(['training', 'valivation'], loc='upper left')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "024-AutoEncoder/001-autoencoder.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-自编码器\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![](https://upload.wikimedia.org/wikipedia/commons/2/28/Autoencoder_structure.png)\\n\",\n \"\\n\",\n \"自动编码器的两个主要组成部分; 编码器和解码器\\n\",\n \"编码器将输入压缩成一小组“编码”（通常，编码器输出的维数远小于编码器输入）\\n\",\n \"解码器然后将编码器输出扩展为与编码器输入具有相同维度的输出\\n\",\n \"换句话说，自动编码器旨在“重建”输入，同时学习数据的有限表示（即“编码”）\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.导入数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"import tensorflow.keras as keras\\n\",\n \"import tensorflow.keras.layers as layers\\n\",\n \"from IPython.display import SVG\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(60000, 784) (60000,)\\n\",\n \"(10000, 784) (10000,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"x_train = x_train.reshape((-1, 28*28)) / 255.0\\n\",\n \"x_test = x_test.reshape((-1, 28*28)) / 255.0\\n\",\n \"\\n\",\n \"print(x_train.shape, ' ', y_train.shape)\\n\",\n \"print(x_test.shape, ' ', y_test.shape)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.简单的自编码器\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"model\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"inputs (InputLayer) [(None, 784)] 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"code (Dense) (None, 32) 25120 \\n\",\n \"_________________________________________________________________\\n\",\n \"outputs (Dense) (None, 784) 25872 \\n\",\n \"=================================================================\\n\",\n \"Total params: 50,992\\n\",\n \"Trainable params: 50,992\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"code_dim = 32\\n\",\n \"inputs = layers.Input(shape=(x_train.shape[1],), name='inputs')\\n\",\n \"code = layers.Dense(code_dim, activation='relu', name='code')(inputs)\\n\",\n \"outputs = layers.Dense(x_train.shape[1], activation='softmax', name='outputs')(code)\\n\",\n \"\\n\",\n \"auto_encoder = keras.Model(inputs, outputs)\\n\",\n \"auto_encoder.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"keras.utils.plot_model(auto_encoder, show_shapes=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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63hoYG6Ovrq6W9ytJZMrEXX3yRv6cnHLVxI5PW5QZRVy7hjRs3wtbWFidPnkRiYiLq6+uxfv36VtfvCerMk9wWIsKf//xn6Ovrw8fHB3V1dZg7d66wjZb1m5iYICAgALt378auXbvw1ltvCcu6I98wY6zj1BKstTGXcFRUlDAjwc3NDYMGDcKIESMAdCzP8KNHjwBA9CQLTfdNnmLy4cOHQvCVq6iowDvvvIO+fftCT08PRUVFKCwsxLfffouEhAQh8fzFixdFN9uEhoaitrYWN2/exFNPPSWUq5JvWP59VFZWtvt9MsY6p8vBWltzCVdXV2PGjBnYtm0b3nzzTTg7OwuP8FElz/CZM2ewevVqIaD5+/vj2LFjKuXo7c6+paenIyAgAEBT0La3t8e0adMwbdo0jBkzBqamptizZw9mzZoFAEICnPfffx+2trZ4//33MWTIEERGRopu9DEzM8OsWbOEvMZyffv2bTXfsL6+Pv76178K30dISAguX77c5vHCGOuk1q7O9wRdySXcGbrWt8rKSrK1taWqqqoe3S5P3VMNwFP3fg84nzVrV1xcHFauXKmx2+oZY23T6GyQJzmXsC70LSsrC/7+/nj06BEaGhqQk5Oj6SYxxlqhkTPryspKrFu3TpRLuPkz4XSZLvVtwIABePDgAfT09JCYmKhVzwpkXSeRSKCnp4fw8HBs27YNMplMtFwmkyEqKgpSqRSOjo6QSCRwcHAQLqrLpaWlwdXVFRKJBBMnToRUKu3JbqjMxcVFSNfQ8iV/GAAR4cCBA1iwYAHWrl2LadOmISAgoM30CDExMaJ7K+rr6xEeHi78xuVkMhm2bduGlStXitJGqE0HxkwYUztNH283b97UibrRiTFrAGRra6t02XfffUeLFy+mmpoaIiKqqKgQ7p5btmyZwvp5eXkEgHJycjre+B7w888/k6OjI3388ce0b98+4bV8+XKaMGGCsN6nn35KACg1NVX4HABatGiR0novXrwopFNorrS0lF599VW6ceOG0s9ZWVl16rhua8xa626KYayn5OXlwcfHR+nTvLW57o5QdqdudnY2fHx8cPnyZRgYGACAcHev/OaxGTNmiJ7YLZ/2am1t3QOt7rgrV64gLS0NJiYmovKMjAzRjUQHDhwAADz//PMAAHt7e5iYmODUqVMKdZaXl+PYsWOwtLRU+K/E2NgYmzZtwsKFC5GVlaUw1Nkduf35AiP7XZKnn/311191qu6uamxshLe3N5YuXQpjY2OF5VKpFGZmZvD390dubq5QLp9uKg/u2sbLy0shUNfU1ODIkSNwc3MTyuQpEU6fPg2g6dpSWVkZpk+fLvosEWHz5s0ICwtrdTjD0dERtra2PfaELA7WTOdUVFQgLCwMa9asQUhICGbPno2QkBBh3LEz6WepB1LbdjY1rzqlpKTg8uXLmDNnjtLl5ubmSEpKQlVVFby8vFBbW9tqXW3tB1IhnbBcayl4u+rkyZOwsLDAuHHjhLLo6GjY2NggKCgIBQUFiI2NRVhYGBITE0WfjYmJgaenJwYPHtzmNlxdXbFnz55ueUCugg6MmTCmdh093h48eEB2dna0adMmoezu3btkZ2dH1tbWVF5eTkQdTz/bE6ltO5KatyV0csy6ZRu8vLwIANXW1ipdX27Hjh0EgEJDQ5Uub28/lJWVtZtOWK61FLzNs2R2xpIlS+iDDz5QKC8pKSEnJycaMWIEBQcHKyw/f/48bd++XXjf1j0TP/74IwFN6YGb6+x9Ft2eIpWxzuro8bZu3ToCQHfu3BGVf/755wSAwsLCiKjz6We7O7Wtqql5W1JXsLaysqLBgwe3ur5cY2Mjubm5iS7GNV+u6n5oL52wKil4O6OqqooMDQ3p6tWrCsvy8/Np3rx5NGfOHAJAq1atooaGBiJqunD41ltvCe+J2g68hYWFBIDmzp0rKu+OYM3DIEynyJ/kPnDgQFH51KlTAQDnz5/vUv2tpbYFoJbUtl1JzasOxcXFMDIyanc9iUSC+Ph4jB07Fr6+vkI6BTlV90Nr6YTlwyvyFLz0/0+Zb/6aP39+5zoJ4MSJExg5ciTs7e1F5RcuXMBzzz0HX19fHD16FE5OToiKisLGjRsBAAEBAfD29oZMJkNOTg5ycnKEfD05OTkKwx1DhgwBANy9e7fTbVUVB2umU+TBND8/X1Q+bNgwAGh3jLEzejK1bXfT19dX+ZmeAwcORHJyMqqrq/H666+LlqlrPzRPwdtSV549KpVKRRcW5dauXYt79+7h5ZdfRp8+fYR8QZ999hmApjH96dOnY9y4ccJL3sdx48bB1dVVVJ/8jxG1kdpUXThYM50iP3NLTU0VlcsTbnVH+ll1prbVRGre5szNzYXMi83JA2PLAGlvb4/4+Hhh9oScqvuhPd2RgreyshKpqalKc3/Lz+jls1osLS1hamoq7NPHjx8rnOGPGTMGQNO+/eWXX0T1yS9qm5ubd6qtHcHBmumU8PBwODg4ICYmBkVFRUJ5XFwcnJycsGLFCgCdTz8r1x2pbTuSmre7uLi44OHDh0JKXzn5U46U/Tvv4eGBoKAgUZmq+6G9dMKqpOCNioqCvb09vvjiC5X6mJKSAisrKzg4OCgsW7JkCYCmYRIAKCgoQElJCby8vFSqu6XS0lIAwJQpUzr1+Y7gYM10Sv/+/ZGZmYklS5bA19cXoaGhCA8Ph4mJCdLT07ucWleuO9L2qpKat7v5+PgAgCgFwuHDh4XUuP7+/kpv5Pnoo48wefJk4b0q+0GVdMJE1GoKXvmNOrm5ucjJycGqVatU6qNUKoW7u7vS+dEBAQGIjY1FdHQ0QkNDERQUhA0bNiic2avq3Llz0NPTE91A1G06cDWSMbXTtuNNW1PbQk2zQYiI5s6dS++99566mtYjrl27Jprupy3mz59Pb7/9tkI5zwZhjHWIfHimuX379iE1NRXFxcUaaFHHVVVVYdeuXdi7d6+mmyKSmZkJmUyG7du3KyzrjmsTnBuEsWZ0IbVtR+Tn5yMwMBDDhw/Hn/70J4wePRrDhg1DcnIygoODsXfvXoUHIWub3Nxc4YlH2qKwsBARERFIS0sT2iWTyXD48GGUlZV1yx2NHKwZQ1NwjoyMFKW2XbZsGV566SUNt6zzqI3pZE8//TQiIiIQFxeH8PDwHmxVx02YMEHTTRCpq6vDwYMHkZiYKPoDMnr0aKxZswZA0xi/unGwZgyAoaEhIiMjheeJ/h7Y2NhofaDWRr179xaCck/iMWvGGNMBHKwZY0wHcLBmjDEdwMGaMcZ0QKsXGA8dOtST7WC/U1lZWQD4eFNFVlaW+h/CyrSK/PegjIRazO85c+YMpk+frvGEM4wx9ntkYWEhJMRq5pBCsGZMlyUlJcHT07NHUlYy1oMO8Zg1Y4zpAA7WjDGmAzhYM8aYDuBgzRhjOoCDNWOM6QAO1owxpgM4WDPGmA7gYM0YYzqAgzVjjOkADtaMMaYDOFgzxpgO4GDNGGM6gIM1Y4zpAA7WjDGmAzhYM8aYDuBgzRhjOoCDNWOM6QAO1owxpgM4WDPGmA7gYM0YYzqAgzVjjOkADtaMMaYDOFgzxpgO4GDNGGM6gIM1Y4zpAA7WjDGmAzhYM8aYDuBgzRhjOoCDNWOM6QAO1owxpgM4WDPGmA7gYM0YYzqAgzVjjOmAXppuAGNdcfDgQdy5c0d4/+9//xsA8OGHH4rWmzZtGiZNmtSjbWNMnSRERJpuBGOd9Yc//AFlZWXo3bt3q+vU1NRgxYoViImJ6cGWMaZWh3gYhOk0T09P6Ovro6amptUXALi7u2u4pYx1DQdrptMWL16Murq6Ntf5wx/+gClTpvRQixjrHhysmU5zcnKChYVFq8sNDAzw5ptvQk+PD3Wm2/gIZjpNIpHA29u71THr2tpaLF68uIdbxZj6cbBmOq+toRAbGxv88Y9/7OEWMaZ+HKyZznv66acxZswYhXIDAwP4+vpqoEWMqR8Ha/ZEeOONNxSGQmpra+Hl5aWhFjGmXhys2RNh8eLFqK+vF95LJBI4Ojpi9OjRGmwVY+rDwZo9EWxsbPDss89CIpEAAPT19XkIhD1ROFizJ4aPjw/09fUBAA0NDfDw8NBwixhTHw7W7Inh4eGBxsZGSCQSTJ48GSNGjNB0kxhTGw7W7IlhZmYGFxcXEBEPgbAnjlYkcgoJCUF0dLSmm8EYYyK9evVCeno6nJ2dNd2UQ1qRIvX27dt48cUXERISoummsA7y8PBAcHAwXnrpJU03BQBARCgvL8fQoUM13RSB/EQkODhYwy1hHeXh4YGioiJNNwOAFuWztrS05MxoOurFF1/kfdeGQ4cOAeDMf6xreMyaMcZ0AAdrxhjTARysGWNMB3CwZowxHcDBmjHGdAAHa8YY0wEcrJlWeOGFFxAWFqbpZmglmUyGqKgoSKVSODo6QiKRwMHBAY8ePRKtl5aWBldXV0gkEkycOBFSqVRDLW6bi4sLJBKJ0teNGzcANM2XP3DgABYsWIC1a9di2rRpCAgIQHl5eav1xsTECIm8AKC+vh7h4eG4fft2t/epJ2jNPGv2+2ZtbY2+fftqbPu3bt2CpaWlxrbfmtOnT+Ozzz7D/v37YWBggLlz52Lw4MHIzs5GUFAQPvvsM2HdmTNn4qmnnoK1tTUSEhKUPpBB065evYqKigp8/PHHMDExEcovXLiAc+fOwdbWFgCwe/duBAQEIDU1Fa+88gquXr2K8ePHo6ioCEePHlWo99KlS1i9erWorFevXli9ejWWLVuGqKgo2NjYdG/nuhkHa6YVvvzyS41tOy8vDz4+Pjhz5ozG2qBMdnY2fHx8cPnyZRgYGAAABg0aBABwdnbGnj17MGPGDHh6egqfkSevsra27vkGq+DKlStIS0sTBWoAyMjIEN00dODAAQDA888/DwCwt7eHiYkJTp06pVBneXk5jh07BktLS8hkMtEyY2NjbNq0CQsXLkRWVhYMDQ3V3aUew8Mg7Hft9u3bmD9/Pn799VdNN0WksbER3t7eWLp0KYyNjRWWS6VSmJmZwd/fH7m5uUK5/Gk58uCubby8vBQCdU1NDY4cOQI3NzehTJ4u4PTp0wCAqqoqlJWVYfr06aLPEhE2b96MsLAw0RBIc46OjrC1tdX5YTYO1kyjGhoakJSUBF9fX0ydOhVEhGPHjsHf3x8WFhYoLy+Hr68vjI2NMX78eHz//fcgImRmZiI0NBSjRo1CcXExXnvtNQwdOhTjx49HcnIygKZ/peVjoQDw4MEDbN++XVS2f/9+ZGdno7i4GMuXLxfalZ6eDgsLC2RkZPT8lwIgJSUFly9fxpw5c5QuNzc3R1JSEqqqquDl5YXa2tpW66qoqEBYWBjWrFmDkJAQzJ49GyEhISgvL1fp+5arrq7Ghx9+CD8/P0ycOBEzZ87ElStXutzXkydPwsLCAuPGjRPKoqOjYWNjg6CgIBQUFCA2NhZhYWFITEwUfTYmJgaenp4YPHhwm9twdXXFnj17hDFxnURawN3dndzd3TXdDNYJAEgqlXapjoKCAgJAY8aMocbGRrp16xYNGDCAANCWLVsoPz+fDh48SABo0qRJVF9fT8ePH6e+ffsSAFqxYgVlZGRQQkICGRoaEgA6e/YsERHZ2NhQy8O8ZZl8280dPXqU+vXrRykpKV3qG1Hnjm8vLy8CQLW1tQrLmrd9x44dBIBCQ0OVLn/w4AHZ2dnRpk2bhLK7d++SnZ0dWVtbU1lZWbvft9zbb79N//nPf4T3s2bNIlNTU6qoqOhQ31pasmQJffDBBwrlJSUl5OTkRCNGjKDg4GCF5efPn6ft27cL78eMGaOwr+V+/PFHAkCRkZEdaps6jm81SeJgzbpEHQdzY2OjQsAcPXq06IfX2NhIpqamZGBgIJTZ2dkRAKqsrBTKoqOjCQB5enoSkfIfcMsyZcGaiKiurq5L/ZLrzPFtZWVFgwcPVrqs5ffi5uZGACg1NVVh+bp16wgA3blzR1TH559/TgAoLLWOY7IAABO6SURBVCyMiNr/vrOysgiA0tfx48c71LfmqqqqyNDQkK5evaqwLD8/n+bNm0dz5swhALRq1SpqaGggIqLS0lJ66623hPdEbQfrwsJCAkBz587tUPu0KVjzMAjTOGVjjS3LJBIJjIyMRP/u6+k1Hb4DBgwQyhYuXAgAuH79epfb1auX5q6/FxcXw8jIqN31JBIJ4uPjMXbsWPj6+uLOnTui5efOnQMADBw4UFQ+depUAMD58+eFelrW2/z7vnTpEuzt7UFECq/58+d3rpMATpw4gZEjR8Le3l5UfuHCBTz33HPw9fXF0aNH4eTkhKioKGzcuBEAEBAQAG9vb8hkMuTk5CAnJwc1NTUAgJycHIXhjiFDhgAA7t692+m2ahoHa/ZEGT58OABo5TS8jtDX10dDQ4NK6w4cOBDJycmorq7G66+/Llom/4OWn58vKh82bBgAtDvWK3fv3j3k5eWhqqpKYZmq7VRGKpWKLizKrV27Fvfu3cPLL7+MPn36CLOF5FMVU1JSMH36dIwbN054yfs4btw4uLq6iuqT/zEizT9rpdM4WLMnyr179wA0zTkGfvuRys+6GhsbUVFRAUD8w62vr1eoS1lZTzE3N8f9+/cVyuWBsWWAtLe3R3x8vDB7Qk5+Bp2amioqv3XrFoDfvqf2jB07VrjA2Fx2djZiY2NVqqOlyspKpKamKs3zLT+jl89qsbS0hKmpqbA/Hz9+rHCGL59XTkT45ZdfRPXJb6YxNzfvVFu1AQdrpnEPHz4E0DRbQ+7x48cAxAFVvl5dXZ3o882D6qlTp/Dss8/inXfeAdAUZABgy5YtuH79Oj755BMhcJ88eRINDQ2wtbVFUVERbt68KdTz1VdfYciQIfj666/V1s+OcHFxwcOHD4U+y5WUlABQ/u+8h4cHgoKCRGXh4eFwcHBATEyM6IkncXFxcHJywooVKwC0/30vWrQI1tbW2Lx5M/z8/JCQkID169cjKCgIS5cuBQBERUXB3t4eX3zxhUp9TElJgZWVFRwcHBSWLVmyBEDTMAkAFBQUoKSkBF5eXirV3VJpaSkAYMqUKZ36vDbgYM00qqqqCpGRkQCAoqIi7NixA1u3bkVBQQEAICIiAhUVFdi5c6cwHrthwwZUV1cLdezcuROlpaUoKSnBnTt3kJGRIcw3/vDDDzFp0iTs2LED7777LubNmwcHBwd4e3vj/v37qK+vh7u7OwYNGoRLly4Jdfbp0weDBg1Cnz59euqrEPHx8QEAZGZmCmWHDx+Gn58fAMDf31/pTTwfffQRJk+eLLzv378/MjMzsWTJEvj6+iI0NBTh4eEwMTFBeno6evfujbi4uHa/byJCeno6FixYgCNHjiA0NBQlJSVISEgQbtTJzc1FTk4OVq1apVIfpVIp3N3dlV6zCAgIQGxsLKKjoxEaGoqgoCBs2LBB4cxeVefOnYOenp7oBiJdoxUPzPXw8AAAJCUlabglrKMkEgmkUqmwD3vS2LFjce3aNa0fh+zs8f3KK69g9OjR2LlzZ3c0q1vIZDK88cYbuHDhgqabIrJgwQKYmZlhz549HfqcJo/vFg7xmTVjWmrfvn1ITU1FcXGxppuikqqqKuzatQt79+7VdFNEMjMzIZPJsH37dk03pUs4WCuh7MIO0z7ymQmVlZUabkn3GDZsGJKTkxEcHKx0Foa2yc3NRWRkJCZMmKDppggKCwsRERGBtLQ0YbhGV3Gw/n+PHz9GREQEXnrpJaW5GLri22+/xdy5c4XbnKdNm4Zp06Zh4sSJWLhwIfbu3Stc9GLtq6ysxLp164TUl4GBgaKx3SfJ008/jYiICMTFxWm6Ke2aMGGCVgXEuro6HDx4EImJiTo/lRPgMWuR6upqjBgxQsiZoE6FhYWwsLDAqFGjkJeXB6BpGtlXX32F4OBg6Onp4ejRo0qvjGszLRrT01racnyzjtOi45vHrJvr168fTE1Nu6VueerK5rML9PT0sHDhQpw9exaVlZVYtGiRaJYDY4zJcbDWAubm5ti8eTNu3Lih8xdBGGPdQ2cfPlBZWYno6Ghcu3YNQ4cOxb/+9S+8+uqreO+99yCRSFBRUYEtW7ZAX18ftbW1+PnnnzF+/Hhs2LBByLnw6NEjrF+/HpWVlTA1NUVDQ4PChZzq6mrs2rULMpkM//rXvzBkyBBER0cLF1HS09Ph4+ODhIQEuLi4dLo/bm5ueOedd/DNN99g/fr1bW57/PjxSElJQWpqKk6cOIErV64gKCgIX331FczNzbF//35MnDgRQFNOhxUrVmDixIkYOnQotm7divLycgwcOLDdvjHGtEiP5o1qRUezktXW1pKLiwt5e3sLWbfi4+MJAKWkpLSbFrK8vJzq6upo0qRJ9Pbbb1NjYyMREf3yyy+kr68vytzVXlrIjqTSRCvZ3eTMzMxo6NCh7W77/v37Kqe1tLOzIyMjI6GPHh4edPfuXZX6pgpoT1YyrcVZJXWXFh3fSTp5Zh0TE4OMjAzk5OQIiWreeOMNAE23k27btg3Xr18XbjkGAFNTU6xfvx6+vr6IjIzEyJEjcfHiRezbt0+4g8rW1hY2NjZCxrYLFy5g7969SueN/vOf/8T8+fOxaNEiPHjwQC0Z2nr16iW0pa1tnzlzBvPnz8eIESMgk8nw/vvvAwBGjhyJ0NBQ/PTTT8K65eXlKC8vx65du7By5Ups2LABffv2ValvqsrKymr1KR0MwqyVQ4cOabglTKdp+s8FUcfPPBYsWKCQx7g5FxcXAkAPHz4Ulefl5REAmjx5slDHo0ePROs0z4kbExND9vb2HexN69DGmXVNTQ317t1byLeryrZVydV86NAhISH/c889R5mZmSrXrwq0kuOYX/x6Ul7acmatkxcY5UlsWstZrEpayMLCQgC/ZWlTprvSQiqTnp6Ouro6zJgxQ63bdnNzw08//YTZs2fjhx9+gLOzM/bv36/WvkmlUqV5jvnV9HJ3d4e7u7vG28Gvjr+0iU4Ga0dHRwBNSWcaGxuF8vz8fJw4cUKltJDybGwt12lO1bSQXU2lWVNTg3Xr1uGZZ55BYGBgh7bdno0bN8LW1hYnT55EYmIi6uvrsX79+m5JeckY60akBTo6DHLjxg3q378/AaBp06ZRbGwsrV+/nvz9/amhoYGqqqrIwcGBRowYIXqcUWBgIDk5OVFtbS1dvnyZ9PX1aejQofT111/To0eP6NSpUzRw4EACQLm5uVRdXU3W1tYEgN566y36+9//Tu+//z7NmjVLuAh3/PhxGjBgAJ04caLNNldVVREAsrKyEpX/8MMP5OzsTKNGjRI92kiVbVtZWREA4eIhEdHw4cMJ+O3Zff369aOysjIiarowO2jQIJo0aZJK9asC2vNvotbiC4y6S4uOb928wGhjY4OsrCysWrUKFy9exLVr1+Du7o6PPvoIenp6QlrIzZs3w9fXFxMmTIC+vr4oLeQzzzyD9PR0rF27Fu7u7jA1NcWyZcvwzDPPwN7eHrm5uRg5ciTS09MRGBiII0eOIDU1FQsXLhSlhVQllebZs2exb98+AE15eeVPv+jTpw969+4NT09P+Pr6wtDQUPhM375929x2y7SWK1euxL59+0RpLTdt2oTq6mrMmDEDHh4euHLlCpydnRETE9Nu/Ywx7cK3m7Mu0aLbcbUWH9+6S4uOb77dnDHGdAEHa8YY0wEcrBljTAdwsGZMx8hkMkRFRUEqlcLR0RESiQQODg549OiRaL20tDS4urpCIpFg4sSJkEqlGmpx+65evYpFixbB2NgYJiYm8PLyEi6WAwARYe/evXjmmWdgaGgIR0dHxMfHC3Oh6+vrER4eLtwt+iTiYM10lnzevK7V3RWnT5/GBx98gMDAQHh6egoPzc3OzlZ4svnMmTOxe/duAEBCQoLWPiw2Ozsb69evx5tvvom0tDTMnTsXUqlUSCEBAGvXrsXp06exbNky+Pn5QSaTwc/PT7gnoFevXli9ejUCAwORm5urqa50L81OHWzC81B1FzQ0DzU3N5emTJmiE3Wr6/i+evUqWVpaUmlpqagcADk7OxMA+vLLL0XLamtrCQDV1NR0efvdZefOnVRVVSW8r62tpcGDB9OAAQOIiOjmzZu0ZMkS0Wf+93//lwCQra2tqPynn34iBwcHhVQTnaWp41sJ3bzdnP2+3b59G/Pnz8evv/6qU3V3RWNjI7y9vbF06VKlj52TSqUwMzODv7+/6Myyd+/eAAADA4Mea2tHvffee+jfv7+orL6+Hn5+fgCa7k1omed99uzZMDExQUlJiajc0dERtra2CAsL695GawAHa9ajKioqEBYWhjVr1iAkJASzZ89GSEgIysvLAQC7d+8WnlUJAA8ePMD27dtFZfv370d2djaKi4uxfPlyEBEyMzMRGhqKUaNGobi4GK+99hqGDh2K8ePHIzk5udN1y6Wnp8PCwgIZGRk99l01l5KSgsuXL2POnDlKl5ubmyMpKQlVVVXw8vJCbW1tq3W1tQ+ICMeOHYO/vz8sLCxQXl4OX19fGBsbY/z48fj++++FeuTpCvz8/DBx4kTMnDkTV65c6VI/GxsbsXHjRuzcuRM7d+4E0JRJ08zMTGHd2tpaODs7K5S7urpiz549uHHjRpfaonU0fW5PxMMgugwd+DdRlTzjREQ2NjbU8tBsWQb8lsGwvr6ejh8/Tn379iUAtGLFCsrIyKCEhAQh4+DZs2c7VbdcR/KWt6SO49vLy0uURqC55m3fsWMHAaDQ0FCly9vbB2VlZSrnSldHPvTmDh8+LAznjBo1ivbs2SNKpdDc2bNnqW/fvvTDDz8oLPvxxx8JAEVGRnaqHc115PjuZkkcrFmXdORgXrduHQEQ5WshIvr8888JAIWFhRGRaqlflQVUOzs7AsSpc6OjowkAeXp6dqluIqK6ujqV+tmSOo5vKysrGjx4sNJlzdve2NhIbm5uBIBSU1MVlqu6D0aPHq1Qr6mpKRkYGBARUVZWVqspRY8fP96pPpaVldHVq1cpJiaG+vXrRwBo3759CuvV1dXR1KlTKTExUWk9hYWFBEBIN9wV2hSseRiE9Zhz584BAAYOHCgql2dJPH/+fJfql6fGHTBggFC2cOFCAK2n0+0IdTxgorOKi4uFx9G1RSKRID4+HmPHjoWvr69o+hug+j5o+TAJiUQCIyMjYXjl0qVLsLe3V5pWtCMPrmjOyMgI9vb2WLFihTCL5cCBAwrr/eUvf8GMGTOwePFipfUMGTIEwG+plJ8UHKxZj1Elz7i6DR8+HABgaWmp9rp7kr6+vsp5xgcOHIjk5GRUV1fj9ddfFy1T1z7o7lzvixYtAqB4YfT48eMYMGAANm7c2Opn5X9oSPNpj9SKgzXrMarkGQd++7HV1NQAaLroVFFRAUD8A1Qlj7j84RLqqLurecu7wtzcHPfv31colwfGlgHS3t4e8fHxOH36tKhc1X3Qnu7Oh15UVAQAeOWVV4Syb775BoWFhVizZo1o3Zb/kckvVpubm3e5HdqEgzXrMeHh4XBwcEBMTIzwYwSAuLg4ODk5YcWKFQAgPBhiy5YtuH79Oj755BMhuJ48eRINDQ2wtbVFUVERbt68qbCd5kH11KlTePbZZ4XncXa27q+++gpDhgzB119/rc6vRGUuLi54+PAhHj58KCqXT11T9i+/h4eHwo0yqu6Dx48fAxD/AZNvu66uDosWLYK1tTU2b94MPz8/JCQkYP369QgKCsLSpUsBAFFRUbC3t8cXX3zRZt927NiB//mf/xH+GD1+/BirV6+Gh4eH0J60tDRs27YN9fX1iI2NRWxsLGJiYhAcHIwTJ06I6istLQXQNIvkiaKx4fJm+AKj7kIHL8A8ePCAwsLCaNasWRQSEkJhYWH017/+lR4/fiysc+3aNZo0aRL179+fZs2aRdeuXaMpU6aQt7c3ffHFF/T48WNas2YNmZmZ0T/+8Q/hc/ILhR9//DH9+uuvdPfuXdq6davoBonO1v3NN9+Qubk5nTp1qsPfkTqO79OnTxMAOnnypFCWnJxMc+fOJQA0b948+uc//6nwudraWpo8ebKorL19EBsbK1ws3Lx5M92/f1+4UAuAVq9eTY8ePaK8vDxasGABGRkZ0bBhw2jZsmVUUlIibCcgIIAkEgkNHz68zb5t2rSJbG1taciQIbR8+XIKDAykb7/9VpgJcu7cOeGCo7LXjRs3RPX97W9/Iz09PYXyzujo8d2NkjifNesSLcr3i7Fjx+LatWtaN1apruP7lVdewejRo4X5x7pAJpPhjTfewIULF3psmwsWLICZmRn27NnT5bq06PjmfNaM6Yp9+/YhNTUVxcXFmm6KSqqqqrBr1y7s3bu3x7aZmZkJmUymcMfjk4CDNXtiyGcmVFZWargl3WPYsGFITk5GcHCw0lkY2iY3NxeRkZGYMGFCj2yvsLAQERERSEtLeyIfTcfBmum8yspKrFu3TkiPGRgYiMzMTA23qns8/fTTiIiIQFxcnKab0q4JEyb0WNCsq6vDwYMHkZiYqPPTNFujkw/MZaw5Q0NDREZGIjIyUtNN6RE2NjYIDw/XdDO0Su/evRWm9D1p+MyaMcZ0AAdrxhjTARysGWNMB3CwZowxHaA1FxgzMzO1YeI564To6Gj84x//0HQztJZ8Zgof36wrtOIOxkOHDuHQoUOabgZjjIno6+tj69atGDVqlKabckgrgjVjjLE28e3mjDGmCzhYM8aYDuBgzRhjOoCDNWOM6YD/AymYAFiIBY2RAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"encoder = keras.Model(inputs,code)\\n\",\n \"keras.utils.plot_model(encoder, show_shapes=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"decoder_input = keras.Input((code_dim,))\\n\",\n \"decoder_output = auto_encoder.layers[-1](decoder_input)\\n\",\n \"decoder = keras.Model(decoder_input, decoder_output)\\n\",\n \"keras.utils.plot_model(decoder, show_shapes=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"auto_encoder.compile(optimizer='adam',\\n\",\n \" loss='binary_crossentropy')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 训练模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 54000 samples, validate on 6000 samples\\n\",\n \"Epoch 1/100\\n\",\n \"54000/54000 [==============================] - 3s 60us/sample - loss: 0.7063 - val_loss: 0.6794\\n\",\n \"Epoch 2/100\\n\",\n \"54000/54000 [==============================] - 2s 44us/sample - loss: 0.6794 - val_loss: 0.6742\\n\",\n \"Epoch 3/100\\n\",\n \"54000/54000 [==============================] - 2s 44us/sample - loss: 0.6767 - val_loss: 0.6729\\n\",\n \"Epoch 4/100\\n\",\n \"54000/54000 [==============================] - 3s 49us/sample - loss: 0.6758 - val_loss: 0.6726\\n\",\n \"Epoch 5/100\\n\",\n \"54000/54000 [==============================] - 2s 44us/sample - loss: 0.6753 - val_loss: 0.6721\\n\",\n \"Epoch 6/100\\n\",\n \"54000/54000 [==============================] - 2s 45us/sample - loss: 0.6750 - val_loss: 0.6719\\n\",\n \"Epoch 7/100\\n\",\n \"54000/54000 [==============================] - 2s 45us/sample - loss: 0.6747 - val_loss: 0.6717\\n\",\n \"Epoch 8/100\\n\",\n \"54000/54000 [==============================] - 3s 47us/sample - loss: 0.6746 - val_loss: 0.6716\\n\",\n \"Epoch 9/100\\n\",\n \"54000/54000 [==============================] - 2s 46us/sample - loss: 0.6744 - val_loss: 0.6716\\n\",\n \"Epoch 10/100\\n\",\n \"54000/54000 [==============================] - 3s 48us/sample - loss: 0.6743 - val_loss: 0.6714\\n\",\n \"Epoch 11/100\\n\",\n \"54000/54000 [==============================] - 3s 47us/sample - loss: 0.6742 - val_loss: 0.6713\\n\",\n \"Epoch 12/100\\n\",\n \"54000/54000 [==============================] - 2s 46us/sample - loss: 0.6741 - val_loss: 0.6713\\n\",\n \"Epoch 13/100\\n\",\n \"54000/54000 [==============================] - 2s 46us/sample - loss: 0.6740 - val_loss: 0.6711\\n\",\n \"Epoch 14/100\\n\",\n \"54000/54000 [==============================] - 2s 45us/sample - loss: 0.6739 - val_loss: 0.6711\\n\",\n \"Epoch 15/100\\n\",\n \"54000/54000 [==============================] - 2s 46us/sample - loss: 0.6738 - val_loss: 0.6710\\n\",\n \"Epoch 16/100\\n\",\n \"54000/54000 [==============================] - 3s 49us/sample - loss: 0.6738 - val_loss: 0.6709\\n\",\n \"Epoch 17/100\\n\",\n \"54000/54000 [==============================] - 3s 48us/sample - loss: 0.6737 - val_loss: 0.6709\\n\",\n \"Epoch 18/100\\n\",\n \"54000/54000 [==============================] - 2s 46us/sample - loss: 0.6736 - val_loss: 0.6708\\n\",\n \"Epoch 19/100\\n\",\n \"54000/54000 [==============================] - 2s 46us/sample - loss: 0.6736 - val_loss: 0.6707\\n\",\n \"Epoch 20/100\\n\",\n \"54000/54000 [==============================] - 3s 47us/sample - loss: 0.6735 - val_loss: 0.6706\\n\",\n \"Epoch 21/100\\n\",\n \"54000/54000 [==============================] - 2s 46us/sample - loss: 0.6735 - val_loss: 0.6705\\n\",\n \"Epoch 22/100\\n\",\n \"54000/54000 [==============================] - 3s 53us/sample - loss: 0.6734 - val_loss: 0.6705\\n\",\n \"Epoch 23/100\\n\",\n \"54000/54000 [==============================] - 3s 61us/sample - loss: 0.6734 - val_loss: 0.6705\\n\",\n \"Epoch 24/100\\n\",\n \"54000/54000 [==============================] - 3s 48us/sample - loss: 0.6733 - val_loss: 0.6705\\n\",\n \"Epoch 25/100\\n\",\n \"54000/54000 [==============================] - 3s 49us/sample - loss: 0.6733 - val_loss: 0.6705\\n\",\n \"Epoch 26/100\\n\",\n \"54000/54000 [==============================] - 3s 51us/sample - loss: 0.6732 - val_loss: 0.6704\\n\",\n \"Epoch 27/100\\n\",\n \"54000/54000 [==============================] - 3s 55us/sample - loss: 0.6732 - val_loss: 0.6704\\n\",\n \"Epoch 28/100\\n\",\n \"54000/54000 [==============================] - 4s 67us/sample - loss: 0.6732 - val_loss: 0.6702\\n\",\n \"Epoch 29/100\\n\",\n \"54000/54000 [==============================] - 3s 64us/sample - loss: 0.6731 - val_loss: 0.6703\\n\",\n \"Epoch 30/100\\n\",\n \"54000/54000 [==============================] - 3s 53us/sample - loss: 0.6731 - val_loss: 0.6702\\n\",\n \"Epoch 31/100\\n\",\n \"54000/54000 [==============================] - 3s 59us/sample - loss: 0.6730 - val_loss: 0.6702\\n\",\n \"Epoch 32/100\\n\",\n \"54000/54000 [==============================] - 3s 62us/sample - loss: 0.6730 - val_loss: 0.6701\\n\",\n \"Epoch 33/100\\n\",\n \"54000/54000 [==============================] - 3s 47us/sample - loss: 0.6730 - val_loss: 0.6701\\n\",\n \"Epoch 34/100\\n\",\n \"54000/54000 [==============================] - 3s 55us/sample - loss: 0.6729 - val_loss: 0.6702\\n\",\n \"Epoch 35/100\\n\",\n \"54000/54000 [==============================] - 3s 58us/sample - loss: 0.6729 - val_loss: 0.6701\\n\",\n \"Epoch 36/100\\n\",\n \"54000/54000 [==============================] - 3s 53us/sample - loss: 0.6729 - val_loss: 0.6700\\n\",\n \"Epoch 37/100\\n\",\n \"54000/54000 [==============================] - 2s 46us/sample - loss: 0.6729 - val_loss: 0.6701\\n\",\n \"Epoch 38/100\\n\",\n \"54000/54000 [==============================] - 3s 50us/sample - loss: 0.6728 - val_loss: 0.6700\\n\",\n \"Epoch 39/100\\n\",\n \"54000/54000 [==============================] - 3s 50us/sample - loss: 0.6728 - val_loss: 0.6698\\n\",\n \"Epoch 40/100\\n\",\n \"54000/54000 [==============================] - 3s 47us/sample - loss: 0.6728 - val_loss: 0.6700\\n\",\n \"Epoch 41/100\\n\",\n \"54000/54000 [==============================] - 3s 50us/sample - loss: 0.6727 - val_loss: 0.6699\\n\",\n \"Epoch 42/100\\n\",\n \"54000/54000 [==============================] - 3s 49us/sample - loss: 0.6727 - val_loss: 0.6699\\n\",\n \"Epoch 43/100\\n\",\n \"54000/54000 [==============================] - 3s 60us/sample - loss: 0.6727 - val_loss: 0.6699\\n\",\n \"Epoch 44/100\\n\",\n \"54000/54000 [==============================] - 4s 80us/sample - loss: 0.6726 - val_loss: 0.6700\\n\",\n \"Epoch 45/100\\n\",\n \"54000/54000 [==============================] - 3s 48us/sample - loss: 0.6726 - val_loss: 0.6698\\n\",\n \"Epoch 46/100\\n\",\n \"54000/54000 [==============================] - 3s 57us/sample - loss: 0.6726 - val_loss: 0.6699\\n\",\n \"Epoch 47/100\\n\",\n \"54000/54000 [==============================] - 3s 46us/sample - loss: 0.6726 - val_loss: 0.6697\\n\",\n \"Epoch 48/100\\n\",\n \"54000/54000 [==============================] - 2s 46us/sample - loss: 0.6725 - val_loss: 0.6697\\n\",\n \"Epoch 49/100\\n\",\n \"54000/54000 [==============================] - 3s 47us/sample - loss: 0.6725 - val_loss: 0.6697\\n\",\n \"Epoch 50/100\\n\",\n \"54000/54000 [==============================] - 3s 47us/sample - loss: 0.6725 - val_loss: 0.6696\\n\",\n \"Epoch 51/100\\n\",\n \"54000/54000 [==============================] - 3s 48us/sample - loss: 0.6725 - val_loss: 0.6697\\n\",\n \"Epoch 52/100\\n\",\n \"54000/54000 [==============================] - 3s 46us/sample - loss: 0.6724 - val_loss: 0.6696\\n\",\n \"Epoch 53/100\\n\",\n \"54000/54000 [==============================] - 3s 46us/sample - loss: 0.6724 - val_loss: 0.6697\\n\",\n \"Epoch 54/100\\n\",\n \"54000/54000 [==============================] - 3s 47us/sample - loss: 0.6724 - val_loss: 0.6696\\n\",\n \"Epoch 55/100\\n\",\n \"54000/54000 [==============================] - 3s 47us/sample - loss: 0.6724 - val_loss: 0.6697\\n\",\n \"Epoch 56/100\\n\",\n \"54000/54000 [==============================] - 3s 47us/sample - loss: 0.6724 - val_loss: 0.6695\\n\",\n \"Epoch 57/100\\n\",\n \"54000/54000 [==============================] - 3s 48us/sample - loss: 0.6723 - val_loss: 0.6696\\n\",\n \"Epoch 58/100\\n\",\n \"54000/54000 [==============================] - 2s 46us/sample - loss: 0.6723 - val_loss: 0.6695\\n\",\n \"Epoch 59/100\\n\",\n \"54000/54000 [==============================] - 3s 48us/sample - loss: 0.6723 - val_loss: 0.6695\\n\",\n \"Epoch 60/100\\n\",\n \"54000/54000 [==============================] - 3s 47us/sample - loss: 0.6723 - val_loss: 0.6695\\n\",\n \"Epoch 61/100\\n\",\n \"54000/54000 [==============================] - 2s 46us/sample - loss: 0.6723 - val_loss: 0.6696\\n\",\n \"Epoch 62/100\\n\",\n \"54000/54000 [==============================] - 3s 49us/sample - loss: 0.6723 - val_loss: 0.6695\\n\",\n \"Epoch 63/100\\n\",\n \"54000/54000 [==============================] - 3s 47us/sample - loss: 0.6722 - val_loss: 0.6694\\n\",\n \"Epoch 64/100\\n\",\n \"54000/54000 [==============================] - 3s 48us/sample - loss: 0.6722 - val_loss: 0.6694\\n\",\n \"Epoch 65/100\\n\",\n \"54000/54000 [==============================] - 3s 47us/sample - loss: 0.6722 - val_loss: 0.6694\\n\",\n \"Epoch 66/100\\n\",\n \"54000/54000 [==============================] - 3s 49us/sample - loss: 0.6722 - val_loss: 0.6694\\n\",\n \"Epoch 67/100\\n\",\n \"54000/54000 [==============================] - 3s 48us/sample - loss: 0.6722 - val_loss: 0.6694\\n\",\n \"Epoch 68/100\\n\",\n \"54000/54000 [==============================] - 3s 52us/sample - loss: 0.6721 - val_loss: 0.6694\\n\",\n \"Epoch 69/100\\n\",\n \"54000/54000 [==============================] - 3s 52us/sample - loss: 0.6721 - val_loss: 0.6693\\n\",\n \"Epoch 70/100\\n\",\n \"54000/54000 [==============================] - 3s 51us/sample - loss: 0.6721 - val_loss: 0.6693\\n\",\n \"Epoch 71/100\\n\",\n \"54000/54000 [==============================] - 3s 51us/sample - loss: 0.6721 - val_loss: 0.6693\\n\",\n \"Epoch 72/100\\n\",\n \"54000/54000 [==============================] - 3s 52us/sample - loss: 0.6721 - val_loss: 0.6693\\n\",\n \"Epoch 73/100\\n\",\n \"54000/54000 [==============================] - 3s 53us/sample - loss: 0.6721 - val_loss: 0.6693\\n\",\n \"Epoch 74/100\\n\",\n \"54000/54000 [==============================] - 3s 51us/sample - loss: 0.6720 - val_loss: 0.6694\\n\",\n \"Epoch 75/100\\n\",\n \"54000/54000 [==============================] - 3s 51us/sample - loss: 0.6720 - val_loss: 0.6693\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 76/100\\n\",\n \"54000/54000 [==============================] - 2s 43us/sample - loss: 0.6720 - val_loss: 0.6693\\n\",\n \"Epoch 77/100\\n\",\n \"54000/54000 [==============================] - 2s 44us/sample - loss: 0.6720 - val_loss: 0.6692\\n\",\n \"Epoch 78/100\\n\",\n \"54000/54000 [==============================] - 2s 46us/sample - loss: 0.6720 - val_loss: 0.6693\\n\",\n \"Epoch 79/100\\n\",\n \"54000/54000 [==============================] - 3s 48us/sample - loss: 0.6720 - val_loss: 0.6692\\n\",\n \"Epoch 80/100\\n\",\n \"54000/54000 [==============================] - 3s 49us/sample - loss: 0.6719 - val_loss: 0.6691\\n\",\n \"Epoch 81/100\\n\",\n \"54000/54000 [==============================] - 3s 51us/sample - loss: 0.6719 - val_loss: 0.6693\\n\",\n \"Epoch 82/100\\n\",\n \"54000/54000 [==============================] - 3s 49us/sample - loss: 0.6719 - val_loss: 0.6692\\n\",\n \"Epoch 83/100\\n\",\n \"54000/54000 [==============================] - 2s 45us/sample - loss: 0.6719 - val_loss: 0.6692\\n\",\n \"Epoch 84/100\\n\",\n \"54000/54000 [==============================] - 2s 45us/sample - loss: 0.6719 - val_loss: 0.6692\\n\",\n \"Epoch 85/100\\n\",\n \"54000/54000 [==============================] - 2s 43us/sample - loss: 0.6719 - val_loss: 0.6692\\n\",\n \"Epoch 86/100\\n\",\n \"54000/54000 [==============================] - 2s 44us/sample - loss: 0.6719 - val_loss: 0.6692\\n\",\n \"Epoch 87/100\\n\",\n \"54000/54000 [==============================] - 2s 45us/sample - loss: 0.6718 - val_loss: 0.6692\\n\",\n \"Epoch 88/100\\n\",\n \"54000/54000 [==============================] - 2s 44us/sample - loss: 0.6718 - val_loss: 0.6691\\n\",\n \"Epoch 89/100\\n\",\n \"54000/54000 [==============================] - 2s 45us/sample - loss: 0.6718 - val_loss: 0.6692\\n\",\n \"Epoch 90/100\\n\",\n \"54000/54000 [==============================] - 2s 43us/sample - loss: 0.6718 - val_loss: 0.6690\\n\",\n \"Epoch 91/100\\n\",\n \"54000/54000 [==============================] - 2s 45us/sample - loss: 0.6718 - val_loss: 0.6689\\n\",\n \"Epoch 92/100\\n\",\n \"54000/54000 [==============================] - 2s 45us/sample - loss: 0.6717 - val_loss: 0.6690\\n\",\n \"Epoch 93/100\\n\",\n \"54000/54000 [==============================] - 2s 45us/sample - loss: 0.6717 - val_loss: 0.6689\\n\",\n \"Epoch 94/100\\n\",\n \"54000/54000 [==============================] - 2s 44us/sample - loss: 0.6717 - val_loss: 0.6689\\n\",\n \"Epoch 95/100\\n\",\n \"54000/54000 [==============================] - 2s 45us/sample - loss: 0.6716 - val_loss: 0.6689\\n\",\n \"Epoch 96/100\\n\",\n \"54000/54000 [==============================] - 2s 45us/sample - loss: 0.6716 - val_loss: 0.6689\\n\",\n \"Epoch 97/100\\n\",\n \"54000/54000 [==============================] - 2s 45us/sample - loss: 0.6716 - val_loss: 0.6687\\n\",\n \"Epoch 98/100\\n\",\n \"54000/54000 [==============================] - 2s 46us/sample - loss: 0.6716 - val_loss: 0.6687\\n\",\n \"Epoch 99/100\\n\",\n \"54000/54000 [==============================] - 2s 45us/sample - loss: 0.6716 - val_loss: 0.6688\\n\",\n \"Epoch 100/100\\n\",\n \"54000/54000 [==============================] - 2s 45us/sample - loss: 0.6715 - val_loss: 0.6688\\n\",\n \"CPU times: user 6min 53s, sys: 23.2 s, total: 7min 16s\\n\",\n \"Wall time: 4min 24s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"history = auto_encoder.fit(x_train, x_train, batch_size=64, epochs=100, validation_split=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"encoded = encoder.predict(x_test)\\n\",\n \"decoded = decoder.predict(encoded)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.figure(figsize=(10,4))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"n = 5\\n\",\n \"for i in range(n):\\n\",\n \" ax = plt.subplot(2, n, i+1)\\n\",\n \" plt.imshow(x_test[i].reshape(28,28))\\n\",\n \" plt.gray()\\n\",\n \" ax.get_xaxis().set_visible(False)\\n\",\n \" ax.get_yaxis().set_visible(False)\\n\",\n \" \\n\",\n \" ax = plt.subplot(2, n, n+i+1)\\n\",\n \" plt.imshow(decoded[i].reshape(28,28))\\n\",\n \" plt.gray()\\n\",\n \" ax.get_xaxis().set_visible(False)\\n\",\n \" ax.get_yaxis().set_visible(False)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "024-AutoEncoder/002-cnn_autoencoder.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Tensorflow2教程-卷积自编码器\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![](https://camo.githubusercontent.com/c2b4e51b1ebacac0d5fae4796bff2572797cc385/687474703a2f2f6d692e656e672e63616d2e61632e756b2f70726f6a656374732f7365676e65742f696d616765732f7365676e65742e706e67)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.导入数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"import tensorflow.keras as keras\\n\",\n \"import tensorflow.keras.layers as layers\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(60000, 28, 28, 1) (60000,)\\n\",\n \"(10000, 28, 28, 1) (10000,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"x_train = tf.expand_dims(x_train.astype('float32'), -1) / 255.0\\n\",\n \"x_test = tf.expand_dims(x_test.astype('float32'),-1) / 255.0\\n\",\n \"\\n\",\n \"print(x_train.shape, ' ', y_train.shape)\\n\",\n \"print(x_test.shape, ' ', y_test.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.构造模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(None, 28, 28, 1)\\n\",\n \"(None, 14, 14, 16)\\n\",\n \"(None, 28, 28, 16)\\n\",\n \"(None, 28, 28, 1)\\n\"\n ]\n }\n ],\n \"source\": [\n \"\\n\",\n \"inputs = layers.Input(shape=(x_train.shape[1], x_train.shape[2], x_train.shape[3]), name='inputs')\\n\",\n \"print(inputs.shape)\\n\",\n \"code = layers.Conv2D(16, (3,3), activation='relu', padding='same')(inputs)\\n\",\n \"code = layers.MaxPool2D((2,2), padding='same')(code)\\n\",\n \"print(code.shape)\\n\",\n \"decoded = layers.Conv2D(16, (3,3), activation='relu', padding='same')(code)\\n\",\n \"decoded = layers.UpSampling2D((2,2))(decoded)\\n\",\n \"print(decoded.shape)\\n\",\n \"outputs = layers.Conv2D(1, (3,3), activation='sigmoid', padding='same')(decoded)\\n\",\n \"print(outputs.shape)\\n\",\n \"auto_encoder = keras.Model(inputs, outputs)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"auto_encoder.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.BinaryCrossentropy())\\n\",\n \"keras.utils.plot_model(auto_encoder, show_shapes=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.训练与测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 54000 samples, validate on 6000 samples\\n\",\n \"54000/54000 [==============================] - 31s 572us/sample - loss: 0.1007\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"early_stop = keras.callbacks.EarlyStopping(patience=2, monitor='loss')\\n\",\n \"auto_encoder.fit(x_train,x_train, batch_size=64, epochs=1, validation_split=0.1,validation_freq=10,\\n\",\n \" callbacks=[early_stop])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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YYY06dOpX2z+F5Bi+M58mzDAfvpl94N/1RnmcZS/YAaKlmBv8X6Rf+L9IfvJt+4d30RyrPkjk8AADAezR4AACA92jwAAAA7yWdtGxMNMcg3clKlSpVsvG5iUulOX36dKmxMdH6ewZZlyj9PXNFUD9znmX4eDf9wrvpj1R+5vTwAAAA79HgAQAA3jvvkFamyGEqY4zp0aOHje+++26Vu/zyy21ct25dG588eVJdt3//fhs/8sgjKrdixQobnz17thw1RpTI3x/3edLdnBkXXPD5/1+d26fkHPmuusPNAJAO9PAAAADv0eABAADeo8EDAAC8d96jJUKsi6lRo4aNn3vuOZUrKCiwcdWqVRPeQ/59kv3dPv30U1WW84B279593rqGJd3b16frXlGTl5enyosWLbLxzJkzVe6NN94IpU6lSef29em4T9DkXKrFixfbePDgweo6Ob9OztczxpjDhw8HU7kK4t30S669m+ngbhEjZXKuJEdLAACAnEWDBwAAeC+jy9IrV9Yfv27dOhvn5+cn/HPHjx9XZTlEsXXrVht36tRJXXfdddfZ+MILL1S5/v3723ju3LnJqo0Ieuihh1T5a1/7mo3d4dFMDmnlGjk8NWTIEBvXrl1bXSeHs+V7aowxK1euDKh2KE2LFi1s3Lt3b5WTQ8VBbB8gd8p3d813v/cRvm7duqnywoULbbxr1y6VGzlypI3PnDkTbMVSRA8PAADwHg0eAADgPRo8AADAexmdw9O6dWtVvuaaa2zsHi3xwQcf2PiGG25QucLCwlLv3759e1V+9dVXbVytWjWVq1WrVgo1RpTIowratGmjcnLbgfXr14dWp1znvtNyzoecp5OMHPs3Rj+/EydOVKB2KI27tFhu4+DOpVyyZImNg5jDI7/be/XqpXLTpk2zcVTmhOQC+W/jiy++qHJyO5BmzZqpXLIl65lCDw8AAPAeDR4AAOC9SJ2WXlJSYmO3y1J2rcpdWZORJ6cbo4ex3F0gP/zww5TriWioV6+ejb/85S+r3MaNG21cXFwcWp1ygdtVPWrUKBvPmDFD5Ro3bmxj+c4lGw7p27evKq9atcrGd999t8r961//KvX+SJ071CiHlU6dOqVy8lT7dHB/l/r06WNjuY2BMfp36+jRo2mtBxKT73f9+vUTXvfWW2+pchSHHenhAQAA3qPBAwAAvEeDBwAAeC+jc3jee+89VW7ZsqWNjxw5onKpjh3L7ch/8pOfqJycwyPnCxmjj6RAdhg6dKiN3XkIjz32mI2Z25FeDRs2VOV58+bZ2D2yRf7s5Zi+3FLAGL0NhXuPG2+80cZvvvmmyt155502XrZs2Xnrji8aO3asKletWtXGTz31lMoFPS+jbdu2Nnbni8jvb+bwBMc98um+++6zsfveyjle06dPV7kofu/SwwMAALxHgwcAAHgvlqzbKRaLRa9PqhRyaeMdd9xhYzmsYYzuqtu2bZvKde/e3cZB7CBaXvF4PG3bVWbL80zE7Wp95513bCx3/DRGL1M/duxYsBUrg3Q9z7CfpezK/tnPfqZycqm42+Uth0Dke+oOjch3zt1l3T01W/r4449t3KFDB5Vzh8XTLZvfTTl0tHbtWpUrKiqysbvbsTsVoKLcd3rv3r02btCggcrJU9w/+uijtNbDmOx9N9NN/m4Yo/+tdKcOyC0/3J2WM7kreqJnSQ8PAADwHg0eAADgPRo8AADAe5E6WqK85Knos2fPtrE7PvzZZ5/ZeNKkSSoXpXk7KJ08SsIYY5o3b25jd7z4v//9byh1yhUDBw608YQJE1ROzts5e/asysl5O/Id2759u7ru0UcftbG7BcV3v/tdG8u5dsbooyueffZZlRs8eHDCe+Ya97tQnmLvvlcTJ060cbrn7Ljk8zNGL0V3f5dYih4OeZSEMXqbAnfO76ZNm2wc9O9KOtDDAwAAvEeDBwAAeC8rh7RkF5sxxixYsMDGctmce9Lv4sWLbfzaa68FUzkE5vrrr1dluXx59+7dKscQZcVUr15dleVuyu7SVDn04P7c5dLw3/72tzaeNm2aui5Zd7h8V90TmS+++GIbFxQUqJzcIfb+++9XuSjuAhskOSRpjDFdu3a1sVwKbowxL7/8cqB1kcOcDzzwgMrJ4dFdu3apXJS2l/CN3Pph9OjRKiefiTs0PGvWLBtnwztFDw8AAPAeDR4AAOA9GjwAAMB7WTOHR4773nvvvSrXrVs3G8v5BHLJnPvn3CWPiCY5fuyOLcvjCebPn69y2TCeHGU1a9ZUZbl0Wb6Lxui5cu4xBVOmTLFxYWGhjcvyfA4cOGDjBx98UOXk8THu0uu77rrLxr/4xS9U7tChQyl/fraS787MmTMT5uQzMib4+W+1a9e2cf/+/VVO/i7J7QiM4Ts7SPJonqZNmya8zp3v9fe//z2wOgWBHh4AAOA9GjwAAMB7WTOk1aZNGxtPnjxZ5eSSOrmb8vjx49V1mTy9FeUjT0ju2bOnysmdV5cvXx5WlXLCl770JVV2h4skeZry2LFjVS4du6/K4a/Vq1ernFze3qRJE5WTQydXXXWVyrlDbz6Sw5KXXnqpysmdyF955ZXQ6mSMHsa66KKLVG7//v02Dnp5PD53yy232NjdkkJ64oknVDnbdrSnhwcAAHiPBg8AAPBeZIe05CoCY/ROr3Xq1FE5OXtfXrdv376AaoewfOtb37Kxu8Pvhg0bbCyHMlFx7dq1U2W5MssdGh42bJiNgz5AUK7MMyb5UJvcibt169aB1SkbuCucPv30UxsH/czc7/Jx48bZ2F3xt2bNGhuzW3qw5Pvx7W9/28bu8zp+/LiNly5dGnzFAkQPDwAA8B4NHgAA4D0aPAAAwHuRncMjd082xphrrrnGxu64r5yr8/DDD9uY3XazkxxbHjBggI3deQi//OUvbcyzTq8777xTleUzcZeiFhcXB1oX+b736tVL5erWrVvqdcbo3xe5y3OukLsWuztLV6tWzcbyxHlj0vOzkr8vV155pcq5ZWnLli025p0OVqNGjUqNXUVFRaXG2YgeHgAA4D0aPAAAwHuRGtKqVauWjZ9//nmVq1q1qo3dJci33XabjY8dOxZQ7RAWOUzRrFkzG7vPdvPmzaHVKRfI5ajJhh0++eQTVXaXileUHA4xxphBgwbZWB4Waoz+XnDJYZw///nPaapd9jh58qSN3UMeCwoKbOzuaCy39ki2O33btm1t3Lx5c5WrX7++jTt27KhycndlOexmjN61G8G68cYbbSy3/HCHEuVWAel+18NGDw8AAPAeDR4AAOA9GjwAAMB7GZ3D4y4jnTlzpo0bN26scnLs0B2Pz8XxeZ+NGDHCxvLEZ7lk1Ri95TkqTs7hqVKlisrJcf1///vfaf9s+Xk//elPVe4b3/iGjd3TteV3iPv7MGHCBBsfOXIkLfXMJvKZudsMyFPnW7RooXJyaw/36A651N/dJiLRdXIJvDH6mcl5RsYYs2fPnoT3RMW4R0bIY3tkzj1qRM6by/atAujhAQAA3qPBAwAAvJfRIS13+enQoUNt7Ha/ydN9p02bpnLZeKquO5xXmmzvPkyV+7MYPny4jeXvwWuvvaauy5WfTya4y4Xlz/qSSy5RObmdhLtlhPxzcthKLmk2Ri+F7t69u8rJ7wn3e0HWc/ny5Sq3du1ag//56KOPVFnuXN+0aVOV+8pXvmLjTp06qZzckmDXrl02dnfgrV27to1//OMfq1x+fr6N3Xc425c9R5mcHmCMfrbyO9gdVvRpmJEeHgAA4D0aPAAAwHs0eAAAgPcyOofHXfpar169hNfKZY4XXnhhwvvIsUh3bogcHy7LWLGcQ+COgzZo0MDGnTt3VrmuXbva2N16XY6Fu3MPtm/fbowx5ujRoynXMZu5xwPIn6Mc41+1alVodcpF8h3bu3evyuXl5dlYHhtgjDH333+/jT/++GOV69Gjh43lcRXyfsbo3wF3KXSy+W7y5HZZD2Oyc25fWOTS4/fff1/lZHndunXlur98Zr/+9a9VTs4fcp81gnPFFVeospxnJb9nFy5cqK5z5/NlM3p4AACA92jwAAAA72W0P9HdqTPZzp116tSxsTy91Rh9inajRo1s7HZpy25cd6mdXFbpLtNs0qSJjd0hLTnclazr3R1Ck8vs5bCYMcbcd999xpjc2UlYLoM1Rp+WLp+h2/WO9JLvn9yJ1xhjunTpYmO5DN0YY+65556E95TvhLsNhSTfD3epsiy77/TSpUtt7A6nIXPkM3N3uU72PclWE8EZOHBgwpz8N/Q3v/lNGNXJCHp4AACA92jwAAAA79HgAQAA3svoHB73pNy//e1vNr722msT/jl3Wbpczu5uPS/JZXjuEvhk84fkmLN7nfw7uGPVhYWFNt68ebPKyZO/3377bZU7dxp1riyrdU/HltsMyHlMhw8fDq1Oue6pp55S5alTp9rYff/kknJ3Dkai07Xdpa7JTj2XS+TdpedyrlGydxiZ065dO1WW39Hu3EaflkBHgXyvunXrlvC6c//mGOP3dij08AAAAO/R4AEAAN7L6JCW2/09ePBgG/ft21fl3OXgklwyW1BQYGP3VOcaNWrY2B1Ok6cJv/766yr38ssv29g9sfvgwYMJ7ym72JMtt3SXaZ4r+9xFL7u13R1A5c9q5cqVNs6VIb4oOHDggCrLJa2PPPKIynXs2NHG8h0zRi9Fl8/VHcqQO48/+eSTKjd79mwby+WziC75ndavX7+EOblTtjGclp5u8mfdokULlZPfwe6/Xb6ihwcAAHiPBg8AAPAeDR4AAOC9SB1VK49beO6551L+c4sXL7axHLN0l6jL+QTu/JhkW9sHLdlW+r6Sf8ff//73Kifna91+++2l/hkEy/1Zb9q0ycY9e/ZUObmFxJw5c1Tu0ksvtfGhQ4ds7B5dIU9o/vDDD1WOeR3ZR37XNmvWTOXk85RHg7g5VJx8j3fu3KlyDRs2tLE8Wsnn71l6eAAAgPdo8AAAAO/FzrNc2t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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"decoded = auto_encoder.predict(x_test)\\n\",\n \"n = 5\\n\",\n \"plt.figure(figsize=(10, 4))\\n\",\n \"for i in range(n):\\n\",\n \" # display original\\n\",\n \" ax = plt.subplot(2, n, i+1)\\n\",\n \" plt.imshow(tf.reshape(x_test[i+1],(28, 28)))\\n\",\n \" plt.gray()\\n\",\n \" ax.get_xaxis().set_visible(False)\\n\",\n \" ax.get_yaxis().set_visible(False)\\n\",\n \"\\n\",\n \" # display reconstruction\\n\",\n \" ax = plt.subplot(2, n, i + n+1)\\n\",\n \" plt.imshow(tf.reshape(decoded[i+1],(28, 28)))\\n\",\n \" plt.gray()\\n\",\n \" ax.get_xaxis().set_visible(False)\\n\",\n \" ax.get_yaxis().set_visible(False)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "024-AutoEncoder/cnn_vae.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2 教程-卷积变分自编码器\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"本教程通过构建卷积变分自编码器生成手写数字图片\\n\",\n \"\\n\",\n \"首先先导入相关的库\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/doit/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n \" from ._conv import register_converters as _register_converters\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function, unicode_literals\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"import os\\n\",\n \"import time\\n\",\n \"import numpy as np\\n\",\n \"import glob\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import PIL\\n\",\n \"import imageio\\n\",\n \"print(tf.__version__)\\n\",\n \"from IPython import display\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 一、载入MNIST数据集\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"MNIST数据是手写数字识别的图像数据集，是经典的深度学习数据集。其中每个图片大小为28*28，即可用一个784维的向量来表示图片。其中向量的每个数值在0-255之间，表示像素强度。\\n\",\n \"我们使用MNIST数据集来进行卷积变分自编码的实验，我们首先需要对图像数据进行预处理。这里我们使用伯努利分别来对所有像素进行建模，同时对数据集进行静态二值化处理。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(train_images, _), (test_images, _) = tf.keras.datasets.mnist.load_data()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 构建数据集\\n\",\n \"\\n\",\n \"train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')\\n\",\n \"test_images = test_images.reshape(test_images.shape[0], 28, 28, 1).astype('float32')\\n\",\n \"\\n\",\n \"# 化到0-1之间\\n\",\n \"train_images /= 255.0\\n\",\n \"test_images /= 255.0\\n\",\n \"\\n\",\n \"# 二值化\\n\",\n \"train_images[train_images>=0.5] = 1.0\\n\",\n \"train_images[train_images<0.5] = 0.0\\n\",\n \"test_images[test_images>=0.5] = 1.0\\n\",\n \"test_images[test_images<0.5] = 0.0\\n\",\n \"\\n\",\n \"#　超参数\\n\",\n \"ＴRAIN_BUF=60000\\n\",\n \"BATCH_SIZE = 100\\n\",\n \"TEST_BUF = 10000\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 分批和打乱数据\\n\",\n \"\\n\",\n \"\\n\",\n \"train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(TRAIN_BUF).batch(BATCH_SIZE)\\n\",\n \"test_dataset = tf.data.Dataset.from_tensor_slices(test_images).shuffle(TEST_BUF).batch(BATCH_SIZE)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 二、构建生成网络和推理网络\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"我们使用卷积网络来构建生成网络和推理网络。并使用x和z分别表示观测值和潜在变量\\n\",\n \"#### 生成网络\\n\",\n \"生成网络将隐变量作为输入，并输出用于观测条件分布的参数p(x|z).我们对隐变量使用单位高斯先验分布。\\n\",\n \"#### 推理网络\\n\",\n \"这里定义了近似后验分布q(z|x) ，该后验分布以观测值作为输入，并输出用于潜在表示的条件分布的一组参数。在本示例中，我们仅将此分布建模为对角高斯模型。在这种情况下，推断网络将输出因式分解的高斯均值和对数方差参数\\n\",\n \"#### 采样\\n\",\n \"我们从q(z|x)中采样，方法是先从单位高斯中采样，然后乘以标注差并加上平均值。这样可以确保梯度能确保梯度能传回推理网络。\\n\",\n \"#### 网络结构\\n\",\n \"对于推理网络，我们使用了两个卷积层加一个全连接层，而对于生成网络，我们使用全连接层加3个反卷积层。\\n\",\n \"注意：训练VAE过程中要避免使用批归一化，因为这样会导致额外的随机性，从而加剧随机抽样的不稳定性\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class CVAE(tf.keras.Model):\\n\",\n \" def __init__(self, latent_dim):\\n\",\n \" super(CVAE, self).__init__()\\n\",\n \" self.latent_dim = latent_dim\\n\",\n \" self.inference_net = tf.keras.Sequential(\\n\",\n \" [\\n\",\n \" tf.keras.layers.InputLayer(input_shape=(28, 28, 1)),\\n\",\n \" tf.keras.layers.Conv2D(\\n\",\n \" filters=32, kernel_size=3, strides=(2, 2), activation='relu'),\\n\",\n \" tf.keras.layers.Conv2D(\\n\",\n \" filters=64, kernel_size=3, strides=(2, 2), activation='relu'),\\n\",\n \" tf.keras.layers.Flatten(),\\n\",\n \" tf.keras.layers.Dense(latent_dim+latent_dim)\\n\",\n \" ])\\n\",\n \" self.generative_net = tf.keras.Sequential(\\n\",\n \" [\\n\",\n \" tf.keras.layers.InputLayer(input_shape=(latent_dim,)),\\n\",\n \" tf.keras.layers.Dense(units=7*7*32, activation='relu'),\\n\",\n \" tf.keras.layers.Reshape(target_shape=(7, 7, 32)),\\n\",\n \" tf.keras.layers.Conv2DTranspose(\\n\",\n \" filters=64, kernel_size=3, strides=(2, 2),\\n\",\n \" padding='SAME', activation='relu'\\n\",\n \" ),\\n\",\n \" tf.keras.layers.Conv2DTranspose(\\n\",\n \" filters=32, kernel_size=3, strides=(2, 2),\\n\",\n \" padding='SAME', activation='relu'\\n\",\n \" ),\\n\",\n \" # 不使用激活函数\\n\",\n \" tf.keras.layers.Conv2DTranspose(\\n\",\n \" filters=1, kernel_size=3, strides=(1, 1),\\n\",\n \" padding='SAME'\\n\",\n \" ),\\n\",\n \" \\n\",\n \" ])\\n\",\n \" @tf.function\\n\",\n \" def sample(self, eps=None):\\n\",\n \" if eps is None:\\n\",\n \" eps = tf.random.normal(shape=(100, self.latent_dim))\\n\",\n \" return self.decode(eps, apply_sigmoid=True)\\n\",\n \" \\n\",\n \" def encode(self, x):\\n\",\n \" mean, logvar = tf.split(self.inference_net(x), num_or_size_splits=2,\\n\",\n \" axis=1)\\n\",\n \" return mean, logvar\\n\",\n \" \\n\",\n \" def reparameterize(self, mean, logvar):\\n\",\n \" eps = tf.random.normal(shape=mean.shape)\\n\",\n \" return eps * tf.exp(logvar * 0.5) + mean\\n\",\n \" \\n\",\n \" def decode(self, z, apply_sigmoid=False):\\n\",\n \" logits = self.generative_net(z)\\n\",\n \" if apply_sigmoid:\\n\",\n \" probs = tf.sigmoid(logits)\\n\",\n \" return probs\\n\",\n \" return logits\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 三、定义损失函数和优化器\\n\",\n \"\\n\",\n \"VAE 通过最大化边际对数似然的证据下界（ELBO）进行训练：\\n\",\n \"$\\\\log p(x) \\\\ge \\\\text{ELBO} = \\\\mathbb{E}_{q(z|x)}\\\\left[\\\\log \\\\frac{p(x, z)}{q(z|x)}\\\\right].$$\\n\",\n \"\\n\",\n \"实际上，我们优化了此期望的单样本蒙卡特罗估计：\\n\",\n \"\\n\",\n \"$$\\\\log p(x| z) + \\\\log p(z) - \\\\log q(z|x),$$\\n\",\n \"其中 $z$ 从 $q(z|x)$ 中采样。\\n\",\n \"\\n\",\n \"注意：我们也可以分析性地计算 KL 项，但简单起见，这里我们将所有三个项合并到蒙卡特罗估计器中。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"optimizer = tf.keras.optimizers.Adam(1e-4)\\n\",\n \"\\n\",\n \"def log_normal_pdf(sample, mean, logvar, raxis=1):\\n\",\n \" log2pi = tf.math.log(2.0 * np.pi)\\n\",\n \" return tf.reduce_sum(\\n\",\n \" -0.5*((sample -mean)**2.0 * tf.exp(-logvar)+logvar+log2pi),\\n\",\n \" axis=raxis\\n\",\n \" )\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def compute_loss(model, x):\\n\",\n \" mean, logvar = model.encode(x)\\n\",\n \" z = model.reparameterize(mean, logvar)\\n\",\n \" x_logit = model.decode(z)\\n\",\n \" \\n\",\n \" cross_ent = tf.nn.sigmoid_cross_entropy_with_logits(logits=x_logit, labels=x)\\n\",\n \" logpx_z = -tf.reduce_sum(cross_ent, axis=[1,2,3])\\n\",\n \" logpz = log_normal_pdf(z, 0.0, 0.0)\\n\",\n \" logpz_x = log_normal_pdf(z, mean, logvar)\\n\",\n \" return -tf.reduce_mean(logpx_z+logpz-logpz_x)\\n\",\n \"\\n\",\n \"@tf.function\\n\",\n \"def compute_apply_gradients(model, x, optimizer):\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" loss = compute_loss(model, x)\\n\",\n \" gradients = tape.gradient(loss, model.trainable_variables)\\n\",\n \" optimizer.apply_gradients(zip(gradients, model.trainable_variables))\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 四、训练\\n\",\n \"我们从迭代数据集开始：\\n\",\n \"- 在每次迭代期间，我们将图像传递给编码器，以获得近似后验q(z|x) 的一组均值和对数方差参数。\\n\",\n \"- 然后，我们从 q(z|x) 中采样分布。\\n\",\n \"\\n\",\n \"- 最后，我们将重新参数化的样本传递给解码器，以获取生成分布 p(x|z) 的 logit。\\n\",\n \"\\n\",\n \"注意：由于我们使用的是由 keras 加载的数据集，其中训练集中有 6 万个数据点，测试集中有 1 万个数据点，因此我们在测试集上的最终 ELBO 略高于对 Larochelle 版 MNIST 使用动态二值化的文献中的报告结果。\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 五、生成图片\\n\",\n \"- 进行训练后，可以生成一些图片了\\n\",\n \"- 我们首先从单位高斯先验分布 p(z) 中采样一组隐向量z\\n\",\n \"- 随后生成器将隐向量z 转换为观测值的 logit，得到分布 p(x|z)\\n\",\n \"- 这样我们就可以得到生成的图片\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"epochs = 100\\n\",\n \"latent_dim = 50\\n\",\n \"num_examples_to_generate = 16\\n\",\n \"\\n\",\n \"# 保持随机向量恒定以进行生成（预测），以便看到改进。\\n\",\n \"random_vector_for_generation = tf.random.normal(\\n\",\n \" shape=[num_examples_to_generate, latent_dim])\\n\",\n \"model = CVAE(latent_dim)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 使用输入生成图片\\n\",\n \"def generate_and_save_images(model, epoch, test_input):\\n\",\n \" predictions = model.sample(test_input)\\n\",\n \" fig = plt.figure(figsize=(4,4))\\n\",\n \"\\n\",\n \" for i in range(predictions.shape[0]):\\n\",\n \" plt.subplot(4, 4, i+1)\\n\",\n \" plt.imshow(predictions[i, :, :, 0], cmap='gray')\\n\",\n \" plt.axis('off')\\n\",\n \"\\n\",\n \" # tight_layout 最小化两个子图之间的重叠\\n\",\n \" plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"generate_and_save_images(model, 0, random_vector_for_generation)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch: 100, Test set ELBO: -77.73490905761719, time elapse for current epoch 27.509661436080933\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for epoch in range(1, epochs+1):\\n\",\n \" start_time = time.time()\\n\",\n \" for train_x in train_dataset:\\n\",\n \" compute_apply_gradients(model, train_x, optimizer)\\n\",\n \" end_time = time.time()\\n\",\n \" if epoch % 1 == 0:\\n\",\n \" loss = tf.keras.metrics.Mean()\\n\",\n \" for test_x in test_dataset:\\n\",\n \" loss(compute_loss(model, test_x))\\n\",\n \" elbo = -loss.result()\\n\",\n \" display.clear_output(wait=False)\\n\",\n \" print('Epoch: {}, Test set ELBO: {}, '\\n\",\n \" 'time elapse for current epoch {}'.format(epoch,\\n\",\n \" elbo,\\n\",\n \" end_time - start_time))\\n\",\n \" generate_and_save_images(model, epoch, random_vector_for_generation)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"### 五、生成图片\\n\",\n \"def display_image(epoch_no):\\n\",\n \" return PIL.Image.open('image_at_epoch_{:04d}.png'.format(epoch_no))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(-0.5, 287.5, 287.5, -0.5)\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.imshow(display_image(epochs))\\n\",\n \"plt.axis('off')# 显示图片\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 生成gif\\n\",\n \"anim_file = 'cvae.gif'\\n\",\n \"with imageio.get_writer(anim_file, mode='I') as writer:\\n\",\n \" filenames = glob.glob('image*.png')\\n\",\n \" filenames = sorted(filenames)\\n\",\n \" last = -1\\n\",\n \" for i, filename in enumerate(filenames):\\n\",\n \" frame = 2*(i**0.5)\\n\",\n \" if round(frame) > round(last):\\n\",\n \" last = frame\\n\",\n \" else:\\n\",\n \" continue\\n\",\n \" image = imageio.imread(filename)\\n\",\n \" writer.append_data(image)\\n\",\n \" image = imageio.imread(filename)\\n\",\n \" writer.append_data(image)\\n\",\n \"import IPython\\n\",\n \"if IPython.version_info >= (6,2,0,''):\\n\",\n \" display.Image(filename=anim_file)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "025-GAN/002-DCGAN.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2.0教程-DCGAN\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"import tensorflow as tf\\n\",\n \"import glob\\n\",\n \"import imageio\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import os\\n\",\n \"import PIL\\n\",\n \"import tensorflow.keras.layers as layers\\n\",\n \"import time\\n\",\n \"\\n\",\n \"from IPython import display\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.数据导入和预处理\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()\\n\",\n \"train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')\\n\",\n \"train_images = (train_images - 127.5) / 127.5 # Normalize the images to [-1, 1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"BUFFER_SIZE = 60000\\n\",\n \"BATCH_SIZE = 256\\n\",\n \"# Batch and shuffle the data\\n\",\n \"train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.构建模型\\n\",\n \"\\n\",\n \"### 构建生成器\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def make_generator_model():\\n\",\n \" model = tf.keras.Sequential()\\n\",\n \" model.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))\\n\",\n \" model.add(layers.BatchNormalization())\\n\",\n \" model.add(layers.LeakyReLU())\\n\",\n \" \\n\",\n \" model.add(layers.Reshape((7, 7, 256)))\\n\",\n \" assert model.output_shape == (None, 7, 7, 256) # Note: None is the batch size\\n\",\n \" \\n\",\n \" model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))\\n\",\n \" assert model.output_shape == (None, 7, 7, 128) \\n\",\n \" model.add(layers.BatchNormalization())\\n\",\n \" model.add(layers.LeakyReLU())\\n\",\n \"\\n\",\n \" model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))\\n\",\n \" assert model.output_shape == (None, 14, 14, 64) \\n\",\n \" model.add(layers.BatchNormalization())\\n\",\n \" model.add(layers.LeakyReLU())\\n\",\n \"\\n\",\n \" model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))\\n\",\n \" assert model.output_shape == (None, 28, 28, 1)\\n\",\n \" \\n\",\n \" return model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"生成器生成图片\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"generator = make_generator_model()\\n\",\n \"\\n\",\n \"noise = tf.random.normal([1, 100])\\n\",\n \"generated_image = generator(noise, training=False)\\n\",\n \"\\n\",\n \"plt.imshow(generated_image[0, :, :, 0], cmap='gray')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 构造判别器\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def make_discriminator_model():\\n\",\n \" model = tf.keras.Sequential()\\n\",\n \" model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same', \\n\",\n \" input_shape=[28, 28, 1]))\\n\",\n \" model.add(layers.LeakyReLU())\\n\",\n \" model.add(layers.Dropout(0.3))\\n\",\n \" \\n\",\n \" model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))\\n\",\n \" model.add(layers.LeakyReLU())\\n\",\n \" model.add(layers.Dropout(0.3))\\n\",\n \" \\n\",\n \" model.add(layers.Flatten())\\n\",\n \" model.add(layers.Dense(1))\\n\",\n \" \\n\",\n \" return model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"判别器判别\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor([[-0.00016926]], shape=(1, 1), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"discriminator = make_discriminator_model()\\n\",\n \"decision = discriminator(generated_image)\\n\",\n \"print (decision)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.定义损失函数\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# This method returns a helper function to compute cross entropy loss\\n\",\n \"cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 判别器损失\\n\",\n \"def discriminator_loss(real_output, fake_output):\\n\",\n \" real_loss = cross_entropy(tf.ones_like(real_output), real_output)\\n\",\n \" fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)\\n\",\n \" total_loss = real_loss + fake_loss\\n\",\n \" return total_loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 生成器损失\\n\",\n \"def generator_loss(fake_output):\\n\",\n \" return cross_entropy(tf.ones_like(fake_output), fake_output)\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"generator_optimizer = tf.keras.optimizers.Adam(1e-4)\\n\",\n \"discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"checkpoint保持\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"checkpoint_dir = './training_checkpoints'\\n\",\n \"checkpoint_prefix = os.path.join(checkpoint_dir, \\\"ckpt\\\")\\n\",\n \"checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\\n\",\n \" discriminator_optimizer=discriminator_optimizer,\\n\",\n \" generator=generator,\\n\",\n \" discriminator=discriminator)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.训练函数\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"EPOCHS = 50\\n\",\n \"noise_dim = 100\\n\",\n \"num_examples_to_generate = 16\\n\",\n \"\\n\",\n \"# We will reuse this seed overtime (so it's easier)\\n\",\n \"# to visualize progress in the animated GIF)\\n\",\n \"seed = tf.random.normal([num_examples_to_generate, noise_dim])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"训练迭代函数\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Notice the use of `tf.function`\\n\",\n \"# This annotation causes the function to be \\\"compiled\\\".\\n\",\n \"@tf.function\\n\",\n \"def train_step(images):\\n\",\n \" noise = tf.random.normal([BATCH_SIZE, noise_dim])\\n\",\n \"\\n\",\n \" with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\\n\",\n \" generated_images = generator(noise, training=True)\\n\",\n \"\\n\",\n \" real_output = discriminator(images, training=True)\\n\",\n \" fake_output = discriminator(generated_images, training=True)\\n\",\n \"\\n\",\n \" gen_loss = generator_loss(fake_output)\\n\",\n \" disc_loss = discriminator_loss(real_output, fake_output)\\n\",\n \"\\n\",\n \" gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\\n\",\n \" gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\\n\",\n \"\\n\",\n \" generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\\n\",\n \" discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"训练函数\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def train(dataset, epochs): \\n\",\n \" for epoch in range(epochs):\\n\",\n \" start = time.time()\\n\",\n \" \\n\",\n \" for image_batch in dataset:\\n\",\n \" train_step(image_batch)\\n\",\n \"\\n\",\n \" # Produce images for the GIF as we go\\n\",\n \" display.clear_output(wait=True)\\n\",\n \" generate_and_save_images(generator,\\n\",\n \" epoch + 1,\\n\",\n \" seed)\\n\",\n \" \\n\",\n \" # Save the model every 15 epochs\\n\",\n \" if (epoch + 1) % 15 == 0:\\n\",\n \" checkpoint.save(file_prefix = checkpoint_prefix)\\n\",\n \" \\n\",\n \" print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))\\n\",\n \" \\n\",\n \" # Generate after the final epoch\\n\",\n \" display.clear_output(wait=True)\\n\",\n \" generate_and_save_images(generator,\\n\",\n \" epochs,\\n\",\n \" seed)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"生成和保存图像\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def generate_and_save_images(model, epoch, test_input):\\n\",\n \" # Notice `training` is set to False. \\n\",\n \" # This is so all layers run in inference mode (batchnorm).\\n\",\n \" predictions = model(test_input, training=False)\\n\",\n \"\\n\",\n \" fig = plt.figure(figsize=(4,4))\\n\",\n \" \\n\",\n \" for i in range(predictions.shape[0]):\\n\",\n \" plt.subplot(4, 4, i+1)\\n\",\n \" plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')\\n\",\n \" plt.axis('off')\\n\",\n \" \\n\",\n \" plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5.模型训练\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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4i2EKQJPQBTPUDpbbhOjDmk08+AbxOT6k4/MSInaysLNeNK973ynwEhmGYIkgWmZmZAO5Akh07drja+mSqhF/96leA3/VY3XPD4XDaKgKdIJ2u44+VcDjsniNTBIZhxB1TBElCu75OcR41ahR/+ctfAFi9enXSxiEVooNXV61aBZT9TIDykAg/yamnnspZZ50FwCOPPAKQVgfgloX8/PyEqR5TBIZhWKuyshCPFl5t27YFPG+9jje/7777AHjggQcqPMaSCIfD7j21sxS3wySqVZkUwU/vUZYfPYiGDRsCMGnSJNfv75577gFg6dKlMbX5CmqrMl2nogqqItfsZ5lHEM8HLt6ceeaZgBfG0zj/+te/FvpaHJFIxB1m0qZNG6B0CazfH+1MS4YJUBoZGRnuNGMlNGmMO3fuPGiRCoVC7v+7dOkC+CcE16xZk6+//hqApk2bAvDFF18EYp7l4cgjj3Tt2T///HPAd+gmEjMNDMM4PEwD7RavvPIK4IXItBtKIt56660APPjgg+V+n4pIZo1HTrrmzZuXexxi7969LFmyBIDnnnsOwO2Oq1atYv/+/QBs3769TL83UaaBuvP27NmTE044AfAPfdXBHvn5+e6wDzkBzz33XLfby7TRc7tt2zZee+01AO68807Ad4iWRhBNg2nTpnHssccC0K5dO4C4HOlnXYwNwyiVtFUEUgFXXHGFs621kxSHlMGjjz7KDTfcUK73jIcimDdvHgCnnHJKTO+pcWs3PdT/6+vs2bMBuOqqq9zR8WUlUYpAyTBTp051h7tu2bIFgMWLFwOwdu1aVwdxxRVXAH5adDQ//PAD4Km/GTNmaNxlGU6gFEH9+vUB77j3l156CYDf/OY3cfv9pc01bRYCfRBOPPFEwHesdenSxTmcRF5enpOc+gAqo2/nzp00btwYKHt2Vjw+IFrATj/9dO6++27AP8/v/vvv57PPPgM8yfvTewJQuXJlN/ebbroJgAsvvPAgE0gZgx9++GHM8ypKohYCyfrFixc7+atxb9q0yf2fTJlzzz0XgLp167rrtn79esA/DbmsJyBHE4SFQPdvzZo1gPec1q1bF4hvPoSZBoZhlEogFYFCS926dQPguuuuo2PHjgA0atQI8BXCnj17WLp0KeDvhsU5iy6++GLACzs98cQTANx8881lGn9QjgLLzs4GvN3xiCOOAGDmzJkAnH/++RX+/YnOI3jqqacYPHgw4PdFkDLYt2+fU0Vvv/02ADNmzGDdunXu/yE+ufZBUARvvfUWABdccAEAubm5zjSyI88Mw0gqgVMEGRkZDBw4EICHH34YgNq1aztbWSGxL7/8EoCRI0fy6aefAiWvoLVr1wZ82xugatWqgLcKx0JQFIHmsnnzZqeM5GeIdS4lkeh5Xn/99dx///2An1AU7euQuhk+fDjgORR10Gkyd8loEnFPq1evzq5du4DCB59KLU2cODFu72WKwDCMUglcinEoFKJXr16Av/OFw2GnCBQSk80v27E0pCSi048vuugiACZPnlzxgScB7RqqTcjIyHA2czyUQLLIyspyocTo+wFelERh4FtuuQXw0rKnT58OwO233w4cHr0HohPfRDgcdolxSpCSfyyeaqgoyW5eCpQc7w2Hw7Rs2bLQ68G/8f/7v/8L+GGkWJEDJjc31z2E8cjYShZVq1blt7/9LeCH1XJyctyCmA7ofrZp08aFA4uyf/9+OnToAPi5FqFQiOOPPx7ww8BXXnklkNgPR6Jp1qyZG39x10M1JR988AEAffr0cSZSvDHTwDCM5CqCWByT4XDYhcSKo169eoDvZMrNzS1WaUhGKyutf//+gKcklJW2bNmysk4hZTz00EMu00yVdXPnzuX7779P4ajKhu5J9+7dD5LEQg7cQ6Gmq2q5Nnz48Lg61ZKBrkPfvn3d/VOC3Ouvv+5M4qeffhrwaw4uuOAC3nzzzcSMKSG/1TCMtCIwzkLZSJmZmSxYsADwnSTR9lPfvn0BP0d9w4YNzmGoRKQ2bdrwu9/9DvAdjnIyfvTRRy7cqLTOdGDo0KHuOqi12fHHH8+//vUvAOrUqQME2+8he3jHjh0ubKa046J9CcBXeAUFBe7ver2Uw0svveTu8eOPP57oKcSF3//+9wC0bt3a7frRTWnWrl0LwHvvvQfAsGHDAM8voiSreN/nwOURhEIhjj76aAD3kDds2NA5C5V//d133wFeltq0adMAv/vP2LFj3d/lXFG22iuvvOKiBDk5OWUafyryCJRTMW7cOMaMGQP42WiLFi1yHyDVVqgnYkVI9Dzr1avHgAEDAD9LUvfp+eefL7FsWh8Y1VuEQiGmTp0KQL9+/co0jmTnEchJvWfPHsBb4BS5ev/9993rtMi98847AHTu3FnjdQ7UsjYrsTwCwzBKJTCmgYhuyXXXXXcB3uqvnU5OJuUFfPLJJ05yymxo0aKF+32Ks6u0c/LkyW5FDjJyKN14442AF0orugtMnDjRhdHkRE0HtmzZwosvvgj4SkAO3NK47bbbAN/5W79+/Qo1m0kmr7/+OuCbPLm5udSqVQvw26ydffbZDB06FPArbWXy/vjjj04hKjMzXuFEUwSGYQRPEYC/yinDasmSJc75JxtJO/3ll1/uQoSXXnop4Dme5FNQ337ZW7HuPKlCiuejjz4C4N133wWKtwmHDRvmbEzVsMtnEGu7ruLeOxmoNVlZ0X1Vv4Xzzz+/3A1YkkGlSpVc3wn1UBBZWVmMGzcOwIXMo52lUrqa89atW52zVEpCTvOKYorAMIxgKgKhEMmyZctcyEU2knrYd+vWza2i2tHC4bDLvVfvga1btyZv4OXkvPPOc8pFqkjtzw+FQmbyp2inKI8iSCekkM4++2y3m1akW1G8UTh35cqVrnaiaHv5zMxMFzUpTo0pQnbZZZcBXgKZnut4t2sP9EIgIpGIk/TKu1bNgS4k+BIqEom4cuO5c+e67wUVPcgyA4CYD7uUs1APV/T1OBzR/Hbv3g14XaGT0fe/rCgDsnbt2s7xW5LpFf18agNUWFwFV4l8hs00MAwjeAlFpaEGHNrxs7Oz3UqpbLXnn3/eJZ5ILsajSi1RiTZydqnqEvzwaEk7/Hnnnedyz7Xb6LwANW6JlejdqqCgIBANWKKRE1SSW0k369atK3cJdiITitQgd+bMmbRq1QoovsJQyBTcuHEjjz32GAAvvPAC4CeLVQRLKDIMo1TSThHIdpbPICsryznG1Lhz1qxZCbGnEqUIlOAUXXmnOant97Zt21zv+zlz5gBePbvQ9VC9fkUalSQzlVpKJHruCoWqeW3v3r1d/wmlE+tcwIo0KEmkItDu37t3b1dPIJUQrb6iE+PAc4Kr1iaezWYOu0NQdeOjZZaKjmbNmgUE2zEYjcyc4h5mSWFlWZaGHEt6sNIFOdI031NOOcXJ5EGDBgHQo0cPt1jqAxP0DkUyRT/55BPXVEUnOCtzcNOmTS6PINX3zUwDwzDSzzQQCrFkZGS4LDMdmpkoKiKZpWCkAiKRCKeeeioAzz77LABNmjQpU3ZfJBJxFZQ6QkyHrFaEoHRrllp44IEH6NmzJ4A73kwdrnNycspdkpvqLsbJxJyFhmGUStoqAqmATp06uW63csokiorslDVq1ABwvRaUMw5+PUR0tpjqJ6Jzykuqk4jnfQyKIoh6H7p06QLA6NGjAf+o92uvvbbMx74LUwQ+pggMw0i/qIE455xzAM+u1u4aS7v0VKEz/uQRz87OZtGiRUDxyU7a5cq72x1ORCIR5s+fD/jREZ2BGfRq0nQhbU2DqN/pHHGJ7tdXEcmshUBhsh9//DGwhUFBMw2ikYmlGHtFGnOYaeBjpoFhGMlVBIZhBBNTBIZh2EJgGIYtBIZhYAuBYRjYQmAYBrYQGIaBLQSGYWALgWEY2EJgGAZJLjoKh8MRCGZRUCwEOQc/nsQ6z6DdT/WzzMrKck1NVJRU3BjLUmsQtLmWlUDVGkQikbS9kMbBBO1+1q1bl7p163LqqacSDofdYhAPgjbXeJO2ZciGV3mph127YTw736YLOilKR61/8cUXLF++HEjfHTzZmI/AMIz070eQTILmI6hRowZXXXUV4LfDVru2ivQ6CNo8SyI7O5uXX34Z8Buazp07NyYlkOp+BLVq1XIt6TZu3Ah4fSpScSaHKQLDMMxHkE6oFZuO2X7hhRfo0aMHAN999x0ArVu3BuDll19m3rx5wOFtJ//nP/9xR8PrBKig88orrwDQr18/59N56aWXAE/VrFmzJuljMkVgGMbhqQgyMzPduXmnnXYaAI888ggAu3fvTtm4yoN6HYJ/ruFvfvMbANasWcPYsWMB//RjtT8/4ogj3M+m+jitRKAIwYYNG7jnnntSPJrYuO666wDo06cPACtXruSzzz4DYOHChQDuwJpkc1guBHfddRd33nkn4J8wpCPHr7zyypSNqyzoQ9y+fXv3vcGDBwO+afCXv/yFr776qtDPqetx7dq13Vl7Oho++tyEdOXCCy8s9PWUU05J5XBKJRQKcdNNNwH+s6ej7H/3u9+51+kErOI6WicDMw0Mwzi8FEH16tUBb+WVEtAKO2XKlJSNqyzojAaZNjfccAPgyftly5YBMHnyZABWr1590M/LMdiuXTu348iUSGeaN28OwFNPPQXAq6++CsA333yTqiEVixy6vXr1AuCNN95w93TChAkATq1Gm2xqxX/UUUdRr149wDvdKlmYIjAMI/0VQSgUcitu//79Ac9hpmKTf/7znwAsXrwY8HbcRB+EUl5CoZBTANdccw3g2foA999/P3/729+Akm19HaAyceJE9738/PyEjLes6D5lZ2e7sJnGVlKI84gjjmDWrFnu7wAPPvhgIodabnTC9cyZMwHvRGep0o8//hgoPg1c16FmzZrceuutANx3330ArF27NrGDJo0XAkn/qlWr0rlzZwAGDhwIwCeffMKjjz4KwLp16wBo1qwZ4MmxzZs3J3u4MdGvXz/GjBkD+Cf43HjjjUDsB7wed9xxgPdhW79+PZA6B1RRVA9x991306pVK8CrCwD461//ChT2muv1//M//0P9+vUB/wOzadOm5Ay6DIRCIQYNGlToe5FIhA0bNgAwffp0oOT7kZub655VPQvKHt27d2/cxyzMNDAMI30VgaRklSpVOPnkkwFczvmMGTOcaaAVWlJ73rx5boUNyk6pUOGrr75K5cqVAVixYgXgO5jK+rtyc3NZunQpEJzMQu3m9erV49hjjwX8Xb9r164ALF++3Jk+ek2rVq3cz0opJXJ3rAhy4O7ZswfwwrkDBgwAYssR2LNnD1u3bgV8h6NMhFtuuSXew3WYIjAMI/0VQevWrd0OMmnSJMBzpnXs2BHwkm7A3ykbNWrEvffeC/iJNqlGdnLlypWdI7NLly5A7Lu5wlaaZ2ZmJlWrVi3T70gWu3btcqExnW58//33u38r227Hjh2ApwiklHbt2gUER80VRb4cPZNHHnmkO9I9Fnbt2uVqEVQ30rZtW8BzlCYq89AUgWEY6a8Ili1b5vLsFZapVasWzzzzDIDbFWVjZmRkuF0o1WgXV/oz4Dz9sYQ4q1Sp4uaiHVN2ZbVq1Zy3OigoUaZly5buXql+QvOIRCJuN9VO2LRpUxclUv2+rl2Q1E4kEnG+gfHjxwOFw4exkJeX5/wM8oNIGYwbN47LL788nkN2pO1CICQVwQ8p3n333bRr167Q65RH0Ldv38CED1UQFc1zzz13yNcrp2DVqlWAt+DpYdH3FGbLycnhhRdeiOdwy40+tOeffz7gSX19gHUvtACuW7fOhXzPOeccwL+vgGvNpnkGMYwI/saTn5/vxqzrUNLCEIlE3Nw6deoE+PN/9tlnEzZeMw0Mw0h/RRDN0UcfDXhVXcpik3PlvPPOA/zqvCAQnf0nlB350EMPAb6JMGjQIMaNGwcU3iFlEgiF14IkmWWeqfquSZMm7Ny5E/DDpO+99x7g1RSoNqJx48bud2g++qr7mchdMl7ofl122WWA32K9uPqX7Oxsnn/+ecCvSJS6kOpLBKYIDMM4vBSB8rsrVarkdo5hw4YBwVICIjs7+6DvNWnSBIC3334b8Bxl4O2UsjHFggULOPPMMwGDHE+WAAAQe0lEQVR/l1GtwWeffebmvmjRogSMPnbkDI3e4eXgfffddwH/WgwYMMBVGmq++/btcyFWtfE6/vjjAc/JGEuzmaLXrqxUxDkpldaiRQsAevbsCXjPq5yLSqyaPn06jRo1Anxfgios9dpEcFgsBG3atAH8egLASc9p06alZEylEQqFin04ZQpo/PqAZGZm8v333wO4PoXFPRjqXrx161ZOPPHE+A+8jIRCIdeRR5GBHTt28NprrwH+eIcOHQp491DXRYt3jx49nANRURFlkY4YMYI6deoAsTVeKa/JFA9TS4VScmS///77bnHUhz8jI8MtHEuWLAHggw8+AOCOO+5wRXTxLpwz08AwjPRXBF27dmX27NmAH1ravXu3y++WoyVocedwOOxCn2o9tm/fPubOnQt4FXcAn3/+OeDlSJRl7AsXLnQ566mkUqVK9OvXD/DVzdatW119iHbCE044AfDulxy8Cjd+/fXX7vf17dsX8K9ZKBRycXeF27Zv337QtQrCfVfuhDpO//KXv3QmgcjLy3PKQQpQz3KLFi2cQ7y4pjQVwRSBYRjBUwShUCim1Vs7Z/fu3d33ZDdNnDiRlStXAtCgQQPAd1itXr06JX3ji0NJTnLwbdq0ye2asonLemJR3bp13ddRo0bFa6jlJj8/32UPqg6iZcuWznFWVKnl5OQ4+18+kG7durm5nHXWWYV+DnxVoZOOhg0b5uzsIKGQtnb4aDWg+S9evNgph6uvvhrwnYyRSMQ5SU0RGIYRdwKnCMLhsEuz1coZbU+rQWlR2wr8yq+nn37a5aRv27YN8HdfeeNTTUFBAddeey3gj7tz584uH/+YY44B/DPxilNJ4XDY9VtQsop20YkTJ1bo/MN4UVBQ4NKgo3fxohGT6FOcrrjiCgCGDx8OeElT8v/oOkR7zbWDjhw5EiCQagD8ugqFR8H3Yemchn/84x/893//N+AnFGl+4XCYk046CYh/M97ALQT5+fn84he/AHwpBf7BHcWhB/72228HvAunhyGo5aqRSMTl2avctkaNGu7BUNah5jFr1iy3KJx77rmA52DTB0QdjuVgC0o9BZS9Z6IW++LQQqe+fvPmzXNzD+oCICTrdc/AX+jV2Xj79u0ut0JoQbj55ptdB+t4Y6aBYRjBUwTg59mrWUVxSAX07NnTJV4Edfc/FJK5CvP98MMPLulGGYaSzHIYRf/c/v37XWhOjTGDECYritrEKfOzevXqbl7Ru2NJqDpRDVuC0lSmLEitif3797tnXI1ailNPMouffPJJZ1aofiFeHapNERiGEUxFoIQK2U3RNqOcfTrXr7ge8emCFIzs+aysrINq10UkEjko97xr166BcX6WxIIFCwA/CQj8+al6Ugqoffv2zomqe/z444/z97//HQim4okV+bx0wtH48ePdyU2x7OwNGjRwiWYKp+q8hwqPLS6/Jc4oZ1yZYqNHj3ae9XhNPEg8/vjjgHewp2LichDpg7J161bnTf7oo4+A+H8oKlqYUxY0di3kWty++eYbpk6dmrRxJANdV81Lh6BMmjSpTDUD/fr1c92eFS2aPXt2XJ4DMw0MwwimIhDffvstAEOGDEnxSBKLcuvPPPNMl/+gDENV1i1btiwwR5cZ5WPhwoWAnzWqHJdYWbx4sVMQqjmoUqVKXM54MEVgGAahZDpfQqFQ+np6gEgkEpMRHY95prJaMpnzTCWxzhPiM1cd4KoQ4Ndff13m7E8pxOhWfLH8jtLmaorAMAxTBGXBdsrC/FzmCfGZq0LD+hrvLkMlUdpcA+0sNIzDCeWBBDED1kwDwzCSaxoYhhFMTBEYhmELgWEYthAYhoEtBIZhYAuBYRjYQmAYBrYQGIaBLQSGYWALgWEYJLnWIBwORyB9+87FWqRi80wPylJ0dLjPNakLQbpexLJi80wtsZ6fWRaCOtd4YdWHxmFHOn5ooxvHpmL85iMwDMMUQdCJPvwzHXc6IzbUtr5y5crs3r0bSG7fAlMEhmGYIggqUgLNmjUDvFbn3333HYD7un37dtfuqmir8169ermTgz/99NNiXxNU1OTzySefBOBXv/qVOzF62LBhALz99tupGVycqVq1KgAdOnQAvJOedBJ2Mk/xMkVgGEawm5cmwpNakdBSKpp66tTbGjVqcNxxxwHQsmVLAKZMmeIORxE6+GLmzJnujL1LLrkEgFWrVsX0nqlsXpqVlcXvf/97AO677z7Ab90N/inYN998M+CrhvKQ7OalUb8L8NTAUUcdBfhnP+bk5LBy5UoA8vLy4vWWwcojiJVatWoB8PrrrwPeIaj//Oc/ARgzZgxAoV7u9erVA3AnvhT9cID/MH344YcMHjwYgLVr1yZi+HFFcn7nzp3MnTsXwH0tjjVr1gDwyiuvcPXVVwOetAb/2gWZRo0a0bRpU8A/43Hy5Mm88cYbgL/QZWVlAd6JUOlyEG5mZiYAffr0AaB169bO9Ktbty4Ay5cvd+dAxnMhKA0zDQzDCJ4iCIVCTJgwAYCzzjrLfV/OlBUrVgDeWYAAEydOpE2bNoBvPowcOdIdo60QjE6g7dSpE+3btwfSQxGUl/Xr11OjRg0ANm7cmOLRxE44HOarr74CvFOwAbZs2eL+//vvvwd8J9u4ceMYMWIE4J+inSwk8aVgLr30Ujp27FhofLm5uc65q+dZz1+lSpXcMyszrlevXmzevBmA5557Lgmz8DBFYBhG8BQBQLdu3Q76nk4HvuGGGwA/vHbssccedE7gli1bnD0mO6tz586AZ6fNmDEjgaMPBn/605+cX2TJkiUpHk3s5Ofns2nTJgAXMiyJoUOH0q9fP8D3LSULhTlnzpwJQIsWLZxztziKOqkjkYjzAVWpUgXwnmuFSJ9//vlify4RBHIhqF69+kHfU0xcZsM777wDeE40OQ6Lc67IqXTSSScB8Oc//zmQJ83EC0UUsrOzmTNnDpBeC0G3bt3cIaHVqlUDvHtclDvuuMP9XR9IHSm/a9euBI/SQ5vN9u3bAW9TKgltWHJubtu2zW1omkPVqlXd37WQJ8NpaKaBYRjBUwSRSOQgeVVQUMDAgQOBsmeUSXLJQXPrrbfGYZTBQbtM//79Afjb3/4GeCrgnHPOAdIjo1AqsFu3bi78q7yJbdu2MXz4cADOPfdcwAu9gecgXL16NeAfGZ4sRfDDDz8AMGrUKMBzXMpxGG2u6u+a1z333APAG2+8wTHHHAN44V7wFIEUkdTdl19+meipmCIwDCOAigD8lVO7+f79+51PoKz06tULgHnz5gG+PZduhEIhZzPrutSvX98lXTVu3BiAadOmATBkyJCkHrtdUU455RTAy4JUYpgSoY4++mjnLJbj7M9//jPg7cbJsKGLOqTBD/l9+OGHgOcj0P/LN9WmTRtn869fvx7wFcu+ffucn0GJYE2bNnWhxxNOOAEwRWAYRpIIpCLQKimic81jRTncd911F+DnpqdrxKBx48ZMmTIF8G3HatWqOX+KPNEKPWm3ShcUAmzUqJH7nnbXcDjs7puSwKKjBsmguBCevlecItH1X7p06UH/J7XQoEED5+sQBQUF7vcpsSgZBG4hyMjIcDIw+nsqyvj3v/99yJ9VKOaSSy7hscceA3xH2bp164D0bGMFXkZkgwYNAFzGYDgcdvNZuHAh4IdZ0w0tciNHjizUjAW8e/jSSy8BuPqJdEYmW05ODosXLwZ8h6O+AmzYsCFpYzLTwDCM4CmCcDjspFP097SjS3IprzxaQWgnAX83UbJRupoEchw9+eSTrsoyep4imTIyESxYsADw7pPmF12GLkWQbiZPcehZjE6UUvLX9OnTGTlyJIAL/6oaMZGYIjAMI3iK4MCBAy4kNmDAgIP+P7rJYyzIiZauO6Z2SvlIiqLdRSFFOVaTXYlXUWQ3F6fcMjIyuOWWWwBcX4pk+3qKCx/GE827a9euLhR82WWXARVrvhIrgVsIIpEIv/71rwFo164dgCvtPBRyCD711FMAbN26lauuugrwPxBaONJFWipvXdcgGs137dq1Ljat+fXt2xeAN998M63yCJSRd+DAgYMySzMyMujRowfgL3jKNUgWyVp4evfu7eZfXM1NojDTwDCM4CkC8OOynTp1Ouj/5EjS10Pteu+99x4AU6dOBfzS5qCXINeuXRuA2bNnA4V3IpXlXnfddYDXyksy8rXXXgN8GTl37ty0akgiB/H27dtdDoj48ccfeeKJJ4DkK4FkIRVQUFDgzATVUCQDUwSGYQRTEZSEVsvSwoHq2Kvw2zXXXAMEUxFIBcyfP9/ZylI8yku//vrrnbpRSLRp06au2lA/JwXRu3dvXnzxxSTNoOJI2dSpU8epA93jlStXcuedd6ZsbMlAGZXR7ctidYjHA1MEhmGknyKIFSVrbN26FfBbYwcJeYWXL18OQMOGDd3/Fe0hsHPnTpc4pYjCM88846IK2j0VXpN6SCTxCKnJNlZ4MDqZTP6ANWvWFEouOpzQvHr27Al489f3vv3226SN47BdCORwfPrppwH/AxIUMjIynKxXxiAULrQBqFmzJgD/+Mc/CmVT6qucpePHjwc8EwKSk0lZkQVAslf354wzzgC8eSv3Qx+Etm3bcuGFFwJ+TUK61owURaXlWggrV67s5vbWW28lbRxmGhiGcfgqAqEqRO2sqUaZf6eddhq9e/cGKHSQafRuH004HD6oKnPfvn2uKWsymlcURaqlNPUhqStTaNCgQa48XE4y/a78/HzXmEZO0R07dqRVclSshEIh111bZ3OAnwQXj6azsZpUpggMwzj8FYF2YIXX1q1bl9Qz5YRWZiXLjBgxwoUNox2DxVUWFkU7Zfv27V2Lq3iOMVaKUwJFVUIoFHIOQJ1bOHz48ENWUobDYXddpOK+/PJL5+w9XHwD4DXgGTt2LOCnTkNq5njYLgSS1n/4wx8Ar78fwMUXX8ztt98OFD5ItSTi6bFu0aIF4OUHKLKhD0V+fj5ffPEF4Hv9W7VqBXgLmsatjLN4PzCxLEKxouufn5/vpK6OMrvtttvc4Z/qSnz22WcDXn9/XW8tJsoxONw4+eSTOfHEE4HCz5gWTkWHVHhWHmJ9Rsw0MAyDUDJlSDzPmC8NSS71Koze7dTuTLttrCW7pZ0xL2KZZ0ZGhtsNJk+eDHg7vXr3pbLlWDznWR5kQnz88ceAl1+hklyVqMeDWOcJiZnro48+ym9/+1ugeDV20003AfDwww9X+L1Km6spAsMwDk8fQSgUco0+pXiij5/WcdOpDEnl5+c7269t27aAlwSVri3V4ok6FctPMmLECDp06ADEVxGkmmeffdb5RnTiUW5uLmPGjAH8U6uSgSkCwzAOXx+BvNYXXXQR4B+rPn36dFfRV9a5p9p2ThZBmaeiKd98841r6KmjwuNBqn0E4IcN9bzu3bs3IaqwtLketgtBCWMod9gtKB+QRBO0eR533HEu1yKeGZRBWAiShTkLDcMolaQqAsMwgokpAsMwbCEwDMMWAsMwsIXAMAxsITAMA1sIDMPAFgLDMLCFwDAMbCEwDANbCAzDwBYCwzCwhcAwDGwhMAwDWwgMw8AWAsMwsIXAMAxsITAMA1sIDMPAFgLDMLCFwDAMbCEwDANbCAzDwBYCwzCA/weSPiQXMrhYGgAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 11h 8min 3s, sys: 9min 27s, total: 11h 17min 31s\\n\",\n \"Wall time: 3h 13min 51s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"train(train_dataset, EPOCHS)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 生成一张动图\\n\",\n \"checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))\\n\",\n \"def display_image(epoch_no):\\n\",\n \" return PIL.Image.open('image_at_epoch_{:04d}.png'.format(epoch_no))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"display_image(EPOCHS)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"## 6.训练过程的动图\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with imageio.get_writer('dcgan.gif', mode='I') as writer:\\n\",\n \" filenames = glob.glob('image*.png')\\n\",\n \" filenames = sorted(filenames)\\n\",\n \" last = -1\\n\",\n \" for i,filename in enumerate(filenames):\\n\",\n \" frame = 2*(i**0.5)\\n\",\n \" if round(frame) > round(last):\\n\",\n \" last = frame\\n\",\n \" else:\\n\",\n \" continue\\n\",\n \" image = imageio.imread(filename)\\n\",\n \" writer.append_data(image)\\n\",\n \" image = imageio.imread(filename)\\n\",\n \" writer.append_data(image)\\n\",\n \" \\n\",\n \"# A hack to display the GIF inside this notebook\\n\",\n \"os.rename('dcgan.gif', 'dcgan.gif.png')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"display.Image(filename=\\\"dcgan.gif.png\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "026-Transformer/001-Transformer.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow 2.0 教程-Transformer\\n\",\n \"\\n\",\n \"这里我们将实现一个Transformer模型，将葡萄牙语翻译为英语。Transformer的核心思想是self-attention--通过关注序列不同位置的内容获取句子的表示。\\n\",\n \"\\n\",\n \"Transformer的一些优点：\\n\",\n \"- 不受限于数据的时间/空间关系\\n\",\n \"- 可以并行计算\\n\",\n \"- 远距离token的相互影响不需要通过很长的时间步或很深的卷积层\\n\",\n \"- 可以学习远程依赖\\n\",\n \"\\n\",\n \"Transformer的缺点：\\n\",\n \"- 对于时间序列，输出需要根据整个历史，而不是当前状态和输入，可能造成效率较低\\n\",\n \"- 如果想要获取时间空间信息，需要额外的位置编码\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function, unicode_literals\\n\",\n \"# 安装tfds pip install tfds-nightly==1.0.2.dev201904090105\\n\",\n \"import tensorflow_datasets as tfds\\n\",\n \"import tensorflow as tf\\n\",\n \"import tensorflow.keras.layers as layers\\n\",\n \"\\n\",\n \"import time\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"print(tf.__version__)\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.数据输入pipeline\\n\",\n \"我们将使用到Portugese-English翻译数据集。\\n\",\n \"\\n\",\n \"该数据集包含大约50000个训练样例，1100个验证示例和2000个测试示例。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"examples, metadata = tfds.load('ted_hrlr_translate/pt_to_en', with_info=True,\\n\",\n \" as_supervised=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"将数据转化为subwords格式\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"train_examples, val_examples = examples['train'], examples['validation']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"tokenizer_en = tfds.features.text.SubwordTextEncoder.build_from_corpus(\\n\",\n \"(en.numpy() for pt, en in train_examples), target_vocab_size=2**13)\\n\",\n \"tokenizer_pt = tfds.features.text.SubwordTextEncoder.build_from_corpus(\\n\",\n \"(pt.numpy() for pt, en in train_examples), target_vocab_size=2**13)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"token转化测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[3222, 439, 150, 7345, 1378, 2824, 2370, 7881]\\n\",\n \"hello world, tensorflow 2\\n\"\n ]\n }\n ],\n \"source\": [\n \"sample_str = 'hello world, tensorflow 2'\\n\",\n \"tokenized_str = tokenizer_en.encode(sample_str)\\n\",\n \"print(tokenized_str)\\n\",\n \"original_str = tokenizer_en.decode(tokenized_str)\\n\",\n \"print(original_str)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"添加start、end的token表示\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def encode(lang1, lang2):\\n\",\n \" lang1 = [tokenizer_pt.vocab_size] + tokenizer_pt.encode(\\n\",\n \" lang1.numpy()) + [tokenizer_pt.vocab_size+1]\\n\",\n \" lang2 = [tokenizer_en.vocab_size] + tokenizer_en.encode(\\n\",\n \" lang2.numpy()) + [tokenizer_en.vocab_size+1]\\n\",\n \" return lang1, lang2\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"过滤长度超过40的数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"MAX_LENGTH=40\\n\",\n \"def filter_long_sent(x, y, max_length=MAX_LENGTH):\\n\",\n \" return tf.logical_and(tf.size(x) <= max_length,\\n\",\n \" tf.size(y) <= max_length)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"将python运算，转换为tensorflow运算节点\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def tf_encode(pt, en):\\n\",\n \" return tf.py_function(encode, [pt, en], [tf.int64, tf.int64])\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 构造数据集\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"BUFFER_SIZE = 20000\\n\",\n \"BATCH_SIZE = 64\\n\",\n \"\\n\",\n \"# 使用.map()运行相关图操作\\n\",\n \"train_dataset = train_examples.map(tf_encode)\\n\",\n \"# 过滤过长的数据\\n\",\n \"train_dataset = train_dataset.filter(filter_long_sent)\\n\",\n \"# 使用缓存数据加速读入\\n\",\n \"train_dataset = train_dataset.cache()\\n\",\n \"# 打乱并获取批数据\\n\",\n \"train_dataset = train_dataset.padded_batch(\\n\",\n \"BATCH_SIZE, padded_shapes=([40], [40])) # 填充为最大长度-90\\n\",\n \"# 设置预取数据\\n\",\n \"train_dataset = train_dataset.prefetch(tf.data.experimental.AUTOTUNE)\\n\",\n \"\\n\",\n \"# 验证集数据\\n\",\n \"val_dataset = val_examples.map(tf_encode)\\n\",\n \"val_dataset = val_dataset.filter(filter_long_sent).padded_batch(\\n\",\n \"BATCH_SIZE, padded_shapes=([40], [40]))\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(,\\n\",\n \" )\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"de_batch, en_batch = next(iter(train_dataset))\\n\",\n \"de_batch, en_batch\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.位置嵌入\\n\",\n \"将位置编码矢量添加得到词嵌入，相同位置的词嵌入将会更接近，但并不能直接编码相对位置\\n\",\n \"\\n\",\n \"基于角度的位置编码方法如下：\\n\",\n \"\\n\",\n \"$$\\\\Large{PE_{(pos, 2i)} = sin(pos / 10000^{2i / d_{model}})} $$\\n\",\n \"$$\\\\Large{PE_{(pos, 2i+1)} = cos(pos / 10000^{2i / d_{model}})} $$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_angles(pos, i, d_model):\\n\",\n \" # 这里的i等价与上面公式中的2i和2i+1\\n\",\n \" angle_rates = 1 / np.power(10000, (2*(i // 2))/ np.float32(d_model))\\n\",\n \" return pos * angle_rates\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def positional_encoding(position, d_model):\\n\",\n \" angle_rads = get_angles(np.arange(position)[:, np.newaxis],\\n\",\n \" np.arange(d_model)[np.newaxis,:],\\n\",\n \" d_model)\\n\",\n \" # 第2i项使用sin\\n\",\n \" sines = np.sin(angle_rads[:, 0::2])\\n\",\n \" # 第2i+1项使用cos\\n\",\n \" cones = np.cos(angle_rads[:, 1::2])\\n\",\n \" pos_encoding = np.concatenate([sines, cones], axis=-1)\\n\",\n \" pos_encoding = pos_encoding[np.newaxis, ...]\\n\",\n \" \\n\",\n \" return tf.cast(pos_encoding, dtype=tf.float32)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"获得位置嵌入编码\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1, 50, 512)\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pos_encoding = positional_encoding(50, 512)\\n\",\n \"print(pos_encoding.shape)\\n\",\n \"\\n\",\n \"plt.pcolormesh(pos_encoding[0], cmap='RdBu')\\n\",\n \"plt.xlabel('Depth')\\n\",\n \"plt.xlim((0, 512))\\n\",\n \"plt.ylabel('Position')\\n\",\n \"plt.colorbar()\\n\",\n \"plt.show() # 在这里左右边分别为原来2i 和 2i+1的特征\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.掩码\\n\",\n \"为了避免输入中padding的token对句子语义的影响，需要将padding位mark掉，原来为0的padding项的mark输出为1\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"def create_padding_mark(seq):\\n\",\n \" # 获取为0的padding项\\n\",\n \" seq = tf.cast(tf.math.equal(seq, 0), tf.float32)\\n\",\n \" \\n\",\n \" # 扩充维度以便用于attention矩阵\\n\",\n \" return seq[:, np.newaxis, np.newaxis, :] # (batch_size,1,1,seq_len)\\n\",\n \"\\n\",\n \"# mark 测试\\n\",\n \"create_padding_mark([[1,2,0,0,3],[3,4,5,0,0]])\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"look-ahead mask 用于对未预测的token进行掩码\\n\",\n \"这意味着要预测第三个单词，只会使用第一个和第二个单词。 要预测第四个单词，仅使用第一个，第二个和第三个单词，依此类推。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def create_look_ahead_mark(size):\\n\",\n \" # 1 - 对角线和取下三角的全部对角线（-1->全部）\\n\",\n \" # 这样就可以构造出每个时刻未预测token的掩码\\n\",\n \" mark = 1 - tf.linalg.band_part(tf.ones((size, size)), -1, 0)\\n\",\n \" return mark # (seq_len, seq_len)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tf.Tensor(\\n\",\n \"[[0. 1. 1.]\\n\",\n \" [0. 0. 1.]\\n\",\n \" [0. 0. 0.]], shape=(3, 3), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# x = tf.random.uniform((1,3))\\n\",\n \"temp = create_look_ahead_mark(3)\\n\",\n \"print(temp)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.Scaled dot product attention\\n\",\n \"![](https://www.tensorflow.org/images/tutorials/transformer/scaled_attention.png)\\n\",\n \"进行attention计算的时候有3个输入 Q (query), K (key), V (value)。计算公式如下：\\n\",\n \"$$\\\\Large{Attention(Q, K, V) = softmax_k(\\\\frac{QK^T}{\\\\sqrt{d_k}}) V} $$\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"点积注意力通过深度d_k的平方根进行缩放,因为较大的深度会使点积变大，由于使用softmax，会使梯度变小。\\n\",\n \"例如，考虑Q和K的均值为0且方差为1.它们的矩阵乘法的均值为0，方差为dk。我们使用dk的根用于缩放（而不是任何其他数字），因为Q和K的matmul应该具有0的均值和1的方差。\\n\",\n \"\\n\",\n \"在这里我们将被掩码的token乘以-1e9(表示负无穷),这样softmax之后就为0,不对其他token产生影响。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def scaled_dot_product_attention(q, k, v, mask):\\n\",\n \" # query key 相乘获取匹配关系\\n\",\n \" matmul_qk = tf.matmul(q, k, transpose_b=True)\\n\",\n \" \\n\",\n \" # 使用dk进行缩放\\n\",\n \" dk = tf.cast(tf.shape(k)[-1], tf.float32)\\n\",\n \" scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)\\n\",\n \" \\n\",\n \" # 掩码\\n\",\n \" if mask is not None:\\n\",\n \" scaled_attention_logits += (mask * -1e9)\\n\",\n \" \\n\",\n \" # 通过softmax获取attention权重\\n\",\n \" attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)\\n\",\n \" \\n\",\n \" # attention 乘上value\\n\",\n \" output = tf.matmul(attention_weights, v) # （.., seq_len_v, depth）\\n\",\n \" \\n\",\n \" return output, attention_weights\\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"使用attention获取需要关注的语义\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def print_out(q, k, v):\\n\",\n \" temp_out, temp_att = scaled_dot_product_attention(\\n\",\n \" q, k, v, None)\\n\",\n \" print('attention weight:')\\n\",\n \" print(temp_att)\\n\",\n \" print('output:')\\n\",\n \" print(temp_out)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"attention测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"attention weight:\\n\",\n \"tf.Tensor([[0. 1. 0. 0.]], shape=(1, 4), dtype=float32)\\n\",\n \"output:\\n\",\n \"tf.Tensor([[10. 0.]], shape=(1, 2), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 显示为numpy类型\\n\",\n \"np.set_printoptions(suppress=True)\\n\",\n \"\\n\",\n \"temp_k = tf.constant([[10,0,0],\\n\",\n \" [0,10,0],\\n\",\n \" [0,0,10],\\n\",\n \" [0,0,10]], dtype=tf.float32) # (4, 3)\\n\",\n \"\\n\",\n \"temp_v = tf.constant([[ 1,0],\\n\",\n \" [ 10,0],\\n\",\n \" [ 100,5],\\n\",\n \" [1000,6]], dtype=tf.float32) # (4, 3)\\n\",\n \"# 关注第2个key, 返回对应的value\\n\",\n \"temp_q = tf.constant([[0,10,0]], dtype=tf.float32)\\n\",\n \"print_out(temp_q, temp_k, temp_v)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"attention weight:\\n\",\n \"tf.Tensor([[0. 0. 0.5 0.5]], shape=(1, 4), dtype=float32)\\n\",\n \"output:\\n\",\n \"tf.Tensor([[550. 5.5]], shape=(1, 2), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 关注重复的key(第3、4个), 返回对应的value（平均）\\n\",\n \"temp_q = tf.constant([[0,0,10]], dtype=tf.float32)\\n\",\n \"print_out(temp_q, temp_k, temp_v)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"attention weight:\\n\",\n \"tf.Tensor([[0.5 0.5 0. 0. ]], shape=(1, 4), dtype=float32)\\n\",\n \"output:\\n\",\n \"tf.Tensor([[5.5 0. ]], shape=(1, 2), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 关注第1、2个key, 返回对应的value（平均）\\n\",\n \"temp_q = tf.constant([[10,10,0]], dtype=tf.float32)\\n\",\n \"print_out(temp_q, temp_k, temp_v)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"attention weight:\\n\",\n \"tf.Tensor(\\n\",\n \"[[0. 0. 0.5 0.5]\\n\",\n \" [0. 1. 0. 0. ]\\n\",\n \" [0.5 0.5 0. 0. ]], shape=(3, 4), dtype=float32)\\n\",\n \"output:\\n\",\n \"tf.Tensor(\\n\",\n \"[[550. 5.5]\\n\",\n \" [ 10. 0. ]\\n\",\n \" [ 5.5 0. ]], shape=(3, 2), dtype=float32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 依次放入每个query\\n\",\n \"temp_q = tf.constant([[0, 0, 10], [0, 10, 0], [10, 10, 0]], dtype=tf.float32) # (3, 3)\\n\",\n \"print_out(temp_q, temp_k, temp_v)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5.Mutil-Head Attention\\n\",\n \"![](https://www.tensorflow.org/images/tutorials/transformer/multi_head_attention.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"mutil-head attention包含3部分：\\n\",\n \"- 线性层与分头\\n\",\n \"- 缩放点积注意力\\n\",\n \"- 头连接\\n\",\n \"- 末尾线性层\\n\",\n \"\\n\",\n \"每个多头注意块有三个输入; Q（查询），K（密钥），V（值）。 它们通过第一层线性层并分成多个头。\\n\",\n \"\\n\",\n \"注意:点积注意力时需要使用mask， 多头输出需要使用tf.transpose调整各维度。\\n\",\n \"\\n\",\n \"Q，K和V不是一个单独的注意头，而是分成多个头，因为它允许模型共同参与来自不同表征空间的不同信息。 在拆分之后，每个头部具有降低的维度，总计算成本与具有全维度的单个头部注意力相同。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 构造mutil head attention层\\n\",\n \"class MutilHeadAttention(tf.keras.layers.Layer):\\n\",\n \" def __init__(self, d_model, num_heads):\\n\",\n \" super(MutilHeadAttention, self).__init__()\\n\",\n \" self.num_heads = num_heads\\n\",\n \" self.d_model = d_model\\n\",\n \" \\n\",\n \" # d_model 必须可以正确分为各个头\\n\",\n \" assert d_model % num_heads == 0\\n\",\n \" # 分头后的维度\\n\",\n \" self.depth = d_model // num_heads\\n\",\n \" \\n\",\n \" self.wq = tf.keras.layers.Dense(d_model)\\n\",\n \" self.wk = tf.keras.layers.Dense(d_model)\\n\",\n \" self.wv = tf.keras.layers.Dense(d_model)\\n\",\n \" \\n\",\n \" self.dense = tf.keras.layers.Dense(d_model)\\n\",\n \" \\n\",\n \" def split_heads(self, x, batch_size):\\n\",\n \" # 分头, 将头个数的维度 放到 seq_len 前面\\n\",\n \" x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))\\n\",\n \" return tf.transpose(x, perm=[0, 2, 1, 3])\\n\",\n \" \\n\",\n \" def call(self, v, k, q, mask):\\n\",\n \" batch_size = tf.shape(q)[0]\\n\",\n \" \\n\",\n \" # 分头前的前向网络，获取q、k、v语义\\n\",\n \" q = self.wq(q) # (batch_size, seq_len, d_model)\\n\",\n \" k = self.wk(k)\\n\",\n \" v = self.wv(v)\\n\",\n \" \\n\",\n \" # 分头\\n\",\n \" q = self.split_heads(q, batch_size) # (batch_size, num_heads, seq_len_q, depth)\\n\",\n \" k = self.split_heads(k, batch_size)\\n\",\n \" v = self.split_heads(v, batch_size)\\n\",\n \" # scaled_attention.shape == (batch_size, num_heads, seq_len_v, depth)\\n\",\n \" # attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)\\n\",\n \" \\n\",\n \" # 通过缩放点积注意力层\\n\",\n \" scaled_attention, attention_weights = scaled_dot_product_attention(\\n\",\n \" q, k, v, mask)\\n\",\n \" # 把多头维度后移\\n\",\n \" scaled_attention = tf.transpose(scaled_attention, [0, 2, 1, 3]) # (batch_size, seq_len_v, num_heads, depth)\\n\",\n \"\\n\",\n \" # 合并多头\\n\",\n \" concat_attention = tf.reshape(scaled_attention, \\n\",\n \" (batch_size, -1, self.d_model))\\n\",\n \" \\n\",\n \" # 全连接重塑\\n\",\n \" output = self.dense(concat_attention)\\n\",\n \" return output, attention_weights\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"测试多头attention\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1, 60, 512) (1, 8, 60, 60)\\n\"\n ]\n }\n ],\n \"source\": [\n \"temp_mha = MutilHeadAttention(d_model=512, num_heads=8)\\n\",\n \"y = tf.random.uniform((1, 60, 512))\\n\",\n \"output, att = temp_mha(y, k=y, q=y, mask=None)\\n\",\n \"print(output.shape, att.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"point wise前向网络\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def point_wise_feed_forward_network(d_model, diff):\\n\",\n \" return tf.keras.Sequential([\\n\",\n \" tf.keras.layers.Dense(diff, activation='relu'),\\n\",\n \" tf.keras.layers.Dense(d_model)\\n\",\n \" ])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"TensorShape([64, 50, 512])\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sample_fnn = point_wise_feed_forward_network(512, 2048)\\n\",\n \"sample_fnn(tf.random.uniform((64, 50, 512))).shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6.编码器和解码器\\n\",\n \"\\n\",\n \"![](https://www.tensorflow.org/images/tutorials/transformer/transformer.png)\\n\",\n \"\\n\",\n \"- 通过N个编码器层，为序列中的每个字/令牌生成输出。\\n\",\n \"- 解码器连接编码器的输出和它自己的输入（自我注意）以预测下一个字。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 编码层\\n\",\n \"每个编码层包含以下子层\\n\",\n \"- Multi-head attention（带掩码）\\n\",\n \"- Point wise feed forward networks\\n\",\n \"\\n\",\n \"每个子层中都有残差连接，并最后通过一个正则化层。残差连接有助于避免深度网络中的梯度消失问题。\\n\",\n \"每个子层输出是LayerNorm(x + Sublayer(x))，规范化是在d_model维的向量上。Transformer一共有n个编码层。\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class LayerNormalization(tf.keras.layers.Layer):\\n\",\n \" def __init__(self, epsilon=1e-6, **kwargs):\\n\",\n \" self.eps = epsilon\\n\",\n \" super(LayerNormalization, self).__init__(**kwargs)\\n\",\n \" def build(self, input_shape):\\n\",\n \" self.gamma = self.add_weight(name='gamma', shape=input_shape[-1:],\\n\",\n \" initializer=tf.ones_initializer(), trainable=True)\\n\",\n \" self.beta = self.add_weight(name='beta', shape=input_shape[-1:],\\n\",\n \" initializer=tf.zeros_initializer(), trainable=True)\\n\",\n \" super(LayerNormalization, self).build(input_shape)\\n\",\n \" def call(self, x):\\n\",\n \" mean = tf.keras.backend.mean(x, axis=-1, keepdims=True)\\n\",\n \" std = tf.keras.backend.std(x, axis=-1, keepdims=True)\\n\",\n \" return self.gamma * (x - mean) / (std + self.eps) + self.beta\\n\",\n \" def compute_output_shape(self, input_shape):\\n\",\n \" return input_shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class EncoderLayer(tf.keras.layers.Layer):\\n\",\n \" def __init__(self, d_model, n_heads, ddf, dropout_rate=0.1):\\n\",\n \" super(EncoderLayer, self).__init__()\\n\",\n \" \\n\",\n \" self.mha = MutilHeadAttention(d_model, n_heads)\\n\",\n \" self.ffn = point_wise_feed_forward_network(d_model, ddf)\\n\",\n \" \\n\",\n \" self.layernorm1 = LayerNormalization(epsilon=1e-6)\\n\",\n \" self.layernorm2 = LayerNormalization(epsilon=1e-6)\\n\",\n \" \\n\",\n \" self.dropout1 = tf.keras.layers.Dropout(dropout_rate)\\n\",\n \" self.dropout2 = tf.keras.layers.Dropout(dropout_rate)\\n\",\n \" \\n\",\n \" def call(self, inputs, training, mask):\\n\",\n \" # 多头注意力网络\\n\",\n \" att_output, _ = self.mha(inputs, inputs, inputs, mask)\\n\",\n \" att_output = self.dropout1(att_output, training=training)\\n\",\n \" out1 = self.layernorm1(inputs + att_output) # (batch_size, input_seq_len, d_model)\\n\",\n \" # 前向网络\\n\",\n \" ffn_output = self.ffn(out1)\\n\",\n \" ffn_output = self.dropout2(ffn_output, training=training)\\n\",\n \" out2 = self.layernorm2(out1 + ffn_output) # (batch_size, input_seq_len, d_model)\\n\",\n \" return out2\\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"encoder层测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"TensorShape([64, 43, 512])\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sample_encoder_layer = EncoderLayer(512, 8, 2048)\\n\",\n \"sample_encoder_layer_output = sample_encoder_layer(\\n\",\n \"tf.random.uniform((64, 43, 512)), False, None)\\n\",\n \"\\n\",\n \"sample_encoder_layer_output.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 解码层\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"每个编码层包含以下子层：\\n\",\n \"- Masked muti-head attention（带padding掩码和look-ahead掩码）\\n\",\n \"- Muti-head attention（带padding掩码）value和key来自encoder输出，query来自Masked muti-head attention层输出\\n\",\n \"- Point wise feed forward network\\n\",\n \"\\n\",\n \"每个子层中都有残差连接，并最后通过一个正则化层。残差连接有助于避免深度网络中的梯度消失问题。\\n\",\n \"\\n\",\n \"每个子层输出是LayerNorm(x + Sublayer(x))，规范化是在d_model维的向量上。Transformer一共有n个解码层。\\n\",\n \"\\n\",\n \"当Q从解码器的第一个注意块接收输出，并且K接收编码器输出时，注意权重表示基于编码器输出给予解码器输入的重要性。 换句话说，解码器通过查看编码器输出并自我关注其自己的输出来预测下一个字。\\n\",\n \"\\n\",\n \"ps：因为padding在后面所以look-ahead掩码同时掩padding\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class DecoderLayer(tf.keras.layers.Layer):\\n\",\n \" def __init__(self, d_model, num_heads, dff, drop_rate=0.1):\\n\",\n \" super(DecoderLayer, self).__init__()\\n\",\n \" \\n\",\n \" self.mha1 = MutilHeadAttention(d_model, num_heads)\\n\",\n \" self.mha2 = MutilHeadAttention(d_model, num_heads)\\n\",\n \" \\n\",\n \" self.ffn = point_wise_feed_forward_network(d_model, dff)\\n\",\n \" \\n\",\n \" self.layernorm1 = LayerNormalization(epsilon=1e-6)\\n\",\n \" self.layernorm2 = LayerNormalization(epsilon=1e-6)\\n\",\n \" self.layernorm3 = LayerNormalization(epsilon=1e-6)\\n\",\n \" \\n\",\n \" self.dropout1 = layers.Dropout(drop_rate)\\n\",\n \" self.dropout2 = layers.Dropout(drop_rate)\\n\",\n \" self.dropout3 = layers.Dropout(drop_rate)\\n\",\n \" \\n\",\n \" def call(self,inputs, encode_out, training, \\n\",\n \" look_ahead_mask, padding_mask):\\n\",\n \" # masked muti-head attention\\n\",\n \" att1, att_weight1 = self.mha1(inputs, inputs, inputs,look_ahead_mask)\\n\",\n \" att1 = self.dropout1(att1, training=training)\\n\",\n \" out1 = self.layernorm1(inputs + att1)\\n\",\n \" # muti-head attention\\n\",\n \" att2, att_weight2 = self.mha2(encode_out, encode_out, inputs, padding_mask)\\n\",\n \" att2 = self.dropout2(att2, training=training)\\n\",\n \" out2 = self.layernorm2(out1 + att2)\\n\",\n \" \\n\",\n \" ffn_out = self.ffn(out2)\\n\",\n \" ffn_out = self.dropout3(ffn_out, training=training)\\n\",\n \" out3 = self.layernorm3(out2 + ffn_out)\\n\",\n \" \\n\",\n \" return out3, att_weight1, att_weight2\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"测试解码层\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"TensorShape([64, 50, 512])\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sample_decoder_layer = DecoderLayer(512, 8, 2048)\\n\",\n \"\\n\",\n \"sample_decoder_layer_output, _, _ = sample_decoder_layer(\\n\",\n \"tf.random.uniform((64, 50, 512)), sample_encoder_layer_output,\\n\",\n \" False, None, None)\\n\",\n \"sample_decoder_layer_output.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 编码器\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"编码器包含：\\n\",\n \"- Input Embedding\\n\",\n \"- Positional Embedding\\n\",\n \"- N个编码层\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class Encoder(layers.Layer):\\n\",\n \" def __init__(self, n_layers, d_model, n_heads, ddf,\\n\",\n \" input_vocab_size, max_seq_len, drop_rate=0.1):\\n\",\n \" super(Encoder, self).__init__()\\n\",\n \" \\n\",\n \" self.n_layers = n_layers\\n\",\n \" self.d_model = d_model\\n\",\n \" \\n\",\n \" self.embedding = layers.Embedding(input_vocab_size, d_model)\\n\",\n \" self.pos_embedding = positional_encoding(max_seq_len, d_model)\\n\",\n \" \\n\",\n \" self.encode_layer = [EncoderLayer(d_model, n_heads, ddf, drop_rate)\\n\",\n \" for _ in range(n_layers)]\\n\",\n \" \\n\",\n \" self.dropout = layers.Dropout(drop_rate)\\n\",\n \" def call(self, inputs, training, mark):\\n\",\n \" \\n\",\n \" seq_len = inputs.shape[1]\\n\",\n \" word_emb = self.embedding(inputs)\\n\",\n \" word_emb *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\\n\",\n \" emb = word_emb + self.pos_embedding[:,:seq_len,:]\\n\",\n \" x = self.dropout(emb, training=training)\\n\",\n \" for i in range(self.n_layers):\\n\",\n \" x = self.encode_layer[i](x, training, mark)\\n\",\n \" \\n\",\n \" return x\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"编码器测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"TensorShape([64, 120, 512])\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sample_encoder = Encoder(2, 512, 8, 1024, 5000, 200)\\n\",\n \"sample_encoder_output = sample_encoder(tf.random.uniform((64, 120)),\\n\",\n \" False, None)\\n\",\n \"sample_encoder_output.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 解码器\\n\",\n \"解码器包含以下部分：1、输出嵌入；2、位置编码；3、n个解码层\\n\",\n \"\\n\",\n \"输出嵌入和位置编码叠加后输入解码器，解码器最后的输出送给一个全连接\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# import pdb\\n\",\n \"# pdb.set_trace()\\n\",\n \"class Decoder(layers.Layer):\\n\",\n \" def __init__(self, n_layers, d_model, n_heads, ddf,\\n\",\n \" target_vocab_size, max_seq_len, drop_rate=0.1):\\n\",\n \" super(Decoder, self).__init__()\\n\",\n \" \\n\",\n \" self.d_model = d_model\\n\",\n \" self.n_layers = n_layers\\n\",\n \" \\n\",\n \" self.embedding = layers.Embedding(target_vocab_size, d_model)\\n\",\n \" self.pos_embedding = positional_encoding(max_seq_len, d_model)\\n\",\n \" \\n\",\n \" self.decoder_layers= [DecoderLayer(d_model, n_heads, ddf, drop_rate)\\n\",\n \" for _ in range(n_layers)]\\n\",\n \" \\n\",\n \" self.dropout = layers.Dropout(drop_rate)\\n\",\n \" \\n\",\n \" def call(self, inputs, encoder_out,training,\\n\",\n \" look_ahead_mark, padding_mark):\\n\",\n \" \\n\",\n \" seq_len = tf.shape(inputs)[1]\\n\",\n \" attention_weights = {}\\n\",\n \" h = self.embedding(inputs)\\n\",\n \" h *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\\n\",\n \" h += self.pos_embedding[:,:seq_len,:]\\n\",\n \" \\n\",\n \" h = self.dropout(h, training=training)\\n\",\n \"# print('--------------------\\\\n',h, h.shape)\\n\",\n \" # 叠加解码层\\n\",\n \" for i in range(self.n_layers):\\n\",\n \" h, att_w1, att_w2 = self.decoder_layers[i](h, encoder_out,\\n\",\n \" training, look_ahead_mark,\\n\",\n \" padding_mark)\\n\",\n \" attention_weights['decoder_layer{}_att_w1'.format(i+1)] = att_w1\\n\",\n \" attention_weights['decoder_layer{}_att_w2'.format(i+1)] = att_w2\\n\",\n \" \\n\",\n \" return h, attention_weights\\n\",\n \" \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"解码器测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(TensorShape([64, 100, 512]), TensorShape([64, 8, 100, 100]))\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"sample_decoder = Decoder(2, 512,8,1024,5000, 200)\\n\",\n \"sample_decoder_output, attn = sample_decoder(tf.random.uniform((64, 100)),\\n\",\n \" sample_encoder_output, False,\\n\",\n \" None, None)\\n\",\n \"sample_decoder_output.shape, attn['decoder_layer1_att_w1'].shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 创建Transformer\\n\",\n \"Transformer包含编码器、解码器和最后的线性层，解码层的输出经过线性层后得到Transformer的输出\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class Transformer(tf.keras.Model):\\n\",\n \" def __init__(self, n_layers, d_model, n_heads, diff,\\n\",\n \" input_vocab_size, target_vocab_size,\\n\",\n \" max_seq_len, drop_rate=0.1):\\n\",\n \" super(Transformer, self).__init__()\\n\",\n \" \\n\",\n \" self.encoder = Encoder(n_layers, d_model, n_heads,diff,\\n\",\n \" input_vocab_size, max_seq_len, drop_rate)\\n\",\n \" \\n\",\n \" self.decoder = Decoder(n_layers, d_model, n_heads, diff,\\n\",\n \" target_vocab_size, max_seq_len, drop_rate)\\n\",\n \" \\n\",\n \" self.final_layer = tf.keras.layers.Dense(target_vocab_size)\\n\",\n \" def call(self, inputs, targets, training, encode_padding_mask, \\n\",\n \" look_ahead_mask, decode_padding_mask):\\n\",\n \" \\n\",\n \" encode_out = self.encoder(inputs, training, encode_padding_mask)\\n\",\n \" print(encode_out.shape)\\n\",\n \" decode_out, att_weights = self.decoder(targets, encode_out, training, \\n\",\n \" look_ahead_mask, decode_padding_mask)\\n\",\n \" print(decode_out.shape)\\n\",\n \" final_out = self.final_layer(decode_out)\\n\",\n \" \\n\",\n \" return final_out, att_weights\\n\",\n \" \\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Transformer测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(64, 62, 512)\\n\",\n \"(64, 26, 512)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"TensorShape([64, 26, 8000])\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sample_transformer = Transformer(\\n\",\n \"n_layers=2, d_model=512, n_heads=8, diff=1024,\\n\",\n \"input_vocab_size=8500, target_vocab_size=8000, max_seq_len=120\\n\",\n \")\\n\",\n \"temp_input = tf.random.uniform((64, 62))\\n\",\n \"temp_target = tf.random.uniform((64, 26))\\n\",\n \"fn_out, _ = sample_transformer(temp_input, temp_target, training=False,\\n\",\n \" encode_padding_mask=None,\\n\",\n \" look_ahead_mask=None,\\n\",\n \" decode_padding_mask=None,\\n\",\n \" )\\n\",\n \"fn_out.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 7.实验设置\\n\",\n \"### 设置超参\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"num_layers = 4\\n\",\n \"d_model = 128\\n\",\n \"dff = 512\\n\",\n \"num_heads = 8\\n\",\n \"\\n\",\n \"input_vocab_size = tokenizer_pt.vocab_size + 2\\n\",\n \"target_vocab_size = tokenizer_en.vocab_size + 2\\n\",\n \"max_seq_len = 40\\n\",\n \"dropout_rate = 0.1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 优化器\\n\",\n \"带自定义学习率调整的Adam优化器\\n\",\n \"$$\\\\Large{lrate = d_{model}^{-0.5} * min(step{\\\\_}num^{-0.5}, step{\\\\_}num * warmup{\\\\_}steps^{-1.5})}$$\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\\n\",\n \" def __init__(self, d_model, warmup_steps=4000):\\n\",\n \" super(CustomSchedule, self).__init__()\\n\",\n \" \\n\",\n \" self.d_model = tf.cast(d_model, tf.float32)\\n\",\n \" self.warmup_steps = warmup_steps\\n\",\n \" \\n\",\n \" def __call__(self, step):\\n\",\n \" arg1 = tf.math.rsqrt(step)\\n\",\n \" arg2 = step * (self.warmup_steps ** -1.5)\\n\",\n \" \\n\",\n \" return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)\\n\",\n \" \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"learing_rate = CustomSchedule(d_model)\\n\",\n \"optimizer = tf.keras.optimizers.Adam(learing_rate, beta_1=0.9, \\n\",\n \" beta_2=0.98, epsilon=1e-9)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0, 0.5, 'train step')\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# 测试\\n\",\n \"temp_learing_rate = CustomSchedule(d_model)\\n\",\n \"plt.plot(temp_learing_rate(tf.range(40000, dtype=tf.float32)))\\n\",\n \"plt.xlabel('learning rate')\\n\",\n \"plt.ylabel('train step')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 损失和指标\\n\",\n \"由于目标序列是填充的，因此在计算损耗时应用填充掩码很重要。\\n\",\n \"padding的掩码为0，没padding的掩码为1\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True,\\n\",\n \" reduction='none')\\n\",\n \"\\n\",\n \"def loss_fun(y_ture, y_pred):\\n\",\n \" mask = tf.math.logical_not(tf.math.equal(y_ture, 0)) # 为0掩码标1\\n\",\n \" loss_ = loss_object(y_ture, y_pred)\\n\",\n \" \\n\",\n \" mask = tf.cast(mask, dtype=loss_.dtype)\\n\",\n \" loss_ *= mask\\n\",\n \" return tf.reduce_mean(loss_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"train_loss = tf.keras.metrics.Mean(name='train_loss')\\n\",\n \"train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 8、训练和保持模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"transformer = Transformer(num_layers, d_model, num_heads, dff,\\n\",\n \" input_vocab_size, target_vocab_size,\\n\",\n \" max_seq_len, dropout_rate)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 构建掩码\\n\",\n \"def create_mask(inputs,targets):\\n\",\n \" encode_padding_mask = create_padding_mark(inputs)\\n\",\n \" # 这个掩码用于掩输入解码层第二层的编码层输出\\n\",\n \" decode_padding_mask = create_padding_mark(inputs)\\n\",\n \" \\n\",\n \" # look_ahead 掩码， 掩掉未预测的词\\n\",\n \" look_ahead_mask = create_look_ahead_mark(tf.shape(targets)[1])\\n\",\n \" # 解码层第一层得到padding掩码\\n\",\n \" decode_targets_padding_mask = create_padding_mark(targets)\\n\",\n \" \\n\",\n \" # 合并解码层第一层掩码\\n\",\n \" combine_mask = tf.maximum(decode_targets_padding_mask, look_ahead_mask)\\n\",\n \" \\n\",\n \" return encode_padding_mask, combine_mask, decode_padding_mask\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"创建checkpoint管理器\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"checkpoint_path = './checkpoint/train'\\n\",\n \"ckpt = tf.train.Checkpoint(transformer=transformer,\\n\",\n \" optimizer=optimizer)\\n\",\n \"# ckpt管理器\\n\",\n \"ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=3)\\n\",\n \"\\n\",\n \"if ckpt_manager.latest_checkpoint:\\n\",\n \" ckpt.restore(ckpt_manager.latest_checkpoint)\\n\",\n \" print('last checkpoit restore')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"target分为target_input和target real.\\n\",\n \"target_input是传给解码器的输入，target_real是其左移一个位置的结果，每个target_input位置对应下一个预测的标签\\n\",\n \"\\n\",\n \"如句子=“SOS A丛林中的狮子正在睡觉EOS”\\n\",\n \"\\n\",\n \"target_input =“SOS丛林中的狮子正在睡觉”\\n\",\n \"\\n\",\n \"target_real =“丛林中的狮子正在睡觉EOS”\\n\",\n \"\\n\",\n \"transformer是个自动回归模型：它一次预测一个部分，并使用其到目前为止的输出，决定下一步做什么。\\n\",\n \"\\n\",\n \"在训练期间使用teacher-forcing，即无论模型当前输出什么都强制将正确输出传给下一步。\\n\",\n \"\\n\",\n \"而预测时则根据前一个的输出预测下一个词\\n\",\n \"\\n\",\n \"为防止模型在预期输出处达到峰值，模型使用look-ahead mask\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"@tf.function\\n\",\n \"def train_step(inputs, targets):\\n\",\n \" tar_inp = targets[:,:-1]\\n\",\n \" tar_real = targets[:,1:]\\n\",\n \" # 构造掩码\\n\",\n \" encode_padding_mask, combined_mask, decode_padding_mask = create_mask(inputs, tar_inp)\\n\",\n \" \\n\",\n \" \\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" predictions, _ = transformer(inputs, tar_inp,\\n\",\n \" True,\\n\",\n \" encode_padding_mask,\\n\",\n \" combined_mask,\\n\",\n \" decode_padding_mask)\\n\",\n \" loss = loss_fun(tar_real, predictions)\\n\",\n \" # 求梯度\\n\",\n \" gradients = tape.gradient(loss, transformer.trainable_variables)\\n\",\n \" # 反向传播\\n\",\n \" optimizer.apply_gradients(zip(gradients, transformer.trainable_variables))\\n\",\n \" \\n\",\n \" # 记录loss和准确率\\n\",\n \" train_loss(loss)\\n\",\n \" train_accuracy(tar_real, predictions)\\n\",\n \" \\n\",\n \"\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"葡萄牙语用作输入语言，英语是目标语言。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(64, 40, 128)\\n\",\n \"(64, 39, 128)\\n\",\n \"(64, 40, 128)\\n\",\n \"(64, 39, 128)\\n\",\n \"epoch 1, batch 0, loss:4.0259, acc:0.0000\\n\",\n \"epoch 1, batch 500, loss:3.4436, acc:0.0340\\n\",\n \"(31, 40, 128)\\n\",\n \"(31, 39, 128)\\n\",\n \"epoch 1, loss:3.2112, acc:0.0481\\n\",\n \"time in 1 epoch:467.3876633644104 secs\\n\",\n \"\\n\",\n \"epoch 2, batch 0, loss:2.4443, acc:0.0982\\n\",\n \"epoch 2, batch 500, loss:2.3006, acc:0.1139\\n\",\n \"epoch 2, save model at ./checkpoint/train/ckpt-1\\n\",\n \"epoch 2, loss:2.2473, acc:0.1184\\n\",\n \"time in 1 epoch:429.6356120109558 secs\\n\",\n \"\\n\",\n \"epoch 3, batch 0, loss:2.0709, acc:0.1306\\n\",\n \"epoch 3, batch 500, loss:2.0279, acc:0.1412\\n\",\n \"epoch 3, loss:1.9927, acc:0.1443\\n\",\n \"time in 1 epoch:426.3838963508606 secs\\n\",\n \"\\n\",\n \"epoch 4, batch 0, loss:1.8720, acc:0.1571\\n\",\n \"epoch 4, batch 500, loss:1.8020, acc:0.1678\\n\",\n \"epoch 4, save model at ./checkpoint/train/ckpt-2\\n\",\n \"epoch 4, loss:1.7664, acc:0.1714\\n\",\n \"time in 1 epoch:387.37333059310913 secs\\n\",\n \"\\n\",\n \"epoch 5, batch 0, loss:1.6616, acc:0.1807\\n\",\n \"epoch 5, batch 500, loss:1.5908, acc:0.1936\\n\",\n \"epoch 5, loss:1.5610, acc:0.1961\\n\",\n \"time in 1 epoch:389.60524225234985 secs\\n\",\n \"\\n\",\n \"epoch 6, batch 0, loss:1.4435, acc:0.2087\\n\",\n \"epoch 6, batch 500, loss:1.4117, acc:0.2127\\n\",\n \"epoch 6, save model at ./checkpoint/train/ckpt-3\\n\",\n \"epoch 6, loss:1.3852, acc:0.2147\\n\",\n \"time in 1 epoch:438.03212571144104 secs\\n\",\n \"\\n\",\n \"epoch 7, batch 0, loss:1.3070, acc:0.2183\\n\",\n \"epoch 7, batch 500, loss:1.2383, acc:0.2320\\n\",\n \"epoch 7, loss:1.2111, acc:0.2346\\n\",\n \"time in 1 epoch:378.90994358062744 secs\\n\",\n \"\\n\",\n \"epoch 8, batch 0, loss:1.1545, acc:0.2332\\n\",\n \"epoch 8, batch 500, loss:1.0854, acc:0.2508\\n\",\n \"epoch 8, save model at ./checkpoint/train/ckpt-4\\n\",\n \"epoch 8, loss:1.0658, acc:0.2525\\n\",\n \"time in 1 epoch:377.7305886745453 secs\\n\",\n \"\\n\",\n \"epoch 9, batch 0, loss:1.0109, acc:0.2532\\n\",\n \"epoch 9, batch 500, loss:0.9780, acc:0.2645\\n\",\n \"epoch 9, loss:0.9624, acc:0.2656\\n\",\n \"time in 1 epoch:378.3708670139313 secs\\n\",\n \"\\n\",\n \"epoch 10, batch 0, loss:0.9245, acc:0.2576\\n\",\n \"epoch 10, batch 500, loss:0.8940, acc:0.2749\\n\",\n \"epoch 10, save model at ./checkpoint/train/ckpt-5\\n\",\n \"epoch 10, loss:0.8820, acc:0.2757\\n\",\n \"time in 1 epoch:378.5437033176422 secs\\n\",\n \"\\n\",\n \"epoch 11, batch 0, loss:0.8609, acc:0.2728\\n\",\n \"epoch 11, batch 500, loss:0.8305, acc:0.2833\\n\",\n \"epoch 11, loss:0.8222, acc:0.2835\\n\",\n \"time in 1 epoch:378.56798672676086 secs\\n\",\n \"\\n\",\n \"epoch 12, batch 0, loss:0.8031, acc:0.2821\\n\",\n \"epoch 12, batch 500, loss:0.7770, acc:0.2905\\n\",\n \"epoch 12, save model at ./checkpoint/train/ckpt-6\\n\",\n \"epoch 12, loss:0.7700, acc:0.2906\\n\",\n \"time in 1 epoch:378.8077425956726 secs\\n\",\n \"\\n\",\n \"epoch 13, batch 0, loss:0.7457, acc:0.2857\\n\",\n \"epoch 13, batch 500, loss:0.7311, acc:0.2971\\n\",\n \"epoch 13, loss:0.7257, acc:0.2969\\n\",\n \"time in 1 epoch:378.34697437286377 secs\\n\",\n \"\\n\",\n \"epoch 14, batch 0, loss:0.7159, acc:0.2925\\n\",\n \"epoch 14, batch 500, loss:0.6931, acc:0.3026\\n\",\n \"epoch 14, save model at ./checkpoint/train/ckpt-7\\n\",\n \"epoch 14, loss:0.6874, acc:0.3025\\n\",\n \"time in 1 epoch:379.3904767036438 secs\\n\",\n \"\\n\",\n \"epoch 15, batch 0, loss:0.6885, acc:0.2905\\n\",\n \"epoch 15, batch 500, loss:0.6594, acc:0.3072\\n\",\n \"epoch 15, loss:0.6546, acc:0.3070\\n\",\n \"time in 1 epoch:377.10075068473816 secs\\n\",\n \"\\n\",\n \"epoch 16, batch 0, loss:0.6465, acc:0.2961\\n\",\n \"epoch 16, batch 500, loss:0.6306, acc:0.3117\\n\",\n \"epoch 16, save model at ./checkpoint/train/ckpt-8\\n\",\n \"epoch 16, loss:0.6257, acc:0.3115\\n\",\n \"time in 1 epoch:379.0886535644531 secs\\n\",\n \"\\n\",\n \"epoch 17, batch 0, loss:0.6033, acc:0.3021\\n\",\n \"epoch 17, batch 500, loss:0.6023, acc:0.3162\\n\",\n \"epoch 17, loss:0.5984, acc:0.3159\\n\",\n \"time in 1 epoch:377.6911520957947 secs\\n\",\n \"\\n\",\n \"epoch 18, batch 0, loss:0.5469, acc:0.3225\\n\",\n \"epoch 18, batch 500, loss:0.5791, acc:0.3195\\n\",\n \"epoch 18, save model at ./checkpoint/train/ckpt-9\\n\",\n \"epoch 18, loss:0.5755, acc:0.3191\\n\",\n \"time in 1 epoch:378.4746241569519 secs\\n\",\n \"\\n\",\n \"epoch 19, batch 0, loss:0.5287, acc:0.3209\\n\",\n \"epoch 19, batch 500, loss:0.5575, acc:0.3229\\n\",\n \"epoch 19, loss:0.5546, acc:0.3224\\n\",\n \"time in 1 epoch:378.284138917923 secs\\n\",\n \"\\n\",\n \"epoch 20, batch 0, loss:0.5182, acc:0.3193\\n\",\n \"epoch 20, batch 500, loss:0.5374, acc:0.3263\\n\",\n \"epoch 20, save model at ./checkpoint/train/ckpt-10\\n\",\n \"epoch 20, loss:0.5344, acc:0.3257\\n\",\n \"time in 1 epoch:377.9467544555664 secs\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"EPOCHS = 20\\n\",\n \"for epoch in range(EPOCHS):\\n\",\n \" start = time.time()\\n\",\n \" \\n\",\n \" # 重置记录项\\n\",\n \" train_loss.reset_states()\\n\",\n \" train_accuracy.reset_states()\\n\",\n \" \\n\",\n \" # inputs 葡萄牙语， targets英语\\n\",\n \" \\n\",\n \" for batch, (inputs, targets) in enumerate(train_dataset):\\n\",\n \" # 训练\\n\",\n \" train_step(inputs, targets)\\n\",\n \" \\n\",\n \" if batch % 500 == 0:\\n\",\n \" print('epoch {}, batch {}, loss:{:.4f}, acc:{:.4f}'.format(\\n\",\n \" epoch+1, batch, train_loss.result(), train_accuracy.result()\\n\",\n \" ))\\n\",\n \" \\n\",\n \" if (epoch + 1) % 2 == 0:\\n\",\n \" ckpt_save_path = ckpt_manager.save()\\n\",\n \" print('epoch {}, save model at {}'.format(\\n\",\n \" epoch+1, ckpt_save_path\\n\",\n \" ))\\n\",\n \" \\n\",\n \" \\n\",\n \" print('epoch {}, loss:{:.4f}, acc:{:.4f}'.format(\\n\",\n \" epoch+1, train_loss.result(), train_accuracy.result()\\n\",\n \" ))\\n\",\n \" \\n\",\n \" print('time in 1 epoch:{} secs\\\\n'.format(time.time()-start))\\n\",\n \" \\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "031-Image/001-image_classification.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow 教程-图像分类\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"import tensorflow as tf\\n\",\n \"from tensorflow import keras\\n\",\n \"from tensorflow.keras import layers\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"print(tf.__version__)\\n\",\n \"\\n\"\n ]\n },\n {\n \"attachments\": {\n \"image.png\": {\n \"image/png\": 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\"\n }\n },\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.获取Fashion MNIST数据集\\n\",\n \"本指南使用Fashion MNIST数据集，该数据集包含10个类别中的70,000个灰度图像。 图像显示了低分辨率（28 x 28像素）的单件服装，如下所示：\\n\",\n \"![image.png](attachment:image.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Fashion MNIST旨在替代经典的MNIST数据集，通常用作计算机视觉机器学习计划的“Hello，World”。\\n\",\n \"\\n\",\n \"我们将使用60,000张图像来训练网络和10,000张图像，以评估网络学习图像分类的准确程度。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(train_images, train_labels), (test_images, test_labels) = keras.datasets.fashion_mnist.load_data()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"图像是28x28 NumPy数组，像素值介于0到255之间。标签是一个整数数组，范围从0到9.这些对应于图像所代表的服装类别：\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
LabelClass
0T-shirt/top
1Trouser
2Pullover
3Dress
4Coat
5Sandal
6Shirt
7Sneaker
8Bag
9Ankle boot
\\n\",\n \"\\n\",\n \"每个图像都映射到一个标签。 由于类名不包含在数据集中，因此将它们存储在此处以便在绘制图像时使用：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \\n\",\n \" 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.探索数据\\n\",\n \"让我们在训练模型之前探索数据集的格式。 以下显示训练集中有60,000个图像，每个图像表示为28 x 28像素：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(60000, 28, 28)\\n\",\n \"(60000,)\\n\",\n \"(10000, 28, 28)\\n\",\n \"(10000,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(train_images.shape)\\n\",\n \"print(train_labels.shape)\\n\",\n \"print(test_images.shape)\\n\",\n \"print(test_labels.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.处理数据\\n\",\n \"图片展示\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure()\\n\",\n \"plt.imshow(train_images[0])\\n\",\n \"plt.colorbar()\\n\",\n \"plt.grid(False)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"train_images = train_images / 255.0\\n\",\n \"\\n\",\n \"test_images = test_images / 255.0\\n\",\n \"plt.figure(figsize=(10,10))\\n\",\n \"for i in range(25):\\n\",\n \" plt.subplot(5,5,i+1)\\n\",\n \" plt.xticks([])\\n\",\n \" plt.yticks([])\\n\",\n \" plt.grid(False)\\n\",\n \" plt.imshow(train_images[i], cmap=plt.cm.binary)\\n\",\n \" plt.xlabel(class_names[train_labels[i]])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.构造网络\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model = keras.Sequential(\\n\",\n \"[\\n\",\n \" layers.Flatten(input_shape=[28, 28]),\\n\",\n \" layers.Dense(128, activation='relu'),\\n\",\n \" layers.Dense(10, activation='softmax')\\n\",\n \"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model.compile(optimizer='adam',\\n\",\n \" loss='sparse_categorical_crossentropy',\\n\",\n \" metrics=['accuracy'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5.训练与验证\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1/5\\n\",\n \"60000/60000 [==============================] - 3s 58us/sample - loss: 0.4970 - accuracy: 0.8264\\n\",\n \"Epoch 2/5\\n\",\n \"60000/60000 [==============================] - 3s 43us/sample - loss: 0.3766 - accuracy: 0.8651\\n\",\n \"Epoch 3/5\\n\",\n \"60000/60000 [==============================] - 3s 42us/sample - loss: 0.3370 - accuracy: 0.8777\\n\",\n \"Epoch 4/5\\n\",\n \"60000/60000 [==============================] - 3s 42us/sample - loss: 0.3122 - accuracy: 0.8859\\n\",\n \"Epoch 5/5\\n\",\n \"60000/60000 [==============================] - 3s 42us/sample - loss: 0.2949 - accuracy: 0.8921\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.fit(train_images, train_labels, epochs=5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000/10000 [==============================] - 0s 26us/sample - loss: 0.3623 - accuracy: 0.8737\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[0.3623474566936493, 0.8737]\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.evaluate(test_images, test_labels)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6.预测\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2.1831402e-05 1.0357383e-06 1.0550731e-06 1.3231372e-06 8.0873624e-06\\n\",\n \" 2.6805745e-02 1.2466960e-05 1.6174167e-01 1.4259206e-04 8.1126428e-01]\\n\",\n \"9\\n\",\n \"9\\n\"\n ]\n }\n ],\n \"source\": [\n \"predictions = model.predict(test_images)\\n\",\n \"print(predictions[0])\\n\",\n \"print(np.argmax(predictions[0]))\\n\",\n \"print(test_labels[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def plot_image(i, predictions_array, true_label, img):\\n\",\n \" predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\\n\",\n \" plt.grid(False)\\n\",\n \" plt.xticks([])\\n\",\n \" plt.yticks([])\\n\",\n \" \\n\",\n \" plt.imshow(img, cmap=plt.cm.binary)\\n\",\n \"\\n\",\n \" predicted_label = np.argmax(predictions_array)\\n\",\n \" if predicted_label == true_label:\\n\",\n \" color = 'blue'\\n\",\n \" else:\\n\",\n \" color = 'red'\\n\",\n \" \\n\",\n \" plt.xlabel(\\\"{} {:2.0f}% ({})\\\".format(class_names[predicted_label],\\n\",\n \" 100*np.max(predictions_array),\\n\",\n \" class_names[true_label]),\\n\",\n \" color=color)\\n\",\n \"\\n\",\n \"def plot_value_array(i, predictions_array, true_label):\\n\",\n \" predictions_array, true_label = predictions_array[i], true_label[i]\\n\",\n \" plt.grid(False)\\n\",\n \" plt.xticks([])\\n\",\n \" plt.yticks([])\\n\",\n \" thisplot = plt.bar(range(10), predictions_array, color=\\\"#777777\\\")\\n\",\n \" plt.ylim([0, 1]) \\n\",\n \" predicted_label = np.argmax(predictions_array)\\n\",\n \" \\n\",\n \" thisplot[predicted_label].set_color('red')\\n\",\n \" thisplot[true_label].set_color('blue')\\n\",\n \"i = 0\\n\",\n \"plt.figure(figsize=(6,3))\\n\",\n \"plt.subplot(1,2,1)\\n\",\n \"plot_image(i, predictions, test_labels, test_images)\\n\",\n \"plt.subplot(1,2,2)\\n\",\n \"plot_value_array(i, predictions, test_labels)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Plot the first X test images, their predicted label, and the true label\\n\",\n \"# Color correct predictions in blue, incorrect predictions in red\\n\",\n \"num_rows = 5\\n\",\n \"num_cols = 3\\n\",\n \"num_images = num_rows*num_cols\\n\",\n \"plt.figure(figsize=(2*2*num_cols, 2*num_rows))\\n\",\n \"for i in range(num_images):\\n\",\n \" plt.subplot(num_rows, 2*num_cols, 2*i+1)\\n\",\n \" plot_image(i, predictions, test_labels, test_images)\\n\",\n \" plt.subplot(num_rows, 2*num_cols, 2*i+2)\\n\",\n \" plot_value_array(i, predictions, test_labels)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1, 28, 28)\\n\",\n \"[[2.1831380e-05 1.0357381e-06 1.0550700e-06 1.3231397e-06 8.0873460e-06\\n\",\n \" 2.6805779e-02 1.2466959e-05 1.6174166e-01 1.4259205e-04 8.1126422e-01]]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"img = test_images[0]\\n\",\n \"\\n\",\n \"img = (np.expand_dims(img,0))\\n\",\n \"\\n\",\n \"print(img.shape)\\n\",\n \"predictions_single = model.predict(img)\\n\",\n \"\\n\",\n \"print(predictions_single)\\n\",\n \"plot_value_array(0, predictions_single, test_labels)\\n\",\n \"_ = plt.xticks(range(10), class_names, rotation=45)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "031-Image/pix2pix.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"_xnMOsbqHz61\"\n },\n \"source\": [\n \"# TensorFlow2.0教程-Pix2Pix\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"ITZuApL56Mny\"\n },\n \"source\": [\n \"本教程使用条件GAN演示图像到图像的转换，如使用条件对抗网络的图像到图像转换中所述。使用这种技术，我们可以着色黑白照片，将谷歌地图转换为谷歌地球等。在这里，我们将建筑物外墙转换为真实建筑物。\\n\",\n \"\\n\",\n \"在例子中，我们将使用CMP门面数据库，通过提供有益中心的机器感知在布拉格捷克技术大学。为了简化我们的示例，我们将使用由上述论文的作者创建的该数据集的预处理副本。\\n\",\n \"\\n\",\n \"单个V100 GPU上的每个纪元大约需要15秒。\\n\",\n \"\\n\",\n \"以下是训练200个时期模型后生成的输出。\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"![sample output_1](https://www.tensorflow.org/images/gan/pix2pix_1.png)\\n\",\n \"![sample output_2](https://www.tensorflow.org/images/gan/pix2pix_2.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"e1_Y75QXJS6h\"\n },\n \"source\": [\n \"## 导入TensorFlow和其他库\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"YfIk2es3hJEd\"\n },\n \"outputs\": [],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function, unicode_literals\\n\",\n \"\\n\",\n \"#!pip install tensorflow-gpu==2.0.0-beta1\\n\",\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"import os\\n\",\n \"import time\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from IPython.display import clear_output\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"iYn4MdZnKCey\"\n },\n \"source\": [\n \"加载数据集\\n\",\n \"您可以从此处下载此数据集和类似数据集。如本文所述，我们将随机抖动和镜像应用于训练数据集。\\n\",\n \"\\n\",\n \"- 在随机抖动中，图像被调整大小286 x 286，然后随机裁剪为256 x 256\\n\",\n \"- 在随机镜像中，图像随机水平翻转，即从左到右。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"Kn-k8kTXuAlv\"\n },\n \"outputs\": [],\n \"source\": [\n \"_URL = 'https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/facades.tar.gz'\\n\",\n \"\\n\",\n \"path_to_zip = tf.keras.utils.get_file('facades.tar.gz',\\n\",\n \" origin=_URL,\\n\",\n \" extract=True)\\n\",\n \"\\n\",\n \"PATH = os.path.join(os.path.dirname(path_to_zip), 'facades/')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"2CbTEt448b4R\"\n },\n \"outputs\": [],\n \"source\": [\n \"BUFFER_SIZE = 400\\n\",\n \"BATCH_SIZE = 1\\n\",\n \"IMG_WIDTH = 256\\n\",\n \"IMG_HEIGHT = 256\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"aO9ZAGH5K3SY\"\n },\n \"outputs\": [],\n \"source\": [\n \"def load(image_file):\\n\",\n \" image = tf.io.read_file(image_file)\\n\",\n \" image = tf.image.decode_jpeg(image)\\n\",\n \"\\n\",\n \" w = tf.shape(image)[1]\\n\",\n \"\\n\",\n \" w = w // 2\\n\",\n \" real_image = image[:, :w, :]\\n\",\n \" input_image = image[:, w:, :]\\n\",\n \"\\n\",\n \" input_image = tf.cast(input_image, tf.float32)\\n\",\n \" real_image = tf.cast(real_image, tf.float32)\\n\",\n \"\\n\",\n \" return input_image, real_image\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 539\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 5739,\n \"status\": \"ok\",\n \"timestamp\": 1564763183279,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"4OLHMpsQ5aOv\",\n \"outputId\": \"939881fd-c747-4eff-b995-47d8e48fda27\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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fUe9Hfb5lkYPlEeKlK2oFHPkXohG0OEA3X75DVs8v+xK6w+m6lHW0wQ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\\nCP3/IygAzSxJT6IAAAAASUVORK5CYII=\\n\",\n 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A9n8+9nleqzPi8harLyqaee4pd+6ZfuKJWvflbNcra3t7l06RKXLl3g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\\nZ3EWZ/G6OEsMZ3EWZ/G6+P9DzxmovI4iBgAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"inp, re = load(PATH+'train/100.jpg')\\n\",\n \"# casting to int for matplotlib to show the image\\n\",\n \"plt.figure()\\n\",\n \"plt.imshow(inp/255.0)\\n\",\n \"plt.figure()\\n\",\n \"plt.imshow(re/255.0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"rwwYQpu9FzDu\"\n },\n \"outputs\": [],\n \"source\": [\n \"def resize(input_image, real_image, height, width):\\n\",\n \" input_image = tf.image.resize(input_image, [height, width],\\n\",\n \" method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\\n\",\n \" real_image = tf.image.resize(real_image, [height, width],\\n\",\n \" method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\\n\",\n \"\\n\",\n \" return input_image, real_image\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"Yn3IwqhiIszt\"\n },\n \"outputs\": [],\n \"source\": [\n \"def random_crop(input_image, real_image):\\n\",\n \" stacked_image = tf.stack([input_image, real_image], axis=0)\\n\",\n \" cropped_image = tf.image.random_crop(\\n\",\n \" stacked_image, size=[2, IMG_HEIGHT, IMG_WIDTH, 3])\\n\",\n \"\\n\",\n \" return cropped_image[0], cropped_image[1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"muhR2cgbLKWW\"\n },\n \"outputs\": [],\n \"source\": [\n \"# normalizing the images to [-1, 1]\\n\",\n \"\\n\",\n \"def normalize(input_image, real_image):\\n\",\n \" input_image = (input_image / 127.5) - 1\\n\",\n \" real_image = (real_image / 127.5) - 1\\n\",\n \"\\n\",\n \" return input_image, real_image\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"fVQOjcPVLrUc\"\n },\n \"outputs\": [],\n \"source\": [\n \"@tf.function()\\n\",\n \"def random_jitter(input_image, real_image):\\n\",\n \" # resizing to 286 x 286 x 3\\n\",\n \" input_image, real_image = resize(input_image, real_image, 286, 286)\\n\",\n \"\\n\",\n \" # randomly cropping to 256 x 256 x 3\\n\",\n \" input_image, real_image = random_crop(input_image, real_image)\\n\",\n \"\\n\",\n \" if tf.random.uniform(()) > 0.5:\\n\",\n \" # random mirroring\\n\",\n \" input_image = tf.image.flip_left_right(input_image)\\n\",\n \" real_image = tf.image.flip_left_right(real_image)\\n\",\n \"\\n\",\n \" return input_image, real_image\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 378\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 6537,\n \"status\": \"ok\",\n \"timestamp\": 1564763184103,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"n0OGdi6D92kM\",\n \"outputId\": \"47787177-8e36-4b7e-8b73-fea91da61e77\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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mmhiARKr+yuLtK+DfNDYLbva7XAkBwaG8JAs5NYskJyoJU5YYDUg5Q9\\nQh3Cpb6ftMQNGPE6RxNx7KGysMKJ+Sthza68xgGv8FT4Te7oq9yWi0T5OGI6Dd/JMz5RkmLsqHvA\\npaf/NgVcdetQQYdj2vIGVxcvYxK5xfsItmCjV2CCaKb7vtdn/+7yGCrzCdM4JeHEZw/8OSpYxuQd\\nrUpQ3EWNlyHuzdDB2F8Id44yQ/AcmAGrY8swDRA7mMPFpxvSnt/t6FaDHpfNi1OhmNTItzkr28Kp\\ngl+1/RWeuVvRrQ39y3npxugCVqseHBM/1TXjvaY6IacuulGefmmiICliOpDzQOwbwmxkoOOYY8mQ\\nRhdgu79Pf99uPNxzwm/9aZqQ4+9nnX5ukJ+Usoam8XEWdjn8/HV+9Y8OfZzGlbtsRl2Z1WG0DzwB\\nl7yGThha9PoSvn4T+uRYdN7K4h0GeOMiGybL6B6xlcfwAGUqQSGTw5pjTyPGwtYs7DY53iE0R+6J\\nb8MpJ1TC6YzMe41BmXSEX2dUyBHEc1lCI1gxYspoPkbCDCQjwbxOi276xb3hapU/BHkMlflonW1n\\nd1VLwbYtBo/Ai/nq6hFq/OWpIRIRTbVqqdUgSI9RHE9sxLm8oaa7ywCtIU3xoGEBaXoIEbExnKQE\\nPCusSASdkUqDpMCQCkNcOdZsAalpwGO9FgsV15+sny2R5CweDTUzrT63ynY1MZBaDMioWHkck2Vr\\nlwx+HWudPx4rPqge/Zx3DTsd6OAJgHZcz0vxJC4JZ3+vgVdfgDYMiNMiBhY8kQkMtEG0Hjt6JrFa\\nPOouvifihfqKH4Iy+R4lmjlDCbassdFoqJbimGBpdir7W3181sDihkvNZkEcUarxnY6KK+1Ot9IC\\n3LwGt3BlMiXT4fBbOoJLcOnCVW7hCFs7wHAD8jeugrbeyFDcC8OVqoV0IvOz4jT1vbGZahohGFJk\\nswiPcOAop2HEE+fXLzKgMW0gEwsM2WHKko/IssKG7DAm4nPHQMQhVlLwwRvN3ZEYYdYDd/wmI2xq\\nLZSGVvbpV8dALfchiRAHrGTP+cuBYmtCUtR6JCoiPYEeEfV5gyF0W482mkGngrkPQB5DZX4PkYoD\\no5g4hdFCh8VjiEcgkRCjB8LV6G/NCRoYusyqi2BrUlwzCz44Mq5g2xBY00EH61tH5NVAyYZ2hWAR\\nwdPZNRQa62hMCZbcO9gWi2CpZi3XjLZJEcjU9hOTcXJpz4AXhM05I/0s1pow0/WpC1U9zmo/WZzY\\nUzE26HrB6uWe9Q3BbEWQhthF6J0XP2bYTre28K7v7kFkNj8yLbgy3pea/Wo1WCRj5UZzBTIptPE6\\nsX4+nXD1aCjzMiJ8VnyCY479+l/ZZGGaV6Y84YHFyQt5l4t+CpQ+oejh3a7c9ng5gdAFh8r6FWkA\\n6QsikRDqWpMD1iePdwTx0Mt0P4O6VNl4D4VgqTbxXZjGloKW2swakJ/GgWwfzWTOGBgNXuE0ElVo\\nbZekgWjFx4kMBOuxELBQFxYzTNUNlQXw3AJKz7wX1u9c5+rHn6I/WNSAr68xKUOzhDf/6C12P/Ek\\nQ3Bl3P9JT1lW7J4GWNexp4gIQbbBz8CUM3LCwBzn44OXx1uZn7DObfMb4EqxQLgJzTeBVK3piIgR\\nkrC+0cFtH73vrAwaZWc2UGwNIkgMaMm0kjhsXgUTjg5fpcQOK27FpGbhE4IEIdEMF4i1tKyJoqFg\\noq68bTMHTbR+3lJKo1U7fd96tGkOWLW+tg/RjUU01ZLYXGaCY8ZMPyrCJwaqiCZimJEPe+wouzdS\\n1mh274IaMD0xdeXd36Eq9crjn/60rcinxaRG/ScoaTzaa2HaNp4pW+nRp6GlH7ZU5pDHtasxMcU2\\ntkWdw7wt23DStCh8txtuw2enWRNnfcfHS85V/Ygn6/aQ84AF9UDJqXtPgeuqrHwB3sCGyqn1xmRT\\nDwW83g9jn5wt23NhWoS2xzlM9/O6KB6811PHeDMzYX/B3nMQtGW2hPVyQFs4Coo2uFersBiLo80K\\n86sQWrfM+6+tq/dR4SY1TGXatUzUfPydqOc0MuoevjyGynwLUjkxqA0R/y9EI2hB+0P29/+E/+m/\\n+uv0lyKxCWgeUIwVmXY/0ZeCBSNJS78+pl18ky70vmORFmYCUSP9CqIkZrM1gzkVMq5XXEq7mCaO\\n1xmz9/OL/+FvMeQByhptB7IYvfVEBIY1lgysQGvYSPkTKksmeDAyJrDsppNVeIgWVLGQiSFSSoYm\\nb1l75tjoONi6BMx8U2vJDtVMC4e5BS0ZEl7SM1eOsXgiyoQlxroAeK7/RuyM72MQekyiqBPUgnkA\\nt1itVIlHlUON/EuP6Vg+du1We5l7X1wYrfKaMTsN6QefLv29iJhi6mOkBGo8w5XTBKUJ9b0U0HDS\\nOhWD6GwsECjtSQ/l5M2YAvqWeBet9azvo4QFdgwX54EY685nXcOqrL1dOIQ3KfHYM1IEXcF580w8\\n9KQn2mdMOFHYUHYt2KYNle+9qVG0eeQRjguOgGJSyEHpwooivoBEnQEzhy+xEwu+iGCtIU3vFnQ0\\npBFoe8wKlpyZZgIhRX9OCmjv6Mv2s+gAdBXqyQ4fieCVV9JW20clPmLm1bCcsPxzauJ9yOlRrtPv\\nquoUQQ2YeibXTvsKT179NeTpbyGxJw+KRqGIECSBGL0dEWlJ6hZWh0JsUIGkhUagVyNWbE7FscRZ\\nKkR2ERp6HRiOfoIgKxbz50ji9UtmmpinOZrF9w/cUfae2KVvB7I6zJejYNbQlADZSCEQ95zXG2tC\\nWklu2bU2I98oHN+4w+XPPEFuINnJ3pgB9hq89ccrQkyQEmalVpAcKyIaUi1KDWMmm049LEQQJ28q\\nOCJwytp+d+nUTTDJ53Z2478MhBixWK0qMa78uTnLdrbhLuMKInCBKLD+7bdhd5f48aYGX62SFZT2\\nwcOR37ME1KGWUWwrMLwNjViEkBFrp0M3fTiaqAkhb43wgE1KYdtr06rMRk+onl4D7tvfIVSoDG59\\nu2d4q/cFhQZbGSHP0RAhW6Vtb3l2pnUc1EWn/k1jNUBGKKVmQp4Iao+eBnLSSxk9sfp824MojLVN\\nrD73qZiLW+mJRpUYjigMTiUUowx30K6hux5pU0K7AF1PDGsWFIZawiOZMAuJGN2IWN26DevaBl37\\nQlDtI9VCCcdYPMZkqND+GBN6OFDKveQxVOZ3k4jXDp+DLVBr0WHNrr5J1DewdIjSIbEQQiBIi6og\\noswlI9b55AjQBAESAUHIaMk0qcFKTwyBrJEmNfS6ZO4wIk0wSv8qT4Vj9o6P2A8NgSXGioZDrvV/\\nRGn2uFUannz6R3njoHFFizBuWjLvenYlceutN0nPXKXXQoleCEnEbzTrBvbX73D8xteYtRc43g30\\n24E3oCvGpXSL58LLsB7QTmlCM1mEo4IYa5YjBavBUjf0s09iBKFU7u13tzRO1qDeLAwRQXslx8gg\\nxhCEubxEH0FDIliomKZgudCmSNd9hf1mB+xZsnm6uVt7PVEMOPg+x8uDEc1zaGFI48YdhtMsM2Zj\\nDKcqiwKwZiQzaxHHtKk8wgI2FWuqbKy6uYl7SKFePzKit3Yq0Hb6uwf+BTqPDRFxSKRSWN2z62uA\\nHSaVUdw78/yILcWVhDHVfUJxLDiXVdUx9TTz6oYKjOfHLQtdqqbXWsa3llrOsQZxSkRSwPo1hY4c\\nB9baUcJA0AHt36Z54p9w8eINoqxYzBK3hjfZe/IiT7xwzQ2kDr4yfIXLF69xZQcIPqpFIvMSCY2x\\nvvwdPvDcR+p7gu6THde/c50Uopd8JtCIcTG/g9ku3foWs8UeEq2+zBHWGhfvh8dk2Xozj6uME8VB\\nyhgTqgEtgUTDlSZwJRoXF8LbGIHIPAY6/EWFIIQaJGxkRk5uFQ30o7ojhUAJLUZCYuNwSXTMYRZm\\nNQ5uDKy4ujPwtP4RT82usbNqmMVDombacp0Phj9gV4x3Fs8wKwfcbF6gT4ZkpejgZWXTgCyX7Lcr\\n8nCTGASVFqyhoaUMKy5yh117GUuvccVepi97mHlFRhNFpGMhcK18lb3h8+ywIqIEjQSLE23SRAnV\\nShQbcI66P3WpNSisMgxc599vwHFcMFzZRAVVfzeFyFqNISYOh0tI3KNLgUikSK3gV5SdaFxuv8Ve\\nWrC0lmwLVL2utlrPXJVHRZnHCFnVs6wu1DiAZleiUyp5cgL3qmCzALb0n22+5aWLHz9Ej6qOeEYz\\nbrwyRRY3Nz9ddOo05j55BpEpu0xr8LjuHmWhsljgFEwjY7DD22XBkS0boK0laaf1PW6uMSQkFtR6\\nh/ZGdskUFJbKKFGYJa9kSoUYx3/Nk5VijERJlFI8sxtFc4HQ8Tf+00/z2Z9ZEcMxu/PEqrxDLz1l\\n7vTAJjR03ROkhdCVI2JKdAYpRJpsNDKjXz5PmMXJCEm5IQwfI5pHbkJqGJbK/LjHfvcfI9EIoZa1\\nnoK/Pzg38fFU5ptoWCUB+ajKNuPW7cTxDeMChvU3+flPvY8dW3JIrMh6IREZKJWjIa5MagHYgZ5E\\nRFEiG8aGUYg0DCgNbhlJtV9dGlK+yX/0Z/a5uhI+1C6RwWux7NLxH/944M3ZwDfnma/Kt7jVzVhZ\\nImYhtIncrbgw3OYgKM2s453hFuvYkGkhRyKRxgaurV/hoxePeOKzC76+/H2u6CUKM4o4XhdZ0ZTM\\np/be4lM/vmRXV5S8ppVmglhKAMiEakWUkJEti8Kkd/xXBQnV7d/CNcegz5hIOm4GEixMbv5kmRtO\\nBY2JIg4irELiny//NTfKBYbo7nOpmaUtAVmu+MyPdOT+kK8tbzLEGWYz9yhEmWUFXnjAg+p7E4mZ\\n0ET0Qs/Bj+6Rd2Gw4siGhVqlULig8PaX1uy8OGfY2wOqXWdj3zlc1n8RyhtrV0hNYBjWfp9UKa2M\\nHpCeTHqEu373NxJQiXUhp84hJVRlFGq27bgIjywUVSM0CbKi7ZInX7yIPO3IRKz3UvwZ0gDv/N51\\n5lcvo1fnIEqjM7dhq85bKLzz6i3aCxewuZAtES0TLGDSErLSlMTR9SV2R0jWMo9zZrlhkIYkicW8\\ncO3yr/Pc818gNbdoQ8Nxd53ZrKHLkZQELY6fK5FGjMEgh0gwmGFQlOEAD5wyH3sMy5kgQihGagK6\\nugo3LtO0mVIE04jVjO8NrHgacrmvSPZ7lsdOmcsJNzQhDIzczp59Pvdrx3z6lrCrS1L+Nk+/GLlw\\n/A5vMUNp6KynSCbQ10VgGs71+q64BcgnVt1Cj1upxWoFgADH4hasoVwsb/KZ7p/ywrIhf07ppDiJ\\nzt7gL5Q/5Jb+Ft8qz/ENucZL+gnWIeAo6ZyEcWG4zgW9zYWUuWl79HEHlUgqPZr8yCvrV3m+v8ET\\ntkLzMV3acwJZGGl/CnnganmbXd4hyOBuYZ8JZFSgSCKYEktBo3DwwWvEeYAA2TISSg3khS0N4bho\\nUkO0IZTE8Hamv37IsG90JRM1VgalghaCLlh3PTvSEnMgUIjZOE57/Nn+IvnIJ4ZuBeqiDQQt7Eg/\\nXcckuWIBVBJBA/DXHuYwu2/Z6b7KhXLE7ZTZs49zazBCYyRriDUAmqxnMcCF/tvszj/M9Wr9Sook\\nswoxKUkzF/Mb5P4mjRptLjTBF9FcinuTWzj5/abUa6Wijmn9zkRK1fCueIltPDOoOLgYZoXcKSEE\\n3m4CMb3ArcUlNMkGthNoCsxEIX+Fg70P8c7TVzGJDAGyKCE7XJR6oL/BlQ9e4KZkpGlIFlH1YPKl\\nIbA4XDK88Ye8EG6wP3yThkOyrQgs2Q+3uXB8gz1taNJNUjj22Tpb0qsySzsUG0ihuEdYufGtRBoS\\nKkYuHU0IJMVx97DjD20BUkbMK6uqRJq2QbVn3q8wSai1YAu8MuJWQPSEjAr+wSr0x06Z+/ipOKKF\\nqnY3rmGoke5xe6eoPvhFhRA2O4FAIm0la7y3cIZtgvc1om1AMCUptDqQtFara4RBjZjWHOQ3EdZc\\nljf4oL5OCUqjgaG6oDM7YqYrZqxZyQ4qLRYiTenoQ8IE5uWYg3yHWV4joTD0863gY+0FM+AQOVgi\\nc8GCEMsY+TeSDQRwyuVRpr0C7HcgPVHUV6tpf9SGTQJKjwO7M+jnNN2afGPFwQeuuus9i+5WW3FT\\nTXfoXhXWry5pitCgRIygPU1eoyRMpCr0ekvLRFMaMtEyZkYJc6RilCU4c+RRkY/YN7ikX+Mm+8R+\\nn/3FRY7UGAjEWhSukWMuW8+l9E2640TZvQp4ILHRylW3wiwrT68/z0LeotHMnOI7/kANQG9zte2+\\ndIXvrmWMtXoET8DabBFYqqU7iqq3AAAgAElEQVQe0a3gq2cJO2buCHHgtXCNVHqCfJgjC8QyJttk\\ndoo/Y0ivcdVaVjZjnRvfdk4Goq0AaC3ThEOke4udCxdYWcZKQ4qepzEray6W15kvvsmLh7/Hc/Fb\\ntOsjWjHEDtlZfY2PzAYO7IBGlxBXzFkgJCy4p93IzD326LkRqc7uocYmJDYEGmII1V+X2hce8A0C\\nsxjpcAv/4mKHWbnFPMxJ1qAlOHniXVb5g7fGt+WxU+ZYqIuhuziefDIWmRIKM5AWs8atmJHzPFqa\\nuEs5jtox6LepmDwO53dz78bXVkTrAhAdRiQDg++dWdlKgjoftzdmEdCBMAxczSsuh7d4vvtGtY4N\\niYGsQKgW2xaXVtT3APLLVtof1NoUGQsRQX05MZlYKYMYi2cSXJvX6xoWCioQrWKe6yssX74BjcHs\\nFibFmWRDZIMF1g20LaFSkJjJdDQhkxuvM8NiDfvZi5QhdTFIoDdZvT4gYapvRw4QpLCb70xFjZyz\\nvLFwnMuwmRiqA7UIICpCeISU+V/98X2elTvcWsCXV7/BbS6yjDN/TnPOfOKY/aHjfc8q3yp3uLW+\\nAkC/1ilZMaA0BX72xTd56vkjFpqJ2WMpQM3WdXG4rDJLRE9AXWMJiGnvSsYqmtFhM7RWUBwxbPfo\\ngiVUMqPKiHUNKKEjK8Q05+X5mpf5Kl84us2qmSNVmUcyO+WIi/2KD352h+v9G2ie0dmCoYsMUWmL\\nw0XX6Pj4Cy2H+XVeX95mFmYEmYEqKpnL61u8mN7gwy9lPvNczwuXCk/mzvnhMbPfLvlrn25535UV\\ns6QV9ByNqjEu62HbVGHSgUIiECs3KyAMDMwmeEXrc3hfJ1L1ziPzJOzYkvnRO0g/MOwYxxjH3YIx\\n4DGO3CkT+CFY5fAYKvNEJFvlQ9tYq9lDmlAVgRWSZqfs1WCfiUxHCF6JuFRtIgjhXZTHs767gjca\\nHxSWnGVSfw81uULF9w0owVxhDR4oc1qeElFmda559rTS1IS2Ys6hSbYZEEWEYFWpWyFLRKJPTq2W\\n10jRUslQ3LIgCCTz1PggMDJBykgt6yGVCmi6xWYS0RSJJh4gm5TCGJkINOYclUTBgn8mCdoOYNmt\\nvuz9JSFRJDOEQCYRKBQRJG3RIYAN/kgtAralvNCRhEEOgVm2R4RlDh/Vf8bPrf8XmnVHjpFuGVmF\\nBcKAmHqijlLHYaCjoRcPPJfqkY07/wjKXlmx0BW7+Yi+CRsDovWCtQUvIWEaagKNEg8zTbtg58oF\\nmisJTWtfj4FgzlTyG9TxrhEzIUlCsvDWt99ElqW2qSpz85hKSQEJARuE3F9kxZwbx1ewtNkGMZVI\\nZCCVNSHtcl0W/Ih9mCHuEBV6aQnm1z8YbtBEuJMDb7bPMcjMPTnxZLXLx9d5n77OB+xNfvT4D+Bo\\nzfEbA70ZTbjNzL7JzxG4XT7GO/011jMqyJmJZiQdpmfv2YyiwWf8CbZoz1ivYtQMGSWRCUQTgsyZ\\nd0suHBdWv/8HHK8CebbDK0E4bj6Bw7u+QAKMxeIcCj7nmd+HjJb4tvqtnVktW6Wh0JLFd1nRrY7V\\nU4EbeO+J4XKC+0t9cWEqEqQImRkaBtCExEivSoigsW4aoIIFY4iOY3o15EAx9XRrwpQUoRIovv8b\\nwSBLtUVCnGp9nCgdEDxzbRYbCA0Shkr6cdd54v+idduw2iEivkiUWtpi7CcZFyubEmEAn+TWQGzB\\n6o7wgjMSsoIkegLHjXAcFpi4rYRFStApY/D0sqnmtXM2j+O9ahIoEmgLZxQf/eFIaytaDtnLXTWN\\nDbO8gY3wMGOsY6UAWp+tSA2QTvx+I9RNxwNeH+eoIhlXPnOAzTo3HqRS+EIDQ+DwX2b6fkV7OcAz\\nhsyWtDVQHUrCN3S2jbNp/nJNwdYLurcy+wfC5ffvb02GulyOrA0t3Pqd17m4hGfLK07vrXBnkYCK\\n0oihHXwoJD61/m1QY0ZPoamLPiTt3WiRSAlztARaKahmcjUiIl5GurGepbi9Mct1y1MKs1hodOR6\\nB8YScJvgPrzbGDs5Z7ex7qRjtuqGK2+TxR0QM5JWZpZAEDuhfXIt3Uut4WREThXheSDyCCrzu7kf\\n9xfNUccwcGdqvFbdLif0ZAJD2GVllxjCHl1ssDgjkjGEAZ2y9Ea8extCuXfLfZstt4K1WgQDkZZc\\nE6VLaFizy17awWJHES+Jm5vAeliiZFKANrVINNbWV0N50wKz0dLeCnYBIhnEKDXgGwnM2hnDUIja\\nIOLBmlAizSwzC7VyXgPTdltC5fdGiNEr26YZtDPPICXUAokOl0g0okTGDcgcP1CgZQiKxhk0C0iF\\nmCoFThIOBieG2YKX28xSdumsoRVBbJcuZMbaz2dV0XUc19kx0ZwLXwRKkEdKmesIuVkiqC+QUuM2\\nYw0+CToN+8iGMZKsTNb7dL0ac57K0o/SFNfuzNx1ieZK3QISXYkNBs0E8wWv9jlyuhkX8eBGT6yZ\\nxaIUg5KCF6VKo4U+ELV6XSMjKUNCaELxZ5RNiWUMpChBEqZwUZf1RyUzeFvYsKGiDkgZMDMacfpq\\nTyBVNRDU+2Xu5VpqTblaH2VS2r5nktBUNo7Whc7v4SDI1LP1Xzv1/aRsB4HdgFDG7H/fXs//0ymh\\nSSvTawO2POg65qM8gsr8tNK2U//e61gcIJF+w5dlK01dE9fnn+KfLK/xpfbDkL/K8dEf85eblzC+\\nVC2g3iP3sq0+728hkWqtlFrtL0om0KI48+Ko+QD/752/yOXhY7RcpTAQa/q2SeF2vMVq5xbtk8I6\\nHWNloJShWjZjMEXxxJE0EUmiZUp1zZPq5M4hA43AXn4Se32X3XwRW3tRoL1wk58uv8sH2zu0+S2C\\nvuWZnpUNESxwLC/w1dsL0rWXOOh+g2wdzXwGnXkfW0SLB8kCns1JMUrYpeMpviLP84X+Kr/QvM2s\\nfJVZNtS8iFOSjp4DPjdc5LV/7z/g6KM/zisX5r54aKzexdixWmGk0yNBalGy7d/cMv9L9/XGHr74\\nmlvIcUDqdFMzhhhI6lm3udIURwknClTJqet5v0SMEgJlLEQmDRIrKN66stcCiYYia6KBl7+Kk5Ei\\nETQoomGinppo9Roioh4jaSLV/E2TOxFjh2nrOmowpIQaGAzkXCBuNnIoobjiD+LemhaSeXwmIyQU\\n0Y0fbBTMjBRqdrGABNgt6s81Yv8m0JvT0hMUM7eOEwi5wkbu04o5zOl7EIRqHJ2Oe727vwGG8UEQ\\nXyPxayha42NGEGjNKyWGacFQqJRlJbiDKw3RHIa1M3Xa9y6PoDL//ixzKLWC/rju6sRnUWv4WvkQ\\nX//GNZr4EYbVC1ztlU+WF7nCV4j4oLfJsd1+KaNsu2PvXmENRak7rkzdq0Qyb4Z9/s5XP0Y7/Cwz\\nfa7ul9n43aTj1eYVnvr5Bb/wiz/Pa/t/xGwePSvPElZd7pFemMOGry1ktBYbiqaIto4fx0xjkQtv\\nvJ9/8Df/P/bfvEo63qGPkQvpLf67j97hqfIys+GQLras2POAkbnb+zqf4XOvfJHmIz/GS7yMpTXL\\nXGi0IXKLoD1JPPnjju2DNIQ40NkBN/lR/nX3Sf72l/bY+akPcDD7Du1wk9BldvWID3VfZPed1/iT\\nr7V8+UefYvnEj/LNAyBCtOK5kluv/CzrfJsr4PuZBk/ouI99HX9QUqSlC3NWTWRo5hTzMTIESKZe\\nu4VECRvYRXh3+0e6reTkcQ3x2ILJHQAuS1O1jHqdEa34r/mGzIMNyFQXxe1I55TLiZk1whASohtE\\nIVBsjOfEjWVrjgYbDqdhjW9yoZEylVysh0qhhHEXe492qyolBJTo72sM6IszyFQzGhuKqseEwA0F\\niWiswd1Sd+uJUEJfdxJrUDpC3QloDJdPmaaM27fd/xjZAC82xeJUNoiTs/GFLJFCi047nY2Q7/he\\nN5Dre+XH3Y88gsr8bnJ/lrlLmP5/XAE9WzHRh4a1XgaUEjNNuEZvF8nWIlKwUq3ekE8mtnxXNovf\\n0d2uhqC1VnrcwcgoPbnschgukpoXaPqnKSEwphVZXMGlHW7tGt+SlpsHB2TJhNm4FG2UOeLPo5P/\\nqlNrorpyUHFrf2YNt29epN97gVtvXyE1+/RAlh2kvJ9mVZD+DrL3LDd4hkF2QQJK4Eb6GV4tLW80\\nP8tCV8TknkJjkYtyg4Nyg6Z/jRh3OFx8gpXNUaCTS9wJH+fl4Wluxj3+67/3ZXbsDdp4SBw6DvQW\\nfzF8m0+uv8Kty59CuEiRHQYpBMwD1nJy67FtNsv4rHVK198CiFs/8cHvWfY9y8rmHM2e4K2dFXfa\\nHfoQyV3PEN2KjAZYM+3JeZYihw2s1OQZUQSTY3KAi5VS+oIkzwZVgRA9Oz5Hr1sTdgmzJUNoaJrg\\nSRCSkSCesRx6agUrUpNGzM4rTMmc1KzcYwpNZSQBoYUhIEGcmZQTJTYMaZehS2QdiLHSG6Mn6OSc\\nawBVaYL4ln914/Lt6ok2XhNqeYGKT8/mrMqABiUrtMxpmuQB5DAWWpuxZkkuM4Rm8kWKZI85IDVG\\nZtXw+u6QRzgx58tUbCySCCFhMXEcL2DMKOESQ9itGaACNFWdb3YcencU6LvJ/Vnwj4gyr6vYiZK1\\nQNiqfU3lMm8DhTXl+KR4B27oPyPcAlghlJ4YE4MJ2EXW/cfoVz/GcATaeqBovOYQpMaCKrZpp+5T\\nB5mOlfCmOuONW9Mo6/kCNSMPHeXOh2B4nWyXyGEH6j6WVrFJckCHhiaDdVdhRxjUkxSmR6sYXSwR\\n0ZpubWAVcihilOqmxxJgEGeO6CVK2afUkrU5X+Kf/u9LbupbvKi3+dXm3+H/av88d9qr5BgJJqxt\\nRuEj/L2vt8z5y47Vh8A8Z5678wf8dPdFfjbd4jXd539d/Aw305Os02WWQRhY0OseEp7njeFTkIxm\\nWNJoz8HwBh85+kd84vY/4vXli1zSTxKPr/FMdB2So06TwSo3dAwYim2y2ccuieauPBZoszise/U9\\nDsGHJP/szk/xxwcznvnFP8e39jOHC6PTnhyC00dhYji52ImpbpPycYlWsfZQWAyF53/vdwD4ifWv\\nkcohMRi5OAPEaOjLRT43vMStO0s+Iw0fse+w6L9KYMCCU0eDycabKQNIC40xrDuCPcXvvdVw1Rqu\\n9AOS3BOwWsYhcAnNc/r2Sf6f1U/S64c4ygfENnKYbgKwevYV4kxYdv0EAVroKOLZnQpo9RqC6pQK\\nb5ImGDEYNPMF/VFmv71E/5Zx4e1r7NgCsVjrtkAsDX25wSfiITu8grBCHbDHUErdUNmXsTE0em8Z\\n34cr9VrqQCCQGLjIO83z/OPuRUJ5kmjP8Oryedbh0nSWl0EwINf3ci9Kxb0U970XgUdGmTsEpngt\\n1cpjnguUJTStKyX1Y8ctm8hz0Ll3VGU0bJyfDQdgw2bxuuJuxQGzPfLxC/z3/80bzPYKKiuKZcQi\\nTWxZRqdDWXYXLYzl70JE1EtlphApsSOaejlNEzRGsvY0pdBKRlJkUKPkI9r5x+mHC8AcyB7gJvgO\\nKmnGbjpg3sOF5S7L6tVRd2UbYcUej3eBxxIRr1Ok2REmSZCLe8Rzg70MyALaObHseZ2TYrwTP8Dt\\nODD0wst8gsPdT3MnXMRMKibbej2UAEO5wlCr1fWxZ70jHPYDfdOxtku8Pf8sh+mALswokjAiwozC\\njGALVAtDCgSFrAPvpAO6xdMsm8/y+q9ljl5dc3MBEjPDfMk6OHRkohQTEoLkQBxmDIuBLMXTsUXr\\n3hSFmTTsHs59q65fuvCwBut7kl/5xvPkfeMvzH6Sm+875nDniFyWFFJN2FGEAnZ353/b0WhGHFoi\\nu6vAG+tvA9B97RKtvsidvffxtdknubO4xKqDGA/4H77UMSy/zX/20pPs8UWelJsOZxQl6B32ZEUh\\noRbI8QKDzV1Zp8hx/xH+/r/5Oh/78JN88McKC3PudWGgsdvsd4LkOcfNNf7uFz/BjebnOMxPspCB\\nG8+/AsC//7c/BYsV61xIxRW0xt4Z3cHcXraR8qi1pIzDEb5vR/WSG6E93ufy+jn+77/zz9HfP6DN\\nyWGk6HGVaNCFl/nP13/In42vMcO58Q6djjU+nT80FvtwuTubhekvMpW4iKYEMVZygdfLi/y3X9+j\\nu/NhFheeZCgLVhiB236ebd1j0lN3U8xn/X6vuOFGHhFlXpuhWw95EDj4yfdz4YMNw9yVE2ys490M\\nqz8eeP3Xv8W4sb3vLiGnrPWtKWKc/JvMWa0/zO9/ocPCc4QkaHa8m8WaZ3/yWfqYGSgModQ6GjAv\\nLW9+7U24uYZ55PJnnnYWjBlJnJAlIpRXj1l9Y4Vog1pwLLufQ5iDCDJOaAugEY6ucvNzx/zDf/Ft\\nmK9xsvkSZpU6GKJr7L6D/rLXOF900BZIrUe8cvH+tB04nLm7OiRYH8Awp6QxO7ZhaVc5lpt09jpv\\nXXiKG1cWfo9tzC8GyNCvD7wGus1Q6zmy29xunudb8zvcuPQUNw6eZBkvOO1w5IGLQFHstc5/lxmF\\nlkEyfdyjCwd0+jG++juRG1+87bedD+z+7DXWodb3q68vFUhdZP2Nd9h96QlUnOlA4ywCMWV1J3Dr\\nN2+4mftL3/egfCByrE9VyoUQ8g5pUGc8JK9RHswt47FkwVlO/yY46Eaeai2NwIJZfA6AX/2fv8wT\\nQ+Z3L3yMX5l/lLfnz3HcK9LssR6eJcjr/PJv9/yfX/oA+/1naLRnbis+rf+Gn+p+h6Vc5DtyjX8y\\n/2lW4RLSQsFYdQd8mTlffNX43X9wTApumTcmfLT7An8l/yuaXHgzZY77T3MjfYxjLnEzZNjfB+CV\\ni0/C7JiVZTcOrJbonVySk/GnyTKffql9ExL7coE7Xcvb8+cY0lWEhqhtPVi9mKPs0Q+3oH+doTlE\\n0gz63jO+EXLcbOO2cYjGYIGe/M4mjhBMURvfk6LNHMqT9PYc1+UZiM9xbPuVXnsdrV7AmJHuBaOr\\n9XUqbL+R9wInn5RHRJmPJK2AP3iB3LNue9q9htu1Gpvzbp36FzpBF7iVLjPO3GX7u4kaGtr6tubo\\ndhaB3mbVJo6J0AhDpYRFrfBcvAx2DCYsZzteEIrqUxTz9OMUMdvBSgskrBSkSeNuVoxQzXhT0V2s\\nb2A9wFqhyVz6wD7hsqFmWJPQkplb4OhrS1bLI/au7dM+sVMr0iiNCSkHQtfw+it3sFApg0PjdDTp\\nodbdMCpkkeCZl57nfU89yyq004I5xsC0wOE31tj1gbGEp1okIywX+4Qnr3Hlg+9jnhbksFlwU3C2\\n3BvvdLDSyi0fa1ELmHs4IbcwzJyPvj6kDA2lGTNyPfqmxdASqMkBE/XLKqU0WqQp0A0722TiH7p4\\nKeUZez1wKzAfLpCD75FcgrtesUTyVpPTqE9OkFp827wQoBQllcBsgMXwfgCG9EF0dYtb/fNcb1/i\\nrfI80s7pNZDCHLPAl1+/xdffbNixK7SWmeVDlsvbPG9f5ZAn+EK+xm9c/jHucAFpgwcpmTGEq7x1\\n+5jX7qwxem9jKVxf9fwEX2e2vs03wxwWT6A690BpjFAzQFPeR5qGXEqtEVShy626PqKVCRbKGQpV\\nMAm0MkPWM1IG0RmIW+UZh6w8rhLAZuT+4/TrXUJekpoZ2i0Zg6EaahLhVJJWnLoIbLaW3EieOPDG\\nhtiiiC7Iy2v0q5dAl6CeFhjHlOYY3ODajAbuqchFHVoen3vczOLEIXdX2Y+GMj8JEgLOwA/RucN5\\nDpo31gsYQxFCTMTYUvrRimzuO0jsGzHUzSoibDZBNtdeAboW+iRsWFOKmqBFCMwcHArqtKjk5ykQ\\nYmTI5pmOKqCNv8foOLqNuwZt7+ggxUsPaAvMkd6weETXtoSLrp+KgFiiX/Z0iwRr6HZnDItAnEey\\nwaoMNBpplmCxIQxztNS5pRnnrTlG2gVjHQu9ZYaLPcvLPcsYKzOkJ1W+uvWK7ayhydBLZeALIsbt\\npmW9t8vxbuFOUygIUsudWhDm6wQ2QFhUDt4A0qEWiVporNASCFnQoYF0gUtrWFqslrlgBqkIsUBf\\nZjQDaM1+FGlJdYN56fGOGqmZj4Ds7Mw5sgWf/z8gXDa6sEJDZk1P3i90NpDCwssqW2DQgf2VV00s\\nTYe1RjEHCcQCSZVWGpr1Dm0/4+hLbvV9NHyQy4tv8E46IMcrDGmvxm7m5GQkuQy2R6bjyAaiQZtW\\n3Bhu09vLdPFpDo+f4Kj9EVZ2ARXxDYqt0MkucQ7Huq7VAEGS8YYljo7/kCBH3G4+Rm6eQOMOxD1i\\nEEpl2uytIsthl7ZSrlOs1TStEs8i01avY/K2mOvBpoGu+OZaZNhbwbyH3XSFvk1I2XWevuSqzBNw\\ngV/5+6/yud9wmmATVuhQGBgYYiRJIejgGz1jZBVaWZC1+DZ51evzktERa3w8NZ24560DJoUoK0K3\\npuvfIDTvR/cvAD5eseDbNQFI4zYJG/1FZbxN/9UMbJrjWiu+qTqpQBtg6CHuwPLuRuujocxPVB+s\\nBHspREk0I2UrpYqfOVuhTbDSgUJ2KEEzmwof9ydThQ8TdwhCgOyrr5aE+W5SHgVXYYi5ogetu/gF\\nMCNops1KTgnFfNspM5oQGbTzd5eFEMWty9C4eT9u5gubl6/BFwg60IHVG7egq+ltY3ZCNnhbIM8Y\\n3jyCo3G1B0J222ndwjqhpRDaSFkeeVEjayBkcuqwlAlBEc1EyWgQLAoqxZOoQkPOAzuzSE+HWYbQ\\noGHwXZlsSY4DJWWkGZAoJNyNVlEKRvz/mXu32Nuu67zvN8aca+29/7dzIQ95SIoiRVkXSrLiOIpj\\nN7ZsJyiCNghyg1M0aJre3ooWSB760Ie+BChaoGnRIL0ALZDeULRoAjRokTZp4ji2HMuX1JZFWhIp\\nUqSOeHg5PJf/de+91pxj9GHMtfc+h6RqRZRzJkAd/W/7svZcY47xjW98XzqEdI5KJaUZyhp8ieVK\\nTcaY1gy+ajIDcSi/9Zs3Q+91V2dHFNYK4x73XjmHw1YCpBIRwhUumjZNfXioiWfFYXnEK//3aRt7\\nUChLuCLMPv8YfmiMhMSEhQgLd14MrHX+3KOMe7aRlcBg1sF6LZz/0ttw2nN8ERXKafooo7zNkA4Y\\n5ZGoVnPDaC8Xyv6kBT6P/ecdF7bHuyfP8No7z7HiGsfdY5zbE6zzQVxvWcPlChr2hsX6nWx55I31\\nY5yvrzAbE+c8RmXRGC9GNYc3LgHw9//llxvE6XC0BrnYsb/y+LxOmjLhYY2gRutyC0AHK2A8jIZR\\nmcV/60dodyeQgxCAgB/x2quf4bUbzwExNWuLM/aeukx6KpgmakZxI+dMOnNuv3gTVhWuzLn66Ueo\\nMom2KbMUYbLcGbjzjRNo8InmDqkdVhPuiyhj87Znt1kPkjTEGl42xawCSXjii8/hn++2Mvetb5oT\\ndCs4eWXk7v/z+gfutYckmO+uVuaYMzj0RJy1MoIkqoW7/DgJLLi3bDzBgz6U32VNqoYRndO2bypg\\nTVJx8mV1a87pDdJvaGdgwqJhq6V5UzhVi8GPioHGB+7Seh9Mjdy8eb74t20Eb1gzXaSbx8dg63bG\\ntQBWLDa25HiydROwSkJgRQpjFziJSozb77X0xw3W9xA5wYcTip5yMS4Zyil1vEfxJbCOwY00A1eW\\ny4KPJ2EUYCPUyjC8S7FzhvGMsZxyMb7LaDPEEsmbJnaGMq5gfAPjEFsrorDwE8ZywhlLrJ6T00gn\\nxigCuXD0uScZI5lBJXKYZJBXcPaNWxw+fY3hMIoeyYGni0Wb4fbLq4b7PyyrHUpyCIM1syGF1ZpZ\\nEeqQsK4GZU8TVisNyUBqalla+9oiqegG4HQBfkRfgjGSTRCLjM7Fohq0eP75swv06QVFpmo01Pd7\\nd/aPn+HOr1zFzrsY4JHM2oc4CNKa63/wMmf9VKyOeNOLEXGuLzPrvzMymrNOAEay1gNKFZZNa6B8\\nnKi211z73D5lzxhmRvVMFpgXePdLrwHw1Cef5ayH0Q1TJVVjYcrqTWP9FQvdewuoCS94CuR7MogW\\nvE1w70E92t5ucg+fHbGcgfQRXK1VBocjoI+2AydzenjAOCEiBrnhht1iBjlDzQGpmAZhYxd68d/l\\n3nOAyL6VilFYz5yTw0iCvElkJDNy13Gph3ExgK8/8CEfnmC+Ob1axJSGUOxSPDdUwwcbnNOVd7ZX\\n9nfznLITTH3n68puteDuzUbrwccOTGuSEPDdp3dHbKfi2Dy2t3+3WPl2jLGd2DsEnPzII+x/dIap\\nR8abBHFl+fKKcbli/uhl9h9Jm8tQp0t0Bvdu7AgFNW9CkROk+zZWX+Yif4Wz2S3O9t+hdN9kTw9Q\\n6RAKirMyobpx0M8501sUWQN9mE3Xm6zlJS7mJwyq7KVH0NSjkjaaLdWNeZqzzC+Dz0iLBV6NUk5Z\\nzl7izuwWZ+PXWftelKJ6COOKtL/PxY70h3mca9GUKBQNxCYGWQquCq50Qru5Hh7MPGmwIHxs4/Ep\\nwaAgGRkj5jnhf6oFOk/UxhiZ11Z4bPo4Qm/RD0cXMGw50GLBAjEPLnQkNgrJA6LrmsmIeUAYJIob\\nJQlrFXIO2MzdW1JQIBfWHvh+2IAWrPG/xY1BB6oMGJUySeW21wkp5BuIhDVaQ4kxhejmmmBJVQ+Z\\ngYlXPrb/ijbKblKKh75LdceWdZP9ateF6Jpte+5g3O/AFN9iqJRiTV/FMGvTpaRouDYDZs0xcIdu\\n44E1zLxaiwGjt8a8b6plRWkKcL+7JcRj1JjoTjmTu4BjfYoPHhOvtT20U79reHtIgvl0mvnOf4ZS\\nYgzZCVqcTBNd8StiErDE5sq8X8D9gLXzVBtaJIDUuMNUtj9vjt9TxpeM2KF1BJcmL2rhvoKjVJIE\\n7Sp44XEAubQUcmPTtft62ikvtj0VxCjdwHBpFmyanEAdWxbqrMD6gtVizmrmsGi6KiWCwqwI4TTd\\nNqrMEdZkfYG//B88wQnUBdwAACAASURBVBf/8CFX5dMsxse4VK7xmcvCX1jcwFxCYoCQya04eGKx\\n7pnXDnWhZmc+XuHK+pOM6Zz1YsHPzd9hUCVZIllMyEnzVez/0h4LncMouCu9HPHo6tM8Ph7wuf7H\\nuec/zl/9a7/Dz//8E6GuaFC0NUAlUqvNWTg1odr3EKEm3fDP73OcfwiWD4Z3LcolbfrjAiVx/O4A\\nFxW0toiXQ07zdvz+3ZN3YG6bDBuD81TCgLlchToweEjH1m5NKRXLFXRJlwrjOKLZ0G4e+4aBJIKS\\nQQ3xgnelSQ1A8YvoWDcxL3yNMmJtdkHVsB3nIU2GMiJa0GSUNFLyOqCcmqjDWbzvfBA3TSncu3Ev\\nQG/RjTrn0hyGyOLfefUM5o39ZDCKsypzuDvEPs77kfRYjPxshouElhQVYBZVpDT2V+hM0Gli5bRq\\nOgaIZrUNFDbY1N1xHUGc3Jqy0jLK5IKMGSkp+ndZInRZOC7thIj//2WlNQIqrk6lMNrITPuo6gUg\\nbYYPO4Vho5H+/uvhCObNGTsCWYNZnK3SnxPNjXbzOpGhVi2QmiHCFMwl/+4yM6FZX9FOu+1zxTEY\\nsqrJ4wT3idLXciGRNS5LICF+CCl0GXDHJLrrNlUL7XVvzFydbSXgbBxZaJ3qiF8GPoKHdlXpFDw0\\nX/rURQZOpK19nykWRg1iOcYhhMggpihYEyRF8g0ODt7i4NIvsZzdZiVwykDmdR6nY7K7gwGloxIT\\npcxrjMpLRcQwetYWk3VO5XGJ5u59FDOLDIvZpJKTMQ/xoxWVb0vFh18jnRVS/zrkzwfWK9PlKSQP\\ntXY1iclOY8soaJcxN+hNkLiZ08MT0D21SShJU70PCvmw59LTidU8qgyXOMvzCMubtwA4+NRjlD0i\\nSEwVio/k0nHya+dQhNwmMs/HY87tgrGc4PWYsTj0ilGwss94UUm9YAXMhZJAqCyHuwzjGW49xRK2\\nvgXdrN1O53hdUOsYipRlzVRNGjPG4ZhVXXMxrCh+istdRPrYp/MrW/zYve3/BOOOsbU2LK3CRoXx\\n5AzW6/Y3KYJyAo5XkK9F8rZR92zqkFOF22ASDHSynJAcd6GmSExE270T6uRmpVkqaostzdCmVeMO\\npJaZNwJWaMdPcKgTrzP7NsH4XeWTDRaeBpcaF30cjTL5FUiTtxZlhB1j9vdfD0cwn/jh91FRJvik\\nXTc3vJnOTmI3m9V0Ob4niAXwic+aplqMFjBHYInLCpehlXCt++yCsI/rKaRzkIyywL3GBmsZtjAp\\nV/jOJzxtAr3vpfoE7SQnMniJTK1lSLHPYvwYFWod283dXrMZ3Uwo1XAPOysUJNVWmk0bNYVynBak\\nB2Hd7CzicaIh7AgD3o6iDIy0LEdKE/BvDSrN4fiDUYiqZILBEjDTjjUF22idr1AJNccYO3FyzqHH\\nkiQYEHhDSnYCgAQlT+Ku3EyCanvN4oLYVN91fLfs5fd6Tdin7G6D6KZQJTEI1FTYaHaYB7YCsIC6\\nH/+39dqhNq37cUXXd6zXLwNwvPcat/feZslLFLlGml8KwTcZ6dMjLFLG3UkNklqnUBTJ/VtczF8j\\ne2K92Me6L0cwrw4yME8fZcGawSqL+yQVMn2+zZ35DfrknNlrVH4Nk8fR/AlMDrgvyDao7/ITV+Dg\\nCi4By9sQZ++7d+4B8Ohz11n3DQCxUBifV+Eua8obRACfKmVgkrltZVr8f/EtsWGTGdSAPadmWfs8\\nTGM3bqHP6Q1ug/l9Syw+jelaTGfJ9ywhMSEKbW+0Z5LNQNEuP1W3kPN3eZqHJJiPBJ1MgHMCDDzD\\n6gIfHqHOh8jMrQNfIzgjcyyfgNxsgXwPmAGXod0aEV4m3DquuomFflASar0A228f0EUwTPLAF/7s\\nOfXx13jiU29RWLG2rQ2duJJr5vxH7zFeDORFx8H1NxjdWrMRah2ZpX38TqXceIYXfvkG916/DOeP\\nBWPFDdE+Ml5dt2qkgzXQd4gUPL8D3UswU2bpCkkLJRlJlFkvHO+9A8M9tH8KSTOERNYKEoYPpSg+\\nP4blJ2C8QkwarijjyDga58Mpe/0KJCzMavNeAdtcs0Qrbad70mGYHGwYY4Bi87WyexgbsPL4JCZX\\nG/cYTg/0vqOKMY7HpDoipSC1Qhmpg7G33gCwJBIyQC5AKvTLQJAsQXFDLU39uqBwlY5NpvfPeolt\\nM7hpqWCaqA0ltDY/4daqtIa9TmnFlprscZgWIDtjeYH/4e99EYCP2isc1if5YvcU//b8iLEd0dr1\\nnPX3cK0hD2uCOgw5suOjccm1k+fZq8rIgjfmgmohWcakY7V3g6WuwSsz2FSXYWk48Pv+0k+Rh5Gf\\nzo/x59JlBjnkF395n//kr6yocmW6CPH6s7Cew3ruWO/QfDZ1BPQiHvfSZS56R7PA6LgbaZ0p3QDS\\nt4g2JUbw3oNbQGskV40S6JukKJRMhTgkBKGoRhB0AjY1RW3GZFalk8QvLa4mj457y6qlMdK+tzRy\\n+lAjoIs7bo0FR26BPRK+REy1JgedKpoPWA9JMAfJipcB9m7C/Bgu38YPVsjBo/hsGZCBzUi+Dv0O\\nZox7J3Dpa5AWUC/D8DT4lZ0u4DYgBJ4hiDqZgpuBXURgx1FWOEuqn/Enfu556mMrar6HZBib5qa2\\nJom4keQy1nQkjIqkjoojqjHGUgvzMmd250nu3rjJvW/dJHGE1YEkkOrkWrIGsdhALqRSEB0p/Tv8\\n1J95Eq6uSNejTByTk0XoVonVp444X3YcXpmhRxnTaIxiJW6SIeFPPM2X/ubb+N0IbFbPkdka8Yym\\nCNpisqH/hZMKTRShXTvZUQbZyQq0/Wz79XvpgJNF2XvzZN3a3rlhPpJ8QOsZlBl69xzObGO+oSLo\\nKIE3nZxy+spb1DTiKZp6PmZISm8zqIXO5sDie9yBP5glFiwn151MsmG7QgppX7OGkWpMe7ZLOVWg\\nYSAc/YtaWmCUnjy/S9qPkfmTS7/NaTrD/Qb7/DpIaobjzpx2tJltEMhJYir1GlZnprgoRzrQE5OO\\n5ok9jborulUVJ6SWo4Jz3lisSWQKbzKvL9CPlzk4+CnQL0JpAjlKJAWjx94nMCUvleqZPglD21wR\\ng4XRJ7cJxdbx92IZt5hx2LTGpgR52psSPZ4gr7Pds4HQBWVWo7fmIoh1YerMGqQGIuaB31uqTSJA\\n20O3Tqsp92uU7+jpPHhwf+CaxtYFS1GNVS1UlWjoN16iT/66cL/BzPushyKYS6eRsXYn/Jn/7Cl+\\n5E98hrO9l6FXVtzjLI8sbAQPrWoTGDUzr0q2z9Atn8RvfpT/8M/+b3DjGtTJorV9qJP9U8rMxttc\\n1xss1rdY1OOYiGwbwOrIUO/x/PFNyuwrjHLK3t4ey3Xjirm0m+v+i5o6pZRC0o5aK5pqBKB14eji\\nFl9f/gpyNrA3fo7aBglEZySDMRlOSLci6xaY4Nb42/xLf+5nWC3usZS7Td8nHMV7m4UpQ0oUG6mp\\nxoEiQqodXirZE4c/8jSv/R//F1fKj5O8p9QlS/0VnuBper8gFOBkmyM04TCX+j4B+PtfLoJ7F9UL\\n1nD0c3J6m0fT1/j8+u+i9ZDVrxg5bfnqEA23ZCmkTW8TtEePaaFotIYuTqqzZjv8538A7+B7X0pT\\nCpymHV2hKFo7+gJ1EDyFBK4XmBVl3W5e9fDwQEJpT3E6qdQBSEqnRpqoQymqniTREI1OhzTl+0RP\\niLKN+AZCm/o/WTxoqFTmLXAXqdF/aGrcmcTImsRWnTCkqhIjhT2gph6So13ZWCACRFkVsKTnaDB2\\nnVFrIZHJ1kN6B4C+v4zIWbB0a2Soud9D5sd4dwHjISZdi96FrXxHUyn12L/hauUgF5DuQfcStT9C\\nc0JVNsNPKiPedzC/AVbw+R4pPxavXUcgoU1bv/QeBh1+COVZkB6na0A60HxB37N2JlqjwJogZAvY\\nSEfQArqOA9zARbEJadQcjysDG97q+6yHIpj7WBDm0ROY3WGdTymzNzAXSIlLXYeVU0AjSKJ4MnLf\\no2LUKgy+aHj1boY4dVfa90bnI/4mP/3UGzx/6SaXli8y6SaYKC6FFec88aXfgMvH0WWula1gP0Rm\\nDmaGaow8S2r/tg/dPRqyHQMH4yv8Se7yM5/aoxtPY4inTVnmmloTJ/Lhkkd8AEmZN+enPHf6Le6M\\nS8Y8jR9PFMkU4lOq7bksLOhQks/R0ZAKh+sz/rVPv8NH9n6ZbIqIc/PgXZ5dzOkYqeqxYajv0Qvf\\nNQX+MJc1uV4lsO+eE/btVX766bf45z/122jJrIrTZUUkhLYCc0/NVahr5gehgadZoBZqVwJCKPMo\\nxx+SYC5jNLHJxN60EFfrPJNWsPCWnFih08y8wEnT6+4s4qnJ1ulqGEdyzbBsf9ySg7Wd43pG8i74\\n4FPvBcC1AWeT0fh2Oe0O8MnkYuenTbe7QtM03xoqSLuvhK51QAaKD6zXPevViI+rCKREZeG8CfkO\\nNd8lz4JJgwgiiSwz6L7Wnj+RbAk5RLGSKZrnOLcgXYHhYzEc5K2hrGPcQ7ZgAqfcBkhzSCPMjrn8\\nzDGf+CN77D9WkKvO2i9IklEPCS5bXrD8kUxZOt2es3hs2VyJCi4wa4yZ5bvC8COZem/Bb/ytMyg9\\n2I6gm1yg+aAxW3bozZPqKmw6aXHVl+DrEBN0w/QRKj3SegXojOpL3BUbeqzcBu5+4F57KIK5zA3n\\nbbj+Hc4uZ8ajS/hs6lQPCEvIIR1peSteM2NGYWS1OMEun8HlE7i5iovccsu4eK3ZIMLV+h2+kH+D\\nn5h9levyT5i2trbNOtQl9VcGPCsiRinlPSdrPHB0vK3JwpptVYpdBaHHbaTXxMdkjuYeyfF4mjo8\\nKX3Vlv0QAlQU8mwku7CWxKv/6d/lwIO6FCIpRkqJ0R1pDS1xC50LabCEd/QeP/Pk/KuD8vh+VAyj\\nCC/s3+MR/QOc1C5wVYm257TJrEEj37XT8n0s33wucWDM65Lr/i6fT6/yifwyXSosu0JmjohGIJuk\\nkM0Rz1jnMcWXQyM7S3iGikLfdRQc+Ks/kNf/vS71FdnWlAJIJbvQSWZ994S738wMug74QivJMucl\\nw0mMwXPrDJ3bBhIBSBS0GJQlqV5gLQinqXchPGBtBpPe/eY1vd8Lfb+D+wGITdmCA6lBl+aGSjRu\\nRQ1NhvhAZkBqBHPyBX/oX3iK/pF95BPnlH5imwSXfF7h1hPXAHjiSVjmDsuGmZAsseeJs+cuc+//\\n3ePrv3iOl4pbR/V22jGSrLbKslApWK1IXlPtJk99VPnjf/pxbHaPVV4hXYdaxs3QWhHJiF/eSDzX\\npvNiEkJ4bcYItT04vkw6fY5/8je/RrIDqJnwDwCVFcnCxL2mBod5gJbbNX09QD6H/gZcOgXOWO+N\\neDffqD9WnUFa4ikhBwv84F249gbws+/3CT4cwdz7U3huyZ//23+O48de5YV8F+WH2gY0hDWREect\\nxKGVTOh3jJI5OHqEv/jX/zL//b/45eBWS+Dc7pl4mxUoLMUYGTFfk+odEhctK40O+UwqOiQY0sYM\\nwu/TVY9lD8S6YFgEx8xFwAumE4SzDJ0WqTGNaaBVUJtY84J5R2UMBluFLs/5zHHBim90r8P3c6tX\\nPpVqofkcV6tKIVl8v6SRnijrelc0JQ58YK9ecC5XGgMlHks2NnlRJfg0/vohLmvIugOJyuRlaXVE\\nyopkt0lDZTFFHs+RDeo20OxWED40U8BGCTNp+jU/mHPon2o92b3GlYsXGxvHUc2MDmk9Uo9LfK8A\\nkkJ62bqtUffd32CczCCIIG2aKKUyz8r58CUW6ePxu26M00Es6X17GN/vEteNJZq6Now59NVdKnil\\n9wue4DV+eKx0dh2AU32dP/+nfwoev81pd4MiFcsKKei0WpTZH4oex2r4OvStGlMNqemizLnEG/OO\\n9A9P0DKLuShbofUQF4tGqjdpHls32PSccXiN37/s+eF7j1D6e9gsU2pUtymFRZ20XGlaU0U6mVAs\\nZtEnKIMhZ1fR42/x2dUL9PUyamkL0/qCIe2xzNd4RZ4kjj4hiBnauG0NGlq8C9df4d//Wz+HPfNb\\nWF5R1FjlU7oalnvr3BJM6Tgoa+bMSKef+cDP56EI5lFvL7lzAG/6AEXpyqIFFEMm7rh325aDOh3K\\nKIKPiaEmLjns2lpt5QmngaLJRKIgFMQ9biZg21EBwUKhLvhNWxe66XF5nx66twYJU8PCpr5i+5/4\\nb+qDbQP59HhRwqqkpuHiaC107Np6RQOmc28Zrm1eb7bWwlTfQDfJG/9apVECvTEmrGVvO43iHdbK\\nD27F9dn0paZsskFWMumOOag2etaDh+bu11MPQ2NeQNvbSB9+HPunXj95/Tv8kYMX6f2M5OEsdWFO\\nl9ZUHRF1dCV47sCE7B3ajBqqjFGVmCMSMNN6KIgI8yy8e3SDKy2ruBOdF2JX/d5cAGl9gMnNK97d\\nyLOLW/w7n7pNLhGgTw4v+Fw55M6dM05nA55g6ByRETFBUERiwKjWii5tc2oLylBgn3M+3lc+9/Fv\\nk2rFa3gNdMGxQTzjCCWFCXYthTQTRr3LJS64/guVktaM9IhMMYHmLhbvZxc2jWWYOqmmzc/z0GHn\\nB/y7PwRz9lviBKAMHLFcPMPXjgf+mzeusPKOwLhDqXSboVdosPDKb5Hmb5DmhexKtoHc7u2cA65d\\npH2qjwy1Mjs8+MDP4yEJ5gJqXPQjZ7MMssfM4jQDcF1SPXDmjTyrN2MICQLHjBmHQgSyKUZu5CwF\\nSKhXOl8z84t2c5Udg9btCg5zfSAN3Py0/U487pZe6o2xUNu5EPKuSGmYwvaQiQw+xc3cCJQuBA5Y\\ngt2Qc3ihmNlWi2vDAIFde7Gt646FKXP7OldQjUEds4Ip5C6uywSnTIF11+nmB7V2D8Bs0pquFr0m\\nghkkCknSJrsO1sz2E7oP25dGzW8ZuQvMXTbsg4dhfX5+gz/W/x0O9A4djpkxWmuENfgj73WsGehT\\njsDSEguXAiqoRcNMXRi7oAkuNPNaXvOoh0DVsbNh8+8e9B/mCsr8BOdsZRscD5XFWsh1yUf1ZT63\\n/xKXx2m6wHnzv/jbdFm4XgURYcwFtxGVHGP6bXQyRLAKE6A5/SwZPF46np5lqheKOOuSWaREmIkH\\na2hIhlTDbSDpInoutwfO/94xohnRDlhtN/w0a+IeRtoijUK7zdAnCpCmHhtHsiSe73r6tIhqqT3U\\nUq9wnH+I/fzD/Hd8ktUmgOsOVt7AqqNzuHxCvXyGLNZkjJnMudDSoGSI+7NEf6KH6FR88Of6cATz\\nGKFoTIoZJoVBADIijknCiRHXjdkPmb5161FlTH2I4+gU6DTohzSzBqKkj8w5VOMgApl6CwbTd1pj\\n0HTKuLevVKcSWAB8Y1hhss2/RaL58WB8jDH0GLsvWtsh0rgkUhHxzfsTG9FOKeMUrHczcTYn0AQv\\nDDph0bZjr6aoKl0oLEXmWmhc1gZ1EFSv+9ni+YFZzg9vpYa6mgaLB/GYznOhpMRqloJ+1+XN5p+u\\n8fYQ2q4oRGLqdmt8ULnCw7F6zujHN1mk09itXql1gsbYVmqbA3m7dg/vGJZSshbwkE/RRUBUQNND\\nr63lHy46HzbU4jIRVyH4MtH6jCsuKIkkyl69YL4+p2+OMjkZnxydwZxcm6yyRJCKhqyw9Xi1lkzF\\n5g5W8HYnalFURmYJDi2hRYmBvuDozGo0jNWBEr0HK5V9caiG2lTpxk1SdVextT3Hg2SAjYZ4adDL\\nGtOL9vhxPaqG+YyldUjtdvFJxOrjPt/VYHqy8Bf/+l/mzaPXWdrHUPWGpIeYWjxmIAjxaiO5Fe8+\\nsIJ+SII5IMNmmzhEYN6hUBVxeotCctJCgYQSjQx3p/o01h8NUpKE7MUmzFpkxHQYPYnarOCcpE0I\\naWcAawrSD3648N7v3ff1A9njfVl9w98zO8+1K6DTzp635/u8eHCZKh31wY/pvoZWTEB6O1yQoQW/\\nGES5Xi/47PEd1Aam2SdjmuYMTE+8b4/bDieRH0BeF2vKGoWQSkgeSntmcLO7zGvPfJSvf/YPcrY4\\naC8p3p+2crZqY/XsXAuYzA2iOf5DJ2/zcz+g1/+9ruKFpAZeAr7DQhCsVRWbSnP3DpWpWzR93e4C\\nt42W06RKabYNrtPOl/v/+ge67jcn1rbvIqPUlm1Xr2gxZgrTCL64k7wN8YlsWGj37zxnYqg5UNzR\\npKikgEpsC8fEs0837M7fT99vvxafwXQR7X3v7elWmt5a2kCw0Yx2aX83QbmAu7X+QWlN2elvJjjz\\nAQhAlpw5HNfEvXqAmNN5HHibY21jVuG4N9zdnYYsvWc9HMFc2GDioeed8CYnGdcqRH6STfxMQihH\\nE26CmJIsx+aZDJLbRdtu8QknU5weo6MwQ9oHUxsHAgHBEboNx9mm1/c+Hf+pSTphlfH1xCFtDcbm\\ndh+EvDac4IKI7sA0Md2JG6Nnjg+v8+4f/hmWdIzy3T+mjWGLGN4eG5TOjHTrdY6//MvsiUIaKV6b\\nY9N04bfIvTSMWjyhD46hxzPxYFqwzeB95+sHf2e7QafnnDIwcci5xzRzJ/fcfuwZbn7hi9zeO4pH\\nahnptCanmu3zGpWKWg+tyXzl3de/6/X6vVzmQsabEXMYTMxcsJY8TBm6sXudY+mmBGs/FHBSq1IC\\nzpuUOTf2cwD6g6mqfCdwgm+YMybbkD7xyyUVbIzsIeXcuN21GS/XkC3eCbDTmm7dbVVim4R2rhmp\\nBXOPIbkuYzayEyER61CrTdTuAdVST1vpjM3jy32SC7uPtemjbR6nRZOd20BMNm8kyZrez5j5BdmC\\nthk/qTuP2RhsqWICS2acpiNUYx8MXndeAy3pcsQW7W087DBLTbB+lMdvg1w8FVlL85moEE4+bbKc\\nVpoKIVrWZeAEjlawPwDrrv0S0domEcr2AtqzZJ939RludDBoR/Y1oQEdm9PGSumEPGmjeIg8hcqa\\ngXkkBNrsphBcK2QJ+hlEVuJtSqzxoQHchX4nFRBC/L6KMiZgtSbPlZI6vvzk8/wvf+JfZznvGfqe\\nWmvLRpwsHbg1brszpkKnHTaWpjUhqGbSuOZn3/g23Tf2ubwcUFtyc34DS1dRN9w8XNHbgbYJx7KF\\nqmggzO5tvN1O2uqdrVXuFICnmzLgq8hositOwkUY0JhqlX3O5Coni49z88o1XnrsR/mt65/j7b1H\\nMaLxLRLzj2BUKZgZqbXbQOlUqEWxJKR8Ruqufmhb8/tdXQUdenTWUX3NoMZ5Omw/DVhIqdT3EQfT\\nBq9sXfACD8aMlDPua7xlvy5LSrPrScR1/rCXEb618cq3+yBuS2//m4gxpZBcABhLofT9ph/kEkyN\\nddsnAQu+twcEwCbxMLRp1vjELKgDqc12hG0cmFZym9gOpcjNo7LpX7HTSfPY+PbAoIXuECK21983\\nyc2GBdZefyTpWwjX76ueGzxlAX+5xmsbFcbUs9Z9hEoSYSXONPvSeSSWVUB1Fmbx+pAPDbFawFuF\\n3/yv77HKF0Hk90pJwkoLQ1eooweVicjGexMWPmdPZ0jJdKuexckelAWTrx/NmTu6ByEzul5c5+Xq\\n2PKI/fERlGXL6FpJJonTcUUnNFaBkkyb2FQwCzpZ4NaEi0zRBOerVTRvACgxdENCe6GUgUQiW4+X\\nmOLMuTVVPFPVogu/TGgWxix87eISZ3tHnKXCKEoVoUs9YFQtaEohOZ17hrGGacF8wWoIbrtX42h2\\nmbcUfvH8x9lbQucDb6Xf4MfGAx6XWyDnxA0o2I68prfcWpkYLls8c/crNgE8cpbA4XWD2099BDZ/\\npVHiOjHsRcdS97jlV/j68hovnjzPm8dPMeo1qIeo97gH+guOe0W6wC2TpRj9psNthWrfSvuBU3/i\\nw9yd39dSGbC0ZJUK76QFbz31Q3zpC3+ci9y1BmLBdQ0T1AVMKprJdNMncAkbM6+Fw2HFp1/8dZ55\\n+ythBkEoR9Y2Tfw+yMGH9352CQCyZUXJRpBOMNKmMQ1wt9vnxaNLFJ23AbxGffW8kxnv9IPehyef\\n3LhmF3z2+C59GePWFmDCvCfYY4JLm6DWe67GTuK9+foDoNTN009Q2E4WvlFtbM+bREFDTbRuxN5i\\ngja31MiSNG2mDAQsHDEktTKskrAGVUUWvhboUKqEYcV3A88eimCuwww/qXzzf30B1sv4IHqDp67C\\nowtYDM0MOMZrMWddM6f3TuHbJ2HyO2ZYzqEcgKaGwU3/NZBS4Pa4xy+9nvj1Kiy4isi6cWaD8lVd\\nOF8sqLbeICtiSpFElxZohfl4QCX0IVIr7VeLTK1Nj5wQDTOCbod1zMj0ywVJYoAnb5o6OSyq0ppc\\nFjGw0DuvrUY+Ug8wGRhcyamjWAIvVB/xsZJ1j7EqZpmEsl6O5NklrIwkdU7P1rw9PsqvvnDI/jBH\\nypLTPecjq0e5KicggmIUp0lKTBBIKCzW+/KPgKl2AzlsM7QtkDJp2Nh9vxnZYitlGzXMyIyygNk1\\nfvX1V/lHX+3orl7imZMrfPvb7+DWYxVo06vgPPXMNQ4P4eWX3sar4qYkdaqfUlny/POPwPqD6Vu/\\n18s0sRZllI6T2RXuXvsYr3/hD3PS7QeFUio1rcj+XiB0or1thMrEqGXFY8OKa7fe4fG3XiDnOATU\\nz6m+e/g+2Lh5cO3mjbLzue5Gux34wkHFNwdNDBSlnSw6Gq5T473qnEkr7e7lx3n3J7/IBXNGjb0R\\n0GCoIj04ffx+q/OC37rBc1/+EvgFnRaK+2bqehubLVhlHiIEtKpRNtOYbY+3TF5k2qvKgzBX/P79\\nNYi36meartVGJCwCRWYYHU7f5mGmv01M41cbJEcV1USyTGp2d6JKqls2VhVDkzc9dW2v94Mv1kMR\\nzM1mcK4wHDS9Y4ejNd3v+yRHnz3i9jws48L2LMwTHl1l1r9zxr03XoKStx0huk3qKG1D+sYbtHA6\\ne5Zb9iQyE8yWcLgADgAAIABJREFU8XOByJAcV+PJn7nOqh+oqWwaTKKZRelZvbnk5LeXGxhMFaqu\\neOKPPs5KR0oqJKRt1Iynyqx2zFeJG1/6DoxHUKdClHaKO+gSozJbzBgpmJ7x2W9dZb+H37zxJsVH\\nVBM5J5arcx47eoTLVy9x7+6ab33zNZBZNIRmCnXgytE+n3n2Ke69AS/2fwrRBXNGRj/lTdnnuXSL\\njmOUSicx7r0podt+qTulorSNuZ0J3GU2wGYr+SQaJS2TmA4IBVeKEAcnoJ4Y2eesXuMd+Sxv86dY\\nfbnyzZe+DJ/6RIj36/S5GtTCvdvfhNWS/MxHKdVAZ1DCfNgH+NW/9hv86vqA//lvf+H73ZYfyrqd\\nPsmN+c8yHhS+1e/z2qUf5stPforz+RFaZmh10DWa51HBdcrYGCrJcwQrb40vMZKd8tjqnP6xVznc\\ne42rEpDSqGcUCW5Jh6AeMhSBwIbs8IQ9W/v+ZM3t9/Fe7q++tvFNESbT5On30s6If8fcHPWee2nO\\nvfxjzPJlAP7x00/zv//Jf4Pjfp/azYImi6NWqcnJkmkTT4jG8F4lDGLcBXFjbs5PvvEOR1/7zzkc\\nzsl1xDx0bXKjttrOfh2rIGmSuwAsoS7E4F7T6CdgWh8MIW+IEhODhNxFQT/h1GZYn8Pqc3S0S5FV\\nY9Q0suQJjvVJ3tVnKC6EN/G4JTBUBw1xOx+OeHSAxfEBBxygMxhLaJbHZ0SrdEG7BrPTctRH33+v\\nPRTBPMqRGn5SRLnBujBinFUiK7fWLbPQbruosC4Odc5Wj6U1OS0MK5y+FTjN3cUFimIS4/5WDTYn\\nc9NSSAPnWjhPI0U1xua1gFVKdQYTvOzBGCepUaBPnFdhmaBojACLhbCRJCN74UgTbrkdPDn8LoHN\\nbVMEUmG99AhQN+f8/L/1Gsia+U8/yyq3ccc6kuojfOfmwHe+8xV49Ary7EfxhUVjAWE+Zk7euuCX\\nf/uVqFS4hpeBpQ/QzUL4ySDVBUiJA4l1OAw1TRYXi5Ht+0renQZ0ywC32GML9jJVrg2i8h1MvkmR\\nTj0PrPnzVqWwz7C8CueHcLewePIRVnslXHqkDV3UGbxzChcVvXYA2Vr30FDv6S/mLL/xWbD5h7g3\\nv7/1yupj/KP8M6wZ+U4Pq/pDdOlyiIbVBWKZDJSVkhgxr+S2N2TMMTBHHKPSyuxR93jh+Ck4f54n\\n/Ur7+RuEmLFtssIpsBmQp2PVG4zWPqeoW99P2TKWbzOjBqVNFFigHQVGCWyXzCgdb4/X+PryCTqL\\nEf0Xz+ZcdFepswUn4wpJMzwpScJ4qUswNgEv0UQnmVqcwaNPpNlYm/NmepJ/cPbPsT9WuhKcfZ8Z\\nuQYPfQq6pRgp91gurT1ewTPqQtAhpysCeVS6GgbtVZsKalliWqijMvM+VA0Bt8JQAj88ZI4NddN0\\ndnHWcpkxX+GV+giufeuUTp9Dw8KnJuxb1/it/9FYHl2w7E9JfeLC1pyk8/h81MEL6j2aoR8zc8ss\\nXOGvPPW+n9VDEswVpOHc3ihmC+jmHbMZSDdjqCOm8w1G1TukbsbQdyGcvVlOwgIHbqV9a8WBCoLR\\nq1ExzNu4vUlLsSvoQLWxjfdmXILeqDhex3CR9zWkFEM+zUjCPIfxs0UG5a5gITPrTuhIuBHE8Qp9\\nF/jeECa1LiDpgKShb1xtDcMTmK5Rn0HqIVXIc/RC6Nln6UvwQ7TrqZpA99EhdEsqM/A5sBfZbZoj\\nKuTFHjOp7PXhSBS+LyHSVbS0tmJqpXdisrwLmCTsraQh47tZ26S06K39FQCONiaAoCIMrOnoWmmb\\nSGmGS89isU/q5mh3gJU5+EiXoOYcXp86A3P6nBg01P1SCo/JqiNp3pp9Q4J0EFXcQ7L+z+88xz/o\\nLrHkhHE28tTBU3zyhWvUFfzar7+OnTvIPMTYrcJM+PQnPwbA17/6LZA+9ks7AX/8J59hnuEf//bn\\n+PLX3uSn+mimJr6JMUbfX4TUuNHezBQq1tgxAaPFvykYWOxisfdjDVPAjxDe7cAZrfSnMCHpQ+4Z\\n5BJfPf80/+VLP8qyRO/ibn2bH1s/zrBakzjCpUc8sWiubq4jfdcE7zqwmhpDrZJzQlcrZFW5vb7E\\nf/TST5DH8E/tuo6iTqpzktcNK2YUx3zN/HHl0lOXGGelKWlGo7RqBGFx5fzb58jbCbIyNuns2q04\\neGpBfnQW5intca041ZSudNz7yu3AySfhcwT3ntzNGa2Lo0mWbNzFwpaeycSZdw74yt/4auuQj9DB\\no3/00+x99nrYNTrRwPUOzNkfEve+fszNv//SQx7Mt9qV7RvWOs8lttwUq9V2W8ukSWhnN8v19u97\\nViuH1KjSuBdiaA9W2ubOPeRCktCgsIkdIIA7SVJ8v58GkVqAziMpG57GyBaJzDxWFHRZJebM+1Y9\\nMIIaMstQJ8xOGu1JttamG/iiwQ0twzWcyYSWRuEMdKMQovwKPms/swjATeLWzFitR7pZ2bBWYnho\\nEu6vYV6rHSGIZAybm3YKAG0sf6e7P7H8y05btCMm9qrEwHdpm9pQel/h44g3bessc4oJ+BAvX3zT\\nMN0Mb0ljGW32gIbZs6ftwO8PsgP4Pa6LdMQpM3L/OGU85ZWvjbzyH/8SdD185GNIv4/TEQp6htwZ\\n+PpL34w/fuRqHOIbNoTz5f/qy3A+wp0D9uo1hkY83yPy5O2nsJtBb8mh277I7j2yC5ft/mwLkk2P\\nNdFETbbfzTHbGhrodAz5Mrf8CcrsY+1XLjF/c0EnC37ntTe4e3IepIMqQXnxkd//o58E4KUb38ZF\\n6fKMcSy4G889+gRX95Sz1+FMn0X7jq7PrJdLSPPW/J6Ufwgdd10z34N87TFW87qzf0M33F3INXN2\\n5wR/W0OZsL0j7we4cgl5PMgJ07x/R4aqzEvP7X4PMW0JT8xC4IoyC0hMj0FWm73YhCfi2HSFsZmj\\nlxJwzFAoQ2JwWIvGpKdN+P7IwhOrInD2wSH74Qjm3mCQzWobcWoc7/6oJdrxLd/pKAibMWja9tzt\\nWLebAQhsXEa67piUlhiJQodnw/WcvZlAXjI0kY84gWHPe0hnnOWzSCE0RdmW1+z1idJdMKZwdFFv\\nIkFAL8rC5qBvod0hQkeXM8XWUPao2oGElKbJFJSl/dfev2zfZqCV91+y6f82Ha92PeJEENeIgQq4\\nYjZnHI9AV6QUUHvB0VQiULvgJnjNG+VCT7GhK7VRydpkJu0xRaGWsCVTb7dWHHq11s17gzYookYa\\nZ9RyAHaI6F4oVFpGusklKCqsYMZM5apC4+f7dK12OQvNEuxhWdWWkPYpFwW6S/DOCHeuwv45i6uP\\nMKg17nwH1rF3UTl/JzTJubzPhhZERkbHX34Gln1AdbNXKfUEICZfW6/fmTLyqf/RgzTm0X0HIUwN\\n+M1WcuW9qVD7G5kgHMimTc1SUOnC5MXj3yqZsduHoX2ON67zi3/xDfAV6fOHzK8/yWpeWxIz42Dd\\n85v/7VcA2PvCJ7joh0iSpAdTXnzxJrx1DhcL8MexOrI2A+uhLloFaM3bk4gDVllROVc40cKobVjJ\\nghIrHi5HY8105aDt0RhApINzEyol+IEtu3Yr9BjVFCTh1lOtscDadfE0i9duTTEvEPD4GRl8ACYY\\ncLqhA424KM5QrY3zSlTuPoA6ZxVGjLC1fP/1cARzaNjTVNI42CSuFe/NmhIgTQBLXeKCubIV05oc\\nLVvTo2XUE28T70ASwhnot/gL/2aH68tNjS4jSRj9nPlzN6hasX6MV2OZRMd87KjHA8MfHBDpIvtO\\nzqgrDj9+h6WugzY4Faae6YhA2tcZdz9+j74GlrboDihDZVx/mi/9ypt887VDKPHhxvnUYKeJ8kUN\\nnqo0GGdz+wWYVKe/o9upTtYBX7lEQFaj2jO8+mpPP58hHNPnyAKWeWT2mDAyIBIYZHc+Z3W6pubK\\n4tqC0Suph6EO7TOLoJ4sYydGXY24O4vHDxlkxHuHUpkvO1Z3V/Rln0QfrYsUGbqMjyP5M9y+s0/x\\nATTjZXITjRLWWpSKxmrweKtMB1/IjaZpiFZ3S99/9kvyXnxefR83p0pg+tmhTzAzVBNmiVQzM+04\\n12C29HkWCYU66onsMRuBzKJqyvssFi1rTHOa5xXgDIwIiY5MppkUN/CrNDhGJA7nvMtlmSYaqWHX\\nR5PMaNVUY9SGwmd0S+LxtGOOYikzEw9bxNSClhXgceCCyoK+zUnQKdQxDEca9t/ZXsPUW0CsEqT0\\n8YjMjCpO1n08CUUKagPuTcM85/ibcYTsuEqww5wWJ7TBn4K74ibgiZGxlZgxzIMuMelRMj6ApRYm\\nDcxWVBujP1XT5pyLbKkjkUgKq41xRjsk4pfYTm0s2PxxUuicvW5GzX1Er3Z7J1O6nOgBm3eM+sEO\\nWg9JMM/tdN3Jqpw2NTctBQ9FNARq02tp6SZbLJBt9mGpXboosZBw9DG/QPR1/uy/8hT94g5Y0BID\\n3qmc6DJK1mSNnpdwQk4gm5BKh0pHrU5KCUuVQW9Fo0XiMaLaD151bPkOGZrji1VEO2zMLM8+zY1b\\nb/PN1z5+fwnSoBOXaFYxyRdssuKAV/CMIBvNEp8SciqTEmJkajWYCOXT/E9/40XyfBViV+qMDJQr\\nyvUffZyxK7hDr2DfMt5+5QS6kWd//GMMvqJKpXa+aXyqQ64dd79xm+U7x6CJj/7ENYbeWOkypvFu\\nDNx99YT5egFD0xJJkZHmckzRl3F/LDDzZZTeamHOMOr03sOzMTRzc9MrAbQg3sXr2MAwD4n/J1NV\\nZdsPZmJXtGpJa4e43QdNTft38x49mpQuDaqzyQ8zb9gaQaVdMVIJBc7UehwxYKUoIyOVaRaATYDf\\nRTjj6WKvhStRIdMRc6xj7PH4KwK8m/wCjMGNMoyhgunbW9loiZfMNnsSieaeeXMFm+AbYIION5BZ\\nM3AurbLcSkAnTNq76BPu4a/LTMM1Tpys0gSw23UVmqRyC+xpRBYafzvJPueKSnMN0gAZAVJSKB6V\\netfFZzDEYYsaUhpqvzF7bhXXfWs3Tk2fNyC1wcY7pAOJiiv5ZDrd3t8HrIcjmCd2yPSNVVIrg1c6\\nh6GEW4uMqRkwRK+y+AhlGhJYATk2zIaLWRpG1Z4kQWfvkroTitxgL3+VUb5E7kNwKiCMkcuAWtcU\\nyqaw1aD5yYkO3Vy8xAOOk+2Cv4e32m0Hama6wHIHq45cX0bWT5G5jjQoyCqk7Kx9DcNHImuYDdSx\\nQJ1j4wrKCZSRoeYoX8bo0peSoJ6Ty11SOqSUEjStIpBmJHme/uwTqGVMDckVHyqnwxMUcYpUkiS6\\ncgblHTgB7n4cswtyCsOAkgz10uzcMv3pHVbnZ3iac1s+wbmdgw30ovTVYfk2erpAW+9C1BrX3qJM\\nH5ys96gautzFD1lZ0/K2EuWmK3APuKCMlyEnfBjBCiNC9gzlHqwScO3725Mf1tpIDXM/BDSV5dAq\\ncd2Ipk0wY/TAgl43+alSCpIX+GiRWVo0QOt4RM2JooaabmzR3MFU8aQUG9GWbZsZ1QySbpT4DLBJ\\nLkEgEYNZViWkMpIz+tDeimM1bRx7XGEsPXU8wmzOzhwQMrWyGuxnNPjnQfiU6Z7ZOVlk5+dK5Ciy\\nc90aISHpOUgh9zPGUrC0plNnkROWV6QGmbrWsMRzpdPEsjtB+hzMGBo0ks6Zp4LkHjohtwMsN+XV\\nPSsc61tI3m+t/hIJY3eNKop7v/3MJ1C0vY9JWwc8PpwJPfBNTb59v/ddkymsfRDv6GEJ5tWBTEqZ\\nWm+2D26JczW6uboCG3CZtY0f2ik5nUG63TjfSpyCe+1E322URdDo61v8gXwXW3+Vtfwys/EjyGKF\\nyIg1nqlSSY2eNw0a6H0n6fexdjZmNGkyR+UFPslvMSxPmOkRIo1/Y4ll2udOgZv1z7DsDyMr7TLi\\nxzydXuYdfxW1ayif56LOSV2HDycc+YqZfYtn+BKX1iOQsZrIgK7PsRQeor2PVIGVJV4fPsba/hj3\\nypyxc/LqHs+uvkG5+BofW9+l/MIpdIkyGipCFSPtZHSd72Gl49beI5ysMsd7R4yWSDbw5Pp3cPsG\\nz4y3WjAPTLdKtEsxQc1xU8bk3JQZx/xkGzxpGZCNiFfI3wQ959KicgqULtHVNdlH+nLCvn6Na/0K\\n+Pe+zw/rw1nRr5CtqbznyAq0VW8CJbET3Kd9zAZKonGHDKDbw8+jWTHUfX7rN6PJqPazDDkO11wz\\n5gNVR6oO9E/MWOcSmuiDMFtmyvmasRYW1/ZZp9XmsLDGXupTTx1AlsC5YcWZPZJYz9pIvVdmwyH1\\n9kDyREIZasJ9j1dffZqq821S5SngCytMMKCRW2/It/cXjVjmIZnhU69rYoLYgvtF1kbI7/LxZ8/4\\n6Z8a0HRK7hPL8YyaHL0EB4++w5DWjNKkoWXbgE+W4fkRjgXxDFVwNYqsWFy7RbkMtXOQoE12NXoC\\nuSZWz6zpZB+KIV4xX/D6awt+4R++idSPBaTb4k6sCXmYgnEzhPbabPBALTWRPN/ovxTN4aC1gYo+\\nGEJ8OIK5hu5vre/A0evQncGlEZ8XpHsUujVSB7TJhamDJWG9dwceexdO9uFiBnIdhu3JNek1xM1R\\nyfUuf/T5nkdmC4aDS8zlnEIN67dN0vT/MffmsbYl13nfb62qvc85d3rz65FDD2S3OGiwJZIOLImS\\nLFt2HAOJZAd0JiGQAwQ2EtlRlCBI4CBIYCCBLQcx4sCBjTiA7SS2lAgJrMlyrMGKqNmSLNJsDupm\\nT2++8zl776pa+WPV3ufc269bTfK19Aog+977zrCH2qtWfetb31ezhzOCNufTiK98BJTBenbsLl//\\n7sxHvz7R5EMyFbppZnx+Hjlot/k/9l7jsDklF9dcETvkiUdv8TXvvcK+NHxKXmeQBQVjseiZ9we8\\n58l7/NG9wBMnR2wPXmtqBDQdY8G5D9E6sgir2PLP5it+pvs8Cy6wKpmFnfCxK7d471bguWGFWV+3\\njwolu1ZNkakQaeWYYjNeWWzx48vf5LVuB5ldoLXE1+2+yHs/dMpzy6O1vKfgmZBkSu9BSNuWrhW+\\n8Oyj/NjV22wvtlnGSJE5Wgaaknnk2cs0dolX+1fYa+akGJmVE1orXMyFD73vPTxyfPeB3quvZCiG\\nWo/p4MmIjTjqimhbLrVsYNKDtU6GmhQEwTn2AtbQFMjF6wqkgIYP8p/9+Woxlz9CUUWzEouStSeH\\nDhY9e9/8Lo5mHVhEh0D7RVh+4VXIHY9/w/OcLk4ZfW4xhyIbInko5NuZg8/dhGHgwrc8xVEc9V+E\\nnYM5937pFWSlaGlcO0SAsIfoFSy4bZya0yvNBlybZDYVsceC/pgVjA1rUms8U3+JZZpcGMQtDp3+\\n24F+mqefe4l/48/so+1v02pmVU4oVTvJKod+BAYnro45tKsV1w7SkG3w7yVTnJ9T2VzjvfTG/N6W\\nxG+LKM1UjxjKdX7ux38fP//TSyRdYLBtRtZarGBvOQ+jjDNEHBXoRWpnbJXDsLWPq1DZe28xHo5g\\nLgb5iHj9Dv/eX/tDnMSXWW0dEq7toLsNQwyk1NJkX82KODTQHF9Fv3iNKydfyw/+jZ/lpZ++AVxn\\n2o9Nw6GbuR3zVLvkuvwWS3uBOU9yUrLLVwbbUKnzFXWk4L0jp4zQirBbbvOu+BqXyj12ihGaFm0i\\nr4XMwZNPY4sLNLLFLF4iaEuMkWgnbB9/jr29bVa5YZEuI+0FUsk0HDLr7rGVD7iy8wWeePlzPGMD\\nqR+wPDAPiYJTFwOJRKSzhu3ZKbePT9hP11jFhkU54rnjX+NDzTFPpU8TtSOl4IuJ5gl7HZlC2SAR\\nuVYe5dbqNZ6eXWM43aZJSz50+us82x7zVH6JUHFPb+P2B0kaoRQYRDjSwOU/8d2kp/c4bGd04rCO\\nqBKs0AxLYhH6xSVW2ZCgBBOiKM2QuHi05Knl7ltf/N/NkVZYWBIsQRw8eKcC6YRke3Sp1nPUoPQM\\nYmDuuuMu8BlCQ7GBVQGsQ0KhGXrCUilxDPw7UBzGCyilzMjtHp2dglwkhExKmdy0RAmE4ZhSeubH\\nbTUJ8GBexDsqRYycDTrlNHUMTY/pRTRWJUQLRG2h7NNoS1uZW1YR5r6/OxVLlRVpWBEkkfIlUl97\\nLLoEJDqbQ/JzTglyXnptJTZuATkcQ+oIwwpTrYqekKWH4TbS38a6OzTtPsKA6y2Nu2vq9aljYntV\\njL4uHhP8Me0G1iW8Cf4oDSaJRsbKQcNQmXhiAsMdFCFqT5DI+JTkWr3w7LwWAizjO47kBfFUSGXA\\ncmWSjTs59UaluTVVUfFhZ7MUIzRK4g7P/ktXOJ4dwo4wGL4NE2XIXjcfMeUhwna7QJ+asXV3Bj88\\nwB6wP0xbnDUC5Yp7W5K40H2BJ9pPcVo+xaxsOyMitFhxCdpi41Y3bwTyB5uVAxQSsQjbsWOrvMyT\\n7HPBAtoFcq8cXNvh3d/9HTRty79+9SrHtMQqpRtlweXlk1hOvH+24ANljrYO6FtpmOk1ttMeV+9E\\nZj/4Ak9+8jdpLZH7VbUa8q10qDuXnJXLd1/gmdnjnBw3FG1pLHOl3GB3ueKqHnHaHXsDRQIRIanX\\n4sYrY4CZ8KTt8Z75P2fIC8hzpCzZSvdYDB27HNOW3ndMo90XmZK8vj80Mw604e7lV8nbGQ3CnOKd\\nkeD3dB5rJltogRnKUGEIa+FwO3Pv+OSB368vd8zSS3xw/s+4MLxISpnTAjdCy+3ZBWJ6N4Q9ljIH\\nC0QKIe9DvAWA8Czeyy0giZkOXOGTXO6OeHzVMS9KGrHg4BTUYE4fLGSOcuDu3mVOuz/MLdslhECj\\nK969/A32Tn6VNq0ov3BU5ab9eLNAUxwFjo1CByu5zK3Zgj7PeZXrADSp8MTq0zwz/Bh7w5J5ymDB\\nMWMphJLo1NksQXpWZqxiw4vlD9LZU9xjj9hESu55xG5xx34BgN1ml44trAleJ5HM++RnWaQXeSQv\\nCUOPxmo+bpljfp33M7C9dZlBjygk0PTmKdiX8ShPIgGaPDs2q9TPVI20DU13eE/4LF+bPs+sO+Y3\\nTp/jJk/jqIDXiRwqGmG0U6DD+02WEBIx7CDWejZehbeQDNJQbI5xCHLnTY/z4QjmMZN1BeEif+Hj\\nPwDhlKnKC3XZ3DhUK5XmNXPmwumPQ3cZuqsQEuSxgjwuw/5BicaLTCSgd20Ic3ftoAOlOp+st0Hv\\nVF4O0FCAoVQSXjaGlGg0EeKcExKvbwVu7QT68AUiilY1tkzi7lYVKJIjAoWemb9CBxKB48Z4bRdk\\nVhjSkja7g3mfBoKOrBhnIogWLtg+e+mOb+dyQC1SdAli5DwnaiRkc3GoooTgC6RVsSWRhJgQh0Me\\ns31MIOYIU+8dXigad80MNevJWLLaRt2TBJbLDik9QZRBnelRy8LIaNAgngWuAKv4a6p3uS/Ld+yu\\nfaljS/b5rq9vuZKWrOYRefw6n9nZ5aVmwWvzjv1wh2G26/OwW/HokwOxdb2VG7ZPH+ZkTcSSWKTE\\nN12/wnsOIk999kVmpwOp8o61jWDe9xAMihpHzYJX4yk/lV5gm6sOIXQH/MHLX+T3f32hWZ2iuvKl\\nsMarFIyYRxW/ATE40MTrMfAjq8+TqgTrPAfep5/jm78ucWk4YZ561NSlIihEOk4rLUBauBkDh/M5\\nP7J3jxcbIcSrUARhYNHe49s/fgWAX7QXCPEaog2qhjWJD3+g49lnI+857rm+OnXj7pyJUbnRRPbe\\nvcKGQ0rsXQrgHbublXJ8ZsdumBlREo8tDvnExy7QHi+488o9bi573GXIh0yJYi2KtwXsFuwdwFZm\\ntXUPne/6sydU6MubACXPsPltuPIC8PH7Ht3DEcy3bsG7j/iTf++PcXfvj7nDNkBlYjWlMITN7ZFj\\nT02M5OwUtngEi7vwD/74z8O9mhHgF2+UFUoSqlNNoZiRachTu7qPQkFlQ6viTUwpvqJR6VlqhVgK\\nTfEGjpkGVKErmaG4jroUIRavfKtlSrWkG4dSCAJNza5i8cKiUgh2ilryBh4NSPCsQqo4/2SDiNB4\\nal3NEgpYteswELqpCzerVpnOXAs0zoWIxXV1lIF5tnp+iamLTda6LY6XG1odknJ0r0eXEDau0nGp\\nO6ELPvFH6lyRqiRXz90bPwrBlmQiRdzI4+Kq+C7tIRjX9YBv6H+Ui83P8OLVPZ74zj/O/lOJvabj\\nuWL0monhttP3MLTvCa0nI4N589lYBgoGjx/t88FbB7z/v/obPCbJcVhAe5m6MkV6hqT0ts2RXOXr\\nVh/grl7zDFyWvK/7FM92r9LYKZr6M1ouuSRiUVQjqpBKx9FwiQO9zmPdh7nBuwBo0pL3rf45XzP8\\nC3bTIa0NG+qHru1D9GD+UlYu/Nl/ny/MIovrM25vLbiDojIn5JbLecn11T4AH1xc5jYFCZXkhvAN\\nx4EnTjPv+ZVPc/2HfoQ5DRFhGDL/IpzS20W2y1cDIzb+zgwbkXeLqCTvSpaAkWjzMVd4gYvlC8zS\\nbR5JX0PkeSc61P6ZdaKoHrT2bvPcty/4nr/8EdLOPfrdU/aHU9qshCJk9cUxhkDOiVAuspu/8U2P\\n7+EI5nYCsyPuXYLXd2+TQlU1k4QW88KPjrrO67eFsMBKInaZRVjw+HwPmk3nkXPspzfc56oEx5jl\\n+l8LnGFqPPBRrc5KFbQavzPn7KKRbWZhcPHwiFxmLBqvFai5mkEo7ho+jlF/xoskqbJE4NrpCTvL\\nnoiRc8YGI2xYCG46CAkjnDdyZd3paWxKGkmaI7bqvFd/7+bupUj9fRODlI3/jpdgynLM+3wK5D7R\\nWOHqK7d4VoxedeLVj8F8pM5J/XtTSr2HkUxD0pbHTg6oaMDv+dC+0KaOdp7QmOnLyrVCZIUG32sV\\nTlEShUBoQ91v+G5HpdAQfI6KUqJ5g0kYaMrxlGdoXBtSCB0a5pBP2V3e4Pmc6W23FidXXEn3mA+3\\nUdLEnoK7Z1NyAAAgAElEQVT1LWoNcjbMIIpwMRyhw8DvJ3PUfca/r/Q8KvtczrdpbagJwGhuXNy1\\nUf08ys6Cl/aMVy4qyyv7EJds06DMCRqxdMTJ8sAPok1cbHfJJlgeMBUO2iO0Hbh8acZTLYRu6d/X\\nGm3syWFAVVEz0oNOvM4M542Pzl5eY6u0Q8k0dsLMjtjSU4Ilxta3NaOlXmDra3NbZomiTcTM9XQW\\nQ3RzjfriYJGGSPHOJ8rDz2apQjcLWM6X/sBaUwOKiyZ6dpcnF3YpAbMls7lympdICKyEWg53bqeX\\ndbyiPObok7i+jFrMrg44Qg7nMbWymaU/wCEU78jUQlbzIK1+jw24sOy4sH/IzSOh09F8OpHVF7cx\\ny3X/xIhWCELJUzC/1HUsVoYtlyAuB5IzVbZ3HW+9o5b6B2OUlQljVk32xsX6kk37sPXC4LufsXAz\\n8Yg3CMeTrZmY06/we5kHP/dWlZiN2z/8kywaoZUx6I+Bn3pulZ+OmxYUGZXXY2VsrOCv/sADvFtf\\n/ghmzKyQ+wPEGtAlnfV0IhtBtDCqe1pZ62rnsQXffyNWCtty2ZF6F2yzNBYDQ91F+WcFPWUmwrwE\\ndpd3EYtT522TlFglcg1/ltzKEFpXNiOoF7UzSrAj9sI9Lnb3JpZXDn7NY65PVHEYjerY5b0iznw5\\nNeH1HXhtF46bQ3LdgbkVQ4dE2N8Z7+8RcNsDpbqa6t0tSDFws4XBOtqglKEjD9T+sELKDu6M1+pB\\njjFmrC0ofb47aq54f4sRK3xbSAyma+nbkVM+ITMd2ABdw0v/5Db/0Uf/FszN/1a62mCl01uRNK4c\\nYPCff/qr73ucD0cwD3NoW2RmxKbUUNzWhC6TWNZWh/V2MIba6EOPLloiC3SJY+nimhHZZGN19IZ3\\noyXZDh0LdmpxTet1LiaYVP7n2LzERsfWAxpqRiO5ai3MwRo0zziJhSMdyHEOeoEX/+cfJCGUaVfi\\nWuFtZtpSa9HJd1Gt1N8d1rhlAxdsxqu7F7GSmWfjepc9GNciotgoPrBeyHJ1g2mqJsUQ+up+4jKi\\nYPQySiTo5AMJ3qAlpmRTX3jDWH2v8rp4wNFK0RpESDETJCIpIjLwxMu3uDxycqv+tNaGEyqeqGNV\\nvzaAjL6nZ23Wfu/HUoxsQrSesDpkL2UuraBTaEtCbXBWkURfYDcCeDAllM0dVOFq13GVzp8CCeTK\\nLolWgCrbwLhAOzTn19Gz5GbU4bdIqUCGafbXAq4uOurZ1URn/DyG+j01cxeZFoFNizYBl4ceLe3M\\naEgEO6UpUjtSxz6DUj1Q/dvrO+oxFoqYG18XIUikt0hbBmYaGAavDzhMmX0e1jnwQKHRWkNTydPn\\nlpE1VGNSofWrVXXny5i14yqkvmhOBpLsfueMP/LnvoPlZVjt+leEAHlI02cqBa0Q7KA+r9/qtB6O\\nYB6jd7aJEGmrXsQ4gwOjaUKo/Vb+s09eQ0AjITTMR/0a73/3btGNYQROuMxheIQTvcaezBE7qjrR\\n60xdKOeu2jmM4Csc41EVXMe74zL39AqruXB7W1jubHE8u8JR6Mg5+wMqZcqmm7zO3jwu+75PakZc\\npnbnTGha5quOmQR2TwbK6yc0ZhuO4/hDucF9LerFzVC5wIMWVyc0N8924+izxE3HtTNiofoqClki\\nOQxMmfXIJcYFijElheSSCUVocTstaYTlUMW58AXdT7EyAaaGMGPkZGv9d6N5BwtgX8ao+iDbxXii\\nCNdv3eP9swVJW2LpazKRSNKSdQ0jgS/UHszXXZkXVz1Xb+7TDL23zU/ZntV6yMb7x1bJjemrlRNQ\\nNnHFcUZW2CxPBsZrJ1Ez8YVmfKnJRNnbbGwd1TtzLpWOB/NsXDw+4ZoOlLqTCFbIEnHW+HouqlHb\\n9bVqlHtQX6TM7nLFLCj0A10Psd10px0DKA++xrVxbiZnQJMpV7fpt/racy2u658MRDhqb5Eeh1vz\\nQ462l9AERCM5n0yfMz7PoSiDQlFPZuD+HrcPRzBPaXrQu/FEWNFjCMqMGT09fcW3gbq6O7xglrGc\\nSNlV1mqqirtjZ5A5WM+hXeUffG6LmRywbO/yF/QRVO9Ut2/q9zpfxG/RGQmiB3a6JkLHjIzxenmG\\nH3vliJc/915e3W3hW57h2T/9bXz+XXNEE6vVihg9Gy14UJde0EbXv5u6gBWZnDuiRIJEViXRti2z\\nbqA57nn8Jtz5s3+frUE3MjSdts4ispYTEDBJZHNx/1YXJE1rpDsbmhpoBEsDc40uBYChTXErrN6d\\ngqIlDOiDTv6GsWacLkeaEQnE3HiLeijVYT04BVoKJs7HVmuxivV6MIsUKQRbOWZrCxT4Hx/Y3frK\\nRjCBoOx2A/GVOyx/6EfYXrQV0nOecTCjTEwr3XjvWYjJUNqikDK72VBzeXzADbbMYR0PgJvJx0b4\\nsXHdLtMfRjAN1kVxmGI7mO8SipSp8KRjBFMnm23AvE5ZDTAUn2R7/cC1/Y75ySlXZs40ilbIuPmz\\nQ5m5nnPV4wGa7FTHIoVFzjx2dx9Znnq+GyHV59ZhyjLJvjzoMT4f4UwI8B2ASKn5t9W6xmgwvXHx\\n8HunNQF1loGyEliFnrLtu6GuH5CmTFTRWKTCOEPdtYHbuTzMwbwEGFqafdgre/TWI5opjecZQ1Is\\nzCbtsdHgVUp0Nkhq2e5a4glQcl1C60zDcIJ+w7J5Nz95vIvMnwJZ8B+GVxlyi8SayW9oD+YNeOWB\\n79pNQeYkErd2nucXLfBz5bs4siVaGobdD3Fzvk/OA2nbGDdnorXq0rqdlqpzekvKYInYQGEBZeEF\\nFfWmhUZdpvOLdy7xS8MF2mHbleoYM/l6Wc8F86IdW1fmcBFMAkmq80sxmiHCkXJ6Z+W4bcykJnHt\\ng9fIsacpLfc+e0Q5gpD8CwYruIlvlX1HSVq8CSkbLQvMDKvt01bvgWS/p4IieVZrBwVVpSQQawlk\\npBhFAwP9QxPMOxJZFUsNF4tQbq6Yl5O6mHmwtsmUOLA2MwAkUSRPGuJYYNYoOgxIFgotfXT4pCUA\\nkSLVb7LyvX3U604t7JfCusM5gpVJn8UXzGppKEauEFsokay1wYkqC43DDX5s9XtweO1UhJtbFaZb\\nzPnM//qDFDKpau0rqSYoru0y7khCgT4482OcI6Eoqc4ZbS8QdElIK/b6BsneV5IrTt9gHuTlQYb1\\nddyhFqPBw0w0T3oQI0ugY0FjOy7jUJNPX/es7qALhI7QLFCBuLWg0yUw0LZ+vTKjoXqicspoWSAE\\nCsN9js/HwxHMAXJkZwkn3YKFLsiSabYCRSGqJ+8iTCyOkDOhRHYF0gm0CYa7uNDFGZ74WlcblCQN\\ng8wQ5pjMyTK6Z/esNSLGTAhG78sHPRy9D6wylBw4yjMgQGkIPcRlAxIpw0AI0QNXzXBLgSixqtMF\\nGm0gQlo5/CDMwYzCkla3CF1h3rdcbAHZJoVtRDakETbm/VhnKWLkubB49ArhCT80Vb8HwaBZQn8D\\nlkfif6yTlOtb9GFOyMrwulFOQ5UhUAbJiPhVdhkArZl3plDcwFnBdPT+dJhHqyGFmBJz6/zzquln\\n4iKtQZzPm0ODhocHaMmLd/Gp+R9Gwza5O2bWtnRJQXrEepIlLCzQbKiq86fDFgDJlhVGi5Uy6vdA\\nG/UESDNdtVsLEok5Tpo3LpbmfQGmQsjVNEGGioKXurNRUhA01xsfMhSrEg3ieHZMkH3OrYdO2uZT\\n01FdIHpVXnp0i5uf+CgARxe2KE1L6TvXMhFBQiRlI0TXDR+TiEhgUDdfiSZYtmnhkBDZPUo8errk\\n+JO/znO/cYfb9gXmajwRdikcuWakdDzInfRmMidkQtWiNK3NQGakLLzWPMfL88dZpI9wGK+hRLLO\\n3X1sghr9Rw0LmhY0NJWW0dAwZ8Ab3jyn16rgEiqcNCBvASI+HMF8JXA38sn/5UUO7BAT4UQ7wuM7\\n2E7rwVjwoKGeaYcEi9SSvniE9EbMM3b7XRi2GGXajNE8TqcH39SDS9A5yRrQyGh7exa9GyNc2djo\\nPrhRKuKs1fiZMIOyRXs6R16DrW7bLbQKdMUw8YlfSkEluMGr+FEmjBwFrQ/nKOHQsMtMFF2B9rB8\\nBVhB0WbKADPUVUvWv4Nj0SHSt1BmTDLpVgsy8xmkBo/K2voFioXTAN1MkQFSdL0OV8wWzEIlzAjZ\\n6n5fzCs/GGn8XQPMarFJmWhZjNBMKqQZznYQp8PkMNJ7IqU8uAf5Kx3HZYd/+FsNv7a6DLblUstl\\nRqIjSAIp9GU2ca/MjFA7J4v0hOJmD2MwLzgk5fSnRKpuWEJDrFhhUqfyBoyQfbeiOXrQbN3V3qmD\\nSkkZaxo0K6VkcrMihEA2T2NiDnRlSZRIpFnvEoDxqVgXqB23HgJ85iBz7fpzAJxcnJFDIPUOx2VL\\nbnKiLSkltGXKKELxhQg1xNw7SYqQ80DTzNi7ELh+u+ez925y/deW9Kq8/1HhKnMyp9RAwYMM5uMY\\ni/cTswqrqroGYcErpzv8n79ym0UvvDZcca/hkms8GsEsj01D6hw8yKmmnOpG7qxTRz+TSCCSGDBK\\n3YHdfzwUwVy7RykvHvPKX7pZeYgCeyvytzzO/PnrrOZdfaVM92l2KmzfUW78vV+FzqAsOeII9AqV\\nu+hqiGXErQKkzhkfqUDKFFVKSWjINCjFCiqb1fDzZY3fabzNrZ24g3oydZNnjQ4FHVxj9Y97fuIf\\n3/Q7syigp8yfu+pysGpQktcC7iW44a29s+ceIwXnqRMFUk/UlvTSCezP3Pm8DCAtpAv+feM2e6zT\\nmBdm1pWsAqlgQ6GTforyGlpKESyP/MgAqwJzgb6jK9BLj9K6YUBpIRWK1h3O5vcSPHjnXL+/GnrH\\njmt/4CKr6NIcpYruNQMcffoe5eYxVz/6Lob5nC5ALIYEISfY6uD2Tx/wZrji7/bY1yf5scMrBL7V\\nDWWsYMkZDsGKGyhoRG2Fad1JVtDYQocUI4epDAmWqhLjDEqhVOErclMx3UJSMPWmlpiVEgS0IDtw\\n6dldBinkWz0Hrx3Rlhk2CChsXZmTLnZYcIaUmdBkb14rR5nudkHKG+d4qTnW+HMpiV5f5gM7T/lR\\nLxJDyUhTyNVgRcKp0yVLpJgLyIHPYRFDIpTkmuJmQhBlEVte7gp3di/zU2WJ5m+hGf4p32Qv8zF9\\nnQVHhNHN5wEWQTdKymdqaOYpNoaQ2OXF1Xv4J8NzMHwHdxd72KoHWzEpio2wLyDLJfEEtmNLkYWL\\n6Kr6HBhjEM7BG7IhMRKAdlD3CLzPeCiCuauJbUHecFXXU5q4yzwGUjsjpYqB12AeVJkLYJfq1a6z\\naewXB6zo2TU6Cs4JXWDSYqyIIdeti3t8jq4kKoXCGiN8eyH9S8kGejee0FSNpQOELb8OEpwaryvY\\nC8TLezQRciwEhDCA5Mxy3wvH4folcgNRCqhhORFzS7qzDXfGc641gMbufyoju2V0R6mGANEqz1aC\\nb/rqg9+GQBq3AFGcEhojbcVBG3AHnWAwo2YoZdoBVHZk/b66iIhnm4RM18CyTRRRihZSFkpQmBnE\\nhi5C1xopCj1G0QEpRrSZGwc8JEPFxcIArCSIggYFiwRLnrQFMIuYGW0ZyYAuf2qhUCq84aWgoeKx\\ni6osOLpqhYoxO+upUBAVECVJobQDcUcoV7fIERiMdDNhcZccB2gy249dYPYejyOZOiUGaBKsbkB3\\ncEpIbwwZ54O5aQcr2O79eT5a9oRYSLlDmPvuZPCdmiRoJbrhOdCEBtVC6QuNtAiBlLwPRJoZixQJ\\nPVB2SGGOsE3JgaYobTXkeAfu4nimbD48HlvcvGYogaxbLGkxmTnnvZziwJG/ajKXN4hpm+Gun/ve\\ncBFp4cigjz1NNVfRIt64FSGtvOh8sefhDubIxkWyzQtWHEPd3DkVzyDHGICWdWY5ltenItLmTShQ\\nCsKAygAMIBv4k1VAGJlof5vvfjvjS8vgKz/1jOb6xitsHV+zQgoZC+amA+r831HUviiT4FLAjQlk\\nOvJQF7jE1EoMvMGxZKIqSn1fxVXVGQxuCi0TWW3q9JOyvjcVCgiVUqejSe740VZrEFbNs0bXgolW\\np1gZebvO3nEZg1qcrZS1zdZcFZt05ETChivTQzIETBtCcLjJm2p6aLNTQKfr6Fb1y1NcZ4UKb6lN\\n5+O3rMVWrv0usr4fFKEfi/7FGRNWYKjicVghxcCqgVWEGAViQ+5bsAQls3IbUtdRh2mh2c443CaB\\nct45ZzSLmX6vBxqu077i7fw7iwVhBl0u1bAOkG3MxGHAgalHZgAIDjepKjkZW1Iz4gLbPcgRtKeX\\nWBWjaIMRabSpU/CduPebn7lODwXI5jrxSMNQAqZzcpF6b8Znxo9s6rW1luHz+/zy33+Fo/aI0gzk\\naMR37XI8W/nFkIDk+l5z3FyOe7ZeG+Avfui+R/lwBPPabOKz1fDuMTeNbbILPzmhYcTRatCYFuFm\\nel+whkL0myp5Q+BegUCWQBqt2FCkRNYcrLpejGa40+X5Eirjk+VTOfu3c7+LNIC4jnf1J/QOAe/k\\nszBAGBjZASaKN3hAEiFIA9ZuVC/98z1gROfqSkHTzLsKY+9UgdLW4zu/9Gz6ptbtoHqLuJnDIcaG\\nG84IkzAa2pb6vWzoqGzghB7Jp+Bjson9Ka5EJyC1UF1j9ti2P94bam0/iwcdoXhVkIaCVR2TB84/\\n+rLHUDpQIS+ttvgmmicbFh+KpNIS4lj38Az44OdvUvY8YO4+f4lu5vFSzdvsw4uw/4V9SI17+9o6\\nM58YJeM9HFkdCgw9VgIdLb0kZ0X2nfvO1uaeoRSyFbI6FFJyDyp03QwbHJok3acAt7n9FTyZ+twe\\n//TfecX/FiK0K9gT4ru3SaWH2EIy97S9W7CbDhm2zz1JbjN56CA0HgdOIL16AMvt2sxdd5vJyI3r\\nu+deiI3SiNF/SYv5l5jJV6aPSsGsVB+ExEDy80wZTQUrnUMwZajf4Kwtswg2g0/u8OonX8WVERXm\\nxiOfeJ7jJ7oKg3KmT6ZdzVh9+iZHP/x5+Iv3P7SHI5jXrM6bfD2A+dAzWR3U+tjG7/5vuv6QM/80\\nTux6cyU6VUhqplfFodz9xVWH15Cgnp2gG9St6bPPB403HNOb/a5QG2SmhWIM+JuvO7OGjFlp3UiO\\nWXZZf2LZeM8a7df128cf7jt/1wyg6VqaTlv+ccdibCTGU+fS+n9ZmTix67b/+pnjR4+L5/Q5407g\\n3HlX3XOQes/qcdr4+T5nxGT6Luc/PzzBPAYliUAb/dxDZJAVFpUcXXskWAsFkppTt2Z+Lt3c6GNy\\niMsgW2ERawEhZmdujRS8Wu+QcWc5znnLzgyKbv3WANkCWur+fWL+WGU2BWeRJEHDDM+lvSFtTDT8\\n+8Yd35skOvEicKH+ksCWsN0Sr8+w0IM2dfdptFlYHvj+anF1j9Xcd1zFBM2wcwD3bhXk4CogWCVB\\n0J6AQLHerQ+lY7OF58GNMbmrfLda7NUqaJfIBE2YJEy2QBaVjeUFWalzeJrmYrhMtydWag3FEnNp\\nEXWSxjpuFWIMzFMgx10GufKmR/mQBHOtgbzaQwFUEaqsTPQ1o5CDG7rO09jGDutAru6uUrd+Prn9\\nM90LdLRlClhZUMplKJdpLGPaY+ps0Dxm7VQscB29uE/9B+rrMhtxaPyHGqDljFlzS4qXycXI5SLZ\\ndurDERl9EsVaLPuk9ezUMCvnFgyPaCHhTucVtnATWJfpL1IguC405rudPFponbkH5wIvOAVGeocG\\ngnfomrllX7AWoQdd1m1hBj2kESFrj4Q5hAPQWHc/rkUjkxhUXh+DGKbmGLsa6Alz18UjS6CXBphR\\nJkaAU+7EcM/L7GIPYmOv8MMxrQFiakjRKqRX/yujyNq6sBnGnYmNu0xPOAKhUtvqLK5aPJRxrqwT\\nH6kwiy9u/fR3KnNILRCKa+64qNrGLrImLCZrv8qxSbQIiI48izFbkDpn7vNAyJha1GPIsd6SqoFk\\nwSUntIAVN1auj/0QoA/ZXYZM6u4urI9zPGYNiAXPdBk3qLHy0VYbB7N5jJsPj8/zt5PDb6YGVt8q\\nUzFzLOyPaqQdKp3LJZTx3aHuSD0LcfBknRyWkLxWJB51rFSIUQSkvnfchb7FeEhmvfshotm5eCTA\\nedRifs+zGc5+cKw4V3Kfz4JUg2FfA+K4IFBvtjfPgGcCxeZQdukSzMq7aCShkkgUhtyR1DE7M2d6\\nRguUypgwNUpZC9+MxR8xn0olGahQzLDo9MFSQHOklYY+F1RmiF0haKRpnya2S6TZxQZFdHzo67mS\\n/SG1wT9IpFb4jc2Fr9SMWzYElGAF3RKaDvKKGBsIARs2J/ubjQJ5CWbo0GNaKFWuJkiDpgUMpzAc\\ngC4gH8PyVRb5OuSeGVswvAorMFq/iWXwYGHncicxSMkLnAosB3Z0ichANxT6cB2GPcrQQk4gK6zf\\nJbQDOSRKqlhEgZXFKvA0+zLn4oMdfRyDSX2YkRrMazFfBKFqqvhDwKjMPl2jums7a2RcC9obV3K0\\nGGP6nHF+eD1o9BR1/TRhsgGsAmVeX9ncBfnCYePxy6hVxDo+vgGuq0NlSoJCiuR6DllZn/soxTDK\\nNJw5m/H8ZPrdwuCgvoywZYFSYcpNOO/M7nZjAXgr6PMth7+/eEQF3NZNxgthAcybiQwlSaxFa+eh\\nT0XZCn8FIE0UwxEdKLV1f21TV9SDvy/AXovCzjw5Z8ZDEszh7DZ/Dqw8cAI6w7PKXKaVuDWP/TTR\\nMdPSM93IzSRzwoDrnyQg2pDKh/lX/sj/y86lAz7wvjvsXH6JLp2wTCve+1WP0sRCzplA4PZrtzg4\\nOODp9z1NbBu6tELCWbhFSsB64eZrtzk5OeLilQtcevQSGsxtz4bAC59+iShPcvf2dW789ocovTHk\\nL7Lqn8LygtERCQoWvClknRYoBN9ZBBGCip93cCw2aCZbDfTFAwShQHuXb/yWA55+5h4hvozFfYZ+\\nbd7whqaheu2KQA7CzpUt7MLKC7BU2zhTYg6E/cLRzYTJRa5fexff+PEFZednYR7QrDSpJazctxH1\\ngl3Io6uNbHzXmHlm3x2ZYTs3OElKkcv8xz9wh1uHH6MkpTQCTSRGIWmEpkVHXW9zsk7XtF/BPHyw\\no0w7xzoZzQPmxOapY4SNPKiW6b3eIg6lwktlM76xsaCPJrZGzeg4Bzd5r4JJ1ZMfRVTwWsx0EON/\\n6v9cABkP6NPOCKaF5M10oqfPrwtDZUe9IRQZa5G0adQazLRybXzl9D47+8cpoI+fVTb+zht/Hn9/\\nAwHhPoSEc18FdbcyHev6u4uMBAqtcMpIxx0/L2xAoLA+5o2z35wbtvnft4YPH5Jg7lsRKYKNBbWg\\n3oGmI6xXKKGj0DpMQmQqcZeILwDduc8dL+I4q4IXA0vE7CoyfCvbYeD7v/8pLj36k7RNZpWWDHIP\\nDRkpHhwpH0AkkEtBojDkHtXxZtX12bRycJWgUBiwIOQy0FZ8MA8fRIaP8XM/tcd/8198hJJ3MZlj\\nOXtfsIYJyiBlmDVA5/i+OgxlIjAp1vUQfV+T6wNcRU3JtQoZ22M+/o0rvulbbyDxN5nt9HT90du6\\nK0UFs4yqt14nRkPpQpJCUwTyHjk9yU/85GeY7ylt+CVmRAYFiYbsFYacUVWkjMFtzKbWw3Qd0IMG\\nlDmz2IBep41fTwwdOShFT4FEaTO5yTC4kYGoc99FBpAD4NqXMgHfsSEmFTIoNZADJRKKuN9jjZFZ\\nCmHSqq6ZoEGRgSg9jRWCZRqBld6BsPQmopGGG6TCcXjRmlwDpQcaC8cQIhIuEMKKqEKnxyARdAA6\\nAu705AqIDqtpEbxbegAZRdNgCub3wczF/JlNlQOfxu42lGBGtkIx3xGoDZ6UbTKUKGT3b6zQkXjA\\nTPONR07rLm9Oth1veCoDSYWSgz8bdfjCtc7QN0GXumxyv0CpdTHJUwmTSSLBG7iEUBq0KJavQL4I\\nJVQ5hFR3Hqneg9GjIZMl+c2ddmtlgrOSjsba49+dzDAqar7VeEiC+YijjjSeDtp70FwghDmUFao9\\ncIoyB4uU2KCLBcwPIF7yYhBjK//5k17fLKtZgKAUJ0CjYZ+2uYGyZBYzyiGBQtZMIDofXCIanC06\\ni0piYNyJjp8twTFON3PITkxSr3U3omgzI6d7QETDFtm2KcVvsLMS1Lsdc0FmAcsHIKcU2a43sndc\\nLUrFlg/BFAmrjQyj1C15BDmt/3ZACDeYbd0gcUhsUyX/jcfuFKr17/45fd3ut7gnZEWmUTEUt59D\\nl4ht0ZB890RxXWfJLl4mRoydX5WxKaYijuPDVYo3NKtSuet+dxqd0dkhFm6S5ItouwPNHWh6SqOU\\nMKCzgObs8IAmZBYh7vOwBHPLTgmdkAXAGEi5ptlKxekGwhDJduwYM1CWS1icItwkllNmjSKpwPJV\\nyI1rmI/drlk2KADgLAnBLLrJSD6BITLjcRhOKF2C5YlDd8MKYkKHDJ06H74UJ24UI9qOGxcPhxuW\\njG9xzgDNHPoK59mOR3icRmgVKqXCpLaheQ9UCLFCOpbJtP57ceaS1B2slRkyu0JseyztEmxOGhK5\\nzKEsMa1Kj8G1UwxBEu6lWwoapZoXOuw17hTFtNoY+mKcxiKxGlasdvEqTZgRkqDasNRHyXbN2Ss2\\nB479+Dds40aIxud9V+9ROwVujxkDE7W66sxr8Q7gUsa6xf3HQxLMfbhiXAd6D+afIzUDJZ4gMqCl\\nx7TzACwtJUS6ECB8CvT9wDYq23Vu65lPXf+3BnUzb6jIhd4GhrICTjBOSdZVSdwM1teMJGJ09WHM\\nE8Pj3ObIi6dI3Zpmn7iAlUTQlsGWFFuBGl0/gFU60zBOFmrNACwPsLMPzTFOHUsgrhZoIuSQINz0\\n18ocZcTavRkphJbUHDLYAbFpCDMDXTHHpiC9eY3WG+B1MJ8hlHpXRvFhFwseFwAQjBBdWz8UX6SE\\n5BXggqgAACAASURBVBob4gt0nJZpz3JkZAXUrE6DMrJwG3E1yIArljcSWOy8xo5+jjy09NfuAj3b\\nlxPN3MhqxJydsqoQeuPw0S9p2r2jQ0PABtBZhSvMvCjc2gR1xKYlpeDwcxOgqddFlMgh/+33Fa5u\\nfRG1u+yVTOnTVBwdMbJRJXGEdVJUctUon5nQ5YQuIkP8AsYKOboI6Wm+98/9T3zTNz2Cas/21S1O\\nL2W02uiUkgiWmadthiM4/djJOeVApu/chOcAmmaP0nk7/9/+mxn4KocJa8KlmLtSiXphqWr5+l4U\\nRKJfn1JL5gq0hqWMTPO3wfrn+Ec/Oudn/tGnaJuraCs88t5f4cLlT7F3KXL90UtYk+lLRkS49/o9\\nDu4es7W1xfXHrsJ8/Kx1wicGjbbceO0mx/eOeO9z7/WXBEACw2D89qdeZxHnyGqbvn83v/wbj5Lv\\nKdp+mKjNOUhnA845E58MrHdWURPRHGiLTOYyqIu06QzKqu5e36xGwUMTzA1qNsfl1/jqb7/Cd//3\\n387+7JBh1qMyY5A5kov7RqpzX7ZLZPv7/yh/68+/zq//wxuU7hqkvTdeyAk/q6tqrRijmagDGgrK\\nioaBbGteKJJwxeQWN8vdON5Jj2IM6j25bu3UvPhmMmC0bvdlg9uA6YpSjglhRZZtz8ibuqMYapek\\nJWhu8X1/9WmaK8ecbL1CDrluc30s+jkxP44YnLZHFHEueA6Oh4spi+Uj/NB/91uc0LLsVswEcjOQ\\nqiHH7zRGvr2zSMoEg5YxSxdlGDIUxV1ehKEMmHZEWop5kC2SMREGWnQSC/KlYfM4Ng/JfUwDmYF/\\n+7ue4qZsM5RAG5+hSY1j+powzRgzYmW3YA1b/9of+J1P7ndplFJg4R6ONEAccMPyAGGGBCUPKxAh\\nNwKymp71ECKZA3b0l7ja/CqUm2g5IM6VXOoCdh8aZpHC3PyCWIXtWqsQngpKx+niAsjzvPv9v86/\\n9WeeAU4hJHotNBYwcamLVgqWvN8gfAmUTw275KOLAPydv31M4qugODvF64V+t1OuDW/12fGeQJv0\\n1kVHK7qxeruuv5gqlD2s/yClfC0nq1M0HPGJ77zMRz/+yzTzGyDOmhqqVIdkg9wwehdYyOt5VzEc\\npV6r9DxRG0o19hDNDNnhkcY+wjwu6JfXuHf76/hT/+pFyO/B8pazczYTys1d8/T3Fi90z8AqPKx+\\n70zdPUvpgW1C9hYFK8LUV3Cf8ZAEc/ATjdBf5PDmNi/8dCDHHZJldtoF3bAk5sCoe5xC5AiIlrn3\\nqkK5CGn2JgWPzSr2ua81F50qtRJtBMoUNDehi/ENa8RttJRbf6Tj/VYNXF2uMlVlvwYzJauBZJqs\\nCNHRobEyn72xJ24tSEOiPwCd76DDlQpteBaRRUklYNWJJoYBM++2a2RJwLf2OlxCyy6zxthqZlgp\\nk2jPm1Esz98Tw6mM7kvj12MwaLQho57FlYFQCk12nepCZIU7BLXjPsaMIB0Te2L8hjern9GTiVhp\\n+IX/y7h9vM1QGkItbjbZr3UWDwZCQsyI1hK7Q/jeh8TRObsKIa1APgU7gPY2IczI2qBEdO5BrWSD\\n2U23zgGkvUKc3UL0gGKHzHQF0kGFuoIKZdzU6Vl2RsC/VxRnkuBBSsV3U23M9GnF1qwnNjcAl2FV\\nbHK4NzVaCtaMLquRTXUSGEuam0HK93INp3Thhh9bjA7RSMCkBUlMTJpQvBNWD/2cw8WK4TscYRIr\\nXfIUxNU+LWcs6Pp7paHvA9puV8XMU+aLeyCvosBAoRH14w9GCS5slfHeEl826o5YcGgVc3VKDCbZ\\nWav9VUpDjxJoW0PDPsWuAS3ytkPqiJkXiIcwN3SRsOjXQSUh0hGyJ0shzKE5gHb5pp/4kAXzAMM2\\nv/0Lr/PXX/hJb6AQdf3bkGuhcwDEsaag0J/Ava+F4QquQVJ4Q9V6c5ypiK8pTWvUbrQ2cybB+bec\\nXQ022aLj/6+xZ/8cO/O3qZhTwYtpC2YKs4jMIPUH6KVt/tr/8BOU2W2Ix/U1I3OhVtAdw6kP8Yhl\\ndjjNSSllFw4y/bfv0uUV8xnkqfH67dKyxteOr69t1mRyMbbLQMuSvdU9Lg27lDCwXIQK3JSqeSYg\\nNi3E5xfU+23T8wjNqPKjP/IbdPvbIAu/71DPX3x+aPFdXalzpNyG7/22L+H83rkhoXUMmgx2CHvH\\n6JXPcuHyHNGWUhwiTaYEg/1HXoE9v9azi5fR9hXaeU8jvjMMUgPNBFFtzs41/BdxTZuAoBIY/TFH\\nrozanFAiIQ2M5MNSHb4CAZdb9Xvgs9Z3z3YumEt953qMVnOdQ3v1c2kPoTFCSGTpESlgo2xxniDD\\nIluI5ArI+TNisYHmHpSAyOM+fUoBdYcyvxBCyYmuCtLlsiSEgUBDXxJowIV1U03aRoXxse3eh8eB\\nWlSe6IMjFBMnsDHZPiozVvnAYVor0Huty7HecbHZ/HkzY3f102LHsHcLwi26cAnTrn57j9IhuoVI\\noMSG1LwKl86TPNbj4QjmU0V8cDlcrsPxHkwTJeIXdLPEE/GtSo9foMZ/nuRdzwbWdUDayCKKOnQh\\nawy5bOi1COsGjrca6+k99nqViZUjEx4t1RgggjUMqlVgrB5PCTBLmNziiW98kvZ9Hbn5GEN/uu5u\\nHB3Q1XnJHgD9QZo8Gy1XwSUhqdLKF5k9+yLL2QlNHIlR47X8nc6rwivYiHLXrkunjbWWmb38Koef\\n+QX+kO4x/6m73F707H7rN3DYjCyEkX9b+wHwwLDJaFP/45lrGZiT6n3/5u/5E9ztv42SG2JoCCWS\\nVMm1kWVshPL3NeS3ASH9bg0beseE7RjCXZ55ruE7/8sPk2avuc2duGTt2JzW/unn6Rt/YPu0ZEuu\\nMePzeNYcyEg1wz7DWQM2dzmFnlndZ/aeB9Tdn+ureK1IEKREcp9RzYTghcGE141gNNAu62fkbUAt\\nRdwmYtU5PJHiDf6Tv/kfkC/cYrm44frnJaC18LfoG9QeB+B0dkCu4mGGIBZZ9Aua7mn+0vd8knLj\\nYsVivHXeRcFrnNBEMIcxrRwh4iwsr8O4b+rYLVzofXG0xeRHC0y7G4evcq3tjIXcWX2uHZFpRUkB\\nNPh3e4HUYdw3snzOXTdRSjyC2S3++s/8y3Q7Nzi5eMJBxBlEZUYJu5gIjRWKdTTdYzx+66ve9Lo/\\nHMGcUk8+OF4MrCep4g0gYyDfoEZJlY6dAoWdi1HjBXwTnElGrM4NoKe/1eC/XkPPlzrXRzGSjvzn\\n8fjG1fisoTQIYiOHuHiDj7sxVLeVHhYnPPoRuPodM7rZjO5k16ltULtXq3PJeEijAXCdjIGCa265\\nv+aOJZrLL3EUVswpbJPO7EPeaog5dSpIck1yGa9HQlIholxOiaY/4lq/IuaOoZmhyWhUfa3Em5gQ\\nJmdXlfHb3/wYRmHRgY7Lv2+Lg5QpRZHGKaClfn4gI9lVdEwMtEPs4WgYAryzrwmIzTC5xtEN+P/+\\n931K2K73UcBad9ExrwOU2hmah8hWNL7h37yObd8mq/vTlrcogvlQwhS8QEgk0WkWzjDaNEDuadRQ\\nMbJoLcgmkiXaGowGiRUQGIvWbw83V4Stpt6HBOkoEO0SWydeb8rMwFqHjYowjJ+72mNWSp3Xddec\\nG3LaoW126BslNpF0unQPAMFjR47E4kCQm4DjBATRCnfmmsZVYX5pcSZN/fv4HI0Vs0mfaWOWSj0Z\\nFLXGdw6TO9RbXJfzC6AU0NZh4eYiL/xiwbYySVoGizSVBDFowEJhFuYc90uiBI6WwLP3/5qHJJjD\\nKAzkQ3He+MhrdbraeYgDG7PyMXO/HwB7NmCc73EYJ8x6JKRaWW1wDzdesw7j/pqNxaUGcRspuJtV\\n64n8N1pClYk36gbSju4Re/otuLlY0s0HUjhmNIHxTtO6SGzQuYoAZeFFWbpJmApJlHKTA7lDkQF3\\nTQ0MG9fk/FXd3G5Ohr31vMflzTCaGAmDEYclW6Vnq/RogtgqpXhxVKwwurCXjW8bA/lbJdAjX6YA\\nJ/ND7pSbDMyI0TnaXfAtukhHm5rqT1n8WtkMeOQtPv13cSwM4l3HeGPh5s073Px/PovTTIszlcqG\\n84duJB7Wg93k3/2TLbar0/14q0VwKijLmOTE6R2jil9Mib2SycMJs9OXacsWSxJJCkHNjbVtfJ7O\\nf9fbgecUQVjlmtEutvjL//X/Bts9qkf1XrU4zJjrZBsZHGVidPl51F1r3ob+GYgr0jIj2zvYqbFu\\nkBK8MWmEIM+Do5swxxhP1s/9G2fjRIPYgFuVM234G+c7fe/bvU6582R02OKv/Kd/F+QOtNve4Ww1\\nMR27wHNwxosV6C/xVz7xp+77kQ9HMD9zETbV9Db0zX/H9uzx3zeLMefx4fUFd7q5EvUCkue0BNxV\\ncdRCKMCiIvkVzydP+FqDIqJAIlNQ2ul7MoBAUzHxUc3YBNQCrbYEK/TiC5G3afeQOsgdeSik2YoS\\nOtJW50JxeNAP43lsoPSe8xtqhV6Wk3/prBhDzuRYMUjrWVV/zTEPcd735rTU6d9llF6txtbj1Qym\\nznCRwDCbcaJwUVrvBWgzWaNzoOlcB6Z+spzbEawft7MFNICegYFIRhliT2mqNo+AnbPPGtrldIWc\\n13t6fnL8no0rzwee+cRj2My8Cc0eJ6UPEzRViQqvKZTgiUCb1SVygZQHmvYO8er/zZhkyLTze5Ph\\n5YkpeI26HlrhsoaBnZfvsvyln2YvXuD7Pnqd8PJvM3/yOkfqypiJniBuvB3qdR4Bw7c3/n/23iRW\\nuiy57/tFnHNv5nvvm7+aex7ZZJMUaZm0KAuGRcCApaVgWAsvDBswV7YWtLTQwiOgjUHLKwnwyrYE\\n2LJF0DJkQDZkyAZtSFZTspqtZrM59VzV1TV+03uZee85EV7EOTfzDVXf160qVqFKp/F1vcx3X+Yd\\nzokT8Y9//COO3KyiBdoX/sznuec/DzKSLc5/SoFJp6XnQJ930mBCxzUS8L1iOOt1jrbHnH4HXvzS\\n90BfaAmXFvlJl1UeWuPw7gh29pS2KymLs9C/+e1Mb09qRrV0i2slWlHGxtQ0pa7ywJdy2j76Gpha\\nbnWAl/4FsJmAjbcHZ9OLirqzqkQ5/NXj/WHM34MhDj579CXE2FEYEHazxEOWaPAczaPn6Ioi+0a4\\n6plFoxXAZ6xl4asEZSy54bllzr2GhgjC6W5m5ih23g50eoqyMgkKYvcBrBnSmHrNgB9Mmq4r3r12\\nESV1gSMGTI1KpWCUxmTQ9r8+LjIU+nt1Wb6J7JBa96XgB8dGcf/Wc8yfeZqvfHXmk5/J7K69yjTG\\nMda8+f5t/dXlCCpd+llbulSoJFPUhlCpE0M8mm3shYf2JfDaKguvDNLeg5Gfg/GPgp8ElTAl5dEG\\nkInkecFwS6rgURQk3TufK+vs3Od1brBj1aBEe1vTo2iDtCCemXijkrowmHBSMzddWG9PGeVlKM+x\\ns8rGjKkV2kSEGYYnAV1T5HFViP0cwHiYwjP/yT/zKV7Uu7iekGs8qynpsv8qhcNG1qk5zvUAXsSV\\nYYCb9+ClfwAvfvUUTg2R1Nh6BWkMKujR7sXoov0sfYM6LJ2Tg5/PG3c9t4mdx9ZjFA5pw299T2Dv\\nEvXP7NduDTbWC3+jsDiK/ra7zofWmCOG5ErxY0w/wVR/HEszc43wVGRHb1TrHh2zDyO3emAtFsbK\\n0ntSwHJoP1VndiPnjBVF7BOYHC+fEnBRJAp7Y2QTGCyqJbtHrs3TMvZ4HvQpUZiFZuAaLt0WQHhX\\nElhhS3/6Ob/28tSNYXSGTA81OzwSHdUTNTkPbt6C40/x69+ckB8/Ylh/JehfgLFqfxevlo6sT8RX\\nziAjTjrXXxI6B2jvCXWJgPfjuM9rvHZtoJ5MzZQo5TiS2b34xRcYSnnoe0VLs8Jkr7KlUFBmvEWJ\\nb2dQ91gzzZvWttlHvmUkTTtuFGeYCr56yGS3meZgj8gQ6PgShbV5F1j91d97Ma4Kvse8KDfeu/4a\\n91Y/oOrILmVE2myqGVMhneNO65JsLe25Zm/NvPUGpd5kMwByCvUMX60bvBKsmdELa82s0wrVYzqr\\nanGJWjSclvO19lRi7US5YGk5ggGn7l0NmReoMbXPHFgzyLXwmIe0QKzxYJV9ru9QU1U51y7Im8F2\\nQK5deJ/2d+elHq4aH1pj7mgUQ9gznJ19mhvTvww6M22n1jy3Lh5K52DskyNhEMMxd5LbeXodKWhf\\nDrNUXCOMniejTp9BbUUi2q5J0+wIrRWPkJFQSKuE7IBiCw5+0YteKu4a3bH6gdcdItjNM24JIt2H\\ntO2gC3fm4LV3766c+30PO0Ul9Lk9sUuwzXOwINxI0o1wOdj2gsv/JBz3/b3MiKWlQCZ5eClqvcmF\\no62LUtcm4WJk+x4OE2vRUfMWW7Rji0zsofhU5FE6jdCaDHTnrvwwgq2NexT/LyDeStZF8FGZhkQt\\nlbMmjSHDSM4wXSILNE35ZQ5cZUwuw2SHP5dkWKtmrE0KOEM0H2/wyPm/afUUHQyRSFA608LaWr6n\\nOTGRruqKnMpcjTEF/bA3dI+/rAf3J9ZxlxI7/B8H/+oBpHn4edbOs5RYw48fV3j4lw656v1OqHj7\\n8aE15gCkYx6e3uW//JW/z7WT32K9yjw63TRj3haedOPdEpetA3rt0RYWlMGDYaKopRDjGRLFSlSZ\\nVsHLA15/7VlcPhHQjTtiIS/gzdtUV3JdYSRyCc9Fm8dVm4bEOV6sJpKN4EIWcLeodJVCriuSDqjG\\n3+29rrcz6AfvLZQ26Is2lFMcb/hnxRlvX2eSR4yNlTWQqcwgoVct0Kh4T2KQzo+xHLGSE1RXJFZh\\nWHxcYC7X8AClFVEBcPRDf827Muoyf5zeuSq5LR4i7BNrAohlVPc0wGQZaX1qY7tuGkSPGcYeSqjo\\ngpsbzsNba+bPfx7d3eDv/f1v80dvPUcRYaY0iS5vkg0CPjBLJepxrzYoFyG7QKmt9ZoPhk4k8X2B\\nAJWTBgvGnNpXAse59ixVfy+QFw85ATls+XgAd0pi9qOAMW2g1oJrid6+ZV9wZ1oWxhe+2oMtPrO0\\n9UOX6KnIXo9GmjKaIugg7ObAuJMJdTch42Ge7w93fLiNeT6hbn6af/qlz5D4Y62UNlpl4aGFQpea\\nFYWtkmzACpATqCA1mtTNuSUxkxNMhRz4TB5oeAsyZ3x3Aqwgn8Q5tKS6C1BHsDvM3xoo/3BgowXR\\nI2Z/FOwrEUwHwBhrLC8RYeuPQpLXokKt1oBWBo4puxusf+ouR8+eMKY1IzeYeBI9cyhSm1+mjSsU\\nLIWEMklFJL5nJ4Xxxg3GlaGsetM+kISwXqh2WZrm+xN4mKMckTnGuc6Lf+chL94/i856AqaF8FcT\\nLkY2Z6/XoQx1gF/6IebBuziSjah3QacGfngDtw5ofkupvOzvjglNSbQ3N2j9Op/g/i1QDVGR3O+6\\nITy4e5NXjz8J9dP8z3/b+dzNpzF91KpFDRoXPQxrP8d0yWffj8ueefiSQ7sH/aQS2vTSJwRRazNr\\nH62pd4bZvuK4b0uB+7doVCzOrWq7qXU50uQphC9g9hTizq4UBpfwegVI8x4RpVeCHMKmkTB1F/Sw\\niQqKN8XWiqJDYmvPYfqJKKQbnMcWLb6L48NtzBf44iSCqzzBSYZrBP7d3YWUwRQeZeqDAjI02pCG\\n5ogbngtcrzBU0NDawGsYcwOq4vcS2ApsDAZWW9neWn7hd+Be4vf+1nfh755GCXh5CEdb0NroSSlW\\nx1Y6qAjPHIXmR8flSg060wOH7Yv84p+fOb59QmEij/ZExjSGNKS7lw21cNRqaEUg7CzYMbtZsDpC\\nyq0YOoxQ7Rh7MyfWxfgfMyZGiidsznz5V7/B5s0fa7StoXlhjZImteUbOo01w7yCX7r9hNf47o5U\\nnFyVueU9EgEJtL7ki6HosBECaUkGTs3I5wV6AZ4IpsoHBsU7E4LQNCmeqKMxzzPbBpWJZESjW5Gd\\nM219A8oXkn4Xx3kDlhu0B00uViJZiefWoDtHPLIY8QYPNYN7GH2K7COYxYPXA1ZUZ++4hWqpfZbd\\n2QnFX2MYBqxK2H4P+YeadpcqjqO/rbSovEMyHYI5+B5pQnoCSUa8Ps3Z2aeZd/eC0PIEcMi7NT7c\\nxlyi/VhlFUyUBKtP3CV9JNDNpb2ihy2Vl+Hhb78KdsSSireMS4V14eaP36AeQUntdxLZ9eQDo8H9\\n3yywiXLjqPhtXGBnn8muN6BcgylB3sH1mac+f4sSzUyoDubOrUl45VtvUt64z9PPfZI38g7DGcio\\nOHlWHt1/E15P6CNh3t6iJqPykDI9LvMew10C10zGI05JKbqOazGOOEZl4L7PzP5Jvv17d/jkM5+m\\nPPppbqxucjZv2Q0Tvna2pTJ64miXSDIuCn99XFXOvxpuUeaEcsL6zXts3/gsPgtpHIMeJnvjJzUv\\nCWuXRJrfP80pkiXG6RivGVUlmcDODvqkhicn1kJ53S25isqO1TST1oewyuMSoDGWJtiNf8+Sc4kE\\n5EaMSSpPffRZCh4bixmqKZyQBVZrPz6xIe+kP1ngl1xWjOUI0xWpQ4uN+ugqQN7XRhx81dAKCOcG\\nc2RZoca+AXo/0DwaIXkoLf7Nv/FN/o+/+1UYNkhSrCpjK9aoAqaXheaCctjOty3JHgF0GDU2jRyw\\nmcC1kyPOdt/h7PQV4OeeEDd/98aH1ph3yl10hWeBO8ogTGOLLhsnWyST5miqy2qEXQOGe38tB9JM\\nXcG8nqk6tMYCiZQlNJC3HloyMtA4gkjVxf0Iz6KEcuK4hl1tPZ+Ne1IouQa0Y4BW0ryiSGDSbzpU\\njQk/lUZt7M12Ny/wK//xd/iV8SHD6jkGeYFpOru6wo0LwXKK5N3tz90mfWzAk1PcOPEjXvr7vw+n\\n8LFf/CIPSDgf59e+PHHdv8hL/9fXGJ++w7M/8Ryv631UMoMn7n3tFfylKTznw3FFObXkwP1tvg+7\\nTzVXFmoNTnHLHbboaG79UQ10CKjgPfSQDsf8yopv/80NMgb+lciYGyXNC6vVZKBKFHsldqSmnmW+\\n45Gdcu3fvsON9XWOAZMng1lEhdK4LAqICLXJNxuOaGZcHZNvHpFTtFWsEiyOQWi01J46lxZZPKm5\\nCEjoiIASt//oiLx6hq07MyVUV1JQCquPnMi15flvdaIipK40ao74ESKJqSplB/O3BrA7sDoOdEUE\\nGIN15mt+72u/iKSfw/Np9MbVFFEd/VhvnpGQ6kD2zIxFYZd5gwmNmjcHGBF0Ib2u3igWToP7COl2\\nRPTv4fjgGfO3xav22FyETBpSm4ujGEZ2aTqujfIkiTy092YPbmunAnZ2IZ1dkaiynzNWlSRAltAi\\n339T/H/fzaVxSIcVzI0fOxWYC8VKGLnSjFQtIdhvBtXCeNXmTtQaethK/H4zQv4c6PPMZQY9psxn\\niytsDocx9SG7MvQmCpv5Blig0tWN3ZSBz4DDve1HeTTOuKwJCOYE8oppPuLBdJPt0RarTjLF/W5A\\nQIsx1/13nb8xuI+hK+YGrNr9MSiNi9+bdtMAaG+VhF4bfvr+KOm3rx/x5h8UxnINEcXdKWLYcBrU\\nYs84A8jc9ErGxX5MlrmX3uDan/o50nPHnJXXsfTwLSmCsI9wNNWQIx4StUaHokxrpmyF3XyC5Y/j\\nnHJm9xkt47amZmGqM56OEVWqOWlyuuzsk46UTzg++zgAv/6f/A4+fZ7VR5/i5meuY8mYcEjCWAY2\\n39tyev8eAOsXrlGHjM2FrIpYJZ059156E310hLvEmkqfo06KjxALsEU4LriO+OoGz/9Lz1HG6PVR\\nu9x/Oz+pkLfw6m9UatH9gpXKbBMMM8/8wvOUcb8mVGMKqkN9De7/9gOYrsefya41hHnvTOoHz5hf\\nsgyHXDVZ1n+ElT2sb9QfV6yGDYuuTi2U03CU1wWwaKVGSsscSkAtDhVqkegpDbFYU0yAGW8dXOJU\\negJ+ifZc2mcKjEMYpbSG7AzjillL21wiAz/OcJZXkGZyVsrQ2s0Na/CJ6AG6Zjg5jp5IugYplJLR\\ndOtASuA8YeqQJePNMKocU7NFAjIpUlPM7CT4cAvyliEdYTj1bAC7AXKCjschup8g1UxNCQZD/CjO\\ndQGMZV97dRGsxUGv7m4T594rBFetqleeBJL/QxszBewItzFQueYs0HqhBsNnRdQANMGwztSoCdE7\\nvPbKTUo5xjil+n04RxU9P7oxn9lhA+y0UsRCDbEoq3lkfZTZFEeGu3z7W7/Jo+lZrlliTMrZ9iFl\\nDMEor45YZjVnVHPMhyccpR4x148A4PV74ImdVc4GpQywq2EVj0SRnLB1bL7p7rUmfrpaGqWPK/Af\\nbKi+ioVpRrXmtCxVnsTSsNQp9tgKpgEmBYYSXcY8ZIA1mik1qLNF11LauqsgE34Eu4O6PumOnkON\\nAg5wifSNBJ7+Xo4PoDHv229/AoDNkYBzJ1GYpba7Px7syAWYGSxCzklnIIFFEU5yb41nh/CmjeYF\\nZmoSloa63TxqBSm4rLv9br+KHSToZtr45e28DaKarH1eM2bqUW7TW95Ba4xhgbknh2qOtgQP0tvn\\nNdU7F8JTHfBDvJnzhvzSa5ubB7zCvAbSUSU+21dxH3wgepNGu7FeLQjS1AulTXKN++mdmdGvO7yi\\nA3JBO5GDhXFxx7lwrq5tVwWwdOWx79mY4l7Muo1u0zjMx5EQt9IivNIsRoXUIi0ICLDc4N//pW/g\\n8ggdFJuEtxW76onETySe/vwzzBkmnRktcc2O+cHXXmYeK8efuIGuBvLw7/DLf+l73P/6Kzx46VU+\\n/vM/w/3rE6Ror5ZPlR/8xkswJ3TOV4JXl4qGXElpxW5uvWY3vxjJexHOfIuZQD4GjDMqiUptFZRv\\nygbScC7auplW8Xy3Uzg4kqIl3VGLjPu3W0tXNvG6GZjVKOvofeXNq05iaK5goYqqRCPm1gAVb447\\nlwAAIABJREFU8gpWxgaY1lAb3IrH+qGC9uhX2+ZsGZK+p1Pvg2fM1aApo4lbYKm0sguFKhlrSnDi\\nBW/G2jVK96v29WCk1u+wvw4ju2uLqXnRzQuA6DY/GI1/bZgUkscpiUdZfRidKIRBdwgZb1BLl0Tt\\nHdxh0zDkbgxp+HJnwkQZgziIOtULwbvtcSGhxGgDtB6d56vKHjcieVbF2ybQW8UR3qP0MpZE9Uh5\\nhZccajI95Ra2ab8RnRdCkvP0DL/0w+NHS1RFx+jDz34/jBwSuLYLV9ANfN43reiORYVFs75fusWD\\ndvsI5IDssOktvqdvAO0/XpjkWR6WM2wFsws2jUzzLUgz03iLmYLMTpk+yqPTN8FPeVCe51QKtRaS\\nKGvLsH0ZpgErY2vEfGHIeXjOTLA87udhuQP2ZsMomhtb27Xi0Y3JDs7fG6TmM6gyzwT0aMKiqjqs\\nYDeHcT9/KktUjAJZF18J4r2A2dPyFwkJn6VHxmUHs6NDqydpx3rfLBZo8sBhbA6KLNP28RvuOz0+\\neMbcekrPyUwR+vcmED40oZ/QfZh0RvAwop6AJhDVPMaaYmH1m1TVIz6Tg5KGlphDC5YqNc2YDKhn\\n1ByyYYmYnF0lj2hGHDhNS2j1dnEdKsBBdiAT6pVsFVfHejGDrkHPQHcMGMX75xlmxuDNuEsT+Wqr\\nLXnBtDxZ8Y40SqRqUOQ8Gi8nV9ANZCG5x7klmgZIAn0ISVA5Qj08rlgGZ3E+Aoc9R98eiz2fnu3t\\nvvpiUVO0hAZNjCmihvfJkJTxwcGP9wZlBdHhyvfw0jkk6eL9OAR73+pedWPYnI60w1ijw5qaSjgp\\nNEw3zVQ9IY1tLXiCdB10i+gtilRkEIo7khP4CbAGGd/aBzh8P4F5XexZHjNlJcFRzKvmcDXZZ3FW\\neWCbIpmYdU1ZotIgKKwUztIIq3WDQiJRSR6XvtDnYbq4X7PCFJhIM8IV04haa+sXQrGD9EvDUETB\\nNLo/GSy6Md0xqzTFbW1rNmQyqAdB0/KcnEVWe6n1f4tQ859xfOCMedwmX0oBYrR4iAiztBlGxJpM\\nK00FLYSM9rfalgch3ppBuMUilNpe9+49zVNtG4FJeKrhlNqC5oRn2l4s23xF2JDy67iUxjwwPM+Q\\nZ0Z1ksyL0ynVGXQF+jKkR4xygyK71tUngTiDKJPeh5zQHokAqoqkvSBRLKj9/TukCJpNkCqD3gZJ\\nZAxVZSXwMH0/Elg6YrILjq44KxkgvYjkR4xaGXXC3Bk0sUv38WSkvG4d2Nsz+yGwxv25tkpYGUl+\\nC7eh0cuE4Jw/vov8H8ZY1u/hv0q70V2H5XGUtguQk1xhCBZ3FHrVbnC141hxa7+2til79GgVQQ8p\\ndc0wSj9XDv77IwY8S5EPglgU+ezlnGtrutJzOIvbi/ZyHm/X3A1l8+gjibM/x67K6c1hs4Z8LlBW\\nc24OjTPJKN4WgbcoWEL4qt/qvTJ3iGIFlFhbhfPqwEs3kNLyQTNdCMRIB/dODj7wnR0fOGMe06Nr\\nWXQltAPvLinVoi8nc8Xc46/yDPMZOq2wnQTZVAKXLkpMnAJME+w28cxTa8e1mWHeUaaZOpRIZJrj\\nUvHaw2KBaQM7AxlD3N5jQ1E1hvQG1f8RVrfgM5IdLzMUZeUfp1hhtok8DHg1kq1h+gbMj8jlEYPs\\n0JxbABAespdXcSuY73nX4gJl3+nnMA/ZX0N7TyvsJqQ+S5pz6x3ipKIwfRNmY/QHWN3hqVK3hZEj\\nmL6C2l20PofudmRVVjLwcHoZilHq4z3ni/bqKuTEXcCOWA8/w253G+yEUoKBA3ce+x1/GCNbFOpb\\n7DzxpgCUoL25NljtMQv8MKlw9c3gnGWjN+lrhtE76zsMXhRxKV0z3rVVPLO3O13Kgta3dnnzsSO+\\no9NMXaVFeN2INqdHCmIpKmGl8+L7ZXa6aqcOt8jVevevuneGXBDZEpvFCg+XOwIBi9qDIhWTEpFc\\nZWkGEsV/LRLw9j1SQHeoH8evlm6HITZWVBt1OTZNsRTceQrO2O6ZHtie0nJv0SibVN+yX84/y/jA\\nGfNKaQ9GqYsRC5lb9JQ7/tuM9RXy/AhKpaqRxJimiftj4sR+mjflLltRkiSwykoyRSu3+A4P59/h\\nmdnb2hHQKCp4cLbiWv1Z7tUVU9uRkxjmFbfKiSaYv87IawzmyATuleSKyszwzFf4L/7Xn0TyWXiW\\nJpBhW88oq69StTWzSEKqMPgYpd5FKfpVfKigwWjxlqwdp8TaVohvzsEqfpBAkwvG/DBclSmKhrar\\nb3GWzkhDCOSnAnnKQRNbfZmdTegY/NuhCKt5wOQBNb/JdiiYGWrKuq5Cjc6eRE5AuVhIdO63PdE3\\nX+OX/82/wvaVjyDzC2S/Tdk9Av7sE3zHuz9cZxZahg/gQnaJJsY1vDn1marzJTt5fkN7e+78vqNV\\nRDrONmAuCqmpAKokSA+BSuIoEMIOoekG8oZEJdsM3hUXE+gj0FWDCh+P94qHwex976UrKHoUApnV\\nRgAwklVEp4AMgYHowIQTcKIJpjkgRzYshXbW4BDZN98I730OoFvi9lYNnF6qoDaAZCyVEIVOA1Bj\\nn/ADKKtRck1CR2deLjk+NzaigGXxpvSuM44zWiGZsjENWMpru9/hqc8+tuf+z2GWx4+OqUZWs73Z\\n2p+VU37iduFj61e5wWuk3YZJCqrC2SDcX9/k9fGbfHd+yJYBkRRrcGdIEj599E2uf/JNPvLwUQsR\\n4yFqgleOr/PG9BXupxPmFOI/rkTR0DQx+szzd3/A7fwKQy2MNA6tDyCF6SMvMuQ38eEhplPjpzvX\\nh8SGKbBmdYo7yaPTuiQhJaF4wWlhtAqI4RVW6xGf997WnmOvl2/ZFbdxpbkp282kHIygYiU6nR8N\\niAk7mxhVGraopJRYS8IxqhrqJZJQVRg0RbLrAqpwVQVovL68yxxWj4orq/osf/yj3yYfF8bNBjbH\\ne6Gq98EwCd+3qeQ3SqosEEjkP+5Fh3b69e8hsCcd3vFejyrdmiYGnTAtiBqDCaMOSP4BrsZKCtrw\\n4VEHJL2Gp1NySqxkBs2Re9EVpO+DDSjrS9W7/ZwPn5962zg6lCeO6RmqmZXcDAXEhuVkUSRt2Whc\\n/6hl2bO0wf+jjpC/T85HLWckAZPaCZW7LH/QPWvvkAf7KluBKNmPdSsLhhI66NGPNKTBvMOpQqPn\\nHsK1Ud0a75SAaUXYC5tZ5Mqgh7f0HJ4vWMG7w3r54BlzdKnydSxmhAXN8Kbd50/f/F3+xO1/yNPl\\ny6znl9nlkftZ+YO7T/NyvsGrxx/nezzFvD6m1sAUXRO5znzimd/n2Tv3+cK97/H02cxNM7a+5aU7\\nt/nmjTu8whE7PUJ8xBGmBFoq6+OEe+XZj73O80+9wo3dGZ86m9HqDO7MWfj9F2ZS+iM8ylASyBiU\\nQ/fKWnXxvAYgS4qmuQJIJeFkD5nYagZJkSw4pzDu47ke5euhNT0si+6vAcSYbApMVWA0Q0VZJ2X2\\nUMTLObNqmKZKhOwFofhMEiGZs9bm4aXo+m520MOyfVc+Z7UuJmb3vdJB9s6TK+LK3emUf/eF3+CW\\n/L/c2mSOppnCAPzFH2XyvOPDi6O5heBWSBoKLWIJ3HA5pZSvYvK9OP7AHTeeDG7ajwPvfZrJ9Xkk\\nh9DUmoRWQe1lrBRG/xh1u4MhMfoJPn8X5gck+yxDLdRJSCIMtoL6DdiBscKvYGL4AQzcf241OkAT\\n77INXo7I/jRSK7U6OStSnVo3ML0OwFg/RvXCMKSGYkjo1pTvUiyRTIKxKAM2PwP8LCkfY7Xpt4hg\\nNsP2jLo9omonBjg9oUoyapkpk0OZsbJBxcPxmGukW3xid7aJKusLOKRbRifH5jN8AktreiGbD2OL\\nSPq/gIu6ynycRbx6pw36B8+YuxJN3cCWZEikrk0dtR3H3OMGrzLwGg/GG7z03B3yf/Cn0ZM11+SY\\nL+iKRxirBGpO8vAyZr9LoXL03Rd586/9DVYvvspDWXP8536JzcdOUAprE1YWVaBzgrVJFBSt1my3\\np2xPzzj6+u9z9N/8KlkglcJahWsOrAeqChnDKQySmKUv0RSsFFJ0UUeoFCrRjsLFSSLkg4YSioIM\\nhPB+b+PcjWXgfXtaFfQwMkYKWYLGwx11iPuJx4LBqVQGcjDv2KBklIRJaslWp3cX6tJbox7oplxp\\nmA6jqctDGsVRBRDlaJ3YpYdcS8YthEEmdvbuUL9+lLGyM56av8vN8i0GccwLOwsGj6czfHyJv/yr\\n/xqrF36Hbv4uVnge7nVvV/15OIoWfLjPziZ8FIbi5JpJJVHVmNNvYclxMcY6kC2qLhm+ypRri/AU\\nnZXxdGAlA66nV7KgzjXcWqK/88dVdWYeMo3fXzYsVYWq5FnRFM978t+irgKG0Bp5kaO6xqbCka7A\\nWickHyiv3uY//FP/C7K7GZRjb9XZGe67cLP+HKe+4tQqKkNELK7kUtFauFV31LN/QDYNXN+CJbOp\\nxiMS4/ivsqm6wEXRJMlZy8Rte53Ts6/wrCdsAjfFipNWI0Wv8Qf1JmXRYR6XvHc0s+7003d2nn7w\\njDnLfkhqj6G2TPNEjRL+YqhVUjWKzMyp8iA5jwbYpAncMFW2FDSoLqDRrwSD0wQy79DiyBE8ZMMD\\n18D2NPDQrmNdBEhK4Yy1TuyysAXmWpqAV8VEsNmZt2dMa2fXJrubYxpNMGyxfDNl6bBjLeG7pFmA\\nNkVcl8lzmdfaQr+W1GrcAISmP9N+X2URQqWwuXR/IbIRQlP+DdWN6HrUPJHQW9yPwuNEvnraqLeP\\nO28olk4v4WCx2wzM8xw5pir47GR5F7JLP+J4xl7hL/zC7/IL9W8z7h6ySsJWDBFnGoRHxxs+Z9/g\\nd9ffBhossxjMzkTZ73p6yaO7CJnF344Os80cjwOzFbIIKSuu0dZw1bpaSYMXPAs6htzAymuw9BAk\\nKXKtd73Kl+Km+M4LP0t0vT8874wyiLD2gAujh4lgUtFRqO2Db+SBjc24OoojqlCddDQQev5x3FgT\\nH+NL/Ne/8AOO6xjNXMywJJSceC1d53fn/4cH9wZcV8zSqaxdzNe4Vh7xs3/iNW7VXThtdc3kwpvX\\njvmej/zfj77NvfWdJeywzURyYyyFT5+8whf/5EM+8eC7XM8J2U1US/jqLj/g0/z5X3uBr/EvAkN8\\n6cIO2itYvtPjA2fMY0+3hlD1vpmHCb+YlupNmrNOrDBuF+fsbMvJEOyCKhlkahStDKnidWQ04Xgq\\n4MLowHbi9jzz7GY6B1csHkrbUqoXVrVy61Hh+m5mkZMVAv+jJXwcUG+UwQYa7lPt7UMbFslBGH7R\\nyz30oM4ZB9ljixc43PtcQ/w+RGyvvscX3+9YcP9db5Pbth2efPJ2zoU3L/zQdMl5QyZGtF6o5y4/\\nvTvMrx9pjHLGU/WbfNq+zMoekb1wajNCYa4j96RwN98maePjH/SZvfxo+9Z9+bVfYMMkCVaTsyNr\\nKJpjjgwxnwIPT1idkSFggMD0PeoFmqa5N0Mfkr1+dcu/K4TSjHoJIhJJEUXijUej1KZNn7TL3M4M\\n0hqni4FmZAiXLOkY69pBfeY6r3JUf5PrRcmmFI+r+OZqoB7dZVw/w43xGJHE1sO5GisIFZfCyjbM\\n29c4evMeN3Zb1rLmvio/OLqDDDe5cyzk8d6yQTow4KRpx01eY3f2HdZHL7M+PeXp3UQhU7lDzjtO\\n/DoZXRp09Bye+F6d8Z0eHzhj3rme0Ka5Z+IyZ7JF0Uw3msXgWVP4/ht896/+NW54ZRiiqCe6i/SJ\\n6WSiWlRJ7DYP+cgrDziu8Fw1vvNX/zp31vtEUDZbuhOFjEuiupHcODFhfbZhMA2MTg8oYMTfJwMX\\nwaUAw4HJ3a+OhYR2BVRxdSB+uNF0loEebHXdcBomtWF7es67eqvRCzXCENhiTON9wVrv0f4tlw37\\nobE/3HgCpknSN4cerAasFAyGEkVIGv+KRHrq/cEyh+oPWJcHrLcvcZx3LRqaWSWYyJTkmJ9yosct\\nQoq2G9AZKhdHj6PgfIL4wvdSyTouMFck63saFo50xJgY0ooJpTRCb2ZAW6FZRrAGC7jAgFwBjUUM\\nHO8rvUOrAtLphnuTtmDH1nIxyhDPdsnnCAOtCYpG16PowlUZCF14kcSQlWsrx+0VTkzQqpQUYnDz\\nn/032P3sF0mrE45Sgt2WPEQF81CjzmRKIOrsHhmP/qf/HfmNL4FvefMjT7P95X+LcnTEJ30k+Uhp\\nyme7lBlnJwtgH0X989z99pt863/4NT71ndewUqlUTrlH8UJhag9mAEsR+TIEzPLP2SyPH75Uex6+\\nOxFGqrWNksZeElhvJ17YTjz7aIt7sAuc6OojzYM3IpkXBIuEqJOLMUhM3S+8eo9P5ai8PBS3rxqd\\neAKe9uZgx2cOkqKAgmCCJIyJzOJ5uYYg1SU3s4V8V169HBxz+DPsPd5uOoWFWuVBwapaIBjqiyF+\\n0sp464lKsbZJ6GI4tCVJo7/lRe+yj6uhEe3P8tx57COMKrlt2oAFDOCpvG+MuZqS6hzeIDvECqlA\\nNcFyheJYrezKWVSS68HGjl2AmDoPoj/9x3l44e13GG7PpVAmj2Yes+zvfBwTtNGLsyfogodRbo8g\\n/OBMZH9Mr9SEfWS46AXZpStYjP1BQn7PSerzJ8wj7rgL01aRWrESWdcqMDtsgTMtbMaJUp28amwT\\nMYq0iBxAnFPdsdPCjQy6ceZ5x4MsnKbETkL33LXrqmd8gKk5jF7hERNzqpS6YZgdTxUrsS12Zo1Y\\nb8JXl36i78b4wBnzQynCDrksb3gz9ChdLlPMgybogVG7NzZJZ2lA01Ru3l+tXUgRR4PSVOdoXeYs\\nHVJcaM1QPCQplvOL0zENShM4Jk7V0D+p4q3praEeOiuHj/+tEmAdJ4/vOB/2Hv5FBNkhJubt83on\\nmDAYTXjoh7jlcUd1MRqJA3PjSvYIuUPn4hD/f+txeMRFzS0Or9WbqFjj1iveBL7eH0OxhiFDh7GS\\nRa5t3+w6erxGnuU8Ji7n/PO9Mf7Rz0dJHgnK/rz7PX0rSG35ndiylFgM+QWobjnGz0N9y99c8R0e\\n+darj/GD96Oq2sWjtqdHMOqE6JwgCNd3M0/vpiZpYSTJrUDIlu/r46laOJpn2E6MDkcuXN8Wshem\\nNKHMeN8UJTMY9HxTmo31XFhhYNGfNHw2P/gObQSEHnXVS+DhOzU+eMbcAx6RbsRb5hsKirLzgUf5\\nDvf9KY6vKEQ0ougmieLWEoCaKBqegWBIAUnDUoZuMgcl0FIrPe54tCKakaTMzf1RB5UZSbFLWxKm\\nATZpywolLH/gakU0kjUHmPfbLePeqjk85L2ne/lvYvMqUgIK0lgoO6KRrxqMLuhVi/EQM+3YvSni\\nI0Vt2RzUaT0UBTNFRm2+e72UwnvcOCxski7D6qnR5BQdb7FV2AwDWneU/L7p50xVp4g2wzmQfKao\\nd2G/qEVgh2mnbJ4PwV3swGS2SO5tv7FHYPu0trCfA51l4rKvAk5PGH5Vqcu86hv44eyKb3SQeuEs\\n337zOfz6t7q2cJ4aZGRtfgkRDIRjjpozSGL9a3+Ha//b/8kqpcDjpc89W87LWoQ+FHjq3pZbszEC\\nT73ygO//1b/OkWRMJkzTAjU6zTGJUJt5rmxn4YU37jGgFJfm0AWkGvi4NG0ob/DvBJ6RH3oVPH58\\n8Iw50Ewy56dGopJ4NDzPa/nHcKms65vxK7Hlxhbz2MmrIzkKMKxWLCvVp+CjDoF/pdYzcNYp6KTk\\nhv+VMNpOqAkmXVpXqkWWPjFHYkmFkp17w6s8zUjX7JCmdVK9FzScv74YffHviyZCC+SqhOP+PWme\\nU/cWQ6wo6ISCcVKEEUHNzi1Kh0uv++clM6YUyzk1vRtveMGUhFKd0/b7JyuG2Z+vL//XTJrIXpCP\\nFa/aM/j6DpOfNLVAuPskX/GHMWwFvorEuwcVcEqQvTUX8UKVkBG+DCdBb0Lcn/TjfbowtqkZHPEc\\njcDbb1W8QWJxfxP6xFCakhr7BUBa8Vk4HlGcE6Jx1pyCy+MieCPYldezZOEP3kvtqryZwUD1Q2Ip\\nMVhwqVydT997yCcfPkQs2DmohEgePR8VZt0FRk0MU5T8ZOCpU+OPf/27rWi3trZ2DTY9gF6F2BBC\\nXA68OJ5WwW6UqAHZb6iVQANaZ6IujP4Ojw+gMZ9Bmx7KgvHFUtgNT/E//sFN/tbXhZGPgewiWScl\\nmgK09IvUSL550tjxkVh4A5g7XqMCU1spb0rx4HfedSYK6s5ghg2R/OxsgzBMzoA2ga4Mekp+6df5\\nz/QY5FWUkABAQ/jeWpi2H4eBdrh3toTy7T0umvNDjzrOQRbjGF1oshmrubL70ldJm5nhCaU6Y3NQ\\nVGN5jrV5LlbxYeDh8ZqTT38Cff5mJMUuGY+rMP7em737lY2v7gEMxMaZeJCe5b/6J1sefe9z5PIZ\\nynSTIs6XnujM3/1RRXEGKitmojzeqOBDyLN5xppkMxJe3SGU5o0F8fY9OC+PYGtFlFVFA+ICAiLQ\\nltcQsrVGC1cwhy6/3nuTVWroghsB5UkH57w9ufOgzUUC5eHPb5X/Oc+cieSqyy7K9CVTycwqjJrI\\nOlC8gFSqKTZLVHCLUOcZ7ZWfFvfFmqSzZ4/PcmE2I2Uhz1NjmClpkoVbH1IxASKqKeuUqUgUwmm0\\nwDOOmOWISMG3HFjLH0gzt+8WCvgBNOY9sUe7a92YO8XhBzyHjLcZsrErD0mmkZxSQyyqRyUBaaKk\\nmBBaBalB0ZrN0bxCSwqhJxU0FUrdMErgNtaawKobswTqmTWDOWXIFKuhzyFGsoTKGdfTy9T0Smuo\\nSyvMiWGX4JULC0D6BDk/TfbG8HDEJ6WD3zshL73yxIk5eu8RN3fOSbm4oN7qlscirhqGfWgnVxWK\\nVLY758aZsbFEbRz88+MqNstlUMlwkCAthkBSYtZjXtJPcX/8GUg/wZmdYOn9wzNfDQMlj0x+DZeE\\nykjRAlUoOTPrjMoIzK2TzwFE0eUk2v39YZBykYGCscqJNBkZbY2UhaoNe3dlqFByq3I8BJOveO0G\\nc/NwXTQ6ijYnwmmK3i6tZP7tTdZhYvctr+tcZTLMrnQNf8wxEabhGkVWTE4A1ik2EhlWlFbBrWuo\\nczh4vbdp7x2g2ZnMyG3tFvfox9r69dad9e6RqEjLchgmcoEQC8aKrVxnl04wMxK94EgP9kYhGHbl\\nsffohx0fQGMOXdM8mLI9xTNCEc64w6QrZisIp6gMWOvN2Z3Gmgvjc8Y07GiuMclKeDA+wpuQNolk\\niUkzDGesPnUEKXTCa18HvkKngemNDTwwVhtl16CUaJwhoU0hW2b7LMyOi2A8RH2giCNaA9/sEwFf\\nMLxDQ1v10JuLYxaPueP4y/Hh4VpviWVK8gGvZ4zbM67trnFr2nJtmjnHMrnKWWOf9C2NASNNtKh6\\nHH6mR6ymI7zcxjXjl/okXjbmtkjVdeMQvHexMEhuI+YDXl/ggW95TT5Lsc9Q9ZgforvZuz62NfPm\\n8DFe5KdItkWTYRRyAl8pD9enHNUbmJwum3i/xe5RhPWjhOSTGMmdZM6Rg59NIX+s3jxbRdwYK5Ta\\n2zzvx1WvSxXmISzbgzHqALyxv1zsHKv0ar38/TN2aJjy2439b6O5TI7vxEETW7nFq8NPcibHjAR0\\nIeLUYaAg+BgTVBy0zSdrSeYukCXMuMMo0XawumM+tYhoJK2JSlUAjyI+I+pGsu7lJSI6Hpj0Gq+n\\nH2NON9EFL+9XcxX8+c6ND6Ax7xNAD8yQARXygDA0/ZuMy83A2ToND6DMMMDxF05I16HnJKVlqQcT\\nNl87pb44U+cj0AQ3hPT5E4p4m8R7WORkJ0y/X2FnTPWYvkt3u1oMRAfuP/w5/sIv30LzlpRnymBc\\n/+nbVJkuYZrX5hNe+/KLrGwFKFszbv2RW9RVoci0UAPEleO64rXfegXdxeZTayVrVHqaznQEf7eb\\nOR4nbpev81f+dYPtb5Plu/u72Bde88S0YY+1OUqpNvqVN8xQnaMxsHK3n+ev/7czf+fhH8P1NkVW\\nuNiy2eBzJJMlMNFbP3OLs9XpksuQ1rkhmbL51gPqG5WhrHCUuT7FGw8+xXZ3TM7XwEZI759pfU+f\\n5y//o0f89/OfQTGKGzuvrNTQfErJf8Bf+nOfQfm7ODMi0zI5RBQ7QMufzDvvG72zNtBX3mD6+jdY\\n7TZgTtaESG42JdquJb3K8F4eI/DoOIzTjZ//STZjdDn3zpjSTiMN1snh6GasJ3LbYQRkaJeOjavd\\nx5VhwretMM0pMvKt9FP8R1/6LFLuIn4cKqQkZmwxwD0aSTWw7lmVIgGRqhuDz1G5bVGrbGKYxnzU\\nAill5pY0zgnUE6UUPEk8L6KFo/mwbAATt/iePhu9d9v1xkVYg1wuEAneofH+mfXv1Ljggca4OnRf\\nnN1Dj7NNsJpgzvERXvcsl9DUkBbu5YZnKiW1wKkzF1xAZlyG9hB74oOLsxRHmeZn+f/+8Rn4LkLF\\nG3Dz+A4uZfG6I+RWrm1O+MFvDOSpFTihPHX8DPPRTJFdS6JGKHlcRl75zWvwiOhHWZyc9tV2LlAl\\n4ZuZPD7iGX+F+osPSel7zVJfdnN7iNypmEvi9eAmSgt1FGG7vcO3/+CUL/3O87B+miJH533xsovF\\nkwdchbtHdzg7OWtqd45YGPNcM6dfu8f88kTe5gj17QTsNuiaaiCa8XJVJvG9GUVWfN9f4HS8iatQ\\nzdjMhaOsjOMG0nW2RzdZo6CtuKrLOXC5bMh4nOGNuzpa4mQW1meGvHnKtWqtIK1SZVow9FwD5X6S\\nYbmymeK+HpmyqzS5iUOU/FDHez8irb+/nv785dKRLEdd9GMFbU2VHdfEQ7nNt9MXwJ6naBhzdaWI\\ns3YWOnHxwjB0R0MJ0CnGTiYGjrGmyxKe/YRbYa2ADMypb0GVXFbU1KIRZbE3TqIqrPLGIibQAAAg\\nAElEQVQJc1Ee5g00CvAe7u3/beyWd3h88Iz5DzMkpliMNqF7q23YG14F94OQ6qoF5US3+v7Rh595\\n7iA/MOQNZDbHy3WQT4A3JM4N4SaTzZQDfmwwB24QUysRxQnC5E8xM1NkpjOHVSDrCHpCr8wjD5QG\\nciaiSbOLgs3k+hCxN1HuYT5EM2j2p+sH5xGTtLbCJyLxJCzaLgpgGSYY022UY1bDT7K1W1SN7izV\\nWnJTogNMaXzx2W8x2SYok24kCxqZecbkKWCm+hgbsyjImqTRYNqM/fN7H4wdx9gw8KgkqI7kEWfH\\nxndxz/PEaVGG+jzuM0m3eCuXcd0LKojHxqb2OMPbPNK6xbc71tMR16YVq2kTz1wAj2YZ4pC9UQ4v\\n7n9XvC47uCbXADjb3mLKt6M/uRtFjVQ7ZbA90zYOV0uwXtpm5UK6IIrWJYD7MQean4iNhExtRblO\\nqS/wA/8sIs9jckT1irR6jbEYkoxdrnBDGZ9ZY1qgNWeOKFLJuzVn39jgOjJaoWpFXxiY2TKmyuSp\\nT2qkCv4yUFekkoNaSkw39SiGwo4YNIN/h1AuSvQ8iAJ1IWWcp6C+E+PDbcyXcTH73hNQ8a97Gt6E\\nuqIJb2msmVb23zHCNjfNNHjaSzKohVh9hViiUQFAciRF/Aho+Lhv2XHCTury/dJmz84T8BTOCDZA\\nEsxWFKsUNCIAAWWKLkPWOpHXFdTU3DE9aMJroEapAyJPsSuJ6iEEJj4gzO38ey6gL8r2576fln0x\\nqgOlorKOzuf5mE19mppuB12vszRcwKZGPIpJXhmZOW4a3aVh8eBkqkWiyn3Vvyw2kiINB6X3331f\\nDNcBSDDEtbo7mo5xyYgWtrtP8Z/+5484OflXgvYqlfp0mLCjT94IipvG/Rcg28g4rbj35XuxmS7P\\nMOZh5C2Vob7M3fJ7/PztM/69n7rBjXGLqjJPzlD31E7Eo5Vfd1zas23uQNtEAIFxfZdV/nkAfvkv\\nfpKX+SJ5OG7nVoOv7m3jpdL3nWe/+Bxnxxtm6uIQZE9gyoOvPGTYKLlVOpe7yvpTx9TUS9DaOaAc\\nzde59+VXw3suA9vTp/H8PFWu4z4svAcc5uRIrrDawh0lfXbdqqlPgvnVXOvhVPDvCFhmosKw4dYX\\nb7AZbuCJ8PDbdeQZHm7uw8NMtRFydD1yYmNqtyyYwWkkKs+jMWiXBQ4D8e4kdT7cxtwbKwK9vEkK\\nzXJ1qkg3yj0ZogvE0imQ1ouFlh6DnVUzNJwsH3yPtMRWxyza6wX2MXZWmvcZRn+hNxagpvBUfIWX\\nKdq/eT2nN20ItXc6r0RH+FnawvfWbNmCgztP6JhRG8GvI/U6RTNGQfwIiEbWgqI+UoRow9WMfO+5\\nWckkCz2Z3Ohjxkg1a4UdgjeO73K9rkGnwWODM9q1xj/vAmQ9Y1tK6+6u4WrWIZgMKstifn+NC1wk\\nhTpbNMnRm/zWbznYR0ntcvzjcdxJuYWpUXXqGBvZMquzNa//xhshWWvNRKQjkEJXYskb4Wl9lWc/\\nd43009cQfwVE90VXBzepQ2U98rsUeTYY0ivU6WkA/smX7vBS/QSyCmOevCKeFhizUGAV3/GR9FFO\\nb2wp0o25kSwjnrn/j19DH+4L1PSTK47sJiXNy3kEnKesHq157R8exbKpCnYdYbycSfAGaXYWiRgl\\nOSUVaNi2NRmLooQN8BQXKkrRqAIvOb67G/MFlu2fu7zebyL7YObgnC7i41cqmf6zjw+3Me/jnPj8\\nk4ToBxO+dxN5WwPSj++rInoIemgItL9vxtyI41zJoswHsI9oTALV+F6v7e/VOUd4OLwcP8i2iuyT\\nVC0HEOfjkBJWKzPCRu5wKs8CH20QCK2tWSs/95GiiklpwmUsOGSVzGpah3eYMqUOPKp32Nnckkx9\\nb2zRih0U//dVY0Ruom2o0haZWLtHC22ubVJJQ4xpuQfvH8z8qlEroHnp9FPLCeiPUZ2QhG2nPvkJ\\n5kb1cuBQRP/Y6q3HaTu2el7mQnU4GW6g832U10m+RmqO7yTYUT0Pk4yg5Hb98TbXFvkB9kuiWEW5\\nGYeVjzPIj1PqUXxeuKN7g6chKAYw67PMumE+QPyTEHIVch3TjHsc66yZ7RqzzovkQBSlKWuOEb8Z\\n7T8rwDqi0ygGidEjCYRDaqOIIC36dkkHCdjLo79v0uGY/tntGRxCscsV6XsO7324jXlP2jUcMYYG\\ntdFZ4k3vtmGxFtaMijeQsLa541Ex1gyO05UY5jax9l6R0xvJ9pmZm7JQ8xC65oj4MikXr8eJ1PpM\\nHKO1hbbdlWgXZ7khSAnGAaamXyFjc8MiwyuApwl0ZMuz/Hdfesi6/FHG8jFWeWCTJj72J55jd7Sl\\n5sqmVKqCSyH7TLJMYoXkaFf32t97hWEzsl4fU8s1Xis/zu8+GEjDNWbGuMfpoCGvebvWFpYszyVg\\nqqDnKVUE8hjwltU4vjU12HtN7zu3nP1m1ecViGRqsRaR3YCloMSpFrjuzFH0qlzmXybRRRtalNen\\nVBrjRQtwNtU44g7kY+rssFoH6I0dFBDFOA+Z7St0vXmhQXNPpBF2pWH4PMVsd8GP6NnA/TOw6MyS\\noq+n+RGFkeIVYwz/gdZeTTLU3KCkwJRnP6HYHpKBaEYykjAb2tpoX9b5xH0zMgIGDZIxS/Vlg0Kt\\nR7rSH0v/XQWZ2z/w9hkmeqAbc+F5Xny+S0r3vRkfbmPesWzgSpmhQwSk/wCEMZJoPdJ26fjPgWcC\\nROlvcy3EI5pbPO/+HQcrKzUvXWWxaSKOn3cNzjEeYiJOZDnGFIraEmKKCKsRzuY54BUSMkRJti8c\\n3+YBq1L8iFM+wq/++n3qnLDpOYZh4OzojD/5x/44u7xju57ZUDGNcDn7HLz3vMKYkFPhy1/+p6zf\\nOIoiqrpikpvs/Cl2rJCrAG1rUUtq56gH171UJ0qz9S33kOOzvPh5AXN/m+f5no3L0IX396vAkMNd\\ndwiD3F1DD6PSoagelRjBaS1t42/H0mBAaX6GVY1qSL1O9TUiK4oXqhSSh+aPNQ/dJJyHbAMqc6so\\ntqgJcA34q47A9ThzG9t6KA0dk+gB4FF23ym+EJdmYli/Lo9Nyz1UI6kjnV7gtSxNyfv19IKlaT8V\\n4l8xyO2+CHsD3yETb1S0DomegzaaFk7biODiMc0wXwryQll0eYiib3PsH+74kBvznrjUC+91K87+\\nv4s9bTPJ2opp7pG3CdpdnIA4G8nKw5N3a1hZm6Ph+et+UgQY3iZgjQntzXOF9n2Nw14K6IqUNCh5\\ngGDgUwsnBcjBNsmEwZPoxCNJlsuIzyUMqg4UucUb/lMh5bkO3HI3bvjm+GOcHRdOV1tOmxdlYoxN\\n9pcxs5s3HOsJD0+e5fT0hBM5puw8uiqlNdiw95sP96deRGQABfehXcv+mK51g5cwFL7GPUHuodPj\\nKHvvkyGN3tocRsmCm4OVlgBmiQBDnyctFaDQWhBabHoxukfaojg8NHFMQEZ2dpOH9hxDmhBx5lLY\\npR3aosWe54l6gcxQM0IBKTFNbU2yOIekt9g0eGd2oVFZDk7Zl/M8Bzk42GFHemEf5HpsYt6lLqXr\\nEklL5LeJ2gO3HuDWttGfyzuBIHH956K0J8Cnnwhe7Qu3HX8Y0b8PAsIPuTHvUAYHhruCBOSAzkhS\\ntNKqOjvpr2XaxZfwTHQbBmgxkAoyIXo9PElpC/CiZ7qEjMTvhPjcNJNkDi+lxdIhbGQx8RUwo9oM\\n6wlPp6A7SGfN+1XEhvC48mvACtJNmNdYjwYXzD7RBGawOrBNT4W9qNu4znHHmwKbVebB0ZqzlrwC\\nDewTMC/k4xvMDxR4DptPeOT/P3tvGmtZdt33/dba+5xz733v1VxdPbCbZHMUKWqiEseyEdmwECsf\\nHMBIoMCOg8CAv0RJDOSDbSQIoMTJx9gIjMSJMnxJAsuIYUNyAEtgEg+iRkuCLYoixUFskT0PNb7h\\n3nvO3nvlw9rn3vteveouNllVT1X3D7Lr3emM+6z9X2v919qtt/kNtasknkAd3fe1mmVkVglCj2hB\\nzZepK4Lva1QGhX1oE+QpYhHrpTLAjQf20YYu3wVulA0Ze8quQ+FCDR3hoSS8sdNq8esxfDGOkdGT\\nsSoBHCtmBchKCLsM5cN89Y7y0791gZ34Bhoyi8URT/3Jp1jOFt4ALkMvc7JEDEFLJBhMYiCkwCu/\\n9DqTW7t0MZBD4IY8C8CiuQbSAhOfQ5S63J35/2Tw0B6QMhjeztlvk/mSiGPP70aPRSrK2NFx9MTA\\nvQYD74BaJwbrQVO9rj4ebfRuwBt/Sfb9WVvZ/jiTePvr1T7EVgTtXgW3NubHysjGK9aSone//Q8Y\\nT64xryFZO5a0yCBLmPwBpQVpFhAE0cGJtJnHqicDTFtYXoA4hSsvs3PJG/2YuDpETFCD2c55Dm/c\\nIS1aNH2E0u8CzWoS8cIdZa1zT9DO4eLX2bkq9LYk1wfVNcbK7l7k8Jk7tMuL9DcaPvzZPV744z1p\\ndkiKc7CA0KCmTIpx+OzA7nCZb31pztd+yUD2oDS13MgZcJIMOYE2lL71w1ksnA7PF+gAw9IoTUZE\\nq64WUvTfN6Fl6BNiS0iHoA2WIA8b7o0kLIbjrKaGePzZmIMe8PTzwnDpFV/xSIqrJCQTDA7CHfQD\\nLeGG8eo3FnDwHJSdu+7t2cNmqGUjhr7xuURBIqtGW05KA+u8iXkzM8UnXzNWi2fqcfa5tI4iz/C7\\nbxnfegco1yAkelvy/X/8MxzO9glNoJUJQzrywjEUsUjM3k1wsmj4/Bd+lenLe0QLxKYjiatZDu05\\nCI0ntUfbWkM8gmGqUPuhNLGrDe2khgwLIXiNaN9Ur2RE9VzG0OTIsKW2qEAyaBx3yKmsO9QDGgmL\\n2Dp0x9qrWRP/+odGCPF4HnPz71VIZvPzTa9wGzN/JHBykytLDHgP5iW2vM6P/6dTPv3jN7DJPqpK\\nXs4ZNKN4SXw8eJGDVz7M//hT/zfst/yVn/4ocuEr9L0b8yEMYIHGMl24hCwu0x18mL/+Z74B/fe4\\nazmdsWpoT3EGvTyELoN+nf/8pz8CF/45R/mQrP2q30ookWkT6Q+fIr91jb/9V3+DT//pa3zyT72F\\nhVveT8bckGuBplH6+Q7N4g6zf9bwtV/8oMc/Bcq4JN14DUJt8D4u09PMPDzTJkKE0AqEzqMyNorY\\n4ipciEbQ4OX0VuOZjGW0m+6wh6O0QAkDZFe6aJMo8jY/8R+cY3/6VdABSIhF9xSk0IQOHTrS13+A\\nv/nXfg1uXIJ4wpifJYy0lY1/pYYoRpjA0GAUTwyLh7GCtR6hq0k+MYg6EEIDZVlzNm3dho/htXIp\\nkDjPITPm8smVucm64IvdRQ4vLHxpOWsoxfu1lMqahUJHYLftuH3xPPtvXaMs3MscW9umUXk1kt3g\\n93QUZWHdKgGKQQyZUgxYSw5dIptgKFWz7WESX7xjlPF6fYUY65yJDTU0OHq6G96u4CxDx9j5eO09\\npKrm652OHU+rK+D3IAPJ4/Nj+ESt+KLSgKi51y5tZfxny3yeraN5iBg7vR1nR0AMSDwk8SZRjwgo\\nSfch5BqLDmjco+mughz6w6NHGHcQ7RFJBM1e1GIJkYjEFuJRDcnUTHtN1vi4GeNw1QiKIHEgh31g\\nH0IN4VjANHonxkax5gLIAqzH6Mm6QJgz6mBFQJmAKkUWricPjzgGYTVcsoqZVnUBobrWBWRJ0QMP\\nG1H1y+ImoCG5a61z96LOJg2/G6uY7D3iuG11VuyOGwyoCWBqW4O6bu1QiGXHwyraehIQOB4qZMWU\\nCw05CyF0VRjocbHeGqRtGCww9KVW+xa8ngBUAzkARSgpQJhh2o2ydr9Ppa8smXW4boUeuO2nNu2Y\\nh6UHlWoBlK/01YAdQLdXk+BgNq8hGQ/LmXpyQU0o0q2NqMr6GtrGuUP9zPxcXPKFMVbWtu4hVjmx\\nMfVjMpy4aKoCmfFZNVZyFoFVwSDNxj7He7kNszw6bD5PY3OjpqHYHAkHKHMSGXSJaPIkp7agCxZH\\nN50dsQM6J9kBJj1aS3fHxTEKhyAzsixYs5I1K7Njk0llGcU7t2X2MebVQBeKNPUBDStjViR7wip4\\n/FAxTNNG0j/XZFct85czYPxGg745+Kt6YsxDiCaKJpTBH3oZRZvLet9y9TDObIB8AxvWZtNNX9l1\\ng/AmNr3Dhz6z4OjqmwDsfvQcWbwcHEBM6cpFuJm4rQbWsurP6htaGXKK52k8GSjkYVET4T2JCSVl\\nl+k1Ro5lNSRDPcaSMsMy+GRh2WWt1lJKNRnRFyPX4uvmIqm2l8CN4+Sb/Km/cNGP6kOvspjVPuNS\\nMAaCgKQWfe4Sn//ff5/2fJUmXn6LncsXfdUq9Taxar4Qxmx6kTu7DcyvQD918qNjVrQefWXzaI/u\\nvkzZvUm83LN7MZBCqkKh9TYn3S4HFwyOLkKaQ3eL0nbQeodLBQ8n+R2AyRz68zC8WPc3tlvgWH70\\nUeCJNeb+HOlaBgar2HVUoGQEoyNyZGlV8iMEjIY42cFVGIJlb35fHzk0N6jVLoJjflWCP3hFjyU9\\nbfMBtAFfVirTWqwxyEBIY5/0OlJ0RknT2gZWfQFmq8lSm6xOyQw6PFGouUFzgDI80gHnfaQr65LA\\nMWmXFtCCWmBczRwMyqSawoLEgJTNqt2zbcztGGX2d+46Zktgr8H5r/Pv/jcfIl+4DsCQX0NKRBg7\\nZ0aiXWZ69L381z9zB/JlsLoAc9NBCbX5kzlrB/fERo8nZLDi819s1woRosek8dDbyN5DS81nBGi7\\nGlMeD9r14i6cORHqoAC/zw/9uXMA5PNfo9XAcsh1JZ6CN61qyJ/5KJ//uy/xk3/rTwLQPPsKSV7G\\npNQqUAglIBaZpYa/0x/wB5+7CeWjsOhgZ1YXYY/uyQTxHE/T8/F//Vv8xF/+Ppa7L9GHb1A0M4h3\\nGQ+lQUzRssM3vu8FfvZvfgH2ev7D/+rPMPvo5ymxJ4UBzULTzfw8esNuf4jf+3zhF/6L68gkYMzq\\nLR3wLMCjk8Q+scZcqARxzKNUVxbrfRkq2SWWQpBAzC0mPa4bb1GbeM5GXY3Rlh0G2yPblCzeG1qs\\nRehRm6F5iqYJ6+oPPX4kK4aWakwuEVJEbVbDy01dOcaPM8gMmJGYuIyrTNGy5yzMIqrOcrWAyh4h\\nT9B82WWKJ8NKDxkrJYv0rN3k4w9ASDvEvEextsoRp4yFHYph1ngYSxrucw26s48aXjI5AA4ByLIg\\nqGIy1CESwCYeXhoLX05KMk+ZKx4NMkV8okHnKN63nzq3YOpNrcTzItb4Eo4lXMd0qLUPy5rbasAa\\nihyuE5YmeOFU7Xc/TuzGyrszXUA8QuI+pgcgCZVIYIkS6rMSMFn4hCqHNN0BEt+syjCvoQj4pJRC\\nhngOiY0LH0aIk7Ka431kkb8n1pgDrBZwqDfDF+q8wO2vTXl99jGi9Fh2vp21R7XHSsDSMyxv7BCO\\nPk5e7HH917+XQ31uVWgxLuYKhaIdIZ+nGXZwmd8Ox+SJNRmIGnQRpIPyNK/82lPMp3+UTO+rEdUG\\nRlCwMCHqDvMbu5Sbc/7fnwn8yueUoImIkXGNupqi0rLEF547uN4Cu6x6xzwA2L10XesT9n9KVzX4\\nGbrglSV5Ckcf5H/6a7eQyQtV75zR0q1WJ0qp0LYt+dYR4eYfJcu5B3YuDxXinolo8lASEDT5OKox\\n9ELjZe+SWSlcNnMPwFpu+ogtuhTGRK6SAPVzWx2mq5rcmBfGboZFl5j01Xsbalapw0gkDj2xWgzG\\nCuqK46q0Uj2GTJYFWY4wqclYEnCIECkSkHAOMw9jUpYsjm7S6hGmC4osCRpIdcIMAbLOKZaQpllf\\n+7ti548GT64xr4zcxmVyTNyYLxOf/5lbfP4f2rgqq8u/Aj57U0Cvw/I2lO+BMuGn//orEPc9Kz+i\\nqBttDWD7sLiFhKtYyF7wMJbeMyYEBXQGQwNlyv/8X74JOwPkuTeSslipeYZQEJtDfwvyh9h/G/Yb\\nD1FQDQHZqnTMu7aRmzpRhLtkbA8PcvfLlfRQ0KwUWl77lwFkb8NQtTBq+LU2LeuDM7YSH/Uz9J1j\\nzB8YUIRxrUhJUuPCbriFQCgewhsVSePJr43ZGSmeKobmqlCxDhNvvTwqKb1ot+Z7BKxUbX2eolrl\\nr0RfE7W2b25EiQUnAPSVfI1ErO53VLmYgQZf9hFWrXaFUJO2NXCXMiGMTbYicbJDpsEYoLaY7ur9\\nWNpAsEzUBmMj1GcwSkgfwDrN940n15hvZv6lrF39dgrlEvQJ4pipT56Dst7ZwDBx138QQtORyyX/\\nvcrx2VoaKBMgeiOoo8S6a/6J4xkHRTuBIYFdcAOsMx+0Fj3Jab4YsB0qMUxIS0G7KaXsw067zrxb\\nWA0whgh3DOj8HM4Kxr7W2RiX5BIClmeMK5n7F+oCHzYmTQfQ7g9J8vM+YTNIz9Ie/jASXgBAl+6V\\njKqeItCWK7T9M5C/wvEVazYHlHL3AHuYUChP0R3+EABBXiBEwYaj2jhLqqdZ0IPnIH+erv+Qf/fI\\nEL1V77c/f0UTWGSWP0jXv+W5JelBa6jjrupNz8PE5TUmRx8l0qDiyVijIaurWYIp07THTroCw20w\\naIenmRx+BvTIE/B5ym7nx6EpY0dP0w4bXdZHtW3Bx+8jHJLy3m7xFltsscUWZx1/2B3ULbbYYost\\n2BrzLbbYYovHAltjvsUWW2zxGGBrzLfYYostHgNsjfkWW2yxxWOArTHfYosttngMsDXmW2yxxRaP\\nAbbGfIstttjiMcDWmG+xxRZbPAbYGvMttthii8cAW2O+xRZbbPEYYGvMt9hiiy0eA2yN+RZbbLHF\\nY4CtMd9iiy22eAywNeZbbLHFFo8BtsZ8iy222OIxwNaYb7HFFls8Btga8y222GKLxwBbY77FFlts\\n8Rhga8y32GKLLR4DbI35FltsscVjgK0x32KLLbZ4DLA15ltsscUWjwG2xnyLLbbY4jHA1phvscUW\\nWzwG2BrzLbbYYovHAFtjvsUWW2zxGGBrzLfYYostHgNsjfkWW2yxxWOA+KgPAED09+1RH8MWDwD2\\n6LiC2Yflke18AyIvvcfYLg/nQLY4I/jOn4l7je0zYcy32OKJxZmYcrZ4aHiAtPVsGvMtT388oLix\\nyqd8JoCtbdn2lm+xxXeGs2HMt0/y4wkVyAlEAQUrbKnoCZwc+/e6PNtn5PHAu7GX75DgnAljHj/5\\nArIdrI8dWhPU4PCtG5SDI8hPsiHfHOD1qUU2/j7la1s8fggPjuCcCWOe2hNJgdPG/ebr8Tsnr4Fx\\nf9Paye9sbufkdk9+57t9bO+2jT/kx2aihGyURitLt/Um7e7dPVkQaJTJi88BUOq1MwEzvypbgvP4\\n4UESnDNhzLfY4vHH5sxYZz5Vlp2u3ll97dgbx9/e+PWx1yf3cNpe38tsPGgu9G7HdnK7D5sLPaxj\\nC0VQE0zHTzd+vUFw3o+JPxvGXAOwwUROnslpZ/Ze793P1bif7X67r+/nve/GNt7P6/t577t4bClW\\n4qFSFVnvd5g+LtgMrwAizsJF1pdFALvblNoJq3Dy9eo7Jy7v5nunbPauw7N7PYOnHctp232fx3Zy\\nu3cd6z2O7d22cRaPLalguWDBJ3NyOXUyeT9O2dkw5hX2wJ7zvP5X2zohGvQGOvhHpYAUsFhvQGGt\\nAdbj07gUkNbfzwWk9wcyRAjN8RN5YK6yuYEssrIPYmDB1j77ux7A+nff3kGeuEli6IaraGIYsrpM\\nipAtQ8lABgnrkS8G1tRfPt56axHBTomPqgQfLjXSuDIAo70/CwTnvb7zIMjJWSI4J1+fUYJzpoz5\\nA0Oq/7YCaUCDUGQOmiAs3eoQwbQa6sK64GXU11WYAQXioRvNEMF6KBf9TgX/jli16cKDmaVGa2lr\\nr0YwzHrUIqAYgslp8/7GNjb/vS+bbsfPyaBovSZSQAvkDgOC+f99Ki1AwgyEWI38uMPH25DDGAff\\nZOZWGZ25y72KwFSmPr58WAQH89twGsFZmYktwbnn7469fDQE54kw5lrDOGXo0TZiYQmzt4Eemk1j\\nLiB5w5ifMOSwZvWT4AMkA0OGt1toLgKGUOp40Ac4bqVOPuvj0jEEtxoE4QHtvPi2N0mF6IlJ8B4Q\\nMCsIWoe3H+uTmut7cMb6flEgZx88AlgC+pEd1Gcjrb+7QtiYmzYmcxOQCE2BHKHvYdbcFR5+IFgd\\n74lxL6eM1/vZFvcxnoH1zHFyG+anXUmXiV9mZZPgFKzuQ9BV8vv94Ikw5iVXphEnaJtJ05v8+P/6\\nIovzcDhZP1C6uncF0JWLq+aDuAhuoAXa6MQlJWh7+OW/9Dq8vA9l1+0s9XmwB/TAGu6viQ8LU6UU\\nMOvW+9ORkWz47La5Afyzb/sAx8EHVq8VJkhpkA0SVMS9SUyhKNBWBpJJDM7cbHzwHtTEs8Vp0FIJ\\njvVoWyjN2zBZwPlTCM7I1L9tgvMcdBerp5p9+NXn54FY9ZHg1M2LQTCj5IRLABWzcH/eKvBtld5L\\n9rFc7XoZJ0epE2G93gInVErrSEAAsrsWfjr3v3fgCTHmo6FQE2wOtLBsYb5TmHdzwC9ymwtZN9i0\\n+EUVsw3WGcg0DFLIJIzCsGwg9tS7sTJQPmgzD8xQbY4KO+Xm35VVkY3fbLCO+0h63bWdcX+VuYkp\\ngqIEH4rqm08ZnyUZ48XjA1KczT8hMfOzhpMEp/0x40/8xXsRnHVoZUtw7oVHT3CeDGMe/EqWAm0T\\nyGruAeqcngPc6TdKGIMkWr3CglANOQURxQgYLZFCYsCAQQYIA5TEKgTxoCEglOrCjWykHB+bFHd5\\n7zkwvxvH6uEVo9QHPSCSMQbEFEVX0Vkf6NUdXcWFN4z7Flts8b7xZBjzUl2dtqGPPUSfvGMOSFMo\\nDOTKGq3O2FKNXE+irMxjQkkEMlajvqDI0MCQqkuVauw6+Gys4QEEhA2hThw12c7k2Y0AACAASURB\\nVAkFW96mDbeIJEoQsjX0chWYOAt+t2KzeyV9TonhyYbcwiz4ZJcLpRgxzpmlL9HxDp0mSt7hjfwU\\nFhXLGdjB45e5Gv++ntG2G/NDRaiPfha0D6RuydEM9vcOOWzWBKcBcn0WjhGcGu/1b91NcLSP0B2C\\nTDzE8TC8VQEhrwmOebz6OPk+QXDu8lZhFV+v27wvHPNW8QSwUQlNg0nGZABRStGVtyoINuYfxHBG\\n39WNpnvs7HQ8IcbcDYUWKHkJZYEUKAuhaETEE4mDuuFWDC0FMaPVyDKDBKUUD6uoJpJ2iATMMl1v\\nMAyQjZhbZ8xkiuoDy3CZ+vGNsbiGJU1zm3//R77Bbn6JxCXulI/zM78ZWMpT2Gj0x+NZJSyPqyfu\\na9+yzimoFYrh/mMT2clf5m/9J8oHZ2+yK28zl4/zZ//yy7zdXwOeqxvwZA/VAzpVaLvFg8UJgqMm\\n70Jw3LfaEpxxV2eT4DwZxryGZW2oN3sRmL4F6WaHdFcBl9ANETDICuwaR8xRVXaHCTrUsHg1PEHd\\nBqYC8Qgou1CMJOJsRQtoQlKsCZfvJgRoXRVSx1Wnb3Mh/D7/0Y/+E57O/w/75cN8c/lv8nO/+Sl6\\nLuMPpa4z+nZK9v20TPp47JufjW9RyYSpM68+08ltnuMLfFz+Mef1JW7xw+zmFzmKFzkYymoDq3i7\\njPHFLTN/qDhBcEYhyGkEZwyBbQnOu+z7DBCcJ8OYb7HFFscgXVWzLBOh9TLzkEGGjrCcoOLy2hSW\\n64iEFmePoaFB0RwIOWAUSl4S4hSAXHqaPHHGE6AMAqJu8KQgJTwAey5AxEgrgtOyz8Re4c//6Ntc\\nHL7MoB/klaMP8A9+Q1nqVcTKdy+8J9QQDjUeVQlOgWlIXA0vcc1+g1l5iSO9zaxcZtY8zWF//ELY\\nd0Bwzqwxvzvj/e0GsTZ/Wt2WGIHzsNzj53/yNvQLUP+MHECzl9iGJdMfeY75xQFKISyU/PoBfOk2\\n0LHS0pYx+TgA12BpnpxGGGOCpol1VSnHT+p9624NrCAW/KooDKEHyczybSb9y1i3hzTXEY4q23AN\\nsMgY19uIE44DOtSin1XKsr6fdf03442R9emMA1ANiUZsBoIe0eiA5p6mVco8c5xqxDropY7/bZzl\\nYcJKDZ0I5L6wMz9Pex0md4BuD1h7q6v6l+qtiird/XirwwwGo8jaGVwZqgciONcaYnGILpjqkot8\\nkaf1l9gvr3JbzhPkU9WQ17j9d8Nb9R3WoriNZH7KFDmkzW9wXl/lvL7KLZ5BbYrYEjbkAd/pE3BG\\njLlV2yercxNsPTEZQEGKrpIZq882hZueoakvZUOOND2+uxQgnQfOH184wQxSDzIwTR0LOjTWoRcu\\nIWWPnCKFDLEmSrMiJpRgMOG4KMNGzWjZMObBBSDZVsnWtVyqnsRqsGxsa8PtEvNzs5CrS2lo6ciW\\nGFiQFJZDQXqhMTAiJm29Vrm6vAm6AmUJ1oLC+Rd2KY1BZ2hofK4qQjMXbn7jjh9Xhz+t/QRMsdKj\\nTCmpdz2yFpIlkhVSXoD05DxQsgAJYemnJtREj1Yp15OJ4+qj+/juAyI4d/7xIT//T+5BcBqXMT65\\nBCdzrPIVNgjOaHsePcE5I8YcRlH/WCZlJuvkxJhhZnWuG787+cYpIbn77iVapXpiTlBTRkKg7aBv\\nIJUBK+oJnFoyfKxsn42mOXXGUYv+ZzllhI7HVcaXdkxqKBsnYhv/9T/9OL0wQmisJVpDMHMxoAwI\\niSJ1xle/mFYMkeI5l+d3IDdMemFx4zrN5V3yrtC3Xo6vVZXZHgBvLdi7eon5bkYV+q8ARwrSUtJY\\nieoqB2XN1taFJpvsfpzkRlXEEwob04t30UH/RytT3CQ441gbCc7qGlfyItUabs4SmwSH+vu7CM60\\nTvYz1gNScc32AQCddcy1QwK0GYbuEokWhuCPp+SaVHSCYxphoutbPFrZsVBmFU4IbvTyBjs9RnDY\\nMPzc4z0Bil+K1bOhpNIz0FNEWQ4FhkAsTqRM4moDQsE0eIl9MHdHQoBuAPb9mENtVaAR+o5W9ujn\\nB9A0NQehHoai90LarL49LeQyrAkOqRIccIIzbJxaw/slOGfDmAurhMyIVT+FyrahGk01VlVVqLOH\\nze0c2+5oLMbiGNl4/5TXongpcyYYTLNgKRM0QM6YDtC2/ru8NuSmAz6repZ+8zC01El8ZBz1Rxud\\nvSvTNizUG2hjBdgpMTPxoSc2EotMlkKvS0rOhBLQElBVihpJYV2gMMYuBfaWzD4IIUW6JSyWR6QO\\njiL0U2rvcfHIUwTCkp3LkeGcIkHgq0P1v4PX/hRAhEJTqwAVkehszQJjwZUPudFY3G+59GMKqZdQ\\nABstdgap5e9kvz6bBGfDQK8kdJuMfVNyt4H3JDg29lrZ6LkCPm5sF4A4gPSZEAJaQyqSFMut/772\\nmNEy7s+fv2O5vLxBcOSkPHHj4fk2CY7PXY1PCsVzAK3t0FhHMCc3yIBaj6m64VZAClYKQoZpD89P\\nIfeV4LzNlU89TX9x6rVI1JYZBaYH8OaX3mLn00+xZIlqof9KD0eRUhrcKC/8HlIQkTXBoV5nAmti\\ns5F8fZ8E52wY87GXCaxnVl0i1jLeXa+iqicqGaypzGaoA8cTBlYZ8siUnb3EjY2zmvXkxGsf0BEW\\ncOPLPcO0h1xYyBSbG7qcISGSs7lBq2GLtcs2MiRb7T9pZa1lPI+adNlse2rU+WZ8OMfzXJf6jtse\\nB0OwXLmtYuL6+DG2qRaR0oB1dDkT9RaZ6CXcYpgMWMmUtw6JMZKWCmlJ5JBJKN7dT4SIICo0IhAW\\n9Ie3ySnTdBFYIFpQFSyBxQyasXAHNGFSC5pIGyxsA6u+Mifc1ycGY/jN72+sCowkeaOO6iESnPGz\\n0whOrah88ASH1YmdJYJzJ2Zow6kEJ16A3DRnguCcDWMuGwYVcKPmA1SsSupWxmC8uVSDUL9T41T+\\nt66+d4yAjOX593ptYMXd0+H6AiZ1kJUEJRCk8Rtk66IHD6ms2YytHpZ6rFLqAzF++QRTWbme4zlt\\nHNCxcz5+g4uUGnfWVYn0WBFaVoYUAgMhvobJEi0JIWGxMKQ5i5uJBYFIB2FOG+cEMQIJjRHLzhRm\\n3QTK17jxRoCpO4UiE7RVpBg0NRYQgOYlRu/mhH+9edZrF3ujF8UTAymrsRAskSVutLpVvFhEHyrB\\nAX8GTyU4C//ugyU4Y+HM6mDWBKeMbzwagjPTzFDliicJznDnNlnOBsE5G8bckidOVsm/AsXAEsUM\\nCXgCzbRO69Q4YqnFC8ZKG5qTx7YAEBfw328iQXIdUAJEGMZB47NoKtkHZUhu4FfGSBj7K6wCnCHU\\nmSSvjXTBO6RJ8gmhgMaO0vfQdMAAUSEN/vuyEbssg+8jtpAGSsju1vYFiUoZFmgoZM30MlA0ozIw\\n9HfQ828zm95i2u0z7SJ9PkKvKFc/dM13t4SvfvWr7O5cY3Z+wrzMSQgigYjQHgp7V77FRz7yMVJj\\nqMHy7SVvv/o20YLrk1FEjMvpBmY79MsDaAck+InbGEbYNNzH2gxvscUW3wnOhDGf/WhXG/aw6sR5\\n+JVEvBKRXSgMWG1YM3qBsUCbYP93srtLhTUD1g3pU+Hu+OB7IlSWszFDirjLp5uyvc2k3rhLra5w\\norlgDHtVGWDVqNP5T1KAmwMlH8B0gLi/VtZMG8gG0noVHVYLn7KXYaf6LwW0xYoSu12sb5EktKYM\\nKJJAmyP+6l+5ymd/ZJe9C79HSQuQzCAH9HaIkQgFYowMvE3JS7SJ9BJoJGCpJ2aF0JLtG37+OSKm\\nNCGgyX+fMYK1xOufonzui3StX/OcM2ZWBTo1tHAMp2W2HkdsemzihKEESppAC0Osja8YfOyR6mIW\\no6tWk28s/HqOBKecJDi42gqtBKf47zYIDhY2lBKbci6wE6+d4Pg9Gq6n2q8/eqtWU8zEuxJqqo9L\\nfSZyWBEcO0ZwCmOpez1YJzi6dPKyIjhpTXDGZHkIle1T3ZJ8jOCkFcERJCrWLwghkcLAoizJOqBl\\noPTXaa5+jul7EZzhq1w6f43LM0DtOMFRYXHpW7z4/MfJFCc4n3kPgtPfhnZZCU6uBGdjXIyL47xP\\nnAljvrjgUiVXZrjb1XT7tFcvcjgxQtd4ErlGLEyAkmiWEcoBESEWZ4whBIYNIyybSaOKe+VBxzc9\\n7qYUG0MFtesihhQPDwjrkmExD3mYGFbZtHZC94HzLF/wiWoVBsXDantLuPWrb3D5o8+jz0YOG7eT\\nZn4/JcFuhtvX51y6OuWOeKitAKGG8EqG2W145Ytv0ty5yY72aMikklgOC4JCk/YJ6TqTeMCi/Cpt\\n11HoicyJ1uBezYCqEpIQg5GT0DWNx8fFCAFQIVnvEszSocF1GLEBcqIJQOkQ+TCdLjhaHrLXTgg6\\nA2soRVbX8TiewDALMIYMzIqTj13v3nnuB875lFc5wdkgOKMe+1ERHPEg9SMiOFZ6JIZTCU7Rr/nE\\ndgYIzpkw5m0+QEoLQLQeyRmWr7DTdoQ2ctRnYgw0VW5VKLSWmBXQ/HWaYrQpExC0qCeRVtnvjZjg\\nyWTQqSg1xxRqIrAg1JjmGJ+0gG5s12PVfuGH4oaR0pDkeXq9gMRxQnFNawSmy8Kt9BpTZvR6CYsN\\nuQUzj3lLKFgPEu9gTUeSTGk8Zh8CDOYZ+Asy0A0v8Yy8w3T+VTpZopKZtcbO8g4XynVmNMhwyG4T\\nKGSEwKIMtNqQLaFSKDmheDVfjBGTTIgGJKQYpUAMNQHVeFhLRMB6hIKEgAaQfMgkzWmCMSZ3RBpk\\n7KmwxQYKTVfIQSiNJzDtHCTLDOrX2svDhZIztInmcqTsgVmzIqjjsFbz1rOjvrlRpZSBTEbUr7+Y\\nelj+lAl0s+fUJuFZERFRbGwvW3uCK25Y1aQ+N+tcldW8jrgbQdHExWemyLMdC3HiNh5JEVfL3Pit\\nA3aevkS8Euil0BTvuin4GhrTAotDSKVgEyFZQcSI2TAJiGXavuHg7SPK/gLMx7MmRUQQEboGpHyT\\nrjnC5F/SdQ3GQOAQSYEYhZJ7RAQl0ARjMAgSXM3CQBeUQQFNJEu0YRcwSimExtBstI0rfYw5XTRy\\nTkhoCNrUtgffjTG0xpkw5pfyHbS0QKGRQ8QSs/gSy8NI115mkoUSlKaOv0ymS4WrwwHn5FdoSmKC\\nG/M8uCvoSVCqlGkcoe9+HCY+MDyp2bgq1wqh+INQalLPaCg1iSE2sn839IVMRjkaphzkfwWVj3Fg\\nipbGJxZxXelVO0Dja1yxlkOMw2EP2tZ/XSVUrSX22iNk+Razc+eYWyKZELWhaKYrxvn8MpPpS3z4\\nzm8x1TdoF2/TiiF2h9n863y8G7hoF7kym3DInAlTQDDtMIRGOhTIwTwBRiEAQxX5F4RGO4J6O9uB\\nJUL0CQdFJdCFwBIhY1yeCl2+xeVJR7BCyYpKJIS4zgHcz8147FBO+XvJbPk1zuUDbsdqzIfvQRuj\\nQY4THMvQv8LO5GMchsjRkJEYiNUiFCnEkpgKXOm/fJzgqJLy+yc4RUYlyXdIcCYNKT7PrekFSpRj\\nxX1Nhk4KpK9ycfej9Fcu0XcNg0IS934lF2IPdv0ml5+6yk1JSCU4Wj16KfDUzSOGN77Ah/QdpvOv\\n0HCHZHOUI/b0NucO32G3NGh/SKMHZLxIpy836eKMbANRsxvmqllvJdAQPeEcneDEApmBibbAHTAl\\nqK0ITpGAxg4tbzDp55SwoGgLNgXrwFq+m97qmTDm/91bfw0lE6ygtcPa9Kpx56hjfhTJ0pC12cj4\\nF0KBWZ7zkU+9yW46IEfbqMFxo66hIAQ0ujFO0hOGAZXIUmsTIR0oYgxiiPSMRS6GIhTUFJUOJUAa\\nkKDMhoZSxqhjJNiwrjxrAikLS1q+JZ/n669fI4WWQkRNiRmCFc7nfaYfm/Fq+jW++tZlXpt+ECOS\\nrVAkEm3gWr5Fp5mDoymvDU+z0CkJQS2hWuiGzA/nf8GnP/om/9q1X6e1OZe/dAML+0xDYXZ0xE99\\nb0O5+CxxucedmQ8+wTASsSR6BWoxydja1LmX1v8WBkod7gCGsXRVDApEFiaoTGmAi/O3uP1r/4y3\\nlonSPMfXuMON8kmO7BzQEWu0NlvjcrK6tyfNtAO0ecHH7VUulK9zEy+hP9IPcFDM9SxnhOCs6fqD\\nIzizfMgl608lOMiASn5kBCdWw3oawQl4A7GzQHDOhDH/8ds/S1glTwQt7uIkaby5pqgHjCsE746m\\nJCbliLZklsmlSMtonP+haxAz6JFP2WHiP1xmDr5wC+1h+sI55AMTaPdZrxFYY8hS8MRLjfWFqqQZ\\nZjAXXv/dN9iZeey7MLqnbuxyEKQYtjCeuTnjj/TnjnVNHP9uWGAlsIwTbrLHouwwKb7g8RA6pGSm\\n+Q4qhSUTjsJlUjWwjQ0MWghFuZje4rztc3V4nRwjy28ORIGU9wkc8pwWbuUZyTqfUDZLkOvVPP53\\nOPHJceagCGJhpQirwxdFCcWYpAGWiZ0eBiZ0sku2HbwPgDIgztooIOOEoLxfbe3ZxyjBDKyutSWi\\nztm1V/iPf/BVPpt/llSH92tv/0MO2MHTZw+P4CRTLOo9CI4f3IMkOF1ZslMWpxKcBUJAHhnB6U9I\\nBY8THKvX5tETnDNhzGfDosaifAjXhceI9Bt60Y0lq1g3yNdqA6KV2isZmC1gMgCLapi9HwhNXOV+\\npM3QHlDqsnGYonnUf9ZMpOCWOtSkaEponNJdEM59cM+/pwE248EiUDL5nUOO3jkiZncxtTaaz6Le\\nGVOyJ+8tsptvkQt09JgZOUyBQizurmUasr5aZ3RDS0+KAUFpbElbBtSMlAavKasKx1HTvql79+lS\\nVnHKtVpixKZRPalvd6nmqlL8RBJtXHDPXd7xcoi3U12tPu7NiJCN3z+2hnwDx4oLFCETmLNnr/NM\\n/xWa7GP0Wm6Z6xTIbqDw9iBGAQ0saUgWKBrIsh53ah4ii5a5mG6zkw7oUcSULIU8FSQaReegwdtS\\nAFkzYZ5o2imzS+c8Jh8XmBhJBbXMqiFVvV9igpVAlIgkIfWZm7//JuAGv4h6rcLIacXbQBdr2MmX\\nuDq/jMVudS1iDgQGYl6gcYerMuWSXuG63CYU6KWt38xcLO/QSuHOEHmT5xmkIyFEFqAQU+YT6Xd5\\n0d7k2eErKMbi5QN6Mxrdp7MjrqLcthlG54oT1qe3HtKbY3szfLT5ei3JkdW75ZhkQil0uWevT8wG\\nSDah1XMkdoEWf2rKxtY2VU9/CHXmJp54UwzTQrZqS8XX4UCMuDJMq/ZUGHgSQgTNVZ8O0BUsHnrS\\npxSI9aIPBQuQQ2AwPKE6bqsI4/JwY7ISgyLB49wBRAsQ6YO5HEWXIIGsuNtpVUGQIcTMxJRcBu8m\\nt9qTtwrQATRELGUmZR8JtZWDAX1fm/+MCoQFmo/cSFbvtWyssWtVkRXrt2mUlP08skLWQtGhcuAG\\nIVAIFBkq2xoXy1NOStXuei2ZsvI0ZCNOmihSKFrQUb1jjbvfVq8NCbGGzb7qUvyoH/+uietHfIQY\\ntDan5Q67yY35Xgaz5AqoUwhOhvsiOIr3xyoCRy1c/uzFSnDUSch4vXPkzq8k+n5Oe0nhOUO6IwRo\\nTdEcWS/ofArBWUxp+ynNAi7dD8H5nZf4QH7lFIJTaMQoS/ioRn5o0ZFvOcHJNF5+fxfBmbwrwWms\\npxc/pC6NBCfThUxTCqleAveZlCS1MtUP+sT929SjHTeyap4YNQk1TyBYfZpC8RxFLK428jVA/akb\\nb0GSqhKSAjL45Gnx26bmZ8KYf2vvHMGqxI94vImaZfREo/jK/xATQvHBmUXQkshhznmmiCyBAtNA\\nru0+Q55S6oVs1LV+Wi+BNUZWgeIGSkxJwZloDK032g8KJSMtPmDVfxsoPmBXJfoNBKHoQGmNXjfD\\nLJ6bV4UiLqWK5je5jPN5XUBXxCNyZpkYGnLxIikRpesNEyUF8yK5HL2njypZeiwImhoKS79GxVfe\\nMK9hq32MGrwh/lgA9d7ZMJ+rxmnG157Joj7EZQwOVOO8qtJLoB2UglKqfwBGrJMLvpjtYwllXek3\\njlwfVwirat1Ux6HWpKJVZcjDIjglcG+Cs3rrwRGcWMZ54BSCU72WR0dw/BjZON5jEM4EwTkTxnyL\\nLR5rGKzbF5zyEUbRsXNepJgxBK+dEGPdS2QDa4LjSbmNTzCEgDHUsvlci1FkbOzUyqrti5YOkx4t\\nvrQDElEZY5dCUUFq3xhvlVEwUZTgEmLBE6JNPQwNYEIY2034Dz3UhhKi12yXsOF5qDfGQkBUsZKd\\nzAtkBA3r2H+dyjBz6fxYVgWeJkPqypkK3krWyHktkwyr7ZS6/zFAoqipT1QrNcn9Exyrd3LMOhlr\\ngjMeTjAIltBV/Yr/eiQ441SvhEpwTnrJ744zYcx/4cf+PXcTa+m89xeBFZMZb7qMPASo8cCuBNBD\\noDDtl+zefJUP6B8guiSEOUhD0NqESKe8HZ8nx5bSzbnQ3K7MoiAhQy6EsVuPmnfCtOzJ17alDFOk\\ne4Yj5kBXA2w9Xo6X/F+ZQTjPQYy82l0h2x59GVxrO8kctm+hqiyGnlIb7AQGsgTGnkJ5NUnXWJqw\\nUtmAu2tNLVoqqliv7A0XaWJHLDg7AkLuuC0HBJswsV0KN+kY2doCsYaxHYKJ3TcT0CpINq99w0xo\\npcPILNurvLr7x5iq0Tef5uX0CRINLm6eeK+McTCLkV2Ed4893c/x3FfxwBmBGwgPuPhCvllaljph\\n3rjBXMQJRiEp31Vv9ZI0VURQIOhqcQrMl20ebEDGBNTYn0UNk+N3Z2yQJWO7CfV8SBF8/DMa+Gra\\nRNyiWQPBZYp51XKx7k4yWaX2mlHwnlYrb1XHFXw47q2WE94q2TCqt8qGtxqUrJ6PsuLeqpZYl42z\\nysTX90dWxvzbvbPr/FORdVBt9FaTGJmWQuO/UDm2G79s6+f87pDnu+NMGPP/5d/+y6sGVZurdNz9\\nKOuGYcMz+GQKxt7CuLKf2f2V/48/ffRL2J0vYTuZO/YMg/plXdoVfuo34NZ8h7/4TOBHn32JC+k3\\nPReXIyaJYkoQwZY9qi1iHYsyEELgyJ7jrfQZ/tFvv8Ff+vB5WnvHQyOaUJZoyZhd5SA8xy9d3+Fv\\n/MYPcyTPQk70oYdnl/xb/+0fIduShSU0e1zRdCCpILV02Wg9SbqSLWkd1G7gBdcYlwLnDq7xT3/m\\nVzn4daXRANY6k8PZTrbX+fN/9h2+P2RabmC1yAMJpMrqlFCHz1iesXkHTr7GXXiEceHcsfuNMePr\\ni0/wn/3yNVhcpDQvMLQfYBmfwhepLawUQ5Qqy+q599JdpxnqP6SxdfMrROXNxYQlM17Tj/PFCz/G\\n4gcvAPD61QssFwuy8FAJTj+bM+naUwnOqK54kAQHEfo0nEpwrLL0LcFZ3+nTcCaM+UE3W3V2Wxvz\\nMa568pFeG3tf8TtTpMNyYNoJIT3DP/obv84z6S1+Z/JxfqE8zzvdNQD6fI5vpj+GdS1fsAOeDa9x\\noXyYSEEzPG1vsbd8GclHLNpneTm8QGbGIJkgDUfxw7w2+RRfihf5ctxjKjcqzyqcz69xxQ5oDxLa\\nfoQje5Hfmvw7HOSrhHaBTQby5eu0n/4gNHOW4tItNV9VPOuGQavd4MIovTFZaYRXTl2EUAIXX/4g\\nv/LULov2PK02SJ6utMGhFIb2NX5s8ksc6pvs0QItSsRycFdWc+2TPSY/38uY11WOzO+RloiQIDYk\\n67jdfozfnH2AEp4mtM9DUrLcWg/WGhtX1JO9q+2ehvt5kO4eIWcLoxu/ZlkejY0chSv8rd/uWOof\\n4U/85J8D4Bvfm1n0CwaVh0pwDo9u8ReuvnAqwZGxQ+gDJDhJFyxTPpXgjGz10RGc8T6Od+DkeLNH\\nTHAcZ8KYt+ZLsXlWOLqrVXF3pHG9Ij1i3qSnCNE6okEbzrF8exdNAzf2vo9vTP4U1/V5AI5sxiJ/\\nAFLi537zVf75V8+z138atcROOeQHyx2+Z0hcKAt+TyOfm3yIQXbJXSRjzJcXWYQJ19/5V3n9/zqk\\nlasUSTQmfGK55CNlzg/2r/BmnPKVxQVKmWAlkKRzUbpMKdIilkkKRbVKyuoqMaUyBAEQhgBUF3L9\\nENdEmUSsLi+Vs0DT0hclEvAuj8VLwvMUGXbRtCSnpyixc7V5WqAIKSxXW3bit3G1T/ZWFu/F7iSk\\ntvI1rcZmgpYZcI5ie8AUzJUGOfeQq5KCtN7eKib57bi1Z9lwvxfW52sEks54NZ8jzRLXu2cBuBXf\\nJFX9vS9cUZmx3dv5P96SXIlEZ7VJWbQFTTMW3yrsLiYcXnqWP+g+w/7UPYE+XeQPwpIhfJOb8Snu\\nsGAi51BJlNygZZ9GXL6bZUav5zCbMIQBIXCkT3MzPMvt7qPsh8xEvToy20Bjt2mt0OZAlimH+hwv\\ndT/CnfQU0zCw7Bbsd+/woedfgOmcRcrE7JWlpr2rddRWxlxMa+hpHY4YxyKSoBFKD8/uf4ov7dyi\\ndBdpsoJ1DKGgZgSBpb7M7ckXWDa3iGT8CdSamK/Z2tX9OikTPC33MYbPPHQ0TjA+hQvz7ilemrxI\\n5hrN9KMMeeotdUuPVm3Oah+r3jffvgd6Joz57f9hwuG5BUsbiDploEdNGcrAhI5m3pCbJdYaqRih\\nLgmlApPS0SxmLPuOoc/c+tpzfKv9N7iWv8g3yvfzuvxx3hRn5uQ9aA1h4PduXOIrNz6O2oBS6MqS\\nb+3/Iqlc4ANyi19cfD8/f+0n6G1Kr63P/pYpzQ6lwNdedW18RhAxXlz8IJ86/BdcSf+U320/yW/z\\nCdK5S9DtEVTI7SEUuLB/gV4ueAY+1H4nyeuSslVVl/l71nr/oBbvS9E0/poIJJhm2D2CXX2Wm50v\\nZVVQkFQnRHdZ/8//42P8wueeIcSLNOqLC/SWGUIgSkLLUNsYREyFkKfe784LsQAAIABJREFU7lf9\\n/5TsCSR1QySxoVkKjQaG3HsfF4mozVgePIWGZyntLoXoCSBViBGG9So65dgDMT5A6q6nCOgCmsPa\\nfa+6pIL3hRl6CDM4MqCuGIOvEWuryfBsoIaP6wtZvxAFphyWyzBpGDsTxmHmaoeQQDJq1L4nfr02\\nU3Mr2EmC4wsywBQBuvA8P/+3v8yL6Sb/ZO97+LuTT3B9skFwhg+g8jr//a/2/P3ffZG9/rMbBOeL\\n/NHlrwPwe/pxPjf5kVMIzge5/s5n+M2/d0gr8w2C89t8pLzCD/bf5M2Y+Opij3fskxxygZuaQHrY\\nu8or55+C7pC5+bgdNfPHCM7q7AGK9zZZXYB6bTQSkjActFyfPM8Qr4AqMU3rdS8+nGSXfrhFWe4w\\ndB9EKsEJywVhg+Ac4+Abns/JZPQYBtJKcIq5vrw0E0qe0dvzvC3PQbhCsKc8tm5vQxmVXamehdYw\\n07sRnDMeZnn1516DT81gWh9iSyu3fxiAl2/DMy1Ma9xRpAqyxTuofe0GHARQ5cI7d/ixcJWlzFiE\\nKYOeY6V5lQBxiYVC0s5lArXwYp46bjRXuJGvsJMjR/o08/IUWSf01uC6qTmuBmhJtZx5PM53ylVu\\nLJ5iGPY45ApHdhETXw3akrmscT7hd/7BAX3sKRQGmUPINTETIBSKJTqZEfKMIRySZImmgpVAiEa2\\ngknHpHR0ObB7+zJ3vjVAaqA0NcY3SgKFGHb45stP8fLrO+RefB0JCsPEoC31OdjoDpkzHE19ZmlL\\nLdxMUOWSY5xSFy0sDZVAYkCIkCITvUjJHWi70UhI12PwZIHQKNuzaswxsIxenVIuFrz7mNTkmtWn\\nawqHwNGhH/Ndg/5eLurDxyrmKnUVqdGdNgEadnfOcZAzX/47/rXhSvSV2iw+VIIT5Qq/d6M/leA8\\nXU/hQRKco/kFX27uFIJDVb48KoKTitDK6QRHJFCEh0tw7oEzYczZf4Hdbod+6vpPsYQkD71MuoZD\\ne4d2eoVhZutlsuqMPRkCi9tvwHARhjltGhDbBWa+1qB1G2GvJec+0TG71jE0vsg8+HMVC1y8/UnS\\nGwe8/vI3mL/TIbQe/wsCmnjqs5dYxqq+KrVrYP395Xni2s1b5F9TsihmwWNoUihNRvIEe6Phzf9t\\nAcyQMvDMD1yhdAMpFgbxzoSNBvq3j7j1rX2e/d7nGOLAoq5WFHKhE6HcEG59xV3ft9I+6B6UdhWe\\n8R4ajpIj2IfJQ4bwvQyWIPa0L0b0Qx0JN4cZHy8hweKX34SFweUdLn5yj4POVQxieC+QYsSXjYNv\\n3KAszvs1qA/8fBj3LJwQXLwHqpFDoFly9bMvcPB9gUztxieFYAWk4fwAd758yPzWHdgfGBcP8UrF\\ncPeEcRZw7JjW7GrRF4gdv//5N/yNsI9XLIeHSnBSBOR0gnOzvwLwQAnOslliZqcSnFwGJrrzCAlO\\ngKNwOsHZWGDloRGce+BsGHNXAtIYBDUS6jKpbH49i1WDIxTzOLn3ZglMDBaDC/C1KBFxd6cYUBuD\\n6znfjxl9AzrxhYvFig8a8QqtRaf0sx1cNIsbazFvIB4yehFQWAooycMOFhExSmuUAVLwNQYz7nb5\\n6ki2XohIdz3mFzPDDNIs+MQiDVFg2SfYbUATck6w2JKlUFQpuRAR8r7R5B1Qz9znXCqVaVxKtpFY\\nLOMKPytNrbv0IShDKUjrKVwzvCJOgFBLrZPrq4ZRQ2z+PRWQTlw4q+ZC4VjjfWOtfkmrtqv3hxpK\\nMcCUoRjLiKfyalVdNmiij3Hvsb6hMh6TgMfiGmccAkkFUgeHO/5etwfpAILSza5gey69Uym+boVC\\nW1oOX3ubye4lhlkhYyuvPACTIhwu34RFCxiRixhTGPupjGErABlgt8BOrvS3bssUSkPZ2eH2W/78\\nDNnJjROc6Mb8QgvWQ1y4eqUmxmFguRRSVspiVJ2E+kyV2he/hRuFN//e66zE4+eWIEdAcoqOQS4M\\nQ4B55yWy6QDarnps/hWWDcthD8r/z967xkp2Zfd9v7X3Pqeq7qvfbLI5JIcccYbz1jz0cCJpYguy\\nHMkwYEixESVBjCAOEDv+kARKAH2QA+eL4E/5EMeIAUOCEyBWnMcXI7JsybJiZaTMSCNxyBly3sPn\\ndDf7fe+tqnPO3mvlw9qnqi55m5qZsJt3JG6C7OatW1Xn7LP32v+11n/9l3F1eeBzmtuNyfaYuwF5\\niCDnUT0D4QGKKBYGth6dER/edulbVXKVz40HxvUvvgrLAqenXHzvRZaxr4VRgYDQxEh+uefGizfQ\\nxRRQJDWoGMsyA5v5NXxHC6QaDQMauPiBh7CP3H1PnQxjniOHN8A60HFFdplontCId/YYvrKgTAFb\\nFxAYSp9bsB0YBrRqgRs9gVITSAMxVwTdeiFkH6FMMlIUUZd9ytHok3AYldIAoVAwshVHL9FYKvSN\\ne/7ObfVoVzShHwY09RgdJSg9Ro54dUT2RA6SsC64cVJlkT1vboPQWC3MC4nDroBO6XvoDSyESp4K\\n5ALFhCH0wKQCWmEV3bNxP5nHJEsDDP5ycbRsmgk29cIEHbBgNDF693VgsOCWfQQB0jtrpRaNFBVa\\nFaAldBHVAo2jQM/6jKKi38GQ4odBEE9kUYih9ZBDPVPdGPhXBDG8AlfWgc36GSdjUX+7w+oh5KqJ\\nLCsAyMokC6WPaFPIFpCQXNfcCQ9IcdQomCfDKzJvBmB/BrZH6Be0eUJSQUoAa5ztNKJojOm7Z4RH\\nZmR5I8DZvv0YN373LAD5UEiSKGIQe4gdD/7AaRYBBoHAcATgPLhIPHTtJsNvGV10Tzhqvb9YG2gs\\nJpDfg59EHRc+tE3eUoamrADONMPi2pzD56/y8HvfTZdgXgFOHAHOK0b3tPPe1TYBTuXKWy18wmoV\\n9haWgVT1Y0LGJoHFBKT1g07N8eTuAITz9cBJ9NMpt2dTRkbjuB6b2QSkAzkFGlANThoY4XgevnuA\\nQ0c3Me7sHpeA9XEy1n0Dpx+A5RRybLwcN7tYfVrA4qU77Fy6QN5ycNvgEy347/DqIQxCIwNB5hzo\\nDW7HOb3ewPQqxZb1ixSzbawkT6JlMHMmSQ6Fpb5Gb7dhuEWXE5RXaxeTCHpIUGMYOnJKoJ0naYhk\\nJpThBjnfZJ4XTsXTG0j5FtghTM84/cmCuwW413Dwyk24usBXRFsNs0LXgE547YuXIcwd9UqEnH0C\\n9ifABYeokmrN8pgoGrvCgOs7BAKTypkdfx7Iph4Oql5INmgtY7Gq+4UITUtXCRgCq642KVay3Erc\\nrCbwRqSUZO1OfrsoeXQ11edAYvSG0vVBj+XtfWzIAr2FSuOSDTdV63+/R6QBDGIxNAasjCwfYewa\\nfz8BjrZGCeVYgLMY7c89BDjemud4gKMRui6+bQBnDOMdB3DGcT8Bzt3GyTDm74x3xp/S4bHhekpC\\nZS8KBKF0vSPYUOO3JVEGZbEQt2i359A5N9mQmldV5lnAzjLG9lQHLBbX5w4ZpCNEP/AKHRKn9aTu\\nCSKEkFYtzSRldBTE0iUhDfVkLVDmRNkhNZGeQqiSBFLZGEEKgVxzeMXrEaQDm4JEzDL0PaSdOhnC\\nwdVbcKe2ldMEAodqzlzSyGsvH3g7pSQ1xWIO+W8mj/eb4QbdPNc1jk07KgEsuhxAUY+p11ZuBQip\\noVihmJBGYokZSPRmGDVqFOozM/BCQ6MafD+koP5/rA9WjkfUdx0heJaX6Ad2jFUs7PhxQoy50Qfo\\nAuQ0orFKOQtAzOQZ9Ns1x1tzPUFg0gE2JzY32Tv1Jd7/xC1SeZqbvMxZ/jUfa+9wKB4DLqFn+vgu\\n7emWUGOv4Gszx54HF7c4tX+V+K7LvOvmhI+ka5QmEHKgxMKF913wUFzMpOLaDqWGjHeGOQ8eXKaf\\nvkSrAw/q1/lE+wovXf4AL3/zJ7EVeqW6Zx3bj52h7J6hiCcYpzUHOL+6gFvXufDRd3EI3iIxGypC\\nMrjzxQN4ZYwXG+sg+chrdFQiFr2qdaXX7m4fokTLKBFRLzVGIMe40mX3j15T3tbNZ+P6K4J6Ioi0\\njl1qPHqf39bjHx80PgFqVewMqExcJ7IE70ss1Pvw19fIvMZjvyNX9m0cgntCMrJ1YO23K+cemVZv\\nlTd4q/s3X2PnfQ8c6602A9z5zCFkoUFJceBwuM0BC4Z8Byu3GUbJwJmieZthXoitoBl0bAEXCov+\\nJv1wAEDWKdq9VuPlEewQKzMW844yS5S89laVCUN/mz7vM+8XZNvH9A6Sb/iNTM+wiotbTeBadEE7\\n8noexlxTqcntOwd391bThXFh1s/cSGLC2ltVvHCuTtiYbikYSHAGDbE6yrn2U3BGiRHo62eZbdSH\\nhlorEg2No5cqtYk1kOw791aLX8cqqQUMw0kPs0h1McUI5gkfJBJUidW9CSuvyZCVAROKBtCINC/y\\n8R+6yn/1nz/J6ckTNJzjw+EUfejoRrc7FPpmjomRspfe+ieCijLRQiLRlku01nJdDkjRiEPAYmQZ\\nv0mW4tlvy2CC1SmUUEg6sNd/gA+n0xBmLBfwm/+y5e/+tweoncFsyioRGRuvhzSwBjQayyKoqSOK\\nJtGZ53H6OKzCGKkIxODVeepa4Sup8NEOj38G88U7MpMDXmwhYaWrXkIGMywIpTRedaoGOkCpejHW\\nutrkOO2rP6sRLo5EZCxZXzWQ5NtbuFLjm6sgZKFQyDHUeKZveDMX+xtbhNWYDEdODgusd81JH4rF\\nAaRZExhW/73PAOds6zIrxwCcncnnAe4twAkZ8vEAJwF3Lr99AGcF8I8DOJZ80u8rwDl+nAxjXlUV\\nYqnGCfH5tVrSbmEldbmaDVHUDNEAKdKknhhvUprL9Lu3WbBPwwEtkVjdRMXbCkcik0boGCgbBQl+\\n9rsS8UDDFksihdBOGFC2aDEGGqS6nt5nx2iJGBYL2oAydxbTziW2dh7zmKU1ddH6bpFQnDQTAStE\\nK6BCGyJ9dY9jwKsnpxBqw1pXtCu1ZF9qwskhvQuhxvWiWSEBrW604YmCBVL/cfdQwJQgRpSGEq9B\\nytBsI/IAcIiEUgFPw3qZ7eNyv40jkzqP45R+hxHC9ZuqLKyYEiVQzHeKmFbte4BUF7z6PckGIvue\\nGQEprYdkV7KA6oiyNk6+XwAnmocHjgM4W8WrU+8pwLEWYj4W4KTC2wpweDOAM7LH7iPAuds4Gcbc\\njFjF481qYzOFtoiL5OCNWmMGMCS4zpmYooWK3FxFO9aTOSF4X++1EL9v90isHFI/Nsak2Qh6RrQX\\naRljYrmSijLeEzAxuFNWv0OrCh4YxqRyo7OYV3TiJdlWWyUjGUs3PIkjgwsaSSE2CcmQUk9O12jS\\nWaZxQRc8nicEUmgYJodYWkKeAlusHvjq9I+MXdtNSnUGFGQO8Ra0N8lth6SIBFm1sgs6ICHB5GUo\\nGdotrHmYGDqQAUc2iRiE3Bq016G9BPndrE4QGzvI1Fjf68cKgvqM2oiM0HqN3raM0K1q4GJ9KiKj\\nWxxBlh5/BcaYou+omhT9nhgbIa3NPVqfh+AGwhmDGwZqw+77z8b7dtpuEGoopANuUMJl+q3bDHGO\\nWUckslXDOrk2I2zqBx6lPKvP/ejiW2IaehoUawJqkVkYWSKZUPcEtFg9YCwWp95SMIOmXbC19R5W\\nGvrjxqtl7LHmDIu6ZpFZIkVxgCOjYsnono1o++ghPhrXzSl+49i8UV27JGur7J9d5X4JPUhBYgCZ\\n1nU68sxDbXBRgQWKx8xtg4n1x17QMWNcy7oCOOsJe+M4Gca8u82dZw/pQ+cLIhSiJrqc6FXg8DbN\\nlT3ibeccl/rgRMSV+PdvEbcOicMCzQs628dk4cbPymoaR6OV73IZ+chr3cYrR9kR4yvOEDMiA+PD\\nirQU7VCMrtyhXxwShzuYKoFtkDmyc4Mf+dlTyJNfJ7fF+bnBZU6DGnons//xfR665ImlYVo7hWtC\\nVCiPG7c+t80zn94nLR7HtKGYEqKioSOWiVcDkilkb0KRhDJ7ng9+KvBzf+OTdGc/xzItkcZcDlSV\\nUArQETiPmXmjC7tTy5VHg7ukCZGg29A/TLz6If6bv/occdhBygxlVrupLIhpxzdaqEaa4nFRqqHC\\n/IALAeQqtC/5YbOzYLFzmdxMiTW8YnGCSYc1gpUZZXoNzn4dwjm49X7QbSQlLH+PMFkA8ARhkbRG\\nbgRP5KVEm6H0gsXkkERhkgNNxSfBvIirIEgIDk5MKX2EGGiCkiJEF9IGlCQ+P7oBcBKRloCFwoA3\\nMfbfCZWc5L+rMmFad4nVBhXuyxYmJIa6MyIdMKkAp2AYWzjBJE+N0HgnJbeXCtJD2od4iCXDwkBI\\nFeCQSNqS0gE5XqVtTzOEBZoKVvwUbFJDnh5izRzyFGWrzmf12Kxy3KudNnFw5AfCGuCUtiOkSAjV\\nWwWCDFjbwPQl0IxNt6B5mCidgw6i77XjAI60gHjhovnzlu8I4FC9tDXAcXXf4832iTDmp3ROd3nB\\nJBiNQFY35jEHoiSaIgxffJnS1PACMJ5OQ4bTcYLqdQhzYqpxO3ExykjceM9bOCwQxLdEsLByiwXz\\nMmAtRAZavcOW7aNDctGpyRWavZf4iZ/9N5nvXqGXJRCw2vlFTJBeiNaC3EJDZojF9ZE1IipMuz2+\\nMROe+8wLTPQ0UhrfENqiKMmWRBu9ww6sIQahs6ts5SXTfJrAc4gNXhiksS4k58Qm8XifqdHEsSHB\\nOIfeTBtNUGYM+xNm7HvxhBVUDhgLuMwKknbogoeGfEW7qpyLi1aKmyzg1Nf593/pp7BT30L27lDO\\nt8x3BtqqkreIkUBLbgI784DdOcvwV6fsHj7O//Q3vgSH78IGrd9zIpb1xhhjuau+N75BTYn9PhJl\\npeqXUBpJLLr5nyqA80P/9i7tuXIswJmWhuX+/G0DOCI9YscDHJMewe4rwIE/d+yzOhGr/snhdyna\\nIWKEjLv6Gmi0WRHuNWeGuF5A3hXEW0FNU+BQnua87NPaGZSxczZVF/qtH3cPfal/v0C0gXPhBu/J\\nn0XKKVKcsT+8QNe9wCnZInQvkcWlOwuDLxYi0SIpNCz7jpQCvXWEELzjS4FZOMMFa3ho8VkuqOtb\\nFF2S4jZFXJogWG1yoR1YJCLM7VmeGlo+0O9y5+bX0UkiF/PXoyOsEAJlOHr4uTFf3+1s0pJ7xYaG\\npKd5YvksbTlN0FribRGxxCKeYsEFbtsld/uH/nUz6POFGJzqePSTCblkDLN9clCWKdPUpFqX3I1d\\nhIad3DAlQq/szAXa23B4AQkziBErx6Cfd8Y740/4OBHG/H/7gf+RHBeICK1O6Ko7EgqoKm3T0Fvv\\nRsWMZO7agFBKpg3GK2fuMLyv4TGMb1pNcoiuXMa3eoy0uRUid1FpVApFCkkCrS74gZ0v8qlP/jbt\\nIJgZh3uBO6eFO7/+TbrG3eEcQMUlab0OxqP6IQRUPblkZo6eYyAMwo9dCfzcJ65yev6bGBliS1Mb\\ny4o2lAA5GCX3TJtEKYa2A/nwNsu/+/c5xYLeGqK4NK2qNwJe/R2h1AQkokcOriZ64ULfdUx0yj/5\\nyKNMw7Q+Ex8BeEk+xmdufB9/++Wf5qA0KD2u4RwYEbrXqPfQKIvJy4St5ylbrzALuyiDJ7eANg6e\\n3tXItJmR6clt59e8dwsWh4T+DKWcRGQ+js14voINXGq+yZn5Fxjl7GNtNh5NKS/8MQDn5u+/OcDp\\nf4fzcZ9ZnNTiIZdHQMYsxFs75K7eavGqSzNam/MQ3+TDQ0GGCnDCC/zcX/5RuHid/WYD4MQKcKqH\\n7gDneQc44XUAx87wyrTh1m9/lgv5I1C+DYCThHl5lo8tWj4+v84d+cyxAKcWbB8Zbwpwbl/hg28G\\ncNIFviaXPKk7KFXspebcamgoLOGR5/iF//3fQS69yDB7tQKc/bvO/4lY9Q93f4CaEIMbEoLVtktu\\nAHXIqGbPWKsn4ZywX9DY0VpAu8id5Xmmy0cJu1Qj7smCdWDmrVvAYzMNUFQiceyPiKdfC7Atyhm9\\nzCl9jmk/MJWI3jQ4bLjzLaHp3WB32hOk1LZdwVtzSfSFLEYohRgbhpJrx5XAVI3tLKgO3vR22ZBi\\nJNARzJOxfQQruRrEAH2DRKVoJqthNhClXyXUalGgE2yOczuqOxJChxLdzZUOsaeJsXVjUudGaeji\\nhHNhixJyba4hCLV57ZjYQaFZ8Bf/65/BHrrD4fYeE4kMTDhkQYieEC+1C9M0NOwDgYYcB9g9z5/5\\npSc4PX8vv/aLn4arLXTTt+w5v3XDNv70f7fsGn/tyRf5memvMuU6AFNaFia0wf5UAZz+9z/N1TQc\\nD3BqGOLtAjijIT8W4NSy6PsJcO42ToQx72xOozC2JaTIaqsjxoqBVXxWbZU1N2yAIShLlGL+Sg66\\nyu47G+JeLN7xZHaDZLV4RUVrhNQPnlgydJkkqYpZZcJ8YFeosbJACyRVVHItW/cFIEeuu2e6zuWT\\nzSgyIQRozEA7XzjisWkBGhU01OA5XhpsVghAWzm3o2YF1P6n4xhDu8ck4WMNovrBUpUsS0HxpJUi\\nqAV00tALlARWXAlw1UEljHQthUlm8t4LPNt33JpfQCZnaczodd1vxROwxTeROZIRUwYi23/pLLYP\\n/Pc34NYZmB98d4/0Xo0jMbkaP7dC0IEz5RYP988w5TUAJhGGrERJf6oAzvxf/hFPxOZYgJNFqkDX\\n2wNwREZG0d2f7f0EOHcbJ8KYvzPeGX/yx+stgXqtiS5rOMB/WkpPp4pY70ns+raxB2bZ/ERhpeMs\\nWioLIoNCFmWZlaEULKwTrCOT/N5s/GMATv2bjtRdM4Iq1oFIQykFjcp0MPpuILnKHZNgRPPwTKJW\\nV67Iqjgv3vIGj7wSIqSBUlY4l1wIkuudO5dEZQQ4zu30nqaVzmwNYnV2xJAV358VoAmvf5QCUg8H\\nd0YMDaBacGhXVrLYJc3oSZQkmDrKtkpsdLqNVF62ce59F3i1zDkYtmjalgj0zO46+yfCmCcSpbI5\\nmgIqsnZhoDayZUXf8lMOIGA5oGGglEJASeZFFqPkeUSPtNV6q4aJ1Qio1xxrXWz+zJVQ0QlACBnJ\\nmajjvURCcGEjrd1kyAGCI5mw2TkGj/PBuIjUlWfVKWCl+HeXGBAxX+T4d4hGUvGsuWuCb8rhBqdo\\nSVkv0iKvmys7Ut5v1UvSUAgV4evq8+q3ahUuomemd5jaHVrN9FalU2u4xLnhyYuowsAQYV92uJHO\\nIUlIRDoyYxFLxVEYA6HUBS2ZXoReCmHa1c+6e2r6JI5smRjGAhFHyo14BWXStWc01jEA632x+UER\\nl1xGUK3eUhrDEmse/9om3ZtQyxvGJs2OQKjFX0ImEis7pBCyMgmsPMtokFTc0111aNLXeavGSIw3\\nU7IZQRIhRq9JUD2iqR+oRiFsvB/zn9fJldWxA1Q78vrxBm/V6uFSyQ++h+vHV966WS1yJFcWywj1\\n4+o6jnhwUjgwuF0it4ZtJJSVt8qEY8eJMOa/df499W9KYKBYXBdM1ZsbRW3cKNnq99t+yQN5zjTc\\n9hMveKihSK7UxNUZzXdG2H/zoavPCtWojYhBQQY3PCLei5HVXqWPicW0pa9CPCrqpdiTsFo4wdbf\\nYPU7NoegTKxnkjOT4op5rWitm5GaSimUUPzkMyFUnerVtjI34lrXsRhHFvV62JG/+vxvmAIZ318P\\nIDPGnqbFdim2Sw4BDQq6Nkqqfh8mAcRlSC0mCA3qARtKnVujEFcmKFWr7lV7QZwnnTTUSXZy3MkZ\\nG/zxjZ+sVnBxrnizMtChsr7znyqAY4h3HzoG4Iz4/u0DOMam5T6iXwQgdl8Bzt3GiTDmf/jnfxZf\\n7BkLHetS7TqFJsSqDTwa85H3vLM4QL/wWR678rQv3lKIY2GWhI1Y1L0ZXonqp6o3crUaox/dzbC+\\nggiHacKVrV0uxy1MqjypANauFsm6K3sdr+tEEslc0DkPHR6ylw+dCCxUUba1QXZNiboQRcCq47uB\\nKI588uaavdv9Vg81jFUfoW6otQYrAddsHzfpZrMMGXFPFM+B1HsrVjDNUJTQeMozUKoOi3ssg0Ai\\nYHJ0g40z7ZoYcEKW9ZuOgB+AjQmSG5j4NXd0HMaGYM2qH2YXChbC2oCPn2HUPM06Xu2/kokpIeLK\\nf1kEE6VIqQEPL6J5q8cafoS1UatG3IMcLsJl9RAO6zdibUPOeUX4yeJ0eRnvE44Y87Hv8aqJmEFI\\noMVG2jZSlCCCF/Y4YtZQ124FPUf9k7URr1t6BXB048s326vXy3fwIqt8qGetbHOF+nFWmGA2cwC3\\nnqTVB0n9LKTG2oNUCQuloOQ32aAnYtX/5l/8OdBtsIKFBapO0cqmxBjdSFtYUbLE/KQTMR6cv0bq\\nCuduvkBJu+TcAnOUREaRVfn++OB9CcWaaS9CXeDUbkZhw6atjfFqo9QqLdmo2kIy0RpPTAoIE0QV\\nydCnhoNwlkkbSO0On7v0CPv/wX/Ivzr3OEMaF5lXfpboreikF1JIFIUQnU7mAnFKlMhUlR+++jLX\\n/udfYfvVLxL7BV1RQjulqal3rQdeKQXCZGU0Y20vZjX56IdiPTijo/wmNAy1LZ5qdgMeBVMhiHgS\\nNYRVsrkhoaIEC8QQKCFTaFhwmiJ7BKLrt+QBY+popAgmnSeIcmBrGtmzPYxET0FI5OwUMhGhSM+E\\nQBsjxcw7TgWvLozDDqlrQHYqOr+LH/p2DRuLpmAMFoEbqSA9Ghcsa1b5mUc/zO988qfpvJUS3y3A\\neaoCHFVvCJHUsKB1vd/LIfWgMqhrImCI9H7fElCi5wOyl+/fbLb5wt4pcpgeC3A2PfFvB+BcXK4B\\nzirTMIY9Qtm0mO6xwtpdOiaEMgK1u43jAM5KtbEijSgBQkFpKXheYEyGpxo80iguo2HOailWZTwU\\nhASUNyVznAhjfrM5jw6nEE0kgSwB0wGNPvESBRkSshGacJ2OHpuTfFDlAAAgAElEQVSc4ze6f4PL\\nyzkP6pw0fYxBnqWIS2lGC3V5AeKn24iUw8q1ClQuBuOCWS+V9eS5IM8GEsTXhBBGWIzVyjcRocTT\\nfGl4N88P/xZb5RRDaflCmGI7P8KzD53mzrBEYqDXwrRtGAqoZtomknOPhISZETXRW3GXMQqNZm51\\njzKNC57tbtNkQ1WxqKQqBKR1Y+esxNRCMHLoKVZIqUV7c/4vitcd4tQxGtcTCl54JWQGFhR1VZvG\\nEiUophnDabK7TDfa0wmmrlBzKJf4mp6jp/VFujpWwV1LD6mw3OOrf19Z7M0Zpj2WMnO9w5146Kyg\\n2s1XtCE2QjskpppIxfstzuMdDrslfGMPFrus+lueiLFpBcbEoGDSMEhiGfeYt+cZGu809KV3f5Lf\\n/um/wmGa3FeAE1Ye1BsBTmbjELlHAOf//lv/Jbfj7vEApwiibx/ACXiHpeMADupMm/sKcO5itU+E\\nMW9ubvHcc5eJzLx5TohIUFR6nnrqYfb34ZUXXkMsonhwLESQ0HPu0Qd45sVTfO0L8P0PFr5/coEi\\nqebSx2zxZtzy6Mkmq9dHRL7ZI/s4k+7vsA031Vj39jPzTulIYJAJry7P83986Rws97A04xv9AT84\\nnGf/oCfMdikqpNTQLXpialEtDCWDTOh1IBAxaxyxR+jLQDTlWj/jD541nv7mOaQXShkIUyWWSdVv\\nqMZ83Pw20M8WHnMLghVqDNM8H2GBZA2TYQIWKVK8ihVj2TR04vH3ZBELihtMIYYJ24uWKFZ5v8mR\\nojUsEJYxUsZ+katZGilyFX3mhqd/9Q9htoQmu87to3twroZNguDoqnE1tkUL1xbwyg3oA2y1oAG5\\ns4X0E/RExczvNoRezvBH/Y/QtomuFv98Vb6P6+27WES7rwCn8imOBThj0vFeApyvnP8oy+nsWIDT\\nRN9xbxfAaUrrjSuOATgTaxHkvgIc/vbZY1fUiTDmey/vcPC56+jhHFckq0T6iaONchNuPXMA0rr7\\nMrpKsmCSHmb/1iMchg+yH17h0PaIVhs5EygixNreaXRDFV0lZzy2VYNzHOXgvj6AvE5G+u+tPbGR\\nhqXISqI10Mcp18t5XpKP0HEGlSk34xWGeI5JvsWwPyFKixHZDXA4h90p5L5HpNA0+KnfJ7IWUopY\\ntyBmRewUr8mTdGEXidBMGzQUJGwRraw24CDm+iyNcvHD5xgm2WOR6qyCEkplGAQOXzwkv1ZowpSh\\n1Li9FaaPJ9L5CcRAkpruzY5k4tDy0tPXiVIb5lb3FQvEZodBG3KhdkwZwx9rdoOfjcE7bA8zsA6m\\ngfOfeA/6wS0GMU92BaNoIgqcW0RuPX+b/Re/CrcE5v5wbDlBdMLdlUbeGe+MP7njRBjzf/GffYXm\\nE+/CZmDR44QA08vG7/7678Pp0/D4Q9AGVt3fB2iWwv/1t74C5SKNPsxce3rdZgulUc/5FRmrqnAd\\naBkdxI2yCamvQe2XWEMtrxeIH/82Jjure4klZ2sAUbYoxZH7oBMWzXletXfB7EHQCVx+mH/+86+C\\nXIZHZ3Bm6vcVI+Tk1/fyFZgv4PsedJS6QrHAwuALV3imPASvfZBbTKEd3dsGQsMK+VoAHQhNxiZL\\n0oWL7M8W5GSIBSJClgGxQNLA4to+BCHkLUwDVLd0ciqgDwolgQRD1EiuNMRk0XKr3a2VQRHUuxW5\\n273lz8uubMQ4nZlSiGCDX7Mkv/cMcAr6wp19pRgUC6zoLybQGEFhXgTuGCwn/i8KYTyI7xPt7jsa\\naxAwQtk+nOKfvnya32xOseAOAA+cfYD3PnuBrQC/+dsvoIfGSnK1Apw/80OPc/MmPP/MN44FOD/1\\nqaf49Oc/xO899y1++NIrPNm+h8hX67eXYwBO8fCjsHF9AWribaTqVUb7kbs6CnCaylxZe7ubwMck\\ncJhO8czhU/wPX/443eIM2ky5Wa7wg91F+mVHZA+TlpbIbOFxdQsDkoxWvF/ACHBiBTj9wYI2K9e7\\nU/zaF15g74VdZICmqQCn/P8DOHIlQgrHApyURhN6FODc+mMATgegtWcpsJLOHZs4D/D0Lz/jVKYR\\n4Pz4U2x98MG7rrATYcyZX6JppthsoAR38cLQsKWwDGdBt2jTNn1NEoXirucEGBYPQDMjTc8Qpq/R\\nTrbYDjvVXRozwr7kDJgwYazRVFnnhoMEhIa8WS4rYbUk7YgjGTy7Xd8cCQiRnp6GlklMGJBiZGfa\\nwHQHdAZlBkMLL2xBbJDTW8y2t1lqQVuBPBBLoNwArh1w6uIjzFPHMMOrYjUw6xrmL20R+pkvjuDV\\nY0UGbLH03ohkCKH2IwxocQ31w6Fn2dbu5KVSICSCCrkELCcYhDKYLzRVaIRlxqkFK2e8YSgD2IDm\\n7AJag88XOngeSY0mTZ20IgGaBL27qaXOqtD7vJp/5mphtzCbbXMgjbMDAJcPNRoiIUFsGzQ2jOEe\\nkA2+3t2b3r69Y+QU1+uUwH6zxf4ww7s7wP7vDnztD74MSe4zwNGNI/AowCm1E9i9BDif+Y++CjIc\\nD3CWBa7feNsATssOOduxAEfbmrO4jwDnbuNkGHPzcIcT11g1xvHXWOeQNjPYlQZEiKCGWnTwZkYm\\nIwwUnJzfVwPdMsEYKLVxQgCG+pqnhAYCYxcSWHddKbjuoNT3jrqMQkTo6JB67i+YE2hoaABj6Hov\\ns9dYO/qEinomGK3fmoVq0OpGscYz2iY1s20VJdXgZJkQtPEDxRLem9CgbRDM21lF8U3aD27gg9C0\\niRiNHIx1yyypXF5BQ3CybrIx8AmSCc0ESQkJXlKhEqHG2psm0I0d10ckJo6SvXsCrGPkhm0suXrV\\nG0NXr6yYjiPUG70lrT0VN2WNZfyl8fuOMh1O9MgDtNuu5Qx+6OtF0J5kLRqUIgqpgV6IuYDswtDg\\nDJd62lnrBiUnhuVZXxeTGUUSuSpPSg1rBTvKM5dVmHGMK296NpvG24mGb5zdWiQz6pWMBh1npkQ8\\nSSgWV+iYZuq6uZIgN/DyWbAl8fQuzdYWS62FNUzY6VsObhS4pmw9eJ4uDJRY5ZM1MM0ty1eW3vQ5\\nbvucFBzWp7rGxx6jANKguYPkIbwsgRyK5+sUbxpjAStgFtEcUXGbYcUBToYqO1wbUEhisEwSaEfM\\nY6ES4oM3FxnnyczvO1bAtZrxgNdINBsLP7I6uGKilBPOZsHq/i+NuzkbVKSxP99m9xAvNqkt07R3\\nV1MTlgtmhaIdSE8vQ+W4jkI9XkQUiAzkDZOuFS36ElxzRWFUd3HR/kwkEUgYA6peceiG3BnmlWRE\\nT2boBhfrqYjFXxkN+mQdq5fsvOLRgOMG3/mr1fhvHmokL88WNtBoXBu9tnY+N4VJ8PUQlRRGgpO5\\n2y7UZKYvNuIAE620rvELimtkVFQZRVB6YgxIMadcNY0/gyx+GASFoS7QIx3JX89tHr+D1bWPD8CL\\nr3R9gLvv7sUhUK8/c8TwjIvkRLaOkzf+vzRI2nIj2FZvQgroFGLrVmGihBBRM2JqmISGQxoIE9o0\\noY/VDGggmScLFzKB0BDSNrGZMptt08TpXQFOIqzWf0BX3mkQD8CkDfM90gSPAhxHykpAROr7hgrN\\nApmBEBoahBATEzEktNDuYDpxo8tFYE5hRisNwcy91TIQNIHtgkGjWwwhUybmAKIIYRAYHiIxQdWI\\nYYJFIUuG0rkHqD2k5O8ZhgpYlGJebGe1calZABU3vuoHwMBQjUKGWMAKKhO3Az1IiKh4o2i1QtEB\\nSl+bUIdqo3wtRxpigCW5LmQXV6j4ntpVoM72hnluha1mQkl39zpPhjGv1W159OQ1+KFWqwORRFnF\\n86h/r6hO3DwNBiYP0i8fJ9iHKXGgiJJKWzvLwxAzOfYMZGgikmFmEzSbZ9CtQ5IjPA22yn6bCWbQ\\n6pRE9M5vbSHXri1D6Wlsj2SJkNXpSwXMztPni/UmvfWdSPQNYQDR+/Tq+DCVYLHKlPqhrqK40APV\\nxZN6wE3wTvTVxZYCTUKGLyP6NZrWaGcNi9xBkxlix258nEkzUKL7Jyq58hUCKSQW09vMp4e0ZctD\\nTjqAFE6FPVI7oySjkAkxE3ohhIaptNxOL5KavWrHe4/OzC4y75/C7LRfX6nIWUYvhA3eUA35jCEI\\nXZeyjz8dfyXXs8kPuMRaaQ5EFSNzLwpivvuxkew9ZtjoZby+qvC+A5zAwHAswNn0ku4ZwNHoa/oY\\ngLNCrW8TwJFZ8PceB3CCx67uK8C5y1o6IcbcD8qkMCQQyaC1U3yZQKm8YcmOzjTRqBCUqgmcUD7C\\nv/j1c/zGP79K4mHf9BpIJUDrxnw5uc3FH34Xw6SgITLVGf2LC2584yXQwmM/+AGGyUCOQy3EUI+j\\nDUK0SHet5/rXXoah4d0/+gTdpKevccjmesPlr14m3VQaJqhCDi15uQPyYJ3/13XtpmAhUsIY0oge\\nMTCPtQWjduS2upkLSONubIlY2FgIpiAdf/anXuEXfuE0RV7wuKAODAQIhrXPeBMMqCEtXcmmjtTE\\nxgKhzCmlEKOgNlDSdeZxwHMI3gqsoXWRJJS9X6wsFJz3rHaO3O/yl3/iM4T+B1nqBGQBLJxqSW36\\nK04BG9t6rUIlIgyM3Ve88MKv2TvPuG6HuhT0yqOqYagxiXTSxoqDbYzVuP7jMUxWh9a4erD7CnD6\\n5CDkeIBToca9BDjhTQDOGGp4mwBON+mJ0hwLcLQxV0K9jwDnbuNkGPON2sxNERu/vdGdhvGBHtGV\\n0AEsEPKMGN5D6S9SyiUvfy4B1UDpKu/Y9lnYIyxyRw6J/SHS9kD/MpSBYfkUvfbkmCm1WatfU2IY\\nlLIoMH8VsnFzeJw+Kl325MVOmWKLl8jzhGpDJJJRiHuIuN6ZSXH30Qzo6v3FetO+ILVqJY8nsoJn\\nlkz99g1gIFkhm7LWqO4I0hHj15B0i7b9Jm0oLPUQCYlEw0BfI6BhVTwV6uGh4n0MwYjJeekiVt1p\\nI6GM/Qtra2k0Zop1SEwE/J4VGPQBZukGSQLCkk304fogwqqQeqzLPhI9l1WJ+tif2ex4NHL0fev3\\nfy8NlytY/V9Fbn5f3gLxdXdkESxu3LnWxOQGuhdB8w43bz7MF7/wAEGfIEen66aSUPM90adD2ocm\\ndCkjEpFemCwS89sHiAizC9t0cekfW6szIdDGltL7GT0c9sQcmZyL5CZ7AwgrTPpdFreWzHJDJNBn\\nxTjFi9+8gMrUz7SNtb9eC7iXgd/HsXO2yresvYx1Tm18j4Jknnhv5lM/+hAh7pPayGIwSmwooeHM\\n4wN97LAAQ/39MWQUFXh/C7cnrqRYBAstZgPtRSGfXlIaz1FJyDSlwUoh6sDy0R0amWHFW9CpzRAe\\n4Fd++QpRt6pGu2ft/HnV2P5qCsZcxRgzH6VJ7j5OhDFPw9MUfZSsCeuio+2u82YJ+mXQrVrmGqEm\\n4ZaqTIqyXb7Emd6YLFsGOoooIaSKOLVm6/3hvrowWjtFDkKIERPhTL5FOoTtfk753d8jJl9YSZRC\\nIpowWEcIia1hxs48cKOFWXuKDiWmROhhTwr73ZIHhsKkuFeQozKUK2j5GogX/oB3SOm146C8D4YL\\n9F0HsxbEyH0Hwytw+BroGVQNcnbLNgQmOWOHn+GCTmsX84FcioPRfMjO/GliHojpMiEYUZarJbCJ\\nV488eDnquJX6s9FYHE2BgUNlFxBrZKBHUZrqfBupHCIL5QHZxvQr5O4MHVOEtnYCLazcydHjsAXu\\nuRTIkaz72DBZScD6ui70ORKHyCLPoRz6e2zPE+FSnIHD8rtcifd/BIxgPRZqgVuY4pm47AlQw+dA\\nMlhDWuWSa/WljEClpanePCFDjoT4QZ7+jPG5/+cAyg+iIRCKszRK8PeVnX32PvUI+5MOLBGGSPsS\\nLL7hAOfSDzzFfDb3390AOK0lyqCUa4XbX3WAc+rPPk4/3QA4t6fc/OJLyI1EqACnHwFOOIdJj8U5\\nwVqwgtmAELAwcXQtMoJSPMwiKz0aEZcnQL3VIlZoinovg6rUaNYRZMkT7/s0/95fv0XYADj6ZgAH\\nHOAQXNvFClGaIwDHCcxaw0u2Bjg4wEk//kaAE/V9/ON/FJB8isG2GVlEqcbJ9Uie6HXhOQn08npV\\n06PjRBjzv/Yfv8az2ze4FQPDZJdlhpQzD+aB2ccSgyy5Yl+mj1Mv87Uls5x5YHHIT56/wuNffYHJ\\n3GNxKkpoPUwhFNeAqNTEL80e5LcXc24Mp+glYd1t/tzeSzzy/Td5eHGNEA5r1t1tTI7mIvbBT0cH\\nETO+1LyLX7uduTY7w0K2mZbIh4bnefwD1/jo8qtMc080I4vznxMdg8wYzMNoC4FXtk/za3sHvNCc\\nJbWnWGqL5YHT08L7P3ST0x/s+Kx9lj7sYM0eIRjWZk4Nt/nrP/4cjx1c56wWwtBRSqm9QgfKEy/Q\\nhgl97HyhveUgtWLq6m5Hix4jZGyckGnskC2u8fOfuEp7cJ6/9+IuX55fZM4TrOLbppXZUjni7XWw\\ny7DVwd4hefdl4myXOFaUimI2YLEl2QTZvoZd/BrcbmDxXpjvQnjA+dZ6dwH/+z/qZlzBaNtwn5WZ\\n7BP7r7LT1vCECAfZ6LXcV4Azk1BpwW8EOKnxi7+XAEdEGKw/FuDEoDC8+PYBHDlZAAe2j11pJ8KY\\nv++vTLm4O2NoEl2l7VhJ7OUEWsgBhmaKkbybUJgwsZa9PvKJr1/l7H/3rzgbPDscApgaEKv+tq7o\\nPDv2KDdvXeVgcoEhzWg45MPLZ3k3+1ziMq3VDHx11zKZJAErEdVMTEbJDTuTp3hheZvX9AKLsMs0\\nD3xo+Xk+ujPnycUfMmEgkSk2xoULfQlIaFhI4uqsZXnpPWxNApN4ijw5Q2KPZIVpWTBNN9iLA7uL\\nFpucJcu2d0LJPU2+yd7uV7h05Zs8eP0mDxFRMn3OLMS4mZZM9RJrut69GbYikgZWNbNmWFGiLply\\nyOP2MjPZ5lR5lKltM1+1RXg99S2DXuHRH9vmZ/6Tn+T69h+hjxiLnUjMwbUqQqGIF2k0OaD75ymv\\nTZnO97jyWw/wG7/6RfLVGcQZcBLbxh03MtPlc/z8p77Fufx5ALonLvHlnV0Ow/S+ApxvDecddR4D\\ncEai4b0EOFdT5M5091iA80C4yUc/VN4BOBXgwKeOvYoTYcyX5y5TgBwKk5jc3WgDfd+TQkSSJwHj\\nGMOuIcUSB9j9Fuftyzxk2UX4i5ejmFStFekZ1CcraYdOGw7jDZY0iCx4Ur7MhXKH83aVoP2qZ58R\\nKOo6FlESIUIuHaYtljM/3AjX9Qo57NGw4Em+wEN3rnFp+AatDd5NpbpRJkoILSqRF3PgkSc+wM6f\\nf5L5Ax/k2tZ5DpgwyB6xFGKec6lcYTsf8sjsQW4xpcRtvLVs4azu8/Fr+zz2uauE3/ojzu3PSQiD\\nFG7lzHzYInLRDesmF/stG6vsrY/q0oqBmSIxEIdMtDnn7GUmwxmmdppZyEdsuIPT0YU0OB2ZXVQe\\n/8SEBy7u0e/OuTXMaUsiqlCCC0CliCdnFWbv30b2pzRXd8h6hdQ+Rh4y4cQWDb0z3hn3bpwIY37Y\\n7CM0uIzWnCADQkNox47VgHTe+w/vmRfF3ZE+DGwzZ2pzILrr2Yy0NQXz/psADy2+waXuZbqhEGRC\\nSZlJ7mkpCB0aoscizd16NBA0YJIRAo1ESj7k0WHOhflXSJMt+jygYSDljkkwZmMRx8pdAggwLCBG\\nlme2efksvPKexLXdl+h5kS0SSxOCRBrgdlnShUixy+yEhmJCFGqULvOVLWXy6hY77LMVOoJ6Nn1L\\nIASPLUZrvNjkngxnF4Mn7sSMIEJWQ6UByUxtziRf4VTzONFybUYBjCzakXeoHYQOLPDKrZ6nn7nF\\n8M0DStsxdEtGVbocPFHYhETGvACjGI0JL33tBjRbzjSIW2j+XhDa8nl7INzmB/p/xunmXwOQf+bn\\nuPV4RuOM80AfCileq/HUQKgAx1JgMFv1mMzBWT7beeATL17mvX/nH/CQ5BX7JPSvAzi1j9zH2sf4\\n2PID3AgXnL0lC57snlsDnHwXgKPJi4zVAc6H4uM81H2Y6/kUOe3R5AVPLr/AQ3qN9/RvAnBC5MUS\\nOPU3/1O+MUnMHphwbWvGAcIgM2JpOVsWbOd9tnPPB2dnuUWkxFGyWjirkR+7Vnjsc88TfuvTvGte\\nAc5QuFUyL9kW2/qReg/3ogZB6idXb9USQXIFOC6z0JYDtk34pH6eSb7GxfxRXpMJc1t7q2uAE0AG\\n2LvG+35ixt/8O3+Jw4vPrQDO3caJMOYuv5mZjaWfllzNr5YwOwMxrWxja05um6jRZkWLkDWgJbt7\\nsyHaLAihNi0I+RCoN213KBmiRrCxP2FZsQSSehEBlpDg1V6GIJKIw4LTpa+Ng5USjSwCJZIt1K4u\\nrtrmFcDeeFqtUGzGQjNLGbzKjwDSOfqn87sNiUOJ1W1bOvXLilM2UfZD4kCUJkaWeaCxQi5eUBbF\\nG/56j5nIqqztrXxeFjYOqir6jyIxeZxQAllBQ6QrSk8gyxQzJWCIbTYF6KDtmH3yMQ64zD/85d+B\\nNkFxhT9GlsbIOnTXq+6fAHILZEH4+OPo792GOxnepE/iSRpiwqSHWX/IZOJrs2uWLKZdrWK+fwBH\\nS+MhzGMAjlQJ2XsJcL75ILxw9niAM9eObPYOwBkBzk8cfwUnwphvLaCxTLCBSKZIINd+mFCNuVWq\\nECM1sSZ7dImhtYEBzkSppbBaqyrWredYZ4pX9qjUqquIWKFIqdlsc3aEQAmerPI0U01dSf28+oOx\\nBEPw5rBj3YL3aA1IMqRpvHs3hWQdqWbMoyqxxjCFehi8bo6CiVeZiWtsJIveLCJ45d8wFEKAqCND\\nQnnjp9yDUQ15PPJdVY2yvm6MYRUXQDC8vd5K+nO64Kd/8aMszr6fvOsyMDFCGfKKXzDG5ZsCQwhe\\nJV3X/ukIzXX4J//uP/MijTs79/6+36KhVkgSyIMzcMqwoGhxKZw6hyNXAgpNpfeogMoo8uy/FdW7\\nXpW+MBRv5CS5OKXdu0fXLAfICHDKEitLoiWCgGh2Y13ztGalct+dDhgsgAYkRBdCQ4jSInnBjhos\\nBlrJlFic/61+sAfF+SJa94b1xJrzy1rogtGJesPnWoQj0gALUq0PWpiiwQvDgqRaIObyGosAh1aY\\nRT/shpIZzG9bxHduqfucTf2lt2i4VRn3wAhwQGKlF4o60SomuqUyWMCkwUzx5KltpLh6Ny5LOPij\\nm/zD/+IfuZG0AbTjV58/3pqfCGP+1CuvkbQnMhAso5IYxr6RdQTdNOYuYduqcv6VazRDj9bmrSHg\\nE2Oe/DwyVpCmfqb5QaFrNSfCGtLj535ZvXlkS5vJ6mEBVUTHVnIKK6p8fT6lKJpBQmFajJ1Fz4U7\\nB2jVZYimFEkrAziWkIRqBUsIG82gle2hsLtYMjVAB7oeUuuyVT50XQR3j5DIeEAWed3PVve/Nu4S\\n1gcqbKZlK/2sGciX4LXpHRazBXkSkZAo5XD1OWI+H1EDQwAN7s4KmUNOcUonMNn3w9pOEpvl7sME\\nFmIUE1Llfe/lwpkl9JK+a4Bzns7XsUS6kIkCyZxXoeKBhrFzTrJSPRw/TJq63ANSudAFC/7DqJVl\\nEcYenEpkVBiFwLDqyVlKLeapdaSEtTcmgITggYkQMTMaPM/SqBAJRFOa0Pt31Gsdy4fqHTtgqdQz\\nUSFKYtDAoIVJiAxDITbeYzVaqSGpsMrzvKXDAmHsJ/o6gOON89zX0dqBSQWyeFQgVoAjOlobg53b\\n/Ow//QsszvIGgHO3cSKM+cd/4ZcwKZ51rwR5LKL18sLIv1w1X/XXAXZNOJ29oCbVTuQahGhjGfJY\\nWUgN3VSjOFZchTUl371A//lYPj32EHUDWxd1aSixoMHj69GBu6N4ARM/RoI4IrEIQ4QcjFNFOHz+\\nRW7/vf+F3aqzEMhe3Vg5qX6f6pxiYEhKKqOxDyQttIuecweDC7OBd2VJo7ihYsGIGNlWt/qWDRdP\\ncj5sJa3V0nLXnfSDTTzkYo032hUFK3WaQp1Pcz2YWFhGyLuBw7aHJhPYJjOW50OjXjTh/OiRGDZg\\nLDGDmT3gjzpNuVu580kbLsMjFFO2K+KevnaT905m5NDeX4BT18hxAGcjZsm9AjinDw65EIZjAc5G\\nfew7AKccAg8f+/0nwphLfoIgA2FjMbhrPdIEPSEwohKppe4A2kSuqWFRGELGgpEsrvojugzuRsyj\\nmu5YGo9bSXUnqyraOOqRUo1U8P6JwY1Ro2GVWIJA0LgS53Jmx/oplSi8Ng1cPbPF/nbDgRilbShD\\nv3Jfg1l1myvHvW6CUe5yUHXtK/MkYDHh+anw8uMzdrrrzA7mPHoTVJXBjCQJpVSW68ZOfUvG5grU\\nGjYRxm5LAQ9NDTLlZnwXfXyUgb1awVlDLqMFCVJDWcbWBGJsaZpJFXBSWtLKdY0hU7BahjFd3VPG\\nmEmLe7MJmslbfL/3boi5MSwinF/6vOZ/8L/yYcu1KvQ+ApwNi/t2AJxv/cr/iUQ9HuDIuI7fATj5\\n9YfxxjgRxvyF2Y8QZEG2jMUZUT3O5fogVRPBFnUhupsUDY+ApeT+Ryho9JZrUZIbc1zLItcF35p5\\nt2uNnviUAUWIMTIMXpmZpUBIhCIQC2JugkzF+bmxgNoqgeQjbJRk64YkgdKHwIsPbhH/wsf51pmG\\n/VNblBiwXKBqVkgM5CJefYqSzbvSp3q4dKGQTLw0mConGhOpJB6/dpOD//fz5GeuMywO6cIVHg67\\nwD6FgEnHEQP8Fow1vtBqCCKjzKhZIRdhGU9xtX2KWf9BFukCg00gTGs5Yz0EdEz4BIquQWIm0DAl\\nc+iiY3iBNVBlEvIKrf1/7L1rrGXJdd/3W6tq73Puo989T3KGHJJDkZQlmZEiRIgUwUkoILIDxYYf\\nUQw5DuI8jBhwEkhAEgMJDCQIEsOAkS8BAgQIEMSIDUdCosVvW/YAACAASURBVA+OED8gSpAtGaai\\ngHpQFF/iDDk90z3dfbvvvefsXbVWPqza+5x7+3ZPD82eudRMATN97jn7Ubt21ar/ev2Xt7qWKdFY\\n8ZoE+Q5pa6+QE6sh+txrx+6EuKfID90W2Nr8NQpS5qrxOlMrG0s3zBWRLhgG3ekbKYOLspXn2ACS\\ngS8QwucSnrk24t5inoFIt9eZOqCKM7RM0o7gijEJ2i7fptUlXq5L9MsEjkQ4yIlVEsbFEjk45iKV\\nXWn0HG6YHmPkDXBTI1lcZ0g0wa5h4jHFDUp3gVsykFhxcehQU9Q3BSRiG/ONT+fb1rbXWPPLeXxy\\nbIbtRRYYizjGpx0vZIxAs0EFwInnDvkU5GjjI4Nuz4Uw/w8+92ehZZStSWRxfHZSNC+8DGhtiFVC\\nmLtXUkpQe1xHitTgF6l5roNZVEMAA/3oeI5hQY27disS6OjYLT2pdLjkKJxrA0f5HiKJ0abNoMMo\\nLGrPIu0CirQir7hS26ahtlGHixrf+MgdfvTP/xh3nr/H8e6alFKwy3nCKVRZkyXj0lHNI9mpVTpR\\nhSqGFZsFuQJisSA/tLzCL/5c4vqvgZYjPv38L/EXkqEcsySDl2+7Wrmlk8zfRRZbiIncXeaoXOW/\\n/OwVdPgU3+ClKFBcrOnxiU2hBsBr+KSOjkg6UHqhYpgLJpO/P8wsYSWPqJ7sUF3Y055yRBhyfaNd\\nnffmItSdF/jt5Y+hKbL6sg+si0aB4H8WgGNvD+CkuogarmcBnBTv4EkCHOt6bFifCXAqJRyjbxPg\\nfNc2wEkXsAZw7EkCHCmkZjF3bR4FHylVuJef4UvLH2Gn/CAH+SkG3wVdtiTHSftqH03petDURd3T\\nBnBGDh/ah3MhzL+48yOkOgZ5WIJaHRGnrwkhUd0o3RAowhVTbZPDwByxHUQ8iktoXCQ5QR2qG1TX\\np0SRECj6dOX6H9pjyJALfOWzr5OGC1Qv0DnXP34R/VCoY1GgGI7H2DiPX4V7XzhCaopiyK1NNrkg\\nk4rPrmts/AKul/CxZxwHiimDHQI7UQKLFYMrUiCJzhXSJGlUAldQCTrRaoZXYdktETeO6x73u+/h\\noF+CH/OSfhnqN1l2C55cmxw7E/VPqNuO49JzVJYs0hVeWzyP6/dxlPcI1sSRZM5UiASagXZM2Dfh\\nyrXL7Ny7TN6Dew5DHkAi4VotWPpyZo6O6I8qyRMXF7B7CBx1EcLxCFX0fLXM6/4if/WXCxc8mARr\\nKqTaaH3fSYBjBtnPBDiaYy49SYCTu55ahjMBzmi10Wy8D3BsKhpzRjsXwvz99n57bzZlkF3ezB/l\\nblvE1hk+ClkyahVXoWoO0igNc4rXSPv2tEZaqJ9rCgdnYx4UDfPIKAMpCUi3JczBW2hi54olgc6Q\\nfbjysQuMYmhVvv6bb9CvF8GRDuxeW1Iur3EVTAX3KDeIGKs7sL5puEs4XFs7C+CYFob0Cm+WSxyJ\\n4XnBca0R7mgJEQdNjCTcgq9TWvWfWlvosHrU92xIXUnsLIKY7H66yP3FSxylBTtaOei+yOivsUNG\\nnqjIUzYBwUHIJSRcEqN1JN3h7uJ5kE9wnC+CrIH1g9hjMp2vYW+5j69gLE5RpdPFQ6singthnl5S\\nxs7QHIk2SNBNHn/tEMzRCz07L+4z9PGcySPcqDe48//dbRVBtAF1mQA7EUMFNN7vwafCEJWqzuEC\\njlMl1QSSqGOOfHEv3M9O7SJjrbb4V3KEQPWaMBOoGgWHt5v7Jn4aQBZw+8P8P3/592C3JW/gwC3Y\\nC+4ZOiVIKjx4dUor6pqHIFhihNTBuoBmWCdIVwKFHvVwZxeGJaRK8Y5UF5gJqm8Xfby16hlXrDTj\\n3nxeuKHCyeWpZ7QFQsc4VPAhkAbeyu5N5uAOfBfuL/j5P/qLYPeClmKhLP/172X1TI13J7oFfRRW\\nCb7yOvzjL8NtYJHDxHLnCgwXidJb3wHNQWXBeovm1bWiSRmkI7GtrWYEp7eESNNWNePpIdoqG221\\nqoKcra1KDm3V+pG8L9j1XWqGVKD8zg6eL1BzaAR7z11k0bTV2rRVmrZaX4X13cfXVlnBlR5kWHLv\\neCBlo9Q1wrJpq2F3lwK9ZMwiQ7JL3aytdtIjpPitCtItyG6kAbB9Slpyb32M1URn2vwG317zyqY1\\nO/l2xApgBL3EaAmVJccscVk0R/ZIGI5OaasOlMT4Zjz7bj6lrZ5nYb73YTjc7XF1Os9UoCtw/MYh\\nHK2xi2DPQum8FSeQSGwc24E+QgkbISJNICtCCZvdVvhWuNoNSczVxTqFlfiUHwwaqmKosxJ1J9Wp\\nOiJuJE8hVJLGrjFdLAJoTwWQJFhfgzeuREdkjCiQD36AvY/uULQEo12NeNu+CHf+6dcBuPr9LzBm\\nsOSYC2LQF7jz6zfg9qVmiyH6kNfg90jiiBqqBaFsoh2+TS2m7FQJMtTehDUq5okadERkoHpPkr3Y\\nkFrMcIRHtM1xKk5RFG49DTwVv+84Tx9/kK+Pa1y7eEYP80nOif2aODxcML4xwO2dzb7iXUi+h3Bg\\nn7smRG0f0ZboBmUYsctCd/0dBjjjmlG7swFOC/R+ogDHj0EOzwY467ajvQ9wAuD8lQ+d2Y9zIcw9\\nQc1RxcQ9BqE2J0BkT2lUldJIQHAX1slItSFYa578WYpOiSbaQoW2M75i90umdBWqpPCKO7Q6XWGQ\\nJbdjUwsJtNjVxZniwJm91Fs76vQyZ4ePzjIsBG8sDpdKbRzFkpSaWpiSdDQOAIYE6zRSukiwkBp2\\n8yjGsWCqTTGl+rtHGFfVwF1dE7iP6+x5vGl+Mq90CkmEqG4u4oiMICNK+9fC9h+j04Fv46MpmqKN\\n4xbbo0grwGDTuOvsw4hbZoLEtKEi0Xg3Twp8fdubIclwcYo3uKW7sLd6FwAODwc4zXH6RAFOf4w8\\nszgT4Mg3Rw5//wbwPsD5+rh+aD/OhTA3mebWJIQnQh5lu4sTH/O0VqvAHIc+xaTPwsDneNjNZrkt\\n8LfahFg4+dNMTDT3Tb41QSHtJXkNATRl0rXizTWCeeexmCgHIkuskfC7tHj4qW+2eVZNzc62LWaD\\nsWPzUH7GZx7x3cPapE6+dTsRIz2fO2GWGFlBwad32jZiKSEDfJPEMl+j5RhMMS6z+3/ajLcjJ857\\naxEo5URNSCCF/CypRCith1ys2j5IhP1VdVwKqOCmjMnoan6og+zEe5vL2DFrMtN4R6nCrePOcBae\\noMggciU2P/JgH07/7RIoDm1TquCSKRIZvpKYBVxPbsg8mDNHcWqKZaRMSVGKlEVsRolYa7IGlwCJ\\nLSvzyRQUPDU+WxmmMRQem8j2cb45T1oUywYL2qn1LBvc+IiEuHMhzMu0e4vMqQgxNSZBvY3k2t+T\\nYJvOPWP2zDUAtgeg8efWljHXNMP2vbKBLalNhEh0CEGem22Lrd1dT9769MKElhxjYEIqGac2BE2U\\niQOmklmR4ZZBfGsM5gedBZunsd27m4VYnD+NWdu4ZprZafFuJtrc3nZ683Yilm0J17ahtkXqKEVy\\ncMrMZXPbm2ybVvjvpcWTt2QWt5a6n+fpb+rgQjLaf9penLX7tU1DwM/HtH6slktHyb6ZTxKRFq7g\\nMudXBuGWaDMlNQVeIhUcC6FnOKPQAEOrrerTBN7MB3HBpwpFU7yzK+qpjW2M8wNzB8ObtjRttnMs\\nuU5zsG2wPhnV4YG1KZMmFZXuqblJIou+eaKS4kW7Rcy6BeAZm7bqOQRAlajPO/VTvNmeNSGe8BZj\\nHtpcbqt3qkT11gDn8VbFFqc/k3Kwtf6majcSG4zKGvXwA8R7SaFxt3Fr5e2ZC1aIhBNDmPXgs9q5\\nmPVdhbXReCMkBJxK2I8CqrZ9LdTwEG+N+UAMporsk8qostnZJG0hc+YJ5lIoqaekiqaJoVA3i2pC\\nwhphUILEpPKW8+YpFs1kHngYGmyCzlMImkj6TEDfhOAU4KeI17Dw1DQL8wlweuvTTBY2j02MSWPc\\nAFfcO5xFIH5ftUWztcmcVU9zFujbvz3k7/mRYwLbnAnqbUx6xBY8WHl8QJhij6OQcaGxOlrejGN7\\nX1NadCttDGRqI1Sbsm1DKKQtkxqcy4LOZzWBIU8PvDVWYu88wGnjeibAmcXEkwM4tc2/swGOvg9w\\ntgDOw9q5EObB3NkiNCVMCMk00odlMjgTqIVKZiR5pafjSN+EtCY5VCLhwNOm9Ju3sg5x6XihrgVS\\nIUsly4qeniM9AOkJIqEgyZkMCi5jbCIWnNAiQjgyy+YB5hdycgEJ4T8qze0fBWwnJOlsKDBCmG/P\\nn3kPahSiJzLXPEVBXNqtJQOJyi5ulzFbRQiZGXOdSNr9W7z+iXvwaOPJlI0GMf1iXACCCMkQxAS1\\nHrdLmF3EfIHMz7q5+vzMU1/mTWQjyadScbGXT2Jrmtynmlgzz+rDN9Vz2gIxbu1gHgLzQT9d2wQ9\\nz0LOBDaR/r559nkKbo3bDHBgqq154hAiWW2KVKnzXqBMKfuTjW970zC0JRFNv28LWj9x3ok22Wmk\\nLRlJ01NsnqH1VyZHSQuRnOf81pzk1Md5zkyCe9scO63sxwY40zjAgwBnOvbk/W0e4o0gF0tRzHwa\\nV2lWgBMgartQ97a14K3buRDmq3nOOaYF6khfeo7lLjBA3YW6A+kme90tvvf5L7HQwqLCjbs3EEkR\\nf1s8koZm/tmwxc4E/Z7pXCheKE8JT7/8FOvhgH3d45Xbld/4XAfDbShrdu0p8piDDloNs4r6SEdP\\ncWHtt0B0I7snwdo0h2kKOIp2e6ShUscl6KdArzeUkTEcsRr2ea8xqSenlRGe4ExbQRUhxybiNVSz\\nukKzYr4LXcftu5/mt359DdygeqHwG6T0Gk7h8lOXWNX7WDbclHJYYIxoieXuAt95UN3sNDOuDSmw\\nOjompcTe1X3GGkROKXXce+OIKxcus7q/Qu0at8fvYamfoNZdlH3gTrvaYrMZiTGlh8fLGWkcofPo\\nGUMTXm1Ci2PVwYKzPRxzI41fFS9TWd7Hm/znoYlL84lMKDGBZTKQqs6Cs6qRypYNFYlizzIClb4B\\nnAUdK70J6fjtAZx0H1JG0iVSWtGVHvROgIRWbDqhlCZ8LY2RrNMADhPAmWf+IwCOQ6pQUgCckibJ\\npyR3aqMjgIz6iFbaHNiam9YAjke2NK5QlvF8FtdyaRE5vo9ZjdqgKlhNbxPgnC3A3xbAqdcwuwq2\\ng3oiuPorULZ8gRmkUpupjZq3xjDEWtGHbCacE2G+7OCgH3EgeabSxRClBH0EyXfuZD3kqeVv8TM/\\n9TkW6Q5ZDuGnVo0jvHEun9phXYwyvR51OiI7rXpCO6XWQ3La4f4f/17+xl//PX76Z/4Y+GtY/hKH\\ni4EODUY4b8RVFTq/gBRO2MmmJqcmrqG4j1B2UP8If/THP4etPgnyYVwyKkEShpfI+NSGBjzEWqeJ\\nEY+NfObfSKAF3Eldotba4L/w+c/9c/yVzx0iWhBd8Zf+sxf40c/cgnSH6veRHtY24JboqtKlHdZD\\nIXdQ0/EWUgSQqGFYiVRtC8GfOmWoA9WNRX8Be26HZbeHl457b3ya//hP3YD6PMLTJJky29p1T3sn\\nZ+Q0Rv1Od5BCr9BbH+OnBjpSpQutrU+kRW7xtjtgrbjvCVTzndGk2e0mOaUdGCOriU/kHQI4h2lN\\nupLPBjjjawBPFuAMF0D6MwFO1XMMcO4dkfr8zgKch9S4PRfC3Puvggd/hGZwUVZWYfk6+Ar6Hu+u\\nUdLXsPwVlulVFnKHjnWk+btHhZ2G2HRKQWtkQYsZhTjVNcqzebO9J6fUe/Q7r9Mvv0K//BVS/xXg\\ngB2c3BibHafHEJQBI0eNIE7blGfb9Tz4Ssc+WM/68A2yZnz3JUYOMemwVJo7XqM0WK7QHYAYki8i\\nqRHVu+KacVWQQ9BdRDJ1PUS8rQPilGGfUi+hfaHLB+x0v8bOzq/gfJMka0aMfe2IcgeRbLzXK3Xb\\ndtf67kCmw9IUdgXuBRFjL8exFaXbu0giYf0OR90C6stQLyO6G0IquAmAbS7mrUnrCrRU/Fxh/z6l\\nL9TuoAmDgsga9R2W0qN1yVBvQncHCNbKsJ3qCUvyd0ITEazKvBKtriHpOw5wahEkPwTg/PQPRN+e\\nJMCpPwySzwQ4s339XQM423PqJMDxSlAovIMA52HtXAjz99v77b3aaq2wSFHaDWBpoEeYK16btlYi\\ntnhwbaaMApIxX7Ma7+NyyG6+B3aLhYxYt6JabXH/oepvN2vMijBZL5y+S2GOcaHTNaUe0u8eUORV\\nut2GBNMBokbnKdgXrdCLQRLMMimfrf6f1TRdwI77MI3Z/cafHluxKUx5BVYdS87MfSs0c2Szy4s0\\nTcBBuqY9OqqNPlkrZd2h+SnqeIQmZae7j/B5VG7R9+sWfTKScodUh9qxswAwstYtO76Dhx+q63q8\\nOLnrMB9AjT4HJLKaWC6usMw7DJIQfQa1y3jdAe1bIMVbAJwYpdldQU4MddKozrGZ5d/+jxbUKeY6\\nA5qoK1j85HP0i8y6W7OWwqLf43r3IXr5LToymUJiEe804naa5z+Gf/pXZgxdQDqcDpcamhqKpo5a\\nluyYs9dFEkpEaStOIoqXCUImYgvqlkJ/VgKCnfgsrHCFtKjUbs3iyutc/25l/6M3KayDqN4WmAs9\\nmdePvoEJPPdyYZAhzKgkvAhL77l1fJdbv77CD66j+VJULzGFsbYJN2KlsK5HFF8xlNvkfEAooR3O\\nGOpaC9QdGNoTn3YuOtVBRanebK9iJBxlEQWFJVN4A8g4e6zsAIZj8AKLxuksk5o4ZcnF2zg5Kdew\\nOOaZj2c+9ROw/+lXeO7S5HgriAzg99nJSyiV8oljjr7L+b2fW/Pmby3Ctr7V7++YVoNxkH5ygN6C\\n/iayuIxoQcnoMoi0bF1h8TrYCroe6a+RF2/g6XXMD1joCmWNMaDiJG3aqpzUViFc5bDRVmOVpADB\\nzVo8+IrdxUDubrTOHqA4WZq2qk1bTcqQ3q62egS7PevDG2jOMB6AJFz6MJlMkTTJMK0g90NbTZfR\\nPLR5rrjkiH6RI5A9pFvi6xrRY1MymiwYhoT2e2iqwBHLnds4N2ZtdW/SVpNjSQnvSyOPgzlmxgWy\\ndpEk1Kg53CvSONc7oCalY0BJ9N0Omu5g/hSwQB9X5Loym1dyheVddKfg+QC4euYp50KY73MH0hoj\\nEiiSGSknuBJFILJWlsOaC6zZWx9jewOl7ZjuZ2dzTRVIYBpiomqLVZyBqtCRcEZGC0ra1boyrCqa\\nDZKjYoEELEhXa8tATF4jpO4xmomyYI8yOkfHI9W/xrOffJ4//Ze+m3FxY3a8SA0HUvI1/Y9fpyqs\\nu0NMSlB9SoRniY8s/rUP8V//e78BK8GOmiCrFfo+nIEmiBQ6CqorolKktZqjFRcP3g+JcMIQ4S2L\\nb46OYONld5rKPkUWjYQSr0hjz1s21XehGZYK6zqvpbjg5O4/A1WIRRzt3j2e+e4r/MRffJ66t+ZI\\nasvOFYruUNXofMR1wEel+xef5X/5wm3e/KJDyWBTFaLvFAeoBJp0gyGqrr/8wws+85dfJF1av6MA\\nx1qF+LMAznI+9skBnKf/8Ir0zKtnApzugvDG5RvvGsCRE6EqJwFOEmmFKd5JgHOOhfkv/52BIiMu\\nAt6TrHm1dT07aupY2c1rLu3e5Q//1ALnEBixR67byTsdaqpMZPAy0VPCAokIlzqw7BTxEWdsYX0j\\n1Ue6do8oY0VbKJvrv1WrrMF69hY9lAU6PIXf+iDa7YIYlYz7Dsgax/DaURXGFCVo61ycOdQzGa7T\\n+yWGNeRFTzk6hrSYUVdQAGemiokRJqWhGjMyLWmdQvnaxudzJaYtq/mJWNtTAYwCiKHWERWdNMTB\\nxKNxVjsdAjZFEdQeVvvcfiXztV9NFFkyutG1yvCjJlwqi26H+8MxWRKpdKxu0RygW57/7xhk7lBH\\nQKK6DGB+i/3LQu3eJKprRWa3YEhK9IseF8PzCOWYXTlgYTfwucieYH52weLNWjEqQWvbMSLWMklb\\nUpBhDDZQrDIWWA+tAEVnLdQ1Qk1jn50Q/xjht2eF+J3RDyXho7FeD1S/wb/zn3yaeukNjndu4I32\\nONUlYOwMHck+ggscLY6DOIzJTZRIFfb+1U/x3/6FX4XcYYc9NIc9/bL110FL1AKdqCZmgONN46yN\\n5Gsq4GgkD6f79FyR1bwBOOIEXQAj7gFwaMVlJoDTpRymtNHfHsDJI3S3eeaTz/ATf/HDHF4+5G5+\\nOIg8F8L8l/7+6wTVGZE8Mg3eFK4lBnUF3Zvkvd/l3/+zFdVJHG1CjObSbZz0KbhMk7vDyVSJHVGo\\nXL43kO8es3tY+Re63+bqzR3s8orbqalZMqmIgraiTZNv/vHyw5TqTu6V43IMl5Tf++1X+e9+5u8C\\nayIGGJB+y90vTCnbIT+n7MC4HtZB+iBcGyhHN2BnB1Y7IdA0dvoqKVQ676Ks3RQlgyFMnHmBxjYX\\nj+Ddk091WjBOIWPa4pzbaztxpAYqmufo6XE6FeSsJY5fX+Xr/+iIv/HnfwdhB6uHTNXX8Q4wun6H\\ncSjBAVId1lfAFgQD3YRsvlOQOaGRdAnxEK73blzmH/2tO6C7FBm2AE7486qWBnA66pjZzc6lXeef\\n/6mOqoYwbJJuHtqUtAVwhEIRnXHiAqdzQepAp47OVXIUpFCpM8AZG8BJtNzEt7z31APBzdjtFlCg\\n3Etkv8Luoc8AhwZwMKG6Ruz76iIZI50COHXYp+/2GcZC3l1sARyP6VAz2ZohSOpDAE4YgGKX6InN\\nLVJ3NiUtfatk5GQ82tjzoxnqG4ATQOMR4/IwgFMW0F3m9jeUr/1qpUjP6Bn++NmXORfC/Af/3c9Q\\nGxmWmITDA6Wv2ig7C6WOdP0tLqUr5Px/kcgoA1vuiROx9WcKdRRp2VlIiHZu3ePWF76MHPf8K594\\nmlu/+U+4+PGn0b2nkJQCKTPNiTBJBGvx4xaeMpJE9PNKjvjEH/tejuSTDP4C2UIEFg3Cr6kSTDRt\\nsTOgFvwsU4KIuJD1AtkTwxcXvPprr4BePiV3bYu7ZjvKwOfrAwjlRNjY47WTk++Et3/KvHsAoW1v\\nGqe/N6C0lE/BD67h9EQI1nReJJWMRzmORZl0q/mS0v737S7w+KSaFLhwCE+9gkukmL9eldf/4UH8\\n/g4CnKgy62cCnMvHnwJ4ogDnr//nfxuWPWcCnPkB3h2AMz3Z1ujNn1qAJ+8kwPkf77945qieC2H+\\n+r/8+8Gy2bzvoxbcFyyqIRSqOqUULnCTp7WwZs0yKlwyPGLyTEhhspmLVPqmtpsnehKXRyUfVp6/\\nd5cri7scCCyOdxjrVdbJKa0iJeIoEwFURlq87rbY8K37br/6whqj426+x0d+6go3U+KOAo0pL0iA\\nFrgUhNqSEQzZKoFWNb6DQGg7Owk9MsovwKu/cxtZPxMJKNVwamMujP4mt9mBE/3UzeKT4H+Iv2T+\\nda41OU/yaRY21kSxNnmVSMCYCikbE83vo5uyySycvhuJVPxJwG9vCNNYdGzsjH38O3NzxP3TY5q/\\n3vUmxrWPXeCjP/lpfBGDoFUoJUj13lGAQ4zoWQCnvhEO0CcJcO74D4D0ZwKczXp6H+A8LMYczokw\\n1z4RBQUqJsEALIyYWzAoqrbxcGoN18uAYYwnhNQj7jB/2pDlO9UTVkYSZRPHIbT6g4FT9FQiSoi6\\nQCCnxc3UEz3xOSzX1QsizqAF6xxSYZ1A2iZBdUwFpdKy95lt1BNXR1NlxWAtK3Kn2AjUEV8f48sF\\nLZsC84g0WGimzwtMcnuWiDoAa+p43hLTMYkURSS3ZwwRLzPfSav00ugJDCW1OAZhQSe7oAuY0pbn\\nwZuE9zZumb5XTk7Fk5EXMRRTL0PkPHDsNFy83UX4LjZx8rPQfz/4XmxsWTP3jwFdk0waKZbjWqko\\nUgyhIlphrCyzc1V3GVmzaOjaHjkCikqQYkBUiBd3EnGvzoS9GhSsV1eH9PIalGcBWFlhUMHESeTZ\\nxxJV6aM6pz8Wn0mskAHjXlrxh/7ES7yq13DdI9eYm1XbTJPmpEXnOSDeZtMW54GY0nWw43Dz/4ZX\\nP38Ih4aqNhAfQGkykcgsMOMZvM3yWG91GqmtWSpnru/NE/mJvzZzV4hQ0rcCONM83gY4Huc2QxaS\\nH+mTOBfCHDakSrAlMGXLwXLyaGi7p28v5lNidNM2gzhFuXiLTT3cEe4tFV91lP5pbvV3udDttNOn\\nHVqbGO1iCsiINjS7nZCh00RxnT+DMmK4FIzCmAfGZFQdMVnNx2R6TCaa+pP9Npnsg+3/CrDGNbdJ\\nslUbkkYsJj3GglUx1hVUYPAjkirHdkQWpTZOGYGoDuPBTWMEm11wo1hoJbNaWxi9nyemumJUsg6M\\nHLXIDJ06+Yh2li3+9CFnXWNS8R//yueizc5pZ06R1JG73OTmfkfdC9OIo5RdGlFT+04m/ShzrwEc\\n04jzHuwN9uqbFJSRqU7oo4TGlq2ZEGOKzdS3Rk8a1izWx+ytDV/cY7Ar0Y8iSJfCKd+ultoVFGvz\\n9HEADjhjsBrKwJ0LN7mzuEHVnnXKWwCnx1RIXk+g87MATjbIepE8KNZdADmEeoR1E8AJt2/vhaVm\\nlunRACfN/d8CODwIcKI0XHvmtg4D4NgWwNl/DIBzVlTLKYDjgOw/9M2eC2E+0VNOZotpD5zsfUYJ\\nlX/mWW4hUb5FPDW3B4mYNhuFMD1y8PUZ95+7Svf0D3N89HH+2l/7Bf7T/+LPUJavUfNdjKCWjyCi\\nGOCKoWQ2DGln3Fs2nx1ve2vGpuEWA09oU1cNZZ2sIZAIgdxEy8TySEzuF93UNHEJG+UcQrihAkWc\\nUgXJC9bjEmGPlBfU6vRJg41TwaRsiJLow3Y4gZQJvThtwRLCWpSpdpW1qIhjFGGf4wLiAz4eIv1V\\nHjfi573XDNQZtWKa2GZ0n975OwlwZDJBnAFwhm5nPEho3gAAIABJREFU6/QnBXAKLnI2wBFm3vR3\\nBeDIGFwxZwGcRrb37gKcaOdCmE/1Aed9ae5wKzTQ+I/FJPgm5l1rcoS9VZtUK92a6GEaGBewLh2j\\nXeT24hnu5X0ieUEbiJqsidttMr08Dg6Rtv8KyobsCAH11Bwm+dQ19MS4wOYuoRjqfK053ExoUTDT\\nUZHksx726dJHKfUyokYthbEoWoPK1NLYyonNFzmxQcokZHyyp0+LPnrkvgAUXSbG2pHSC+R+l/KI\\ntOP3W2uNenBTnCUggJ5AbvHvDHAsVHjfEhwbAkJligM/2R4EONtmwJOiXqgXdli89CL5/lN89le+\\nxvf/yPXorka5k7q9XfhEabxB+yf9z9vfTdrAhEW1bQTbR0+9ySeuY7NA3wAcPeOsk0/H1pqIqJZq\\nSqlOtUSpiuYYL1HBSiBz00Ky5kNyaXdSpigzm5hcfeMfqy2JSTz8cYpTNWEopRopKXX9ZAunnA9h\\nbqF5bhw0QaDfxovkGRhI3mgkt7r96DjzaHlrN/M5fTYjIhRPmDirPLJSYcyJLmVE+xaXDtsptwnC\\nQ/4ANeaJJzp5fxSxjGpu5RSdqA6UWyWdRJbtZ9FQs30zYeeNrtmpBSVZRMvOfKkijRXPwFtaj32M\\n9dEexW5jvWJVqOZ0noLqNK1n4TEtmEiOktnBFX2c0kwm4R7nVEmRSCU91TqOjj7CuL4TwdFvI737\\nPdlcSdajLsE1Ag3ZnhR+sAVw2rybhOAG4ARHfkS7vPWimNZa8NnkOZrDEA6uXcIuXefN1ffycz/v\\nvHzpqTgn3aH6ZKJsxgWZKtJ3zfTyOABnmkUJpYtCGHED1KfNLVN10lbbJiATwGmBAGzGwlESweI4\\nTo6lqRo2NDt466FcR/gEtTyFirEuhSQBcCJi4CTAsYcAHHdpevLmfTkBcCqKdolSO0w/FLb73Lj7\\nHyMW/1tp50KYP3vwgVkDqQ2laDMthmoP42Bcl6tcZcXuXs+Cnl1JjBy+5fUnz3LEbQi1FbNQghO8\\nJoGkfPj7XkYXqSGcRGpOwNhnQ9EtDFH4o5kXHqcldhh1l132uDx+ELMPkPtnKKaYhZOGotDXIA2r\\nPW6GdMraCjn1aFm38opCtZ4dgbyCLEDehUVCzJvDrANLVBb893/1m4gOSL/Y0GdWggtEBJOEEDW4\\nkkL2RCEoUzGPzGpxxjSifWSvea0kV2p1zBOSWg0XKVT/IqRPg56LqXWOW+hrqTi5htkBJrBAlNF8\\nRwFOoM+zAA6pmSafIMAxMUTOJ8CJMXoYwNlA7Xcb4JyLFffLf/K3mU0m3sUu6q0grNPUmxEW32Dn\\nwj/mJ//mHrWrHMvQ3A+PbpMtuhA8gZF6HC+/MFDpsLrHYK+zshWZY3otiBWUFiqJEKm9YW3fDv96\\nq9bRMwwr9rjA//Fv/QL4DwQj5EUJYufcSkZZCGFeP44T+wK9RlyiFCg1fj9egl6Kvw/3YHgRbIeJ\\n1hkAT4glinwC9IXwEF2uUYpLgTISKcWK1w7GRLnVUyd+ahzMgiagG+HpHFSsalBLOIoirxm/D+Nh\\njw4RKnhyir/fAB6oXOMBvy/kq1y7v2BsP/UCjCFE3kmAI2QK9UyAIy3e/UkCnMv1OXSxdybASZaD\\nY+YcAhxESDmfC4BzLoQ59z5AkLRnnA5kRCzNu2MyGGwNqbCbXqLjA5TjW9CPrOxovswJG/OWNAly\\nHRiSUWslKfQenOZmhUqm+IepdpvCJYRLlKEn5QgLG80QVao5WqJEmsljwJ/WPO8BSxivIQc9bs+x\\n2Pk4l57ex1LUdhzV6EvHoiivfvVr9LsLltf3Keq4KVmD41lr5u5XjtH7O5gZbh0p9dRKvM2ZnV9b\\n9qBD6tCLS659Ing+XMDGDbKRGovgjTdrMLrRzqMCQS369Ef3o9h7F3uIKpQSC6fehLu/fYB5pPKL\\nrHncTMD3dHPl7isrPv9/HiB9mANSTZgXqrbMV8Cko0qg0sRxGDgsWBO/YTd58dJXyf/mgsRA5ai5\\nKB/dpmLfUyyFtLvFv9bc/h2rMjIlAhVGplzGscXOTG7QgDePvyZK4+UWlJv/9D7DomflAyMl+FJS\\nwmqE9O7JPkrC3VnpqkUDVbI3KgKHJImxKr0Lh18G1rsgXayB9sRTrc1SX0K4DmXEuxImz64BxuYA\\n9VbxyUuHe8aYapE6lNrk8jGotRBKi1Jw7iAZN6dY30xgmcjDeLIQ53wI87IPNoRbRXqQJY43c4gF\\nCi4J/IPcvgF/+t/4+0gO52Spm2C+sLf5/HlqfjG+6z54IQh0RNgblqxuDByPB/R7MOTf5+nnX+aj\\n3/cxenuB7vU1esUpnVNaWPPCetKxsr61DoQinHBobCdlbDtv+n6fozWofhw/voHKVdZZubNQSucY\\nHaijI+x1gArD3gJ9ao+xcxKKuSPV6GrCv16o7M6rsJYC9RC65Wa+CFFppR1jatxfCKULCVzH0DRU\\nKlqFThV0qqU6UXO2grt5YLUDQx/O/VIGRDrcmgp721sujyNVGruivI/OgQdtyFutdtjv7HD7S4W+\\nTCFnQhHD0pqzAU44lgPgZO6kN1k966Q/8QMclVvkbxHgdNKQ5BbAGfxlnEOO7C4AXblEzbHGRl8+\\nAHBO85Y/qqW8h49LdscX+ex/9QV8+DiLD17n0kcvPABwjl9ZcfubNwLgPL9PUcMNsuoWwLl9CuC8\\nTB2UoApqAGcqZq09vriIXlty/VNvAXD+SaUWnYV8hIE5NR/x9A89R+kbsHkIwKnD7gxwfCsK7Em0\\n8yHMq4E1djMpTQU1Zs+IN/+3Z2x9jZs3fhDtogqJl/ub62yrsls2QeljFl/un2bQETdjJ13i9tFr\\njH3luQ98gLWsuJWE/+F//SYXeYav/4Pf5MUffIlyKXEox2RR8qEyvDFw+4uHUyWtt3T1qCsprTEV\\nxvVXYP39mOzCWCh1FQjfBXKEpx3XBF7ARla+AlGqpRCqAsU8xur4GOjCRDOMsLsLdcv1NRU3nsj7\\nVRFxyBnTRvPpzCiHtPUkUyFI4jxyH84xhSoVzx5l21QwA01bGyeEKSi9j8zfsokxqkG5xFC3wlbp\\nwtz4DgKcbMpOXZwJcD75PXtx7BMEOFh+KMDp+u5dBThC3xKkHgQ4ZTesoOcB4JwPYZ4zWDrJI8EW\\nG4IQ7GcA7INfx4YHUcBcKNiNaVaJCK5BKnQo+5Qc9vP7qwVD+TAsRm7bJYbdkeB2/lgrZfUDHPqz\\nHI2FYa9CNfZSTykFVkd4neKsN23qr21/dqXgQajkmax7iFRGOUK6vhlCWwp7mvaiDrRDZBGLuZnU\\ncUdyCuGcdwjpLTE2rU7ipDLP48bkd6iMOeM1OK6pIZzNlVSFrgLFSdZ8/l2Ka5cK6zh2UIgK7d5O\\nBq1hWp+K1kZt0wQmUX/Xac83PcR253wTbfBexPGusAKoTSsCOoNxN+bwOwpwEgvbPRPg/E//2ysA\\nTxTgkNNDAU6x8V0FOEJErpwFcEYBOycA53wI8611PAn0k4kPpwX3KRS5fZkYufbFlKY7JdxskjMi\\nVjoxExdNYUXkQAW+g7ngdJgF77d7wj10Mp94VR79ONPdiG16Cu6bYm83C+1ErH0T3AkJ1+3pm0wS\\nW2gbl29s1HLG8aeHymnqYkMpW+PrcxX3GA3k5O9x0GbaCPHO6lkPPvVxK7KYFsJ14p0/vMt/wFvL\\nl0gCFoCDWsDHpq1Oan3zgLaIDCAGzNqmmi5C+S6sanNub4i2Nrv7FBLX1oLH5nFQLmALGF2woWcY\\nL0MaObBnuJ8OkdEpwwfbtT7IQXmO1aqw7iOcb2mZMo5wfICVHtCtsD7mqWNbKMNMsNxHhR46KFfB\\nbgcqUNmAiDYzzKyNRySwMZb20wiqIczHsllAY4FuAesR8uKEjJjnmhFmoRzeA5sytJovVCTNZyQk\\n9tIUdvS4taNdoHLXFAmME4/M/KyyuSlR2nCjtUxx+WdQ2n6LoYvnR5hLaWPevfXK3tbXzorC34an\\nszBhjg9VrEUuVfBCdhgsUsLEpf1mc8yvNLl24gazMN36+oSw3Po8ZVJKzDfXEG4ysai1a4g1u6aG\\nKi1sBKp6RLuqe8BhnRZnc2G5MtNBUud+m033bptaC5uCY6BHrY/TnKjqIkSUik0LJmKJk0NnlSqp\\nLcyoTarWB/GTrFuM2E7TigzXglePRecDoaZOEzzNA+izoPmD2LaBR3vZc063ICnjnYPvxjHGxlfm\\n/Ty9HySCPPXF9jpIDxwcF95IMUixeWi3S00lhBIZJEMaqSxJi7Cl1El70g7RyyGXeqM0TTHutg5z\\n0Blde+C7BOYtssqV3GfKQoJBLi9ibqcOdcPEWeSOlS4g9WRdUib+3aYldn1mTD0slrH5TY7K3Afw\\n2jL9TDQeCLgVBslQm4D2yMbFNar4VaDYhivJY1G5G1jwN9m0t9Ycr9YiQC3SvnV+505cb44LkC0B\\nMWdwb82LzVmP3c6HMKduVEHzCY+29qgkBM4UAj4VoSAEuJ9mMPM2lhICRtv1JtbAcHyvQSD5lPXo\\nmCRMDXQItel0eu6DkHzT59Yn1xHXMQQhRm2bCBTU0iapqDGtTVgqNpQWaYKFt8YWbXbUICTzFJ9Z\\n46otO3MztqkllwhQXFv24FQlZXJ80u6h7aYD6DEm+7NAiTUYKcujSlMfY+KqgVEQN8T6BvxzhGXV\\nOvc1oUFvppXTSP291qSymaMuG5qOdxDgJH84wImC2XHOkwI40xw9G+DYuwpwCjQt6EGAkywSis4D\\nwDkXwlwps4xWL5jUEJrAJMw38/NRiQlT2wjzyLxoKIRlkPZ4m8S6JoRf8DhnCZujuIOOCFHpR33K\\nMevihU2hd48RfieuaJ2UqbiPU8CDYL+4oV5AKqmZViJEaiBLDRSAtn5IOLHa7wEBBMzDZKOBSgyd\\nhcE8IWaTX1s0tVEWqFG9tH2phgnFfZ5f07mbd2UU3fy1OWBro5GCi9N5IZly7BC28QyNujhIpPq2\\nkt+rktxJ7mHO2+TkM9uuJuE0bfCPfdkzoPGc7DNJyu13x9mgSOJNbSgj7ATthjdBbDKBDzahM5tH\\nPOOzb/1juNYmjPOmrxIhkuIpchpaCb0HqQK2dgk3sI5JeE9yP56yhR1aAzhNriSbLx2ZLm3Yk0/7\\nwBa9sjeA440FEcIsyaxkYEAVOSEaxAJgCgbexxBps73XGAO8mWjmdczbXhbnQ5g34apuiN6hphWq\\nw4k6nvp25rJvDhYRakt66PQI0eBH6LVH8g1cjYXAKGMT0EavC0jfJKfExLdpZnS6wHXFOt1GWQJy\\nol/bIWCbz4pKmvnPVSYUUVnIpVZnVHCNzaSXDtI3QfcjcQnHVWbKg6Qdx/k1ct6hukTyhjrYHsY+\\nTiNFctosagK2cWhMJbBctIVKWdNS2iQltVqgUXTAJ8ebEOgf2VILm+YDTBXjg+ujzshJTxhQJz0j\\nyuTKltb13hTnNmtpM1qxnuyC6TAvdPXxnxng+BbAidJoq3ZG1yigY56S7gGVxCXUK6JKmgR9PiJR\\nSW5kD/5yJQUo0sOwObwtgBMPIIy4DODQecGsomoBcKwiOrR7HNGxB63KkPrE1yQBsDgOrbkBHKQJ\\nTG/cLRIgLZLwALGg0W0CWUsHkrFUqBQsBXf+JIzZ2tTQIP9Ljd4gAE5sLqFpNPDiwTLpGnH5vREA\\nx5SonuVtvEeUxkg6OVrfZjsXwrw7foWd3iHd4sf/1D66/HqrQm2bdFr3MwW6naVSnmpytfETP9Nh\\nHXg1dofE6hMrqlb2r32F49wqphRhJ+1w+8Ovc+35rzN2laGNUl87OHCGTw9Bjs9JADv15Eys2VCN\\nevAw26XK4pndKGGniqijo6BVuPuRm/T7u/SXF5DBkgdvhAnJEocv3WNnlZFqiEfxjXL/o/yDn7/F\\n6vDDmIetU0msxyNGq3Srfer6KmuBlDNaoNiAJCGNK7qVsH/8Klk6Sil0KKpKRbhzdEgaLyDJ8GJR\\nnzVFuNWyKN3qkIOjV9gvS1JZhEYpTt/tUEkc1uOINsDB88bl0xYDbFjS32vNJAqHzGzZQvht3mGA\\n05nQa3cmwOm1xSE+QYBjeoRqPhPgSFpx/C4CnCgY45wFcNybg/ocAJxzIcz/w6d/FnaOGHZe58/9\\n5CdZX/0C6+WNE0T3tmVT3Cx7P8m45icnz/x9KwA7NnQj5iw8kywoL4sYlcgM9Rpk/Ys/0jP4GGm7\\nYuSU8QLJMkvvWPv4WBsJEOaOxi0xf9Vl1jY0LS4cNkHcI2TJkU9nlSJOTUGF5KHTskNPrZXqgiZD\\nrad/9VM89YUv0L/6KVChmuGaGDAOun1WFy7yzcNnGKRD+p5aHLEgK1KrXFof8uHnv8ASj4g4i+gb\\nF+PVC/v8Xhk4HHbx6lG8o1EdLMeRF/qbXHvxFS6t7rI7Ol6FDqUudjjgOn/nG5e4zWUqlziBOKS0\\nyi+8bfvgH4jmSq5G9jsstYUTWmVdwa1/RwFOb0JacybA2W3O2ScJcNbdGt3XMwGO3Csc3Ln7rgGc\\n5D3VxjMBjvmAqp4LgHMuhPmfe/4XKbuJ+7vHfGBducEtSrqPbVF9pq1pccKfufXA4jo7iWXahdkI\\n9j7BaAVRQS2oLE2UpIoStvKUhFpHakptN4ZMDbSkiooyeuNTf5yHk6k0m+AnZnMii8cubi08Siqq\\nipvjBsvUobUwiqHVg45XFXPFc6h0VYTEmuvyRT7zwld5lruxSZlhSSg5cTNd4G6/xxfLPq4L6irU\\nTjVIzX64r/f59As32bXCshZSXTK4sM7wu4vL/NL6gLtcCI6L6phFJu1iWPOR7nW++8V7XFvd4jlb\\nI+sBMWdcPMMNPsJnf/V5jixzxOVmB6b9N6GQ96AgBxBD6zFX5XUurb8KQGJkbUpOS0q5jes3+DN/\\n8jMsnzrEuzcAThQUntpJYqgHfz/dSotrX6U13gtdcXLNpJJCc9Q3GHNUre9rE4Q/BHQ3qWrUDIii\\no9LVzHLd4+oBlrbm+cOShsRP9rGqMzIw9FFjVCQEI1XJY5iUJB8z+Boy1OY7dxeSKXmt7OgCr20w\\nvKO8UfnNv/tZxvUdquRIMpKEZ7hrQq7XQfY5tIqUIN6rKLlW1AuXfU0dP082Ddt4JGYEEM9CLy/G\\nhgysrUZpR4eFGVe4zXL8EpeGSk8fYYuuUBJF9zngEiX3MEy+pKlZbGYOb3ddnAth/qn69zDb41ap\\nXEm7HPbOOodbcuJ7sC3RqfP/jU2c5iZm02cHz8kJM2Ism31M0ggpMXmOpwqWTqLTjoGCSNAJdSSc\\nQmku/IqT58Jbb5kiQeSraTtiitTxOZLAUm0x5UaloCmhKaNuLDuJaJM0qW5BFJbpGBv7Y5eca/km\\nQ/kcHy8JqbGoR+BLP/YvkV++Tp+WfKDboWuL5NALvQtqzpggsY8fJK4djiz+1v9OZ2tuXb/I3mf+\\nCDsfuMAPdoniI6MXXIxBozzdzijs+wUWPrJ3eBH72b/HJ157A6VyuLrKbv4BlmVJz/Mc0fwSCOIL\\n1NPbIiz7g9bEjKftdX7mh36XH6o/D8CFcp+VGF7qDHBeti9zQ29xd3m/sQtOc+tBgBPy5CTA8TMA\\nTssZYicHwMkipNwc4qKIKt4AzsSQKHtpNteYVwRBUqBVyYKfIU7O9KNvAZyp3xmlE2HpjqjPmZQm\\nFe3jzGpwMXeMtTCmIO+NosxK2gnHv+O4CH1NvMCv8d/86Jd59uu/9XCAc6cBHDkD4JT7fPqHb3K5\\nrlHzBwHO/a9xZ3m1vQrHjrcAzt7rfPcfeTjA+emffZ6vDi9wxAdPApyJcFDefoTXuRDmeVwzHFdE\\nHatrbDSsHym6ZTZhs+MX2RaV28L80W06okV5YxSUCDEybK5qNLmKpu3h9NIJu9nEDBiC22TDejz3\\nZM6KLNRTfNRzNppMPNJMKSRMpdvKFLInU1XH6DkOVQqlLazkAqOi4yEyBL91DdI3bj97idc+fokD\\nFVZ93/QJwySxckjeN+eQ8fphx96tNVfkkK4ccz8p/rGr3HjhOmsZokh004uj5BaMZtwWWNWLcK/j\\neGfNd9U75OLs9Eu0rjDtGDQy+KTutriXSp2LN783Bbqr0eU7PL36PC+XfwjARTnkng0sqe8owIlN\\n4GyAQwozy5MFOFPU1oMAxwBB3jWAs/srn2P3d37zTICzKke4yrkAOOdCmL/f3m/vzeZYWdFLpatB\\neyzdigUjHcY40NLuC13uyK0G5STAzk4q2a4qdBYuDgE6VZvtmIq9GU7HVIQkkVAqHYlhFs5KDu8N\\ngf87Gp8g4A8xPM6BjO1zmNWmzWeypW/idGy+Pxi5ZUFUauRQSti9MyHQK751Toh1QckofXIy62B9\\nNqUkIQHHz17i4GPXuKeJstyjrFekLBTtkQpqPUMCUefw/j73/l+gHnKBFevlDvbSVe6/cIXizsLD\\n3VPFseRYiTFbW899v8jFw57SFxJrKCsW3R5ZhjDbqIfd3JVNzsrGIfp227kQ5rXFRIuAynHE2atj\\nJ4hp2ifZ3t3BziiJdbptO2G2LbSpxXZuao06U/XNR101ASab2jBzxBKwjXw29stKVDrZdv6dkca7\\n1ZRQMc/8TQwP6170x+J6JmDZ8Rqx9FkSL3/1BtdRVhqR3Vk3dUK12S1N4vkvFufy3TV5NJbAU0cj\\nF167Sx5eo+RwBptPtVeDSyd7VB3vUC4PxuG6wBh1b5JBlhbKZtPIteUvI1Ba2Ju+J6NZIttYSTVy\\nGgCwVdD4OJhWKI7Vyvp4ZNCHa6v+LWqrsNE+N7EUCj5iGKNscH4cEyGN2yJ6+m3jxH48bZUztFWA\\nOmuim/MEKJSoxdnO2aQWeqOHLwzuwBp3YVgpNhasCLhTBUYPOpwjLRxqZYWQFxGXbhJaf2jqgDiH\\numathYsZ9NgZxzVDgsOUWMsY5nkqqDNKRns4spZrUeEgORdTpdRjFtWpqWLFcDJDG3mxtKWtfuvr\\n4JwIc20TxTYxoTPT8tRsnkEnFcXtY7Z95rL17WZyn1BNJfb1zUJ4/IGcaicqtMyyqYBz15IprGGG\\n7YqMj44HtlNP9vD+NBzi0pZfJDQkQC2TSmQOZhN2fvGXqL9oXJCEapBnVY+JO490G/+lKXlwLlpF\\nR3j61hF3/ue/zWXJCENUoZFWnV0iOid7xYDc91CMZ1frcAjVyM/Y1Lecwq+m/rdU8Xl03oumFse1\\nUrWcSCaO0n2cG4CT3uMAZ/fmHXYfAnCQ1LL8332Acy6EuXqDG+5U6WPJN61sjr/dimeZFJGzo0ke\\n/Da3E5NFcL/TIlnYqHiKkhuPw1vHApy8T5Wg8JqqkkeR5uhliMmTy0DhJCqZz3t4fIyeCt2LAhlD\\nu79SJVM11OfcqsGYVHYOC7sK7iNYe9Z2qeTMTJPWVnVqsE9EWY7G0+NxK2y9WeChVm6ukSuIrMNp\\n5hHyiEhDUeEJsNk+Hg4emXwG6lvOn/daE8QSznL+xgXWKRyX7wOc8wFwrqyh87MBjqk2ff7dBzjn\\nQpgbmSKJSmpKWUiWINr59t1HPbKvIiU5hG2V9sKRUK884kE37bRCubE9Tt+41BYxcNLZ80j24u3w\\nMtksm8fbSLRtFtMZ8XcRpUiml47qwRRUMawKZkbOPbWOsTF4xS02CZOIJxcVRKcKTEbKgo0l0q0J\\nYn8xcLMZSUb+hNKnTC2R1acKIokiC0ZZnFS0t8KuhAgt23AXv/eaT1mCW5u1SczTmIbW3g9MiSqb\\nwdpEPOhDQYif+Xk2X/hmY57vT9tE0MfA+NstCgZGv+Oi6ko9kRX6KOF92r7+sONOHz+1xPYoeHMs\\nujuJRPXgado5WrNIELS6xOBpzN0pvd8IYNdrohuM2uzy/QhXfY3bgHjFVeYVWBsYS964WiW02WSB\\nolRPvqOgKdgKf9ii3vhW1sO5EOZrXWJ5wagjKj3w/7d3fr+2Xddd/4w551p77/Pr3usbO3bsJHbs\\n1mkSF0pFkEopILVSeShSKRI8AKpU9aF94AEJCYkCQoVSeMhfABKqCuWRB4T6CE2ltKFRUytOozix\\naWonjn1/33PO3nutOcfgYcy19j7nnnt9XbDv4Xh9pfvj7H3OPuvHXGN+x6/v6LyzqmqjACDxRHzw\\nvTggIr7jSRJipzQhoHgtaxluhgWa4nHKE00XJ+q6Nl8PkulZXBeloK7tspWgirY1T/NBx/cw53Vi\\nfiT0tWPN9xB3v/uwQ27mdOavua8ZkWYG6jHGMActimmmDFmCWr4WUh3tWxssshlG74q30oAKSUEp\\nhCq2X0xRcRWb0ziWfdZx12c6AqNDPy5WwbVgwOsMPlxwiWyvDSl1fmZhgVJq+jFSbJBk9p9xgqMn\\nPmMbD8t9hrr0plTPblx4CuLzcQN+v8s9TUgPJjiGDzGJoqO2kMowf9e92DO91TPPQu5LcE56q87M\\nN96q19aso9CFSAoNuWogFQ1ov0Vw+t7LK81b9+MWwbFk/lkm9JXgSJcZBOJi50lVE92av+u5kHlM\\nPkYXQweCw4JeFtgwgATG/IFUczx6rA/H7EacC2P+m/pPCHnJqnuLn1k8zZF8E+MtNgV7DpPhxp6o\\npH3Xz8910V3qCpcP16SiNAKm4qGJKqLTqidftlUW77dsc1/QlDgOyu2rO6w9eFPdwlK/98Ts7jM+\\nxT/Z7gmvbDMTO+EG+48rsB75roaWN+OL/I9uh9/vPktTWmfWJaIh0ndaJ514WDLV8qschF58qmNU\\npV2usBgQDWiIqHn5VymGlEAICRf18kdMc4+khhxcJTEMoSaL3vhhB9zOT3Gz3We13GXUdBfFhnI0\\n+zNQkAsCMZg1DTm1dOZj4yQoOWTU9AMlOIkaIjuD4OR0aqP9kBGcTpUkvtmeJjhlraQYzwXBORfG\\n/D99/QlSe5ucrvPnj/dpr+5guMvvrrgbNx1v6yDyAw8THxwE/ftrt7n2jddoc4/0PU2IY6zZ8IVr\\nomeKzp2FlSjHi4bZ5z+HtA1B4okYn50+jlOdREjtAAAW8ElEQVQ3V+95bZh0spUwqu8P5t3/9WC3\\n1NCgCtyVJ/jvr3yH/MZjiO1gVohEenzS+dDsISJECYS+0IdAluhaGaY01iOxoZTtvEJBotVBOALV\\nq0kRtPcDsCgYPf6wBtQa3xBtRkfilh2gza6XEgDjkIQhNPVhbOWvWJXEzebjvMlLADRyjJIJosg8\\ncXd+xKIckLkLrMbOSDXzqULDMzE+BicJ0P3Q1TU2L4GFgR17W7oEr/oYxJfbArmcpIgbSnXy61wE\\naRo6jDutIUMRo206uQfqboOWyQn6uXm+3aBuP9164n3///a7BiEhiL8cIiu5zK3mU3yveZwWz8mJ\\nGKVpyAg0G61zEyWKVRXIUJmyIvSYQSvekl/Mh/cpBawlzgEzQnRtdTVDo1Ask4YGRRjX+Cpe4Xp8\\nkT5eqrHxjYdiD3nv7odzYcy/NPtZFu0Rqf0611rlsrxD4G1Eg4vx2KD55ie+lRo6Jd18tjFfqJ/m\\n43fusPPOitm6rznkgkrnBlOMpHDvhPGzuXknyrKBm/NCWc/o0x4lBLL25DraK6jHCz1nXmt5BYZR\\nXgqVwW7ii7b1q3yj1hNhHwPEhEhEzBufRPa4yXN8M+5yM/5tctxFNROJZDHmxQhqlEbpQoFo2KLH\\nR7zJJo+QGmwZiCwIWX1YRVKyHNIGgaJ0IdTxdkrSRNPtoWq45LVWVh68c08WRAKrxduwOq7vD1cx\\n+NSmMc754WPoJolb4Sm+8AeH/Of+bwGglllboSFUgvNt/vU/fJ42/jHKMSLDWnJ5Bz1h4OyMKPfZ\\nz8QgVRG+f4PuG68xWy9BjRQiskVwpEAMysOgxQnOetFw8PnPsWwbogSy2MZkSwARNjIcYTz6jWmv\\nNE38/+GUkdMtGrdNcIwVVaOLLC3/O77Er315TX7jJ/6vCI6GBCpnEpwYE8XsPRKcy7wRPsq62fW5\\nc9v5JMkMw6ffK86FMaeP9M0euf8I69Xz9MsDQv+ZOtE6U2JmnUrNgRuI+vADC8QyYzZMlx1wau5h\\n4E79PV9CuldJ2vsUKvHQVMBAvDLj3vjgVqDEGJN1SRKBXYpdYtn/FLePniREj8tZ8DKoUEJNinh8\\nzUTJMbu2RSgnPACxwKyfEUusfEbpk1JCJsftSl+AQMoLmhxAM4Fd8uovs+y+ytr2sbIDOgiBRToF\\nIZPTGj52wGPPz1nPqIvHa5WjRtq7wjsv3yAfBk/WSEfz5B5XP33Vs/XZ2GkjqtAqcAjXvnIX8gJX\\n5xpvDyYZTXOPomTxP8OQU/PlOwSjPsxYyh6vxZf4k/A5AEoKPoxYwwdKcObrXOuOHhHBwVf3fQnO\\n9m/8gAlOWC3cjpxBcHzGr54LgnMujHlY7FM4xvRT/P1/cBM0EcPTRIGu7WiebNn9zGU01EaE0IMJ\\nYoGdox2u/84NtM912o4R4mJkvgFIy+8A8PMvHvJvfvZJ2vZPPdm3KqQ6HGGcoiPu/rtcwrYb6FN5\\nhnUcm0us5WPM4o/wkz+14rvlSdCILJpxAIbVUqNMdrrQKk//pWfod3uOU1ddTd9Vks24/TvX4DCS\\n6rIOz8648sLjHC6Oto7DKx1mh3Pe+aO34Q6uz6y3kfh5rNkFiz4kG/+xEgFpYWbQZnQGx4seKlsQ\\nc5d2pwDzBRzNXTu7Nfq5cXcHcnImEwCJ0GVoAhCTj5lLHlP0sVoCNhvC4548DdENvg2CBRVD3fCH\\nMtQSUVmgrCnUkWt9hLwG7T5QgtPQj2M0HwXB6WO5J8Q5EJxUhmHgj4jg/O5NCOlsgkPwBs5zQHDO\\nhTGfMOHDChPxjXYo/lcjzGaYRggdfb7Kr//bN5gvbtFEJUigixAfm7F49qA2F3nDyRCb3Vnvcuur\\nt8AKoZbmDUMjPEYcaIp3cv6N/bf4xZf2SM0x7SzQd0ZTaxWHeZlSWXWsZHEzU/MkwUnzSxz3++ws\\nfoh//E+/y1tcoWhmNmtqwbESNI511p5shY9+9hmOd5b0lE1sXY1kLXdevkuzFFL0MsN8teHKJ5/i\\nsBkIjp+XGCz6fa5//S1S31CysTp6g+Xyk9AsnEQMBMdA1ZxktAaNYi10bc3NiYzzQmQOtAvQ5HHy\\nppAbZTkDi6F2nvrts943P2KAbD5UeviFJgjbBCduCE49hypKzJ815HgujLkPKJlBgdA8jeYnKRYo\\nUqCs6DWytH2KeV1KMSVo2kwsKSswb+mlQJE5nmBTikHk4wCI/R6sXyfEGVhPtFJLEf04vIkmVgJj\\nbPR03YH1Ko6aHAxQSovyEVr9Ueh/DLJgzQ6lJlxNUt11FchQMku7RGfGMT1aWaqY0ZiA3QYar76R\\nAiWwzAes1Cs/hhhiUgg5QX8byXOsxoysNH7Mox6qDdSJjds2JGVqQ0edf3iytN4Y61/FJUlVZHM9\\n6uXx7z31+rbq24c8hPLuGC7iqcK8AJAo2kG4xCuvGOhHiGFx0lvND/BW/9e7eat+bz7z4h7xh/cQ\\nexvEy/NOHttmOTkBD/fI145nUyDGfUr3OH/45cf4bvnkw3mr8RmODlZkKfd6q1+5RrgrdRhQ9VZX\\nD/JWF1ve6j4SHz9R+zacmm+i1D91WlDM1VsFrd5qHlxRq/M5JWBRyBFy2jw3EusVG+YTnKp7GHJh\\nW1mLreM5dXyn5A0eFufCmHvFSiQ0O+i6OFMpBklAZlDL6nQsuk+ohE0QxARIvjhiLe8gjJ0UlrzD\\nrgRFEYqkqlteavOLeiwP6nbsH5nKIARU6r9Dcin5gg4C0tAXXG1OPGPOMAA3uEuH1qnnrH0nr7rK\\nQ6OICPhcwRZsOPcyVi1508VmgeQQfNRhaDBaxlUysDurcTjzc9tMQjkJDUDIzlTqeY1GPHhNrq/W\\nWjaDX6M03DTwJ5i0tU8MzUyTIX9v2IQLSk+1Gx8cwZHZLvT5TIITylZH4vtEcI6cl59JcFQar1Z5\\nVARnDCXdS3ACW1zmEROcc2HMh11LO4MmVmNkTtk1gyas1NpkwS+KJbRO5sHM3ZqBd4TBqA73oBYB\\nSgNxB2WGFBft8U5Jw6cBbZcEBnxejqHi8W0j1fpTBW2wMqPElmiNH1edPj9k34s1vivk3ocv1+Pw\\nSUAFbz92466q9XwNkRZ0jZXaoVa0xvKqq1yg1xphqzF+ilVfb3v7rzG5YWKtbYkbEarBH7bE+vVg\\njG3rz5ZxPrEUhXvZ+rBg7fQ3T7g/Tm20g4FUgdCiK4VQG4dC8HiHeDneqJ1oXki32U/DJjEhw19b\\nPRvBY/TS1I1eBGoJ4YmuawMdxxnpmF8ZP9cGph4RCkpPMSWrebghDqGGU6bGGl8rYnW2caBYDTlU\\ng2CmEBsnQ1vnJYk6BFvGA8lS0zExMeqqh3rOfjHH8xlregcvYPgeqfqL22WUm7jIhuCMG4Gg4gNx\\n4nidz7ifHxDOhTEfw0SpZgs0gygSkrtIEuuNb/zCBsDMM/tQX7dqyGq2IdSbpJFZ43rM6+ZHeHP2\\nBDflezTpkBwK19I1cuwJ4pOEsq4pwcgSSaUloLSNYAUO8mUancO6R9MOx+lj3JW/wDosCE1CUyRY\\nLbsajCzU5LR/XZSaIBzEffy4C8OKKM4qxP/vWgNSr0EtWQpSL1Xrn2NAqCxaNixAdIiX1uOoi7LA\\nWLvv1G24djAKsTwgbHeCiQwiHiN5myz4e8fpeuvh8gfvtW+qx1Qqzc7FDZyaS0kM3zswcRXICnlI\\nHAZnpPXeDo8K4N2QYZ9ic0RmZMsUcYITyVtDpEFpSHqK4MgWwSktUvYh7hO09YXSryC1hOotF2mA\\nuCE4xTmQiqLjPDyqB+uqkZTWh1CoYCU7J2lsc24YpsEbg4YYvuDXIG0RHMPfGEImBN9UbCA2Jyf+\\n6DAgYiA0I7kZ7tnWcyKn/t3cxOqVv/8E53wY8233DTbhEatxtRBGFTNke+EPlRNWf2ZgGfXtev1y\\nDXt8850r/Nbv3mAnXiWmGavVMU/89U+xnvksTitQrPdsuTRVDB8WKRKJvPGl79EuFyxKS4mR4zJn\\nlTq6cMlJdzEsNCOzkaEsybIbu6bZSDFE2RAlcRfPiquwubiJQZSRzI/nXJstfCZAjY+oOeuOie2k\\nkBOcuuhlWPxnsYbtSe8MT9LmiX/QW1Y3mgFDp8cDRMMmPACy9Sx8gATnhr1K23ImwVlX2dv3k+Co\\nNhv2fA/BkUdLcETdIJ9zgnNOjPnWTbboLiUJSRkrRwBEEq7qMMS5jCDFL7IMOzRbYQEdL/4ab5X+\\ng+s/zsu//2OuyTATjuyY53/6kxxducNxOqKVFqnt6l0Io/bDrjbsrud841t/BN/bY3a4IKU5Wjz2\\nvZL9uvtmDKn3zheUiwJGd890zc5sRp9gJYa363ossAkt66ZA6Ihph7IRy9igLnZBa1y+gPTjg+zs\\nIflDPgQzh3j56FIOQkiKWMSsfi+V1Q0upsR6TuHEYhw0kzYe7rBBDCtXNw/Ye5RpmgCb/AQfKMHZ\\nTU8QYjmT4HTigzPeT4JjA2s+i+AkcQ9lIjgPxDkx5nAiu9vjN6PBy4lC1VI0F8GxUAe8mtZqo7JJ\\nesJWCGP4TL9Zqi0dCTOjWwsqDXd7YcUOXZNQa6qIkdBHGG50IaIlwewAmgPWJdKlfYhCRNDeqhZs\\n3b3DsMP7aWAthBVQ6DOsuh6ZCz4HUYHiiSF/WikrIEREWpLgXX22UYQL5k09xK2suaX6ZA9p9eH1\\nYWFFkI4hRilVetbEBTwFI4Thw2rhrAqnM+qiisQaF5MaOxxT+sPvHDbUCQ+P4XptDSrcVhtUkJQ8\\nXizHIMkv/8BOq2ESMSSIJ+RHK6Ob5HjF2jyW/crbn+A71wD9KMRMZ2v+3I+/xDotsV2jlTl9Pgbc\\nmIslLwMkMl81fPHlLxHfadlZ7pCaGescic1jHNnTHu+2qj1k9RwNBMNCzfn0HU2aeUmfSNVFUiQY\\nyQJdU72SsUuy5ghk8Djqy0Oj0WgGbOs5OLUWo5MX5zz+OcH8M2zIM8Emjzn8J6S6OWwZZdn66r7M\\nfZPcfj/jLOfEmG8ZIAFCB/EmxnVIxxAipdn3GBZgorUONKI84ew07rvxUdt85niz/bVSGkpfILX+\\nXmooEVaa0ThHU2S99vkl3hidUYGE+FSTtcIaoMVygiJkCUjqMdZgDUkDSofhanhIAXsbmiNIN7DZ\\nDs1uoJPixlwyZgUpe6C3YH7FEy3BMDkkzp7A4tJLzEKd56iRkvYh3AEu4y7klp8nuBEfN5beKZbc\\n9k0nAuGuu+shYxYqmd6HdAdC9RjiEYQGCZkQvMRNgmEhYcEwaSEs67Ue3ND4gEU94R6MXuVw0eq9\\n3C7/MwFrsLV6nBkD6YnW1m/bJjiZGD2BTkmbjdyqJzcaRTdYR7zAUj49mpsSVnxtdoXVfk+36EjW\\neE02nCA4cyJ77YzbVy7B8QE3lhEJ+9A6wckDQw5uwC1uCI4T4xnEFcQ1GKRY6u/xFnj3OudAhl7d\\nkIYGQWgEwihGVQmOeiJys95x73U4//F64yw/yHjtBsPuwylcLi9UbRYnNtUFKnijjw1FBYzDLKTq\\n2rjhHwZvpPfTdt+Dc2LMt1FAD+GZr/HP/v3n0d13yLMlR3oNsQaGyhNLYA2XV8/xq//zVVjvVENe\\nkLAAcHcTGG9miL4rW4GmumGAxBnFQpVEqO3R5gkfABPxmx2jtz3G2tFYn0HPntfOxiFuB/VGKrTf\\n4uf+0Qu8+FcO6C6/jjSw6mycJoP0tLkn/cIP8K0vd/y3//AKv/Qv/hr25HXC/NscS6GEnuFBTCVy\\nqXyctHqSX/+7XwN9Gro5zBLBct1IalXPau3hqvY7/MpvfJbl4gZ5/n2O9LpvRCiiDQFY2JLm+Gm+\\n8Pd+D9qOn/7Fn+DKc9fp9r9BbjJmRksiNjN/XlYLvv3UnK/8ptScqY8ADpSqkz7Fzd8dNawyrNUh\\n3PCBE5w5aHlkBEeT1S7JMwjO7BMQ5o+Q4Ky8qPwMgjOKjp0DgnMOjbk6K0nX6Xb+GNt5E23WxLJG\\nNCEoJhlIoA2UKyBrd+2l7siidYILvnhHkjP4ZKerB2TLK9vOZDDK7p6MqoUTP7q9/d6b61CIR3Tt\\nG/TzHp29SgqBZFrbg9VZlszISchNIMxvEa++SfP4q6RZJvSZHF0OU0yI2tLm2k8fj/1Yt5XtbCi1\\n8vMQKVg6Juy/Q9p7nTi/DuUGvVTVbG28Pll3iU0H7V3Y73juhwJ7zxyS966TY4+Z0WgkzhaUzgh5\\njzvfegzW5jIAUuOhMmhHTjHzd8fpcNQZG6AYxB72bvPsS5nV7Ag96Nh7vqNUGVyT4vfQIs2NXW5F\\ng363hmGqEdv+6DLIKEgt/+vdGojWohehZKOIbSJtY1g+UMzI2TZeRPE5mMQIBU8mhlg3Eh3luf2Z\\nVFgc8gM/ussnPnOZ8OxNVjtVZ1wUowcppJwIT/0gX/yNt6BA+1hPufI27cF19pqMhjx8IlEjO80V\\n7l46gsMZdIva8VMPfDCq42U3QrNEL71JutyxuHJAaW/X2L550tYC82aPwwOD4yuQlzC7BbMO2gbi\\nRpxaonsN1rYwA7pPAc3oAY375/vIb86hMQffSdeYHEI4AtYUWRFD1UaWWqPNzA35kODZTuDB+37x\\nHh6FEjpU1hCWBAIiq614m1KCEaSvMe4V1txE43V6yVgyCOv6vQHKHJUjYt/Xh0kYbqUN5VRjKZRt\\n3L50jKS7IHcg3CVIIrKuMfnkI7TCsXssckQzOySkdyC85cakzjmM7JNjIecjJM0gLTanKlQGVonR\\n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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# As you can see in the images below\\n\",\n \"# that they are going through random jittering\\n\",\n \"# Random jittering as described in the paper is to\\n\",\n \"# 1. Resize an image to bigger height and width\\n\",\n \"# 2. Randomnly crop to the original size\\n\",\n \"# 3. Randomnly flip the image horizontally\\n\",\n \"\\n\",\n \"plt.figure(figsize=(6, 6))\\n\",\n \"for i in range(4):\\n\",\n \" rj_inp, rj_re = random_jitter(inp, re)\\n\",\n \" plt.subplot(2, 2, i+1)\\n\",\n \" plt.imshow(rj_inp/255.0)\\n\",\n \" plt.axis('off')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"tyaP4hLJ8b4W\"\n },\n \"outputs\": [],\n \"source\": [\n \"def load_image_train(image_file):\\n\",\n \" input_image, real_image = load(image_file)\\n\",\n \" input_image, real_image = random_jitter(input_image, real_image)\\n\",\n \" input_image, real_image = normalize(input_image, real_image)\\n\",\n \"\\n\",\n \" return input_image, real_image\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"VB3Z6D_zKSru\"\n },\n \"outputs\": [],\n \"source\": [\n \"def load_image_test(image_file):\\n\",\n \" input_image, real_image = load(image_file)\\n\",\n \" input_image, real_image = resize(input_image, real_image,\\n\",\n \" IMG_HEIGHT, IMG_WIDTH)\\n\",\n \" input_image, real_image = normalize(input_image, real_image)\\n\",\n \"\\n\",\n \" return input_image, real_image\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"PIGN6ouoQxt3\"\n },\n \"source\": [\n \"## 输入管道\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"SQHmYSmk8b4b\"\n },\n \"outputs\": [],\n \"source\": [\n \"train_dataset = tf.data.Dataset.list_files(PATH+'train/*.jpg')\\n\",\n \"train_dataset = train_dataset.shuffle(BUFFER_SIZE)\\n\",\n \"train_dataset = train_dataset.map(load_image_train,\\n\",\n \" num_parallel_calls=tf.data.experimental.AUTOTUNE)\\n\",\n \"train_dataset = train_dataset.batch(1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"MS9J0yA58b4g\"\n },\n \"outputs\": [],\n \"source\": [\n \"test_dataset = tf.data.Dataset.list_files(PATH+'test/*.jpg')\\n\",\n \"# shuffling so that for every epoch a different image is generated\\n\",\n \"# to predict and display the progress of our model.\\n\",\n \"train_dataset = train_dataset.shuffle(BUFFER_SIZE)\\n\",\n \"test_dataset = test_dataset.map(load_image_test)\\n\",\n \"test_dataset = test_dataset.batch(1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"THY-sZMiQ4UV\"\n },\n \"source\": [\n \"## 构建生成器\\n\",\n \"- 发电机的架构是改进的U-Net。\\n\",\n \"- 编码器中的每个块都是（Conv - > Batchnorm - > Leaky ReLU）\\n\",\n \"- 解码器中的每个块是（Transposed Conv - > Batchnorm - > Dropout（应用于前3个块） - > ReLU）\\n\",\n \"- 编码器和解码器之间存在跳过连接（如在U-Net中）。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"tqqvWxlw8b4l\"\n },\n \"outputs\": [],\n \"source\": [\n \"OUTPUT_CHANNELS = 3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"3R09ATE_SH9P\"\n },\n \"outputs\": [],\n \"source\": [\n \"def downsample(filters, size, apply_batchnorm=True):\\n\",\n \" initializer = tf.random_normal_initializer(0., 0.02)\\n\",\n \"\\n\",\n \" result = tf.keras.Sequential()\\n\",\n \" result.add(\\n\",\n \" tf.keras.layers.Conv2D(filters, size, strides=2, padding='same',\\n\",\n \" kernel_initializer=initializer, use_bias=False))\\n\",\n \"\\n\",\n \" if apply_batchnorm:\\n\",\n \" result.add(tf.keras.layers.BatchNormalization())\\n\",\n \"\\n\",\n \" result.add(tf.keras.layers.LeakyReLU())\\n\",\n \"\\n\",\n \" return result\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 8082,\n \"status\": \"ok\",\n \"timestamp\": 1564763185677,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"a6_uCZCppTh7\",\n \"outputId\": \"1a0eaa18-9a0b-4444-a230-db4881b87d6e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1, 128, 128, 3)\\n\"\n ]\n }\n ],\n \"source\": [\n \"down_model = downsample(3, 4)\\n\",\n \"down_result = down_model(tf.expand_dims(inp, 0))\\n\",\n \"print (down_result.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"nhgDsHClSQzP\"\n },\n \"outputs\": [],\n \"source\": [\n \"def upsample(filters, size, apply_dropout=False):\\n\",\n \" initializer = tf.random_normal_initializer(0., 0.02)\\n\",\n \"\\n\",\n \" result = tf.keras.Sequential()\\n\",\n \" result.add(\\n\",\n \" tf.keras.layers.Conv2DTranspose(filters, size, strides=2,\\n\",\n \" padding='same',\\n\",\n \" kernel_initializer=initializer,\\n\",\n \" use_bias=False))\\n\",\n \"\\n\",\n \" result.add(tf.keras.layers.BatchNormalization())\\n\",\n \"\\n\",\n \" if apply_dropout:\\n\",\n \" result.add(tf.keras.layers.Dropout(0.5))\\n\",\n \"\\n\",\n \" result.add(tf.keras.layers.ReLU())\\n\",\n \"\\n\",\n \" return result\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 8075,\n \"status\": \"ok\",\n \"timestamp\": 1564763185678,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"mz-ahSdsq0Oc\",\n \"outputId\": \"500642cb-3ce1-46c6-e161-bbe6b787e038\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1, 256, 256, 3)\\n\"\n ]\n }\n ],\n \"source\": [\n \"up_model = upsample(3, 4)\\n\",\n \"up_result = up_model(down_result)\\n\",\n \"print (up_result.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"lFPI4Nu-8b4q\"\n },\n \"outputs\": [],\n \"source\": [\n \"def Generator():\\n\",\n \" down_stack = [\\n\",\n \" downsample(64, 4, apply_batchnorm=False), # (bs, 128, 128, 64)\\n\",\n \" downsample(128, 4), # (bs, 64, 64, 128)\\n\",\n \" downsample(256, 4), # (bs, 32, 32, 256)\\n\",\n \" downsample(512, 4), # (bs, 16, 16, 512)\\n\",\n \" downsample(512, 4), # (bs, 8, 8, 512)\\n\",\n \" downsample(512, 4), # (bs, 4, 4, 512)\\n\",\n \" downsample(512, 4), # (bs, 2, 2, 512)\\n\",\n \" downsample(512, 4), # (bs, 1, 1, 512)\\n\",\n \" ]\\n\",\n \"\\n\",\n \" up_stack = [\\n\",\n \" upsample(512, 4, apply_dropout=True), # (bs, 2, 2, 1024)\\n\",\n \" upsample(512, 4, apply_dropout=True), # (bs, 4, 4, 1024)\\n\",\n \" upsample(512, 4, apply_dropout=True), # (bs, 8, 8, 1024)\\n\",\n \" upsample(512, 4), # (bs, 16, 16, 1024)\\n\",\n \" upsample(256, 4), # (bs, 32, 32, 512)\\n\",\n \" upsample(128, 4), # (bs, 64, 64, 256)\\n\",\n \" upsample(64, 4), # (bs, 128, 128, 128)\\n\",\n \" ]\\n\",\n \"\\n\",\n \" initializer = tf.random_normal_initializer(0., 0.02)\\n\",\n \" last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 4,\\n\",\n \" strides=2,\\n\",\n \" padding='same',\\n\",\n \" kernel_initializer=initializer,\\n\",\n \" activation='tanh') # (bs, 256, 256, 3)\\n\",\n \"\\n\",\n \" concat = tf.keras.layers.Concatenate()\\n\",\n \"\\n\",\n \" inputs = tf.keras.layers.Input(shape=[None,None,3])\\n\",\n \" x = inputs\\n\",\n \"\\n\",\n \" # Downsampling through the model\\n\",\n \" skips = []\\n\",\n \" for down in down_stack:\\n\",\n \" x = down(x)\\n\",\n \" skips.append(x)\\n\",\n \"\\n\",\n \" skips = reversed(skips[:-1])\\n\",\n \"\\n\",\n \" # Upsampling and establishing the skip connections\\n\",\n \" for up, skip in zip(up_stack, skips):\\n\",\n \" x = up(x)\\n\",\n \" x = concat([x, skip])\\n\",\n \"\\n\",\n \" x = last(x)\\n\",\n \"\\n\",\n \" return tf.keras.Model(inputs=inputs, outputs=x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 323\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 10279,\n \"status\": \"ok\",\n \"timestamp\": 1564763187890,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"U1N1_obwtdQH\",\n \"outputId\": \"3eb5f090-b74d-4ac8-8187-ec114c755e6a\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING: Logging before flag parsing goes to stderr.\\n\",\n \"W0802 16:26:27.173293 140096643995520 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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IL3wZUJWFFZY83tJfHH4JHmu2a8zCWFZ8314ZTfPLg9phnudos/cNxf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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"generator = Generator()\\n\",\n \"\\n\",\n \"gen_output = generator(inp[tf.newaxis,...], training=False)\\n\",\n \"plt.imshow(gen_output[0,...])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"ZTKZfoaoEF22\"\n },\n \"source\": [\n \"## 建立判别器\\n\",\n \"- 鉴别器是PatchGAN。\\n\",\n \"- 鉴别器中的每个块都是（Conv - > BatchNorm - > Leaky ReLU）\\n\",\n \"- 最后一层之后的输出形状为（batch_size，30,30,1）\\n\",\n \"- 输出的每个30x30补丁分类输入图像的70x70部分（这种架构称为PatchGAN）。\\n\",\n \"- 鉴别器接收2个输入。\\n\",\n \" - 输入图像和目标图像，它应该归类为真实的。\\n\",\n \" - 输入图像和生成的图像（生成器的输出），它应该归类为伪造的。\\n\",\n \" - 我们在代码（tf.concat([inp, tar], axis=-1)）中将这两个输入连接在一起\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"ll6aNeQx8b4v\"\n },\n \"outputs\": [],\n \"source\": [\n \"def Discriminator():\\n\",\n \" initializer = tf.random_normal_initializer(0., 0.02)\\n\",\n \"\\n\",\n \" inp = tf.keras.layers.Input(shape=[None, None, 3], name='input_image')\\n\",\n \" tar = tf.keras.layers.Input(shape=[None, None, 3], name='target_image')\\n\",\n \"\\n\",\n \" x = tf.keras.layers.concatenate([inp, tar]) # (bs, 256, 256, channels*2)\\n\",\n \"\\n\",\n \" down1 = downsample(64, 4, False)(x) # (bs, 128, 128, 64)\\n\",\n \" down2 = downsample(128, 4)(down1) # (bs, 64, 64, 128)\\n\",\n \" down3 = downsample(256, 4)(down2) # (bs, 32, 32, 256)\\n\",\n \"\\n\",\n \" zero_pad1 = tf.keras.layers.ZeroPadding2D()(down3) # (bs, 34, 34, 256)\\n\",\n \" conv = tf.keras.layers.Conv2D(512, 4, strides=1,\\n\",\n \" kernel_initializer=initializer,\\n\",\n \" use_bias=False)(zero_pad1) # (bs, 31, 31, 512)\\n\",\n \"\\n\",\n \" batchnorm1 = tf.keras.layers.BatchNormalization()(conv)\\n\",\n \"\\n\",\n \" leaky_relu = tf.keras.layers.LeakyReLU()(batchnorm1)\\n\",\n \"\\n\",\n \" zero_pad2 = tf.keras.layers.ZeroPadding2D()(leaky_relu) # (bs, 33, 33, 512)\\n\",\n \"\\n\",\n \" last = tf.keras.layers.Conv2D(1, 4, strides=1,\\n\",\n \" kernel_initializer=initializer)(zero_pad2) # (bs, 30, 30, 1)\\n\",\n \"\\n\",\n \" return tf.keras.Model(inputs=[inp, tar], outputs=last)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 287\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 10677,\n \"status\": \"ok\",\n \"timestamp\": 1564763188298,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"gDkA05NE6QMs\",\n \"outputId\": \"f339ca66-3e1e-4a27-fb42-0c1abdc7142d\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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kybI6LJ5qVm+HkVkomemU3KggREVAimVtLspCxIQEQFIMuzmSJyAkAv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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"discriminator = Discriminator()\\n\",\n \"disc_out = discriminator([inp[tf.newaxis,...], gen_output], training=False)\\n\",\n \"plt.imshow(disc_out[0,...,-1], vmin=-20, vmax=20, cmap='RdBu_r')\\n\",\n \"plt.colorbar()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"-ede4p2YELFa\"\n },\n \"source\": [\n \"To learn more about the architecture and the hyperparameters you can refer the [paper](https://arxiv.org/abs/1611.07004).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"0FMYgY_mPfTi\"\n },\n \"source\": [\n \"## 定义损失函数和优化程序\\n\",\n \"- **判别器损失**\\n\",\n \"\\n\",\n \" - 鉴别器丢失功能需要2个输入; 真实图像，生成图像\\n\",\n \" - real_loss是真实图像的sigmoid交叉熵损失和一个数组（因为这些是真实的图像）\\n\",\n \" - generated_loss是生成的图像的sigmoid交叉熵丢失和零的数组（因为这些是伪图像）\\n\",\n \" - 然后total_loss是real_loss和generated_loss的总和\\n\",\n \"\\n\",\n \"- **生成器损失**\\n\",\n \"\\n\",\n \" - 它是生成的图像的sigmoid交叉熵损失和一组数组。\\n\",\n \" - 该论文还包括L1损失，其是生成的图像和目标图像之间的MAE（平均绝对误差）。\\n\",\n \" - 这允许生成的图像在结构上变得类似于目标图像。\\n\",\n \" - 式来计算总发电机损失= gan_loss + LAMBDA * l1_loss，其中LAMBDA = 100。这个值是由的作者决定纸。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"cyhxTuvJyIHV\"\n },\n \"outputs\": [],\n \"source\": [\n \"LAMBDA = 100\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"Q1Xbz5OaLj5C\"\n },\n \"outputs\": [],\n \"source\": [\n \"loss_object = tf.keras.losses.BinaryCrossentropy(from_logits=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"wkMNfBWlT-PV\"\n },\n \"outputs\": [],\n \"source\": [\n \"def discriminator_loss(disc_real_output, disc_generated_output):\\n\",\n \" real_loss = loss_object(tf.ones_like(disc_real_output), disc_real_output)\\n\",\n \"\\n\",\n \" generated_loss = loss_object(tf.zeros_like(disc_generated_output), disc_generated_output)\\n\",\n \"\\n\",\n \" total_disc_loss = real_loss + generated_loss\\n\",\n \"\\n\",\n \" return total_disc_loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"90BIcCKcDMxz\"\n },\n \"outputs\": [],\n \"source\": [\n \"def generator_loss(disc_generated_output, gen_output, target):\\n\",\n \" gan_loss = loss_object(tf.ones_like(disc_generated_output), disc_generated_output)\\n\",\n \"\\n\",\n \" # mean absolute error\\n\",\n \" l1_loss = tf.reduce_mean(tf.abs(target - gen_output))\\n\",\n \"\\n\",\n \" total_gen_loss = gan_loss + (LAMBDA * l1_loss)\\n\",\n \"\\n\",\n \" return total_gen_loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"iWCn_PVdEJZ7\"\n },\n \"outputs\": [],\n \"source\": [\n \"generator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)\\n\",\n \"discriminator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"aKUZnDiqQrAh\"\n },\n \"source\": [\n \"## 检查点（基于对象的保存）\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"WJnftd5sQsv6\"\n },\n \"outputs\": [],\n \"source\": [\n \"checkpoint_dir = './training_checkpoints'\\n\",\n \"checkpoint_prefix = os.path.join(checkpoint_dir, \\\"ckpt\\\")\\n\",\n \"checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\\n\",\n \" discriminator_optimizer=discriminator_optimizer,\\n\",\n \" generator=generator,\\n\",\n \" discriminator=discriminator)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"Rw1fkAczTQYh\"\n },\n \"source\": [\n \"## 训练\\n\",\n \"\\n\",\n \"- 我们首先迭代数据集\\n\",\n \"- 生成器获取输入图像，我们得到生成的输出。\\n\",\n \"- 鉴别器接收input_image和生成的图像作为第一输入。第二个输入是input_image和target_image。\\n\",\n \"- 接下来，我们计算发电机和鉴别器损耗。\\n\",\n \"- 然后，我们计算相对于发生器和鉴别器变量（输入）的损耗梯度，并将它们应用于优化器。\\n\",\n \"- 整个过程如下图所示。\\n\",\n \"\\n\",\n \"\\n\",\n \"![Discriminator Update Image](https://github.com/tensorflow/docs/blob/master/site/en/r2/tutorials/generative/images/dis.png?raw=1)\\n\",\n \"\\n\",\n \"\\n\",\n \"---\\n\",\n \"\\n\",\n \"\\n\",\n \"![Generator Update Image](https://github.com/tensorflow/docs/blob/master/site/en/r2/tutorials/generative/images/gen.png?raw=1)\\n\",\n \"\\n\",\n \"## 生成图像\\n\",\n \"- 经过培训，它有时间生成一些图像！\\n\",\n \"- 我们将图像从测试数据集传递到生成器。\\n\",\n \"- 然后，生成器将输入图像转换为我们期望的输出。\\n\",\n \"- 最后一步是绘制预测和瞧！\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"NS2GWywBbAWo\"\n },\n \"outputs\": [],\n \"source\": [\n \"EPOCHS = 150\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"RmdVsmvhPxyy\"\n },\n \"outputs\": [],\n \"source\": [\n \"def generate_images(model, test_input, tar):\\n\",\n \" # the training=True is intentional here since\\n\",\n \" # we want the batch statistics while running the model\\n\",\n \" # on the test dataset. If we use training=False, we will get\\n\",\n \" # the accumulated statistics learned from the training dataset\\n\",\n \" # (which we don't want)\\n\",\n \" prediction = model(test_input, training=True)\\n\",\n \" plt.figure(figsize=(15,15))\\n\",\n \"\\n\",\n \" display_list = [test_input[0], tar[0], prediction[0]]\\n\",\n \" title = ['Input Image', 'Ground Truth', 'Predicted Image']\\n\",\n \"\\n\",\n \" for i in range(3):\\n\",\n \" plt.subplot(1, 3, i+1)\\n\",\n \" plt.title(title[i])\\n\",\n \" # getting the pixel values between [0, 1] to plot it.\\n\",\n \" plt.imshow(display_list[i] * 0.5 + 0.5)\\n\",\n \" plt.axis('off')\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"KBKUV2sKXDbY\"\n },\n \"outputs\": [],\n \"source\": [\n \"@tf.function\\n\",\n \"def train_step(input_image, target):\\n\",\n \" with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\\n\",\n \" gen_output = generator(input_image, training=True)\\n\",\n \"\\n\",\n \" disc_real_output = discriminator([input_image, target], training=True)\\n\",\n \" disc_generated_output = discriminator([input_image, gen_output], training=True)\\n\",\n \"\\n\",\n \" gen_loss = generator_loss(disc_generated_output, gen_output, target)\\n\",\n \" disc_loss = discriminator_loss(disc_real_output, disc_generated_output)\\n\",\n \"\\n\",\n \" generator_gradients = gen_tape.gradient(gen_loss,\\n\",\n \" generator.trainable_variables)\\n\",\n \" discriminator_gradients = disc_tape.gradient(disc_loss,\\n\",\n \" discriminator.trainable_variables)\\n\",\n \"\\n\",\n \" generator_optimizer.apply_gradients(zip(generator_gradients,\\n\",\n \" generator.trainable_variables))\\n\",\n \" discriminator_optimizer.apply_gradients(zip(discriminator_gradients,\\n\",\n \" discriminator.trainable_variables))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"2M7LmLtGEMQJ\"\n },\n \"outputs\": [],\n \"source\": [\n \"def train(dataset, epochs):\\n\",\n \" for epoch in range(epochs):\\n\",\n \" start = time.time()\\n\",\n \"\\n\",\n \" for input_image, target in dataset:\\n\",\n \" train_step(input_image, target)\\n\",\n \"\\n\",\n \" clear_output(wait=True)\\n\",\n \" for inp, tar in test_dataset.take(1):\\n\",\n \" generate_images(generator, inp, tar)\\n\",\n \"\\n\",\n \" # saving (checkpoint) the model every 20 epochs\\n\",\n \" if (epoch + 1) % 20 == 0:\\n\",\n \" checkpoint.save(file_prefix = checkpoint_prefix)\\n\",\n \"\\n\",\n \" print ('Time taken for epoch {} is {} sec\\\\n'.format(epoch + 1,\\n\",\n \" time.time()-start))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 651\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 29872,\n \"status\": \"error\",\n \"timestamp\": 1564764405058,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"a1zZmKmvOH85\",\n \"outputId\": \"e890387f-8934-4569-ef5c-825425c3222f\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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WAxw87eqjQp0tctqU9kEZJV+k2HESE4R28zPkHwHucdOEMwDmuEKBmT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\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BuHNGvSqSN2BmUzWl2SbSBbhyWht8/Qfc8YU8mNE4epKtyqYZ2EbneB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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Time taken for epoch 31 is 37.78854441642761 sec\\n\",\n \"\\n\"\n ]\n },\n {\n \"ename\": \"KeyboardInterrupt\",\n \"evalue\": \"ignored\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mtrain\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtrain_dataset\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mEPOCHS\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36mtrain\\u001b[0;34m(dataset, epochs)\\u001b[0m\\n\\u001b[1;32m 4\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 5\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0minput_image\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mtarget\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mdataset\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 6\\u001b[0;31m \\u001b[0mtrain_step\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0minput_image\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mtarget\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 7\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 8\\u001b[0m \\u001b[0mclear_output\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mwait\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, *args, **kwds)\\u001b[0m\\n\\u001b[1;32m 402\\u001b[0m \\u001b[0;31m# In this case we have created variables on the first call, so we run the\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 403\\u001b[0m \\u001b[0;31m# defunned version which is guaranteed to never create variables.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 404\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_stateless_fn\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwds\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;31m# pylint: disable=not-callable\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 405\\u001b[0m \\u001b[0;32melif\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_stateful_fn\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 406\\u001b[0m \\u001b[0;31m# In this case we have not created variables on the first call. So we can\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 1333\\u001b[0m \\u001b[0;34m\\\"\\\"\\\"Calls a graph function specialized to the inputs.\\\"\\\"\\\"\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1334\\u001b[0m \\u001b[0mgraph_function\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_maybe_define_function\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1335\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mgraph_function\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_filtered_call\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;31m# pylint: disable=protected-access\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 1336\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1337\\u001b[0m \\u001b[0;34m@\\u001b[0m\\u001b[0mproperty\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\\u001b[0m in \\u001b[0;36m_filtered_call\\u001b[0;34m(self, args, kwargs)\\u001b[0m\\n\\u001b[1;32m 587\\u001b[0m \\\"\\\"\\\"\\n\\u001b[1;32m 588\\u001b[0m return self._call_flat(\\n\\u001b[0;32m--> 589\\u001b[0;31m (t for t in nest.flatten((args, kwargs), expand_composites=True)\\n\\u001b[0m\\u001b[1;32m 590\\u001b[0m if isinstance(t, (ops.Tensor,\\n\\u001b[1;32m 591\\u001b[0m resource_variable_ops.ResourceVariable))))\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\\u001b[0m in \\u001b[0;36m_call_flat\\u001b[0;34m(self, args)\\u001b[0m\\n\\u001b[1;32m 669\\u001b[0m \\u001b[0;31m# Only need to override the gradient in graph mode and when we have outputs.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 670\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mcontext\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexecuting_eagerly\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mor\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0moutputs\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 671\\u001b[0;31m \\u001b[0moutputs\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_inference_function\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcall\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mctx\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0margs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 672\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 673\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_register_gradient\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\\u001b[0m in \\u001b[0;36mcall\\u001b[0;34m(self, ctx, args)\\u001b[0m\\n\\u001b[1;32m 443\\u001b[0m attrs=(\\\"executor_type\\\", executor_type,\\n\\u001b[1;32m 444\\u001b[0m \\\"config_proto\\\", config),\\n\\u001b[0;32m--> 445\\u001b[0;31m ctx=ctx)\\n\\u001b[0m\\u001b[1;32m 446\\u001b[0m \\u001b[0;31m# Replace empty list with None\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 447\\u001b[0m \\u001b[0moutputs\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0moutputs\\u001b[0m \\u001b[0;32mor\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py\\u001b[0m in \\u001b[0;36mquick_execute\\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\\u001b[0m\\n\\u001b[1;32m 59\\u001b[0m tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,\\n\\u001b[1;32m 60\\u001b[0m \\u001b[0mop_name\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0minputs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mattrs\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 61\\u001b[0;31m num_outputs)\\n\\u001b[0m\\u001b[1;32m 62\\u001b[0m \\u001b[0;32mexcept\\u001b[0m \\u001b[0mcore\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_NotOkStatusException\\u001b[0m \\u001b[0;32mas\\u001b[0m \\u001b[0me\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 63\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mname\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m: \"\n ]\n }\n ],\n \"source\": [\n \"train(train_dataset, EPOCHS)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"kz80bY3aQ1VZ\"\n },\n \"source\": [\n \"## 恢复最新的检查点并进行测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 431\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 3578,\n \"status\": \"ok\",\n \"timestamp\": 1564764412900,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"HSSm4kfvJiqv\",\n \"outputId\": \"773f73d3-3282-487d-f7d7-195ea5ef826c\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"checkpoint\\t\\t ckpt-4.data-00000-of-00002\\n\",\n \"ckpt_10.data-00000-of-00002 ckpt_4.data-00001-of-00002\\n\",\n \"ckpt_10.data-00001-of-00002 ckpt-4.data-00001-of-00002\\n\",\n \"ckpt_10.index\\t\\t ckpt_4.index\\n\",\n \"ckpt_1.data-00000-of-00002 ckpt-4.index\\n\",\n \"ckpt-1.data-00000-of-00002 ckpt_5.data-00000-of-00002\\n\",\n \"ckpt_1.data-00001-of-00002 ckpt-5.data-00000-of-00002\\n\",\n \"ckpt-1.data-00001-of-00002 ckpt_5.data-00001-of-00002\\n\",\n \"ckpt_1.index\\t\\t ckpt-5.data-00001-of-00002\\n\",\n \"ckpt-1.index\\t\\t ckpt_5.index\\n\",\n \"ckpt_2.data-00000-of-00002 ckpt-5.index\\n\",\n \"ckpt-2.data-00000-of-00002 ckpt_6.data-00000-of-00002\\n\",\n \"ckpt_2.data-00001-of-00002 ckpt_6.data-00001-of-00002\\n\",\n \"ckpt-2.data-00001-of-00002 ckpt_6.index\\n\",\n \"ckpt_2.index\\t\\t ckpt_7.data-00000-of-00002\\n\",\n \"ckpt-2.index\\t\\t ckpt_7.data-00001-of-00002\\n\",\n \"ckpt_3.data-00000-of-00002 ckpt_7.index\\n\",\n \"ckpt-3.data-00000-of-00002 ckpt_8.data-00000-of-00002\\n\",\n \"ckpt_3.data-00001-of-00002 ckpt_8.data-00001-of-00002\\n\",\n \"ckpt-3.data-00001-of-00002 ckpt_8.index\\n\",\n \"ckpt_3.index\\t\\t ckpt_9.data-00000-of-00002\\n\",\n \"ckpt-3.index\\t\\t ckpt_9.data-00001-of-00002\\n\",\n \"ckpt_4.data-00000-of-00002 ckpt_9.index\\n\"\n ]\n }\n ],\n \"source\": [\n \"!ls {checkpoint_dir}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 4739,\n \"status\": \"ok\",\n \"timestamp\": 1564764414930,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"4t4x69adQ5xb\",\n \"outputId\": \"5e3cfdf4-3ca8-46e3-8aa4-ac46ae41cd88\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# restoring the latest checkpoint in checkpoint_dir\\n\",\n \"checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"1RGysMU_BZhx\"\n },\n \"source\": [\n \"## 使用测试数据集生成\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 1000,\n \"output_embedded_package_id\": \"1pMHZiYmRLUAp1xlUS63ONvBz2LyNdjwD\"\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 5456,\n \"status\": \"ok\",\n \"timestamp\": 1564764417195,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"KUgSnmy2nqSP\",\n \"outputId\": \"98fa7939-e502-4bac-9a41-4a15517277c0\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Output hidden; open in https://colab.research.google.com to view.\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Run the trained model on the entire test dataset\\n\",\n \"for inp, tar in test_dataset.take(5):\\n\",\n \" generate_images(generator, inp, tar)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"9kptHXRrOhbU\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"accelerator\": \"GPU\",\n \"colab\": {\n \"collapsed_sections\": [],\n \"last_runtime\": {\n \"build_target\": \"//learning/brain/python/client:colab_notebook\",\n \"kind\": \"private\"\n },\n \"name\": \"pix2pix.ipynb\",\n \"provenance\": [\n {\n \"file_id\": \"https://github.com/tensorflow/docs/blob/master/site/en/r2/tutorials/generative/pix2pix.ipynb\",\n \"timestamp\": 1564757118227\n }\n ],\n \"toc_visible\": true,\n \"version\": \"0.3.2\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "032-Text/001-word_embeddings.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2.0教程-词嵌入\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"import tensorflow as tf\\n\",\n \"import tensorflow.keras as keras\\n\",\n \"import tensorflow.keras.layers as layers\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.载入数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]\\n\",\n \"[1, 194, 1153, 194, 8255, 78, 228, 5, 6, 1463, 4369, 5012, 134, 26, 4, 715, 8, 118, 1634, 14, 394, 20, 13, 119, 954, 189, 102, 5, 207, 110, 3103, 21, 14, 69, 188, 8, 30, 23, 7, 4, 249, 126, 93, 4, 114, 9, 2300, 1523, 5, 647, 4, 116, 9, 35, 8163, 4, 229, 9, 340, 1322, 4, 118, 9, 4, 130, 4901, 19, 4, 1002, 5, 89, 29, 952, 46, 37, 4, 455, 9, 45, 43, 38, 1543, 1905, 398, 4, 1649, 26, 6853, 5, 163, 11, 3215, 2, 4, 1153, 9, 194, 775, 7, 8255, 2, 349, 2637, 148, 605, 2, 8003, 15, 123, 125, 68, 2, 6853, 15, 349, 165, 4362, 98, 5, 4, 228, 9, 43, 2, 1157, 15, 299, 120, 5, 120, 174, 11, 220, 175, 136, 50, 9, 4373, 228, 8255, 5, 2, 656, 245, 2350, 5, 4, 9837, 131, 152, 491, 18, 2, 32, 7464, 1212, 14, 9, 6, 371, 78, 22, 625, 64, 1382, 9, 8, 168, 145, 23, 4, 1690, 15, 16, 4, 1355, 5, 28, 6, 52, 154, 462, 33, 89, 78, 285, 16, 145, 95]\\n\"\n ]\n }\n ],\n \"source\": [\n \"vocab_size = 10000\\n\",\n \"(train_x, train_y), (test_x, text_y) = keras.datasets.imdb.load_data(num_words=vocab_size)\\n\",\n \"print(train_x[0])\\n\",\n \"print(train_x[1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert is an amazing actor and now the same being director father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also to the two little boy's that played the of norman and paul they were just brilliant children are often left out of the list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\\n\"\n ]\n }\n ],\n \"source\": [\n \"word_index = keras.datasets.imdb.get_word_index()\\n\",\n \"word_index = {k:(v+3) for k,v in word_index.items()}\\n\",\n \"word_index[''] = 0\\n\",\n \"word_index[''] = 1\\n\",\n \"word_index[''] = 2\\n\",\n \"word_index[''] = 3\\n\",\n \"reverse_word_index = {v:k for k, v in word_index.items()}\\n\",\n \"def decode_review(text):\\n\",\n \" return ' '.join([reverse_word_index.get(i, '?') for i in text])\\n\",\n \"print(decode_review(train_x[0]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"maxlen = 500\\n\",\n \"train_x = keras.preprocessing.sequence.pad_sequences(train_x,value=word_index[''],\\n\",\n \" padding='post', maxlen=maxlen)\\n\",\n \"test_x = keras.preprocessing.sequence.pad_sequences(test_x,value=word_index[''],\\n\",\n \" padding='post', maxlen=maxlen)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.构建模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"embedding (Embedding) (None, 500, 16) 160000 \\n\",\n \"_________________________________________________________________\\n\",\n \"global_average_pooling1d (Gl (None, 16) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense (Dense) (None, 16) 272 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_1 (Dense) (None, 1) 17 \\n\",\n \"=================================================================\\n\",\n \"Total params: 160,289\\n\",\n \"Trainable params: 160,289\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"embedding_dim = 16\\n\",\n \"model = keras.Sequential([\\n\",\n \" layers.Embedding(vocab_size, embedding_dim, input_length=maxlen),\\n\",\n \" layers.GlobalAveragePooling1D(),\\n\",\n \" layers.Dense(16, activation='relu'),\\n\",\n \" layers.Dense(1, activation='sigmoid')\\n\",\n \" \\n\",\n \"])\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 22500 samples, validate on 2500 samples\\n\",\n \"Epoch 1/30\\n\",\n \"22500/22500 [==============================] - 2s 78us/sample - loss: 0.3730 - accuracy: 0.8780 - val_loss: 0.3887 - val_accuracy: 0.8592\\n\",\n \"Epoch 2/30\\n\",\n \"22500/22500 [==============================] - 2s 77us/sample - loss: 0.3496 - accuracy: 0.8828 - val_loss: 0.3711 - val_accuracy: 0.8660\\n\",\n \"Epoch 3/30\\n\",\n \"22500/22500 [==============================] - 2s 69us/sample - loss: 0.3288 - accuracy: 0.8884 - val_loss: 0.3561 - val_accuracy: 0.8712\\n\",\n \"Epoch 4/30\\n\",\n \"22500/22500 [==============================] - 2s 71us/sample - loss: 0.3110 - accuracy: 0.8938 - val_loss: 0.3448 - val_accuracy: 0.8728\\n\",\n \"Epoch 5/30\\n\",\n \"22500/22500 [==============================] - 2s 67us/sample - loss: 0.2951 - accuracy: 0.8978 - val_loss: 0.3346 - val_accuracy: 0.8740\\n\",\n \"Epoch 6/30\\n\",\n \"22500/22500 [==============================] - 1s 66us/sample - loss: 0.2808 - accuracy: 0.9026 - val_loss: 0.3260 - val_accuracy: 0.8768\\n\",\n \"Epoch 7/30\\n\",\n \"22500/22500 [==============================] - 2s 69us/sample - loss: 0.2686 - accuracy: 0.9071 - val_loss: 0.3218 - val_accuracy: 0.8764\\n\",\n \"Epoch 8/30\\n\",\n \"22500/22500 [==============================] - 2s 70us/sample - loss: 0.2574 - accuracy: 0.9098 - val_loss: 0.3138 - val_accuracy: 0.8816\\n\",\n \"Epoch 9/30\\n\",\n \"22500/22500 [==============================] - 2s 69us/sample - loss: 0.2464 - accuracy: 0.9143 - val_loss: 0.3090 - val_accuracy: 0.8828\\n\",\n \"Epoch 10/30\\n\",\n \"22500/22500 [==============================] - 2s 70us/sample - loss: 0.2370 - accuracy: 0.9172 - val_loss: 0.3047 - val_accuracy: 0.8828\\n\",\n \"Epoch 11/30\\n\",\n \"22500/22500 [==============================] - 2s 71us/sample - loss: 0.2283 - accuracy: 0.9210 - val_loss: 0.3009 - val_accuracy: 0.8836\\n\",\n \"Epoch 12/30\\n\",\n \"22500/22500 [==============================] - 1s 66us/sample - loss: 0.2203 - accuracy: 0.9233 - val_loss: 0.2985 - val_accuracy: 0.8844\\n\",\n \"Epoch 13/30\\n\",\n \"22500/22500 [==============================] - 2s 67us/sample - loss: 0.2123 - accuracy: 0.9264 - val_loss: 0.2959 - val_accuracy: 0.8852\\n\",\n \"Epoch 14/30\\n\",\n \"22500/22500 [==============================] - 1s 66us/sample - loss: 0.2054 - accuracy: 0.9281 - val_loss: 0.2947 - val_accuracy: 0.8836\\n\",\n \"Epoch 15/30\\n\",\n \"22500/22500 [==============================] - 1s 66us/sample - loss: 0.1989 - accuracy: 0.9302 - val_loss: 0.2927 - val_accuracy: 0.8876\\n\",\n \"Epoch 16/30\\n\",\n \"22500/22500 [==============================] - 1s 66us/sample - loss: 0.1924 - accuracy: 0.9331 - val_loss: 0.2937 - val_accuracy: 0.8872\\n\",\n \"Epoch 17/30\\n\",\n \"22500/22500 [==============================] - 1s 66us/sample - loss: 0.1865 - accuracy: 0.9348 - val_loss: 0.2909 - val_accuracy: 0.8880\\n\",\n \"Epoch 18/30\\n\",\n \"22500/22500 [==============================] - 1s 66us/sample - loss: 0.1804 - accuracy: 0.9371 - val_loss: 0.2909 - val_accuracy: 0.8908\\n\",\n \"Epoch 19/30\\n\",\n \"22500/22500 [==============================] - 1s 66us/sample - loss: 0.1753 - accuracy: 0.9404 - val_loss: 0.2916 - val_accuracy: 0.8912\\n\",\n \"Epoch 20/30\\n\",\n \"22500/22500 [==============================] - 2s 68us/sample - loss: 0.1705 - accuracy: 0.9422 - val_loss: 0.2951 - val_accuracy: 0.8900\\n\",\n \"Epoch 21/30\\n\",\n \"22500/22500 [==============================] - 2s 73us/sample - loss: 0.1655 - accuracy: 0.9443 - val_loss: 0.2910 - val_accuracy: 0.8900\\n\",\n \"Epoch 22/30\\n\",\n \"22500/22500 [==============================] - 2s 68us/sample - loss: 0.1605 - accuracy: 0.9470 - val_loss: 0.2923 - val_accuracy: 0.8916\\n\",\n \"Epoch 23/30\\n\",\n \"22500/22500 [==============================] - 2s 75us/sample - loss: 0.1560 - accuracy: 0.9486 - val_loss: 0.2940 - val_accuracy: 0.8900\\n\",\n \"Epoch 24/30\\n\",\n \"22500/22500 [==============================] - 2s 69us/sample - loss: 0.1516 - accuracy: 0.9502 - val_loss: 0.2949 - val_accuracy: 0.8892\\n\",\n \"Epoch 25/30\\n\",\n \"22500/22500 [==============================] - 1s 66us/sample - loss: 0.1473 - accuracy: 0.9517 - val_loss: 0.2984 - val_accuracy: 0.8928\\n\",\n \"Epoch 26/30\\n\",\n \"22500/22500 [==============================] - 2s 69us/sample - loss: 0.1436 - accuracy: 0.9524 - val_loss: 0.2979 - val_accuracy: 0.8900\\n\",\n \"Epoch 27/30\\n\",\n \"22500/22500 [==============================] - 1s 67us/sample - loss: 0.1400 - accuracy: 0.9540 - val_loss: 0.2995 - val_accuracy: 0.8912\\n\",\n \"Epoch 28/30\\n\",\n \"22500/22500 [==============================] - 1s 65us/sample - loss: 0.1368 - accuracy: 0.9548 - val_loss: 0.3025 - val_accuracy: 0.8912\\n\",\n \"Epoch 29/30\\n\",\n \"22500/22500 [==============================] - 1s 66us/sample - loss: 0.1328 - accuracy: 0.9570 - val_loss: 0.3042 - val_accuracy: 0.8920\\n\",\n \"Epoch 30/30\\n\",\n \"22500/22500 [==============================] - 1s 66us/sample - loss: 0.1290 - accuracy: 0.9596 - val_loss: 0.3055 - val_accuracy: 0.8948\\n\"\n ]\n }\n ],\n \"source\": [\n \"model.compile(optimizer=keras.optimizers.Adam(),\\n\",\n \" loss=keras.losses.BinaryCrossentropy(),\\n\",\n \" metrics=['accuracy'])\\n\",\n \"history = model.fit(train_x, train_y, epochs=30, batch_size=512, validation_split=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"acc = history.history['accuracy']\\n\",\n \"val_acc = history.history['val_accuracy']\\n\",\n \"\\n\",\n \"epochs = range(1, len(acc) + 1)\\n\",\n \"\\n\",\n \"plt.plot(epochs, acc, 'bo', label='Training acc')\\n\",\n \"plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n\",\n \"plt.title('Training and validation accuracy')\\n\",\n \"plt.xlabel('Epochs')\\n\",\n \"plt.ylabel('Accuracy')\\n\",\n \"plt.legend(loc='lower right')\\n\",\n \"plt.figure(figsize=(16,9))\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(10000, 16)\\n\"\n ]\n }\n ],\n \"source\": [\n \"e = model.layers[0]\\n\",\n \"weights = e.get_weights()[0]\\n\",\n \"print(weights.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"out_v = open('vecs.tsv', 'w')\\n\",\n \"out_m = open('meta.tsv', 'w')\\n\",\n \"for word_num in range(vocab_size):\\n\",\n \" word = reverse_word_index[word_num]\\n\",\n \" embeddings = weights[word_num]\\n\",\n \" out_m.write(word + \\\"\\\\n\\\")\\n\",\n \" out_v.write('\\\\t'.join([str(x) for x in embeddings]) + \\\"\\\\n\\\")\\n\",\n \"out_v.close()\\n\",\n \"out_m.close()\"\n ]\n },\n {\n \"attachments\": {\n \"image.png\": {\n \"image/png\": 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\"\n }\n },\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"放到 Embedding Projector：http://projector.tensorflow.org/\\n\",\n \"上进行可视化\\n\",\n \"![image.png](attachment:image.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "032-Text/002-text_classification_with_RNN.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2.0教程-使用RNN实现文本分类\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function, unicode_literals\\n\",\n \"import tensorflow as tf\\n\",\n \"import tensorflow.keras as keras\\n\",\n \"print(tf.__version__)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.使用tensorflow_datasets 构造输入数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001b[31mspacy 2.0.18 has requirement numpy>=1.15.0, but you'll have numpy 1.14.3 which is incompatible.\\u001b[0m\\n\",\n \"\\u001b[31mplotnine 0.5.1 has requirement matplotlib>=3.0.0, but you'll have matplotlib 2.2.2 which is incompatible.\\u001b[0m\\n\",\n \"\\u001b[31mplotnine 0.5.1 has requirement pandas>=0.23.4, but you'll have pandas 0.23.0 which is incompatible.\\u001b[0m\\n\",\n \"\\u001b[31mneo4j-driver 1.6.2 has requirement neotime==1.0.0, but you'll have neotime 1.7.2 which is incompatible.\\u001b[0m\\n\",\n \"\\u001b[31mmizani 0.5.3 has requirement pandas>=0.23.4, but you'll have pandas 0.23.0 which is incompatible.\\u001b[0m\\n\",\n \"\\u001b[31mfastai 0.7.0 has requirement torch<0.4, but you'll have torch 0.4.1 which is incompatible.\\u001b[0m\\n\",\n \"\\u001b[33mYou are using pip version 10.0.1, however version 19.1 is available.\\n\",\n \"You should consider upgrading via the 'pip install --upgrade pip' command.\\u001b[0m\\n\"\n ]\n }\n ],\n \"source\": [\n \"!pip install -q tensorflow_datasets\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import tensorflow_datasets as tfds\\n\",\n \"dataset, info = tfds.load('imdb_reviews/subwords8k', with_info=True,\\n\",\n \" as_supervised=True) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"获取训练集、测试集\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"train_dataset, test_dataset = dataset['train'], dataset['test']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"获取tokenizer对象，用进行字符处理级id转换（这里先转换成subword，再转换为id）等操作\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"vocabulary size: 8185\\n\"\n ]\n }\n ],\n \"source\": [\n \"tokenizer = info.features['text'].encoder\\n\",\n \"print('vocabulary size: ', tokenizer.vocab_size)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"token对象测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tokened id: [4025, 222, 2621, 1199, 6307, 2327, 2934]\\n\",\n \"original string: Hello word , Tensorflow\\n\"\n ]\n }\n ],\n \"source\": [\n \"sample_string = 'Hello word , Tensorflow'\\n\",\n \"tokenized_string = tokenizer.encode(sample_string)\\n\",\n \"print('tokened id: ', tokenized_string)\\n\",\n \"\\n\",\n \"# 解码会原字符串\\n\",\n \"src_string = tokenizer.decode(tokenized_string)\\n\",\n \"print('original string: ', src_string)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"解出每个subword\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4025->[Hell]\\n\",\n \"222->[o ]\\n\",\n \"2621->[word]\\n\",\n \"1199->[ , ]\\n\",\n \"6307->[Ten]\\n\",\n \"2327->[sor]\\n\",\n \"2934->[flow]\\n\"\n ]\n }\n ],\n \"source\": [\n \"for t in tokenized_string:\\n\",\n \" print(str(t)+'->['+tokenizer.decode([t])+ ']')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"构建批次训练集\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"BUFFER_SIZE=10000\\n\",\n \"BATCH_SIZE = 64\\n\",\n \"\\n\",\n \"train_dataset = train_dataset.shuffle(BUFFER_SIZE)\\n\",\n \"train_dataset = train_dataset.padded_batch(BATCH_SIZE, train_dataset.output_shapes)\\n\",\n \"\\n\",\n \"test_dataset = test_dataset.padded_batch(BATCH_SIZE, test_dataset.output_shapes)\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 模型构建\\n\",\n \"下面，因为此处的句子是变长的，所以只能使用序列模型，而不能使用keras的函数api\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# def get_model():\\n\",\n \"# inputs = tf.keras.Input((1240,))\\n\",\n \"# emb = tf.keras.layers.Embedding(tokenizer.vocab_size, 64)(inputs)\\n\",\n \"# h1 = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64))(emb)\\n\",\n \"# h1 = tf.keras.layers.Dense(64, activation='relu')(h1)\\n\",\n \"# outputs = tf.keras.layers.Dense(1, activation='sigmoid')(h1)\\n\",\n \"# model = tf.keras.Model(inputs, outputs)\\n\",\n \"# return model\\n\",\n \"\\n\",\n \"def get_model():\\n\",\n \" model = tf.keras.Sequential([\\n\",\n \" tf.keras.layers.Embedding(tokenizer.vocab_size, 64),\\n\",\n \" tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),\\n\",\n \" tf.keras.layers.Dense(64, activation='relu'),\\n\",\n \" tf.keras.layers.Dense(1, activation='sigmoid')\\n\",\n \" ])\\n\",\n \" return model\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model = get_model()\\n\",\n \"model.compile(loss='binary_crossentropy',\\n\",\n \" optimizer='adam',\\n\",\n \" metrics=['accuracy'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 模型训练\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1/10\\n\",\n \"391/391 [==============================] - 827s 2s/step - loss: 0.5606 - accuracy: 0.7068 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\\n\",\n \"Epoch 2/10\\n\",\n \"391/391 [==============================] - 798s 2s/step - loss: 0.4964 - accuracy: 0.7725 - val_loss: 0.5414 - val_accuracy: 0.7545\\n\",\n \"Epoch 3/10\\n\",\n \"391/391 [==============================] - 788s 2s/step - loss: 0.4347 - accuracy: 0.8076 - val_loss: 0.4571 - val_accuracy: 0.8075\\n\",\n \"Epoch 4/10\\n\",\n \"391/391 [==============================] - 795s 2s/step - loss: 0.4185 - accuracy: 0.8129 - val_loss: 0.4733 - val_accuracy: 0.8141\\n\",\n \"Epoch 5/10\\n\",\n \"391/391 [==============================] - 806s 2s/step - loss: 0.3016 - accuracy: 0.8824 - val_loss: 0.4644 - val_accuracy: 0.7839\\n\",\n \"Epoch 6/10\\n\",\n \"391/391 [==============================] - 793s 2s/step - loss: 0.2672 - accuracy: 0.8996 - val_loss: 0.5584 - val_accuracy: 0.7959\\n\",\n \"Epoch 7/10\\n\",\n \"391/391 [==============================] - 796s 2s/step - loss: 0.2302 - accuracy: 0.9144 - val_loss: 0.4058 - val_accuracy: 0.8326\\n\",\n \"Epoch 8/10\\n\",\n \"391/391 [==============================] - 797s 2s/step - loss: 0.1922 - accuracy: 0.9305 - val_loss: 0.4489 - val_accuracy: 0.8362\\n\",\n \"Epoch 9/10\\n\",\n \"391/391 [==============================] - 794s 2s/step - loss: 0.1658 - accuracy: 0.9426 - val_loss: 0.4936 - val_accuracy: 0.8366\\n\",\n \"Epoch 10/10\\n\",\n \"391/391 [==============================] - 791s 2s/step - loss: 0.1333 - accuracy: 0.9548 - val_loss: 0.6117 - val_accuracy: 0.8199\\n\"\n ]\n }\n ],\n \"source\": [\n \"history = model.fit(train_dataset, epochs=10,\\n\",\n \" validation_data=test_dataset)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# 查看训练过程\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"def plot_graphs(history, string):\\n\",\n \" plt.plot(history.history[string])\\n\",\n \" plt.plot(history.history['val_'+string])\\n\",\n \" plt.xlabel('epochs')\\n\",\n \" plt.ylabel(string)\\n\",\n \" plt.legend([string, 'val_'+string])\\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"plot_graphs(history, 'accuracy')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_graphs(history, 'loss')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 391/Unknown - 68s 174ms/step - loss: 0.6117 - accuracy: 0.8199test loss: 0.6117385012262008\\n\",\n \"test acc: 0.81988\\n\"\n ]\n }\n ],\n \"source\": [\n \"test_loss, test_acc = model.evaluate(test_dataset)\\n\",\n \"print('test loss: ', test_loss)\\n\",\n \"print('test acc: ', test_acc)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"上述模型不会mask掉序列的padding，所以如果在有padding的寻列上训练，测试没有padding的序列时可能有所偏差。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def pad_to_size(vec, size):\\n\",\n \" zeros = [0] * (size-len(vec))\\n\",\n \" vec.extend(zeros)\\n\",\n \" return vec\\n\",\n \"\\n\",\n \"def sample_predict(sentence, pad=False):\\n\",\n \" \\n\",\n \" tokened_sent = tokenizer.encode(sentence)\\n\",\n \" if pad:\\n\",\n \" tokened_sent = pad_to_size(tokened_sent, 64)\\n\",\n \" pred = model.predict(tf.expand_dims(tokened_sent, 0))\\n\",\n \" return pred\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.2938048]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 没有padding的情况\\n\",\n \"sample_pred_text = ('The movie was cool. The animation and the graphics '\\n\",\n \" 'were out of this world. I would recommend this movie.')\\n\",\n \"predictions = sample_predict(sample_pred_text, pad=False)\\n\",\n \"print(predictions)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.42541984]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 有paddin的情况\\n\",\n \"sample_pred_text = ('The movie was cool. The animation and the graphics '\\n\",\n \" 'were out of this world. I would recommend this movie.')\\n\",\n \"predictions = sample_predict(sample_pred_text, pad=True)\\n\",\n \"print (predictions)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 堆叠更多的lstm层\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from tensorflow.keras import layers\\n\",\n \"model = keras.Sequential(\\n\",\n \"[\\n\",\n \" layers.Embedding(tokenizer.vocab_size, 64),\\n\",\n \" layers.Bidirectional(layers.LSTM(64, return_sequences=True)),\\n\",\n \" layers.Bidirectional(layers.LSTM(32)),\\n\",\n \" layers.Dense(64, activation='relu'),\\n\",\n \" layers.Dense(1, activation='sigmoid')\\n\",\n \"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model.compile(loss=tf.keras.losses.binary_crossentropy,\\n\",\n \" optimizer=tf.keras.optimizers.Adam(),\\n\",\n \" metrics=['accuracy'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1/6\\n\",\n \"391/391 [==============================] - 1646s 4s/step - loss: 0.5270 - accuracy: 0.7414 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\\n\",\n \"Epoch 2/6\\n\",\n \"391/391 [==============================] - 1576s 4s/step - loss: 0.3900 - accuracy: 0.8320 - val_loss: 0.5222 - val_accuracy: 0.7726\\n\",\n \"Epoch 3/6\\n\",\n \"391/391 [==============================] - 1568s 4s/step - loss: 0.3559 - accuracy: 0.8538 - val_loss: 0.4429 - val_accuracy: 0.8162\\n\",\n \"Epoch 4/6\\n\",\n \"391/391 [==============================] - 1590s 4s/step - loss: 0.2504 - accuracy: 0.9059 - val_loss: 0.4470 - val_accuracy: 0.8091\\n\",\n \"Epoch 5/6\\n\",\n \"391/391 [==============================] - 1608s 4s/step - loss: 0.1977 - accuracy: 0.9280 - val_loss: 0.4762 - val_accuracy: 0.8222\\n\",\n \"Epoch 6/6\\n\",\n \"391/391 [==============================] - 1622s 4s/step - loss: 0.1619 - accuracy: 0.9430 - val_loss: 0.5484 - val_accuracy: 0.7808\\n\"\n ]\n }\n ],\n \"source\": [\n \"history=model.fit(train_dataset, epochs=6, validation_data=test_dataset)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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RORkZGkef16dOHq666itGjR1O/fv1j64307duX8ePHc//99x9r2mrYsCG7d+9m79691K5dmxkzZtCvX78Tvt/R9U3eeeedY9tPtG5Kt27d2L59O0uWLGH58uWl+ZWZKkpVWbJtP9OSUpixfAfph3IIrRHMVWdFMzAumvhmYXZXeQXm6852v9ahQwcOHjxIdHQ0jRs35oYbbuDKK6+kU6dOxMfH07Zt2xKVc6LzOnTowF//+ld69epFYGAgXbp04e233+Y///kPI0aM4M033yQwMJDx48dz7rnn8sQTT9C1a1eio6NP+t5jxoxh0KBBhIWF0adPHzZv3gxwwnVTAK677jqSkpJKtEyw8R8bdmcyLSmFaUmpbEs/TPWgAC5q35CBcdH0ah3pt5MgVja2HokpF1dccQUPPPAAffv2PeEx9jfxD7sOZPHFMmfE1cqUAwQInNcygoS4KPp1bFTp1uyoymw9ElMh7N+/n65du9K5c+eTJhFTtR3IymXWyp1MS0phwca9FCh0ig7l8cvbMaBzVKVdYtY4LJFUIitWrOCmm2763bbq1avzyy+/+Cii4tWrV4/169cXf6CpcrLz8vluXRrTklKYu2Y3OXkFNA2vyagLzyChSzQtI2v7OkRTRvw6kahqpZoyoVOnTiQlVc35Kf2hidUfFBQoi7akMzUphZkrdpJxJJf6taox9JwmJHSJpkuTepXq/5wpGb9NJCEhIezdu5f69evbP2wfU1X27t1LSIg1b1RWa3ceYOrSVKYnpZCakUXNaoFc0r4hCV2iueCMCIIDrdO8KvPbRBITE0NycjJpaWm+DsXgJPaYmBhfh2FOQcr+I0xPSmVaUgprdx4kMEDo2SqChy9ry8XtG1Kzmt9+vPgdv/1LBwcHH7sj2xhTMvsP5zBzxU6mJqXw62Znyp2zmtZjbEIHLu/UmPo2Nbtf8ttEYowpmazcfL5Zs5upSSl8t85Z26NlZC3+fHFrEuKiaVq/pq9DND5micQY8wf5BcqCjXuZmpTC7JU7Oeiu7XHzuc25ytb2MIVYIjHGAMfX9pialMIXy1LZfTCb2tWD6NexEQPjojm3pa3tYYpmicQYP7dt72GmJaUwxV3bIzhQ6N2mAQPjounbztb2MMWzRGKMH9qbmc2XK3YwdWkKS9y1PbrGhvOnC1rQv1Mj6tWs5uMITWViicQYPzNjeSoPfrKMrNwC2jaqw8P92jIgLopoW9vDnCZLJMb4CVXlpW828MLc9cQ3C+OpgR1p17iur8MyVYAlEmP8QFZuPn/5dDlfLEvl6rOieebqTlQPsr4PUza8Om+BiPQTkXUiskFEHilifzMR+UZElovIdyIS47HvFhH5zX3c4rH9bBFZ4Zb5ktgYRGNOavfBLIZMWMiM5ak83K8tzw/qbEnElCmvJRIRCQReBi4D2gNDRaR9ocOew1mX/UxgLPCMe2448CTQDegKPCkiR1dEGg8MB1q5j6KX8TPGsDr1AAP/9xPrdh7k1RvP5q7eLe3+D1PmvFkj6QpsUNVNqpoDTAISCh3THvjWfT7PY/+lwBxVTVfVfcAcoJ+INAbqqupCdaaLfRcY6MVrMKbS+nrVTq599WcU+OTOc7m0QyNfh2SqKG8mkmhgu8frZHebp2XA1e7zq4A6IlL/JOdGu89PViYAIjJCRBJFJNEmZjT+RFV5df5G7nh/Ma0a1mHayPPpGB3q67BMFebruZ0fBHqJyFKgF5AC5JdFwao6QVXjVTU+MjKyLIo0psLLznM61Z/9ai2Xd2rMxyO62+qDxuu8OWorBWji8TrG3XaMqqbi1khEpDZwjaruF5EUoHehc79zz48ptP13ZRrjr/ZmZnPn+4tZtGUf91/Uivv6trL+EFMuvFkjWQS0EpFYEakGDAGmex4gIhEicjSGR4G33OezgUtEJMztZL8EmK2qO4ADItLdHa11MzDNi9dgTKWwftdBBr7yE8uTM/jv0C7cf1FrSyKm3HgtkahqHjAKJymsASar6ioRGSsiA9zDegPrRGQ90BB42j03HXgKJxktAsa62wDuBt4ANgAbga+8dQ3GVAbz1u3mmld+Jiu3gI/vOJcrO0f5OiTjZ8Qf1sqOj4/XxMREX4dhTJlSVSb+tIVxX66mbaO6vHlrPI1DbZoTU3ZEZLGqxhd3nN3ZbkwllJtfwJPTV/HhL9u4pH1DXhwSZ0vbGp+xf3nGVDL7D+dw9wdL+HnjXu7u3ZIHL2lDgK0TYnzIEokxlcimtEyGvZNIyr4jPD+oM9ecHVP8ScZ4mSUSYyqJnzbs4a73FxMcGMCHw7sR3zzc1yEZA1giMaZS+OCXrTwxbRUtI2vx5i3n0CS8pq9DMuYYSyTGVGB5+QU8PXMNE3/awoVtInlpaBfqhAT7OixjfscSiTEV1IGsXO75cCnz16cx7IJYHuvfjkDrVDcVkCUSYyqgbXsPM+ydRWzec4hnru7E0K5NfR2SMSdkicSYCubXzenc8V4iBQrvDuvKeS0jfB2SMSdlicSYCuSTxO08NmUFTcJq8uat5xAbUcvXIRlTLEskxlQA+QXKv2av5bX5m7jgjAhevv4sQmtap7qpHCyRGONjh7LzuG9SEnPX7OLG7k158soOBAf6eqkgY0rOEokxPpSy/wh/eieRdTsP8PcBHbjlvOa+DsmYU2aJxBgfWbJtHyPeXUx2bj4Tb+tKr9a2kqepnCyRGOMD05JS+Muny2lUN4SPhnejVcM6vg7JmNPm1YZYEeknIutEZIOIPFLE/qYiMk9ElorIchHp726/QUSSPB4FIhLn7vvOLfPovgbevAZjylJBgfLvOeu5b1IScU3qMXXk+ZZETKXntRqJiAQCLwMXA8nAIhGZrqqrPQ57HGflxPEi0h6YCTRX1Q+AD9xyOgFTVTXJ47wbVNVWqjKVypGcfB78ZBlfrtjBoLNjePqqTlQLsk51U/l5s2mrK7BBVTcBiMgkIAHwTCQK1HWfhwKpRZQzFJjkxTiN8bpdB7IY/m4iK1IyeKx/W4b3aGFrqpsqw5uJJBrY7vE6GehW6JgxwNcicg9QC7ioiHIG4yQgTxNFJB/4DBin/rBesKm0VqZkMOydRRzMymPCTfFc3L6hr0Mypkz5ul49FHhbVWOA/sB7InIsJhHpBhxW1ZUe59ygqp2AHu7jpqIKFpERIpIoIolpaWneuwJjTuKrFTu49tWfCQoI4LO7zrMkYqokbyaSFKCJx+sYd5unYcBkAFVdAIQAnhMLDQE+8jxBVVPcnweBD3Ga0P5AVSeoaryqxkdG2rBKU75Ulf99+xt3fbCEdo3rMnXk+bRrXLf4E42phLyZSBYBrUQkVkSq4SSF6YWO2Qb0BRCRdjiJJM19HQBch0f/iIgEiUiE+zwYuAJYiTEVSFZuPqMnL+O5r9czMC6Kj4Z3J7JOdV+HZYzXeK2PRFXzRGQUMBsIBN5S1VUiMhZIVNXpwJ+B10XkAZyO91s9+jt6AtuPdta7qgOz3SQSCMwFXvfWNRhzqtIOZnPHe4ks2bafBy9pzcgLz7BOdVPliT/0U8fHx2tioo0WNt61ducBhr2dyN5D2fz7ujj6d2rs65CMKRURWayq8cUdZ3e2G1MGvlmzi3s/WkrtkCAm33EuZ8bU83VIxpQbSyTGlIKq8sYPm/nHV2voGBXK6zfH0yg0xNdhGVOuLJEYc5py8gr429SVfJy4nf6dGvH8oDhqVAv0dVjGlDtLJMachn2Hcrjz/cX8sjmde/qcwQMXtSYgwDrVjX+yRGLMKdqwO5Nh7yxiR0YWLw6OY2CXaF+HZIxPWSIx5hR8vz6NkR8uoXpQAB8N787ZzcJ8HZIxPmeJxJgSeufnLYydsZpWDWrzxi3xxITV9HVIxlQIJUokIvI58CbwlaoWeDckYyqWvPwC/v7Fat5buJW+bRvwn6FdqF3dvoMZc1RJp0h5Bbge+E1EnhWRNl6MyZgKI+NILre9vYj3Fm7ljp4tmHBzvCURYwop0f8IVZ0LzBWRUJwZe+eKyHac6UneV9VcL8ZojE9s2XOI299ZxPb0w/zrmjO57pwmxZ9kjB8q8VcrEakP3IgzbftSnBUMLwBuAXp7IzhjfGXBxr3c9cFiBHhvWDe6t6jv65CMqbBK2kcyBWgDvAdcqao73F0fi4hNYmWqlEm/buPxqStpHlGLN2+Jp1n9Wr4OyZgKraQ1kpdUdV5RO0oyoZcxlUF+gfLMzDW88eNmerSK4OUbzqJuSLCvwzKmwitpZ3t7ETk2C52IhInI3V6KyZhydzArl+HvJvLGj5u59bzmTLz1HEsixpRQSRPJcFXdf/SFqu4DhnsnJGPK1/b0w1w7fgHz16fx1MCOjBnQgaBAX69CbUzlUdKmrUARkaOLTolIIFDNe2EZ4125+QX8uGEP05am8PXqXQQFCO/c1pULWkUUf7Ix5ndKmkhm4XSsv+a+vsPddlIi0g/4D85qhm+o6rOF9jcF3gHqucc8oqozRaQ5sAZY5x66UFXvdM85G3gbqAHMBO5Tf1idy5RaQYGyZNs+piWl8uWKHaQfyqFuSBAJcVGM6NmS2AjrVDfmdJQ0kTyMkzzucl/PAd442QlureVl4GIgGVgkItNVdbXHYY8Dk1V1vIi0x0kMzd19G1U1roiix+M0q/3iHt8P+KqE12H80LqdB5mWlMK0pFRS9h+helAAF7VvyMC4aHq2jqB6kE39bkxplPSGxAKcD/Dxp1B2V2DD0TXXRWQSkAB4JhIF6rrPQ4HUkxUoIo2Buqq60H39LjAQSySmkOR9h/li2Q6mJaWwdudBAgOEC86I4M+XtOaSDo3s7nRjylBJ7yNpBTwDtAeOLf+mqi1Oclo0sN3jdTLQrdAxY4CvReQeoBZwkce+WBFZChwAHlfVH9wykwuVWeQc3iIyAhgB0LRp05OEaaqK9EM5fLliB9OTUli0ZR8AZzWtx98HdKB/p8ZE1qnu4wiNqZpK+rVsIvAk8AJwIXAbJR/xdTJDgbdV9XkRORd4T0Q6AjuApqq61+0TmSoiHU6lYFWdAEwAiI+Ptz6UKupQdh5z1+xiWlIq369PI69AadWgNn+5tA1XnhlF0/o2Q68x3lbSRFJDVb9xR25tBcaIyGLgiZOckwJ4Tk4U427zNAynjwNVXSAiIUCEqu4Gst3ti0VkI9DaPT+mmDJNFZebX8APv6UxdWkqc1bv4khuPlGhIQzrEUtC52jaNa6DiK1WaEx5KWkiyRaRAJzZf0fhfHjXLuacRUArEYl1jx+CM4Owp21AX+BtEWmH02yWJiKRQLqq5otIC6AVsElV00XkgIh0x+lsvxn4bwmvwVRiBQVK4tZ9TEtKYeaKHew7nEu9msFcdVY0CZ2jOKd5uC11a4yPlDSR3AfUBO4FnsJp3rrlZCeoap6bdGbjDO19S1VXichYIFFVpwN/Bl4XkQdwOt5vVVUVkZ7AWBHJBQqAO1U13S36bo4P//0K62ivslSVtTsPMi0plS+WOSOuQoIDuKR9IxLioujRKpJqQXbjoDG+JsXdguEO4/2nqj5YPiGVvfj4eE1MtLklK4vt6YeZviyVaUkprN+VSWCA0LNVBAlx0VzcviG1bMSVMeVCRBaXZD7FYv9Hus1LF5RNWMYUbW9mNl+u2MG0pFQWb3VGXMU3C+OpgR3p37ER9WvbiCtjKqqSfrVbKiLTgU+AQ0c3qurnXonK+IXM7DzmrN7JtKRUfvhtD/kFSttGdXionzPiqkm4jbgypjIoaSIJAfYCfTy2KWCJxJySnLwC5q9PY1pSCnPX7CIrt4DoejUY0bMFCXFRtG1Ut/hCjDEVSknvbL/N24GYqqugQPl1SzrTklKZuWIHGUdyCa9VjUFnNyEhLoqzmobZiCtjKrGS3tk+EacG8juqenuZR2SqBFVl9Y4DTE9KZfqyVHZkZFGzWiCXtG9IQlw0F7SKINimajemSihp09YMj+chwFUUMy+W8U/b9h52JkhclsqG3ZkEBQi9WkfyaP92XNSuATWr2YgrY6qakjZtfeb5WkQ+An70SkSm0kk7mM2Xy1OZtiyVpduc9c+6xobz9FUd6d+xMWG1bOkaY6qy0/162ApoUJaBmMrlYFYuX6/axbRlqfy0wRlx1a5xXR789ZHtAAAf9ElEQVS5rC1Xdo4iul4NX4dojCknJe0jOcjv+0h24qxRYvxIdl4+361LY3pSKnPX7CI7r4Am4TW4q1dLBsRF0bphHV+HaIzxgZI2bdknhJ/KL1B+2byX6e6IqwNZedSvVY0h5zRhQFw0ZzWtZxMkGuPnSlojuQr4VlUz3Nf1gN6qOtWbwRnfUFVWpR5gWlIKXyzbwc4DWdSqFsilHRqR0CWa81vWJ8hGXBljXCXtI3lSVaccfaGq+0XkScASSRUzOXE7r87fyKa0QwQHCr3bNODxuHb0bduQGtVsSVpjzB+VNJEU9fXTxnFWMb9s2svDny3nzOhQnrm6E5d1bES9mjbiyhhzciVNBoki8m/gZff1SGCxd0IyvpCZncefP1lG0/CafDi8u82wa4wpsZI2dN8D5AAfA5OALJxkYqqIp79cTcr+Izw/qLMlEWPMKSlRIlHVQ6r6iKrGq+o5qvqYqh4q7jwR6Sci60Rkg4g8UsT+piIyT0SWishyEenvbr9YRBaLyAr3Zx+Pc75zy0xyH3Y/Syl9u3YXH/26nTt6tiS+ebivwzHGVDIlSiQiMscdqXX0dZiIzC7mnECcprDLgPbAUBFpX+iwx4HJqtoFZyneV9zte4ArVbUTzkqM7xU67wZVjXMfu0tyDaZo+w7l8PBnK2jbqA4PXNzK1+EYYyqhkjZtRajq/qMvVHUfxd/Z3hXYoKqbVDUHp0ksodAxChydNzwUd/4uVV2qqkfn8loF1BARW9mojKkqj09dyf7DOTx/XWeqB9moLGPMqStpIikQkaZHX4hIc4qYDbiQaGC7x+tkd5unMcCNIpIMzMTpiynsGmCJqmZ7bJvoNmv9TexuuNM2fVkqX67Ywf0XtaZDVKivwzHGVFIlTSR/BX4UkfdE5H1gPvBoGbz/UOBtVY0B+gPvicixmESkA/BP4A6Pc25wm7x6uI+biipYREaISKKIJKalpZVBqFXLzowsnpi2ii5N63FHzxa+DscYU4mVtLN9FhAPrAM+Av4MHCnmtBSgicfrGHebp2HAZPc9FuBMUR8BICIxwBTgZlXd6BFLivvzIPAhThNaUTFPcAcHxEdGRpbgKv2HqvLwZ8vJySvg39fF2V3qxphSKekUKX8C7sNJBklAd2ABv196t7BFQCsRicVJIEOA6wsdsw3oC7wtIu1wEkma27H/JfCIqv7kEUcQUE9V94hIMHAFMLck12CO+/DXbcxfn8bYhA7ERtTydTjGlL2CAkjfCCmL3ccS2LcFuo6AHqMhMNjXEVYpJb1h4D7gHGChql4oIm2Bf5zsBFXNE5FRwGwgEHhLVVeJyFggUVWn49RsXheRB3D6XG5VVXXPOwN4QkSecIu8BDgEzHaTSCBOEnn9VC7Y323de4inv1xDj1YR3Nitma/DMaZsHNjhkTQWQ2oSZGc4+4JrQVQX5/HdP2DtFzBwPDTq5NuYqxBRLa7PHERkkaqeIyJJQDdVzRaRVarawfshll58fLwmJib6Ogyfyy9QBr+2gHW7DvL1Az1pHGprhphKKCsDUpcer2mkLIGD7iDPgCBo2AGizz7+iGgNAe6IxDVfwIzRcCQdej5ktZNiiMhiVY0v7riS1kiS3eamqcAcEdkHbC1NgKb8vf7DJhK37uOFwZ0tiZjKIS8bdq78fW1j72/H94e3hOYXHE8ajTpC8En+bbe7EpqdD189ZLWTMlSiGsnvThDphXPPxyz3/pAKz2oksHbnAQb89yf6tmvAKzecZWuImIqnoMBJEp5JY+dKKMh19tdu6CaMs5yfUV2gRtjpv9+aGTDjAbd28he4YDQE2SSlnsq6RnKMqs4/vZCMr+TkFfDAx8uoWyOIcQM7WhIpLPsgFORBSD2w3035UIUDKW7TlEe/Rs5BZ3+1OhAVB+eOPF7bqBtVtn+fdldAs/Pgq4fhu2ecxDLwFWh8Ztm9h5+w2fn8wH++Wc+aHQd4/eZ46te2CQKOUYXFb8PsxyD3MFSrDXWjITTa/RnjPI4+rxsN1Wr6OurK6cg+J2mkLjmePDJ3OfsCgp2mpc6DjyeN+q0goByGpdcMh2tehw4D4Yv74fULoceD0OPPVjs5BZZIqrgl2/Yx/ruNDDo7hovbN/R1OBVHZhpMHwXrZ0GL3nDGRZCRAgeSnZ87V8KhIqZxqxHmJpWYohNO3SjrvM09AjtX/L62kb7x+P6I1tDiwt/3awT5+AtO28uh6bkw6xGY/yys/dJqJ6fglPtIKiN/7SM5nJPH5S/9SE5eAbPu70GdED//gDtq3SwniWQdgIv/Dl3vKPrbb142HEh1mmAyUiBj+/HnB1IgIxmy9hc6SZy2/NDoEyecWg3K59t2eSjIh7R1bk3DTRq7VjlNhQB1otw+DY9+jZAKPh3P2pkw4344vNepmfR40G9rJ17rIzGVxz+/WsvmPYf4cHg3SyIAOYdg9l9h8URo2Alung4NC09I7SGoOoTHOo8Tyc48nlSOJZxkp2azazX8NsdpNvMUEAx1Gx9PMoWbz0JjnJpPReuvUXWSqeew29SlkOuuKFE9FKK7wHn3Hu8Urxvl25hPR9v+0LS7Wzv5p0ftpLOvI6uwrEZSRf3wWxo3vfkrt58fyxNXnuTD0l+kLIbPhkP6JjjvHujzePk0p6g6/QPHEk0RCefAjuMjk44KrunRX3OChFO9tndjP5x+vHnqaI3jkDtvXWA1aHTm8ZpG9NnOUNyqUtM6yrN2csFoZ3SXH9VOSlojsURSBWUcyeXSF76nVvVAvry3ByHBfjw9fH4e/PiC0+5duxFcNR5ie/o6qt8rKHD6Y/7QfJZ8POFk7uIPE26HhEJokxMPEKgbVfJkmXMYdi73qG0shn2b3Z0CkW2ON01Fnw0NO/rPB+rhdGdAxrKPoEEHp3YSFefrqMqFJRIP/pZIRn+cxLRlqXx+13l0blKv+BOqqvTNMOUO2P4LdLwWLn8ealTS30deDhzccYL+GjfhHEn/43m1GhTdX1O7IezdcLymsWs1aL5zTt0Yj5rGWdA4DkLq/rFsf7PuK2dk16E05474ng9V+WRqfSR+atbKHXy+NIX7+rby3ySiCkkfOPcHSCBc/QacOcjXUZVOUDUIa+Y8TiTncBHNZ27CSVsPG+dBTubvzwkJdRJGj9EQ5XaK12nk3WuprNpc5vadPArf/5/T7DXwZaeW5uesRlKFpB3M5tIXvye6Xg0+v/s8gv1xevjD6fDFvc6cSs0ugKtehXpNij/PH6g681QdSHFqN2GxEN6i4nXqVwbrZjl9J5m73drJX3w/hNkLrEbiZ1SVRz9fTmZ2Hv++rrN/JpENc2HqSKdj9OKxcO6o45P1GSdh1KjnPBpWivlWK642/aDpApj1mFs7cUd2+WntxA8/baqmTxYnM3fNbh66tA2tGtbxdTjlK/eI04z1/jXOh+Twb+H8+yyJGO+qEeYM3rh+sjMy7/W+8M1Tzv1HfsYSSRWQvO8wY79YTbfYcG4//yT3PFRFO5bBhN7wy6vQ7S4Y8Z3djWzKV+tL4e4F0HkI/PCc8+8xZYmvoypXlkgquYIC5cFPlqGqPDeoMwEBftLeXZAPP77ofAvMyoCbpsBlz558CnFjvKVGmNO0df0nTu3kjYvgm7F+UzvxaiIRkX4isk5ENojII0Xsbyoi80RkqYgsF5H+Hvsedc9bJyKXlrRMf/P2z1tYuCmdJ65sT5NwP5lQcP82eGcAzH3SGUlz18/Q8mSrPhtTTlpfAncvhM5D4Yfn4bVeflE78VoiEZFA4GXgMqA9MFRECt9i/TgwWVW74Kzp/op7bnv3dQegH/CKiASWsEy/sWF3Jv+ctZa+bRtwXbyfjExaPhnGn+80aQ0cD9e968zgakxFUaOeMyz4+k+c2vIbF8Hcv1fp2ok3ayRdgQ2qusldAGsSkFDoGAWO3ukUCrjrZZIATFLVbFXdDGxwyytJmX4hN7+A0ZOTqFktkGeu6VT11xg5sg8+vR0+Hw4N2sNdP0Lc9TZ01VRcrS9x+k7ihsKP/4bXejo3f1ZB3kwk0cB2j9fJ7jZPY4AbRSQZmAncU8y5JSkTABEZISKJIpKYlpZ2utdQYb0ybyPLkzN4+qpONKgT4utwvGvz904tZPU06PM3uG0mhDX3dVTGFK9GPUh4GW74zFlA7Y2LYO4YyM3ydWRlyted7UOBt1U1BugPvCciZRKTqk5Q1XhVjY+MjCyLIiuMFckZ/Pfb30iIi6J/p8a+Dsd78rKd2XrfGeB0og+bAz0ftGG9pvJpdZFbO7nemfttQi9Irjq1E28mkhTAs+E+xt3maRgwGUBVFwAhQMRJzi1JmVVaVm4+D0xOon7taowd0NHX4XjPrlXweh9Y8D+Ivx3u+N6ZvsOYyiok9Pe1kzcvgjlPVonaiTcTySKglYjEikg1nM7z6YWO2Qb0BRCRdjiJJM09boiIVBeRWKAV8GsJyyw7W350vjVUoGlknpu9jg27M/nXtZ0JrVkF1xgpKIAFr8CEC50Zb6+fDFf8G6rV8nVkxpSNo7WTLjfCTy86fSeVvHbitSlSVDVPREYBs4FA4C1VXSUiY4FEVZ0O/Bl4XUQewOl4v1Wdyb9WichkYDWQB4xUdaYmLapMb10D3z4N236GyLZOlfTMwT6d0G7hpr28+dNmbuzelF6tq1ZzHeBMMjj1Ltg8H9r0hytfgtpV8DqNCQmFAf+F9gkw/V6ndnLevdD7UQiufH2eNmnjyWRlwKqpzkyy239xZpI94yInqbS5rFwnacvMzqPfi98TGCB8dV8PalarYtOkrZriTNGdnwP9noGzbrERWcY/ZGXA14/Dkned9ewHjoeYYudJLBe2HomHMpn9d88GJ6EsmwQHU507WTsNgrgbnCU4vfyh98hny5mcuJ3Jd5xLfPMqdN9EVoYzT9ayj5zpzK9+Heq39HVUxpS/Dd84tZODqc4qnr0f83ntxBKJhzKdRr4gHzbNg6QPYc0MyM92Vk3rcgN0us4rTTHfrNnFsHcSuat3Sx7u17bMy/eZrT/D53c4CzP1fMgZkRVYBft9jCmprANu7eQdp3aS8Ao0Ocdn4Vgi8eC19UiO7IOVnzs1lZTFEBAErS51mr5aX1omH4rph3K45IXviahdjWmjzqd6UBUY+pqXA9894wyDDGsOV0+AJl19HZUxFYdn7eTcUXDhYz6ZR84SiYdyWdhq91onoSz/2BltVDMCzrzOafpqdHrDdFWVkR8uYc7qXUwbeQHto6rAcqdp6+HzPzlTnHS5yekPqe5n094bUxJZB2DO32Dx21C/ldN3Us61E0skHsp1hcT8PNj4jZNU1s6EglxodKYz1K/jtVCrfomLmpaUwn2TkvjLpW0YeeEZXgy6HKjCojfg678536wG/BfaXeHrqIyp+DZ+69RODqTAuSPhwr+WW+3EEokHny21ezgdVnwKSe8738ADgp3RXnE3OKO/Ak888mpnRhaXvDCfMxrUZvId5xJUmVc8PLgLpo2EDXOc60542dYFN+ZUZB2AOU/A4olu7eSVcmkOtkTioUKs2b5zpdNBv/xjOLwHajWAzoMh7kZo8PsOdFXllomLWLQ5nZn39SA2ohLfjLdmhrOGes4huGQcnPMnG9ZrzOnaOA+m3wMZyU7tpM/jXq2dWCLxUCESyVH5ufDb105SWT8LCvIg6ixn1FfHa6BGGO8v3MrjU1fyVEIHbjq3ua8jPj3ZmTD7UWdsfOPOzrDeyDa+jsqYyi/7oFM7SXwL6p/hjOxq2s0rb2WJxEOFSiSeMtNgxSdOf8qulRBYnUMtLuX+tR3IbtaTd4adWzmnh9++yJnufd8WuOAB527doGq+jsqYqmXTdzDtHsjY7rXaiSUSDxU2kRylCjuXU7D0fQ4t+og6epD8Wo0I7DLU6U+JaOXrCEsmPw++/z/nUTcarn4Nmp3n66iMqbr+UDt5GZp2L7PiS5pIKnEPbhUiAo0781rNOzn7yP/49ZwXCYyOg59egv/FwxsXO0MAszJ8HemJ7d0Ib10K8591hj3f9aMlEWO8rXoduOIFuHmac3/WW/1g1mOQc7hcw7AaSQWxZscBBvzvRy5q15BXbjjLadI6uNNZWjbpA0hbC0E1oN2Vzg2Psb0goAJ8D1B1+kFmPercgHnFC9Dxal9HZYz/yT7oLJq16A0Ib+mM7Cpl7cSatjxU9ESSnZdPwv9+Yk9mDrPv70H92oUmg1SF1CWw9ANY+alTM6kb4yzhGXc9hLfwTeCH9jgjSNbNdBLbwPEQWuSClcaY8rJpPkwfBfu3Q/e7nb6TajVPqyhLJB4qeiL516y1vPLdRt64OZ6L2jc8+cG5WbDuS2fU18ZvQQug6XnOqK/2CeV3l/j6r517Q7Iy4KInodtdFaOGZIxxRk3OfdJpLRg+77Rn17BE4qEiJ5LFW/cx6NWfufbsGP51bedTO/lAqjMbcdIHsHcDBNdykknc9dDsfO98sOccdiaVS3zTmazymtehYYeyfx9jTOnt3w71mhR/3AlUiEQiIv2A/+AsQvWGqj5baP8LwIXuy5pAA1WtJyIXAi94HNoWGKKqU0XkbaAXcLTn+VZVTTpZHBU1kRzOyaP/f34gN1+ZdX8P6oSc5iSPqpC8CJa+76zrkX0A6jVzEkrnoRDWrGwCTlkCn4+Avb85E8n1+ZvPp7k2xniPzxOJiAQC64GLgWScZXKHqurqExx/D9BFVW8vtD0c2ADEqOphN5HMUNVPSxpLRU0kf5u6kvcWbuWj4d05t2XJ5+A6qZzDsHaGU0vZNB9QaN7Dmeur3ZWnt2RtQb4zU+93zzh35F/1KrToVTbxGmMqrJImEm8us9cV2KCqm9yAJgEJOMvnFmUo8GQR268FvlLV8h3P5mXfr0/jvYVbGXZBbNklEXA61c68znns33686WvKHfDlg9BhoHNvStPuJZuqZN8WZ82Q7Quhw9XO+uk1wsouXmNMpefN3tFoYLvH62R32x+ISDMgFvi2iN1DgI8KbXtaRJaLyAsiUn7r3ZaRjMO5PPTpcs5oUJu/XOrFaUPqNYFef4F7l8JtXzn9Jys/h4n94L9nOTcOZiQXfa6q06E//gLYvdqZ4uTatyyJGGP+oKIMsxkCfKqq+Z4bRaQx0AmY7bH5UZw+k3OAcODhogoUkREikigiiWlpad6J+jSN+WIVaZnZ/Pu6zoQEl8NCVSLOzYEDX4YH1zvDdOtGw7fj4IWO8O5AZ5bi3CPO8YfT4ZNbYOpd0PhMuOsnp4ZTGadrMcZ4nTebtlIAz+ECMe62ogwBRhax/TpgiqrmHt2gqjvcp9kiMhF4sKgCVXUCMAGcPpJTC917vlqxgylLU7ivbyvOjKlX/gFUr+10wsddD+mb3aavD+GzYVA9FNpf6azOdmgPXPR3Z+3ogCqwKqMxxmu8mUgWAa1EJBYngQwBri98kIi0BcKABUWUMRSnBuJ5fGNV3SHObIYDgZVlHbi37D6YxWNTVtApOpRRfSrAQlXhsXDho9DrYdj6o3PD44rPoF5TuP5jZ9ZeY4wphtcSiarmicgonGapQOAtVV0lImOBRFWd7h46BJikhYaPiUhznBrN/EJFfyAikYAAScCd3rqGsqSqPPb5Cg7l5PPv6zoTXJEWqgoIgNiezmPAf52pTqwZyxhTQt6skaCqM4GZhbY9Uej1mBOcu4UiOudVtU/ZRVh+PklMZu6a3Tx+eTtaNazAa5TbdO/GmFNUgb4WV13b0w8zdsZqusWGc/v5sb4OxxhjypQlEi8rKFAe/GQZAM8N6kxAgDUZGWOqFkskXjbx5y38sjmdJ65oT5Pw05uB0xhjKjJLJF70266D/HPWWvq2bcCg+Bhfh2OMMV5hicRLcvMLGD15GbWqBfLMNZ0q59rrxhhTAl4dteXPXp63gRUpGbxyw1k0qGMz5Bpjqi6rkXjB8uT9/PfbDQyMi6J/p8a+DscYY7zKEkkZy8rNZ/TkZUTWrs7fB5zeqmTGGFOZWNNWGfu/2evYsDuTd2/vSmjN01yoyhhjKhGrkZShBRv38tZPm7mpezN6to70dTjGGFMuLJGUkYNZuTz4yTKahdfk0f5tfR2OMcaUG2vaKiNPzVjNjowjfHLnudSsZr9WY4z/sBpJGZi7eheTE5O5o1dLzm4W7utwjDGmXFkiKaX0Qzk88vkK2jaqw/0XtfJ1OMYYU+6sDaYUVJW/TllBxpEc3hvWlepBtpKgMcb/WI2kFKYlpfLVyp08cHFr2jWu6+twjDHGJ7yaSESkn4isE5ENIvJIEftfEJEk97FeRPZ77Mv32DfdY3usiPzilvmxiPhkJaYdGUd4YtpKzm4Wxh09W/oiBGOMqRC8lkhEJBB4GbgMaA8MFZH2nseo6gOqGqeqccB/gc89dh85uk9VB3hs/yfwgqqeAewDhnnrGk5EVXno0+Xk5ivPD+pMoK0xYozxY96skXQFNqjqJlXNASYBCSc5fijw0ckKFGcK3T7Ap+6md4CBZRDrKXl/4VZ++G0Pj13ejuYRtcr77Y0xpkLxZiKJBrZ7vE6miDXYAUSkGRALfOuxOUREEkVkoYgcTRb1gf2qmldcmd6yec8h/jFzLT1aRXBjt6bl+dbGGFMhVZRRW0OAT1U132NbM1VNEZEWwLcisgLIKGmBIjICGAHQtGnZfODnFyh/npxEcKDwr2vPtDVGjDEG79ZIUoAmHq9j3G1FGUKhZi1VTXF/bgK+A7oAe4F6InI0AZ6wTFWdoKrxqhofGVk281699v1Glmzbz9iEjjQOrVEmZRpjTGXnzUSyCGjljrKqhpMsphc+SETaAmHAAo9tYSJS3X0eAZwPrFZVBeYB17qH3gJM8+I1HLM69QAvzFlP/06NSIiLKo+3NMaYSsFricTtxxgFzAbWAJNVdZWIjBURz1FYQ4BJbpI4qh2QKCLLcBLHs6q62t33MDBaRDbg9Jm86a1rOCo7L5/Rk5MIrVGNcQNt2VxjjPHk1T4SVZ0JzCy07YlCr8cUcd7PQKcTlLkJZ0RYuXlx7m+s3XmQN2+JJ7yWT25bMcaYCsvubC/G4q3pvDZ/I4Pjm9C3XUNfh2OMMRWOJZKTOJSdx+jJy2gcWoPHr2jn63CMMaZCqijDfyukZ75aw7b0w3w0vDt1QmzZXGOMKYrVSE6iWXgt7uzVku4t6vs6FGOMqbCsRnISw3u28HUIxhhT4VmNxBhjTKlYIjHGGFMqlkiMMcaUiiUSY4wxpWKJxBhjTKlYIjHGGFMqlkiMMcaUiiUSY4wxpSK/n729ahKRNGDraZ4eAewpw3AqA7tm/2DXXPWV9nqbqWqxKwP6RSIpDRFJVNV4X8dRnuya/YNdc9VXXtdrTVvGGGNKxRKJMcaYUrFEUrwJvg7AB+ya/YNdc9VXLtdrfSTGGGNKxWokxhhjSsUSyUmISD8RWSciG0TkEV/H420i8paI7BaRlb6OpTyISBMRmSciq0VklYjc5+uYvE1EQkTkVxFZ5l7z330dU3kRkUARWSoiM3wdS3kQkS0iskJEkkQk0avvZU1bRRORQGA9cDGQDCwChqrqap8G5kUi0hPIBN5V1Y6+jsfbRKQx0FhVl4hIHWAxMLCK/40FqKWqmSISDPwI3KeqC30cmteJyGggHqirqlf4Oh5vE5EtQLyqev2+GauRnFhXYIOqblLVHGASkODjmLxKVb8H0n0dR3lR1R2qusR9fhBYA0T7NirvUkem+zLYfVT5b5MiEgNcDrzh61iqIkskJxYNbPd4nUwV/5DxZyLSHOgC/OLbSLzPbeJJAnYDc1S1yl8z8CLwEFDg60DKkQJfi8hiERnhzTeyRGL8nojUBj4D7lfVA76Ox9tUNV9V44AYoKuIVOlmTBG5Atitqot9HUs5u0BVzwIuA0a6TddeYYnkxFKAJh6vY9xtpgpx+wk+Az5Q1c99HU95UtX9wDygn69j8bLzgQFun8EkoI+IvO/bkLxPVVPcn7uBKTjN9V5hieTEFgGtRCRWRKoBQ4DpPo7JlCG34/lNYI2q/tvX8ZQHEYkUkXru8xo4g0nW+jYq71LVR1U1RlWb4/w//lZVb/RxWF4lIrXcASSISC3gEsBrozEtkZyAquYBo4DZOJ2wk1V1lW+j8i4R+QhYALQRkWQRGebrmLzsfOAmnG+oSe6jv6+D8rLGwDwRWY7zZWmOqvrFcFg/0xD4UUSWAb8CX6rqLG+9mQ3/NcYYUypWIzHGGFMqlkiMMcaUiiUSY4wxpWKJxBhjTKlYIjHGGFMqlkiMqYBEpLe/zFJrKj9LJMYYY0rFEokxpSAiN7rreySJyGvuhIiZIvKCu97HNyIS6R4bJyILRWS5iEwRkTB3+xkiMtddI2SJiLR0i68tIp+KyFoR+cC9Ex8RedZdQ2W5iDzno0s35hhLJMacJhFpBwwGzncnQcwHbgBqAYmq2gGYDzzpnvIu8LCqngms8Nj+AfCyqnYGzgN2uNu7APcD7YEWwPkiUh+4CujgljPOu1dpTPEskRhz+voCZwOL3GnZ++J84BcAH7vHvA9cICKhQD1Vne9ufwfo6c6HFK2qUwBUNUtVD7vH/KqqyapaACQBzYEMIAt4U0SuBo4ea4zPWCIx5vQJ8I6qxrmPNqo6pojjTnceomyP5/lAkDsHXFfgU+AKwGvzJxlTUpZIjDl93wDXikgDABEJF5FmOP+vrnWPuR74UVUzgH0i0sPdfhMw312ZMVlEBrplVBeRmid6Q3ftlFBVnQk8AHT2xoUZcyqCfB2AMZWVqq4WkcdxVqELAHKBkcAhnAWjHsdZhXCwe8otwKtuotgE3OZuvwl4TUTGumUMOsnb1gGmiUgITo1odBlfljGnzGb/NaaMicj/t2PHNADAMBDECJQ/0u4JiJPaxUaQ7fS5M3N+3wGveG0BkFgkACQWCQCJkACQCAkAiZAAkAgJAImQAJAsHCyRLsfXkDAAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_graphs(history, 'accuracy')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_graphs(history, 'loss')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" 391/Unknown - 125s 320ms/step - loss: 0.5484 - accuracy: 0.7808[0.5484032468570162, 0.78084]\\n\"\n ]\n }\n ],\n \"source\": [\n \"res = model.evaluate(test_dataset)\\n\",\n \"print(res)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "032-Text/meta.tsv", "content": 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apting\nferry\ndesolate\njessie\ninflicted\nrocker\nprojection\nirs\ncambodia\nenthralled\nensuing\nleia\no'toole\ntransferred\nexposes\ncompeting\nyourselves\nsentiments\nkisses\nstray\nturgid\ndeclares\nnuns\nmercilessly\nit'd\nexceedingly\nted's\ninsecure\nben's\ntanks\nkusturica\nspaces\nspliced\nsheila\ncrowds\nbalcony\nmenu\nlamas\ndiver\nsecluded\nintegral\nredeemed\nhalt\ndecapitated\nstealth\nbudgeted\nvoters\noverweight\npraying\nstevenson\ncleveland\nstakes\nmattei\ncharity\nstalk\nolympia\nolympic\naspirations\ndecoration\nslack\nbullying\nbum\nmo\ncapitalize\njameson\nskimpy\nwicker\nstarving\nfrenchman\nfrye\nate\nmonastery\nwb\nhayden\nbanana\ngrandparents\nvacuous\nwilly\ndarkman\nneutral\nrumors\nsomber\naunts\namateurs\nradar\nounce\nbagdad\nstud\ncloseups\ninsisted\njed\ngeeky\n64\naims\ncomplains\newan\nexhausted\nday's\nweaves\ngladly\nmisogynistic\nsoles\nmichel\nuniquely\ninterminable\naristocrat\npaul's\neverybody's\navant\nanswering\nsmallest\ncontacts\nenlightenment\nmurphy's\nemploys\nunforgivable\npunchline\nculminating\ntalentless\ngrabbing\nsoulless\nunfairly\ngrail\nretrospect\nedged\n" }, { "path": "032-Text/nmt_with_attention.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"J0Qjg6vuaHNt\"\n },\n \"source\": [\n \"# TensorFlow2.0教程-注意力神经机器翻译\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"CiwtNgENbx2g\"\n },\n \"source\": [\n \"训练序列到序列（seq2seq）模型西班牙语到英语翻译。这是一个高级示例，假定序列模型的序列知识。\\n\",\n \"\\n\",\n \"在本教程中能够输入西班牙语句子，例如“¿todavia estan en casa？” ，并返回英文翻译：“你还在家吗？”\\n\",\n \"\\n\",\n \"对于玩具示例而言，翻译质量是合理的，但生成的关注图可能更有趣。这表明输入句子的哪些部分在翻译时具有模型的注意力：\\n\",\n \"\\n\",\n \"\\\"spanish-english\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 415\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 10605,\n \"status\": \"ok\",\n \"timestamp\": 1564762374047,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"tnxXKDjq3jEL\",\n \"outputId\": \"d4c18730-6b30-40b0-bd7d-1cbc184f52a3\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Requirement already satisfied: tensorflow-gpu==2.0.0-beta1 in /usr/local/lib/python3.6/dist-packages (2.0.0b1)\\n\",\n \"Requirement already satisfied: tf-estimator-nightly<1.14.0.dev2019060502,>=1.14.0.dev2019060501 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.14.0.dev2019060501)\\n\",\n \"Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.11.2)\\n\",\n \"Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.15.0)\\n\",\n \"Requirement already satisfied: tb-nightly<1.14.0a20190604,>=1.14.0a20190603 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.14.0a20190603)\\n\",\n \"Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.1.0)\\n\",\n \"Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.33.4)\\n\",\n \"Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.7.1)\\n\",\n \"Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.16.4)\\n\",\n \"Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (3.7.1)\\n\",\n \"Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.0.8)\\n\",\n \"Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.8.0)\\n\",\n \"Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.2.2)\\n\",\n \"Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.1.0)\\n\",\n \"Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.1.7)\\n\",\n \"Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.12.0)\\n\",\n \"Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow-gpu==2.0.0-beta1) (0.15.5)\\n\",\n \"Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow-gpu==2.0.0-beta1) (41.0.1)\\n\",\n \"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow-gpu==2.0.0-beta1) (3.1.1)\\n\",\n \"Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==2.0.0-beta1) (2.8.0)\\n\",\n \"2.0.0-beta1\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function, unicode_literals\\n\",\n \"\\n\",\n \"!pip install tensorflow-gpu==2.0.0-beta1\\n\",\n \"import tensorflow as tf\\n\",\n \"print(tf.__version__)\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib.ticker as ticker\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"\\n\",\n \"import unicodedata\\n\",\n \"import re\\n\",\n \"import numpy as np\\n\",\n \"import os\\n\",\n \"import io\\n\",\n \"import time\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"wfodePkj3jEa\"\n },\n \"source\": [\n \"## 下载并准备数据集\\n\",\n \"我们将使用http://www.manythings.org/anki/提供的语言数据集。此数据集包含以下格式的语言翻译对：\\n\",\n \"\\n\",\n \"May I borrow this book? ¿Puedo tomar prestado este libro?\\n\",\n \"\\n\",\n \"有多种语言可供选择，但我们将使用英语 - 西班牙语数据集。为方便起见，我们在Google Cloud上托管了此数据集的副本，但您也可以下载自己的副本。下载数据集后，以下是我们准备数据的步骤：\\n\",\n \"\\n\",\n \"- 为每个句子添加开始和结束标记。\\n\",\n \"- 删除特殊字符来清除句子。\\n\",\n \"- 创建一个单词索引和反向单词索引（从单词→id和id→单词映射的字典）。\\n\",\n \"- 将每个句子填充到最大长度。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"kRVATYOgJs1b\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Download the file\\n\",\n \"path_to_zip = tf.keras.utils.get_file(\\n\",\n \" 'spa-eng.zip', origin='http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip',\\n\",\n \" extract=True)\\n\",\n \"\\n\",\n \"path_to_file = os.path.dirname(path_to_zip)+\\\"/spa-eng/spa.txt\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"rd0jw-eC3jEh\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Converts the unicode file to ascii\\n\",\n \"def unicode_to_ascii(s):\\n\",\n \" return ''.join(c for c in unicodedata.normalize('NFD', s)\\n\",\n \" if unicodedata.category(c) != 'Mn')\\n\",\n \"\\n\",\n \"\\n\",\n \"def preprocess_sentence(w):\\n\",\n \" w = unicode_to_ascii(w.lower().strip())\\n\",\n \"\\n\",\n \" # creating a space between a word and the punctuation following it\\n\",\n \" # eg: \\\"he is a boy.\\\" => \\\"he is a boy .\\\"\\n\",\n \" # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation\\n\",\n \" w = re.sub(r\\\"([?.!,¿])\\\", r\\\" \\\\1 \\\", w)\\n\",\n \" w = re.sub(r'[\\\" \\\"]+', \\\" \\\", w)\\n\",\n \"\\n\",\n \" # replacing everything with space except (a-z, A-Z, \\\".\\\", \\\"?\\\", \\\"!\\\", \\\",\\\")\\n\",\n \" w = re.sub(r\\\"[^a-zA-Z?.!,¿]+\\\", \\\" \\\", w)\\n\",\n \"\\n\",\n \" w = w.rstrip().strip()\\n\",\n \"\\n\",\n \" # adding a start and an end token to the sentence\\n\",\n \" # so that the model know when to start and stop predicting.\\n\",\n \" w = ' ' + w + ' '\\n\",\n \" return w\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 53\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 10574,\n \"status\": \"ok\",\n \"timestamp\": 1564762374049,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"opI2GzOt479E\",\n \"outputId\": \"9597fe66-d08c-42a2-a9cb-2d21d72cc240\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" may i borrow this book ? \\n\",\n \"b' \\\\xc2\\\\xbf puedo tomar prestado este libro ? '\\n\"\n ]\n }\n ],\n \"source\": [\n \"en_sentence = u\\\"May I borrow this book?\\\"\\n\",\n \"sp_sentence = u\\\"¿Puedo tomar prestado este libro?\\\"\\n\",\n \"print(preprocess_sentence(en_sentence))\\n\",\n \"print(preprocess_sentence(sp_sentence).encode('utf-8'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"OHn4Dct23jEm\"\n },\n \"outputs\": [],\n \"source\": [\n \"# 1. Remove the accents\\n\",\n \"# 2. Clean the sentences\\n\",\n \"# 3. Return word pairs in the format: [ENGLISH, SPANISH]\\n\",\n \"def create_dataset(path, num_examples):\\n\",\n \" lines = io.open(path, encoding='UTF-8').read().strip().split('\\\\n')\\n\",\n \"\\n\",\n \" word_pairs = [[preprocess_sentence(w) for w in l.split('\\\\t')] for l in lines[:num_examples]]\\n\",\n \"\\n\",\n \" return zip(*word_pairs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 73\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 15504,\n \"status\": \"ok\",\n \"timestamp\": 1564762379003,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"cTbSbBz55QtF\",\n \"outputId\": \"b12a315a-a645-4cae-a1f5-1cd7715719e3\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" if you want to sound like a native speaker , you must be willing to practice saying the same sentence over and over in the same way that banjo players practice the same phrase over and over until they can play it correctly and at the desired tempo . \\n\",\n \" si quieres sonar como un hablante nativo , debes estar dispuesto a practicar diciendo la misma frase una y otra vez de la misma manera en que un musico de banjo practica el mismo fraseo una y otra vez hasta que lo puedan tocar correctamente y en el tiempo esperado . \\n\"\n ]\n }\n ],\n \"source\": [\n \"en, sp = create_dataset(path_to_file, None)\\n\",\n \"print(en[-1])\\n\",\n \"print(sp[-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"OmMZQpdO60dt\"\n },\n \"outputs\": [],\n \"source\": [\n \"def max_length(tensor):\\n\",\n \" return max(len(t) for t in tensor)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"bIOn8RCNDJXG\"\n },\n \"outputs\": [],\n \"source\": [\n \"def tokenize(lang):\\n\",\n \" lang_tokenizer = tf.keras.preprocessing.text.Tokenizer(\\n\",\n \" filters='')\\n\",\n \" lang_tokenizer.fit_on_texts(lang)\\n\",\n \"\\n\",\n \" tensor = lang_tokenizer.texts_to_sequences(lang)\\n\",\n \"\\n\",\n \" tensor = tf.keras.preprocessing.sequence.pad_sequences(tensor,\\n\",\n \" padding='post')\\n\",\n \"\\n\",\n \" return tensor, lang_tokenizer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"eAY9k49G3jE_\"\n },\n \"outputs\": [],\n \"source\": [\n \"def load_dataset(path, num_examples=None):\\n\",\n \" # creating cleaned input, output pairs\\n\",\n \" targ_lang, inp_lang = create_dataset(path, num_examples)\\n\",\n \"\\n\",\n \" input_tensor, inp_lang_tokenizer = tokenize(inp_lang)\\n\",\n \" target_tensor, targ_lang_tokenizer = tokenize(targ_lang)\\n\",\n \"\\n\",\n \" return input_tensor, target_tensor, inp_lang_tokenizer, targ_lang_tokenizer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"GOi42V79Ydlr\"\n },\n \"source\": [\n \"### 限制数据集的大小以更快地进行实验（可选）\\n\",\n \"对> 100,000个句子的完整数据集进行培训需要很长时间。为了更快地训练，我们可以将数据集的大小限制为30,000个句子（当然，翻译质量会随着数据的减少而降低）：\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"cnxC7q-j3jFD\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Try experimenting with the size of that dataset\\n\",\n \"num_examples = 30000\\n\",\n \"input_tensor, target_tensor, inp_lang, targ_lang = load_dataset(path_to_file, num_examples)\\n\",\n \"\\n\",\n \"# Calculate max_length of the target tensors\\n\",\n \"max_length_targ, max_length_inp = max_length(target_tensor), max_length(input_tensor)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 18029,\n \"status\": \"ok\",\n \"timestamp\": 1564762381576,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"4QILQkOs3jFG\",\n \"outputId\": \"a63f393f-6e7a-4650-8476-159d80fecd7b\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"24000 24000 6000 6000\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Creating training and validation sets using an 80-20 split\\n\",\n \"input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(input_tensor, target_tensor, test_size=0.2)\\n\",\n \"\\n\",\n \"# Show length\\n\",\n \"print(len(input_tensor_train), len(target_tensor_train), len(input_tensor_val), len(target_tensor_val))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"lJPmLZGMeD5q\"\n },\n \"outputs\": [],\n \"source\": [\n \"def convert(lang, tensor):\\n\",\n \" for t in tensor:\\n\",\n \" if t!=0:\\n\",\n \" print (\\\"%d ----> %s\\\" % (t, lang.index_word[t]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 359\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 18013,\n \"status\": \"ok\",\n \"timestamp\": 1564762381579,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"VXukARTDd7MT\",\n \"outputId\": \"e8406fbb-6ba6-4d75-cd5e-ca1d5948d0ff\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Input Language; index to word mapping\\n\",\n \"1 ----> \\n\",\n \"6 ----> ¿\\n\",\n \"52 ----> eres\\n\",\n \"3361 ----> aficionado\\n\",\n \"70 ----> al\\n\",\n \"911 ----> golf\\n\",\n \"5 ----> ?\\n\",\n \"2 ----> \\n\",\n \"\\n\",\n \"Target Language; index to word mapping\\n\",\n \"1 ----> \\n\",\n \"24 ----> are\\n\",\n \"6 ----> you\\n\",\n \"1089 ----> fond\\n\",\n \"59 ----> of\\n\",\n \"809 ----> golf\\n\",\n \"7 ----> ?\\n\",\n \"2 ----> \\n\"\n ]\n }\n ],\n \"source\": [\n \"print (\\\"Input Language; index to word mapping\\\")\\n\",\n \"convert(inp_lang, input_tensor_train[0])\\n\",\n \"print ()\\n\",\n \"print (\\\"Target Language; index to word mapping\\\")\\n\",\n \"convert(targ_lang, target_tensor_train[0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"rgCLkfv5uO3d\"\n },\n \"source\": [\n \"### 创建tf.data数据集\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"TqHsArVZ3jFS\"\n },\n \"outputs\": [],\n \"source\": [\n \"BUFFER_SIZE = len(input_tensor_train)\\n\",\n \"BATCH_SIZE = 64\\n\",\n \"steps_per_epoch = len(input_tensor_train)//BATCH_SIZE\\n\",\n \"embedding_dim = 256\\n\",\n \"units = 1024\\n\",\n \"vocab_inp_size = len(inp_lang.word_index)+1\\n\",\n \"vocab_tar_size = len(targ_lang.word_index)+1\\n\",\n \"\\n\",\n \"dataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)\\n\",\n \"dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 17993,\n \"status\": \"ok\",\n \"timestamp\": 1564762381580,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"qc6-NK1GtWQt\",\n \"outputId\": \"49776563-8a3c-4d28-a23f-2aaaa1bb59a0\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(TensorShape([64, 16]), TensorShape([64, 11]))\"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"example_input_batch, example_target_batch = next(iter(dataset))\\n\",\n \"example_input_batch.shape, example_target_batch.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"TNfHIF71ulLu\"\n },\n \"source\": [\n \"编写编码器和解码器模型\\n\",\n \"实现编码器 - 解码器模型，您可以在TensorFlow 神经机器翻译（seq2seq）教程中阅读。此示例使用更新的API集。这个笔记本实现了seq2seq教程中的注意方程式。下图显示了每个输入单词由注意机制分配权重，然后解码器使用该权重来预测句子中的下一个单词。下面的图片和公式是Luong的论文中关注机制的一个例子。\\n\",\n \"\\n\",\n \"\\\"attention\\n\",\n \"\\n\",\n \"The input is put through an encoder model which gives us the encoder output of shape *(batch_size, max_length, hidden_size)* and the encoder hidden state of shape *(batch_size, hidden_size)*.\\n\",\n \"\\n\",\n \"Here are the equations that are implemented:\\n\",\n \"\\n\",\n \"\\\"attention\\n\",\n \"\\\"attention\\n\",\n \"\\n\",\n \"本教程使用Bahdanau注意编码器。在编写简化表单之前，让我们决定表示法：\\n\",\n \"\\n\",\n \"- FC =完全连接（密集）层\\n\",\n \"- EO =编码器输出\\n\",\n \"- H =隐藏状态\\n\",\n \"- X =解码器的输入\\n\",\n \"和伪代码：\\n\",\n \"\\n\",\n \"- score = FC(tanh(FC(EO) + FC(H)))\\n\",\n \"- attention weights = softmax(score, axis = 1)。默认情况下，Softmax应用于最后一个轴，但是我们要在第一个轴上应用它，因为得分的形状是（batch_size，max_length，hidden_​​size）。Max_length是我们输入的长度。由于我们尝试为每个输入分配权重，因此应在该轴上应用softmax。\\n\",\n \"- context vector = sum(attention weights * EO, axis = 1)。选择轴为1的原因与上述相同。\\n\",\n \"- embedding output =解码器X的输入通过嵌入层。\\n\",\n \"- merged vector = concat(embedding output, context vector)\\n\",\n \"- 然后将该合并的矢量提供给GRU\\n\",\n \"每个步骤中所有向量的形状都已在代码中的注释中指定：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"nZ2rI24i3jFg\"\n },\n \"outputs\": [],\n \"source\": [\n \"class Encoder(tf.keras.Model):\\n\",\n \" def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):\\n\",\n \" super(Encoder, self).__init__()\\n\",\n \" self.batch_sz = batch_sz\\n\",\n \" self.enc_units = enc_units\\n\",\n \" self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\\n\",\n \" self.gru = tf.keras.layers.GRU(self.enc_units,\\n\",\n \" return_sequences=True,\\n\",\n \" return_state=True,\\n\",\n \" recurrent_initializer='glorot_uniform')\\n\",\n \"\\n\",\n \" def call(self, x, hidden):\\n\",\n \" x = self.embedding(x)\\n\",\n \" output, state = self.gru(x, initial_state = hidden)\\n\",\n \" return output, state\\n\",\n \"\\n\",\n \" def initialize_hidden_state(self):\\n\",\n \" return tf.zeros((self.batch_sz, self.enc_units))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 53\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 17975,\n \"status\": \"ok\",\n \"timestamp\": 1564762381581,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"60gSVh05Jl6l\",\n \"outputId\": \"4718a2ce-5217-4dfd-ea27-1e56fcbfb975\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Encoder output shape: (batch size, sequence length, units) (64, 16, 1024)\\n\",\n \"Encoder Hidden state shape: (batch size, units) (64, 1024)\\n\"\n ]\n }\n ],\n \"source\": [\n \"encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)\\n\",\n \"\\n\",\n \"# sample input\\n\",\n \"sample_hidden = encoder.initialize_hidden_state()\\n\",\n \"sample_output, sample_hidden = encoder(example_input_batch, sample_hidden)\\n\",\n \"print ('Encoder output shape: (batch size, sequence length, units) {}'.format(sample_output.shape))\\n\",\n \"print ('Encoder Hidden state shape: (batch size, units) {}'.format(sample_hidden.shape))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"umohpBN2OM94\"\n },\n \"outputs\": [],\n \"source\": [\n \"class BahdanauAttention(tf.keras.Model):\\n\",\n \" def __init__(self, units):\\n\",\n \" super(BahdanauAttention, self).__init__()\\n\",\n \" self.W1 = tf.keras.layers.Dense(units)\\n\",\n \" self.W2 = tf.keras.layers.Dense(units)\\n\",\n \" self.V = tf.keras.layers.Dense(1)\\n\",\n \"\\n\",\n \" def call(self, query, values):\\n\",\n \" # hidden shape == (batch_size, hidden size)\\n\",\n \" # hidden_with_time_axis shape == (batch_size, 1, hidden size)\\n\",\n \" # we are doing this to perform addition to calculate the score\\n\",\n \" hidden_with_time_axis = tf.expand_dims(query, 1)\\n\",\n \"\\n\",\n \" # score shape == (batch_size, max_length, 1)\\n\",\n \" # we get 1 at the last axis because we are applying score to self.V\\n\",\n \" # the shape of the tensor before applying self.V is (batch_size, max_length, units)\\n\",\n \" score = self.V(tf.nn.tanh(\\n\",\n \" self.W1(values) + self.W2(hidden_with_time_axis)))\\n\",\n \"\\n\",\n \" # attention_weights shape == (batch_size, max_length, 1)\\n\",\n \" attention_weights = tf.nn.softmax(score, axis=1)\\n\",\n \"\\n\",\n \" # context_vector shape after sum == (batch_size, hidden_size)\\n\",\n \" context_vector = attention_weights * values\\n\",\n \" context_vector = tf.reduce_sum(context_vector, axis=1)\\n\",\n \"\\n\",\n \" return context_vector, attention_weights\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 53\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 17957,\n \"status\": \"ok\",\n \"timestamp\": 1564762381582,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"k534zTHiDjQU\",\n \"outputId\": \"164cb62a-4f36-4d80-e6f5-f1c0f67f3b04\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Attention result shape: (batch size, units) (64, 1024)\\n\",\n \"Attention weights shape: (batch_size, sequence_length, 1) (64, 16, 1)\\n\"\n ]\n }\n ],\n \"source\": [\n \"attention_layer = BahdanauAttention(10)\\n\",\n \"attention_result, attention_weights = attention_layer(sample_hidden, sample_output)\\n\",\n \"\\n\",\n \"print(\\\"Attention result shape: (batch size, units) {}\\\".format(attention_result.shape))\\n\",\n \"print(\\\"Attention weights shape: (batch_size, sequence_length, 1) {}\\\".format(attention_weights.shape))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"yJ_B3mhW3jFk\"\n },\n \"outputs\": [],\n \"source\": [\n \"class Decoder(tf.keras.Model):\\n\",\n \" def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):\\n\",\n \" super(Decoder, self).__init__()\\n\",\n \" self.batch_sz = batch_sz\\n\",\n \" self.dec_units = dec_units\\n\",\n \" self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\\n\",\n \" self.gru = tf.keras.layers.GRU(self.dec_units,\\n\",\n \" return_sequences=True,\\n\",\n \" return_state=True,\\n\",\n \" recurrent_initializer='glorot_uniform')\\n\",\n \" self.fc = tf.keras.layers.Dense(vocab_size)\\n\",\n \"\\n\",\n \" # used for attention\\n\",\n \" self.attention = BahdanauAttention(self.dec_units)\\n\",\n \"\\n\",\n \" def call(self, x, hidden, enc_output):\\n\",\n \" # enc_output shape == (batch_size, max_length, hidden_size)\\n\",\n \" context_vector, attention_weights = self.attention(hidden, enc_output)\\n\",\n \"\\n\",\n \" # x shape after passing through embedding == (batch_size, 1, embedding_dim)\\n\",\n \" x = self.embedding(x)\\n\",\n \"\\n\",\n \" # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)\\n\",\n \" x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)\\n\",\n \"\\n\",\n \" # passing the concatenated vector to the GRU\\n\",\n \" output, state = self.gru(x)\\n\",\n \"\\n\",\n \" # output shape == (batch_size * 1, hidden_size)\\n\",\n \" output = tf.reshape(output, (-1, output.shape[2]))\\n\",\n \"\\n\",\n \" # output shape == (batch_size, vocab)\\n\",\n \" x = self.fc(output)\\n\",\n \"\\n\",\n \" return x, state, attention_weights\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 17938,\n \"status\": \"ok\",\n \"timestamp\": 1564762381582,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"P5UY8wko3jFp\",\n \"outputId\": \"9da1a49a-181d-48c9-f87a-b5f3719f6a7d\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Decoder output shape: (batch_size, vocab size) (64, 4935)\\n\"\n ]\n }\n ],\n \"source\": [\n \"decoder = Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE)\\n\",\n \"\\n\",\n \"sample_decoder_output, _, _ = decoder(tf.random.uniform((64, 1)),\\n\",\n \" sample_hidden, sample_output)\\n\",\n \"\\n\",\n \"print ('Decoder output shape: (batch_size, vocab size) {}'.format(sample_decoder_output.shape))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"_ch_71VbIRfK\"\n },\n \"source\": [\n \"## 定义优化器和损失函数\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"WmTHr5iV3jFr\"\n },\n \"outputs\": [],\n \"source\": [\n \"optimizer = tf.keras.optimizers.Adam()\\n\",\n \"loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\\n\",\n \" from_logits=True, reduction='none')\\n\",\n \"\\n\",\n \"def loss_function(real, pred):\\n\",\n \" mask = tf.math.logical_not(tf.math.equal(real, 0))\\n\",\n \" loss_ = loss_object(real, pred)\\n\",\n \"\\n\",\n \" mask = tf.cast(mask, dtype=loss_.dtype)\\n\",\n \" loss_ *= mask\\n\",\n \"\\n\",\n \" return tf.reduce_mean(loss_)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"DMVWzzsfNl4e\"\n },\n \"source\": [\n \"## 检查点（基于对象的保存）\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"Zj8bXQTgNwrF\"\n },\n \"outputs\": [],\n \"source\": [\n \"checkpoint_dir = './training_checkpoints'\\n\",\n \"checkpoint_prefix = os.path.join(checkpoint_dir, \\\"ckpt\\\")\\n\",\n \"checkpoint = tf.train.Checkpoint(optimizer=optimizer,\\n\",\n \" encoder=encoder,\\n\",\n \" decoder=decoder)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"hpObfY22IddU\"\n },\n \"source\": [\n \"## 训练\\n\",\n \"- 通过编码器传递输入，编码器返回编码器输出和编码器隐藏状态。\\n\",\n \"- 编码器输出，编码器隐藏状态和解码器输入（它是开始标记）被传递给解码器。\\n\",\n \"- 解码器返回预测和解码器隐藏状态。\\n\",\n \"- 然后将解码器隐藏状态传递回模型，并使用预测来计算损失。\\n\",\n \"- 使用教师强制决定解码器的下一个输入。\\n\",\n \"- 教师强制是将目标字作为下一个输入传递给解码器的技术。\\n\",\n \"- 最后一步是计算渐变并将其应用于优化器并反向传播。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"sC9ArXSsVfqn\"\n },\n \"outputs\": [],\n \"source\": [\n \"@tf.function\\n\",\n \"def train_step(inp, targ, enc_hidden):\\n\",\n \" loss = 0\\n\",\n \"\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" enc_output, enc_hidden = encoder(inp, enc_hidden)\\n\",\n \"\\n\",\n \" dec_hidden = enc_hidden\\n\",\n \"\\n\",\n \" dec_input = tf.expand_dims([targ_lang.word_index['']] * BATCH_SIZE, 1)\\n\",\n \"\\n\",\n \" # Teacher forcing - feeding the target as the next input\\n\",\n \" for t in range(1, targ.shape[1]):\\n\",\n \" # passing enc_output to the decoder\\n\",\n \" predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output)\\n\",\n \"\\n\",\n \" loss += loss_function(targ[:, t], predictions)\\n\",\n \"\\n\",\n \" # using teacher forcing\\n\",\n \" dec_input = tf.expand_dims(targ[:, t], 1)\\n\",\n \"\\n\",\n \" batch_loss = (loss / int(targ.shape[1]))\\n\",\n \"\\n\",\n \" variables = encoder.trainable_variables + decoder.trainable_variables\\n\",\n \"\\n\",\n \" gradients = tape.gradient(loss, variables)\\n\",\n \"\\n\",\n \" optimizer.apply_gradients(zip(gradients, variables))\\n\",\n \"\\n\",\n \" return batch_loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 1000\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 457194,\n \"status\": \"ok\",\n \"timestamp\": 1564762820867,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"ddefjBMa3jF0\",\n \"outputId\": \"f1966e2e-dd6e-4a83-b1ca-ae5d4b26d905\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1 Batch 0 Loss 4.6147\\n\",\n \"Epoch 1 Batch 100 Loss 2.1792\\n\",\n \"Epoch 1 Batch 200 Loss 1.8256\\n\",\n \"Epoch 1 Batch 300 Loss 1.7484\\n\",\n \"Epoch 1 Loss 2.0445\\n\",\n \"Time taken for 1 epoch 65.14264225959778 sec\\n\",\n \"\\n\",\n \"Epoch 2 Batch 0 Loss 1.7078\\n\",\n \"Epoch 2 Batch 100 Loss 1.5201\\n\",\n \"Epoch 2 Batch 200 Loss 1.3324\\n\",\n \"Epoch 2 Batch 300 Loss 1.2268\\n\",\n \"Epoch 2 Loss 1.4118\\n\",\n \"Time taken for 1 epoch 41.88753700256348 sec\\n\",\n \"\\n\",\n \"Epoch 3 Batch 0 Loss 1.3513\\n\",\n \"Epoch 3 Batch 100 Loss 1.0342\\n\",\n \"Epoch 3 Batch 200 Loss 0.9434\\n\",\n \"Epoch 3 Batch 300 Loss 0.8773\\n\",\n \"Epoch 3 Loss 1.0038\\n\",\n \"Time taken for 1 epoch 41.1470890045166 sec\\n\",\n \"\\n\",\n \"Epoch 4 Batch 0 Loss 0.9539\\n\",\n \"Epoch 4 Batch 100 Loss 0.6765\\n\",\n \"Epoch 4 Batch 200 Loss 0.6487\\n\",\n \"Epoch 4 Batch 300 Loss 0.5726\\n\",\n \"Epoch 4 Loss 0.6843\\n\",\n \"Time taken for 1 epoch 41.780235052108765 sec\\n\",\n \"\\n\",\n \"Epoch 5 Batch 0 Loss 0.6601\\n\",\n \"Epoch 5 Batch 100 Loss 0.4600\\n\",\n \"Epoch 5 Batch 200 Loss 0.4444\\n\",\n \"Epoch 5 Batch 300 Loss 0.3771\\n\",\n \"Epoch 5 Loss 0.4654\\n\",\n \"Time taken for 1 epoch 41.11035752296448 sec\\n\",\n \"\\n\",\n \"Epoch 6 Batch 0 Loss 0.4556\\n\",\n \"Epoch 6 Batch 100 Loss 0.3045\\n\",\n \"Epoch 6 Batch 200 Loss 0.3213\\n\",\n \"Epoch 6 Batch 300 Loss 0.2689\\n\",\n \"Epoch 6 Loss 0.3225\\n\",\n \"Time taken for 1 epoch 41.67611289024353 sec\\n\",\n \"\\n\",\n \"Epoch 7 Batch 0 Loss 0.3330\\n\",\n \"Epoch 7 Batch 100 Loss 0.1870\\n\",\n \"Epoch 7 Batch 200 Loss 0.1904\\n\",\n \"Epoch 7 Batch 300 Loss 0.1787\\n\",\n \"Epoch 7 Loss 0.2293\\n\",\n \"Time taken for 1 epoch 41.24461650848389 sec\\n\",\n \"\\n\",\n \"Epoch 8 Batch 0 Loss 0.2096\\n\",\n \"Epoch 8 Batch 100 Loss 0.1414\\n\",\n \"Epoch 8 Batch 200 Loss 0.1424\\n\",\n \"Epoch 8 Batch 300 Loss 0.1320\\n\",\n \"Epoch 8 Loss 0.1658\\n\",\n \"Time taken for 1 epoch 41.80285120010376 sec\\n\",\n \"\\n\",\n \"Epoch 9 Batch 0 Loss 0.1480\\n\",\n \"Epoch 9 Batch 100 Loss 0.0893\\n\",\n \"Epoch 9 Batch 200 Loss 0.1017\\n\",\n \"Epoch 9 Batch 300 Loss 0.0940\\n\",\n \"Epoch 9 Loss 0.1245\\n\",\n \"Time taken for 1 epoch 41.111262798309326 sec\\n\",\n \"\\n\",\n \"Epoch 10 Batch 0 Loss 0.1155\\n\",\n \"Epoch 10 Batch 100 Loss 0.0605\\n\",\n \"Epoch 10 Batch 200 Loss 0.1058\\n\",\n \"Epoch 10 Batch 300 Loss 0.0665\\n\",\n \"Epoch 10 Loss 0.0972\\n\",\n \"Time taken for 1 epoch 41.78199052810669 sec\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"EPOCHS = 10\\n\",\n \"\\n\",\n \"for epoch in range(EPOCHS):\\n\",\n \" start = time.time()\\n\",\n \"\\n\",\n \" enc_hidden = encoder.initialize_hidden_state()\\n\",\n \" total_loss = 0\\n\",\n \"\\n\",\n \" for (batch, (inp, targ)) in enumerate(dataset.take(steps_per_epoch)):\\n\",\n \" batch_loss = train_step(inp, targ, enc_hidden)\\n\",\n \" total_loss += batch_loss\\n\",\n \"\\n\",\n \" if batch % 100 == 0:\\n\",\n \" print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1,\\n\",\n \" batch,\\n\",\n \" batch_loss.numpy()))\\n\",\n \" # saving (checkpoint) the model every 2 epochs\\n\",\n \" if (epoch + 1) % 2 == 0:\\n\",\n \" checkpoint.save(file_prefix = checkpoint_prefix)\\n\",\n \"\\n\",\n \" print('Epoch {} Loss {:.4f}'.format(epoch + 1,\\n\",\n \" total_loss / steps_per_epoch))\\n\",\n \" print('Time taken for 1 epoch {} sec\\\\n'.format(time.time() - start))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"mU3Ce8M6I3rz\"\n },\n \"source\": [\n \"## 翻译\\n\",\n \"- evaluate函数类似于训练循环，除了我们在这里不使用教师强制。在每个时间步骤对解码器的输入是其先前的预测以及隐藏状态和编码器输出。\\n\",\n \"- 停止预测模型何时预测结束标记。\\n\",\n \"- 并存储每个时间步的注意力。\\n\",\n \"注意：编码器输出仅针对一个输入计算一次。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"EbQpyYs13jF_\"\n },\n \"outputs\": [],\n \"source\": [\n \"def evaluate(sentence):\\n\",\n \" attention_plot = np.zeros((max_length_targ, max_length_inp))\\n\",\n \"\\n\",\n \" sentence = preprocess_sentence(sentence)\\n\",\n \"\\n\",\n \" inputs = [inp_lang.word_index[i] for i in sentence.split(' ')]\\n\",\n \" inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs],\\n\",\n \" maxlen=max_length_inp,\\n\",\n \" padding='post')\\n\",\n \" inputs = tf.convert_to_tensor(inputs)\\n\",\n \"\\n\",\n \" result = ''\\n\",\n \"\\n\",\n \" hidden = [tf.zeros((1, units))]\\n\",\n \" enc_out, enc_hidden = encoder(inputs, hidden)\\n\",\n \"\\n\",\n \" dec_hidden = enc_hidden\\n\",\n \" dec_input = tf.expand_dims([targ_lang.word_index['']], 0)\\n\",\n \"\\n\",\n \" for t in range(max_length_targ):\\n\",\n \" predictions, dec_hidden, attention_weights = decoder(dec_input,\\n\",\n \" dec_hidden,\\n\",\n \" enc_out)\\n\",\n \"\\n\",\n \" # storing the attention weights to plot later on\\n\",\n \" attention_weights = tf.reshape(attention_weights, (-1, ))\\n\",\n \" attention_plot[t] = attention_weights.numpy()\\n\",\n \"\\n\",\n \" predicted_id = tf.argmax(predictions[0]).numpy()\\n\",\n \"\\n\",\n \" result += targ_lang.index_word[predicted_id] + ' '\\n\",\n \"\\n\",\n \" if targ_lang.index_word[predicted_id] == '':\\n\",\n \" return result, sentence, attention_plot\\n\",\n \"\\n\",\n \" # the predicted ID is fed back into the model\\n\",\n \" dec_input = tf.expand_dims([predicted_id], 0)\\n\",\n \"\\n\",\n \" return result, sentence, attention_plot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"s5hQWlbN3jGF\"\n },\n \"outputs\": [],\n \"source\": [\n \"# function for plotting the attention weights\\n\",\n \"def plot_attention(attention, sentence, predicted_sentence):\\n\",\n \" fig = plt.figure(figsize=(10,10))\\n\",\n \" ax = fig.add_subplot(1, 1, 1)\\n\",\n \" ax.matshow(attention, cmap='viridis')\\n\",\n \"\\n\",\n \" fontdict = {'fontsize': 14}\\n\",\n \"\\n\",\n \" ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\\n\",\n \" ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\\n\",\n \"\\n\",\n \" ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\\n\",\n \" ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\\n\",\n \"\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"sl9zUHzg3jGI\"\n },\n \"outputs\": [],\n \"source\": [\n \"def translate(sentence):\\n\",\n \" result, sentence, attention_plot = evaluate(sentence)\\n\",\n \"\\n\",\n \" print('Input: %s' % (sentence))\\n\",\n \" print('Predicted translation: {}'.format(result))\\n\",\n \"\\n\",\n \" attention_plot = attention_plot[:len(result.split(' ')), :len(sentence.split(' '))]\\n\",\n \" plot_attention(attention_plot, sentence.split(' '), result.split(' '))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"n250XbnjOaqP\"\n },\n \"source\": [\n \"## 恢复最新的检查点并进行测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 457912,\n \"status\": \"ok\",\n \"timestamp\": 1564762821673,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"UJpT9D5_OgP6\",\n \"outputId\": \"86ee1b5b-f73b-4a48-dcdb-8ed290147eb9\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 66,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# restoring the latest checkpoint in checkpoint_dir\\n\",\n \"checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 677\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 458400,\n \"status\": \"ok\",\n \"timestamp\": 1564762822187,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"WrAM0FDomq3E\",\n \"outputId\": \"a5d67baf-a8b1-48b2-d6c9-01df7289fae1\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Input: hace mucho frio aqui . \\n\",\n \"Predicted translation: it s very cold here . \\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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yrTLpxNmf6eXDz63WPZudb6e4tjTOb1jkznm/9UktdlelPePdMvnA1l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\\nDGECAAzj/wLFQzeyW1lQOAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"translate(u'hace mucho frio aqui.')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 677\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 458390,\n \"status\": \"ok\",\n \"timestamp\": 1564762822189,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"zSx2iM36EZQZ\",\n \"outputId\": \"3e76fb22-6544-4a19-b631-f41f697fde87\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Input: esta es mi vida . \\n\",\n \"Predicted translation: this is my life . \\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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+xx1/qT5R2f6+eetdFXlwAANsyr+5ztl/DbL7s+AQCGEmoAAEMJNQCA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\\nAEMJNQCAoYQaAMBQQg0AYKj/HyzTthGxqa3hAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"translate(u'esta es mi vida.')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 677\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 458790,\n \"status\": \"ok\",\n \"timestamp\": 1564762822599,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"A3LLCx3ZE0Ls\",\n \"outputId\": \"ef26532d-dcf7-4b63-ce8d-c144d247ff2e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Input: ¿ todavia estan en casa ? \\n\",\n \"Predicted translation: are you still at home ? \\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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O6TV38qBvGFJI/awvJHJ/n8Ks8ykjOS/EGSD1XVVRbLds20q2tdWZyS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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"translate(u'¿todavia estan en casa?')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 70,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 701\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 459670,\n \"status\": \"ok\",\n \"timestamp\": 1564762823492,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"DUQVLVqUE1YW\",\n \"outputId\": \"9ab39734-7a3d-4555-abd2-62804f0b3be4\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Input: trata de averiguarlo . \\n\",\n \"Predicted translation: try to figure it out . \\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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z70+jjD8W+PZtjp5CEMv+kvfEFe7p4G/C/zL4MIQ3T2nv44ParqNzfd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\\nsSRJjf4/nX/okW6JJV8AAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# wrong translation\\n\",\n \"translate(u'trata de averiguarlo.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"RTe5P5ioMJwN\"\n },\n \"source\": [\n \"## 下一步\\n\",\n \"- 下载不同的数据集以试验翻译，例如，英语到德语，或英语到法语。\\n\",\n \"- 尝试对更大的数据集进行训练，或使用更多的时期\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"accelerator\": \"GPU\",\n \"colab\": {\n \"collapsed_sections\": [],\n \"name\": \"nmt_with_attention.ipynb\",\n \"provenance\": [\n {\n \"file_id\": \"https://github.com/tensorflow/docs/blob/master/site/en/r2/tutorials/text/nmt_with_attention.ipynb\",\n \"timestamp\": 1564755135167\n }\n ],\n \"toc_visible\": true,\n \"version\": \"0.3.2\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "032-Text/text_generation.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"t09eeeR5prIJ\"\n },\n \"source\": [\n \"# TensorFlow2.0教程-使用RNN生成文本\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"BwpJ5IffzRG6\"\n },\n \"source\": [\n \"本教程演示了如何使用基于字符的RNN生成文本。我们将使用Andrej Karpathy的“循环神经网络的不合理有效性”中的莎士比亚写作数据集。给定来自该数据的一系列字符（“Shakespear”），训练模型以预测序列中的下一个字符（“e”）。通过重复调用模型可以生成更长的文本序列。\\n\",\n \"\\n\",\n \"本教程包含使用tf.keras实现的可运行代码和急切执行。以下是本教程中的模型训练了30个纪元时的示例输出，并以字符串“Q”开头：\\n\",\n \"\\n\",\n \"
\\n\",\n    \"QUEENE:\\n\",\n    \"I had thought thou hadst a Roman; for the oracle,\\n\",\n    \"Thus by All bids the man against the word,\\n\",\n    \"Which are so weak of care, by old care done;\\n\",\n    \"Your children were in your holy love,\\n\",\n    \"And the precipitation through the bleeding throne.\\n\",\n    \"\\n\",\n    \"BISHOP OF ELY:\\n\",\n    \"Marry, and will, my lord, to weep in such a one were prettiest;\\n\",\n    \"Yet now I was adopted heir\\n\",\n    \"Of the world's lamentable day,\\n\",\n    \"To watch the next way with his father with his face?\\n\",\n    \"\\n\",\n    \"ESCALUS:\\n\",\n    \"The cause why then we are all resolved more sons.\\n\",\n    \"\\n\",\n    \"VOLUMNIA:\\n\",\n    \"O, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, no, it is no sin it should be dead,\\n\",\n    \"And love and pale as any will to that word.\\n\",\n    \"\\n\",\n    \"QUEEN ELIZABETH:\\n\",\n    \"But how long have I heard the soul for this world,\\n\",\n    \"And show his hands of life be proved to stand.\\n\",\n    \"\\n\",\n    \"PETRUCHIO:\\n\",\n    \"I say he look'd on, if I must be content\\n\",\n    \"To stay him from the fatal of our country's bliss.\\n\",\n    \"His lordship pluck'd from this sentence then for prey,\\n\",\n    \"And then let us twain, being the moon,\\n\",\n    \"were she such a case as fills m\\n\",\n    \"
\\n\",\n \"\\n\",\n \"虽然有些句子是语法上的，但大多数句子都没有意义。该模型尚未学习单词的含义，但考虑：\\n\",\n \"\\n\",\n \"该模型基于字符。培训开始时，模型不知道如何拼写英语单词，或者单词甚至是文本单元。\\n\",\n \"\\n\",\n \"输出的结构类似于文本的播放块，通常以说话者名称开头，所有大写字母都类似于数据集。\\n\",\n \"\\n\",\n \"如下所示，模型是针对小批量文本（每个100个字符）进行训练的，并且仍然能够生成具有连贯结构的更长文本序列。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"srXC6pLGLwS6\"\n },\n \"source\": [\n \"## 开始\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"WGyKZj3bzf9p\"\n },\n \"source\": [\n \"### 导入TensorFlow\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 397\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 6412,\n \"status\": \"ok\",\n \"timestamp\": 1564756501128,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"yG_n40gFzf9s\",\n \"outputId\": \"08cf79a9-4f5b-4ee2-afb7-209179e1949b\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Requirement already satisfied: tensorflow-gpu==2.0.0-beta1 in /usr/local/lib/python3.6/dist-packages (2.0.0b1)\\n\",\n \"Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.7.1)\\n\",\n \"Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.1.0)\\n\",\n \"Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.15.0)\\n\",\n \"Requirement already satisfied: tb-nightly<1.14.0a20190604,>=1.14.0a20190603 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.14.0a20190603)\\n\",\n \"Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (3.7.1)\\n\",\n \"Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.12.0)\\n\",\n \"Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.33.4)\\n\",\n \"Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.1.7)\\n\",\n \"Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.0.8)\\n\",\n \"Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.16.4)\\n\",\n \"Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.2.2)\\n\",\n \"Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.11.2)\\n\",\n \"Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.1.0)\\n\",\n \"Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (0.8.0)\\n\",\n \"Requirement already satisfied: tf-estimator-nightly<1.14.0.dev2019060502,>=1.14.0.dev2019060501 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-beta1) (1.14.0.dev2019060501)\\n\",\n \"Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow-gpu==2.0.0-beta1) (0.15.5)\\n\",\n \"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow-gpu==2.0.0-beta1) (3.1.1)\\n\",\n \"Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190604,>=1.14.0a20190603->tensorflow-gpu==2.0.0-beta1) (41.0.1)\\n\",\n \"Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==2.0.0-beta1) (2.8.0)\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function, unicode_literals\\n\",\n \"\\n\",\n \"!pip install tensorflow-gpu==2.0.0-beta1\\n\",\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import os\\n\",\n \"import time\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"EHDoRoc5PKWz\"\n },\n \"source\": [\n \"### 下载莎士比亚数据集\\n\",\n \"更改以下行以在您自己的数据上运行此代码\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"pD_55cOxLkAb\"\n },\n \"outputs\": [],\n \"source\": [\n \"path_to_file = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"UHjdCjDuSvX_\"\n },\n \"source\": [\n \"### 观察数据\\n\",\n \"\\n\",\n \"首先， 观察文字:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 8756,\n \"status\": \"ok\",\n \"timestamp\": 1564756503495,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"aavnuByVymwK\",\n \"outputId\": \"9eabfb6e-a151-4ef3-e8da-4c3df1c045bf\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Length of text: 1115394 characters\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Read, then decode for py2 compat.\\n\",\n \"text = open(path_to_file, 'rb').read().decode(encoding='utf-8')\\n\",\n \"# length of text is the number of characters in it\\n\",\n \"print ('Length of text: {} characters'.format(len(text)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 287\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 8749,\n \"status\": \"ok\",\n \"timestamp\": 1564756503495,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"Duhg9NrUymwO\",\n \"outputId\": \"127f9e8c-71cc-4c8e-e09d-555e7f20164a\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"First Citizen:\\n\",\n \"Before we proceed any further, hear me speak.\\n\",\n \"\\n\",\n \"All:\\n\",\n \"Speak, speak.\\n\",\n \"\\n\",\n \"First Citizen:\\n\",\n \"You are all resolved rather to die than to famish?\\n\",\n \"\\n\",\n \"All:\\n\",\n \"Resolved. resolved.\\n\",\n \"\\n\",\n \"First Citizen:\\n\",\n \"First, you know Caius Marcius is chief enemy to the people.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Take a look at the first 250 characters in text\\n\",\n \"print(text[:250])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 8741,\n \"status\": \"ok\",\n \"timestamp\": 1564756503496,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"IlCgQBRVymwR\",\n \"outputId\": \"d430e2fd-8c5d-4798-eeec-8d24ce3dec5c\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"65 unique characters\\n\"\n ]\n }\n ],\n \"source\": [\n \"# The unique characters in the file\\n\",\n \"vocab = sorted(set(text))\\n\",\n \"print ('{} unique characters'.format(len(vocab)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"rNnrKn_lL-IJ\"\n },\n \"source\": [\n \"## 处理文本\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"LFjSVAlWzf-N\"\n },\n \"source\": [\n \"### 矢量化文本\\n\",\n \"在训练之前，我们需要将字符串映射到数字表示。创建两个查找表：一个将字符映射到数字，另一个用于数字到字符。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"IalZLbvOzf-F\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Creating a mapping from unique characters to indices\\n\",\n \"char2idx = {u:i for i, u in enumerate(vocab)}\\n\",\n \"idx2char = np.array(vocab)\\n\",\n \"\\n\",\n \"text_as_int = np.array([char2idx[c] for c in text])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"tZfqhkYCymwX\"\n },\n \"source\": [\n \"现在我们有一个每个字符的整数表示。请注意，我们将字符映射为从0到的索引len(unique)。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 431\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 9081,\n \"status\": \"ok\",\n \"timestamp\": 1564756503855,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"FYyNlCNXymwY\",\n \"outputId\": \"7293ffef-0ed8-4ae6-9a54-3397f989b83c\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{\\n\",\n \" '\\\\n': 0,\\n\",\n \" ' ' : 1,\\n\",\n \" '!' : 2,\\n\",\n \" '$' : 3,\\n\",\n \" '&' : 4,\\n\",\n \" \\\"'\\\" : 5,\\n\",\n \" ',' : 6,\\n\",\n \" '-' : 7,\\n\",\n \" '.' : 8,\\n\",\n \" '3' : 9,\\n\",\n \" ':' : 10,\\n\",\n \" ';' : 11,\\n\",\n \" '?' : 12,\\n\",\n \" 'A' : 13,\\n\",\n \" 'B' : 14,\\n\",\n \" 'C' : 15,\\n\",\n \" 'D' : 16,\\n\",\n \" 'E' : 17,\\n\",\n \" 'F' : 18,\\n\",\n \" 'G' : 19,\\n\",\n \" ...\\n\",\n \"}\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('{')\\n\",\n \"for char,_ in zip(char2idx, range(20)):\\n\",\n \" print(' {:4s}: {:3d},'.format(repr(char), char2idx[char]))\\n\",\n \"print(' ...\\\\n}')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 9073,\n \"status\": \"ok\",\n \"timestamp\": 1564756503855,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"l1VKcQHcymwb\",\n \"outputId\": \"0fac4352-89b7-4a9d-c2b4-8e9b38db5714\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"'First Citizen' ---- characters mapped to int ---- > [18 47 56 57 58 1 15 47 58 47 64 43 52]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Show how the first 13 characters from the text are mapped to integers\\n\",\n \"print ('{} ---- characters mapped to int ---- > {}'.format(repr(text[:13]), text_as_int[:13]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"bbmsf23Bymwe\"\n },\n \"source\": [\n \"### 预测任务\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"wssHQ1oGymwe\"\n },\n \"source\": [\n \"\\n\",\n \"给定一个字符或一系列字符，最可能的下一个字符是什么？这是我们正在训练模型执行的任务。模型的输入将是一系列字符，我们训练模型以预测输出 - 每个时间步的后续字符。\\n\",\n \"\\n\",\n \"由于RNN维持一个取决于之前看到的元素的内部状态，给定此时计算的所有字符，下一个字符是什么？\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"hgsVvVxnymwf\"\n },\n \"source\": [\n \"### 创建培训示例和目标\\n\",\n \"接下来将文本划分为示例序列。每个输入序列将包含seq_length文本中的字符。\\n\",\n \"\\n\",\n \"对于每个输入序列，相应的目标包含相同长度的文本，除了向右移动一个字符。\\n\",\n \"\\n\",\n \"所以把文本分成几块seq_length+1。例如，假设seq_length是4，我们的文本是“你好”。输入序列是“Hell”，目标序列是“ello”。\\n\",\n \"\\n\",\n \"为此，首先使用该tf.data.Dataset.from_tensor_slices函数将文本向量转换为字符索引流。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 107\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 9448,\n \"status\": \"ok\",\n \"timestamp\": 1564756504237,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"0UHJDA39zf-O\",\n \"outputId\": \"9e7df249-7f19-4689-804c-8705f79792a5\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"F\\n\",\n \"i\\n\",\n \"r\\n\",\n \"s\\n\",\n \"t\\n\"\n ]\n }\n ],\n \"source\": [\n \"# The maximum length sentence we want for a single input in characters\\n\",\n \"seq_length = 100\\n\",\n \"examples_per_epoch = len(text)//seq_length\\n\",\n \"\\n\",\n \"# Create training examples / targets\\n\",\n \"char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)\\n\",\n \"\\n\",\n \"for i in char_dataset.take(5):\\n\",\n \" print(idx2char[i.numpy()])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"-ZSYAcQV8OGP\"\n },\n \"source\": [\n \"该batch方法可以让我们轻松地将这些单个字符转换为所需大小的序列。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 107\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 9437,\n \"status\": \"ok\",\n \"timestamp\": 1564756504238,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"l4hkDU3i7ozi\",\n \"outputId\": \"a95fabae-b3fa-460f-b664-7d66d13cabeb\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"'First Citizen:\\\\nBefore we proceed any further, hear me speak.\\\\n\\\\nAll:\\\\nSpeak, speak.\\\\n\\\\nFirst Citizen:\\\\nYou '\\n\",\n \"'are all resolved rather to die than to famish?\\\\n\\\\nAll:\\\\nResolved. resolved.\\\\n\\\\nFirst Citizen:\\\\nFirst, you k'\\n\",\n \"\\\"now Caius Marcius is chief enemy to the people.\\\\n\\\\nAll:\\\\nWe know't, we know't.\\\\n\\\\nFirst Citizen:\\\\nLet us ki\\\"\\n\",\n \"\\\"ll him, and we'll have corn at our own price.\\\\nIs't a verdict?\\\\n\\\\nAll:\\\\nNo more talking on't; let it be d\\\"\\n\",\n \"'one: away, away!\\\\n\\\\nSecond Citizen:\\\\nOne word, good citizens.\\\\n\\\\nFirst Citizen:\\\\nWe are accounted poor citi'\\n\"\n ]\n }\n ],\n \"source\": [\n \"sequences = char_dataset.batch(seq_length+1, drop_remainder=True)\\n\",\n \"\\n\",\n \"for item in sequences.take(5):\\n\",\n \" print(repr(''.join(idx2char[item.numpy()])))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"UbLcIPBj_mWZ\"\n },\n \"source\": [\n \"对于每个序列，复制并移动它以形成输入和目标文本，map方法是使用该方法将简单函数应用于每个批处理：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"9NGu-FkO_kYU\"\n },\n \"outputs\": [],\n \"source\": [\n \"def split_input_target(chunk):\\n\",\n \" input_text = chunk[:-1]\\n\",\n \" target_text = chunk[1:]\\n\",\n \" return input_text, target_text\\n\",\n \"\\n\",\n \"dataset = sequences.map(split_input_target)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"hiCopyGZymwi\"\n },\n \"source\": [\n \"打印第一个示例输入和目标值：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 53\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 9411,\n \"status\": \"ok\",\n \"timestamp\": 1564756504239,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"GNbw-iR0ymwj\",\n \"outputId\": \"4240109b-6f9f-46b2-8126-609fee3b7bf4\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Input data: 'First Citizen:\\\\nBefore we proceed any further, hear me speak.\\\\n\\\\nAll:\\\\nSpeak, speak.\\\\n\\\\nFirst Citizen:\\\\nYou'\\n\",\n \"Target data: 'irst Citizen:\\\\nBefore we proceed any further, hear me speak.\\\\n\\\\nAll:\\\\nSpeak, speak.\\\\n\\\\nFirst Citizen:\\\\nYou '\\n\"\n ]\n }\n ],\n \"source\": [\n \"for input_example, target_example in dataset.take(1):\\n\",\n \" print ('Input data: ', repr(''.join(idx2char[input_example.numpy()])))\\n\",\n \" print ('Target data:', repr(''.join(idx2char[target_example.numpy()])))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"_33OHL3b84i0\"\n },\n \"source\": [\n \"这些矢量的每个索引作为一个时间步骤处理。对于时间步骤0的输入，模型接收“F”和trys的索引以预测“i”的索引作为下一个字符。在下一个时间步，它做同样的事情，但RNN除了当前输入字符之外还考虑前一步骤上下文。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 287\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 10188,\n \"status\": \"ok\",\n \"timestamp\": 1564756505026,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"0eBu9WZG84i0\",\n \"outputId\": \"0a467f77-f745-45b6-dc7f-559a4f4a05a5\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Step 0\\n\",\n \" input: 18 ('F')\\n\",\n \" expected output: 47 ('i')\\n\",\n \"Step 1\\n\",\n \" input: 47 ('i')\\n\",\n \" expected output: 56 ('r')\\n\",\n \"Step 2\\n\",\n \" input: 56 ('r')\\n\",\n \" expected output: 57 ('s')\\n\",\n \"Step 3\\n\",\n \" input: 57 ('s')\\n\",\n \" expected output: 58 ('t')\\n\",\n \"Step 4\\n\",\n \" input: 58 ('t')\\n\",\n \" expected output: 1 (' ')\\n\"\n ]\n }\n ],\n \"source\": [\n \"for i, (input_idx, target_idx) in enumerate(zip(input_example[:5], target_example[:5])):\\n\",\n \" print(\\\"Step {:4d}\\\".format(i))\\n\",\n \" print(\\\" input: {} ({:s})\\\".format(input_idx, repr(idx2char[input_idx])))\\n\",\n \" print(\\\" expected output: {} ({:s})\\\".format(target_idx, repr(idx2char[target_idx])))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"MJdfPmdqzf-R\"\n },\n \"source\": [\n \"### 创建培训批次\\n\",\n \"我们过去常常tf.data将文本拆分为可管理的序列。但在将这些数据输入模型之前，我们需要对数据进行混洗并将其打包成批。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 10178,\n \"status\": \"ok\",\n \"timestamp\": 1564756505027,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"p2pGotuNzf-S\",\n \"outputId\": \"3d934eef-7b8d-4391-af37-67de1d850b7e\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Batch size\\n\",\n \"BATCH_SIZE = 64\\n\",\n \"\\n\",\n \"# Buffer size to shuffle the dataset\\n\",\n \"# (TF data is designed to work with possibly infinite sequences,\\n\",\n \"# so it doesn't attempt to shuffle the entire sequence in memory. Instead,\\n\",\n \"# it maintains a buffer in which it shuffles elements).\\n\",\n \"BUFFER_SIZE = 10000\\n\",\n \"\\n\",\n \"dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)\\n\",\n \"\\n\",\n \"dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"r6oUuElIMgVx\"\n },\n \"source\": [\n \"## 建立模型\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"m8gPwEjRzf-Z\"\n },\n \"source\": [\n \"\\n\",\n \"使用tf.keras.Sequential来定义模型。对于这个简单的例子，三层用于定义我们的模型：\\n\",\n \"\\n\",\n \"- tf.keras.layers.Embedding：输入图层。一个可训练的查找表，它将每个字符的数字映射到具有embedding_dim维度的向量;\\n\",\n \"- tf.keras.layers.GRU：一种具有大小的RNN units=rnn_units（您也可以在此处使用LSTM层。）\\n\",\n \"- tf.keras.layers.Dense：输出层，带vocab_size输出。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"zHT8cLh7EAsg\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Length of the vocabulary in chars\\n\",\n \"vocab_size = len(vocab)\\n\",\n \"\\n\",\n \"# The embedding dimension\\n\",\n \"embedding_dim = 256\\n\",\n \"\\n\",\n \"# Number of RNN units\\n\",\n \"rnn_units = 1024\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"MtCrdfzEI2N0\"\n },\n \"outputs\": [],\n \"source\": [\n \"def build_model(vocab_size, embedding_dim, rnn_units, batch_size):\\n\",\n \" model = tf.keras.Sequential([\\n\",\n \" tf.keras.layers.Embedding(vocab_size, embedding_dim,\\n\",\n \" batch_input_shape=[batch_size, None]),\\n\",\n \" tf.keras.layers.LSTM(rnn_units,\\n\",\n \" return_sequences=True,\\n\",\n \" stateful=True,\\n\",\n \" recurrent_initializer='glorot_uniform'),\\n\",\n \" tf.keras.layers.Dense(vocab_size)\\n\",\n \" ])\\n\",\n \" return model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"wwsrpOik5zhv\"\n },\n \"outputs\": [],\n \"source\": [\n \"model = build_model(\\n\",\n \" vocab_size = len(vocab),\\n\",\n \" embedding_dim=embedding_dim,\\n\",\n \" rnn_units=rnn_units,\\n\",\n \" batch_size=BATCH_SIZE)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"RkA5upJIJ7W7\"\n },\n \"source\": [\n \"对于每个字符，模型查找嵌入，以嵌入作为输入一次运行GRU，并应用密集层生成预测下一个字符的对数似然的logits：\\n\",\n \"\\n\",\n \"![通过模型的数据图](https://tensorflow.org/tutorials/beta/text/images/text_generation_training.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"-ubPo0_9Prjb\"\n },\n \"source\": [\n \"## 测试模型\\n\",\n \"现在运行模型以查看它的行为符合预期。\\n\",\n \"\\n\",\n \"首先检查输出的形状：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 14644,\n \"status\": \"ok\",\n \"timestamp\": 1564756509531,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"C-_70kKAPrPU\",\n \"outputId\": \"c23fee0b-d816-4d3c-b363-e26693450ccf\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(64, 100, 65) # (batch_size, sequence_length, vocab_size)\\n\"\n ]\n }\n ],\n \"source\": [\n \"for input_example_batch, target_example_batch in dataset.take(1):\\n\",\n \" example_batch_predictions = model(input_example_batch)\\n\",\n \" print(example_batch_predictions.shape, \\\"# (batch_size, sequence_length, vocab_size)\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"Q6NzLBi4VM4o\"\n },\n \"source\": [\n \"在上面的例子中，输入的序列长度是，100但模型可以在任何长度的输入上运行：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 269\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 14636,\n \"status\": \"ok\",\n \"timestamp\": 1564756509532,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"vPGmAAXmVLGC\",\n \"outputId\": \"d0b9e5b6-33ca-456f-eec5-0295777b91d0\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"embedding (Embedding) (64, None, 256) 16640 \\n\",\n \"_________________________________________________________________\\n\",\n \"lstm (LSTM) (64, None, 1024) 5246976 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense (Dense) (64, None, 65) 66625 \\n\",\n \"=================================================================\\n\",\n \"Total params: 5,330,241\\n\",\n \"Trainable params: 5,330,241\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"uwv0gEkURfx1\"\n },\n \"source\": [\n \"为了从模型中获得实际预测，我们需要从输出分布中进行采样，以获得实际的字符索引。此分布由字符词汇表上的logits定义。\\n\",\n \"> 注意：从这个分布中进行采样非常重要，因为采用分布的argmax可以很容易地将模型卡在循环中。\\n\",\n \"\\n\",\n \"尝试批处理中的第一个示例：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"4V4MfFg0RQJg\"\n },\n \"outputs\": [],\n \"source\": [\n \"sampled_indices = tf.random.categorical(example_batch_predictions[0], num_samples=1)\\n\",\n \"sampled_indices = tf.squeeze(sampled_indices,axis=-1).numpy()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"QM1Vbxs_URw5\"\n },\n \"source\": [\n \"这使我们在每个时间步都预测下一个字符索引：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 125\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 14593,\n \"status\": \"ok\",\n \"timestamp\": 1564756509532,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"YqFMUQc_UFgM\",\n \"outputId\": \"6046fe90-c765-4edd-bb56-2e29557b5fe8\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([41, 3, 61, 21, 12, 44, 40, 24, 54, 19, 57, 51, 53, 45, 9, 52, 62,\\n\",\n \" 46, 58, 12, 62, 54, 62, 22, 25, 23, 25, 0, 42, 53, 43, 5, 45, 51,\\n\",\n \" 64, 57, 20, 13, 14, 14, 36, 55, 19, 5, 24, 15, 15, 62, 9, 53, 54,\\n\",\n \" 46, 28, 37, 30, 29, 32, 64, 29, 51, 52, 0, 54, 51, 50, 7, 51, 37,\\n\",\n \" 49, 33, 23, 22, 46, 56, 25, 43, 52, 26, 9, 33, 56, 45, 7, 10, 11,\\n\",\n \" 58, 45, 29, 64, 61, 37, 64, 24, 25, 62, 47, 9, 42, 57, 18])\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sampled_indices\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"LfLtsP3mUhCG\"\n },\n \"source\": [\n \"解码这些以查看此未经训练的模型预测的文本：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 107\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 14578,\n \"status\": \"ok\",\n \"timestamp\": 1564756509533,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"xWcFwPwLSo05\",\n \"outputId\": \"5bd7b655-3417-4209-b5fb-394f0e62ab1f\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Input: \\n\",\n \" 'g still, in cleansing them from tears.\\\\nNow sir, the sound that tells what hour it is\\\\nAre clamorous g'\\n\",\n \"\\n\",\n \"Next Char Predictions: \\n\",\n \" \\\"c$wI?fbLpGsmog3nxht?xpxJMKM\\\\ndoe'gmzsHABBXqG'LCCx3ophPYRQTzQmn\\\\npml-mYkUKJhrMenN3Urg-:;tgQzwYzLMxi3dsF\\\"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Input: \\\\n\\\", repr(\\\"\\\".join(idx2char[input_example_batch[0]])))\\n\",\n \"print()\\n\",\n \"print(\\\"Next Char Predictions: \\\\n\\\", repr(\\\"\\\".join(idx2char[sampled_indices ])))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"LJL0Q0YPY6Ee\"\n },\n \"source\": [\n \"## 训练模型\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"YCbHQHiaa4Ic\"\n },\n \"source\": [\n \"此时，问题可以被视为标准分类问题。给定先前的RNN状态，以及此时间步的输入，预测下一个字符的类。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"trpqTWyvk0nr\"\n },\n \"source\": [\n \"### 附加优化器和损失函数\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"UAjbjY03eiQ4\"\n },\n \"source\": [\n \"标准tf.keras.losses.sparse_categorical_crossentropy损失函数在这种情况下有效，因为它应用于预测的最后一个维度。\\n\",\n \"\\n\",\n \"因为我们的模型返回logits，所以我们需要设置from_logits标志。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 53\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 14568,\n \"status\": \"ok\",\n \"timestamp\": 1564756509533,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"4HrXTACTdzY-\",\n \"outputId\": \"90eb3df2-4df4-4732-e28a-4a3bd3473f11\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Prediction shape: (64, 100, 65) # (batch_size, sequence_length, vocab_size)\\n\",\n \"scalar_loss: 4.175114\\n\"\n ]\n }\n ],\n \"source\": [\n \"def loss(labels, logits):\\n\",\n \" return tf.keras.losses.sparse_categorical_crossentropy(labels, logits, from_logits=True)\\n\",\n \"\\n\",\n \"example_batch_loss = loss(target_example_batch, example_batch_predictions)\\n\",\n \"print(\\\"Prediction shape: \\\", example_batch_predictions.shape, \\\" # (batch_size, sequence_length, vocab_size)\\\")\\n\",\n \"print(\\\"scalar_loss: \\\", example_batch_loss.numpy().mean())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"jeOXriLcymww\"\n },\n \"source\": [\n \"使用该tf.keras.Model.compile方法配置培训过程。我们将使用tf.keras.optimizers.Adam默认参数和损失函数。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"DDl1_Een6rL0\"\n },\n \"outputs\": [],\n \"source\": [\n \"model.compile(optimizer='adam', loss=loss)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"ieSJdchZggUj\"\n },\n \"source\": [\n \"### 配置检查点\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"C6XBUUavgF56\"\n },\n \"source\": [\n \"使用a tf.keras.callbacks.ModelCheckpoint确保在培训期间保存检查点：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"W6fWTriUZP-n\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Directory where the checkpoints will be saved\\n\",\n \"checkpoint_dir = './training_checkpoints'\\n\",\n \"# Name of the checkpoint files\\n\",\n \"checkpoint_prefix = os.path.join(checkpoint_dir, \\\"ckpt_{epoch}\\\")\\n\",\n \"\\n\",\n \"checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(\\n\",\n \" filepath=checkpoint_prefix,\\n\",\n \" save_weights_only=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"3Ky3F_BhgkTW\"\n },\n \"source\": [\n \"### 执行培训\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"IxdOA-rgyGvs\"\n },\n \"source\": [\n \"为了使训练时间合理，使用10个时期来训练模型。在Colab中，将运行时设置为GPU以便更快地进行训练。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"7yGBE2zxMMHs\"\n },\n \"outputs\": [],\n \"source\": [\n \"EPOCHS=10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 377\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 153379,\n \"status\": \"ok\",\n \"timestamp\": 1564756648387,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"UK-hmKjYVoll\",\n \"outputId\": \"794f87e8-ff8d-43ef-d4da-472f94f0cbba\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1/10\\n\",\n \"172/172 [==============================] - 14s 83ms/step - loss: 2.6193\\n\",\n \"Epoch 2/10\\n\",\n \"172/172 [==============================] - 13s 78ms/step - loss: 1.9240\\n\",\n \"Epoch 3/10\\n\",\n \"172/172 [==============================] - 14s 80ms/step - loss: 1.6648\\n\",\n \"Epoch 4/10\\n\",\n \"172/172 [==============================] - 14s 81ms/step - loss: 1.5218\\n\",\n \"Epoch 5/10\\n\",\n \"172/172 [==============================] - 14s 82ms/step - loss: 1.4335\\n\",\n \"Epoch 6/10\\n\",\n \"172/172 [==============================] - 14s 82ms/step - loss: 1.3709\\n\",\n \"Epoch 7/10\\n\",\n \"172/172 [==============================] - 14s 81ms/step - loss: 1.3206\\n\",\n \"Epoch 8/10\\n\",\n \"172/172 [==============================] - 14s 80ms/step - loss: 1.2758\\n\",\n \"Epoch 9/10\\n\",\n \"172/172 [==============================] - 14s 80ms/step - loss: 1.2330\\n\",\n \"Epoch 10/10\\n\",\n \"172/172 [==============================] - 14s 80ms/step - loss: 1.1915\\n\"\n ]\n }\n ],\n \"source\": [\n \"history = model.fit(dataset, epochs=EPOCHS, callbacks=[checkpoint_callback])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"kKkD5M6eoSiN\"\n },\n \"source\": [\n \"## 生成文本\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"JIPcXllKjkdr\"\n },\n \"source\": [\n \"### 恢复最新的检查点\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"LyeYRiuVjodY\"\n },\n \"source\": [\n \"\\n\",\n \"要使此预测步骤简单，请使用批处理大小1。\\n\",\n \"\\n\",\n \"由于RNN状态从时间步长传递到时间步的方式，模型一旦构建就只接受固定的批量大小。\\n\",\n \"\\n\",\n \"要使用不同的模型运行模型batch_size，我们需要重建模型并从检查点恢复权重。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 153371,\n \"status\": \"ok\",\n \"timestamp\": 1564756648388,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"zk2WJ2-XjkGz\",\n \"outputId\": \"9088cc5b-bafa-49cb-a3a4-ee5341b578ce\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'./training_checkpoints/ckpt_10'\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {\n \"tags\": []\n },\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"tf.train.latest_checkpoint(checkpoint_dir)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"LycQ-ot_jjyu\"\n },\n \"outputs\": [],\n \"source\": [\n \"model = build_model(vocab_size, embedding_dim, rnn_units, batch_size=1)\\n\",\n \"\\n\",\n \"model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))\\n\",\n \"\\n\",\n \"model.build(tf.TensorShape([1, None]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 269\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 154683,\n \"status\": \"ok\",\n \"timestamp\": 1564756649725,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"71xa6jnYVrAN\",\n \"outputId\": \"66ae1561-327f-47b9-c1eb-a6226417d126\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_1\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"embedding_1 (Embedding) (1, None, 256) 16640 \\n\",\n \"_________________________________________________________________\\n\",\n \"lstm_1 (LSTM) (1, None, 1024) 5246976 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_1 (Dense) (1, None, 65) 66625 \\n\",\n \"=================================================================\\n\",\n \"Total params: 5,330,241\\n\",\n \"Trainable params: 5,330,241\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"DjGz1tDkzf-u\"\n },\n \"source\": [\n \"预测循环\\n\",\n \"以下代码块生成文本：\\n\",\n \"\\n\",\n \"- 它首先选择一个起始字符串，初始化RNN状态并设置要生成的字符数。\\n\",\n \"\\n\",\n \"- 使用起始字符串和RNN状态获取下一个字符的预测分布。\\n\",\n \"\\n\",\n \"- 然后，使用分类分布来计算预测字符的索引。使用此预测字符作为模型的下一个输入。\\n\",\n \"\\n\",\n \"- 模型返回的RNN状态被反馈到模型中，以便它现在具有更多上下文，而不是仅有一个单词。在预测下一个单词之后，修改后的RNN状态再次反馈到模型中，这是它从先前预测的单词获得更多上下文时的学习方式。\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"![要生成文本，模型的输出将反馈到输入](https://tensorflow.org/tutorials/beta/text/images/text_generation_sampling.png)\\n\",\n \"查看生成的文本，您将看到模型知道何时大写，制作段落并模仿类似莎士比亚的写作词汇。由于训练时代数量较少，尚未学会形成连贯的句子。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"WvuwZBX5Ogfd\"\n },\n \"outputs\": [],\n \"source\": [\n \"def generate_text(model, start_string):\\n\",\n \" # Evaluation step (generating text using the learned model)\\n\",\n \"\\n\",\n \" # Number of characters to generate\\n\",\n \" num_generate = 1000\\n\",\n \"\\n\",\n \" # Converting our start string to numbers (vectorizing)\\n\",\n \" input_eval = [char2idx[s] for s in start_string]\\n\",\n \" input_eval = tf.expand_dims(input_eval, 0)\\n\",\n \"\\n\",\n \" # Empty string to store our results\\n\",\n \" text_generated = []\\n\",\n \"\\n\",\n \" # Low temperatures results in more predictable text.\\n\",\n \" # Higher temperatures results in more surprising text.\\n\",\n \" # Experiment to find the best setting.\\n\",\n \" temperature = 1.0\\n\",\n \"\\n\",\n \" # Here batch size == 1\\n\",\n \" model.reset_states()\\n\",\n \" for i in range(num_generate):\\n\",\n \" predictions = model(input_eval)\\n\",\n \" # remove the batch dimension\\n\",\n \" predictions = tf.squeeze(predictions, 0)\\n\",\n \"\\n\",\n \" # using a categorical distribution to predict the word returned by the model\\n\",\n \" predictions = predictions / temperature\\n\",\n \" predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()\\n\",\n \"\\n\",\n \" # We pass the predicted word as the next input to the model\\n\",\n \" # along with the previous hidden state\\n\",\n \" input_eval = tf.expand_dims([predicted_id], 0)\\n\",\n \"\\n\",\n \" text_generated.append(idx2char[predicted_id])\\n\",\n \"\\n\",\n \" return (start_string + ''.join(text_generated))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 55\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 159152,\n \"status\": \"ok\",\n \"timestamp\": 1564756654219,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"ktovv0RFhrkn\",\n \"outputId\": \"e7139557-b924-4656-da8e-a7e3f15ddc98\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ROMEO: VINJBYOO3U$zZWV?NU3UYZfUj&XYK3XKJjUY&&QKPQPQUXOzW?WGZJZJMB&O$qUQj3QUFJ$Wq&WxvRZj&zV'UY$NYVYDYXTrzJxY$QjQ$X&GjWOIUQAq.;QxZXVQzq&GGACjj3qGV&ZDWZzYYYU!&qZX$GYq&Z&vYVX&QCPUzzCZ!UQZVYzGBqY$B333&zk3z$J3UVE$OG!EE!NUY&PXjqVjMIfUYXq33?MjOQ3Y3XZzU$GXXWLZzZUYYG&KENvN&jJ3MQGjYWB3QxjxAU!ZQZ-JDJ$RUYqXDZ&!&jWTjJUWEV.YqYZZ3xX&wL&z3j$z&jUVQzJQj&DjWxYU.jQjY&YQD3j$G.&?,GNjjVY&jZWlQXUzz&kzZUJZjjUcUG&&jZJGJ$zVUjQ-VzS&G3JN&3&qWY&zxZZV$DQzYzYQjUvBAQS-3YY&XDVjzBNBJVVU$&QUwZxIjA$zQDQqZJZVU&J3NBZJVJNzTNqY33YW3$qW&DVEZVOQIqXXVON&&YXN&kJ3qY3&&GBxY!XS!Y&SUJ$Jz&kEU3&UzUZUQqNGqD$XjXUyYZN$Qj&WVYYEDXzqOWXYPU&zVEUjUY$DDJO$U&VVzGqjUkD&qjQQV&Yjjj$W&JSZY&&GjQ.qYj$PYUDY$&BWzZqqYXVROJW$YYzAjqQ&$&J&ZUVUqK$3M?XMq&UTZq&XqjjOWjMYX$&j$jUYQGEUMwUMDqYUDZjJYYzUqUW&QaYZVWjAV:UQ&EvPDxZ$Z$zqOUTzUYM&YFUHY;ZG&U&bx!jW3U! BUzJQJ3AUG&3NjJZ&3&Q$J-VYNSUz!&ZMqAYQUj:&UqI&X;xzQBUQQAG$z&X&zxXUIY3VUWE$Y;&U&Z&XZQq3Q$V&YJ&Q3Z&KUQDJbQ&LBBT?X&YA&xG?CjD&&DGVU$YCGJD3V$SGxQU$HAJUXUZDj&jUkxUqZ$DXTDG3YW&ZXjqO&GzUX3U3&OZUj!B$3x&3j!3JXXUQ&YW$UDkqPA3DZVQXJ&G\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(generate_text(model, start_string=u\\\"ROMEO: \\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"AM2Uma_-yVIq\"\n },\n \"source\": [\n \"你可以做的最简单的事情是改善结果，以便更长时间地训练它（尝试EPOCHS=30）。\\n\",\n \"\\n\",\n \"您还可以尝试使用不同的起始字符串，或尝试添加另一个RNN图层以提高模型的准确性，或者调整温度参数以生成更多或更少的随机预测。\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"Y4QwTjAM6A2O\"\n },\n \"source\": [\n \"## 上述训练程序很简单，但不会给你太多控制。\\n\",\n \"\\n\",\n \"所以现在您已经了解了如何手动运行模型，让我们解压缩训练循环，并自己实现。例如，如果要实施课程学习以帮助稳定模型的开环输出，这就是一个起点。\\n\",\n \"\\n\",\n \"我们将用于tf.GradientTape跟踪渐变。您可以通过阅读热切的执行指南来了解有关此方法的更多信息。\\n\",\n \"\\n\",\n \"该程序的工作原理如下：\\n\",\n \"\\n\",\n \"- 首先，初始化RNN状态。我们通过调用tf.keras.Model.reset_states方法来完成此操作。\\n\",\n \"\\n\",\n \"- 接下来，迭代数据集（逐批）并计算与每个数据集关联的预测。\\n\",\n \"\\n\",\n \"- 打开a tf.GradientTape，并计算该上下文中的预测和损失。\\n\",\n \"\\n\",\n \"- 使用该tf.GradientTape.grads方法计算相对于模型变量的损失梯度。\\n\",\n \"\\n\",\n \"- 最后，使用优化器的tf.train.Optimizer.apply_gradients方法向下迈出一步。\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"_XAm7eCoKULT\"\n },\n \"outputs\": [],\n \"source\": [\n \"model = build_model(\\n\",\n \" vocab_size = len(vocab),\\n\",\n \" embedding_dim=embedding_dim,\\n\",\n \" rnn_units=rnn_units,\\n\",\n \" batch_size=BATCH_SIZE)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"qUKhnZtMVpoJ\"\n },\n \"outputs\": [],\n \"source\": [\n \"optimizer = tf.keras.optimizers.Adam()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"b4kH1o0leVIp\"\n },\n \"outputs\": [],\n \"source\": [\n \"@tf.function\\n\",\n \"def train_step(inp, target):\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" predictions = model(inp)\\n\",\n \" loss = tf.reduce_mean(\\n\",\n \" tf.keras.losses.sparse_categorical_crossentropy(\\n\",\n \" target, predictions, from_logits=True))\\n\",\n \" grads = tape.gradient(loss, model.trainable_variables)\\n\",\n \" optimizer.apply_gradients(zip(grads, model.trainable_variables))\\n\",\n \"\\n\",\n \" return loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 1000\n },\n \"colab_type\": \"code\",\n \"executionInfo\": {\n \"elapsed\": 295217,\n \"status\": \"ok\",\n \"timestamp\": 1564756790330,\n \"user\": {\n \"displayName\": \"Will Chen\",\n \"photoUrl\": \"\",\n \"userId\": \"01179718990779759737\"\n },\n \"user_tz\": -480\n },\n \"id\": \"d4tSNwymzf-q\",\n \"outputId\": \"894696d7-6ac7-4e0e-f469-ce07f98ac3d3\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING: Logging before flag parsing goes to stderr.\\n\",\n \"W0802 14:37:35.460247 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer\\n\",\n \"W0802 14:37:35.461703 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer.iter\\n\",\n \"W0802 14:37:35.462915 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer.beta_1\\n\",\n \"W0802 14:37:35.464316 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer.beta_2\\n\",\n \"W0802 14:37:35.465240 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer.decay\\n\",\n \"W0802 14:37:35.467100 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer.learning_rate\\n\",\n \"W0802 14:37:35.468499 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).layer_with_weights-0.embeddings\\n\",\n \"W0802 14:37:35.469516 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).layer_with_weights-2.kernel\\n\",\n \"W0802 14:37:35.470415 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).layer_with_weights-2.bias\\n\",\n \"W0802 14:37:35.471374 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).layer_with_weights-1.cell.kernel\\n\",\n \"W0802 14:37:35.472301 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).layer_with_weights-1.cell.recurrent_kernel\\n\",\n \"W0802 14:37:35.473214 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).layer_with_weights-1.cell.bias\\n\",\n \"W0802 14:37:35.474144 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).layer_with_weights-0.embeddings\\n\",\n \"W0802 14:37:35.475064 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).layer_with_weights-2.kernel\\n\",\n \"W0802 14:37:35.476021 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).layer_with_weights-2.bias\\n\",\n \"W0802 14:37:35.476972 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).layer_with_weights-1.cell.kernel\\n\",\n \"W0802 14:37:35.477811 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).layer_with_weights-1.cell.recurrent_kernel\\n\",\n \"W0802 14:37:35.478764 139895554488192 util.py:244] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).layer_with_weights-1.cell.bias\\n\",\n \"W0802 14:37:35.479741 139895554488192 util.py:252] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/alpha/guide/checkpoints#loading_mechanics for details.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1 Batch 0 Loss 4.174371719360352\\n\",\n \"Epoch 1 Batch 100 Loss 2.323850631713867\\n\",\n \"Epoch 1 Loss 2.1186\\n\",\n \"Time taken for 1 epoch 15.880910158157349 sec\\n\",\n \"\\n\",\n \"Epoch 2 Batch 0 Loss 2.0843210220336914\\n\",\n \"Epoch 2 Batch 100 Loss 1.83076810836792\\n\",\n \"Epoch 2 Loss 1.7715\\n\",\n \"Time taken for 1 epoch 13.457166194915771 sec\\n\",\n \"\\n\",\n \"Epoch 3 Batch 0 Loss 1.7104278802871704\\n\",\n \"Epoch 3 Batch 100 Loss 1.5949002504348755\\n\",\n \"Epoch 3 Loss 1.5931\\n\",\n \"Time taken for 1 epoch 13.458112716674805 sec\\n\",\n \"\\n\",\n \"Epoch 4 Batch 0 Loss 1.5357296466827393\\n\",\n \"Epoch 4 Batch 100 Loss 1.4776993989944458\\n\",\n \"Epoch 4 Loss 1.4852\\n\",\n \"Time taken for 1 epoch 13.257809162139893 sec\\n\",\n \"\\n\",\n \"Epoch 5 Batch 0 Loss 1.4319688081741333\\n\",\n \"Epoch 5 Batch 100 Loss 1.4038314819335938\\n\",\n \"Epoch 5 Loss 1.4126\\n\",\n \"Time taken for 1 epoch 13.24284291267395 sec\\n\",\n \"\\n\",\n \"Epoch 6 Batch 0 Loss 1.3629602193832397\\n\",\n \"Epoch 6 Batch 100 Loss 1.3476572036743164\\n\",\n \"Epoch 6 Loss 1.3545\\n\",\n \"Time taken for 1 epoch 13.140316724777222 sec\\n\",\n \"\\n\",\n \"Epoch 7 Batch 0 Loss 1.3121309280395508\\n\",\n \"Epoch 7 Batch 100 Loss 1.2977547645568848\\n\",\n \"Epoch 7 Loss 1.3026\\n\",\n \"Time taken for 1 epoch 13.17615532875061 sec\\n\",\n \"\\n\",\n \"Epoch 8 Batch 0 Loss 1.2648319005966187\\n\",\n \"Epoch 8 Batch 100 Loss 1.2559295892715454\\n\",\n \"Epoch 8 Loss 1.2583\\n\",\n \"Time taken for 1 epoch 13.247912645339966 sec\\n\",\n \"\\n\",\n \"Epoch 9 Batch 0 Loss 1.2237274646759033\\n\",\n \"Epoch 9 Batch 100 Loss 1.2122169733047485\\n\",\n \"Epoch 9 Loss 1.2092\\n\",\n \"Time taken for 1 epoch 13.323989629745483 sec\\n\",\n \"\\n\",\n \"Epoch 10 Batch 0 Loss 1.1861408948898315\\n\",\n \"Epoch 10 Batch 100 Loss 1.165980339050293\\n\",\n \"Epoch 10 Loss 1.1561\\n\",\n \"Time taken for 1 epoch 13.36545991897583 sec\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Training step\\n\",\n \"EPOCHS = 10\\n\",\n \"\\n\",\n \"for epoch in range(EPOCHS):\\n\",\n \" start = time.time()\\n\",\n \"\\n\",\n \" # initializing the hidden state at the start of every epoch\\n\",\n \" # initally hidden is None\\n\",\n \" hidden = model.reset_states()\\n\",\n \"\\n\",\n \" for (batch_n, (inp, target)) in enumerate(dataset):\\n\",\n \" loss = train_step(inp, target)\\n\",\n \"\\n\",\n \" if batch_n % 100 == 0:\\n\",\n \" template = 'Epoch {} Batch {} Loss {}'\\n\",\n \" print(template.format(epoch+1, batch_n, loss))\\n\",\n \"\\n\",\n \" # saving (checkpoint) the model every 5 epochs\\n\",\n \" if (epoch + 1) % 5 == 0:\\n\",\n \" model.save_weights(checkpoint_prefix.format(epoch=epoch))\\n\",\n \"\\n\",\n \" print ('Epoch {} Loss {:.4f}'.format(epoch+1, loss))\\n\",\n \" print ('Time taken for 1 epoch {} sec\\\\n'.format(time.time() - start))\\n\",\n \"\\n\",\n \"model.save_weights(checkpoint_prefix.format(epoch=epoch))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 0,\n \"metadata\": {\n \"colab\": {},\n \"colab_type\": \"code\",\n \"id\": \"C_9USpdHlYtt\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"accelerator\": \"GPU\",\n \"colab\": {\n \"collapsed_sections\": [],\n \"last_runtime\": {\n \"build_target\": \"//learning/brain/python/client:tpu_hw_notebook\",\n \"kind\": \"private\"\n },\n \"name\": \"text_generation.ipynb\",\n \"provenance\": [\n {\n \"file_id\": \"https://github.com/tensorflow/docs/blob/master/site/en/r2/tutorials/text/text_generation.ipynb\",\n \"timestamp\": 1564754299619\n }\n ],\n \"toc_visible\": true,\n \"version\": \"0.3.2\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "032-Text/vecs.tsv", "content": 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0.3748178\t0.3809782\t-0.33576423\n0.16730495\t0.18694583\t0.11264387\t-0.17133361\t-0.12585372\t-0.16964021\t-0.13284881\t-0.08818946\t-0.11995234\t-0.17370445\t-0.12671416\t0.1660907\t0.09425982\t0.14395632\t0.13485321\t-0.17166063\n0.17211181\t0.22831716\t0.1427893\t-0.21108553\t-0.14546722\t-0.14950803\t-0.15467247\t-0.12565\t-0.18501736\t-0.1759996\t-0.23533522\t0.16720578\t0.15928347\t0.16238682\t0.14377385\t-0.22900407\n-0.09844178\t-0.11813971\t-0.082380466\t0.11460061\t0.11353986\t0.13168502\t0.042318806\t0.098044075\t0.10272004\t0.102135405\t0.035340518\t-0.060670596\t-0.07564883\t-0.12121707\t-0.071936816\t0.061797544\n-0.353256\t-0.31365204\t-0.3423187\t0.28485942\t0.26596296\t0.36301336\t0.30832455\t0.30665272\t0.3217656\t0.26021528\t0.31121337\t-0.3282725\t-0.29951987\t-0.35679245\t-0.33283097\t0.2944888\n0.09557401\t0.11040455\t0.062366728\t-0.14816752\t-0.08649048\t-0.060809657\t-0.09826634\t-0.09122247\t-0.04291284\t-0.103320494\t-0.06506269\t0.08313984\t0.09958201\t0.0766117\t0.13594885\t-0.12394716\n0.22076766\t0.23856255\t0.28212392\t-0.27496493\t-0.2650253\t-0.21906403\t-0.20583603\t-0.27171993\t-0.2243455\t-0.24520318\t-0.2630342\t0.28748265\t0.30035448\t0.26945126\t0.27231282\t-0.2648981\n-0.26178905\t-0.23123066\t-0.17059687\t0.24246006\t0.17331144\t0.23848061\t0.19613414\t0.25414184\t0.20394436\t0.18457851\t0.17106725\t-0.19159918\t-0.18142118\t-0.19057241\t-0.2279575\t0.18009008\n-0.32768425\t-0.28834262\t-0.28543025\t0.3193619\t0.30401438\t0.35598865\t0.27872545\t0.31923312\t0.35210848\t0.26735306\t0.29239962\t-0.26833776\t-0.27196905\t-0.33080983\t-0.3155614\t0.29282966\n-0.061283875\t-0.09610441\t-0.12440909\t0.09233796\t0.111499175\t0.14021982\t0.18738696\t0.10652652\t0.19264178\t0.07989433\t0.062116217\t-0.09040109\t-0.14484245\t-0.14455484\t-0.061370738\t0.14847969\n-0.29092202\t-0.29519406\t-0.29347\t0.26528737\t0.32597938\t0.31210062\t0.28947404\t0.2974233\t0.27641353\t0.25596392\t0.33027983\t-0.29609922\t-0.2669858\t-0.2528167\t-0.24166454\t0.31322578\n-0.049371004\t-0.041566055\t-0.068900384\t0.027055902\t0.059390116\t0.046479933\t0.09655665\t0.09360679\t0.08452492\t0.051164255\t0.10555634\t-0.120448\t-0.01862308\t-0.114907436\t-0.08340374\t0.07310928\n0.29669037\t0.29657802\t0.26240554\t-0.2507971\t-0.25030696\t-0.24753271\t-0.20298408\t-0.2031658\t-0.17927332\t-0.22671525\t-0.21535109\t0.2769483\t0.24309462\t0.25141537\t0.2763686\t-0.23724942\n-0.4870288\t-0.49496552\t-0.5162831\t0.5324786\t0.48681042\t0.50995123\t0.523936\t0.49771443\t0.43469426\t0.52462524\t0.5171067\t-0.5075429\t-0.53888726\t-0.5407957\t-0.54086345\t0.5077934\n0.002714574\t-0.07430135\t-0.04094737\t0.05398579\t0.021480666\t0.05755725\t0.0059852735\t0.00784574\t0.060526606\t-0.014323548\t-0.0033613537\t-0.01636727\t-0.026364267\t-0.058416292\t-0.017130844\t0.018158386\n-0.4026749\t-0.32107916\t-0.3400852\t0.33337677\t0.34216687\t0.33997452\t0.35523343\t0.41323712\t0.388985\t0.37938893\t0.34581184\t-0.40852115\t-0.388337\t-0.39489514\t-0.36062554\t0.3693877\n0.19059554\t0.2746848\t0.1930803\t-0.22040743\t-0.20812583\t-0.19253407\t-0.2076416\t-0.20903301\t-0.2202245\t-0.2281389\t-0.2551947\t0.2647238\t0.25283864\t0.273085\t0.2127186\t-0.217298\n-0.020012654\t-0.0136160515\t-0.022779804\t0.035056714\t0.09105661\t0.037313428\t0.01716523\t0.004587056\t0.042347364\t0.04647645\t0.08992345\t-0.029832685\t-0.06762832\t-0.03157709\t-0.006611651\t0.028769482\n-0.24561451\t-0.2153521\t-0.2828842\t0.269064\t0.25741333\t0.27715564\t0.2394385\t0.23193753\t0.24786057\t0.23029432\t0.19502175\t-0.2160294\t-0.21499625\t-0.2524299\t-0.25245166\t0.22613302\n0.42375103\t0.3453722\t0.3973933\t-0.43196732\t-0.40248188\t-0.3558746\t-0.3462997\t-0.38530952\t-0.29530233\t-0.4408556\t-0.4092908\t0.36517796\t0.39697984\t0.36469355\t0.4028387\t-0.36142483\n" }, { "path": "033-Estimators/001-boosted_trees.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2.0教程-Boosted trees\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"本教程是使用决策树和tf.estimator API训练Gradient Boosting模型的端到端演练。 Boosted Trees模型是回归和分类中最受欢迎和最有效的机器学习方法之一。 这是一种集合技术，它结合了几种树模型的预测。\\n\",\n \"\\n\",\n \"Boosted Trees模型受到许多机器学习从业者的欢迎，因为它们可以通过最小的超参数调整实现令人印象深刻的性能。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.加载泰坦尼克数据集\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"from IPython.display import clear_output\\n\",\n \"\\n\",\n \"# Load dataset.\\n\",\n \"dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')\\n\",\n \"dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')\\n\",\n \"y_train = dftrain.pop('survived')\\n\",\n \"y_eval = dfeval.pop('survived')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"tf.random.set_seed(123)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"数据集由训练集和评估集组成：\\n\",\n \"\\n\",\n \"- `dftrain`和`y_train`是*training set* - 模型用来学习的数据。\\n\",\n \"- 模型根据*eval set*，`dfeval`和`y_eval`进行测试。\\n\",\n \"\\n\",\n \"数据集设定的特征如下：\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
Feature NameDescription
sexGender of passenger
ageAge of passenger
n_siblings_spouses# siblings and partners aboard
parch# of parents and children aboard
fareFare passenger paid.
classPassenger's class on ship
deckWhich deck passenger was on
embark_townWhich town passenger embarked from
aloneIf passenger was alone
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.探索数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
sexagen_siblings_spousesparchfareclassdeckembark_townalone
0male22.0107.2500ThirdunknownSouthamptonn
1female38.01071.2833FirstCCherbourgn
2female26.0007.9250ThirdunknownSouthamptony
3female35.01053.1000FirstCSouthamptonn
4male28.0008.4583ThirdunknownQueenstowny
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" sex age n_siblings_spouses parch fare class deck \\\\\\n\",\n \"0 male 22.0 1 0 7.2500 Third unknown \\n\",\n \"1 female 38.0 1 0 71.2833 First C \\n\",\n \"2 female 26.0 0 0 7.9250 Third unknown \\n\",\n \"3 female 35.0 1 0 53.1000 First C \\n\",\n \"4 male 28.0 0 0 8.4583 Third unknown \\n\",\n \"\\n\",\n \" embark_town alone \\n\",\n \"0 Southampton n \\n\",\n \"1 Cherbourg n \\n\",\n \"2 Southampton y \\n\",\n \"3 Southampton n \\n\",\n \"4 Queenstown y \"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dftrain.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
agen_siblings_spousesparchfare
count627.000000627.000000627.000000627.000000
mean29.6313080.5454550.37958534.385399
std12.5118181.1510900.79299954.597730
min0.7500000.0000000.0000000.000000
25%23.0000000.0000000.0000007.895800
50%28.0000000.0000000.00000015.045800
75%35.0000001.0000000.00000031.387500
max80.0000008.0000005.000000512.329200
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" age n_siblings_spouses parch fare\\n\",\n \"count 627.000000 627.000000 627.000000 627.000000\\n\",\n \"mean 29.631308 0.545455 0.379585 34.385399\\n\",\n \"std 12.511818 1.151090 0.792999 54.597730\\n\",\n \"min 0.750000 0.000000 0.000000 0.000000\\n\",\n \"25% 23.000000 0.000000 0.000000 7.895800\\n\",\n \"50% 28.000000 0.000000 0.000000 15.045800\\n\",\n \"75% 35.000000 1.000000 0.000000 31.387500\\n\",\n \"max 80.000000 8.000000 5.000000 512.329200\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dftrain.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(627, 264)\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dftrain.shape[0], dfeval.shape[0] # 训练集，验证集数量\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# 年龄分布\\n\",\n \"dftrain.age.hist(bins=20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# 男女比例\\n\",\n \"dftrain.sex.value_counts().plot(kind='barh')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAYwAAAD8CAYAAABkbJM/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAADgZJREFUeJzt3X2MpWddxvHv5bZdwJat0A1uluqwdaMpBZbtoqC1IqKWVi3oKqjRxphsIvjCH0aXNCHVSLJYXxBSJCUCpVRaRYiEagSEgkGl7tZ9K1Ba6Rq79iWtaWmxVll//nGeLcM4s/Pr7Mycc6bfT3Kyz7nPc8655p6zc+39PGfnpKqQJGkx3zDuAJKk6WBhSJJaLAxJUouFIUlqsTAkSS0WhiSpxcKQJLVYGJKkFgtDktRyyrgDLKezzjqrZmZmxh1DkqbKvn377q+qjYvtt6YKY2Zmhr179447hiRNlST/2tnPQ1KSpBYLQ5LUYmFIklosDElSi4UhSWqxMCRJLRaGJKnFwpAktVgYkqQWC0OS1GJhSJJaLAxJUouFIUlqsTAkSS0WhiSpxcKQJLWsqQ9QOnT0IWZ23zjuGFoBR/ZcMu4I0pOeKwxJUouFIUlqsTAkSS0WhiSpxcKQJLVYGJKkFgtDktRiYUiSWiwMSVKLhSFJarEwJEktFoYkqcXCkCS1WBiSpJZWYSS5PMmtSQ4m2Z/ku1Y62Jznf2mSj6zmc0qSvt6in4eR5CXAjwDbq+qxJGcBp614MknSROmsMDYB91fVYwBVdX9V/XuS85N8Ksm+JH+TZBNAkm9L8vEkB5LckuScjFyZ5HCSQ0lePez70iQ3JflAki8kuS5JhtsuGsZuAX58hb5+SVJTpzA+Cpyd5ItJ3p7k+5KcCrwN2FlV5wPvAt407H8dcFVVvQD4buBuRj/wtwEvAF4OXHm8YIAXAq8HzgW2AN+T5CnAO4EfBc4Hvvnkv1RJ0slY9JBUVT2S5Hzge4HvB24Afgc4D/jYsCBYB9yd5Axgc1V9aLjvfwEkuQB4f1UdA+5N8ingRcCXgZur6q5hv/3ADPAIcGdV3T6Mvw/YNV++JLuO37bu6RuXMAWSpI7WZ3oPP+hvAm5Kcgh4HXBrVb1k9n5DYTxRj83aPtbNNCvb1cDVAOs3ba0lPL8kqWHRQ1JJvj3J1llD24DPAxuHE+IkOTXJc6vqYeCuJK8cxtcneRrwd8Crk6xLshG4ELj5BE/7BWAmyTnD9Z9+wl+ZJGlZdc5hnA5ck+RzSQ4yOtfwRmAn8OYkB4D9jM5XAPwc8KvDvn/P6PzDh4CDwAHgE8BvVNU9Cz3hcChrF3DjcNL7vqV8cZKk5ZOqtXMUZ/2mrbXpsreMO4ZWwJE9l4w7grRmJdlXVTsW28//6S1JarEwJEktFoYkqcXCkCS1WBiSpBYLQ5LUYmFIklosDElSi4UhSWqxMCRJLRaGJKnFwpAktVgYkqSWJ/RhRZPueZs3sNffaipJK8IVhiSpxcKQJLVYGJKkFgtDktRiYUiSWiwMSVKLhSFJarEwJEktFoYkqcXCkCS1WBiSpBYLQ5LUYmFIklosDElSi4UhSWqxMCRJLRaGJKnFwpAktVgYkqQWC0OS1GJhSJJaLAxJUouFIUlqsTAkSS0WhiSpxcKQJLVYGJKkFgtDktRiYUiSWiwMSVKLhSFJarEwJEktFoYkqcXCkCS1WBiSpJZTxh1gOR06+hAzu28cdwytIUf2XDLuCNLEcIUhSWqxMCRJLRaGJKnFwpAktVgYkqQWC0OS1GJhSJJaLAxJUouFIUlqsTAkSS0WhiSpxcKQJLVYGJKklhUtjCTHkuyfdZlJsiPJW5/AY5yZ5LUrmVOStLiV/vXmj1bVtjljR4C9c3dMckpVfXWexzgTeC3w9uWPJ0nqWvVDUklemuQjw/YVSa5N8hng2iTPTXLzsBo5mGQrsAc4Zxi7crXzSpJGVnqF8dQk+4ftO6vqVfPscy5wQVU9muRtwB9V1XVJTgPWAbuB8+ZZqUiSVtE4DknN9eGqenTY/gfg8iTPBj5YVbcnOeGdk+wCdgGse/rGk80rSVrAJLxL6ivHN6rqT4EfAx4F/irJyxa7c1VdXVU7qmrHuqdtWMGYkvTkNlGf6Z1kC/Clqnprkm8Bng8cAM4YbzJJ0iSsMGb7KeDwcN7jPOC9VfUA8Jkkhz3pLUnjs6IrjKo6fZ6xm4Cbhu0r5ty2h9G7oube52dWJKAkqW3SVhiSpAllYUiSWiwMSVKLhSFJarEwJEktFoYkqcXCkCS1WBiSpBYLQ5LUYmFIklosDElSi4UhSWqxMCRJLRP1eRgn63mbN7B3zyXjjiFJa5IrDElSi4UhSWqxMCRJLRaGJKnFwpAktVgYkqQWC0OS1GJhSJJaLAxJUouFIUlqsTAkSS0WhiSpxcKQJLVYGJKkFgtDktRiYUiSWiwMSVKLhSFJarEwJEktFoYkqcXCkCS1WBiSpBYLQ5LUYmFIklosDElSi4UhSWqxMCRJLRaGJKnFwpAktVgYkqQWC0OS1GJhSJJaLAxJUouFIUlqsTAkSS2njDvAcjp09CFmdt847hiStKqO7LlkVZ7HFYYkqcXCkCS1WBiSpBYLQ5LUYmFIklosDElSi4UhSWqxMCRJLRaGJKnFwpAktVgYkqQWC0OS1GJhSJJaLAxJUsuyFUaSZybZP1zuSXJ02H4wyecWuM9vJ3l547FnkhxerqySpCdu2T4Po6oeALYBJLkCeKSqfi/JDPCRBe7zxvnGk6yrqmPLlU2SdPJW65DUuiTvTHJrko8meSpAkvck2TlsH0ny5iS3AD+Z5PwkB5IcAF63SjklSQtYrcLYClxVVc8FHgR+YoH9Hqiq7VV1PfBu4Feq6gWrlFGSdAKrVRh3VtX+YXsfMLPAfjcAJDkTOLOqPj2MX7vQAyfZlWRvkr3H/vOh5corSZpjtQrjsVnbx1j43MlXnugDV9XVVbWjqnase9qGJYWTJC1uIt9WW1UPAg8muWAY+tlx5pEkTWhhDH4BuCrJfiDjDiNJT3apqnFnWDbrN22tTZe9ZdwxJGlVHdlzyUndP8m+qtqx2H6TvMKQJE0QC0OS1GJhSJJaLAxJUouFIUlqsTAkSS0WhiSpxcKQJLVYGJKkFgtDktRiYUiSWiwMSVKLhSFJalnog4ym0vM2b2DvSf7WRknS/FxhSJJaLAxJUouFIUlqsTAkSS0WhiSpxcKQJLVYGJKkFgtDktRiYUiSWiwMSVKLhSFJarEwJEktFoYkqcXCkCS1WBiSpBYLQ5LUYmFIklpSVePOsGySPAzcNu4cS3QWcP+4QyzBtOYGs4/LtGaf1tywePZvraqNiz3ImvqIVuC2qtox7hBLkWTvNGaf1txg9nGZ1uzTmhuWL7uHpCRJLRaGJKllrRXG1eMOcBKmNfu05gazj8u0Zp/W3LBM2dfUSW9J0spZaysMSdIKWROFkeSiJLcluSPJ7nHnWUySI0kOJdmfZO8w9owkH0ty+/DnN407J0CSdyW5L8nhWWPzZs3IW4fvw8Ek28eXfMHsVyQ5Osz9/iQXz7rtDUP225L88HhSQ5Kzk3wyyeeS3Jrk14bxiZ/3E2Sfhnl/SpKbkxwYsv/WMP6cJJ8dMt6Q5LRhfP1w/Y7h9pkJy/2eJHfOmvNtw/jSXy9VNdUXYB3wL8AW4DTgAHDuuHMtkvkIcNacsd8Fdg/bu4E3jzvnkOVCYDtweLGswMXAXwMBXgx8dgKzXwH8+jz7nju8dtYDzxleU+vGlHsTsH3YPgP44pBv4uf9BNmnYd4DnD5snwp8dpjPPwNeM4y/A/ilYfu1wDuG7dcAN0xY7vcAO+fZf8mvl7WwwvhO4I6q+lJV/TdwPXDpmDMtxaXANcP2NcArx5jlcVX1aeA/5gwvlPVS4L018o/AmUk2rU7S/2+B7Au5FLi+qh6rqjuBOxi9tlZdVd1dVbcM2w8Dnwc2MwXzfoLsC5mkea+qemS4eupwKeBlwAeG8bnzfvz78QHgB5JkleI+7gS5F7Lk18taKIzNwL/Nun4XJ36BToICPppkX5Jdw9izquruYfse4FnjidayUNZp+V788rAUf9esQ38TmX04zPFCRv9qnKp5n5MdpmDek6xLsh+4D/gYoxXPg1X11XnyPZ59uP0h4Jmrm3hkbu6qOj7nbxrm/A+TrB/Gljzna6EwptEFVbUdeAXwuiQXzr6xRuvGqXj72jRlHfwxcA6wDbgb+P3xxllYktOBvwBeX1Vfnn3bpM/7PNmnYt6r6lhVbQOezWil8x1jjtQyN3eS84A3MMr/IuAZwG+e7POshcI4Cpw96/qzh7GJVVVHhz/vAz7E6IV57/Fl4fDnfeNLuKiFsk7896Kq7h3+cv0v8E6+dvhjorInOZXRD9zrquqDw/BUzPt82adl3o+rqgeBTwIvYXTI5vivUZqd7/Hsw+0bgAdWOerXmZX7ouHwYFXVY8C7WYY5XwuF8U/A1uGdDKcxOvn04TFnWlCSb0xyxvFt4IeAw4wyXzbsdhnwl+NJ2LJQ1g8DPz+8C+PFwEOzDqFMhDnHal/FaO5hlP01wztfngNsBW5e7XwwehcL8CfA56vqD2bdNPHzvlD2KZn3jUnOHLafCvwgo3MwnwR2DrvNnffj34+dwCeGld+qWiD3F2b94yKMzrvMnvOlvV7GcVZ/uS+Mzvp/kdHxxsvHnWeRrFsYvSvkAHDr8byMjn3+LXA78HHgGePOOuR6P6NDCP/D6FjnLy6UldG7Lq4avg+HgB0TmP3aIdvB4S/Opln7Xz5kvw14xRhzX8DocNNBYP9wuXga5v0E2adh3p8P/POQ8TDwxmF8C6MSuwP4c2D9MP6U4fodw+1bJiz3J4Y5Pwy8j6+9k2rJrxf/p7ckqWUtHJKSJK0CC0OS1GJhSJJaLAxJUouFIUlqsTAkSS0WhiSpxcKQJLX8HzN/2PLgtLgeAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# 大部分为三等顾客\\n\",\n \"dftrain['class'].value_counts().plot(kind='barh')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# 大多数乘客从南安普敦出发。\\n\",\n \"dftrain['embark_town'].value_counts().plot(kind='barh');\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 0, '% survive')\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# 与男性相比，女性存活的机率要高得多。 这显然是该模型的预测特征\\n\",\n \"pd.concat([dftrain, y_train], axis=1).groupby('sex').survived.mean().plot(kind='barh').set_xlabel('% survive')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.构造输入特征\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"fc = tf.feature_column\\n\",\n \"CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck', \\n\",\n \" 'embark_town', 'alone']\\n\",\n \"NUMERIC_COLUMNS = ['age', 'fare']\\n\",\n \" \\n\",\n \"def one_hot_cat_column(feature_name, vocab):\\n\",\n \" return tf.feature_column.indicator_column(\\n\",\n \" tf.feature_column.categorical_column_with_vocabulary_list(feature_name,\\n\",\n \" vocab))\\n\",\n \"feature_columns = []\\n\",\n \"for feature_name in CATEGORICAL_COLUMNS:\\n\",\n \" # Need to one-hot encode categorical features.\\n\",\n \" vocabulary = dftrain[feature_name].unique()\\n\",\n \" feature_columns.append(one_hot_cat_column(feature_name, vocabulary))\\n\",\n \"\\n\",\n \"for feature_name in NUMERIC_COLUMNS:\\n\",\n \" feature_columns.append(tf.feature_column.numeric_column(feature_name,\\n\",\n \" dtype=tf.float32))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"可以查看要素列生成的转换。 例如，以下是在单个示例中使用`indicator_column`时的输出：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Feature value: \\\"Third\\\"\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING: Logging before flag parsing goes to stderr.\\n\",\n \"W0331 19:59:16.792093 140069985818368 deprecation.py:323] From /home/czy/anaconda3/envs/tf2_0/lib/python3.6/site-packages/tensorflow/python/ops/lookup_ops.py:1347: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"Use `tf.cast` instead.\\n\",\n \"W0331 19:59:16.879169 140069985818368 deprecation.py:323] From /home/czy/anaconda3/envs/tf2_0/lib/python3.6/site-packages/tensorflow/python/feature_column/feature_column_v2.py:4307: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\\n\",\n \"W0331 19:59:16.880531 140069985818368 deprecation.py:323] From /home/czy/anaconda3/envs/tf2_0/lib/python3.6/site-packages/tensorflow/python/feature_column/feature_column_v2.py:4362: VocabularyListCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"One-hot encoded: [[0. 0. 1.]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"example = dict(dftrain.head(1))\\n\",\n \"class_fc = tf.feature_column.indicator_column(tf.feature_column.categorical_column_with_vocabulary_list('class', ('First', 'Second', 'Third')))\\n\",\n \"print('Feature value: \\\"{}\\\"'.format(example['class'].iloc[0]))\\n\",\n \"print('One-hot encoded: ', tf.keras.layers.DenseFeatures([class_fc])(example).numpy())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"可以一起查看所有要素列转换：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"W0331 20:00:18.112254 140069985818368 deprecation.py:323] From /home/czy/anaconda3/envs/tf2_0/lib/python3.6/site-packages/tensorflow/python/feature_column/feature_column_v2.py:2758: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"Use `tf.cast` instead.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"array([[22. , 1. , 0. , 1. , 0. , 0. , 1. , 0. , 0. ,\\n\",\n \" 0. , 0. , 0. , 0. , 0. , 1. , 0. , 0. , 0. ,\\n\",\n \" 7.25, 1. , 0. , 0. , 0. , 0. , 0. , 0. , 1. ,\\n\",\n \" 0. , 0. , 0. , 0. , 0. , 1. , 0. ]], dtype=float32)\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"tf.keras.layers.DenseFeatures(feature_columns)(example).numpy()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"接下来，需要创建输入函数。 这些将指定如何将数据读入我们的模型以进行训练和推理。 我们使用[`tf.data`](https://www.tensorflow.org/api_docs/python/tf/data)\\n\",\n \"API中的`from_tensor_slices`方法直接从Pandas读取数据。 这适用于较小的内存数据集。 对于较大的数据集，tf.data API支持各种文件格式（包括[csv](https://www.tensorflow.org/api_docs/python/tf/data/experimental/make_csv_dataset))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Use entire batch since this is such a small dataset.\\n\",\n \"NUM_EXAMPLES = len(y_train)\\n\",\n \"\\n\",\n \"def make_input_fn(X, y, n_epochs=None, shuffle=True):\\n\",\n \" def input_fn():\\n\",\n \" dataset = tf.data.Dataset.from_tensor_slices((dict(X), y))\\n\",\n \" if shuffle:\\n\",\n \" dataset = dataset.shuffle(NUM_EXAMPLES)\\n\",\n \" # For training, cycle thru dataset as many times as need (n_epochs=None). \\n\",\n \" dataset = dataset.repeat(n_epochs)\\n\",\n \" # In memory training doesn't use batching.\\n\",\n \" dataset = dataset.batch(NUM_EXAMPLES)\\n\",\n \" return dataset\\n\",\n \" return input_fn\\n\",\n \"\\n\",\n \"# Training and evaluation input functions.\\n\",\n \"train_input_fn = make_input_fn(dftrain, y_train)\\n\",\n \"eval_input_fn = make_input_fn(dfeval, y_eval, shuffle=False, n_epochs=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.训练和验证模型\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"在训练Boosted Trees模型之前，让我们首先训练一个线性分类器（逻辑回归模型）。 最好的做法是从更简单的模型开始建立基准。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"accuracy 0.765152\\n\",\n \"accuracy_baseline 0.625000\\n\",\n \"auc 0.832844\\n\",\n \"auc_precision_recall 0.789631\\n\",\n \"average_loss 0.478908\\n\",\n \"label/mean 0.375000\\n\",\n \"loss 0.478908\\n\",\n \"precision 0.703297\\n\",\n \"prediction/mean 0.350790\\n\",\n \"recall 0.646465\\n\",\n \"global_step 100.000000\\n\",\n \"dtype: float64\\n\"\n ]\n }\n ],\n \"source\": [\n \"linear_est = tf.estimator.LinearClassifier(feature_columns)\\n\",\n \"\\n\",\n \"# Train model.\\n\",\n \"linear_est.train(train_input_fn, max_steps=100)\\n\",\n \"\\n\",\n \"# Evaluation.\\n\",\n \"result = linear_est.evaluate(eval_input_fn)\\n\",\n \"clear_output()\\n\",\n \"print(pd.Series(result))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"接下来让我们训练一下Boosted Trees模型。 对于增强树，支持回归（BoostedTreesRegressor）和分类（BoostedTreesClassifier）。 由于目标是预测一个类，所以我们使用BoostedTreesClassifier。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"accuracy 0.829545\\n\",\n \"accuracy_baseline 0.625000\\n\",\n \"auc 0.873003\\n\",\n \"auc_precision_recall 0.858218\\n\",\n \"average_loss 0.410594\\n\",\n \"label/mean 0.375000\\n\",\n \"loss 0.410594\\n\",\n \"precision 0.793478\\n\",\n \"prediction/mean 0.381616\\n\",\n \"recall 0.737374\\n\",\n \"global_step 100.000000\\n\",\n \"dtype: float64\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Since data fits into memory, use entire dataset per layer. It will be faster.\\n\",\n \"# Above one batch is defined as the entire dataset. \\n\",\n \"n_batches = 1\\n\",\n \"est = tf.estimator.BoostedTreesClassifier(feature_columns,\\n\",\n \" n_batches_per_layer=n_batches)\\n\",\n \"\\n\",\n \"# The model will stop training once the specified number of trees is built, not \\n\",\n \"# based on the number of steps.\\n\",\n \"est.train(train_input_fn, max_steps=100)\\n\",\n \"\\n\",\n \"# Eval.\\n\",\n \"result = est.evaluate(eval_input_fn)\\n\",\n \"clear_output()\\n\",\n \"print(pd.Series(result))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"用训练好的模型进行预测\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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nt+3qSNDfaDhYDbA/cU1WnAWubW0VIkua5to+qPBl4E/DmZtHWwN91VZQkaXTanhH8LnA08AuAqrqVGS4blSTNL20Hi++rqkpSAEke2mFNkrTF2JTxwlGNFbY9Izg7yQcZfCv45cAF+JAaSdoitL1q6K+aZxXfA+wH/HlVnd9pZZKkkZg1CJpvBl/Q3HjOX/6StIWZtWuouYPog0keMYJ6JEkj1naw+OfAd5OcT3PlEEBVvbaTqiRJI9M2CD7bvCRJW5j1BkGSJVV1U1V5XyFJ2kLNNkbw+cmJJJ/puBZJ0hjMFgQZmn5Ml4VIksZjtiCoGaYlSVuI2QaLD0hyD4Mzg+2aaZr5qqqHd1qdJKlz6w2CqlowqkIkSeOxIc8jkCRtgQwCSeo5g0CSes4gkKSeMwgkqecMAknqOYNAknrOIJCknussCJLsmeSiJFcnuSrJ65rlOyc5P8kPmp87dVWDJGl2XZ4R3A+8sar2B54CvDrJ/sBJwIVVtQ9wYTMvSRqTzoKgqm6rqn9upn8GXAPsDhwDTD7fYCVwbFc1SJJmN5IxgiRLgYOAy4Bdquq2ZtXtwC4z7LM8yeokq9etWzeKMiWplzoPgiQPAz4DvL6q7hleV1XFDLe3rqoVVTVRVROLFy/uukxJ6q1OgyDJ1gxC4ONVNfnM4zuS7Nqs3xW4s8saJEnr1+VVQwFOB66pqncPrVoFLGumlwHndlWDJGl2sz2YZlM8FfgD4LtJvtUsewtwCnB2khOBG4HjOqxBkjSLzoKgqr7Obz7zeNjhXR1XkrRh/GaxJPWcQSBJPWcQSFLPGQSS1HMGgST1nEEgST1nEEhSzxkEktRzBoEk9ZxBIEk9ZxBIUs8ZBJLUcwaBJPWcQSBJPdfl8wi0CZae9MWN3nfNKUfNYSWStnSeEUhSzxkEktRzBoEk9ZxBIEk9ZxBIUs8ZBJLUcwaBJPWcQSBJPWcQSFLPGQSS1HOd3WIiyRnA84A7q+oJzbKdgbOApcAa4LiququrGiRpLmzKLV/mgy7PCD4KHDFl2UnAhVW1D3BhMy9JGqPOgqCqLgF+MmXxMcDKZnolcGxXx5cktTPqMYJdquq2Zvp2YJeZNkyyPMnqJKvXrVs3muokqYfGNlhcVQXUetavqKqJqppYvHjxCCuTpH4ZdRDckWRXgObnnSM+viRpilEHwSpgWTO9DDh3xMeXJE3R5eWjnwQOAxYlWQucDJwCnJ3kROBG4Liujt9n47rUrY9PRvNJcvPHln4J6KboLAiq6iUzrDq8q2NKkjac3yyWpJ7z4fXabNjNMjrj+qw3tXvGf+dueEYgST1nEEhSzxkEktRzjhFI81QfL4fsY5tHwTMCSeo5g0CSes4gkKSeMwgkqecMAknqOYNAknrOy0fVe16SqL7zjECSes4gkKSes2tIW4T52L0zH2vWlskzAknqOYNAknrOriHNGbs6pPnJMwJJ6jmDQJJ6ziCQpJ5zjEDSBnEsaMvjGYEk9ZxBIEk9ZxBIUs+NJQiSHJHk2iTXJTlpHDVIkgZGHgRJFgDvA/4zsD/wkiT7j7oOSdLAOM4IDgGuq6rrq+o+4FPAMWOoQ5LEeC4f3R24eWh+LfDkqRslWQ4sb2Z/nuTalu+/CPjRJlU4P9nufulju3vX5rwL2LR279Vmo832ewRVtQJYsaH7JVldVRMdlLRZs9390sd297HNMJp2j6Nr6BZgz6H5PZplkqQxGEcQfBPYJ8mjk2wDvBhYNYY6JEmMoWuoqu5P8l+BvwcWAGdU1VVzeIgN7k7aQtjufulju/vYZhhBu1NVXR9DkrQZ85vFktRzBoEk9dy8DYLZblOR5CFJzmrWX5Zk6eirnHst2v2GJFcn+U6SC5O0uo54c9f2tiRJfi9JJZn3lxm2aXOS45p/76uSfGLUNXahxX/jS5JclOTK5r/zI8dR51xKckaSO5N8b4b1SfLe5jP5TpKD57SAqpp3LwaDzD8EHgNsA3wb2H/KNq8CPtBMvxg4a9x1j6jdzwS2b6Zf2Zd2N9vtAFwCXApMjLvuEfxb7wNcCezUzD9q3HWPqN0rgFc20/sDa8Zd9xy0++nAwcD3Zlh/JPBlIMBTgMvm8vjz9YygzW0qjgFWNtPnAIcnyQhr7MKs7a6qi6rql83spQy+pzHftb0tyV8C7wL+ZZTFdaRNm18OvK+q7gKoqjtHXGMX2rS7gIc3048Abh1hfZ2oqkuAn6xnk2OAj9XApcCOSXadq+PP1yCY7jYVu8+0TVXdD/wUeORIqutOm3YPO5HBXxHz3aztbk6V96yqLeXxWW3+rfcF9k3yf5NcmuSIkVXXnTbtfhtwfJK1wJeA14ymtLHa0P/3N8hme4sJbZokxwMTwDPGXUvXkmwFvBs4YcyljNpCBt1DhzE487skyROr6u6xVtW9lwAfraq/TnIocGaSJ1TVg+MubL6ar2cEbW5T8a/bJFnI4BTyxyOprjutbs+R5HeAPwOOrqp7R1Rbl2Zr9w7AE4CLk6xh0Ie6ap4PGLf5t14LrKqqX1fVDcD/YxAM81mbdp8InA1QVd8AtmVwY7YtWae35pmvQdDmNhWrgGXN9AuBr1Uz6jKPzdruJAcBH2QQAltCnzHM0u6q+mlVLaqqpVW1lMHYyNFVtXo85c6JNv+Nf57B2QBJFjHoKrp+lEV2oE27bwIOB0jyeAZBsG6kVY7eKuAPm6uHngL8tKpum6s3n5ddQzXDbSqSvB1YXVWrgNMZnDJex2AQ5sXjq3hutGz3/wYeBny6GRu/qaqOHlvRc6Blu7coLdv898BzklwNPAD8aVXN67Pelu1+I/ChJP+NwcDxCfP9j7wkn2QQ6ouasY+Tga0BquoDDMZCjgSuA34JvGxOjz/PPz9J0iaar11DkqQ5YhBIUs8ZBJLUcwaBJPWcQSBJPWcQSFLPGQSS1HP/H6Af/DWFM1tyAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pred_dicts = list(est.predict(eval_input_fn))\\n\",\n \"probs = pd.Series([pred['probabilities'][1] for pred in pred_dicts])\\n\",\n \"\\n\",\n \"probs.plot(kind='hist', bins=20, title='predicted probabilities')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"观察roc得分\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.metrics import roc_curve\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"\\n\",\n \"fpr, tpr, _ = roc_curve(y_eval, probs)\\n\",\n \"plt.plot(fpr, tpr)\\n\",\n \"plt.title('ROC curve')\\n\",\n \"plt.xlabel('false positive rate')\\n\",\n \"plt.ylabel('true positive rate')\\n\",\n \"plt.xlim(0,)\\n\",\n \"plt.ylim(0,);\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "040-App/002-style_transfer.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-神经风格转移\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"本教程使用深度学习以另一个图像的风格组成一个图像（。这被称为神经风格转移，并且该技术在艺术风格的神经算法（Gatys等人）中概述。\\n\",\n \"\\n\",\n \"神经风格转移是一种优化技术，用于拍摄两个图像 - 内容图像和风格参考图像（如着名画家的作品） - 并将它们混合在一起，使输出图像看起来像内容图像，但“画”在风格参考图像的风格。\\n\",\n \"\\n\",\n \"这是通过优化输出图像以匹配内容图像的内容统计和样式参考图像的样式统计来实现的。使用卷积网络从图像中提取这些统计数据。\\n\",\n \"\\n\",\n \"例如，让我们拍摄一只这只乌龟的照片和Wassily Kandinsky的作品7：\\n\",\n \"\\n\",\n \"![](https://tensorflow.org/beta/tutorials/generative/images/Green_Sea_Turtle_grazing_seagrass.jpg)\\n\",\n \"\\n\",\n \"绿海龟的形象-By P.Lindgren [CC BY-SA 3.0]（（https://creativecommons.org/licenses/by-sa/3.0），来自维基共享资源\\n\",\n \"\\n\",\n \"![](https://tensorflow.org/beta/tutorials/generative/images/kadinsky.jpg)\\n\",\n \"\\n\",\n \"如果康定斯基决定用这种风格专门描绘这只海龟的照片，那会是什么样子？像这样的东西？\\n\",\n \"![](https://tensorflow.org/beta/tutorials/generative/images/kadinsky-turtle.png)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/doit/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n \" from ._conv import register_converters as _register_converters\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function, unicode_literals\\n\",\n \"import tensorflow as tf\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import IPython.display as display\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib as mpl\\n\",\n \"mpl.rcParams['figure.figsize'] = (12,12)\\n\",\n \"mpl.rcParams['axes.grid'] = False\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import time\\n\",\n \"import functools\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"下载图像并选择样式图像和内容图像：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"content_path = tf.keras.utils.get_file('turtle.jpg','https://storage.googleapis.com/download.tensorflow.org/example_images/Green_Sea_Turtle_grazing_seagrass.jpg')\\n\",\n \"style_path = tf.keras.utils.get_file('kandinsky.jpg','https://storage.googleapis.com/download.tensorflow.org/example_images/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg')\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 可视化输入\\n\",\n \"定义一个加载图像的函数，并将其最大尺寸限制为512像素。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def load_img(path_to_img):\\n\",\n \" max_dim = 512\\n\",\n \" img = tf.io.read_file(path_to_img)\\n\",\n \" img = tf.image.decode_image(img, channels=3)\\n\",\n \" img = tf.image.convert_image_dtype(img, tf.float32)\\n\",\n \"\\n\",\n \" shape = tf.cast(tf.shape(img)[:-1], tf.float32)\\n\",\n \" long_dim = max(shape)\\n\",\n \" scale = max_dim / long_dim\\n\",\n \"\\n\",\n \" new_shape = tf.cast(shape * scale, tf.int32)\\n\",\n \"\\n\",\n \" img = tf.image.resize(img, new_shape)\\n\",\n \" img = img[tf.newaxis, :]\\n\",\n \" return img\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"创建一个简单的函数来显示图像：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def imshow(image, title=None):\\n\",\n \" if len(image.shape) > 3:\\n\",\n \" image = tf.squeeze(image, axis=0)\\n\",\n \"\\n\",\n \" plt.imshow(image)\\n\",\n \" if title:\\n\",\n \" plt.title(title)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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+caD64bQ30d+wh8vYa2PQiQ9YENxnOo9m5hCm6gdvO9OPye/infzoMtLPs2ovIk2H6FAe91kK+WXwCVpeGuvttTjNG8CiVlLkq8QV2Y2oPcucfHmFtiYE7Hl3OKy+sZNuGf7H6y9+nt1LlBUVh/R/v4oLLp/LN8/P51Z1v8+qWGTj963nwR8fx0g+38bH/amT1X3fTffZUXrAfzylnXMnyX/+OuCWGXUSoKrDwzCMeYv1WhDJEXIujGhW0OBikYJrRwsN/KSXhauTZjZ+lJ6bhL6nAsN1Hq2k129t7CMUsxAbjPPftLjYtdxJuq6cwWYCaaCXUtIIa1YAloiFNSfSiPGKiFm3qTLrnzeEM23a2PPUMyfYAx92UT79hCp6NCsHn3PT7tiNDffSccD6POIyUFJUTGGjGZLDgjenkuYrIL6qnwycxGvPR4xJh0EhEFexmJ7oeor/PT3llKQZlCCmH0BNJbC4rx9Xm09LRTpNXxR8JYTK7aP73ryb04h4ZRD/WeSrYFzueOUVX9DFT/H1EIzPzDZ8Pa52ZzlhAkr5GmljEaGfmgZyfw+VGQc/ow6iLGepeFmdjdlIbjmhJa9iKknIuDvcN9kUZjSJQ9jfJjEea2ZzPw2aV4fuU2Z+x9RqNo6+PZ4IaPs+MijqQXONhOLImq0ltf8JP3a5U+2Ojud4j3nHosOpUUWaaeLo5zqr+szkj/ArdwsE31X66g7X4OzvpWv9rFhW4ecFaQYexkqEda1Ed0D+YwGRWWLyshnDSg8kmEaqNWMRE9yaJr19SVQQe7xBhdYi6OUaSLgtLNq7D6ugjUvBd6rTXGehN0u8bIu74OdbOQnpsMRJxDWuhg9CcKkonn0LXnkU4/e0INYp5wESTNwwOWGp1oOoxklJlUC/gXzULaNLDJKmgsGQekcHNqEMxBkOtRDUvigjQamnB6liCz+dj+d/+wIPHnk7Rl7/Aiuc38+Lqe5g5LULz03v4zGPw8P3fJBp5hgLTx7H376I2FMazzcHCmybxy11H4W0z8Ouf/hGLt5e4yYKzyErTxgBzFi1h+4sv4UkYkUmFZFzD5S7CFA6w7Cor5fFKHrx+iMn9t3JeuIke22RCF88nf8EAP1bz8Ogm+gM9bP9aC2feZKFn3jqcxtlsXFuIajuD9ngAe9hCSE2SP/koXvOE+Mzio5B33EpPSyf1jnwGYhbC4oskgxo9r7zK9KmVtG9ch/fYhfzVVoBfCTLHHacxr46BJPTsasMf8qCabJQVTiIQjxDVzdisZiBCNNFHga0IS72LcHSIYHwIh9XOUPtmnHmT2NASJh6MYjEVUV3qYHNLOxMNejoyiH4YIyQxTrKijNbksn3xFWUUYQlFQU7EgJ1BPllj8Y8gpBaU6fsRplDVdOjiO54Opf4Pr0tIXxtlX/+wYjxT0bvAuwkdVpI6dTPdvL7cRCwvxHHuIaZEXDxrncmJS2o4ZvABbKXV6GEjRTJOvSFEsWsBwhjHoHsJNvfg3dGJajXhCwewWVRCfi+TGx14S4JY3W6m5NkJyRB5pgR7LHFu2+3mes2Cu+Bt1nfPobK2nDZ9KwttXQSSb5P0DILiIG6yY68w4OlZzT+O+x6a0U1e1xBdwQKaixbR7lMJP3w7FRJ6jHZ6axuJn9iIeSMUl9QyRbZR9cZGrMlBArEY/aYopcWTKa7fyJ6Bqdz4tc9Q293P5k9dSV6tStuuB1h//0ysea0kYkP0eRsY6tqAyWbH2fwWW+4b4Ik6IyccdxV/+ctjWAM7mBUrYKknxvTShby143WkNY7RaOd5YxRbTS2GVh8D8QhSMSLDfiprVT59qYkffmyAreddzfq9UNe7me8nVjLrrntImIqY3/Bxnn+xiUqrg7xEFeZkNUN7VtPXtomGimoKT9TJry9gVxOs3mImJvuY9upLdD/7f9QaBcG4jaZwBGNFA56OTnavbqLBXsXW5SswnX8Vm4rLCHbvpsbhRlMEQUViHvIxtaqESDxKcWkBA0kjTqNOZZmN1lACs9WIJuIkNQ8R1YRBxMmz5hPZ9RbCv4VL+5q403ommrmaeGKAWE+EEoeNfoN6oNdvBEcG0cvRYYAym3YKo0lsPOccjDIpDGvs40bPSJkiyGyaXzqmftTK2GyDwHj26oy+TRjD9RoyH82+UMuUWBkrTTPkkeOFkB6E+EeZkEaJPs6Ad6BQ07EziqwhkROd3WSp8yD1ZK3rEI7XUsqngacnnD+i0bmine8au7FOaqbaacVb4mLhG+vpb9nMFpwUBL3MmWXBpmtE413syJuC23wcsYG7iRDFOyApmGRj2pJJeHqDFFiSdHT6iBpMKIWSTm8M70A5ZllAvMaFZSgPmdjDJ60a31v+AOdf9kkizlPppxrT4J0k9XyC4TBGd5wh1UKRuQhl99PEq89jubcBo93MljZwKP0MvvI8dcLKgNPEr45J0Lx6gMKGpZSU9FPnGmT+8q24dNhocdKjafzPL0J4o1XohgFKXtnK6zLE/LptONjI337USN+eFqbN6SOhQGFBKwZDKa9cu5maGyex4YoFbF8V4/Ptm7mweCZKt4fmwW4qtCoGfYJFJafgT3hJJlq5YqtEmKfxi9AKpCqxCAO6JcHPTq5n3SVeXl58Ea/+7ksEHQY0b4LZJ3yXJ03/JhE3snP9LhqPOYsn73uIs574DTtXv0h+VQWJ4GtMOufLJDxxdvZsJtqh0Nj6Nq4378Oq2dDLqhgQRjxSo7SyFs1QwMp73uJjC45nU/Nenl18KdqMqazd3U+BJUBhmQtrXGEo5iUc9lDuKkZEDeQ37WWat50yXwA9AT3FTtoKC/CVFJHwhslzWdF0CxVaP/1tL7BkyYn0DG5g9pbHOHXGfOwNKrGgFRkU/CA5MRPmkUH07xaHUuPOZvJIk+ZBtdr3Gko5AbwjTf2Dnokcap/BWGQOwsMmpiN0tjUW5nwzJ55mJnF6Cb5gHG3SBRRseRjL6QK/0FH9VWyNHs3MKUneXrma+iJBY+0OolNnYByoodTkobozhiURw7+5C3vUiN8fId9pI5kXw2Dwkm8tprJIIRHbjt0+nb9OOQnTq8sRrz/M1JO+SKK4gs0+G6EwHF80FUOsCYvLjSN/GTGxlL0xgar28PxbhZS4ChlojzCnqI0i0YZU43gTJTw/24a/eYDB5PHYLBq2wgSXm/PRFSOeZAJ7cQHXfbOOkNlNq+cETGsepr0zjP+oECeU7KWtq56mp/oQshXf0W5MRMgL17L9xztZ+sPjOWvlPBy7Bzm7fxfmEzxc3bCQ5tB2qopctPVqFHStI5I/lW61hGpjOfECE091D+AsOQFb3+s02wLccfIcJj20ldurz+OlR25BEMESthDOM2AxO3FFuuibdhIv37qFyDPdXH3N93g7Hmbyx88gSiEN530RXXHgSOg0xmeyybiVsvseQLE6iLuLCCkJfJEgloYKogVu9LiLBdOr6ZB9DM2dyaKTT2BypJeOgQG6emxs2zbIlVOjyG1NOCIubB0hREJHCcSxSgsxo4mdU+uJ1JaTDEuqhZUBlw0pTKiagwZLH1/8f58mUVrJintWc5RupHvVa7j31nHcxYuIWU3EnvgwmW6EAKHss7OPB2WM3T4rssTKZ7PxDpNztqibsbbpsUQ+EiY4LJeaRWscqwVniphFIx2Zhch9/8eGiB5owMm24Eg5iMY9Is4YE9BEncQHMHFlrgHYb7Y03JeMVcgHxNgVxMOzFiFG7P9CHjYH7QERDkoirSF0RUNzNCA3PIvFP4jfZ0HEE0wxDFGdF+OGfzRw6qwZnMibdO+2E931IvOu+l92/ejrRBJW1vkT9E9fRMeuXSytdLNAMaF0dmGKwYDRz0BAUlao8K9FZXxp4DHsbgNKezcXOXW2b9zCwin17I6V8az3KvKMCfKTHkoxY8rT6BtK4AnVMsXQT6HswGXooiK0h6I8O3tUEx3FcU786izuuN6PrSgfl3uQomiA1t/9gWMTggKTHVdMpWiqF2fh/zLU0Yph0x48SyLMnrqJDU84sMz/Em9dWMOJK28mvnYd5kmSZ298kweOncmG5ZP41JvbOcevEbFUU9zrJFSpkwi/hLvHzVR/kKBRI88XwFkwi9tDebRGAuQ57WBzUOg6ncGmJznv+R0876jitcI5RAgihJUhrY/FZ/2J2wKvElxSzfnXv4gvomIRfWzatpuPnVzN7k2raJhzProSI2mwkBAKLd0q/7rhFi41BBnKr8JuclJTnI9Uh6j9xOl4N4QZ6PPzxopXmHzVOVTOnk6tcS0GSyc//fgCntw7Cd+2FZS1Po5dFZTENEJtGsGYARE34rQVsGn+HOJz6sgPK+gWMx7NgaaVgoDekkF+94mPs/W2a5l3/hJetcK0s2ax4ck3UfRe7vr1nUxbdiZS/TAR/UTwXhS4bESkKMjh6JFDVOdhqeN9wKGwy39gtv10OyNjjjxkJvlDAqPFSUtsEQP5bo4/5Rx631xNYnoRUyoqifg7UIJeLKqNs3sfYeULDm4vuICvLTRz0aJZvNmzi9rrHifRn6DCN8j2rS+RXFLFXncdnTWzaW7poticxK8FsVmCvL1hDRf1FfCS3I6h1IjJlSDx5x+xwF5DIhohWWGmfPZ8WqadTriwjM72LdQ71jC/vALdoZIMaCQTkpryIjSPRmfMSON9n2Llxtf400/WknSejcnUCWGVDe1bOeaoyXS29eOJGxFn2KgtuIjB8C5OsZay5SSNSXN3seWfedSZGnmhx8IrHaspPe1PeJ85nZdbzGyom8upm3xct70ZLS+Mbi1FFhSw7ulBzjpGUnXe1ez8+4M8Xaxy6fYIrdZOFpWXo+1oIZY3i1ZjAdJoRARbedVqQSb9rC2fjdNRw2sv9/CHu59k78ZeVjbdj3amld3VDmKrIIkgpho5qiGPjbuMLK0+isDgdszmYkw2O0N+H2oezLdHEfEifEoeSVuc8PQytthLqI3m8UZnF9MGB5nzuYspOFrgSDyJM7+ELW+9RHnlNq6oqsI0OY8B70loba0k3+zBqgSoCkeId8XYaTOydbqT8r5uIs5SNM1APKxR4BrgnBOns+oXd/DCL79CgSHEmrUbmPW148mvrmJ69RQMZidVVjcuUwD7c2N3j86ODw/Rvwdk3URM1989ycMYLfNQ1HEE0dPBdqN8p3W8nwPacDtH0O3LhBKLEGoNsmDuKTzzzRvojPdjLHchjElOOnEhpmiQSLCfhkKdupJePuXzUN5dx/IXffSqNSw4PcQxiQj9HZ0sqljCgDPJ+teeZJI+xGSznbrSPNa17eKNles4/7SvUV41j+Iln2Xtqts42tsGdUGMukrSYcVR20BrkUKd+xWKzaUYC6roU4/Fp6nYlRCySCUZjNExEGFXtIbHntnA6y/8G4P0UTe9BuGXmGwdaAENKQL83hUgXhbBpnj458dmQjCM1VDCznVraVy4lWQ4gdkv0E65hBMcFlb2bqFU62KHVs2mjTG+0NrMsTNmoRYlMYo4ezES1hwUFBby/JO7OevqRRT+/e/MMNpYV2xhiuaiOdbNDVMM/N3TzivWAgJanAGLgyeKa7hscDtn97bxVGQH3/6vnSwNNXOb7zH6pqvE87z4DTUkbWZkKIbAwDEziqivSmCIxun0GnBafBjicTp9fcydVMxmk4JEobq2kHh1FbbLL2Bo5VP07eylhgSxyVVsfnYbM2sWYLLmExrw47LUUmAsZPvuzZQUuygodzNkKkOi4zNDqCOCLxZm7eRKQhFBd3CASGAAk55gcuPxOMtn8LsntjG58kKSCzfi2+VDKbazcMllvLWlA5eji3gyiEP1YgjFGRrKukP0fjhyiD7TRKIoo80OQmQPaxxe6apkCbPMyJO5gGdUewcTaYypaJSWOuo8S51jzUXZzECZS/XHEup4ZiPYt5nbwfowzoKmg2IiIZoHQ7ZBbLjeLFsxZ42jP9jq17GQEqlnuV+HcRCQThuJi04h6uzi5C8V0hUyoGlm8szT6N2SRI3XIEwNVJmN9B/dRLElTolT4H9pLV1zasgvnsqtj/6dixtrcFrjPL+jnxOOWcaTr95JbV0tm7uCNO3yMm/2pzFay+mQXoJRI93zTubJUBCrzYm/bw8uGUPDW6+ZAAAgAElEQVQpKcPm66Cm7GhM9pns7fXhVsFhtNEfUen2m3mzo5v7H9mK223FrXiQkU4ceRZ+8M0ZfPH67SSCUymfbsJqsVDoLuf02fWU2vqIRrch/C+hJ/spm1WOR9FQFMnd/QOc/NrfmDx9DtfRjdGymxOuWkT/tx9FRvtpD5aiOPPI1wz0B2L0BKA/sJfeDo2Fn8nn1JtvZOdnv89qi5lbGvpxVJRQ0BWnwpKguKuFoWglCZON3zS3ci1GEmxgVc8WnBYNoWu0n1CCnBSj0mol4c5HTw5iIE5MsbHizdeItMZwJH3899JJhBSwChdmqwtd1/CbLCSsCnrYi5y8gJ/ffytXzV+MY68Pf9REKB6jYfcqHNoiUCcxFNpOQXkxPQkvdXMXo0XjBHr6iEYFBZOmETWYSNRHMJh62WB2k+fxEvAFiYaSHD25AX+/l67BVdi83djzC+mdtAS3pYL8QBNt/UO49TWYTI1ocS/Ftjp+d89aEll2fc2GI4foJ4KDEJvUNJhguNH7jgPJ+k4J7FDiCA8d/ahB0SNMKTNhan2NZq8gHptOZ1cxqiyhyqATNXkZGtAxlpiItlaSXz+NN9s1rNg4u3ESkY7NfK5hJrdufJjvLDmXRVPcrN/6Nj+67H9oad3FjtY+jr98KWaTwGVNEnYZsQx1M8tYhcepsSMwyNz6Y6isKKfAXUaRSSXa0Up/IEh55RQef72Jzbt38eKajehhBXQTl/3gezQ/9yeef/xuPn3GNK68ch5xrYV/3HoKtzxq5Fc3XE1FeQxzohkl4CQU28nenm4szimYBvbi977Oy1tvQDe9Rkf5Wm7pjGEeaGZ+32R2bn6MP/ysk2tuNNF8uYpr7XOEv/obyrespXkwyZBvNyZnPu6kGbFrLd5TL8V6wTxOeT1I48ZuOtf0svmiWbjsRkzrt6MXFWNISAKEua+wgGOMCbRqF5tqi4mXuAg5wrz8VBsza4O4r7iI8slP0r3zTXQtxnmtdzN13iA3bzmBH7xqJz/cxLWX1FJvVwlGwlz87WvZfOOPcNocaIoZt6uI7SufZ8orXgomz8T51FoSxBnyKESLI1gTZhLBXgxEEMkewmGFogozMlpLa6cfa2ExycAe+hZcgWntHtpaN9O0a5BYMsjnzlzAb279Cb3GCPmRYiadcBy/f/5Zzlg8iVMNIRyah35/B7jqSPYN8Jv1ZoTVlVL6JvIevs/v+cQh9FRksiJJ/RCNBKmnFk4pGZrgqD1ZAEUgkamQTEPaKSr2paGMcZwOYyzJDufNOGR6IZUUpJfaZywmGquNZwtBHOvAHW9B06iZgARNRyAQkpH/6GnjsyR7e2P7JjPyZx5jteqDDTrD/TjQcVAoqZuoM3px2Rinr8x8vpnHKGhpQ7wOurbvvowskktvBjfs7IVDGl75TuEPJvA1v4XHDw7VQjzuwGEswZZI4A/E8IYNbHplBdv+8Xc8L9/Hzr/dwqTYUzRMmoHPVYWn18Pb+gDfOfZkfvjsOsLCz9Hzj0IJDlJvLuDseQuZUZCkUHTjMiYI9bdTbyggIK3YKoqYVVzO4ob5zKpaQKmzkeCQjaqGaQzqNv7fPSv56Z+f5uVVTehBnaTQaTxhET09g7x07x3U1bi4+fqjINlLbVk9Cyzb+f4VJ1FXuIeY7/cMhFsQYQ1DU5hJDVdQVHspxsp8imURBa61rN7sJ5acRWeLgQsTjewV5eTv7uaFL/QQqu5jdSJEVGjof7qWHUNmTGqYQlMCc6KPcruCsX87tuQ2Jn3rNpjRQPXMOmrn1nHJC51M/ec65lsN5OXbCPWvIEGSa4MKn+izc25bgK+/McDG5hLmTf8GVlcSkcin8K1nuPUTyyg2FWHT40x3meh78p/87CwvBYluVGchv3nqNYLCgNGiokybik9KjLEEVY4imnZ10VFeiElPoL61CkUMglGhJ9KPS65Ft75ASBi5+RebCBsKUZwavV1OAsEdlJWEeKvlKN5yX0ei5Fju+NaV3H3L1/nlLTdj1+E7P7qd+gU2lp1/DZd87RIef/EFTBXlLNW6iZlLSJJAiem49QE2rmnDNOCjpGcnhgmaVo9cjf5QhiweKM59ohhLau/Wdj22zg8C73f44wcJRRnfFDMyaH1g0hwUJlcxzU++zsmfOJO4zYxm7qEjEWUo6cTk8mMorWfKC0HCRo0iSzV/qFzIgu5unu5+hLy2NzBKna9e8AX+uHMz63cYufySPsL9fpJqHQGhkZ9vYzDpw+QswlFUznENM0jEVIpMEjQD1nyJohjwDYUw5bt58431vF1cx65YJTj9XPDlY+keiDLo1Wgss2MwGdi6fj1mc5zf3/RxEvEoZZUuoj0bCbQMMDT0K3qnnYNVf4JBrZmN/3UPU+JObFefiunC+YQ6d5N0hFn51hA7ApV0tRgw+ozc1+TDpmh8ydPOnF4vwWuruWh9Pa+c1spxfX42VbmZJRTqkgNE4gkMsQ6a9ryB6dFm5v+/+9AXzCRvwSlo/k10PrKFo+adQ9umt/Fu+xcy1IwmwK7acAobS41+zLE4c8osPP7ooySCOrgiWLwJ7v7b43yi8Qxcwd1YxAbKhYPSxB6+f/UyvnD78yxZciKP7+nkE6VmtGQc01ln4Nu4ha67/syXPnMaT3S+QeSqUzH+9UW8hU6Ex89xtSqXf/0xQrEylMQq7LY45378WU5dUsYXvzSPZ9YVI8sWUVxh4vpvXY0S8VBXbuSnP/0d133vy2iKER+CHVtiNM54jfrSIqzHncc1lVEcJ82n0VrMmtfuoGHaXLydfbTKStq9nYSEGWGYGIUf2UR/KMgUDs0Kz7FEnzllUt/lbTwUTs+JYFTfP+REPza0NRMjGvyRM1HVkhoLLrycV1a8xsVXXEpCH8LT6SFcUIKmGtm59t8c7TRTPZDkOxUz+Oe5TQSURj73lpFdpXZuW7OJO199nDc2NnLG4lZ83hgVhcczJEBxOEkaDTS43CDM6IkoUa9KUjfgdlsZSsZRbAWEkyrOwkICQQ+LT1nCqj1xNu3oo2badKyKlWBfG9b8ON5BL717BkkG2jGaE+QHdxBTkyQjVoJaIdu9MaaeOAv00/Coj1JXZcJ99QUM3rea0Nq1lJ0WodAUoNN0Fuu8ZpI9LtR+G5Pq7BhCQa7Kb2dapx9jAgpe9GKJLeL05Um2LYW6qZVom3fSI/2IZAiSPTQcU4ua6CfS/jqGufPo69hD1xPbMBXNorkjxNH+PRDbhVkFg+7E7a6lNKHiMLThNg3x27ffZntE4dXPlmAss/O72/uZWns+cQRzYqvQVCtREeLNl1dTZDJzvFshYT6Drl1BIu444YCfmnNPY/PeZuzhGNWdFvb2hOg0bsMys5xwd4ItRbU8cv2fMGrV5IX6MTjNGJ0lTNEl7S0a96w285kLLuCic07AWdGIyV2Le9EnmdEYZXL9HB5/9DHOu+hiHIqV408/m9bm7aixfCoNEVYZGjnDa2TDupuZOVXg69qBNaSRp+ej2OxYowm0RHxC7+GRS/RjVqFmJeuxDr9spgQhUg664Toz8w87c8WYnyrMZlIZzzTzTpyiY80k45H72BXBB1kJOm7aeO1kypwp29iVtQdrcxiZ+/KMh8znmaXO4bj7g+5Nk7H1w6jnlqEYjDhkD7b24AOAURgJOCzMv/hM7v35bznjC8fjZYCYjBKK7eHsxj7u2mtn3eRP8plzDOQre0k89ALbZIRIwxn4k8V0tw1xxzc2UWprQ3MvonrSZUQNAo+3j/6+JqwuN9F4ApPBjMVmJpIEsybRzSZ0RSDsDqIySiIeRrXbafEE6YuYkC1t2AKDVOclWL6pCzkUwWSJI+w1+K3l6AYjK95MMBT0UVU0ndMvvARn5XzuXbGLutqFWAOr8Ji9FC+YycBQC72tG3DbjuUH//Qw9OokPLoZQZLujl6mG428aBWcO8tIoElH6Q2y8epVzPv9VMrvgU1f+DGzFl9OlTmOFotgUIppW7Wc+jngbWmjft7RtPe1s2NHlDune7hGk9QNdPL7+ql8pXU7ScWM21GF25Qk1r+bl2I668KQL7sxekOIIoWqmkL8ZifukILNFcabjGB0JMnXg/j9Sc6dpnPTnt3gi9OnlaOKOGFnHjPPvoCef9yNobeHqxZcxL92vMTcOVUUlWp0m+yEeiRm2cmsMy5j+ZYmPLKa4mmTSco4LdsEW/repPHkrxJoXY+xbxPdL2zmoZ21LK6qoLN9d2qX0oSZR55ZweKqegqLV7G0oYKa42byr4ce4Cun+FCNbszJADaziWKrD7tWxfoqK4nt/wkrYzMx8qWemMa63/bEH3aTxqHAsF37SL4XI3b7CcooxOE15agq3QMWNK2XGRcs5a8/u48Lr/sGr29oZXHFOipNfk6+4nyG+mooaX2Gpu4EG+qm863Nu/jJgAlzeAfGkJv/+7uR716tMs/7Jn07zkHN+yq2gk8xo14hoTrx9nZSIEE1qMRicSIGBS2hIpMh9DwjsaDGtu1b2BgQrNkpmOzUKfdvI5xXw2ubdqB27+T0z11JYfl0ti6/nfM+VcFrLU5u+e06XCdcw7LyYwlt7uMcW4Jjql10tC4hME1gmfFvYlXnUO+oIGGfxc6mtezd3YHPk8Rqt6JqARRidIoIPc06j9dWc9WcfnYxRP02P63LXmXmg5fQVP8U+a4kCXOcClc+be4A9Z0qtcedy/rEfOKPPMNTP3uIN6fPpkcbQNdihIJBTo428UBVNd9sitMhBZPDG1nR306gsAQCrRylxvBskJiraymfXIQh2c6r6zo4+pgpPD/QSdBUwgtvxQmv7aEtUEae6VUiccnqFXtwAtKQhyXfQazuuwhpwrDGT9x0GR2GKgbpJxbXUGcKBloaeMW9hK6kBZsZerf0Yw2/jrF3LZucVhLmQhKmBsoqTiTq9jBn8UJMVpVovxm3uQjyjFx57unMDD1LxeQannjayq49AywtLWXXtnaWLlJIxgKEDEamNBazZcNmJre4ed3imNBreOQQ/VhyUSC1yjX1X0pSK1AzTToik5hEOm8WjVBRUumZcfSZyv3Ib9GK7Nru8Pmw2UBRRsfgZ1uBKseUy8QBtMzhmcvwlgdi7N41Uo5eBTsRUj5YKOi+yg5g/z5A/QdbjyBGbsZoGTIw8jxGtTMmXHXUfTvAVnWZobja4f1JU0UI6medSHz3SoLxFj72hSuxD0SYPc2J6vEQ0BMsVB9gbuM2nnu2jYKYwsOTbEyniGmJNZjd1QS9O/lRrYsf/kRl4XnHsmzeemL+B+n1RbAXnM8kewCjwYYvGkL2xlCcJQinHX0oiiPfRTQZJ6HHqa0r4/ktJuJhL5tbdmGdUc2enVswVjRSuPBcers78a68hBOPqsJlqcTuqGVA30aeNZ+VmzexKWrltt/cwda/XYbXm8fWvQs47eQTae35Okp0JuHO9dxwZ5KgdzZlapBJipf+cBdCMRHo76Aur5Tnm0yEm8NcsKiG5BleKnYrbPjsI8z44zQ8t75FWf3RbNS9TF3oQDMb6DOfhTsQwvfq22ypK+fVaC9J1Ueo2o5R5BFSFE4M+JlBkK26YPlgN16Di0fmO3mkKcCq4FGc3V0Hf4wQ0ARKvoaMl/L0Gomp7tPIoIatSmBWCzAMdqIW2rEpIeyOWmK6AVXz4/G3YLe7UYJNJGNmzMV1aJEmtI4n+PYxZu5++CmstTPo21iAzZQkFExicpYTDFah2QsZsk3GWVKNaizHK2JYSu3Y9F4mlTewJryGZcuWsXDyQho8jxFSkliKL0ca1hOSbShVC/AnanhzzwsUFzsJodCu91N7+mR6/92CoTMxsffw/X3N3yEOZgrJFoWhKBMOMRpVf6apZ2wUTdZoj4z23kdzgNyP0FJyK6q6L5rko4x3FM1z5ENPanS0tOBNCBLSheoq4L77HiM/rxiTzYYwmmixnYo2uIOpxzq4w6AS01S+WVjOM8KAxZjkBydMxikEze2D3P6Pl9nQXsnHr9nNA60lNN/5c9S4RqHRgcFowWgxY5ARtEQSo82KjoaOwsvr1tESk2xfv4VEMMqMxcX4O5vQyxaR1J0oq75JY+/tfP3cJfzPrcv52o+eZ29iGtaIH5fdTFd3nOc2SLzU8dbjj+Dp66Jp0IlmWEj9zL8Q3FHHv9bo7G6eTn+nnWLdhpLQMEpBIBFgekkjhdJEWI/ytjdBcM0Q0aeTiIZ6Jn+pgd5/b8V6di9xg5eGC04hHF+HnGXG44tSqgdpVWzsLjITT4YwFJp50dCHp2ohxTGN7jjcUlxE0PM6wXCYqkKF2ZHdbGgJE4v60EJmolEjRpOdwoBEt9RjrT6JfHcl5WUVWBJJDPkujPlWouSjGYqRWhDvhh9wZcWfuWHek0wP/obgnrvQB57Av/Z/OFr7FTefvBk5tJ4Bs2RS7VFUN06BpIbV7CBhGGBxnhXFPQ1ROYWkUkA07iUsg+S5VablK3TFrNz205/SQz7K279Fel9m9mnf5N4H16CYnFzyyYspq6nmtKUX0hlqZP1uK91hlV7NztttAySmNBCdoKp+5Gj0Y6Fpo/a2SWm6jCbhYVtseodJOMgsfVgjzyT5sfvNZO5SecC9ZfT9B43hNsbWKTN+Um94RjCeTT2zHvbZ57NtSzxS5t1g7M6cGW1mxUSJ90Bx+iN9G6fceOGn4z2v8do+giCFTiziYTCSoMBiRo1JLvn6FXj3eFBtRbTqi2niVPILX8Pp6WD2QpXZ+l7KbHl0bZ7BdcUvU9Ad5qtN1YSdeQhfmIra49Fta5kTi2NdcjQdr6zBddxCkkE/+TVVWPOLkLqCx+PDDPjiMZwVbpo7NIyWPMrUZja90seUGjfWzh/zmYZ8Lv3lt7EKNy073kDErWiKwv/9/qd853PH8FZlI33bW1HMcGaeSkXDhcRjTvrx0n7zVxlavYay2fXcvLaSaFDHnegk4jASDESICQP10W4uVdvJGwoj5RCVTgdFTiN+bAy+WcDb7XUs/s3jvLjuPs45oQJbTT2Dz3XjVT/Ftu2tLH/oOVZaJZ1hH5rByFRifOV0H9cUfYzr1vqZtW430dgAyUQMi8mByefnoZJJ9CgdvHWh5ELjeZTWVqJ3b6JHTKFsSNCWdGCoNuP0RincdCe1xgW84ZqLpsRx+O7lM2d6WXRKGQFLDSLez/TSCAaTGzXch1GoaIYkg4OSuuq5eGJdKCaF+d4tFBokM+oc5Hl81E4q5YtLF/PjR7egVVTjKlQpUULMLo1z2uxp+Af7eG5rP5/z3oWobGPaBb/h/gf7mDGnDptVJWIX6IkoQTmARyym1zeITW9lS8sQlZUV9IckyQm+hwcleiHEXcB5QJ+Ucmb6WgHwIFAHtACXSCm9IsW2twPnAGHgSinlhnf65Ug3nN3sMQ7hTGgP+eHBI3PP+rHtDZNfZhx2traPQFI5IvBuNfGPiAa/H6RAS0I8GsPoMjAUD0B/mPv+ej9TLv8iD/Ufx23nuih+cwGeRDuNukCWNOJWZ3Lx0J+oFxFsNgPnTLJwe+cuYqrCFV/7MfU11Ty+tZ9666vMWtPHxaediU2PMRAcwqXasJp1nAVmvAND/Gv1KxinzeXp51ZiJ47Ax4JGO3N5kmPPmMuseXMJDvXj9XRSaGhHFyaiWhCJg4eWv87c/3byg8st/PcfrVQtKKC57Rlm7R2kcvEcIuoU8o6K4j92MSXbBX2JTswFDiIBD4pQ+cY8nbeqC3mo+hhONLs53lzGQH+cl0vLOX9hLZajJB8XRSQNOlfUfJ69W/5IoWUOZqPO7h4L8V6VuF7Adv8eXKqNgFXHmKiionwr81rXsLliKcrUIPZEJScWF9PnDzNj1lT+sGkP319m5+ddvbRFP0++PpPtzYPYYjb0hpuodeYRD3q4fPYQ3SyiqqoeuWcTO1asYM4UiSNYR1u0BaOrGGveZGLxEBX5KgN9fvq9HmwFKlX1c3lovUCzlLPilWe54WPLaCxyYYsOwdFTsOdb+fWDf6M5Uc1ZyRBB6eDGz12KPdrCvx9fz9HL5uL57a3Icg+zz/013/rxCkrLrFTaS5lUOxOjkBQVFeIP5tHb243PYqPFU0xxqRHVpFJvtbHJeOj2o78b+C1wb8a164EXpJS3CCGuT3/+DnA2MDl9LAL+wLv98eRsYXQHCrkcWVZ/gI6rairfsFaduXtiWstWVXX0fu+ZbadnFsARuVPikQAlfc/0zGcnOKhDdCSC5iM2gOoIVEOCgqISotEenBYjfpPCJWdfwDUbCxhyeLFGBrnhNZ2zF38MV9RPeI+J5pX3ckmhhwEj2GMaFxibOe17c7ngxjcY0vJwu6o5/tgSnr5vG7/4wc088Yc7OO7q2RQZi/BHkliNZQQ8g5TlObEWFTKwq5U6u0aDPUx7yxuc6zQxZ84cTMZ8AgNRzNogka57iRUIDFoIozThad5DSDHi2rybkwo34FTOIxIYwnzXA7xhVTlx6kJa6quoPXMGK3Y24fX4iIoh9D7BRTVBltaZkZaNqA6VAew8FYuwtchLha0KT7gAtaIMsyFKPNiFwWElip/8wvMYbFmOaoiREDp7/r0OT8McSuIFDA5JzAVm2rvasYd1vj5jPfeGl1DUtQDjpCIWVob59MkRjK56vpF/KXfd/DOeVfuYUXkqnaEEtupCApte5eyaZroNhZxVpnPJJ85COedsuhNDzOnppffCT7Hlnh8zN99Md9BBeVURWtSE5krQtPZxhM9IjzfG0ZVT8HVr3PXIa6jmAsLxCP/z8k7KFl6GTYbpfvLvYHMzqzbGFdOG8PRt4/5r/81f7vo+ajzGtDmzWfW9P5E3dTfzF32ez1x3H/XlTlSjDU0m8Q72ErMUEFct7G7bwVBFI4HWDUzSd3BUzXSkSaegwMryZyb2Hh6U6KWUrwgh6sZcPh84KX1+D7CSFNGfD9wrU+r1WiGEWwhRLqXsPmAjgvSK2H1fcqEa9/3YB2mNXRWMOFuFGPHgCVU9cJx8pjNQGW5QjHYiahqoKloyYzKkZhL98L/0LGO/rRYkYtgJKFKrQDNnGXL4euavOGVivMFF1/fFhWczX4yYN96h01EAUksHsAxr08PyZhlMD6RwZ2jj+nAdmQ7RYaIfXq2bifQzHomCGjVApM05w6ubM8tnmoFGzD77pBiBKjOe/wcPHY2EppJMJChQbYQMMeLWSr663fT/qTvvOLuqqu9/T7u9TrnT+6QX0gghCSGU0HuHR5AmiKCIHQsq1kd8FFFEUHgQlCJVpEMINaT3ZFImmd7n9nruae8fUzIJQwj4qLzr8zlz7+yzzz7n7HPu2muv32+tzRVSG5OvWshDz71ET38t2We20ZttoSgV4NwpafCaSAkB0bKIhgS+99cYA5bEjMmN+GvKWfHEfYQLTRTvRKwV36P2xiz5jmcRHNXkxO9hWjX0t3ShFARY8cAfOG5hA0e5dvOlz5+AmjUIuh2kE9uQ+t9FC5jYC6axfKOC6tmMYURxZ3QkJDq6t1A2x49oJnm2xeQaRSX0xnLiBb20PfQ6G+/fxcO7IM9iHMQpdA2y8JIivJk2OtIR6pyl7FW3Y9NlauN5ZjrsVC84GYfXR65vL3958gnOOXsRJcVlZCJJbv/OL/nv7zmwr3Myf9Lx/NHWxHS7lzejfRQZbhoaaogbG5HscRaWvMVLOYuFK97FcdLJXH1fN8mMTNKWobRwKhMKMuSECLVuG2lV56L/uphiWeW2sxYQ7+ig541mjOQg5HSmLz2SjNDMeT/6M4nW1di71rFrw3KKppyOIk+gP1XMuvWtTK0U6IglSclBUkolPsWB6i+jdMr5ZDpWkxzsYN6x83D2voWJyZVnXkexBc88/l38po5SNpFk1xusK88Sb7Lzy+UPUe0uYV9vmBKnjK+mEofsJPnOanr8xWxPidT43uPCqc0krTRYOcoLytEUG8qhDNsx8knB2JIR5T38GRourwA6xtTrHC77ZPKvDCI6WIatdWHseqljgdkxIOGHLfj9Afk41qkk/XPZNP8/F9Mw9s/YRraPK59CEFeWZLKGk1Q4TyIfJd6T4Ycv6SRsxTSlRBaVh/nH5l62JAQez3tYrlVgk7djU5LgcmHlddKCRZMaZG1rO8VuN65qLyX+HPGaDGfOWciGV9fRnkmgGh5kMUmVlcW99SoK48/xxJ73eOV3v2Cidx+nFHVz9DFHY6kyfluOVPsTuPKvgzeLbpyIqjZwRM1U7r35HBZXB5FdLtJKln1vPYbe24LhVgkno4SXTaY0tRWhew8ul0nWUU6vYcMwwqi6QtavoCUitLXvxRWoI2cqOBJ9XNjgYEnIZHpjOaFiN1ZmF9NmXsielkqicRksLwsXX0U+mSevFBJ9sYMdtgTVJUXkXS6qQ0XIqQznLvHg8usQNFh2zo187rpT+NWsyYixNo7ZHWahCo4JpXik1cxucFHrSTLdmeIzx8ygftI5XHTCTWxs62VdazuSx8XqRAbf7DIGzX0U4iSSGMRw1eOVC1ELy1Ac0JVz0XjUYo47ppjauXNICwF+9Ie3cSkyqt2OFGrELxqY6W6mHDGLonw/clE9d//6T3Q+fgd9z99HV28njoqpzJ87GZ/eTPNgOxedew7dfXHSmSiuqkr0aDfZFc8TufdH7N29nt1xnfZIK8fVRcjm0yR1LwVKjowjiCr7yWv/mVw348Js41YUhOsEQVgnCMI6sumRwtFtdEWl8ZgwY4N7RvzpY/ePrfdhfvWxoOfIOYf3i6KIKIpII+HFI4PAQUp+5PvB5aP7D15C7wM9Mw7TZ5ygqgPaH4eR808rt/GA53H652Nt47U1+j+MUmdlaf/Mabx+GovVjJfhc6TOIQeG/5zy13WdvT1JdEWnvnw2L+zYS8RRQ0npRGrPOZFfnv1VBvKTMY0guwtn837tsfjkfjIOk0Q0jq3ExmCJzH3bk1SWFTF1USMee5je8CqtqOcAACAASURBVCaOnTKdylgx6e3b6An6yeqTcIeOpNvWjNOjYqp3owgCxblmjqgoZN7EcsxcilzkKaK9v4fAXrKOo9ClmzCyxWSTKk5XmLMXFfDAbeey8eGbmVfh4DvnTqHh5Bu465uX4NnRQ8FzO1DfXEvGY1FUO4dH2vpIlpSBYIKgItvz7GndRcLMkRTyBDw+Tq4TmOJNMm3CdII1sxANF+GOXuy+cmqnhZg9bQFvrthFRBc47ugAsZ4QFbVL2OnNM7kkh5rdg6n3EBSdHDWhF9Vup8yAdCjLb7qX891Tt3EK3VykBTkxvJzjpe186cRlnFQ+gy8vuZgjJ86ms22Q0t5SdgxkeeX0E1HueohuS+Vr37uS4y9aQjqxg1bDSUt/nFwyTknITyJioaZbcHtNBo0iCuafRquaJi06yNWcQEopRiqaQVXVLJp3vEpNfS2BQhuWFuXXP/gFqx59jGnxdmJiO4uW3kBtTTlq9/M8u70bj1nETd+8B1vejtNlceHeQer1ah5t1rhZO4m7wtN5uSlGznIi510UOlxUCzDYKzIYLWRjq05esB/We/hJWTd9Iy4ZQRDKgP7h8k6gaky9SqB7vAYsy7oPuA9AKKn4UNP3gBTEh3L0HoayG113dcwgcSgxh90K4jirUAnD5eZHRHIess545/8ots+nQQ5nYDl4gB3rcpIOwkcAwRL3D9j/7Ln/mfr/1yIIqHYYzNpZ2byHC5ctZHGkk3bdxRWhfZxa0sgtwc38VmoglnVxXuebTD2igG4rCQ4PraUz+cmfVvONK6/EVyeTFtMU5hVibU7Sq12QyvJm92P89z33Eo4kaclfQWjKWUiJe3mjYzFdq95kx5o4x/X0kijfgm/RSkzdgeg9DTVRiVNPkomsoCue5fhLb2DzI+8jz4mxY+tyqoNX09or0NQZxhzsYc+Gu0g3BhG7HKy/81lS1efwzcdUMnIIxUxjKx2kWI4zs7yW+gkqkYRBfZ+boqMqqayoxuOrIR5wkB8wUXO9fPkrvyXS18e6N+4kcsZUbvzcV5k9tZQTj84jDE7nz4bO5JoQte7n2CeneWddDF9mG54fehCiKj0DoDbfytfqo+SUGMoFWdpt1XSvr+ey2dXYlWpi4T3kjAC+oIuGgTwrvnIRFQ6JWQsWcnfL6/R+YwMzTmqgavbpPLBaZFZDDrfDQVdHG+XOXja80YqoZ6ktfhOv14UllDClro7P39OJNelMyiaIqIO7yXSvIljoIpfr4Zip1aSMr/OdB5/jhu4/0rtgLqLrOhz+XgZadvNk03b2xhtIdUcwLYmMlebYXgdrgn6eb7WDbxIlPjexzja0cAFC2XTu3OijPKBj5GQ6dA9yU5CAZGEdJlT4SS3654DPDn//LPD3MeVXCEOyAIh/pH/+I8QyjKHNPPQdfZhVfXBbY/3rH1l/TKj9aNmooStgCh/dxiHPM54FfChlJxxi+zfK2NnNh93b6L6D6KKj13rwjOHwT/7x6/8HxTR1XDY/guggmxCIqn5Sfes5r7IJoXYGngmnsrx/Gz4ty7nWdk6QdxJNxLEcTv68LYO12ctP6ufR5wuReEPB/ozFO3/YzdYX1xPrXUdf5m2++egTlJSEePXOP1I8u4xbbvo7Ax2zOL5tJT0vv49q5uho62fbi3tI9IcYDB5PfG0nsZcfJPXWM6RXrmXeuXX0d7xGYWM5zel1uKx5/PTdt/jVdQuwl8/l7vV51m1vwtu6nI3OIP6MwG3X34s2GCcZVak2NvL4MZ2cLtfT2fUCXTmN6Wtj+ApCBAJTyKle2lSDXQ+vxhkZxCXKtLR2MHeBl4ryela+3U9aMJg1vxG7v5rBTTkGgw6W91aAYmP9e/1UyS7sdWcSNzIIfhtFlVBc249YnoMCD71uFZttKe++l+Pk2/o44Xsb+epde8kWRMntCNN85yN87rLPUnL0qdzV+wrqtDKMCgtnzTLsylnUVM/npbdf542Vf2ew7WXCUQdLFi6gyjeZiN6Dmrfz24f+Qar8NPKOUoh2MLDzDczEHvRUhkkVPu755o1ceca5VDv7ucH3NimhlHjoSjLFDpR4Py9v72PHYICurXF6EyY6FoZssjI4gecMP5ajgAm1i4lrQWySh7SSxNHfjGRBOF9BzFZOlbOSqE1GtuKYeuKw3sPDoVc+yhDwWiQIQifwfeDnwN8EQbgGaAcuHK7+IkPUymaG6JVXfczfxX6RYD84OPLjHgZsBWEY7NtvKY+qRlFmVJt8wGUgIgy7QkxMLIyDFjgZOnR05jCscI2x0ZWCNJS2GAvMoZrjqhJxaFHxIRB25BaEIV/0iHI+KNJz6LChKF6L/YOKNXqfw8Ckbh6axfJhbp4P2/eBRV3GtD8K1o4Z7EaPHWcN3+F9B+SsEcwhsHgUFBX2u29GuloYebZ8oK0DrtUcc2EHDbT7cxqNQ58dR/4d1GETkXJxFyUVFomeftpbWplaFyKX20cw4uVzJbtZHTiCM97/Oy/nl+Eu60L1+ijOW1wxx0vMXM+qE0+H2Da++LPPIHhrmGeEUDwORCuFkUjhsDm4/WvfwVfqYvt2N7eeGOPVLR283TWbN4RWrg6UE1JMBrYaFM+8jrYHn+bxytlMmH09tbrKaaefy1OPrycz+ADFdT7efHYmr257m/S+fuovu4qXX3se6+mnSEhZ5LqT0AaDBNPP8vuzjsVzAmz9zXPMdxTx5hM+7k27EL1XEfvDOs6+81ZMZ5KUuwC3quOzeUjs6mVDh5vzHv4ei45uYPrco5g981hCJU7u++0d3PGj37B++gz8oTls04rIu3r508p5ZPUtiKkMeWUV77ZcwtI5GpIHYpkMPc0JVr3UzGmXv8Af1nfT+PnFnP/0T3lj92J2FlUTyAts+p/fc8ZFl7AWgxf0PdScdhFdu1dTHPIRaUnSsvN6OtsqmFhewLE1LqZikOYVKhoNHny+mxXvhhGcm1l63BIcip8vnDubn95+O8XFpfzXZy7n1JNm01g3hT2b32XvM3+loH052vnf4t2K/+K6oh4iiXU89GwHqpZl3xtNtKYHEQ0ZERB1aI5Z5K0MvloXYTOBJOcIp/rxylCW38gsn53uXT3odh+ipVBkBXh/Xz9RLfdRryBweKybSz9k1wnj1LWAGw/rzJ9ExigpUZI+NInhx2przP8jVuh4lvyIjLeAuTWOphc+8bUdzN1nv9I1QRSHBimEYaISYAofchGHkn+VpTteuweX/V94pkbYWP/US/Cvpw7bRZUjS97CTOagSqBOcNEZV0l7PfRE3+OV1Z3ULTqaJd/4BtMf+F8qbRJtmRg2NGwFQcJzLqFnT4Yf33IUqrMVQenDoc4kGfFimipuDb779R+z6MzjCSka1S1X80Srn3lH1/O9r75IUd7GkcVByBj4a72k8wNMvngG30xW0xlo4L9/cgeivYcptSfwh5UTeOLnP2dy7XSWzl/KM7v+yq8eegpJjlLqy1KcVPBms6yLGhzvVKlZsZ4tL6nMspyoBYUEiSB4TBximh/e/33ec+cp693EJNOGrGfImgp115zHP5pdZB/+C0gqq1evZ+V7m5hcXUmfYXDcKYt45+EXUE8+mWIlQ9jSadmwkTKHhGrp+NQYf/zBOzxUHMRWU8ekwtmcdfIkzrjBxW9fbEH1+hno1wi9u51vX+rlBy83ct3t73HeZy9BOX0xXS88wnk/+V+2b9rIGwM7aY0PUB9YzRHlVeiagpyLUIZKPteMbpvDLT/+Mz1RB/7SALFYnONPPguQ6O7qQJJNLkqnuPUzpzIYKOSePz1Kjb2HEnUfvoU309RUzHXzetmzYx31ky9i8bHLefTpd2iLxRAlgQkVFexqb+M4xUMw3cQKZyPxjhYUrREzkqBcjLMwE8MXkggLEnl/BfZgPat3dJHQ0qiSAerhhUx9eiJjP0Ab5EDXxkHA25D/fAxL5aCo0vGyXYpjWC3COIrCOmjfeDKq6MejQ36EmKa5Hyf4sPaHo2gPkBFlb1pYhg6yAOJBQ4IhjhrMhxU8Np58VP2Rfh25j8OZVbAfZ/nI2IOxz+3gqNjROmPqfxTI/RHy76AO24w0PWtTqDER02mRq6+iumI3jkwKNR1DT+WocsnsENwEfIVEzV6K/S58ssDurM79a/r50XkLSe9oYntnF9OOm46jcCqqYeFK2Xjg+ZcorHMT6t5HxQwnt/44wpdvnkBbs4VTdeE1M8iajlkis+zui+nOrSJgr8QSZjJlMMAvb/khpy09mrPPeZOGivOw8kl27t7Bjua1KFIRDrGLO353O6fVm1x26o8J732XROlJ9MSgOJdnmk1jo3Ma1w7OxtQzFGVUvrOwiiMdA+yM6XTGZIqCWZSKIvzBQjATbIvITD9qGa07/kFMNQiVh/jTUxux2SdReu3lHDXDywZV4LIpQR7evQt54VwcLQLHTpwMyRxXffEKIskorfvCWAWluFzl/O7Z3ej5eiSHjNELyS/fSfieq3EU5njoz/fy9J8fwKs7qL3gJrb19BPJqnSmBmnwezly6kzisgsh2cMFgU34M430+Y7kK997g75UEH+pk2w6yte+9S3sAT+D0QgP//V+lvp9+DI5HnvgAZZd9WWSA53ssSnYpl6DL1TPUVNh87qXqa87k449O1m3qpOX32oGRcEhCuzuaEcWYYbkwZOL4NR3sUkaJJLaziQtyQ+9Hp4LZliXkihP2OgI59i3qwnNVoCme/C4JJKZ+Ee+5/BpUvTw8UG+sTIS0XpA1Y/R3scFQIcHoKFzHnSsNWYgOVz3yXCZJDPs8hl7nDj6V7QEhPxQIiOBocHDtCwEux3zX5C6aNzUEsPKfshN9rEaG/o8RF8fmI5B+GTZNEdYOJ8M1D6AOiwIwkdRhw+p6C1dIheL4fCKWHIJT7RWk9uyiRtOX8rgumeZPifAuxs2U657qFPSFOZkRCOOWhgiPWkyzhUiuTXv8dOVb2K35aicOxPZbWFLxtiwto2upvf46fXXsOqJ/+HGn63hqLocebWPrz3UgShnuLxwBtHCLi548Hws905s8TAGQTTTTU/TalLBcgYsG79/dhWyvBlBkvCqUb70mTlcO6OLsht3YtgqyPZtwLTdg6GqmMlu2twhfJkkmC5+H6jHcru5JDiNL15Qi1QZx18vcHTWTWejm55mG42+Yr5//0qSrhI+N6sOp/1KHvzpSwh2G5UBgYbzl2J4amnLZKiVi6nt7sBtdPOzhbOIxzWci2dRHHQQ0+10DGg8+8idLLvoHLanCtnwZgv9Qg0VnkEGunPYfEH8Yh8vnHgW/GMtS8+4jqMrj6Ov6+ccecE1VL/0NxQFcqTQXBWY+SRLCqBB3MxW8Xqe2ydSIbo578IKYpFOqr1FrNm7E5eviGhU5XvXXsdFFy/B+cJbRBrrufDKa7jvoUfx+wqpbZyIbC/ENtjHitfeYuG8Odx97/288M5WumMRJEPGbmnkRAvTssAS+I3WyyRZZo4iUWP1sgAngtvgB6JJOi8xsdxJv6rSnk6gSz4SyR4EyyKfSHC40+NPkaLfz8IQRXGoE4CRGxFEAUvc//+Qoj3Qxz3kD5dGJwGmaQ4pYnHEejfGLAUo7FcmQztHW9nvBRljlQ4rG2F4pzVMCbSAUeh71FIXMSVxSDObBmLewJQFBFPEoYtoloUuSyDqCEaeIlNHiPTjVGw0Or30h3vJGQaS14dg8yBoFm67jYDThkMUEXUDTVOHgnwxUWwSai5JVpKIWhZ9FsRsdgynE1QNNAFFlBF0UCyTvM1Ak/Uh56Alg2EHQR2+X2l4prS/P4YfwBB+YY08pzEYyYcMZiN4yJCBPXbGNXbmNtL+8PcxeIGAgDUyCxvLlho5ThhzDWNZPAcET8H/EVg9XisfSh0GrgModjmoKsni8oVImR0IaQ/zZ3yZh176C5curudcv49Lb3uZ6tIjeHnjOmqnlaJ4pxJMxnlgu8b8Bnhq9btsiOSZ1eAimbGo0EReeegB1JJJfOGz32Hv+3+mOVDGL8/zENcuZXssR05vwmlK7HUm+MVjVxMZ2ICez0KyGtG5DzXrpynfw+e/eBknnjad4+ZVUeaqo7tT4tSjSim1Zdmz7XcEhVIUzUV7ooZkJkyhq5oWuYWHLJHCbJbTCuCKxDN8efpRBI/KUDLZTldXJ9nwLJz+BGWpGHKDQWtvjqc21yL4HaxLOCnK7ODG8y/hiYfvY8PGOPrWVtx2kQXzj6D4rGuY76vEVuSmd8NmLDOLvWQue/va2LNvJRMXLqXmlNP58p//St5ZyeyJZ7CoJAThCE6fG8Q+nn7iEewOF9d/9mLc7z/G4/Enmb4xzAMrP0eLVMull8xnhr+cI+oauCCUZaC9mefVL9CkVVJUqLC6vY3POJxMDFWTTZu4L7iKjn37eOiBewgUyDT09uGaPYXzf30nTz75Pvt2DCK6DcoqdGpKZPp3v8XsKSE+/9XbaO1RCQVNPBYkBR3LAt2SQLEwdBPJgB2Czk4zhcuSEYUUZk5AI42JRTxvY1pVCQUOOx0pEbfSSDa3DRMbH3D1foh8ehT9GL70eFbwx3VBH9j2mM9RIHSc84/3/aB6ItaQT3xse8bBx1mIhgXDMUCWIKLoEpoEWZcOuo4tq9GYzVPjdePXNbx+P5KkkNEsiuxO0rk0mXgMU1FxOT3IeY1YdytZ3cDrtGGzK5imiSiKaJqG1+3GoYk4TZEKXwAhUEg+Fieu6XShkpAEVCfkRTcggy7hytqRTIGsLGJKQwPpUD+P8/Iciu1yCL/8IWc0Y4vGi4w9HDmY1fPPy/8pdbgm4LcGohWUBvvRsnnC/e0ce/FFTFWbeb15EEXSOOWka2lqW8d1Vy5jx+AMTrYleUuYiOxdR9Oqt9m9s5d+xYtfKSWdqcVUiplSbMc9uYGBV/+HvrIGHHIT/dF69s44mQXl7Si/eRodlVyui/Tel9ByQV5d4+H3j73Nkw/cgeYcIDGhkpfe34C09WtErQZOOf5KfnL7Jn7xaj/1rijV3gk0ina8lkBhscG0Uyr40qXLKKxygCdKgCCGvYwVN92Lb+NepDVJui/34z/5SCyrh1giglu3cKQFvnJ/D4ZahzZgEuh6k4tjK5jd+R5nGi7W+C0ej+fYa0jsbG1h/u52dleYxDb0Yy+2mDVxDqYpQmofq/a0c0fTIDbRjhxchMO+EyHczmCogMb6QsyBFI+tXYlDsnFcdR2Jne8TtTdwhfw+T7b1cdxlXyO7eiV33/MkF912C2a2gHXtEVbHTiXvqKYkvYs5BVlUaTfFioNuLUdfxE6wtILnnvgbmfgA554wlak1Bcw6/2q2bwrTvquTyUeEOOuM01DjA3TtXI3PIXDz939HzuXAlBMUlNUwmO7DllcpFCQsy8CNjUmKjXrRQcrSyCsWr6ZTDJgWkmRiApYh06vZ6NjZzaRQEYqho3sySFYFppDGyHQd1kv9qVH0gihi6fqQoj8MjvuHNzSsrIQx3z+k3oFW3xi3x1g//IjBP/xpjKKswigJaFRGs22aYOnIpoiOgKmABmDmCaYTTLbZKJU1JtUGMNNJWtubsVdVs2PfXjp6IiyaPZ1Clw8sC1mWiSejWJZCQVGQ0vIaNqxdg6mZmIaBYVkUFRfTOxhGEkTsokiitxfRYUPyK9iTaWrzJkXBImyCQsSjEMag24SowzlkvY+4YYYX3RaQPmiqCuOsxDXadR90GQmCMJpx8+D9B8RGjLJ0hkfFMWc44Nl96BgzdF3mJ3PTjCcj1OGf80Hq8E2CIDzGEAh7WNRhRZSob+jCSGvkRJm2AZHXtrQyN1TGaa4eUlIDQamYO7a+zNt/idFxznnE332EaHUXA3t3Md2eoqG+mPlKknO/+n2mlkxBT8a59cW1POyxKJ3QwAOPP8llly0iHDqT+NYqSidUsnS2Qs8GmYKsRtg8j1fWDHDXfY9QUuQkI4Vw2bbQvbcUIr+AgR6OnF9Bl6Fz/PlT6Hq5iG7dZFHNRaibf47oyICtnuu/+CP++Jd/8N67e2jp7aWhcSKF3hZ+9OvnkJ98HkfzyxQss4gbbxKOhnh2xTaeea8JKZbEbiuivmAtss3PkXQg5yLIegEvBO2U2/2ku3YwSzGZOHURheUVqNEwC46tRUka7Ni6hgmVe2nrMNhpW0KVUEjCiJDqL6Fg9hy2ClNY2bSFZTaDVeog+955iztuuYF9q1/HME2ibbvYG/VyVKmNxPaHOX7xHWTiD+JMVYCvmFf6g1zT0EV2y53ku3bgkXy0DyboKain+MiTmHPZZWzvlHhv7Vv88uvXoOSamX3a2bRGC3jx9X9geTQWHzGT7l0rIdMBRoqLbnkMMWDn9BMuJD8tifv9dzjGVEiKIi5rmN6ty8hWhoAsUG/qhFSRGY4Af8zF2GfJiIKMizxm3kAXNPZEetF1Gw4asfkE1MFmJMl2WC/1p0bRAwiyjDVspZoHJxGDA6z+cfnnI3UYViBjp/IHLxZiU2CsBTmenjggX8uI5Thc3xhyC3ndHhLpzP465hA10VQk8phDOXQsC7QYdYqdJZodqasbu91EjaSJ9vcxtbqGlrZBZlRNoWaKgMPUUUydVCIJokwulcQSJApDPvY0bSeXjCMZDjxuH6qqomcS+GQRt8+LYFew606aB7qpyBaiiG6i6RhJPYbT5kQKt1PhD1Di8ZCRBXrUPJ2yjCHYwRKQTHGMZT/mvi1rfJD3oAF5LDYxujzgCIvpUJHCgrnffTPyjM0xz3jUhXMwHjJ0XYIoHgj2jrRzCLD830EdttDIZRUENPL5ElQ9yHNvvELpFBvTrCpcxY145CwBex1lEyaxJ2PDs3A+7z/6d7rsWS49q5H05jbytmn4nRWI8b1cevUPOH1ugNXL3yE5bRJnHFtCS8zGG+9v53OXTcI01nHNuXP4Y/t6YtFKrr75d9zyzZvZ+u69tDQX4BUchMP7mKa/RpU3zJZ8Bjwyfo/I/KMLqHW0kI/YeXdNnNannezcFeHht39LWjOxLBOXz4HdUcFgVKQpvJszTz6DlEPn3fu+wxm33ILqdnDusku5+TNf4fYrBDblQiQtO+QkfH3bGPjW1ZTH8rwq6TzXEeYkT5Z7QsWEPv9Ftg0+gEf1IYYs2le8RNCbpkKBd5uOY82uGKWOJJvdYc6aPoXn1+4i3B1CKXIw1RfghbZ3mGFzcsWXv0Mmup7KGUvZvXY52YEMtmAFHUWVzGkUeWfVM/j9ErbEHpbUhZildWH0vo/fL9ITBVVRmeiQyJp5XOU1JAdSrF2zhQkVDQiRfRxz9jLy9jIef+RxXI4cS49dgMvS8LgkuvLlbA7HuO+xF3AFA7iNHA5nmp4/uXnziUfZpGXQJZPFqgs3JpYgYdMF+kWLuGhi5nI0KjIdukDeylFRADOLRRYceQrf+uvrWKKCrthRtCxOVzGpVORwXsNPl6K3hmmTH5qg7GBmzsH7YNhSPwiMsw46VmC/Aj7UzOEQ+wRBQERAHm/2YYpDMwRJA1OjXNWZjR0xGiaTTKEnI8yfNJf1G7fQUFFBKh2lsMBLMhGmNFhBPpUnk8mBZSdvyARLyunq6UY3NUrLS3AIGm6XDYfdRSaTIZmKU1hegsPhQtDBa9lxIVFkd5JMZ1HjEVw+O5FUjIDXjxZPUywqZOIxKnw+amUnrTL0WCZ5SeAANtMnlMPOB3SwDCeXO2AgP1xr/WOe899BHRYlJ93CRFRbirXREnRDxaGpFBX4yWdT6I5+0thI9WTxL2rgs6F9WLE2XiqLMVdReHEVlNYczztNG7hKMdj63ItUV8sc459L1TKJB9e+w7SJtZyw+HIm1r7O2hU/5huPPMb//qKevqjOFDHN03/8H7rzBYjZ3SAVksysx4VKXUkrnfEA1VNC9ERc1Gdaad3YRs2cEzBnzuScpV6mzLyKSCaKJRQhOZ0I+QEiORG3q4CE2ovfU4zkk/GInVz63adYeNRlnHLC8RjpLJ//4c/Z0dGJvbwEMach5waY3u8kNHUJ/3j+GW4vDPKbkMaaRJaNqknF9uXUuQaR2h4FP/g0EYezjlXxhZROnMoSn8yqnhjbe/28siYBio+8ohHob2K9cytXlk0jXVCBGd+DHHmbbfn5NPgCTCicxDYhTXu4i1DxF1i0qI233n6dR+/+Pde/uIiE6GNraxGqZeIJlJIfHCDt8zH39GtIBGaTlwr5y/33cO+3v0Iu/Dq7uvt4/bVWLjh9AflMEpxu1nVEmHLEAhomh5h+qodMRCOfyWGYKaqDNTz5zKMM+Au4cV4jb77dRIWVJqMVkDSzgIJDdlHvq2FfZC9zBTc7rCgXH13O6Q0RfKrKy7HdSIIFgkxAssgSQlVTiIeZsO9To+gFy0QUhOHAmhGLchgYHQ2QGgbrRtHW/ZbiiAVpCSNAnMmomT6a9XIsy0VkNGhntM6wWGNAxpEBYtQyHLZYRQtDgEgyBsowKGIZiIiYkgWmgS0tUp42mZztp9gTQLR7MDWVBcecyebNW1EcbiR/kI5whInlVfTt2QWqn627dlNbP5mgy053exuy3UswEKKgMEhvbzcNs46kc9c2tFwc1ZIJFPgRcmniySSWIBIsLqXEFaS5rR89l6W6soK8mkWSBExNon8ggqam8bhl4qkwxaEyCoiTkZyk3IWsNtPoooJT8KHpOrqsgWCANNyHpoJs2jFkg7H+KwuwRvpxrNIdscJHLPsDMPSxwQDst9wta388lmlhjQStjcVHYAhoN62hbeQRjYKxhzAM/k2SlG18v/N8BgYjpAwFObWHmRMdeIwk7pJC+ppbqVy6jMWJbppNhbX3/4gzLlxMxZGTuWz5Ru4URZpSnWz66wP0hXsInHAcfzr7C7Q++xuW/fIZvvujl7jpG3fhuu81XruzHN/RCSaon+HWL6/kqkvnITe1sfb+Ozjl29+ke81e/OUV9OtZsB/Nvn4vOdNFx4ZWlp1yOWuaFE75JwAAIABJREFUe1Ea5rBzy2Z69vTx7dseIi+ZBBwFpLMSmuzAXTwFIe3DIelMmCayuz2G3VFPXnMRMXp5b9cgb+97n5OOOZpn//IXrPAAupgmls3Q+/3f8ZathSOMYi60mzwSixBVYMG8aTyxdSML5h2PpkbwJpsYSOlEvGWs3LOIOcvOYaA3hezRSXXtJh3rR1cU3KX1KKrMwOBKZhYVEVcSTBckykp9rNdmkd0+QHeDxc33/JJTknGkrg38KV9Kx54+bM5yKhunoOZNCv02JrsnsNVyoYpJikudeOx29qbtBMtCtHeHufOr/4WubkcrqueFl/pZvHgSA0kZZ9EM6qdMoWaWiVsS0UwDLZmhub2LxM43WbLkFJrfX4VryUlsWbuRn93+de46+fN83e1jfUagKyYhyzYqS+sQXEWIsX7spoCEyTmzLGK7VZ7t9fPntgSC7MLvq0XFy8XHSLz8bpJB7V+b6+b/VgQLUzJhJGLSgpFfrSCKQ9x1ceyP1hquK436aA9rhamPmxFxZJnCj8gqacsb6KKIKcqYgohomTjVFNVYlMsqQkYjqw2QjkWoLitm1Xvvsq+tkzPOPInenn6cHhuRxCAOtwubLKFYJpKh0tvei2jquGwSoqKwr3kvfo8bh2QnNhjGI1toNhfxTJaiwgJ8LpnOrh7sigPF5sAli3Qnoijo+Hwe7AUFZPMCxaEi0moOFTt2bwAMAbsEomBhZKKcInmI2228Y0bBaQNElLwdUTcQRJOcBLrN/PhBYeMBtCMDwoibbGza1WE3mCUKCHww39CQjMPg+RSJIJiYag+pfAajayMFboXz5p0KqomeSCOXlvPe5k6QvMQHm8kLBuGuLnJxP6FLl/CPouPYNqOK1u5OSrNhbvnlUzz+1TOoPGkuX6w/Fp+iI8iDZDIKmuWke6+FfbvM9J4IRy39BZc/cTlXlhcgOwL8+N6X6RNf4dvf+Ry58HL8UjtNuwWefGE3D/1tJdl8GkXxk9OzeNwh8p4UlhikdloNO3YMIOt+TEtG1zQSaivb1jhxFRtYYidCthIpX0E0sYe6qe1EImF+eOvPePaxx0kODnDtKcfQkOzA57+E8pd/TMAUmTWngYlOgX3FEzizrZ/fdu1kXnAeDTYIFli8uGMOZ599Mf3dHQRkB3mvnSn1DaxssaOpAl5nBUKyl7LKSZiRXoqmTcLhBG9hIda2lcyq7mDm0R5Sjr/xzKNdzK+eR6Gnm6jVS4/YySmnL8btdJDJ25g2uRglUcYmS0TL7sZRW0fGVUVWtHH9DVfx6HVL0aZPZPlbgyw57hgm1FVj99ZiagkCYg7T1FEtPzlryF3XUBdg1d9eYtW2v/Hfq5NMr6nltm/dgL9oNheceArWKy/QFUuTFwoRBIlYxkBwS8xetJiVK99HtEus6ndy9/sK0cB83AUmZqqfDG4Uh8HTa/yI0hw04/AS0n86FP2IjLXWRozAkVWhLA609hjBT/fngbfGpDE4wFofaXisD1c6DKU/FhM4FPfbMjBFYUhJmTqKYXBifS1SyzYc2Si90TiWV0LSU+RSNnTNorGxkc0bNyLKEscddzxPPfN3qqqqaN61k+qyEPlkFI9TIRFXSSSjGIaBqeuIoszmjVswTQubouBw+9HzItlUBsXuxGmzI+o6HrdCxIwxbWIDg4ODRMNhQqFiAk4Hvf3dlJRV0NY3QE1xkGhPOz6XG8NIUuj3ouQNnKaTpWVF7EomiBo6OYcDwRRRLAssnaEZ0sd0z4xhJR1cJozMlA4Oqhq2zC3BGq1zQN+Pp+j/VVG/n0gMLphhEMnmCYcDHFVfgSrqlIZK6e3pwul088Bf/8qNM8t469XHUKxiqipchCpr+OqaLVzSs5mrf30nL4YTqM3NPPDrG3nmC98nsNTLxIL5dAy8RZUcpD/xBg7f+bzY9jxX3nY1E+06+YIOHB4XsVQ/X7/1YW599DU6m96jvfcV9OQO3EqSRdPqufq069E8F7KqbZD7H3iKnbvj6EopiqigGhm2b5Vw2r2k890YaQ+iZIFYje7OYKilmKaMX87S3/4Opt5Hc6eFvKGfa786iSXfPImTzrqa/73kfMqFSnom6jjPvQTh7ccpsCeQdYlYVuVufxFTUy629HdQvsjNs5umM2/uAjata2JCbQnNuzaB4mFOQwPvkaArVYModjGv6A269/UyZfZFtIV7MMUwUmIvl5/0Hh5PDl3wky8d4JKbXZhmmFrLzUD5AKfOO5vZcxaTSLbjsskMim6Kihz0rHZS5K9jb1sL3pCf2275Nr+99lyMxqnkhTI+99kJ1FR4yWZS2GQNSQrSMxgmlsoQKvGgpVKgWRimhXDkdby+eQcrnppDKDSZ1tZWzEgLlZOSpJ4T6QvOxJGGovIq0h27KNBivNAS5xUrQouW51tPd2M5iqgqraJj+5soThkBP4Zu4bT1EOvdx+GuQ/GpUfQiFqK1HwQ0R6x0URgi+wmM8tWHXDUmorh/NagRzvxwBTg4bwrs992PJwfkfhkDGsrygb78MemDRz410QB0MPJUZlPMKijE2rieIrtJa3s7Xk8RyWyaoK9gFPuNDvZRU1tJRlXZtr2ZyTPmUlBYTJsJlp5HxgJLpqK2EVOU6Bvop8ATIJ/TkF0ies6B3etEtzvo7W2jyB9EMCw8Hg+KJJBNxgh5nORyCarKSsjmVbq7erFkkSkTG0hms9SWFGFmMyQyKoYp4nHYSUYiWMV+ZD1LTXuGAtlGtriQt/UUacMi73CDKWLTTLRhN83+fh8neGy8Ph4Lco+wbkaKxhuAraEKFhYiwkHPetgqEIT9C9AckGfnP6v0BQHi+QTZYCkut5sUOoJpohsGDsWBxyUgWTL5XB9BI01HIkEsOomF9VmOOHEGE4+8HH+gkBn5JL/41Sp+VSvx2d/fzRn3/oXj6xYxLRYjJT5LdU7kZw89SJ9u8PLTf+T0+eVkOnRqll3D8icf4uLfnsyuxB7KbAOY/kqcPieGmkAqLiGa8jJ7XohVzZ3saurCEoKk0yk8TiemEUUTvaS1HIYIpqXgcAuYmV6UfBuJ7r3krDwZj0yxQ2FxTSNzfE7O+9JUgtMkbKXzMG0B5uxRiYsbmba0gT/VHU11/TTKVt9DetJkFp5/KXPbVRwz61i3ZRV797bQuHga+XieSVPK0cKDFFeXEE+JNLWsZqmnn00ZnQLfGVy2+BQ8Si8PPxXl7LkdyMkVTJwSRjWL0HQdQc8ht/ZgyX6yxiBiJoHHG+ShFhN3QqXKtOOQNWTZRE2nOXFKgNebMtSW16PKAlVKhsajFiDXzkR2eqhQRDTdwDJM1HweS9KRZTuBQheZnIpoKYRzcUJONys3rKJ352oGexpI683YNJVYIkasL0JZRsMr9JEpKKY50onml9hX7eavW/eQNnQQbIjoaLkI+fbXCEj9aIYHRdDJpTOcMLeW5d399Pz/FDAlAA5RGk0SZiKgCUNpgI3REcscTnQGgjjEqDD0PE6Xi2wmgygNBUohCgjDTuD9U/2RA/cDfGMHidGL+MCFfTg/fNRVJIrYDAvDgpAlslhxoG7cSMAfJJ3SUDx+3H4HRY5SbLKEkQwj5FQchollipSEqnD7goh2N6UVlZSXFNHT2kxkMEw0nkG0u9FMnaqGSfT3RwhVVpC3LMygnXSkG12wqKytw+tyk4jFURQBmyxhkwUsU0fPQ1dnO15fgEAgQFbPYeoquUQUm2zH6fOQLy+jtyeMZqgEA078aQHJKZNx5DFNg4JYlDNdBbS4ZNarWXSbfIAhYVnWUA5/SUIfCR77sLQFB/8/6o0bp6/Hguej+nz8tNEHPJNPkUVvWhZ2p0hi325UUcI7oRrJ7mEgkULWdTRVo7SiisjmdQRKJmI4YxQEFcycgCcT5LXVG6gpdtC/ZQdnfeY62jZvpDn7NOd/7ks02n1ov/46XzhjKb/7wz7O7xjgVV8/VzZcQkl9Edf/4jHkKUfjqz2O39xwK9/73S/49TMvcsVnL+fxZ/7GrTddi83lwucoIbx2Fbff8Qh+XyNZRSMz2EUs7cBfWIBsWiiym0RcwZdL0de2CnumDdWmEbRAsSssapzMsVMWsHrPS5x/15EUTDgDRW7ANCG9bguSkKD2vJOpOGkZzqZ76Gg4hxWlX2dZmZspJVXsdQd4+YWHKGqchFZXgiOpogRltHCUzTt3YJWVIvsnoIRWc1bh6/j2zuCN9ct5Pj+bgJbkyLl5HPlXmTKxgTxH4Mz6IZ0kJ/ViCWHspoKca6DrzTxWSOKqqTp3vrmLm4+twO10o+syWn6AgpI6GoRqgkXF/OLhx/jRbd+iqKgR0W0iWyKCkCCr2rHZfJCPYdjsZGMpFJcEoowhGyhCHkmqYoq/kY1d7/N6m8mZaGh+A4ctT28kjiXmyOJitT2AUamwZftbaJvBFIbWaJOsPKbkwDLz+L0ijrrZ9Lfspqwoy459Azz+jy68BTWQaTqs91A45BJ8/yYRSsss/2evR9KH1my1BMhLIlktj2K3oSAOr0AkothtaKYx1BnDjJecpSMiIJlDUbWCJSAoEklMDFFE0oYsQJs8FGRkCUNZBcVho1+RZFTLwLCGgxRGQF9r/6zC0ocV2Eg0qSUiWgYCeSxNoiGfZUlRgPDWbWjpLIZmMmnedLr7Win1u2navBkkgeNPOInt29uIxUxqJ09g9hGTiUd66G3rxsibBENBZFEi4C/Gkl14/MVo6QjJRIJ8TkXVNQTRJJPX8QWKSMUHiXS3YcTjiFYOTc8g2m243UXk4gM4fD4yhoUoypg5DUGR0TQNTQdLkMlrBi5fkEQqTv9AL3ank1BhKVo+g0UeQ5QRnG4slw2b3YVqd7JV1elDIWWzUCwJyzSxRAFDsFAECWNYe0sIo3x6A2s4mthCNEEWRHTLHBpsReHAPD3mGODVYkjhG+YwYD6U/mEI4BUQ9OEI3jHZKgWEA9cd+Ot9WH3d/xHtX1VVal13xemUl9aiJTO49UEayxpJ52US8TDeUCmv7Iwy529/RC6dhvf0WUguyLR1kewc4E1nEZfMnMj8Zcfz/oqncTbv5sYtK7n98m9jFlfj3rWJGt3G0T/+A0/NMyDhpOjKr/Kr5/7Ms3sUTP/xOMzdOOObEIt9dDRvRcbB3GNO4LIbv8WE2nLy0W5ySoirr/0WWIUYRgK3UyYbjiJ5C1GzWTyoRLteQhI1RNnGnCm19OeylBSVc9ON13Laqcfy3vsruem7t/HCY/eR7lJ5fcVzfP6Gz/DqWZdSUOxjWqeF7doj6XG08ZsXBqlfehYv9hTQ9ug/uOvWC6lqmM3dzz7Lklnz+NuDvyO/oJ5sezf9/a3YlApy9uMoDuV46Fvb0SOfwSXDg4/+jq5Ujp98fjGtD09Cywn4NQWHH+JClorKInp3tZMLiwhynli8leJgLRVHN/Bkxs67wSJ+MsuLaGlkFZUi0U48G6Y946esqp4Vu3p4c+3fuOv0U9DKJqIIKWRHEMM0iMdUdNGGYSrk9QyCKZDNDpV5uvax+eX3eEXYxOCra2iKa5y/+BRuvOkCTlp2Jv/r9/Db8+9k/WuP0935yv7Mq6MOAxHTsiHJMictOgJpYC+daQdd0TDRZBLRsmNYJgYalvXR4aSfCoteEgRsmHicdizLIqdrSEgIsoBoGdhECQQRSZZAsHDaZLK5HLIgYYkiTpsNPW8gSRYYGg7FgWaYSJJI3rSw2xU0TcPUVFz2oXMYwyQNWRrivrsVBdVQ0XQTY9hNJCANbZaJOcwHlwwZJBsWFopg4cDONMGkwu2nc/UW0pEBHC47mpSjp2cv6ViYzj6VxrIS0pkMW9asprpuMjOmlbN9zRY2DnYAJoZhYLPZSPZJ5NUMeqFKJJMjaxhE+7ooKS8hHo8jChI200C02XHKEBsYoGHSFFa8+ArFxV4ChR7UbAa7qIHbQzqdxe0PkM1mkaShPrQME6fXhs3lRtd10tkEbqdIY30d7W3dtLTsxed1UlQYwJRldMvEpYr4ZJG+jh4WVwyBVDuyKSIOi7QyNENCF4bITMJ+FpQgCCBJowFN5jCzyhRMdMvAlIYGVmU4UZ1u6EMzOcPYn5zZMJAEETQTUxoaFARBxNINFMWGZVnouo4+vN6vxTCvXhSGBoj/oFimjmRpuEwBdJXOvS2I6RyN02ezvSOF3Uoza/Jk1KNOhFWvoj3ejFkeIJfW8DZW8e3v/oDlT/wF/1ubmVt0FDuyL2Jsmsia/0fde0dJdtX3vp+9T65c1bl7prsn5xlpNJJGQgGhLEAiGCEwyYDI6RqMsS8YsA0mGzBBiGwTTJAQAmUJZWkkjfKMJufp3F3VlU/c+/1RLRnf9x7W8/J9+O61ap18Tq06e/1q79/vG5wRvMmEk1f0c+CaXyCsGVQ7TZ+R5VO/+gH3zK0hzvr4yRRq7D5aBQtzZhIZKyhleOrJx9hz5at5w9vew+aBddx2fB/pwKQlDEydodlOMMwskoRo7gGq/jRCKFbLHItPPIGvXHs1xw9FrFqaRdQOMD93iJ5SDynb4+bbdvCx9/wF1/32l7z3Mz/j/bnlNB/ZxbVdkt98906+9KW/5HUbb2R+7hmueMWZ7Fp5AdccdMne+z2KZ7wMWQw5fsIqxOw4E3u3ExqK7twS6u4k9blBLv7Ldbzpih7c+Rlee/kbOX7P33L7p1qMrqvipzRzjZi8ylEatjnYmCTf40E65NCuMQoNk+n5owjX5aQNoxS7Ctx65AjnDXksynQzmUjSI1tYamf52Jdu56kZk9ceu50DWzfQX1yCmbI5eGSWSEocw0PHAa2gRiNss2rxKO2whWvajP3yfu7NHefFL/gANwQP89lTVrCxtoPzL3g50stz/aazmM8v4ZSXX8iNV91G5Hf8hUFiGg5JEpHJw/lbliKmZ/n1vlmSKCZjm/Rni7QtC0c4TM7+H8SMlUBGSgwVk2iNLTSGUBgLuux2EmEaBgiFihM0AtM2IFYdzlMS4RgCUwuElhg6xhOSVhLhSImIA1wpMG2bZyGbsdaYQqJVjECiwhjHFESGwLJtwiRECIMk0SgVI4SB0goTSZxEmGi8oEXJMumOfJzEplIr4+RT1FXASH+BjGdQsIpElSpd2QzLR0ao+SHS0kyN7YV2jUqjQiab7cCypCS2DPygThKHpLt7mJ+bx7Gh1aoxPDqE32gRVWsoFfLgHbcxODzKfDnHJZdexjO7nqJSL7N8ZARVm2dsusLSJUswhWQ2DBbSHgbSVhiWZG5qgsUji9CqgcLCMBwWD/YyWZ5BCoFteTT8JulCjnqtjpARPT1ZglaVlJthSy7L7vY8c0CERiUKExu9oJ9saAMlkoX0m+RZaYUEQaJihJQdvR4BluiIs0nLQgsF0qIdhwiVIAwDyzDRhoIFJ6pEx8SAqTVJEhEnnetNs8NGTpIEoTUIif9foov8n2sG0O3ZNKuTLOnppz5Toqerl4nj46QyRUInRTkJuVZFXJrpIe9XsCZDks2bOL5oiIusKV60YYifXf1j1rZnmT+hl5Ne+R721SPWmiWOqxJbztlC9x1lfr58Nf/y9E6GDy3nw1sEk0cqfPvowzS6NKnMII3je1ACvCBEeBkCM8e/fOcHbHj7O1mibWCGtAiotS20jIhSBuH+azGISQn43lvezOGleUrLVvLpz7+TWGnm9tcw5yd45wc+xcte/3ZWrlrBB9/3IdauWsw/fPYf+OQXvsrbLjibXX6EMyf48eUbmf/eNSx5yVpqe8r86vPX8pY/fxE7djwIQ4tRjubjX/o0S9ZezIEDe7D8FqVUmonKQxRyfbjdBktWnMH9u+b5yHmr2b9rG7ngnSzNtbBHqrjFEj1mH6VCjK89HMPAM+eRSYPhszye/kELMdumVS7jlFayYpHJIzttlLuYY1FIbOfozebQ5SrbghG+eFGF6k/HgQJBENBsBaTTBaZrDaSKUa0mUhjk02kmZ+s42Ryef5Tf7n6cV70uz1W3b+cdvTv53oGNtOs3849/9272pTZw+54GW1N72V8ZJwqT5+qQhjQQJJiWwfK+Rew7NMO+yRmKhocjfdoklLJpepcvY3pshpnK8+O8/LdI3Tj9A3rJG6/sFNoExELjGg7BQmAwEViyc0zrTqBWArQSCMMkETw79sa0QCa6E0CkgRKCZ8eG/5bDhShRmEJimSZCaRL0c1T6WHdsSQTGgpZWgm27nWerkERBVkNfojBmpsnZCX7NpzxbQaQlpWKakgm5dJq43qI2O8uSZaM88sijnHTSyRS7ulg83MeN199BsxGhEoFj2URxgGWmcV0TK50m2zdAIiTV4wdRrkX/4ACN2QoyhtnZaaRKyBXyNEONKU3sdIZibwkdtkiqc2S7e5k8NkbOswnCENM2aIcJ0jAwbINEx4SRjxQm2WwRw7Rptn2EZ3PgwBEsZZIvFUjnPfxYEwRtLNfBcfKUKzUKxSxGscT+8hwVAS3TxFDPjuoXIJESEq0QWmAYEgnESpPoZEFsbsFkRSWQdN5BQge62ZlZKaIoxhQSoTWSThoo0QotBMbv1UzCMMQ0TSLdmSEJIYjjmNkf/4BoavKPkroZGezR73nrpfSnXOxGSLbQh7Rdyg3NuO8TRB5aZoiXLmbHL37KlkvPYSJu0gjgjDUbOPjIDtYv6kXuv5WB22/jHYUUJ77iq9SbZbb//Tt5y99+jRduXEre1Fz5nvcyOm+jtcXr0hX+ebrFTZkCjmoxvHQlB+69FV+6pDP9GI4LlkGP7fD43b9AiQjRVWPT+r9mzsgR+zMExx9C2SGXb9rE1R+6hCc2Xc6n/+bLnHbGidz0i7+jMmmw1suwZsMiPvez7aDbqMTAcyxWbF7NhiVL+danPsf7zzmX6w4fJjEzOEmDf7jkhZx+wgCN2WfotXIEpoPrOTx+8lvZ/cgYjy4p45br7JyfQMzvoTDXZm+gEEkvdv6F2JnNWNksi/QcX3rlKg5+61v09k1ReuVKhBljWlXKcxrDS+E5ITJfwDEM2uVxwtuH2fHLbZh2D+bl5yHWLGdirEzcmuecc7cy0l2inbT58q/G+PEDe7i6eA25LYv55c+f5FUf/wqGm0NLh8QwINJU6zNUak0KXXlq43VW9wq+9dr3cs6bT2FgfoIDe44gH5rg66f/GfbMb3nkqQPk/Dp/dslSXv2OTzCafoCyuZHV57yXRhBjCoN8xkEaDmESgZ0hrs9SSGt6e4fZPeaTszSz5QqYHqg6cRL/h337v17X9j/RpBB4lolrGni2RdZxcKQmbQg8AxxD4pkGttB4psRB4wGeEHgIUlLiAK4GpSKE0NgmSBUhVIyjI1xiXBQuCk8rsoZB1jTwhMLWioyUeDohaxp0eR5ZQ5CVULAMCo5NGkUaRc42KMqEQRmQnZ9iSIXYSkMUkbUlgymL1YP9WMIhbvjkcjmWr17DfKvFy1/9aurNFo8/+iiTY5N4/UV8SzBba9AMYjQS7JiaX6MVh/hKUewdRJguIGk12pSyReZrTbK5EhnPw5+fRzWqmHGbwb4SxWIXm089k5oyaTRbxHFMtVzBb1aRQpHNphFSYwhN2K7RUywSNnyII8bHDuFYEZmMzcmbN9FstDGFJPJbdJsW3ak0aWmRsh36untQiU+m0eCMgWFGXJc0MWkUKRLSQpERCVkBWanJGIqsTsjohBwJ2SQmE4XkVUxOx5S0poAirxOKqrPMk1DQim4BeRVTMqBkQAFFyVB0GVBC06UVxSSmG00xiSmEIYUwJB/7dCUJ1h+xOKtUwtBQL8cnxrBscHIpqnHEZC2k101Ynp2nqO6ld+oBNl10Hpvzw5y0eAvn9Y/wi7//JKVjd2HNP8PGC15Kz9lnMlJaxjldB7BbNi//yy9z62+v4dKXvwRRivnX73+eq371bf7+hu/wQ9o8PtCPEzeQscmhp7YjnRzdmQKYJok00UnEVV/8CFrvIps6yD2fexmB7CFqSbygifJCPveCy/nsB88nuPAUfvq9G/jwez5AND+GCAwOHZxj5fLF7C4HEPsILSgOLeaD//OvOH3tGr7x5Y8yfugu2tPTvKA7S9tIKPXm+NLdD1Cphixet5bQaCJb8wR79zP2+APUM0NMHm1z1MwShoLybEBptJ9Qm4jSAOZAL2ZXAR3NMxe7fPEb3yKOp/AGekFn8CjSjtYwtO6DZJa+F5V7I6KShsDGy7s4IxZhYBLVHdoNSVQe54Lzt3LRRWez49HHEHGE6eW55+nfEgctxKoeUsF6XnDRJcwYDuPTAbaTY+rQQY4e2I0Rgh0r8rbLoqLCnj7O6eedhzF2jOrEbsrawf3EK/jKBQH3b3+aG6/5LmdedgZfu/cA3//qd5lonsumc95JM4yfDYYk0qYZayKVIa43WDTQRXeuRK2mSXctp9xSSOniZZz/0LP62fbfI9ADUilMKZBxjJXEWDLCFkmHPCQ1SmhShsBBkTYlaUPgGBLXAFclnQBuS1LCwhISSed41jLI2A6eYZJ1bFKmgSsFntC4Ehwh8CyJawoyrkPGNnF0Qs60KLgWeUdSsCVZC7IWmBgUtWZAJ6j2PBOtMnPNBo2ohp0SdBcLbN/2MGvXbcLJeHQPFOkd6OL0006GqI7nhHgZi4ef2sFwXxEZN+nqzhITgiNJlCCVzeJ5Hi3fJ0SRH+qnp6uEaDeZK09hERP7TRItcVI5oighCX2mDh2gOjHGVd/4JieffSEz0xUyhRLxAulsdnKKVtLENE3q1RalVB4rUQs+JpreUhd+K6A5M01tdpKe7iJRFCB0RGy0OzMb08ASTWxZJpUrEGlFs1ZheSHPqO2SNR1SloFtRqQsg5xpUrQMCqYkZWgckWCKGNsGxwLLUDhS4RDjEGPrCIcYVySkVIhNiCdj0pbGlQkWEWlT46FxkxBbxZgqxtIJjtBYOiFrSnKWIANkTZB/xFmrYQgmj0xQN10OThxlrlJhvtUm8ne+ktHnAAAgAElEQVQzPbWLyM0R1bJkRZkTUkc5HB1lbnaS3916B6/+0F9zoHsjk0dqTF7/z0z37OeK/hPpfuYa1gynwfEYHF1Hs2eAiUkDw+jhUDLLd77ytxQ2nEA5chGmIMkZiFadD3zxyyS2DQgMnXD5i05ly/Imzcnvse/6d/HAYwaJ20venKHcfIoz5WJWrm3jbvRxpiZpNCZARGy7/V95ZPs0y3uyzLgud97zOBpYtPEUnFyJH/z0Z1iLT+LoZA177BjdXT1cuHQZX3rn29kz7bNXuJSVIqw3qc3Pow5OMbnvIC9+/Xk8sO0uUtJiTT6NaxYZHb2AUKcplVag6gFJZZr61A5k0GTVcA9u3wjJMo29PEUjiDk+YyELf8LTMx4zdQjMUarF1zDT3kqlnKWrK4vpCur2MYqLipx71ulkrQjbaHP2Wedy9PBu6nMTHKv0sWygm6UDy3HLHmNPX8vUTT+glLOpVcYZ7ivRk3WJatMYYZukVkO0EyzZplSvUA3WMd1ehgpzjE4Ps+94lteecDGXvPRy1i/p557vfYF/uu8u5sqTdPWsWJA7d4kRtMKIoBnQblRxiQmaEamwi2X9I6xeMoSd6iVSATqO/52Z0h9q/y1y9EKAJTUmikgoDCGQSi1QcgRSa6ROOtB1LVALED7HMDvWekJgGBZaJ6S0ACSJUISyg7vWKsIARAyG1kitsa2FVIz+N4RIh0qfIFko9j1nSK6QulOMtXSAnfiEoY+XyZBoidIxnmsxNNRP1G4yunSEWnOOVDbD+Pgka1eso9FOmCo3OOPMc7jpppvI5zMc2buHv/jA+/jcF7+GsCz8OITYx7By+EGLrv4BYr+NGfkQtdFxC6XBTbsIYSA1RG0fz/OATjHy0OEDnLBxPeOHD3Hhqy7nxuuuI5/voTE7SzaXpzk1y8DgYmLDotWKEdLCsg0cy6Dph2Q8Fy0hk82RSfWwb98e0ulsB79vGjTrVTydwvYMGs0GKWGStAKkEIzkihi1Bk3low0TQ8RIDYFKEKoD91RJghQSRAcJJRZ+YtOQaDoFWUWCWEA1dUA4C+dKMJAkcYJrGCR07h2pBKk7KKwEjU462x25+v9F+uL/56a1hqTGQL6XephQbk3SaMYcmqgzFjoUZ3eyedQkaDbJDvVQrk+g3DZDZ57IDU/sYengCjKmj3vnbfxoSrPh5CbRdU+z9ZRvkFuzgtklWS6b3si+2+8nWpzj6l/+gmldY2tqOWknpB5J3nHCGl5/2Xnc3ZakhCAGXveqC/nwW9aj5E7aR+9C7qtx/Y6lhKJFdeoJcsrmDS86na3vX0LbrmNY3Tgpj7VLPO7dfoyeYo7+4RLHx6YIKWKn6uhWA9cLePWZFm973TBHD+znRzuPEg2nsHp7cR99gj4XXrFpLVu6VoNdJjXaol07wOzJb+bHP/4q6eIsqlFj8uEeRGkRditiX7iM7v4UuXyeMAqoBk0GFmdp1rfx+vNS9KjV1FWOkuPQSg3SiOuYiaRdgaaYxfJ9EjmATE4n5WzntE++gDndj1HsYWquSTodkjTbTDeqeFIRzYyzdqDCjuYAVx8eYEnpaaxjERvXj9CM2nSlbHQU0tfbRbWiMaRJq1rGKzr4rRxDlw2xeHgt/eb5HKgl3PzAIzx9yw08duwYkXL41HdvZPdhBxF5XPb69zO6eiXmuCQWESKWhHHUCVaAm0kRBibVoZhpb4KJXY9j102EMAlaTYTx/EL4f49ArzWuDrEMB5sEgcAUkkRoEq0ROkYogRQd2Jw0DQzDIElC0BKlFZbqoDiyykBpiA2NLUDppGPmrTUGHfimAkgCpJTPSSvoZEEi+ffUM6XQz9UEniXqWHEbWg0qs7NkMlmEbWKEIV4mQ7sVoSMYXrIUhYnj5PHbmnKtgW3YZDMlJmZrbDpxM+PHDiJClx/96GrefuVruPqHPyWMNH4Qo+oJpt+mHinSuRJJdQJbxLi2QaQEhlxAuSiNEgkYEIaKVrNBojX7n9nJqg2Sn1+7my0nnsS+HTvoGV7B0cNH6M/k0Ymi2ZjHS2VotwLymQw6ScimUgRxhN+qIxKFlCmWjgyzd/8+RoaH0Dom40nQMZayyVoQtH0cU5C0fBxhsTidZrYR09AxSkckSYBtOmgdY5kSJTvQSiU65CbL6vAZlOjwI5IEEiRC6U4xXoM0rc67EqoDw7Q610Y6QWqBLToonecEngQorZBCEuvkv8h35D/ZhKSYzlKJAsxSH/NRmdFFS6gledYuWo0/sZdcMkZ3wWDu0EHSvSuYrLeompJcb5pyvsji/T+gItdy1/EZ+paElM67iNGuFJnsCMvPOJunj/wTpfqD3PjDx7hy8cmU2pq37N9GUzl86EUbufIVF7H3iadR99zIRz74LhavXs/GjTms1u+YmhrHG97K3LbHOCqXQvMonm6wYfmJLF2ToS2gzz6ZdiNkzbBLPLmDdATnXbwaBxO18VK2Gjfx6M591IOYlbkWy4e2YAW3sHxoBLd7FQeye7ljzzO8avMZvDt/EZ+54wZ+tu0hRhYN8OM/u4RkI2yXQ9R3HiM/kGaq1iKVs7mg6PK071LyJLYYplCKSXspuqXPSOo4hZJLd0rhGStp6jKxzGCbRcJQEzSb6DDCtQ0ymQz1RhNljzA5vp/Il+RXr8cyNY4RUZ2pErVqOF4aK67i2Gm+cMVmrvh+hWv2lAiSDbxuEYg7r2XTay8gjNsUU2nqrSau66JCH8dJ4TkuwfwcxqINNCb38zc7W4T338Kb+x22rBrlzRdv4aQXX8E1Dz/JN776ZXpLJcanZgn2H8JJFLF8lvvTgY0PDgzQqsxyWt9S9hertGiy7vyTiG6r8PT8QbR2kTp6Xt3wv03qJmUIHBHjoHBQ2CgMFWGjsIXGFgJH6I4mSxwi47CzLTsfixhTxyQiQpgJrtlJ83h00j82GkPFyCTCSGKMJEbGEYbqrJsqwdQJpko62zrBSBSuITBVsvAdNBaStOtRzGbIOiZ5z6Kn0Eu7EZDPdeNHkno9YWa6BWQp5gfZvWcfUbOOpTXpbI7egUWsWreeU888jSgJmJw6Qj5lY8QxsUho+U3aYUC5XMYwDBpRSD0MqVQb+H5IO/BpB2380O8Umhc6SLPZpFAosGR0EWOHD5A1LfqKRRIFa048EbdQwBcpWhGk82nQEX67Thz4GBLiKAAVk3E8Qr9BEvv4rSab1m5EKI2JxjYEhlaEbR8Rh5hSMN9uYKRcgjhCtwJ6cj0QdwrZhiGQcYhrgKnihfepMFWMjENs0dm2dELKknimIG0ZZBwT1zOxHYlpgeMamKZEGhrDBGloTEsgVYRJgish7znYUndqOobAMQWeIf6onVzFEW6mG8vN0qQHrE3MNA36Sr00axGuV6Tl9DNemcctpijYTbrjhCVS4JuCbKApzx7m1l6DKgVEn81uv8kjn/4Be+ae5I6vf4yxkUm8c17K6AvfjLluK2rrGppBBjXd5DVvvZIZ5RKfeAavft1lvPGC87nqK18mqM5j6izFzBr04MVII4OjDZxUQhwnZAqLGVw9hK0GCOIAI7WLczYvZX8yxDevvYX3fvm37O/exOO3fJ/tzzxJ0qpRqRxl6+YRdk9O0p0fJF+cJPvkzew+tJ/jXd1cc3wfP9p+N1/86GeJ0hlecfEruOiz36ERr6Q4sIX8yz7PeVtPZePQJk7sP4HiE3U+uXqUVxR7eftpMetzLfqdMmsHIlYNKlYPpNGJSyBaODJLxbfwwzbN+SqmCkmZirRWBH6b2FAkIsNMdg0rT78UVxhEzYiZ+SrFTDdpy0SoFlESUGs00DWfn7y1m8+/0mFZeg+/PnoSYf+p1I35zsAlSQhabcqVeUwUMq4S1Q6TmBrTSPGOH/icsPPn/Gm+l3E5zOy6lxNsuZzajMtFPT1UGi36Sx5Sa5ZLi6UyvTDxVDzrtjc9PU0c+ky0yui6QIqATN7lhIGVGCpBB+p5kwP/wxG9EGIx8M9A/8K3uFpr/RUhRAn4GTAKHAYu11pXRAfa8hU62t0t4E1a68f+8DPAkKJDXH3Wok8rLEN2UjcSpNRo1AI8sDOiV4nuuCwZC9vK6KgsKjBNjaFAa4H9LFpHa7SO0QpMw13wW42ResHyTigMoyOUlogErTWOlFgSHNMiDiMSaZH1DKrjdUTkkikUcUQWr2ByfOdBzFSOlJmjp+BQb7Y5dvgAJ65fw4N33snq1StBaKYTRaGrm9EVBc466yzKc3OctnktWTfFv1x/G+0gptaok0iHialxhHRotBLMWCMaLbSoE0URruNAlFAqFnHTkqFSF9lMilDZDI2spWegwNixPfSWPO66/RYMJ8VZL3klt9z4K4qpAkZrFlNpxqemKUYxjuMQhhGu62AYFlHQwjQtZmePMjlTo1DMk8ulyOdKTE/NkMk5CKnozrv4jTKuWyAMJDEt+vLdTNfmSLTCMnxE7JMyTKSThkShZAymxDY7GHuVdATtDMtEoYi1xog6cDMAFcVIIIpistksjUYD17KRXgdxEwUhse/jmAYqTjp8qkQ/d/0fqxnSZGL8OKGxiInaHLlCN14qhfLrHC7Dja08g48/wodfNsxkuYWc20tgLyLnQDZZylldVQqbtnLuujcxVLqTrGxyLG6x8sOX4e2PqI02mU/O4MBUmy6zh0i7WOk0jnELmeFuTr78/QwPDXDuSSOc/dd/TmIFfPLv3kijkaJt9TMRJTx6d5PNpV6SEFpGF5ES3PXEjVyVH+aDmyPSuQ1Mx1t4wxs/wqUvu5zfPXgH73/rK/jc265g12/7+POvfoWWaZBXJl//9gM8tP27VKNteHor51x5MS/9+CpO3LSF6nAew83w5O4HcAzNJ7/9Dc4cyRMNryA/OMiZfo3uzBo2nruWEWuQuTMSjnzhk7zstX/CoRv+iQ9f+S6unwlxVUCs0xhtwXEigtDFyLSwjBhjfpaRyQM0RtZT90yipE6+WuTRZsCdO47x8Su2MhO0CGKFX68z2JOj2TiMNg1CP6A+tY+uoRJauGSoUSof5B1rJujP/xPP7O3ijht99LzBS1/6EmwrQz6TIkkijFRAuj7NDXsFX/jFg3yjeR/RslfwwMnLqWmHPdUWZ/ghXrVGMF9j/9QYjUoaQ0BJuJRlayFbI9BILENzypazeOCBOzhUnWTpzBpK62zCdoOh3BrMOIXTXaJdPfq8+uHzGezEwAe11muArcC7hRBrgY8Ad2itVwB3LGwDXAysWPi8Dfjmf/QAIQWOYy14gwhMU2LaBoYhMEwwrc4yk0nhehaplItlGaTSHsVSAddzMC0D13NwDItsOoUhJKY0cCwTSwgMrTEBW0pcy6JjdNGBWEqjo0f2++u2YeJYEikUaIUUikzKwUuZgKKrp5cloyuolBvsfPIpJo4cw0gS/EaddrXOfXfczu6nnuKKK67g8JFxLnjxpZx6+mlMT07QbNXIZFLcdee97N69B0OaHD1ymJnpCV583gvxLIPuQgaDgMivsqi/lyQKCSOftt8gVCGJSGi2G4RxQL1Vp1ILmK20mW/UMTxBI6rS8tvMTs+xZHgxflBncKiX6379K5atWEkrgHYiaceSTDaHRFKbr5H48YLRU6drKJWQci1O37qVJIwJWhHtRouU69JstrCkIGq3UGEbqWNMo8MiNrQin8phYWIaLpZhYwoQOsE0BIaQnXdkdcirtmVgWxJDKAQKU3akh1WcoOJkYT3GlB1MvW1anXRanCAR2LbdSe8ojes42LaNbVn/Ttn0j9K0Im2lkcpkUf8QhlY055sEEeBWOb8nz9c/+0lU3MQyY3Jhja50gIotXDdBt2YYy6zkiR27GCgkHK4LRnu6edDv586bbqBZyXL6yos5uXctI04WV/t89ov/yDVf/gC/vvpDrCi0SVvzPPXYI4zXn0GrOstXlDhw+AAjK1dw0tLTuf/ae3jn155BllaDzIK9nLAd8FBlllplhF/e12bzpnexf2YGb+WpXPsvP+O8V72fjSOnkKMH4Zhs2LCB115xItf86it881sPcu996zk4tYmDFcG6tWtpNavs2DdB1Kry+svO42uf+TsUFnccaXD97sMsNVsM5+oEY7OUJg/T2r8dY+4QqX1jVI48yWDi8Mx1v6SUyyB0jrGKyROx5Hh9kiRRyHqK/c9MscI8QKlYYKbhUw9ClN3HL1otbtlRZ/OGYXKmQIsI4oDFg72kUzZ+o4ablmSLPSxacRKxlSZVstDSZM2qZZx44jqCKY/BbJazz7qAiy48H8twqJRnMIipTh0n1VLsmZhhx9gRvjY0xr3J2dy/ZjHnnr2ZD77mAj5++SUcO1rhndddx8Wf+EsMDZOhJtIG+TBgRBiUOh0GhCZWBtu33QmGYNmKRTSbFepTHvVbZnjwwMNYbozGR/1XiZot2KVNLKzXhRC7gCHgMjruPAA/BO4C/nJh/z8vGDVsE0IUnvXg/H97hgRsKUh7HsKQSPlv9HghxHOj7Ewmg23bVCqVjnx5khBGEVop0uk0AK0gQC+QZZ7N6VuGgdCd/Hwcx8RJ1AlkSqGf1UH/PdcqaRidoKHBNAXCkOgoJI4ihJUgpcBxc+x6Zj+FQhepfoN2eRbPtFm3ah2PPP4o/fkCselwz50PkC8NUOwbIltKE4uEkzas4cjxoyxZuZ60ZfLkYw+Ty+WYb7UJKgdZs3yYlWvXM19vIUyHfYcP011wGB4cpVmrE6qEqdkZnIXcdUf2wSKJI6bLs4SmxbK1m3CweWjv/fT1FBgeGaTVrtCTT3No3z7q1SpDPf3MTk+TThIcyyRsxxgWJFFMvdkgnU5juzaWYTExNsZAXz9TkzOg6hSLBcrz8+TynRE6CtrNJvnuHHHgE7YDPMcFO4ufBFiWBfho1UYLG5FohNDosEOOQ0qkEPhhgJfyCIIAU0KsVKe+YhhEicJ1bKJ2A6lDhDZIOyamJVCJwMQgTkJMJEpFHV9Ox8WQf9xA32U6+JFC6ASpQ9KWJJNOY9su+xPNDx54gFdtfAV7Hv8d+558gs3n9tA2fNL1h2hYm7irmuXxRx5hWcnEliH9WcncbMyZZ5xB7kDMweh+BrtHmTx4mO7ekHe9/HV4VsRwt8dNP/0i803N5a/5Kyxp8ej2vYyuX8PMjMFURXHd7+7m0ZtvICmOYvh1UmYWc2gjydhhDk0/xLnveBh/ahJn0Obzb/oL7vzuRxmzP8GSJYL7/UF2tivgZvnJ1d/kO1f9A29441+wccNqpuoZbvvF3/PbG79HOu1RDRNIYjZt3sS7v72NSbUI2xTEKsN3vvlV3tQ3T+rJR1g70s/swzMc3b+bSsbkpNdcylh5gon5JuqFL6E8l0Z4DfoXdcPYDsTcXfh9Z2GbKV4wcS3TUYad3pl87K4befUrX8YPr3uM05du4JKTFvHSdf3M1ZukTYe855LNpDl8cC9DgyMYAgKRECd5ivk8reZxapUqSVzH9Vo4PS+lNrUb0UwwI4GvapQyFs2ZKVR7mrrp8NFrd3Ll9EGOlvuYvGg9b93agzQC9s7OUswu5kuf+SvsaA4jrBFZNnZk4dgWVtTCjRIWCZs5EXZqhlHCS84/nd/cvZ2JuQrKBOfxFqoakFnps8KyWZwb5YbZ48+rG/5/Sl8KIUaBE4GHgL5ng/fCsnfhtCHg2O9ddnxh3/96r7cJIbYLIbb7jSaEIWGrSdRs4jfqqHYb7fvIKMJSCiOOCWo16rOzJK0WSauFDnxEFGIkMX6tSmu+gtBgCEkSxehEIRfQNGpBcS6JYgwNtlaYSYwRRzhojDjCiCNShsQVYCYxUiUQBphK4YiOk2rR9AjmW+S8IqN9i6DeIGXavPTCi3DQHNrzNCtHhzlp6xkYdoaRJcuZnZnnNzffxdN7jjG8bA1Hx8YZXLyIHXuOs3PXfqIgxHFdIi3x/RaGSLjrjpt5/OH72PXkQ0S1GQqe4NjBvWQ8h7Rp4CpF3G4SNGpsPXkzpa6Q089YAaINiWB6osVjjz/C4FAvQmhKpSIHDx7EJmZu7DhJu0Wt1WTz6afi5nuotAISIam12lQqVSzDpllvMTdTZnZ6jjhsUS1XaNV95ufKNOsV+np6sAyTUrGISBQ6SWg3KuQcG+EHxNU2OcOlO9tDGJqAi6HAocOENXSMinxEkhCHPqHfwkQT1erkTAtLJqQcSdo18GxBIeviWmCKmN5SgbRrkE1ZRO0m2VTnfOIQFbZwpMISmnzKft5Srv87mtAQ1OdR2idtS7x2QNJW+JFEZ2363QYVo4c7Zrp47MAcG1blyEuPnDiGOztB/eADPP7QfSwZGeB4U7JjzuLOIyGek/DjR8fg7gfIhDaH9z5JIWOg6haGqFAoLKM2WyGbtRg7PMmfvfXd2Lk+tj1xlIkdLo89eA/3Pf4wh2UWUzRoBybN9gyBNnCFwg9jpsf2U5sc42vfv4p7rruRU/Zdx2NPPMmrP/EaNr/0tcxNPMj19/0YNTPD29/9FsaO3M/tN3+D9Ze9i0PTBndf+1MevOUBwiBACINCSvDVq/8ncw2bXLKTIIzYsnIYbdn8zbcfo3n6K9mlPT7Rt45Lipv4s/oQN9UcxndOMnT5+7jpmRp7j86Tsfo4dugQFaGwl5zE5vnvsWn/ZzFXncodR17MJ392PzXp8PUf3sQVW9YymAk5e2UOrDZOWjIdKcbrVZpRGaV9AtOjpUA4JpaToV5PSAKPTLqI7dl05UsMDq6jUevm2MEHyVi9JHWIGm2QMalsiWM1j8yehyj6kiAnUD1TlLqbzEYlpJnmV7fegBnVsbJD5LrW4Klu7HSWVtjkMZ0wbcGI7iDp0CAsyTW3P8Si4WWUKw0WrxpEuRGjm1bTqk6yqiBpNcf/nV/SH2rPO9ALITLANcAHtNa1P3Tq/8O+/xu+TWt9tdZ6i9Z6i5NKoZVakJDX6IXpOEpjGSZxGKGTjvxB6AdEQUgUhIR+QBxGzwVxoSFJEnzffw42GccxQRA8RyzwPA+xoOXuLEzvTcN4br0D/xOYhoEpO38aUnYE0FzLxmoLsmaW+myN8sQEecvg9FNO5ebrryefduku5bBci4MTs6RKJfYfOUTbr7N4dBWVWkg2243n5jq+49KhkO+mmC9wfHyc6XKFmco8bjrFGWeeyZIlI6g4YnZ8nGI6iylMbr7hJo7vO0jGNJk5dozRwUFy6RTV2RZH9h9l1dJhzj5zPTPTjxGENaZnxjh48CDlcpUlS1Zw/MhhRNDGL8/hN2Z5aNs9OMUiA8NLwLYxPAeQtNsBWndgq0miyWbTpNNZNm06ga6uHsLQRylF0GrTajTJZrMYQtBq1GlU5+nK5nGlSdhooVRMqdiFIV1smeqolCaqIzymNJ7T0atRcQJxgmkYC6kZjSHANg1MKTAECBSea+O3m6gkIo4CMmmPdquBISGbSZHyHGzbJOU51Grzf9TUjUIRKo1IEqamJvBKKXzZpq6aRNN7aLUTpo5O8pujmqGNL+auQyX++b5DTAZLeDT3Hv71mRm6enu588Zt7N0RMlPJUa+Y3HPjQ0RnfIp71v0p25p5DNsjdCAMTezFaeLiUhy7SH2yTdfAEMuHB4j8kAfve5BtN/yOa2/8Cffvfpx2zWDDizaT61qCiA4TcYRG5SCO20l97t99F+dtVHT3HcZ+91u466F7uPPnv+HGW+9k1cqAFd0uqb4+7r3zKQZHi0x73dx6X5Pv/d2ruepr3+UFg2v4x098Fttx6F/cQ1uv50OvzPHhi45gpFxaxw8T+oolw6u57eZxHuo6gbMvvYgL166ltxBz/fEJVnz8m1wXeLQLg6yze+jvyfCSU5awuLSKilrDHbn/wTVDb+cbNyf87uYxqgUDN7Z4wwUnUNmxjQ9dehKLF/XSMB2MxEW1EtxmlWByClOaKB0hlYmlMsxXG4BHZAzgN5voxgwHHr2bYGYnXjYmCY5R8w8TmQ2CJKJNlmPzbX776EGunGuys2Fy8EWn8LFzRzByp9BrpDg23+DDH307nojpGcrjpzTp/m50qqNpc0RkqBou3XgdGDggY0HWiKhM7sYPfNLZLlK5AY5PVlnZNUiUM2hUa53Mw/NozwteKYSw6AT5H2utr13YPfVsSkYIMQBML+w/Diz+vcsXAeN/+P4aWyqSRC9AIQUq6iiURCF4jkM78LEsh7DV6miWa4VpmoBGqw40shOQY4jAlBKRgKnBkBrTNDvCWFpjuDZadajaQiUkKsI0TSzLIvAjgjBAC4WSBj2ZLuYmpugdHCSMA9qHx0g5LlOTk5QKeTzboVopc96FL+GRbXeTThcRskBhoMDY2BjXXHMLF553NvVKmScPH+LYsQynbF7F+PhRJo8+hWr0EraaDC1ahh47Ssnt44mHn2Lr6XkO7D1GuVxGC8n2R5+k0FVg9bo1bN54Ar+77XYs0+HJpx5FGgmlQpa+vj5qtRrbH36Q1SP9zMyWmYibjM/N0Y4Scqk0li0hbRPFLcwopDJf5riAQlc/o+s3MnbkMLpewUCQhAkOkLIEQTum1QoIg5hMocjkeIt01MZ0TPwwIpfP02q1sG0TP2hihhXyuSKBsmiFCttzcbLdJCpH1ByDJKR3oI/Z8gymJRG1BNuyCCOfOEmIopAkDjFsG8d28H0fy3JBd6QiDGF2JB0aTaSl8SyTIGiRyaRoNZrPyVrkXPm8fTX/dzStBUfGD9I1YpOzizTnDpKJ2rh41K0+VG2SlX0lnq6P81jS4PQL38nPd/sUUz2cf/An/I1cQ3jrDt50wiCnbTmR/f4hGlMRtdE/YVurwoO507jCfIKPR6exfuUyXjXyGItPyBMevZv3/fU/8OnPfR4nEzCyTDF9eIK7t2/jyPEqiWPjPbOf6+56H3OBR2HFFoz5IwT7f0Xa07zvLSfx6je+nLzawRMT01z9iXu49uc38tcf/SAbTzuZl192Kd1ZjeVovvbjX3PlSy6hNLiydtMAACAASURBVNpPPCaIxh7kDW//Dkd37GY+FTB64iVMTu8kmt7BkZ23km/8hCm/n55Vq2nV0my0D3HVA7/m0+/5K37wo8PsmLyGd7/mHL5w480c2Ntie1UyIjKcec5K9ux4jIHMehoTbXoGRljtOTz1xE9Q1iR3HE9T6yvh5ft42QsXsaanjzXnvJBaolDjh2kf/B2L+qbJuR6WsIntBNoRZmgyOZ+lW4NjRzhaEEY1gukn0e0xiKpUjo9zwsgp3PDINuLhgK5CF6GfsHdugmeemWPo4UeIu1fTfdkF9K9eTLqQwzcmmZg5wBte+xZ6c4sQ6YQDTzzGkJVHSQ+CMqYQtOM6exOJJS2EBr0gHWIaDkHbQJgx09Mdm9JsVGZuKmHmQJ1GIyJ5nl37+aBuBPBdYJfW+ku/d+h64I3AZxaWv/69/e8RQvwrcCpQ/UP5eehMK0QSYkoDLQwsYWCazz5f47fqpDyPIGjjuWZH5VArLKODplGqg5Z59s8tIemQsEwLKQ2i2MexJFonSClxnRRBEJJEIUqH2AtqiX4UYxsmBh1BLyKDZqVK/8AAtdkyWW1hZ3LUynN0F3IsHV7Enl27OTA3g2ll+JM/fT2/ufdBLC/P9FSFTRtP4pGHnuK3t/6Oy15yGYsWDbJ7/9NsWL+cOFa4tkO1WieJNQ9ue5RTTj4RaWgOj81w3wPb6O7vI5UE2LZNkkR4nkUYxjzy0D309maxU73MVqZQlmLZ8BLSqSyLFg3z9NM7iNsRBRdWnXkK1910L+Uq1OtNunvyuFGKdrtNtVJDJop6eYZ0KketXOnMpnLdvOiCi3lw22McO3qYLjdDsVGhmM0zX51nyeBSEr+Gk8oSJRGO6zE1OUepa5AoaqNIaFXr2DKF55mEfoKMY7SwsN0887MznLDxNJ58/GFcRxCpNl05l1qtQSmfIQoThErIp1OdGVmz0ZGkX1C2NEwDrSO01uQzLkJ0ZnKZQgbfb7Fp3Vr279+PlLKj2vlHRN5YRqdQeWxunnqYoAOTPeWELhUxmK7hJT59di+1okEj7mJXFNI9IBmx6qQaR+iedKn4ZXrjEl/+24/whle+lJQu8/3f3Eq7/0TWb30Lzm23otduZunpgsITR/nEvffw2Y+9iq9/7gryQ3kueePXGVnskl40Qt4a5PGDu9jQXWJ9f5tfGQ26e07BLY8xU92FlBrPTHjvGy3KcopH9/Vz4SUfw9eKgWKKT3zmy7z/ba/Dck0KQZtDMs/mLaeyYl0fB598iu7KE7z2/HO56mdHML0hKqktfOktW4nrRxmrWXzlO9ez5+l59hzRBGmHuLqbI54mndjc9tNf0FscZf3oMP/0tZ8TiuUs3boFt32c/4u69w6W8yzv/j9PL9vP7ulHOurVkq1iSbZlyw0bbIwNwdgBU0MLEF5qAik/TAnlNSW0XyjG4BBjwLhggQ3GsuUiq1m9HekcHen0tr08zz79/WMdJpkhif94k/BeOzuzs7szuzN77XXfz3V/ru93LHToUGJsvHgVghqnWEvy/Onz5Iwx1o8eoitR5vmzWfaujChXbbKZ61m+MEdGCCmdfIiYNEyvNoVfUZDNRZTrJXQTwsChODWElt1MuTSFXRjGiGowN0BzdC924FNzbExZZabxECtScYpzL+DWuylPi5w5M0ax2GTZ1BkmZiUSqST23Bi1Sg+xsMwPv/4dlIROvTxFZ9FnvRgwPyawU/X5yPJXsHH9Om65607ykUBJjNBDEEWfrCES13VsIaJs+VSn52jTI5xSnpGmR0XSUF8ya3o58XJ29JcBbwaOCYJw+KXn/ppWgf+5IAh/BowCt7702mO00MohWnjl21/OFzENHcdxWyZQctiaahRa4lRx08S2GyiyjO8HKFILy5Ok6CVPaZHADxHFlrqhoirIsorrtiYxDU0lCPyWeiMCgd8kDJqIQkset7XhE/AcB8VQcBoOhfEZcskuJDlCCAOcaoNEZNAILSRZRpEFBk+cIGzaLX13QyLUFJ7bv4e167YSuSEvPH+I177+ZkRRJww8VFXm+T27mclXEHwLw4wzcHKItnTLN3J2Nk+hNIesG6A6iKpIza6SkhIv4aMhyWQSNakzb34Pqm4gmxehqDpnz5zFsUZYvXoV/f39SJLEsQNnWb5yNR/6wHv44l3fpOkF2KFFNp2hrbOdmfNjhH6IHoksXLCA4fNn0UQR222w85nf4QsGqfZ2tl25jaPPP8ng+BRmMsW+E4NceumlPLvzWSQZVFUlnW7DdV3aMmmcGQcEBbtho8gqa1cu48Wjx0l39lC1HDo7F3L8xChioNOdbWnqC6JLd2eWRt0m15Ykn88jSBKmrrYG1RQF3/cJPBdd14iQWkNSRIgiCJKM5ztkEinq1Qq6qqCqKookEobBH8y5/w50OAgDAjekli8x06gzYnVw8WWXM3riWdqaNp3tORrVPCExzjWzzDoea+cbSHPjBFFEuaPK55au5Ae7H6HiW+zcuQfVn+Puv/lTvvTgLuzSIAohGBHR6X1MTDd47uldeB+5hUKxipBwGaoW2PvbUW5851ZsLUOcSeZqRXKp9czkz9GeSDE98iT4PpGgUbAs9owsZvtPd/ODx79JEAaARLkWEKHxve/fy/e+/NcIlSIf/sku/EqTptVk6xUfZeOGW9imGiDN8cgTTzN95AG+/Pcn6ekWue0Nt7J1Q4HNq3v52BfP8soP3c/jn7mDUEjx/ne8h30P/DPn1y5m5OkfcvGrX80vdoyydnQELSdw62vfwdk8fO7Rn/O6levpW9WHUj3L+OSTbOqX2f/tAh1GH9PpFBs3XEw08BTq6msojhxFK/6chpQnMhz8skG800UU+ltQQbKP0ekK8uwBBGuWeVKe6vQAo8fO4dVLeKKBVWgw3LmUcpRjcDYiUT1JfXiIoCFTbMuhnN+BIqZxO1RcU6FLEYgshRfOnOHRZ/YjtXcS0wUuEwxUJcYx10PMO1jOIL8eGUETBYTQxRd0kqKJE29y8co0B041UMWQNjPO2PhZMBQEJ8ARDMq+Q7sYIb4cS1ReHnXzPP++Oeg1f+D9EfD+l/XpL4Ukgi4LKJJGEAlEUYCuinhhiKxrLeVKP0KWBUxF+b3dXBhASITrumiS3OKxQw8CEd/3UKQWgicLIYauIADNZhMiCadRR0QiCgJUWQPfR/FDGvUi3bkOgpqF43hIgkRfLE7BbnLF9TcwMnESTVE5tHs3qqySTMcg9Fmx/nIGz4+xafMGrrpyG9/7zn0kMzk0Q+Sx3z7F+rVrOHz0KCvXrCfXMQ8pinGukkfRFCzHQtV1puZmkQWRQr5ISES9XiWRiKEpOpIkMTdb4ZZbrmHViqU8/ewOTh47w+rVq+leMh9zZYxCocSC/sXc88O7ueaaq3ntG27n4V88iCfs5U2338z9D22nWrep12xS8QSJzk5818V1bM6NDKMoCqoUEVgOVmGaZiDQns1yYt9ORmerbNxyFfMXLebpnc/w00d/Q297Bl1X0BSZ8fFxJFHDNFUU1SQSRBqNBoqhs2ffXmKpNkr5WRpNl/S8RfRku3CaOqdPDtLbk8O3IhJ6HF/2IfDo6cri+z6e5+D7EHoOhqYQIeO7NoomEdOVFmUjQCKRpFqto6oqlXIZTZLQZJmEYfxHfcx/QYcPCoKQAA4IgvA74G200OEvCoLwCVro8F/xb9HhzbTQ4c3/UW6Losjo+dOsX7mCTkuhR12BbPl0qBKJmMRILUAK5tjY303UnKFZ7aEwHvJUuo/19PKP7XWWLlvGNx8QuGnTJbxw+AjVwOfk537B1ttvYue4ThCYKGGTo8ePcdmaVfzNZ79AyV+N2LcMp60fU0mx+m1/SjXyqcye5hu3ruMbBwJqExMkc0uYG3qYuK6ix9aiG1Ce2MdbPnaW6tQ5AldGaOmFIgsBux74NFff/mnu+NDnSUoG3esX0puFsyMWv97+Q/Yf/DmhJnPj1o/wwfe/FSGKkVDOM1Mosf/BjzMXjvKtL1S48c8/w+ruPransqhNj4ODQwwu3ogmjNK17jqmXZ9be2dJ9m2hWJgl25lgcmKIhPoT9j1/kD3nSxTHxzE6FT54ywN8QnqUREaic9liLkuWufTYe7G+3s7hh2t0TMc4LBd459feSqNrBr96ijC9kunpKbJtJxnYO8jyeJWGZTFiRcRjHZzPLuNpLmRaWERe6SCVTCO6HkW9wpt7J/CkU5wZLiLPzXGV3kYpHxCm0gwEId//+j3Uig4u0PQzLEh38e5LL+BrP/4xanI1BTvPlraQ71QnaOSbpFJxKuUyR/wGogDz1BRThYB6o0kyl2NydoZQEnj7TVdz6oVDPD1RIyVpRIGL+//SZGxLz8Zv6cgrIpokIggeihi2VjrfIq6rSFGApkqoIuC7CJGHGHokTQ1dFRFDDykKUV6aigwcmyjwEEKPZqNKvVrCbTYIHAdd1kiaCVQpBr5ETo0j1R160zmmxlti/qXAIxOLsefx35LVVB5++kmOv7iThT1trF65Ct2Is/qiTZzJFxmaniWSRdIa7H361xw5cohkMokbVSk3isyV8ixYtJDZQpmTA0PYbki1XsB2a8zMjjM9N0atXuT0iQHK+QLNegNTU0kn4ti2R//8pbzvzz9Mrepx+nyZlRddy2Vbb6I9M5/BQ4PEzTS5XAff+97dvPKVr0TTNHa+cJAtW7dx7OBuZiaGuGLLWpxag3KpxMj4GKOlOZqaiGSaWJFPoVqgYpVJdsxHS3fT2bMQMVIoz8yQFC3OHdvF7id+gerkWT2/jWRbBs3UiMU1Nmxcy+Yt69lwySVctHkLthNQrNdpeA62L+A7LoYQ0WlqVKfGiKsiUgg98xaQ7ZpPoygyM1plemyGyG8ihDaKEBLXNZJm6y6GAR3ZJElTJaEraHJLEE2RQkKv2doQuD6qKKFJIn7Txm/aLSeyPxBRFE39y448iqIa8K/R4Xtfetu9wC0vPf49OhxF0R4g/dL51L8bnt8ylBken6MjkyQnTNMZq9CnJ3FIc/GlVzM9epLM3DPcusxjzYISC1OQtsvIyT4UVeF9X/8+hlulo3s+taaFRJOcpjA9PUZ9boRKKLJpsUDf+k3s2vc8l126gcf3HeDDX/gGt77zf2EHJQaeeJ5EZh7r3/khvn3/IWK2Rt1SaBQnkb0GQbINxY+IyyCEMrWx3Vxx0TLaYiL9MYNUWuPXP7ybYbuNMILeZJYXd36BuWJAzS0xMnWQBx8doljO0CgXeeSRv+bvPnEt//C1G/n+t97DMw9/lBO+z3332jQW9PCbh3/B8K/+N8/87Mc8+MvHOTEzR23yAIW6jCrC+P49zKa7uXxtja1r1jC550n+7KrNxMJ+kourqJqImslhlAx+d2CI+kjIUJfGZcvXsDp2klRvG109IVddvhjBK7PKETn+rn/ito8KhKlXkNSU1qyC1NKNkZ0mp0oqO5xX8cnSB3nY+ShngkuJG4vJ6RJxasRUl47IY2dJQ1+0iHQ2S121yIU+TiTyi/PHePTjf0s9XyWBA2oT2ZQo1jzkuksitoSx5hDrcnDKNvHqLmvTKp9755tQ4zEygoCpyjQaFoWqR1tbjnrDQ1R1hCDi/sefw/ZFTAGMwEVGeNkyTn8UWjcRApKsIQGSJKFILZ79X/ffoyhCUkWIWiYkst6yxJNlFd8DohBVgkAQCIOIKBTQdAVZiXAbFrKkkkllsSwb27YRJAUlFNBUjcJ0ngnqxONZzLYc/swwblMiDOYYGBsFIYUZh7GpSbRYD6OzNm29i4mSOR5+9lmuvuk1CIHI9JyLFutkbOg5/vy9r+PY6WGOHjBY2NnBC88+y/LVa5gtjrJiZReWG6KbnQRhBceDtlQnQ4ODdLan8QLQjRQb1l/L0SMD3HzjVqZLU2x/4nfk4hkmJ2bombeAvnlLcTwXvUMiEAwKs9OoxJkYreMEPlpSZbpQY/2mV+CWQqbHz3LNtjU88dxeQsHEd3zKhRJSPEZQLpNLppCCkOLEeSzLwkZq+coaBqqmY9seoVvH9WuYnWkqcw0MSaHhB1RKZZasuoBf/nI7qhhgmhKJ3hTZzjTZJjSbLl093YiKj10RiKwAMXCx63MMzA0S03U8F3o7FpPUJRTVQZJkfN9HVVt8/OxsGVVNEIYWkmgQhj6mqbfs2wIPx7XQNI3IbdC0QZZVNDWG8DLUbv4jdFgQhP8MHf43Z1CCILyb1rAgbXGDTCZDtVKFSGRmZhpTdBAUlUjpoVCsI6s6WXeMxtQeFssZDEWjai6jvX8x0mzAdTf3Mnf0ICdO7keWZKJAJN+YY+/hPMvn+7zQ1sUrzu3m+IKLiXW2I+gZtu94gfxclaIV4QshFmXM0XuQjwzz9282+N/PHuOeF9OsSNcYNS6gPjeBohSZHqkTaQHbrrmcuJRic2Yr71u+nB33b+epb9/DgGqxb/BxpFKBb/3Dw+hymUXXvp/Hvvb3TI5WOH7gOb727btp2keYv0BGpkCt7lIOF/L5u54npS5GT8l84bPv4oarrictxfj8p76EY49iiBEZ16IgavRd8hZesM+zdF+Nmy9pR+1ow3PLNEoJdPEcsyWX/kycW6/bzNe/fZKaVKVv/UZMv4FcO4iY6qPpl3HMMeRARvQ0RNHj49MD/MWXHb78rmtoy6XwHJsLlq3muweXc8hdhysvIaM4lGt5EqaG7xdxIhWrJpCMi0gdneRLCkUhzYNTR+hs2AwLIU/5s7xi4UZ+M7EfSYkwQpHz9TqRqHLNyqXIqS7G6pOIPhxVJGhMcltaZ8OWS/irr34XXXTJCAKjgY4iBVQbLiW7ipjJkl11ETNHXqTqiShmDCmqteYZI/H/rUIvCBGB/9JQzb8q7qqioCgKtm1jmAbNZhNN0/A8rzVNq6j4fkhXTyezs7OomkGtbr0kmAWaItNsNlBliUQiTrNp49g2uqoiihKO3UBGYEF/L4Is0bQDGvUihimjqzF0NUV+aooVq1exbFmOdGeR+e09PPDAg1y85XK+/YN7+NAnPkkyE2P83CjHB4bp6TJZsmIpj/7qV2Q65xHQIPQ9QGDevH7MRBxN1jk7OERSFIh8kfb2TsrVIum2OIpmkp+a4b3v+SBPPPEkl2+9itPHT+IILqaR5NpXvpaTAyc4ePAwh0+cpVAsE4kBr3rltdQcgUVLL0BAplzMc+i5CoFnIYgV3vK2NyAmkxQrc1y8YQsHDp8gCDws2yFjGlRqFXyvSToRJxUzScUM6rUadbuOFVjYts2C/iVY5TISEV7dYt68bmr5CjOFWdLkOHn4KDQFBBWSqoEgqRTGp+jI9ZNty1GaKtEzr51QtZmbmyOWiOGG8ZahSrmMa/s0Gjazs7PEkxLLVywkCAIMw8DzRdra2ohCUJSWHaQstxb7Vg4Jv+/jS6KKoAQEgUuE25qC/g/z79+iw/8Bjvmy0WHgewCLurNRrVaje/5ifDHJ6s4xzowVCcU0hlzAm6myedEKGsYMUs8qnjk1S8/ULrTkDuamFzBQXclrlrbRHa3inx/cjqgCks8XP7WZP/3SBGK6n7kOkfPDu/B2PcORbDefXv1drn/L26iHHqE3gdiUyETzaA5I1PsvpJAu8ZfXTzETLOLxgw4xs5N0I6Q9t4HfDJykMLkPT7S48Zbbue7y63jd29/Kfd/9Dk99/bucGx9g170PoSWzvOYNF/Gj5/fiThzjym3b0NvTZHoX4Eket17/RopTs+w8OMbBg/vZsm0eRm4paUMmXpnl+D/+jOX1OOOazI+ffIQOyeCzn/k7vvHATmpzI0STe7n29tu4bdu1xBeYJEQXSRS47xu/xadO5EFp8gFe3LefUkkjYdTZtqibzalDxCdc7LBIICgYURJDmsMT6tiBTk/NR9wzifSuPHKUwhF8jjkGw9J1uOE47eF5gjCNTwq9YRFIFrqZxg7qFKohWbXMm3IR9z3yIN16AjFmopddXhvPUCoOcbkeoWSSNLQ0AyMnWbV8CbPPPsbThzI4YUQm8Giv5ekJdK5+9et4xyM/YZHo8ovlBvXFKo+XFD6/q4Tykoes5gio2YUsWO4wdvQwEw0XkRheaCMIf/js6Q/FH0WhJ4pa1I3SWqECz0VXk1j1OpJpYioKdq2GLKvgBUgh+H5LvCxwfabHx9E0Db/ZQJUkRFlGEBQEIUA2dHTdxLaauE2HTDqJ77hIUUCsLcn05AwnxsdQJGjUXGLJBOXGHJKQpC/XyYb+NViuzWM7dtEme8ypp1FClzBs8tZ33sHJsyfoaM/w9JNPMTRa5Nqr1uPVKnR1dSHqBrJmkkrEWbViDSPDw3T1dDN85jShW6Ozr5dysUg6ncTxHVRdxXJ8rn7FdYRhyPzeHl7c8zyO47Ltqis5duY8O57ZxaVXXsGxgWEuWbeeg8dOgqoyMtPgNde+hod+dh+CLICgsWrNAo4c3M/6DZso2ypTdhIJB6teZdH8fiyr1tL78SMatoUohjQrTSp1BUUSkEWRZMpEkUU8OSRfncGzbfrakrRl4shCjWxCICZmkXWDmWKF3vZuzg+dIhPrJ67KtHelmZ05iWelqVVrNLMe07MFIkGiUKxSbVikM1kq5Spt6SyGpmEaItVaiWq5QhAELTnY6SK6qtFoWCTiSSrVMm1t7eTzRRRFQZZlXNcliiJUVUU3NEqlApXqHFH07/8h/qvR4ZAQSVWZnBynf3ES3y+QcqbxM2nacu3gN0l2xmiULYJinu5chrPJ12BYswR6O8uWLOKuT36ID77zjchKRDKCmqyw++AZ3vBald3nCjR0ie0Tw7yCNv7y4x/kkTvejGKoqPgUTw4ihhFazGAqvZqjj+5hj+Rx9aUrqeePcXQgZNu2bla97lPUD+7gcx9+B+/74B464kt4/P5vo/VehuYEOJ0JFl16IZV9Mt2nXJZ9tANdz3HlsgX8pjLEovVrKY7PMDVwmtCzuHciTzydpllvoORMBo4exPQrfOUTX+UvP/Am2kdzPH3Pj/nF8BHQRRZvWo+iqiwtTXFCVxAtgRWdGczOOL4rYgUBthQhC2WSCsye3E624ww//b5FzZ6l99XbULwyxtRxdDEikk1U30JSEgRMogYqjuoTUWdWiwG9OM1J2trmkw1PUXc9PBUankRKD+jpMTFDjSA0CZU0A4VpkjGN69USXVMnWWGfJyyOMO+iKzEPT5DUNaIwwPUkFvdm+fqzp3BjKmLoMY3M7tkpEsgs1joRwzwdC3N88qEfYvgxfvDa+UjhEJmUyEIhRpemMO1FbL7+BvK2RWVwmMzKFQgnTxBXYVxqoIYRXhSivExt1j+OQk9r9x36HpqmEQgQBDaqCrZdQZZlBAQUWQVa3LuqtMSwdFXBcwUMXUUQRSzbRRSklg9sFCAK4Louqta6OgiCgDDwsEplit4sruuTME00CQhkGtUGHd1dzM15bLn0Yn75zfvpXDqfecsupK02xcnBUwhChKZEaCmNockpJu0SA8ePYYc6QuRjNyr09MwnkAy8UMC2bR57dDvr129kfGyIjmwSvIjjx1+kqztLrVqnWm2SyyWJhJZW/uDgILVaDYSQrddcTr1awbdrNDyLs8Pn0TSFE8cOc+TAYap+QCqVYnbiPDddfz2xuM7OZ57BKkyyaf1STCVk6NhJTgzP0LBnqZem2LhuJelUSxysVCmRTSVx3SaEEAQgqzLgYzU8FFVEFlVC36ctl0Y1ZFw5QJMhltLpaDeQNB0zrWDXK2y8eDkdXZ3MFuqMjw+xcEkfkzNlxmbOUaOC3YQolGjLZRHFCMetExFSLM3Qns0yl58hFovh2k0WLlxIfmaWWrmCJEloZoxarYGqaBQKJQzDwHXd35M5oijiez6VSpOOjq6WKfy/s6H/70CHBdmgqWokNBdD9ymMTZEWS4jyNFZdw9Bd3JqJoBgIgcvmeBXGIzYkBTwzj9NcwBtv+ROOTgzT3ZbiyKzP+abA9v0WK9dkODN8lEWXXEB827WceOxpvJlJDCVCKZd58cmnIWxdhtj1Gi/u34MAGD7sfXYYLddNuU1oGb7s+huKhYDpn58G0eC5nb/jOz+6i+/d9XHakm1UCxOMduvgO3x35xE+srAT7xKLG1+9luGJ8/zqp3/FirW38/qb7+C5fc+TTagU58bp69HYJuW47W1XEYSrEQ2TrJDl7MXrEKoFNme6iMlZrrjiOp7dfYBlb7yBb7gp/urpp9h3vMyrbjDIaCpiU8dpWiiRh+RXUNQRZiqzeNoWFGcvzVSaeeEg4dgpJEMiAmQbDt59kgWeRB2FjCgw1ruM62/JEUYekZphYuQQ3f52rogrrOjtY9KVODSm4zTB8RPk7QgxOk06kebSRSIPP3I3U8f20iUIXHvBIhauXEvj1CHsxiDzmwKCKpOvewR6Atea4vTQEJZTR0dn08bVvGXJFp6tjXBs7DyyDT99hYwU2nz1dIYfjzQI7DJEPglB4NBTT7Hwks1YQZmOpMHaG2/COHYY2S2jxFSyjSbOf3K1+i/xx1HoowhNklAUBc/zUCXQpJCIAE0WcB0HWZYRI/clXfmA0GtNuxp6DLtpIZgqrm23TEsAUYh+L8rvRRAGIaZhEoUChWoVRRCwnCaGrtN0HRq2R0xNQyQzNT6D1dQpVGbB9xBkkaHRabKFEbr7FnBmcABZETg9cJTAKeORhsBDFjSmxkaJYzGRHyZSYrR39ZLNZtmyeSPxeBwjaTI9MYwuBfhegzBykWUVUdBbBt2ayJLlyzi0dy+e3eSSyy5lIj9No1gkldCZPT/JyRNHEMOQytQkvmOhaDGq1Tl6utZy11fuYvmKldz2tjdTPlslP36GRmUKJRFhrFzI/b8eYPWShfR0dzM+M4JpmpjdGm7TIqYkEIUIx3MhjNA0BSFywQ9QRAlBFHFdl4rgYEcC1UYDlVYLTTNULN9FkxWKlRqF8hS1pk+h0uD0+TF8QcfyFPTRWAAAIABJREFUQ/TOOAgekiTQaDQQFZFCcQpJDkjE4jSsIoIQ4nkehbkqdsMiwseM6dTrdbwwIpVK0Gg08H0XQRCo1+uIokgsFmst5oqHgInVcEgkUiiK9u9l3n85OhyKElNaF2bhJD98ZDuXLTZRRY+Fff0UfB134izFWoVFa6+gWGpQnrNY6FYpPryH2PJ23EQRoaeCHBYYP+UyWG2STGT5h09ezu1f+Dk5cRueHNBrdFKu1vjWV77Ijdds5cGfPgjIIAgEgg+hxGJJpduQyCfjlNraKE5PIeHw630+8VetZaw8Q7VmoZgKye4ET+58kDPHx/jAn72LRrnA5iUbOLl6gpwT8fiOnSzt2MxzRyo8cs8bsWfv5bt3fYRv/uw5Tu16nGZTpBScJRkeZPTobhbnluLUk+QXbeW17/4oz+7+DZu3XcV9+46zRrf52Ze/zhee/zUzM0WOnz3Fdz/2Lj5y5z8SWVchSxkiX0YNwVQblOdO0fQgoS3Bqs3SsXkTS2MdtNd3YNUs1EglGVo0Bnw6PJ0AF6gisoCxji6uvEjHkSo054aJ2b9Fk+q8vuefKRUUtHzA+gtew1mhjUdOWqSVBNUggetU2XFgiAPHB7mgrpK3qwRNiUgsknenCYQYeVngWcHk/Mk5RqUIXY7hOTaJ0MTr6+XaU2Po/cv5zeAg9fOnefDG5fymUuI7z+cRLZecLNJx3RvwZIdGFKMnlWTfYw+z5JI1iPkSliYzXa+iiApy02e13MZImH9ZJfaPotBLkoiqCPi+S8yM4Tg2phZg2yGaauLbNRRZRlbDFlmjiTgNH0cSqNVqaJpJuVxG00UUSUSRPWynhqxoOJ6PLMi0pRJ4bqNVuP0iiqqgaxGO2yQWS3JuZgzXLSKqBg0XEhmTz37tPq7tWc3Y5DHk3BYkpYo9HaBLCjXbxvUdFN+m2nRYt2kVyWSmZSgexBjYfYB6ILNmo0Zv/3x6FvWTz0+zbtF8CufO4LoNipU6vt+S3W1Lq6iSjW2F+I5Pd+8i+uZ18/RTT6ChIZgi1736aqYquzHNDOVqiUzfAta2t7NkySLGJmZ5aPuTdHR384GPfYJjh/bw4skzbNu8nshrZ2DwNLIX8O7bbqThh5wdGWVkpEh/v8LihVmCqkYUeghiRCjH8TwHRZYQQo2mbZPNZbFtuyVNQQSRgu/ZBIKI7Xlg1QkjEV/UqTaaTOaLeL6IpGkEnosk1REEgdkRH1U3kBBI6gbI4Koigi+iIlCu5FEUiYiQ4KWbqirUGw0UXQNBYGp6lkQiQYRMOtmDY08SiytEkUAm1cHkdAlZ9tAUD/AJwj98YvXfgQ57QchsU2JowmPZsguZp41Qskxk6iSyHeTHAyQvZGR6HMVIonjjCIlluGtWMhTrIi66qHoDudFg0zU9TAsRTiDx/e1P8vZXJ/nBz35Cf+JTnP7Vl9iiiWxbtYZlr349L+45QrlSwZA1ukUDJ7LYrOjMrk1zfqKOPzeO5vtIqT6U2CKeO1agYqTJTx6mTQ9YvWkh773mKupnIpp+CUdfzjd+cg+16UHedvvrcE6OU7v7Sb7607/m4NlBstnTrFyRYfjMixw4+CLZ3h5+/eB2vnLPbi68QOTxH21kcupZvv3xj+NdcRFmsouvfeNuru3fSH3oMEijfPXTd7LryF5+s/0pjECm6lS48zPf4q4vfhbFbZAQyzTLg4S18/TENO78/0fx9QRty1fjunmyQh4ySWTPpdGMc+ixU8wPdARCtJ7lzHg29ykxbmAIqWTRJp+g7jaYMzpQDYUoGCWW7uXpX/2MRf1jvGbeNWwfW0it5rGo0+BgoY4kCXz+Ux9jxyMPkK+MsKa2hv15hVGSzCyI8QarzobI5ut+HAjxLQNPDLg1N59sdJCPPfoAs6rOomySP3n8NM1QY4XRxtK4jhJZHNIssokUQrmK7Wus3nYToVjj7BOPs3rDOjpEjaros0BSiPs1hP9b6pX/HSEgkEwmWxLBIqi2h9Ns4rkWmiKTiBsYehzFMCnMTRFJApJoIkYeqhbgODUiIkAlCgKqto2iaFiWhabpiISUS0WEMMDQVSRJw9TilGs+nusyVpim8ZKRthQKmGaSWqnI6uXzMRQfwizFuTLdWZWabeP4HnPFKrYvM1eDRDyFKMWwmy6SKJJIpLhow3osJyKTNBk/e4amA4EXsnvXblRZxGr6ZFJpms0muto6YHbDiHgsxt49L1CrNlnQ38/GDVuIJ0xePHya88MzRG6ZICgjOwHZdAeX3fBq0rl2Th16kWu3bWbnrgN8/jOfZUH/PF7Yd4BiscCFq1dRbChcffUNnNr1GJIKuC7ZuMGrrr6ePYdeJG2KyIJAFEqokkTM1BDCANexyXV3ISoiqmK29P9fmiSWRI0QCUU3sN2AQqHM0PBJJE3HCwUUzcRrNlFlgSDwEEWRUqlALpdDkhUct4HgRcTNJIIc4AVVRMlDklveAy1F0pB6vUo8YbZabp6Lqqn4gYsoiZwfGWz14EUNp+kSBAFmXEeRBPKzReJ++D8qaqYIIQs6kjTNKzH9GjOpbvLDRdzBg6TXriCTzlCcG8YtKKT7F2HE45wNZjnWcTGdYhUr3INCGzmjDzeo8Bevnc9XfnKcnz2j0bdb4jufWMW7d9dZcc1bmHr6KXY/f4iMkGNTvIPVF7+Rzp4ssmXD6QGM7jjfqe2lb8Myzg3mqeSnMet5pKlznO1IsERbRqEyR9SWQZpRuPv+7QTpldyz41nE5/YTNl3e/+43gjdOV24K752b+fjffZnjO4apGja/+s3b2LX9UWSxzl1f+SGffMcqPv3NR9l9rI3lb3+ST928kuuvL2IsvIhH54osePopTpfP0rl2PsqxAscffpR3/f3nwJQZKdTY9/2vcqbe4KHHnuH1l11AoEWtCdV6Dd0rc3Q4CymFgh1xy1IF50QZoZ5E1BsMfmuQXldH01QktYP81dfzvrPtvOWq5yiIFm3aEMenU/zyV21cfVUb7b1tBHIVTXCZ153jiafPsnJxk8/ccB0npQRffHyYQAiIdS/inX/7WQJ84us3ccrtQbvyem5N5Llw9BBfKXictnsRlVEiISKTSvKZWJyqHPABR8MVQ3ob0BcZLBOSrM50k3dmyPUuYC6nUmq6mKaNogt4VpGmECMu+ehSREJoMhvUWYHDascgQkB8mdjNH0Whj6KITCLNxMQIyVQcUzcwZA2CECHy0NTES33YlgWoG0b4bkgohiiKTKYzydjYGJbvEdNlmpaFHBchiJibnaKvq71lSacqILawTVnT8fwA23GJJ9M0mjbtXZ3YlkOtUSWbyRCPJ8nEIoIZCT9fZsKfpbNrHqlUjHg8SRMBM9WFLkstNyjRolDIU61WSSaTdJo6ZkzjxPHTdHQt5sorr+bw3t9Snp0Bv2V9p2sKRAGmoSFJEuBSLMyga0kmx8c4PzZMqVbF0LvY+8J+4kaTcrnIbW+4g53P72b/3r1ctHEjo2dPMb7rGSpNkVqphLlkCdXp06x7zbVUi3mef+J37NvxNLfecjWuZbFsUT9xcwkDx44yPVGlfV2W0KuQMhLYtTqaBLG4iRCZEIVIkoBi6oSCSLVu4/khjbpNKKqUZyqMzxRwwpBIUNFVE9duYlkNBEEgYcawbZsIsUVSqQKS4KMqKulsmtlyEU0G0TAgckklU0RRRBB4QIgfuLhuazJY1lRsq0YqlUJRRFzLJmYYuLZFd2cnU1NTZDqzlAplkvEYDcsmCF4+nfB/PbeBpemIgamT9HXPp6tvKft3/jO5pTKCNYknRihJE81QqRVm8dILqOrzkWZmMZKjLO7NIsgqjdlZgnSKdtfiC29axl/8rEyuJyTb10ubIzKb6sIcnaRfVjh88AU+8Y53ckjuQnOr7DwyywVeksFKlU2LV7L1ltfzgQ//f7QbnYRmg4IS0Nu+hUJjFkERuPEtN/HeSzK87fO/Y9+v3sHowG6qlYhlF/Rw/44xth/K874rHVwO4F7VxqXFdXjjNtdf/SUeeexHlCyBytFpdh/q5b4v3s7f3F9iXmqGzz98jj3fvxmhPsMytZ+3fr+BVszjq02k1RuIhoa4+opbsG2HTHsfpeEj/O3ffIpfPPgrxMoICWsc36kSE/N85ZEG58op1m28BC0IqZT3I4uXUhgJaHvsF2RtB9dIoiayPHHzrdw3HEJigldfGKAra5jKh1iqzYHJM2ycbUcsjSO6GoLqEjQcmpM1Hjw5g6sKrLv6YpZ09TNZmcadv5TUktXEwybtTLE5VaHjwJMIQZL/dcijV42Tss+yYM1CDu6Z5oOvfg1Rb4Kv/eRHaJ7GZiXDIiFkRXYBCUXDicts2XIToqrzt2efIpPN4bsOmqJRtywSKYnqdAkv20GFEKVhs1jWUWWJctN52YJ9fxSFXhREirMl5vcsZm5uDkHVW1rmQYkw8vGiAFnSKeanSKRTTEzOkO3oRxUSCKLP+MgYhhYjCECMfEQhQhYFKpUKMcNAjUKqdgMviPADgTCKsJpVjISKpCoMnxtj4fyllMtlevu6KReKGIZBfmqS+KIcgVAjqTVZu3ETUiLBzMQ4o4MnaDQ9mr6AKMm4gYciSnRnUi2CRvFx7TKjMzOsvGAdl166lbNDhxk5f5yw2QQHItH//Q8VeC0N/AgXkSZiKuDgwZ0oikRHeh5dC3qZmMnTtEpce+3tPPz0s3R09LBmw0Z+9fiTdKTiLFjSyXU33MRP7vsxB/bu4A1/cjPDw0P0dM+jvSPJu956Bz+4+14WLlvCFVdsZfL8GQLPYfmCLg7vO81b77iVqdHjCEodogDHqiOGLactfJEACAWFyXwJx3UZmysTRRGO6xO8ZP4h4NOol5EkCZkIWRYJAxdVafXQTdNs2RQSYspxAl+lPRsn9Ju059qwbQdNVVFkFcuuYNk2pmkgirQOVpsumqoiRB5WvYGqhrjNCpoaZ2zkLLohYVctfEcERcGxG7TUDf5nolmr8fAPfkQs1sbAM/tZuuJCYtokmtBBNT9Id0c/ZbedmBQnIM1zUyUGimWk8jQXberD8ibJeQrtbSYzNZumV8ErejQr8IqtOc6MTzB2eoZ52dtwATEVo2ZX+O2PfkTv7e8jFSugnNnDU7UmU6MF3iCv5R++dCfv+7O38MUf/yNWVSAeJaiWpyhPDpCKt9EonKWvewV61Ma3vv5z3vPuJfR507znff/E+VoaV5zlr+54PfXTP+UbD4xz5bq3E4S/5q3xG3n0oZM8tvMX5AY17vmH7Xzvoffylx0bufdnj2MaIsXaCOLob8ku+xMuuqCfsb2zxGoFzjo2shJjZHSQzUsuRNIMlHmL+PE/3YtbmsWkSUVOoVSPkQ8XsmNKwqscZQU+xgs7SCwf5/HsB1n3+B0kIxdj6eVMbbqE9587z+QJm0pY5sNvkkhKKkXnFEJ8IQlH409u38iOZ0+TM+bYdtFa3Po0A4N55sIIw4vx2E93cXRwjNyWV1AS4ly2NMGSaJapgf3kC2NkN99Bx8atfODXL7Is2cn8sMy+msc8rR1Zn6anUOSLv/whZTPGtdl2+uo2Ccemc9USMsksUuDR0ZFlIu3RPDVBUAow0jlsp0FM03EbeQKvShhJ5CcLbEbBiFwafpN0Ik1Ut15WHkp33nnnf2miv5z4ylfvunP9xtVEkYiqGJhmkpGRYWJxHdu2kSUdSVFaHqGyRDKZZnamQDwWo16ttNo9qkbghWiajKqoCBKUymVipoHnWIRhBIJEvVEn156lVCwgSwqz+TzLli1hanISRZEhCOjoaKdUKDKvuw83CmlLm+TasngRPPz4k/R0txNaVfB9JC/E9T3iukqzWSNutnqC+E2GBk/TlslRqYcoqsfc3Cgj50+DD64bISkClm1Tq9eRXmqHCFFEGATU6gVEOSAej9HXs4TTA8cJIpea3WTFmk3ULAfbbjI6Mc073vYO6vUmpWKBo4f20dfTyatuuJ583WfVRRv51ne+T//CeaiaxpHDx5grFvnT229jYmyUKPQwYyr5mQb9/Qu4cO0Kzp45Sui5BK6DXW9gO01cX6JctajbLoPDI4xP5WlGErbjIIgCiiiiqQqmqaIqMkIYkozFiIjQNK3l/KQriEJIzNRIxE06shlsq4EkSyTjJt5LeKTruNhWi7qSJQlD19FUDUmKEIWWvaCu6biOjec0MEwdQRBJJpNIkkQsnsV3I5KJGNVymQP7j/PRj3z80/8Tuf3luz5/59aV/WSMDE66iiMmODY0jl9uYmhlevtX4gkygmBwuqriZZeTUnQKhQJHhwqs6e4gHeUZLudZnTNJT4yiJGQaeZufn6tRnLD5u3ct4KEnxrnm+suoDJzArdURpZDRfbuZOXASQYg4XR/nys7lDIYRG1eu413XrGfJgsXsKXSRW7YF2xpDmJ3F9S22XraFbWslblzTxpcePkJWyWHmliMGOscmXVJ9a9n33MNsWmWzctVf8OvwBoYWXEGtTyIKT3Nk4jSGHGNVYjnrLujj9MR5hqs1PnrzZmZ/9AL6ixYzLx7gXd/6Gt/+x7vZlujHa9rUAo9kTmPLFWuxQw0hpmNIIposoPk1JG+MM/mI199rYk/Oscqb4Jpnf8bFx48THDtJ5sMf5/7cxZzaeCufjXr5XcminoyjpQ2uWj7AJy6fYGxmBEVeS2THqJcm8DyXsyMlBFmHMESQUhhGlunzRTQ1Rt33cWsO2VSJ/U/9jr079/Dc84dItWUplmsMHzxCRYgYmotxhTNCdX4nQXuCF4+cJm5kOD48SLx7PlelOukrlMmlc3R0ziMWl4mZWWLz2tl7agc/P7mdmCEgehFqzEAwTNSwid8o0yyXCGyHC5Ihl+sRhBZSGGHGIg4GIR/527/7T3P7j0MCQWhRMoHvMDc3QbU6RRAWqdXLWHaTumODBH6g0ZHrp15x6Ghrw2oWEGQfy/YRRJGABlGURFFb2GAmnUCRRWQtjm4mEEWReEyjUS+j6S3XqZ72NmQatGc1+rrTGEaE7zVYuqwfT/QhEKhZEXO2xXS5wIc/9kn6Fi0i1CMiySUKmuh42PUCihyBIlIo5ylVyvT0dZOKayxuj6H5FiNDp5mdrTM8VWTGajI2W6RQb9IMRKoNj2rDozJXo1YpIUkSE+MzBKLOkXMnCZt15HqNdWvWsGvnC1DMkxEcxCBgx+PbmZ06R5wqObHBmUN76O5byYWrV7Osr4fVC3u46bpXMD01xW13vJa+nm6GBkfYuGUz7d2dTI+P8aGPvAcUiYPHx1h38VZWrVlL04oIBZPJvMXe0+McOjvJkaExHGTi6QxEAZosYagKvh/iR+D5TQQxRJQlGo6PJGuEYYiqanhugGoomKZMNhOjbpWJJ0ziWpxUIkbcVDD0ENP0kKQqbSmDTMIkdD3imkkqliGZTBIzUwS+gGHEyCTbCbwQIp9kLIXfNHDqFrIaMFMYJ5Z6eZOx/1URj6Xp71mEFBRZp11Cun8eyspemtkYca2fsfMzxC2bgbkQS00zcuQwqtfkqov6+NDrN3DPQ/s5Jywn19Swi9P4mobZaPDmlTkWhwL7ZrpIJ/8Pde8ZJUla3vn+wkdkRPrKLF/VVV3t/fR472CGQTC4ASRhpAsCrcSuJJAuiGWllRYJKyEJaVcSHJaVkBAIhBVuBhg/09O+e9pWV5d3WekzI8NH7Ifqc++5e3dX8+Ve0JMnTkZkvifz5Ik33/Oc5/0/v7+KJoU886Uv01muYisazbaHKYfYBBxpLHH9xG5OLJ2l026wdHKZL3/6Wf7LXzyN28uw4ohouZtwYpEoDpHGbuDzPx5FN/u5847DPLq6yskTlxgbzLB3VMJvi1xcHMQsFukf97h7+zx3jMzTUJeY21JmIJ2hlptFtBK+8pkXuTxznl/amWN4MMUrXr0FMdrGYO4A57/0M+zZC99ozDOsx+SjNldPHOOLf/MVgiBC9CKSRMBz2nhSzGefcHj9H5xBtUKi8TH+/L8+Qte6ytmXDbDl2//AR//6R5w9O8PTz03jR8tIfSqeXmJf/iK/9WCbWqtCKr8TN44JwyZuKGEoBuXteWbXu5y4UKHaaNH01pi4roigtpgYG0EOweqlmBgeIok2G/AyloiUxEyMbuOyHSLFXYqDRY43GuycGkWLUuQ7HW4tFdi3UWW0WmH70ABbB0bZue8AfeU+SoUKSXyCZ2d+zFKzQyJl0A2f1uxJhrtV7LkXkRoV/E6HYuJxg9EjrzSwTJ/RUQvNCgn/F8C+/zF+Kko3SQK+H9Lfb7CweAUEH88RURQdXdUJgoh6rY1lZTl+/BSKomDbLomwSXPUVAHPC8mky5t6/DhB11KomkwUCsiSQc/pYJopOl0fw0yxUbcpZdMUs3nsTgtdUhBliZSewfN8krhHNqshiSYICa1mm1qtzZc+/xkUNaFQyiBnLVw5wIhayImGIKc4/vxx7F6bQwd3US4PYlo5BsrjPHPkKa5evUqSJJta7zDEjzabNuPIQ1MSkjAh8RxGtwyyVm8jyhpHjp1D0SV2jk9SyGURkoDuxlViwSdKm9zx4COcPfI8YWeFihPztne+hy996R/5+t/+OZ7nISgKd95+M4VikZe/8mFMI+bqzAaRkOLF6TmOnDzNa1/1Zh5/5iRXL0/z9je+mXPnnyNOBMziFPuvO8zZy5dYfvx7RP6mgXgcx4RxQClj0e76xKFESpfIWTJBnCBJIlHgY+gq6YxG2tSpNdrouoqlSuALhH5ENpMikzEJQzYplEKCkMSYZoq0ZSKg4Lpd0mkTRZUJeu4m0dQP6Ha7CGKEZRqoioEsqaiqjqY5JGIESYgsy5tWkT9B4xFJ0Ri/8WWUG/fzh//pd3jZXQOMlUvMLG7QfO4KkzcXUe02taxFJpWntL8fr2sTuzbPnjvC2x45zFNLHbLxVl4jnkJHJYxVFrPDrNevoOZ9SmGZmw4azGt34J58AZw1JMVA1CVEPyEzNMT5ixe4rdRPM5YQRI8XZ2eY0rLMU0NbXIbcIKLsIUQJR48e4eMf+BiRuMib7l3iT//xCI+uXuK9v/QKnH8+TX9+kY//5j3IQsBCw6SY1HCmj/H+b/8Q4+6E/rurrHoJIf1cfF7i9XduQ1mF9sI83z1zhXveUaZ6WSIt3s3v/tZJfvFXFljzYh768t/T/dpX+D9uvYVFISQrKTS8NpIo88h/epTpeh5tYB/S3HPkpSo/99GjrO9+kK42gv/JM+RjlVh0CfslymGWathh5+AS73n5Cln5MmfW84zoProi05NjEhkqLQ9BLxLJDtVuDS2lUWlVkPvS7D6U4+TRK/TlimwpxCyvu2iKhh7IPFvfS6rvJraq3+eJ8zmG82W+7Z+m3oAfPn0SBIu7iwXEdpsxqY+JsolWGiZK60i5iNr6DI5b5eTCBRqGSiQkhN0KPTVhoDDA+XMnsEwDd6FNuVTihnGdfGeO0FLoGAZVL6ElRSQvcW7/lGT0AhBz4eJmW77v+zQabQI/xvM8bNvG8zzC0MMwNFzXJklCJFHZxCXIYBg6nU4XRRHxfJtyfx+eG6BpBoahk83kUVUdy8yiSBkK2UluOXwPN193J0mokc0W0VWFtLW5yBiajiIlkHjUq2uUS1kyaY2ckWCqQJzg+psm3aq5uXFy9uyL9Ow2W0aHEIXkmu+swNXFWS5evvD/8MDVdZ0wEogSgSgW6doeThDiBT5+mNDs9qi3HZbXN1hf3+Dq0hJOFHHu7BmEoEMS9Wi0qly5chnHbqLKIo+88c089uMfIRCSFh10wQfXZm1hnnplncvnX2RpZp3b7zqI488zt7DK5NabKA7mcfwO192wl6PHH2d0cJC0ajBWGqAyP0dtaZ7bb72LLcMTpPT0Jh5CMtAlmz27JpCVGFV22Fi7hCJK1zxkfeKoR0oTCbw2uiEhSgmaICAmAXEUQPJ/H4HvQnytwc318F2HYrGEYWzW9aMoIIoCZFGikM+TtiwMTUdVdFQlhedFLC+vkstnKZcGMAwT0zQ3qYvSTy6fSRKQFIOeL1Oa2MfwcMRo9wqtkoYxXqQ1c5Hmli3cffddxHGIIkE+X2Dw+of4hx/UETL9PDBlEuVl/vBRjzVhmJ8/4/KBJ84QyRK6JPIXL97Lrx2aJ9yynSuv/hX8X/09KA0g3PNKUtcfZmG9w1Ra4Wi7ii06FBsr3KhWeXDY5k3hPP+mKCAvPU6MTigDoYelBgjSIFkr4gNvOMSb7r6Nt/3Sp7i8foHP//q9qAOHaKr3cWFWZedn/xjpQouvXncf28bOM/bW93Pdz7+Xra+aYffra/zjh/6ERHc49Sc/5IabFumWDSrDy2TU7TSPvp2/+y93cSg3wl///pc5ebnBt7MTCF2XK0urfOOZKrf94i9z5Lsv0j5xCWdxgZ4go/aPks7dwNh4mnIxxMrW8PWQIA42PRDUdd5y3RL/7obnyHurTC9qDFgFaiLM12DGM3h8ocu3js/Scnx27Syg6RaXKj1SWYt+y2DHjhJ33zLM1AGR2KuzrS/m8LjMjvuymLmtOOoNnDirYKiDGLLOTcFWbM3go+/5VaQopuD77Mmb7NtVxJV6iOk5arUfsDz9JRY7R7l45RR2Ksd8KOBHIo6fkBEz+O01ysUCYlsi0ydjt+Zx1R7+DXfwRHEv19/9KoQ44tDoAOpL9BL8qcjow9CnWlujVCrRqG+qOTKZDI1mHc2QSYgIo4jZuTVEUSadNnHdNmJokDIVgrBD0LEx9CwIMa7bo9GISRIBEglRCokDn1qlgyhK5AtFimmLXErlyPOP0z/az8rq5oZuEITIgoqmaQhCQBAEHNh3EEEQePHMOXZsG2BubgEh9FGQ0HWNhYU52i0P4oSR4RLFUoqstak0KZdHeObxH2JZFp2ejaqq+EFEGIYkgYtmGOiWRatahzhiaGSAxaU5rHSOwIdsOoMf9biysES+r0RaguFihihJTDgVAAAgAElEQVRySCSdarOF3bGxRkd47IkfE/Z66LFPEPQQUVFkEUGIUAhpri/SWlvkvgfv58K589x52320ujYf/8if8I53vpttE+OcP32cleo6gmqgBCFxGHH/PQ/g+RHjpS08+eTjdLtd9uzfx4E9RY5fXAMhzdryeR581StZqDgsLy8zOjpBvV6lv5xneeEKuUI/YZLg2TbDY4MgRciyhCrLZMwM9cYaYRig6yqh75HLZ6jXGpgpC0UR2aiuUi6XceweayurBEGAbsgUi0UajQ6lviEWF2dptRp4ngByRL1RYWpqkp+g7whRFOL0mkSJwVpbwjfGGRCWeDAzycUrywxWL2P3mrx49RSDQ7uJIpnJjEqkBnzoY58gXJ1jI5xhZ1Hh6q47+Tdf+x4ZPY2ViLiahRhPkZZAtMZ5uHSRs22D+ZqI+HPv5PJGyJaMwsS5Y/REmW1vfT/zvRbu+eNIC3OMd9a4sZjj2IXz3FEw6W5APRSJBZlEUzFkhbXOIGlthf37B3ji7z5AT7FYU0qEUczC957k+rECrcsO37znDWSkb5Ba16kHh9CkLoPZUY6/8Me87bcf4W8/+lle/cADuJVlGmv/jYlD/5bl5b/HOnQr6drrCO49w72BxKPxGMc/+UP2DzVwhWGee+zPEZeWKQ7dQ3V5nvZil+JwGrk8T0quYIQKYrtAoc9isVahUBJxgwYPHa5y9/AlTKFOLewnW84SNkOeWgp45ug0uZyKHwrIQoqyZZGEqwxM9HHy4gXu3L+Vtcoya50G4wP9DJayLD5TZd+uw1y2n+ex5W3og8NkjTaVtQ3iZJUtnSEMEQqNLl999gi3FPvQTBcnleKIu8DAaJZqZwUzp3G53qSbTiFlS5xs9RBEmVxGJoWA0rLp1h2EyCHc6OGkDaQkxXRDpnZ2nV2zNeZba5Rl8GsV4F9R6Waze0+mWu0yPD5Kr1unWa1j6CaqqiFHLp1uG8vKocgamazF8spVMlaKRnUNTTWI45jIb1Ot20ShROKlGOnPEUcePTfE6ToYikzgQ+z4bN0ywMULx5Fll45dZWR4nNmrV9F1nYnJcVZXV1FMDcHT2Nho4fQ6yFKCYGTYffB6ZmamiYOI6fOniZNNNK5oQDFnYmkGAhKe53HyzCnsnovvOUiSiCyL6KpGx+6StUxCt0cQdBkaKjA6NondrqLrYywvr5PNlanXmximCnHE2bNnkWK458Y9pC0T1/URCTD6S9TaPUqWQiJ5uH4HwdCxxIR6N+J1b/5FLs0usu/6WyFwEZMChXSOR7//dS5fqvCJT/4plcoVPvTB/8DL77sfNZKZXqnypre+lXNHn8T2BWTfp6BE/MzN+yDyMbNZ7nzoIcTocY4+dRmzvJNHXvNxvvzVL/COn93LU898nfOBh+dCKttHkDj05TOUjTyGZdLuNRgZKeF2A+x2j2IhTbVeY8/+AzRbbVqNNpDQ6dbxfAffg2qlg5DYKHKCpoooikS9VkNAol5bgSShVXfw402YmampVFZWCcOfnLwSwIsiwthBK00wTAPHCZHblwmUAVqKR+z0WG1V0LURUrlB3MglrNr08j6amaKhTuEdO0omp1IobiFv6CxUL3L7vb9GNdI5/vg3GRZC9tZF9rW6jGcynPc8vL0j6PUWX/cSZE3lfC6PHXcpHb6J7q5xWo89RWpplnKcIl5aYmda4wm3g1fv0Gq2yWcUrMwOqi0Nx64ihj54PaqdU8wdO07qUpt3veFDfHF4mJ4ekbNfxkzf37BDdumK8OWPHkOfbvKZR/+EX/jkR3jm688zuu6z6z6ozXyW0b338+z8M3zhqEf320/x1G0/y13zj/GdqX4u2jJBuE7f5B0stL8G1VlkPYOe0qhvSIz0S0j6LH0ZEzUlkMFDHbQp5XU6cT/3l08zmLi4ngamQrOdMOLZHPvRWeZ6BabkAt3AIacJjOZ12lKBVbuKFKd5/EdX2TIKB0b6ubQ4zyF5HDVvsLh4lcC3SY0dJpPEpFINanHAcEqkmTSIjTJbXYvWc+e5Z2yMitNCUnKsdW0qXQElZ1JrtjCzRbrNNi0vZt6JIJOQhOAGIVJdwas2yeg6tugSBaAjMKyLHFxsMaD0qPtVLDkmpSkvuSz5U1G6SZKEVqtFvdVkZWWJWq1KnER0Oi36ikV8zwXAdXt0Oh3WVitkMlmcno0sClipNPlsjpRuECebcsChoRFCfzOT79ktAr9LnPgUi3lcx8Zzu9hOB9vp4doBrtMjpWkMlEoszs1h6jqJ30VMXHy/i6yImGaKVtvm+InNfYKECCudQlNUzJTOQH+RXC6HKMqESUwQ+czOzSAIm9bulmURhgFh5COJ4Lg2d995O2OjQ6R0lbMvnkaWJYLQQ9M2sQTDgyXCMMTUFbKWiWGmeO7oCTYaLcx0lnplgTC06XbW2Vhf2DTqIKHn+qy3OrzuzT/Hj554nHNnjnP57HHOnTrGEz/8AZIQoykykxPjnDt/jI/94ccol4Zx3A6eXUcVBT7ykT/ggQfu5/B1BxjfvR8hncdRdKRCmdgsUmvXEMQet9+5j1c8eCdHjj5O2bI5/8JFJkfu5p2//H+SKxYxZZ3+fBFTVfHDHiQRuXSGTquBIMaEkUuns8mymZ6exrFdwijBtjvohoqVNshkdTyvA4CiCiQEqKqC7/t4vo0oxYiiwMDAwCYKQZKRRYVctkzyv+iM/f8jhCRBkjSqtXUcKeGL/3QEuXwbNxQttOwINVdky/kWvhszP3OKK0ce5fEzz+HlfIRaGzuK8es1BrYe5vyJs4SdDSKnTk4ZYGZxhcMDIiPbDR5OJik8s0TmwixSu4F66iSWOUB6pI9+bRgtTrhnZRktzrEmWMSj13Hx7tfwqCtSDRsQdvn5Qpm3b9nL9JkTfOvp7xCSgOGxEUGk7mCstJvPffbd/N773sesoDNR2EPXlsjm0uQyJstCgc8vvJbe1z/OF17zIbrf/Hv84xu85xfex6n//E+4cwphUKJXiVDmVqjbjyPnXsGZtSwlN8btppm86wK/9eivM9Z7Ckc0sDspxrbdSW5gHdmUCGKD0nCWWNYoZwzarQZ05smm1hiRztEfzvPzU8dJ06XhxZAboNfo0Be3SfVv8NG7VcbjOjsndfZm2zw06dFeegE/bJHOSaSGSkS5HCtNGU/to1zM49pdlLRCr7cB6nUY6hh+RmT2hUdpRz41N6ZhxzyZtFmXbGzZ4xm5yQnf5azXYEO3mCfkxHyVVTdi3k54drnNlV5EqZRn71CZjO2SsyNkp0NJTRFEAal8P3sHxzgwanLnSg89XSUeijBjFTmS8IL4JevofyoWekEQIAo3gWBdG1GUSKfTCGJMo7GBZVmkUjqqJm7WgzUZTU0zNDh8rX4b0Wg06PV6EHrY7Sory7P0ejaOHyCINpIUYJk6ge/TV8gwUDaw0iqSJBF4DqoMqZREo14hbemEoY2WOEhBG01ySUIb1/fY2FgjX8jS11fmhhtuQFUlUpaJ63tkclkkRQZRwA8DFhYWGBgoQxKiKyoiMdm0SSGXRpFiDh88SLtTZ9/e3XS7LXRdRpFFtoyNMTkxiqFLlPuLTG3dgq7JSGKMKET4icSl2UXqzQZOr0N9ZR4haaMYCU7iE8UiUQj3vOJ1/ODHT+L1amQkB92rUFBdBH+dbqvJnh07sQz46pf+hl/8hXehaVkKhQKjE0OYlkzgNzh+5Em+/c2v8pFPfBIlUyQyiuy84T7uetUbyRUGKQ0NEUoOkRxTKlvkSxZ33HEbTjfga1/4JunE4lV33sdgpo+J4XH6SoVrd11EQCEMI0QpIm2aWCkTQ9XIZ7IUCwVkUaLbbiEkMbLsk8lJiNf6I+I43MRVCxFJEtFs1Zic3EK1WsF1fHwvwkxlcZ0AQfjJTfMoifm1D76fT33mj/BnT9FJXH7zL/8RHZPt6R5+apLLSxcRI4n0pUUOvvgi+y9VOH3keVrNBYSgy2TeIJPpIxVvWjS2Y4ODt76FG7anKFtneeaFFS43XYQ7Cgz8u7dQP3wrmUI/rm8SMUxn9xLWtime+sxfU3AcRD1GNTWa23eSvP+DLLz8YZp7tvNsr0smjtArG0ylctR7EY4XU3d8Fq8+yYc/eA8bzQxefwlzaiuG4nPy0WfIXzfA3tqjpPwVTm88wMzSEYJMg0lB5fZ3vYVHv/EED//+26hH00i9R4nWqgiOiHJhgf49KvZ8jeV+mY9P/CVvdCRMBX53+J/42s9Oo5dcwsxusqkFsE8gRj1iZC5e1ql7Za4bybI1JyNhM5HV8BqXSKsugZ8jJKTd7GBQI5tuE8QbhHmBW0d0FFRu3DtGSQnY26eSCeqk/C7ZnEwiemhahn9+5iKrfopmYpArWPi5QWbTRVTBRVFdJuOEGKgFETNhl14Q0kFkMfC5dHmZRJaQLRFPSfAjiQgVRI2FlRqSpqFoMYgRjl2nkMvjNtpYKRXSOoJl0A4bWHqLncUtLAtzaGKAWGsTSCJuGJFKmS95Hv5U6Og/8YmP/sdbbt+D59lIooAkyjiOjWlqiBJsbNQ2N9cyFum0iSAm9GyHjJUiSRI6nR5huOnJqiQBkggimxK/OJEgsBnqH8B3fSI/ZnRwkGMv/AA/DvEiH0WMUCTYqKxCEhIGDu1WjXTapNms4rgd4iSkWW8haSKO7bJRqZG2NLq9Kp1uQLm/nzhJECUJQZaYmV9EkEQUVUZXZUgENh8RuVyadEZDEmUyKY12c4M4DhkaHSKrWzTrdVK6yvjoEKaps15ZZffOnSRxjOv0iEhIZzOsLC0wN3MJ4ghDT1ivbdBzPQRJRJdFur2QJPEJ3Dpq4iFFLooYkygxt9z+AEefP0ohA2NDg+w9dBOipFPuS/PEk9/nhtvvIJtSEb0OmmIyWDR42d13Mj83TX11lu985QtMju/j9Jlz9PVnaDVDGs2EvokdHDn+PFpYZVyXycYBi5U1+ksjSEqKqcmdiJJEq90kl8sSxSGyDL7rYxomhmHhuT6GnqJaWUYSROIwotnYYGhggFqtAUKILEtEYUwQBfh+D8PQsO0uYZjQPzhAo14nl0vTbnc4cfw8v/7rv/ET0dF/8IO//R+3bZ2k7Xrs3b0Nw9GY78xywz0H6au52JO3otSuYKZMrmuGbEkZlGpdsh2Hq7Q3y3JrC/z9X36KTvYQmfEH2b7/DvZPQSk4wqW2yXJDJejL8LJ7bueP/+hbbN1eYHlwkmebJstVgde8fA/d/jLTA/1Mux2UsMHu8RGaq3Pcd/gwVl+JnQ/ezeRr387ut/88n/74h/n9//B+Xv7yRzhX2+Cbn/8Y68/9NRVhiDXXYPfLfo4bzH4yT/yQcnknO258FUMnvsaDgx2+Kw5wLjjAe7Y/zuDL78RID3LgXa/h3Lc+yo3Zc1jDERoSPV/kh8YY7/t8mfcd2uDuwgxTcoZjK6NE83W2f6BAWXiBO/NVTLWPFXGKPX1nMdo9VlMlRryIV+58ipE+Gcmukgkchj2JnXu2YeRkUhq4oUTiQ0aJidwWkixRnihR6Pl885IMQpuc1iMbwlBRwHca2FKWlWobOS9T7cLMSofZSgNJ0Dm7XKV48G4qbgEz6DC1cIw3jt3ElXqVluLS6Nq4QYDlG/zn936cr516Ej/qoUgS7W4DUZJYWWkgqCnyQ3kGBksoQoAkQS6W0AWFSruDLcU0/JAdY334tVXajXUO9plIcYdYEEirCpET4IQ2z2zAe//97/7r0NEryqbvaD5rEfgeIgKpVGqTfSMlGIZGHEOr2cV1e3j+prQuISZJNksiIyMjWJaFKokosoCRktE0BU3TGB3ZTtoqIksqhUIOM6UyNjJKvpAFwUeWfTY2ljFSGqIUI4gRpmXQdCIEw8IJPMrlAqoioKoyURThui5xHFMq9RGT0Gg16fY2s/6rc7PIskw6nWZ0aBBDVRCTmMj3GCiXSCKHvnwWQ5PptpsUCzn27NlJudiH67qIosiu3TvoOV0kKUFXVTy3h+vY1KsbHNizC4kASRaQFIUde/YwNTVFq90mJMF2bQ4e2MlARgWnRcbSSGSRHgK9KOHAjYc5fvIMMgJep4XddIhpceOt+9BUEytdYnFxmcrqKnkrA7HIq1/3CJGksG//fprVNfoyKlemj5HEDbxohb27xymkIlbPP8vW4RHS2TKO6GMLNqKqcPDwrShinuefPIfvxZhmmjjepGFGsUfatHAch/AacrjTamNaOrbtEEcSngMXL8wgiAkJEaIEQegRxyGCmOB6PQQR4sRndXUJRYVz509jWvr/ZU7yk4ggDJmdmSbybU6deIFZR+eOoRFycg897lGWVnCsUY499jS2OUrDsMC06BN1ypaGFYbQafOOd7+BD7zjNu4/aHF99ixPPfqnZCcPUcjFJEqDrz5xlM994js88vAr8MSAyYzFA7kF7jUWqdgFpgZHMTJ9bElSTKYVOlePcHhqiJyzTlo3EJ2IdBzTnL3Koy9c5L0f/iNaUUhl5ijl7rN0M3kO/NKH+fAff41aBE9/67u4HZ10scD0o3+D9vSzrD/xfSLDp55s4TdW3kJl4zTWboGus4C+MU2cHUALBcRWyNp6ii+cfjM3FWbYI19CSAy67Qn2vz7Hm66cJHfz4xSuf4rR/jneOPpdPrW/gbKm8LeD8K23n+GHH/8qb74vZn25SRBLSBHME7GmjtPRBljpCJj5IQpGRCEVo8Y+aiSiOw0GRkVuya6RlUoEbowkOsR2lS05g3IW+gbzWIbMjp2DCElCIuc5trYBk9dzZSlCMBUGV14klwtQogY/u+UgaReUEIqGxUSpiBfDRq2LZmawPRfHha4dsf+6AxTKJrlcQDnbZe+ExHVbJG6Y8MkUNtCH01ilPgaHspi+z4CaZTgIEIIuQawg6yqmIaHLIrpqIIr/ilQ3nhcgKzora6uYqRzdLmwdLmPb83TbbVLpIotLi2RzKTS5iC5mIHah10EMIqziCL6dcHDnLs5eOo+ZFbBdG8vMbbbYDxY5+sLj7Nu5n9npCl5pCTvs0ljrIKdKRElCygypVGtMTk7RabZwHZsgbBIJIqphsFLZ4ND1+5heuEjUK6PGNrLaod2yObhviumZefYfOMTScoUkriHJASndQNMlRAkEISCbMdFkifGhSfywjZxVyaSLzM1fIexAsa+fjiyg6gnrlQVcr4XQSpCSmFzWoq+0i57bxgtcNENF1QQ6UsSxk6coZGQkSaa+Uaevr49v/egxpib3U2+uMmxNIqoSohijpvuorElYWoRodnDaPu3eIiuzayysnkURXB76mdfyxDPn+YV3f4AXnv4ytdoqP37yx3S6HoqmMjw6jOinSFkSxeAAa9VnWF07Tc7MogsqYXOZphfhyykE4Mabb+PYc8doN2vITpNeVydTKuD0Vhge6COMBEShiGE6BLGLH7gkSUQSuZRKCo7tkoRQHsiiKDK2HaMrKRQ5Jow86vUGVqaA7fTw/ARF0AjCgK3bd+C5Di/xv/D/SaiyxHDRQo8TWq5DtpAnaS3xg8fOM5YZAud51C03M1RdJB4YY7aywK54iW4ioTcgKDeIVBld2iDXrnNjXw4/08fPDTyAKLm0A4Ftw0OsOS6H79rBwsxZ1LxMLz9Nn9oi07eNSrPH320cgcjilok+ul4TPfLYUVzEjSRGtOtRRA8jvgK9FTaaJdalFB3BYO5vf4f8eIbZaY9Ss8NpbQbFb7PnxusZENOc+IP3MCTVCbIWz2Z386vGc4iZPpaXEo42ttJ56hvoVxIyKxrdZkI7Da3Cyzm342VcemyO6as38vZbYFelyjefe4KbXqMxKGdI9ywaVoO4o9IJNhgeHOUXm5d4uj/N9d4TBM4amhERBiqqWUBw1zk2myMp7mK41UCPt3Jj1mHUrCB0a5higuj6VGYi1LTJz2xz+fHsFQqFmJQuIShZnI5Ht+MwNjbC7Nwq7fUq45N5VioO6tCNrApTBEI/uucQOydJ51N87/IpBuQh3nXwbj57+klEUWT/6G7+/ad/H1kVWVzYYGigSODakPXoKR2uu2WQATWB6lVG0yopUaBZabN3+zDnjnUR4wZ5K4vRa1NQErYMF+h068iqjh/0SKQsqiURhjIJ7kuah//iQi8Igg48CWjXxn8lSZLfFQRhAvgHoACcAN6aJIkvCIIG/A1wGKgBb0qSZO5/9x2SKBIFIdl0BtcJ6OsbwDLS1Co2mUyJSq3D1NR2fLdF7MWoskwYRxiGSspUqXerFK0h2q0qihBD4BO7XZobCxhqnueePoWRkmnUK6R0k5mrs+zbczODpQVm1lpEnkQYhJu/N4k2saaKytaJPq7MLRAi4/QC5mYX2LJljKvNDca39jG3+CIj/XtB9dm/dyeV9SWiMGJwqA/XaVIuFdE1GUUW6CulyeVyhH5Mt9NClGKsnIwoQS6X26RGyhqZrAJCFt93GRgYYueOPVy6dBEh8dlYr5HSTbrdOmNjQ1RrG4yNb2NudpG1jTUmx7eytlpl7969PP7EY8wuNNi5a5DllVlSWg5Fha1TU8xcrBM5a6iSj6ypbJ+c4PLlM1hGCislcv7ik6iEPP3tvycIO+jpnXTtde69+z4qaytcPPUUWQNCo8P8FZ1MH2hxPzI61z90B616gxFZp91uMzI4xLNHXqAdi7TihNxgP0lscu7EVUa3ZOkkkNIy9OI2AJ7nIwgiaStDEiY026sISQpRCrDtLrnMAJIs4Hg+jXYDkWjTeQyBYqGPaq2FFHvoqRy9bsLFC5eI459cRh9F4HTbvOGBfbxsVOD3nkohrsdcWlT4enOR33nnjSyf+gHZG/ZzdOWfsZcCFjWHYmoA6cIKM9/7AY/89i+zJNZQrRES0yR2mtiyTKNWJ60GDCYJ3uAQ5V0GPUlk7+AeElPEbV1BsSKuC0yebxk8vO9mou/8A8YdKcaGXyQnp5gJIhSxxkBrDVOvMiZFdOU6teIDfPuUzJAc840vrlMe1nlD++/422dlhqWYrz/1TT4RjiGPv569zhF+63W7Ucdu56gyjiHJDL9c5ZO/8WsoBY3PPXuWxVzElBJz+brf4QPHTOx/XiQKwZXWSb56Ertqs29YR0o07J5LNt9HsLZBLdQYHN+CoObQTz+JdmkQ8a2fZujmFdYu/w7FjEnUs+mKOmvl16A4FvVWk7HiJB9/voXfu47bSp/jZSMxe9MqkX6OxKuBrvLKgxJnwjRBkEbrdbi03iOMBc5fmkbUy2hZjSBqky1OIOXuQBT7wa6jLDxGIoc8v95hpGzQL8pEkcP+jIWnZzg0Msk/nv4xARGDo4N07S5jO8cZm1DYmRfpLyasXpmhXxJw1tss2wE1V2I5dujLlzDx6VXqDMgCExkRRfDIamk6fpeMqtDxWuimiqJtGiy9lHgpozzg3iRJDgAHgQcFQbgZ+BjwqSRJtgEN4B3Xxr8DaCRJMgV86tq4/30ICe1OC0EQyGQs2q0qtdoqhpGi3ejRlx+AWKCQL9Hp2HiOTzpl4kc+zWYFQ0lIaTEDgyWGS2mk2GW0nGdqrIyhOGydGEEIJXpOC0FK2LZ1P41qwHplgeW1i7Radebm5hBFEUWSsVLmpmPT+iq6qhIHm85X+VyO0BXYujXLyJhBudhPp1XDc7vUG+uoGkhySH85y8hImWZjgzDwKJeyZHMGjtvCSitEUYCuWZimSaVSwbZtej2PfL60uUEjBBSLObiG6s1mUnTtNj2nS7EvD4LHemWJKHbodBogOjz8qleSyWSYmJjg6aefZnh4GFkRaTQ3WFqepbKximEYLC3OE4UNJDlBECX8OABNR1dCAnsdIWgzPjhMSoiJqnOkZBfNUhjoK/HD738XOQ5IAh9d1+nZAbISIYUBcuDjthZwOx7f/f4zPPWDH9Kttvnmt37E2twKvVYHUzepVBtUVmZJoi4JIc1mgyh2SFlg9+rEMUiSiiwraIZJ/8AYiSATETE2MYLdA8eNiCORMIjRNRNVMfC9mOnpGbrdLnavzurqKoVCiVyuwE8SajZUyvChR/Zxa3GNVHMOb+EsLVHgeGWDO2/J8aHP/JjxG+9n5sIx1iUVY4fMSVPhBaHL9hGXX7k1zdOf/iv6JZPVTo/qRoUkiqgtzFJdXyEn6bxhaJR3338TuXTI9dcPoeurTF/coN2xMVId0Gzed9dDHI6zjO68CakSoik2VJ9iwHDZWhpAy5boNW0iZwlZ9KmvXWHp1Peph0XUlMqf/fZvMXdpg1v2VBgcrFPY2mWvNsek8H0urF7ioyc9ErFOZWWdRJWZ3hggu/sg4y+eJ4pi+nsC3XKJz84W6boyMjaKIqMrCr/A/fTHAorQ46snd1L5wSNc+f5eeOaNWNk6Yk8gK3XJJgW2ssRrP/NlEutOcjveSRJ7SGGbeCmimtlFNdxge7/JVPoCDw0uctPYWVZ7t/K5pdfws0/dyp9dfpgna6/kK/VX8uGTO7lw+SZWvyNz+tur7GY3fX5COVtkeERnfukq7RWb1N4HCGQZISWgK12WVi9ype3SDByWQo8TSZevrp6h30rxlr130K5sYBiQz1tgxkzsGWZoSuTgFovEbbN48RI7YoW87dGpOoiqhZ9K0RDyDOoxcW2NohYzkk3oz2r4bkCsSgykZSLfRzQzBCH0HI/oJSIQ/sWFPtmM7rVL5dqRAPcCX7n2+n8DXnPt/OFr11x7/z7hX9AAxVGEJom0anUkIlKGQBx1MFMapUKRlKbj2D28nkMShahKgoBPKqWTyVhoYoTv2lSrVVynRei7EIe0W+tIosu+HXuQY5Ver46ihTSbTfYf2EWzVUXXVRBCBvpLDPf3U1lfI5/NkE1b7JjaQRwnlEtFinkLYgczlSUWe4CKRBHDVHB7HbJZg8BvI4o+Il1UMaJoWdx64w2kNMhYaYaGhjBSMilToVTOYzt1UqZCPp+lr6+A53mIoojvu0RxQDpjMD19mQ3Hio0AACAASURBVOsOXI+m6PQVstjdBvt2T5HPpPiZB17J2NAg9911J9PTlzZtARsNTNNkYmICy7JI8MlkDRRFYH19FcKIVnsZpJhIhFxfCrfXIWckEDZQxIijTx9DCBQK5QGQPRxngcb6DErU4uTzj/Pww69GTefIaMMcPrSfZqdOIrm4wTR9RoepyQxlfQmhd4btW1MErTVKVkxn7QoFKSar9ygXwQlqJFKHtbXzVDaWkCSJMIhZX1+n2aoRJSHtjkM2VyadKeCHAWbaJCKh1emyuYmjks0UabU610iaDqKoYFoaM1fPYaWVTaDdTyiksI3ZPk+zZnOqF5J0K1yqzHHDjq28+c6388F3/1v+7M+PsV0u4l++yjOn6wQtl5Tr82TTJUhluP/6fuRlh1xOQDVkRC9gqFxkV9lgq+yT8yRGewHOpYB/+rPvocg5Jg8uM7JVRvOLaH6K+Scf5fJz3yXVi1l8YQUrkPDCdSJ7nl5jlZaUxjO3UJOKXKkM89nPHaPWbXDpSAs96OErg7z6LX/FkRdEFuY8gkpCpDk4QhVjahs3vfwOhPoJhqfGCFyX+aWz1Gcv8VpfIIkEvEQgkrK4QYjWcnFVjY4ekqQLOKOH+dN9O0n5AsfO9lN3euSTS/TCRYSoQC69nc+97cMU4g2GXZVzv//bnF0u47XvZmRrP5quYKOxq3uFfQMSQvMyfm+NgrzOqLJMOTfPkFbDMnQWtcN8bu0gzy9q1Jp5Tr5whpnQY9HYz+VT6+yJRPaLEaVWl/vGRjn41k8jKyOUx3dCmJBvHiVTsogyBrYiE6WKVHotgmxCeqJAq1dhrr5IzXWJZJsdW9NsHRAZslIsXZ3BWaxQFGViOaSX0rD6h6jo/VSSEYqRh1OtYQoyBc8lLblUKmskEYixTxwmWKZB7PSwRAFDSFAV5SXNw5dUoxcEQQKOA1PAXwAzQDNJkvDakCVg+Nr5MLAIkCRJKAhCCygC1f/hM98FvAsgndYJgk3teDabRlZFwqSBZSrg67hBjCyDLMVoakwmrSIrXeIYFNmgVWtQGhvH6fm4YYBupen5EVu3TrBeWaa10WHL6E5W28/R6CyTzmZ5+vmvMzQ0QtJ00GWTopmjUq0xNjZCz+7geR6NisvE2FZWqzWCsIuseoxNTeLYEhvzAq946F0s1Z/gxAvPEUQuhi5hiAKDA3lmL19lavIApqaiyQmWWaLnOCT4DA7naDbW2L13C9PTV7Dt3rVbEUIis2XLlmsKEgdFFJm9skwxV6DZqXHo4HYqlQqDA8OcPnUOUYKlpSVWV1fZvesQI8NbNsmHZ0+QSpeRJIGh4T6ee+osd915L88+9SQpC06frHHd4YMMDJTw7AaKZKPKEZenpyn258n1j7PeBF3NIXUFDK2GnknRwefx559ATudQ3AXml9foBR6Tu27mwqV5Tp29zJX1FuNxlyuzq+R2p0kNJay2zmPmE+z2BnIc0zcyiC3JJHGIkMRoqsFGpYkkpEiZCo5bR7c2kc+e7xKj0Wz3KGYtam0fQRZIqRnarS7NWotcLs9GrY6qaxBZ2G4VUQkZ7B8jSX5yrbFRnCBtuY0LwW7OPH+EF8QrpPq3oWR38Bd/+Wl2HnyEZPQ2/q7a4MHE468yEZrXIWnB6QMjTLcaTKhtgnOPY2Rvxso6VFs2KT1DJmuhRBpJtUn15Br6YIlX/ubr+civ/Ffe+x9+hZIxxonzV8lsnMG9fBnbcfH8FUYn7uXZH32Zg/dpKO46aqpKR5zk7Po4SavM3NWzlEWRKIGLksA9N1/H2rOPkbdUXn/z2/nTf/o8G9U2I1t2I4oF7n3F6xnyz7IYbqXemMOpBrwlo+DNX2Eyjhnss7C3Z1lsdaj0tehttIiGSmQR6Hab6FR51LqVXKLwxGNf46GFKY6N7WbHg9toXWqz/vkv8oZKgIFIq5ymWi7wuS99n/sy30KMBbJ9GfyNJl9832FWKzZnFn3Ozp3FxkbsCWh2l5TRZpucY9HVyQsdcFdxp2c4tG0bx+bW2KcaVDc8bs8rCNV1in0FhAGR0zmHDekgzso85Y0nEOMqUhSgWBl6bkDbjXC9mJQpkZVTrM/5BOogL9s3QXmriFbU8Hs91EaTrWJAkImRo5gNNC61PeaCECkJMKMOMS6yF3N4wMLwwOs5xCiYhkYY9vCCCEMTiWKPKJE2F/k4/JcnIS9RdZMkSZQkyUE2Xe9vBHb9z4Zde/6fZe//r5QqSZK/TpLk+iRJrrcsnVQmzYEbDhHFLnriIYd5DHUHsRQTO2365CxKAFtGthBGPRStwMB4P2baYv+erYS9y7iNCnt2phkqCxzaM0GvUUUKIkJfI9EamHkwBBnZqxA5s5jqEIMZn//O3HuG23JedZ6/ylU753RyPjcHhXslWcGyLMk2csIBaNNt5sHdJowBE8bAAAYbD000YzI2psc0GIMxDnJWTle6uro5nJzPPnufnXPl/iBNegaD+nkaxutT1ftW+LJq1XrX+1//v92wMLt7jKQnMe1VkrkY9f4KshFF1S1k0SZm5CgMZVl6cQO7I6AGZBZWz7NfHpBN58hnC8TjcSLRAKX9PaYmjhEMptksrhIOT6GoHrZTRxR0AuEUWsjAdiPgR1DVGKFQAFVxCQYNdrebZNNTpFIp1IDJfmuNYNgnkwoQUQ0KQ1PE4hFUsUk2IaIKLnfccQeJVIG9/TK+PCASSyGLEuMjk1x86RpHjhzBc0yOHp8mnSpw8OBhIlGD4t4GlcYqxfo2gu4xPZVFk3xEr4rb3kGwLdRoBc+0cb0BYsjDw6fbMKmWTJr7m0zmJrhy/ot06jqLyz4pQcA2ghjBWcT6HoahoKgSgg+GIhItzFIf2DDoYfg6wVCEZqOHLIMgd9ncWqJU2me/tEUgEKLV30LSPYq7PSrFBp4Joi1gd/vgeUiihm1JOJ6N5TRo9nYxFBFdNijt1RHE///AZV0lzDONJF//5F9xZqGKmn8Xv/KGUe6yz3OxY/Olx/6cA4kiqp3gid4MH1ttQtehW2uSPnIns/P301GzaHaVi994ArmjoIs2jU4DDxFdsShMpfjcQOfzz97g7R9+mnf97C9ie3F6rsyxm04zcux2pmeOkvREemadTFzj+OzPoJgJ+p5Bu9xjYz3A2aUtnnnhm1h2GSlkI+sDQoUOz7+wyOPnlvjGlz7LRC7Gb/7Cb3B47ASlhTLH7nk7M5qKhYYZHGek2eT9Sh3nb/6Aw4JL0vFoa13agg233ocvRZGC4NT38XGIpNPc9da3s9t1KIVGUFyX49o892XeytHX5fmeH00RuDvCwqhD51fvJrJ1no9+7Ce5ufslBqk5ZNuk07V44I57aQ8UwhGdO44e431v+yEeuPUtLK+UEJQojiwT10QG9WUsASQkDh+aohKQ8Iwg+3oQNz/BVza7LPZVrjV7VEptVv7yZxhyXkBpXsNZ+AqR7jZDSg+vvsuBmVmMbAoLmyQ6cV9BFidY3b9IZWGfob7PaK9EulWjYPdQTBiKF2i0TZZclx1LIKLqSJUdFDzitsyM0kJu7mH3OoRDOorgIskiouzjISHqOolkmr7vM7At/FfJzPrf9QX4vt8AHgdOAzFBEP7PFcEwsPvK8TYwAvDKfBSo/XPPVRWNqeFRGns7SH6Pvb01TLNJdX8PTdYYHhrHHthEYwaK0sf1Wi9j2Yvb6IrPzu4GiWwSTxbp1FKE9HG6nT6C3CGdU2gMXsC2IBW4ldnJ0xw9cjuzM1PUasuEIhKK0SSeCCJLJrtbZYrbRWKRAiOjQXCynLrpbkS5iuKNE002SaZFzEENVQOz7xCOBBmacNEUgbHCcQq5HPvVEsX9LWzXQQ6IjA6NYihBcH0a1QaKqGK1qgzaDaKBENVai1AiRTQaZ2x0ingsg+cYBI0cM/NHERQN2wdJe7kjV9d1JiYmkGWV+bmDOGYPu1dEEHbAKeL22+B3GZhNDhyaxBq0sKwGR4/PoOo2lt1mZGSIdrtLLBolFk0gSxoIEqlMhvL+FtGkg6i0cZwBejyC6dtEogZ4VXS1jhxykQyL/FAcz23hWnso5hrd6jaypGH7Dj23huu0ETCxvAHhdJrWoIIveeiBAJZnIikBNtb3GPQ92q+IXyfiWdKpIarlGp7t0+v0abfbtNt1+v0OgYCGYej0em2CIZVGs0zQCOE7CoocQpFD5LLDWKaNpqn/PW7+P9RUSSVvJzAC4+y3PN4S2eXW6A5/8cIqQ5pAMpHkx3/uXbzpRx7AHZrj3MwdnM/MURsqIA/fQaNXZavXQwmqTKkWfsVHk8PkE2k0c4C3u8tnSx2+akmc93yU/Bhf+vq3+cNHn6I18Gi0eniRJEJimKptEnItFi++iKbkWD03gqHcwb42yte/8Y/o5W8yl4oxncsxnRvH68TQY0HCt0a4XLqE6k2zcLHEwhPP8qb52/h3D96G1lqF+g2G+gPeGXE4XdzHuX6dmTfez4bu0wuANxnBL8zxhV0fq+viChKSCJLn0ekOePbsZczkFBdmH0ScO019dIrC3afJjv8SUvQXSP7um8h9+T38wNkGn/7oh7l1PMJtUyJPPfcceihCkwnmDj+AovtYtkiv20N1fGay0/zGhz7G+x96L1EnhGCbjIQ9VMmn6QcoywoXtpexDZsdu8VCLstCdprzXpS/rel8rRVnp6uROPPX3Ff8M35oPEFKgnGtzamMT3PlLJpQp5AM8HP//t1EpQChoR4zx4NkU3lWX6oQa5nMeQqpvk21LXBurcFLbZFaJ8CJoRmUcgvbD+DYIjl3wM15jahuIAsqjmUTjUQY9Pr4nocRMEASKZWrGIEQnijxnSWP/9/2alA3acD2fb8hCIIB3MfLG6yPAe/gZeTNfwC++MotX3rl/LlX5h99RVT5O5rrWVSKS8i+jNkXmRo9RsPcIxjQ8V2PRq3K0HAeR64gigLjw1kqZZNUNIjv2IiCR7PdJBBN4sktBnYXTdNIJg6CG0ZLKoSjHUJhg+eeXCKgHUewuuhaE5Uoxw6F8GyReCKA7U0SDMVR5TjZtML1+nNcX1RIxUcQpC6J6BxW1+G2U3NUqnv0Bn1G87ewsPw8x44luH79HP22ztBQlIFl0+2YdLp9Bt0auWyW/f19TNNk0LdJKA6FTApHUEmk0uyWdhEdGVUR6fdskokCV65fplztICsemq5Qru0TiaXRdY1EMML+fpVux0KXYzjugEIyy4EDczz0+kMsb59nY/syB6YPUq91sO0m/X6VgVlldGyYbqfFyRPHWFtawA8oVJtdDswfxTBUZuaG2dxdBUEhEkuiGgZBJYAqO7hClV57GyViEA7GaXX30QIDzN4umujQM22isVmqrSrQB9tGVByiiQShaIZmcQVZDAMBJKVNPJ7nwHyAwaBHt9vGsgeUS00syyJkxIhqATxErI5Fu1tB1QN0ug1ESUNTxZf3a3SJUDjGSGGSbuvlvYqW5iJLAVz31S1v/zXMGvR5+OHHeK5aJXToB3nm6h/zrQtw21sf4PSx23n2zArv/f6f4qf/1w/zvnfcySNPJfmdS39HOizx6+Fh2qEi0aEw9XqPoyGP6u4G+40wc0MtLLtLJfVa/vMXrxCRorjpOLHnn2P74Bzvfe2DSBEVpW+Tnz1CNBrEs9tc/Ou/JJeYY2N5iZGh+9ndbWNGbI6NtLCdIC09zVpEYqu6hHo6zET6F7m+ZSNbH2PD/gI3nogRMCQ26iu88XSOU5MuS2eb0B2jnZKpz44Rlw7TNMskTswR9Eq0cqPcyJ6makbwKgKOpBJQQjRbLQQ5gC1YTN1yktVajURwiqeDdV43k6Zb6/LSxSW+8Pl1jLjCB955D89dDXHP8S5FO8RbJ3o8e7WNcu9Ps65mSfo7GCmdwMDEt3q0PQ/XFQiGInzfA28mHpXpovO5F5f429VF6kYQKWxguBIpQ6XbWUDRDZzZDEcMlZrjEp8cotYscywTpWG2uG00iuy7nN3ZJVEI09VNJiZug7U0naZCauab9MwitbzG5q7JE8/JjBa6nDg6T73XZKXmomZGibgtLq1sMrAcDmk2d4Z79LsWbVfH87rkM2kalRqm1wVRwlM1XBd8yyUZM2i3eviSjie8OoWpV1OjzwP/5ZU6vQh8zvf9rwiCcA34rCAIHwXOA5965fpPAZ8RBGGZlzP57/uXXuD7IqKgENBCyJJHq9OjZ3bA75OIh5AEn/X16yTyMRKxDIYiEo8WsWwX15FJJDOYZo9YNExYmUGJNkApUauvMOgJFIbHcC2DzcU2M4clJLaR+wlGhoZR5AyhqEwhM8zC6uPEowlsr0MqPUWntcfMfJa1lRKypGDadXZLTRLxMUqlTYr7iyRTeRqtPZRAjeJ2hJnxw3R66/Q7IgFdwrICTI3nuXx5BVMRCAUkQoEQ+BKWXaPR7jM6WqBX2sHu1RgdOUan06Ja30aSBGZnhpE0j/29PULBIFa7i+u6JBIJ2pU9JFFjZHiUVqdJOjXMCy89R6Hgc2n1GRIZg0xiCM/RiEcMzp2/QTKR5+477wJEREHjicefYnY2T7f7csCdPzRLs1YkFk0QiWbZr1bIFtKsrN6g1eqj6wGCRpJcbpqOVScUkvAHOooUIzcUYzh1CCWgcu7Kk4zkR+h7Do7kEBRBUnRKe3vEEwkS0Vn2ihWQWpT367Q7XWRZ/L+4+lVVxbVNiuUNYpkgXbPLwOwR0EVEGfq9LvFEkFYT7IGNKPi0G3UU2UBRZPL5IUzbJ5eJY9v/NDLh3wI6rPgy6506+uTtdNa/jael+ZvPfJJQb5EQDj//O//I62+9k6996rd538/+Co0jOZ7g/ZQv/ibXN7dQ/BgHMiAGfJq9Pm55gWODES49ssM5p8AXG4+hJw4g2l3c1WXScyNshePIkSQXSg0OxSLs7q0TyaUIzg4TyiXYW77BLbNHMIbH8FcuUZg9wLrjc+zgbezu12lIPeZuewOr165QKn6Sewv38dTJ/8jlZ/6cqdR13vmDY0wRph2qs/fNp/Clh3Bec4rLboPxRAG3HiAaiDDxUx9ksPwcbuYQ118sIYshJDNAMNDBcWyy4xP0HBFXlFh66XkyqSiH82Fe3LzApWvr/ON//TRXr71EftbktYfvoLe5xM2nb+HLe2M8uhFkRM3R7y0wU4zy15s1fureGL4r4jhNJFVCUzVc18cIJam7DpX6PoFIgf90252Mzs7yKx/9M8bGp2l22kQiKdyqTzIawvYsQpEwI7khnnn6USaGUlhzacKKgN7vMOiVmNMEag40VsJE68NYKRch2saN9piMyji6R28QQQ5InFsWaA9azLxpjoFRxewOiMoQDhoIgw4Fb4AuB/F0mXrTwTUFZK1DMBKg324S0RQCYZ1yo4ftySDK+CKEQ1Fcp/PPud//7ev/QrL9b2Iz09P+Z//iN9krXsbym2yWNrA6HoHAMJ7fZ2o4Q7lUxfRUxoYOYA+quNJ1qt0A46NHCIp9apUN+n2N0akDiEqDRnuTdkOgWZMYn1MIqiZRbZSW2cKzp5gfmWWv9RiGOsdedYlEeARLvIpvD2F6ZRr7AWIJj9WlCoWRBLvlLSRvBjn8Iu1aBF+GVDpKvQoWJSQ3RSoyQjRoUKvsUxgK89TTj5HPH8Lym/QsCccV2NxeIhiI4HsKjtthZvIYrWafcnkDz23TGsTIFxKU97dRVYVwJEijvociCaQTQ/Q7LnI4RXFnnaAM4VCSbHaSveYSMzOnaTa6tLu7ILh4XYVMJsTmWomTJ09y9coZkokMwbCMbQ9Q5BCdTotwBDzPIZnKsrC4zd13nqS612Vq4jXsljbZ2LtAIR9ldWWLkdFZdD1Gv2+zeP1ZMtkosdgQPhrpTJRWI47HPvXaBZLBgwQjLpeWFul0GgQMA2fgEYyFMHtpWs02HesaAXUCPahh2ya9fgfD0NjZ2cYa1IjFcvSdGr1Bl0Q8R69VJRhO0uuaOK5ASIsQj4VZWVskOzRKt+MgSQLp1DCOqzAwXX7v9z7B7k7x/7PGfQUNFvR9vyMIggI8DfwE8EHgH3zf/6wgCH8CXPR9/48FQfhR4Kjv++8XBOH7gLf5vv/uf863Y7rua9lRmo0+o0mXj/zMB0hFRWy7xRt+5Ld488lZGvIk5UqT0Tj82E/8BGf3ejx/ZoW1R/6C//RLH6Sw8VkyUgkrmKG304aQjygdQ8yPYVz/Oz59OclLfZnhvQZbN2UZzo1BzGdMT/GGg4e4WRJJHDiAaNo89pFPELlyAyOYZFuVmXnra9naXEJ783244QBdWyCuuHiqgY+N56v4dhn6MlP6KO21X8VYfxHBjdNv9ujd9mOIco6iKFCqbTM8yOMvwty7R5GiAapmnb98+LOMp6bxo3H++MsRLEdGFF00uhhuG0W0iHV2SGdD3HIkwi/+2se5+ViGH/yB76dt2yhqBcESuZ59B5vbHUqlOoO9He6eD3H7yZt4+sw51NkDvH9axFDaxDQQAi6CLyPKOr6v0Ov1CAhgehqm10WRIwTyB/jhn/45irbFwHewOl3mskPIgzpa0CVohIioMJSL027soQkyUdGn2veo41DZreC0BxTkSYJ7ITZS5ygbA2xDJdAXCAckFq9tc8/UGKgKSUMlGu3Qb2b41jPPMBVWGbOaRGUVTRIZdPsIwRCq4CMrEvZgQDyRoFHvIrgWKCImCvWuSVBXwRrw0UseGy3nX6zffFdQIDiuRXGvjens02sr6GqKmdlJRHeN7sCm7YQx5T4zEyPUq8uUHItc6iBDiTC15uM0GzIT07dw+PBdDOfS9Bo3kActcHZw3FUEd5po9DZW1wWCeoKIFkeP65T2LXb3lgnqUTyjiutkuHb9BaJhmUg0R1AZ4eCBSXxHIZ84yNCwQzioEo9EyUZn0UQdc7BISp/iyPwMqpSkWoNLS8sMRJGJqXGCQQNBUYknVDSlyVAmQj5tEA33iQVUquUNZNkkFFWQgyKa3kNwA8jiyxm/Ioloio1tWgjoyHIEwewQMSS0oIpoyNR7DVrVDtcuPkO9vILbsxg0epw4doztzQrDIyMsLi/Q7gpMH74dST1BOncAUTY4OPcglUoMV4hiD6Z5w/3v4wtf/Qa+EQI1gifESGWGKO8USWVyqCGHtcUWWytnOXLTDLVWi+XlKoIg41kpJHxEK8Gdd97N5u4FNrb2EJGQvRaJQJxgJEu1WMIxtxkeTRALT6LIbUKBNCMjE0QTKu2BjxxUUeQY4YhBQAsQCyRRUDBCQXpdF8sto8oBQtEmxXqNoeGTKKZNPB7Fp8FucY1Wr8jQmAbeP/0d/FtAh11fwGmUedPtJ/nNX/koQb/OyuY2b3//x0hGgvy7Nx/lrfeNsrx0jsWywCc+/kdIS5c4cdM0+vg0ASGENP69FM0gpuBTFTN8fe8QL5Z3+YGPPwlTJ0lUt8gVt3HuyPCT65tk1Brd5jad/Rs8tnCRluvjVZvUd8tI4RROSGVz9TzhVpn6s+e5Zf4kgU6NbkfmcknBC+XwfYdUJEBc0ai3E3RNjaq7z/DQh6lVQljuOPL0XVhmH19RcFoOkUGChB3Gd118TyafSvK//87PkBUuUy0/h9c+z7tvL/Pv7xR402SdO5Nb3JGv8rZbNR66J8trThc4t3gd1bX4rY/9Z2RVJjcUwqslWEy+hWJDohDWmLKLJBJ58pKK6Vs8cPokYadLw+liKAKCoYEeRDUieJ6POeiAO6DXa2N198H2cG0Da9Dnd37+F3F3tonoSXKJCTbrNitCmo3BMJeKOktlnStFl52mzI3agG0pTEmI0bSTSPFZgkNzlI0B2yNlakGdkHYEtzVDTRthbTAHgdt5rJJmY7vM2vVniGsup29KEg8kCSoRMskCSlhGVkXyiQCyLyK4EnpAo2sK9AY+Ph66oeOYPo7p4vcsEAQERcb3Xl2PyHcFBYKqSISNKG40zN7FPoGkhGX6HD7wGi4ubNPc7pDLJnBN8JUlVG8CszdJvXuNYPA4khjAsTzSkRHi4QhufxldazM7GeH89UfpdDfxnNsoZOfJpGVcK8xescz8zAHWV5dJxTUcJcOZZ57hppPH2dreIZXM4uIxMp4iVNumtFsHXHxnjJnpm2m2r2N284wW+vQ6Kpubm7h2AE2Jcer4a9hc2Wa4MMza+iLZMYXipsnYyDiXLl3AHAwYGRlhZ7uMbkgM+lW6nSaNVoOR6RTN2gKe2CQa0XCcOnOzhxFQ2dttYtk+seAYLWuAI9bodOs4joFpWUyOH2KoME25vEfP3GV1ZYd42sWybJ5/9jq33jHC5auPIrphAqEQjl/h8sITTM0fwPI3OHZkjp0Nk5tP3MPa2grD+XlE2cbswvDoMcrNVUYLd3L94jd56/e8l/XtGrH4y7w/vuRxbfEashim1W5wZaWILMQJDy2ilnPEUofptmRsocPczFEcVM5fWSAaDWM7cGPxInOzB+k06jhtD9EeIEkKjXoHBAHLcmnUq0RTEqoSRlR0QnoIy7KQNXDkLeIxG0scp+U20T2H+u4GmiSiB0Pf0ff+taHDmiTx/h/5GW6aT7O99hKSovOh/+0vCMXy/Mkv/hCVVofLqypjKYOHXjfH185v8Df/+EVCxrc4df97+NCHPsjrHriHD775/fzBn3yKnWGNbq9Fs3mNO9MpvnLtZvodCe3wLD9wY50jeoV1u4ISytFwJTxriWXvIOJ+CcOXyU1O8dTqNaKeTWAAMd9j5ex17BGZwG15kmqWoJxH6Xg097sUyzUqXR9D8XC+fh4p0Gb0yM/SSy9hWSJpLYQkWxgxj5bp0NhaIDNTwAjGEASHd732IX7jHx/FdytMTIkUsj5m6yohSWZ8KMK5s09S2nUodTQQwrzx9jHOxSVEx+fpayVmRvKM33k/f/0tG2HrGTZElQPpCD9/1zwZ3WepXSGSTjPRc5HCY+hRG9eqIksGlikgSAq+3EdXA8gBgU6ljhFIA93RFgAAIABJREFU46ohrO6AmKbw1MNf5uM/fBPf6ETwE6fxvSBdaUBxINOWw+xUXAQ3REyLsbOroBlR9usDVMlHdgc4Vh9FNZDlJNuVNoqg0u1HMar7ZLwLTOw1iWkeTjDO3mIVt/4E/9P3HeWpc4v8/bObjMYEVFVjKBYkYrvoCrjVKmPZGI1uB3Dp9010TcdxPPSQQrnSYjQbR5FfXdf3d0Wgx5PYbz7Ltcv7HDw8Q7G0THYqS71eR9dTPHjnKVZufJPV4irHT9zH2somXWuHbHIUNSCSihymVn+aoFxkp/Ekd9zxAItrX8SzhxkazVIqrbG0lOKWm09TrpZJJHQigQTVao1EYpzd4g56OM/03CyqCp4zwuR0lpderKHXXBavlzl6+CYE12LgKPTsNXCjVGsr5HIxopER4jGD69cucP7FJ3BsiZkDBRwvQrlaIZpIIokWuzvXCQQGRKNx+r09DE3FHtTRDZWgreDYUVQSqGKP177+TZw9e5ZEPMTm8ubLNWtXoJDNsbG2QCqlY+KytVUhnzvEoLdHf9BkY2MFjz6xpMigV+fUyQd58dxzzBwO0uk4FPQMA2cfjZuxe5u0e1e5eu0C4XiPa8pZdGmGiD6FlBe4cuNhxoZPUtpp41guDgMWr1SYORCltN9DkCUCoTDhWJCpqWMYqkXf3WA2cYhG5QCLy9+m2eog+z7DIzfR69q0emvsbFfpWh6CKNLpNQloIpGEiCzp5LMFerpFJllgYXOHaFynVN7GCEYRxA623QBHRA2EqFUbZLIZ5ianuHzlKZSQQCiapjASp7z0LJGARigYp9VpfUfX833fBY4LghADvsD/IOgw8GcA0Vjaf/zhzxHoTHF1vcE3H1skEQvy4Z94Dy0/xGo/zn/5+1/mV9/zvfj2Nt9/WCRw562cXbL5+rPPENcMTt90J4/c2CSTGePCF79AdDhEfnye+955kBcefZj3vUHlhRev8J4/+EM+/9k/4N7LF1nrnqd7983UAmkeW/kS+sRdHAqPowZtwuEk7twhBsUayy9dJh3bYXr0Deycu8iB1z3IfrFKTO3gWSZRwWPEanP2tz/Nfd/7ZiwhhZsPEx16iEZrD6e+ieeXUVWPTEFADYIh9omMxHG9AaePnObta0+AMspOQ2I4EGK7tMZQOoos+rz+1AGq9SKtvkg4EcNw9pHDSf7qWzc48bafZ7VU5htfXURBIJvSeffpm5nRmyhBlaJZJJNJYgo66ZhBw1Op2Qohu4pf6SMFBQRJRtbC+IqM4LhIaoB6a494Ooqua5imi92t8/7ffZIjP3orf7v5FVaNWSJDtxDO+Nh6jGqzR8uMUDFiNBtVUvUmZsvG4WXZQoUw3a4JjoRnWkyLRe5tlZl22vjCAFPzcWWZRtui0uhTXA3gvfA00xMTjN0xQWosSqXvUqv4bNRatNoSrZrIaKfHaECm3zcZzSUwByYhBURZIqjKiIMe/qvs+v6uCPQCJq26wNz8SabnPWLyOwhEH2ejmOHmu6bodA9w6PgqDa9ONJUjuNZkaPoUsv8SVy+WSN/S4vpyjdG70mRFjWfPPEvXKhHWRtmp6BiBCNOTR1jfuYZvjTM+Ncz26iqh0DzJ9ASCbrO8cAanK1PptxEcWFlaoljcQZInSMRuITd6L5XSFdw6VCpL7C0JzE7fwUhBwzMEtldtbjp+iqBic+7sFcz+AMGLMjE+jTOogKuSzycYmDqNRhtr4KGrQcSAQ6dfIxRM4FgKnmUwOZFme6OEoSRZWb5AMhohnghRLhexHJ9ApE2787IwsIiIJPV460Pv5dLFK2iGR6vVp1qWaXVrLC1v0urtk8xpNOoNEqkW3/7WZcLhEBu7SxSGMgiWzGh2htWly8xNioxNJ7l+rU82ciebK7sEoju48hhJ/XYef+qvuOWW13GjskQyqpIM5zj74iMElSQ7OxvMHJriyktL5AshRvKTXF/Y5+67b6Zad9naXURwW4yMzLBZ2sZWBlTrJXQjRChs0Wx2aPUbGFoCNTJDrbpLrV7l8NF5ri+8hBEysMwQkdAwti/Tw8cTBqws72GbBUIZjUDE5frzC+TzCRqNFo2mjeBZ/6IPvoIqe5z/B3T4laz+n4IOb79a6HA0bFDeLrFcOsLdr38L56/8Lj/5H27Ham9gJU/xmx//URKOzYExF1cMc62u8/xais3mHs2tiwj1Pb706Y9z8tAU/Zm7OBVWaRefY37CIVxf4n65DdY+b7wpyt9/6n9h3x/hez/yYc594NdwnrzM9Qfu4qJbp9gZMO5bJDI5hmaH2IyG2a89S7DXw+uF2bu6SPLYHOztsz2ZRFEEonsDnvzlX2dOtnhNfJTt5WWS4TBm0UDL+8ihOJFgFLNdQfJMBKeLqQ0IhQNIgoTZa2DXLObGb2K33uFIZpjhpEjUilLvK3hGlEqtyOTMIeL9NvlMmHb+BNPv+Qx/8/mv8NULZcobW7iVPlM5nw/dfzex9g2U7DAILj09wL6UJhsN4UkCtb5HcSAwGQyhy00kUQfdwFMkPLtHv+MQS0cROy06rV0kYwTX04lbLp6mMPPjH+fB33svfmSJ1d3rXBeT7OoF4pJCRA8g9YeJeT4hRaJv2NQ7FtLAxO12CTT3OCXsMxMbYHcHqGaHqu+RSUoEdZVma0DCV+nYHpZk0+z3Wbq0jqqIXF3YIJ3JYKgSVr1N0FMI5YMUuhaGDJqq0e6beLaDJMkYmMiqhxqQXzVh33dFoHccm3DYZmX9Gq41Rz5bpF4SEWWNSjHO9IjGXkVlvxTi8aeucGy+R3ErwlThBMNDj3DlwhqymmF7Z4dgYIKRyQqVzVNs1r9JSD/M9OgY166vMTkXp17zufjSVexOm1AigKL2efSLj6AoO4yPhTl24B7+6189TDSzzPR0hsnJGYLaPNcvXWDmQJz14leJx5KUw2dw/QIm13jk25u85vib+dpXHqaQ7SAJA8y+wvETr0cPz/BXf/kbxMLj4KvIYoxENEmbDuFQnL61x8DyyBWSREIygipSrS1RqzikEkPMzgzRazvU6xVCEYX+oMPU5CyLC+vIio5pF3HY49FHvkEmOUXIiNLvueQy47SXFtmrXGV3p0QiGUcL9CiVK6TzHhvLJSKJIXZ2LxKTJ7l4tsSh45PUKks49oCZicMAWK5MpR6m0dxEdkIcPzZHb1Bia6dNYy9BKh1D10IkEiqjE1PYZp5oECx7mXj4BJl0jeLePqKqMjIxzrkzT9BvXqXvWNTsBr7gYlodfNFl0Nwnns3Q7wvcWNlgenqcRCbKpSvPI0gKgiTRaLYwJJFWr0smM89+/Szg06iZ7IgObnWbZCiL51iEY1mWF9dQvoOA8r8FdFjwXNLoXFpe5pHnXuC+u+/lT7/hsu9kiLgfIYLHn/3yOxkPtjjfnuXKhsjzl55kf+sKM5EUv/9b7+NHPrXNZ7aOc3v9HPOnj5CcPMJIoo9QfZbJkzHaRYOSZaM727wp5PCZ3/sMR/IF1LVFkg8/Q/Adt9Ax99HiozgDm+nRCardLc71VY7KLo5rUlpcYX5+ntLeLiNBi+XPfZnKixe4w/XxHYmG1mUsFGVzawn92AhYDnowihwO0nd9rGYRy3aQVAGHAa7r4pgdPG/Agfws6xtXyGXyTMYSxNICS406iuQTHT+ILrhE50/xonoMT01QvrFN+6W/ozclYbdUXKfIRx64g4C4TyA2Ttf0kDWDlOTQKJl0ex0UVcK2JFruADcSpWNvExi8/JNX42Es3yOciFEpbuO5FoN2G0MIEAinGTBAxyd35GYGjRh+t4krwT2xEG1nnU3LxhxEKA5WqdsiBcnArm8z6diEkyoJt0teazGdCtNWVRpOl2gujGSB1e3T6jlIgkDQ8PAV8Lsd8qpIDx8hoDGomWy1uuiyTVJRCMouCDVUCRzfRcOn60EsEqPfamKEZATRJxSTeJXa4N8dgR7Ro9vUOTx1G/mxMpFQnO3tHLKoko+0+dbXP0NuKESldpZMfpROO0coXGXy0Cz9y0/RXX+W/uAAiZjEdmWJI8dfz972PxDRp1CEKIYY4sG3znH+2WtMjxgoqkFbDONF2qxc+jRJV8UNh9kryhSLzxJI9TAHUxyev4WvPnyGgwcERFqce/I5LK2LJp/k8NEiN86to0QtkokcncE6+XiB4sa3mcjlOXO1ztce/Rqvv+11xINT7BXXOHbkXZQqLp3OKhE1TiLtYdoROv1tVheuUsgfYnV1iSNHjpBPOexuN7D8GL7TAMHCcnySyQlaVZNoJEHXtgmG4jgDiVrtOXqtGrn065CkPsXyRbKZELoRpxMvk4hpXL/WQZsU0QNp4iGF/dJlNBWUiEI8rNN327TsDhHFp97sU6v2WVlfY2Q8QaO2ycLmtxkZS3Hi+Ckieon1nR5T88M0XuhjO0FK2zoSImqgDEIaUzRJZme4fPnrzM24dGoqhUIBqV/j9OG7eOTcearNBcp7u6Szw7jCLmY3C65Mo1YiPJpj4doejhmg0S0jyl1CcYX1zWsMz+g0ei26bYFYZICuVkGJEVQMBnYXhSgKMkHNwf3OXDf/6tBhyzR554d+iY/82k+j6FG+tCQSsEb42Afv4QM/9klmJ1McOH2Sz33jDL/9iT9FsHwCcYc/fN+7SWsN/uTb+9x2+jTve/Nh/o8np/jcN55D8M9zU9TinnuP0o1OUS99nVMHx7hx3qKfqBLY32XoPb9P/ZtfJ3j9DO8Ye4g136crCmRCUSqbW9x39ymq3T71qy9ibq8xrI7z/FefQLEdet1NBG/AjOfQNxS2On0y9TKl7XWEoEYv7TJ5IIeLTr/bxXICCIlRwlIB3dxnYGookoAgSTi9DhtX9nj3bfcjqjLlyh5+IEk2MguhMF54iKUWlDseO80Se26bpy9vMFHr8Cc/bpAPBml2bsITRdBStCQV2dAwBReFCME42JbO2q7FvuUxFJTY2rc5OprEEbtIOniuj66HadeLCIpJOBZGF3oYqkO7vYXshxFFHcf3eMcn/ojP/OpP0u5WCXkucbfP5m6TW9M1AppGLKIg0KPudEFRkQWHthNA16LYroDbbBONSmQMAcfv4YQkLAVUSaHpungiBEUZSVLpd3u43TqTQ1nwfTzHQpAMZKuH64u4mBiKgCSJiI5Nu1EiHgsgIdL3LHQV8F9d6ea7AnUjCEHGp4ewexJte4nnnqvRsQLI6hEq1SpvvO924tHz6FqQo0cO4HkDdFng6SfOsLHUYm+7TbPR4MXnL2E1K/z+n3+EtpHj/od+gbvvOIgtpbl26QZhuc/6Rg1XHSdV6FBbljDEI7jaIhsbz7OyeBGz7TOUmuPahRv8+R99lXe87YeRIwvgpbj19gMM6jbTEwW6tT6JBCg4OFafKxfbzBw+iCtITE++kfF5lbAaplV5mn7Z4t1vewNnz3yFnnWF5FCEO+5/kFz2Tir7Bon4OAcOzuC7dSaHZ7E6HqIjEzECxCMKvX6TVCpDNj2OriZp9/ooqoRl9omFUziOTjSaR9Z6bBafo2dtsLm5zur6Gi+dv0ggGKJaLzN7cBTcEp7XIZYcIpk5xfzhN9JsdTFNCUPP8ZaH3ku16XDh2hUKwwkmhnOM5gpksnm6/R7dZpJHvv0S7X6Rcv0liushHLfP409+FXPgMjs5gdN3OTJ3AtGWGB+ZYmRkiJ3SJbrmBp7nEC8cZGvfolXvEFaDTA9N45oCEhK7OyuUSysEgyblSh81JKIEbVK5YWQ5hCJBLOaQjueQZesVBtAShhHAsiw2NlYIRmzWty5Qb24jyR7Sd6BA8H3/ku/7J3zfP+r7/mHf93/tlfFV3/dv9X1/2vf9d/q+b74yPnjlfPqV+dV/ybe1YIQ/+uSf8lM/8B+Zk8MUrjxJa/nTfPADP4gguDRKFne85cN87BOPkk1FmJ9Q+dufeyfD3h6OFaOtFPj1H7kdo5DkPbfJ/PEvvJZPvv9t3Pe27+Pvvr3EF54ss1JOcq0eZLvZZbcR5vh8gWtPf5PoXbehBRMEnR6TiRyC30f1ZOaPnETPJDlwyxGkoQlkI4PZbeHWN3GsEgaQM1TUgEbfU/DMAf1BHzEkIUejRINhXFdiu2nS0SIE4wkEPYKJxkBL4MlBZNFFcMGyuhy96yhts0Fl5Tr1i+fx613M6BgvFR0iko3ZLRM2wmxuNGltbDAxqPKpD/w4mYFBpQiio5EOqgR1n6DTJ+ANCPsCmqISUXSCik9AktEFh0p7gIWMrMg48suQRDkgo6gujmMRDMbxULAsn4Hde1n7wPHwHBvBHyAVDjB5zxs4ZCRZbOzhpiK8PidjKA5SwELQu8hBkURCZiwbRVF0BlYXK9SjJ/aR1D6irNPo9cBQ0QIGvu/R7PRoVjsotoln2vguRHWNqCwgdirEpQ6aauN5bSQNWvUasm0i+fbLanm++3JfzqCLpHYIhBRcx+fVqmR+V2T0oqAgiiqRUJiR0QkyobvZLr2ALRTxxX02FzKEwoc5fExhaXmR0aRCtbzD+EQcqyMQDkvoUY+Rgk6r0SIWCZGI5Tn3/EXM1nn6XoyRyWmm56J8+SvnaV1Yp5C06JnX8FSFlmmSSoWo12y6vU2qNZ1IRKbZ2aPZgFz6EBVxkXYzwcjQPOvra2SSsxTNMoZmEA6U8UwFQZPwJR8jpL5M0JZZ4PqVHrPHApy/coZAwCAWyHP9yjbHj99CUClz/twL5DJDzMzMoSkxPv/5zxAwIsxMz7G+vomqyhSGRlhYucHOzh4+DUaHRtktbpJOJFlYXmJ6bp71lRcZHZmi/wqVcbtl02q1yReybG9vIys+/UGX07fcwgsvXGBleQ3P06nUd8nn8lT2S+hamKeePEO32yWfT1OubJLLp7BNgWbLwXZkPNFBkCQKw5OUm/tUq1WanS0EdJrtMmcvDBAEjUg0TS6ncPHaFcp7HYxgmGS8wOZGmUIhQlCLk00nsM0ugq9imm3C4TCGYRAIGNRqVVyhitQLoSpRJDGBawaod2qMFPKE5CEkYZNutwuCR7fbJZWOI4geu8VVMtk0Aj6Oa/FP7Jf+m5ksyzSaDsHOHu95y0G+cmYBqRFlp1chpsfoWyazoyG+59gU9x+bZ3tpAeq7tPTj/M+Pi/z1h+9j19YoVUpsb1cpdXzuPjrFrN3jA+//fvxulYRSZX/tcY6fOoHYh+3iiwxNTnLl8mUOvv2dCI0tpGQOmTDdXh/XMEhlY9imz6k3v4uvLqzj93dIyAYMBjiWTc2yMZQAdbOOKVmIUhiz72McHMcSo9TtAH3dwPQlHE8ETSMajBJVfASzg9lr4g16CBEdx+pjpEMEUiES6SimAuFciMdaMTZKXf7o0TJu7woBv88vPXgY8cQBglqRriojiQK+7GD6IiIStuMTTITp9/u4noIaiDDwTcJhi32pB2YYXzLw2EGTfSyzgisImHKIYCSHbPrUW0XC2stypeFQENu0cCMKgmthWw633vdG/uGlMyScLpXiPr5jI0cE/J5FRxIJhn1ESaA9qCHKEvF4gL4N+47DUDxI07JIB0OUG1U0XyEUDCDp4DpNZN+DgI4piPR6LlFDRhZ9FE1DbVfJJhI0WwOmx5KEBQ/btvB9n7iuIssCWkhHlGxM30JTNcRXWbv5rsjojZBKvyVgDwRWFzvU+xcZHhZxbYN86l6ShQB7+2WCgQj5fBrX7SHYJpbZxHNswhGdUNRj4cZ5HKHLZPYwve0dLHMDJZhHE1Y4f22fqpXmdffexsSYjhx+WaT64uIaBLOYHYkD00c4cSJPo7bG0tUNYpEhri6cobjj0+vI9Psy4cQYriBQGD7BwcM3IUlDtKoCY1N5kpk53vmuH8XzRYJRkwvnrrG4ssqVazqRTAHHnOP6RYtyeZ3P/MXnWF58mLn5+Mtsk1fbDKx5JmfnQAoQigwRigwxOn6MTHKeWHiYe1/7Oo4cm+bIiZs4cex2JE8ml07h+y7RmMrG+ha6mqBRb2M5FVyxTbNbJZ5M4/oiihZkfdOn05OYnBlGVncZG5dZXy8yNjHOjcXrJDNRxkeymP0q3V6V7FCBRscjFMxjWSqCViJbiCP6eeamb2Ft5wn+W3t3GiPZdZ53/H/uWnVrX3vfl9k5MxySw50SqYWiFNKSYDuivMhGbCCJEydCENsJYOSDDThAYFh2YASKFCu0Lce0w0S0qI0SRYrUcJ2Fs/X09DK9d3VV177dW3fLh27asmMntEWxZ4b1AxqDul1AnVM9ePv0uW+dZ9+hJPHIKLniPG3XRjEM6q0OtqRguhYdu43rKKyvFfBFg1Q6ysUL59Bkj6mpKYq1DmEjRDQcI6iFKeSq6EqEqdFB+pJZ/LZHUG2jKhV6U2M4ToOF+SXWNq/Q6bRJp5MEgyHq9TqyLOh0OngelMtl6vUK3h5+KHBlq4rcewfBWBOjpnLvRz+NMTzEvpjBF3/ifXzpsVv5jw8e5cTABJXlBsHsUT7z8p38yqsuv/N4EtkIUy0tEZJkGh0fPRBia3WJ2SsXUdwGg0mJgbjFVH+cocEgTiZEMzJE036DC+e/hRTf+dCS0txAkRRaGnjJBCYuozGJr3z3qyym+9CGxjENjdDwOCsabIQzzPoSy2hIUgpl5BDbqQkaaLhCRwQ9tHSGWGKYRiCKG0hS9kOUOlHKch+OJVNrqPTufx+BWD+iblNe3qIV0OiJJ0mtn+cXIpc5ErB4+gMyTz6U5o/uGmHcqzOUu0Kl3sY0m8T0fhwzgW1p2I0OrlVie2MFx2ygKyqOZeHIgkxKYTQZJ+CXSIdtXJGk1ZCh3sDp+Mi2R1DTkQwV32zR9kwsK0+puImkKnj1Jr4kqOQ26EgpHvvlXyUTNWhXGsiBAEEBmqEiaxqukLB9F0lVsIWDHFIRuoSv+ejZKJGwhq56hLUIsiohAgJPtsn2J1FVj2jQJxmwiBst8Cxc16VaqxIyghTz23gdQalUxcfBxkELyOgy+KKNEC5CCGzbw3EcXOft/d++Lgp9vVLD6ciERl7FbhxiMJtl8ZULNJpFzp+boVTvcOju26ltzxOSEoQDH+Do8ZP0JwcI6BFUL4lTKjHSH8NxUzhmgf6IhuIGCacTrKzOUV+dY25FYqHgoMs53nz5HB7XiMohNtfmOHzHT+KF+tCNk0iqguMV+O53XKYPJlm5ehnHMghkBjHUOLKq40sJFleXufDmNVKhLEPJcYrbHSplieL2LOWcYGrfFD2Tce66+wT1tTCh1CrJrMXEhKB3fJlMeIiV+RzLq5cw/csEE6ssXSlx34O3sF23sDhMevj9XJzfRIl0eOrPnsdrj/HKqWdQjBquHEOLF5HkAsKNMj5yEL8TJ6h3CKkJBvrGmZ+9Sr2+SjIVwm6pTE6MEowkqVsurhdhbaVK/5CM0/GIhZJ87at/hgSce/MS5ZbLa2cvsrZ1lUiwh7ARoZCvUTXnmJlZY2X1IolMhcJagFLtdVK92+S3FzHCcU6fO4PTCtCsVnEdk9tun8a266giztkz3wY2CBo2MzOXiScV0skMw8P7KRS38bwWAUmwtrKO2a6h6yqmN09AB1ku49hBUIKEIgYoAWRVwXWqpBIRJDXOseMfxO4EMB2bcGwnfWqvKEGH8OQAv//mJL+/qpMaGuba2fN88gPHWDU7VMJBquk7+fnnBvj0lX186sU0SX+BP/zXt3LktuMo9RVcp4lvFjg4YHBiJEpAsblrfy8ZrY3ke6xuNekEs1Q7OplwgOzwMJ5tEOtxcBpFbC+I5TWwJI1sIo7iOJj1NmFVZTodQ/cdGopO04ixUiuRSfcRVmQUI0kgOo6x/xid4XF6Hvsk9eQgNCCqh5HcDqqQSMdihFQJIemIWApHTdO0QE+lMKUw9VgWeXASqX+CQN8EqyULxwGpUyPgXqXlSaT7exAZn3hYZ2QwSn8yzfbWGisrV5ACMp2WR7HaQFUFUcPAUGVEq05EVUjrKknFZyQqOD6WQfaatCoGXrMHWYpDpURrfZnGdhnbhGgsiREwEJJGMKiDqOPZDWiVURWPgCKj9RzHSWV2cih8D6HI+G4HaKEFVYx4iHrNQg8bNF0POSgTN4LUqi08bDq+hxJSEb5Ps9Wi3W6zXath+oK2Y6NoEromo8kgST7RkEEwGMGVZCTNJpXQUFVBo27heB4dzyMUC4Es40oakidwbBtxI2XGKrLgxG3HWCqUmRjfz8Lii0SSKmVzm0i0zfqmT3o4xsjACcrtEuF0g4XlJRyrgp4IM2Dsw7TWmLt6mkN9EksrU1REnUc/tI/tK5vcd/fjXDh/he89/3luv/0g61tX6RnMcmTqJ5nYd4Gzl9b5/B/8Bz78gZ+kVNhG0SLcevvdlJoeS0vnSMaiBOUI85ckBnp0EtlBVtbWkWVotRocmrofSa0RjxzE9a7hXwsRiLnMzp0hyAiyayH7KsFwBjUZ49qywtLmEg8d+QCKEeDEbce5urDC95//Nn5wg62lacqFa/jGBV76fpHRsUMsLC7zwL0PMjN7nmgshmLeweR4lXMXCzzw4I/zv7/yJRynQ6m+QNjNogdzbBdtevtjpLMRKqUalmUgXI16uYU76iLJLq2WTXOjTUBdJhrV6Ism2SoU+PgnHueFl14lFgwz1B/i0JFhvvlsh0xgkPXVc/iWxb4D09Rbp3EsjX379rFVuIyhpSiXNkhFDzK/8BxOyyOgh/nGN75Of+80mhSj2bZQFZ1KucmRw7cyc+USmuKyuLBCLJEmoLpY5Q4HDt3BxnoRI2mT3xomYNRpN4skEv34IkatoFGtlIiHA4SNMKocJxPr5cqFZRzHR1d0kOq47t5FCaqKzKMngrTXTDpOllQkhsDkzFqR5+dTBKIj5K0CcUXQ39zgUw9N8NOfeByHBrnKNuGQwC62CRoJopEIiqwiZ/ZTKm0iNBctGGI0cRipvYhTyqP4HTzVR9UCjGQCtK0mBhZ+sUqyJ0DDNIknssgPvYqrAAAV+UlEQVR6gETsKAc2ciQfi+KvVWkVNzmybz+1soMZ9DnZM8JCcYPDJ+/g7Euv4TdrtLUmylQvUtAjEjawO20CIZ3trSqBYJxGrYYrCSQlRKnWQFMttGASzWnQkk2wZHQ9guhYGKqMq8gEJYN20yQe7kMVMp6wCQZ09o8fIEASM2Qg+2CXWshCxnGg3qxhhF2Cnoah6kh+B8+uobKTUGZbPpYlIcsBdKWK6cmYzTaupaEIBV2GSDSB69nYZgHZi+BbOrphYAkbzy1w7yf+OXPf/G2cXJ5GpUGqP4Mr+7i2jB6wCCWiqIaC3DIReER1nWa7iRzUabRtgpJPKGLQdi2ioTBm20aLCloNk1LNQpZ9gkYQ3dCpVRvYHQtDkVF8F9vuYEoaw8MpTMsEVcbsdAiHw7StDuGwDp6NJN9AN2MREtvlEitLZYTvkI6PEh8aJhbv0DMI6/nLYAawpTCJyD1s5TQGRqMocpX1lWUW5kosrlSJpDP0ZUcQ+jUuXlqmsJakXstRXNXIZoMMZTZo5Gcx20FqnQaLSxm2tgSi9n4eum8fVy++wcylU7SbcSp1icmJBFvLDWq1HJa5SqV0CkvWkXWDVqvF8vIqqWQfejxAs93g5deexbHHybUWSCYn0MUBBgZjzFw+i2Y4DA3ez+r6Co5bZ2pijLLnkCtvsbZSQPFl7HaDVM9BWm6OpnmN9fklvv/CF2k0zjE1GWd1/XvY/uvoIY8Dh6fo+DP0ZDPkCnnue+Ae9o08jswgru8RD95HNptmanqUpcUtTt72YaKRNDOz8wwPD1Iu5CjXynQcn3pTokOLpl3FIciZM2d47jvP4LsdVFkhnyswt7KCUDUiEYNmucqxQ8OYtTSNZpH5xYvUyhr5fJ52zcHrBJm9+iqbq1t03MsUt7dZXV6lY7XYyF0iHI4jULEsk4uXzhMM6gQCBhPj+0klB7BMiITjnDt/Co8Gisji00Fy+1BUg0arSLG4iiZ0BvvDNJtFEDJ4Om5HxnN8ApqB78i0Ww2E2Lutm4SmEG5dYDyyzCcfOYikpQgNTPHCSoIHbxnipz56kB6KfOGzWf7g37+fD3/4GFpomGonQSKcJZY9SGjwOJHUCFPj+9B1Hcdxdj5PEjKQXBvL1PC1JOFED2ogSKfeJtqbJTNisLVSxgx0EGGZllQnmu7dyUWWHHLCoufRuxk6mkH95CDph0e54C/T/sAtDB85SfD2acbfdxenzp7jQ7/0OK3QBh/6pw+T/chHqRND94Okwhpms4Hr+Xi2jeZ1kNt1AoaKCEgII4AZjWGHsxj9k2x4YEZjtPUQbSmE5ydIxDNEE3Est4OtqgjDQA/GsFwFEVDRPAer0yISDRAIhJFVi/7BfrSAQUgx6VhlzE6dMA5BAREVavU8iWQEWYrju2Es18O1fVzHQ/gxits1FDmAbdvQcrDqK2wunyOUSOOaHWh7JEYOYCsZSq0GkbhKW3QouBYOIIUVlESGzOgkQtXwhaBUrhGOxMhmeolE4uDa+KrADyjYwkc2dPINi4rlEohF0SIh5KCOabeRNRlFl+npT+HLLv0j/agBGWQXT3KwfQtZ0qlVW6iShqTLmK6O4O2F6lwXh5pNjo/7Tz3xB4yNZzh/bo7xkX5eenOGOw4PYOsRvvDHX+affOwXkcNlbM8iJB+kN6bw/Dd+AznYYHTsEU6d/grZXperm2Mc6zvELZNZXro8S1sb4yN3THPm4reo5+cobUfwjX6SvRr12gYDg1FK+bMM9k8xO7dJPBmksh2j0WqRHqiRTRzHx6RU8IhH+4n2x3HMJo5psrBwlnvv/gQBY5xK4et88zsXkCMNHn7gEzz51G8zMjZMOWeRiUS57djHObP8FHfediudUpInvvZ7HLvt4yzPvcBE3ySXLs3z0z/3C2xt+Tz7/JfRglucvO1OguJ9rGzMEQ1oVMrrbBfq9AxNYNo5ttaX8SWP225/gMWFPIdOSGyueuQKlzh65D6ef/4V1rfe5I5bP0arpjMze5qpA0fQFZvi9ibp3j7CiRgXL7xMNBJCOAFqZYVkEkq1a2T7xmnXBGZ7m5HRhzj1+pc5dDBLzIhSL6RQAglazmWcThBZVqjVV5ka2U/HEqyv5QlFbTwnyGZhgVR654z4RrNMMJgh25dmK7+ELMuYbZ9MqpdwaIx2x6ZSXEOxPTIjac5fOMf+g5MUCj6u16RZaeGJbQJaio21KoOjUCyVsFyBShjJ01GDYDYN4vEo+eJV/seT32crX9uT/RshRB2Y3YvX3gNp/sZxEDe562G+I77vZ/5/T7outm6MYJjR8X14TptIWCK/vkww0I/U7CU97DNzJY73aBPXrOO7Edr+EqvNs1hSiPGxSeY3ZjH9Eg46wwmf5a2LZAc/TG9fH9dWZnjmuQu4qNw/fZzT7pu8eOFZBtenKNS2wd2P3dbIHL6PWeUJavU2tu0x1DdCBxWzPsjJB4ZYWVkDz+T8uacZGxoht95gcnKSSKKfldUZxmP7sDt/QjwySTyWQTY1yls1thsr/KNHfp2NlTLxbAjbHGB961XCER3bbGMYAcZGDrOw0GJhtcrm0mWCoVUOHbqPN09bVGp/xOM/83N86Ylf48cf+1k+OPx+Pve7X2J8IkU4pBIIBbC9Jo5fpnDtfk59/2ke/th9/MWTCwxMGSxv1lhbX6S87ROKWWxubHH44Cjl0jaVkkeusIGmqFQLTeKxIPhVOm2de+58iOWNPHMrs6TSGvXaDL5b5vjhzxCNhnnmmW+g+Hkmhh7i8vyz+KJKMj5Is9rBcZtIyiZbazrRWIf+viQ+LulkGtdpAgrNRpupqf1cOD/L+Mgx5hZOccuRKZqmRSQSQu64mI0QPekpXnvtDe6++yPUGlsonkGlmSMYAlnIlHMNEqlRLN/F7dg47QZITRRNpVZrMTSwn53PN+2ZWd/3b9vLAbxbhBBvvFfmCjfWfK+LrRvb9VFlwYsv/RELVx2UmMz6/LMUysvMvPwCd02PEskOUW922M43GRgaZy2nEE8cZ2WpQkirYdYtrs5uIFyNyraMovqsrz9Jwofcao3b73+M715eYrNaZGBApm2/TihYx3dmkUWExaU3iCg2fj5AsqeKpY8TDAvalkuhVieWvoXNrRiBwATT0/dSbq4RjExxYfEsHjFWGosEIwYHhvv58z/8AlrYw7Y6TI9P8MIrT5FvnKWdT1LOKaysFtA6CqtLL9Mxk6R6T9Dyqrx26jtogSyPffiXufzqKgG5DqLKt555gsP7H8Ksj/D815e48+QRNClDpSmwO/00ayHufuDHyNXOcuBIBs9VCfbNMzv3Asl4jI3Ni2jBLSIRCBstWo0mshwByeGOk/eQ23LpSU2gKB6R2BQdHOLRY6wsLTA0OoCmDLJdfB3T3eC1sy/zF1/7MvFoG+RNzp7+Lho+TrPK9toswl6jXfQIGSpHjhwhnsxitRUaNZ2mXSQUPEZACWI1NSw7RcMUbBVyuI5gbvYCpe03CQYzRLLHcVoOKhojvUdZnL9KOW9Sa2wRN7J4doNg2CIYdLBabUpbJh4S2d5pbDtDqbaJEihQqS4j3m6zcVfXTeq6WNELYfH0n/8xoeg21649h2Ls5657b8e0isitBQ5N7mfx2jlisoTRm+f0+WcZObCfcm2GlPUo+27dQsgpDKOHVmsRpCJ2XUL40yQnTD42eBuLl59GsVyCXGF9MYqe6uXI0WNcWTxFPHgQbTBJKvZZ0j15DH0R2YK1bRnLnGF95n5mV54gHo0R8Tu8/tLTHB6+HdobeI0SVzbmOH57DF+GgeGj9PTcyTdf/E0azQ1C4ffjeTa6ruK6bXqGGiyvB7DsCC0/xT333ssLL5/iIx/6eZ793n+iWIuxOBvjxIn7sJxNcmfnkVSJpSUL7DHCoSozV96kZTYZHZnE6bSpV2uY1TZeW2V07BCO6yPkOJoaJh6aJijphMIKI2NJlldniMQklldKKFqIc6+/SEit0CyeYbDvKNvOElpM46VX/ozhgcNcWz2NYRjcd9e/pPjMf6dWr+K2whQaHSLJKFvFWeKJAWKRHvp6jzB28Cjzs9vkKq+QGbuFxVdOIbkyYSNJtbaI3/KpNq+gB2OsvjFHMqVSrCwghEKrUaZnQCZfWCUUMsjlrqAGfTzhoSga165dJGio+LaGopnoQQ23HaEnO0Yg5LKaP4NEi30TJ1jeuEK7lQPfR3q7B4J0dd2krouljtWuE5Q8bLPK6JgglQyi6VPccsudrOYu0qg4xKId8qsF6nmdZDJJPD5MLDmBUDNUCgZCczl94QLCr5AwDvLmxTlGDgxSKR9l5FCW2ZllakUZXQ6haHVqpSrnz7wOjkM5X6WwOM/8lQ2sUJ1K4wAXrzzP1pJKJF6gUPgL7jgeo5kPEIkG8KQ2tjtApVbBdSAkC1YWi+jaKJp+iGT2IP3ZY8jyGJ7s0Zs+xMBQglh4CIMH0BjH7khslq6yVagzt/gGslHBFWC7FSr1DonkJK+8eoFwKMDGukcyqxFLJhgcixNLhOnpGSGbOsDmahUj0uDU888QDsJ6bom21SC3tY4sa3QsiZ70BK6tsbaWQ5MnyG/Z6FoYs1Wl1Szy0Uc+RWZolCPHHsWIRthaM3GcGnedeIzh/mkSCZ25uVUC4RqmtcKhYwGMmIvVVolENdZzm4yPHyXbO8ncfIONfJkLFy4wc2EFWXTI5fJEo2E6nRZ9/WmiyQSapqDgEZBVerMDhCMxTKeJ6/gM9EdRJQsUjbbpEYsnyWazDA4Oo6oKkuztbAVlEnjC5eryRTbyy4T1NEEtwsbGBuFgGEkE6OkdxPP/9oSpd8nn9/LF32XvpbnCDTTf66LQK5LA8q+RSd7C5MQ0hVyO9fVr/K+vfJVo6CCaEUDXx8j2Cw7f7vDm5T/nueee5MLpdeqtOTbWC8wuX+HIieOcOPjrZIYER070sHGth3vu/yCnz5yjI0d59NMfZyMfZavY5vj0fqb69nHv8TuxakuozSLbS0+xsXgZLaaR3j+CFynRrLaxGjW8RoxEpky12aJsCkxFAr0XWc0QjvhMTk5Q2C5hCY+l1W0k2SBopDg89bO0mxJvfK/G0HA/5678CfNzq/Rmp9AC21y7Oo8q5fn6N/4L4wP3MNg7zdjEYXLFKolMH5Njt5JJjRDQR1he2eB7L7xJODqE7QpeP/Ms8YRHq+Iw2J9mdGwAWYFicZ2oYZNNDzE6OkIsEeCee0+SSfczPfEQtZrE4OAgnl/j2NEjXFmos74dYHa1TL4cYd/EXYwOHOc73/4WKmkWZwt4wqBdzpBNDKB7UzTbCrZlsf/AGHfe837Wc1Vee+NFeqMWd94yxN1HHmBr9RKuWSeTjZHLr1JvlMhvL7OZ2yYQiDM1PE270kEliRqIEE8kEGjYbg2zXadhakTjfeS3ypw/dx7TbFEqlWg22nguOLaHQ5tMr0pPX5hEeJhK3qFVqVAsbCNJKo4rI0l794fr7pHF7wnvpbnCjTXf66LQG8EYRqJOrZ6h0cyS6G2QTTdIxWOsbdQ4cOxB4j09ZAYGefk5i8ODv8iJ6ZP0pcoszj3Nqxd/A8k9REj7CGvlWWbnGly62CAQriPHVshvnGFq1GDmzBskIi75xRCFdpBCvZ+ZBZ+21iI78RhDxywGA/tprn+PpYuXmJw+TiozTnZAZ2NjjakDMXSzSG8gRcd8hY3LOfxKkVpdwrYitJoqr75yllJpg3arQ6uVYGl1G9NaAWGzvLxMwHAYGjPQAjaGmKavpwzmFof7P0FxXcJr91Eqt/EUj0Qyy9J8jWa7SqvpkSu/hOvDpUvX2L/vJFPT0yQSCarFBpYZZ23dIRafYiCzj5iIYLd6qVUcms0arYZAdscotV9F6CvMXH2BSDjIxdNXEY0t4rpMvfZtTkzcwqmXvsGLz50mklogEYuQivViK3MkMjZBJUClUWS7vEIiLeF6NoWcRTzVw+BEgLlrr3Pq1TeIpI6SGLyFlcoGQ8NZYnENjzots0Dbq9Iwt8GrIQmLjc0VJDkCkkSr7hGPhGnU6liuzXYtR9PKg1LA9grEomks06deb2G2XZKJQ1imjqYGqFUryFoZWa3huRZ9vQNsF+r83WeadXW9N1wX7ZXvsRY0uD7ast4t18Nc31YL2jtJCPEw8DlABr7g+/5vvZuv/6MghBhiJxy9F/CAz/u+/zkhRBL4U2AUWAJ+wvf98m7M4ueAR4AW8Bnf98/sxdj/oXZPNn0DWPd9/2PvZHD8u+l6KfQ3TJvSO+G9NN/30lzfslscrgIfZCes5HXgU77vX97Tgf2QhBB9QJ/v+2eEEBF2Ihh/DPgMUPJ9/7eEEL8KJHzf/xUhxCPAv2Cn0J8EPuf7/sk9Gv4/iBDis8BtQHS30D/JOxQc/266LrZuurpuMncA87vHHXfYWQE+tsdj+qH5vr/51orc9/06MMNOju4Phqb/zTD1J3ZD2F9hJ7mr710e9j+YEGIQ+Cjwhd3HgncwOP7d1C30XV3vvL8MEd/1gwHjNwUhxChwHHgV6PF9fxN2fhkA2d2n3ejvw+8A/xb+Mpg1xdsMjgfeCo6/Llwvhf6GuXv9Dnkvzfe9NNe3vK0Q8RuVECIM/E/gX/m+/3cnr9/A74MQ4mNA3vf90z94+W956t8rOH6vXBcfmLqR2pTeCe+l+b6X5voD3goRf8sPBozf0IQQKjtF/o99339q9/KWEKLP9/3N3a2Z/O71G/l9uAd4dPc+QwCIsrPCf8eC499N18uKvqvrZvI6MCWEGBNCaOxkyz69x2P6oe3uOX8RmPF9/7d/4FtvhabD/x2m/jNix51A9a0tnuud7/u/5vv+oO/7o+z8/J7zff/T/FVwPPztwfHwNoPj3017XuiFEA8LIWaFEPO7d+xvaEKIISHEd4UQM0KIS0KIX969nhRCPCuEmNv9N7F7XQghfnd3/ueFELfu7Qz+/oQQshDirBDiq7uPx4QQr+7O9U93ix1CCH338fzu90f3ctw/KrurvV8CvsnODcsnfd+/tLejekfcA/w08KAQ4tzu1yPAbwEfFELMsdNp9FYr6deARWAe+K/AP9uDMb/TfgX47G5AfIq/Hhyf2r3+WeC6qmV72l55M7ahdVvQbtwWtK6um9Ver+hvuja0bgvajduC1tV1s9rrQn+jt1/9P3Vb0IAbqAWtq+tmtdeF/rpuSfphdFvQ/pobogWtq+tmtdftlTdy+9XfqduCdmO2oHV13az2ekV/07WhdVvQbtwWtK6um9WeH2q2uxr8HXZO+ftvvu//5p4O6IckhLgXeBG4wF/tW/87dvbpnwSGgRXgx33fL+3+YvjPwMPsnPD3c77vv/GuD/yHJIR4H/BvdrtuxvmrE/7OAj/l+74lhAgAf8jOfYsS8I9931/cqzF3db1X7Hmh7+rq6ur60drrrZuurq6urh+xbqHv6urqusl1C31XV1fXTa5b6Lu6urpuct1C39XV1XWT6xb6rq6urptct9B3dXV13eS6hb6rq6vrJvd/AH8nkWsysBZnAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"content_image = load_img(content_path)\\n\",\n \"style_image = load_img(style_path)\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 1)\\n\",\n \"imshow(content_image, 'Content Image')\\n\",\n \"\\n\",\n \"plt.subplot(1, 2, 2)\\n\",\n \"imshow(style_image, 'Style Image')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 定义内容和样式表示\\n\",\n \"使用模型的中间层来获取图像的内容和样式表示。从网络的输入层开始，前几个图层激活表示边缘和纹理等低级特征。当逐步浏览网络时，最后几层代表更高级别的特征 - 对象部分，如轮子或眼睛。在这种情况下，使用的是VGG19网络架构，这是一种预训练的图像分类网络。这些中间层是从图像中定义内容和样式的表示所必需的。对于输入图像，尝试匹配这些中间层的相应样式和内容目标表示。\\n\",\n \"\\n\",\n \"加载VGG19并在我们的图像上测试运行它以确保正确使用它：\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"TensorShape([1, 1000])\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = tf.keras.applications.vgg19.preprocess_input(content_image*255)\\n\",\n \"x = tf.image.resize(x, (224, 224))\\n\",\n \"vgg = tf.keras.applications.VGG19(include_top=True, weights='imagenet')\\n\",\n \"prediction_probabilities = vgg(x)\\n\",\n \"prediction_probabilities.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[('loggerhead', 0.7429766),\\n\",\n \" ('leatherback_turtle', 0.11357908),\\n\",\n \" ('hermit_crab', 0.054412022),\\n\",\n \" ('terrapin', 0.039235264),\\n\",\n \" ('mud_turtle', 0.012614699)]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"predicted_top_5 = tf.keras.applications.vgg19.decode_predictions(prediction_probabilities.numpy())[0]\\n\",\n \"[(class_name, prob) for (number, class_name, prob) in predicted_top_5]\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"现在加载VGG19没有分类层的网络结构，并列出图层名称\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"input_2\\n\",\n \"block1_conv1\\n\",\n \"block1_conv2\\n\",\n \"block1_pool\\n\",\n \"block2_conv1\\n\",\n \"block2_conv2\\n\",\n \"block2_pool\\n\",\n \"block3_conv1\\n\",\n \"block3_conv2\\n\",\n \"block3_conv3\\n\",\n \"block3_conv4\\n\",\n \"block3_pool\\n\",\n \"block4_conv1\\n\",\n \"block4_conv2\\n\",\n \"block4_conv3\\n\",\n \"block4_conv4\\n\",\n \"block4_pool\\n\",\n \"block5_conv1\\n\",\n \"block5_conv2\\n\",\n \"block5_conv3\\n\",\n \"block5_conv4\\n\",\n \"block5_pool\\n\"\n ]\n }\n ],\n \"source\": [\n \"vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet')\\n\",\n \"\\n\",\n \"print()\\n\",\n \"for layer in vgg.layers:\\n\",\n \" print(layer.name)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"从网络中选择中间层以表示图像的样式和内容：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Content layer where will pull our feature maps\\n\",\n \"content_layers = ['block5_conv2'] \\n\",\n \"\\n\",\n \"# Style layer of interest\\n\",\n \"style_layers = ['block1_conv1',\\n\",\n \" 'block2_conv1',\\n\",\n \" 'block3_conv1', \\n\",\n \" 'block4_conv1', \\n\",\n \" 'block5_conv1']\\n\",\n \"\\n\",\n \"num_content_layers = len(content_layers)\\n\",\n \"num_style_layers = len(style_layers)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"风格和内容的中间层\\n\",\n \"那么为什么我们预训练的图像分类网络中的这些中间输出允许我们定义样式和内容表示？\\n\",\n \"\\n\",\n \"在高层次上，为了使网络执行图像分类（该网络已被训练过），它必须理解图像。这需要将原始图像作为输入像素并构建内部表示，该原始图像将原始图像像素转换为对图像中存在的特征的复杂理解。\\n\",\n \"\\n\",\n \"这也是卷积神经网络能够很好地概括的原因：它们能够捕获不变性并定义类别（例如猫与狗）中的特征，这些特征与背景噪声和其他麻烦无关。因此，在将原始图像馈送到模型和输出分类标签之间的某处，该模型用作复杂的特征提取器。通过访问模型的中间层，可以描述输入图像的内容和样式。\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 建立模型\\n\",\n \"tf.keras.applications设计中的网络使您可以使用Keras功能API轻松提取中间层值。\\n\",\n \"\\n\",\n \"要使用功能API定义模型，请指定输入和输出：\\n\",\n \"\\n\",\n \"model = Model(inputs, outputs)\\n\",\n \"\\n\",\n \"以下函数构建一个VGG19模型，该模型返回中间层输出列表：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def vgg_layers(layer_names):\\n\",\n \" \\\"\\\"\\\" Creates a vgg model that returns a list of intermediate output values.\\\"\\\"\\\"\\n\",\n \" # Load our model. Load pretrained VGG, trained on imagenet data\\n\",\n \" vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet')\\n\",\n \" vgg.trainable = False\\n\",\n \"\\n\",\n \" outputs = [vgg.get_layer(name).output for name in layer_names]\\n\",\n \"\\n\",\n \" model = tf.keras.Model([vgg.input], outputs)\\n\",\n \" return model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"并创建模型：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"block1_conv1\\n\",\n \" shape: (1, 336, 512, 64)\\n\",\n \" min: 0.0\\n\",\n \" max: 835.5256\\n\",\n \" mean: 33.97525\\n\",\n \"\\n\",\n \"block2_conv1\\n\",\n \" shape: (1, 168, 256, 128)\\n\",\n \" min: 0.0\\n\",\n \" max: 4625.8857\\n\",\n \" mean: 199.82687\\n\",\n \"\\n\",\n \"block3_conv1\\n\",\n \" shape: (1, 84, 128, 256)\\n\",\n \" min: 0.0\\n\",\n \" max: 8789.239\\n\",\n \" mean: 230.78099\\n\",\n \"\\n\",\n \"block4_conv1\\n\",\n \" shape: (1, 42, 64, 512)\\n\",\n \" min: 0.0\\n\",\n \" max: 21566.135\\n\",\n \" mean: 791.24005\\n\",\n \"\\n\",\n \"block5_conv1\\n\",\n \" shape: (1, 21, 32, 512)\\n\",\n \" min: 0.0\\n\",\n \" max: 3189.2542\\n\",\n \" mean: 59.179478\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"style_extractor = vgg_layers(style_layers)\\n\",\n \"style_outputs = style_extractor(style_image*255)\\n\",\n \"\\n\",\n \"#Look at the statistics of each layer's output\\n\",\n \"for name, output in zip(style_layers, style_outputs):\\n\",\n \" print(name)\\n\",\n \" print(\\\" shape: \\\", output.numpy().shape)\\n\",\n \" print(\\\" min: \\\", output.numpy().min())\\n\",\n \" print(\\\" max: \\\", output.numpy().max())\\n\",\n \" print(\\\" mean: \\\", output.numpy().mean())\\n\",\n \" print()\"\n ]\n },\n {\n \"attachments\": {\n \"Screenshot%20from%202019-07-28%2021-11-00.png\": {\n \"image/png\": 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\"\n }\n },\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 计算风格\\n\",\n \"图像的内容由中间特征图的值表示。\\n\",\n \"\\n\",\n \"事实证明，图像的风格可以通过不同特征图上的平均值和相关性来描述。计算包含此信息的Gram矩阵，方法是在每个位置使用特征向量的外积，并在所有位置对该外积进行平均。可以针对特定图层计算此Gram矩阵，如下所示：\\n\",\n \"![Screenshot%20from%202019-07-28%2021-11-00.png](attachment:Screenshot%20from%202019-07-28%2021-11-00.png)\\n\",\n \"这可以使用以下tf.linalg.einsum函数简洁地实现\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def gram_matrix(input_tensor):\\n\",\n \" result = tf.linalg.einsum('bijc,bijd->bcd', input_tensor, input_tensor)\\n\",\n \" input_shape = tf.shape(input_tensor)\\n\",\n \" num_locations = tf.cast(input_shape[1]*input_shape[2], tf.float32)\\n\",\n \" return result/(num_locations)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 提取样式和内容\\n\",\n \"构建一个返回样式和内容张量的模型。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class StyleContentModel(tf.keras.models.Model):\\n\",\n \" def __init__(self, style_layers, content_layers):\\n\",\n \" super(StyleContentModel, self).__init__()\\n\",\n \" self.vgg = vgg_layers(style_layers + content_layers)\\n\",\n \" self.style_layers = style_layers\\n\",\n \" self.content_layers = content_layers\\n\",\n \" self.num_style_layers = len(style_layers)\\n\",\n \" self.vgg.trainable = False\\n\",\n \"\\n\",\n \" def call(self, inputs):\\n\",\n \" \\\"Expects float input in [0,1]\\\"\\n\",\n \" inputs = inputs*255.0\\n\",\n \" preprocessed_input = tf.keras.applications.vgg19.preprocess_input(inputs)\\n\",\n \" outputs = self.vgg(preprocessed_input)\\n\",\n \" style_outputs, content_outputs = (outputs[:self.num_style_layers], \\n\",\n \" outputs[self.num_style_layers:])\\n\",\n \"\\n\",\n \" style_outputs = [gram_matrix(style_output)\\n\",\n \" for style_output in style_outputs]\\n\",\n \"\\n\",\n \" content_dict = {content_name:value \\n\",\n \" for content_name, value \\n\",\n \" in zip(self.content_layers, content_outputs)}\\n\",\n \"\\n\",\n \" style_dict = {style_name:value\\n\",\n \" for style_name, value\\n\",\n \" in zip(self.style_layers, style_outputs)}\\n\",\n \"\\n\",\n \" return {'content':content_dict, 'style':style_dict}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"在图像上调用时，此模型返回以下内容的克数矩阵（样式）style_layers和内容content_layers：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Styles:\\n\",\n \" block1_conv1\\n\",\n \" shape: (1, 64, 64)\\n\",\n \" min: 0.03488168\\n\",\n \" max: 26723.258\\n\",\n \" mean: 780.9614\\n\",\n \"\\n\",\n \" block2_conv1\\n\",\n \" shape: (1, 128, 128)\\n\",\n \" min: 0.0\\n\",\n \" max: 95840.21\\n\",\n \" mean: 11674.933\\n\",\n \"\\n\",\n \" block3_conv1\\n\",\n \" shape: (1, 256, 256)\\n\",\n \" min: 0.0\\n\",\n \" max: 296185.9\\n\",\n \" mean: 7241.375\\n\",\n \"\\n\",\n \" block4_conv1\\n\",\n \" shape: (1, 512, 512)\\n\",\n \" min: 0.0\\n\",\n \" max: 3164086.0\\n\",\n \" mean: 104884.49\\n\",\n \"\\n\",\n \" block5_conv1\\n\",\n \" shape: (1, 512, 512)\\n\",\n \" min: 0.0\\n\",\n \" max: 66307.84\\n\",\n \" mean: 650.05994\\n\",\n \"\\n\",\n \"Contents:\\n\",\n \" block5_conv2\\n\",\n \" shape: (1, 24, 32, 512)\\n\",\n \" min: 0.0\\n\",\n \" max: 939.0783\\n\",\n \" mean: 8.983593\\n\"\n ]\n }\n ],\n \"source\": [\n \"extractor = StyleContentModel(style_layers, content_layers)\\n\",\n \"\\n\",\n \"results = extractor(tf.constant(content_image))\\n\",\n \"\\n\",\n \"style_results = results['style']\\n\",\n \"\\n\",\n \"print('Styles:')\\n\",\n \"for name, output in sorted(results['style'].items()):\\n\",\n \" print(\\\" \\\", name)\\n\",\n \" print(\\\" shape: \\\", output.numpy().shape)\\n\",\n \" print(\\\" min: \\\", output.numpy().min())\\n\",\n \" print(\\\" max: \\\", output.numpy().max())\\n\",\n \" print(\\\" mean: \\\", output.numpy().mean())\\n\",\n \" print()\\n\",\n \"\\n\",\n \"print(\\\"Contents:\\\")\\n\",\n \"for name, output in sorted(results['content'].items()):\\n\",\n \" print(\\\" \\\", name)\\n\",\n \" print(\\\" shape: \\\", output.numpy().shape)\\n\",\n \" print(\\\" min: \\\", output.numpy().min())\\n\",\n \" print(\\\" max: \\\", output.numpy().max())\\n\",\n \" print(\\\" mean: \\\", output.numpy().mean())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 运行梯度下降\\n\",\n \"使用此样式和内容提取器，您现在可以实现样式传输算法。通过计算图像输出相对于每个目标的均方误差来做到这一点，然后取这些损失的加权和。\\n\",\n \"\\n\",\n \"设置样式和内容目标值：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"style_targets = extractor(style_image)['style']\\n\",\n \"content_targets = extractor(content_image)['content']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"定义一个 tf.Variable以包含要优化的图像。要快速完成此操作，请使用内容图像（tf.Variable必须与内容图像的形状相同）对其进行初始化：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"image = tf.Variable(content_image)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def clip_0_1(image):\\n\",\n \" return tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=1.0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"创建一个优化器。本文推荐LBFGS，但也Adam可以正常工作：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt = tf.optimizers.Adam(learning_rate=0.02, beta_1=0.99, epsilon=1e-1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"为了优化这一点，使用两个损失的加权组合来获得总损失：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"style_weight=1e-2\\n\",\n \"content_weight=1e4\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def style_content_loss(outputs):\\n\",\n \" style_outputs = outputs['style']\\n\",\n \" content_outputs = outputs['content']\\n\",\n \" style_loss = tf.add_n([tf.reduce_mean((style_outputs[name]-style_targets[name])**2) \\n\",\n \" for name in style_outputs.keys()])\\n\",\n \" style_loss *= style_weight / num_style_layers\\n\",\n \"\\n\",\n \" content_loss = tf.add_n([tf.reduce_mean((content_outputs[name]-content_targets[name])**2) \\n\",\n \" for name in content_outputs.keys()])\\n\",\n \" content_loss *= content_weight / num_content_layers\\n\",\n \" loss = style_loss + content_loss\\n\",\n \" return loss\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"使用tf.GradientTape更新的图像。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"@tf.function()\\n\",\n \"def train_step(image):\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" outputs = extractor(image)\\n\",\n \" loss = style_content_loss(outputs)\\n\",\n \"\\n\",\n \" grad = tape.gradient(loss, image)\\n\",\n \" opt.apply_gradients([(grad, image)])\\n\",\n \" image.assign(clip_0_1(image))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING: Logging before flag parsing goes to stderr.\\n\",\n \"W0801 08:52:51.591864 140178057602880 deprecation.py:323] From /home/doit/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1205: add_dispatch_support..wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"Use tf.where in 2.0, which has the same broadcast rule as np.where\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"train_step(image)\\n\",\n \"train_step(image)\\n\",\n \"train_step(image)\\n\",\n \"plt.imshow(image.read_value()[0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"花更多的时间进行优化，就会获得更好的结果\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Total time: 94.1\\n\"\n ]\n }\n ],\n \"source\": [\n \"import time\\n\",\n \"start = time.time()\\n\",\n \"\\n\",\n \"epochs = 10\\n\",\n \"steps_per_epoch = 100\\n\",\n \"\\n\",\n \"step = 0\\n\",\n \"for n in range(epochs):\\n\",\n \" for m in range(steps_per_epoch):\\n\",\n \" step += 1\\n\",\n \" train_step(image)\\n\",\n \" print(\\\".\\\", end='')\\n\",\n \" display.clear_output(wait=True)\\n\",\n \" imshow(image.read_value())\\n\",\n \" plt.title(\\\"Train step: {}\\\".format(step))\\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"end = time.time()\\n\",\n \"print(\\\"Total time: {:.1f}\\\".format(end-start))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 总变异损失\\n\",\n \"这个基本实现的一个缺点是它会产生大量的高频伪像。 使用图像的高频分量上的显式正则化项来减少这些。 在样式转移中，这通常称为总变异损失：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def high_pass_x_y(image):\\n\",\n \" x_var = image[:,:,1:,:] - image[:,:,:-1,:]\\n\",\n \" y_var = image[:,1:,:,:] - image[:,:-1,:,:]\\n\",\n \"\\n\",\n \" return x_var, y_var\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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UIIeq9E1L4Pr7uQeG3Kp6i65vrWtXvTh/Hv7k00lNmZt2+6+7nNMzTTpuwjD6GyhuhtKOp/NLkGWvQt0Z1evvoefCm7yu0vETp1JFptGGhCOwjgYRc8NEooTxqWcrH01Hc9uwqgC+ybyCE3AXgLgC47rrrcNcrXgFQCgj7oO0GoIHpLKHtUVrMkHHKmW+0qjohin0SAmCe4W/6/M4YUkohFjWmNnx1+sY89D2EtlEHbtvLGIc6qDMWdeblIuurqrPJ2C26vqryi55/ddquU5bSyTo1dQX280d+5Ec+Mn/jEQ5m8ilgOq+ilCrab/62Pl1QxkABcM717wKUJeX55OODLAF4Pvs+QDEjdzrZ/RUCglFQzgGqeRDnk3lp/jbl7O9T2xaqPjPH7U8fTJ327y6fZBTga6Lbbl+q4HtGzv38PRQUE6FiGnIOJLPbEXSKbBLUnwayWcaBdMGiZuj4UqbWjT0fm8qZoXOSC3Wv3R1KwTkHtfuzBFDGIDgvXBOcgzpjRgUHBy32x9evGn0N5VHLUj58qlRBDZNSvg7A6wDg6U9/uvqtwYTIc0WYkyRB07p8oHrgJ0ylSDiDXotLjJcoDI+ZzLLaWIbQ26RtH7MHpgoDK8eylMBVbBvRSIHwKQrht3rByhV4ZS5fE6tSfHzwCVwR68JMPgUUedXe3l6BV4kaTNll/q4gML1wHePBlGrsW3wLxuZRjBU/5+3PNNgKkRCbuQNOE3pQhUUaYpeF0LnPqH+uNUGV4bZNBkfheWjfeDGmlKlFNu2pj9aZk7ZxrKaBLBTLUj6uAniG9f0MgGtVN0spPcJ9PSQ+y0wIQtuzlAxhE+hQ2P3Rf/smw6IEI1PPuj0f3mds0kYNCGcs1o51ej7CR90vsAeXDqyx6TvxWJ68xNXrDGlGRBc+n5Zk/YqYiVp8ahHgWihoOgdnwRhd5iioPhdpKfbB8cScONQYe9tAtux5sTYs+rFC5+RSZJ/A+3xsz3OZgjbkrx74FLGWza1lKR8fAvB5hJB/AHXS5tdBnYZcCe5oan5BomLwrBCmMTGrI7xNc/uOL9eYHm5IlhCgxiMzb51zYt2eD58G7r1vXO1ECa0kxD4rnGd8vdaEhQv1NbDIkLqaFvSqMJCge2eFTcy6toyQL0/fx4YBCy5dmd502Wrm67l3ZoRa3dx+t4whnDLU5lON4AltoMIjy3jCnESeg9revhnCF3Vphett59Z3rWBQSsHzvMirhFAhHE79pRAOoUNM7D5N66Nut/Ar54qOW4oJ5xw0RHJdloJUumSFzXi+GzDGkOd5kFJh11Vse77nWYhC59KzUO9Onpd4MUsScLFYyz5lDCJ3whA9HjpGKUzLAgA1smETupvnZTru815U8TLnOq3gMz74FFXf+650NDntC1jzzv60aceSeNRSlA8pJSeEvArAH0BtYfjLUso/nVYmSOOfsaBWakWZ1o4bVzfr/pOMwIlLnRjOWfNhvPgME3MJUVUdmy7sWUrHON48qJiHkVbcW6qzYsxs75p1MbBHVY2HlQ8Np6prSXTnVa3SjmDZWBGLWCrm4VONYCkAlFJwIcIEa0DTRsdIFhKuMW3+Ox78KiNZI0PeNDiC0CZY/YUQYInK0+GcKyUsKeftlITEugLvOo1kTWB4uMWnFq14AJjw+xnjakZx7vfgRSBdT1jJQ08Bte5tBSDPvWHE3pY9imqwgUwVLK37dWFZng9IKe8GcHfo/a7gUFdwNFbP2uFb7n0h1nbX4lPRT2rdI5oIInZZm9lMG6NZ960Koc9orBa2RX+K0OsmSAU/4aYSdcDL4OYtC0yxqwUSJ3t+A6hOAFyC5yP02esaI0qJd/UKTz6rylaMUQtn26lAXT7VGFqhVaG79cqFeOjr9qVU90KEsxMMX6IzY8oa7kHJqnzS4XjkqzxDTTHOD5qhUJh1to73oIyDfqW00JeabKaxItUSBWRpykddNPF8zNplJLhOQ1Sm1RHy0vVuJm4oUWMETJgJYysSgbUh9NmN8mGUJl+S47hKj3s/VLnbZCbQ5HnqKL8l9/+U/oR6PUK6KWbUUWp7Rr+EAKU1SZw7r+qWncXkKp4v+kNOB2wjmUCNDUF8RrJZZafQUPO7maXmLi4EmDt3axls9L2+HbJ8cHfWajtocXezyvXcVJnbVCOZ4/VdVh5LQfEI7c+iBO4mBjKPgVXUTMhvPJ7rlgk1WqN8hHggqgadQwkiQn8yaI1zLP2bNuzK9PsWKE0GL+oQZK2V2y4/5rF4Ltw746tz3cQp9Bnd8IEZ4+sKvb78hcrFH4J1j1sV5lUq63pJbIUuREkZXypfUzlYYU0HbcJgFhdHkWCb24zQJexVGIh5Q8h8CrCvfJV3iGK8FaawdT/PtYjNRMlIVued+u4PmZ8zhK2FCoe++T+DVxV+bSvNteGGus0KfVtYqM+GwPesy7Cwh46rEChs/zy1TsxmF3UeI0SGsftXt95551RL5mJ7lI+AAakS1qnzRzlh1t8GBYJj7SpRFfpkE1axxC1vQ/qzTk237kQPud9D3EIT25suPG9eQXDhZkJkOSHR+d0VVG3le1qInosGCmzTeR5UXgDaylBkBszzexMsKqzFRtXzCSCHflXGA0StnJ52GKsipsD3iuzzFbgQasqakBT3BkDNGU/SLnWTuc29rqfOte5OtfY6cwwqObjgjRACwuNB8W5WkVmhRwFrp6T0GEFetyWM57LEzz2CZE26UwjbqVew3v1uG847miiixXdEKYNwciW8iqKXrNdIQPZ1V6AUJu6t09MfQWkpx5UC4Po2ph+Jq4WgyufmfdZQ0kJpa8LKa6FqPYS8WmraCein0P/MUuHWd0rVd1AdyldjHrrP7noZhVD0w2fQLY1bOJMUQiDR84DreppwyPYoH4vATK11CRy8KhSG0uLLd1/8MqwBVf1ZEUrEaFnPWBNVBH7euuYv3GyxzkJJsWmHgWM5oM6n77cNQ4EYt8Q6FbE4jA2r5o8qUhT67hdBW318aZ6+LALr4hUnzDPRyECGBRrI7OuOYYwK63VThHkdbLRArpgJo6PYz4kp31sOSic7h40VzAbr5mQpH7OwDKI+TQN3tVEhmp162rQ/S8Z4Qi77GefFAsahKWEv1Vdx3VvnohnkJhDw04KCG/6UhWpErAeMKW+FPddCwwc3HKWtiNeMkux9Asd8kfCd1h2xAiyQP52ut7cqT8M0a9KyiUobQqxaRjgXeeDiopPn1sn2KsMYW8CMTx1s4S8qHqcbq/J8hObErYqer5pvzErIXwMWkYOzaAMZ4DeSVRrIWjamEQvEAvnT6VI+lkDUl3JKeRO0LeejRVjIe2nbu21S3UJri2gE1/ocFZCIZcPEitsCozlAzfYKhO5ctUlo6fPUPiogFCdMBIhejzVhgfOyHW+QEH+sqZ0oBOVqM/d5T3e0mbeBXUfowLmM34QSOf2BlXhT6HqaFtvzCBJTQxxN2VkCiG8Bhp7q7StbhyBX1Wk9d2k3k5D6DDM0iZc+TOu71bb9qW6piEet89yNcj7mKOvOuVKVav4XmJXw7CXua7vOuRy+pDY3RCNgfZUSFC2vFBCwJszfoczHtx7qPLfv3mUooNPoX0S74fIpJ6HYfApzT0G5dDhBHaXTPXPCw58Kny6YPgTN/d2hJSZ0vQT3WZrO2dAzNBahOMzbT1cWMHwq5MRru90pfKoKwXxqVYaLKp7ivC/7rA/3ZO5CPU37zLlKtHb5hKi3vtxd6Vxv1Mz1MG47AUIOWORc3TsvfB68ZUX5uHL5xud8SBk0eDO3U51FbEMxSxCwFot36H1Cs9OnqT0MXYy+5/SVWZUb3yG2tYR6+5BBI2jWsW44ffedBLo0BA9bg/GteA7vKeOh3q8677uKudacM/Z6tZVT99TXUj9tRadOuESdOdTEa7gMj2NLrbMRLcFJmx+b4LX3GTHX2W+fANiyeeENJVuWguST0+rAVjKq3mHVmM/b9qreV9U8XdN8aYfy0TY09QA0atqxAE/ryzoFo1DMswhnhZ2sou9VbXiVu+V2ZRp8p70vjajbXiTGauhcRaLsU5goZnjK6iqjVntT+7MotE1IiohYNnye0JOOTQ33qTTXrxZLPWncPiTaPrS4pjwnUIwosEuPPZiecqV2WsYTKGOlIwlU4n4Dr0sDbOhKWjLWOGmEqz0voi/rXASBbZdiXde9cDeEkdJQha0pnPdh3OhBcEMOnRC50vXAPgRhRfPIm+S5IXMoYkOxxvnlDXduXmn52rr5wCz4QnvM9VW13/KQTW90yhLmLqXOeV/6+zxh1QUDWYW3fiaECFP4Timf2Czlw35HYvKx6FdXZ7JWnr7coG1XAK/qT3BS2hqJeuhY+s4IafzcTVDH89EWLFsBKTRV0zvos5KGzssGz7OqDSFaPCsiIpaKhZ6S3nIwxsCnnd8VMRtLGi9hwoDtsPi6nqqQ/KUqvtXyjRp8BzGvM3G/HcqHL+HclzfR6VgTQgvm9qmY3E40cmIz7clgW4vz8gTjdEoitymv62BJooSZ8T+o0yrdtu1cBmiC7XnxeZ6X8hUqD/3xCmqBCbKhJ2rmxcVc+CyhPLm5r21evmZbGqgeL+5L2EOV4Ou5Zi/+cb6Ap6h3fCxXpN2eN4mQT94tpdWuec9zV8I35lW5RL58CHf+5XmpTspY2dNUFd6UJOq6mcdZ5l9PvvFxkwDdMnb7SYUL2H6mGspDY0XDtdRVMSUvQ6rYMMEHmqiDuChV651SYDQCoOcTq1bGI1oMUfhYKBptHb4AI5nr/WxYpX9ttciCD2ASimM/t+c9rEwhq6JFLRu3SgNUm2CPmx3G7GLBeTaVoVyLRsvyg9qhfPgSzn0CUNXuRyXvAy1L7AITwdzWXzyDT30zwQijRsHQQhbPHbcepWUhUwi4ZJnCcx8AZisP435WaeANduIJFYSp1ZHxwgwrOi5euugZc0+8ZD3iPUvB0r8HbD5Rqs+ed1WKj2/XmdJtcyx0lxjOqtMW0m3iaYR6S9gvhfhN6yPnZcXCXaNVfQzY9WWMqjW+LvjGN7hsvVhaYdaa0O3YipgQlcp4RMvgpZnWvCkYyVA08AD+XZ84SsRUzBIyHcV+HDJieFTKgMyxzvposyesiPuMZNU9CUOVkSyEV+X6eWyhsakQzkXJSOfz0HstynVDfdx+g3tkGJ/wWCEnle5DkS+4Rp9FoYp++4xp0ww71t/evMYkmb6ToeGD9g5YpmyVR8JniLP757ZRVd79e9lwn6fOvBd5OJ/yvUPrvdSVbtqhfCwDrgW3qQvMp+DAI1yvcNL5nqZl9o7NhU8wn3V/WyxONmMpMDS0o38R4dDv7zSFtkQsENZ6L53qHWnB5iPEIOVDGzwP0wTmNvHTiGp4PH6hOLnKhztx173QlgCfJTQKKGvCrLCcdfXFfDpCyFoPwowIhxWOGHECYXs5DD2vPMeoZkiSz+DmO69qUYg0ZeWglIILK/pimlLherBWlZMwL0+M82mjUFemOPEcrWpnnTkqKscsOrH143yFFYF6/kUsBt73OG0OmZCGFhBMas9VT0hFVDw2BLZb26E1EREREYBlTKrjzWgzLXHC+SJOJtrj+bDcwZWWWV8C8TStmnMVKkupSlZ3T94EIHwxir6kVzde0lZq9D/jiaA+RSVUmDXXQnIIqvJirPYppeoZrfEqbUln4Iv31ae1CxMraZ43dJeEqoS4RcNXp5vobD6NNZkxmMRJF7meF8wkWduxpC6s90/Nvb55Na+LfBo8/bG3FxScF2NmUVQ+vKfMBs6NUnJ7DeZH7X7Yn01OFPe03XgLXF95s8mE/btNh6pihac242nH/LGpZwucUgjUMAQJlVdAhVC8yqwBX5ivO0VC1oW9Jp35WGvb0BC0IZTnlKHEv6regS0PrFjxqGqzcNq5unGsRLmnjEe0HHN40drD1ayOV062uoIJYxPimmXlosB0RcOHKRbIglDj3lNXgAoJGfPV6YyR8Ah0ogaTEO641TldGlgdM/L1yfPOC0U4r8yRYa7yF5AIPX7/voTRyk4sh7B6FYyK74V+VPXHd903JoGKi/D9XjVXGsyhxtYzD32wk20BVOaD1cHUfkbm20pMFYzsXKtp8eta2Re6vrGx6LL4AAAgAElEQVThwqXZnJenFWP+9WbXXahEOD9XCKkhvMs8E6VFQ0IdOLxO5Dlokoz7MN5Ry1WRPH2kaQphdpczglDTsKJV866CEQdjY4wZX845mEOPeJ6DujtXTjF2jkdtlUrijLlRMOQ43vlCrlsTA1lAP6r6FpTsHipTVRnIVjFPDb2w71+UYWuO/rdH+VgXNoGxt6mPberLArC0RN4TNk7BOGmWT4+CtZIT5SMiTjqctdOEDnuVn5OwLoWAWHE4d8SGocpg0HI+1a7eREQsGYUcoGUpHhEnH9PCPSMiFo3QsN11Yh5a6pxIPTfssThJYYrGAwDHG7+mfpwInKRnAaZ7gew11TJZ5wSt0gjAEwrQVON14tfHuQQbuoCF8zxttAhEtAhVoZ5x3kRERCwJ6uA5J1wv0psIH3y8yMybFs+ddigfhKhD/AR0XOJkMM2Bf4ICFLycdDcNJvHavASTgGzHE4a6a0VFsrsPeWA8oi+vw912EdWCvu+EdBUDyop9dJJhC4dN2Z/e5HtWKM/1OPgfPXCCT7vNXixV8cxegbDiIDtnjAXHZHzMc4XGj1ZhyontxX6X66xsJTQnZ1Yin/M5s4914Cvvy6GadVK99VnKMbJ/d6/Zsbye3KaFoGpumLbMPMqy8npKJv0Z0zF30wuLvkVsAAjUotXTwn5vzHNtYaC0zPtaZgCqmxxcSnhvaiQrGHlPSKKyu2HAmp7pRJ0zdBLmhQ17Yx0Dkzd2Uj0fhJDLAD4N4O8AcCnlMwkhWwB+FcA5AJcBvFhK+cmpFUkJ8AlTpkA5oayW0oGxIlOox7PbVfALqXPyeGhivHsIov1pN13VR0+dY4XE9X5Mq8/8XpV8bycvw8MEJzdX/VD/NiHgPb0VqDjx1jdGtHR5XOUirQJepWI1C903N3zXlnImjK984CncQgivcBDcJ1dxXxZhnbX2fHPIXBtNFCkzKvbzURGVjlVhcbwKMNtZCYgCXzLvsh6vohgTKbN2PMJEpZFs2nX300Xu2RhjWvy4DYvPTNhhPaGudHK6Y62t3I0ry8q8KmEAimUbC+tVa9NVCDwbBMzcDGBandC2NHfMfQn0oXSPi+rnCYDf1hg2vpXGU9cwtqzcS7den5xTgSoDWcnwW2Ugc1F3XoQg1IBuG8jG/aFj5lTFj5bFpxZR5T+VUt4ipXym/v4DAN4rpfw8AO/V31eLdSu2hoDa/zwYs522aOKB/V5rf9bdpw2FOYPG/te2/qy7TwuHUYoqni8qHitHu3iVMZKtC4F5JJSxcijvOvMP2pb/0rb+RCwVJ4pPWfPUa9JdooFsGdW+EMAb9d9vBPCiJbTRblhCxzSrbMH6u0zrbSgC+73W/qy7T5uKtilxp0GxPOnPt/mIvCoAhTy/FseQR0REbA6aUhAJ4D2EkD8hhNylr322lPKjAKA/d3wFCSF3EUIeJIQ8OBqNGnajZQgUOkruyHUT9LYJS6dBQF0V2qbEnQbF8qQ/32Yh8ioXgRZ7w6cKh+dGT8MEbetPxHJxkuj4GudpU9/p/yClvEYI2QHwh4SQx0MLSilfB+B1ALC3tycb9qNVCE3uLYRctWF7wLYR0Rq5LhEzEBoXuipUvcOTTthP0vNtFiKvmhOlZOM1Jj5HREScDDTihFLKa/rzAMBvAbgNwMcIIU8DAP150LSTc6GJ1dEu4+4YEAATTmX/m9lOG6z6Tb0Mixpzd0xOo+dj0c/dNiv8afB8RLQGS+VVnqTU0oYEbpKyfZ/5Ny3x1pM4K/K8nLBd0bbw9Sd0Y5RlGaWcOtQp7nPSgGXwBV+yMlC8ZsbRbb+uMTEkgdkH3w6Vy0Bof9rGn5fBU0LXiC+xfBkbvzTaVGG6gbdEN0q3eehKaNPBdzoghDyJEPJk8zeA5wJ4FMC7ALxc3/ZyAL89bxuN0EB4Y1ZyKBUVO25ElNFEYD6tSkYVomAeEbEQtJpXNRDsWZIAlBYEgEglloy2RQesE9Gzu/lY9M5bNdAk1uezAfwWIcTU81Yp5e8TQj4E4B2EkG8BcAXA1zTv5hxoQCRybU2ggN6xV3jP1Ihw0IQwR6JeRCTsm4/4vtqCdvOqOUEpLWyhfWLOtoiIiFgNhFjOtlMBmFuillJeAvAFnuufAPAlTTq1EDQgwsw6QExAeT9OtSAciiZjFMe3iCi4bj6iQt0KtJlXeRWGunNEhwfFmbUC+PJdTuua9oWlxXygzcIa5YyTa85vwPjtrQVNLXE5BWABY14seopHPXo+NhsCsy1KxrUaETEH7APQKg/ki4iIiKjCGmWKk0uvGuQQcM7BOS8mjJ/ckZqgaZ5BgzH3Julz7v93guB77vHpvDHnY3NB4V8PhWRVc298txsNb3K585vvPi5U0rgNSitonDV/dPlcJ2iXDrD05Uj7+p3lJdriNfh4+uO9jzWbx1QICLetJA3qDzod/3to1CHPu6iq013D0xLBTR1u4rpd3fjyjA0CEs+1JdATr3HQ976rkvQX/W6aoGnbwsOPfXUm4cbYJonb/vF2TzOnJofAudHaUMm8T5smsIn84euj7wT4UGyW58MeOHshrmIi12nCcT2WtipcNUzbNmHYBK/CKRDKTtRpqacFZn3PG2IQ33nEMrEBpL0R2hbOyHlZMfDtgLXB8B4VEEOs2o1FhME35XVTsFnKh4E9IG2FZTVpRS8LVrnp26u1BlVJ/m3rZxOEWtIi2gXb0sP55m5FGbFctFkI9fDRVhjJbHg9LB6+cMI84hERjdB0g6SCJ3U5SuZmKR8hrq42wOdWXSdszXXaRIp5BqtHHN/Nxbzr3F1ncQ6cTLT9vVKddGQrz+tWPuy1sSmW9bZ5YpaBquTyE+9m21BQWnw14/fXnve1WcoHUFwEm0Kc2oA2xFrWRVXc7Ena9jh6PjYPTd3ZPiNKfN8nC5QC3MrdAlpHe81WvbR4sT39rFoXVWFOfEUH7kVEtByUMQh7OdTlMacm7ErKMCsipZrIaBdrnpfdQ6i537lxi9v3O5YgOm0bQ7fflELkedl97XMLp6l6Bk8d5jkAlJPw3Lbd5/FhSnJboaxrAWvqzrYnrZ106/bH16fUk2w4rU8Bz8itU3TpNKFgnoU6q7w3KS3x12me0adou5/2ePrCgOp4uprCNQ7Uhf2c095PjXdGrTVcuZZqQOQ5qP3ezHi769bup/m0tvEeX/c9M6VAlnnrjVgj1Fkh9ZiyTfOsMpRScBG4SxVjgODF9eXyKVQkgpv+up+GFvros400VXPR9ANQ289bzyGEAM9zMLrAcI9Z99l5la4SVRe+6IAmJ8BPQ4Ayyq3NBMZlfOGddcI9m9APX/hgHbnEN/8W2b86MO82NCLEhe/eUKPSDB7FhZisrQYQeQ7QdLKmK+ZzYZe8aaH505LL5zSutEP5ICRssF0htULQrrVFq28BJUmhHVtgndo3/em91ydkuhYcp9/j56hqu6mA7Ltn0QJpVYhJSOhJXUExoO/MN2eaEr06DNPFrGec5rHyKRg2sbEJa2h/mqJpnfZzTnufNdoZ7yC2IFCbPhiDiNNeuZAz72cphEJ46VBExFIRajQJLRsREbFetNSz3r4etQEOsV2qxXETw6Ei2g3XExLn1vKwzLFdJR2K2Dw482EhCnZVMrftuYn0JGJRiDRtefBFRiyznZqIb35VsC3SFgGn2l0eBYuIhcEWEFpq9YiIiGgIe10vyCvmPc/BV7ePpkQ6E1EXUZFdDtywXvP3sjAH/YnUwoMSAV7AS/MeJAeUPiMiGsMTXx6xYDTJawluYvF0KOLkYFl8ytfOOG9qwSGMERERS0JVDtfCm5mPHrQ358OboM2KFl3z6Wh2QgjQpKOum4RuxsrJ5UIAVKi2TD2MgXMrYZxSdcpjHc2OJZOy+pP6dm5KEog8BxcqWY6aMiWFxBobK/nUTqwfJ7nOiiU3ddjJ+kZYZVXjizDt2feTndcybRE0ZGjuJgOUUvAsKwmJjLGyJc9NpqMUyAOS2sfXGnS88n0Ftu3DeA5RVQ+tedZAk+ex8xTmCSmkgfeKsE6K8bRVuViUsepd1HzJst5noUV6wvPS6c6KnNQYSEpL484zTYeirLd52NR3Niu3bAoSUIg1PXi0okZEOJiRUD/XmhHzFiyjHcqHvduVgY/g5fY9dPIpitcoxUS4NgNvFAwXggJJR/3NOZBzUGYlhwv9r85QcdPODEI8ynTYlZ04bD9HRfKffi5u7VbCq+73wZPMWjk+pk/zKgcrOoneHgtoRYR6kvyFUUStcaRw3pQQJWFyaahMBm9SqXDKu9+XiCrBPhSBSkUojC5DDZ3IOSoHIyTBffy+LCOGJ06+trFJiBK5oGOlvWZdEevHNAXcNpKxVL1fYxAxSq0LKiZb9wJjGjf5vf4kEZQBcM748O2oxJjePcfZfcn13hf6WxR2xsq/2+9SpxxLbZ4XeZVrJBu353SgrsXXjYuvKrsAS3JpLBwBsbALo9sn4cyBUQaaOnzO5+1uSkO8spPnvnk97XXHtMS0a2CVBrKAx3INZI0xNqTbdWmZxl6TAGpPDPd202euZYwa43hy2Zqr8U0hJlT/G5dbZR8blLGF7dqTNkTZ2yBQ931XQDieLy8zjGEFERERq4Ir0FcaJCyhlPPqbboXDV+fZtFIj+Lh/h0El56vIIxkmag1FnbkRMV9MVc0YlNxcmeuG5ZVRSwZA+d8so1l2w+w8xFiW6A+rQhRNFEk1pFwR0RErBWlMOAAi7spswxe5Ws/NOTYvs9nJNPXa8WIe8NcN5duh45FIYTYuibUH3aFS+lnRMSycXJnrnGjttnzUdf7MM0NW9fdWdeS1Xa4no+A57HzZag7rhERERGrwixe5Qit4xCpNmKat2LDPReNUXMsbL7k9dCf5rGM2Gi018zvjTF0LETmRG7nNGhKKYQdv8rY5LRgX51JMiGYWVadK7CIZ7KfwW2nSmg2z0lp8dRjO3/B/udr1036dhPOTSKuG9vuehRMrKodGzxlbGiSTBK8LWFf2ONg7qsbf+mOpZtfYjNBT515nk/ibwGwkDAGX99WxQAcBXn8HlyEnsxbhSbPM23dzttG0/F150CTcMd5Y5rdvkREAHo+WHNqmjfDvodSiNFo8d5b3/zmXJ1ybq8f3zy273MVqSZGMpfXbLLA7TMYBiggtvyxsNyAiIg1or3KR6i71bN43bj+qS5q11Kuw7DKty1AADIEx27X1x9fWVdwEgKUMfgSqCvbt+ETtKed/m21W0dJEJ4EZF+ipFeIngV3LH2WoSlI5omZXifR983zqvnSBIsWkJsqQ4vqzyKUqqg8RNSBz8Os6RWlFDxhoNwy+uS5EuBduII858pItmjvR5Xxa1p/7Pu0QYTmOdDpjO/VKtb09eNTUHze/iwrJZ0LztUGDdb9pXy+KYK+EGLSP/N+DH+1ytM0LfM0H9+1lSaPAW/cH8+nmxdi+sB8u0ZSCriZ1+s0kJndLX3vZpFoYgRaBg1flHw4j3LtWyNiysYqLUF7lQ8ffIt2CYuqtVYFS4miAHiej63f4x5X9d3nCo+IWAXaMNeaeiwiIubBLKHCKziscZ6GGDSq7rGUqsbC4brHISJilZhX8dhgbJbyARQJ0iyiDsy31esyLAdVFjADJ3SsVNZyPVNMvAfCbDE7q58hmnUk9hFLgPfU5BXNtcL5NwFhghERC8GsECX7Pnd5NOFTTWF70oHpHvoqaz9jpa3ca3PPWWMWEXFCUNhy+RRh85QPA9eF6YNNIFcZYz6tTndHkFAmZSke40t1w1li6EjEGrDOE5HtTQVOI4GPWBM21XLv85DPsWbs09Cpzadn1eeGKUVEnBKM18op4VObpXx4cgUq4fM0zNNOaHuh9dpE2Gc5mtEf7xZ8If2zy02Je4045fCEEDeuso7nw751Af2whSDdcPNKVwXzLk4+Hzp5CA0NFgKgrCzw12ljkWCsnOCdpsUNW1zviIElOFFKwa11Z9bhzJBmJyfGRyfG+RkRpxNL4FHrbJ9SOj4keSM3E5iTR7VD+SCkvGvTLOuI/feik+6W8fJDFY1ZsCxCpWQ6ICg5UPj6s0EohZoBp8JSsBIsJZJjjkoX1I+C9XVy0X/zKuZQnZDHqHi0E7ZV3nciODDhZ2YHQVtYtxOLO0ydDmzDw/9EDtCk6ClvLKhQUV5nvhyU3HPNx3OtRHAjTNknotOKZxvDlgFg0Q17vIUA7XRK7VPnnkJ5u99OGVs5KtxDPTsJUurdQMWH8Y6bBrPCnV2PD0ugkoZnQHjmn68twZWSu2x4PFbUCcFrjKZ0salXzVdU6E7N6pdnfVHGQDkvGsimGIhLByq7G08IAYCFjRGFf/6tgBfObIEQ8suEkANCyKPWtS1CyB8SQj6sP5+irxNCyL8jhFwkhDxCCLk1pBMEUGdtALNDqWyCxNjmaYlN4Yab+ZiC+89W6mzlzlfHBoCarYcZm8QXR5wMCCxUAfIq6Gbuu/9WAd9anBoq6fyLqMQqeBWkBIxFf0aYLF3H/Fo3fEp+aHixT6Gx+dWChSLfrpaLwlhJsXntBhv8Woc2LKcFGsgMGm/WsA7Ma0cPuOcNAL7UufYDAN4rpfw8AO/V3wHgeQA+T/+7C8DPh3RCSjkJjZg16MaCwrmyKrX1oKVFwiK4Y63XtgS5oVxViob9T/9GFzjRS4rgshaQy8w3aaFGrAe24LJO5WOdbZ98vAFL5lWEkMmXqUpj0cBzKt+wzZd8f4fW4VplN4neu8bBiAgHBe/bJs3thpi5GqSU9wE4ci6/EMAb9d9vBPAi6/qbpMIHAPQJIU+b2Qtb+ZjZYzpRUjhfqvWijXDdxIW4WNsbYP+rUEgWHWO4spwRx7Mz1xkhEZsNGvZvHPrRFsZf1/MREYxV8Cop5eRLW+ZUW+BRtIStNJh7quZ7VciwsPI/Fj3myzSQ2Z/wGOciIoCi/LKh0SjzYN4n/Gwp5UcBQH/u6OtPB/CX1n1X9bUSCCF3EUIeJIQ8OBqNJmFXwOywK4uQ0dPAuF23HCaCvhtWwigt/aOef+PQAbeOBlhZHoYbU7qcViLaDDccqfJfWcCv0lVWguj5WDUWzqsAi9ZVvTvHSi9Owzv2xfvra94tr134+JBjJAuKjpiBEr9bhgzh1kn9yfMRJxihS/6UTotFU0TiuSY91yClfJ2U8plSymd2nvSk8emoAIJCr8b/TLlFMvNlCAdOHV5CFNDOLALGtTeIcw4uVOKf8SrZ/5aBUM9VMKoENd87L3cmvJ2I2ah6F/Ouk5DyddddYN9866HWmmjy3HU8H1FJWSbm51XA7Hdi5lSSTLf2u7DDjKxrvqTdIMPbLEOeCyeB3ngNS7lTUzY2sb3prrGsdHK3x7BWrrZ4j7tW63rvC31zn8/+DD2V2+d5N3mITr3eXfdcRbaOUdXXx7bxw7ptz0vn6qyxJjBr0fcOp/XJRy98884tZ8G3MQLcTRB81wD/DnW23D3LoOLv0Nxl593+4GOEkKdJKT+qXdUH+vpVAM+w7jsD4NqcbawPy3TFakJF4SGai2y3DsMrFJswh6qtDuepc66tTpu0HQW1xWLRa6KqPtsy6oZs2GDUf78Ppo4514S3j6tAVKAXgXbzKs4xtw1wGTTOEVCEEACjRT61gHXUNIzK9MfsqtUUBaGuLq2oIex724lYDELHsildNQq6q5C765jRiffdJ/jbZZkzHxZhQPe10yLM+4TvAvBy/ffLAfy2df1leieR2wF8yri8l4pQS2IolmFxNP2y6qS+64uCY2kKLya8f8+LkgKzaOu4QaDlIGJOrNLzEbIeBNQWpfanJ5iKmnVmt9sUq/JIRM/HItAuXuWgbaE4JXq9JAG56vyOELgHGC7Sm7+Sg0hNX+N6PvmwFdlK2UV/mvtaRhOWhZmeD0LI2wA8B8A2IeQqgB8C8JMA3kEI+RYAVwB8jb79bgDPB3ARwAjANy2hzyX4CE8ja8gSXn7Jy1HlfltgO/N4Lty9z5sSdZtB1K6vbiiM+YwWpcVjVZ6PWV4Mu3yAlZJTgAqrzCIQPR+txCbwKhfNzulYjlJQ4lNVW+A2gN1G6TT0GuUX5fnw9auW58O9d1rZyJuWg1Bv1aI8H4xhrDn4ZA5KTYjLdM+H5lEcC9x9dAM8HzOVDynl11f89CWeeyWA72jaqbooDXPo2qaAL9mn1onMNZoKabsp7ANoQvosjMyub80hxp1lC/Z81FZAZnk5StcaKCBN+EG71vRSEMrg61gvS2VVBRUF9A3Uutn1cnnqVOwhpE8cwVGoqyLsUUiphU3gVSVQ2i764aObnE/CQqDW7sKEfstQFlpnlYFsEf1xjWVLQfR8bDbGyoRzzfc6A41kAIrz7hQYUNtzwnmIxTOxJHZb8Ej0Y4xfFgXyrHyfSa4x17iYxI5b4G4/KAWliY7kCLTMutCni457KADOeKF/QgiVWOjCDoWa1Qw3k1wlmnPOwXxJaSzR9+t6qSK8qkuqFc4oaA2pnI5z7CbPlHPHEwOAsSLDKRFj8xm8hS4DRaKKUaqqY8piJ+zuU0CAQ7/hCYRQz2m3W2vhLyHpz1fem9hYkfhZusaLvxmLpjPmVcohd09hBuB7buqba55+c09CHAXUGhcCgOV+tgm9/qkUhiRE+aRoAILSiYI9bsj3bhOU5kUVfCcPi/JY+vpTncSqr9v0qmWWqgioQwaBgPBOD59yhRBKVWKoPScMn3JPLDbx4zZcYWZcL6z10nwOcZ6B0QlfGvMpd13X4FN2GXNvJZ/iuVOkWDutqcCVFAxL0LN51djeMUsx4h7B0ys8Kuv3WISwb7c9tFXmEiGUHEEtWjfNol4oy4rGm0XAJ0xzXqZxCQPyGtvhu3U664ZSCi5E4KMwlOh6U+9C4ukjs04Up6ZJD4/ygFKqvB4h0hbl6l3OA5fWAOq9hM6JKho2J9qhfOhTY2cit++xFQ1zzZZQnEczlERYZZl/EpaGU+j/xPjLHBClHsLZ5JOOJ21Fj2jALhjWsFDql029VUMTXlpcQ7XATblJycSuxOXHAArvSTifNXogaF5+Pe6zC9M3n8AOS6hlfsGxClUWjybwlfcqJKHtGAXdvlQeByHcm6qb9iKw314le/yj81n1u3AuVtxP3S75OfuUBqs6YF/yzGOPYWOmsDqu72RbvU48gpflErxoi9ZZQw0pjlDXOGzXVqIWd+saoQwUNpurXuUeT6wRFwpaS8ueegPCfZYO82LryBDBdTcQ2VvGU9qhfCwDVZZjYzHVmvNal0Udoo4J3XG3LyzeOnuC1X1mEThn6TIWWzCcTtbpSsW9PrnAh5UdcbiphH0ZRK9tz72p7yai3TAC/YYdpErh50/LgvEchPKq9cH2VKGC9wRIJuN54fG6+MCwWK9HxIkAZQzC58lfAU6u8jHNArvMeM46CCXOJmdi/LW63DIIfslyfOIgCkopoKO29K/m+d38mJUzuk0VZje133XgecaFb4QRcfLgCxmywyFNmAkX6zNc1jWS0Tk3GHGbQD15uf2KR0PYA2JCXkyY9QzUyGhrhmiEmaAqD2RdaNm7ObnKx4yBbsMWh6GJ7T4iXkXYQz0foV6fWsS/BWM6F1yllAtwa2Xk+m8m/N9X5T5bxkYIq0BlcnkTtO25faFlbetjxObA9nYLAc4FkqRNkowfi1I8Tj0EKzNfLlR0pxW6lSNDR3SQaZ6UaouZ+13VF99HRBHCbCaxBrRD+bATzg18JzQCk/t8nyZJ03ZT27/r5CxhnfqdpGm5DTsxx67Xl5xb1U8HLE1hksoBjNsvwZecaz+HhhDCm6AXSvC9d+V56Rl5oLmt7vQ1z88YU64/3zhWte1LiNYMb+Y2w04OCjV5Hjo5nwuhE+KT8b3MKctMH4TKA/LlLwhfmEQd06U36btcp1fZnKcda15Se+7rNkSWTbxD5lrgMwb3u+K6d24Enj5c2LXGSSoVNr2wfi+MfWA7hTATO1G+3KGqjnoqbb+wGVEBX2Km/QlUW0ate8ysSNIUEM46yPPJactuAqiToBvKF1ialjaDqFqTJbpnCcV22XmVEAGzW52DPAc0P62LuqHWpZPZK567VOuUZNwJPUL1M4w9HNbf1liaNGqWJirHW1fDDY8Squ4x/2YJKM8W6x1alXLp5VHl/CKeZaDWvKCUgue8kI6nf2jUdzWnnS7muWrbbSdgto2fZ1xm8ilMYr07l+z5FbjpgHfjhDXymHYoH76E80Cm705MAMUQGlt5MKeLW0Kq96U5wlfpb2sRhL483+4+Xnieu6qFhVuWDFG1PQE1ituTelbf7Hcg6u4u5bmXKWoLs9gF4Oc0TsKWoGr3LSGAhCVQmywJJfDOioWkFKCJXwj3oeH7Cg7ZmacdRzC36xFCTIQcjTpbbdYJNfLOm1BaMKttXTe36ID+oyy0mb/nUean0ZYquDseRZwOCAFQVq2gTIN7n6uAzNufEFRFFqxCmKl67gB472rIe7xK5BTFY95tfJUgSgtKBWWsOulwDjq2Mag1Tz2K4aIROi+aYFXrKxQN+9IO5WMZ8FmD6qChdrzRmNdS5VgiVul2p0hKfahV3ihCess7QRNM9g4eN6LHxjJLbUaWY3M447rJuQvevtveD9fTuQrMKUxFbDiqBNd53v9pnTM1nzsBXd0mIQ7syIeiQdO9s7xFbJqmEIIjh54yXCDnFIlxEUEXEWKyzSQHQBkop402StoIOOtGKXle39nGoHDoYJX3Y1X8YsH88OROR5/3Yh437YqF6LZg3ufexPHKRyMIN2RrNAT1UGv7ZF1AnUqa9Lqr62zE0lCYu+tQrqLScfpQCsOqMQdsz13N8KoqNDoAdpWeD/MpzLkPPOxMqvFQz3mYYOhzB9Rpwj7rHOAqrP4njILRBILmk2M/KJBzDioSHV2gogFydrKEvbEXyIZvHN0zc1ThxXdomevBCqsqzdd1GMkW4WHFyZqPU1E31rMQcup/vnoAACAASURBVHKaoCfW3HG6axo34cZDG9jrQwC+Kc90rCYD1fkeFAlL4ZsxjDGYfBWhvSCDqrZPEEqCTdtcwHXgKBiVW1evy/tx2mjOaQZjkx2LtDBaSyBeoOKxUbCUtCZGsqWfZu5AiByUJop/AGCF/A9zl8foJaAPyKVIKAM3ySCj4fjAYIMkSdRRupyPaxJdijxjSNa0reoqUHqXm8yjDDz9H+eBTLlnaXBDQxu03Q7lgwCT/Ut1yIMVAz0WDigrM2jXaqQt2Gbv4vFp3UKohW7Hd0+OoizCcd2pPlj9sW7NdS7HeMJX7RxgnzGiCsCceGo9qLconfa8bjOeay5hZUI9FxPWgUfU7rvt6gMAzynkbg4LZQDrQMDKBdCPCJGr85VyLbB3GEATUK534xD6neU5RlRAMH1aeZIA+QiUpUB2DHp8jL1+H6NrVwCegeaK0Oz1+7iwtYdON8GDDz4Ixhj2Dw/Q6/fR628DLIVIEwiagPEhGBg459jd6oMyIE0ZkjTB8PgIvU4Px8dHyDhHzrl6v1QRcsFVUjoo0O/3wShDPjjGXreHnAuIbheCAgdZjgGAQ84xzAHR72AkAJGmakA4BzKd3M8VE0mSBEwwlSDHc3STFLnIkTOAd/QsFTnGR9KrNwnwBGonk8y8SR0NZq0ZmHdbnOu+RHAmoJiZIwwJylRSnLV2RD4slQ8mRdY6sPN/zGHGdvK2P6neU6dPCHGtREKoZFXrOxdcjdl4DbPJWs2yYt1CAKl/gwE3sb1EW3yn/7p9t/u76YwzYjZ0TLote3qFYZ987fGSUFpO+q6CHUA6vuZZQ75Z6N/BTtgBqYXPJjCUa9ziWPCZNEC5CN61J0cOig5AHd5tR16aNvUgmejaSXju5PokFJcjEYCAph/g4BCK3+i6ElBkRqYQFJxO2qOCQTCuSIyYtKE+ORKaQoxVEAHBgX6vi6OhAGWKTgmeKuWDHwMQEKIDADi4dhU7WzuFnS4FNb2mYIKCgoNTs6EKrAnCAKEUGU4n5angEJSNo46FsDdnsU7iHkcQVCg+gjl8bTo4BVge9r4pyuthKcpmZSjv4mk49az7VaA0bifC8yGBSdw81YcsTJKQJ+Psm6AOwxcqFMYIX8Y1Nx4ns6jGX7yktfyXbfS17mTM2eGgqkpX6BgTLftaxYSiFKXnrHjvzFeHc01ACZmcimJkqX1I4Hi8zA7hRQKNpOM0wgHB1VI3jIFDnz5vBGFNuXJ1P6fAkCpiwkcZaELRYSkYMnBBkQLIcoHecB/9dAuiyzHcP8De7jmc2d3Bzu4usizD4f4+PnD5KoZXhtg+fwMODo/R3T2DAc+wnwsInoMf5WCUgqUJ8myAPM9BB0dIOymGwyHybAggweHgAL1uDwAFYwm63a72egpwDoxGI6QpA8uOADAkSYKHrh1ixHNQTtHpdtHf20Ov04U4Psb583t4/OIl0DyHSPUuaoyBdlMkSJANBwAVyEQGQIB2GQQYDrMhaJqCUgE2ypVSkCQAkjJjoALgZmtGrhkWVaFk0EyA53CXO4PnlPExZxLjC9RwR1FYPOM8m2L5QII0FubN4zhC1Mz6fArJlLaFUAoyBZAX6QgFLW6rM20Xr7HC6PaGTspbYzcuIwSAZMqJlM76img3PNa/gvehylhkf6oYGf3z5MUz7zwOM5LZuQS2QlPykDD/PPYqyIIBcHZC9Cop1P/cgXC9N0wAVIixwAtYCgAX09dKlYBGAYoUAnlR6dC0jXIlf2RUIOkkwEgZJphubsRzJGkHoGIc+pNubeH4yhXs7W5hcHwAyhNwCmxv74CnCbLBCHmWoUOBwdEQ2E5xcHSE7f4Ocs7R7fSBhCLPOJhgSPVw53mOXi9FmvYwHBwh49l4DxRGKQTj4EhAmQClKTjnyPkIw9Exer0eklR5WEajEc70+shGQ4ikA9btgDOAZxyXr13Gmd2zEIwiG4ywtbcDMRgB4HoIBRIqcHg4wNZ2H4ypHZPU+AugQ5VhF5ZxlZsxhZYDOgDL9IUcEIkeUKZ5FNQcYzyI/ileqCMT7HnPGKhNu2mFgayu0Kz7OF5j3MilYqwIeOv0KUc+o7HrqbHvqVg/wjyHEGVDsDGQOXxM+DaJETkmua0eA1ihm3Tq73XRDuXDhkvg5nCLrn0760W3vwQt12zJVykLjZucYakdg4IiB+NM2WasfAlB1VdBlVWokwPcWDoSIKFcEXoI8MExKM/RR4Jz6IAlAjTpodtjADuLo24HAgwPX7qCg4ceRtrtob+7C37jBXTTDliaYAcMeT4EG47QGWUQPIPZQvaRxx7B7u4Z9PodbO/sIe10cHQ8gOAcHAy9LAOjFMeDATgEjvK8wBApVZarLMtAKUWv28XWzg52un0kVCAfjjA8PsYo50h7PTz8wYfwkq9+MShluPrIwxA5BxKKEecYIcMxExAUGGhlLIfa0rbb6wK5bjftqc8MSAAk40NIcnCtqOdMCb+2bDwm6hRolG1Yx9KyKotMYDsmTA6zLMLL6PYMBhKxwfAw4lMV9rRCcOqcqdRQ9mFCYEShTSfauCYAMAbBlPcmsewsgk1IcQIAQoAJ5YOgPEF++RL2tncAqgxWnU4X144OkaQpBM8x5DkyngOU4SAfYitn6KUd8DzH1tYWRpyDZsoTnTDFB/s72xgOjnB0eADBhuA8x5mdPSW+50OkaQ9JJ8Xx4T5Y0gFjypsvhECapuPvJjJDi/1KIM1zMMEgAJzfO4e008Hly5dwdm8PO50Orh0eKV4tBHLtudm6fhf5MFfeLUr19vjqZSQUQK5zMJgABB2bK4UAhkkGCoEOBzhj4JSDCTX61HgHCppyIIyQHrrb5LxwFIaFhzhq7+ciQ5rm7oeJDlhB261TPmwHxfjaBoUfLIA2biwEGHLGkQg2dt/qqKrxuFDKMUy0L0VAW/4oMBoAlOJMwpAmHWz1u3jo0Udw/fnz4ADue+QhpP0tnDlzDgeDY7CdLnpndzEYHuNdD9yLvb09ZIJjq9tDr98DcoFOkiKhYpzLIXKOFzz/K3D52mUMh0M8/PDDOD4+xjDLsbOzg153C7fcegsOj46w1+0DIkfuKB/9fh95no8J/WA0xOD4GAdXr2E4GCBhwG5/G0eDY5zb3cGznnsnXvNTP4Fu2kF/dwuZ4EhA0et0wECx1ekhYQw7CUNCKcAZhACuIkM+lvmHaoxSdZjUIAU4lKs65cYqp7xjgjL9F59Yn+pOSlvZWJOLd1Ew72nVsd0AJpZmLIFhRbQPLVsrvp6MfXIzHAcFhAqGDddWVX+Z9UNlCzWaFjDC7yQkKmdKseBgYEyAZkA3ozhkAIMApQJjpjYaAaDYTrtIegzX796IBy9dRML66PV3cOmJi9g9dw7HwyEOh0N0u130ej0cjTju/Io7cHDpcQxHGS5dvoJuR3lDczAwLsBZDsYSXD24CowEulvb6Pf7yIZD5FBeDM45cjEEy0Y4GgzQ6UxkJMYYjo6O0Ol0xtuhd7tdXDk4xM5WDwzKOsW5QAKBUZ7h4NIVPPf5X4p77r0HD9x9N25/1rMBRsG6HYBzjPIMyDPtpVAMhbMuaNJDZ3QAIAFYqizoo5GK2tWipQAAmo9DuibbAmilj+o/a4RdqfetuNy4eJ21N8c6tbeWD6blgeuBUeU9KpRZFZ9yjCerDOlqnfJReKmuELQB2IxeAsJa7OM+C+cTQPmEnipwFfkjgIxyUKEtLWCFiLrxPy7MzQDPkbIudhjFbrcDLgRGgwyi18U+z5ALAba1B9rv4FDkGFKGTpqCpwmGI4b9bITB5cs4PjjCM2+9FXkucPbcGTABMMrUgqKqvXvuuwdndnexu7OD89efx2AwwPvu+wAoKI6HhzgeHOLypcvo93oAY+h0OsrKY3IfoA786qWpWqxHFHu7u+A8w/FghCzPIfIRkk6K7euvx/5ohO3z57C3s40bvvgOHB8dYTQcIj8cgGYcg4MjdDhDj/eQUoZEJGAixzYbAd0uMghcy0YYCY6hjlHmTLncASgLHmegKtAKk+NOzG5vJhekxlK316BtiVkgSkneS1rnKsFT5YApwlp9pozJj5nZg2AeN2FSUfE44WiZ4jELp9lIRnWOgcm5ADRLssK7QLW3g2q52PDLUY5u0gUTOdIOQ5qkePChB3H9M5+JDz72KM7s7KG3u4ODwwPs7J1Bkqgt269e2cdxnuGLn/0sXBEcW1t9XLp4GSbXjAkOkTAdDg3ccP48PnD/A0jYLo6TIfLRCN1uD3kmsLO7jWE2wsHVfQgKJAlXh/Vqurazs1MwmuV5rj0mOXIukFKGXr+PnOcYDY5x7txZXHziCXTTDu549rOQixxCdNBJGBioCv/qdDEaZRCCQnDFB0cMSGmiaCwYGASgtwM22T9CG8gEVYodNanyFIoWN/HIu+FJGyIj+hBqIKNLWLiFdldIw1qnfAA6zrDbBc+yiRsIGMejmt2JeJ4r62JSjFkTQkxyRkz8qmdyVhp0fG48+5A1PonTdduGqzyZT7Ndmn2fjlMstFvnVORQeCazP14fwPExOjs7GB0egna7E6HJDSFxvwtt7WB20juHyDOk1JwWrpSAhGcAS9ATCdjxAbqg2Ov0sbu1h3seuA/oJLjzRV+Kx+4fgPEebrr5Zjz66KO4++7fx96ZM3jmHXfgYP8Ijz3xMLa3tvDdd30vjkcHuHDhAu57373o9fu4du0qtrb72N/fB6A8FlevXsWjDz2EZ93+bAw5x1af4/D4GOcv3IjRaIRuN8Wvv+PXcfOtt+DM9edweHiAa/tXVVxvmoIlDNnBCNkoA+cc3V4Xe3t7oDzHcDAEINDtJBgygVxw9Pf6yNHD7tH14BT4uf/rl3H7rbdjq9fDcACMRjkuXH8TjvYPcN/FS+ikKbrdLmg2AB3sg3W66G71ccOFC0h7HVw9PMBwOMC1wyM1nmmqLE5JijFFyqFj3JTSkaIDMIHMt8tJFZHT3gJ1yxQXsLluW4QCTzO3N2qg1joV7gYO2sVfajrQ1U51PKsylCnpwiiT41hZnWBOzXd73bqWKErLMbYVsBWPcaiCSWAvd9QUmny6JylHtAYF5dkXLrEJisg8oS5T61v8cwufRdzXRKiRjHLwhILmFMYmxS3yRKn2xFMgpwJpBoDmSHPlTe52e+iwLgb5MYZZhpHguPGL78QDDz8IJCl6u7t4+N73ob+7i15/C0g74DnHg488CJ4LfPDcGexfuoRXfue34/LFy7jl5pvx8KOPQNAUlOfIGUVHcAyPVdhvp9+BoAmOh4cQQuCJi0+g078VV65cwft+/15893fehcEoAxjDaDSC4BxZnqPT62Gr3wc4x8GB4hu7W30I5MiyXO2ShQw557h6eA1ndvfQ66Q4ygfgGQfNM3Sp8iokGQc/HGCntwVOOQb5ABwZkI2QdLvIcuAwy8DzHNs7W8gzlUuamvfElcKnjtxgaowFs8IhMjDBkGnjZZBsTc3GQs7uVkFF63mhS/cGKjm+dny6A89HoHrHsokh2M9zzSYBizaQjU8/VxfDCjdEK5UPSiny4bAglAAYC/C5ZuBUCw1eQcRWOgw8E8ELnwJgt6F/p1WKhjtZfUlBph77RSflJKHaqBISQyAEsLWFwXCIZHcXqRAYucJSlRJCkzFRYeB6dyaKlKY6CUFMFK5OFzjYxxDAzWfPYrvTx1avh19/x2/ihltvxNnz5/F//tIbcMez78Sjjz+ODz7wARweD3HHbc/Ei1/ydXjnb74bx/vX8Nzb78DW9jYSkeHC2XM4uHYVZ3a2cTwcopMmyIc5ur0eBKcQIsW562/ATRduxtXLl5EjxWOXLgIAzpw5g+1uH/3eNm685Va8+Cu/Eo88+hjO7u5iu9fDMNNWJJ6jp7/v7+/jaH8fly5eRq/fxVavj62dbW1d4sh4hne8451IkgTnL9wAliT40e/8Xlx54iL2Dw/R76Q4f/YM3vDmN+Gmm27Cs150J/YPD3C4f4Buega9ZBfD40PkYHj04uMAF9jd3cZWp4vd3hYGeY794RBDQYHhSBFzyoBOD+AjvVaAAR+C5QBLkvJZiFXzgk52n5oKZy5UEXTf9aRKsPZcDz5BfhZ0P3iWFYmszxBgsIC2x4Iq59UKhc/tvgkC7GkDIeM/S0wbKCuuZv54+JAQQiXIugYsn9ABj8DhE1CoVY9px9q8pfibMe1bc61qNzbrrIGxkczH51YBIYDRCEm/Dw4gZUzxKR+vLymFWuNgidrlDgCEClcaj4cxzScAjjj6nQQ9Hb6UIceDjz2C2++4HdeOj7F/cIAbbr0DV/cPcP25M7jnnrtx223PwkgADz38CHbO7GJv7wxuuvFmHI6OcHx8jK3dbdx/z73odlI8+PBDoJTi+OgAN9x4AdeuHYDnHFf3H8PO1jYS1sEgH2A0GOK2227Dtf1DdNMOvvll34irl6/gxhtvwt333AtKcxVqxVLsbG/j4OAKjniGNO0jSbu4fjeBEBwsTZAkCUb5CFwIpN0OUgCDbIROmmI72caIqxDo0XCEJEnQSzt497vfjVd++3fi+OgA2XCkEuHZSO30202w1dmCyAFBVQ7jSOTIuVLYkPbRzTMtcGmZLqdQyefKaMmoUBvSAGE5u5aBbIwq2cvMVf17XX7i8sJc53sWDGQ+RcNed+GNTT5temDGZtp99m95Hrb7mx6XoJBkdy01RCuVDxuUTo6SMXFx5rv59M7VmtpwAbPK2BbfKQLc1LoMk1g0wV5AfRRAB1CLSrj7EFWBA3kKUA5KGRJjsTJjxAU6QiAVQHeYo9vbAqMprh0d4ZFLT+Dxi1dw5wuei4cuXcY73v4B3Pnc56O/3ceFzs2AuAk72zt44P778apXvQpf/Jzn4IYL53F8fIhrl5/ATbc8Ew88+jAopchyjmw0RG9nV1nXmd7xIxsiyxLQ7RS7584pQg2BlCUQgmIwHGI4yrG9vY177rsfaUIBwcE5x3B4hMPDQ+SZwNmzZ9Hf6uLWW28EBANnHIPjAY5HQxwe7YNRiu2tHTx28QrOnj2HrW4fKVLwYY53vumt2NnZQpJ2McyHODi8hle/+rtxZf8a7n/4QVAmkDOKw2yINO+g39/GYJihQwW6nRRXrh2AcY5+2kWnk+CGbh+CUYwocJTnGPEc2eEhRK+jLe0ULGHIqY5ZbZEsu84wpEqyuYo+hbQRFY/NhMWrBDDxvq87JKSOUOEDY4B7lpFPsPJqSA1hb9lq2tBewTxXuXrHx8dQokyN85bcvgpRvCZyYHCEvf5Z9FJgtH+EAT9GnlBs7+1AUIqLly9id+8M7n7n3bjx/A24tn8Vt93xbPQ6XTz8gQ+i21d0+OhogBtvugFDKvD4ow9jh/UxSkfI8xxpt4NMZ7NzUDDKwBKO7ijBsVAbm1zbv4YzZ84gyzIwxpCmDI898The85rX4F3veDu2967HYHSAXr+HfERx8YmL2D3TVwYeTsFzDq636jXvmTGVG7h/cIA8z7G9tYX9wQB9pNjub2FEc4xGOXKd43Huhgu4fPWSUka2+hgMDrC11cFxBiAD0g5FLjIwpCoEmFGMRiNAcBx3KTIO9DKOJE2UAsio9oioIecc42T2Uwl3ngPFNbluGmL6Yz4X0J/WKh9jzVILv6VcEPeaD/MycN+gcj7RJO3fvZ4Gp22vRo6J8mG/zKYTrInnQ1vqGEswGo2UZ6eqP6XnsYm/mGiEQoBxgURQdAVDH8rb8fhjj+PS8RUcU4Fkaxs3fukdeGT/Kh6/ehm33vZMdHa3QTsd3Pee9+C2227DcT7CtcEBvvobvw5HR0c4yo5x/vxZDLIt3PfBe3Dh7AWk3Q4Gowz9Xh9pkuLa8AgppeCCIh9lEInA8eER9vZ2MRyOkOeK4PVSCp5TZMMjXH/+PO6953245ZZbMBgMdc5HB/2tXajzLiguXrqKS/fcp3YnSVP0d7YghEA2VMmAInscZ87sYafXQ9pJ0E8S9LZ2cdu33oLHLz6Bo8MD8CFH2u3gzb/+dly44QLufPaz8MTlS7h05TK6nT7Szha6nQQjMYTIc/CEIulsIc8zHGYZOgIAy0Ezjm6ng4QlyFiCwyRDPhQYpTrBL6GgoIWkzTagDpNZmqIS4vlYdNvTnnsTQ3cixrzK5IR5bpgh2M/JyKvKmG10ZxnACsL2FCHHeK6NJ2dZQlAdD32aqO1rOx0Mjo4ASsFFgEAjtIeDcYz3ehQAtKE4UdQSiRC4cP31eODRyxj0eyrROqE4PlIKyHvedy/++Td/Ky5dvYjbb74ed73yrv+fvfcPkSQ98/w+9frl5XU4CMJBkuTmpZO6crlcLjdNu90MQzM0wzAMYhCyLIRYhNhb9vbGAi/LsizLejnutOfzsixreRGyfMhCCCHWQiyHEGIYi7mm3W6Gpmk37abcNEW5XKSTvHQ6ScJB8PL65d23/McbkZVVnd1TremZHXnngaysjIx4M3687/P7+T784Aff4Zt/+Zf0ugOuXLvC4cExvV6fcjHHGEHaURRZTiJT5lVNlmXoNCNUFUJp6toQcGiVQJGhZMroeMxoMuL6q6+ymMaaxtu3bwKw+/Ud/uqHP+IP/9k/o6wEdWWx1rKzu0VVVVjjgTr2IFSa4Dy2MgQR05B9gH6vhxIa7y0EiSLgnaHCYY0BKcilprvZQ6cJ5aJmUc7o5znWBAbdgnlZ4suSvIhpzkmSgBR08hSEonQxUmakw7qIEia1QCuB8o1TWURo/V9VlvfRZNQqzvv5r9b0tnv6xz85w+Ql/tan1viA0wfaFvu2+Oeiqb8Iq8r7y/3hp7eJpxn6M3MHz1uIz4uOrO77MuhFFoFYYb7E0LN3DploFtMZQQiyrLjwWDo0JQfnTkHGwSMYb5D8/P33yXsD0k6PUloWBIQDX+R85bd+C+kDjx7sczA94O2vvM39Bw9YHMy59PpV3r33AVevXSFVCT++/QuEEFy/+gp2ZjHzCu8dQkpu3r3N5SuXUZmOhdwqwzfIHcfHx5SLkuvXr+O94/DoAIA0K5iMx+zs7rC5ucmT/cc8fPAA6yyz2QwlFcPhEJ1oOp0CKWTEhCegEk2mE0IIqABVVXNYHqKEJEsywPPB9ICsk9EtCnrdPt4b3nr9TabHI27+9U/p9wa8uXkZ6wPH0wXz8YwkS5FaYVxA5hlCFQgRqJxnVpZsSo2pDE5KghKoLCHNEnA2YtJ7olR9IXibj58+2cjH+d9qmflZTPi1N+ipVLWPeioXdJj8qkrhv4N0vlt2C+t9kWe5bAq4KifWpTS9+EldTDZexNgN4WwKx2oqy98CiVZOSUld1xFO210wlSZAkBIRGmfi8hpCNDyCQMroMGNuCFpjRJRrKZKty7vcv/eAy5evYOoaMy/5zs++z5Vrl3HGUhQd+t0uD+4/Yndni7KcM53M2Rz2yZKcTtYB56iNpbKhgWTPkWUNIWB8IJcCa1wES8GTZxnlfEG320UqRVVVFEXB3bt3AY81nrxI8d6jVYqpHVW9wFqLljm9Xg8yialrCArj6gibK6OxbL2hthV5WqBFrMfTaLyINSGVLVEh3hudKhKXUNmYguyCJ3hPaMBO8jynrmuCF7GBLwplPamWBJXig8c1Y7ngogrUPoIm0+LvHAkP4Tk9s1b1yIvqiR+XLfIS9dVPtfGxSqedM8Vapt6mcrbvzzUOPoTWdm9dffCrYz8v0tCmiX2YkXJu7I9EF4l8LAu+zj5+1zBf70UsYhYCL8X6SbKmQZVRTWqPbzpk+9jDQ3uBCjHP3ivILl/iaDKmXBhUqun0+ywWJZlOONp/TJ4VBGu5vnuVh+/dQ0jJdrFN4jK++vZXGR8fMzo85srmHoPugPl0EiMPIXZHl1oh8hSnBM4LpBJI4ZAKfFB4a8jSBK0kKIG3UZBJAouyYjgcYoJEF312LmsSBUmSoHXKvKzo9Tr0eh2cC9TzOePjEbWNaU9KKVSWsbM5jOmtQlPOZ8zLmtdefZ3HR4fMpjVaVHSLDo/2H9Hr9di9eoXx+Jj90WH0lrkK600MTcsMj8c6S0qCSpImRQtMluBNCaZGeoEqJVlRkHiH8TAngJVY5U8dLCGG3GXTLyQ2q2KFYT2rEy2AXK6zi5Jfs+/zIh+rURrfpCe2Amq5znkR/rr2BJZpMqdr8xlrZ3Wqv8B1r96jVQG7fufza/SzyMevGrVAJ6J1KjWRgtiMjuWzX/YEDRCUQPiLpraepWcWtIZmdbTz+/knvZxry/qV83O0NcxXjZSPY26el6frUlEAoRVhWpIN+synU3SeE3xYH91dwyhEiPxOLvHgA2loGvypQAgKjWJ0dERZJEgbSHJN2ikYTxf0egMGwz6LyZR7dx8w2NskC5J7Dx5y+fIlRJB0fETZGx0e4UKgP7iKcIEUycxakizn4cOHLKqKr3zly1B08CEQvAUKZlWFwLN3+RJKKW7evMnu7i5pmnL16lW01hhj+Mm//Bnf+hff5vKVaxjjUAqMtXS6Q4KzOGewpmJc1/R1hI/HC5Kmma11LvbdQDAvZ9FolgHlNP1+H28di6qkauRz7SypTlBJxtHxmF5aI1NJNy9wHqyr0alCCIkxNXWwXCt2OFocYxAELdFKEUSEDa5dQKPIlCIodwrO2HY9b95DM+1OZ8NzZNSKLvhxUTvXbHNCyoVGX2q+9w0E8IVHXDdRW37R8gfBaQOD1UPbi36RK2gOXQFq+LB7dgYR6++K8bFk5g09hcC0zLVtbk7LK1f3iQeuG/zM9osaLE9BhH7Yvh9mFLwsZn6RcZ4Bbyd8ACUJwZOmadx43shYSrO28/nyYERsPUqoK6TQZEpEg+LoiOAdnV4HkaQML1+id+0qdW14ePcDjvafcPXyZWSiMdZy/OgJ1js6acbOYBPnPbu7uzw5OOKD/Ztsb2/z1qtvMJ/NcNZx/+59XnvzBnjHeDLDzixvvf05rDGYusS6mmAtwXmSckDrRQAAIABJREFUvEOadpES9vcfobVma2sL6wzBQ6fTieggXpJlBYlWmGrBYDCgLGtE8Bw8OeLg8SHgqcsF3bxLqnUsuhNgrWVWLvAu4In3sj/cZDwa00tzalFTVhVF1qHbGyCkxAFZt4exhmldkRaKS6/sMTo8ojI1nU6Hg4MJzkzI0wwpJaPJMZeuvUKSZtgQUNaR4Cnrkrqs0UqxN9hECMH+fIb1HqxDJhkqTaiDWTIbCbHnig8RRat5ptBG/JZ74SQvlMYl11kKz0kFCef0D4RYTjUR4mmIdd2ZL+wVWuElq5/XnVN77suvLs7kRSt+VsdokfKeOqVzg35meHw66eQEOBuVP0MyQouvdlo+82yFaAzo5ji/Rp6tI+sgUWfmhXduiQS3BDVYFk1HmXMGjes8KXnaqbq5lrVnoORZwJRnnacS67umP4vOn9d5wJV1ckpEwAiZ57HIXAhsVSGTtPntJp2q6VTeRmxiDCHW6AlTI5MUESS+qiiSjCxTjfNpgfNgVEadFHSyBCUleZphKsP+/j5f+tKXqMqSmzdvYn1gpzdkf/8BVy9fo5rPUKluajM0V69c4Sd//dccPO5z5ZVrHN+dkKQ5KElZ1rzzzteZTmK/j9H0GOFgsViQpn0W8ylVvaAqDUmSkGUJDx8+AhHo9wbUdY3xim6RsZiMUEnKZD6n1988Ld4WAiUlzCxeOhamQtqAzROkFKgkRTkbMwaUxHuoy5IgA1r3cT7gnGNvb4/ZfIL3sefJbD7j1Vde5e4H79LxHYwyGOvo9/uUi5IQAlrG5of70wMSrchVRPyrnSPgKYoeZVlR1RUEj9OSTqrACURlGl9XaKZzaFubxzljXeziHUIjFFSUX0JAiKnGBLDKIhso4Ofx1BdxZglO1f92hrbrrB0jsNLg9swPrdEFA+vX1HlZIWCt0bVOdqwbb902KcFZEPIsr1jZd+nUf8lZOp8O42PjYlbqhxbMCnH6pF4kf7Q5dukFXfEE/Z3D5z9/ua5d3G2n7GYnoVdWbOuh0vG+yYDWEUfdhJq8X4CSsWbBe3724x9HIekdVWlxeH740x8jheDa1VfZ3N7i9bfe5IPbt7h65QoPHjzgzr1bzGYzhsMtrC/ZP3yItZbFYsGrr9/AWk9V1dg6FvL5ag7WRchC71FK4YF6PiXLMqSMsLZpmjKbzbDWUhTtdpDOYhYTYsHggrrqYquaqqrwLkY4tE7QWuM8KK0oihxaJHPvqaoSYz3jg2OMqdne2cHWNdV8zuHxEUdPDrh+/TpSgVYpWkqSJItpVfMF08Mxvd6A0tQsFguuXL2KEgprA84bhjuXmE+PsUoDKbX2BKXxiYqFgALwEZljT+RY4ZmxoMRiXMAlMeUjQUXB3dqTrftm2Sukecai+SoGU1D+BZwtL7KMzu97PvrwrLEuulafxTzXuYmViMxXtucRXuBaVh0fZ3/7Y3TKfUZ/y/RcObVqaISwYtiv2efMoKdKwRk5xanD7KUU635MiG8vnc5f6lJOtd6Uc0rUkod5RJGDgeAsWZIhfGBWzVFAXnRwIjCrPaUIFA7quqScL0iThO3tbXrdQTQuspSdrW3++kc/5Lfe+W32958AUB+P2dnbpdsreP8XtxgOBmiteXjvPq9cv86tW7dZHC545513EE0aVez1IXA4rLV0h32CtzgbMLZiMOgxGk3QWgMeYwzOGbZ2LhGcQyUJu9s7zE3Jk6Mpqa3ppIpEpzhTIZFkaYFEYIzFe/AYRJA443AuRPBNINGxjubg4IDNrSGXL+/hTEzpsq7CNClhj/f3GQ62cM6xKGuCAu88zjukTgi+xhhHb9CN9ZDWIowlSVKC1JSzOTpLKbIB1SICupTCouqAUBqlJVJKnK2b5oieZapskkdUR9E83NCso4jnG+1QEWXUC1kVF+HtjTq0/L+db6vHC9av7ZexRtcFSYi1M8sI60WvZTU+c+FjXg59qPGxsbHxfeDzwOzk5ORSs+0bwD8C/u9mtz8+OTl5t/nuvwD+IfA3wO+enJz8jx9+GidErIdIgZVn2rzHW/SM013LrNd5Mc9vk6eGBpxl4OcY+0emT9qIaRnvR0zfUDKmvHgcPjT9JASnL8A38LqirMB7Bt0cnMOYEp0mZJ2csrLsP3qM1JJr117BW0MgkCYJMknpdrrM53N++vN3+eDeXb7xjW/wla98CQgMN4fRG5RnpHnCbDYjyzL6vSHbW7s8fvKYPM3IEs3utat0ig73Hz2M8IneIj1IqSL2edYj+MDxaBQV+/kcVxuqqkJLFQObSiKloDQ11aIEKbj9/h3yTk6n10EJyWwxbzxUKb1ORNY6ODikLEvKxSIWrJsanKfXKVBqQPCOXq/H5UuX+Frva5Rlye3bt2M9kxYolcQ+H0KQ5hlSKibTMVol7GxucXAUO7MnSR7D7q4kU0l0CGmJJDapMmVJJ4sdbhfTCUoJMpeQioTt3oApltLWlKaJcQsXrX8ZU+1ifm/0okoBBIFDLPN6ETFF4ROjZ0QNPzR6AC+m5bd8+Jyh4wnIIOIykuLiDHptJOXstlOb5DNz5KPSJyKrGkdZ0+MTL0+foW622edEBqOTc4UnhzUKyvNoZd4/1ajzZdA6efFxya7zxesf4Xdks05bFCXaAvkQ01Ta1E8ZJKopoFdCkCWKxWxGlqYIoai9o7IOHwQiGCKCryAvMpIkYXIwYjQ+ItEx+myM4fr1V9FaU5ZzhsMh1SIWkz+4/4g0TdFaMxgMECKiTJVlifeBfq/Ho/39iGKFaMBNBMZLrLV4G+VCp9tlPJ4SrKXIc0TTvTw4i3VwPJ7y9t42XniSJCFJEhQi3l7vUFIilUI0MLjee0xtkKmkyFO00I2e4yIylvOxlqOusWWFTzKGm33quka5KDOUVEitmc/HFEVGkmTM6wXj8Zh+v8+iqnE2xP5aoxH9Xg9hBQZPqhUieEpvCU7jiF3dM28IQlAr8MEinKAQelnrS2gjfxLhYs1OIHYwDNKDVSCabvSinQdRb/zbcCF/0r/5qyZDLqJB/AD4NvDDc9v/m5OTk79Y3bCxsbEH/DrwHwJ94P2NjY2dk5OTv3nuL5xsxK6jDa3otRc70bVMa92DWK/EIEL0xAuWcG+rBYRLheSiz3ZdGOd5NR+fYgoCLKHJDfTI1mATvrESPZL4fDKtKIouSkI5H7O5ucmsXHB0eIwL0O/3UGnCfDGHIEgTRUBSlxWLxQIhNVevXuWNN95gc3OTo+MDvvvt79LpdPjab/4Gv/mbv83du3fp9ze5e/cut+/c5bXXXqOTF+zv77O1tcX+k0OGwxqdaEwV05UQEfbPW8fYGIyp6Xa7TGYTkiSlO+iT1BlKJUvUj4ePHvL2Fz7P0dFRRO8YRiPMBo8nMBhsEkJgPp8ymYwQKJIsp9vtk2UJt27douhk5HlOrz9Ea8lkMqY2NX7qmS9iQeBw2MMYgzEOIWPjK4THucBsPl/2w5hMJqRao6RkNp9TltDrFI2QMkgdYR29qdFKMCkrcpmQ5TnWGMY64EONappF9ZOcvgssgmNqTeNR9XghMTrgQkAFFdObRGgQs1pm3+adfkIGyDOUkqdWz7PSpi443tKwOOMFiY4J32BCrk33ehE6t+Y//RzgV4p+wMcuqziTG73aFO1UwX324QJOHUNnk9hPaV3ahGybsj0tn1b7jVwI2/959Myoy0cb9rm/8xLqHZV3SCmwgBMaFfPZYLVJYSBuN7EnRqqgnEUe65DUxjGaTEmzhP6wTxEEpQkEoZBS4YwnSRRHozFlWcZgixBce+Uq23t7PHr0mIODI/I859GjR7zxxht8//vf59rVV5lOpzzaf8BwOCTTKWk34fDoiDsf3GY4GGA9BOGjA0pJRAiU9YLeoEuv1+P2zTtcv34d8GRZwWw2I0jo9wve+1f/E++88xscHU8wDpyzdLsDjFtQ146sKEidxIeAtw5rDSBJpWY+m+Nqh0ok3W6fytRY7+nrHt3eABcqjg+PePDgAcNhH4A0TXE2MK9m5FmG9+BcjZKabtHh8eEhg96AtJtSzhdsdbsYbwmAEoFgDV4KsiTD4gjOolRCThZroFLJtCxxPkboN6UG28wNqZoAV0ykE03djvfghItNqAMI0fSiabMFP25G+6y0+vP8/qM6yNr9V+WUWAGtWK6lC451kbKBFzy9i9KHXvbJycltYHHB8f4T4McnJyf/78nJyf8BHAKvfITzezE6l1v71GvtISJKkpVX8Jx5j5Lm6f2e+fo00Etg6ABWiKYZYkynipK1qaZqXAuyYfRKiSj7nCeg8FJxcDhmXhscsRlRXddkSYrzlvliwWgyZjKbEpCUi9hkzznH8fExOMHv/v7vc/3GDf70T/+M733ve+zu7jI6OiI4x9VLl5nPZpS1pZMXpGlEhnIOOkWBcbHbqw+CQMzF7vV65HlBkqTMZnNGoxFlWUbjxFuEUKRZigtQVVUsZLfRQ2e8YzgcUhRdQnDUdclkPEMnOTKR8fNszJPDA/YuX2Lv0hW2t3axzjIaT5jNFlRlyWQ+YzqdUVU1k9mCeVlT1jV1bahrg6ktaZaR5x3Gx7GY/srVqwwGfa5cvkKW5SRKNZ3ZK4yzhOAAHz1cKkF5iQ0OYx3GGkKe4FJJLQKVWWBNjRSejhAMlKYQikQIEhGRXnQb7RKeIAVBeOzS4IxP/RNz7VxwLa/bT6x5PZPCyvu5axNNVse68Z71Wv8bYf3rM/rI9Cslq1p6AQdUWxvaRjqecpLBr46T7Lzx8REj9BKPCwHfXlM4xydCK6tAC0lR5FS1oZyP6RYdKmMpyxpUBBYRMhZPB6FIE4WQitrGpnvOOZyL9Q1KKRbljO9++7vcuHGDN998ExCIZr/ZbMaiXKCUQgYoy5KtrS0AqrJCahUjFVrhvY+9qQAXPErFBz6fz9nc3KSua0CilGrqcxRHB8ecQMwGSDKqOvY8scGilEZKha0rjDEoIRBNk8EkUTF1LNEkiSJLC7SOdSptdC725ABrY82JtRE8QWsNIpClGVVdL7eFIBBN7UBtDCFEoBbvQ6zHkAIpNSE4gnV478llgtZJU59Zx0wlH9AqokemWrMIjiq0UTKBl5IgAt7HSM7qvJFhFeDkE3SQfZL0rDX+cURCP0b6KFzldzY2Nh5tbGx8f2Nj499utv094P9c2WfcbHuKNjY23tnY2Li/sbFx3xgTN64K4nUKR9sRfPW9CSmiVPzc/r+OzuezrjCnVYa+1lo9z4CfyZCfkR/bnvM6JerDjKZnvc6lVoXVz7+Eorb2tIWMUaEAwnFalCyAIJBVRVdqdjodZPA8vHeP/YNDnFe8d/MOVgg6vQF5r4cnhnNLY5FJgU5zfBDUxlFVFQhFp5NRlXNMVVLWVVOLUfB7v/d77O7usr//hNF4yle+/FW+8rXf4PLeFdJMEwSUZRXhCLVivqjodLtIFeFvkySh2+2Tdzrs7OxgnENqTfACrVIGm9vMy5q6shw8OWJ3Z5f9xwfkWYcgPNPplOPxiCcHh7x38z3KqmZre5vPf+nzKK2oKkO/32dnZwetNUVR4JxjNBnz+MkTDo6e0B/0GAwGDHoD5tWceTXHe0+WZQyHw2X9yWQy5fjwmNlkTN4pMOWCO7dusVgsmIwP2eoXXLt8ieCrZaOty9eusrW5w2wxp7QWKySVJd7rtEDmOarbQWQJJngqaxmNxiymc/pZzt6wh3CO+WgU60S8RzfvwdXLmrjGDKeNbD+Plkr4R319hPkb1rxavvHU6wLUeprPvJ6x/cLn/Tze8Bm9DHq5sqrlvc80HOV63r5Ky+PWyIu2tqOZky107+qcCiGcKTiXUtI2Fr0YPaMAd9229ppbepah8Iw5H0I4LVhXKvIEuXKPvI+vdXJ7Xb1JA7W/Kv8qYxBaIVVzD0JoO9cBoUnZdgjjqeclN25c5/69D9i9com7D+8xKysqZ9jc3qQz3InNZasFiYqOq7ouKTodSmMpF2WMElQxYr+ztc2snFLXNQ8ePGBnb493fusdfv7zn1MuavI8pZovSPMOiUxYlDOyNMG6mt3tHZRSFEWBkJoQAmmmSZRmMBgynU6ZzWZsbW2htcZaGx14WcZgMOBb3/lL/qN//+/z3nvv8ujxQ/Z2L5FmUXEfj485OjrCOMPu7i6zckGwjrqu2d3ajhEMY/EErKsjRC6eXq9DCKLJHOgRhGA4HOJcRPAqyxLnLFoKijwnyXKMjc9JSYlzjixLeLK/z2JRkXcThJBUi5JyUdLr9On3++AtIRiED8hgqRSUpib3sJmkdLykozQEQeUdo6qixECRMfFxTgYC3gtEkKgmO0M1QS+aCMnTIFJP630fC61biy/DuD9/PecdZUSAhacOWyPjQvv7Z+qjVtb3+feXSL+shPvvgH8XuAL8a+C/brZvrNn3ZN0AJycn3z05Obl2cnJyLfk3kya9o/FWBpC+wdpumIz0Mf1BiIgFLdt3GubcvJ7pWVxl3Kvwh+2NOOMlOTVEll6lVaNoZZ+n6QJ1KatjnH/wH3YNq6/VySLEaUj/Il7VZ+13fiFIiRSq6cnUun8DBI/2gVQqMgEa6GQ5Umt6/R55N0dmBU5o0Bqpkpi6oyKkrxQCneYMNrfY3Nlt0n8FSE1A4r1nMplQzisOD4948uSAhw/3efToEaau+clf/4Rht8dgOMDUlt5gQF50UFLjXcAZS7IUZhKkwnnHaDRhURlAsLOzy+6VS8yqBc5alJToTJPkCb3NHi5YJvMxi9mc7b1drr3yCpPZhNdevY5KFA8ePuDevXv44Oj2OvggKCtDkIL9/X1m8wnW1fS6BTubu1hjqY1BBE8iFBrJ1rBPt8jwweKMjXOfiIgjE02qFUW3Q69TNIhdgXJRUlULqnnJoNfn4GjE7Zt32D98QpoVaKEQSXy5IKitx1nXFAMGDILKWbyUVDjmdUldO/pZzk5vGA1Ma6EpQlRKxmkRIAiJaKB6PyzysVTCf1Ve56m1tNbQOn5xYTqvmF70fD6jX4Zerqz6t5K4NomIOkFET6sX0esaPa9++fzaWdLOl6dU6eco16GpiWi7UUMj65ptvpknq5GPM72vnqs4PMMrfE6mLP9flQ+rsucCJIQ4NSyce7bCd1HFbPX62mMShXcGnI96Q2wuFe9fXaGtYbs3oN8fkCQpP/6XP+ULX/4Sm9u7HI3HFJ0unX4fKRTGVxhjUUmB1ClaCZx3MXqAaDqHWwSCPE24v/+YvZ1LjMdjer0eUip+9Fc/ROuUf/LPv0GWZfz03Z8jJGSZIs86KKkwVRkb+QGueY4q0SRpGmsunOf48AgXPIuy5NLeHrPplIPDQ4KA8fGIvb1t/viP/5Cb7/3PFEUPnUjKaUllazpZh6LXQSuNNZbZJDYAHAwGzMsZk8kkRmQUVIsyArU4hwTSPON4dMiTgwM2N7fjfkmE9x2Px4QQ6A/7BBkrKtIsRREw3jLoDalqQ783RKeKm+9/QGVqet0uveGA8XTC+OgQIWMH92ANRVGgOzlGBEJZIeYlifckTpBIRbfbpTPoY5zjyegI302JAkmBiFkNSI1oQFCkhxaidx3U+5m5yYeKsk81heWfNd+FFUfYi8gUsYKU13x+2fRLxaROTk7+r/b/jY2N/x74efNxDPw7K7sOgMmHjbcRTbVoQAhx5qyW+bMSsO6ZE+mpzet6tuAibHSjOQVcxAdvvEahMZcjXwvASli7Ub6g3aeZtE890Gfc0vPC4LwnafX9otQKn/bUnuW9vSBTXwq4lX1iEaVc4lnHBS8pbEVAsN3tU03nHB4/4biak+gcdMbDx4dknQ4yjd1hax9LlrM8Z3vnMrNZiXWGebnAO4fWMZwspSRRirIu6ff7JDohdwUCQV3XCAm//uu/jlKKt976PG997g0uX7nCaDSiNhXW1QgVe12U1lEtZmityWWH2lmMNcxmU6RS5Hke0acWCw6dYzAYNMVtKUeHR0gpuXrlKl4Ebr1/h9l0ynBzwMHBAbt7e+SbOzx8+JBiZwvnPMZW8fy1ptjZjsq+cwgRYj62h2o2R2nNcHsLay137t5FCMHO5g5bO9vkeR7TzibHBBs9m963Xs5YDK4kOFuTpAmql/P5vbeYTefMywXOWCyeTtYhSTTOOaytCaOSNElAaqpcYYPFehAIqmqOmM3YzAp6eca2SxjXhjo45haC9NGbKCQBf9qjYK1ecBZ1Z9lE7WXTRZfKi/z4+XXSfhScGec8CMULgVKs3puVz2uP/swA+cj0smUVbCynQ4T/FE/xzfg5emGfCcnb0ho+LKXEGfOUp/J85GN17Kfm4HkDZC0955xWj5MrvQV+mTl53she3fZRaGVcpTXWWYTwsZmUFCA9wnoSpVBSEbzFu3gli8WCG9dfZTKZk2QNGiMNT/MOgaKqLVkea9ykh+BjzQdCAY1xiCRVGaKjmYwnZGmB845ur8vB4RHWWq5du8Z7773H6OiI7NIlArYxGqNXvjKOVAuSVOEax9l8PqefFmRZRpZlTKdTHtx/ENO+mudtrcU7x/VX3+D/+RsoihRrLVtbWxhfs1jMCM5xZe8GP333DjuXtoGYxjXod2PdS5piHXgdmrE9znmUFE0zx4CQYEw0GqWUsQaSWIfYGsJaSrxXsbi8M2A2n5BkGT5A2skxxgHVsgA/NKnczlis9EgXQHt0a0g4H6eht+B9jLTohDwvCMYwPh7TVSlBSRKVxKncoF+FENeg8lDL/18mXl2YVvXUFy1IX6Z1Bj49xsfGxsavnZyc/Ovm438K7Df//wz4q42NjW8Si/j+PeDehw54soES6hTZM5wy2tXCPi9BiqfzXJ9m7A0DOkfxHp4W57QGhhBNKJhGSQTa3MX2/SyfFis8+IIPZVUYNO+rONC/VPivGcd7fyqoLjrOmv3OXmIc24boCYlWm0MESeIdIsnIRGxoNK0WVLVBJQlJnlLWFVInsRZCyIhmESRKgFKaxWIOBBIpSXtdgg8sFiUEt8x51TplOp0iZYmUkqLTQycKnaT85Cc/Ybi5ye/83td59OAhm8MhRafAmJr5LBoz3luCCCRpTgge19yjJNXoRNHJC5TS2Dyl6Hapy4pO3mW+mCFE7P3hrGW2KHHWcOnSDp3XXuXWnQ/Y3BzS7faYL2bknYzQ1KhsDYd08oKyLEmkorbx3imp8XhU0uTENlGI4KDb6YEQse9GXTOdTqOXqNOldBZvInSwdYZESRKpm/zbmI+cCcn4eMRoNMKHQKfbx1YVVVlirSbPUrRKkUqQIDBComTAITGiRgFZlhMSx2S+YDqdcWV7l6KjEN5RVWWseUmjwFlt9rd+Sooz7y80J1+EPkm9/LxNEp4u+r34WGcVyM/o46WXLatOODmDtLkatziNToholPinDdOzAteDSs720GjHORcdDz4sc/19E7lXK2lKZw0PTo99Jj3HSbb6fyOnWrn4S0H6rp7H8459ASdZa/y156KFxAPGgZZRTmkPikA/L/De8eTJYawX0JJesYUVKfcf3mJnd48QDEKmEExUpHXKlZ3XOTx4TKIU1lmQElcbdB77IDlnMC5hUcU0rHGY4IOnXCw4OnjCF774Re5+8AGz8YS3Pv82CTAajXDOo7UkTQuQgXJWEbxnsDnE1ibW6OGQGnq9PiEEqsrghWW4OcS42KAPJCqVJGnCf/wf/H3KxYLRZEFpHb1en8tXLjMbz3n8+DG/8Ztf5Qff/wFXrl5ia9jH2kBZzWNH8qqOxfrWYkwFSCySXq+HDZ7xZMxkNOXatWukA81oNGIymzJMFN4FKhtTf711JEojZGgcX5Y871CHAL6pf5mXbG1vI2VACE/QGuscdV0hraKbZXhvqUIsYPfWoqWOAcLakoSA0AnDbo/J6JjgAyWQS0mSpDFFEJaoy6dO49VpFp5Wyj8OXrzmt9dSc64f+bdWPzbXdP5aX4Q+1nvDxaB2/wfgdaCzsbExBv4p8PrGxsYVYpj6GPjPAE5OTv63jY2NnwCPiXz5P/9Q9BDgZOMEKwKyxXBue0rAclsQkrjkIzKVYAVaj1PvU3zYyVpUGu89Ugp8ez8DnHZR9U0U4TTVShBzRgUy5tAFd+aWCSFwrEB+nkqfs1YTcQx8m+rlm88R4tQL30R4ZDNRVkVaZKpBrNyb5RjxTpzJDlnDrNdNOnEmUVCe29cvQUJifEOR4VAeUlcjfCBLBFIoRlXFTElkp6CTdwgBjqcRbq/2JWkTtlZCo5p7q6VFKh29PrWh9rGw2TqHtQElNWma42SFDYEsSXAiIFTMbfjeD77DYjbnD//ojxh2e3zta7/Ob3/9HbIspz/oo4SOMLOLBQ5PWRqmk0ns66EkaapJpaIsFyAlvU6HKjg6mUCrDr1+l4PH+3S7HcrKMJktePz4CVmakiWa/YeP6HUKUqV5/933eP2117iyvUVVLzh+MiPNMpwMUWABwgtCEFRVFbvEBoeta9CKTrfAE5hOx+A8u3u7XNreYjQZU2QJoXk2bTFiENGQMlbg5yVladnd2eKVK1cpqwX3Htxns1cwns4wdYmpU7SSZDql9KG55uitKtKcuq5ZuDpCTfY6BGu4Oz9ks9sn1ZJtEqxzTIzBIwkqRgGdOE29Wp1xbUQkhBCRa0VMsQvn9hGsRwdadjB/BjNeLqvzTV5fhmPmgkzWKxqYUIBwmmMOZ/JvRew21wjA+Byj88O1ocqmLjYsPze/sHI+69Tcz2gdfRKyaoOzxsHqtFvq/ILTiPR5R9m5Y9Y6gc4pDG1xMc1astZG40PrMwrGmWaFv4xhfP6c1sqNF+8pslpP+TLhgc/A4jvfpPhHXiNDhFxNhMKZptO3NQSdIEWCTDRlWZL3e4yOjun2CkRowSJijc3j/SOSVCOVXKahtI4flaax15ME4+J3nU4nRiO84MGDR3R6Az7/9tuUizlHR0fsXdpjPDlGhmjMWWsRKsp87z3lokYWaDC3AAAgAElEQVQpSNKU6viIYmsXWztqY8mynHIyjg1mA8znUzpFjhRw+4M7FHlBQFIUCWZWUy3mJCJDCUGn3+enP3uXgG+yCzSEiuBDdNI5UEWC9B5rPXmuMSbgPSRpQlWWMUvAOagjH9Jak2UZk8ls+TyifhVhgpNUM51OybMuRadgMp7E77VCilh/U9eGRCcoLQhBI1yD2kbABI9Usqnp8CgkWglC8NSlo9PtUeU5hkBZ1bGFi3RRR1y1n58xzdo5fHY+/4o6g9pTX7Mk1zoDLzpsc2+kELGHyEuOfmycnKxNc/1E6df+3q+d/IN/+A8QgG5QKqQQqJW7GUJAJQkE16TP2JgiI2JOoGqgQHyTU6p1Gj1E0DSEi/mKUsomXMoyWuCsiV03BSRNUVfsDKoahTmOYb3H1nX8rukC7Xzs+Bxk64XySOdOPTPQoEokp2gWIRos3sfQoiBeh2jCjbGLqCBNkphuoxU+EJXPpYEm8N5FxqgVvkGdkMvu1OfSO1YFkxAE11pfNO+q+Vo06W8NkxlsUy4WqGpBHhyFt6Q6IQSHEYp7x8eIzoD+5hZ+ZjieHtMb9nEhooEsypjJkKYZ9XzB9Vev8O7P3+XNt15Hpwm9wTb37j/g3Zt3uHr1GnXpWVQ1ZVmxub2FF4FOmmBMyes3XuXw0UO8M9x8/30u7exy+84drly6zN0H+2RFztb2LkVR0On12d3dYz6rKMsZUilGR8cM8pROp8N4PCZVEiUle3t71HXN8fiIJE9ibm+ALO+ge1sgJdOF4YPbt3jr9VfpZjnf+dZfNk0Rr5LrGKLe3N7kwYN7BAHTSUTtGmxusf/okBrPKzuXGM1GbA03mVQLtFJsbm7hjKWqy2gwTWYYU7HZ30SnCWUVkUoGg0FspGgcWaeg1+ty6xe3SIRg0OuglKTb7/H48SOORoe8dv11KmtZzCOiSkhSjKlJhETVBtHkLgfvY56tFHgtccHiXYWeL+jIhNe2duikCbNZydw7Rh6C0oQspzbzWGel4jrx1i2FvyMQPITg0VIukaLimozzbpnJfq4wTxAbJrURSlaV++ZvmwKw5A/+nMKk2h4+p46JZd59m2d/Pt8+0afLYZVW1o7QEl9Xy3NeMuQmNB1WjwmxP4houncFmvQ5ooBti/Hbe3Omtqy5r+fpT/7pf/m/nJycXHvqi8/oE6F+/9dOfufr/4gQWleYiVF5ARALhp2I8OMBCM6jpERotXSItc85AFmWUZYxuttGNqqqIk8zLDHvv1WSpIwIdsF7FNErLNNYaLtEecJjnEWJiGrUNh7zbS6KUIgQjV/pwYlW4W4jznmDqmSbuR2b2okgYnfw1knmAw6H1holNFVdk3Ry6moRZTVtNkHbbCGmKi3BSs5lATQ35lRute/OxWOXWqSKHvPGexgTQSVpd8jk8UP6nRztPZKAUClSBSbzksl4TH9rl6zImUxmWOW4ceN1Fos57/7s51y9fBmQ5Foxm5fceON1vvGNP+X7P/gu7998D1d77tz9AIJGKcWVK5epTcVkMsZguP7KdZyp6Xd7TGdzkiRlf38frRKyPKWcV2xu9zHWoprC8oiaFTg8PiZNMrIiRwm4/uo1fvTD7/D6G19gdHiEF5ClRUzr1Ypu0WE0GpEkCQ8e3OP119/k/v371HXN5tY2V69cZjweMR0fIdFkmaY32Obx/j6vvXGDw6MDppMpg8Emx8fHFEWBFBqLpZemHBwdUvT6GOvRiaKaL+j3BlRViVISicK4kv5wk9FohBKnc9cYh1KiKY53OGfpb20jQuDxk336WcF4NqUsS9566y2Ojo6QEopOgasCPljqYAnWodMUrRPGoylaSTY3t6iMYTI+ZrC9iasdQglMAGst0/mCLMtQIVCkKQgoyym5zhqI3gg1rHRMXYuOLonA0UoW2cioJUm5LAdwKw4FFZP4TyMDSi4RvZoJHFPH1Lku56tzvIV2t7G7/DIz51n7N8MukVNWxWa7rqREEPDOIGRylnn5cPqbjYMkAEo0jvzWvR/MiqERe38JbxqnYqOVi9VobSM3ic7WP/mT/+pCMupTkQ63EUCJ5qY08HI2eNqW71JKvBBUpkQFQVACnaZL72cQAt8oPUJoBBITTieC8z42fAMwBi8iKoMkIBFYpRBSIn3AIUizAkLAh2iB4wPexy6kKsuQcV7FSaI80gu8qwm+yY9rCnSFiEWJvul2fQaKE2JjuJbvNvfChehJFVJQ2XjO0oUV/Ucg2wkuG9QP59E6ASHwLlqqLPl5WKaahdAw/6UVq1dcyKGNZcf7Expj5PCAnlAxUkGCy1MWwPF0wmQ8QeiEzMPo4Al5N2cw7DAaH2KNo5dnpD7BBs/dX9zh8eN9Lm1f5hv/5JtU8znD7SG7e5eYTWZ86198h8l4RBo8eS7ZfuUKu8MdJtMpDx88oBqP+c4vbpLlOVJCJ+0xm5RcvfwaLjhee+V1jDGU4ynzoxGH4hH7t+/wcP8hu9vbfP33fofF6JBf3Hyfw4MnsRZDSmprGvjcgkRpsjSl6Ha4/f5tbty4QVXf4vvf+wF/8Ae/y7/4s7/g9//gD3jy5MkSLnHr0mVu3rrF8eiIzv5j+r0+x6MR3aKgW2RMZiWb21sRUClNyH0fFxRFWsSi+tEkGs86GtZ5t0ch+ljvcdbihUSnGZVpcNK1oC5nHNcLdve2MKaiNDVaJ8xHh1x+5RU29y6z//AhLaqMrSwdHT14WipUFnODK2NIixQdAmVZkirFZj4A55n7KbV13JkZUhso8g4g6FqDNYZ6OqHQCk9k5BBiil2j5Kgm5z0y/cgxBQKt9DIy8rwULilOC+9pBEVElooFjrFjffT6+uZ7pdRprwN/qugs9ZbGS6zadEetTz3SgqfSX5oVd8b4CbVDiBWmfsaGb5wN0QUbz689NMSxhIiMerWEClhGic50Zv6Ywt2f0S9PG2zENF0fIajxMhabN6lJQkRZ5kXk255omLTiucXraLmus6cRCk9YztEmaEZojI52ewzGx347KtXgmx5VbQhSiOjBl807zbiiKTH3IaYvB8AHpGqjkI3zzkZADN+gUUnvo4ovogESC+oDUkuc9U2GswcRYuqMkI18gaWWI8VZQ7/9LhAVwjbd2nM653Wr1NHcrNO6k1iIH2V33NtjjENrRdIMH2Fm4Xgyp/YedIaT4K1n79IODw8PmM7n7Gxvxr5PeYfZYkaSdNBJzXe/+x0+9+brfOMb3+DSpT2uv3aDWx/cxVtLlmccH4/xwtPtDNAdQafIsVqz//gxtq6QIjb7C84jpUZqg3MBawxORKMSGS9ud2eHNM0YjcaoVEclXwjm8znWu0Y/rTHGkCrJaDQiNDz7i1/8InfuPeSrX/sNHu0/5hfvvstw0CfV0SHrQmA+L9kaSK5eu8zNW++DC3T7PRaLOf1+HwcoocDBoirxzjfGhMSHxqHkA6lK8MLjmj4hS27czk2hCA3Eb2scap1SzeakRUoiBTb4aGQpFR2ATVPdyLcVtoo1kkomCDymKtGJgiApK4OQnk6vSzmfUagC6z0qBAKKIsvwBJwQVD46AFSW47xHBY9q1iqNgxUiOITy7eSTp4AvIeBUlFu60Yuilr4SwWzWuUCghcQ1mTKtUSDORSXbl2x5eyv4pFgRIR8hlbdNtZKNY/0Z0ZDzkSEvRLOxyf4Rq04vD4imz1v7GZZoeWcydV7MnPh0GB9CkIhk2X1TAKlKo7dGRoVcelBokI1iE0BIIoY0YG1YKf6LipoIslHWY6qKkklsYtN0Bw1BYwQN0lZ8YFpKnLWtrtDUmgiEbVBHXMBLEXtCNF7NQGzIFmS0IJVuhEIIEbWi4fxCyghd205O/3SXZqVEsxjjzBFCRMYgwlLZcq71AovmJoSlB1VLuVTUZBNRCeLUYxxT1Ro0CBUXgG9HWzqdokCTKDI0SRPedwSMqXEEptM5CEmSFaRZSiYEx+NjhJQM+kMeP9knEBhsDbl75w5f+9pXSbOcR/fv84W3P8frb77Owf4hTw4O+eaf/zlHh09I05Rhf8jx8RGpStjff8TRkyM+uPsBxjnyPEeIQLc7QCtF7WpUopG1ZD6bIqUkL7oIGVBScev2+1y/fp26NvzFP/8zik7BoNshz6+RJAm9Xg+Ax48fU1UVQmt0EmEKv/TlLzCfT5HAn//5n3L79m2k+jd49PB/pcgyvvW9H3D58mXu3rvHfD5DS83Wzh7lYsFrr76KMZayXNDJU2prkSj6/T7WOYw3JEGgZcwltsETShvnfhOGb2ENo0IjCE1ET8hAmuTxO2diTxJnSFWCQTAaT9kcDHnl+nUmk8mSiU0mI5RSqDTH+5hzXNfR+5bqhDxPMZVlVI/p50WMWniwziBKj3RN4aZWpGlsdljWFoRB6ui18Uqhm07ggfgMWqVKLJmXaBIFw3K+rZJseHKARgELeHdqIMhGaXFNqlJoDHlJzLFXyGZ9RIaqhYz7choBdCvK/dLT5WHZSKH1LsHScyUapQrpY/TRn2OdjUepxZlvI5pn0mea319HqwYSrfPgM9vj00cbG8tHFJV2lqkiSyMihBiJlytKjm+QGVvlx0UDw7lYnxBU7NPjG8XLO3BYlJBIqWN6TLOWlJAx4u1XjXLi/GtTa4RGKLDGIZF42ayX4FodgyRNqK2J4zbzVPrGOF8uhYANHtWs1aWTzNplzWJoeJRWOjawWxr04hSNksa4bsdpnGytnGqL95cKWiPXJQInBWIZ8Y/7Lw17QAZFx1qqJMN5G/mBkDhCbBrYK+j0e2itccFzNBqhpWJyPKKuFnzhC1/g7gd36ff6LBYzauPY3dmlNobPff5tDp8ccHR4yObWkDfeeJtHDx9w8PiA4dYmN157lfl0xJ1bH5AmivliwaDXx1lPr9/n8ePHdGW0oRaViQaBcwTnY6F08/zSPGU33yNPEr7z7b/ga1/+EvuHI7rdbkyNXSxisbZUJLkmz3NGoxEP7t+n1+3z8OEjPI7Xrl/n6uUrjKYjfvCjH3P1yiX2dvc4Oj6KUffdHRKtmc8X6Cb91tZ1bPqnFYtacjweobMOnSKjtp40TZkv5nS7XUJtsT42aZzP52gVu7wLIUjTlLrhp0omDWhbwOOYjCf0egOqqqJIc5LekIOjJ0gEvV4P7wR1XSGkIJEpUjYNfb2j0+ngfWA6m5ClGf1ugUgTRpMZxhiKXpdukZH4lLKKGQTO1HgvsZkgI0Y0nAbvFDI0KcGACpLocm7S34jrM4Qoe1oDA1hGLhVxmyNEZ3gIGGuX8kU2ckSlMWU5xvrjGtA6gsBEB28cL1Ga2tlo7DdjtOus7e7eggws5UQro5poR2j05ni4X+6ypMBSP24jtaKBRVZixYAkGk1rhXM75vno5C9Jnwrj4/SCGhjBVmCLRpgTlXohW29HjIp45xH4ZYqG8KrJF48eGqRHIAhBNuiwNua7i0AQkaH7RskWEkTTWTq4iO7j8Ugho/KvWyFim66ZMeDbenUhKj5aCJy1S0VGN/lyYQVRK1rEgAxIL0H46CnzTegNHw93TT1KXAkxOiRUY3S6RgLG5kVtHrAgNKHEU4v0tHu8J1ES33iRWrQNRTNeiM17ZAAp4r00tsI5g1Qx+mG9ozY1MpGkeYbSulHuAltbWzGaUNe88cab3P/gLvfqu2xtbfLNv/gm3V6H166/xu//0R/wj//wH/OlL3+ZV1+5yvVXb7C7s4UnMBqN6HQ6/NWPf4Rd1Agp6fS65HlOt9vFNrCxVgiKtIPxsYt5vzdAIqidwfvowf7613+HO7dukuYZr3zuczjvufvgHnmSgQjM5lO0Snj77bcxto4Fd3WEu93Z3kYAIzPGWsMbb97g+rVX+MIXv8Dx/z7iW//tt5ESNvsDfCPQJXDl6hUePXhIp9NB6jQG8oyhsob33nuPPM8jwywKsiTCDztjqa1pUENEYxzoaHg2uPjOueX/URHxMZWvMUBCiMpAuZhzfzal0+2hhGTepEGEILDOUk/HCBHTEIssx3tHCI48yxAhMC9rxuWcnZ0dUqUZ/X/svXGoZNed5/fxyeFwuFwuN0VR1Na8PF5e3rx9NE3TdJpO02maRlE6QjgaoR2EEEYrtIrGM3hnjXC0WjNoPF7jmMEYYxzjDMYYxesYIYzQGq3QaDudjuh0OkqnedtpOi+Px+PxKIqiKIrL5XI5HE7nj985t6pb8oyX7Kz1h25TvK5bVbduVd1zzu/3/X2/39/hPotFRdNWWGPoFSV5bE5VaoULGa13cTImVikUNmqcdKITxjESgqBDenXi6ipxMhkkHCYBKllm5NpnqRNpmzZO4HT0K+9lHNmVyVETlgFgSngU3b6Uv6fEoJvAIzrsfdvNQQDKiSZptVqjAl1A52NwpNOcFpbBF3TAWbe4dO+3UqF5qBL06BM/336324MHyypFvL5dTGhNCqKVjnQG1TVY1jEZ7ah1ab2L2aqAq3KdGGu6JEZHwMmgcKkaoiUor5paBL4xGJLrVOZ9WWuSSJzOsSbVSQxSKQxOBLodvStS/RJqu3pdwjLmMcbKeIrnmd4vjaMAyzGQbPTj9qjJyqMob1onZczrLol3XnSOyoB2slZJbSeQhTb+DgqrlVSOfSArC7I8R0VGhTLQNDXGZFhtaKqavCc6hLXhkNm8IjQt+9NjTp04jTaG999/n6+//gYnTxpcU3M8nrCxtcHGxgb379/n45s36JUl/d4A5z0uBnfG6iU1NAZ+ydGx9TWJddA4ATtdXVGsjXBNy/37e9i8pCgKAOq6xjnHeDxmc3OT2WwGwHwy4/S5C+zt7VEWPXRh2L1zh/5oyDPPPMOd27e4ev0aZ3dOs76+SfCB4+MxG5trlFmP3ft3KUwh8UWWs1YW3LgBVTWnV5bkudDXhTLedLoOrTWNd1i1omVTfvnbWYP3kULY6zGfzlCjIabNomulYW044s7dXazJKXs5TVOJVTGKtq0lESly+fq0VO29b6jmsLm+zaxaCBhbOVpVo/OM3Bq819QhoBGLYlv2sYCLiX+rPOYRcCcJ1NP1uKprelSnFGJ1Wq9WOXjYvDooOnp/CAI+mBXmC13TxJUqCqvVkPi+LiYFGpYK34fHYkr+O7ASMfkxq2uGku8wtI6wMhZXTY+6z6jUb15uYhX238X2mUg+QniAcw6tlaAuCupagjGnwCgtFYYYXAjo6eJ/tJS5iUG7WlnIg47V3ZagoHUB6zXSuyKAthJ44wlB0zqPDw1llsdkJnLgYvCtEETIe49Wgob6qBVRGAIO77WgL7FcrrTGGMkw0+ITEA6uicE+JKqGoE4qJQRKo5QE8ypePOki0olyFUvq2iwduEJwcRJIE7mLA0j26eBwBCmFB92J2YNrosYlEILw90yRE3yg8i2+cczrhkXb0l8bCMWryMhstN47EnvarY1NDu7f5/yF8xhlUATOn7vI9evXeeLJp5jNp2ydPIE2hm++8S3e/tWv+PFf/ZSdE9sMBn2Ch+FgRGVr1tbWGA3XaNuW+XRO41y3OM+bimZRMTc1ofZ4JEC3maEoSumS2h8wGg3oDQccHh6zs3MC7cWho2kalFbc2b0jncibwGQywXvP1s42dd0wm884ODiiLAsunDvLYxcucu7V81y/cYP/8V/8C/76r/+awWBAExoW84q3f/kOJ05scu/ePXZ2dqibls2tHXCeurnLcH1N0H3ncT7aFWglXNVIGwreC5ISk474owJCVToaj/Hesbm+zryaU5Yls6oiMzmj4YC6qjk43GdzbZ3ZbMZiseDJJ69Q1y2LxaJ7H0GoLE3d0DaOst+jHPQ5Ohxz685NbFawvb5OuTZkfHyIRzH3DXMfsE4zKPoYHyKTL0Cs1hjSZJpOWyL5blIPUQMRudsaE6sYy/LtcvKX76LtBNs+AhRGKns6jQGFDwgSFY+jvMwfKXXQq6LveD4mpMk34lPxOTrIXFMYG+kRRKBDtC2gurGLQjRUKk2osio575ZJTpzOtVopqyxTj1gtSujzkqKS9ql/RxP+59v//02owDEZZonQp8109zVB+HwsKYMZYvIu3arlxcsx4EMg+FbsSFsjV65r8HE9kWnf4Z3B2pyAAGrKGem54Bscwp230SjF4aUSshLwKOWpWofSNgYsogNzXoJjrVqcd3isXP5BPq0k/xrnPDqThMA7IjBiULRS0VBLAExrAcl87GqtNNHePnbqVrGqqzJC8HHct5JINS2UhTAHAIcjJ6NVjdDAEBpYqBbYLMNpi8oMR0dHYr1rLFleoq3oX3xoGA0GNI1HZ5aeyTnc2yf4wHS2oNfrceXKY7z00ku8+tXXeOfX79Ib9Png+lU+/PB97ny8y3sfvo/3Offu32P39sd88eJlqrahaivW1zeomobgPNWiphz0OzH8sDcQdB8nlXytcErTzhcM+iW39vbZ3tnBGoPSSihLTQt4ev2C+bShzHvMo338oByw73ap5qLJuHnrFv1+yc7WFvNFzY2bN3n1q1/l448/5ta1jyhGJefOnmNza4uPrl3D+cDm+hpKGeq6poo9vbZ3TlLkFqUCo9Ead+/sUpY5dV1Ld3Rrcd6TG0kYrLWx90mgLLKYdEhX97LoMx4fyuvnCwjS5byqG4Jr2Nk5STVbUFUV/X6ful5Iw0CrsbFBcFWJ1qXslUIzXjTcq++yvrFGU0hCtpjPcDPFcG0IRmFCoPUthRaATVJsiecyDF652JfHy9wrJbolCPbIeJcx84jDaqSjpVgPJ4F9AiLqthVmhk8tJFRXJTJdlX1ZHVUhAWSSsCutUEZiTBXoNCadnmzlVJcURDk3Y+wS6Yqb976jdlrzCOX4Uwya0tYlORHsAyUGQPH70ElC+W+ZTXwm8DT1H3wBFRzBtbRtjasrrFWE0OJdS91WLOo5yooXtyJglSEzmdAiFATX4p3HK4VVhoAiaI9XIq4Lradf5CgdZSXaYE10tgBUcGRG0csLapcm3haCQ2tN3UqPCOelRN0hoTq5+TihiUURHiwdigBpohMHqbWWXlnGIEoCEh2TFO+lA+m8rvAh4LwkZVmWSTLWNjR1JdUVIDipRCitsJklyzOyvEBpmcB81M4QO5U7LxOf0RKAtq4WFwsfMFZh8yhkRGG1ZT5b0KrAvHHM2wZTFmSZxZqMte0t8iJjMh+zd7jHqZMnuXD+PPd2d8mKAqMMdd0wnsz48OpV9g72+bNvvMGffOVPGY1GHI2PWdvc4Lvf/x5P/+EzZHnBl19+haMjaUB08dJFprM5N27d4sNr13Aa8izj3t59JrMp9aJiMBwSWsdgfcBobY0szwHN0fiIj3dvMRj08R7e/fW7ZJmhmi44mo7FscQIBejc2XM417C+PuLSpUvsbG/z3rvvo5Tm9KnTPPX0k2yur/Hmmz+j6Pe4cesmX375Jf7f/+f/5qmnnuL0mZPsbG3z4Yfvc/78KbyH/mCEUjCbHNM2jnv7ewwHI44OjinKPpPZnP2DAwa9PmVRYI1U5Lxvcd6zsbZOlmUU1uB9S15kUmExkBlDZg2tdxRZj6Zq2BxtYbOc8Visek9s7rC3t89jly/y/HN/yNWr18AonJcOuePjCfW85vh4Qm/QZzAaMJ/NaKuWoldiix42M8ybmsl8Tm8wxCstxgo6w2GYLKYyuWai8ChNhgkK78G5ICJVgWYkOPdgtSWzkpBqpKooqLA8X4BhT9f12INGo7VonZQyeC8l+aatqRYVTd3gqoYsjSe0HAvhA5sg/VFAgkZrpcGjnKvDGC2uK9rGVjlSMc2UoW4a6sVCblWDaxupeCpBtuXpXvjmRm4SmEoDzsyY7nk29rAxSmGUIjOG3Fp5rTVkRnRoKt7yzHb7CJ7gP02T8vn273MLDx7E3zd2V04LOKsIfojJfRu10hofHe9CaFFEHUCkPaTl3XuHx5NlOcGz7NaN6lBUEZbriJp7CeC9rGMEh1caE81DXPBdDwaCj+tccqnTeFyHZCdEfhWY6NDUlXNUCsR+Hrx3KC1jWilNWzVkmZVgr3VYm2GtwQdHcLJu6tThHDrL4FWB72rwpwPozHb9OZSKNErvKXSGq+ZkmVCRPA5TiO1tG6BqWo6nU8peKW5KQFvVVFXNsN+j3+8zPjxisViws30CH5xUHO7tcuv2Hb741DPcuPkRi6rm5M4JTm5vMej3ufLEEzz95BNcuHCepq3IMktRDBiPpxidoY1l0OtzcHAg+gabdUHjeDrBWJnHlQqgRSORFRl3d++T5RmLxYw7u3ewysTfpyUERVn0WVsfUpa5WJ97ODo+5Iknn2Q2m9G0NeCo5zWnz55j/2CfJ574IgcHBxweHvO9H3yfyxce4/33P+TDDz9gc2uLpq7Y2dphMCwoMsPB3n1ufHyLxgvFOc8ybt24yebmJv2ixDc1GNE2udaT2YzGtbSt3JoYu+RlLnoVVzMYDTu9a5YV8dpxWKXQJqNeLCjKjLZtOTw+4sTpM9RVRb83oKoqoZ01NUZLRatpGnqDkkXbcjg+pGkahsMBJs/IjKKaLTo3RY1mlOcUmWhB5osFPtru+wjMoi0OoR8HHVBeYeI/rzxBB2kTgMEhrpw+Hp007hDbfB3NEKTXjAJjwSs8Tlg7IcISKkgjUnREykX7I7oqEfSjha4WXGSu6CCAeXBRbyxwt7y/lmMoAcUBEdYr2x0Hr9CYCMx5YboglE6hNHpCkPUVZO4izlMhyGcgOHxQOC9AHMF15+MlCGcZGf/t22ek8iFuO8pHWpUWqlLSR4RYggox4FZKgupUKlNKStUKoo2uoCuho2QEtLE0bdvxbIW7LUI7TcpqofVCtRLESN6gDYE8K2LNOyyrFx7MEhbqNhdA2SjaCQoXYLqYr3xghY6fJfH2UvaprFjh2bCkgqz6rFuzNByuo6e1jlQvv5KZp+9JEptkKRdxWbP8jqwxcj5KsvWmruiVPfEbr2tMUVLVLXmvhxNt+ogAACAASURBVPOe2WwmdCtjqBcLXNMwKntsjkaMD8aM1YRLlx8nOM+v3nmHLz3/IvcP7jEYDXnsyuO8+Yuf8+zzz7G/f8jtO7ucO3uBp595mnPnzvDcc89x8fwFvvu97wifdXeX3mgA84rBaMR+pGTtnDqFCZq6qZhXFcWgYDafEkKgKHooIAsZZdGniWj8xvoOi3nL+tam0CNA6ELes390DM4zmdwTVFvB1s42x5MZVVWxvT6gN+jz+BNPYLShzAt+/otf4IPn669/jZe+9AL/+n/9X/iX//JfceLENt/8zl9Kc0LnufTYk9y5fQsXYDgaMRgMuLu7S7/I6ZdbHB4dSLKqDc7VWKUx2jCeHUt1RFs0MJ9OUcbg6pbBaID3nul4Qq83QOuM2XyOslAWPQKeNtTsnNjm4PCQLLNcvnyJj258xObmJsErNjfWxGpysMZsOiUoz6A3xLmaPMsYliWN82JPCSg0ea/PbDqlrWuyLCNTcDSbMOz1yY2lrVu89uROHGgc4g4VvJSoU6DRiavTcJCLVZCVpGFKmjcVn7tSLNAm8qT00g/PdwTYFboHYjaRjqFi9Sh9pvQ051pa5L01qqOUhLhPaI4h1bijTqTpqjNyDPHKD3E8KgUhJJMACfiUijSuWA9XrHR7lsq6LCbp3JtaqBoyyLvP9/n2u90SvSL9dI6HTT5Wt4dowCBAmJb9rlt9I0VKS4XLR81SUKCcB2zURdDpHRL/20eUNdH9HALAKR8IWnr1uKg9CStVQEjXdDzvh65xlpGBp6NXrYLCWq32upLXqLhuLx9PyYogxKuXrwQ6iWcuyYy1iqZp41q1TFAwBnHXkWMWuaVeVFibyditG7Jhwdw1aGtxregSjMnEDan1OBoyK9/j/sEB9cIxGPQYDdeYTCZcvnyFUX/At77zTbZPnmJra4MPr13Hmoz9+QRH4NKlS9y+dYe9vT2+9rWv8eJLz/Onf/JVJtMJZX/QIdveOfK8wEYqWkrkDLpLJpJbZgiRUquj86UxZMYyGR8xKE/Jd6zoKgD9UoxRjLHkWcbNW7fZPrFD24o+aG19yPVr11EqsKgWlHnGxsYaFy89xtWP3kdZww9+9H1Onz7DF594kjt3dxkOehR5wXxeUfRLLpw9h8Jzd3eXoihQSomK1ojLmcxqHhub2KJ0BEZbMi10bWul8tW6WuK4kCyMFSoYvA4oJ9eMUoYsy/EG7ty+w+bmNgf7+yhrqKqK3GbRzhxx32pcpAslCrKsA72yx3RWMZtPMFrTL/pM6wZrZCx4q/Bti1NGwO20JnihmigfQ+d0Dcf/ByXXtdFJ07W8vlcGBMlERUTo8loxIPqb5+2UhKd1alUPldaYzur2U6oz3fO0wne27UjSkyQH4uYACG3fozCRAu1jPGRiVSREF0wxi/GxYqrjAuZk/etq/DEpIsR9v/0a9ZlIPr6AEr2DVl0ZO67iHasilaRlU0sqKSo6R8RkQylccMRCtvBQ44AI+MiZVZKRKvliPZHORLqg0o+R6FwKR0twSwqEYKORgBu/bx3kx5eeIA+X6x4mckcXn6j81j4GXDpEe+HkJAFeiWCJiPzouEj46MygVpokCGdYxYGuIHF+fbp4Y1YcqT5JaBRXGCrnMFoWS+8blBLBceMc1AoXPFlWUGQlbV0BiFtFUPjKUTcNg7U+s8mU1jnOnD7Nvft3sLYAC9eufsRzz79A2euhreXUyVOM1kYMh312P97lg/feIystr3/963zv29/heL4gL6wghxp6hfQSwUvPjyzkhEi1oki0ljhRt45mMqEsSqzNEO/whsX+jKBEJ5QQvs3NTTE1QKwK2qphOFqnX5ZYDR7NdLpgsajY3FyXiVWvMZvNuHP7Dj/88Q+4e2eXZ/7rJ3nzf3qLSxcu4ENg7+iImx/fIAQ4d+Ysw8GAd955G2MM61unhBJ1XGNaoeShwPb70lF2LhOsVQqdFWgbsNkyCUUhgUbw8bfVKKdwKlGfDEEHmvmCauEZDkdcOH+JGzducPr0aY6Pj+V5ztHUEki7rJZJ1gesNmgCDTkGxXw2J8sztjY2ca3n/t49TFkIHa5akJlckNNW0+iICsfrChf56isBSVRpLe9HOpZTYaWATESXosJKgY6ijyTJe6harBMKrdKAiIvWsjzu/XIOWT1OTAW6RSOdWwh0qLPMBFKbUEF+MxfHnEeshXVEhV0EE0xErX2s3AREbAzJFoMu8UjfURI6alQnaNYJqft8+0xsCroGZinxCHolySD9XqFby4iBiY5xuBI4Hx08TcedlwRVXpv2iZtPmiN0SNfqcj4PxCSIgEtv6aUSomLH7o6OGISqpLzcCYSuItK6Oq4diRYia5xSggCHSIXUmRg7SJLjOwARp2IfiSD0MKWkMqMDenU9JMQEY2nza0xG2/o4J3hcEF2iWtGSGALBCTWtXw45Hh+gTMbMFEymc/qDdebzCdrmrA3ztCBEO1iLRWFNRlY4MlvQ1A33du/ith3D4ZB+f4h3nv7mGo89/jhlUfDqq6/x2quv89FH1yAEZosFX/jCF/g3/+b/4oXnX+DyY0+xubXGwf4BzouVrlJSEZpXM5ommaWoaJcvYOa8WtC0LZnNxT3MeXBIT435Iga4ntYHtDFsbW0xPjqmresOYDl99hx3795hc3Od9ZGsSee3tmldTV031FVNkVteeOFLjI+n/Oznv+DPXn+DyfSYr772Ok9eeZzcWuqmZmd7m+2TJ9g7PGB2eMzm1hbr60OuXr/B1uY61uZSdSoKWkTHGJxH57GK11aUwxHO1WRZQesaJsdjtjdPsqjnsp7ZSEEPiCOTVrRNI1TprOS9d9/n4unTbG+dwGvP3bt3OXHiBIumwprAxvo69/cO6JdDFtUkXk/C1JjNK3qDnjQHnshja/0+08WMsijIfGA6nVG3C5TuSVKgPSpX6Fao+Cn+FIfQuEaEpGUh0glDZxjRAV5aEdo4z6cxGZZGEBJT+q5qulxnpEKJUmlFjPvkNZK3pGhQ1sLE9k20ZudaOa6XSUlFEC/Fy5JbRTqYUksjCAV437WsSMtmcuRLNDD5LCwBjHgOKSlaylQ+HYD5TdtnYzX7AsKF0vIlGK3F9SeWNVTUTnjV/Y6CtMRMMET6TOoxQLQKc1rjkuBLISUlbVIpheS7kw65TG5ivqOWj7k2lsV9KrshU71vCT7gfKANrfyNojO5iRhRpPHpJoiHVgqFEc6uEQ5sG4RS0nq5eS8LQdDJYUEWJQXYIkfbHLRFKYPWRrJWE+LNgBX/bq89LeLrnpwefAxugla4WF3Kiz7zRSVN5PKcLC9YH43QVkqPPZujnTRvUkCoHc18wWw2ozfo0yxaxuMxBwcHZFmG0prHrlzmiSee4O69O+RZzpVLFxn0h8wWM779rW8zPp5SlDl167l18zZv/uznXLt5k9Y17N7bE7qPh7wsODg4YF5XzGYTWifl+KqqonDaR7qcYn1tKBNOZrHGRq5qzmhjxPrGBpubm5w6dYpz586J9sEYhmtrbG5usr21Rd3UoGQhKIqC/qDPaLTGdDZjb/+QuqkZ9PtcuniRd995h5sf3+Lrf/F1BoMer73+GmXZ4/TpU1w8f5Ezp05xcHDABx9+wLlz57h48RLXr13j9q2P6fX7FGVJrywZ9AfynSnDYDAgz0VsJ42rWhbzBYv5grpu8A52tk/GCpzQ+6T87ZlMZhxPx1SVNA8syj5Xr15Fa8WZM6e4e/cuzoljiQf6wx6DQY+mlVKsb8RIwShLFl1ABoM+vvEcHRyzWFSMRutoY1FFRu08i1rMAWxZoq1FWRs5sRqvFSgt2hCWiJEYuq3oO5TMbEElkwk61KkhjokQaL24yLnI13UhxP3yHOfEEc4FnywXuuOm26P7vIp0sSChnCP5SujOgUwpjTJKpGRK481ybtJxbnFKzlWQXr38HF6ch4JfzgnpzT2h64vinfy/jZoTFUvzafx/vn0GNh9ihT10QUlak2C5MHfizUfW5JS4KBXNJKJTjWJZTUnPe/j/0gMK1eXZkWISAyAEkQxdpTt0SWtnZxvPVa+c0d+e1Kb1bmVP+nx6OZ5X3Ru1iter88t1VKnl97VyH5ZV0VWrUaWWtvJLXxdNXVf0ix6urcBrsiyjCdAEB97jPFhrKIoC5wLGaGymadpGqJMRcJnPZlhjOHX6NG//6i3ee+89nn3uWdCKxXzBD3/8I4L3nD93nhs3PqJpGh67dJnvfOtb/P2//5+wvbPNN7/5LVk7I2VtsVhIBSDLuh8zOXUlDVvSCLStrF9aG3q9HgRN61uMNrTRZn/196nrOgbwwtfXWnM8HjMYDKgWC+pmQb8omE3nqBArrkoxn8/Z2t7h+vXrvPzii3xw7TqXLz/OrVu36PUGEoQHcG3NbDbjcP8QHwKj0YjFvAYfWMznKCVzapHnKAyL6ayzmZZgXZNbjWsF0FRo2kgVTYxRpSIIGgPzTIuTp3Oiwbh44QIHB+LyBbCxsfFQ0pZlWUrDY/+k5TXTBmncmOc5xlrm0xmDQSGCbB+wCM21aRwhJKF/6GLKAIgrY7pGU7WvG51S+dZGKgdL9UWk+abnyLg3XSUgIegqHj+9qxxXx32K0K2JiXqbSLgdNXn1fIKPidHyfWQJXQITj5o5BJYVh4caZD9yraXXpvuaCNqvHit8cg75twHIPhPL2QMS8ik8agHjwzIwICwpGTFBCVotbQsj6iQAU6QaGfHBMHopDO0EN/hYBVCCDAErsUCHxqamMm4FHY3yCXTMYxyx0hKC8N58kAGBE9RIeXxou/+nW1A+otTy/+7mHYTIG0Qe11ouJtGHQFAholjLx9HxwvVS85Gbir7VJnIJ0/3I2QWpKkRuW9HroYym8R6V54SgmE6nzBa1TB4aglEyyWtDmYmGZm044MT2Nk1V0zSOE6fOsLm9w82Pb3F/b4833vgGb731Nlee/CKvv/4ql688Tl0teOWVl1lbX+Oj69fpD9e4fPkiX375FUaDIe+9+2suP36Fjc0NqsVMGjgNRwwGfbLMoG1GnluKMouTTUZAUdUNTVUBmv5oKFzPZk7bVOAC08mMo4NjjvaPuHv3Pnfu3MU7aFtPUztc7ZguKhQiGJvNK5rG0TpH1QgqaK3FNY5FXdF6x3y+wAeFNTnvvPMOzz7zLAd7e9y4dp33Pnift956m6ZtyKzBOcf+/h6tc+RlEZ2SJJhxoWU6nXJ0dCSOVzEwN4rYtV2S1qqqaV0t2pxkuOD9krqnpHrTLGpcFGWurW1w+/YdNja2yPOcLMtwjUzW1uaYLKd1XlykQosgk04sfhtZTEfrQ5QB7xtGg9jNF5nQXHDMm1r6BUSnLtBg49qmvNBSDeI8o5VgBDp090O8jgVwkJvWyP4YiAQlDkFJwBdMQOuEXUQ3ER2T74h6tsrhtBchrhIkyGmP0767r/DdayK7XuYIEydUq1GZgAVKG4I1svxog1JiAKGVJCFWLfcpYwgRbUJLMiJNX3S3X2sTJ5WILHcaLdUlLy4K+z/fPgNbRDdTpTytt4ne2lXy0tpEDMxjMiDm6yki9ysGKXRjuROdriQxxAp9x9SLqKPQoujWtpQmSH+s+P+QHtWdGYteEY2m433aZhOFeGXrznGlmmGjEJnohoRL6+0yuVBpoZWjkDjrShmapiJx6IV7rqJda7TaVR68aKn6/QEH9w8ZDgfkxtBoRdEbMKtm+MaTmSikj/OrCorMaIKx3Ll7n3rRkBnL/f09sjzjuWef5+DokKPxmL/6q7+iahpefe2rTCZzTpzcYVD2McZw6fJFXnnlJd765S9YX1vn3v49/vuf/IS3332XXq/HcDiCoNlYX6d2QoXKUoNh33Q0LO89RdEjL8X+V/kgWpCg6PcLpuNJbDWgUVoaHjdNI3O3zZhXFa5p8EBbLbA2o9crqKuKEKQnCB6yLKPX6zGbzSJA5Th5+iRXr17lhz/+CY2rGR8es7+/T9nrkec5W5sbbG1tcfPmDe7cvs3W1ia9Xp+gJFkwsTv8cDjEKkmixLJVtDZChYKizBn0+uzvHS6vlxV3JdfWDAZCaRZgWLO9vcPFixf48MMPuXv3Pv3eEBeEzhWcZzqbktsc6z2ZtQQlzZ89gfXNDRazikVdkWUZ/d6Qe3t3JX5qgbalzEsG/SEuOBrXUDkfwWDJjlRwiB+oMGJk/fSiSVaGEB8Rh1IhYBqM/LZKWCziLhqfE6lPoIVpo6SJgQ66iwUFK9edtsPjupgtxYVCn9KEqO1QwUWakyazUt1Mmg0dhFrVehljOo6xEO1jk5ZIeUVu8k7z0T2nW3V8vJ9MiaJBUaxkorysxQign/b9tttnI/mI1J+2baiqirqt8XgJfFxLaBr8okKbgNEOjUP7Fh1CykWI1zWtcjRK/KHTfgN412K0uCBopIRklI6TtiQKmQLrPblW4B0WsfVTeIyyWGXAa5xX1A3UjQzu3OTk1jDMMgZZQaYtBhXdFBS5kWx9dZ/2AdsGMjSFMlgPoXGCFHVd2gW1b5s2ZrhI18xI53CLGhonx9CW3EBuPJlusarBkG41VjXoUGNo0CrQOPB1INRQNo6h95wocsZ3bxNUxXE7Y9/PCL2SBQGXaWoNk3qCsgFlA63y5EWPybzml798l7qB9a0d7h3ssXNim96oz8bWiK99/ctM5vusrZd8643X+fKLz0M15Sc/+DZrReCxiztcf//n3L5znUtPXuHLX/8zvvvTt3jnrTf50hevcOnsKbRvyIxjNCzIMsNo1MfkGcoa1jc3GQ7W2No6wfb2ScregLppGB8esb29g8KQlyUHx/sMTu9gt4ZsnD/NYNBDuYZBbljLLB/fuMrd+7ugNbkN5MqRW1DKUTshOhS9NYreGnnZx5qSXjlgUc0JrsZXM/LgGOaG9V7Bz978KU9cfoIf/+znXHz8Kba3TohBwKLiwoULDNZH6CKjamp6vZJBr8/x/h7bW+vM2hlzX4M1ZL0+jfNU85p2UbHeG9DTGYvJMcNRSfAy8bf1gsGgZNQfsbG+SV6UVPOW2awhVHN21oZce/99zpw+iQpwPDvEBc/x+JDpdEJZZNRuyixUTP2CJnh0npH1SqrZmPnkgMzMGQ1hb/86Z8+eYTZroezTmJw5LXeP9iitZlTmjFRgw4ENBq3yLljXmbiNEHnOQmPSGA/WKWzQ2KAxftm7w6Bj75EgSVKQ6kTQGm+kCWkbKwouBIKXW8QCpNO4B1w0q9B2maKLlAvjA9r5roomsZUnaDBGqJBeOaq6om0a2hBNJiI9wrctvmm7fjt1XVHFACEoumqNi0HFsgqD+PcbTW6XoIEABUFoJ85Hl63Pt9/1FkOLlfuxihZWKAoJCVyhYqlUDYiiTo3vLgAn/C2AJV1jZYu4qbzTCtWhO4cuA0pJTegoGKtbd1/5SEzWJJHpMujXLAOQKFAJmqAlWNJBx56BXgKxGLRobbtATSyyhDKl8BKwBE2IxwlRX7UKMqbrPu2TkFI6rBMCOihQnrzMICgWzRybZ7Qh0MwdeSFmI1pLAibAirAcQuwTVZZ99vYPmc0n0rciOGaLGZcff4zhcChWr1pzdHTMu2//ihs3b3Dx3AWqWlydZos5xmTs3r3Hl7/8Ff7pf/vP+B9+/ENu3vyI1rWsDUYUZSZAkfcEpWTuiMmXI+DqBt+0ZNZKp3rnpIms82hrsFmBzizeifMhTpK2xWxObiw6txH8VsznU4q+iLPbtmU4GnGwfxC727dLYxocGxsbzOpZ5PRoqmrBY49d4Zlnn5eeGa5lbTBE62WVZTgaoVTg8Hgs10sESGtXM1wTe/tlZctQL4SO7YM4rBmjcK7Fq9SjRnVXV+uEdphZHXtLaz66fp3BcMTxdEZb1ezu7gp9NRMGR1u1KKXJcksgUuNXjAvKXg4+SO+rIuPu/fsYXQgY7Dw6wHDYj85nSEnGewnqg44ic9VRlVLfJtEkx/GHf8h+OtHdIT5XrYzZleRepfsR8Ope7zz6kVB8te/Gp1lRr1YYktV2mjeCEr20WtkXTwYVVvrqkGJnHcew9BrqEo6VRCQE3VG+1ApQn4CPdB34T8xcv3n7woMHD37rJ/9dbb83+r0Hf/zKHz1UwoVlNQTixB1d09PXHvsmyRegDCFx5wKdf/EqOtN5bneblLtdUJ+8mFI5WMUf20vGuvo6WNqfqThhinuWWUG/Upf2VYRJfvSmabvMUevkYIJkkan0HFFehYkIWOQfekXbtg+VHa2y6A65ZeVsH74PgdYnkNmTGrfN5wtQDmxJTUOLocgyxHXXRbthWXAODw7YXt9iY3OT9999n/FsyqVLV+j3SvLCcv3mDS5fusCly4/zi5/9jNY7Br0eShk21teYzqaMjw5wXrzD//TV17h39z7T2YzJdMGTTz3Nq6+8wrlzZ7h69Rovvfwlbt74mI3tTbGGjeYBYjtsaWuHMpqyzDtXMY1ib28PpYKIp+uGzRMnGY76tFXN5voamck4ONjDeU9dtTSuZjKdMCh63XUXdJqYIMsE1feto2kq5vM5w0GPEALTowltaOgVPabTGYfHY/63/+N/51/96/+ZLC/4yY9/hEFTu4rTZ85w+/ZtTp04ibaG0AptKvEvm7YWr/TWAeLlrwFjZUJfpSl0AYeXfVkRu8Y2osewWcZiMqXfL/EhsL61CSgODg6BQK/XFwH7dCxNBIsMrYw0EXQtRontojJEr/WaRdPinWF9fZPDw0OKXk5bN2gNuTbkxpJpLe5qiOjcIRW7NHZCCMtqRaRUPDpEvWJpN8xy8g7+YdT2ocl4ZSyr8ChFJnziNR0dAt2hzZ3lYTyeiOQRxzFtIoKdqqYBHakVXkWKlk8BGA+936ejyx4dTETS6NyKlFK4KEo3cfz/s3/+5//ngwcPzn7KQT7f/j1so9HowSuvvAL8Db+n8rH4EANn6Gi88ju6LhBQ3uF16t8jz33Uex/Ah6RLjMcLAWOMCI2jg5RUIkUfkBKeLnhaWdPS9Q6igUo0IOBTqU/eh676kegv0gH7YdrF6nukbfXaT5/vN1EzkvNW91qlqL1h4CtCBAcDhsFgyM3bNyl6Q7TRKJOz8I5eVkJYUM1qbKbJ84Ldu7u8+OKL3PzoFgdHhxwez3jh+ecJYY5CsXt3l7JXUuQ9mqpG64z+sM/x8TGT6RHDwTqZUdy7d4/zFy/z2JUnuXz5Ma5d+5C33volOzsneeLiaa7euMUf/ME/4J/8439Kvyyp25qyV+J8RfBipR5CoGliU0ffktke3opJyny6oChyev0edz++AcFTlj0ybWjid97r9RmPJyxq6RPlXMOgLGhS1+0YWGaZZVELbautG6ksSP9tjM0psx6z2Zx8UHL9+jWstnzrG9/g9a+/ztH9XZ57/jnu3Tvgyhef5PjwkH6v5ODwmKKX01Q10/mMnbNncUFxeHsXmxmsznDB09QzMpNT9IsoglfMa8X9+7v84TNPcXB8hI0C9dlsRp7ntMGTG1lj6sZhtMWjmE3GTCZjnnjicbx3HE2OyYxmPq84tXWG8XxOXUu/kzwvqduK6C+E0dA0gazcZL44wgRHL8vFCjeyRxZ1LZobpdG2J4G5EmctlTQTMWlGp0TZYwwsHfAfHiurfz2JybMcA582Z5gger9Hj7E6fuT1QTQnTh5PdrePWt0mLbLygVbLfGJ97AWXLdcW4wK182TZcp36zZ/HkayxV8fw6vmlsfvnf/7brVGfCcE5X4iTFxHd99HiLyR6ETEgR3QW8WWhu0VuKzpS7oQj1E1y8a9bsfSDyJtNgZte+aKjx7K8SUxclILunZeTZ0gnEZMc4ah7oYjFIMYH33Eeu/P2wl9MaJkLXtDZFPjEz6WcoK8p8Urci6CI3trRUV6JKLtqmmVw98jZdvdDwGq5kAOBloh8lSXGZGSZwbpASDoK3Qj6EHy0CvR88coXubd3n5/+7KdcuniZy2uPMxj0cM5zdHTI009+kaZxfPPP3mBra4te0ePevT02Nrf54OpHNE1N0evxzFNfYjadcuniFZ55+mk21tdwdcVffe+7nDx5kl/+4pfsHR7yq7ff5uz5CywWc8re0g6yddDWVTe5B+dpXM08duAtezn93lC0OWhyo5ntH+K94/r+HsZY1jY25ZoxYFXG2mhNXNDCI0Gtki6sIBWvpAdpW6EpjbY2AWnMeP7ESV4+KX7pX/nTVxn2B/zlj37Imz97k+FwyP7BAWdOn2U6nWGtYT5d0PqWUzsnyXLD0eEBxmvKLMepgGtaMBYfAm0tUmUljgpkWq4jjFjHOre00CzKEuccWyd3aJqGer7g/t59NtfWGQ4HTKczJpMJRVGwPlwHbTkeH2OMJ7MGdCElcyMJb9M4girITEYTWpqmEr1IVcckQuFcoA4eVWaiR3IN0lBTqhFpvGktwVlqgObxVI3n0SBmFUgIXadoqWSEbkKn60Dr0pAOSDk0jd34NzVfSyNDwIoM5UKnK0tGFyGIu4tSShBDo6PY0Cx7b4QA0TddaCXyuI+T+WpQ+amBV6yKdOgZyeZUKCeBhAL/9qjS59vf3fabguwUxPvosCalOo3XknBLE0K3rDTE54ffELg/enyrl5Sr1fd8KKkISz0ILBHWTgMSA4QQVOw/1bIaEKXzX0Vd5fpdBi1pf/r7SVDvk+eetr/pMz4a9KCE8iJVHwEwgoZFROUlOfcYH8iMxbkGq6Uxa/Dyvuvr6ywWC27cuMG5s2epGoexmsz0uXdvl6PjI3ZO7DCdzDuziMlkgtZw8tRpDu4fYHslvX6f27dvc/bseebzOdevX+fdd9/j8uXLfPs73+KlL3+N3/uP/mNOnDzBB++/x8bGBs41tK3/xPcjU5JY53sck8kEFXTUqIhVf7OYUi8suhDd4XQ6ZW9vn/6gT0+baOfvqaoGndnutyNqTtP37Am0dcNgWEq7gKbBYDvtiHQI7zGdpYvCewAAIABJREFUTrlw4QKntze48dFNLj72OHfu3ME1FU1bSwUmBDEXyXJym7F3fIiJDmJyuQdaFyiy5ZztE7CTflPn8ZnGxz5WTSN9acosxxhB3OvGkduC9bUNJpNjOefMYJWlrhfdea8mzMk5zWiDdyGugx7VtrLe3r2DRZEVPbQWEX+eZZK01y3eNVhtYgAeacFAwKOV9NsRHVNKolcT+d90Pf/t1zzxe0uNbj+tytE9LyDJTzIKWpEawBIIT1lIqiZK0qTkMyUwPz2u9CfG56eDKqle9cmkI73Or1x3v8322z/z73J78ECCv7hwq9jvIPXgEHRUSr/J+ksRuXDKR4empW5CGSsfLKy413zKzRFSYVlU//GvlKYkSBcrNv+JW0hin5B4q14mFSmC4dsm9h5pIPYw6W5evN6NEU57EhOJoLCl8/TvuHi+e06ki0vOFN879SYIXmzPdEzRtFreNwqx042alRDbKwYn9nNtKw3RQnC0IURuvSBqmTJoEwNJbciyghAUB3uHPPP0s3ilWFQz7t2/y/UbV1nUDQeHh1TVnK985Sv0e0OqRUPbeO7fu4c1hn5/RDWv+PX77zFfLHj++ec5Ho8Zrq2zv7fPlccv8pUvv8LJk6f4y+98i2f+8A/xrmE+m1ItaupWkGVjFBhD6xtc8LQrwTdAZiU5ODg85v79+9y+fZvZfMZgMKDMS1AwPj7keDxmPpsxX8yo2xartNglG0Fpki7BWvvQAp188Z1z3cCbzyrxXl9U/PwXb/Hs00+xubnOxzdvsb61yTvvvkuWZRyPp6xtbDBcW2cwHDIcjNC5QaPRradt5DMF19K6JuoCHY13tK4ltFHHFNpOY6S0OMFIN9qWTCusgrZ1NE3L+miNtvEcjadsrq8xHPWlw3pV4RB6gtXRQQ4FvqVpK5TXBCeTNt5hTE6eW+pmIciJkeDIaI0Lgdo1NFWLEv4BpLqhUhC08FK9OHU5nDTki1bb4rSluptwtun+CuMjdlb2sqDhooNZ3Kfi4+m5q683Sne2uukWnMfrpVbLKYfX8jdpuIIJBJ9sQMWquqtDR/560oRJJUVuJmpSkg/6J24+9vLwDhV89//g2m5ft//z7Xe7feEBQctvprzuekl55bp9mOhcI1Fzlwz4lf93v+QjAfdqsrq6mU/RXTjnugpEWviz2KG8o350GFroALZEIwmt76glTq7w5diJ1BCDYtgbCIoahDb0STpK7PbsfDRF+eRxElXYqmSe4kkSr/QcHZZUlxAd8/IodAxBcDcfrT5tLsYW3hHHv8O3C0BjYyM8FJw5c4af/eSnPPf8c8zripM726A8h0dHlMWQc2cvsJguaBqHax17e3scHh6yvr6J9546zsHnL15kNpvwlT95he999y8Zj8dcufI4bV3zi5+/ze///u9zfHTAH/3RP+Ty5cfJCumPkALkpq6p6zr+xpIg1a4mtHIlFGWGdy3j8TGlNQTXYo3FhcDx8TFra2soY2laDyrgWoe2BqVtvIyWTI16US2vh0x6oS0WC5qmESq3tcznc2bzCY9dusTG+gbvvvMuXpT6lMMB3/7Wt1kbjmi8o9/v0zpHM685Phqzub5JP8/4+PpVvPOiXbSKpm3o9QZ4pWV9CkH6RPiWIrfdObUxCWqjML+wGVVVMV1UTKdTgndMZ2NmizmPP/44t+7c4e6dXQbDAf3BCK0sh8cHtK1YvltrmdcVw/6AtpEYQOscYxVVM8Ondc856qrCIG5sRmtKW5JnFqVD7Ecj+kUixYoEigffJfLzpu3G4KdVtZeBuXqIorRKKUz3VQCdib5p9Xf8tE2p5VhdHXeNdw+NS4jzjEo0MQHebTJBSAAZCB0uArl/2/unbColKKtAxG/Si/1N22ci+XjAA0H1fIhBhY/N8ZY8UL/iGCE0pJiZpkwu7u+QHe8in88Jgu8lqEq31LRGeScuVkFuievtvaQmzrc41yJd0D3pX5d8KLrERZqjRT+qiMwYrdFKdc0FsywTB6Isp6njsb0TPYqJzdRSoqHk9077TLTrXH2OUqnoImI8E8V+WmtMvD163ypNpmNWrGNCoxRt62jbmnpRC7UpomImk2qINnSIybvvvsvJkye5/fEdLpw/z6A/YrS+wfmzl2jrhuMjEXQf7B9RxM7Z2mpuf/wxTVNz8tQOTz5xBRU0B/v7XLx8iRdefJm33/oFX3rhS/zld7/Hm794k+9///v8+t33OXvmFKdOn6I/7Elw5xz1Yk7bSG8M71XUCDnhmHpP0zRMjo45PjhkLTpZnT1/njNnz+JCwOYZRa/k9JkzXLhwgbPnzrK1uYMc3uEbaXi5aBY09YK6muOVNCMKWtzNFosFRmdkWUZdi9Bvc3OT8XjMrz94j16v4O7+fYKGH/7oB0yPxrz55pv0en1OnT7F8cEh88mM3rBHfzBg9/Yu7773a8hyTJ5R1RWeQK/fE890bSmynNyKE5ZzAd+kuq3GYjE2p1f20cowq0QEPptMwAemsyk7OzuUZc69+/fRyjIcDlABFosZk+mUPM8oiwJcK02z+kO8k0U/yzKhg4VanEqCYjKZynN8oHGyPzOWuq2YzueCxqLR1pDpDGtMRPGTNkOCi9a38RoLGCP2/snYwVrV3TcpAddgjPobb4qlyUMycdA60hl16PZJqVqQoNW/mc26m9ES1GhjhO6hFdpodNSDrP5DCZfWaEOe5VibSddZFMvZK914aK5I90NYBV+Wc9vn2+9ue/DgC7DaTAyD8KFtt0/Wdh3X6g7e6gIPSEmBj2Q/aSiXovGluHSl8ViXpISusp/QyBCWYtMEzImIVDQWKTEGLYl/1Ju00YZeqrweHTxe6dioMHT3Za57OFlwkW2wmjwoa7qAaDWhSMlEl5gEF5+jH3rvdC4OtTwf33afQdzyEsK70qxQe7RysZIahf3GYIxhOp6xtrEJSJC1vrHB3Tu7TKYTnGs4Pp7QHw4xCsqyz8WL59na2mEym7K/f4TVmr39fULreOzSZSbjCW+88QaDXsnR0REHBwd8/3s/gi/A7r17/Kf/2X/OP/4n/430IUOqu0ovbU/VIxWuEKThrzFZBGWgcR7lPIPRAKWUnCsCsOiYPAQCRmf49pOBcJZlHT0u7Q9uKXRXStyjnHMM19Zlfs3ETvloPOH1N/6MV155mTfffJPcZEwnkw7tb+pakrK6Jldx9lJEwHcZkHonMZvMY23XxBLkemjDshqcmm2q4DGZpSgKvEfWLGXwTc10OhdKsbG0vmEwGAiNKAbRoXXxdV6KitF9DKWo65rMWtEHxe9FgTS+JZBZ2yWr8n1B50YV3Qil3UEUmFvziTHq29AJxkPUU2ltpGIXj9A5F67cj2RqYfjEgZPmgxCbFab3Ic4Ff+umHr3GlgnSJyqX0qzuobG6asyS9qUsKsXjq86RnVYrmUD9lttnQ/Pxe6MHf/xHLy9pB8F3DhGr3CGxRRe0GeIX6IMMTLliZF+IJSaVFvFUFhLqkgyCxOtr0+HlmPHxtm1RyECOfK9YGYgBUyJBrGR8PiI/wsFNJetUzl4GFd6H2KBQ3liKK5LICH1Ijidc9zSBpPNOXP/4tSgi7UeeI1Qu333GdNzVU9WkZkdxgCnBaTNjCSpwcLCP857Foubs2TMsZlMGo3WMMR1F5/btXbTWfO311/jqV7/KY5cuc/v2LfKy5MUXXuKDDz+gLErqpsFqTb/fxxY5bSWuVdeuf8h4PI49NuCdt3/FM8/+IV/5ky/z8ksv8dFHH1H2cl544QUG/QFvfOMvGA7/Q8bHc157/avkWUGe59FmF1lXUdT1AoAyKwlBLPzwgbpt0q/E4eGxGAXkOSEENjbXqeuayXhKWZYMR32xQLSCKrZ1I82fskwEc1aEdSlAXCwWhKDo90uMMdy/f5et7S2ODw65fecOG+sb0ltjMuWZZ5/nta++ygfXP+Dqhx9y/doNtna2OTo4ZLqYc2Jri6puCK1jPp9LT5CNEYeHh5RlLp3hj8b0+32qqqLMM/KiYDKddghEURSdYDDPMuEZe7meemXJ0fEBeZZzcHTExuYWJ3dOcO/e/VgxCvT7famgBEfjWnCefj8uhJMZCij7PRFAlhl127B3f4/nnvsS165dY319nel0yvraGr71tLVwsEGqQtZmOCUiVNSyYpgm/DRJpgU0WUuuTp48grakxx9FgtUjVKdUFXv0eBqNC22n+RBamLxWI+ci4jpZLBtXS0KmNb1eT8STcR7xsaLT0SCiPimVpl3TMhgM0KmJVl7SNJUI5L2PjbciBTMGDWnfX/zz/+5zzcfvcPt7v/f3HvyjP/pHWC/ar0Y13WPWy3UadEoOZEt6IeWjI8yKoDs4h9YZbkWolGsr+o14CO3BB3F2SxoS42IlJJf5X7c6Nit1cs1oUEFEuahI0wyREaBlTgxh2espvVemYiU3BjAq2lYbbfBWqKyqMdR1jenJ/Ge9ACHee0IWNSEuk30rr0nP8bZFYT7BVTdhWf0JRhb80uZUbd1VOAOQlT3qukZZ0bwoYyiU2MjjA03TsrY2RBnLBx98wOb6iOl0wWtff50XX36JH37/B9z86COOjiZsbI2YTucyhuuG2XRK3ispypKPbt7ipeef4/DogOPxmCLLGAyHbG/u8F/8V/8l/+Af/AH37u3xizd/yYfXPmA6nXPu3DnW1td56+1fopWJyLYghAZFcAGtA9pmLOpGkkllsEpz4uRJ6rriow/e59KZHQ7nDU3dkvUMrpXfa7A2wtUNh4fHDEcj6vmCvMyW81kIKCO/T57nsv5Fu2ObGVofKIo+zjX0+0O0DsxmFb5tgMC0quTKbVvOnTvPiy+8wDe//Q3Gh2POnD/H+++9z+bOJoXV0hTS2Y7iG5Rh9/ZtTp060Wk6iqLg6PgAayznzl1gPB5Tt43QdK3BoGidY9DvYzOhyzWNE7G6Vdy7f49Bv8d4PGY4HLCxscne3j02N7eZzyqcj/Qro3G16+hXWVng6opgCqwyHO3vceb0SQ4OjsjKgjy3NK4VilduUUpT1Q0kxk10G9QqRKOEGIcB2mYQKzu/SaOhlOr6/jyqe3q0umBQXTKWnpe2hyhRQsTvgIgQn2cTE6MDrMQ4QgdoYmXDerDWUkVn1qQDqaHTkBon350zy1jVJH0JQhdVcb9bSWxNWq/j9/AXv6Xm47ORfIxGD/74lVdITfEibyF+kelHF/vcEFZ9xelE3jpoQvyCAqJNcC6Vk5KQZhmIdNUCeSNSv4HUpC69ZzoHQTNT75GwkuVHR5EgqJYOWvoL+BTcyFuk+3IeIGxMWYR05NP61PHYr2SoXWMo3d0XXp/qOKPdZ0KQ8CWa+nDytbymA1Xd0MtzvHci8jKK48mEtXXpgTEYDNAGbn+829GKpMSZ07Z1LN1WLOYLjo6OuHD5koh7PVy/fpUvPvk07733Htpo+oMBVisGwx4ffPABzz/7LEeHR2RZ8f+x98axcZx9ft+HTx8MBoPp3GazWextGGJBMAzDEgrBEKqqCKoqCK4qKIpq+FyfahiOIriGo3MF1+cIrut7z+f4XMdRHNfx6VzDcFTXVQTD0KmqT+dXp9MpqkCwDMESLMPwCJYlFpvFdrvZTgaD6dMHD/vH88zsUrbfe1MUvbfoO8CAu8PZ3SF3nuf5/b6/7+/7pdvvkaYpx06cYHVlhZmZGWZmprj6yUe8cuk1zp07x8mTJzhy5AhZpvj6xnWOHTtOu91mc2uTsUaDpNcnKJWL4NOixIJWq0nkGtF67b4zR7Jc60DaZAJPYpyK0LBqRp6YyBxBcb+PY9vzkdOvctdXhCCJbbnbE4JHjxaYnp7m4Pw8fhTSbrdZXl5mY2OD8fFxjh07wfnzz3H9669I+il+YBGtrZ0m9UoFgw3MdZrR6bSI45hGo+FQpz7CBQlCiCLZ0FoTBSGZVrYaJCgQrtygUGe6QOpD6bPTbHLs+AkAlpaWmJqaptNquftOFk12aZxa9GqI4yutA6OlThlNmiXMzc5y587dgmstEURBgEAQukb4OE0J/MDWEN1Y08YGJEAxYQsh7PdjcvPNAa81cMFdofJhzD56S/4+mVb7jPpyLnKuWZ4b+ikUgQws4qP14L20LrTspbEoq5VPddLVYpCYAIWSSC57TD4GoTi3UrYca6U15XKZOO4R+tFANWaomvv4sf/01/6znycff4Jbvf6Lexde+OsIE7gkNRtQFowNuIWnMNoGmwrjFNHcHKzt+mER/wzP91GJwniDdS2U1jna5EGAMpZK4vv2tVoTGNdkLrULLuw8ZYRTRhPYtVIIhLRglXDKbsiBylZ+X+XJRp58aGnHldT2fYQcBDvCzYupo5rmaP5wQJT3Q2kcC2AoWNHCKup4jpakpR1nPnJf8uEZQ5amRGFke+20scBPkjE+PUW326PT7YE0hEaTZBlTM7PEvb7t96tE3PjqBi+9+BKrS6s0JicIyyVCz2dra4f27g61sVHu3r3L0aNHCT2fVreD5wuWl1a5ePESN25co1KpsbOzw9knz3Djiy85+9STZEnKzdu3OH70KJubTdY319ja3ub0qTN89NEHvPLa6/y9v/Ob/Prf/k3WV1eYmzvI5uY6o6OjTlTEIESA0hlGekgtqNZqKJ2xvbFMPfJpZ1hTXTJ0ZggrZXr91FG9LWkz8gKUyWwC6qoI3bat5mht7wUrEmar0pVqHSMFSZzgCcHE1CSdZhc8SRR4bGxtk6YZhw/OMTo6yvTUFBfOX2BhcYF333mXGzeuM39onnq5ROALdtt9UgSNxiitnRZaa+r1Ou12m1Kpgu9LmrubVGqjNrHIMuu74SoSfhRaep7W+L4Vj8FIx0Sx86kShvH6KLdu3ySKIg4fPoxWGf04QZkMo0D6Ap0JgsCj2+9Tr1atiEqcEPk+KkkwQlItV2i1WxgpqNerKA0mzRD+wN283+shfZ/A88hUhjTCBegC7Shz0nm+ZdKO6xC7vqZuQAX5ve3OAfBduKk8m4R4aoi27ca6lxvQykHwn79GaGWV5vL1TQ7FyWKQuEsPPPwCcB0WpCjWP5MLq0ikdKwjtwlh6eU5gAmgdWYpdEOg3TBFdLgy//+5hnNLgMub2nJUc+hYodk01PRSlP2sBogxuWCZzaYNWATTJTOZUgX8n0+6hcqUa/ZGmkJJJ/+WjXBmLw4FtW8nnEeGqwULg9BYjjhiv9oWebIz/AV77vfeoAlXWNrVvnOFVyCo9lpc6dLdwEIONQIajZeXU773M/PKEgWPU/gB5SjEKMORww22t5u0mjssLS4QRRHlUoWwVCJJEvr9PkncIlOKJ598EikF9+895Pz5CywsLLCwsEBjvMGJ4ye5++A+c/NzeNJj/uABAj/k27vf8NRTT7G5tUWj0cBow+h4gyDw6Pf6HDtxnM+ufsK39yOePH2Wy2+8wceffMIXX3zBzk6T5eVFglLE+QsX2N3Z4dbt25TDkH4lpRfHGDNQEzEownIFnaV4MqBchTAIrfGiLfW4oo8hdpN54IfkLqz5+wwPMq01Y2NjRUAPFKhPPmCllFSrVZ44dYr19XV6cZ/lu3fJsox6vc6lF19iemaG9957jxs3vsT3fX709lssLC0xMdHgw6sfcvPGTT779BpJ0sMIQegHVKu+a7SL7QQtrBFgTvfynbdGmtoJJzU2MAjDEM/zCsRfkyA8H4MmikpMhgHrq6tUajUajQYrK8uMVmsYLEprXbkDonqIymwQZDm1EpMqhwR5rjnf4+HCIsdPPsH21ha1sVErhZhmxf8sCEOiKCSJrUcIxUSaS2o6uMFmzo5aaQpUJU/0Y+UQ56Hkw3M9Svk9bw2bHEUxr3IMJep5hQOsnK/CFJXWPAkRziukSASM5c8K4dzHoUg88iTDfqZtLjbumOc+NwhD4iShPjoKWlt0MAhcA30+xt01PCZC8X+HV/vz7f/5TbhqscEU/hxgn0sp3Xw++M6GE0ghHQ3K2ApI4Aeo1FYrwC3inrTU43wdGgKNhNtdXlvQc/NADUfFNcIgjE3ZtaOZCHchdjUTKKMJCqDPBTF+vm44TM6NN2HYV4kxSg8aXnV+LQLl+kqKKoZ0RARd/AmWO+kUIz1PolwlX+RqPUKQ6rxvcTBmLbDj44chndYunl+mWq0QBj5ba2tobECrtKvY9u2c0Gq36MZ9DtVqhGHI519coxxE9GJbvZ6enkYpxU7Lqv1Vyg3GR/ssLTzC90PCUoTWlmJ77rnnufPN15w++xTdXpevv77JpUuv0O62ePLAAY4ff4KFpUXOXzjP3/s7v4nKFJVynU7bVtUtlUUhRUDecSqlBTLiOEZIqFar7G5tElVGnbeZZ++HRBXA5yAhtOFLoT7mEsCib8jNX7Y/MXDzsK0me269iEpWrKXValoXbGnXv2azye7uLmEp4urHV1leXiKKSjz99NP8pb/0l/lf/sf/gWs3b6PTFE9a8DcI/IKRUC7ZBCAIQqRwibiyqpTSebb5QtqeP3fPZFmGELaXUxjItGZ3a4ep8XF8P2RjY4OzZ0+zs7WNRb4ERiiEsx7MkXilXPXE84pgz2hDahTj4+Msr61SrVWQRqBlXgG08ZTvh/STvvOrckCBEDg2rf1bjF0bpLuntTCDBm/cHC4tyyUPXY0L0Au5W/caZTTCeIPvC5u8i33P7fpqrW+/m3Tkgb8QVpTFiP1JwuP9KEVcY2zLwPC6CVbkoNVqFfFDlmmCIBfLGIzTx1/3r7L9bCQfe/n9kRsX4b5ogdFq8AfmlYfcEEbmjX05spS/oUBlaYGE474cPVTaKs7MF45cVUszpCaQT9oWncnrIflPLTUmUy5jdu/n+IYIF7jlk4Awg+vLJ/qccyWLU9wNR0FBs4ubQXhDz/OrsI0m9rmr7MhB7uEWQeOuZUgqRdjkrN+3CFElsopIKyvrjI3VMcYw3hij2WyyuL1DvVoniiIEkvGxBlIK0jTBIAhLIY8WH7G4tMzpM2fIMs3du/d46qmnqNYrbG5u8tZbb5NlKeVqmSfPnGWsXsMXsNPaJdOKsbFxHi49YrLb5sjxY9z86gblagkwfPXVDebnZ2m1WszMHECpjJtffcWhw0fY2d4lLpctTU6naDchC8AjxChLHfM8jyAI0QZKYYjSVjsoTVJbBseWx1WmUCIljQXCcyidCzByhL7f7w+auxzSVAS07j7N5S9Ha1XiNGFyesLef5lip7nD9u42SmdUShHbu02ePHOWxeUlFheXeO6ZZ1lYWkB5UKrWMTql2+3gSw8/9EmSlND3Ucryvz2ssWLBqc010Z3xk+XJWtM7ozMb5Cp778VJRrVSYWlpBa00hw8fZWdzh8xYg0OUwHgSCaSZ7YmyHFQrGZrqPp7wXLDtEwaSXtyn3+tRrdbY3toGCZWohIetykkNnuejTOoid9ukjfQQxrNiDHmwJoTz9tCDgChfUwtPBJenyJzKMjQWhyt/WFCjGD2OC5OfA5JMpY9NqHkV0RRDRzskWJusOFcb7apjg4k9DwKlGB6ftiKn+jFhvQp4tFoZYVglzZKBV0N+lUPS4cUf+/PtT3gbAWRxL+Wa+JCDZqqoSha3z9CW31d5w2mWpXb8uN8LIej1raKPHAwEfN93Smy5wo19H2Xs/eE58QJlxOC+li7ozGXasVmCME66wJpYkXPgYaAIObRUoY3tNyzWGlw1Ix9vQ5W94dflf77IzzFmcMwedOCd3XOqotbaJfWCwLf0GM/zENo6k481xml1WgiToQzEccxoYxwhJL2eraRXS1W00Zy/cJ4PrnzExMQErVaLO3fucuLEMZI4pddbpzY2ijGGdqtJt9eiVK2yvr7KM08+w6cff8TY9DTbW9scOnKU3VaTzY0tKtU6Vz/6iNdefY1Oq8Wrl19laWmFA7OzHDp0iN3tmEePbvC//a//O3/6z/wC/+Ev/w2ENDTGx8l05tb5DCMDpNAk/RgpBJFXQkofGUbIKMBoRaZTPGPn9dikDsX2ME7kIlVJUYE1xpApxcz0NNs7O7Zqj7DhqrTgSpqmaAH1Rh0JbG9u4fvWNDCKIutzYmB8fJxbt27x8OFDDh06xPrmFqfPnmVtbY2LFy/xj//gx7z2+o+YPDCDxKBShTGKxvgUAkm/H6PqKWmcUqtU6fS6RJHt/ew0W2TYeT0PnEtu/er1OggR4PslBzxpoihifWOT+bl5Dh6c4+rVq7x88SIra5b67chIlMoh3W4PjOshCXxU1scPS3iBX6zN6502x44e4dHCAsIIGmP1gRiA5+GHIVIlKGP7YX3jhEQAndlq0vB9DaDcmp/P0MIOdrd6FIPfCrI4INMXg86/fPxIN0fkr5GPjRsjRJFkFaCbq57kgLdSLkl11OE8Dsjpy/sSkDSDUmDH9JDn0G6rydTMNM2tHZI0pjZaJ076BSNA5W1sgoKFkA3+0p9qE3/8Kf8vbCP2n2rEwNFXA4+7Aiuj7C5ss1qecSgEWue7Q4w8H6SHMpBp6/PgBSHSD4rdC8J9xig5SpVnrQPqk2tAzVEYlyAZbfmVnhzaPeuRkDtUCmTxvNiF2/NmcOH2vCSWd9YNf5eGokJiXLVFIIqgs+gz0Vg0wIllGc13rsUmeJJ6vU6lUrHNg77P9PQ0Wis6vS69fh+E4OjRo4yNj6OxHhMy8DFScv/+A5aXliiXSkxNTvL6a6+wsrbC9tY6H330Pquri7x++TWEMczNzRGWyhyaP8jW1hZplrK8sUalXqfVanH37l1qlSq7rSaPHjzkuecv8OGVD3j3vfdotlrcvHWbo0ePk2YJOztNLr/5BueefYYXX3yRubk5kiwl8EJCT+LLgMCz2bo1FKpRqVSK6kW307PlSGUDbN+XeL79P3q+RUCkFK6yobHqf4Yo8qlWS2RZgslSfF8SBB7lckQpiqhUKkxOTjIxMYGUguvXr7O4vMLubotqbRTP90EKJiYnCIOAc89OfLQUAAAgAElEQVSe44mTp3jjjcucOXOKV199lV/+pV/iqaeeJO71GC1VsXJWVrGrPlqn3+3Tj3sgLYokhUez00YL6HQ6tFotpONBS2wPD1j5ZUvbCAiCyN6/nkW3dptN5mfnMFKwurHK0RPHXeN8ZnmoOm9ohdCLiMp2Iu/H1mxLC4NWA0RldnqG1dVVNjc3aEw0qJStAVaaWdGGJOkTp1YDP4oC/MA2YtuGSBtwGOFhhIcyAmVwY9nu+dgVwkfKACl969Du2WP2uN2FsA64SA8jrFttpq2vj8a+tzKCVFl5yrwBVgsJnm+ljd1zhSAzrlFSWCKmNlb+VkhRiGTku8EU151fgzLQixO8IGJlbYONrW0qtTpxmqBzpTajUVoVghvaBaqD1e7n28/S9jh/GwZjIa94SOH4/kORiHRgW15BLYAMBvTP4ffLaYOFwSyWOugh9qtKuffeR937nqQ1R2ltcuH8drDJx3DSYJMU4UjCThnOrZG5Wk9xj7oSzPCx4XNwylX2mBwscTliz/6kRRpsM6xWSOlh3LycqpSpqRnW1taYmGjYqnwvJYhcnyjS8vjx6Hb6NBoNwCYpURRRLlfZ3tkhiiIAwtAnKkXUR8c4fOgomUpYXVkiCEKSJKEbx/iej+fZKvPszAGeOHmCu9/epVKr8Ud/9D/zwQcf8OILL5CmKdNTB7h//x5XPrjCr//ab/LNnVvUqnW8YJj2Pegp05my/yvpvMoAfBvICm2po8ZoapUqaOGc0i21LwiCffecdFWL/HEQBDYoVkPyu+7+MsbghyFRFCF9D63t/ZXTdpvNJghBqVSi3W7zwQcfsL6+znPPPcfGxjadXhfhS6rlKkm/izHWPkAlKb7vkSZW1UorRZbpAgzOex/yQHnAEBmwCDKt0CbDl4JareYMIwNmZmbodDrs7OzY+V7aa8VVvYRn13PbjC4KLzjyz3ax0PbuDgcOzCCQZGnG2NiY/T+6HofAD5zCnKuIK+HCTceCwCUCJifPD6QlChjMxa65saIRgiTLEHl1xpjivOHXDb8mP2dQJfhuvWCYZVPMR2JArcp//33+QZY2CWizb8zGvT7lyK71yoFrwv0v8kb0fC4aVrST351qfnD7mVjO9vbIha6GCsuiOIaQ9ndSYmSuEGADFWFsdm+kBs+WefEMTp/A8W2N4xCaAqUxaKtuhQThoY0ofg7vhXCtEdaZ3Ag85dAul4gYKaxnhrAOmRpV7LmdvXHHhyWBrWIWRVJl7wvrd5Af09qirpnO7HFlkyuNxgiDUvY98mNaWBUTi+7ba8j/D0booqk2jXtkcUIWJ8T9PnEc0+u1WVvbZH72IGms2dxsEoZWUrVWrtCoVpkYrTMxWqcalgiFYGdrC7Sm3WpzeO4AWqfcu3+XI0cP89VX15manuDixQu8cP45jDFsbW2RKUm1PMb4xCTTU1M0RqtkvS6Hpmf56quv+PKLL2iMz7C5usl7b7/Nobl5rn32GZ9/9gWe57G0sMyZs2f58stPOXJkjqTbJhAQSK+YHLIso5slhepVL+lZypIAX3sY4+N5IcIL6XZTev0+/ViRKI0S9jX9NKOfZsRJRhxn9HsJudpyv5/Q68W0Wh2a3R063RZx0iNOeqRxyoEDM7ahzw9IM0gzSRiVaXU77HZadNpt0qRLY2yMSrnM6RMn+OVf+g948cILlMtlJuem8IPQBbuQaYkChIhASTw1QBtzulc+8Vjkw0N4kjAoUQrKDpmXBdVICOGawDVRJbRVi26TJG0jvQAtJTnHVRlbOfKC0E1wnq0ISYmHh9DWfVsYRZb2qVci0m6XsVKZeiV06hiWPiAAVOakY10wY0DkzbpioOpTyGo7mdlhZ9ZMK5QzVct0SqZTQCGGxh8MKd6prAiMcLMHDBuqKduU53ZPGQIjiucy08UxTwirKmY8pJb7fuaPyewcU6BMbsHBIVSWj61sydu4BkFhJ3hLyRT7jkHuRP3z7Wdt+74EBPJK9qA3SOaeH0UA6jFwE7ZgB2jCsOR+lx+T+9SLftLnGnB2cpYGPJwBiNx+2UnYf58b8eOfMbh/ZTGuJB46G6xrufKPdKo9w8eG1X/yY7a30dJHjIJc3jrwIwTW0BVhV664lxD5NqkwWiM9j3IU8ejRI5566im++PxLVtbWqI+NkiS296FSigjCoECDo6iEFwY0Gg0aYw1u3rzJ8uISz5x7hmvXrnH06HEmp8ZZX18n7nUYHx9nY2ODY4ePUB9t8MzTT3H/4SN8zyPLEt59/worq+tMz0xz55tv+cM/+F1u3rrB3NwcnU6Hqx9/wNGjRyxwozRX3n+fr25c5+onn9n1wygyQ5EABKFH4Ef0kx79OMYPIzzpW8DKBXomM7Q7bRDa/o+wVf5ut1NQrjxs/1+n20UY25eY02b6WUIQeESVMpEf0O92SeIMT1jts3JYod1usrm1hUAzdWAGgBPHj1Mp1zh64ihT0wfo9/vMzB6gPjbKyZNnWVlZptftsNtuUg5L9LsdWs1d6pWKlcWPYzZ2t5GBbyVhk5RSqUTZMRY8XyCwZrpWaCgiCDxUmvexWNGN0Xqd1dUVlldXee78BT768Cq+BOlZvxQh7RpTCksEfkS326fTajLWGLN0VukVUWN9tG5ByH7K/KF5mu0W9769h5Q+QSCtsW4QukqBQitjYzGlnTRthp9X2ot+Fb+ooBfiIplysanASNuXjGsMV2bg9j6gRFnVOturZecCq+rmFXPDcNKa78MiKkVSOVTlGK7mDye/AJ5veZEau0YJl+yEpRI7zSblKCLwPLa3t8ipkpawYPYBBTlQ8a9S+/iZSD5GRkb2BU/DP4cfe56HL62EXkEzkQItLeUIRPET7A3pB16BhKZZgtLWMyHLNMKVpIY/K/9y8ybrPGNUaYLOtKMZWglAlWlUptEqL7e7L9dVKYw2aKWLSoalWWEfC9eYLmwTkdACRWb9EoRf7MIHPFs697B+CsLHBX026cp3aWyQ4mFcauohhW+vVdu/WWtbjg9LASKwXhaecwSPylVmZg+wub1DVClz6PAh+v2ETqvLrdu3ee/K+zx88BCEoNvrsLq2Sq1c5tatW7z99tu89Cu/wkSjgc5SKpUSl1+9xAcfXOHWrW8svatS49atW8wdOcTK2iqHDx+m3WqjVMbG1hZ3791hcfERp0+dJk169PodLl9+nfX1ddI05fXLb5ClisXlJbI0o9Vq89prl7n02ssF1W1+bh7fQnhUSyWC0FYnxkcblEoRvvQolUr0u03SuE+tFFGrlSmVSwhp5ey0sdm97/vU63Ua9VE8Ka3cpG8b1XN9cS8MEJlPkiiau22azSbdpE+aJjTGa3ieJNMxlXoZI6BarhGVy6yur7O1tcN777zL1OQ4qxurXDj/HBsba9y+eYt/99/6Kzz77DM8/fTTVKs1PGGrOkmaoQW4WxHPC/B8n1KpRKlUcuZTgjjpkSTWYwZPWOTQydoWJfpUI6WHyjSlShllDN/c/pajR46ws7mFyjL6cUy5XKXfj2k123Q7PVteb0zQ6/WsvKYArVIypeg023h4jI3WWF5epNPqMtYYoxKFmMzRwzzo93ukSWK9Z4REiDwZyRvihDNyrOTEJ5ROEdqiZVJavwCbUNigJtc7zyfDTOdkdOk8a8BF+25Cl0hHgxGusmmw6O/wPjzR2snWVUQdcheGYTFvDMvhqkRZVFPpAsESnl1oq+UKfhjS7fdBelZm1/OsRKi0XgVSDJBp44Kvn28/G9vwQg/fTQSE59YzMTivQPMfP9+YAhjLtfizNLVKbVKinfHccGKQI9w5+JA38FrEV1tlR2FfLwCtnPBCTr91nlBFdX+I8jT8dwEEYVDIa2qsbKuVaRf7pDaLPhBB4aNTMAsckphLcuZrrecq/wJhK4hKYXDVfykIyyVLHQ3tPGVUxtbWFpOTEywur3L2zFnm5ufY3tikXC5Tr5bRmeLRwgLbG5skSUJUKuN5Af1+wthEjSdOHuPwsSNc/eQTpqamMMbwwvmXeOHCBT779Bonjh3n7/6D32Kn06bb3uHN19/g4PwBtNbUaqPMzs7RaXeZmp5jemaG9bVFPrzyHt1ulzt37iCkdGtbk9pYhd2dba5/eY0oKKEMhEGZ3d1dKtUqW7tNpOdZupsTfsnSjLm5w/TjPpmygIz0hO1pA5uYqQyTKWq1GolzMxeumh2GYfGdZk7hs1wNkZ4g7cdF/JRmKf0kwWgIy2Ub6ErF/Nws9+/fZebAFEZp7nx7B6ENO7vbnDp5hg+ufEAv7iOigAMz81y/+TWloIzSmqQX049jPE/iB5JGtU51fIzN7S0qpbJVcXPgIGBjJxdUGyRByS/YH4WapVEkSd8qPCYxQVhi/vAxMIJqrW7pWt0uoR+glKXzhkFAkvaJk4ypqUl67RZR2aoKpllMpVSi2+3SbrWojdap1RvIILSqbs62wNIDbS9JmqZkBjtH+47ui5Vy1zoj8EKE9F2PJCA8K83v5u68T6xcrqC1cuQZm/7na3IOPEspENKa1CqtLKCOwGSuyjJUGR2ubgyrQ8auWTz3dek7KmcuHJQnLELYHmaLi1mg2/MkuZVEkqR4nk+5XHFACfuSmHwue7yn5KfZfiaSD7sNDPNy0z6bCQ4QIWHsMa2zfXvOrSuYCTly6p5Labv1fc8jDAKr+ezbJqIg9PEDjzAK8APPJiueHeyWfSHtcz8ovDp8GbjdGzIsGyA7OUo5yFplgXDlxwwW2TR6UELPUZ+8jKUFFgUyNtCygZV9TWHw5F4jsGVpT4B2TfYCqyzi+T7S8xHSmlBJzwch8aRPzl2WUrK9vUsvTpmamqBUikiThCxLaHWazM0d4IWXXqSTdvn404+ZO3yQZy+cZ217k2MnjnH+wjl2/+ifsbG+xpfXr/PuO+8yN3eI8cY47XYX3wt5+OgRn316DakVlVqZS6+9Srle5eTZp5idP4gMI27cuMGhI7OMj9dZWlpic2Ob+blDxHHC4tICo2N1vvnmG/wgZHxyhmq9wXPPX+TgwYP0ezErq2vUxhrgQSkKiTs92q0WaEWlVGZza52xsQpHDx8miDy2tjaolAIqpYDxepWxapWJsVEmxhpUyhHddpOd9g7Cg0o5QkirrIE0VmUkTVH4aBOgjYcxIRLrBL65tUXcb5H2O7R21pACNjY2qJVrVCqj1EYbvPjyJVqdGKMl9xce8sknn6G15u/+5n/On/3Tf4be7i7l0KdaDkiTmKmpBp24T1gv009TPE8S+L6TbzboTONL6cBOg8o0WdwnSWKbkKR9Emd45bnm+16vT5poSlHVVmvIaIzVCQMPVEa/08QX4HvWpFICSsVF0CODEC+MbLIbRighyfBIjSBJUpTWZCZDCUWiMpLE0giQtnoihbHeKUIPad4LlJGkKrO0yrwy6Urf5KpvckADkcLPybP2WqTvKpuO++EB5Px2axyY4Yz9hCya/wpd91y3XO7/HGVskKWxKl/CsyaU+eu+7zXDwEZ+fF9/Sc7tzfm+QrhGXLe4uMTk59uf/PaDCcfw82FPj6JXYlD1yL9rrbVdX4SVGVWZrcjnsqNG2zUojof6uqBAW3Mhh5xiY+XALdimUc5slAI9FUXiDVmSfj8N47HAIkkT66WhJVK76k0g9j3Pd6HEvmN5JXD4HKkd5cooVx20a6MnQKPwsM3RQkqHfvtkLvizSZgg05r6aJXd9i69bo9avcr66hpx31ZAx8bqNMbHicpV61guJM3mLstLi6g0RmUKLwyYOzjPw4cPqdVHmZs7iPQEb7/zPn/449/jnfffod1u89GHV1h1RnfXr19n/tAhzj33PA8fPmR9fZ1SKeStH73D5cuXef/991lbXWNrY50XLr5Et9ul121z/8EDLl9+hWuffsat2zeZnJphdX0NozRpaivGfuDhy4BMGTbXNxlrTLCzvYN01JcoisgyhVGpjU88n91myzpvO6DEF5IsSfECfx8Q0u9lCO25ivIAaR9tNJCeod1qU6qU8aRPY2yMdrPDna+/oTZaZ3Z2hizVVGt1vrxxndNnnmR7e5fx8XGWlpb48osvidOEr7/6mq9v3iIKAqQRRH5AZqyRZSmK0Moa3QaeBTttohRRiSwwp3RGmjjrA2n7U2yiZINo6UHaj9nZ3uXUqTN8e+8eN764jucFzM8fot/v2v+PUUSl0FkCLLvguUy/08YIyOLEBdiKNFFUyzUq9SqdVpNmu4UQPhhFpqx3SBSVUcqQqgxtDF7goU2GzOdlKen3OjbRNhb+1Sg83xsqPNrEItXJoOKPJFMK38s9a3KYzVYtrS+IRmiNEBIh/ULBNR+jwyDE8Fzj+z65H4uUslAgK6anIWVP22si9tEiC889BvOYncMGVMmcqpmDc3bZ++lTip+d5GMo4hZGEvoRUVAiCkrFYyE8POHbyRX72JfBvsA/L/8GXkjghZSjCtVyjcboOL4MEEYWr0n6KYEMQFmUUiWKLM5QiSL0QipRBV/4hQuppXhYtZ+CH+cNgpYkS4nTZJ8BSxF8uPPygCX/0j2BdR93BK99TuhDe04f+cm71WQX2tLWENJKECt7M0s3aadphtbWUNAYQxCFeEFAuVRGCsG1a1+wvb1DY3yce9/etT4P5TK9TpfJsUlOnzzD6uoqK0tLHD9yFInm9OnTnDhxgrn5Qzz79DmePHWWe/fu8Vu//du8dPEl7j28z9dfXSdNE0bHJ1haXOQf/Vf/NcbA4uICs/MHOHnyBEePHeb+vfvs7OwyPTPLu1feJ85S3nzzR5RKJaSUJEnC7u4ueD5RpUqlXuOjjz9mrNHghfPnmJ+bZrRSoRKFHD1yiONHjxAFEb1OhygKWFhYAGEYq1WoVMp2gDtHc+FJVJI6ipZg9sAsR+YPEQUhKs3wpFU4S7OsMCzyPasHbkwGaEu1Uhm9ThejJZlWlGs1xsenuP7lDT7++BOXEGvev/Iuc/MHOH7iKCefOMn7V97hmXNnUSrlx7/73/Lw4QM+++wzlpeXOXXmDFoZgjBid7dFfayBNraR2xOeNR6UksAPCYMIzwtIVUKqMpcoSDzPJwwjyuUKebpuzSUVlUqVRqPB7Tt3OXHyJMLzqDcaNDsdEmUVSZASI2xyNTo6Sq/TJXOLhC8kYRiiM4XvpIzjLKXTsdSAyYlJwsAjinyL7GTWf0UbQyDtwqu0bQaXXkCWpiSJlaIEYIiqaE3TXXRmDBjlEFhZNMXLAnV2JQ/l5BI1SCORwkcKH+NU64xWBekTo1FZOjDzxJl+gjNcNISBhyeh024i0KgsIU36qCwBo/A9ge8Jm7AJq7+eu51LYYrdk2CydN+u04TQkwRSFIZvwqGYP9/+5LfHq/SPb8OUph86BwYymDkfO6cK2QomhZHaMJo9XJ14PFnAVQy+T+nm8W1YqnP4Oh9//n2O6z9pG6Z4DH/24Dqtwdrj15bTh4qqCIJeErvrtFLXnmcr18JI0kTRbXVRWnHkyBG2tzfxfEm1akVTVJbRbu4QlUJKlTLlao3Rap2HDxe48+03zM/OsbW9zfjkBO9feY9abZRz585x5843KAEH5+a5+sknHDt2nHqlipSSt956i6XlRRYfLjA7e4DqaJ319W1KpYgPP/yQe/fuFeDORx98yPj4ODs7Oxw4cIDlpRXeeP11Dh8+zJ273zI5Ps7E1KSlkCptk0+ToVWKUlCObE9d0kuQfkin07HV0Fx9SQjK5XLx/8vvjTy2yBvQjdYWccf2guJZlSk86HfbJJlNdpM4QRi4evVTHj16xOHDh4miwMoDS8n1618wPT1Nt9vhwd37pGlKo9Hg888/4+KLF7ly5QPWNzZI0hSNxvNDEBb4DX3pRFAGQh22N9H2NdhKiNzHNsl/5o7ovqvwA+xsbTMxMVFUUXZ3d5mYmPjOvV6OSnT7vaI3Jr+HlBoIGdlqTIbn+cV15eOjkNoXYtBoLq2holEK5ai1+fjMLQmtrHvwg+Pj8QR/3+YqoblEfC58AwMvj+8b+4PHlo4/XMnM55Ph+yP//fA1Db/f949d9v1++HU/1F/2Q9vPRPIxggUrB83fZiA5m6OIGHzpWVlP37OZvQsW8x0pisf5e+VNnHnQ3+336CdxkUDkz/M9P65cA2iqMjc52D1N0+JxIV/6mNbx932B3/1pZX+dH61rfqXoHXl8t424g5/KUDzOj+cJUSH9aWzSE2cpqVZkRpMpRZZaIyGd2UwebTDK0E36qCylWq1RqZSIuz0q5bI125OCcrVEmmWsrq8xOzPN6GidJIsplctcuvgy73/wIeMTY3R6HVI051+4wO/9dz/m3DPn6LU7jIyMsLq+yUcffMDayip/9+/9Fxw+eJBypcKnVz9jeW2dXr/P2PgEfuDR63dpNne5+tFHxHGPM2fO0G23uXjxIkePHqUShaT9Hm9efp04Tbj06issLq4ikEzOzNCNU5ZWV3j0aJFu3KE6WuP5555ntF6j1dwlzRRVRzcyKqHf6Vk5YZ1Rq40i8FhZWWNtbYMgiKjXx6iWa9QrdcZqYzTqDcYaowS+JgolURjgSUiTmF63RxCV6CUJBp9qaZS7397h5q1bnDl9mlu3btFp95icmObFFy7S3G3y5fUvmRifZHl5DS/w+ermbc6fv0Cn0+Hzzz8nS/o8/fQz9Ht9onKZne0tKyuZpO57t1XBftontyaVnkcYRbZsnKX0el263Q7dbocsSxECS0fzPXq9Lv1OFyEEGxsb+L5PlqXU6zWUyvbxzqMgsJM2doFTWYbCkMVJURWQ0iYjvV6PdrtLt90jSzVxqpBOGUSnmWvQthM3KrP9WSrFD0uUosgG3kKAkUihXXIw6BOzyIssxmy+W/XSIS6scOPPmELRyxeOhqL0gJ4lbXUw7z3Lj9v6tCEIfVuFcRULm4xqW23xPMfV9lzVJe8xk8WxvLKRH0PklUzPuTxTVDbxfIz0ika/n28/vI2MjPy5kZGRPxgZGfmnIyMj/9PIyMh/7I6XR0ZGfjwyMvJH7uefcsdHRkZGPhwZGdkaGRlZHRkZmfvjPmNvb69oAPVdQ+njzaa518UwPeLxNSF/nlcPfd8vAqT19fUiQMh71iqVSrHWDKObOYXGGIsUp2rgeD28DVda8vfIvYyGfz9s4Fsck/53go+crvz49scnHgCDBtkcGTbGkDjp24EAjLLVVwDnMSSEz06ricpSdre3OXhojoOzB7nw/HMcOnyIwA/RKoUMEpW5alBItVqh22uzs73D1NQk5y9cYHFxkTAqc/v2N/TjhJdfucTduw/49LPP+Pqrrzh49CCnT59mZORf46133uXGF9colUocnj+IF0qWFhfY3d3l9OknOX7iCZrNJr/6q7/KuXNPMzbWYGy8wf3793ni9BkWl5c49cQJ1lZXMMbwzttv8eDeAwvMSIt3J2lamOZOTk+gkdTrVXZ3d4nCkF7ct670ykmkGvDDAO2ayfP7JZ97i4ZuIO3Zudv6YiiUUBao9TxCX9LtdTh9+jRPnX0anRl2dnZIkphavUJUCni08IC3fvQWvX6Hzc0t3nr7LW5/fZtut8v45BTvvv8+l994nZWVVUYb49y5dw+DoRSVUL0epSDAoPCEFV7J6Vc4iV4pJaEf0Ol091X1oigiCIIitvJ9H5Nl+FHIsWPHOHjwIJ1Oh83NTXtnycFYEkJQrlZZWVmxwKUnCRxFtpfElEolgiik2+2SJAn1epXx0THiuI/B4GHl5BV2rASej9bQ6do1TkiJZe0qS/3XuU+bbXb/bo+etkwWhgJ+addPkQsBuWqDAAvguQqG0rqIg4dBAq11AWAMwIY8ubCU+yyzvarlcrVgC/m+tAwDp9yX92Dlbe/5eYO/I++RFAz3qA33qWidFX/bT7P9sVK7IyMjfw64BtSwFZdP9vb2/v7IyEgZ+EdAA9gBnt7b2/sXIyMjI8DfB04BKfD83t7e8k/6DLO3Z3X8jf1CMqPpd7v2C3LHjLANxcMGYkDBzc+PCXfMGIPJFM1W2wbeWcbEWMO6fWqDF4WEYUi/23U8yrynxA3aNLWccewik39GPiHnqFFu3AIUicf3oU3DC44xBmGsjDBOLaC4fiH2BRmPG6d5rulW58jbkHmhcXGUEFaFwQgBbpGxDY9WRs3a3wrbdJ9l2IZmTXu3aQd84HH8yFHq9TphKeD4seOUSyHf3L3H1oZtvOv3+zzzzNPcu3ePhYVFvr3/gPPnLxBFttqUxCnd1gatSouXX37JNmsbw3/523+fv/pX/31ef/Mt1tZWqVYrXPvsC44fP879+/eZnpgkqvj0ujHSL7G6tMIbb77Bu1c+IERSrpW5evUqo6OjfPnll4RhyLfffstLL1zgwoUX+ermTdAZ3357j5mZKQ4fOUStXmd9fZUH9x/w0ZUPeP6FC1RLZZTRbKxv0O70QLuA1Pep1qpUSnYiOnvqNArD3bt32djYoOrMiwppPhQTEzU32XskicF0BVG5ThanCONz9PBxtje3EHh88snHnDlzhnPPPsPyyiKddo/t7W3effcdZqZn6fU7hJ5FmzbXNrl27XNmZ2a4cuUKR489QZL2Wd3c4uuvvyaaHKfZbKISRaJiSAVx3KNer9teH5MVE1QURYUk3+PIR26cWC6X8cs+ou+xvb1tEbo7d2g0GlSrVdJEkWUu+fY8PCEYHR21BofGEIUhaZYRSmtAlvNMx8bG0FmK0ZpatWq5wYmVls20RmptsVAjiCpldJLST1N8EisykctXozD4CJSdieRgPBWTIKrg0OboqR0XgyDQYHnqeojmZM+3vR92PKkhCmgeSLn5IPCdD4ywPTceZCqxlBdpOeraZI5K5RYoYQUlbN9Xfkw71CUHLPJFyc41vV6MlLgAKioCw59vP7hp4D/Z29tbHhkZ+deBfzIyMvJj4Hng9/f29t4dGRm5DFwG/hbw7wF/3u3/JvBb7ucPb9dLA2IAACAASURBVCMjuGkVsNLJhYGstsfyzPh7KxPs50h7nleMvzwpiBxYMFwB6Xa6+16Tv/cwBWM46P8hTvb3VTeG17OcOz58zuOPH3/P4e2P434Prs+OKxuAWaXKNHOouEtGFFY9SQ8pNQE0xhqoTFujPC9gZ2eDKx9eYWFpiY2NDabGxgjLFVS3S5bZMXX08FFeevlF/vD3fszv/s7vcPv+fY4cP8bSwgIHDx9ma2ODE088wcMHD2i3Wnz88Sc8//wzXHz5IqeeOMkX1z7nX/4f/yeffPIJL7zwAre+uc3xo8coVSssPFoBHz54/z3+7X/nr7C2tkYcJzz59NMsLy8TRhHr6xv04x5RKaLdarK+usrJUyeYmJri2zt38aWtUHgywPclmzvbbK2uUq/XmZ2dpVYp0+tXbAVA2Oq2xGd5ZZVKGBEK2yCd6Wwfym2TR+n6Yu3Mp9IMZTRGC6r1Gu1Oj1Jog/PtnW1OnDjGxESDW19/xcOHDzly5Bj1eo1ypUwpKjM1PcM333zDaK3O7MEZmu0Ws7OzzM/Pc+3aNc48+SSLC4/o9Hr4fkroPMlytU7tgNt8DcpiSw3KJeKzzCaNOWI/DPLmyannC3Z3d6nVaiwuLjI5OWmBYZ0CUZGUawMqtfdVmmWUwjLtdhvP3e8qy/A9HxnIQr3K9zx0ahC+FSnyNNYUVypS13+ltDN1FAbjBWRJ31Z6XG+HYJBEDzYb2BsjQNhgX+Ch5WDut/8gW72Unizkb43WhbqdGqJzfn9VVRJFAXHf9k8hbA+ypXhKpGerSUJrAt/5zbhLsAquDHoYrHbFTyxTDKrA31XT+knbT4Ol5RP6XwQOAX9zZGRkGjuB//7e3t6fB37fPYf9E/oL2An9J297ezbnkgKlMrIkIXCJgJQCz5OEvk8/jtFphi9kgTBGfmC9NlKrMtyPY3rtDv2OlYtVWhNIj0Z9lH4cE5VKeL5PFickvT5hGBFFJcpRROiadZWyjTYqTdFZRpKk1rzI3XhBEFg1pH5/36DIF4M8qx+ukOTlvdyJ2g9DvDDED30i3yf0PKQv0ViOnzTGalp4Ai/w8IUAJzOIBF8ITJZZlSxheYiBtK6cnnBUmiS1ModphkpSS3FRCiEk/W4flQlKQYUgiqiUqyRJQugHfPTRJ9y6/Q1PPvUUX934ir/wb/xFMm2Ynpik0+vw8qWLPPnUU3x89RPe+NGP2NjYYGx0lLfffovDh44Sp30ylSIDiVZw794D2r0O9+7e4z/6m7/Cytoq5VKZubl5Wp0OtVqVN998k5devsTq6ioGxeTkOCpOKdfqTE5MIrTimeeeYaIxzonjx2m2Wpw/f4EXL17k+ImTfPPtXXq9DpMTDYTncfHSJaamZvjs08+5fuM6R44e40c/epvXXv8RnVaPtY1t0tQQhhUaYw1evXyZr7++xZX3r2A0vPv2W3zy2VWeff4cF84/x5HDB3n3nbd49ZWXufzaK7xy6SJPPXmGQwcPsrm1yYOHD9je2WJm5gDz8/OMVisYLTk4N8fq8gpaQVSK2Nzc5Nq1a7TbbaKwzMrKCseOHUHpjK2tTdZWN1hd3wABXuAzOztLuVzm7NmzvPnmq1y6dImTx47yzJNnebjwiCgq089itBaMjo0xN3+IJNP0U1tlqNVqlMtlms02ExNTTtEjIu9Daret/noUlV2wK6mPVlleXnaIUYW1tQ0mJiZA2Hu71+vheYIkS+n1epTCiFIYIaRktF6n1elglLKJZBTZJj+d0el3MMKQmZQ47mKwhlqBF9mxHnr0O22QkmqlgtLa8pkFZFmCh6ULCOEhfVFQFT1hJaR9z8PDpxRG6MzOI8L3QGmyOEElKSpJrVN5bjhqLHc1CkOM1miVolWKwDYt+p6H7wkwCgGUoohOs0vkR0xNTBSKVR4egQxQaYovJfVKnciPkECWJKhEIbRAaKtwEvo+0kiSXkIax6RxnzROrMeQsoo90gWxpTAi8CS721s/5bT+/89tb2/vn+dA197e3r8E/inwZ4G/BvxDd9o/BM66x38NuLZntwWgNDIy8os/6TOcywcaywE3Sg840nnlXUik7xH6QdF0jbTGZGjjFIbs631plX3iNLGIpyepVapIRNFP5EuPLE339RFJBFmSFu+DHjAD8t6QHPnOAz0hxL7f5fSS4W34dQXSrHWeUhV8b8xAhEEKa6qZexgod74nB4qQ+e8HpoGO8qWsbKmUEuWSD5MbFSKI4z4Kq+LkhyFKw0cfXSX0A55//hyLi49YW1tlaW2FuNfn2rXP+OzzL+i0mrx35QrdbpuN9XXeu/I+Txw7zq//2q+xu9tkdcX2WwShlYbd3t2hVq3yG7/xG5TLZZ56+ixT09OUohILi49YXV3iv//Hv0+1VqbX63Bgeoa7d++ytLiEEQGlqMLc3Cz379zlqxs3OHX6FLu7u+zu7tJutUmTjG63y8EDBzh99gzffPMtOztNOwd3WghhmJ07QCmKUFrx8N4DKpUKjUaDVGVsbe/g+z6nTp3CSMHy8irLq0uk/YSJiQmUUkxOTjI9PU0QBERRRK/XI0kSdnZ36cdbKJ0x3pggzRRpYuh1ExYeLrv5N6C10+bLG5+zurqM0innzp1jdHSUleVVHj1aYH19nSSNWVx6RLVUodEY5Ytr11lfXWN1dZXbt2+jEXz99dc0O10uvvwynU6PclRha2ub0dExNrbWXdJQsQCNNtgGesXm5iazs7Osrq7SbrepVqsopeh0OsX9mSQJk5PjLC88YmV5jYWFBSYnppFSUqvVWHi0RLO1g1KKickGKs1oTEzS6XRojI7RT2zDfavdtkpoYUTiqvpZGpPEGWOjY/SSPs3dJn4u2++UIqX0SNMMX/jEaUYcp0RRRJJljgucN89n3w3Gha2PZlmCJwMrCgFEfvCY5LCjOmo7H2inJhlFEVoMFLDy3ui8YqF1Rt43naaKUjmkXKrS3G2TqYQ46VGKKmgFvhcShJ5V50wUKjPWVNgDm+LYXuJMJdbTCo9+PyHvG8sy7Soptqe6UinheQFxnP6k6XP/XLq3t/dTnwwwMjLyO8BHbj+2t7f3z92EfX9vb+8vjIyM/LZ7/N+48/9Zft4PvWf9F39x74W/8df3HXv8i9DaNXZ+T0kXBtlXXn0YnnAf58jlr80n2J+0FZml9G0g76oh32dtP3z+cIUkR5OGuXFKKUu4+h6u3OPXPYxUDyNpjy8wWsic1EF+2Sa/VjP4v4JV02nuNjlz9gw3b33Dzs4Oly+/yvLSMvfv3+PQkYOoJGVmZpY7d79la3OLiYkJ1tfX8YOAIPAolSImJ6ZQOkNlhjuPFpmZmWJ2dpaFhQWmJyeoV6tsrG/S6Vqd8ENHDjMxcQCw7rOnTz5hF0KjCPyAmalplpeX2djcIIgitIZarYwwNlBdX1/l6NGjGGPY3d1FCI9SKWRlbZV2s8Xc/DyVSoVPP/kY6UnCIGRm5gAH5+aI45gvrn2JcgZxY2NjVCo1Fhcf0e12kcJndNSqmYyO1uj3+7zz9tuMjo5y4cUX2NzcRAArq6sEDl1aW13lwOw0zz77HE8/8zSPHj3i/SsfMjU1Rej7ZJkaqJBEPlpb40nlSq3T09P0+30q5RrSE+xs72JQ7GxuMH1ghgcPHnDy5Em2NrdI0pTNzU0uvHCBJE249vkX3Pz2Dre/uo3n+2xub1Kv19je2LTBu1K0u308X2Ay2xNULpeLeyvnvvZ6vYKGgRSgNOPj49y/f5+TJ0/TbDZpNptMTk7S7bYtsuZhXU+lR+wMPSU2+KqUyvR6PTKtCDyPbr+HLRdb+dBSVEYKV3VJYqtsEoQI6UwihYeQkjjRJGmfUhgVY8O6Hkt7Z4sBHSufhLXJDZUshSXJMoQcjE05hLck/ZgwDInKJfrdGOn9MA93MCbt4zjuobWmXLYKLrYZNNtHi8n5+jmPPwiCYv7IfwZBYFXLhEQ4OoLvWXWSWq0GAprNJlmaUalU+LXf+Nv/ZG9vb/4nTlo/3xgZGWkAD4AZYHdvb6809Lt/sbe396dGRkZuA+/u7e09dMd/H/hbe3t7S4+91wtYII1f+IVf+MuXLl36zuftCxrk4D4bnrvz9SavOGqtKTnVnWG60zDym49V3w+tks8PbPk9p8GBaHZ9y1xAX3hKyf28+mGFnJxf/50Ki3aCKCL3P2LfWpTf84/7DRR89cd6RqxPwH73AmGwXhOZpT3n/8/Ag52tFoeOHCZNFffufMMrr77Cl9dvUK/XaPfbZP2MuUOHefjwPlONMS5dusShg0d44swTIAR+WKFaK6OyjKSXkhnNsWOHeefd94mCkCdOnuDwoUN8ce0a87NzXH7jNV574zLHj53mow/e5/XXLvHWW2+hgANTUywur3D48FFOnjnN8+ee5djxMxidcPTwLEsLSxw5doTNrR2k59NqtVhYWuKVl19haekh1r3bA+Fx8+sbTE9PU6lUQAgq5TLGGFqdrk1KVUq9XieOY4wxbGxscPKJ02QqYWNjA2Ngbu4ACwuLVKtV20yd2D6Mbt9WwGdnDvDWW2/xzLNPsLiwyrGjp1hb3mB7dxfhCUbHGqjMUBut0G626LQ2qdWqCHzq1TLCVXmzzPoglaOQr2/d5KlTZ1BpSlSt8OD+fU6cOEUU+aytr9NudzkwN0Mcx6g0Y2y0xhefX2P+yCE8fNbWVjl16hRr66uUSyV2mm0mJhukiaJcLrO4uMjExARJklAqlWg2m0W/h+/7/F/svX94G/Wd7/ua0Wg0HsuKoihCKKoRRrjGdZ3EmDQNIXXTNE2zaZpls5TLoWwuJ8umOSxlWZrDst1eDk+Xh3Jot5fDtj2UpSxlczkclqXZNJulIU1NSNPUDcYY1zjGEaoQQlEUeSyPx6PRzP1jRopJ6bZ777Nn732efp9Hj62RNKMf3+/n+/nx/rzf0XiMqckpUu0djI6MsnbtAA89/ACbN2/BsnRy+Qz9/avJZrJksmXWrulnfHyMRCpBtez25Jq64am7x1FkEd3UiUSiyKIb4FiihOVpU9mWjCpLSKpLkavpBhFZpmpoVCtV1KBEezxFsZTHRvL6+2QkJYSul4HzUHxVVSmXy831LUkSpUqFSCh0vmpour6uJElYuoEadis5k1NTRKNhGh3gF/qdC5PgjfuNHllJkkilUmQymWYSXVGUJoID8GQVgpim2bRJDT+hAe0yGwkL77zRaBQsyw0QJYlIJMKf/9mf/UZ71K+FXS0cnkFfCfwEuKgRUHgBSMx72jLgFwtelvOOvSv4uMCgu3jqBUOWzzv3siS7VQCPxadxvPkhFhjRZgnclrzI83wg4MInoFHwuTDwWFiyvtD58Np9FpTSPAMpis0MkO1lURcaYwsPg+d9Hsn7QbFFF1XovQeRCwIu18sBkaYjZdu2m1nyNohGeVUUJWRV9lhEPUrPxtsUXeXuRplRltw+mXy2gKGbjI2Mc+uuXRx8/iCHDw9iWQbdXWlkRJBlhsdGSaZSbN68hUcfe5St268lpKpUjSrBUBCjqqObEqG4yl3r7vTwyjZ37bmTwcGjICsUKkV6e3q46aabmByf4PHH91IqlbBt2HdwP13pNHIwiAg8/thjdHV1MTAwQDaXw9CrDB49yoa1Azz55ONcf/31PProo1SrVeLxOOFQlGwxSyyaoD2VZmxinGA+yCOPPEE6neLZZ59lbGyMfLFEoVCgo6uTZCJGMpEgEo5RKBZZ1beaVCrFxMQ4+XyetWvXkUomGB47ydCJ45w4cYLDg4Ps2nULmqaxdu0aCsUiIiKGAaFgggfu/xa33rqHdLqTO+68lWuv3cKeu2+nVCqT7mhHVXRMvaFjgScEZfL8888Tj8cZGxsjFIrQ0dFBoVCgs7ebUrnMzTt2sPepp+jv76eYzbNx3QDHjx1DFiX6VvSybs0AO2/ewfbrtjM+OeZWISJhylqlWcUwDANRcekCNa8EraoquUKRYDBINJ4AcDNllTLxeJyhkWH616zlxPAJNm/eTLlaplAuYNkW5VIRVZWJx5MUCgXikSjFYhExqGJoVcqilwE23TJ1RzpFuVSmXNaQZYWq7qrewnl1VkPXsUyboKS41TwdYtEgcTFEuaw1g+kG9aFhVBesTBNE0PUyqhLENfY0jW/TXliA6GaUJUkiFIkiIlLVjKaY2UJbsPD/8w7U+XWMKDaZPvIek0gju2zbNmow+K6go1KpuGV8j0632czoyq27uF7v/YUiYfLFQhP3HIlGm+Jhvx3/8hAEIQj8PXC74ziaiwR+76e+x7FfysY5jvMI8AhAIpH4pccvTDiJFtiy+C6hv4V/F/YILmSqaZyj0fux0KG40IF/r4SVbdvNDf3CZNiF124855f2Oe91C9fN+WrF+esshGotfN17nevCBJ1bOZJQOA82sUW8hNz5QF+0bFRZZe3AOp57dh+5XI679tzJ4cOHicailMplZEkikU6zb/9+tm3dzE03XMuX7ryXtetWU9V1Eol2JK+qWSqXsQzIZCZpT7WzYd0AJ06eJKiqPP3UU2SzWfpX9PLg1x7kppt2cfMut4n5yb0uLPjE0AkqlTLRaJR0OsX4yCjZbBZJVmjvSNDT1cX12z/DPfc/wOE9d7F522ZOjowAEh2pDu6//8ts3bYZ0RKRZaWpWq8oCqFwmGwmh6aVWb16NceOHSekKCiiRFkEw6P01nWdySmXVlhRVHL5HKtXr3ZF87q6GRoaYmxsjBUrVjA5Ocn46Bir+voZGR5DVUMUcnl6+3splkqcGB5yKYGLVRBFSuUyQQ+e61L0u1pEpukmrSq6m80Ph1x7H/JooVVFpVIqoKhJurp6GJ96jkK+wIZNG3nu6WcIhULcvucubr/jVjas3QCIZLNTSEhYrjqdl1Aym1SwwaDbYN+ACy+cd4ZhkGpvp1KpEAyGyefzdHf3kM+7rJzZ3BSW5QYX4WCISlX7pf6mUCjkNvDLOkE1jKVbGEYV3RaRRRlVVjElqJQrLuOchAdXp7mOXPvtMlDphoYoKsii4THNuc75u5eX9S6YfuM8oVDYRbwsWMuNNWiIEAoGUVSVqqYRiUaaa+S91ngD0iiKSjOJ0aDpbsA7F1LC67retDeN93MhfLMBhZRkiUatNKgoaJVKM5FqNZA6v2QFfvX4jYOPf3ODfoERbOCx4YJKAOeNWcOQ2h7+27ZsDxsOsqw0Xwu8S4hl4TkvNM6SVypr+u6N45btiby999d7oYFtZLIWvs93GeDGe+aXN4iF1wcWhDtuANYQprEan2XBbaExbwwleH5yuVBzm/b2BN3dvRw+coQ799zBwPqNWJZBsVhAUVX6OtLkcznSqRQT4+N86YtfpD3dQbVSolqVCAcjlHIlSlqFsBpERObkyRFEy42a79izn3R7EsMwaI8nqJquyNFTTz3Fjh03cMeeu0km4oSDIQ4eGmTHDTcyOjZMT08P3V3dVKsVEvEoR4+Ok06m0A2NfD5PIefiSyVRJByJMDQyTDwSp1wqMDJ6ko5kO5ZucPfdexhYv55V/f2oqkpHRwemaXJw/wF00yCbLWAjEU+61L3GofOQuJOjwwwdP8Hrb5wCYHHbIs7NTPM3f/tdPvaRj1KtVrlu+3a2XXctnZ3d7HvuCNuv60O0LYZOHufkiRPsffJJ9h/cT6mUZevWzaxbt45ILI5W0dGqFSRJ8ZTVY8iy7EKhbDwlV4lSsYJRrZLPl4iGY1iGTUWvEE3E2LRpI4ODg2Smctx5263c//WvkUx1sHFgIyeHTyKKEq76t8fJb7t6NwtxpZIEsuzOGLd5zD2mygqxmKtKbpo61arO4OAg8XiCcrlCOOyyekRD4abhMmwLNRxqwhBtDZeVxOPtNw2XOjMelVz2KtukUi27rHWeHoItujSjYlhBtUHH099BIhwOY5ouZMqwLGzbaK7zptNjg6IGm06SpKgu/LBcaiYgwCV1aAQDlm1TrWoYhkE46DKFNddg83uSPMrbxjo3ESUbWVJdhisLREkhGDpvbxqCaIZpuzoiovu9mxZNCtFm0kGSkbymYjdQUZrML41mP1lWkSSZ37Z8/PohCIIfd5/6O8dxnvUOvyMIwsULqvQNHEcOeN+ClyeB/P+T6/6SI7Bgzi1sCr2wkt1wYhrzZmH28sIqQmNr/3W4asNrRobzQcTCHsXGtRsMQgvf90KWoQZ0WFbV5vGFgceF/R2K0uiFenfP44X7GzTQ7zT1Q9wvyjuvB7uSRJFivkQwGqNYKtHR0Y7hqg6Tz0+R7kijBBXGRibYsHkLU1OTbBhYT1W3uXPP7QyPjnrIAJ2qZhEORTDDEtv613Di6EE2b7uWbD5DKBjk+PGjdKW7GR0eRrctntt/gL3P7KNvRQ/ZzCSFfJZrt23l5PAYlpnnwMGDrF27hm3btlHVDQ49f5AjB55hYP01bBhYRbqjl8Hjx7ykgcnxE8dJpVwYkCgpBBWR3l63wm4YBmOjo8RiCVKpJM899xyKpBDrShNvT2HlIJvNcuzEED3dvYyOjrJ161a6unr4xoMPoK5XWbFiBY888ghd6U5uueUWjh07RrVa5dqt29i7dy+6rqEoQXp7+hk9eZwV/d2s6O8mlowiSRIPPvgAd9xxB6qqMjI8hGm58FRJdKF3uWyBcMxN1vWvWsHE8DBd6TTHB4+R7kozODhIKBrBNi2i8QiRWITjR49RLBZJ96zimWef4+sPPIQlWjz8wNdRVLe5PBINoyqqR+tvNh3mRCLBsWPHiEajTUHCBuGPZRl0967gySefYuuWazly5Ah33X07Dz/8MJpWpb29nXw+71WAoFDIk06lME2dXFVDsiGcCBGJRLA9RXdVlinkCyiySiQWde08ErIoY4oWpuEJNosi8XgUrVBCFFXCYbd6UC5X3f3WIwESLQvTcml0m7ZBAtM0mr5pMKigaTpVXUdVZRcC7PVBNVNdkoTu0dUHg6pb8L8g2FiYfG+sLlE6H6iFw2Gq1WozedWwBY3E1kKmvXK53EyYNQIlSZLQDcPVj/NGo3pSqVQQRdGtvNn2vypB9hsFKv+SQfce/39l0AUBZNE+f8PNeiw0drZtY3mTs4GBsz3O48Yx0XazKo3ysWWYTaVMy7IIqUGCikpQcfnQgyGVpjAg784OLYQ82bar5uo2iDb4qVydgMaQpfOADlkRzwuFyXgqnt6XbbtNykgLGl4vuJZL4XOhOrPZVIV2aUxlryH2fHBlmHrTkDdph21XRdYybEzdbS6uVCrIosTRw4c4cmAf27duIREPkmyPk053EItEKWsammHQ2dnB+g0buHnXbtauXctz+w6RSneTyefQDJ3O7j7iyU5kJUwxPwWiRbGQYdPAKiIhl3a1VCqRjKVIJNoZGT3Jmv5VrO5fwejIMIPHjnLbbTsZHDriQr1Mg0OHDzI+McqRQ/tIxIK0J90sxbcefYxc0c1EdHR2YVlw3eatbNm4gf6+Pu689TZURWFyfIpVfavZvnU7mzZsYt++/YwNj/L0k0+RzRdAltCxeP7IUZ566ilEUWRiapKOjg5uuOE61q9fx9Zt23n11Z/jOA7Hh4f53Of+mE984hMYuo4SVLll906y+QzZfIY163uQVYN0T4q9z+xly7ZruXHHzWzatJkN67awtn8jO66/hXKuQFAWScXjrkPt6bVkC0VsW8eyDXKTo4iWSUhViSXijI6P0rd6FZqhIathyppGpVhmRXcvhXyeqbFhrt+8ieeeeoIdN93Arbfudh3skIooq5SrOtqCBvmGwTIMg0gk4vYvVSvo1QoRVSERi5HJ5IjFYhSLRVav7ieXz3jUwG7/U0U3yJRKaKUyoaCbAQmGXMcjHA6jKq7DrKohgsEgmakclXIVUVZcNXLbndcmukszrYoEVZVwOIhWraJ767aQy5PL5rx+KxtTDCIqKqKiElTDXplYIRhUCQbdADMUVpFk0Dw9k8Z6d29KMwu00NGLRCIkEm7150LH7r1oRm3rvFp841wNFrwmhfYCTYYGVaRL/ODVUD22ENPUwTI8XK1LxYvoZtKCIYVYNIxtGVR1Fz732/Grh0d28jfAzx3H+dqCh/YBf+D9/wfA9xYcv8ljvVoNTP9L8OBfNRbOkYZj3xgXVhcW7jUXwnEvrEoA7wpMLgxw3mtuNioG77q/IMm28BwNx2Xh+d8rEfarEm4LH38vtMDC6y38/13HFibJZJfVjcZ+aNuE1DBjY2Ncd912YrEEmYlJnnzyMVavWYOmV9DKVTo6Uxw7dpzb7ryDSCTChg0b2LZtG8ePHqOil5FEEcVLToLE84efJ5FIMHTsKIcPH6Wrq4uO9nZkWSYejyOKIpOTk9x22x0888zTDA0NMTkxQTQaIZFIokZUOlId5HNF8vk80XiUQqGILEv09PSQncqQSqUIBoNehd9mdHSU3t5eMtl8U2+hVCpgGAY9PT2sX7+RweODHDlyhC0bN9HX10cmkyOTmcQ0XTjSddduJ5PLkkgkyGQyTEyMcfsdtzIyMszg4FG+/OUv09PTw8qVK4km4kiixNGjR+nt7WXVqnVUNR1ZFpnMTJHL5UCyef7AQWQFbr31Vh555BFuv/V2RFQQaVLQNlAWlmV5AnU20ViMUqnQnEeRSISg6oryiiKMjgwzMTXJqlWr2H/oEP39q9m1axcd7SlWr1vLnv/85yCKGKYJpqfabttgWk09NZf5692VP1l2SYca1eWGXtXJoRFuvPFGMpkMsZir7ZJMJjFNV9+rVCo1A+5IJOIej0Zd6LFuICmyV2GQm/29jcp0gzmrmcDGQlZkLLvh/7mV6sbjpmkANsHg+QS0KLrw4AZhjWEY2B4rmW66wqDNyqlHpGIYBqINhuH2HMcSyfd02C+snAKIvDeV7kIf4Fcx1jWgVo3XNfZK0zSbxyuVSnOPu7Ad4Dcdv/bZ/0sMugMNkTS3imGDZBNUJIKKy+9tWRaSIqLKbbdU5AAAIABJREFUrhiRqkoEgzKKJ6wXVGQikSCmWaVUyKPKCorsTmjLw7trlYpr2CwLo6pTLVddGkHOBxSWqTf59xs8/ZLoUv/KontrfOGKKLtsB5bbbCRL3vM9JivbMhEtsHQTSQTbMqlqFcyqgaK6TT2mYbuZYVFBlRUkbFRFIhQKIstS82aaBqqqoCoSplF1qU0NA12r0KBKkxSVYrFCoehS5IqWTSGbQ0EkGY+DZdHT2UM6nUaxLUq5KW7ffQuWobH/2WcoFbIYZhVTthmfmgIlSCY7xa5dO7j++u3s2bOHu+69h717nyKWbCfRkebk8DCHjw4zNVkg1dHBir4VVEp5Eoko8ViIqYkxDh06whOPP05XTy+SHGTTlvUcP3KYYrFIf/8qHvja1zENk8lMjlVr+tmydRMdyQSKqFMujDM+OUpVL3HkyBFiyTgHjxzjoYceJRJUeWLvkxzY/wwhSeLxhx+hr7uPVStWsGpFP7fuvp2bbryZI88f5djxE8iKytj4JJPjWSzDpqMjRXfvCkQZ7v7iXWzcsgnNMEh0JLl+5408+PVvIAgCPd29fPOb/42RsQluu3sP69atobOrmycefYJMZpzx8eMMrF+FaWvc9cU9PLd/P4V8ie3bbmb7tTsZO5nllh17eOChb5DJjJPJZ0By2clEyWXXkiQFEYN4IoYqgxJSsESbZCpJtpQjFI/S1ZVG0zSmsnnKJY0t6zci2TbVcpGdN95AJBpi67XbuOOOOzAtkUg8jhyKEEskXZpl20AzdCKxBJWqV+kxTKKhMMlIFEvXKOQyVCuaK1aoSIyMDLNxwwY2bN5IvD1Oz4o+OlPdrF6xBlEVqZTKZCbHGBkapruzw6UZRKSQy7rVCtMi2d6OadsMDZ1EDQZdbRUZqtUyk7kJDK1ERS8RicWxqzbVskYkGCEZjxMNB1Ekt9RviZBs70INxqmaGlqpiK5VKZWLpDs7ODx4iFw+j24Y5PN5NL2CaVpUSmUsw0SSFLBcXLQieXh2WcYWRcYzGYJBhYawqdtU5zbYGUYVSXIzVbFYDElyWUEUJYiuu+eVZRcqcJ5b3cQ2TBe+aBmItokiuT0rlVLRtWEKVLUiulUlGg5iWzqjI0MY1QoSFiFVRsSkqpXQtTJWE2r22/ErxtXAZ4H1giAMe7fNwP3AxwVBOAV83LsPcACYAiaBbwO7/zUXE0V5ARU858VfG06EbTfpeA3L5ehXZRlVljEsC013dabcX9ll27MlV5xN9lgQTbxknPhup73hOFwY7ABNunXRFpuQKdOi2eze0J5SpAXCuJ5oqoQMluj2xcmgykE07TyM2L2W21gr47LLLXRiDMMgHAm6sJ2qBQpUtBKGbiFKrkCgy2pkIVoud125WCKbyZBsbyfd1UlF0+hZ0Y0iQlx2kwWGXkUJKRw9eYybbtlFRdOJROOUKiUsW2TLhjXceccerr/xZlIdHQwMDBAKB2lPtjM2Pkl7excjQ2MEgxJKCAYG1rFv3z6+9sC9ZCYyZHIFYrEEPav6kUVX8+fogQN09/by14/8LSghprIFTEOjWtHQtTJTU2OsXb2azNgJOjra6VrRT2dXD1uvdZ3gcqHIurVriYVk+vp6mRgdoa93BWOjExwfOkl3dy+33XYbjz32GE899SQ3Xr+dVatWcXjwGJrpJv5KpYrr1AKWUaGrM00ykXJ7BjDRTZGpbBatUmTfs88yNDTEd/7220yOjTOwbi3hcJh8doqJyXH6+lcwOZmhPZUmm81SzJcQbZunn3mOUFBh+3XbeP7Qfp7Y+ziHDh8m1t5BvlBx57ltI1sG4aCKbJm0J9uxZBklqFKqVIi3J4lEopRLJcJKmHSqk2gozPj4OKtXdFEq57nhphs5OTpOV1cvx1/+GUcGjxKORiiWKkSjrg5Xgw2qXNLo7u52iTkQPfspoUhQ0Uwm82XWrVtHuez2i1i2wUPf+gaPP/4EugEjo5PkswWyE8exbYOSVmJ8ZJwV3T1epV1GREFSRAxD9xAJVcJhlXS6w2XTmpqkauroukk0FqFaNWlPphkfmUD0KuguHb1FoVRGUcPkCmVSqTS2JCGaZQ9R6yac47EkJ4dHcPWqbLSKTjQcQy9V0cqaR6qgoptmM4lui67tkD27oWllNE1rOv6NwLRBHuFSd8uUyxqqEkJWRLRq+ZcqnQ14lqEZTUax81B+FyIci8VQVDeR3wgIbcN0120oiG6ZILv+uV7RXHa6cIjfdPzahnNBENYCLwKvch6NcDdu38fTQDuQBX7fcZyyF6w8DGzCpdr93y9s4LtwJJctc3b/0efcaAwDyZI8NWLPqIpuGdnyOIkXjoXZlIWZHNM6TxfYYO1YaCSbfRniBdHpgvO+O9O0MHPkNsu5PyCePL3YpMxcKKTTeE2jTNW8huiW2JqVFdv2PqNXh5YkZK8q40oIeEqWpn2e99rbWBoib6ZuYCESi4apmgaxeJzRsTHKWoWB/tUAnDhxgtWrV/PMvufYtv06NE3j8OHDrF29ing0SigUYmhoiHg8zoNfe4B1q9egqirFchndNFi/fiOdnZ3YSHT2diNaFg987UEmxye4e88eNE3H8qBXk5NTaFqZvlX9TExMkMlkSCU7kIMy8UjU7aExTMbHJ+nt7eX48eOk0x1EImEKhQLprm4sy6KQy3rYaPe30o0qoWCQVDqNVtUYPHyEF37wA1oXLWLwyBGKpSK7d+/mrrvu4vrt1xEKhZjKZhkbHyEUCpGdmqJUKpHJZLBtm8Ejx9iwYYBoNIqmaRSLZcrlEr3dXfT39yEiE4lHESWRVf1rACiWShw/fpzeFb3kMlk0TefZZ58mGo2yb98+HnzwQXbt2sV9991HZ1cHRw4fZWRkhG1bt7L9uu0MDKxnw4YNngCfhK4bYIuoYRVDN0h3plFVlWq1St6jQI54DYkNnHg04jbil6tuQ2J7ezsHDhwglUqia1VS7R186cv3UswXODo8TCgSpVwuk81lKRRKdHZ2ks/nqWhVkol2SqUSuew4qzpTFIsll17XMKhUKmQnp0gmE0xmpti5cyeDg4NEYgmKxTLJjhTlgtsHEg5FmcpMYNs2sZgLKSuVSk3NGTebpHp4XhWjqiPJEtFYzBPoM9xSuOk26iuqgm2JrpCiJGMYBplsjmR7EjWkopUqRCIhgmqIzNQUqioTDIaJREJuxkY0PdYcDdO0vLXTKBu7wYKmuYrHjabbBnSk0Y9yIYW26FEjumtQ8YITFybVaIK3bdFTqRa9gMbNujbwtbquuZ9JcbVBGpTHDTvVgOkAzebEcrnMV77yld82nP87jkQi4ezcuRNsCVu2m/AmAGxXyVuyLQ8y9+6Kx0IylMZmH1TDTUrsRhWkkV18F4uiKLosiAsgEg2iA2ABnMtNILvXdgOF5j5kucFCE3ZluA7Kwup7Y84vzHgqagQRw3vvOqbpioLaso1luklD1zlR0bQKkUjEZVY0zKbQqGqLzYw1okypmMcy3B6VUEgllkwwNTGJHAlhGwbBYAi5aiJWq+Qkm46ODiYnJ8nn8+z0NDosw+C6G25gciLDyeGjlEoaA2sGMEyTiYlRRBky+Tzpzj7Wr93As88+TVdfFwf2P8P61WvYu3cvDz74EIoS5PHHH0ORFLZs24hlmxzcP0gsHKejO8lnPvMZPv8n/4nt269ldHiYYDDI+PgExWKRrnQPx08cwxYlurp66O3tJjOR4fjQMVavWYOqyvR29/Dc/gOsXtVHT28f937xXiRVZu3adcQTCU4ODxFSFVfJvuJCS0ulMqosNYX0cvkM6Y6uJvHH008/zYoVPYyNTBFPRqlWq7S3J93+UA8m1NXVQzwR5ekn9rJj9y3ctvt2brnlFkzTJpfNsXpNP7IsMz4+wfMHD7F5y0bGxke44aab2bVzNzfv3IWul5E9hXnbNAh6tsgUPX0pT9bbwkYWXdppWZJQw6EmAiVTzNKZ6kTXTTrTnYyOjpNMxWhPJnn04YeRFJXe3l5KuSwiImXDYv3AegrFHCK4ukqGSb5UIBQKY0tBCmWN3MQYK/p7yU5lSLQnsQyT+758H4JP4IorrmDHjp1IouayOMqh5npRVZXJiQxd3V2YFuSzGeSgBy/LTCHLKqapk0x1oCohiuUiyWgCrVrBREaSRayqR62uQqFQplqpEG+Po0pqE4qk2kU0I4QcCWIbbnU8kUyiaSVMwyWciYTCWKZEtjDpJuU8v9KFRL8bxm9juj0otkg4HG6ueTcAOu9z2raNbcnISkPnxUSWVG9faWhyuKxVtiESiqkuMqEJBXVp4d2gxmN5lVW0arX5Xi6Efi1MTvz5n//5b7RH/avZrv4txrJly5w//MM/BM47/Yro8TZJC8vUsJA5YGHg4Tb3uI20Cx2Ixg/SyEgu7LuwbbuZYVoYbDRKeo2x8DULr/te2Dsb083yXFCCWhgYncd26wuCD6+EbovN3nu3CUvygpnzDCmN6FXFVc8WbdeZ0qoa6WQKUZFRQ0GSHSkeeugh1qzxAoh8ge6ONIcODxKOx4gnos0ouru7G03T2L9/H/fddz833XAdHak0HZ0dhNQgVcOgVCxT1iqY2DzwtQfJT2W58667kBWVSChEtVLmxhtvZOfOnRiGxc0330Q63Uk0GiEajfLQ17/BtduvxTR11JCCKgfRqybjE6O0JzsYGhmiPZlEVYIYhvt7uvAaFdOyyOezaJrG0aPHufGGG1GDCls2b8UwXVYKNagyPjrG2MQ4A+vXo4gSO3fv4rWXXwHgyquu4tptW4hEYpTLRcbGxohG49xyy81UqwaPPPIIlmWxdu1aNmwYYCozybFjR+ns7OS67TcBcPDQAZ4/cJBEop3Onm5OHD1GZ2e3l6UquZmmfL6Z0brzzjv51re+RX9/Pw8/9DWyUxm2X389XZ1dPPH4Y676ajDsCsuZpssqYtsEQyoxT73WslzDrhk6qqJgVPUmfC6ZTCIBmlYlEgnT1dmFJEncddddVA2N02++xe9+6pM8/MiTWJLI0MmTTIxPIEkiJ4ZPkogmCEbCFAsFQqEQiViC3PgJIuEwqqp6Wh0q6XSam3fuZPfu3W4527KolPVmz4Ysy+5mJIvEI1EymQwiIpFoBFPXKJbLmLpFUHXnWqFYdIMBxWPRsCw366NpiJKNZVoYhsu5HonHsA2T8alJUukOhk+OEo7E2LRhE4VintHhEZLtCcLhINWqqw5eKhapGjrRSNil2/XWoKsILL0rc9xwBBul9kapeqFRfzcMxm46l43/ARdO6TFvSU1hNqv5uOUxfQGe2u/50n4DCverYC8NZ/Av/uIvfht8/DuORCLh3HzzzQAuJaXkij9KouuEXbhXwK+GXrlzzvYcgvPPaeDeFzJLKXKQqlV198UFEN3GnnL+WmZzvrnXcik53fcFluXOOVGyyecKtLenmvO/cWsEyI39BltGlCxvneqIpiuwJqmeE2OKICvonnMj2m6vmay6wTymhWboTXY9XTfZuGEjJ4dPIMsKnZ1p7rrrLu64/XZKhSJTmUnS6S4y4xNMjY+xZvMmqroLPdm2ZTPj4xPsO7Cfr3/9IXbt3klHezdr1vRQLGhIioptQXsqjiWaTExNUdVscpMF4okYqY44ifYw61YNcPD5AxTyJY/ufABZVhkfHyYSibJhYBPHjh9j+w3b6Ozo4u6772bX7pvJTGSQZTg5PEw+l2d8fJJke4pisUIkEmZFXx+ZiXEODR6loyPBHXfeQS6bY3homJGxETo7O5FQ6e/rY2h4FN3WKJfLXnO9RDgep6u7G0Orcvy4m7ONRFRSqRSKEmR8fJxMJkNHRwe2bbNx/SaeeW4vmqZz2223Ua6WOXTgeZe9KRRl9erVHNj3LEowTFdXF4cOHSKdTlPIl5Bk2LFjB8eOHUdVFaqawX/+sz/lz/7sC/T1D7D/wH6ef/4QO26+Ea1UJhGLoZs60XCEYqVIqr0dXTOoVjVyubzr14SChFS3udsWXV0yS3F73eKxJGOjo5QrFTrTaSYnxrAtg+6eFfT19SHLMvsPHGBsdJxUewpJdtdIR7Id0zbRymXUUBDTtCgXckzkiqxetYZcLkMoEsGoVgmHo9x775d4+umnOXjwIL29veiGhqabdKY6ODE0xKo1axkdOgmiSCyeQBJB08pUqlW6UknC0SiVSpWJzBTpVJpC3oVwVQ2dSrWKoVVRFa+32EtmTU3liEVClLQKvT09DA+dZNumVUzmdE4MHae3pwdFVSiXyoCIZNtYnqPX2dnJ8PAw7r4RJBZz9wg3MSk3+y8Wru1GokCWZZfu3gtGztuRxj7l9jw1RQxFV+jQre6rLsuibFGpVJv7uMuqd54ZLxoLI4kKR48eJRQKvYuJTPOEQRuwPF3Xf+MEme+ee+75tQb333o8+OCD9/T39yMCkt+PLMvUrTqSKGJ77euCUPdEZAzmzTnmjZqbQbJ05oxZBNHBsYUmvaDfLyCKEoIgYNsmtdp8877P50MUbXyOD9Hn4AiuE+E4dQTBhyiKuEGZq7DiOIL3//lAxu/3e+e2qdfrOE6det3GqlksWhTG5/PhOA6SJBEIBJoG3TAM5ubmMMxZbMfGqpvYTh0QEEURnyjit/04gg+n5lB3TARBal6npaUFx3EwDYOKpiEKAuHwImpWjcVti/jxT46Tf+tttOlpDr7wA770JXchvvbaa3QkL+HU66+zvKeX+bpFMn0JRs0ksijMK6+8QndnF9du28LmTZv448/dBo5Fm9pGzawxp8/iV2UWL72IRaE2jv3kOPsO7Gfbtq0Eg4swbYONH/0YszPz7P27p/nWN/87xXfO8M1vfIuXjr3ICy+8wE2f3cGb+bcQsXnj1BRKKIhWqeA4Ijg2baEwalAh0KqQujSNPyAj+eAX2TylMyVag60sWhTmv/wf91CpnOPVkdf4/j99n6NHBzn5syEOHf4hyVQHS8JRRkdfZWJygurMLFdetZwHHvwqK/tWcvzYS5w58w7hcJTly1dimjpf/OI9zM7Osnz5cmRZxufzsfLKD2IYOuPj4/zspz/jpz/7KZnTU1xyaSdbt11LRatQN0x233or+//xewwPD3P69GlyuRw//vGPueWWW3jnnXdYtmwZpVKJu+++mxv/4D+w/IMfZOznYzz3vf/J5z9/B29MnOJM5Qyz07NIog/BJxJqU5mdnuGd0hlmNA3LrFGdmyUgyZSnK8zqs8iSn3gsyZw+j4CPubk5zp49y9JolNdee801SJFFPPCVr/Ch1R/m8ccf4dG/+Q7f/sb/SbCtjatWXcl/vOmzLO/txrbq+H0SWlXDrM6gabPILS2czhZoDUepOSJvnMqwc/du5JZWZH8Lk5NvMG/b+ByB/DtF7LrJvKlTN+tEI1FCi4LMmzq1Wp1INML7L7uc9vYkAVnm1VdfZUk04nKE+wPUbZv5uolf8CErPkDAqtUQJR9+f4CZaY1Z0yAej/Pm6QwfG9iENqOx/3v/iF7V+fSnr0UOOLx5+heofrcUHl68hPaL26nq0/jkFhRFRVUVZmbmmJ+fd50q0WLOmHW/Q1+9aT8URcHv92OaJq2trfj9/mYA4PM5+HwBnPkalmPi8/kQBAe/4CAJMkggCn5kESy7Tr1uIQgOoi3iEx2mK2W0mVkA/IIP05inqs1g4+D3+5mfnycQCDA/P0+9Xse2bWq1Go7jsHjxYv7pn/7p7XvuueeR/+VG+rcDgK9+9av3LF+5EgEQRB+Oz8GxQULAcRwcx4fLhGgjCD7vmHur1+uYponf70eSJHw+H7pu4PMJzfklCALz8/PN3qRGcrBFbcWc0991roXPaRDAOI6AINS9PQsEwZ2fjuMgigL1uvvXJ/qpVmcIBtua53Acdw42gpvG+6zOTuPz+dwvwAG77oAk4hMF7LpNHQEJCcNykGWXZELwiQQEH2bdYs6Yw7Fs2haFqBRLzFt1RAFOZSYIRxYTjSwhk8mwvLeXamWaFb19+PwiP3v1ZXbs2MGpU2+AT+Syyy6jUj5LPv8Wv/vp3+OJvd8h1Z7iAz3Lmdcr2LYPxACObSOINrOmQUAJ8NLRY0hykDWrrwLBQZJEsm+exjJtwuHFqGqQ8bFTzFTPASKBFomPrRvgBz88Qt+KD/L3f/8PfOADV7Cy74PMVQ2WLUsw+cYkxTNFIovD/PDwi6QuuxS/HGDZxcsolAr85MWX+GBfLzPTGq0tLSyKRDlz5gxLl8Yp5t9BEBxm59zE2zVr17Ls4gTvvJ1jeq6KIsrouk5LSyuyLOP3CyxevBhNq5LL5ejt7SWfz7NkyRJ+/KNDLL04Sa1WY35+np+c+Bkf+EAPc/oMraHF1ObnmZ2ZY+uW3+HAgUNc2b+SlpYWzJrFihW9fPe730XTZrDtOn0r+9n7fz3J//z7/0GpPM1VH17N+y5exr7n/oE1H/koomBjmvNIooDh1LBqBm1KG5YATs1CbgnwdjbH0ngMq2Zh2XUcu05LsIUZ3SARX8ZPT5zgI2vW0tP3Ab7z6N/wBzfcxLGfHKelpYXR8TE+vuHjdF/xAfQ5nWw+y+yMQc2uMV05y8XLEiiqyrnSW7TUa3T2Xsnk5BQ9vT2ogQC6rnPmnbOs+8ha/ug/fY7f+eRm6nWH1nCY06+/Tnv7JRTefpsl0SUsSyT50Q9/hODzcWlqGWfPTmNjEVRa8PklLMtmpjrDvGlQr9ksXryYd97KE10WRURibnaGer2OJEuEQiHefDNHW2sbc8Ycl1zSjlaZpjarc0m6i8zpNzAMg1ltlqWRxVh23aVgEn3NfWZ+fpZarUYg0EpLSwDLspienkYURQKBAIZhEAi0YNsmguCuxUbi4dy5c00SFUEQ8Pvd5zVsR8MW+Hw+6lbNvS+CYIv4cPDJErOzure/+ajXay4ThONW7ZVAEJ/P5/YJARdddFHT5szNzSEIAq2trU2f90c/+tFvtEf9fyL4+KuvffWeD63sQ/D7kCUfkg9qdg0EG5/LH4DgOAiOgx8/sujHL4FPcBDrIn5BIhJaTIsiNXstAn4/ovdanyCAbTfvS6ILorJFh1rdwuc4+AQRWfLj94nUayZO3QLbQURAcGycug12HcGxmzefALZdA8ddZHatDraAT7CxzHnqNRPbqoFdx9Bn8Qk0X0fdwYcIdRBsAR8iPhv8kg9HdLxzCgiCjQ8Rp+7gc8Av+vAhYBs1rJqJ2tLC/Nw8pbNlFEWhVVGIL0uwZNlFXJZo59hPXuTQP/8zN1z3v+FX/Pz0pZ+y/KrlnHrzDT7Y80GqlWlqtRqKorDyyj4++Tuf4i//8j7eKvyC5X0readQQBRgzrIRHcEV0pIkFrdGeOGff0D2F1lqlknXpWkc0+SHh39AZOnFfKCnh/d3XU75XJFVa/pZN3ANyUvex+nTGRYtDjMzN0synsAn+QgtWsRc3UKR/Jh6jdaWFubm5wnKrVgOnDtXZM6aQ0TCth1enRglqLbwsU0fo69vJYvDizg3PY0c8PPYt7/NiZMn2P25XeRyeZYsaWN6epaJ8Qkyb07ygSt6aVvUhlWv8WY2gzY9y3333cc111zD/v37yeVyXHzxxfzHHTs4dPAFPrZhIx/56EdZsngJLS1t/NevfIXHHn0UVWljbHyCT/3OJ1m3bj3Llr2PM2fOoigqsViUvXuf4pZb/hC/38/73/9+Vl75Qe66424CAZXSOyUs2+Hrf/VX3Ponn8eeM8m/U8DXImLO17BMi0BLALtWR/RLmFaNed0gHApRN2v4fRK+gJ+6ZSIHJAQfxBNx4omLOfnKMN954m+48wt7WPa+S3jh4A84fuw4n9j8CS6KttHefgljr5zk7596mpGRYQJ+ib6VvRSKOVoFgeVXrsCoOQhSgHPaOa64oovKdIW2i5ZiGDoHnn+B9Z/4BLNGjZaAguU4+BUJY77GkqUX4dTr/CJ3mnB4EfV6ndnZGbdyplcoT2vMGjXUthCzM3O0tbZyrlqm7tRZsriNueoMhjFPeFGYUGQx7+TeoXzuHNF4DEVRKBbLOHWBulNjUTDET195mcrMNAFZQBIVOtIdLAmHqAs+xsd+zulfvElrOIQs2rS2BAj4/Ti1GnVznoDka4rAiYKNnwA+se7ZCwcRG8s0vNf58PsEfIIDto3PcbBsCxEHnyDg9/lxHDd7LVgWPhzq2Ag4iIKAAIg+EBwBqwY+wSbgl1AUtblByJKAIvuxzHlaWxRsq0Zri0LAL2Hos+jVGWamK7z04+O/DT7+HcdXv/rVe/o+dBWy73zFQXQcHJ/g7hWeo+9uxCKOA/W6iePYSJIPWW7Bth3qdZN63aa1tQXLAkGo4Th1LwEWQJYVbNttRBWEAH6/Gxj7fA6OU6dBJe8484CNIAQAEZ+vjm2LzSSb66i4kD/HqWNZNQTHjyA6hCNtzOnz7md4D5gw0Aw6GhlVQRARbQef7Mfn+Kg7UK/XEeU6Vk3GdmoIjs2iRYvQpjVmpzUsHJZdFEebrTJrzJGMJZiZ0/C1tOD4HGpGjcs6OvijXbv44p6/4LXhEV4eGuJjWz7OwaMv8rldf8g3Hv4Gn7nuOl4c/BG///uf4eabP8vH1n+M1CWXIAcCOPUac7PzzBgOsiIhtcrU6vPkMnnezL7F7X/yJ7w5+QsuvqiNi8JL+N6+72PiI9iioKotTLwxyisjr7Hmw9fQf2U/h370PIpPJF84Q9uiRbxTeIvw4jBXr7maoy8NcsX7L2f01TEuft8lpFPv561CjkIhT9+VfczPmhTPFdn26U/xwg9/yA033MBf/7e/5sNrPsSpU68za9SY+PnrXPORa+j+4BW89dZbnH5jgu7ubtLpKzj0T//Mqv6r0I1ZgkGVcrmCZdl0dXXx9ttv88ILL7B+/XoWLVpEIXuKyLL3EfC7mW1ZCfLy8BDhxUsRBIcli2O8cXqSxYuW0qbKnM6+SSAQQNerzM7Osm3bNubn3f6/wz/q54xiAAAgAElEQVQ8xLe/+U3u+NM7eflnJ2ldFGLk1WGefvp/sGnDRqRAK6n2BKZt09qq4JcC6MY8Tq0OjoNl1Vne20s28yY10yS0OIzf52e+Ps9bv3gLWfLhD7Ryzdo1TL0xieTzobTK1OZqRKJLODtdIZN7m8nJU7S0KNiOj66uNH0rV9KiBvnu3iex5+b50Kp+JkZf5fLeKzl16nXWXn01L7/8MsmODi6/LMX3v/99tm75FHv37iWdfj+ldwogC5wrnSMejyM4Pvx+AX1OB0GgVQ0iSRAIiIy//gaTk1PMzMxw0bIEgi0xZ+lEF0cw5nVkn5/y7DnCrW3kCwW0WYPI4iVctCyOrusEAjJnz5SILlnK6cnXKVVmSCYT9K1YwdkzJQxjjmBQ5Wyl4lLWt7TiV0EQRAT8KIrMubMznKucJRAI0Nra6lLkazpLlixmeuYcVs2mra2tWbVv9Bo2Gt5l2VVON2arOAEbucWPYIKIQE2AumMiCjJ+n8B8vYYkCczV5pBpwSeAJQpY9Rpti8IEBBm9OkvdsWltbQVcaYxoNMqbb76Jqrp7WKPaGwgEOHTo0P+fgo+/uqevbwU1pwaWhV2vIysqPlEAnw+/z4/PJxFoURAEAatu4QggexWFeXOeaW0a3Zgj2NZGWyiEX5ZpDYawHXBwaFFVHEQE0UfddrDqFnUbdH0Ov08AW8CYM6hZ85jmPD6fiIgPq2Zh1gzqdYtAQKZWM5mb0/H5RObmdDfLYkPAL3uZoBrG/By2XScQkGlpUSiXz1KrmcRiS5mbcyfo22/nUZQAgYBMvW4xPa0hiKLraM7PMz9Xp7W1hbopItgOtugQj1/MbLXKqddfp1VtxZF8rN+wnn3/8D02fvITzMzpRC6KIfpERNHH2eIZRGy+8Cd/iigLaGWN3g/1cc36jxKNRhn84Q9YlkwwNjbOqlWrWLp0KZdeeimnMxmWxuLELrqYykyVdNcVFM+cIxyLs3RJjMsu70TwQVfX5VycSDD80x+zbu3VDB49ypatn2Z8fIxKZZpgsJXnDx/A1Ku8+toY3/vH7+OXZKLhRfz3b3yTM+US/VetYfvvbednPzlBsVjitdExPrT6Q/z4JyfAMQkvamOuNs/SJUsRfe6i+Mpf3kfLojb+7vEn+PnYGMsuvoS6AJddcgmfvekmJNHHXHWWcCTM/V++n3jsIrZ8+tOkOi5F8jlc8r4OXhkeYe3V13DFFVdw7733EY3GuOaajzAzM4soStz5hS+wftMnWb1mHROn3uTk0DDxeILf+73fZ9my91Gv16mcO8dnP/sf+OxnP8vJoZfpW9nH1OlJvv3tR7Fti7a2Nl5++WVmZ2f52dDL/OV/vZ9yucx0ZZrw4jauWXs1997zJX5n6xbK02eoVuYJRxch2gJnzlRobWlFnzNoC4YJtrZxUTzB8v5+zlVnMQ2L2bkZCqUzhFqDtLW10dvby/TMDP0rl3PxRcs4c7ZEazBALLqE18deZaZ8DkmA7vd38fH167h6VS+CNcdffGEP3/rmo/R/4P1s2rCR913ewysjoyTfdymv/fx1+ld9mHmjRt0Wuf/+B5ifr7HlU5sJBmS0mRLnyiXXaNXqSH4/dr1GS0sLPkcgEl7M4LGjqK1tHltKkFOnTnHlVf2cLZ9lzjDQtGlagyHkFhVR8jE2McaJn5zghs/exIevWsXbxbd47ZWXufzSy/D7fczP6Vh1m00fHWBlzwc48uKPeGXkZTRtmu/t+x4D6weYNXQG1l4DPolKqcq8YVM+O0NVn2X11R/CMOepaNMgCgTb2ognLkKbnqWtbRGaVmVmZpYlS5YyPT2DplWp1x18Pj+trW3UrDq27eA4gttbJoBZm8cvSfgkibptI/kkcGwERBzboWbV8Mt+REFEknyUSmUMY97bRGB+3qS1NUgisYxs9hfU642sbCtzcwatrUFUtZVDhw//Nvj4dxwPPvjgPf1XrUbEQZJ8OKKMU68BQpNjXhAEL+CQcCvadfe5jo0gyK6jIVg4jlstkWWF+fnZZrWhXhe8v/MeVFjGNI0FvUcOIFCtzqAoAQRB9OajjW3XUdVWZmZmEAQBWXbhvfV6DafuI6D4QfCqabZbuQ8EAkiSxLlz5xAEgXA4TLlcprW1ldnZWaLRKIZhUKvV8DkCcmuLKzbmcxECqiwzZ9QItS5i1tBYvGgRM2fPUTNNwpEIr742yudv+2O+dPcXWffRAbTpKh3py3nt9WEWh5ewLBHjO999nCtSl7P8gytYumQJ/7D/OT6+dSvVeY03f55h1pgh2NJGKtVBvvAL8qff5tJLLyPQGqQ2O8fxl37I6o+sZ+LNt7g4kSR+0WL8bUEGX3oJY9bg6g+v4+iLR7j3v3yR667dzsWJyzHMWa54fzfDI8e5/fbPM33uHKFglAM/+CHLe68gINd57h/+md/9/d/lxNCrnCsXyWZOs27tNTz19F70eZOJ8XFSqQ5k0YcjCvj9EssuTvLSS4N8/vY7eHFwkNOnpriko51SqcKZc9MsamslfUU3J0/+jDPvFLCtOoLgYAoOljFHKrmM18bHcZCYm5tlYGCA0dFRDh8eBGxuvHEHL774I1RVJbI4wMq+NRiWxUsvvsjM9Dm2bP09anOzbFj/Me67/wGMuVnWrrmaN6ZOUamWuSR5OZElYebn51m+fDmnT5+G/5u994+Sorzzf19dXV1TU1NTFE3TNG3TtsM4NOM4Ik7IyBIkyBJCiBLCGq8xrvG6hvW4xvg1fk3WdVlj0DXKGkMS10VXDcHEGOJRl2uQ8CUTJGR2JDiOwzgObTM2TdM0TdNT1NRUV1ffP6pnxNx7z825597v5p6T55w5B6aH+UX38zyfz+f9fr+AkZERlixezAP3f4tnf7SNN37zBu0dF3HXf/s633/i+4y8O8yW7z3GHf/ta4ymRpkRDOLaVWbOnIE14V2CT+ZyJC+6iNSRI9TcGtVKhZokMEMLUh4b45PL/5IfPfsMflEge+wYCBBsVsl+kPXCFfw+mpumIcsN+MVGiiePT0XsX9x+MZcsWoiqBClPVNm9azeWW6FScQnOCnHs/QwzZkxnzvlzOZnP0dzczHu/f4uDgwe5eP7FDAy8Q1dXF5lMlr6+Xlat+hTpI8dQmmTeeedt2i9KYoyZzL/oEvKnThCbOYdC8SQTZ8+iaE1EI7PJnTjBkfffo3laEFlSaG2ZS8WuoDY10Xegl0BDI/EL5lCxLMKh2VTtCuPOOG+9PUDhZJblV17JCy/uYMvjW3jllZdZvnQ5h0eGOV3IE4lEsG0Hv+gjGAxy5MgRGhoa6tONUySTSd5PHUXTtKn0Lr/fPzVNnZxGiKKIM2FxdmKcgCDhF8AneE3MgB+vqe6rIQaasIwyjU1NGKaJvybQ2KhQ9TnYdpWGhgaaG5uwLAvDHueC8xP1CarAiRMnUFWV6dOne77cnAcf9vl87Pkjz6g/ieLj0c2bNy5e8gkaxQCuEMB2XAI1Adut4jo1zAmbiYoDPu8SXsWbRFiWxRlzjEZVQW5qpCEgMTY2RtkwcKo+xkreBuj6RNyaj0YpgOj3ExBFpEaVgB8CYgP4fNhuDdfnHQYzQjPrI3WRWn0UVcOHXXURxAYC/gYqFRtXCCCIEj4xgE/w4+IDyYdfbPRStIQAruCnedp05s+7iBMnCkxMONT8IuFImIlKhbLhpQM1T9OpVF18gohflGgIiJw6cwan5uD3+1D8DXwwepTiqVPMnjMHpVll6eLFvP6rPczrmM97R0ZQGjV+9+abnDXGEUUYd2wuX/hxPsjleLP3TU6PjXHdF2+k9z8PoCkSP9q+nXffOcznPreWRx/+Zw6/PciEUyEcDvPBBx/w3nvv05JMsufXPVzccTGR2TOYHpnF6hVXMjQwwFf/7nbu+todHHzzLeZeeD4/3v5jRo4Ms/rTV4Nb45KFFzNWPkPq6FF6+w4QCUbYt/+3INQwrXHOO+8CgprCtp/+GKVRITl/PghwJJXi8sVdFAuniEajaGozxdMnOXniJMVTp3jqqX9lx8928JnPfApZlkgfTWOUz1AqGRweHGCaptHS0sKvXt/N6tWraWmJc/To+6RHRvAR4M033+RjH/sYfb0H+clPn+eCC85neHiEnz7/UxrlRrou6+Ll/203R48eZd1Va0iNHMEwTQ4PDHLH127js5+5mncH3+Yrf3sLBw70smf3Xj7z2U+x4xcv8thj/0KpdJrx8XEOHz7M8uXL2bVrF4888giS0MjKT60gHJtNxZhA8Iv4AyLjZ03u+8dNvPXWm8xLzMUwz9IgBTAsE7N8BkWVkZtk0ukjDL79Nvp0jeCsIE3qdJRAIxXH5ng+x/4DvyV/PEd41mziLXOQGwIMv/cecpPCjOAMVH06ihTgdPEUxzIZjudOYRgWn1v7Of7yyr9g4z9+i+9893tY4wbfe2QTcxLnM3r0CI1yI5d0zmf1p1bzN1/+Ev/ttlt46Fv/xHktLVy//jre7OtlVjhKkyJROn2KpqYmBgcHsZ0KDY0yY2dP41bhgrlzwecnNH0Ggh9qVFnQdSkzpgfp7+/3pgBSA7o6jTNnxvj973/Prl+9xgfvH6OxUWXCMala4zQ2NlHzVTk9VsIYK9O96GN88Us3kM1mMM4YPPXvW/nSl27gvdQRRoaGMSZsTpfLqNOaaJ6m85+9hzg7bqI0NTNNDyL4/XyQPcpZywSfiCCKNE9vwhg3wA/4RCrVKpZjMm6eRWmeht/no+r3pJZjhoFQrYGvhu36qNQc7HGTatWH41bwi35kRaVBbkSUGmgIyKhqM4HGBsrGGI7tMFF1OHX6NCdPncIfCNDU3MxEpYJpWWi6jiCKnDh5kv379/+5+PgvXJs3b964qOsywDNm+mpVKr4aguurFxc+XLyziooAbg1HqFCtuYhSANEN4DpVLHecWk1gmqpij49jU0MQA/j8IrLgR6CGI/io1kBwqthODX/Aj1N1qbo1fG6V6fo0zAmbGj58bhVqLk4NApKENVEhgA+/DyZq4PNDAJFAQGDCcXB9FQI1EV8NRMEPkh/TGqcldj753AlK5hjB0Ax8FZeJij3lT/L5BW9S3uCdm65tY7sufr8fMSDjq1YZM8pMmONouk5jYyPz5yVJZz5gwp6gUVGYMS2CXZ1gfHwMVWlCkhuwDZsj6ffJfnCMv9/0j2zd+iyfWLEY2xrjP175JSFd558ffZS//uL1zJ/fRmnsLJFZs3B8Nr5qgOCsIAPvDPGJK1YgiC5z2+ey8KKLsMYNnvnhk7zw/HP0v+V5KG7/2l0svPRjhGdHGB8zuODCKOfFYrz+y93EzpuH0iBz4sRR/nLFEt4/eoJPXvkpDvbu5xNLriB9ZIi25Dxe/+UvufDCFhx85I/muPSyhez/3QFmTJ+J2qQybp1F8NcIh2dx/pwoQ8MpT9Lr8+H3N9CkqiiNjciyhKqqKIqMOWHhVl0E18/0GUGqLjQ0BDj6/gecKpaYN28uoihy7IPjfPaqzzA0NMSFbXP5dc9vOfL+EWKxOeRPnOBfn3qKv7v1b7ntttv46u230XegD0VROX9OjN+88VtO5I/z1FNPcdnHLuGD0WMsXryYrVu38swzz7DtuecYGR3hu488Ruy8OKtWruT1X/2SmZHZzApHCIVmsu+NHsYnbPw+genTdE6dKSGIfmbOCHH0/fcJBoOcPjvGtOk6VbvC2bNjNDU3YtoVorPO493Bw0ybpqIoCqeLBXLHs5wXnYMeDjFh20gEmKjUOGuWqEx4kfDTmzVyJ49x5MhRjh3Pc/OttxGJzETXZ/L666+xZOkn6f/dAdZ8dh1v/K6Hn/7sBWSpgX/94fc4Lxrl/k3fIjQjxNy5CYaHU4RCQcbHxzHOGsybNw/bNpgTuwAA0R/g+LEMynSNUyfzKNOaccYnyGazzIpEeG/kCJHgDE4XS4RnzeL06ZPgFzHHDPSgynRVo1rzA1UsZxw/PhoCfjRlGr9/600iM2ajBpvpbL+Ed4fe5Uh6GCHQQCg4C11XKRSKjI+PUfNVma7PoLm5GcsyEcQa+Fy0phDlMwbadNmzHLgN+IUATs0En4vkF2jWZnKmcJqqVKXm8yFWRAKCDycAVdfvBQRUfbi1GpIiUnUqSAEVn1jDsR2qFYeAX0QQ/ZSNMXw1UHWNUtGTeFWr1Sk5cqVSqVsdPNnwvn37/qgz6k/CcB6LxWobNmxgMp4QPgQHiji4k4lUjpc0I4oiSJ45RkKcyoN2BM+AJyFglMsgyV4kmesZ5wz7nDx2xwZEJM1LpBHqqSAu4lSakEAdDiV62lrXdjxzoFDPZ8dFEMT6VLs+gsfrVLnUKdZ1U2Ch6MmiQqEQbj3ZRpAEVEX7SNKJrutYpoPteLAWxwYcy2OG1FO7JFHCtC0ESWRoaMgzGQuCl3okyyiiDKaBKLikhtOUy2Vaky20JjvY9tJLKJJAR3sbkuvS0bmQB+7fxJ1338XgwCCi7JlfHVvEFRzS2VGuueYahodHWLNmDRdfOJd5l1zCwvZ2BocG6Nmzh979PTyxdSuq5hnYBwYGWbNmTZ2eHWTvvj2UiyVa4q3EEi2UykVM0yIaiRLUdHQ9VDdraxTLBq7jUCoVWLCwg9Rwup6wYpPJZgkGg7QkWpAkieHhIYpFL9quVCoR1oOoesjjJJgmpmmSz+awXBNN1ZAkhXAohGGaXurTaJZkMkk+l2f5imUUiyVSqRSLu5fyV19Yyxe+dAuSIGC7Lo89vInNmzezbt06BgYGKBaL5NNpHv3hv/CVr3yFVCrFvffey5YtT/Cznz3PebPPJ5NNc80115DP53Ech7ZkB9ffdCMd7W0saO8A4O4772BgYADTNFi7di2DQwNEQlHypTK2YSJrqpd4lc8RCodxcJElz8ysaiqqJFMoFIhEIpRKZSzDYDSbxbLKRKNRuru7KZUN8pkciqJRLhfJ5/NEozGCmkyxWGRgcAhJEFjQtZBwKIwkSzy86SFiLQm6u5ewe9dOisUioXCEWCxGOBSia1E3L7/8MulshvbWNm6++UbS6Qyvvvoqa9etoVQss2PHS8QTUYrFIoqis3jJUkZSI+RzBUZSQyzpXoxpmTi2Q1trK4ODg8RbEuTzRTrakxh1M5sqyQgiZDIZzLKBKHmm8FA4CqJAMZfHME1syyEajxGJRnlt52uIskRbSzuaplIql+o8EmXqZ0yPDnm8Ez2I6wpomubFD1oWsqyeAzGdNOQKSAgUywVUxYMFypKK6zp1I6/kxSq6Aq5jIQoituDi2CAKXqS2XU/xUhRP8iVKIiIChXJpyvg7GWs4aeCbjHQURZGvf/3rfzac/xeuaDRau+WWW+rGT8fLPRTrNHNXrMdo2giiiMBHQZjn8jkmw09wBWRFwXXNqY8rl23C4TC2Xa6fRQp6WMcslafYUq7tyWwEqR6c4HqeQhd7Kihh8nuc/NqiIHsGecc7X/T6fj1pNJ1kAU16SVRVpVQq4QousqAwiWx3bNBUCcN0kOS6WR6JfCZLsr2N4fQIkiASCYcxHZuFCxZwx513cu893ySbyeDKGuVyGVV2sG2HcEgjlymSyowSiyUolYrcecc9zL/oAp569jv07Oonlojz7W9/m+d//GN2vrqTWCLu+ShdEU1XyOXTdC1aSj5nkGhrYeuTP6ClLUY+O0ok3MLaOoxu9+6dhIIxorEWFi/ppj2Z4KWXn6Oto407b7uLa9ffzrXXXMuLLz/JwgUt9PQMc931NzBw6BCDQ/0UCxlWrVrNjh0vsGLFctKpDIIAw8MZli5fweOPP869997D7td2E2+J0b14EdlMloGBIbq6uigWi5imiVG2ECUwbQtV9rhBruullUmiTCLeSiabIT2aIpvNc+MNN/LqzpfYunUr11xzHevWrmP/gR5KRhFdDZJKpbh1w81sffo5AO6//35SqRT3338/kUiMV199iY6ODjZt2oTjeFGqmzbdz8hImqVLl3LDDTfQ1taKJMk89tgjbN/+Au3t7bTE4jz82Gauv+Em7rr7Lvp6+yiWC8SjCURBQnQtLNchHo9jGh4p3SiVcXA9Q3KxhFEqIGkqoXCcttYWXnzxBXRdpyUeQ9M1stksqVQaTQ8iiDICoIc8AKBteXA8yRWIREM4CJQMh7JtU8yOEg7pPPDAA6xfv56uri4O9Q+iKhLJZBJRlti1YwdXrV9Hsr2dXTtfIxiOkBoeoat7ESPDaVwc4vEY0WgUwzQRXdFLCC0W0cMay5ctJzUyQio1zIIFXRzoO8jylSvZ/epOIpEQ0WiUTCaDquhksmkcxyUSCaPrOpKkYBglSqUCpVIZRZERHIFQLEQy2U5Q13n44c2EQiFWr17Fgd79mIZJPJHwMAqWies4hMMxXNcmlyvQ2tqKIAgMDAzQWj8zI5EIwFSDQJMVHFxKhSKoIqqq45QMLzlSknBci3LZRHZADerYjonjCGiKgmnYlG0vhEhVVSRXoFQqY+Og6Tr5bJaWlpapszGbzeI4DsFgEEEQMAyD73znO3/UGfVHE87/v1y1Wu1DyNEU+duLonVdD2nvul6cm2s7uJOgwfovYDKK0isYwBYcBFnBcWxs28UVJVzB44JMbfySiiQKOJaDK9i4iEiix+2YpI46jldAuJaBomg4gO264E4SaqX6Rm6Dcy450oskEz0KIKIkEQp5cXjZrJcKEY1GMYwPc/snkwuMslWPQ5RwbAdJhlgoQTaTJV8sIgsialAnGAzy6ms7SSaTjGYzHl3WtnDKoKseTFEUvEvNgs5OVF3lYP8hrrt2Pa/t3kkkHMEyLV7csYO7772bPa/tYvGy5R6ts1CitSXhFU0lr1D4xte+Su+BA3z2C59nyaIl7N29i9Wr1nDPPfdQyOcp5vIEQxGisQhr165jZGQETdcxSmWuWr0Wx7Lp2bePzvYOBgYHufmmdezv2cczzzxDIpFgYVc3mVyegYFDKIqGpkhse24btmXR2tZGPBajQw95FzLLIJXOYZoW7e3t9Pf1o4dDpDKjGEPexqEpCvGWFto72lBUfSomcvv2F6Y0kjfdeAuSLNLWlqSv14sXnoyr/Yd/eoAXdrzE449sJpVOsfmxx7j22vXccccdrFi+El1TKJZLHH3vCNF4gqef2coDDzxAsVjm2Wd/zNKli1m3bh2/+MUvqFQqbN++nV179vLCtm08US6zYcMG7rvvPsLhIHfdeSeWZZPNZFm6ZCnRWJyf/ORFJFWhWCxi2zYtiZap0aZlGdh47BnB9i4vpVKJbP3xWCRCKLoA0yixb/8BIuEQsUQL+VyBUCTO8hVryGQy9B48QFDRWLpiFaLr8WxwXX7w+Gauumo1lmUxdPAA665ajWO79B88xMLONuLxFg4d6uPlV3fQ09PD448/zuDgMFu3bqW9vZ2O9gWAM1Xk7N27l1w2y2uvvcYtGzawbOlSJAF69h2gbHo58uVigZVrVnPfffcDDrqqYJommqaRzo2g6zqRcBBLURgcHiKkh8hnR3EFET2sE4pGsGyDjvZOMpks69atQ9d1Nt5/P6IgE41FsEyT4eEhFi5ciKJKtLYmEIIC5ZIxFQtpGEad1FycuoRNRm27rothG0iyiqrrOJbXRBBFEUmRMW3DYyYACGI9gU9EFOtx2aLXbBAAy7QoO16xIQKyqk5lt08WHYZhEAqFptJO/s+gcn9e/3PX5FklICJK3tkkulBPGGAq6Mp1cfkwqezcNKnJdBgQMYwSiqAgCFK9YHBRFBnXAwTU3z4kBwv1yHdB8hpgovMhPXny8cl4aM+3IXpcGtfFcW3EySQsV/xIkttkkMokpVwQhClwpqpqSJI49f2pqk48EaWn5wChsI7gupi2zaKFC0hn0gAomoogSwimw0s7XkLXdXKFPHo4RP/ACOFIkKCmg2MTCkXZv693CkrqIHBooJ//9a+/QC41zLM//hHHjh3jyR9spe/gQQqFEuFohGAwSDgcAVxGM16a1mguy0hqmGgkyuhoivVrruLuu+6jI5lEEgR+9atf8eCj/4JrakiS93PlC1msfgvTNNBUDct1MQ2bWCRCJrOH7kULuHnDzWy8+076DkJ//0Evhty0sCyTQi7PgoULOdjXx5o1a3BMi7a2JKouMTIyArbXUCiVSjiWV6gheHuNKwhYjo0kCKiqiouDbXnPk2wdRphMJkHwkvpM02R4aBhFlWhra2P3nj1ctXot+Xweo2wQiUTQVJlcLsfDDz9MIpHggQceonvJYoLBID09PSxevJje3l5WrlzJmjVr2Pr0E2x7brsXBb92LbFYgse3PEZHezuxeJxMPserL7/MjldfxrJMbttwO8uWLeP2W+9C00Qkt/5cqRfUoizh1PcrSZHRXA3TctBDGqIgTj230qMZYm4EVVVJJBK4CISjMcySQW/fAUKRMF0LFpBKDyNYAvliAREJV5ZpTbRQVAUvHS2VolgsUiyWuGrtagq5PMPDw8iyzMaHHmbDhg0889xWFi3qxjItspkMu3btor29E03VGBkcwqm/LHTRa1SJkohh2Gi6R0JPp73mrlEuEw5HyeVydHa2UywWUVUVw/Aipk3Do7BLkkTJMAjpOo6rYpoWouTdMQuFAo7tsG/fARYvXkw0HOFg7wEs0yKeSGCaJQr5MrFYFNMyMIwSUj2x8VxcxLkR8B/xa+HimobH03FsL7hJFDEMEzUkeal8daKDUP+Di/shSOOcfQbOiQmvB1FMvu8PgbqTxc8fu/40ZFePPLrxY4s+hiRJ+AJAzYci1X8hQg3XtnGdGrYz7nVffC6CH8SAgBgQaJADKE0yYkDArTlUHS89pFp1qVScuq5WwnVrVCoOjlP19HGCH/weFNCPD6ouPtdH1a5Stau4FQehBkJNQPQJVO0KPirUqjVwPXCgv+bHrdgINR9CDfz4mKY1osgSbn0sX6tWGDtzGk1V0FQFSRQI+AK4PqicreBWXORGFcm4efgAACAASURBVNEP7xwewLZdmgIBqj6X6c067x0Z4aw1zvlz5tDQpOCr1WhWVSbMceYkzmd8fJyxsTH04AwqNQc5INHgE8kXCsgBgXQ6RbK9Az0Y5IPsMbY8+l0+s/rTJC9s5a1Dv+fUyZMs/9Rq9PqY3K5McLxwkszxYzzw7W/z+fXreeU/XkXAz6XJTlrnnk9jYyM9PXv41PIVFE/m+e4Pn0SbppM9+gH5wikAPrlsGe8cfocLEufTpDQxPRgmf+IEvztwgOuv+yIvvOAVAj6fj2OZUWRJpFwqIeCiaRqfWrkKURIIh2aTzxfo6dlDY2MDoZlRGgIy1kQFY3wc4+wZTh47RbPezPwL5zFrZpjLPr6Io0eO4JdkjFKZwYF3OHE8z6xZUZZe8UkuvfQylCbPw/Pqyzu54pNXsGPHDlKpYaY1T6PzkoV8a+MD/NtTT3Lm9Clmzwqz89Vf8uUb/5qh4cMc6utlw623svJTq0heNI/t237CD5/4AYoic8stNzMyMsL3v/99Nm/ezPLly/nGN77B66/vYfasCDW7wqligTfeeIPB/kPMCEdpmZug4kzwk+d/xpdv+DLZbI6TJ/JMm65TqVZxHQcCflxqSHIDos/HxLjNmFlmWlMzxTMlxs6cITxzJidOFdAVlYaATMWyMcpnEeVmMtkCmeMZ9GkaY8ZpwjNmgShRyB2ncCrHyVMlTuZP0nVpB+8OvI1bsWm7KElzo4I5cZYLk60MDw4xmj7CZz73Odau+SveT4/iw09fXx9/9YXPMfB2P7/4xc85fPg9Hnro21xyyaVcffXVfPGLX+Sa9Z/nvaHD/OLnL3L77bez5Xvf5Zmnnmb7j57j1VdeYduPtnHZpV3MvaCVcdOgqamJhoYG5EYRyx6nSg2f6Ge6poPfA1xpzQoVs0rm6Cg/f+U/GM3k6B98i8HhIQ6/N0Sz3MTcC1r42GUL0KdpzAyFwFdhrFTG5xPw17zLWUD041Yr1FwHrbkJwVfDL4BfANHvwy8AtSqOK9S7w41Uqg6WfRZBDCDJAtWKD6daBV+VQMCTociNAQS/QLVWxa1WEEWBGi6Vmk3N9eHVNX5wbHw1z4iOW2Vi3ERTmxAFH2dOF6lMWJw3O8KrO/+cdvVfuR599NGNXV1dnqxKEPFVq/h8ntm8VqtRqdTw+ar4/D6oCVSr1al/e+4lwefzebHqfhHbHsfnE5CkBqpVFy+4qoZtVxAEP4GAiFNxEXwAXtNLEKCpqRHTtHAc10uoqRc74+PjNDY2MjFRJRDwTfGCqlWHQKCBWs2tJ1v5qVa9FMVJfsiZM2f+D4yZfOk002UNq+JFADcpDfz+9+/Q2NToGWKtcSIzQ+RHM2SyWSIXzEFwakRj5zFr1ix+9T/2sGzlCowz9VjZpmaO57NcnpxPxbKo2ib9Q0O0zW3j40v/gpOnS8TnzuW3e15hy5M/p7t7CQ8++G1y+RPMu7CdObEYJ07mEAWJ+PkJzp4dY9EnFvGVv/0qX/5fbuLjl3dz9Oh7rLjyE3z3Xx7jnq/fTc+vfk2TonJe6/m88cabnD5R4qrPXc3x7BHa2hNUXZvTpbMcSZ+kc/5l+NwxXvmPF7h88Up++P1/Q1ObiMXn0DF/Pk/+6/c5Lx7l8kXdjAwfZnZ0Nm+9/RaX/8UneeONX3NhyzzC583Eth363zpEomUu77+XIj1yhPDMWUybNZ3jx47T0NhIywUxmvVpTNeayRdOYlsTNDQ08soru+m4eD7zLpxPcn4rAwOHCeohfr7jZ+ATmHfhPHp+82tu/ruvwrhBKn2UgF8i4HdJXHABL/zsp1x5xRV893tbSKdT3HXvN7DHJ9jwla/w69/8D46MDHPixClkWWbVp1fy+XXX8OCDD3LllSu49da/pXTqJH/1+c/zyCP/zJw5UT696tNcvvjjXNSxgO888hA/3vYCN954E+cnZhMMBj3itSyTPXYMWVMRAyKVqoMiNyLYNaaFmpkenkU+dwLHqSCKIufHIljjYxzP5YlGZqHoEd78z7c5M17gk0uW0SCJvPXWW0xXpyGJYJ018Ik+GiQ/Qs0hlz3OhRdeyEWXXsLZ8hhQ4+yYQaVSIRgMoWnNPL1tBxu/fT/bn32W8XGD5iaF3/62lwvmttDWlqTqVlEbVd743W+9xE01ROFkDn3adPKn8vz+zTcZPXGUoBbibPk0F3d9jOHho7j2GNFIiFNFL+7+3cNvE5wxE7lRYnx8nDNnzjBz5hwkXw3HV+Xs2DhVp8Zrv97FX33uCxx5f5jRoxkuuugifvzv27ns0o8hKj6ymSxVp0oorEPNJTRzJnJDI8aYic8ncPz48an9IxAIUKlUAM/oPZmmJ4oitSrIahOnTp5CVZo8b6PeiHV2Atep4cOTZ54dG0NpasQyJ6jWBM/r7AMf4NgVKhWoCVUEfMiyxyMLh8MYhjeRampqmorctW1vYrtr164/6oz6k5h8IPgwbQcsG1lRkEQRVxAQHLNeaQmIoossePnH53I6AERHxLa8ikySFBAdTLNMKBj0+BflMpZhTU1GZFFAVZWpbH1ZkXERsG0HAQm3jrRXdQ2jXEIUBWzBQdZkDMPCxUUSNQzTQtVEJMnjboTDQXCh/6Ano0m2dSCIIqVSiVAwNpXJLIgSgihhFkpTXU1dlclksyQTrcSjYYaHR2hpSaDrOgOpIRJtSVK5Io7j0r2ok927dhOPhRhK9aPIOtF4K/l0mlXLOsiXBPb272ZgeJC1q27D0XOoQZH29g4uvujjvPHb17l/05MsXxkk3NLNrbfdwr69Paxdt4pc2WGBbfDCi9vZsu1ptmz7CUu6F/HE1h+woC1JerTA7r17iSU6Wbl6A+liFlFVWdDZSf/BAdasWcG2bY/T09PDpod6yWdNFKIs7OxAcwtsevhe1l1zE/feey8ArW0xDvX309W9iGKugBJUWdixiEODQxzqTwEali0RCsdZd02SoaEhDh3sR1WCRMNxSqUSohTBkVIsWryEp7dupWNBB3Gxg85FS+nrPYRRtti27QkEAbY9s5svfflaLr10IbffdTt7e3bx7L9+j299+x+Zc/4FJNtakUUJxyrQu2sHAwPD5HMFupYsZ3AkxYLuMjfeeielcpmRoRS5Qo5EW5Kj771LZGOE4eFh/unvv82SxYsR0Lnu2g0Eg208+eQOVl+1gqef2UokFOVg/zCPbX6MXbv28+STT7J4wVIy6TTXr1/HfXfcyR0bbyFfGKRs5NA1r8NXKBrYpke2l5AouwZ6OEzWKFMqFGiJxVFFmc6WNmzLwjIsdDWMUTYppQbRRZt4SCV9aA/lQpFILIppOtgWiJKAqoe8aZdpEUp0eMZTw0BRVTLZDIWSSWtHB45js+Ol7YgojGZy3HTdTex8+VUWLexGREGSbGKxGCtWr2JkZIRSwSTR0oamCCxsb2PxkoXcess6TNMhFo1iOy6m5RCNROg7sB/JLSGqMqqqkBnNsG7dOopmmYOH+lAUiXA8iuhKvPjCy2Szo2iqwpo1V7Fy5TL29+5DUeN0dnQyMjKEGtRAFCjlc4iKSyioY1kauuLSvagLF4GRkQE0xYMSCponuyzkirS0JupaWwkBiEWj9B3sRVYVyiVP3oLjIikutmnXpVYCiqJ6GevI2LY7BWlyRBsXPJCi66KqXjfLsiwc28E27TohWkfTNUzTRhZVWlsT3hRqaOR/1o785/V/sXyCD0EE1xawHctLSztnsgH1IgNhauoxuc6VXdU/kFBIJ5PJASayrNWnFN4Z5Lp1JocggWN6oEE8/pXgup7pu/45RUFAEkRsF0/29wdSr8m/n8v5CIXCZNKjWKbpyW0FCAdDnnzCsSgUCmiSRjQUxrCsKSYNjkuxVKQ16AHwDMNATiQ4MDRINBEnEgqTzWaxHIeQJtPZ2UFYD2Igcah/gIXLFjMyMogo67jiKIW8SSySQFc1tm9/jh88+Rztne2MDg6SObiXna++yk033ki5bOJik8sU0cNB4okYkUgEQXDZ9NDDrFqxgmIhx2gmTUjXCepR7t/4AD/Z/hNUWSEzmuKBB+5jw613Iwsej8ejg48gazq6opNc0AWizZYnfsCqFd2sWL6SnTt3EgqFWLVqFT94bDMuIq7t1GGKDhFFprUlyUC/x/GIRCJYrleolcsmmcwoCC6qqiGrKpFQiN5y2YsxlVpwBBfBgWKhgKJoRCJBNm68j96+Hg4NHPTYV4qnsujZu5/1669i3/4eNFnjxZ/soDOZIKhq5AvZKfJ7a2srQ0ND/MPf/3cioSi50TRLFi/ihhtuYOPGjWx75jnCsRjXrruGuRfM45133uLeezfy8b/oJl/KI4sKuXyehx7YhKioPP/884yMjJBsa2PlypUcOHCAn//Hi9z/zbtJtCWRJMgX8oTDYRzLRpQkcL29zRUcwnoMEZFyqYimeXttPldEEF1kUcJyXQTbJhwJYVslBgYHCQY1Ots7KJcLuA4IkoJju7iCg23l0YI6o+kssXCEbD5HMtFKoVTEdV0ikQiKohBPlvjBY1voaGvhn771IKdPnUAPR9izazf79u0lFIqwcuVyenoPUCwUKWp5yuUymq7jWCZtnUls00FRZURJZbB/kEXd3RiFINnRDK4gIgAtrUkKuSxaMEQkGsIoW+zvOcDadSvAEDDNNJZtsLR7KZZlkM3kyOfz6LpOZ3uSQwOHaEm2EI1GsWwTUZApFLOIkkg8EqVsWbiiQymbI6TpyIKI6di4ooDguEiIGILrwSFcF9MsEwnqH+FTTUIhzwXnBoM6LiK27cEL1TrA2nGon2EfTm8FQZoCoHrwXHcqYcu2PaBoKKT/8Xvpn4LnI3petLbhKxvwdI8fksnPHemcu4lPaqPPBS1Nvn2o03ZxHGtq8/bkVucmhgh10IqAKMqI9XGnVZdUKYqCY5ooqooruBglEyQQBekj38ukRtZxLErlIq7twQGluhRMdEHXdURFJpvNehuQIFIqFxEkBVkWUUSJfKHgdZzqY7NiIUc0msAyTOSw7ukRBdEbE+fylMoFotEQuKIHNHMEEFxaYkHyBZNd+3axYOEiinmLZFsbquqwaOEiMqM5crkUJjL79/Vx2623csUnFvHfv7GRQiHP9Tdex5Ynn+Dnz/4IGhv55a5dZLN5EtEQB3r3I0sKB3sP4bgy4XCUaFRDlhzSqZRHsEYgnR7CAVpb2olEEuzZvY+lS5fy9NbHuf76dQwPjRKJJxAE7/AtlUuoqkLZtIgEI5RNg2w672ndFRlHcIgEQ6RSKU+yIMkIgohpWXXpgMvq1at45JFHWLlmNUFVY2hoiGy+QDQUZePGjTz33HY23vcAp868z+mT48iKzKLuReTyOa65Zh3lcpmOjk7E+jgxO5rGxWX9NdfQ3b3Ek7CVDJ588mkGBgYIhkOksym6FnSxaulSeg/1UyoUGB1J0d7ezpYnn2D1qtUsXbqU++67j2QySSQSYcOGWzEsh6GhATRNZ9fu11i/dh2t8Rj3P7ARSRTJ5tLcdvstmJbNyEgKQRApFj36tSB4HibLdRAcl0yx5JkVzTKiJOHWqcLhcNiTMLhe7r5jF3FsG9u0vNcQLqmhFLKqEIlGkWTVe7/rENIn4YZglg3AQZIlbMumVM5j2xaOBbKiUCzkuea6a2lvbWPd+mtpb0uS7GzHAYaGB4hGo8RjbYiuy9DwAG1tCQYHh1ixbCkIUCyb3tg91oJdl4XYtk2xWKBYLGO6DplMhngsRjQSplAqUip5gKN4NIZt24yMjGDbBsOpUVoSMcKhCJIM2Wyevft20dmxkFAoimXZtLYm6WjvRFFV9uzaTb6Y8y4RpSKariHU5VehYJBCvlCHZTkIooBtWkiySiQUpmgUpySclun9DJMbs2VZIIBlOh+R2kztY8JHmyeeHl9AkmXyuQKWZRAMenR4UQLTsKc+x4MPPvhnz8d/4Zr0fPzhOvfsAceTCjsfFiZ/WIhM/t+bZhlNC06dZ+d+Htu20XUdp+4RnLy0eR8nI0mexlqSRFwXZNk76yzHxrXrIDLRAyFKkoRlG9iWl0pkli3vAiE6qIruUbFtm0gkRiaT/sjZarsCuirjWjZqUKeYLyAIEpFYmOHhYcLBCNFomL19vbQlWtFUhVKpSCIRZW/PHlpaWrBMr6gSXRHDLbB48XJSQxap3JO0RZcz3J+jq7sNQclx1517OWX3UjNMrl9/E2pI4YkntrB86Qpuv/M2Fi3q5uChg9x7373kCwVWrljBpoc3sW3rc96E2BEYSmdQFJllyxczMniA1PAgmXSW1o4uIq1JSpkChmlSssokW1oR8IooRRLZ07OPxUuW0RJvZeGiBdx37z0sWbKElSuXI4kSd95xO6FwiM7OBQwODJKIhhFVjZHUKJ2dnRilMsuXLaPnwH5KpRK4LvlCAUFwue666xgcTFE2DTKpLOvWr+a1115j2dKVRKJhnnhiC6WSQVALcvc372TLE1s4eOggyWSSRe0LKZZK6FqQkZEhXERi7QlkR0LRVSzLppDLIcue5NOyDBzHIR5NoEgOmx55hHvvuZ94S4J169ezb98+dvfsxSiZpDMprlq1Gle0iIZjPL31OTqSSTK5HLlcjoFD/bi2zbIVK5BVBUeweODhTSxMLkVUBJYvW0FrrIVCOYfoip6EV/R8s2VBQnJsYqEIxWIOHAFF0ygbZXDBdRxcAVwcHAQE28VBQJZFr0iUxHph7XiNGlxkUaBUslAUiVtuuYXnnnuaQqGEpileI03RQIBUHqzCKEu6F5LL5hgaGWbp4iX0DRyirS3J8PAQK5YtR5A0Dh7Yh4OFLEsIguj5LhwXwRUJR4KIokjZMHEscOwyhmF4r13BRUHCES1y2TzxljhG0aCQdYi1ahhGiXK5TCQeJpMpIIsuLYkk6UwaWVSI6EFERSM9mkaSREJakKJRxMUiqGmIskixWPAa3oaNpqlTQGNvnzDRtKB37uCllnV3LcSwbM+PoqqoqowkeVLmSbmUKIHreGdWqVRA0zSAc+SV6hSwe3KPs20XVVGQZLEONwwhScIUAyQej/PVr371/z+eD6bqH2dqAz6X+jtZbEzqySbHwpPgvkmji4ePl87ZwL3N+NzKTZgUx9azz0XRo646goCsKqj16YSnnQMJcB2QRNGz7gkOjvOHna66eV1UQfzQgzI5ys6Xitj5DynKk/4OTfPG4YKLd1mxTBBFHNOiY0GXV01qKi0tcRzHxTQNLMtGlUREMY5Zv0hOjuElSWbf/j5M0+TaVdcxNDLE0kUL2N+3jxXLV7Hx/k08tOk+Xtj2HLZj07mwkwfuu4vLL+/CNHJ0diTZu2sXdtngu9//HitXrmL79m2Eg0H2Dg2gqArZTJZES5yBwQGGhkew3SSdHR0s6l7Cjh07KBQKyLJMIpEAREZHUyxesoCykeP6G2/AMkySnR0U8wUi8RiyrBIKxRAFgfTIMBICVslA1SbJvA5WuUzOtpFliUQiwYEDB+pVdohQSCUeT7B//35M02R0JIWQaKWrq4vrrruO2267lXBEZ8uWLfQd2sOWzY+RiOuoms7DDz1MLB5n6fJllIoGsqZSLBRxgXR6hBdf3MEVV1zBtBmzuOfuu7AcC7NUJhSU+P53HwT83HvXPdxz7zf5zeuv8bWvf5OybdI/dIht254GIJ1OsX79ap7cupXFi9ayft3NGIZBLB5iJJ1i7EyWPbt303voAAXXwiyWWdjVzeYtTxPSQyxbtpRwOMTBgweZJGZbrmdqdRCIRUIokkQ+73qEcNnCBQqlIkWj7BmbJQkkFUkSUMOecdkxLSLxVhzLpmiUwbG9Lo4Aw+kUmqYRDocJqkHMcsl7zsoiqt7qFe2uSDo9yA033UD/oQF2vbabFSuWE29JIEsKlmWyctkqBgcHaYlHGU2nWbFiBQMDgyRaWjnYP0IkGkHTNIr5Mv2HdmKUyqiqgiypntdBVdGBIjLZVBbTMJEFCU1TsWyDfb17sCyHcChMMBxhTXs7jiNiGGVUTWdxSyddi5ZSKBQwTQPXgdHRFL29vYiCF1RRKpeIx+IkWuJThYcsy+TqXalSyfOkSKKEI4KmaRiWiWV6HWRFVlFkte7vmNzHvBCKSa39ZFEyWYB4e4Y7dQicW5hEIpFzCha3rrn3GiulUun/zV33z+v/4TpXPnVuQfHh4wKTVPE/XH9IQBdFearYkCRpqos4GTAA1N9nAtpHpiuy7IHFLMusc0G8M0KW5fpZhde5rH9doe5bMi1zEpnrnVXY9eerWO/Wl6eKacvywk4kUcFRhKnXQ7lcJGjrKJJMMKRh2hYiLoZpEAmHPZJ5uYRpWvXnc11HLgpILpQKBfK5AqqioWs6lj1CItHGgiVLuOOO12hWNPbs3Ukhl2PbizvZ19PDsmXLeOmllwiGIqxZvZpbb7sVx7bp6uqiZ1+PV6BJMgODQ4iqzsGBflavWY5tOyRbWynkiuSLBaRiEat+CQuFvGmvVNesFwr5qXM7k8myfNUSD2Zsex8fi3tG92Aw6Gn3HccrEIUP7yulcolyuTh1D7Ftux4uIRGJxBAEiZtuupnbb7uLQ4cOsXnzZvKFMuvWXkU8HmXF8hWsWb2G3oP7SafTKIpCsiXJocEBdFXDNCxCkTDtbR2gCZTzJfr6DjI4PExbSwvxeJTeQwe5+447UDWFp3/wNNdeu5pnnt7OjTfdxHXXXEt3dzdDQ0MMDwwSDIdIJBLEYjFUXWPPnj1omkY+X8C2bWKRKEtu7+bWDbfj8/m4+uqrKZTzPPfMM9x9zx30HjzE7t27kVesRgspuJZHN/cMUC44DqquUSyXcBzPHjXprRMRMG0L0yjhChKWayPZLkI9+MZybBRZQhJFTNO7Hzq4iLIyddcrFApYloN7bqCQKIALV121hh3PPUHP3h6i8RgLOxdwsK8Pq/76jEWijIyM0Lmgm/Rohq6uDorFAunRFF1dCymWDEzbQRBlFEUiM5rFsUDVxHMa2Q6FUpFoIkQkEqZYKiPYFpFojFBIxbIMCsUCkiKj6zq2WZq6j5qmScF2Wbm2m0w24/1eBAnTKhMM1gnipkg4HME0XMJhbcqrNekj0nWvcSjLXoM7GAyi6zr5dGpqD/LkWPLUc9LbRz46IZ1spANTHsNzG//evdagVCqh6V5amff4h3viuXvb/9360/B8PPrIxkVdl1GrEzHPffNRQ26QaJAC1Nwaoq8B16mB66My4VCZcJDEBmyrgmNX8dUET9NWE/D7RHB91Krgqwn4agK4vql/X6s6zJwRQhT8+H0Czc0KerPKmdNFHLtKICBSc1wqEzZCQwDRBcdyoOpSc6r43JrnFanhpVoh0Kw0UXUmqFZcmptUZKmB8XGTM6UCevN0fDXvMAjqOg2BAJZlUHEmaAwEqAo1wkEdsUkiOENn4O1+zpwZw+erYZZP8cHR9xkZPszRY8c5axiEwjPwiwIB0Q9CDbEGpUKRoK5yOn8GozzB5Vd8nLWrr2bTPz9CZ2cHn197FX3/2cff/8M/8Nv9B+h/+23ak/M4e2acee0X8m///ixv/PrXHDue49ix48ycPp1I7DwOD79DeOZM3k0NMyd+HsPvHsF2qoT0IBV3AjEgYpQNYrEY5XKZWq1GPB6nqamJVCpFJBIhn88Tjsxi9uzZvPteigsvnEdHWysBOUD+2DHS2Rzj1gRHM8fw16peShB+ZgZnMmFVQPAIvEePHqXqqxLUdZqaGpl13nmoSjOBgERr6zxSqWH+5m/+hiNHjvDlL9/E3XfdyZdv+BKv7fwlbw0cYuasWTSrjfz85V/w1L8/wze/+U0e/95jXHzRJegzZvKb3+zjq1+7k9PFE1x77Y2s+eyneePAPl564SVPmjX/Irr/4nKS7e2cGTvDlcs/zceXLEUQfEiKwkXzL+ZH257n/NgcxIBI24VJbvrrL3N2wuUTVyxnYdclzI5GuLhjPl/86y9xxfIVPP/iT7nqM1fzzb//B2x7nIoFjYEGThVOMTs6yzsQHQuxVsP1ufj9DZ7JtFqh6sDZibPY9gS1ag2lScIwLcbGz+K4E9QEP6JbQ/AL6M3TkMQAPgmKp08xbp2lfOY04xWbZk1FU1UqtoUU8KM3Tycg1HAcmDZtOkqdR2CPVxkbO4UkK2RG3+fKK5aiKg30/v4g7x4eoq2lhVwmy4RjE4udR6lY4IILWxgzxqm5Li3nz+VUoYBIlao94X39sXEaVJlE9Dy06QrT9GmIkh+fH/wBAUlpQFdVpukaPnxMVM7y3vD7nC6eJB5P0Nys8+KLO/D5aoyP20xMjDMxYREKzaSvr4++vt9RPF1gbGyMaDSC4BeYGQ4TCAQwzloociNnyqcplTwp5JkzZwiHw1iWNUVuBabo447tIvoDuLVq/bIJtZofvz9AY2ODZyr1eQ2Ixsb/nb33D4+jvu99X/vd0Wg0Go3H6/V6vV7LQghZCKEoinEcF1zXOI4Ph9KUcrmUy6WUEg7lpCRNkzY3zW1pTnqam/LQlPLk0Dw0J00oSR2aupyUYziO8SGOcRxhhFGEUISQ18uyXq+X8Xg0Go2+O3v/+O6uTZ977+lzn9vb9nny/UuPvF5Js9/5zufz/rx/dLWTo1tp5a308la6dLVS47zr0WEIEgIqZ8+gd3YSLi+37bcXA5+jP/jhTzUf/4zr4YcffnBsbAwpJZ2dnSSa9uwtbUfr4ZsUHe2gv2QySRzHLC8vs7S0RCu1OJlMticZHR0dJJNJlpeXCcOQrq4uVlZW2rzudevWsri4RBwrPZxlWjx/WE0V/OACXYYC0c4vLtLVaVCPV4ilhkgCtPZdHSlBrkR0d/dgdHYSRpKenm5WVlZwXZd6fYWV5QYiqYoNXddxHIfz3nlMvZPz52qE9Trr1qYIwpD+gcvx3jmPpneRSKoCUEaSt0pvs3b9GnzPIyGSJNBI0EmyI4GRTHD86It0W2tJHAScYQAAIABJREFUineYP1Xj9v/lLs6cOcV/+MJnWVkyWTj1Kn/04P/Orbf+CrfcdjMvHptAxiuMj13DNe/fyp9/7Wu8/dZbTEy8xLXbPsB3/8vfMTpyNT/84YuMXPUeDh89ztat7+ONhQU2ON1cs20bsVxh9er1vFl6m/rSCstRxDUf2IpcjiicepNcLsfpUwuce8elb+Nl9A9uJhFHrNuwnmLxFP2X9ZOUMUd+cJT+yzYy/+YbDFw+QLwSszaTYeH0AkJo9Dgmf/WX3+Dqsfdi2za60HDSKVKpdczPz/NXf/UEn/rUp7nx397A2swaPvjBD/J3+/+Orz7+OENXbubQ84c5sP9pznk1+gYH2ZDN0KhDV/cqfumW/5mrR0aY+NEx3AsBT/3NNyjMnyFuxOzctZOZ6VdJJpNcv+t6vnf4v/HayRl+53c/w8N/8qcMXnEFSdHB4ObL2bQpzzuVGiPvGUbXOim9Xea1H/+Y73//JPNvvs0Vl2dZXgpIdsYMbb6CUrnI5//wQebfKODVKjx/6CCiobO5f5jS26fZ88EPMvP6azg9PTSQF5vkegOR1EivWc383BvUGyqHxvcXlbBAS6InGnSYPc3A1wSdeiea3tE0l1kiWW9wYXmJZKMBehJNg7nZN+nu7mHDhvVoQvDS8QnyvXnk8pLS3TUSJOoJFonR6iusz6ylUjrL3KlT5Dfk+MC1O5g88RLmqlWcLZdZllCpvM0bM69z+aZezJ5ulSb/nqtIJrs4vXCKZbmM67ps7ttMnGygd6iGJFxaIpEE2ZDksuuhngBdo3q+Sq16liDwuWLgSlavSfHfDj7H2MgWIMHSUkjfpk2shCHHT7yE4zgINHQNVq9ZS7iknpdvLLyBrnWyZ8/1zMzOcN69wKpVqygWi1x33XW88srLrFmzFsuymJ+fZ+PGjehakuo5l44OdR2TSVVrdnZ2tUMBG3GCIAibZ1SCRCKBYRhty9z2WZZMthuLer2O3pVES3Y0s4QaLC8v09HRga7reJ73j7ba/Rcz+Yib3VjcRAMvRZdkU+GvrAuVHd2loFK1WsY0TUxTvzgiavLT2shD08lDoRjq/TRdUK6UkTJERrCwoMaUg4ODeJ6Hrun4gd904VI2iq0fbFp6mxunCYWURDIiiEJ0w8CylCVnq3u0nTTCAMdIE8cxNc/D92qkU2k8X7ntWKZBsVgik8lw6MBBTNvCNnROTp5AoGHbNql0jrxpoAmNqZNTNC8MsYwRQqc3m8MPa4yPjjM0MsK+/d/i3qfu47MPfp5Myubp7+zHshy2jI/TNzjAJz75AAeeeY577r+f/fuf5s7bb+e+j97H8YnjPPrIIwwP9XP44CF6e3NMTkyQcmxmpqcZ6O/HtHTCSFFlZqdnsW0bx3GaUw9lwyaEYOvWrSwsLDA8PIzjOBiazuBgP3NzM4RBDdtKcfiFF/DDgK1bt5LLZUg5DinbYW6+QM2tUqlVyeWUy8QNN9xAtaq+55VLFMsVojBWLleOzaOPPsqWbVsZGhjk1Vdf5cCBAzzwwAMIAelshqGRIQSCarmGH/g8/tWvEgURe2+8kckTJxG6zoHnnubw4RfIZjMYhk4USLZt28bDD30B27aJJBx84TD79u3jsa98jbRj8/T+p/nO/v3Mzc7Q35fj+MRx+vv78TyfF44cASTT05NYtsHg8AjZzCgAjm2TStmkUxky6TxbxrfwwP33se/JrzM4MIhbdRFCw7FSBDJCk4ouKDQdQ9fxfa9JvQrQTZ2a5+PYJnFsEAaSoFbFi5RDTrFUQhMxpmmSz+ebaHuOMFIc78D12g1kuVxAIDAMi1KppOz3bAPTMnBwOPTMd9m1ZzfHTpxARhHXbt/K1m3XcmJigv6hAQLPR4Yh8/PzlColNCHI5rIsFObJ5DJ41RqOY5JJ56iklB3xQmVeFTDEyFASyRAnlcG2bY5PTmKZiiZm2ha7d+9sTgR8Tp48wf0fvZe52YXmhEAQBCGHDx+kN9/L+PgoURTh+z7FYhHHsTF0i8HBQaIoZnpqkoH+PnVv1mqkbIfZ6RmyOWVj6NbU9KdarhDKGNPUQSitmWlpeJ5ESoXm6XoKhKIIXIpet1xQ/qHzEUA2l3nXa1NOGmKJbVnU3Bq+XyOTzv1/fvT+dP2/W/8QMbz0s2v9+6WOMJf+v0unF5f+30spxC10svVa09Sp1eJ3vUZNOgIM3Wy6tUl0oSmqZYtaoRnEsULudd1EiIg4VpM2GbZe03JpjBSdw04TRupZaBgGlUoFEIRBgG6ZxL7SdJWrVVzXpVgsMjA0yNzcHH19AwhDJwpD9TxIOUSRQsCFAGJlLz02Po7varhhzPjoGMQBUoCu2wwOZjlw4Gm2X7sDYsmNN97I9PQsg8MjHDn6Avd//D4ef/xx7rnnbizDYHp2XjlHEREGEQhB6Af05vPMzk2hGQ7pVEo9/0ONXCaLKQy++8wzbNu5velSlG473g0NDVEoFOgbGMEwDDLZLEePHMVxHPbtfxrHsbEdhyAKyWV7qZZKhFGIZTkEQUA64zAwMNCchjY/M6GMLTKZNHfccQdTU1PcfvutFEvK6nbr1u1MT09z4LlnsHQDJ58HQ6hnrBcTSsn05DR33XEX8wtzeH7I1q1D/P4f/C6/+bGPIaWgUiqSTmeREo5PHOeGPXsIZEy5XOYbf/XX3HLzLczOzDBx4hg7d+7EthwK8/PIpnNbf74XSHPw8HMUi0XSjkPV85iZnWF0bIzR0TGOHj3E/Q98gi988fN86rf+N+647W527d7FoWcOYdkWc7Nz9A72KR2CrjdrNUEYRUhx0a1JM3RkHCOkBF1NxTRNwwRoWqlrze+FMkLGMQgDYcSgG+3gyziS1KpVYqko2LpuoJQKkijWyGbTnDx6hJGhAWzHBl3jxMQEmd4+gvCiy1hhYY49u3ezb9+TYOjkMikOHjzI8Ng4cRwitJg4AsswqLpVTEdHE8pFFTQsS9nf+1FIFMcIYjKZDKIZ8xCGPpWq0rm0QK0oUs5Y6VSa+XKB0cw4NbdGubBAShPtqbmuGUQyapud+L7fDgJtUa+EEO963rTOiRa40TorTNN+1zNITWDiiwygZr3aeg8VDGxeMi1RExYZxYSh36aCtib4YXTRwfV/tP5lTD4efvjBsfH30UgIEkkNRBJN76TegHoDwmiF5RVJTIOYBomkAJGgo1NH0zsIwiUMs4tGAmIa1BsxCS2B6BCsxCs0RAPRISAJderEiRjRIWgklOJf6+hE6CqJKZEUdHWbyLhOsgNWZESDGE3ASj1BIxmTEA1EshOR7EDW6ySSMbFoKC58fQXDMBFCY3FxiSAISSSSCsGKFDrWaDRYWjxPT9cqUmtWIzo66da76LJ7KBRLdHQanHvHZePGTdirU3Qa3RhGp/rrGzFvnzlHpXqOzZuvZM2ataxJrSWVSpNOp5n9ySxvvfkm1+/+EI2VOt959m+59999hLt/9SMcefFF0ukevvWNfVx/w4f49G//Dl/588d5//u38cTXv8amyzZx5513kM3k+LNH/4zl5ZALwSKvT/8YO52hdLpEek0Kt3aBy/rzdOkW4WLI2rUZGokVeqxVSCm5+uqr2bBhA6VSiXw+z0svvcTo6CgnTpxg9+7dDAxexp//pz/n1Nw8e2/8ec6eq1IovUmP3s2m3k30dJvYq3swui08/xxnzym72bVr15JMJllcXFToYKeBY69m3doM67MZzK5O9n7oQySTSZ584q+48847efvtt5mZmeGtt8tsvvIqgkUfkdApV0pszOa5775f57/+/Xf51lPfYfHCBW6/+3bSmbX0b+pn2V9k6sevcvC/HuJTn/o4n/70b9PZ2cmBg4f500cfwersYvb1GY4cepZ83+W47ju8/NIxcvkc3d09pNau4eTUj3Fdl1W2Tb1jmS3jVyFI8MqJCXqzKfL5XpxVPdzxv/4q41u3sP+bf8FHfvV2RkcGec9738ezz/4Xsrl1TL36CpazikSjwUozaAwgWFoiWAqpr9RZWoloNBIqIKjLpNMwaSRArjRIaIJkI0EcrSBJoDWSlEtnOH/BozOZJCGS6AkNzehkaVkS1xssBRGio4Nkl2D9unUYnR3UZYIg8FhyPca3beWCd4H6yjLpzHoW/YhT8/NsHr6S9dl1PHfwIEJL8r73j3H+nRqr16SYny8y9/qPufKqzSQ0WJ/dyOp1q/EXfVbkCps2ZVjV08PKckTNq7EULbEc+CzHyySTBrn8OrZf97PIqM4PXjzG84f/O/NvzjN4xTCnFk7z93//9/zwhz9kaWmRKArJZHJ06ILnn3+el19+WaFLTavTd9xzSCk5d7bK6z+ZxQ9UArqM66xa7dBIgEgmiRsNwmgZRAIZ14kiFaiUSCTo0BVFtLu7C8MwMAwllNc0wYULFwDa6PjKSjP5vYkkaZrWRsellCQSCYIgaHN3Ozo66ezqIAxCRMJg1apVPPvsP85J5Kfrn2Y9/PDDD77nPe+hXq/T1dVFK2QLaE+zEolEu+FIJpPK2Qra+04loKvXrizXWeXYbVBM13U6OjraxUl3dzdxHJNK25yrniMMFcWXRoMrBgcoFBfIb1hPJCOWgmVs22ZFxjRiSTKRpMs0EQKiSNLRkSCMIoLFkO7ubpIdnei6olItLi6i67ridPcYbfecMAyRQUi3bVF4q8zGjRvo6NR45eVX+Lnrd/HNb36Tzi6DseERZk+9idVpsmZ1msUgILt+LdVKhcVwmY6kjkioJPVOPclrr76K51b5xG/9Lo6T5rN//DmuHBqiN5/jfe+5isTyCl/60/9Ep6XTAE6fPkVXVzeZ9VkksGXLNczMvMqBgwfZ/9TfsL43w/cPPM/Q8AjPP/s9Bq8c5HywzGWb1vOT13/Mh3bvovTmaSZnXieKO8im1+KeP89V7xnhh0dfxDv/Dpdffjkv/eg43T02P7P9OnrzvfzpIw+xNr2WzVddyfeee5bpmSmuu/Y6xsZG+cH3j7Bz9/VUy+c49P3nMbt7yOVyXDm8mSsuu5yp12ZIJBrUG9DV1UVnZzeZdJZXp17h1ltvYfv2HXz723/NH//xH7O4uMRfPPZlchvz2D027vkazpoMi17IpvwGrtnyAcKVZb7xxDc4vVBUTkO2w2d+7+N852+e5urRq3j++ee56667yWbTGGYXz//3F/AuXKB6psLdd97D22+d4oMfvJ7Vq9K88eY8l/dvQmgaS2GElhDEUnKmVmPj+vW8/trLdJg6jr0Gw7a54rJB/uDzf8TSSszYe7dy4wd3EPo1Oju7+dgnPs5H//2vE/gXyKxby3K4TFxfobPTQOvQEQnBchBQj1bw/UVsx6KnZxVShiSTqi4KAo9Y1unUOyEBKzIk2aGyJkg2MJKdLDWLeFmP6ejUSa5IlmSM69YQWoLVXatIiAbJDkWha9ThnP8O6VU9NJYi/OUQKetc3ncZxbNvYXev4sUfvMh73/seguVllpZ9rnnfFl6dnGRlaYntO3fzduE0PT0pjG6TYnGBwF+kx4SVpQaLi5IleYEL/nkSDegd6KdaKjM0dDlDQ6NMvjpF7WyVWu0cqVSahNDIb9pA9UxFZV91r2Lz4ADnazWcNQ5vLrxJGEbY3SYrLLM2vZalYJnM+nVkM3meP/wsO3ZcS+FUqZ3tYZomfuBhdikdSCvlvJFQmpmOjk4uXLiAbducPavczVatWkUikcDzPE6ffov169dhGAa+77edYcMwZGVlpU3vulR/HSwtshJJwjCiv7+fRLLOqYU36elZRWp1mmefffZfT8jghg0bGr/8y7/c5pypQzBqIzwtdMl2LgpmDN1qjuW89nRD10zMplq/HRR4idtHqxNsXUjHcSgWi82fo/i3rdfquiCMQkWfIEaLRdsLWi2lKdF1Nf1o/c6q+9bbXWsL/fq/8uo3NHBrPogY1w8I/YBcb76NarmuQltbAS6qMw0xNcXj1ww13QmCgDCQSBEx2NfL7Xfczk27b8IPQpxsijvvuouZmQViDaRfo1yscO212zl6/BiOZZHOpYlDeHLfEwwNDTK/MM/4+BiDA4OcOHES27Hxaz5eoDyuNQ1qNY9ypcTI8KgKoEs77WA2NYVSI/vR0VEmJiYwDMV3/PrXvkapUOTjn/gorucRA7Vajd7ePtyqi+vWkHFEOp2hUCwQhAHDQ8OYps10M/wnm82STmcgVt1+GAWkUg6O4/Dcc8+h6zpf/OIXeeyxx5ifn8c0zfbNKqWkr6+X7Tt2MHHsGEePHmF2eoZbb72VZw4+hyZ07r7nTj7+8c/w0Qfu47Of/gwHDx7kwQcfZGF+nps/fDN9fTkMy6FSqTAzN88z332aC+fPc9V7rmZ0ZJQnn3iCRx97nFwuS7VSJZVOMT07gwzK5HJZNCCTyjIxeYJSscyRo8cpF2sqk0ZAf/8Q6YzN97//PT7wgevYu3c3vb29TBw7jp22SDsZpmdUaFWLd6wQEo0oDgm8EF0DUK5xGhBc4sEtZYQQWhuVi2OB0OImT10n5WSIkYRB1LwndVzfJWjmYCAlXhDQn+8lDH3l/43iTVuOzcBAP/l8Hh3B9OwMlWqJubk54ihifHw70zMz2JalMnmQEAlcr4aUEeNjo4RIqpUKIyOj5PN5giCgXC7z5S9/BTfw2LVjN6HvkUplKJfLKkQpjtgyvhXLMghDJR6t1SqkUimiKObI0cNsGd/GHXfcwb59TyrjB+PiWVGtVtv3vuu6jI2N4bouCwsLFxEoXW/rzBTX1qFUqpBOO1imjR94beSohRLpzYyf1s+wLOtdZ19Lz9Y698rlcnv6GscqbyCSEULESBnzR3/0hz8VnP8zrnw+37j99tsRQoVStj6/1r6o1Wq0wmKJlfAXLWyHRiKbvGt8ZKTOI8uy2joPz/PIZrOUSqX2nlA6jRq6Zl2iWdTI5TIUCgU1kY81WjpGoSsDlaaRf1NrqPambhhoQqD0jgYg8b0A0zKazlVhcyIatzVGjqEzu1Ag39dHuVJmbGiEuYUCmUyKkyensCyTof4BCpWC+j1iHdMysG2dIA4pF0rouoOhGyAMUpYkn0nTm+vnM599BDtnsuOGHbiBjyV1PnrXjTz11AGOT84wODzAM889x97dezn0wgv89sc/yY233oxl6jz3wiGeenIfk8cnyWZSGBoUi2VmZmbZuecGYk1jZHCAvrxDbybFt771FIEUWJlegpqrNGXZFLVyBUMXZDIZFuZm8YIQr+YTeCE33LyHcq1Kf1+erz3+dcbGRkk5JrlsLy8cPszQyAiRF6KZAtf3qVQq9PcPIgwBEbhuTeldhEDKGF03MAyNgYEBigsFbrjpRh5++GH8mku+f6CtA9V1Hdu02Ll7F/v376evb4AHP/c57rnnoxw6/Ayf/u1P89yBA+y+aRff+do+ctksh48eYWR4mKHhYSIJNd9DQ+D7NeIgIpNLMzI4RDaTY2p2imq5xmNfeZTZ+QKf/OTHKZerDI0MY2g6pVIJt1pjbGyU/QcOcMcdd3DkheNEUYhuGWQsg768RYRy5duzZxe33XYbt91yO7qpY1k2U9PTDA8NEUdQrpbU2SckmjDQTY3Aj5SeSNeJo4hQSgyhEesxxCp0EaFCdcNQogmB0MB1Qyzbxq9VELpOX76XE5MTpOwUqbSDECqfzUmn8YOIKJZKhxL4RLEgk8uqJHHPxS27zC7MYRmqzkrZDnEs8ZuTO8MyAQ2/FhBrIYYW46RsZmfK5Hvz2KkUszNTiFhDt3RKxSK2Y9GX6+PE5DSObZDL5dSEgJiTJ6e5dttWpqZmmDhxnP/4+c9z6JnvIkzFLrj9tjt5/LHH2LJ9jKmpaTLpFE46xdzsAkKLGBoaIpZ6W5dVqVQwTA3TUGeRbdtMTk6261vbthUjxDYJggg/8BgcGKJUKuH7PppmkMmk2pOR1vTkUk1b6+tMJqME7LZJFKqpaLlcxLZbAYMufX0D/MZv/Ma/HsF5AiVCigHLaLpJyWZoeCyVGE4IZBARh5JYSiQqACkOJcTKqg4RNQ9hga41k2dFSxwOIJtFWdwMVIswm2nRLSRSRhLf89FTDnEIlm0gNEEQhmhx8/0FzaZEEofq/UBZnmmxElNJKbFsVdyoD0pTjkSXiuFjnThWtnSGLsAQ+K4SbGczObQ4JpARRivVPY6RoaDi19A0nWwmB7pyI4mJMTSTVCbF/u98C90yueXGm/CiGjOzU6SyfTiWw9EXnsG2bCq1mnJ0iGN8z6fm1njoC19g5+69TE4c57O/9xlKxQJpJ4tAx/U9hbqJWFG8kOScDI5lUKupVHfbtttCcADP85idnW0Hps3Pz1OpVgnigHK1hud59Obz4MD01AlFIWiOBf3AJ5OyKRZDTENxK/P5rCr0LEcVl80bcHhklGKphESjWnHZs2cPhUJJ2ZY6DplMRl3fOCbWDUbGRigVFpiamiafzbFlfJzZmVk+fv9HEbrG/v37+fgD9xNLwa233sr8wiy7r93JnbfdSqlS5oWjxzl2/AiaMBgeHmbnjh1MTU1hGSajIyNs376do0ePctsdd2Cn0szPzDI0OkLaNpianKJQKHDTTTeRSqUYGx4n7WRAawlGY1IZm1gYOI7NxPHjjI9uYd++J8nnctRKNfp6+7hh726OHDuqBGKRxNTSIAR6bBCJVkAZIEOiGLU/RTMILYrRdCVFlTJGECEj0DUdNPBqFRWoGUllQx3rZNM5YiL8ICKOlUuPZug4pqnsq01DNSk1l5mZaUrFIql0Cj/w8N0Q20yR6k2zY9d29u7dyfETJ4iimEqliuvW8EoeQRhRcj36+nrJZXspl0tUKx7HJ09QLBQYGhkml82rAl83sW2nSSEB284yPz+HZVk4jk0un6XmVjh5chonZavwwdICDz30BQYGBolkyMSRE0gpGR0dwzIdZOirex0BkaS4sIBtmAoIEcpeM4oDUtkcum6qULBmY6LpAl3qzfG0ymKwLEWF8zxV+Nm23QYkWg1Kq3iFdwvS9aY41XVdSsUScSyoVBb+yc/in67/59WiK7RArn8oPm99T4lCdXRDR8qIKIyawJeGoRtoGATNENxWQNelNKvW+7TMCopFj2zOuiggbTbYtm1TLhexLAcE+H6IrQkV0avyCQFJGErleqUpMC0MI2JNR0iJYV4EyVIpu2mbqbddtQxNx9QNNEPHDwLCIFBntZFTFtJSEsiItJ3C9QN8PwQ/bhol6Bi6gW4a+L7EMUWTDh0R+i6jI1voH82Rytm48wFxDFFUZeLYcRZKNXbs3UEcyaYDkbL4vWHPHpxsin1PfItCoYBuCNLpDHEsKRVPKPTYdvB8j2K5yB2338Cxw4dUYzA0DqYJYdS+tlEUIRDUajVFrSwUqVWUS1V/fz+zC/Ok0xnK5SJRNEi+d5RKqUgun8et1Qh9n6gmGBodpFQqUa1WGRodoVwo4nk+hmWSS2cUNditkc2m+dIXH+Izv/fZdtiwEALbUrRwdGWnvOPaa9m3bx+ZTI5HH/syn//c5yiWquzevYdDhw41qeEmMg6Zmpoin80xNDCIYRjMTM9QrZZJZ3L05jJEnvrMCoUCN95wExMnJ3juuefw/YDdu3dz00034GSy2IYy3Al9n5GRYfr7B9i9czezM3MKJOwfoFxcYKg/Q+BLSrWCIsPHMV/9yuNUKzUOHTlMtreXdCpFqVKmL9sHkWwi5yZShsQRqmlu1VHNBk3XdSSKvq7uFbUPW1tZExqiSUmKRfOZHseYhtUMaYyQoVD7SEbKQtqXigolBLomcGsulmVgWzaxE+P7HnFkYhgGKctmZHSYk5NT+GGI0BVt0XVrRASkHIuBdJqoT8fzA2JcBDqVWgVqEk0X5LIKQA4j1US5rksulyMW4Hl+UxBuMzo2ytTUFIWFEmPXbmFubo5qrYJovg4RU6t6WI5NLpejUikShxFCNwjDkK1bt3LgwAF27Nj+rvPDMAycTBYhlbNdy7RA13ViTzVmLWDfcSyCpltjCzi2bbsNPFxKz2pNPlzXRdcM0mmH+fl54thtPgsFrlf9R5+l/yJoVw899NCDu35uJ7Ztt4WZXV1d7fG0lCoocPHCBbRkEpFIsLwUkmhAl2HQbZqssm06khrLSyH1ekSn3onKalKBKiKRIFxaots0219Xz55RfNmOBJ2dnejJBCLZoF6PoJ4gKQRyZYVGPUY2YmIJJBPUZUxDQEyCpeUQLdnBUhQiRBJzVQ89nR3Y3Sam3o2hddJldLC8FFFfrtNtdJMkSUMkCKNlkloSIZI49io6OnTWr88hZR1vKcRa3U1SE7xVqhAGdZweJbhV6FUnb595m7NnzuK+U8E0DXRd482FOZ548gl2/swuzp47w5r0OmTcYGHhLbROwek3F7B60rw+M8P2n/1Zzr1zHttyWGV384MXX+SjD9zPnzz8MPfc82vk1+XoMLpICg2r0yS3Psdqu5skKvjvpl/8Bc6dO8+6tRkM3WB65rV2wzE9Pc2pU6c4deoUFy5cYHFxkYGBAe68805++7c+wU/mfsKaNSlOl07z1unT/NIv/k9cOXQlHZ1d9PRYdHYlWfSXsHp6sGybt0+/RVe3zdq16ygWy2i6weV9/Qhd58jRH9BjdZLLrWPgyisYHOjnz/7sT5EypH+gjyQNFs9fwFxl84u/dCvf/vZfs2Ztmm//7VO8f9tW3Oo7aIbO0krAC89/j27D4L3vHeNHL01w5ZVXcv2u64nrdaKVFfzFgNVOD//m+j1sv24HmzZtIJ/NsvmKfoQmGB4eYs26dSSS6hrd/+/v5/p/s4cTL5/g+p/5t4yN7eR977uOSsnl7BmPudffpMfu4bJNG8llVzM81IdfcymdO0+1XOHXfu3f8cBv/gZf/MIfUnyrxGWDAxw5cpTXZmb54O4PsRQsYxoWDST1lYhYxhjdXXQaSUQSEpFANARdlo6eSNCIQSQTNBoJOjpm0tx8AAAgAElEQVQ0EnEzrDOZJNGIiVG0QC2ZpCEaxLJOo5Hg/PnznPcXWVxcJI7rBMEF6ssNllZW1Dh9eZnl5WWWV1aonj3H7Otz/GTuder1GNMyWLM2hXve5Zvf/GueOXSQ4qm3mJudw19cRNc6GB4eYejKKzhzxufI91/i6b87wImTc7w2t8DVo1u47rrrWV5OUnyrSkLron/DRjzPJZPJcObMWVavXsM17x/nHbfG3BtzTJ6YJLU6xY4d1xIuLXH2XIXubkXrm5h4ES2pMz6+hTVr1vD22yXc8zXKZyu8XTnDutx6zlTPcuVVV7EULdNpdtFt99AQCfwl5duv7A4rZLN53njjJzQaDWzLIa43WJEhnZ0dmKZqXFzXJZFIIKXk/PnzalrXHGu3is16Y4VgaZH8+k04q1Zzplzhx1NTeOfP09NjkU6vJr1mHQee/fuf0q7+GdfDDz/84DXXXINhGCwtLanCVYg2tW5p6aIoPJFo4PsejUYCwzBpNBIkEg0SiQZRJFlZWcG2FVJbr9cRQqh7aHn5XanBKhCwE5FoYJo2UbRMuBzheS7r16/nzJm3MS2Dzg4Tb8mnS+9WU02ZIJFM0GgoiliHlmR5JUQkdDq7u6gHkgZ1Ojq60DTB4uIiiUSSRh2c1TbvvPMOjUYdHdB6LFKrHGpnq9RpcPr0W6xZs5ous5Pu7m4VTigSGJ02y/UVVlYi0GIay5J6IkkykeCCv8LlfZtIre7kF2/8OYa27GD3B36F0fcPUrfqHHrxOFesv5rzpe/z2Jf+knR+M/Pl1xm76r3MzMzQP3A5a1ZbFItvMfHyS4xcPcLA5QPMTE+zYcNmGlHEyycm6DS6Gb56lDPVs2Qya8mvSzHz6qtctmkjaN1s6B8gszrFqUKB84seK+EymwcHWFlZoafbxPUu0N+3ict685SrZ4lWIhJ1KBQW+KVf+EW6uzv44cQkmYzDq1M/Zo2zmjPnzrFpUz9hGNPd08PU9AwdIoGmaaxepVzzznvv4Dg2u3bt4rXp1xi5eoRfuesutm3bxsb8Bs6WK4QrEYNXD/Mz1/0chTdeY9+3nqKRiLlq8wizP5mld1Mf4fIi3zv4PX7+xg9z682/wJ889CWu2TKGvaqbcFlyYuJHRHXYkN/I+lwOGUkunD/L5oFBjv7oh2zePIy9ymF2/g16+y7jscce4zd/87c4Wz5Dsl7nmvH3k9Aa9JgWp4tvks1kmHr9J/Ru2kTvxo1cCJc4+cqrZAf6Gdo4wPypWX786itYPRa/+Vsf4yP33ssPj/6Abdu207PK5uTEJMmOJLazCtPUWVpaIRagoVGv11lZqSuKqxB0iCRR3ODCooeW7KCjM8lSsEQCSIgk9XpEQutiyQvoEKDpHXR3GsRJZfxTp0G0skSnaZIgSbycQNYjtEaDpK5jGgb1RB3X9Tn91mmSHQmuuKyfVWtWYVs9UI+ZmX2N3ssuY+2aNO+8UyNeSSASCbROQYcmqLkXOHvmHS54Pp53HoQyZOnv30SHZiDDZWJNsMFKEcYBuVyOt956i42beqk3Q6BPnnyFjRt7yW/YwLlKhclXp0ilbYzOLqpnzrAmk2Jj/jK6u03efrtCMpkkDBcpFE+zYcMmVlZW2LhxIxMTE8SxpKtL0TMvXLhAHMck9U5Sq2zOnz9PT08P77xzlt7ePI1GgnPnFP2qp6eHdDrNK6+8wtq1a9vmKN3d3SwvL7cpwfVmkGoymeTChQuYnT0sRyG27bC4uEgymWT16tV0dXVxpnyW48eP/+sRnDeAKLpoq9uiMF30GlcHsZkyCSPFh7YM5UkcyYhYgB8GRKEaXZumqQQ6TbENNLtqwyBqfU8ILNtBxjFRIAkCRW+IwkjRJMIA01AjUATNgl8SR0KByhJAYhkmMpbYptXMJ6i1EbDWqLv1OwlTUbcimkJBw8DSjfaYNZQxfhgoy18EnqfGYn29vUoM6LkIXzSvU9wMY4yxjBSx0PCCiO3bttPXO8DMzCw116VSq7F9x7UUFkqEnhIT57J5+vryzM/NYJkKiSiXy2QyOb78yKPEMubo8WOkUiliz0dDp29IBZ0FfkQmk2JwaIiZmVmOnzjG+PgWFhYW2Lp1qwqXatqVxnHcTr+UUuK6LgcPHuTpp5VgStd14lAyMDjEkWNHEZpgbnYOwzAYGhpBxjUMQ8d3PfwoAF+hMiknRUxMsVBENzR2bt9OKGMWZgv0D/RSrVbp6+ujr6+vaQ0p2LlnLyKGwwcPYZo2k5PT3H/vvQz0D/DlRx/llltv5etfe5wtW7fz4IOfY3h4hL6+PgYGBpienqFUKFJzK1imzejoKOlsjmqthucFlIsL3PThm9GnTvKRj/w6mzdfwX/4/T/gP//FX/LAvfcxtm0Ln3vwQb74+UdwKx6D/QO4zSAj27Hx3CoLC7OEkcSxLdANMo7N7vvu4tixY1QqJb785S/T299HqVRiy5YxhBA888wzDA0NoRmCKGghpRAFIZqhI4SOYavJnuu5yFjtGcdOgYDAD9BMlc2CQNnRokKfNGGg6aJ9TxqGodAjTd1PmjDUvRirW0ES4zaLrlw2T2++F4SilTjNsE/dNBkb34pbq5HJZkFeHPVOTEwSRgFuVZLLZdl62zY0TaBrOguFAi+8cJSRoUH6e/spFRYol8vNvRUwMDBApVJh/3f2Y9spRoZHkFJSLpc5euwoqVSKO++4m0qlQqVaZseOXZTLZSYnJzDNpq2vaWI2EaVyuUy1XKG4UKC/v1/9/aa6jqlUioGBgTadr1ar0NfXRxhKKtUyuq6r6UwcUSqVcByn3YS0pnBwUXjZOitcVwVpeb6rzjxdZ2R0FCkltWqNKPrHI0o/Xf90K5FIvItWB+8WkQNN+mKMQnU1IEIZpcQIYRJFUolYhda0v70o5mxNNlrPwBaiqestqtTFoELTVEi5pmtEYYxuSSzdooUni/boQ0PTYkDds3EcoQuTil8jnbLfZW+v6zoyujhxUZTmGDOl7KRbaelDQ0NUq9W2kYuu61QqFXJ5G8dK4YcecRQTaSqBXdMEmlBWvrrhY6T7qAO+G1CteegObN26hdmjsxj9RTKpNOlMipFtw3hFl9nZaQaHh5TIO68oRmHoE/oCPwjo7+9n6uRxTN0gbk4PhSbozWc5efIktVqVsbFxZo9Po6cySM9v3tcmenPqUSwW2b1zB4VSmVjGpFIpSr5HxkkxMz/H0NAwQsDU1DTICNO06e/tp1op49hqGg8S2XzuRVGoQo/1iwLeGJUCPTY2Rmmh0KZpI9UELJVV+Uz7nt6POzfLTTfdxPz8PFu3jTM9NYvnewwPDfPII4/w4Q/fyI69N5LLprFTKWq1KqZhYKcyVGoVgsAiDFPoCHI5lU20dctWUnaKhx5+iIMHDxLKiFtuuYWnnnqK2267DRkP4tZqTJ44wdaxLQShTxiH2KaOritb3Ewmw8nJCYIoZuLEBNl0mnQuw+4dO7Esi+npaTK5vDL2SKdIp9P4gdfcy03BedM+KCSmOfYAIJBRe/8L0QrGjNENFWkgJZiGhrBMgsBts0NAmQAFfkAYSQwrbk711YqI0aSalvi+j5NJo0dNRguiLW7XNUFay1KrNZF/BDERmqFBbAIC1/OIAiUDQDOIpLKX9kNP1WRERH5APp2j7CkzpPn5eWWosrDAtvExDMOgUCwwOjLC1Mkptu3awezstKJRGQalcpmBfptYqr8DwDJNqtUKlUpFTeiqVQYHB5mdnSaX621P0i+tn1up45VKqU3PvtS0onVPt2rtVlL5P5QpAO04i3QqQxgFzYmJo6jXLebQJbKC/9H6F9F8CJEgbhY1Ag00pW+IiZVfsxJkEEpJGKkRnWZpTc2HbOsuYgQITTkQCInQNDShwgQ10XpIyOY4WkMTGlxyyIeRakD8MFBKf2I0QzU+cRQhNQF61C64UNGESBkRi5gojoiikFQq0/ww1M9Rm8GHOCaSqhBXKVAQibj5jBLYpsFCqYhtOfT29hILrZn1kUKEPjW/RuRLRYGKIgb7BokFKpFb+sQI9h84wq5d46SyfdipHIMDNocPTZDL5UinbQpBFcfROPD0MwRBxODIIHZGcRINw2Bqepbjx4/Sl88RG4pypqtJPcQgtIhYl+i6JIyqOFaKMBII06RSLOH5LlrTf75UqTK3sMDQ0AiGoa5930A/tZpKQq1Wlfe1bRrkshkM3cQyTIQUhKFPFAlCGeD7NXXYGzZCg3J1gXwmh5O1cWsec9NTDA4OseBVKRZ1RocUX971A6JYYhomXhjg+h4Ls3M8d/Awo6NDhEHAgYOHue2uu5mbneWe+z/JDXv2cvTISY4dmyCMIk5MHMf1A268JU/ohYSxj1uucGJyAsvQuPbaney94fMAzBSKNBoNilWXfNphemGB3oFerv/Zn+NTH/sYP/rhy3z1ya9ycv4IjpNiYGCM+flZQhGRSqXQZUQQBji6Rj6b4cihg+zYtZtUJsvevTdxz913MTg0xnf27cdJ22RzvcrpzTIVNRGBEDZx7EPYdGYzBKBoeYgYGUuiWEIskaLJC9eEChwTBgKJZlhN5JXmODXCdT0EAh2BJnQ028CMIlUM6Ro6QoEAvstCcQEZCzKOohlNl2bRtGbat4iwrTSTUwXQYkTTmafmechAUaAQEYXSArZpYlkG+XyKsZEBTp48yfTkcfoG+vG8GqYpmgnkJroG9911L1/9+uPMTs8wMjpKJp3Bsh2+u/9pyqUqI4NDKnMnitqBg7ZtMj+/QLVaxnFUE+KkUxi2QbFQwrQttNZImybuENFuyFrAgXLSa3rP6zpxGGPoNgIDGQXEElKpVDtDSEatglUdwZqRRtNAygAhNExTY35+GhWQGqv7pBkC9dP1z7dadKggCNo0upbDYktQLuMA16uhxQZpx8aLAlw/UDqQEEI/QLcFKSeNW/No5bloitH7rkaGuKl5FDrIqO2Q43s+W7fuYHZ2lnQqTRCoJiSTsXF9r5mCjuLNoykqFBLbsomimKrr4th2m0ahCauZLSKIorAdLibiGCeTQ1gmC/NFcrkcU1MzQJVsJoeMY1y/hi4Mstk8nlcjjAEp8GMPoSnnSUmMqSuacRQLiAoAaCJLpVwmm7WIpIFl2xz47nfp6x/m7jvu5XOPfobd1+5mZHwM3/epVEqMjo6xbes4hw8d5p6772aopU00TayUQ6FQQtc18pkc/X0DHH3hAIeeO4xhOhw/dgwZ63i+i2WnsE2DQrXA0GA/5XIV07SQMiSTTlMszuNk+1RB5gd8+MM38q0nn2R+dpqR8a14fsDo+CjPPFPGNjUKpSKDfUNUvRqFQoHhwSH8MEC287g0hodGOXr0KHtu2Mttt91B4LtEQUggA7Zu24ZpWxw5dpTiQhHpe2SzadAEc3NzpFIpJiZPkE2n+dKXHuXwocMMDA6Qtw1KCwvkevNUKi6VWoVcLguaRq1WYaCvn727ryXtOHz4ltv41r6n2LFjF3v37mV0fISZqXluv/127rjrDj79yU/zwL0f5ZkD32XblnHiWHL0yBGGhocJAknKMXArZZxUikrJRwQ+Rj7Nnj038tUnvsYtN3+Y3t5e9u1/mttuvRnf9RnozzE936ypghjDttoNvBaBjKOmDkrpETWJAsiQTfMNgYhBIghCF900yGbyzM15WI7VdGUKCcOYKAYZSzShE8mYKPAQRtMdTkoiFDAggxCEhm1cpBzGsWqupIjQI4uISNWfEfhRoABpzSCdzYIdqkesBMuxKZXLOI5NHIfNNHZBoTiPYRiKrtaXQ4YR27ds5blDBxkeHsL1Q+bm5rjp5pvZ//R+7r3/PirFEpVKhYF8r6JL6QbZbEblfRCzZ9duCiXV9C8U5tF1ne3bt1OreW1Kr2GapDO9FBZO0pvvb1KoNKJIgVyWZZFKpahUSkSRIJfLUSqVsG2b3r48vhe8yxVW5eU1G5MwRje09n5WcgJBFElSmVSbNvyPWf8imo+ESFyizVA8WF3XEZqSfHiehxCCXCaNpWsEYUDQtAm0dINm2YXUBZGUxH6Epis+eyQVOitMgWnY6Bq4boBEEssYtAjD1GgFFbaK8BZaL+OQwIuwDBMNga5ZyOYHIWWAY6cxbJ0ojJHNIBfXdRV/1jax0ykCV914lmm0wo1VJ65p6IaF5sDCfJGaW1NhOZK2mLFarTIzPUck1fjOagqrHcehVvWoVSsYlsHIyBA33XgzN916F45t0Jvrw/VqPP7lfeR6B5mdmWF2ZgrTNijOLzA+voUoCglk1Ba5appOJpdhcGSUtGHiuh6+30z81EIymRQDg1uIooCvPv44xWKZu+6+l0LRZXh4jHJxDhlH2LqDaVqEMgLhoAv1ecaaQo9z+T6OHTlKOuPguj6G5nH48AtN2pISl8sA+vr6VLEW1Uhlc/T39wIwOzeD27RA1g1BPp9jcnISwzCwbZMjx4+zZctW1d03NT3PHTiA53lsGRnj5ps/jG2ZzJUK5PN5Jk+cZM/evTz5xD4efeRR+vv6OHjwAG+++SabNl2OaRu89uqPWb/pMrZt28Lf/vW3+f3f/12Gh4e5u9kQ1Gour776Mr/zsY+19/W5s+d54sBX+Pmf/3mefvppvvy1xxgaHuD+ez/Kg1/4j8rSr7efqj9NpBkYRorZwiQCg6Fekz17duIHAd/9zn6m5+ZxPY8777yHa3ds574b7uaJrz9JvjerUNFYHQRetYrppBBSEiFR2z8im80RRUHTOrqJjnhBM1RII2raxBqGg+9XCcKLdrA0NQyqvdeJpEpwjkKJ1GJEJIiayKaGSS7TRyswVEqBaaWJwoCa52FYJrEEy7bRdB3T1IlDmjxZhV7FsY5lNrnAUYgXBoSei2ObWGYO09CaBgJN0Vuhgq4JnvrOU/T19hFJKMwX0E2NWs1lfMsoum5ScWsEQaCKLR0qlRKuq5NKOaQyaYLmoRvHMQJNZSg09VstZNh0bE6ePM7w0CiaplGpVCiXq8phxlCTk0KhgGVZ5PLZdnFo2Qblchnf98nlcsyV5tA1k4HBAapVlzAKSTsXmwvLMtEFhDKiWK6gRYJazfv/50D+6fq/XSsrK5im2eZEu65Lb29v++EdRRGe75LNZtGkrhwQJSBUU0rUEs5qVN0KmjDRDWUxGxMRRxIrpRLIDUsn1mJFL8wPQBziVlX4pe+FvPDCC2zZMkahUGjbYoZBrVn8KNtYyzYwDJO5uRICnVTWRI8gqFRw0g61mkJaZRihW2r659ZqFy1RhUYQhZTnlO7ID5QVre/5lCsVbMtCQ8ewdDzXx/NDpJC4FZdrd23HcxVn3XcDhAXzC3NUKmVmTjzH3/znX8Ob3YaZO8lC+SSzExq33rCN/+OPzvE7v5JhdmqGKFBgSTaTww8C/k/23j9GjjO98/vMe29eVOrKhUq70Gn3thuducZkMh7MjQcEjxhPCB6Px+PxFozCYxhFt1kIik4QdLq9jb3Z23W0i428UARBkZXFQt6TlY0iKIogKAJDKDpZIGSBYWiGGdMDZm5uMhhPGp2+drtTKRcqlULlxbvv5I+3usnds73rP4Izgi2IEGc4PT3T/db7Ps/3+f7odBfZOHmSd999h6Wucxc6e3aTW3eOSLKMzdNbNPYPqUo3ndnf22X/YMjGqS3SSrOytIypStK8oCg1UjnzEiOgKkvCICSOY9I8od3tMEly8jxldbnPO2+9w/PPP8/lS5+nLEun34hjKq3dJDxJ2D/YJ4oCTqyvkSYZRZWDNnXKtGvwkiSh3++zv7/HE0980QU6BhGLS30+/vhDbn5yk163T9Bq4zdC9HjE0uoat2/e4dmnv8zBwQF3b91hOk344tOPcbhzH2NdUR8bQa/f5cMPP+bUqVMcjo6YjASPPfoWJzZPcPnqFeKoMW9mjg4O+c3f/Ef8J9/4Ondu3wJgaWWFr33tOYSQNOKIVtxhlEzwvZDB0ZBmq8UKlkj4lLJFb3md5194yU3jlEfPCr7z8it84QuPcf78BYrQBQV6CKZ5TjcMicKY8XTkdLlWzsFe3wuRdZJ5pQuwUFUGX4Z1pIAg8Hy0qfCkQ+OV5+ybrXFTRIGb+gtTEjYDppPUaZaUoqwc66IsNWFtOKJ16YA35eN5kiwvyXSBJwVJUiCFIG41CHWA8hTD4YBmHOEpHzxLWVXEUUBe5PieRxAE5HlOFDdcY1mWNFTAaDSqmxzHMhDCY5okBCJg/cRJDg/38ZTTXIxGE3dfCo0xs4lkxWA0xFiBh+eaelNRVU5fOAssraqKVqvF3v278z2q1XITwOXlHllW1AwdRZmn9DfWuH3nJmurGwhcRMLOzg5PPvk0N29+ytbWafb2D9DaIKRhPDlyAHxVMJpM6XWcRf3dO9v0up2feC/989F8HAswngvqk6IezTlUVkhnGSalm2hobD11MG7SodwbYKUb33meh5BO6OO6aw9jDVb7pGVFEARUSKx1ieZVVeImf3ZulekmKbNJTC3QMxbp4GWEEUjhgZDzkdjMhUtKSaA8tCjRGnRhKLVGazcGniWuW6tJJgWe76gqcatNq91x1CohHA3NWJTyiZoWKRv4yiPyg7kASEhLHDv/8sPdQ164/zzdhkcrjLl75yNGkwnKaxGFHqYCg6TdajJJE1KjERa0wSn7hWTvYM85AFlBgWRaVHUCrCLLC+cfb4ekacrK+mk2T0dMk4I8T9EmR3mKxd5SnaeQEyonRlYCJ8RPcworSKYpvhegCAhDy9LKMocDN5YU0lHjJtMpTMfkeV4XwSV5Pq1FkQ3n5hA38H0fz/exSjAcDhkMBlRVydJSnzwrQVg3Zeg5H/ogjtDTKaUQREGDbmeRZrNJgCAZ7bO02KbTDvnql58higKuX/uQsiw5feIER0cHhFLy7/07f5tknPDUS0/x6KNXnKtLUdIIJFbAytIKH1y7RiMOeeGFFwD49NObvPvWdf6H3/6Et9++zicf3+Dw8JDxNCEOHWpxeHDAuTOXePONN1hZWWP/3h5ZlnHyhGWp2+T999/n+vvf5/IjV0jHA1bX1zg6HLKxsU6n53Ii7hUpvmfderMOMRJ484IaW4+SrSCsU2Ir61KMfc9DKouvHMUKwFTON9/guO3OcMAgEFQmR2o3kQQQwgklHZVI1v8HKSwqDFhvN8iSnDQdo1B40hBYhVWucbcIAiQWi56bRLgMGwQYXdNTahEtOEcUg0EIDxkojJAIzwWdWe2e3yLcHmAd6tVsNep73t1nk8kEIZxDjzUP7n1w04qqqsjznKp2+/K9EOf/UKPVQlOWeZ187FyPXONVoZSg2XLrdTweAzxoZHy3dxmT4fvSvX7WIc/Tae0spzW+SucC559e/2Kv4+PjOZ3hYTGmtWI+BQuCgKos6ym72+9D5YGGylZoND4+QilHuy0zN5u0BhX4SKHwlKaq3Dr2lKKiIpSKstAo5ZK5x5Mhw6Gz3UzTjDzLCIIIz6s999HkpfPdj6IIay1pUiBs3UwjKKucfncJ5Xvs7u5iGyFBLUAXQmAwFHlVO9pYRqMRjUbMeJrQ63QcspsnjIejeVr6YrdHEjbACFqtFrdv3SKIIuK4ycryCusnNrh86QSQ8NjJ6/RNShaMOXf2i9z45DP+9t/6l/nss88Im6c4f/YCZVlSWpfqXGlNkaVcuHCRjz/+iE63zd7uPsZa56ZkLYuLXXzfOfHcu3ePs2fPUhQFk0lCp9dm/+CIssiJWl0nTK6trbvtFlnpKCQu6b1EKUG/30Nrzfnz5zh37ixbJ7dYWl7kYH/ANJnO6TRx1KCz2K4nNI4a0wjCOYUFJDdv3iaOI4qiYmtri7NnzzEcHaFkyGuvfQc/8On1Opw/c46D0S57e3tYnKC61W7z3e++xtHRPmdPn6HVikmzlMFo5ICP0pCVBZU1PPX0k3hKcebMGa5df5+NU6cQVnPr1i2iqMnJjXWMNTz7zDNcPH+B69evsXVqi6989Wt89PFHfO3ZZ2m2mowmI27eus3S4hLaWpqtGN9TfHb9Oi+/+AL3Dwfcun2HtfV1kklKlk755JNPSJIJr7z8EsYKdu7fpN1qkCWG2Lr9dpJMCMLAZVcYMTcLqsq83lc9PBWAcFRi6UnyJHP1VeiaICsE1mikcXujUBJhDTNnUI2lSBKUcOeyM8qRDgyY5c7g6IzWuKkLNQBgdUVpFUHk4wkPU1kMjk6+2GmSFSVGOqOCmclQUDveOcMGn6ys8GoqlNGgEFS2IgoitDWURYoX+OTjFBV5zgCobh4ss0TyoF47Bovvakgt5hQqKb16ujJFqbr+VT7bd27R7/cRwlEKAfr9Xp3740Asz/NoNtt8+umnnDl9FoSb6K6srOB5Hrdv3yYKY4SQ7O/vc+LUBr6F4XiAECCUotlokpcFRhviqElZaH7S689F8/ED62JhpJL1C+3X/LWSWQVi0UwmI7ButC0AjK2nI24hzRT92kBZVPUBH1AW1VwDUlQVSiiEB1bIOliowPMCGg3HX/P90I3QMCjp1VQKsJWmzHO01XjSuSNgNUVhEdbQbDTqTatAeR5FmTOejImiYF5IGVPNi4i4GSOVIs8zdKUptSb0XKDMYHpEFESuIZKKXq/H0dERk8mEWdBM3IxQ0qPMU+Kad7naX2HvcMCJzQ3WtCYdSUo9QMqANJnS7bXnYWlBGGABVReK7XbPsausJEtSfD9ECHegeL5fIwcahGQ6nXB3e5tWq8XprdOMh0Ms4AWKVtByWhch0aacT4ImkxHTNCEIGyy2FxmMjzh9+gz37t1laWnJaUpKNyVqBKHjv9YFclEUc5eGoiioioq98X5dEGZu/NzscjQ4oixKvvLlXyZqRJw6uUVRZKRpTr/fp9mKOX3mDGVesLO9y+3bd2i2Yj74YEyr08bqiuHoiCJNuT0ccvb0aaZZyvbOPS5ePEent8yVq49x5fIlnv3yM246Mx1ShSFvfimNYjkAACAASURBVOVr9JZXAXj5pSmnNk6wtrbGB9c+5Nkvf4kL509z+uwWVVlw6ZHPs7m5yeuvv8Fjjz3Gk088yVeefYKvfu0FOt0uB/u7dDo9Fhe7HOwdcPPmZ/h+yM1bN/nKV7/MeDzh/LnPc+OzO1T6gdXtUn+ZwWAA1jXnnqg1H0mKEO4QsxhspZGeO8yrqiQKQjCaSZoR+L5DZ4VABMrdX9ZxwJ3rGs7JRxqUlKj6PhbSo9VqYbBY7Rx2ZkhMkk749NNdFntdut2uc6hD4PkOdDCqtgStDw+h5JyC4nnuGQSuSSmqEqmcm4+vFGHkoyvXHLkgPxdmWJV1OFTtPmQMBIFHnpdIpVCeW/eeCucNvxP5lnPqy3B4hO+HxM2oHt27/WcwPCTLMjqdjnuuqiJJH3Dgi6JgOtmj0+mwt7fnrB+jCM9XDEcDWq0Wnudx9+4dGnHEYtxFG43vBQipGI0SptN0bns4O5B+ev35umZ7uWtG3frxhKIqK9zxagFncUs9nXD0Juksq6sSJTwQzvLWUx5ZWtBoBJjKYqXBkwHTaULQivF89z1N7biVZRm9XhepcqrKEIbih342UWtEhJII41DawHM0QSHcxNTpsdScMx74/vx3cRTFgiB0hU4QhERRg/v7+zQjF6pnMPOzNwxDkiTBCyLyssBiaHcX3fcIQo4GByTTjMuXNsEaDg7v0T3VxHiGopwyOkq4eOEMX/3v/zFZkYMt8RsNRFHNf5f7u/tsnNjg2ofvk6U5xmoajS4YKEqnE9S6oixhY/0kd+7cYWlpCd9X7O0f0u/3aeQluweHaF3SX+pjtEZIN52cWeyPJhNW11aYDCecPLFOmiQMhyP8Cz7SSMqyIE1zOt3uvHYpioI8z+fhaw8HtoGd60Im4wmLi4s1og1xM2IwGLK2torne4xGRywvrzCaOs3A4PCQ4XDIrds36LW6CAlh5JNNp4RhOM/UqqqK1dVlnnn6WSaTCd/+9rcJgoDVpSVu3bkJQrmJgdZYXbGzs8PS8gqtO9v8R//w6/y7f+ff54nHn2ScTFhc6vLkpaf57NZtiqJEBh7bt++wdeY0jzxykevX3iXsLCGxJJOURjOi025y+/ZNRuMRd7e3uXj+Itc+/JhLFy4zHY2IGw3iZkxROCpQPURHoGrY6YdphwKJsSVa13kTUtah0zOHLAcCIRz1/ke1B1EQOlcq88ABsixLpPKx0tGItXs4Rjumi67pjUqoGiR1bBtjnZupVApj8jn4MHt/H3a6C4LAFehIrOPZ4AlniZvnKda6GlZrR7kucdooIevz76HSXGtdO6y6cxd+OIDUsRceaM+sNT9kAz6718fjMcvLy7UeydE3A89pEoPQJ01SqtKglItKcO6jHmla6w2tC+bGGHLtbLnDMCArK0xeEkQhWfKTaxP/XDQfx8ezjAGHdlpr6XRb9WjbFS9Ggy4KhJJuIwe0KdGu1MFah96XlcFUGrfU7LxBUSiiKHB2sdaN77Qu6qlK7XdeOnFtoJ1lWpaW+L7L8qASGFyOgstPAKsNSCfmk9LDAEVVkRUun8PzA4wF5QVYqvlm7lAlRZpldNotlBROp2JK8qJwaEt3Cc+Xtfe6s+uMoohZCmu71SJLUoIgoECws3OfU95Jbt+5SyvukSYZ+/v7nDpxgWySY5RAJXJuKxjEEUJbMAblOyS2224xnEwdpUxI4sgnDsP6pnALe8ZR7nc7nN3aJPAjbt76lP7yMlpXJEnGylLT2axmubNim4vwGlgEvgrYOzwg8kOUkORJCqrmNmOQgN9oMB6NXFptnY8SxzFhGCKUxFcBzXZrvrnneUZlSsqqAAUra6tIKVlc6nFwcESsFMury6ytrfDxxx+zvX2f7bv3MZXhpZde4tr191jsLNJoBBwNDmg2IrrdHnfv7xEFIV9+5itYoThz7hzXP3iPF194mf7yEuPhAKsLOn3XdOze3+bixc/TbDb53d/7XxgnKe9fe4/bt+8yGY8oy5yrVx9j8+RJbnx2k7e//zqvvPIKv/gL/xoAv/d7/4SXX3oJT7YZHB0SBRFLK33UoeLu3dusLPexVvDI50/w1jvv0VtcZppMSe9NyPKUsshYW1vHWLexultAIBSAcv8ZhfHrjAmcbW1V7/eqplVJKedivVn+jfu7dBugMAQEDzzBhcudSfMMXTr91DRN0FozGg2RUrC6skyzHTv9R1lh0OQ1Vx3rGhZwqbhSqvmBLYSsi7YAIVwB7iGpTAXCAwllqeuGF2ePaBzHPK8c8iuEdKJFXJo41o3bjXGolQD8wAUQKuXPOa+NhpsoVaWbZOiyRPl+rRlzaKM1gl6vT5Ikc5pWFMZzmkU1meJ5kk63TZpkCIybFmoP5Tk7yeFwUAtUA3wvIApjClG4pODAx2IQPHgffnr9i7kWFhbq5oE51SnPc1xB4OgjCg8trUsmrvVFrVaLyWSC1i4dXErFeDQhK3OWFntuqjAcEXRCMMadX6LWawmDqqdrQRCQZRlaazbWT3L33k1XHNg6c0rXadCAtpqGauB5bt/Xxk2hZ9OZwdBx0vMid0LudpvJcETYiObCVadvMQRCMhoN8L2QSZqw0l8CDLoy+IGHtlVtW73Ku+++x9rGSVeE+x6mKmg22xzu77G5eYpTW22GhyM6Ddj5o49o3t9g68oJxsPPiMMVHntyjaf//j+mrEqCsOb7G0MjDJHKww8CKmMp84rKalqNlrPfr/Wcs7BEYyrSTNOM2/NizPd9ptMpBsHyYg8rwOiK4ZHjwu/v71MVBcoPaLb6HO4fYnRFr9/nmS89y4svvkiRpYySMZ1OjyrPKCpLp9uem8hEUTTP6ALmZzZoVldXGY9H3Nu5RzNucevWbdbX17h65TGefupZLlw4z+bmIuPJgL29A46GY5SERhTwve++yvrqx5w4tcZkeogXrCAMVNYQxy3KsmRxqc/y8iqPXLnCE089xebJTd555y3y3ph2u8VGbwVd5tz45BOk9HjvvQ945NIlnvvWr/L2e++STBPSPCNQsHN/n9X1E2zfvcfJJ08wmia0Oh2++NgX+Nmf/Vn+0a/9h0xERGUqGs2I8WBMnju74pPrJ7FW011c5HCYUVQVjTgibPhMxglFWRAphRC+A5ilQtTgiu/XqdnGoqlq22pLqxGjsYzHLjOkyiuqWiuMdSntMy2eA6EUaZLX6xNXPEtnepAXFbowVPU5gFBUukRoQ6vTRqk2pa4pt8pDGoHVjgkQRN4PaRtmNuqzZnMm4g48D1O6XDZwxbqsTSLiyEcEDcajAUQKW9a6LysdFdk6GpWomyqsRgqBkh4oBwhY6yYto9GAKIrn2hWMIWw02Lt/D6XU3DRlBlbMfk6tNWnpKICHB4copeh0u3VGVkqv1wMkg8GQpaVFijJDGo8gaqCzzCWmeyEKgfEDhATzZ+go/lw0H26TlXPFvTGGO7e3mQUNOuckTbPpOP9KzQSyGmtdPLyxLnFxZmPpgpvKuqtV+L4kTWv3KeUhpNuci6So/fgNxliCICLPM4RQ2FoMK4RiOp0gpZxrQmYbS5akIEXdJBRY4wJwbGVRoZoLjh72cnfNR0Wn3aSq3GQgDEO6UYeycsLAsizQdaphGAZzr2hwQVbtdpswihgcHFKYgpObm2xv3yEIAq7f+IgnnrrM2bNnuHt3h6VOD4smbjXQtVuXQKJxU5ggCAjD0B2iuuLkiQ3CMKKoKibDobu5anexOHad9ng85tat2+R5Rq/XYzAY0Gw26XQ63NvepihLWnHkRPaFxlle1y4xomKpv0in2+Kd994GYKW/iFAeeZZTGU2SZHNUcUazmoXNxXGM1s5NaIasN+KQKIzY2tpiMBiQ5wWdTpvte3eQwuPEiXXu39/hnffewvND+t0e7WabIAp55ZWX8AOPpBtz79492u2YqN/i6GjEyy+/hB+EXL1yheF4wuryMkL5vPHGm0RxzCcffUy/12J1fY0ojMnygnNnz3Fve5tvfOMbDIdDrlx6hK998zkEFbdvfkKr2UBXFe1GzM7dWyz12qi/+ld59dVX+Nf/8i/wf/7BH/L8iy+xurxEZTSf3bjJ0uoK6ydPce7ceb73+ve4du0Der0+r37nZc6ePc/FCxfJqpzhYICQCqXF/F5yUj3QtqynRzPqj48fBE7HMQtLazbQpa6bCuqwHepk5QeolLEeo9GBczHLcypjsUbT6/WdKNtapHAF0cXzF7DaZb2gDdbUoWbVQ/e/NBjUfGN8gOLY+b4wo0MIoNLaUaRsha6okdWAMIzAaJLpFCE9rKkQCnwvAFwjO8mnSCnwvBClBFHUQAjJeDyeo1czpzwhLdY88DgPGw2GwyH9fh8pJWmaYozhxo0bLC8vzzf4qqrwA0maZLRabZTyONg/ZHHR0fwODw8xpmRjYx2QVJUT8xWZ23u63S7DwSF5UWHLHKMN0vP/P9l///9yLSws/DzwFtDC9ZmvHx8f/+cLCwvfAv4u8H/UX/qrx8fHH9WP+TrwHwA/AL50fHz8Wz/mOebgkbFVjXQWCCkRpm7opUNylVJUupgXTzNhulKzYqQgqBveWXagkh4Vucvjwf0WxlikYn5fzUNrdYWnPNI0rxvdAF2700gpUQbszDGIGkDSs6BRR1/NihxPKrLC5Q/MAt7mYbxCUpgSKdxab6+2KUuXw+ArH2sLrBToQhP5AUXhtIkKdy73+30Gg4E7n0unLwyikMm4ohl1+QH/jOG4zWJ3iQ8+ep2N7mV8b4r/F9wbGEah05ZpTRCG+H6A1XBwcECz2cZXDgDxfMV44tzmqqpyzVg94QHBzs4O7Xab5eVlDg4OnAbBD2oUvRbdz0T2GExNkwbDytoad+7cYn19jdF4SKsRk45ylpc6DJJpnS/xw1kIRf16zj6e7SdlUZLnBWniwiSnyWSuH+r1enhewGBwyO7efUwVsNhdJM2nPP7kUxzs7dLudGi2Y7xMMhzsU+SwuNrHlpbTp0+zs7vPV776qywtL+P7hjTPePrpZ/nVLz/O+UtXKc0eUdhkfeMU2IrpeECapuS54fd///f55ee+Rjtucu39D7DW8uabb/H888/z3nsfcPXKFd74vnNQ/Vt/7a+Q5TmZ8NxaF5KgEaGUoNNpsbe3y/LKMq+++jLvv/cJz33tKVpNj363S6vdIdYRWe4aVikk1NRayQMqI8JRhz3PBbNWWj90HrlJmKwd44A5Vfjh86PRaFBpN/mY5S8dHB0Rx7GLkDOaymh8BVEUE9TOptNJ4kyLlAOclZJUhvqeV3Nx98Nhsj866ZrpWISSSAtZVRKHAV7g1/Qv1+CnaULgR4j5z/1gejmbRgrh9n8HetQTI2Hq+7iJNiXCPjjHq8oZYhRFMf8ZZ9TdWaCu53noQpMXKUr5eJ7CGua/g9OKtN057guMNpRVhdEOCAgbDaZp4ohrNWDny5+cGvxjv3JhYeHnFxYWfnthYeGfLiws/JOFhYV/UH/+WwsLC/9sYWFhp/5z8aHHfH1hYeFwYWHhf11YWPgbP+45ju0x+7v3GQ8nRGGIr5wAbnVlhX6vj9WGVtzFVhWeEC5tO5viCQ9PSKSV9Dt9PCFBG3q9HmVRYLSmzAuqIidJXCZAEIYYYciLklYc02rFRGFMM27Tasd0e22yPKOscoLQccCTacbq6iprq8uURcZkMqLIMsbDIVEcEng+fqAo8hSLpBXHNJoRaTLB9yRKUqNdTig7c0VJkoRplmKEE9Xf379PHEckSUIYN+rAHUuhK1Bybh+6sbbO0dERe7s7lLqg3+lhdclyf4nFfo9zp09jrCArSppRg1JX7O/dZ+PkOlVZEUYRk8mYPE2xwtLrd+kv9Tg8PGT79h1u377Jzr17HOztcjRwIvI0SxiNh9j6MBwMhnS7Hc6cOU0YBoRhQJomLjH88AhrDO+9f4133/2ARrPF4eEBh/tHtFodPC9EKuektLjY5cKFc6RFXjtlCTqdDkuLy7RaLVpxE086K0JhnbOX1U7YFng+oR/Q63TRRUUynbJ7/z4C6LRbpEnKdDQhTxPeefsddrbv0m336LVdavbGxhqnN7ewVKytrYCVrG6sIj3FrTv3uHTlET69eZeTpzbJCs2lS5dZXl3n9OnTbJ05w3PPPUevv8ijjz9Fp7dMVuT4kU8QRjzzpS+z2Gnz5ptvIhR89OE1Pv3wQ/Jpyv69ewyPhrSbMa04oioK1tbXePXll/k7//bf5PTWCdbX19k72Gc8HnPm/DmqvCBQHp9+epP1tRN85Stf5c7dO5w+dYrB0RGf3fyUMssZDodUeUWWl3i+T6fTodNuU1SOwhgGHqEXOjoWhjwpmI7HZGnCZDxicHRIXqQUZUZeZCSThDTNSCYThsMxg8GAwWDA8OgI5QVkeUm3t8ypE6dYW9tAVxWD4YjxaOz0Du2mW2tlRRA28FSAUJI8dchglqXOIlL6SCmIooBGwzUFQlg8zzltOLpUSlUVNOIIP3Dj56ARMhlPOXv2HFtbp2l3WgRRRJplzho5DMAqJtMpWZbj+y4l2g8CgsB3eqYsZzAY1Ch1Vbt8ecRxPC8iPc/9McbQW+wymY6ptNMUBaHPo49dJctTsjzFDzzKqmAynhIGPkoKyiKj3YoZHB1Q5ClbmyfBGkaDEVWWU5UOJIlbHZRS3Lp1i7JyQlXPD/GCED/46eTjx1wG+JXj4+N/AzgF/L2FhYWV+t9+/fj4eL3+M2s8VoBHgV8ALgCvLSws/IU/7Qms/QFhGLLY75JnTqvViBtgBXmR0mw26XYX5we9Q2JdseDAqwZZWjgb5mbMeDRwiKV2dAeXnN5A1iGWzqI5pMiKuTYxCAK63S63b98kihwQo6Q7/IPQw/dcoyCkRAmXhtxsxIBEeDiXQaXYPLlBmbuzxfOcUUq315/bQqdpiraGKIo4HA7mYE9RVjQbLfb3d+l2uzR8d8YgrQvYq214+71FihpcGxwcsrq2TJYV3L75Gbfu3uX7b13nH/y9v0urGWG1ZXVxifFkwK0794hbf5H+Sp9pkrK3t0sURfTaHXrdLjdu3ODOzVucOXcWzwvIq5IiTWg2m8ydyCIn3J1Op5RlQRQ9MHDpdDrOAtuTBIHPaDzg9LnzRH7E4eGAIIpptlt4UnFi4wRnzm5x8+anThtiNIPRiGbUoMoLwigmjMO5Ve5sEuZS4sWcSgwOZMmL1NFsyqJGyAPW1zb44IMPOHXqFKPxkKPRgFa7TavbQyjF2soauizYPLXF6uoyYeSTlyntOKbXWeTypUewWL71/AuMh0M67TbNOCbPc0aDAW+//TYvfvtbVFVBGESMR0MCTzGdZrQ7HTY3N3n7nbf5tV/7Ne7c/IyrVx7h8uXLbkJtDNeuXePypUcYHDmA8d133+XDG3f46ktvEIUBaTKhyHN85cKA87yk0+syGY8Bwf37u/hBk5MnNzh9+ixHgyNnf5xlxHGEchU1o9HAFfxCuFpOOCH57NLm4f05qGMGAmcBryRVUVLqmT2vwVjBZDwkzXKk9PDDEGMqAj90IZvW4vuKOIrwAh8l7Dzs10oHeLkoBcdWaTZiNzHR1dw+fcZkyfN8bnHr+z5RFDFJpmAlcSNkOJnQiGLX4BtDniQcHg2Qno8SgspohLR4KkAKD4smagSEQQMpHWBRFGUNtovaettdRZnVGmcxp2BNpmMajcZ84joD5NM0nQvikyQhDBVKeDQaDYyx88l9v99nMpmwvX2Xbq9NHDUdK8kIrAFtXaOtqwJtQJuKqnSvy096LRwfH//pX7Cw8HPAzx0fH99bWFj4GeB3gUeAq0BxfHz88o98/Qrw3wAngTZwA1g6Pj7+wZ/0HJ1O5/hLX/oSZVmSTF1R0l9y3v1Gu0mH2xBjJ/YzZS0Wsmg9o2yJGl1wGpB2s+FEotbg1VkGxloqy9xq7ODgiKYfkCQpyhOEQYM8T+eHhH0IZcpr1wojQOBsTIUQ6FJTVAWe9AkbjhaVTsdzz3SH6kqSiRMX6WqWiEzd9U6dUK/u/rOycPSwvEL50uWHVGLewWrjBEEHBwe04iZal4xGozmXb2fnHtNpSrffp7fYZXv7Ng2/Q6ernLNU2GecHNFpd2g1Ynq9Htc++IB79+7SbXcIwwBpHWInhHAic+O6ZC9wqFJVlDRbMUJKGlHsJlHWuYdV5oE3vZSO43h/b5dGo4EfhAipOH9uk9HhkJ29e4Rhg7xIqUqn25lNwWbcRiUsD4844QH6/iBdWDpdgpxZSzp/+ygKagG6uyl8XyGlywiJGw2W11yGx7kLn6fTaRN64VwzlGUZ7394jfffe59Op0MY+GjjeJuNOGSx2+f7b75Jf2mRpf4qyWRKsx0zGY2RCp599lleefklbt92wq8rVy6DtTxy5TGeevKLvP7GW3z3O99hNBxw7uxZPr19k167zTSZsLq6wUsvf4dXX/kOt+7cZndvj+WVPs2oQZIkcz6rJyRWaHbv7+EFDe5t7/DMM1+aI+9FmRF4wtE1oggxc8Spnawc2mrnDmwPXtMHSd3aOlqjUk4wmdWbrNEVYRSDkoyHY2cZqQ1B6BNHISpw41iXb1AjU8aZMqja7/9BWnPNsfUeoEmzdOeHU6NnH0+mKba2DS2KnFbcdBt6nqNxSNHiYh8hYTgYEvo+nvLm67LRaM7zRWRN9zPGoGsPemdL6vYJZ4DheMXzBNnaAvth/n5RFMRxPC/slFLOQUdXdWGp5pPT6XQ6n+KkWeooYn7Dvf7GrU2sZZIkSEXN2bVIIfiHX//G7x4fH5/4Uzftn14ALCws/HfAd4Ff4o8/q74OcHx8/J/WH/8W8K3j4+Pf+ZO+58/93M8d/8qv/MrcfEB5Dn0tCldk9no9fD9kNBwhalt2iyHwGxhj3GQ9y0mzBE/5nNo8wfb2NlK5qUkYNLBIkjTFV4q1tRUODo7wlWY4ntDudOYUwDx3+RaDwYBG1MTzJY1Gg8loQJqXrghqNKlKd88Ox2PazTbGVLSbMbosORpPaDabFEWFMZXTdUQ+zUbM7u4uSiniqMlh7VK1sXHSFfCLPYaHByjlozxFWeSsraxzODig0XBuShcuXODGjRv19y8c+BY3yYqcw6N9dKG5cuUiOzsDpknC6lqLZNjk3CMer732PsY22No8QZWlXHzkMt/61rew1tJut/HDiFazRZJOCPwIo50eUChJlWdMJglCuYT5TqeDrF0zjcQh5Q46RglJVpTkZY7Rlt7iIr7n0quvXr7C3dufcH9/h8lwUiemeLQ6LZT0Ub4TIntRrUOwdb6ImDEmHuxdD+9rQgisUUjppltLS0tMp2MOD/dpt7sUuqLMcqQMOLm5yYmNdZ7/1jfZOLnB6PA+nu9SqcfTMU994at886XnwMCjVx9Fo0kmCUVZcv7CedZX13jyycd59POnGU4KEm3otnrcvHUDKQPef/dNvva1r2EqzSSZkEyG9JaW+d5rb/DsU1+is7TIwe4enXbMtevXOX3mDHmW8cqrrzIeDbi/e8j1D69RVoZ20xW76yvLfHrrJlEjosorRqVicHCPy4+cpRW7HBOw3L61DdYQ+AFpXrDc75OmUwzS2UTXNd7MLtqdF+7vYdggy5zGLq9KwiAgmWY0mjG6yOeTpkbURCr33s/2dRUoPOm7LCxjHYj0kDZiph2c5ZSVhWt6VOAqC692WK2q6oe0Ue68kHPK12B4RLe1SGVyoiAkDGLu798naARgBX4QYI2lzCa0Wx2KysxzX+YUKh5MSpMkodt1k8fJZOJc0mqdVZZltd27nj//LL/j4QTzxcVFPvnkEzY2NphMJrSbMUejAY2wjfLEnNGzt7fnAFkko9EIqRSNuEmZphhtiWLfuYIVFYEnQSqsdg3ON77xk51RP3bycXx8/AfHx8f36r//X8A/BT73pzzk3wLePT4+/n+Oj4//N+AQ14j8ac8xp0tJ5ew2q7IiS4v6c85ZSkqB8lzAmdaGLCsoyxwhFMk0I8tyAj+g3WkyySakZYo2Ze2moJxtrJA0Ap9kPEHVLlaNVpMwalLogsqCCnxKU7pl7ntoLJ6UUIvKhPTqDlRhhXAOI8HDb3QDKX0qA6V21DBVh5RZtPuZrBuDS6lQykfiUF505dy0JJjKFYKN2AlevVqbkaapKzCNxgpJp7eIHzrrN61haXWJKG6itWGpv4RUHlXlFvJ4MKLbalEUFb2lZQaTETs7O/h+yImTJ1haWibuxMSNFp7vO0GyLhlNjhgcDRA4NDsIIgLf2UPmhdOq5EVKWeZEvkcYeGAse3s7rqlpNCiqihPr60jh8entG+zuHeD5iiIv5nxP5/Aga/2PRVuBtqJ+LS2VASuUU4ZIDysUVjgXmVIbrLNXQniSonIfK99H+gqUT+D7lEXJ2to69+7c58MPr3Ny4y+zutTn6mOP8s1vv8BHH33M4089zQsvvMyF8+d55NJljIXID+j2upw6tcUrr75KGHisrq5zcHjANHMb/nAyZHNzk06nw3e++xpbp7dod9vs3NtmOE44Ojri7vZ9nn76aZ565hm6S8sM04Rup8ft7W2e++aLbG5t8Tu/8z/yyndf5f7uLk8+/jhUoBFkuXMzsdZNxJpxk62tLaS1eL7Px9c/JIqaDI4OMFVFnhc0Gk2KLCPJCrKiotQlpS6pbR4wQmIEWKlAObTLSoERBiHBq9d7WZToqgRriFttDIajwyPyPEMpj06vQzNuogJnlyjqxnFWjHsqwFMBSvoU9XRGVxpjzaxfRHkBQnoYKygrU9th+gjpUWnrNF3auXNpXaGEQHo+43SKkRDHLVrNFsZYTOlC17QVaGGRfoD0g7kjjPAU0vOwQji9VlFRVWa+cYe13mkWtFbTaams+2OERCOorHtvhpMpRkhG04ThZEqapejKuIBCKZhOU6bTtKbsGIajIboyztVK1L73QiCkRxBF2Fr4ruoGu6zKH7dd//Sqr4WFhR7wi8D/8EIncwAAIABJREFUVH/q2YWFhfsLCwvfX1hY+Ffqz30O+N8fetiIP+ZsW1hYeGphYWF7YWFhuyzLeeMaBAGB7+zCnQuPN8/gsGiwwtGuSkeldS6ABQiL5/kEoZt+V1WFNZbAjwBqGm6jzkLKyPPcTQylcC5zuqAsNe12u14ztcMjLuCwMg8a/lnT/jAgNKOlqIcKYnD6RSeI5gHtSin8QCGlIAhc6JmUkixN3b6SFxRl6Wgs9dRh9voAHB4ezsEjIQR+GBB6Pp12h2YnRnghvicpi4IssXiBZjq1SOnj+4K8NCyvrDEYOsG153msrq6ChaIsMdpS1tS2mTW+FB5hEBKEtQuScHlhxjgQQevCuWMpD8PMBCCg0Yjnxi/L/T7NZoPPPvuU8Tih3e5wsHvI6vrynIs/ayQe6DgfvOYPf/zgax+Aa1KKGsC0FEVWi6BdtpJC4HsRcSOmEQV89PEnFHnB/d37xHGH/vIS7VaLVrPNl375l2nFMVevXubgcI/BYMh0OmV1bZXNzU1Ob53m7NmzJNOEyXTKYqeHlRbfD7l86SLPPfccr776Kptbm/hSMk7SObg3TVMuXbzIf/lf/SZKOYF0VZScqEOFn376Wc6c2eLi5y/yzFNPYIxjeBSmosgz56ZWlvQ6LTCOdre/f4A1hk6zU1OdQlrNJs1m7HIn7IPXkHmykqMtzRoPcEG6gDvrNUicDrEq3Zk3szYuq5Isd+tRAL7vKGJKUp9RYk5TnD2vMcaFNyin7fVU7ZaoDUoqRwWswafZ1Ww25w0PUNNuXYAzxhBEDcoyd5ogP3IuXUIgrAu5NrVWSQpv/n1+FISbrbPZzzqTJOR59kMC9Nm/PRywPWuoZi5ZM/MebUrnrue7piPP3f0rhEAbTZZlNBoN2u2Oq7uNRnmSRtyqJ0MS6Xso5XJaZgDeT3L9mTQfP7Kh/xJuQ/8isI0bd/8RbvO+89DD/sQNHXgK4Gd+5mfmNoWtVotZemMjDvGU0yM04yb37+8AbgF5vl+PsyV5nrtQHQSVrpw3s1Q0aoH5DKVyIV9TJ4K2lgsXLnB0dISSyglvhUcUPegYrRVUZVWnqpt6TDdz4tJzUd5sQcw+N9N4zFKTZyFk8MCNQQU+STqtPa6dKNH3Bc1Wk7JwPs0zLunNW7dohE5s3e12qaqKEydOzDnqSil2du4R+iGr62toXbKzu0dVlZzePEFvfZHxeNflCVSWRmORldUut259xt7uLltbW/i+YjKe1NxlQxCHNAgJo9gJnZRCW02eFxhdYaQT/ut6zOnsIA2+8BhNxk5cJSSbm2cotbOoO3myx8HBHrfvjjDG0ut12dvdQyLx48DZ1tVBWwiPMHCj19nrPbuZHkaSZtdsUlVVJcZqTOm0Dkq5Q95TzrUry3OuXL3C7Tu3uH//HqdObHLq5KkazevXN7HT1Tz1xBN0Oj2CwOd7r75CkuW88fobXPvgGh99+CH7+7vc+PQmV69e5fzZLdJck6VTnnj8C4RhyPPPf9PZ9G5t0u52eee96zz7pa9w9YtPcjg8oNHsUKQTACprefrpZ7h0+SorKyv81m//Nm++8X0OD/b5pV/6K/zGb/4XjAZDx/0XUGqQCoajAe12h7Nnz9HtLjFNU8LQ47HHvkCaTLi3s83h0UGta5g58ziDhdlh/OBwrHUdWERlsMIJ0I3QTCcJwoIfhaRpxtFgRBg1aIQRUTfCWk1ZZATRg7TUGYrjHN5U7QTk7LKDKMTqEqUClFejPFaQ1on0c0cgmHOiwW3QcTNGSFGnOguyLMP33GFjygLpB6STKRqXA2N0LSp/CE16GJWcfS6O43nRBTCZTOb37Xz6JgxijsK56+F9IAxDGg03xcBUaGvRxhWiMzqKRSONot/vUxYlR0dHNJttlHTF0sHhHuPxmK2tU0hbZwJVJaX5qdXuT3ItLCwEwH8LfPn4+DhfWFj4DeDXgOP6//8Z8ASw8Mc8/J+jAhwfH78OvA7wuc997tihgy7tXgjBYDCg1ezg+ZIsK6h0UU+3LZUWc7DJWbn7lIVGCGfTfHi06wwfKkMUefXB79ZbEAQcHBywtrrG7Zuf0uy0MdaQZ3kdKNh0VChrEMI9t6csxmiXs2RnhitunUsk0pNUmZlboVtrnStejfpmWYExjprleR5+FGJNRdSoEWthCKIQU2m0MfPPKd/jzp3bdLs9hIBWy00lZvt2t9VmkiYMhwNncb7YYzQ55M7dHTAVWyfOMByUEN7j3vYqrc4ivcUuWWZJ84S3v/sW58+eJYoCx1m3TmPmBwGmKtEWAs9N5k1VojxnrjGzHdaVnlN6tHYTy6wsnPOdUAjPoxFFrogUAmMKvvXNrzIYjPAjn6kSrG2skGUlnvAx0oL1UNIglMAiXRFpHkw5Z3vJg/PKuKAAIzC483FmbyqlJAqdHbLAp8wLzp5b5e2336bSJSvry0ynCaU2TCYJyTjli48/xa//+i9y7sImxhguXrzIcDhme/sOezu7vD6d8Mqrr/DWW99nqdvhytUrHAyGJKMpcRQwHB7x2mvf4fIjn+fe9l00zv3s9e99nxdffBmhBI89/gWOj4/57JPP2N/fJ88Seosdvv3tb9NfWuHZZ58lbjbR1vDSCy/w6ssvcXB0SKfdcq6IEqTNefTRq9y4caOOKQjY2d3jxMY69+5tkyQVzXaHw0H6YL9Vcm6c4D5ha8AXQFKVJcpXzlJdCJAuD0SJCj8K6hwLgRE+QlkazYhIRkzShDDwqapqXsyHgQuWnE1XdFG67F0EuipQclZruiy0aZIhH2oCZna2s/d8JngPgwC0RXquoUjShDiO3frw/BqIEESNqLazDglDj8l0hLW2DpV1xi+usdbz80rN1vU8CPAhByzx4Gf40SnKZOJc1qZTlxc0GU+wwulPpPDcGqRESIvvec5p7WhEVhiiKEQJj7LUDEdH5HlOo+FkEkVVUBUZwv7kQbg/8Wn2oxs68BvAXwLWgT/AbejwZ9jQj4+PTxwfH58Igr+IHyiEtIxGQ3Z2tl1uhBKU1ZS72ze5u32TuOmh/BJDgRAlthbtRVFEXhSk2RSERvkSIXyUcpy5KIyJ4xZKeix2F2lEIYvdDrdufUZRJBwc3ufwcJ9KF2ht2d7eYTqdkmUJZZXi+YbAjwjDGM8L8f2QMAzrUZYbt1dFhiedqKe/vESvv+Q6b6PnzY61rhjyfEkgnH7BVx5CKDzPx5MejTBGSkFVafIsZX93l9OnTnFiY5W4GSKsZnBwwODggCDwWVlZYX19nWazRRAFjMZjxuOU06e2uHj+81Sl4Nbdz5BhTNju0luKeevN9/ibf+2vc+OTmyx2+4TN0BVqvkcQeihfMRkOmExGRKEH1pAmE4zRtFox7XbTobnKicCySpMVFZV26FJWZ5s0O20O9nfBaiaDQ97+3mtsf3qDwd59oiCkEUZkSUVWVkjpgwywwqOSIaUUpFVJXlbkZUVRaYpKk5cu8dRa52A0W8JVoTFa4yuf0ItoNppuYzLOZaIoNFUpWd88xWuvv87h0SG9bocqzxCFZqXbR5UwOjgkzaY8+8STDA+P+OqzT/PFRy7x6JVH+PD9dyiLhGYc8bnP/auMB0NWlhe5evURllZW+P5rL/L+m9/l4ukNXnvxm3z/tdcYHOxz/sIltPF58vGnWFzq8R//yt/nkbPn+L//6A9ZWFhgYWGBjdVlFhYW+Movf4nD/V3u3vqMbqfJ0soiv/d7/zPvv/s2ukwJQ9+J4Gr0NM8yiqLg4Og+7Y5HEML1T97lzXff4PqNT/jCE08RNHsg3DRJGI2tSnRVURYFVVlSZjllqcnzkjQvyY1kaiRpaRgVJUle4kcxlVJURhNEIZtbp2i1I+KmR6UTrKno93tY6zivMxMDV3AFeL5DaaTv1QYSFoMi15o81+RFRZY7NNn3fYyGPCvJ0mJu8GCMs1UuigxbOgRGICiKjHanhVSCcZKSTRNQHp7waAQxnU7bUTV8Z8sbNwMasY9UFkSJ8iAIlfOY1xoJKCHwpMKTjhPrSR9P+vgyAMwcQXaTOg9jXAM8nU7nfNrheEqea4daC8ne/n3yIiXwI6wVHOyN6C+u8/QTzyCMJi/GWHJ6iy0ef/Ixjo4O2D86YDwckRYlvv/ThPMfdy0sLPxLuHPqvz4+Pv4A4Pj4+A+Pj49/cHx8bIHf5MEkfgT8/EMP7wDjH/ccs8azLB2wFYYhxpqaw+/he6ErbiqnnVBSkeXpP4ewUhe5fl1wzZr2dqdDlmV1pkbDCcOzAl0WWGPxfA8lHUAXBAGypgEqqeamBFI6tLi05bxIceGi/vxnGE9cPoSsi+UZZWSWYzKbAA4Gg3khArLO84AkTej2OnS6iySTKcPBmKjWKrbbbfI8J45jNwVRgjzN8DyfZrtFMp2SpGmtc7F4gUccdjDkTMYlnnKug2la0O/2ajqbM3MIPWdzX9ZJ7Eo68X1VZxL5ysdUJZW2KM/D4pByiUAqjyB0dJXJZOL0MJ0mvvApdEVWlFR5zmQy4bGrl9CVptNpk6YpjUaMp5zl/MNWqG492PnrNkOrZ+/zA6Dih22QZ3xXY/RcNO9cBgXtZodxMiXJpoRRg8APasDV5V8EgdN0/Jt//W+wuLhEnudcv36d6x9cY2VlZe78dOnSRdCGz3/+Eh9c+5Dr1z9k7/5O3Rym7O/u0e/3uX79A77w6BWWV9cBePGFF1laWuLUiVNcfvQqH3zwHpceuchnNz9DIP5f9t4/TI76vvN8zXdKpVKpVCqKpmja7U6nPW6PJ+PJeCwrOkWnKHoUrZZHp4dViKLoCCEcxxKSdVgfIRxhMfE6DktY2/FxfjiO4wixCWF5OEXHEkenaLGszCmKPNGOx5NJe9y02+2m3RRFURSlUk1N9f3xrW7h7J3j3LN5kudZ1/PwIOmRnpGmq771+fF+v97ceOON2Lolk7J7PSYmJjh06BAPPfKpfJhSJE7C3LOmsbK8xN49+/OiOGR6coalxjKu16dYLPDMU0/KeyH//qT/yQRdZl284xtIOsyBIkEImVMVRRGGPkRAg1mQzx9ZRhDK50fKvq5spIZSYxlKjJTWD8Og4ysNJUCcfffGY/gZv3MLNnyWRP5cCaEShwGaJp+9vifxyUMKXZZJ6XMUyRBgSa6TpLksTSVcJctGTUhGgsgB99IWYHzXlo1MSvj/ptwvSRJM0xzJsIbPuKZJSfXw3/VOefCwvlZVVQ6ehw11lGCaOinDXL4hCTPj+72+r+bj7/tAX19fJ3B9LN1E11SKThElE3hujyxN2bljGwqQpD0qZYOCrRKnkdTtT1aJk4hWp4EXugglI04ShKLLrUicsrC0QtGp4HshzWaHA/v2AVAsmChagm4J7KJJnKfTTk5NkaQSQZspEZlIcIOQMFLw/BhNLzA3u51qtYpAMDszRcFxpIZUkUVTFIbEiUSeFYplOXnNUlQUTM3gzJmzmKqBoeskkZxcRXGG2+tTq06gKhmdTpOCpaPrCr1ui9Dr0+t3mNs+w9RMnb17djJRK/HSF08wOzdNuVzBsGzqkzNcOHeexmKDIFJI1YjFlVXaHZ9+2OfAwcP80b//D3Q7HRIE3Z6LoirEIsWLQoQCxVIZzTBwOx3IYmzTIAk9Op0Wnh8QpTLhNlUyNFWSs1qdHm4Q0ul12b5zF2maUqyWCHodTENjbrLCjqka1WIZy5Bpv0IRFAtV+m5Mu+vSC1LCJJLSmETgeh5uPsUwTVPyp5OUOJXwURSVLFUgztAxCAKfKPJJ05BquYqCIPJjNMVEFTovvXwKs2oRZxm6olC2bDRSgk6XZnMZQ9cJooCPfuQuOssNjh44wB1Hb2T/tlm8ZhPihMD1+MM/+AJf/OJL3HXHHdxzz13cceuNEPcp2gqp2+Tg7m1Ml4u0V9soepFWx+c9P3wd//OnP83Vm7fw2Kce4WO/9sv8zm/+Oj/7T36S5eUGg8GAarnIvR+9K5+ImMxNTvDM5x9jdqbKjUdupFIt4jgOs9OzFIol5qa3Mf/yKQqOQc9t8dF7bmXH9ik6vQ5Hb7mJTz35NJ984F6iJCRLAjIRE0cRaRRTdUqIOMW0CpSdIpphSCa/YtDsdGn2PCltU02EpjE5NY2i6kzUp4iiEE2A2+2wZ9c2qmWL0HdRSIkTnzSNSYWUIyESoiik3W7R7/fwc7SnqumkSYobBgjFQjMMwjiVcjmkf8QuWPieRP9ppkGhVMSplEmyBAUZ1FStlfF8F0TG7OwsO3bvIosz0lRhtdHA7XXpd1uSpBKHqICSZRgaFCwDU89QCFFEiqpmZJk0CYehRxgEckUvwNA1dNXCMCyKxSJDmsqQGNdqtUhTcsy2gmUWIBVEgSwwq+UylWKZJIrZtX0fJadCt+Xz2OPPkok095IlJFnKmfnTTEzXmJiuo5sGpIIs/kHOx/e6xsbGxoD/DfirwWDwqXf8+nXv+G3/DFjKf3wCODo2NrZxbGzsh4H3Aue/19dYX1+nVCqRJOkoz0FSCKV8bvHiEighmgau54OQ2+E4yqQBuNNG1QROqUQmFERiymLaNFhdXWXHjh20VhtEgcdqs8HS0hK9fgfNAi8I0TSLYYKz5wWYpi4zaNSMgmMRhRmaVgAySqUimoC+2yVNEwoFB9/tEUYRmVBxowRFUUnjiEyRG0RDUzA0WfgUCgWSMGFiYlKSblKZWCBzTAJUQyWIfcLAIw5DDh89gue60qsZZ/h9iYc/ePAgtdoEK6tNdF0nDCJURcc2iiRRgOOU6btdzq+ewDLrFJyENIoxVZsLCxdQVZVHHnmYCxfm0VWFdrOFqoCaP1tpluI4RQlMSjNikYBhoGuqPOtSIKcVZUnM2TNnKTtF2u02+/fvJfQCVCXDUDTcTodOp4tmaLQ8jz3796GpNlGYMjE5QxAGZEomJWgkJKSkcUqae1q0fMqtqioi47smzyLNyGKZz+EFLpYlN09xluF2exSdMrpuo9kGSlGns9qgbJo4mkYSx6gIDMfh4kqDBz5xPxdXGrQuNvF7LcKgT7VWZvfeXbR6Ll4U0lhZZWnxIn7gcnZpmVLJYa5eY256Ar/XJvRcPvfE03T7EYeO3sb03C56bp9bbruV2blpPv7g/Rw7eiML584ThCH79l9P3wsI44j777mXIImxbZkxUik5zM3NAXD48BEWLi4hhEm355K5TdI0ApGyc88eNN1g5569/Kvf+Dfcfe8ncJwaR266lWeeeAwN2SynEXksgEq73SaMU3TTIggCdNvBTzU8ty9N04aNKjJ0TUXVNUJfQg+q5TL9XotapYSpW8xsmySKA+lhUCQqXWg6kmoak6QRcZRvxvPGQVPlwCdNEtQ8X07kwVYiHUod8y1EGslGWstlSK58riI/wLBsdFMGRHdafSYqVXxXSuyTJKBcK+IHrizkhUT3So9vnG9lZNG/utrK4RJyM9JoNEjiDNO0Id9gpmlCkLlkqcz2KBSKo8GGoij4vptL0iRhURE6hqGg6Rp+IBtyQy8QBCkvvXiS3bv2IESK22vhBj2cko1ZcGSDBKSpQhLKBs6w7O/7vP5bZVff60AfDAav5j/9mwf6M2NjY59CGs7/1gNdUVQKToVuz8MpVtByWocfeGRJQrvToVwtEwYJnbbE6iqKThRlRKFMKJ+oTuUdpIapCVYbjTxx2KZeLbO6ukwQSiThmfkFyuUaCxcXcoqGNMSphlzDaapCOdfTikwhi8DUQYiAOA5prnZZvCj115VKhZVGE8jTlLNU4v9UBT3Hj4kskaa7yM+3ITG79+7DjwKyLCJREmmITWN0U8PzJCWiVCyj6zrdnk+CimkXUKKITrdPqVzmmedeoN1qMT09Tb/XlxuYwKfp96hOlshS6PcblJ2SlITFKSQacdBjqrqb5cULvPDCCyOKSarIgt5PI5QsA1VHtXWCJMbUVHS1CFGSh+wAGThGGa/fZ2W5ganpRKrg8KEb8DwPxymRpilmqQaAU6qh6hr+SoNut4vv95mo1+RDnmRkmUaWxZCAqShEWYyh6blBT8qhFEUdTc6TfNKlGIIwiwnjCN3QieKYwE8xrRSzVEH4Af1ASmhsdPqdPrqikugmnlDRLRslgx2OQ7FY5PSpU9x+581yUgmAoBGEpKmU3U1UyvihT8/tcffd92CrCpkwuNBs0+gmzMzdQCcKSIo19m3bzp3VIl77Am+9/hqGXQDg+WefoVAosLi0zLNfPM3ExCTf+MZfj56J1976No3lDpGf4JS28dxzL3D06F184xtLvPKtBqvNJSI/o9HqsdQOuLk4y5Eju7jnrvuZnz/LE489zgP330+1WuGhjz/CasPjwP79HLzxBp595hn5wq5U6abQ8XzivofrudTKMYR9Zsqy0YvjUJrY3Bg3dikVCgRuG8/1cZwSkxMVLpxv51MUHVU1ydKEJAGGxbIKitApFk0QKWmSoekqaRqhqTIPgcwjSwW6kKbrTIUo9kniDN3QURQdEWeEscT16pZFFGfYTgXPDfC8ECFCPDXk/HlJ6NB1jUJpUk5eFTnhVXWLOJdH6rolC8NYGTURQ99Wz/VRNAPdNAmTGMPQSEUCIsP3fLrdLoqi0GxK3LDMoNFHskxV0dHyzWgQBPT6fWQiu+TgH3/pGUqlEqud85i2RoaGopoksZCZBaEg6KfItF8FxRCjJPkfXP+f148DPw98dWxs7GL+a/cBPzc2NjaL3MC3gH8OMBgMvjY2NvYcsIwcq/7y9wKjAIyPj8vzMkkxcgT60CehKAqmpaOqGnHSJQqGUgh11KAKoUgkaBzKULwkRdE1uo0Vjhw5wmc+8xl2795Nt9ulVCwShiGtVotqrUbg55AEocnUa10FFPlMxdKfpSoamuyuiaJAEqvSCE01c+OuivIOyaCSwyWGaO7h2TqUkCRJRCp06cuKU4SukmRSsmnomhwKZimKprHaaKJpgmKxTJxExGmCbRWYn58nihKmpup5SK7AMDSyWJFbH5ESpj6OY+G7kCQRtqNj2g4HDx7k7Ll5JuoyWK/T66IqOmQJ5DlGSoaUnqUJumngRxkkEUKzcKwiPbdPHElqmJFPdS3LwrLl9Fc3VLk1SVJUVcMyFHTdYLXZxA1CkjDCMCy52TIsKQcS0tOWKQKRRGS5ZEdFEJOSRHGeqn0FTkOW5dlCJqqSB0AqMrguyklOAgXVUPGjCBHG0rui6iRpim0VSKKYBMHxUyeZmp5mqj4loRSZlEsnQsP1fXbv2AlJSLlUwXf7ZFlGz/WYnagShZKKqOsmU9PTtLp9gjjDC0JWlhfRRToidGWk9Pt9jh07MpKTnj17jgc/+Ql+8Rd/kZ/5mZ9hol5l4cIitik9S5ZlYpsmhmUwbU+D3+XGGw7w3IlTeL7PkcM3EEchP/7jP0671eGFF18iDENOnj7BRz5yN4EPe/fuR1EVXM/lyJFjtFqrNDttqrUap8+dp1Ktj7YRZAlxzAj4MdxshGGIoemEoY9tFnC7PQxDR1X1K/4MocltR45tV4QM2E2SGMjybZ+KGOaJpBlxEqKrDigKqSI3EXGcoOnKd8nrdEMnEwLdkobwKIqoVKooCoSB9KwIkVGqyOG0JFYx8j9fke1J37Bp2jnt6wpeWNd1Wu0WE7Ua0iMDKQmWXiRMhlu5FIE6kmINE9PTNMUxi4RZQhhF0gSv6wyBM2kKpmGxutokTWM0U0NXNNJU4sXjWJ5vSe6FTjORZ3V9f9f34/n4ez/QYUAUR9gFmzSNiWM5QXBU6f8Io2BkfAZttGIKg0AmfZo23U6XNIvz5OACs3PTRJEM52t32tTrdRA6qqrQ7/fRPIVisZBrUoffiiENKJLcY1WXBtM0xsxzQ8JQagWLxSK6Jqcf7/SBpAlkajbCdg5Nt2kqw8iGnbViDDM9hTTZa/LFJFntEUkgJ8dRFOAUHFAFrbbEgU7UJqVZCYWZ2VmCIKDf9yiVHNJMFndJnBInEaZlkMQppmlhlKTW8eLCRbbv3MYnH3qElaVlkjjFLjoyxVwBPdVJFfKU6xg1ywjjWN4suXlYFRmqbdLvdVhurOB2PWYOzEhcXK7XH34PhuGIYRjSWVm5olvM1/pypRcAfNfqT0euQNM0RcnDgFQhCKIEFSVn14MS6yh5Er08LCJ8z8N3JfpS1VSCEHw/Rldlerqi5+nuWUrghZi2RalSZOniIkvLi2zfNitJFJl8eOMwIkkyeq02O2+5mY8/8CDVWoU4i4gThTTxyRSBZReYXzhHGqXcf98DzM5N8cyTT3D61Bf5/OefYnZmhl6vh1A1Hn/8cfbs20+SJJRKDvfc81HCMOTChSU+ctt9PPnUE5x8+SSd7jK79taZ2VaG5CA//G65WPzYb/4O09OTXJifJwgT7rjjI5w48TyKUHn4Uw+ze/dO+q5LpVrh9JmzPPP88yRZxrFjN9Pv9zl95mVWVxokmWT+F+wiBcchM9SRSdyyFIZZKkOaRpLIiT9JTGrEkn1OSoZCFEstu6oIlFwel/KO9NdIhkCZhp2HhwZAIn0oIsU2bKI0Igwi0gTI8wkgBU1BTVIiAUmQoOhS7hTlhldJABGyAFSUvPnu5lAHdUQkuZIfZJBlUo8fhuFoJT2cVg4lKPJ+lOeOmq/ms/BKuuzwxfJO74f8elFepMliRxJXrpgG0zTFKdoU88mv7/t5Noo1knRKpCMkSfp3Wmn/l3gNBoOz/L/Lfl/6Hn/mt4Df+rt8nSEYxcjRx/1+n2q1ShxLr0ev2ydNVTIkZUrXtHyCKSlVqAqWZaNpBo2VBgCWZdFsNpmYyCmPaUqlUkHXdc6cOcPUzAQFq8TqagvTNIljSdZKU2mCDdKAKIzRNHnf+J6LqmrouomSKCiKmhvNZUESRymWVZAb7zgc3VuGYeB6PTTVGBU/7V6XJEpAAd1wCHwf27IJYxdijTRLcvmwQSyCkYG6Uq6hanJam6YxhUIBXTOIkwifJLhpAAAgAElEQVTX9+QWIH/m4jge5TcNBwBp5uMUyugFixsOH2Hvnj3EWYJIcgpjklyRjImUru/ilEsoYUSiatiGSRxFEndqaZw7d44kitm2YztGwaBSrrGyspJjcWOEkBh5gSqlrMsr9PshSgZzc3M5ye6Kp00IgaKq+H6EqltkQsqt1DRvMMlIshSRqmiqRiYSYi+ncgGeG0jyYqGA7/v0+14+wLBlQZ2lOIUCSn7uiixhcanJPffczZkzZzh98in27N1NRh+RCeIsI458pqYmWVxc4o5bj3Ho4EE+ctfd9AKPWqXKmXPnsU0Lp1zL64Ye1+/fR7fb58QLL7B712727NpBEEQ8+uijVCoVjh9/kVOnvsjJ0y/z1FNP8fAjj7C0tMQv/dIvsn//Iebnz3Pi+AluveUYAM+fOI6l63z00N0UiyWefuyzPPvMcUoTExw+dANfPHWSz332s1x//fUcf/5Zfu/3fo8vfOEL3HPvA+zZfwDbKOJ7Ie1emyAI+NznPotVcKjVKjRWW+zbNceFi8uUbHNUawwzwKrVqiRgBQFRFFEqlOg0O8wemuOJJ55g37599Ho90lTmNykikcOBPIhaoBBFAUrelMjsNnGFOqgklIyyJKSqMhcjSZL83o3yOlBKLotWiVjI5vbiwmI+OO1RKpXodDvomoamDyWXV3JiVDU3z8dyszSETszNzdHtdqUKQNMIQylJrlRqxJGs9UDWbSury9h6NR8gSs9zfbLG4uIipZIzomTplo3bbeVSyoQ4lD5N23bodHpMT0/jBz4TtQlenj9FnGnUahNomkans0SxWERRZAq6JM/+Z8z5GAwGZweDwdhgMJh5Jyd9MBj8/GAw+ED+64fesQVhMBj81mAweM9gMHjfYDD447/ta4yNjSFlqCme59HLw4Jct0ejsUy7vSozO6IEkIWSbTlMTNSZmZ4jjmOqtTK1Wg3PDVhcXMDzPGnWm5mhUqmMdGu6rlOtVnMcqWwWOu1OXjgINFXGyruuTIxWhMS4drtd4iRmYmKCyclJecPHwUjnN+xUM+REPooC4igefSBZllGtVkeHVt/z6PZd+n5A35NNTRQm0gwnDAzTRkVHM0x6/R6+H1J0SkzU6nhen4WFBSxbIutqtSppGtNYWkYRCrZtYRg6hm7gFJw8pNCj2+kSRzGO7fDQJz/JhTNn5YukaMsiJy+gJNULICOL5ZSZJCPNwM6pXn4YsLS4TKvTplKpcP3BfVJ+kEk0myxY0xEGrt+X0xdUGegkhMBxJPJ0+DBpmjaiVAghUDWZ9mwbNrppoqoaKbJo1AxDaiiFRqJILJ9pW5TKZQpFh2q1hl2wUTQVXTOxCgVMQ8/vIdCVvABUBYqhEYYJJ154kSwT1CfqRGEoV/qpNAbahRLFUom7776XbqfHwUOH2LNrD7qi0mm36HsuqAqddofPfeZRnv38E9xz90eZrE3w4onjaLrOLTffTJalOE4B27B4+KFH2D43w5NPPslDDz3E5PQU9ckpFhbPsWv3HLfcchNHD98IwuCmG2+nudLBLjj869/+bf7sL/6c3/zYr/H5556hNjHB7NwMh48c5InHH+PJJx+nsbrE//ixX6c+UWFl+SITEzX2795Fr9vm2NEbeeiTD1K2C3z0o3dz4tmneOSTD2MaCt1Oa9Q4+r6P67pXyBg5TCEIAnl/iCw3k2cYui63ViSy4VZB0RVUA1Ttu8k7QggajYbMJImjvHGUK+NmZ5XADxBCUu8s20QgJ4cCyBQ5XUwyidzudtvyIBMi3z6YFAo2rufS67XRNH2kn43jmG5XpvCmqTxrQBJahvdpHMcyfyfXuw5pXb1ej0ajwdLyMoEfjSSAiqJQq9VG2vxRfk+/j+d5hKEvp8BxzDA5PfAjtm/fLnMJUhmAJhBUyhVKpdLohRrHMYEfIR8bKbX5wfUPe62vpziOk2/JZMEcBIH8XINg1KCr+X0j0fDpaKs2xHNmWQYZ+IHL8vIyp06dGqE6h/fx8M/outw8qKqKQE5WhZBNd0aCYZgoQhZgURh9l857eK7qupFPPGWTEcWBDCeLrwTfyr+7DAMdJnQrikIQhST5+01VVZIoRAHiKJYjh9xzolsGBcOm2+3K7Ul+1hu6PNMlbEESJFWh5EOhgCiMMA2TKI4QisAwDaIwotNu0+32SdKMm2++mU5HGnGjLJWafCHP/TRLEJmUNaU5BWmYmWLbNu12i/Pnz1MqlahP1bEtg8CXuvdhrgEM2UpCekn9EEUVoy3Q0AgNV1DvIyOvUOW0O03kECaFMIhQ1Hw+LfIwPAVUTcdxHEAWtmkmG0/DsIgjWSSGUYhjW9KvIiBN5AS+77rs3LmTfrdHGiecP3eONI4QZNL/mEFCCgnUJyd46qmnaLeaBL4PcUSn06JcqWEWHIJAbja2b99GksS8ePw5Ar+HKgRhGGNYFu1ul6WlJbr9Pvv27cO0Cnz84x/nmWefwbaKHD/+MhMTZR5++JMINabXa7O0sMy+vfuIo4wjR27mxPGTWLZKwTEJPI/PPfoouqKye+8e5ufn+cyjjzEGtNttdu/eRb/fZ3W1iWnqTE9Pj2qthQsLNFstKnkCt6UpV8hUijKSZQ8bkeGgKQgClPx58Dwvh+QkeX0xhI5Ib+jQC5UkCaalj8BDSRqP3gMChSRPx5WeDKnGiKIot4bnxKwslUG7ipIrNBj5KExThgxLmZbIk8KHA2zpC9bzAFxNM/L3rASyKEIjjmOcoj0aYvX7fXhH3ISiaFiWRZqlo9yN4SDB9/3ReaKpVwb5ktwng6blsy9GTVySyAwSufGIR36V4cDONM0R4vdvxiJ8r+sfBT4lTdchE/R77sis2m43AQXDNNA1M/+byomD7/u4Xg/P9/ADX+ZLRHKzMDS2zs+fRwiVXs+l2+1Tq9VxnBKeF+RGwRRNU7FsO08yzfIH33/H1D7K0WKqZCor+ugFMpx+DD9cgZob0XVJzlKk7ltRGD0g70xHj4JQmth1C1u3sQwb27AxVRPLKlCwHWmCSqXcKgg9OeVXBWEYYRgmYRAShRLtaFomTqVEr9+n15MEJUVVcT1P3ty6jqbrpFmGXbSZnz9HpV5FUSXrvFiwSfLND6k83KMsI8kS1Aw0Q0NXpeEwTVOWl1dod1rUq3XKThGhqmiqcuWlmK+YgygckVuyLMHtdvB9fzR9/puIuHd2zilSajU8vBWhyM/MMtEMHc2QrG4lAxUFEumZcbu9PDVUvsQ8vw9JJj0GqTzgoyhCFRrEkt0+PV2X08fGMrYhsYtGQSbqClWTLxJNYWl1idNnT4OARqtJGIUUq1XKlQqOZXPo8A00m02uP3SEdqfNwYMHOXjgACWnwOlTp5mdmWP/vgNS99vvcevtd/L888/z4IMPEocRs9u2sbK0zJ13foSCY1CrTvOpTz7LNdfaTE7sg7TIoUPXc/7CKf7yr/6EP/rDP+CJpx/nqac+x8rKMucXL9D3O/zKr9wJwPTsDLt37aJWLeF7PQxVcMuRw6hpwtGjh/iV22/GdX0KlsINB/YxUa5iWRaFQiF/KRqjdW0QBOi6TqFQkAehkqEgJ/KqLtBUgWVKA2maSo+JIEUg8udFmtSGulZVk74WXZer3DAIqZWq1GsT+WYlIwnj/FBXJCklzcOnckKWDHHT8Lw+cSyDu5JYFgC6YaEoVwyB8nmUjHiZEB1dyc9JEjTNwDTtfJuS5AVHHnqlSxyhYWjfxWJvt+V0Tm45xRXKXb4NHRKEWq0OUSiNtFEsp3TyRaDmf0cJa2i3u/T6HSxLkm+C0JNGYU3KNn5w/cNeY2OyWEjSYbq5bFbDSErx6vWpHNvqYFsOZAqWZVAqlRhmuwghcileh1tvvQ2AO+64g6effpo777yTixcvjoqsxcVFOc0VBv1+j1K5SJSbUnu9HoEf4ThFCoWilIBFgaREavI+HRYPQG6Cv1KkZGlGRjJqFIbNBsDFixeRIaAuQQhBJFPXDV0lCkOsvNmOwpBez0XVFVy3Q5jIhHPTNGk2G/R6PTzfHXlIfN8nDkJ5jtg2cRQRRjIQNYlj6W1QFERetMVBQJYK2p0ue/dfT70+hWnZCE1F1XUgI0wzlNinWHbodPsIIZu0Xs/lpZe+SMkpMDMzh2Fo2AWbYrHE+QsXKJXKo+C1JElI4yQnDKWsNBu0Gi0sy6Jer4/8PcPB2PD7KclDRi7RlWGRaXYl/+e7SI1CYBds6vU6cSyHO3qOi5eeg0QixgOfTrshKZ/FskyQj2J006DkFHnumac5ceIE1+/bR5zIgDmhqRJEIHRU3eCOW2/h+HPPc+ttt7KwcIFOt0OxWKTrBfT8iNtuv4Obbr6VW266iReff5abbjxEEPgcPngoD0ks0Wy26Hb7qKpCsys3s3v27KHf6bN48QxHj+6i2404eP1hbjh4MxNTO1F1jaJjMzVTBTXi2Wc+R3M14pFPPYqqCLJM0Go10VWNfq/HsaNHyAYDmisNnj/+oqx5NLml7na7rLZbHDx8A5/51MMIFB5//FHOnT1LtVz4rsHvsOkfSpZUVdZsShZTq0/QaDTRDIsk/71DBLOW11RCqEShlBMNtwVX8jLkpl5mHSf02m2KxTJ+KA3iI6yyoqKoqvwcE4lij+JASihLjpQaZjJSISPF8/JhRSzPf0meS4ijNEd5W6RZgqGbo4F5sVQcFfqe51EqlZienkQIOWDr9XpouSy+UJAS7yCQ3ishBP1+f6Q0sSzrP/ne2ZaTU2EDpqbqIxT4wsIClXIFx3FyKadU/IRhSJqAlUMR4uj79yX+o2g+FGUcw9KYqNcQQtDr9WRBlCcMT+QP6xDB6zgO1UqFfq/HiydOEEY+fuDSd7tomkaxWGTXrm2YpkoUeWzfPsNzzz3Ns88+hed18bwutVqJIPAQQL1eI0nivMhQEErGrl278s7Zgwxs2xx1z91uF9d10Q2VRqMhtbhJPFr1maZcfc/MzIz451mW8cgjj5BlMh18dnYHhmbg2HJKv7zSolydRNENKpUap1+ex7ItnEoJIVRmp+bw+x4L5y4gyFAEOHmmgYJKEqb0uj0MVSVwPXrtDr12G6/Xw1A14iAkcF0KpomuGVQqJal/1DR8r8+F8+cxdQ1NkYdfEPgoSUySQLvdJ0lAMyX68fjx48zNzXLo4EH80EMz5HSt1W4xOVkn8D1Evt6PglBKpwRcWLwIinzxTU9PS/lN3jm/c+o8DMzJkpQ0SfD6LkEYjjjjuq7j9fqEfkCv00U3NaLQR1VgYfECc7PTFItFOt02KgJV04iTiDSL8hVpkcXFZcJISv2mp6Y4/sKLNJsNsjzcKIxCwiCWzZemsbzaJAxiFi8uU6vWydKMSrWKqhn03ZDFRpP777+fOAy5/babmJ2qsXf3DjRFECcJ09OzaLrKwsVFzp6Zx3YcnFKRpYsX2bdnL5P1OmfOnOHzTz7GXR+5h5PHV5md3M3i8mlmd3T5b39hBvSTpNoCXj9ipn4DBWM/g8GAVrPBZz7zEP1+B9KYZmMFy9J45Zt/xX/9Ez8BupywxomPUzRptlbYvmOWT9x/D3fccowzJ1/k6OEbOHPyJe69724uXlgkS6BarhEFMQoqumpg6hblYoU0zqhVqqRZxuzcDJNTdaq55M/1e/Q6bdIsIU5idFVBVRR2zG27krsRJjimQ9kpEno+mhDEQcC+PfuIo5jV5ionXnwBoalYTgFVN0jCiMbqKq1Om1KpQBT6BJ6PZZhoiqTDmLqFgqRUlYpFAs+j33OJIxny5PcDbNOANMNz++iKjiqgsbyE2+vj+/4ovGlqaoparQYo9HodfM9Dy1filZI0jZNmbJudY9vcHIau47tS6lewbNI4YXJyksn6NJqqsX37dlzPxS7YzM7O8vLLL0skdJYxN7dNBpkGgUSNCzNPn5XY3kKhkIMWgn/gk/oHlxAy8FWg4Lo+rdYq27bPEoVS+tduN4mjDNf1qVTLpKl8bzWbzVHDCowK/dOnT4/ShLdv387Zs2fxfT/fCkoZseu6TNansSybTqfNMCNqyONvNBo5kcrEMPR8uJPien1MU6dSrYxkvyLf+KqqSqu1iu97TE1N0e1I6pBp2sRRyvTMJM2mHACmmcrURC2feKrs27OXleUFZqZneeHES3S7bUy7QG2iSJaEuK6bo3oNLMvCNI1c6iW3okmW5u/KGMMwSZI4l5HVkKZZffQONXWVTqeNblpcuHgBzw1GRX2axmQiQ89zC1QUOt0ulmWRRBGnTp2iXp8AIAx9KpUaRcchTkJKxTL9vkutOjEqCO2ipFotL0spchzJULZ3SnuGE+Th1Fd+Flpe/Al0XRIpDVPDMgtkuWzXLJgYuk4cxtRqFRRFsLq6iqpr7Ng2y8KFRZkq33cJwwC/18X3YwqlItu3b8cPA2ampllcmMcyTKbqNRzHwbINhKaTJjFxnLHaanHs2FEeffRxpqamiMOAWqUmiWwI9uzeyyc+8UmO3HQT9913H/v37sVxbELfY6peo9lqEUQJ587OMzVVx7Atut0ey0syPPLFk6e48diNCNUi8AwWl87wwIMf5bf/zcc4efJZHn/qMW6/7U7qtR3s2XWIIBQE8RL33X8X9Xqds2dOc+PhI7RbLY4cOcLBAwcZGxvj8aeexDQsshS2b9/O8soKq6urGKqGaRi8/PLLNFurHD58mIce/iT/8tf+FZZlUavVRoU0MIKADMN4l1cu0Os0efb559i1czeNVhuhZBSLBWzbxC7YlAoFvDCiXKnRbDaZnZ1leXlZ4o9tA98LKRYloGDfnn0srVwkzpJcAj8hrQFhSOj59LpdVKFgGybNTnuU+m3ohtyGpClKntBeKBTyDfsVeb7b90f/ljAKCIMoH+bJjJMgiEZniGVZaJrG0tJSPnyLR8MNy5DUueGmtVgq0u12R5EWwwbGtC10TW5kHKdE3+2i6ya+7+L7LjOz00RRIMOiFxfl0CQfekjJlfRnT0xMyGYk+v7fUeMPPvjgf6Zj+f//9cgjv/PgB2c/RBQFrK2ts2XLJtYHa2gbJYL2TT/A2LyFRqPJ+PgYb70V8Ib/Os611/C+yTqDwYDBYMDY2Ji8CcI8nFDZyNVXX8Nbb71Fq/VN6vX3U6lU2bx5C5cvX6ZYfBetbzZHHa6iCLL1Ac411/KXf7nAq6/25Hpuo0IYRtKEJNaxtppMTtW5+qpruXTpEuvZGoPBOlu3bmHL5quIL4dcvrzGd77zHcbHx9my5SqCwGdychqBgn3V1fz1ytcpFK7BdT0uX47YuW2OS7FP+V0lzp0/hxiHLfZW1uJ12t9q0eu/yuYtJqXyu6m9572U3vVu3NdfZz0bIJRxVG0jWzdvZWyDir5VZzA+hrHF5vLaOgtfXWD7zh0Ur6vwV3/9dTaMQ3I5QhkXbDY2oagb2bhJZZDC+iBjMBigKhsYH8u4+qoCY+PrRG9FeK+5bN68lQ998MNcit/mcpKwxTRQN25gw/gYGxUNt99D3WSQpGsS+TaWoW1QeTt4C//NgLH1deqTE0Rvx1d0s4qCZVls3LiRjRs3snXrVsbHx2GwzrgA6yoTRcDly5cwDJ233wqI44jitddQftd1tF9pUSwWCOIYErmV+Vb7Fa679lrSccHGsXE2bhgnW4cPzc3y1ltv4pSLfOXCn/PWpbfpv95H36RiGlvZvMVEUQQiUxjfpLJp4xbW0zWcwtXopsmltYTorZA0G8h7Is3ww4if+ql/ygsvHOf4C/+O/+af7uftIKRWq3KVdRWdb3+Lb32zhbbZRt9kMLZR8PabIW+Fl1BVhSDw+am9P8nLXzrN2S//GVH0Bpu2XuJrK0tsFDr/4l/8JjccuoUPzx3goY/9W+5/4D7+5I//D07+X/+Ox554nMn3vIff+FcfRwj4RrPJh35sB680v8lLX/xjfuEXfo6f+9lb+Miv3oUY38C3211s2+bVV3v80A9PcPbMPB/+8Bw3/vRP8xdf+Qrzf3aOT/9Pn+ZLXz5LFLyF9/rraFs2s2GjisIY7muvMaYqdL7d5gM/8gF+9zO/y1Xm1WzeWpAJ86nCjp3/FW/5AR/84I/SbH2LX/2X9/Dyl7+MtlnnP5z5EowLfnTbBzj/Fxd57/vex5fmz/DBD3+Ip7/wNPbVNm++8TpzP/ph1rN1Xuv3JcHscsTOnT9GySlx8T8ucU3hKmznagbjGd/peyTpJS5djhiMKaytZ2SDMbRNm9lqGWSDcYLwLbZeZTAYU3jtjT6b8ns0TtYwzK2gDPjQ3ByWZfLaa9/hzTffGK31DcPEsmw2btxEHCeMjQtc73XWBxmavolvNJuspSl24WrGNyi88abP+iCj25F0uvr7JvF9F8uy+c6rr/K616dQKKCMb6B47XWMjQlc1+faa6/DcYpstTaztrbG5s2bGRtf5xurXycj5X3vrfNHf/R/vvrggw8+/g99Zv+Xen36059+8IMf/ACXL68hhCS8bNa3EEUxGzduRNM0BplA2TCGZZkkawmbdSnlHR8fZ21tjYEYY5wxBoMBly/HXH31NajqOK77GoNBxtpaxrXXFlGUMdbWEsbHVcK3I/RNG3nzzTdZW7uMomzguuuuw/d9Ll26zNpawjWFa9lsbOLSpbdZW79MmqS8+4fezaaNW/B9n8vxGuPKOIahM1gfZ31wmTfe8Hnf+95HEARs3XoV3/rWKyiKyiZtIxs2bCTLMgzrXaxd9tmkbaD67ncTxW9z1dXX8NXlZV577TXe9/73cpXp8PVXvoq+UWN8fCMbNmxgy5atjI0NRrKmMQSMDRgbyGBhMQ4D1knXMgbZGN/pv8qPTL+fb3W+yXd6r2EZFuuXL7PJ1BmMC94OIzZt1BDqBtL1lMtrKeNjChuUMYwN67wZRQRvJhSukUW/2+9zXelaxsYGjI8rXH311fI9kqa8Hb7N+vqATZpG+NYlrK0W49oG2s0W/Tf6bNowzuYtmzEtG2VMsGHDBgaDAZqmE779FuO5Pj9eS1i7nCLGBiiKwtraGmODAXa+5QFZJIZhyJu+h7phnPd/8EOc+pNTuL7LD7+7yvve+yO8fO4sha02l9cuoapjvOtd1/KmdwlFHWftUsx7au/hq3/1NelLiVMGY2tsVDexMRtn8+YNvHkpZVzZxJuhz96f+Cn+7M/n6X3r67zLKaOIdTZeZfLtrss/OXCAf//iSd7wv0P1XWWu2roJoSiwfgllfAOpUHj70ttsGNvImFDIssto2kbiy5ICeOSnf4Y/eOYLXOs4vP+9P8KWzYJvv+7T+tbXOfvyOf7X/+V/5y++8pd85tFH+aXb7+TwT/8zFi6e4ueP/SLeGxEbNIMv/emf8mM7f4z+q6+SrMXMTr2fl09/iSd/7/M88cTjeJ5PnFzm6q1bmZqe5s//73Ns2rIRMb6Bpa9+lZ89eoxf+7X7uO3nf573z/0oq680+dD2D9N7pS1rjWyNy+kaV2mbKV67hdf9NxmIzez9yb089/wfcs1VVwMpr3yjgRCCb6y2OP+V/8jFv1zkTf91JiYmCMOQHTu38fxzx5n70CwXvnKBH6pU+NrSItrYOGOqxvjYOJmAN9/wuOaa63g7eJNri9eQxhmdXod3Vd7N2CDl0uVLiLFxxHjGG95blEpl4jhmbW2d9fV1VFV6CqMoYuPGcYRQuHz5Mpt16eFYW7uMqmpkgwFCwKuvfnu0bbn66qvpdruoqsqWLbKuTZKYzcZm4kspGzaMoygKGzZsZH19jVdeeYV6fYLX3NfQN+mMDTaRZhEbNmxiff1yLomHtfXLbDHkBnUwGDAA3r4cUi6WGR8fJ8sS3n77Eps2beKt8C10XUPZsIE0Tfjyl7/8fb2j/k4hg39fV5qmtNtNqVHTpJa6PjGF67ooisi7tIRjx46QJAntdns0KZc4smHzoKPrV9IgXc/l3PkWhm4wOVnHtMyRGS5LM1yvj23buXwjlrImRWGlsSxXnk4BXTdlaJQijXW+5+cTUrmOkvkBJmkqOe+RkJ2qlJfIFf3S0tKo0/UDqcNzHIe+26FYMIjjiPMLZ1BVjeeff4Yoidm7fx/tTgPP8yk5pXxVLldow6nYsEMeUh+EUDFNHU0zSRM5OZ2amuLYsWOcO3eOVk8+bMVSBbVSot2SBnZDM0kymUBKlqIKaZCDfLWcKFekaFFMrEl+t1xJqiRpShRLKUAKaALCNCNJQ3RN6mpdz4MsoViqoqkGcRaPijtgxLse/n8Y1jO8hmasoZygVCpJDXGWUSrY6IaBnmVEuZ+kWKyNNk5Zrn00DJVms41h6JiKwtTklAxqtO18dZ4hEhmuowz58LGU9ghNR9MMFCWSgBlNQJKCIrjhyE34nsuZs6eZnZ2l02lTKhZJI59M0SjYDlGSIDKBZhqITBrlsySl7/bRDZ35c+eIo4T9+3ajqhq2rrMYuNx8yzEO7L+es2fnAYUTL77A550Ct995B7pu8vu///tYho2h2fzzX/7v+drX/pooijg3v8CNNx5jYWGBP/uLP+ej99zD3ffeC5rG4sJFytUqszt2EkYJXS+k6y5SKtXYsXMnNx+5iW63y+EjR1E1nQd+417mPjDHjUeP8YmHPzH6TLI4Y7I+w52/8isUyyXqE1MoisL5+SUKjsHZM+fp9Tzuv/d+pmdnWF1dpVarUSwWOfPyWfbv38uJEy+xb+8+5s/Oc/jQDWiqxsLCBXl/m+aI8FEsODRWGwSuXP23Oy1SpNm2WiuTJhmO40gyRwK2bRFFwagAkB4PKRuzTHs0rRpKAoUQPPfcc5imSa1WG/k8isUiwyA5IQSlssyPGa60O53OaDU9PT090s4D1GpSCtNsNvE9j6npaXRDQ1V1hrSRpaWlXBZpEgQ+aSJz56MooFwu4jgl7EIIZDQaK39/h/APru/vGhuQpAlZKrAK1mgKPpQmapqBYSi4nvT89Pp9KpWSzOoIXBQhA0/TJPTyxbkAACAASURBVERRNEqlEt1uD9NUMQydJIkplysIoRDHQe6zkH63YeqzqsnNhecFpKnMkVFMNaf2pIRRQL/XH52nYRaOzlSRipHe3VKNkacryzJWV1cBhampCXwvRCjSo7XaDrF1FT+MMCydzM1YXLzAwvIiB288jKaqtBttjFx+NMzukCnnuZY+Ewh1KIEUOCWbKJDekmEmSLlc5oUXXmDnzp1EUcRUvU4WhVxstDAdGTgcpQmOYdAPoxwkoaEImfLeWm1j20X6fQ/LtJioV0eSySzNQEh0aT8P21WFih9JiY5lm/hRzEpjCUUFpzKNbZh0fBehaqOJ8miwmf88SRJKBYd+Xw4UGo0G5XKZ1mqTcrVCr9slHuY+ZFCfmiKLZfZXvVYnE0iaWbUqtfyOQ5oFREGM0FWaq22C0OOWm29mRsDSxQXSNMIyTKyCRb/TRRMlsiQl0TMcp0IYxzSbqyg58jtJYtJU5eZbb+Nzj36Gs2fnufnYURqNBrpmY4oMPwhIggBUE8Ow8ml7vumLYkgTbrv9DjrdDghBtVokjNu0GhkXl5ZZWliRG4zHH+f2O25j164pHvnsg5x88SRHb1jgzjsdDN0nTWJOvXyS+++/n263zbZtMyRxjKLq3HTTTdxz3wM88eijBJ7H9gMHyJJ05MuplUtoqgz5S6KE/QcP4fW6TNaqPPX4Y+zde30O9uij2RZBnHDypWe57fa7+fR/96vMzMxSLkh/n6YrlCtF9uzazcOPfJYzp09x5NhtWGbGuXPnqNfrLFy4yL59ezh37jwHDhyg1V5l547tLF0QfPbpp7nrrntotVvy3bKyxK657SQi4fzieSq1Gs1Ok1qpmMsbJYnVtm16vR6K0HAcm5WVFer12igEV9N0hIAkkU2r5/VJkoxCwcQLEgqW3ITbBQtFqDlpT25ThkGHhmlgmRZNt4tlGcRxTL8vZckzMzOjzYnMI4Fut4ttOzRW2sxt28by8iK2bcsNqaYQ9SLCKKFUrFAsFmTtmyS4rjSkG4bFykqDmZkper3O932U/uOQXW2QjGFDN7Bs+Z9MJc3yUBgLTdVYXl6m0WjIl31OFoA0TwqFOA5HZqMkkfjaoRlOCIGhG5CBoRvYto1pSGqIrmsUCjaaqhFHMQKZbB7FEUHgE/gR8/PnWLy4TJgTRRzHwXEcer0eURTklK5Eas5HSed9gsCjUChQsCUdS1EzMmJUTTA7XScjwzRVKuUiEJOKmEq1iGmoCJFhlwyJO42kD0NkCiITKCioQsphFFSyRDYKvhvg9jy8vk9rtU233eP48yfYsW0nB/ZdTxpnpHEiDU2GDKsSKhi6niMBNXRFQRUZIkvodvsEkS9TMDNApARBOPJNZFlGmkSkGURxItHEijryuVi2KbdRQYCqm6iqMjLxjQhIcTwKpbNtW2IQLQslS1HJyOIIsgRNFWRpTME2IUuwDZ2ibeEGIQpCEqkiaeRK4wTbtjFUTWJ1k4QkCkcrTM/zqE3UKVeqLC+u0Om2MTQd07ZIUyG/r0IlFSmpUEhjSVvL0oRMzVBSMVqzoyi8fOYMtUoVp2hTsm3UNMH1Q0nxUkBRNYJY6jRd38cPIsIkpVAoMlmvMz8/j6Gp2KYDqcbZ+TY3H7udm286wgOfuI37HzyGZvf4kQ98ANsp8vzzx9m5/Qb+9cd+lz/90z/h4KHDDAYD7r3nAR5+6FPc/ZF7+PyTz3LkyC3Ua9NMTc1x+uQ8nb5HqVrHMm2eevppWp0OfdfFsGymd2yj0+3Sd122bd/Os898nk/924c58FPX0+50+PXf+B8YGxtDjAm2zW7nkUceYWbbDGfPneGjd93H3Xffx65du9ENnU7Hx7QqPPjgxznx0oucm5+nXCxRcopoikK5WKaxssrU5ATdTodatUrf9XC9PlahgGbqdPp9PK9PliV0enKFrZkGfb+HpivEkUyTTpMM1/UoFKReVTfUvNnMm3NN5FS1YcBbQhDkOu8sHj2zO3bsyvX1KmRXpBWGIVPYVU1gmQW8vi99KGn+LKkaBcvOE3FjQj/A7fVRFAXbdtB1FadYyM3DJkJIgEW73SaKAoSSUavWcQplVE1iSKvVCYRQZaBimhsb1R8Yzv+hr0E2gAxU7Qqp753AjDiKRz4KeX8lKEIGeWV5LkCaXAkuG/qWhsWHaZoUc8Tu8AyVZ2SCqmpomjoyJg+nn8PQwTi+on/XdV2aTt9BqhsW0GkikfByUKURBMGVc1iXDVUUDxtzi3JRDgA0VZ6DvX6PZrtJsVikUCgQRjGZmuH3XRT00TAsisKR30RR1FEDlJJh5IFr/w977x8cyV3feb+mp9VqtVqt1uzs7OzseDwey7IQQhZiWdbLWtlsfMYY48e3MY4x4DiG8hECfhw/hCI8Pm4hhHMM53BAiM+PQxweID7j+HyOswFnbXxm2Yg9IYQsC1mWx/J4PJ6dne1ttVqtnp6enueP72gMV/fUpZ56ngtPFd+q3apVbWl+dPf3x+fzfr/e25ji7U1ZoVAQYIfFRUZHh0kmTSy7hmeL1HJZUvC8AAmpu+6HYUjd9sQ6Zui4jkPg+UiS3PFgdYpWnuhUh0GEIgnvouM43UKEMMQHqLqJqoqDDbwe/rj9Prc9M12juu+9rvtHbOpSqZTw2Hh+V7Jlez6ZVBrHcboSSykU3h1T03F8r3sfWY4t5gtD+NBm5+bQFJVMMtX1gxFGJDqEMD/yIYpQFRWvM5+psib8m1JEYXSSZDrFM089xf6pcZxaFUPT8QIfNxA48yCUCPxfNA1vS3RMXaeQz3L7bbezd+8+XDdgdblKMqlzxRUHCYGPfexj6AmTo0c/y5VX3MCNN97CA1//JvX6q+ydmuCqa67iwQe/yRVXXEkURdx5550cPfppcoUCmqryzMxJVFVnZKTA8PAI5bUSlUpZSGk1Rfh202nsap2vfunL5PPCn3js2DGuuOIKyuUya2tr1Ot1MpkM+Xye66+7jmy2AMQ4cXKGffv2Mjs7A0hYlsNacZkbbzjCffc/wOFDh5icmOx4RmQcx0WSxXUVRdgQTTcwdAXH9VB1rVNweB3/77o+kiyTMHWIQhEFIQGR+B3T09NYHR+urpsdX5aF0vUw+h1KndYlSwqss9QtcHieg9oBKtTrItMEoF6vC8mVkWRlablrmN9e37YBDH4gPovv+xyYPkgUiaDenw9TjCIBl9kmtgpftNx5Ty7bAbuiSCx3aaWvJ67/j8cvhezqnnv+9Oj+S/fTp2n09Cj43havvXaa1K4kO5JDbG01iEkxIEKSYOfOFImhHZw7d46zZ8+haTo9cpyIiLjUg6r2Ee+RMAYNChcWSO1K0QgavFJ+BX1ApxW1aAQN1F6Vvj6Vc+cc6nWLVqtN/0A//bqO1jdAn9qPLPcgy6Jlm0gkuu3XbUNSoVBgYGCAvr4+4nGJvv4e2lEMwxik0QgBwXdvRU2scza7dqbR+vtxHYfSy0XMHUMkU4O8sFbkjFUjndzD0A6T5196kaGBfojFSCdz9PZp+MEWERGq2o/Wr9OvD6D09RKLSwThFmEYAHGCZpPMnj3EJIlXymUuGhnhW9/+NsWXXuLSAwdYe+UlfvLsHG+4aIzEUJKz9jqtZhspHqdXjhHJcc45NvVzFjFJImEOEY+B0iOTTKeQZZWezkMZ+T49cpyYLBNthbQkhLSg3WbXrl3UajVefvlFWq02+fNyNJsCoxiLxYjH48RiQn5QrVa7D8j2Ddyj9qH1q7jeJq1mixgxztbPijZ4n8pZ6yx+0GDk4ouIwha1Wo0LLhrhDaMXE+9RePnll2lLMYhLxFtttjyPXrmHHlXII2zrLIY+wK7UDtwNl+KLLxJFEYY5yJl1i61Njz6ljz65H8/bwDln09Or0CsrtNst+vp6mZp6K1+8+y7W1x32TV3CgD6It+ni+T6mYSL3aDjeJu12jIEdQ/RI8e5i1m63icXjvPFNl9DYcpmcejOvnTlNTG5y+syz/OCHJ7lk6lI2nQTpnW/mt9//+9z++/8bP/jh95j7yT9yywffz5Fr3sWxvzvGgNlHPjdOqxVw/Knv0a/r9Cgy//FbD3Li5A84s36OMBbxawcPseE5tFsh+y+9FKlPYd/bDlB8scgffvJ/55xzlpvf9wFmZ2fZtXsXD/zVA1y6/1Le9ra3MfGmCS550yVcduAy/uWR65j/yTxRq4fRsYv4+l/ey3nZ87nx/dcy9Za38ts3vo/f/K1304718gd/8Ptc/e53s1p8kXPrNi+/8goTk5ew5fv06xq70ln8ZoMYbV5cfolBY4h2ELHheSRTCWLE2Wr4pMwke/K7WVku0m636e3tR4rFse1z9PX1c/z48e5i+eqrr+B5HV12S4TDaVq/kJHoBu12jHwhy+5d53W186+99irxeC9hq0HYatLX19c1lO9M7kLt1fjZz36GYQ5iO+tsNXwGBg3MxBCGOUizFdKKIjLZPeQLF3Dq1Cm2tjYplUrCE1Us0Y7ayD1S18w/NLQDczBB6ZUSm+46zQ5ysdUKqFtV6mfr9Cg9tGmjqipPf/+ZX8mu/hnHF75w99G3vGUf/f19tFsx/MYWjUaDCy+8ENf1cTdtBgd3sLnp0N/fj+O6xGMCZoIEvUof65vrNLwtstnzsG2H/n6V3t4+HMelp6eXc+fOIssSIBGP99Db24N1boPkjoTYiG96yIrMzmSa9fVz6LpBTIqxteXTbDboU/tpNJqoqoIUl3DWXYaGhpDjGv5WAFIAUpONDYt+bYhKuYbapxGPx2hHMepnLWxrneTOFPV6lQsLWVxnnTe+6WLmfzrPj+d/TEzqJV84DwHyaYEkoUeD0BNHkttIkkRjK4RYRH9/P3G5B98X0IoobBKLxYiiiHPnzrG5uUkqlSKTyfCDH/yAI0eOsLCwwBveUKBPibA3QzY3tqDVpk/VaLcj2rSI9cRwbAvHXmfD3yCXvQAJhVr9DFEsYnNjk6HEAObgAL3xOK1YRBTGaWw0kI1+HMfDb/iMvuFiFn72LMXlZcydKS46/3y2NrbQ+3W2WgFKXBwAW60W7XYMpbeHdkcyHLYjIt+nX+/D2djsgiYO7Hsbr1RepXK6SsI0iUkSGxvnOG9PlhdfWsNybCbG38ziC4vIksSe83L8bGGR8/M5GoFLX5+GsyHkM5Ico9Vs8sLzS/RrfezcYQqYiu9hpHfS9EKaUcDGVpM2Em964wQz//gDpLDJUL/M5JsmqPfu4C++9mX2jpzPpfvexqsvF/F8j6GhIWhLRJKEFO+h1Y4Tl2Qa3gbtWBxd7SVo+pyXzXH//ffx9A/+C5dcMkHgu+w57zxefLFIvnAxO4Z28C+vPcLfPf73FEby/PAHT3Pnp/6Qd73zKl4r1/mtG3+b//z3j/GWyTfz3HNLPPfcAv/1R6fYu+8t3P35L/Br/+IQXhP6+hUKmfN5/rlnacZAasfIXnA+6R07qdZqzP7oFGpcYs/ONEGzwcLyIr/9O7/DI48+ymhhhHKpRLyvh/WNDWLNNi+tPctHPnYnj3/3+wQtCJtN2lGDdrDFbR+5le997x84P3cBdXeTEzMzPH38uxQKBcIw4tK3v5V/94V/x759+3n++ecZGBhko36atRdfJtZncunbD/BqpcL4G0cZ0nfwwksr9Md72bE7gVU9xwWTF+Ke9Wj4W8TjotBgGAk2NzdoBFv09w9h1S2Su0RoYNAI6etTiSKxv1xfX6e3L44ia9TrdRLJNFEr5Gz9DOl0kp6eXiqVCu12C0VR2NpqdA9BW5suqjbQ6aIE9PX143kuyWRSvLbWz+DgIDM/+jG702mqlSojo6OUS2V61R50Q6N2pkJPvA/TTBBGbWjH6YlHbG457BjaycbGOq+8+jJ6v8Hp06cZHb0Yy7L44Q9/+E9ao34pOh9h2CKfH8FMZHFcUNUMhpnBdgJqtkMke+gJCdurEoQOxbUlbKuGrhqMFEZJmWl8NyJlZgh8j6XFJcJAxvdkHEtCihLoaparr3w/YSDjOj5hEOH7HlIU4Dk1EobBcHYUU0lRL7nYVRfPcVicn2FtdZFI8vECW8hz0BjNTzGcmcKpR53ug0QqoVEplpCDBCuLNRR0UmYKBYlcNs3U5Ail0jKuVcW2asiyjhyoLM6XWVosE/gampHE1HNMDR+gWvSxViAMJFKpJMXiKq7r4Ic2yD51u4xuyCwtzZFIGMgyrDkl7NBhrVJC0TXMZILl1RXyhSxmUqVWW2V+fh5ZzTK/6uBGBoqewgpCIlWh7to4roNj2URBiKnrOJZFaXUFU1dw62U0xSGVUDEMqYMZVKmWHWw3IAplNM3oIA6LHZlbQC6Xw7F9wkDq+ms8zxXZDqbOvr2TZNMp6GATRWquh+cGGLqOJqtIUUg6aaCosDh/iunD03zkjo+SG86zuLpM3bWZnZ2lVBIsbE1RWF5YRFc16nadXD5NQETgOgS+SzphktJVysVVxkZGODh9mKoVMLtUEnQRTcN1fRzHJaEbaLICto0uBaimTmZ0lJPzs+zPp7hyapTQqvHdRx4ilUySyGRxggArdDFSSbRUgrXlUzhOhVw+heX7TO0/zC0fuZObPnoUPbeXuTWPA1fdxPTVN/O1bzzBI8eeoDAyzdMnTrDvwDif+NQtPH3iIcIgoLImKg0fveOj3PaJjxKLxTh0OM83v/lZ/vIv7mJqMo9VK3L1tYcwTLimkGW/LPPAXXfxb37/YxTy41x++WHmTs6wcPJpTFXiumuuZmpqkqq9ysFDIxw6MMwTj3yVtaVjZPUaR6Zz3HztBJ++/WoOH0xz5Y0TfPyOz/HXf/E13vuuKQqaxbf+7RfYW5hg9uQzXH/DEYpFiUL2BmKxGB/4wMe59foPc88991BcnCefMqgsr/L4Q49wYO8VnDy1ipadpB4q2CHkhnNUKiUsq4yhQdUqs7q6SkSALOu4tkNprUwY+IyP5Tl0cApTl0VlyqpjVWsEnkc+lyKdMnC9OqlUgqn9B8iPjGLbISvFMl4goxspisUV/MChWl1jbW0ZSQ661eeFhRVqVZd0qkAQRKQSBqauoikhqhxg1daQIhe1QwArrZbI5/N4jsuHb72ZA/un2LdvhOHhJKYhknJz6QKlVQtF1hkdT5HLahRySTQ1YjhfYGJ0L2PDk0wMH8BUspTX3H/WefpXA9rEkGQZGRnPdfB8UZ2WVQXf9Yg6eSyKoqBrZmczIOH7IZIio8oKUiiKUqqqUqnXuvfYtrR1m5aG1JF8SqDKCo5j4diOAJEk05RKRVRVmFllWZiqg2BbahohyypR2KnGRwiSTmAThVIn70MhZSZwnTq26+KFAVIY4dkOIkJcxrMd5ufnKYwMoykqpeKawH9C5/0KqZnruuzbN9Utyv23FVBJirpEt5Coi3oVZKGAwBOwkWQySRj5XSRoteaSy2RAkXFcB6Kwg9cW9LvAE683PjbZwZJC2AmIE0UeCKOIUIpEiJskcsnlSKSdA2i6SF33w4CkYaKbBn4ogte2rwvQ6Q65nWBEiQhQJRUv8JFklcDzUSVxUCmurXW9A2ulEoWCqOZbtkWpVELp4FmtjjwuYZpIdChfoVBxGKYprrnlkE4m0c0EKysr2J5PMp0RqOBqHUVTBYnTFxlluiEyrIQuRMJMpHjisUepliskM1k818YwDU7OzKCoKoqmifuzQ2MjAj8ICMMASdaJIpl9B/bx539+Lzdcfz1rq6sYhsHMyacxtAS1apW1tTUef/xxlpfmmTn5DAemDyIBH7r1VhKpHCOFHP/qdz7I7OwsX/vavSzOL3LNketImClu+dDNaKrK3r2TzJw4wePffYLpK6/mppvfzx9/4U/4yK23UBjOU6lUmJ6exvM9osBD0SUKmTyri8tcMX05ru+STKXIpNNYNQvLsbjt43eyVqny4x/NctMNl/NXf3kfB/cdIlcY5RsPf5ujn/8qd931AIXcGGNjoyQTQpJ1/71f47HHHmF0dJxTp2a49tojlMtl9u07SN21MA2Bwk2ZiQ7cZg1N1vFCFymUiSTwbAdNURGRgwGqrDA/ewozIQIKA9dH7RTLfC/oyMq1LrHL930RVivJ2J6Q3ibNBKapE3Q6GqIjJ6MoWjdc0bJqpDPpri1BdDPCbo6O6wjJpyzLndwqpZspItDdJqZhosg6YSRiI0JfRFHI2zlWyEREOLbTUbHQ6aj+04mMsXa7/f9gCv5/d2TPO6998Nd+A1PXGR8bp1yvUC4LPNnJp5/hyiuvxEgaBK7XbSOJL7ZDDpFUZPl11j+yhON4hOF2SqS4OPPzs4yPT/yCDk5VZGRJEfjPzkUUGlSrS5zRdFV4IsKQwBXyKlUxUFQZ23IwdAVVVUiYOkYywezMvGCGyzIy4uJHnfZwNpumXCqJQ0wk2pq2K0ICt2+QkAgCCV0z0FQJy7FwXJdsNtsNONzGKBaLRQrDOdFSMxNd1Ge1buFYdSJZwVC1X2gxq5oIT6zW6yJ8pjCCmdBxrDqSoiAhFg5d16msVYWvIptiOxlzG0/oB0IXur3gybKMHwQomkwqmWDmxAncICSXTmMY5i9gdIGuFGBbvoUUEnoirk6RQNUN1tbWmJqaRFcVlpYXSaUyFItF9u7fh6ZqHDt2jJmZGY4e/Sy2bZNKJ/G9kOPHj3dzRBQpImEmcHwP0zCoVOvIUdh9WE0zie/7WK6HHwSslasMD+eQwoggDEgmtvX9RQxNPKx79x9A0Qyuu/56PnzD9URBgI9I61Z0TfhGNOEb8T2R35Iv5KisreEGPp//7F3UbIcrrrgKSYKkkRD4WN8nCEIRahmGlIoVkALS6SSzs7M88siDHDlyA7feeiv33Xcfn/7snfzRv/lj2u02D3zjPm6+6VYAlhcW+fbDD1Gvl0kkk5x66hk+fMuH+NK992N5Hs899yynz5wjlTT5+te/zsrKMplMlkqlQmF8hNkOQ/7yw4ep1ypiAxV41Gv1rnQCGZLmOK5dJGEofOKOT7K2tsYDDzws7lEl4Jmnl5H8GocPT/P4Y09w6tQyN1x/A/d+8x4AHn/sKWbnTuJ5FrlCnnrNp1QqMVLIoyohqytFEskEkiKeBzfwUGWNKAhRNR1T17CsGssry4xOjAMRvu3huA6KpmEmzM5kKyNJSlcXm06nGR+fBFliYX6Beq1GKm1SKVcF11+TSZgJbMfGqjtk0lmUDtI38iPcwEVRZBRFxrJrTExMUKtandZ51PUBKIqC19HuJxIJNLWjFfclbMcm8MVGNJVR6TwCoEhEfogkaVgdcpEkR4yOjvO7v/vBH7fb7b3/n07Ivxr/t2PXrl3t3/3dj5FMJllbK+J5Pnv3jjO/ME/CTKAbOlaljqYbQqraoSOByAdZWJyjkC9QKpdJZ/KCAEWAImtIspAqCRRnROB7gjKoKiT1FOV6pXswCUMhjXBdl1Q6gWN7KKqQSVWqVZJGmkpZ0HmeOnmcsbERVEVmbn6OfKHA3MISSmSQSCrd9xdFkthIlauEoU8mk8OyLAzNYGw8R7FU5KmnnmakUCBAIpXK4AURqqLguy75dJKqZVMulxkbG8OyrE6110BRlI62PEGtXkeSVSaGRzj+9FMkEyayqpBOJFkrrTE+PsbM3ClSqRzTB66guLbQzf9Kpkysqo1mCOnz0pLwZ2qKiqwKadfP5w5sZ/GYpolt211cuKabSIpM4Lnousnjxx4ll86Lzr4U4Xbk0m4HHWoYBpZlkc1mqZRrXSnM9sHEtm327p3iqaeOk0mnkWSV/fv3IykKxx47hu265NMmV199Dd988CHGxsZ55pkTVColrrryatwOScv3fLzIYaSTaA9CNia6s2IT6XkeqVSKZDJJSouYWVxDN0xqVg0Zjdvv+Ah33P5xMtkMhVyWUqlEYDkk0ykkVWSChL6HF0YomiEOYZLA8KtKiIrK3PIa1157HUhQrpR4+NvfZnJyHFWROhRSgUv3PUNQlnTI5lIkkzrvf/9NOHbEx2//LJZtMbG3wBPHj3Hs0e/y6Ts/zsH9h/nGgw8yPj7BH/7hH/CZP/rXRFFIebXEjTfewufuu4/9e/fj1Et86OYbKC4tcs/d95AujGLbDvv2TyIHIcvLKxRGCyiyRippslosYSaSuJ3vJ0Qivz/DZOZ6vv6lTzM8sUZpJU+tErJcWeaGmyYJlBVqp27j8eOf47obxnji8ePcfMtNXH75IVQtwR2f/ASPP/oYV197HZNTU9SrVeRI4fjTj/PpO4+CFPDU8ROMDBeE9D0M8IMQPJ/MSIGlxUWC0CUMQgwj2cHUy5RKZRFkrSokcxL4EisrJVTdIJNM4/kOtVqNwvAY9VqZxcVFrrjiKhYW5sjnxUGsMJyjUhbyXtNIYjs2hUKBZ545xnAuTyCJ3DqI8AOffG4Yx3E6YZkKnhswPD5KaaWIbqi/EJ5pmiblUg0zYWDV64xNjlIsraIGCmEUIakiE8fzIiJCJEnF0BVSqRQf/OA/bY365TCcN5tMH9iH5zksrcyhKhpT42PU6hVuu+0jzMzMsrq6SjKZRpZB08WpbVuPl0wmcWwHz/dF2Jv4reLvKCTwIny/xvj4GPV6Fc/TME3hLfA8od2TJL1TrYlEOmQigaKJQ4dTq3c5/sKXIEJWRBq5jed7BFHAWlkEnqVSCZBCQc+K6G7WFUWlWhUTr+855LI5qvUaUeAzOTlKEIRYlo2mKKDRCfqTMUxDZBt0gse2TYKmaZLJZJAlVXhPXI/AsSmVygwPF0ikU6KqFLqiAhS+HhSlyDKmJiNHCmHgYNUEgo0wJIzEZBuFoJtGN0jJ9fwO71yE4YjMBO0XshI0VUZTNarlKrl8DglhQNxOdFZVGd8P2E7a3Z7At4MlNU1D7fx8+wFLGAksx0Y3UkRISLJKaU1g43K5AsPDoxw79l1AvO8g9JiYmMBxHHRdFRphVSOTSLK2tiqubee0D2B7wkA/0qkWGLrK/MISLZqKAgAAIABJREFUuik6OKEUksvmSCQM5uZPUcilqKyVePqZp3jgq19mYXEeZBmj8zhJUqea4QbIyEiRjKEpyJLBwcNXURgu8Mk7P8XczCmOXDkNCN64oqiEYUAUhMiaiq4bZDIpVlcrLBeXmJyc5LojR/j0J0f55sPfJBaLESPOk08+yRe/eBdzc3Pcfvvt2JbNGy55E9/61rdw3TyWZXH19TdyfPYUH7/zE1QqFdKZJFdeOU0um0XTdLLZLI5rcfnllzMxtR8inbn5eeaLFhEKCTOJF/nIiSSKqpAyTKxKkVCqISsyajLPLbd/HFkJueLKK4n8sBPsJaFGaRbn5rnr7k9RrpUpl6rcfvtHmJmd54brb+Ho0Tt55uQajz/8CPmsAoaG79oopomZSJJMJLFsm2qtLnxQ2QSe5FKrV7FsseEaGZvAqtvdlHFV15FkCdtyu4duXRcblm2U6YkTJ1BVlYmJCYGbdFwURcIwhFa+3AlLi6IAP7Swqp4Io1I00ol0t1JtmkmWl9Y6mSgasixwj9uhc4oiEQSRSGxXhRk5mUwyli2AJLG6UiQKRLs8JAIPUUkNAkbHRkmlUhRXi5w48dT/tDn5V+O/P7bnYBEWJjxsAk0pUr+3TcmabvxChpEsS91MqJ/XRycSCeq1MrIcIUFHx22TL2TxJJkgtAhDhSDwunpsoMPqF5kEtm0jITbeIgBUHHgjsQSi6xqVSlV45ToJzoZhYFUcklJq+5OhqkoX9KHrSSqVijg8bBeIwgjD0AmiEEUWc3rk+0gIzXutVhOdGdPEcRwSiQTFYrGLUh8eHmZ2dhbV0NFksSEyDGGcDcOQRDLJyloRx3G6VdpqtSp+r2OTzWYFCj8ELwi7csntir0X+CIfqHOdtilT24jjn78eoR+QSiVxiSgWi12PjCxLRN3gwOj1bkDnukURyIoouG3jkn0vxLZtxsfH+fKXv4SuaRycnmJxcRG1E3Q3OjbG5GieUqlEvV4nnU5Tq9W49tprCQOBTRZY/gDPFWtlGIBhKt1cBhCeMVkOurlLgWV3Doxl4dFRxetZNeHbMwxRwLvxmiPMLc2hRCqShADoSBGmblCteiRSOpZlMzoyyiOPPMa+A4cxEwae4zIzc0IEayp0fUOe7+HadscDI4sOjaMwNjlCtVrl5ptv4dGHjzM7N8MTTz/EtVdfTaVawjTSJJNZ7rj9E9x+x0f4vY99kLvv/hx33HGHQFVXLBK6Qa1WIfQ8VhYXuf7GWyitVSjVLBx7GdtyWVicZe/4FAcOHOSxRx6jXq8zPDKM7wZd6W0QQhR6BE4d27WwLAczkUTXdOpeBUVTCAKFtbUVNDUim0kiSyaEJtVawIEDeQ4fnkY3VRYW5/GDOvsnD/H0sTlGxsaYnZ1lZKxAvpClXKmQNE2IVKIoRNc1VlaKJJNJPF8mDET2WzKZpFrpFPAiF9PMIElQqwvEu9yBlWwXGXRVw1aVrm9I07Sup2V7PxoEAZZtoWvCKx1Gksj66BSEwzDo+pC3c7G2h+d5aPo2fAVUVScMQ2zr54pnntcNDt3Oq9meM8LQQTcSuK5Dve6RzWb/yXPpL4Xn46tf+crRC/N52q02e1J7oNVGjoEUxelT+mhHLTRFpU/vo6+vD62vn97ePmhHSDGJ4otFNM2gX9PZtWsXmUyKc+vnUHoVEU6nSITNEE3rY2tLJA3HYzLWOQtN6+tW5MMwFIiz/n48z2Nra4t4PN7dICuSjESLZsOjZtU5Wz+Dfe51Yk7vQL9ADcaaxGMyEsK42hOPsXFuA0lq421uYQ4MovRpZPO7yO/J0mg3KL5UpBW2iCQJWYp3N+t+sEUyuZP+gV6kOMTlGOvOOdq0GDQH2PQ2WHlhmdE3jHBh7nwG+zUGzUHObmzQbIbkclnqVYtYBIrSg7fVQJZlzq2fQY73ihtegWbQ4syZMzSbTWRZJh5v02o1O0hJiWazied5tFqtDrqth56enu6CF0URvkBR4224vPTSSxQuGCabzbG5tU7YDCCKiNoS7TYdXFtEo9Gg1WrR0xNHVXvp6ZGJohZbWx6K1Ca7K8dPlxYpFotccsklDPTrPL+6hN6vYxj97Ny5g3K5QtJMcPFFIwR+gxve814eeuhbaL299PQoeJsOciyGpvbSaEakkiYxIqyzZ9jyXIZMgz5VgVYL11nnzOk65w9fRP10DaI2l719mvx5e6jX67xSeoXEjiRnz9aJtWJEsRA/CInLMeQeBdoSbtBg0NwpDMlhSJsWUdQmpI2ZSPJf/uFJZk6e5KLRC0gNJpBliEVxouYmEm1AoieK421tEvgevQMqyaE03/72g7zjHe9i5+4UJ558hi//2b1ckJ3gbx76zwzobb52719g7tiiXH2eu/70Ll4sVvjMp75Eq6mx1VznotEC9dpZbOcMP/iH73PRhRdgGkM89OCDnHjmhzy7uMTDj36HpZU17jr6h4y88c3ccPVvcMX023mhdIa4ohI0QqQoRiPyeHn1JYZzWRrtNrlshv4BnWajwWuvvsrPVl7E0IbInpdiZPe1bPk76TdifH/m61zwph6M5FkmJvewZ88EL6+2eG05wYC2G9f+KaoxwNn6GZqNBgnDJJTayL0KrXaDuBxn3bFJp7JdSlWr1aReEyjRoOnT19fH4OAQYTNg4+xZ1F6VRDKJrmqsbzhkkilSO5IMmIOkkykGBg1arRa+69FugeOsEzVbKHGFHimGqvYhSxLelkO/btBoNumRe3Ecp6O5bXdTqQHK5RKut047inX8TfGOvv2MIK/1GexI7OT06TNseT7tdptW1CImQbMZErUj4rE4siIW9Vqtirvp0Gi0mJub/ZXn459x/Omf/unRsbFRmo0Y7SgiaodUKhUO//oh6nWb2ukq+QsvFFlMSh9bm5tE7TaKIpPJpKi8Kg7rzUaLRHKI55dXGDR0JGT8RsDWZoORi4epvFZGiikkd+xgfX2DwX5N6PI7c3G7HcP3RQGqfrZGr9JH0GxQq9UZ0AbYWN9AIkav0UfgNym+tIrW24um6zQCj62NBkOJPmRFphUKP8PQ0BDLSyto/f00tloM6P00ggbvftdv8MLKi/zk2TlMM4Ei9+GHEdl0hvVzDlK8h1jYZs95aZ5/folEUhzwTdPk1VdfRVVVVlZWGBkZYX19nQvz52HVz/Hyy68yPDaKOWQiSWBZGwRNn7PnbNK7Mqw7G+Lz1atsbQnCYdAIabdCYnFB/Gq1WgC0mtCOiSDDM2fOoGkau3fv5uzZs90DyZkzZ2i329Rsiz27d+FteCw8+1P27Z3iJz+eYyAxRJ+i0mw26O3tJWg0iMs9wrPZaglKkRzv4kdbrZbwipVe4bJD03jeJktLz3HJxASP/KfHuPDiC3j5xTXS2SytRkAsHmdl+XkUVaW/X8e213n88ce4+t3/C8vLi+zZs5ueeB/n5ffw1T//M6YPTmNZZ2k0GsTjcRKJBEODO7DO1XnttdeIxXrY2togaMUYGRnhksm38uz8Kf7ub/8WbVDj3e+8iu9//ykGBgYorRVRNQ2lR0OKx/BaTWhK6MYApVKJXbtShO0mb9t3GSd/9F/5nd/5AF/+91/hhZUl3jT6Roz+XtpNn161j76eGJvuFoWR86lWNkll0zSbEWuVl3j1pTWef6HEV/7sP3Dfvfdx6tQpnl96meXlBdytGu/8F7/G5Nv28vZLp7mw8AaOHj3K8b97kunDv87unTsICWnLKtUzFc6deY30zjRv238pVr2Os+lx8NcPk8lk0I3dfOGLf8y//+J3eM9738nLlVd48ntPoQ8lcJx1hnbuQOtVWHn2OJdeciV/8zcPcd4FEdbpAZz1OmNTb8LbOs2OpMLpYo6f/vgHDBgtDr/zMh5/4j/ynvf+Jj9dfI6//buTyLEd3PK+T1B84Qzf/+43+M137eGnqxZKby/57Plsui5DQymqtSqtRkBciTMwYNCrDdDwN9nyXYYSQ9BuEwQhzobNgG7Spyk0wwatpou72WBnche6NkAjbBKXYH19Hb/VRu/ALfr6+onHY5w9e5p8vkA7auEHPuu2Q6vVZkdyEMuq06uoyHIv/fogvr9FoxHSp/UStaC3t5dms8n6+joDxgCv1WoMGQMdwMW237BF0AxJ795JGLR47XSVV199lZ2pncSabYJWE1mO09PTSzzeS/b83dTOnGV58WeYQwZPPvnk/39Qu1t+A60T2FOpC2axG3qEUcjM3CypVApFN7ppxdVqlTAKuqFfyWSaCFF9mJubQ5bFRB8QoqgyqqKSyCewbZfh4WEsyyIiIpvNYZo6tdov6m63CSHbxAFJkjA0VRjmXF9UsRQNCchkMti2S7lcfl2rq0ggic5MGIR4fsDI2DilUolkyuxUPpIsLyyysloEYP/Bg0hIWJaFJInTpiqDqigsLy+S6lSCkokkkR+SSCaoVarksjl0RYMg4qlnnqGQzyKrCvsmp3B9j/n5+U6FTiVhJgmjald2IiF1iCigyBrmpE4mk0OSImZmTorvRBWdC9u28QOB9xVa0oggeJ26Yug6hiQRErG4IjSh27Qg3xMdBlmWIYxQZbkbkrWdHJtMJIiiTnBWFJFOJsgVRqjVagSBx8hoAc/zWFtbQ1VUdEN0r7YRqMlMAiSJwkiBO4/eyWc/e7QjmbGwLAvbdlgpFjl06DDLy0u4rksylUbTBO1lrVTCcUXVLlvIUKu5jI2NUqvVuPuLd+F5LiMjIxzYvxfLsqhVqoJ4lkwSUiXqyCAiWRYaTbuOLEkoqozWJWE4HD/2OJZlMTY6gmFq1KwqqqaQ1E2CUCX0A2RJUFsiKcL1fKTIRVNMvvyVP+fpp57m5ptuwEwkODVzjK/e/0eMjFxMJF3HSGE/qpwjk3O55ZZxnJrHx++8mv37xnE8nYX5VcLQRZEUDh6aRpFlVotlbrr5/XheQDaXQ9U0Zk+c5IEH7gdkbv76/Swvr3LuzGn2nH8Bd33+8+zbv5+RQp5TM4uslZYYHpmgXC4RBEKKATA8nOTk0zOYZooZ67OYwxqWcg233foE33jgOA/c921ePrsA/AVvPH8H9z5wG0kzzde+WiaIQj75iU9w8tQcc3NzTE5OEoZ+l9YRRj5rRWHidl0f09TJF/IdSYWoDluWhSRDbngETdOoVSpUfZtkQsjvZFlGQRLa9khUnYMoFDjuqocsiXvcMHRcz8UPI2RFRzd0NE3It6IoIpPJACG1WoWggz1NpdIAKIrUJZBomoaiykQdSk6pVMJ1RXjVdqVOUUSVKooiNN0EIup1txM2l6JQMP5nTsu/Gv+dEY/HOxptj0w2hSRpqJpMuVxGUYWPo163Oqh10Q2WZAClmycluq5Wt2uRy2aEJyKIuvSm7Wr99lqUz+eYW14SvoAOlMMwDBzH6Uq7dE0nCkUauePYqIohZJKdxG1JlgjCAFnSiaI6kpxAliSCbviZ8DKEIRD5qGpSBO26DsgSa8Uye/emyeayHD9+ksnxCcIgBFWEHiYTBoqmEUVBpxvkomkCx55MpkXl2UxiWzXy2TTLq2WsWh1VlcjksizMLmIYRocCJ7oapXK5i3XfXotVVUdTwLIcLMtC13VBfYr8bhDgdoUe6BLFoihCUmQUT/y8XKng2EKqsi3JMowOSUqWkCMxl4jMMLkTsqgiqD/Cs6IoGoVCHk3TWFxaYP++fWQyWcrlMtVyjbVyicMjoyIvSFUoFteY3DvV7ezXarXXu/OSBFKEoRvkszl8X6yv25/fsqwujUzISCPCiI5UN4DQ7/6/iYkJ5hdOdYh7CULfBhSQJZGiG4j5zbWdDlXPE3KgcpHrr7+OSqlMZa3EvoNTuK5NMpWgWnGRCQlDubNXEYVXx3ZAiijkRlEkIXn+/Ofv4v4H7uOG627kyJEjfPeJh1mYX+LwoSv46Ec+xhfvuZubb/oQH/rQh7nlllv45oPfxFBVUtk82dFJVp5YZWp8hLW1FQCeefoZ9GSShblZqnUbI5GAUOKWW69kcX6J645cx/TBw8zOzlKrhSwtLUEYcdU1kwShj2XXWSt6JI1JIhU0TaJUsXFDi4PTtzF78gFqpSr69D6CSNA3V1ZOMDk5yeriKnfe+Sl8z+XqK6dJmDJHrrmcRx99FN3UmBibYGF+AdNIiDwnTVCpIknp0jU9z8d1PFIpEcGg6SpBGODYNhB2D8hBEOA4DhJCrqgbBqHviOiIerWjzpGxrBrZbLYjyxXzynYhOJVK4VouQeDjeT5IIZqU+IWOx/acIUtyZ58m/K3bnrNUysSybJaXlygUhrGcOrIiI4fg2h6KJgh+lUqJMBLE2Uw28wuktP/R+KUwnPf2KszPzwut6PgYmmagqULXms0JNF2tJmQ22/keYRCJ9o8kERGKf0M3jTQMIQyF1CHotJ18X+D0qpV6l4Neq9Wo1+vUaoLLvv1nWwK0Pek5no/n+8iKjKbrpNIpUtkM9s9N/oZhiBh7VCRkohAkZISXROr4UiCMfBzHI5lOYzkWfuijqSqOIygmIEyLkSSBKhEGAY4rCEqO7yKpMmEkMHt1x6JULSOpMrlMFsfxqdXqJNJJCoURZBRkVVTNvMhHlhWQhFGoWqtSLJZYXV2luLbCY489xqOPPsyJE09TKq9hOxaKpKJIMjISiiR38aLbWFtCgU2UFAVZVZA7B5pUKvVzMgAVWVbRFA3kCD8K8UMIIglklSCSsF0X2/Xxw1B8bhnKlTVK5SK6qiJHMouL8ywszJFMpvFsl+paCcd2GRkd5uCBQ4yPj/P448fEZ0Tu6KgFilKsQxHF4gprpSLVag3X9XBdj1yuQDabR9dMZEnFtlxyhazILFFkDh44wKGD02TTKeZm5whdn/xwgWJpjaXlFRzHw3YcbMvFtTwCNyDwPIIoRO4sFLIsk01nmJ2dJQw8Ilm0vnXdwNRNAQcwkxhmAsUwKdds3DBA1RXqNZu5pQUCz+fQ9DT54WEOTk9z20fvYGX+NQ5MXcmX7/kit3/8Jib3q4yMRfzW+36dcqXI//p7f8xHPvxFdCXJHbfdTr5QYO/+/Tz62DEWV1Z55sQz5DJ5hvMFSqUKcgRHj97J3KlZvvv4Y9xz913cfMN1fOC9v4UqS3zgfe/l4gsvYHx0DKteo1qpc2D/fq686hoOTO3n4PRhgkAil87zoQ99lNFCgVtvuodP3vYQST3Pp++6nc/822tIZIr89nsm+U//51/ztS99DbuocfJ4hcOXH2ZqcpxPffrTFFdXmZ6eJpIivEAkDgvsY5qpqalO8rdC4EfIMp1nTO6gTgOijoQkCAJqloXvuMLv5AfU63UWlpdYXl7GDwLx88hHVgROUxxsbIIwIAzozC8Rnht2ENoOvr+NO1VIJFIib0Ghi04Vk3kkkmo9h8AP0XVhIrXtOqlUEtMUvqMolLroTgmZMPSpVMpAiGnqRJHIQvrV+OcdzWYT0zTJ5TPUazaVqtgo+4FPFErIsooi0d04hlHQkTHprHU21b4XsLy8jCzLTE6NA1IXCasoEsViCUXWuoWoVCqFrstdROe2/Gr7PttOGhYFMwXXqeP7gZBIBaKoNjIygmkkKRaLXRRsGArj6TZmV5ZkDhw4gG1ZZDI5FhbnhfRHi5g5NYOmaRw4cJB6zWFkOA+RkHCJw4bOU08/RaFQQJIkhoeHqVQq5HIiXT2VSlEsFjFNk9lTs4gTWci+fXvxg4hTM7Nkh/Nksnnqtk2tZqHpKpIknuFUMiM2SXqCpeUFpqb2Ydt11kqrHV+VMMpWKpWO3Fbvati3v6dEIkEqlSabFxv7erVGLpcjiiLq9TqWVUNXhTcsDCPhBUUgRwHS6WQHu+p2wkd1kZKdzrCwsICCOEQtLS5z9dVXs7KygmEkRCHD8xmfmuSmW27G90Lm5maZmBhnYWGBIAgE7MILKZXWqNdq3PHxT7C0JA5jIktM72SQLROGr+cMmYkMgSs6sMVikYRpMDE+ju8LuZlVqwtpn20TSsKALw5XInfEdgXy1+ugnY89cRxJlrn7rrvZN70Pu15HJqJWLZM2heTW9x2QJDzXRzNkJFXC84Wn1Pfg0OGrOHBgP1ddcTkPP/Jtjh69DS/wKFVsapWQUzNLKLLJ8vIS999/Lx//5K3cftuHuO6GGxgZm+LhBx/kqquu5NTMSabGJwG4/tqrcRyP4fFJpiYnkKKA6264nr37DlCuVfnspz/HzMwMIRKJhMFVV1zJVVddxf59V/DQIw9z7ZGryWSHmZmZQ9EDHn30ES6fvgIp1EinNIq1WaamJykVQw7tvYlD+4/wyIPHMOQATbe48fpJ3n/TNFGk40eTlKslzJQpoguiiFQqiW1bFIslRkZF+nypVGJpaYlisUQURoRRQKVSQVVlglD4J2VFIpvNoygKS0tLeK5HtiOp8sMAp26h6Bq5XE6gdn2H4eFhFheXURSFtWKRsbEJajUL3/cII590KovrBAIVLEUYegJJEj7E7WdjG5ubSBgUi0XSmQymaaIoYr6pVCoosoJhmGiqhm276JrZkWjZRCHi3pYFktg0TXI54Rn9p45fCtnV3XffffSaa64Rp7cWxGItzp07x+DgAPY5kfyrqioL84uErSbnnXceQ0NDqL19xCVw1jd58cUXaTQa5PN5dF0nFhMJq0NDg5ytnyOdSaH0KtjnLPr1XswhE9cVciGxiRHehVgsRhAEbGxssLm5STqdJpFIELgegwMGQ0kRSliv1/E2NzEGB4gRxzAMQa+wLBxng/4+kwHd5LycqIK89tppmq0GW16A1qeLB97ziEs9XDzyBl57rU6jsclW2KLZ8ZcgRXjuJkOJnTRbAfWzdRpbTczBIVrtELlHptVsI8d7qJ85y8bmBl4rwvcipHgPf/+975LYuYN3Xf0OXi2fZuotb+bVtZdYt2wSiZ0MDJj09MRwXSExu+CCi6jXa/T09HBBXqRzr64W6evr48yZM/T09HDhhReyubmJbdsoisKuXbsol8tIsoSmqGxsbLBr1y7CMKRarQr9c6cKpcgS7Ri0Yy3kuNAJ9/TEiCLo6YmjqAq9ikYYhdROn0GOq9COGBwYotHcImg1MQYMdu/YRSOK6FV7GLvoDWxtOPztsb/nxIkTXHbZZaTTaZ577jli8TZP/sPTFC4scKZeJS7JTIyP89prdSYnx+nr62dry+fFF19k9+7dlMtlUqkUL7zwApvOBlqvQjJp0quobG1tcO7cBlEE9fU6z/1smYsuegNRW2L4ogvpiWu8+uprpNMpgrDFWdti/1veiu06NBs+itLDsz+eI50e4o1jo6QSCYKmRxSDdhjhbzVxGz6RJNNqw8RbLuGn88+S3H0eQeiTzxUYG7uEZ5cW+Pa3v8Xiwhx/ed/93PqRf0XQavGxj/4eL724xmc/9xmOHPlNPvOZz9BoNMhlskxOXsKP5+Z4xzveQXr3ToorRd44cjF/9Md/wtjoCM8+u8yWHxBGLd70hlGWlhYxBwe44T3v4Utfugff9zi4fz8feN9vIbcjLn3rJfT3KXzrr7/D+cNpvnD3F/j0nf+a/3DfVym9tMqrr5T5ywf+inu/ci+nZn/EXZ//Pzh44CqefPIfuOyyN3LFO9/O+fkBUhkFq1ahVHkNu9bE9UOavQo/fW6J0ZGLeMvUWzGGBvnHmR+TSmbZsWMH3qZPsxFw+vQZXim/jGO7SHIcVdGona5TP1vD3RDmWykusfZSiZmZGd72lreyK5vphJI26e3tZffu3ZimycrKCi+88AIX5M8nbIaomkLQCHn22ecpXDCCLEvY1ib9/TrZ7HlIkszm5joDA0Ps2rWDtTWxqezr66EdxWm1WsRisS5QQe3tQYrFiMd7uOCCC+jt6+WcZeF5m6ysrvBS8WUGzF4afpMoini5XMQcHOpqbKvVKgCXXXYZ3/nOd34lu/pnHH/yJ39ydHp6mmq1zJ7sHqxzFnt259h0t2i342iaClKcaq2Kt7nJ+efvoa+vn7NnT3OmZtFqtTCMBKOjI4RRi5defImRiy6kt7e3AwVxGRwUQJTBwR1UqzX6evtJ7ejn+eLL6P0DAMRicaTOIWdnagdBIyQuSwxqOmfqFoo2wICuETQ8NlyXdruFbW+iDmi0WxGqIhH4DnLPAO1WHG/Lxd0Uvo4g3CJotNiRNOnXNQa0Jj/56Uu8+c1voVR6hXYMpN4+As+jR+mlFYYQgZnYgee7Ag0dxmmFLYwhDXfTJdiKUHtVlB6FC/8v9t4+uo37PvP9YDAYDAaDwQtBEAQpCKJoiqIoWpYtWZYVv8iOoyS2k7humqa5aZvmZpOm6WbT3tzsbpo4L23SbLpJkzRJ27RJmyZNm5c6ruPYjuMXWZZlWZZlhaYpiqIoEgRBEATBwWAwGAwG948Bmd1z9pzb+8fdtufkdw7P4R88BAkMfpjf93mezzOUYcOsEQv18MK552m7Dvv3H2B1rYjfJ1JcXMZpO2hqmJ5UH2urZTo4CILEammVRCJOur+XUqmIWW+wvLxE7qq9TJ57keGrvMNPX18fhmEwNTXF2NgYpVLJu7/AZXpyilAoBD6XbDZLo9HgZz/7GUO5Hd2m6DAb6zV8+OgIDj78BIN+6vU6qrqpNrkEgwGWl1cQkEj0RKlWqsTiMVZXSgSCQa699louvDJFSNFIZ9PIrp9PfPwTFFeX2blzF729CZaWlnj55Ze59ebbOHnyGdaqJW6/7dWsrVdotxx03WTv3j2cPv0cjgOmqWOZNmFVoV6vI8gKvT09OO0mM7MzaEqYpt1gvWoQC8cYGOij2bIYGR3iypVFrEYTRY0iSyptWuzZtZu19So4LaIhmUT/IEuFJUKSQCKqEQqFCIVEDN0gIAfxu9Bsu/h9IEsxlstl6vUK0UiCYmmFN7zpzYRFmU9+4g+4ajjLnvFRououWq06B6/fw9ziFIPZbSwVC+wa3sWB/dfxxc9/hWxuiERM43fe/16uXLwhDqxPAAAgAElEQVTAJz7xKY69+mZ2jwzx2juOcvbsFAE1TmWjyq6hbcxOv4QWTfD617+ee9/yZvbv3891112Hrq/ywpkXsW2TYrHEr7z5t9iWGWNdX+blyROM734N19yQZL3k0JfK8MrFVwgySLg3wsc/8Qe8+vZbuPW2CbYPJrn26j0UFhZRfBHatkyz1ia2Lcql0mUG0zkvI6iEmbh6L+eefwlBDBCUBSRRYnW1TLVWRd9YJ6xoDA5m2JG7ilqthiC6VNd1/KKPvr4+VpZWSPb2US6Xuea6A5RWVrjqqp2srZZpe2NsstksJ06cICSHaTQapDNJrswvMTCwjWQyRqNR5/LleW67/ShrqxuU1tZpWBUURcPna7O6usbAwCDr6+uIokyr1SAQkKk1aqR6kqxXylu9OJsZSbPhDVRC4QDbt+9gavpl3IaDFAzh0CK3fQflcpm2C1ddtRsfbYJBhccee/Tfj+3KdTtbdApJ7gaTFQXLtnFdT2K2LZfMYNojQG0FwDz7lFfK5UmfqqJ15VO5K3d58q0iqzi264W3XRFZ9tSISkXfYiNvkjG2CoK6f5NlWThdJcIyDBzLQnCBLp3CxUVARpZVBEFCxJuYVytlNFVBcPHwfC7YrongSmBbWKaJLIrIstyV2mNeKNx1EQUBWQRbEEgmYxi6jGvaXsje9QoH3a1GckASsQSR/RP7MS2T+blZJvbv5/iTj3kytCzztS9/jUNHDpFIpyiXyiiiutViuolj8yhieFYsGQxDp1r9eai8Wq1uYRBF0ZvGZbNZbNvm9Lmz2LbNxMQEjgBqIuYRt3C8SZKkgON2qR3eEgQJUXS69BZnK8wPLoriBa0UVcWuukiihKhIWK6NqiqkUklEWaQwV/EQh5JMTJGRcJEFmD53HlOvMnX2HENDQ8iKwsOPPk6lWmJkdIhsNoMowtmzp5EkT0GzHYtkKkEykUCRZcoVvRs8lLbgBqZpIogKtusyPTNNIqFhmAau6HHCKnqV3NAQx08+ycSYN91KZ9LEkmpXbTG2SsdUWfZQkE6XtS5IuIJEuWiQTmdxHRtJSXDkyC2cOHWK+bkCQ9kclmGiJkU+/Ml3MjM5z/d+8DDTUwWq5TSKpjI6muKhh7/NsbvHOXf+cSDNM889z3/50AcRgLm5Of7zRz/G9ddcy/0PPcjCwgLf/vY/MjO/QGF+hscfP04imWLf/utIJpM8+MAP2Fec4PCRgyRjCX7w4EOoskhMUbj9jls4fPgw5XKZWEztqo8yI3cPoygix8+c4C++dh9DuWEee/gkhw8eQnTsLoBQRxQcHEXHdkE3Kpimjm7bHD95kqHhEYqFAiNDQ2hKClXRePzJxxgazm5N/1zXxbI96o9hdFGAkrg1ca1UKiAKVCsVDPPnErUsSdB9/0mSQCGfR5IkMuksiiSTG8xslYSpqoQsSlTKZSrVKprmqSOFghc83iwQ3KTKeX0i8hYFbvMgkc/PU60aeBQbFU2LYVkmmprY+tmYkcCjIyUBdyv3cfbs6f9te/Iv1v96bRbjbe7ZbtciZegmsYSMbbmUqwVURcP7kPCwqS7uz6EcioKmKRRKRRRV6e79ItVqlaGhHHNzcx4iU+zuhYJnMdrskNgMhYvizy1F3rDKey+oiopuWlt2IUHw9nMXrwncY/9b3cmmhiw5qJpMpaIjdktgNx9fVVUqetWbwGezzM3MeA3mEjiW21V2PRUmNzTEuXMlRBE0NeWp+G63VLELGHFc7/t0apCFOa8NOZVOcf7cWQ4dOcxjDz/GwcOHOX/uNNnhIRbm8oiCV6C4CZGJxWJdAE2SdFonX5hH1w1SKS/EvWlhyefzW+/NTauSZdvolocC3gy1W5ZFMpnsok5lT80EXEH0bpC2MMGeirT53Jvd3xOLxTAt438AywhUq1WOHDkCCKRSCY/0JWrMzc4yNDpCJuMVFG/uT4lEimwux+zsJK4ocOb0WdKpJJWyTjabZWZmxlPGNQVRlLeQxlQqKKmk55rohpKz2Swzs/MYlklMVHFcGM7mKBZLlAomhXIJTVJxsZidmUFWFaqGQzqTZuLgYb7z7e94n9+qTKlkIiAiyRKuK3g9Y4KIhYCh6+hGCUnSKFfKyEoCQZA4fvI4XvmzR2wbGkkxOZlnfqFKTEtw9513c/zJs+jVEg89/G3e+e43c+78SWRF4Z3v+AAApWKRdDrNuXPnuP3o7Vx38Ajfuf9hHMfh2C0HkUVw8e4/VosrJJNJHn74Qd58772US1WSsQT5YolCscLwaILpWY1CuUoqBrGYyujYCGfOTKLJGooieiFxWePVr74VCYXDB8f4wf0Pks1mEHA8i5BoY7sygqwyNTnF2Pg4pVKZoeERzpz5JB/84Ac5fvxxXODEk8c5euftlLqN35ugA0mSsC27ez3+XO2enp5maGjIU6Bcl3w+731GyV5w3LP52lvoXE1NoFfzVKsVhoeHu6+9gaqoFNwKlWqVZLJbrtlV6jb3jc3vXdclkdAwq+bWZ9SmS2Nzv/EURBvLMr2ukYyCaVlUy1VSx7rXdTLF7Ow0mqry/8F19W9D+fjSF79w32tuezXhkILoE+k4HbSwwtLCMpGwyvpahZZjEVIUxICfZrOJbdtkMoMMDgyyvFwkFot79orVFdyOj2g0RrNpd5WNHk6dfoalQh5N1TAtA7PueUN9Pj/VahXXdenp6SEQCLC0tERfX9/WZu/z+YhHY7QaTdb0GqIsEU1EkYIytmkjBRU6nY6XBZFVEj0pOoh0hA6mZRHvSWI328jhIFo0jiQHMRoW61WdPXuvplKpEgqFESXYqG7Q6V6otm3Rci1OnnoJq92kt78PvxQgqIQIhIJYjk1H8BHWIvilAAQkfvTI41y6fImDNxzm0oWLHL3lKIFggLGxCW697TbOnD7F5QuzJPqSNG2LjusjGAwSjUZZX19HVVUCgQDrG6sE/EESiSStTpudVw0TiWpMTb+CIPpx3Da208KlwwsvnqVcWuW6a/aT6UvjNG0aRh0lKNMXixEJKThWE8H1igwdq4VVN7EbFpJfJKyEaDdbNIw6QdGPEpSxDIvK2hqttsva2hqFQoGB/gECoo+aWcNs1KkbTRYXlhgfHWOpVGR41whnXjyH2bTZMDc4dufr6MukqTcbSIpMQJYoLpcY3j5EYbHAhQuvcPaFF/jle++lur7OxNg+Zi9Ms3dsD4lYHKthUtvYQFVC+AKAzyUWTRAMh3nf+95HJBLhrmN3kZ9fYEM36Ev2YtVNtmW3c3n2EuO7R3nhhZf4D+/5DyQSPZiVda4ayuG2bKymhc/1E4nE8PllwEdbCGA5bToth8WlAnJYYaW0yhvufiOP/OQRpicvMDiQJtmTQPD50CL9TJ2zyA3t48D1uxjYLvG2//M6hMA8q2vz5AvzuM1thPy7+dgnP4EsSzz8owfZtWsXExNj2IbFG++5h3/+4QN86Ut/zkc//Af86q+9hde+8Ze485fezFJpjbZPZGWtjOCTWFmrMrJzhNmFRd76y29mfP8uJq4Z5cLUJBvGOpmBHvxim1uPHmFbJkWzaTP58nle86o9rF++yO+/9z/Sl+7n8ccfI54ZIBCUaLba1Dtt6n6Bhi1wZWGZoZ3DrK+Wee2xuygsL7JnbIJ/uv+HnDtzlqeffooD1x9kpbSM60IwGAA8asfCwgKdTqdrI6ljGDVarRYjQ14J5YZRY3R4hKXlAjgOdcuiWqkQUVS2b8+x46odaFoPl6/MY5hNRkZ3s7JaZmOjhiSrmGaTmdlZ+vs9Za/dbtNqtYhEQzhtm47ro9lsEomGCCshms02jmPj8wVotZu0nBZWw0JRVOJxT9loNIyuzcPGddu02zY7dmzn1OlTdFx3S8GNRDSazQbPPvvcL5SPf8X1hS984b5t2/pJJFJsbKzhui4rK+uk+3sJ+EO0Ow6JuAa4BCQJv+DZPkPBMH6/iCwr3WGbg2k12TawnaX8AuXyCplMhnJlheJyiUwmQ9vt0Gn7kEMi1fIa60aNvtRmRsihXC7R399P221h1EzEgB/aLguX50BW6E1EcQWBVqNFPBElEFKwGg0EXwc5EMTv99Pu+AipARLxJH6/z5v4C+BDBKGNLIsszp4nltxOvVGnfyADPhef4+K6ntoi4GDUDa7ky+wcyqHrNQIBP4GAH0XxKErgEomEPR+6DcXiKmFVJBwJUVpepr8/g6HrDA+P8A/f/ntuuvkmjj/1FNfffAOV0hrtFsihIGpY49KlV/AJPlQ1jKwEkCUNywpgWhtk+lNbhYC1Wg1BEPD5fNRq3l6gVzeIJ3vw4yMQCLCxsYEsy/h8PnRdZ2BggEAggNPuYOgGvu4BMh6Pd0PnK8R7IuzI7WD24hwrKyWSPdtQwiKu0MKqGVx38BBW08ZybEzLIRaNEg5HuHrvGDVdZyC7DUmSCAR9NK0WfX19PPLIj9m9exfXXHMt/3T/j1ivrFNZW2V87ziXLs1yzTXXEI/30NfbT9P2HBmjo6Ps3jXCUz99mmbLoD+TprpRp2na7Ng5xMGDB3nDXXfxn//rf+XgwV08/cQZ9u6bYGGxRMd12LYjS1SLsHDpItfsm8CsW7x49kXUWJzi8iIIHVQlQtXQufrqaykWlmm1XRBcRET8gQDJbUkkQWTftQe4/sANfOB9v09I8HPzLTfwwpmnicRkfvZCibGrx+l0TCr6Ou/89bfyxPET7Nw5zMM/fojfePs7OHXqOW551S3sv/4A1157kF97269SWSshuvCbv/Uumg2Lf3jgUT758Y+gRCS+/GdfJKpEmbo8T29PD3azjmu5dDoukuinUq3gFwT+6Sc/4eabj/Ebv/kW7rj1VVTWZKTIErRi/NVf/gN9yRjPPPUSd7z+ej79Rx/j+9//ETcfvRVRDPGa2+9A31jDaTu0fDVEUaLVEoj1D9IfT/LS+Z+xbWAb/X29vHJhjtOnTnp7e0Lj6muuZXLmOSRBI5VKUG80aDsu9foGgUDQI1X6wLZbpFMDuE6bRrPB4vIyQ9tyHH/2OL09SUKRGJVVjwQajoSomzXclkiHNiCSTvdx6tQpotEIum4wNrab559/nvGJceh0cFyLjWqdnp4EtZpnW6xulPH5fAT8QWRVoa6vI8sqzVYdOah0DygukihiO238Pj/+YIAOLhIC8USCkBIkny/iOgKj42O8cPYFBNdH221x/Pi/rAj330Tmo+26FMslr++iUqRUKVI1TLSEV94kqwqKqhKLxdBiKuB1fczOzjA5OYUkyRhmdStolkzGqFQqXayYh1S1TIdkIu2VsSgaqqYiSiLZ3CC5XI50Or0VGB8fH/ceq5vh2JyYuKJAOhEjpiUQu1MAUZa2wrCWZVGpVrYCTqlkGtfxUJ22Y2JZDo5jAQ44NookU6lUvCyLoVMslLEsx8uomDpVw8AwHA4fOsjY6ASKrCEgUSnrlIoVZElFllRwPdyfbZjcffcx7rjjGAtz86RTaU6fOcP5c5M89NADTE6eI5XOcOTo7R4qUZC3UIWWZW35/cDzoNu20w2yuZi64YXTvL0HUzdQJJl0MkVCizE8OrI1ldvEuQmCgCtKiIqKICsgyTgI2LjdEitA9oJvsiYjx2R0y2ahWES3LBQthqLIqKrMUDbL8MgIiWSa/FwBRdbIDmbIZDI89uTjHgFDVVFVtauAxTh18gzHnzyJ6wicPXOeM6fPIUsCDuAKAlosyXUHDzM7t4Bh2szMzYIoUdENCqUyhmmSHc6RzmaQJZVELIWmaaQSCR588EEmJyfJFxawHG+aWSgWmZubp1wsoqkqZd3gzruPYeg65UIRXTcolyuoqoasqCCKGJaJbZs4uAiuN92zcRgczFCulsnlcszPz/HwQ48xPjFBNpdjoZBH7YISjh07hOBazM7NUS4bCE6Ge9742/zuBz7MQw8+zvh4jtmZU/T1RNixbYB7772XbDpDLJbkjz77GURRwLBMbrnpENdefw0+n4+vfvkv0CtVTp48zcS+fbzj7e9kdGzCUwhUjdzQMPc/9BCzswtUjSqy5uUoNC1JqVRiemqGXG4I27HYf2g/IjHuvvetvP8D7yefn2P/xBiCa+K4Jo7o4goygpsASWNsdBhZkZEVBcMyOHXmHJ/85Mc5ceIkhmMwOT3NmbOncWwv5D0/n6dcqnjTWEHqTm68UGoqnUKWZFzB8zY7jsPUzDSapnkZJVFEkmWqhs5CPs/01DT5wnx3MmShV3VURelmpCqYlkEmk+niEz1cajY7SLViICBhGF4wUBQ8FXSzO8hxPfnadYRuaWWlqzDa3TwUyIqEqnkbvyRLpFNpUumUh940dRCcrXK3X6x/vdXpeO3dkuxlLGKxZDfwqXRfT7Eb7gbXAdO0u8qGB7awbRvX8XIDsViM+fl89zc7W9mDTMbbb8DZUk/S6cxWrnFzSrmZb9xUKgzD2ApcS7jenms7WK6N0z0sbGY8NlGxlUoVTU1QKhVB8OzEoighyd0Gbwcsy8GwPcS7K4Bteq4AUQLB8ZCmAgLJZBpJVn+ed+lOV//HtekkUDWvfNG2bDRFoVIpY5kmoiAwOjbKmdNnOXjw8BYMZvN/3PxKJBIeYrgLoSgVCluQGMuytu4FTNMkl8ttBfMz2UGy6cz/pBiZpumFsrvKxuZ0eRPFDo6H+O3mVBzb6eLUnS3XgCh64V0tESOTGWRychK9UmVsbARDN7r3K7Nb9xblcrk7lfae1021NJ/PeyVy3cceHh7e6k2ZnJzcAhGo3fshSZIQRLHbZ2RtQV42p9im7T2/1bJOobjAQqGELAkkUkliqorlmCSSadKZQfLzC1R1HVESUGQFFxHdrOJudZVtKrhehUAqlkTXdaqmp/LMzs4yMjTE+L59zE1PMZQbRBBEDh08zPzCAslMkpnZKQDe8pa3kIglSagJTp86y4P3/5jjJ08iIDA1PcU3vvEN7j72OkzD4NyZM+SLRWKqypMnTnHm7Fn++m//mjvvuZdKscDBg/sZGswyOT3F5PmzaJqC49jMzs6gaSrz87PolSqz8wvEYhrT0+dwcRkbHWc+X0BWZTKxGJnBFB//+Mf5s6/8Ja4DhYp3vVuOg+0AooiDjavrIHggo5tuOco/fu9+ysU85XKVffv2cf7cFIODGTLpdFd1kxAFCVnxum4kyVPmRVFCVkSqlQpat4dms5xTRNgqCdzMLMmy3KW3il0l0KsW0DSNSrUCeMAKSZIYGsqiGx6gQBQFdMOzyTuuBQhbEINqtew9muj+vJBU9JxB3vvfuw4FwSWdSW5loVNpL8OVSiewTAtVUZBk6X/Cgf+/rX8TysdnP/vf75uYuGbrROj3h7pc7yyWaRNPJAkGQswvFrFtl6gWQVElOth03BYbtQ3CoSiRSAjdqlKrbtDu1DHWSzSbG5RXlrnx+hvwuQGqa1UEXwehY2Nt6CwuLOFaTap6lcJKHqthsrKy7t1YlXR6YyqBjsDslSv4AyrheArLsSmtrFAza3TaPhxHQFFkBGRajkXfQIrLly6yWl4gv7TI+rpJf3+SeqOAteGjrtfQWys4wIWLF5BEP0GfQL1hkU7nCClxAnKSUDBMPNxLWa+xXC5SWi7S6fjo7U0RDivUal4GodMBUQwgin5eeOYnlPOLTOzZzUJ+kVtuvplDN92EXquxulZBjan89PGfEgi0EPwtJL+E5IvToUEymiIkBVitXsGqC6xV1lEUkVhCpabrtFpNcrkssqzwh3/4h+zbt4+vf/2bHDp0mIW5RdL9CRzLwqWN3+9jfX2NeqOG07Zpuy3spmdJCYb8BOUAckim3fLRatgEAhIBn4TjNGlaJsl4HF1fpW6s0bRsgqqfpq1zZeEy6YF+lgtXOPHscYJCgGbbRA1HWVlZ6crsFu12i9XVFcbHxxEEH/G41+2xXtUJBYPsHMmhhjV6euJ0Oi579oxx5cocw8NDTM9MIYUU2vhwOz6qaxtEIiEGc4OcOv0s4ZDM97/7fRr1Oieee55oLM6OnSMERZFwOMTK0jLBkEosrvH4E0/zox//hGdeeJEjt9/Bcy+epS10qFsWBNr4RAWhLSEGg8iiSCgko8h+Vo01brj2JnbtPMB///Sf8Cv33MXlKy9QKc1w4PoJGrpOWIsyNfsivb0xMuntvOs33sW+/Yd4+aVX+PznPsen/vCj/POPv8/H/vijfPFLX+G//fFneNe7P8jffPMvuemmm/jpMy9hui5Lxcvs2jPKr7/1LpK9EZYKZWZmp5m/eIG//cbX+dMvfo7SSpGPfPSjnHvxeaZmXqFa0zl44Aa++90fsm3wCD3RLJfy5xke2sdnv/hXRBJJAorEjqHtvHzhCpOT82zfPsybfuku1irLnH9lkrrZxCcEUaQgyWSMWKQHvWqxnM/z3vf8Dn/6+c8jYfPao0d521vfSDE/y//x1jeBa9LTl6GnJ0VvbxzX16KytsS2bQME/QHaHS8Y7MNPWEti1F2isQTbB7PoNYNAwE+n0yEWU0mlkrTadRpmnVBIQwnJZAZThBWZpeIc4XCIRCJBRIviTW8VLrzyM0aGrkIS/UxNz9DX10dfXx9+v4egBs+G4rg2+Bx8HRGfD3w+gR07diDLGqZpIAgirVYLy7IRWmA7Ls1mi6X8Msn0IHarAa6fmKYRDkfpNBs8cfzpXygf/4rrT/7ks/cdPHiAnkSCfL7Irl07SSYz5BcKBIIBOu0OheUCfekEAcnParlEq9UkJMucOXOecEhlYKCfpdIKSS2J47pslPOksllWi0UOXHeAK1eWabttwCUY9IZA4XCc+kYVQhJ+MUgkpFLd0LFtix4tSrxXZfFKgfXaKpnBHD6fgNO0qK5v0KZFo+Zgt5terqnZIb80y++897389rvfxxvveT0nT55GCsiM7x1mfv4SIX8Yn9gBwaRp2CwVVhkd2UWpWGKpWELVwoQkhYbdwKrXUOUQoViamllhpbBIX18G8OHzuRhGDVEMEgoprK9XsewW/o5LKBSi1WpSqRpkMgP0plLMXrrI9oEBcjt38shjD5HbmUXs+JH9Gm67hdFcYXhwD70DMU49exI1HMMfgIX8GtlcGl2vYTct0mmPrvXOd76DN73pl7l8+RLHjh3jmWdOcuDQQV55aZJYIo6iKFy4cIGxsTHy+TwDAwOYpkmr2UaUAsiKD78QJCSHqdXX6Un00WjU8fl95BevsGPHTs69eI7s9gEURWF4eJiIqvDC2TOsr9fRVI1StURUU/nOd75HNNaDXqsSDAaRgwrtdpuhoSEuXbpIX1+atbV1SsVlDtxwM0eP3ITjuDz04weZn5/n1ltv5rGfPsb6eoVIJML6eg2fr0Otuk5POk2xtEJv1GtBv3hljr7eFF/5yl9y9xvvRglCNJrgqaeeZmAwR196gLnZacJSAH9QplGvcWH2AgN9g/zzP/8zot/HyO5Rzr7wPHfecQ8vTr2MnzYIflxcfEB+tYQPH7fecht//uWv8KMHfszRV93A5cUZdu7cxmqphNno4HQ6aHEZuwmCHKWu23z4Ix+jf0Bj5/ZeWm2Tj33y49x5172cef4C/9f//UH++FMfZvrCBb7/jz/g0A378ft9VCpNenujXL5ymZX1Dd737vfw6c9+Bjkg05Pu4cZrD/Orb/klzp47h76xwc7cTkZ3j9Cwm9xzz1v5iy9/jWPHXsOGMcf5s8v84P6/Z/7KPNfvv4Gv/NmfEhJFjtx+GwuLlzn5/LO89NIUE9dcT7vTQVMiaLEkltVgcXGaYn6Vz/zxZ3jqqeM8+dij3Pyqo1y7fy9PPvUUR48e5eSJpxk7cg2z5y+RSGjohk7A7yeZTCOIUDMtHLtBf6afleUaTrtDPBEhHJLR13US6R5WV0r0xhM4/g76epX1yjrxeIyenh46HcFrFw9I7NxxFaVSiXanhaqoqJrC4pV54rE4nY6Pufkr9MZjXDUyyvLSKtWNddLpAVp2i7WNVUSfgBqOYpom4XAYRVEpl9fxCx743205uIJLKKjQAZotm5bTxG463HzbrVycukhvby+CKBAQ/f9i1O6/icPHZz7zmfsOH76RhYUF4vEomhZDVbsTjFaH9eoqNaPOnrEx+gez4LqUVgr46JBIJAhIQVaKq5RKZUZ2j5PbmaXTdIgm4zSaNlJIZm21RlhRMQwDv+jH5xcR/B0E1+/hLkMi8XgP4Pnvtm3bRqq3h5dfeZlTzx7ntXe9gYAcQhA7SHj9FAJ+stksS0uLSAioSoi6Vee5Z0+QSCfpS2kMbtuG6FOorJdRQ0EkSUMJxdGbFWJRr/lbVSK4Qoe4qrG0tIyDjwd++CPueM1rqVRXsZsmaiiCGovhOjatlk0gEEISZfC1MYwa0CEY8NOb0OjtTRCQRDRN49xLP6OwtIIckrj55ps59dyzvO/d76Uv3cvPfvYyiVgvO3du57ajR1DCQfC5rJeX6AhBmvUmI2M7EZw2uewAgiQwd2keRVH45jf/jscee4xkMkEg4CfVl2RteQWfFAChQ3mtTDwRx3Ug2dOH0/JCwnv27iIcClMz1nEcFzWkIocVjI01Wk3HUzoiKsvFZQYGBrCsFrt27aLdcZmemmPH0A4Cfol0OkNUjWLUTNK9A7Q7Nq7rkEoniUVjWx0t9bqB43QolQt0XB+C4BJUJBobDWzHot12KJdXmZ+/TCwRQ4t6atfMhVdwWx164gmmZ6cwrRqNWoODB69B8ItM7N1HPB6nL9nD8kqBeEQlGPQ81P0DGfbuvZpKeZXlxRVS/X3kBgcJKgotHHw+kU6njaLEmZ2aRg5JbBvI0Gq1KBWX0I06ff29rJV1Tvz0afbs3k25UkDVfETCCvUNi3ZHwunI2M0WQSXEr7zpzbzvP76XO19zK6l0lH37Rhkd28WB/Tdw4cIM//C97/HwI49ww8Hr+NKXP8MffPhjdNptREHk6C2vItWzHV038PslNvR1tqXT3HX3G2ardRgAACAASURBVImoKjceOMSusZ089uAjnH/lPFcNDXP13gkCIYm1is6Owb1cs+9q7v/RP5LN7OS6fTcyM7NAtbLG/msPMbs4h6AGUXqC/NXffI3PfObLfPGrX8AvChSX8/T2hAkEYKNSYnz/jVx77fV84D+9n9G9Y5RWigREEbNVoyfTx2q5xOq6SXZwiMWFJRYWl7EaTUKhMEbVwKFDKBCmtzdDRIvRE0tSa+gUlos0nSYb9Rq9ibiHgLZM2m0f/el+Eok+DwDhD7BeKaHrGyghlXBEQ/QHqNfXCYUkNjY2yG0b6kIamiS0HuSAxGq5Qt00qNVqWxOrmr5B27VRw3GCQRlBENnYMKhUSrRaLRQ1hhaP4OtALKrh9/kIhcNEo1F27xrh0uV5GnWTvlQPddOgQ4cnnvyXSdq/WP//rE996lP3/affex/42rxw9nmSPf3YtoUaieDrBKjVN/D5/IzsymFa6zjNJlazjhpOsrK8hprsIRoPs7JcQBRFtmfTgIOiqihyhOXldVaXymS3D1BaW0aLREBwCDhBVtbWCKsaTsclkUhS15u0LAvLqhCVI4TCIs889xLXHjiAZdaIxiO02y71pkUoGKQ3nuHFF1/ArFsMZvu4dGWKF069TCQhIPojHDhwCFlaR6/XsIwwvekoa/qLLFVkrrlq2CvBXavSdHzEYr00my0yvUk+96d/wrt+6x2cm75Arb5Bq2Fw6NCr0PUaL754mp7eBG1bwGm2CIZCqIo3rEul0pybmiEoBVDDYUKhELqus75W5q433MXffOsb+Dsu0VCCy/NrZAeTNFoXWSlGOHLzGD984EGGtl9LJA5ty2XX+Ahnzp7D3DDJbs+xtLRIrebhc3t7e2k2HJKpCPOLV1D8QXYM70AUAywvLzMyMsKzzz5LIBAgmUxSWi0SCooEAxotp+kpHAGRVCpNs1ZnZW2FwW2DxKIxrIaDX4SV1QLbc9v56lf/nF279lCv18ikB8nmBrlycZFETwK9prNv3wSvvPIyyWQv6XSSQqFItbrBjTfeTKvp0JPsQRL9nDrxDBP796EoMj09PVxevMJjjz3GXXfe2c0OySwtrvLS+Re447W388QTP2X3rt3EY3EEUabtuPT0xAkEZJSgS2ZbjvkrS4ztGkcLR4jFVWRF5fjx42wbyHiOBPz4BAHLqqL4A9x09CgtuwWtIHV9A0EEx7EJhny4ssjEjhsZHd7Ht775dbLpGNFel1CgTb3RQhTjiD6VWErg5amLqNEEe8fG+eu/+GuS6RhDuW24rSaNVpP+9ABje67mA79/H2ooitqjoym9vOmXfpl//P5fcer0E4zlbmBhaZptO3K8cmGZX37j6zl58nnCqkarZdI0mpT0JXZu20mptEw4qnH2pRl279nLk4++wMc/8jn++lsf4NJCidff8SucOvUz3vM7b+adv/l7vPrWV5HdlubS7CUuXZzku9/7IY889QRaTMPXbtOhTau+ge0XSPTv5OC+azlz6jTf+ru/4w1vvJOZl8/TqDdI98eo1jbojfew7gsxENPIL15GDHSQAyEq62tcNbybCxdnaDVhR/YqXjx3kdHRnayUr3i2J7FDor+Pml4hEJBomjUSiSSW1cRxbCrVEr2pBPF4nHy+SFBSqJt1tme3UygucNVVQ5x/aYrBwe2eehfwE+zI9GX6uDJ/hWSvVwpotxx6EmHspk293iAe78V0LNZKZQIBCbfdxmm3CQVlJEmk07aJx3t4+umnGdoxTLFcwtQNhnbmOHHiJNvSA2yslTnx7Ml/P4ePz33uT+675ZYbqdXWCYcjW5NJx3HA58ms4XAITQszNzvN3NwsfX39RCK9lMs6PbE++jP9BIMSteoGC5eXkEMRaAdZW1mjZbk02y2EgIDrcwmFFTr4qDdatH1t/MEAQkCk43awbJtEPEo8HkFRw9QbBpKioldWKRcXoGnRtBrU6wayX0QJBcgvzKNEVJpNi1azjmVapHt6sFsiQUkhILkUiwXUSApV1QhHJZyGRMvykerJIIcCSAGJQNDPQLaHiOLHaRuEgiKl4ipyMIRPaOM6Ler1OqZpYtsWdssC1yWseg3Vqb40a9UNNowWrbY3tQe4NDvD2OheXnj+NG3Lx0qpyNCOMYx1g2ptnVBE4Er+MjMXJ5mcnORKfoPrr7+eZssiIAZ56WdTNJptdo/uJRqN02hYLC8v02w0CMoyogRu20UMiQRliXbLh18IoIRU4vEEGxsbtNsdotEwbceH6zoEpRARNUIHF8syWVuvUa3p1BsWHSQCkoQYkDEbNtFYD9V1g4WFPLFIL+vVKrbt0HElxKDE+MQ49Xodx2kTklV8Pj9r66vQEfH5giiKTMNoo4QVYrE4dFx8fh/lShm7ZbOUX8FxXMJSmPWKiaFbhMNhHNeiN9nrgQrUOI7jUNMt6kYDw9BBaKNFYqSSvWiaxssvv4zrugQCAb7+9b9C0zS257axtLhAcblAbzxGs1YnpkbouC0kQaCvr5d2u8Ps7Cz1ep3+/gHi0Th+q0OnY7O8eon0YJy6adHphHAJg1+h4/OCpLbrw2n7cTsOr1yYIZPNeYc6NQy+IEowSNtqceMNE/SnVYKBJplkDyM7BvjW334Dv9PknnveRjqV4dbbb+PWW25l/9WHOH7yORauXGYol8Nq2Qg+kbAWQfbLNFsOx48/CTTYMz7Ek098l3zxArfecAvvete7OHzjft547A4unL+M0PKzfTiKFHLIL+c58KqbaAs2iXSUnkSEwWwfG2admlVHiydJxWNcujzLwtIiO4eHSWUGUPt6cYNhNhoOghwl3tOHFlZp6HVCYoB0ut/Dcm8fIhiIIAZEQEDfqLFRW6Hd1hF9bRzbxud6JD1d16lW9e5hwaVcLuG6QjdQ3KLVaqMoEUKygiQJWLaNGg5Tr1u0O1AxdAyrQctp0XBbNC2P+79Z0imK0Gi0CAgRlPBmKZkDeHYIb/IZoNmwsCwDLabQaNbpdPxsbNRpNlvUNjybXkTTsGydcEThJ48+8YvDx7/i+uIXv3ifFg0TjyZZq1RZK5eIRHpoNTs0W3XPRieJOLRYWbiIFPTjE4IEJZV4LEkgJFNdL6GGVaSAn/XlFQKKxNraGmElgqE3GOjbhtms0/HZKGGZen0dwZJRe6M0BRuBII1Gg/yVPLfeciv+jsPQcD8PP/QQ7/nd3+Pi5StomoLPsSmvriMIfo6++tWceeY0o7tG6RtMM3tlkmqlzMC2YcJqh2TPMNVKmYX8WQKiitOKkc5EKdeex+fuJSo0EYOwtqEjizKRRBJ/IMj85RkOHzjI8sIVsrtGMRo6G+UqCU1DkgOYpoUUkKHj4BN9JJMJQkE/pbUSQUnDaniNyRv1GqI/yK6RUU4cP04orLBzZAfRmMp6aZ1gMMnbf/1e2iwSi1xNcWWWS/NX0OLbmJt5idtfezsnn3qaayb24Zdl9EqVluOgKCqNeoNwTCXZE0MKSPhwUNQott1idXUVTdNIp9NMTk7h80EymaSnpwerXqPlOkiSgih6LexrK0sIgQCKItG0mhimwTVXX8ezp57htqOv5gff/57Xl4Jn0YsnethY09FiCoOZIc6+eIpkMomqRlhdXcZxHfQNg0AgRCAQYLW0yvbcIIuLV9DiGo7gElHCnDlzBkkKI4kC6XQaWZZZW1ujg8DeiT3EozHOnDnNkSM3UiiusO/qq/nZS5MM7dxJobAAfpuQpDA9M8d6RefAgYOUVgssLi6SzWZYKCyC2+Htv/VePvnJjzNx9TAB0UeiJ8n85SVCQpBgKIBp1XFpEpBlrJbJjQfu5JVXprk8f5Fr9g4RCjVo+lz8QgRQaXdEVtbnSPWmefM9b2HX8AiXLl8kocXZPthPoVik1WwhKyEqhs3pUy+QiPQS7dngytwq+obLgQPjJNMR5M4ImVwUx20we2WBwVQ/ky+/jKaq1PR1EskEl6dnWF4pcOjwYfAJbBvZznOnzpBL7cHYELDF0/zhR77Kt//+pyzlF/j2d/6cX/vV9/DNb/05Pekoqb4kmcwgf/ZnX+O/ffrTPPrjRymtrZOIhvGJfmxbQq9LxEJ+jv/0EQ7eeD2F+TnCUZVoj0rLdmnWTTp+gdff/RZ+8uA/UV5dYXtuEFEMoioRQkqYamUdRdOIahpWo8lafRl/p0NfXz+X85dxLK9Ql46AIIhb0IFMZhAx0KFer0EHRCFMp+ND8LUpra6R7ouzVq4Rj/TQaDdpNiwCgh8xLNNpt9FrGzQaDeLxOIGAn9lLr9CX6icUChMIBDHqNo5t0mw2iMWjJJIZiisFjGqZPXvHWVxcwnFdQrLK7l0jHD/xOFElSiwWYV1fpX9wG4888si/H9qVDx+VUpWR4WEK+ZJH8lFV9Gp1y0MriXD+/HlyQ4OkUimPoa2oDA+NMl9YoFwuI0kS+/fvR1VVqtUqOA6S7BX8JFSNqqnj2i4VvQzA/ol9WK7J1OQUmqZRLBURXYHRsWHKFR29MkcqlWT/dfuYnDxHQk5QreqUK13/nCxDCSYmxnFxmV+YI5VIYzsOakymUrYo5gu4jsVQdhgXkWJ5Hr1qYppeKQ8IlIo6sYRELKFy8sQJJFlEkjxyhabFcLGwumVDudwgkiB4/uBKBQSwDJ1zZxYwbYexsZFu14GLKHjkrv379/Pwgw9xyy23sFDwrGv3feQ+3vH2t5NOJ3n08e+RyQwxNpojl8uRn9cRRJfR0VHOnTvLTTcdplgs8/CDD5IdyqLFNIZyWRzXIZWM4eIRWSRBxDRMHMskNzhIqVShYhQBYYuyUK2UkRXZC006FpWK9xrv2zeOaVrouoVlWt7zrxtks4PMzc1h2zZHjhyhWjVIpTK4ro3jgG2J3H///eRyaVRVJV+YxbZdFFlFUkRkyetqGR4ZIplMAR5jX1VVSvkiZcNm/8Q+FhYWsF2QRc+3m1+YIaGpVCtFtG4GwTQdqpUKExPjFArzmKbNQmkOx/HKJsfGxrBtm/vvv5/x8XE+//nPMz8/z5e+9CU0TdsitGwWSwmiwPz8fJeRncFxXApd7/LQ0BCFQn4rNyRKIIlSl7oFjgAiNrFYgpuOHOEjH/4Qhw8fxDYNVFVCEiViioQoe4WMDzzwEKoqkcvmiEkiEi7vfsfbmJnN89UvfQHTFTBtgdGxMf7447/PRz/yXzh2+5185zvfYWRkmPyCV/4niSJVw+Dtb387C/l5Hrj/IUTBpZBfwDZdPn/wOuam86zVV7be39/97hPceftbSbxZ4tz5Ar/9vfdz/MlZpicnOX78OIO5HG9567tIJWJ86EO/gSDB6173Os8brqqcn5pEUWQGB7MeUrdQ5PiTJ6iUi6hajJtygziuy8zsDIbh8e1VWcWxbSy3giyLxGJJBDy6mix7pDa1S6eyLIuYlsRxPW95TIt5fT6KjOPY6LqJZZpIoudnFSWVwVRsC4dtGAZVyyGZSHq0K0vHchyy2SEADMPwCCeil4navHkoFote1koWKBRKuC4kkxqSKHLm9DnGJ0Y9IhegKDEqlfL/xl35F+t/tXw+sK2f5wWEbimtIAgIroSqKAgimFUdwzRJplLYjvczmcwguq4zW84TU71rL5ZMYthV9KpOKumVdDmu5e2XSF6ZnSt2SYMVUtlBigtFZC3BcG6YcqWI3kW8Z9KDXcKV1+VkWzayKIHq3Qwbhk4iFqNsGsiyhoBBIhHDdY3uZ2YZyzIBL6fh5SsENC0B5KlWdVRNwTIcckNDTE1OoqoxRMHGcWxm5mcQ8DzjkiR5+0W3VNHtdnKJokf5KxeLgIose5lAQ69SzBdIxGJbxBzRFVCTKU6fOIdrlpibPcj8/AJzky7jhzQSiSTDuRwnnryfO17ndLs3FHKqyvzsHOVSmWQigazI/Dx54qKqCrYlbOVFFhYWKBaLVCoVRkdHtor6ZEVBkhVcByrlCorqlSUu5BeIJVQkScYLvHj5EtsxuyVvma0CVFEQEUTPm692ATbQpZQ5HsVzk5hl6AYIXn/I+Pg4s7NzXi9EF4E6MzPD+Ngo+XyeXC5HMpmkUtFJJBIIAiRiCSqVajcPU0GSJeZm5xgaHqJUnEGVY1tFc5sZoQceeIC3ve0t7N+/nwfuf8ArqDMMstksZjdDUiwWOXjsOh5/8klkVSSVTFEslUAEWZaolivdzqIEhqV7yFZBBEfAFUCSZBwHxsaG+d33fwhdL3Pw4EEUWfQyTQ6YVYvp2SkvqwLEVA2rG3GTZRVEiQcefZTcsMrb3nEHyXQW23GwTANDEhjK5jh37hyZVAwkicJCHsO2scwio6OjnHr0DLmszS2vO8r3vvMDREHi2J3HePTxMqoqs//QfvLFBYZGxpg8N8XQ0BB/9EefZt/+CURgdnoayzJQE1ne+fa38tlPfghR8TK7qcEMruOgV3SQXBLpJKqiMXNuEse00GIqsViKQmGewfTQVkGlKEheHsiqkBvMUil6/VGqopJIpLx72O4SBLdbRCtg2173nCLTLSqtoChefmN8IsfM9ALJWJr/h733DY7jvs88P9NoNJqNZrPRbAyHw+FwOBqNQAiCEQiiEYahGVqhGIUrK4ridWxv4rN9Pp/jcv44qbpKJVldypXaeF2Ob8uX5Lw5b8qrVRSfVqdNdFxFYRguQ9MwDMEQDMPQaDQcDYfD4bDZaDQajUaj0XMveoh4r7Ycv9js7tXmV8UqFDGFwTS6f3+e5/t9PnEc/i1oM06SX2VZJAj+Nv1tNynv3vuIMcQihqEThjGhYyWviWNEUcJ1PRTFwLIscrkM7ZZFI11nbKyM3/B2Eyh/kPHfhPPxzz/32WfKIyVSgsTmtk9K6NI/ILOTihD7BWJ2WPd8wmCD27c7BJ5H2hhG6N9Dx76NpuylmD9C4UgBy7rLrfYNHMfG8z0kUcZdc9na2aS/X8Tz13tAJQl9v86mv8VWd5tg02egL8UeeYA1bxNZEhEGBtD36dyx7xJtbtEv9uO5PrK8hwdGHmBIU7Hu3GafpnHnrk2wGRBsexzKHeXAgQPs001E+tnp38Hf3GQ7ijGGDAb3DvCOsUdwtza5075Fqm+bu3dvsRPFOK7F4OBeAn+bxx9/kvSBYW7fvkWqL+E/rK9v0Gi2uH3nLqT6CMOIgT0K3ZRAf2oHx/XoHxAhFtlYu0un1aZ5+wZHiyVcb52TJ0+wsDBP/mAWWRnk6NHDLCx+gwcfeBB7fR1/bZOdnRR/9epfMjAgslfZx9q6R7NZp5tKsbHh0Xi7QbgdJtBFUaKvvw9i8DY22Nrx6QoC7vomqT6BG80Wh3IH6RP7uN25zeH8YWRZ5tatWzira8gDEurgPizrLkHos9/Yjz6kIYkDBFsbaJrO3TsOfWKKbDbL9vYWm5sbCEikSCHJAnfv3mXP4B7Efok4EujGMDS0H7FP6inQW4hiH7IsMTP7NW61W9Suv8XGZsjGxgZj4w9x/e06qVQXM72fra1NvI117iuV2Qwj6BPodnfYq2lcr9eIuzHpg4fwA499usaQbrK6urpLe8/lcjz88MMcPXqUV155hddff52jR4+yurqKoijcvHkTRVFR9sjs7HRR1D1460ku+b59+8jl8mxvx1h32gzuVQi2fGR5gD5Botufgp2YVLzNdpQi2t7hvgfuYzsK2d6K2Np02avuJfA8un19eI7Phutx5scf4+Cho/juGtvbICsKn/yFX2QorbO5E/FD4z+EFzjU3ljizOkf4c///M+J6XLs/jJ/c/VrHMia7N2zF1lVkAcGCLa2Kd33AIWjhzlaeIChoWHEPhjau58n/tE5cumDfPjnP8DUxA/xa5/+NPsNlcceO8/62ir//He/wH51Dzs76wzKA2ys3eWtapXa9e/w4+96hMcfOw/bIc2bTd749nd44vx5HvmhRxgeHmY72uKtN1boI6RP6kMe6GPPoMJebS/aoIoADPT3J5Hd8SZH8geJdyDY3GRQUQiCbba3Q3x/A1Kw4XsEWxsMqoMM9EuQ2kHdq7BnYBB/c53AD1n3XFKpPvr6knjucHOL7Z0uO3HIxtYmob+FLA8w0D/ATrzFdrBFN9pBVRW63RRxHNJlh+5OH/Iehe3tLVqtFr6fiCz94gC3br/NgCQzMLCHQVUhfSBplle1PURRgNiXPD/fnJ3/B+fjv+L47Gc/+8zP/OOfJtqOWLVtjh17iBs33sbQDfp6948gChzIDrGzHbK2ukY31U/hSBnr7irr3ib9ewSM/QZhuEEqlujGm2xHSfLNsJllfXOdeHsbe+0OQ/t0Hrj/KG+91eDN6yuUyw9QeavGAf0g5rBBq1FnK/RYddcYUAb4V1/+N/zoD5+k9uYKfrCFeSBDKt7m5tvJIUUeVFheXOC++4/QbltMT08QhQOs3d5A1SUkaYdbtwJ0LcvWtsOQIbK1/QC3rv81m9s++n6Dw4cPc/XaNzl6XxGr1eHkD5+iulJhs2+TcGOTQXmQI0cLrK+uYvcExJQgICLgB5uEWy6eH7LPSPgk3Shp4s6XjhBsbFIq38/bb7/N9s4Wr88t8Gu//Gvcvn2DVP82P/kTj7H0nRscPnyQv/j3f8W73/0TbG9vcv1GlWOjx/jWawtsbgQMHxzGumMhpPrIHDRR9wwS7OzQl0r4UNYdmwMHDpBJ53ht/puMj4/TaLyNJCmkUtvs3z9Mn5hifW2Lbioi3A44dOgQiiRz585ddGOYdrvDxobP0NBeDh8+wrfmX+fYsQfp6+viugF79+5hUB1kZ7uPxo0Gd6wb9A/0MfLAKCsrK4lrnk5z9+5dMuksq6urHM4fpr8fXvvWN7jdvoO5L81btTd58MEHGTv2EP3KHm7dTHpTun0pXvvGVR57/BwzX/8mhqGztuYlKURxxOTkFP/2xT/l5ImThN4GkdBHtVqjfP8xvv3tJcJtny984QsoisyH/ocP8dGPfJTj08d5/k//DccfniJzcD/xjsDGxhY36y327x/mjt1mjyLTJ/YznDnEjhfxRvVN9L19xKkdJBHCnRT9sUTYTSHEMZEo4Yeb/Ot/9SztmzeYnHiI735nCWNoH/0D/Tira4iiwHcqVZxVl8H+AQpH+9he72dA3MeLf/HvWN8MSXXv5/WVb/AzTz3K7eZbNG/c4Wd+5qepV6qsujb7jX3EURd9/xDWqk2p8ADr3TVkaQ8XX/4G5x97D3c3Z/nK//EXfOTDv8i/e+nfcvLH3sHr89cx82n+4H/7l/zCpz7NP3n/zxN3Q/aqCvV6hRv12+xP76c4MkJmWOdPnv998oUym0ESmy8CQ+Z+DH2IPknCtle52bzNnfot2vZNDmTS/OiPnaLduoNl2bv4iH3aPvx1h4P5pLJhQJTYv38/jUazl4aVQlXVHptDBPqAHTa3NpClAQYHdaKdkA1vh/UNi1u3b/Kud53iW3MVDmYeoK8vIJXawQ08Ij8kFsA0hmh3WkgDIvEOFA/nCcKYjY0Ndra7IPQhpODIkUPU6zXu2g77NI3+/oiU0EccDRDFKcLNTbajiP7+HXaigImJMe7YNqRS/MUr/z/ifPSL/eQLeTptG0mUd09k95gegpic0mVFxtRVBEmm0+7g+yGqqrJcWcF1k6zsYj6Poso0Wy1kWUVRIgQxIW3X63XS6TTlchnPc7l8+TKSJCWn/MBhdGQUUYJqtU4Y+ISRgB27tFoNRkrlhP0RR4n62k6I6GPj44iigBqG5PNZrE7iwLSaLTptjzCOMAwVSYrx/ZBWq5nQj92IMEjy3aemJpidu0aj0aBUGqFarVAslfnVX/0kkNBpNU2jVCohSdJuYobruj2ScqL2RpGKoSkIgpyccs0MuYJKxjC5OjtDOp3mj/7ojzhz+iSOE7K4sMDcwixPPvU+rlyaYfrkFHOzC5RLk4xPTuJ4DoYCSCJTU1M0W01sy+b48WlcN0nmSrLXY0DYBQqqqkK1WqdQKDAxMUGluoymGkxNTe0SaAHS6TSGYeB6NoQxvhew0FxI1Gdd76WCdAhjl8ATuHr1KgATExM4jkOt1sA0dSYnj+MHdpIAoyTJQQnJU8bzYnzf3QVAFgtlZFlmaWkpUZs8j0ajQS6fwTQytDtNmo022Wyaam2FykqNcrmM67k06i0+9OEPMnNtDse2cV2HE8dPMjF5nE6nw8LCAq1WC8uy+MAHPsBDDz3Er/zKr5DL5XZzu9OZpPEdIcb1PFzXodVqMTJaZvrEWWrVBgsL82Sz+V66hYAgiaTTJrblI0QCIJDwtmNkET7zmd9mcmyUXDaDIudwXJcw9AiDANd1CCO4dnUeXdfJ5cZotqo0WxYnT59hbHwUI5sjCD1yeQ1JMJEFmU9+/GNJwpTT4X3vf5KF+SUsN0njyOfzOLZHtdKm1a6jqkmvS7tlMTE5wcLCApMnxllanqdQyPP5L3yKrz73h3xr5nl+9Td+ixPTJVrNSywtLNBoNpmYnEDVxqhVPOavBCwuziP0uByqobLc48dYloVhGpw6dRpZjBmdHoFYoFpr0GrXkjLNWERRJdKGiabrdKwO6XSOOIp6STJmksvf431IkoiimJimSbVaTZguiESRT6vd3k3ayWbTPQclQ6PRwPcc6CXFCQjJde4llciy3FM+fQQBRDFJqAnDmEIhR6PepNlsYhgJ2yPhEiUZ+SsrK8hywi0SRXBsl2q1SjqdxjTS/+Un538Y/5+RIgg8iMUeQ0PoMV1UgjDGtlvIqkanE+L7HqVSCccLaTSahEEEooymaIlbZnsUciZhLJPOKNRrTfK5MpIgI0ghaqwmZTm+TxBFZM0srVaLQj7fI02HpLM5Ws0l8vkRLl18Ff170iDvUZEtyyKKEsJ3q5Wka1m2S6FQRJJAVQyatRrFsRLzCzbEBrqh4vku2WyRS5eXKOoiYxPjvHLpCqdOmUiymCiuskoQhD02l0QMiLFIHIZJmo8sJ4qtICLpyecmDtE0DcMwsK0GiSmSpE4RxuTSa/Jh0QAAIABJREFUJvrYGC+8+CKnTk3j+TbpdKLmW5aDbqhUKhUyuSxWp0U2k6flVahXa5hmBt3Q8N2kGd8PfAQkJC35PRAjnB7XwPMCfL+2S2FPQLgSkpyIVh3bT66xZhBFiVvR6LR24aGSlHCZLMsinc7gui4jIyN4vWQhVdETt0hJUhh9v4P0PVyve+ozCH/rwOo6rmuRTWdYXrrK2MTkbnqVltaRPXnXmVEUBSOt4bk+tVqN48cnmZmZIwphaiohg09MTGDZHRzHQZcVms0mj597kiAIKZVzfP7zn+fKlUt87GMfwzAMZmYSJlbiAGu0/ITx0m60EWUJUZAxTZNavU7UsZEDK0lQimMIQ+IoAEEmEu45ZzF+LPDUuSd45rd+nVOnTvVcQrAtF82QiaOIIAzxvAAv8HFDB89TEGIjuc6yimEamNoIK40rLC8vMzU6yR899yILC/N88pOf5HO/8zuUR4sJN6en5tu2TTpv0qrZpDM6c/OznH0q29srJa6B7bQIg4DISRyhdCZDEARUKhVUReH06TMogsorVy5RuTJDxlRJaxr5fD5hZAQugRtw8dJFnjj/BK7VThwsROLYT9LXohgiMQExazph6BNHApIIXhyhGxqe3yYIInw/RJaT1MNkXdB6vDqPbDbh2ERh4o4la4eHLCs4HR9ZktE0LSGfV5pkRkSIYxIzI0bqJbPFUY/3I8YQC71UqwhRjIjj5EjQaCTgXN1II0kCnhti2w6qZtJotUkbJnEcUigWqK6sJHubtNH77D/Y+G/C+fi93/v8M8eK9zFsDjHQN8BOuE3uYJY+UrRuNlizbNKGSV8/fHt5ieHhYd6qNpgcG+eN5e9y7ifPM7z/AN6GxwPH7mePrDKkarCzzetLi0n8rmFwfyHPmnM3ASz1i3gbGwwPG9y9u8p2FDN8wKTx1tsMDkrcvLOKaSbUybGxccJ4i+vVKmKqH9taZSsMUdU9PDL5Tl588U/QBpNm9r37hnjXqXezsvJths0DROEOnmdztJSneHSEWv0NtkIPISUj7xmgdH8ClYq2Y2Ii1lZdxh4cZ93d4MSJH+Wxxx5je2uLB0fK9PcJ1GvX6VgdarW3UJU9DJlDaHv2sL7mMLBH4c7dVW7dbqPp+0ilEobHqrtGIZdhZ3uLTqfN+sYWQh/kjxbo7xdZc9c4d+4Jvvqn/xfT0z+Kt76DOLDNoYOHuNG6zcHhYZqtmxw0D2C7q6y7LhtrG5TK9/PgsQf5xte/hrN6F21wD0cOHyEKI4b366zdvUv9epWJByepfHeJDW8NRduHs7pOJpvBWVtjUFHYDneo1WpMTU1Rr9c4cGCYzc0tICYINxgdGaVYLNDpWBw7dow33niDMAyZnj6OKIrMzs5wbORBKm9UEVLQjbv0D/Sx7m7S39/Pvn1DuwCmej2JUj106BCrd+4iyQPcvXWbx88+Tu3tKjfrN3jnO6cQUimiYIcHHijTunmT6akf4dO//Cv89cXLvOMdo3x3eZkTx3+E0YfG+Nmf/QDXrl3bPTBdvHiRn/qpn2J6eprt7STH/d3vfjevv/467ppHsZhnjyyTAra2ttg/bLBv734qb1QZHh5mevokb73xJqluEvmqqCq3b62ysxOz19Bo3Wkj9MOGt8b58+/h9s0W+4f302w2eqU5XZTBfdTrNaSBvbjuOlbH4Wee+gDVypt0Y5h65BGGczl++n1Ps7FmYZg6O+EG29tbqIM6Q8YQ+zSZauVN3vjuMo9M/zDrts2xsYdoNNscPlLkje9UGRsZo3XjbTa9dfL3HeG7b6xgpPdTb7zNgbSG462yevstsgcU3nhrmeIDGUyjD3lgk4cnihzOK9xfyrMduzz8Q+Mczh7lp37qCdL7h/ixMyc5mjtM5oCBQMyZU+/i4Xe8g7evV8jmhwmDLYaMfdxxOty11ziUHWY4a7Bv7yDz8/NYrVv0iTLVN1e4/4FjuGsbfOQjH+H1119n9e46G/46qqoyMzNDo9Hg4YcfRtcNbrVvc6vdZmx0jCFjL0eOHOb69etIkszAwACF0gh0t9m3by/rm+tseC4HD+cgTgITun0pul2Rfdo+giDAMExSqT583+X5576KYSblD0fvO8yNxk3uu+8+tsMdhoaGmJqaYu/ePbz++uto2iCFQp5uN9WL1jR45ZVX/sH5+K84PvvZ333mxI/8COFWhG3fZXNzKyHaC7C9vYWm7WVgUKV9s8o7xiZ49cJfcf/9DzD32iKn3v0Y/pqNtbbKO0bLWJbN1/7m62Syw9xqNxMl/MABPNthcJ/CkL4Pf9PFsjoMaWmO5A9Tv/EWx0bGefutt9ja8Nna3ma/sZ8hQ6V96waHDh+lVW+yHSe04Uwmw61bN3jv0x/k0l+/Qj53hH1DgwTbMSPHHuHFr/4Lykd/gtdef41sbi/pQ4Mcyb6T7a1tbtxc4b7SUb797Q0eeWiQw8XD/MG//BKHc0ew17ZJp02iYIfN9Q32DEj8+ysXOP/Y4+xsd1lbXUNSEn5GGIbIyh42VtdY9zfol0Xs27dQtINsBlvssIOuGZi6xtqay83mDe6u2txXLNInDmK336beukFXELj/8AjLby6iqRminT4iIqQ+gQ3Pplg8yqCsEUUhiAIZM8vbjbf5yfOP8d3vrLDqWKiDg+w3DLLZAtevX6fdbpPJHNg9pH38Fz7My392AUUfYuU7b7BP38OAJGMaJvPfeo2hvft5u1nnTiehmfu+T39/iiiOyB/OcfnyFbLZHJqms+assUfZg7bX4I51G89zGdwr07rZQZZlVFVle7uL72+yTxvCttcoHD3M9etVDmUP8s2Fb3PfkSJ3797h0KFDCN0UlnuX65W3KT9Qotvto/zAEapv3kSWk+jfsbExduJtfN9nZuYaY2NjPPLID3Pl8tf4y0t/yY//+I/zVrXOd76zjLOWxP2OjY1yu3Ob9zzxHv7oy8/yzndO0m7XGdq3jziVwrqzxvLrS+zff4AzZ0+TPZjmG9+cZ8MLUfoEttlmUO2nTxIIPZ9gB/pFhQFZRBb7ec/T72dufo7DBw2IIg4MGzQaNxneP8Ttdptu3wDu+gb3H5vk6ad+lq/+n3+MuOcmh48c429m5/mffvFDXLn6NzxQPsP//dKXOXpgP63rLVKyzPDwMGt3V5l8eBxnfZNvzsySPpzF8wLMA8O02qvshDF7ZY1Lf/kfOP/e+3nt6zfQhw6BsEEYXUdVcwz0byP0y+Tvf4ivz36Dh+7Ls3ePjHvXYvnNCid/7HHsdYcDeo43v91gcGiAjY2EibPhb3C0kCRORdvbHDp8CCElcujQIEbmEPIehTDa5m7HJX84w1awjZhKIfaJCHGXWqvBoKKx4W7xrdde54l/9CTP/cm/hlhkQO5H0zRarSYTE0lLwc2bTaT+Ae7eXeW+o/ezuPQ6qrqHgwdNzj52hr/5D3Nsbe1heLgPBOgSo8gK+4YNou0Id22NvXv3oexR2fA8XM9h//5hHGcVSZE4YB5gaGgflUqF0+9+F29+t0apdBh5UGGfmsH1PDY31jhSOIKQ2iHY9Nnc3KBULlN7622+9rWv/0BrVKrb7X7/F6RSMnAFGCBxSl7odrv/NJVKHQWeBwxgHvgn3W43TKVSA8BXgIeBu8A/7na79e/3HrlDh7pn3vUuJFnk1KlT2LZLu91GkhJlQJaUZALTJAgj/DBC03SiKEaIoW21UBQNUYRGo4UgQrlURtNVmvVWok4REcUxsijiej6SlNSB31M20+k0jVodWUn+PyFKxkmtfxgThz6ilNSzBn7Uox6rLCzMUe4xLsIgQhFF4ljCMBWaDYtsNoMXuji2TRSLjI2XkESJlZUKZtpA1/REFevlrt/7fcLQJ5fLEMUxmpr0CciiRCxArVpFEASmT5wga6b5s1cuoMoKjudy8sQpEARmZ2fQtCQLPFFZwHVdZEXEshxEQQKSRCxRhEa9TaFYxnEdfNcjW9CSz+oF+L5HWk8U+47rQBihGhoSAovLy0xMTOC7SY1s4Ad02hYIEjExY6NldM2k1W4TRj7tdpvRsQmq1SqGbhKTxO+Ojpap1+uIYtJgFYQBURhhOxZhGO+qyUndrNDjt4S9TPeAVqvTawwEVVUxDIMgCBAEkGSRWq2C63pkMyVOHp8GoNFuEYY+rVYLhAhd1ojFJAPb0BI4VhCElIolRFGmUqkkWfe9E78gCFy4cIFz58/jed6uo5PU4Aq76sv09DTz8/M9B6SekHSVJON9avI4y8tLdDodcrmkbrXRaPD0E0+TL2ZYWJqnVW9gpLOoukHg+ciqQqvVRJEFFNnAczqEYYQoiciiAKLEtdkZHj19FrdHVHcsO3H5igUcL8QLAqanJ7E7Nq7TxHccRCGhrXY6DoahJxR7QaLebDA+dRxZUphfWsL3QkzTIA6gsrzIF7/0BQRB5LnnnmN+cYknn3wcQRD56lefRZIhpxbxg5BsJouRydDpWBRyJq7TxrFdgjBAz2QQRJHAc/B8FxAwTA1VUVleWsHUdAqFAp6XKLjNdoug96wEUdL7ougateVaT7GRe7yXRC1qt9uUy2VmZ2cpFotks1k6nQ4A+UKWdrvN4tIShUIBRdEghmazTTaXJZ1OoyoqV69eZWS0RLvdRFFU4jhC6zFsfMft1TdLSIJMGEdEQbjbJxLF0S5pVhAEstlsUrerShCLeF6IbVmIcoQsiTiuDbGIqiqoqkmr1YFY4H//g997rdvtTv1dE/t/j+O/xFp18ODB7kf+x5/HNDL4vo/v+5immTCebBtN01A0lVajTrmUY2WlxtjEBJdfmSGdzxJGMFLOEREzPzvD5OgkltdCllRqtQbj45NISPiBSxh7CGKEKErEgUqz3SSdURAFlSgCSVAT1VkIMU2dxcV5MqYJiLQ6NlEUMTExRqPRYmRkhGvXZslkTBzbwzTT5IuTLCx/nrDzOKru0XHmUDQQwimq1XnOP3GSSnUJ1TjNh85nee6l57k6s8Dx49MsLLc5PnUCz7IZKZZo1FYItJjA9ZmemqbRaODYNplsFsMwsFptmp025WKJKzMXKeQLeKGI1bGZGB9LOBCGgSSC73l0rDal0QmW5ldQ5ICJ8REymQJXL69w/unjvPjiJcLQJ53VCVyBYtmk2Wgx2XOhV1ZW0FSdiclJXn75JWqNFu9/71N0WjVU3SQOBJrNJoKqYZgaZ8+e4XPPfIZzj59hsVrF6nicOHkGq1Ulm80yNzfH6GiZjJHh2ee+ysTUOHEsYFlt0hmVdttC19I9OrSN74e9mHcglmi1G0SRg6ZrhIFAHIfk84nDUK81yeVzLC0uYaZNspksuiJh+wFXrlxCkCVOTZ/gytWrnD/3BJevXGRiYgLDMJClgFcvziFJIk8++SS1WoPnn3+Wc+fOsbCwwCc++TGe/cpzTE2eIIojLl++yMT4cWq1OoaZRAPPz89y+sxp6rU6L7x0kSeeeBTHrjM2OornR0iCwcrSCsVSCddz8XwbRdGYOn6ChbmrtNptdF3HUBVEVcHu9d8mDBmN8sRZlhevEHkWsiyRMQ2qjSaiIJBOF3B9h0ajwejYCZ568r288mcvIcrzvHpxiXNnfw5JD7lw6c8Yy32Q3//K7/Chp06TljIUxkZ4/qVnE96ZLPPU00+wslwhimJeeOEFTp05TVrPMDt/hZ97/ydYXmoxv/zP8J1RTp96EtevU2+9wmjpDIaWoTx+nNGpST75qV9CcduUionTGMUS0+ffy9JShWI6y9yrl0HzKZRKpNMmjuMyc+0qJ6dPYeZMrl6ZQdd1ZNVHUvKEvouZyRB4AWEUEPlJL5SuyFQqNUZPnmBxbpZirkiz0UJWRDRNByIsu42mamSzOSqVao87F1OvtygWyoRhRLPVIp3WWFycR9NlHj39XpaXWiiaSxB6CZdGVoh8j2w2T7vdRlVVBElEigXiKCIWBRqNFrImk9aNngs2w9T0cRq1OrlMmnoz4avJqkrgeT32lEwYOAR+hGqoEAv85m/+rz/QGvWDQAa3gDPdbvcdwARwLpVKTQO/C/xet9u9H1gFPtJ7/UeA1W63WwJ+r/e67ztSQgoza1IsjaBqBkEYMjI6imW7tDs2mqmhp3UkQFKThj5BjIgCn0gIkUS5p1w4KKrMSHkU00yjyBqu55DOmPi+TxxFSLKEpqsoqgJCBCTNcbKskivkMcwMsqLgeh706vczpk4+n0cSFTptexdKZBgGsqyiKjqqooMQ40cxjWaDVstCUlQ0I4Nhmpx59FHa7RaiKFBv1PADF9vusLQ8T71RQ1GS0hVZ6kHyVB2xBy+yrE7SWOS5SbOypoEgsLy8zOzCPMVikXQ2Qy6Xo16rJ4cDSSEIIiQEvB44qFavIgoSURhimBqarhMRYzku2Xw5aYoVRBTNQFV1TNNEUVR03cQLIzqOSxSGiIrM8tISFy9fplBMSnZaHSeBYEkSsqajKCKu7eD6IbMLs3ieQxgEpDMZbDspkQt6jVeKItFqtwjCAFGSkqb+jtWz6AUUWUSRZRQ5aRxUVTmBfIVJbGm7bWGaJoaRWPn3QFOCECPLErKkkM3kyaSzZDImV2evsVRZopBNwJLFUh5NNRAUSGd0JBkarQ7zi0uIsoyeNrk6e5WLVy6yVFmiZdm0Oh2uzc7y5NNPU6lUdkv6dF0nk8kkjaNRlCy8lrW7OQmCCNf1GSmPkklncV0Hy7bwgwBVlXd/95dffZmFxUUa9Sa25+G4dgKBchxCP0CVVcbHJgl9D8fxCKMQiPH8AFWRkZCwbKsH/REpFLJ0rCZjE+NouoooCgRBTBjFqEoaUdYJQwnfi9F1jVy2kJQw6TrFUplmvc6VK5d58YUXqDWqtFpNlpbn0NIy/+KLn+PqtUtksmne/76n+PIfP8+LL77M+97/YU6eepxccYQ4kvAikUqtheW61BsWQawQIGA5Ia4LKyt1BFkkXy5RGikhIBGEIemMgWwotGyLSBSwPQ/X83EdFz+IkWWNKITYF3YhR74HkqAlpZJBzPLyMp1Oh6mpaQQhKRE0TR1NU7E6VmJZK0mzt6HpaKpKGHhIiHi2R6NeZ3SkjCLJiIh4roXvJkAvXZGTuUCUkMWkZEORRBRFIox8OlZyyNU0bdfivjckMWl+FxDRdA1ZkhHEBDgoKSD0SrYURUHT9R9guv7vevy9r1VRlMBqC4UClUqFxcXFRLwAMj2omONYIEl4roumqRCAIIuosoauarTbbRyrTSFfpNlsIIoii4sLlMtlqpVKL2RFIorj3dKcKBRxXZtMJpeACuMYw8zsbiSy2Syu69JuW1hW0iQqiiKGYewGsOi6njSuuxbEYq852iQMoFgsUWvVKeTzBEHEyZMnaTQa2LaLqirouo5te4yMjKAoGo7t9dZPUDWJKErgibKs4HneLugvSWZMyqzulSX6vs/Y+BiFfJ52q0VIjKpIdGwrKcGNYXx8nGqlSjaXRRYVojii2WqjazmqK4sEPXdDN1RUVaZRb/XgoMk/JBFZUWg266iqyuhYGcdxaDY6yKJIJEAQJWBXz3YIPR/bs/HDAEWSEm6XIPXKoTxEMbleZtpElkVkWcXzHJIwlaQBOIqC3c8p9yCmvhfg+W7v++yCIe+JU3EcI8kSAgKiJGB1LFRFQdNNoijhlaR1g7m5OSanxvEDF01L5owgCHDs5DpPTk7iuj7Xrl0hk8lhWW2KxfxuCfOFCxfI5XKYptlb15O/6b2/STabpdK79+5B7YrFIrlclsWlBU6eOIltuUiyiOv6LCwsUKvVmZqaQpIE2u02iqrg2C66biJJSg/6KLG8uIRtWUCMKIl4no8sSczNLTI6PkahUMK2XQQZFhYXmJyYZGlpiZhkE+7YNpoiY9sdZGKiMEDVFADyuSLpdJogCKjX67TaHa7NzVIeHSWXy2G125w9e5a5uVnOn38c207gnteuzmPoOtMnTtCx2rTrLVYWV9BFCPyIKA7IZbIoahKYYHUc9LRJu9PESGvopkEchXi+iyBCq9FCkmWiMCnnUkSJdrudlPFKCqIkIyCgqjq+7/aAgjHZ3n7I6iRluydOnCAMY3Rd3YUmh2FEGAW7f5tMNr0LdK7Xk/tbFCVESUgCF4o5Go16UsYYi+i9dSMp6/L/IxDgvWc0CWtIOvyTezXqvXe4W+IlCAJh5COIEYKYrGMxIbKkE0Vg226yr/4Bx9/pfPxHL06lFOAq8D8D/w+Q6Xa7USqV+mHgmW63+1gqlfqL3tdfT6VSItAGhrvf542y2Wz3E5/4BFEUUalUMAwDgNOnTxMEASsrKyhKQpBVVDlp+hYFvCBEVfXduum0YRIR0+m0sG0XSZLIZrMsLS9w7uw5bNvera2M4xg/CJLNraJh24kKem/iPHnyJJ6X1J+bZoYwDhBiKBQKSZ9JbzNTLBZ3la84TpJxVElF7DkogecjSBFXr13jzOlzXLp8gUzGpJAvEYQuy8vLiILM2Pg4AgqCEBOFICsitfoycSRQKBQQhORmFkWRkZERRFFkZiaJ7fM8j1qthqFq+L2NzaOPnsW1LSq1KplM0sswOTHJ0vIy2WyStmPoWVzf58qVqxCJKKpMPpvjiaeexvGaVCortJsNZEml2WwShiHZbJI0FQRRotqSuEG5dJa2bWF1OghCct0VRSGKIpaWF8n2JkTTzPTUIZcwTA5xYRhSr9cwDAPTTDbr2VwaAYl0xkgOfEFSp9vpdBIlsPdwKGpCM280WruEdkkScF0X27aBCD/wyaQzhGGEZSU0bFmSsZ1OQhOWZTzP6y1kKlEUsriwkhww9TSLS4sQw/T0dDIBRCFWx0JRJWZmZjh58uTug3zvYb3n1IiiyIULFxAEAdM0KRaLOK5FrVYjn8tz7/C7srKE43icPXsW0zSZm5uj1WqRyZiEUZiQUUWFfD6fOBKiyOLyAlndxEynkSUJSRFRNR3bcgijENt2mVtYYmxsDFMDy+mgaSaaWSCKQtqWg+84GEqyCBmqgijEqJpIEPoIoozdsVBUHUmVAZFYSFKgGs0GIiGe55BOGywuLjJSnmR2dp5XLl4kAn7rt36HTruDiI/jOoyUx7A9N/mbthqosoxmqDiWjW6ahJFAELmEQYTreoS+g4BIeWSEOA5ptTtICEShgK5pqIqGKIi4fgiSSOB6aKaBJCT2fwQ4rkWpVEIURer1+u5mzvMdMuksgsCum2KaJu12B98PkhQqScJ1EwJ9LltgenqaF1/6ajI3+Da25eL5HoGfpNUkSUPi7n3wty5miCzLpNPpXmqRs1v3LYoiYRAjKyqCEON5DoIQ9kjnArKk4vsxkGxAf/Of/vI/OB8/wPj7WqtyuVz3ox/9KI7jYJqZXdGoVqsRBAGFQoEw9vFDj9AOSBsGgqJRq3aAmAgo5A1iIcbudJBiGctrkc3laLc75LNl0mYa17dptetompIQraVRavVFimWddsul07HJZsqk0yqu5yBrYi8tSmVlqUJpZBRINhLNZpNcLoeu61SrVdrtNqZRxJAL5E8sUb/2KGOTMr//5V9iZKLEyfFfp9OZw/VrFItTuD6YQpu5pUVKo2NEocRStUHazCBGAbmMSXV5BbNUQhQFmvUauqphpE0cx+HSpUucO3cO13EwTJNrly9x6tGz6KbBzMwciiLj2Da6aWDqKmIk0ug0yebK2E7AWCFHo7PAFz/3HF/4Zy/zxeef4ufe+wyiEnNt7mXqyw7nHj1NGAJiQoaWVRW5lxA1MjKCZVmoqorn2GimQbNukc6kKeSLzM/PYJgqf/ilL/GpT32ceq3B8enHabQsxLhNo1kjnc4AAhk9jeN5ieth20kvmBLjeT6BH+E4XrLetpxEmAsCojDG89xkQ5vLEgQhMSECyUbcMAw8z0cUBRr1FtPHp7HaLkpaoLK8gmFqrCyvMD19Ei9w0VQVUUiShy5cuMD5808wNXmcZ597FlEQCSOffD6PbXd4/PHHefHFlxgdmcBMm1hWG9+LEASRMHJ3e0U83+Pa1WuMjE0zNlZkfu4S01PHyRYKvHphBqtpJ/08CkxMjFGpNBKxUUwS1AzToNW0KeSLicsbOrRaTbKZHJ6jUqnNk89p5LIZ2s0ORjbN8nKVOJIQZYlyqcDISImvPPsi58++ly+/8EsUsicYKU7ieCvksgbLCzLLKxeZOp6jnB+nabuMj4/T6XSo1Zu0Ok1KxTIvv/IyH/zgB/Ech7DVwCxmaHdiHFtgYjoksEdJZ0e4eOmPWVh6lumpJxgxy8xW2vzhs1/kfR/+BGN6yNTYGJ7rYochyx2R4ydOcuXCS5yeKFBr2riOhaRJ5LJ5qpUquWyWVitJWAsRUJQQSTERZZk4FghCj8ZKlbGJcexWk6hHFPfEmGK2yOVXL/Ho2bM4joUiK9huC03VqNUrhEHMqVNnmJmZQZJENM0gnc5QrzVZWamQzelomswnP/VxvvSHz/Jz7/8k/8tvfAJVVSmXyzSbTTK57K5gdi/lSlNUGvU6mmn00sp86B1IAERZQpUV4qSVhzD0CYmRiImiRNyQhESMRpIpFvN8+tO/+J/N+SCVSvWlUqkFoAP8JfAW4HS73XsSXhM41Pv6EHADoPf9NWD/f+JnfiyVSs2lUqm5ra2AWq1CEHhMTk5+z0Ygib9TVRlIDg2+FxADIUkjZ9LwDIIAjudgWW1s2yWbTTM6NoIQh+TMHKIkJoq6ZeP7CTJekdVeGUSM6yQxeUQxpUKRKErizUwzgwCUCom70LY6VGp1JEWlUCojSCKW7eK4LohJQ1ahXKCYyxOFIa1OC9uxEQWBpaUFiqUSZjq3e3LVdYNsLgexCES4jofnO71GVglJFsnni7sbFVmWaTabvYZqlYnxcWRZZmpqitNnHyWXy1PIF+l0WlRq9cS5yZhkM3kcx0OWJGzLIkak1qjSbjZRJBHXsdE1A9t1ePmlF6itVIiDgDgSCEMfSYB02iAZg7YyAAAgAElEQVRtaMiiiCyLCAj4no+hGwiyiISErhoYmoYoxFidDrbVppQvYRoaxUKJXCaNJCm0Wg0kUcE0zZ5bkMUw9WSCkyRkSQUBBGTiGOIoqVvUVAOlt7EXhOR6JypSUqrmunYSbRqGPRdE3W0ilHrRdJIgIvZUp8hPHIlEfZZoNdu0W8mhcnR0FMe1CILkYGI7Fo1mjU6nRRQnSsvU1BTNZnNXcSuXy5w6dWp3Qwv0IpIL5HI5gsAjnyv2DnRt8vksmUyGQqHE2NjY7gY1Y2Q4OT2FbuiIgkQ+V6DZrLO0sozl2KiaRi6TxwsjJFXBdl2a9Rau4yKIApaVRC6WCjni0EeQ4fSpk1ydvYbrdYiimIxpomvpZLPtBQgISJKCFwY02x00XSNGIIg8/CCg0W7Q6bSorqyQz6QRxZgoDhgpj/LUU0/jhQFziwuMjU7wSx//VT7727/OZ555hvJolpOny/hCE1lz0fWIsfEssuajGaCZEGJTHNGJIgE/DDHTGkY6g6JptNstbNsnl84hqRqFcgFJVjHTOTQzTRAGCLFALl/AtW3aLQskEUVVKBbLuK6Loijkcrkk6jNOGu7andZu2UwURbRtmxBwA49mu02lXiMCjp84hajKXLh0gUyuwMVLr9BpW/h+0tSvqr0GUlFEkuXkcBwnzeKimAAxZTkps3McZ1fgiOOk1FKUSFQ0zyMIPGIEPN9HknQEMXGuBAR8/x+idv+u8fe9Vq2trZFOp0mnE6fhe5XBIPB6CmHSaB3HMaKYzDOy3Cu/6ymYYu8Z1TSNdCaDKAjYlkuxVKSyUu0dXkNkOXG+XNelUChQqzaSCM04id5O1MkkTEMgCdvwAr938BW+p6k56rluAYqi4DuJqi4h4Hg+jWaDdMagslKh0+lgWRaKouK7Ln7gsLCwiKYlUfNJY7ZITBIx63keggjFYlI6m1QSJHO0YRhMTEwwffw4C4uL5HK53sbYRhREgsCj0Wj1VPlEBAmjANdxkGWFTK5ArV4jCmJUVafdssnnCkRxiKGpvehRP4m1FRMhQdFURMBxnB5MNNh1oVVNJXADfN9HkRMatB/4yLKKgIDn+kiyjKrK2G6A67pkM/ldl6LT6aDICn4QoGsamqYBMZl0FrXnao2MjPQiTYNeiXDSJC72MoTjOO6JqffiUyVc1+m5mxqBHxEEUa+U2iYIAkzTpGV1eu+XxCYn4l7y8yqVSi8YI9trpneRZXlX6b4n6kqShO/7TE1N7Ua5lstlZmaSQBpN03rBMRKO47CyskI2m951+iRJYmpqiiiKWFlcQVEEEMBzPcIwYmWlguc5mIZJqVhCVsTd8JcoCvEcDySRbCazW7Fw7xrkchlGy2UuXb7Cz33ww9QaDbwgwO64iAJEQYih64nThI8gCLRaraRcWxYo5ItYjo0oykRBmAQ/WA2yaZM4inn0zFlmZ+f5yleeQ4hFTp96lI9+7GM0mg0uXb2IJEdU2yHEkM/mkv1Ru4kkSTi2Tdu2KJZKNBtJkqqsShALhEGIKquIooJpGji+hygI+E6IqmnEJOI4Mb2DpkMUkuxJJKnnxCf3quu6PQezTRiGvRh4Hb0n0qZzWcIwIgpjOu0kgGV8fJypqSksu0Ont6bVa0tJ/HMY7rpbSRN8vLsW3QsrMsxknxIE/u59ei88SOg1qd97hoIwAAJkRSaKAnTdRBQFwjAJXgl6h5YfZPxAh49ut7vT7XYngBxwHDj2n3rZvbn6+3zve3/ml7rd7lS3252SJJGRkRJ+4OGFHnrW4NSjZ5CUZAK3Oh1cq4MYKkihTlofJ3SzFIunKRRPUK9XkcQIMY6RApnJ0iRe26Y6v4jvgySrhEFMoVBE0RQy2SKl0hSeL3D89HG8wCVXzDExNsmjj57BzAgsz79EdekiI7k0KgIXX/kyslDnQ+87xSc+/ChTo2kmSlkyqkynXmEkV+LaK1eIvJDLF5d5+cI8TWsRMx+RL6YpjKiMTkJG1SmlS/hWk7jtU84Z5HMaYSBit2ImRrLEfoNOrU7GLKOpOl/+w8+zPD/HmRPnePL8OaDBC1/9ImfPPk69UWNsyqRtN5lZWCRTKIIMftji5IkCS4tzzM3OE7girhWTMbNkzQwrV2vMvHyVqxdf5YnHz3LmXJEQC0SPjrPEF77wLBdfnUeRZHzPA0HDdWCl0sHzk4k1wkdL61ydnWF5uUq1WuPazBKjo6dpNgMiQcTxfVpOAycIieIML7w4i25IjI5kqVRmewqzAqKK48YEUZLrHRFTr3tcuVpBFLM4jsjsbI0wkhCQyWQKaIpEo7aC43RoNuvk83kKhTKeG6MpOUwjD0gYRppWq4Ykx3TcOj5NfNrk8iq21+D41CimKnPpwisQRLhtnziSqDU6iBJ89MMfpFRQsdqL5DIwVjZx7TbjoyVWlpZQdZNqo8HY+Aksx+c3fvsZqo06z7/4HLMLM5iZDGYmh2N7qLJB4IWMlyeYnjjJb/36Z2g320iSxGipTL1aYebqJa7MvcxytYKmGsiSztNPvZ+rV2f4nc88w8c/+mFazTrXrl1BS8usVOfRMiqRJvLy5YtcunKZ8YkyubxBNivz/7L3/rFx3Ped94vD0Wg4Go3Go9FoNVqt1+vNZstuWIahGZbhsYLqqnr8OK5P9WP4gjx+gqB4UARFYQRBURweHIJ7ckERtEVwaIMgyPnx5TEMwzAMR4/qOqqisKqiY1mWZWh6vdms1+v1er0ejUaj0Wg0Go2Gzx+z0XN/tT7gaa9A7/sPl7vL5YKc/f74fN7v13txpsJoEHL2tYs8fuoUK/VFMmeIGPQoKAGi5DKzqDKKW7jpkDgUqNZm2NjcxihaJJJIp99D101GfZfIh631NqpQx1Jn6fd6vPjc8yxUl/nD3/saz3zpJLbhoE7IVI/arG/LlKrPUJJOUUyLDDe6rK13UfQFAm8eJXsY3TVw1waUtAK2bGHJJYhkNMmgUpxBFzXiIKJql0kjgen5k2y2u2w0m9hFjTgYIJFQNC10LSeX2MUi/X6fer1Bu93GcYZkhDhuB8OUiON807a+vonnBVw4+yqbFy9QLVWpVWqIJEzPaHz7u7/P9HSJ+dl5NjeaWHoFbxiTJQrDvoPvBMiCROwGDLsDhCzDMgxWlpZRFQ3P80kQSGIBRdaw7RKCJKAbEiO3TRgPcbwWmg6aIpHFGZZe4sJrl3j5pbMEfkAqZKD8s4AT/rMe/9hrlaqqhGFMkuZ+wDRN6XQ6RIGPJIhkSZzntWhFDKvE0M1xypouIgkhmioSeBHuKMC2C5i2hO8FKIpBpVKh02sjKgLdfg/fiymX6nhexPScjeePckJWoUSxaJAywvNHCILE8cVHaDX7iBI0ZksIgpxDCDOJglVGFBSiKGFx8XhOTqxPYxYquRw0vEyYuBxfWUFVEsKgj6mVSROFXn8NQgmrrmNYBTxfJApEZKnP1uYqYSTiOgKJMOTS+fNEXsby8jJWSUBRUzbWd/jSb3+Z1869jKGnGNo0UaIxXa+Qph4CEYqUkWUKSSAjohJnPotLM3SbPdprPkZBJBUinvrCo2y2zxAEBqOhi+cFhH7K3HyD0bBFGIW02n0USaPdbZFlKVGce92iJFcUqHJe9R2OAhqNOdbWLpMRs7W1xuknHsfSKiSpwvrGKoXCCFnSabe71KqzNKZnOfP66wiSSJz4WKUCmiIRugKyaiDJIjMzM2xtbSEKGVmWIMvguw6iJGLZNs8//wKNxiy6ZuF5PqZp0u/3iBOfkdPDMBS8wGNjY5vhsI+IQNEu02jMQhKztbGN78XEcb6hXVhZxneGDEYdhv02SeIDKeVyhUKhzIXVNQzDpFSpMHJ9Bv28E/zCi8/mSoeyRZKk+F5EvT5Lr92m121TKlUIgwBn5LKxsUmr02JpZYVLly6ND8MO9dkSJ44/RqvZpVisMBwMKdgWuq6SZXD+wjlmZ+Z5/KlTFC0Ty7IIogRFkOj0enzxi0/Tau8QBbmU+IWXXma6UWVxcZo0svitz3+R0HcI4wRFLVKb0ZldnMYySgixgCSLJFkusXNcH9M0qFUaFIwCSSKgSnkhaHtnG9cbcO7Cs3zuqa9gWhqDUYu1y5dpbQc88djnefj05/n853+L9dfO4HQvEwsuqSwiGxVksUbiu4S9bUolC6sxh+/5GJqZe3mTmHKpSPxzGd44Z6c0c4JBr0kUuMTRiND1sO0KkiATZylhEGAWDI4vHUfTNayCjmUZmKZOsVSgaJcZOQ6aZiAKAqoms7O5BZmIrGk4joskCWRiQKu1Q6nc4Ny5jVzW6QypV6rolokgCFQqFUiTMbE1uye9MnQTPwyRJCWXP0q5XzZJc1Kjpoq4ngNCXhDVNZ04SEkyEVnV8D0/J/GVbFRNzYvwH3H8N61mu7u7/sTExCqwCOgTExPiuGJUBIbjpw2AY8Bg3Mo+AHh/3+tOCjKKUqBYUJBkETEWeOl7z6HrGqIAVsEiIyFJYpzAI/JcVEPlwrnncL0Rj59+kjjKW0W6JRMnCcV6jSxLiKK8ndnubCEK+WMiEttb69QrVbZWN9CMvEqTCTGD0QDHiajMniLLMjZabZaXl1HNBs4g4tvf2qZYLBDFMSOnjVmQkYyM9ugyjz09x2g0RFNHFKw6/Y7IsBtSr2jMVX8H13eQ1BGxkKJaRfpJj5IxPQ4g81C0mK2OR2PhOHEcEQQu5UKZMFzEtou8vvoKmmbgODKNmZNcuHCONIUoNMhCEdOWEdMBQpqxvRXQ3u6zvPIoYTQkyXKdr+frxBFU5jWKs9NkqUC31yQMBUqFAmEUMuiHfOtbX6PfH9Bpj2g0jqNbPlvbFwmCiFJpFlV6mDgEyzYwRI0w9kgShaXlGVq9SxQrGkmUMBhEVMtlFEWh19ugPq0TuhGBlzE3s4SQZrjOAF2SsIpFWu0WuqYzaPexDJU0SfG9JoYuo6smgpDihwGIeepnUVBw3REzM3NcvnyZ5eVlCrZBEDi0t4aYpoEkGqhyGXcYIwsFZhsLuSzJd9FMFceLkXSTZ/7t79PpdpA9h9DPq8y2bbN6cQ1ZlqnUlhiMHDq9PqVKHVk1ePTxx+k0d5irV/jOn/wfVGsVLC1lZaHGudf7SGnCwkyddruDICYMnC5xFCNJ+cH6a9/4d1y6eJmHj68QRBHrW2tUK1VOLzxFu9NGEFJcv8/TX3wy757YZbRxxe3pp7+IkGY4soU7CDANk8+f/hyu69Hc6dPvd/niF/93wjjCGPhkLgyHPmF0mVKjwurqKrOzs9TMIlEQUbQaDAYDzEJ+2FBVHQEBTdQQCxnuqMficj0PXep0SJQhcRqShgLzx1fY7q3T6/b4ypd/h9HIYfFXelSqJQpmn/alr/Hyq2d45nd+j9J0jV5/SOgMUY0QWZLpegGB71KU8kAu1x9RLFrohpYHQKYZKQLdQT7NrF9+BUOTARnHjRBUg1GYZwoIqkSaZAxGQyzT4sKFC0xPT+eBXbLGzHSVIAwp2nmVr1bL9eWnn/wCpmmxtraWa6I1m14r5PGHv8TG5S12dnZ45nd/l1deeYVMSBDSmHK1TBB4DN0BlWrtXleDNGFzZzPH/loaSZIRBC6KaiEIGaqoEQYJBaOBrIiEQUTox5hWmSgKcbwRJ06tEARBjnZNZbLoo2MM/6WPf6y1ShAEgjhCJA/kyrI8Jy0IfQxVR5FlgjjB92OiOEG3cpmp77sUK2VC1yMMQjTLIoxGJOQJ2oPBAEVRKJgWZ9ZeoVqpI5sFer0BoqSQpT79fo/p+jTdfh/DkOn183BdWTI5e/YsS0srDPpNDFOh1xkQhhELCwuIgoTnj+51QAzDQBQFRq6LG3sU7WkKBZuUCNf1ELKEOE1QNAlN0dG1Ipd2XkYVU2YbpwiiAE0QsQwNIRMQZJXlpSWe/eNLzC0u0+12scsia2sbhGGC74cgQZiEeH6EIip4fojrdZmensF1PEI/QFFUZFUh8bLco0kGaQJC3iW0TJ1Bv4OKTa06zdr6BQqmjWHo9LwASMnSkCSLiCNIswRNM4iiCMtU6PbaaLKCouhYVoQkyiRJjO+7JFmCGqX4UQyxgFGUiGMHQVDRNB3HcUiSiIcffpg0jRkNR0zXG7iBj24WkEWZJE7v6eCBe11Wo2DgDByG7pD5+Xk6nQ5hEHPixEl6vQ6e52AYJkkS4XojLKPKk089ybnVF6hWqwhChueOCMOY48sncH2HjY1NZmZmQUywNBM/CTAKBr1Oj/mlhXH3Pweya7pJlsH2ziaNaoN2p4Vh6szOzOQH5zhEFCQUWRtj5UfjMMQcOe77HpViA9u2mZub4fXXz3Ly1Ak67R4FKw8TLtplTjy8wrlzF3jkkRMUChaSmFfeXzv/CiQxkmYQRQ4Fs4QfJ1y4cIHjx4+TRHmnS9FUtna2qVfqRKlAt9NHEFN0TWMwcIjSGCFLiSMRKUlBkUnSEMvUc99klJGmHoqi5rAX18cyDDJJQdVyz0MYxszMNOh2diiXixRsm4sXz+EFMeubLU4tL/PoyeO0ulssLZyivZMglyUEUkb9LtXpKr4fY5cKNNttikUbUVTY2tkeS8By1Uq5XObs+QtU7QTSBFlUEESBJI4QRGnc4ctRvGfPvsbS4jyGoeG6o1wWlcb4nkcS5+9b0zQGgx61Wg1RUFBVFatg0GxuUqlX2e52EYYeX/ny7/Hi977HiYWFvPs/Bp7EWXqvgxaH43VEFIniAEXW7nX2kjQB4b/yd8QSiiwjZDJB6N0LHRXSHLAkqPnPibKElAm43kdfo/7BzsfExMShiYkJfXx7CngYeAv4EfDE+Gn/G/D98e0z4+8ZP37h79PQQm44V2UZXdXQVRVJEiHJkCURSVaIwwBn6OKMfNIkzhnnYYCoCJTKVXzHRRBSdENlMOjhOHmWhO/npqlypcKg20dWJHTNIEtiLNPG8QOG7hBdM3LjW5SbWJMkQsxSsjSlVq/huA5uMALRp1ovUq4WKFeLVGtlJElFlgxkycRzBHpdjyiUiGKoVErYRZNWZ4uh00VRoVQqY+gmul7ENIrYxTKVWp3+oEd30CMIQtrtDkEQkGYZGxubGIaRTxASRFFIluRpuZ7n0O938TwXTc9TNU2rSLFYZG5+npWVRWZmG6iaShiERFGYZ5JoMoqsE4X5xT8YjQijCGc4oN8bYBct+v0erXGqZ6fTotcdoSo2sqQxGIwIwhFhMmTkdBBkGcMsYY3TLS9dvMjapUsEcYAiC3iBx7A/QNdU5uZm0C0TUVEIk5QwidEtE7tcwgsDMiQU3UBSNCAnMTiui+eMiNM8KEdRFDx3xMb6Zba3tmhMz2BZNqdOPUKWCaRJytbmDqViKa8w+S6qquXkFS2XbQV+QBZlnFg6QZSkdNptnvvec/S7PRr1mdwTFEW4rksURbnxz7LGkgoVTTPIUoGNtTWCJMJxHebmF2jMzlOtN+j1Blh2AUVX6Q9HOI5LEoWkSd5qT7ME3cgP16qmsHrpEq43olKusbXdZHFpCTIYOQ6jocN//OafsLJ8nLm5GVZWlvL8lHYbfVxRsiwbRVKJoog4jjAsHcuy2dxcxxkO0AyD2dkZBoPBOP1WuCcXimI/lzEYBuVyGU3XKZgWsioRRSlZFiMhoCsaZBlpmlCqljA1jTRJ8AOfJEwQBYHPff4Jvvvcc7xy5gzHTy7nqfJCyvRMDV3TOLd6nmZ7h5E7JMtSBt0ezqhPpVamWKrS7nS5eOky0/UqURgwGo7IMhHH9zBNA8Oy2Gk2xxO6gK7oqLKcbwbTjCyOkAXQVQUxzShXymiqge/79ybYbq+H5/pIopjLR9IUTddIkwzP83JpWadFpVLC932CyMf3fTzP4/nnn8+9GLI6fr0EQZDQNOOejlbX9XEOgIgkyuNZNiXNUnzfJwqTfGMFhFGA7wUkaYIgCnmXI80o2uV7/jTDsCjYJoWC/VHn9X+R459irRJFEd918nSjJMVxhli2mVPO1LxqPRiNiKIQVVGREOn0cplsGuXd4CRN889MkiBLMkmSJw9LMmxsbNCYnUdWDEq2zU6zi5AJtFpbzM81GI1cCpaFH4RjT5lMlKScP3+e5eVlhoOQUT+kXC4xP9+g3dlmp7nG5tYa3e42xaLFzOw0Fy+9zubWeWZnH6HbDei0PXS1wuee/BKaIVKwZEQpJsli1tZXmW3MkyYp29vrxKGPrJksLi6SJDFB4EImIwoKBdum2WyiqQVefvEM1XqVF198HtcNKVhFfNenNmNjFUw67ZgokCjaVRaW6lxefxk/GCIJBUgtECJqM2Fu9tUKRGFGGCU8duoResMRdrHETGOec+dWOXnyCRqNOknWZ2PrNRRFo2wvMV0/RRjGNGYrtDqXiNOAKIzQVJUw8nIISamIqZkUigWiKCCMAkzDznNVRoNxXkne7S+VSmxubhMEAVEU0u8NIIuI/ICddhthnIWhaCr9fv//yxCRBWanG5imyfb2FoZpsLOzlRd7LDM/CMXZPanUYNjnxIkTaLpCu9lGkHKKljw29NZqVSBDkhTq9TqCIPDUU0/RbrdZX18nihI0VaU+PU0UR1y8dIGiXWC7uUW33aJSsnnt3Gv0BgPWL6/xuSe/wPnXz+MFAUgiSZKRkaDrFrIss7y8QOCOeOSRx9ja2iKKYmzbYmNjkx/+8Adsbq3T3Ony+OOP8t3vPoeiKmiawauvvsrS/MK9UFXPCyhXKiCkWJbF2qXLeaaRLHFxdZUkChmOBjjugJXjS6xd3uDxx58gimKCIGI09Gg2m4RpvuHtd3oY4w11fbrCcDigWLIRsxTHcbBKVYIwHgMCPAIvwrIMikWbfn/IxvoaBcuiXiuzsrzA2qVNRFGnMbPI6+dXyYS8wGBZBoom02118rVFFLFMk8APMCwDMpFef4ikyEiKzKA3pF4roihGTmuNE5I0uWfIVhQFAQlRlDF1kzNnXsMwDbrdnNa4ubFBGMYUCrn9oFyusra2zsmTJ7GLRdbW1sc5VRJB4FGr1JmervPCCy/wzDPPcGH1HIIgoSjS2EsYoqpq7jn1/fH6JNHr9jAtPfc+BjGe5+D7IYqcr4fBWJKoqnm2XhzH+TUah3m6ve+PYTsGpXKJ4bD/kefrj9L5OAL854mJiUnyZfSl3d3dsxMTE03gxYmJia8Bfwf8p/Hz/xPwf09MTHTIq0hP/cO/4i47rc2cZpRlJEmMYRqM+h6KpqIa+URsVyx816HV6hBEIbqWb7x2Wi0sy0JR8nAWTdNw3bxyvbm5SRwnLK4sj0PGDNJMoFiySOOEaqXIoNejXpum0+8iCDA9PUOn0yYbt6tGoxFzc/nXZusi7U5GGCYEQcj83ALHl59ge7uJLKqcfuwRRCGl1+uytX0ZIQuZXagiS31STM6e3SLwEx599DGSuMcf/+G3KJWLuZks8gi9iChMKNo6aZaSJn6+gSOjWi2ysb5DoWihaQpWwaLRmCVNpDzEMPB54YUXqFSqRGHC1o7P2ddDVo6vYFklshR8Lw9rVLUCZWkGQZDodT1su4AoCuhWRByHPP/8dymWihSLZQQyssRGVSpUZ3V0TeK5F7+JIkp4QUK5uICqKriei2nqlKs1JCCJQhAkRHKTtiTKrK1fotcdsbiwTL1uE4bhPSpEvz/Asgp4njPWEjsIQkbZLiGpSo4tFYR7wVWypDA9O80f/+E3KZdKpEke9lWrV3n89OP0ej26vRZxlBGF+cZQVgQuX76MZVqEScg3/vAPqNWqFEulvBUpiqxevoSp6ShKbur7uVa21WqhqjnFo9ft5X9HXcfUDKI0zj0lokyWZBiFIogKSRiiiBLHHz5Fv9ckiELKWgnHcQj8gFYQMjszi+OOGPSHHD+xzPz8HL/1xd/mS7/zW5TLFb78zO/y7W9/G1GCOI558cWXkSSJ2dlZvv3tb1OpVFheWSSOUkajEaPRKO8AmTbNZk7j0TSNKBB57PFH2N7eptXsjP0qQ1RVQ5EVut02iqIRBh6arOZwADIEQcZxHRRdwR0f7O2CzSB0UA2dmmUhSSruaMh3v/McTz31BHESs37pElbRpmyX2Vrf4OmnPkcQpQRxil2w0HSFZqeNF4YUMhtdVzm+soKiSmxsbVIuV+l2u5TLZarVKoORQ+i5LC0vY6gSvh8SRQG6XmB6ukSrtUOSQDIm1CRJdk/LXKlU8TyPfr+LrhkoioyiKpimTbvTRNc0NjbX8DyPpaVlyHKaXMEqkpFgWRbT09M0Gg1GowFplpEmGWnKPW/Pz6vXPydaaXruKfM9H0mS0TUdQcgXDF3PD+qe5xJHaY4uzjJazdaYeuNhGHlg1HDUZ2cnJx39j/H3jn+CtSo/gEhCjqsOkzgnAIoiWZISJQkkkI0JZkHgI2QZpVKJ7VaLgmViGDadbptiRadQKiJsNvMCUegTRQEFqUgSp/hBjGkYpEmGZAiIIogC4wJDzMzMLK4XsrnRpN6Yod1uYxdKqGpGEHh4fpYb1hFxXAfDsBiNcr+XJGdUa0W6nWFeXReh2+5RLOtIkonjuWRCimnYDBWH4TBCklQ816VSK9MbeSSSSqvdx7Rs2u08YM91XHRdZzAYosgqszOznD3zOqouYNpFgtBF0SLa7QFxmFGtzUDm0ettYxVNAs8bm7tz+IpmpHihRLlUYfXCBqah0+l2KJeK9Ac+G5ubGLqJquusXjpPvV6n3qjz0nNrCKg8fPxkTtyKyHHcqkYQ5Dr4brdNJgmUrBLrgw2G/QHF8gyNRu5BfPa7z1MszSGJOXgiyzKOHz/Oq6++SrlcJAgDBEHEcQaIYk7uakxPAzKFcpHt7W2azSZWMYevhFEw9vdVaLd7hKHHE0+cZmtrC9cdIkpQtCt0uwO2m12+0HiE4bCPIGQICPR6PTwv3wAeP36czY0tQmdAluXyOdu2x8G9FkHgoakqkiDgOA4L87NcvHiRp7/wNGvnV0nimKOar+gAACAASURBVFK5iCRpSIlAu9WmXCpTLBXx3E5O1/R9EEMM3WLgjPB8n0G/z+LiIpcuXaRSrTI/s8KRI0cYDAYMRx4b62lOntQtgiCg23V56qnPj7HtIoqi5PjaBFx3xNzCLI7jYJgmjz/6CI7j5sHCZMSxj6bn72M0dNAsHUO3iEWBra0tTp5+jFTIkBCQBAnf98iyGEXMi7WqquBHueHfNFTSNCMMImYaM7zwwktYBZ1ypUxrexs3DpDk/P/ueV1UXWRxaYUkMgmjHBBSnamwvb6DKhVIUyhYFgN3xGCQ+3yGzghVycOfvcBDNgTIIEMce6DUHAxkGPf8xnmHKiZO8u5TGOa+MVES7xWxhsPhPY/gYDAgCiN0RSVJYnQjL6Y7oUscJKysHOf111/mkZMnGbjROIwYBIR7HpCfH1riIJfo5ZCUvKsUJtk9n5Iokitr4hgkaYznzQlwyvhA0u87JONwTGlMcfuo4x985u7u7vbu7u4nd3d3Z3Z3dxu7u7v/fnx/d3d3d2F3d7e6u7v7v+zu7t4e3x+Pv6+OH+/+g29iAuLQI/CdvLKfgR+EJAhIsoaETBxDHCX4YYiqKVTKVSQyRv0BlUolby17Xm6utm3SNCYIPQp2gWLRJgwSVFFDTAVEJJzhAEWRGQ0GJFlKq7WDkGYookq72yKOEwpmfkDRdQM/HuHHQ4oVhYWVOY6fXGZxZQHHi+h0hiSxTOCLDDoh7aaDJFroqoKkiBQsEVlNWL3wCsWSwfxCg+ZOjzBMWFk6QbXUACCNQJR0CoaNJGlksUCt0qDZbOfJkakIQoY1/mCbuoaqqoz6PaLQJyZmeWGF6ek6khQgiXk1YHtjgyQGUZRJEh+BkOFgRJYqDAYuWSKRJiJ2IT/kDPt9Hnv0FLMzcyRJgG7qZKhsrndoNnvEaUa9VsQuK1iGRBoFuEN3vCimFAsGxZKFJMqkcYSu59jeKI557exrFIsFfM9j2B8hZGBoGhcvXMBzRghZShTEqKqUT1ayimGq6JqO5+dp4K1mB0EQUTWF186chSzl+MqJvJospKytXeb57z1PmqSkiUClWkYQIE4iHnv8MSRFZOQNUVWF4w+fyDcAkoiqKyiKyPbW5jizQ7yXGZIkuXzA98Mc7azICAIUCkUWl5Y5sfIwplZA0wqUijVkUUZXdWr1WU4/8XkeOXWKEycfQVW0HPmMjCQp2JZFHEZYhoWqSDSbLeIk4ku//UXWLq3xx9/4Bs9+91mefOJJPvfk5ymXShRMi+lanVqlyvzcDCICzsgjSRIqlQqGkVdQtrY20HQTWcoJXpaVk8MURSUMI3w/uJeQqmoKYRjjhQGqrBLGAVEcMnBGdAaDnH4x9uMoik5MiqGZZHGK50VEYYAoCszMTrO5tUWv16Vaq5MlKd1+B1nWeH31AoPBkCgIgCT/vJoGmqLQ6bQI/RDP93Acj4JZIEuhXpsmEySCKMxJG3FC6PuEcYSsiCjj62Jja504TZAUEVGUEUQZwzRpNBo5MaTTGU+OIqKU5/54rkdGnieTIWJbRerVOoEfIogZs9MztNo7jAZ51W44HBKHeeoxWUYOu0iRFRFBzO7RzXKp1Ph147yDChmqrCLLufQljROyNEMWVGQ5JyYpioJpFTANmygOyQBJUojCBMd1SNP4I0/s/xLHP8VadevWLTRVzSuDaYquqAyGQ8rVKoWCjaUXqE6XaDU3abZaJJlAEqdompEnY8saGTkfv1Sq0Gq1iOII0zAIg4hyuUKv3UGWFdI4RVIUdFXD80Z0e11KpTLD0QhV1egNh7iuS7FcZnZ2jsFohGkobG1tIskSlXKVQd+n0+mjazZLi8e5uLpJp9NjaXGZjBDXG7Gzc44k6TM3P48sGJw5u8rGeos01kgig82dV4hCgUq1QsHOpU+2UQEyFhdnKVgqrWafuYVZSkWL2dl5vv2d7/HEE08hazLTjSrV6hzEEp7fx/NHtJs7zC3O8fJL36PT7ROFCbVKjUKpjDNycR0Xw9QoV6t5dpTrEYQec/NzVGs27d4OQegiiiJ2qUKv18W2pwkDgUHXZ3G5zokT01y8/DKSrLK9OaBeOY4sa+i6zPR0jThLUSURRdGolCo5nETKEKWM118/R606x0xjjrn5GXw/Rxh3Oh0qlQqlUiWXvlgWYeCiKCKe4xJHCUEYsLW1xdzsAqKc5yZcuHCBF194CTETee211zF0Dd8PiKOUVquF7wfIkppTuXSVz33ucc6dP89oNMqlPkHA/PxsjgYOQzY2Nhg5DoZm3jsYtVotHn30USQpl8bESUISZ2iqxqA74Le+8AXWLq1iFU26gw7OcMiw10OQZVTdZHp2np3tLdY31hgMBmRJRrfbx7R0xAxK5RI7zW00Tccu2KiyxsgZ8Mgjj2CaJqdOnmJ7Z5u5uXleO3uOUqnEk08+yb/76r+l0Wigqiqnn3qcdrtNqVRienoaz8s36s5wRK/fo1IpE0Z5ztSZs2eYmZlmfX2TRmMOVTEBAc20qM/W8XwXSzfGGW4xvuNijCXJw+EIWRbxIpAUlWqlAog4owFnXj1LfbqGIGY4gyGSKlM0FYKhwygMUXWdKBNJUlA0mSiOcbwRmqKiyirb65dzAE0YUi5V88OUAKZpE0Y+I2eAJEpUy0aOtw3TsVpCo9NpkWUJkiQDQt65jEM0VcuzukSR7e1t7EIJTcsPXtVqjXa7RaVSpdPp4DgjatM1dF3D81xkTUNVJBYXFzl//jxPnH6KrZ0NMvI9sywp6EYOcxgOh2Nsdp5jpxsqSRSPi2UGQgrhWL6saQaCKOJ7Hn4QYBdt4jgey5elcYRAiKxItNo7bG1tMDs7+5Hn649+TPlHHGF0E0XX0K28Guj7PnOzc+OQHhFBUCgUS5x99SymYd3rOkiqhmkXkKScp29ZBhvr66yuriLKCjMzi/R7Q3wvJvRjCnaZJEuRlDx4rtfvUZ9poKg60zMLOR3Bc1FlDdu28X033+zu7BCFGtXScUgLrF/OZUhpklGuGnQH68R0QOpzefMFMtnFc7o8fPxRTj/6NP1ujCQUefjESdYuv4KiDHGCSyiqQIpAsVTCHQYEUYQkqPQGHmurm8RRSpKInDh+ijiJeeXMi1iGgR/lbbOtnW3OnHmZ2nSNze1t4iilUKySxTFzczVOPDJHEnusPHwCMVPRZYNqvUQYu7SbTTRZR0ZjeeUknVYPBIk4Tjl9+gkurV0iDj1k0UZMTUxDolhW8Pwe3/nOt6iWFzmx/DSlch1ZD6g2FBbnF1AkGVmUGHS7FAo5RWg4GCDLMu2dJs98+RmiKGFmvo6oZLS6TYbukFOPnWJucY65xVkEOaXT6TM7v0AYRGxs7bC+sQGAruvMzs2gaSq/9/tfYXNrg8bMNF//g3/P/Hx+4VuWxcqJBV57/RXmFxrEccDyyjy2bTAc9uh3OwSey7A/wHMcdEWFJMYZuqyev8jSwhKmqecoXyVvg6+urrK8vIg0zksZjQZsbW0yGAz41re/zeW1Db75H7/L+kaTre0eGxsdFMUiCFK+/gffYGFhme899zzuyCcMYqr1GqcffZxKpYEkqSBInHz4MUzdxHN8Wu0+8wvL7DTbSLLKiy+9zIsvvYTj+iwdX0SQQTPVvPN2fIkkDBCyhI3NNYqlArKmMj07Q3/YI5Ny2s7Ozg6yItNqdlFkg7NnVqlVZ9lY3+Hi6hqPPXYKWcgQxWxMITNptzosL6wQJwK9/hC7UKZSrhEFAmEYsby8QhbHuCMfAYVadYZKuYbn+czMz2MWbGS1iKCZlKoNkGVEVcpD9ISUIPApFmx01cLzAjTDRNcMREml3W3jBQFZnOCN8lTg+dk5LMtgNBhhaAV8J0RTdDTZRJUV0jih1WwikJJEMYNBj4XFBfzAZX3j8hg5qt4LN1xf30CURDbW10jiGEmR8H2H0WBIf9RnulqjWq8hiwIF04AsoVQq4gc+spLnsvieTxTmpLWfd+2KxSKCIGEaFuVKDUVScX0358EjsdNs4joOqpbz2c+de50LF84Thh4XL17Atu0xEz5iaWmFLz/ze/xX8SD/Y/x3GhMTE2iagannlLj5xiJhmBJH4PoxmqGTpCHHH15CkWUqpQq6rhOEXk5xtPJNQLVaRjN0BoMBhp5LExVFYTAc5BkX3VZeHOsPKFZsZFkjHssgBCmnOhUKBQA0XSWJIkIvxI98lpeWcF2XXq9LpVKiWCyiKBpZJuQZEI7P5tYmg14PFZWnv/goupnR67ZB0MmI8AOH3/7t36HdC3nq9Bfx/JQkjmm2NkgIma4v0ukOGQy7NGaqzMycpNfbIEz79PsdCkaF+vQcSRgRRAGBF1Is1hk4bWq1Cl7o0O1cYmm5gVHQyFKdNLGwjSph6hBEPZxhxOprbebnl9ja3MG2bbY219FNmW53Ow8KHbnMzcwgCQUUuUCWQafTRTdk+sMdFpcabG9tsLm5TRxmyIpBEEQ4bh/fHdIb9HF9n8FgSKFQpLWzheuOkCSZYrFBt9+k1+vdo0u2Wi2q1Sr1eh1JVNA1k3KxwPbmJpKoEEZJbkgPQmqNOmtra7z40gucPHmSJ554glarxdNPP83a2jpPP/00I2fA4uIitm1TKBRJ04T1jTWqtRJxEFOtVWnutKmUy+P5ILmH8jbN/HfLskiaZly6dAlV0dB1k16vQ61WY3Nzg5WVEwCsr68jCAIXLqxSq9XQNI3ffeZ3sQs2zdYOg36PUtlm0O+ys7PDdnOLxx49jSBI9AcDOr28W/4H3/haHl6cCWMaqcrGxhZbWxt85StfoVqp4fk5Ma3b6dOoNXDcIZubG6QJua8GePa73+P06dOkSYpuGjz68ClMS0eR83V39cIqWSYwPz/PxuYaoihQLOWyvijJcIZDZFnFdRyeOP0E/UGPLImpT1eJ45DR0EUxVCRJ5vz5C8iiiGkYqIpEGkdsbm4gCNDrdvBcD1EGXTXxRgGSKGEVbERJptXsYOoGm5ubiEKGLAhkqUC9MUMY+Sws5NkcrusRJRnV2jRZmrC9uY6qWHTa/Xuy3GrNRhAzev3e+PqxuHxxlenGDK1WHnKbdw579HqDMa1MRJaV8T5Kwi6X6HY6lEtVJEnEGQ5IMgFBzCgUCqyvXaJcqxIEOfEyjnLke5blsuKfh1lblkW700FRJNI0JonjnDAqSJSKFZyRR9EuEY2JoO1u915Xv9vtUatNjzNrfr5fUO8R1D7KmPzqV7/6//sE/d86/vRP/vSrp//1U3hXr3HNv8lDs/O88/4HlMsPsssuGbskSUz9F6t4zgiySXYnUiYmJhGQuJPexjTvY2rPHrKJXW7eDLALRbK7dxj0cz/D7OwcH4w+5NbtO1wPfPYqE1y99iHOhwGmXcT5YMTBg4c4evQIe/bsQRJEVHkf2j6Zffp+3MEtbgZ3ENmLgIgyJXJg/z6uee8j7bnLpHAH1+uzuxuj7d9Dcjdlu7lD++0uBbvK3/71GxwtH+NYxWT7rU0OHDjAB+87WAeP8cabTeYW8srAn7/+OgXzMA8tPcSVq9cRJie5dTvHcyr7pjCMI9y6FdNuN6k9UOHw0aO8sbPDsWOHKViH+GmryyH7CB84A1pbb3F/tYH34U1UTeUHF36EfkAlvBXw0EMrvHrmz7gWXGVycpf7H7BpvvUG6j6Jv7r8I2Zn58kmJnBGMVfcD3jjzR9zI/oAdjMkSebM9/+KXu8KJ3/9FHHqocgirnMLQcxyGdWugDSZ53H88sIS7lWXKWUPkjjJXnmKd3vvI0yIxLciboY3iW5GiJMiF//yMoXDNvMPzXH58kU+8H0e/exjpMkdfC/kmH2MVrvJmz95g1O/epID+1WUvTKmdZg///M/5zOf+WX27VP5YPg+v/prv8raX19kn7IPyyogCPDan73K8RMr7JEmOVo8hn5gP7u7d2m1Otx///0sLi5y7do13nnnXXYn7nAryiv0Bw4cYHNzk3/9G7/J7STlk7/0KSoPVjEMjQerD7JLyicav0S5fD937sCv/Mqv8Oyz/5m33/kp95ePUTx2lE6njaYfJLx5g/3772P7zTf5y7+8RO0XPs6eyb34N67R/tnbmIeO8t577yOKAktLyywtf5p/89T/ys/e/inuFYfu2z32qzqjDxxu3bnJkeJhrl6/zju997AOHeZmGLH06Ye4/FcX+fr/+XX+1dKn2WnucCO4yaQgUS5X82ThT84xHI74zGd+GdPU+dFfrlL92Me4m6ZMTe0juOpy7GiJtf/yX/js//woH//4J/jx3/wdx44+wAFd45p3nVs3YxQln3TefucdtIP38Xa3S/nYg2RZyt1dGH0YkN6ZJEvv8vDDJynef4zh8H18L2C/rBHFt7lx9RqFIwX27JFJ0iyXERSOsn+fypUrV9jdMwlZRu/tLpXyg1QfrPLjH6+zT9H48MMr7E6khOEN4C62XeDu3UlGH464eecOV0bvY1k2u7t3uXr1CleuXEHaK6KpOvu1KQQBjtiHuc+8j/vuu48pReHKtRHhjVs8UH2AJLnNu++9S8ouKXcZvj/EOlRgN4O7uyn71fu4fv0Ghw4dRhRzlKUgCOzdu5fr4U18/xp3dzMmJybZp6k41z7k2LFjyMpeBoMRe0SJg6ZOuXw/R4/a1D5e48MPXAqHD7KbCfxka4u3Wj+lWCzxwx/+xQdf/epXv/Pfe87+lzr+6I/+6Ku2XSDwb9Co1zEOH0bcI/Luu++ymwkcKx1hj3wXz73CB+/dwC4d4eaNCEmaZGpK5j5lH8G1m8SkTExmefkvneBOmnIrvo1lHsJ1Pa5fv4F+QCPNIkztILfThP37DnH9yjWMgzpZdpsD5n14V3z27lVImcyLV5O7HD5k8v5wQJrdoXD4CBMT+abj59fpxETGhyOH2YU54uAOUwfucvPmLUzjGK3tDoo2QXInpPzAAzTfepssvsG+AzauO+KXPlln9KFDekvmoGGQZTGCANJeA+96m1vxLa44AfumDGr1j/Fur4Mo7CG+C7t3gMmIT81+gvfee5fqxx+k13ubOIy5vzTDsH+VKXkPwys/418tfZr1v/kJJz7zG/Q/6HDr1k2sgsHd9A5pFhHd9EnvZBiGyYEDB5i4Y3N3wqfT/Vv2Tu3h0MHDJHdStH2HOHTIIr59iztZxP3FEtf9CCZjbnjX2Z0UOHK4yAFVZ7+2n62tdUqlInfvCOwis7ubb/Lffvun7N9/ANM0uX37NlevXqdSKSNOSrz55mWu+Lf5xCc/xfCDEZZZ4G6WcC3wuJveRprcy5EjR5iammLwwYggCHhofp7uO22OHj3K9vY2165dYWlpGc/zeaPZ5LClcePGLZR9CtKeKW7fvoWq5h67SrU6DgSU6Pb7HD1oceX6FQ4eNIhvJDz48Y9hGhbv9Qd86qGH2Nl5k52fbLG8ssy5H5xnZmYGdb9Ks/kmgrCH5k4bu1Dh5s0bBNGI6OY1TPMohn6Q+YUFfvD6D9k/pfNA5X5673Z5q7XDb55+iu03foJ58D6mpqZwXY/PLH+a9fV1ghs38P0AwzDQD9xHcifAsgq888577N+/nzAMeeCBB9Dv02m3f0bh8FHupBHfP/t99AM6k3smSdKU9/sfsH//fnTd4NbNW9xOY957t8+DDz7A+x++R+Fwgbt3dzGMgwyHQ6xDR7h21efKVZdqqQwidAfvUas8wOj9AaI4SZrs4ROzs/ys1WJqSuLB+oO8sfNGrmI4VCLJZA7bCsg3cYc+u9k+4ug2u5MhwuRdjhRs0mSSKU1BliTCmwHGfRbdbhdh714OHdC5cT1AmpKYFBOuX5tg754pdicSRFHAv/4B+/frpHcmmJJzbPI7wy5LYwn0jRvX2L//AHv27GXfvn1IksTknl1uhrcIgmuUSmX27b+Pt958E2XfHm7GMZ+c/QQ/e/tdlL0yN8IYZUoAUiYElYmJXSaECeSpKT54/31UVWV3d5c0TTlsH+GtN7YoHi1z5+5dBEHkZhhx6/ZN9u1T2KeoKPsUBv0P0O/bn3duFYUbN26QJHe478Ah3h++k2e+KfuQ9k4iTIj88Ic//Ehr1D+Lw8c3v/mnX52YOMD1G3c59Wu/hqYf4pp/leDmFTIhwTyi8fZ7b+GOruKMXI4dO8qeyb143g2uXvuQB47dz/VrHn54g8kJYHKC5PZd4jjm/tLHeaD8IFevXssrP4cN7iQR+/cpKKqOOLmX8GYKCFxxXAI/JL17myC8wa34BpI8hapMYR+tsFdSOHz4ELWPV2Ai5fqNa9y4cZ2RM+Sqe5W90l5Ktfu5eycgzUKOHrVRphR2tv6OvYrCHhH2KhPEt25z9MgDTIoid9KUxi9+nNGVLvGtkCd+898wuXeX1k93KD9Q5FYUMyWJSPIe7qS3eKu5g22XOHzkEN71DwmCgPlPLXJ3N+KnP3uL/fsNrrrXeehTJ1n+zG9w6Yc/Zu8+heTOTer1CoKwj8OHipy/8ArzC3UsWyO5EzEavUua3WKPtIdDhwrcuQNJPIF+UGJqH0h7b3PwkMahQ0XUfQbqlM01L+DChQt89pFTRDfvsP2Tt9grTxHHN7lz+zaqvI+jR0r81dplVFnh1379FHEc032nj6btw/UcDmj3MT39i0TRDSRpDwfu07jPOMBbzTeZmNjDJ2Y+gTty+Ok7b/MLH/8FWu03MVSNA7rGHmmCqX0qw/ff5+O/MM1nP/tZ2u2fEUYuf7v5N7zxxk84ePAg6d2I839xDtf9kOlGg93dhLt37+I7Lil38a5d5ZcaM/zd1t/y9ltvMftLs1y9do1JYQ/3338/SZJw9epVisUif3nxEqqi8oO/+DM+eP9D/nr9x0iqyN+s/TVTisLPeu8S3LzJT5tv8uDHisjqFD/44VmueR/w6YUlPvbgx9grabyx/Sa349tMTe3j7e5P2StLlIof48KPLrB7d5Llzyzzbr/Pnj17eav5Ft/4xte5m+0yJcs8+LEK+9X9/OhHq/Tf6xJFN5mYmGRCSDlWKnFA1/h/zvwZv/4/neQ/fP0/8H89+ywPVh8gvOGzZ3I/xoECB02L2sc+gXXoCH918cfslaf4zPJD9PvvEwXX2K9qY1NlTKlY48z3X2Pw3pDagzWuXr/OQ3OLJLcT3uu9R8E+SqFg8/+y967BbaX3neaDg4ODg4P7hSAIgiB4FUVRFCWx1Wqpb2632+12W247PZ0ep51kJnHGk52trZnd2t2Zqsl4k9lUaso78WSSTWYnmYvj3Hxtd3zpi9ytVqvVulA3iqIo3gSCIAiAIO44ODg4ONgP4Lr2Y745U5O3Ch/JQhWr3pfv+//9nueR+VPQARMrTreblY013E4PM7OnGRybYXx6hoWbH3H75gLBgI9GvYLH5SQWibFfqmET7eT2C5QrVSrlGhZLl3a7TSQSxSYIFAv7SJJEKpXi3t0VRkfGmJya4sTxR8js7CDarAfcdA6EfwJBn5dMJkMmk8br9eL1evH7/ciyTCaXpLBXxO12EwwG2dnZYWNjA9M0sRodguEg2ewudnvPOK4oEpaulb6+Pjpdgy4mNquNvf09goEQW9sb2EQ7hw8fplwus7dXwjA6SJJIt9vFJkkUi3u4XV4KhRyl0j5un4Nmq4ZsV2g0apRKZUzDgiTbqFZquN0u/H4/ok1kb2+fa9c++rvLx89w/fZv//ZXHjl+ElEEp+LhzfM/YnZ2hu2dHX7u5z7HH/3R/0OpuEOtUsPtHUKWXaQzKSxCk+FYlFK5QL2h06g3qDRqmIJJwN1HU9VxyHa8fi9bqxt4AzK5wg6jI8OU6kUEaQDJ7uLh6hJDQwMoDgfl/RK1eo2AP4Dd5qbbbkLHTdDvp97KYbW1abUaqK0KzWaT9fU1XE4XI2PDNIw6T589xerdEm6PQKXcRFMt5HIaJx8LY3caLFy9iTcQJfWgwj/68pcpFLaJRJ08WF5lNPEE6xtLhCNe7IpELtnl6Y/F+eDqe9TKbRqqlacef5rlB1eolGvoqoXZE2e59MGf41C8ZLN5/AGFwfgwVuzcXdxkIDaGx6fQrOcpFPd46sln+Is//wtOnnoUXTcI+H0sry/z6KlZLEB6O8fIyCE6XStB5zT71VXur71LNNaP3zFObtdAcfSz+fA25z5/mpWVD7n60ToeTwibXaMv3Eena6Fe0lANjVqlwfGTh+iL9LN0b5P797cZG+vHMDU8bh+tlsrW1jbDw2OcP/82hyamcbrtbKzeITE5w7GjxxkaiKEbBoODES5fvMjQyBAeu0Cr3cZu83Lm7BNYrR02Ntd4+PAhfr8fwzCQpJ57IZvN8vRTZ/ju97/PxOgkuWyeof4Bmnobr9fL2sYG21s7nH7sLIIgUFbrSB0Tu9uO0+GhWi6yeHeFZCrF6FiCa9dv8fQzZygWs5z92MdYvLvK7OQ0//EP/5Dhw5P0R6J4HXbu3rpHOBwkNNBHMOAlFo5Q11QsXRulUpWR0TG6nTa7uRT7xQy/+2//gI31JD957y2ciotDh0ZYX9/k2LETrKwscfjwERo1lbv37hAMiJx98klq1SYbGxs88cQThMNRFq7d4MXPvMCDlQ0sdBmbjPPhpQ/47Lkvsv5wk6fOPE2pvI9h6GynN5ibP8bskSnefPNtnnn6KYrFKvH4MLncHvH4OO+99RZzj5xElmWqjTpeb5A33/w+Tz39GKnUFp/+zDlufHSbVGoHq2xjIOInk93h0MQIM7MnsYpBHFjoWGqs3PuI9G6G/azG9MQsgYBMvpTnibOP02pY0IwmzWYTq2BhcHCQh9s7dNs6dKHWqBH0B9iv5Aj7J3n0sce4fecqyeQqNtnA7XJil1zQdVAslBHsbSSrDbvdTjDop1ZroGl1XK4ejbGltXE6nXg8TtrtNlaLRLlYRm83OXp0go+uXqfP58dqtVGvqjhlGbffTqvVpdvtYrFA12rDhpX0zjbjo1O0jRbJ7QzRcB+yzYFdGYzEkQAAIABJREFUsfVgGmoVm0XA7w9hESxY7XZy2TQul/OnrjmbzYGAiMVqoa+vD7/fz3Y6TalUwCrYuHjx4t/ojPpbEbvSmi2efeYc/+TX/xkvvvgS8/Pz1OtlfAEXHpfE+fNvUC1nkRURl89Dta5RrqtEor1+RyDcMzDLiogoiwR8PqKJOD5fiHg8RigUIZMu9OSCZk9IqGp1JAkK1TqYBuOT44yPJgiEfETCUUIBD7LiQatr1MsqydQKS5uXub16kc3MMql8kuXVZUKROIorgqSEkF1RIp5YT45YqCKKOrKsYpgFXIrB0vJNMukqmD40TSSb6RW0q/UCiiATCgfIZNYxdR2PR6GYz1KtFlheXaJaLRKNxhCF3ojMNARERDweF+nN9V5HJRJleXmJbKbIzcsrXLu0zO2lzV5BOOrh9s0rRKNRNtfTxBNRwmEFXS1QLKYJ+CQkSeyRjKJx1LqOVlfJFpdJFxYwTANd64mw6lWV2elRXn35JcKBCAtXVolFpjkxN4cI+Dw+QuEQyUyafDmPYPZESBcvXiCfz+JxKcQTccKhCCYa+UKacCSCx+djfHQUl+Ihmy1gCCKbm0kQZU5Mz5HJpNB1A0mRCIUC6LqJIvWwdUtLS1y+fBFNqzM3O08gEDjA4AlEInEef/IMiksmFg5RzleREBEkExEoZoukk0lmp2eJj46TzeYRRZFUKkU4HOa5555DFEVWV9Y5deoE5WqBubl5DFMjHI5w/vwbfPcH3+bStUskN1d55eVzzM7Pkc5myGbTvPrqy/zyr73GqVOnyGSzBwU2CcMwCQV8zE3Po9YNLl88z8zkNMV8hpXVVSbHpzB0nWq1yrlzn8fnCVAolFm8vUyhUOTFc8/zysuvkoiO9vo/sodiPstoYpR8Psvr332DmelpYvE41XIZl8dHuVqgWC4e0DYE1HqdcDhEvV5mZXmVSDjAzNwJxsenmJ8/3QMamBqvvPYq82dOoZk9G/ft5UXi0TihgA/RENDKKjcXbnP6zBn0A9fBC8+d48TcCdS6RiaVZmV5iStXrpDK5lF1DZenRzSTFYXp8UmioQh11UDTQZBEVN2kWFWR5Z60LOwLMB5PICLhcXkIhMJk0lkKhTyG0eth9ER+darVKqLYk5zF41FCId+BWKnc62TodTyuAC6Xi2q1SDqTJOBz4XIp+AIelICPVCpFLBpFkiVM1UAye3uMIPTEUrrWo5eIgnSA84wiSvT6WKEQAZcHn8vTQxYLQo84oniIhCK9AqIoE3KFcImuA4JWj4RSV8u9EbsiUSwXKFd7efBemfXv1s9yiaLI1PQk44k4xWKWTDaFx6OAaRzgqV0kEqM/JRdpeo/Kpmka5Wq5h43W1R59DYFYYhS9rqPIHkBicz2Jy6MQifhQ1SqC2Is7JdN1dNVgcmqKTDpLXa0jCGCYJqrW6xCJooioiBSqBQIBD/F4nEw2TXJzE0V2Ua/XUVUVQ4fp8Tj5YhIBBY9HwTQMUqkM0choz2FwIH/zeQKsrqwQCvdIa5Ik4gq5MHUBtd4rr+qaSrWuUVcLhMMBJqemuHb5GsVyEUlSekJaSUQ0BEzJPMDmxvG4IkRCEQKBEAsLlzkxP4mAjicQ4fSpJ9ncXAQpi0cJkU5lcCk+Qj4fwkFhNhKJsrmZAsNkYeU8ml5Glj3Ikg+QwZQYHx8llU5x8+ZN5k/Mcfr0KYrFPPl8FlHqiRKr1TrlYpV6tRdlK5Tz+AIuyuU6Hp9CJp3FF/D0SE2JRA8/7nGxtLzY69ohImAyNTmDdiB8NA2d8fFRquUyhgl1tU4ymSKZyhAK9eKUkizwjW98A9PsgVL+v70om83y+RfPoWla74JRLxKLxbh08QrhcBTd0DH0Hn51M50iFArx9NNPs7y8zO3btzFMk2eeeZKVlXVOnTrF+fPnEV0Gy0uLPP/c86iaicvn4cu/+mXWV5cpFAskxqNEYyFckkJ6M02hWibgC7C6uk4oFEIUentyPp9ncrLXUY0lRgkFAgdxwQyyLB48WPWEzbFY7KBcraDVVTw+5acxLU2r8/b5twmHI0xOjrO+uUkmnQZB7P1jrQnE4wlisQT5fAZN19CNOh6fi0DAQyqdIhqN/hQkk0qlGJ+aPpD0qT8VXY5GYuQLWURRIuALkS0XeeULr7C0vEggEOoRS0UoVDWqao9CmEytIEoi8WgEDBNN1UkkxpGFnvA4XSz0zgBd7QmQTZNELPpTOmaPTtrr4Xg8vS5XKBTqyTgFs9dROYjOeTwBFFePWKXr+kHZW8Tl8tHj6/TODcM4oLyqKtl8gVA43HN2aDqT4+MYmAcPbhKmaCJLroPfd7APqdpPhYYmOh6Pp3fZyJcRDuSXqlpFFHs912gkSiqZwtR7P1+tVg/OPfNAYGqiHsicNU1DlMSDuJz0N95L/1ZMPv7kT/7LV0biE/zgjdf51rf/itu3bzNz/DDrqz27piwLuLxOuroNl8tDx4Rms4bf66WjG/z4rbeJ9IfxeHt5WtFqJ5fZodXs8NHVq2ynNnn2Ex/vScUqeRSng1Zbp1ys0hfoo7xfZvXBfRSHgtkxuXd/GRsCXaODqmkYnQ6PnY0RHVKoVXYp7G3jkj2MJkZYu7eLRIiAe4hStkup0GFkdJxgIMLd5cvk9lN8/JNzhPpdiJITm+BHsccpZAw+/uwTCBYdmyCS2d+hspfjwfoGmt5AFAUy2R30pobPF6SUL5DKbBONDhMKhSmV9rHJIoriplit0KxUaBltnLKTSkXj4vu3efe9i/yDf/ALTI4O8tHCRwwMRtnL1Jk5OofHD0v3FugbCCOYAi2jdygospdiqYHN6kCw2uiKSayCQTz+OF5HnLam4PMH+fH5r/P2xW/y9Nkn2Ms5ufCTG9SaWZ77xNNc+vAyC9dv88rP/zy7O1nOnum90iB0sdkdRAcH+fDSVex2mVq1SUtrsZffo1FvsLGRYns7TSAQYCjei93cW1rG7ffSaetEIv3UK3WK5QLSQczO4XJisVjJZvNEYxGW7i5z9vHHEUUrfcEIxf0i9ZpOdCBOLreL2jDodi0E/X1UynWOHTuJTZLRtBZul5t2u40gWrFYLPz+7/8+77zzDqdOneLIzDRra2vE43FUtTc67nQ6fPLFT/K1f/d/MRgdomuBn7z3DoX9LF6Ph05XY2trk+X7K7z3zkXi8QSNRgOXw4loE/ng8kWuXf+Iz7/8OSSHA8lh597KfUIeH+ViAUm0YMXCe+ffZ+bwFD6vB7fDSUdvY7Ha0HWNTDZHS2+DxUIgOMDGww3+3suv0u3C0uoqh4/M8Nipx3jk1Dzp7QyLd+5x9+4Sya1NSuV9IpE+LEKX7Z0t8rkilWqVmx9dJ5fdJZfbY+74cS5f/ohyqcTE+Dhds8PG+hqXLl2k3TZIZ9KkM2maWpM/+7OvMzM7Q6vV5lvf+QvS6W0MQ2C/UuTBvTu88OlP88iJ46RTKYZiA6QebrO7s4vL50dvt8AKHrcTj9tDW9dpaRrraxs0SjUkl0xyO4XDBsPjk+Ryu2ylNlm4cYPY4GBvPN1oYLPZDszPGnt7e7TbHWKxKMGgH8M4oBXZZLxeJ8FgXy+3arHQ1DQEunS7VhSHE7/TSyafo6HWsTsdKC4nskNBaxrIsoTFYkFt6Aem4Z4/QbCIvceHYpH4yCiCYMUuWlBcTtAN6lqNUqVKKBTGBB6mtuiKHWqVJp1OB0EQ8Hg8jI2PktstYHa7eL0e6JqsbTzg+vVrfzf5+Bmu3//9f/+V47MTmKZBvphhcnqEv/7rN/jsyy+R290lt5cjNjBEtajj9YUpVfaZHBshmVzD5/PQatQY6B/i6q3rTB85ympyHcX0UqqXiISjSHaRTHqTqdlxsrkMVrHL3u4OqjGG1yPT3yfTtbQo7ZdI7SQJBIN02yJqU0NRHNjsHdK5JJ22js/rp7zfoqnpTI7N41T8WK0Obt68wyeen6ctblPZdlFrX6dltvArY4QCh7h4/ccMx8YpVmWGg8cIBzYolBzcuPU+ktKkVqoyMf4CdruBxVrj4qV3UYQpxqdhe2ud/v4xPrq4wadefIbrCz/hkVMn6HZ8pB5mcXrzHD92iquXltDbCvlcFVm2cPqxGXazK2zt5Dg0fIJqtc0nz02zcOePiYWfo1iq4vV6ECWZ23evMTlyhHbbyuLNVeLDcS7e+AP6A3H6/MewmV4sggWHU+bq1Qs88cSTDPbFeP+DRXxuPy/93HNMTQzwL//Vv6LRaPOlX/t1/P4grVaNprbPTjbNyMhxzK4dv0dAliWK+0UazQZjiUlWVnsRSEVx8jC5weEjY6yvbfGLX3yNr/yr3yQY9FErlXB5XBQKWYLBAPVSmc3dEuOjY1jQqNRKOB1OAiEfieFRqtUyhcI+kiRzaGqCxVtXaRsCQ0ND1Bs1REROnHyUeqWC0+nl4dYm3Y6FiaNHWbt1i/3aPpVylZnD08wen+erX/23vPTS5/joylXGx8ao1FcZGBwl+7CGYVrpWlpMjI3SbFbp6+tD09sMj4xSKZSpVur4gyFOHp9l4cYdarUG0UiM4ZEonW6HsbFBPrh4lbmZ46ytLzM2McbGxhp7+X1OnpzF4+nF1IYTQ2SzeyRGg5RrDWTJya1bd/jiF79IKpVhbGSEr//XP6VYr/GpTz5HOrnK6PAof/5nrxOOxHDY3ch2kdT2Q47MTHJvdZFmrcGJ4ye5ceUmoiizuHSXU/OnGYgO8O77HzI+MkbHbNFst7h55QZuZxu3y0sg2I/X4+ejqwscO3YUt9OLVYJbt2/x4qdeQHZEuH9/g1x6k+iQwnDCjaVtY3joMO+8fZ1zn/04y0u36RsI8+7Vm4wPDFCuljDNDjabnUatSlc38YU8qGqdZk3jsY8/xYPFHZaXl4nF+7E7Woj2FlrTQJGDCBY7VkGmbSnTHwyjaTp7e3soigu314Hb6UeSBPb3S2iajtltEQr1UyrWmZqeplYt0rV0KFTztNsmNkFiIDJAV2izv7NLqy3QxSQY7KfVbpPP5RgcGKRY2sdmsxEM91PKFHC4HMiKg2K1RKVQwGq1cWzuOEv3FjENC13TRHJAKBjG6XL3aHk2BwhdwEQUZRxOGbVepVgucv3ajf92Jh9NrUGhmiIUkUhMhTCFKgtXrjE9c5p49ATppIpeFShU66iagSxDwGNSrS5TqK8zPjmLEohiiALZQpVkPo0vIhCKSyQiU4zHT3HixAxIOpqgYAoikqggmiI+H5jkSacWWU8ukc2n8YXBFYkQTkwzMzvP9NwJ1tc1CimDYrZOcqXA5uoKmWSakM9DPBplNBFgPBEmFA7gcyWQPS4wY5h6AkObIpuVD6SHddRyHk3LUKzrLK6sILhAkFxISgRVU0kme2Wj8fEppmZHCYQl8EC5oGHoCgI9rKuuuqgXNeLxCIIYIJ8ycLnCzExP8uUvv8RLLz9JOpNkZT3Pc4+/gmCGePb5J8lm1tlcraNrYTzyOLIcQy0rxHynCIemexbmSJhoJI6gxRGNKEbZQNeqmGavmPTrv/7P+PKX/xkryTyeKETGRQr1Mv/693+P3/jt3+btd9/l4oULFKoFFm6vsryaRpbDZLNVNFWlWFynXE4hiToBnwdFEZAlEQEVl8skFnVRLxbwKAorKze5fPE8gmCSyaTR0VA1DVMEVVN/eqMvFMo898wrvPjCa2h1EUOXyRcKRKJRnnvueUxTQHEFSCQieAIuZFcPZbe6uY6qG9xeWiadKTI3f4ql25cJuUR+8bWXefHcszz97DM898LzjI5PcXNxkanJGURJYXp6luxqhmvnF3jzu99keeEC1WyK3/mNf4FRr1LPF5mfPkUiFMfUVKbGR0lERxFFH+FwjGeeeZJCOcubb75OMrmCgMzzL36e8HgcXzRMulzGF40SHU+gSwKFeo9AVaj2iBS64UI+QP9phkm9LnLu+V9l4coqkfAoWlXj/JvfZXklyeJiFkkJMHdqmvkzM1S1MqJLIJnNgimRiPU8Avl0hieffY4zT56hUE5TrueZm5nB0DWuXFrgjW9+F0nMMzsfIhqPIMoCkiISCHk49+KroEt4FJmXnj9HPDbL5soKxfQ6WrVANVtmfbGIJMgYuoBuQllNkk6nSGerCIYLwxDAVBHpfUIeFy6fD0lSEAQJwROgmM9i6EVCAYlAyEBSer4NWfKhyD2ykCjp6HWNTCZPNp9FFF0oLgHNUClXq2wms6wn11F1HROZSGQclytMNpsmk0lR1aqMjo4zNTpNNBRDFGWyqTymoPz0u6iqiq6bSJJJsdhDRvt8IdRykbqaQtWzVDUNzQBBihBwhVFEAY/iI+BzIblceJQwHo+JpuZZX19haek2y6tLKB6TYnmT5aUF8tks8cjkz3qr/u9+dTodTEPDF1ZQq71JlsvlIR6Js7i0QCCgEIslCHhC+DweTL0Igk5ZNTCRkRQXCCr5fB4DDVnSkWUfWlnoIaNnEnz7jW+DKaPIAqau9craoyF0rc7K8irx+GRvChBRcMkyLiWEJIkIkoAsSYhoZLN5MukyvpBIOBRDVnyEQ6N4fCLZTBodgUg4gS7mSSZ75JvRRIx8IUNdy/fcFLqMYcDcmTMkk6vMzE6ysLiEhMSVS+fRD0iQoUAMSe69vCYzGVKbRcbHZxkd91CsltENobeve2Sy6SrZdJFCsYwiy6j1Mivrm/jCHhTFg4SAx6cwPz/H8u1lCuUqHp/Mibk5ksk02XSRqcQUkkeiqvUmuKYpkc+uYqLiUjwIoohpHmD3fQJLi4uUyyKyIlMuZnsTi3Ke1177ZeLROK9/+7uEPGEMU8YVUIiFYxQLKeKxAPlC9qAIbhCLRVlZXSafzyCKIuPjCTRVp143iERiXLh4hVg8RjgUJh6Pky9kURQFxaVgCD3jdqFQZHl5lde+8MtcuXKJUCiAqlZBMJgcn0YQBC5dusiTTz4OcEDQc5FMZwiFQge48BSPP34GSRa5eOFHJGZG2VxJ4XL5ePzJZ/ja177G5Pg05WKVV19+hWtXrrCysoRPkfnLb36DQMDD7OwcN28ucvHCJd69cOFAKZDm9tIyTz77LDNTMxTLPbyrKEK5miWbTfPaF34RTRURZImvfvXfMH/iBFcuX2Y0MdrrTUSjPUpTvnc+KYqEbshoqokpGOhaEY8SQBBcXL5yiVg8QrFYplhOkhgfJxCIsb65iuv/Nw1IjMbxuHyEPT4S46NcW7jE9FQcWfZx+vEznL90nseffJrPn3uJ24u3EQUXm6vLiK4qnoALTQXTEMgXMng8CpHwOJgCsgKKIlIoG2QyaSS59/ofjcTQVInNzRXC4RDZVAZNSx+cE66eK07syS8NvSey9igeJMVzgLPOops6kmEgiD3hpKrV8XjCGLr006morAjU1Tx61aSqaURjYRLxKQrFNNlMHkMHXRdA0JEkoecFEQTKxcKBG0hGEiVCoQiyKGEiouoaal3HFYoiiD3xdi8uJSOKGoapI8uenktmc4Xnn3/24PySkWQFvd6b6GhaGU8ohKaX0UyBsCeEpvcmhKrWi1/JikwmkyGdTiIJMgFf9GDq+DdbfysmH7/7u7/zlfhQhL29CkFPhOHhCDu7a+ym93m4ucVE4hDVWgutbqGvv5+WustONo3bNQgdkXq994fFIlPM5cntZjgx9xiK6MMf7FBtrPNL//iLDI+N4XWGCHiHELoiub0UssWLKdQJ9NkZH59EtvdxZHqeD96/zK2F2xRKZdSqxuqDVSyiyLnPfZ6uXqOQLdNpdzl+4hDvfvgN8ntriJ4yulHl9t0tdHWf4fgcc0efYGNrnwf3tgmHvZgtcHhdhAdCLC2tEB/uo1Fv0izr7GRzRCMh7HY7ExOH8Pv9NOo5Zk8epz+QIBIeQ9XqvPnDb+GWg5yYf4zh8SCp5B56S+Dh9hb1Wg2nYsUfkWm1y1i78Cu/+iX+6ze+zuTUJMnUIn/8H77G1PRh+sOD6LqBrptMz0ywvHafnWSScMRLubZFvrxOeb/DJ555lWpN5Udvfo+BWBCXx8F/+uP/Qr2lYZoW9ksplpbu0N8X4+iRo7zx7R/zz3/jN3jtF18h4AugtVoMjcb4iz//Y2ZnjlOq5ZkcmWRoMI4FG5VyBa/bj+JQ8LhdYFrQWzrhvjB72RwBj4/xsVEC4QBOlxu13sBi6eB2+VleWeaR+UfI5jeZP3mCK1c/5KMrl3C6HKQz6+zsbLH24AHb29t0uybNZoOt7R26FguFvSLtjond5kRv9yIGCB22Hm5hk2SaukG3K6LrXX70g+/znW99m+hwDCwG65srlMp7NJoqIW8/t2/fZWw8gdPpZmlxhXtLywxGR3ns9MfoDw2wtrbOJz79AunMDlcXLpNKryLaTMbHxzk2O8fIyBg2m52FG9cpFnLYbBLFcpvcdo3+/lHOfeZlbi58RMjvZWLsMKFgDNneh9fpZ2VjnY7QxKu4qJbr/Ml/+i+MjiUoF6u88srf55FH5vnN3/rXaHqVvj4PitzrhDz5xFOsrW0yd/QYwX4vbbPJuZfOUa/rbO+ssZ3ZZyAWRK3VOX/hPQTTgeIyKVd2+NV/8GXi8SluXF/G5w1SrZV44okn+M63/4poLEKrpXP7zg3sooihlaGj87/80/+NZqOD3lLZy+7QwcQ0ZLR2k75IAMPQsNttWDtgEw2cih3ZptDt2LCKFiRJwSaJiBYTh11m5cEyomzB7hCRbA6uXrnOyROPIYoWbi1eQTC7/MIXf4WmquJwyExNHSK9k8bn9eIPhjDaBkLXwO/3YLcpPNx4iGF0OPXoY3j9TrSmwf5+BUG0MTl1GBGTbCZHYW+fYMiH3WFFtrko7OdotVtYAEG0UinWCYR9mO02pUqB7d0CakMlEPSAWacrdNnNpFFbGoPRUaw2K0GfTDg8iOJ0E4lGCAeDmJYWZ8+cZXT0EJauiMW08Pa7P/67ycfPcP3uv/3aV6aOTOJTJDS9gt6RqOxbkJwuuqZB19pgYeEWR44ew6hVaBkr3F9dJZE4S7lixbC2iA0K+L0RJJdG35BOrPsMW1t1mkaeyw/O8/xL57A141jNKk6aeMQgA8diNLV9PIrC2kqV4eij2MKXUashPji/ydTxCWxig7uLizzz8VkqtQa5NZ2RCYlSRSPsn2Th5hpd8RZ2d4SI9yTffeM9Jif3CXo+hc/Xx8r6fVw2hYGJB3RbQSp7DgJuOxu7afzhCsX9fWjbsVjdPPXUo1y9dpnh4XFCwT62ktc5Nn8Upz1Cer3DjSs5AoMXGAjPkSvUmJlT2E3a+PGPz5NIjDE7dwi1keXI0WMEwn1cu3iXF158lQ8/ehd30MJXv/ov2Unm+aUv/nMqjSql/SIYbqamZqm0dzDUGqurtyiVGqR39vnEx09waOIJ3nv/XRrtB9TLcQYiIdrdPD5fmK0NA4d/DcNw8vTHnqKlb3D7gwrBiBO7bOPDj1YJOGNY5VXu3kszEouBdYVSQUTTmqiqQWywdwmJx+Nsba3hdDqp1irYxT6yu1n0VgOrKNIVreTzu+xmdvD6fGysbfGpF1+gsl/nyJHjxCJjvPPOTxgeDWCxQLW2h8/nI5nco7Rf4czpGd7/4HV0XSEWHWXx7h1eOvd5zp+/iMvtRhAs7GZ36bQ7jIyKlAsmhWIJi0UkHB5AbYPRahLsC5HdyzIUG2Iw6ODBepJPfvJTrK0v0W7r7GznOH7iUcJ9/T91TmAxUCQX7164Sr2mMXf8GIGAQr2VZ3Nzizu3l8hm6lhtHV790me59O5NEolRwMLHnjrLn/yH/xtVVzlydJZu10YiPsKH799meydNJGrDquwSC71EqVhmYeEiP//qy/g8br7zg/+DUsnOP/2fvkImu8ajpx5h8c5Nfv7vf4H33n0fh0Mhs5vC7XRQLe/xxnf+nJOnf4FiOUm9kWVu+nF+9IMLdJo2EsNRLl//Fo8/Mcjo4SeplEV20xXK1TUmx2f41tcX+JUv/wKXLn+f0ckQH7xd5PDhEeaOJaiqKv5AH99//T12iw949hOf5s41nUeey2O3xhCFaQr7Jbr6Oj6vjfTODuHQKKl0CcXnZ339IY+cnqXZqrO7to6zvx+bZFCvN/D5ItxZ3GBy7DCmqbK8dJePf+wMuV03d26/x+lH57EKIRZXf4jD5kNvO7F03DTNW+gticnhk+xsr2Nz2HG7FB5u3cfrCRxIMwM4FZn09hZ9wWFyBZVA/z4edz/ZnRptXcPurODzBCkXrYT73XSte0hWqGgCD9MlnA4TuTPCgwcXmZgcoVzVMJUaXSNBuXyH/oCDeitIs2lgdYpozSKDg0EGw0MYqo1bN5eID8R474Pz/+0Uzn/nd37zK/Pzz+BiEKPjpVwqcfREBHeflW5HR9clrFYb8yeeoT8UoKHvQifCmVO/wMTECYrF2/gcISxVHwFfC4dNIbvhJ53ZxzO4RNeRxKrZkbputKqKzz6MJClIHpNKpQ/T2qJlqATCfVg6AaTOJCeOTzJ7IoZe36NR1vnM58/idNvIZnLcvP8mE1OnCPePMTThIV18D19wkLWH10hlbjM8cIj+/iEePLhHtbJLpZjj/vJtRoZmkGQvaqPJdnoN2W5hZGycVqvNbiaNQ7ZRL9dxuZzYBAvLyzfoC/WzcHuNra0C1YqOL1QhnW8xMHQYt6vL9YX32VjXePTxeT77c58kt79LQ1MJD/SzePMOjz51ktdf/zpPfewU3/r2fyW9uc2v/pMvIshp2p0GTa3J4tJVllc+pNHaYL+2ydDQKO6ASiBYwTSD5LJVRLtBbGgIu6NLW2/QF/ESDYSwtNsMDg6T3EzzsSefo1ZWefrpJ0kMxblz6y5qq86jp6d5sHqNWLyPcm0XKx4sViddi51qVaNjWqk3dDqmFbevj67FTq1hEovGMbom5XoDq81OWzcpFQs4HA5Aor8/wm4mx8TEGGojS6dbQ7YbiGKVbreG0IWT/5XnAAAgAElEQVR2uwlAf78Xr1cmEIrgdvXhcQZoaW1K5RKCtUu73UKwdrFaLQSCPrqGB7sUxusJ43Y66XQNbDaBjlFC0yoIGLSNBvVqGQQ7RreDrst0TIUv/tKXGB0/wn6pysBghHurd9hKpzh+7CniIwN0RZW98gMqtSI2MUByaw+j7cDt9hMcMAn1y6j7KiH7OIn4HDev3uXi+xdoG3kkCXYyBe4tPmT5TopKa4fBISeprSR1tUpN3UdtFfjiL/880Xgft24u0W7b2Sve4Nt/+QbPPfcU9bKJpgrMTp1FskoY7TrbqRXu3f+Q1ZUVFHsfJnlkaxQMG6raYHz0EKLVyUbqAgOxMCNjn6ZastFodWh1Oj1GuGhlcDTC1vYqklPg7BNPMD17CNFlktrNkc5W+eHb36FhbPE//s//lInJOTzOBKpqod5eIV9YZmxsglozRy5fQ9dlqnWD8OAAnY6FdqeOqtZxOp20OwYO2Y/X3c8j82cJ+WPEhuLsFdJ0gf5wP9Vak1whQ99gP3RgZWWFrmnBbOq09Rb5bIam2kQwRWqVAjbZhsvtIBDwYRPtqKqKxWJlb2+PVDJFQ20RCPtp6SoOh4NGvYnZ7SBJEh2jS7cr4JAVmnoFLAJWq0LHEJFtJopsxypVMAQREwFVyyNYFQShjVpvkNzVKRR7cUKabVp6C78rQscUuXH3Lpn9fUqdBgsffvh3l4+f4fra7/7eV46OzuOPBClX7tK2tMFUcFj7ECUb9WYOl9JPJOxGbW6xk2oyMDiNpMhcv/YR4XCEgN1PsZKl3KzjdA6jdH00Gw9piWXWt7LMzT2DbNoRui0Ea5F68yFdx7NozSyGlibgnsPaGmJwYp9mXeL+yioDYS+6JtExVdw+aDRKvPXj7/LMU1+gXG4RHfLw1oVvMTU2T2G/Tn9oGJs4yOhoh+W7DcbHB1i4cYvMVoGJGWjWfeQzbbqCgdMr4lDabKd2yOcLhEL9FDLFg0mLzMraIuMjYzQsbYyGFbVeZ7+YYmdvmc+88Css3b2MZqRolc+g+JOcOjXP7cUbYBicfeIs3/nmdzk+f4rcToZKrcqD+4vMzsyiUyQYttLVEtQqBg9Tq8QG+0im17DbRCS7hKbVMDsNpg9/HJccJV9IUmtk2d7SmRxL8HD7Bl7XANtbFXBlee+tGzx25jTZ3D1sZpR7q7cYHIyRiB9GEKzsFq7i9Q6hN7t43R5UVcDlUtjdyTEUH2Rvb49oNMrefg7Z7qRcLjN/7BQLt68S7uvDLss0Wy0cdhu53C5j42NUylXiiTiPnDhFvdZAdshsbKxhtQkoCpSrFegKiJZetDPU52NvbweHI4pDkdhc32Zu7jjlchEAw2hjFQU8bg8PH64xEImTzWax2ayIdhGnx8na/WWGYnGsNomO3mbpzhU+87lXsZgONjYfEo/FCfb56HYt3L9/j2QySaFQYHh4DIfDQbmaxeg0KRYLGEaXp554jruLy4yMjJDNpfD6XYyPjZNPGVgtDjLZJFZBp6Hu0O7qtFsSuioT8A/RtDzk9q0FbJKBYdY4Ofsyuzs5AiE7Pr+X61eXMCw7dNoC+d06v/alL/PhpWsosp+JsXFe/8GfIVgM9oobxIaG6LYlGkWR8KgHi2mhVCpx8sQJbly/y35Oo2MpYHSrPH32RUrlFjuZCsW9XXwhCb8/wP17Ofr7FUxrmdhgjGJOptkqkNnJYnZsbKfXEaUGCC1mpz/B5Xf3cUWvYLbdNOsKM7OnWLz1fXweH00VXK4w4XCU/WKJRmMfn99FvV7CbLbw9g1B12RoeAC/L8TFD99nIOzH7fZSKu6hOJwkRk+wV36IS3FSKbfJl+5ht7lwuCK0Gi2axgZedxRF8lCtFpCdLgYHB9lKbSBaJTzeHnnSZrMB4HS5WHmwydCQA7UBAgpYujRbWWTJi2QLojb3keQOtKFQ1sEUcXvsdFodivsPmTp8hHSmiOzxYOh2tGqaxFCcumpFazYI+zyorTItTWcgGuP61UVKlQa+kMKHl/9mhfO/FZeP3/t3//4rn/nkCwj2FpEhhaPHEqTzBe5eS9EoC8SGw1gElc3kAy5fvszszGkmJia4d/8OKw9u0rXYSW/tkkrdpS8QQLYrhPsHodvEImnkMzVe+PQrhP1BNLVAOlOiWC7SbOcxuvcZ6HsC2XqSvZSbvXIaxb/KwrWLbD8sMDhwlFBfmKVbGlYGqVU7JPpexe8dJZXMcf3yLmeOf5lDk0egNUAi+hgWs8LyyjpBfz8OhwuX4uILv/AaTd2kK3SQFZHHn3gMmyjy1g/PY3bavPLaLzMQCODzeKhWi6hqhUdPP0out8vhidMEglHu3LzM6Mg4E5OH2NlZo7RfxGh5OTw1QS6TY2lxhelDcyTi47QNjc/93Od45/ybDI8kuHtjkfkT81gEK4OR49iMeVaWJWzCEJ869yz9YS9Hxic4PDFHqzJIJRPGZh5BkgMoip38Xg7TNMllUuwXsvQFwxRLFYJ9Aa5fvsk/+cf/A92Ogd/t5ut/+g0cLpnBRD9NrcGPfvg9JibHqFaLOBwequUWiVgCzC5Gu40FA5/XTb1WoVapo8gO3E4n77z3Nnq73St+iRa6eh1JFLBYbWCYdLpdYrEYP/jBDzhybILdbJZsPovNLtPpCHQtAmPjk0QGYtgkKy1dxzTsOBwOttNpZo/NMj46Qb1Rpdvtomka3W6XVquBIvbR7Vio1rPU1QZuxUoo4sVpt2GXHXjdDiTRjtfXo4tpWptTjz7Ko6ceYXPzId/73veIxWLcuHGDSjXH408+zkcfXuXdd9+iXivy+NkzzByepbTfpLxfYW11lXCfj0Zjn2/+5Z/RUPNcv/s6m9s/4fi8m09/ep5KNUcs0Y+mZxHlIqJrH1GCozPHcLtdRGIR3F470Wg/33/9r/mrv/oeicRh1teSPPv0Of7kj/8jv/M7/yf+kEg4InH1xpto7TxTU9P09cXwexOMxGcYHIxRa1QwzSbBwBBuVx+bD9fI7i3z2XMv8/df+RJv//gjHj7cwTCqhEMBrBbodpvcW75BLDqIWm/x9f/8V+zuZFi8dZ96tUFLa9LFwOd3YLMGePvty3icAfR2g1R2AQGD3E4eq+hgdPgoscgILsXP6oN11GYNj9dDs9nAbrcz0D9MpVLH7/fyne9+j/MfXCA2EMbp99LQGiS3knh8XjxOhQs/+QmZTBav10u1XEY3NMrlEk6nRDTUj6mbaA0N74Fl/P3336dSqWC12hnoH2D6yDRm10R2SNy4cZtQKECjUaPd7hXrRVEkEonQbreRJAmH7KSp1ul2bSBKOJ1WjLaESZGuLmLpCsSHo7iUEKViBVOUqDUNmqrKQCiEVTQp57PkdnM83N7l0ORhnv3kCwSCHr7zF3/5d5ePn+H6gz/4o6/0haboC/uJDpfoWASWb5ew4mdkYohyYw2/Z4gb168xPNKF1jii3U3HUkdyWIm4h8ntbHPy5CQP1h4SHXyanfVFohMGq8lbfPzJf8SFt6/RbpXoGFYSo05M6zKLa2GGYoNkHuaID45SKO6A411czhHuL9YZHxunqWqMjo8iSDVW7q+xtaoz/+ghXv/r1zl79iy5QpbnPjvNG9/8EbNHn8br9bCduc3WmsEjj85xY+E6r33hH7K28xPaTSc723XGxqPU1D3u3bxGfCxGqD9Gavk+DsXH8oMlxsaHsNLFpogcmv4Ymd0av/Vb/yu/93v/gp/85Cp+fz8PU2sMDLix8wma5lUefeQ0T5w+w9sX3qKLSWxogLd+/BbTR6bwOHzobRVZkVCcQRS7l52tLidPzrCWfI+9/B2GBg/TqAtodRv+oBVvCBTHDLvpLZqtIv6gjaBnAn9IJNjnY+3BNlOTc2xuXGP+0U/yiWee4Yc//DrDQ48xkPDw3vs/JuSP4g74sDoyJJM5PPIQVsOJL+Si0Wiwm91hdHSUdDpNf38/NtGOaZpsb29Tb1bxeLw9rGxLw+y0qOsaQV8Qq2CjbyDE8tISo5Nx3nn7x+yk80SjA+ztb6OpBj5fCFEU6XSs2G0SHdNKIjJCuV7D0hU5feYU2+kdtpIbiKKAJNtxOGQiAyEWPlxj9pERrl69yNThcarVPGpVI7n1kNOPPUpb61AqFTl9+jGqNR29ZTJ95CiNukrbMNjcXCUaDVOu5vnc53+OZ576JNdvXKaupZicnKChqtTKVl7/znscPTqNLyhjV5rcunODmaMztBse8vkCCCqHJxOcf//7JEYm2HpYpJBvoiguMnurHDt+mncuvEEp+5Cff/F/59bqDY6dPMryyianjjzOO+/+Kb/yq79Gcb/KH/2H/8hv/ea/YeHGNWp1FUHo4PP5WFxaYGrqMMVcm7/3uS/xow++htcbJ7NdZe7YNKX9Gt/65p9hSiX+83/+U9LrIj+6+BanT57mwqW3OPvoYaqVCjevL9Mym3iDdrJbeV58/gtc+OhHIMDRidP84R9+lbnH/LjsTtbvy3iFWer2P6VaK+D3HMbWGWEnt8DEyHEkm51ypUO9XqFWyyE6BGr1EmZbZSA4huJ3YXa7FPIFdF3j1/7hL3Hz5gLNVo1MMsNQIo7dHub++keEg/0IXTeakcLtDNHVbTRVk4GIiWwPkd4pMzVziFJpD1mWCPhDZLN5rIKExWLjzp1bHDt2nLt37tM/GMJiqbBfqDPQP4ricGNYchRydRLDR0lu3afR3CU+OM1+Vf1/2Xvf6Dbu897zw+FwOByOIGgEQRAEQRAEQRRM0TBF0zTN0AqjKoqiOI7juI7XdXpTb2/qTfP/Jm3WN3XdbJomreNkU2/qpq6T67qu67iO4ziKLNOyTFM0TVM0BVEUBUEQBIEQCIGj0XA4Gg6H+wI+fbs5e/Zsumf7vMMbDA5mzvc353me7+eL0NjI8rUWZOkC86bNzvYUc7VrWNZqFq/VcD0bzb+Ka6bLWv8qZBkKhSzLbiMrzatZq22gd+BWDh/+Jdkz07/RGSX+vyHY/1fVJPpQ/QKiYlGqZRh90cAxouwZuINg2Mfk1CEkwLQKtHfUU1tHxkaBCnrN4sC++5GSVYIRmYmRLJrfR6k6BJJIcSaE5m/j8EuThIJ1+k0gJOG4Ip7XiWs7VCtZRMnB8xUQHIvhoyKK0IksS4yPH8ZwMrR1xDkyUcCsKgz038n09ChayI+nlxkbt/EEnYG97VQqZQrjNRRVpLevk3yugOs5vDz4PMlEmkKuQCCg8rkvfZZUIsXeA7fhuR4PP/QNIpEIsigRi8WwbZMXX3gZBIGjR0ogCAzsSWIYAuGwRDweZnIigyx1EI6KmFNQq1k8+9yL74W++Hjyiee4+547GRk6Qnt7B64nc//9X+WOO24jFA7Q39+HbbsMHzJR1BRGxUVWoFQdIhR1qZaqCERQ1DrBxK/68Pn7qNZKvPTSS6iij9HsEL29A4xNjvDSi88gCCKdXX3ksjOoSgdGzWTvntuZnMyQTCQRBRm8ClMzYziOQ2dXGteVKRaLCLKLILhYThUckYH+PiRRxLAtME3c9xxKjm5g2BaiYRAMBmlra6cwrSMIGoIDkhtEkiQUxY8qBLBcCzwFWfYoVauISLS3pdCrFSrVCqqqIkgymr9Os8jnC1TcKrKsEAj6wXVxPAeQsBywLRfXdhEEkVgijOuUufueu3n88cd58skc1UqN2++4jXxhEg+BUCjE8PAgkiITjUfoaO+kWjWZGJ9BCyjsu70b0zT58Y+foKdrN4OHpnnk24/iD9aIJfwcOjTE0SOP8dkHHqVc1lFklWhUpFgap1q2efgbD3HPPfeQy+cIBjTC4TC9Pd0M9PuYnJimt3c30Uia554+yje+/k1++sy3iCfCtLWpWJbNkcGX0fxxBDeIYVeQ/RYhLQJCiUqpgufIZKezDOxPIItB/vqRp6hU88SiMQTBQbcsLNPGtAz277+dmZkckUiM++/vwfPs9/ajw0iqRTgUY2JigmSsj1Syh0JxgkK5RCiYolYuE40FwJMpFktIilnfRW+LUK1UqFbLiAiEAkHyhcJ7ZJMan/nMZ6iUyxwaPARePfCoLZFE13WOHB2kvaOdcChOrVolHAxjORaKDIGAhmlZIEIs2UahUsZ1XXr6+iiVS2jBIBNTkxgjJrFYDNMyaU+n8RwL15XqAU62+++BVqIE1UoNRZUJBEKYBvh8KooqUqua2I5LOJoim5vBKxkEAhrBQAxJVdD8DrZjIbgGhuHS1pGmWjVxXJdSpcQLD/4JSkD7Lar0fxa8R59xdHRdwhYNdF2sk+f2tRGLBRkaL5Irm/j9ftpTaQYLFQqFPLZr0tffh2uLODUP27EQJRk8F8cTsewahmmg+ZX6C6YkIQoSjufWPROiCzbYlodpVbE9m0QqxejBKorsJ5mIc+jw0/i1NNGgH73mEot2IftsVMUjX6wheD6q+iS2Z2KaJh3pOGPjJfbt+wKSJCCKElOZaYLRIEbFB0IFvVZmupCjv7sTQZGoVmr4gyFKpTptyK/50fUa5UKBjp4InjcJQHtHJ+nOFPncDMFglFrVJBlxqRghpqfzWPYE4FGrlnA9gc72FE8//Qz79xxA9dUbdn7NT7VWoVwuY5oxJEnCtqp4noeASE2vEksIuJ5ENBqn5HmUqxni8SSiF+fll1+gp7edtrY2yqUS0WiSr37hS0xnx9FNm0KhRHtXEFWV8fvrmQuKohINqRTzRVLJdlRVolarIYoixWKe9vY01WoFQRDw+/11ZHepRCrVXt+Jf8+Lp/pUbMPEtm1CWhjDyJCZmETxKziGRbVaJhyKYlkGfr+vHr5XqyEoAq5lUdRtOjs7mZqaATwymQz79+1leHgUWQDHMTF0lUQihSIrWJaJrIgYhkU0Fkc/VMWwbGzbwa9pHDp0mHRXN64jEYnEcd3Cv5+/fp8Px7XA8xgcHAREJElAUSRkScZG4p577uaHj32b/bf109ffxcFDg+zbfRvfOvo4ll1BEqBUrZJMdCIg43kGkuSQy2Xo33OA6Zlp2pNtGFaOmlVD9ARi4TA/+OsfELgtSSAUxrZdYvEoL774Ms88+zi2bSDLIoV8md0DvchigOHhUXb37COfz2LqBpIEfiWAqgQYGR0ikdK47757mJ7IkZmeprurl3w+RyIeRvX7qdZK2K5JdiZDqn0vZb2M5RiMj47TN7CbXHbqvTM7gG0ITI0bdCTiVCQRRQZRdhl88TCxziiioOAio+tVRFFAkEDzq1RqFSTBQ/H50WsGnd2dfPub3yKZaqPTbMcwDOLBevin64AguWQmJnng/gfIjOvgQVsqQWaijCJpCJKEbTgIYp2G6Lpg2TaSKOLXfLiuiywreJ6H63jYjomiRFBVFc+t07I8D/yBukfYskwkScITBPAUJEmipjvgWATbIGqEME2bQDhErShjukVURaVc1REdEUERsE0HUVQIhiOoiooSDGGaJoZp/uZa+v+sNP/fK6FhhVxuipmZKVQ5QmfHAK4jIEgGheIkB18eZGRsmlSqi0g0QrGUoViYIRSM0dHRhYtB1dCpVR0cPFzBQ/X5UFQJFwdR8SFKPgLBGIKnUa3VqOllKnoBx63i2BUcV0cVIwSUDgRRIpwsowYzKJpIb8+9dHfupaujnztuf4A77r6TjlQ/ASVGItmFpKrYVpBMpsLoRIZQKER7qoN8Ps/4xBjFUpZ9AwPorkOlmEfwPA7s289nHrifUj5HrVJi/959xMIx0p29WKbNyPAYfi2CTwvT1h4nnU4STSTxPIPBwaMMHR0hFm+jWpnh8OAolmuwf99u/vD+e3nwwa8DEgduO8ATP/4pgWAcWfHjUwMcOHAH3/n2Y3R130ZXVzdtqRhlfZCZ7LMk0ybRZI1oAmTZh+sGmJ7OUa0WScSj5AsFXnzxJURBoburj3Ashqz66O3voVouUa4W0YIaoVAIWZFJtiXrqbSTE8RicUZGJ4nGY+8dHDrVWgXD0AGPSqWIbVv4VD+iJKAbNYrlCvlSGV03ECWVaDhKLBrHk0CSJCRZxnHd+ipDMIbqD5Hu7KOjqxvFF0L2+XEATxQRJBVZDdLelmJicpLDRw5RrlZItrWhGzVMR8f1BCRZIhjWsOwqtlOrh2hJEoIg47kigifh9wcJR+IEghEAXM9lZHiUjvZO7rrrTnr6uki2JcgX8ihqPXlU0wJ0dqRQJJlSsYpjOSTjSUJBP/nsDPFoiIcefBDTcPjSl/6E0fEpKmWRocESsWg/99//DSbGs8xM53AMGcENovnbqdVMYvE4rgu1qgGCRLVW5cc/fpxiKUswFMC2DTITR/EH4IWXnqejo5/Hf/Qsmakili1RrtSwHJ2uvjYiEYWujg4SiTgBLUyuMM3o5FGCYT+qEmR6Zgqf4tGeaqNaKRINa9iGSSigUc6XGB0aJuALgOORn5lGFiVyuRyHDx7m4Yce5Jmnfsr0ZJajg4NMjk8wkRmmmJ/BMcW6EMp+XECUPBLRBB0daSKhCKqiYNsulmNjuw6VchE1oJIrFHFdm6lsBtuy2LNnN6lUikwm8+9I41AwSLFQwHE8tFAI1efDp/nqoUmSjKr6URUFRVLwKQqhQJiQP4Qqy+B6aD6FRCxGSPPjWMZ7JC0Hw7BwHAdF8WE7FlBHdzpOHf8oCCCKMorsx+fz4TgCAh6yKGNaHjXDwDQt9EqNQiFHtVg3ulerVUzdIBoNo/k1RM/Dp/nY29/z2xPp/ywA3OVFqvoYgmpSmFHITQmk0z2omstPn/4OkWAY23VJJtJIxBiZOERAk7BMMAyHQj5DR2cdiR6Px3jmucewLBgZLdOe6mJ6ZojdfXEERGzPwXIEbDeO7HqUilPIfqeO6nQ8nnzsOSKRDgRsLLtMIAwd3VGef+EwiVgP3/zmDyiURlD8AoLnI6DUMbN9fXFGJw5RyOaRpTDBYJjnn3uB/t09dPX0U7NVJo8cZfdAN65g8IUHPkcknkAUREyrrtH9u3cjCR56tYIW0PBEgReeH0WWZf78L/6U7JSOJOjkipMcPTJFNuMjlCghSx2EAsl6x/09FDqCx5NPP40kQCQeJRSM8PLLh0h3djJTyNOWClMsFaiUXSQxQcAXQlYcavo0Hg75bI5ywaWtPcWjj/6Q7HSeWFwj1R7npZcOUioVmZ4ZZ6BvP0/89Kc89vhjdHR0Y5gwdHSMAwcOIEkCCC61agVJUujsTHNk+CC5XA7DqNHX38P09DQzM1P4fD5UxY9p2ti2SzzShixJ2LaFrIj1swmJsbGxutFXkAgFI1R1Hc3vx/NcAgENQRAoFav41CAIAqap1/GqokBNryCJCrIs4ff7aEsmOHT4ELIi4vf50HWTfCFLuVIllUohStSN4aaB6zjYtlsHALsCWjBEMORjcHCQ5557jlpNJ5vNkcvlGB4aYmS0bnzPTI2j6wa27VIuWuRz9cZcMhVCN0r09KYxdIvnnz3Mlz77DR5+6BE60ipa0KNcK1Mu2dx919eplkWCQY10d5DJ6Rd56fkjCCh859uPIEkihq5z4LZ9fPazn+Puu+7j8KEh7rrzPiYnspTLBSTFxrDyTGQOEwgq+HxB/GqczvRewoEOxiZGEQM1fEoSzS8xNT1JueiQzU3x9W/ext1338WLL43gYYPr0pHuoFwuIYl+crkCD3z298nl8oj4qJRdEsko1VqJZFxjpnSU9lSaifEZcjkTWVZwpRwHXzqD5wkUZqbo609hmeC58nvNI5nuznZGho+Sy00jCRCLxXjxpUEmMhNMjk9wx523USwUeOyxx0gkE3UE73uggWeefZLPPPAZJsanMXQTwXMZGR5BAAJ+hUqlSqlcJZGMMzw2iuvWgz3HxkZJd3RRKOSwLJO+vj6yuWni8Sg+RcLzPCKRGLWagW1b5LJ5tECAUrFYN7fHYsxMl4lGY+Da5HMlBOqAh8Ejg0QjUeKJLgRPQPap1GoVHMvAqhWoVEp0tLVhWw7lik6lVuKJJ5/k/k9/+jfW0v8Qa1cP/8WfPXTzzTuZPHWG9WtT+JQofbfezHT2VSZOvMqO7TfxwQ/8Pomt23n33THWh5Zpa+sil7NpbmnlQvFtHvmbxxl75wJf+5PPcnL6OBuDW7iydJXOdDfVuRL+1TKZdzMsLvipXJnDr62hSZY5PTXB+vUq1UqZxqZmWBGIb4lz+uwkgthAT/fNzNUMjr0xybr1O8iMnePnL/wc55rLoV+/xZIl4W+N0NXzYTTfRk68M8X69WvRdZvrduzkto/u41+f+2feHjvGkmFzuVpjV28369ZoPP+zFxg6OozU1MKW+DbWaiG2bEkybywQjm1F8Wk4XiPL1yroV+b4YN8+rjZeolZapLHBx5JpEAxpzOZnyZ87gSQv8+n7P82Fc3lkUWF86Ai7bh7g/vv+gNeODHE6M80Xv/jfyBXPEY9EeffkMGvXttDY2IQkKUwcP8Xi4iKJZJTW1lXMla/Q1XUDNDjMzs5y9kweobGBkZER5i+bjL09ySfu2se3/+q7/O7vfoxP/ZdPMTd3CWuhnjI9NTVFIrGdymwNa9Fmx/brePHnv+K69h3M61UikSjT06cZH5+kqUkiFNrEGr+fk1PTNDe1sGHDelZWGrhypcbCosnitUWMqwYLVxeJxqK0yK04tsfEOxME1m1kackjM3WC7dt24HkrXJlfQF2lgidSvnQBXZ/Ht0alVW0mEokgiiKjo8PccstNbEskyBdyXHOuEQptoKW1kSbJY9G2kZslVq8K0djYRHxrG+Vymc5daUqzRWZnZ2nb3sHU1GmOH38HLbCao0dfQxAgGFjPCiuEw5uolHWUVoWOnV3IkoooNqGqqxgdexVJbuD0zCn0+Svc0tuP0trEyRNvs2/fnTiOyvKSxGtHjiKI8zS1WFw158lMThELJ6nULvKpT36SQq5EckeS8IaNXC5f4ta+W0juSDBXvcTlSxd4452XePKpH7J6lYavdTP//c2rN60AACAASURBVE+/R0BL8u74ORrEa2xLbOF45hU2bPTxyqtHcd1rXLfjfbx88FlWBIM//fpXeOVX41yulXju58/wu3fcjbNskM9PE9+6BXcJtsZTbIxsYHDwMOaCjesuUyiehhWXVw7/io/evo/MiSyL1jVOnjzNbDmLFhCIRLZSKbmoretoam0kHNzEyRMzlOYuMHe5wk+e+jsu61W2bokQCATZvv06LpRKmLV5GhuX+bt//Hv8q1pZHVhD9swMFwoX8PtX0Sw3EwoGaVGamavoxGJRGhoamZyc4OLFMovLHi0+P8cnTlK9arJujUaz3MLSks3JzGnWaKtobhbr2Ti5LOVymR3XbePMzHlWr16DYei0tqrYlgsNjTiOy7p167g8N8+yt8K/PvsMyeRWfvHLX3FyJsMHP3gbxnwNqamJrVt20KI0c/pshsuVMi2CQGOzRygUxufz0+RbhW4sMH3qFLOzF9nVvo2x0TfJnDzzn2tXv8V69Ad/81AkuZr5ssNNnf+Vq1dMIlsEpk4fQ1hR6Wm/j53X7+QXB39EPjeKIt/I+vVb+ODH3k92ZpL45uuYr8yTTAY49Nqr9PV+hOnzk6xfm2D7tm289srPUZv8RMK3sC2l8NTPfshSw2pUR8LFZHvbdRTON7Cz7Xe4/n3nOXT4Ddq33czdd97Hf/9ff0o4ohIKb6a1IYS2wWTw0NO88IsJ9u6+i+UVhbt//5PM5I4zeuwgmzYmWB/cTK3m0tm9kX/4h8fZEu1g9NgL5Es2H/n4x9gRX8e3/uIxWlohdd115M4W6Lm5nytXDd555zjxZIzjb48SjW9mxd6Autrha1/+A948eYR3hma4unCNG3b2cnPvARoaLnD50jVy+Szf+953+fk//5TdAx+kfKlKk9jKnbd/guOTx7l48QIf+eDv8MIvX2dlcRW37vfzd9//M4qlBTaHOijPXmTXTTvxr23lfHaBjYFObujtJJsd4+l/+gfOnamy1HCJC+cvEo/t4N0TY7x/4HreOpZDafVYEa4RjURwrRAfv/ND/OtzT3H8nWk6d9zAmfMjrCz5uVyb5+b3bWV8/CRb43HOF86z7C6zc+f1XLw4y1ylitBYp4utXRvhXOEczc3QQCNr1mtMHs8QTyQIr4+SPZ+nVp2jYaWJ3r5bGH3rGBsjG4jFkkQ3xRk+NkzmxAm6utLYjom7ZLNxU4Ajrx1ja2ILuVyO0myRj3/8Y5w8eYpWVaZYLODz+WhulihXzhNavxFvWSAa2UalOk+1fIW9v3OAJkXk+Og4a9dLdN90M469zMLCApqmcnrmNOl0O1KzyNKSiyy1cGr6FC3Nq0nvfB+2vczMmVMcOfoC8aRGILCeVw4N8rGP/h5///hTtKyspvvWHcxfNjlzJkdHexcz2Rwrns3Z/BSXyhf56Mc+xobNq1hacjg2NoRZneX2D/0Rn/3q5/nMH9/H0LG3uWv/p3jxlz9k/YaN+P1+whs2saNtJ1euLJDa0clsqULu3Gk+/OE9/NV3v0kymWL1mhCxTevYtauHQwffYGLiOIFgE1qghezMHOZCE/t+50P8xXe/zO+8/8b3GkzrOJstkEiG+clPfsIf/s9f4fg7J/nkvfuYmTnNLR+4gfLsUb78+Sf4yZOP8NX/9l1ePfwq3vIFdvWtZr7SyLYtu6iWztKirsfzWvCEFQzzCi///Fm2t28hEgqT7ryRC2eKFGbnWRts5ezZGdpSbVQvVbi+qwPbtGiRW2loaGDTxo0IUhMbNq5larLAev92XPK0rlapzi2wuLiM1HqN1sb1nDl/EXNhjm2JOLOzF9HnDRYXF5AkGVVdhW3btLS00NjYyMULNVZrEFizidLFea5evUqr36FhRaRJ9OF5i6w0LnDol6f43Bf/iF/+6lekrkszO3eUi2cX2BRL0Nrqp3xphbMX3mJl+RrVSyXWBX00NzcT2RxlbHKaFt9aWGmgcnmO9WsU9PkKx4Z/M1/if4jJR6MIjzz6twR9UXxKjMnJLKLiYtol9h/YQ3/vboqFCuNDM/R372bPvj1omo9IpA1ZChKJhXnwm9/msR8+hWmZdHZ2Uak6xMNtPP7TH/CdHz6EFlCJJzrI5wsUciU8oc7m7+4awO+LoKo+HC+P6WQYHh1iX/832Nv/MJWqjeqr0ZboJjtZRQvK+JQgmck8XZ097Bm4DUlUee6J7/D8My+RjPXh90WQRIWJyQzPPvsiqVQboWiEmlHj/gc+h2eLjIyP0Z7u4KdPPcnd99xLpWpS1Q0e/cFjlMo1goEwnd09pNOdtHUkwXM4MjzK8IvPUSoVkQUFUZGpVWdoT3cTjQcxrAoPP/wwDz/0dSYyo2SLJSrlMhNT0/T17abnwO1M6mVmZibI5I5i1MqUqw5mNUzYv48f/+BxPn3Pt+jtvJvMxDgCFTxsJFVBURTa2lL0pHs5sPcAogDtqSS4kO5IEk8mGRsdxbUhGAxSqxlo/hCFfOG9QEcJ13FJxGIISO8FOQZRFV+9s9PWgWkaVKpVkok2gqEQlmnj2hY+VcXv84HjYJoGAc2PaZgUc3lUReSOO++gVqkSDmrEwlFEweOlF59namocvVoDz0aWBAQcDh16Gb/mQ5JF8oUcqs9HNldgJlvHvep6DcexkUURXNBrBuVChYmJCcbHJ9B8fnp7+xARUCSZL3zhC2j+ENFIjHQ6TTQS5cEHv04w6Mev+QgGIiiShiT5qOkOtgeeCKMT44xOjBAMx6gZDv5AGElVmJyeBGz6ByIcPvwsE2NHECUbWbFwvBmQsqS7NXr6EyDZiB6MjY1jWhaVYpVUog3DsMnms1RLFUQsgiGN227vZ++ebrq7uijkyrzw3BH6ug7Qnd5NOBBDUQQqlQymXaA9naKqlygVqsiigCLbTGcyyLIfxS/Q25OiZun4lfqkIOgP4JMVVFliYnQMVZbRK0Vk0Uav5FEUgVgkRMAfIRKME49HwdPx++rIa0X0oYoaoitj6zaeJ9SDxGQFUXC499772D3QT6Wmc+jwID967DG6u7po7+ggnU6TiMZpT6eJhsJowQChsEYwFEDzBxAlAb/qR5FkpqamKVWq7yETBVzHRVVU2tNp+nr6ESQJ0zIZHhnFcuprUoZpks3lcHGQVYlisYIgeKiKSigURpZUXM/F7/MRCmr4fRqCKIDn8YWvfo58OUdvbxf7BgZ46fmDZPN58sU8uWwevVYiGY/QlkoQDPgJ+bR6aJvjUKuYyIJCPJbEryh4QCIS+W1L9f/vS9evcMdt99LXfxvT03lMU2dk7GXS6XYS0TTZGQfH8kh3hZD9ZaYmK4wM5xAEl2IxS7Vi0pXai8+nYtsGzz97mHDER7lcYmRkjPvuu49KyWYqk2Mml6VnoIdAIEBACeKTFErlGXw+GQBDl+nvvgPV56Mm1hDEAvFkkuxMmXQ6TbloUjUqtLdvIRiIIgoqE6OD6NUqqVSCaEKmWq0iKxKeV0eH6pbFwO793HnnvVRKZUYnhohFowSDQUqlGnrNQlEUXnr+ZZJtbaiqghYKUK1UECWD8eExwMfUxDOIYpyQFkf2gannqVZFAiEHn9+HBYwODeMJAhPjk9RqNfbs3U8iHkfTFGZKRSyrik+TEFyHcDyFbSqEAin27z9ALj9NMCiTK0wg4DB0dBLTsalWdfBkkrEYrmdTq+kIokdXZ4rpmWk+/YcPcPToUXLZIqKkUi5XCQQCSLJINpvFp6iIoowAxJNxTMOo+w0mJuvBsq5LIV8k1Z4iGq1vA8iShOvYAHi4eLaDIitIooSu67iWSyQcQZAUBE+krS1OPp9neHiY6ekZAlqIUCiM7ZiYpoGLQy6Xw+dXGR0dRVVVQqEQAOPjw/XJiiQRiUQIh/zUdBPX9RAEGcOyiIZi7B7oxzBq+FUflmOSzRYwdAvTMur65VMRRVBVP7puEI/HmZqeQdd1ypUSe/bs58iRIQBC4fr0vFIp0dXVRa1m4ZNlEikf+WwN8BEKRQjHQsiyiSC6pNraMQ0BU/eYGBtGVSUc28WyXYKhAL09nZSKeVLJNgxTR1U1BNFj+OgwihwgFIxzZHAMzR/k+ReeJpfPEEv4kWSbzq4eBC+M65hUqwaiKCEpJnfdfSeTkzUkUaZUyJFqi+HoFXS9Wn9/SaaxbZicHGPt2rWYhonmD1IozKBqfqYmJ2lPtzOdyaIFVUzTJhoNowYENDWKaXoIAqCaRGMxxsbG0A0DRYZovP68uY5DdiaH7brcc++99YA/26RWq9GWSuBaNrZtIwkgiRKiIGPZOtFwlOxMEcOyEGQRXa8iINZDTdtSgEw0HMWxLUxTp1arAh7hcBgEt/6dsoDruvhUDdMwCIVCeHgEtBA+n4qqKLiuh6L4KJfLKLKMJ3qMjo3RlkyiyDKGLqCFgoCLqvhQfG79ufYEwEUUBBAFdF1HVf1oPg3LssCzicbDVKqV31hL/0NMPn7yk3986Ec/fIxs9iI+TWLzFh+HD/2C2z/2eabeuczQ8Ahr1yzjLM/z9tjb/O1jT3N5zmFnKsnVhXk+uPcOXj86ys9/+Ry2I3DqZJZTp06yuLiK7l2dfPUrX2TxmsnI6EE+8JGNRKO30ipuZ40WILzxEmczVVqb0/ilm9DWbqHrliBPPvVXjI6/QmBdG++/5RO8+dbb3HFnN/ryv9GkVOkd6MRpuMTpsy+zKByhrecKm6IbMKyrXJo1WFmW6O7uJbJxEw0NDYwMvc1HP/oJfvb0LxBEEaGhGa5d48LFEq+//gZvDg+zuLDIB/Z9kA2hCBfOX+TixQqz5TmyJ99hyb5C/sIZGppWc3N3L7t2dRHetJ6tW2/g8pUsra0yxtV53jjyCnfe9RFM0ya4fi133PYx3hh6nbm58xiXShRmxglu3cHMW7NcuiQzX3W55dYQvvWzfOUb9zEy9nNeeHaIP/+TJ/jQgdsZeWuEWDTI22+/Q2BdiMx0BnXVGsqzJc6cOsuGTWsJBNbx+T/+PJtjaRJbbiCfv0j6+hu5uniFbdu3UZ0rsbRksbBQwWWF6dNZ2pO70OctFhccohsTWFdcWpr8CJ7MkiNwqVRFapZplESaFYlGT2ClSaC5SWHBWmTN6tV4QiOzly7x+tHXiG8PcGE2x5WFCwwdG+TmvhtoViBfPM25CyeYN0oYC5d4X/8AZ06eQmoU6b6xi4WrV5m7NMvZMzNEI5tRlRaqlTkql01WPIV1/jCyqrF21XrWaWsZfH2Qk5NjnDj9DuvDq/nKFz+P565j0V5iraZxxbjK6FtjtMirafAk1NY15HKzbAxHGXnjBJfK53j11ZfovzXFtUUDxxKRhbUIyyqFczkqczMsXrvEJz55F83KNdauX+Zf/u1HLBgF3rf7/SxaDq+9NggNV0HUSW7bRq1isTEcZcPGjbx6+Cg72rYyWzrPydPvsuQ6WAtX2LD+OhLbdjE/b+IsW5y/MM1fP/pntPhWaG6Bycy77Gy/CUVZTQMSVy+LXFsyWa01csOuXXg0sLRko63ajKKs4c3h11izah2rfGu5VDY5n5+lUCxgLZrcsKud9RvXsMRlmlpttm7fiNLSgipt5d3xDLZ9kR07N7Bxc5Arcw1cnlti0+aNFMvnuGJc5nzpAjf23MSCbaJfqWEuVGlqbGTDhhBbk0n6+27l9ddfp1Ip4bousVgMb8lhpWmF1maF5uZm5q9cZW7+MnhLHD/+LmvWBgmF1nPhQon269rp7OykoaGBUmUeGuHcmXNcyJ/n8txltm6JsXnTFhSlGbm5CbGxEVFoQJFbWfFWWLt2LScyJ1lYWCB7Jovc0sJsuYTcrDA7e4lmqRnLtiiVTtPRvo0zMzmMhRLv3/0hVN8yW7dGOJM9w4WLp3CXF7mQO8PSooPt2CCKeAI4KwLTZ8+wYNRYH9KolUusC/h55cix/5x8/Bbre4888tDSkkSpZNDfuwdJXiAcVVAa/Bw9Mk1Y28HmbWuIJBZ44Zf/guJ+gv0fvp2J3C+5ses69HIr1644NKkF5ubylCstXL/rRqKb43xgIM2PfvAI7iKsWRdhYdmmY9cNFC9VuH5dgZdfeYvwlj1I0jIbE6v55avfpHyhmbDWhbRi0X1jlJ/920t8aF8Xf/Po91kldHPTgMK5XJXV6lbM+RUW5Wc5PbHI7R/5Mpl3h1lxA2yKxildeptEYiuvHn6XvR+6hVPvlvjggVupzeeZPVNi7HSGQu4CAwP7EQWJXLHEHz/wv3DszUG85SXcpSVm86c49PKvKevvcNV6l709j7LrpnZcapyeKrE21EImc5Q3R47jXl0ikoxz4sQZihcucfddv0doQ5SiOUfl1DvMW1cY2H8PT37zx6zbKjBbqpBKJGnfmeb73/8Bd3z84wy/eZRYbAs0LZMrXOQjt/fxDz96is/8lwepmWO07+zku3/5fR79279AbK5ybmaFf/6nJ/nUH3yagf4P8PZbF+m4PsI7Jw+jV64ydSLLR+7s4bmnX6H3/XvQ9SpL9jKuZ3Mi8y7/9Q//iJ/97Hl2dtxAqXiR4+PHiW1JsNq/CsvW8WsqtdpllgGxuRU8F3txmamZaW64Pk3u/HmuLV1l9epWDr/6axbNFXp6bmZ+Xueas4hvtcT8fJWGZZGeG29lajrDqlWrSSRjvDVyjGQyiepr5XJ1nq6uLoaGhjCta7S2KDTQxIrXgLAi4jWucPLEu2yObWbBNnEdk2/++Y949dAbrF0n8+7k27S2trJ27WpaW1dxzXZZq21krjLHLbfcQiqVJBQKcu7cFF7DEh+9/cOMj51AkmRu6d3NyLFjdN4Q58yp47j4+NlzL7AsrLBrZzvNPoXa5YvMVebp6LgeUZbp2NHOr156k3gyQSigEIt0s0rz8/wvfkIwuJF1q6I0ypdwGhrInDjD1Ik8rCgUzl9k+/bt6FdniWxaw5p1DdRql/jIgU+RPXuN6alhZiYvIbUusW3Har70+T/h+X85TovSyNyVKqNvnef2fT3Mzs/SogRIb7+Vw4d+xZGhf+JrD36DmdOXKJ0vsHaDwVSmiHXV4Ytf/gL3/+736Li1DK6PLbFOFqzLNDauJuDfzNE3D9PeHmVjtIdsLkejuMyl2bMITYtE1m1E8fuZr1wluHYDly/XEOUllt1lmpRmNgXXceHSRVbcFVpXt2JdsWlqbsZcmCOgrWfzxk7encgzUzzIddvTxONtrHitqD7YuLGN+arBpUtnsBavEQoF2NV1IwuWyZbNm3nt9VfYvGkLoihyzfFQxLVs3KKwMbSdY29O0KI0scw8W+M7yM6UWPYWaG512PfBOxh/5yQBbR2haCtvHz2BsLLEjutSjB2fxKc1Yi4KVGeytO9I0LSqGX9gHRdm51Ca1xIIbcDfInLm9BjX7Ksk2xIc/vWr/9+ZfFgLNg8//G080UUQLUZGDiErCrmpEqPjWfbt20tbOs5MPsNEZoq777yf7u4uRiePEor46x1l0yDdHsPULXTdpj2dIpnsAGQOHhqmXKkRT4SpGUUyEyOMjB4lEe0kkbyb3Xv6SKZEirUhxseGKWbh9tvu5dO///vgGhzJjBPWImQyVfq6Ps3A7tuoFSGkdHPX7Z/DsaGQBcv0kZspkIgnCAYDBDQ/0VQbP33maTRNQ0QikYpz+OCh+u/t6WZo6Ai9Pd08+CdfwR/wUcxlee75Z/AcA9cxqJQLKLKP/t0D7NvbS3dnDxOTEzz62LcoFYrIosqRwy8xnZkhEohy/2f+ENuxiYSDdHd3ofoVisUZ8pkpCtMThKNRqpNDFPUjVGoj+Pw63X09tHck+OwDDxCLRwiEJbbtEunsDWM5JSYnJ9E0H6ZhEo3EsG0b03Lo2d2DbtgYusEDn/kcd99+D6FIAFyPZHsb0XAU13GQFYF8IUcoEiKkaYQDQWpGDddzUVQN1eenq7eLcDSKqqmEwiF8fg2/pqKqKrZlY9gmAmL9s+1Q03UURSEUCtHf00u1WqNSKRKJRBEEME0DyzJxXQuAUChY3/0slUh1pAlGQuTyeRRVxadphKNhZEXGMB100yKZaCMSiSLK9Z1dy7ExLJtQOEx3bx+JZAwPh/6B3YyODTOVmUIQRDraO7Ftm3A4SiAYRhBkwuEwiXgbAwO7CYfDBDQNSRTQAj5yuRwgUCwWsW3o6GhHFH2UsyKeq9DVs5v+vn0ghMhNQ8DXy/69nyYR70IS/YwMjSHKIlPTU1QrZZ544jEODx7F9URcGxRZxXYcnnv6JcZHcyQSbUQiIQIhkWBYweerGyclUUXzx9CrHjPZGZKpDtra42iBIOFw/TASRBtBUtF1uOfuOyhVyti2Ry5fQNcNwCOZSlAo5REFMHQTTQuRjMZQfT78WpBQJIKqBUmmUnieQCgSxuf3EwhpRKIhAuEwoiBjWxaeYyGrEp3pLoKRCLWqQalQ5uDBg5Rr5fpzaJpIkkBN17F0C8+r78N7jotI3XNhOw7VWoV4LEk8GsUyLPKFIorfj6wIOLZDzajiuCbRaIS77r6bcCQAuO/dyzDVao1CMY8oUr+uYZDP5RBEkCUJn6piGCaSLBAMBQhoASzLwjBMtJCGrutMT0+RncmSyUwivNdBKhTy6JZJZ7qdeDyCKIEqK0iSgKaqlCsVFFkhGovXO0z/Wb/VumIYdHSl6UqnyUyOkysU6O3ey8uHjqBIHorP4ejRQXLZCsnEFkTJo1Qt0t3dQ6lcYnR4CE2TURQ/hlHDpzqEg11IUt2oKisuem0GUXYwamDZGulUP8GIgGcU6iFzxRJ6zaSrM43lWGiaRjZbQInE8MoWsk/Ep9pYdg3BC+JXo4iSyXR+EEGQiMY6yUxkSSaSOI5Dd3cnajjAk888QSwcIRSIoKp+Bg8OEgwFGNi3m2KxyG0H7mB6aoqXDw+STCaZmJomkoijaAFK5TJGrUg6nWB8dBTLlKhWyuQLOVLJNPFYO0eHn0GW/KQ72uno6kSv1Rjo30cgGObee+/lBz94FNGyiLS3I0gCpcwkJ2Yz2JZEZ2cnvbt7cJwC8WiSoaFxKhWL4ZFB4okgsqLi2B7BQABVCb2nR3BlYZ6+3j6GhgfpTtf/51Syg3w5j09R2T3Qw/DwCC4OM9MT9Pf24eJQLdWo1Sz8fg3HcVAVDcs2SaVSeJ6LZZuUShVc1yEWr08lbNtEQECWZWp6BdNw8PsD/+4BEz1wHItarUwkXJ8mBQIBbLsePizLEprmx+/TKOQrRCIxVFWlkC/guh6mZfy7Sd313gu3U/04joPreriui+O4yIqIZVmoPolCcQZZFnnmqTESiQ4SyRiJRIxcfoqxsTFM00SWVarVKol4G6qqkkolGRo5RCwRJBaN8PKLR9C0MPF4gvHxUW6/YzelUoXuzr1kC1Moqke1VGJoZAxR8NGe6iIU1vAwsGydmckc8XiCqclptECQoaFRjhw5SCwapFopovqUumfOlUmmOgmGfBSLeVzXZWJigq7OToLB+hp3NBqnUimh+MGoSvTv3sP42DBtbSmGhg+TyUwRCEbIZXPs3tvB4OERXNvDsR1K5SrhaIRYIoypG0RCQYrlElrQR0c6xdhEFlmIUyiUEBWdtvYw5VIVWfSjVz0CwRC53AzgUq1UicXiuI6Dg0Mq0YblOhg1HctyMC0L3dLxPA9FUXBdm1rNQJYkJFnCc6nfN89F9clMT09TqVbZO3CASDhGtVZBVGRi8QiVShlV1SgVi5h6jVg0jt8fYGJiFEWRGB0dfW/bpIbtWPh9fgRBIpfLYts2CNTPVNtCEAQkWUCSRUzDpKYXSSaT5EtFCrlpouE0wVAAWRExLRPdKuLYDp4nENA0LMdGkEREQcYTYHJsrG7oj0WRRPBs+zfW0v8Qk4+/fuSRhx763x7CvbZE764Bru9Kc/z428ycnGBHYivHJ6apzOW58eYdSM0wfXoaGk2WTIvx0bd569Vj3Pm772P+6nkqcx6JbTtoabIoFE5QOH+KNwdf5aMH7oYGHydOnqNr1w5YucLXvvp5xn7+a661LFGam2bjRpHNm1W2Jz7A0NExFowreJ7B0MvPc6F2hkaxiV/9Isu740XS7d3MTJ3k//j7R+lo7+WGtk+yWl7HwPtvZb6S56pZ4aG//Dx/+c0/52fPvsKx4bdxF68hiVX6+3fyD088xi1dbbTK63jz2DvUbAtJWGb3zVvo74rznQe/xvjYa6xbtYJpXcZtWObs+cu0NG3iS1/779z1yf+JR/76e7xw8J/p/0A/bckUoizz/M/+DWghmbiec9lZTp0ssHqVSDjZzJWrsywby0ydHmPP/hvp2LmLddpmDr54jMLFKi8e+hci4XXMXprn1MmTfP5zX+bXh15j8vhJ2q/fzmTmNULh9bx9bIy2bZvpvqmD4tkznDydoXAxz2uvHCQzOUW8M8HPnvkXrruun3NnZ/jwh2/jYr5C7uws8bYITS1LTGZGkZVG/P41eMvw+pERWppXsXatj5HhN1CaLK4tuqzQQmBNAKmxictzNWrVy/i1VnyqxGpVZmnFwnJ1buy6EUVpZc3qIKvUNSxY1zDMy+xsTxOPJVhyJBauiOzquIlfH36Z4+Pv0tDQwPKKzdLSEpHIFtylBlZWmtiW6ODc2QtcKlaYq1WhYZlmWcJtgM1bwkxMvMuO5PUsLrisVhU8zrF6DTjXFjl8+BB/cN8DFC+cp1S+yKnpd2gQFyhenOL0mQkc2+Ljd3yU//37jxHfnGBHMsnFYp4tsc2EguvIZ6to/k384rVXCG+K8fT/eI4tm7fT17ubef0Kbxx5hVUtLSwtrFAsXGbDhi3MG5fZvCVG5XKF3/v0nWyIrKG9/UaaWxVWrVZYF4xw24FP0KIonMycpHDhHDf3vI89H/gALatX0yi2EghuorU1zKKxTGLTKirVCQZfe54d27Zw5eoVXvnFG9xySyeDh37Nju0JBn/1Mhs2baVVk1lqmGd9dA3JVBvlWYd4fCeXLhv09PexBo4wlAAAIABJREFUeWM/f/+P/wOxQeGVVw6TvrGeglqrXmPinbO0+tay4FzhYqWCJzShNDcDy1zVZ5HlJv5P9t7+r637vvt/cjgIcTgchBBCCCFkWZYxxhhjjDFxHMdxXMd17tzEydK0SdPbb5dlWbdvr63r1qzrsl5pl6Vp1rpZm+ZK0yTN3ZLUc3PjOsQhmGBMMCZYFrIQQgghCyGEOBwOB/H9gXz3837r9rj2+gv00Dnnc/N+vd/P14Y6D+krGvMzy8zMpFGXFpidjeGxVWCzyKyurJJfkdCW8xTki1leMVgxdIqKipFLyyhRqmhq2s6SmuXV3z5NndtGeXklH18YZHLsMqFLQSaiAyxkY3R2bMLjqWDw3HnyQG/PENU2D6urJty1XtZ5/KTm1loDm7c1YK+ysm3bNtJzUzgcDqJTIUrLLGRzc+QWVEqKKtGyCyzMTFNtr8FaJWKxVDGbXaSyQsZebqPaauWqXZ2MxKaYzSwxn1nGXCBRUVbKQjaNu96BwRKFRUXkCxXefffd/3E+/oD65x/980PXXrub2EQSxVaMuagUVkowF5fjcDopLLnEorHA0EcjtLVexXS8ANlSRsPmdciymfXrXUwlQrx+/AVuOrKPifEh3n+vjBpXCWc+eI5Hv/8dXOvLOHHiXfwbD5KIFxK4PMKhFlgqhvNjY1RZtiCVbGT/wWYGzka4/eYDlMoFvPT0C9h8bTRt8lJdU0h2fpKLAyLTiQlmloeYW+ijvemrTE6ssLuzjbmZIB8OjDGXzTA1NcSDf/zHDJ6NcX7gLJm5eQpNOp71TuKXRkirFVTaq8gupCkuKGDTpg0sLy7ys5/+BKfTwfb2Ft797XG+9idfYs91t6BnK7n7ns/y5slfc/LtD1hddbFxxzgjfQLbOzr5x3/4O2odXurXN3D65BncdR6iU8M886ufceOhO9iw3sM//OCb/NHn9rCSXU+daxMULlCqaDz38ut89si9lCoV7Pvydfzqr76IXvBHXLr4IZ+742ZefP7f2bStjqeefJojN36Gy5d76R98n8HBNNdcs4++vrMUlZSi6TKNm+oxjGluOHSA3778Dl994G7efut9Kq11zM7NIplNbGttJXBxlGp7DbqeJxaLsnPnDjJzKWanE1gq7KyIq6wsq0Qn45SXSayv30DfuQHaO3YRHLxI2842Euk4W7dt4tTJk9z2mdtJXpllJjVHfX0dcqnEasES2WwWW3kNJpOVmcwEXq+HixcvUVVVyVRimkuBEbZv38nvT3Xx5fvu48qVeULjY5SXSciKlezcDP71Ht47c4rrrr2KcsXCxeFhUkkLy1qWT9+ymxd/8xJVtmpuuOHTRKITZOfmWV5awe2u57nnn8fuUPA31PKNP7+f8fFJvPVNLC+ZuP76/QwO9XPPvXfy8UcxtAUHlpopdu5sY2wswXe+/TB/963HCE8kue++mwkEP+SjwR62btxDKrPA0qrG5g1uopdnKKs2c+DgPh770RPccuB2UpkgJfI6sjOL7Nq1Bam0mKd++S9cf2AfWU0jMHyR+ewSjmo3/QNnCYV6WFE30LTZwTO/PsaXv/IV3v39ALfcsZ8f/dPTfPrwDbzw0t9iLPqorpHRjQWymUJ2dbTzy2cf5eYbj3I5HKG3r4s//vpXeeP1NxGWLdzxRzfz1M9ewbPtAsKKh5/+81vsvfYQo4ERJEsxF4Y/wOtfR3zCoKGxkdjkKK3NDQwMnaeoYJV4LIVgEhEopMHtYnxqimxOpaRYZiI5SbW1knx+GVOhQCo7i9tZj6ppKGXlFBVauXQpjFE4TsOGLfzbK8dpa+2kf7AHd62fC+cHuHZfO527r2YyEmM8ESccHMdqs+B0OpnPzVNcbGJ5sYjxsRirRVMUrVpYzZdweewybTs9vNfVw1Udn2ZwqBuhUKOwQGJuJkd5eQnLqzlWNRubNlYxHAyywbeFpZVp5OJ6mjf4sVVbCAQv4XKvQyotJzevkteWyQtLbPC5KCkuIjIe4+zZj/775Hz83d//3UMNLR7CoTjhSyvoukZtbQErYhzFWkS5yYdSVsHmZjdj4yMolgIKBYHv/fUPkVZKScUvcebDV9CW5il1+BmLZpgaHabcmmJ4pJvOXVdx7sIk750cZveuL9D13s9hdYy/+fYD9EVjxKYzuB3r0IwI45H3mBzXWFgQuBQMs9nnpqNzE5baRQpKV5kMWcjrFmLR91jXYGZkuIurtt7Jm8d7Wb/Ozou/fhpNy1DlLGbXoQ1cdXU7+lwdnz50HeHAx8jFCWqqRTrbWpgIXcBRt4WqqvXUr/dTW2fi5Bs/5eK5d/jrP32A9i1buPrqLVyaDpNbMtBzTtZt2MnX7n+Al17+HVu2t3L4SBPl0jpUfZEr05N8/s778G/Yyo9/9H84f36MzTurMQtFNDSusrq8wOJMKQ0b6kklDayWzQQvpvHUXkVscp4jn+lkdCzIolbC8ECKbLFMtcXOP3zvW1wKDtC5z8Pzv/gNiqRw4PrrCV3uZnDwIp+9/TYcleXccNMNbGlpoayqgMDYGEV5N7nFGda5fARGJvnyl/+MkfE+JGmJomKNcDjI9Z86TIEgMTGewlJewdneXvZe00q9W2YqaVBgFDE7M4tFsbJ+gwdBXGVjg5v3Tp9kcTnNynIGXVsgNjaNsWRw9swwG32NTE2GKSmSKJNk5ucWUDMyZWYHseQERYCzuoZ1fifxeAK3x81Y5DKRyBVWKWD4QpixwCjF5iKslTYctW5qvW4WFmYJfDzKjp07OPHG25RLDk53/zuHbq1GLNQYH09z+r0P2Lq1jWTiCko57L2mg5OnXkRdivPZOw5y6u3f89JvXuaXP/sN2XSW0dA59l6zneHz50jEZqm1bSOTnce7rZC+3iEWtBWWl5exVIhs2iCyucnBiVdfo6qyihuuPUr9hq0I8iJWm41lYwG5Ahx1DobORygSJaprK+nuOUd88jLTiTHIi9zzuQc52fUh+nIh584OsrWlncJCkQuBEPPzs5w/042vYY54dBZbjYXJiTgtW69hNDTE7h1NnB98n4pKF5ubdtLT9+/ceNPVlJSsciWZ5eKFBJkZkbffeoctm5rpemuSvVddjbPeQ3FREaHY77DJDcTjq3z/kX/kyGduwlwJCCY86zZx8fwISmkR8clB7rrtM7z8wr9RZqqlwlKLYSzjdNjYt3cbK0YKuaScdev8VNeup2DVjJGHeTVDKj2D02nno8EB0M3Ep2YIj49wzXWbGPl4hGb/NrY0b+X8R+dobmhh2Zik1lnIwsoUqwV5piZTZGdUtmzqIBnPMTObw2Kxcfq992lo8FFQKFJaVkKpLNM/0EeF1crF0QGatrQSmZhgx669VNc5SE+qmIrKKDItEL44it1RwMSVZdx1G1hczmIsFrKqZVld1Rn4OIy+UkB97UbeevP3fDw4wOamWkrNRSwtrTCdnGNppZjenvf+5/LxB9SPf/zYQ/aKdSTjZjr2VhENhzCWDSyKh8IlNyZLGLMk8tQvf8mf/vnXONe9yPp1mxjof4+pxHkWtBQOx3pS2RxlkpmCxSsUlHmQreBzeUhcSTF5OcZsFpLJabwb6nDUVPDbl45RVePFbq9nycizoulU2vz88l9fYveuZlKpKHO5GIGxCFu2dfLar/+NKqsDpXyRCoeJzGyaL97zJ/zudx9z+NAh3v7dq9Q6ZZbEIjCtMJeaY3/np0mlpujuOU02E2f3tTtIJBNMXhyhuuFW9CUD/2YbmeQFShEoooBLY5OMjoW4/uar+bD3Y9p27GFlWWa9bwsvvfIcpXIR6mKStvb1hAKjZLMailzG1sYOZrMLnOntIzWdo25blty4js1tUG+9kayaIBofZLVYotFzmEgkxqYmJ6PRPhYXM8yll5mYWOCOL19PItZFSfHtfO1rh/jR4/+LOo+TlZzE/PwCe/ftZDIxRvf7H9DSeA2armOv9ZAXRG689WZ+d/wd2nbUk00nEJarOHTrNazkC9m8xct0PMjissCSmmfTxi2MR+LMpmeJToRxu+yc7/8Af6OfuUU7pWaB2toyQuNjbPL7SF1JU+Oqwb/Bz4c9fTQ1r0EyJkIxlldUPPUbGR6+wEpeZ73XhyAIjI9PoZRZUZc0qmxldHW/Q+PWJlYMlRKzTJlFYXEhz9LiCorVzpVYjMKVFYyCVYokExs3+hm9PMrszAyLqsoth2/le9/7e+pdtRy941YuXR6guqqWN985y5898ACWCoX3urtobPRw7twA29taWFpcJhgMUVRURIXiYGdHO5dGL7KynGcqMY1cWkKxWWb44gXyxQtUWi28/+4ZDlx/mHRqhjO9Z2jZ7OWdd49z/YGDuGrWkRcrsVhl6upsjAZCbNy0gelUkpHhANHxCRbm5+kfOMOnD97OaHCUjl3buTKzgFRcwpamFnr6utje1kZ0PIQgFLOYTSOLEnd+YS/neodYEZfx+7YwMy1gFI4THpkmMZOgbfsGqquuITbdR0dnC37vDqamJ3ny2P/hsX9+kh/9+O+5dl8rgtHIqfd/R2l5GRWFmzlz/kn2XXsVWtbJ4UP38JWvfpE//dYXmEmlEAWRk8df4ZprDlNRXoyeL+CpJ5+iuroWZ10tg+fOsm6Di3l1nqXZBKX2jQhiMXK5haXFRVbzS5iKV9EWVBobNnO2bwAKRCRF4cr0FDvaNxO8dBHf+k2MxeJcvDDC1uYtpGZm0BenmcnNct3eW+n6/YcsLedx1jjY1rKDxGyc5k1N/Pb4q9xy6+28/34/u67xUJS3UCDIVNpsxK98iNfTzPj4PL6NLmZmQ1SXbGBlFeZzM0THImzb3oy1as05Sc7MIBh5BEHAVePknbdOYqmqIJWYpbfnDFKJRL5gEUkqRSwsJJ3JUlBQRE9P73+fy8exY48/tNV3HSXF5RSXJrgcyFHMHg7csBtHbTlXMmNcWbjIzx7vpcqyGy0nI5fZOP7uE8SXPqLj+s3M5kqIj+eot1iosRUhlhQzP2/CXrURsagMRRZp2lJLPN5Py9bt1NRu4YMPBtl3bRvX7t5OkcmgwCijwraFK7Nx1vsr8HgkEukpVsUCBM3OzOgcndfWcejWJqrr3CwaJdS6r2EpD1vb6omlhnF6nVzRClhZNnFgzx3s3NLB+aHfc+7DUxw4dD39Qwk0qrFUdSJITUwlokwnztN/9jeMhQNYHPUsFVVyeX6Rc4Esv/m3C3z5c/+Iy76F9b5KDGOMzU1F1Lo0fOvKCQ6PUan4KCtq5DM3foW33uqipq4MQVzhL7/1Nf71l9/h/fdPM35pHa3b/ohpdYjF1RVm55awWsxoSxOMhrooLZlHMlkQVopZ1bJcDp3lt88fY3HhCj/56TFKTNUszddQQCWyxYalqpz3PvgIVS1mfEJHKLIxs7jIhx+M8OZLZ+nY3sK7Xf+EsLiBs/1vUb9O5MMP30WfKyURW6Wp7k7u//K3+dXzx4hcTjA3u8DFS/3s3ttCerYSuXIdRuEoZU4dTZhhpXCRyxOXWc4LzCaX2dJwNVqugnJlC4sLEtnsLJq2QtuOHZiKTVRWVuNyr+NSIMjKap5ySzHq8iRzC9M43dUUFReSnE7hqd/A6a4PkMwV7L16Dx6Pm8pKmfWb6pGVIqyVZUwlIoQvjeJb56Xe7SKbydG6fRu5hTm2bO/g9ZeHSEwVYTYr3H7nZ3jipz/E5alCEExMJWeordlI3qhiIqFir/Gycct2Pg5dInA5TGWNi8jkDBs2b8FaU8GsNkHz1s3kohVkE0usq93A1OQ06Ss5GluuRiixUVlnpaSyhGdffZJLoSEWF0EUTExOJTEX27h0cYpCUcThrOHtt09js7pwujwolZUs6Dq/O/Eii2qKyNhFpBKYvzJN48Z1LM+nMIkG9RsbWDb8VFZ5yS/XkJ0TSFyJEp9USasz7Nx9Dcn0DOlsmBqbn+GBWYRlJyMXRrj96Ke4MHySb/zFl5BKTMxmLnHu7Dtk01M0NvnQ5kowm0UW9RRPP/0Lvvnnf4GYF6ivcVAtm4lfCWIYOmKxidf//TgNWzdwaayf5GwIU4lOeYXCpbEERSUuVgSJcnsNmfkZ4pOjpNNxrHI55eVWFrML1Dk8VFYrlEsl1NXUsjBbyLq6zaRmZgmPBamwlmOsaqxbt4WNm65l2diAmikjlphhpaAAoShDeGKQldUrzC7EaGx2c+58Lyt6mpVlnfHIKAWsUCSsst7rZ2FuAbOpgA/efZNoKERFlQnFUkK9dz2lFVVksgJlUinLSyrzmRyiqRCTIjOZSOOsqcBmlZi+Eqam1srW9s3Mzi1gLrVSYa1aw/tq83R3d//P5eMPqMce+/FD/g0t1LttTE0WUloi0dBSSO+Zc4wFqmjouIywUo+z2sfGzZXMTK85EKUVOrXOCkaDcSZCKlU11dTWySykJpmcF6lUKjCXFLK8qLJhQyMjFyNUOW3U1dczn13gs3e00XfuI8LjUWzWOsbiceKJPJt9PkpL8yxpSRo21hJLLJFO6uzbew27rmrnucd/yKX4GDt2XENFeS3T8STzmTluvvEGek73kF3Jcv0NBygwdH706BOsc3q48cZD2OuqCI1dpkiyYjeXMhxPkc6Os76+FLFwkcxUiujELDaHH7ergayq0dHZyQZ/Az879lMmYmM0ba1jNpNkW2srv3nhV3z1yw/R093Po//0A7769Tt48Jtf42/+6s85dfIMz//qKbT8OB7r1USjwzz/2k9p3dGKUuFm8UoppaUKyyszWO0ab774PEJJLVWl67CWX+HN959n8rKfGmcRbdvWYbFW8cG7F8gX6FTYCql12pmfM9HRtg/3ujpc6zbwym+6eeCr1/Pc609TX+Gj2lrJqe4f46zfxuC5EMMfD3Ku7/d8729eJzYzxEcfDbFl4x4KCzSEkhAlYh1lFgt79zt58hevce11u4nELnNlcoqVFSgxl2MWy3ns0Se4/Y47uXBhkFqXnRMnXqRUquTWW29lePgiogiBi0ESiSTrPOtZ1ldZNhYxm1Yps1gJRyLoWg6lzMrEZIL6Wi/l5RXMpmbQjGVKxBUMQcTlruVM34f4PV5yuk5fzwfs6GjDWelAW1rmnXdeo2lrE6PBGOHQBJWVMsXmQi5cOMvWbdtJJZMsLa2yyjJOp5OPBj5i//7r6f6gmxtuOICu6zidNWzduo2vfPF+/vH7/0TqSo633/k3BEGkTJE4d/Yc33jwfsyKQN/7pzFWV1lZWuGeL36dYz/9JQeu20WJJONw1pBMxnFW17Bj+04Gzvfx+c99FY/XTXYuy5VYkpdfeYmDBw8zEhxix/at/OzJf+Wh73yH7373O6yrX0dxUQFVNTZOf9DH5sbN5POrFBdVYrMVcvCGw5jEIkpKBC6MZMgLMfZft590QuaN48+z+6q9ZOam+Yfvf5vXXn+W372S4LoDW4jHJvjyPV/lVy9+l+amHVwenyc2scDNN3+at37/LInpWSzmYi6NfURzcycbGzaQSiTZsXMnFz4apWHTRmbn0rRu30YyGWfrus2UOtxsb23n3996g7HLo0hmgfJyiZW8ANoSudwSno1N5DJZ5nMpysrNqKpGVVU1C9l5ap0OZtKzJBNxmts2o+srVFXW8t6ZbirKymnd3swvf/0ramwKAxcG+fzdd/OzY79g7969TE8PYXf4+cW/Ps98Zg7FriMUVbK0ZGJq+jImyaB8pYYym4W5+RQLC0tU2spYyRewuLhEJp2m1lnPxHiY1Ewaa5UNRZGZmZmjYLWQDQ11ZOfmgDxikYl8HoqKSnjvvf9cgey/xMyHWLjG2ldkCw6nE6/fjp6PMzwyQDgcIZVKMdAbwNdoxddoIZVNkUzlaGjYTZNvL/GIhkOx09HZgrdZwWHPA3ksFgWHw4HFYkHTVWKxGF6fm1gswdDgMKIo8+xTr3Hq+ACRgIrJ5EESvThsTWQzIlZrI4MDMV58tgebxY630cvgYB/f/e53+duHv8fLrx7npkM3sXfPHm45fJTOtg6CI8NYJQs2m4OhoQGefOoZZFnCYpHp7u7iZNdpDEMkqybJZKJIso7TZcPlbEQ2+UnFZdDt5LU8upHA4xXJi2mC4QEQNMxmM4l4BhErWk7AbLJxuus0g4PdPPzIQ6iaQTqVI5fVeeON12hrbeZLX7mbPfs60DSNw4duwufzokgyiiJjGAZ2l51kJk0sEcfj8uLx+0hmUgiiGZfHRWtbE9lcjnAojGxVkBQLDU1+vvfw93jwm/cjyxZGQmGSsSCtnQ7M1hRW6xqv3GAEzYgSjUdwOOxAinvvvYWX3/g5D33vWwgYWBWBYLiHeCJIVs0imHOEIkFMZidaWiQbz5NLG1jMdmSThKzIjISGcLgUkokgmpbG5fHS3rmbrKqS0zSS6Qx9fYPYnS4Uiw2rw4LFasPQ1xjrqVQKm82Bx+PB52tAkS0EQyFGhoMkE+m1WRVZIZfTEDAhSRLR6Npsg2FopFIpZEVGzah4PD7y+TxOlwO73QaCgaqtMbz7+/rI5wUsFhmMPF6Pj872NiyyRHNTA7qq47Q76O3uJRFL0NLcTCAwTDIbRpR0VD2Nz+9m77792Cw2TEjEYyoOewN+fztvHj+FGROB4WFsig233YMkmPA63SQiMfbv3kc6mcDrdiPkRRw2K3nBQDAJSLKZVCqD0+PkxInjpNJpRPMaSYe8gCSbMJkkZNmC39+Iy+UGAdLp5BqVxdBoaW0mnUqTTCZxOFwMDQ9isSqc7upiYGAATVcxyRJ2l51oLIaBQE7TEPMijX4fvT1da1SxZOaTuR4Tfr8PWVZwuhykU2lcLtcnXHyRXDaDTbGQzeXI5XSGBodIJZJkM+pa2JJsxiwK6JoGQh5DNdDzGqJZQjPyaIZKa3srNrudnKqt9YrndUKhEFbJis1mo7W5mc6ONiLxJCbZhNPj5vDBwwh5M61NrSQSaWRJIR5L4XA4SaUyqLm1+RA9pyPJFiwWG1aLnUwmQyqVwWw2IwgCkUhkjYBjGKTTaQRMuFxuDCOPqmpoqopu5IiEI4iiiFkyYTIJ5PP5tf7d/9EfVPmVVQxDx+rIk02LyGY3siwgmjR0XaO/f4DASISO9v2EQhGsVgWf34FkBlmSATOCCexWC3nVYCiY+A96kSAI5NQsumZgtSnoWp5MJgOGzkB/Pw6HB1mxYFZkREFEVXMkk0k8Hg/JRIpMWsXlcpDTssSTMQKhMBmzhMPmoaO1HU3TafB5MUkGz73wHM3t+7HZrKSTMUKhEC6Xi1QijtPpIhSKctttd9G5ew+ZVAZB0CCvks3k0FQzWU1FkkREMY+ua2CI7Nt3iMHBQRoavRjGWpiZLMvEoglSqRyZlIrX04iqadjsVhqb7EADFquI1Spjs7pJJkQEKYXfZyMajREYipI3NEZGhjGZRBKJFN7WzTT6/EQTcQKBILpq0NzchKZp5PMmhLwZkywiCAJHj95EHpFoJEoikSGRTqDrWfxNXvqGUxw+eIDvfu+7BEaCePw2JEHG4fKQTuU4eufnCQZiuFwO8qgYgom33z5Oa3szXq8fp91NJB7mwIFO9FyaTCKNzWZHzWlIkoV8Pg8I+Lw+TCYzOTWL3eVc+59TGUwmgVQq8x+zjZJkQpIlEskYak5DUWQSyQQtzW0kk2mCwSAmk3mNqIUBeQFZMSPkIZfLoatr/fgmBGSrlWg4htfvA0CxyAwNDaGqOdo7WjndfYrTp0/T2NBMJpNBUawYhkEkHAPA51tDyqfTaXRdJ5VKo2kagUAAh9PO8FCAVCKDrq6Fo+qGjsUik8+r+P1+Tvd20draip7XeO7pk3R0dBCJRlEUiaamRvKGwODgMNFoFLNZout0F36fl2gsRjgaIRIOk0jF8bjdZDM59uzuYGRoGAMB2aKsZZqkdWRZxqLYyGQyiCadeCxFX18PPq8HwxCQFQFdy6NILn7+5FM4nDZkC2u0JknG7fGQy6mYTRJ+vxebDRKJBGazSDaVRTStHZEtFhuS2YzT4wQKaWjwMTAwQD5vYBJNeLwuQMBslpAlGavVTjqnIRhrcz4mQVzbHwwVRVo7C6RSaSxWKwJgCAb5vEAikcCiKJhMJkZGholEI9hsNsySCV0Dt8uPbqhYFRmrTSEQCiGbRVwuN+0t7QQCIaxWhe6e05hNCr29fRw4uB+324HH4yevaejqWj6NIlswmUykUimsVjuCsLbP2Gw2rNY1ipVhGFitVkwmEdMnZwNd0xBNIoYOFsvamT2fz5PJrIWX/mf1X8L5+N8PP/bQrs6rcNSUMz8/S74gxZWZMXo+OMvMzAxen4utDbfgb3JQUJSh2l5HoVBINjVPYjJDNpOnuspJZ+cuxiO9fHwpgMPexEp+FVVd+4h3dbSjWGTee/c9Nm3ajM/nY/TiReo86yguMWHkV/joo37CY6PMzUyz3lvP5FSMbduasVVZOf1+Fwvz8xiryxQUFvKVLz3APffew3vv9pNMznD6rdeZTEfZsWMHC4s6cpnM66+9gcNWjbVMRqmoQJQkdu7sJBabZi6bxayoCMICpiIBVipBl9i/91acbh8rBXOs85azYszx8itPEZ8e4VIwgL6ks9HXwQfvXyB8eYqN/k2466vYsctDKjNGdGKKd97s4hfPPEmpuYS5+TQjIwFGA5e5PDECBVBebiG/vEIml2PfgWvR5nM4HU4MHd493Y2m5/jsHXezY9dOkvEYJrOIWCCRW1hA0xcpKl7hxJvHOfbTf+HNt0/SufMabrrpJlxuD6+8/iKt2xoZPh/k/gceobHJQtPmq6mybCJwMUzH7q1cDgew1SxSW1fM4uIy0zOj7LiqgUOf3k8qpZPJjtPa1olo2FnMSTiqNpKaXsDp8LKyIhAcHSGVSlBsNpjNxVjQslTZnBiGwYcffsjc3BwlJSXIpRLa0iKZTIbL4VH0pSWqa6o4++F5Nm3exJVkhoH+AbY0N1EqlSJK6ZubAAAgAElEQVQUFlBsKsIsSVy48BECBZSUlGC1WilYXSU2PkFFZRWiKDI0dIF8XmA0Eqau1klZWSkVFpmLgQA3HNyPo7qGbDbHpsbNXA6HuJKcYWVlhcHBAd59912i0bWEWau1gqqqKqqrqwmHx1jS59nYsIELF95l19Vt2B2lqAsqw8ND9HQPMNB/kauvuonQ5Qzt2/dw22du4dxAPzabHafTyXgkhiiauPDxx7RubyM6Pk7nrk5+e+I4bqeL3/72Na7/1H7aWrdx9dW7KRQKmByfQrFa2d2xi/BoiFWhiPmszupqHmtFNfNz80xfiZK6kuWq3W1cuvQxZnMp7voaIpeTrFvXRG5eZTIexOmsonHTFs6ceZ94PInDXsuCOo/b7SGTyVBQsIosy7Rua6O+to5wbIKKcgVTcRGGvorVWsOl4ChCoUABq8zNzSFQTIFQxIb1DczNLVJcXEaJXEFhQSGx2CSjgY+pqrZRUWFBzINQWIi1sgqbtZKFZf0TvKAJQQBV1XjuuV+xvLJMgbDKRDSKo9pBfb2bS6MjDI9eYGlxllgsTCHgrHaxbKzwzlun0TXIzGbZurWZdDrL1TuvIhwNo5RXsKQZ+Nx+LFWV2MvLKRRKmJvP0NTUxLlz54jFYtTX1yOKa/jE8vJyAFRVxWQqpNwisbi4gL68iNVqo9JahaZpzGUyXLkyi2EY1NfXc+LEif9xPv6A+sEPfvDQZ247iKlEJxaYh0ITH19+hTJFxOXoYFmVMFhh165tvPpiF0UFTrZsXU8g2M3x42+Smljgb7/7DRJXenntjffw1hzBKF6mtLSIygqJpfkMmUyKhYVVRLOEJFWg64sEPx4ivwLWKifL+UJGzgfZuq0Dn78Wt9tGLHyRM71n6Ovv55r9V9Hd9z4T8Wluv+2r7Ll2P5eHx7mSmmAu+TGjo8MUmSvp7p3kmus28/orz7KrdQerWp55dQFzeRWLWgGyzcUHXV1ER56h2ruT4kIzarac3PQqP3jih/zqN48zmbyA01vFbMLg9HtD1NW7WVpQqaqsxuur4+TJdzGWVzlw4FP09Z3j8I0HeeLHv+BzX7gdk2mOb/2//8JvXvoB1uoVes8E8Jbfw54blpmaC1CYtyIYbmxVNg7ecB3nzp7k7Efvs95Th6m4livJBdp2uknPR5iOOdm2s4HRwCiphIapqACxZJ7u3hN46jfQ3xfhMzc/wLUHtvDoE3/Ozx9/jD/5+iMc+/FjvHH8Of7l8f/Npw58hlBgGTU/w0033sDIxxNcHk0wvzhBSWkxHw8FefB//RnkF/nN86e4976vIxYvMX5xksmpKcKhEa67fj+L2gILmQVmsxnu+8IXyK+usLK0SHBsjK3NW6mqtPHRR+fYunUrg4MDbN7cjLGiUyCsoiglpK6kKTQXUFpShm6sEJ+Is2vXVVRUWjnXN4S6qGK1ViKXKpw+c5yGra0sLyxikCc5OYXZbMZWZWfFWCE3P89cLkfDRg/LWp4ypYIr6RjuurUq9tLyEkvaKmJBCUtanro6J8HRAO3t7bz00its8HlRF5bYddVOfvvG6yiKhdUCncqKas71nWX9xjp86/3MZRaYmVH5whf/H7refZfI+ASudW421Hs4duxJRobPsXXrJurq1zFw7jybN2+mpKSEzs5OJHMp87kcP3riGLfdeiv/+tQxmlua2L1rN9GJCK+99ip/862/5uFH/pEjNx3lo8EztLS3cOFcDkFc5PP33MM7b71DVruMSC2XgmfpO3cau3UTjpoSnvnVs3zl3r/ikYd/zeZWE+0dDQhCIb/4xTEOHryRImMT+aI4e/bspqx4PW+e+gWNWxqYnRHp3Hkd3b29fOr6VhbUZZSyUspLTXjWb+RXzz6F213HaHCMa665jvzqCqsreaYSSbLz82QyBkJxESPDF7BVyJiKC6mrtVJWIpPJzBMdm2Tr1m1MzsySN/IYusrk5BTVdjv68iIrK6AoEpqqUygW4PU0YSyvcjkWxO2qwmwWGR4awlFTjce7gSvTaRZVDd3IUe+2sGNHJ2pGR1as2B1VzGWmqatvJJVZIJdNUlwiU2gUUl1bze/eepP29nampuK8+OJLHDhwgIsXL7K0tMDGjZuoc9cSCASIx2Mo5eV4vevRdYNcLkdufoF8HkpKSqiuruatt9767+N8FBaJZLMZIpG1FGKXy42/0cl9993HkVvuJp7Q6B8awmlvIJvUsFvMOB0iil2jud1BMh1C1+Gpn79KKKCwf8/9GIaO2+WmubkRk8nE8RPHOXXqFK1NzQwN9tF9+hSHDu+jrdVJOhNEEFIcObqXW452ct/9N9E3dJxIvIeTXc8hmtN0tLfgcNpo8DVy9MhRMPKcePU1hga6CQ72kMplEc0Wnn76WU68/QbPPH2MvXv2YTIrxFJZTrzdRTqVxWZVuOWWQ7S1tZI3HMRjBpFQiryRwGLP0jPyIuncINlslq6uELmchSNHb6OltZn9B/ZgscogqPzw0e+yb38nOTVFKDJIV/cJnHYzX3/wLm676y4OH7yXL33pfmIRHY+7kfseOIgoxtGyAiNDcYy8hNls5sTxE9hcHtz+RtKZLA0NTTxw/4MEgmFSyRxOh4tsVkPTMygWEZvDQmOjn5aWJv7im3+Boas0NDkJBvo49pNHsUo+opEwjc0Onvn5DwkGYzz07e/z9snjiHKa090DPHHsJyDmCYSDdO7eT1vrHhq9zag5SCZzuJwNDA/EGBocIZ3KoufzqDmdkZEAIyMhZMnK0aOfR7a4uPPur3HXnfditVqRJIm7776XtrYOBEHA52vEotjp7NxNY0MzNpsDs0nBarXSdbIbl9ODz+8nEAgSCASJxSJkc1nMZrjlyFE8Hg/pVJZQKEQe2L1nN8lknFQqidvlRpZNHNi3F0kSUCwSiUSKwYFB+vtHOHmyi3Q6w8svvoxJNOF0OvB6vXR0tNHR0cH9999Pa2srLpcLVVXp6enB7rBiNik89tjj+Bv9CILA0NAQomiio6MDi0XhwP599PX1kIwGOd11isGBAY7ccoSenh5isSih8Agutx1ZMvPM088gyzJvv/0mwcAwz77wDPv27SMcCXP8+Bs8+sNH8Xg8JNJxkvEUp053o1isHDx4EEkyk8vlsNtlPD47u3fv46ZbDvH000/h9zfR2bGPgf4RJEnC6VQwSwa3Hb0Js2QiGAzQ2rKbtrY2LFaZ/fv3f1KVXAs5GRkJ0jfQz+PHjiFLCsMjQcySgp43aG5uRtcMfN5GbFY3+/beRFNTGwJmAoEI8XiSdCqFoakYeZ3mZj8er5vGJj9uhxNdX0PWGkaeRCJNNBIlHA4Sj8cRRRFFUWhr68Dn89HR0cmBgweJxmI8/fQzJJJB7FYRn9+FaBI4eOggzc3tNHibObDvMCbRjKYJJOMp1JzBq6+dRDJZsShrhJ1AKMpg/xDJjIosyZjNZp78+U9wuVzs27ePRCKB1+tFURQEIY/NZiGbyzA8PEw8lsBisdHgb8JsNhOPx7HbnVgsNkxmAVEUMQzjD71U/1+vZWMRSTFIxTPI1gyqmkEyO/E3eBGlNJmEG5AYCvSRSeskkymikTiKZY2xb7U60LUs8UQQj89NOgN5TUfXdBwOB1arnXA4RDAYwiyK2Gw2EokUHq8F0NF1HTWjYnVYCQSHSSSSpBMpNCOPw2HHZnNB3oSqq2sZA3qeVEonq6mYDIlcOo4iC6SyWRDMZNIphLwJSTaRSScxmQUOHzpMXjSh6Ro2p5db7ryFbCZBTs1iaJDPW3jx2adoam7AYjOhmAVyORWP10E0HEY225EkG8NDEfp6+/H7PciKiZyaxCwJZFUNWXJy6u1B3G4IhmN0db1Ng99NR0cHLmcD3uYOLFaZ559/gaYmPyaTiXxepKmxjd7ePnp6T7F//15EUSST1pAVgWQshizZP6nWyvh8PiSzyMhwgExSxTAEotEEjU0tUAKiOYsi23jtxWfIplOcejOOxSYSDg8jKwY+v51guJd8XkDX4MDBTh555IeIoptcVsPvcxGMieSya9+mpuUADUU2YwhrVCtZMTMy1E8gHECWbaQTaWDNqVj7nsVPClEquq4Ti8XweDwgmAiHwzQ1NaGqa88yGonjcDhwOd0IQDabI5/Po+fU/1gbTCYTbrcbLaficNo53d2LIJhw2B1oeg6ff61CryjKWjaEyUw2m0XT1ohcJpOE1WolGAzi9/ux2qzY7BbUnLrmLojgdDqRJRga6cbnW0vrTqfWsjqisSTJZJaWllaSiTSpVIbWNh+pTJhQKEQinvwPOlMoFEaSJDweLyaTiJrN4G/woigS+/btJZWIYxg6FouFF559mUOHDuHxeujp+xBNy0BeIRqJ0tDgZ2gowIEDe4jHUri9Ljo62iAvoWprznksmuGWm44yMjKAqmYwm9cymww9j91hw+Fw4Ha6EICcqiKZzBy65SZOd/eiaxqJRIKGhgYERGLxOE899RQdHe1ouorVZkHTVJKJNLIsk8lk0FR9jYZl6GRzWeKJGIoifUJAzCBLEorFspZsr2ZRczmsViuybCabyxCPJ3A6nFit1jW3RFEwyRLxVBITeRBForEoHR3tOJxu1KzxSfaHCLpGNpcgHk2Bae39yiQTyBY7kqSQSiUQRZG8YUJRzJjNa2noyWQSWbZ8QndMIcsygiCsuV+GhiybP9kDdTR1jTQpyzIWi2XN2RfX5kP+s/ovcflIpaYQJYF0JkkqlaH79CBvv9GDKJoJh6KYBZl0MsxPHjtGNJzjxTeeZSTURyDUwzPPPI5Z1sioKRTJxqH9X+fVF3rIIxJJrLVsRSIRhgaHMFQds8WM1+NDNsv0dPcQCqe48657kRUrfl8Tsmijp3uA2267i0xa55vf+Db7Og+QSqfQdVAsdvr7hkjGo/h9XiyKiXgqQl40OP7qy+zZt587bzvEgQP78Tf6OXTTIfoHhrnvS18nmojTO9BP16kTdJ88DXkriuLF6WpEtgnE0v3k1AG6+1/E5rLT5O/ApjQxPBDF72lGy2rYLBZGhvv4xoP3cfz40wQD/bgcLgRdIRpPc+jAXkYGh1DVKA984wE+f9fXcbub+P7Df8ne/e0M9Q/Sdbr7kxfLhN3u5C8ffJBvPfgXHDlyJw0Nfh555BGGh4dpaGgincuhZrM4HTZCkSDtbQ0g6GiqTjgQ5tgTxxgeGmAk0kv7HhetbS4aWxyYLSlQBnns0e+y72AjR4420tppRc9nuO3OW4iEMyTjeYaHQjz77Al03QqGDTSRvKGQjSewKCJtLR7efO0FJEVAy6t4fR4sFgeaJnL44F0MDyTQdYmuri4ikQixaIRgYASzSeKN4y9/EtTUTyqVIZPJ0dfXz6GDR9izdz+hcBBN1cll1U8WIBdms0hvXzfDQwMoFhtdPadIJBL0dffw4nMvkEwkMIkismImHIrQ09tLOBLB0A0aGhs4euddmE0Sra0deNw+Xn1t7TfIigmXy0YqlWLP3k5OnjyJpmnE43Eef/xxfD4fdpuTZ599jpsOH6GxcT+JlMqB/UdoaWvHLEnIion+4W4sFgOzrKFpMYaG+0mlU9x3730MDAwiiiaGh0fI5tLY7DKxeJiW1ha++c1vEg4HUBQJwQBFtnDgwAFOd5/GbJJR9RzJVAaL1cH3f/goslXBJCk88sTDhGNBurpPkdOyHL3r84QiCZ555gWaGtvpOnWaQHAEh9NCKh1leHgASVIwSxL5vMC+/XtIJGI4XHZS2QxmReLA4UOEw2Hu/vxdeH1eDt90iLdPvs2J42/y9NPP0NzchtXixGF3M9A/jCLbaW5qo7enH1mykMlmUTUVn9dF3sjT2NJMNBghkUqi2KyIokQ0HiMUCeH2uHC7PWja/986oOP1eiEvMjw0QjKRwmySUCxmmhpcyIrIc88+Qy6TIzASJBwMEY6EyWWztLe2Y7HIKBY7JkHhwP6DuJ0uXE4vh/YfJJPJMDA4QmNDI1lVI5vNctuR29ANlYGBAcxmM8FgiHw+TzyeZGhohJbmZtxuNyazCZNJJJvTkMwKzc1racc5NYPd5sTptBMKBf7QS/X/9TKXFJNNJ0gmE1isEI0E+fznv0I8FicYHqSnK8pjj/4tmXQMf5MXm92Enk+Q1zX0rEhH2yEGukO4nM0kEhFUIYrVYcHtcTM4OEgkGkCRZPbs7kAUzSQTKfwNPvKCii4YBIaDeLxeopEQVqtEPBbi6WeepqOlgwZfM7JsIY+Ize5CzxuYZQvkRUTMIBg4fB6cLiuSWcTjdRIPJ7nr7vuwWGz09Hazb18nDzz4dTLpDB6Xn4e+/RAZ3UFg8GVkIYUiq8SSQfqHhrFYHZhFO7FIFovZQTzdjdttQ5JkDF3EarHj8zXw2huvYhJFWlqaOHXyFC6Xjd6eCB77ER7+/sMcvqWDzt1tRBJ99I68zInXcrR42kmlknzuj+4mHB0iFgvx8muvYbd5OHr0COl0klQqRiyW5MEHv83wyCm6Tp9CkiT6BkfYf+gwVquCaBJ44oknaW1pwe1wMzDUw/BQiK2NN6LRQ2OThTeP96DIfmLROKp5BLvNybFjLxMM95NMRhkaDCNbHHTubmfvnk5OvNFDMq2iGhAdSJBOBGlrbmJ44Bwms4lUJomWU3G67AhCHkmxEAtHaW9vIhKN4vF4cLvdgICuq8iyGY/Hh6rmyGQyNDY20tV16j9wwX5/I6qqEgyuYXPzGNhsNgLBQb730MOcOPE2x4+/hsNqI5vNYjKZiCbiSGYF0WRCNEuMjPRitVoIBaM0NfsJBkN4/j/23j/Ijfs88/yg1Wy2Wq1WqwVBEARDEARD8HiMjCfj8Xg8mczyZhmG5tI0T6vQDM3IWq1Op3IURqs4Wp2iMFqtopO1iler0+pUCovhqhieljtmuCyGN+ZOmPF4PB6PJ+MRDMEIBMMQBEGtFtRqtZrNZrPn/gDjSu3txf5nc1eV+1bhny/Q3YUf/bx4n/d5nzebJZlIoSgKjuOSSPRlomtrawBomka71SWTyVCtVi8PGOwPQJZkkQMH7qXdbqMoGrOzC+zbfyf333eA4eERDh8+hmvbpHIFJNHnwIEH2LJlC1/4wheIx+OYpskzT3+d1dV1VtdWcF2P3Xfczr1fuYfjx14hiiKW15bIZLKMjI4QRRFCKJNKZtm5/XM4voMi9S2eBQGGhoY4MTPL2NgYZ8+eZHR0HM+R6NkNRkeH0fUk09PTCKLP8ZljgIihpymXy+zaPcmxYy/jhwFPPHkISZCRFJnZU6dJxOOkMxlMs0u73aLbNdm1cw+NRoOvfvVBVFXCcRyKxQGq1Qrnzi0yODjIxMQEuYFBknEDz+khSgJJTUWIBERZxvNDNFWlZ9q4voPV6/YlTMkkYRhi2z0Esd8+YBgGvu8zO3saVRJAkqlX6yiyQSKRodM06ZomltmXRdq+TSGfwLL60jzDUFDjGoXMKF3TZHFhjqnJaaxODy0R0WhWGBgYQNfiuJeToDAMMM3OZdIwoFIuk8/nGSyVEASpLz3VDXo9B8uy0HUdXdOxLOvnxtL/T8iuDv+Hf3/wk4MjJK5Pk7rxRpx3r+ITpUmqte/RfqOL9XbIV373c/zkx6t85GaDjxVzbFwSufbqcQZzv8qv/uPf4O133+BS7ANOzR7jgwtv8dGP3cpN8RTRRsTVV6t8/GMfw7juWrrdd/jwfY+IS8Rim3n9R2/juTLXXpXh9Mlvcf6CQHBhE+90z/P47/2v/MFjL/Cnx06RK+QIOM9m8SpuuanAdbKK867J8vf+T37hUx/nhsQN3JC6lU2br+Cdn6xzhQBL33+N5e+ts+MLv8bLf/qfuBgG3JzSsd99h8FP3sxFOlytq0z/8j9maX6dm64v8sX9D3K9UWQT12G/3+aDS3UGb/5HvPVmhHf+fYKLDpuvdtjx+c9y0003EkUR2lVpXnv1XYxrUty+51e4Lq4RvyFBvdbhzbdNrr7yWq6PG1y8EOPa62/i47d9Au+Sz/kLHtfdcB1f2v/P+MSv/hNWzi2wWVHJ3JzhI9kC//k/f5N08lZuueVjfOe73yGbvZHOO2+zKboSWbmBKLqS1EduRhAuULz1U3z49kf5YbmMIfwy7UYc8y2Bndt+l2uuS6MoRcTzO2h2X6dRf4vH/pfDnD8fR5Yvkkl9movndT6aL3KJd5k9uYZ6jcSr9Vd45ZV/g3TVh9z+T38FNsC23+Xm3ABz3/wuxrW3Im0y+Mtzy9ycu4YNIeIa/Vps5338ICRzc54LwQVuK36M4GLIzdmP8snSCB984HFb4WN033qTn7zxE8Y+PcZmSWT007+Armv8wtAn+PT4FPXXm3z51/8ZieviXKVq/OLIKOo1KokbbiAIAiZ+aZzya69yc/omrti0iUb9J8SizVytXUt4Ad7svMnyd1fJfOQmgovn6b33DtenPsL3/+qveMt8i5Xvr/HB+Q+5/dfuYJO8mdrrdQY/UWLj4mbc96/G7L7P2urrdN56k9de+yHSVR+iXhVy/nwb5aoP0a6V2bFjN2f/y7e45jqVRPJ6ms03mP/2X/CZz45z3fVxPvjQ5dUfrnPtVTKFW/NcOP8Bm67czMK3vkU8eSMfuB+g6tdyc+5jfGHX5wmBgeJtvNluY73zDvu+9DmiyOeWzBBvdmqIsc1o6nX8o4ltWNb7nL/g4gc93A/fotNtkr05x5stG/s9j5uzSf7sxH+k0WqTz9+GEIth9SzqtTrJ1Efodkw0XcW/dAFxk0zuo7fyXq9DcNFl/dU1zvs+V8pXs7r2ffzzF/jkL34M4YoIVdvMN8/8OVfKm7l06SKvrf4AWZUoFvKcv3CRD/0PuUKUSGVuJp1KcN01OlEYcvHCJVLJFO+9axMGlxAQiMK+vOH6RAJiERc+jPhI+hauv+5G1td+SK9n07Pepfv2T/ju9/6CzUqMoU+WuEKUsHrv0Wi9RueNn/Dn3/wmH37o8Nlf+kVeLX+fDWKEYT/huSV7az/QJ5O8916PMAy5/vprSSQTvNdziMWu4F3rfZz3Pa5SVN566206nbe44F8gunSJIAh5//33uXgx5LvfXf7/ZVf/L64nn3zq4M6dO7kuHuPUsSapj9zGJfkH1JptTr78AT9+53/j4Ycf5pZbb8F1bd559xKZm9LIm0OuuTbOO2+8z0Z4iQ3Jo+e+SxS7gv9hahrvvEetvMKN8WvJ5z5Ks23ifhgiygqbJQXnnVd523TJ3DzAex+8z4VLG2zEBHK5NFOfHeFb84us/dX3uPGmDIEQsFmUSVybRrlC5u13uvzVyl8iXrHBNddcyY/bb3DLrbdy3TUaq+U6u//HL/K9pe/zOw89wOp3v8PK+hqbYjE+cC+wY/s0I5+8jVbrdfbe/kWOz5yg1W6x+9f38NqPXuP9dz3E2Cbef/89Hvztx5idO4uyGa6Nb+JC+C47Pred4m1DvPbDHzP6qWnmzn6LiV/+NIkbbuIq6Ta0xFv88AcVpCsDFOkSb7Zc7M5HCKKfsDz3DT4x+nk6b60z+UvTfH7b59lI3cTRx36bG2/7BLfe8gv8p5lXePvtJp8Z3Ymuy/zFwjk+PvAxbsqmMa7azHr1O5Q+/ineNT/kf7pvD+fPX2J+6Zss/Jc/50t772F+6S+off8Nnnz6GY7/2dfYtfu38M+HbL7qA9a+f44vbHmalrXOVZvgmmsylIaK/M+/tYedO+7hM5/6DOb7S1TKM7z9ztsgX4u06WquEHWO/odv8Gu/fheev0Hmphyv//gnXJ+IeLNbJZf9BBfOh/xgfZ1f/OSnmP/WOa6//jouXrzIq6++Srlc5je+dCeCEOO373+AX/7lz6KqGu6HH3DfffcwMzPDD9bX+MY3/iOPPPKvCS98wK7d/5SFhXNMTk7RajXQrtG59ZYcP262eb361xhXh2RuuY1220a44go28AkvRiRT13PhYsB5z4PYBu9aPTK35rjh+ht4/fW/RpY30X2rx8VLF5j47CTf/d4SyRsSfCRZ4K23Qj7x8Rx33ruXRw/+Hm+98SbJmwy+MXOUL+2/nY/edjMXzn/Infsf5fd/7/f55/fcydvvvM23FxaJxWJ8ce+v8cMfvkqx8AlO/Nk3+HGjBtFFsrlb+fZ3vsUHjseXv/xlvrf8V9yQSKFoBp/9zKd5/OmDJK9X+PIXn+ZfPXkve/dvY/4vfsDO7V9mefUM+UKCz47tYGxkG3/47D/nzi/eww3x2/jO4ve4ZdDmrdaHfPTWT3HmzFn++KU/4fChY7z0J8/wyL/8V4ixG/n29/6UG26Ks++LX+Gdro98pcA7zutY5gdY5tu8022gXXs9g4MlfvSjOv6HEZs2bebixQvoRr+37/yF93jzxza3FTO8Z9v4HzpcdbVMaXCA13/c5NIlCVU1iG2+kiCykWMiFy9eotMx+ehHb0WWZTY2Nrh0KeLSpRhh6OH57yFfKdLrOlynX088meJH9QYbfkgsFuOKzVfg+T6fKN3CwGCeH3zP5sPzLldds8F1msF3lmt0flLns5NDOO9f4AMnxqZNb+B5G9yc/TjSJgn/wnkU5WqSyevpdt9gI3YFVykKQRgQXgy57rokb7zR5PV6k+GRT9Ltttm0aTPXXtufN/Puuz2Wln4+t6ufWfmIxWJyLBZbjsViP4jFYj+MxWJ/cHn/cCwW+3EsFlu7/Bi6vB+LxWLPxmKxeiwWW4/FYsM/6xofOOeJpwx6vS61ShUjLtNsLWAYGlEkUigOcOrUHIgSkiCgKjKKKNPr9qg1O6ysltE1hUwhRSqjo+sykR/QaDbw/cslK1mh0zXxHIdIiJAVFU1VSWU1MjmZnlcjEDpIiks2p4JosVZZwjLXiBsAHpqsoCs6IgKVepVqdZ0tU1PEdR2r16XXs+k0TDRDZ2Jqkla92R+WWG+QzWbYOjXFtqmtjI8OocoitVoVq2Ny7PhR1is1LMflzOlTzPeY1s0AACAASURBVMyc7LNX6QTjY2Os15dptMrki0UkxWBsdAeOo1KrdzB7HoIkkiumiScNZDmB7bSZnT1GvbFEt9XEMi3MbsTqSpNsNsny+jJryytk02la9Qadrkl3fo6BwQLZdIpkIkEibjAyNIRnBzSqbTTVIBHP0G1aNNsWIgpCJPcbwBWVSqNBz6nhhU0a1gKt7jJrlQWS+ZC2ucbq+gLPHnqInlNneLhAub5Os76KZVoIokvPqXB69hC22ySdC6g05nF7PUpDo4yNTSErRr9079pEgYuagGp1gfXqAkHYJZ/LYFsWM8ePE/g+W6e30G23kUWZTquD2TFpNRpYVo/l5SWOH38Fw0jS7Zj0bItUOnlZXhUwO3uK5cUlVpdXWC+vUG80cDybRCpOtVpFFkSSySSzs7NoioysiPie12cJiFBkldLQMD3LxvUcXM+h0zZpNjusLC3guzYjpRF279zJru07yKVzuJaLhIQkSNiejWm1QAjREiKGLpGI60yObyWTLmDEU3i+SCaVo7xeQZFlauUaVrfHAwe+wnBpCLPTZrA4QL1awXNsBEli6/btBIgszs9TGhqiWa+jyiqJeBKz1WR1bR1ZkjE7JpoiE/geC+fmMDttCEOiIIJIYmVpDUVV0HUNURCx7S5hFBAEPvlCnoiIUmmIeq1JuVLhsccfw/NdTp44+dNqgSRA12xTrdVpNlr4voOuiRSKGeIJlSgK0FQVI66jayqaLuH7Lp5vkkwY7N696/LgvjJe4IEgUKnVSKVSZNIZ2h2TWq3Wr4Z1O0iKjJHQcX2HIPJxXBer5+J6HhEgImEYGQaKJQq5QXQ9jqbqTIyPsnXrBDt2TDE1PUo2kyRwnf5AsLiMrusghQwNF0ml47ieTbNVwzJbaJqKpvffi+vZdLvWZcmVgG27BH5IREAY+qiqRhRFuK5PIpEAQBQlQAQiRFHCiOs/C0r/Qa+/j1i1efOVdJpdXNdFNzLk8hkC36NeM9HVHD49Oq0q+XSOVCaF2XHo2RFBJFMqDaDpEcghbs/G6bmMj0/ieh4Q/pThrFTKQISAgCTJSKJAz+2RSiZotdqoqkqxWERVZVqNOuNjkyyvLCIIMnv27UWRFXTFQJFUTKuNa3dJplOocZW22cP1BIQwotNeZGR0knNzKxRKw7g9B8e1yacTpDMJms0KwyPDnF1cIi6nLpskWJSGCpTLZVbXqkyNT6CoEglD4rlnX8ZzIlRNwrTaGIZKz+4xNzeHbfsEPsTjSQxDx4jrtFrr2JaLpiVJxDPkcgX8sEcyqTM0NMJq5W0IQjLpJLVaBUFS6VarjO/5Aoamk0jEGRwcoN00Cd0A3/eAgHwhS6PSQo+nkUWVwcEiiYRG4EOl0cVx+hUFy4wwuza1Vo0jRw5RGhpk/mwZUZFptmqUioO0Ox5zp2bJpDOsr1fI59Nc/AAGS0U6XQ/PdzBUiWQqSSKewrZdbLuH44VMjk3R6zgMDgzS6bRRJIEocFlcXCQiIpvJMVgaZGho6PKwP5lms4mq6qiqytraKr/5L36TmZkTDA+X0HQN0zSRJIEgCFhcXKRUGuUr993H4tI84xPjzM6e7lfQmk06PYt0KsXY5ASloUF6PQvLsghDSCbSeJ7L/Lk5TLNLJp2m3eoQCVF/UGsYEjeSaJoBgGEYffa/1ekz866H3QtxAxtFUog8n3iiP6BQ0/u43ev16PUser0Iz4v6DLmmUSzmKZVKRFHE9PS2y3I12LZtG4ODg7z4wguY7Q7NVh1VVUkmU6TSGbqtNpEgkc1ksSzrp9+jKAYoioIsZWh3aiRTBivLa+QGoNl8F1EUCUKHILTRNImtW3fSqHUZHhoHYGFplqmpcebm5lhcXGb37ttZXV6hXK3QbDUJggBZlrB6Jpl0Es/1EEWRTsdEllQUVWN1dZlkKo4sy7iehaYauG5Ar9fDti0kUUI3dARBIJXK4Dgu9XoDURSI6xqiKAJcNikAURRxnb+RNskoioZAhKJpqLJM6Ed4vo9lmoyNjyIrIpqu0+o0kSWVVCJDFAr96rlr4wc2yUTfbElWRGy7RzKeRlEUIiJ0XUcQBUBElkVc10UURZS/STyCkDCMLv+fNtA0BcuyiKKIKIqQJKkfr4SfXxoc29jY+FmAHgOu2tjYcGOx2CZgAfgt4F7g1MbGxvH/6vXbgd8EtgOfBv7txsbGp/+uaxQKH9sYn/oUURAwNbELVfdZXJmhVZVJpwbZvmsn9VqVybFJ1teWOHNuhkIuSyY7SKvTpt1qoMcjXMcjmxslnUghSiGiKLK0tITruhSLRSQhIopEIMALA3zXx3ZcGo0miqLz1QcfoGt2WV1axA1cWq0OO3fupNM2qbcqRAJoYpyEFieTSiBKcOz4i+hJg1y+xJatexEkkROHn2BhaZWRie1YXZfRiSka9SqCECAJDqoiEwUhSHEKhSFUTWN1+QzV2jKRGFIcGGZpocX05BSqJtFp10gm03Q7PTqdHpbpYJpd7j9wgERC59nnniCXT1GrNti5Yy+pfMjS8hpSpDA2Ok15rY1tRUSiz+LyIe677yCCKBIEHiury3i2R+CHtNsmWlynmMuh6nE816NVN/F8l/HxUj+omCZ+FBF5Coau0+40WF6fw+x22LtnH8ViiVeOv0Aul+eO2+/k5ZcP8fgTzzC3sMjy8ixbtuyia7Z58fknEUWNA/c+hO22OXToKfKFIkHgImsexXyBfHobjh1x8uQJup0O45Pj1Oo1iqUilUoZx7GxLrvB7LvjXjRVp1KpoOs6y8tLDI+MkcsW6PVMFEUBwPNcstk88/MLaLrC2Ogoh48cYnR0mHhcp9vt4vsutWqH2/fcwdLiIrLUTzBmZ0+zd88+rF6PRqOBrKn9m8/3SKVTKLJxWdPp0Ww02blrO0uLi2i6TCqdZGR8jPLqKq1Wm0a9TRRFjIyMUK81QehPYRcQiUSf9coKkxNbyGfyHJ+ZoWdZDA4OoSgKnufjOC7btu1gaWmh7/IkywS+T7PVYM+ePczMzLC+vsqePXsoDhSortc4Nz+HYzscPnKYxx9/nLvuvpulpRXq9QbDo30JVSqVIp3qJ2KJeApZ6xFF0Kh5yHLI1NR2Ou0eR18+yh177kCUfGynSSaTYmV1iV7Po5CboF6r0ek2URSRbtdkZGyCeDzO0sICk1umWF+rUBwYoFavICIxUBqkUl6FyEFRVARBJpnIEAR9nbvj9igW+y461fUqlu2RTCbRtD5w9yfJqiwsLCBJMpNT00iiiO+7OI5Dp9NBEAQMwyCRSPWbz90A3/cRxb7WudWs94FV1wi8CF1XWC8v4vsusi4RNxTiRoJWy0RVE0iSgqKKiILI4uIiw6VBvCCgWMwShiL1RhNF1gjDkCiK+u5VsozneXheP4D9jV7WsT0c14EowojHEYTop/hoOxaBHyCKMl/72te+v7GxMfJzI/w/oPX3Eatuuim98fFb8wwNDVIcmMD3PXrhKgsLNpODTxHFv8pAaoTVtQqL64tsHXuMsbExXnr5EYp5CSIBMUwiygFty8S2RSa2TLG2towQuAwWMljtFnagEaGgJ3LIsoLjHAY/zsJig+dfeoHnnnsBLZFEUxTsVhdFFKjX6/Qiny1bdyEFfX12z2ngBT10XcF2bDQtQyGbxzRXKM8foh7sJ4jgq/ffSb0yS69TxnM9FC1OudLh7gce4tDJw4zLOlpc5cy5E8wuz/Po44dp1TsEromGRyanYpkTIPZodZZZL6+xe/cOzp2bZ2py22Up0xEOPvo4p+ZOc+ddu1lZOU2jojA6XKTemsfqNcnqU7i+SSjZJFNZPNeg2TjF1772J/zJH/85zVaZUFjG82R2b7sX2VB57JF7yWd20bXXKY0NsGViG19/4hgPP3aAl144SDpl0GmZpDPjzM3PkchHHHr5/2Bq6J8wNj6CY3Y4ePBxzK7JzJlVDr/8CPXyGl+59y6W51MMDIk89dTD3JBJ8e+eP8jCyimGBu5idGQLzcYZMpJEL4q478BDPPLYYxDBykqFvfvu4tTMKVRZYXrrBKurM+iaRrMV0mp10A0dVZEYnZzilZdepNMzGRkZ4Y479vL0E08wOT1FJAbMz52jWq3yzLPP0Ky3mTt7hoGBEo7jYegl2u15JFHCDVzGR8YIEfEij+3bdnLfPV9hsDiAGK6iJ8cIgySCJJJMyqyvrzI+MYzZM2nWW2hqHMcNKK+tk82lUBUDURJIJAxGR0d59tlnMeIaxcIA8USGbjdiYeEw+/bu5ZlnnuPRRx4ll8lzZu4MsiLQbjdJxROsrYk026s8+NU9OLZHtVZjanKSQ4eOsHv3bmRZZnl5mdAP+vL14ye4fd9ewiDgscee4OCjT6PqKt12F13XMBIBguBgNkuEYo2tOxN06jnmTwnU7Hs4ePBJHrr/KHfte4yW9yw7pvdRq7gcf+UkxdEOE8MP4tsar8y8xPiWFEcOzXDvgbuprNlgJ9HSa4xNDlHMTbO42KJcXsMPq6TTOY4efpa9d2xjreoyNjqBIIjUqg1S6TSebxI34lSqayiKzJbJBzkx8zSSIpLJJBBEcJ0eQRhBpBGFImHQd87rdCxSyVzfNTKRxnYsXNcjCPy+TBhoteuMjY4SBTqVaoUoAsezCXybbC6JqhusLi+ydet26vUaSjiAJ7QRJJNEPM2ZE21yAwaS1iMKVDRxgI75MunUEIaRx3Z6mKaFLAtEUYjn2RjxOIqs4ro+kqjgBz7JRAJBFFlfX/2pS186kyQK+45Xf/iHf/hzxaifWfnY6K+/8c/adPnxd2UsnweOXD5uCdBjsdiNf9c1nPfP02rUSacTSJJIo7lOKi3R6ZroWoLTJ07Q63q8+PwpymsOA/kSkizQsVaIohaSalIcKKDrCSqrLRYXKjiOR61aRxAEMpkMhmFguz6u7+F4AZ4b0Ov59EwRXRkglx5leaHNudkqna6A1ZUp5CZZOFenVu0RhuA5Xp+NUiS80MWyuqRzWYqFEtl0luPHjvLiM08CIrKqUsznAJ96rYKqaciS1G/CMns0Wxb1msXZMyucPXOW5198nLa5zrZtJfIFnS1T45SGJhnITxFGAYoSUK9VScaTiOjs3nE32WSRarnBcGkIz+1QLGQxrS5Or0eva7KwsMRKZYlqbY12p8PqygqKKmGZbVqXmxq3btmGpChIqkqukCOVTNJoN+m061TrFSLBxzAMHM+mWqsSEeH2bMLQQzJ0REnE0HSmt2yhVu1SWRHI54YYHh6mVukSTxZ58fkFVuYbhPg8dfAI1dUGk1sGGRlNs7S2yHp5nkceux9EG8tsMzFZQFLA8wUaNZN8YYjxya2kM1mMeJzAtyF02bFrkq1bh4gEH9dzWVxZYGF5gWanxb4772ZtfZ2u1SRXyJEfyBFPGciywpkzp4GAZMKg3qjhOD2OHj2KKMooqkw8kaBrdpk5foJOu41u9C3ppqe3sbyyQhAERAQIYUCv22FgsEghP4Bh9C3yEvEEyVSC1ZVVkqkEruvS6bR5/utfp9O16NkO+WKO0fER2l0TN3BIphKouoppd/FcF0FwKQ4ksbwWltmhODCA54ToSgqr7eN0Yf7MEo7tI8sinufRsy1arRaWZTG9dYrSUAlZltFUnTD00RSVdDpNBMiKgmXZNJtN6s1Gn3lTZGSlb7985swpmq0akqRQKVfwPZdUOkl5vUIiYTAwWMC2bbodkyAI0Q2DkZEJJEEl8H0kScX3AzLZLLfv3Us6ncLp9di9Zy+aFkc3DKrVdTRFx4hrOJbJcGkYSeonV77vIctiX5YVuGTSGUDEdT1S2SStdpN0MkkmlaJn9S5/ZpBMp1F1jY7Zxr3cqGloOsl4Ak3RiQLottsEnoemKohCHwRVRUQUVayuhdnt4Xo9JElganqaqemtjI8MMzRYQo/rFAcGL1tTBnQ7HXq9Drph0HNcTKtLhIjnu0SXBzRJUr/BM5vNEgT9JOlvLMA1rW8HrqjKTzXqjUYN3+szSP3vz0BVdRRF+llw/Q96/X3Eqg8/fJ+xsRHymUna5iKr5dO0mg5bp/Zgu23iRoZux0FVJCTNRZBcBDkk8IO+iUAySb3aoVGzUFWN8YlBLNPEdhx0XUdRFNrt9k+vFwT9BNnugST171/X9mg069h236QlnU6TzWYJwxBB6LOTSS1FKpns94p4LrVmFT8ISKWy9LwA2+ogST4r5QqZTJ6FheX+71HXCQMX1+6hqTLd6jphFEfXVVQtolz9SwoDSTRVQ5TjmN0IWYwjCzLN9lK/CbvaIpct0W6bjI1OkE7HWV1fRFUFXK+NqiQJQo9cQeDs7Cr1WpdMPkezbLEwv4qsWgSRRSqVwfdD4skUv/jJTxGGIUEQ0bN6SJJEt2sxPzdH17IJ/QhF1vADG8fpEUYhPcvuEzW9LrZl0mrXSKeTEBnk0x8lksoMDRdQpDjxRByrJ1JrnSBbyBPiMTqWpdFaIfAjmp02Hx/OkUrFcW0Bx+4wMJhnaaHO9N57qK13KAwWGBjM4zj9+BhPaGi6wtDoEEtLS9h2QLXapNlq0u60GB0ZQdcT+I7Flm3b+Pa3v82u3TtZX10lmcj0ezwUiUw6jaYpHDt2jH379pBIpDDNHslkHN+PqNer5HM5XNdBUWQURaFSqZBMJlleWSRfyDMyMoIki6iqimmaRGHfxMK2e8SNOL7vo2oyIyOjZHI5JFEmCEJSqSRhCJ2OSRAEDBQHaLVaKLJIs11m166dxONJ6pUaaytVFFnFtjxc18P3QoqDRXxcdEPGMm0Utc+Yd7td0pkkZ8+exTRNSqUSi0uLrK+ts3P3LsZGR9m+aycAZ8+e5ezZs2zfvqOP/6kMlmliWV2OHz9OsVjoW1EnigyUsoiiQLE4gG3bpNNZbMfEcU10Q6bb7XDu7CKOE9Dt9uh2GwwND1Ao5AmDkGarRSqdJvADnnzySWzbJIwiZEUkAhKJBM1WmSAIEEWRRDzdr7qXyyQTSTRdxbZNgtDn1Kk5SoNFdF3HtCzCMCCRTPar3kGAomjE9QSiEBH6Aa7r4vt90lySJOJxo9/M3+vbHOu6htVz8BwHSVIRxYh4XEM3NHzfI/QdFEXC9QMCX8LzIlzXIQojJFGiazpkM2kct0cykcF1XbpdE00z+nbAkoIkSQRBgCT1Cb0wDNG0vkmP7dgEgYfj9Ps8kol+5USW5Z/i1N8mzX7W+rkazmOx2BWxWGwNMIFvbmxsfPfyU//6crn6j2Kx2ObLezcBb/ytw9uX9/7rc94Ti8VWYrHYynmvy0Bykso5n1eOv0i7Y9Hu6By4/zG80GN8Sgb5LPc8aGH5jxK5Lo6r47UrCHaTyB5mdTag1VYY3p3ggWe3UVk/SaN5hqSi4VkBR16ap1UvEDBOMj3O9p17KRYE9u0dRjZqGOkajz33Kxw5cZCE0SQVr6PJq2zfJTM5oZFQSkyO7AfJ4eziS8zMHkZOKDg9AyEYZmGxSb25iKBEnFns8NCDBxGtMrtGDNbPHWd5bpG2ncSM8gRKnuKgxq6tFsvnHmV+7ginTixy7/7neWx/jZcf7rB86mWeenwXn/7sMNvveILTywrb776XdavOg0/dx6FTT1Gzz3Di3BPMLhzBMeN4XpHf+Z2nKK+McceOe9g6OcrxF15k3+6tTO302X1Xgvu/+iRNM6LdbiN6cZbONeh0KyiqgywEiFJAaTBJrbKCInrI6grFYRNNFEkrSeRIoNOp0jSXaVXPYjkt7r1rN4MDKXbvH6DtP4euJHj+2VdYqZ5AUhs8/Mwv8cLhL7FrdC+67/LSwcOofhqraTE8NMiJExUeuu8lcsYkpcIdnD6cYKT4LN22B7KLaa+CEDF79iT5fJpUIsu27VtYO2eS0ybZPbWb2nKHkeIYTz3xFR5/4m7OzZ9ibGKYJ58+wf57nmZhzeLrL5yi2hLwRJ9dd27lxLkX6HqzHD7yLMn4ECcOR8zO+Bw+NEsi7uK557jn/mm0BPgYZIojFEfSpAZ8PH+N4aEUmXie6nrAyRPncO2A6nods9OjWi6TTPn4Xp1UPEUuMU6nFhHaMhljEHxoVCqIUQdFckglZOxuFznU6dZ8Hr37ME8cmOGx+4/ywINfxw1kJrZPUBzNMzxVpLQlhxU2cMNlxieTBEEHQ1e4/94HWF+tUVmrM1gc5NjRQxy4bx/Nrs3CWhM1kaPZdYnkJAtrdeKpAgcPPkK3U+be+yaoVU7QaXbZvXsSIo+EvA8pGkNNz9IwZ5ibW2NmZh49ZeEzR+C7jI4WOPzSYRrLA2T0/QQskimWOXjwfjx/nUZlnfkzZxkezrB9ahpZUFGliMh3GcjkGEyN4nU95k6fYTA/wbapu0glDKanx8mmkwwWiwh4VFY8BG+Y9WWbsdESqhFgew3SOZWRsaE+Q+SHqJJAyhDRNZd6bR1VN5A1g9179qEaOpEUsrg2x9LaaRQ9QJJ9Gs0aoiaQGtBIFQL0tMdKZRGr18ELeqzVmlTqPuU1CatlIAk2iXRAOl1ElCbIF7ZjJItMb9/PyrJFz1LJDYzSc0Jk1cDzHM7OvkKhoKHKIYaRQNfTnJuv8Mor88QTBQyjgKqmGRwapWu3sdwO7W4dTdfR4ymCMP5zA/s/1PXfO1ZFkYBh5Gk7ZbIDsG3nOO2aiCTIxJMNbMdkqJRlaX6duD9GxYk4N/8KmiJhxCeYGtzCulWhZq+ybXqU7lqVcnWZbKKAHs/RaIVoiR0YKR9J7lEa0AjDZbZN7qdcbZDJ6dx59920agq63COb1GmbLbywSyoxgSDlSKXTRFKVcu0kZgPGxreyttpFCLL4QgsjGaEbKc4tvc3TD91Fu7LM+vo6rZ5IYXQvvlQkmUlSKui06wu47TOUKwZrFYdDR/6ETCLLQ/cdQvR9Gu0zyNmAFw9Z3HXgbmZmX0YQfdqtGtnEADPHj5HNqfzRv/l3TE1OM3eqztSWu6nXe2wZfYCJMY9DRw7SXDN55PEnyY4oLK03ePShF3n5xaPERROJPFNbt7K4eppCscjw0FasnsnC4llq5TrZTAZB7jA1NY4hFzA7LrnBNGfOnUJSVMIAut0mA5k4qiFSKBb56x/8NTu230mzWaVcW+LrX3+BmeNHOHDXkxw88Ax37rkPQZDZvi/Blp1DVCpddu/YxtraKe688yvoepqlpTL3PfAQD+66Gy/ooCgqtu1RbcyRTGap1dYRIpHnnznDAw/t5tmvv4jviNx3YD9bdhRZWGzy8ssnEcUsTz55nE995jYmJ8fR1DiqHOfwy/8efI/F5ZMceuEwkV3Ease5c//9ZIoRPaeKFDXYd9ddpAslCvktPPfccTrmCqLQ5eDDB3jowEEMOcfCShc/iFBUgbhuMDIyjuvaTO8Y5vjMs2TSBaRogBMnz7Bv/17ihkajvkphMI+kRlTWl7h95y70eIYwUnFsH0UMUbUkTz71JI88+TjpXIJKvUImE2dxYQkBgdMn5zDibQ48cA+BL7Fwbp3S4CiJeAa7Z+N6JvV6mVRC5657HyCVHuDw0cOMjY1RqzWYOXmKTCZBqZDjlVeeZ3lllrOnFilmd7F77wivvfYqcXUL88tHMIqHGBzYTnVVZ9fOfTS6J1hZnsG1Rebn55neaXDH1mOcW5hlvX6YRAr83ijl+nGGh0aYOX4WQXY4N7eO50GpVOLokSOIiGhShqWFBVK5OPv274fQYG7hJZJpmUaty+zsPImkimX6KHIKRYHiQIQfhThO31XNsQM6bRu756GoIp7XA9GjXu2xddvtaFqckdFxZk4cBUJ6dgeRkFw2jyZruA4srSzjRk3C0AVEbNtF1wwkOcByTBJGkWbVpJgZIJBXCIUeoZ8kCouUSkna3SqKJGH3LBRZwIgnaTbrJOIqTq+NLAsUi3lsJ0CPZ3GciF7PQZYUfN/Fd3xsy8S0O/g4+HioooomJvECET8Uf26s/rmSj42NjUsbGxtDQBoYjcVig8C/BIrApwAD+N2/wer/1in+G+d8cWNjY2RjY2PkGv0ahkYLbNk1RmlomInJrUSexHplGddpo6oZxsd2cvTlMiND+wlFD00LKA3vRjeGsN1VTp48goCNIaW5f++jxBMTjJT2kB3MkckZFId1coMWzdopFhdmOHz4WSQ1ywuHX2a4NEm74/PEY3/MSy+8TCo3SjIzgqikOXz4HMdOzrJtWx6CMosLywwUtjM5fTunzy5i+T2SBRdN9dFFncAO+PqTT6HrSSpNi9Vai1Q6QSYv4VoLJDWHfFah2XQ4PlPn7vsf5qmnnmJ+fpX5+UWOnXmCXXcPcMfd+4gn4/zRv72frz91P1nD4aVnnqSYTrM0P8vI8AC/9rnfwLVlpibv4PY7d+H7VX77X9xDpzPD0toCeiLBxPQ2Hnz0UZqVgMhJ8dxTRwmdCEnW6FhNmu11Muk8tuOzWlmnUq5Rr7UYKA2jazr5whihn8ANoNqpcXbpBKIMu3beA1KcoeE8Lx8/w9Jyg9lT6xQyowRCl+HhLI16E8/ReerhP+Leu/+Ahx97iMHJNJO7Jkhn+8H27OwZdu2YIl8ooGgJfD9g564xXnrxcbq2iSRJ6FqSMArR1DiiKLG2ssaRQzPs2LmVYqnI2YU59t63lcMvP0cqOc3Tj51hy5btzJ1d4umn72SkFNJrl9k+PUzg1xkaHOPYoTMklWHS2naWFpo8+NV7qXVfRIt3USSdkZEdbN32AJE3RK+lMD5SotNqUlmxWDxt89D9R0jFx1GkOIahkE6lqdTWMFICuQGdXXdsp1r2UZVBREmhZa5w4Kt34YU2kdDDdS1CIaTVtQn8iDAQSKYzCDKMTJZ45fRRZEPgyNHnePTh+yHwWFxc4MXnn8OxTFRJRNc0tk7fyYsvniGZLKBqcZqtBn5oo2sSPdNkx/YdFEojLC8vsnvnNgr5LO1GnVIxR7tapV4uo6sGgwPjPP/0ce6680GyRQNZ+6v9wgAAIABJREFUSZJMZQlpMz4+TELbQVwZo1DUCaMWU2Nb2b3zAB3TYnFxjXgigeUtoxgmgpAhn93JyZNzDA9tY+HcKrffsYeFpQUOH32RXL6ArmcpFIdYrZxjrXGGTDHLxNgWas0aZq+NICkcP36GxaU1avUG9WYXzRBA6jEyPojvy7TbHpYVEfgSjXoX3w/Zun0b+cIgnY5D1/TYsnU7QeBRr9c49OLzxBMJMtk8W7fuJp8v0W50CfyIfCHPfXfvZ3pyFzum72L3jrsZKBYIfAhDA9FPEvmgKiGO28L2AryeCIIAQZd0UqbdqLJ8bp6R4QFkNaLX7qDIIuurK5iWxcDAMLOzy9TqLRzbQxQEspkMqibRbtYQ5ZCQAKtjMlgYRJN0VF1nrbxKtbyKKDg/N7D/Q13/vWOVeMVmvADml84wODzI0tJyn500OyhaiCwJLCyeJZvKQSQj63Ey2RTddovAjaitt5naNc2+e/Zw7PARhgfHiesq8YSB1bXoOR6ZTB7HcogASY5IJeKcPn2KsdFJqo0KY0OTxI00mioShH2WvdVt4wchuhynWq5wbnGV8fEtqJrIyvIat+/Yxf67bmd9ZZ3yyhoIMqJ8I6oS0W3XkKUIy/ZREikct4vteAwMZIlCF7tTJxREBorDPP/8IWrVJlNjo6yuLZDOJFlammdoeBrXk8hl8wSezejoKJ7vsbr2Gg/efz+f/NQ1REGE7QacmjmDoqgApDIycUOh2TFZWlolX0jjBworaw1URUaSHexegOc7KIqA7fawuj0UWUNV1L7kMvDJ5eMIKHhOQLtdp9WpEY8n0IwEfuiSz8eZm1tGFCSWlhe49tqrMVsWPdMhJERRJWRBoLJaoddzyOeKlNct4kmB1fIyutZ3KvRdj3qt1nfU65rUaw3yhRy62p/ZI0sKzWaFVDLDmVOnyOUK7Nx+O8deOcT5ixd55LHHOPjwQe6796vohkixVGTL1ASJlIYgaAjIrK6uEY/Hufvuz6LIYJk9lpbL7N65gxeePUm1UafTsYgiSGdESgNjBKFIPK6TLWRBEKiV1xmfGGO0NERlrYESj+M4Nj27iyiFuK7D0NAIZ0/Po+sGqqrhuCbZdI611VVs1yUSBHpmv9+v5zoYyRT5dJ5Os0M6nUKRVI4dO0ouP8B6pczRo0eYmBxlZX0Vw9DRDB3H8RgdHaZRr3L3nftZW1tndXmN0dFRSqX+gMNsLs1apYwiSkxOTCOIInPz57hj125q1QaiIDAwWGB1dYV2p86evXtZWS3TaZs/vUfD0GO9eoLAB8+BXq9HMiWRSKaZnZ0jlUogSg6+GyeTSWH2GpRKg7RbNoViklajxYEHDuAFLQIfxsd2sry4gh/Y9Kw2qqaTjGcI3YBDRw9TyA/gBxaCIOJ5/QGjtmPjuQGaZhAR0GhUkSSFMAxRVRVJUshkMiiKSrvdpmu2UXWFQmGAdqtDp9tFREBRJOqNKqIoEAU+dtciFMBIJBgbG6PTtSmVBkAIsS27TyTLOrbpoqgSURDhuz6C6qJIEqAReAL5YpZWs4YiaxiagWl3iRsJvMDDdW1UTUESwfMCVEUhbqRRVY12p0fX7FAoFEhmknQsi9Dz0FUZWRCIpAjLthFCAV1Vf26s/pk9H/+3A2Kx3wc+3NjYePpv7U0BD25sbOyIxWL/O3BuY2PjTy8/9yNgamNj463/p3MaxnUbO3Z8DkkS2Lt3H8ePnySXzSNKEaqqcubMGcIA4vEsgiIyOZ3F7rZYWvTZsX0bil6msmbS6wVoioGmJmlbNoqsU2/MEwoB+2+/G7NXwXNFvLBLvVbFtEK2Tk8zf26RsYlJuh0bSZAYnZhgfX0VPwzxQ5e4YRBZy2SSCbqOwrmFCk4kUyzlmJoYRgotFD1J/ewix0+cZuvue1hbW2FyyyCqBq7TZGF+jlQ6id0Laba6FAqTjI/vpWMtocgSM6+s8fijT7O0/iLFgRRnTqzwwP0PE1JjbPyzDJZuY9vUHggFZk4fZ2x0hOLwGBJaX2LWXCaVVMll0lQrdcrlRn8myb47KJerrK2VCb2ITqfF9JatLJfXKJUGSSQ12s3u5ZK9QDr9f7H3xsFt3Oed90er1Xq1Xm1WaxiGYRiBYRRGEYShEYbhMQzDMjqWVVWeqmo0qqpTXY1H1fn8po7r16/Pl7p+fZm8juu6rl9Vr6PTuD6P6tGpqqKyOlVhWZZleQzLMAwCwwiKIDCMwDAMQ2sYXq/X69Xy/QPq/fk288717mba5y/MAjtYzCyeZ3/P7/l+viFM06RYLBIMh0glh7Bdj067hiTadLsmhhEkv2FjGAKOb5EZzNAz25TKZTTNIBwKIPgi0UiMYrFMr9dlctc4ZrtKqVohlUqxvFgmEU+gBRxsu8fIyG4a9SalUo5iaYN0cgJFF+h1TWRVQQDKxRLT03so5NdQZI1yuYBp9Th8YD+XFmc5fOh+Du17lMOHj2G2ikxOZnGFEkZQ46UXLtJum2iqysTkbnzXQVYlcmstfEFiZDhOx1qmXrVQ5AgLCxukU8O0GyZdu4emOaQHY6SSgwT0JPWqTbPVQAl2iIQUdC1GuVRBNxTW1ucwAgHSiXGqlTbpTBjL7tBo9sWEkigiy33ufzY7jCRJLC+v9gXHdgfHcTl44BBnzpwhHA5TrVaZmprk0sVZookYVs/Gti3CkQSOIxLQAiA7tBpViqUc42Pj1Jt1AnqQ7NAQkihz4cIFHMvmgYce5uTJk8Tjcer1Opn0AMVimWAogCSIrOdWmNozjuQHqFZrfeSfIJFKjtIxq+iGCYLL+moF3w8yNT3B8vIlUukUvV4Xx/aJx4aw7R6NZgXDCFCtlml3mhw8eJBSscLC/DIDgxmCQYN2u06jWUOWZSRJYXxsN81mHVGQWV5ZJBwOMTY+SrtlgnCdS9910NQAhhHAtvv3T6vdoF5vMjkxgSiJNJsNHMej0agiSRLZbJZGo4Gq6oCPqhoEDB3Xc2g0mjiORbNZJ2BEWV5aB8FjanqEgYFhel0Ps2MRCASxnDrtdhMBCUmWMHS1P3drWySSA1jdHuu5NRRFwfMgEYuj6hpmy0SQRTy37yLb61kI0C9GssLqxjLdbpdQMEQwFKDb7SFJAuCB0Ecuep7H1/+v3/9nzcdPGP8YtSoeT24eOXScQ/dM8fgzBxmIT3Li6VVmL17h+dNPYETqKH7/QViSBZbbGlLV4tjxUb7y2GPsih3FGfXJxGS6pR6FlQ5Du2NYbodiqc7BmUOUqxu0azKBCBQLa6hKkFhMZy2XZ+/BvZw5tYTZ0HjiuUFOPHumr43yWsRDR4iFLVzB4dSZHMfve4x29yWsno9gmxw8NMy5szZmcYmXFmZ55IknaWzkkYMBFE2nZTm06i0mRlK0ayXiIYNOp8PQ1IPk188yMTbFvgMzVCpFHnjkHhxbwLIVBgcGEUU4d67Kww/dQzgoUMjnabaq1GslZg7swWyaNJoepWKDRGqCQ0fHSERcjt17FJEo0fggoiBji3XyG1VM0+PgzBC1+iLpzAPUm0sszC+wa2I/1eoSw2ODzM+uoygBVMNmZv8R6iWbSm0dSa5y/lKB556+xMLcBRRlmXo9x+KFIHsOT2P6NcxGlcnJSWrVNmMjMzz5+NOMjA1z9J69rK4vUak2iYQSmBQYGZ7m/JkrTO0aI7cxz9zSCvv23Uep4nLi6Ye4cuopCq0N8pU2Y6PTfPWZ/4Nk7AsEDYnp6aPUSwKe9DKuL1LMd/naE89z4ewGrnoRRQghqhAIORSWwugBiXQ6zPyleR58/AD79/88x+75LSLBQbpOi9MvPsXMzBGKxSpTk2PIYgtBmELTRHypwvzCeRxLQFF0nnziJJVSiYcefgxRL3H/fY9y4tmXCYeDmN0m9xy5j7XVAqFQENurX8+VEeq1LulUkHqjTjKRoFavce/R+zj78gWq1QbDw0O4rkckFKLrtLl0ZZaAESSeiDE6PMLZl86QTKdwHAfbctiz7yCXZy9y7Ni9LC4usLGxwb69M+SLG+Q3cjz86KOcev40XVNgemoPlxfOMD8/x/yVBWYvXukLmgVYzy/y2KOPsVHI4XgOzbLOU7/7IO2rdXZND3PP0RnW19cZSB5A00Kcv/gs03t2I/kRVtfPkhx0mM4ucN8Du3jw0f14jsTzzy1z5AGHqHGQRt1FkOqsrzbZN3MvxdIaC0svowdEIqEBZClAubrM0Xt38fxza0SSLcyWTiw8iu2YeLSwujLJZIxC5TLp+Ay6ITE/P8/Y2Bi1Wg1d10kmE1y6PEu91uTIkaPUKmVCkSjVWgM8H1n1qZSLjIyOgu1Qr3cQFZVgVGN6aj/nzswjyzaW00CRFVKpIXL5JvNXzvPwo0c5f/YihhwjknEQRQPcGL6noRsOqxsLaCpoWhzXkmibq+yanKJYquF0eyRSGTyuO60bYWyrS6vTQVchGotidnp0TYtASMY0+14g+FLfUBGIREN8+cu/8d9H87Fly5abt2zZol9/vR3YBZT+fjb2ushvL1C4fsoscOQ6SWQEePf/K5kDbNu2jVAoQGYgw8LyMsFAkJXVVQTf58UXXyRgGIyPjZFMZ8ikMjSbbUBAkxXm5uepVuuUC3m6PZN4LMnq2gKC2Ma0qoTCCbLpKWo1gWpVoFRq02m1kTWYGE+QSkUYGR7GbLZYWVmm2qhTKpbpdHvge8hiX/mfyYygaCEESQGpTTwWZWRgnHKlwMpygyunX6BjdTh8+B48xycY7DtOm20TAdi7d4ZyvkCz0aTT6WIEwpx7+QKep1CuNhkZnmJto4qhpxGIoEhJLpzLc+5MgYnxf8HUxASzly9y9uJ5YtEQ5VoF27RZXd4gmRwgFouwvlEA26BTF4gEBgmFkrQabeq1Ep12Bdevs3//CLGMzOBQmmKpTK1WRlEVEITr7PQOjUYL3/cxtAABPUFQDyNKXfRAAE1L0OtBoTRLz26hSnEct8vi6iwTExlURSATnyIYGGZ5qcDCwhX2HhgmkdI49cJLSL6O1XMYHh2na7sMDEyg6WEChkEhv4FuyETCUdKpLJag0PM8RFEGPFLJDF2zi+f7FEpF0pkUY8PDNFptTj//MqFAnJn9w/hCldX1dZ4++RiiEObKxTUOHtjFVx5+AFXVuXTpRb7yta9TqZ+n3VukVtngwoWLSKJBLB7mxMn72Lt/F3v3jzO9P8aDDx8kM5hkODuAIFapVJe4MneZVqtGwOihGgK5wgbR6DCHDj3A7pm9ZAYTWH6FofEgiVQGRQ0hoPZnaH0f1+sTl1ZXVzl/bpb7jt/PvUePIgoyx4/dx9yVBWq1BoIgMDQ0yOrKKrKq4Lketu2gKBqSKBAJRnA8F3wIRWMkkikst0c6PUBmcBDH8RkeGWP24mWWV1e4fHm2bxrkOjTqTaq1CtmhDLFolEKxSCgcIZ8vIqsagbCBbigkEnEE3ycWiVKtVOi0WqSSSQbSgzQbDTTdoNlssbB4BVESCAR0bNslFo0jSxLpgRCZTJLCRpux0d0kUiHqjRwds4YiR5GlCB4dRNkiGAgxNjZGvlBgamqaRDKO1bOum4x5WFYXBI92p0Wj0cCyeiAIKLJKIKDRbDUxO32RvACEQhF8X6DdMtH1vhmWIuu4jk3HNHEdD1VVkGWVRqNFx2wykI0xNJwmHE6yvpZjfX2VntWhYzawLQ9NM5BkCddx6VkWnuvSs/rdI0SBSChKwNCIRYNYrkWtVsMXfTzPp1qr0G53sOxe/1zfR5AEwuEgkXAEH592u4OmqX33WNtCkWVUVcN1nX8oXf+Tjv8RtWrT9wlHwiCKiJ6IYQSZGB/n3IULtNttkok0rWYXWRZxpQ6K4NLt2ji+y+SuIRRBRlSDTO7azaXLF4kno9iOiOv5BI0ISBKtThHfFVBkAYe+cdnAwBi1ahOz06Vj9ojGEoiyTLfXRVUkRKm/CyeJDp5rIxsBFpYvI8lJxscmyQ5kEJ0AXiuPaXWJhNJEogO0zQaRqEGtWiASMjAMlUatgWX7NNs9YvEk+XUTUTZpteuEjBQIGvGkRCis0mt7xKPDOG6Fb//NObKDAywtrWLbPooMPcehVmlTKjYJRyIEQgIrK8vgSlSq6+iaQSIVQ1NkNnIrdFodkqkU4YCOrAcoV3pY3R6xmEa3275OCSoRDoep15uoqko6PYAshnHcXl8ka4SRRB3b7rG0tEwyMUSn7aKIdebmzzOYGaRcarFrYh+droUsC2SH4+zbv4eLF2a5NLtIdjBLMKLj2BrVao1oLMSLp0/z5FMv4Ho24UgAVTXQNSj3KnTbNQwjQLlcJhW/nVajguN7hIMhgiGNVrtFJBrj6adOUCq2OH/hDMePfYmvf/33mNw1zGB6HEVzGBnOUq02WVxeYW7+Erum7iIRT6JIEdpNh6HhMHpAZe/e3Qxm41y6VKXeKAAehUIRy+pSqi4QCcdZX6vT68rEkh6JZJRuzwPPIJXM0jFrbOTnsO0enieSjA/16ZumSTCooekahqFhWTZmp0s4HCadznDw4AFOn36Bw4cP0+1aLC0vIMsqyWRfLG12OkiKjO+DoigEAgE0JYrj+vg4KIpEr9eha3ZoNBoEg30d4a5dU7TbbRRV4tChQxiGwfz8ItVqjWQqweTkJKIgcf78BfSASr1ZIhJOE7stTrlcJBiIsrZcRFF9SuU1NE1ClnVEIYaP0BfAN2s0GhXGx6bZs/sApVIFXVdxXQdJhk7bYmR4ing8zsvnnsM0m8RicYKBBPVGlW6vhqYZVEpdVE2i0+mRSiVx3B6apuF54LrXDR99EccGWZaQJOm6XsmmY7aQZRn1+s6f53o4jkOn0yESiaCoMp7roxsGntenZ8qShCD2jTwXFxeJRCJUq/2mmg/0ul16vb7UrX8dDoLY10e5joskCQiiT7fbJRAIAiK23UMQ+8aSznVAlaqqOK5Dr9tDkTVczwFfIBgMoOsaltWl17Xx6WsZFVXEdV0QPFRFRhD7msyfNH6Ssatbgb/asmVLHvgO/TnaS8Afbdmy5RXgFSAAfPX65y8DVaAC/Efgvn/oC7YrCol0Btf3UaW+6HX39AyaYfCVrzzKnulpXMdBFvvCn1bDp161UXQTTXcJGSNMz+xC1SUWFpaZnJhBNwwy6SixaAJFVskXFmg1OsQTCUaG9jE5ci+Glmb25VnK+Q3KxRz7p3YxmIxy5tRT9BoVrFYDDYeQBoWqSqUZwXJ7HNw7Cd01vvbAXryyjSHEyO66l3Kvx+XcEi2zihHo0yQUVSOfr7K+UmR0eA/7Z+7j3kMPoqsak3sMms0l2u0cSqDK/MpTnH35JR68/3FiiSjl8hpd0+ehB7+K60vcc89RDh/eTzQWZ9/eGYqlPJlshitzs9TrTXZNTnB58QyF6jK+1KPWLDJ75RyNdoWRsWESqRTtbpcnn3yKSqmAEVAolmpUahVq1TK+71MsFmm3m6TTA3iex9lzZ1jdWMU0HeqOjSoLiL7L4GCaWCKGT498rsiRA8do1NqAxOW5c1yafZlkKsy9x+5ldTXHIw8/xtGjR1ENhWq9Ar5DNBLlkUceZ3lpnfuOH8f2bKrVCoGgzoUL57ArJWRJoFKtksvlyWTSnD17joHMAJnUAHbPo15vIAoBYqEpvvKlh4inBFxxnfOXnmB+bpbZC0vE46M8d+Ichw79Ok+fuI8nnngKs/khe8ZnefShC6iBLvG0TywRR1Y1ZmaeQDN0dF1lbOwwG/kFhsc0HK9NInyYwewEUwcFwpkmnj1MNRfAahtUSy0e/tJTrM55CNYYXjdObqXH889cpl7QyQ7sol5r49g+IhqKpNE1PWKxFI888gQnnnsRwwjzzDPPMTk9SSwWI50eYHFxiT3TexhIDZBJD5BKpYglY/Qci6X1y9huk0otj+P0UFQZRdbpmDbFYp3VlQIHD9zHE197lJm9k6yuLZEdSpBIhskOxhkbHkQQXTy/x65dI4j4SGKYjfUNWo0azU4Zy6lSb6+zlltkaGiCkeE9GIE0iqrjOA6ypBKNJDiw/whds8uFCxdwXQezawIiniviux7tVpXnnv0ahqaTTqZwLBvX6ZJJRxnJjjGQyrK0uMAzTz3NgX172Vhbw7FsWs0Wru3RajZpNZtosko8HqVntbHsDr1eHVn1SSZj1BtFzF4dXZfRDZloNMju3VNoukI0GiOVSuL5Do7j0Go1aTQbOI6NJMrsnTlAICgRSwoEghLNegffF1A0G9Vo0WoXsHpcX/CAL4i0Wv1FkONaVCoVGtUG+D74PgIeeDa6ISGKfSfb9ECcSCxAMKgRCmu0mhXW15fomn2B79jICLFIFKtrETRChIxw3yDOg3Aw+hMn9n+i8Y9eq97tvYvj2Dzy0INMTuzlzJmzJJIR4rEAqVSKQqFCKJIgmkqwXlgmHhBJJxJcmVsmOxBlZNDghZfnmLtc5NCBAyzkLqBpAWQxhKZEWJibx7ZsMtk4qVQYvB49q8Hp504yOjLKwuIquyZ2o+oB5i5fJpXJEE/GiYQNFEUANHxfQpRrxOI95i/Xmb2wRCw+ysnnVkkOiaghm2RygPkLa6SzSTy3jef3aFZzhA0ZPIlIJEWt1sUhQKNb4tSLzzJ7aY7p6UO8dOoi1XKO/EaBoewkV86vEIsH+N/+3QTH7t+H1RXpmB1M22R69ySzF5fYs+c4lUqBeEoBOpQLZZ4/eY7MwAi6rlBrlhgYTBAMBLDMDqPjI3S6Ho88epL11TXazb6zdq1cYXx0DMfqC3QTiQSGHqRScUikYwiCSXpyNwcPHKVSLuE4Do6jEgsPIYctonGV/FqRH7zaoNOGSqHORn6BSFyj3amQy5U5cOAItmsxv3yescED6Fqcl15+nuxQhlJujaFsHLPbZjA1zuIchMMejupRLOXxHI+XTp0llQpwYN9eYokYnmcTDMWIRFOsbRQYGx3i6H1DHN33FFff+TGPPfwE+TWHSNylVFlGVnyanSKVvM3TXzvDM8+cZKMwT6W+yIGDe6nXqpgdj0rZZHhsjKCeptNr0GzlMAIi8fA+UokRao0Nem4NwUkyOjrM2nKOI4ePI4kGiirw8tnTqLrPiy8+T3ZgElmMMjySQVJ6lMtF9uzZS7lcZmRkgscfe5JgMESr1SYY0gCXaDzK6vIaIj6W1aXTatJqt1AUBcvqEY1G+w+kjkc8mmAgk2F9Y5lquYCsKWiKDqiUSjV8XyQU1MjlVijkc+zevRdJ6YNUZvbs5cLFi0RCUdIDKc6fP8/k9G7CwRSGoVFv5tg1voeB9BSNWpVQSGZsZBjB15ClGPPzcwyPpvrgl1aNVqvD/NwKoVCEam0dWdJYXpnj7NmXqRV9BjODKIrF9N4xbEdAFSPoqowvmQSMIKHgOOvry0zv2seVK/MMjwwQjcYIBvpgANezyaRHiMSSdDptdF37bxAESZRYWV0mk+kjlgWxj7n1fQHX9dk1OU0ul8OyejQbres7EDqGruP7IqXyOrXmEsPDQ/iu0f+NiobtFhkYjNGsO4RDcQS5gY/SR1y7Fh4mPcdG8EUMPYwiiziuydDwMPmNHIYRJBJN0un0G16CIOHaNrbr4vRselYXQZCIxWL0eia5jSLBQAjfdxFFkBURQfD6i5GfMH4S2lV+c3Pz7s3NzYHNzc3M5ubmE9ePT25ubn7y+rHDf08ZuU4O+bebm5t3Xn9//R++jC2IgsDE+Di5Qp6Ls7NEw2FmL56nVCgwvzCP7TgImPhuj0qhSbclMTk1RGYoxuWLRQLBKLHEAOFolGKpDm4EzxapV4vYTgnFyKHqLcJhm3yuQWlDoLJgIglBhoeHePjBB7C6TVaXLvOVR+8nEdFJxQO4TodOrUrTbNOwTXQ1Qq0U5JHjR3n84aPUigWePXmYhx5+GCM0jmSkkHWXjtWi3enS7jgcPvglNCWBokSpVU3uf+BLrOVW6PXKBAyNVDxOu30FWVlDD3ZYXX+JavM0g+NVJGOFldUVIqEMq6ur1OtV9u4/QL3aIhKOUS7mMLt1MskMg4MpZL1MZkRkdDJCOCqi6RJD2VHKJZOAPki1LHHs8OMMJNPUyzkG00m6rTrxWIhDB/cxnB3C8zyMYIBas87AsEarWSSkjuKtFEGoYjklRC+M4rv4co6B5Ai4KbotA13VWd84zZGjSTTdI7dR5PlnlpH8LLlcgUo9Ryg4yOraInPzpxifDCNJXbLZUTQpQSq+B0NLcmXxD/id//t3sS2b8ZFRZElkaXmFgwcPUiiWkBWNfKmEh0i+WOLxxx5meDKL3daR3DjPnryfyckpjHCLenOOyxcKXLr0Ha5ccLky2+Keg4+T22iytpZjcOAAp08uU9+Y5OEHniMcGMYQZlhZtnj62SOoYoCvfqnK9PBTXJg9SaWYo5FL4TZHKVc3mFs+R8e7RGz0EtGRK0QyTTypiqQ5NMx10uNNqvbTrKxdImBECRhhbNvBR2Df3v2IgozVswkEgywvLZOIp3j+5CmSyTiSJDA1OcX6xgae7/PccyeZnt5DyIgwmh1lanIIWbKoV4sUcus88ZUnGB0eQ3ABR8DqOqQSSZaXVjlw4CDdbodYLEa9XsUTPE6+cILLly+wvLJAp9vEdhzi4TjF4hqBoEwmE2F1bZ5EKsDM3l2USiZPPf0SnisiKQ6uZzGz5yDFfA3PFZmYGCcWD7K0fBnDUGg06/Q6Bpoc48CRDFPTGTQlRKsuEg4m8YQCHmUsU0EV0zTqVfbvm+HihYvs2bMbs2OiqyqhYICAHiQaidAyO5TKG/j0QLAxu22q1SKFYo6RkSG63Q6GYVCv1/F9gVyuP0N98eJ5qrVyX0dk6LSafb8G3/cRRI9yJUcmM0ylaNFodPDFFvGkhmHo9Lp9XYggtVA0GQEBQ9MJBAIkU3ECAYOe2XerjcSiqFqfc69rGq7rU69XMM0GZrtFtVpE1QQ0XQSxRzxukMrEsF2LRqtnu/OsAAAgAElEQVRGKBJE0VQc18NxBTrdHpVqHcSfSKL3Tzb+R9QqZbtMPB7m4MHDvPTiBe45cgxBFLDtHvFEBNsSmJ+7gieoOEIE0e4xkDWIJgdZurKIZb/M1ZbInj17uDS7yMFDR0kkFBy7g6pa1GpV3E4GX6hTLjVJRidRNRVFKXFx9hTpRIpmq0W5VCaRTPa9jBSRZruNKqtU2qAYIcIShOQQM5MqsxdnWV7LY8t15MAEB489QigocmBiiG6vh+d5TI6PUljPUysVkSSFWrXNkydOg6giaUvoSoZwOEF6MMra+gqjI4d55slTnDt/CsdvUSn0xwSnd+1mZXUJy+oSDod46snnmNlzsP9/Ejwsu8X+A1leOnMC3w/huxqVahtdD6AqBqoSAVTarSb1eoFzl18kk8kQDAaxLZtEOkkgHGF5eQVfFIkn0yyuLFDv1GjUTMJGkHb+PKmEQi5fIpUZZTW3Qq66yAP3vchjDz/L8soVbrtd5fQLT3H8vntRpAiTowe4cmmBfQdGyRXmaTTbTO26n2J1g+ee/QoH982QjGdZmC/TNS0QHBTNZXr6Hkq1DqdPnsVQFR568CE6HZ/dM2PIkkqpUMGlw/DoJItzeaxemy89MsXLZ85x6Ficc+fOMrPnMPXmCm4vzj1HHuDosaOcfvElUslhHjp+humZSRyhgK4LXJntEtQjfPk3fpW1jQJBI81a6as4TgXXCtOsGCTSEstr56lWWpRKNscfOEap2OTK/MsMjem0OnlC+gQP3n+CaqXJ+GSGC7MvEApGiYczFAtVNE1haWmRdDpNdnCU7OAIzWabWCxCq9UgkYySK25w5qUXcF0b27K49+i9dE2LZDLJ9O7dLC+vgiBy4cIpLLfNiZMn0PUAM3v2MTkxTbnSQFEMVMVgfWWd8V1ZRidSrG+sMTOzB8fxicVizM8vIAiQK+SIRGKMj80gC3FOvfAsobiMqvsUS2UkAowOjVOttTl3/jLZzAQdq4Ek+ziOi6aEiScNZFlgZXkdq2cxvXuCTtvDsUWOHtlLs7fM2sYakfAEq0t5ZLlHq7dKKpNCk2KEginm5+aZ3DVMId/g+PFjrK2tEI2GCIdjeH5/JyAUjNNqVTC7Zv/etW0MQ79+n6sUi2Wi0TiXL18mFksRj8WQJIlLly71vTUckBUJ3+svTmLxKKIo9vVFAVhdXcFxfEKhGL7n07NamN0OIPWnVoIGsqghiTLdXrOPsP973yhJQlZEEGwqxTKDQ1m6ls3axjqKKhMKRmi26sRjSUIBHVHw0fQAoiBSr7eYmdlLIBCkVKrgeX0/qk6nhWl2SCRiP3G+/l+imm36m0jInH7+BVaX17j3nnuZW1ggM5ClWCng4lCpFWm0Gpw58yJD2TGOHjlKrW7SrLVJpCMU8zVq1Q7NVptYPMDwcBxEm27PpFCooMlJhrLjFHMVOq0yXWsDR+tSb9WplBvMLy/T7LbZd3A/4UgEUREQZQlDD2G7LsXCEpdmX6LV7DIzM8XMvvuJJ0bIjsU59eIpjhyZRldFnnr8MTLpDIMDGWb27UdRdfKFIqKkIMsKkiYyNjEFnkC3JZOMTBEODBHSh0jGR0hEh9iz9whWT6DWaJPLl/AdlWq1STKdZP/Bg7iuQzgaRTc0ItEA8XCUSrnBuZevsHv6ONPj91DMdeh2BEZHp6jXW/i+Q6m0QSAgYPZqIPe7FWuriwwOZIgEgzz37Anm52fJZrO4tsNodoz11XV8HPKVRWzNwHUMDD1BNCZSrFUo5evoAY3wQASfLng+Bw5+CU1PcvqFl+hZHdKDQWJpDQ+B8dF9xMIpNnJrCDLIqkggFKTXdamUa5gdi/m5RR548Nf4P//Db9KoNymXSoyMjKMIMj3LwjRNqtUau3fvYXBohGg4zPzSeQYGBojGg+iGAl6YsYlhwqEoA4NjHL7nIOVKjWKxQrG8SjqrMDwu0bHXGZ8Y4b77H0MLChy/96skMwp1cwFoMDayDz2kcvB4lnNXTqBpMc5dvEC1dYnJPUGsXotjR/fx0Jcepl1TSUUmsEybUiFPtbLBvr2TpGJZHn3gaYqlCo1mlUaz3t+GD4dptuqIEgiiS6Gwwf4De5E0gcmJMYrFIpcuXaJar+ELIh4Ch48cYX5ugWbTxBVcqtUamq6ze/ceJicmePjhRykWi/QsC8vuMpRNEY0ZTO4ap1atc/z4cYq5HOFgGFmWyaRTRCJRstlBHMcmENBxXIupyVEcx8OxJKYm91KrVrkyt8TQyCiTE1Msrl7h5IlnGRsboVioMnG9Y9NstVFVmV1TE3S7XWRJIRSQMcI67baI7QqYVhMjLGL7PVKpMXw/gKhI1FtlhsdGafc6DAxl6VoWQyPDdB2Lar2BousIYt8U0dCCxGMp4vEY2ewQg4PDhEIhqtV+QSuVSkQiEdrtJpIkUKtV0DQN23aRZZFKuczuPVMMDmaud0ctorEIzXqPaGSAaCSBLPs0mzV6XY+AEaNcqiGrPnge1vVukCTJtBrtPj43EkZEoFavgu+RSGZAEGk32siKRijYxzIm4nGazQbLS0uUK1XW1/PU6w0ctz8mMjc3RzwWA8HHsrvIskIslqDTsv6BTPrP8Y8dm5s+S8sLnDx5kq88+gRDQ8MMDadptWtkMhm6psWX7nuA5eU1XFdnamwE1+3QaZvouk5Ad6FlUav6DGRSpFIJSuV1ioUSjmMRCcdIJoZotSq0OyaqZiCKIvVGFUWV0IIalm0yOjpCuVyhUq5gGEEMw6BWr9KzfWqtNuNDoxycOcALLzzFi6dPYAQCxFIhller5AoNjt17lFp1Fc/z8Dwo5EsEAxrdtommaSiaypW5RZrtDpGwykB6F5bdw3O7WG6TdtNjaXWNZCpCPKZSyDUZy05h9rpEogFGR0eo1ZuUiq/hAo5j47vguSKuZ6LpEqKvUa81AYFoNMLG+gaeI6BqGngeuqYgqyIXLpxFlvud8oBu4NgejtMjHo8jyzKKrJHLLSFKCu2uiSZLGAEN17UR8DE7TRzXZmhkN/HBETzPIRTSiUSDDA1lmZ2do1KpoqoBPM/FcXpEowkkQpw7/yzxZLCvj0uOsLRQ4PjxB1hcnCeXn+PwvbsYGZqhUGqC5xMKBsmvFQGHarWK67oMjwziWi7droWHy+HD+6k3athu67rxX5d0Mkqx0KJabiGKCuvr/bHU3Xv249hdSsU8x+59mGhwBE3vU+9ajTKjI2M4bpNQ2KBabmAYcdZW14jFYsTiYbrdLutrBcyOyaHDe1nfWKLbMxGFIPlcmWqtTDQaotczSaeyLCyuMDgwCPj0eiaSpKBp/c68qsrMz8/jOBam1WM4O8Li4iKKohCLxeh2u6iSiOODgIjn9w2hN9YWUeW+zUA4HMbzRMrlMj3TAl8gEAjQsy3MdhtJFDB0nXw+j+j7BMNhzE6bUCBCPB6jUChiWz6aEkGSbfCcPkZeVXF9B8uWGB0aJ7+ex+x1qBTz7JmZwup52JZIo1EmOzSA6/79+JCHbYHdcxBVAVn2se0ezUYH07QQJQiFddrtNq4LruMRCoXwfBtR6KP1ZVnsXy8qsizjez6+L+DYPVRFR1FFPN/BdTxarSYg4nn+f1tY2Havj7h1XBqNFuFwlEwmTbFQQtNVbNumVqshSUJ/fMuWARBEB3AIR4K4jkQ4FKNerxGOhBF8FdcBSZYQRA/L7hAIGfge2F0bWZMxNAXdCGBZPXqdLpLaF8f3ej10TaPRqFKv1ZEUCVmUsb0+urfXdTCMAOFwCMe1sG0bRe1jem37Jx+72vr444//983O/z/ikUf+3eM/v/vn+fTQ3fzsz/4cb7aabBNl2lfrbN0qcNttISK33UG59AqTn/+X+L7In/2Xi/gf7cDsfoChbaVWdUAwGB75aW4NK+Re+Rav5L9H+JYw8g0f4/Uf9rhB2Y4WvhXzao7mW+t899Ul3E2V7Od+hrfe6nBr9HYq9Qq/9/+cIHnXTzM39+fcqO0kGAojyx9wk7KF2279OJVGgZtvGeDf/OajDH7+dt656jM+fjd/9/0N/sNv/e8kUhE6b5t84/mXEBBgEz62Yyel8its37HJ7XfcxM1BHcHZQX79R5Re+RHqx25F2Ayi3hjmk3cNcdttd9DrCnwq83n+6I/n+YWZX4It71KrvcZ3v5PnhVMvEgwFabUaXPuox22hO9mhhVhbqdF43Wc4O03izgG+98o62c98luDNKrfdvpP3nQ5mp8o3vvEH7P+lfeBv8uEHNq+Wf0D01tuQ5O1EY3fy+htv4G1uYccOlRsVEU1yKXz/R/z4tfd4rVrl7mGF9955k11f/FXK5R/x5V//Zb76+G/yWq3BXbHP8Pw3Xmbv3t3IisdPpW/Cev9NZn7xHtz3b6RcfZXEnTrZz9xN5QdtbjXS7P75SXbeLOF8+A4feR+wZdu7XH3bJhQIo6karVadrdt2EL39dnZoN/LJzN1UXvsRzTfbBG/R2a46fPfb61Rf+w53JX+Kj5ybUVWFj6mDdM1N7kzp1H/8Go12md8/+dvUW3ke+e172LFT4a/m5im8usKOm21+ds8I0g3beK2+xhe+MMg3v/lf6L3b5eCvTPO9/N8ADr/4i/+KiS8OcuIbv0MyFeSau5O339jG3Xf9Gv6HMZqNd/jRayVGPjuGa8tc+M/rNF/fJJ2+E/EGh8BNOoah8U73bf7kwjmUG7fyubFh/nZtEdfrsba6zHZ5J3d/5m4kfwvv9t7ltlCELZvw3ns2hUKJnbpBq9EkFgnzxpstQsE7eOftd6lVXsd53+VGTWELFh9cs9i27Rr1H1b525XvEo99gtffaPFO931iH/8pPG8rH9ouru2xCWzZ3Mobb/wd0g0OH7zn0Ht3Ox+525Dl7fSsTf5mfoVOt83Om67xK0f+FZcv/zls3sjrr7W566441WoJTZex7Xd4t9dD024iENpBz7rK3/xVDsf5AD2wlWhMJXTLLdx15xfYtvVmhG0f4vsf8MYbb7FDVbjjjgRvvtnE7LZRlZ203zLZutVnc3MTy7QQZZmPPvLovfs+3Xfe50PXxfe2IWzZzh2xO7nllhjbb/gYb5tv8EbjdWKxJAMDA1y79gGVSpVbwyEajRqtVpvPfvaz/PjHbyBv28ktt0SJx1KI4jZ+8Hff5+O3/xSf+tSnadTfIvrx23j3XRN5+w62btnOJh6edw39Jpl3rr7LTTfdyjU2ufr2Vdxr13Dfh61bVOQdH+NGcQfKjRo7dxq8/8EH4G8yPDTCZz/9WdSdO9jc9Ln11lsJ3GwgbZN4pVAim82yY8dO3nvvPa5d89mhavzlwtybjz/++Kn/2Tn7n2o8++zvPf4bX/63qKLM6280+ItvLbK09JeoO7bz5us/5vNf+CK/97tPEk+P8amfOcS73/1Tvv3dPD/3r3+FOxQBwW/zny74/JsvH+TmbVXmv3OJXP6HvP2GS/j2GDd/LMg26TbU29/incqPabz1BvXmKzRf/4h//1tf53u5NSKhFOIWjVeqZ/nl/b/GX8//BbfcejOScCff+0GB/Pfy/PwXJigWC9Rfq/J6s8irpe/zgXcjn/zEMLlXf0jUuIG7P7WTle+/wcrSKoOfTKOJ29A1A/NdG18Sqb99FfNqmzt23gBbM7z7TpNdv/Bp/vO5P+SXf+lB/nzuj2ld/QFbfYvtUpq175fZ/bOTfPvbczgf9Xj7TQvX2cLHb4/y3nvvsWWrw05Vw9u2yVZ/B4k7Po9+k8pbV3/EW90qkeBtdK5+gLzNZ3llgcwnU8z/5V8SVJMo2jW2bt2GogRh2wd8L5dnz8/eg7xDY8uWD9iibGentI2rb9T5UbXONUHilVe/z73H/zXPPnOCX/2VByl8r8EDv76XP/7jP+Kxx57hW1f+gq89/TskUx9nc8tH9N55h1tv/SQ/9wu/yJU/W6Veu8q3vvMCv37fL3DquW/y6Jef4cMPRL793StM7/0CbH0PSb3KD3PbScbvYPXb3yL+8c+wXY7wV995li3Xwvzpn/4Nv3bfL3ODCKoqI2+TuD0Updursl2+hVa7TTb7WT52w12Uf1jl4uw3uenmEPlX1xn9QoQXXzhLqfTX/Pa//zrf/JMc0188ysLKeQ4cyfKO2eFzn/llnv/G1/nbtRWOHT/G7bd8nPjHP0fjjQrelrfI3H07vTdvZ4tc47ZwnI/f/tOsLv+A9y2bwC0KNwVEPv3ZJM/97p/w/B88T24jz8cCDle+dZmfmfiXvNezWPrr7xC+NUH77SYjI5/iU9kUb7z5Np9IfZa1//oX/IvPj/DBBx/w/Y08O3YG2PSuUak3yGQ+wQ3bVTrtdVrtDsKW7dzzq/fywh+epfXjBjt0A+u9d9gm3UAwGOCWWxX+YmGOHXqY3rvv8Ebrbe6IxFj59irvW1f51N138UrxB4hSkLX/WiR2xw1sbnmTeDzDtWvbeaNR5/uvFvlU5osUfvADvvDFT3DLbRrFV79P+JY76bzlELxlJ3/+rW/yiYE7sd7rYr7TI3Fnhq2bN+DYH/Bu1+ODD99nu+pwg+xxwzaDyS/s5YU/fJFt4lZcx8eyIJEW4KMo165ZdHtvc8O2m2i/aeNudpC3b8P/cCfvuW9x086PIWzdpNVqs+nD5jWJG9XtGMZNyDfsoPN2hxs1F+8jkckv/gKlV1/lR68VicfvpNvt8OnBT/N22+RzXxjn90/8Lp8b2YUi3UnlR+u8dbXINd/htnAUNm+n/GqZT2bDVMpF5G1hPnS3Iogu2264hnety1Y/gKrqfD+3wScHouzQJW7gVhBEXi2/iqEabJG20XqzwVZRQNm+nbsScd41u7zerqJIKlu3qFy92sX9sO8F0mz+GMv6EFHchrRN5uabb+bP/uzPfqIa9b/Ezset4VsJRkIUSxXWNtapVeusr68gej5BLUC93uDEc89y5NBhdk1Psr6xgoRPPDrIxZfnUQyPeq2DZ3n4gku5ukE+n6NZ66DqMtFogOzAEGPDgxSuXKZUXqbnVAmFNUbHJ1DVALFEhlang+sJPPzgw4iySHJgCF+UqLdMGsUSh3bvpl4tAh1ytTIvXpzFiPeFqZ5vkQj5RIMip06d4ulnnyWZThKOxuh0eviCSywZJZYIkc6EWVlfYj2/QjiqoRsSZ18+y/LiKk89+QyXL89RLpjghLG6Ml974ik6HZNkOo2qKqytbfDVJ59CFhUi4QCD2UFisQSRQJRIZADHlbgyv0QuX2Z0ZBpJUBBEGceFeDLB1Mw0+/fOkIzHScRjJJNxDE2j59hIstKnVPScvii31abTbjMwPMjY+DihcJh4PE6xXMK0OoBAs1Hl9OnnuXx5lkJugxdePMPilUXq1RrVegUPm6npKRRBpVargdCl3qzSqLQYSu8jO7iL2SsXcGhxee4lgiEFRQoxkM4SDodxPYdAIEQo3F+lh8MRPM8lnkgzODDImTNnya2tcenSeTIDEVzXxFDCTO06gGnKzM0VCISDmE6d/YfGWVldYWM9z9DQGJnEOIeOHuLJEw9y8J44M9MTdLtgmT6laoldUwMkEmnyGw1SsRRG2KFYXmN1qUM2PQ1Sl2bNZe5ShYvnikjeIJcurJLN7MZQB2g3VDRpgAtn12g2W4SCITzfoVIt0bO6pFIJfN9laXmeaCxMIKChaSqiCPNX5iiUSwSCQTRDQ1ZlItEwyWSSSCRMs9kil68gCwatukmj3mZ69x6GR4YxOw1c1yaVDON5HRrtLpFYivTAAAIykqigqjrhcIRAMIQviPi+hOu56IYPooduBBgdnWTX5H40PcrYyBi7984QT8YYGk1hOSbpdIpYNMnQUJZCoUg0GkfXVSRJwnFsXMdF10RChkEkGCOg62iKgOdatFt1Lp67xOriOvVylZ7dF8/ie9i9HqLgo0gKAd1A11Q0VUcWFXxBRFV1fB88T0TTDJLxAQKBEFbPxuw4mJ0ugiCjawGMQJBKpcT6xiqGESQej183TgoSCAT6TrzhKLlchY2NDWr1Cq7rEY9mqVZbVMo1ZEWgXC72t8MlCVWTkET5umBTQBBBlSXCwRCKrIDv0mx36Jg2qmIgqzogocg6QSOMLOuUyhVmL19iY3UDs9s3zZKlvkhTVWUq5Rqe66JrBqIg0e50/ucm6n8ObrzxRoqFIrKq0OtYDA0NMZgdwDAMhsayXLk0x8T4OKFIjGKxiSbKHDh4mCee+AqGoWKWPLg1xmASzE6RZqOG2XQJhMMokoYoCAyPJiiurFHp1JAUH0mEgcHDRKL/L3vv+x3Ffd99vzQajUaj0Wg1WlarZbUsi1gWIQtZyLIsEwVjTAh2CHYcJ3F+NEnTJnVbN23TNEnb1O3V9k6bNKnT/GqaK3Vdx3UcxyE2wTbGGGNMhCwLIYRYhFiWZbUsy7KsRqPRaDQa3Q/Wdx73enCdtufu5x/YOWfnzOx+35/369WJ41gUSllc1+aenTvxcCgUDCRZwrZdJNHlkx/7OKIiE0348Qf6+M3PfAbNp6ASIhj20GSP/S8ewREFcvky/f2DxKKJtwvzIRRFRBJdRMllaGQEwVKIhMMU8kWmU2kikSjPPreP3t5edJ8fUZJQNYWdu3YzmRyjoyvCdHICWdJ56KGHK6fDioNfD+C5PhzLo1S2yOUKqJpKe3sU3e/DdQ0Mo4SiSgiCh1/38erzr7N3z31kMlkikTCTk1NMT2WRJAVfIEipZOEP+CnkMjiuwY4du5ieKHP06FEkySKdmWT37g9im34QbHbv3sHocGW7bmwiydHDR1DkSjk+HAsTDMYxiyJmPovjZYlGIRJsZ3DgHv7u6/9AV0+AY0cOkc0U8ek+yuUcfi2E7VTIdaOjowwO9tLb20Mmm2Fyepr2aJAXDx5iKjmBZZUYGR7lYx/fTSopEA7FCPjDPP74YXbf10kwpGAaJe67fy8T49NkspPo/iCqpuHziyQ6oJAvcPDABONjaYaHj9LdHWVy/CKK/LaksjCMqCi4toYk+rG8YdxyDNcKMjmeZ/fu+8gVxvjWd77K1sEe8jkDnxZiaChLvMNPOKLg8/nI5TI4jofu878N9zDxcNF1lfHxSdoTAVKpFADhcPhXAkNFUUinskQiEXK5SvIsCl5FVly2KsSnRJxMJoUsi7S3x0ilphgZnSQYCNPX10cqlWLo+BADA1sxTYPR0RFM00TTKomW55qIoockKyhykIA/TCozQcAfwB8IMp2aQFZN8DzKRQPB86H72pmcnCDREUFVJQqFAoVCDr8vhk/3YZkO+ZyF69qoPgtR8rBMMMsKrlPZThBU8BwRTVOQxIr5GyAej5NOZ3Fsu2L6NsoEggEcx8F1QVU1NM1HvpCnWCwSCISRZQldD2CaJYqlPKIoIisy4XCYSCTytj28QjkURRAEBVmugAAq4mMRx7EYHR0lGomi+RRyuQyqqr6dMHlYpoPr2siyhGmWKhhgT6BcKiOIDrlsEZ8WrCQ2nocg8HbCYmNaFWGnbZt4jovjOpWivAcgVESXgQiBgB/LtN6WJP7HoSj/JZKPRx75i0du2nQT/uZmTr41Svv6GJG2Nmyv8mO4NRDij7/4Bb71j9/lr//mb9k62E9VDbz26mk+/skPMXz6F8zPKqwK6MxkT1PIldne92netfPDzNrjjJ7cz3Tmlxwf/Se27trJvXd/iGi0i96uB2mLrmd4ZJSZQpr6ugba2tZw6sxpDGOBVYFWDHOB2Rtz9G7Yyr6f/IQ1m2Rcr4ENt6k89Buf4rZbPs/Qm89y8MeH8a+6yuDtm0hs3s0f/t4fEWptpU6q5cMf/iDnL5xFa5AYHn2DyakzvOfuQUJti0yce4ZVYYt33HkzXVva2Hv/VhIdIW7MliheL9IWWcWBFw/y1a9+gZ5bbuahzzzEhek0hXyR6Lo2lpxFZmZmKBUMrpRTSA3TyD6ThsZqFhavcejlgywvV2HN1nNx2iabFrl05gb9t/Vx6MjLrF+7kelLGVa1hLkyM0O4bS3VYg3ZbBZr3sQXsDh39iz2ZR2vyuO6eQK9Wab7pg+h1PtZcM+zPvoOLiVdlpdlfv03PkogZvC5L3+AxUWPVY1xyoUWJk8V+cG/PsrFyy9hLEyya/v7cRd05stBbhTnqdXS3Jg9Tf/tm7DtZbzFNVy6nObNN0+gKSqKXMvVq0Wam3xcupzCWXI5cWIIUVhmTTRBT1cv937oFso3Vmjy+fEFGvjqI4dQZIktPRtp1BWUhqv8yz9e4N8ff4l1awM88OFb2Zy4k+K1m7Evt6HVdvLVr/yYc8lhtvRu4X9/dz/N8l5i4dv52b4nQFBoUd+NT+5lZbkKxwFVaiXaMUu0w2DG+B5PvfAw//jYHn7x8vc4d+48hRuTfOBTq+gYmCIYCDKVzLBgL9Db20uxeB2toQHNV8f83ALr4zHq6uqJRiPMZK+xbt06Nm68ibm5WVJTF7l46RI3btxgybGZnj7PunXtbLl5C4V8Acuep/vmbkZHRzh+/DgDt/XS5NNIpaZQ6uq4c9t7iK5dzw/++fsMDg6Szc4w/tZbuCsrldW0m7agN+tUV68QjbbgLYtYc/DiCyd46aWXuZa/QiZ3iRMnjhFd08rpU6+zOO9SLJiMjIwya5R4xztu5Wohx1ujIzjOEj3dAyx78KPHDpO+fJXIBovVa1uoEdah1CZQGwKgXKBv63qCqwZYv/ZWrMUSi45NoVjEnDfxVla4fqNMY6NOU7OP5RUPqVamWChizJmYpsHKcg2CAP7mIKraSFOTzoULKWS5hkXbpq6uFlGqQaCGXC7H7OwsgUCA2tpaFEVhajqJtySxurUDoWYWa/ESy8vL1EsJ0pemOXnqCBs2BVm9OkyVGyKXu4QxZ1ItCKwKNDNvzVFfo3LpymVqJJmmRo0Vz0NrbGXeWuDG9TLzloVtW5w/f458vkBLIIivyUcgEKIjsZGl5SWWlpbJzRRYXFwmHA5TvlFmYbDMDoMAACAASURBVNGiTqlHqVdwbIfXj736P8nHf+J87WtffeTOu7Yxe30WSZSpU+v40p98ic//0R/yvW9/lz/78l8w/tYof/H33yVx2310ChfY98JZdn/qnVw5+UuGv5dj5O5P8Bd729n3r3/L9vd8mkatla7u1fz7k//G2vVhZueG8Mlb2bw5waWZ85SvyWy4+QFm565jLEyQvlji/ns+xtiZf+fY0ZPs2n03sUiUQ8+fpa8zwdXSOS7euMb8oowrzvMnX/oqX/r8F5m7PsnIqy8RWiUTaG3n54cvsPOuHbw1PMTigsXu7bdx7LWXaL9pA+eyWQSpjlnrBmHBJXV1gcj6NVyZyVNdpXDvA3u5ca2M51Xhq/Nz547d1Det5dFvfYlPf3oPH3r/B3nkT3+A61SjqB6LiwVk2cdcsYqic4EqT8BdjFAnC1CzSJU3T0trG6+/eo6161opFq9yx/b1OMvn2BTfS3ssSDZ7iUuXZikal7lz57sJhxMcffU1br0twePPfI3iBYf3v+cDHH71ScSGeSJrN/L+Bz/Gsz99Gm/J5d3b/gyf4vEvT/4l0UQHalMdDQ0ily7NskpfS42o0dSk8qUv/SHb39WL2jzPb3ziM2TSEncOfJRLl3/G1//pU6yJtnH69DWcxQZuueV2nLllJsZeJdBSh2HWMHDzVr795G9zS8/7+cXPf8SnHvosf/lX/4uOWDvXrp8lPz/E+XMGWwffzfWZRmJrujk79RqxTTL9t7dxOV1DfrqPqzcepXMLbNl8Ly36u3GXGzj8xvdpbA7w4oHn0BpdVulx3r/31ynPzXHirZcpzJzillveSzyxmVT2MkYxwy9Hn+O+e+/j+edfYnl5mXTmFNt33cTg1jtgqRVhZTUdm9bwz//6RaJr1/H8z19hhQUGbh/Ap7WyuFBNe6yDYnGGAy/+jBvlPMXiIlXLm8hfPoHa1EBtbS1nTk1SVS0iiDLWwgKr16zh9FtvsWtnN1dzc+i+Vspz80ycOklP381IdXC1kKNGXmF+3qRWqIfqek6efpO+3n7OjE+g1Kv4mhqoFqrY3L2ZV187SPl6gb3vfQ/jk8cwTY9oeIDFRYs5K8OW/ltpqG3l5Pir1DWlMK9r4ClMT10jEtpMnVZiZOwQmlaP59YQCLSi1kW5lErRtqaJppZl1IYGrDmROcPGmLvOlavTtMe6aGxopa01QubaedTG6zjGGpzlElpjHUuLNVydcWhuXeF6sUj5Oiy6JtXVLlUrVehNOq2tFW/G0NAwt95yG2+9NcKdd27n29/7Mu8Y3E6odSOvHjqCokh0bFrP4qJFcJWfwtVrXLicIRzajFC9gu0mcewlagQdpa6JyzMXaQqscPfdd/H6q6N0dW7m1PgRasUgNVIVYu08i4smzeo6bsxaaKpGYfYs1dWLlGZWo/pqMRfLrFQts6plFU1aI5Ik0dRQT/HGNXx6I+GIn9MTZ2moa2VhYYnImjbKs9dZuzZCsDXIrGFgzJosLS1x/Pjx/z7JR02tRMko4ngm8XiEYEjlO9/7AqWCxaGDo/T27uHZA4eI9/TwV3/zCLpkEAhJfPyTHaSmn8NKmUQSPno6JLYNDrLngQdJF6Y4cuw5nnvuAOmMw7atn+WB+x+nZ8fDDCXh0ME8U+kSuXQR2yiQm0wR8al4Ro7sRJJEJIFbdnDKLrLogSbxqT/4PInEXmLhAR7/yotcm76KW9yP7CbpHYjQ1/sQ0fYHSU/m2b/vEKNj0+iBMM8dOcK+I0OUPI1YtB+/Fsc2FXCjdPfeh+qLMzo8RSFnc2DfASbHjhMKqth2mUxulB/96O/57U8/gC5K7N6xk9xkkd7OforFaQrlaSxPw9/VTVf/NlxTI590sAwXWZQAj+TkNGWjgCCXkeQChmsyNp5HUuIIWpRiWcQTJbRgEElVcVwFV3Iw3CLxaBcdHX04mosvpjC464Ns3/0g+/ft49knjtHfcQ9PPvY1fvjE50CxSRfTqHKYcl7HtiQUWcbnKzCd3k9np86u3Xu4Z8fHyKQMioUylpXkuf3fIhHbhpGPEQveQznnUkxP4Fl5dF0gGAlgexoIfnyBdpB8DI0erZx+CAKp5HEM26Ez8SCR4C56uz9JPm2z975OAqEcrjfG0JFDlPMqW/sUJDmJKotg9DN8vMz0yIscHnmKsbERUukx4oko/QOd7NzVi8sIcnCaHfdsQ/H5OD7yDFrQpG9bnExumo6uQYKh7XhOlER8Lz/41gtkkxHuv+fLDA7sJhpp58iRSdLTMk88+RT+gF4pa02mERCZSo3jujYDg5U91Fwui2O6CG6JYiHL2NgQluUQCoeJRqNomoqqS/h1ke6eTgxTZDKdRvfrRGPtuB60d0SYTGcomiJ9gw8SSQzw7IFnyWSTSHKQXNYgEo1iuiVMM4/j2jieB66CJOo88fiT5ApZRM2jo0ulo8dHZ287uk/CNMaJx0WikU6isXbCUT89PWFyuVGmpsbw+zR8io4sqlilEqVcmv4BjY4OAYUIZl7HLJdITg6RzU4jCSFc20++ME1y+ji2bSIIGpIk49gqRlnEtEs4Yg7LUrBt39soQhW/FkD3BRFFBdMSee7AATK5FGUzj08XUVRoj7fjujB07BDT00lEoYzr5Aj4ZRxHwPagPb4ND5VSYRLXdlClALKsIggFuro6SMQ6SU7kKJdcjh47gO1YOE4ZTzCQBBkZH44Htm1gmHlMz8byVDTVRpaK4JVwPQPBdRkY2EokGsT2HAQUslmTY8NTqGoCn95eQS16BoXiNIJcplhKk56eJJ9NAf/xfdr/mf87s7S0RCQSIp1KEYmGKBayfOY3f4upqRwf//jDHDz2LKVyhmjQj+iWcBWN9kQnw0NDFMsuZdHHQCIMgOzzk53K4lgmZTOH60Ii3os/pEMoSDKTZWpkHEn2VeRdnkkunaUr0Y7tlDAtl57ePoqFIsWSCYKJ6dgk2mNMTaVwHYnxySGOHj1ELpsiVxgl4I8Ti3ag6X7+6dFvIikKgWCY/v5+Uuk0z+1/gcGtg4RDfnK5FKoqY5kGZSOPbZvk8wbxeDdjo8OEgjqxSAemKREMy/zlI3/FZ3/ns2QyWZ58+ikG+nax5/69lJ0cxXIOWW7HHwnhVyVymQy5TBndr6GpSsWxJfpQVQ1JEPE8D9fx8Ps0DKtMb18Pk5OjxNoDyLJGOBTFcV0yhRSua9Pb2cX4xDTpVIZI3M/AwCCRUIRD+w7xkQfvp7snwr5n95POZvnKV75OT1+CDz5wH5oa5IEHHiAQ1JmamsB1LEKRMKquEdT92KaCWQbLNLE9m89//jdIJif57GcfRhYVysUik8lhyrZBoWSjyn6KJXBch3RmCgC/5iOTSVIqlthzz17u2b2H/fteJBrx8/3vf4eJiVFCEZXDR47TkejE7/ORS2coGkUGBnejSH6SE9PomkgmnWXv7l20x8LoQZmt2wYoFix8qkbYH2RkfIJsNoVtOFiGRXtHlO7OPhAr+NXJ5AjxRAxZUioAmKksmqqTyiQRpYozSfeF6e/bimk4eJ5HoqOdFw8+R9ko0tPTQyDgJ+ALcvjIfkLRGIV8Cdt0CITC+PwB8rkMiiLjGAY+v04u65AvFtFUmXgsRio1zcjICI7t4NfDRCNxLLtIsVigp6sbsywgizLRaAjPMwgFg/T1DRKJxklPZyiV8zz2xNcIhP0EgyE8V8KwbFTVh1Eqsu+5p3nodz6FKKgEgjr5fLYiicwX8DyPgN9PLpdHkiUCgRC5XI50OoUogSxpiIKG6xk4joXqU3A9i0g4jucJuJ6D45iAguMavzrpLxTTBEIqqhzAspyKgFB0cR1wPBdBFBAlibHxIRRZIRDw47iV/ojjOCSTk5RKBXR/GFFyK2607buR5CBIoGtBCrk8kuAgSFS8WIqKgIiqaqSnJ8jk07S3dzA6PkY0FkIUBXy6hiwqOI6Dg1X5DF3HdQRMQyAUlCkU8ohIIMkoooSkKuTyKVSfjib7KBQMDNPA9Txcz6RUzldwvoKAYRoVY7voqzg//g/mv0Ty8e3vfPORcOsaaqplTLPE8ddf4ubuPiLhDfTdtoVf7P85J4Ze543h48yaN+hMJKhrbOHG3JssORmW5xV6tvUjWkWU5noOvPxzXjv6OOcyw3z5j77Lgx/4DD/4/uO8+kKS6toFfnnkOB2bumlstPHpkL2YY8/d9yKLMk0+iRWqmbvhUCot0dHRzbves4uXj7zB2QsZjNnLVFcVCTa08L+//UO+/eijvO/Bh2lq8vF3jz7K3/79N4iva6O6epkbxRkM4wb/9q8/4sMf/ARP/uhf6eoIEwm1YhYWuFpeorZ2NdVCC8HgauqVZh786IdZn9jM8NgkqqYTXhugf+BmlIZWNm/eRtfGW7hyrcCCYzNbNllelgm2rGP42POwbCJVKfhX1bFgL+A4Hmtjm7m5q4szk0MU8ufx+UQuXD5Lg9ZEQ10dV7JZaqprKVy9TGRtjNniLNlLl1GVZRRZYGz0JI2qxKnzb3L//Tv54hf/nKtXr9G+sY224BpePXSU+KYGdu/dyQuvvsH1q1mG3jzB2Mk3SZ4+TyKeYGT8OLFIiFV6I6NvnWJy8iyFQhljvoxYM0N7QuX4yGGEqhXSly5RIy2jKDqrVvm57bZB/KtCrDgSr7zxEm+dGmXHHffx4AfuZyY3TXu0lbKRZc26TfzwOz8mP2PyysvH2dQRJxqvp1a1sJZnaQutwyrXoK6aw9dUg69hNRMjixw68mOKRpobs5cxzAzrN0TQFI9QS5BVLfW8cuwnJM9M0r4+ytzcNeo1mdHxl/nKV/6GxgaNiVOTHH31OPNzi9Qsr2L2usTJ4StcnXHI5i9RU1eP49ai1UdoaQ6xaeNmfI2tVK00EF69lm3v3M5MNo+iNFBdpTCTvc7SsotUbdHatgY8l7FTE0i1IucvXkL3NeB5S9QqEv/4zUe5kpth4B238PqxQwwdf5m+3h6s+Tk2rN1AQ73KyZMjXJg6x+pogMuXU6SnLxBdG4GqJVqCGhfTKeYtk5/99CfMLZi89eZbfORTv4Xe0EGduIr0lVP4W+qpqVFZE0jQvjbG4mI1xdws7uIimtLEIgaRtSGadJ2ps1Ns2dKPVq8zky/Stno1Pf0DOMsC+bxJbW0zxVKRecflRtlkw6aNnJpIoaoKrx17jXVrY2hqC1cLlwk0tyFUCUiyi7Uwi1gVQEDCcQvM3rhOTY2AvbSIubhANpdCqlqhpU3n0sWzlBdKFPKXmbc8asQa2teuoXVVEEWV6ezcSHI6y/mpFFViNdPns7z51lFaWwSWl5aoEkW8pSXspStUV6/Q2NxMNlPiSj7H6ojGpo7N1NQIrACFa3kWrEVy2StE1wW45dbNLBgumcwVlgQXsaaKjs6bkAWZRl8zk1MnmZmZQZSrKBULlI0Sa9fGyeevUydW094eoV6VOXvuFE2Nzei+Rhoam0CogmWHo8f+Y6dK/zP/d+ZrX/u7R3a8o4/GhmZeeOlxJs+fJNw6SHTdDsZGxlm96QyjJ06zJXEzLOXYcusDNLaIpE8PIWubSa008OgffYSADo8/+xTJ4V9S3bCKDRvWENS3MPbmNWrlamrqxpnLXcdGZee7Bzk9fJLL2dPcsb2f0ZNvUFNXxio1Vag+t95OuWjwwK+/izVtcU6cOMd86QrXLl/m1p5bSV0aore/hRNjJ/jpj8c5+MIE1XUNPPyH78ewqmgM1lJTU803Hv0H7t3zAC8efI7RoaPE2qLUeiKLy9VogSbM8lVWt7RRWxNi57vDvHLkVV58fgLddzM0vUxz9Q5KpTzRYBNjwycpzkRYs1HBXD6DoNcwk9rG+psvs2ykKc+4xNrfy1XjBHqLjLdSxUx6nmibnzrfIuKyRLgtQfL0ReSaJuobBUbPHOF08iB3bPsonlPHTPYKK1KWhYU8l5JX6e9+N7J/hfse7OSnP7rAa68c4oP3fobhN4ZYWBlj9to8XkMVtrjE1ekp9v/8OK0tbbz+2mGqakq0tTewbDeRuXQNTWtgdOIwunIbZ05NEghJuMuL1Mp+speL1KsS5ycnaWrQqG+qJRBpprHhJgoFm7J7hVLpJGujcV45OE6D2MrJU0/T3tnDbbe8j9yFee4YvJMawaZoTvKRj7+bJ3/yBP3dO3n8iZeQZZvVkTxClY9qZzelq7VIjVeYPL+ftaF+5iyDOnkWqcbg9373y3z01x5k7/t38eJLx/jQh/6QZQqcPpWkQVbxqla4NDXLHXvu4srMBYr5GW656RbOnM4wdOItbr+jB0GySF0ap1HzISy5NDfpNPvXk7tcRKiuIX3xPHL9EoGWJhRF4XpxDrWhjlhbM1VKLQtGFTMzReo1P+vjazh77gId8RgnTrzOrnveRepcA5aTo6lhCZbrEYU6LLtAtaSi+dYzeS5HS9jk2vUZ6vDjOauRal3eGP4FoXA9c4bATZu24tge56bP0xIQ+c3fupe52VqqhGqS05NsbO/ALLn41UYuX5kkFl1HtatT7VvBv2oNhw8eYnNPF7ViFTeuX2djvIupqTS5TB5VrqP/nUHOTma4nPbjWo2s6TnD9RsW8+UaNm2KcyFZzexchvTlk+y64y5y2QVmF9M0qi1kZy6irDJobNSYKzaw4onccF5j+5a7mExmWN95C8nz52lfGyU5eZq2dj+uZRHw+zk1epJ4+xaulyqglGq3GT2Ypk7VuHgR3hq+wsBda/GWmnhp31P0bgkjCqsoz9sIyw41sohhmTQ0BJkcS3JLXzdnzr5Fe3wj169dx7QsVFWlpqqROWsWQbRojUa4MFXkcsbi7t2hiqV9QcWbr8G2Z2lsqqNw9QqNWjPVK/UY8zZLXg3N/gaa6qto0DxKRg5xZZla0YfrrOB61ax4AkK1xxtvvPHfJ/mQalbwbI/kWIpjxw4ykRwjHttKvpRhZPhZCsYRZEHikx+8nx3btuF6MfJpl3weXFHH3+FDcWRyZYkDB55i6OiT7N3Txdf+5iGSY1ke+94+/JE8/dt8OOUJbHsCVcsynTnMk898hXhXO+lCkUB7N44YYGh8hD33388DH/wIkUiMQwcO45f99Hd10NEBRfsoppwk1BHg69+c4nMPPcMzz47TvU3kif2/yc5dCUIBAcktUsyMsTXRQU80TlQVyEwd58jhpzg+OoSLg2V72A7IaphIPMHffesxHvrcI4iCn207d6FqUdJ5FVGKM5F2mDJs2rcmUCMagWAfKgk0p4yVGqE8PYzllbDI4DkmtgGC6cMxHSyjgG0ahEI6HVGR3k4fopciNXWIXHYYGRnRFJBcAZ9mEfRpKJ5EwC+TL06xZ0c/Tz/5GA8+sA1ZKuH3SThylu0P9BOKd1MoCjz0kc+zY+c99A+E6dsaZPf9WymYRVSpC8eKkyu56AGBPfdto7c/QiQqYjgltICIJ46DdhzFP4Hqt7BcsAgwMVEily1je2l0n8nO7d0EtSBj4wZ4IiXHRA/FcOw88S6V3fd1EE3A0NhhhkenSE15lAsaHjKy5mFZCqFQF6ZjEYyX6Bn06Orpprd7K4lOjUhUw3BhIpmmVHKIRbqJBPvo7elgcGsfXfFB7tuzl89+5rN88iMfYe/9PRRzU7RHQwTDKsOjh4jEFETVQJZFVElCcgUkT0XwNPI5g0w6XxH92QLFgotpSNimhm3J+HyVU3ctHEUQPRSfTldXHFkR6YiF0DQdRarIH+Pt7XR0KcSCIuGAiiqDqnl4jke5mKOQn8A2xsEpoIoSAX8ApALp7Aip1BSdiW341DCJRITe/hjRqMY9e7YR9nVTzgWR6SSgdSF5fuyyS6Ho4jrdeE4cPRIC0c/w6CQiAQR0UtNZunp6SGWypHNZ+voH2P3AgxwZsvAFd/HAQw9ia0UKpovf343jKQiyjR50kZUwA317kUQZSXZQFAVPKONSwnNlPFvDcXK4XgbPc/AEC0F2QCijyCauk0fzu/gVCV33EfAF8Pl0TLOAYZUpmg5Fw8QwBQpFUBUNSfLITE8hCnl6uqKIiowluthuCcstUTYsLMdEFF26uyMVXKGskcmkKn+eyyaSLOHza/Rv7cFxRKaTJRzHJd4exi4LlAsylilSMkws10TXdXw+DZ8io/lE/LpHQDcJaFAoZpmcnMK2BLYPfhDTEimbFfqI49rY/yWe1v//HmdxueLpkW0KxQztsU5cRCwjg6y5mIUAiY4e9IAfTY5QKBSwnCwIBsnJLJIQIpevdHeGRw7j02X6+7fimP4Kwc4rockJXK/I5NQ4u3btYio1yvT0JNu27sGxVaLREPncJLqu0t8/gG2JhEJhxsaP8fTTT9MeD2I7aaYyI0hCCM8O4bkh+vu2IWpZBraHGBjoYnKkSGoqRzadwxNEopE4H/vkw0iqxeBgL6VyCd0fIxJP4NcCCIJAJBIhHAlw5NhxkskkO3bsYmBwO/l8lrFkhvZwvEKM8ooIoo3nuFhlKOeKqHKRfG6aWDyGFvQjSiVc04dj6/j1AP6gwPT0eIVilwhSyGbp7hoknZ1iYnISvz9EQG/Hr/sqrh3RQRAkjLKJIJuUjAI+VX+7J+cjk8khiiIeHt3dfaQzGcpFm1K+jCeXMI1MxVEgBsikDEQUSnYWWbEpltL09w8wlcxQKpYZGR0iX5win59GURRkUcc2VSxDRfX5wHHp7mknl0+TyWRw7QADvdtZVetx5NizOA4YZbuSBOSK9PX1o2kVgSEIeI6DT/OhajK6rpPNpcjlMoRDXdhOEX8giFWKgpRC0yRsR6RULgNQKKXIZUp0d3eT6FLJpkCSJArFLKUc+MMqX/jclwnoYQa3bmU8OYFP0ys0wFwezVfBgut6EAcHzedDVRQMw0IURULhIKZpIggC4XCYUqmIKHqIoovgCsiyQLFYRBIEQsEwxXyOYNCPz6djGQaxuI/e7m6SyWmyuRy6X6WndwBVUXA8E8cx8DwFXfdj2Tap1CR+v46q+Ekkujhw4FmisTCp9BThUBTdF6SrcytHj0yiKgGmpkcQpcpaqyxFENGRZAdBKuGYHqIg4vcH3r4mHx0dHZSMSmohijLdfb3kCxadXZ10dGrkCxlSE5WEL50dJ9Gpk8vlEAUNo2RQKqVQVBnLspAkGdM0K3hcx0KSpMo953mYVqUTa5oGlmUxNTWFP+AjFm4nW8hjWRae5yLLKqKk4LkVkmJFqgye52AY5q96JZIoYdsOhu2gSBXEres4BIIBNFUjEAgwOjpKLBZDEIS3OxweoiQiiiJ+/9vfsy+AY7uAwNjoJJ4roGkK+XwWRVUol4uUSkV0vSIaVBWV9lgMy7QrNDlNw+/344kSiAKiCLgewv8hCv6/RPLx5T/980ceeng7S0tZPLuNnXd8jCvlIZoaHZa8Ku689UMk4iHOnK1iwVLo31FPc8jGmltFuPV2NMWHUz3CqpBLalLiz//4GRqWP8TzzyS5ei1NLFFD1y0uZy4+x/HjGT784O8gouF5jdz+zgHs+XpeOXSCXPY63b2b2fbOfn789M+w5+exbZsasYbBdzVw/uJR3jxeZM2qnbT638GSU8tM8TBVjVka9TruuuN3efSvX2Lz2q20trWQK52hUaumuz/Gyy89zoMfvYeVJZV1kdsQl6NU1XiI1RKSVMe8XSaTOkfZuMED730fZcNm/zM/Z9mx2LpzDbZZZuqMSM3yGrzlRa7NFvA1LbFh4ypu7lrNTevfycJcC2s39tGySqOm2mTRLjNxdpjhN3+Ov1Xh+vU85yav0dQY4bXX3iC8polQawhvpZbqKp1wa4waqYZFp4DrLlOFTHzdHXRu2Marr0zywb2/z4+feoWeznej1ncQbRtg/NQ1IpGt7LzrXp56/Btks5dpbGrDWZSpr29m2fMItvmYWyiyObGFVfWdXJ5u4OLFHDt2bOf2d/QQaOrg1s4/oW7pPYj2NhaMJsLrlkilz9HQ2Iwo1uNvViiXr/DjJ3+Gs6ByevQE+dkJhk8cobnZz4H9L1Ao5HlrZBxZkhl4x3Y2bNhI25oIa8JhJs6MU1VdRdnI0urfQldnP0MjP6WluYPFpRx1ikv6vM2N+XPUrDSxb98zzM7PcdPmblLpKcZGplhY8Lh6LUPy7HkKpcucPf8W3d1d3HXXTt44+jrzpsmG9Qma/c00tzSxqmU1QnUVNdUiy8s2gUALl2cuUyPWkoh3sbCwyJFXj9F/az/np8+xtOTyzsHbUerqKV69QaOvlTXhKE2rWlDq61hagvPnz7FkOyxYJvENES5dGOfE8Bn6+2+nLdLGvOkg1azCXrCZXygTDrciChJelY8qwSOxMcyuu+6jqTHMhfNZ3rt3L6+9/gKtwQB1dfUEmts5emSSgvkiF2eOomnNzN+Awe39FEojVMnTXEiPEQ32Y5oLtK+LUqdInJkcZXP3zZwYPoFcp9Fx00YK164z9Ms3keo8LmZSPPfTU/T33EJ17Sw3rEnee+89nD+9TCx4CyfHnmfBy5GfyXPx4kUikQgg4vfrlZeKLGMYBnNzc/j9AZxlh3qljqoqAU3TEEWB6Jp1FK4V8DU0U1MF7oqHXw/S1NhEfV09NTUgKRLz8wbXCtdoa1tNZ+cm9GYf/lVNiGIttUINi55LdVUNVbLEouVwvXiDFUTWxzeSSV9AbdSoEatRFIUlZ5lS6TqZzAxro2u5erXA4qKDIHgEmtvwPLicvUBdfS2iCLOzszSv8pG5lMZdXqYl0MzUuYv09g7Q1dnFSpXAtWvXGTp+nPj6dmqqJTwE6upl1oTDPP/cL/4n+fhPnO//8/cfWbIFZouwe88dvPue7VimzanxK7xjYBuXCyfwnCZagiHWdgi8NXSGU+On2XLTBsrlWY4PD/O+932cb/7w2/zOpx7iri0P8NNXvs+LLxxm821BujrX8t3vPYvmUzl25Cy/+/u/x/HXjtLdt57z50q0NN3Emalf8Cd/+Wvs6GmOvAAAIABJREFUf/J55m2VRnk1+aszGOYks3NFjNkyakMLa1Zvpj02QCo9zYGXn2Ti9Bt0dXUh1cp8/2v/xofv/i1eOfsjPvLhe7hy6SqJtTfxud/9fdo3Sbx4+DU2b3g/ly/IqOvzhJRuynNFbupqR1Vk/ur/+UN2vPNePK+FzPkrNIZHuLX/91gy6jhzZhy9SWF27gI3zDQb1oVZsReJh7o4dHA/DjWsuymKu5Ri6swKc7PLBNtquXrjl1y8eIPujXfSvmENszdmODuaxBcUWbBdlhZradLi9PX2cjY1Rm39EuVrN1iYr2bwzj6uzrgoWi3ve2A7f/uV77EqsJpPf/oLvPLaLwiv0cheWqavdzNV4gwTqWMkNtxENQ3suecTlMsl1HqF3EyG7q4tnBo7i1jbzM/2/TOf++PPsLBgoiotlK+vcCVXpH39ei5dHqe5RWbJC1JdLfOxD72Hb373Gxx++VWOHjrGP/3Tv6PoZfa/9BqbOtfw/ns/xqnR8/g0leqqOr727a/z+c/+Pm+OjtDVtYnHn/53NsbaOX9hDFF0uGP7J/jJsz8g0bGOy6kC3nI1Qo1JXW0VmVyay9M25qLEn/3pw/yvv/gHPvHJT1C6MYVjrGXy/DHqtWoWjDru+8AARkHkC1/8LA//wW9TKsyxvqOD5qZV5K/mWRdfz4EXf048FqNcKmPNO3jUElwVY/Xq1VwrXuVb3/oOd999F2/88jX23ns3R19/lTOTJ2ldvZaAP8S1a9fQdJ1Tb53kpq4uFhYsFKWe68UbTE0doFZSCQVixNa3cik9Q+8td3CtUOba7DT+QC3looOISqPeQOFalh077mbm8lVW+QOUzRzP73+KPXv28C+P/Rtf/Pw3+MevP8nho48Ti4cRRQFhRSK+tp2nnvk3zpw/Q2NjM/mcwfHho3SsT9DSGuT69QIbE2s5OTqMOb9CT8+t+H0hGpuaePLZp3n/h34NaUWjeK3IC69/gwZFJ7H2NvzNAaqW2khnRum6uY1y/irVtY3UqTWIVbW4ywtkriTx+9Zgz9Vhzt+gpr7IkrNAvRJl0bFZWrA4d+Ec77prG1fyeWqq66mrUcgVr7Gq2Y+9ZFNTU4NluCi6iSzpVFU1sGAsINU7eEsKCBZe9QILpoNYI0HVMtbSEqHmEFJtLaHWVkZGRlDrFda0r6UwU6ShsYEVrwp7YY6Vqip8ms7FSzn8/gB+vYn6apnslev4W4JcnyuhB2QuZzPU1oJANUqdzMKCRemGVYFaLCxQLBVYWVnBdRyqq2tYWlximSrq1Rrq6mReeeWV/z7JR21tLbKkEI3FUFWJqekksqBSKptYhsVUOokrePh8DogGRw5OMTluENBVFNkik53GKPvJZxU6OqI4bpr9h79MoTRO/9Z+QoEu9j2TxSf3smvw43R39eDYLgFfDMdUyGTTjI0d51vffBhbDfLYY09RLmUJxjSm0hMMjw2xb9+LZFIFRE9nbDTFwYOHmE5NcezYAbZt7Sfe3s7I8RSf+4O/IVOeQJAMymUP09L5wfeG+cxv/h1HjifJ5Q0OHz+C7C8hijai4CCJLqVCoWKalGVEEcxigY9/8iPs3L6dscNJnn7iaXCnkH0pkpmDCF6ZqckiyaTNsy8e49DwcxwdfY6RY6OMDaexXQHNLxCOikiqSHdXB319/bS3x+jr28lnHvoddH8EQZbxPBlJlnBFEcuFfNEiXyhhuwLZTJonHn+MXbu28cxTTxCPVwzR+VyeY8dexDRMZNEjnZkmmZ5AFCUUIYiuxElN5Slkc4wND2OUc5RKZYZHx1D9Bv6AgqaG8NwO8lmJ1HSa5NQIueIwE8kjeK5U8V8IJs/te4Zn9x8knoijahKF4jS9/TFCAY1wOIxjS0SjETo7u+jq7SHSHiMeT2BaFrlshsnkFIqiYJTLtMc6KJdN9EAQWdaYSqaJxzuIRhMoPhXNFyIYDLJ79262D24lHIzR1RVnYGAHwUAEQbTw+fz09HTh0zWeeOxpfIrCfQ/cA6KH45lMJsexyhaGkSeTTWHZZQzbpFDMIksKqqpVxHjpJLFYCJ+uoigSmiaTSqfxAM0XQpE1bAvymTwBPYwoikSjYcLREMGwDp5AOJqgr3c75bJN2fRITudwEZC1AJLkR1EDyGoAWZSwTSgVHQrFIrbtUCyVsC2XQqHMyMgEji1y+MghDHOanp5O/H4Zxymg+Fye2//42zvMHooqUCpl6O3tZGJyEkWR2TqwnUMHX8QoW6iKjG24jIyMYRkGjmMieg6d8RDFQg6/TyM5Pk5qahJRcLGcEl093UgexOMdBINhcrkcuVyGZDIJ8PbJjR9/wIcnuMiKhu0K4El4HiiShlk2kWUftuOCpKDIOo7j4roulmX+iuLm14OYZQNJFOiId4DrkkpO47geiCKyoCAKCkFfgHAojKqplEpFotEIgWCkctL6NnErEPATDAYIhQI4ToVKIoogiAKGVcYVbQpGHtM2yOVyAAiegl8Lo0o6oqiiB0IcHx7myLHDZDMVYkogGMS2HPAq5ljbdN52IvzP/GdOTU015bLB+FgWx5XJZlK4Xhmz7OC6KqGQHwQFJBlJFFBUEU2uMPADgQAeDqIgks/k6esdZGRklFLRrogpFYnx1BFCYRlF6KKzs5ehY8MIgoaomFi2xVNP7OOHP3iap44dIe9U9uR3792DYVgUCgW6u9pxLBNFCmPbEqqqICmg6yLRjiiOI7F9+330dPUQj8ok4n5e3H8YVQnywx8+yfe+9TU8LNqjMdLZHKFAANtxwBWQFZWp9CTTyQnaYzF6+wcYHR5FkiRUWaRcKHL02BCDg7uJJNopWWlC/gCq5CcUiGKYU1glg8mJLIFgAFk1UTUJ1zXAc0lOpQiHQ2SzRRTZTyAcwadD2ShgWmUsE1QlgKqqGEaJUjGHLCpoWgjX8hgZGaGjs4sjB4//Skw4NjYJogKeRCLRjqbL/OkX/prurj4mxicRRYmR0WHAo1QyiEU6EASZcrlMojNCKBRi187diLJOPmfg14OIooDrmQRDGvFEmEKxQEAPcezYMbb2dyLJLplp2LHjHlzRhGUIhxJYlksqlSIQCJHJ5lAlH739/ZilIh4ellUCQBAgEg0SDXVTKI1y+NCL6LpOd2+cfNYkV8wR1P3g6dh2mXAogWmZOI5NQO8klRlBFEV8mh9ZtZhKpujt6WHLlgGOHR3Cdp0K8ahcRtV9lEp5JFHGdr23SUceoqDg82nYduWauzq7ME2DqalkpXeoKoRDUQq5PImOdpJTE2iqWnEUxSIIgoQkVRIRw8pVSGyKhm1VTN+5bAFN1TGNPIqPyvvLcCgbBqqvQlzq6OhlcjLF7l07GZt4Hde16exMkEplKeYdtu3qxTILyKKMJGkUS3k8LPy6QqFgYJsyXd1dHDp0iIBfpz0eYWj4OCATjUbx6yHKZpmg309PXz+OLeDXwxhmEUUF2y5zzz33Ewl14ffrxBMRpifH6Orqo1Ao/OqZ4HkVX8f/l3q4no2AhO04aH4dHBefriK4HpIkIrzdaRIEAc8FPBFZlit0SMcCr0KeGh8doWyVGOjfhuO6pKZTeG6FfoUHgiChiBLFYpFCsUAymWTnzp0MD49gGGVUVcPn03Ecq9L5cJxKL8YooSgysiJj22A5NrZrEQ6FsMqV9Eb3BTDLBgge4FI2TFTVh6JIFdeIY2M79tv3q4iHg+cKCIL0H36W/pdIPr7//7L35tFtnOe9/2cGgyE4GALgEIRAEARJkIQoiqIoiaIWy7Isy7KtON6SOHHaJM3mtmnSpG7vvWnStEmaNulNbrPWzepmsRPHTmzHqyzbsi3JkqyFoiiKokhwB0EQBAEQBEEQHAzuH2R7cu8vp7+c36+JEgufc+YM3gcvz3meZzjzxTvv9q37Pz02HiE6tcCGjQ2s8ThIzsts7dzO/gM3Mx0eJhbLkC/oVFV7KTVraGXVzKejvHj4OcwmBadjPVMj86zd0MSTzz1CqZohL0/TO/gU45On2LPjrZRKNZw/aSKtL5PLJJhODnHmWB/ved+72HftVr7wme9w7IUnyS/DnW/byy+efBDBJFJT3cSdBz6Cx72Jlra1bNhcx8hkFxPTQb7+le/y6st9tLZcw9REGBCYLy3Q1XUU55oGEimBD977Z/zpX72Pf/6XfyE0YbC580aOHD+Ez13N1NQkC7MxXA47Xu8arKV2zhw5TlmZiReeeoqZmQhVtjxbNzWQiIVYTs+wbq2f2ZkIWoWN6HyUkVCQ9Vt3sf9Nd2KWSrGUlrMszLC8nGFhMcm29utwOlZWxro83E/fpRB9ly4zNj2JSS6nwlPHsgg9fRdJLy/iqvRQYrMjl6hMTgZ59wfv5G8+dR/rNq9jbZMLQ1zkUvA86zfUopXL9PafYKCvi098/JM0+lsZ6A9RkEpZnFsgLyjsvX4P1+3az/RUnJsP7GMxN4FrTTkvvfQqP/juDygttXDu/KtkjRA7dtfjqJCwKqVoFQX6L5/htje9k5bmFsaHh2hrb2Nk+BxjU2eoqasmk8rjrFxLfZ0Xp+ahRCzl3JkznD93jvnkPN3nuug6e5a6+nrC4TDL2VKmE3FeePYZ7rzrD1DsJZw+dpnZxDLVHjt11c3MzS2yZo2L0tIyBvqHEaUciUSKxx97HI/XQSqxhMNRRmNjI+VlGs+//AwDg/3s2NrJ9OwseiFLPDFNfCZGNBKmpLQEQV/EXeVFLhGJzYY5132S5NwsgeYaLvSewcjnKSkVOHz4EGaTQmXFOp558nleP3mGKk8VwcFBpsIREGBk5DL5Qo7xiTBOd4Brdh2gq28IpcxFmbOamuoASBLpnEFNdYCWjTv4xePPoGmVWK0KfRf7Wd/SRn7Z4KGHHuJNt76JvL7Sg9DQUE80NsFSRsRmqySTi5HJTjGXStLSvAmtvI5yRyV9l08xNjbB3XffyWNPPEp4cpJt267BbtdQFAflDju57BKDY4N87N4Pc+7UUQ4++x0kFmGxwB+89f386Ns/Zik3zvPP/YT5RJIadyOZpTQWi0yhUGD79u14PB5isQiCYMLtcVFmtZFOZShTK5AwI0kWBF3AJKmcPn0GV0U1M9NxytRyymx2wEAUZUTRgslcQDKZmRiboiFQh1JawlNPHiSvL3H9TTcRnV5AzJtZzokUDBNGXmQwOMxyrsCePXs4f/4itbV+TCaR0lIFQRBIJOaIRKLU1NQgyQIt69owm03MpxbJ6XHsDguW0gJlSgk5IY+RK7CYyePQ3CCYyS0s49AqMbIZTLIZT7WbCqdGLqtTXqFRrpVjVyuQzCKJuTmO/5rjaYv8Zvjbv/ubT7/19ntwOJpwV9k5e/EIkiRSXb2VyWCUNfXL5HU7DQ0dzEaSlCkWAuus/Ot3Pk+ZpRXV3Im5xIxWu8CFs0+iz2eYTlmwmp2oVisudxn2yjw/+46Znz3xBX7ww08wNLhA83onX/ziA9z3px9nInqKV595kEsXBtm1ZzcPPfRz/L4mrtm5m95zJzCbJDZvfTMXuoModoPY/BCf+NQfc/LEMd68739wyy1/SE/XYb7+lT+gd/ggJ49UUu3awrvuuYlP/8NbuPWe/fzgm8+yZcdNLGVjlNlVohNLWK0FUqkZUukkZZUWhi9NY7c1UaaWo9Vf5Nwry6QWDTZ0BvDWFXjh5WfQl+yMXV6ktNRG1/kH2H/gLkrNNfjXNhCNniZvWiSdSGB32LGYbDgcdl57NUhj81bWVJiwlE1zsvs8db51JBJWNLWW6GwQQ5xDMOYhb6WivJoTr/6EzVv38uqRU5TZJZrX1fLDB58l0NhBx9Z1zEZniSfnWZqf5EMfeRvZZZmpcIa2TZ0kY2H8gTX4qiop6BU8+MPHecs9nQxFf8p17Z9kZqrAoWefZ9s1LRw8+BPufMseavxVTE8l2XfDPvrGz3P4YBf79tzAiy8/hNvtZPC8QtM6O7MLJzhxZJS33/Fx7A6V6poKOjbuZCg4xjvefTePPvQjfGur+Pb99/OZz3yGqalJ7rrrrRw7eYKhgRLueU87XefO0tG+j7HhCJmFCGs8ZhSbk+/ff5DPfvFevvCZH9LaVsuF80H+8q8+ypFT38NX3YLdbmfXDV4e+rfDGMs67VvWYXOUk9d1JiYnqW9opKKinAs955GEZcqsChWatrqvUxmz8TnmEilmZqb4u898gseeeBSbzcrkZJh33fNhCgU7Tz79U8wmmbyR487bb+P4yVdYWsqyadMGvL5qsos5Nm1tpjmwk5889DDdF19h05ZrOXd2lLUb1vLo4/ez5/ot2EvaEYwc23aspabWxlNPnOAtd94DCDz6+L/yjj+4hWNHzvLOe95HOikiWcrwNcxTZi0jr5tRSq2UyArbdrSxc9deLl4YpMxewe4dN9F17jShyWHK7KWcO3sGf1MrbpeX5kArwaFRrGVmNrS38fH/8QnKyyoZGbvIho1Jqqs7+IdP/YD3v+9dfPubP6XKJ6OVK+iLEI7FcFRY2NaxmxMnjxBNDLOuaQflNh8T40NEExdpathIXU0zU+EI9XVeYrEoXWdP8PZ3votnnnoOV5UHS6mVJdKQF8hms8ynY7grHZhkheVlHafLxWx0jvPdfey7aSfR2TD6oogoCZTIMiUlJdg0B3OzSfbt28fZs+dQlFIu9J2huX4LWqWN0dERZuNh2jdtov/yAC3rt3LuzDn0PMimDLZyFwuZDHXeGi6PDtHsD5DXDSSLGSNnYLMpmJUyJKOAbmSoqCinzOpgYnyK+voGSq0WYpEY8bkYurH8a2vU70Tj4x8/94+fvv22O7E77STSM0xFZqir3kJkapbRkXHat67D7tB4/Ugvw0OD+OoVXjtxkKnxHNfteStuZx3R2WHKq6wMDsXIzJu5/vrr2bSxk0pXNbe++TYkeZ7zPa+BcpRf/PzfqPW7+dcvf5Xbbn8HmUwYyTKP6sgTmhih0d/I6e5TlJhVrtu9j8DaDRx/ZYro9CKp3DAXB0/gD7h484Fb+eD7/4bWxp28/OphmpodXBw8RnhklP7LQ9gdK5srbd+xGcUq88gjT5PPKyglZiLRMC5bDZta1xKdC+NvaqTGFeCandfjsNswl+m0btzMQmKZxfl5rIpAuVZgcnKUYN8wM9EomtvBurV+/A31zC0UOHryMM+9+P2VH6W2GiTZjoyGJFQzOBSjrX0L6ze6EKUcJjOsqfSztJjHEHRKSsEwFrFYzDgqXFTYnJhMZqIzYeLJWbZuvQatfA1joRHK1zjZ3nEtdQ1uxkMhjh87glbuouv1EcaCl5leGGFxIUr3+X7+6J4/5tGfvcLYxCwLC5OcOvMKoxeXaG5up2BKYVLGMUwT7LpuK0KhQP/FKUyCQnp+gezSPKIgc+FsN90XztG6fi3mwjLeOheTkyM41Voc5TX82XvvI7uURxRMnD/fw+133k56aYHUQpKCJLBpYxtTk1OUKmVs69hDaUlh5WVY3sp8IsFsLInfX83Q6AC/+MWLBJp8LMwXWEjnaGqqp5C3klkKsWFjI3OzBVKZEMu5EgRDpdJZgbXUyjXX7mV+YZHsYhabasdRuYbwxARrG+pp29BKaamZ1JxOqcWKu8rF/HyCqupyknMpotNTvP0db6eyspLWlnaOvvY6eUQ8a5zU19cwHZukrrYawZwjNZfE66tG15fYf9MtDPbHeeaZp9DzIu4qF5MTE0zOjJNIRlkbWMfRV06QSi1QWVFGNrNMU1MzF3q7ePinD5LOzOGrq8JaUomiqPRduojZbMLn9WMYKotp2NzeiVhQcVU2UtAdhEbnCE+mqG1qYHRokFwuR4nFxF13voX+/kG2bt3O2MgU3d1nKbFL1FW7uHjxLOsDdXzyEx+ms2MLy0smhkbDbO5oJ7OYpnKNg80btyCZYDG3SCwWpa2tna9//cv09HSjaU6y2Tku9vYRi82wceNGTnedprLcjVxiJjg8QpWrkrZNW1icTyNIFnbt2sXZk90IFjOGriPJKsvLi6TnF1nWl1lcXETPL2Evc5FOpzh9phvJZGEhm8Pj9VFWZiEyHV4ZsqVY6T7Xw1wqjskksLy8TDgcZnJyEpvNBsD0dAylxM5cKkF3dx/xeIwqTyX9g4NsXN+K1V6GsbTMusBGPN4aJkOTTE9PMz8/Tyg0QS63sr784ZdfpaurC63CufJGWVWZSyWYS6QpK7Px0ksvFBsfV5Avf+Wrn961ZyfpuWkkSUaWy5HkUhaXo+jEmBzL09cdxqIKlFcYjI1M8NRTj3P3297Ms4+fZGp6iYJFxONRSEwNU6oLZCxBpuJ9rG1aS/+5HBe7DO448GHSRjfO2gSY5snMtfPx+/6a2OxlxiZG6e66yLrWJiwWBataScDfir3Mz4G9byc01ctA5CiaJnL2bIhbb7qLd7/nXt73no/wuc9/ju9+8+t891+/jmzYoKWfzMwurCUq1+yqxO2d5mc/fo2llI/W9TuZXxrHZTWzbOQolCxRXl7Fuqat+HylRCJRHn/iVTrbr2Uy9hw1FW7MksZUuECJrZJI4jVc1T6qqxtwqDaq/eV0nVwkm01jtYpc7hvBUb6My11JdMLMWv91NLaUcPClx2iqbSMamyUycwGzYieVWEK11uB21TE2NYDLrZKcj1FW6iQZz5BbPomRt1Nf30agxU/f5Zd49WiQv/rIZ3n+xceZic9x9uxpssllRicGCUcvcLZ7EKtZo7Z2E4GmBp5/6RkyqSTPP3+Qd/zRbTzw7W/y2b/7Gvf+6bvYt/86znV18d/+8m/4xv1f4cCbbuVHD/4blpISQskRXGUbySfzbL+umsGhPm675T6+/I2/oK2jhqYWK3vav8imTdX87Il/w+9vZWRsjNJSgZeOvkh8JkQml2N5IcfgUJCNG7YzMZ4EI810ZJE8CyymoGP9Bwmn7icWX6Sj7XoOHvw5//1v/4ZHf/IEZbYSojPTvHKoi/HxPhoaWkinF2huaCQxl6X77Ovsuu46ckuL2MsrSSTmyGZzLKaTiJKAr8bNiRNH2dK2g/S8gFUtZWEhi2jKs3//fo4eO0pueZ4DB97M+fPnyOc1PJWb8VbBP/zT57nxpj24PRpf+er/wlNdibOynDNnTrGt8xr6LqeYiaVwOKzMzEaw26oot7sZCl7GW2tjbHiQqXCB1kYfR44eYvcNWzn84kkmxiIIFHB5LFRVVXLy5BkmxqLccftbuTx2mcWZZYJjQfbc0M7FvvNYSyoxln0kE2mmYr1k9WE89s1IpgKT0ct4fD4Ui4O9e97Ez3/+GLHZaQqFPHpmnunZOSpsZtZt0FAVN4dffpZSczNaRRn9w4dRLU1YSiQKpiUmZyawWssZnRjGVVHF0WOvsq6tmrJSN0bOQS43j7u2wEM/Psib972NmcQUS0uLbG7fwHRsFrFgomAq5cYbbuKJx55EKstRYauktLSUpaUInjUBorEZbDaF1GICf9Uuzpw7wrYdnbzwwkGqvT6isShrG+voHxxkITVPNpsjkUhQVqaS0xfQnHbmZjM4NDuzszMszKfJ5nI0NjaSSKVZyi6TzWaodEvYHeUsZpawWFXAjMdVu7LIjVlAls2kF+bILS9TEPKoqsJcIkksHsdbXYvJJGEymYhEpxEKJmpr63j++ed+f4ZdmaRSbDaRbDqKJKooipccaZLpLNm0TF9PlJ6uM4iijk2xcPzkIU6dOcIdd72V226+i+HxMEgZJEkn4G9m957djIYH6O4JE+nbTs+RAAYSLZtd7LzrBIGOBEeO/5j9e95JncfH4YNP8vCDP+T4sacxxCihcBCX00tbcwc+byPpZBrJkkVWAN1Az4qokgsjJ3HbgQMcO34In9dBPDbOs08+iKQGcblsdLbtZlfnHZw5HsHINZNLukglhzly5mFEOYKkSPga/Xg9XuKxGL19/fzssSfpHRhAs9mQJYNde3bjb74N2dJKf+gYydwZ2jvd7Nrejl9rQ8p6efaJIyCF6ex009LsornFg6a5USQPhu4glVrxWZJXNqdxe0UMI4Oe1cEQsSkKopjD5bSg2WTEnEg0EicejeLU/KhygHAoRzImkk2rOCQ/wf4IfV0RcikJRXLR0babdDpDPJ1EteSQLRkUm4W+gVHaAtuRdAuqTSKTSSOKATIpB+2te/D7vfj9XkaHRxkfj5GKW0gnFQxdI9gfwsgadGxvR1UkgsEBxiNhPF4ne/fciqb5afF3cOT4KYLBYSKRODnDYCA4zPjoKBg6HqdGPJXE7fEiSxYOvfgisVgap83N7l1tdG7eRzI7QCjazc7OvQT8fgxJJJnOIisKff09jIfjKKrByVMvsn//rbzv3XeTS+c4dbwLHQPJotLfH8RiUWlsbCaVyiAj4XI6sVgcREMh9EyOndu3k9MNIpEooiiSzRg4HAqqzUIsFiEZT5POrHS96tkIkdQwhiVLOh2jq/sMGCIYOUQRDF1Ez4hIokg0HkHTLGxubcehKhjZGOQyyJJIJpNDxiAVjxOJDBMaDeN2uvHXuWhp9uBxOdFzOTSbe2UJ3P4gw6MpstkY6Uwcck4kvRGHFMBhcRMOhxkOBnFaGnG53OT0FKpio7+/H0OH8XCYuoCPvTfvo2NzG75AHTYlSyYb5ZGHD3Pw2eNEYuO4XRZcmobH2YzD4mc41E/v8CkQRRRVBVHH6/OxZ88eLBaZQHNgZTM3p4OBYD8dbZ3EU3GS8SSqxUI2kyU8Ok4kGiWXSXHo0IsgAbpOJpvFyGUQgUwmQ3tbG35/AIuiYojg8zfSGKhDlKWVZYf1FJIs4fF6ESUD2SIiSTlEVoZVrUwadyCKErq+MqxL0xyIkoiItDKJ1WJBRKHRF0DXDXKZDLmcTjKVIptJr0yaN7JIsoiqyvibfWhOJx2b29m9cxc+n5dcLksoEsamOfE21q1MSCxyRTGbTUQi48iqQi6n4nR6SWfSqLIFh6YSiw4wPDrRqsz+AAAgAElEQVSOJEnYnAbd3b0EGjezs/Nm0rk4Nkd29X/HgUWxkEwF0bMaesqNx+tCVIJE471YtOOEQ2Gi435c6k5uve1WHJqMTpr+vh5yhsTwaAxZtuBze6kLBIhGkoyPRlYnokYIRQZo9DXicnnQHF5CoQg7d21m5+5OHvzxt9G8EI9Y8PvaSKcNmpvbSGWyJJNJZEuOtB7BEJO0+vfTGNA4deoIHreXdFIkmYyhG2ki4WH6+vtwao2MBsMYeoTxyBmSyTheTzsSTpobO5EsTkLhPvwBFw5VRDIkbGoAm8UBhkQul0NERZahsVkjHOkhncrQ1xMDQyWnrzwrdTGNarEgYgFDJpvNoufS/7Gh6MDAMA6bk9HxATo2dxJodtLT043X66Or+xR33XE3/QO9WJQcuVwcSZYIBmPEIhkcqkY0PoA/oNLbHUXW9xBLduOpM+juOYKmuRkNJsmmLYisTPDNZOOEQt04NZVMdmV4p9frIZtNsnNXJwMDo9R529B1CVlRiCcjGIaBt66OSDRMLBrF7XaTzWRwOjX0LGiak2AwRDIZw6FZ6OjoJJ1N8cOHP8fOnbeRS+uEo+Psu3k/o6NJotF+LLKKLOZQtRh3HPhznM6VDQDPnAzj87moq6ujp6cHt9u9oj1ZnWCwn0gkgqFnUW0SABabxuh4iFg8jqoqDA8P0NziJxaL4XHX0dLSTHd3L0888SSBxkZiqRQWRSKTSREZD7Fnzy6Gh0ex2RykkmlsqsqZriDDwTAdHZsBkaeffvo/NK+jczf9fcO0twZ47IlHiMYi6DkZRZEZDw2g2lQUi5NcVkTTXAwP9yErKg6bl3hqFJ/bQ3v7ZiLhJJIoYpAlFg+tDJP1t9E7cJhobGWzRMWiMDAwjKeuDl1f2VAvlY6T1bP0dp9EVR1gyOg5HZ0s6XQKm+oil5OIxSIgZkil43hcjWSzWZxOJ11dXbS2tCBJEr46D5rDSTweR7EoeFx+Muk0siiRiifBWNGKdDqNLEnI0srwKosikdN1VIuEbmTIZHJIkmVluGMuTSZtYJHllaGHOYN0Oo3H40RVVAwdIuFx5NXvdV3HoWmoqoNYLEYkEsXp1BDF1bmRogVZkojFYoiiiKqq5HJZVFVB17NkU2nisSSKRSEcjhKLRZEkGciRJUs2o6MbBpIooygqhrEyVFlVFRAhlYr/2s/S34mej69/7Zuffvs9t4ApjaJ4kKRKQqEhnNoaNGc1A8ERFK3A5m0lODRoX/tOPvjuv+bwa9/nM59/Ly3NTTiUa5BkL5HZwyQzo+TSVlLpLAVpkpGJHh75ToKXHptnz7b78Nc087G/+ACVniiKVWc2OofdZiUcCRJoXoPmrCCXcqMvVTAyEmcyOo4gXySZvcCFnmnySxrGYjlPPvE8GzvW8N5795JIjfLioYN84Z++zMyIGSkTwOuVKHMMcejVT3Hk8BM01tfhqrRy7Za9VDtu5MTpS4RnxqkNNBFfyFEo2FgWS1hYzDFvJFjMwGuv95A0phEcBtfsOIAibSMVcpGaW2Jk4hF6LvyUe97+dpZnq1iYVGna2MIal5PlRTNLuRwVa2TmlvopsVg4/fpFLl2cJBy5xFpfI9mUTqlkhuUEkbFegv3dTE1NEg5OIWQXsVnylFir0HWR2MwcJWYHdrvK/PwCpZZyQhPj9F8a5gMf/ACvHX+JhoCNvJGhbeO1mLCyf/8tXL7cw6WBI1wafQ1ZXsIoZKhfK/GTHz/KqRMDLM3XEQmJLGRDeGst2J0LJJJRsikX+264i+WsSDhxHotZoMRkorGhhVNHg2iOZi72xkmk0kxNXkJVbCwsLGIpFbEqFvxNPjy1VaSTaRxqOXW1fpbzS1y761qGRy8QnZnkmWceY2wkwe7r64lMD3G5N87GLY3Yy8wMDo6sLB+oqiwtTlNR4WFdoI0f//R7nD7dz94br2d96wZOHT2DtUxDK1/D4ReOMjQU4o63vZP5xAL5xTwLC4vEZjIU8iLhqRQ2u8Z0ZAbFasVuV+i7GMRT5eOF548yP58lNpPiut276O5/hj17O0jPTTI5NcLi0hzVa6rRXBUUlk1YzFYef+opNgaauWF3KyMjFzny6iHMpgyx2QGcmsTocB9WQSQ1N4IgF1DKrJw528Wmjiac1RaQlqhw2QhNJEilU6xr87GYjbOx3U+gRaXKqxAMRvC43Bx66Rec7n2am97cxjXXdvDc8y+hG0nsZXaczjWMjIyzZfNOMgsZEokk6USCF19+hqXFBQZHz7Esihj5cgbGxilIInaXxlhomjWudty+RmLzQ2hOG1VOH6WKRDQax2wuMD4Wwlxior/vEqn5eWRZxlqqEpkJkc3OYxg5SstKmJ6aQitXca+pQHWqzM0lkMyg2BRyy7CYXkAtK8VqLWMkOEwsOk0iOUs0EkMuMbOYWaKhqY6KCjvR8DgDgxe5PNRPpascyODQrNgcNlLzCfRlAZfLg6JYSSaTuN1VFAoG58+fZyI0RmtrK3V1jWQzMBlKkEikWcoUqK72cuzwYcLRMGpZCY7yEtw1Kms85WQX8iiWEhTVgbVMpdzuJDozw+zMioCkU2kKQoEjrxY3GbySfOvbX/30wMirVK/Zh0XewEJ2EiM/S/P67QT7xigoR6n3riczp2AUyvDXNNDR4eeLX/okiVgFtx74K5bMi4jiBOuby1mYzdHbn6SuYSOGnmU2McWTTzzBDXem+MiH/5H/8bFPcvHSGcYu2xkZu0RTSzmvvPwMDc0NtLXezPKCRkvbtTT6Wzhy4mcosk48Ncm5C72Ul3uwiB4GR3q4aX8HP3n021it5bz3Xe/j0Yd/SHlpjvt/cIK3vP3NzEQvEhoexG71Mj+fwWRW2bJlD1pZDd/61sP8/ZfvovfCeZIRK4upSvrHHmUpl+LSpVG0srXYNPD5ylkUUswnRJaT9WjaPOHIMN09J4mE4tx44HpmphwsZqYor7QyF82ysJgjnZzDYiknGosgWSQmJ3px12qMD0zir2plcjaJ2VyCTSshONRDhcONpbScVDiJ01VChWZhPm1GkDROnOjiLz96Hz995rOsbzxArX8H4xPdnD83woc/8kekozFq/WWcH36F6/a8lZGBBB++9+/JZmYQWeD4yYd4z7veyVNPnOe6nR/m6MkfcO3uXRw89AsO3LKfZw8+jK9mLapVI5koYCpoVK6NMBdeRi1TGB49zY6Oa3n6yV727N3OCy89w4VLQ3zojz7Etx/4GonkFFu2XkO+sMTF3tOU2k341nh47PEn+JM//jPiyQUuXBpjz7X7OH76BVwugxpvJ4mZSVL5F9jZeh+qVsqp0y/jr6unbdPbeOTHf4/H20BLWyOVLoNYWMOmmTEMHZe9lfGJ0/jWtrIwn2C5kOdS/yC3vukAklzKyNAlLKY86fQcFrPIHXffy49+9AgCC6xraWAqEsFmszMxNsH2Hdt46MFHWRtYx5qqNRx5/SAmIc/Wzk2UmBWefvIX7LluD5KkUKFVMD4+gVW1sX/ne3nx5YcZmzhLbcMGhi+PUmo1s//GG7BaNPSUROsWL+FIhEhkjkwmQ8uGRta3NDCfSuNy+rjcF6XW70aS8szG8mxsv5PzF7/GzW86QCJaSjZhZnD4EjMLPWzduolk0kRkNIuijlDfWE/H1p1MRWa5ZtuNnO+9SP+FfgLranjp5ae49fab0MqtVFWuRZXX8cLLr7C2PUlg7S56z81R6agnkY4xEnoJu82Cx76Jsel+VJtMIraAz+dFUhZZowVQStyMjg/g9phZ0m1k5hawlduYjIRYnE/jcVfS29ePr2Edzzz1LPtuuYV0LkoklCbQWI1JniUyKhCZnUI0ZVhmCcFYS6WzBIsKw0MjpFOz2NUSlhHI60t4qiqhkMcwTIiiiYJgoqVhExd6eojFo1RVu8Eo4HRpjAyPUWZ3IpksrKn00NtzilKlDJ1lKpyVzEaTnO06R0vLOuaSIcqsZtxVGot5kbnZNEKhgKO8ErMsktcLDA6OkMqk2LR5C6IhEp4Kcfr06d+fng+jsEw8LuP17CSdS5JKD+B1B1BUJ/HYytsUUVcYHc4iW1y43Dqnup5geDjCfX/+P8npEjlG0RxZdMNGeDiNaMjUeT04PaPUNccJx7r47Jf+nAe/38vNuz9HOrKdVKwNt7sFp9tFNJZEttmQLSq6kcNT56W5pQVFEbHIMhbFhp41cDktOB0ysmrg9dfx8INP8+MfP0F3dw9fu/9LhEM93P/NzxKKrE5yjhsc2P+HfPjej3P3be/E5XIgWzI88MCXaG72Mx4JEY6EABEkicMvvkgqHUe1KPQFh7EoDtrb9uDztJBKpwlHB8CSpqW5kV3bb+Pd77yPYPcAoeFeXD4LvWdG6TrTTzwVRhczGGISgySZXBxFFQkEOgj42wAJSQSLRSabA0W10dLWjt9bh9OloTocpHJZ4qlhcsTwBWzY3BkkW4ZQrBedcdw+EYczg6ImiacGMMQ0DoeDVDxDPG4Qi0TZvXszw5EubGoWRVFobGwlm83Q3KaQo5fmtpWN7JoDbfi8rUiSikXNofn6iYT7SGZSiDmR3Xt24vL46O3vJZszCI9HaGsOMNDXx/Ezx0lnM/QP9NLT3U0sFiOdTBMZjRCORtDcDkLhUXJZHYtFxuW2gZjG72/hrXfvQ1W8uJ0tHDv5BMlkhn379hGPRenr6aK1uQWHZiPYH6K9YyetrQE2b+6gr6+PI8cOoWoasWSK3v4BvH4fjYFmXnz2WWTZQtYQMQwRr8+Nqjk5dvI4kijj9XkAcDrdBAIBPB4Pnds7EEWJXNagr6+XTDpDPBrF76+jsdFPNpNFJ7syqUuSEWUJzaZx5PgRItEIEgbR0CjtrS0oEtgUBVVSUG0q6UyW/uDKJkZujxNJlhANGVWxYegimtNBJDJOLBZFlHIcfOVhvvLP3+Vnjxzkh9//R772wMfYvreOP/nQh+jvDfOznz2C6oijaSoOzUZ/fx+KonDk1BH6+/qQJRHVptDZ0YlT03BqrSSjOuFYENkiI0k2RMONLGkEY2foHz2GrqtkszbCkTCZtM7m9naaA23IFhGvx0edvxGX042IRHdPF+Oj43jrfNT5fXhdTprbGsECkWSMUHCUOr+Pvr4eFFnBoSrkdJ1sNofL40aSZVxuDy0tbbg9Hnq6ujl06FlOHjvOaHAAh0PD5dRwOlQMPYuuGxjGytKHIhKhUIhYNAYGyPLKGyyMlZW5nE43msOFxWLB5/UjSRKqopLJZhgYGMbt86KoltW/k0klU6RTWWSLBYfTSTwZJRwOr/S4yDJej5dUPM5wcABD16/oc7oIGPk8mqYQT4ZIJ7PEIxlkxUY6GV3ZaMztxu1RcblcuJ11DAyM8uIrx7j1wG1EonFUmwuvx0fOSBELR1AtGk6XE1GMo+sWDh/q40tf+hJd3V1s66wmGhvlyLFnaWl1cezIYfr6+mhs99PS7CcWyyHiweNsxtB1ckYKmwOikRCSqKBITgzDYDwU4tChJ7n3T95NKhXnK1/5El2nTvLNb9/P7u0fwOuTiSf7SSbjZJMaRs6gP9iN1+Omvy/GzXe8g1h8lPb2AOFwDD2rcPzUSZpbfHjcGumsjk3x0tHRgSRnScfi9Jzpx+f10+jzoirg0FT6+0ZxOrw0t7eRyaXJGSu9prJFwaJkCUf6iUVEVJsPp+YgFgsiI6PrEhbFRjqbJqdHsKg2sikdw5CRLDI6KUSLiqJqODQNSRKRLRLjo6O0BGTCoTDJZJK6OjcPfP+7eDw+jJwFp+YhGkvT0gw/e+wRegfO4Pc3rywpXOcjk0kRDkUJDoyyb+8+orEwHrcXVZXpHxjAqTnJ6VlcjhYi8Rhup4Kq2OjpCRKoa6Gra4COzj1EQmEi0TSyKrN953b6+3tJJ2M4nDZEA7r7etEcDkRRJhaJIorQ2bkTnTjxlIHH6UZWDEKhYQ4eOkgkHOWO227Docm8eOgUN998K62tAfr6unFpfh784TdWNnwzZFLpGCIKkUgYp9tDOp3B5XKQzmQQjRzJZJx4PImiWjCQMLJZXE43O3fuXFleVmLleSSKSJJMLBbH6/GhqAbx1AB3vfU2YtE4Bw8exOutQ9Nc+P11ZDIrk5HD4TDZTBa/30c4Mk42k+Pmm2/D5bbh8XhIJ8FX10IkOk5joA6X00M2A05Nw+d3kjPSWGQHquIiOBBEUSVS6Qi9vV0YGZFkMk33mS4cLo32tk48njrC0RCR6DiSRURVNFpb2gmHI2QzOdwuD2IOXB4PsiwiyiK6nsXn85JJp1cXhABNc+Fx+0jG46SyKdBzZLLplV5tWcYiK3i9XhRF4eSpI3i9dSRTSdKpNLquE4mGkEQZzamhyDJkM6QzWTKZDM3NLWSTafYfuJlDBw/idvvI5XQymSwOh4IoW8gkV35LOV02xsfDRKNRxsOjeL1enE4HsmwhHo+AqOPUnKvXR8QwdPScSCy2sqy7LMvkciurRabTabK5NCDh9XiRLTKaw8fJUyfxuF2kM0ncbjetrc3k9CyKqiJLIqJoYLGouN11aJpGOBImlzVwud2oqoosyYRDEbK5ld6gX5ffiZ6Pr379K592VfgwW1QmxwexKiXEo8tEpyfo6zvLhnY/msNDZ8eNgIVv/MsX8Pm9tDfv4LmDr+Lxualyy0xFhlhKibi9tUyMTrCUm6NxncLoyBDvvOddPPXsj9nQcg3/84tf56mnXmRxaZmx0WGWcstEIkm2b92FIJbRvraTy/3D9F44Q8+ls4ikmZqYYGhgBEuJmem5CLORabyeSu666w7WNbfw5rfczac++qfU1Dexc8dOtnZ28Nm//wyZbBaXq4lTJ4ZY1nM0Nq3h4CuHeO/73s3Z82eYT8bI61nKrFZisSn+5I/vxVlpo+d8NxvWr6Ohvo7Xjr3GiWPHKeQXaV4XQDAVCI4MUsDC2HicXbtvwOIQOHniJPVrWyg1szJvxGwiNDGK31/LdCRJVVU1Bb1Ad88FRJNEmWpHtloo18poDDQjChYU1UG9v5b17a1UlNmw2sw4NBkjv8BMbBqLWSevp0nOzbKwkEItK+Ff7v8yzc1rGRsdwyRZGBoMkdd1RsYu85OfPMBdd9zC8rKOzeZifi6LKAjU+st569030zdwnsHgRUyCnfDkDBNjUVSbmcZmO5OTSWp9dQwM9hKdnaO+pg6X04u/qYnLly+zpC+wfdsGtu/axPDQCLOJCNfvvZb+YD+lJRZefvllrr1mF2MjQQqFAhVaBV/96teob3ABJvI5mamZfupr12Mrq2RLx0bi0QwHDz6HqqpUez3MxmdY1tMsLYk889STGIUcl3vGsWtlNDU14K2vwa6o2DSFkYEhJiITWC0SCwsLdHaszPW5NDCASYR9N+7l2Gsvk11cwGZXwJAokCcYDOKv8xOJTHPj/j0sLqVxu11YSlQe+enjbN60g5vefBujgyHS6SylpaXkcjk2bFhHo7+B5194mj037MPf0Exkepo1VXXYbVV4fLWYJAFBkqjz1VOuOamu9jAxPsFSLodACa+/fg67zc7a5kaq3FWoahklJXnQHWzetIN9B3awrsXPbCzOkSPHsdvW0LltE2vc5Wxq38FA/xgdHZ3U1tbQ3LQO/9oGxkPjnDt9juoaN5JoxVZWCch4vTbc1WtITmf5/gM/IL6QQCzMkF1OU+tpxCSYyS4sYHNYGRmeIJtdItDUwuxsguh0hFQqg0OzceDAmyhTy3n9+HHGRsepbWggHIqytrkZoSDgqfFxofc873nHexgYGkQQCsRTcdKpOexlDi72nSe3nMXlclFZWUFH51Z27txONpthaXkJCibMZjOqVUEptVMiKyxl8xSQqXA6KRgCet7ALAuUlFhZyCyQXkjicrkwm0oYGrnM+XPneerpx6mudiOKAguZNC6XRmVlJbK5BETQjWX0jI5RAKtaxtjIGOWOSirLKxgIDlIo5EnNp9nYvpGmpnpCoTCvv36i2PNxBfm7v/3kpz/wkTvoOnWGdU23UeGqRquwcOilb+JwZVjftJeecxNUuDR+/viXufWW9xBobGUyMs7lwX7u++hfcTE4yOXh87Q1N5G8nKRynZ0Db6nlG198Do/jZrZd04KYq2D7tht58ulHWVNVjrXiAsvLOfr6Rti0xc2Fy2dwSjcwGsrT3LSZqZlRTJYu9u27hn/+X1/immtvYSa5RMC3ncaGGj7wJ7fz7e98mQ986I/4zle/ytr6ag4++Rx/9rEjPPTzD1Lb5KTn6BQb6++hbsM8JWVL/OjhZxEX1mFx+UkuPsiea/fy+E8uE49r/OTnH2Ui3MNIcA5/1U7+4A/v5qvf+gaXg4fpaN9GuakDj0dmPNyPntcp5K04nCZicZ2ZmQTh2SFSsUtsXHcNC/kUkdgwgQYXo4OVbNmyhczSKC8++1Nu6TzA3LIFbY2d5fw0TpfC+bNhWJZxrqlEts4z0HuC+g0bKZEcpBML1NbXIsgX+cWjPdy0//38wxf+GzfsvZ1nnv4BG1rWMxoew+3103NuFslUzrr125GESbL6NNaSCja372R4NMlCJsWNb6rD31BD19kBtm26leHgLMlEjKlYLx/809vpGzjCuddq2b1jL4NDB7nu+j289tJZfDU3YXXInOs9wp7rdzGbnKe2ppxEfJZnDx6ktNRESYlAoLWZNc4Kyq1lZLIm7KqVM+cHCY0mueU2jYnL9dx0024+9amP8Rcf+yyPP/FNFHUNhrjArbeuZf8Nn+d73/wMk7E+IlMx5qY1BscfZ0PbNQwFo1S6FmlovI4ffv97vP2dd/H88wdpDWxmJhrCQMBRXoKtpIzuC6dobgxw/Y138/73vY93vONOZhPTCOSZngnT6G9mKDjOR//8owSHh3n0F99iw1YnFVItdQ0BampqWVrWGewJotjLcLurGBgI4tZcpOMKPRef4hN/ex/joTkq7BolpTozM1HcrvUMX55gYOQggyMhKisClFkd2LQCoriEr8bPww+9iNsZIC/OYFULhGZDTIZGWYrJxJJpRGkR8svkKWV0OM3o2CR1/hJqG610NN/OZz/3eZoCTdT6/Tz/wusk59JUrbFxse8UDY1ViCUWZF3h6NGjyFoWpaSOidFxFFsAe0UZNTVzTM8IeD0VZFMpqhSF8loPsXiEyGSczGKSsfAlzKKd2WkdxSogmJIMjy+wnJjF7fHgqixndHyM3Xuu53sPfI/1LZs48tpr3HHLHYRnp8imDGYTY3jcUKldy9TUEJ0722heH+D1lxJgipFKhyiz29GXs7Q2r2MunSCf17FZNfS8wUJ2HpMko2nVnDzWg8fjxKGVMZ+ap2AsMb+QxqlVsbQsIEkmBvqDmM0y69YFmE9HGZmYoN5bT3//IAvpNOUumUIuhcNWTom1gpGhEInULGWlNkpKzIgmE0Ojw5Sarfhq/cynYkxMTHDu3LlfS6OEQqHw23hm/+dOCMIMsADErrQvVxAnV3f8UMzB1R4/FHPw/xZ/baFQqPxtOVPk/0QQhHng8pX24wpTvEeL8V/N8UMxB/9Z/L+WRv1OND4ABEE4UygUOq60H1eKqz1+KObgao8fijm42uP/Xad4fYo5KMZ/dccPxRz8V8T/OzHno0iRIkWKFClSpEiRIm98io2PIkWKFClSpEiRIkWK/Fb4XWp8XO2TKK/2+KGYg6s9fijm4GqP/3ed4vUp5qAYf5GrPQf/v+P/nZnzUaRIkSJFihQpUqRIkTc2v0s9H0WKFClSpEiRIkWKFHkDU2x8FClSpEiRIkWKFClS5LfCFW98CIJwsyAIlwVBCAqC8PEr7c9vCkEQHhAEISoIQu8v2TRBEF4QBGFw9Vy+ahcEQfjaak56BEHYfOU8/69BEIQaQRBeFgThkiAIFwVB+Oiq/arIgSAIFkEQTgmCcH41/s+s2usFQXh9Nf6fCoIgr9pLVsvB1e/rrqT//5UIgmASBOGcIAhPr5avmhwIgjAqCMIFQRC6BUE4s2q7Ku6B33euBq0q6tTVrVNQ1Kp/p6hTv1mduqKND0EQTMC/ALcALcA9giC0XEmffoN8H7j5/7J9HHipUCg0AS+tlmElH02rx73Av/6WfPxNogN/WSgU1gHbgT9bvdZXSw6WgL2FQmEj0A7cLAjCduCfgC+vxp8A3r9a//1AolAoNAJfXq33RuGjwKVfKl9tObi+UCi0/9I66VfLPfB7y1WkVd+nqFNXs05BUav+naJO/SZ1qlAoXLED2AE8/0vlvwb++kr69BuOtw7o/aXyZaBq9XMVcHn187eAe35VvTfKAfwCuPFqzAGgAF3ANlZ2CZVW7f9xPwDPAztWP0ur9YQr7ft/Qeze1QfXXuBpQLiacgCMAs7/y3bV3QO/b8fVpFVFnfo/cnHV6tRqPFelVhV16jevU1d62FU1MPFL5dCq7WphTaFQmAJYPbtW7W/ovKx2S24CXucqysFqN243EAVeAIaAZKFQ0Fer/HKM/xH/6vdzQMVv1+PfCF8B/jtgrJYruLpyUAAOCYJwVhCEe1dtV8098HvM1Xwtrsr/z6tVp6CoVRR16jeuU9J/obP/XxB+ha249u8bOC+CIKjAz4GPFQqFlCD8qlBXqv4K2+91DgqFQh5oFwTBATwOrPtV1VbPb7j4BUG4FYgWCoWzgiDs+Xfzr6j6hs0BcE2hUAgLguACXhAEof8/qftGjP/3leK1+H/yhs3J1axTcHVrVVGngN+CTl3pno8QUPNLZS8QvkK+XAmmBUGoAlg9R1ftb8i8CIJgZuWB/lChUHhs1XxV5QCgUCgkgVdYGVPsEAThf7N339FRVfvj9997ZpJJJr33QBISehIIhA7Se1F6BxUQEBFQFATpTUWpgkoTQem9d6SX0CG0hFRCek8mdT9/zPDcXL7gxXsF7/1xXmvNSuaUffY+mezP2eWcedoJULaM/3/5jettgLTXm9O/XKahXtAAACAASURBVAOgkxAiCtiAYUh7AW/QOZBSPjb+TMIQ1EN5A/8H/ge9yX+LN+rzqcSpf3hDY5USp15DnPq7Gx+XAH/jUwRMgV7Arr85T6/TLmCg8feBGOaXPl0+wPgUgbpA5tPhrv9VwtB1tBIIl1J+W2bVG3EOhBBOxl4khBDmQAsMN7MdB7oZN3u2/E/PSzfgmDROqPxfJaWcIKX0lFKWx/C/fkxK2Zc35BwIISyEEFZPfwdaAbd4Q/4H/se9ybHqjfl8vulxCpRYpcSp1xSn/gtubGkH3Mcwp/CLvzs/r7CcvwEJQBGGluJ7GOYFHgUeGH/aG7cVGJ6sEgHcBGr93fn/C8rfEMNQ3A3gmvHV7k05B0AgcNVY/lvAl8blvsBF4CGwGdAal5sZ3z80rvf9u8vwF5+Pt4A9b9I5MJbzuvF1+2l996b8D/yvv96EWKXEqTc7ThnLpMSqf5wLJU69ojgljDsrFAqFQqFQKBQKxSv1d0+7UigUCoVCoVAoFG8IpfGhUCgUCoVCoVAoXgul8aFQKBQKhUKhUCheC6XxoVAoFAqFQqFQKF4LpfGhUCgUCoVCoVAoXgul8aFQKBQKhUKhUCheC6XxoVAoFAqFQqFQKF4LpfGhUCgUCoVCoVAoXgul8aFQKBQKhUKhUCheC6XxoVAoFAqFQqFQKF4LpfGhUCgUCoVCoVAoXgul8aFQKBQKhUKhUCheC6XxoVAoFAqFQqFQKF4LpfGhUCgUCoVCoVAoXgul8aFQKBQKhUKhUCheC6XxoVAoFAqFQqFQKF4LpfGhUCgUCoVCoVAoXgul8aFQKBQKhUKhUCheC6XxofhLCCGWCyEm/935KEsIESWEaPEK05dCiAqvKv3XQQhxQgjx/uveV6FQKP6bCSEmCiFW/IdplDfGCc1fla9n0n9LCBH3KtJ+nf6TWPr/Qhx+EymNjzfQ8y7KhRCDhBCn/900pZQfSCln/Oe5e76/uhIXQqwRQhQKIbKNr1tCiDlCCJv/IL2Zf0XeXuJY7wkh7hrznSiE2CuEsHrd+VAoFIrXTQhxUAgx/TnLOwshnvw7MeJ5F/FSytlSylfauWKMxfnGujxDCHFWCPGBEOLfujZ71R1uZY5jKoSYL4SIE0LkCCEeCSG+e935UPzvUhofiv+YEEL9d+fh3/SVlNIKcAIGA3WBM0IIi783Wy8mhGgCzAZ6G/NeGdj09+ZKoVAoXps1QH8hhHhmeX9gvZSy+M8k9qpGJf6Ejsa6vBwwF/gMWPn3ZulfmgDUAkIBK6ApcPVvzZHif4rS+FA8lxCisnFaTYYQ4rYQolOZdWuEEMuEEPuEELlA07I97kKI3cbekKevUiHEIOO6+kKIS0KITOPP+mXSPSGEmCGEOGPsCTokhHA0rv7d+DPDmGY9IYSfEOKYECJVCJEihFgvhLD9s2WVUuqllJeAToADhobI0zy9K4QIF0KkG3vcyj3nXA0F+gLjjXnbbVz+uRAiwliWO0KIt8vsU0EIcdJ4HlKEEBtfMru1gXNSyqvGvKdJKX+WUmY/Lx9CiE+FEFufye9iIcSC5yX+R+UVQrQ0jrhkCiGWAM8Gf4VCoXjVdgD2QKOnC4QQdkAHYK3xvVYI8Y0QIsY4OrxcCGFuXPeWscf+MyHEE+A3YD/gXiZmuQshpgoh1pU5RkPjyESGECK2TExrL4S4KoTIMi6f+u8USkqZKaXcBfQEBgohqv2rspQlhPgF8Aaext/xxuWbhWFEKFMI8bsQomqZfdoZY1O2ECJeCPHJS2a3NrBdSvlYGkRJKZ+e+/+TD2EYnR/1TH5vCCG6PKccf1heY0xLEEI8FkK8+5L5VfyXURofiv9DCGEC7AYOAc7AKGC9EKJimc36ALMw9Hr803QtKWVHKaWllNIS6AY8AY4KIeyBvcAiDBf53wJ7hRAOz6Q72HhcU+BpZdjY+NPWmPY5DBe/cwB3DCMAXsDUf7fcUsps4DDGoGasGCcC72AYHTmFIVA9u9+PwHoMIymWUsqOxlURxrRsgGnAOiGEm3HdDAzn1w7wBBY/TU8IsUcI8fkLsnkBaC2EmCaEaCCE0P6LfKwD2jxtlAlDL19P4JdnE/6j8hobgVuBSYCjsWwNXpBHhUKheCWklPkYRnsHlFncA7grpbxufD8PCACCgQqAB/Blme1dMTRgyhnTaQs8fhq3pJSPyx5TCOGNoYGyGEPdGAxcM67ONaZhC7QHhj/vovpPlO8iEMc/Glf/qixP9+sPxGAYSbGUUn5lXLUf8McQU69giBFPrQSGGUdeqgHHypQ5QwjR8AXZPA+MFUKMEEJUF+Ifo1AvyMfPQL8yaQcZy7HvOWm/sLxCiDYYrglaGsukTO36H6U0Pt5cO4yVS4YQIgP4vsy6uoAlMFdKWSilPAbsAXqX2WanlPKMlLJUSql/3gGEEAEYeqJ6SiljMVTMD6SUv0gpi6WUvwF3gY5ldlstpbxfJsAEv6gAUsqHUsrDUsoCKWUyhsZMkz97Ip7xGENQAhgGzJFShhuH8mcDwc8b/XhB/jYbe4ZKpZQbgQcYhqkBijAEPnfjyMvpMvt1kFLOfUGapzA0DmpiaMilCiG+FS+Y+ialTMAwatTduKgNkCKlDHvO5n9U3nbAHSnlFillEbAAQ6NSoVAoXrefge5lesQHGJdhvBAeAowxjgxnY6jLepXZvxSYYowd+S9xvL7AESnlb1LKIillqpTyGoCU8oSU8qaxnr+BocPmL4lDL1mWPySlXCWlzJZSFmDonAsS/7i3sQioIoSwllKmSymvlNnPtmxcesYcDI2EvsBlIF4IMfAPsrET8BdC+Bvf9wc2SikLy270EuXtgeEa4ZaUMpf/oLNR8fdSGh9vri7GysVWSmkLjCizzh2IlVKWllkWjaEH4qnYP0rcWLntBCYbL5ifphv9zKbPplv2gjYPQyPoRcdwFkJsMA4XZ2Ho5Xd80fYvyQNIM/5eDlhYpoGWhmG0xeNFOz+TvwFCiGtl9q9WJn/jjWldFIZpbS89fCyl3G8c1bAHOgODgD+6MbJsr1M/njPqYfRH5XWnzN9cSin5F58BhUKheBWMF8XJQGchhC+GaUC/Glc7ATogrExddsC4/KnkF3WavYAXhtHe/0MIUUcIcVwIkSyEyAQ+4K+LQy9TlhcSQqiFEHOFYfpvFhBlXPU0f10xdCxFC8M04Hovk66UskRKuVRK2QDDiM8sYJUQovILti/A0JnYTxhupu/N8+PQvyrvP8Uh/u/1hOJ/hNL4UDzPY8BL/PMTN7yB+DLv5Yt2Nu73K3BcSvnDM+k+O2rwbLov8rzjzTEuD5RSWmO4sP6370MQQlhiGMZ92liKxTAkbVvmZS6lPPuv8mccLfgJ+BBwMDbwbj3Nn5TyiZRyiJTSHcOIw/fiTz4u0NjTdhTDUHm15+XDaAcQaJxD3IF/HnYv64/Km4AhAD8tnyj7XqFQKF6ztRhGPPoDh6SUicblKUA+ULVMPWZjnAb81LP15AvjmVEs4PeCdb8CuwAvKaUNsJz/LA7VxtD4OM3LlaWsZ8vRB0MHVQsM03/LPz0MgJTykpSyM4YpWTv4Nx5eIqXMl1IuBdKBKi/IBxg6wfoCzYE8aZg6/ax/Vd5/ikMYrh8U/4OUxofieS5gmMc6XghhIoR4C8PUqA0vuf8swAIY/czyfUCAEKKPEEIjhOiJobLa8xJpJmMYKvcts8wKyMFwE7oH8OlL5u+fGG9wC8FQ+aYDq42rlgMTnt6gJ4SwEUJ0f0Eyic/kzQJDBZxs3Hcw/2ggIIToLoTwNL5NN25b8hJ57SyE6CWEsBMGoRiG+M+/IB8Ye/i2YAiSF6WUMS9I/o/KuxeoKoR4x3jfyEcY5k0rFArF32EthovqIRinXIGhUwZDx893QghnACGEhxCi9R+klQg4iBc/an090EII0cMYuxyEEE+nBFsBaVJKvbE+7vPvFEYIYS2E6IAhzq57OpXrT5bl2frfCigAUjGMKMwuczxTIURfIYSNcSptFi8Rg4z7fiwMN+6bG8/HQOOxnj7x6nlx6ByGGD6fF4y+v0R5NwGDhBBVhBA6YMrL5Ffx30dpfCj+D+M8zE4YbsJLwXA/yAAp5d2XTKI3hvtG0sU/nh7SV0qZiqHnfRyGynA80EFKmfISecrD0Kg5YxyOrYvhJu6aQCaGi+Ntf6acGBpX2RiGt9cCYUB941xSpJTbMcxr3WAcsr6F4Zw8z0oMc2czhBA7pJR3MFSy5zBUxNWBM2W2rw1cEELkYOg1Gy2lfAQghNgvhJj4guOkYwi2DzAEi3XA11LKp6MZ/5SPMvv9bMzDi6Zc/WF5jX+j7hgeBZmK4Wa/My9ISqFQKF4pKWUUcBZDR8+uZ1Z/BjwEzhvrsiNARV7AGNt+AyKNdaf7M+tjMExPGochXlwDgoyrRwDTjbHkS/786MFu476xwBcY7l0cXGb9nynLHGCSsQyfYIhr0RhmF9zhH51UT/UHoozpfsA/3xSeI4RoxPPlY4hvTzBcI4wEukopI1+Qj6fWYohD63ixF5ZXSrkfw/2Gx4zbHHtRIor/bsIwdVuhUPy/TBie1nIXcJVSZv3d+VEoFArFm0UIMQAYKqV80VO0FG8IZeRDofh/nPEenLHABqXhoVAoFIrXzThNagTw49+dF8Xf75U1PoQQbYQQ94QQD8WLv7NAoVC8QsLwbe1ZGJ6LrsyPVSjKUOKUQvHqGe/ZSMYwBfnXf7G54g3wSqZdCcN3DtzHcMETB1wCehvnwSsUCoVC8bdS4pRCoVD8PV7VyEco8FBKGWm8eXkDhse9KRQKhULx30CJUwqFQvE30LyidD345y+CiQPqlN1ACDEUGApgqjUPqR5gRkySD95mAvIfE6PJI8fEFctsiUpKXB20PMqIxbykAmamaajJwCZNku+oJV9rj6PQQUE2qZlxlJSzpjg/gzytHbrMbIRjCdpYU+xtLQjL1WNpZYlTZhLFoogSlSV2qkIySvIpcANhAq75lkRG6yl2ccMzxxa9NCU/K4x8aQJqV/QaNdJcD6VmeOWDtUMmMcKBgAKBvjQPra0rIjsRMtPJ0HljU5CHsDRFljwgzwqy8l0pyS9CZ6Ehs9AGkh7j5l+RvOIctNlZaPK1aD2KuRoRh6ubDpGjw9yigHxLb0yzUqBAYqIxQa/NQF1gQoF9LvmlRdjE6bBQmaNxMEWfnUemsxuWaXEkmJiillnkZZWnuCADb7sSNAi0xRoiVeZYykTcS/Uku1mRpLenXHIKZo6m5AjIs1Ojy9YQm2WKObkEOHkSl5RLSXE65e0KUUVkk1DTAZlRhE1WIZZFgkxLe9JszPGKSyfVOoPiXFueFFviVpSOu3cFEotiicssxVdVRHG6BameErfiJwi9jhzTQrKtzLFJssSkJJcUa1v87K3QmJgZPkh5MSRGJuNm7waoAS3u7qYUJaaSmpeFKNKTlg3q8mZY5OpwlD5obeC6LVSkgJSbt9Ca2SB0mTjgSpG6iFSNGuuMEnKLLSksUmGvySVVnYKtrSMFqiQoUqFSq8jW69Co9ZSIQpLVztiV5FFkbkGOVmKXmYejxg99QQ7FWXEU2puTqtcSWJCIiY0N98wKMSm0xTO3EFOtHjJMyXQtwqbAATQ6kkoysFPreJyZiM7WifSCDFxNIU2kY28Gabk2kOuEZ1EWaZbFWJeqyCxWY6rNRW1qgS67CI1JHndNbXAuKsZOlQ8m5ghVCenJOWTZuuOapydRnwputmCiR1+sxi4/C41GR4LeDLt0DS6WEEs6KcWOoNUQUmRJuIsO22wo0OhJVWkIiY4j20GFlVoSlpmHh6OOtKJ0HHRulOSWoEGLtjQNrXkh9+1s0CUU4EQeartCLHN1pKTpcHSFK2ot1cglI0eFvcxDI50pKLEiRZWFoy6FTE0+sTZOWAoVZil52GBFfq4JWm0JuaYC85JEclXlMBcxpOsK8E6yRW+Vj0leKZnmauxyrAnPK8HZWkORiRUarRne1imQp4F0c7AyJz8hnjs5qWiBAmGGlZmOAAcfSM5Cb59CnsYcCk0oNFOTnQt+TqaotHmQmUlYSgVCzB6TlJxALODoAPnSjJLyNuiENT4JGTzRFGCZWUiJvTdWsYlkuVphWaylUGaTqy5B2KUSrbfDuUBgq88n3zMfy2hzcq1KwEKFZZaeJ+Y60rLscS7Sk5SekiKlfKkvGFP8S/8yTsEzscpMF+JjryUr14xCdSnSzJSiEg3CtBStKo3kUnecS7IpUOXiVmBOppUgO90BSSE2ugQyzO1w05sQlZeNc3Ex2eb2lEtPI9LPlCrhJdzzc8MqKg07V2vuOMZhlWOJR56WhIw0CinE19uMrBgP8kugUJuKj4s7+clhpLg4YpGZQqxdCNYpd8lSaQlQQyZaikwLsVLrKdZ7IIv1uFlnERbvR4hrLHFFeqRwo0hbiBAlWEdlk2LlS3GGHifv25jlCPKEHa5mPkTaQfaNMFxCQihNLEYd9wCrSi48ThHYF5Rg4pbDkxQtAj1mrhpKzL1Qxcfibu5Fluld1PfySHFzpkSnQ5Vnj7ltFDZF5Sl+GEacOdjZeVOQHofa3g+r0nhQ+WGizSA/Pxu7AjOKrJK5X+pLSWkhvrl6Sqz0JJo642AKDg+jeeDiiLuJFWkqS7Kj7+Jc3g8nbT4FsWmkiAy8NTaElffB/P4VSnTWmGozcU5zIxJ3PMz1ZNvlYmuVi1O8ICwjBQdzJ8qnWxIWUIS9UFNQmIFXkQ2iJIlE03KUmmRhnpuDWu2BS340BWkl3PLxJbg0h4L8UhIS4zB3czN+ikxQqQuQWk/cCvQkupgRFxaGhxrSHRyxLrXGw6kIsmJBDWHUwClHRbKrHqe0NFxUuWSqSsmydMDnrjUJXjcBW2RsFl4OjoS5ehFy+zrXgrzwfJJBtrU5tg+SsQtxJyw7jer37XkYkkvF+wWk6HSoLTVEm7jilxeHKl1FfIk95W3vc8fEC12OHTqzZJwskykozCc335bcPEvKeVtSmFmCPiuTRPTg5IFtdjqUQp6pDS4yjccOApssiUOcBfnuUaQX22KdU4hepyXTwhGbojQ8k3PJsHUiEw0ehclE2XtTqtOgzszFqSQJE5UHqY8lqXb5BKmTCbMJwDqjAF/9XSI8K5JdWEBQUTKazCKyTGxJERp8s1SEVbbHLjOP8hEJXPV0p0JcFA9dHAhxT+f2tQpY14xEn+mFf/oj4j39yEwuplJmEioLU8IcXbBPTkOdBzpbgdYxg6isAKpr7xGW6EFFi0Qs9YIItRd+tg/JSzUhXOVDSGkq5D/mio870tSJyg/j0atSkM4hmCdHEiuscbeFJ1npSGFPtpcgMDmaFNNylJjGkJhdgZDCaMJybMG2BAeVBptSyHdQodNLItS2hJibkxTzGGdVArluAdxNtILEh6gdtZToTDExkRSZqqiUlo6ZyoLwbCucbJLJs7fFJy2OmFQXPB0kIl/Hk8wnuHnlkllQnoc5JVS0SidN60VqSSE+8Y+JqOSDU1oq6aam2OUV4KxzITYjFUfLQuKK0jHXVoAMM1SeyTiKx2RnuZMlnfAQ2ZiYpFNQ6ExE/L2XilGvatpVd6C1lPJ94/v+QKiUctTztq9Vy0OGudzjt27nmBqtoVx3PRUup7I6vCv2NcxpuOwrhg3xo/+VQMyDwim+25imR21Z/dUtBlSPwdysHSab96LPusZxb0dcd56koVsLIkw0VBMNmZpayqKchVwaoCbtrXTM1vsRWCmIeeP6sH3afHrt15O9pRIjm8Vw03cnHTOfENa5DS53BjPfRI9ZxXx6d0nGvLEDS+udBUc99G0I+PIjHlyNh2WuBYhdWuLeBs+u+2jo0Y7bQdD1WDLhVc9xJtuTXet/4G73ysjMcniOucsjWZPdPruwiTrAoapjabr+Go+cmhDVygK6LGZssI6lp/cyamgTGmV25dc11+nf4n0u1StFExTC5A2DsTK1IjtTTzPzodSSUaQ87sfOIzBo4iomvDOYdrtW4fQwCiIb45bZEs/aDem0czpxg3bxqL4Wy/RHvLfnS97NfcKqwlCcHPQEFfxKjvl71Nh/icTaTan5zilyiw+SF3aVyOC9fBYMC8Pi2XzmB5jzIXCCjvMy6OG3lRsWYThOTmGlP6R+OI9ea30R/c+h+dGX00E7+fT9w6w98it7u8ZCTC5NnsRhG67G4XQrbntc4pGrM8HZ5anr+YjYOyasnvEx/DwW/E2hwTwApm6cjre/B0Vm79KzMth6dWPVwa84mngc3YFy/NSqOsOauCB+rs2y91oDlqxc0RzNkFDuuU+hxcVfCU98wONtvYjJyCHbM4jrRyKpVLEKljMkW+7dZ9LvYyHyGmletwhMf8AHJZYEefjQ+NgDFrt9zrJbSxjesDduR6uQkODBO31Alqxjp208w6SK7SlW/KCdz7hIwcOMrkzKeZtqIWbYebTAJz+VhMRdlLdKY8/YXO45xrMwZjXrj9ehn/dYBtzbSMKDDFJzt+BUeyQPb6jIatqF9nFnUMXqqVthCNPT9cx12sdVv4r04ndMHkGwvwdjG5nSNDmB9hcKqRBan8j7MXwUUI0PTIpxtDHDuVVX3p7yAdZmeRyyrsnMA+lEJm7AqsNKPreMpuGqr2kZ34ovFzUDT2tamrrR9Xwq13O2siznIYsCG/F1hVRsTiehq96KqGXnaZhZlautGmFaOpriWnbUbngKn8BmPJhykZjxzthVqI/LjQOUdu1HWuF5fnmQyvh+jcmY2Qz7fcnMbXKVrX3fIeHhIExVj1iie5s2siP9bLZRuak9Tb4poEs1Jzw3baWg1h6SWUhK3mN81IN4+LAr94auJDT8LKHh5/jJsjYnt6RDaBHvixV413ybG5pKfBhUn2TrMyy0GcJpU0eoCbV+7E0/zR48DvvSPegUTOpJTVNb7E+VUhq6kdzRN7lQ7RF7ijZjla3h9nurscrMpf89C0LPQhP3j/jx9yZkZu6APv7wbSlYhIBei8fqVsQn7WfIjAsUDCqh2eNQBnkGQ34C3fWPqfw4hsPlSnn32GluhnizXxVKQGUTsh8lUt0rHLmjCfmpyeS2LcXFNZrPKsKX2x+xasqWMCllrb+80n4D/dk4BeDh7S4fl0Ry/oMlfLLzU96pVptYt5XYhp/DZNR1MmtNJLtfSyxD7tAzQnC8TXVUC/YybZaGTfd+xaR6ddK3H6J/xjaGzfyC2i2WUeo9kcujz/HdhgTe1o5ng0wn2Nee0E/O03H0FA47/cLdI5fZlPUt62cLWkXZ8sPeu8S3g3LqCBoE/kg3fSuG753J8nYVcNvoRsN21Wny5DjxhZIAVTmCigp47POQDud+oOBuQ7TR65ADWnDgbAJ7WsSytJsnXx96l9YHHQnMuU03nS16Dys8nEPp7HaV8KzFhF1bzOcTDjO7wmAq//I2bgN60mr3dcp3rIg8uZNp1wPJLQklvEoL2twayshhWcxZ/Q0hTsmU5D1kz0gvvlf/zHfeN0nr4YjLlLnUuTGVtQvGsjfpO+Lf2Yv3zvY0rWCGa+kivhRNuV/3IHvvXuBQXEvGBA4hVu/F/qzOZGzpy7jy/UlfPpTtWZV5V9+K3aOX4rm+CTbXVWj7rOGD+nVgXVtmuFaivnsczfrVQnxxAfZNImz3Hb7pk0HerY/4YXId7jRwo9mna4l1molnUgMGf7KGNbbw1fmviD2Xw7EMS+blfYn9+W580ceOD7+34J01EYhD72JVuQ0Pjs7FZdcK1m5YTuTUFKZN6017RlNrih+QwNSps7lxWs3oCSMZ6BPAhKE7yPjBnfetE1l8Lw3R7hJybHM6PZpOd9+GDGjXGModQOVhzopmp6i7ry/vVe9NfPI8Lnx3hF+XDqfi5Yc8Wi2o3O1LWnR3Rn63gTat2nPg698wudmdQ90cWTTCh1Pq9+l6fTAqnWDWzuZoaz6imb0V5g7rOWG7FdFRUrnjPsL7t+eHresIj+lLs/pLOVv5UyZE69hzpTupNiPYMrk6jXYEkL6rEfHvdqTu2SV8HnUUt8nZfPDwKm3kcRZXyyH7WE+Gb95JnT4zqHj1W361Gcf5Kh/jd8maGa0gPM6edYdj6NTakoLUFKo4NCe5WNJzexEbKoxiSpNIftmlp/3QG/RaOpohXSdSL6I+jjt8GTr+Bh/0vUS1A4N5b+UYGgVYs9/pY+Z4xbLTYgM1PPK5OrkfWUcHYXNiJNYnI3i/+Apv2x3h+zGVWfLrVuxn1uaL3p/yIPcB6gs5MP19mny7iequ6XS06M6XSRU4U2c18SM9qXbnJo07p3C9mjVfp/sxvsYCVrVeTI99H6Ez68e+cl14J2MNdg6j+ORSKUWn+zExz5zIKbG0uT2AOnnJ7G+/H9XRIFZn96VB1fHMvyDYVHElv761kbTlB3k/pS95dUM47+GFvztMezCXbUVNSI9tyictrGkWVsKeLX2h3hM8Fn2CxeavMVkGs68JrOZ1YbTvSG6ZZrOrYCqlK9pgO+AzapvX4kO770gd2ZkVJoL9N2DTqJ+5O2gA+6415Had6Uwb78a1tz9BePbnw+OxBGeFEqcZRUrNd0hKqY1baArBSYHsTZjC4evTqG7bmAE58zjsEEz9fUU49f6IUk03Lu01xX5ZMBHzcqnkG8SkyRVeKka9qsZHPWCqlLK18f0EACnlnOdt7+4SKFvfTaAo3hqvmw1xP9wJ3zw1l7r1JOPJCMJbViD/4FqamU7AqeoOcpdUh6qeFNSPxuH8LmbZVeRM6io6Z3gxJ3gaGTeOcaX+LNy8HdGozuNiGcbpWR8Q73+aNM02+j6K5kaiLVu6j6FNxGasI6rT4a2WPDmzFt+oK/xqM4G7ER9jUfE0QcXVmNmoEUmRD3Cee5hLulJCC1SsTYTI2ePx9v0cN06y9rNf+FbqWb4wg+4du7Ez2Y56JZH84uKMl/9EdtdyZsX48wAAIABJREFUxLmlL55evnx5uYDt9ddSahvP5nc28H2UL1vXR5FWdzAnppVnaovaDIgKY6aFAw0aDyXrRBJSBLMhZDqj4uqwrPlaks81oWbdYVRvsgT/mSPYecqUlhMisE1eweXOX3IjJo91EVuJn3EW6byAqXo91K5A1sM86rRYTaS/jgnLt7DVy49Zdj9SRReEyf2d5Kq24uO/l4TkAt6z2Mse22B+O7uYOHU9rpS/zTvvTiN6ywIuPCoCUysWejqguX0PVeFk9vZqTIf0KkzeGkZ6ri83Pb1YWEfFW3t1nPUP58LApjBwF17nKtFjenXioruw0LwpB1wT+SzwaxbdmcGq8t8iV6RjOyiD8CbN6L/0N/ZsP4km6nc+XrgTU5OaVIq5SHEFJ/yyYrhZms79Dz9EDB5Pswkt+fpsFtFzfVg3Oh+GN4AZYxGRgbDGlVXno4lpnsDtDa2ppbGndvoP5Ll0pHSLhsbaDdh8UwKrfmCs6QZc167luN1e6m0Ffd0kdBEnMa39mJOp2ZRLjCG3bSDpNrepvL4XX7jZIHLW0n50VbKv1cc//DwBZwbzbVFrQtt70b1pBxbn5vKD3MnxT11Alc88sxVU5gHfPPiFe7PuENsjiFmplfgt7UdWxB8h7kwo9qqLxJ0eS/8qcynOnorz+1W5mb6OGx4azO08qTbRCUenLxjSIwW/AUU0K1+OYz/k0bqCjrA8SDGHAXqo4gCXr20j8F4kiWc/5YOK46hmWo/s6FNEB4ezJjWEPI2KKVUasiU1nQ9v9+bH7WD+fTT92k7iG7cqzNyQgf0d0G9PoqCCNX2GtKDbB5lcad+Rg2s2cVAVyYUxKu6/OwNd8XBUF7xZd+NrtlRZg/nPXRjcDgYkjqfe+K/g0SnqxWh4oi2kfuI6fteN5fMaTbjp/wvqjBUsSaxKw4bbmeWSQJMiCCo/ElWN9gxrdYaI3bvYdDyFjl2dcSk3EqccMzod0NL17ab4FV/kdn4oDSvnEDPXEq/aYFL9Fpkut1hVqxf71oUzfkBl9AtGEBG2DDosw+mEhpq1b+FQs4D1FZZxZOYsjjW/ijYwhO2F1tzQf0DQJTUzm4YjnbbxaZ4lJd+c4XeLYMamrSJuwHICLrfA6/oCCt7+mNxFUTwKTSVL8wva/IH0iZuCS5O6jAwoZnq6PT4ZG1jy0QB6L7+C57GdfOGQwPCSFVyIzaDIO5B3EyPYGO3K7R4Sz9h30GsSESdcKWg7Em2Xliyt30VpfPxF/mycAnCx85MzQgayLLQF0rMB+Ud1FPnl8pF3E1yjXMh7/xxRaXdRtTwGs4poc2M5WfnvYjfmDMP9FjN+4nSqZaylZp6GfcGFnDr0FU86H6HxIw8+f3KPSZUWE3x4HDPnfsq9E3sZXimVyMdZHPeoxaDsdI4evcmUll3YpD3EfpP6mDxqxjxLN4iZS1jqPHppMxi5ATrt6MnwCRUY8eMY5u92YI75QM4F+VLRbQotV+wnZLYz08csJNPdlHZh5ZmXv57i1NaYqhfygezN/klRuMhWHDYrYWOzGSTu+ZVhR7YzsVoFLpzdSnaFuUxptIa3e+3G3XMYPbwOou1qQty1YWRefp/H05uwKyEU+v+IGKBjSNvm2PZ5QsDKWjRf5cS1ri05WGhL04+qk7XrEEOXf8ki0xjGqRowsWkrrBOyGVsjnPmdLLk3pgSPFFOWaeKp6mfNu+pJZN/3omOnkXgegVo1splSMZZQ68W8v2owwvIIP7S4hGupP5qFszncvw9Na62m7oKKuKkzWX94Hj0+HUeFqxWJz29JHbv3meA5iHUdj9IzK4OTR7ZjcvgLGtyYTaOFkUxwCWbZsUhWFLtxme7Mr9WbhUfWcs/iLRJPLKfJnRuM5xoZPjWwTIA6nw+j48czGDGyJv2DY9G2SmdYzi6Cuo9m08PPqGpxk+GHQtjb6gy/7ltJYdsT7F5ZlwuH3ubUpgvcXvElSb6Z/Fzblw1HPuLjhTOp0DCbUx+NInXpeKYfeY/FSz/m+qZhHJt9nDk/dSbX/kfSH7eg2KkV4xb257sj87AYcpJGgdWxLz3HBVGbpSKAATZP+GTzNLb3OUFGuCd99QF0q/k7jSK20r5WKpdNp/LJpK7kV6pE9d41Gfz4GjGF3qz5yIbUd7P4OXwtDzwtSe6mY/9hE2bl3uZg+Q1UN02mnmU0T6ofY8OvnZibns2CZk9IyDSnoveHDNvliVfwCJxjMlnU+yrONzdS9UI5bjYaxmaTVJyiV/JjZF+CO6QwVTuc/Ehbbu5qScWR73Fv86cUdT5BzI7d2H8xGov3m9Hl451YP1jG58ljyc5oyTa72cxtuZcbuxzQVCvCe483OTE7yPj0I6rffkRgkoq93zbA2TaQyp6tGGo/ktlfb+N2zj6qPKpHk4HzmNgji98fmrBr2HA691hCxyc+jF/WlE5Zn7Pry/ksOJdPW5OtLK/alwoFiwgLrk7PHXspDCtGa+3C1mphzH3wEWHH/Dlb7z6NnN5j7Z0f+bSDD8di2vLxCVv6Vj3PPr/5zImOp3vQNuYfmsHYZqcJZxSa7Jnca/seod8ew2RUDQauj+ZoPT/GJy7hw1tN2ZfSEo+Gzfm2ci5PClZR/8BghpxUkflle+JL6uP6o4Zy7Xtz2P849jzh4QVzZmd2p/EyN947vp3CW16E7PkS63H1CcvoykirSiSsE0w5NQrbUW1YsN2PpYMnE9F2NHMGLmf9qKM8KpqO8+4hLLSUTK10FIcjMzFp1BG7y7GcMA3lSos8/K+m0LtjMAHrl/CTZQ2+WjTjpWLUq5p2dQnwF0L4YPhym178wbd+umriaFP/a6Iq+yDrDcOh+yF2n/alsUkhDewWc2RSFYqDb9DxgWBOHnTuDNeLwfG6MxHe6zh2dR4+yyrg8vFiYjJDeeAymDrxp7CLjKXlhQcw8FNsr7/LaTmSOrl1iQ5wZtaRTdRN6YxFnDn2o4Kx2j+P+fW0XKnRhsHh85jvGsimK3o6jaiBv087lqTXZ/pB8LisYl+NFIpjM4gKHkSLyHM0WPU2bXy2sbrNHBqZbOHRiggmPnTlYtd4fupwn4u3a9GsOJXwxGucK25CucCHDNl9klmLDzOm621ihmXT9JdpaPMuMLa8Kbo7fbHcWJ5rLVeQ6b+SVDmVgtzdrC72YWGl0fzkf5aZh5szacxl5n/WmZbfjeeLPatZY9eNCTYTmLT6PrVXrmBwXAS6yVvoef4RNeuZoonaxAg/F74JCmDVTz05qR2BZ8xEdouf0OZoUUe3okDbl8M+H+FZoqeJz3Bqn2xBDbO3EW/1YHriA3rMNKXwWhFXOkGjBq3o95GKtrUPcCJ1JKv3DwFVO5a1fUyavEdP749peSIF7qcTXjGA+QGL2c9t2js8pFZOferdW0pR7j36VPLinWvjmV3lCyafnkhx+9b49ZyH569j2LDuIk29z1K/VwVapWj53esG7h51MYnfTqfytWhtkULTH+azeo0VrWw28kmVe2Q38iB1zQBmLF/AQj9/2D6Y2T+8R/eIThy6ZMP84U9olLyH1M+9yT1+lKBKtYgurk9p936UvlOPbw97MLlHCG9F+XF1dls6FoSTYJFLatV6LNxxigq5YSTEgVsWMC6adfX3E1gbjo4Hk2wITRvIjlqfs77bbB6O1lHl1EVWFzlQr1ZD3tJ9hDkwM2IgNTMf03TseJqdtILT2xmq/pLCDsF8nXaGkKZbQX7GxMA55GWZ0rLOUjTpcykX4EiLX47jfQ76NnXA48FJ5mafYMIMaOkMR6x1pJwHpy8M/1unH8+n4YrvSS20RNPyF75euIJqxfPhEdxzdeC0jzUPqhyhSpOhDBjSloOT0xk5EyrGw0/lLVm5ugGjbx7ig7vlsCzvBm9/CZnh5HddQNT3Fqhjf6HdDxNpdfwbVp6aiFWNGFpbWlNNW0QX2/1M8qrHofevkHX/JI/LBRK/En65lImLJgaqXuBiy8q0L9hGnbwA+DgMcckV1a1pfDfen49+Hkq7yh2IPjGQbq67WXgmC6/h/uzpXptfw6KYfOttRvlUJG62Oy1XbKdWtjNnm13HJ6I+i/uuoLChN2sKArHUhPK2LpkVNVKo+Z6Gvo0G0fq4pJmzhmZqyVslbhzeH0/+1F5EVTbDNn06zZZNxqlzCCPuFUFmHKXjfuLKzQAmZ0wmgxT8al0krziazb5fYFE6gIdfDMd9rCBokA2m2YFsuVmFx9qlOFSpRtbvp/glPJ/6m06yKmYx+Sv2MupJB/qqrEkyv0WUqZb6+hT8O19n5fUIpvvf4zd9Euev3Kdq+VwCmxex68lqvpm0mqWvqMJ+Q/2pOAVQpM2jKN6aJ5n1qRHugvw9kQMt7pIRNwK7epPIOxiHupkFjlW3k36qLTTwZEvWaSac30XE1js8LOnLFxl1sKzRkszrgym8o8VtlCMFJnm45FzGLVVNvP9pbrpP4bf4s/xoXwfXYZ1pvXA6HqpA2r/Vkh9/2kbNjvcprTOCirvXYjHwXSrqPqe1OMv31ucIDN5GWnJf/Asm4pfoQOQsDyr3rMeNxgmsHZ/OkgOBTP78Lt2SmtBJc5d5gQUMinJm4bGFjB4KERGHKSe9GH+5kNVBFyk1f4RJ9xy+n+aLqn5D9texJmH/XGpP68j05hnc22JGg75DCC6KZooIRrbZzai4Oqzr3IV+REGtXlTrs5pra9dR6UkSPhMvUXJgM7NGhNPM5DzrIrbAiQuM6reRg7a7EBcekRlcFVrvIdK2HvNTfibIawE6L3eq5F8jOnYWWlVrvrPMZUELH7T6DUSmjyLy1mKW3M2kXPfbxNaoS9sJ45mZXJPiJrbcvKkm/WoknoErqNezEsPTKuO1Scd3zcuTrctmfmExuw5Z0XOkA2MTQE4zfFF3aM0LxJ3tQmBxBF+V/4agtPM8SA7hVK0Shm9cQ+tvaxMA9Du7gdVR0GdmVbJt2pKfFklwbgrDysPZG4V83d+WwT7zqZirolkHN6Ic7hBMR+q23sz4JUdpwWO+2XSBER+2ISunPhmd32dO1lp+3/0lHg3bcL0wleq7zvOgU008Cy6yOro5Fv4fIlnC4aihHDiWzYhGJTjeP03md4FcG3CGkV5VOROipuGyXGak3WLmhsHsuDMN6zY9uR7XlpU54wmpVoFBG12ZVu8gHXNjCMn9GNMWBWxxVmHr3IXpnl3Y9sWXrHHN5GrtYBz1wXyyZQGl9aBBlKTA3xa3rLU4hoyjjXlD1tSNxfmGlot3T2FmV4vgB9Hoag4kyTOdJW53SQtoiHpjHM0tT3EzYBx3zd1oFXmPAoc9JDTJJ/BMJC6Da1E9bCNpdX+HmOFE17iDxyYv8nRq7B6msyUVIo4/Zvs3n7NXPxmxWE1/XRWqNLiI/ZxSIk1LcPG3pkHHHzAbdZbZRwPJn/uIjM1rmP7JEZa9O4NlR6ahuhCC0/35fNFnDc0tKzG4rSvsq8Wdlp1Z8Og4fVy/4qTpDRYUHeJ33QDGPNnO5mZ9Cd40j2/rhmA9MRZNd2sank8jXBuKuoYrOtYRsbQmRcN96VjXicelD3DKqUyrLB/m1bqLX9Z5Jt4vom3wdXaW9sA7tJRDFnFYW95iZvB3iHVLaT1mDIH5h5Bh4ymqJql6YjkH1LNwqNGIiUUf8+vMT3D7UItZoOBgmjOtCcDxkhufjNayKHMbjSPzub7tHD/17Ef/R6uwaLcPv2tR5Nxexo1h3amx6AjVtzci68RNonZBn9zFXPQexEj/Yg7c0zHiWG+WTlpB69PbKIrfyddp43hfd5qVGQmM8Q7C9OZvbIzuhnUnPXVLGxGv+YZ2np9zu81x7IQ5LJrxUpXvK/uSQSFEOwzfRKkGVkkpZ71o20AvL9l20hecjYzjUbwPjXVbeBjqT4s9i4i3u8GJghIOtKpBaexKqvZ7jynboKcXVLkPHXcNQX7xEzt+qUv18m9h6RiDrkFDnB5GE2o6j89On4P7o5h1dAPRl7fz24maBPgG0KQggW/jt7CgYwhF7T8holNXUnxrElR4hNspQaQ4+eEeOw5tlwiOppdQfGkXuY9XkBr5LqTmQkEk5RroiV4zjzv7PyH2/CVy69Xm8935LBhkR7uHSZzz+ZGksaB19iP5nQkc94/lols/uperwKbbXxN88QqNfOshExNoFpZHeOwimv8awZYTqUT9fpbeeh0V83XM97jCBScTNq1KhpMrGRu9gIbfh3M5cwgFV86gPt4R90mbqZ3zE8sftqV5hj+be62kQZWFVNv+LV/HnaRrjdG4ksZ2VTmyy9em+sWvON99Mp+0CcV6ygxS6/tRKUBPFR83VjXuyMCvN6CZDFe+aUrdGcepZlXKldWD2VN9KIlmgpmXNvAITzjZjQt1mhIaX5csX/DmBhncAXrhvDSTvPt7Ubdyon3Nlvy2+zcm9tbxzelv6JA6mtgMyTt3LvN7xWCeJJWSkteFb7XpVGg6lRz7WjQKHc4I7/LI9weS6wJeAydy8JYp7xzeQY+Oriz/uCtmgYHMPBjD+mbVmbn4BBvqdyW1uQO66aZ06alm2KY8NNc1jIzrjf7CLNx9+7HIxIqrRx7gELuY/VmXWPEuLF0ISeFwCPACgubMQ2tpSlJ6Gg3tbbGpfI6YrU4s7ezKqOZf0nvBIpJU6ZwolNh+Bl3flZyz78VHfStzTN+D+vXG8wPfos6pTptT0XgmFNFqbQcOJ74Nbgc50qGUQeMWEHpUg71JV8LsmlGrXHm0y2KoZfKIgROmMXFIY2bnnGCaVyj2F77mp2FjqXKqCb9/1oluefCJL5R71J1PEx35ZelyarX7kSOV7FhefjlTZ2XgE9OXyPJxNLmSwbiJHXiwr4gbFlnUW3MPgixoWxNWA4OnVqTa7DvcnDgDMfNDuFsXClxxfnAIYWZBjyE2rD2URqZaDWgwHVuTT9duJdm3LjGO9vQ5cJP+VWrgeeE+8UmeENLL8F3Ae2fRytOXQw9cqTPQnAtH8yAyj7pDOnBefQ8c7zLOuRqJO/JwPlmdIJNYzl225tR7Now5f533mhSxL+UYKSMCWLhBS8q11gQ82cfJFjX5LGU+hQmmvOVViyUBjjgVhGOuSqFq30lc3nyFj2u7Yn1iJr7VehIZeRe/C0OgaC/c/4XltUZTO7UqkcEHaGbuy71HoexKymeu5RHIjaP6zg95Mm0zfhG1CAhIZHNcBfIT1uJZM5smhU/Ia/8WCSce0sq5mMw95Vhy6hTO+gCaBebgYpJPYzcnIuO92VGpHMXJmzn7/jeQAfLiJMRn1+g1KJn3VjcijiAGN/oMWm+lrvlg2qXc4+7VyliXqJj6gS1Z0UnY+LXjvZM+HArUUWCbgXh7vDLy8Rf6M3EKoLqzvezcbzKznEJYcr8zH66pQc2fqzHxiYZyZolE2KyHbe24UjGTed3b0u7yEERMLzoFHOe3iBDsI5rT1r6U33JTGVWvIQNLY1lz7RHFjwU9vupIjapR1JvfndY3OtDYy5G37L7DyjKMBssXEtTalRpZKqrkHiDQ+yamd6/y8ZDarNFU4qMH4+gY0IqL2RaMi8lhbv5e9t8IYsK4OMYO3Uyz87dZ17Y97oObY5Efy7cHi6ny1kjuTF2O6ZSFfBV4jCUjxjOx2hVMciZzvzac8w9kZvd3ODrvazLSVxPj9wV+J0twt/ViesXJHBx5mjFqwQn5hN5dfqBmFRvciks4bZOI9d1CvvrlO7oObMU2teDDC3nUqlGXO2u+ZvO03vSNCKCpMxxrVYXZG8ezK+d7Om7bwer5trS+UMSpgTZ4VZjGug0jaTLre/YOmEd6K1+6XLtDtokXu+LimdwlmOzfv6Jjx7bU3NmcyZNDqXZW4l94FTHyK9ZXziWuTXOCHm1h5KbWDOybSU6XyvTdGEvgSHA+Dx+vgjY/qfhpfnWSHiawdeN20B3mnbZjaNrjOz6qNItNmsecumvNWwc9uDb2GPmLXWjdri6Zb1Vh6i34cEVTPtiTS9BbU1jfDhZVGoHe4xC2MU2Zlu2BbfE0bvw8lT7tT9HhwQV6d/Jn7MVZfHxlE99dHEHFpNosF9PY9Ptm6t0rIDz1DHtDrpJ9rweD67xHRvh3DDwr6ObWk9k289mScocrRS1xdHJi9icjGbPoQ+rbF7Lu9gcE1P6Gws3dCB5YmakHTjFxeBVsN5wjvLULXQ8eYqp3BRqdUZFWrjrT6cKtST+TfeUs03dMJshrJ6MdbvHpCVOWLVqL+5ipmB2ow0Hbugxrm4hfUgd6mViyN8cD20RBbo+uePZ2xMp3NmZqwaYpzajiHENuZigdNs7h7Tqz+DSlCvN6uiNKt5OS58eRe6dxuApmpgnMkdkM6VJAcVpjnBIj+G2RF7NvqOmp+4C1N+bTYEc6h0+oqdgINgLdpqoJ/nAwV5d4IibfZ2X6HFZY+9M4yYes3I1UcXUmwLYceW6J3Bj2M1NaZDE0YDjmWjXXTUfi7LEFzyfOzJ8VD7ize8zv5MfUY0ppeUJ9J7NF/T0nU3f/f+y91VcXDNd2O390d4t0N0qrgIWgYiB2t2J3C3Z3Yd6KcVsoKgoqiEGXoqQgSAjS3bUP9vkezz7Zz7vH984/4rrGWnONsQj+VUiXURF/2lXxGz6cvrZK/soMxLzaAMtcU+LezcXSRJ/ZcxagllvD3rRiyjzHMENiMF/MxlPRHIxiZDvzjOP5nVbMBa1uRCUPoPf3IQnaqkg3vWNx8FbqAq+R3dpLmONSZJL3E6aey7+Zc5kl94RlTa+JVf3EoPYaXLtDMFHvQNhjKeMapxAhuY+VjVqMmf0cjjgRJz2IkwUnSavxJ1OxBSWJy0i+FsP+9nSmuBzj8OtoPMpVuPV5KYoVg7BeqouK80qEw0+SsPoAb4c6cDRzLAY2MzHas4Z/XZbiJ/uXbQvqqF6RxM10E1AcgcDJic3fDjAttIBXHfKUGM/n/Rdltph9xHhXHhoP3LgqD9fXLv/vnV39v0VCWrO/c90PMrUPsnRXIwmukszTM2ZYtzB2st8pNBrIasnPfG+cj+aG+ahcmY3Yt6+Uv/nJ7AMBbP9bhLbmRPIyssl41k2GfA9uShYcXfMOjYIhvHfZRtuLu0gpOrM/5zx2rvrkpkYxTk6VgqbhHJzwEccCfQxtfhOav4vJ1YtwdIsl4408t843Mp+hTH1lQfihP0i1T+aTxzdkzl9EruI0tUfbST23i1Tg30WrSBEKxT/9L5qrVNEvDODOV5j35iMwFH+/GeyflU7ajU9UCLIZquXBmtvHSBcFOgBdXSguZv/IWUiwnTWuwewWVmVMRi+jjQX8XLWJd8/KWR3xEuu8O1w+YY/oaDXSH2gSsnobx8d7o9Q2HY0V8VxpvUb3rb10DpVil4s5FwpTiV0ohLv8PsrHPuXCcX/W3z3PCFFN6nWqeJpbx9/WWbRlpLDt5Ay086CqvpL1WzSonHAccrZyVgQktu1jgKULtg76pJd8I+1mHS3SYwnOKCFwhIDtzW7Ej+jHVeslKHrzTFic2upHHLwTz4Gw55z9rsfI5kp0EypQifBjxv5Ebp7/SE70Vk5W1nF+Wwg/b8/m2YtIri3cz/hHT3h/yY/8P3sYL6eGTm4d6wVpFP2MoEXtImLVcmxzHEZ84k0cjeTYJdBFf/QBHk+ZxCu7Jah8GIjC0ix+jh7M9ebbmJ3P4U8rtHVARZ8KyR2raBoPVcHTMRwAdREdKHUrsSchgL/m9Sx4dhpj6YUMPpHD1Yi3jE31JiEY9K8cIFt8DiMl9WDfBwKdBhFh1EBysiZHYrYzwbeJ4L6/bGhs46zCTM59OEzuwaWknNzFPCVnrGVG8nLnYfTvwpdR+7h2qZ6opHA6d3pRJ74UTe0sNvwqoLi2D2c1Idr0fdDvfUflD2ui7szFYfU8HnX9xfR7P9aiu3D1lyEjO5uu7lTCEq2JsvhGt58pV+t/0SZryTbfWTiKgPrpfxBv/U1M/xTqRqvC22AOjwxCqwH2R0zGQsqEVyJV9B4Q58Xku/hdvYqe7xVyDtmx7ep7XDBkVn0PT80XkjJwIoS9oM6lkLUduvzq/srzM6e5s0GHxYkiyOmuY6FKAprfbLgpnYXOZi+eCprRyTHjbPEq9nn6onN2BlqiOxBIzUAi5iNS637iUJ/MekVn6u/k83PLHmaEnCdp5D/Mrctjh+8yRhxy4eHYZ9iJ/qQ+ew8trke5VFNJfmoVZV3ZTPYxxDbKgEU7O/Ad/52MXld+nh2LUF8GbiVhdLWPx6lGGjnLGAoVm/ldVIK0hALtKW/QcRQiPMMLA29xZOpN6JmRT22fOHJ7o9k8cBVXi2vJyI7EcYApcvUyFPRPZfq0O8hsGkrY0wRcmzuQcrXj2ssjlP4+xuRxCby4eRg7eVsEZflkTjvIzUuhhKk/59S7F+y8Mombj8ei4HSMvhINnkm2sdnFmpZt0xi8xpq6adN4XqSASGI8JilRnHl3+X+Hj/8iYpIK/UoL31Bp4cb7QwI2yECXYz/zF21ARCiBrTU3EEy3pr+/n71vQjnwIpbfC6cxeMlwToblMOVkOAdtupkg1cDyxFrqPVoI37AQe/9uBql4MWlwAWrff1ASvB6PA//ypKkYXaGXKDSksyZyOfa231AzVECmshGhj0kMCXiH8a/9pC6/zvH2GSzf0sDpGSr4r1mKyLh2Hj1O5GHbPr6+m8yCBk/MFJcS+FSTStkYqlRDqO36wJN335lZG8j0xQ58y2/kiMZF9FVOs83bjNwwJRRqz7N+zhpyUksxLwwhTCyFLaaZdPv/IvHnW7RM3vB1dzdHFCRYky3Lh5PajFZcglDGV6bG9CJIOMkQuWJe+a7H/OIMLI19UJBZSuiMyfQ+G40IFxE5aMaEdUcxkMymVUgb43VaiEav5mrQCaZP3Eyp0Dom6IshKV/HSClX7khOo2a/E5Gaxrw39GZUQxTvP6ZxddoDAqOKkMQBAAAgAElEQVQ2kumny7lcCbZJijJrhxHKTeqMzpnFrLjFCAqEuTain0dZ5URf3UT/b3kESwazSu8NgRvjmZp8g3FxchyI2cmCLXIM75tDd+pqvuk4YtrbSJFMGRu2HMMk9B1cjuSHxRlq9TOwqHtGmN18MtHHuVqHFCUPlMomUPCulQ+DLiKZ0oDd4iik42IQ9bnLqHEfkPy8lvseF9AMDMS6QpZPqsOotXxIep02brdiWTtrFWYiXgh6jsLzKk71SOO0SpgGBrL0eipO1stwFTGi182CRTdHs9PmK/8sTmXf84VEi61l3OPx2Gx7yne5CGaVK3JmwTVEziwmZrAVqWd12e20nyX9ytytGE6aQgYOPQrsvnqH2ho5VINPkdCfzo/yf7FSraBWdgoPKqpwv5eD6ng71tSb82d6I9InOrjsIMb8li4yzx3E3eoWtWvF8AsrxMjZnVaGc0dWnbPPDxFu9wPrcfXoJm0nuWwuGVHjuDLnOzklQox1mo3S6imcM12O1iFP7iSEsTjbGCUnYTwAaz04YR6E96llTHMZx4JwWRRmpXDaoZ+b5Zok3fnJ7wkG7HoWSVZjJo/G3SHuizOuemtI/TCbyQeW8VRtCJYun3HqT6UwwpRD79cwv9sIjc44NMnglec7Zpy9Qsj1AKyf52DXtg+J1lxyWm6jLRwEdzq52ReB/s2rVCl40Vw+Bd86K15ku1IxVBfz9OVcLW7l6dzvzNutzbmLgdQVB6FsfpCQg+1cD/CkWfkj21+bs6TvDdVKBpSqWDFCoo70pavQPmpAedVCutXT2XZXgiFb9uEu1k5PxlsMBv6iwGA6hgm7kF0Rzy/nIpZ9eElclQe/E4KRNnDm/cMjbHdqIbq5AHGFAGz7bbn/9SuWEqPRfHgW4yORXKs5jYGIMMeHH2T7agFKvy8T6aGFe54G41avYYhMNr7jjzN5VRGaqwdz/dlRlLoP0tV8EMmRzmwPN0NbrZ5g3adEH3nMwBFxeC1awOeO9+g0VrNiwaz/qKP+R3w4F5cVoXrbZqwMu5kVJMtutzSs9bRRMv1NpKCRKdkSXH1Tzsf1E4i9+oOalfc5ES2LWmkP9xecZ9GnJEojz9OgboPmJF1Uh1sSErMGa79XRN27hcye5RidmofgmAv1myexX8iTV/6hLKu2wLTjC0mWwzi4ZTqjhukRb7OTLftGciN3Hesre9m0CixmtGLre42AxNds2VhGxq17zOg/Q0pRO54VczFflcIs4KWYJSuHnKT2aD95G7ZT+EWCScsNEVyJZFOgI7biETQuj0DpmxozGi7y9FsLjtH9rN9xE2nvpSi5j2DJx6eYb5tPp4wN44VVmQHY2gojEBbi05sy/uR9QzBlNg1WVrgm9+NysZrn0dsIuPCJP8bKDIy2IcJ8KnvPqnHgywG2ey0mtFgcBQ1FIppVMNq2lwtzR7H/xEVcBhiQZNdPZJUMZsO8sJGqZrC2MMEf7rMx7Dgzhv3B1w/8322lf+VW1mX3I1vTQU5iOy/OjOXi3YtsbWgh+vVZtLVleOTtRt568DknoLduIpOny3DR1haTgzcJHW7E5VmPSU8N4kR/GqtdbjFj3wG6+l+yeI0GJ8NmEJMQTmf/DRYM9+H8PB8eGnYxOsCPXA1wMAxgw/cCFDJfk576CjvvHzhVVSNvLcHRF1W4+rxjdIsAg1F+NFa9w14P5EeH4JI7CgWTFTz7lILxnRwUSjUJuLeHsD+9JN3MpT8vCNlXQRgOMCc1VphDQjnw+xqpiQr4PhvEkDAXuu/nsPeZgLHu3nhP7+fumUIG59gw8ugZnD9thNyR7AtRpMFCj/X9u9nu5UJm6i2+KG3DwC+EvypRrL7oz3WZkxgHDmLnWEUurlDkVNRY+udHcy63h0cV5wgIGE3tgcs83mjO2iETiRY244uaEN8wY8PmWcytdmV7TyCTt2ylqusn00XaULVfQkPLSnpC59GQcI/Vqq1M9Y5CUPmNJe9/05JvT/lPLe6PcWFKeD8+4R+JaZrLAtUO/CJ2gQWUPnuBIHYHun/G8UqrmBUHpDHbXsSaa3tYtvMrt4OWMrjDFJ+du6nzdMflxXv2NCejsT+f4yFTiTO0wsa/g1zbgegd1+FE1QFEBqzh22B71mnM5uO0WNSnCrBLKcJ03VsG51mhKZJHbvJ4Sk0a+aseztmWH3iEK3LQx4PJm5fTXPMLgykbMLy3gfUbnmByYxM7h4qjVHqNfucV+I0Qx7Swj4MOF3D8EUNpmx8YSGGhMZ4rr+uJPlfE03MtfI03JDr4Gt0uc+gf8IVekzVEDhXgdt+M+TNWMtt1ImULxxMl+x6vBUFo1DvjozcEWVEPUgT5KJ14xaIJ27FLlefT8UnYCYZyuW8RpqNXcGe2CprRD8mySiAtZgzt39rRlPGhNmQUb5qeIauvh/97F4ocg6jxFyJkyyaMmkuYPNubGsVlnBgmw9/ieFxlPajpTseqbRer9T4QWrSXnhRnpg4ewqxHZUSqOjBzQzGZywb8N2P6fwFMNeX4O+AgJDYQ6yrCciczhhaCnex35DtFSZH4zEOBNJoHN7BNygi+feXK+fsUHAhA/3UCAWI15GVksfniAbLkexDpbuDomnfUv8kjwXUxVRl1OCk687anhjGt6eSkRtEh505+03CuV17GMV+f6ZMVefZzF9Uq05G3kCfjtSqZW/YzX/4hp5+fIOFQOXt3efDJ/RsHZ+9D9vtn0i4l8fXjv6zRk2RfcAMpQqHcdqpFc9UMxn6BsGwYe+ICzb+HYTlNDy+Pk6Rf/0zVn0CGarlz6uwqwkWLSPqSQZn+GHY+qcBz5T3SguG7TQi7hVXRrpFllhHcUFzC20SYGv4JzdzbTFt5AlEvdZpet1D5Ho4v3EhruTrYp5GpMRFubSHiyj12uVjQ1p+Nc5YKYmqrqMgz4dOJzbx5HsJIUU1K1Qt5mlsHsUW0ZaQyZtFEmkT0oT4FxZcCCmJecu3WCG6LKJHmDQMsnSnzcaAuSZiizBqymvqJ+F7CFusnLGt2I2qUI/CSvNKhiBmf4fbnn6jeecwMOTFCoyoZVViJt1ojKpGTmHZTl+8F4YiVfCZrSyBvgellllR9OIOaGpg/egK7IV1en139+7jefpJHUnOpks5EUvoTCyRF0bB2pUEiBgfjhXwWNeXmvevEedxB4fhyROr28dlwNhqGuUj91GBgxHtEeq341RENYss40tuKzy5p+keLYdTbRe7nRia46lHZHo+vgRQqpww4NHMBoYPeQelaIqpySJluj/+U2Yx9vJdt1WnoXGmi0mQhX+PPMsxGGJlnW5hovgKfrEvsER7NxQ+HkXKtJ375cZSN5TBvfoiG5gri6pQY+6aehEpHfjx9R6l4Mu/El1LWfQseifHIIQdRURlEFdtI4xNTpmgRvP3/XpAN8DTH5AcEDPtLjVkDvlbahEbJ4evxgWuTTWiaYkqyiAZtMlYsfZrELqyQXL2L+tpINCXMyHRRJ+5tMCPsx6DZOJK+iDOoS5swRdid/khR+n4J4WK3HfnxVxg9RZ3tV99T3fIH1boeCuSdSBk4jpCOdoY7FhEq1M9r2nm++yw6Je0sTxTBRn80C1UTGJ6ayE3pLGpnwcmUbHRyRFCvjmTbcF9+lZ9AS3QHuRLzkej7yBwzGF6fwP3OBmyr0vm5dQ/rk16QNsIHw14tdviuoO7FBkJ9TlCdkkhddjDrGszY5ZDIz5RqCpr/YbKPIQdLJ7N1ZweWay+T0euK75RjCPUV0vqnEUlhTxRqpJGQi+SXYjPC1QpISygQffM+Ok5CHNoWiKGPODEfPvMo5ji1fWJY5UZwcuAqstpFyciKIPVHLnL1Moiq6aHrF4lM4FAa5VVxae5AcoABvDxGnhRMGhdPlogmtvK2vKjpplvHndBLoSi6enP63EuE55zk1mNfQj0s6CvR5HuvBPNdnCmLUMfE1Ii6qdMoaRqPaEIcrUlh/3GW/o8wHwLBgH6pSzvZMGwVh4Z4smvOSzTU7/Nt6GrUbYQQWTcS0XGR5Hcmo/jhBn3iQijJqNEgJs2vkiJeuUsTfCcK7V3XuVx6hoi8SDZeSKGRKHSb++nccJTYdSOYkCjgpTF43G9FMECPc6IizBIV5UOeG2OLluOkE0nfwH/5YQeeU2TwwouY8OsM/+jHS9/h1GcG0nf3AcUv1vBo8U82aNlSPiCS5N7veC8U56JBJSqOVjwz7EBTazL9p/+yY78F01vOcEMzmxtZdkSkS/BnqBavNaWw9dHh2818DB2qGIo9JTVnaemezg/dwQz+lMUadT0aAQUjPbZbL0BitwkbLJaxr+cXNVLGNJ+PZVenMrtP+HG2cCJPdLfRaVjEwYn3SdHfisOvYwTMnszq6b78NS7m7mstjOUG8O+uKbhc3s2Nbi+eKwZT1lpFdto7Lh8ewqW4RmSGfaNjlx9f/pgyz8eakN+22H97yV2pGTyVOY+RhRiaGVrM1bXiQshtdIcOIe/qCgCUsGI9IljI70awUYVRjv4cFKxivF4yS2OOoPS5AQnxXjqlDRimfhCzhDKeKZ4n1vISvj97yYy6xNdyWLxkHRryCnx2A4sxg5l7JIM/BjWkjxIwKj0b+5winNs3Y446yrNseHwwHOUha3DcZIXNLDnMjl3G/swknmsaIKmkRc97LyJvzeP9D2h87U+NpjbXHZ1I6f9O9I0ndBYVIv53DQKZr3RNUeZKqR8vz+3G+/MStjgEAuKAIgWx8bzrESV2lyn/zNvI2SMHsXoZQYW+AkuCXFmgPw0tESs+/VLgVG8Wz61seagszaLSJAL3H+XiqGV8mLQVa+n9vM9wxq+kljSzAsZOek6NbidfN+8lIe4q3TMes78inmQJMOYtUecScVrsxu55AQi2H8HXdx17am6zPkOa+ND3XDGDAeNPkf+xmvHjPBAe/BXZtnaGpHvy77m17H8QQmOKD9mG96lWimFH5ieWzLpDvLghua2DUDMzIX7vcTQUSzm/cShrxynw7LUmZTTAwApWjNUl62oxV3rKsNo2j8axM9hgAFPPxZEq0clASWlSCoKwmK/NmqDx6A/9yln12SxuKER1wDTGyO3C7WkBU5yXwdEuDm3WQCHpLbWecFusiouajzksmYGi/HHCjYuI/XCGfwdp0ysxEvWk7TBsPHzZC9m6FDACj4lp3EjNR7l2AnKfmvm5rx6xuGSmG8/BJq2dp48fEbDlO29LjFGQ7+F3tDs+mjN43fAI9WXqpCQ105bezPr+LuKH19FpOZ5HoWnkeDhjlBSLoWgjji3LETlbgY2hC6mnvpJvZoBEYzThTjkc+S3HoM9l8EcPGZkJZAm0kSybiMT3KkZ2zAZ9W1K91Cm2c+fe+zd8flREyDQ/xn15hn9DGWYXIjjUVoHIZlDWW4mn+xRy4luIH3QRaZF23HJjWWAQzxNxd5b22yGRosp0BRc6jgX9r/n4L6IhEO33Uuwh52E/qesFTBqxG7WGAwQPsGXXoha8H1oxLegNzkeqmFQ2nLtGFlj1PSBAewtxl5tw+XiVyBUOrBy+Fukbj3GyjCdAI4m235nEr/6A6yhRbnsJWD3WgFvaY/DYGYPALR/lCgFtWr/oW+GNTddSBq4J5JknUAP7ZOFA5QiO/3jDPL/HSC4opCX0Aq5Lo2mbnM/xjtE8H6LOpsSdTO0bRzSdPP5nL1t2hDHz1mkGJc3g6h43tM3L6Sx1J/5yKtUfxHh2YxvzXc+RPrIX/4IodO478mOKBnq3L/B5qS9uEZp0+dviGLUfKX0PPlHC5UHDeWk+F6NLx7k5rBeL8j+4xJch8F/DxDljGHzsKmPNmvgksoGNdq8RKI/HmfEk+YQztlmdgIgDHJWNxL8/lKHbNvO4UIRpYWdZe/I36f5J7Lwfyl4RWzJlFZkUthsfvWO4i5/lk4YXc1RKWP5Rj0uyZ6jyP0Lj2WY0B8dRL6NL1sZgHgZuI3/DMUYs9sXONJ+sI/6MszvE3SunuP3lNpczvuNkuhkZPTXGKIni87UWf+3ffHtyBhGLKzjUWWMzejy3S99h0ltC8I4AXP1h6c5A6t/D2YDZvD51l+9BXoSuf072GWscTr9C70QjV0UHsX2JKgHeuljuXcT7K9cINJgPG9ox8L6Hd/NCMr74EZfgxLH922hynMglqRcM2T4Wjb+W3JqjSlBjF2379iCVb8DueWs42PQTwn7T/1Gb4UnXUFFPQiLUn8kWD3lRcZbE0d78LDckKCuSoJBjrJvbx64KIVSfhuOQ9YkVb2ZTu9SW7XdXkiIhx/heBU5W1zH30W7cLRQIW9eCYmYiE+xskYpfy5AP/9JnsxDv8fOZvHwyeQoq7JUezeCaVAbO8sGO3Swas50tzcb0Fzoxv3E9alIT0fyTT7GiBQszjHlWdwOP0H28C4lD/3Ivw0b1Yzz4L3aVNszePBW3ud2U1ulTtmo34V8LGNr8nU3OXlQP28SLU/mMX32LytDlnNJ4hfHlWrzsJ5Kp8xzlG04056xBt/czUWO2cG/FGgze7eWxpDCH7fcz/nAtkldC6JJLQ7CqknU3mvE7503UGGf+RgmQ+bYPuUNDmRQmSsgNDZ4FjyNASJXdtUlQJMS2z90stOtjbvcHBJVxSItL4byjlfmZP1CoOch3/Ue4n3SGtP0Q9ABk/Eh+28zgmSo0ZN9GbMxk/gR2MHdvFidOibNkvyItpUVcnz+XwV8nUn3iACtN2xhQm84t9WF4lIrwS/EFFfoLCG66zPkcb5Iqv2JnuY1hqgtJnZfE+dVvuKG0m2OrN+I86TDyB+fw9pEYjv05jDR0YeeQdPQbVVHvsGR89SoiC3QROXMV1TvpSP1O537BMF6pB+Jvv5p/nuYjY6OJ4IcEzo7X2eW2kITrq1gvXIm6jy2Kd9tws9jCZpd8wh9tpERNAVuDEHz3bEcrRJk1rgKsv+9gZ1c7gpln//9jPgYPaGZ97Fru9K/H6Ml6qrSW0lmfglPrMqJLHnJZuZK3aZMxLz7GHIUENivVECT3h+3NUrxSHMjEflP8hkiQd38L3pKD6Phli55gJ6qCTPYu/5cBbpex2bWE1/n78TyqhpmWHtI1OuyrlsKz/jPuK714vbqIrymlFBZ5IvIK4kfb8choDpcHiTHl5BXa8vSR/P6bFNUKGr3W4V9RSa+UBkNE9XHy2M6ZW5J4rNVCuDWWQa6ijBgQjqD0NMmqBtivOsOl9f2M8fzKeisJ/GxLWPn1HpY3DzNs9Gb6hV6hP/8sOvdn0qkyDMn0Zmq0RyIiAwpAz5wFzOmNpdFXBr8RxdR0BZOTcY7nDqNx7pRj/KUcdpX/ol7vAEOS+njYDx9zzJly+xWKU5yRlCpA6WMEp9XUuZf9Hvk4fdIi9zK//ja7v1hiVSHM9HFBHF66lfBJ7WRkZlEtIcTEYUYERSnz5kM1avISxE+rwbjLHoGQDxWXluHS8ZOwvnfkdqzAzRNKkmJ4HTqRo9VfyYrxINx8Pb7PjmH5s42yGFe2Nl4nvfglPVoZTIvTRysnnOuDHVCSfUffuxTWmmziazlMNZnD9OsKiMeA5TM4dCYC9SwV/so7UF4uwadfIhzJ3cLE8kpMjIxRjnuJr/UXun8sw0pzHKKqr8mL3svoKQfo+j4ds3hVnmq/5eH7FRyz7kBeL5q8wU5E1jUy+1c1HuPvomOzhveJ5+n3dEJM+xxp6onEf1zElmGpOHhfYLVNJzfvncbx3lECZg/E6sR0xGZo8VZ+HaIxh6ncMp85oqe4/aWHQzF7GdvaxdmeVnq6pVmeMZ+AjsvMENGnysAUrR9N7Gt4zlCToey+85EHlaGYaHVSP9mQwvjjvNswgt0ZVwlUs+IHAtwk5jN5iROnzsUjsJ1D7KQwPKrdcarahb7nVOJCJnE9ZAOHre9hslKHCItnnOI7U2TimPDkOjc75Vj04DGTTUtRf2GJ4K4mX8SGM/FxEsf2XmD7nU1UzDRAReEt1z4HkHr5DGJKDfwxzqF/SDW/pg0izXYb/kcv4qdpyTFZLzZn1GN/MoFSLSUGjvhA7aAmVET6iNpez0ZZdVYNGENw7gWuDLair7yXzoB88lqCCSqUJs74HvbH/0FbuZjqhBL8Xv8hTWsndln7GfPOm8fnvIl5cgnnOdMY7a4Mny3gejT8mQlNVajFPaTueASbxmWw30kIP+8HbJAMxkhJiaiWr4zK2YVmgAGVsucp/UcSvXBbWhWEGdt+lKq9vUicPsa1HhvMvqViGfeIugJTCj6+YvqYSewtV2O2SDLTjeVJLvUjLLkYv+RCfIUjmfbnGSaGung+GQXPZHB8Uo3jnWbCH/6hvlSS8mPVjIroQxBzHcGt1TgGTMXfTJ2wfQtpVN3PL0EabxQX8bRFG7m8CQQL7WCl4Vx2y9awozaaW60p+Au957D6fXxEAkhOLcb153QCWpKZaBrBPN3h/+2o/j8eZQM9Ng67SOoaTa7O8WBs7UFK3IsQWAsTFbId0XEvqKQHxeibyKgaoSSrjjjwq6SIxVZ1bFj3lgG7bzA1KhDyInkop0qjShQTZOzo2HCRgb5qTEgSkJVfhmdkMoIBemAygFmionTn/cGnaDm6VaL0D/yXrnoQE4VpJl7sSZFmT6wfr80Maci8haenIr+3rublTyHWa9lytWInLZ5eeC+UoEVCAGqWnFi0l8FajhCUxYr7lowr/4cbWvGk335LQM5AWoZAobMKdsN16S4qxcihijIxa0pqcohWcSFTdzDj0vZRo6RMNbDayIe71nMJvWqDt/kyziospEbKGD7V4dWpTEnoeVzz43iit4FN/xxDfNJd+uUhsf8OZpVSrJ7uS1F3M+pjJJFLh7BrZThf3s0ho0m8VrqCx+U4stPeMs/JmpNxjfgmVNBjPJfPf8yYv3UGIcW2jDVV4p7kDAw7ezA2F+N0eTEzhqhxcPUXjOWHkXlsCiP63uNu/IONI35iMWY2VqIV9IsO4XIPlItsI/C9ApfTi5AQ72HqAEeGqR9E/eJepPT6eSdzCS8FRU4/3cFQE1/m2m7hlAx8cgNTYWNy6zSQ08tkilwLTV9KKO4zYWHnBPzsJfhobox7eBzKQ7cy9/QKOJmN2e+PRHT/oV3XlQiBA6OTFdnamodouiUbfu1GtrODU0OG4R76ksCGTpZf6WcphdiMdKZIvgBwA49rDAyfgFqbCL3dc/Fzes+dO0/4+XQfIun3CTQuBr/daPgJuJAJPH2AYc8sOiS+Myo1Gmt7N0LkJrK4NImMAhveSCnw0y0QRcF3kHqOcmkV7n1CdNslMknfkTNXrpFTUo/miI2Mlf+DS/5vokSnMqp7CWdcT/Lg72WGnnjCad+1rFYW48Z3ab5dv4WW9ycuDPHAviuTVwka9Hq/Qq69jdFdvVw5t45YoCHZh0P9t3GrjybbUICavDPxYnZk35iDmoMxpXvaUFcsozT8JsPGrmC1jjD3/jYz0OlfTj4OJ3PEVIxuPER32zzWiWewwRB07+VQpjWAytpCfv+ywGJeM7efnUZvaDrbTFewuKEQafeZeMnFYvTtPNIee0g+2k3LHDvkk94iX1XEC/EqWh4/5ZBkBsE6cNi4iZ2ZIYQmvqBXfCQKBxfBUF947g3ZuqAqjsfEVFrfXEK51peWyO+Yre1APC4FI60p2KS101EXTcCW5+h8akJBrofycgUsNWfwN+sTloaOpCQ3oyM6gPV9XUQOaKPLajy/kz6R7uFMwu7jGIo2Ul+ozN97EdhYulLu/pN8MwM0uxsJH5aDcmMr9l9K6StvY1CZDdmCgeSVpiHxowoX0SYG6dvCSAtK7Nxp1W0g9VERvnZ1JL35iV9jGX+7XjO9NQNdKUuU9Y4wTmEFOfEtrPB8irRoG7JWE7ARL+GJxEbU5ZWQSFHlTPx/PlL8jzAfDqra/WnzM9FQf8FJiW2kF+9GTU2H7fn6iFlac0ftNi8r65FJSma6jS2x2ZMIqjdjodU/ZDZdJvlKCh5BAXzeNJwnsU0Q+i8pIyei1PAar6ErWXPkPZ6Ng5AQkWTUy35anGYR6VLN4tyzMLCRcacusvfYJgY1lSDaXsN23jLJ8ARX87pJXFZCp9B4vBK9sH4zlwnLg3ktKmBQ6hMyJdupiutkmro9suKDeefxmvgnWST+aMV6xGxmu5ZzOXQk64duo7ZSnlzN0SwRbWVGWja7PPYiFJZPQKETMYYjyHS+QEuPFsvKn6Lctw523eP6pq38Dk1B/OQbfvU6kF+eTpqkHl1VPSh1xeI2ygiX3lTk3rXROHQ/u/u70NtpydKgZTR8jqTfcAKq2qpk9LRj1TGUXWGbOGLuRb9JL293jkF2+FWaDw1D60gY/6ZIIFvfhtmFYLz/StCk8gILgxIoksdzYjef1VcS9XYXDyeNIDo1GZ/ZcVxc6cA6hfWMeRWG3sQFvCpORsXpD7Lfr9A0q53kHTsR3DMifkwIemEH0FIQ8O9SP7rHS/Apxgo3qXKUQi9yZsp4ytMaKHgRS2pgP/urP9H9NgbVDeDtuA6JFetJc7dF2tCWyhf66Cdfps/OiEMKKazU7qK0t4bmCnl0xixBs+sv7W26hLc2M6r4FXM77vPvsgu0jZnP6VwInKvFI6flrDDQR0Y/jOpfaXgbW1BwcgYpAzYimVeH2uDz6Chpkf0qmF+BZ1BJDGLOGF++KLUy1tkdbYEVgneAJgSWwIMoiDH+gab8E9w7DYir1gTXgVwdtRm9bjG+OQ1im5oSDFTGwy4P9z49LN+VkjbkK8c3JGGvU46mKlSEf2VzrgjWfxNZrTwbnUxvXHpvca79IjoZQ5niMoS425oINvxDfaMx2gkb8TjwmfMXdqJpvh4V8Tck94qwRsOcyozfSHe18+nEXcL0OvE/thf5/VfYeN2dpyeLCVSuIHr0RF5WKfIlTRN/xSYcLb7S3VPF7XcFDC5fQG2vEEmOY/nR/ZpauXDj67IAACAASURBVCc8LMnDTnoz0b8UWTU2j6NlMdSnniLRbg2TLKbRmH0QebsccgoV0fhlTpvwWETTK9g4sAzVvylc6Klgzd9PzJ1dTc+EKnqLHmFtdRi/5HD+TT+M5vJAVusLw20rAvNN0TDKRbWjEsnPNykZ1sCwJA1itDajpKyKsPIQRn1LZInMZvJ+BHB+1mj+KTnLaDlnugaY8k/GfQzHapG8U42x5oORd/3O6M/XyBOxR6EhFvGvMRAwlZ3t2zFNiGdkbSPiZQro9nnypWMDW/Q1mT57DMOVf1BWeZ+GyvtYln1BSrWKbyJjyDP7QrehCvESy1DNP0v1mz0wLpKFYQoMGanM7pYHfJCVo1LWkE06yojI19H+oIppSxcRewvGzYLMQvjSA5f04aTXU7ze1iJ06w2KnlPoFg/jup4YfxqGUyK1CIlWOBW/gVWnL/2v+fgvMsDAoN/cNo1oFDkhJ2CEqw5Ph59lfMxOkvpz8KyZyck7D1EeupbO1isMm7iGuWmj6NJ6z7SzqTz9cwvh9E/IvZuJ6BMD5upWIvOsn7HSQ3G7sZoDTbJYRXzl6iYrFoUlkt+1gJqRoUwo/kznc3liPj5mxJmtNGRH0+xqzquXS1ltOxYvz1aW7ZvKOtNS9lsn0pE8ANsmCyR93iIjGU9izDqoyaJBLoDdUVU0FbqgIl1L0mSIbgojcHsXlz5e4ucgT5a2g3BDOA5/v2HfpUTTaidWBp7hsqkHxi/vE+IkQdD48Yjed8JD7zBblK3ZseYpR67nIla5GSuH7/g/b+aLogV9hTL490WSYPYAf/dI7lbqkZQljEfmTJQz5JE4l8nPlxsJcrZjb2UJR8SrsK0cS5fEes7FbEMmSZ6fIdK4Bf3gzo3tPP6bQfTZJZz9K8SZI/dZum82y9afZndXG24REtz1jIXJ5xCcsaLfyovduhMxejyXBfb22PU+RDrNgD0m06nNHs/neRb0HX3J0YUrUTk4FlqnE/PvPARdsD92Kr4hHYjkNBC2eBqnBnazYIQG/2j7YG+vROBAY7rv7yUl8j4RCs7cnwrzFvcx00UfBcs0DFIC6Pv7iO76nzg7OHGxv5xv5ZVMGzaJ4kNnsLi2A5MzJcya8IJ85XZMjjvwOWkW31Z9oNbGD3wMEElczW/BT4qun8Jm8APaLWJYujeJV0ta2Be5Auuye/z47Uupry2ThQ/iqbMHf0UpVIK2Y6SrRufT83wT1sJ98z/sm3yTbf49XDhiyv0LxjxKFUE3wQklpZlsNivmeOQ19ldOJk1Lm5Ci5cgpzyDwUBCfv53n9pEHKD6Wor/wA+7OAurS+zHYvYsl8o38ifUiLzgGhdc/CeIoPm4R2L/qYYzfTkL++GDh+IymOWps3XueUd8zMRdZztLBUQRd/Yiz12KMy++S0+fE4f3rEKxZjJXDCpxPhLDrqIBioUUMb6jnhG0vZrnxHMx1YGPjByplpKiN+0iSdB8me6ZgFnMds4JPPMx6wfK50YR990FGQ5cxoluoVNnIlMPpnJmUT6efIZXvvBCq/wrSKTS5h9OR4MYA68v0nl5Hn38w2D4maMw24rZGMfSgMvdCL9F8sReT+T7Iazxi+vRQrmwK5LAlpPyQZY6MKH/7pXhhsIT9Tx7SveAp699dYmZjIm6+Z6iUmsOS4CWkrwrmyIc0tlmMRqNek6Tkw2gqWfHtXi5W55Zw+8tDJhppIMjNw0FZj8x2OYSHL6f8QwDG/Qu5s7mP/R8ecqXqJDL1e9EVUyKpejJuYnfo8PHHKdWE8jprSgxvYvU8A8VhtTR1jaKt+g1JPfP561zExo5MHoZfwt52O6uqjnLbK4aFYuYE5J5HU1QN44wKpnuvQHuREW/+TmNFiBq+Ui7suBPBwkGtNBe7kjhZg7y6B1zKjEDG9jTdwYtYc2oOd81jybl4nQ/6U0jZJPiPOko4KCjo/4PI/n9m062TQU9NCzlTI+DL9SpUl8nzm/e81JHhaOkuNn++TLv5HBbf+snDt69I2ezJEemX7D5gx9Lx+0kmgt1q9uxbVcHGgUP48yIDuTV1FF55zIH7pWiN0sXZ+yo1Y+5Q7Z3Hi/Uu2LZWkDhChQxBKS9lv9KVHcpkl7Mc9g7Arm8y14evY2GlBXsDnTGSHMxQg89UmgxCt6CRuZ2S1Iu8QLQxjWHThvDdVIjyhRn0pj9nzKcQvH5Y4JgWS+kEYRQHHkT/132Gi8RjWZvFTiFdTnuq41i4G2+DTJYuvMBm2R7Cc+N4L3acypEe2A8fRf3FflzD+ogVaedSeBrzX/diWxrHAI0mNvbEo994HSM9a3ScC+lrjKVtyFTWFm9lkf1ushqdCJbLw8btAG9//eT68MmEdxdw9tQOitMjCX45HKvz6RyxL2C00F2KTE6hq6RJ08wniFck8/vvGpa7jUFTUICsuymGSvpIfTqM0PtYnGImIZGSiKb9Ch4oKDMu/xnHqt5R/6aYk+PLya/LY3jvFFzlRmAfHEHmWW16TTy5NFcJ/xepRJeFUSk6i5jNV3CqXsE8x/s4O0zjy+GlBM6AMJV9yAlSsBxsgZmWJdsTsngWuAl7wQfEe/NZ9TeEVyP6ULcppc9Un7d/vzBD3AzZ+Fw6ez5g6lmMZuFdzslcQXeTEmpLM5n2p56uEwYsL/zOgeGmvOwsRvzaY4b+mY6I4VyOHHMlY1wt/6x/zKfycl4vn4pnYRBGOkYoJZxB1t6ToQ436X94m9yvelgeGIrvmX3Mj9+HmIsaP8qcyPXSIGG4N22XoxFRdWG/9FuyvaWIvWSORtVJxtvZYSNXiWODKXHp5Rxu3MUPoyW8uOvOowsRdCkGsaYsHK2PaSR3vGHC2w0Uyc2k1eojIpWZzNIx4VveTUYsfEjHq9FYi77Be+p+7n+8jVrdSByUKtH/kcfej1WoJC3GTKqTOxdzOTH7PUoXztE6/QaT7TJ5btRNk2c2C1TlKB6Yy8C6eGzmlyMtEkrf+RSS8sswNVqEoogSGsaFOAmF4mTcx3RZEXSN7BCIneGX2ycClfLQUCll3aVviEeZU9yYjO6wHDp7R6GhNJV6m9uYNP9mq/VOxF0luK9bwFKfwRg/EcFG7R1+lZfw8VqGSFM0iukVrBSNxbQtkcMRPZy1jiJqiBrKYvVIN6VhOcka7axGbg6/xqWWOfgPakJd5DNZKV/Ypy5FUmkpaplmjPrVxaem8yRmxeCVY45Fyx8GzOlDZa08uutHIbJgCqeap5DmMI2MsUr8bitnktRJJEUciDL+Q5tvMVmz3lBrv4rNjqNZ11SGRncmelKGvL5zkxuZ5mj8eM7tjLuMinairbGfSTHrcSx4xOZZR5g7tY5BHikcrWlg8Sgznv95hHTLZ6b+/IuOkDr/fJ3PuG3bqTCI4kiSEh4G6Qyb/gYJw/dYXqjDyVqHw12uPOovoOR2O7sCVjMouY3LH/dwLPM2z0u0iS1IqAgKCrr2387s/1M5fGtz0EwzExLENFH8XYqxWykPqlRYbGiOfHMOrwsyuJBSzWn/+WSURHNCYwdPWhvp6N3FAOELnE/4B9NFc8g+lkn5p8sIxA2wWezPdPU/bDmThbiWMjq1PYTUa9FX9JjkUZropV3FyKKfxR1zeNIJQgqp1H7/So7WFAa1p/L0Tzy/hmzGMKaL9ao2TE0NwcZtIA/LW3DsFCPbQASt7yW46Zrzu2c4H0QK6RFrwK72GTKyxzBuMeZhvhAtg/P4XeeDmsIHVqkMwvvlXSYYzSCx6C5j264z0O4MsQ3TcBYeSbHoC+RLbuPd/4cLidWUPkrk+DNhSmXrcDWpp6JAkowmKSY3JVIu5oqtPBQV1qC4cT3noh7x2VSMtWPHs/1lJlqmrthUR3JNIErpRwHOagI67eyRERPhu9NkygovYrZ4AqKvUthbo8lI5yLq5u8jtlGFtT1ZDPRQ4khZL2ttxViVX4B943eW26jy42May1K1qDLSxr1DmGfFJhj56LFNWY9yuWZO5Z9HfYgG826/pcPaBv+ajwQ25VF9ZS6Xpq7im2QhWM1CNamQgTk/sNi7imPbnjPPKhff0QkkZi2hTcKLCUNB124d0g9u8sVoAE0WxpillSD+NZpEywbK+6T4LG2BUuUD1EXtUNFwQlbQhaStGvZ3QlD++5d9Tt0YeO5gmKYlZuEbmJiWxJpORQrFRZlLLMnWg9nb+42vzdJUCG1k1JN7PN7wnMdIkzB3NuJ2bWzs0aDCaSLPhX4wQtOP38/uMUHahdtFrVTdmYzYwZ0oxx3n0ZcMGmaNZUxpL3+bvZkbOBqprUPYW25FsOAZ903VGFb+ipzadey1TOZrqwY3q0NIEKtC23gcXpKe9H84zO5P4K69ByWJUZQ23KRLdCYqKuUIdJyRE9UjsSIZuZcX8Vz5gLg350kUXYazszmpspHIRYgxSOg3UkLVRF86Q5kc6N09TcuCm9hZVKBk7ENSSBQOjkOI7hElr+03Y/ok6JPOoKO1i/iot2wecY/LlW9R1tKn41cUdqoq3OuNQkxjMUl5UiglveCUShd/OhZgqnedTp9IKvObGaDbhJCKNXKVlbQpXkAhpxaVkVdQLLbhqswPVj9xw+yqIj2q/Ux8vZcBDY8ZXX8NrfZ/eWI1k/IuDWTeWzDDUIKGpmgkBNKkv3iDkZcEAzpUaRD2IvpvFqZDdXDs6WJrzkcUFcdiZNrJq4KrGH2SoNpQivyfsZhYG/PwrRY+nnJUVPzG/10oYY4+qA38hMivdtJ6f/FPy3F8q/JZ3v6Zr1UbUaKWYI5xesBG3pt/RfhnA2IVP1BVdkGpLpfsAeLUSbjzq+81CuoqfMteg4zWeUre3kBreSpbLlaARRTjFcUYri6gvVEWqUYldPqysdCfi45oOzkVjVxV10M9T5265g9sH6TDnJHd+D8R4XjNL4aOtGFrzTgG5BZh6yDHTYMaBkY5czptP8eLsv6jjvofYT6sNBT6l/t5Um2yDOFnPUhtKqG6soq+xkZEu9zwlxjBlucrmFToRKlqJPN9R2F1MZjj1z+zdZg++yaN4YO1DXvqJ/Pnx22UApbgu86JckCrHxAAz+ZzJ7MbFQc7xg3Lp7bzBjEvF2Hnco5VV04yDMgvSWLr4F1YVb5mlrENpspZ3Ak/SeH3brRz+/4vds6yKwu2a7fzoru7uxGkRSlBBUwUxcRuve0O7L7tLrA7MMBAVEykEaQEpEG6G/aHvX/A++159xjP/A/nscY51jEXQThS+e8osvveY6ezCI/UCu65ZqOlLcqOhE5kvedw2dwdkbVHadEUQ+JOHuc3BPDOyx9ThR5mHlrNPAsJskVE+COvxD9119msE4tpUhGxBcdwGSSgUd6QH/7+TDnijv6PkxTP/IfI9NPESzxjMBKY9+oj8yWTrUsv8eJ2O09S+/F8oELXJm1WTp7C3bgovKwX8cTHnoMmooxrsKXlwmrWtXjQU7WBEo9cYv7xQ2uBDApqHiSLmOCa+ZzQIAsqxBYS1+BAV6s33bnHOFY3kIONH4it6WVo5TtsB45le/QFouW7SQ3tR+N7H3O5jUDzINOtYjgif5monFSyD42jZeAgpowZh3x3MfqiZ8gQCmXPg1SUh2xh/ZUIdN5uZGKjEZff6BB7xomwsFFAF93k04Ql6kigONmBpcWGJMS9ocHbmdqDS4gW98PRQJwbdUdRbxjA4DIrOjVVUPOyobbuImOklmAnE8eIX2UIZD+Q5+GEZdcUXmQnsqnejgN1QhC3FER62L6oH8n0G6S3RZJkMpAy9S3UrwMxgYCwsDDAFFG1AtLeVXH/0WlmF8C7TYcRU1+HscV2qtuESFYbCCJj+ffwUlq9NRk5QZlEMVEu/b5DXHUeUkYL2Rjhi7TpOp60T2ak5DRuC32gYFMgBA9jkmYQ9/NTcZsdwfejAk4NXUiIjBrPfXaRHruLt0nXmejuT36fAR1aAqySP9PYLk1HuzlPhBoIaVlFofV5zo07xNfYuej05FEpCODPhTB+OluQOsKXDdcfUhEyCbEhPQzc+4lz3X+YsWQL7fWp5GS8JEvFB+eucTRUSrNpsIC7fwuY6t/AWnVNmuVfM6otmCYpJx7Sw0J66PjwD575miga/4On+glOWzoxoGfV/33Mv4CWjxAXB/E/YIIYv+r2INz2gTsdhewYfphuBxD1UoK+ZvBzY523M4d7JzI0vJ69jp/wFQmlvU0dPOu4KFLPsa9OBC6GI1aLKfWRo8rxFjskyphT5EPQ23YY/ZMN1a3UGZqhIlWMq68b0V/ykbk1l8IxRWi+DueQ7wveH3tE54ImzLvSKemYS3RDKe7yv3lXbcpg7Unk1d2hTUSUPUeWIC12nQSBHM7vnekWFUJUV4E5+hcZiBleZ+5jUzMXIScPNv24gW+cEpl1evgtDWCskhQewXeovZLLs8RYSkimyV6Uz5KOVHiN50NGPx/VF2IgMpO7n3t5J1eAcf5ALhto810+j136HtjGVdMiNJBxKfVE+Fbw9Ls0XhZSnM7SoeDc0P9uPv6DCKSE+n/a9GNrs43xeQ95tLAXA7EczveZ0KI4i+IULRI/v0BSXZfv7c+5sK6A6KgAHDVeYev4ms46Y/4VMeFFdD9uBwS8cBai3yScZYlr0Fm1F01VB24U/yVGSpxMgRShcgq8ibQi2nooLz0f4hQeRlbESWKCBBwbtp97ySm8eSrBAhdhTIPNUP+hw/oBUmwvuYCs1TkOJq1mjIspFv1zyEk4x9r8U9j0mbJeaTIpy8fgf3c2var3EE0/zQ+JCmqKpMkesYBR9avRVtak4p0ai32XsTHpH6qUxeiX28xU6RdU9BQRKjcV/4pfGGtZYinygktbbNBaP56P69/THvebh5/0aXyyhCHv+rkfo0T6239Qnu7D50XejA58SqXJaa4GnoY1P9DzyON90HKGnrtJ8eHVnIwYhGaxLzEfGjEOTka7q4hHEpl0J+1hjtlD3tStY1bFYn6MB5NUCz64BjI4z57XBw/Q6z+SAeIq+DZv5LTwXLbMucK9BSsp2byQH2FX+Jh2mDO3T3PtQziz5JJZYhqL6fOv2OsWYuKSRVPLXcQbjjHVwprp6Y1I96RzdVwE9r4CVovYI7/FGQGS9KGIEwLiKkoxvnCJinsbOSJ7ALcPFzmdeAML7W3MND3IlyQtNimlk6Y0GFPdYRj3thDdtAR/yQsE2shR/uM1WuNGYx4vYJZbJ3lXXrPseypyM6cT0xDBp+IeFEZswyCuEpE/x3m82oLPO4dyvGcyp0VNmBaaiPCH2WQL3Lm1r5oZtVdZcH4P6wq2MjYgCunlD1ihUU7Y1nUM6HvKphVn2Kb5hLR4aQrLGhn65gxd+i7k5u4moHIXEfJvKFXQI7z5Pr0N5mwLvMZT4wEI7GTRWridOaq6dOafx1JkOnNU61nR70p4xgHmWPujZuTJ4OaNNAhs+e0RhqROP+uPhjH741qyzSah/eAgAxffpK8thcflQbiUXKXVQ48Sy324fFmOkeZ4mpcIY/fyAp0uObS/3kOy+RCiq1/TmD+MxRr1pJgkEKKgS22aJZMFJfjXKaHm8pr+GNhtEc+ZYRdpSvTEIV6DzoEO5Badp6lzNom54rTr6HHWTQWlFjHuiQ3DP+Ew4sKfEDS6sGL0S1K+aiIYX8HGz9OQVNMmMs+KYc0fOHC1jkEPe9DLM6Pwmgo/xjzEozQA/Vkbkbxxk7XlFzgrt4xHskHMs73AxMTvZOueIr93Orq1NkS3BaEjmY22+VdOmcmyZGklIy56U9G+nj3VD2CAJxaVEwmucSYmx5UcJSVGtW6nW9eby0luWLta4V1uQuPAfhp+LKXOdD1pXfrMeS7gV6gr33MmYrDtOjtCb7F8/HsUrUwYfekiT/+2YGU0HYXCZqTWJbE3ToecG5O5NHQWz4YeQ/9GJnI21ujG3KHRUYtNbX4cDvnF2qgkKDRFbaQ6dh+VuVM9nBmmu4guV2SjsTwtRm9obB/M20tG/LIp5ejlLYTKORG5cirrd/9/dO2qpleE+uJIfN0TKBhwnq5EfwxLGsnqUqe4REDo4/ssXf2IFbFfeDPeiPPisjhETmL9RENm31jJz6GvkY6H5fky5LiZUZF7ku8ftjP2tCl7d/kCUcwff5CZ024z6kkXu29mcnCXNonWgZhuPMIAt5nkWWrwqbGXkr4XjJ86ha73OYTGy2BtdIRJvt4ob1hD3MVC9mvsRE/TDcU/zdzprEPxvCx3AwzZ0nwZtewe5sX9pvb3R96k6HJ+uxxz+27y+dkp/A7OZY7CKczdPUiRmsS+hrVo9KTjkfmYGXSzUEievRtusdbKA7FDG0j2ceHY/Skc1huPmPxrIosVERLTx6JXi9sFQzlmasxktVEYjqmjK/UPDa+FCDt+m8l7IqkYtp/L59spHvySruh+hPVD+Bi4nk1KPewKXcu/yy/hVHueyLeNlFetxcNRl2MxklxIcaCkE9r7tpDgORBNwzi+jnxGie8tRs4NRU9jJ7ftU3g3bhlZ2gWYeDvTZJaEoL0Cn1vDeTnXgP5DQYRdvYPN8+PUM4auTuj7fZ/1rGXRuf0stysn9Ot2RDYfI+7jGWLPCAibmgRYAdGIYokL0CMzlvkWpsS5vOTl12UMn+BPdb01ZeKZrBD/Rml/CNa2o8heacy71ZMpflRNRuUqVBp6WZG+jteeK3h1vAGfyhgCxUaxxlWKg2WFbCpejZO4ECFFYLGxBOkQD6QCzjBAdSD77k1BTyAgbPN2AJT5SlNGOZMnLUEgcEewMhSdbBny/55meZcPN2wnsUF6NMLTdNic+pPtx7biOGQxkm81uGzwjpJJxawYvQW3fUrk/6rHybaY3Mxu1kqIcmy+Nt/Pf+T+01XY9ibSnL0KD6dNnBxwiLVSocz5vZ/jy+Zz3WY+YIyxVBuyUlaItI4A+auYfnQnMNmdgMEnGSflw+7ZmSQL5fJTfjoTdq7l/NCLKJg7M/21Gqe8PyKvV0fFdynue61gdOcVfnXpMy5/HGc1Minpnsvj9j/kyL5B78ctUD/DwVNZzPgrz/lX+ozY/Z6pu85x4pMCzZvuo1U0mMMeN0m4UIn8KR1WR3yHLyvJeDWawovGmD77RoDsDTYHuxN0eydHq7o5YKxCq+MSAj+nIhvWjsm8V+wYMY1xDn2o1fQjuD2BRfqT8XlZh96NxZz1vYOR7wLWeawl3vskMv9awEMlCqu1Sbiym8W73Amvv4dgRwShWhZ0DQ2ix7yY7W9n0H7TGve6M7jp27MqTRL51utYpGVz+s5aDtlK81bWiiu2hYwX80RzyEAWmVXTJ1lD1vBgjp1awM13TznyrgTnIjdep0jReGsNekVPsVnVy8ipn9ieMJ3BXQPZ8baHEvMKEjdrI2n9kbx/Y7i/9A0Rq5Ip/a2GwDaKYR71RNRd4sXviWTdfoHFy+s4Xl8AETdw+xVG57f5zPg1g2kV/Rx9mEHfw2T8uvLJKdrB9QGzULh5j1DlGlbb5xFu8N+Fx3+cXgHVZhMgPAWpNUX86lXHuL0c0c7BuKTksu7xImTKyylRe8UdM1eGnP7EvhkejPe4x/Hx46j4LszW+uF014FSqDc2u/ezpbcFJ2tvVC0KiHmwjHcZ3aBSwtHqHLI7TzHuHti7neDuy/d4AMO6YL3jeqJErjPBpANz5UxyrUz4kN5F/c96gmoGc89al5y+WOhXwyO1nA5jSaQt/VmWAL1yElw2H0/LUGdaNcQZplLN+Q0BWEnYYa/QjKiyKEuGeZAlLMJfw3b+qbtBsYo1pklFvNOyxWWQEKprXEke78+rzJGkxz2geOY1jNXE+CH+jCW3lqLTp49SczlzlqxhY1ART9L62bpOleHtpRycPIyal8fwslnEZ8U2LlmK4SxuTOuF1bj+OQcMp3vqYl70fkRrgQwVf6xIFjGlp/QVu4LM2dS4jo8NrvSWziYmHY7VBSAmbkpsTQ8HbOyxdRjD994kouQhQDKL0CZvWuUn0Cf6mxmWMbSKQFRuKn5lvbTOv8aO2nbMWkroVIhgguY2Bp6agKfhItZfiWDGURGCG41oLoVdU0Fqy1j6SKAXRTSAfiTw2unKAuBVRQ8tD69ikl/OVDVHjkpm0FKqjbGMMtUyU5GTeUO+ihi19RcZFbif+n5xQrIKKZ0xHYqyOK41jMB3+xBJbWWariy362WZNP4LbupDqav4Q6l8GtXdWXTpPIYPy0mV0WSQ5zCsm6vJM27Gu9wPNK/yfKoAKf1VmOhG0y/9HauX5Yyv+k6dnx9JjRFYDjJCdIoeXx9W4PokmiVDTLhkK4eU6VrEu4YjK+eOZ48B+c4xxA87xtNRhqQny7Bm00LKHxxhpaEHk6XVUNLtArFvvIqex/7gaSRV1NJj3khPswLi6lJ0tGvg1FlISOsq7F1EMHXSZDWrEW+8iaqMPz+o46ezBX8tLdhw4wEJHETMo4eyt6psVLmCfvlQMoVsyc14yQhLaZy7xlL6ppFNQ4QIfvqFLl1JFluV42idwciGYBTcA3iY0YNfbwHtUfuY+KoKJeN/qJLWY2xvLMNn7cHdYCI5v0xo6j+NetwnOlWqkJwgxtrgWQi3fuKLrB/bNCZS72iPyrUGRve1UOtrxzpvZ4JKPbgf3sCyybpMFgmFnn4eb5jFJeF6LF79JGDJRLSvePDZWx45lxJ2SKQxr3UoU962M2ChBPvFe6gzsqJKRg5XXzdyk0Dm1hzeCRrRfLMRtwpD3h97hLhuISL1zyjpmMu7xgbc5X5jmN3AYO3RlHd/pE1UlGka5aiIPaHHdTEabceJFRVGxESS2UMuopVYitf+B+zpXoS+kwfi9flolBeRWafHpsr3TFSyYdS2eGoO5HJaqJxXgmRcXfP4IuEA3q7cvPCdHPUF1Gd8487nXgQGiajkW/OqKZ9MuTyEfqViE1dDt5QsEfnDOeVbScdXEcwspLiQF/A/jtL/39vN7AAAIABJREFUFbWrdcdO7hif3ISvtjfdLfV8an5HtrIPC6TqEFX9gknDFR5dOMaIJivcDQYiPnEFppJ/kLsfiKbvbBwGubDLejY1xwMIsVVH0saQkbHypKUOIVjlDDPvHcDh5nTcvG+xu/YYHiWWnGtcwqWsOuotf3NWRpKgOke21rbgpytF8/mJjFyqSebT1xyRMqAuMAzfxFZGN3vy9pEm2o9PY/XHh6PZZiRIpHJggSKdGUvx/HWQI4di8Vu3DKWWC8z+XURwdCfh3YGE2Sjh2hSG9Cgfvt0eyHH58VxoW41aew9BagOYM9qC3av/4dX6i/QI1lDxVZqylCiU+mZj+nQrncu8GHHqDsd1ZMieVIrnzQ8oOmxC+9+hVJR3MPxQKp7qm1C8IEJBYyp7Pz5njZAY06pjCdoegVorFN8OJ3S1KEcbVRAEGDFCUYcmTSt+VDqhJDeEL5le9A6IJzmvk7abQ1mQ+5EqHVkm5jwjQv8R9lN20mauQ8wQBTY/U+fCzVV0zbjI3cc1ZKuLIjNpAD7uJczVWURy7i2uvNrHlEI9BlQ18XOhGeJP21iY5kRL2XPqlNrI/fOIOVPUQHke0ABY0IYaUT2FhG+dhYj5MiJuZ3DoXB7GJU+QdYziWk0uD2RHI6YggqtsAQbtLRi4ueC12JGJ6nf4JVaLua8D6/KziV69nfBfW+noek7VTwUKXrzjU94KFgx1JVitn0d7smgvW0bjk71YTJjM+opPrAmeRpNwHvUt9bxqUCLEz4fgXd6sH2nCp5pIpj7vZO70DJ7V7mLvyjuMcE4gKjKekB0DsZa34GNBGYdMNzLFIprflxaxNnYfdxXFMFi0nZfPOpmlHU1kZR6zsuvZtn4JVzv6WDLXCLGSDzjdusWJtV1c+RqLuLseR06NpdJwEYKuQqiuobmqDmOUqRTcxWCcCJID9tNsEcIU2TBe1iZhpDSQjVvXMHOoLG5KxehkmlES6MTS1jFURvdjrefJoH5l6iVe8nJNCZGerfjf28xWyVjGSF8nX6KJyfnK3DETJ7RNnBeRn/iVI4+fjy9ZH4fzrbAcrcxE5JzriC7exN3pUvgnPObEZ1G23X3In7ggflbLYKw5lmtJLaTPFCbNoorXtuMokc9F2/Q9wRkPcCsvRL98PjvV5pNdXcOQSyqM1nHki9487NSFCBhaglRsC8c92xl4bBSzFlZQr7IAg57D7HTroOHwAV52SfKmpAqRH/EYKJpiVSRLa+QyIu03sOm3JrPsjpA0u5zpX6vQM/rGVMVmel/nYZkXgIWhMNMbuxFd6IyzVCvfJtjT7VnMyl/vyD4xn+0Gt/jUI8nVGAv+ldrBpqxmptXb8qu6GxGVIIo7KzDXrKBa8ARPYUUKm+woH5BN8qw+ZB2m4bnRA/f+9yiP0Uez4TZpjr0I5X1kselDkjIeU3Q5mYtB+1Cf+hP5HnuCzIq4ViCDm/UWXpQ/IVPMAX3mYtwqReNQG8SbylEuV2OI7zHOXdr539rVf5DDl6/seOGhjcK6p6wWK0b/dxQrhm1mT2UpehV/eeilhIvoFG5FpnGiQxqh+xv4IaFO2cwRHGhO5MbDYUjeTmLHloE4jPJih1QsUaqpfBq0gb0B87Ez8CdtaSiKU9IQeSfMcGc9Olv76a1xpt4snzLJRGZc62WdmQRHnecwOD6DR7ZlvLgax6WBK7jcqEuf9Bach2kir7IR+V8xlFfWI9M2mM6efoaEiuJk7kfN4ZmE/1XC3esffo5MZaCRH1k7M/gYYkV5+gDmy35DrvUNsa/UMfX1ZEtyDNoyTZyotyTy9Gf+XR/IhtmTOaQGX3qdqb46hyjXS9QUZxJsLYrZ8Xia7mwiymU6aYMU8JgQTZTYaaK3xDDJWwfTFc8IOmiIQ/EK6BPnpqQhi/rvEN3gwLQhLvy5UkNuXTWd5Qa0Kx5h27R+2vKksXq9iAarU8gUvUR6UC22dmOQSVmFkPYMcn5+RaBej456FPLxcONtHgvHWzAwT54CxXmoJ61kyePviLX5oyEvx6RtYxhv5cfmIz+oVP3JjBnzuGIqy8CuGl5+fcWAaavo+avBSAd/Bq2Tw9M7AiE+A55oIcHjvnL0xcUJCVjNMh9/ev98x1UvkLbaarTlD3LL6Doj+pvosctjbLwiQp61eP+RpNF+MV/jWrG11aJPKw/ROwZYvzyKbP8Rnvf6k3TzLznZkdw/dQwVEyFcDWTJOLGd+6KDUBAbj+g8NyaHvURPR4UeGR8+NdShZ1zLpo/p7FwczMqvrcwc5cNWpBniJ0uabD3PrieT8XonR0f8ZNvAIoZs0uXsdXHOjB+A819TFj/9zrgt5xE9k0HEr3HsTNEh0DCfRaajOWWgTtnchSg2LmVGWBPTIlQ44LGPY5ojSPx1i/Dgx1xZGIRLkDojxVT40miNiOgdbr40pdFKlhlOT6juNUHLax/GSfN59iadbpujxFydi7XncKSyg8maqcvokse8qc9B1M2AaXWK5HoeIeJEDe72+jQUp9HS2Y1fowZXDJ6il+GO25tVOIwJYe4gGWwunGOkry2OUgOIOWDDRxcTQhUliIyvpzBoCdJKXTxSsaQyLAYXC3sKOiXZrG+BRakdi4Uaqfhgh7vkBTSn1qLSKknlvSlY2kUxUDyFQ31+RBV9YsiHEl7btBKubol5rQxtShpMHTuOP6dOcuLUGg6m3CVCQgW7cCOO/0nh94Jj3HmXyMyjz7i3r5xBeob4qt3gc/I8hvRKs1VyLuuLbyJs0o6s1hRK339AfJo73ZniGCho0qlfi2apDhV6Urhr5dBU2Muif5bSmvCcDMVTpDkrYpGejvitUZhqXefRpxgmDnyGRnkWUubq0J3PhJIsIuu6MLMbimpnCpY/ttEw9xmJnc9QXL8fLb1Ufix1ZcLDeNonO1Gespl0rSvsWZaOboMYEuJv+ffSQGws1dlgJIz3r1P02B4jNes3+y23srk9FNu2D5hI53PRcQVOmSf5lJ32P5pR/ys+H89OndsR/lOZ6pt/sTSZgbqWC1/VXOn7qUJrXhMF7s4YK02k9Zsz3hUmyIwqY9WrfGYlP8EzupYri66S2SVMp7oHEUESDI7pR8hOFyvDMWT17UVcxAgxgQ2frwfzcdN9bjyUJFTpATVj41GVMcLt6g1S6q4jtOwsjz5WUGQdxq/vJ1EzD8XLzxrF+Dt4KGfyWKIUmcoNJChasEcuhTMhkYzdMIDDH3axcNJwzq5JZ76lAnJ5QkgKb+W5TwNL+u0JCBQmXFGWyxodzBn7D5+W7SRp5mMWZp9Bp9UEI7EO3r8vZEKFMdUlGehIKrJniRcqsSrkXbxO24Qo9qx9wNwZboyddQWfKa7sPDKdve0WJJxfQ+adftaE5VFkVEjBn+Oc+nuUGJuxRInp0SHfxsRdW0htW4rb60scaC8jNFCIqkvhlPbuYD472fM9lvBWIa44O2D1MgKn7HKiE+t4pDOYN7efUiSwJ7SgkqxlV/h4o47ln6KReFnMLtO9BN36jm1REavmmGEvkENYYh8t766x4cBddgwaxo8R+qx0zudPcTQ6RlCjF07rN1WUdfRJel6NpfNIBIijiIAOQJa/TB1qj6GBMB1bIomY78KA7yVUOS3A2PwsAWmjqaq8gbWxCYb94owU/EVMWYHbeReY3PUGPX8XjnRGUN3cyN+aNoocynndOAiuXGK6di2e+qGk5evzcq4NTUpJ4HuN4/vP8/7+HbzNBmGgrogYisiKKTM6eD73wp6SlfyZE+PmsrvMitcL79I2OpNZIqeIe3iCudJeqE8Yxfy5miTceslUMX3WXLtB/OkSchOX86E+lCaJGpyyJTCo/8ND8yuozArh8PNOhG+loHR2Jf52c4na9wbtgR6EnzrCqREuuPbspXF1B9n7DiGrK4+wkTkMciW9JwY9vzL6uxqotlJDQ/ULH0qsOCoqyqCcBN5mlfJcWR9LlV3UBGjjWbmQW5KTMFtpiXVBMjXSG1jPebTW2XLvdxn6Hh5kvOrihYIXPwwUyH/bQJjSAJyeJRJ7/C77pf+gr6qGrXoG4jVfSA5Yj/GhO6zDkeG59bQaDWBrmiZbu5XpDNBjWPwbygKEeKXwFs3LojztlcRbTgSD3Im47+rAqPorHkrvkCiJxjJJkcGxDYhK5eHaeoFyEVF+6vjwrNQOKyM5Vrb/JaOyBLn8AsrjdnFp+ycyndvIytnFOolb7Le8hKlaM8Nu1qKe3UC4x1BW1QVxMawRC5kRKNRUcNfgIiW+8zirWMy0MVp8sHjMp35hbiTEQWUaH/umYnq7lKCEdlwp5t6gfcy8n86Sd/ocEvhxND+QAHc/koyyWefVzIdeKZxdxHEUSeefxkb2yOwkRf0CVS3+yN7LYW/kRl7b/EC9OI4Jgt9QLsz4H158TvMj+qgqsYrOfCIOc2FjupY7MedVLE/zEnHzakdBZyt/lbajLz+Ym7lZNLSrcFVaih8BIfj1x3D1aSxZCf91Pv6THNm2ekfpzKusD9Glc+huFiZ/p7LBDo3mHXRr/GHfWTecCqRx81an9LAqF+JeY/5SDC2pSiLr9uAf/AmD28sIffMUtafVmBkn0JYzHqXSSGJNWpH6eBbfjkN8GNxJ6/EusoKbMM11Y5uaInYdJoR0ZuFcu5AL7Ud52JBLe0sr/Xe1+axpz2XXAEKLv3HEOIt5F+x5bR6GmcMtTktfQUZJhw/CZewwrcF2xB4CT19CPfA8MrlZDHZMoeJxKHVr/TDXi0anzQWfiO34mTkx4qgGugcHUe30iYm6o9H7HcIjExEKpMZzoPMlc7Ue4v7Bjv6qOOytfDgr8Zpjw/NoCIpj3t9m1IRuojxxAS9kP2EasZDHs8eidecKDsIF7BEppqsqkbJaR7wMpIjO/YWJiSXnfhUyVzUP25k+VF+rY6hFCkPGNDJqw1VULJbQ+eQSklZxmNutZOomEw6MLuLKo6XsVdBFQrOBX92ruNc+mIhhMcT9OwbTnQ0EV17nSbgMhXNccFDKQ0TYlklF8ux8uwjRmgZUJhqz/3kiCan9pDl4Y9ZTTXW1ELUpTXx4fIqhY8YiSTOKqKGFB1m85PqiCBwCbdkqe4aW8yLcz3ZnlLUa7j0/6JEay2j5Jyg2F2JgrEmHkiz3p5/g6pdmzpfPYJiGCdGtfzj/1YyLz6vo3H+aW1drEH/dxptIaxYOW8z5n7acK3xOX6Mzy/dtpiHpA3XOmZgqzWOAnidpHKa1q4u7y7czYtAk7m2KYNKQVlbIBhAcsQwroQouHl2E3c4M3PriuLL1Fh5L5lLs9IrCcAtkNlRQkreHlGcfiR/5ld/3CohLLeeM7WDOSV9iww0prtdVMv+KKaE/7zP4tTSaG3exeO9MzDuFSG37jrfGZ7y/LyKjII+hgyfSlmtD/yI5lBKckWgejrTEHUICZyMRfYasvyoYFTQz6/o33n+4zJHNm2lXtMdO5xd/e9IwUtDBvl6HZpFurnT/obP3NgcMZDGKy+GVrS6Jzu9pTJfE6WYdpqv66P0WyLnkVYwaFMJ+C1cCPovQ0/CeThtzJlYs5KHDZFIsHXFQeMHlmhD2XUuhw3wcI98eYuEDU7xPreWq6DQO28oSVZJPd/t+bN8c5XXRPAa0qJNtc48ZPedp+DwejRgjJoY40OobjWiTP7IdZ1HpcqSyuIKuwfI4/PxGlBl8dwtiWWMe/9ga43JuHRLmjgjJdaJRHEBqkzWbPEPQlbvANkc4mVZLjelzhqb18WWCP42FsgyTVqBIsRdh8VZUo55RKnUAHzMzalSkmfLnPouKZyJIv0ZZfQhKOrlMLvvBjNKNpKg8p6pTmM3mXnzoSqRZvZy+7F4W5jwj3W0dZW82oDZAi4ribDb+FmFAag+rxd/zboEyD7ob8fshy/akKvS0jHhR/Jwrb0+i5T+NqRl+nO1KZ599MX/VNRmr9ZnSlmOM6dekS0oekXdqPFpfgmaLEzKFidhZO3I/Kvp/NKP+V9SuUpqb6Lcfz4+yYVwrjKFSIMXSjO9UjDpLY5AWr8N3Ul8zk0/za/h3ewNf121i9oVaDl8RsEI+lWm/45k3VkDVjUWsPv6FY4IOfKtDaIv9zqBfVtTXqHFWZgsJ5pnIX7bHcYwsx2XtWft1EW9zTIlc8Qn7aRE81FHk0WEBUw/680nnM+dqfVh46BDS5yrYoCfEbcffyCldxN2uiuy2u9i/Tyf04ygevBnG3dEipPWXIDt2Kv1TVWD4c7wrJxGolEL331/4fl3LmMg8ZsWfZvXRLHrXrGDJy2FIdDxAtFkSc7sO1PyT+OZoTVbFHcx3x5JwYQlLHLMZ93gOpUceMq21gRCFHwgGDuHiTm2uJp5Fa4ITeycHMGT1Xi6vOoPmi6WcSvBmo1UUU0e+R1SsjtUbJxOouY2NAjdc1N9wb9cTioeOZEvoD+ITdEl1/Yf28YPJyk7niOokLsuOx3rcc0rlJPlY1Y/whnjCug6x6Vw/n5eIEjDChQE/xnLqQDKzBvrQ7bGdBdIjQHcQY02eIIoJi+7VEDVEBf24dLac0aMsU5/cvR9JLWyhY7IW5Q3COI+WQEAyIKBFQhZj8Vo2blDDx9ud13Mz8V3USdbby6wQLuXut5dsfjyTgmfLcFweis8QGVwXToGQtWzMvMD6EcfZEbaTMcF/uLSoDZlZZ/kdORXBlX3kXb7E8YVhuM2ZBeOm82SSNyVPxSGqEKKPcmb2C1ZsHYCXQwgGEkEIEGKHpy9T3YywGu7PlrBUPgt02T3cDIFpCM4r9Ph8MgKlFfHMLehBKfo1erLlCIz9EEydhNwHCG25xOw5UUzfuBA59XyGNU5mcEADbz4NIWPaC56YGXJ0qoDVB08wVs0bYa/DXL6vxKrF/SzZ/Jfe9CRyh05DaGQf0EJXiQYBKc+5v/okE72Xs9ZfFb+cBcxXPIRIYiSPRZdinv6d9aNmEW4cgOiuCHoUIjBuNURlwnra1ujwSmgyw7L+5YlcJSJH+zmEEF/fPuKokwU1lzPZdkGMH6pGfKzx4tX4dQjNXULJ6i0M8xmPj1QT+zp2YvMwni1B6/EUG4vF9QZef37FxknpGDue54OWLMbSOah+L+Jly2jkEtKJ3r2XRSfGof+ln+mfrrJX5Axjy1ZzoP8Wa7NGcEmtlKtWQkz2+IcRUj9YkO3NOo1nCAuUGKI7nEltPewwu03mkKWMv3uVlbmd7Dk/GKvT0xkvm8+7v24MW7eMsmXNRNVOxkXECMG045x+fpaMmgHkdu9B9HM4UUcl0Vq1AHGhXnYGb+fLka+o/hxEZvhW9vwq4GxpKBHJVdxJP86vABW+jYlHcLmB1KZXzHn4g+jp6aieb2PDp7tcjdpN3tVOBGvPI3xqBKsvFxMf/ZMZy6bRf+0O/U+3seHODGpMHcgNP8v2ssucHhnCkvH3OShdzpVjjxhdUEHsGluGJK9m35NjHK7u4FisLRfHfEYq3xxcqjlxoJ8ivz7C7u0kbaIcJe6G/9mg/i/I9oJ4mwBJbxFkdJ1J6HdlUIsHBuaGROV0Yl/0gRzHeP7x9UJ612FOjB5LauUENr6IZczpewyOD+N6Zy/whpjsJAKF9clVkUNqSADjJItI/TaD4/5GjOo6zeQaPUQqGrmtXIwq5WhEfiJTfCxI3KX+6yL69JQRqnzM3X2LuPOnhDPNG9hsPAJvuTEUDK0h8G4gfsXX2Go4lEItIeRM5qHpdZrtMoMQcQyl5MwtBErXeW3qy4CWXGoz9mBSr0xJ3F5KpeegaqBL9eNkmKJMWVYTNpqDGae9G+MB1ylQDEelT47PTc7MHN/Fb5MMxKqSOfZXmNF26qSuvkzzRyNW+4uwihok0oMIVhrKgaRdbFVYhfeQDHrzq1mpGwVerbT8/oKf+ijCtt7G9EU2GfabKD2phfiAFyi5+gFbqHXaR6ZYE4tkxHhd0Y/hr+NIjnBCoqwSbTN7ZAaP4dCbPD5HrsHf8gy3pbWoNtEgs8wSf5kvpPnbYFcxmIruEoarmnJTpxdfFLhKAl05DdytVCHAtANBxU9UxDWwrXxNa/xFRJX9UKIVTRQpop4Skdu49HuCZi4CwRj+KOtwQdKPTZX7EVpmgc7c8ew6thNFWzv+Ogei01GPhclkIrb54jdzHKW7LanelsLy++FE2NvRp6uFqPJRkn6AYNRoRAe1kmHgAepX0OMPxYKY/+cgwlQzb/TstLDWtyWzKAMtSzv0VAVYh5qS0PGHX916/M6Wwwb4qtPKmQLI9Ipmyt7TbI9ZzKk5yQzJOsaE0dPRFjrPX40mMvyKmN6tRhlC4GTEr+poJhlOITK/iPSRlniX5/HbxZcdbXWcZR8KlknENA/FpOo7uwI3sFz2JUOMoJ1OlLx6KXkVzoygKTRJJOLa1oa42hlklgfxp6Wcp7WOOMiBvqsGxuXtOLk9JL0mg2mlbeT4m2KU2YinfD79pqoUla/k3UNT2vpH8LUsCqPav7QWJ7PAT4onvzRJ9NBh5mRf7qQMpr9qHBnCC1mo/ZfwWjuGtAahnLiWwo4CYs4HotVninrHJ4xkfvJ8wwU2HX5BpNUyuHyN9y7mXAnTQKPiN/pD88mPDqJLew7WTcqMLOhB8KiRsGmqdOsm4Sfvh8Y7IfQwJAkJVBUaKVLUJMpNlPGS3myqFeFwfyGFA6oJnbSR6Lw+TpvtRL/gPbXe/QwQTyJZ+y4KPjvpenSa/KZG/GyNWG0+kDvRFymubUShQ57Aj/FIbtxE9+ZU1LL3YdEnxFxJRWqEHjOlqp5EsS/U9q2isaqYxSLtFIrA4NPTiD56D7WWOvJyFZBTFIHPxugNGUvFFncai5sQ//OMca7/Iho0nr+mGRyeeAV3RQck8xr5XaKIqXEcU2wkOCnQIkFWCbP7k8lwlOVJajJX3bzpcE7g2f7xGLW38bG5lP05lbjF+DKyNwnZtDw+KIj/j7P0f4Vw7mRh2287cQYSOs70vFiBz/CjdFQ/pVqvjsqCJN5/lsXMazGLlbJZF3uEiXuT2TjRge33t9H+LIYPcdbMNLqE4ZbPVE5/xdxPSfQHOcGTMmau9qNZSAqVvw749+oxoWgsyg6ZTLQLxlN0NvH3QtETFeft0BN82+XA71PzsbEdzM6El3j9foNlaCD9XT3Yv9tFu1whMr15TMsaR6f5ajSz5QhvM8bHvANVVU2WFp+CmGxa9OYic02ECVxAVgr09MMQVZ6AoccLMsvckJJOpaNdmX2v73PbXpwMTRmq/WfT+9sbXZFJVFUORrc1iS2V7xgYPx+/FaIcmiaNzqpJOD9qYHelFZevnmG2RA5XzLUYJjqO2B4LEipucbXHmMYQRc7v/Mia4l9s0Mpk0sndeEwdhIHQMq6aKJNHK5Xdh3m8F5oS2xi85zgp8XHs9ZWjNekzItOCmfe8FYYbci1yG9bjDHEf6MVFqWr0tzhhaghtWXBtFXhUgm433Or5w4VeGTbNliBUtQTL9N9oLxVmjl0ojdaPUOtYRUnbVsSnthKbU0VM/gHShMcxYsAHOjQqkbAUQNM2wt5/4eeNMFZUrmTs2CQahy+g7d5lJj5by8tCiJOWJ6YsHe+GFo4XhvHs9yYWWyahdnEB/cOakah8yub75wmWGkZnbgOTnBWQoZ/HAytx2XGemh3zgPXw2YuH7yrJ1HvDli3dbPYJZMRSAb5O6xEPPcjmgzvYrQ894vkod7YhLyyF+E81iq3Ps35HG777/Hk1XYPBK6PJahBlrkMgvd3CdAnlMEw9DIvMDGZE1TBG8JVEq2a+BOlTP2Uif14UsC+7k9S/r/j9+xCbVxcQ9yAD61YpoofsoCXOHjvVLgYKCvg3wo/xwQ7oWqnQnn+OaTdTiP+jSdnduTDsGbRvhYqdlEvu4vboyQzUUsF32ng8iopJ83PFyBd6D4HzrwtEbnXnRpo0cwTKGKaFICcUyWPPXlI6PmAbb0+Dfy/KhXlY/BQlT84BvBpR0drP2F8z+SzjxJeGdZh9r6H0rjci/CEWdeQWnkbYXYj6vzEsbmrh62tPpnrVcdGoHR0JF+aGOvPj228WBIyB5n5AmDCsUPGUQfF7E7d6yij+2IF3yjIutY7lSqsYZYP3I9tmzZK035xy/k5bVyUbTpTx3XIrH5VlqVeyx/hPG8mSrcQoJdDUV0+N5XT6ikVAp5XMku9oF6txv62Y+SndJNp0Ynb1AvN3GjNCRA9D+9kMvRwK2SB+rJK/vfJsCrfifaMWExVmMK/nH/SSd8Dsm4T0BlOTo8a7hlVM8fgXoa99iCv7cDW0jsk5WsjwkqYfWzAKbUPTrgKdj3eoq+rjbr0UzycsYfLda4wxtCDqxxXmeXsjXXCf90auBN2Xw9iknhsNsfw8H8jZpJmMWvkACcUlCJ7rMixsGFFj/uXp9zx2OyTTl5DE2LJEZup5krb05H+F8/8gTsYa/XEjgzi/5ByCBQJWzZqOfsMKtv+OoOfhN45ZpCNs4E/mihDiM7RQrqxlQUsqqf621J+aj3OyAT9WnQX7cA7/CSG9r4CxEkFUJ5kgGuxI3rVpJDQlMPFvKAYK58gT/84Kt4EcbNpDdsJyzIyTcHP/yOWQaTiPVOKAoxdb4oVZFnkQwdYpVI1rZfWDgYjVShPtVMeMujb6x8miELuUyxcquDF/DaKzCzh8/R5ud08idlwTUhcQ2BXHv5qNuBif4c4hH1wLZvLJJwMl27ns/bKTv+u9UDhRxSPjWJoc3jBmmzlxVaNps/yLt+IzJslU0B2Tyo8EdRbv8SWxVxjP3xbE3NAlaPRKPLXuUDdjCabX4unOtWZenIAhi51QSLdk5c4QojM+o7TyGdsOW9Bbp8Wyk8qcWdvBeVFLFo50pPWMCs52L/DWzmH0koeM2X2Jf/9Gcqu4GG+TrTz/8A+SJiLsELghL99Ne4sCX/9Cjyf4XIZHQyFPDIaKG1LdXcexAUrMvpuKfpsks2nLAAAgAElEQVQxjdICpEwkqEgtwsStGmubOSTnHsRNyITL+W109ofjpJ7AzrJ3eJrm83HaX9B2R7BAmdlZ6YS39rNRYz/DF3RQkXaFI6KJeG3J5FjkQL52ziFjSAwLX5rRby/Ezj5Z+hcMZ2caSHtD6wcwXziTafKD0RDAz5xGTj5ZC2SzY8cXQA14xY9drUT3XyPsSQqzmoXJkbnJYFlZZIdtY+/2I5QJalDFCohCJbeDTN0nFBav4VfSUeb86kR7gxXXMhzxG2pIff8A9CR/0JAqxO7zy0HckB3GAnZWesOYfmJWBNB+2ZQN0Y08OuXE5Jh72JJJo+IgfHLdmbQknoePF+M9PZkX6eexeqDJC/EYdJ3OIVR4k+0RKlw62kPZtT3gfhBsnEF7LjfLp3D0zT5y0gW8CN5KsFIi9Ymu+I2GwcoLMYl8xLsxitws/sS3Xg/86tOZUKdHqMCO973beJSgzsk5jmj0SWOTkYmvsAu1Uz3I6DKiNmkVh/85xpYbOwjOF+aBegH7XlaxOjqahIkf8Tc7T5CUgAFCPpiJVLLSXMDzPHPWfEpmSc0vlm/Xw639KBv7bVlRHoBG22T+ybjKl4gLXFOfTtRqSXz/WvDI8yfmGbNQMqjglrEXa/ru0xFfQ62aC7adu+jICiajRxsPCXdGZV1G8PQ+jNCjzMCFLYbDcOqJhWRDasQ3EXa7mgmzVSnrXcP3sRtxmWmBzMluZg+cyIwTlhSN1cIu/RaN+VHMXPgPo7868zNAFdX3a/BQ3Y3dFGv6B5/hk1sJsTYXKPggyg+bSM5VbySF6bwTSKNu/AfxlH7MNT5T0roL74PDqVv3mt/Cd4mpSsUroowDVHP55AXKS8R5Vd9I6JAqFAZ4ca5xGSqpUcxa5cmotAqUHsewM84P4dh+GO2Ajd1MPPd+QHD4OsEa49A36mXc2Rl43Jr3/49wntTQxujxRpyTXcSMGZMoMe1in8kEbjxcQbfhDALnXaTvxivcIraSKNGP+qq9BI2Zxi6DIA57+RH4fTHRFZ3ECBtSeEGZsOsOuM9RZ+6lOBz1H2GklYTp9BVsHLYB/ZpnCL8pReSNGFMfRaB9cAWFmxbiWf6CZctvc+lbG5VZT3h26DFjc105f3sbglubeRZsQcb0ACZpfOSB2wy+C+uTrSCKWogCigYDiEMSharFvPswHZkmHXiuwT/rTpFtf4vdooYcVL1JuGcZ92ZIUTHSlXED+7h19hxTDa6yb+I1lncfYFbyChBx53z5TRL2LSTMfg/Jj8BfJhqZ6pXUFa7DfcMvTh9IJDrGhrBGG7pSSll8/B7mVwRs0drCHJclvD+xkAmS4lhbjaJKYzf1G+YT6R5GQpIy0oA40uiLQlPkCTB8xxetXzgOHMAqt/vsnfmZx70jMDmxh5lSqZge28mI8iCehaeie88WyVFw0RqeB4NtChy3gPmAkv5BBFraCKZmMjpUB8HeFhx3qVE7oYhBNk8RGZ+PsMwIHBeFs/DxvwxSTEBhbjt/R07ndastSDWDxnokXB144m+Pt+ox9lYU8KW7BefYFF7OiuFz1C+SL1xCpEqZB1Xh+BzX41FBESFDFtBh1U9zmgyvKx0h4QtuPuqoX1YgRzuXSzZSXPcMISgtBtIVYacrvJMlE0koH47VZHvWiJiweMgqduw7SK6lDXeD3ejqb0ZH2ZxAEXu+yK0jyfz/sPOWX1lA39ru9dDdXYKUNChgEqKAYmIriljY3aICNnZ3ix2AWCgiYgACgiIgKSlS0tI858Nvj/fz/nLGfs84+/4L1hhrzmuNed9jrnqqle8wH33k9v7i4d9rSFXBOpdFaGoNR0VyBloKr9mrNoXTe4xIHySEKUd4XtrFmHp9gkqc0LFfj7m8AcMUduOklUXWOQcMZdupnCrDhJQQLCQu8zUuk4mzD9EZ5oq+Zy8IUpA2CSNy9xgqzo6EDlO4rwext4gmEK/MHzhOheCAoVh4ddNX1pOORlCphkdDYY/5Yt6nWCMir4lqVhcL/L9xSEKWtE5lnI7X8kZBnes16xgxLgqHCScYM3QGGtEqNHzcgnl+APsaFrKwezxO9wwRn9jFl7lSSE8I5YzaDk7mnGDQMC+OawQxdEYU16tbSf3eD3J9sF+ZTcUJAyY2L0BPr5aY8plMzwtCaXw+szXf0y96Hj4vl2DYVcb1PxdJb7zD3ZhBvLz6G0HaGjrjV6JQ/gb5kHcUGv4gq6Ue8+FlRLvfw9Avna1JOczMbOXl9uvcfjWPMRe3c1e8DkXRaAYpR6HvPpnKgX7YfQtnvlQkkdJxmNbNIcy+BAPreDra77PTQYIwFVtebShg5N/r9PPo4HTLF1JCsrHdOZ+W33VcapqD9OcyRmz04WpQAUGJAmYXLiXE3Jn7N/9hKfcSU2UNDtzOQfKxLAN1iqlTjCLnxw3qfn5luGAVCbFfkS0YgYFSBodHq3D6Xw1TPNfwL6kP00UqiH45HPMxy/DPKCO+Zw2tlxwJVc/Ebls9Erpl2I/awVbxl//TqP7/vTKKapDSc2K9EIZ7zYaqV9jNaeNPURq//s7BzG0Zl3yGcSBuBwPnejB6yzRWz9xG29NYxP8OZLnNXVj6lrr4WWza/ZUZhjU4nq8g2nA0TQIZ3IV6jO4ZxfGA34weVMoUuyl4xTaT/MCfeQMdeONxgsAbCwg/uZHj+dWszo3BtfA1gXPtEHZ2o1kUQJvCL7pFTZidMwFfmxK0fyrwtruEIYo5qKtrY1mWS9ab72zM7ER5lxg/ap8gb1iALbaIqU1mzYAuBqU6IS2XwUppFfa8fsg7eWUyteWRbR9Id4EHDUqvqPozlPqhXvy0tmR38kI6NcWwA1bbrcXpVgM7DkyBJ5twtBVyxVwHv3YXdPpYkVK5jqygIzROV6Kw5gcjyrKZ0beJaZt3syFNk+sRqrRvAJCismsu2Fvz69M61H4lkp6cwGjZYrrTPlIop0CjmTN4GTK0ugSrieMRbxnGRZlqRF+DSV9oLIfPprDpA0ziPybZMakE9k2URn3GMdZ9L+B4uxHZhsnoyVdQo5tLe8Et8qfpEZdXRXjvXL6JTCSkXwamZsPREY3nrW44ZeEyaP8bjGfTQFjRSaOXP5llN1mV/BiDIlMGFJlzX+w8ZnVxLGt+T6DIZ+gniUiyF0Kv/xi+rvHgN9kIX1M9xJVa2KFcQX8jUeABIdUdINcMd8fzKPEw8cIbyI6ZytfPC9gaPhZv382MfK+Fi2swlQxFXfIGEuLlSPeaoTJ4IBmNq3h1yYCyGSvQvn6G+A55NNraqJW0Q7k5E/EuZ9x0R5MhaQSUwhp18O1DpoYP8180USusZZCeCOe/vuCpdT5JnmI4J2vRYt3E+VM/sVPvRLLgFEHOh5j0JgZ9Kx0QdrLz2GmglYp8ZfCMhuo2+BMKmp6EPrzHhKhX2IyzQ0NRnk6J/xhkerkwIPsCHZNauPlNliXidRg1GiModiKn6jvp7fGEne6mflQvbZ0dWGSKU2BswL/yfGQGavA+WIuPso4sPPUF08Rabu0+ieiXOHycdyDvG4BAtZv5ZUUckfpFj8po/FzrCUsaip60E+8Uf5KTWEBvbSIxsb28LlHHTlIOteVyDOleDMCku0txS1/Bzx8NXP4nQbfxDuRarQg/upQlf5T511nJrlNfOWJxhDyTPtSr2DN2YAB/pVuAQuht4KKaIr0SnfTVa0HZ1Aa9Uk1u7VvBwfQuTPqUwplIUlpz8RYzoLpBm4Pic3n9cxvbra/zo0eR3IIs3jXqEPrHmIXd8dgFfQSrcF592oHLeiNuNBxBXk8L0c+9iGbrcmzuX+R6pZHlObop3Sjbe6BtV86zt8+pr+rhn4wc0VPO8Y5O5LTgl3wlC93dma1Szd+8BnwfKmLsMJPHDXE4R+0g5Ko//S6mI6mSitIodTxPejHt7VUqWM+S/umo98gwoTyVCx0S/22W/l+RfJg4agpXbVxB470M5LRH0sdTkagzL9lmUkp5lzotvxS4qXKN9/K2bJeYzZv+Ezn/6xkbPz5E8CkR8zAh4vWi7A7v5djeFbg5KBMaP5AH7bOZq7QCP/kOvNZ+Z+voWPZp+7FSfTIDHq5knugsyoaII9IjiuLzCLb5XoBRVcQe/0zWmqFs0W0jrO80RoZvon+dLN72Axjx+AzCtS+ZcEiZYTZaaG91Ii7KEs/HV0jP9mLtMHUu/NcezbRnJ7F0UwP5BPLvl3F/hiim/nbcLUsjcpU9o2u1uOot5O75rXRMmU32TTnCm9+y3MYLy2+1LFcbSpDgL3s2rGL1qc2csK2HtFt0eUGsEIplIsnduZfj/v4M/h7HZ7kGVmrYM+RbC6sdR6HflovrdUUWyndirdDAEAcYBshgyJSQm8QGvsP/FdQ4TaTOQBTbgXa0ZHXy58ErdGVzOLj3KrNSc0kQ7yG+XUA1QgrtDHD6VkoDcvQ22xCzbABGBdeZ3yTOOZdRNGtPJf3RaewNXEHhPai5EX//FMryptjNGQVVBaBp8n/u/sTlMHLbhWhElsLbc9SobELV6yBiHqoEVotwuUALc4taBpk10Z19lOa2j/RV6GVXpzcaCvEkaz3Cvd2LFRsfEVxlyDyfXt7O2IHoX1F6uzvpV9lB0nAlwhftQKeylmeTJsITM6Zziv4PF5P3V5IdLS84+6qLsGnDESw6QvCeK1TUP0JXzpmQkC1AL4/O76WwMYy2tvn/dXJFoiMOM853CwpieshJd1BY9gNkHQnbuwwaQKAsQAp4WS7kbYweQztXkPZ0K0EvhOxwu0C3+3faul8h0PzH6NYpfFLoprdOD/cv3YxXkuBs+Fp+hewBJmAZmM7j8VW0p51BZ+Nr3t60JV91HUGmZxErn4r5SAMytH5j1keAqO98Aj7Kk/zeDg+T+5jK/2TMfXUkulSI6CxnQXk295pXcm5wNLWXnfFgFHyqpKrlKJc/puO4K5ZYwS4qwmrYaWZGP5WJCJYbMNvLA7VuJ6SN3dj3dgZl0mPZ0yyGsD4WJZXfHNq1kbniTXS0xnIobT5LVgYxLtiO3H+DGJ2VRnXAEZJTnyE9aAIHe+FVx3WKPo5FxbWSb+Ka1H2V4eORNOx9wazsGIZjn9DRrYv09r5YMI/KDdcYF5RIwfXThBc9JnreBUb0lWdJy21ixOaxtNaS19fn8s9sAb77TjA6eTuiWlN5hpCGNw+pUdZE8eRi1M23wFZ/TBfbolNdxUFpJc7KarHPeS7WhxYgsvAOf9Mb2bpkPW4lK7nW4M3mSinEPgbS7tePfktdOHJBlLrIcDYudCUnRobFok9Z7WhBWbUE6YX1+FVrUNH9Autx/iT8zcRQ3JySZh06tVVo7YwkV3EQTYm9TMEa3V4xzFRvkqF9j5c6ElS9cme8rg8Tnn+hV9OI89PcSS3Q5IL7TYZ4Hf7f5ON/UNaOjsKBbWnYZAnJ87/IXKcyjvdoclLiFtvWf2GizSOuSgVjsGI4qi2/KZv/GJc38Sy7dpU1cV2ILLlLSEgzKdoK1B0TotM2lPeZAxBdOw2xl0/xzNBn4p1/WE35BuqDmT3Wh9G9mogsecnD4ADkk2O48nkR66b+5MCXMFLLPvBhziSq4+YTN0tI3efJ/BAq8OrlKgQ/+5PzOoXH3ZFUnRtO3+KR/Dxgy/lHnRjc9qB0kzohHiN4sPwQOV0lWMwIZo/beiYN10JgmUjJybOEvy3GO9GB5PuSLNNU5M7MJ9RMXoTynHHMHaSJjfR2tA3f8rqnH6suLOcE4Lj4MvsG+JArdxHTfy8Rf9BB5JqnaH2fykTRHqzbz9JdvI11ioPxkjAgUDCFDE0XbCVrubVMk+BgX6QAZ1qRdDiE4PZResvXozLCkbQ/s1Bqb2FSnw5uufWjNzGZwcn3KApYzoHVEBoIrYCk8B9KAhn6A/v0YXItDBVAYK0hifbFMCsEgSAE3WAljik4UafzC7MPZgTXhLB4mCgTnhlxzO4ixyTWUz9NidXzfqNsMRSmm0CWA9wM5d5PM7o253Hc0JBXknJkV6Vi0fOMbXM8mN51kBunLrFErg5Z+Vr8FdR4Kl2LUcBJuHCY0CWbSbj3isjH97C0HE5a4y3mD7KhY/pa6vc8YqzHatzDbXj//i/7NGZS6yUDHjuJHWTCN2Exzm4upCR8JPjgJPrYPaThjC3NNg3UZC3DasARUtoj0Bc7AswmOmoa4yZsAWlpdNGhoL0YFam+GKuaYDvSFX1DWCEQcEUoJObDAk58G4C9xzL2WwnY9ewqnTHv0XO2Iuf+O8QdAlAkCc8BZgxU/4Lgih9cXUBwsBgwm//sEAsICfEEezm6w5+gMOkygWWLGLwllp+M5Kx/KNVbqjBUmIrcBn/UJTS4Xz+WD4PFMdJRZGZFPB3avty/aUaxnxOGi+4hfegwj/vNAaUilpXM4OzW+Si7GrDx9kjGXJvOUqmffJ7hhUAgwGf/OdZIpOHw0x5V0ceUWLRwMns66+Zks2K9Jklr/fhzcx6L9foRK34TiTNDWBJqiLqiPXHaolwe84WcG2nsj3zCrQGVnBqjw5NXlfSV+IijRgr5g/rxdLc7/hMqkY97w58+7Ri0r8FkWhml0jksstpNxvbz2OVGE/hwME9XlSEy1IO3bc/wG76Ro97HcNf5gEByC9sXrWCPXCU3ZP9RuucNW1+YsmRPAupFdVwuOsPs1ULSgtX4M1uSs9qB6LhO4VnabzonLyU/0R/p/VWoBphhXvaVz7lqtNoFYPjrKvMLSlB/uIHL4bko5Ikj69aKyojFeF30Z+knOQ4OmoWo1EPkt0axsqcfanMG4SwZRaZsAOMk/5BZNopKi1oSHC0YcHEpZbpTyW+ox0dVm89VVZh23GBcrx/15ZrE/bPj7ukzTO1RxuJBIwvDbv1/J/loqhSHD89oGv+Y6KYphKfIIlizhac/nbnl7srPC+l8C17B6mVnWRMfy4YhStzv+Utfc236zw5hurkaJnphnKoFEc+93Dx0mEmtA1l/YxGXuwr5VKaKq54kt63HM2zMdqQdlYgd8IRBbq6km2eh+0cSuZmbWOUez9cX48jqCYP0fA5cmk73MwE/bJMJaw9B/sg4Gpz7Enh1KiHqRUSfy6EqaC4uu18yvngek+3U0baUoPHrApoeCLgx9gxLJVvh0wVM+3swYokW04VHmDTUnvmtJzntmoD22TWMnedIc4QRQm89QsVy0M0+yQuLbCz+6OBYr4l37ExOqpVxReDGFKk73I10pO7IZL5cvonR8gC0P9agq/sIrcoVvI/ZzhaT9SxpvMM80a/cX36B9kExLHOAPcBhNqI8KoB1R98xqmES0QcyOO89B7vFI5AbmkrZbm3s3ymR8vUZh4TJ1J/pZI0J9JEWJc9hKqPi2vmDIV2yMkybnYPVmUXcru5B9U8jxldfgXgH9hkn4fNQWrY2smr9JB7ZSNFhZkzHEi0EOR/wOzWPJAp5/CyK+qZwrBt+gUsnCNM5uyKM3fcisFKoZYfCPBSqilFOrKTwjysKshfIthchskmO+nfX+HjbAI/z+ay4uxSqIJRi1I/l4WdtxQedeJz5S6pELfWJ5azznsfzjeuQUNdHcDqEb2vEsW7qx8g+q4j5NQyHrWsQXMzl884t2Nvpoys9nJEYAlK8RIZffpuoVduPQFYaMAZqGDfuEiBOdWM+ucVKSA5xZYGNJwKBAIGygFEO8rQHvObX+mg+phzlQlULFfqreJhwGZOtEZjZfGRP9mpEXszj/rfnCG60I/q+mV9nRtDqocHcvPlAKfCbb8tPkG+2CMcTaZz/5k3+XxvKsuvJjzrPaadb7P9ZRPZvfwqfW/H1lDpXoxRoNr/Eq4spjMnRRcvkPsYGKzBVLeTNiP7sHXsD2zM+eMQYwIbF+BRfQOtGC3bieYwyEeC8Vhyj9E4KZPtQUPIOJI2ZG2XGsWXr2DfcigFOyzFYqIiDngEiPZsoy7rAwZP9UT7owNZL49n+TwepwFV06y2jRWULC58nUWWqga27EzF5jqzcd41NpZe4+CiCpKevaQ2bhHbNPXads+XSqO8Ev65hrso9Atf8pWeGOy/mj6FQRsDriwf4qRVMaHc6dg/imfO+kpOfNnLjUjGia2bxWSuIkw9gycWnWIafZ+HmiWSmzyFeNYIF91cRLXeXTypFWGbtJeZSJu+/1eJvHkyqylmGfv3OxBGzmFqZhPu0YViVTOCaRA8a5V/4RCBj5mzGSdCPUz7SdCTuZ9l8ewYXyBM5Yj5Pzo/l+S5JXP5U81TOgh9d4aiO3svx9FgMFPvR2COHe7ctr35+I111FmVry1nxV5H1n18yKHoHjR1lrOww5LJuFHMCb5AjUGXl9k6KBt4jPUGdma/OsiH20P8cpP9XALR3QWM2mAOGnoo0P87DS3oh5d1qDFWFm8obMdv5gu2Ss9k+Yjnnyko4JKhAcDeC/a13QW0er4nkwyBwc4A7eS7sbD/FcH1n/OQ6cU1X5PboWC5+juKucAb9H64kwUiOGUMlaFZZjuLzCIpVxoE3eNXe42PRM+br/iPa6QReB1Wxqk1hlP0Anrtp8X7tOyzmOjHMWgvTyGf0BJni+eQ7ZExjzTB1CAkBEVGmSdmz1U0NKalHHAotg8AKTK7B3bI0tgX14F2niU6FgO4bW/ngqkj2TVkOtpejY+PF2OZekn5nsUDkL77AloSbpIr+Ru9rKPH2GcQKazAes4vcnXvZ0HczccJcHsrXs8V+BUO+tWBa8Aj9tlzKEvqzSK4TY3zx5r8GCPoxZQIoVWcyJ1GG2oJJ1OmL4N6eR0tWJ2LJ73HuhbC9V/jxq5NE8R5+8x+TLLFFD+dvpdxADnkDEZaMdcA2NYUFTeIwayZ1uSH0PWaCU5/FKCvuxzHrO94/XrDZwQih6yrkRfLoHnCMPRPqufZWF2Wrj2g0KcG775AVTM3SXsR1FBFKKWNUlc/lfC3m2LYzVm8UETFe7KzfTHif5Uwpe8kHKyl+Zfcyz9ibt6UQ8tsEY3rJT8ymn7kNhZ1xjKkbgsJbIWfu7sR1+2pOlKTz/ncdLPSAuWYcllOh8c9tnpRlQ9YxMgyHsMH/Lp9+P0FXu57VEWGEvu1FNiuMP22vUeA+4AAUMm7MPkTFtZEVilHU2ApNxSzym88UP33M705jqZcRH++GQuEbCn/40lzym7zvkQB0FW1HchA0fIzBxnweeulNdItqM/BLNwyZgp6VPD6DFwBiwJj/NEkRWJvMJe6GPxetdrPJ7CwSBsb8pAMXRIkw6WFzlyimGVYEvndnibQ0pvI/OZYlRKJThQKpehQelDJtSDPu0repuzqQ/fVHICYe9SYhlz+ls9EulIYvr0kbVUVZ4VccVLxoz/NHZJgnLQIzpEzcaBv+nkipsbiIunFgyC82RxdipD6EteKN/DWr43DqfIqS8xi70AmxwSsYnZ3GpnYhNA9BetAECn+c4FDHdbpTulF1rURH0EJdmgySJwqwmyQgu/gGGq6hdHTr4h7micWZBby5JsvUzStpf32fZ0VxRM9azYi+8lyQKOdGiw9n6iwoLnlGr/kC0BtAiC/oeelwtz6W1VsWUoMm3e+j6QnvR9C2iRw7HoxOTR2HZP5xVk4Ti/Y2Nh3yJYZf5KU3oSZzlT2dH7jW4M053TeIfQykSt8Yw6MujPX7RF1UOGcsR3IgQo4lvCNIw4yyagne5P5Fqlqd7qpybMb7o18dj6G4OX06i/knJkprRySa0sU0JvbioWqHrlAMO9UmMrSvc//VdqpeDScu2QLLsl56NY3YaapKaoEmc91a/tss/b8i+dCWdhRKPulLScJI9pq/QjGlFdt3b7k2ez+d6sO4HfiYL9dnsCn3ALM0bIhIa8G2tZwzqdqI2Gvx6LANX1ceYfMIP1aH/KB3325OH5Dl8upUTvx4StCr20xfrUDcqKOMONXMGeNy9JtSyVUfSqt0KSHBDWwZ0ctFs1r+TNSh+E0SL0aGsDnWjXEp1khouuBltZr+HycyuMUaof5z1ih+Y5fwIQt132JsNo6HDd180+uLr6MXVvcekPWjF7unqyhU6OH7uMf0l+vFdXkb86TSME4xZdPW/RwqUUdhazpBJdPoEJ/DscgT7Kn2w6ahmn1RbbyJqMVU6jTPj9kjxA7LcdXkGGkyIkWfC/WqNPno8fbnKaY938ExvzpWGyQyo+8M8itE+FtryLz8O7B+D9e8FRAG+6MBLCMUCYOLdCYJ0Oit48euSrIkVmBaZsTGvKmYK1mwrsIEoch3RplK4T10PwCi+3dx4/dTFAzqKW9zway0i2w1KxJr/2BcqsyeaUKuH9xHw4Ae1mra4Fnehr6nB9tN+rDp9hVkpYQonX3N8ZIfuHg/pzf3MpmTZrDOyBrmBcHNDEIPnmC88jXGnIa3Zz+hbfaFhcN0qfxdSqrbDH7oi5N6/iY3W5ORSajjsfFNQivbIf0GG49PYNAUQ0wePyAho5k5g1exO/o0pv10+WLxFKWisWyS/szoze9xDo9EJfkk+45ZsqIuFr+ymdgYeeLpoUzFgBvM1xdD6tcRlK2keRcxkuEjoW9vGJ67XjN/wzWcxNWoUahCrNuGzp57INYHcU19WqKu0cfxMEtCFgHg6n2UlnZfvKclY57ehZ+TGmdFG7hX8pjE3Yd596OJlMofBBjPQiNtBExV5mSIKDZFD6h0jidPcjhqi3pZce8qeElz5UYH4V09bBv9DS3LuYzJMKJ0QAlzhq7E06kfuVpijJIX5+rK0yg6gncpJFyZRV2rEyUJZZRbZ2EvF8y+8X0RU9DkwqEFSHZe5aD2aDzrmjjZ1s30bznMmHaAvXJnmSOxlJsbn/Pn2wu2bvNnzN5l5KfvRk7ZlSmPKtmhsYauLzcJC+xh1CsFxDavp8JEhGqFLSh8TWd5zHgKwjtRmHwRQcdcJmsmUqLbnz1Bp0kK/8O9DrD7WoGFqy4nMwsZYtrBlZM0+A0AACAASURBVNjlKJfHcz3KhflJKeQ5KjJytBNR5XXML7hEkfkNNr3IhQHbKW45QbxGJy8Mp6PTXcMxm1csO1mP6aADrLvyGapecDEgkNeCeKxNlTDUmEqAxBSmFvpScPgoXzcPocdJlJ3xxznb0YV+3lbqTV1wtXKn74NOivQHoTz8IIW5D7lr6U1KfSGftSQw8GnGYr8/e3PSeVY2E79h61gwIYz9B3SocKgg3M+D8Zc2YqvRTaBuHwqjTvN+nzGW0Sq05cmi9j2L+g1bOFpWwVy9fHI/5+LX4IP1u3xu2bUwR0sC0aFOnP7bxgM5KcwTjjKmdwN1rRbciHD83+Tjf1B95TWF36RPoXBrGg/EBDR9nElejT0HyjaTPgEGBBqy4dwvBrU/ZtyLI0wL3EjU88XE/F6HdwEsPaKGTnok/S2NsXnZgu/o9cx+tQq/eR0cfmLAQdPbKPybS6OVCAb/rLDZHsio/lGIbqtGKy+FzI2BDPaMZZbVcya4fOPKJUkm77zCk0fbufb3Ks+DthPxKZyUmHk83m2Lvu1Tjsx7iK2GIc1x24hpuUK92mqUTynRnxASdu3gcIQDXTOK6ckrQUGkg7JfA4kfpEiOihh6o04R8MEPI+MGkjosaE0rR6ogh8G3TnBvTBCu2yGhcRhT3pqz9OAvBP5HKTMuQO+bJ5bnJ7P89Hk+1Q1lo+U/IldtIy9iPh3Pq5Bs7ofHiEYWJV9H/3cegbNvsKMgCEFpMLg4wOsJhAVDrZQlCRf2kvi5h/cD8/jomMeEgl8s6fZli9YVHuRakXBcmz3dInz+pEjfx51ssl7L65jzlJl95ZXeQqb9UyK/cz7SHTN4Wgxxl4Kg5QvrjhpyVimOHpt+vGoyp8CsAOUto5lZ/oX3z3UYVu3F429yyAe18tm7AX6lwXwI9RNgLSlLw/Eo4id9ZoztTpxUVjFyTCvtwweSnOvAYIU43lX8wr96GKNln4DxYxo/e6D4+R1jhoZwrp8k50u/cLb0HYv6rsdMr5FXrQYsckvFSf0G21WTMHBajswEZSZ5bCH+VgFzsnZy6cotdrqNot1pAavs7Oi10MB/3UhMUGXH4hB+JVZRaSvEz96Zr3UpqEm6/qdwzR/C05mo24lQrt5CbVkquhKqbLBdTnLPJ26WSrNnpRb6n4qJr5pAwVFnFMesYN62xejvWM9ap3FEl7zCOVeH58mJJH05Al+30PXPjL2tcoSOWsKBm22I2XpT/UED+RRT/rrB0PpFLDO7T4qWE32cZ7Ji/zz8e7T5NrwXhUufOGQgxvcUFWymGzC6oJne7xaIj75P77+DuGRa4nltFpVrNLn2QoeD44cSNGEV1+850v/sd7wPHsfxdST93uzDoN9VykQXcGHoLAIvTudR5CvCrzxHIq0UgpqwrBxHSLQYF9fNouNDDVqiF9k3IJqMxZO4GjWFlFxrVM2zGd6iRk5nLZK1nzmX0sGILg02PnmMj401KWctGflgFcsnRhCn7ox+/UXENrpTNaibg0ILklxfcCPLDUNaKeYH50IvkX9NyL6OSShrzcM3O51zDUImDzCjYX8bMZoLyLjawJVaceQK15KfachppTrul0yntjiKmeNy0Zn3iLUbT9Ai/553WsEMHpDMbH1NzC6Xkd/Zyr0xRRSdWI/sgDLu/ptNmNgyGhYIeTGhjKGnLzLi6kGWLd5E1CtwTl2Dn085LVeP4XM+Han4JQQsqeTvxLGs9hrB8m9FjBtgy7AaIRW9eUxYOZMRSUYEJofhKC/gSKsxe62aeJc/mI8iIky+MYggC18edq1Hdvg+QuQ7sU3L5PqZqv/WGyX2/zKr/1sSin+nWD6VlKDHyEV1k2v4FruZB6HsEyI2VrzfMgHnW09IneGP/L1wQm/a8/KLJIW39yCZcojnF6ejGVOAS8Y17m06grP7InYeX8szlxv8PGXLjB0FzFDMYGfmH/wlE5nScpnUqX6I5L2g4chHstYcIagln7NTTyI+ADpfC/j6WY0H8V5oDUtnyKHh1D08h6TTeXw3evEn1ZySnWIseD6b8SJptMc85fvMUQgUrGBlPdI9S4jMf4ji2HT6V2aQG3ccm9ZsUmzO0GS9meT2HMpaxhE27w0nS0zJtMqma/UqZvtMZpTtd0SspOh9IMlGPTWGBK9HKHIId0E2j2+t4XsRdEvL0TRFgXuqv6lUh7gjifzps5Nx1oO4pXuClwVuVIvrEHnwD1bOdQjHzCMMOAbkW87E9mcMRTbmyMpXYhpuhKj0BlyNHVn+ZQdjZEv5VTQBM6U7/G2ZxTiZJtJzdmE3QYH0noGY3dTnq40Oxb25TKkvodpEDyXvHha1C1h5vxu7k3AyKZH+VkJu533iQK0v9gX5SPh4MX/zDwQHfbGVhPRbUbzOP07SkMGgdI3Qg/PBXIDf+U5evLvDlUA4oHwJ38KPmG8/ifj7FGZkPOO2JTiHNpAyUwmqDLixXQpB7hYc5XKwyF3K6WfGfBpogMS7kRx2PszzV8eQ7bXiecMnQhynMbLnIqpfStlsdYpd9xL4elrA6I8T+HrxOUkDdxFtEkVqfQbu1lsY1F/IzMbLbAj3YYnmHGIW6TNYzYA37XU46WjS/ksRCelVmHU28rkun6PPagFxeL4Xwby3HNuhSmr3XwzlSil21CBMLBrFhIEkqgxEZLwCSwPec0Z8PzpZ/3B33sa6T99x4DE/HB7w46M76t+g9pOA6utCljZe5vEyUxZU9dJmsIB1wNUBewh5u51wyQ5EJ5vhVCrNbpNZaLwZz+1mT5I0Mlh66x6i7e9pl7CmZMIyFqh8oE9qHA8+nkY3240/laMxnOuJoeEp5r/yY6dAgx9thbwoH0RYyT9cvpVxKCaZPhE/WDSpmAUBu3gR94MpQ2D3kErwb6cjSxzHp6kkjLiI14h8Ko7fwWhjMYXZMhjLOSDXNgS71Ehsj3wGIrgZoUHzCQEzJBwZe9eT4qQcZAM0iK4tZbSBLek9Lvj7jkfM4RkVtWp01naz8KILhnrnsOvsZOqgGDRmT0JP3pc9ZapM+3mW5yLrsd1bw/eZsZx+7MHUmZcZ3RbMk5wKFJSGY5zTS5tgN8flbZmffhTB0J0cddBHsvIiEUatbM5KpmCYC73Ru7CTyaF/35cUWvRBw1oZSXU5dla+QKfyEvJpetzdeQ1d4U2snD2Izp2OILkEqSMuDADefIFwyaOIJF/Gb+gp0hreozYjCqclVxjcp5Znf4ro4+3GsDurMW/XpEe9D6G3n3J+ZygbdDw4YZtGfcFGfIcWMzdTjU3Nn5HZ5Muok++5MF8fIv4nSf2/qtFtwiV3JNJF4Sg2gnubEd6Om9jVcR/rr208uTUBlQ5wU7Cltl0f29ZyosRsELHXYmawET17DrB9qz8FxjPpdflNmlMgqc9jwVJAUCCEKcggmHoUp+wMjsrfJLYpjRz5Loykyzh3IZc3I3rpW9/BH18dpszay2/FTp7EumFaqomksQtyhv1x+OTLqOLvLNQr57vVdHb1PmSmlgubdb7TngxSZgpswRv1g078+OEFCnEUyqtgPHQI/TPfoqj9htjmtWgVynGyw5LZTepUL57EyLOK7O0t41LkEo4jQ1ZDNTU/NPiwsoytG0fiOtaAU4o7cWsZSaFhC1slrZhTr0r5WCvevvQn9IoF5yU1sTCI5EHbTgp+X+Fy71COFtwhUniCRzvAYxX0VW7gcn0qEmqOdMaKMF3hL5m7/vH141xMyy5j03EDJ5WtdDdaIdUkzigTKWzt9fgevp1N2QuZp/KU0oo2KvRdMC0tIa37BlKa01ErE7JvGny7tgu7eXLcv3wA3xnjuSerR6bxFx6f+42+SwIBQzvoCd1AQOQ7hu404deyu4hmz6anOwvu/ARgf1QLx94Hcj7alpFz+zG1Xo/XLgIKG5voY63KopwSigSV3Nb3hFYZDnfcZkS2Gpd3XqVSRRT91HR8TMW5/28J+motyKj1Q+WnHMKugajGFvNU5TeRD9Zzxa6Ykuw/bM/sZafqLJQe3KcSWK/nDYI7SDcY8Tp1OiaOIKoNfSrOczvbhMEVWshMlKGk4hfakhJI5FqC1T9qWtOQzlBm+b0RyHc4IT/yGcbCs1zfaI8+gTzu7uXulf34BG7gn3gnlRMOclg8hQ2lzeTUxnCj9DfJKW+h8yxYLCDv0lgIGEowuoT5S6OXEsDvOyk8l8omwfIJJxONqBpXSkicLWDGZGMRTI2H0PrmGO4e/nyPiyVrtip/653Q+P6RVJsfDESMZYbaxAngy8W7mHTCF5mfeP4t5l2HEdnfcpjBeFJro6kzLKXfn0rKchfx5m0CFo4CLp6WQN/GlcUtN/HRcOD4Plu8AgWMne/C6OQuKizNUOh3BIXTGaDVREF4J5JLuxHUzsVDTYHIsxE8ffkWzwmbudEei82GE1y8f4Qp+4sZ4tTB02gFDLyj6H2himblcz44TcNIt4qn3+rwKhDl1ZgbBJW/5hyXUH5wjfjADlRjYwjWvc5tGxtCE4sYPugGC9oWMKXgBdZWM7n4K56+JpsxFPEgxeEYIqnjKDi8l0lzFpLkJEJFtAuXlbuRjlrF7NBK8kVeYvjwL4omI7HXOs2r3A78XLVJ+V5Ib1YZBj7q2N66zN6cDFQvjyR8mDGtuic5c92SYQ4V9ComMv7SRvJZxyFdQxTz1vJl73EUlhfRK9mBWlsORor9CS7/wV9rSXLvZrLb0Q3rd1HsKG9gwSRffnRCuaEJd39LM/9yK2PXbKWkYSVQ9d9i6f8VyYdmX33hTXF/RlV/hPWRTIi7RMpgZ+Z3JbJMs5Fo3TB83B2Yu9+MOPftrIizYcTYsSw4VE68nT9P60S42/iI2AXT0J64itMR19HOjqTs32vaqh8hUitGuXYTwvx4Tsl0sX5xCJ25r6n5+4HB8pcIi7vBuQX/kJ30AU+8YUAVjJ/OJCIYUXAYz5ur8HB5TXm9Fqe85eg9moH3ornY60jz5vsz0ocpslJ/FiDNwciTKJstw2G6MjOfvWKwujjbRwpR+2fMyYAgaOgG0Wq8DmzkefpVHAXxfLu9ls+RZtRVupNml0TQexuM6+tJmzuHs8XTWPztF6W52xjU9p5P0tKMTpBk7fM3LJxnSISYPlMvJdF58QLvhr5kmmUUk6MTcMoDFAQEbd+BuN5fGKfKFWCBqRRfJFsZKBTFIWUq7b0pBHeoE2SaxEFZES4OfY5/ZiBeksWUNckxtzWee99leSA7kScH1TkSEstoAvD5XkSKWRbtSmpEV2ez+ns2/qYumP5IpVS8FMP+Y5lTnUpm/Af+jFxN5ogpWLwfxZaLV5FK1+JX5QqyWhVIOvSImJTh+B5NwrKqCTeZE9R86+Cy6CgGZOxm99yzSMt0gtkz5CKO0WfEEwrSA7i0P5FmUQ26EyFuMCw+XM6d6KOoJnhiLg+nVivi1OcRM8o12aLZD98mMVb2E2ORbDfW6WO5WbIR8T8GJLRLETjYm993DXj0o4lwJNjxtplGMTUU3UAgkAba0cEB09WXcX8wj6Z5ExGVMkFaXIqelo+sXumGhuYkQpb2gbwaevtsxFHfmE/i3UxV3c2eqhVUln1AJ/0zdru2wI/z7HAaxN3ZVqSOcaY3+QBvgqJw7jzKIv9tpO4S8CUYmsy8cMx7Teh/9cne6WXELdBjdyEo3yjGIuk/X69Ocinl4Dh5jO8tIvVGAKXWY/D+C7Kx36B/BGjpgpwdhDlzxPIdzaVh8D0GWA92I9mqN5p3TRvwfVuCiMY8QuU9eeL0hUDpDHaqLiOgJIPM+VeZdWY7Z+IisFIuobfNjCUKr7DXvEeIOgyfDFXm6yneKMc7ZTOa3n1mkpoNjbbfEE9bhMIwLYIuz+FJwFsS0+BX3UZuiv/D4eovfCza2J1UC0b9SfAYx+iH2xn5TxGf8cNw159KhvldHrg3YLfrJvqVStj4jOKF7iCU/hqTWbaNZ7aTOV0UQ6SlN186g9G+G4jHnfkUnn7CjMHxVCVLES3ejzaruZh9lSU/wY+Zc2eS0TmOw/+k4VEMlzQSsJQfy03R78zWt+FVbzKNjatR+6JGWj9p9IrWc9bbnZCEBkIWjEQsIoTp3kYUPpBlobseCy2HMMdTh3T9MA6X2bFV1JcFr6JpKvzCrS5dnB/K8ntYOeubfvBRQwf1HQc5lprM3ahEcmbLMP9sKchLcl72LSlL+yOTuRGXr22kF79j5u9YfsmO5JJrLM/n3fvf5ON/ULqGOsKK5YEI1DwQO+9GlyIIXIVsrDXhYFEhAjV57us7M61DjNCTWoTsU2NMZgXPy58RtUmNirRwlm2eRc+BT4hu0efAFh+Gr12L85MxfH0dwOORPYipjif6WjOhYos4NOkfU1I+cO7vInZMfcqHLF/cC1Vod+lLc/MPUnc3YbIkn2MTwrBf1sjlvX1YvHAJ/Svq6f/jFhKz+jFx0AMEF2fgeiqTNSuX4oskPwrhT/QYZJOVMWjUYm9wH4w2naNEwY4hhwLJa86GsBLupx8iL/srPeUtRBa7kLHAldCdsty5Ls1M30wEh/yY7w5X7oTgdf0q8z/vpkhyNZOLImhbNQt721IGR0Ci7B/uqdTxcHEDj2Yu5FpCDBtuzsP8iAjljs8oK33BkdzVNItmETprEiSU8cEsmki/lch8uYncTyc8DUQ4Em/HcdUw1Ctc2KafwVTRzzxvPElkeRkbuxZxozSM2zVKjFYp4JdXEj5/PLAwUyFJ4ROT2wax2daFsrQShH6TiZw9k80ujYwecI6aDws4X12LgqwCgjEJPD2lhcpzX/6c0aM36AV5Da/ZLnACcoh93E59mRQPMo/h1x1FafVu0i1O4O+7gwjhJdqOOtBmok/bRS8er/RCaOlF2K8KiBWyrc9P7sm6sLtZmhnGX9n2OJwtWv4kDTGiZZQGU3sSeTAsnD6XYnjSzws+SjJLXIk7pXYkbn3NEG0BBBqB3hGmi+RwT6UexhyC6tMI5n4lPOQaJUWFPOvzHdfig8g3jKVXToT2HinEWmMIPXiREPTp7LeCL2YiEP2AhnEKTF+xDyXxEhYp2VMcuYmXV+y4ciyY9LP59Kps59/uAfwb20L2qJsce1nD+DtqBDRncrPdD4GtNoXXY2hiHTkvSxiv9p2/8p7YjDHDYtIcbu/IITPrEP3bd5P+ZA1rTruQkHOEdw27WD0mDfV2TT7pKxBjp0+2RTYvV02l7ws7YgIXEllViNHrzTw47ovbKy/aa9yYPlrA6LTJpCgeoEXkL/cMp5GsXkJwVRrxx9s5nvKcgDVL+f2vL5n9zLneO4EtpS+x78gnPP0os5Yuo29THqJTDrDDehrbNh5GvyWcEeqT2C8zlAWqEuhPucr1VeIoZjfio6PInj7PUVRSpv5tDWMdyvEZu4wlgwdjFF3Bx4jlmAsHc+GDHFMXfUBDNxWRqxqIW17kT1kBAYF6yLR+4mWBBuMFPXzKymS36n5kv10gKn4bEcmXKfj5k12xPkS5zES7qZ3eihCCSqrRUvlFStc7BH2vIpNtxI5zLay//wiJ6BrkpfRItWzj7UBXWtNj0XfO4PfvDATf+xBnK8/TpC0kxGwizD2ExoAf2L6GOveh6Iun0cfjJgZH/nDq8TRCAgZQ/fMrrx3L0In2o0LjEyHmA3ma+xnPExuZfFCKfheOMuTMKPp3ibIv8yeubUlEq2qRXdGFZ6YljrbV5ErJ0ahYwqMN1/+/s/MhXtrIk+ua5KW/x2GHAWbz1HAf2IL6yNv4D+5k8cMBXN4jjYrSMObWvCfKSUhckh7y6i48dFqLdbED4pG2GBR289BnIZHbfpOSosWQpuU0qCsjPfA2zfIpiMrLM0bkHwOXRlK0qRkfgTbS+guYH/SXe0+T8fQ349KlekgLx/5PLE9S1tIzJYzvxxxR+5iEm8lrTI6ascpAgSM6sVTyGlUdVQLKRFjaKo+irCZJ5h9Q1tZg88IX6Oa8Jm7NXu7Msmb2FSFsleWJ/E+SFP8SG/wA555pfLd/C7PlMPjVSVpzNmPXl3Fv12XaPlagWZzB1EhTDEqG4KsTirUgB+deHxqMvxI8eggfy82peyvD+WkejN4cgL3VeszezKTjTjBbC4Nwi4lEc6wAHOWIUzag3GQHpJbiPbAZ92d3ODcvEV3TLj7q70X2z2M2yH3lYYYXJ+/VIFcLrc1dvH85Bpe2FWxwqWZSgg4DRxtgtnYOKX1vIz5QltyftphLDyBOTAzzMa6kzLClykAake5vrPhdTYSKAfHm9hRymqZ8EWqaJHj/UZOs5es40byYmJTr2NON17cDuA9MYX2SCWkGuvg7SWGZbImf2BSEGz1gbBBdgmB697iyUPM9c0Q1WAasGgyPNtowKeUSn9as48Q+H46ELyHA4RFZxQJSGUxo/8fMrJdCdYITA9+NRU5/CBd1tBjWKc1pt3Km+xTyVbCE9Gfy1M3bxDvrJJQe7oKS70A7wf4rWbTjKe41CRCojoL4W2S7HtBZfwUzU3U0NMdzsqIBrE/B1Mt4Wacw3lKb1pb7nM9LZuBPddb3bEfCKgz+VsEATxIrP2KV6EFBxF1aJ+uzcuZaBs/VQOHsIyZNEHJ3q5CGvFZCiUXoWIqQSoLuD+etl4AhSwX/Z/AAR04HN/FYMpRFafsRVR5DWw+Uqtzk2jQ7jE1CGCG3iL+nnaHhD6v/JkNeDXQdgTolyLrAlF9CVt+VJKmvB3frk1li/ZBN7+LQ+1NDRMgh9E5/wW7vJ7wny7C2bQn28YdYLLUPQfx93jXeYcMHAa73XOmXe4AAzQImyGbx6Ws6ojQgeNCNw8SFVD4Yx7nVVzndUkZm5SWutxrS2yGDyeeX7PbrS+Q4ZYSD7jOhMwHXndH0nJiKekt/PuddJ71JgtI6T84pC1gQ0It/9VgGx+0hcFICn4xX4p87iK/a5ogVaDH7dwn2sTZ0briMvFYbp8tHUyTlhVzHMHrv76Pp8zU6FCahKubG63Nb0To5mvu+5Qxe70OiaQ7WwyYSIJmLtbsb9r1XKV58BTnOItC3JuD2TEwXvcMru5FsLwkaTuxh1FZNogbmIJC+RfP1O5wtq+fg4Ymo9ehRWaFBe0MvttIP0V+cxYz/h723fMrCa9v9Pxfd3d0gJaECoiKCYGMrdisWdgd2F4odmNhdXxUUKRUQCUnp7g6pa7+498zv3W/umb1n7mfP8xx/wDmzZs75rDnWudY6JOS4r+9Hk2gmegfOse1WIrdm1GNW/oCn20djqFSHnOth9DYMoullPFMNTFjiMpVd3u78M7CGRU81/nOQ/h8BINkrikVVFJOHDmH+D+hasJplTfUs12oEbxEGeY4jJnU+j4buQGLBIAaPXktulR43HOYg/1eWlxppPJk6Fe/YY8QcPYbowGiK2z9QVn0f0WoxijuGI8z5wnEvayKcB9GZFYnqg2G4ycexL2wf5xe1YfBtOPO6fGlbfJDry64yh0/4/fmH4U8CufThMx/rQfrNFXr3JFFt3YaHjjSfko7RR16HCUgiumkHB5/HkFVdj8M0Fb5VCHBTl6A9pge1NlOyX3aBjApCkWqmjreg99d1qCwm+W4IsetUoXwo02qeIYi0Q7dGiYqSUuoBh6wkijK3oS/6nmhpaRwuC1l76yMrJojzWkyfV8E2jA77wudB79CUKmHS868sUvrCTEUp0iKm/uuQ7LcEuAzFvm4D8aoKuCCKctEy/vbGc/WvMtst4tgsM5jj7m9QEooTJ9JGcZMc4xVVeZACG8W6uH9Fnfy4a4xkHpJmIOhspU3DkLLUMtakZBDc2YEgtQE3gSxGTmMwST5N0tDrKIgnkz9tJj7LBYx9sxKp31qkltTxu1WB6jhZzLwzmBd8m5zo4Uz26UdOYT8iTdTISElh79wLrB6kTPz9LvylzyF2fRGZjtC43x/B0kYEDoGEK0TjW6pG2KuT6CgNRE2hnePzFdE08GJoXBoDZN6iWzmWn8Azuw4yWi7w83sngw0GkrZiM79+1gNjWbQgkOD8DrQ/d0HAMQSGAngrwW4ra3Ki1OnMvIxPcixSHWPoUsqkrbUGGZlmFi8cyzliYcMhumSkENNYSTiVGMrPJK1jAhXF0SS9jcPI1pv6qf0Z4/UUp0Zv6PlOuPUaWv3sibUJJGtMPAmrBETvf4KIxn4aNYdyURtO3U7Bc/MT/GwOolEej0poOhZrp2CqFcLTvAlsH9aEiKweCze6M93GAleBPMM+fSOvoYxoLV2auhZSd/0oP+T+IiO8SHJRI8Z5KtDXi6llY/jcdJHQm4qIaMxnkbsyUfXtVPukolaXwryCJDyOt+EfsgMAG+VC9BU+sFTxHZs6RjG7JwEfrUZ88goomC+PZY0STRGx0GBDo30yFbXdKAzSIuKqgD7GYcSRx49HS7kl0Ybrh2ZG9WknT04HZzcnRpsYMPLRTh7ePIxpv3546E/B+u8Dng+tZ2af0RiUB3LO5yBv9Vz5m/CAtKJt/M4QME3cled9fNEvaEA7bA6GfVTI/fiMbSqaVH6XplZHjXbruejKLEcqLQj/ef70qC1gUJs0Ux+/543GV648z+CWaAr97S7Q3PuNbN0haHzOJrGPNG2/JFg7Aorlgwhe4M11X02G+BrzNlWD5swMDlgP5F22OkkWx/BeacNWL5i1sYqmPz941tlE/4cyNEtIsEhKlGgNHSy3ruPUlzhMGu6QMUsa3ydRSCtI8FZXjB8Bzmz75UB98m+SCr/gIPGRAtnhNBnq/tss/S8x+VBUVxPunnqL9dGjuZi8ngdjTnByuDMOrwdyseYsTWu/MCjPnEbVSzyoq+BwhoBimZm8PqZMSvhFnEuMqa/5iO10fZJ//EGhRIZheuoMvd4GcalcMDpOl/9yBGLTEZiVE2Krz9pvOcTUW9BfyhjFij+kHjuEzY5w3qz3Yks5JIyX4Z75fcrf/sSINvaGlGCGEtHCNkr2RQAAIABJREFUBvZZWWMX5YYEP/jaKIL4zM3k37vPRDNFbonqwrJhaDIUo9XVfB+7DA6qwucEAmjmgpkkeAoYdK6L3tP7GR4VRJpYLn36+pAeEYm4mR7S+qCjAHJrfamfMhAJsy30yRjAn769VGk6cX6FAj7Hr7Iqq5dBvvIoHe3ifPE9VtgHw8enwBVChQ3MBQRfK+Hjc2QUdXimZEe2xRNW3umBgeNJenMAtbZW6r0V+d02jICaCVg9r2bklO88k1XEb0I7WY+byFFywclenPsNiUwX8+JKaTCzHUHsA1jWGuLcr5iP+T38zX3GmW3B0GcoVH+gWt2HLGDSnq0kisVQ0v8G9S9vE564hxP7GwhSVETqyVY8h/fhqt8G8u2rMe8byQVhMw/G5JMdmkzvpLGEhJczRXIMiYWPQDaD7cuOoL/LhLlR9SQDTiNn4fctGbH6VFqHOtPfwxZixZDZd5UoB+ht6EDiz3FmfjqFmeJ1dmbAM/NKJDXm4V2cSWPwMqz77GTckHvk6Pnzrf4HOzVFEayJYPOk0ThZtNDW3oSUsjOFEi/olcwlKaSRv5PEsSq0xbzmEfPmw/7HAmgaSrerNWI155nruJt36Xkkmojh+9CJpQ7PCaw/Ci0P+eK5lJKOy0ySPMTRxS5AC2AERAOKIF8MzUBfmOUdgKncKGaqdWPuaQY2ttwAPOumY6QyioWv1cj8sYZA2b4oZc1CvymCC3bj8XGSY8HTGBZKXeBw7nYs5U/g+NSB+1tMMLFpIy/+CMgCh16y0CWBq4415GJKUmsCH6ssEMhbcbTrKh4u6hzePATfLyuYf1WIoe5eFru/xOe6JTorZ5MTUYCysQXORw7h5fSRsE95TB4eTtTks+jK7GZ72Rc+txZhKzKOoemLCFQfRZxiGfJSA+isqmVoXTfNjX05mJPMHCNnmkUqSTAqxynrKaK260iqcmJQ4WNumz5Dzhha3n/mr4cnLn/sCHKwIO6nNy9szSj7HczB3/WsHA+LynYQozGC0bm/kVKMpqA8jrYabcyExshYu3Licl9ufbrBNqUH+F2qxE5DjRMxCuSJijDL6gf1hUp4iQUQNe0n7tqZ3NdZj4J5FxvjM9G/YsJqNzGulhvRtr2RFSeHY1oYQPGELAbubMNpoBurTaWYGfGVKx1L2d7ewXe/OHSblfneUE+2ojbibbX8I9mHPZXO5BS9waxdjRKBAJQHISMYwNTs7wz6vZA3H0IxbVvGNtHB9LY68dxvzv9MPv6DMpc3FOY8fI1gVCjRwo3cGaiDxJpDWHUU8OPpRSb4J7H52H2O7/PkU/hLyqobeZjsSajfN2KeXEXtaBdVN2Yw2s6RxMoUzIPvM7foIatujONspRTehqkMKYzinmEAhyRGMrHuHYEaJ9FwfcGb7hesGO/Dx9If2EoJGBvoyq7w4QxbEs4P0xNUn0pi9xRJjt8ajYXBHoL+vGTYqBsMIZO6ObWcvQtzdgbhuE6MRa7ryKnbg0+wM6etRtKbIsJMn2RObxjIegNJbo5uorlBkrdVYJQfxObTm7ge30qr/Sxk25ezZ48WLHFhWgcY6ILajG1sWneBYS6J6LZ0c6RXGtFvEWjdsCN+sxPXtKURHdaKWaomrnrPWaa7h+vjunmv68Hx2t0oBFlwqu0nZxuWYDlfD43lsjj5CjCz2ogwfwG2qY/x3ZLA+i+29G/bz5kJ72lZVctk51t4KPnTGV/CSdsdVJkVsC3yDgpXdzB3zABCv43k8QqYMsSGZZfCkDJU5tTRIwjm3+GlSy0CY3X6ht1g6Pz35IqtJ/ZSGMEuPdyIExBqkEjqoTlcEIYiKlhPYeZSdlo2oLBsPzqaxuSWbyBJ9jPetnqoPY3jrYYODhUiKGwezIDybIqKpDEVqyO6VZug3atY62+LWJ9FdEl2YpSZTnNfEWZrqZNZpoDOSHMevy3jbXMg8eGTESQ9oTX4HTJvqhG0zkF4ZTSXO15z6T0kroRr55YgLhzF3HuR8O00OMPmkXeQFO1CQAFirEJWPJSqgcV0dsIJlTGU9PdG5mI/7rpMpvbKFsjcy57P/wovXH4ti7SRG5lyyJiVztUECaug3YPsVHHCHj+CajECrx3idPRWjOS9mdPegJ6GKwZ+08g75MIKrzTMgwcww9+bf2WTVEFbCchcRS7OFdnT5ugGjUPrriEGjCPC/jbqi77g0zuJMrUDePVcZlLKLvbLhhAsYU/3Hmtyp4xiqMVcLlRXoD2sjLtHXqFadY8zGy4w5Hsml83SWGBzi28Rh8hVUSfyTyAvH1fQcGgKL19epXzOdTz6NlB24C1Jqy6h+ekqJqNFyEgIIflcOo+L1nLPYSUR068z0WMkpS88uJwdxI8nh9h91Iy9m9exY3gwc+sleTt5EDfLA7mUuw3BjumcemRK6igTpoceZ+k4d17lHWadnC/TUn9Qm+KPsecXnKTekiOYTNY7b9z7zeO3agm26ss4fnsttbErGVO4GpuoEl4VyLHOuD/5YZHkTM2hz8sUlKV20hDix2pFFRqfFLHt13H+qu1i+7g8jt66SbbaOdTHvcQr9Tq9uWMJE2/mVFspH94OYE9GJFtO91J+5S4C/SbKbLpY8fkeGzqS8XYegpdpKnflT3Ezbx8DPd6xZ3s5D9aa47l+OhmzntOiqIenXQVODVcw8spGKzGAE/nBdCb+otNakeO/P7HBpJmIIW7sDn6BzddIDthMIN/RBvn6F5y6HPtv7VH/JcyHgbq58NZlKzzf/uGpUyBOpTXkR3xj5WIHbqd4k2pew4uizWj6PkW98CeSOeFsz1dC7ttPWkxN2eU/l/rOOFo/pNN32AC6e2/jOngp7glC6DZnat83PHi7lJcWyviF1eOrsYEBuiLIVl3k58sKHlQ5ArDBWcDfrfup/fgOsaPRDN4/CivZAQzOFMPd2Q53kQg0pcfyVzqbxsJKfqNMZcBcPLu7ad1wjG9TjiIiPM/SL6swrPMiscWDYsVY9CePR7sog3l5Cqwr6IOG8V8OTZnDzY7NnH1YgZWKPHvrPqPSVskfsZW86QfVaiAI2YdoRS9Fw0twsdZF8+U1xg+TZ2HERTzcB/FiuzEKL1M5XmqMsWYtCpVZhAeE8fXBZ5ZMHwYJ7UzJ6+bx1Cc8WzSe3LL1jJi4mLMNpbTue0NiwHjyddsIjJ/G8b7xmFj8RqQ4n4yGTIZre6L3qYIxG77SR+s11lHHmRJ6iVJNb/aucqLcMZBRlfkczQiivcwJ/8Z6vH+MJCegl5va76mpnMHOpEaUVPJ4pz8dz65Ydr48z3FVZ9hagPDLSTwO+WC1zZyHhp4syfXlyFAl5s8aib/7KBLuBjPA1Ic3YmI4XTNE+Oou9V1LUSpw5Hj8RdLuX//f3SPH6qPPEK+NR1XbCbPhzahae9P6VorOr3d4b6vJ9A/BFHtWslxTmqzuE2wNvYSHsQfVtod533aA6XUJdD1ORqr3Kx4D+rGybSXhkf5QDitWrqdXUYE25TjM61Soqy2ipNuePsYqBG3fB8DA6+uJ+1nHbpEb0FHEZ6d/+BoQSFb6V5Zd+8DCTg1ey9SxulKDgXaOoNeXkM4L+BRcZpL+DibkbwNrP36lZyJNL31YQDM/kUWDfxmSQfhq/+KB/jAKXOC+aj1SKLNr83pSj1phM7qBO9HHmL54LkdU73OpuIWlXddRyH2Fz4DxpCRdxrnRF+fyx2xQOoJLjhvfR8mD3DEUO+uZUm/O2MJCImaPBZsIsmPKyaq0pY/uP7wJVONWvBrP8nt4dkCGdyH7+ZI0AakRU+CJFyonl3JquAGVNrWsle+g7vFlXO1u0CZZTKWRHEoSf/guOZbH3+5yYJoMZvmZhGcps9tFhob2cr7nuGL3xY0x6bcpjQ1j7M6v9DdIp/rPfqSrRqFjW85ZeX3Mk2UwUTfGv/sUCoPsuSciiaTsRj7cekLwvCp21/6ir0wIh//xwWvkFNp7+7CoyQY5k0KmbAhg1SoJtIQTGCKhx2C7ZkZtaWT4vlHUFiZyy/wUQ1RBJnwWGukleE2ejU5+KHN6ZuDaqk9nbxrtDe2IWNpinT2YHIUbfKzdxtLOxcRumckKf1HajJKIHtZGpPY7FHOWE5v5kF1i3mgbiKKb8x4PpXImaA/C1FKFgKN90Bi4lyArJ1ojZjMgqRZpaUmEcvHYjJClj2Q9i49nsi1yIXt3HaXPujBGt72gYkUufnn/89Xuf1JmNvbCXPFU1rx6gKPbCQaZO+M67zAfPw8lKvQnMgErkDUK5EdPCMKoGk4JV2K/3A3XJ0ZEh4sicX8zk289p7l3LvKVc9j+KoIjI3OorXJkwL3ZOCTdx6HKmjEJlsx3aST/Wyqv36ozeM0IiiYeoE2/GUnF6aSlfSJ6sx2Tjwrps1if/FhDDuyewsolNaya7k697We8ex6gq/mESSI3Kds4lSOp6gQ8isV4thtOf/rTNKUFEbsnxEwEBdpY3ZKGoP0RW1xmMy1XCh/dZvr2UwWLdXi7CnAdPhmv8fP5un40cxJnY/LOhN2NMGaaLs+V1zHf4g8xvpnIqwcwof8WjE4dJ2B4B8kKU5kRUkublR3TX0UiPOvPS8PxPNwsybbwQIw4iqVgOKp8IuSSkJA+ekzuXEfIt3Xszd9BzUILRtz9Sd9zGjS9F6U7LJKz8gNRFJMiSiyaNhMHUiI7GD9Rn9hH2YQ9CebGpTXMEz9NUF8he0Yas+b3EfoFRVKQVULg42vIKaqhH76Cx4/bkPtowrETcfy4OIqpMuns6beDceWevDybjfB2C4LOQL4anOH7zjE4nt3Ibc/RPHGbzjGjKoq6JFmcM4MFS3qZ7pTDy6li6AZbMKy7EpkmFU5Om8vQ3buRcvFkSGc3Lfe6SDKORHP4WMSrrVD0V2VMzFkEAxwYfuETypKqbCxbRf89AnoS2qh4uhzdqlDa1xQidX4QhBRxJeMFOwvd8L6yBceaGzRnyyFYup6iHgn0ZZtIrZHCTg76KA/gaeY51IZup8fJnfSTI/j6JJGgChsaz4eQVRdD/697MRx3kKEVk5hltJVl0ikYqJ2loP4NmYmirJ5SyugyH35NcmD37hawPgTpSVjwmNdMwgJJYBawGtBmzOIJrFx6mPdbFnPwSAPHXn8gu/8DFj2VpHtOJntnbyF19l1SdI4TfiCcWcJrtL+Up9byAI0yM5BtFMforgnlsxegZQzxRUIm/t1EeoURw7sv4acxm22T1ShdeAKdwNn0FLzDvp8YsdknCDl4mbgHf3DS8mX3AluUtp5m58a3yCl9x83/N7+Xd1AYEsKbekf+WN1mzVoBPnuuMFJTBfnGMJY/lmffgllsWlbIo60ZTL0uyxb7e3SWruSYfB2eE2yo2SLCw0/feJ71l9Z/hnAwohxaAhB82sL64w9Jcv3O1gwLTC2WsHxEKoOGrEFN5i0O53W4riFkREQ5zbfLOLvSlJNmR2kUbUMpOoBB728huH2DTV91OCLyHeyLEXhdI1hlMDEntBlRtwkHu27OhKykV1YXgZ0dIvol6ObKoqkgSbFhHGPem5DefyNLq214lWjPIR1x+mdMpkt1KpXDLGh78gKhcw7L399m1b6JKCfEMqukhsKmAKaqJlCm00aIiA3TBO3MTrJAwfU2w9a9RWnhPRZW/iLfqIWKfhoMELai02xBUM03xC0qSXtew2qNhVyUzOW55X0sAgr/37l21aAuzs+IAAKeh1Pyqp7PiQXs9BXlqe9a8jblsz2/h5nNhjiG/qa7wJPYMk1u+8qzPy8W4eMr7BlViVHdY6ys7NHPaGSd3Hz+PG3nRFo7Nae9ufl6LAIxMzL+0UQYdIX33f2YPvwFzlNakP4oRDDIGzNfa9wWnOLsR0OWmIxl0vrV6Gv1p94pgjzdemJ+P6e97gUdRvGYGjUj0ryPjNS1vK4+x9FroUzRliLxgwvxoSvIM9zOt2GeaKlKMUx0Gc2LXEi+fpI3WeoskM9hQUwmU7cFUpEtyWdna2z6HyZCIx2Rm7VYCcVJzxdhqVsQeibn0LRJQbb+B8+OChg0t5zLhSFYXIxgTssqzPzn45MyigMqq9htFURJQBiWLdAQGY5QKEQYI0Xy1DMs3Z3OaZNAXrr15YJ6PTI/lzBtwjh85UOYXDUdTzUBW+sfkXU+HZdCZ/xEVZhZs5018zR4tm8Hh/x/kR3XwiN3Dd4q3uRy5GDeDtBj69MoOsOUKVNs5qSBHpusVxC6dSJ6t1WxuD6Ju7VbuFATQnLjYQJkf/CxvyvSndIodJ1hyj/NRPp/xP1KLW9H5TBfUYl9r47hatXGu+rVdEcforfzAw5yFbxdcR4tsUlI1b0mJnE68wc850AQHN8znz5LJLDuU8iRw1uRmOLEWVNHxjV0E6VeRI1WFlLZ4/jo507yl5FY7lQlJW0Bd0rNiJONpVL6D/3kxHguNgipiA8sK/uLS24M+jmnuPlEyI0KISZZVdhZevO8Woxarw2MC3LhY/5jgvz30RwLjrpDWdluwyXxSfRUref3qDaGpzRStvsPlnOuYXDclgrttcwSf8t3w3hob4Yzb8jJNKb0z0Ym/JgMZMGDp+jltrJXIRBDgxJmVPlA8WWQkQGJ67gt7MI66Sgu7+Bw0BmCgoLo53ICbTUhxd/SKfhURUeHJBfUFvO6bwgj2lcRJC/KhsFbWLR/AlPnx6G+yIuYb65wJZpnRt+4Mm4Z7623MmlKAzJ7kllnMZ/TYct4q/ucGx4tLG/8g3DCIWafdOfsZnc+DrvNB+mD2HYpklj6ixl+QpwDcsmX384l1ZMsUw7HYmsHxx7ORtzEAb+GCOSFOhQVFjJHNJ/NdmeojB1ERo0noTHDyaq7TWNDMrum3eD86Vxab8+ixfshO0u2027giPQeAw7aV7CotZd3c9zomlLP9aB+nDIciUa6AY3FL9g6o5s7yW1E9cZzuDueS4ND2e69jyiJN3yU3E59VxQjd83i7Oqn7CgKY8jxYwimXGTGWk8q7ibRm9vEALu/BEWPYedfHXyrq8gWkSaoz0zOWgRjWpjH0M+BKGS1oRHZzYayLZyI+cVQma/Uq5jgGXWJqME/eGWsinq5JBUtw9EOV2fvA3FU9VJwLs4kwsSRHc4zkdePQbZVjDy1Um4niWHwywFVw91kXQunbl0Yj+9JMudvNI9yDiJxtwVh43uSNWuIjY6g+/UvajWq/9Oo/m+vruJihAHunF7SgcbTqxQpFVEtVKBv6Bf652gSbm/KGtlWxAqFyHhOJqjlNYY5z0ifO5OVSxbRGadG10A9ZHqzMHQJAsppttSnxrsv0SGpVIcvp1UFHvypZlBeJZebf/LatQxZozscF7YSquhJTGAuSz5v4LK5O9O/xZHqqsMPYihRuIHD1z7cHTWC6sj3NGor4xfYRp1lJae+qaDwcSdD58/nx9tYvDpakAL6jP3Ln3H19K/+w7em4aT3sePMr2QkX3QjaaDFKjM9JDrBvxMSKh9TmmGNYAxcl6uAieAwDaxZjIrvU2KqHtIq3oLGqkC+/RGlQFjFFoPVFEy1YUxyC8cm9yHUxRSi9Ll9/hC3Nz9lzcVj9AOK+YQ8oOpzEf1OX1JiCxlfvpv9n54j2l2GhI8IN8PdCJAwRWJ8IX0L6ujR70JUXoZvHRkckxrCyjdFhFlmw8AFzLv+kbHzK9iqGgFVBcQ/iyJNxAeV4XUcL60HQMLcnC4JJ6KXWmKQp8EshTpkvyayo3sGL5PrOaS9jIV5bRD/myhBCRsGalLqpEf7NG/8ZSoptejgm9c0TNY1MeCyKFa2+xg0ohKR0HsU3G/gwJtU/C8MpFNDkvyOPGLPr6dZ5S8jt63DU8GeWe0qGE9fyYj3kUSKGeL7Jhtz9yTudt3DS3UsVeE3kU4qQm8TdDZ3ovq8GAqL8OzjR2Xmd6yfGpD2FRzPHKfEwQoLublMqW5G6GqIrIUPQef3MfPoHjb6KNDzEnJvtvPQ14VZIl/oLg/k3e6ljFi2k3FmXshulWWloJWUvExa45OYV9XGSzsfep4/ZYWoBqALWIClNRVvkvmdPw9Bfg693/siom8HPACG8vZKOb79FFAadB4ZZzcEwO7BVrzS88NG8xFedsrcv7UDfBcwrWgtJXHZpKnVMt1nIsYKfQlsDuOESTv9VGai1TQcqbY4cpXb+eJZgXDyE2Zmfker7SdGhz0xirJgTF4guh2f8DG153JcJ27T3tNYeYA7FsOZJdSlWw9GFkQywkCUhamfmaC8H0O1SC6LxwDg0atJm3QFVeIT6ZRqRU22kw3MI1VzE/P6f6e5nzmV/VU42LqK2ONl7Im+iVq9Ix8HuLF12FPetIqRjCjliurkChcTLhPLzfEXiYy5wHvrSUiqBpGrqMmDtBRmfksg9t0h5vq20xueQ4cijEl4wy7FfLatdSFUYh5HNGwwPROE4NR9oBfPwzaYZ6tQ8/0TV1sjSTefgKP9YOajhI2ENsfdh/Jdoh/q8VY0W4uS0j6dnw21lIppISUxiKLBAWj0WuCT5EpPv1bKexoYoOjH9C/NsOY6/b9ZIN9PmgIpfZLKJXBu8UKEz3w/L8JuuVncTU7FDSdsY+R5NjcQnV8FqLn00F9UG3sByNYu5sgAW349WopNy3RO5Y76t1n6X8J8KFS10SQ8zMDqw/TzqqNV14So/s+xNN5IxSEPNBMyCZY+yJf2dpx/imC2ZRbHfrljtSOHskfvqNaaiYhqNPkfzlJhBMekXNEbvQYfL1te2wu4IfqDBCNP/JcEMiy6Hek9ixkmfp/Hek/wvvGSEzU+rFqbzrfSZygP/MCN5DrGX2tgZNhLmhdfYk+ZPDVRunTHzGPzW1mmbX+H8aQdnHoiZOeH+bz22MiLMQtQ//udFRZXkVcWpbeljcQZ+SRpvEMrwQWf2wpc9ykkw7ibwiVl6Hi64lz2myCD0VwSCydNYMsuBrM9t4vx+uLMmW2KmVQAf4vtEMqp4rI+i5RCMUxHD2f92imEXp/NKut9bDCXpPh4GhtsBP/Kl3BQ4b2ZBQLNCxjUBeG/eweaRvMYWluId3wMqlujMdZ9Rf4AIUP+8eGWWjlaWQeYYOSEQMeUIZYdhLeNITwnmv2rspGqnsMd9VEkr5JloYQV78b58kOsGrxjSf/1F839qxnzp4xZH54hVunJA+/ZzE44Roz4EBK7JVmercn5eU85fr6A5AO97KOdhaImrJ+phJNNPNlD7dkZZEOfWVN4f6UYN+lIHn84QOkJQ2InlJOm2oeoSjeW7ssh+ogumsp2NDWuotPxJxby+3m4rQJJoyzEBAIE7XvR3vYKj9BXFL/6QpmsMcOW3UTybzpjZyqTlDiFiFhVBN7tfExNJ9hfwKl5k/lYWoXIj3iGrb/JmCHf6Fl8n005IDu9hIrrqxCv0+Hgxou8uH8Wj7vrqVtSie3a9SgEKvLPj73MWLGAJe5K5I0ZjWj+UZKP+nIhoA7hN0tC3zbSoj0Zx37ByPYco147htRp1zjdvpJHetcJMt8PWPCrTokQjQgswhbScuwGL3RLCbqWhkT0OfyWb+CRWQEJDXLwZxsdj5pQ6w7ldcE5NAI/sLhiDIKwo2iN8OR3kQ2OvaN4538V/VEZqHQd46r7IiYsTWTuOVcmWnngHxpPklEHTQ/rmKkj5Fb6R6737OBqhDhMXgAXtRF3gZiRc5g5fhGC97/RtRiEaE8H8w8rUmjQxYzcO4xsuonM+Rs0zc4mu38HdXHarJsjRtCRh4i2KXBTazdHFTej3eKKe39VMh/Np8NuPk6mXlTGh/I5NZTE6h0sb9vOuMD5SC825LGHG/tmXMbbchKNWZIcdd1DQPUT1I0/k2+fxJjvj3jUlsut6bux/DOaBQoDOeOqyopGHxa3zCU3x4msWwmszxjEOLFNJLUt5131NEasnIWb2jVOaG/G7dEN4gvyKR6rQJyOAqnTpIi88Ab975JMOxbNgFufCJsmpPN0AKqOX+ie6Ie1cSeffu9j1pBhBEwZx/UnQzAYXs+d7JMUvPDAqMSOjO5CPJ71xbJQi19Ol3jX4sK3saWUuRtTqiiBbew63N93UjarARNXbTas+ssbSz9aBX2pS3pP+6VD2KkW01gLH7doMHH7TSxVHTBM34109wqe1Ob+p1H9314yor0MXlbDzndmXEl4xm+Jd7waDAKBA10fN9ORHMXC8IcsHjGXNslYcpfE05seRJRjX5bv3kZ68DkUMieQ/M8lBo/05tSrI6R5S2Bkn4+q3j5Kza6wVS2Sx/ozuVtxlFRVX974n+b77KsUvj7FSS8L1iocQj/VmCUa/uxLrKBXooEcS3C/7sWnLi1mSO4gctws9uOL6tJUcuL/cvcfEf4o7SLiYzADzRSxvQ9vgUc1mvjn7KND15reX/aUVcmj1a1DqmcBpyrkMVPIwj4EDsnAQ7sN5Ft/pXcP7Je3YrwbuBOE2UR7fhcPx0K6EiXXKbz+EEZU9Wd8vc5wucuW9Y6XKF7ggH5qDc/S3lHlsQlnpS3c3bWZzcv+4HjpAk7AsN3iPC1Rw2FGHC93BqJU3oK72yZ+dXUzTrIvmb+uIF2qzAikcVMfjGRBPHsN72Gl+wDHW38I6vgGjV/IOzKBk17vmZewlZlXNuBMKDFLziGYm85LxYMoiF+CtETy41UYVPyDX4k6iJi08MrXGSnPauJ2RXJ1x2sS5h3nsEYlQgcnZPPjOas7hN+rnlDy/BOFBqr0euzB/OY8tofew9j+Iilv13C52x13p2Ja5SoYadxMecUiZuq1MvtFNkbDRjHpghOzvMQw6p/C9NRtvLHzRKgjRuzQvvjqD+R7pg1DHcUp7tuIltJQLvm5MjUuDN2V1whtgL3Zv9AgGMPfiuhMDuLMWNB4NYSZT66T8/sZ98QMeLZuAS+ls8jIiqPuZX9WbRzIU2nQGppPs5ME8YsGoHhHDyfAu3kZ8x/1kvTPR3qjIlhRAzm/AAAgAElEQVTrcI1zIq1sSBeyuPsE+ppv+PP2/8sXcqh7SsOlaxy0c2aBZDAik9WQrWhCy0ODCbfu01tlR0EmgCqx129hpiOOsDWXA1vnc8XUCiSnsNTAEXmL97zMEzJ/3WX+GT2GmISZRIUt4am4Cw9/2HA5pgqZ56WsMj/PHK0w/tbtpeODOcv93JBeFINpqjLXx0mz1eAztR01+C1azVJDEV7yCI2pWzlQVMQKY0NSFlhSxW1q5eeim+jCc7Xx2DrMZsz1Qdw4coU/Jl9Z1tJIeP+x0DCGosZ3jCeZ77UxTFUPQ0vkDZLukYipB7O49hBb33WiKTaUu4/LeF9ZzadTsuzboI+WrC0PPhzi96PD3PlbxhcJR7zrXlO9+gdBdGMVFMaPEkesjUdByjx2nmhA/eNNBqv+xStFkrCyWcyxT0ewTg/9JZmcEZvNudNBzGqwpNfamucSEpxLKeZo0S8cDQu4Vjucnj//MDXvNWKVItQ2edBRV8G0clUc6m/zyGw8FU7qqN5eSb0BnFSfR0KWMW5ifXk+5xJdZlJMP/CQr/PNKS1spFw2iT8aozEZvJKi9gFkxzYRmPIRh5RMzKfqE7zpMcdj4pFWOcje5zW41ycyorQvKZsK2NFWgMvyYYg1jGOc5YR/m6X/JcyHQLSYOrNxvLzZSYNkNLK2hfxSXo/HcG2kI6aQoNvJPN23SA6MY5KrIW3nRzDz11jee0Sh++U7WqsvUvRGAUfnJcgYTibvnTdzD+zg04NLbFrWS2Z5LqNrZtA/OBavuo3cGxfE3Jp0dIo7iDzzmXXNVtiu0Ufby4+aHg8+Gclx8tBXto2yQWz2Eba5gd91MYL6grhULLp7J9K7zwDxlbtxrYNzrwX4SixCRcGCyLIGLmjLs0wpDpE6e0rGqRL3ZCwnVc7zxF+KKdvE6W9nCj3+PMUG26927IgYD4JDYLGQ6Z4CTIaOwEVhLWuYyAlnAfJGHuh35zKuwpIbWQ2MMZlHyaBhLMp15ObLA0xJ6IfTThkmXT3MeK16ImoaGV9ZSfEeQOkyWP9hqPNlyge7YHJ0NLnSb0nLP8ZjvxwEgePoO3kkl5/FIiwoR+7qXfzL39En+xY7M6O5p6yJq/pgxF/7s0Q3jE85FwnoTmVqtCFOf6PZckuS75kGqLVooPjbjOni5gjUL/BRQZNv97KJOPmE3jIx1K6cQUQQyIMPaYjukuBA+1SkUy7yfL4NdYVzEZ404PWwDo6oGbB3fzT91jtjURCISFkdk630mDvAHtMV5oiqORA0w42gwY6MNb1C2qcDzLM9ygzNDdwxu0lzmwkTszqwtPtGQasI0WfmoBpzg1MHniIQLICEeBbHfkcpQQkHtUDmPWxkS9loHh5NRbewBgNtVUz8gnmT4YDF6QxEa5p47BaD/OUteNe7sXCDDt/61PP75UmSl57EOTmGv/eDmPJ1DEeyzJj1z2/EZw/CZO0yXr2Yxd2yCSxITGaunxQxk6axtm8Vmw1NuegTRoi2LKzZzmhmsNhjKUL5VrLmF+G8NQkbL3/2nn7ItofPWXviMJ9Oe6DqeB8py3Sy1n5GvGcIh2Uk6f6rymDxyey0q6GnM4aBfTK5ExDEtOgRtL2KxmK8Hw7K47g5PpAZ035wwbyJx3Mqkcy8SNAOBarKEphr38T1hKvslVQj6Koj6+cPwG35Uvy+LsD4bxp8qmP4niCMRSV5+/g9qooNDM7eStx+NRo2XmFKqTn7N0Xi8OgLgmlb+ChjzGeZQLZ+mskXKRvU64NpPvODfR0jeZu9nQqlRfSuN0DeR4whyn4k5K5j1JMABEYHMH4ZToJWI1UN8owoLUV4cBulmjUkNEUy9NJTrjZ34jUqnBLOETO9jXFNqQjeGWGrNZgrryZgMD6RW923qFmizhfVT9h3JLKp5imj1tfir7EAh/XV7D1fQW/1GIx/DsG/zJRLrxNZ9FjIkwny3Ho2mzy3JDx3N1LqIEacUIMc034UP+3EY9VFkrRaEIh9Y82tTXTUh1DY5y1cu0rJrACsvHMwW/uCtBvLsLhhjrfFHhpjregIicXq1ku8vH4x8NE2FvUJxHa9DZWJvYxX0ObGrkT2nRch/HgTqZsLOPrnHqca4/G33MSCBHG8fK6zzno1dutu/qdR/d9eUtJNmPs68npjDEkZyki193K0AYJnb6erRwF5Z18alIww63AmZ1IdERdL2Us3KP/+V4GSz2RfGYXhmMW06PZnhMwMNKrCyCv8RJvlDtyVgtEwdmJuaS4q2nooVkCKjgyDwzsI2RqO2JkdtCl+5aqlERGhXxhn4IbskGwCXX1IHfaADylf2WUgxsDcUUxKeob+fgO0gp7jKa2DZ2ctv3rTabLoRlYF4oGu5h4Ef11R+ChEdsY7zJvF6H2ijku9DEYGBdQBY3rAxf0ZT7r+YN3W8a91GKngOlkSq5x9uAnPkJKYQlr1H1RttHAQRiK072bOuzLuxDRj5OGEi6UH9h/yee70i6xrEYxbIWDm8tN8TU1m6NJKxq4GjEpweH+TyCmHcD9+jXy7FWBUyYzMfhzyncEc23p6x5twtWgoYe5KXCkZyABhGe1fX3OD45zXauGcWwDvnmRSNPoi236vQ1V9JWpjhCAKSzvkUMkvJCerC1RFWPEjg5UGY/Eye09K7jDGxIcS/Cif8MBbiHb3UnroLOrLbNnWfp48hXZsxJI4ej6DsQNuMlp6IAaOSdgY6XLuXAytWRtJepjL3L9y5DZ1I6qmBWhhb6SOeLiAAWurOb/nFCDJs3hl+CCBtYYRJZk5qDaD3nQ5WhRkGSNhydHlF8jOEGVx+1d6qlNJuh7K0tnOhK8N4Z+Ih9Rjh6npEAo6nnBJRpXk+dl0esqRlPAQ6yMzoBiiJswnDnhSF8rJEabUFazmwbgiiqt/s1HenA4VaSQ8TRmcC7NdLqEqKGSW8jqyy8WR6nrNGPN83pZo07f3PV0mk/539xfyK3Io5i+LuNiiQrHORALnHsLStJ6xnp70mMzlj4UneX1/AqA3IZ8cVTN02r5wZMpeeJHDzlgRLj4vYpyOAofE0llfco6eKHsO7BhAvuF6XuoH4x6TgX5rJrMdthKbUMs530E4i2lD/w0MTN/KomEm5IoKUJGLI+biKV5lrSZ9hCvLFr9nF5OhYD0vpsUyULSX7baBvJ8whaEtUPwIdL4/xWjKQY72vUtzjQ5Nebd5LzeShy808bLZhfhAQwoaHSht/UtX2ymElpIMFAxmuqMEhnvhz5MSDB4oEJIljbxNE0/rFXBwsYGETszHTSXM+zJLI5+zpLYMM51dUD2EnaPL8JOGhyLt1Mk9hZ8FGGumkKL1HBdDG6ytRxFd00Dn7yTQBR2FMLp81en/PYsxEh2UlzewfHYPVIri0dLM0OlrWRL+GfeP/Wk3rqLQtRJBbwuiOkZcjYpi2XBbRMTEGPrpDtcuh6BZMxOjcjB3kaBYMYauLG9cnomQpaFLbXU1o4a8I9XUiO7WNsQr5tBSm0r5WFAx7mLm7CFMyG9gkIUsGWnXiZPpxCQzgYFRIVxoFDLJT4D1oLEUN4ezQu4Za/Ou/Nss/T/K+RAIBAX861lsD9AtFAr7CQQCFf41gzMCCoCpQqGw/v+vjmSvLFntw7CYKmBzwkWcxsFmr2F87pQl1NOWQ+PFERFTQinpCKdMg5lm/g9ym/cjr+PAqeH7OXv6N9K6i8n6m4iYViMy/ZsYJBJF5a9CVoUcQ2DvhbKWEnj94ENvOVFHLjPmaBASCnfZJT2C3Nk1DPEKwamzBtEOaxZl3yZr/FtKvuxCSc+WZ3XQ1QUzhsFDv5l8y32F/YO+XD0hjojeLqyy3lPztIb0Pi3s7xuA3P1dTDp8gi3XKuh69ovRC1YgkubBE91SJL7pcuMGmH+GD40rmXfnFD8+zYeTl0jYLkDCSRs1BU0eivZD/3sW554IuX9HQLXTa2LjbpHxRUi++gq2pmUxa5Uxz54d5Py++9xqMqL7tTsvjPwJFNOgWnCY3SmN9J4xReT4ON66niJZ04gXxo9YZFmOnNVlKj6WseLlTBa9daKreysl7Xv4OX4qJYevkD17IXd15jL1bwahubkkTtdnTtFHztg2EXPwKFlLhBjTxlrRz9jXd3BEW4+pqjWc09Pi/M6xbO6ACtsNiF87Rkn1MU4a30XYWUPau2Qasg1Ye6SL5odj6VzTjmy/19wqSCb27Go+m+hit0UVM/UPxFZJk62hRh+lNJzoIk1ZlUUDq5jlNoI7S4opP7Sb/VG38FcRMPpsOLt7fbk9Zzg+S7Zz8VIlNgEP+ZWWwMWWxbRuPMb+tnw+CS6g0XcaAfPM+Z33Buc3N9D0uI+5cV/69AxCu/sLqsdKaba8y6HmbJzn3WOSpwVnahaj1E+I20UhbjuHs/GLkJNJZSx/okOn/kMe6TTgs1VAPnBTUoHN8u2UPwkmt3MfJzeYM3jIGXx9LAkz8UW0x413kevZ6m2MQEYAoyU4dfEDj2USSZbORYQXRN3Ux/CvOyM3f2ZUiQSVucHsWviKvbmXqf29jLYKeCmpTX9TLT4LL+GpcIEJI7sR9UlC/NEcfF4cxmDUFojqIXPCQLTt0/D9LkNw3i+iSgaxTucFTYdeUDL3DsL3SjxXcOZO8kTeCN7BPx6czFsM8dX4z67hvkQl6gl/CHUcheqw+5jOucpoo9fs8zLlZ5svsUsEnD6xGJXw3RR3VyNRk4G46BYuiaax09IOyZ4ePtRMIi6vlPF1llwvdufhBFH+hDlxTK2XuZ7GxHccRnCqH8LWGxzJr8FapJtIxy6Gz4ni7JlnDA5JJcgulcCqG7QfDifi6lzshdPonqmG9nJblpzaxokNA0j+HcoQ3+k0VIuiIm/Ku/4mvKpZyepnRtzxvMjsuGJsxiljmH4QFdVK0msnc8YtHZbmsHuCGa2vhlMoPM8No0vgKEX3l32kFBsx3OIcTm3hlLeYUt3VwW0fT2AwMycvx3CAH/qt/jiNFyJosUKYUoiMshvLV1gDEA54AW7v7xMX+YPlsSd5+FSTWjtXZj0eR2jPGmZu/oiuQQrXXmWwRdOMlT570S7vpsFhBKmCTBTHDyDy7tz/E1z/t9b/rb2qUdIU7X+MGP6ymdx7EljM/kzUo3WcpwrblAFUyaex2u0ytb1GjH/nyLsKS1L7thNxxxEU3VirZ4rsjEHU1v8gKCuZ0B9J1PeHeqkO9C5vpmxkHGt/f2CYSwAt3omY+IGgFQaOvMYS20BeZp/ET3iI4LwcItwH82WLNjpPfjCr9hPpRm58A2q7INJPGfHOp0iklXPTCtYXZ2AtepJNEsO4Gu/M3gcw/go8Ox1Dn3HzGD16BDInpZDpuwDl6SKI+JgzdVgdOuzkAzPQsvOnxCCPnpFLucU8Ao9o81W0ngWROymSSOfAU2mkQqww/r2PBw5f6Y28jGOqJZHqvTgMDuRC5m3G99vJzRW7yQsxoccUlA8a8DPzHq/swW/XFLotFDGbd4aabefxij/OGdd59K8RY+eHS/wKMcH/6Fy239pFaZoIZ4pPERYnx9ZpVfw1tMFq3R52jEpnxdt/8DT8idPKmUxcHEdz1QkmmN5FXMOJR3qhrFGeTrioPT3PG8gsVuN/sfdeQVko27r285ElI1kyiCQlIygIIgImzJhzDhincZrAnHPEgBmzqIggiGIABJQsIAiSg+Scv3Ox16k6F3/9e9Veq87NOu/N6OqqHlddz1vdo3q0tmsqr0Mzqd67n+FiIuxSNmKP4i8WPpVgku9JWju2Udo5FS+JY0y7eZE4q7esV7qJXEIcXd5euLsqE3TdhjKVRJRNHahrb0epZzQ+uwegFfiFjSm5KPRtIlCwhOhnWzH1HkeOlQW3DwQxN/QBhyY9JyH/MIVSOtzP/Uxrtgw6AwcjV6iHS2kMkra2dGtMo//vE7zI2YLKMhX6ltVg1/ocA89A8tbV0Htam+RIcY6q7qU3+jHPo+YwSyDgfo0QlxI/3miO5uAULWzCe7jeJwfPVH/OpAQQnlbC/bJ4PFWrsQhYit9RIcP2POW2rwYecT+plbnLLTEnfJrmI3wVjcDHh1njx+AprkKphhSRHuv5tmw8kvLpXCu7wHBPPXoOHmNEUl/Ai0WTj2Cer8rmQTqE2E3h7mN3Zk81J2TPZSpLNzF72UDU31wguO4ertMGodL0iUKhPduep3FzkQKKGpa0Pz/LowcdTHVSYvLrBUyPSyZbNpJDAy0QWPyNcKI0d7TvkOBmyaVaCxYXBXBC3x+JT7LodzeSIq7Nk4o0zsvn8yXNkMuT5pG77jHUl6NhFYJx7TBUBg0iTvCDNXp/sbbAkZ22kCvcRYF9LbLCaSh87MurXy+Rm+NDjNtL3IL88Q6sQ5hmxBqdHvS7+oL3RIJy/bmoPpeaznRqtQVcDiul5sxBugRueI0spk+ZBJtC01Ge+J5M/1rmLVjAgg1hfBmnTJfvL/qcL6F1/V5OdGRDsxEP7Z2xLvmOvJUYoTndzMr2JkC3BbOq3USNsqbhtxbGhT1UVNvTtz6KSNF6XGeMQ/XKQ9qU6jjm/Qf5wFASyuZi4BRJXJ80+pc6IlR9RNGOCqaGtfOiqJABQ8/zd/g61mlrYmq2EdEZriyNest3lXuoOOvA9kM83B7HyHHL6EotZoDNGry/fSDcpYj6dmW049OZgB7P6vMwNx9DNo//KSb/Oyof7kKh0Pr/eGCyDXgnFAqN+S/f3fbfJWjU02d+SyDnAmJYrljELcEw5ra5cq6/GRtERBizLACZ8A34rI8gR7KKYUlzOXf4EUnfmnmfvZOV487xBE9kxSYie8UdxUeXCFb2xEdnNKG2Ikw2bqLJ3ZvsjhjkGrtxnj2dSonfOCctRn/HZxLWSZETMAHPP9YIhtZxN+8Pr4Mq0VA+xQ4ZPeyAjkpY1gFHBFPIWqlNdPBCqpq06RAOoUmhlEhFVRxX3qOw6iPxxoYMmx6Jua4xrutO8kNdjVe6uQjey3BrsS/b9aFo6XB8PqpjfjuQuBpAUo3XYrYM7p7E89AMVMxe4mF0juH6xnQNFNL8W5Gd5mcQmV2N2KQjpEV3MPHcXi4m2JFs4MT30sGE+eUS8uYCLfvUcUvMhZyviFzTgSXzGTS9F29BE9n58ujGjsA64D7i0q44SjRgk9DKYF0rdKxKkfoYQ8FEfz4mL8G0Mo+bowby98gw7AOzkNwbiOfUqZzYH0qnViE6v18zpPYDPwthRxpEipdyqeomq0YvYebMlQjT/XBZlI9/dQp1309S3e3AxGFLMNPpRTYvlAk26fSbGc/S3BGsOreWKwJlls5ZTvKJJkoXnaBI3AAt+1YGYUK+jgKa3Q6o3W9nas1nOKRNvzuBqA+ZR2qxkC9zPCi4upN++uAdeIAbptb8LHXg1LRukkccwU3TkogkdYaPGc0QcS+MlOUZ0StCj007z3PdaRSKYhgbRtW8G6wZ8AgHVRVkd/XlrtESjsvMpvl2Jr07vnFx/98IjSM5NlzArQ1adODPiQdWCLYLiDwyA4sb19h9wJD8HQP4JbGPPUdfEJToxJfmRO7IjSI99yLXU10Z73SMxgWT8fT9SLyyFKKpbahnZmF7LJ6A8PXYi5liml+AW2Yz74fVo3kmhBUv/0CaOCNS77AxaguHNUdR0D4erQserLZfxbB+iozaZMvVsiTkPVwYrnKMoXNDmFTdystUS16OaMXq70CCKpPoLvIA4xCqvzxFd9R9bjk389rkLrjPA98RrD6pzeGXNmQZtjOjJxQPxz4EXIG16WJcPOXLVb1xTAgMJmCZMxfGPEHKZAOvBzsTt8QTS917pMicYfcLVZ5kvGS5dBVj/ZK5uEGRbtE9zJP+myfbV5BVepCR4tF8jj+AvTAfDeEiBPOF/Pp5kBAVFfxTNBl2UJ5tmjfpN+YxfhWnKWo2waHnND16EhRo6VOim8zn0QLAmqdvVNEKt8b6mi4uMRVM7BKj+0MnI8Yq42v5nf1HFqHgGcrHjHLata/yYuBpckb/YWFYK4vrjqOqNplWbRF6fw3GJ6EAmSBPjLRlMC6r59JfI/Gr3kLwzSzC9yYQu+ozwsxc7m77wGzrTXguUkdc0wULfQsC5JXY1Pcua2oz2bJnJ8kHL/PppDdq9V8RvJzKu9AZdLQOgc/P+N4ky2yH4XTEvmLfVyPa+n1igcVrJn+KZOufwVRV6SNWOAC9sfasMb3/b8D1f7T+Za8SikoywOc9g+46Yjd9Oped3QmMbCDIRpkQ4VdExBVpj7iKruZxjAb0R9ZlPx9rqlGXmMQGDykkxQTkdJSj2KtDR+p+ZPp7op9SyCz5cYhY6vJN1xIapzCioJdP9QaMO+aPdHQwAxmNzZRL6HqocMa1m36/zemr+V/2XfphNx0q+nwDDAC5Fhhj3o/4H68YKHDgWlgn+RI96Et149BbTbCLAvuBht8zqTw8iey8Trp+RGG+yR8RwWju2+URFy9GUJQdTVlC3pbmIDJtMwnvFgJQICKChl05ylbb6Dmzi8RHF/AaY0Bz+xl0XI2ZHJtAVo4QZIcSkBGCm1gp8s87sZWyQ/NbHF25zlxuAQ1DNV7JmdN/XQO9Px8jEq5Ba9x73GwNuKUYwhnpdnpNdzFD2I22hRQ7hn+FgjsYGITzZsJLvjZWcjHuLtV/zCltr2BtSz7depp8KPrMRE0VvhxcidjomyR3fURJqhv5OEnCNHtQr+3Lfht1TlZUcPN7J2Ve/ajMD8dZ9jvCmpfI9R/KkN+9iPX+4cuFaJoejCe4PJdh5vk8o5kCifekDwjETV+W0n4TyavX56e0DIoinyhU0qBJpYsfD5W4dvMicgG6XLPToOPKaUaGJRK7LwEjYS9ywd+Y0pLOvIpuZAoG8SW7hWEzEvggOoiH75+xcW4llaqzadHsy+/Y2/RRLUJe+wuND0uR6zcVja5UupMqEDhsQzmhA4OLT+kwF+PyRw9alYsYNsqJgn3xbD3SjP5zKTrqVfjLchMXb43hZlgAq+Z7MFhPgolWU7j46THHlrjQlAC/hkxhfUEmjiP/wvjPZWJLyimw2YHAJ5J4+4NUWdnTpgWijS9YpzEP+7Ju/oi4odBYjk1TBeJzndgjMYG7wa24VMDmosegX4T7vEX4+x4lKfUPG4da81n2Ghe/Z2A09CBFecn4zgzCZq4atl+l0Va6Ra3vSgoUxXGpcSZ7wV0EPVl8lrcgVnwIxzsa0Ok3Gs+LGQzynITQ6BfjDSsosM3moy4UfQfTeePIKpJnv6ERhQIZvixwwgboq76d4j5/6NO/BnFRKRK/FOFlI0rR1yvk3lVglEoiiuF+3NZX4VaGHrkP7EitqWe+uz62PVeZmGPP1ZnVDDebTVm7IeeU8/CyvM/XXQW4FGUwqd2KdWK/iFQZjNW1BfR95EwsPSzwfcPX0JdMdxRQU1eAsoM5X96/Qam2gEjVWu7UjKfuyxXuuO/iV3A5yk39qP+Rg6rmYH7UTMWiZjS/l4szsjqb5lcGCEuvckrvHe81bOkpaaCq1QDd8kpspuhRW6tHfdcnHNr6oJUvgYVDKpMmDUW7xRS3XhtEmk0pjVdBXmkIoxcuxcrHgEcW85ljpc6HR4txTBVn1ZcT1PQxp3TQSPqFHGJ0z2hC+9syWTcVzahstqlPJFmsAUGfatykTAg2HoC8axrS96v/aRj/S92u/nGbZC8UCqv/j7kcYLhQKCwXCASawAehUGjy/5fH3qKP0HD8Yfra9OV10AM+L3LgzYs4lF0n4fuymfm6fZg0ZDUPNhlQNmU5Y+rzKMm/TvVydyxb3lMhNQT10tX0lGSiq6GOcVk6MUOWol9UR/iFORxZPoNTsQkYTllMmfZ6lvyUorG4HvmVf7G6q52Ojk7eOQay/T4cSIYpfeZzyD2UlOJGir+t50upLruba1jXBh+urSSpuoJzSfV0/urgcHISd5TM6WitxGnwCsTzLemaFITCzxtIFsXzsm8JdWWraXXWYbTtQsYW7MaiLYEL+ZXEMw1hhjSI7UPMdDWRVnDlehtdjhocD96I/lXQk5mPbqkWo+51sq0wk0nq4hwaW8a7T3nYGO7ETlaPRqseul9003hnPQ1nf0F1CX+/nUfp0EUoNA0ge/1IqrQELO3IR/ZQKOtfpDLLdxtRJuE4SHfj+LOJtjoFzvgfIezwUtalWaO8spjUmt1sFbuB2PWNvPvwm4XDX8IzH65n1fG5YgdjxApQ/1RK4sRndD0JI6LUga2OMYyydeF1+kfaMKZP9g3CEWV7szyBI8rozanCoHUDY6XDOSbxDKHAA7PhaznVnovHLXH6291kYoM0l8SGQJsIEsWG2I5tpqRVwK2EXJYO0MDeuIn1O04wcu4RpK/F0XBkBQHu3tAG644KEOpD4AJof7GJhTcvsGj0VV48XoP1+SBmSk1gzhkPbIRFdJ0cTxd9sIrO5mJqD5FxDWywHcjx+lfsf/8bXYE+n54NZnPQe35Vt1P4pQmb1duIbtKjcVUMz87Crw0PMNu8jJWjD1P0QAvH2aP5UTYLpY/PcewewB2pWM5Z/MWMlAjG9C0hK/4huULIeA85GJAaeJPMO9dJlJCltXcmV5BCaZo9zs8Psm+gGeUtk/l1WUggYPFsOLaVBmiaDuLE0XCSpSLJ69+X/h2esGIns1wGESwugkJdLw1dgMQgbk134uGwRQzNGYKp7n5kK16wc9gVLiePYOHEIJzSu/gauBi56JfcO3yIXx3KpPbVQEbhDWNly2n5cZLi/locjfxKt/tyjnxewNKs06TEmALguSeWvclDKZo+HUF0P7raO0loqUPUdzcblMMpKyxmn0w1popLsMyIImVrKQ2/3+JSMoKC8iT61HiQ0TKVJx0u/LDYgOHD50RUyODa1YenI+Zxqs0Yn9w0pjTcYZBqJnHG8KZsIaVrFnHRo44ZSiX4X9VjQdYHXEd+x+PkQD2NqNcAACAASURBVOrlS5CTzeW+8X48P46nbMUsjC80M3tUN/0ih1KmqMien/UMMU0ku9iR6O4IFrjOIfJPXy5NnYh/zApyP0uwX3cZl8v+5ohxFJF2gTxPiuXiUhFiJadxNnEXA/rfp+LDIa66XuT88yT6JAdTbzyYopp6JnUIqe0bTW/tTdLV/Bmgb01tUQgRIenI2FgxZcoopHp+EyPRyTBVDcJDbuOqY01aTxEivRVYLR5GUlAJszUGYV7vynGRTJbUeqG2ZeD/63b1P9C/y6sGaOkJr9rYczP0KTdXe/Bu1mTe3IflM2SRtyzE71I9Iws0iartzyNhGJ8nz+F48gNy+2Tyo/46nBvAvcCDpCZ04r7AmVFWWoReP8av0YMp3vwANk3ixEo7dilLIznzBprlNbSVPWO1+x5GDu0k6mYAgsMwb7w/jY/90fKrR2L0E7xeXqbNWBexh2bYjhPjrDl0uPvTHNTCus5bHFXoR15nCBeDVhOlW8+poV58snvArygt2l1KSMoWpf/X07jjwxuvRp5+Vifi8jr+yhMwPESEa8LtvAgbzdykSO6GQ0W3PhY9C/C6fIbDb9ZjuBy6p00HE/hV+ACr22f5ZlbKj/peJvsfx3qPHz6aTtgZzKa3MxP5K3d572FMhvNfeNzyYaJvH8yGB7JXKKTv6n5ony5D7PptziSLYfX6C23nn3CzdgjzHuoRaVPOviNhLJpUxx8RZ97uPc+FHz9x1HHn5KZFaHW/ZeH5ctbbaZC/fjQLhw5E48EXouLiuPDkMgalhTzQ74+VmC59e0cS93Y6X3S8KV11HjeFAehufYCvGaw/LCBq0FnuS67hsuE8Jr+dxe72ZyypakBitSQmBSW0Mpyvqgpo5XcirSlPb7Mb+i7buZdiScSmAAQCDyCa/VpgtWoa+1PUENcUYeroPczTFOGPVSmPLjYwVbUQ06pZ+PsICdATkHT5Pjm78xEfJE2/2cmc7O2P8V15dh1YjYzdaQQB5mhJijI5Pgnd2hamWP8mtb8ik9asY6urO4etF7Fm3QGqoqYiY/yEwlwfotfnUivMZsjZJ7hWNTI5Zh3vPPaSWSnGxcVr0UfI9RbwCRvNbfNcnl8awBK3DTzt8qI2KxOh7g8sNGRwq/jKp3cQGqKNwD+SuVX7aT87gO1XIGK8kPkfihCuKiW86g2rrr9mofwYestGkdu2m3v10fy604JOkDGPdMYhsT6QaXXwdPYsTrIee7vxuPbdiFRLBL25lohmSHL45VaWhe/h9raz+Px8TnxZLzoH+qFimoac6AsiJV2xEyljRLIoX5pPUWy+iJMm31mpk8yCvdNwepaFz55d/Jyjj2WYGsPr/BDpEBKdJcsjKzMODBuPVVwXc2udOFJ6lWXjRFDTucXmW1H0qGUg13cjGjlOPAtYzH1dP2YdlOVeqT03LRqIb6jjstIQouOjWee2mtImUTzeJxI0I4ExkfD1WjApAW34lsgzxMEPyYBCGu5OZl22JXdk97FR2Qj9k57sicrE/PUWKqNkGGfhhOErVQ5ZyLGpt5ZlinP5IZbNtsQkDLccpyk9mDpsSXr4N7H3ZlJwt5u2Z52sUDzKzuffEKhc5eTfDfia/2HLnQiCBx5j/8BQDo8N5s+WWYRn1RBl6MbrqU0cTdIlVu46xh9MkeuwY57wBmYRGXhPWYpMmSaiE8VQa5ckJqWAOTYOVIseortrBlX5yhhqFfMeC2I12thrpI/jpRKGNp+k+M3P/yvdroTAW4FA8E0gECz7x5y6UCgsB/hH/P/8GUsgECwTCARJAoEgKSVLBb2uMq58nIfHfW/SKrrwMlWnTvABqSUN3FIazsTNpuyqLODCzFcUqF+nVk6bh68X801KyPrSEpSUvpE7TI1wmSo+rXQlN2orq/U+I/34DONNffnk9g7p/BYETxNJybBkYMpcDsln8FvzB8scV9C76Ax+rd28bBRwebABmfcO0fZ6GwW9zij6unNpPXTbwaXuUo4J/TG0+s4y6cd47a1EeuZDNvic4McgfW5POcejiirOOs5hnM4yPpgXYOO1mAHqsmx52sKMGl0CK0RJoBUpPrPPOJ/nD2uJ9HfGtwW23R7JJ5mBzMjqx+wuITKhuqiOVWT91lbSprTRscmTA5/W0nSrHuXu+yhUtaD72hgFtduIvdiGeoIthnUTyNY8TWlkKuLnTuJcGYXs7j2MWReJRdlaZu9vJazVjSEiasi9quadQwN2izWZ4/uM6IJVuEy7S4m1Nu0T6jCK00LveS1t4ZvpfuYDfx6z+MdbSn+Z8+n3c5q0Z2NR/Q43A1/eX9qJwGEzHtd/o5Myj5bYCiTdzvBTZA2TzFSQye1HjdxAlKlFw2gFqY4beNDZwZJbD7HN6aWf8yMW/RrHXzUjkZBWxllWA5GlaYxvVKEXb5Y+Lqapt5cXHCLmM4jHvKfkzQY2unvz5KEzjX1A6cl3+j5/i8nZ2Uya8JGg4PusTXuNhoMqczQKiNcFJU0LbMUuk7ZDH/GdSgT/esL+qjMIigejXdDFnP4rUNl1APERJUhkDMV8YDihcbPoZ7GbJ68P08dvAMdEQ/i1+QHTTq/lx2EFHN3DOeZ7nOaiBVjrnuDXSEsijkciM1+V0GdStEnsILzxMe/znNH/qc2mT8Wc3jmXXxo1rLcaiIO9FXaKkvibluJaeR1DtSSmRe9EedB+/PbuIe26GsEx3YxRLWNO2kGuD1fk+NgIHigk4DpgC4gP5H6JEHtFMxo8/wZxDazmWPHEXhubT2MQaoznaXcF1xoUmRAaSrjScr6lrEQ0LQKl6JksunKWeVVP2RW2nJoieZLfuuGbtYupVzKIvCyOpoU3vKlg/u9BpKqvZp6zJZGaD5HckEjMvCBi5UyRsb+HmE8lWksN8Gg9z9IfM3isMIaJ0lNJvOVHv5ddlFgNYu7zPBoD3RhZuAzDqrHcFd7iUOgunCeW4jr4IaOWH+WF6ThUS8NZ3ORDwIatWK7IQLC5Hk+7OZw9o4ug24VV080JWVbJsEvPGP84i927rZGWVKNGvI3n2VvwTe0gqXs3JDaiqelB/y9/se1oO8sPr8ZkaROHV1rTaBbOZt0JvIur5ZJeCnezVtNU8RS1g6nEer7hSOhrru3qwHNnOga9fdh6TIEn+y+jt7GCAa4bCLrVj70hcfw5G0D98D58sTPjuXIH0X20URY8oqrpFcrVQ8m58xOfhgm4rziM7WAtvkR85sMrBcyP5XE7rIJZE/ZxX8Wb6I4jvBPZQ6uUNEfNHLGSg23d2/GIv84Pk+3/Iq7/o/Vv8ariinb62vTl5vAHRCxyoDwvCnnJdvq/aGZU9DsmDdnJpfbrlE2ZxEvtZkryt6HsKodlyyeks40B6CnJpEVLBOOydG5Jm6FfVM/LoF0cWT6dB1laMMUFUbMfdDcsoaE4kiqt6ezs7qAtv5du//fQcZTbyXEM0gdf4zaSixtQlVjGl1JdfCRqWN8Gzv1X8rka+v1IpjO/A6PC36T7NtHeUsmwyfWI51tgpBmJ4s8gpHukeNm3hJ8102h11iVTcAade5EktSWzyhrimIZDhhOI7WPXHsAK1O8l0O0IIW+2cloWdGV38a1PGmX3Oom5tY9J6uKYSrzm3cc8XFb/xF5WH3lVV3Jj/Wm8s4GfCw9BdSlOCU6UDl2EVKYoLUBTgD81YeXoplZzrqGT2b6zkB0hjYNMN0OKXWirUyC1+DGvryxhWIs4DGwktXY3gto5iF7byFgNK5YNv8CduAvsy0ijyKAE8+IhVBfEIacvQteT10QktbF1cAzWUiOZkJ6EpOhBfnsvIr85maA/hTAder+mod8KrzRucEziF4JSFXYUrEdZYzU+umVkqUbwtm0oebK9NGa3IlFsyKCucLQGt3PwXBHTbAX/tXkuvwOgcxy0u4zjkrsD/cLOUidzljNPROjzvZUwMUvOX1tGnjc0L/mCi4k25V/ruFYVg+pyI8rsLXBJLqOxphGZ65dBchbWmDCl/hiXip6QN8uZ8qldvLFdiOrbS6zMW0LhybkoVoRTtG4qQWMNKXgjz82ISJQe7Cenzxf6R+vy0swFK90apPKHcLd5B5NSPlAsANmsAhLrf1GJK9Vn3xF6cgnGes/4Lm1BT/dvtC3X4zLNnsm3PrKotx0DS2O0tMfx8fRhynMOMPLDDlD+yomj4ejWtJLSfyY9NYpkrJ6PZq80Ma9FiZq4iiuCzWy0XYHBwvU8HLYYDw0/THSvgWQfrqrtpWiAOpsWHMIpvRvxtFvI/bxEwYYQfnUqc1gggxKn6ZQ3oSh1K8X9tXhe8pYe9/n4mTZzLus0SseXs83oKVtGzmPsgCwGP5pH0zstuto7MSsdhqjvbrIWzaWssASnmGhMFeci2vkN5ubQkPoRlcyvFJQn0dmqQUbrFGSb+3OMCJ48qyasUoYP4pLEDtLkZrsxI/58Zkr9XXwEr4g0VkCW35T5LWI7BszoCy6BxUQmh+M68jvz8mqply9BqlaDYOM9EJVN2ey5GKcqsmZUF57xCtSXD2Z7TgOfjK3JLnbEVFefia5zkLJWwHnqRJJ2HyP3swQeIR9olRThdf8oPnd1EZMay9XV2sRI+jI9RQtjo3s8bnhG6lcZnj4vIKhKg7oBDozLLWFSh5Afui301t1E4G6Iib4N/KkgqxVkbC3Jn+CNVE8BJYq1DFPVIPLFRobpWJOqoIFAWMlYc1niS42YpT6Ixu+JHBdkMMrQ7Z8G8r/05gNwFgqFZQKBQA2IFAgE2f/sQqFQGAgEAljpKgv79yZz1SiQXycMOWpmyaj379lh9QTqbfCK20Xsyr9ozs+novc4gUZDmKEfxoSMFxh8K2dgYzRf7W9RFx/H8Km3mB32hQa7n2wwW0TJiwT6aEVTnhVBuIiQ6YP2U+U8hTnlTVQ9kGK1yWxijzxilJ4eBS988Bm5A92tzxjlup5Wm2/IaajxJzQCk1PqfPBaiPWFYtxCryNuGcs2E32c0hNxr3DjkEEhvarbmSMoxOO1DIfe72apwURaSxbzeoI5ajHpFORc4dWiADYeHEOGmiZb2kDwKRj2KPJ8TiQtotLsMFJkyMUR7IlpoiCig+VjliB5Tw8CYMRebzz2vMa0OpwVu/agLjIT0dEm7NA6TEhtBLGJmeh63EPKqoO4a80ojDzOka1rUbP1xWLPOuYqp9PiL0TLfx7eSeqkZN0hM9sc7Y5heHQfxs/HBMmObka0Dke45jKCN5MJ2ryOYX8aaLicwqa8vej21pFemM3xE+FkutxFZUw8jhkPGWJVivY2E6xMj7LRV4rbQXP4oilNToYA5aJ9zOtYRouyLtplmwn1MmJJYinfn41DcL+FTVOkOXZyM5RsZv2S7TyaI8FuXUMeuGTioLKTj2ULcOgahO7xRjRrWhG4KXFtsxBjn2Z0ls5FQXEt9t9TMSidQnD6fbw4j/mRElaSgEBqEmdxIWGlDv4n/xAc5UvY51OIos3XWa94NLCGe9aWzFFOZ+T0THpjQ+i/NI7VOxaBQyE16fdwabiO2ehM4r8IORl8n6eavszbOp/Jkrqk/HlKwDBnWnVTcH1wFBm8cJ/ngKTmVozF7uIUs4SmIeuYWxzC4aoN2E/dj1uPFMHBm7g/pAbn6L7MGtnEh7cJeE9QY/fbSaxzga+tRvwcrMAfkSucbx7KgrpVzBisglrhdOq++VEj54SOiij2baMZ2O3Og0sa7Ff6zQqHiXiLGnLGfykzFZtoTWlAEH4YK6cMnBrEydZYjTDhNX8E9nRldNL47TpTZ0wgr8Cdj9Z78RxQQW+fCYir9DK1Kpvy+arU1CzBXmYEw10tkLZJosDhEOJzHDnlGMPey6lsLZ6CYd9qrlgksrBgECu36bOXxwy7eQlvyee0y1zkw6Nohvkr8zlAh0cb9zJvQxhXhYOR2yNgOmBsa4rsaldW1S9lruhYNKbKExytT2DHexb9Psiw2Yp0vHOi6dMufDsecrrlDtlFNxmIBrt/Z+LrY89m1+MM73uKpJ/yDPX15GnoA6apl6EkaOWF3jB2mojg5tdBk5kDMvMT0fdbTa3Tcs7JvcZpnSP+fodRzOhCbmY4WZ+kyB34nvSNBpTsyKfq/mM2tqRzs6aDqEGbqS+8jbXmEGaXfuXX20e0rp7O2pIspEXfMFa/AJnhmrwInYXNlJE03wph1CQbwtWl+JWzlaESM/npVU5T8ysqi1fS1CrOvTf3uTx8LVoKxlT10Wbv5DLmTDPgsGwtCgnXOOSiiOo9wb+I6/9o/Vu8aoCBrtC/oZgPZ7TJm74X+9EG9PndQvMMH74PGUjO1Nm0LlmEcdQEYkROcrLViKE653E0mErLifMIBJCtPA2ltHsY+T/k5d5AXLrfcq7rIsG5HWjdL6C0aB0Kh3fgkueNurQ7ybnwYGglhkOP8H7KSjhyn4W3t7DkSgYuT/M57STC96Zv2E1uZ7vSKmx22eBzbCgTzI2ZrOWBcbY9e8cPI+DeMQaqWnMlJIU/Jm58Tp2Di/8Hjj0WRaJ3FXaTznNNToCs8X7qR3/EXtWG92EggQndzybhzQ684paAvROCby8Q3gdrOlDYL2Rp9yDsMoYgeBqPcIUozp2HuLsikATbEMaoXGCOXS9bYw6xSySKgt0/aTnwgZxeTUZpe6MQE8AVhQOYnZuEMNeLvwqE2Pn2wWTbdWJDj5Gn04hNuSN/moewZn06NWc8ke8exWWDMQjWZZP5rYYtywVcqRXyWykZ2flCsktvsStxCaOXRnNgoRmOBk44a8xgbnMKW8xOEHpwAbFGH5jeq0Vz+hwkPzxBAAhNlSEDnss+xWQk/FJZyIr0iwRMOM7KxBgU4x7RZeyAnswYhn5p58tcGRwVzXFa0YPb76mYzg5E2VyKZhHIXnWJs6LVbH3zHMHKOVgYy9A5QYmH8T6oLXRmldtavqmqcMDnOO5jvzHd0YSDgcXIJizB52A2ZnxguP0OBNei+Lt2F5eWtTCwLR7PSzq83dSLZPp0usTEMN4SzYHnqwnbmc9cn8803T1PlGwp+0eOxhK4fvAhP6Qf4mGZw5cYKz6pmHHfzJhRw4T80PRjnvd9Gm4NxXiZA/M+HWbaqxwWDd2D1xxf0pYZceakAEdhCEo9suRLK5MSXcGlhE3MvZRDRucUuoe/YkndXmqe3aJIoIBs1gAiCk1R7JqPYXsCZRIpZAxMZXmSFE80x3E8Yir5UaEEVYlxeawyRgr1rOI4rx4toVEmhTl1f7htDgo5eTR7pWInasX0a+KMH/san1gHAgf/xt2il7bacdQ0C5naHYpclQ11fe8hDDEh3EgDlZ/DyZ47jmFRT1CTOI1ZqwnZba64iTzkhXkTzX+6CZ5lwS5e4NAcxS+TduL3VDGGPCCCTxPdaEvPoF59KznmKRSLvCFsmAOH3f35WV7ABGd1fBOKmGWvglfuOsYZLca7JYIA93GYeX/i+69orr0Ed41tmHUsITPMiu1qL5im5s+Re69pGpbPY9895JeHsn36C+QGLUJG3IakXhOuz/Vj56k73DC+yojFQvz2fKQk4hhTi7x4siqH9fNtUZzoi4pJF22p0pR5L0Vk+TIGn/DBo8SUnrG/WbRlMwOnzuHd2Uqy99tgtXMXw8YaIxyzlUftD5AQpKAS20qG7l+If3iLtX8cSbdF4XM1YkBrYzWesvcpznPDQqUJkY4YVF008X0rjpXlamSKnnBb4zh2TbMYnfCOz8My0HyhRXQ/i38ayP9S5UMoFJb9I1YBz4HBQOU/Stj8I1b9d3nS20Vos1/I6MoE6j91Icj7ye17ZgiYT2raLSpFRPBqbMV2dyVRN/M4Wn0A229rOFD/mZaiXNruG6OzYR9Cx8V8FLvAJtk0Ar4vYFPMRGTlWtCU0kRPOpyFwweiXiPC2IfrEIZHE2RuRkGmFBWdKniVxfNk7Q/EzZexeMsKRg02QPDDA37K4GplRdEYScQSLBjQXYCTZxDTBIeZJpiMYW4VaRrfGSXoT+Hd11RFfGaHzR1ampdzSuE3N33yKTPNwVWpL2ts7WnctYGmRCvmiH0j/Jw1J4a8JVEMRNSqkN8IA/JTWSX1AGI28zvNn0vvLyN8JCR0dS+62eGss3uMx4S/UesHfv5CrBRi2RC1jY7YBKS+bSTi7lIEN6w5KLGNtZ8XE1Bgg0XoZgAEYsokLNrI4143akcfwneKN/PXaLCy/DOzpNSJ79HikXtfhOUHENx+C5WwYP1pPhWuoe3zSWb8iKGzq5e0JX5MyNzPiL8suSJlgF/JN5aGPuGssQLZcYqMc9vIyZsxzMkYxqJvLVgkOJLbqc4olQA0LR+yL8oPUTkX1AZHkhSnyrEnp0j7kgXBp1me68ZYJQF6TSJMutuPKG0DLjZF4HLlAhHjPiKS2wbLmglTysL2ynwMMkJwFE7nxIoWLmjMInblXnj1F0YdjwgP+gIysJbPlKod5mn0A54teIJRmxL6/MC75yMxfx/kcoQ5gSJ32bhtMWUKMbgeqeKRZgrzB//Ed6Ac/SozcejOx/3t3+iOHMnUilqmGGlwX/AHJbGHBMkqojx+BDfCZxGUu5Ta8o+M5CSHdj9icc0dHrmtJOf6eW4UVLFm4jkuqc7E1fQ1iulNBFV683FpNYssjQn1n8QVfRj6eyK7p9+iY5QHlYbu9HbLcu5HDn4d91g6/y7rNJs50ecqYUd2opKzGe34p0wuCiMj8xETRdQRU+vlQrkxA35I4TVMitfez7mUN5trEhN4qfGYNeZJpDY+ZeGIJVjHXKXGw48F+V95+sCcHZWu9AlbjVSrAnlKmsh4SWJi6Yni6wraq+9zaMV73ps6oWE8lNcafqwZdIALBlHsUw3Cp/Y2tRlayIZWcnSBGG5JxnwNPszni+sh5xFelv4cetzESoUObp9yZPEWAd2fhTBmD1Hu8/nWNYATKqdwbn7M27haRrZ1E9j+iadVZiycWYTnozDGfOlk/Y9dtCc9RnyXkAfrmpmSmILXpescdxcwVnkIA/tYs/TVLqy96qmMek+l8mx8rfP5lfubi3emkRL5FcOtW2g1F+D5pJGxmuI4+tfx5dNKQv9qokttP7PubYNVijwtKwBPC84M8qWosoJ+5UcZHXEbMctZRNl9QMs2nIHT+2Cl8JIwuXZW3CxDvNCInxlhTBgmhpHhByQWrOIvxb6smD2VMb+DaSqUxffVWMakeaDy1w92Or/hyOx1aLk7IPgzjz1FC7BY6sMpbQccxT3pmlvExB45Rhj2/iu4/o/Wv8urypq7ENGehltLG5/NRLH6JcIr74lslR4AwfOIdRvNuM5YfpdUkRj3hKE9tzA5H09/MWNS8ePV1RBe/vxJl7oT92vTWGn3ng3Oh7GRnUeWVjMJqS8xFgjpaDhIjdoA3pc30d32iskms0i/n02Wnj4vsz9xZiQc9nvGooChtOo9QUrbktLoH6xQ+sTfiHOjcCAyoebk2osSYtKFc1U3Nh2y3De8QxDKeFJP3c1ontQfRrcsn9bmTKoa7qEWk85amVxu62gD8DED/NsmEsA02sflcKrtHYhKA6c5sB8mmhsx/DesGFPJRY8ISICTcrp8uiGAySk4+wwC+iE+fDJHJiZSr3WXuMREFE22IWnVyfsYLxQsnKmTforqmhBQ2sNe4NvjZ0yVTqAwMZ1fAlt+ZJujLuwiq/sw0smVmHf0sLgugrxPf2PxJpgrfx9AXQmC9oZy2/AlhvK5pBfmYLnUHXmjraiYbudDTxKalkaIJmzHykSHZjMl3ge9YLyJAeu/lSAEaBEFXVB5t5cCrw1U1Czg2zddytJnc2y2GjIvDuAueYqzwyu4YCQgS8kcUdc2HFTGU1+SyM/6S3jN9GT9yJ9EG0ZxenU1F4p+M+V3C2ZjzHgef5xZ816ywO4Z4aG3CIvToeb7C04nDsD503lOxa4FxKHrBxsC9jL76Fu4FsUIr1IW8IdV70zZuvQp1advIfFjFE9VftG9LYGwZbmoaFZgXVWF2Zga1uMBwIQxI6nsiETYboYgvi/Z+Xm8jX+C94JVdEp8oqn9PN4jDPnm7E5vcTqDRwxCURf6S8wlWKwN2jeyvnQJjRXTeJ+VwOBiOb5J7GbduJGUSJ1jo7g5fpql3GseiHi1ArejhYTNUUWrJp4aeSfsTNRxyF+Bg8hvTOvyUBNzIVJhMO0JEDzBhZ2KTZi3tiIIMaFsiwdSDeKkua+iX/5mSqvd8VR9z9Jvj9HclEiZvjtZhk6MbqnA+U8dtentiKv+pET4mobaB+gNd0cWc/qaxFAweB/99mjwTu0dusoyXGjVoajWhxQzeRYWDEayUZKk2lyMXgzEW/E5N8UKwMWM3fzipH8R8QotuHnb8l3jADtTDQk7UMrOd6/pHToM+SUONCqPRc1xLtnSalzomMU+rXJcZitCuQlNb2SR6r3C6RZALYwSPQ0c4t8yV1KHQNfrjLXt4V5uOmMyf5Aa+gBcG1CS/0NOqz22JiIMCj3MedNqLBY0oAIoOTmjIHsep4WO9PACxfRuMLImy1gKdDXZc3gST2/FIBJQi0pzOlQNJ2rQdFA4wTzNIUh325DXX57WEXbkKL4kU18Bw/ZOZPQ90GnpwGaIB8YRaVh5WIM9dAPOEjMQaSmjqamCpLJaqmzEkRDTYOfwNdxqh0CpVlyC9NgyT5YjcrUcfPKUZBdFHEST/2km/48PHwKBQEYgEMj97zHgBWQAL4H/3ZZlPvDiv8slFJMiKT4OLQNrZGLica30QnWZG39bbOG53w9sl1vyXLaAmtxEbph6sSVgJ6fii9g1ezvGg9MZdW8el5QEdBfsQKm9FTklPUJWHCRSIYoPye/peSfCwl3RFMd8pqYshL1V/egabEBiRATBnx6R6yBARhBGtKcfxW2v2aPdn4qmSobmvEAgY0BOmgpKx6oRuMxkhqoz75ST+Kv/WDp27UNHazsqWNU1iwAAIABJREFU3aNQL0rBdfFl3luNJs1kNR9vjCWvWoz4L5Oxer2CpKr1NFRLMXvdcgxm+FF70J40VCnS1+OjK0zQaaTGwpiIZhcue45DrSqclvZqmg2WYOf5BH1tAUEGlWwKsUEBcQTj/EmLNOLruXqcyx5QHepI0f0PGBsPYKf8GCxFl3F1sh9/0uvgQy5IBIBcEWV3LlM42I4r7Xf52+QPZzxF2R5sT7viDHTya0jcug7zFRNo7REiDGtD8tRCzne6cHttNy2lmlTHKbB3WjQVq9J48ViFWZ/7cuHQMRafy2HM+sP87nzPwVfNTDnyGbuphvTYihGS5Mbt01I4OT+golCRm4ZaGIRNYYnZFFojztMzW0BPhDnK7zeQOe8gz5rk2dMujqaJI2suv8NvTS0TFs7nxvl0PBqU+XlyHyWS6/AUU+CldRj1mSlMvaJK1/rj/FAUY+RsAfaFD7GcPpQ9u/cR8EPI+wAHzJWm43JuAvsSKrEb8IlBZstwrdTh47QHuNvMZvS0CE6J2eIltprpJ+O5re3OvY9i9Ca24xg5E6doHTYlq1IkzOZHoRQOhvWICPcye1w3TkbiCKsncW3TQbrn+BEV/4e+O86zMbmC4jeD2blqA09vj8K8QoIwI0UKopbzqmArg/p85/uffuwpKWBCdAMqfTbzdcA4bGIjWFu6hp/BfVibF8t2QQt5k1dSGtPBB93XBPe3pfZJHOrDxvH30P1IjFuL/9Y2hvsMRbMric1i4lz1HM6pG8qMM7mCXOd3Ymc20HlDD7tJLxnc4cSj0EFI2M5lypKRfH14CtnXC7jivYTeDCXEsmLxe/kR/6zpbFpxhTkvQlnxdyKRt4WEvxUyK2cJ7U2niN3xFyay1agPcGC8WggSmx3hdSETQlQRH+KItkkKw5RH8GCzFLKKyqi/X4HauKnYHEmi9FI9iS6jebrwG7IJJ/Eb8YfENeXsCFuGzr7BiDekcXmQMu3xCXgMn875szsx/zoQndo9tCf7onLXiysnPIhbO4KRi9K5cVWIQNudVeNOs/FVAZNPJKB9MY9mlRYakrtpSLqJRMgtxl2byZiJ7TyatowLvvm01G2iXN+c3P52uHQkIdlRR1pjFKJ9+0CUP5lLlyNpKeBMZj9GF6ziqZUoTj9TMUyagF7sPhr6jsGicx0NzYrMWOzARUUJEos9WX5mDOuz57DY1424G0s5dDSbX5OOU6xaR+j1W2yar4hCeRPRX5voSUtmlp6AMtGpuEgvYmX3QpLHLsNr3CE+391J8i9IsWr6n+L6P1r/Tq8yVaxG8+4zQlQ9edcvgx7jbNSbpVhz3p01Bg9ZKBpPjOd2JmmP4OX1GXSGbEY0KYLC4sEEL57AiaCJNF8IRUo8k5nilsyJyKVgqh+HyvbwLf0c+xVus2fmfMYY21BZ+IGsnEqeBtexY0sVqW+/oin+jZ6aQLwnniMxazcBqnPp7reT8AQb+pk4cEPtGXCClZGqDFWqZsrQQgK9DTm05wj6jhuRlDqOc0wVfxkJ8DFdjsG+MaiZFJC5toMaOVsmlMex/9UsDhgYEzhbi29jiplz1IZ0BWPUpptQemgZhXE/QV+a3F2QvewXvcdBqWAuk9JWgtUPHlzNJERqIUsHOaDAbOpmQ58UGY4c/8mpj++Q6nxOVGwSE56uRmyTHllFyjQ7SfKRCND2p3eWgDxG82rXOXYt8mOjtCL5mha43ztG4LxFOBwewp8JL5jRvpr+IW10bfTjhXYBgoHzGO+6iBzjHMpNFcm31qTIKQPPQSpc6f+A98/bMM4rJrypnXsXDjJfoh+Pr4xmaW4vCV3rAZB16MS/7Bs1qtN4G9LAoenyBJauZ9XmQ6z9ps76wE2Ip7oy2v0rR8dasuhrB27dhszcMh7nY2dw6w+y5muZO/8ULpue8stAmtTGm0RdLWDt2VMU6thRHu7MluahxEflsr35O75XREm7JoX44QGc31/FnvrzoCLO0t2+3L+cA1XwaWgOf9k08dt5Kispwqx1N1tylElVV2TLgS9g/gpd2eMoK7gg+riJSZPnMX7KBJ4baGIrbEK6KZVXKbUIZMQpLnyF+LU6Hr66hc3D1Vw9/hJjkePMtGng85YqbqQIeaJ2ip7MDbQotnOhnyNTcuOg4QFhOWPY4OlK/jxHuhuG4yj8SWhyNG9zZnG9djapbcdQWrCCX4lf0a8y4H+x915BWTDd2ub1kHMSJOckSQSVIAIqImYEI+aEYo6vWcEsRoxgFgNmBRRzAFRUVJCMSBaQnHN65uDbB1NTU1Pf7H/X7Kn693XY1dUHXavuu6pXr7UCkmxxrJhIstVIPqqaIi8GM03XE90GnhpX+f6lmSIdJULV/uDXJsKyi1HM9dFB6flqFF7v55JqDIKgJmaGX+XQKAfik04z6Y8u/QJ86bEaiNh7VaQVv9OWpUxOgxK6dcnICA2oKO9GsHINU9W6eDhuHmtUvtPvx3v8qupxzHlDuX0jAxPz2FliTFTkeELnxbHm4Rq8mEJsaQrf9udw0TmSttuVSBwQQlc4+/YGs7Uil81Om3j26BU+2uspyk5FWTWV69KinOwJRThcGs0dYVz+8IplWQJuT7XntfZuvha0sSBEk/ZXc+jzSwsLxXGcmbeLXYOG4v7bgJnL1Bhq5EFGaiwnr9rDyRIczN3xnQ6DxpzF1ngXYopadF32htlz6NWQxvbXIGSvZCA6T4NfERcxH2+Lz/xpzK4/RLHzMqrK5yEyYimDPx+lc+Z3EprrsC3oZuPjRD797sfn2mJW2t8lOzmE76UD8N29hhvO2zl34Rx1K75R+aiNgzax2Jq7kqlgwJa1T5gi6EGgeZezrp0cydPk5K8UbH4eZ5mOAiryQjqUNP59Xf7PFpwLBAIj/vWCBP/6vhUhFAr3CwSCPsA9QA8oBqYKhcLa/6ezTHX6CoPdFzBZuZWBZ2F8SBknMlWRiM3FSejCU5F7LJu0FJemBBpbzLmS1cqS9j/4D1Dj7sDZ6OS/o8N6B/ElfgS5r+JxuAsOK8qInruK3caOqNo70F/+BQsmuxG3/SHTEx2xOT8Swa6FXFBYid3rjxSc6aT3fCZ7lMaRVf6a6LNj+Rmbi3ytLb+tFdDVqECqvoYvgjnotsqgfDearVYDGKi0iakvenjc6M/ZLe1YqC1i7R5xGpNA/pmQMaKFZG3ZSqmrD77ybxk18ALCIbH4aUzkgsU6/ANgiCdkWUBo3gxG+NfgpjWUssFhWP8OY0BaKnXGvRg6zMZ2zU0wmEHgm9sE6ULsgrsEt/Qw3b4Iack3JL59R02nNrN3lnBg0XB6bJUwsi1CPzoZVm+Av75QuBybaU9RjTiEVOdWbne+od/xz8jFyKIdegzD4T+JbI1D/swaFlvdZfWgFu7d28d+v4l0iCowUTaK8FY7BjposP6bJJkX9oDFVNaUZrM0/CsWFtLEHj1AmL4a2kpCFD/+xH/vYaoKtnItdSzGSlHIaFlzz6KRG6nKnL4rh5X1IDZGWvKnbizeR/xYOcGJfh/28jzKmN+1gch6NxA0WZHn845TJmhgRdJuis8e4UGiJMvTV6Oro0vxnuL/a3xy0EsFD73hJDT0UGelzu7A8wiFXVwYPIbUlgLOZOb+x+4B0JrC4FX7+F7TA1GBiAN91AT0aRai6h7KpucBKIZdQ+NVMMaBKdTXLiYoXh5TBQV+5Zez/PBRDFv7ICkNQVc30CSpSd3nHLKH6TLskCpXMlNp9vWipcQHSuH+tKfERadxJm8rHushOkhIxqNH7DN1I6rsAdcyz/Be7SBUXsRM0R2xBmMUOo1Z8U2GQe7fmGPTDH/i6O+hx7BkFagvxe1VPnFtcVxxWMMfDQGGn+rQCGiC/T+QDjQkuc9w8mI6yfmlxFcNI2pXjKBcDjRkT8LmFji4jUZA4f90j51Admsp/X/bg+0VvmFOKR0o0kNZSS4mcg+QVPrGgOYqxpRlo2LWhwFF4gzKa2LEKQWOR+5i/StduPyegQ5GGLXoM1RNHpG8H+Rad9BUmMirvvNpM82Ed5+o0fTklHYKq34ms6/vA1pETqO34g7KwDUvAQEr93I/JpenjZYMafuB46QVHJjvzp6R8OornL47FUdsCB8xmSnB79jT7ysOv7T4nlVGd7cBspvzKLl/m6m64zA1nsu50QUkRhxjqVg2ZhpnmRkxGH2drcRLb0C85Q5GPaNpElcloLuFHv6wVkGO0wZy/E3uol0tmdpeO0Yp6SKW/JTymhS6+4rzUXQoo3pGYyAVSk2zFedOn2N02HqSCsqYZTuczDfvcfQ6Rcfr+7yrbEOvXxoHDVqpmnuWh0v+4bmMkEsGaZj9eMWosVYMih1GdsFfBsuIMPnNg/8pOP9/yX+lV9mamAn/GTWCOcPCWPdNwIzq1QiVTlLWNJdWq0zU+uyg/s9Omsy/s22XN7ETBJyZYk7104EM1ankx29Jwm+uInjjLswr8zgw3YGxC+8SpCHGzlUbaFx2lBCTUSyyu0lTjx63TmxG4rMFjtu0qOwPSesFHF94nhZ1H4472BGok0TDmWHUzFyFvlgyeiKfKLg6jANxZ9FcIWAee9kmugM/JS9iBBtZnZCH1NyheFbb8PLGIob2ylA2y4WwubW4zIlggc4ybhtd4pvXeT4erETgNYe1iXMJaYEHd2DKAhBeDcJBAN8QsOmqIsP6JTPGsACqtzHj3j8Mzehh1cPpBBYLCdKFybviyK/N58zgEGIfw4lv3YS/zaCjKpVPZ9ezfG8wb+8Es3TXfQIvWkJEPrj5ExQ0lzibwdhOOYe+ThZPJDt4H2FGl1wyVfMdUR8twpUnS7GetI7Ow528P/ABYV0mV6cso2vSFzou6pLq7kjsyx0opx7EfrEiQkSYp91LkaMNDdJDCRG84lR6BBNTa1nc/Zuz52RovfcD/eXqaB+PJXnoH3YuX4zVlEP8KJ7Fj3faOPfbicAIjvUdR0S8C6mCvVyqmkfAnKOkPXnMnVsfAbDZeZmVsmGIKS1GRFeZ2LA/hD/ZQPr1y7xMOMX987+RuneBuBs3Gf7kJUfCwph8bBmFOb0oxE9kRpg0F7zvIjgpIGw4ePk/xnDtJGJkpjDu+UNkHSawoaEPBca2TNVLoo/cK4ZsL+fFzZlExhlx/tFRFtd2MHrpZ+7OrqPfkivsOXMfgYcADGDCik049MjQnt2IqoozN7Q08G8JQMv7NR1PbciuKKBpUAVPw0OpajzOimtCgsof8WjpN3yjujld+Jzz6un4PD2KldwM8kSW8npRDLIxodzJXsnbTgd8YoYR5foE75ZDfFU+jKNuML0ZQQQ8+k5pVAQnbt/nmsdz1Hf6kb6xGssuZ2wq7Hhx8wbNi4bzR/Y3FSs2YzXIi3nhyYxInozi/M+MJ4W9L4dz5fU1SgRXqBbJ50n7M2boVCFrcoMhaZ/5+dGNvyNf4aOXwe+WAczV+I1xxHuiJm0k55ktGTnvuTZRHOFkawTjmrEcM5yaEGf2er6gcHA8DvY1fPgyEF21LmpEK2itV0FKeQD7LFXx7Aph7SNtxs+0RLf1ATJfFdk79wDTLPoiTC9GZ6Aeo8RHIjFCkrCop/gKBGRHl+Lw6Ri7DzkQO76QeVs2I3NqNiP8pIlyuUhrZBZni22QZin6rXKYHbfiteEa1hbWE0gEQU/8YMAZhly2Z6aZOzdvHGel5HKMvP5hlpMSBXZnmRo4ngcKQ2H9AoI3XUHG5By1mNM56harV87Dy0kHf0M1Yh5aMk9fBlO9gxzXmszNGYvYmriK2v02GA25SWq9Jymle1jzsovIGTNxGpqN4ZQYmrb+5mPvE7ItrLGL/khXdAuth/uxQeYWd/704GQ6gBkL/j2P+k/XfAiFwnzA9v9mvQb+I+f3b9ImKUL1rTF4P81DX6sT+Wd2vBJ/StFUGzIrmnEovIF+jjxb9LooYSiTPX5Qom7Knr9NPDq3hAG1Pygdd4wFV5cSNCUO3RAhOitEebIsh9P5b9Bo7MQ1+wjpOYcYqXeTHHctAsKCuKi/lDc90dwIUaUiWoaqbY7cyfxEmbAvt3+2sN/VnauJZ1G5WEzWnFQKrmUT33aJt3cMOfPtCyNGVZM9tI4ou2JuqaUj//YoO1r2MfRCFNJZOZhkrMb0+kdu/LOWFoE+K9RGIvQ6j0BMncqEdcibgOYt2B0FNS/7M+VaGAZjT4KEEK0JcOtLM2vaTqFe+5vwqj/gVUSg/XAWqhvjvbEe58nTGXkpn17BGgp1thJwHNbGXKU6+jZi9tpMdXPj84qlzElrJ2bnblpWVNLRMp+oiBuURI7AdX0GWT9TOPl8Lq4m9Uy6EktjUTc5E6IY2VqFSOZ+Gm03cnTBK2a/usc/i7vx7+lHbKoCbf7BGOt78nr3R44ef8GQOZIMWTmN0L5qFJnooNHzhdIOeVxmj8BsWAhT1V25mpyC/taxaAvkGbf9EvssXWl6OZeEODmOvo3Aop8foWl53P/RxeOPZjxotcV32Bt8V9vRVSlEvK+A7Y5hhKy7gs7No4QmT0V4qA7EMgha4IruKB/CqqQxiT/FOdMIRIcr4B8azJm1M7lcmYuwNQOBQJyM66f5Ua8A6S2gIkvQ1QY6bQdhMcGJb5IevBory/OoTygb29Dk0oOBdh41kSsRVohjNzGLbdfOUWW2iTq3FKpFbPBcIU2/61/Z0VbL/uz3rGrOxkLNhAxzZVx+2aIy5CPTB33H2VOFBsEO0uPsmXpiPOPD/UizXoiEuBbZGSUM9snB8OtTnpX1ssVkE8/fpWI/5DLCqiwEJcnI/qrCfng7fUPFieo3iH1Vm3m1cjT0n84Z4/t80TjLUJljLLd6i450OXH+evS/60iF71y0j2UyyL0acaMhnFA6j4j+WgZPH85aL10iJhbRv2wiw+duJ63nDE7DZxFe+oML0o2MqTyFXWsa3sqKfIy4S0LfHfzon41uTS9TFXvZX7UZ55ZPSBbNYtXYOyytVySv25rQqjOcixQSchY8LJJovJfCImMjtAe3UGJ+Gcf5nyk9dw3Z3xcIEk5ikcZSXjRDRvIGVj0by4mV1wh/X0Dx8VwGlerz2Ow2qj7d+JzciY+5C9aZj3h+TpGV78bReX86i9UaWTXgGXdGrMX+sygLJK1IWXOZwpcpuMu/RV7NhBMmjbjv/wcDg0vsjT/O+X6TmGhzCLEhU6i02Up260C8W8bQ7+VGFn98xTf/BuJNR7CqOht/Ux0EJX/Q/KtGWbollCfS0t+NCbqfuJ70nCEO6pgbLaXonjTrS95iJ7qMMS4OaIfsxra/J916fpyW8kHbT47JfqcY9/gCN1IKafYSpVmkmUSPmdwOHYam/3qUrqRy6dsMPsmPROrvJ6qlJXCSWkiUXDrw4D8r2f/b8l/pVQ3I0vrPBHiVya9Gb+pMsuDOUaKcBGxVDuBE0T7GmQ9Asm4ZlWpn+SyYRfJ1ERYvamTrEjl2jk0mvFGGTSbjkE5YQpvdTQQ/1zBzeQ3fy0+yea07CaGBtNpaoaGxkYSePL5uHcmSsUI0oiMRSxLHo2IJdzpa4MJYvu5YzQuT0ey48o7Gw6HYy3tRqvsvW3+29xGHFgr5pdvOs4ataP5eiYeZD5otp8gqH0Han3fM9zlKmUEJE+LMWazhRLJYHWOMXbGq2gaPtoOghRMxkPwfvQ6sxkvDF/jGARZsA+lfDYyZKwr1jhz/8JVFYyezMmQBTHAlSBfOfZmJbYcHc/pJ4//Si4G557Gxk2XcFxDYvubX6HlM8vfH2coTU4B4UdC4Bs1rAGsyorXJWBjHuZGGFPKcNM+ZVG//wEDr2/gPGsy1Y+l4nDjNpnUCFqx/D8TxrWgyoX0sEGpsw++vFc7jxnOj4wu3583EflYpy4MteaXTzFWgLPcHpqJv8XXywPFtJ5GumxBTSGBB3W1+K8vzTrUX82ox/HrVsc9XZ3b3KNZtbcAubROCGvAx3sCLqK0M3xlGyZzzpI9wBGDytB1slclj+7CFuIQFcOOTEJG4MxAZzPHGBwyRXMpXdsG0OQiFLzi+eDex8VU8yjlNwrfvNLpFIwgx5UJ7GXrm0D8gHIOPz7hpWMm05ikID09AObqSN18PYm7ujYF8ESVDrxMJqLTfJqn0BMFblxC+NwEpGzU8OyTpkinlZGMzYwfAs5+QUhfLgFYfCvVsUC7vxOdPIg3KtsyIPYuz4TXmt7egk7CDgSqODNkbhmH2A84eDuXxzeXk3/9E25r1nF0nwNMnmq4aHSLalIj785Kujkru1/aSzCUmlbkg63cUxZvNOEreYxKnOftCCu2ADTyMqkRo8J6Kv7bIur7jVPc0Znzvps4wiWoneVQ7JHm6dTX9x95ng+csdn6sZ5DYIN7sMiBdpYdvCTJ09eQwtG8hYto6rKpfhW5KCNZtZRS3zKXXSpI6mXIqZZOZ8DWJ8PT+BDbEUWvXi+zdTvS9rhBYZsfL3DSU1yWQeX45ImrjSI69jaV+G94OPUSlCNAxnYdepjk92pcYUh8Pa8U5tk6D/Vay7H0jjU2wJW3ejhyfug7MAStdSs4GsHxxGoMnvKYrO4nHXv2JLrxAvkc6ftOOkxCzhqilYCh+C025M4gmwasyGcTExpNRF46sexIu380QHTYPGvQ49diJoPEfiL9Yi7d+N88LhjBPWw3ZH/N41VePwrYJYBGINFJo+3lQ+q6YbtFslAraqWk7iKvlDTa11DFbbAYD5GIoGWiNQ9YdCs3GcTNyP5vWOZHkI0H3Gn/8h59lsMCDGYN+4W+VwLoeC5yrbcnLesvULbNYZlbEP95qjFe8yOjCy1hOa+aB90z6VNTySsWUf9ej/n8x4VxDRpGi0r58js5EtkaXnquJHHUbSL6BJb3tZdz12keh+TPG/w7hjNMbtLytKO0qZsAoa/z8IjBYvgUXyzR07M9ScXorKaPmMkh3M8/ET/DT5RiNBdP5/kGWy5f6sfLgUAyfrcUw8yNHcuPxaT7DmNBh+Ftpk30xmI6ubHoUYHT1c7qjS1BReo+IZyLh680wrHBi2gJPRA6sxll5A2nLdqCsZsbnB++JDfPERioNBYXjKBSFE5/VjmSpA/PTDPFQMmWPtwjL7RIRCByYErSC/UorMXljQ4O/BDVbDBGsc8V5/UH0ZGaAdClB3wJJfL0LihwIWi/B6H0m/IiOp2KjIk0nFrM0MZC6M/PRUNiBSME3JJPXc3nnAaz+/EIss5AV5rt4cPA+sx8XsG6NFemjf6OVEQ+qbbz8+IQq5yzeaJoxYnkA18qeMu1HIaIn1xJU0cMFJMmJj8AzchP9ZWUptnxBwGAB9V8DGVqhQesYb9bH7idw7xY8I7/htSyXXBdbvJI2kuaWyOaVy1G60J+7G08yecIuhqm+4JNDPHsCaji2uBZ/g0CUh7WRlXAFj3U7GHDiHRXVfsw87sms9vuIpX6lso8q7pn3MV44EoNaGXZsCqXg4Gam+6TTN6aQTbVeyB+GYxJ/Cc0uB8NJ/LngxO/7ldx5+BdDszaq6z4zedF1bqcnMk1JjSHGPgh/RXKo/A92hUXMtNFGoC0g+tVFvt4ei8iihQhWCXj09DYyK2bxQNSCUeP28TBmIHNvq5Op5M6Wu0848G4FPi+Ccck0pDliMfN4zu3GrYxRvoZH32wq9N24NXQhpkrNdGqm0TCqEjF9U77VxFIXXc8wvWKEKULuN3tRJneZTNOxbFqri6bvdc4H3+VEeTmDy9cwdqgTeypOIzh0i7Hlj3DSauPHted4X+ymJO4gxvuvo198C/tFF8gcORw7wU7akpSofjKJrykeSH53o0/HQQ4MNkbfaBDpjw6x7HMCq3ryuCncSEBSLLO33KAtpYTk69FMfHCARxGNLPcP5c2HlcRobeaG6GXkRjTSKpJDXfN1VGMDCQnaxsMLfsy+lULMh2dIJnZwrs2Vl4sDkHrYTcJtGzpT9CldsI6V3wTs17xKotCRijYrQrMeoPV3K3rCAiT1u4n3vMOqurMI7gpYPsGVDZLv+NxPApnvzbyKmECH8DsJx4pRT/bFQb+c5rcHOeDwG89NPXiU+CHdZy5iq/YyLPEkhzwEPAkLQXfRaELD5hHQXwrVaan8U7+HxmlrUJf8RXPTFNZufsEclZ2sHSnJwPRAVIcY8VfVlkdJyQS7ZeETkcTq5uMcEz3JpoJHjFvti1HIKlqKJFhuaoCm5D9I+Dajml1Nb7kcF4yPo/jxKkM+dJIr18AsqTLU6nbhsaqdT0YbOBvkRZCOEO153wiLH8b2R0YorL5P6lYXbhkOxWz2WgIORyJS7cgnX19M+jwlekIP3u7WzJTYwVa38YjY3uZqWM9/t1T/b09vRy+jnw7mxFJLqhvlGSSXS9e6bHRsFvGk0RMlWRl+5lYQnDWCKxM/YG+mQVtbKklBX1g2Vp5qrnG3eQfklTPqfAxDtW3ZOCcVj/Y0+ht950dJMpdqe5nUc4xTFv2ZHf6Tawb2pOQ+x2OiJR++mTNsD4wQuwZyWwjrKcajI5N9m7cjttaYJ/VBXHypC4C5ihHFUZN5bybFslM7CFmajn2DOsPqlDisPAzfwSdJ7CPCiuhI1Af35/Tb+yS/baX6+il8EmsRCKYj3LGMxhCI64KYS6CRrIZHJMB29BSEsGsSlL3nWOg4LG5JcO1ZE37rZiOMjkdrmj2NIoup6KtH9uMU9ir0p6+jD96qNYz6EU5QbScDQoKJDU7iybnD5AiFXFIbD9kxdKrY08UAlhedo021lDtiPqwzvcTcursMnLmMh/lyrPo0GK0P/og/XUXghUC2HRvGyPjHBC/5zPB3jxB70cLeypl4Dirl1+5ThJzPZ8PIIo6desaLT1lMP+fE3DEjKX1nw+30sdzMNiQ93hmJ+X7UTzmJSk0CF09OR73ffVYdGc2utT/xWjcR3cgcfMoiaPuyDaU3dzlmOJYbknBn20Y2mPbBN2w/Ay1fRCTAAAAgAElEQVR/o6SlwfH0SroWrCXh9yuKDFZSlJSFVs93pFNdgBrY78/SZ3dp/PWcZ5MzSNhTztZHjdzPOssrq51Yxs7AZ+FxBgYEcvrSJ2aLSLNxvjOCNwepf3qUu6cHky79h6Y/XzE9tAu9t7eokByC19xEemIXkdn4A6t3Xfy8N5fl3xOI1opibG8EPvSl5UIi0baWKClnIC6miYhpB+IdMpw3ssS3Skib3QUGTvbgxgYt+suO5em3W/RzNkMjWY4vTx+RPv8j1tv2sj/yHCslnuGX2oTovvFIpgzGb/Jw9uzKoCssnXle/oy2mkGCcAg5hbK0rZ+CR81LfDNcuNWriGbhfIYKO5Aa48wCj+V8fv8Z65UHEPn1BvPdE3F1VGDp1/MYG4jiGN2HpD+l1NenMzQvhuflOtSmu+H5Kp8+1kJktNYg07Qb1c7r1A1KYee369THLaVCxZbh8sYMHJ1JzvG77Bwymil5AXhameD1cQS7m20h+QW9VTGYr5fimXkWz3OHcfWMP/YOFRhkvSWhVJ7ld0+zae1rLueXIdygxMNOI3K19vIcJz5bXAYxXX6+aWVYkgmllLLA4iVzL71E6JdK7sAg8l69ZJfXbfquasT7QRCyQWlcOreSD6Nhyu4h+Jcu5+KtFjLeh1MySIhBkz7Sj4sZFvCQou2hJOaNpzxiKc9ybVnwyY8S/UlQ0olnURyb3Q/wt6aYzk+9BL6TozNrNlUbStG0u0Jbyk8cpx6mpHUM+vK1pGrL8aixguEHTrJG2Ea+iQ+r/Ybyps90Bg5azLfSIiq5CRkdzLcr4rTYRLIqGhh7bj4bWxVIixKwbEAfVI7FM3fUF45sOcTO6RMIbfr3Pep/tdvVfwk/Sgp5EJXMfnFdOHabOVIRTDj9iPPPHlPtlM04Gw26UoJY5z+X8sK79AkO5ESpBq0d/RkU+JK2xFysk0ZTOauEfvbLiTTSofT7JbqFQtZpjma2USH10RsJvunErJOvuOB+hZt/ZDgy/DbJA5QQ7d6AeE0r5RkrmJCdTF6PAyEK8ihIVuD0cAbPXbcTGDgCmED4IQ9c5cDfIp4Cva14S/QiuGePsukoxqn2Ja7KicrvE2ltf4e1tDm6pFM30ZU4cWXCe7wo8XLh6I5/EAieg4UvuYpN7HnwjZxiNez9jrHR0wZhXwPkw7s5aHOKwdZWjBvrzuwpo7GnjJaZd2hp8kMlLwM51+vc9rLETcEcm5jvmIwfz6dnLZxs+8RguU+Y+ppzdO52ivXyUFHdwMuTDZhpirLG7xznUk6y3zQfpaRNuFW3cfFdJkvOCFmzZwH9eUHmtQxCnqVyY0AuzuK96HXnckNUSPs9ayb+tEREuB21SXDEQILextk4XktEJymOfOUbCNw1AYhevAwd477YtQdxnyC6FeH2wg3YeBuQ9Ws0VnLmdHU5opH6lFWtQ9DUeMTWK92I/v1Of4tG3umkE7NjA522cKDqKwcSlWivy+aWvSm+HXr8LtqEXVc4OyRCmJDuzld9IQ0J+1kq78HD/nmYK6fSVLEB0777SfwyC6tTXpzIMsde+QyxZW9YEBXJ7aPbSI73/FcgjrBjmPMympTKkH/2EkdBL/EnGhgYHsCepVcxfuPDfmtXBO5V+EpGo990iQGaEwi/uoZ5Zme5r3mfyV9z0PQeSb1lFRMi66jsPw3T/O1c1fyEv58LNOjCxhNE7xKQlOHG7n/mQyucXWPN1Opelt6Soyq2kVMbldBu/obai88ILd9glwuLHA7T5ivDkjMfcTz4hwW3x/O0eRb9fnfxQi4S9IzQts6nrc6TblFtFETfYSzYR2LLFj5pqpExfiw3W35y3ysVHz1HPnMepaWa/CiZiN1kESZVzubutOnwKYk1cgsZf20DM0458zq5D8/kTjFcKxGXodVk9ZmPqroVi+RsCEvwxKHbBemIuTx0EuVkxXX6GArxkQin4S/sVxAitt6N7qtDidB1pPfXVibOs6J/+B1Sl20lWVYJF3d3gs1PYnIxGoGVB3Yj/6B/fxwu76fjvQLGB3zl6RlHdk6qQBh6gZAXSxDzvcjU5ynoSzZzJN+U4+Er+fr9JILwAFRvhDHb2wChey0kjEJ3kDYL38nwpqI/ewc6IdCYjDzQZDKKmQdmMmP+fD5PEGVqsQstzllseLUOHQslZv+Ty+xbBtS6C9A+9BC/Nl/Efx2gXN4ACekQRiwJ4JHuaEqeHsVP/BVv8v5gpAvWN7TwYTaLAyGgRJ39buVs75NC0L3H1KWu59R+aNlkwKXPoahrD8I9wZ8NkZ20N1RQVn2I3t+JDJerRSfqF/liJgS8zeGynDlB5+oI+m/S6P/hPxAv49KeK3SN34LShqc8DR6Mwgx5WmPek9JtzW5rdz4n78Nq0yv+PnuDWdprtOpnk/VBBVfDVFBcwogPLbB8IuKOdmRIaLDauhE1FQGqt2p4dekrN5FGwcMT3wQTSkSH0NDen23aY8h89olh+3MR7pvDRof5gBTvtB1x/mPJ258vqfvTiPz7IPxX/CuZI5M+FhEbOHx7JTUO4gxqiOIQVfzz5ycBTqrotxRwIymQkZ4l7PrnEWlpGST9cwFbT0M+pN9gmcMx2Cvkad940BfnitNUBBslENaDsbwDSECQdBkPc6/RWjIW542OjJ2kwA+hkPeEEzvAhZAYGV73K+dk307EpH9w3VuWRQdVcbfIwf6XDvprdnLqynomTZ7KrZhn+DS38ZcCZEVHEkoKq00X8cJTyDSlTmpLW2mS1mWN0k+Omwaj5W/Ptn+Wc3XWVNwev6MdUPqeRI/YBzTlNrPdXhIfix4GPxfwQzsFGYMC2lSHMsXVmfxdptzNEWK4aSNGKbXsMspEMHIosnmSjDAX4fydKKYHjcZGeTmRkSWM3OaOdGMf1CwGcMdSj4evo/DLKqTGVJNdV2+B/CeUUtWYOrWHBJXlaBWL8yE1gSbnAazzU6Q5ahQqcslcbTeHWAdCVD7hrruBgDJx4mSq6bZbwJIBP0j5lsqIZRZM1XPCROok7X8a+Bl6GYm1j1DM/YK7uBl5J7aBjB396eS1rgFGV2IYkvSG/TqyOMh9QbfpG6G/nvO+pRNYzPesOFwj0oBo6iNF6e+vzMszWvT9VcmIX9EoWIjwy/A3ymmdyPW3QrLJje7UD8xdMh3dryJsyHjHsUW6XHkUyeVgEL88H1cHV95mDaexZg6/Tdew9/41BK1e4LQHw5zVVPRdxMWJ7wm7uxfd/noMWCNg0IbBiEm4UfQ1HKHuPAQK+YhJazJDuQ7T2RmcKX2CSOJqJDoV2F6xgOkuS9h38zLTdFq5On0vObfDqJqQzcDQLPw3rWHzZmeil3WybGs6LQv6EVxpx8DaWnqyH7BYJ4nlPd4s19pMs44Gz+qasezK4WatGm0+r6j+YYhAvpOX1QeBbVybcYWrG1+xgAKazf+y1GQ9Y/J0oA7erVuOdNpcClwL8Vq0kpWPKnAfsoB5XXG0pSxmelk3G4evJPzWWT7XB2HnKYuwWIjg3Ea+6XsxsPoQa6/vIeQtCPycWHHlC1aL/RhxYiDfxIIY67OKOy8+M3ZTAE8/bGeuvjrTLeYTss+E7w81aKOIR6Eb+bZ7EU+LyxC1Wc0y2RD6qUQwraaFXQV5HF19lN1+AuSuXcdethLFn8do0NxDV4woGjxkcFwQ8zX60c/4IHsbIaZJAZuqOSzS3M6l7fo0l5uTKeLNR6ta3jz4QXXjLXBKY477C2IEemxyNeTZjXqG64ui1fyFwmUbaXY0ZGRLH/TLe9ji7kfwzyuc1xryb0vp/9KQwf8q1PuaCn8dmYmSTAXnv2czbPIUCiJXoWT4CYu/EOPZzvmYWFprB2KlqUBegxTOlWmsXD0HicL3aEzQoV1SDKkadZY938KMJFl2l3vyXkMAJw4DcdwQ/iZ87iHMdFU4e/IIghlreTi5gryMXoRVSjwxn06Hgy6ZDzppwRL/PsksLb/OdWEIJw/cAmYC0NGYwJWUaUxwLUEXmMlZbuEDbW0IBu+FjDsM15dgtP8sliybhVRsAqJ6M5j57TPiL7ehEnKIvzOnoTv5Ew82pmK+MoBTzqVYz9JhW8VprPtO4k91HfXPSqmNaEBkiTV9U+8TBHy9vBu7wK1I6HyHP3pInNfBbiVUzpelGSuU6cX4dBx/RjQwOOMiiz0m8nqWNCR9pXZXKCopNaD9FpG0aox1K8muGEqH4XhEVJTgbwSfw57Rs9eXDebuhCcl0Jqdi7OJOj4RH5jn2oaEqCTS1cWolPfQPDCVznoJlluLk/23Et7fIM30E1Y9Psgo/sLLbSaxv3+zrqWNkcV3UbbTIsdQip7HKQzoPoKK/W3a3oVxcV4+s2/9wHexMQWVO9hQncTEfit40paKyaNfeBrsYYL8S07o9+V4p5AqV0v8Uj5gmvaHwsgcSoSOHNG+xz+GQdB8nX0mAYgYytP6tz+FXfeYpqVM/utEbI4/oLAZ8r4GUPz4PDdTwVNlMMkGhtjo+OBsXIjMh49EyNRwxNiTV1othCRMQWpWPVscxjB/ty0nxs4lU7Ub7f27+Op7BtcccbSkQ4mr2IxebhnXF2jyxG0MfVxG4rzSiyIDXRKLbHjXPZ/NLhswnPcajwgl9vwUYUjAJuACOVtG8bctn2FTG4k1usCNjA0cuHWUQ3Pmcy3JDDvFSSwSP4KmaiSqZ9X42uDN4mEj+OKqj/Os6djXa9ExR5rjErIccLLB99c5vuotxjSnkHbnXpylZ3Fx+y5i3GdAbxMHS5qp6r7BZ4d9zDvTRJ/JlQRryoOCFB6hdnSN/0C8uAI/xrWzKGscl+qEkPqRURQxfYo0i/ZPgVywXb8Q4ZtMUiUbqbAfTP70L7yrncbdG3Ks2LmJorI49jcPg5PgpVBBY3srw31KaE94g7CuheUzmxj98zx5WxOh0ZC20J2Ezd7D6cehFFhFMnunGwu33CNa/CwG04NJMjrFS/l3VKRsZf70M0jeWYHBzn5sGT6KVX++0DdLkV2/FPEf9IAbp48wv/wfwoKCOGhlzpbYUA7d/4BnaSKDJAIInjAFVwtRPt5vI7gghMLZ9XzqBNPg1fRbFMpaiVWck1LH8UQn73buAcUZHFSYSYyGJTOKX9Ph1ImMfh3zL0bxKU+O1SsX4J0ahAxe7IzzQt/kA0HqyjQM0MD8tS5jGj1BbSx+0c+RLRAy5C9ky4TjhyJ9kx8TGKnPeFURyg3H0Jp+mFqxYl7+ssRFfQXtcnqUiccwU+cRjW35LFmb/T81H/+NKKoaC+fpB6D1YwJb9Sx4NiKUyNIwMJOnpWc1s8Vu0aUfy5miS0wQ5NHpWUXheU8mrDTn2oXxTFYSQflqBMMbrBHY67Ikv4QDb6LwmiPHj9QR7FNzYa5wOB8jRxA5Sh3Rjc+5fTeEB2dDiP38igkiCVQUzWVO/x4W8B0XOTdCy9JJCpgOu98yNe4QZ18L6esjIHj6Vla0dCCrvoMNs37x5sB3Jpa8wtd9IqeWyfPlwgCG39tEorUTO+aZccnrKlMO+ZOb7M/btCr2fxHiMdmaXZYZCLfEc1/OjdpZk6m89ZDAC0KwXs/+pSe4nCBE71I4w9fNJzAwkJolbZy5VciFaFVsLY6xcOchLB7AnoJWLB1+0ffBQI7XltLmHs9ntbtYy5zn565zGN4dh9yFfjRLioGGKv32bWRGViiCvLmExgZApTmfx47n+go7ZtRaYr7OnIUm05j4t46ZYk1sCtzEg9NruZdWSeOE4wjjBuHqOJaM74PpU5FOS7sC+mblqKfV89bRgJGqfWmrEqXMErIi43C8Zk3zUwc2nLtJ35j7PCOdG7NvMTGhFMXAFgQjhzDj6TSM3uai/fc1LTanOTO6iWPeZrQuzEdmYBpyOQfIsjBjaYsmDVJ1pP5VocHUip19ynnWKoHUyfdcGCnK9wPB+O94jFNJEkUGcaRIz0Gl6BXGzVn0v5SG/ND5HJ07j+FiUlTlRhF8MJjqw97EqU2GGS7ctgikbDQkTO6D8IIlpz2fUGMxi80Naux68ImOgFZOOwUzS24/YmPGc+1ef+AiVwSVhCxox3CINLXXxVk+qpKGAAPy14gTGbuenKx4BGYunLWaQptVH1LF1Ak/vhWBuQDh5xAU+hyk0Xs1zicNeNowi/VPAwnvtSPyzEt8ypVwXTCNMU6vGfrjLGpmnjwYcpG/e4dS5LqW+rpIpH66kO7sibqgGbevBUzs74un/RaYMRP93XGsrLrMFeUZrLFLx9T5BrvXJ+ElHYJEfz2qGlv4ITuJiY2P0LW9TMznswS9TCNiz1G2KEaw+ulbhumI4uOwnW/Xt3C2sw7JCfuYlHwdxeYXnGxqY67lPlLGp3F93zb2WZSwOH8TZ3T0UbQTUiB3gDUeaqxafYBTJyewuL8NqtPaOLQ+HRXZw/hNUkN2cBuHS/X4NGIR759sYaCuErsHjiZV9zKz1lRzcPA6QopGcEBrAhsyjChcO5R+maH0uXgEhfxEnmwJZVrsMxwN6nBzdKekIRAr+yAyn/rCm3IWFHxm1I3rBN27zhw9F3z/DCE2wIFl+tvZ2nAW64NHmHXYg8VLB6I9YB0znj7mrtssbt/YT86tF+yJ0uJ7700GGj8Az/2olzcw8HchdUm/ee65COPP/uQ7NqK1yoqIAC+cVIoZOboPPq6GzOtZzoCJw+k7IZbo9mxGnfHlzTBrrq67i1TSecZGJjND8zZHjL4wtvI1hcLrGL/2od4xAjs5ba5Jj2fUCDeChtv9fzJk8L+EPqI1KBfvRlDtxsfStfx+J436svtkLHFB6c4mnoy+hOLj+wxqGc+feFg7RZ1R15egl/OQX/vG8S5KDKmnljA3irBdtQw7exJHf1f+MTCmoDqSj0VFcNmUk9n3uO1QhODGblzehHDznQQVae6IFQXh3qyET8EGOmaXoeKQyUVBOpGCE5w6uBV/u1kIyWf23c1IKgxhmWsJlbzin/Y3rD6XQPWfbSDdgTD9GqXCduY5XWTToPHcfhSH8V9rFtTdR7fvH0TkdPGMECXQcxGbG1x4KAxA6owA69vS2LtcYEzflXQKvBgbEUV1ZgMTbUYTO3kqALeDgigM3MLrzweZfz2Xfg2X2fdjNwsW7KZQ0IKqYBwzBRNwXH2eedHJXB37i7z6GTDoLmxyRuX5PlDbCSJCdok+YbbtVcYp32GA6D60DG4S/DqV5V4LmSsrz6/CcOz7ihHZVEq7QJpdRsUcM+/PlWkBaEm1Yua2HqvuLUyeFkqooQ/8PY3oVEn6z76MbGBf3MJ38cEkBx/rMjg9hTdRb5g0Zg42Wdr4GI1Cve4LnrkRuFw6g7fmeX7HTiGtVpmNP4Zi7OhPQPMvJgVEs372CrSUX5GvK01guTRvpHcTtiGTa+JnWD3gPHeKNVlZH84/tbMwzElm7dNKajpaKPtZgEB6G7o2AopTJUjVNMKt+SRNbTuYotxBreMlwjrW8zrnG17NmdioWZGibMw5WVlG1i3jlmASfkOcmX70HdFt8UhdXshhE1tUPkfzfl8NkbaFjE8Iw6SujOmMIbTnLkXz9RBam7J+yUwenTrCkamBHMkbgaNaPM3d08jPesXyq0JWJqiz9nEpORu+wR0f/qam0C7zhsjqFtb8zkB7wFfOz11I3c9NmFlrYeZ0EJWRlhR3TefLC2WEEffJ8NbnRh8YKTUM+ZsnUDfx4VjPdkzDnCgzGYJSy09a3DToLjXlmSgU+p/mp1kv1yc5U9IoTWLPaOyV0xAJD+L+iAqmdf9kakUKyqGjUNT8wvi6Tg5+foKoyEIM03/jJ6XBQWlNBt/NJrR8JnuOVJOZPAYXNROEugPYMUCDOZUJ3IoTZf0kVZamRPGy2ZSTrkKC3wnxjsjhy3tPOp9kY7dtOl8ndnNfJYw5hkLuH+jgY14R7xaEsqbwPJ6jdzE8fhhND05SJiwj985Thqd9YXJADsrau5gffJY5d1bgGxbGigFR2Dz1YmXHatoU1BE+foBI8VA+lXcziMcsDgpi67SZCN6XsEVqMf0kvFm7WoGxQ3SILJNE80oHVRfPsiZYiPRRITeX/ObVmFHUTU+kefANPjxSgpvDMN10nUedxZh2R5Jv+QEJMSE+Eec5KqPB32kWLP4Zw4H0PM7fzcHpvTe+o+SR1DJgjWE9yeNTeP1EgMGpDg6ECtm96RLaJxIIVjRkQLkKL1+Z8KwtC4Pc13x+cIvPtf1xv9vGgiJPjupfYe/AY1g96CDxzHoy3hz57xXq/wEVGTEmy1Yg8yMRJnvT1f2Tj/4/sfgLsm0BXIiJpUHggpWmPOpCc5wr0xm2ZAgSRbFEeM1ge0UaRs/VkZ3twtR8OS4ATzQE9J9ynxUHvmD+cQnhHodwmJTN0Z8vuD1jI9YuQvIyezGXMSLafBpzqprIfNBJJ+WIN+extPwGojc/8s+tgySVwm4L8AxYz5XUg8jOP4jHry6O54ZxRVjIvqh2xp88QWbwHRZKVjDafxYvjQyRiv3MGJ8JLPj2mTHj3TCbdYKyFcvx3Z7Og32wR9aVU4ZQeeshK88/wLwHil/XQRqcDs1G4G/N8KkQBMQvPozGP3fxH98X4R8pRBpksFsJSQoyaCnaoZLUy2/jUkpG1KOc95PFHmcxlACSvpJrPxI0akDkJyKNgG4lUzKy6TQYj5tyNfy9hQnQs9OXwXpunGkUozUrF6GwDYphakI+EqKSvHykiXJ5D6X5g9FS+ISPjhv6Rhbw/gYVJs1Y9/ggVVCPl9tMuuvFiNEwZxcHMMoq4sVYM7LeXccOa+Il82gruIFF2WaE2huY7HSTYR+W4Wrhz+TRTxgd6c4tA1s89XfT0lWJq7IcPhXNnBNE8eGnCn87JemIScPYQYO60lPkth+hT/N17IZtQ5DbhxZZbdQMZPHoFOebnDoL99yh7pIPtTOm8uHebqyMpBhZP5iRZqY8jdfEWLWAv4/L0O03iDVdKpippjBlcx1FJQKC8p8Rc30VnyYPRSUuFpzWkqZmjJbUCwA2vbjPsiE1mKpbUR10GPMPr1F1m8TXP99x0jZj89ANNHzpg8PRo+hp3WTKNlPc1/r+K/BzIP5DJuN8dZik7sSBW0eoVnJDqrmTF7+siTtWzOnjF9AtaKftmR79emyJdzNmZ3glIyu1eOhbwSEVbSSb6+gn9QbLlBwatatpd4bWQiOu7g/ET6OKGb1NhGl8wkxvNJ8KjlKmUofKlApSWkRAQQrXr510jf9AWpMNQ8SKOeOlTmfnCnxTP+JrUoHPVGnafw9FIleIyYJV9L7JROpZLEMkf3PYegnndGdw74Y8o71UKP77mD2rqyg5eZWJTRdobG8FQPL3EGho5tLGxwT/3Awyg9DEkLhuLUJn7WV5QxF3LSPxNjvJaEVRvopNYLalHUlTT3FJt4FP8SoMr9BG8uV1hj49zN7ho3giKEOhp5bIHCUGjrPh5l13Tq4z58Hu3Tyyh0uxj7GfbsOoN4msmrOKFVa9DLUQRaAQz+HUELiozMfOv0SZ/cPAxaFYNcI5aQ1+LRzEI/N95Ch6snCYFzEaFowwL6HTqQtD5TrmX4xEhDakZw7GOy2IFR9KOR7vRcNSL4LUlanvVaXfaz1ym5azWvQA6yRiIaAG57KLCEY444ciPQmHOfpYn8ka/agwGINi4xNqxf9Q12GL90B12uX1qLFuwk/3EaZqj/5tLRUNCgr6L5Ll/zy7r57+P9h7q68uoHbRev7oBumQ7m4RDEBEEcFCRcVuxe4W7O4ubFRUTBRFxBakVEJJ6e7u72J/41zvu3efcfb8G9aYz1jjGXOtkAeDO9gprMGakjCyJYzJLpMgdtEAbnk5cs8+kVXf7Rm9oAxDpZloZRci9P0615X06RJ3J/2vBAU3YjnpkoL/n4lYem3hh9xp5hZPw8ToKCqNmVzcWoFO6BX2Gs1B7LsGRpNEUH1iR7KeEW4tJxG1S6d1cz5OlTK4tN7hl/1Msq0UOBWijZfWPHRsbiORuYwF5nIgA+KK9rRo9OdfgRLGMt8wy0+lpluB4vwcErvGkpQVS8JNO16kFbD+awrST9oYPy8M4bjXeP45yAsvfYSPLaHCNoFpFucIHGnACBtzZuu9ZOeecOZaB5MWaYLt8mCCJozkRX8d1mwaRqznTK4p3mBn+h4Kn/ancE4XUWP6GHzrDEIrd6GWf4nEU/1o318O/Yag4OxBnXItGHgjKS9Nd4MZCaNPslNegrLa3cw5uIcl224yftACMtrvYJzdwL/8gfTGPmXkVCv6GiWR1HZk/8cualKKWb78CxEKaqx0kyRXrYnLXpdwn+SDu/sQPMxTGazxlpJvJ7Fsq2fm5RpitPw4lPaEccNcKClPZIjoT9p7vyFqvYeav9IcsxanZc9AxluPRzk9FCIqCAnrh8fsRbhXVTFLrArR7iIMhIU41FLPU4NOIo8HscDhC32D/3CqeTrhNca06XXSPn0YIwsEtMkU0iHkRD1CNCj1ouWqT3vsecziH/Kw0Q6U60kMDMbDYgbCKv6M0Gil408VrkP0mCH6B02798iW/6LodAFLZfVxFPFBTPwYuXm+yJSsxKUrDYFRFyKybVxLi+XMJy3Kwh+iuW8sY0Q0WX5zFG9vFaOW5ErSpSza0715mZlNRPxHzJy8GWinzxYxTdZI6HKmqgbB9CG8Ta1Gt76MgWWXCC3Zhkj7d74UeJGXZcONGBDPqUCqwJHcTDnWvRhJwa7tLF/ZxrqsDN5XzebrFAXSRidz7+hjhqa2M05WioTIRA7HToeuUfiofuLC8xcsODGI+w2lTFLOwSw9Au9V0VR1vWeFzltyGspRsU6lMd2I8v6GqAr9YcmwWLoUs6kx+MSJ/NHoLVzDpuZTbLeqRVgVwnIvoqDkhE3PKYbfL+bR1xxyTr+g7N5qUg6F8jQ0lKisr9CTjZK5AuoZ7ug0htCTVU6wjpZqpi4AACAASURBVD4LDozD1T8Vf/mRnP4lTu23q3i5dqCf2Z8vNwKZMGM/GbW7EbSFoy66gpMj1fi66jT9PXw582kNgxHi5i9pZum5EXD6MoEexby80IxD2g6id/kzNvULtpo9vLOUwd/VkRpJPdJN6vgp1sgpOTH6y9XR5OTB1ePPmXtgKatKs7k2RB/PZwMpHq+AndoJTG/D4I5jyHe10KsVT3NrHdFjl2Ac2UyekTkvhZ8zv3oSTiYDcRzljkxlET3q2rjtzENHvIk1M60Z0l+GCqFxXNg2hp2tCYQeUCD0ozZXzFzYHrcEjQ2P+dnTy/yURJ56VKKhc5GZLRVEN4jiMDsJUxFP9q+tpe38o7KQkJBL/2ln/7/K7SN7QhJF9DAbuh03oalUp/fgkjWHy26VlLx6SYaPGZJdy1FW+0fwQFHuNFsT9zgZ6W2mZGyN5rnDSe6lRPA6fAj1zarsfPEZXzlT9n/aTKSSCYZvb5FZbcKnOeuZ9a6QyuBduKy5h1v6Sz52TUdDfAlvdHYS5tiL/ph/bIxvpTjBn/CluxGsEJD6t4+Ik5E4rF5FRI0CIYdVURkzlKlHw9koP55re/rTKNuCQ5wMO+rUKesxIr3+NmeT7ClV+4JifTti7ZL8LqnlaIAa8+e3I3HxBiPN/+Gy6jlX++oou2DJRs8Q7GbLcuzUTeYttSfL0o9b6X18cZHlQ3Ugqr11tObl8qTWHZkDlhilWPO+6hnieUVYW+TgejmbEZ8UmBali9jahbAqGKSnI9MaD7pF8GcxwRP1GX+jjkeKc0jwOIz/vKXYtqUiqdNCm58fQnU2jBeIo/vhIF/NbClK+UCvdSYtYb1ceClAOCIG55g8uldf4fp5HaZ0fSNllR/VP30p73xJ+URDEq5EEVBSj1bAWi7pyLBv9y6UrIcQICLKDck+HjQksW7hOGwrpDh7/gO1P57ww7IXa43jrF0iysiMZIICPvOtYjYdkvXMpYbgvmYedSxB7ogyCgWnEPPUYHiiANXGDmytC+grUGaiWzPF8sLUKQpj/0ucW9ql3PVcwj8zc8ZEPCJfM5cbj+KoMLBFt7IQoTkrMKp+Rbogh8trxHGd5kSBzDGe2E8lcNUVVq28yJPEHhTEOhBI/+GvkB9/Lp1D2Nga5wGleHqMZMSEQ1xP+sY3zZusCG1n3qR+TPyRw9S2q1zdUIGxqR2b3ozi3AQtZqRHEJqqxFOVfXjqtfLgcS++9llM8tzGqD+deDd2sv7FaO4OG02PxENM4wIxUBJBuv8JUrpHYVEhypopQohO96FokD/tSp85UgZns23I8T7MJ90R+H7txkkxnf6O3ykpkudqQiAda4bx3D+ZIZe2cm78YuZpBVIT8wsdVy0iu4QxMP9H0yddpMVVEXT2MFnkBrmFI5HtdKRUTIyh3w/wO3M1Az13M8h0A0eFMylt1yPMbCwpvbVodbmwdaUXJVMs6Al/hOTV5zQsPETAtVZuyZRSmXATRTtV0kUP4FV5itB7xwmJ3c2u7HfM3biUGulPjL10j4KuNuw7Wrne0cBYpQQ+bL1HuWgeK/o+8c9Einkmo3l6MYHZE+wIe/iDgAGqGJeeY/S4O8wIVmPyuyxO7f7I3jOHeOObxfiAfM5KOvO5MBu92QNxT9NFV0yWhXo1VCZLsM5MQGnsGKRa4NKIUrxr+9NcI+DaQ1UixyUw3W0hUuEN2BqsRcFWB5G8VhaPK+b40WVUDi8jqUIakZevcCm4gq2+JhbmeqioNWE+OR73pZKcUvmJjeNGNkxZwePyH3Q++YX5jHyKnlkxKH8rR70T2J50FimPIUT+NsSz2Re5AZnoSh3lZVMgCwpF+ONYjM9HQ069+/TfmlH/IzYfwm3l+F3awt6WD9wZIo5WayISFdEo/s7mwt8IXkob83tZI5EdCWiN2cej/nOIv2KAQe1cZqaup3ekMG9WlSK22guhT53Id71lVrkjyzPAaV8M8tcyuPomkRGyD1Gx+EXD631QFEDVJGs29b2jfPcjglM+EBhkjoZOMcId3jTUhVOoHkDyleHcmyiMuvp+pNIHMOjoaB59OotSRyMxf5O4ImHB9rPLmL3Hg6LF6Tz8VsfjPluMNjQzIDYWlWd5dH99QoGCFdPmSrPW1p95Dq9YXtuHnPMaEvNuoTQthHE5//XhbvsvXZ65pIGeA+KOOgAY27pxOPwUHec+c/7UPCJ+XmWHhgGtb4qxahjOmH4uHL7jRXXGTaJlouibfpquQzK0yD8m/s0fpIqtGZh9AZksdxa3xRD0KpasNlEmiiqgt38u72saiZO8g7GCChEKbpjKV6AR6oOgRAdXYC1i7FgmzJmnS3gSe5EdvR9J7almnehU1k7z5OdCeV4tsGbI+lckX1hH39tIzG4vZPfs/hxuGsk1PVO8OtUYO3Ao/p1bGdW4kkn3c3BWmYL0/X+IFG6g6/BkPn/P4dWvb/Q7KIXTlYH4jxmOQGIo++xno+LrzESS8cWD9V6Xcamxp2LbLbY4TKWSIrwq1HF7pc+z/mo4KHowqTKBJd8q0SuFbb9ViDRwp9NnLXXaruiHleGUvBahNAFa/RIJr/RDzSAKeYUiPon8oiU7iYe/7TmtJEumy3ikJK9ytGIGZuZN9Dfdwz/fFJSQR4Acrx7G4zXDlIu+z8nbbc2nf+O54dFH+vorTJv1kNoTj8jyvIu7QRXak6bDgnxaJgoxWPseZZ8XQ6cIty4K8f6VNvEfj1IpdAVPwU9cun4wK20OSZ8MsI8/h9zPffy4MpvLtwpoutGBahUUdQ7hXmEdaq19RM3pYesBFdbNX8euVR48thLFbeNJBoT38VRlCMoykmT51zH06k6urJlNgEk/Oife5WFIGIHyk6mu/snwu6fxWb8UaQdlemLE6XdjJO9eBaPZpMu3dGnuyZ1gY/9jaBbdZ+zvrcwSc0Cm9THRhvtBR5hFj6dTq6HGmm2j8fWwZ+S6aTTuXMAmjzXMmfIEX8ObrO4KJ00rm6qOV0RLHmLuchkq4zs4l3GDaUgS5buUdTWzuL/NigrbDJa9/cOEDAlUy+V4IiqGYOttlNavZm/yHYSCjdDriGGsXyO1SmewGBeF+qNarBO7eNivBrdpzzkqp8INUQPq1oABIPwPPAuM8Kvt5eJtcO7t5EjgVPT7krAP3Y3vT2e+OqmS+KkTl9QFjNsPNnWAoi0oeiErs4ZZylMRu3eET2P6MBWvIXJNI0Nu/cFbqxBpIUXEjAciItbAStNGdESaOPt2Crv8FsFzSaJm7WeuQIJu+7Wo6F7lyPXjCGyLmHY2lAPbNtMwRICClAJNxhrMebcfzfwRpL/04azJdmr2xf1nBP2//B+qOiUxFjNj+ecj2PSH/p5zeN35hz290mRpwtazBsT2KfGncDU4utAl1szdv05oJjQx0z6UaJKJ8/7FlXxlqp31SVAECn4wXa0B2kZw4KYck/dcYVVZMRkxZYjoPcPolyHf7Y1RNG2kcYw0yWm/aPEeTU+tEH+lm+knYwh3z2P8AVbOO8vclePZuisQLOvh3B6igdYuBxLK0hkp0MFezhzNhl7cKivQrDtLa4M158XyaK1/zcBSKTSnzGdF90UGVW/n1dQcAt6dxzxICshH699tjk/P4tDBUI4kfGaG2jiUKu8hHvhfs+qSoxNfxU6iK6vL0uoSPL7uo/SIN1k1SXg2WOHlcoTcakOqDncQ88CIbebSdG2bRppUO7nEg4QpYm+laDD/r27QTUgCMi1YccCYTZezaS1bgNooW+zKBXwUzqLy13EWz7Mioq+N34DcMG88pXrwvuvJzqqHpA8V4Fuxn6v3dyLmYYbYz1IC2q6gIv6E4nupKLQY8OT7P1Sak4mnHIX2Vm5/LIbKSjLTisFwCVV9QlD0lSgtVc6a3kO0VZofBVLglMfwozPZWD6R1dJ5qFOGFVrYo0OEQJO8a1v5lGZNelg4rzb8IUrWl4c5b+nWVUXpQ38c5Lr43GdJc18j/XoU0bc3pfLlZZDYz1EJUXorHbGMfkqDrjsDcs+h4jGfV6mFBMxR41b3L9yc1aH6PaPUHBmhJ8S2ygamaN+nt8KT33U2BFNKqck/BMjRBEyespPmCYZc3DqBTElvFlyv5YhmAEbZRpggSpn6Db62d+E4eC6Fmv34PsGRlKsvIPwoN3RcaBNSRPVDLEfMJVjTcJYLzt/YXx7DPrnDJIjUUFnuxAiVJtLVHbknXYPUy290mHngXeFIvK4a6pbXuWdVic8LP858nslPIw32vioAeXsCLF+w1vMd2+NLUVPSYdDp1YweNpWpBQnIHtDnW6wIq7tz6GoXQ0zbgobvc9EqFebn8H7YFn/mR7EOYjUxJL/2x97Si031h0iXcuRoYxVf2r0QU9sNxlugTJWnQh2oN1cSeOowwb5XKG5voEe/GfOMTThZKOPoPoMmsSx+Vgkz1W4+g6Jy2HPUBGPpMWh8SENqfBDjnJvpcPmInKI2oydt53N1Joql8XRoLidsgi5+d2UwnjuPG39PoNE3iA9lBVCvzcofHwhdLErCdw0Ge77meOwGtEwiaH35iyExcL4RPgBSwvA5v5N1FbacERJmQkwPnwx9CDexZlXGI7a5ajH2iQaHX+gzrGUbBpkCVDXyQVmB+oLXCPUY0uZnj8PWRKrFlHET+sbOFE+s6i9io9xOXV8yikaDCHB4ycoRvcQaXOVAD4AiOPhw31GPtS9UcdJx4cDEn6g8DcLGeTW9X1MY053MN7vfmFWm8rR7KNoydtRP7kYjbie3rT3+2y79H9F8qEsK973fvQ3NYXq8e5/Ez5SPTDFZza4fezmyeBEdOQNJ8xjA2IqNBH0sQqlenJOjhrP0ZSh29kYMftmP9NG9TCr2gvEjCEndQc2/lyj3P0xSswmzGpXJT7Gh+QlkKMPovM3kx4rR3KvMlMh7+GpNw1NdjgcL7Qj5vovyP9NoNS4g8GczJ/ur0okBLkkRJAlySO+3mrLK3/S1babKB2a9AkuXu7yLLkV5UCb9jZQY/fUlUWIBWCmH8qPsBOOG+JJY8BCrNiXqdLRoCPvGzqdh4NoDI1bgkxjB6d2pGDsIGK8J7u47Sf0biq7/TpaGbOEgzZh8qUM4tgSfmC/stJWgzLidO/8+8qJzMBICO3J6MlFtaGbiquu8ry5i3AgjEj5nUfh3Ld1S8zj+I5eFQXVIWs5gWhMc/zQE9xgpPpQX4Whug6xGBhu7lzCtsJSWgkwSdZwJO7mRRqCj5CUqYXMw/VnF9qAKZL5fo1WnF8Xv+cSP9Cb88y/WyNvRWljJb0EHKf1TmCQ8gLEjeul5UMzFMkeMLFNZtWc/D6/uZVKtE31dX5l1YRciyte4VreCU/vysJXIJKZ6KG2spuXMCUyfnmaaQI4zn3WZNdodw5Ov0fEbxIsjo/Ac/Rn/3IGEvTfitN1Bfilcxo1mqlVL6JUwQiZXiPhvLdxcPIrPr8/Q47kG7cih6K9Yhub9EaxtiqLLUplGRXUMSuIp1JShp2QEmg2FiOmZsigllr7qAkaIfsDKbBHfGgRY6Y3jinMTznFPSZg2gNpXk6lZ9IhPoSdxsoYyndFUpYty69N1QqXcmOGigIlkHXuM6ih5/oAOlfMc0p3Fzt8Z5AntpMzAE+HSZlT/5qMg70T6Xzm0cjNYVv+P4jH1HGifg7oAunPvkWfhgXyNFsVeXehrTcVsBMQvWoqiri8aZi/pV91E/yhV3rT10rGyP5/lR1AhqY/7k1O8UzanIjaQ0DFrGBQ8Bu8PvXxwH0LOg2TsKtWojC5F01KIe4aaKGndobp4PbpSIxgm/ASRmqvszm0ibJ0RI553YmMZhH/ySfLUnDCrSuWw4lc0T3kw00mM3MJX6OTcp2L+MHR8Q4jbfYqbLuYYp36jrtSAOysseHhgFcuNB7N6tDnDcy5zT2M6VpUuFL1WZXP1LS4pqiNtNwtfo0Qisp7TauCE3fOf6M09R+c3T0yWefMwuhRLRQni9mSQtXg6W7TCSZCToG3xUCRCUtBqqiA08w9Gwj4MMt/MskQBgmQL3s6UQC7vMAMU4Q4Q1CwLsS3cHFTBrDPnUJKAmk4wDj2Bh/pKFNpuovn7AdVK0Ui6myFc28aj+37Y2eriveofLm9Ocbn4BAlphnRpl6LaYYFBay2O3b9o1x6KlFgbUk2N+JhJEtmjwoQjx+j0jWfH/n8Ep2Tx6NhUrHQG4aXoyFfz0fR1nWNxRiYN39SYYezAc9kygpo16F/2hk7hH4hP+kvQGMH/Nh//QUxUjfs+DMxG03ALcwbW4vSqFwdTNzbkLuCjojNqh1Wp3GLFyr1BSB15xuH7mmTau7NdfhCyLZvoyHZEa8YqWnfHo5BzBflTjshL2DPXbjjPbwgj6IvA9qwss94IY/xyARpX8pAMOYuc9j3c3oWzWWETUafXoxtSwf6GBfRJShHhZsnyvgFkF9kTu24bixx9ad2yC//0Cv4sm8CjVduIUzDEsM6GCyutGXhoAHp1F/Cs70c/3dvM0rgC74KY1eqLavABFLIPUZOvSMdqOwQPpTlMK8hU4OM0jtd+ChzLFWWb63323lmD1FD4LiEgrC+NlwIrHtb84PrPg4wTH0zkPgELctr4uGIgI9+9RtGkBnVDZd6XzUb1WB2dNy/RdVcbNXN9NO1O0lLyF/NqN/r8VpN/+Qar1+znhJEBGccMmau5ilPP2hlrZYhY+HOePlpK8Y81xFQPY4WSPafe9HL45gg2pMlw9OwP6mtO4jHVnB2vzNlj+IL3qU2EOlQwpcqeTnU3jijHUFToQGCtOs45e5h0NQnNLYNo6a/CjD2vObxgAouEdAmU9OX+0iEMFH5C/JijBCh9ZVytK6KLlXhePQlVx0g6Lxby1sCfbYaDKN0exJJ5owl1uYz3i3N8MbbEsDAGJZl4ztQ0UKI7EmOU8VXO5IvKQIRrWtCLbGTs+DYmTgjmUdxFrK+EY7PqLb/9dZg+Nw1liT5UbC7Q2RBG8Osyxq6YyOlYQ67s1qR9pQrLB9rgYNhIT+QW1ugZEte+hZ+RFzH4d5EiYUPsHauYM/EAS98uIcZMwDX9hUxv2ECpUQRN16qg/SYXXnWzJcKdgR+kUd9/EDmZL2gL2ljh9ZTYKV50nv6Kum01M71nIudtxoyVSQwTL8JjzC9Sngxk+PAX5DybRbRCJ8Y9Mrga3OcAMHnCa6rWTSN3gicuDVJUxZfRNfot/m3SDDeYwe9rHaywSGCmYQumj3XYGfqNBSeC8Q8bheSx6Wxt0Oc9Usx1rESwDKpmeBPXWUWKSgo6G41ZfamCMhFNLFvf8ajfPpb9fk98vzw4+Z6OklFU5XciHmlDhIoii3M+E2M1BcW4LC6fG8/xTXGsCvpL0uW/zLRdQoSkO3ZR05lpNZ8jFstJeBHNj4V9bHMSsONjNlFJtSQ+K+GAVSYFcvtIvq7OjEMRBE/rZt0oH+4MksDvlwptvV2sVdrIZs9MZF9Xse1GNffepGBbLeDa2ARUlQMYGrqWt67jkDgwkFsjerG3DiUlOwLXn0p8LxKiT0+SSEUoBU7P62HEE2FOLQ+Bw54IaseheHwV4sePUfZhNWv6wGjwWVI+HcTQbw6+6ie4vcWegzJP+TIvmcM9JwlMfcqxDxuQ9NPATraH/u6Z9C5ToH20BjYKLUxw3wFtURw+lckGWT3mX5lIw7MPPLAeyv7hkjzfNYbgje+4vruagDQT6lR06e5QRS91Njvcyrls0Yn6tw7Oyatxfv3E/3uaD2WBFE8j9HBOGcmJbF1+iTnjLvuOceobuHqkAsOOaMzaxTmYeYIZ1b6k/3TDIvEzrQM0WCjcy4uBxZiLjOWeZjCo1xMSNInilmDC/w6jsdqCyxUljNbpo3r9FPyEQ1A07gdrT1B2txlnkVnYpyyl5Op0bB5b8bByAf2zTvDvVzIvY5X4c2MBCdO8SM5ewShLZUrP+9FnJkdIeiNP4wwIuWhEY8I0Dk+NQrInEJt1NnRp69Goasmapbo07F7NxU1+CF18wIOE59TGJtC72IgHX+/QOe4QfXSg7DQV45UCNl1finvUcl6Lv0VXYwqjQqDksTP9e65yNNOGRL2rBI/tIqFpEv5dMgTZ3EJloSwmJndwnN+O7KbDxNq1YuV4kqXX+jAceprWzrNU5G7m7ZAiyq6o8eB5AYePZyCZv5Hv+vswGLqPmA5jVsXMxKchh0vhZ0gQtmSGrRQAe1IhIq+LwHtzmGMUzse4Q/yuL6a1opDecYFUxeaw01iLu1KlFLuUYOCcjW+NLdXiiYTli7B85C4G+/6mtvsucTsmYz5vKwf1w5hmocPRMAOu+W/h4MmVTLMJpvShOx8v2SBxWpSgsRd5eySQFSVjCQ1yx+AL9HGZS4mPsAkPwMtBgLHDZKQvWtJlvAOfAdCmIoOiig5uTq4UZLRy3tobwYBzLFk4kX/nAmmS70PowBJWtcVRsHA97oqf6E3L5Wf0REYtGU5RyS+SbK2Yf/USfSu2MWn2Ot48/83urBByx+4kqkUbp1vv+HElgXSLSSiJZBF5YwIjpF25/KeTj+8vktj+legROyg0rmVdji/LZ4VxObaS7u4Qol9ms+vcKe7EZ6BgYoHFzcMMtoimaFgGycalZNfNZMsIHyyWbkBvRiQ1mp+obfhItJUf5i1RvFbNx/XFGea/eU3XmK+s+6vO+K69dCXmcF9EngejuvEVN0QkR5QdLT+Z+XAzigFb2Vfdxbpl66j5Y4f6lgq0i6TwfXCS6h4b8oYkET+mhpMO9aT37yXcdB0av/qoVvFibvlSZiuOoEdHh3k7RBlS+xftg0l49K2kKieV3bUjiPyzk7NROrjY/6U4ZC2Dl0Qi7BHFgyc3Effbwa5aN/7pTqdYbjwaivos/9THuTo/HKNnEhr3mVtNsLHpFfr2Q3i97QFKnuoYla7i1fW3mDd85kvGG45Pncn5XfPQN5zPj2INvFv16Lulhfuc1XhL1vEvV5qUsGv8kW+jPeok/vcnMPr7ME43l5E/2gxJpxc4TivBe3UIA8aFMBAYjjqrZRo5HWxAq6IKa+3FqFmwgZOBwbi2T6Ln3hfEE3Vp982gx0keY4EJHg2u7FYw5bKaJBOXVrKx5DUOz25TpPYRWeEsAqReo29eQWJxL5dawwivPErEnCeEh4/laOA9Pgo9QMvzHzKHU7g6aBX9njmwSWshgnU7GfRiG4PHHWBiWi3vhvnypUWa4eoG1Cl9YODNlQjP/0tMcdx/UtP/C1AtLk90ONwvdKQ4/jKW0k50NBkyf/gw7AxseD76CbTGcuLuatY69+P8kX7sW6JDuK01JZVfcQxyYUDeLXS3K3Gc+axZEUpV/2e8vO6Cf2ExiT8vcTBkGPJDlLkk5srD1re0tajSG/OWO4GPGdBbwuuTAVxLXILocyXeOtew+5gVo6SMKY1eTFBwJMoBM1Grd4cFT7n3MJuubTOICRXGYNNKEBpJhYs00993M0hMlLmih2jfdZWNYjNQHzMUoaIoGsRtcRhpSFN5OS4NxSCez47iZKIToyhBj6vH+9Mmm0HbTyiwFqC73gOwYHRqO+8mrkAsVhO7exWcEv7B+HF3WSxzDAOrAuq/2pJe3oJ421b8TzymOns2Tzr/4eM3ktcHB1DZ+oAkTQuOLR7NyHP34XQYO/O3MEl6Abk13wnXdSCltYSbf68jumU3cxK8YLEyp6aPY7P6OdRdZVnlq8laHWlKbaZgHneOmW4dzCwywkCjksftHkgJWSLbm0yxoJy0wmz89SRZNGsuzvFf+CExjrFfpVjZFMXi81FYnj3A/fFulN4ZR/z3Zsour2KqnA+SRp+ZcrONeUPekBX+FhPvHywOUmLGUi821VZw5ME1HDs2cED8AyE3DtKloYBljDhldrNYrdOFQX4ztpZmSBx/SVSyDDPvl3HcvosUdVFuXSzgzG5Xfv8UBcCm4gSm6iX09kii8VzAgiWp2BaO4tEwV84H76G74hgOf8pgSwciFmIIW89jdqI3Cn6eWCgOoqs6h4ToBpYuXMpJ+ytkjvNC93c1xvXa2Bf/5ZhxKe8F5wmO24/lnUrKRe4gJ9Ofzk9v8Zoyh2JxWZo7FTEKEmaI4jiS60xR7rjKceE6pvnFEZaqzdDc10gXC3jd7y/posvIbG2ivaeGMt3XhO8UIJgjoLTFnOe2uWgXDMY6fQhlmWW0lxfzLL6SHdLC1MYl016UTIz4dXqTAhmTD6PW3EZ7VxYTlBMpufOZLlcJ5KrCaffIY1HOD0Z4q5LYk8RacQG3vsRyeHArEzO/s1AqixuKdWieFKO8XJWKp1MZ0tpHjowuk0oiuHxjA97DnSgSqabyvRjv5eWpevWRgpiBdB4qQ9axApGgYgrMl5Pxfi/Bg8DitxEdL3NYbfACedkITEXU8CqpxNPuOSdwZpJTDT419jh4h9B60R3V33ko/JQGdRferHLBP76QjLwI2jzS2WNawaKsMySaJnHScQ5Z+usICj2GYFEYC94/pG/aYHT/3kQPdcroRDFLk6dmxdy4vx9BxnBOlajjOraA4f/GE2IXibjZdXIVHtBvuC76P4R4kHKQg/P16PabzMnaesjUZ9pJI/RMigkQ/oeitCzSVxWIHa2NMLVozBCje9UClrj5ope0leTLAdT4+GHw8zacfkHe2kt8W/IAWR8P7HeNp8rVE7/uQ+QoFNKh2MPsolSGu9kSF6+Nj7PYf9ul/yMuH22yshycNYd9532oHdmBea4a66c48VVHjhDD/vRdLqT1hDJ62QJGTuylcYE3i2pGYWczCTu5OYjnfcN67myOLRZCoDGAgDNhjNB4xOyWMShlDsBwgDAWE6I5e8idFTfkkYuVJiSzHkuJzexqXIjrwPkMnzCKBb1HCBA6wW7JQvyKHZFfEcgciTusP7QVN4Elxg+aSMnsI+TWPvjYx/oTDbiMn8H33B0MpOBUMAAAIABJREFUPzqT47nhLNtQhNjCF0Qts2bnltk4rwftgWZ807RFXHgWdna7kLmnj1mBFS2DZ6FIHtNoo29HJvv9z1IXaYySdDo4bWCBQBaHgNWsUW0gK70F/X8yJLfbckxUm4K1KxDtyOD11SJ2l5tS8aIGuaJ53Lrqi8OC75g3v2SrdyV6SdcQ6xrIu7Y8th0fTPKUASgvzONqxCpkVH5SIlfM7vRKPsbu4HVbGWUbduErPhVlRVveaZ3A8sgiKj/d5/aWHjrVL1GoPhZFrQyalXVY9/YMpmbaRJRLYtbah2/LK/RLmsiSqcM3rYHagmayf0owNSGJXfcLuZsWT96ZLSxUdkNVUhaVT+8IPP8KxS8j0T6/h2cLP3NoejNy+xv5qzqGsbeHYRa8F4FAgM4kAbaSq1mFGcwRwJO7bPEXpsV0PlnBh4kqVuWK60TmRw5l2r6zuG7QYZhhGA9/7SXt21Vm7yvFbg3410/jh0wbSt3bWdfmSrvjePQGVxN+uoZ+1wYgZPmRE+l70BolT+Xja0Qm5KG9eC2jdK5TodSf0ap7uFoeziifMdyUzEXi7E2MQg+iFpvPvqfnsa5ZzJS0zwQeeUhc8lG+X17ChJZuGiSskV+tjI9UKvfyF7Iw0YPpi69xqfoadXUXqG//itl8R4b6+ZIptYieoVLcu3qNNybDkHDehKm2NZIXklhn8AXLF6M4GzSS5dq9nIu2plzxPopFg7k50wfDQRJsr6yh/4s49s/xYd+JSdinSTDqWTMBKh+wfnObuTN82By4isOfbzDtox2P5TL52VrN06ez+TRWlRV9e5i1v5NeTQGLXobj1taBvogw0n++4qN5jhc9UzmZKMv3dmGiFCO5GK7PzpNKvDkVTWhlC5++dTBRV4MPcXUky3sT3RpGz+Icsl3HsPFhBEsjN5JXM53j7i3EVk0gZlcUfbXDedy6EK/ta0ncvZmg9SsI7TzPBvODtGWuJXDlON4xhaze3TwTdeC4Vygj1Adx85Me49/O5cK9SWQG+SOas4L87QX0jARn/bUcM1JjjbsyM//sou+jI+4VR8k1VWQ/j1iXE4PGrzAWZ0GxYDNjaxsQljJkSMNFhCaVo+j8hK70OVglHKTN5w9Xb1XiM/A7VdGhbDIZj8uvPaxI2sFqhcFsYhYpAmvqzxZR69TCMFEr9h5+g/VtdyIXPmK84glWPeyjf8xmoh3EOTKvjS2zDLjy6CCv7izhe6ozFyNSeK7SwDmJ+Uy+0MV8f1UObD6H/5Y72MYeJczK7D+t6v/nkRA0YSYNgc+v8dZJheKKWyRPEEW/zJ0x+o58E1pPjNcXPO5Go9QjzIv8PJ7pAPEFfD6gSP+oL6SGWvC3bzCmsyyAGrqVncmTb4SJqwnRMELtuRSTP+kjl5hEuug6PmT2p9r4MlInroKsHt9Vv9EjOZ4OtXJuuKgSp3USS2VphLoMSfnhxq9PrQxoKyRFXBtG1YDyHkCZFRoe1CXs4l/dZ/S9qkgQmPD97Hp2Kn6D4REY6AmjK1OLSqeAtIrfuBr7UCoqT1BkPt3PngOfWHTtDJOUNkPfM3QkQMwQkHIHNhEy9ShFKtaM65JiW95vmm2t+OL7ijphRaRkO/jkGUR3nxwjC4tIYjHDTYupGzCT7vcl6HrIkFWUzfnR23FoMMEC2BBZRIL+SlaUlfGvXYQDSgk03o8i6JghOnW6FGfpg/hyPjEFA/FRiJmsI6WqCdotUGndQXWjDZnVNwnQUaEkuZL3DKNXs5kmDHnU54iHhgyZEoWY1hzha8IgDkTJEmEykkk/T3B7aAN/eE1Tkjkvhr2GydNRu11OwJMoJrfIg+wbmk376EGIbO/fvIsu+v9PiDWrC+BJzhV85wygM2s0V3pFMMAMd10xmvXkUWmWZICYFfkDyplh0UR0ZTsRfzuQpgHNMUMpuH4dRNVRYgllGwX4NT3moZk1Tc7BjMu4i+4zE1T/CIPUQAJepeETeZQ9blWMz79IrtAo1hwPRG/PZ5xi/QE4c2QjN+xC2NmajlL7TDozxKmQTuHar5sYNmmjFjWJ6tIcRG0VKCEfgHNfXuIxPpRTUbcoqykiozKCMJ0FVFxKo/BpFaVtkfwsSkHWfCNhZspYfsnmrexeeqXuM3SULEbuOzB7d5MmOwGCYjksM67ikyVB8YhoMmV1yVMejPjffrxVuoqOxCMCmoW4tHkI7pGbmFJjQkwUOFPI4lVeiH/s5L72Ytqt8lmad5vuLn/coopwu+BMvyRjjLNPMts8iLPbXJm7bjKY3EF/iCkBQ9Kwf6PLpikDOa0+leTXqtzTcGPQ+J20tyQy1iaAZOEWLiTnoeU4jFE9VuyqlOG4bA2yTekIrTpFVM4Vzn6BYQXNKGqU492piFplCxcUrnEJBarSvnAy+yPyXY38bJDB7sVu5k48z81jm9hQuZ31gggaheeTpw5n2hv4nPSGnYv2M25MNdW1xygvdsZEfzLrc2NQ2LiZD5n/OPMylZVnQjGgH2+V/jL5yV+myjTh29TBAd06Noln06MjilVFHtL1DQirRaFo1IJqfRla2UW4Ww0DUWni1JR5qWrAs0MnQGo9luae9Oj7Iqbykj9dtmjTyHaG4qq0ibQuaQwSX2H18QdvI6Mof51GQMYXbAZIERfznekBO3gipYW7mgQ9eiOZ+WA1+rWD+ST+FGnVvyD7lyM6Dox5/fW/7dL/EcH5qkuXQ5w8ilGcqITK3rN4jQjl39t6lEQ6uVTfgGtAA/ECUUyNhnCmtwbPf2mIN5zDNNWatuYXKLuI8PvhI7QXQaquDm4nftCzoIi6bzeZ4eHBm6IwbBUEhK77QVfvZNJu7WZ/+RJuioXTdvESJc1TwDiHiu1vKdJoZNSwIgTV0C2chLDJD0gxYcTCeQzfsonjo13oWvCBfTbKtIsMoC99Im3KzwgWf8frdGe8HiaityqXuN6JeAwLBlEjZG0uYrt0AlrjLJk3cC21xo+IufyC9pQnOPVTwVMgh+z0CdwfHEK12UUUpXoBJcxK0hktPZw3QS7oCm7xsE8Yjdp0GidG4X5+Dcqb1yKa0UiAQRyFI06g89eDed1xDNEdS2dNOSLqXRSq6TNWoosbGV/xd5jJtKQN/DNWYo6TCaZP7iPUpcVNu0Ca+mLpThTDvDqKAQqXeRp5hrwFI+hOiaJbv5SiAl18e1SREYogRnwdv84p4ydTh5niMyxlBbSLW+FSLKBAvI1ASQElhmPYPLQMmcbF2EnmslXEnztKo3iWr0plrwJOxRKoR+ylNe8LDySyKK8PI2DGUT64VNCZkITlhaN47orCufM44zXdOKYP2efG0DNjEG+0hzEpMIhnCkPp+SnNk8XD2W/uhVhKHk2Fx5no6IAgohnJ8lpcOr/ip3qCvgBFvIpDqbBUxE9oNp2pb1BQGURZdSRen/YirVPNzKs3mFk6iBORaTwXDGJszk9+zUzggesR3qqNYuqoJ4zfHoZ3kSxrlk0j8sVDXlc/pCS6gJNSgwkWE6CqVIhQ5T/cviUSvWos3QkPWdw2gm2VfXRUryP4xhkENcPxNOvC+r0cHoiSHbOOvfPn4trex8MxluS+tWLCg88o1TnQ2XCPCYbrONL3hvExT9jg+5uxEtIsKvuOmE0PA72mIxLni0X3FHRjs3gw7C8/Gz2Z2NOL+NEdiKf358+oV1xRHs0g6wkE2U+hQNuejgQVosWfMuTneoIMfyKvWMyeIlmeG1sT5CrK59Q9bBs3i0keFbysjSX2/QM+9Xen1K0ej973qEs7MOfrLGL0FtLRlIdgnAIjGgp59uUx41SLOPOtAUdxAYrKkQgGryZQr4YrM7ex2ns/rXJZzPgVTPVwA2SPeTBF7xzbO68z680vjHJHcqjmPSn1dxluIUVE+HseKWWyJ7cVU+W7yNsYsSx4OTlyUayQqOC39W9OTVzAGu1BOMS9p+vTOBJCs5CtPc3IL7k0LfVj999C+lwsuSVZiHWJFekNErzChGNDFfhu5sJ3+3aSpvfRd3oJ9LOgzSsSzfZYhLtLaHSTJrPxPhobduI9cz8fvoZxauYh0rqesUziCTnBdhj2ZaGptISYj9IkD5yJhWgJEpoy9P6IpVXQxIvPyykd+4VjyRG8snEj//t3fP0G8UUtgpfzp1GS/hKZ2bq8aYtjQpEOPatfsV64FRX5QdD5Db8vbtyX+MhutR2E3ar63+D8P8j1I2tD9q115FllIHNax9HeeIqervWIfblDbfZ+9sglsV3ThBTnjWS2FeEbEYpQ4wYi/i1H2fEme1c4ErdtLUJuQsgKmfN1yX3Ejjuy68AKnilN5ntWEgM/HCd78Bgie2sptw0grmUkr04OwTF+IWJKxlR33KF7WBgarxQw1DOiaagMEh9zUMxvhOKDrDz0lG0FeWR3jCR0fhAfQkXp+3KPayrQee4R0yzGsKX4BeMnHcHFUh6PDbnQs5k6IzN65a142ZnOPr1OJkbcIGJqMJ1/mmBMPLsEQRT6zufwpLVorSxEr3Ul9FdDmHDcPa5QnDKPpKlz2Nt7EcPfI5ms+Zc/lX9ozTfBNOUK1maPqKjVZm5nBVW/LrGr+CB+cupUR23Aq+cZ7RbwtlcXqXwVZk8bytRL0hTujOePwgg6THUouNPF7ZSz+AbOI0tEgZ2llYgM6Eei8nJy3j6kelYdRec7yXfyxb6yjIHkkSu0Aot+okzVT+GtqDNlYtqsSvvDQS11vva54d7Vx0TXXNaHfkDMqoyRe3fQ/f4kf5SXoNHjQaBKFk5nU2BBCyF+GUAa0pMH81bsNXc7xjHzVTnZ2YMQdO3FU/YZVk7fGDzAggurxnBi+wMSdo/DrT2Of5Y/WTH/HJNWT2ZNngWZvxIZP2sZg95dRd9lKo4WWjzdVo2VdBJqzlOJWDiWk/YHMFKT4oCBHh6p33H8GIuYjxcG9VX8GjaCH0IJZOwPYtTj6bwxHELsHHd+35ZgRNd2FrxfTtHuXKaMtsesx5A1QZp0moxn+fJEHuw5iWWzA36vzbjotZD9rt5sV/fDo2gkSj9PwZ9khvxS5In7JPrpGiJd8Y717nNxbF9Nu/8FDHK/YfnXgDWqJgy4m4v5bnU6zRQZVvyeP+Jy5FsupCrEnA3bPYiLH8mMTk/qGhvI0ZmOdf1vtjVcJ6F3IsVaYNN5j5SUMj4ETSfOvJjthnaYHzFnqV0VhemS1AfX4fz3ALqzt/KvVJbBaQ6cFojzdqcNTlcuIZpTi97AUCTLEwi+JY3nzv4ktcpgX72aoUcnMVfpD/ZTBVT9MSK92ALDXZW8OVvBSPNe9OTzaJfLo9lIneiy40wNGo7UZ3FkGjeinx/IqqX6nHy9gheHj3LjSyVbZ/tTJXuVGUdS+bB7Eg/0bblT3w83eRuGa8lysW4u2ib9EPY348zlLXT5uFNj6IP0i0SivQMYkziZgab7GNo8CvGKCkxfv6S0WAmxW87siN9AwUtxfJXqiXctwultJ1V/v3Bg4VhKS6T5ldPJVIEYMakL2Dy4DzHDL1S2WyLdl0WDpgTqKa/oFegj4XIJp6/uvFAZS0VOAov+beKW3k1uz3uIb/M/1io4Y/xLEiEVOTQ1lVB0TaExvQDLG/c5k6FLjXomO+Lv0fVFl0W7bdlSI8n+pGa6rX8jVDuSJ8PbUTrwAonwi4z5eQpTq1AKpV6T+FCAokQTN+doknH2v/coyv+IzYdjbyke5/4iWrYRmUnvOd5YSJRiNAKld1wfk0d1uxNCy+O4lByIzM31SBj6ICFpQvPgP2gqZBL9o4mFMw8gUzWXo49XUjz5INr5y6kceJ3YxxJM7tuIfWIVafciGXzzKZ8NUhgl1EejoyypOyLYmTmHkM87ODq6i9tlHvjX5pGqIU5PjiaWcyJIaL9EsYInqzKl6Nl8kMN7hnD9ax/P38Sx47woje9f8rs4mWcdZWyaPhmRtWHs3LkeMpdgabOVASkHUfiwm/XyGcxWrUTJZxC7OucyIe0fy0NOobVzK75iF7iYag8Y0ksF2uPvURtoyPqieQiiLfE+do0DFQbIq3ljmnSFv7c3YXuknvGTnqJv6c6yk+kMUb/CwfPzUU3oYlbdTNbWxLK58SW6f+czN/ApAld7skb38bhgL+f+OmCo488rz4UIbrhzbMtQ7u3bwuXrL/E6sQiJRzeQKLtMu2sZvJ/Cv3ZHjBfuISV/FGZtssirq6GrYciQnEGYdvZi9P0p9gF2zDW4RYOkOod6qnB9t5jFO+RRd60keMkU1o22xzE1DMfCOrxSP5D/+QVDfGayuFWPK3XqnOhr5P9j7y27sgDb/st90d2dIiAgIBISYqBioKKE3d3dLdjdhWJ3YyBgIKgoIQgC0t3dHde8mA8w95uZ+/mvefZnONf+rePFXme2QgQKvhkEb6gg7MFkJGYlcjnJGz+5SxhuXkfpvD/cvKDKgLAFbH99Aa8b47j9Lgqj+xmU1qRgajwERDWQda6jQFDF7Q4reu/NICC6iB2vnmIic5xgyaGkqy5laHguey1NmGi0kaWFLzBQieaQ+kR2BqvyYPcYpsyWQ7grAoFAlSvCejZ+Osvjba850gxWQiErYv6he02Izo0wOj5sJmPSTRSs4Fi+Dmf8bjCx3pX1Svd4YnMKG5uZ2OjEIzjuzY2jXlwaHsYWu1ssXW/Nw2eR/FYaQljZRja8C+Sv3zEy3m9Bwf4uOsssWTFXinH2r+mYtADXdd40PM+hLvUeTWVD+JV5C8EwZzJ8gjkrr8Or1xmcHe/B/EniPPRUYJHyZ/ysx/HYClza2mj8vROpqsGIj3THIT6WAjkh659c48HF+SSJWRIq0GTK3V3E73/HpxJTLl8t5XTaZy5e+cy2wRasCtXmgMY90uOforN0IZfaKyhf9AiDmhnMVJVh8RJjnn/I44yDH+pe5syoz6Pu4hXyTpci5joHR7dlbLMfwcDQKp6NlmJV8lxmy5rype40t+RuUWo8gjeDTvK3cx37rqkwd9tTDvVG42Q0mcv9opi39CXCkhCEvvNZWKSEh5wVMz0WsQd/gmYZsXBADGZjThOl8A8x+zTm5nSzJVuFtOr++ObuQ6dRikM/v3LqoTLk76BI3gvr5h6yaz3Q8J6PomkevemyFHaOR6LMjI4LX9BvdcCoXJffj0IIlt+OyItYnn5I5uye81h8ncfvUBvCIzeham3AcI3HePvtoEpohO/+16i2i5AW2MxChUR2KWvisu0Q1Z1ZtH/rZN6fhVjsc6JecICGz6/QSShlddNJMo0TODdcgjN7Yrh5PJ0lnMbDJIN/rf/+26r+/z2SktpQPB6Zguvk/FtFe+N07otXUaEWhIOBBy9CO2CCNia9OZinlVAx4xiC4u8sXLIKly/bADizQhOxK6/4nKEP7RD8LhP5TkWq/yTTZSjBt9pOTOZd5Ft/U/LEW9HxlWXvVXcCjZbQ+acAh2crCXyVwm0LRRTfFhBScRJV6S+0BG3DZq01pJ+nS9cVyaoMMOrH/ecN7PQvpeX0Td6KOfHo4ToqJptyv1KPTRuBpYoQ+wYaJtP0WwTLVjkuPb+Hs4YZJ956MyshiQ1qYUhs9uTSlIGsP/iMkgwQYSoDxDeS0JUMqDBnZBGFkssJl1/IQdckamsnotIrS4H2JGZOOU+pagErB+nz8/UmMqSmoGJ/ALXK4xwrt+bHwFUUOdui934l4c4L4BLUfJiMte1oPgjyeJZ5iKDIMGYDzW2Z+OoNIGKlJ377fpKcA6g6QVE4Q19sh9PzqO87lbuNIqSLhRJ9J5QMcx8UGrM49T2VVpUSntocRK7FlVzJdu4m5BOelcDBbZJM/DibeLk9VFq4IN7rzZJnnVAhz9S2bpIe3ADXg8SLnkfLJROEGrwW6WHCHBf6JZXS3GNBRcobhF6TURwVSofLJ7IfBxI3yJiPhWPBCpryDei+4M4IbV16mn7wS2wM9Z8LaEgopIsJLP1wik45EaAcTZW/VMfcY2BTf2rUqxDMGM2/9i4UnZ1QqYtEvLSLEXP2caM1joX359NEK+1fGvn45zIP1nii4jqfuhtq9EydynOxKmJjD3DBcziV10W44fYK79p4JhVUoxkdj7X4O1xGWJDJP352GPFYvwOHXUfY3G6JiY0uSR9iMHP3Q/zKNbJE73J27lFmG0xjbHspz9aNJvf8A2SjLNETrkLuehDc3cHpxR/ZbgNxMqHk/elBrDUQ8aChcPEmp2uiSS11Z17DSNREHyN54RyDpX7zUbkTRH7Rr/kTsYm6lBzZT+c/TeSfBaDQ2EVgYS9zdFsZnH8HPQtN+n9S4+ukUWgrdgEpDFRxpp+zIQezBjFnnxrCf/cJ+e1CGivIHVWJ2YdP1Mzcip2aGU8qHnK3bBHeUt1Um46ntagXV1cFAlQ/sCf5NmNHB1D7pJkNU+Dd1V1ovPag0GwvrL6LnutVSgzeUf2pD8LeERiIpSCT9wALNQFozSMtOZddooPg716EFU18bRmMqNwEjstfJHbaHfaW+nI8R4fjTupUD+9Peswh5kk8YltBAf+elBEtp8e/KZa8O3yO8l41JsjFQ0Q3q5r7UPrzMwaGtQwSuU6LnALK2Vpo9G5hnM4A0kTyqVST5VVHX+QaVZCJAn3tCHLaPHjfpcqKsCgyZLSZp1LJrlk1XDXwZ3mQGHWlf+gbspbuw7FMECwnjQS+aHczI9mAhJFzUTKqo8tOHMVuCbQn+fGonySxSjJ4n85GpdMTz6oCUspfIPWy+z926f+I4FxfzURY9MAT1xt6iJgXsIRIQqYORvTGNdqc3jAj8SQ/xkni/OYAQeVBzHdIoNxtLl35olSU9eD37BuBM/sxu+ghx9Q8CSyWYOewJDTNGshcYspzMRcqDNKx1rzHvnN3uPDoHnEy/TlToYDOK2VEI2fxcQhULc1H2FOGRpIozywOofavFzW5n5zxi+FrST32sZ28PjuCu/2kSfPezfuPEXznIrYFreyrTOf4ialsPxbEtKnbER8zmZkBJ2grDMFmQQcfJavIaBWy7lsjuq5LKfNV4vCCaRRZLSLbr5uwkgn4rz2HUDUSuIaYVhibZHOQHXiACQ0JBH/JAb+pDAZ+Wh2EBkvGNVQT2uiOqc4fVo6YRs/rOQwwTOK1UgPqMwdxsF8vAz96MsxpIDkn51PQlIjHsXMoyPYh604Rdx9a8iRJh/TNq0BPlrcn1uKV/gmz1HekZfWHEluoEAeLJnh6GE+3+9R2P6bcWZ2IUgvE8yMJmOLGvbRqxrR1E6fRn9jfLnzovcaeJb2cP1nCqNzDFAxTZkrXOWZLD+SQ9ilkDMYxs6CVW/lhpMrL88Q7AOlsL1YP/ULnmmPMnzmEh6oL6ZaHwrwBWHndoey4NyZ2zvi/e4q99SXUdQz59s2TDMFEJprtwD5hJdgvwNI+mv65yljqtPNAdS1zRw0C4ENSG4IHUuz9I8Dn6GiEUSMQkfJFRCkE1emtuBRIsPKfEzk9frj8zaQhYxias+bz4NAJ3gVcxNPSEpW5vdSscQVHC2ZuK8BO4ENm4EUGnD3Auf23GH3tJE++fWRoURZ6LW1sXCzKsY4kbgsDMP50nuNrhPhanuZGTiXlxv1Zwx/kKOT6PzVy1FWwk/2M9AZ9BJlXeGVjjv/Q3fzY5MP8wih29bNlWqARL2TW0/eGIuGTfmL9JRxD1cFkCN7SKSPLEdNGhG0zEGolIggMRLCqEBwz0WcUEgFjGd1agd3yMSBzkhLAPRFyiufQ+NqRhqZEetTeMLlfDff+WlA98DSmf3+xqKiNO4aDKF83DVUTmDxlFJcMw+gzPYh9rZUcffcXvcxYTnS/Zt+GbrK7K3HwFef5J3nsHlsQN3wD9u5LmZd1G6u8Qdhdm8bfFljZDpab9vOrMg9//3vsbxQi+FlEv9Nfqfw0n3KiOMZmth5Upi63EdsOFfIaG4j6C88X7uWZMA2d/Fp6FIQEOFZSWWDMz9YclA3F2RhXzO5hb1Fr7qYhbiLh6BGV/pmhs66j7PGFg+HB1LTokFXlwoWiu2xWf8W3Qar8EC6mTiEUnQcnaXRORySog9W7PSBTic3XipitmcwjKzn8K35y8ok7/vuWsqb5ECJy2ihLW1LVXoDryAl8XO6L4woflM6XUCAViPbpFBY+2cxGc0nOtikwWWIXPlm/qTdvo1ZLG154Ms79GvXWnni828B4uSEEFR/HxWs6+tdK2Kgqxdf8HuR0/dnywvR/g/P/Ihq6msJjyRUs3q3MkcR6Jg+ZyuE+kzAxs2L55zvEjpZFr8ic25n5XHZ6wFmxRbRn59Eh+Ib/PUf4c5fQMFkkA38xZ+ZnAr/Z43FzOM/sgvjwaDImN6zpX5XCPuMOFj1bTc5sWdZLfmDKThWmno8mtGQtTY4SCK/dZZ5aKntULrGg4jIemXU0BMeR+bSOQ6mdXM6ZyJq6CfzZ6Me1uCNM0VqKVEM4QyOqGLnQnfDfASilqLBmrytzw6SJFpdn3s4FiIfcIEdzAUcnabLzWRIn8+4h/+k7u20Hcs+8H41fT7DjdBvj7QsZNFGI17pKFthZkZi1FsFBEUZElXBm7HwGul+iz5tg+ktuw+XnH1LTZcm4AA4hv/F5/YLMWUt5tlSK9XHDOHt3GP23JBA64Dmlw5wRZpziQ3cz/ldPsHPvOzqCVVlxbzHaWbOYZjyQuSNmoNPZzoj6Hi63f+Hd75No2+6nTMcF8pqorTnLxag9bNoux0u/ZF4u6s8iTXGSxJOo3W6E6kd/lt/6TndDIGVXZ3Fg4TESxiwg0nUYrtJF3FodxWqpO9QMf4swcANbl7mgKyyg+O0jKD2P1Z9VSKw0Znd8DjkTVnBPry/T2Mr7gH6cGb6a1ycGcbpgMPvvJDG8aCDzHb3wrvbmwtQxCPpps99dE5vpNiRpe+KX+5e57zpAM4KTNwvQkgCBQADucETjN54WfwiP7sXsRAbcjGOj6TcJZXc0AAAgAElEQVSOVyxDpcYQlceHWVYVh95xK+pOwf0qIRqCW1y5bcHguljW1j/GQjie7C5P3MfPoeffJsZOT2VQyxmU9ATM2noEHX0hYkIT/jVa01x2FV3jRtzbpyLnVYp69TlyXizlq8ovPORO89fgIT/+mOGbWE9x13Gu7hzAgo+TmJ4ziqtbtLhqcIn7/ht4a9WNwgUB3StXoWVYi1G5GWk5SfjKjWBq0QhKDYz47juRMzaRFG8UMkXiGU1jdDmWO4Ed9leZ+qCBDVgjV56Io3UDCqUqxGvVMbY5hUjlsaweWIrfe2dGO0rQUXIeMcF2fhlPY+wDK/peD2KEugCtBCELLruCPcj+cWWF42eeD17NntX17NnojWJlCC2tA3EYF494Uh3GCYmEmCqiqJ3MvPEy1OplsM3FkNRTcXQZCnDZvx+/XX747VkDfa2pjl3B9dvadG1tQXiigYwpAq4rb6ZcqMK/L1GM8i3EqhSi6pyIcdRlYrYnMcaPCBDq46IrQo2UPmf+GqAvFEXdIIQ1Fhf5K3uWwJ/j2Ztux4BqeXSMnzF1YSXjY7eAxkvyS+swDz3DJiM7xFp6ESxLRXBIjo45ybjlHeKfvBxmnSGktsXglVnGo9sW+J2awKSm19jyj1HK9lzLLmP0wiss3HyPtHN1BM6VY86bco7EivA8KIsx3ko0BPYSo7aem867CO7dgX7xYJTUnmNcvwf/YbEsWXeCHW+s6Di7gtlOz1l7/TYK1lm8k1Yg1fclz0b1+Y82Suz/C2H/P1HdU4BdnRj7TIM4YzeGBZUHmHLdHa0/y+mMyqdsqwIlLyzoiQhHQ+sk6Qor+bxiKUZ2G7gqVUfINlM87n8iZPRwoldVM/XGXdynxWKWc4GkrEwMZxdwvrEvI2cEo7dqCr3TfBn3O5gwvfeInMggRDcKb4kMvAOSeJcvzz/nHCa3vENGG7apnoA8J2YFK3AnsAD1PvNJFHfB4qsss/uN4YToLhbo6GBFGRHBW/HdaICb2C+snhbwyeszgoHW5Lxtxae9H/pvInjjfI+bu5W41CBEPzUM6sfCZZglsMdGIYG2jSegM5r2ulJskgSsL9/JOSM7MBkNWUIwhjs3m1i4RJ5awI0y1l/14fvKbrxbehlaKgLBWfRvBELE0R/SyofEvtzZ+hVXEyXQXgYf+8GV1Rzbe5+W2JGIjzrErFeqjJ06jedSOmSb3oZ/vTCqEIyj0UjPoDLhKX41UbQXWvGpKpy+g+y4lG3C8vpQPgsukKpvhXuZDzIeR5mVZIzTtS5WekVQvkgT26OtyM22JWrfZobsLaP6dwcKysORGjUSw9RiVq2xxX90HNLtr1gQvQeNaUbU9t/PcX8I0T/Jx4qLeNru4U1CLScsLenbdpNvIru4YdkKRikMbrxHx3BVENciJmEoLfVCnH+Is6xqEcK9+/g3VAlLG2kwluG040pGWntR9u0Gl91SCc4OhFtSDJMElbJleJdXMf5UIcsXz8LwxGkwHImnpQX7zxeRmzWDvyo7yH6by7OQGTwpW0fp4Gp2FqYweIcnAYsGorR9FOO6DAjvakTRfBK3mpdyq90WZk4k6ekltsdfoK3rKxeOK7L04xhcVGJJbBZHpvYOGQog4TmaHEktemRcGPDjJ52Tn7NAIGDYTSEkrGWJxXIuKHQz7YMJX2aIc14riL0fT1FbcY1TyX0wmejKHes1aGrac8TpIm5q44l5PoWEfG2a+x1A8cwVBpjPwlxagpf/8umv6U32qAfElMlSYxfDygmHcfPJJjlnJdn1BZyw34ViQSj/3iQSfukoL9Pf0e/bQH5VHWVL6S12ioVwcnAsNi0nmFJjR4GhG9azTtFko8qfDb0saH2O1O04Vp2fAdX2TAuLwjpbjujap5QHB+IbXAysxF9BgAPQEz6AP8E7CBPPpHzJblquKRI4bSiIZmBro4NG3xeY/u3BpHstaN1EVqaBzH7W1KrqovLYEuP2P7R6apBpK8kHZRfOiV0iMvQ9o58KeZb1mGiFQmKXHaWuVhvTH69Y1HER4TZrkIDs83k0mVzg/eBedL65ILu1iblxtTjLtXNaRcC3OlG8JbM47WSOhEoJO7PnIrFAltavCUiNEeWyqz3CF6PxTvvEtev7cEs5wNSyxfx6/h069Dn72BDlyzOoifrJJ3MJ3CvfUqveiNSql6zYmodL/9+krPHlQfgdtKZd5uZEH84AwweoIKI+hulle/6rnv5foEfYw59w8FZIpXa2LklVzwkv2sPC2j2EGg4m+elJQt2iCXObS2RFGK2iHuSUBeI1wBriCxmjAEU5zix4OgfJJ1fwiHgFDfvY1Wcj5mvVubX6AOPO+XDUvBKjLgX65NbjaDaSxVXJyBl+ob1GhqaGOPRtJ3Ll40U8x8+jr1oZdhU3yStyoEv7J5Rd5HKWI0NsrEg6v4E/2VVce+uB2o2fnLQcQm6PAF0pIXnqSYy1fcO3T1eoEWvmdt1VTod/YZWUGr4ej9n4PprOq3s41iRETkTAlXuXqFseTv/TOgya6AbDQFxFCUXRKmgUxfDALnpcTxCX+JfNQ1aweVU5i4c9w3XvMZZmjKREZBMFiYtYN72SVXKjqRGvYrrJbrL6BvD0wW689zpwWbaXVmDDszS8DPKxGyLJm0ZnjJPHculYAed/T0BM2glpvYtEnOzlmOskkP5IWYcmlb1yaPwx5YMpUHQdwYD5LBF1ZJ1mOh5GyVAyhQtKnZBdyiBRebRF+zDhQyDjhG1oDujGqrYEYbY4A4v3ku8ixbuwKjomiTKtsJZPhteoVOuHRucq7u5Nw+GgBb5TYUWvJs2LWvh+vQX75rGk11hgHOpP/rgwnOUv06q0g7HFxxCUnGVh3oH/+xEZz6FcVoDB2xLYdZ1XB4ex+eQQfoWW4z1JCz1J6DPejd7vytT2WCEI9sf/0Ut2Nl1gUfIfIocPpO6lP4csTLAMDScqQ42jO/ejIdBnIprIJ/Xj9CgF3kqqo/RPhGEooPqsnbyU0/Q1WAaxr9A5uo1OUXFE476SqfGWOr1tLOnvhJwFGMbUMzPwOwPMldDgBn4t2YxRfMq6fCFdegr0S72Pi6EjQYV9kNeNIaejCsemDUi8D2Bstyb9RBvo7CNO41sxiqbpsyBcnd9TJvGko4GT8ucRXmsmz9mbrZ5tKEfPY+uk6exszODYiwYGuHmTaXwSw0+PUBI0oy0pT5mZJwv//KVC7DZO5b+ZMekKkulxFFjoMrxWnu8NQUzxyeHU7VXE/AKU4GTsKVrVDUgRjEVc8hYnFZaT3tWKZcRxdB6vo+mZOsLDfqwzqsZGcwGBOtrI6nQSGf2CWvqybdMmhnX2J0sPajgIdON3xA+/JZfwa0zm8gYAV8h9AWSQ6ZuKVnIgw7L+4OWzjdVpbpiYq9KeoEf2QHBueo97swLJ5srIJeUxXSmLClUF5gpTmVO7jnVVilglzmSqiZDtRQ/ZeMcZ07UH2XN5AoGHoPriHQoqZ3NKpJ0XNZlMFczkzx9lRrjH0WbfhXbsG150HmJ47x3u9+lka3oyfi7TEIuPx1C1BVR38S55LkaWmzDJToeIe8jbpHLK6zzxpYbIRH3jqrcL+c37OV4Wj7LbQMYJ40jvCeGXuDpTcgtwWuaDTnER0iYDSPJ/TeTUmXj+LUYoG8NTvdXcE7PHM+L/sObjybVbftdGpTFLbh3jO+PZV/mNwdYC/ux7RJVDLxbBAwk1FKNmeAktk4PwuvWMQRUllNjsYdPlFoZovUSoZoJ+XDtvXScS3f6Qi5P3UBkziPEjS+nSGsBTdQU0IyXwd5qOo7wZalKW1L64g2JlIh5SzWRcfIjMqOF8KviKT7w4/bR+0Wo6A3mLBTR9m87jVfaUqLxmgLsbM0Z0M66tnA0xYnx4p8aric7MVZrPeMNWVrk+5N3z70x1bqDy9Als3twnQM4OrY56kkpDEXdayoHTl2kMHElbiwLjzg/h4ZVnlGvmIjm1C3nDj8ibWaBVbUjBCEVC80Rp73Ajomo2Ec8D+fwlAJGcLEKLI1n6IYKOiHgK9f5yxSuMH6KtPJ1wC8FPIT80TXGqCcL+2Q+MM15StEmP23ENVA9dRct0I67db6RVfDrKDhvYJaigbkAqKXkybBXpT/i/1bhNjKC9PJgy47GIvAlBYDMO1bAq7pp+Z+1rFyzkExme1YfSeH0uTJtJYnoQul8HIBsfQ4jMNZSa+jIz7RXzrLXweJhNzOdbVA3xZsTV60R21+EuuYGAA2vYVKSBlNNDBB42nGh9zqmWFHS376I5opwINxlWDHekTmMD5Tkp9DmtjTxGTNh7mk81P3lhYM7AiiJ2hTXj2dRJfyVLVE21qM2Zy/FFa/len4uBbTMp4bL805bCdtds5rXmcO58NZL9A/GxG435191cVkigsPAaDRLNnP+pio7BKG6un8d3sRz27z2N292rkKSGstCai7H2VCu5o3XtHMusu7i50QWRlyUYd2kxNv8X8yxHMeDgKYaOk2XX07e0x31nu6MUpnaG/FDbiahxJMsOFXImWZoBo9aSbFTFFQ9XtmuMxSc+kJjWIdwpGkj8HA9GJstik3aSOJ9H6L8YwbweC3xDf1MhOYl6RXn+lFsx6YYmqTJpKP4bRd8POoR4feOciSKaxl64OjuwIUoCzUlOXDSqJ+jAYwpUtSBqIAZqK4mp0OJNcxkFn6q4ZHGfzJAk8tRF0fZezvduBzYs34T5l4Ooux4j8cMl3MecZfWj18TmzCKn/htOU94wMNWTEKl2XB3NGH78LG0Vlzgx9hTT2pK55/MGn5tZ8HUvZq3XWTV6KzIFPXQIPlIWdwrxwfOwiWlDoFrNsaH9yawQJ/RtLASt4r7KIRJb2lDVbWOBTiPj1QOZLGnJ9twxNDvGomkVRaOdPmNGujP9oZCDYp3IbZYlyECU2dkqjGnaz6B2J/JiffFxm0VL5XPslZ6zI7U/M/sV8uLeboqmBeE33hC2hBMT/x3dTUe4fe8GOgbpSA15i9PPfzjni6GTEkfhxDAOjsxjz40CasodGSFji6n3TmbpiOM5ZxlXDHx5cfsfQe3DSFQKQEkox74731h/7xMV4v0Z7izJzkxHrJvz0S19h3GLOx9zLTBsXotEWznWas4MljUgpfQEJUrFfP7hzfdjqxgqOZWRzrKM1DfmZrcq7SkR/9t8/Bd5/vicX9CHDmaMH4+L+ilyPppiKLuVHm0/HLyK0W04RlXALxati6LPkOOM2bOdjekWhOw15++v/WgovMNx70OkU/bQIZNMzay1lEqPZNP5r0gfk0C7Ro7B3qOQ+HqS773rydMvwtnMnlfZ7ZTbZDPfso6uq7Z4NI3jvWA585Ik2GwMDf2NGK5Wi2O5JYnXb1G8YDd7tGfj+2kff12kKVzuiKbeXLbOt2ftmBncDr+Nt/19fuy5RLFHJpmKBsyTk6DjoRC5UZ30Rj1BTL+R259j2HlwC9/HzeJm8m4ubR6IbqsV5m6BCJr60rc2n8WJFxDmDqVtbARGwt00lcZhOPgvOy7b867ZAPt+RRxu/UbhijZuXpWnT9ADQsMc2JX7l+0yq1iWd54BRqlIlCqRfusZE/x2cshalyu3kpAZ08Qyn0Aqak/wtPE6N00PsDQzhcliRSjIriDiRQX4NUNtG7IOplDViXCiA+k/Izlmp0xGvS+jf8Twd6cd3lHbKVp5A1ubK2jJ6OO9YQv+hhuYd2UN0wMruS82jsGFh6mzncFoiXwk/qgTeqYDuz77GBurguyn53StjcWqOZVJFg/YbXgLF9yQjQRH0Z+MnzySoPwmFKt+kCfZn9keKzF7Usi4nW9YmnuNxaZt+N+OwWu2GNHqo6nLjCb3wl485h7DbUIP5eYeuMoIODn/C781C9AY6YOHmQmxirWo/ErhrGwfZnQ9x1CQRbJPIZvl3AldVsgGsV9oykQy1qManaPvWdH9lQd/Zbm//ANVb77gmR2M0979THtTSENyPBMPHSf2bjwr2hXJ83FgsYcxV6RaaSs9zph2fczj1zJ7sAVavgrsXV3D71cpqBcf45FQHSe5Omz0ZMiSVOf9qH18DHmDa7IGYYdm4evaSa9EP2zawllvuYuqDhM6Xt/m1s0lWI9vod/mh+iUv2Zn3wUcNVpDhWwlb9Reo2fdh3tZw8kLuIrThUOYVr9heF9PYkoKMNdrJ6tcnbDGO7xWaiNgyHfeX9jPpfQ01M3TKOtnSd8nLQQ4FVPvNYErU205fPEWiioN5IUs4LXMPGZ8CyLSs4xBHdVUpxVR0yqJpJEaTvJVlJvp4J5vz4HUXvatf0595EU2Rx9BtnggqX00sbtuTYOjOBZ5LnyN+0GN5Xd2jJhLt/FBWqfEUXXwIXu2HcKZ19irl9D2yRmJgmPYx5cRp7iDzc1+ZM4yJUHek2jtbEYkZKKdW4joSFH6P7nCX7te6sV9Uf2rgVxJKSXCFlZuMOS8xUaGK4zigGQIUtlqzB1ThURVKouuPGG4hhpp3asx6VJHfvZPRFOcUar3YURoIk9n16OuG4hPmD6TtJ0wrKpDdshwdMuS2TdDlZEzPNh2QpI9k07y8Hs9ClbW+LZr8vv0er5VHWWznDIeiudRlfKlVOkjw3oV+C0izbRmCTo7pvC9KZ+lmlLI6liQb6nKsoUbWZU/h85n9li0f2Nlp5ALMZ//o436H3F8+B8+5FeTYsGKlKFUvDBArlKOLqdUxv77gmVJJ4+6BPQTT+F0RQeyOUpMMBZl+rkNuEVtpM6jCLt3WUzc94uT2RlseK6Ix9N/NPkMJSApk3PS2VQ16HDfrg5dvVpsi2vQuxHM4FYh+SObSTMTpfphNGNsNxH0YQXVmRloW63GTPInJblCfIuu0/4qkdZ18xGXvE7h2J88ebKSqIYyEjYvY4T6YIKWOjB0tAaREX4Uau1F48VDEjxUqGwdjWeJI8U295hvvIGadLBz2UbN/Tp+WMhxtvUrqqVjKJjqxb6YAqoHTGD04CuU5rWgldnAMEMLdDXg4GJwC1+E//6xNGZIsNhEmuYcIctlwfAYzArfgWVJCgKTeKZHOuG+LxkvG232jmnFfZEy45bPp+ndEny8pBl7LIQ7LyPx9zSjsiYYjc6/5K0xwPdbL/KKJsw0ESdSchxf7mQxUsYT6zgVyutVkDi2BiU5F8bmreKBYQwH4ooI8pGhf340M4XVmAxrZWBLHyxaJrFnTCkZ2hIc0otFvOIrZ0xzWTpgEblVrzmkv4C2/gmUywWyf+8WajQek6mqgGTzF0bUeaOae5zWkbKclpzLt1lS6HctRVvvM+YLp+H+u5X2Z8vZfuQRjWuvIKhdwjzxCrKESsgbVPOtax1SkS40yG8grSaN4c2yiE29zkKDaJYWmDGoQ47VZho8t09i9AQz3jx4wTy1DBp+PGSBhYBopV7MleV4OHc+voeX4+X3G4p8MHyyBNvuXuxQYFXddTpkjqLvXkSpXDUpv2cw0sqexo50Pt06z5kJGiRu2cHFlHp0alyYGBXOiL4TkfjehXPNFfq4q/Dxny52CUfxHqqBx6V52N/pw8vjP0hzq+Je5iHce7+SHFXAnMnjmXZfitR9Uxjq18D14VKoSszmZP/ZeH8uZVTAEv4VzMTcfxAmdYPZ5VjJ8vQElr6owMpeHd0dP1jbUI/G45Mkr3pOX7V4qh2CeSv3mOcT7MjgMY6/AzihUohAMprASffQ7v8TB58V3D19iUEvN1AcVE6PSAWHm/XxkPvKwuBthMftYNe6+YikPWbr0hm0fmvg66PPtE3UYdLZ3wT66fFYRZnOUSYo/ghl1dEqBgZkY+87A63SPkh9S2OwSQ7qBUEc/nkGidwMCjWHMcTNHIXXnfz2Pc7GhxpIjSzArPwOFmbxjJf2xFQ6BPF9KUQH3MWgOJj15n2YkvIJ3U2/+T3yLBsK+qDRm8p1I3cc/7zC4voB0iXH8bI8hRGZjxj6bh+PUxbhlnSKPu0mhF97xujdF/mKOjfSFcmpecjUQWsxStamqriR0jZJws2U+K5RxnbvIaye+JYJVutZObYvrYutkTDtxabbjfSL/2h7KkmbwJRB7Q2IPvWkRHYA4nluBD+JxXG+HQ1lcrQ0jcNFGIGajSZ/e68i6+iASnwWbzqzUFIsJkXYS7LREER7RPFpyWB2WR4fkn4z+5c0qk2FmOlrExr3n4n9f/l/h+sBV/0+1z0na3QBe2pOItdryNt7A9HRTMXxUyOHnW0ZE1GEw+b98HAte9+G8cknkEURt1iycBZZNZnY6w3iPhLoVIzi6eulxNgc5dabO/iu92Rz8RA+d4hTkX6JVKkhGPUm0E4zpR2tdGgo0PTrAbp9/Al++JxxC4YgJTuKwQM+oWQaxPm4h8y7eZYcU18M5ndxSbOb2X+0mJz6F/W+IqyS6ubJ9Z8EZRcR4XuY2AI1zvVLICTxCdr9XUiKbWVDsSzBmd1EZN/Cb/8jpI5v5IuOL3XZx6iK0SZuvS5jQz6hNPUeCtpLaWiSJansOcJ/bigthvIfbjDAgW7jG3z/7EpE33KKu4VEAtPmS/M1spMRM3Q48jKNrlgLUpd9QP+lAf2lYylT6eRbfTmS3UWMHmRC9mgfom9kUppjgZz6b6aMH0+nTCsi1XoEaLcxr6GBrsd7yZpgT0VaI/1/fKBxhBeFv1SZaXSDw198WWTXxBVlEfpVJFOEGMPSFfna/RMrkWx67ntxw6yJUmk5Ot904J+fCKMMeC8my4C34Sw02cE4iVqePFlOeYYGoy1MiLQOw8xwPG1JisSdfUGnmzVJAjWeajQgYlvP9XN3GPLiOPv+DObWaS12TnrIGnk4/yeI2Q3VJIf9Zc3mjeQYuDI2xJq22QOwaVfAeVI9A+5sYnf0MkZqlLCkq40TKQo4WT5hsK0DalXD+dzzmK76cCb19eRx/15uDVtF38Pr2L/4FVLjNsFDF1BxJaZRDrVec3rPJFOs08ak9YOJPtFEbmcpDy1ECY2JQ2faFlos61FMScQvKYcEAxfemaSw+7kboppixOWcZlTTPL59OozawiFEVUWyIKQMC+OhLCi5z79udRIC4tkxVJmmOg80tL+zTW4JxrYWhNTn0VmQhYFCP27pChgjMETQdyjGYr9RbvzDramOODn149DeV1xeLMHxSgemDNVD49pVTkj6MXlIFt0PGjAxc6BD+wtvmvRp/fWII0tD+NmmRtWXA4huNOVpUSb20mY0FHXyY9NeCt9JY9UgQGmFIS+bBpEbdZnJu39yJ3AjE1XN8ZunS9Wy87zfNIT7ExZg+mAh+kb2+Kfp0K+zCE+jt1hO8Ue3NpYaHQFK6ZvRt/nOvooGnvRIoddPios/v7JligKNNXWUJWXwN2cp/lMs2Nk/DueKq1j+WoPSjy6WyUQxyMKOIo9e7B1Gs6FqDDdsxLhnmoO+/x2yDWz5rOFNk9V4fkc8YKOdIc1qrbSpxlKuaUNkcQV9roQxYt8BTLlFytRQro7TJL/mEEnfd2E6WRUjNTHstF5hkjYOiruIF8qyeXQ+kWGprBLMR8PYnB4bK7QKTzFSV4cCQzu2xmUglqaG6rYPSL4aQq5kB0NKJQgtzcLCxRHZUCOC7uYgHdKNjbIkCflVCDZp4JDzEMnqPRQppKFY/4EaDRVodEfM9THjqvUoVKilukyLvAYlPv398B9t1P+I5sPBykg4dZ4kDQ2KVEuZkic3kkHJbki79OVZ3lpW6GmiF6lGT2MPxzPXMDlVyNXjeyh2GYinlQujHx2jpmsU2YahuH5UpLDvEDqzJtG/cDoPbD2Z1VSKxK0A7o/LYbi+MZaly6iqtSZ4YzKjZS0oCrvD19uvOHv2IuW9qbSlxzJFp5qeAh2C9ddxqO4p7j4TmWfRRf7KVXwwOU197kcSW76iPWYU6xUH8vZlEHcXTAaP1dj56KMzRpVgmbG4yCezsHcHhe93IrZwN50hOSQPKiYr1xbn91tp6hjFMK0v1KotA90Aqs32MrTammZxXTJ/XeOnojFv/okxbLI1Kde98Nn4iPDkbDb2rSIlRRlpbwEpvYa8rEniach52pSgIBTu5vVjyHoVfjR3w+MSFFrLaBSA8JeQjWJgGHaDWoU83KR7ef/oNsN1B3BIXBVbezOqYoVYmJlytCoHRgLnYfGbaJ5/DsO1aQUJfxQI6buWh9nV2MgZsTxGERPDCDSao/DI62CLRyOBBtIIe0yY//gVD7rHE2UZTUlVNVvLQ9m/cBBFQ2dRsEDAqIcW5P9cyqLsmWSJtlInXEpdZxIL1s8h9eZVlA00kb6+lB5Te+5luFO0fDZelkP5m72Y6QklDFCSQayiiaoTAQROGsWIEUPZ1zODN8PMOZ5shVy8Ly/7pPFakEFrz3yMpmxi7HRPyHzPp90H2dhnKylndiMqL4d07Xa6Ezfiv8+I1txviMlPRU5aDamS37yT7cOiHmUCNa2xVemLYctR/qnsQCnyO3HOO1kzLIo7L5zRbWpiQ2YfgjUtMLfS5JyRFAveVuDb4cRO3WxCKz+yTFWS62ylu3MLskYjSM8Yw4OKWo5/SeXvDmtkKu9SdqsaW7sfhE+F2rQI/iiJkzfnJXO/nkHLcQWGbsrkU0Da7Qq0R+cx/8I4Xv28wjiH6+TqVrIx8hcPZjuyVC8ft8svqRDrS4yqB6VGAhTVRSnO18Q17BLhlt+RMD6MUnsO38e9w8WhmnhHK1aPEKNkShG5k+fiKBqB6lVv/kV0MbAhF/PtYWyLhYmVfnx+E42n8kG8ywr45KVC4ouXhOm68PnNXJaPnoqOlTS9xpMQj3yFl4kvx/8FU2uTynvBTjqwQ6plFy6RDxgzRAS9GU+Y+GMC5mY3GHBsE3cPxKAm4YDC1KGwexF8PgtByey5LWRKVRIaRbuJjlVD3/oufb27uXvSgxYJW/4M2cGrrtvYKv9AwleanG4d+odPY6KCCTvfdnGoOo9DJuQAACAASURBVIwQySQOzMygJ24imkkRqA0NpauPDf9E2vj2ZzgS/8bT2eFGquICspvuMkHEhF7XTURZZZASa8ILzf7YhAYQM0mCE7K+zGz4iPPfqzxzmM++5F+sEstm4qUVhHbYoXP0Ia8G+rH9+1OK1ZxpzryP6jBzPinDsqYQooqm0dTigmFPBaG6Fpgr6LLVdATeWlPR+N2fkXEpCAXiGLb3Mv/i8f9tPv6LOAzQEQ7p+EXNLEN2Gwuoxol1iTPZU5RD0UR3YlSPY7vpGFsvDeWv1C3iVTTR3/WW0RKFuHZPZpDKD3LnKzOg1ZlZF4qwqEhDS/IeFT9Xou3lwPjXMgS2zWDh6iCURXTQ6PsRS5lWJmXEkrR+A1dCXnLngQXXvfUQ0Umn6Kcy6wbnETK6gMbjy1h0OZA3AdZ4JSdBVDFlgq1oPbZE//U7AupF0LccSoXEF/aduMSV2ErmKKuzeFsVhU3SOBjUoNR1nYjgGB7vvUT48TGIGs1kwWAdLBaepHykNOG5uWwZOoxl04eyoqAd57holAzlMWtN4dkjGwLU9DHdqkzkl/fEO3nzpfI55VHD6RGFT4r+nJ/1hPfDKxkyu4wU0USObOnH88JTxCTI8furFqtnXuZ1rz+fT8iRXxyOISAQDGLI9GaGmK/mnf9alAPi8Si6zqBOFa736+SsmhxHDtbj/1aJwJdzyXQzRjJZwMLrMNuglCXqE0nTbGGM4jvEREuwKq0i48MSfKaaIxDcJbplIdmGoXzP3YG73FtSNU5zI3MEJaOvMFw5B//GQVz4sppRr1bQeKidus/pLLR0pI/xefTdJWhd/oohA9VZruZHRdRMTvetZsKC7YyIbuN6yB3muCTwNlESd/W9LN2oRVSPkJgFbgSYbmGQ1l0WlZryzO8ozhOmcnnqDcZk3ES6fjchg4qofzqZ0O2+rH5YxyyzOi6qjcUy6hU3b95h15gNiMzywv7rGe77LGP1iQ/EH6rCKM+CtAwRtMS0SCzVxtErh8E5k5jXvZcfbn6UDerHrAWn+GIbypekRp7NjCZQGEhFw0vmSYQxUPINEbceE+3dhZfiTT5vVKH5bSI+SzZTtnsXLW+CuWnaTGRCI5/TRhGRsAT/e/DUbBsjj3lxY90PGtaGMMf8AJWjJvKlyppj+odQ2LQKU+vF3Bizmce7E0iep4l1ky2Krx/T8FSFYcdsKXU4yMTRexlZeJL3Z13oinjI2KsljC++ydm6Izz+NJaNyuPoWydkqZ0AQ7UxPL8bhsOG9RS5pROkOJnJnxMZOPcGRt1CumtheNQA6mIV0NtyhVW3HvFjiSdjswu4aa/BXHdPFnnNQKtfClKDZyISXYwTc2mt/8Wx3r8M1RbDk2GMHD6NzbcXMmRcLlFhAfgbm+M58SSyPwoJGOmD85kqrngUUfpASOo+aY46rKHy8lvWLxnL1uMmPBJ/grA1lQan6ShJhxASVMWRQWt4H3yDaeZK9HZFUz4gnY83J9FzoofaSDvm21QSeeIccZtG0uerE+OufeTgslyKA15x+OknPqbXML5jBttUffgmPMWX0KdYiB5jhuYhTg+vJGfiAfq+PYtBoipqvetxt6xlT2MwXonfuT1qIIuajNgQbEhv8lZmvVLk/rVGZhStR8T3IK1aRlSEy2Ob4E7csmCcDL7h9O8u+5OTGT5wIWUWSlxZu4fFLauxmRdMovwbRPMVKDFS4/byrf/nfDKYVdFCdUQ7HdXeqA/9iJHEX0p0SzHtTmGOz1ji7uRg+WwgGn+f461/h+KJw3j3sy8zb77i+iY9Epo0GWnvhVftIVYOukxXVjCfVxxiX+Q2Vkj+Q360BAYLF9IUqodqjTGvDtQQ+bOMdz29ZHa3cvLCK+LVZvK+6g/lbaV4ysqyVPc2RwaupujWE/rVdtDZK8OdT/C17z/G4oXPol2ME7ckpDsEjc9/GaO1kqzPo8F1BiMKJ3CvbzUfnPJ4bb6XkbG/OKigyP6v0phLXsehuppx73YgjN3E+4Y/rA8BLIfAuL3IiTRSq5eOYvJ+GDgNxzFge34jSiKmHPU6yQXrEk7vvEt1/0rGKfeSub2NBLNe+l6KJy7RjAO3fLAftgI73/sknI9G5OZvNraO4+CLIJrun2Nt6QW0rhkT/zqewhpxgmTUUG6r5OyMMbhP3kTL+xY2yWyjN1WfQ7ozKemG9A5wX1fHz5zBfMi+yhJDDVbc1UeUaURo7GJnzxPUerMYPrSVv9Ofcs5WDtv0Z7QVlSMx7xbdUmvoEObTX28FxQsOsL+3iMuvknm79zlW/k/YmLOcQpMy9NVlMKoIJVK5hWnR55mgXYZjxAoa5k5lR8M8tm5rZ4XaeTJmLWBXdBt92rX4HqdKziIbPi5WZLJRA2qK4wl0OcyEijfoLUnlqaGARU+tmVcRRWm4HFK5n6i+uh5hjZBo6U3Y5RzBfv54phr5MTxRh40rKqjuXgUOexEaptPVuRMV80x8rWpItkqlX+dQLOQe8V45Bbl6UawtJ/LOJwoLlyRafupilBnDuuJg+plNQr4gloCXAcwTieeI4BuX34SgX+eL6u+FFF4fwoQscX7vP8qIRyOY+NOX7xb7GB/ujcjLHOAHcjOO80vqG48HKOGal4d01h7uDBTSIu/GjU0f6BkliUXvDebdfMHtfUsQ35fDSJGP/BzViti7ozBzH/IfDPAdNpTBfn/YaNyJRc5qZmZWcKRRmzzfX2ireXI9TJYTNUfYf+kH2zcdY0mrNAfan7Eh6yNfl7SQE+CJXVoPKml6uB/25UfwddbcOc2UjQc4ELccrzBHtnrf5GDPNo5sk+Hzm7nMN3PCZJAVssrroPQ29TZueIj/Qm/BWuT1VyJQ00SKUISr7vMzVvh/sW9WbUEw2roddHd3CtJIGthSiqJYCCp2oNjd3Z+Fn4GBXWCLraAiEoI0iHR3d56L8wf23drnOWv8iHc+c853sO90H75OM9FolqLp/DrmWnpysnUu5867sbfCBlZnwl9VNp+azvqvm3gvFkmIkgE+fudxkf6FUvVWPm4dzSmX49iGJ3Eiuprw3iEsupnG8dgMXr/dhUzHNvb6XmfUGhlUPAYzPf8irnWtWOopMUT/JPnfM3FrciW1yZxen0sY+utjaDyNroYitDz2YeyuQUz0BqYNnU21mj8Fgea4BD8nuPg1qRI/yF7SwDENMc4s3MpcxyB+nbzItdy/jBZV5mbhEl4vLWCgJRzByRsoSzVjbJUuasmWaCfloVR9k9GiwXg0HqU+6RRNj6YT+iAfm84FtBtrkiolwnMF3f90VP9/T3dLN01NstwdBAXS2rR0T6CoQ5Sw/GA8JFPQ/qZCVv8AnX9ksXZZxKXjk/nTKcQky2GskfIHu+381X2Pf+wzigxdoDUG86J1sMYGv5YKcv2iqZGoRyX9HZ4RulTXWxGq2I2DtBn9+QN8uXGTSwuWUNmXQU5uBNu1FOgr1uRI2QgONT7k89AYvM164PVHML5OXpIfWW1RlHZMoa1UmJdPnzOhtwPTqauwFXyOpvseNuwKwlW6k7KfKRS/3sF9uX4sn5aR5ljKvb4yhkZspNrRnFHqP1nktBC0HlGlK8OoWmsqxkykNuEWP+SM8M5v4L63JastwH7DG86fqGa9YQ1fVQSQ8BFgsBEEvM4h/O0abHSnUPS2nm9RFozzEeZx3UQyY8roPLOYVS0TSU37QizpkJzMoJWKjJEc4NGrfxkNSNVUM8R+MIV9QnR2yzCzJo9oaXlUzoFJkBFhn+DhfUj6LQJdkhjXJiGlv4j4vEEY6z/jZ0k0ngXdCCgbMV5XElUpc3JTxflpPpYGLQPaU+J5n2oJozcRbZRC7AIB7IST8ag8gFZLNkrCq8nr7SS+r5uLk1fh1nIYBV01qt5+RmLmYvIObWNRy33qh97Gv0Kavops1KUlqc2rQsLGk2vDBTBwMWFHTxkjx7ny6VsvU4E1M+Yy9sluWmpvYLM8jx+Hx5D+6h2tcVWolifz4OFjHLQmk5mqz5XjUGkD/L1MziBz7EqqSXKXpuyHEvEdkqToG2OgU0V/9g1MZLz4tOI9sslfQS6b3e/OMHLqJgRPfKbR/RDOZcF4V1cxuGso03+/5HdVDdUCSSSo30O8Mpnv/hNYG9OHt5YWM5NSSVMVRLL6NnoKP2hLOsdk4Gl3KmPye3k7vAIzwxi+S5kz0k2eReOf0X+migyRBHJkwzn07RrfFu7lRTtYlT4j3s+JQ4UP4UI440YpkKfoiZjaVnILLbB3/IPxjwy+WLwgVekCCp15/MlKYui8agSOluKpL4yhfj69r9/gJBTFYI0GMiJ7ob4f07FPeZ59H6/qSN7+lMFNcxtiHVIcGQ/J4U+xH+/HummuzLVa8H8PZMbzEI16gpPNSIL60mmwyaBTYBv52LNQ4hArvy1DwEAb7TmPCOofzE2TeKrfbEFlfyVyomPJDp3N2JmLMLH34OH+NKbLgEpNNJtyDxMbr0RW7kd+Ki/l1klPKjNEyRi5mqsl73BT+E6EZjt5fZpYFE9hsqwxtI1isuFn3oilstfemd4EL5RqXqAy8h0BCl9BEBqe9yOa5Umv6B0ONH0mtyWED8rb6B+xgfCed6THGaMRX4rN2yv8ERPnRr8hc5o+kNInxGOHLQhU7CNQOBd9cQ2iW+2R76rh55B96P5oR6KsjtY/t+kbpMwnBRCtKiC6ZDZGNabo91VRbubFYDkt8j3ySVR1RSXRHJE2GQYQQUKs5n+cpf8rPh+DNDUGTjm/435ePzaq+Ri6V9KTe5ZPRyxQevidvt41aDbIsG3cepB5xX07b/yQ5tXdrxyNf8L8+nZ+9Q6gKyVGj4w+Gg5jeDPXnAMSJ0hcm0lJXx5Hlg/j5xUbNon+g2tREn+0wewY5AqaMuThW6ouvcROTZ7b71/QErER2dxCbPvT2Js7hTsThvPvt0Pk3chEyCkHl4Z2+hQOoWTSQHJYBgnuYmwaVsc/B7/BtBv4RaRzvyedH+5dvMltxt/jJ496zlJ7ZRtvilQoaDMGfrDTdDZi50zZZCZH2807PM5pomn7Yj69fcv84ggmmSnjPvohg+qP8cjFH4wOQP5cxuzqYH6qODJ6AzjFlVAS/5LvUWfo3KRD1/bvpMnV4JjyHckiZYxsdSj5m4F2vykxsqoYCp9gvOwmCjOf0SL5D1rJV6mefIypf29g+kWeU0seY+I5lZeJ7Th8dKTpbzzfHCDuTB/O4wyQdihkQMwdAS1RlPSKSH6XjZxII8q546hwWcPApKV0X3nFO80PnNiyHX9S4c5VSDFG+dkonoR18q/rSrwH7cJq3DSUClUJL25k7PJufv/uo8d9G+8/hPHYKgpfJxM8E6rJFLGjW1oKi6RWqhosKCYSg+IKFFtLqVlkxa47ccRO8+BFagqdXaaYtkQQMliDU3KrqbfSZEjCKLaFmjM9KhLZLBnKLfOJ6TEkfIECq9QcKWivoj4qFbNddiSRhIzIQiLGpNJcZsy1H8mMEDLlnLsnbgZlxFzQRHinD6+fhNB4dA8j1p5k8P1+4qJqOPd6B8aNz/hY95Zg7zC6IpYwRqiAzLh/sSi/SZxoCstM1TlSXob23w7qtcbjWq9I9Wh5BJqqCUndTPDC1WSGhOE14RZXxR+SH9mBlZsfa82biYxKwHykJ6LPt/Om3w2hpgks7H5Hj00v98bOxmSkICvEhgGgBxTpyQON0O0C6uXo/y7BdHow9sJqdNhbMbBFBCWzO5T7zibUSIVXa/cyY9AEenvm4r7wFOnV6fx5+4Cgvzu5M9YDL3d9LD+PIzmth+KGPgqnb+JgwyHWzriPzrmn6Dn8ZXrlfcoGHQKsadAVQnwgD/GwWgakQxEYuR/jtZPxQooQJGgszKAzLAv1GhceXQlFtPk9Y7ZcwUnWlBk/c+k/1cXlrZZUhu9n4MEJKsI2ImHUyni5CyjbdHMnfBsd9UKcLXJDw6oJObtObHWXMlzDGo3Q9TzNaudSkD8nXkfhXfmMMtlqLBTECPUz53JME1onkvBau4TPT6oZPGklERIgrPOIBrUEfnQs40v6cYTEtUEmDZuxk7gRXc+YTAO0W5swN61Dp/8qNz+9wqfdGQ+v5+QmniFIyRvD3sWEKvzh4bB1yCc18aJ3CitF8vne8YMv27wQiJBEc+AnJnnmhEhNR7TuDXnW+tQlt7DYTgbBT1YYtWURK6dLZX8hcxVSUBvkzc7Ujzy79d/Px38SB1WNgVs1FVgm1/M49Tgtb3+x2GIZcaq+OMca8OXGfBrO9XC925bKTXW8jVvBsxNDGGGiycnmA1jbD3Bo1Ft2nvnJpouf0C/rxjtOh/M+89C5o0zxUytU9rxlpu1sLDaX8Y+cCMMqPjHq4nl2Cnqz49ESxjY3Q0oxMbv8uPUkj/id+ZyZc4wNR9VpirnKuSRf2np3k+k1nXv+AmwQXc7V4AnsqJlBpyKcfuuD8olnOH/+RHCfHBk5b4kSEuafv8/Jko7HPGkANced7Go8gtIZG/yE1dg3zIH+FXIoSLWS+yybCwu0+VK2irhX+yiRrsZDu4S6vC0sfBZCydU3LJ6ezOg9ogwuOs8az+WUvz3OTVtdFmy7yt43PkgGj8HHro/17yIxn/4Sjd6nGMjGEiGqx9XHP2iOvMf1dBOM1wcTPFyWsa7hxI48jcHmlYxcqchZjykcUZzKbp1HNN/axHN3T+qEwDZSlGXswP9oDRFHVFG+sJxfBcb4NBWRWeeHvOoFpur0ceb6I8THX2dqynWORfxgpZkdupfWEljjy7qnS/g6TA7Zrel05waiceo6z40uM/30CxQDW2nM1GOkzQY+S/ujWT6XzUcdaS5J48TpbchPfcCJsiXslpQmvnEfzmVtJFuH0lNzArFFkxHQeYXFhffECt7EsNUNjS/BlF2dTOqAM8pHQ5FQCUSvKwW/CUJEvannS+dQDsYn46UnwJzBc4k6qIneoiD+KuTg069PToofpiJHSDEKwrfoIxdrczA/4kPJgSisFB6yuuM5eqmXeHXSmfu2gYQdPYv2ycM8mnKSV0Yjmbs6ioB0AZ78FmFAZyTPs18SVZyLmLkljyvjcHGwQykxk1eiU9DWi8Vk7DDkriXQNXUOoSvvEugnjJmiLe5H31MZvZxQrwvseFzP9iOadCw/R07nN1LvDcVMeT8L5BPQb13JrGfryLh2jnMxGajJOBPd7cU2RS+CIxW4O9eT9qPPWdjtz0iL67TMlyRxw3mszO4yt0QU9bNmPNgSTKjMITQ/rGJG6QVuJr6jIXQvFM2j4E8mZw8+RLzhKkEluTzJfMpNMV+WyqbhqXGMQ9f2c013MiNt9sKgfSDrRFVpOrp6qnSnC9On3YxMbyXoVGFq7UbWUB/eh+1hYWYm88z9WFRoSc4WU1w2b+ZY4ACojOdHqAdhzr9QCDuHoMV6dm88ypidyTjqWNFYIoJv6yFuKN1iQnUs0VVOCJfvoEJjHa5zjLFs/su79GI2KF8g790nlq4Zx/GMx8hfe8zIpNu0HorD6vwNvAKcECl4wUmTcoTyDiNeZ8Ns8f2MV95D7rpHaFw4SJvRM2bWVrB11CymiUUiLCNH7uMp6KQ94MXUsXTUjeJdSgtdnouZ0pfPkhxfTnWoYy34h3Ed15neuYclxmLMyP/FJZ9GxiXpUqL2h0jhGdhJ1BGSJgcG6Vg7RLHGyI0lOgcRWOuMm8EYlj6U5LJVOZ9NhGHd/2xG/a9wPk7/c2bfFQ91MsICGKK6g9SwcJYtm8TvdANeJ9qw2j6FkBZ3FhReY1ruX84v80S1sRap2B/oig7mW3kRqf4ldP+K5NSzKFwKbFmbF4b33l2s3OzPUreVRB14TpZRLT3H51GencrvL3UodYSS6lWD7aUHbJ2Yw4e6TSgZW0NzMNOdslmp+pE5fnl45e5DJ72AFqMe3q3bSGrCRZxNrCibU0y0jSL/rPdD8+4hlL0kmLXJjTfJT9jnno1dTQr9zt4clgrlTmIacYbCNMY8BdrYG+6H3MWFLB4YTXj2AGpti3maXsgJ03Zmjspn/pBDbF1xhqPhaxiqLcTouM+s8FTi9GwLmpKTqPTPJPxDFz98u9ikPg+XM5aIDdNGIOkiLSJ/EXqwABvdWXRm3Udc1J0kl2Ky1V9zbud4BlzOsLFoORKOjxGwXsn9kwHMkznDuclfKfilz7nwJp47wPeuKnzUwFQFEkYMUNH5nCYzZWSN5MgNns0kYRUai0TR8XIlv/U5Tc+3UZpoS5nBNuSyRdBtnsjitVPRK/hNU/d6ig5vxu62Hk4lmfi6DONGygR6tZoxHzeMLTl2iJnr0n17B2a+VjgFVzDWy5mir8o0FE8kJa8Vk3Jpiks6yJZdT7pgOyttW1lx+CJTvURR1/fi2ed6Jngpsy9hMIGfdBlwzWSWTzptw+/yO2gLE38cokvxC2bF01g6YT8B224hUqqJwL1zjG0PwWiYBrKOY+jz7eNarTpXdhUyatdoflSX0Nquy/QuHZYsXsIkYWnWW49me+A+cn9HcF4rChZo0d4TQaTiVZqUB3NP6QqD2h25P3gDRmbFNA22xnBUOtvlBfGaOAeHmi4kTZzIse5mnFktu36eZ57WJgpy6imv/46/jSF/xIUJeurDqvRIcnoyCGj6RXFGMEN85pPXZsGjiWnYjDqOjrw0He9jUK0CleG7mBs0nUk61tiPV0cpz5jMnHfIVgoh4eRNneBrXrXUkaimS8zSITRb9zOnyZ7ysrcc0t+Ah40oi3ZPQibDD4ehw7mdq8D8VRoIt6SztqmSA8k3SNFQRmb8L/ScGvmgcYhyxR8se30Xh5hIZPyuUt+nBXJCyNz5ByExHQSUdTD2vcla8yHYvOhD4lwT5kICTHHXwa9bg6iXtQxa04G1RQaypwNp0fuAn+wwFpWqcq3RjTE6cxG4PIaECF2CB2zJGSnFJNyZYaBHnZcwc+xvsax1HKKRKXSYTaZBxgRRuXF4yxUhWprJBD0tRkzVoGWKKalFAnTKzmPm7U7Gr6sl/8Neho1X5OefgxzWP8Er7Rk8NRPBXiWKKnN9PGJkiBPr53dcD5N8DTh27DDeX68xyK6EgB4nBDcmcickhvulIgRMfsBgSzuCZb05ICfBj1UmpC0yI77kKQmxwsg7mqH8JQaDIY2U1A0mqrUPcbkkhswoQjitgi47d2ILkpCdl8cF4280FnUy36iNX8oSRHV/YrB2B58/pPzX+fgPEnLy9D5BDw+UHfRwTnBjpK0//xSpYbk3HA99RV5l3UI/4DxSA/nc/9DCyDZLrgbMZO3HB7S2NKC1ayHiD0IZlS7CWuU7JMZFMnF3KJPObmCtzQDLii6T2uWLjqUGrQKZ3Ki+x9pjf3k8xR73iS8I6jjLmbJAfrxYyNeJH5lrMoOr1w8i15jKx0V7aPttxswTJ5noeIc7V5pICThG5eTnxO9TYdvE4cx8NZFqnTS0/d/i2zYIPZtH3Dj2hGCbo0ibw+n77RR9ckTDIoFeYVEE3GyYfOkB0/qG0K14hGXpvkw6t5N9Nx9y4OVeOmtUUasLwHLReQo7BZDRG86oL8osGjGcXbe3c8+slZzoOlq0ftH3rpv+io8MVmhiYPwmshwNkTnSi/uCThTqisnvHELNyHikMw+TFRnPvplnmb7kDprOeazsrOCOWAqaugd5FXGADYp9XJYZgfaUVuzeSTP1phiPlkPL81Ik3CZTcOtf1FqNyBa9QuZrfWpl5Ym0MUP7YgSjfEQJtxVimHQICb+H4ZCmSv77CgxjrrFDNgLzFHVCbZPZN8uNgNo8zmhswdsnic6iJnJ0RAgs0eWO8l1+m1ygeJc9M+aC4AtPBqrcWWKtSPa3MH7YLMJcqBOHrjBmqgkT6FtEv1cdK323Mnr/CwZvEIPGRlyTbdG7vY/5FUOZdcqfV5azmZtThZl7LSey9Xl/ayqBvlt4dC4If5PzaCp0U7bSE93X2mQdlybTuwBxDW3MppVzon8Cikhx/WAJ179cJONFMndfZKGe94z187Yy7MlDFmt9ZO+0hfwrvYtFL+dRXNdArVIdPj/F6G3NQNi0AgslOUL+qiIRU8W05h9IKmnwp0EH9cY/zG99zju9s2Qes8RkzkJOSL5BV0Sc8csN+RYpzJkRupisPsu94IN4frdle0AZt19N5cb463inFzBZfxwrBbTx3LSWyN3+bDNSY0yXGXsCFnBwkQ65FrOYL2NM05dcrB1c+VKfQ7DiI3pkfzNFaAcLPz2meagNiqlBuNnpUqUpwfw0GfZLeJM90oOvst2oCPjyoHo+CqNdsJk6DHvZ5VgaWTFCs4Whw15haefDU3Et9GVHQ/V9uhxHYJDQhsoMIewdFvO7Spr5rYkI+m7BV0CA6wv28DNkCHNfPmXC9wc4BQZi0yjEO9VpROXdwm/eVDxCQsBtOgODrjDG5CGJQjGcnWbK+re3yFOr5q+EHPJH08gxSSfCUY/AQQOM3d5Hdtd9Nm5O4XzoF+p85tPolMSIpAyme+lic66MDwMF6DbUYidoSIqBPOOcpNilNpxDddnY1hnwtdwM1eJWXhgXoNYqjNJgEc48y+XGwE3MhknjFivDnI4mWiL3EXuyEC+HmZTIlWDQEsew7pkYZVyh5bgE7+ONMYlT4INhIRX6AshJq9AuJoNYlAyi9rcYap1KyanNNJroUiDwhkUmM5EKKeCQajo7h2qiEZNJ9NAR1Cdn0pn6P5tR/yuWj8NnQvbJL5uK0JNeDgxuI9ZKj7ikGBLuNpPq8Q9Wp99gvXw8F8VrSB5ig35hCucVW5BwHcWBw7uJzoshffliHnjuZYG0FWkB2uywNePOXh/Gf6oj+lIWi+ZUMX7rDkzbYtjaLYmjghk6laqYv+5G08uBfc0NiHiB1MA3jke3ceRAPxZpuyhu2MXfkgKGdDljvGgqwzOEcc/s5bXqB74nbGVEFaDYjwAAIABJREFUdyGGu0Mwrj3K4KouDJ5uotJzOJ27x1JoDzXZH/ny6BMNH45h8usXq++VMGn1SHbG5TD8lSjr/G05l53PifGPyBa6wrfUUMbdT2F1jikWh+3xWzCTGc1peAsNx+rxSZRaXqItX8J5oXlIKjzj1dX3XAo9wOyyOUTJvUSkyR3tKCmkvcqokfhEl4IOb8WMiFPV4p7NPAIWjkH3rgtOs8tJ+fKBOrnv1Nx24l7eZPb8CWC0UheSlqXcfuGCnoE4mnLNzBuqgn5fB2+u97PNvgavyrPY+i7ic20h7m7NqKSdZI7xX9pUNFGu9SD18igEhpVT8tGK8zLfcM2XwK/rC7O/ODL/WSv5agGYjmjgcvMcumOGUf3MmGdR2aiynxttCmTcPMOjlGVM3uKCf4M+A7rdaFhVUqyTgsHwOJyZyYi56ZhVfWDOqCX8LtPGsiidUf5aSIaKs0NNHPfxTljVVSC9sQpJwdG4tAkzUiwWg491qC6Ix0HtCtLX96PhokzajCY8HunhfAcm14C3iwQFfTKsum5AVpUorQ66VP+OIuK9GsE7d/Lr3ge6Xmlwye8MfutciHn4lCztl/jUyVNS6UKnWz0uL5qR/ZzLvdwv6Gf9ZlDUAwQlKpjVIM1ATTMKKv2odn1gRdVDSqvV+GZmRHlIGjU+65k0bwLv7Pbi1DyZz/qxJDuakJV2k3XKfgwL8sTx3nVGpGTw+nU6Em1aPJLej7rAMNLaejmzfSolEasJkzcnsqqLr4aTeOWuhIu3G4N1Ggmd4IKaeClTbH8zrriH4XG/yNHKZMgIfTzLVaiJfkx/hSSHLd9TskeA9+ZGlIqnoKKfx6MfZ4hc94nBs2YwxGYsi9TNqG+I4J/eXBb8u5DWzUvwvjKHePUNOIwURUneFouOJH4YmWD5OZwE0TBOjBzLjEsrOHF2PgONA1y7+w+mswcYbzic7yOesuZWPVcfpfHW04fOuI+sHOmPgE883B2BgYoxZ6xbOSURwqfF9cwbOEmUkirmDbcQkvNkRFsFKi3miBQVM2LULtJ7MxnQHcbOhu+c6PjJ2L40lMu/cro5hD6ZEAzdTUDRHEVFJwKU/LnS7oJoxldkfmqz+ccVsv9MYIpqK+qxKhTKSRN65SUlv1+xrrkUya9VGDv4MqDkQYdjOqh0kR0zD5HmONrKrLFDiIJZWTRkF7Bs0FSe6I9jSnYvuRqDqU2vZqDWFZl+Daao/GRC6ztsqpeTlTCbVwtf4/mpj6f3M5i52oovkfGkmylBtzLC7Zr8jPqvcP6f5Ny5Y/tOBbmw5F0lF8cLwJ9c3kkN5tun55hOd6P92zZsumPRsnyC6RgjKksGULh/io0TJ3DxQTYqiw0wzv7J2MK/HFzjwdau61R3ZnBgpBc3ykRZ1FKFj0ksVtaOxHSI0XbTD0XHErxdJtCW2oHZ7EpuvP1I+EgHapyt6Mp1QfTaDr67D+Jn9zVkP25DV+oQoRU99Lf9YfRQE1bX3KfTMQeNyCqUd/tw6mgYjo4mdH1Xw3vuEn4532XM4zKeiZVg8O0T3WsOIHDCi7Fu8aAXR9e3aOwzLDh8bz2mqju5pvIvPhZt/LWNQfu1OnGtGiiKPWZUkQTXV5oyKt+Plg8niFyci9UvNdS6RGgbMoW7N4KZtcuPWlFlZO0H6KuyQDg9G3VNXfoQBuQRthzA/oU8Lx648/ViC8qRsYjuhZ8XzxGhD5Ns7rJCoBCJzlhKrfdzzzCFQWkCTI4VQN0F9v9tx2yWF0F756B14DDD0+pQMgsgNusQM827+CnginN7CUUWdXy444tmyXzkFCfzzawf5egzaP38TnWjHc87pAkKlEEwRom8ADtkBc+iFXkWdGIx6wqiQW0tR/a04rRmEO2nYlk/xYHDMmkIqdTz/VMZFmZmpMREsdqhD48YHWQa9OhXfoWB5SpE69LI7Rei8ZoKgTuESZPQRN/GGskWdbSMjFhvHILEXSE8ZBuIKNRgVXs2hvqxDPEezFeJFcTfg8s7Yc9nGGfUxwZTQUrGgnjORPZbjeeWvStOsuClHcEUGy/2JQ1lwc75BIo85cPvF/QtOEfbiQKSW6IRNB6KoXMXHRJWqHf00PbnNjX9mlRUe2Pmk0bqwAg69KTR69bFBEGumEmiHR6Ogs9yRtUp0G9Yx7TKSAbpODNo7mKCGwuR0mlnePselK5OorY3nTIjDSY9OM87qbWIGhWjITWHzC8nsHOczUzVP9yV0OHLwxQaS9MYZ+lH/IkeAg7Lcr5EBFGB5zR0WvKnxQw9sWx2DXtGZN1iUoUscG/uonSxO+cPXqAIG/Q1pWnpDudvSgGOx15h/rqMVg9fDm0zp3LqQqYqGxJY8ocQixk8Xr6YmY4OFE5fw5j+5zQXFyMoZ095ehldxQ/IHmqPr1oT+4/d4ta9W7g9msWmKcXsnLOeG06JDG8zZ2q7PaPHqMKlREa5fUDg6h9oD2HM5okIvv1KvPgyFPp/MjkrAYECO/pNbfhpp8FE8btoN9tjImjBr71mVCSdZLnqApZ8/IDzhAYMSj7Q3d3Mvq8T2TiQBuP9mdm5hRRxNZy8gjmZac+/ufc5ywr2KMrQOHoqhuVPGX0qGunNKzkTmcq+i2ux9A/k88ytXE5ZwCALd3ITo/Ea647uxVnMXWWGcpgFh6r/ktpew9OiUNxlbrK7J46koSoIaw7nV3IGI1qnkeidwqxPP2nXXEqFoBEjzc/hqFPPrL1lZB+dT2C3DjK59dRIdxAl3QM/kv7fEc5VtDQGVsw4ztbzctxx/00C6rTaW+JV+hmPeauZ4qqG56wFRF68TMuBefR6T6M6N5fN32Kx0r+J21kVTpx8zZZAH/JrYzEcMpqBDykEvyzDRi6dP906vCn5xogRL5BMrScw7xjoDOJbaQVmwvkctZUiSkUK05xw7IYk0tYfS9raD7TsUUdr/W18G8P5tS+LjhxbMv3CeVevyMXaz/Q0vuTYVXcWLjtE7/61BEw+SsDALdZNeUV9njGlEq1wx4T9edsJGLMXfS1wXmrL7+ZT7LjhztLnocjE7+S0yVAk5cqowY19WxOR6rZnf0k02PwFuYW4fnqLusIAUvLxmPiY8vJRKQEaCmT8eUbCvTcsOxTC2YEkphum0HF3A53qo1C0WktPZzU1Xe4oUo5WlQG7Cz9wZPxanmklICVeh9tnW0w7X7JY0h2rEb6Y/gpGsVaOTJVOGtTbEXgty8vnelj5ZOIztJv6w+8ICPAiWL6RkGJBTk4cTV9LJHmfv1JPIZntBhikFGBz2wmBZgkSP9ljJd5FmWglislCMHqAbMPlZPckUddXhXdfJrGX72Dc6Y2OlzLZOt10VbixwHQof7Yu59/tnzGPPsHLdYqcSDmLtKECWZk9rNKeQ8tfXYJabaiLf8nZvlAkNy8nYWMNeoctKYz4juPlXSx//Jp1ioPYYNmMbmAcyABhrZzlH1oB39ODafYQ5KrpDCacFsCnF562wVp1aG7vZ814AUr2GDHKx4jVH4txHzMVl8xuJkg0Ypvhybx0EW4XpHB9fQ2Ssv/y6vMiDle/52FAJT/lpbCKHoF+Uy7fxicgrm2MwbVRFGnX0K2XjPX3cppdVpHS8QF9ISXyrkbS4WDKuKWa3H2Uw61hm4lo+BdbsX6Sdm1i3PtaVPNj2HD0N1YJe0hth/3J8LENJha48StnLD3KPRiJKfK7oI4v/voUVWvxx/wdef2z2NecgUJ2GqXDVXFuKUVHLpq2tBY+qRlx6pcnPwqFUNR6hWXfMHoePKciaAHKKrc5XSnFqFfqvNd+yqlZrwmxGsPfO6tJlW0gcdML4k9/oTP5BUYpppwfaOb77E2MrLfll8G/uOu9JUpLjBlpyqiUTeCNfxv5Edc45noSW6TZ7H6R3fNakUWdZ8V3sGIwn4xGY9avy7njK3F+vIuOhBvQXAByldDfg6GeEWkfp9GkMQ/9IH0GBb2iY7wgmu+PIWwzlFJJQSQK9KnpFkOu9xYSDqZ819fkn6xSrn+TZKJXH/p3VOmWSYW6MK5K9KMlk8ld3y/ck47h8M/3hFvf4or1Y3IqY4mQd0Ipu5klEtvJT4Xpl66gZlrEZtf7iMRtQc4BTOWW8LqhG23tvZi2LUH8zXl0rPtZtS2LEULWKGxMxUjJiW8Fnmw3SeZO0V8UbS0wlI1FsVSbe2Irsas+i53rOuqLktl89iJS4k5oH1QjqMmQux9rqVQSx9nMlblbpv63dvUfxEFHZeBX5WxG98xD+eEHDmsL0fVViYDucqxuH2DWrVpWRmdz6Z0Lkw9dY+HVOEI2h7B/znE81jriYqrMktWruBZ6C4GVc2H9ELaWdnHr2QArHVZg6XWS5OvWHHCXwj58AMklK4kWG8TX6OGMljiCm+tOqneJ89shGtQcEfzkTaBAHi0nYzi4+C25MyMIb10G0sO4tCmc067OVDnYcjxNloHlm7l1J4SC08fYH91K4sBb7D7eY5+jLqx0ZD/T8H82DeMv1qAIf6TS2KulgNm8Bv58fEJ85Wge/FnGpzRrXqkswG3ydgTCx7DDpoUgjXNorDnHQPRVXvmVIn7NFLfpiQTt3EXrXnVCS7vY0TSA6vHJvLHxYcjpaYgvi0Js9zRM55uRIzaZeoypFMxGd88/eH5sI1lqFQ/vLGacUgialXe5kiSBlNlHfna30yuzCQGxkQQMHUrs17+kyuay4oo8oedUcPylSoLRd6xtNEm9rMBGpQZkRd5TVttDt8ZcnAfkKa+tQ9VYjdq+PlpVPtJYu4QTivt5P/EU/RbHuHz+AO1Of0mM+oOA9AGul46i08iLi0myTBl3C6ftmylfl8fD9A6e1PlgXDKC9ccv0ZfzmoRUG/w7R9DvfZ/i5u0I3pxAZlQhllO62b7sClaT4wi4OhnVsWXM71xFgTGsv+TAj5J4lApP4/G9kTLDDF5+e07X9QGGlKuwZtcUNgrcIJ+9BPZCxhE4rgq/krJYZG7G6RJD3AY82FT3gZuT53L0N4x1dEKzyJtpM3JA6w7Pth1gqqku9plirJiQy7VXGRjLj2J3bBeLfLQZn1dAlvQfRMtdkVb7i2RHFg5FctRaptNY4YBqexMnJQo5qOHFl8aP7Owcx5T0fwldN4Mnv8s4PnU7Akl3OSg7nF0inYz1fY/KxCTG7jMi7eQQqrLWYbjiFV1d0ZzfZcPht0JIppaw3m4OAu7bYaQAp0yN0GmqgxsJHCh/he2/i+jPMWCrwQQ2ioqhr/oUkSczubKkmH7/abyI12RqxFTWvzGm6Z9MAqNuYT0mG9Gqfqb7jsHAfwSdd3IwferG4+bDrLWvpPF0CEtCb3A2d4C14zzZfbWeoX+j8PiWz+JHv2mKKMT0hhhZLSoETF7I7jUgv3cdEnRz3DUdBC/y79Vs0vrW8s+tUk6IrsBhJuB7D+UpvqyuvUrgLQHW7T6PQNtMJmprsH3VA4K/62I74Q81Lt/wCLNAXzubz/rFnPG+wLJbzVz4mo7sUgE290eScsMNlYzX/DT/xguBU5yx/ECC9S8Sxkfg43oYezMBYoUv8/qqHB9O1/Bx13rkT7SQHWtF6CVzlm6exZpJrQjKvSND9AhB95XR/G7N0PGVXO7czIHUgzB3Dn39Kxj2fT4G0Ul4mt5ldcoB4vbJEl6fz+n3KmgZGtI34TO1rWUUP1hBpe8C9muc4szOzVyIk4G4DTRtbGVulxR6rtAircKtHUH/7wjnQu09HGwo5fKh3+SrGRHx/hfOdqUsVpImL3wHQpW9PM4sYM/ZnYx5kUFqXymje2RptvTgdIY9ba19XAhcRH57B4YO8zF30mXevwlIFBhyMa8eG6nfuGY10HtsCaskZ8LNSYjNuU66kzwX9eswKy1l+p1eHox5jVz2Bd62fKLUPpOUfzYSuqME97BdWIrcIlu7iPhrt1hQd4yY5hwMP4wmYZkwu34LsG/oeRr+CWbXiHZyy4O4KzGf/fvu8yNvO7snxePsIMnw8e3kf9tHSYILAjZ9jN87l05jKQ7VCxF/fxBSIV8oEQkn0Wg2d7WOESVQwDGxOawa1sy42jx2T1Tnyc85TBTVYcGvVE7dy8Fr/V4OfH7LfA19OiKaYUY/4maPeZv0jV4hHeIG91Hm3kf1BX2Spp3AUD2PlxrSHJSQQVrlFY/+GpNVs5RFP3bS7DwR4bUbmD97PzNLJVm2aBCdcvlcaO8iaegyTtbuoVzlO4HzvFlWKobI0nlsnXWKVb4bOHr6J5leGpy/eZWy+8JYZM3GM1WDwnxtPrW5Imq6lYdyHmw/O4vS6lA6grfzMOYuwdOmstJfDP/JoiyWH8zBHA1+PzWmZ/tnfFy20nJDlWUvRWitWovWPV/8v05m8r56jle0ohS3kL3BdTSOX4rbfmFCtg4m9Usao9dY03j/EM0ONvxtz2V+4GkGDGFTJWyf/pr6JVOJnu5A3gQjhpyYycXhAghNAB/XqXQFClMyCw6r/EYurgjbyacQygonwC4eueXH8fQJpkruKHtlxmD8eC7b9waxOOAWDT1pGC/dzKRFVbyv1aN0SBopN0I5f/0oAxNKyTJ9xrPp18nwjkY89yJZa37QLrmL4l87iEw+h/93fz68lkZQ4RPTfSVImBRPqYQWc3oU0Nu1lx/HCnnfEcmnnXvovHEY57i1hFesoT/9Ca+bLyPXI8m4CA1i+uuZZm2Mc3MiG4R3IfF3LH41f1FXNWPO+tMoHv0Fl2XR3WyMQ8hETp/cyV2XOMpdMyiwX4agZD0RHz+zfXkvF+adxmvBEQaePOHsxRzetIcQGpeIeaYDc29nUhb0DlcJPS6rWRK9Tg7nolKaTgUgvuALBjnnyahrY3rxWNosLHigU8DPRyGEDV9KepgZO228eTvrHEYIUsE6sjd2EShVwtWaK/xT58ec5ES2fk1h3zBVUKiCjO1ICTVx5OlkNhZXkad1DNtlAhT9WkzxvSpEbTOIcHuHeGMHOcKf0NRKpMvEFIfoHqbe+MtBpw3cHiaB5qU+3ovHMEe+gDXDn1ExYRe1bhsR6v7DmJgRiDt+5nfZftIvwFzjWzRf+UyblDWNmRnw9SytrVOY4upMfdVVXkeC51w5huVqslFKhI0PExgnp4jXokSujYhh1NotuB0aQseUZcTNsiV3+xCmT/cm1jeDCZqplIkp8WvABPE/8zm18DmXGww4lT8ak9XtDN+9Ao1TphTEvKDWYTDtg8bzsbzwPx3V/9/T2idMxC5T+u5qskTYluDnaTR31ZDSFMvKXXn8GamEWoQb1jc+cXzuOuKHLmJB4W0CfQTZGrmBpNHWlInbEycqhF1JOsdSwqioNKSisoWX6b94d8+QA0OKIUQD77e2fI9rBUdFwtrG4edsjIDqApaPfQwT8xh3OZyBAFMGrZ3HaK8faH8Op2aYFIfl9LiUa4yN0UlejZpEWHEmsUmS0KdC/BozpLQkkPMFAV8PmFPCx5wG9tPA+lnXMXyggsmYSXRaVyJYWcbgWeNgtw2/xsHrrlRKShrZ4wblahZsffCb2fpi6CWJ0ZGeBq7D+VQQivYoQV4qa4CULqIFT7CS7QLPDVx3u4WcsA/GSpmI9/9EVGoqyq2QVW2JP+18m1THbflY9D9VECEmRcCqMKLHKZHYoYe5DFyY2UF0qjKh9vcJ+7sDGXMrBudW87CyFxdtKTZ7CIIhuNaI8sD9PsUll9ms850/D1JpGdLANN1D5HSVcKInHwm5UkxHq1AqWIOqpQWtJ6pQ3GSGQHE9jioCiMnIMG97CqNlbFkifRoDVRGWWQ6QYahKe18ArsrFdMgmMNakjRlhrwm2XklCei4fyseglJaAhlkGARY+3GiF4qRCJPcOotBsLmGR9Rh7qHL1ZjIfC4RAqAKD3myUHvpRKxRF4+JW2isESEzVYs14yFLfykynJcyL1mEGe4l3AJ1q+LYQ/MMgSLuWn1UJtIjZUvg7n7TU+/hNh+iOYEZWr0HdrAbZ8G4sRnhy6VsPNfGnmC1sQ1lmKpIzjSnqS8JURR6vvLs0tDegW2iKWl4bTWYJCLYnI13gjOzACAZkTfhS8xRXDQ8G1FqZZgDVdRIY6C/h0fEoxtQUwZtwCNjIbpUpEH+P0U4ShNlfw1xjOGYqSozTGCBb6yHVdcN4Ml6Rt4fu4SjgSVX1v2h5TWG33QDnKzKYfT2egqNzaX8RzGjpRgSW1PBaSJw8+RLcVATZ3yjPQFEyoMmf7zB5uj9q5i38OjkRkf50kvcdgqHC5HmBwZ0O5N/EoimRQ6OAHiIKKbh0iQNnMFCG6BkVpNUP42eJIP0T4pD9do+yij+MsN5MkUgjp/bAj23qxLANVfq45ODHpYaf9FalsX/PQ0YBET3q0H4FalupxRFidqFhZ0aPdBBWvGfpCj8C/0ZhbvgNw7hW9OpqGTBUoK26D4Eqdd43i2Hj0cTD9hDiO3KISXDl682xDB/cg9ioci6XGSPcOIydlUUcRodpmhHsCs5nusJKaG3i8AEn9Hzg5Y374O7I4w1JbNIVpKrgPdaTvuIn6k9ohz1phT8RbdTgdeIeNiwQY1/xaj6YT+VjSxEl7bKsSP/NthleSMvlMKyrnzYRB7Jq41hdKoeWgC6qiaKcngv79m/GeLoxMJwx1qVc/ynLJfMndEsII6/wP8/S/xW1q6OnT+2LWLwCkUJVZgntYcomez4eKECjsYzAM1epKn5GmGEjReK17JFS4lrJVGZcWIY4ZbSGpXN99wP2v1nPWSsN9oduQqp/NhfLL2LHRjrr9UkdXEnQCD9mOnRzcKka02dGcNjFk1E3b6Nm9J6cPlkcNN8Tk1ODbf8rTOStGXVpG+3u9ZQcLcU1fSybtLbi9fc0Bxd9YZLiGH6FFtBY38jOTfrcD7DjsKEuo54Gcn9xHU6xmVRFnqWhZCEeATsRsdtP7Yx2okJq+d24CqUX9VydAxOCBJj0qBTHD61oXTrLmkcncZ/hzZI+C0bdO0nx4G8MFeukK3SAZ7ZK/DMpiDFN4FB2k8dTmzngux6X+4n0jT1MdfYWJIfuRfLve1pL/kFFOxA76RZqpUSxjPcje3w4HvEp/KuXy1UhEZYo69DeJIielzjzTO5zt1qbscl7cGAPcY9V0O7VxO+zBALzupkjL0G67ngEnqbxIDgCczttlHQqWHsphKjrvpz8JoFCmwzvjPqJ+HyKLkk7Gt8eYa9xFY+GadOrK0mHTgemZSpsrXvBn4dVLBxXxdP2pVgrKZAsnIhjhzfDe3uomrIdb60IEpI/UKNSRNK+U6wcbYiWZBvGNerMym+l2u46NWXfOdeXzLRiDf6tquLzW0jMzeKKsRZvzvawtHofzZfbCBY6wtzDekTdFMZWKATztBIqpvixadFY0gNV0XkJ4UH9lOeDatgxZsmN4qJvK0F9Q1DMaaNfXQ+pFx14tkjj9rIBw75wzse0IiNYimhNPRNsZKh9XoC1pigP9J4wIj8D6+hwbidpUvIzjSHRitgvrmDab2PURR4xL6USzdJR6AtuYuLzQ+gtOM+/W70QbLOE8/tpFT6PaZ8KYfN2MdW3E+cGeTbQxrup29iGGFPkGvha3kaGsCIa2TWs7voBsgeJdJEmymo4Y098YsQbOzYHppBTP49B2d3sTMon5msk0zIv4eN9EPvJFlQIKJEilcmv9Aocq6z4lZNIh9wmVi235IGiLvVbWjG8Kk3mi1eofN+LQ+lInuWqYZ7VTptoJRNk5cnTzWdRbjBjeu9xLOIBPxaIILt0AG+lbDYW99MgtZDitAr0izJwFm0noCqIeWX3KVbXQ9L8DoeFd6BPHbfdgvhZo89UYUfs4jRxd7JCMj6Ae9eMeFJzEdlKJ/rV5FBVzKBcpYTAwQFoB0ZRMOQU0SZDcDW34G6tK0ZV8vyQUERDWIrC0YJ0VIjjoFTBQFUX6aX1xPd4o9d7jrAxqfh6LadNLJsKcXHUpX+j06rHjNpExotHk28VhNWACBe/nOTSzo0otffT3paN7noh/oacY+abWpoldTEunEDpqT9ICa3nsm0+slYLGNbVQJJ2NGEt7sSljeG7+HSSFd1I1+wnqLKVvdJJ2H+dyOOo7WjFCPJErZCrJoU8mi/Fs7gFjBDUQb88Hj0bG8IfJfLdyZlEAW2C2mGLx2L239n/39rVf5AbDx7u+2s4CMnPbSSKqNFklYlxvQOPj3ni//AGV+ctpXbuaKbcquLDz2+MnuRMjpYh3zZv44D4eOqedWDq8JfY8gr+fCpmq70xbb16zLJWYvEs2IIKOqWNDLgPYchPNc7IRVI5+RP4fca4yQvR9lpWW63l868zaLhpYrJ1Ldd2jmaJ7VpOf5ej9usPaiLtyJKQYYRWCU/OH2FrmgGOHxxQeB7Gu8JWRnzX40ZiAplZDSj+H3beMyirr9vy/T3kqOQsQZLkjCCiIIICKmYRxYA5YA4YwZwjJjBnwSxiBEVUzIIiSXJWoiA5PffD+XfVW33P7fNWV9+6XXV7VO1aa489a869v4xZe641V+NDDu+xJmKBKyL9bqIQVITwhiJrlFdzIyaRibaSRLj3IHzpQVpSPv0KTLku8oy+HjoYVVaw/dQETjkMR8/xLRe2JFMz05YR82cwb5cRp3sKGejwiD3FcsR0WmExbynZDcG0Kgyjs1OTjvY42vs7s9FOjUAHCyZ0W9F1dDtrt++n7VUUtqYdDDAZg0qJBqI/X7NWvo32ZznMMHTAb0g8713laYk156q8GfeyTmLg0JvckEgm3hvIrcUbsXOYzzylKpSWqPPprjNtz5NxO7SGy2umUdnLiYObw9iv64EwJYPp4SKsfubJhrD1qFxax+WjCZQYSJBkOoti488YiTUjpq1Amf583mSfZXWyFMvtm+ndSxv5e4fZ1tHBo4m2nA39xJbBY/F2jeRDhxs75s7k+XhpYl6KoqrixvJxlSi8EufYCRWUOvrRp8IG0eLWElR4AAAgAElEQVS5OKkUcPWzMenl/lhL/ODNz72Yz9hC8MBOEh6Lca+jmwm/RbkXCDVnwayunA/35ZCXmkW1hDhlo+6yq66bqb9M0JutgtObOjzL43lpNp6QHx3QbsWEukRGjslgrrg9wtcNFGoLqVCW5Ff3H+TTR1LlDyJfvmHcV4NW1FEzH06XXicP0xpoDA5E3m8Nfb/cYIPSeCY0pBF/7CQTr2/m9Mad1NrDaGl1FvnosC9fF6u/r/il1ERy/hPOmw2jOH89z7oXo250DcXy73Q+U+Pc12L2zR+Hqu0QrF/H8FX6D/eD5rIzzpzBm4fT8uoiLRl2iHQE0zKwkYT05/g1taImVYLRov58eimkPOc9ykpbMaowoEltKFuujsL2uyxGBm3cUNRFI2kXxUaWhPSIsVDUCPFXtbzSrCb9rwb7r9/hUcxaRF5LY3/dDoHRD0Q181AfKEN+1AEGvQqmcrk2IWJfUPIx44feHwyPOpDb3Iyv2Ru6GswpspTlxpZRpGSDy4DLiBlZIFY5jWDTZexZJI5eyDY+PrXgvP9s5PqE8NPBgr/v3DFqT6dTVoTivFaWGZlzIqOJ3eb6fEzPZ/7PfMw6fqA5ayI6zc00lysSqp/MZbkBLLQZxiDZr3h42zHmYjv2l/fyR2ccQpW+mJz+TE9QN3Ki4jwPfYbrQFvUz0qS376UU7cGcd0/kTNNUvTyPImvcxVHPw2gqKucWfKavO4xocHpEyIiFfRT+EHGwQpuOFhyNz+JJM3BVMoN5cyVsTybEofcwCloSYqh0dybojvRTJ4m5H7vQSwRsaPN0Zh7F879Wznqf4uVD1llXbL+upKpPIqpXTZEV1rQGFzKgWMHiUl9R3bAWAY8/kHTwitUtdlSp5HJ0DALag5c49D+i1xdoMbj8A3YrnrAmRkvUC68zgLVDlY5nybnbx012RYM2fWGF2fHkaVUwBmbsaxaF8vWhSsIy3/MFsONPPDfSKbcNzJrdZl8dDqvIhbR7XwXpd2n2B6/jZgyN/oqbWT3t7187NBFasRdPk5SJSyimg97/MgtO8vLGmMudkmw5N1LHjaZE7IbtNybkG8bSPv11ZjYjcbrbzKRG/V55KCHYKkH72xt+RPQwe5ZMjx3baBj0RIsjl1GIDOT5a+OMXMf3F2gS4uyI653U5EViyTa1ofyjhbcY0cQGdSHQuEojBVOUv/iIkVVykiUt2NfUkZ1gz5z7ubS8bkbycj5fFyrj8VXP47HdTDiyXN+Z/3iQKQWS/LsyAicx82l17GXqOPL9cP8qQ3FyHMc37bN58TxxaTeVeKdTQ2W43QIm7WYZV6miCxyx8R6D1ne/ejo/om/+EhOjswldcYxDNOXMk3dBavhazBMeE2fno/cGiPCDuWJXB8YTPHFWCxX/WT0uIkctvLHdvskgo+/Z3fRcdb3k8C9vTfrU/7w1cCfnxkPeVCUTLtIOGuPFPJc3BSFonwODHLlq7QT+608CBEks/VMPolvThK26Bbzhocye9RxdBJXkNm/mqKRVmySM+O7/1ish9px1vUjZRuUCSww50wfD+KF03hvHc/BiJesmlxDVNlnHt3qQ7zFUc7ti+bimT1Eh20ns98FZh+5h4h8AC6lKiS+eM1sv91sML2C2ZciRq95RdGAFmYn7+Hyj6P4/V7JhHmTGDR6GaGO3xi4KI1+Jj5M2OGO4dc6UhveIjiyn8iYSLZYXCEyK4FTp3Zxs+wZRXUtlMkWoAg8Ha1J3QUBxl/FMP7WyFVpIQ7Z71ArfMDIxmIiy0dSkhFOQOMNTAWjsQ+VJUx9LwLnbIqMldHQ1sf59TB2pGcQuMgZUZV6psz0YfE2KVpc9Dk4axUGKVH4TzyGx4KlXF+1k12n73C333w+yd1m0BFvElVkeR3QzqIgVfYvyOdCxDs8hySxV34kixRGsvnkHtyirRltcQDRjecZMNCFHPcBuNZVo/z5E4sG70ejlznv3Ys4BFwlCXWLbkTPJfBuzXwO1rZTslSUj0Z9qBY5ybsds/im3UOHwkvodqZ+vCH91Ycy5XsNn4PDMU/5hnOWAvmymYhaJvBTrQnZYkWqpPXouO1HQ2l/pjmtIci+kaF6W1B42p8B7m4Eqjby+awn7RcPMe7UbLpeBmPS7zh6E3bxydAHa60s3gXLYWg6h8h1fXFPlMB35WZsrc+zIn4Mu0wHIWZXRUzJI6bY7eerfgwDapYyNE6O4Ix2dAL3cPX3GBJCJtHbcjcfFHcgmriG+ztiqXimjHbFNuZKnMej4RY6M5K44wOHw6J5UKXGQ5MmPvj9QTF3Opvf9WbFESsGLLRnmksKOkVh/19L9f/WEAgE5wQCQZVAIPjxL5ySQCB4LhAIcv8ZFf/hBQKB4KhAIMgTCATfBQKB/b8To64JehwLCe0/CPGezaQKfZF3D8Ryuz8b5m1mb1UFLyMTEO4ezBwXI2q3hfHrejLWkqJMur+AFrunyL6K4fqevciaW1OX9IQHHSKM09flT90XqkZ5cHnYfexNHKiVe8qJUcEIQibRr5cq4mUdVFfXcepjOaMk/hK25ybX6uBHhSxPIi8RaZgEHaWMNigit68dr4TFuM9+Thhf6KshQZWsJkbqo/DLnwx68LrmPPOSW1g/RpmeoKHQlsbKfp5U9avihZM4A9XUKe3VA/qmpHb9xV9OisP1l4jO/kJ3dgkJ98tIP3OGMrH7CFRPAVD9MZhu6hn/7SmyHkeZIHeesu5aBiVF8/41aOgMwbC6CPWJLvw+n0dEXwMOJBTQpWVJaWkG8q8zmVCQxGyrnxRJT6M4I4PxUoa87/eL4XWq7JnUwfmHd9jd7MvDuAqsVdyIzv3MDr1s5o2fzIV9KUyul+X8JWO8UWbU3jr8ipp48u0egz1NKbp9i6VrdpL5KBphbCBeVwvJvdafPTdXMs+kDz815RAemM/TRaBhJc6UgjKCBuwnrN9SGpLj6cip5oz0BELkfvHdYhJ+glbU/AYzyXc7J+6cxW/qRF7/iUVjVCTuv5t4JimO0dltHD7jgZuiDus7ejhi1IWI4XLaysoQs4QJIltRLWlhpm4DtaVXiHsdjgPg5aDOfqcKGkxlqOzVyRPVKqpzdNDxdyEpJQ+Xcd2Y2tZiduwbF+/J0T11PtZ23exAmRvf4nlvOgNBcg0lrZL065VHkt4frKsFqOo/R9PQhr7tStxL/Upqvgmxki+5I/cRP6M6uqTEMdSR5kvaZ2LPz2bkwu3o0sC5mOlkSAxgbf1L3hoaYuY9B3HtD/SfsIzJYl6MEWugKmcWRX8zUXKfTZZgICFtssyrUUZZ2QDnrEfo1IQwcecQxLwrEPCSPSMM+XnlEwckamhU06Y9ZzdqNfI4Oqgxy3oMt1SlSG39hIj4H9A34k9SByYW83hpMIPfb/ri+r0O0b6NnJGzRVLLDlVlM6bv1GSh9hSC38oSNjKTZv15CCrmYTndgb6lIqRXz2K48xV0Uq4wFUX8x5pzZEgSHyrzsHksifflXuyOc2ep6mY208gGJqOqW8HKXXfZdc2JFud6yl59JjXQj1OL72G0cB7q7j6ImXXiqF1GVas9rfr6zBXswzZrNT4L5djS+IpvOSuoSJWmSFuLNq1y8mgmQM+PI5qiTNf1Y0RKCR//tlE7dwWKbTdRqJYkUcQFR+u/nFcX5e9D0Cvy5LL5FaY9f0y2hg7ab6oJq5nC5a8X6OPrQfrdMzQW9+Mog3HIK+GugiVXZztw4zj4pdQzwsUcq3fWRMrMYklmBYHuVqxuEdJXrINC0V6M7rhP4atSbL0s2aujzlHlA+zSfo/yIAuubW3lV1krEob9KZDSReLvDe4bQL2KB301h9BgMZLknIR/X6//q54PgUBwDhgBVAmFQsv/JuhADKAPFAEThUJhvUAgEABHAD+gBZghFAq//lcvoaQgLhx+8htmf85xLEUHSyMHen0+iUn/wRj+aGDuilDQluanmQCTFCFPtvliZrCLbwlP2RS8lu8np3F09xx0TdQIMC+DIzmol+twSrIe244O/oqrYt0rl++lKVhPXEXZ7veI+ThxdO9gnritY4J1D9OsPbj3aDI/7brIklnG8tfpxElmclJsPK+HDmR5igxYe/A5MQYdmTL8MlswG9BNlQmoX+xkAqbcGKmL/qlxvDJQRlFrDmg205r9A00HS85JpxH4U5sJk/y4KdWBSvl95qXrQHQpBOrDgZPMcDNGImIP5hmXUFZUZWp6LVG9pZH6E890s4W8zuvhrEM3Fl1vGFNgz9E7sgxds4rmrIFkD3LE8fBz3llsQV3wjV0GzsyR/4Lz93bMzfuQ16TB9BHVuNxtJdvWgoD8Tcj9VMLCaxFfIpPorRKPucZK5DYOwE7MjF5va6iyHc3iY29wcgajH0+YFPOaxBPLeBn3mIiN09k68SZezV8R96sltlMUe7EiDNtbMKpajHK0Aie21JD+sxTFQb0YMjYTr0QBkXriLLlbARpC+LmCjW9k0B57E/NLSXwZqo9J2w1G3DHEd1wYjwc64fQnk9T0FK7pDGSZmDqSdy/y8E0X9asz6NDwwUgRLoQ3s2mjLEgM4d2XEj41POVhXSr9JfPZEJVOzIXDFK4+DuECIiojcIou4Jf1VIbpVtDPxYZlty9i35jO5c0DePuhlrOjxvA1/SdnnF2ZlTAR1K24myCgblI7BbO6uWuYz7BV86jqVOGacAgLr0VyfPRbgustkSr4hpvqe+SVFXDPd0ItqB8zHOah8Wsbe15XEWJbyNk+HZRMrCDQqg9e/R9Qn/eBzcPmoVZfyeqzWkzWXcMqx9G8mOrKisURSKiJcABRBK3NhJrH8q18HGsftVIyUZVf518Q8L2VrWPFMF3UQ33RZMZZ1pKcrEqqlDy6NV/56y6GzpcaHhhG8q0yhq9xkqyQzueNvQbzHTVw3F/NzOfPOXFzEb19Hem6vpBv90z4lLSR183GnOM3Pl9PEnonjjTFVbSVZzFYppCNJzuxqN3GqZNnSRNIcMXkHmMLjUkxO8UjmaE8u7eU7JYv2D51wEgyj4hReRzdsI075FIVMYxIPBFzXkxUnRQVBb/xciyg8Mkw3g7SIaIWvO4GsdoJipU/YN8xETqV+CJVjkpBb0LfLOCM8kF8auS5b1iMh+xytqsXM6HKhIa/CYxQv0033lyU6GCdSTerU0Wp6RBhqFMt971vMl66isQMLTpr1rPhvTfv0w9wWKEvg5OXczZiCTM+qyP27Rf3j6xE9GUu2fWm9E7rRsHlLktVmxn2MgXzd+asPO5NU+khxBRaMZAMoLlKjdKWBEKy7jKgtRGK1vH4xk7OffBg3PdXyDeFcXCwAS90vzNx4Axi132lv2pf3LvfYWLei7L7y5jgAFYf1jCssx9P46YS7CLOT2X4EQzNowX/p+fj/wECgWAQ0ARc+pdctReoEwqFuwUCQRigKBQK1woEAj8glP/IVf2BI0KhsP9/GUNUTLhyYxe96naRq3mcNIm7aH7pRFNkAStnjsbGeyvvC4XkeypQcbCUpmw1NMxa6Zt1AB/nuSjO70XA/kw2l9cx/u1K3soP4YqzMvfelZMlbCCyVB4N5w4CLopjsriSYuNW1nWFUDhxMTvfPiKr1I6txwpw7FnJoZQ81mSEc2LyONxGnufxl6+UTxzA4m/VTNbMZ1KnGuWbNgC+PIp0RtInghNJs3G23YlyYQezD25jiW0uipqDkLaKoOvZHGYFzWTXvQIKup4QN3svl20eE5wcQMbtM6zLf0Fcew+heYXo6ElzIuw1Y18t4eCuy7BAGUGfF3hm3MI1cAG5laFoVSrQ6SrDiWHH2OZwmYc2tcz7/oK6y17UhCxHMH4lodIyxJZAP2NJ5iqu44BbNs+615EUuZCg/OtkGYynj7yQe1nnWLUmj4P7djPeLg+FlUswPXKS395aeF/sokDjEzHpI/jmc4mBTx+Tk2eKsasbh/ZE4CYYybwUWy7GX2O7zgrWPcwgadEJnB9o8tFjCQ772zGNjsDlhQRL+vfBca06KT5DeSreznjR33Q4G2E3pB+Opw8yQG8KinWejJxkjtHmCdy6lIC2gRkas45zJH8Xl/K+c2/La/ociWNpaRrPzb5QbTyUC42K/JLz4FrVPRKm90EQtJfr0y+iXJBLqLYzNbuncDJqMxMXp3FwqxzLAwO5s3UOewsMcF6xkrr9QVzpOwZBjgQLfcex6ZwZP4Wz+aI6munK99kx/RDHMyHD+CCC1zeIUHuBfexdfji+Yal6E9beO1iXVkrQlWCaBvUh7eVodurrcK/jJNtstLF1XshvORGmR19gQa4Wo4Ja8dCbR+url9zJncVH03BG9u4m9fRfBs1dTFyiMzUp9WSnpyPQsiJxk5CpVwRUXBJS1mJNn+owDnYU8e5jM+PHyzEpdQ4Pnz/iaNciRNYPY8QRZeRGa9LiOBZRpTbWnnnK4FHa3Ds5C4GykH2/t9AuJYKe92QuHjuAhdUTDq8t5GOcC9GRsij9SGTftVDO/HCgQfwC686H0PHrCMLMy1yeP5i0kAtc3bqDK3OdKE6zpHNJLguWnCXn3QNMT21neasMh1bf4WZCHHktosjFdCAq/o5ij0/YrCrHwvIJ0xMM0Al3YIboHsajyD4Fe+RtvyLufhtdF3W2vo9nbuQJ9sXGMaFpD11+/XGc3p+7w9azbLUQK+3R3Dmbzjifi5ztOMvzFgPmiE7khUcBZk/+0lHXg1nfHFyX+5B3cTK+M1TIsxZjjLsGgz0D+XZWhm3yOWgPOM4T7fFsLtTio+hjhCuciVgyjsRCedw9S/lR/Q6VnArGDjdn9lwVZgsmc3lIGDdn6JBl/AvFUU/Z018PP1E37ne1YGV0At2hN5HX+o15zVkaLo4iUPQmW/Qs+OwWhsRjZ3RUhrLJVQbtrZa0JWsyY6cCHUWQLTzISH8pjH56EbxvLicvzULsrQf1jbvppbeaBToxEPj4f1nPxwVg+H/HhQGJQqHQGEj85x7AFzD+55oLnPw3/CMpbcTComO0Z1dQ1daMrMIHBk+ZimlhBz0mQxD0lwH5P5jcr2fyKWnUB/ehkgICbq1ls9gx5pnVUyxTx8OjxxAIhiJ4cIJrSj94OX46M4QHuaBTQ8CzfEbqbcY34zF9rGPQvDyYjwH98Y6dg7J4PTGPT6HcOY/xLycxcsI2/E7fp+FxL94V7OJ8/H48nN5i9daBFrMoSm1yiP/zBmkviE2EjmHlhM1qwvHTYXKWTMXdIRY0nwMgbfiVAl1RvI/psX6YBMZu/lgrSjI0fy/CYdsRbgklNn8wq7svYW77FotSVXQ05JlaWoijx1tSl/fme6Us4RWPOZ/piesSb0SfDWdCdBDadjJk/PpAqbYn5leOcHqyI06qyUiKT2bhw0LkYkBPypfEoh6ahqji8vwqzh19MEnO5X3xBnKGD2XTt1HYug5m5KhbhF/ewhRlHw4DnW4q3Op5g3loIXc7w7hVV0LkPFcu3lhMl+w0woHNsROoWdDCtAeBqMSPIaHrEZ4/5fn4xZYxvQMQNhYTZDyA1GtrSPPSZZTzYZZYdoLMAZhzjSFfHPljeYdvb724MGE8pVJ+TI1s47rJX/bKNCM8EYrX1XQOCP1p1LZBsmAlsh5NmE+7x8BeI0j4DfNCYGOJG5WjQhgp7kC7jQGv7SPJGPeDKJ1iVFSNmbH6OIR/IEK9B5FL+/nUcgK7GfH4NyvTvfMU4muD8Xkbhk31UxZKNONz7yqNHl+QUf7MoeWHeGX3F52T6aQ3rebT3Wns3RKKtXMSWvalbJS7gp/mJeIO2HMh4hIRlzRoqhpJ9aPjnEvZTfy9gYw7msL7XbMpSx9Or5RCBKHl6JkLGS97GP1Ht3Ar3cva15PZ/n0bAo2hxGl3U7R8HZIu0znob4tqVxc7leyY+9OV/Pej2Tq4lrEzyrBJ6yB7xX2sDo5kkaMSNl98MHv3k0t3oENRHZs/f1DIzcP3oDmXBZEsf7AdpGYzelYbA0wUmP72BjktjbzbXMfqi7/5cWE4ri/HcbRuHcajF7Lqk5Cfwt3YChtYmtiBolg1x0qP8e79bq5dbEZv7nfGb9Bk1KEEFqY84HxJC4MLs9l6KYg7RUv47RCNiUErjaM/EBtQzOUUK+4on4aF/oixAzHOMa31KPMsriCicoWurk980nCjqWMDxwdEkLl/Dt761zGxtaFLMZ1PjpfAMYemYVHE+GoimDePr2M/49yvi039vtElk4qI6mnaHKJRl0/gavZabMYGIWgIp/RdBatllmDx6hPTv3kgfradv7WzOSe4yf1++iRrRvKpcznVvQ5guWou1o3VvNl9lEGBQ5iYupExbt2EWOezX6GE463dxHg747FVn3PxK5jxpi/dL/aRldKLZo1+BMT0Il3uAgITIZq6w2kfbkv2Z0dOyn3iqIECIRk92OdOwSJdjHzRBs7Hb0a19S1aim/Y4riC3p/0CFaW4eeIVo5t2co+7Vno2a1l76+N/46c/v8WQqEwGaj77+gA4OI/84vA6H/hLwn/A+8BBYFAoPlfBpFWxbzhOgn1PVgZO7Dq4Rr6jRiAYXoDR0s+0Rd47yPAN+UPq8+pYN53NxlKsCl4JSZrl1DvaYSuiRpRf9+jcC0HmfIM5kjW86X4Pn/FVfCfac+tkhQejDyEb1QTYj5OXE05wFNicZc2pJ+VB32rk8ix66KM3tgL/MmWBLPR/cgZ6sap5BSwnsOLAh2CZMrwB5DtRl0NtO/XMQFTemXtRf/UOIy2RKGoOQc4RWt2AR152pyTSUVNUI2kkgY1Uh24Esz+dBUsnylyONAAEhJZNdCY4rXnsMi4hLeeJqVZL7mhIMNRAxUWmS3ES1qcm/bdyGlJMqbAAQDvtVMJnF3D5XGGOB55TpHmJNQF34kTKDNX7gvDkr9jbg52xdU0l+jg9TUXaWtxRudvoiJJFHOvRSzoFtJbJZ5fD+SQmws2rlpcGhPAb9tx1L33w8m5GTtDIQdvlDJulyNJcY8JB0KdtMg69pBps+uI7RSlt10CdEBLfDUB0YoETdNFEJ2J4spW4lcX8HnYQFa+/cyUu+VMdi1H/ucKpgZYov65AeXMNKqrkhEBBuiNwtrBnBNqMvj9yaTwdyfLZh6nz6WtDK8qwTxDi8wyb0Y6Cci5Zsq7n6kkjIunTWIIyE5CUVyMy78/IW0uy/Kl75k4LIDwoB6Wj1sBrGVcpAbvRzcQmdbCFSNdvu/ZhsX1Wbz8mI2Grh+etpKUOVSg1PMe7xdHUO2EXa+q6Vtnx+VVn1Fb5I2dtCFWNnM4+P01gvQ0rBpdqO3+y1UlFU6I3MO+6BU5PZs5PNAN9V/b6V8YzVd7UeJ7cvF+qoXv2ZVomC3Fspcbgjp3Nk325XrsLibrbUTaLxw0LUkFhvy9TqVREDy7xFn3yQxUHMzyR63c9Lan8twLRv9+xnKFYhQ9DRGNt6TGwhLfZEiVkqexRZ2/7qKInWkjwfAIJiqw+kI5C80NkBF+ZIGDOjoPPJh5JwHjeQUo+DkwoDYLkToPUpIr8at2IVa9iuduJqy7+5BWw7G0lWfh2VeeoSc7MbASssDMjuGnV5KvEotWgTFDJIM4LePG9a+tZLd8QS/lL0aSeeSoFKO7zJ87VXkYAEfxYFq3LdfrpDj+7gteDvlUZxvwdtBA5qW5Ehk7hdVOkN2nHfuOieh8K+azNCjUfYM3C4g3L8ClRp6YFikE/SaxQ60YnbwOGv4mYNt8kW6hD0NJw9CkGxnyELaLoONZwe+uT0yQ+U1GyS+oWY+nTAQf0w9gPWgiNO0ibowtMz6rI/iYSFyoCaJJebz9kIVzYTc7gKWqzTgX5WP3zozcnYsQk6pHTKEFGXclmqvUcZEsJCT7LvpZitQXzmHdeU3OfTiM+scovOR3cmSQPirWPoS4zUAQnMR01b6o9ZbBxLwXX7fFMMFBAUENDK2z4lbcVKr9A8lRksFBvOPf1ut/67QrgUCgDzz8l2pSDuAhFAor/xHsJKFQaCoQCKL+mV//7+3+R/7VBX2EfWauJEi6hjbbWlSczBl9eTXieVOZr6uM1mQDDseewv7wSr4QzPWJg5gbq8nBSBmqXhagOHAZxys7sK4pxtBWlcyOcuor07GRG0K2QRVPQzZzX/ic0TcT2fj2LQktQsYHRfP+3DjM5H04oqaFrJo/MwIqKSswxHHVRgqWDONI3EqEN4cxa0YzM6ULmOo+mRXVwfTuVmPalgkUWLqzd99fon4Kca4azUdjO5aovGWn3GwkNLPYcSwCAN0xQkJsIP/SJMb3/8nWfQc58VWBUn1rSho+oO7qwXWDY7z4UY282nQU2zJY9W0B/obnyI7uYtuBFVTWzqJ0sQunzgdg/dEB3dwgfnoFUlfzmeGqzUQrpKFa68SA3ZM5mS7OqRgxtEW9OTv+GkFZ3cSVzqTNTpkdV/dTVZOOk0Qe2bnD0Z6lw3CtQWwdZ8inpUY4aW6h3tiPqZfWkuA3hKg9k0mZCOVOFvgfzMBvySmq2qNAajeKor8493I634wCiSp250Ovd+hPNWaF8WZ2VdzD6NVrxDKt0CxX55rce3TjssB/EaTHgtEKaHhIl18nwrY2xCoMaUGawsGWrI+6w/jba7EvD8e+soueAztYM8WD6tAxSBWmobJQBqG/CgV20KAZyKo5syhxe8vlP+dZraGPQsdF3O3m8zzoFROu9sPTOoD5L8RIHGLPfq5gebGFFBltXu45gLvCX9zLa/HLzsNv2XzeVlXhs9uQB1OTGaJ1gqzs6YRNcefw+2NE6jkTrd3Cz5wm4luEXLt1n/Kp0xA4rudEzUmGJ9RyNw1Eo+YQ2plAZEI9A739KZrlS1kbhB06h8TnHfhaOON64Dpuc9YzOf4yl9JL+Qj49d+FfO465s/wwuObgOtmFph0e3A3MIOB8bn4CvTZvMeR8RNH0uY4nsdaO9GR+8bBzPX5VccAACAASURBVN5sWufN+3wfDgV0gulyEtoD0PQajeXXI6SrP+JWzwe+ajrjV/SLruNjcUqsxu7EH0zvvCH6hRmHJe1I7TjI8T9q/L4ViXNzEMkN12iUAEsVWUTdjvPj1iNSgici0foXYWc9ZTMa+aIMXBuMc7IC91zOkqE7A+8FA6Cng8Nn1FhaJMnlBlFGaBtxpFADPr4loLoVR48R3EWda9mZnOzjwYphN1DMF8G2zYmsogeIlDlz5N5thu6pQ/31dcoG90Zk10RcFkGmwn6udTQR7tLGE+dOHOISkRf9xpAiSO1ny4O0CtwmnkVCZwSv5Poi4+SDlWM51bJTqPm1ivlRkxg5RYW9eb25t+Ej74RzsWltZ1LtK9Rk2qk8msBXEQWef0mgadZCqM9m+6i5eFVo8FFjMFnNO2n4A9ctehB+ymWdxHoaPuihZi5Fad0MPOeMxLrGFq81RnjOg5sKWcSeVuaDwUXGzelkYVw7qdp9QaYdwTsFdMboUKpxi+OzK0jIUEIEBfq8MMThOSR6vCPXQIRdsRWU1nYy9dDT/7Py8T/Af5Kr/giFQoV/eV4vFAoVBQLBQ2C3UCh88w+fCKwVCoWf/xOfc/mPYhq9kXMQyHkz5vQeVCQ8sJIaRvaDc1Q1mzLZwgTtOXGYPlyMYP1xhDeXwCY5BIk7uH9gDiWN88izz8Lm5jHiFEPQVK9HoCuLvsJ69LbaMXt+Mtu3uBB6fgJWYUUI1irTL2Yv68ZmMqezGP8Zy+gV1szDrni2uKxl8Im7fBbZjMPzpdDeQlixDrvvH4Wv7ZS6b2RS7lXeHT6Np784NsoOHNKxYOx4Te4kzkIgfhrht08Izq8mPDz8P76TdvrIShKyOoIlAgHfhELOfryE8fxchCuXMThYhWShDg8XbqJxShWn7xbxSMuaSmlvWuc/J94uEfEJOzEVaOCntw6hmgV+4Q04tG1H3fsrP/o8IOKWOZ5eV5jbFEzV7gBGWwj4M241CrrB/Az8i0ZcPB2+Qey6Z8aYrscUP0iFgX8Q1L7lsWQlPm96yNTZT88lJQa6R1K6dyz355/mndMsLhqksOfGdfjRTvgzU5iylXbhJnZVx0OzPoJJoZy17OG1vR5ar66x88Q3QsP9EOtjg/LP0dRr52Ao68HGoxcJjU6EH0rwcxBILEUsdw9i88KQrNGif812NOq7cR+pS/n451xQzOHU1A3sbDrHTYEWnlofMIsU4d0nEXxv2lMoAkfHRmB29AHbPu/BwXIb6z4lk9pPwM49rdgO3kNaAnyvEGJVdBnhgDlEXN7Ai2RFuPOBw4e9cOzQQThsC4KPyhzeOYmBlReYF+LC2K1bMNnzhNffD9I8awvOz7ZT3DKc36JDGa+2mC2mI1E9FsN6s7GUlB9nm3ExVuk+nDbRRPaHJMeCnSiV+Mt1WigZt4pVw2W5u3Ibm37rcvGXNl6vHmLgWU+R2gi6333mQWMcW4JfEXR1HDaKY1is2kKK6Dl2JeUgkyGAEDOEihH4h6XhtNsOvdsdnJ6mhss1Hw6agqDxLFi0wN9UpoxZj2jaVS66yiD3UJmn6ua43fBEcLIAb68ADKo+oue6GIcUC1IvjcTj+GCGbTqInZUeh/VsuJnpTZ7XCtpmP+XB9ir6DvjLie2BFB2M5eUPbW5Uf6XTU5V3AxyI1vCiae49UkPL8FA0ozVqBYPe3eWD6isEB024tKEfY4/tIuyPO4+aBnH3aQADUuHS+kesMH3BnisuHDDTwXLaTiIrNZghjOLK7Aj8N7dScG4vb+VHE8QyUgOe8Diuji/eLfR+/pGIoTO5LzKEGqt2Nm7aQ6WrP3ritykKtMDlwVF8lUsQNB+Eh9eIdX9DUep6jBZvIybDjO7dizkypjdZrUpci/xGdrQUR15EM1vxAJ1BloxqVKas2Jb2xvmc7rOEkZ+e8/lPPn37PEP/1VIeOv0AQzMEfr0xvdcb7T86qHh04EMOwyM3IRk9g5PvQmmdN52clGfcyTjFnqQyPJo0+XrkPQs+unGkcx5LxD8QFH2T5KlTKNT5QtbseM7r3iN30CaypeXZ9XYuEwLVMdN8QNmvCpqCZ/6/etqV+n/7ofhnVPuH1wZK/8Wu7B/u/waBQDBXIBB8FggEn8V1y3AXXU77pFfcqe0mc+9S1Hq34+d6lhhfdQ6fn05M3ny+ik1DMGg+YrHJLH4VQ2WJJYqTFmKdNY8NAmn0xo7lhXsTx3wHs6PtEdu0ungaspnqsAPULvYm5MlpzvaRZYX9YPrciaKjwo5lM51Y/DSVwwMsGHjsJgvfv+def2OqH2fQ50ZflnuvYKCyOFVSmmz7cpdqsSv0KtuMRGooQcvKCXknAc+2Ivp5P9cfDsZ5xxXkwmbyIjsNTbnJ6IoPw+nlHKYuWYHRdAsClm4lPOM1T1LjCHh+i785KigKHMjY3As91bW4vDxHjvx07HedICroNob2GrzRLsGp+TJNN6aw408HZe/8uPcxn9mzYxnk2wtDFUtUTZJY3jmAk81zOBv5lkyxm2zkOCEZpjT++cuiyqOMfLYOU71m4icJGTZ3FK0js0iMPUWQujT+gvHUvvfhnJ4xF2NPst/ShIe9XxOxTxbvN6A9PgNHw3I0Ay8wQTQMruegmSHC94duLFG2xldvEV90FbF7WsTT9+Hs0PJmpog8XTqVJMgeQ5uXcHUUZGwHv+NQug16RyF2Ow9xjR1UVcrSR9jO0nQxVrjW8zT2NsdXbKFq1Q5a1O9TuSsJl+KlOC+pQXh8G1v9lrBTM4JNMT2sbHlAaNJ5dv76w83LM9CJ6UXm4g8kiIShY6TC06QR/JE9heBOJAVfT/Hn7y1WicUxPsqCR4ukSP7Qnw3CfTSZ56C0fRgyL9RYE+NDrzVJRE2XpWrvCV547eKV5UcmSgg42W814cm6GAwIpaEniXSVtUTnH+F+/hdmzTdgRftG/ry/iNDrEi/bwrE4Ppxey/siaN1PP7NgXG9cJ2WBgNCWsyzWLaV8hREJybepnSogZt91PBX9uP5oCpOHH2blQH183rcQJa2HqlQGSeEjCU16RlSkOuO/rMfueCqzXMoovDqcw1+2EbfUC8Hgd+xatIJCbX1GuoVzwKaBiP4XSOnvilxLB4uxob/XUBJvj+fVGXXOLryGcmkFDh1riJTeTKJ1CvaZ1xhjuJfBqbvRr/Hg03Nlltbn4HotkDkiXfjF3uO0jhjC5fJcKFDklnMCKypncnXcJeYOtEEyy4jn6zcQcv849v0V2Oc5lPNL+4FzIfbPXHlgms8Nkxpmiq8ntbGTqTV7Gd57Gq42E5CutuVabjJbL92nt8R9KmJSENBDqT+kRUN75CqEeXAwQgrJPb5M2nKO3UvB540zAU+tuaqawF6dEYgsUCZ47XhU101AW24A06aVYGMYyqyvtpiMtmOpmz6PRsURfXs9Xp2KiEn050WlKD/m5lItSGC7cyhOZwW43rFj44kSEptVqKrfi5ywEpXy6Wy/q0HEbV081K4R4BNOl0oIWusmcjxXH5uAPdTkTOfvijQ6hRJ0H53G/gMWDPD4gPOBTZwePY2q3z4k+PrwtJcVAteDLM64wYhVJ9i8XBTDrbKkiIeglTeAZSe9ONY1H8u2M/+Tcv1/8J9A8J9w/2klTigURguFQkehUOgo76DEZKlq3uTVo+JkTvXbD7wqgTO6yuj2s2B37A2QUsMnxAtB4BM+3dVC8Q1UvyjghrojTz5fxLqmmI5+fcnqKEfob42N3BCcJq+kcY4loXfec+hqIsvWSJP+8xcTgqLAOAnzXoOxWTofWXV/RpU/prTAEF6AiIcUz+tXsufyKXabAPqOhLlPRk23g97dagznGS8t3ckd2sGmR1lsrXLnV2cNl1SfkCG3GkQPAbkAGEyQJGQG8AUsdkWzNfIE++5Igb41U+Xfou4KUM2LH9VI5tWj2JbJkY+/MZifQ05SFwXdHVTWzkKwThnOB0CtLrq54aiuvEhd9WdcCwo5XzAG1VonJHc7sqsblMRBW8ybCvubBGX1UNM8BdH4G2xtuINsYwZOEnkk/LZHe1YfkDrClnFraLgyCUeGojtrO59Tn/HCz5OY5BBSJinivHQm2nSh5ulIVccpJF2Ocsx/Gch+5k72eNYVu+Ms6EY/2AgwJfLSI6pvHEDrrgo9ziK8On2fYA1TuJoP6XegrAr+PKTLqBPpH9ClNIS8jgbEfNwQ/5rG3PwT/F63FLvKLq5VRpNbX4tC+GysZ+eQPdiWEg/41AB1SbAzKpRE2/tc/hNNka8VO5N0yVrhS1oJbHzgwt/LR6kpXs22SR8RJOUyxM2JhtorfK3UR20EEF3HjjGBRLsoUB5YRYjtADYcPcf1VwOxVx/FnZcxlEpXkyzUJkcnl+Hdc5nbHo3rqNnc78xntNs5Sm7BvN5uJIraIXPckdO/5Gj5UcKT27dJOLaa/Uld5N/Yw0zDTjJi1rOpTBHJljbGvbzJng+XcRo4mpfp4SyY4UefsmKejBpD6s+fTA4DagBpPWgTIIoDrQ4TCC8XR0cujWZTd/CD+cND4KIZO+6kc3XAejS9Qvj+aQ6Z6o+4QANoOhP6Io7qoL4oDjXDS1af4c/fMHpaHock7fHJ3MbxBjVetVTx/PpM1o/ypdlxLooMokBvAj9uPUY4WAGJ1ka2NH+gdGYj5buE1Mq0MfLMMJ54V/JDdwZxlSsx6MkjUmogK4okSUOMEdrGdH0pgMYcAqrhVPg07qJO1NBsTuh4IuHbH8V8UaarupNV/BZTfehISGPonqGolt6jzKMXei9n0X9xANsqrkBHEzPc2kgb0IlkUjrD9YrwKrpJevcu4tLKadAaAaP7QPhhRJ18aCycQrXsVJ4+sOOGlzGzDqmgVdXA3Q0fESnqQ3BrBz8UhqMq20FmkwxfRXqj3TSbvMCNRP3JJnm8NF7lGmRkS5PVvBNKfrPNoofXToXkaBrS8F4PuY7flNbOQtteC5VaWx7eDGDIlGcY1EZzK1qZ7y2pOEsqsDCuHaFVFUkyIMhuwHyMDhkVywieXUF80R9E9KBo4kH29NYg0SOFq53O7IqtQD3pyr8twP+rG87/pwS9tqcPioHR2O/oy7SX8XSrXsdHQcgvuUwWlXXhmy7DJLsCOm4KEd6ZwxFdARs+POWIqD5rcn7hPmcjVr1vsXj7S8y2jEd7+Ek+iM4jMGwxjUIh75NXMtNLiOP4+SwRGnKsJJNCJVGq3XqwT1nGiIPf+bBzPH6FLrhcusyc2ud4nzFlxI4hyGX2J36vOlt6/vJlYBadThmkzdfi08/X2Msb4NCgxfZf4bgGDyUqLpnV5efw9ehFrYgt+zxzCNkgz5fubuz0JRmEPd+kD9I/1RdhiZCOZ2upL7Njoe9mnEuy6L1+B8bPhExapcWp9ze4E9TOORcpbo9MoW9VFM5p0wgsCuNT2H6KG4Qc3rOe1tQN5Pyx4FuAA59VHVh67iizZezRFhri0k8SaclucowHsXqhNEkrZNE0uEmjtJBhdfmIq88h7pcWO9y1CQ+3pNb8B9OD+jM5KBmdb5sQnPlJyMNDiLgKmR2+HVwfUbD1ASdFisHmGwnNUznsMZmoJ2GM//kJzUed7HirjdgaY/qH7sTWwId2z1X0+qtMsW4MPKuEnTrQLAduJmCYhdqAKAS9jRnUNIp27UlsHdJITroMJntfkSayEu2SfriUvETPM5DH8XPJH9RF+u51aGqZYqA1i0lP6hkb8Iy3i6wpkJLi+YBhLDPpZPHkKAxnHyIj5DY2n70wOKpIxDMljL+WMTAviVPvJTGrcyVe6QCVXo48W21FYE5vmHoe/5NHUJh8n+G7M6lAFNExbvjmJTJ89Ee6bRxI+HgAt0N1lG/PwWVgCIkrjEgzf8xa132cL8jF+qYVWosfEfJkApN/ziRAaQ0zLAPZdbOU4ri/zJwmZGHhTvTEepOy6SAxBw7QeXIqMV6F7Fc6wWX1YlqsdiNYJsAmbR6DcnUZhT67sWAc4XiYHeB4aBQBTr547S4iTbuM0pyBxCbGkfC3ghH1zfiJWbHtcynFhQVcyPlJ8M/zRGXVk+2TzCQVNwQbo7nFRhZqVOMQqU3D91O0f1rG9CATbP/mIjj8hQuONcyeZcJ276XEK7zEK+Q+x806GZ0+m3lnX+H5sD9Runkky9SiqaTGuyWqqN3qi9XXI+xbvRHlvp2kxI4izT8Nc/UwSrZpMSX9OB8CWkn0f8Ggl08oHuVIa/BSfr6/zzZtZUxW76BKeQAmK3YzaYA8q+2d8diog+OQG4zWWIvFtAgGTUhmv5I6K84K+KakgYLxLZKnhBNh4suNPdO5EJxMhGhvRCziyKzbh0FcLLnlChTNyKShpoZLC+9h9XgSIctCedbggJSmPaOSFvEj8xtBjyRY+Wotlv6VxHpsx3PPDkyGDGaeZh0qlyxp7krHJ0WKGepTaelbjY+CKJdqnRhevRfRvU9QM/XHV60U91ML6fusnidCcfy185n8dAwTNviyd/xi8m2vM9fzIepvVpO4SAHjU9sRnotjWdQKHosK2a45lE6pR7iWZhMc1U1bgwiiwi48ND78L5br/1/g93/bTvXPWPUPXwb0+Rc7HaDiv3JWVtJJ49YDNHY4M/tdAiuUMlmtm4HHPScW7jfm/JuZjFS8hbdsAsKns3EedQynD6DeJYJ2Wwo5Xl48GzUC64IUouMkaFb1QP21G66rRnNb+IOVJ90QYS3HX/Qm+lMHqd/O8ehqC4t/veChTRXzxd3oo1nHoF7DwT2S+z3fKTllSmdDNzEXLUGhgbD2F+xomkCA0gueAB5HK4kvVEHroj0vZqYzdlYGzc8a6JQXI+xkDRHqhmx5q4L92yImex/Be/cyrl84xp2hhQR37uXOtAkYHnzPpnFjmObZi2E/N/I+8jNdEZt5Mbic+Fdv2Ov5mpNJNkQ6jGDQd9iY78+sE2PZ/geWHlOhKTOfsuxqui89IfnQWj5PqWaFsxEbA70pFPFGU68S0S9FCOJ/02Ytyb3+GZi/zOf/Yuctg7pu337d40d3t3QJgighSBqAiICCga3Y3YENdou3ddu33a0gIgoWEoKKKEg3SHfDb794npm9Zs3a+/nvNWvN2jPPc7z6xjXX9e445zvf8/wUKC/kUE4hpRn9ydkahfnyFaQW1ZIDOF9SRLthJ5HmDyk00ODD/i/45pczP+wH8uOvYnalj7qfnjisDmbqs78I2jKMzX3SZLe44N3zh6X85uYxIXmmh7g1PZJbL6bS4rYQSa9h4GkPI+6C/mqwrkfMeDuNyafoPj4JOZvV6C+YxsaToVy4s5nm6ssM7knD5iGMklWmLz8b4Z9pxPp+R+G6HLOlTZj3LoG7JvuYJpHHP4t80Whu5+AgYywPDsd5xwTCBSvI8jJn8YAoFobcoVDGhpITZ9l/4j2LD49gy569aJxUIGv3b7aHq/Fe3pb0q03MFQp52H2WN+qK7FGTpa9PDy2H8XS2NJH26RGiH3WI8elE36QfEhs92TJnAcM6Ymhx78b3wy86XRTwdbJigrgNiRKHWa4/nMjAL8hdvcTQM7F0GNfwQ0qcZxa32KA+hw8yaaDqgcXvRCav0iQiJweL31uYJ/ONC8A5BuPFT54dnMChvmLCj4kg/lWG3tZZDErehrrpCtYNvEeRaB4Lz8tzYEcGq4cqoi/aSKZEFbpKaxkuzGWklhE2g7Q4ImIO5U64LArFJyyfc72d7JbVRJhymmTR95SXzSbkn03USz9HRLcJuaYcFotnYDrzBuEq2ghSxZgWXIMEOvxUek5mlStpPXHsXfmIvsuirAg6Rf9Fa1B4mc3tYnlcPPPodLmE1KVY5iRew6fuCUs/7MbZTYWklp+4H32Mebcdm7bspnGRJs+8amn9uogaFSguyqUnyhSz9GRMVMPAq5zPGTAq7jXyyk4kSf1EOMQDBdVIxi5+QJbubMJH34XLzfRX7qZt7DAUvF7j3DmR6A0VjF70h6N6g+i7XsqF7jq+B85F2nkVwU1nsamP5eNrU762+qNzbC/Ky7y48nkThqsN2SzdTae/I8/MHZn2+zK30gSMnNaB1xgNXpjmoyTnz8GzeiRmfWLf5C3YNuuTnJHLhLUTkQnoZPeaUo63jqJgWTsyURmodVykJXoNt+pruOM6k/yxEohofeSn+TJutlehrSSBXEIbFcplqNl4/Mti/v9F25WslpXQxieSly1bCR13kwcdKcTcv8jfi6bwhnqih/3mWV89RUfsOH3yEA0FG1ghVcTgxdmI+frSl/GRb7LOGIvocevpCbw32OB88B7O375ju2UprcaHGVMfjW5mLL5izbyVtuWDpgwLzi3l6V9XGHLDj0rvfez3vU1p/DwGuu1hRkgMJs79EUgfRsVzKw+nLkLgZIl2sT0V0u+wUZCgSGMQxjqPqe5az5fcswj6XqHYNJd3Rw7RZVfF+2w55je/Ye6NAxz8x5htU3TpE33KEIt5jF25nvqiT2ydH8Zp+07+0Q+j0u49mz+LgP8WhKbivCKYkOzZnDX/wu0FQrIanfluvog1d8sI2XkM8Ttd7AuqwSOnkxhVB0ZLSrEkOZGtihKgo0qHZRkhZwayW9cFT91XdOQ9Y6nmayIltOmUkGDC8wrotEf46xZgyKmgCRgeXMzb1S/4NEIDyZpGptqY8GfGNgj7C9Z3U9F6Fq27exFUpKC4UIO+q0M51XKUhhp70nc3sDdeA42Z82haNovj8/qT0HgbopcgyKjhlaESSD8CUX3QkIT2BkSXzGfbylpu7KrD8E09c1WSyO2MQjgdJkhpsMDdhMHrJyPno8P1FUl8TZbmkJouSvEVCK6FM6L9FmpK37EuzMQ43pIXtkO4+2oI02fGM66kjqutPkSLxrJRrg9tEXVGvqlkqf9iPJXfIOgLxbFBH5/1r3g/cBzbzTS5OXg4K+ynoZX6ljuJJ2jwaII+FebXjOLi70CGldxmxlURwnqs6BCt5eShaCx/DCNf5QqJ8+dyRDmdiGhPijhKUIctxQ15lEiNRllJk9mDA5jxYhoyGo00Ko2ju8KCwZ5F9L8ZzdjdT7l1oxtp9XP0CGrJcbHkiO0YuNyCwCsbzO1YeDSc8wCnCjihEE3l9NnsHVGGUvpcdpblUdziRdCEfN6XPeF7Tj9GuHnw8cwyLDRUmNNgTT/Jk1zNAvOFQvQVEjArv8DaxtvMtxyP4Z/hiLVPQrN/HRcrN5HZX4OX1X2MeNvN8AUmfPN7TvAIIY/3dlPYm4recx12qNzH885fDDQYzKXKTwTKNrDbWIyd+27zNaEKtWPJlHl8IrNAjsFi3uy7tYwh5bbAHzy8U3gX82/u+ZUWyuB/+jiccZjm+O1oBDmRJ/jESI2BKAs+cTBBndmLxGj4s518wrAhjwn9Q1EVlaCmoZim8gSOhdsDeRCZCpuugcJkqHxD8owNOFu009abS47Qm6akcninRfTERESOnKPLUIlgv188janl3rZVJOa6cUtkJ6G/HtFg+YQ+HwPkfu7hT89Tms00yC0wR7H8Ptua7zEfBb7JjoeNs5g6dzlmX0/RGnIEk+JIErt00ZuezoDH35EzsGWcxFc+1u3GbeN2jBhEATrs1nrJtptCUnRg7elk9ro1sSKwl3Q0iJC0QbjeiYf9vXn0oBAN+VZQ0Udw4fR/tV39v/A/qFWHgdr/ZuBcRSgUbhQIBH7Acv7vgfMTQqHQ8T/aX0NeIKySlEFwopV1ppoc2VmFYFYFPgcsUZ21HG1FB4RybmQ/UuP5ysEYNVyl8KYZj4dt41y7KaqFP5j1eyKPbNIJ7N6DqLIIshtTebT3NI11JVjXSvC8/Apeqks4oenASvtU+mLVyLm9k50HN5NUWMIL/z6+blrLkIwhXPbQ4qDCGmptT9PPpwi9F5sI2m9OrPdLUiYa8XvxYa7qP+fLnHOcFJ1PTelvVHV7MSqfiHRINj5O7RTFefPj7U7SbtznceFAwsLGAwNwc7zAP+97uHLgOnV756FZeJnhNs2M7HVlR30XUw3XUlBmgkzlT1rvn8Rt51g0MiVJ7fHAPcOdlS3ziH9wmm3GL6n7p5XtDVpItFnwsqSR21Up2KeeY8/8dD4FCVndcwrfloms/XweW7swtjWEYPBFDs2qxSy78Z5tg+9RlPgPvuvXMtjIhsZl4QwdcxGBwROCdGQxk7Dg0KYkwsJeQXgpWpOuU2tRRGLMNKaae9Hrfo5dA0I4dOwpNTfieH2qg2v15zB/KsOs0Am8CtGlcPYitIeJMvLXd5T8/eH+Whq/iaE4NQJ9R2vC9qRx1gIaRiymNLWa0E9ZjN3dQmXaXs4VJ1BnVUDqfTnOrB/C2teNrGxXwWb/OgbGQLLYVcIe5PDYvxDtK0N5NMeTijED+LlBh1WCdCLNfekp34HceleeXnPEtsyA3X/OEle6iIsTf3EwJZm2c8/JXfeWe4fG89cYUwxX1KKa9QYJrSacW63Y/6WG/U6GnM55QEnpFKxK36GxS45311pouvCVhdIuNB3uwOSXK4UGMoiUiNApI0VZ32NOec9g+i8lrCuruPVlNi7uwRhLjSItJxm/QU6krgigdqoJSuQzqDoB8aH36QvpR9U1FYxlA/n8czLbLcWJ2xdF8rrtbEQEwjMJZyJTfh7Bcu4OIjsTSZ3+hUJBNZapeaQfP8vfITDctp3IoTlUlerSU3cM267DbP0diLFJNhdUbyJ38wnNm0+yJuYq03p0+GzriWNAD4sfiDNGfS+2N/Xwd9ckPSyJ5rQQIis+cyvGhsqf1kyw/YpXfRij1ydxJEEZG5Msdn46SmvzWly/7qKpWRQ7y3wGOltyvcqPwnQpFndX8GT6N9T7DUaxKpQQP3g1Zzhrm/QwltXEJfYoqnvjyRp5itG/S5GPHodv8mYiP+4kUhBKc5gkxoQT0n8nR6Y+JNxUEqHKXa6PcaJpWyJkKcOyhHj5jwAAIABJREFUQaA7Dzxh5zwBq/9JRrIxE53MTLKlXRlllMLY4l0MlQ3D1xB+mwn4eLeDXL1bvA11wnf+KQzqNRgVEITO20soqQ+hXnYRKorXUU5YT/qntRxZNBABw0HWhDUGOShY7YINtmhtmUnlijK+jHQhRGUDf0r8SOgo56dOGDMcvxN7exYumrGYloVQs0mEldZF7HjhRsu3MRzTlsFAZjVqNbN5PMKSvSPj6dm1g0tvXFk0KQztY5XsTD/3v7Xt6hkw+9+vZwNP/5vns/49xnAo0PgffXgAqAiLmOYyi2e+4ixUW0F8+j6kldrY9Xg1544XEVS0mY17ZiDuMYWIg8s5tt6Aifv6kJ96l34tf9HeOQHZtGfI5roR7pOAQexNpt1Np3GFgAal35w968iTsIn05SZxUs+HhgHJjCkqoWFDHCaXKogckMvTuBm8OjuXjM/PMD9xH5eIuyjOX4HVoGDEMiMRcVTFPiOPO2l5vPugg8XwxTy7PJXjXqtZ4JRP1qObZD6pIfHtIYoyw+lJ0GD17HwqHZ7RIy7NtjXjQMuD3mGKfM15xZ7ov1F2deX0OmdkCh2R/fSTpn1deD7fgeCMOCdfwueXI4iSMOTIzjDiVoQzzq0VD4MdeOV0MyE8njUTTjGxKYBvHsd4GJrMgnPHWZVxmF7DsxQKqul/K4g+yXPcbHjIK7Vqluq/5tIvZT7eH81f+515vGcXM/YP5qvgC3NEJqLweB7T+lqp6efCuhwBIzqWcGLGYmTCzrExdz+L1n8l4KI3Tt1fCK29h3rKdd5qbmFV0wOODjvP7rd12PxJ5O5fdxitOY7C6HxkrwZxw8cT9VlhhEeuxiMnm2bDSsLtj4PCDHpPjubapunMnNCIe1sVc3dE8dfjrbgLwgh4tZByJ2/KR69m4toy+qrNsfWQQLl9EXILw9n5/AGLHucTsekzBg1zWPjPQYrj5nK+bQDd4pkUtVqyxLWU2cYNVDsMJsFhOrvGVPB1nSx7cp6wvakRDacK5j8T8OdxFmaPllKwYyxvNS5QuFSCd1qqZGq+5WbhKAr72lgr2E5/DTGEexuZ/eo+q9NKUR+mTVjgTyaMFuO6ZS0bW9pQmKxCyMoYNphIMXPvEZ6f8Oe5VxV7O4/gN/sWZqNOsc7vDGZLTxBxKxbLnoMcmdbKsNYFRJc/JE5dCr8PBRwNns/F0HQSc1SZs/485cPFmCuwA+dqVrYuYO8eZ+pWzWe+xAf69U/hqMIaLqTW4t2hymShOu6PKwi0N0NSz47LZhLM7/cGkx4hz6/NZd7LUi5+buJX7W8ivoxk8dd2ZidHE9f9lAt1+qhmi9Pc1M0U1Rh0L2SyNHEKavt3scAsFSULVUbmDsAvJ4Y7YtU8OyLJ8rmHuLrrMrYTruC6YyO343eybVcHjguncsZ3HsM0mlh3YzX7r/5k/60x+FXDUIE/AoEAx9HP2fqyGyubwyQeHID0GH1cxadQU3WN9IYiLgxche7SBmZpg8egnWQMesui3t/sePCWhb9OoRguQ1yYISXTLEl6f5WUoR/ZeeAOO/dX009iIz33nBFNViSpVpTBAVbIbXBhbOkIbC8sx+zGUtx0LjB/9FTitjcg7/OHV78ErB47gfDS6yxNOEW3yEA0E+qYfC4KowvvUf3awxj8aSGElK9RBNw7zlztblRcVqFY3o3IM0/Gd4QjFhTL9CIh467NQTotgs22MpSd/skJr3Fcu7wcudO/2GKWzKbYy0ybZM2wVgHp7/+BE0682nMFHeWRjF3+ENmBOSTOXYm6yZD/SV3/50AgENwGPgP9BQJBqUAgmAccALwFAkEO4P3v9wBRQD6QC1wAlv4rZzR2QNH6Sfh3QY+VOXgMY1NfD39qGshrjqJGtoNTB55hPiaWh+8aEfySwENJkvKqFAr7iRBcasTaKhGKi8ORNdjDqKvVWEno0XvwIFomN8ksLEV85C5utJexr/4ZKcqddHaMJAeQsrSmZWAx3T12DJFvR1PSBd5XYiDTQ6GdDXVJe3EctpZdF7/h2anMlrg/yOjrEaP4k+fy0vxWfINuxzu8BCMQDWlkmpMf+dOmExj/krj2hVQUlrJuXS3QBNQTX9fHSL1axIG/fjWB3gSa/hymufwoS8QaOfXjOe11P1gsH4CTuwdJqkICFPbhOvQzo9RfQVoSw7/k4ry8F6Nji5BszmR87mcsPOzQ7bNEujsdjaozNIjEETFoOZ/lCzFTecK9SlfGtF0hvyaXRpFTGHXqMXa0I8pvFnFj7iESlz9HpLmMk5LvMLPcQVP3PdTqtwGvUA1/huUKJ3oDpWioUyTIIp9sqVU0SYUgV/GGzrnjEHkqiaOJJo+rHehjCSVfdZll3sGIh5dpNtdFSyQb9cfuNDW6cXziJxwWR1DyUZ7XwfDoexYSvcU8VH0EmePItZ/D5N05qJers9H6JRsfvUHWuYBiJXGuVxnyI2UXG1ViMMooomqDJepX7DnVXUdFVSRjdOG38VVGxqdysKwfncql3Hjxkx5zcwpKqtBufMs6nWp87wQxREqPDX52eOY/pFAwnbtTJBj3yBeDZ7Gk9LynNrcN07Zubmdd4GSQEZjq0OgaTndcD8uUbXHRr0HK+AyDRfr4mptHslIb9xJ/4Cpli4GeB1qmqiTOEOeV5h8GV49CNj2S6C5Z9FpMyeuvxrbkVFSSS6nMmUm3excBUq786CmmQqYRtZIvOFyLgdgoqluCqBZt4x0CfvcGEoIFh1I08HeupX/GB8rVHHgnnM8inQaM0jM5b1zAvEZRwl/3kT1wLIm5Cux1CiJLHOrj9fiZegeL2nS+FzYhaTqevrZa3pdlAJIUiFSQrqNKRLs0Xh1COLCQ+XYHmdyhiblhJMnSH3H4sJLWmr0cfR3Hl7R35P3ThsmUADpl93FBaMJb18kM+6pOVUc+i6WLiLLTpp/uN1alJxGmZsUqP7BY4cMm0xq8LY/hWS+Gnv5QLvfFofBOB0GRNlFtf7CcqU7fHCG+HvfwqIcakY8YtkF6rjTTQvxQT7GjaUcaxMVDhi6YTwHxduKtCgEo7M5GI1sP/bJMvvd58aNrBFt9bEisuYhjYhOrjbrYGLCLo6tvkYIxDAU9kbfY/cjgydpXlImb0Xcuh/i+uTQb9+I71ACQYw/62LsPYM7rt9x/kYBwzmAksjZhssmBmhI9Mqz3siw5gZmTDvK9owZphZfY2WnSbeJJ0vRZWCmO5rrhIDS6Qug5/I4rhpbo1uxjtrYtJTEvqVp5CTNXcZbK6GJ2O5fh0wf/677+F6J2bwPDATXgDxAGPAHuAfpAMTBJKBTW/XvU7in+LR2rDZjzPxrg++9x0FYQGo5YwVA5A+4rFyK6UBzTHHeu9xaAniJKofmsGzaMIJF0mop+oiruxS+FITh9mwYOAZwrGssniQjGPn5FvyBt6kbo8bDqFk0v7rPcRJoe1fNs3HWPQ3uCkS6qIE3aG4laU4q1e1GZrkh61h8URJaiHiVKvpkQQfNEgkuXk9NuhfTEaER+ZvKiWRy10nqqS09RKwfuBn7Yqtnju9YSM+0pxJOCbtMfEq9Bb5kvIXcOglx/AjMmYn3oPTIyHei7J5Fz/ztyCodpfJxI60QLItaFsfiUFd8LJrJH7zuD+hR5GN3DtdZfLPRwYI9iPE+XzuRZ0Ck69S4QUfeC+qf+8OsXJxaXobxYmbHTHJh1oQQtkftUxNzDrsOYfLsTyP5cgaqjEkOenGWlzSaKzUbD/mF4VU+gUEENO+tLNHfpk7V7OHuuD2b6PG/WjLREMXYtshlduMbMYHyYJTedFHkbuJ5eVTFuDJhEf7kCOvu88QtRJHnTDILbbvC59SKiLn0su/mEhHJbFg3ai560G079JnHN7CjIlPCl7RyVYyrozDDjpOA6dtfMmWh+lGtfXnJk6iNmvU7m0fXpZI8Xxdy3nbPnwVkVrLTbed5VjXjDYr4MSCLd04O+2DI03Iv5LrOYmn0PWRpYytY/J7h2rY4xx3X4S1OPnopLiDbuxqnGjjo5M2IOLqVvXCi/9F8xqVSLV5YHOdd5GiXdN/x9M5Vt8zKhT5TzFo7Ev7qIzGBnyle48q61hDOBvqTpDMXteRy2qgXcaLBHvHM6jxekMCXnKy+zzfB3HMuGtmIuW+VTUTQAdRbwzO8W3tGpLK5uI8WkjjD2MCQXunqiaOnpI6bzHrnXrjNtt5BvGbXELJTjfvUpqlOtiOqNY1HTW+brh1C70oVNSUfpkz5G5cNjxLy6D0n5AAiPTUFgvArsenmRPJ2F3dOZ/EMVrT/f+SNuyAxlEe4riGDXokdYggN3B8iSqvyMSsFQ8iRkSY14RtqgDFYaB2E8QB+b2BHkma+mXahFTJcyQ+XnElwtjtnEC1yVnM5sqW94znZHbuAdNurZ8appKLG5cpxy/kxIxN/82LmcB6NEkTB5hJxqKZ+7HUhtkuLhteugoUm4OISf/oHg/SNuRD4iIa6dsfoqfMrJJXVPH9VF8Vje7KFCv5e+1hbktIYyykSewhO5bFVo5PLPffwSVaDpw11W8A03/xSuNqzgjaoVU7svEmOwhN/K9byU0GTloRYs/Mv5YR6M7O/LNHZ1Yz28Ej7Nh5o2KhfcpcxNA8Xn+ujdcKUx/yx7/M3wsWxDEGmD4R8ppqp2c+SQGQ17pXCQuk6eggkpHn9Q068n48oU9lsEUiBvh/TzFwQOmcHGO/3Rbo3CVtyOJ24CTG4vZmA9xNyGz1pFBBQasNFtGj9qxLjTLs676moEjnr4fPHBNSOPupFBdKV8wUTmEQWeGuTEBrM4ahaeV9eiYL7sv/58/B9EViAtbD19FrcCW9wPT2SnkTEvHOcw4WMMs1bOwpRSPN/94q1mFKPrK5k1P5JxKasZrBfGSSUFBv0IZfCuz2S8c8O8ezvLsvIYF/OT8142aMeUs6VxM++L3bAcKkqN/ARMxXSQ1kzkWfhh5i16QI+cKB5bs1gTkojmF1e+j9zEROF5MqReol9dQl/QQWZERvBeYxCJldks1RBj24zdhHR8ZYLXCNzWL0OroR9LsgP5O6iS96eO0k9hP8HfRiC5NwoF0QJ6ZD9T/7IUPetIqprdMZixlsUu0/nABpJmL8at/xm0Bigx/24evqbaOElLsl2ymMVy84jet45Js4xYl5bK06XZ9PcX8NfmkwyRM8LZp4mz76tp9Szlxb7DjFQfiFz9BOwN2rDSe8+N+lms9i1j/4G9FDecxKn7BkeHuTFW0pb62nSWSUtwRNmJXnVL/HxMsImIwF3ChuHx5ah2pxP8ZDU/gnWR8J/LIfvhbH8Tj0ypLSk0cPn2A85c+MYvj1eY9aQzK+Q0kwbMJ654GzWnBZQcGMu9ShfuCHyZI72HvbenIL5lPIEiCbS2PkHRwBXXNe8Iui1g5axjeE4bzM8ObwKnypD+GKZ8/7eMZzlHU0rEb3CpPJqdIXBprA9V8aa075FF6BvBM7VuvkuHcffGLiYv28CSz1NRyHnCKJsTlA/7B9esQKJ6ZPGU9eX7GFt0pn8ha7wjq16Y89miGVcpEPvQxkqzanoSHmIadBXN3L2cklVjuGUxmvLT8HxzjXcj93OophFDhUpm3NlBr3wpXyI7eXBIyFavhaTEWnNB6MCD7P2cafvAGad1ZEp1sVG3mikJa7h4U5a/Z+vxvE6LCeb32PTzA26OhczYoUL0+dE4/EniU8lXnr/vYJf1UCau24TTtwNkjt3MomN+6FzJoyFoBmP6B1OnGo+vwgAE9+MxL5Vki2cX1je3UjkzmX43LrPLV47xbWa8sG1jwlUZXi1tR+sRjO5axA8nRZZe38OYrHuYRPjy5fYoEh794sv3UCQkPjC+MIHcgxE8cm3iQ98L9H4b0yZ/nzz3JVxJG0eg6hTCq43RDLLD21HAj2tCBL9ccXU34WOnCBbSyixqysAkfQ3fGq8wtbeFXxKG/DNEg11Z4Kg7jNXhI3h+YzE/LM6y+/Yqfvzph6ZDIeN+dtJvdAwRUf5cenqW36oqlPdfTe4oe8JVBnK7YS0pjx/QuTCCzdmPmCS7FFntfcz+8YPtbUI6+97yxaeWfmOCCX3uTsOh1fyW2Yzp0lAs85VJff6CsQ6XSdphwWiFydwa70Xf3AEofwyGSfsJ7asjcm4V37/OpORSCM8WaDJZSgRxx0pMXXJ4ixMBZu50FifzwSQetY+nsZE7idtSTfzCe3C3WYDmjRmwYyqf5Bpw/WkDtRMx0fdE/MBLcjNc6CycTF2UOaej7qI9dRF+78SRN7yLnKs+Yg8eE9RpwEaHIdin5uJf8ou/b63iuu64f6lGif1HC4RC4dT/h1ee/4O1QmDZf7Tnf092szy71w8GOS3WzzlNR/UFjmn/IubZNxzMkzkoWUzGywxUD9ry9GUhzVt10Y+5QlamgKmCdvYnmfFqvTZBhnuoXz6Xt30buX16A6sNtFg5bR01v3S4FSBNv4GjuG9vjWyaBJipIer0nRGnjnJklzXhs1cyxjqI1RnmLPFTZ6ZDA7sj/+Hb+8dUZaVhm+jN6tk70Lx2kvTe33j8/gTvtRiis5J7uzwZ3jwEQWwA4yKeY71iOLt4h1wYKM54QbFWAEpaa2j9kE347vuQMJ/FPUrUxrWz8IIsZxfsJ5SLJK6wJ60imvYmXTy3/sHyog7Z7bIcfXUdM/2xnM0cR6A0pBoE8Gf6SGbZ1HNy/VcUNaOpfByHX1Af5dIC6O+HTLM3f7xFOBuaBgIP+HQAiGRagCMzdIdg+3cZdzQH4zItlcM5s8hPauCfSTHcjDlFaaoCdtrfqPzqzJFbELoaTo2YyhNJc07XvGJ033MeBjykbVMq10znUB94HvGX+cjckaJqkRs3a2DziLmonpjNMdWx6KuuQhh7D+F5R9b8HYNWpQjxr5RIXPaQoTNP8AuQF0DwyKFkBXRxWgAnl8HPcEjvEJKSvoa5n3tZeqEfqFsR2LGN94ePs2uWGs2OS/jttZ2fj+5irDeJJ8ums8fpLEkmTUhY/cV9qwryPFx4/WYg10M9qYrNY4PEbjRUspheY8OhPCUUa6YzVK6NpuXrELEqYqyYORPGPaMkPYPuQ/e4VJvFa8lW+vJvs2T7VBoKJOgpV2F2233WvRGh2lAPEfcYOvTkCE9qoSJFSIX3ICS2BnLm4jbqnoWzJf8DlfFW+IpLM8RnLeVV0mQ2DSdFUoLiSet4kTqTYS2O7BobRqeDHS0O1dTWdZPqeJXOEgX6jqZjr7kQ8cZoRGz8mDH7ABq/w/Hw3Ypz0C1Y6wyAP6tYgxURrltBQxY+DuPuznbKosM5NHIdR6dEM3DnBmaGy2NZuhEfYQpHB9ry1cOFXJWpKH8Kolr9GM6JSxi2opCxqYs4kdfIP76NqBb1sVjxOeHVRtyaJY2mxCrOqY/H+dJ9jJyvEKO/hX075DhWuJuMpy5cjDTCqC0BrRH6HA+dwsiEFH5aGKDUIU/OkRFESHrT714Bpxf5c/z4HL5MGcpKv5s0KPTwV1QtiUDTZhUaUy15WNJD6/5SdstnMrvhAEFZMuQZ/UKx1QXjoV7sDfqGw5cnXJgcQwUwZryAhbkw8NpoJAdZkSEZgUmBDuYPetiSmAcr4glIHIXwQx/XaqrRmTuQmZWzkDGbRVR6JhLDvTFqWUGNwXVO951gQ54Jb9cdQfX8HCL0XzL56Wsmuc6hsy8Q0sqZYprLnsC1bL/Rxfh9C5lZVYOWZxBmYpJYtEeSpTyD0X+uo2SWQol8FX2fBuFoUc+Nc06Y2k1hn0YpOysTOZl+GzozuKmnQJ6TD4ImbfZIreLoQXOki3T/v6r1v/hfjIiGNqrXl7PrXTOVnyqRCFrFjeyJUD4Fp4LJdG3v4Ou3d6j1i6Rr2EYmGD6mn4gOl268Z8ggb45fjqPmqQp341VYM7KLMr1jrLR0ZIGmDulzNRl4WA9/1VYkyqag638V8lajiiVpgODtG4Q5oex6Xs9WY0Xe6/eR57qAzvRkfl7vh/esWdy75kPpyFuISSnivWQ2UoUltPa0MVs9is2mm5i99Tp2tjbcm7AVNZsI1M3u4Z80ApXd0N3zmU7BV8zcR1CU6UB1zxH0ffdR/fsTT/YsIjDqHKdsr/C7Sgq7ZzcYP2Yj+WUJ/KzTYlBbCVNk4rg0eQVdtV84Hb6DNGVI3rOZ6OAROJtZsVjgz70VOnQvlWRKsCF9VbYoarjwRysejx4jQl4eZfXmbVjXynLgkxR1y+wJ9JDiVV4dHt/8OSp+AZVaUQy6/AlWg0QPWTRjsnkdD4dK19GwZiwrlrqxr7OdyXkpJFeswWPyStRPu9LQ4YxFzyXs9CYxLeozeslFDKr5yYPqRN5FwjDPTIwj7PHq3UqCQQvlqCDV8Qjz283M8B6FhnESi1qngIMTkvOcGCcxmlgNGCeEnEY44SBAyngq67Lm0Pwuh66hX8DTgZK6D0yNayNU7AiCeYP5/vUwky/H0eygi26+EpM/bOWJ635aGy+ioRLIjjulKAzuR7ytDDPSPjFgnAJHCqzwUY0mtU+EabkBVD/7wrTM4cyIz6frcxo1ynqM/yOJtIMmYl9e8KdMgZKudgLEvxAivoeG8cM5vToPEyM3bB9CqlgycubOVATmcf3bJLRbdSnIEkVHOor53xZyvFUci0F1DKvUo7nRlCklSoSMtUK/IJ44Wy3ePt6Ng5UFtQ2yZCr3R8dFhDtL9nB8sToui10ovmONvNtQur/u52VPO4iJ4btchjfWcthOFMX9YDs6HRCjW8Wx/o6MFUQjfq8RpZ3mBD8uYlNqN6vGjOV80R2iRcXwq06lXcGO0pJafBxaEf51lRYtAcN901GLk+aQYw9aRroo7v+AudxbvtoLcfMXRa7sARWBIO60hLLwV+xOXMH1hmrUdg/Bct8IEtrPcsC8hpOyw3ljD59TWgjvcme6tBWa995w110CM9k0DlTp0OBylu+V4FZdyJsAFazaNTE01OL0x1ouXU7hVLw8H7bNxfy5GHnybYxRliIp1JtyRwkEdaqEt0gh3/wOW7/rFJaeYFjLTx4tHgl5IawGeghCTM0QOSVP9MaW80fkC7uXz2Spx2XCPhoh6N+OmiCWoueqKFs2crLwI/zdQqj8apxOVjF86DiKZwuRNyxDXy2W7FdfWDd3Hhtr7+Ju0INJsTnxYlEYDGjl6KMfOPftpVj6GXLO/shmRuNa7gBWX2D8XWruv0NkykU8ZFs5uMOY6f7a2PYbgujvBg7XncbXfyIj7+QxeIojt68KmNT9h2ZlJbJFJvP7cPq/7FLR8PDw/02a/te5eOxY+NveYDYqj0ShcBV7o84RoO3Nvdc7SZ04mmxFL9xu5/F24Dy8zGbSE7sEo7YOvoxbzInkF2i3ZlEkM56U+jg8amLYvWQAl0yb8NHqxm/vCkZ+eYO06x62fJ+HqWQlcsr2QBqr+nuRsdEQQ5dVlP6KYpzxaPYOlKeptwGnYzso/KzGqPFBGOuM4kjscdQCqthZ+huzGgGG8QbEJuyjYdN80sT0mPrhOjUhxzHLC8fi9VUURoog53YA7SEdKBQYMTTtNX/EixB1LUND7wT+7iqoTPtNdlYI873a2SSdQN+7XC60eBKX+h3tV84cbZRht3MAO++e4ZZcHPcn3OFmRzXxgY/YUb+VWUc/c7MhgbViyUSHDKThURxV7mcQKT2NhMtiJFKaUUjKosm+jEBbY9weOvDF2onRxRn0TLFHr3Uyb+pmEvGmmGXG0mjdjaU36yjrE2WIPXcAr3NOxN8tZe2Vpcwz0GeiJjSnd5Mhp01hahCGAxfTODmdY8VlZP1M4Jd9F1dNdGlU6MeeAaFEx0Rzo+0jN/p62Kp6FHepvSSUqTB0bDtyM05jKdtMZKYaJvfbOH95ByKDTRjeI2CBKTy1hfZCaBpsx6S8TBap3sPeNBu7Vx8Q/PpEUOIM7shuwEhKiWnvD3PPdA/j1DpwL8nFJNGMhGtSxMs6oG4QQlZqEv4DNjDoeyF7TrcwXeEBzRWqxOeLYDNrCtWNPdTLudBdux1rg2Yw8aRIHaRFnekS7eTl1yp6Wz4zUb+X/kkfURL7iqXSDSzlBrPzsSoipt14GNki1f6AWR+zeHz+KPJiSfTOcKJ/ywmu3czBJmERKkU/GTtpJOpvb/B41FI65Mxp+JSNa2Qrt8ymodc6hd4N9/AR9lDW2MBAVS3UW5V5JF2LX0EFMvKPsRjeTkGNKR0FOTQFBuMsIs2Ce6I8G72Bi4nBDPq+AIO/ByLzW4BMa38mDb5KzPc+rju6od1ZT8DxPXjsnEI/g1Q83t1Cq0US+xMZ6GgK6FJzYoGbHsFZR4jXPkDyz1oGuGwnWPwmN3K86dX8TKH1K5BJYfWCvaxukcT+9xHEx3dxt1QR28a/Kbifj7u2EY3Rr1CdYYapxSgKe6uISHnGjAvreTzMEvHRciQEujP0Wimbnl5m0slTKFf2YHT8NbnFF3mWk0SKojSzP41HQtaBx1fVGRsgSdXKNgqldDFZGMP58A/ELJtPd/ZZrIKDyKpdw66YmWQdfU7pbAvEmmHveg1UXFwIvZ3Nmcj3OIiW8UrNkqFCH17H5rOxXxPWKYqEWooyLuIiqb6qtNWKkRL5iAVvrqPrLqCxZAu20QJKJ0ci7WrL+4lXmByfScacxzzPEKVX9B7jMeZbPwsU5B4RcsaY/KZXbDI0pnL6ZwLTy7HMbMHm1C10JWpRS/mDyRQ4mGrM3uVaKKj5Mau8FmHjJ6I9HflY7khxmjHGvZV0ZNrSdqePrKyNnPsQQVHhSa6lUxEeHn7+/7Sz/7Ny5tiR8Oh5Rxm3ez7jjRcytEqcvJIGejeZ8BlFxt+SQmP8F4piB5HXlIlTRz49MgLEHzRzpOgjCxdsJrhOB83Q8xxct4x5bfLEVA8kTahNbt0dClMr+S44g4dJGuKdRkhradApc59houJsOeSGvdiyT7gXAAAgAElEQVQgxGWXEiDWxx+t1xQkD6Tsij4By+/TliiHkbszS4MriVEejXl3EVcfpjDrxQvCVPpjki5BZ64Viz4N4J8lc9k93p6WGSOY2c8RLe+jKH4qwUFOk0vDhIwrF2WheDCOWx2JS7LGdv8cim+40dr/Cqc8BrDm2H4qKjSZf8aOAztvM2/qDFpW/SF/ZgVDzBfiUxdCT2EUUbYivHY8yvt3yRTMmorPAhs4eRR9qzxka89Tr6FDQ8M9jN+Yc8O+H8XKQhxLvZF37SK0rALtXEvyLOQRHZFCwL0eVsZqE/XChcNAydVX3OxYxzYZKVJvzkMtLIzdt9QZradKZY48w0y+caHfCGbiSXTTLkLcJ5NyOQFz1QH0Ds3nSbsxwdPmYPUthpMqu2ixbCfp7HecvusQe9uHrfZKTGw9w72ZxbxQKSPJuo4FusMZ5WiK5x1Y0wUlEvCmHwgsbCl8LYLj/krsDDRB9xl3Khw5t7CS7CEmlDeP5kPpL6Y3KqGkIEH2ih8sdf/JyMn+TM0ewqEV58j9GMHvNnvOefnQJrOToQ6XOXDpEnoWj0judmZ4pgM+yrFkxB1k/ty/qNjeiJ1fBl2dK+kwjaRetp4+kVyecRw5wWtKhr9m4E1DHLVX8OHjTQxnjWJEkSKvvRUoNRBBSnwBDbnnkBNmIBPwD5WRJRQ1yxLS+hdxG515kGrFrOEtmL7JRlL6OX99qyTZZwsT7X0Z0/CBD1lCuhulqBlmjrukBomf8jG1iOZKnSnd1aX0t7al+NdfRITFE2H5lPfxoUiZe+G5wJouLy9WnZ+DnnAMAzQq+D3Ukrp4WVYOnM76XwsorK7HbrQzOdGfcK0o4O7QAOY5WYDzD87f92BVUjQCv8mURqYzp/9A3sXZMtp7HZf6tjDaUoZMmT/IS1by6J0V3YGaONVYY6TsyhfVfHJeC+hvNwu/mBg+29kjV5NE3YtkJhyeypW8fH4XVHCQOAIjH1I1xJv4xExaB7jxUR9EEt5ifEicV7P20lduhJRZJ1GDXHDpdOaXvAR16r0kbani2twUdqhEYr5YmoCIJrDuh0/jPCY8uE/vTDMMVqzBvH4VBzeeZFNpMHd723BL+IeW/qP5lTQIYw9ZXnrmETq8h+ZQY8rHKlMrAQvzH5Jts5URmyN4sXYqm150cGCCPqu+f0PqjQTRm58ivaMImff1TF1jR+hvG2w+xxKutoHbwxroaTFAVm4gGbmPkJYvZe3nhxQHXcJBLoPIYm+sMu5goPeNc6MX8fr0A5IvulGjJoOtnRVvHW6ztX4AsZfjkFMNQc16DZ8VrqOQVoJfVg3qgVspFbUgNfbLv1Sj/lenXf1PUa2kzRyNIhakOyEcuAnh5r3o+fURcv4YZQ2mXAoJ48IhGUwyLpJ4PpimtCU8KrhKulsjus1DOXX6J4cy7ekas4QV3a38ZfKLAIsAzmsq8TQ8Fq+gOWy9poRCcCtKneUgmQTCUs6/e8vwUxX4hc/nreFn5qX0sD27lMhIffZPW43/22God2YTPtEc6bkCPhTakRx5i+mHQ1iclkLN0sWs/PWB8vgwrjpp4LpIGY8bO/m46CpqUb1MmLeMw7aBmIyTw/xwPAZLYrFO2M/2TUNZeX0IXnXVnJ4mQobLGFiwjoKNg7hn1ciuhlIqXUoQC5qF189vzDX7m45mMwJmC9ARvqMl7wT+smEcuZnCKMtlHH+pRqKRMuUj3lOZNwtZf3sGKq5l7rZ2DrZnErH3JLe8HvL3bXOiBlayL6o/CSsGsG3ESMYd9EL07Up+3t+I84YLhDqe4+J3H4YOXIDUgAOYnnnPw6hmsrVs6DaCAd8PMudXCeZaeXT22nNzixH/xE7lqtQbXI6bcKBdkrmvPvJg8RQaHJVwvaeMXGYcK3Q30LvuANbbdvD29k626oaC3Qr8PZcx5Pp0Rk8U8OyNAJFgaRbag6YYnFodzuPCQubOn0Wnw0l29cngYv0RwSBvUvs783XvIC74HOeN6yxGTJCnXVoNzcFfKRgbRVFlFs3GF3hXFs+f5k+cU4ji5NULyD4qI1QxnP2692g9uYDqks/81kxCTbmFq0tTCdSaj/z3Wpal1qL5+QfqXc8YOiIGW78BXE5zx1cxFxfjN+xaGIBxRzHLxqWTbGhEd+E3xG7P5aBKBF3RURTW+lJzspWwaan0DZ/DW8llFE6QIf5gFOvEPZB7MIKSK7bUmffwj91jTIvukZBqSfPXH6x+WcX6Ow1sL/yKneh+fM22crxtOcFjr5ByQRP99AtU57wm4dwDBBrKCE88ZUSAEc8+udCV0Y3p5Rr+ObiKT7e34jZpIcIns5l5qYWoAg02yBqyLG8NH0cJeNw1jEUSphQ/MeVb8mzeno9k3tNqgsccpduhAz/TV4hMm8QKMxMEzltYYmFLmvg49jTUk+foB2OOYmpQwdX0wbypDeWoWgHCUTPJHGpN8SJP+ms30H9QGAP875MzWYfPY6xJPTKJyg4Nwh/UET59I3fKhQgPJdCyZQVpq0wYczyIC2W/efGxhD37p5GU2En4Z1nuDNMitHk8EYfskGI5eyJTedGznU7/WwiP3+HvYeZwwpA3QXokC3tQ6D8JNlfxcGQ7f++Ixn5UIqql97Ho9KBGUM7a2eeZ+bce4ednskhOgNa8kTi/t2Dl3/dI9rzAxskryRB6Iy4KD2bmUVMwnL4zS1l5dQp9Ur3M171E6LTBaC/NYqu+K7XiZiyonoviFVuazl3ndWYe6cOF3DaoZfbOXASHvuE79T2Sbo8QTAGHAUeYvGQTw/otRTnoAfaiohzN2sa6Kys4YXcFv8mabB64lZPLBegYrISf/XCpX/h/WtX/6RHvg+7aKiLcI6j6FUtE0Rl+uPqgoWNMz5Mw7NbasfvTKEw8ldhqLYlC5y/iFTfT8+0m7vO9ySr6RrPtYWTJY7HJeBq/5tA3updAYQYXOn0QDRBi8q0UafFmpCX/gHwFYsGSrIoPRqLCDY3eCXhP8KHghR4743/Rl1nOOK8qylyAiZrMmLATh1X7GSP4xOXSx3x89AeZXmnS37/DYfpA9oreYsPEJHJDF+DrAjsey1Orr8HrAxfIcezlsZiAkJRSfFILYYsxnq6BSAUoc9Z3DbLSRrg6vyXw9RN8ZQwZ9vYK21HA3nsPl9WXMCLhMHJ/rmGrb4GFVjebuxcSECdL6/3pVBqaM9p1HHkRbxiFF8qq1/imvhChzEdaBdbcNBVl8AIp5NThvqCVCWbNJPzWQ1a7AdWAfG7ssMJtnSnHmz4BbWwAai51sbkcBkqUcWPFWR6U66JuDOmFMMh4CI9y3zE04Scn3Beye38cfu/TsOitZHuwNs82qdLfqIGHv8Nw/i7LhL4etssV05HbioqNB8+7l1KVHcwuLz92p8iyK+wWGw6lg4sQ317QOwcNX2EmYNQrjWKpOCmbhlD5WI+DmWXkSJjhH/WDeMcQ/F+f5qbrNxQVrJFIes8glTKGXNbnttVgLlvbsrmlmr58az4ZVDBtxEByu1opHjCb7Zdu8MyoF698M2y6JJia+AP7unuImfnC63cMG1ZBgZcejW1JoClDx48u6tLdkVN4T7FZM9u31CBl44lXdSH/Fztn1ZWFwnbri+7u7u4UUBQQUcRuRcVul7lsFHWpy+7u7m6UUlBQUJDu7u7m2Qf7D7wHe4/3O/iuHzHnmPc95jwaDDLeefwyycRsoBvVjAk0t7ylZcJGBFUyuL3uo7/bG+nOMwiUbfC9mU3PBEX+UjVD0F+F7flcNhSl0q3niHnNVXpEe+mSt6ZTUxbHek9iNZpxqKjBv9gTIs9zcEgw5fEvsVAzAu1W1jfmsq24HB3XMhSstBgV4YmPoAjRm9s5L5LGrZttXLK0Zd68p0xzv8LCxEo+/3xHQK8iE0esYFiANtU5hyhM10FF+TXCX5wgr5F23YnIyf9GvSONZ5lP0DAxZ3RnA+W1E3hbtplVsp85v2koVhO10LstQ1LjBUQNDan8/YGnsyZQIPUbsWJzxhmIMHLBVvSETKlQjkK76Re39c/AQC/obebCg8fIPI5H32kRIsviCfiyiiqtPMoV1ZDoNuHejZt0eQrRp6XIo4/wRhlkFwwlxekhnsanKKww4H2iPWhokxiZhN0UcZaun0jKzXg2V72AmKEMkhvHlWkJfGgv5pGwDg17Y/n5rAsTmaf8PifgUr8QXimWtCwYxtviH/jf+EHCyCyE7asRazMmuyYJ84HvCNl8wd5lGY+3p1HdHop+oxZtfW/oyG2gtssUA+vJuCr8QkfJCtvZ/tRV5NH5NZxnZg2oymqQ7KKHu3AkpbdmseLeLOZ+ksNyrR1PekXBsQSZxWPx8q1jzmMRUruf0DuQwf1RmdzX90JTTuk/1tL/aO3q/zfK8gaCmVvOI9EWhKT1YZ61qWAs/g2RnCsEK2tgOcISR0k7zt1/zL2uarydF1O54z5/3zyFBPN5tHcNxU83wuONzOychm+zJdczj1F7qwtHA0kKjWTJtehHsUecXn05EDEgLFSErUdauRtxjxi7o/SWFvIw5y75U0sxdRzH7OardPVuJqIpE5rfIDyrnFVfZyNTdodppWDjmklr6kfCZY1RKWrk9d15HNltRpXfa1JewcfEAwR6+mNStQuXERksmTmRyDNDWVmawuLMB7j1xvIno5O/9p7EtziWZd2qDJTP55T0ItS71JFujqLWTUBS7yGu9CsQ32/ADyZj/vYWPlut+PQ+HW2BAy4q77ihsg3D255kfDnPQjEjquNGUp8Wxrihf9h6yJK3/a2MCQjhwffr3ChWI/mpL1fNjzJoySsSOkB+Cah+gs8mkLTbComkTPYnj8FPygHZO6tJ7VZALf4lCr3ZDDLZxc8UUA6B60EJjCz7itv1jyT7D6L41j7+Wu1LRUQVI9THsbX2J0UGmtRXaDBskBwXYp+wsHMEereVuPN6PAnLnTgzdT9x0jAcUVIZ4ORwLQbaXBFWtIZXiTDRlV1f4EFhN9vWFxFSWs64/DQ2flYhWzODfMl2tL7K0uueQZGqKQM/DLCTKqXZOxlj1RwidApRUZajaKk9Y5dk8zhrIYbfVGjc6IxqxHv0evtJb3xHgW4OiWankUqXZo/FZ3zzujA2taTuvRJtqtI8aN7HIMEUPF1PUeaaTdHXfVyVO0Vy10K8Otzw73IlfthGpuR7c89nEBcqZ/PtRSijO3IY3/6IPnfoezaXF84SOFU9wsVJAb1fveiPXMxAvxFd1ftZaBDMy5/JyE8PovjlBea9EuZU/08yJDVIUWpn3tmjtLmPoHX1GmruvEepV5yJn9IJTUkn9OcWvKYuR+9SFPp9LsiYiaDSkkWT0FU+FMM6XtJiI4yy3j2a202xtpqMaLUwkjIyPNoXjG57ENIufxMjdZfUSXJUhf/gbxFX/iq+wVDZEPZ73yH/0ShytvpiodDJ6QObOHNqOJHzpBmzJpArpetRMAlETtuH5V/OEvPsJQGJ+uS532Jz/SV6x0xjtu4NJOr+IvfPK95s7aGnYAC09dmh6M9S4Rh07aeQSwLJWNHIHZYwjbuIofPuHGv7BIyyl0Ayt5yrAW6UjXUGV0e4lgXFscBi/CXgUTec0oRb6n+YM/stOHfD5ziilFxZbLGe2U8CebBuLYc0j/Owehvj55ezxH8169rS+DDkOW21Oly4GYmuiRzXu9VwlR+Lr7IxLkhQElWHa64ocb0V5P5xp7U5gb6aPPwn5bIGcMeXE+nXkbS5xKwQc0Zt+cKJgipYOIiiapg1cx3fh41heswLjHstWdSgT8uwbUR2pKKh+jcDzblYSuXhNtyLu8d+0CjvjPGPNBrNdPj29DtnzW4jlDvifzsf/0WUZIUEn9duZ3qnHs9VrmLa+ZOVRUW88Y1nQkIGU/VqSdCey4iTN7gx6CL6awQMftlBTqoMizuHsjpGBceaZ9zfNpFPSXlMNt+K3qTrdO2QoMrJFhWlchpjLbD3LwA0mDNBGBNHIY7PWMj6vg5i137n/NV7DHxfh9KylRjpbUSsogVB2gNkxsxAOE+NhQZL8U1aR7SKIxubXHB1TET/CUy26WN8fQpRT8+gvMKbkbWr+WRjw9WIZ1iOG8OB5J1s2b+FxUJ/cyp2C6NiZjK8pJ0JJdt49d6L0I0vQPcT8YUjOCSUCRbOeCvkczXanykmXui4ZVEZnUpQ2gPmPc3CTm4/Yw8acK4znNAsFQJSdrPvpx8zvLK40N+GlM9itNyzeXnVgrnTxHmf48+ax2sJGjuCt7JmfNtzjhWCOs7vnseqnjLsy8D+r8/cdRVi0VsBc8qWMsVYhclx9xlTMZmXK6wwff6EZsYiX/YGoRkH8f1+lM9OgQwue8TGpH9xvnKZJy8nclk3m6XHbyAlIcG8qxbYfD3F0+uaLNLtJle3kdb3O3h9W5RYmTAux5xlsU8dm3cJaAd6jGW4bNfNbZ8BZisLeLrHAenCuyygjElvv2JT9Rc3L1kh99qasS45/Kqtw2igiVHCbVyxd8TyoQYGDo+58bsXTRtdJgu2cOaRDReTQrnzaRxb39zjwqpwNI+oM/vwE7xurOJcuyq7wsW5W3QS2aVbkZdWoevDNZaaOtB8wJHvf6cyviAAkSBQ5B3ZVbc5MrWBJ+sX8LnzNu4jR5OqbsnQjAV0F77BL1iU49JqnBCexfLf9ymS0sGpMJtK9UcME/ZBK0eZ5wOpzBWSorP0O/dXWGH8CY5J72N24F8I74lGxnMjhpWiLBzlwu3K+XwVCeXbli5OHZbG7+dmwi+/57qVM6vLB+h4+IJ33RM5Fu6EYEI4jjvU2XX1FwO9R/kZvp2+5BCOVquz3+QRWTJ7+aVRgZ/bJuyz91LeIoVT836OqK5DUrSNf1oMcMs2JzPElvNyqTi3rKM68SFbcp/RGjqDtPV1KEyq4JjRJBquPid1izIOj7+zISCYy72lhFzawOXVZSjJR6L0uxWb7xeoNByDf0cSf0eqEO8tRsKRlZiJ2RB24Qj0nGJ3nBcjPReh9t6fQ4caeBN6iMXrC/EuEWfNZ32mCPXRu02Se+c6KFQ1gg0pfFzrzIGhF7kR84mbMsrQ38xCnx04NHynsa6Q/ITB3HyRQdjq2WAWxu2VHyh4dhfEd3Ik6Qh6HzYQdnwLGV6zqX2eiFq9EC/eifFD0ogD7aPRb/JnvucQXGS7Wd9swwODDp516mOSXIKlZD0v8m8hcW4uOiLydE05SuqgDZibarJlXBep0fV89NtKyaolnJqpxWbRSobW1LJocyl680cyaY019UUivKtU4k3OJOIKo8jUq+eJRirrCl6QoNaFvLo9AvNVSKRkcEgS9jWNpP3PRg68fPb/de3q/ykDqg1MM41Hd3wtKqlJXMi4zqh4Q/YLBfLpezVF6Z7s3HkGDSsTHBbeJzzqOyaz27AdsoAlt4XY9mEki6KmUm9UQFDsZY4JbWO+WBKbLswh4Iw7O1fPplyqDx11R+AOYaELuP36NqVdDfyz8DjLhHuwMLuJ7OJBDCr1QDi6l3mPookRGaAkWgGlirck/hPGktsCdtQ44CQymoTm0wzkpjG4S5lXd+fRyULGbs3iUN955i+fwPb9tbh5VrFy8mo6E0ZxY7UXHctgmnQJEQsXsNpgOCJ3N5Gdp8pA3jDG5+xErVgB3z4pZrxL5WOrL2ZFPRxMmMLFeBX8uqYQH1vPuP5BHEys4YfHdHb11zOm15nGA54sPi5Afb8fufMKkM78QsnNCuzGezBu/CQmK3viK6uJ94sgqseGceGuOFOCj5KcD7TA6cJYLOJjiVKHpJ3p/H64l0lSvpiXWbL15yv2//lA4pQZDMzeRcDdV4SlnSQyOZTIfxIQvVJNmbchs6QfMnXjSja8VuTu9hX8NnhJkU8APgGO2J0Yhbj8HsIsM3i09CTrP14nIdAJ06KVREi6U4M935EmiuEc+jAX4RMlEJXEQkdXLibl8F4XJoj3EHLegvHxfiwoNOWt/mNMFTtZJizOt70GiGppMzErg/7R43kvPQuVAh9KYwOwPbUGlawitPTjmDHgx+aGVh5ukqC4J4uQEsiOPkiJ1FO6Lo/B7vEdFuS+JsNwLG8NNzPueyCqybWUp53Hr3s4NaL5LAgVwvSlLTdzdIhJXMrMYbMwN2rnl+AJs8QcWahZhtuNT5yLGsYYa02OGSyh6M8V6q69QlvGg7lK3aiH7OBzVzw/B9wYnChL8JqnKOhdxKfwMx9U7JEqus8dvwlYFF/kwoWrmOx+ycrZCQwILOjaOI8dsw/SK+3J2rZ2XDWkCUirY5fXMoSShLCdPJUWL3NSGjUpK/Xmw00tCJdCeqIRHdlxZBa70uCixOu0ZZytvcxz8XCUZ7kju9UJYbVbLJTxRX3XWpx63Vjeewk38w/Mkz9B9zF/FDseE5Tdwundd8k4NZMD33XxHa1LdI41+61GMKtdgvy7p7hZ1Y3bmFFMtLdgkesNLNV6aG5o40SmL1IbrXlmKk5PqgQrGwPZ7buQimuHKX2TgtGxz1R/6cZi10xEbOq4F6pOcIUmPqP3kDRuL4ovdCH7PJ5jFiCq5Ph/py+KK9hlHcEewQWORW9m0fr7OM0TcLUrF3CBOmFidk3j3+EHkP/yFwcCtlGWV8W+yjWkC2L5dO4YsvLeNLYc4vsLLTKM7VmxSpHhug4ILd+DtX4d8h0viRQ7j1vob1LeHKPmxScm/HWGJaM8OHg7mDx/M/YQRsRfZ/D6mIrjegmyFmjy+qgxq54MRzVUgNH8CpTURrM7/R3RroOwjtWipb6AH516dHn/y4VuORpFh3MjbgLXfHQRl7/GhKEytJ4+xdNGMMkKQezt6P+qTv8vIKYC4m37MJReyvM2FS6JOODy8yKzlTVIzt6Ds4Q9R99t4V7XA8YPv0Dljs+8NcxFQjCeox5KTCp5htDjbTxqf8GUZkus+7qou96FqLYohUayFAu7oNQjTj8yQDQmjiLc/fcLdz/fY3+DDX1lRdwVlaJgahm9plrUxz2jq3cz0R+yoeUNjjO9uPbFFz+FmYwpqcTSvI/W1A+8M+8jWMSS18fPECwDVb6vaWzyITzxANNc2lGo2oV8/3nSr0xET3EOBYPHsiSjHTfTufzJ6ODHjDooiUMytxpB+QLkNKag3uWIj1wOcwYpk9R7iEW1DcT3G9JqGozZ21t4ze/5v0eyhkm4qLwDPQkU3mWR8SWaErl3VMeN5MGkRYwb+ofMMiVu9beQPWwo1UYauEQL0LSrZ2MTjJA2I7x9MLOvPEPtUxgASbuFkDLqYHh9PX5SjiS/XktatwL9zz+g0JuNnaIiP5Mr6LH0Qq4zD8+GSvwFv0l2G4ReWyQTnX3RSOwnQH08/fp9fJACt4Ie7poLaL2WTbumMnq3FXkCLFm+CjDiPOqcoYHLstKMcZJnTosFmO1iSnEInyaGkuIxhsCBFzhtcEI1s5vF4rl8aYlB00KNlprBWGYnU1UVQZGaKckvs9Ax6CfdIJUfvzbSOnYcJdfVGPipCa4OHL/tSt9jUbTexdInIcf5C78YNr2S7f6bOV9sSUC/gCFW0YxQ1eLFjRqcNC0Il06hofItxXVdzBx1hR23ZjHbaDBPu2SZKyJNXostX9QuI2GdTYqGH+lW7ZxImodpqRYe5sb0uZciVSFLnu100sRe4m2vRIeIAGsfY9ok/CinApXqm+R8i0V+xmgeRKTQ+Pd6bizYQcrQFYj96MVr9gTMtbwxSxFQfecDElLgiRDDGzKwfBbEIL2xCO2awkLtUdz78g6V1mxqKmR4WVwGv97SYiOMm9w7VHpqKei1Ib2lBAlpGX5+fY5OexDaSirESN7lnetbrGKzONXRjGnJMLbITCPU9BH5j0YxoH8GC4VOroyz48yp4ZTf18LVxxItByNG6gxBTtsHjet7iHn2kp7XMuS73+Rfocv0jp2KhVkZhgMW5Ka8QjBlE68KBggbq0uo4gjGlJZze/U4tJtL+W1hxa/6R9gUOXIoR4wTRZ2s6xvATV4VydxymBVC2VRnfHv20n7tJdjPYJfYfGb4DmW12hJO+WxjtPcrMM6HoUIQEU/B/W5GW6xnZ+RCjo+x4LDGCfYmjuSfxeWIeY/kUVsiXoYZtNXqgLwaeqZyDCvNYJysP77KxnTFV1MSWYeoqxtxJRXoRi+nrekbnR/qGD2sjaPVsKVfjD3vrmMnsZeN7uac3HaHXQXVDM56x88iTypNx6KXN8Dc6BfYZ7izqEGfVC2I6LxFg9hkBppzETM1ZKH/YCzMbOi8tQ6jn2m0/z7Otyvf8R3s8R9r6f+Iz4eQuqpg1v3zBH++TIqoIgZ3aikeMYWEflVO+QSy934gbnNP0fvnAXJ9BcgOfsq7kj/kiMDymjxm7Z7MR/sDBGTIIHjygRftJjyRGYZNQh4jTr3l1+C9PB/3DYXalzy0OcP0v6+gFrCSM4pZSAvrE2l0kijVZq4NHGdJii6tsnupHiyK1a7frH30hpAbxVwpPsDxBBvuLPSmU+4nc9vqaKuMZG7YZ2wEK5Gilm2PRVH5lcfKklCWdX6g0nkkKlJdlFtqk1dVRnBjHE7d2RR0j0DRyxLP0rFYRcKLb+OYKGSL9PFAxjstR++bA52x4qhjg1qiKPtmaFKakYjg5ztOSOQwLsSH+6ZC+AhcERsWwvuYNci35SAk5Yth4UfEGq35ZjeaT9lZuPdK02+sjGZtDP1FXkgOc0C7vwc5gQBjx1lInITQM1746jaTvTmdZXqbsJkgAVoGhPSp4e1RgoyGCIl+VehlHaZvdC/inQo8+baUdxpdTJYYzuBKGTBXI+jMGloHQkh1CUNlyny+bdyB1ghoTAPlR/ZsZyJwFRPFMsaMF/D2+xvyLigjq+ZFm60AEOJhCEy7tZF/duSzOamTBPFUFK3nEidahegcMRpSylHP3cNAbTPRq9qIi29jav2/CCStkCbdHhoAACAASURBVBDPplFKms4+ExbGrGN/UxnGasNReCHDwHaoeJ+FjugRAgMKqWyJxERqN3ltfVwqs6JGs4kZsqVkiMQhE+9MrIoqd01lsYvah5B2MikKtrTKG5Cjs5Oo3310n5hGEsI8LX3F1fB2UrOnEy3syrmsi+S+cGG3xUReZT/n6ZAdRMlKc1I3mfNDXZn46TsuVs95G74KSU1TDvw5wM1xz8lpnIHqnIv8uHIDA0UrCoYMQ/dcCkYqKmRY1NHZ0c0Zo0IKxSVIam4n8O/N3D8chvGouyyZpY9ZoCN1CZJk9LykaOEPar+cpSrfkP7h23kTspr+N69Rch2PalEs7pFDEJYxIaP/KiVWC+jPS0LTcIBO5SaqhRUY3+bEyPRmlHx1OfP8Jdf8XlEs+pjZIwoIS81k86AuGubpYKtvRG1NBgs2XqT7/n2e5EwjO+Mwt3PVafq1m+s9Enh2iFP6R4LsdbV80K/DvKQTbSNt9tWPZubFy3Tp5GEXaUZt4mBcpIzojZxInbEual6DaRrVR+f5sZS8qmLeqETWJQvxa0sXu3qHQ5sS1I0G1WhkA2qw+teO++bW5IzQYcC5Byv5BF7/WMrY2pss1CljxQNT5gQp8alZnfPdvXz89oyxgyZy+3Ytu9xHMMXgCzaqFlj/2UWvUi6uU7ewN+wTE5y8+SbyCHuxjdTK36Bz0nDSW+MJvmgNNabo+RvzT9pg3l0/QahbIT31faiNCCHjywnO1sYxQsIff2kZHGrU+VPXif/MH2g61PJEVZ8CNSWGXfubifm2TNx1hw7jJga9kCRWP5r+y+7cUasgkxIumxnSn2/I9YZ8FK8d+t/Px38RYXl5wdyXp3G+L4y5XRI5Fiq8+qeepfuK+Rj9HKHSKAJEHhKnOg2Jrt+YVXVxWLaXLntt8kxVSIu7iPPKYVzfm0J5jQmCWDPKFzXjZtvG+vttJDSVscnKhaGqzWzYuRud3aOZ27yVvyv7UBnnwov4QXTf3890lwz2haRhmWKNxMGVaGyfguu+m8zP1uNExC1WVP/CxPIEdz98Z6d1Gle3RPDV9SGCn/14t85m2u5ecn6O4q6xGAor9HG/XsfUnI/cd5Hh7qwTbLklhLz8IFQaPPhke4LpuluZO/pfBKv+xvqrOZlq5izYcBnPmh6Kw/thsTZLc9cgO+M3h+/cY1GsLhsN+phQ6c4ztQLO/NqL6RQB3vWBOEhUIht9AC2tTgTakzG3+0nlzrd0jcmmw9yNV0EG2Po+x3msIT1zlvKptZXJud84/boC+3Jp1MemY9ArwHeqImFZXzgaugjFfm2W/RVDRrcQzkJLGGl4mHhtJXbHf4V4D9SDx+IhlUDD17vE6aXjkfuefqfXeK0/T6LbZWY6LqKkww+j9gCG9FzHbu5n2KjHIXknVF2OEdJ6mTuqHqRIqtDrMo170v5o1vrx6JgN+841Yfm6E7vf//JN8jqFAz+Q8NlFQNJuXuqKcKBMljBrI/7+E07eZB2ynxtiOK8dnT+7iNPQok9+JekbzzB9kAFDxWahPm4SQh8P8NqnjaCHaQzvzqVHYgpyA9F8cFfhVLE13mnFZE5Lx0frKp0tpdjf24Ns/znMx7XhUZrJJ+lwXhsv4ViFJY9/mrHZ/CXj5KKpj/zM86lLqNJZxdJcLZ5KrqS0cjZDzxuxYf1jzpYdp0B1EB9cVZj7/id6kpNJjd2J7KV3dG/9F+dVLsxXWMXajEVoJqmyd6405yqUyT9qgqpmOEeWOKH5OIl1f99jsNkwbkxtwXd+BIdjBchL2vAjfSjWZmPpzJmN+txDhFy+TVSEK+3S16h79Z62fzIxmpfN9wFHDOPjcPbfjeD0GNo9T3BD7jUT4wMZGLKNuHGSTFwbw9xnnUw7pU9fwWCkWmehKSmJTOAV9o59BELrmOSYxpAx86iW0WLU5jRmT31DuuM0xLPCWLzxEMuyJrHyfggTV1xEUHsdqf440l2MGPujnf7cFKLPVeO+UJknMtN4n7CbRYve4acTR+uKTvr/Mqf72356FhrRGXuJt0/f4TP3Hm9WRpK1Q8CuXCHwEkDOfLB+jtSK2WyeNcCdrGZq9MZzc7gaKbJ+hPXs4pGsNSe6vBm1+AzJWzN5+soakfui1J5MJuBjMpcnjWOZjC3S+eeI3D+VvePm4pcxnQbBRQo2PGW75BjcZO+TLabBwaYoxuYHM3pIFhliv5FZKotvySCaWiRwP1bFRat4wmU86FXZi8TJv5nq0EOw+Ge2CeYTbrSCkW1hLGnsJMMwhJfZmWTZzWVYhzjeFy8iolXH7EOyrJhTQO7RerQyh9LaJImnyR3uCBvhFa3N1htb/yOP+h9ROD+/6WBYv+4uDr8zQ3K4C0mn96L+/j5jg4sYFHKWTNftPI+7QkDxeLLu3uCNQIoxcuJ8aamjokeSrFwBOgWK5K4fTWLMKs5/0ca1LR8fhwEGL1TF1iAKIuoJ9XJFdYYIK/UDiWquZvrbdzxTymJHnT0t5bMQTqyid24WHaXg8S6I9wP3cF7kS0nSfGINV7M7djPXBwVhnPOKkh54HhND3G03lBoeMPLgT2zjRtP/xxqLZim+rRrMrGcncCtxoaRNk7UNZVhkxrGo+z0/G9yor8lG7vEghn46Sb9QHeU3jPBp+YF/9HuME3/xc4YURkqdaJnH05dXh4zWYM4/vEiEzgCrVjyh/kMiY9658TxeBdHDTWR7DmLRrRTym85yX2IC1I9GqW0fcvahTHmURL+uIrWrK6gqN6eho4lrVUkIHbHnYmwtK0OLiDBswCFwHuFRPwg4EIqJWwCLl1nwKuAd55dI4ytSR5xFCOrnd+LwUx0Ri6EcfJuHTocFiTXNuNTFU2wshHjTA1yi+/A/nkGqhC+jZYejXTqWEF9nghZacknzMu7KC/gSspxtS7SR6hcw64UF/RUvGEjfTpzTI3ZpnyCwNYBXskJoadrxNBq6hI2wT7qDRoc+1dlePE6upT31IiNqNemtcaGrajTNuo70XI3DVHY6fyeaMVPFE4kh4kTr3iBnsCOL5qoiL3GOoIODaa99zQYrAY86vmMxYiJ6H6/Ra1LKyLSRCLSrye7P56zgOEmeQQwquo59tTJ3a18xuWIt3ubi1HfPQtJAGd2HJnyQcGVUwAnstLTINJOm9sFljprJM3bMKvziQxi83RitxjqsC8+j7RnCuQQ/JKYPI1V4Iyt2R7JC4TZ5I3dQ8Opvwn+MQbpYDJvef1ghV4tq5lHaA3pZUnGZKVLu3JdrZ0uzHRlvYvkYHsOLXXHMiIjl5syZSIicx2XEHE4+Pk/SigCO5VeRariHcekD0L2c3rRyWjuXovqzG2vZYQg36lDfp4xYfyeGlW5IlFmhUeHBr5GRxB05zWSJOB5Y+bAgawz/WKTyMOINxpHmFCY+xDc5ipJgFcb6LODonp2cbu5i481SGlUyibLfTp7KOt6dM6bH9RBLDq+kr1QBnUxx9C8u4velT/Q7biLq9heyzqxn6uAa9vQs4EuRFpL6AWhqdtDSa0avIAzEhqG0PoyQG45Uhp0nYNJ39Co/gONMMFsKnpqYVZUzQXUCk6zKqM3Ox6tFld+JCqSP9+PPGR9ufhyHilgNAnFZBrd+5JKuAF3FiVy/JkR7RDAW75/wWX888hI7eCZ6Av2HFhyMjMJg2BRmFNymo3UmCdd/UjIwkb6jJijddsVRQQJpVTE2z87g4IvBeM+XJBFn5FUnY3CqDyMzD2y3HaFA4x1TxntyekgbDrMckA1rYr7MUCzbZDHpdCdG3YerwiVYnSuiK9oQtTc7GP+XO70Nhjzs6iJHWIeygVE4jY9hjLICPIn638L5f5H7Ny+EWUe+IWtuMIFNWqR//YmVjytfHvcQ0HIM6TEbybm8hZrc88gve4tZwUPOaRpxsamUOymvmFx7hN5ZHmzo/4zPKwmilOK4Y9zK0uxwxqkc5o/uLja5yjL+rwNInL5No7Qtw85NYPGM/eRofsaoq5LDJbuZGZSOkHA4zen1tCTvQ2PtY5auHMZjNU8U5mxl/K0l/Pr1EnOxYn48Xct0WzVen7zCjCNz8a53Q+jaK47Kh+G06zUHrhrQLTeO5qHhKGe+ZcSy2Sx+r4VKTyueMhHMfPEd/49HoWo/cfvCCHDXwKv4JqZpmXwR24x8ci1u/eW8GhHEj/4Oas5c4NmSPp5NfoTLoRl0Gs8jw08UycUbUFo+lCMPiriqtYtMGS2cZh8hOuIyfpafcPu2jEQzEep/DqfTzYVgR38e9aRSVHUKoaXfsfafRalHJiKl4czMXsqvxAbOe5pj+ecBQrYDnFmji6amF10vY/nzcRPj3Q7z4ct0bvosoLVGnOWt7zCwSefDKSls610xH7GJlWUafJwtR2ZBAqMjdpI3OJ9Vqu780BPlcdVa7BxeYlIcyjNbc156/KJQQZmghBim3QzAf3I9J7WqGZaujUDxHdUKZvSmZWB7tB+JG9so6XdG0vsjpf4/cI2+ha5YIH8X1GLhmUtyVjvK5eqIR0ymSOQLJiHj0Z8fgkRHCXW3g8jzEuZolCYpTvr4i5chlaZIbuB51o46RGmiDT1VtphI3eBIuxvJVVZ8rTuOr3UP40rfMSCbQrGZA0q7G/nWnMRYHwdShSfyuPorTpMEXDhYzAhxSb6WiuPwOpo6EwFdtz+x7ukhBEraZAU5Me2CG54iFmxTdiXQSotzIg1M0ZJBPX0Xvz9so8MhE6vALnTv6uAskkWcuzI/pM2JsXTAj1I6q3RJVZlOTNN+5ozq5t/Pi1imlcXUqRMImTeHpLROBAMNXFwdTZzoQ/wXjKMy7xcoWyMQ/YZragSqz0/xTGoRWY6LEWuKQs/cAp20JArNxAiMquZ9/zCyH75BJ/gyLc0lrLau5KFwASesI0moPIBk7naMXiwiNyKeY/8Es/RyLvqGJyjb5sL1cW4IkpM4vW4R2zZt4fvpcJ5tCCIoJg7pj/OpjblGstR8tEdspaX+ClcmDmJz6l9sUrjHED0PyiqyyPfYTJLTcpT3Tgcde+5/duGbigUrB1/Bp/ApaERB0Shwz+S75AAG8XbkGgpzT3IeLsN1UVbPINxlE2Mu/IuWnS3XfnSw0qGDfvl4MiTtEK8rxyhfmKgZ03iaU8ry+bZ4C+cRU2pGzfXjtIy2Rq4zjs1PPAi1VWJKQxm/Z5ZhXbCKzNR3THQto7jfmBYlPbQuh3Hcyo64zjlYVbfQpCRLvEI6X5uVEC46ztYZU4iIlCRYzIsBkbuEb1HDME2H37p2SFqIMP1KDUONnXBzEkWsfT+SV7yxCJ6CnGsMf1qFSNMtwdUoC0G0FpGZcf+RR/2PCB+Hzu8O26l6mecPaxgxIMCoJYJZy4axM+g902cfZoUgkiHD/Giyr2bksFEM63/BVeU37JvmxKbqeFStvDG60cilGdO4ODWCjqmLmFPfy8/vfSg2H0K3LR5NdzMmzCnhTthccr4LMaovk4HxodgGyOGd3IWoSiT21ncR/XkGoz4BvUO+EjRsMoPe23BZXZPmRVMICj3CtFPBSGJE3LnnTJAxpdtkAmF1QtjH1nLpz2/KrHIoGtNKYF8MPwJqaNC5hWtnJhft7Nn9+QIj/HUYrXEGU2FvxCZM45lKJabrpuP1RB/ZzvvgMY9q2RZsKvSo0dUl65UJ2u+fYbxdhkNX/mL94n9Qme2Ab99wXohEo2Tzgi+LZpIVGo2ikzNHA59gWqfEWAUPjNwn8K21l82TNiP/byA93yYxIlCX/k/vyO/uw/ZWNVPHbGJ8ZzrnTLYil7GH7KYhLJD4jJL4Rd4H/qAweQouEe2c2OiP2t7LvPVLIdd+PqfepNFoJI+r3UvsNLMYUO8ksOwC0i6q/N1rRY1yEK83GWGiupBCQREp1na8GtJITrs918rycc4Yit/9QlQHZiOhI4V082hse5vwLM3HuP0wLa5/CBQRIrJwNF0Pc8hvKuRxiwYZDfWIORdiLGXPjknf6JCppy/jNprKYuQ/UWSqhy+ljuKMsLxJo8ouRs9UZ2XaSrpvb+FR3Bs+2qmxv3Uy9nX5zK36yLKR7ZTVxJLKIsbH6NNgVojqcA126JkyInsaS35kYLBoP4IiU5Zcu82m9bNJ7FXHO78ee5k5rFpdxLCB94i9mUVPWj+dfabM2ePOsfQG1t14wuapB3jy6hcLLr7iTb4PiyVmM6vMhO2aJ/F3n8GzyWc5WLCUxsRfSI1y5vHBx0T5WhImacduucNIuGpQ2dHLi+l3Ib4SazEpas2yaNQu5IOYMu6ru9Cqjsb/WS1PLRfBqQP4iC2jYvM+vIPjGJKoR5WQK/Jyl/jjJYx6Xh1x814RMf4Uuk1fqXTNxlv/OeoOTxGWcuJyWyk7ExaTEnSOmqRyVP7E454+HoPTcxD//JD0lLO4twvTMbOchiNiKHp/IzRnLMslpNFOOoLh4JlMmPQYoZQ8bqSHUpl9kfVDVlF2bCWq0o58TRJGaP1hhvbK4GM8mANfvzMzdDlB2pNIabtI92cpsqcVY1FUAA3B8OkzYQUv+G29DCPL7ej2Tmcg8Rx8WwACPYS0VlD5dD+P/6gTO2CEl+JsTu1+iHb/IrbNucooj1QUB5wRMohET9OSgze/YNfpQM7Nn9j9I8zHY7eZ7WePW/wqurPWIJvQwZ7Tk5B1NkI8uZJyjbf0q3VhsGclga/OUuVeDoP/UCMixqPmZOr7bNl5vpgp2sfYLl5CnkY+PaJJzE+bTyhieCSL8ivHAGuRJgbfOsfhbWuZ25CP06nr7M99i+pAEv5jEvCU0qZnQJgdy8/TWwmXfrxDPiiVlpJeZjm/x/bcBKoSxCnMf/m/4eO/yMmdB8LeG+szNfgAOus2Un41EEflIOQeT8XOcC/X61rp7RHCbfE81GTv4/FSidNmqhj1myAn9YroGAPClnkiJmhk99adLJ/8lbKK/SgJ2/EoPxgfw1Sm/jUGmwZZqp6LcD7lDXLD93HiYwqSFr7syV5DUv8QFC28qe09QofIKFQ9mjF2+htnh3V8KM+itVKOcXLVRGUU05JVyf7cZM79KSTq0mcWjN2AbmUPdztf4fFHgJJoA0bJHqjKVdHo+YEJKR1ojF+HbdVDssUkUZKtotvsBQ9u30Bo4Xtml3xGxPMUZacnMDA/DInePmTDP9Mxfh55kQkoln4i2HgE8sHdOEtNoqPbGYkgf1RjzrE+ro0Jx4YxONOWFdf8yJ4TipBBGnYFPTQ0mqAlkKHW4Tcnc51xHMhBU80A7Y5BPL22kuFZRnTPt6dC+ivTu0T5KDmd4txUcrwnId9UhJ5bGZk/5NF+WoLk8B1cV4yl7tEpjutfp0X3M6kmaQSlJ1GSIYmslBHF4bFoXprJnrfCeKZ3opWrjL+7FbZSWZzoskDU+CU2zm3oR5uSrW6KeKsvcpZZmN8ypEVZFL+KLiJkpEjo6MI8IwJR4ec0J87D3aOMFrUMvuTtRjoxGk/N6/zVrUVAky/1pbrkD9JGfEg1ylfv4Wt0n+/JCfzx/I2RgjGjYsU5/+cPXW0JCElqotAxjt9qafSpi6LWZI+l8ktepKng0G1BqxIY1Xym0ViDf9vS8SuewCAFFfa2aXMw/g2HQ4aRoKfGMC11Wvf0k28fhcLgjXi9WUFp6D+Y3A2ncs86PH9dw2fOA0Q3LiLTwIo/VZpcV7zNylZPVpfps2idJV8+u+Fms4qy3x8xknNjcFoG/YqNiAglcjX9DwUCGdrlGhCr8WDUeDXin5Yg2SOMv2IzTe/zCFqxGJuEEo4N/GGjmQmRN2JYINbPKstgPsVHYTOygEaJoch4KKD00o/yIdXI/TSjT9sAVZMv1NQoIK1VQEWVOzXCWsxXTOPXdylsdkzBavkmPM4HIX73Fa8Kh2PkZIxbShGrzv/CRO4U+g7f6OpdhmHvTXbd2U6Otw53yjQQLDem8uF7jmqb8zE6HkO9Qloq+qmsNKTD6Ad6cglkqm5AM60EKn7jEzqP0H9TWGZTi7hZMR/qA+gKSkA9rRkadsOFz8Qsi+XM3vd01B4CiUNgshPUjkLqE6QSzAgVOUAyvxjeDTOCkjkf/pOrLquYmZJJCwVoFkeQ3lpE1jAXtNt/kmi5CfOGX/h2lHOj2oQVw2V5kylgVLs8tyMbycutw9fOiG/5M5mtG0GrnwLiRkMof5BLfq0CeuNfo+XignaEFAM6U9gs8g+D+58yrceXX10ZvLQzxOLPK5Yp23OjoAorOwMetIG3yFestZpo/tnH5ZPPWLpmPzmXywgXqmCiziDuav1itlMuc59NobomBSmdXqrERKnVEMM6yYs3af+ZR/2PCB9nL10O0x05h1FFZhS/eUiG9GjOrlZkm7EUxWoNfNLyx7E2hsL0aqLyfqMcWEGhzVhmXTtKl4wKOjb5ZDUnc3HQNHI2i3Bwoi1vf5yj6YA8I+6MZLCmJ9PWnmHprjass54ztmo3Dr6dvBrw4FiLIWURaSRJGhFcmUJdpwHmJoOwtjiGp+9QwjpymXXlMa6JP+jsvMGiLeZM/pRJRFA+T5/NJ6E3lgar51zuy0dikgJDdYToqKijrWEUDoqR5JnnUm3ggJ3TC+59KSVx1TPSxVW4E9vB0602nPXrxvpLHy5dEUgbfKb110WEUheTV2yAbt84RBNPYJf9mxV6oiQwDLmuD0y0sGfq90j8/dTILconVWQGD639KNSoxMSyF0f3U7x+1oZH33da4qsZmVzGl2GtdOu00fE2B31JWco6vqOg0InSmCgmd8yg8bss8eLJfFN8gne7GnfLH7D1ZC7j8x+xJlaJndolTPEazfyY+5iqRKGzOg8nbyeOdj0kXDcQN5exTC+4xLT1c0hdVYW1WzNBG1yIzKsgd/gcThZJoSc2CL9n/zJqiAV96se5qypGkm8YnzqScb/3Acv08ahY/cFtjTjGDS8IUW5BebAS0x4cRrjVlOE6prRqqNKt0saxQVcpbllOXucfXKTCKbI6wKGwFA6kfcdJ3ZTo2eVoeUzhfvBsbp7uYsHBDfT9MsEjMxAHmUpeVqtQ+HIsj25YMjbXCXMZEfIrqnFuKacsXobCo88p0rXn47x9bP1nJeZzzdh+r46cojamdEujId5P4qRslhlmkl3+G8atpTv3OdKybUQoz+SunhBK06czueo20VdfIj09BoM5XextvUbmJhd2H5tHSlYaYxYnEvF5KD5Tr/LrgzQDmeLktAk4eO8565yrMLxtS7GYFVM/VDEqWp0huRmISOVRJeZCe3Uz9S+r0Iir4LG2KJJ5SvioSUHzDbr2z8P91TtEFwgTEv6Fn45GjG/K5k+THlOqpTFq/c1pyXUEV6QRE5GIXKExzVZfOaGkj0zkDhZXT+XTp3Q25hYwoXkk2jOgXCiMYR4eSAYlYV5WTePUemQKn/Hk5BKkJkuyZugqliQ8xEPHCNeuQJJGtvFL3xrRUlc2VzfwS/EJ4wYsGGgoAL07mFsuIa9GiDmjzFF32cCSlVqcm7kDz+SPaDp8hFVC7K5+wV+vpfhh/hWZ/NPwVoiBqipQMoUkWwRvOugpymZg4TVWmRext9APPf9u5OV0wPEIakbhtHgdZejHDNr7kgk1XEJv0m9GBtmh+uQrg7wsmKsYzVD7sZDZh739VWb8CmbGdE9+fHuCXLYeDVJGdPyM56OKBkEWdSgIFZHs1I9ahyT9MTX8sHiCS0sGMVm7kP/0Cp4Es3zqedzDtyHkOA5J2nH4VkLbNUN015xF988kQqdtZMPsI0hVJlErPkD3sh6EDZK4+66KH6visOl3Q0euCwVvabQeqZEhfZVJYfXcO5v5v+Hjv8ixk4fCHMNDeHQhCke9yaglRqBw5TPtm+YR+XM31o4uNOTI46vQgXNfKP5SGxlXN5qxRbVk28uT6nadkKVVmL6OxaY0G7k7d/g2p4AJN7VQ8CvhSNgOov5yY3qsEaVafiT7RTBVtAkR8fUE/0kla+RnbIR7udiozlnhCLJaFrF3zSScr11ifVw2c14+RGKSgP2mheiuMuN60QkGN6WwfK8mjlmnOXEmjmNqsQRoxHGmw49F5ub8VPuEUP9HlFQbybSwQNbrNWKPzlF8MwjJNDGKiwX0GBkwymkQRysluLHyOz+e7cO47xNKa8yQ7oaKjVsZHveCFV4R5HpMxDz0HnPGRnOzYQ01Iy9wp+wKZi5RfDu+mZr56khKgV9fISUaeaz2aGH3TllqRzuTrF+CrE82gTHuOKtVEiiRwI6D91n4YD0JLSKcZTyXPuaxRymVGW3VNOq8wtzMlkFH9NBvNOHl9FJGlavg9Gw59zf+zVUFbcZ/n0b0CjXKenwxF9ZFWTmNNcs9GGV+COuQABIGGWH1RZ8CfTFiV0QilhKIZZUM3/SLGPc2B7FqaaIXduPyUBVK1bHsekbvzI+c/T6E0YOjaWtbhlilL2WjE1B3VELwrIZvv6WYumwOXloPSZLtxOvSOszo4vOOr0wVdeHtnj9Ymn0lKXAYtWLa+N07SKKlByuXr6WsPopfyqZsiJlJyvrbtA2XQOLKEezV49E5cRiBjxwd4i1o6iqySFqaXf1y7FFR5/6fVq5PdWKhpgpfmvs4mplD/ldbfjik4frbkh9GD1moOpfj5xZw2dGF0A9feW2zE/GKcCRHjeDp1wZ6baR4UlrOXjlPwkUTKX86CP9AAc9Wrsf0zCHEq8qRNEsnIj0Y17oPXDJaxUoFY3YVlRIcJEbelU8oVVtgPbeQzHY7tLUCmekugmWuIwab7Dj7TzIP/iSxy+cLVXcs8Vq8F+vuw1g+tWHPqiJcqrXJK+hhusFLMuQc6dToxk8sHdFfxzGxO0ieTgDuSX0YZkbwI+Q4Sk9FMZpYQnDsNiK+L2Wt+STGXd1O+PgQhstW8rxThdsnZzHd6SBFmXm8XjkK134LNj6oZ5v1cHw3PGFamgFCTmFcvanMl3kaqGt3UOilxQaneAAAIABJREFUiG5UNTaCKnqCs/icYIB0lx09hhLY1u3ERe8eStE/yf42jt33XhET4s5+ofV8fe6AimQovWdMEfXNgZ7noGdOb4w+eetTWKXbxsq4ELpzN2Kh5Ifmj3V4GQmxSXwNi4s+kzZ8Kl2v5QnUMKdOzhwNwVfetomiZxiJd1c3Q1zHUFx4FetBI6gxtiHAyh09sQc0uc2h8H0cam+f0Ou+hviqj2wZ6fZ/2HnLriAYtdt60t3d3SCpiISSJiqKiYrd3nYXdnvb3Z2gAoqYiIoKonR3d3edD+8POM+ns99njD1/xFpjjnFdi08/q3id54v4s07GXJPhRuUHdmSbI6fUytfyKoq/dyOvuwqLviFYSl1jna43zxNFWd9jwLjaKsqdtBil14y5TRlzzR14ehK85JrIKo9EMHouL6d001+ngp9APImxBgh4y/E9NPJ/j3xcPHkqZOiG/TjenIj0o2XY/C7h4LhhbH/7hr7McXgrNhFZ0cm4qnz6pI3ZmH4Zv3A3XsqL8cByLiE/K8lwnI5s7njuBwTzKUeM/SUyWG2dz4g/NrSM8CQtfBideh6UmDkwL0iTPVkCxJQsYWtMJVcdnZHsm8Gj9IUIdk7gaG4J3brJXP9QzbR0L3Tsx3N5mACrAgYz0eEy5yavpHSVPFKj8tC01+HKdUve9Hlz82U9oS1+zOmS4V2HKdpZnhSkbubY/j7UirRRX/aUybNdGG7mz1oLZdT9jXgqlUWRehF/Aq3pE2qhI94MISNp5Er9aJPNxO35c/L3C5J4I53pC74i+rqaqS/OUq6jQ4XoR0SsFlGjkYJJ4VtcJNt58HAaGbvHM7XxNtEF1cwQ20CVdR0CFDDkWzvCBdIUGhtgO9BPz6QQPiq7klTmToqhKKXKq/CTkqBxlBWqL6dQXi1Cpu5ONqp3Ie7dxLG+FPbo+pAbNwRD8Rr6P31iTHYE5mEjufsyg00SgqR9juLeOQsezZHHdGwPqwwCMN8znZVDNXmqHIasyVf0+m25KiVGe18ezgl26Nc20/2wmuj+x5T2niD6lRXJkVeZUNKNTsE38guvYxCghNK3JViWCfCu4i2TmmfRGWuFeVoCyufukpRQR77CAWQfWXAnZCm79v3F/GoWLb+UEDlRy/JIe0ytvLEVfEe0vwQGSzqJ0PBDT1iIgy+Xo1DwDovkSGbnOvCneDjF2wP47u3B8Mpmbs9YwpGIYAaX6PHoZwz2rV8R0SvmrthlYl+YYn5BDo9fTpyx3Edk9w0OqhbT0WXPZWdTCNjE+6WC7Dg1HsEb41FcOJzTf3exd3IqE5QdqDlqygbPJtLnK3NmZgBvPW6yodaPsPapFGU3Y2RXzA+LOFTNY7gS+IviobroVW/gWpcHxZ+zcetyI79QgO6lEwiPO8qIVgEax1ygT/EShWZr6dh5jhPTXbDJF+dx2yycxJp5lzMIgZxBDLfbRG/xYpICwugXms/bMhkEhVU4MaqDSZPXo3pqMZ9b3Cgak81T31JiJ2+gu+EI2hI+hDr2INE4EpFkZ3ZX/eC6cD/TQ17wZ+xS0s+MYqufGyd+NNHZFEBHWxolDolMVUniXXMtWhJGVCeexVh0IpOPn6DmVi8yr6RJ3WnPxYMxKMuPY1d4CFGr1jJp+hD0mpbQ0SqM3sUJVMz9hah9Mway8UgXPKHHXRWBwRqY5e6kI6kU8xpHfLS6aHARZr/EaGJFfbFOrCN8yHK06hVJTRIm1aGBUOVQAr22Ezb3GKoPb7EvqZ0g6z4c5f+QWjyDf95osGfuGm58KKJLfSyREVbItQ+mJlEM8QIP4jsH88/3GiZ6deErPZ8Ql2FsUBzFQS8D5issY6BCiqsLDDjbJoZ7YTLFmg3o/mOJ07gxJB6oxqZLiPsvNrC0dT7dnTb82tyI2O0sms31KHWyYqZZLOcNbOGbHs1J3mh0+FF32YWouhv/lY//IDcO7wwxGF5EU9EhJIUHsJb6jobYVr7KnkcwPpiheqnojval/VMiniqJPFB1ok5HhuDrEURKiFLmYsV8t2yE++yRi3xGsa8/DVV36L76jrMTt3CpoxStV2W8+aNPwjhDHqg345EVwecfvbj0ZIBCKvW1ngxSa8RcxYq3PwwZPbOZcQOV+NxRR9Van+T7b/DQnUhp1AY29w5QhBDqTVt4J65BXcs6lEXF6FZopVbElaniP2gvX0tDlxyO/yiim1RIspwNsoLGLIw6gaKeCz6Wl7gW+Q+WfnrYCKZw0fMa0v+aoJudj5DdFwS646BpB6vmf+cyIdSnTiL5URdOcztZ+MORsLR3zBcV44voWsR+vearsjCWCrkM0u0jqkaPL+Fy6GnE0Sj+BQX3SiTLZbCpaaPU7gyPGm5y/sUerpjWMyLvX86LHERJKxZx1804vv+MkaMaoimFhGe6IW1ewc2NTqhGx9M9t56Fsi6s/aLBSa9b+IW4se/pMpbYlpDaD9M/+HIqaR+epn782ZBEz6I+TB4MRep0BXPKpXgl38KHdk1WK3+kbEgfkSXqDGmvJVYsk0n7izn6twRB4WG4JeuQIaqEbLEIaqKqlFuHUVI+jUGu/mi83c5fRRWE5YppblVjyLOL5C1VI/ZwFKFDb3EmIYsHqleYiQ0NyX1s2XOEk2H96IgXYdx1iznq3iwepMCX8wfZ53UZwRobCnWiGe/ayb0CV0ZLRXM2t5dg56nsTTjAfLeLaBYc5oS0BXl+27j06gyFeX0MWaRMorALXiMk6LnvidjCV9w70onAvz54lDXhc7+F9oXqpG3cxrxJKwnNOIFI9gCBt5VojI5imFA2x62fsurWTOxTspAZFkRuZjnZapWMyDNgkGA1TjZDCTY6RlLCVJTkBrNB7AJ5TTKEuOgjXhaFm4kpo+bPZfZcY54+/c2I0ZYI6Jygx+cixZVz6G0uQ8mqEtNMCb4K9aCo1kZndTuFFaKY2g2mrkWEYuejNIYPYYO2CNoG8tBnSJRFM/+OXUPeYnl+ddrjNz2Hbb6DqXj1FkfrhahpfcTRPYCqyEbEdMUJCNLB9OA1Jq8fx5HPexmwGMQ33dkcLNDgr7glF2tPEa+jRV1LOj3FSxG1aaVG6CfTeqxI7NdESegPhdLdmOaPJLgrndKfheRP8gWvPPYYS1Bcn0Lvtxw6lNegZhpGt68dvL5Lv64eQ6UWEJEwi6qKwYw2bsdM0widjFHIaZ/gMcNR+6lPhnsPNuIdyKR9Qr/vJ3ebRQnS0+RtRzFjFp5ip+1TOl31MTN5yJF8M86diKYuZCYDCZdxF9SixcQdXdksOoolMaaZ91pedPW9QrVAhrEieVzJSGFzkRJXrMxYWWbJZ4Nq7hebIKLehUdNGdeKryLQFoCRWTBV8WuxXLABTa0fdN8+S5WyE0ObvcnLf0PN9nwuJIlQoduGgkYLC0rukKIUQG5lKlmxv//3yMe+/SEht6VGkCD9nawl6YxVEOTVo2v4fXZGwPokVj1ZjDbt4Gd7EhVmuSg0OxCf9BEzPUu+RTxgi5IcGq9asO+Tw05UnRQDYVY+s+aRzR42zt5H28lVPK08Rf31csSebWFsgxmOxb2se/GZLndNpGXyUKrXw047CW/H0ziOyWfb0LGoy5gzRjCClg/7uJvynj+vZYhL8meWRw7Jv4OYNCGYbS90GFD/yg4ZC2QkijH8Usar0aNR7byGWacW+6XrOLU8ninLjDC/MJkTEw8iF9uIevYD0vIqcNaahnabEM4pHWT+aUdD7SJCslEIaM9m32FzjPHlnyOvGVKnxNFWBTInyjJL+zriE+owbtai2eAt1z+bEjVfl7gaM4aLfsBj8nfuTLuJr2sHH0wvMMj9FwWKjQwsCaPbLZ3TBb34GRnT03IHtZZEuoULsOvJwUd4L35NgngrJTGh8yNJO29TJhGERrA8WnF7CWhTJX9gGJOkxkD6M7T0F+KomcRrnWdMW9ZCQtUOTra34CDViVGeEDkCvizfFkhHyTKSej8zuKydcdWeDN31Dy12npSojWbwdTlWbrFHJTSSRa49tFzLx6DGG+OBsxhKdSA/S5sjjtfJ+lREnWs2ETX2TF3ni39pMkNjauj2cuJD7DOCMizwWAeTk9Yy8mg0mZr9vBP4hupmfRQ+LUYpVpJVE75zVyoau8wq6hbux23+AlycAzljZoH+oZGMcLVBSkyewaGCqKkaUF9bxND4epJ/3aLk3GOOa49hzxttjgoc54b4D4Re+zNVIZyUIcXUuQ2wvOQ7Y4a6cr7uDc79OUzuqcU6Mp6JYpEUvnlNU/8WRnwo5GHHEcJ8HlEW3cgP20XsNZzBv4o67J64mLN/b3EkdTBWjnFEWWRS5/eR4J6pfA9VR3PEYNzVN7GzSpx56iW4Seag4KDLkukyXMk0pfLTL5xMjiD0bhwK3YOQeGCH1MoNOHd9I8/wGwIRT3AfGsIEjVjMx8/BtHkXiQ3XsRSTJUM0AUOXTmTSwqkz0iEv1xvTDyLMWDqVsQ9d8LcURvLMBexEtiBn3oh4jBx68UPo93ZHpyCDCSGJvBpjgFL1E8yCklgl+5Wjosv5lrOXOdY6DO8v5m+cAxVIINFkgNSUoWQrSBEk9J7PXoMY4Tqf8WHy6Ed00C5iQt6rKJoHJBko/YIZa2m7lc03Bwu0K0BW3JA/OkMZpyKIScNXkgTUke02JVd1GiXqB3AIcaWqO4tgkwxSYtfxRXIuxg3P0FKSRUL/PQN+tYwzGcPsyRMRr33FweXPEDl7koeawsywXMb2DbLkK1WSPeQnr0fnUvLva6bsUiD0bymD+2WQNP2BUL0IsRJ6JNc9pMR4K4FK8tTWPEfAeAUP5r3loIYT+++EMaqwjFLNzxSrWtI9eySN573JyHqDxNASlmkO4UTrDSakSuGW5UR52CgaRnWjI9fDc2dBAhIU0FWoZLTPNXL0i9hw6wt79qT9Vz7+g6w5dCrEOlWfuwKzONUbSorFdZ7OnEhPxC+M2uup0Gsnteg6aQYjeVA9k8B3f9Gb+Y341wPMChYh+/cQlHoc6CiaR6vEZiT27EXLZhuHPkexO2oslZF2VIeLIq3/mDv/WtJ4Oolr9xTocxfk3PRBzPoWQGniTlZPyeLCyzuclTvMHHs7KkLPoh7+ho/lV1gdsJmXX34xcuds0v/u5L7QMr6HemH9MRpxswUkK3Vhla/OoqsriXu4h1nvXelzVsbK8AwOfibkRN1h2YFxeI0R4ImYLuP7BHC0EMWkuxofTVUOC2YSuC8LUb/BCHQup395HZZB2+m6eoHxqrXcPqGI6g4Jmv4GITsmHaeKamqni/D0pDi/pnaQkqmKvKEYzmU5VLRcR3wgB1efWeSN9qPrwTjUSrppFNIh5a0ka128cLX8wwXxDHY+XE+w/b80iGUyU9IbMd98Mi/r4hQUQqtwLu+GCnL++GZ+5OxB3VcLi8hJzAj4hXLYdeb5xeJeEcWiJS+ZdyOaLb0+/DG9Q4fdWBSiFrHa9QuflEXxe5rDMjchjoS1oXynkY/nZCko6cd3zTQ8gsZRr6PEn+gRGFf4IfLMCImpDzGtkqFrQS7hZzJxid+AVEg2ibWH8XaZRN2mJGJdXZB2OIlpRzu+QksQ+XWUN4PmMUxAAeGSPEQ991Mfegnf1SsYbfeAYZp36bfN5IHQdd7s8GWXwwFkn5+krkiMZf1F/KKQ047CTPbdgE1vGQ4VvyiOcmXSoEhWbtuBwolikn6/QP/rbbI3jCD2SwQe9kF8uxVBr2ACOV7bGSOnhEvEGk66zUdQzI2Rf/fRMXY6LVnx/DbcykznH5w+2sxLL1dWpYkwpeYgmUEzMNRz4Z6AHHevL8ZnuisqYtOJrVyOsa88+mWriOzzIivoDTn3t9Nu04lI5VNWqi7ixcskHotoY7vDHOG94WhrzafP4A8dqUEYiAmT4fIbxdpffD7pz6bdseh3RXLtyWCeumlz5kM1phP/kJ1lz7iKXGxz0ti+txL/2AWctt7F58ZgUl/t4v2+YVSGiuLeqYeqYS9vU3UIKJ2J0nQFjiycSsodf4Zl/0VGWxTbTZfoX+uC6KcmetsaEBN2xlH+Ba+bJYgxksFffjpdXWHYWq9H4v0jWjWN+NuthE1gB1MUzThW8gvNOAGEy74j61iIhdhQlF7voPhdJqoH3ZBCFwEVAVzi8/kro4aAjjRD52ylNMaFisrlrE8fxcg5yhS3ONDZ2YBIrRwKcm2MkewnViANwalNGHZJIhAmQvWkKTxasQcTm7lMXdjDNzdBAievQG1JB9bKN/ASLeTIIXfcZ+TyMNSCCXL3ETboIct2BHO/pZPod4r20vksCFrHp7S7rDSdz9ZufwpGjiQlJYDDtyZz3PklaVJWiPsNJrglnaNbfdjcJkjU22ZkawwIzO5i2eVGtL/K0p8uTaFkEnbOqoj+3YZVfSwqggNE4sFWU2Guhr3/H3WU8P+LwP6/0SmtyRLFh6TMk2PXi+s0DjJFYcxsLPfOZoIMiLXCbt03SLQY4lWTznILe/QmwOnEl+w0FyRBR5wJwjUk1O1Fr9aIN8nWyOb9fytezdPUeJ1/nK3SszAZ/h6l0g6+R16hwk6L7Xvnodr4lLXWmSTOF2Ba+gMYfZqwS7kkDpNBxUUajz1z+btkD8YCWijowav857hJnmTFlTKmR+9hSuN3nqjJoj++Fqfrn3GbKYJwZx0nSyvo3R/OPqMWbJuM8Vl8m7Qj/3D3QgEGZpXs3yqEuq8mRp0TmNs7hX9VJBHNLER6riw9hrJsNVjJ0S3K5MtPI36wJa1/q5DdLMmAkiTCSrUU5MtzoTOPubEraTQFz8/lSAqPZaugCd5yWhj2hxEq+oSAsANEh8/hjYg2PuMfskV2JnlNn9FOMCNGZR7qgo6Iqf6LZetwHgkm8iZHk4HYImasXE/YFRtwv8qfQw9Zr3aWyXPOcNU8gbrQ1Uh+XsmZrnwmCu7i5baf3Nj9CpdBKzh/uYZv2aNZoqSLiV4HFQv2o27zmlxxS44PFUJCayYbT2bhkC2Ot94ZXhY2c2m3MYHbHnH2WytZflexds/mrJg6gXnzWRCXzcrzz0guecfwfVMYpJdL49lwzEUcYZc/g4TSGetdyI/Dd6gccpXazRdQip5KiPUi3NwCSbz6nmm5Juy8dgVsFEibm07/V0/uPBlAoPYLTJVheZQApUlJHG2L4pjHNm4eW0nD4xaohvaJjvz75zfmAiFcWzyfB3fC+NjTgE/QehS7D5KmZ8xNPU16hrkiMrYNLr5guulaoj68RFzRAYNdwVxIf8Gwh46UJ/cjKhCAQPRzXklsondxJ+G56XiKOiC9cQUfT5xDYKQxh+81E598kz2ZQryvmcHsaBEqlB4yEPiZ3TbHmG43gX9j0xllY8Z00W/MKlMgd+dOPFTrsfW8hKP7KNodvPlg9oJ20TcoDpzggtsEfgWV8m7dKMY61ZDTvAblcis2yKeRU5WGbNV6xnd0s19RDNGIEaxOMmNn+lnsQ2djsv0S+xTbubMrCJw8AU8IrmHDwVFsj21g6/0CRhr8YvyGIYittGaX8kGs/vYS9kWc4KWDeNlpTXG3M+Gb/4LCNpyWHkfquQXt+ScI2riENMnnZHlL8jH8I49TynHp1mfB2Bimmg/jUOs4WhqnoTrBAnVNGfp6fKm/1YKfbQexYiUICMxE7I0918Xj0DRORH9ELYNupbMyqoKw+6+YoFdHR4oidpW/0PXVo7A6ggMaJTwL0qY/ZzltYZvYNCqPwi012N0SIDami8V3ZBGPr+f51d3seraIveHCHP76hUvTmgnuiUJL0pc5D18jpraSIL3dKDS/wuhtKIkpSxmbpg4/hKja54qC6lK8mmSZk6FBqFEaduL/YjX2BK+uNNHmqs+6rGYsi+ZRXzGIxwfCEKl2oeLzOZ6fj4GQf2DrFurpJLNzHP5N/Qg4ef1ng/q/ICwqz3JdeW54S3InMZurD1tRSShihZQaffOqMHjUjIHjAf7mNaMl8ZdyJwc8rhRyYU0zLeq/EInzJl+tC2OhCSDng6TvWDaFTAZaCVLOYGV3DrccClHw6mDdiQLGdw3BtHwX6fbr8S4qQlHXhS41G87k13Fxpg+pWX+Qf6WCuoMXDypWcPJiCqsn2hDXOoBZO0jurGOVXCtHcoSIFUgjsfAt06Z6kJj5F787L5AcNZXWCXr4p/1ieaUt0+liXYY3BzYYE1EziX2WNyg5cQMTGwXQusjULAe2+/hQ5ZpLR6E6pnNnMl/6LbMOPsKizgwaTal2Ho73py7ez1ch/ftHols0cf0tzjSbGAw17VmZ9JKKNhsuiBeQFhvDZh1LBLrcsRCS5tbf4diNbqD+awlZBg6ot92iWrMa8So1tMQbkTS2QaZFjkT1cQiWVvNd8QvG7b8xq7lCdt9qgptvkZu9jR8vhRkppc+qbw8I6gvFIS2H7IQANIzOUNM8iigjEbbMMGByyBosYmQQiDyJbJoTmVYWqJTLIlOgSsC5rQxLFWBH/iyqRslhxSxGPbnL89HzyfqZTb9UOt4t2tyW7yRWdYDjU6q525DJpxXNKK3fzqdFO9Ae58FK1V50M7fiHbaWheemczx8HJ/rvInxMMa+LJ/q2GCmnAxBDLCkiPF2mrwwNkHr4xzUdRahWNfGwyNHWPGshaB0RUZrrEa60g3BT0txdc1hxF0N9sh/YaH7IUxHD+fU6Ld8dSrD6stD9rarYF2jSk56Dq8G0ig060bn40mSJQ/jPOYBfnGhzM9MQEdFnl+md/AV8iMrvY3cRjnyVdV4dnI/m/Y+Yo2wKmvUFDj3M5aFB08RFlyPgr4e+fGhqLrORyRxGKsdbXj65ATbVqXRJSJHV1Uf5qpuXG4oZOJQT4wlEsj5qIgUgLs63UlKDBNL55WALobiYZSWH2bt4nFIi30mQUUPh3GqrPwtgoNbGY2pbUg0OiGPCSt1f/P+WTQilinQKkafaAIqPhl47TjCwsHaOL1dQo3DbGSUfmC+P5CUO88pppjruJLwdBvxp75waOUmvOvSmKrvx8PoK0z784ybZ6Ux/SrEDytp2tFn3uNjnB84gGphKYHb1+N9cBl633xg2mS2PvdgTJ4Kk2SaGFJdxypdI4xyBFDbIw3p7TBgy0BmLpkFK/g+9QO+Eo4IH99J+OQPuCYBI0YjU3YdRUUr3v+xRa5Dmikep7nSqcHEbCsSe3NJ0nIhZ74Ej7uh6HcuW3ydeDqggEpsIefFPmHSLw/S0oyMT8fA1pdVtRNws8qgyaIaLRFhhFKH80v1HTPjDNmrq804pZ3M/eTBSNXXJE1chKn4Hhz6/EB9Eg2193Ao6kTwxxUyvA25iQBbauG50X0EVl2mJ1uYJebSJO0txEy5G33x+Wjf+U1X6nYslqjSrzQcsVRxBo+fB2z5H2Xp/y+mds2VdQb+9XxPrFkKjgbuCEv9pi9mLXWDB6hVsiL76QJGyYQiK6tGlIIyNROcaf8ShpVgBkriUwku72NB/QXmSdgyIeQQ8AGdZUF4FYGB0TnAn6rOGtKMz2DekkyazwkEM9/iVNqFfUEnH/TOMLjyNmP8hDj7u4953XIEmCqglvCNOEcbBlaOY4GAODrTZxNyQ420G1f5IxTIr7hu7umvxCPhMCpdVmRqDUPIGkbYZOKXqYzElCS+RksSW+KIragxty524jQ6k4yWc6gdfoTyo1tMdS7hoaQupruG4/0mlD9HhHDuaaK/YTIbXIYx6Oc6Rtan0Og7gogBG9LMZJl47zqHMSatS5fTqqbYvpuG0Xh5yuTd+NbrBZ+GUd7zA2mhLpTWrOBqDCwthHviEOzWxPEiOXJkf/As6RXFla4MG/yFeu0I3li6017ayJoxB8h59YrKX47IN0fiIGlDuIQBKvtcubJfgNaBXRzXfYJkqRBjTAP4Gx1DQaUgCkXWHB+/BJMHMoytrKD7bBnvHHyprr+D093POMv7MvPMMkj1JG+xEz9midAm2EC1oCTlbwppTf/Bh82rsHo2CMNDX3gqX0Dzusc01w8w3amDWTqFmP1TgOhHMcy8vMm8IcCe7748vPYIr8m2HJiykkOvQwkX9GegU4BYCW3c50wBT0nOv7vOCrHx4KrCloM+3N71gUoFoAFe7T2Gjo4uN+594axDMmR0QcQv/C1uE14jAQ3aHM9xIfNxPteuGsGSpRzt2s/mcyvQN3Ln+1If1DWVKAyvR3GuIKsm7+FFUxnNOql4JNVQba/A42nerOseg7DtPOpMMohXXA/X4tko1YYCLvi0zML51QauBV1BqW0ypW3XqOmJJD4oDMdDG5n97ThmwzbiucaLmtPfGaOjgpWiGDsqClEWqMbh7HX0V0KF9G2k9UopeXeUAL11TNmvRmduPuKvjKHjINuXuWJxVJ6jtetwsAsnumA1/QZ5eAjcRyL/C/nKxXityUZq0WyOZj9H+W0mNpLuONctYuPHMkrcLBFWn0Bdvi5W6lDzaRe3q6YiWf+MqD276Dz6nhPinthMEeGS02E+CRvyc7gD8tZX0JpyFKVl6xHTyWTQuKkMMfHFwFaTzD+7OPo+k87WwQhVqbOu5zkqc5w4tr6Tbh8bPPVvMyCpgrLzLPozBKl97k1JbSpS80uRe5eBuukAn3uDmJg6A1WfOFIr/Wkac5bVTzfxKPYOw9ysWWCryy0RUbQmhkFoAwh00/N7B2nV+ixyP4/Fk/doaURgKhJJolkPo+Sl+PDenqLZZlRWLSAwawSL2luRbtvPPYM6Zp9ezgwfawSGnWHblumYGeXyfWgm8TPkuJs3EtkRHeRGDSFzyAQefH1JZ6cxh4WHUWVSC9rqnLuegXxmOrHBqly2lePk71YUxGLoufiePYrBbHIo5p+uT6RqLWRtkT1rBPtwd5yM3BSt/07t/geRUJYdWPdbn5JnezCuuYtMuSEPX5ozK6SQ1qoq/ootRzu5hSzfP0yq3E1HsTfvu48yaFIUkxqVHYiPAAAgAElEQVSM2J2fwDBJR6b3iFFldg23pocUyrny7vwRCoaKUSU4iEt6z3jzKIjV+x+j9yoRtWQ5LC3DOWW8mrNC3ZghxUk7TfZFLWWF3VRsUx24uCeI9IqbqGucw/PQYD5uGUta1jDuRkBc1GqahgxGOeUYUyZuosDEBK3fR6lSayLzmRWBzyXo2mdAnYIRlc+P8qL7H0KGanJdZDG7nSexfsVhghdW0toaQsNvVQL6N3Pv1h3ct2gydUowIfLaVN0XoqZ9GndPpRPyczyDDKWRffWRdlEvRv+dwhCvDIRiDHAbHYPaR2NyZPcysVucp51C+IiAs28mjrX3OFD3jTHibjTo7Sd1x0L0ll+mt0uDJ+VCiIr2sNcjgTsZ9kg1zKS1dwMoTeZ9xFVizfrYJWZDu+MEBKcspuScAh53P1Lp9ok9XV6oLR7g+efDfK7+xMGCt3QAFx12MevDPhYY70TfuImRLXU8HCrBx4TZLFH4SPe3JYzPWMii9L84bS5j0MBU5M1c+CPqARlhdFh+wVHCgOACaw5f3sTmsyt4tGMdqQn7uTisk+Oy9WhnJhFxqBLTyg/4jnEgaKwZAe5ufPsUR+TjcpZFTmGSiQP19RXEOali57qDeSue89IpkDnzYYrnIuT87Vm6YBTn31dgX/uWjCWP+LpsMwZyuZSXP2Sumz/ByVO4vT4HkRuyXHsyDX3v2UzRGoOt9lTSBHeTURvACtNXCBxt56W+Po8NHvH6RjMfFn7hiqw8tgqnWNbZwOScZAwEtqGjL8Sb9I1Eva1GbaYb1eMNObvsHFW71/H1bCvb3SwY07uAXZKBmGe3srjLjaj6aoJGqvDTvYIlY9sQXmdCkKQ2x6TvMqx/AoeEVRCY7cv2M4nolB3l25xCFh5YRIN9Lhl5mpiOGENCwzMOCcqS3tuCXFwo5VV7+WD7lUwzXfR+KKFvmsnrhifYdz3lzdc5fLHWYL9UMfV7d7JsQx5zkruJm9KHiUoto+JjKG/ZRMn3acQXazHoUCkH9WbT8S2Jp6OSeVyxGIH5V5l2/Qytv0zpC5jF2NyNvCkww7HoCk4eCrzvWsa4sa7cfOXJofGf+Lr2FsoWtXxkN+ZVTyjvEOHKpJdM6fZG9MVfxBdH0/1yHi2vl2C1eThNGhN592Eo/hmiCHk6oTVmLh2V/xAVf4MCZQ123t9O6Pwf6NU706KhxIHR/vydK0Bh0xICfjSx/0wogW8j2Ht1NoPmHEa0OI5BS8LwCddhg20sa3Oz0CrZSeRjP2bsO0iSuRgmFmLMsXInxqqH7HovRl84xJZXCbiFhxK0P56iiSI41U7ifFE969oKuBeznC03DUnYpoHriEd4U0mGXw5GpV+5uWMsBYvCmZGSQVlXJJVGQzm2ajtf5mqgbFfOdZVEIkSy8I+8wWKlBXzwE2HZqMD/PVO7t86eDKmdI8QJayU2dhSyvz2ZZLmRhKZLoZh+A/GeetIFjOmwaEZCzQbjKy2sUf6Hl3K3OCo3jdmP1Qhbb06qtTaPk/vwKvbGv+MA96ongUs2b3aGYuhwB71WEbRaFyGR8pBCIQXsW2UoExlHnmYDG1UtiCg3YXdzKK6yzxG88RTv7FhMseFkhDKbrtrTNNSR1+u0+FpoR9jrajoH+pEpv8WJwc+JyCrgkWAsEtLa2JpYcEwsHpdIX2qOtuGmUIh06ktSZQIx03qLgbQ9xs9uQbU6dYXZuKrcowdn3s7dTK1IC12DMghNb2RS4mOsMxSYahrKrbZInFYsYOXleG5UOlDSq0uH1VGmJJ5D4MR6Ll6cwuE8A7yULuPXW4xOVyXWAoL4Gf9kstU65IUimFeUzW8FHVqTJ5FU8YCyiYMxsjuDgVQDHZXaTD8liq+IGKduzmOyyG6IuE6Jnw/vFGVRyHvLxYM3ODXiOFI/xvG6YAvvctwxyT5Gjex2dOud+etggEv1U2afisR/fgL2tt9xyHuK+occjGeXE5fuz/sb4GVozfEOa+SfB1D4NQjXSiF6xbLxmAamw3vQ9H5JW2Q3Gyt+supIFI0K15ixKgZ/pemsqV3KM4MqNnnaUFX9DKtZ45nkOR6PwEUsvbgVybUuLNwQTLpRHOlLHfF/EM/2R9+IdCqE+PEk1rzh7M1r9JqthJyvdPaDvIkXQhW32Jb9gZM9GwmRGsnkI4/QW2CL84kGho4WQuZ8MW6NVSgb6OJxxA1l/XPc2TGUY/GrKPuRzM1LcTyQPsCyQ/sImi5PkNdgnm0+jueyADxmziBS4TFr5OJZM/47hV1nGHz2O79H7yb5Ti8OWroUyvjwr/Z53j7ZicOUq4yIOM1Y+RwOl+4mcNJ3tvjvJKQogPTn74mz2I/v+jSu5DQwwmod98VKmVgTg+fgPSTpKqKT0UaR7WKezJxE6lEPlv8VI6o6Fk2VDfT+FWZ9sw4HS4tQ4SVitvL8MRmErFQi6XmdXFJfQM8bW1buX8W4zc+xOTKImfH6ZEx5zRzDXcQlSnMi9Dzv1ZKp+PiF3/0VLCm/hvuiYkzCs9lsYcibw4M5U11B2nhB1oyoYYRYIP+O98b71nqEjDpRa+tilGQD0V+OceFuIQYaB4jL3MLHmBx65gYiL+3Mi1vP0DbegvOkRgqKk+hTMUEt7Ql1JvKoPXKhQyKVEkSRUophZPYj6tfrESu9med9y6lsVcbtWSSrWvto334ArdnHObVvJxXfxyEjcZJ/jD/R/vMxb5evZq2oCaPfdyOsmkd1Vxs7JH/y18ibh+Y3kYhr5onXYA5+qaA3oRX5/iCmDsjQqieM0SgTWrXUeX4rljUjTnPGcQ6FDs8Rr0xDMMyHey9GoNusiWT3Lza+76PxSwKbzLqZHifLlY41/NvYiOdCZwJszQl6F4rmDGE6fzzid+Az+hqlmfptLOFl/vz5Y4JejQUtUTZcKFcjPe/Af8+u/oOEXrgeItQbyH1LaUYYqREVf4pOC2MelwhiaOCM3kACgl0DqFU0oKG5hmTFKua1+lJeuh7lARvuxCjwyF2WTLd4nMsj2KXjwommXlrqtlFYHENkx3ec5MQozZTC1Poquk0aNMrqMVwwnF8NGxAt66TG+BuazfNoq27GaZQeA8m1NMU00vXtAKWlw3ke4MqnIaPZc/gUbeUdlH8MJXmMPGrCeswM/UO+cT4WH0SxHyXHqHle3I96hpeoB1HdeYxmENvydHBVz2LErnK6v0qTlxeIn8xX7imGoP/BAY1x/fTLOJEcNx4BjyfkNdbwPCOFHys/IPNhFgeGVXP7uhreVao01YSzWf8k0tXFmGnLIWTQwum0PPocdbB/VIOVYw23nR0oaPmG/3d5lAWTEBhfTWvUA3odjlHV8AoZLxtqokTQ1BKkXiEXjUwRItvbmDP6JXOeBvJymSofXhyj1tGMWz8jefPnC1PFCgly7ceqTYUXbcY42bgT9dWWU4ZR3BYVwFJKE43cXzhvimG2+Uvc78aQoXmVqSMquRI9F6XiOmo7mtEc+x75ujoaY1XZYiTKlPt9GFslIClzBOmmIlY+nEfviM1YqdbT/MuIKs801vXCYlUxXOqusrj3Fr/66ri3fSXKe09S9vYTlhLDiD/fyKOen5ibixOb1oyGvzvPf8tRuuM+Qc7t/Cy0Zs269wwoTUa5/AY3t87mjWYzQ+o7mbH1HGl50ajPKMd5rCHlWRpkRAUj8zuKn6mhTFqzE129LkK/9eIYp4uz73AuCt/hZ/4DYqXPcMDoD1N68rlV50/e3WiUplbzo3AQSiVbCVyQieI7mBP2lJxIcwQetrHSfz3bo54S5L4E77IjrFn2L+nTt7Hq1FRiLj3G33oMp4ZLMbdNgeOKL5naf4U4mX705MyR6ivHb/g4ivM1yct/w8/YL7jp6ePYVsNb+3Vs/xxJyqkzmEc3U1q8h80V1QzMv0BpcxdVca4UZ9qjo36BDEtdbrUsQKw5GUPbPI6XBJKjbYp34k56Tu5A4WE8Ape+cfsfDVTkzqErvgmr1KPEsQEryRSqqhyY6jCJ/t5kpH8+J01yMp/KFjJBAuJSzDEIVMfTbAIeYhqEhRxlkKMiSCqjJWFCYdVTHOVMWPAyjeXyC/Do3YDTJFEEPo3ldOEbZg5VQVdWmLYSHYyUOrgpaYq5lA0iKufQnvyOtlYX6pta0Igfh/jsS8j8qqeq6wKPFU9yJ3gwB1J+crs/jy+zfLidkINotj2jU5O44tPHjsSDKAWuY17aF8b8uMq62SH0i6WQFXme5QqyvFCyYPTP7yzcXciEt8LkZT1B33oad66WskFMEZMh+fxqk8Ff5SIXXW4gcaSYAYWvOJiosue1AXe609nUasfbWxKcmNZMQ2keUpqbKKxUY/JoEUZH1LJKQ5l5po4I2XWR49NLRdsnNl48zfANj0jTEGSOYiEedW5ckPiJsGQLMWEx/3t+Pk6c2hLyYb0Nk/sa2ChrwJ6UZlQrAzHKnUZ2qyBKKjeR1i6kQ0IaoWtSDN8pxk3ZDYh/mETYLDX+cX9OaHg83R2qmJl7EljRjly0LEfH/iFZ1QC3JGEulF9lVGU0EQywSDoVwTJhAlQcaSptQbYxg59VUQx5v5HNia1ELrTD03Mn82WasR/4iFZeJb8tZtL+U5n7OrfQ9C+m/t87yA5xw6fGkZ/eu5gRPMDjaRNZp9zGu4VjOVEjzwmjWDpmZFMvGwPerVgvL0LXRJX8ykqkk67yLacMueYOXn/cyiq5vRg+TEJNPptf6dXs8JFDuecqBXtnc2nyGNpt5uE5qYx3znm8U7WhcKEIknmFaFpU0lQ2g7sWElRrVlMi1sZDnzpEBtSR7Ezgr+g0DB9/4efgAMQkL5PX84dBAY/R9BiO5JXv1HYEMzirn1ffoylYMJYMaWFKz16mXPEOgsMfEFqyEpveEoINRFHsd6XCcywFwX+56GiBVHouFwc2Yym5lMVr7BimF0KnsjzBWydini6EW54bPwrMqTAop6JsIWYPLbmvO4GM9lJsrK2IzP+LvnsGb83LSHF6TI9yJ97RL+icMZadjxUxLbjCtuD5JPW/xVPtPQofHuI39jQ1YkeQ15rPXkdTxtx8SMPsAG5uaOJHzTSGSUoTrR+H20Z/3N92YFCmR3nmAX6rG+Im387HliSchI/hO0GW4wX3Of74O4v1OlAIr2NMYCh+s70wWW3Op4ho3tvLYhyylvyXW1FS+kOR020OZflQzDYmzlzGqku1THL0YLjFLaxXeiPdb8XinQ8o65nHn6YaPI8eQuTSW9YdjEIzNxGNmrNU3DxIk9x9vtSoM2dHCD8O3kE2rpzl+3ZSXmdBp2grKyuLIW8Hz/ReoGj2llrJf7DfFsV741oqlGsoNQpk++Ii9KQPEaldz+rwaPY6j0NGzg2f6i4ybWu4O3YUs6P8ic+vQu97D083g9H3/UQs0iTXqh0fS1C68ZP3X4wxqtpPadd13k6YTWOrJC0tYzgmBgbPnHljd44rpULsjFTk4oVNDFbrJb+5l9hr3xih/pdNH95yuLGUPWuKeStmyrrvL9h3YhhqIvlYBoynS1YIA5lg1Bf1cqIohOSjK1ghZUr4oDpWz31M/Y8sclYP5V5rIHO8Tfki9JXYX9+RmTaN4LrVqFcr0VW2HeXkFHaWSiDTs5gUlwCEhizCMLuXsopR/O46j/A+R8rLXzLQPIVRpfIM3vwKwS5/rAplSV54hEWOtahrVfN3YCwNheNobO4nN+ogmQOaHJYwpJjRiDoJsk/Pjp9HN7L2hSgPR+YzJjyfOZUxNEiUMGN7Px9+rUFMWQhRRSUyD7+ncXkQAWnnGfiTTmKeLYvLDajQiOHZug+Ux9RRmqbPk4h5zFymy7czpRh1tFIWbYvgCmn+bD7O1T336IrOJvFtHNGxeyiOFiVFYDRt8ofo1Y/nk1M5Do7vqB+0h/0e2zgaxn/l4z/Ipt0HQ35P3o9Whx3hv4O5GKHOF+/r7O55RX0LdA16S3mTMUptmhQ03qQjXxNRO2Pmys9grV0zhz1E2fG2h9yyMrqqnHDVFGHf03BGfDtK8Cl3qlNXEhwRSraaK/Pqw7kzVhqlFAFUB0/Gc3AtnbnZZEjUEf29jzOjZRjfuoOBMAle5rTgLJODip8mM/Ye5KetNG/sphP7ci4b2wowM3ck8OdmLq7eicqsCXS9WoV6sDXPbq1mRuM0XuUpMEQtG4HcHPbHtxDotgfnVl1aY52wNF3Eae0wfu7IJ7Y8ipKuXmw0vmOaVUODRhNZcsrMDUtj9LAeIj13YFt+mu1K6pwc4ohNtDIunWos6P/MdhsJbr2VoP0PfHBtRTZHmwYjTaoE5Jge+wMLnySu5HrQk6lP9ghNop+fQMDNEZVGTTyf3OXJ1EaUFS+T/ewJui7NvPYYy3QDWdpThei1ToP6r4TZrSa/w4idiqP5FjMSVeHPGFj/4oNUJw2b/jJzThNR5j+Q/iJNtcoFTIzlub5rGG3uFey+95kVxllo9IKH4EVKvWZx45/fLDp/hVzfbJ5nb+G8yDpO+JxmqMh6chckMWHlDMyNX2AQMIuZn+vI7ROgXaWCkDkfiExRI2KeIPM1rPj3+WsWJU3gzMeDPBL5jpG8Ll2r/WgJ38C6fjtiTM1RWH8Hf+fxeIou4vLwl9TfXo2UtB319sqMMBZlXXs3l7qHsbLvNh+iDBgxJJgfX6SQsSskvCqCseJJdMg+oF5sP1LfdelQiierZTFhqfKs0hpC0LlTGK9wpzLxF9VThrPKuAmV2BUctGpnW5QoA6ovmTnvABV6zxjYFEHLoSYO+d+gqMGNC5UeOIh9QUNpEluL+3k8M5qz5/+wyjKXQ86dBP5M55DSCM612BGXkswS3290q/hw+es5gqW86Yv9w4LTWzGkl1EOu2n3EkFPphfbxTtReA9p+c7YTznKxUo/HKuS6CxvJl5mHqVzytBNLyVnQAbL7hyiv9SAVRypIyxoT8hnsqk5h6v1cKGGiy+fY3r/PCk7j2DnVs6ZmKmcGDaXtqpFBI1y4d1VUyZZ1+AnaMNs9Urm5IhSXt6KipsdrXI7sGuZzfBfZSxu6Sdh0miuzpjA1ur1LNPYimGrNLtXj8DhzBECdGzQExHmyYkC9BacZFTnZjJ78xH+eJhW2Rg+2+3Gt0SKYkE5vPMH0ZeVQouYBR193Xz0HUzW3mpMpPsYtFUc5TA5igJHM2aQHZWbJzB75AaGVRUh6iaF5BNzRI0HU3hqC9+HLiJ0ujdaL6Po7JZgmNtfPncI8lprEf8Y/0IyLAFbYVV8fviTbvEOYwND0qQLWCwpjnCKI49UGuhx72HUw3LEWsRJrJ7ELKFuui8qkfZhGTdMzjDhYQU114R5FyfMaRllIp81oHW2mNe163kVPowfp56yvMCIWvknfLj6nlGDcxmbbY9lXzEpgrKI2Zhyo+E6La8r/0cdJfj/IrD/bzS0qXH3oRNFNyuQi/7EvXYRzKT+hZkCmG+RRO5SI/ITupFIfIf0haf0MgYl0S04XZRjVgR83PqHmpumqL97gH/9RTzr/2XVY7CZ+5NzuTs5FpZL60UZdp7TZV2+GM/eS+CcnUuIgAlyRj8x0FbAXD2UudPPUbNlCz3fBHk51QNOJaBWugSZoaps/3yBzAhTNhia8HzHX/wkBJHNj6S+N4pmW38eDw5A+mkUkeWfSZ5wDwE5K5bGTkcpypdO+wls7F+MzF49Jv7IZY//Uva472CF6wDeB84yeORBkmuf0bpTA/W6fFxHO7E2bRtqdYeQTFjByokx6Ctm0XN0Kj8S9iH+xBvBgNFslw8mo/I19aW3WVrxE2vzMt7ITaayYBqZVNHrMpTU2CKGz/tKfloM2+Sj8NXayOf/w75brmWhKFzX4yalu1tSQEoUJCQEAzuwMLG7a6nY3R2IhbUM7AALg1BKuru7u75T2P/2/q73GQcx5o85Z2QEHsGy/FFZTJ+IMutGGnHr3lrsm59TVPQU57nnUfXRpd5gNxsT/Zj3NJltqT9RWdtH3Q8HZj9fz7ebhmiK1nBM8wvWl1/xKy0VZdkT+NQnoD+9Hj8WsjHHmoFN35mbWoXfxUT6O9IROjmNlbOekxk1jfGn56OkvJ/J8atYX3eF5tzpfKnei0brSZ535DIw04lGm71Mk3zA5nkdnF8fwEzKSYhYzT7JblJqmyk0rkIi7xlDD11EbuQqhl4L4ICnJ+OuumI2biRrm3p4KvjMiBQLgucXkiGjgqRjCaWvEwj+k827H7Es+1XH4o+P2bdTnzv/nGGnsYD9z0fzTF+V9edzkZRdhc7wZtY1DObU4gdsnFpFencVJpOcSR3mg0pANNOSvrFjghcZr49S82spam+CkLPTRamvnkFP55K0oJ+6UaGETWlC+Z0RT5Un8ONlP4I5Lug8PYb9ER2uTRtHz97BTHI8Qua6Z2jeXY3ZkhvIfhHl7XQL3Cw/MEvsX94+Fub+xnUMOy7BaIlqVD8+QWKyInPVshgun8KSz4H8LSwgRPMqSjoSaC9vQ+D3Eg/MEDgaoXlzC/evFfB0+mrejpZGqqKXxTVX8anRQ0fVnbIcMb5/GcQq5W6OX/ZjTPlPDD6bIJh4hNdFhdgL+eDZrMvtN+kML5lDnuQZ/q74B/Wnt5HUUsbK8Q4xGz8RsquSyDJVXN5U8WjCZ0K2T+ZS+GN0dV4gL/sPY+dtYGjTIUqv9tOffJN7J84xattDlhdX4Rs2hwIzF0LlJEmuvEmC9wiGDJ5Ad2wy+r9cGB1azbsBAoTbXfhtGIzqyzU8vLKUDQG7GX9kLcGbbtJRJUR3ajI7BsYQ6TKUNVaLUU0s4OkFFxaGq7In7wRNDTUMb1mK4YBFCJpuc8Wsgqypa8m94ctRwXE2lM/nfk8WP62VWPrxCV76uzl0OwajmPfM+zmT123TaTM2R9RQD9tHCxk/4itjjQJxF/qDvXETNlOs+OBhxUv/uUivcWL0djMO+C1he+5y3h5s43PHGr5af+X0QmGMP0hgLhPMDMtjDHHq4ucCHeT0hvD2tyEHq4cwwPW/P5H9fx1lKSHyGqUIF6mA72Wc1nJGrDqPDCEFbEbdR0i9hyrvHiQe6LHntClPG6+jbWlO+xZDPCvDOR/zHHG94XS5K9Kd3Y3rs0u4j12ChP9HNNpbCZIbycuy89j3bqZA2ZS0En2GtAuIShfwMV+Kvr48PJO+oV6rA/1Z1Cs08yDyJvCeAVkgp6gKaeC1fAPudu1o5dajMvMUSlJ25Jg8wdvSCqGeX2zOqyfh9FWO20zGQzERad1eftUUIa8jyyA/ecqkhhD5qQLl9N98rhChV76X/I7lDNF8TOUPT5L0evjaU83sFB104spJWBTCr7GH6Wo4Q1SuMAaJSZxOus3scV1EFZgzSek7XzsbeSlWy6zWEqyUXpJi24Z5VT/qeSmkyZrj+PMNUv6SJA90oyq3EnO3RUhI5lAZG0GRgQeTa5O4kvaD2UPc6O9oQOVWC3cjj6Mx6BbFA8pwk2tg8Q8X3ibqkiNpSqr5vxgpPqKhXosw8cl0cpjjfv+Q6KWJUNlQnj9dxEnjXHb7iTIo8Rbfp0ihHFWKYrwJXyyHIWT4nJ0ZvXy4n8sA4w5sjeFbyz5G9J1GIcmVebuiYZMQWmq7MT7tg46XCJoqOznzUhojBBhNf4Tqyj7mhY7nap8r28a20xo/i9vm6oQ3x3AzwYFXwQ2YXj3PIb/xTF+3gcKmZB4pjmZAxX5mSYzGW9iY3pPaBMQks95YFI+2NHJ9/8XXfyexTelEfOlHOPIs5n+KMep5h6ixG6eKZyGbpYv/nHtUaoLkzHJC4ssJGPGQ79f+RclWE1GFApLDYevEXPjnO8XnnbB3P8mPZZtoN9iFdJwHYwbP5OnDRdiPD+XPtMksPvmSwvLNyDqZE2j0mxUWKbSI+NLt7InmryV0zqin1cyQZHV5KpqE+HI2hH4JU765tvLdsA/oYJwPiEtEo1ZhjEu/KNmvP6DTDo3xGhh3P0Hm0S3iDG04Fm2IuHk5LR9TOd62hKBR38g2/ILv3ACKs5RJ+/SIdtM8fqgZ0iukR2JMAvPpIHR6IIE1EFIuwNfEE9dxcL8B/u2exgilc0gKdPDpzKZzwxXyMksplxyBWHEV2trjmbO5jFHxv3EdFcXDuBASK2LZ6PaGTymalO73xK9BmXljpfmlokzW73a8BeJ4a3QgJT+d2HgFpJruUFWdwWjrHaRIvUZdZiClgh4KWlQwahUhzXYhaUZDsXQPZGJzDw3iu/jwzRetBG3ygh7RYw6IKVFjMp4/zR0o135HsvQKnw0W45DTTmnbMFSHmBAlVsvxPZ7s6zWkpa8ea8E79rVmckDbi3W7TTnzOpgIi+nYlhURuu8N4uqKpBd9ILREDh31xWyUVSFONYuq+Rk0BUWg3TSAk3PP0jk8iTrzEqQseujOgmmGl6nYFcSgZnBFnhK/UtIadbhx2JAUY1PEhZ6zsaGBCE1ltGXyqK2JoFRb8T926f9E87H/xNl9efrrsItTZ4V0G50TQ2ibeYv4458ZU6/Now4rooXXcUHEE71uS+wG7MHFcy0Koy9RVHaLR7I2ZCzXoVViFGeey/JE2YgCyS4eL7nK9aazuA6eioasHnFzZEmMy2XzBj+kFaeSJPGXxpxhXC6v5FN4CP5Ocxgv1sazXQqYMBV3592gcRt78w6G9szBXbaXQztPkeCjSOrx9SiYeyFkbIJCzUC2X9Tg9olM/h7YSX5iEIUZX2g0W0JFhxuj16tx1ywP22EzKBbSIr5OigLjSsxbvlL5JoVZ6vN5sVaLXuli5trFEfZLQDm2LBdJpPpxKMnq4jQP82Fr43lOOOyjYZI3UvuD+JT2i3itV/jrTeBEljAWJhtQeB7Pkl9LKBKSJqDekO0T/zLu5yPKooWxef6TVNUBrNaUJF32BN3jNhPWegfv42+x3v4Zh/eVFFXVsPhbHAnjq5nY/JtJ6ho8NX+rSekAACAASURBVOplku9F0i9s4GbDDuJf9bBePAirwevR7g3nzP1YAnv8eLWwh3frL2AkdxMF6Ub8HGw5PK6D51n7uTCpjKFO5Uy8fovCEwWYSmsw+WobX4tGkbXyL1vcayhveUXHjq+07Q7DSMeGW+czCBgpRfheY8yqw3hjcouEvkrEd7iSnSyPznT42JuEvuxi8mRU+HZ+LVcExxn6YB0zF08gdaAZQgqVxDlXkWScwNtdsSycKE3zoGdUxLylcVMgd3NTmTC/jjMhR5nzPoqTGf5Im77AId2FozsWIWWShJi4DeLFo3jRp8HvjBisBi3kqn0F48YPY6rgIaIib9DwnoCIGnh4jyfBNooDO4IZLpuN/XAPVs6ag9CXFxRGb2L929uUz3qH4vs7VLQFsGL2Oconv6IuKYOTxzeRdG82eUG3sJeX4LTrOzbmvma5/RN69k7EzVQexWA1jHcuJ6C6mvsbx3NSToGGZlOO1G8jzc4Gkyohpnhf4fhKPcIufMeqJ5Hi6kj+sfzJqGwDoifH8OLsP+RbTCWuuo1/yj5weN4WIlP0eaksh0r4Jx6qPEZXq42UlmzuiR1haKYZzm8sEG4awDY3JSb1RPNqw05aXacwPHcil+wn8De1n1Eb5NC3W4i9VRfSyo0MuvWA3QWidHpZYdmviIyUJBkKI7maqUTT3dP4aynj0Z6Mt5gqyoYmGEy/w/D555FW9Sd52CmU37/GRNuNKUlV5GeHsbjpMzWK7+m/eIdMlURUPptRbNKHSetPRid4cPzwNFSGGBJuqYjLnzZafmvj+7qOAb1PiXhuhltYJBf67iDQ2M3InrOs0ijkx30thl5fx8iQFq4fNyJo+2+Wyd6n8eEPqjzHsNzrNBOm9NKQOQDRsHYuRsixWMWAcNU09IODEI+DMw/Cedy6Hc1hBWw8eQIhMUeM4oaTo9SIYdm/hOk/4J3+OgYbHWDEw1pKRJag6ZFPd8ptlp7SQmpgDmffXca3yZNe3dU0an+hO9YWrQs9HLqeSpmBE/elTZn5zzloSPq/5uO/yL7DF/dJjjrEJ6lYTjU/IrL1PN+WdqLeIoZ1TzJav+wJyCtn5SBTonrG8LzkCHN3KJA4qINhQy1Qf5qMks8JnqlaI3EvF8l5Ezjw/iQnrt9i5wkXrn2wZ4j+RNoOP2PS90gMfe7TXFSPns8vHF4pUD8oHWt8UZk3hAdhE9ha+ROjxi7cb76kT1+XpbMUkI8pYt6YFpaujGKTjQX1pgNQ7RqA6+SZ9LTV4Rg/jstRk0jauBObhHiazR2xuJfBk3GydAX6M72nhhMackjbnsEg0o9rajvYoS5Oy/OlFJevRnZIIcZRmWQv2ozpsHDMehOZqzkXVX8VPkim8CdtAdMHSJCf0MTI+CD0N07ArtSCy7+DUFSaQYrSLV52/UBHsJcBHX1sL6/AV3MyovP1qU3cg+PHcEZ4rkFk2FCyDdSYVwzng1Yy//0Vki94427kRru1LYP+DqbMR59b70uRK4nGNcyIr+WurB3uyF95L3psR6OePw4zgRlvuzt5NbUPmaPXaem2xjkojuBprmTJK/HpcxgjvRfSMLKIjKbdSDVE4TWunbEdG8j8dJ6l6rHYS4nwLXoVRSOHg1gcq9dNRuK0MPIhh7GttuL1kxxWVMfSe8oOv2Pv+Ho/EslFYrTXz6Sn0o8pdQ/YH7+bpGszUDopSeHbo5yrf47bGTs6ptxE0u0lPkV/OGtag1rFcNIjf9LUUUbj0M0odH2iXVePrAdJDGxegJV+MvazRpKy7y6Ti4bQ1TKbZzNHsrQhkImyDrzJimZZ0BKSvr6nSUONolEajDX9jM7rsXTpG1GuUY6I3AgMHtezoe08NndXUxDRh53MbJS0iigOCWOX3CG0hj+g3MKWpLXTeWlQz2S3ZGJs/sE9dBqLxu7n9Oz9DD7xBYNp77Hu/IcTI0cRt/8ybqNEWBhjw7SB9SzZsAPHYdqoy2UwqlCHplEOiJko4Rccw/v9O6i+ehfHgyOxmXOCeTcuc/vcV3JzazCfYk3U/bO86JjB1u5+En6dwm7HHhokLXn/6yGq3X7UDrNHQjqYMb1q7BVr593ZadhnOPBCbRltgzdx9P4sbmzR5l5eLodLMnHOUWBWhSJpI3TJk+rBL2ItG5qCGHvYnSNOc9iqMYbZNvLIy9ZR0m6Laf1stq2X5YSpARYq9TycmUbm/S8IShrQrrZg7CxdRkxK5dilJ1wROBNrXsFkvRY6Pg9DNtmXZ7vSaSoyw6U8i/2BOfiK1OCXvgDrrHbqEz/h3BpM/uI99O/YzIX7ntj8CsfE9xFvZn4g3jyOnf2TONn0lQrtXC6E2rJpnTTDjmYQPVWLEbHf2T7Vn92l+9k18D5r39fiPbUVh74WXkj40N5ViJafLg/Vk7Bxnot3jDQfrFZwpuQHJu62jOAzZW21zAmZS0LbPir7n9Ipoo6d7GAund3FCc9LfH17l/wLB1Can46zZz5HMo/gv2E3M95rorqokJ7OHFrHz0f7SzVmDroUlX4ictZN9h85/x9l1P/E4VzLQKO/dEcF0yxcsMkbTpXeZIy6Uqm/K6CvV5xBjxWot7Whw6ubBo0EGhtfIfa+hr6qqwx0r2RNxxeW3V/JjWe3GbA6gyuO6nwdrsY/dvb8+tNJUUsJEbE6uCoGIdI3nACbKkaa5ODa10fFixIkQwoxdN9Kzgonzv78Avdk2ZvqD/ldkH6RDhkN4ouiUC4YTPKdz6QISZPSdounV38zU8GBbdHLeHs+kHP9/XzjMraip8jsUUNi6WbKbSz5UJqL5qsGlro/ZMLlD/wDhK39zTaTOgY5aJLZbYWzeCxPWwXcKxzCSlfIK19KqOhRdH/up0jbAM+ut6D+D2XZ3UyMjqHWVoXyha00NLwmLWIy15U/sufdYjKVLAiUekd2ZReGpoWox8tTaD8QqbJVaKov5E2SKmk7JjDlWC628TdxmNrP5k1VrNijwODkSQS8VOeK/z8I/azi9JlQ8oNfYGgxk+iWJHSMu9hm1oB7iy1OT6aRILOBKA9PBl8IZ+ZgdY7qmWJuUMziQgUKhqUSkdODdelCeovOITZeD2oDaUo/wxBjXRJ/ltD52oFDEzSQ+XGaPZOsWWRuw6TlN5n+uo72XSfZeHAtCQnC7Mg+x9wHUjxN7uHs4CMkBlaTPtqSpNfXKXnjyacz+2i0ABte4z+riiGpeUh5dlIQ3UWaRgn+q3cRFvEUnQ/x2Lud4omVPpr2RfR/V6Dp8C+OK8VREXeSif4z0dFYwOi07wyaLyDvhRKJg0yRk8mluLqGz3rKCDeZ0J8fx7a5w2mLLcfmowKyk51ZkfcOrZLXBG56Qp1sMYEffDi1KgUSs3n+9CPTfGSxXvqYxRfkCVcwpOdLGTPtj6PjpkLuzX9Jaewjak8tO1/00x6nRpPna/oSzDgulsSNpRcZ+e0IuaLnedJegPi4TdToDMDW6TgS2uLEpO/k3v0dFKxdwVTtV1TpSCO1XICc3k5+rKnHW2sSJ43Dyc7NRkplKYn3PpLhNZ/jj26xz86PY9rdJCZd4HHUcwJGG6Mqpse1KgWaxarYVNyA16q/uM1fSY+CAeuF8yH5KT9n7udChjiednJ8XShEeM14wu6u5mPhMjo8hpGy6imG139zfOsp5v8NQEjmCoUPFrGksIwGS2u6xb5ywjaAkP52VOUS+XpWgbLcVhoHFiKzw4nFSl1cPhiGnZMv70q+MsVBl/ScfWg4uPHMKgzblknI6LxE9tBEJlduozHQhrezS2mTCqRPdQYPtxxDVzKDz2YxkAmtX/sZteEGEUU/ONjxE2XRIr6snMOz18aIDx/MGwNp8kIayVynRz3PsZWeQdERRZJt3+EgncOskl5KnEw5NjqCp6vTea/WiIrZca7XWfGyMg07hSbiPpzgvMdSIpQtMKrbzWS1vzRq9zDqdyqPZO9xULSG1OkeHHtzh7hhY5FJTke98TFvLRaR7LQRbquyfW4XrReCic3X5pBePNe3DsPy2WZ29/oiJFyFYH/Q/x3O/4uYair3y6+vwVo1GHnH+VTbHOCOhA4vr8qjMqSR34IFZKxcgflEWVh3goq1O/jb9YZViuOYaDefFF8lnIZq0hMLrUHHWKR5h9uS02HEbjZtVCT83Cj0t2gjJmHCNv09FJnEEhZXhvqzYBg9EQPDYySWjCG3T5GXO9Yw0qMNF63J4BOGpc5rmkrEKPr2ml7JUEz3BLNolBMmcRCe3s9lpfmIm5ew9eNXnIoF9E0BQ9vxOE+dTVOFAa0RM1g7aiNHjBoxif3C+gZv1v67lwlj1zJqjCc/RDP5hAbJf6MxTmplYMB57pjZYfyjAOVADyIdDjLB8BOm1ZqY6GYyK9qOBV76tCi+wPfLI6z1CtgXG0hKxxIWfItl7vQoXFeE4PlKBZlfp/i08xnDv7QirKFCSJsIt0ZL8O7wBTpU/vIkUIImk31kzn2I1kNLJu+3petSGh/eB5FWD/VWBtwdsh1hrXz6It9St2cMhQm9mBaPZKD5XSKtp2O75ykrl81DTLOWpMFWZK35xsItDYw/IYdyrhFKZ+dDuDkkxmBuJMDKUZm11VF82vCNyP16xAQ0UjwyhvwJ/1K/8TJTb40nq1nAbNtffAyMJedPKg/1klgzswe+aDHsuQ8nLruwtPghApU61tUdJG9NMAKFRhbtWI/XUkWOPjvN3xe9nJ3SwYPS/ZzP+weVZf40aGXyMe0PJpdb6JUeQuG5I9y55sSyXl+EhBJwmyLEEu+NNH85yaXCav4UaGMt6OdLTQFT/fVRPFrA4H8nE/rpNU0fjZgyt5iEKgfST79E2dmei4IgBF+0yV7+CoOEsehLrOZsewQ+N3r5MFOOuBG/kX0nzDZlA/Iun0FSy4URsx0xVKhlquUdql9dYaD+N47kP2S8sitLZE+xZ48zhwDnEyfZcPc+2ZO8KBlohqDyOZ9L1IhecJf7f2eyZtAZBO7XWDqnBesGC8yOfqaicBNN34IRFVOlwnIIYevGkdc5gf0HJqJcOpgWiyD8sorYpKrJmYohCEs30Gv4nKEa8bg8EiDm18/sX4uJVZ7L+vi/HCsoJUNbmqvzTdj6IJd2/RIcZIYxLOY0JuZprDZ2xaswBfnc07SJ9jNO/QAX+oeT37IRiYoffLBR52bUG9xNDdF7K2DYMm302yLIyXCm3FsJp259try8zpu1PnS0T8V41AReP7ZkZeQ5dnjF4FGUwVcJb+Ru/8U+SZv6k4tJr+klxGc9gSq36Xg0D5GEXHLnR1K17SNXkqC/uJ/ZB5bTVGiLnKkQwZXLMfXfRe4YEc70Q8uRXuJ0DZD8U43kjy5MnTpxy8mjapkSKncv4SfzBW+HH2RGCfHkSzMqz6TYnmWGdmkny4R0md+8gtUv5Pmi70DOtB5WtV/FqWcFd6PVWGg2kk7DFUjUPmOP8iA+vPekY8xcHNrLsPrpRLRNK9vK/5Ji64q5RgabClJZ9GsAbaMMKJc2ZkD0EyZaDsHcugWB7db/KKP+J2ZXvU0CyjLFeH63E4MqByy2d/JH/j5TFulT6gU9V3/j2r0Z3fg5mHXexWeQJBbTehimMI1OlyL4dydiq+R5rpLCVbdmej3KCJ54lTiLC5TyhjSjD/xyL6Z2shf9Zu/43tiB+Lc8VE9/5fvAFLSvWlFrGMre3DbyTOzIuiACG1XhrR0mQiJ0FH9kfOwbpnscYPHjyVxbpo6Pty8rl4pQMuIXe9fcoL+jhahND3kZLMLe7nxO99/j+69g5tw0Z3XXbLpT5yFZK0yrJljv2kz2RQeO/97NwvZyDG7NQPHUDUadust8yzCs48ZDaynX7mdwMnkB95NKCKgYz7jIV1zW2Uj9gst0OrxG8V4yF9OOc6pcg8S0cexepoLfnJ+wUMCktiSao5KJMaumRz6ZTKGDlLV0s0D3L+HtooxWKKFKLgJxvV6uffXhhp4v4m5d9P9uZpLFdNKvnKLyzkqExYZg/NId24KXCIu3s+bPKjY9E0HDdgqb533F5/MSFKZM43p9MVvkJuP44DDb9IJIL6pgS1gZXuHbEGuIQjg7kzG57RgZP0RMYj0nVhVxPlGDB+ufMnf2FoQqX2G4XIW179250taOhsFxJhz4ytbnwiQ+OMUWXrIn/Bb7BXtZOUybyb/X8715P+nH9/En2JHMtS85LTBhiEoJIbOj+dfgHPlLK+kweEnpI18WClx4ov+dmJku2CWk8j7gF15icYj/nMD6u9U8X/qc+R29yPw4ydJXi+jLnolEgxfufZ9RT24nuUkT8yhVFj2I4nxhPlWOvpw9vADPtxOxX6KE5ds1eBuP4qDNAI6Pm8om7RTs1HR4tGstqa1JjL6+gQOPbajUluJ2dz++ERoIfw0nUkuBBfU2tFan4b1JB49bq1jYJEl2/BnUkkTwTYsgZ9MfFqWdx+jGVj7/GIylwwsGuHzDd3Qqn42/4b5cljHXr1F30J/TpvlY56/gXv82/Av08WvXIbrVmviwTlyvhxByXZzgKBdiIkKI3uRDf6UT14UrWSvmSOTk06w13c6clE3Mr9vFuWBJJq1ZSsNKIQy83nBv4ntWL/rK43NDuO8aj+fkUJLHXceiYzoDXbzY653BgLmPoCgH44MaVFcVorLDmrP/RHB9rx1/2wNY257KmucXSKkuI+jtEk59TeJ5ZQuyqq/RWJCCzGJNOH6KiuidxHoIc2LWNYarxyJzOonz4jW8ubQMk+FP0NylwNYDrcxb9YgsD1tKOwTgdppBSsHYNVgwTiqYckEMerOn0385jappm7nqeRv1jwZovSkidNNSpkhNpj9MmkrNTbyrKEJtYQL3P+xk6KwJDGh4S2PCV8KMJbFWaiDcJpaYvh7aY8zQ/TGFH24LmdBZi0PBdTZoxOMbep+Nsul8M1iAincbyYorWdL+Ffm00YjN+8IXFQd8hVQIvPKE8jxnKjOWkNS4gZ4rzcxaPoVVfwxYUfmQr3erUf09g8FW0VyY4klRWQOJEgN49v4orwpe/rdV/f88LWgwTroZKRQxKYUpmlGwWo7G+FZ6k28iKCknT9EaofpK9J9fJlp1MMfVV9FR8y+3fS2xdNVkm8MK/O52cDbsCm69tiDdD4gjE96GjP9KXvz4hlWLNmohavT2viFP6jqLhdu52laPm9luTr95iUSvC0fPZiOr9hHqgC+g2FxI7pVSpJ5KMXxjB0l7mqmP7Ud22mV+Bg5in8oZ3kzawcOvD4h5ADnKN1Ev0aQ8/y64DGeE+UIuGmbxpl+S4SNuwJMDfJphz1/FX+x3nYIr1gwTNcLcaQonKt5SYBzOb8V9KJVbMGiwKTN1bHhZWY5FcxJR1fXMjDyPUXsGtoZ5LPkym1+tNbjIKtOfbsRE4QQMoqVp+ilCydBG/opn0vuiDw2FcjTzy/Bo76NKupJlwSOIfKyPmos2iv7dTE9ZyP3lGiT+7iDLwBRbIKmwm6CBOnwubaVNoxl94S4KYsXpNFuPSH8pDVKWDK99hcvQhQR3hWBTaEdX6UEGyyXTEqNKjfAfrlqq0S9YDeLr0EuModZ2HH+/OXLwXhGXDwaRq5HDsYhQRPWvkR7YRjPPGdjaT0avCJs/TCI35CoPzBwZZyLNRf8AorJf87O7Envnh2T1SLB2/hHMDBVpP/2XsKa/9Gl1EvBuKJsEOog2bqV8zQ0663vpbxYi8tgELMMHsnLeCvKG6ZD3JQE/oSX0SxQjmZFNreMgHhbcR5el1NdvIe23LcLVNogkqDBtmBBHH1bhMXIeD+9dZ1CmI+8StnAu4Spx374wxMuO9L+HWDf2HMfcVNn39xg3Ah4idekkCUOHI2UXiLznBCKLslHwdMcxyZT6uBBYvpBBWitREBNQ8+4Us9pFWSNui7hoKYME4lyvnkDSmWdUNRXwy1MOX1t7EDRirZLDJemXZGr4cil2IZ1VA7mZ8A3Muulu/kKL6kuOWgXhJxuHTGoEPpr+hLwP4P39Lxz+MIctO5aS4TAEu99dJEuKsKGmh8WyShzwGgYjNrLyxHRuNTlz1AN+hVuwtPYGW0b+4ZL4FAa6SOCf1s1rY3t6W9Nw6YjmtsN1kIePTdPByAeNlvP4zF5M4aMi5KWb0UveQ4vTVtzrvjFE0g3XDnO22yrx/Ucbmim5SEjWI9TdAKrt9IamkucyHedRTxl7xwoJ4XZEOrooKM8jt6OG9O79mFspsVzkGSv0LmDYPJKPNn34nOtBWu0IpeOUCbxSw5UhsHTFRvZvu0tqfyFjFEN4JDGQLYEq+Mf3cUkbJrKP0Z0RJDf3ITN7JsLKr5nWUIpKUjUTet7h6H4LF9lW1Lu/YieTwNcaKY5nQ4NkBrma0lzskELg/IH8o5VMVNhCek07GcLGYJrFY731XGzTIjo3jT0/w2n508Gf0Dhk6tXRqJElemAS5iq53PpUQGmfMrVJtXQPykZMIINQjAGTKSRNUsArEQNIUP2PXfo/MbvafeLsvrqhxdwoVmbDnzBs2kTIyXdh7dYoTByK+CZSS56qED9mKfHxbzennkTQ053LlU8rickuxehZOC/u1BKdqI+CxHp8rbah2hbA/TYvLl2ZSlNqD1opsQw1WUlJrxZvy3qYnLWQlTHQFDmCLTsucNFoGGE9v3kRncAnHqHl0ISqtDz16dqICXWT6jSR9DnOTOneRYsXqNmP4NTp5fjNuIyNkjt1RU+pl3+Bdu09RGSr0NTKY4qQG3e6c7nxMhvrFi8iln/kdNU5hi0z47qtBf6GvjR//0HP7xw05GWJLlOjwqqAk8/L2fSzi1MyJnTXx6Gs1kineDpP0sX59WcaM8ZtpTIonwOp5zBJMcdOsgKnwOsMKbmHgbY0r280c9LSH6W+YagpNfH5uyeVMiqkeQs4bD2KXQtH4zswm3G5G9kfs4eJZgXEdIKaTSNdRTI4R7dQWHiRaWM0yJCxQshdjNSfgxDIt1OSJUN82SzW2iQQGtqKiUU91Y2TcHKxpSLEHp3Rq1GOXY5YvwwJanJ8sbFCXVmBntBddJZ+Rrehjslyyqz59wIqifs58vApOREwQ66fiWpl+I98TuYrCQydDiLzYQB2S8dTcqOU4RqzeXc0DLPYN5xeWIj4NSdCNY9x5PcL1PPnsTu8iFYXBT603aNNdjwKf/dSkVHBGC9vIs6lserjJGaYBrAvbiZ/rzsyxXomh80OoPxmI355Pwm9fJdOxxGUKSRR5qmE7PZyPKyfs7soDyPRBWxReoW2XiIW7h4M/C1AZIwXQl7qWIhK8CSnGDGD1aTm30Q+aCdPv5/nzrNrVBeVcz87F1sPURp/mdGXqsqNflku1Kpyca4l0nFhdA135+jfJHrkJFGUaaZZfwze0q70h0QgZvebcuU1/BEJwHuAAKnkMYj+dmTErud4lxQwuaaICeGbyWlQwKpCA995uexTucbvtDDOf9tA9E0FfGtmU318O+OWbUNR9S2ldjL8/LWGzspi5qSJYNktQ96vR4i7HUZZL5+3fxswXdGAQWMyof4zGVvyjqyBMzg+ZDDjFdtJiXyPZIUZi5NzqZHpwrP6FYYf7pLzRA8udiIdHYqgPIBsyRQKF9mjUH6e3zdzmTnImTdDPqPa3c7RSTdZnWZJVVYvDuM7aExpJEUwjFrxZiSrr6PqPYAk00M4i3WTXWyMarsNKRu6WRr9CWtXZ+RGS7JUqIsXdSvJ/X2AJbaBVN3vISM6miPPunj0uZvoMS4orRjJkSnLueH2l+jR/2AnVkLtzzzGjPFnvYYs+mmi9PU7Ivv2FL72sL2tg2rVDLZeSeLz8PmMCaxG1dSbzmYRNFO20vuxDo/CPnTqO4gom8gyKVMKhssjJTqQchd9uo36UYkNpLM7BxfrRoSrrHk81IIjjfvQG5pIrcpYtoSX075jIR6X4jEudUP10FVazAyRb17CgDwLjEz9EHI6ioS1AnUhRaSl1NHV5cClV/3kJBpTRub/za7+izy6fXHfIOGxJI0RxzgoksdzpMm6qoX1xTJ0lOJwW2NPSYAUtTl5mDKYvp7fNJVb0tVqQuI0F15OvsArHWdMw54xetMFRsneBW0fdC69ob1pBPrCj1Bcd5Flz9UxbBfmjVI/BiuVSGhpx2mdI5ee5zPOwpDs3/0UqJ2lSOYh7rgjLLmSjIEvMTgvg/zndrQ+ivD5uyut/rLME0thYbAKDmLq7PdPZdbRFDLdpyIxcSAOnps5meyObfdGllz7w/C3jlS++UiutTfzAyYg9amHH0e3YeuVT9mLJSQ7tHC1ZBJJk2+Q8fgl0k3q2OhqMD+snFc2xcjIt/LspypzRhWg4uDLm1uJ9Kk5sLRxD6bDBrPsbCxTz6Xhp7mJoOAy2of04NrzHYHoJr6VaGOkGEtjaTnSVr649YcgK6rE7P1G7DtyjrSmH4z7ZEOQXB7nDB3p3PON8/umEhf6lobaiXgmzqSzwhfx01ZU5N5lWLIiLTOymPNpCqP1DzBacRcdTb2kPqvDb+QYErtayVLrwv9VE69WVeIeKoL+vSJG+q1jwIoAfFr+EtIrxki328yOHs2Q9pucNvJii/s2FDdLMTzKEiuRLgp6usF2O9VTyni+8QYfK81ZLWuASfod/hF2Zu7xkTiYDuZpUACn3t1j4t2FvOqcxhwLc359kaCsZyNBK9aQnzSHvbErePr0E1XvtRlU0UaKZgTaglgGas8jTUwah2lDyIq+wbJt7kwceoK/k/ewc9wzxIVsqNLPZAI/2erhz/ldTbya9pURwT2oLZvAnv6l9G9dx+FDDlyc/5TL34IQtNxjaPxwfI6Ow3y5IlGhkHD0IOPq49EZ0sDI0F6GpM1hikcNiwLXMsBMmUPyVVzoCsVFaQ+j1VowE/xmaPcN/gh20yL4RrSaPAZ3klHqraK+6hv/mi3F82cgNUtWcOFqHs62fczukeJZSyMxQLQ+ZwAAIABJREFUQ9vIqDNn7L0injcmUG3dxtQ76/AZ9B3j/N8cjThK8FQ/An9OR15nDbdsN1FVeJ2IBmeuRAbyp0GH2Iou1lpKEhOsxVKDDu7opqEd6Y1fhzjGMjdI1FRCWK8MPyk7ikPA6Yg+jeMNSI9zRHfhdPpH91P94gvW2gnMaD3MeoOjVO3ejtmItTxI3MGqxhb0F0+muPAvrb1edHhU0iD4xNAkIaJe29AfVU54dTPXv/dSadKJmokCjeXe2FVrs8dwKfM/PmOL3xzWiKZTk99Fy79LCRZfRE/kagQ15XzZtZHVDjO51O9BrkgP+uaXaNSczulkOT52DuTzSSXuXpRCyUybYy1lFJQrsazoMn+FtTHttGXKDCe6JcbzMmE3DSmN5AnJcH2qC21m5ayMqyNM3hrNZH1quqPozkhFMNYYuYIIqgYnYJNVj4aJLyZiwaR2tLOgOptwB3/EXohTsdYBoe2j0LOUQNakGtHor4haetF/t58Sr01YZWkSZ2GKVJ43KXZ/sCuKo3/DARziA9jfdP3/P4dzZeFOftpL09XdydDjMtjsvM5Z53es2uuHrn8SnqlNDJTqRyJZH78eD65sC2Sf6TtWPLfl6dx97Mu6j/CD18gYPOCh5XHu/vnKvTsvEHryiRCDWj5rhDFl4RWkNm5naFsYH+xUKatZQk+2H2X9Afx75yH/RP0gqjKd6Wen4hhjC0XbqSjfQl5mEjrTr3FHfB26dp1EyVbiITjNIsESzGTfEbB+JfMuO3L5hRmCr+50c5jV+SmsOtPIbm8nbhVsofRzP3oRnxjRe5BbDk/QTbnOux5Ljgbc5vT5EwS8fcoPESeGjhqE9LXDWE3xRMkpnTzXe3zcFUalVwtxi4bh6ChJVlky/S17meHaTprSUTKkHhJXu4h7Tks5J3Mbu+vG1Am5sdXiDH+rpvDDXJ/hNh8Z/vs7DkGabL6dQODkHUREveSijhRbtt7EN6iY2MhkzNbJkr2sntVBcmx+r4ra4ngSBybxw3kbItOFsOxNwForEvOZk9he9opA3RZC/qzEOd+Dg4lDEJulTtCHX7hH+jLv0zSiB+phu7oT9dIIdGbP5ONlNWSOvsHk3you51/is8M8QpSTkJ+qx0xFL8zXTmCB1iIWKWtzu80Ax7I1mK9XIcTVnS+XrpC38ya7tAS8jPAlIV0XU9VV9DR8RDlmCQaW+ejIbOCpSBOyf7ZxdeY2lItKEdbdR9zsLDa9sOTC2FiMjJ8x5tRpNrk8Q/1GKl4zXvE13gO749JYDupi5ITnbFpxgLuxj7ml6o+yhQql9j/5NHgy10LlGFN8mUHhfVyybEKy2QODhT7Ehx4jPlONwZJT8IpWwUUYDuxeR82n8zx5uJXuATNpNhdnsE4L6x9I86HLC8U5k/i7yZymHYfoe3aIDcWtKAy4zLR3FxHuD0fH3pRqUWWEuhtJrvdlkk0ivesriZn5jjcysVyZ6suxkF8EqK3HctRUIm4Mo/dUJILrf5ic0cfIb0+xH19A3G9JPhCI9Tx9mp/lMKb7F0+fbGWUcztVr0ZQvvkTPn5z8a3cQ0l0CWuk51H85AnNKm0cfOvF8h4rktOlWFTrxaJoJy7N3ISnwVFO+4RTOUoOx/R+atNreCDiQ4frNgRDd8LQOwxSrsLsjSi6feewWD4WEuMRO3qHaxUbOBIUhLFPCQHuorxMGMtV/V0MsZmInLA17ombsYrZzODnqoi/+MLoZj0kNbLYciEYteHGnNc/irbDDgQLOjjkO45ZDmnsPPEaaZlE5l/rZ0BYJqYXr1PsGs37iE6SNn/lQmAJoQ0BWFXtJShhMQfW9TLQSxL5ugrMMs7SskOGz2r2lDhf5aLKEqb2/aDsbhDt4tHM1jzFge5ywncvZ8e3TBYbyHP6ZwIzZaWozX9PU08Kcs2RKPV8RYwVFFvF4KQ0juYIT8w01Sg474KZaC5ZoeqcaAvgt/9C3F7lob4ti8EOAprkgjG3Ok3LvPfguZiG3ZLkN3eTlT6HuSrjuTrSHbvBTfzbaIvlDvn/tqr/DxrQvFHAu5CB9PboU/BZCAr2w7OFZKp1ktr0Ha134VR1WlP7OZoeFw+kvTOIauhHcfcKlOy8ufHvKQqra4lp6IMh9XD0Ii3qvdhp6nK7qpxhMrq8VXzBe/FKdlnLIebbjVO5LMZSXvTmdnMtuYfvr38jMaqOadLZtKuoUaGgSE98JdlXJJHuOU1DUTtLf3oQJFBmuJoLLtpmjJIs5lvpTdyNJFn4KBPLynNs/QVtBUK4H93B7AV7OBFhz8SVM3kgnk9i/yIGSrVgLl/O0ObtTBs3mVCTEqTmfEI3rB7vtlm4Rk+iMkeGfWYmaCdXMrVRCAcJUYx0VpARCYGzryCdYcomyyvI16VibdRGbnULc+KHMt1uBHKFaZhJbeRLqzEGGaq0tUrRPDqTOtlnKGeOBLF7aEvXIFbWhfDYOlQ6arCwLOW9Uiy5iJPz+RW+C+S5xTCOyMLE2QaEXomhQjoRqfr5DBUy50b5VEbUpdGsXM7gpKdEzNjF0yIhUrrN8WmQIaJDjIC74kiWxyG7oAVdg1mI3Z4Jvgfo6dyOxbQ7jO/bg7Pbcz7c8idU4h0vSxWpGwofvrSz4PABxOIfc+bWQ3Ss5vPWbTQR928R6u/KUs9ULBrywUsK0xKYsnABa7bksMF5BZ9OqKEuNoQzybFsvJFAafQLbkhFEX2tjsHr3xG/5hEZi8Zwzmstii+F6FBu5Nz5C4zNlcaqdzW3C88yh3jmCJbgMCAeD80SfrW7kf/qFP3jLUl3GM6PDS5IBFzHtSif7MOjmPgui5fX52My5jY9xpf48OMt8wvLqL+Wx6Eh8xi+bjqOqiNoqTRl0Kl5DH1gyIS2RHSj6nDz3AL3/vJL0Rbn6Dbiqu0ZkKBGGyCi08eBJiU0ktIZpLoajQmWFPwoJchSBVn7acj3H2d15y/KnOaxOekVZS93kvFtNL0jVGhZbc7mOR7IxI2AZTbIDQ4l+M91ToxPwjfgHguu+6Kgp4Jd4VtGNfTTMaiJtL5cXMTOc6jGlZ8N7QwXBGPz+AyF8m4Mk/9Kvmc3LXlG6Kdl0KzUiaWqPt5dnsiUHeRVE6wyABxkSRWvQrRhJ10qM8kUK6fp9QKGzVEmdmYM2Y26jB28gxPdfQxLqkPFdSF2LdUYqrdjfU2bqE0RhOo+5sEbae73zSdG5TkNOrK4PTFFpXcA1WHGzJ/6iivZxxjUsIGJIiXsujcS81hQcdxK9Fo3ThUd5p/BaXD+FTH9cSx3X0vcZ2jPT2a6TCdpi5oQpK9lCFr0fMsipDaRij8hTNQeRbvVbxz++HFKLQmfhAK2iwzhvvgXsGmjKm0/l63k8ZKtQb4zhwaXHBzU7ShJEkdaTJ2WbmVkx1iQ8zmJEeNjmdDUQqJRPYi4sz/NFl89W1Y4HqDbVR2tykiKRtoi3WhKy/YByISpI+Vqj19cMxq/IyhvbsK+0Jjmpc5QZPsfm/R/ovm4curUvrTWC4xRqCH7uzj5PfY8VHxC0ZgyBgSrM1Y2lXfKP5ENF6IlYQAegkwWxW4i/cRNwsaqsUt8Ah1nrlKqOINVojaM3baCPTXX0WgSZV7uRdytP+D9jyyF9nU0VCgg/+Qpqt1meDp3I3nyCNXmMRQ0FTOhR5U/5+YhnVaHhMMMJEVTqNljzaaSSHpMNGhQLGNJ+D1SVyxGX8WW4vKnnC8KJCkjAvd5GiwQ0UW2VpztsQsZN9iRKVJV7F48AbkLG/A+PRbfLRewqvmLk5QjH0W/0iVhhVJLKIqFLyjYv5lb54+yckMaJomtfKjYw3mJfibHNzO+z5jySzIIx21hoslYRItL2NZ8inFdSciqCXNNrJbogWKYhsjjMMgaF+O3qDkl4DNoNQePbSXSNwYrLzd0y67R3jCRiaGpFPsq4v21GfFPucyQcifqtQ6bp8uxao00guWwd7sJLpedeBZ+Hr2GCVjURiJtsAmJ6ONMEnjzrsGAXQ6VCLvmUB6tjGO9LgXXgvn614yT481I/mNGQNF1KkPvISxkj4lAG5nyXirLTFBV8cHdVQ/lXBO8lb3ZNW81DgslCQw9R+IOH1KVeznm5kLVi9koteXwuCuTQy7+zJrwhujJF2iWW0+OvCSR3fdYctif2TGq3G//Q6FCK0csLFlu+QaRzC5WmpzDTduQgGYbBi615tMzP/qdvZAf74xZsoDJ7qcJCKhG16oaUaknTPV04n3oQQya5jHMeQoq1i95UacKmm3c3H+ZObILmDimGVdSkS2rQVtCj9aUzUgq6tKcPh5hlTwefDzM+q4ydAps2KoTia+UOoo9ZlROLSZVQY0REZ+Q+RPJBrFGPKs72JXwB/PYTvb6GuIpcwJ7rVbqpMbh2LmWEgMLbKrqUZSfj8MgF4YcHk6HZC/nE48go1TOFvF83lj1sPljJnppOtzYEcjZfcvQ2HgCl8Zt7N3ygE2NO7jgNxQf+93MlX6AUNyC/4+9swzqwnv79PWluxukOyQUFBBUkLALFRW7O352YncnNordigqKiooKSgoo3SDdXd99sbszOzsb/xc7z7M7+1wz5805Z855d90z5zNzbpLKUmj/85Fq/VA0u5J5UAliGuqo2vvSUXOU071nEWZK0ayzk2XhHzkq/Yc3+imoplRz+7Mjv3vjUfR9x6KabvLVH2MnsZnk6mLM+vlRGb8VBlXD21acopZiUVTIZJlPHI49TrGGBGOa4/CSUMVaNg4JfXXGxUazVvYFtscXY1IURbTTInIMRYhU/cHTM8rcdq4iWiOadp95ZCZ9xteumwl5SURm9TDuUCCXzFsR1qWzNOwNtsnnWLn0NGYuv7hyfSEz7qqSUw3O2VC1ToC2AAo7YFm1NMs0xLDLMMNBOp+DTfGcclBG6+o4VAcIGWkQiaeDIi3f2zH+1EKkiDLKqeV8TtNhX2o6Uyf5UsEEzEe/ZPRTJeorjWgxrGZaUz0a5nVUtAdhWOjI75hwQovvcqsqFsxtiFp2mwkeUcQZFaHakIaongn9knQoSr1Ha+dZpJoT0bz6EAX79TR6F6KWHUXTrSwkTaxJfeTHt5wPVGX9/o/k49+RvScPB4/U7iDWeSrC39uZJFdHcmcRbt7LsC2w5nJNNUP7T0DpsT0BknUEVYYjLO7L9B8riHL4SeCqlfz54IRF37WMHKHD4XojHj6rY3xVHfX9dCiSSqNj3k0Grg5CSj4dw2cf6d04i7ToIl5L9mCX30WGgQpujTcY0D4JlRH/IPj1i/zi7ZzekMuQi5ex2X6csBUDuZ90FVn5VTTEPSRsVhU7ErsxzV3LNHcpnHqlGe4Lq0RG8dpXlKQnjthFhJLfdpz6GDe+1O1l7lIngvykmT63iWuTtiC4O5veBZ/QWjMcjwOPeNOchEu5HBba5vSI32Sq6TMO5NciWmOAUESUnN0F5L/ZzsB0LcqtTGk3DMew8QbmsfO4M3UEqbIRDJ5xl6qtAzn1S5JV47oYIaaLjlo4T8SDmbtmPQU7zrBn61ZkL5uiubyWhngp3s81RUGtlisqRnxszWd5dCmXlg1lih5MqgqjCQVC8gv5Z7g9Ww5fpHZkDn0e/4LeXk4qDGDJ1+ts/jSdD72fMS6KJK+fBjKSPjho6tES2EpZUh3SOqfZMDWQzo+bWJn2lXypBobp+NNQkU6l0JqLYtNwtS1HtfU4n+UGovgrn+FbH3B+9xTeJ9YRcMGOzZsesz9vBX82aiFI8cDh4S9EP4XxdNlfpL7LciatkPKZTrRs82VCpS3zV6tQ+3My8y5ZMGb9Q/q5PyRksIDXadcxK7mITHEH3aMmMC/GFqvfM5lgvpohU5QJ2DmH1tn7OHY8ieGmZfgvVierJxqXv/uIPbuU2XMuIG66hV6xOahl/MbK7TRS12L4ZmSCzR5lPm5YzyKXQs6oTme4pwRZc3oYN8GK6LLzVBSosPTTKYZYf6Iw+CadU14SWNhK1mAbFKr/EDfsD+MTvhIpuokUmVr2rAkmeOYScqYOw/xHPhVvZPA4HMDLHc9ZscCFk2buDNgbQ4xOMdcOrSHdIYoRNYZwJotZzad45PCVL6J/0J2gQ/vPMCb1v421WBGLDp3Ez1CMn8oeyFQqISWdzj1LBwrf9fBqwUiuvvhOx1xXzoTl8nj+MOR+nSN7/EYOVH6k/uMJTL5k8kJ0DMMd7JGMe49b+xgEsreheQmaNTexaP/NSysj3rU9ZsnWIWxJeUxmn3ZSi4dhVlZC4+BezB8n88MqniZlb/o3m2CqJ0N97x9iRh5gmMxxnjX/YpvMNJI0S9FV+EVZ3mj+vG5i1qwSrBNsmSkxmSWzmtkpq49t4lmqa204us8G2WHQLQ+fveHbCwHPLoBOA0QJtNiv1InTpGIGZnxEtzuLHG0P5kl40xYTzmyrk0zKfYy1+AmSwz34kBFF8JTZ+EdK0+6pS2dmKtYWNwmPXU9Jn3ak3b7iHvuWJD07NtmfpSnsB+a6IPPqE7eCLEhtV2HXwo2cYg9bnPuhE/gO+09niG6zRbx7N21uMqheykQicCo1aem0V/XHpNeTdO2fRPlnMlTWHEHOC84+TPh/J/kolupmjOJYNOtn8tT2PMW13pw6VImHjhdjB0/m4dGt/H1+FpdGOdrzjpJ/9ji73DcSeE3I3vByvO6+YsaoGKTenGKFXBqLNkxjQIsdaR4yyAX8paU6jxsz+pIvXMUjWRnM50gwNvwmezMrsZWextxrfyhq0adZ2gi97V8YdTmQJikFQr2sCfg6DNkCbWSWGfLFxom3CX1wG/QLZQ1zPi/14uIoCVJnSaKQW0mg9G9mib3lwyYh63Is6R+6g5mHdhKovYn0Iz8YGPKHBUMmIyGpz2OcCJwWzJS3ekx77cW2VY5cC8hBPtqbWMW7yGnF0BLTiWTjXNpeSVL+ThMj3QiKGmcxKT2ekHRfho51I0QxkwHm4byRGc6ZxRqc9juOSd1VJgWEYn9Ej4kHbVHKXkLT9ybKRFZjMKovb5fOJOF4Kfdj16GeMA/BCCHC+3KYOA1g8Q9Dvqc2o38HRlyRoNjTnNVn8ljSMJNpjYqELb9CjJg+j43LkTs3huAGGXrdIxCbuJeiNecQfN2Kns9WEjcsZnJ7M+WLPXGznYaX0l+WT3tAxZfZVL+awQ/VNTywjOR5n+XsfrOfbambmX9fgvkL5hA9OZ8k40F8fOJDa8dmTl8/zmapvxgPXMXpleNJfWyK/JjNpN/xIcxTH+WGAYwet4V9rV6sezCCz//koX99M/cO+FHg148rhXNYrjIL92uOLA8fR+u8NK50W6PaEcem+qMYCrdiPeUJmgm3MbHqi6K0JZGzfPBauxX9zw9xK0qmcpIUpqNmkr3YCa1NQYx+3Y1EZwV5j93JFibidMSJ8DPbMBmUyNXeeewa/IL5X7+Ts+AvsTllTOmcySqjpUTMHM2V2Wuxdf6OQGYCkT0N5BacYf2tdG5t9OBRn9foygfSVO/N+FR1ZDSM6VT6g9aziziI3+RU9Ga6dC6Qe6WWRVrenA6RZHGFJfIlv+j75TYzPx8g+NZh9p2Sx8NkBI4TCkB/NBd0a+gwy6Kv7W2y9FaQVlKG8+oxjHhwiMlfzzC98Bn1l8eyytqB9a8WEaikxsjuZARes4ketA+h2R4eu5ixbnAmku5rMaxN4FBGNP6e3djO3onb+3UcHtlGQ18jBj6TAjFDcmZcI3ihLOp+35i1/yceW+9xc/g6bngpolDszt5iLdoHm5PinktE2iVEIlYgHHWUErUX6Mx+zJzAz8w5HYVPmi2661JYFRHJhAuv6Tzcnz6TrInbOJu6pm7CTP5iFuaIwDuUNTdmMqZ2HEOvgUAId0Jg5zaIngi7gL0S4GUPC+27mPv6BlvipfgnewGas+dzx+o4Aw9NJvHHZroTI1F8k0/FjMd8EcINI3+OjLLjwRAD7jx9SO+a6fwePRVRSUPatKPYJ97GHdEdSL4NQuXVeN4VD+fXG0ucT+tT47aR938KEArnMDxlELeXGxEorYmjqAFNMa+oN7qB8rMldMr9Rfy7Dh1PdBm2ejAX9axo32qMVlklPbqS3Ba1/Xc29X8gr6BOSZMBe048JS9Vj6tyOfTvC1qqEfT000DNxpcL2YdoNEvgtf0Rls+/j8QdQ1ysFlD4+j6oKpOQmYSVRgOLXl0jb0URO9viCavPo6k5lB1jfEnAn6joM/yxG0liaj3BAy1pdfSgPdOQW4WhuNh70FrdQoCvL9cG1+P5N4OS6S1QeA3xTzosVxfnbqEkGzvvcSwrgv21E7Fz74eq2A0Ky2rhYRQ7fEch1qaJobEs762ySJIMwdFBlCU1LxGsccRZWgH3EjsW30wHs5Uc0GrGcPZQgk4tQ8T6KZnlK3FTaKLCsJDSLitssKPizxcmz1dnsFQ+pp0ayArU0DB4hbVUKGmyCaxWHkw248gsk4PXdzjnLI3GQSPsHXo4k9cL+/pjKOVHlWV/HHI0mVOnhOHRTECJG33COLffivMdm9kr/5gb8hLohTUysFcEpdPdTCrIYsxcoBPeydewcuUBek6+QmKrAi/Ki3jeKIVBexcmXqMwNhalOT+EFmcvlK1WU3TyMrYdeXSKWuLUPw+PhWG43/vKwdNZSE4/hpRpLB7KK4nIDsFmgzN3X0xhjns8HVE36DFRo9nlM3+b/mKeEcdKvznc27uIHc5HuPNkBXWXlyGd2UHooFr853aQXxXFmYSVCGvkCG1dyfya1xgODeHjDxXk2iXJvR+B07XJOLX1R70jnvhbMYi+XEuOgxstEk9YXjKZnOwYBtvep8umBU7e5ZaBC3Jzcnii1Mo5UTd2vBFhrkk9Ob1luEZcxU6tk5O5Gbxv78RR142MyBxkbAbgZ/yVmNhG2ixGMef0QtyljJFR92F2ww7+9JSzTNmZ5thXrJo5A6H2TSL0NTFzPMwzKSsCfopRp5DO5SorHA2DmNVqwqE/B9lV8IzmtqNMnfqaGUID5vwzlAHrxzBq9lako9spiXxDyQMHZDX8Wb33BpvkD3B1ZzChhuk8lbrItO9dyIjVIPY9h05bK74UzKW2Q4u+p9djYGeKgYo56ZmXsDSwh8sxGAzzYe77F2xsy8St1paLyw+h/fA9XRb7kGiR45bvPiSL9xBiqcMI/SFs+NNIy9RFpBkBjSEgKYa5rhwkl3KvZxTr1deieDeVhB5vclRbcKpI57XeVQx6Jdkxxw+XyGpOpFhxyvMJzXmZ2NRsgfwoTNyckCrVoT63C7+CI9TYfmOOw3Rkc6NYUO6DqkYseXKjMTAuZq7fI75k+yH6AbrU4WQSjEyGBHFYmASYw9p/4OhEIZliimwpfUe4zluSFY1pSijj95TpqOk5oiNsIVvZFMczqiR7dfFCezLDPnYz06IZPd1SBpha0KfZH6PmR4yqHkheTjPKeq/RkYnj3odEOvq4EZ6cQ+jeCDamnKFVfRZQSL9Zxdg7PMV62TdMFSfx+WQQxoIgypqcaTVbR1dTPfKbXRFVzUQnZxcZsppMb1diRb0Byx7f/pdd+n9F8nHs+IHgOYNqKbcpYmfRTGbe/8zrHcp8GbyME2UJtFxzwrb9Ljo/HNn+9xPxTyJZv2kubrqdLBg+jEEz+uIWtIZZu3/R/HULDe+VUP6kSV6ND9pGs5gnt5yoY8YYTb6A9K5CRo8aSe1QFRZsScC7rxhStvfIbhZhfHsLSjI1pHR0Yp73iHGKK7l0yQLN1F9MGuLD59iL1Kr1x/ndD4xk9jLkuxLL7rUz9EUhR0YN4IrjbQ5VaROX+5ahbZ00yE8nt1mGSMESuirf0F3fxLYMeaa9uUFo/XU0ZSDx0VsOR+fwfp496vE1VJkYMUBdGbvMj3ioV1At1cV6rVp0htWRujKOsKCvaAtziGrbxrmX5xkqkU9han9c/4SzRzqO1EY1qlr7MVShBMGPfOz0GikIecwINWfkihr42HSLb0qpRKw1ZeRdP1IGBqFR+Z5H4+6h0FuAsrGATR5d+CwQ4m8Hsmq2PBh/Gb1H75Ef00zO26fc3DiHRyHZDNp6HEsLf9YmF2P7RBm598ropATilbcTk+SjWA+ahvTQw1jU3kOr4CLXrJ3RzNIgUT+D19U7mGO8gjXPiznR/Jh5i9RIlNnIuzvX2HEnCF1NL1x9RzJl/DDeXWzAN2ca32T/4qzQSOXTAURPGU9XqSHi7krI7xrN7FBXrMX6MVI1mRzjzRgq32Fs+BcMfL+SQCfnQp5g0JRDalsMLUu1uCerjlzELz6ImGOon0fX0uNMzD1DU5UfVn7RaA0ej+/WTnpFrEjN82Htlvmoaa+hX5sGiQbKdIj2MNeyjUfu3Uhcb2TdvFmE/LMEkXfexE8fjGxBBko+I3kl94WzFhMw+GENKRHkzwxnjEcfbtzfxb3knbiNT8dWVY0axVSurr/CiZR32LYp4iVcyArFDHpzOvFpkeawwzBcm7KZbWCE+ScxquYEcMV+DsuPzKHo2VtmW4zFy1eLxjWL2H02g0Fj56Px6QIbr6RSs82IQwHHyPq9nxde/TBti2fa6EEM9BrK0p/dbDeyZ1KLCq42bjyNuEn7NgOOdxXRXmCC4bmjOOYXUy9Wj4RSFkdcV9O/5Qv+GfW0/PrG6J4ShGI/eFgah3VCKf4ZBsQaStFucw3EhVjndKD1oZq72f2I7TOORGlNMunPGrUU3grOYDkhhItdWxgZ3w/xl8NQ36nC/GJLtMPlcdxyl1cSzSwsrmXqmusskhRDe04wC0I12XZyNbayKeyvt8TJ6RiPMm4QfPMEnd9s6XccFpbD2kwwHQoPzEFwEcJOgOkQeIgIW5QVEbxvw265OPaTprJUVorTCvbkd93k0vz1nI9dyouQ6TzTCeVEjRyLZ/xD3uFaphr0MmrEGDT0FUl2NCN++DqqdauIkFVgUFspaSYTyXdpxcJnx8GFAAAgAElEQVSzlAsfanGMu8YyyTdMET+Ej98JMEyAU+K8al1LWEktm49twSMxl0zTVJxLP/LrZQ+S+UcZIp9Kr34zanlJLJx5lpV1UvzT4UdV3K7/SD7+Hbl+/EzwDc9DODa9p/tJHL2rdmP7MRelCV2MDZ3I25YjHLLagP2+8bgOq8dbcyh6HiY0lQ5Eb8Ir7u67jJtnHYE6SihO+8CUPmb4jJFlgGs345/2oCHoQ8aFPQxacJ1xPk8RD/5Nz+7dlPdbzJNj4YS45rNUp4tzVg3E/VTle3QQtjces8F4EC2ltbzJy2H0hBu8D83nSXMp+f6BHFW0Z0GFP33j8lnlnMBh9Xpux3vRPk2RyqMRfNW6R0zJQh7GneG5jy8P0idSuj+SVcdf8LQpnBVzjTjw+SEJEb9pWt6J8K8qRjXZvHfzZPZZJ7plEqmz9UE18yxfZKaT2bKFjxpvORPzDc+ye9xqeco17W9E98oR9nkZkepvyZjXjDAqgSPq8rzPziTmSTYFuxJ5OA5irfIIp5SIVBUu5CrxePckbmvWsfr5U77J11GpOxK9v9pka3cjebSdeRfFuHE1CQtpVd55hFE1bTMyq+SZ0teC3/VfmannxvqBI/ERbSCrRpbwHDM6h6Rg32rD9dy7WKamsTHlFRtCbfAnBcdLLowWZpKkUcmJ/ruRVHdhRz6sMV6H6wI/Ci8cpbffcQrdHSh7thvDT284WBRK6eDv3H0yluaPmdi/uI16nwaqi9Q4lbabSX69rJueRtamWKr1rvIw/SQ99qcYmBlBoMUCfjkfRbRhEMI/PQg8tPht0Jf+Ns9JrNoH909idPwC6vXqLFkzD/U1H7i/yoUFl/KwV41jxOMYfndHM3KFHWZGRoRvEWVMWRCqIjHkeCTTZ6sJEjOnEfnrLSdNmnn5vZTcqX5UfrqMefkvJg72oanTGZ8xZvQkLmdqmQWBXvE4+/6h30AJPhhFs3bGV44t62JD8F7CWq7QJ6OMybmDWaUZSR8dJbJ/BOLodAnjfkroLbZCfNRfLsg8QSUVvk8/wETXybw96E5EuwN/ChIovjUJ7+FPqdk+FOv7K7FTamF6hhg99+3YYVvL6SYrmnQ7WJWezQAZGzZ33KfBzJy6InlcZesZJKFBbNkelhqW4uUbzve6vozUaeNP8xOmyRiSKiPLcTl11r+Ygce4QchXZfNGIMWDtxPgzz2y2tVp7JuHWbsrKfU9iMsN5ft0SeZ4hjD0pBUf1KRp6FDGpDaCSMMxHFPL43tnNQb3b3DpTgLmc4dgalpFXvs4TqyZz6LhHSyQ3IWESDIhc5RYtuw3kgpnqN21DenLQ8jvewV74U1kz0tzQEQMVyUhe/xhsxLY1MPINti1G4pXgesHkO0L2zW02bGkGIk1WpwbY0DohL/ofzzB8mW2nP3Hm+4jsbxz0icyaAsNu+ZT0xRO1/S1aK82pLdFgaLzD0gcKqS+2J4vjcFc8Nbixmo7rM3K6eehT0FFDjoBM9l3bwXjov5BQ/oJs8Yb8HP/dUq9JqEhEOVTUQIHIs6wvX4L3798ZKJXIHIet6k+8xut2rOccvRkrVQRn+On4FFhx22FJrp+pPyfST4EAsE1gUBQKRAI0v6buWCBQFAqEAiS/8sY8d+sbRYIBDkCgSBTIBD4/StC1xHIsNdoF8f0fJmnuJjth5fTXH8Mj89JBN9KYZHmGHRnrkTsyjwEAgEJL0MZ/Syf6ts3CIs8zmPHEJRqT3LkdhqWPRFUDG3ln9E5nI4dzKh4b1a+8WHEvlPIjBdQWexCkVUDKVM9mO3ahtDcjOdNZ/jo3UDhEH+KmvX5GXGWqYe24Vu+mgM+Wxgi2Yp2lwHTTNP56vyAtj0qSHVdxEFCniHq3uw/UoK34i0Ko2woD2sioWwoy/+8xDq9i/h7xvg4HeKFVgiqaf4on+jGtnwoNf27+XI6mIX3ytl87BSyKYM4L7+U7NJFRH+w5a3RYEZ7uvNmkBpmI+M5MCiGKxGGnHjpzG+rySSLXWGz8V62iETyVnopvX3HsdQuiE+qu3lcoIXeP7vR/n0O3y9aKC6EwxpvObjAAbNQWQS/ZJkdncmt/AdYeDfi+2wB444mMOTrQdR+T6RZaQdvdtpzrUSbS22KXIsNZvxgf06906dCeJi5k73Z+HA2Y4LXMyZXjEtVS3gmtpqx0j9Qr1tKRYcaLj7z2BYMyztMkUlL5GnrRUJaLxBUnsHzQnNCEpoQPJrBG8s2hrYPQLjjMMLDY1HVdeLkjjYWeMxnhIcliVXFbN8+kctvZvCrr4BthfFYnZSl5GscaZ6JROtN4X72YhaunIHlrmlYaj/lkLsLrxfWspPvXJaQ5nOqFd5Pz5A0bRpXFYJxvHqSFXXDcXlpgOet/mz6vom36+NZs70fHVMO05jyijmud7hT7UtqdQVfKrs5qn+MpHmRrPfzZui6MnpsRRmaaE77wKssnLUZobCGj3f2kfsuk0FJj9EXHY75iDf0zZPHtWIHiyum0vwliW9G0mj/bkB4ZwNT9hfzes0XXsXLscmqPzFz93Dk40YyTxbSIPKVAQ/eM3SAHOfVDqEVEcja2ebM6RuP64Zi+LSEUc/PIxAImKDahOKOeYz12Mr5T1actvGiyVaVbePcuC4SA/U/qXxfRLreGfwP78e+dheHX+mxyXIOY6wmYrBiBJdMhhGepc1jvWf0ufKIaH8Hdvgk0b7DGs31PpSJvuR0UxZ/RZ+jlhrPFv1WshwDsf99g037R7Jx0gq6EqeSkj0d8RxHLFMcEVWPR0z+LwZyBhyXj2fgnzt4p3eg9kmM4NLjdFm/4M7Bvbg8v4Wd3XaM9sOtxRU42GYSKrOHaevP4d+Uh2xDNLGvQW7lBhbpD6A6oILZFUkc2+7DgNq1hBSmI5Cfh2B9NN1uwbz0W4z3YRiZCGkRsPa4DGvOBRPaGYxTcDDuwTsQrF9N15FrjBpwgXVPG5HtPwKBk4C0y0UsEt3Ir4v+CAQQarkNwTBlFh3IQdRShps/BNw5nEuGQgo/Eq9xNfk4yhvssTgUzJm0AWxQtOJbWiV60aIITr/icYQTO0tXIOww4fywGwQ/KiJ4phQLf7+jj3MXCrPcuT1jNbM1F6G2Zj0BJ0eztTGU4vm1TPlcS47UE4I2GhL35yg6j23+FZ3+f8u/Ra2iRxTvahn+sZNBQy+D8p0/sOwbSO37dLIe7sVm4DtqXX8jRi23jOFokBiXt+4gW3IfQ6vVkcoBcOLpy4FoGI9mqGM9a9TN+J7VwyiRsQzLPIbzods08p7whL4sM4ENP48xYIUnTqvm8PtvJ3oCBcxUV2D2ZxEAP0U/o1VaTW/ULdzNJUkU5GP85zYZdjpUC9oIT/hEYvlJ/A2+0Kc7kesKxRTvfIfA+hxvVa7SyAhGWnwGTchW1OeGYgKnklx5JIxCvGIKS6YBmd8QnDAhttYAOxUp5DWM8I5N56drD50iiozo/MmzfEkcLMZyqbIemb7Tya7XYWeLOKMdFPn0OpwJaX9gpCLExFOUV4yTgSuffigiYriSQZVGMLGOyudXmZIdQYqeEhVKechrVeFZ5MAyV2PWytjx2TGI5rXSiMv9g6DtFcVhJZz+psgYFXUOp15kpeVgRLBEdJY0GcYG3JKcSouuBII917j4LJITxTV8th1F16AMikJHUKDURJ8TQ3nnuxbxO0q0C7ey9e9Jbj88jMb11cy9/wqxxDJiNJpxf6CE4/idtPZ/g/7eNXiezyZY0Zu8b32x6bAnLGgWQR6xjJskw7NmDRJTdTlsNYhbTScZqHaEc0otJJru50b+LlosxzLS0RP/YgeS++wntlyPkeEXkBlXw7n3oykumorOBFX8BkViUwmPinuRHOrMzwGgO+wAmrf3IC7WRlS4F0XTpsHqW6hdl+S58030yjoYaD+QvyNSaMoUIGXjQGn4RepvX+frOzt239rI4/STDNz4D5YDVhL3+xs67bHcPlVKaaMyV1cZscO1CXrTEOh7sr9wG4p/fdg104lFwe/QrOrExsGeTyIJjCtWR8zGj1H9rNjY/BG1l/YYqq4jv8YE76VHyLl1ANqUydKSIig3lRNFD0kTduEy2JOky48YvHoOhZ1/sA5/B4v24cA8AqS0cG7LorjqOnde2XNWfxy/81UoKYpHRk4C1exyjio7UlB2AM30T0yUN0JHPxOB2FiUpOUpaZZiqPEIot5dw6Uwi0uN2fweto0+nSOQEcTjXGhHq5cQv5wZfG2RgP5qiBY0M+hlJISKMHDiHLw0PyErro1abQCDJBZjX9DIj4H6LMuGJam5GMWLMfpNN0rpp7C5eoXZ2nHMlbUltmMbL84lMbhAlm/tX9jeI8eLgFzqOj9wf/t6Zm92hz93UCrvwtoAbmTB4xtwvA2e+4FbI4Q8hwnX9fGqS4TrJpx8bssPPSFjxh/l75mLTPcXha3NTLbrD8WH+Rh2iD3hZ9E4GIQw0J4n2c00FEczxGkC4yM1eDxeHpnrhzmz5wO6wbaEl/TFqSWSJj1lXla0oaukSOKQKtybalBKGI7UWmsGlXxGrGoiD/SDCGuVR6t5CWqG+cxy6iQn8hQiKeJ8aTcgf4gu7UmvaNQfTp1UCP1VpP91X//v+nwIBAJPoBm4KRQKbf+r0IFmoVB49L/baw3cBVwAHSAKMBcKhT3/qzuUtHWFCkO8SGyWxFtMk73aipRobKAi5g646bGrJg474w4sJaNQL6tme2kYF0Id+JvWl8thqdw5KGTq22imHFrEKsU+HFJ0RcN2Ps6ibRRXiaDZdYyrgh8sPnSeeI+J9Oos42taGo5X7yLzYwuykVbMUlckzaKC2IaLuB1MIGnqMgb6i+Mk/oGwiklYihbxN1YWed18Uuot8TSO4127MbfNvdn3J4gEg4H8TRNgrutGzR9lzNufUvs9gcZVLoz+bExzlQjzvhoxb1w+hywyaEEDh3Ij1ptAfosEc4rk0egS4eOcXKIyzNBuNcUu1Z2C33VUTMwn5NtELhkK2RINHR5N2Lgf4Fr7DdYI5ekZfASxsHAcnA3RiTnC09w6egWq+JWuwPJEMGl7RBk1fhbVYjb4iRri2V7D5+wSFrRI80NEFO2fGvyW28iqT4/IfhHPbc+bFO8YwcWAPqj9UqC2poYLlg/I3x3DBMNX/LTLwrfaBJ+Zv6l7/pCysUO5k3WGHakddHq8YX75cDr6CDkwDkqARk8jbPVm0JRVStyIPgzoqKSq0opgmUDC1HzxEj3ED6lbrO7bSMb9Cr4NWcnczV2kL++HffRRtAND2WEykYDBARSL11C/x46q5HiKtnaypsOdbzWZOHmNpy53P++/h3GjspDxnvOwExvED+fxOC1r56COL92xLezsM5LYmpNcjtUi7J4xxTXtTOq+QuTV04RWddNTIsrq+WMIeZGDZHIPtv4J+BSo8P4fT+bqGrEjx5eHX8bipyHkU9Z75hRsYOMxJc4W+7N8sQWSI7fycIsGn4pKwaULpWot0jclMtivlmMhXfQr6Md2VTvsar4TvXI8Sco7+dDhSHeGPJo6Ukhjg5bKXt4/T+fAvOGkmeiz6NhOpkRPo3jjLUZ0rKJcT51dUodQeX0EpqzgioY/V3754PtVAg8PcTbkP6IzrZEprU7cstpGgOJNUr3bsVLQ4GiiD/VFyyjtSCXJbR1/nxzj1vAVODpuRStsDh3V5ggHzUVBrJSy+kLUBk6h8+t9gsjHyX8VfR+vxFNblVH59/nYsgTDAZpEXwlByn4Pb4RNHDZrI0ryOXJ6Y/COekBhlyy9WoF8UrSgvUUb1+Y4KiyiGJWswPK6I5iPSkOuN4Tm6ASGuUxjS0kKeExi9NMtNEltwrffFzYrnWLtpbUc2XWcQ5/T2OIcQo+zEcPdv/OuWoT8gT7EyP9FQV4HZeUsXmdWY+fhhUTTIIweJKEy4QsdKyawMSqTKRtdUZlzB6m0vdSmuTBB8RanZNei7GyDWPJ08vMVeaOXx+8hQ1ke8YGRmSepUhnO6J9iuC+ToMQ1kmECd5Z9SMNRJoCTOa+xMm7irokpun9FSe9Uxyf9LfXdM5k0YCOHTO8QePcur70CeJdXRK+bDmoSomzsf4+M5MvU9C5GYP2AC/GJ1CXL8UQlkrkyDxkq9hNzkzGIhO9l8h/IiIPfb4Xs8RX8R5+P/wn/FrWqv4OpsMXjJ3J9jhJ/Zj/HTWfQMPgmDPYjuHAhfYps8ZF/g/TRNQhOhqI+/weWO88yZeJ+qu46UbTJH83um3jFzOLQrtf8co5hpvp+AtTmk5h2mfedw9h59j2BQiHGx75hlN5LyagrfOu5Qf69tUiM8cSvMY7nObrMiTzPhjc3cZrvx8V/atmYe4C2LiN0zYoQj7ankwpic4azw2QSXYoLOD9lGlbhmmSo2CDI7Et10TBGaOZRN2AlA5t/0Dx6HhUnvWmSkObBfmW2hNgwRleXfH8HHgcuor9GLbPrHHCxNmZcViIarskcq5vFhFGJTPumQM+3XSjObiM7Spsu2XFY9cnn9Ld9vO6pZZ/oavpXG3LJdzx1r/eRs/QiEknjeBAtRFvpGXL7xlNpIOT53IlYp2QzRzMPyQZFFoy7yHoxSULfZfFiaBrLFBt5s6GBHw16VH8S59yLvgR3CPilVEdQcz5NIy0IMFjNg4wsGockcXZ6Ks69fnTIPiVgext+p2RRLhvMZJsx3PfYTM+r5xRePIWrSgz2zvNIE1zmws5A1N9OIkhiGT4mSlyKfc6L1oVIa9WQ7SzNwIjzzJ8/E9lXT/BzVCZipyRXos4wf3x/yoUJXPvwlDiXWxxUX8RuwQH0v9dRsXc8Dz01CVyRS2vQMuLNDFHY+5KWHXlo/7JDR78BFYkHPL/ij03zG5Y5uXNwzxfy9iqwbvtMPuxayiYrGY5Vu6CHEeE3O+kzVcC+iXMR6AWxfmUuh39b4asqh6fbVLaV7UDQ35cl8d5c+DGHiKd5xPp7s2nlN6SmqfMl6Q5LQuJ59Lyd5E2eiJvHcf61HoOTY5jx5SZGwlCEHdasdr5BW9QxRp47Q6fob4wqZFn/PYwRaw7z7HMB7mb5lGWIscymmjc9vuz+VsOULntmiO5iq74rxmOd6Uz9xMt3VwkJuIZlSwHd4g6EFwZy6rYXd5c2Mby1gVHN6+jyNEE19x6KVcq8G7+X6uh1uIQpMO6YGx432tksm4yrpx0qv2UoN1nMwKlHiJi5ixt9JzIu/glqy4Pxn3mIopc7eLz9OuKBi+i9s5zMMaFU9Aah+eER342OYfJwDZr2sSxz/YpcXwWkfu5gwC9NKkdNpGVgCaMTglidZkCV4QUe1hdiIuGJmu5Qymsj8HtdTu7bCN7LbGHGY30GOWzk+YlHiH3y4dlTIQGT/Di92R0Ry+X8WDyFUNESdi2/x6e3SUjrN/GqswHnuy1sGRCLzdVoyrb0I3PfDIoG25Cfu4s7vx+g9fEyFydeoP+SxRhV/8Wx6xeN+45QcugtOye95myRM/u7z/LmXB/ytu4m82sSFhbnMIjqy7Ih7lxoGc5lpXn8aQ9l4S8Farv30GqlQUfGcYQbdzCl5QJBue94U3OEGAkPHOLiaTEbwpOiRq5rdHDwwn7at07mVdEs+mp+ZWdzMk+yN6NyZw8Zh97y6ctAdkz8zo6KVwwrTcbFwJzIRlOsbT+h2HyfmYtj/8/0+RAKhZ/5zz+J/yuMBe4JhcIOoVCYD+Twn+X+v6RBQsBKw262G1mzqK8OnfMVEdl2BnmTtwyPusbd+xsYuG471rUbUd+fhuoISXbddmTw4jSedpswtXYIxzImM334RKwz1Hj+9xx1LREsajTFtliSgm8uDDqczJ2HH5kqFoxpw0xc5l5F0BtBRFEvCvsecXJ3JKdMb3FhsBdjkn8zNaYMpaSHrC/wwEP2CN8+L0ZQLU9e+1Qmd4hSXzCCCQqarCy8y5p33oQtrGKk7lhKP6sxqCuLkua+uFcf4sVJC6b8GcTnhCSODN1HoUQ9anKmGASO44ZhO96p6ohl1LJvpCXBVr+ILzdBQ+k7OyIjcHZ/yfLVDzEt+ATGJ9G4/JX3FlAW7kls+gumFgczsG4lUaPGstz+Ma7thzC6u57wunEMHe6KQnw92+4IWLjvIaEnHxC1/RzXqjxY3DmIk6OscVopyoTJvzG83U39pwru4kGanzoVB5x45DWdjFFd+HxtwPmvkEdZ3rxzmETM0p8YZNejXmjJ5VN5jJIcS+vUx6xK2ED9qcnMD0jkhVchvqaP2TVuG4UhUNl/NqpTRagdVMJRAYyT0mCx83G0F+9noHgATSNEUSvdRMVdGTr0N7PniRkK7zKINnzIzpN1aM0FxWYx3n53w94mGrmGel4rvmdrhASX+3Xx6ZIom5Ys4P0dEwQvtzDpVjhDR5phPH4JkqPOUMVR3LsX0zjGmSEH+tMhu5lV443wLtOmvt4Xr8yH/Fo/mHaTv+gF6bLtbTr7FhthulmWmZY6lB6qxrAqhZeXo2ltmISl4xjULYcyV3QRGzcq8HK5OMtLLTgz35rPLvsYOygEhZ+RHL9zga87NuHi7MuPyp2UfdyPQdxnxs75B3b1J0bzK8mJHdiWK1Nq3EaGzSva91xlp+F4Ts4VcsR2HYWl2+nntJr7O4dw3fALTgryvAlrQ/WRD5de7SWwciri+TOQzy3itV8cG8UbKEuGfYOWc9FThW3OcxhoXU5ndBvvtgWw0UUbRblgthVMZbqnJOtGeWCVno/qQS0OSrczTv0Um7JO4bj4C7bP23n47ADZtg0oKnkw2kkTt8cdVBzN4ERvLj2D/VH8u4uww/WErprE6MH5dApuMLijiYFVy6mqsaSp2Qyd7Lt4lf1k1qcYJmT+YdGHHu6W+yFRWY9DzgMC5OW4F+dBz8oyotWTOVcyl1ljJBmYkUxqYBe7DwRhpNzD2HszieoSRfCthaWPOtj69SHCV/fZFviOoNUajBE9j4efCAeu9lA/7iV9+91AuiudA1+OYRUQx9U9TUwzTeHDur1EVdsxYYQtqsNG4udoyMyqj+SP/8bKsXqYSPYytv4vliobYGIT4v2k2HdDA9vRAvzFjdhzVJMwXT98e04je1iFj5a16BcewyyxnWepZSzrkkV2WgsWtxQxjnTjjuQ1DqtPIttlBV4dUzHrPsDDooNUT0ugrMoB3csnaFttTFTDVbY2+uF5+gfrmnWwSnvBAYlxPPv0hBcJo1lUNedf1PD/n/xb1CrEukhskSK+jwkeWpDXo0tFP8Bdl3AVWSKNO7h+9iLqZXBSx48L385S+g3OhZ1EfbEPIgUljD20iCwVOKTgilmJB0aioFtyBZGuJH4ZDWExmvz9lsyQhW58TU3He8INZH7swa0jkpkaiojVSBPXcJHHy+aheeMdrsPFCZNdT1jFJJpF8imPlaVDOYpfdZYoqD3hXZsJJ5r7EfB8PAkGIwm9IIeFrju2MnmYtz9F5802Ghe4UHopjeYqAZf2DUDJ9QVjzDP4XJyNI/A1K4P8ZglmiRmg0SUgTfo7URlmtIkq8/tuFwW/62jzd+HEN292idxkc/Rf3qs5YeN+gP6aP1kjVADvvei+lMXBxYiYluc8zS1nVJIP/iXjWCW6m5z3Y+kOgoOFqtwyUkBU3JbP2SXoCvOIExHl44kQfsveJdEljWwSOJuXRG/4Ty4G9OGDkTq11TWcbqrn0O4MREcr8FM+C/2uDNwTK3hZ/JlM26/cyTzD4cpfdHicgeIDRPVJ5+ZYKVy3fuad+FQW603lyPuj1AknMaCjkrRiV4KlA4mLucGr4T2of7zLszp7LN++RfVHAYuf6qE3fyn2H5/w5fAqhlbORkWrmsf6JngvkCWsqRLPQ0pcKHqDtIwJsq0e1OUeIEN9OeIBxxlVmUbJUS9KzxXQ0nqPOyY5bHEIJbzmAYmcZ836afTnO4MfaOM+YQ/vuxQoaahlhOsazGxzuH9sG5i3kZaXx1OHHvp7e5HVlUpkwWa2yKvwUKqGVeJbsVB8zMIx89n5KpzvD6pIEHtI6oblhJrnMkVZn7PXnjLYt4aKkZc4barDtCfaqO/Yx8lpQ7ieuwOH/iKI5JjR2VOOR8gRtFX2oqXhx44p15GSS0axS4vdpxfi9Gwb99viKdF2JljqIG92v2HGlBWIpNiiEq/GEUlHFORvY+84iwCzKQS2OvGx5xwBijcp0H6PlYIGEo06VJVuhV4Jfiw2ofzJCjK1FHB03IZ6QjAdNWYoSPQlfd8RXHdO47a2Mp0xKdgKMwnwX8U/rVF4aqnhWZ7Ol5Yl6GjX0RjXS13qJVLMdNhg2oaW8gHk9Magf+8VBV2yqAl7iFa0ILNRhHdayVRaRDFZSpXw+ms4t6YjJwzBV1abYS7TaAaee0yiIek+FSmB+Dp9YXjrFQIu3aa/rhqHPqdwYdUebnsaod8tzZNqAX5lLpxsOI+Cgg4p41z4klnDyWunkWgahHj4apTHxzDpeCjPorJYXBnECM9ypNKCEWjos1khjIPfzqDsbEN3igT5eQrI/r3N7yHDyf2ghHZbHVUq+nw1dWLXOglm9/vIMIEbk74UM1DGHYPSOkw9SrhrrEbJi3rSO6G2KY/6npmsVcrkoOkdNArFeeMVAKol2EyzRl1CBA+LEWSkXOFAmwdYP2DWQw9SkqTY6lLPHBmYIPIHEyMNRF/tQvnWQ/687KXaYPa/7Ot/qcO5QCAwBML/u9ek2UAjEA/8IxQK6wQCwVkgVigUhv2XfVeBN0Kh8NH/4MyFwEIAUWmZfpGX97BBuw/+QluUKsQwkbzMrxurqVyqg1RwDLqG5xhy/y4iR4/T1n84stVWqA6G7fMDGKwynBA1cfopyFHQlk6zsyXKrb2o1twh9nUtmS4jeOQ2nD9fcwnXl8MncRass+PewSzk3RfiLLKVvsWXWWTWzD+Uc/tsB1viLnFm+hoGanbQp0aOU6cTgOcAACAASURBVO6umJbX0q5vQGWeGJ6VQtI7nOmj9A0JHWN2JN/nuPUINMTFSPn7lhLHd0h9smO8rDRiR/OpuxfCnRtbOTZ0H0/rhfSpFKD77TjagUK+mM7Fun4dcjKV6J6cS6NLEtbScTQN+cLV+v64ipvxvdcPNcMFeDeZMTrhAhv0/tCv/C+hcVcwcZ7OmUQXGOIJ6Q+5+qKLeQl2BD4bh7RSBflbntG64DlilmpM3x/DgaQmzPr0Z5arCLOMnRDcWoFOiSfmEzYTvcAQOjuYV2nOfRVplneAUBcOdXRhmibKKIWt+GUn8qhDnj4aBVzU0OfxGzNcT+xncWgkIfKiCK5/5VmVPN+cYjkvFoCRfSaGzf1YKJfAKCUpkGmHEXAv05yhp++gGTCZExfTqXNWpdLNCKXqUjTai5E26odUpBU/BFWkJYQT634e96DLXLU7y4SdI3ndt4clEj1MbnqO9q/x/JZRIzO3GJVlJpQ/SMNPu5tfY39CShD+l+txtfpGVN4gmhSkqDWoJSFGj37i1nTYFfDTpA8BGUXkKE/mQ3st5qODqe8zCMU9vfyYUMYkm0GEXK1i4bej6A2rRr/oJ/1H/MH35So8+qex9/AJFi9Yg2FBGO+vfSLSyYy8IUOo+xLLdXMPVmedxyCgjVh9HbQ6m4kw+YBD2ib2Lb7GnavPmXzbGVvnQdjW2DHL3IPA+idYtToxXFkLK88UCu0s2Pc0GE/z69x++4KV4+OYMuYx2PkR2H0E09t2+PxKZvBGWZpXmiD79ClhY0WIiHzKr4T+DJ+vzyFVSa4ZxTPvuhhSM6bCtbu8nxuDW5M9TFJmRlErYRbBnP3+hjUvSgjqO4mVGdv5bjyGu2/eISHaSaCqM+r2bmTrnOZvQRROqZ6IWZVwsGYwvjtjMV97g2KDH+RUitLwV5N69x/ETA9CuAAWXYbRC5ZyVtaaESr5fK23w1yjDknJbsZXtfO3ry6XP5Wj31eMxbG17BfLoFt/LCNbaplmaUb8t5P8TTJCO2c0z+el0DzSFfWO8ZwfsYXqu2rIlFrgtb6K+DkTqL6+jzXBE/llLSA+5i1jNX2Jq1xG9fYA+j1sJK9RnexFpRikCjCM8EXZIhmj0yv5Y9uJ3K+hWPhqEiTSyed5/py+VEqF/wAmpdynsFEBu9xetk/Tw+bbf2LvrYK68L9979eX7pbuBilJsUEwwUTAwO6On93dgR3YhYEYCBaIopQKCEiJgJQ00s333JxnznNx5uz/7Nn7Oc/M3u+rz8Waz+XrPWvea83ywEkjG/MJA1H/8Jnb1XY4a+jwvGgtgVkGyMlPodRcjTzJJj5UxXOq7SSSZRIsT4FAwQSutlVyeqQy2V2iuPTu5ZmYEfEXClnAWTrGWnH8eyE6gfNwd32GUXQcEbciyW7L/u/k4/+g/2yv0tfTcPotPhrBs+usvSHAo3U1gZ4nuZsvx3dzfzrfLqHVQASFIBOmSL3lzf6ZmFp/xXe+MguOnMJPV5E7OVU4adeTUuiBVJMpUMlSjzes39KNg/c7KqU8MXruzt8pESibXmf9PDXuDxbwYOs11g7z5cr81VzyViWpej6+6+0QClO5mOCD2L1ACppUyHIZxIj0SFTUNPgmJ4pf61XeVq1BXLGIv3ba9ChKUx/+jnTlXWzNf0uRvCIbTCT5Z0Ea9ssPEiE3gt/5Zmx7eQID1cd8GqZAq0EEO1XiqFXw5ZieBIN+b+eg/HNGPG6gdlQDEzyFzHuViY24EhVZs+kaupu3eqEoSeyn85kYR3J2kDt8Ew5OjmhtnM7ZUZuJkq7nkYEhU/q6cn33dEZcyuPefg82qYxlRa8yztYVxDR9wdBuChpHr2O+8R0evXKUvXqEdmwU/xjkctwhkMNbYtn4Koen3RVorezL+iMXmCE0QPdPBdFtq7BLKaNzWBD5WodxP6fA+DexCAYGIhxfwviCGrpfribSeQ+nHWKR+BhF7mDoawox9+GeEITLYU/pOpzkSyhUO8yNR2oMNDhOhVwgU/6+JTbmLB8eNHNUNJ6Dy/exwO4aWR453B84jiKJu5w5ACtOqbFh3D/oZIQhNiEbeTbiJX2WZ3W6ZCrtRCJ8Lgseq2PtE0DcxiSalGdy6+xMEnQzeDI6medPz5HTVUybnhxDTV/wqecB/dZWs/21LTsdtBDZbE1O5wj0gmS5/MMV7wPJyM4OR8LtKYey2xG/bEuAuIBJ0TNZ4SvAWK4Uj2ehnPO4xQKbYqws8hAYRJJY/YadMu1smTcLC5d0SgKPM2dyFSKps+n+qEXkvhp65zzBPkcB1WkWTGxMYknLGNa61+F3PIG08wfQ236Pq2MTmHbwNh5jG7gvbsxGv1hyzz7mcY8akXofMbKv5IvmNXR/XqKwKBdDT2POl8VQXxzG+MYYOp1jGNu+kcs/PpOuPB1N+T3orCwg62I9qxbFsTX4DFu7FJEu6OTTCm2Wldyj3vUOH/sMxGaPEQ+FM7hkK8HH9x+YnLiXmduyscgKRZhziZt/fZngJU/My/eIBp9k/bRsri3V5fZMU4Ten/AOTeVYRwPbbjrS91V/pKZ+pLoogsHqTnx7X0taxB0kbo9A0HCDSZ15tGW/4sufNo67qOCir872vHEIeu7x3V4Zl/chyDuvJdmlL7IxXWzKW4LauMd4zpnA6tunGTban4hjbvj9VWNIWTY7hhzENSOfEfcO0GfJZLqq9bA3SQenBvTOmhPs+pKDidsw9xRje/1XVL+5oP5kM3bJSyl4+pbd3wMJCghn/hFZSi2UiNCVRSRIwOmcywhsirnU+4bC7XtYst0Nu2w5TmYpUCHfS4dpBf313ZEdaE7G40qMP35k/5e+LLTSoVVxEyXO2/naocrcit90+vgRdM2EEEs7DJ7tx0+zmBCpDUjM2MG7R4mcCT7+n3rh/AJgAjgAf4Dj/w+n/ze1/9vuRigUXhYKhc5CodDZQUOTnTPWseX1Pkq2W2P7ZC1jxHxYsCyMvl+c6TOnCKcH94kMG0xhySAGFN0n+rMRB+M+ETt4N7MnReHd+w6FyhsMGayERW48Sx7VsS1vMAG2lcSYvqe84CbTTZux+bMRcedRdC/UYnxCJVaHP3Nu5i5wzGJO/m+OizljlhRM6Y0ElrxvxqtRji4dI0Iq3zK4roWhWRlsa4tjYLklU7qq0aurR7nuL3vr1hC2RQWjGyF4OUQypagTh52FSDnGIX5Gle4XEzk8UIN4sRX0H/sUy/zTzO0jhknFK8zK7Ql8MwqHZcZMuOHLXgV7nOeNJ6zuIgu/W7Ej9QTijZqICruQtPrG4nXNnGlwJ+BmEvmq37iS5cRA2b+sUT7LFIMufi9XI3hQPL5qlSx7+Q+eF75R3OmKsLaRcwGJDH7jx5hthryIM0UQFc20IUvY6e3DhrZLrFx+m4D6dMq/LWKK9B1sb25hzTY76mNqWJBUh+iV6RxubEWvU5M6xFgpUcXcU4cR6ImyVfE7fQ7fQSjMZs8OS6S1n9BsPw1XjmAg9oo4Gbhp38CVICUidD14V9fMnGehDBphQtK+CjT7dmJSC+qltdS0jkfrURYKUt+pq36Jq1sF+zSWIrFuLkp3Lfhecgsd42YmTT5Ecv19jlrWU63cQe6qbsTSu/gwcQiPAtpoyi0kx0Ubp7+l3IpbyWuvj7yb049DBXIMUHhFaecKsluj0fq8jQqVmzQ7jsdo+mBSYzIpe6DOZ8NmJqa6kmrxkPlPlmIz+iLdVWVo9I9BsGoOj0JSEF2+h43Fa9lrKOC0wm1YnYpAKZZTVXuwOj+E2VPU2O0Fh4o96e9mgKGnG+IPB1AXHcyxnAU4LuzHPLd5jBebQ9+CJQiueaMnJslP2QAuTB7P9cxj/P5YjH/UcJ5PyaL9ujtbnWciPP8X4d7l2KxuYu+y2ZRVnaEnJRq5YFEWHTMiPvY67k7LkZzQydySS4yTH8PsOm2+zjMhMMGe+odWDJyTif8HUwQ+y7gtvpPAC+v5fP0I8b4ZODu787mnGYGqPlN6q3npehNXjfOIWzwlb1Ec7xZlcEp2IU8q+yOaegiF9Et0q4Xx4Ppx3MsGMUD8PZqdjczasQBjFxGWe43jjZYCOrKfOK4oieLCSIR/7iLV843AoW3sFrqSZZ+GuJgAt18LcVOajdPwP1y2V2HSr5vEWo9kx1pzHIt/UKT4E+mwaSg+mMT2mRqsKDBG7WY9Wfft0Fz6HNX5p1jRlEzH9c1cMBRlcs58TjbIIeJ/B5U4O452erPlUDYb058hX1XC74TJPDDx5GGEJ5nj3/OxOg+dj+kMaJzPFcvtDAu/gm/xKDKUBxN1w53Xpb8ICYpjUuI72t424CJQZv2KT6hq7MW2agivFR/h5PgIE7lgjMOOs7AonSsFG5hzU53JIipcHTCBX2aWrKxuImPAC+Yq7MRaSQFT4XVe59cwzPAlx25Us1RLwPGGKqx+tfDaNf7fiev/0voP9SppJCFjLdQUslR0EZXNj6C9jUvO7pQtqcDvnD5fs52xHfqTL7WrOR3dzudvxlwbOYJdHn2YfgPsJbt5ruJImoQ4Num9uEtfYXfYFbTmLsFRxgmTNHkaZirjbdVOm7YaKUXQ7DaFw6Pm8Hrnfp5MViDd0hjf9XYsAt43KFHpbkajqC3dJrlU/lCnpcoM5V/t6JT68UnSlizvj8QYSrPyRhGOuWmEONyixvwgeSqv+V0zmC1JrYg9+M6PYzdI6hfBzJljSVWFyKeOzPk8nyH+DdSOGEhGZX/+fgpj3cyL6ObtRHRBJAFirTTHdRCobYm6oSbzg7ZzVLmZ8JwOjkQ2IVXcTJPYIBR+N3PJ/Dm2Bw0IuGlA2jNFni8YzPr+ZcxuGMv5rBDWjw9i568aUi+vRPCqkcBuJ6ruRmDevwePMAF7v7zlfFk+14aOYbiFCwMV5vBhWgC+H/UYrtzG7AnDSX8hRdnXZ6jKuyKTPoKkP5/5M3Y2xn0KGP94P7vCT6EYn0uWvyb4LyWt9DfO9uJUC22xsldlVF0K050FyHooIowcjMmkXpQ7qnkksYGKxZ0ssh3OaRlQb7SjVqkTeR8TxNN8qSkIRySohl9dHznwx5aQ7T+ZPTIAM79m6LyO/Pkj9A7uQrbXkdLEbCK36ZAdmMZPmasoqwtIN9CnMf0Yq31NqC5/gJmmO5vWXOPHqBFU+9TQJWPKMNNnuOW509HkwFPzLbwXP8IkHAm7nMXZ7GLOfZdBucSZfcNVOGkTS2L5HYoX2jKufRv71r/jc3ALbcvns2z7Oc7N0iRdy5hVHoYcKJBE5LYNDTa30dNMxmrTCA6XLUaQpMHX87/4pehE+Jj92GVroVYzhMNyB5gwYRx7FYRcKL3Jk7t2PJ76DKld03maIcknzaHM0nmF74YYHo0cQ8x1AacrEpEKsifCqYm85770sTXlbPoxSmqWsjaqlVsDzqE04SWblAREnZrBCpNI7L8/JWGrE1ttl3B272KiK/ozXiDA/kETiU3T0TF8iaijL19G3OHC8TSWD4mnT0IdHStHMe3wAZ7G7KF7WS/2Dup0CPfyQ38FA8wn87Anh1HaUai8TiFmaTwzOnwIO5oM7orUXT3LEUlZwjxKkXOVRje+Dg1TI4KdbGnTWY8WsNzeHb0IAclG6TSvMsVhpDHLe3PxShPl+B9tWtzcUGx+gGbLKBzqL/NEtpXNEe9IuKKDT+4T8i5acivkBJ0XC3lhYYCrTA/rDtwhtXYnTw+K8WZNMK46Higq5zKo/icRL4NotVZCKjGZR5UPqZ/cySDLhUjaKKJ96Tn7lpWw6sAjon70UKSWjs3mDpLOwACJhXgfTePGjOksTnzL5aQoJFNamfNRmUHfH2EsYstrMwUMXyvxWeo77y9UkbstBD3rbmYOFSco9yGLlUWY753A639e4iSTSdD8dCYlb2N4filzPEuxtSpCy7mdgoWncE9V+5fB/O9qPoRCYaVQKOwRCoW9wBX+V1xdCuj9v0p1gfJ/67/0+kZO/FmMn/9E+iRC0vtETlWXoVOsw7LEfJK+BrHGRAajRD/izGNwuZbL3VxHOv+GMmFoLSWOU9n7RJ/5hTMwuZHL2TcG7N99iPtnjXlf5805LW++dujilBOGwzw/ihtMkHWopnrCEhyXa2Ir2E3DmFNIVqUy+s4ljI5EMNJ0Je3Gp5GILMLh7TvqW1zxabnEYdk4GmSuUBh0j1iHC6R41zNE+iXDN69C8Hclp96l0xw3itHlQzi9yQnBrSHwehzaX5O5Z2CKj5ssGrmrWT1vFeMDUpjaPB7hh2kEa6Rw4aAIy7+dxrunksUvSggo7eJZRwC1rgLG1bxAflsNt5cJabFKZXlwILONhrFc8IjVIml8epSG89wUrK3nci/THcGiEZQ/fcMIC3lUrkjw+mcHW7P6saz7KD2F+uQ6j8F2oTZHdx9Ewes7cQff4HNcGenJMZTV56Lmoo7wdBghAzsw1phP3wItnnoco8P1IYLWECrPp2H9yYeJ9jPY+Xc3uybs4tq3F4QailLklk+M9mR2B83mtcVh/DYu5dDeIsols2j+7YCi5yNemvuj0xjJ3Fn2+IcVM/2nI8GmM6kPyKHI7CyienFcPV7NJc1HjBs6Fwm9Q/wpWsQGk2+EhR1ixdMiVqUH07lrM5Y3jmOxfAZdZ6Yybbkf4m/iWX37GaGrFJg1ZQ2igbV4XB3HrIFpHPMP58qzXIxU1Vgle4znRau5yVw+p7nz69cowu+l0HpIjckKk3CvSGGSTC6XbGLxW6NHn8EXCH45GsWdA/k07icdxz6jJC5J2OR3KO9ZwIqC45ilmuDWYMLFVaMxbPuI+/FJzBt/mFy7In4tm8v+KwUsPV1CvxHneWWzh0vCfD52L2HK5910aGfT9uscaia2qLq4UNcmx6ub3rx9+5tXdxN40R6M2NcuxnvGsdfnJN4dxfQ5oggN5byu/ovM3HsE7Yuk5EwopQfWQcB5Tm2chkPceqJWb+GFfiNmz2DR8Bks7PFnVcBe5jg+ZNTQAvRdZzJdW5zQ6HGsuV2Cs981FJ6M5NXkMywJ3snF7q/c/e3CkuLFjF2fTkufpfS79pd6pXx219xnW28QoVt8yO88xrsZw0hT30mHiBt9D+gx1NEWjfRTxLSao6hTQ4e6BAu+udJ3kxvthPL1xXkcns1mYsVv9O6uQv6qHs2HdIkQucoA4WRmRnYwRewCt1tcCKj2of9YU2wNPNh7wY3gL7dI6yNHlOkxwgaVknXpMXsuiXJZZTiD1hXxQqDG/bXvGCY/nxPfhOTn+/Kj6BBN+Ucpj19HzKcD3HVKY0JuH4a1OiI8E4197Ud6nS5yKmgjEdcOM7QpgKspazgrJsbK753kebzjjPYy3C6/5UPQbES8Ylij9pMBmyvwaJvGcoNIgtwe0PI4DP2SUtRXrEEpUZ39i75x+t0L3vvOhZmxLNkpRh/d2fi0Lqa/0WoOynzA7L01tjnSPFe/iWp1G+0dXtxK+4G052hs1ir/e3D9X1r/0V7VLSsP1x9hb6/KGSkXPGSlael6wdwSR/yeHufa58UYiQ0jeqoqpC5nvdNSZCWlGb35PtODM6hxNGN/ozndupYskHvB/Cp5ouLeoO5uRGFxKCJyGigMd6F5ZwhNY+2p5w9F5yNYfew64hXAp1N86BGnsNiEOUB9mJDvsdeY3+PP364cfsr20l5myYC6Ktos+hDT+gNTNW0GFFSRJTuQQiUTAmPFWd86iAKu8sXMgnatS/SaDIO8iRAXQ1rtQc67FSHZkMCqKmPmebVgayQKZWocFbayqUKMtNAOJOw3YZN2H9Hxfuil3WVbsxOHpZ1oaX5MRmIv8b/PIRKigEyQPCd79nPA6hiD1R5zUc2JQjNd7FUHc5t6KB8PMsqcTIxmksYHcpNjcBqxDXWnJO7+7GFAuzlHc2Px863C6Hgu7hPTuOCXSVpPIcsKq1HW/8ZM2UHMjxIh930Mg9U0UP1Zx+84We4Uv8V2FIwtFWWh7BPub/4K36DBrognhusxqvlIOZeYTBRd3MJmdhYj7AdzpqqOcf8MIz89lpkuX0iSC0NEUQ0pm1Zac/vSr+0KZvcjsDRoJr7JldqWHiS32NIvJQFZ4yCKO87wMdMAOeVYGkZ5c6kK9nATHCX5blJHi0ULIhNsUS2qQb0hHy8LN7y6ylmprs9B3kCNGGeHuBPrvpEg7UGIS5+ko0eNHrlhfMp+T5rRVzz7K6CXKE8sM6jyg/WBvbjbNLHngzYf2mQQmnzHKDwC3dRGYn/sJ2qiFM8+RHEwpIb3pxZjtHAnicZG7Ct4wfwuDSpOPmHgj768lp3CcQkVFi3eiKBBGtOkZHbdvcvZgsVMNn1PU8YMvs7P5PjEacgInzNo/UPsflXQaN3DaH9tCuWaeCupjeLyX7TujkRGZBjSj7dzr0OKZ2X5GOdU8LfgI5bl4JIN+oG9zFK9TOztc0Q2SBK3bSEJSfP4PHgb59x0OXKmg1sReQjb9RBkidJ+VYq7CfeQKr1KQeJEshJfogUkf1/Aevu++NoP5IYcPPwcTeLEfQwXg+zHuaws6UFDygq5Oyf406CAUP07UmUNiCgO4leTJWMkqsD8B95d0FEoT+gvI740FvPCxQJXld+0/NTmXVsGU4BfajPp13YDs+IfhFy/S4HySnrLa9jpkE3fwpOMcx5MbHIVA/U20XM5FgdFZ9AtwfthOwMVtVGUqsa8wQrODKb6WwvOGn2RS9zBX5nL3M3O503PNRJTUjHXsCRe2QAf3UL8TIdgNiGap213KC75Sv63nSTlFCFd1Emi/1A2qkDfyY1olXnDXktWSHjRPKmDbscNzEaFoaotmE/5ipu1Ff1sB1LYW42o8W8Ob8vklY0ZtU4exL5PJtk8hb6CNq6H3MRqmCV7zDOIeVXPgSUKrMp3wN7jPU4HDYhu+YNrRw4LDa2oeiNNj+plhjVv+pfZ/O8du9ISCoV//ud7DeAmFAoDBQJBX+Ae/2uJLxow+zeX+JxthT0yDzked43UCDcGr72Gv1cUg21P88/iFRyZMZL8lykk/52DCTFYsAullZbcHVDIo00TkSkayu0rq3jQR5WccDEGifym5dV42v7A/sOnmZNUy+m/pRg69qesez8mygYUhdbQJy6OkJl6pLxsBmBPXCTdTXqoqDZRvms+I/yOse5cBX9SizEyFsdyjQXPLgm4YapAWucJhBZqyBrZo2/STWJzAl++PafygghD7+6iZ3snDSN0mfalPz8mPIBVO8jOjcEtKB19QwNuKr7GVLedB87GzC8fSUdTOJkLo3F8bIfR52oqB3rzIe46/q5DqGgvIaOPLDmybugVTuTrj0r+bCvjzeInSPWRx8z6Da5eS3i7rYj7vWb8PmSG5KcfSK5MJuK+Kt6im/B+95JupSz6detzOC2X9cWy7PHug59BKZmVsiTKpuKq2YaYsAuNnxa8/fOHrjXLsVMaSOmjUmSUvqPqowCfi2FrEmbz8vFSV0Jj6Qd2qU2HZfdAxY7duenskp3Euax1SA14Q8JWPfp8DiY+YiIKCwV8eTGVgjfBuEyzoE+IF5me/sTrZrHIYhdavOa6xB8c/hqSlzOA3637GakC8RxD+v1YOoZrUvOugi51KzSTwWQAVB3zYZ4wgoHA1NFgnFuAqutTqvRF6bwYg8nKb/iW9jLXKQjrjhzE1sfScWAavfc/UbBvOlV1ckyTCMJ9WDep5b0Y6rVjL/uOn/raRDhWE+V9lqtlY5kXMJppB1by9+U8JLKaeXk6mYwSNY5sm8vn4CZuxLbyesAg9tWM5X5VMYvU/DhhdBixheAbdZYBYn/JeyLLld1riPwDlyOCcNZWR8V5CgXpsVxQmkpQ6j3sXuuS/uMCtnHraImt4dSpRQhmC+C1GMI7oewtDEJ7ngdSKYl8/TKClN1ifAifiZ5SF5Z5Y1iRH8oX6d8s07YlvkaFB+fdGScGocp+TPAcwuXOBIZMmMPj0HiWGFeTW62Gp2QfToi94vo8VaK3x7NmTB4Vp1dT5e/EiaqDFL3LJnbxWSZFiTPVwgv/4DmcSvvA4C/FfD5oip1rHLtqRHlflk70DC0ONX1hoLw6fJwINsk8qLfDzViKm3qfufNxM1KaM/l41p2V5m1kG0QSpzgV6aaHaDiqEL9CgHCRCHvyyrituIns2p2MLFXAydGN3FGylDTXEJPcjGfgGqQ+3CfZRoeb0s9Z192fJzXueO0IJsnAnh027vy1MmZ+3hd2bjDnU2waKjKq6H7+hAZ5vFcRxaD4Cz3aHsQJ5JkYIIPaNy0SM8VY6BpBc74XPZ+0eGdTy7PqbCoeb+BV8jveLb7C0pOz2bJGgKJDB+bWEnyNbUf0+040SrfTPCSV1H2rmSJykRep6jQpC9iVkYh9TS03jS/j3juD7ra+5BrFYSKuQ2L2dA6WDcFVN5vRJQ0gO5qOph80B8igF5/K9dQXvLQZQr/ei9waOpMJk9b/99jV/0H/2V5lZ6glvKahzY2kHYwThLKsqImsXaMQn9WXIac9qS31YIT5OhQ9CiB6G2b7djGm3ZfTx6+jrijO0rJ0js3aTdyvj3wtTWBwbzOaHaPQVOnAWvov75PEaBivTtfshfhtO0xEgiLKG/swtXgKeQm9uAcK0fN6wOZdtih/Pcq3wJeMzg1ntvA+87Kq0V9iRVfjEoo/vGe3UwqrMmzoZ5sOoj9oyQlEUTaUsNJe1j82QD5cilrPbiKO90P/8AIU18+DFRkojF/C4Iw2vie04RCbSvdTFe652qHwW5Ee7Rr6j1/Km9AEzB5lonv4A4Pz3REp6mKBWS0jujJ4ZDSFoDsWlCsdQNUwmsLceeSqrMSh5jGtfRsIirrBDMdidpPOjZM7sGIO0wQtMMGGivkbOLggjdkTr3Os9hCRFps5mXSf/vPeExS7gWnlZZg5h2NZ6sZLDQNkiqqocZ7HjjJN7A71Z7qsVcsZPQAAIABJREFUK4bNI2HDTziah9m8KqaHJFEFpF5KJOHNDGTLgtmQ5ENvzgs0mrOpdt7Ay4x5PFihzM0wWWaegMLXu1hmKkB03B1EHhgRO/0aw/42oCY8w/v4eUQuXsms/YmoaKeQuO4Zfw+kctMwiPUtBxGbXkxluDkbO7+wwLaXWHFd9phWIXzXyem4IuJe+LB9/0KU+44lsN6W7/kCcofmcK/gEjOTp3HxajCOJmsR7urHrMw21GOHUhE8mx9/X3Ht2TOEk3ew4FcwA+VeMjt6Hrvseti9ZgLev2cg83UBeoIh9KjuoH/sW0I+HGNmajg/RysRbl3Ja01j5p8RcHizI59+DuXrpzHccd+HZ64jW6xusUtsBIfsj1Au60h9jCYnM26zKfQbIeMmIZ/bSVTIYNZ5XeDu6jQiRO6wM2YstbvXkjT+B84TnvDFfB8cHsLqm8kMTTpCSPVZxB7/4bDQBCt5H/YsiSagaTi7ho6nz+xNtGyu5m/2cR6vP8l6QQVjTEazedJIjg0y4X21KgpTbEiN0MVI8Rl3ftRw160vC+sFzH33h+H5x/CxPUhR2CZG7LjJ6zOzcB49Gv2lURjGjEBHbRT1/YypTy/nXE4iA6f6sv1sGc/mfqYgRoio5EpcLzTDgdOg0snor37EavZlsHcTG5Q0GTizAD/vfI46xiOVP5sJNV/4OESX34tWstP8I1cM5Mka8wJ3kvHcICT70l7OlM9g+Y1F3EnP5WbNFkJVowm84Id12lqyVTM5knyO64Uf+PBBFcGmOu76zydp3SicD81Hp0mKtc/b2L1KyLDEr/yUnIiCWAm/9JtwKx9PUed68msmoZpRis/KDex98oyFaVpUmf5EzCIK/aZOPDbcJzjIhQCnVWh/uYVhgAxHHhpjp+LDmeI1GG7eSYtEAjvSwK84knF6omgkr+bNlyuY2hhRMiaEqLxZzEpuwHmzH0WvtVn6OYED87qJ/C6J4KMJzietEfw6yLrEFuaXDiVR5AVFRV4I9B0ZapDBh2M7/mPGrgQCwX0gAbAQCASlAoFgHnBEIBBkCASCdMADWAMgFAp/AA+BLOAVsOzfgjkA7c2MHBhK6AoHPNwH8GmKPrHnhJxw0uP14/24BNwlqb6aneeP4rjuG0eW7KbdJATBjyieh8Wy9qABIwtCuH7amdgKB2qUYrkaMIXsJ49oTDzG5vlDUHS14F7zAlLnFlJVugJhQCAWIVYEu+5n85w1JIeksvOQKiZ+dry+FczsCf5kGVWjKbebmged+M1QYXKkHmtHRWPtfJvFXd24DDOiZ0xfLpel45Jiwqjqx3iObGPII1eqZsminzyGY0FXEW98wt/AwQSuDMFyRi2/tA9ia+5Lc48t9hnmfKnZhtJVJcbveUBnRgnfraPpLHBgnkw5ed966FfRTp+yLjbViyNTu5K32g+YvucrEwcuYYjHJQJyCwkNCaXfqylore3B9fkD4tWqcZ9sg3tGEx0lJvipriTw6EWc5Ut5+X43Vja+yCmIEF9rjVZmMA4nhpLSPo5f12W5fa+R7qFpeN7cz8DQ3UyxqMZo2DMkTD7BzI3wRoTpbqpoLNVhqyCI3TPcub9Hkd3p9pj77EDSfixLix5SvEYaiXslaPhOYn22gDlOTUQ9tMDZuoV7019wJOo5WXkxeGwJ5nR+O2dO9KXP7gH458zj1StLJlsGU/tQhl659ahs34edqgrqs9xY4S7ANWg5+72s8Vws5Gr6LvoLrlOh+w/xBRO4dcWAa8M1uBpqx/uXV5l84wVBvksZXWCKXGQMDp726C9ZxpVxzkwMrMHE5B+KFBygfTE7flaSevwPBy8fwmxbLvYb76J/SJ1UUWe+/N7JTzUpZOesYtqdctJFYgj8Zz3WHYGcHL2MfQfWc2fODQZt20OloTlnXSqZvWQ5JxIlkG4DU5t1vLk1i1cVi5jk9Z4srzTqniWwfc8m+jcs4kuXNsWTK3l+Zg/7B+ggW78Y1zEChOuECIV/+NbXCiWVAxQ+GsWdnLlUjFPjww0ZxoXfYdPvnbyNteOb3EN8bV+xs/g6Qql8Bs18wpiQbKbeOUqHbzQONTEo/KzA39Sa/sU2JKXGIM5NlujqsHylKD+GnURuuhOcCELWsJvMqGycR+gyVGIze/xj8L9oxcuX28i4IKB4mDSl49W41u8W/f3EeXDejFuCdrRNd6BTmYbs4hCkvcpZ07wQx5ebiT5xmvctu6hsbcBpznBSnHxoUK+hRn4u6+1ek/b+FD6LZJCUbmazXyv3dl+le+cEjrwbyhzP+zTGHGRmehRO2qewVFnArUE7MdFYRWScIUMrC1givZ4pTQNZtfUoIn6haH0XZU3HWwxKjjE95xS1Stvo17KBNzJiiHR+4MbMdrLU66hsKeTa/VKuJS9F0f0gWz9rcP7nd5723UGTeQGVb7YwSyDgxIYNtKZ4MCb/CKNntmPYWcO6R7HM8LqD74sdTDwWjWBcDJvfuHMnqZWPil3M+/aJR/tXM2L7XrriN1NUNoWwZhfWxL5j3AdRDtinEjokitHyhagESmAmIoOi5WO2e1hT9cGQBf28eSlQxnZtOhM+if6bKP2vrP8vvEriTweqAY6ImBxA46Ifc6++xEvGjbw/qRgtCCIrW4ycuz549w3grX4T0xVFKMksZdfzB5yIDMM5aBLCxmLGWWlw+480Pz/FIBa7nupNq/h74wj25i1MDtMjQQ6q8tRQ11NBunkoDnMv8FfFC/1rMyHrKCfyOvjSWI1BZDUHMxv4/noMI/q/I3JvC4W1F3C36OTrmwcoi1ghHiGNvsVQRrbrIIh/xV9zE4SjM0k9Voj0UA/kDRJRV/6LuF0NsqHKyLj4Yru0H0a/DEgR9uN7tBzDK3JI1ZAi1L6aC++16fMtmJeu3aTI3kHDZynP2yrxbKilSlYSsaelGDabsd1HgLG2LdULBcxr3IdtsxiZYhXYpVQiUQ8lNgY8e+LNfZ7xe6kBj9sjGBG3jh7dGcxOW0v47XE8v16MQESciuI2Dkr1MjzgIEodC8i12oj3bR0eazpR57KYUNlUVg15hqFHAQw4D/eVYG4EzSFJwArUD43lmbou6o0CRKofYgS8WFrNGicbQAmvHF1urpZl8iXwGm7EzDm7qNQX0nLpKe3P5FH0fUN4mDZFKgfoldakMTOGjJ9V3I6B05tzcTtrwpVcP34V+SL7TQGb1B+EGehgulYJkYLT7Oq3AYFuNwufPydKKZbqvBZMyiextSIOVfudtB2SocfzKfXDx1HzSZ0g19v0tVanRN6UN1KapERVoNUUzlQjDxLiW8nRtqbmpy8R90ewZqoBAHkCAZsNfmIpDj0pHcSrBtHuNZBWS02y3QxZUbsO+eoeFi3SRi7agsxUD4SjbrDwUADuS/tR1iRJP6cfJEVn0vtNhJPFAgKVbZiULMrn4aks2nSQVTGvuUoE306WMD/yGC+PFDBHrh84dfA1C9i7FbfRk0h206XDYj2etjIE1qxEWP4cEysX2ow8Mc8woddBkaBVqaTkjWPNtFUIRl1ifHYEyac+cmyhL9/s7WlwHEbFV5jW3EpOZjN3htTxpfcy7z1GsyrxGJ8ihYiUvmDgA2feDCnisMCM9+OuEn7ChpOn3tKtu4qUjzs53/UG+o3E8dcHJpgaQ4wWr7M1yTC0pPK8OdhpIZm5iC8yP5meuZZI8RpM8woQDPdHQSCLWJMhgwp62CqQRiieS7QY1BplYKwlwtxnAoTtP7G038AtLVihNhGFH+V0LhvN1uc7WDFSnmCJ+yhOFeN5jhp1DS2YDlhJbWI1cQZ57C//gYOhHYUe1zD79pGw9q90h5TSe0md77KNSCsXkKxeQWmJHmZ1g/GSm0JB/DvKGvrxSawWB/FGzhfuIa+jmZnrPvJw82QGJfTn13IPeoc2sF/agdtK7rRIqtJm7o7K7wjGPdPngakHu821edBow4gBVngaiSEyUAe5jFRSBSq0SliwYvoluqR+o9lVzb0yBTzby9AwaUX0XSmS6U948GI4i7bZoWToyG6tzyToPeS1W79/ndf/SvLxny2BjKpQOKwfBRoiHHTYyMVVP5kzTZ1bi5dBQgWC/5nkhFWuRSfjN27Dp5KbJMbqU2XElr1h7YzlvL36D9alvwgvF8dYtR73LQeQdVGl77UwTI1k8Xpohe/MRTx0Wc2VLEPK89KRVTTH32QKs3t6uNKdzYmvmVx40EFY400mr7nKKemjJGtewyBYhmrfMJSsjHF/+om9gSGct32Kc1kF4hffclhDiiTTYbgn6/C3bhhJio9x6XeOUnt5bLr0qT0xFhk1fYTD4E39OQz7OKNxRoqGwj6oTfnBwj5fOHXSFuPpanzR/o1Lowhf+sxBprMIWbMsntXdwEUikO5N8ShMamCkWgEFouLcsw5BuQIylEbQYgdcuMUIfy1kSgfhUngbUQMNLBvTyYzrYb/vcI6qSKLfeJyvSNB77iUt5+upL9pHeflAZMTL0NMwJsfVEVWdHsyjV1ApYkx3QhO92Ye5pu7NgZtv8QHshEJIEmXOTkNulNehkfGXSmU3ZFUssB93E6X+kLJ2B38+mfPpcjGCrk7uZU1jtGMoHunZuDxXZGHyYvROHWKH4iDkzwexx1+Rxx1HGGPzD+++fqVFT4Dn2sN0t5mgprYZ383KYG1IWXsNf0Z8pjTsGa11t/g6fiQKm58gp9dKE8spmLuViooX2NKXCCN59AzakLnTQmuxI1pnYHQhxOW9wUdjBGEXzdA21MBBfAqtJ+uRXpqHVa07dxpkyJkkyvOiXqSWjEUv/CpDvpVi4KfA77uHuSaAut+HSLNQ4UtLKQ5hIZi/n49R3B6ct8xhWf5whBerULN5gU9fEcJTyxiqkEPoWyueJW/AyFGaKGII0BNl6KjzJA/ehO2Hqay9OpbbW0oZNuE4oTqvwWgmv6T86dn/ix2LHvNtzXZCTu1nTFs//lhfYdb+cCSlBezT98HcrIQPy8YhusOczmkzMG5zJ7CfHRPe6XFy5RKK74ag1d+atGRLMkt38NVaGfuHqqxabsndKg3uxZXyj7oWu6KCMcibSs7FEB7aT6UnVA7ZSEMMJg3C/oiAM5s9eO83jyen3sLtmxxIKeXXi9VU5r7Ab6wOU9IHs7WyC1O3PvTthj9/nlLVEISw2409Q0Sx+dbKelVFdDu2EW6cQmbGGrTVhcT/ek6KvQd+osMRLDJCYsU0JAerIp6wgLceBfg22xFplEu+UIr1OeP4cfclinNL8UzMZUK0Favcp6NnX8Yo021k9vUi61E1F8Zs5PRaO9oWHEd4QhR7GUX2TjZgWOIHKkRskSEZxWdCVMd08DS1GvHp3kgO7KI2NYEFtv7ofj/FqLWRTAI+p12g8twSAnpPIulYhZi5DG6lrYTXazNPKolrTRFsiBtEhbgsdUo5iOuq42Uyj8vZWmiUy5FXV47P9iaUE37Ra7SN4Kr5xDXuw+b5aK4L8wiftx3v3M2YO28gIV6MuTcP0KUJcwea8mPiLrRPRiJQ1OX4wyP/nXz8X5Sjoalw89Yl5Nj5sT3dGrvIFj5OmsSSsG+ESh4iPCeDiYMPESlXgKXKCg4aHaM0RZPutJFMXLEDDWkZpM8vo9x0NRvsJNjJd/Q7xfn+Lg+7yfMIffiL+Z6NlNjostp9LqOnTMJquAu7Fm0iYNFhtlnLkNmWz/TNpzm5cScp/0zC68wpRsq7cqn7N0ENrTxXW0CL5WWECovR/SJCsVY8PdIV3C635tirW9xpcMVYZCh65NG0aCXtay/Rs6IRka+1iA6txnPUGqKiLuJqoY95swvXp1zE0PsD+lOaOWj0AK/dsfQsSaM9SQaRyRs41naG9d+GUKh8AC29M2is+k37jF76/Y1mjcZfSszOs/NdDv1virOr3Yv+h0CpS4h2fC8S90Tp+r2X4i/G5O3dgs3p5RD+h/fTJuIzZQe/3Uqx/UeFxuenyK3XwVxjA3X+CrTfn4p8UiYKn65Retsbl+piEqOGEOwshuD4QiJvbac7aA9di0uJUk4g5JA/2IHlRHGk/vzDWsN1BBm/wef9NNaqryMtVxbDPj1oeX3nUZ4xxg39ORGfQuCVvnjc7eWogS57kmq43/sAhbEH0Yi/wIqkPxCrgopwEHN2VaOt2oI6zkSn1DPMWgFpCW+C3/kwUz0atwNveCprwUj7U8R+HELT5yS+n7fAbX4180OucVJqDh+dHJisaMvyF9pcjxNyYX8cZ8oesbYrlyB9NbQ6dJi6xB2x4K3cllnL9cJ63halkr9jItY7/XnpcBjFTQtouDKGBZmWFJpNZkNxL8qeXdyqLsDndRHHRoiw1lcR6WsDqH/8mUoHQxbOyeNNXBXGI2S5o6nFun8ceTd4LDWFi6jQqOd2vy7OVYdTlfmOooMb2VjwkLTbv+gRP0qjjTt637UI3/CCkdci2SS4xyubYcyL3Ifn+BvMkK0jaJg0SuEP+DTBHzd1Ab4n59C7RgZby1b6zwKHzQ3khIdyaMcq+s1u5OeZBpjgje7cgbiNjydzXRub/rTiX1HOUJlptAzrIqzrApsuGZB/8CRnFrujueA4MWNcEciJ09glZOfFHUxo8+B7xw/k3U3Q2bWKmo37yRHNpGW8JomrBAyzSOVbjgW6Ay7QEmKHo8JqGheHUNsxiXPW2SzacABrz0kUm9xAmLSRPipTmdcnHo3Lxmw8VYNtbC1f/QoZuGw8x8w/4yemhanUfbTPBXB+shO9rqeYIz4Kt4yfTLXfQL97k4i3d8K+LoEGWSVs34lSPHcJzT+KEE4wo/n2WDRGrkA+2xTtjw28XC2Lf28rRU8MqZvsS8fbU4SbDGe31lmiUyyxtV3MloN6NN37gXU/LV6fCiawcB1XBAP5tNidhIhuVv+u47nAG6Mh31kq6KbnXAOzRsogI5lK88i1+ITlszlBn0DPBhJ+x/KBTta8ecQvfyFTRIPIwIDDfz/xj/IIwszVuRyzgKcyf0msOs/lYV64pAfwaL8/l441490sRY2gL0dPzP2XPOr/FxfOz529tCt7bQB3zFSpLPnDng/1KHcuR6rtDLe8nxB3cTc7d+3iyvEbzD/8ArfnA+h5W0Tv0IMMsElD5GoDLe4RXM5eRNTLeQzaeopF5W9R6F6Gjt0cVEYEELylhVNlrmzdEIhplSsdg/vjdb4Kuxsr+af3Ngs+R6HXNAiv25pc7J3DYdlG3kn08EA+C9vSKBLCpJma5o9XwE8MfIfhER9BW6cMByoPI6t+Hx2pSlSL9Pi55DFjJbfTLOOPc4wYf+JXo++RjGu1JMmWw+kYY0rdTx1CC5fTf6MYIdZivDF3RNZ+M71FtYgpR1OrpoO5SSkWZfXUD6ul97QEzsaD0fpwEJXeEg41SfA+t5KFzo2YVm1nwJrdDFt4i2HvrqMnJ4tu3WUUppSi06edyanRDBH04iy2iqjfmzAZVUdDmxwy1+KQ3GaHovEbTBVTseoqpWWFL8YaQtQOKiHQckGuYDquxdUYDWrm0ubjHDh4CU0jD2xOaLAsvYU0YSOOw4r4ue8Iedr3Uc0uwFb8OMWt+3DVzCP3jBn31btYXhmHdUc5bTuHQFMmo7b/ISI8koG+fxjXZcL01d68ENzFYeJFvtVvwiPoDIp93jJv+FC6ewfw9PxZHgoG8PnIV45VruFpwlVOlX9k0j/7+fAxnU82BlRPskBxzk4GpzZh1dedee7qbBoUS/73VromZhPu8YxJr9MYn34Zo7Wa5N0LRG24PN9CDRnUbwC508pplLWi3VOcMaYLUXrgQHiEON11JgQIjbh1xo239ytJrVKApP4ULNyKuNw6Jpy7yxvtcuJuqfHkoy+7lolye54qTvXmZGXvRsRhDWJiQXysXQ7qB5g15TwG3hY4jtzAhsljsXAW8HVIK1Ftu1h+5hSJT/5hrk8D70cvo6BvGcmqHzEuDySwVprg8I1UmsuhY2jC4uxMAo/+Juz3W6ST7XhYspTRVmOpUsxCr+seZ/+8Y1eCA2Pt85EsOU4PT9kbk4ulnzSPlxxjRkQHjsc3sPvqaPyLzXFX+4uPuwvt4vdYZxnM31NPaerqxOd0IrOW/6S4rJiU1Ei2nZ6E76dWAjN/0Tl/HCOv3yC804rVCoPo2nycHK8BDAkpJuPXMGoUDuOifoXkjjB69O/TX9DLnLrv9Em8z/beSaQ+0OJ5ZRE/WxK51WNPafxN7J3G0HzsNV19vfhcm4+Gqg7T/0SgUhvLgNa/zBS1paTqKboScdx/loHl0Pk8cktEUy6XIxKNXFzqifSxZPQm6+IZnML38H/QuB5MhXo0HUEG6OYXkhGggMnzDLpcc8mbK4LV6wZsapWwsXmPxdn7zG8z4NmZ9bxp+8n0JSlImRthXNyHjcay/KYKsyRlrLVfEaZUhLRiMtUxu7GRDiBhnAaveroIUN1DnrCeJblS/FUR5UreffqLJ7G5vppFJUcpVn2O9ISxmBl+xd7EgWgxD1xaS5DtLeDBg++0DRnN0inRGAdP5m/7aM6mnEdVTsj+a3epg/++cP5/USfP39p1wcsf5ZRUPvSJJfdufwZ4bMK7dCzbP1WyOMSLtLR4UhTr2J9XQ1e3OsNLIzG1U4b55hTO3sc1O3fKutKY3lBDUnMQTWaiDM3ywefjdkYZzWFCwBUWPVFGIr2dJdKj+NSbwxSf8ewJrUO+VsiiY5tZPm0VFh4B3IuJxl9mGLJNpYipN3DXqA+7c0uZLnBhZfMnJtdIsCLnCYV5bwn/5sGP/F/YeztjdNeRzLknUWzMp1d2KWIFvYiPNURWpJSa/8HeWzBlwXBt1+ukQxrpbkEEpDERQQXFwsDG7u6Oy47LVmxs7EZBRGkRaRCku7v7+wvPfPPO3O8z771+xD5mzd7H7FwhvEq8+TTiCTYtmoi1ypCj9BeBXhFxnyQwLfxFk70CVhkZZNjtQ26MHTHf/DGzHs/jmRFYh3cjVBHD+Ehz9Oxm8K5YHu9V1iQHBmHuIoSEiD50z6c5vgWZtccRaZhI2mdp+qvzsbYvoEtVmbjf8uh3uiKpdInfW2agGjyQDmkFpDuTaLviD58CECv+ROf6eORqy4geKI5hw3oG3xzNLR+49Y8xjzv+YJhfw5X1c8g/cYj+s/306ocQJxhPU2ArPls6mVMi4LyhDl/SptCkVsqrwgSmmVpQKrqOv1fHofmkAckJQ0n1/ML0w3uQdcxEJDOPMqsOIoODEa8eSe9ML/YZDKRJuRjd95kknDKlYNskyjb/g/mVM0QrtrPOzJqQB9/QGbmatIUumGyehGiMCtd7n3Fk9SLODHGhyFGe/FfhdKu9w3xYG001UzC8cJaIXnM8XFRI/BmOu5Q7cdFC9E3WRk1zLxnygzAaswb17T95IOPG0pJfROncYIDBM/78dsE3oplk/QeYSAzE3XwvoeNkMWyIoeHBM4LSFxLjWcA2nclYGIgSM2g/PZ9H49d1hvj6bkxGZxN95yXWc0/hm6xBRuYn9LSdqJCRozw3g1WtUYgzlbhc+CU8GPmRH+iZtZbtBguYYduAT4EqHxpUaPioCXMT0f2UxGQdEQRuPjSJTqE47wFlrW/4rmdJr5AYk8cqYNRtzK/gRlwuLuHy6U2IxzYjvfk0lF3DPMUX57e6JI98wXu3fOyXiVA2o4HnyXKU/Ynlo683rYcOsdNuDgnzfei65sbAIGVurlrH2E/rkVQchJCINikSIoiPscCn4QPN+p4kxT5ki90z3jU1IawSjrmUHlVZlpR3x2JnOYmEpxEMt9QioL6Yt6orKbjmxFjNIWjVZpCX3Ua4uizjVTciW3qP3ya9lPiNRfL+INoGpTO7zJzkqBe4uC3CuLecM637mbV2Jy73tOgtUCd3xh+MN97j6Yax2A50ReTEQ0Rn9yLdLY1ScS/FYpUMEkrmqrUyQ4Ty+CKYjoXaHxwqylEYp4fVPFMkLPzwSS+guGA0on3puNtv47nqXqZHVKBTL0V3qRIv3IPZUlnDRWU3PFTEOafhxEMFGTKirtEk60t+3mskbU7xWygT9RFjEAq6jJ2vH+tfPGPKLGV6jJbRVSDMKH1VIj5fRVpKmQpDKWwTM8C4Do0najQpuZFql0HOqx//o4z6v0I+jp05cHCFjy2LLnxmpJIYO009CBswiR3u80hNaKBLoZaW4laCorp4d/M112pS2Cgezq52TxzvzeSJQwETbG/x4fFXvLrVGK3wk6bYJoYKv+eMyi2mWM1i6k9TvkuEIiceQPdlRxRT2pkVsYV1awZwJ/sujybqsuHWKTzEshFpK2RZeBeNmgPxUZzOrykT2eA3DzvDJq5scqPxrzj3+rfxzXkIF6Te4/OiFFNDRczMrjBYNZjRxVP5llxHksM5RLs3IDJOk9TdtZzvdEXbN569mW0smOTFovTf7HaeydupN9iy2YTCH7PwiPemZiwMHZGEOqp0LfuMyN3lKBrLY645iQHJ8zGYtoPpuXNoMa5lPVH8FCiBgigcWcsWiQjcD4UjEpSBe28EfmmdxLmLUTngNdtkrMm9m4PxrHcoXr6BZe5iZFI0cNUJQMa4A79xD4mPrWFihCUOXb0Iq0YxPTsYy7JaxMZ0YlgWxbwNP5g6rJXYmxnkehiSOfQ4k5+toCfuKAenZ9GuVIixpSGS+4ai3qjOINlB7D/gg+aSciaulmPtkPPUmimwdnYY14KW01iSR1m+CVmmavS3xqCiDteSZuF8sZRrm6vZ+e8TIqScSZR5xe/fN1HLiGNgvzLWU525GPwIX9nhVCTkIxWXTX1hHIKBvXQIvUJylBu7y8wYcG4PeqEShOc1MXK6KIOmv+UdtzGZLYXf5vUsVOjmU4E834otUNfNJLb6LKuat1Htc4jC69Yc1PfGa6Yjew82c/CgBwvmurDD0ZAlhTZ8o43zTx+hUlnIUTUhVoQ0k+hhzj3pWLwIcOwNAAAgAElEQVQHt7AnbhlR/llcuaxG5JvVTDDXJKDtG/4HrqJ/r4KzG2Zy+NYK7P3UWTVoAgbvm9FTEaW7L4qezCYSB2RinyvLvMCjZNR8xckyCKUJwsgo1WPvfJqltmqEpO1E0kqCX5VTMfVbiLxoCPk/knHpncv2+cZI3RcQHGxJVccZ7Io6sBppxWQrW5Z9fsCSazfQqEzF0C4ZQUc+a9UKWKzSTdnanUzd8J0vIR9Yc30mK8/JE1y8moqGEMapVbH42iLmV/3krNtIfsTM4lGPPpv7jLG8fBXRnAyUb57Fxjyd5C/mOEzvoa+xmRGqA5jZ2oBe4h/uj9fGy00OWe9R+JoepS53Ele1hNAYLMX4MeMYKaGMV7QseYsXIRY+AyUJW1oU66m4n0jv488s842l9XkhE+zGEy5bRE7FU2YMiidArhYfXSumj3bGLvUgNpO9EPg8xNNyBA0GrbQEmyOkBF6RO2g4J0ZpRh5ZQtJ8NChCcpIQesnvWabawr/xDbQ5OPO8ugbpPgnUrx+i27yM8DINxlnZo9kcS0+nCC3NdnyT12Gl032an98hrc2C1fLfEBcOJ/ldO6OGaSCcF8SyeXMQKIYwTnsOUe370eibiOiP91jXehD+axJtf3Mp6coluW8Ki5a+ZmTEBT6re1D6VkDu+0Q+DLxK4dg1eC2z58O9wP/Kx3+QS5euH1yXUMHurUcx10ynuUKbRXo2TKgXoG8RSWRGKdFlLTjauTN5xAW2Ru9klmYFskFd1FaKcsZkItcHFWGw7ixTu7tYmOxJtNsrhuQ7Y3UnnX3JZzj6SpqND0qYtLsCs18aNIoMRXG2LA21zgxXK2WWehIfZidhcTyJW3VGGM3NZYFQHVKxvQwpNWZF3Qi8jr1gxJt/2CPYS5n5Roa+SSGuPBQtX2faoq3IX5GAef059lasQYslCH19hty992iVjiYuKx3tnBpkxUYTJvKKK7IOeG+eztHRV7Hcu4WyJdNxSSsiykOCVWM8adjymUPJ+xmzLwCdPwYY1FRTXt/Fn2Vz0DybgO4BA/akWPJ27E4GTKiBQWKMSAvHeVsmwV8q8TNXRCbQmUptCaJFHJjVtZsz2mcQaRFFW90Y1adjqfLYj6NSDWXt2pSMcyFATQLXaYsJ8ztGe9NQHv6qYdewPwifv8+DHHEcdgSyee1rfp1eTeDLn6xZv4OBNWWcexPGYukBZPd1oTw0EQMra/J2ZqA1rokOBRN0uh0Z3FhC6RAHqiRPoejojejdodwYoYWX0zkmP3Hgqn4sM6SHEvzrPY5VomReWcGw7w1kZF7FUHMTk+OTyZL2pKc7jN7+YI6qTednyi9Chlpw/fJzZnruplXZgS4xf7wPvefHqDn8FGiwXrkQI3F1/g18ivjTNVzxd8I4dhHnle1pyesm1WMcSknVbJxQyrna4RRLxOH2ZyGbAj+xbtU9JpYe4983v5HpLSJrcB/6ugY0Nz7ntfYBxv95jcNXCT4IWZBTGE3BNWl2dAtjF/gaYy1YozSEwoo/WJR+wbegktOT3lP+V4dNgp28SZ1MoZ0lso3hqDjX8+/z5YxW0iM60oAehXekGS9jqLI/c9Js6JKV57J/EssVk7nWoIXhhwSG/WPEUBVlll7dhW+jL2VkEVVym/kJTiStMmLaXylKpESQEpUgLjwbR5dKUt4c551/C+ENXxmuI2Cv1y5+pknwfrA6FkUzidlhSqLHP8z/fYWEFg0Kq1qpz5/Exx/9KP2bxKv1hpwIuMCGDmXCttty5ORtAg49oM/CghoJLSxfhpHfEc/xF6/4GvCYh7E/MFYvQjB2JTu332Sd22BkMSYrV4IFdf28bn7DOV11RPoz+axpiNmVH8TqVxOwdjizdxxBtFyRVv0foD4Sc7F5vB3sQdYSCQ7nxrLjzAbeBgSzQE0ETZE2luyR44H4X74pn+LyoGAs373Hyb6Zy3XFDCs9S2t3LGNk0slt24Raawyfhw1jdqoaTr2SVJn6s/+tMG7Kx8jSvo7W4QrK8lOwytqN9dk4epNrEPX2RuJGHumzFvGoqJV7TWmkxt0nLkmIIq2JzK/dg/XAy1wJ8+fjiC0MSCjgkWoOee3fOGt/iL0lT0kxMKO7cSUhVeuob3dALO4bhaYPmS2o4ZrqcTyb/fEdUkeYeA8ZV2Q41nSL3ZNUmTdsHB8vHvrfIx8PL547eFOigXG1Lhxf5EzgzxKikttRLm+hvbKZBXtsKO0rZ/7M8ax9uIuaQ1JsEFfmy8cJNHoMQt5IiPHqFvhmZCEfvR4Lk2FcykpGvU+HttBAGvv7ySx5wtNSB3bkafE5KB3jilgCTy1CLGYkDzr8aeuO4PSNNDbe6KDj/Ek+yTahEX0Ru+HmDI4sZdLS1xwynIr60zU4FX+h4bM4Sr/nYKwcxKhaDUZn/GHx2MnUFs5lTZcF+jL6GKauRsm2CnORTVhW+rKnuYb3Q8W5EZtK0PMnkFLKGJ1vrHD35fv2/Qil6JM3agziGoYs33WAGKGBLPHsY3xzM7q/f9N9WJe2J7E0XxpG5ZRi9IpKedXjg3D6dQrnCKgOn01bsjX/PtdnrdQJAqpMGbdvGG8/m6Ava8VRIwN2x/VSFx1Nqvs6+pwaaUgKwU1pDecSRLk0ZgnLc2yJqVqOvEUnn6XM6DcXI9LclSWzPvJbbQ/eSisJvNPMnpTXrPMbj1aSEIMaVHH2laSiRo8maVcO1x2h+bgq75Cl9VUDPcWm9Jz2ZFxBAF9tb/BqaSNLHt5gsUom5uk1VEoIY7Z7GR9CNzO4zwebM9sZMHYT0y7sY6r+BDLvTmVsgj7j7hVClxCPm84x3v8pLWXJHNV6RVWrHYPTpHCYNIL4sBj8z18l4Ooh0lLOMNrJiZKx50h/NIE5brYMHduEyAxFyF9I9Bh5Wn5mY7VFDI2/xeyMrufkQFMMGqwZ2riB2N06JO2YSVhUF8PKWjkTk8HDCadof5GK0TwXNPcFkR4kRGXyHjLlUrBxn8Bsj2ZuSY5ks4IIuq0CJmvO5ZHiCnI9ZDEzisCh2R6vGYPRWmSNy4V9hH74idWAf0hZHsGgVGsCil+yIXoRy65GMatIhLK7jTTnqqCodZySCHFshYXJ6Q7BtvodElW59Dd3U5tYQcSKIYi+iiA5ETT0lBGdYkxqdBGVE9TJGSSKjGoPZQN0OPT4Ic7X/WiUKiHoyHOUkSbjaxvl41WpKfhDUv+/nNcbgo30F4SbNDHMgj/S4ei8SkTyphY56i/wDs1htbIAS89yJrYcI0BsHDrCJxCfGMKtmET2PJ2PyYidrBpTTOHZLQzXXkTN4lOod/vy3cOK2/VCeAgGUZB3gSfDQwkSLMFWZSt/a5poT+hm2QAo8jXB4I4Ie5p3YHfmKcNKtRjrHYOz3SIKy8/zR7OUqIkrSHnaRcPXBJp/qHKnrJKGZ0X4SX3h9ZASyqP/IiaqzO9aXao13OkdkIpVqhmqDYaI/9GgUe0RNjmGTOnrR6dekqiM/ZzqtSTO1YSFyT0oGa3FR/4J9VuOUC7bQnvzDIw0RRnmsI5j/wThgh3yqULc73SneYAWG60VWZvZQ6roEKjX5PxoW5QGSbM1RoqwrqEEZUhxWxBIfdxNYk12EFYah25vLqWKf7F1nkR3lhGHStp5L1lJ06caemRX8ltYhXTVUD4tPc+g8jSib//9r3z8Bzn776GDG6eIEOfsx+PMWMpy/nC/cyg+1q24qs/jV6M3z6UzOG/pg9nExSi/8EK6KJhA20V0Sgk45JjLBFMfKnr1cV9xgq0+pqx7kYuznhzHDn9nx0R9fJp08bMW59b+F0jNWYtUbirFDjKMUzdGPbOG9V5TGHLkX5ya8/C2XsJJpWLiG1JRFxMhst2FZ77OXI9ah2WMDdEWTYwsXoD/Uy8i5jSjVDcMobHgWnWeHonp9OgrYny9AZkR0nQ5fqQytBqf7zZEdw9h5GorGlP8Kb/7C/O7OyibooR2iTlaub3UDfuLeKs2iztHUVZ5nbctLkyRv8f76nquHQpDf5slFn7HSHAWRtXWg6aLt0lwn8T0xEHYLnxEfZUCMboqjCiaxJA+ZxaG3sJljjeR/gHIDJqBVOsbpg1uIS+nHtGRJQgMHBj8PAOjwZbMeCZNsJklE21e05lhy9mOGKJWD+FWRBiKDULUPY+gdMUK+qKCeX2hBFGbdix6TbBQaSbsTQUWQs60aaWwbuk4fK8NZ5dNHsrnQvkkK0VnSCJ/c15ydcNwskxHMrGkia+es9hR/gLn48aEnYmmsVcEk0gPMkJvEoUXqw5aEjb1HdWDjCl+lcqiAzNIf/McUUcR1Do1Sf9lTPDIPAYor6U35QWa8X10rLNENPIPNh5eZLnqU/nxHuozNBHkGfOmL4DY5IFkzvpOkskEQn2mk9MchllKK9FO4/jxbinKHWa4W+nwMuoqy2Y9R8Mpi9cPjFji3459STfVHltJfN6OuMlHygKUEIlUZ2CCFRoSqehISBCmmodHmhnWrrWcVjBnZVY3c1ylib+cjPikPjJ+qvDF3RLJ97+Y/ekM7a+6UJ7mjpTgM+sEvlTrqzFOxRFpy1FE/f2KZkY1FYUdaO1bR5ZDKUNLjyA0UIjOgeWYasogVFZHSWwNZz1nkGHeQ0J0Pu2/hBFbYcezHQ84sHIWQRotjFGbhZJgIO/CrhJ8s5od533RlZVBRceK+6HBXPXU5F3NfSS672EiO5j0BWvpKjLF3nk91Ro13PyeyKnJSxH8vk3JhgCubH7O/ZZ0tEfKUysqx4quv7SO9sY8vptP8sKM7bNE3M4exYwwEpvLcHecxKy/xexWFaI5+TzlE/bh8DoaramLCI+LQ1kujTahPpY3ruVw7yd6FvkwTqgcfdUz5IbepFVGh6yQT0wq0KBZfyNZo05i4GmH2tdq3nWA1rdRfHw3ht0HPhNoXs0Pze14vdtC/qBONFR0aevURpVoXG885+tCV6SeB3FVUhkf4+nUxu0lXkcVjQwd6pWSSBGJZ86BVCRvjCT7xi80pmghrvcOx3ttaGmk0j1JE7miBPTrdFEwruFU01TMzXXwsO9iY7gk97U9CTMIIbh9Ce65c/A2mcc5+Zs4vTQgdIQpLX8UGGRnTWtZEzpNA8hRa6Y03pHL0w1Jb+zjS7IGX+1s2VprS130QnZ5/aTJdRpPDv3P5OP/is6HroJm/2+rwSxYHs7eM1PYpl1NgEkJxkKj8HCxobU3n7pEGZyD06lVu4nz8Fu0pUagNmUedzuFCWAoCyLrsR3UiH/8TTYHxHPwmgqyeWdZs7iBotA8XJBkeckjruiOI6zbnaHS35Eoa8JWNJ/suCR2TLRjZdcTrumOg8rpUPOWp0FShC86TYPTax4fj+BFzwwkOwNIpI5m7TF0dCkjrvmNySmSfJQfh2bdBXQHCehR3UZVxx+GTDNFRuEhe86XoFUiy+izN/CS0EVIKIsLjX8xzg3lw/2tCO3sZWv3Y0y10ynlJm/fSyJEEXHlFWiLGdLV8B2hTnHUh45kouALeqaaXEkMYIZ3NyrtY2GbJJsvN7H2vD871Htw01iI6+7HnFNcwKyKShpd0ng6TsCq0lqOlOoyOlCF9WZeeOgNR3ZoOQ6hHZTWNXD7+x5qPw4iKv8ieQp7iWzqwbJRjzTBEoYl/EPC3wSmzRPheGIn65TFKBGyQLQfWgVWiPZps1c2EHq/E2zagrpmJt3v3NDvH0KkZxlrMuczsaCXZX+SsPGQ5XliGkqKc+kT+UO63GrOb7yGy+bR6D4xp/fyEU52PUX7dDqDxvXjNBry3t7Edq0JiYcPUOc4AVERBd7U5nHMbirZXk6ondyGZIU6ir3fMS6RIUWjjZhWY3b3BlDl5MC3FjMSEiqpfKXA1ttSiF+5jtW8WuqUP1MwIJN5ovCmzIsD7VEoa/zGosmKqpDPGGv380/Ra1Sq1Vhh4Ms3azEOr/hD0NsF1Dr2sfXmTcKk/PBfH8LwbF1MXxRT+K6DKf9sRKUqjlK1h5SrrGHu5SyEjA4j1xfIUc1svgRcwvHiFcryG+i+t4X2masJbJPiht9oAoyvIvliMjv1lThTuoYZ/Wso3LMHo7F3GPS4lmPuhWh2P8c49RFdv/zJP6qOjeoshNIr6FJvQrEtiQQzAUMUsxmb08qwR3UUqI5glJMju5VFuF7zkKNZGWz9KIHz3q2ktZoQscKDbfF3qT98HOWRlpSWDsZdI5Ws4NMM9c1n2RwbdhbcwX9jDmVvOqlbf4aBctPJEX1Fy6R+HEfYc+OgJ8dFtajDCO8N/zDyoRjXKhyRv5yJeEUgSm4fuVBWyRxlLY5pFnGgNBnR9lYmaCgSZdJGkoo7KwI/kO+gQaV2MvtE1alXFcJmgDJyrSmE+fRAzEd8MtaiV3EHb3NJSkt0yRqijFNrMONLYYvCbCaGPsU1DVCDFBUvCpqFqTVeSOanA6z6OItbF2Ip+iXCBuOxCMu0c3P8CM6f2MflBSm07q+iYeR4hh03w+qGDH9knvLI4DSLhk3GUg5kgpcwoECNlkkTET3ygLTNbvQ9N6d6dyKJ+yqJ6VfgQ1QCq71rsLwZSpZ6H4+7u2nR7uBxdCdTrZ1BxxDp1odYVgxg7fAVmOzQJjpMjlml3dQfXs6faijcNQxFbTlaJb5RF9/Bw/tbcZ2wjus3df/b+fgPMlBWu/9+cwLv+pNZuS+Lk3H5bLMQZnlbIlOtfKkuysLAVI7Iz1+wr1LnSF0zo+3grYQ4j7csY07YdXYobEEtaxGvFh/B7W4624Lec3KqMUk1/yAt5s9L0/l0z1oNVvKckLDn/hI9RsSLYSznwJbeI5wtWsbhro+Mtn/N/LwZ+PT9ZvoaVTY8sOOb9QJcW6fSL/SdkpLdVOn0oSX0mpvv/5JbfoJbrl+42DKBkgG1DLnzAZUT11CuvkuhsAOjVJ8TVqaH9qFvcHUXYhkDSXHLxLavFfEoKVQUO7k43pR/pimQozCH8nejEQnbia6ZOQ1h4xHX9qfj0zE2BDxhwz83qB1oiEFbJaGbEpnbP5PWiSXM1f+Dw8p4AjdKsEr1G+oKYrxeu5i9/VNQGP+MgTvlkAsSodYUOqoP0+6+lYSyJFJSfnLM/COHstOYUZaPipkteaYTaO56yMbaBmpG61P7bDNNSleJDQnh+gsbBh4qxelbNyYL2pAFshq7UJYdgpVqN8ar51M9S4C0bCjXdI6g3zSbo3MeInCNpP7qXI50HEApJI7KwUHItA3kqNR52NtPf/89XHTy8SnayQGHZ3itscOjzYKD18U5Y/mFMIeJKKrvY/F+McK1TtBm6ktoXQOzjA/yTSYcfS95VD+sI0t8LvLXGwnJsGbTZhkeDVHBVfCIuYlnGHBBEbOJJuzRyyI8UsCwyZcxGxrO8oND+ZLYgF5/OZ32yRzYsQeFR8UsmhRM62MT7qs7sMt/Jx+Oz8DyogqmjY2c7smB4X4MnTyV9w82kGGYzrDh1ojN0qX+mCU/vKIwudjI9FDwu1ZHwtOvvGyRZcTCqQSrphJv8wTFzyfYqryNoHOuNK43pVLBDfGJgfx2a+RbgwTfndwJ16+jRS6VwoGiyMetxzHpJDembsE16R8kvEMRm+DG14/9DH9TiaGsH3FfW/BVcuSGcjYZJr+YN7aaV2sHUG2iyFaHQfitc8Z45ive1b0mUsOJyNldbPUJxFog4EraVYruruJC61xqxnVwI3cwr8L7cNxmgtLeFXz7oceXCmUGzL2FkbwJSVMjEKwfyXXvUJpm27AtxRmB1FOOZjRhEvMZsSMNxCVZkKVuhdXPkyxXzcRTbzrx4dG4+m3lyuUvlK9oJfLFSvQG3ueGujZmGu0UTn2H7bFLbO+9gsqfbmaYuDDfZzo/zjxk2jRj3sYX8qUsijktqqjWa+E31gInuZtE/vjBGpdgksovoNcxlFG65uRWJpIVU8nAp+OonvYE93+rWZMxFscWAxbWyVKRcIlLa1aw4WAnLExhwNMopKpcyVoqQkNqFksK04nxGEJmWwMTXC4ScOcmR/fZEfcnBMG0Cly/qLHG2I4oh7l8XDKP890eLHxVz7PV/RhXdbO56AenLXX4pHWE/VELaBWS4szON5j+/cm2a048Lt6E4EAkcr0HMN7+ksQFFTiUreG4TSMPPvVSM30/zSoFRI47wHHBof89nY+Hp04e3DpvEQayYvwJqedsoymmMoM4qSPEcGUbZAKr2WO3lWxdYcx1P2OenYuiz3SmhkZw18AW6TnW3PXsQSZpLXuc4+m3GYbnrjK+b6ph9Xx7dIbLEduWyPdSZ9RaD7JYkIDB2xwEnaZUtbdTNTAK6Y+lJJWs4loZLJ+3hYc6Q7gy2Yr5O+ay6JQwv2z7GemiwMn8bmydhtMl/YnHOlZYpw/nl5ka+dWSWI+rIVOmlfD9b+hpiaQhwB/vVCVmm87D89FBrtmEIvzeig95Ckg+Fye5cRdXD4Tj/TgF5Z++BL1Ow0ikAtOkCwRKX2dMixpBQTmc7DoBUZ8wHLqEJfeFeGnVxsWqVqQHnwbREi57drCr6QY3u8y4GBRMceV7fly4wZ+iJ4Ta1nAir52U3xtRslJFRl8Kz/OaHI8+hYHSWcY0phFbqoCiRSU/Av7yXuQuGvXbGNV5FCWdEcjd3snxyBNEZc1jptM4Vv5qZYL7PErrRyHen41Rvxc+yj/xdP8AEzTJrXhEXI4da7/OJty0ma9dJeguMeWImwkuYVe4/lqKNaJvGdw6jPx7BxCd04hZ51wyP3yk3fIWGmMsuJ8Qz0lLI/51d8RU8Qrvq47T6CjLs41nkFAr5+v8AwwKGUD0kqVEBf1Fu2EUPXlteMhYMkBvAPc1BNialaNkmIOZQjjFOkJ80Sxl+F8zZox8RruKAUUy+YhJhRBaOZTN0+wJpBJb+ywOJLxlV5EQxolH8TZzJemtAXmlo1g3fAYiWfoM66tno2cL+k8XoZ/7iT1DaolbfwyXw7105Ybxd/Q5XIxaaTMs4bt5HMoFGux2y6TUSBiDqn5ah02hodWZfb/c8Lj9m89XH7N94b9EHF+LlkQCSQsjOH/wAzFea9H46Yq8mg8xOeVUDd+Ckmoz81T8GCW5FmnF9bwS6qVz8Sj05J4yvlWdB0e7WLZyBt8b3FD+nkl26ASO//TmpG43mzaNZHOtKuPXuHFXaSlz4zXx2alHbHg2F6bI8SDoPnecNRlu7srN0Axe3vtGassH+j9fpb36J0tW+nF3Ugmvb+2hdLgnQwLc2banjqT3JSyZcpBlc+dzbJsPI0TsEdFXJfGdE0H7/pL5+xfhU/v4vSIR8xOWTJkqz8qWED40WfBE3IHB5g/xi16N2SR5Pn1sI2b9GEJfRuHjNo3Vie34p1nQXX+PjZpu2M/KRmPgGNSrvrO7sJbk74OQF4B8WCTZeTBAHc5WTyW0YxTxmsP5bLqUYLNYVi/fwd/KpQhmLOX6i72cENvG/VmuLHfeQ6XJCxQaO4ieP4u1GS8ZeXc7Hi369KpP4neCJLrS6qxT9MTw5S5kqreyoW0RJeaqnB7ryTobee6/+MV4oe28Wa2BQnUqU/KqMRmbT1xPNoKnBXgJ2pBavBQXCS80x6+hp/U5usJOjBazRjs7ihMGS7l0ooQgs604+74nJ74S2TnrKVROpqC9kT9y9qyyWIar5VIKpLuICb7w383Hf5AjJ48f7FhRz672adQOqERMPYk9Im4cnOHBq3cv8NG/wI/EifQrXURQHEyYlhxjI/5yav5q5t2RYpn9NO727+XFL0fWnwhhiZ4c9mtO0Z99DDkrS9S/d+JeXkKI6j1GDh/L1KcJXNi/mTn5D6gwEuabsB77N01lZEM1Wk0tfOxO5XSICBNMZjG6P5MEUzlitbwpqilniGkFP4taeKM4lvU50firTaGzy4S+bc+pbxKw4oUXlVtLmdjTyWLHqSy8q4vxFTe+3WvDNfYdiQaHEEnLQUPNlJXSboS1BOHSYE6w8gocFFbzb1AlAUqlTEmLJDhaBjXtCn69rcbVezKhX56xfo8tC4+u5NHysZztl2DOjxIKtLeQMXcFcyXLuTHeBUOJ8UQmtpIbGcT54lymztqDZMdPXoXrc0OrjcTXQpzUFmWKYQVhZwfS6NzMg/wdPHtqj0XmKao/HuSn6XXaLnriK7Kek206qMhEEd+/m0rRI6xWyUNFpQehBi9MhJSQH1nH/No+5pbfYvPe5fz7/B2Vcoc4O7WH57sjKckowVmhk3Md1aj01qBqPRChC41YXdpDcdx1tiwR4mm0Gi8GOXFlzyOu5q1GvdaCJpVjFNea4XN+OXe8T1G9+SYqKmM53HGdWK/DnNQZjLOdOQPmLWCadhFl4hIkufViL5bHkh2/STu6E/kGE47Fn+dvaQs1yX/YflMEjyEziMtfzftBsYQYVbHUcBieP2u4pC3Mm+JK9BUaGSHZyN7nhjz4dxCXCxUwvFxN5Pl+0uROoZMZybLBGQj7HWVn4go8cyOQMv1AUnkzq76a0ak3kBv1K7GpX8imilTODg4hzfkYG8qn8/3lK4w7+jj8M55FYbq8UIwmWvgduyPPINylyjnBCb6szqY/6DQ1eQNRXWiAkkw6GuL7kCyzpkn/F2LRcpg6llOp2MQWkWskKD+jLL6X8z8usq3kEqoVKkjJZZNW854bfonopsL52m1oG65gurkuMcoD6dedTlHWHVZ7HSTebzNxH4I4JiRHvkQlSq8rWam5Dj3/e1Q1paOcsRqT2cuIOpaP6Ix7zDx9DeXIEZw//oC4FH02XZJi5cbnnLXLZv3FCziMmMmy9gZcCjKotjFle7sem8QruIM6cjkj8DHoobfgM3eFXuK/WIepl9s4LJrIy8IYLm6ew5seI6YLKrgnVchWiwkkBxXzj+YXzH11Cf13C4GK81AWyPI7eT8v7vgyveo9isef80/5I6ZL3ifAeSFDBQYsN5lLpmEoIUjjJr/ygrQAACAASURBVNKFfY4VO5R0CNb9y5OaJNQ1lBkVU8Enj0JGNQpz5GcB7pH/MjBjPKOK7tPlFEZa6lyu+srgF/6SmfUubDpxHoGeGmdN9+KleJEDPzZiJJ3BuIY6coXi2GFcQ9DTboSXSvGhPI7OgYPZKT2Im4qyWMhOJ/ajPm6Zlfy2vo2EfjIzF7/Bd7APdzbPICkvkBLPZgoRx7o+n9e2WbzfFYOhggLfX9z/33N2dfLU6YP1gpk4LVvCn5BrVFHIymXr6S1SIW7NSoQlMtA3aSDhwk6EmyV4O2UTwtMaKMhUQacqFImdDqyNUGOu/W5iXx2iJieXy7cu8C1mNgeGmZD04CHbemKwLLqKo/56uls9sPWw42ZJF43GhmgHV6HjNRUhwT4K2mtpvLqfPpNR3Ln+iMDPe9i5ajEefcm86a5mpvYxBMXRfGw6yGphK+LFq5ku/5hqzUuYtSrhIpPHqjsHcJ97C+elOjDxAtjYg+VjPEcvJN3xIEssXdF2tyGk6CSTMvupjY3BMnsRYcK+/H0TSkVpJfPfhNOUuZK6urvcM2wl8m8xPbUvOGkMNd+vUSauyI0P/hQl3aRv8y2c167AYshiGt0nMzM3AOdHJ3HSXIF2az3bWpYzbU4WaY+D2SP0mLmrazG/OwHxxmzMC76TJ52Lv8VFys744voqigNR+9hZ04T1/R/s8q2m4LgM6VWnKK+IxD3dkf23tuA5MxsvvYkkyPzgp2Iu78eM5cA1BVJfeCGcNICxyw0JpQ5F3RPM3qyK7303Ojb+pfNgPEEbNVH01OHVS1cOPLjB3R8y/FgRiJnOAE68H0zB23cwRZ5LkYcwrspm3G5V4relMWH9GbSSEjiVfYht5xew/MIYDCpcaPJ/R7miBeE1iuwU0aMXFa73hqOaNZZDdXmY+QuYpFVFQ44Wzp6b+BbXSOGoDUiOVmW62nfOh39BSkSRWxf+cEfDiOxmWCJqT4nNb/wuqNOyZjRTvqqwI+8Lys93822YDnM+W7L98EQy5yqjmxmE6qBAErd85m63A6fcPvH1+jOcTn+kIy4QvaGuHH/USnLqRtR2miB29g4PE94wzHsuHwSnWFOdz/q7JWhIFyKywY2Hw7Ow6ZPETrGMj8E3sBm3BIvP72gqiuadVToTjDSpjshDrMUHydaPWKc50NaXxJINoiwvG4toUjMKlj60SOdiJ1lJrTEMaRnO7poreA6dyO5DuzBVHEt1ezUNlwu5dleUBLUVPCnP4N2NI3h9v8CZ8RJYrPjE0VUHcRXUE9oky1YXd+YkD+K7kTfzbXcwouQwq0KDOVdQiFSWOIee3GFvxjDqd7zCqlqeaR6+OMpkYO12kZe5Qey2G07sADF+N2ej0JbEvPb7DKxQQW5KPeZvktk/PIyori6GaVlhefkdm23+cKHann+KPPnwo4xl4+9SEK+Ohu5YXIsVaBq8C035Q3xo2k3arIu0hKnxxPowCxuNeCu3nVvN9vxqvInJrxIGF1iz7Z406ze8piUohPbPX4mPN6e0Vpue3sHYZz5hlFwuocs3MvmnDpoSUVyt/06RsSOC9COUfX7O3+AaKnO9cQm7w5+1+TQ8+MZ6L0WW+h/hV5MOCc2v0LDRQjTLnDtibrQqWRNruR+ZHiEkxZz4kZ6B74nNGKtFs7PQhGsHvpAUMoKD5aLc/ZSD8ImxuA69TXmcJDmu2zHe/pu8Skd2f86kXraXWzs1OcR/5eM/yZ3rNw+Gp4wg38UMV6lHVEkEIat4G4M+A/rkU3hwdzmHxuvw69pPNPbeJ+yoH+oVSbj/yKHFIIdTnlN4nhvOINm1yAzzITI2g8UNNpRpKqEj+onC9slMpJu0fiEGDDbnw9c/uCiPQGfgZYRbxRHrFidCqBC37DKc+/r4MHMEhy1N2L7sPLtdRfEvG8f4BWokBJjQJpSOkekfMqTl+bu/gBKhZiZ7a1Od8pv5bc7MNg1hSlAqIloW1BZlcFi9msln5zKsPZHDVis5U9iE0nh3tHp/siiiAT1NSPtUhlD7N/6oqCJu5cO84S6MuvmFHov71CXeJO38MMZvz0V3jitGvVd4XXiBfxebsvH9Ddz0jJGsT+NNfCBHt3Sye/AK5laYc1Q4BH/p52yQXsAdscUY9LxGWUiMoSvb8T6YxoAfHgj17mdZxATEjb6h6fqNwqQg3OqeUibrh2jhHTa3vWJmujNvJ82lIPERaia96EqaoS6IYI6IA4mCp3RajcPl0CMileo4ue4qr0nlQ2EWl8IbEdpuiveCJKYK/UWip45fnjZIj/WmJiGJ2qWOTKsIRNMuiW/P7Fh0aQmiK9OY7C/LpsUT8Hvdz8j+WGprRKn6UYxN9U1GqN3kbtkEfjurc21XBCOGjaS3vR7FodPI+v4DnWIxNg2uZ2OBFia1M/neLYK4eysWWSm8C3nDsAXwta0PCQ0HOn8NYEOXMelO74moiaZOJB0TCXmGH+nn/WQ7er7Us8qwgh2N/QzqaEZ6khHWy7TZfNiGiohWCrp34Hp2FL+3JqOj1ELH1vcYvn9F1UgLAhKGcDwnkJfB37HNNyTr62q0Hpdxd90+po8HUcuR9HSZ4+ihTEjLM3QKi5D5NJJ5eh85NNyK2vzXtDlPRehBHG0KDTy2tUHvcjS9glr0/1nJ1YzXWDwPxmnlEor73mH0dBJpDjJo/rBGxmAPdQ/GUDRgCEPPj+fkyX1o57tjrydLR2gmypNteVgRiKAuEKnMTtC8Rc/fu6w8v4Ok8FckX/ZnePR8huitI1N3C6K3hUgTt0FnWiau6cG8ZCSvRX5gvdeRkkeW1H9s4eP6E0xcvIRyLW9mvIthdul3lg92pNJJkssZrciaebBbJIo3P6xZIPKDdG15pFqNMJFswMhLBKXAz8gJp5GpoUxutgktWoVEiz9jtXgH0l1u/HxSQtnOnYzacABlhZM0Dr6Llfhq3KwEXIkbx8Ol9eic7sJWoZwKKyumazTjn+2KfdZWFspdRaEknENPnvHJQwzHiCnMbMvFwk4Hg4yDZBdEcd70PXM6R+NScpOuNkvaOyqx7Y/kVf1okjtv8/n2XxZOL+ZE9kfcU1ayfO9W8jtGI/Z8BV8L5vNbrY6HUr9QH+JIDDmo27jQl9vBcJuhmA1Qx6Q+iwlCaQyLNWSfYivj3r8iU76WqvR8LKqXMUV7CNf1n7FrpjE3o/7QpmzPmNIayiVX0dcbi6loJZ9CvvyPMur/14fz/9P06WqwbVoCO+/vYMfFj/TtCCStqIknUdHY+oVQrTaQ1VcvIeK7gl0n17Gq2I8j8bVo5PxmtLQG2feeENX8E7d77oztDiE3+l+GDlEjZnwsX699xm/FU0w1J3J49ncCJLUYIaGHjLENtyvXYNydDr5a9IU/4oLZU0K3POf3qA2kbPLmmeRPsi59o2aUPT/Vx6GgO5ypSq94sHkuG6dac+vXURIOTSKiKxqpgev5m6vHvvNrsTzszbdblVxnMdsRIHtBGEOVCIaIT+T2ywisb61EU0dAa/wDjFasxzsrBZ8v3dhKnGDAlMGkausSLuPHFbPPvJAPho4GFBRlUVqaxkmFYlwH7ORvnQUPc9+w6u0B1v28wzF28/ayF8J+r1E9r8I/WnKkunxDtyqAmoWSNCVuQFNZm0lNN5lsuJ7yO7tYJzjDpZ/PuG21jFffbVnUkoVJwg12rxrL1fpWjpgFYvbJBLHo18j5NfLapYL1yj9Y+HUjq1otCa1Lo7F7AJUv9Gj4ZxLzMlswLYxHzeEQvV4jSTW6TkCeJL+GOBEcmAV9As6P80VcMRCTVbt55bmG/swd+H2dy4ayZDbtugqdOggEY9j+5xGFksfZUXKPKV8/oCgTSPzVXwj3/Iuu+RuEBGPonnAUGdVRHJ06nFjVg7xesobpHt9oV3tM/5AfSD/ZhXRDKkEW/qyO9Mbf6w5+sQGorzBBLkgC1fhIPgzsJFeyj6yOTNbKbwGBFcpTJDhgtoFdEbuZ8vgwTqu6MS4Sx3uUKi4/1tDlns9sT30uv95OScAh9mgZMi+ilnmLDDhmmM48qlgmEFASockRsRji7kpQFHKcwysEKN5cz5iLk5liY8iRmq9sXp7Px2WPOXnWlJiiMTTbxvC18ANKpzp45ZTN5tmD+PbtABPttZgqYoHfy8UcG1ZNf8MbXO0V2L7rAB8FucRYf+W0/BVeml1F5kUoIxokcBOVQPn0N4p64rmxcgl7012wK6qm5OIBvOPtyUuIwSlMmjPv+5E48Zn+nQfYa/OBrLsJXL0Zhm2+JiMviHNP1h1rFXBpfYYuVzieYsOQin6+Vr5jqE0MRxXKyLg0kQYp2LAW9l6p43Hjbuzt3zNj41Y0Iq7zSiCFpvoAvn2KI2i4FU4mZsgMPULDSDWsmnvRdI/G/H0Ol753YRRRS/z2+WzFho2Gb5gR94R1I4fhamLBGmNHjrx4i7qQPuphr5m/9wBLmhOxEtbk4fud+EX+BttvjDfcRLLzTqaKDOOFmgk7Up/wdIQqMw/Dv0k5NA8eyzS9xTgUtiIu5cqNornIR4xkwcY1SHcIEKy9w9XDc9jkv58ThmMYLgK+52K5pZGCsEYS5m+HYDJkOKFf4tl94w1DliRzZd0T5koO45hwLOeLC5CtFqKvJ5Yl7Uk0/NnN0jVTuLYlEI0cWbaVt3Bu51E+GAUj+H6WutP+qHY9oFHegost+5CRElBksJeya0tZPdSB2e2WCBov/adH9f/z5NaXE7ZBlYZv+fSndWBsqkNaZhG2/vC1diBadrY8y8nl9j8XuBjxjINOxsQobaVXvxTFqLuse5TKd7kYxq+yYGvoZVIvmWB9o5iGJQHEGC2hWSSbiTISnPpkinqrCIO3afHhyBo2Lz5FgWEeraISqE9UIsRxPFYXHlPxuIXHbQVkfxiHYL8oHl1hfG8rQ91iK2ZaRfywCiD+8RMS507h6eWrfNQfjFXNWqr+9oFNGF9nHmBlbQGpfbN4Y3yIy7s2E/kwChwqmVCVw/y/FxCLfILOlRzK6uoJKTtA49ZU5hqZopbzG78Xj8nU28WJXGf6nEXpv5WNz/EbjGx4R0TzbYrtB7G1sIrG9nXkHHqHSOlR5O5cRFHdAEFgIx0lZczfs5zluQvYNyycZM1/0TawxlTvF09eqKNQ4E/dxDVELI1G1vEqY5r3YrAaOkNjmSVXztDsXcgXL+bMTDN8vlzCRPCW3B+a2N+ZgZhhFAuE+rjf+AApqSGs706l5tIjXsb9hOvJdFyRRqGnlsEf96AjGE1D/3vO9enhu+gneoJG3gxzR3zoX743dTIuXB+J1cdJWWpJulAzxz3fcDsvjBCPcFonheJsXICL3xxszFdTY5qCtP5A3o18yLiBRahuGMHZ6uP8/lnIqrZTxHtYktykTs7Am+Sqf2KbWCiLR0TwV+QDNnPPsHbcLaLuwzLn74yQyMJm5WX2v95EU/Vm9EtMqDkxiGtZHxF2F0E3S43Fi/dwfZE4+y/2IX1PHwNPO8yLjVEQ3OX08wW0TdyA49h+DKT9qPjQyN2OB8TJmJMx9BT/H3tv2RQEA3ZbL7q7G0SQBkFQUJCSUrBFsRPF7hb0tjuwuwsbQbFAUBFERES6u1sa3r9wvj3nzPusH7Fn1uzrmr12InzLL0d+dijaWxYyrrSag5vHorlmCfv2CqChlsZAVSdlArG4DaojR1aN6hvd7CgXY8txQYTqjTF9nIaez3hOCg6mJVSA6vk/cAm2JEZ8PYMlzmK5NpzFa24hL9uL1oy32Eva86QoGeme3WTUXMfAdAt/HlUz6bcRre7V/DFXYHHcByrvRjAqbyaWmlI4LX+AtIEEvqe3ouU5jrW71pNrpcb0oJvsePSQGQXX2GB7ifw/nhTbJKDtI8d+xXUY97YSOc8Bz+AiHOs7OFYzgJuTMJobBDhmlc3tYA/GNHxl384TbCmpZX/Cd4Iu/UR1eAWtjQIMEtIhWbyCb84eFH+pZlNrNh1jo6kud0ZKsY9/ZdGU5o3B6uhffKdbUjIXiu6rsrriI0vbcyjMruDWL0fq3qrx9e15Kt0+EPQ0nQmaGcRkqPK+z4qhVTO5IT8S0/f3Ue5dy6NpwTjYJGFopswdne2Iy61gVL8OwqmPuN6ize30zRSPaUTZdAGG4sfZp+7GFVdFbk0twHtFPsoOR8gRGovG2QGOXPiEmNJ8MhxGsXeECgsNehgkOIBPXj2FimPJ7y2iYuQYzGM+cqpFnMNfS4hOyGa21lXkYnx5MXYTVjZWXJw6lu/XdPg4aQ9/PTyY65pGgI4cXyNnczr8IEp1iuy2DmG51tr/4yz9v6L5OLZ7Q9higalID9blc+sT2n1bKL5SQZ3RNq46lLHFfA8ZV8exprSZ60eX8WFyBDMvvmWt43KOFMqRsuMMbtVhRDWvpkN2C60hJsxOekpO00KGTJnNhj/3aR3ex/mypQRZjkDC8Q8n7GuZJj4csaFWXGhzZnmrMFebzjN1/heSZ5kRsDwCMZnRXHqznOEbHyN97RTHkmTJfNoLDQYcnOfCdWUZjmRP5uYqfyblujL79kKSZzgQ86SYZOlsrjkN5uv+44R/HYVzVBKacxdz5cB7qt8JMX56IImZAiiN8idJvpd/E6ahWKPHKMc6JPz6eaWkwS8vY3xGx7Gmfin1nZ5sD3OiXXcuSnaOKMzxxqXWHjejPZwMjuDdLmvWqYpxZLsUhtPHUJ3/gtIHwmxbNguHmHt4r/SnyLCPOVp/Mc7cwLwvD1lbJckf53ZG5g+lMUeYhDEldKn+oiX3FYWL1vNScS+GO7ZzJcIYu/kTCTx4BRepM4wYF8avE0/R0DJi6q8qhBfpMnHsBR63XsZyviHnGq6jKZVATHoXk4tcef2xAoWZxwipquZo3lECvoWja/iVu0EqKGiPZ/zbQALLl1L1ewmbjrvT+2IDY/yb6PX+Dxc+4GdqxY1TAlh5FNPWN4tDiklMcw9CKeEL4oYZdDb2YOJrz8wYLUZdrGbqsxiunt3MKJtrtJ5Qwm6+Ay5t7Vhr2LDYYiclh1cxbUQpyeGtCG6yQkpkBO/qs3E206Yk5SwKEhcYnPGNCYMEmNFWjIPsL67llBLs3kpmaSee/3Ww9vR+5oRr4F9nzqdhImSJKCFvFI5c5A60x3sQf+kVy7/74bzJl8dng9kevZ1M1XnEewzjb9d01nYVMc8xn4kViej8reDGr0coGsSSfVGd3dFbuRL0ErftB1D4moxZ6E62VeThKOvJL7GPHP3wHRslAUrX3mbD3V9YCh+lun4zj0MaqG6xRH/SKDbFXKYiwJS1ZUPIe6TO1sULUZFbw/4xo9g1ZQcrQu5wQUOcDy+fITZkNXIZGTSWlDLdahFef+6SpqlJtPB41qgvxk96PJ4Z/biauOH5o5Nnbcsxu7WQ5kGTcJiTzPuGcmz6xAhP6SKkxYPm0VfQNlgMr/YTW7KUWeHr+FTxgFoxDdwVZmHR8ovvnv0Ie4uzI8udb17rUW0VwN5xAtm+Iuzr+olESgvV/T2ke5TjM2oBIzrfcPWLOFH1rnS4TEBNOIhyhwBmhujwZIszN5w/IZb/ly5hf6LeGvIHVf77oE5v3lAEVCbwqQTSJ/bRI+TJWRFjtta/JUSjh+8xD4kyXYA0NUR7n6WkfIBJNr84Me4kEe8j8ChNJMjyL3Eb2hB88RlrQxPm/JFDU3wa3YKSVNssZ1ukBWpCxSwsDkauKo5ctzdcURGm18+Z8XJvSLD1QtXZhdXvb3JR3opnkZb0rW3glVQBfX9PMjqhjduZ4iilufH5thKzdZVZ4rGF/h4LFt3fxKPIU7x3VubaKXHu/Prwv83H/yA3zlwIs84VxGmRB/ez3tPur4Fg2jxmjk5liYQ/+57aoBR6Famn01Bsek36xJl4Py/GybYLn0INNtROIWpQGa/zGuhUX0lfTxozJXR43ZTNYJFFuJel066pj7XOH/6Ky6GtF0uC0Wf6Uq9wR9eAS4WC9LoNZ6tpDnJZ1jTmvGXpbgVUGiRJqzNg8cJV1MUdRKdDkXP5C3g9WZ4du0JYHDIa4+hufupU8LPrPrdyL1M7/g1TH/Rw03IzbVn7cTex5HuHMFvNtHkS8Z3af7IcfnCTzil3mGx8C1+BUty0FxMl1k9L2TvkekV48WMARaVSvkjNYVBUHvarx6Ba9pqUngGu2tpwpX8PJkpjeN6djOS7CgSmOrGgtJ5vr6poCF7EpOowPmuP4mLSAgaPaiLhvTbGfX2klOdj4DSGv23/aLmZxjtPPYysfZkyLIjMSbY0dbUza4gON172I+Ziy46yXYyXCOB8ehl8/8Vs1euU5k2gaEQgo/st6HCwxuDXP069OM+nTSNxiKlkdMk5JBUdGej7wY17aYTZyHFBIYr2SnGGyVxANmY3r84GIeaZw1pJMYYJNdOyYC7Opns4GLUZhxtDGXwgin9TVChok2Pxg78M/m6K7OjXtKeBbm0MJQNjGKjrZ6zUJNSqLWn2Vce24BcTNMzJ+VvF07EK+N/zRN3sAbn2c7j04CGWAgV8SvqKhPMWhDLuYa02Dc1sR34YaZKodhetylE4D2shw7gJ8bfmqFif50nuEBx/vmColCrTGcDqdBQN7f1MmqhDhf1wdA9dxTp1CF8dqilLvckM8zj+k1yAk85y8j0nIJRqiFFIO+Xzr/O8LBDfwFBGf13KgpbnJI/ewKjiLcjmrsNx5n0+xzwh69I22p4IMNn4N0dLWhDtLsJVVwpxIX2kfnbxXLOZwaNCET19hSL/YQyV2oWTxWGO9mSRdvkNzt6p5D605kfIERa4GqN03p8OV0GcjUDv+Wu+6cbSZTSFB8+3cCX0AI4TXAgO3MLOB07ULR+K/ZOJRGzx49HVx4gGruPpK3e6/9mgL5pGcZM0+ntkKVlWgGmdEQ/OlCKsVI2yQTnVX0qIrtBljfcHBn4nE7g7mrLKN7hah5DQLsO5tMMctB9NvPJC1BocKW8/S8C4HEqf/2RpQAivqhIoTD3PAhMZclqF0NTIx9a2Fe3OC3wtP8sj4cHo/PmGmfxVOgMeMDhtIyMMFTleb8S5TztpnV+A3o1zHFEZhX/hJoaO0mdD6lA6mj/wa4o3e//KYOHwm9IXccyvkUe7T5XYxnfsGJLPHyU/JmSuYrfVYZpvd/BwUCVbWmfzw+UZih+yOVe+htGjY/kk3Ydvkydd5YUMUSjgTf04lur18SRfCR0DCcwHqrhf0oG7yziCbCRIuBQADkuoz7uJi3MgD/efY98lO/reWFI5UY8TbQ1Yt2rhWWvEB9cHTE6p51jpCnxF17DBZivpbyOwMoC4exUE+W4g8tH1/3fOrk4cvRfW7eaJeEURcpJDUPvege9gFQ5tGIlFzSPWd7WgdPs2X8scqb+0ldLKGN5M3UfZiyiENRsoSbjERY2ljDt+msQ7gdi2ZWDy+gwvrKcQ3pmGs4YAQm87GFAcyvHqa2yRHoZgxFSWDRnB2KxXGJ7LpE2ll+uKc5hkKs3k31vZ19bK73XP8Vv9g17h0+SVxdOjVENc5GGaBp5gEryGaB9jYk8u5XqUD4NmajFXbCs1n7dikbAV5acfWXnUjlcOpQzLFwMzH/Irs0hT7mTRuAIMX5agpm5F59d0MqUGs8TeANP6NqrzZJB9rkxLSy2W4u8Z/92X+7J2qAT+ZbvXHvZK/2b0QrCPeUjU5/NIHpVmgnIU89vfUF4twdZmf4TeJdPReAoNHWGSDKcwWEaRKdudWFD4lr0yq9nQPJby5qesmO1DS2cxl6xysVbP52qWNntD7xERb4vXn/Uc1MilaVU61/s1+GhdwbjqQlJbhBG6VMlCBV2+8p0zgca0hCxnWk4TWjKBODV/QTTQCZFpRghPn8HlSf9xeqwvLgMeSBxT4eLAb1osfbnRPY8SrYW4HNpPX99Wwob/xs1KgZqXISTn/OLDMEUCNviSbfgN78HKZKtsQF78IcKts3CpsmbIbzPUJVJwLTLnvflrppwaQbdwFxH+0jSNEiD11DHCHd9h8UwO6Xg5xPuduXA7nQkBHvRn3eR5mwxNa66hMteBt9lWONnNpen6XhRXXeVamCcy86XZnXmFwZ215DabIXSjleXJeiysdmGGXz0622IZ7yaHWI4aEs1miH1MoCG5lp7BMkiOXcSmdD+2hZlwe7gPWTb6mLcPoJgqxNX5rZhVfuaSoxaz2mYRGtVKR+kPNCyjmftmL+Iya3A/6Irx1EG4HE/jh505ogu2MzngCtclAhDVXM5GBXEu3olgpPVmBpfCAT8nFCsVOe6vi7hMLSebHnDggh6djz9z8IwkF3eOQujTVta5LuZxijjyxX95oXUUKX1lvigMIevGG1QqZDAo0uP0uyxa146n/VMXik2FfBS7QkZcBq+HdaInX8KL4FoM56UQtMWY5w1RmF+sw6fYhKd9ggyWFeR4vCdbpkxE9e17DgQfYJ+bGON2BtHWr4KA0j/MpypjPmkDBhcy6L4vzpAZLaidPEjlxd8YNSaTVvCXBWq1mI1WpbiqCbPBY3iW+JNjI0LQKYvF18YXyfgXZObOwbLkA7I3irFN7GD05WouaHaj4efB+vL7zFUaj2ZzPctNE5n1fQ6BTy8g8yeGx4WTCRZIZPW5k6wvPcrPjavReJjKVGkdJndtY4fWcZL6p9KbmoFIkgDOm66QuCSVmW2rMWkyZEGGAaLe0xnq1IkFpXhLb+Oy8TvWCVRwS9GW3v4AZvlJIVUejXp5Alskz+NXE4Fz6Qd+vKtjs/QI9GvSac19SWaZLLZO/7FVbRkOGsro3ctBw8wCvcYm7tbV8btsGNdTVRlhEsBi+SiiJc7y/e3/7nz8T3LhzMGwy+bdLPfXpVe5HY1r5TzqauaH2Hsm5yvwRzWJKcUyvNdpoSG5Bpe2KYzuOIO/iRjvxFvZoVLKtZW/eHJhHyZNW4h7UsPm4AzCfw4jq7oT5yoBSQNs8wAAIABJREFUNhodY/kYaUrKxiMoY8fD4yd4Z/Udi0vdLNRMJ0NqKPIxFSRaGXB08h6CV2ygJPs55Wu28jz1IOdk5hC4YzbxUSdYKr2e3E2CPHn3Dct1Fygt+Ino7z+87CnlXZkKMoLg3a3AHIMrnL6sw6YFqkz+M4ghr6r5GdCPl88OzgnJkVD+i+OW1biqOSL/JB0z7xpqahX4laFIuNBtblc0I+qrxi/7f9T8m8jyoRoEe1RzcP9gMiv8qbv+jU8eQfjPuozfNjN2nj7Aj/hf7MtKZYf+MGLGj+T3+bWcGeVKd7ox2t51CDx8y+3Sr4yyu4a07Gck6jXYmH6bfclFhK88zbGDrdiN0URR5SkV/UsY/s2AOilZ1gXPJi3BCAvv+wTm/qBmbihdiScx1Opi/Sg5slu70ZRWobhmLe6ljbxYOo0tVr10Hu/mqMchYpePI1/+Do6XxqIhsQnDOcps17lLte0HNt714OwMc3avzCXJVZb3HQWEFdpzRj2JGYdOYNw3iJo2OWyV3/ETSDdQY+TfPRwWgTRtFfTrN2Cd3UfiP1n26Vlir3mNEWpOtIvY8UmqC/OaaYQKR7L7XRJBVk+pHHoO8W8ClOZvJnp0Jq5J8njlVrHpTjQLHt+h/FctGkrKuAh2oDisg/Xvaljg04nhE3scV0ky850UIkmKXN/mRvDNP1yxXYG96Hd+Cykz39aHi0E7GWP1h3cC6XjozsSwSgLhOAH81aR4pHaF5QbaND2sQ9iglY+yU/ja8I/rQpNY5irOrpDlbNg/DmkxY/7JOSKQe4hC+TTc81ppFClC+aoCEkfDOLV6NtukKzhSVYGTxRG6ewZjIPWalUahnE6J4eMvMW6ormbxo1Ayj5dg9MibcVMfIjHlORaW7exPr2HbpFTy8qNZPXwPuS076dFWR+5uC7Emo3jkI8fDq3mo6nmiaCyDsBD86Wwg4NBJErwm4jVCmoDWbPbWjcBvjjcij7cyxnM0i3d9YOfn3Vw49oPxQZr8LqjGw1kRu/Rx9I1XIfnOKVRMzEnVc8XnqQLijtaUKyeiK/2J2pxmlNTlqDYzxMirC8cJPpxZWYfu+ximaY/DJqOUywrVWKmbsrCli7xp9jxcfZGH6cbYiBZzUC+BcqWh7MlqZkKHMk/Fo9Ep/A997XOoZtcw43oTO5+HszH2FYPbIqkRnsMEIU0O6BkR2rqbj/9CmNIsiGF9BNkGjsy/+h9j583lzzVRMsO/ss7aj/aIOvT70qkwsaa3WpERg7L5m/yS4moPJu9Yz383ltDUP4SsP5l0zvbBKec+85q3kmt0lKo0A84pF5Feb8Ik+WMEyieS3miKTPgA2dPWYqJ0GTefPKpzsih2O0a/qBnDRgryN/sc8V8z/9+Rj3uXj4WZjR7FK7sGrP7cJ15bk9TXMElhK01fTZkUW8/U3zl8lk9h842X6Ahm8yR1F18CXqCprMbumqV4PPlBVeIiirZOZYVZMe4VsWh0VPPe2pOo2r/YWrTwQzWbxC13+dA/gyKTHC4btjE16ydm1+xQ+/EDu+d6DJYUxctaB5slCsQfW8xAWSIDnRLs1xnHaVdJ7J738fZCDhvOPGRSqybftSyZ767IzfSXtNx4jIVQLa521xAK20Ph3buMdT0HkkeQEyzBc5MYA6qlnOnNo9bkGaoabtgPimVs+U3WWhqi+c+S7qF+9JdIIdqfgXOkBaUuagR22mH+dTTDg6sQiQ3E8/MRvud2UVYxHzmrrVR76nIlUZTEK7f412rEPL0qstVysNL5TsjneK7VCJBr/Ry1ohBWSe8n8dtI1ujK4NU/BXndINzveOE0WJ3GfnO8kypREBrO43NBrItypz/IgVZ5WxST42nPP8WDjM8YL/OgQ7gIc7+L7FrzmEc7F3CjxZxdAnKUEc77ogSuxt4grvUY8bPOcz45ntVt8rz8YY3b00DG/BjK29TrKKgpIhaTyLbTsZy4eIQboccYc7aE0Aw1zsxYhffRD/h4r+aeVDK23Z+pHbYbQ6103qq0UaPeQeaQNN4oyuPQ8ZYWr+W0ujoyrPEEnYLzUH4znbYL3ui4TUDBwIAr90ZjZytNgPUTtjduIVdLn6jB33k7cIl7o98Ts/8DPYPNOPe8kptb7nJLPhl7/bn066qSZ9dHTlkS+pJrsXitTu+Tl5x31aDCNo6J7T1sGjEcJbcmhNxbEbEwIuNnJJeqJ/F4+2uaXwzh9JqX2HTdZdrux+zIj+H9xA/oFUnSJadFr281d0WrcN/+mFd2sxGY086zoDMIGtmgSCftsU+Y9eUTuWG7cLOaTtzZjdjIyqEvl4q5yF8quh+zLjcVl+DjaMlKsevBfIJlXzN+w3wquso4O2MeDukbafG8hfyNUiaL+bBn1RgmCPvyWsQWQ435tDf9Q6q/EI0p5/EZ+weyjZBQKkJTUZ32iEKazH3IbLInTOoDxV2HuC1Qg8fL5Wi8aOTYwVs8iJ+Ei91YmlPvEWzazZP18/g+bDt9B9VIfn6Olq3J7HR3YXSGEeP1BDl36S/jR/fhNXkQKmHPGTfEm9C6Y2g7yBLwJ4eqdgFWuObhusiSZ4+aWTSyl2cfIzmh9IAK3Qh6F0qjVpHBFF870kYMwttYlKM7G5H4I8RdgqhzWIChQgyyhpk09Dqj77uSrv8Ws6hQgN3az6g5couCBY+4NHwHZtXvqdKZyfaSPD7L6TE424vOvg6sdJLQddBGqvso21ymI1m9lAM/rAg++YCEpPNYS9TgIviOr05fyHyeRlS5EsI9ergP0cZ51Vyu/UxCICcRu8qrGNX+wkhoFgHSL3DrKiLWUop3+hHMLoxCUy+dJ4tgzoy3RJ8uoKq3nTyLCtq7ftInfxW3F0a0puuQouDPTqV1HI88/r/y8T/I9fDdYaP09Li1UIH3EYIc0GjHskmf/T6qJDUJ0OZxBo2kHZQqTWd7WRdbtypgcuA023Uk8RRJZb32WSqUrVjee4mdSdoErrSj91krOwsyWGTrzZ3hz+nRUkdYMovc3LGsinhHqM0U0tzdCVPZwe4GDwT+VSKrVsNwEyUE2yxIThBB2F+D+vYyJo405kVMJu9XTUFolT8N71cwZm0pFxdc5Jx5PK8W7sbW0IRkn9loyPzlTNYhnvUqUSTxipseS1FvnIP2hy/sUJSnwc6LgupFaP7QRCGllrUrRnBEK47TzeO5kC6Kjqw0tf15iBXGslzOjyJF0GwSRKA3gOGT33NkUxHa+Z1I6/zgUp41XnVVZLjMQFhfmsQaaxYW3ifebDci/fnYtjlQkbmGkLmruXPWFGHdKIY0ZDBJO46TQhZktrkjbj+Xby+DOOG9l+DU6cgJyPH8+lZmec7h8K18XsooozalkZqrtZg4+iPPEX6oefFVUhW1hFc8KhWiT9YS13+ZfM1WIWZ2Ntu75pDDbfqF9yKaHcyHdjMslJTIevkGmc93iesLwkq1DYspK7FotWfuHGm+Xf9MYJA8yROM+LhdgLDldqTvP4Xh1hD+W++NsNQuZgjNQF55JDdH6TAxQ4E+ETdEFcZjW9aMhIIpaRrOyDZPxE1pNvduJiDW0oddkBmigirIRR7gvMMe9o+fQ2OSCmNzKvALymFtQxWfc4+gbPeLxznJ2P+Rw1NFhMI2OQoqDChf8hOhXnvskxppHpPI1txMKiYF8etfHToF7xmn0EvqAn+SfyXTLiSPWvZfLjlr0vZfOAlbp2Iw8hbabTVIWs5HrOg8gvpVDF0Qi8rz4RS07aFXxgmpgXL8lMSQdVAkbOt6aqRUGGiV521WPMW5kRy2n8D9znYGagvxMt6NaXMD01+EMnaKJ78SplG1JAcCN9Lf846HBqU4uswg4kcEZl3lWGy8hsDAAK5tMkQ3DKe+bx1i9+SR6Omm6p02G8/6kzU+nEtmX5i50IXWN0MoUWtikJsWmoLqDO/XISR+GzoN7uzRNkK9MI7pMxcQnHmJJeLP2C1XQ0R+PrWag5A6tpfpWQuIWPaNkYpOHO5r4KXAcJrUVXGcFUl21DJUa8/S2JOPpHg5md2/WVQTx/kyJ1J+mnIwcAQKxeMp6r/A5AYzXulBR/cPCnIKERSUJLc1kchB21h8+wsFcmp8OaTK/FB5Njz8x/TpztT/zaXb5BPGJoPo/bidmPwQXIxLuC6hy35RQ0aUHGT82smYxNTgNVgWc/Nu4m60o+53nzl/VrK8p5d4GVvaJOKoyppKi5Ek/U0mbJjpwpuO0dwyrOBtbRMO3rLcE/vMx+hOlupXk2/YS12zLtN0hxL65jzOKzfRmleITEcP26pluThxGnVvP6PYb8JGkXfsCDiLcl0OU0daU19liPjLMgr05ajOV8dJOoWYfmPKB+ywE73Nn8sCCAUp8fnJ/1k7/3+FfGw6cSXssKECCslymK1x4XliIzP3z+RFzDcEy41xNVzKwegvBOx4yNG0fGzUh+I8dCpP81Wx6VbnsM1bfEb5c9fDBbc7v3hVN4g6h7sskhqgoPANO3oHsbfFl2y99fSqGlCYtRuPnnLSW0SJ+JaDWHcZbVM1KMwyIr+0E9G6xQyKD6DreBKynpOpKJJmkGQ/NAXx2MWJW7N8uXL6FyM3dnHMfSVXuwY44BjMp8BJzFfoZLn0A16u20fe6SvMSpTDQfYyF2S+8KzBgl+CKdyROsKXzE9EnxjNupDpBHgs5rLkck5IJuPgVcO0WY4420mywbyVYbKmXCkajfBkBWbtf0awfQ8nGrqQtM7h8JilvJHs5LmnL4ttW3FVv0SUhRGTR6zBfmQ0D5tmszsvjpBB/Wj/KeCkpSHZfQ956rqWY5IguTiPP4kfOaizkag7kQRby+PyZzKfu6aRscwTYStHUndFMrkgDwO1ZA6tG89My5Uk/vgPOckznL82n0sR8/kmE4eqRC2drhrEFIRxwXYdfpVNLL1xn5B5jfxMrkTgXyqi9fFIOyXSqTgKhaEx5GQnc7C8mO7bW+nT0MB0QjePB/ezT86CmKUmfJQSxMRcgtZlJezqn03B535yGtaR2GjP+Ppz9FZ346fcSKOWEj55/WhHR2AwcQWyPfHMGvyKFavbaSuu49u/JqbrTiNNUZ+fDzsI1H7KGIlyJqRcYksURB+vQERpOz3P13JjhB0bsy5i0K9NXUwYrf0WuOQ/wvTtIqb8i8Sr7QoL9zjj/bmJgJZALAeEyK29yRM9f8g1IPfJDNw1LTARrGDQVUneD3/BB6MVrFs9D2/1ATZ4n2Vx3l7CXuxjRb4Q1ckN7NLuY+rHJEIalpO09AYfO38j091Bn4Q3H579pihvP18XulLhOZtFWmOp/duNiU8zttVWdN1/xpHluRxwXsrx7FP4j17Dw7KTrJsTSmmxOtN2ebLXJRSfgut0iOkSXf2CW14OiBhOJ/1XL26tbWxRKqPm+i10LD7hVbeX13qfQFuOxux6VG1X8z3mFgIZJ8iKVsGhJo2w9ancXjSIkGRphE9MJN7fF82/Z+lbcpgjj04z2n0SLWdXcPTnACovYjl1y4CBUTnEadfgc+oT7ysjKZRp5LqEHjGd7tw1bCZjkxMBPW/4PmsVSgpTeZ2rQO2uUCQmabP33FIs9fXw+XqQxn+rEIkuYZirHoub+9HT1ic8fTFelr9xUpOg3qec0gBLgtsnca9+BcYl3bRsLWbt9fM8qu/GU2cHdfeCcbd2oDw5hTdZg/gVcZ3ZAavxrdMiOOkHcoLSNBnZU3Gyji0NoURqVeKi1olp1Upu/Q1CQsmNee2v0cwZQs2jdlZbNyJusAzx62a8S4zEZ6Yfq71201pZRe/0IC7kPmWkVCUpgydSKGOPeEcKe7UO49T9jmcpQsQECxNutB0bqUA+vPmJkoQLvpnz6eiIRsRAicJ/Bnh9kGZ7shAlDfv/Vz7+B1m1Jyys00yShqnm3DymT4DpfsZkPiMpr51SIz8u7fVl86611Czej8z1axhal5OurIpB9Qx6pVPRua1ItN40bj3cwvyAn5j0aPOxIo8Hzub8SJVF2+Q39iPUcdoWwZ5med5OGo2jdC6W5/dQFupLyf7vGKmep03/PnZuu4lsqKficxz+E15T/j6Pa6nmbLXS4HHKU3KEurgu70aAnzVRB5aSrenDPSk/Yvv/cEY9hgcK9jTdE+DyxVmoleuj+foTa1QfUDsigcXHruLleBk3bRmKts8m4UskOmV3KTHdzsszVxH7bzhHX8nhZNDBlvh4Tq3bxLkuKxaO1EbINJ6bivN4bTCHhUky+Em50xmtx4db/Sjv0+Tc7RKmjX2H1pihiOXfIqXZH+FDh5GfbED9u5WUa0uxW+AOtQ9W8mXUJ/Scn6F6JxTpvr80p95g0LIj3Hoch3DhNA7FHiHyVgwTOzI5JeHD1MIThARqM+y7P0bTn6DSfpfd9R+YoVeIdl8fkVGiiOw1ojNMERHZIq4+34Vgz0Rim1wQ7txKpo4Z7WpmCO14wcRl73lfF4zH6HH4jJ9Ei74EGplSTL/2ikFDmhGbKkJQ8RaszTV5X7WV1VI7STw6kQ1H31BjM4ZnwmZIKNdQJfQb82R74me8YMM7ZdKEK7mmXcOyllDKuz/ToDaOupx3+Jk85mKUG7SUc97gDf2GIxAtymWBWC0pwxZy/2EDdVKtjCjfSZPfQ9LartFg3Yeu0E4Q/EVMpTYGSqXsPyFLs9MTnhq6YhYvxvGiFHaNLiXznxfmrUKMUviHeWIdue4Tsdl4jPuGlYSdfotU23W2PrnM1NFzGBjcgKqFG+vXhWCuKc4h+SY2z1dkXOVfij5KEOvdz635QTRFfWOQxhLUT1Xi3beXLnEwCDtK9sxVGDq+QqRXi4P5sRQ/TWGv32uWRR/De9MQwud/wXPRBb79/k6w7jd+Jyuz8/JczDNHYNEpQdRpO7wsL/LYUJpdg+0YfqOK761P0b+2GPmykZza34SpYy7/fgujly3Fxe4sbGVUON0YTsaml7g/9uP0iOfMW3Ua0dZuAsdJkhW9lsLlR+jZs56Nzt+J9P3HYBt12ordcP96lq7/gkk4K0NvRT8noj/j4+OC7KhgtM8eoHaFA8MK6/CUruHjtLkMq+vny7dE9hbP4Y9iCBu/P+aN3DweN0txb7wwSg1NfKyfjLVwJuLqTnyy9EO3fAuHxzoQlivG1DFjiUu6g7iCG4O7xZn+RJ05LjYIf7mCdYAun4U6EcxzIrD/H7EDZ9hdf4oepaE4a7/mzqFyWrbMpcQwhwV3itDaK4mfkBw1eXWceHocM6cQMtIesKPnFLbNVkgJhPLn23QSR1zm/V81DB2i+CulzoS6HoZ8W0Dm2mdM2OfDFZ/f1KSt4ZaoM6dE/1BnqM6Bl+e5NHwa3e+i6Qwwp0dWhnoFa+S0rQkovskFqUeMvi2BWIoo1pYqjDDT5uKT2//vPJw3C2dxyaOJ5nUaZMyrJTy7h7yb5xnil8Yts7NEpItwusmXxmQv9gSGMux5F66XpOhKOcGS53q4XaylIDyc0Ln3kPr8CI12AbIjA3F59Yxfu6tZdP0mtypS2bBzEf6Bh5HaNMCLkSN4gRI9Djac1tNiQ84UZO1OUb/qEdGqlkySn8bumhec+j2N1CRTLmtnECwWzv0BY1zul1H0oYqP21qZcaUPiynbyds8Dz1rMRTUR3LgtynnZsjS8MaKU4ZDURUNJTouifQXxdxZEofFYk8yHO8y+/UWktqTmHNPmHndR1k4cJpx7Y/5ELEGKd+Z7JPYiHL2PkZ7jcEuSYDMacn0ZO9BpUOWstTTeCZ786r9AiX35/J0II/Lrm1ILLvBj6xJGGW5oPXdHm9HRxyK3xMvvBKDrHPI7hTntqYrEzUns/DOB941dGK6by2/D95H+3g3yy3yiDOcSfTzA6SWOxN5X5TkA0ostziA72c3zF2+s8onmHmfl6O72py3k64ya9YSbucsZMblDvTvxHJi+DUmbS3i28JsXpuPRGONDxIX7hLktRWfBaYUlp+iWSOTn0a+/HwQjd/BVdSZeODrFU6KbiMdH3dTPt+IgWVnSL1fjdaXueyTkGHB0V08GChA3sCX7zY3GbA8zcFWd8yN77FIw5+VY6dhs3I9/s9+srZ4Cgu3rEBi2Au0c6Mp/92J9ptE8q2dOPBTG6Xs3XQ1BPDaXpcb2Rn8iRHHJyyQY6WfWDdWGq2yHsZoXcWsoIRNv86Q3F5Pcu8XBA6549FZS+u0NFb7PMMhQYYrca7c3B1OwK6dhApGId5uQ3DuORqMZ7OsN4uzPgLc3BhG3sBu3g5pJdSqkcIpczjorof8oTMEiIqj2WaPgOEqBEamEyKryhql40gUnyZT8B7brv7kvMsyKmsTmLW9GlXxdHIfz2f+lRj+WdSw7ZkAeXk3GL1hGLLR6XikGyO9rJChHyQQvBrPvtmzGHU/CYXmaoapD2X9mA10TWolQESJjJoH7LD8xwoZG65GVvJS5y8qy2wY/UIf9bo4xAYtZcJhSUaGWNC3SJQ/JreIuyVHSoIKD5RPMHC4hP6qg3QPOsyl2UkEL/xKqK4korPdCB2Tx77Y0YzdPJxY9Fh20pUb40YS4L+TnWUNlDwOYerQXHxHCHIwchU7BK5inxHM0NIwjvRuQER0IR9f/Yef3HoW1XwksOclC6ojOSLiz2NZbc6qSLAjs4D1gZIYT++n0aKH2FdVzP1wkFUfomjImE2tVRyyXyPxeWRGppIt8p8XcEPTmc5KW2akGPIlNY7YhxE0Xz9LV/Vb5vlXUb5MBd+YCzxaEkaaxnpKzt+h++dOXAZD7v3x2FZN4rn+es4OHY3D2A5SzKbQLluAz/EzjFvlgL9YF1N2BXKxZx62D0QxvhrEh/22eGXupfz9IPJf6IDiXN7U2eNnNZvVAn5MvVnKnyWRCP7zRPrlLd6bpfDzz0KSBrlgskSALnUBPuee+p+O6v/fI9bYg2NzKc31+ox06OF68R2ut+kzznkmk9osWV/pgJC9BGm6pdQOU8fqUjvCOZkc0behoqqQnGZ5ety0GbNhOPOkjfhZUE/yIDHULSKQaf6Fd0YhH3osCXWOR0HPH1X5Wlp7vqO+/TkW228z106BVAERBKJ7eF8Lrb3CjAuOIXenI3OsNhImMIZa6XAe5QfSJWSLZEIGMQcuo3viNIENBvxITeONxiBKP0biWdGH8fhRaG66iopAAYNEBYnsqEXsrhjn5xyF1oMUXl/Kt+4Ettd/oVI1nah5xuyx8YTU39yw+0pPZhvDTNdTLu/PcoU6ZrZVYtilyO68LZilL+OHxQ1G9LkAz4jtNeKkagregxVRGLBA1LaYb42zWVu2iQvDz5AQY4pKTST16u0U9zrh6bqYIfVjkbYMotX+FAFOjZzNA2v1BzR2ezJoiBi2o0YzzHAx6d2JPK7+jLm4Hw6V7bQ93IHYaEcmN79hnccg6lUDyJr8kLdDtxCy2wQ/q6OYSYax60IwGjb2LGsVQX7CYspkbXj74A7+y8dRKySMsWYsIvXlTABUradwt2oDJvoWLNsZy2fpUmSvvIYh4rAkDJNrLjDtGvMNEuj4ZkarZiMmlcW4SqmT5VGOsJAlmmIVOIqMJyjIkMTvO1D+PoLHuV8xddVHQS6G0H/aiMvnM/FRMqZ5WbR4OdGn4cyXg0fQX7qYn+cXUhXkj077NOwM3BkwEEW1YT9ijXXkWrQzXnAe0XxBOs+OosRzmGdP57bXO5IzHagRdafSEvqeCWLg6UPTQDXHu1PYFjCPiO+PKLlrgGvoGXb/noSAWApmj7u4XT+C9QaPKKeH243SVIhI4W+QyBKFbqIiX6FhPJQ+YlFDj5UZR7h1/CBDEOZ5axeyNT8RbXyMpEQJJpRxxGE1W9T8WXFyNnvs11OzegqjfNtpiJjKgrHzWbliDWj10K1Ux+ojzkRn7OGkUT9dH56z3r6aKrvB3H+9m4If6Sy2G0JeXQYyfzsQwBj92Eesau0lZclXXPeuQnboAQyVs8maPR2+fKGuLo9ti9Yy5OtxshJs6Ol4Qa/4d3QedyLXLY+InRD7PbYhFb8bP8+hZIr7g+lQPNLeomZhRn+6Aa+EfTj3zwrpzmIEih7RKXoQPIbzMX4MceU9XKuU4GnVRyRrVJnzQRdra2m0dEpIH5+OtfA3tHFlT3ova4Iz2H05ig1eVtTlttJg8IOD86NpDnSlvVCMf1JNpJmbYCZbTe75Rywa+oSsc+dYsEOVuyXufP2nRWbqZZx0h3B8ogEDWfKYCH3ifY0WvaKSaJpJcrVYjKG556me4odrXRhfTOFNajsJKkZott+j9FsSCkPW4L50BhFHaxHwe07Xm/k8ckthb2EPFcIetIhZoBl4nNQzT+iyCqG0y4/M4pvcSHjNzbEq/DIciYJ8BY3Be1GdpUqVgzfno17/H2fp/xUjg0aDhAb2zL9ITK0oHwN76E2qYm1CA7/EZMj5e41Dv65TVfqAb039jFHooLA+lPCB83jYGNG/cAXVOjcJ3PmB5iQXlua0cSGvhQyzYahIDHBDxgvdki423X/Gby0tEn9nsnCuKl2XXpC69zb/bvsjpvWQ3JEpOHslo3qsnjkVZ2kxX8RL+VHcK6tjp9IiEtrfkfV8F4Ibonii2cC2DkPWuMK8mQ4Uqs3jc/F3Fk3aQvLfEoy0FND/l0aesCJaSZo8VBOgTr4N3ftbif9vLN+F5Ogf40LgWi/ibLNo1umjS3kf/Vmj+bZZgw6pi1wsvkVkWiNDDR7woOonyZlDMPkUQ5uyPUbtecQWTsYpZQFOcvLUFRgwrXE7HzanoCf9iAH5PchueIV4yGTMs3/Q5zuB7KqVSBb7sux2B6fUh+HfcA43K2G8K6U5G76C0q9rGTtyB4cO7Gbccls03yjyZ0wdNXGViK23QCXeDImDPkxSV6NBYSFlPlJUN/8gSnwh9iYLabv9gHtPq8Eugrof8fxNAZdlApAMYA3042wjTNYwJSatfEfPtocIOXxFcf9j1mVOAAAgAElEQVRpOFzEQQcZ5Jfs4vHvcL5FR2L19TGZo1XZ9kCUGmMn7g0ah86r4zjeXEvgMnfa9cajkpnFKENzLsasZNmlNO7dvciUAnOs0GHygwBWb7mLcmoeVa01FP6njn1VP3LNBZR0KlJU74uzXT7xiYVINZ/ikKktCRGivH9oROvyl4xQCiW3uZoAcweSGk+Sr3qdv0VVTDw9jISzeQgM3EMvy4EkM1UWNcwhuMWTdyNV6KzUJODEOTxMuxjmtJA8svj9pRiBdwKcHhDCm5c4DZvI1x/PyHjUhEW4ArgfZI/uTP781uah/DZ+/RzFl1djydWAsnmmRIgbgbkQme/iWSwdhrpCHd166xlcc4Hv6bfQmjuM6dHmHD4cxbHPG7h1fQq28z7idkWc86KnkBdSYaC/GzNbA7Sz3iP3Yx4PDe8wWeETygvseJlxhcZBImw/o8c0xfUI1WhxNGcVcULp5BSEkmjlx+XwNHTswhlzfhFyl1NQW6yEjpkJj+6F8/MMRLjLwT87rlpJk7d4FiL2T5jsJ0HdVB2Uo6oJumXE9xOxCPWsYk/OC2yfbcB0VQkzwgwwjl/OoYpZjK1P4G34Gd565qJdfZNrw2+xr1eQUa1iFJQOw3u3DWM+l5GxaAixl17iKhDAppvlaM7qZnaKFEqa3ui9tiDIpZplzfsImXodn09CvM4JRVWvixpbe16WhROe6UlDTiTt38/hff0Qh8rV0a9qQSE/lhzX9SgVaDC8Xxq17p3kqXjQI9fPyZ+P0RrkSp7dVkTL5GiQ9+JkXAsjZh9mhUo7E5tvoiPbzRyJECZtEsNibh/1zT2cy01iS/BMVhva4m5swdXRbxFW6uHKIQEuqUfx5vt8vB9sY/7mExxaNZMnC+8g+vUKj1+eIGDPdnLjs7HpyybNoJZG5Y/ccOV/Rwb/B5EVGDawIkCLgnnTmPQ+kZcbfLk7bQvTRAywNR3M6lmjcLsQxE35/xiS38DtkDbm70jnQWYsj246Y/9aA8lh43FVdie8IR6t1mmUqm2gXzmTX/cHc8BWjNvDDah938VrayEEDq5Ea8Zh7sWH4qI0nVC7Vkp25CDRo0BtznK2MpXwK/8xoFqDVs00fndHUPbnNKIT2pHv3cQTXXle/rUlKFWZR0IPCfmZTMDOh4S9HstpQz98xL7xt8OOT2IOtKYkot9bSVyGIc79R6hYcJzH1oOQ/n2Vu1O3U1GagpuSGZ92/cR4oIYzgy9RrmBLl/lscjuvMV/KguIoBeLEPVkWkEp5XANGlbI8q9mE/uv9BCaPoeVVBKKe87hR1kJqowIha4ehGvaRL21jOVm1iJK/L3i2IYJhmOAQN5+8wrvkTLJn3cNxSHf/Rrf7Ee927mNzWCkdnjX4OCmi9kSXi3o59JkGMCtck/7sYjpD9yE3MZ3zajsxSd6J4rFEltyv5+mGJnKStiA7MohRww+zWWQDRWtPEbRsBHpC3xB5MoPBy734MlSZqT/VkGuK5KXuNfT8xnI2fDBHExVxn/2Xl6abkYnbx651aTg8Hclx53YGBprxF5Ag0n09vh93kL/fi4utRygxd+SlnB4jxJrx+zWVm9dXUrVvBU3X30H+R+Z+quDRqpk8eBDMxQhNqhqO8uFuJLpFjbjFHOVjVjv9+zdj3fKNt7d3MXHeFFpDehC/r8AD5z9sUR2BVuYBPqzbSvbrcZg3HkBWUZDTlc8Yee0fG6ceY0jfQTxMLChUEOWM2GFuidznZdwbgnNn06sqwP61YxhoGyBWuoOG6tcs8JNlx9xQ/lvzjYf3VzM9/zQDvmW4D7rMp2u7KV09wNX1N5CeGUap3mqa253RST3LqtQSLi30RqnDkA6tyWil7eO99SLcXL0wGkgjgVm4vVOl8Nd2hmmL87H0BhGSK3D+fIFhZ4LJTa8i5YUGiqZvkPp8mRc/mll3M5sL6ZHEPQyH2LsM1LRz78wLbIVb+ZdeTKtyNav0zIgyiEd7+ldio89w8PIhpkYcYsKWEKbENKCzN4pb49woDD/PyiGeeFWXMmOuPYm9ndx7+AKlpig2mj1mnEsrl85dwXHNbnatn4rX4lxG6l9k3k5LAlNG0eHsx8Gkn/iHaHNaQhy3z7nEdxcxTdCD5LxMHA3cudxSwNJ/UuT0ZHNHSRjZ+EYGNltweHEdlpsns+tqDEk3qiDlMyEjMtj6WoC9zfu5rm6KT90EXmarMSFwMy++65ApI4LJ8+nMX34Syb+fcKpK5I/JIYQKrnBuwIrUcX1cv5/OjyFgprUa7fZ44iq68LZSZkmnMKaVsiwZOZa0YmO8oyeyt24h/x97b9kUBANuXS+6u5HuDkEFFFAQwUAMTOzu7gS7u7uwW0BRJLwRKRFQpKS7u+v9C8+3856Zs37EvmbNNXv2mem5+MQPsNuxE1mXCTw4EsKakXG0OL2i/MV5pk9bxuEnU4j968CQRaH4tO/EI347t0fcYoVGHhkFs9lQl8Vu0Qf0FtcSPcKOrX+y+RYTgOgyLbYssfh/ulH/v/h8tDfq4F5wH41BHwh4XIxHdy/tEyczTUYKh3NPGbt6OrrxM6kLVObj1QFUf5sxriaXawZruKE1B4FfPbTaLObt/RbmPRaipiKf0Hdn2P+6gbFn/TFLC2aT3QO6ZmThvkKGTa+raJ3kjPhVL84uscCq2pRXLwKJ+uhGwKEA7gpNxvhJOKsaDyMq0cUQZdj4p51rai7M8PdF2uUoGz0FEBCypjlejt/PVvPs7Dbs4yZzxvEtU9QuoztkKW83LyL9mQtiwhPY8HMXm3Z7odOTw7RB4WTKOfB9fTiirKU58Sh/BOupK91Hz/USPsx2JvfYGBoeP8U/SZOem4ZovizDwnEll9J7uKH4jNUTKxC90UrzhSJ2b/Dl2uM0cryU2B2fhuqpn5xf3YJV42XE7L2xF+zmvtMFFpvkIBAkwcZ9PhgfCOON9wpap/9jXcsjtsxax+Fjd9m09zKNf0bz4/cPVvtroCm9FoHjhky0OoeRUQDb9CaRYD6Gv+4yyHkPYUIsKN3aideedgYKHLkTu4sVsx9Q/OQo2e8HWKOgSfNAKgOV6SzQcOeS4GYa50ymv2YFN89cYGTnAKutanhbOIqHtwUZ4zuVF/++4Dj8Kj0bS8m7JcGJ7Q04/LViyv3BaPiv5JtXFY8033NysSgjZcs4P/87ETNtmWX/nMgPl7hSd4i9J1P4oSOAkXMbsva5LOoTovuJJ1p/7XAwWodQfQbxU97xdEkfwtcnspNpdN1PoNZbkjMGSfyVLWVLcwFpmrE8MBNkcIc2ASu3MzPfkal1W/FvbaMufi2OnEHWxJ2bNmr8NFLhqlACpiUWrBy8DelOQYbJiKM/1I38yWGkJ39nb+IoNiVLs+SLHgvvKHBruRF+Spk49MxiQc9IxMTLse98x+rNUxGsWM/UqlA+5Thz4+Ucrq9I5/Xz6+w/8wKNJxNQ0D3KSn4zbuYdXp/YgqPdWlzOZZBcPQezvzkYWH/lUv9LKgbVYLPYgciiP4S6T+T3tRDa5r4h2GAl5w/PpyF5H11Re3k1fAVDL2WiZB3OmRkCZE9PZp6jFxGDymmft5IbnwZw/RuPooU3OgMriA9O5azSPg4P5DPmbCP+9gKEif3m0ZnvnPS1ZLGwKpN//EJfaiGvd1hhZRXC3NQZzJq5nS2n/pL2ZCHz9Aw4ujKERYnnCPmezfcZF/gcacKd/sO8MF/BngWFjBy8Hw/V1+S8yGGCiiKn1/8jKWcCZT3VnPAdxptzs/Af3YPS/HXs3JDEK38F5hqVMq1mC4P/E2Kjsgwamfs50LAVS80RCFuq8nbdeHzXhXM7zAQTLUtS9BXwmBGK4IGVTHfuZFVTC8ekLpCvG81swW6m9dzFvWwMmdl3OPB3P3N/iLHEJJeS78+xdjyEQbIy85/rI/DlHksDbSmotcNDLYF3J0aQtMWBsd4CLDq5mpzA20z0nI7NGSm2T7Mm4cAkFg/Ik/QkBOcafR5ECfClbClnpk7g7IrT2IuaMqpYHrevLhiP/J9O6v9DXauVCa1qRCr9JP2PEo9OxDBn3nXmy2gi4ZGKROgZ4qZfYWHBM65qaXIsRggfvRy+LBjG3xwN0qSlcSpvIHK8MO4dGsyS0WdN5gfUGmfRPLiGX4P08GoVJyzrO68lb6KheIEV6n0ESdvyTUwaKYlmJjvYcLpbCAFpFcxybnFnpgn3LEq4UnuLoX2NTFGagXHyCUz7JBh+9Qznt23mwKhMdIYNotV8MaX6orS2ZzJcUoEPpaYIN+ny5k091wsvEzdJitkznfDckIKXlz0/su8zdsR7vF/E0qzWQlVeFNHmXTSXxTJjwmEG/2nHvewNZd+0yej4jpKAMZfr/nBJS4NFQx4SF+dMiRDkCYdgX3ePt/aLmJR6hEizlQgkGFM42Jv2umcsEZdnvrIFiyUMGP97BjlaA1xOTiF1oSKOg0YTI2SLp6IgXir9dA1byauyRlzEe5kutZ1X9+8Q3Qm5f0cTU3WTRTHJfOjWpr4U5lpZ0E4M5RIVdEROonScKB26MlwIOsP38EVIhqgSMXsG3TVvOLpegKCYJ1z9sBXrVDEKZRqJaPrO7pabZFn+Ya9wDcXfghHwPkNrbg72/QrEGvVzVE2ANVP+A7rZOeYrRMYws3UzxRHR6LT7EJ8ki3uuJ64yH3leWUB2qQB9Nu10uOZy4WIZDx5vILHpMW+P6mNm8pvULwHsMJMguKOdmXXt5CecYu3+lSzyjkF5+zkS7pSjnhFNokQD4weq6C74SniDBnuq3qMqAE3RtQiM1sMuOgOP8cNYVnKE6JrvjC0xZLFFHW+Kk1iobkFnVhTYi2BntwWRMHjjBi8bJegwvMvVDR+5dj+eqQsvoZxayuzha5mZV0LxMx1olGZruzuBCjVsdi1AXvwbfn+TEHpnyvVRYxkamkFISyma7x5iEJZJb+lH6rTmMVbbjgU8InXMWfxLEnD+akdVxQBre2+S6CbPszFzCC3/zDHlADTietljNogzQyMoaJmGR20y+wZLAb3EJCbyrqaI6u+NZIhbczrtIscsqhgjo8nXcVt429xDgKUBvtNeEmLxHpuxHixqjiVY6DQJPTqEdHdzpuMIaTtX03rhHRIFkRiouBB+6RABq8bivC6N2hBDsoQFeVL7mqlrDmNsMZ6T5d+JrR/NVDs4WyGG8btqlvT3U9GcSnzVL36InOWPYhrz7auRbjbm7V0ZZG4qETDaDA2BlbS4XaMtxJmkLU7w4hoYuBOS8o6Mkv3IFR5m86IM6kTg64QAmvJ6ed1rjathJKy7iM35FMy0ZvPA3hO/BUbU1xpRv02OsSVhfJB7wzxTf4YLB1FSJMsyM21yMgqINJjGNPUy5N9OpYzVXI+MQMpoC0Kf82gebYWoZwc/wn6hn3iVyXN2IVp9nqz8OGoe1LNq7jWa4uNYMH4/ktnpfCzsxzgpkh8TOylOK8VA3ADJGn2G6LqTf+wGd5tKUPPvpKMg+/85S/9/0fk4cPluYJnaUmLfFnFh8iVkCn/xMvUGXzxKmFK1k3ZpY9yUg5mQK8Gax9qY2llTl+nG1BehRGYJozwmlCyFVlzq9UgUDmactjM1GZ2cscilKdeYbEs9hveO4tfHrxx1W4xxkTfx9tXUi/uwN0KSAePRbOwyxb5WkO40Ya6e8GSlrzE53hd5GhGGwKcgRpVYcF02mYabOaxY282r4tnM3e/C9SPXKAwcIP/YdTLPw+Hnlbx6EUbx/SAOvrzM5X3v2OQmhs/K8zjZaZMgq8XY0xIsiPPm8vXvVNT5cDmiGImEI9R0qPNNtIhl8hb0SXwmXGou2rljOJaVgpV8Jrt272OG0RA+X9rC3dcPCbkfhHH3KGYI/uJL8H9IJrRy5L+NBNWVoLwpAuHIS3zvnoOpeC4bBHJ5LmqJum01nxKq2bYonMmdV7hQlEqu/GG27vrHybgAbgQsZOv03RxIquLK83s0i0kyZORFesoFyel3xlnFkN2n9PgUoUvVG2VMDyiR+6+Qz61SqD6tJf9AMJLeErRJWSAZtZ30Q/t4u8GMx0+DUHLyoNltDKrl4YiMDKc+sZO0yPE07pnL7/0vEJ7mT9iKGWTPvkidVy3DGjL58VCO3Ye3onJEiO+b/In+m4TaFdi3S4aMYSKs/tFPuvw/Js+35c4gRfaPNkfFHKIjo1goY8ujz6no2Jlz5+p7zBYY8F++MENetPFWKpgDfloc/6SHyDI5upTTUIiZRvUEYWKtwqgtnsW5dmhuVcXJzYXqDkeaClvokZhPU88jHqU0YuYzg/LiNuQVnPERLcNv2UWMXkQRf+EknVLlJPWIo1xYzZgKO4LK93N133WGfxmLxMZKRj2YB3NCqbBZh0XpJE432GGUps1Wzf3csdyKgKQ6VhrhbK7yIGXBb3aX1TAiPZt48SbOyhkgbz6Tp9steDjQyJOlcUgGfSNfMYR/gsrYpQ6jqXwYi/piuVHrzaKhRsTFZkFaDNLCcpi8LqLihwsOfiMpdGrn5v4oLsz2o67Dkw+2qexTi0GsoIORs9M46J+BhbIJRxQ3Ynl7C0M89+I30Mu+smSyhiylZPECrrgYc/TJe8o9AtlRDSa/DyHYJcTHBDe2/QjBTsiS94NU8VMSwVt8MMGP17H3x0OUD57DNT0JiVGj8LmcxddMU+TqTxHXdZIn4tLcakhDOOwPQi3TKLMzZbPdPMSuSjNx6SqOdeVwfECc3Qud2d/ugKS4G+5HdFDwbuGO7EnEj3XSK6nFxEv/8agF5oz+TnO/CZPKk6jr/Mj2WQK8yXNkWGEZU0f5Y6g8gP/Z9dilBbNrxwJOjWlhVVEkx8Ru0K96ifG1IdRWxlI/XpTlHUu57LeP1cl9KCy/TFp3JieP5fNo1A585S2obGhnr7kSZp0FXH/2kuNX5jNp5EbeFCqScbSOwr0BBOmF0tuXR/AgDwR8NJGsVWFNuy570uczaup6bt++zDhfA1pFiziRL4zEQisk9L35HBX2f52P/0ECL94M9PJyIju0ikdj5zHL1IqG1mRut2UwxcyW8qbxqFZ3oGJdxPXqwehLV2Gqks8hiQAyakZR05tH9thUzB70U6JeQH+5DhcaJTD900qLiRBFmY5MzBRA3nwLTtLSDLRFs9rSjqioSiLlX+Op5k2oiRtC6mVcP7SR7VZpHPF2Yd3ecu79FuSYmQxhLctoHygio1WdNY4atFsMZeOBX8QPk2LypTUYajWilR/Ld5HvRD1xQd+shzaDHzgca0AiaiG6pu/Q1FKhWS2L4NCVnOmcyqrmm7hI/0VfupaO/9IRWRbIu0ermWEQyBIncb50tzGz9iPHuMOZEXG4HFpFu+JjnHWLSReaS3nSacYrXqVKSIbCanUqjTYyTdAPgeuhvJn2hqNVlwmZcAeLu5u4ueQVxbkbeWphT1y+CelF5Tg1azLdPofA9D8kPq5j4RVvUl+HoifTRGiKJ46W9iS0KnPT7ih/v1chqrOF7eMiOL2jG5kf7lwJVebs+T4kY+azfO0/Toaso6tbEzXJXJKqDXmmepwzy9Zyfuox7DZ8wKLpIpeV5qH89g4arqL4uAcT0tNP/R13vh17iJPXeZrkxIg9ewC9jnEEnG3HVcMctdMTGXZqIRtWHsBxhDIhmeqYVyvS9+I64aNdUWysZbbNaB7JV7Cvfg/5CRWMkEjGfl0Pnv3bqNGqR7brBPW+cshJtBJSJMWkH9NIOuZM3ZZitMX3cIw5XLZvZlCRFNEWyUhZGaOcZ4uirxjzw47QuXouTxe9ZdP2Pax8sJXxc69i2vODrr5+ZD03sW+iHsZ6hizccwiF/lg6h/Th2TvAJ+MI2gRm0TwSBk9RYPngRRQ+z+F3TCfd3vd4vHwpn2VDmZ58kLDmgzTquOD/JwVx4fM4nfBjYMMg1haL0eYvS39pEa79g/i2dyzCy3LZq3+NwP/e4DFcA7WPfUTKn+T11jOMzHbDsiEG570vcN+igabsHGKSXlBbcQC/U3c5evoFAenCjJ3RzKTH8RR9e4TqeTPaVxVSObgUu1YBVKZeYatSEqF1Bsy0F8Tk8WkKpOyom2JJ97t8nAqN8OxZwt1db4ib952LAWnIltQQaWrO5Qhvorf7g/dSMiQM0d69mvflF9msugzvwmZmXPhM5r9OxhsJI2fcx6nD/kwO2kXdyk7kIoVZ2aNNmaYjuUk17BpopltpPNdmi9Dwshe32irGjzch68oPNFsC2FG3m9gv4tR8WICHZCZKzv+QktNkxuad/Lf2Ed+f6aAx+TQF7xqYWZDH03Jtjo5/z+NEeaassSaNavKcdAmzns6of/EMW3GXKxZ+GGtO49vPq4yoDaPE3guTvgMsDZnGDJnxDNYOQ7P1FdIyZ5k1W4BP5Yr8MVah8mcHzcmSyI5pZorvWu7O9qPNuRmZ8NkYbhKl2sWehptT6GpqQ3VhDoWJM5FdVMuJCEfEXu3HREaLzv5BZIoKMNWhnyHeNkTurGPiuj7uPAr931M4v3JhVaCpoxV2B69yKSKEOx0DrJ06EofIbKx1M9iW+4C2zCw8GpZwarIFP85XY9zyk1mlIpwvK6Za5j+6tW3421KBprERN0v/4e07mLA8Wf5dsmRwfiaiudlY+LmxMHQKKd7/4Sk2jZSPu1k+bDCFSg3YmJpCYwXv1DqZO9SfmacfkS5li2tVLMFVHXy5o4eBpzLD2n0p6Kwjdv882jOjMGi9yuQoI95FxiG2uBz17f38npeE9rERdJe8ImpgM+9uP6fhZQe3QyNYEn2TVz5+vHnui97Yi9T9Gs6H5b605SWj5wR+Tw6j9PAuZ2VOYKr4FI3IqZy2siSlxRJTDzMelxmDySBErV24qtSGu4MIN7rEaZ1sQKJ+PZ5DdqPmPBFf0QR6HV34GvoV0WGVLBZ2ol5tNIOv5+Ex0EPg4kG8GxvAZBUVtj6RZMOJZYTMu8ejV884//oORz0TsG2WRyw1HGshS8SyJ1L2TwL9kR5cvS9N++uX1EXkMlK/gn5VbZa6z+d+xmjuV7UwynkuFV21KDis4XfwbSZLXEXFdgGbQyORFP6Ljb0wyhZuCJm7oTFsLgMb3iDRpIGYwGQ+xM7A0MKLVU5yBEcpk5sqi2dGMK0HOxBv0qFjzjRmf52MgXwpbSXuSNSOQrlyFnVZAihXDPC3SYqk7ersXObGr/ws/tUORbe9nNqRYhhKgt02eWY/CuCpZwAf7o3EVDkdtfwniBaoM073G5/iUxliMoFm+TgmHFLCmXzMH2gR+9GMgaxNtB1tpLl+EifcD3K3OprJI/153hZJt9IoxFvcqB1swrkkFT5Kz+eBx04Ku4eRXz6cmD8PEdi5jJsmUTgumcvRTwpMjT9H4HxxunQMkA99x5pJlshXrSFZ5QYPapoYI/+Xh6pHSE6vw0NejElOahww7ua+0hAUu/oIyPjMA99liLRYs/r8bGqNB6OodZZfu6vIV/nOendn/hQ8Ybu/MfbB4fQPqCDfa0q6pAOTjfvYJvQLv94mHvfZME9zNcU96Szzq+b2a0f0ri1haboDQ4qikVf4yNXObgTHOaIT3o1hrSG1N0Xpzv5HovwjngZdYuFqBfbHrATTxYSrXaXQ7hj5c+oQ08rm7qTzjBzVTnjjRQKb5vKiVJYXB15SXrIDuRtbkTqYyKujeznyfg+3jmxHwXA/L502YSdwid2d9dwuXktk3TLmtV6lXVmKR4XRzBP8RWNuB4uFD+PpVYai2EhkvYSJfZeF77PDvDzWR2LTAd7VbmOo7EVmC10h5lknnh+7CZs0lB7pv0jadZK3ag4nwitIzTBg9fIszD81M9VsN6i4csm2Hl+FPnKETnNHfzgSc1QJet2F865GMiOz8DebSJn2BWq7VbixcA4ypWaMr6lgxrWpHPh2gmZJU5a2LmfU2WZ+lQmRuP4kN4OdURL7j0DlLgbG6/L7gjAaF19hJ5lNuVA5kgFzeDdpB0eKRiCtbstPZXkcJH2wzeri8e+D/Mts/D/5+B/k3OHzgYOnneewjzCPvgSxzLaJtuxQlhptJFOlFiXiyGo5TEJ9IUtODKEqvRSt5tGcOLcJXw8ZOlXGoR1fjfL4MvTrXOj0MUH2VySFmkNp6WtmQks/PYMaaBvei0iJBAK2UkjnvqCqbTV7He/yvm05OoajWKxYRHT1Ng6HvUOqyRNdIWk6BiKZ2O7He71oVtaVUqHWjKzSJEp/XOOtmym5koIU7XiGWa0bP1S/Iq/kS6d9KgZ9avgPsaI9Zj5Kg62QEnxJlPAEpmZaEFUeT6vBBjTvj2bcuhM8uKSNyvSTaMWcwyJEjJNq3fwzMcXp2leWi2ohKunG1ezbSDlfRPXfV4JGJtP8PZbxh/PQqptAqO4oBte0IOFiTmJyDPL7z/HqxFiOt/oRs8cJL8vR/Hk9hOXW4XyMTMNOq4GesTNQP76V6U4vUGr+yrJJTuh3dBGqMAm9/g9cehHJqtIKJIO1kTv+hfA/E9l1cxnf/8ZS1KSAwoQBFm/woyrhFkrTfEjY/oxtH2KZ7jycrLI+9k8tpCNVmp6Il3TOSuL1xZMMVZdFtfgmBi4+XNm8ny5zLbpttakcORL/TyvJnDAVv0eOOCh7sOJgJXpOltx86M7caQm0XonGa9QO9NctRcDVgm/z52Mutxbd1sOoKIXxXL+dvjJNFmhNo6u/lC+/BInuuU6ZbyDi12PIGRGDXqMzBw6G88LClhfPluHyKpxfBzPpmLQN55Id+IxVR7NuC0fyH2CZX8rYTn/CnoVRPFWTtoy3+BZe5q/gSVT11pGflM2qpvcc9LJnmNUMLB22MrAmmXWpzliYJrDFqZ3o4Fr6Gr6Q6FrG7E5TYjUbmScM8Qt8uHKlm9fp1QQe6SRg/Dk2OJ8gx/kU1xN9ea3Yx/nqCBpUpaixFuKHhAbJ5hZ0p3Ujrz2Lp9vH4unht9UAACAASURBVHbIlfXtLoTeiODUVVcmDRgiX95BwvM8jMfIkXs7mJY7AYz+LE1iYz3GpsYkjFZi9qXvHNEt41S/Eqt2RvCzKJ9FZ6WwVJ7EsYxLiP+3mCUlsQRnRfPXaSQRz5qwExDDpdwdu3op5rnm0uo1h2nmLUgUfyfwSRDKxSl8n7QLk8J2Oq32oqnswo9bx1n44wM5AyPYdH0hsXNOYbRzLKfO+XBOIItTrh5ob36L+eCXLHy1gH0jFmAtUEzQ42ykB/uzIv8Dwv26xHuuZFmjLc8iA/g05A0/s69w8WsR484pkZkVy4hKI/5JyvLwmw0XhSr5r/oNq8zesMU2nHMfL3LCW4h1t/zYVf0Qf9MAbh0VY9fjGiwbvfDr1EevKQ6P5gom2Oxl9+UPHFhsQ1d6KgOSfmj3N1Aq1EeTUT5irQLcNbuM39DlxIb48k1Hh5ubFzG+ahw5ghdxNm0kTkCCZVYpCAmaUZhkhdKQPtRNrIg710ZvUDfHPEJ44X+JVLdeTl0OYpulAMmNZVgWe7GoTICLP09xIrqObmtdHnhlQ7Q1s21U+Jl6mNDkhv898hF89k5g4GAthsf74rFIkfc171hkboNIwSJSP2US3hHEn+XZLIsN40ZnGC3mNmR+uE1amyk2k3aiUSFATW03xqIqCEsqoioshaF+CbLaUQzNyGJQeQoYqpOvd45TwRP426aHTkIZRgNd+Mq9pbn5O5/ad5BSeIxz4rHs27+Nlr+NzF3riUmmNDPHiqHy3o2vwsNZ++kd6YWaaEV2UtKgz5L2DgZ/jWbXpVOsLKxgxR9tkpzr0HjthJx0KZq5s7hWaY/F1BF8+PqUTEtl9pS0c/eiG6O0l6PirEFnkznvCkrpD3+H5Es/3I/coe/DDyaIhdOsN4zHHRn4l9xBymoET9pP4GfUgbbDYEpc57PZ+R7Wcjo8Cn+Ns4oEKZ8ayK2PRae6DCdlEWxur+LE0ftMSb7KuFZF/mtKo9cGZhiFUHDjJydEIslyv8aM3Pu8fniXqQWZDPy8h5fAJm7+ecLaoPOktMuQpzqHlP47VEr9YpJdJA/cHjFyajttWnn8TW9C5XMCffU3iHG8ylun+4y3LufBnrHUKTTyZbk/DX8KyBgejvFvQ6KzP6KqX46VewSvf/ihUFuP9nhthDubMGncitfBmcwzLOJyqgEn5fMYe66EXcECbF7/AM31SrgEfUb8mC3WiX9Z0PcKmf6ZlAgsoT++FhvrMUhLvMTmRBe3I4+zc/ZUnmREoZZTiGjRS67Pec/ec1d4niuK86I3PDDNIX7XPQSltFhTqckRY3mu7i5gtEwtUq05qA0bIGJQHcpBKegvTiLaJBPzxS5cULqCZFsob6Kf0O+zCvHgGyiIS1Hb44nomFsYaQpRZhpHzuWfDHr/mtebznPLxo4W1QQ0jTu5LNLMpTtLEf41jICIC9wb40Z1UioSgVZMVJZAc/x0Hg8O4paIDf156pgYrkY14Q7HyzTx+nWGsl/PcAr+zaKXYSTIpvNi5nSmSxrTb2JAdIUt3wRm83jHY4KHH2Xvh2Qy1JSxcx6H1CZXGneMJdZvHJMLtGhXn8vCslBK+zSQrn7NmTJzyipHU7lGBwtHFVRfyNGX+IrDD6byTXsVL3rusLLsEotmWbDXVhvff84EXd9LYeEBNoeGklmjzMKwFfxNmMKOCU2oJjvyctEMPm16wOlpsQT9MOTI40+YGgtwsGcHofGSpNr1ERP1lit3rvP0YjAdxXNoa89CST8OydXaJKf/4pDiI1T0TaiS0kDzchVBujJUvp3Jgmv7qDIxocChBukQGNKShbjkS64oezFa6CLdF/sRS7Lmv65viAsrc32uP3oiRbh+1kCtQI/2/7ay0q2IYRV3GVO7GfvGcQi/Hs0v2WSKI91QrmlieucGZuWkI72pnjfTLvBn+npu/2qnND0f0e1fyVO7QHz0QmSmChJvI4/PPEPs/EM5fG4Svfr19AUYsyw7D69hWlwL34uM3lE2/X6JuvgFZgpJsl16FXkG2lQpViMq/RJHe2neew6i3qUX65YQ7s7MZtBYba7cuE9f0f/tfPxPcvnyrUD5MVuJivRkr3cAUwsP4aVrhF/DR56nOSPfP4tnsfIo6ZZjX/SN6o2HEX3+igddDXw3MWFi5mSURr3kRYIOVq7KtEar0VGvyudRcfTKzyRL9RfeUe1UinYjWPIVKbMJXPoez7kFAUTNuovdyxdMsnVl257zPOzdx+IYHyTuPEctRQh1o6EkDdVE75cGDUo/Edd3wSirDgWtoST01zJMT4CopFos3JToFB+EcVcMHaaGdP7tILWzC5e+1bQnzuBBmSvlbZVI5xTTZ97NMhFBgrLjWbFVlff3PKhWLuHwgwKKNAbzepkdxYPtUSx0wNFVjobyTDQVuqhKV0LZJJNB2e0YWSwgc180mUKFlPekM860gg1CQjyON+RmwS0cHNUouRHM0cBN+ASvJ3LvK4Sz7+OgfZBjf66x2KGSIe/3EK/1hXDvemznJxH86hYPE0tIyUjh8uHjWBtvIfnHFkzNR5Bp68ic4ebI+49CdtICgtXjQH4bC9yF8Tq2lvmdxiiIv6R+/jNUkjORiUonLM+MtRVNPFcxRddwPJbCZzEWOktqST1tTe+Rui5Fy9J1FIQ8RsXoImk9lxB7OBjX85s5E+RLicMyKiJkWPckirBMQ+zXrcGyPJtNsSFU6hah91Eex4DttCTvRLPrIErdb2hpGUN042fiPuVhbxjHown1nI7Zwco1S7m7/z+Cnh7kYmUAX4cXEyWTxLQh6tTcaUY8yJWvEXG4qLqQ4dnKMBMRbm5LYfyMRXwb2EGgwSvC0t8xYrYyRekxWKpbYvLfF6Y7CzK98QdPvB+wePlmhAa5Uim4l+okbzovFGHnc5Crzpu4VLyPxBRL2qr3YVHqSlpsC8PO/iau+DO75nnyZr80ccuNOBH6kdUiEvS230bU3JU2eRU6WgdhlfuKRuUpRF0OYYLoSxaMG8WCGidk+nrYZWRBXXomMX8LsCkbTMPqIQzr7ebaz3oMBETJLEvGcVQj+0u9CarSx+2GPT9XvKSx0xGbQCf6Tg6i0eowGcpDsf+wgA9yA5w68Icf57pZOFGQ5tSnFLSpMV27gmsxj/AV2U/AzBzaXtcyd+4pFoi0M0hWg3kllxDVGEfIThcW6XuD5WOiK0s5ddQPSyEDDh49QVDYV7qU/mD7Ihp15+vM//saqd/dCCm047PJkt4+O8pbftIQkwd2ykh2plJlPIGThcl4Z8qjumYrCzqEyajXIF5dmZIGY47PbuZPzTgUemLR0OmnpLGBhPh/DIw2J+atCq4GbvS2Z9A3dC3ZSlcx75nP+OK/JCsacVP6Nlvzf1BaOo+52onEVu7HwjCQj0fP4Lr+JCEFq1GsGEbsuHQWWSzjyrRF3AkI51B1OBuGy5GlfJPe7g/8poT+nkg6KlWZrbuO8Y+m83TMYPybnrJxpBNbYy4T2ujK+G0quJVMpUFcke6vCYi4uDC0ZihXehOZ5iJI7Sx9Hgo1Yiakz7aTmkzd9oZbq78RFBT0v0c+Ll29G1i1UBIJVQs0X7xmip0m3y5H0eMQRMH4IYgl34Kd4zi+0BcLYUnCvsfTPs0Nd9sqOrhEbFo4o4WH0vOxCHmVeRg0QX6/CJ3ilhgXzkakr4PuVAMKm3ZxdH4p/iHnuBvZTIDrZKyLPakrvsV6eSkkBJ8xd+QuPoqZ4Kxsx4TuaOQGiaBSacqe4mQWdv7mo8J8tlQ8Z9sgIzx731DbmE/7hlASk38j7WOErO1dCDdH7M8BcsrdSBZOYubIEdgqRJEzwpzkwlT2S3Qy9forlrwbjrP5Gh68foCIXQX3BpJ5vz6Z2t9P+OzhQGFSM18y/4L1E9THb+f88x08n3+C0oHx2Kv8Jru0nXGCsyh/nE/IguM8D/NmwOwEq/r0kFURZUSfBDLaxnR6myAtlMDIxUtpzSrgt8FlkgW7sQr4iJzRWiQqzLDPdGKWwh+S5h9ixnwnkhS/42U8m0cnblBfZEZZxBnCLc8wRcSKh+vT+LTZn0tfv3DJ6xAZ9yOZYjCGiQJaXBw7gyWj1ai9Opln7hcpEOhho1QespkmTM5rZPz6U9QPMuZP5w5yk0yR+jsX593KDLyrpFlaG1VrB97uEkXQxIbHF8wImfCB+F2tnPNS5u3pQ3Q6h+DGDE5PTeTBfCMyE2sRsVWiSkEA63HVFH/qQXmsLAkF31huvoyE53epCxnKRq35LPtQzCmPUVx5q8GIXgnuvJ9H568h/LlfzJ96W6YkvKIquYieXmHkPjfz6VkGh5+lIGfnwOI0D+wyS/lScZeovJPMGRaI2iMpFo/dyZvIHFY6NzHcQo+wvPWsGK5M/9Yu9orp4m1dT6b9MLTLZyF00pGVVX5IZF3h+4ITbLJ/yIIPorzbkkHb6YMsDr+M26hdeMuXsT3cBalUqP6vgWUdgoSkRNEyUo2npmoI5WogvHQyBhIhPBqSh6KVF6vitKhL+Id0hjkj/1UzrPgtc4VGISYeiKhvCQUpCgy3aqIk7QrXZx5kcb0hhUUqWPQ+xffITh5H+6Jw2Jnm2GY89R2537+RmcYQrWaATNUDloZNpCAxkvVWkmyy+soKKQt2vHClT/sf83LOsnphELvimwg4oI33qY/0aXXTvHsDh3W20qDnz77Be9lwfRcZ0vuwlLxBYPYeyqIqIGkBUf5d9DhUc+9TClGdY4n9VMIODSXsR3WQ+VOH/7SMWOV5j9OBzejahmAXuBrv+79wPibJnpVvSPnShVSLNh3SVaT/lcNgiB65zm8JtfWg2qwIARMDpGSm8dVZHL8hJyDKlvFKC4m98Iu5GosQyznFb8WjPHndi5x5GBssjzFQ7kVblTry+upsiE+gebw0U3YasDcpD9cAYxYe6uad0EkWWmyl0u4GQ5c48uPPHSY3ZNET1U9VWSb7rFRZu+ESwXM9GT65n/3bo1k++DxFz3aRc+Ih4XccsZ6aifeJUOytP1Mhnsjstj4Gj2ikIEGeT5X1lLTORi8JfgSn8+dDCvB/8vE/ycajhwNPW8qRLivJR0kJnsXpMFfjGj7lhqSUJSIqG8tfW1WyOx+xWcWarKxMlJMG4bNyHm/F3qM9yBeh+FeorzyGaM4d3JUL+Vb3l5HZlpT0vMI614RE91KcsaQ16zdFBXNxnB3BdzVBumP9sVZ3ZM+3LoZPcERZQ4jez5/Y55XH3FZ7/MzlCfsUhqPPMPrMzHHN3EVwrQQLpdIpKOiit6qXtrHDkE77iMfAXV7XhaH27Ct9nbKkqAtSafCDjaOvI+JWw5GSFO6XT+CjRSKmGSUEPHxC3vFbSM1I5Pm5owydtJ0LYxZgc+gS3jTwpUKXQhEbHgi8YX6EMPt8h6PtvgoVqXC+FJoQq/IE02ZNtq5V4+EnHVYq2+GULs8InymY2bfjuPYzh6a6M3OKJyLnYjhuVMPG/gFSX73ivuBR/ihnYij0jZFVOvRv34jBkescsZdDMlyYr42JHP68kux+Z6o6L3NiyDS6nsXyJfE97Sb7uXB+GhnfWykKi2SDgjaukqeZu3AingdUsLHyZkRCJWcWGiAutwLPYfc4+KWKV3smMu7JVpob8xkk38eYoCVkhHahLlYD1VFYT/hAS1ocV9NK+HdYhoht+2h5aMWcxIlo+dVSv/4eh0d6UN5pwoFvSvjafSbEoAMxNqHX9opvmsLMWrWQXOHTiDyeiKFxDMveRCB3LofNO1wIiX/O858F7HW2JC1dmmsXZyD0MYMAsS5qfuzFWCqQ2VdimKS3ktdfn6F8wIiOrJ+cmTQTL79LDIz3QkZchDHOLryJrELXu40PhQZcm7oQ5wRDHk16yYWEbLzXqHBC34eaoUac+HefkTXCiIRVoug5EztdFcqVPDl6y55fTUXEnmljn9Yseh5ak5/txQjJu0RkDEfOsZnBWVc5K7yO3BGzmZI6j0MhabjZFLLE7gYCt6YTae3KL3kBCuv8MVUvp1gggVFhXRgUifBtcR1rKx5x+qcBr9cNYYNkDo+2tjKmNIXHNTlMVK0mrMiE90KlDNXLZ/OWanwdlChV/kdGczUKn2upjPNFeKgKF9UfssCgFVImYqapT3ekG6ONJzD9TSaHOq4gcS+JqpD7fMu9hPrzYQTKjWNCiwkDc9spcM5nbUwGbTaxrFi9iozbVgSYTSD+8XzilvlyWMED5Z4ArAT6EZGS4kxNGk9M+/nSP4t7yds4Of8S2S/CcFxUgtytKSiqPMBefTz/fvbzRP0rZr1CTC38x4NaI3oGZ1LursSIGiOcQq7TqD8HxQFNnqvXM9ygkZU/JFmpboH3jUAOLWjiZJkACqV+mGur4KlvhLilDK8lftLzaTPOE02p75yIhaA/IxJH8KrZhLTMpYzTg69/1XmiOooxw1uZ3fsRmzkuOBQL0dUtzje163QJXuPrxvfIbnenaP4hZs4/jd+3R9w7JslH4R4U/2zBtL+bOpsRiOhoEBOXw/HRUhh11BP6z4ByhQi8GjTI7a+gxUGGVpJ5e/F/0c7H3kPbA7XEAvCvWYGX02n0yoVIUxyLRX0pbi2NHDATQeGENkVnv7De0R3DzqHMbVZEsfYFMXWazNx3nGwDHcZttaH3+x2svbPpF9HFvTmW+h8TKBfQoNA9mqFiUli1jqBvuAmaAep87GvF09af2RJChFQ/Q3fYdky+1LNOII9jO6eyWsyAb7/KaZIyQTQnAQG3XyxQ6kbGoZe430qUtrrjPUaJ69ef4iCZwOHsJI4en4xarTTvzLZSL/uTFcYTKMnQ5trfYtZWKmKlt5ub3WYYK9hhFpeNbOw//nTeRuV1EYOEzEj6WIOs8hICDOXoLW5C3syRmdpZ+IwUYMvKayw1tUXg3Cl0CqTRdFbme98IHgX5kZVwjuum0ggtdmHKJDXmWk4m3NaPl3cF6Y55iIywHT8F7/Gqv49t7btIbLbjpHoQYnFXOS+ug7LuOP4VxeFgp8+2sHQ6qroJz3zDtANXGG8wnEKTO+zLeM6f6josl27GaY8J4bfesXT3YJSeHyMwXJvzTtfIm3yCsqAZdF2u5e2RPBy7/iGeD6PkBpMYsYR/fwcQiRChIKWIrUPcWBdTRsg+RUY9vwsyX6j8EYrsTwmkt4Qj0t1N3Kpl5D3/yd1R0ijoByPx3pvTOfPYZz6IdyLBTD7tSli0NoIyOnxP6kDLxpukzRW4zF/NqrFBmE30Rsteia7252yd6INR5CmCxkxGOiOH7vEFLHe9SrDgfwTG30G114E1Ws5kO+jStsAFL4d+htuPRCHzNHffaZORp8jEUW+44xPI1yPnsLFeQnxMKpLCE2le4sfHR8sI0BnCt93vuFS+js2zHbk6WguRp4oYRBuR3bWHphYvTj1tYf/67Xy4MYXy5tPsXh+IsOR6Or6ls85+BZrjrqO1rY2Zl6w4oONDpdxv7lt8QCrdG92+DGYqW7PXwZzWXWFskthLXXkaT3/HsMxRg4ndhYzCmK9pYWj41jFWTIiy30NpsvClqcOahM1vMTzjza7N+7HT70c7/y6PbSzxx56a5yVsdrXmy6p17Pgjx/XOfmqC8/iimcVpM13UekrJjJ+FprQcYdnLiZhrit6FPZydPoaK+F1s6v/K78IU5EKtkXjYxl0HF/ovRnDu+BukdMXwu3iAqFFTqL7VzYIRwZjFbuR0pjMBW4y4/UYdd5vrBDauoXxEMdV9gwkqnkvch8XkN21ki/AbLl1PQjbWgPu6Beg77GBW2iRGDzJGV34MhPgwT7GPtmGNWP54hsqnzSxMFKTd2Zlg1SOIDnXANaKYi+cSqZtQSn3VQ7acVkOlJJ125xiik2IoFlGn3bQb6baJjOu8ifVqMWL/3GGjnxXHz5jy8EUgK6fM4OPXX7hKb0G4ywhJoQJqc16zpvQPV385IOK4hfst03Brq0JebT6Lj9xn9YYodE1moTFLGwV1Ieau3cvO5duYeXYShn9dOTMhm/Ceasp+6DE9biZqPitoqw3BtM8NRWIY5D6YX/OWYKLsQ27E/f+Tj/9BTh3dGzjEqpt92YIcs1qMaLYmuj7G/Pm4hMk1Y4hTauCPrTy1Xxpw7zHgiW0rmdjg0xiIqXAYIwbuoG/jxYlfVpSWxbOu5CRvtY9TWCnIJJkyxAY8OWShh13PNWbWqvOwuIuODeC0vZLChjm0KyahAMTJJaPXoIpB/T9cDQZYG6HLBOFKtAwq6ZZ3RkSnjfgIKVaMziCoaArmkU4oL+yjqTaXseo2xJurM6qkHRGlMvpEnJERHMBSUov0DhGk3T1RbY6hJVYRq4DRLGx7wNxcPXY4LOOwQCgaxsY0rz/H/SZtJuzQQvNaOOgK8bJHBSG5Bnr7ApBVPMRCKQ2unIhn2Cobdut0c/VqPjpG0ymaoMCuR6fR08yko1eaOUVvqflPEV9NQXLeZPO61Ye3W6cjFPCA749syQjTYKq5ACdiMqn8d4vAY3e48vQKuy09MMmzJG5yC855i9AdqcF2+9n0ludj0u7PkmFT8cnYwJZCN24tu0LuqPn0L5PnWrsU/bemsKFzDovThuPm8ovRI+w4vvIz/3SW4bEphUOmq1kQ/JBra/fSVKfN0Rlb8VhtQ9SpdGTvjkUo7B6NZR4MET/Nx3UfUd1yELmn9qySkyIpYgyi2X0oG12gdvcmHMOL2Nf6GztJIRLrRDAc0EVf4AD1RSMJDH5GUvxjth8dwV8pKV5dWcnEKfsRUhzgyoZr+KhPIEfIgYy5P3G8+RFNfVmURGxQUG/BT1aNqLR8tEV7sLL+DS0+KJf04OTdy62Pf2lQXIlI3ghib/XgdESeJoEyyuutWJBrzd+OrUyfmMihKTMYoaPIwx47BCsiMXk+HHWvCu5UbqNp7GncO8O5G/yEgQRbrBcX8zXAnPvPDhF79zhRWYcwVFqJ4+csBDyiGdw/kvSmj5T9OUWe+zDGd1hiMLGJm5+jmT3JktgBcfb2JCDrP507xGOW7s8XVR0WjY/gY8gQ1JXzQDmK9QaPOHztGqfnr6ZKNo4/jTpMU6wj38ucZdbD0Qup5KnMX6R6pNBSdULL9hWSWpP5lvQPNz9PFMRESbqVho/KUeb1+lKgF89Wte8YOs5i9tdo1BJ9GZgkwnDXcJJvC7F8lweVAjcQz01BbLoMU2eeIeiVCqOmLUQkJpkXq1Yz88twSsbd52TKcpSkCqhJOoaTxwQahZsoexRO0qQRnN6Ux6Zdm7iWtZJRhmeIrFzHq8w95FX+ZrZ9O+JWypwW1EZHZzRr+6opk0zn5u85FKRdQxM9/uXroTG0ganDjvE08xmphrf43aJJu8cUZFLeUSeyndd1othmpZD+qZTiSeaMr/2Edqk6LyqqMG4y4Zu7M2ujgsnWXYGEpSzSEumc1bElMNWDVM3zIGJD6ZfbyNar8lTQELW/D0gr7cDctIvCqjY0hilwadwJRu/wQO/uQXaUaWPmOo0g827cFydgaibB8lm7+fS0EQV5BxrshFhb60HZrXRWKqbxaVAZMfdz//fIx/Hj5wKPDlvA97oIpjaGE6/UyW1lVV5aJfJY0I57+ince9PBRN8+5ESvYeiSS7VkLZIi3xkksoYnv6rZsrCNuJRDRAk+ReFfK5FN5nzO0mS5xlNaxIWoshajKSccy5/+DKQGY241GLOIe6TXXeXtz2VIUMGls+t4mHqfoE+v+XF5FBKR5cTnt5FVGUfCdS8ebL/LnOxQHjQl4lmmgIKkJg1ootmVRnj2IHx7rdG3rKPYzorivm8Y2k/k8K+feOQr0aIlR5uLFsIhbxgqVEGvyhlirXQJEb5Kolgvu39XsmPZdlYGWFFunsmxwPsoL36JjpsHk8dpcGqVICPLdNi/9BhnZZUpmD2evfr1TJbLx++bLRvHVqE4VIrii8pUZI1j64OdLGsX5maIFYGa+4lKbUQ/VwaNzlRWKRZRVlRJv9VrGhS2cnLKapR60qlMaeD+s5r/j523iuqC68J9f3+6u7tTQEJARUTFwFbsfFXs7u7u7sB4MVDAQBBUFAQBaQQklJTulPR/br5zecb+Ls45+5yx9zPGHGutOeZY8+6Za435rMUwxzbMmiZwWHQcR2X2o9HwmWULRrBWQ4wktTg6DI1YunIs5T/P4tK3iKiYL8wfWsXjN5tQC6jk61tx1g+MZN0SNz5VdqLpWsJ5hQ349PhQZJhFc0oir44l0fRamXgJUYIX65IftgeRDyL0Fv+md91H+hpNUBb7ydcjiZw4uJ+WgmhEN7vReFiWyRsX8bjbgsJKLRpqxNgt0c2DyiyOevyhJz4FsWl2eMU4o9B7lUJ3b1SV/mW2+GByBS1smf6QSpNPxKnY4aDyjlahKF+ve9KwWUixuRTvrAxplO+lvsKacnk9SrOeYOHhy3fdX/xKlKV94gKmDd6KzcF1NFQMZZanDm0/JKn0L6VKy56nlQXY73/Bt4cdDLebQ/3HfNxe9PG2qxXHw0ZcVf/BQA9xNlx+zdlTDkRnfCbs5QDGOzjTIn+SURrSBEy1of80U+J3HyZuqCnRdVksfNKJqdt14tXGsW2oHge3HKdQU5VMqUS8dRo49P07KSOW8CPjOZ90PzL95HIa8/No2NSHrKYccjZ/6PilwrSZPuz3msnyAzsRZJ2l61425uk1vM3KYYi1N8ftvSn2/kDTzF6UFFWwO2bAoZFO2G44RFtzJxmTC9Az2EBzyndWRWhSavqHorQCJCZmMUN3I1ru93FTO8vpr2mELHpCla+QFaKfeZd1l9JNPiyLTUV7jA/e3WfQGKbHrNbvTLnwgE8Gd5gh5Y2oxzUc5eV42JPNZctagm7fxzDqNNaWvlwZuwAPrxc0vStlaoUY7yxU+fbzCSJGiST3ZfM014SxthmEWS3H5LE5JXFGzPE8y6qoV7RcBtUtkwAAIABJREFUGcTn7kksc46nsEmB9iYXNkycw/pCGaQanrNTpgFHs29kN3/k7PhpFDaGIBb/jQk6huS81+DWgCmccVnP0jOXWD56JqL5YajkNFFZP5vNG7z4GreW/rJ6yL0IxUE3BVM3H4ZnrsFsmDb3n53jR0MzHj+jUS7+xdbwtTy5v4nqd7f5+KqYSQ7meKk94IrpXD4oh6BVBbklv1mi1cSBgrP8WTsA1fPPuD1jHUcv/Xct7f+N/2dw48q9A+cMnUgY2k6PdAUDStU42noEw4qJZLh34t82i419H1hm30Ns8nga5UqYrhROQudTklUa8GwsRK3pGWHNLayx1qakvp3qvG70XJz4JFtBns5XpL/a48ZIano3UzDlPB/O3UHD2A/12rP80OwjKz4cxdZ8XKyXMHP4cBwzoLa3jovTF9KsacGSwEU4/W5lnn0P/o+z6Da7yXqRWyzoOMNQo/58iColp82OTw0CSsxARLYA4xINpO/Jsn7FS5xiirl7LIJiZTtiSp/S86KPmupAst3qcU0fi9Ufdfbm57N5oC27FVNxw5NUd3fmm33lzTF9RrpYcezhbtIGqDB7wChmKr3hc9M8DpVOwMEyglMjV5Ha8I4TYmOILrtE38hIdFKf4GU5hwj7UurERXHuUWRV+WmGiBmxuu0u92MvMXWLGaF3bnDAQZx1x+Po8/xAyrd2Bjk0s7V0O6/r9xMuv5aOsyKEdfYiuWcu/b634mOWwvQ5oLJlB4Yv67lfcxOrpemY7L/OyqQ4PlfqMSBDBknz36jlP2d3jhF375xC+roeu99cIP/GKoS2ugxwVeKr53Tytq3Gde4MimRyUTM0ps9fnsSezxiuOEjH3ERm6Xzl5bleMr7XozQ4BglXI0TNyinxE8V69EoWS0ShIm2F0uiVaEtHYG2xnHgZNbxCkhmk35/Lut1Ma5ShvvsR+ZN3MCj/KFb+TghtW8g3nE/+t9880TXis2o2WRbrmdu3m6c/2inqWELrd1X67U1EydUb0x8hvHDUZJf4LI6XieNm7Un+J2mk/oxmqe4tNtVVYrW+iH+6hxH9Upt42Sr0Z3aSml6KiuIBEnKfci06nI70XLZuuUJc6lcKn3dwZsUppgbsoaGfPVo3a7FYHMCDoHJeDUug2Xw3hzJSCOuqRj48gd2/E5CcvgL1HgliilJZfb+Tc7nBJE7XoeVeKpa2svTlfeKt00q626VxqxEjO+YEHjuvM3RxP3Q9Wpi36zzf1LYjeLCfiifvYfVxXpzZSMi5CIJvLsdrhR8KvWJkRX3EeJADPS2KHGjLYYDWc1pV3jDn9T/MP6jG+ZxuYhQSmDtIh6zfJRxbEk3mgs0ssh6LRvx2LG3UGe9TSqVKHKWf/+FP1wseXDtOw8GDXFimztLGOBysJ9O7dyJnDO+yaVUIHipaSEslEXX1Gae2LeXvqyS0pr3gWupt5hkuwdzsHFESiviqWmN3vAiNsXIM02miOriIGZk2fJA349yjZoIOKqLdu56itkhyC3WYpvgb5+J3NNkfpnn5WsR9zbG//QTbI51UtEfg/F2HTzWHmOIdw+2QV8hoTSC1K4kA0y88b1PAwHE0iWWylGSeoLRLDTfBZ+wSlPArCueQpgZffY4wRkGa5T3fmVMowgwLbwb4GCLX2I2Xhz/P5ZM5YyGNv44eftVOBH+5xZpHU9CM6yS6K4zlh7ey6oQFOu4m3IjYg5r4PgrSfuF0+TkP/0vZ1f8nvtpVUNJCtiaHDjtN8nPEmPsunut1KRS9noOJ7H7mPbmKl5gt2nHqFH9YT/QjW8QKp7Lp213UVVtZ4hqFmvx7pD9vRrjgMPK5R2koVueXfDO7pmnRM0VIQ5Qcj8OFNL5bwY+aCN7eOE/pisnstNImaf9jgs6cRCgUIhQ2kSMUclUmCsWnwez8/YFHEx/y4Fcw7hc2I9w9m8LtTZQKBUjaBDOq4ScLfwrpcGtGqvESugr9MDC4zlfNNpzzo5mrX0+naALVGQdZNewk42+H4nY5gAdXfxGen0mRyEQiOucQueMaa7deZPzGDQxTa2H7aHkaUpax5uwGTPvvQc/tO1pv5qATZIGi2V1KEg4w6VgcgrdxNIfJM37VfIyNlzMmeh6eBV44yFdyYGICt7f9xFsnlh7PJJTculBQOkp4/VSqDfU5XfEFv4/fcPSU4d7RHQSkb2DHmTnIG5sg8S2Ly4M/YNnZjcRoTVb45xKwXg/rhvH0XTnByBc9zAn1Y8f22Yxz0OEf74Ps1XvA8WdCJI1kmGVcx8iQDM4bevJjuxR7S/zYfi+MlycTCPqVxNKSODbPeMnsFQLejhNl39todnU/4+2aeUgb+NP3+DHt1kV4by8mR2YWNhnWZPr2Y9cSNaJL5NH47s3E947I2XdwI1mCe+2dvEkIYZK1J/XyHwj75c6vgbdxO1PDztBFBCuYYtESTmPkL2o0j/FBWRGnbyMQ/B3OzXUK+KSO4XzEHAxbDfie8BVDhQTMTWoJfyqOTOIkOOiMz9p6lP+p4sSscWi+UMXIRciuWB1mL/iX4VODeX/xH0ZrprJ5ZCJdX+o4u+81pgE95PSlM0GxhJ3z1PkVKU+I/HiiN8ow1yyZCXPTuBcoR4DnRyaMeMa594eInL6QbZeSeTMxmvxQdS407mCOjxtGnauZ0/GHuQ/jCRnpyRrvKZgt1mJk3Ehy301ko9pZ7g/rAM8h+B6rxeiPKp/3VfHWSotcvwqOZHqysOIRDx6dwS2jjMHuY5HZYo5EfB/2FfosfiFHxoWNPDMLY/8KDdb0u4XsuT9MW7ORnc//RbjkOXMaZ5CoupcB7hmsn11H9hYvojyUCAw9zsa/i1B+cpDab/0RtNWSEHqKIYMiafm7hC2TZ3LzuSnv6ldT8O035q0WuH3bwOKao7y6GcPHooucCHpDUM9hEhpdMMnOYu6XBF4JtPG55s0Z969odwygSfUSA6aWENv6ivNV49GRvIameBaSM7oZNes0IeelSD88lhuzXrJ1yVCUT+dwvn47uSYPaOvzo0ZDiDBzJSpFsQRbjSM4LxFJRWksKuFaxR/iJY+x4dwG/Pqa+aJryl6Nlfwc1R9nWS/Gy9qx4JwWvlE7WOqUj8eVIpx9LzH9+GL8pz7lSPdnqmaYkS8RyZOqUBYtisKx8yKzbFSoSI0kUH8+T7+bsEjgR2N6Gto2U9kSYEfd6WPc6y/g2vyHXJiiwUFpGYYMtSO49zvD2heyOEQU200+aA+V/59N1f/LQ0NCQKrFb5LUftJeZIFDrBzLCisw7MkiyrQbLfVWehKziBG2Y1idy7yv30jvs8Rf+Rv9fn7GxrUI9whZfOyVuHstiQ4tN4YaatCW8p0cl04291mS/EmbX6WH6VpsSErwJ5ZpzyFKPBNxTUsQKjCmVB0Dtf5YD4nmmvQAtB6WUF50hjF6b/j3Ry6LbCbwXuoAUQ4NVE7ajlPWHk6KG1BrvJF3vW2IrvZifWg6k6oVWO7Vx+XmS/zpX0i1WxWvukSpknjFyXcT8VE2ZY3HClY0DmHXgA085wFBP3LIKXzLggH9yO0nSXOLNfFmAzBtr2b9iQjEbepQVc5EpRKGDNhGTEEmUaVmjLtwC7mrtliWO/Ls2k1ERCcw98NZevW9+LDOhDFm0swQ/YaG8A/eDsfJLLEgRt2N5YrKtOmZM2V9GfoTlbm77z2TFh7GyiKDvNLJ+E0fTcY9cT4MfYz+bU/KYzcgfGrEdPUKtApFMNSVY9ikO2w4vBSp2y0Mfb0bniSSH/ScR1+0+TXCg6iy85T3FDFKayDrSmtZ0yeOz4qDiJRmYa8v4MUYX+LdxchOkWCfZSglFXKYNdnQv9ODepMueqWfYGaxlSzBAg5lfIABHSzBloHT19KoK0JbSDUm+XIsQoWn3y7yUXUeu8q/Y6bTyIH7RniZLkGtdC/jftVwL28YEkEvkDJQ5F9nY9olFPjU2cwwv2Y6ugzprQ8nY7oll2vqWVLoSUXtc34pD2fD0eX8cklBdk8pPQ9dMO9VIFVGAeVCTbIcuzFKySd/jxy2Q2bwz1U7JkfOR7cvHy+lI2SUSiNm/4xZ8in4FwRQrrQIU+NG9FVK2V5jwqRpHzEYsocVmr20CgNY9mAHn++MpSJRCZeNIiiHVqPhYMOP1fUM+NKKmJkCtj5t5A2WQMnOk39+FaAspo1VQRvyaxSg8ioub76jPd2Svbev0FtSir3EC4apaTCkVsCTZxEEPbwLbi607W/HSNUJT6W3mFjOxmxeIrmy+syznUlrzVMmnzhCSEEGpqKSPLdzR7JlC1lDh3OkZz6npoxgeeJpMlTvk/4lif4LLPgbpU1MdiCfny6AtJssOmuOboQnOmo1vP9ynkzJXJZZfcfVZSQjx2gjEAi4FPkb9qZwviAMqXZvzLfKonYZNKMzKNJxpFEkmSlyIGwvo6qvhZ5/vZEwWcVj/9scDzXDR0aUWwGhjOruYbWyEzmyXygxbyOyagkzJAQoc43yRaZIqotj2V+alt8tTPypwve2KgbmJWBqk0677Fa2eA+k+O8EnNs/0DNTCo+cBO7k6PFq/3FcXEyZpj2VbU8McZwsjVRHHM+lx1CWrI3haBMkR6lhLG7F0oaF7An9ioiiPWVlNWw7E8mS8ctorJJAueAPt+R7qG94R1Hkb+a3yuEhksSMwA3slbxG8q9+2AjSaRdPhqJ7rJ2bg1RhA6MXTiYh5wBSk6aR9Sviv+bS/090Prac234gYgp8/mDC4g4JkqRleDBkAL175xMYdghFI1H6YrOpth7Cr94mBnoYEVL3DZNblTSduICr7grEzFuoqy0nK8mU7LYk7Lzq0dI34rqYB3HPjVG0CGXYtT2o7ZrHBr+dTFi4kK6yWmzm7qXYzBRVsXySWlXRbVRGKqAIb5Ug3Ctl2VOwG6Vl0izuuUC/Qgsem4kTsm01NtKSBGRosO7mL15K2ND98C/NPstIbU6havkzNnZB6t1FfMz9Srv9IP7mGhP1YyhLDp9hzo597O7dxAGNxbRLWLPr1wCKXWMRCAzwaU/m3U1x/tUdhY7UIc5b/GaFxkMKfsSjapSJQYMVvn0VjP3TQ4fySlbqOzHqmA79RmoxqC2FhipJHvhdIysxDMe00yjJ7cR6mhuyfZLEprbg/vsla9QPEdnoinKNG09VYuhqmsKhAfrkiP5DZYECXy5MQn5jJvMTJTh5+zfj6+ajIhRw2K0d4/poBvY58PTGcR7NN8F28Qp2XqojWSqQWc+vskvwmo2bJvNo+DZsj90l8GkDE+MeEDFkIrpAy9kXqAxS4+jRCQzv7WTXtS1UhgpoMnHDf5Qmk9qrufJhBUKT9agE19Ot4shvcyfsty9GZpwFTxpV8Hp6j4nhS3k4o5Bb/0rSvU2K+Ys8kcvow9MlhQm5DuwYrsZ0N2nS+s1imWEFEg21BPXoYuSagOPhSzzu3IiOXQy5PT3kyCpwQLmEv31nKQqLpJ/rMKqav5JcVs60Q0uIl9zJ621+5N2O5dOSGfgnPcBF2oLj/17lj8sflGQcmW+xiCm6A8hXtSXguj8qVoOJCN+Dl20fZcNK+OWrQm+kGssTu5lQ1EPq+36ku1TgsvU1P3rhmPxDCnXs0JIbg7hDK4YRZ9AO+IHwWg2djtm4CyfztlJIblEP/jRwSviXky2ZNEeZsXGBHAEnf6OZ7sPgxirOb/xKxZFVfM8Jx6pGFndrGXK9Qwncd44nmx6zZuBEAm6dpMloBrsvliPc5Y6iRRTDSx5z3cEGq7SvDGl/yPUvu7gbr4zixGusfbOXFcriFHzNx6o8nVKJNFxtz/AleiujaquwGFyH2JcUtPWNsBMexUJrLKFi41GaqIyedSK5kZcZJGVA2rjHMCiOdSmiDBC7Q3SaKEP6LWbRtFcM6xvJhfrBWOlep0AwnkUiaoiGnmGh7GZq39Vwcr0bzX/gzo6DXAg/zYXOjyyXVqH53W56WoPYKXTgc60lQ0QPIjOlF1EjfV6Ov8zN4oeI/Q3EQMOZL4I6rM1mEyvzkR2T/7JQ4ihDrTy5MEiRSJ2d+DRWkxWkgq9OGYO1vTD7+hy9miUssX9Oy5j5XD4Vy5s51nSmaHO73Af9rF6GS8qgnimNmIMasoqilOu10lz+i/55TSywfEm/5kKk/rQxWakYNoxivp8KmZF9kCpKWtJAtr/9gYhwFsMW+PPnsxP6EZGki0ihGbYECVF1Coq0KZx1kQ/vcjly7393Pv5n4sTR2we6TVfg6tVLR3UW6X3GHI15iszOU/ikZzGwJRVRkSxSe9Xwkx3Nh9JcSk8rc9bvAW57B5Ee94YrPgvJk+hgaHk1b+zSaa2zZqpiB0fS1DD7XYuoxBx6ttjw8+czpFSGc/neB+56+7G7LYAp7Q2UWA9lgKwZN36U0NWiQd+fZ3zVkmSaySLmxbwipsUcxb4HtCZb4aAXQoKUOX/TGtFqi6PXVoK4vFhURebz+4kSf45FoxO1jXfuzhxr7ON21QkS1Nz57DqA/snO/DrxkVDd8WRpdDHsWyjvbf4wKX8MFgPEyXsWQb7TcKK/v0ZZYwwjxNUwHFzG3oREIi/tpCMyg9Z2NRITqwgf1ssMORl6j72hL6GSa4PkeHHiEntPpOJ/MJCeTDmaHH5wuKaYlggnxLyV0Xmzl6O2wTiG5zJddBrW8y7w6tBxlp85jf+lTIpqptPVZYabdT9Gtubxccw+IqMTEDYrIdr7mrYqRXQehZA6xYodV06zSb0Rg7Zh7AgRUjfRmec3dfmedhxp5w+Mb1zAk0MzGVS4HhnXaP6qBDNrihc3Ap4huVGB6EfSnBvUzvOdD3m18A23RRNojxbHsvw1tYe9kJJzQU/1BYXfJqB4w5ST4vFM+TCXlO5g8g5LMXO8OhLLRFhoYM2Cu+fYZKdDesFzbslPwOPaU84MmsU71Vz6Fdvyt78nl21VONJ9ik2JN9DS3MSMjnama+kSU1hDQGsXP//AGnNZ7qv1MOxTGel9esgWg131LNIKH/FXQxPZf2wZOnoVn6buIVChlTqV4dwvdUUpbQyOkqNI83mH9NVeYr+103OgmgHWTjz1q2bDgalIxYWRs9cfhbH9kFplTWqZPzOtj+JkcwPFW/upGuOPqbgLyaWirJi5B8HBVOzII08+ltNK6lSWZnFb0QgTWWk+GTfyIFqLG+2n8d84iLQ7VbS4ZbHKewNbjvnxoXg4vgdO8eLjYxw/NZItJc7ERZvYaWuC3NoF7Li1nkt+DpQ8vUL1wq0sdLbm1Na9dAc9Zp9mERuaHRk1XIMIyWouKm5m49mrmA//SVaBMpqS2vRIv6FkeBk5L6NY455Jx89bzHbaQND3S2h5/WS8lz8VlhOoTV9Abv+5DGs9x8C5I5C+dJwgrRSmf4lH8rguVqf28ThlB6/drjPd4hUWG/7lr34dTd1RxMboEzzLmsvTRGiJN+fc1QTuXLjDuMMPuBpvTtcQd7KNa9kkp8v2sbcYYTmXZJ0UIjqDkNgjRoXSTQbUPeKHtTt6tarMsDpH/Q9lJNUnEOxgRkT+FQ7k32eKjTFrkqcj8sWJsO1t9Ct8z4eQSExsjvExVgzxMdKkxgoxE69lV6EPT8ZNJipcjAjROMaPOku7SAW+oumUjnMjL86TufbtyMjHkRBkzbeKMI6vX8arMiH+mp/5beLCjzQtjv8zgWVJwyhvzGB15kqUR7XhkSrOi6QYvvyMxElJk+kO1jzJT2W+01xu3jv7/yPZ1bZTB5afWYtRcTm7TPYQbd6L4rXr7P5cwOhNNixQb2bT8hOEtH9BbKwp6SElWJmbopR7FauFN1kvpkDK22o+hT+j0yyWgUriFJy4i36pEqarVzN8tBYd/yageX0WPiGXcL+7l5sLD1Lic4o0kWccOevO5qfm6N7t4e7NKBJMkojq+oBUM2SXC0jNkOVOaAz3I/LYZiBD7zAXvGeuZeySuVw8MIOm/goY3lnM7KALJHf7Me/6H8ZvcuXfHjVM5r7FboYHKkYSHJvRn5vBMzEKqMSltJkMmQRsCibyakkRvc2DGRGxn6fWx7mirk29biuDh7zla943UnhM0eYFOGSN4bvFOHZGZHNzxE5kDL5QI6xAfYMjik/liTH7B50Ru8iNScRJWglRNrM614+oTXdZ6fuNGk11sgapEtW9kpOeFtin7eZtxwJGbZ3EwH3zWbhjFPp5czB/FEazeCHHsOTro5somZ7iVsdPTuV34eU8jqiBA/mx35WNVm84YXae9S7miDkF8LsphdGjT1EVO47C+Se5fquSv88kqNFdg8y86/wr8Y7qkrG8PL6bkogeLH+M5JBqPmZiFoz/lYZHri2XrRRJlR2Cx+iZfHo8A6ORY2l8LiTu7Cxi49p49SuUeWKpJLvNprHciiu3+3F3WT2tMY/I3hrKcfmbFETk8Lz/DC6JNHJxzgDWKLtz520iI0dk8SdmHA16k2k2syGoO5r3M+SY82csx91lCL31hVgfWT40GZJU78qKsQcQKmbwNUOR8sxWrH0nI2lgRYzHTeblevL+iB3JV9/TqrWOEl0tplwZS/z9T+RnjuFtxRhunTEjWySMTRmmxB025thJNeafPYDxvbs0ztWlV38oejkuPJM+QqhIFjr543nX3YdGsxnC+P0sVbRlxxsNVHVvc35EGaucfVlYdYJw1Tssl5IgxGQAQ6Q/kBVThOmXyWjXSmOg0sIMIzF+9qaha6TMDa9x/IyV4H30Z2rMpbDXWcrryCxcLX0ZPMoMO0MTpO6lo9B/KoU5qeT9FsPa1pULevNJk1WEJfvpPHefkL23WBx1gRhvRSY0+rLVYz7L34gQOlKBkpxkZCNGoWGWSFVuDVQXI3/RFLckKcrDUmkSWUen1SasZJ1ob//JswJdGlsqiWuYTWbwF7ziowm7bsmtB/+wYPZrpH9J0y0ym6SyLDQSG9n3z3Q2mnrz+qQNcf7NeOy6y5mwLDSaw4m3+cvaa5nsSjPG/ZYqglWabBrnxZWPezFUdkTcRZaukig+CY15Gt3BLonpaEglclhVhxs9vrQJTyOdWM7nozbYnZzCxpWTUdIOprV8O/HtYZi16dPQt5K0J2GEHlZguVsJ90q1sL3niW7DadpM7DH9vpW8LiGdiQ28VOjE4/p8qooaqbXMxflZABaew1lp+I6L+Vb8+HOZG/FvkDY/wI26IgY7BLDIdDt8ERBu2ISedy+/PMRR9p5ISddbiqapkP63g/bRe1k4exBQ9b8vH/8TcXbb8QN3zaeQJbcFgcRtcvNTUTFyx55g4gXylIabM8hVAVXZ7xxPK0R6oAu/uYFmhRLxA0sYpL6I/fkVTCuIIqnDC9kpyjgH5/D3XjIqB1w4NugvC4a4oZ1kS1PXbiZbmtKbVsKuanXezs5AvtaAyDZTvFtyaFCuIrZNFo3cLL50tuEhmsJTMVv2Z3XRZPKd7OpJ1A/pzyCFQMSyxzFr7iLuHjjD1aAskoPseOQQgPE/IRyP9kLFUAe1V9GE385jS4soZu2GLO5YxGnJGE4UKiKv5M6I8ouUrLNh8bXh+CpKclFZg3LJEqIdv/EpxYelWxZyccoUBh88heTDCAZrSiCseEzywH2IdjZTTjFKXqUo00fTllOc33CAa6MHcqPgFnUVDSyQliYn8QHelYvZ0O8JPrXVfDOX4nyLEse9Ovh01x1rLzeintfTo9+FfEII+sZ1iPZEsm7BvwxdeoYw3y7ihH/xrFbihbgyW9XGcjAnm/tGSzmn/omnq1KZo7kf99P7iA4wQ6T0ATJDfpF6u5pOD2kkSsT44m/C0nkhRBZM46vuRV6PvYXVGlWWbmlkxZShiGYGUK21EuW+N/wuESI2ayd9AetIqsukU+E3A03/IbL8LCN6BaQPNaZcLZllu9RpzBIFNQcc6poIYBsjde8iGS3DHRcv/B/JE7hGh6r6QD61FDBNrYffLwWEXszFasxAlkrCXD0TrueKEjxZlIqSULK7jZgo0Ue4nCszWl7xQfUbebEj2KS3kKSk/ngn7mC4hzpmE2t59iIHhc46pt4/R135YB4XRjJn3AB035UjtWAnefnb+P1EjslybWg5xvNH9y+PFdUI9p3Io8VvkFzjyoAiMyJUCxhY+Jy/ChWEyUvjZjSey6Gvcbw4mA9GfvQ2PUHffj+PC09xRGEWP3U6eViugVVzFj46zuj4ipFUkU9ergbrnCdxI/EgSoMGYK8rxpiGfiQWZBAj9QlpPQV+JoTR2NXLgy1bQVKH6/svYFqhSKZ4LKVfjFCZP5u0zHvMHjYNI50xWGadZ8wgN8aM8+UpqUwU24+YRCh229t4HaHI6LRMDA2dGV3gyt42Sd7U7Udm2lYcatpQHDkSu0U9POu4jcLwbqY0/qHSYDzhYWWYqtkjt6KelEMRTNr4jLj9C1gjocI3u6Nkls/E3GIc2YFZeA7SQFChjGh/W0REf6MqaYROQD7GeQdQcLbnnOFirprncOnOfcT7rFH+LUW9tB69k30ZMz6FiFQzljjbE/RPOoOfLGKxRy5L/6aQKzWEOd25uJs78lWtkKs7Q1m+fCQdg1RR/GOIQW4aIkb6FBWZM3k0uMlPoEpeh3Mjn6DnH0rCzLX8W7mUzKIqav42o3nRmh+XXRjb8gE/PX1e2MylMe0yviu28TliMypVlQgN9yHf4I/9V10O1ZvSryuAXrsLDKhSJEL9DaPkhdSXS7DfSRUTXXOajkZi7TkG8dEfeXgp9v8e2ZVAINAXCASfBALBD4FAkC0QCNb/x68iEAjeCwSCgv+Myv/xCwQCwSWBQPBTIBBkCgQCp/9RDj3JXsZuvYph1V0ctDahJ1GB36uZBF5axI/nIsR1rWVn/FlMu9XoiixjxfJduIbBnoZwlNfGE2Wsw0EvN7pTyik6cwfDCZYETBWyZ8FBKvc7obRCwMkB4vycooXQzRztM6tYcXk91aG7mV7G2Pv6AAAgAElEQVTewGzdS/yjOIm+NG86J9fR8f0tf945sLuoHwN1hmOrVMA6xZ903jBkxOs8VN4Xc+nWK9xHbWFTQgEBgWv4GehO2RIXhCecWX/GFJWQHnwXrUG95A2S56rQPlJI2NLzNJ2bxzJHEfxUrjNR9QybH/1FP/sPUReLmS7hjci3Gkym2zK7rZMnl6Ox9zjLrSMxBAS+I9Kxksq859idO4GjWicD1W3RsNJmwbUlTJ1hS227F/lJhlgfrOGg3Bt6J+2i3CmTizXTiXmWzuAyLybrDaFptSINzclonbHA3LKQL/HR9BY2IXHiGJM0jWm8mE6t9Swe7pnA9M0O1LY9wrzWjVFeFli9DGfwfX/CHbdz3fQt9i/NOKmyjv1lJ7ly6gvW587yU+cpxdObsLZagup4DX4sTqdn1AZylpURM7CWJIcpJAu/kWrQiN2NcDIHnqQ+xIY3EoHY511gdfcntC/P59ih86RGf0E2+wQ2DrosH5TAC+/RNJzTRbjvEyEztRFvbSF5SSdv1UcidBBg1feZOcvCONgQy5gvxRxYsJ76xgRC/92MhelWlt2bh/bKFF6MGEL6WDEa4jXhli/dA06zfvQ4nsgu5NoYff48Wc6htJP8uWmMuOYA/M4vQz+pndpcRbTfHiV58Hec1zxl6aw1TFF9j1F5Gn7mtqx5/pbXfk3UT6pgsd1e6g93cqBMCWHuVkQOruNjlC36FZksj3qI48VydKO2MuSzB3/f9UfJJIE3Rt1MOD2fuj0Z7NwWzfpJl3k5cTChiXU8/u2EcOUVcvvO4K51gpWiYdxVscFP1pLMxx0c9M0hxsyCKG97xoikkFNuj+f5egzEk3AUOlNY9pN689cMDE0nVH0O5qr9yA08QINHDqVV75Go7CTU05tGW2/uv/7Dhc5R7ApLQnS8DdS+YKX4QkYFmnI18Cn7mmJ4WXqc2b9e8032Dqae6/A2b+KgrCMrVm7E74AN9bp1aNmpUNPyjuN/N2DzNYPWxKM0nh1GcU8sQY6+zA/TYLPFV8wfrUBX+AhfrQnMb7DCW7SG4VIz2NgdQknbbnJSl7L7azl6H1rRl5nM4n5pBCiu5Ve+kLNbx/Fe/yPOU8+iJ1aGfruAdu0P2OcfpdbgLg3VW1BTdWKdXxeffn9D1+gmQrFmIl+f40XMUCaVvsF81nWyQ34zqtOCHXKL2MEdRDlD/iw3YrUVSZ3pytIDxfS46bDEpZpTU0Yz69Fmvn9YT8CClygqdDH9kAOF8xthxBfsPNsYXqjK7d+yyFspMBp3lvepUR6wi86o6YhsTeSpWjdF6VN4H3mdAVPkiDQvIq+4ljmDTdCf5cGi0x8Q/bWBq+rRSE44hP+Zjf/F8fh/Xfy/UasUNf/y7EkiD1yPECrzgbl693DS34WcYzvygnwsNKToELEn3UCZyM3bMXj6gIHKSyivcUBTyYlLxz+xQVeJdN028goqcQ/qY6p3G+HX5xObmMG98B4W5MgiVN9D8wdjEvduwGXhag6dKmHq+mVERuRgJ+FD0Ewn9FXL2Ff9AwPT9UxznESsej7byrM5NmwCLQ2F+LkLac4S4WyFC5qf53Jt/mgcct4ybdheZptXceLCSrR96wgKrKc1OZqepn783bQF37eB9F8TiVfACB5V7uXOzGxmjBvFodK71L56QEzeKtZ5z8B1tjIzWjTY1zODJTaLKLWVQeaPkJLP/hTLORFu10atgyWHpiuh9LeJpjupNIX1MLfUh8Id5vgu3srxi6M55aNNUkIgbdYfeDx9H/NHx7BfYxRpejNwDC2FUxYE37yItpcFD97n8cX1N0sbt+I/pQEzi5fod0yjoska/YEjeSRU4Vy7JM52WgTqmzKpNRa+1zIxw5p/2q8y6mEf129/4VlyFipyh1GxWoOZZxPf/i1Fx9aVPYfjWDO7j5bfl6g+ewh3fynGuY6Bgnp8dg9GNT6DBwf7k14agkapJiMGuiH/cR/JLjtpfd3IOIlJvPDw4KPFMKpsCzA4mcSQy8c4vvcJIqEiPP72ntH6h0jrf55l/lm4ndBibFknZzeOJDDnJtFy5vTZLeFPgjlGg2w5st2YQXsOsuBtLU9jq1mul0H6hwZ6RRzR1NKiSleT8Do3DkqH4/zHlrD1ZgwIssJ9bxUdTiXUPhRQGAL9xHbQPt2KZYtciY7Zwwg7LYJ3FTCp9QJv8mT5K2/Fo7P+tKn+ZOH1JARS+ZxRieFg3GLMjd2Qi7FDcuYqqqKbqVIsI37UFqr2lGIoJcFw9+fs8o/kqLsyllWuNNCBq8pcbshuw2vxJgYXhSLeIM4nM0M+9y3FUUUUPaMftJokUa0zkL4uJ4xz3hGZsZMZcYM4Nz6PwL5k1g/solLLicmOVjTX5WN0aRPZ1x5RP+kwjQtdkby2ibUu11n5sZMVBw9TpTwZwYZjnDgykfWPlQlIr2Pr9t2kRNeje8yEWx3z+NP5AotnTljTRM/0aCq8fCmMLCHzRTm3vVo5bSeFXlkHHgEfWTByAcIJYmjbvcAkqoo9V/9wIVmVqNc5XL3lg6vkfb4K8xmpVoTdUTGGv1Qi1NGNeWF3GdkVx6daGd60teM4rI+WPC1inr2jX2wpyaVzGOjujuwkTfYaL2Jn8QXuatcSnl5ITXoq3139qbEVcFhsNePjV+MrJaRTWYuikk3ImE5h2b2LJKSH8fqZCyWJiXTaTOP9V3cMR1RBpREHfm7jp0AZwbbvZL1dyZGrS/m8OpO+2ExEk2IwG/AWn889ZKldoCAuhAvHy9g7sx+F6aMx/DGK3IIuasQjqQ/+jozTWw54tdJeARZ7fHHOGMHMtFLUl/ajn0cjG1V/cP6FGHfMzTjrNZf4x83/NV//N28+eoHNQqHQGnAHVgsEAhtgB/BRKBSaAx//swbwAcz/Y8uA6/+jBHKmlihb3aKu1IfAz4WUVUQgfiINh0maDKvLxlH9BxKVzRyK/Mo5w2IMdgqQ1drH4+wUIjyNyQspI770LXP32sATb2ZcGsawRB1WB21kcOMINs6+zrjgdBZHruF5xEyuxTtw6fMc1K36E7jSkJ37DpHVl8+1A0m09i2gWF2VersWpJv2oNcZz2WxqZAmxpkWa8abWJNlpUT5vS3ca2hg5J0HqA505MLTYlbcTGBlwjXmHBrMnuJ77JwgwPXLIS6Wria1fwfEmPFwWinVgVO5U2DO4ktDEEjtRy58BOGFuohrCokMmMr5jUvo3HcY9xcjSB2+ntsdaQQr92F3ZS4dt2az0kuPxAs5WC9exqrDYXx1O8GqsFHcrr/GqQpV1H1307EyGtmUPFZXnmL5tsNcbH7FlnNB6AU5Ema8liX/5PF3gjMHTm1l1IBaFrrqsXJ1GMMLjDggdoB/rTcySMyZzDIPPMZc5ZzkboRF0G+iGzb6eqhPPU+s4SReJhWw/WITLoc8iBk9in7D6jDpsCG9vxf+3etIfeKB9YqdqP6UY7BVHV0Lp2J/bQEvg+9hs0CBrazg+tWfZMTnkRsazMNRZ2iWNearuR+ft99hfeUQ+hR1ad8eSMXbg0yzqaSnSw3iHRhedpigL3GcKlfFJ9iaUfsPo+8uQHO2EqKLOuhoU+PAw3Nc+F5I8tsojrmK8g0YXiCGTaQ9/QfvYfHvIoRPfyDsTuDC0ZPM2rOGqMRHSHGJqEP9OPbIg8+jFPm2wxbRY+O4k2CDnGkMPypiESYISb+1i/OXD7Hn1DNuX4ljc9QHLqc/5plDMOWmyzhobMcgeUtcBQLeza7m1o9fpAcm4lElwNheBkH0SFYpvMHtYhsmGgEEi3hTfnwWXxJCGRJ9j3L54wx5PBO5fzvJf7WMNSHLaFm/hbM+7+ivswAvj00IVv7gVOBdKm2Daej5zvcmKyZ4b8TIIJmjrcnMr3Hi8S5zitz/5XZkOGrhw8h2v4K9+Cw8T8/mltRILmi6obpuHQuvviBq+VgUlj9DSyuCnZOaOVQbRv/eAsJls3ia7oPrcgPKzvjw3qKQylvjWbyqnUij28wvdmbzxW9sjDiCdIoFBuOnMHrzGq5knGSJ+V+Cxhazb5s6TTcWs6lRwLibusjndZMwUoN/jrmiFWTDooJ72Onbktd7mk2rZLhxq5bW3+MYcfgbcjKe2He2kFNijVNdF6tjZ7OzURKH6gByr1fwLmgmFtIVnK3II0H8BKtlrfh7yZ9ZFfMIke9H/NsgrEJPYFKXCv26Sbdz5YvYLdoHaSMi+gvjiC5swrTZ3rWaozKt+NQ08/X4KUwaLrNOsxHTIaLIh3xnVc4Yts79l2OrH6PvHo0lV7m99DWf/t1Kgu51jiQcpBE/GrVKGC/6CKXoEmpvyqG4xIRJE2MQjDfAasRXAjedJVDCkZWHTnBE9jjdSxZi6uRI3GsBi/Y3cl9HBfXiEUS2GTIjMI8Yk/7/NbH/L4r/x2tV918xlPI30P/yZz5l2TCsW8igzjdcM5Vm97wkDA3749CnRKu8Or4f3+CpZEPY0yeMsGvkfnQzIyfKIZYqi1zia6Y+mozvYEe2Z/ei2JCApFANt9IO1vRE4R7dygbLI/St/8yXNz95k9/NRbUQ7n00wNNcBVGlUIyTTFFoNkVMvYB8UydSD6tRJ69F2e9orBd6Ef+pgj09Jvi1NrNxTixq91dRlP8Vl37tBEcNZrT5eB52/qVbW5PNid9RtDPil7gxeQM1ufyugnGbYtG985mIyGrGX/FC/VcGNvmnWdbwhvgfwUzgL8m9XejElnOpT5MXOiux5yfOhpYMstWnrEhIUWoF3fv3kVH5AUNdLfwDX3FBeR+z7edQ/aaGU0nr0O6vytatcZTdP0B7jg5lwxtYee8u9V2XMZRXwN8nkmCVRhr0BdhN1SbXMA437Qu09rXgOdcGobwOo/un8MB3JJdrH3BeSRxhLQjkmlh3vBeBligznZ24nVBC0fQjrJs0Fzxqic1RxlJvLP7V99lzo5ZAAEsDwhousnxlP4Zs3oXBBA9OXNBm/uU49LNzKTPZifm6W0Sk9lFuIoaK0h4q7ebz9nUSMb6bEIQd4Tm+pKjmoOIoSbOJOtWDxFEQ88Fgwl+cD0oxRLgNzQkn0YppYa/rZSwrkgi4EkWCvQLB87WQzlchOeUYxT6XcBXxYhuwYIgqN+Y2IT5/FR8PZXP+4GJ0TO3pHz4c8zg93O0PMmnCFpzXL8bk9RV8jn0gNCaRqi3D8FKuRVNPCoNyKVr1puCx8DaqeX9xtbxMbXch14+e5M3ZWHYG3eL1uwom9E4h5MkghtS10J19mpZBSxjiNAqtJ3so695D12B1Xp/Jx0rcjXehz4krtuDQoyk0b4/F7NxxSkyyERbUcWrwIjIPXuBzayURYt789sxleepxvhUGo2Sgg3yQBHzfiPLoJtqaU7FRGozGyJ0MLQlnxLMJTP7ZhUFxDvXeSqirjaE+rRxFJw+q84OYV/cFUd9hXL/zhhfV5UiuNEHPUI2Ic9doCJ3CkIg9XD5bz4W9k7m5/xka24ZTFVfMwQAd3EQlSXj4lW+t4awysKJMXJXizGfIWnmQ4TKesTrBbGjYwpfc1wxXs+N95VPOF1ryIGoj0rRyLeQcSW8H8an3Gk49+ykZUM0mLVPOSKlQeu0ON6+sR9NgAjfScnCwP09Iry01lUJ+ttQxuiSaPb/d+FwbxfAB0sR2PqXh4i6C6v9iMnoNw4c+Y4t0M091janulWOURjYxrbr8kf1IauslZGOuUvOijh/Cv0zRmoWqvjNpHUL6ZFT5lT2RjI5xzDez4ZThcxIv23Eh+Bwaoxz45dfNmMGT+XbSi3SpdIbLOqNcaEWw1gqWjrTmx4992AwZzKWEz6wpMeJ7aRkzx01H2TCQlraHVIzYQNERGPaPC7N/9tFyv4TA2J9ccrUiviABc2ETs0r3sedx/H9N1gKhUPhfBwMIBIJXwJX/mJdQKKwUCATawGehUGgpEAhu/mf+5D/xef9n3P/lnjICIb/SmXYhhjl5ujgqifBUrAIvo2iyxMT5mVXOCYlhbHdJxvV2Ee16i/h1Rh3VFy9RkbfiZ7w2B7YFgeNmPLdNoKLSiKlrttFs283NhPU83QTZNpAjk8Hgwdm8bZvDl09VWAr3kPXan6ED17B1rguF73eT3SRCebs0+cm5SJppMvCnGTlOsZR5HsY8opraiWPo7izm2IU1TCkPJUzwixP3QvhktYHnj27xZPp8fjTdwSNXgw1NM6D+KhfMloLzUSZ9ksXg11Ic45YAAqbjReAmP9LGmrL8UCfOXqk4zPLm4dK1xMuZ4+LbxaDGLHYXv+X0o93ktyXQRTHvfndAD9j6TWSZsgGLzKehoPyR8tElPL4ymOXRUpTM72LyxeWcuzef878KOKk9GTeZNk7J3kSkvz/axzS4ErSbS/obeZ26B+fdk5hy7yRaI4Op1BvNxLxtdJvXs0fzLplfTjPm4QRMdluhbHmATuNeuC1HqYcWU8VlmaT4jLe+P/B6roxLhy+vJ7gh3uLIwhtRLIq4j2eagIZ9Z1lcW0nqs8nk/5FhzJ96ZCcuY9yrk5S8vkZ4zzhuPj3H2vJe6sbY8CfyPS02buho69DauZCUzDxGh1sy5wVMme6HIypoLejBy1mROEfIvpaOl+UszO4XsbEE1vks4L3Dcv6uWccR3UZa/D7R7243reuT+PwoC5HLltz43MCbWz+Z29+LqX7zmLY2g6xXU9EXTGLMRD3iKQdbNRoW7SBP0E7gpf2cE00HZXXYnUPWW2/67YXhV2/icWo5RsabGbv9MJX1c3nSWINN/TKsDi4kaO5ZMgY3oN7Xw8pC6KvxZ2j/dL6Z/h/svVdQVt2X7vt7yVmyiGSRIIISVYKgICiKgqKYcwADZsEcMGFWTJhFQcWcBQFRREElKSJBRJEcJCP53Rf9P1Vdu07v/rrq9Lnp/VStWnONMWvMVevieWrMOWqsb2wqX0xaog/zAkSpqxdi0TUN93VWzJIVoGgzn7pWe4zmv2Lzh1JSMlz4UScguf4MHyLOYLrzFVkLHNlgMZa24F3kSD7Aqc2NHqN68qUm0afpLWrGbzEu0SPJdSIN/b5we/EPonx/svL9LUKEJ7FbEcq0k+YofihkhKUOcv6rUP0uj2S/fCwvt/FwxGpENRLJCu1P9ufZ7M57xe2g3jjdE3J25DyGey/As/Iy9rU5LM/WZrTCTFhSjLF0E3MP7iZVt5XHa6ZTHfWJPgqVZKnqMassi+iuNZgFSDJxbilO/d04IXmRgo52zDNPsn1wDB4Sq/E7JMOnB0K8Tryl/KM7291m0WERjr5oB3bdJ0gd9IUWCTOq7t9lrNtwnDL8eF1djby5JSNPVeE11oL3M7+yIPY32rEvMLapZq3iZN7cL0NS2ZL2aQ1Ma4bME3+ZMtuDTd3rqRl5ihObZqDj+YVlKdbsmzUb4/IgyizXcPzVTWb7P+DOkmlolcIkx9Xc+BDJML9YepKrEb4bwqOOp9w98Z6TSXPQ813Dag0hi3YVE+A/jslfjjC1wQLzb1rc171OvpoMGb4LGb9wCo9mfeX4USNWTjnCaZO5FP46wohbqpjWlKFvUs1ynW2cnnoBgcPeNKFQaPNfIu3/ofjv0CoL7UHCVVpqtAy3RDB8BDnfvuEs95YdsgrMlPFibYgi/UKSGV6fiuW3AGrmtiBeqIfG70RqPnxDIBuB2rRGDHJPE5UvRX8zkO9oJO3BNBqajyHdW4vO8TrkyF7B48tKsi+/w2+FHf4rw/F0U2CT4wxel52m3dsc/d42SFx6SprjJJqza7Cxns/XsmnIq+3l00YDPD1moJtzmkUm/ZjispnvAzvYl27G7aqBJE3N4f6yEk5K6eBu+Z2UtuV8L/2GzLgddAWbcdSkL4I+C1B6tYqbyto0+7WTp6nF9YIvzBkvSlmqKANMGwg4dYdGZT1miuhx3y4Umbu6LB4zD+X6Krate0LpnnfM/jCBC37b0PrVQWBrFit6GbAuuYWphW5Iro5l1d0jnFkjwdPt39nl/BXrUQ/xUP7B1oBHSCR/wWXVNI78WMR6y3Qs+o7izpGFfHR/hFDLlDWKO7kuncprk3yKxi9ge9lF/AUCtuxez1MFGS5e0CPQz52GkfE03glhx2E52lPNCLy3kxVBBjzYdgk9n704Z13kTk4Xyy64sX/PAux/9adYvT+Shd95tLaAWW0nuN2WwDnXsUz3FmD7RciwRTd4krqBZjt3UnUusl3yBedubcA0cy2+h+ZS7L2NRDcpPmlW4bVElY4YCeqtf6E7wQnL7bI4lkfzNHw90Ziz8CC8PjmK1Kf9GZHij3z6BRIrwpD6OI2+NkMJSYyh9mkQWz7EMUnamz1K0cgei+PJ+zSEQiG3L0QzZYcfJ/dHsHx5Cd1jxSieJ8edzjjWr7iHud1iDP984t72NGK2DiMhbiTnBXsJmPiIoEleHM1MZEfWCF6E7aHf0BaGuHeipOjOh4HS+KS8oOWtNq9/lhB3v4OKOCfeeuVwfcwGhA9bcY9zJfbEW64ssmBu3zloLF9P1f18nM2NcHQVEPchgalmFuhuV6Uzbjzte3P5NSMG85VXCNzwlJGnlfFscSfilzZtOnH4nHiH4elT7BM85KTcMQyEheicGMnZoV5MHOzMLlk5BrZa80b1PEpLtrBh7lJWF59CUXQ+1j1ljDCKRm2BPEuPhiC1eg/T721FLbMXLx07WOh+j6d7k9jaPQqfhlT65QeQO3Ikww1TMCzZSvDdHQwJ+s2Mye2oR3jyyGkAdy7dYpKOLx0xP3ng1IJ9hDOCG1tYqhbF+EPwcKKQComr3Il4hLaCF7vsvTh99giPGyspeHaRBS+S2f4qjbkj8/GPXkOk5TJef9vEPqlZvHQ4R/wuEYy1HuPj2Iu9yjWYdfqhnl+A+rQiJqekseLNIPItfrFFx4E+elb4vmzH800KnevaSD2Wh6WHJhbfb2DgUY5H8k4SNOew46E+jpIXkLasxl/jPi994FBwAPplg7EMSeHUkFv4XRpCS+5HPEum0r7sL6+Uq8m/LUZoSinL9L2YI+nDq3fXcLdJYHnKBY6GdyM9twj9+FTME62p+lNMect+lvzs4uToabiVa1InHodgTtg/0qj/UvIhEAj0gDfAQKBYKBQq/jtfnVAoVBIIBE+A/UKh8O2/7PFAkFAo/PS/xVrMv+02oaOkY73duIk4O1GGepwg+fNJErKF7D82m/u7ZTHseo9QtQFl21mciAwjOGAydw7GE7NEj9MpbTg2qfNBKo3t0t9YremNYm08TqHJlHmsZ++bW8hccUQpKYoWz0tcPRTK83sKBPORM/O3IkSFdMtmiovqGSCpRemv+XjNesGY0ZIQI0qZSBOv45ci172T3/01WbbEl1GTHRi/ohyJk15kKQ7ksK8YicVWyD0ow7HKnZC21SioPMZbYMI88Weoqg7D2PA3z1pvkF56nQtTdBmssojfXTdRUnlM+PMODton8uDITdIMbjBI+yjFtW8Z0jmaCosMnpTEEWQSRIedKhd7P2f4wzGsPFPGo0Wa3LbezKoMG14+yyHo/GaeA58wxuBCPmrTNdgwN4PY7Rok511FOa+dn3LqpG2NIUz6LCvO7SRMqYlzX/xZvNQc4eNWbg46yJ1TsejJ9WJ84jh0hOM4UH6IYzuNeZ9wgCJjP+RyYYCqFFu1b6B2+DNnvxxn9ZfXJA0ai8l7ZyKdpzB/WTqTNsRy8dVnBikok/vrBn7IM/nVQoxMavj6OJihfcpYcETIke2uDB4pR7/61RSqV7B5nB3CHRfJkPpIkYsq58Ktmag6gkHnX1OYaIjEuLvULciix2Y8prekqVRuo8lZHLmvP1mqqYsG+QgKmphQBA+LHsAEQBxoAu9SaM+URnTcEbpE+vHi+1Xc3J2J25KL1qAnDJEayP3Ue/QMA7IAbaAB9nnvIHhpFdMs3lMmnsHXTqgFZr3ZyNj4er40lLLQJhWJGfZsEdyn37Ev2NVG0iDzh8lVXkSbm2BnUcr8G5GMdT1HtFw2Pu8+Iqmeyo3b0fgqTESqWkjHvkO8eTAbr7RKRGKMkbwfytXzX3gpOpmdyqeYHPiDj4ebeW3VzB/lg9yfL8cZgROGa0dzx3cJO4eo0l0HR+7+oqmxBP0JCSQl/GZqcwtfbp5gmUsr+zpMcDdXp/LCJM57fGN2hyVieq953ns2sT1uRIvr8/VhGGq+BiRuuM4ISSkiGIermyTXBCs54LqS9S8uc1rfnccfXMk8VY1t2AN2Vt5m7VFrJkUfIy/nEpJ9rBGthjTxR0hKDEfkrgEq42x5OwiEd69ROUgDncxGXv3I58JsF/JEknF814Jp6yoUDqiwKaovd6/248p8P2auusozYTUX74WimayPrGQWchq+yNuK8G7XWvZ0+dMw1IYor1sMt/vI7AIhhY5riUwJ5ad+HZ/8lLAQdeDan3scGRuJ976DJFw/i0zKTZRlpQgrDcdZLpB33zI4HLWJn2m3+f5Hh+/570nP6MS0IAWHEVt54hLG4O7pfJLsoaEhDatPtehtXkbQ3BfcmKJIe1EDI+/6E7noPI+DPtMUoE9b+T1cqmRoeuSOrlMuRYILFAdVkDFvJ8mnVVjlvZvJq54gXraETN+p5FTKcOf3McIWNDJ9byJrnWdQ1jkH198NzNx9+v8mH/8A/11aJSEvYW1tO4sD52ZzynMSapufMuXvGgJj9HH3NEPkSw97e6kzqiCC2aGRxI0dxPIgF+xum5Os383zFYOpvB2J3iAnVJ4WMVi0lsMj/ZB9V8HQoCE8X7+O/vM2siH1Jk4Xjajy34nMg1QmqxcjMesI7fesaDTTQapEBCeHR5C5g7r7OUR4fmJO8V1a3Gez/udBhNPvgFQ/nkZbsrtZSNPPQNY6PaOk9DuXBNmE9gtBqj0TOclJOEY3IGlwkM0yspibqyE96BtHzbyYmTqOflIzyNj0hEzTXrxres5c548xW40AACAASURBVAlkPt2EvlUhvwuEdM5ywvl1H55YmDD/hARKxyyQlf1MrPAk4ik/uTm1lqNLnvHEczF+mXv5OMiTgVr6nO21gdlVFQgfPuRz6HZq7P05OF+DoOjdHN2Qz4L+A1iiJ4cdK3gkPMo6tySsLBq5eTQO4S8h1z/PpPG7GH/G/ETDZBUhXs24WGdywVqBA03bqdfeQke4KP08XFHuVKfongmbr34kck4oL88NxWTPbGJPzSRRGENb7VH8w6VZ2teTIWt28uB5PHERDWiZWRDVx5B3WhnMezMOk5qVpCUMJ2DmaEJ6DuO2UoBrWhvH7ktSZf+A4qLzKDzMQcd5LLE9fdix5QUnBY6EV0mSkVBFn8xuyht7I3m6N0O2P8IDHxDJZ+fiH0Se3sspYThxpn8YHNOKZfYIoubGcKV0N+tl3tBjakr5dTWsV4RRJjWML7vD0ZLTJKZiHPqGbWy3ryUlx4aBCzfQ3XiDD70jWW74GklBB231z+FvIQ6GDuxapUzSo/XkSxzA5FgyT1I7WHeino+O1XgfW83erSU8O2uPvfc1rl814MbOXYTFbsd0zknW6g1GtyKUgLL5GOfPZGZlf/b07mT4nV1YDr5O+rL7bBsmRHvKN0qcjrNafQBH800R3urLMIsYglf2Ia9mPVoaInS9+sW+eFOstTTZ7CrJNO2NnGvNYUXpEmw6p1HSZkLBmDq2PWlDT0EWTz0tjqtdpPhEH04t9Sf4UQ7vi7dRP18Bz89XUTDv4LKCOmNLriPbkYZSUAZXhq1grq0K01Zb0e98KAEes5DCn6lSiexybEZmxVqkC8LZoT+BLB9/Lica0fuNJ/1e1vPEJpXxTQJybqbRu0KdunBrpkw9ysnqDITvJWjWO4X+fm/2SYVgPXMbt8y6WR29kEnpKjycpMepVDmOHr2EhNRjmmyLMFt6kSfacnyZOwWzB7u55tzK2cRB7DqvhIZjEH7jV5C3Qpu2pWJUv1IidH0CQ+Jy2THUg+ST0cQN1cWn+SPivw6iOLCUA8t1WLtpH452a6iU+I6wfgXNyaeQejGNzn0JyNwtZdwKV3ZHjafvuBAy85+iWmeJk/Yw7HTquPNWDr+5XbwvFiHirw1KWUuYrDmYuMqVLNC7xMG2FuY1S7EvL42nTq5Mjm4lzaiIlMoK1k034o3laFbOCkaiZwZfW3fwaBHs7UilNeUgu+Pv/H+bfAgEAjngNbBHKBTeEwgE9f8BoT8F9v1vhL5BKBSm/Uex+1rKCGdKa+AQMBqF+d00TshBLLSN8rvrGfehg4J+edTqSPA47izTBy+hR0eBlMoIkpUj2L24ilO3Uon9EcU+eResnzVwY6QyxxXmMSsqm1MhqszeeJf2AZ7EVpyj3+gdDPpmga6sgD9PLmCzO52EMpBu1qRUaSbm/S4yIPM7/XUlaNNJRumYJ036HRTmfcJQ25/gNDGstkqjcs+Al8UBOClpUJrhxuWvdYTMzKbmeiFKCmEoun3luW0/tP5OIq7MmpYicw6eCUelP5z4dI713yeiJdNKvYkO4h0dxLSdYLDNIlTPhdGeV8+P/mrYij7FrMyYS7bNDG9/g4KmCzVFCgz4nQqtGbB9MdS5MW3RcjzvuDNWqIbitQREfB7S3aFL3c0TlHz4TM5QZ4qz/9L8sw/Fz16j4tWXo8dXc+7Adlob6llVvwKGGuKuHYfZTzfkHzai6JbFb7WvHK0PIKp7II97m/H70gn6eJ5mUTu0XzmE7xB4tOQho1tegdhP1mzSQfLgXpw9HzNlZhATNN5wO+U+bVkxJAnvUDhlNp5Sx9nqm49b6UuEBrYw15u2wCsME/ShO7eRc9+28GSYNkObt9Nv6Cm08hIY1nSbZSlvEC2PJa5KnFPCqbQ/bSVtajw9QSJwcgo3Fv2k39B6eo31ZkLVNbZlL+KdYjVtjxtpzZFAdUIXzzPfsGu6J4Nq1FA8FY3bkBLM1Sxp/F1NDSAyU52VP5ZCYxllMcdIExzlrpQlLca7eL0/nnD3ufisVablchrt8ekoB7ZhltuKq9AUmfP+yP+ZRGtoHNrGK/A3fM1iVxkyvKLYsHAYwoGX+OT6HqzX0+9Pbz5pdpObYoS2+WpC6nyZWTwWywNRxF5zw1dyKtf183HUFuJU1MNXmRT8W324ProP13uWUnYzkjOfDjD/0UiMBh1j++LJXNb3RH9gE/1bYrG4r0TnFUfMN0jwSbGOT49VUc5XQP7gdfqkeyKdZsazttd0G1YgpSik4PFLWrUHs7a3FM9FjehMyub7KCG2KkLkMuv5oaKMi01vqrTrkIptY0BiAnnL/Lh86Bf6B/sjXySF4Ec+ip1a2CrYY5BRQOEESWKWSDI5po1D0oo4pGXzcmA6A7MT8NY1okW4hKHpFtRLviXF/DM6Ld/JjReiOSqVU1snkbx8H2sMwzjidZEhQ9Rw9qomNyEFc8s69g6SYoJsAa57niGiLsdy6TQk9xtx/ZUG+wPa+CQcAQxnz8ZEZnnPpC2/hrkVm+nosUb9kTbSZl+JTVKnn5QdC/Q6yH6WzZiDk7mYL8fVLYsZ7V9Om5QjFsOcyItPZdmaakwKevGoPoZeAkUU335AdtIeNiWJsMBKGbmfIdRJD0b31i/ULLcg5/yShPQcZqyJRKwsGuM0bWjRwnffZsbuNuWHiTGPr91n14lQZs5dzfIVl4me24tF0w5SZlKLzu8mFqnuYk/GRP70f4OGciCbbbYQliXKhiXK/zf5+E/w36lVKqbyQrO27RTcfMWGN+J0pcuy/kYkC1fPx9t5LffbizBuaka1LZRG7Q/U5rxlWsdh1slMpKCkjMt/r+FnNY4ATVk0Eru4J9aK1V4/Pu3YQPjuJKKWLsJj7VGCg/Mxl07i3fBHmHzbSZ+GBJqMJBgl3cZbkQrEvpxFYt4b2p8lYfSukZpx1ZQUtdDc9oUax7ksf9dATbUl0277sugEXLh/l1PjTrHUeTOCO66clwihQm0byW4XkAnJpFhqAx/dX3K46THRZwaz2aEH6SHePG4LRrd6I5/zjyI/YhX10unYTUnnzpkvWOZvZTg/uW5RhqxGNTPSnblRVo+8TSs61ZBaM4AtZoqcfLUXzykjSS7ywGLdSoqn2vN9rhTdm44SlTiDYyWq9Pbex4cBISz0aGXF3x5s+zTSyzuT+5JTWV8g4Orq98TfvcmAID0Wvt9Jx+azVKNCU5EO0zes5o5zLWPtvjAiwIooMWsGXtjOj7fQe+YOJqx0QctjEls18qloX8z7Q+u4Jmjlw8vL2Lr14/HDo/Q0a5L96z3Xzg7D71h/Ypd9YvXVh+x/XMia7jpET1eRsCUB0xR/qkaYor3mIctlBFRdCWXdSyvSzc8xqE2evLY4hv+IQDN6PoUNQ2g43A/HXFMSbskybvQGsopmkmtUgV1BHAOnrmfy+Gam3qjEUa6eRE1pBn+WxbmiCTWDVk41aaL10o5alywO1NzCx2w8FmL/VinvIdVNwQBzIvYUMmbfRnSiNPimaofqfmeW+dUSfVKHv1G1hJ+6R3J4EAwZTmzrV94FPePvpVdI7ZhPRdJB5p57TcjiZNYNnMFuq1UM/3ScZ8lDKZBciGHeN86f7cubqw+JdfdmjWR/Rs4di+GKHajI7aT1KDgeT6GhbT4nS46RVLyA08JY9hmsZeYKcz58dsBuySNCt9rgtXwJl9YMp1hJlAHiy5mX3Ubr65XMCL6ETt4jHEZU07Kzm+zy0Xzd2IbtUFVuH0hlYN/llNccY0hvRXa46tBQI8rZcQORHHUaa69erBxoSpbEX4Jy4olQX86augSaGp9g1j+G6N3LOXZqHZu3u/Pq3nCmpyzD9MxCQoa5sttiF4JGLzj3ksj1+bh1jmN4+TPuHxpD0bTn+Mhno2oRw/2h3tgua+bDcF2Mjd2Qb1iC1DdFHgRt5KXbebJlZ6NeZ85wHVU8pTOx8XOlOtaXP1WzuDXxOZs7BOx7ocmrwYWs/vmZ3wNkuJv9Gq7vZHjPbV63zEYgmIhQGIu1WAZX9gZwrGYL4/u9JltzHndivjO4woxuTwX63HvI4MlB3HqxkiUusXz+OQP7LjsS1G5yfIsfOwN1ULcrQTojmXvOR3C6/4ABo2W4+KCK3kFNzD0vwYIaNYbIrOVvcSXafp/ZeDqQZf4HMSqp509FCapGQVS0GNGndjaxldKUVH3mk0Eedt/l2bDzJTdCBJTvv0Jd7BDOKEZxqmokWl2RHIu6wAixS8zre4XZ8W/+kUb9o/98CAQCceAuECkUCu/9y1z5ryNs/nWv+pe9hH/bL/5/oAWU/Z/iNxb3Yelof0a/7qTAdxhZ3evI/3GLX8J8ZoYUEqH2lLzT2/DxvYH7DS2CYgt5qX2UPx/TOCxYQ+eZHFbKrySt6QUWqx342GJLo1YGxSYCdqR7sF1vH0O6S/H8ZIhk9Bnkhvoja7IbpVtlXL/1nWGiYyjBE32FPFReqnHyRyCl0a+4eWAbk7dMY6HRcVoWz2KI1A3OzSrksJs4m0WzsV55hhd2NSRtsEW4oYaolS9wEY7lT8Mo0setQuf3HMaqhnFK1JlXRRqM7IIzAx5wv0SMTElVdhW+5XoGRCGBlOE6VgAf7mxhr9hkHuS7YGY0jxqnAThkO2NYepk/EWkMKBanO2kVN78lsj+olSPjpnDjSCVKcwaSMiuT3IrtRK+ERapDObojEPsydzY9SEIntTcPxVeh/qAf2QOvseq4KwtW7mTHjRPcdcvh5ID3ePZkcuqaBSFavXicW82xO9/oJSFkp3I2+2K3MT2nN4t7oFYCzhwNYq1uIEYPQ7j5YQ4RP/fis96X7ZnH6bX+E1Hlvlhmq7FGMpRNWlNwErjysHd/5ve2YM4EKZKEGixfn82UCj+6Eqt59LmC0yInqFXTQut+J+oX+lJ4v4eclKP4q13DwMuVyh451jKSqgXG1IU1IbF5AL1Pm9BqeZfnIx+TMNKfor5jqYoQkGjzHW2rAYwMLmfceSmG9oxgZ+AmhJKdZKZOJdFIHlcbacJ/3sTMywwFM1h5cSkrnwUiuNqXRi0pvLTLcZv+ARl1daqOH6TwuDg+AYacPLSJ+D99mLdRlq/7tQgL8+fOo6HUz26nuaSeXxNC2HFpPdvfxvDRVIUpw/7gJ7+fA87KbO/dhVRLX3zkAzCSHkJMmihHosF4aw+Ct2PwESqhOcyTkuZwBpc6021sSoboEwbfbabpWiN3rQQIDswkuc6GQO0sfr0v50dOF2KhA+g1wA/LFxN4L3cHvUtSPBdeQiFPg2DlaiSnRNE7diiZGv1R9frOX+dODH+AbZ0e4+d5cslYgeRKFaR6vcBrZza3DMsJkJNnMJNZIkilPDkF1YNy2NXK8Z4eVkjbstJJAo/LRaxZKSDgRyC5FJJkd4znKzLopaaMqlcRwm1tHBxbzkfpxXx8m4DvanjS/ocBD+6SmnGBkDV+/MWXr/enYZaxi2TTJYw6c5iwomXk5Dmw8Eg8IjbpJDZb4+v6i5+1TVwVaWVKWgNFgbOocHXksLs6y7PXMLl2AlFCJ+avlUTU6zc1hTZEVInhGrUNz8oBOHQ08U38AbL+JVjtiWJSUCTvVr1AIkaRqJWr6K3zi6m+u5DtrmHKvHmINgcia1FNulgwQTKVSPzZzNvPh3miF8T5+8Xoae6iK/0jeYXzefBBnFEVMykyEGVx5Tj+DNnMrwhxcsPysMqtZIqaJEsPfuO3gyhpJ8/hP6iOR5fPM3fmcGpOL+BImBCCNSnJlUbZXMDGxhOc6zHmw815mEpOY86bG2gY//0ndP0/Gv/dWiXbooTvWAM0fbTJ8yjns48e5kYjUNH4y+nY/SjGn0PidzI3H7uRVXWTz3Z/mfJ1Ijfqg9nXS42PigtZJkhF5vE4qqR0WGndwy2/Alwt3XkwaxrvJtey78paRKpqMFcqY7iELuXCY3RJHaezlydxpbb0XraUcY1hCF/8ZdC4NB73HU3Uu6m01AxEQ7+Wi4V9uFKUz7Tbw9AytsLBshaNRF/eXPJn8/GTbLKYxsIPXtyS7gcfvjNI/woLdPNxmvYVtXBrhq5x57XZLxxddvFRaTFVVcdxcbFF3iiannojkv3783yUK/ekj9DV/pCoguvo/daguW8DEaIl1AqHod0zCd9eRdwVnqRmnA1PdtygZKgdPX4NjG/NoL1Wi5Gmxcxfl4p0qRwvDI5Qbd9Bd64JiiIiaKhaUaXQwoe9K/mYUsmzuzfJnnWT2X4BpCwXELFImq70UNoqmrgiPEWzQQ5PJBexIymMcFvYIwTvDjhwLZRXxveZOjSVsPS5dOtWsOenMpYzJ3JS4T5xPkHc27yW98npyF9djZrDUvTe5GE1rI2/2UcYM06NGzFGTNefQ13OM+xdNiK2+TR7ZSyp+f6O4BZphvScZ/BHeUq3XmSeTStHoj0YKxRl3bsTJFz5RlN7HgZX8nll6of9O3EmrTzF82EenBdXI8ZqFZPS+qCSfpYZ4urIdPXlxignDkjnsa10J712xFPbLUHY6BDeVufg3gkLLXbwt/Yas3+FMNWtg3WD4LfOKBQ9l7F0zAP2+N+mePJZRHta6d3pxqMRBYTNvIWHWgO9ZQczeqM76hLfUX68h+ejQxBfmMBq1yw8NI+xY6ovH0eIMb07lVEBPlz83cbvxZepeq5Izs9gfl7yZJbtH1Z75LLT+CA1rev4ZKPG7q/xtGnN5s60XOaaT0XzgDxy3l6ITzQkSbKNe7HwuPARD3or4ix9CvPgHMaXWNLoFUGYmD2Cy51ELRrLgI0FKFXpE/e2nPNyxgzP2YO+lTbFZmMQFRqirPCdh2r16Btpc3VVOGvjg7l8Ro4m43hs8EYgNGCUwwjKwlYwKSeG/uPC2Lv5ICPvLUf0xzvSxy1l8iRj9OvNUPbwIVg2jNNHpTj9RItn00yQ91nCF4ESxExGr3I4vWLGcr6uFfGKK1zPe4/EORMeG08k2v8T6bNVCbXP5/3Ghby59wenGX1xdXGjVK4edbvLrCh+iGl9b+QFbawUTaJ2qD19z8gj/KZPSNM4ZA6m0uLxmU27jhP+zge37hZK5eW5lHSC3sraXLhei2V3X4bZS+AQLYZ1qgzXC74RYrGCJcfnYb/uHhI2GVRVWXA2dgHxvw7T9FYcZeMBKOVs40dlPRsbrrPQyoJBedI8EYqyoNWC5PCjGA2tRLzyO1d2qyKMt6T1STQLjKdQWRtA04wrRN4qxbTHF6+wq0R8lWCR6gLSEi/idGYyTSvzCOj7g+wMZb6UXkS2JYFHx9oZ2M+DflFP/zFX/5NuVwLgIvBNKBQe+XeuR8Ccf43nAA//nX32vzqJDAUa/k81tAAGfZQYdkVA5Dp/Ura94fPyFPrefEOLvDSTwswoSBqJxNR4ZqUo82yNMYvM+zE//ydzJLM4lxrBNXMVsmJUsfuzlSU1CqibmiAYO48njwO4nKvP0qEL+SqhhYmyFcOHp9FeMZRvzz0piCyg/6BRlLzLYWn7UzbLyGCtOZhV7yLxib5LamcrI25H4pjvzJaD4QjkfnD+YyQCwSY4GcBhdyeONVzHJn0jLyx16CwdTVb+c0xOnaT0cznhRlvxnrUYo559fDociNK7p+zVucLNQw3YNizHu0eEU+92kbl7B7lLdrDXcQ3X0+R4MfE7Y9ti2Pn0Nzuj33C3O50jHUKcYgfg8U4V6+BBfG57g6SiHu32IVyMvMxXoxekOLozoauD+NkzmXnfmaeLA9k74zmW8yW5fSyN4JAFNLe04G7VwN8VKjiaT+Hq2Z1ElyewZrI9Fa9SuPRqMs8NrXj1RIRYXXWe9axgbCPk9J6O4Y7tLH0hS0SBLJ6G1Rjaa1K92IuG+HUk3B1C7edv3HLQIq04CRc1STIltrJh9WIqbSIZ551B08lz6B/cg2WzGUqN5bwYr8TN63oYTutDa1QahV4j2GzhzPIuD2zYj0pkKMPCkwgwfo+d6mi0t+nyfvcgSCtHejtM9hBB82kH3nN0GO0yiU/SJ4h/c5t1w9XwMwgh1uAA8lezWPjoBXbjXZlTaorgcBsy4xzRGRqH/ahuOq+bELEtjfqvsCvVF+3AMGpM1mLaLWR8xVXGzhLgN8Uaz01DWS9/lft3zqC+cBMpo8zRXtuBQHsTwqLXFE5fh4JkBEqay3ky3o3O9FCenxvOLb2rrDn+lUe5xxEcXEDO5EMMCdrCszJJmL4HM+lgTDRd+LZVAtOC0Yjmq/E90gF/BCz0NERXbgke8TFc0l2HctNbLl0oZkfsM/SejMFy4R4MPHdz9Wor60M3MzrqOM8l29E5OJnsn7LUi2lxde8gFszt5KumJnfNRXGZ0JtTSnlM+lOP+HAZctTf0+zYwtSl4xD3FFJsYcwrxhPU5MYt+b5cb37LYdsDlCmuo90xmHeBZ9BJK0dwazrH+v+kaX9fzq24ynrvh7gHnaH5oxMuel18qenkuuA9Yrd8eOqehLxMJ0dYwM/xmxDP38EyvXkkyn3mzcHnrIuLJ3mtLg/OxTHrqQv7Hx+iarw5uxsXkFErzy797zgV9jDn2lSi9P0YrhKNof84jh66xcFCedaWrOKQpx4dmz15H7cfQ40tvB2/jzbJQUy4LIqz/2DeCBIxdW5FteAtS5sW4f++P6N+SeFdqINY5GnWho9DsWAxaoO8CXbzp/aoByPEBuNapYv7IX90LhYjUx6N/6ooJg7LY7FiPWaNgQQZdaOgvYvbs4zwj/lAou49Lms8p+vhEDQlwjH06mTT9Bd0NvvzoNsLDprzzCcO4f7hzK6bR/uXUvpOmUpi1EbGH1Dn7pRtDBRzZ9T2HcSu20tqkB/51d9YpNSCekrff0zs/xPx/4dW9ZKRpPZhGCqDbJn9fhJv32ZgHHiU1BQlPGytKZLUQbSfFy/V+6Jb04LOjxvYGcznQLIVNaVFiBrWUyA0Z7z6ZZ5IgMDci5a3L1j/NIRfYqr8ye+Da2ZfOsS7eFUjpKLnB5I9e2hWH42S+GFEfzRw6PIyLKYLMepK531yAlM7B5AZc5oxQ6ZgLd5BUnIc+fbm5Ofu4dCd3dyM2cLUwAWc6T8F9d5JKE63RZputug7cTD/NWUG/tz+do1nKrq0jp+ETMU5Buk0szRNGu1rFgisBtLyq4F9980pr62h0whkG+QoOSFJ0q9kCrp7kdI0lLza4eRrZODy9hGXa97i3ClE/Z4MdbmKOCzVQcZnIKb2Z1DVU6HXAGkEP17SVjYc8fgE1AZrM6XzGeFxkWg39cJYXotROz/ivlaTFt4xLMqKIpMGHr1X4trZGDw8xzDELJ4KiWxW1m4hvmo+h7vq+dByhT+uFiydCKGmaoj0FtBcF02RlDKDD7VQL6whvUuXkuuxDOlZj3/uQW58vccfPXGyhhzi6fobiM3yw/T+Kuba2ZN01J7LhUPhZwR37h3iaHwX7SfPMNTDjk1PU/B4oonii2jKBtaQuHsDH4W9mNlrL0YoIjl6Du3FXQz2FtDjVIOwuh+Td33iys2N/K1TxltKDAkuoTSghFhdkG3uz6jkCgoyfVlXUcGtcbmUfSvAeHoLez+UYypsIVYctGxqeNRTS3WpBrbiApTudfN91TU0j8/D3fAvYgcm4ui0gutiyizRyyfteR2x97eCxVcuTUlHeLyHGsnJSBZVkWndiu53WzRqW7m+dAx74qdzX/Itp1seoWx2jeYWSe6l72H8jLnoOk9Cz+kWRQ3dPLp6HrVNSTzb+BaXXC3MHU2pUawgu1CCsbkfiXtWTfz6EP6oW5L28jqOrfCgnyKZRm/4VuKGclkKf4njceEcYsvzeTVzPi96qvmV8IOrbXXYxEdgtSKQ36PGIska6qouw6edpFtPpTW/m85+vejzIBA3p6OIGFjR06hIT8MaesRyyY69RLmOCb2mC/B1amXbuvX87e+BILGKjpGD+JLTTkvJF8pOvMXIdyY6b90IWfUV5QuL6RV8kPWFwXSvqePgD1ciW7UYKa2E0uiVFPa0UTnfAp0WiF8iy9KHtxizo4zTey/A8M2kPcsitryHUtFR5AokKVCpJF08jpFZqznez5FnoglMNxbQeXMO93ZG8bNxBLJSI/jr1I1VZgtuU3cSkxLHmmcfOHviCfbefbg0MxihdC8emefw3agFvVJFCjtg1HAZ0kTe8SpWmlMeU4i6ncByNXEqml6RlG/NQNGR2MzUJ0DSiqBnV7mW9QarrOcYDnVD8/5HIr59wbhMnawSG9A6zj2FkXwo/kRbLwkMOmMIVL5DrsoqbvvYYzjiCm9afqA8T8i7wC+EBehT8aGNFeGrqV7hQ11fX7Ij59KiqM6eFXr/mK//ycmHAzALGCkQCDL/dXkC+4FRAoGgABj1r2eAZ8AP4DtwHlj6ny3wq6aQ0CvV5IfdZMeLbDruRfB7Th+OfMshcWUH41umE1nwggXGB2hLu434ATVyI1T5kDGAg2mVDPN05+rADJJ+/aJygAt96zK5tD+Bio3hzB48l7ZOaZKk1rLt3Am+Cp2pqfrAULsnqPTqg1FjI7UCcRLzlTjx6Bq7I/awdO9CTIOaUOodyZAeEWrDDdhsv5Mrw3KpDU+nR1iE4kBJwt9eRHLrPaKmdNBW8o7wba3oBdvTWfGMcJ0AdGNFcDp0lf7nUjhn9ZAREp2MGnqawhWBlEbq8OXaN7Rk29mkWsys0gjKpC6xJl6ajPiZ1Aw6yg1xRWpaNbAoM0Gt+glOLnvQMpuOQUUU1x6Y8S4ghKw+E/luNwwf+pE1YSg3OhoRfz0A19OnyVJWpOTxMCyy13PacBdJMyo5K7uO5tDJtBpC0E476pJ7yHihzJTt1uy7GoJ5hRjSbWrc7rUD97tbcBCNRckBPvjuI8XCkXdfjFn+UBGTwL7UvejPkAfyfLy0njE3dhM3fCOiYycR6JSB3LJ6ege50DcrhlGe8+iZd5yubQLCFO5yu12MHd5jsjqIGQAAIABJREFUsNpzk9oCR/a3vKNUuQXvM1/Y23qPqrA1+M0r4YFdH9LCB5D5wIPn8ZOpT7hHz8il5GQdp+P1ZD7Gd9FcpUrNEkXWbm5lotkABolVoxeuSeXKxTxqOsT1JZYMCBFD1McP18oMlov6czVAFbvlM5nqoYmZjxbHho9i7ZIdbEwwZeeojai6qnHUSpTHs+aj7Ay+F+y4IDkW4aktaCz+zfzHrzm52oqQ9VoIHaIpqVpFS7IET22WE3WpGW3RLhLTihAv8ET+2G3WSM3F9FUZQqEQu7tqbH9/jt59fvL1iwxn3a2QcvzLh4xHbCtKJ94tg/XblzLA34d0owHYud3lw4RC3joE4K3txUB/R0LedHPg+AAeWD8ka7oN5uM+MdatmsDUDmbe7qZ9TyeXHopi8DeKaeYrsTn2FnMRFcZ62ZLhbkxfkS+kaQpp6KeKlHwH+cd/o7M3gbsZIuje02bBGU9E3r5CtTWb1tCvOBgkcepoKk46PqhGe3GntymLdfbhK7GLTyGDqJmiiYN5Gvq7FrCmuptQ85+Y/27Del4R6t98mHF7DWKNeQxuFqPdTQ63LzoIa4wZo3Cdor+3eNXcj8XPYzB8KMIzy098q+5BvmkDrjfqWdj1Ec110hy+fYTT82PQC92D/jZt5KLnIMyOJu/sDIR7fLj/OBddMwN+ZO3GoNyUYXcnYSkKDUPsSfPOZkXvBt5vSsSpXZWhkr2YtuwA5mNN8RuoQbVlK8/zTjHYWB0Vhz/kjn2HfehwFAwCaNb8iqhtH8JnqJE2xpE3MRlIq72iUDUbSw113D5nsWPUb5ZILUc89z3m6u0ER17mwqTTbClJZP0vExKuKLK5vy6jLB+SkD8ewSsBotusqDE8hI+jNoPbbmE60ok10mA41ZOgG++pf+6N6yZfrJK/M07xLylNYXgE/deag/wPxH+7VhXXtjJjzgimLX/OrDfdhCm6oiL3nj0u84i6X0rr+Cb0y7K4IRBjZ0IyJbL66PY9ydbYbcjqOFDyrp1nYq0csbal94dCwrzn8eK4AcvMOqnSl8aqvD9NGt2IGOaDSi6jfo5GpMsEG6EtpffU8NATMkR6NVfv/yReWYRg7aGMnzyODP8AHhx5wZlEA35tHcWYIcbEnnMievpJHAeq0HpCjI3aJkT2zKfuYRHO40Q4nfeb9j/TkKuWw/nYCby8CnFoOoOCtAbJmZoM1p6Cb+UDXuVZsq/chd8FpSSmXqbXn4sEN5xl/XIvmhSPkSw6Fa2vN5ggVshfFUsEFaW4udgyb+g01DUrmb5gM50HM1m31JPYby4EbIjA45AEvx3nkNVcSaOyP7rnU/lk7YvTsiUc9wqmWkaGy3ekiEOJ4g5lAqbpkZ+oS2nBYoTx1xH6FKHrX4HNE3kGDFWlwKiRAuv+2P3YjbjuZBTTivljW4nnyzTOu17icY8TQVfykNeOoOtyKKbC57yavIVRg7YzTS0P5xdqfFbVRj4ri+KLzzD4E4HQ8Q9qXYX01j3FmSA/gnfBrVcLGP3rE3Iq6gQ6itG+0JJto+CkxVg2v6tCX9SS6yKFXBytSt+cblYYP+RtBEg5+9K3Porhw4fjUd+FZ+ho9py9SvUXKX57e1BXGUimgzZ3Bz9D+yl4X+igR6aamEg5FF83scL6D74K8xC+z6JeZwtGI47zuFqVtpx2qF5O8FIdfige44SsDt4BGhQ5XKSCJFQS7pP4voyW2+5cOdNOW2QBpdOUMHJ4we1nsNhXjX79ZiEj8ZchboFoVX3l73tg9wIasseQkXiLZCdnnqw/RnLlYHq/CWeZ83SuT5LGLc2DWyeFiHxqIS7kD+uPX+RN92t2SDcScEOZHkN1NtcoYXv7ExnvdnPL5i7L7UJwUapBLnwsL3XcGOkoQdKgUD68imP83FFITpjLlgtv6XN0Hw+fLidao4Kq7xeZ3CMLvVfTnqeAXpsA8bcdFC68QMSsKp6J9TDCbR/mec20nmmFwwkkfGuhzcKb+XOP4m2zg/Q4BxSdlZBNKKP23GsyZGqxcYgmp7sM4fGr1O+ezdKyzUid1gWd8bS0WKKedoOrX8P5Nb6Bl18N2a0iSr3IFzQL0rhyKI5atQXc3WBM/QR79k+qQNAQwEW/j9zLN8frZyAb51uRli/ND3d/jhyvYXn9IibWRnLhiwMKTyLQzLqGU4EzvdJuYJd8CDvHNwh7rHk4MYbQpLckfIrm1pdAJGRtkbL9yCbl9bxeeIAHejHYi+jwOfoMIpYTWHLzJavq3/FaxoQjpl70MU+kr9Irft/8Q4HhLkaZa3BYdg93BzuR1uTOkJ8ujOmIZ2wfAefPHkVsmAnq5p5IaRcjLjaebTP18V13HJtsDZyD5cjpcw1lxQ7il75GRWYMM+LDKNf6TMr3jzjcyGd0nT4b7Vtolg/k6a2af0zWYv/ZhH/Vwwr+A7fr/8t8IbDsH78BICErhtm7fkiLfmTDt0V4tv/mx9Mkro5QQS7vDwfLT/Mx+gLjg5fx1VQGi31NSFeXIdpyAuufIRQ/ryJmWTzayvmoBF7H2WgBc5W3UznEAZt5M9FxWcrHXztYISpH43d5xIPLSNwtyaLZq7iQfZ4JM3YgqzOV63vamDl1EZ9OxJHz/DB1+pd445JCr03iJJQ0UHKoP6JXT7DRN5es2CQOnljGzhhZVCMOsSXQFX+jAjYqaPA3p4zk2T95VPWeVo8KSqYVI95fj/hGKWoMtxLaWY+e00+M/ixlU91qZqQ3Uffh33pD/gh2Ywh1jFISoc62DSO7CZTVR5Ag1Z8lmTd5L93J9qQa7m4/j6r8Y5QuX2bB1cvImJ4l6GUztjsn4WiogDC3im1ik9gl3PWvr3yG1Z/7ESjYSphdGcueR/MkIRTVai2WeqkjLmnP6GXnWGVwFO/fiqyJkOXI+FZWPPmDhGoPsoUVqES2YnPwJFM2z8YwdSM2UnAK8ALyr23gZNZvOv1+0h1cR/BSP0LPuPL6oJAHnzfR3LGPV05GxD93R+FNPiKhi3Hb3MLDt/bYCt0oMRlBVcFw7Nf50CutAOuBqujWeNAu3UB91h4UjVbi2pLOMfsJLFg/n0UJemyO3Q6ijZRvy6FjcC/We+tx59pHRnTt5eubk5yIvY3B3h80SpdQmHmHOVNuQrsvcQ4PyHP4wtfoXfTFmyMbHFiTcobrZyK4/fUaL5Z9Q21wEXH6cdi5B9OZnsIuP1he+4KK25vpri7mweZibH8vYsNjCZxCLhGrvQyBbhPT87eyc5orj5v+8HJ/CSojS2gOyeSv5nOSRg4kas8X9IfN54vLWmauvE/M1Yk4TBDy2GMd4VFJaCnMoCRhM7mvN6I4JJTEFGkMI7RpPHybizK1uOybzeGPxvTIPiNsaigPXhWxs96DZxGRuPgGE+XmyqeHnYQJusjr2kZa40xeFEmTPleF8FFeqCzYjsZDOT75fkYi1wglMXtMzerI1fPBdP8axHVG8270Lq6si0G5cy2/v0jxyVGBra+d0DY4S9fXlYx+OoEVkQvZ93AUTuYjsUvbx+LAAIbsOs1NiTqmf93L9F03sZfbxlXdX4jMKKJE34SCH1Ukunjw63ckGw1iaK4bxF75aQg17cmNi2F8wzvK07ZwOSYGmekGjNvyv9h7y6cg2HBf96K7WzqlESkFBAsDFezEwA6s18JXVLC7uwsVxUTFQCUlRBQQpKS7u+N82J/PzDozZ6+19ux1/RG/33PP/dxzCXB54DklS4NQHSTMI5F0XFZL42ObQck/Gjz668zaMS74ReUxSXU2uqaDSejPpFLnLM2LvqI2ZBUjlkbxa2AWaQIhnMOEN+uHcXbWctTVIW+KGe+FtYgxOc0KRQeUu+4juXYK9vpFxItmYee5jcFmm7heVkaWkS9O8XvJPrMGk6m/KTEyoqoiiLlOwmx+PAobXRGcG8cS9WkQ/wSG4hq6jtM7fLj7JhQPF2GqGl3Ifr6S4DtZ7BWZQEreIXSl1PHP7edg12RE8x/Qr1HJU/8vxObfQ+LZV156qvPjWyYn/47jcWIozdXX/7/E6v91/Gd0VVtfC0KbXakOzsBcz45vjWmcHlrHgUszWO7/kpUuduw9vJfCwTNRqUrFsATkpaVJSn1MUsFY6rTqWSdqg1W7AW804vAZcwHvU3t4e9KTfyuLUPoqTXWvGPPtGskXHEbumOcY+3+i1+AXLutW8Pn6darleugc1E74XxMSL7by+rISLemedCmuw3vCMtKuC4D0XG5czuDeSyMMDX4QPbKKxZOsWR5syCGtJix0XjE7fwsq19/ws/wiAjKNTHKTJPPXVz4XH2JNdRnPEhuQqrLCWqyNYQJf6RYoRs3xFOnRDuja7Kbtbx16duYI1n4jwqwdb5UGxEOzqZKeiaBQBIJpuSjMGkX6w7Not6nzzLaFiIE9KIcUEd/zllP7nBkYeMrJ+y8omJeHd/VqjvSVoGdyjvLl9byPg8axx7FseYMN5ah26bKkYyf8vcan5idMXhjJ3h/3ODP7BBTJ8ublRtrFBZA+Vswr/znU3p1BwZPrrNbWokXTkXMHV6PhG4KgoC7RMgLMuezH5amTONU4gSpPQe4Y9jM3VwDNsR9o25tB+7nTaPV2QbEBHaJpKMlCB140bvYiY3Q+7T8VKX50geZcUFmZz7fqOmQyBZg34zFJ5auxunIbgRTolfqC1DkVGjYsYeyKCvqnDyB+/Deyxm1873Dh59ebTA+JZ+3SPDKtjlLU4sMw1xFkxr1lWuAAw8LfYdU0EbGj/qwPLGJHwRJeum7GJ8KLLyIq7FcX4kL5WHbUBXB2iC/7Q+pINDTl3OaxxOdtZe0+WwJnb0fIaC+XPXZz8V4+NinxbLA8jWH2PvqX72LwHzkyrJy58yeAw3q7CXt7iCnHsvGPqGD/mVjGrb7KxxYvguxtsP6jyHX1E1hIzqZVxBBjgTauukgjEgsT845z1eA4U8qGo9lpgMdQdeYB43sGs9j9MQ+3dlKmOJ3TgR7w1BqVI7eRczZih0A/k2ac5tz2s1zYXk/tQ0msBxeRX9qMR4M7pfLjaHVKxlFdB1vLPjJa/mB15BRSLMY19ThK92RYdfUaV78m83rbcBZmbeLTYCNcrazIDXuMi7kcCRGfcJDfgZmoAENcBLFUqUf99zSuZi9HSHwc1uGXWDVrCB25nxneE8aHxaf429mH/PObmDq/pFHqNmUyS2mubGehmx3PTywhpj0J0+PfON5zlxUKsxGt+Ym+dS+jFCOozvhJ/89lvBvawc1B9/F7mcWnRff4kHAJrekaHOqswScflDS9YOsp4qvL+CU6ly2Ld/LpyEk+ua8jJvs2xWI9aNh4wZwxaB/fhLJLBF2Fe9Dpz0E+qpgGazUMGpWwTl5L/pxDOFa94lORFl9KepD82IiBZBZJHdIoibcwoGqEfkkeYQZiPLQUJyBzFK6f6nARL2OR9FM0B5LREbRA6M97hDQX8E6kg/iIRG5U+nBjhQaG1woo106nZ0QiL2+PpXxgHX/z5TjalsicDmXGH9j9H87S/xaG8yMnzgTKjVjJx8QcdIxsUJXLxtalFuFKWUpychC0ek1qUhxXUvoZE/oeqdA6xJqMMRBV4OiLPegGKnH74GgS48Yw/fx9igxeIiOiRmFWP1Wn6hn06y97j/njpC9FTm0Wkqk/URq1kOvBVuxft4kVo6fTXh7PrKW3uXp+Axn7t7FzwQKc5S6zqrcZNWUbPrzcwHozR+b1dDJ+x0pGLrdi1KhjnNWeRrGJDdfPbuK9hxBfXqTjUebB9QtGJMwzJ/vcC6T0anEqvY3dmBzen3vG+OBeVtuW4un8lGsXulHIVGTBTnB6H4BdpRlqW85ioDcPm54q5ra+oe9DKm7GQ2jWysK9rZczucOZODARPdmvRN5YwMF/hnPqgzv7/j3KsxsHGBMwhZA1d9n79AGBIWeJfPeeyMg2fkd2cDJ2PyfP1pIhspWyCiUqLM9z2XIFjit0cJDt46hSDQPWP9kU3kDR/m50bMdSuXUQFdXRiLRCy701CCWO4qBLA0+RJ3yDMXffbyS5No2T244walcm5ybNwF56D5ajOkjKnsXTnROZXzabE1HuBPyzEsmiEMI/3WRFYzumLoFkWExC1rSc/OXeSAZrEapcguTP8Xw8NIHyEc689VTBTnYc39Qi+HfwOsIGJpKu7sr3xClIfhmgLbKKs6f1+XZTGqc35oQ2GlG6VpZj1+5RNHEBVVWX2CbwiSu1GeTstSZqmi57h3zl/KEKGiOPMGlxEjbZGdxRU0JQsJweizL06itojl9F/oNsCq3dcBLQxrnoNaMlz1JWVUrXLynOivZRLHEOwX19TKrWYqm2FpomB1i1xY8PxirUxmjxcsY0tOJ0aGyIpWPdfKpGb6RGoBY7zU6Maj4gMNKHwDB7nuVfYvn4CTy5fJyyvkAmGmqQ9NWYZkMpkvxjULCRR0V5KzENhcxpjiW58SweW+JpT6nj0jhpcg8/YtSGdWSIzGTmrgssHeFCg/ZnVI7pkLsgjJ075emffwop0Q+ULA9nS4gGksolPMj6i7WmEOdvXWNa6F5O3fLllZkj2x2CsVEyxl5YkMGSInTW6pOWMJ2G39J8j/IketJ+qhOMmFaky6eODjwDh3Mo+BhrPK35vdkGe7lheJBNgeBEuq2zmaZbxjz9bZyODsLn3xmcrxchTjqT2I4CYhTv4WiyljeKa5jhI0/FRWPyFzswU38GToI5lJdOpKrRjLNjj+PrqMnNKzns8r/OoGBdjpXco8lyDn+6/6BmqsUJXXNMX12gVmQe853mwa8L7AgM4sPHDiQXqlIhnIXx/tEkyU4mq2kmg2r6qf6TTu8CTUxdCzC7V8CZRUYUNOdSNNCLiYU0eZGRCL/3wc28mLl+2jzR+gf3my7YmhjyutYPs3wFDMulUOq8jJelGWZfbHGs1CLi41bW7FnGkyRHbn4OQa5HhL6YVrLDyzArl8D4byW9zfFkSQgiYFXP8BE+FNW/YGhNIxPqRvIiWIN6HylWVLdw9eoEGrRtCL1y6H8M5/+FnDx8OlBF+ju6antwKbvJK8UaCiOrqakq59rXNhb7HOT9K39OGDrTtk4FWWlTeqW3IJwkiaaXA4gKE/+lELUKaVSVQtic+Z2VBrakZVciXOeD/vRuBlVl0lZeQHYe/G4MwEjIE+eVYXz18sbrqDKGWSNJCfXH2/ccwamh+LtYY2FjgL+yI6piYng0JSE3ZiH6+ycTcMSWmYPnU9UcyQ2Tl9jlXeexw1Fa38/AKUaXb/oR2FiMpqBmGypd0SwSLsfGeBOhBQlIVfgivnwjzlqH+bRzPAl/OlGx92DMvJGYRJSjPmElio2VjJboZqbhT/Q+vyR88iVmFjQTq95Nb40uTtE9xGtcJ2eiJ6MdTnDS2QOFnAWsujYMYYVCvEd68XHGZyTsZCmqTkO7uYgPAX+4+uo+7eodHL4bjH9GJdMN77Mx8whzzhSRL/eV5NYTDD/xjdDBeTx2tESxfjdfzv1g8VQpan90c7hPlPvdI2jfO4zEc4vJXPuAvlMqiG/zRPh4OW5np3L7pREdKr/Y5XGDfbfTGZoqwDLR0Yw2lMBzaRDZjbtY4veRBWqTkC/8hONMAXJFdOmNDURkZDi370nhbL+GWQs7aR6likrnIEL6C5j7ew5tvj8YKfiONDUjhKQ06QuopXBMLOel9KlTn4Fhzg/OO0xHVvAFA7EdKHrM4fW7dTglZbNbrolkX2cef6oidJ0sI0t6OFhxmHmmt/GojkHuUgbdTsO4vvgoQ2bqYnDiFZqu0IQnL9WTyZkZhJBlEK+37CW/LxIdxQc4aFggfD+LbGV7RN3/kmMmSfLPiwwTCyPERQT30HD+rJXm9kJzVilb0pIuSFJrD0LrnxA+fi4rXoagamnPUbNa5MLkWD1RmQ95Fbhb+fDreTwp6l+pzSxlx4rr1BeXktGWxR+xsySaS7O6zZ9LBo9JanbmqfUihqhbc6p1OQWrXmK+5Qu8O8+h8o8oXlTBZH0VOlI9IJ1Ls3Y7kZ9nkJiVxzArXU6V9rOoJ4F1tx6xZqovXbUm/AjL4Zf5ZdYumUTh2Q5Suz2ZrDiNVPlV9DUf5+yVZNaNHcMLhXSMNPSQbz+A++Jmzl7xIbrsKubVMnQ66mIsYEacQAeDO1vRqpCma0E7mtWZ9NVZYFFqRJS5DF76K2hJi6bXxwWhL9fwXmBIkZYnsfpD8Puai4heBla2U9Fqg1/KW5ndHsvDhDz090Xz5K4Vyxb08Kw4j7mSf0l/qIyUjwzHTj4mR66W+pQ8jJd6EXZKn/ttyWgJidFe6cnWMUa0/xVk9refBEn64bj4FtWSdcS9GEfJ3SNMMBGiXHAyd4+dx8rPFSW1Muq/adM2biynq30JG2HEM+nLmKReQ8OygNSuL0h9e0u9x09OP1jOKws7CibO4munGxJ/tuDhPAvBDVM4GqfESJka+qwz2NdijFOTD0uG7CVNfjbr3X1Iiwrj/PRKbsf7U+7SychOfVKHj8VdV5NXD5/+/2M4/89AokueXtNuOhpaqCqpozlHnyuL30GRLR8/m/Mr9j5Jn305123Jlcw8/m7wQDFlM2sPHEL96kMEOxahYdrGqCBJtr6dwZHnPfS/s0BHfS9P7o6i3GImAjqyiByYTrbqKB4unUR6dhzOE67h4/eIA3nTENDcTX5GKIaLFhHwJYALaXsZ2LCRhUW17IhvYfKGOuYJLmb4a0O+RVayZ1AVsW1xZP52xfVxJEcXXWV9hA8uYkuZZjiF5TvDibo5l0iX26yfUUXI5aNYJHjjtqGDvQ9zUJO9QuUBE7z3XkYz8zlSm+sRy+6gT+ITkmUGmJoEkhUTxQfhv4SZ7iX+Ty1vyoZTWZWM0ukqPnzpYba3M7v/KmLjEoyY+xWc/o1DrCqFmpeemNT1MfhBIMfCohAtn4dioCO5hdksnDeMhGUbiNhlx1JRYQ5sTkTx0nEibxnQ9laJUAFJnPyPoK3YQ2WzIPv8NpOYGceDFwNkrqrELPY1u9erExD4kZTARXw8L0z1jxS2bVXgyKozHB9dg4KpPhlnviCgX8uFl108fj6BzpQGrq3xRcBVAJuDOaQ+GyB7SgLCvf+SLNPI505/dpX78DLzMXaR+Uz4p5aQB8Z43BhG/ewBPry7x+Cidg4IbGG0cwqSuUfp2KNKa7IKykd6OX1vO3cK56M3+yDcGImAsRzi/YoEH5Xjj+UBMoZ/4+ktB9xdbVmWKExb/35+ln8g+GwaWhc+4Grvi8Cu0ximarJIdTYPUzUpMDyC27jXlLdtpaWwmTemm9iUIk5gwgtsPJdRFW3BJGl59Bcd4uuHXi7dWc7nYYlIijqw8VEdOu1l3HR8RIYtlDoFs+THaR69zSbk31g21Ppxd0446XMnsnvONNYOrMIw/RknNq6l0OgUxwueYPzPXN5nvye55AnBK2bSJJyKuJ02t4x0EBj8DIEDN5DJ/8y23d0M6Hcg+PkYbj0pJAe95N/jsYQ5bYXxFaTJmnE98z3P3A6wNOUVM6eVoahuRt6qmbTphuLzXIUSVU+2Z2fSEfmT4FpPXtec4haXUFUXoWz7MaoFfiKgchulD+9Z8G4PQ1SdcIzpwWH7QtSmRZB79DgHZx4m6PlRbM9c4bppIP3W9ih8XoB7QRHZrUfxVz3LvJa1bJhxB/FCPcZFL8bzrxvH0wPQ7axj1ucPfLp4n9bSIn5bjcNO7hmLmq+iK5uPlV40ul6zWV5VirdDOk+UR5PW+Q5Hj9VEmphRHxTFgl/T8Y8I59KOBoS+HWWgtoHXxy9jrrsbRzF1fv11xjj5X+KaZTk0ZgiGytFs7t5D96xfiKUsQvpiGmN2JbNH3w9N52xKAodScD+L7UbGaE6/TazADiIrn6GeJEuKpyvn+g8gVj+CfxziGZAU5vemFnZ49qKmc5D1W/Upd52H5daL+Nvk8DP4Il/GJ6Nqq4/RXGH0lqSQbFKL9aDBCOj1M1NOirq2eUjnRJM64QcbhexI2iOKZnkC5n46HH0+nDmDEv+ro/r/ejqaarDVXURKmQg73FxY2lPBn/R87CZMQN5Ni/qWT2y/VsjoY3OYIdhL2+G3CFuc4buJLwl6XfQUleBQNBOrUx3kKlbiPnQBucmSTN62ii8fr/KPsBcrD90F43ZElezIq0hFz2gZ8kX5VJR+w2jkRXolSri8ZisJdw1BypNf6Q/A4gBOPw5zZtMtMnTGIVi4gfN2b3l0r5Kfxcl86yhmel0y6nITkFE7x3Cj74zqdyVaW4+EqneYa6qwoWQ2C2ulOZK5lV/hX4lUcGG4gik+8zppNnTGYU0P/b336Htsgq7dvxwpi6EteoD8RZ4EPxfhvso2ctJ+IJmeT21VMXOlDYhS0cVknj9uTjN4ERKNn/lpprllMvbsDXTtXlDbFs90qVT6D3YTFB/EX+EK7gdcpp16TpxS533dBcw1zNDPNKe7EixOXyJCzB3j22nsytwI4xbRK/IbVTtZnD/VUB3SRMxfaww1E7GI2Ex1YA7ymFE5vopkr3y6LS9y76Mc1bMaeXbLhsLDP6j68Yi1J13ZOHgT0VlCfClRh656vryyAYRx0R5P66CnfC9XY5KQL7ZaA5jqbCf6SwptwotwM7IhYOd3GkVFWN1YjtJYFT69rONs1z4CVXvwu7cLjhxGx2sp05o6SJ85nzkLB3Oy6jbvpKUIGlrAySeFjNY6zXfhXG452TKqpYzzsxSw0EpGRtuLrJjr6IfVkKU0g7yJHjy+8If9h2Zyr1yF+UsUuCW0nbuiIfQvcmDdomOo3/clXr6LWJd8slXncO2DGIYB8/l0dR8Oar1Qvh/pzwZIxlhgESrLTrVcFrwSwGrTDzQuuUD1Svx8RdBbLM3q7c1szpZGYpcEq8saGTJBDzRyMDYYRu9GccpnP6I8OwGEJIgPXYFv9mPdU+Z/AAAgAElEQVR2t2miKedKQUkt1wfHML3QBGuLlTScmERoayOa135i6yVBTsxh9N4m8WT6YlQFlJn1swEL9y5U5SoZ+DYWox55VAvikRVNQ72uGnz28udcItq+81B+k4apvSPrFPO40N/N8Dl2KEruIks8ivrfMbwKWMobVTt6m/qR/KLC6xpJMit8GKm7neEqC7hmD4POWbM89T2F4aNwdJlORWwdl4Y3U9KhQ2StCCcrlRitYoqWeSYmMcdp0xiPdMYNUrSduVU5QJvRB94lXSQ4Zjbf+qtwkcjh/I1CPO1V8V1xhqff1mK64xMTfOZTYzuUSu7wWfoahqNvMy/9CcOABcnpJKRHMcZQiOzGabj1WFHVKIhU/1fch5awzSANpRx7vq/tI2VGGSOxY26RIXMmNzNc0YB8D0esLOfwKauJtGwjjtXKI300GUPrLUzL34d54VoaZffSV9ROT548oq6C9FDILjk77gmpECkQgfCPt0yINGB4fQ+ZKbEE1bxgQU8vQ0rMuNZYxmE7eUR6YxAT82bZ9gJmNJnwOr4ac5sEpL9kMLzPCammwcyZNfs/nKX/LTYfx87tDjypVoGaewmf0y9hkhRL8q6NvCp9wgr7SXj5/yDgn9WoNu9hQcINlu/0ovP+EbYqi6CZU4taxDHySuJZLLsAhdbbGHYMxmJaHy05okwQf0uPzRLaRYay1tQZNQtdLk75jIH3Il5FxSAunYiquhwyLfvoH5xJbuFFrKdF0tLwGKHsDPYf/4Cd2TbOHrZic/JPbpY/psqshVipJZyQO05Y2m6arfsw/OLLQGsYT4SDGWNly4ZJq8m9X0BVvA/xSe5EhaYzVm0mxWNPUjRCk/K+JtYUVDNX+zvj78ohGL+KuXm2fFUbjnv8Jq529NHQ/Bhn87dMLJ0Gw0cgUvSDY7ofebz8JlEbt1Mj2oyA0VL85JRIfVbIH60/zBFsoF8yg4dWffg2FnP37mzGnnJHz7cE8dR+BER10F3hjdanhex9vRJtUWFG7PDlzO9PiK3zoEVClix5eZIM63GeJYLvNS2cfLt4GKhH5u95TLL3IezMOySEqjkfdIPhlLCyuIU71afwy1mEgPwPUqVG0BRhh+Boa/5JTkOl5yBCQ/uZ6OmKceNRklrTWTdej7GKyhwWOM+CVmnqyiQ4FdeO++Zv9Fi04lqciWKVNHfegp2cD1Pd5yObn4f3KFM+PlXHUt6V8Omb6LluivTxcUjMUmNVdAVJg4XxXDqMpaPVCYt1ZfBtcXq/hiD6jyIvUlW57CiP9+SpLK4VoLe5B/1RC3kUIMkyDxOmz93CvxUWjM37wEblFnyV1LhY/ZcZbpMZml6Psmgv9k1jUUuRpCZEEs8LfyjbKkjZu3MkCH/EZqE18JDK4nvE25qjliuA4LIApLRluJNxDePAYRRX/sT9fgHC6W3sim1hytzbGOpd4JTeIqRb69j/5S2aktU0tz4h68UNhF3y6a+VZ+/9x+j/0mZJrwKhirU8GF/P2JYyQvN7CfOuw7vKmYCCX5j5bkWiX5Oz5YHsXOPAUO9ApnlcYmWBMUE+GYzcfpLfue78FFZlY2whK+8N58PwMdgoDbC9OpMJg2Zx8dRdtlkqEmg6mZ2/79J/9weaT+wx/ttH++or/DyTwXjt4WweIcp2z/PcH+xBq/QkRmoEodX9m7mmnkzpyOf7s8vc2BPGz/Rv9EkvxP3LI2Jcp7PAcA+mEU5o9cLMaGVkkgNpk91Hu2c74qIjEE3JpeCiOy6rwhiZHoC22VBmW48ldpY5TmKNvFnuinFXKa+iHxGb/p5XffdYuOo2VupOtAnW0S/dQlLPQdoEtKlNycF5bBsrrk3DKCyRlFULMfg8C+PSLAqU61jf9g921bXoFeoxTg38dEcSWd2O/A9JXL9KYt5nyY/8QVh1aGM3JJPF2x/yYMkPZmsNQUahCDHzENx+3WHEWhtEn/dS96aTmTvjUTDej1zJVmZI+TGip4qVSbZ0ZIVRkf2FhLED9H2SwLRTk9l3LqOnIcndq468PNjM3ei3/P2gRqqVOCNSNqBjewyLiXsw1l7EvDpBPr4J/Z/Nx38hpw8FBTbW5BAiGc3piiGErOlkQs8QFCvGcTHqFGmTH9EXsJTQk5OQEZBGs9+ZWOE6rl0Nxas+E5mfduzaM4PX+fGURpkxTqmcP60HkDD7i7n0Bb6nGrPMeR4Ng805sv8up+afQ7v1I+mxL/H9tIyvW/0xcs7kc8x7WkM68KpoZ5mPK9scd2N3OQ3Z5j4q5EXxcx3H+gM9rBgfgs9CA3aGv2S+jh7DpdRYH5fCQlURQupaeH14CyknXjA9uoINZZlkWuvwpsOatqxrXNv4mPrfvzh+aj0OVjU09ATQN/ED2xP1eK56HWNxA8Y5drP62lueBexCqCifeJshrBB9wVOxtex+NB/riaUcOj6Ile+6uV0WS+3QFwgq+3HDED6b/sOTpmQkEqoxmixKSPA3Lq86QNi0NRjvncyd271Uddbi5axA1Nab1Gb/i+Lyb4SabKCl7g8PX0WgldTGe3kbCs1zMN07lJYVskR7jiViki8ywMeiZI5eOozOlGay3hYhIfAHQ5mRFC2NQGNxNZnHbKBsA9dP6fEt4gbbr/5ls8thvFfrktj8hZKNDjhdvIWkzUJGVK6kdGwSpZrppH7ah4m0M+IO5ihVxfHokTBvU5Q5u0eOuPV/aBWci6DdDIQKH2B7eDZNn6UpkZOkZc8cdC6dZmvoM87J2PE0sQfD2XZICk9D92M9Gkck+VUnxvFpQ6h4WY1KdR+eVx4RcNmFeK/dnBt2B2v7WiItKnFXlCdQNIuN5QMYpP9kQtJESqbq0SiYisOxxyiu6UYhRxXtwGAMnXKJWVLEpMMBlIqvRE1pNwqDAmmMTaegIp0oWQ16k5PZpHMcl38vIZFaQabOFsztFOn6sJ0JAuVcHzqbkHtNiLiaIGotCc2b8VC/SkV6LJ/Cu4n9WshkVW9uvnmJ0hQHkl1dWHrlLxcHm7JeS4Wexg0IGrygvlQDzys/+dTnw9KoLr55LCH9swcmA2aM/tnFtCv1PLhbitg5B8wyp/D039V8X2hLfD2UexTSnGqE77zd9G1OxKvyH0J37KRr+TPqG1Jo+TUdQUlLWr5ZE0sRkbHpXByizz3Nn0zqvoD7t8scaUxFWm8PX66tZ7WyDisl7BnVpkLm/EEsaBYjtS0Aw5anLHbtxSpSipbBo3BS96V+RBQF0YdozvdD+kAcx9ze8/bNQoxWqXLe8xgaI9qYdDeZ2h2rkb22DO1XC/FTFcAm6wKPjs4m5HQ9OfeCuV04FPHNMxhyWIuQ9Q95oVmFf95LMvVNuLe3kfMe99mUoso4Zx/+zY5H4FUR0pr6REr/5YxJJu8rdVHouMarrzrIydVx/5smk4QFqVMchti4F4wrusufcS9YLt5IdKk60rJdVP2px2fSCozsnnIoJB3vyYJoGA9QYjiGT7G5PLQywV9oFX6XdqKnPorcnQKMSO5HslSU8ekJvL68kbsHgoh80Uzf7Z2su/oY8TVuxGcvZ63wIz7+uMrADBgvZc1AQtR/qKP+WwwfZw6fDVRasoisH2IEXT7FK8NuluumM6pBmNTr+SQMUuOc3FtCTILpzu9C9EArdeZTyQlajo+cE1Wd3QhLzyc2K5hdSzZjpWPNDO/lVCkZsEZLGHdXZ2y7fLBzgwMt+0mb68+iqUVsNZPDpdmNAytvkyQ8Cb2KBSjUBfHu1QUsZq1m/SwphmbcY1CWCqkJ/owP3IT3UlF8nGfxKiiJZznV/FU7jkZIDsd1FdFV0aLhuxQBWiexnBTIpldXKbRoZ01YA5Gx53BSu8QGrV6ML7/g4bd4ZvgNpzVhNZL6egh0mBLh2EveoNU8Lj5CaZ4Szl5XuJwXhrjKcjTnzmXB9geI3ehl4jwt9hm5Y180lt/pGbhdP4SxaxDelYq80zbHe0QQElcvUl/qRERsLJstlFAaNwGBz1c40TSdqB23sN36HQURZyJGOLL37GfmiA7naZs3qyeu5cNpJ572gGRcP3Whlfz+1caXqxXMcH6EnFsXEUqiSNQVMXrTA2xFXCiKsCBzSTB98mEIndUmNX4P767bc/nzcOIS3zJodDQnZ0USln6My8GLSYqRJuheEWXRyVh8W0O5yW9uHDhNeMksWvr1+FlynNKoGmKsPLlfJMhYR1MWpmvzSU0cvpYhMP4hLb8+4bfQAjnLzwwc2Iw0kGNTw4bNETQu02TEXi/WrRtKmdR9lOPVMFhXzekh7dz5GM3XD4L45dahdOoWiaqjqDjYxY1xnoy07yO2NIs35ycxa2Q3H5zb0dozlewmW/ZuvMB+6QJSOg/Sr+uHywdf/hx4yNNBDazao8aukx4gVcvZbakohpxnQlIAFUPrSDuTjoGNLW3d0rwo3ERsxyBiShwZV/SGHOOFxCRu46PbJvYNl0V+kBFljU85qCnMhJZqpptOxEHxAEc2TyFGegllZk1kH1yGaH0C0wumUNv/BlWNB2jUKeOjrwynNhD/w4tL36RR/90K5LBriANTopzwKjDlat5hbm0KZ/enat5HHSIqaCS+CyfT2CbF98ZW8ktUeTxhIg/fPKNdXYbZphOZt/UZJxYl8s6ug7wWKRwmHEd4giuvgs4ivXI9DhZhTItU5n3lSPbXrCS1azAHd//FrvQw3zQWMPr2RtbbHcGuz4os/TX823uViqUZtEU9x3fOB8LUyxhsuYgo4zTigudgKfUcyWEK3G9JJu7VKq6/HuDTWxW2aXUh0xJHyZlcfi5zw2YUfOoUxLltFd8+7GDV+fEEaWpi/mQM04ftY0zZEprTi0mqsUKsTYclUT6EH3Dm7rwsQvX8WFPiRcy4xchE7UfZdgJV24fSMFyTjoIepvR3sNzenONlk4iTq6TRTQ6BugvcibUn4dtenJJkObpvD+biebyI9+HxDDdacq2Jej6LYJcuuuq0sDikiaWLGBEfwym1mMK+TToEfveh1LKMj5GPmK9mhrC+Fq6WV5Bpe4FopzGLD17H1ec2wSJPSau7iYHsMCoFdbm+y4uK2nyGbVzD7aCg/xk+/gu5ffpY4LQyTXas3clkiXDMo0NxzvDFtLmBjJN76H95FBnBz1xvdiAp7Smrx+nRHN6DGm2ITJpER0spqc3RqMSrM22iELdLMlDuSMWlIgpjB1NmJWozLOEGQvkymK3xQWfgBzFZj6gqV6LhjyyV+3dSeS6FHoV6Qi8cwd37Ck52tjQqy+IhZI7ryBfsqJPlxAgJ6usXg8hv2nojsag/RpFcKk8r8/EW2U/T18/09oiivkMC3ZY5qGt+431sEF9l2zk15jHXHSZyLbYfHXc/EoY9weTGb8xVMykbNYG//UX8sXai5VAK3R1KaFj84U9tEEpZx2ifPoxRsxYQ0HmDISs/MlramHGTDDl19y0HdNcj4XmDUcGaKBhNY7KTKR89F7HA4wFpNhmEP/+Cq9kO0jQCOX8xnqaKIkx0PJjm6ILdusOoCkRhaNyMuec8ft4vY/6muTg0SaGxUoIcAZBoa0FQqo+u2l8sKtBlvXkF8ofiqDSKZJnOHqpFRzFyxA9OnxfCMF2WlEEK3Frvx+ctUsgWCfCg6yueHoLoyu5BPG4ywdM+ovhQhtBQOZZtCufkcj8cjC2Ia6hkx0hHfjfHkJfmxGi9Am7dC2Xq2Hc0LHzB58zfWDVU89YzB9/XrWgUifJNVw7N9WNp+HqGRbMkyarbx/Lf/vRuc2OWwzJ2rB5GbHsem4uE8C4RZb1pJuLxkmyN68e44T1G7rN5bjGTorWOnDMsYPswXe6NdWPFomxOh9ch8WwkUoOv8HxLLwsVvGkO6kFE1ISW0wnYX0hkzoRKkovqeH1kgN6ze5iqvIO/WfG03W9F4IATpj5lHA9eRnFTOCniO+lSM0fojScdGinIbRnHt4qHNE7UZeDXQeqj7dlo50SstBlvHspwaL4JC2eMYvRWF2IlCxmhE8yeM8vZUm9HnNQx7NsMeCmRgadiFe1yG4lJWoJbgjnNZT8JGtqEWVcyVp1WHPyRwrv4e+wIm41T52TqNJUwTBMnZEwyteUyhBe14WA7gwc1A2TWZ/Dv/NPQcoRZ1aos2K1HmY4jOTeT0ZEeioyrKPYHCultUCZbfCvls/fRceQ7bgu+U6Q7HOEkK5ZpanJ00lO8nkiTLSGITHEVBd11ROYm0DZWiN7EOkrmjkG6L44xYXZcTT1PRN96rLaFYSMhwbMDX6ixLWVauTaLVG9i5f6a8zs6CHWoxmqbNC8aziEzcS3G10q5tceLg4JtdIbvREZjAEW7Lu4cWciX9FCE6u1wMBBh2cXznJz1iPO9mwiSn8oTv/1sE3+M7jF/ktzHI/NLgSZBZ/LsEuhNrGSTrT2PfgsxZ30I9YJrWXE2kmnetRyzX0785gK664IZs1SCiBcK5AlmMEzpE0Ef7Uh012Pmg3SauuoYn6RDfr4xDweHkHWviUtvD6Ke9IK2g+F4DW3n5uBFlDs4kbHMgktDBri3czwLd4+nLlgUyYfmuBsbYlMylo6O7WzRL8N3oRM/j7/+P+fbVb+UGCpVbWwX0WP/v5/QvCdJ97HnzPg5GvEpOhT2J1D7uYWO0AcYiKkg2dFP/N505sw5gJ6dCTIGXlxavhYD+VEcj23jj7QtJzY8pDqiEv8/hnisW0K3vRSapWs5EHuYBqU4nneXET6oFZulVoQWrWOm1EV0taUJmqiDjHIN/qOm0Gcfyey8XiotShBYtgOHmXNIeGWI4+gABkp6acxNISd8LQ8GZaPcJ0g2InTOyKFILBQtpz8gv5pfp87ifmcLloUD1Mqvo+d8IhWW9zAVnA7rFelQOsj+9vOstVZjZ7QMEeME+P0wjQX+slRfs2HUj3e0LHfiQcBTxjsaYBd5F1GnBRw00idRS4MpGwfRHXkK87DPaCgu53W0OF/7hbHcs5kHI/qQmfeaOrW/fJH8QYCAAO3bBqC6AVtPT7b0mBPwZT+vE/I4mzAPw60j+HLwIVeHljEtT4srVar02uszYK7DRH9LxOfd5dyhleS+KKRvXCe7VyihqZpLQedUupTPMndIKBMujyBhYD/2f5YyT/gQIYNM0Ltiwfk5AvRkeFORXMTNntuwNYAb4lOZ+FGEpCoBfkUp83HdFcKP13OhSoKRjVH0VD7GY24n004lojD+JNlkITRMhf7idVy3nISX3QqyLr7lfP5PjAYG+Ln9DWmnJfF69Ar1ye4kjNzNWfEn7FBXoC/aH4EOU26ebETXeRAnOp6TmLAM49aDBCrKEVjxnV1Cg2hZNJhgiXP8HtjGq60HcUidzUoFJcJ6PlK0I4DBShfxsDmN+2tDHvqMZHjjfvZ1NxF0rIX+P3M5+TYKxV22hL+1pSzMg3Ee+6mJUuB5oTb91+VxDLZh7KHhdIwuQV3hCaWjJhJT3E2JewVtpTp0aG5n6vvpXLMLxqs9iTeK8Wx5+JOpmsL8XnqS4lfzMatrwXXMI/62WNArGESf02z2VzeSNXkuu4oPc05wE+PjFDjdrEDmqZ1cT9qE5qpq5g4T4Gj4BmIbpHg2cJ9If7ghvpr3tSIUHRMg7OZuXmsIsN31FuPDnTi4VY+BgS8Md7hE5Y2X2M0VZ8uOIQSsmEdo/Vcizp6iymsZokMl2XQ2hMca2ZzqXc1Zv5N83PqAiHcXSdHdxrGkOrLu3+NqVRr7tV14kGVI8JRE9IJfM+AXxtrFz3hLNMXTZ6GZJ0JTRRYr5qxC69ExjvyRQSluOwJGi/lxuoXATU/IvGrK3o4ZzF8SyIwdTggox/AryJUBz0rOp68kR/YFL3450CTZidjYF4ixgR/+CShoiyI/PRfzR99p2ZSD54tLGBwMIL33K91bz1Ipc4VR0rcpN3XHQlwXm4nZmKgVM7xpEf8gx0mHEnxWtnClNgGvE+fpmB5CkVUx8f229BkUs/puNEKbDxEtsYjP/1ynrSCMTR8OIij1kfeJMxGeOZ5RV0t4PrCcij9d9EpP5OhxJX4KLyNgjy3J4VMJvfOVQ9KfWCRUgabCfeLu/MPqq7Mo//WebaL/b7fU/8N/Fg19/YxrFuFInQGGf+uws/BG4PlRtmk2MGD8lAYhbeyFNmEtI8Zyy8d8zJcATXOGaStS9kcGFR19dlwMIPZ7HlbDvTHsaQJ9V/JUVPjxQw8he3mGZlsi1mbMn2kSpBjK0tDmyZze0di3W3Bj0ybGz/BnqVk4Hb3WvOiLwVNfhG/FkXwqLaS2OxiPpkpmPtyDoA50vtKiRlycjAZ93NTV0FTvJzH7PR3q8WhIS9NtY8H9zMl8eemF0raN1Kk7opewD8ITKJ3zh1evghBMFyNPZzvvWxXpefiapp9FOH/0pWXUMjbeWsESQzXEJYaSYmVBwdQApl5/gtTcYmzyP5CRHU7IRzGUhsZx7ekYFmtcRcgkh7cSEWhqwsTZCqx+n4TssP/1VlnVvw0P15sMfCnh/vc72C5RYs2KBYzZ1cssJX8KvsQzTMEU49kWzPkqitLkQexM60MuU5Ry+hESHEBl+VwGZuiTwwoGDbEi+zZE/tpHYfZFcmpW4uckyYBhIQ7Kf1gZsIFlM5Vo0G+h/EM4tlumU50zgdNxV9ncuhR950Qqn91kilcZesMBlY+sWHaLcz9r+Bb7FAX5Cg7l5zCgr45u1xt88sfR25bHS2tXhiQUIxj8mNzDp3hT8YvuggG0NAbz4WMyXtmnOfChjX9Dopgf8YqJbrO5091L1UwfPKrekHuuk9iA9WT1nmbI0nCi1nXwbc5ppkxQ44moHreSBDhx5A3bcs8RPNiIW903OHjjLnun7Odp+UXuRo7h9pp7vHkqQdVdF6ipJGbVMqwMx6AqoM/56wHY92+ie44swrGDyXh6mtn9f/hd8QyJ+6/5vjaFoPJVbPjbTXaoP6UqIKGxnKB9H2jWquT3+14sagWQK7vB1/o8DPW9UOhoYNn0XqINPbEYMQfr0RIcipdGU0ySgg5ZLAc0eJFRyoax6+mTfol4wRa659cwxm0eVbUV+I3dyr58R6ZNKebTyUtkNpew5dEvDhrsZ352Pt4hZXw/VUjEn9+k7Iyk9poLw5VFGKRqxEaceaa0g/FbltHUWU/GoQ6uuyVjVbea+SHQ0jGL8b9W8zLgB8L52yhs1uSr5X4aP7Zg5zaZEdO8GV8QR5K3H5O0OrmrKUxNSSJtIXHEaSkTVjWMTqPV0N3Iou8TyRb9i9TcQYwSTWCc0Guixe5w6c9QIrP2kTrlJQsLB5BdYIpeZgJmbUKM839N2z/eiA7M4XH6Yzaig5PtaDQte/laW4F60keq1jtTfOshT/tMGCO0G01GEXN0LB827cEjswuznCaK+1Ww6+5AceF9Dg10sENQk+A8AUxET7NdyINidUOe1grRalLBkaneCEgbMneoFKuN5JCViEW9o5nrKv3cnzqdAVVLJprIobvPFytRfXRMEyH1IWmNcigO0iOrdTLaE8Zw/dIsRizvpDdugKOXHzCxL54f99/xWzGZBo0DvL1vg/KovVi3Dyb/xvL/cJb+hw3n/ztRk1UemBmUzVi7dczTXIj309dY5JYh29tA4UxhaldEs2OoKwreF/ibdZSbXzJpU+7h2rpAsvP8iZI/QltqEupimaR4rSTe9zraxZ8w9nUgriIO7aWbkPwaTFaLFj4Fc4hX34H90wHc7hwnXyqH79+aGT35MFsfzyC/OpXelwMIC/yvot+hoIzCP5cQ1FPDy7yUwW+7uGkpwskzC9nv+5qa7zm8SNqKobQcZ8xv8jsiigczniAjKI6jWCFGfKFP9BVGuUJs8R5KrtAjDERa0Nwbw0jpeRwvfkSkCkydZklVw0jm9pajqdTKWO1dtLm+5dS+Y+y5Npqg6+c5FnMQXylhKlQTMVu4D5+OdgZXxfP61HeMBl1Ec2QacnpR4PIQamYSrzIPZ7eZ3Nm4GM/eOzQ/sMDoTSZ7PwXiNnYv8l/6KU6AqNSllH+/y7V/zpIsNZ6MknzcpNL5UqzBP34LeVv/EmsnLQ5hj+7awwwbN5Z7B3bw8YcB/gkt+Dkth7fCTDi4G3kNT0Ke7+LI0cv0/Eyk63EvzaeOIjkxgt7oZsxabzO0xpIm0RpkxFK5U3+Oc6427KhfhlWkPFZ5hsQ5WNGc24KsQz+ag90JCXzMIo1EXk+6QE9ZDNOWKDD6ZxYjlmpTc1eCgJRkGp7eRmVMADEPtqICTN18DNdiQS7OV2S4ty/r3SahunQq4QVKtH0K4OSrKYRecsL3X2Nke68zN+s8lfavuPUsg64ZO/kgOYmEDh9E18xn9oAx8mkXUF/6lkGqXghU+1EreYzF5UNQyVtOfngG9VpdFMR+oc7SmrahG8m8X4TNzm4Sv1vS5dfMsKkDbE1YicAqOz47h/I64zJKVsto0fFj0ZWLdJSlMMc3lUWG1lApgrCsEfFDj+FtvIptd9xYsVGHulEvODVKnTQ5Oc53vsd40gSGZP1g6UA3bdP6yZV6wJqvW/k6ShaV+nqqPjRCfxsGLqsYqZFNTkAyt/SvE/1wFHbWaQzx8we/eew44YC6+lKkjv/Foleeokk7mf/+MCJNn9m9eSqtD+V4cX0OxlNh9eF8yj5H4WKtSU+PPaVSc4noqOK87Bc6n17miX0vOd6B9JSYkZCcyfdKVzrE4pCw3cS82DZuXcoHU1HoE8JisD7zwzYjb1XD2peCsOg9MQvuUH5pAsmv4hBb486AnQpe+vkIx8xkobYnFtImaJWZcdB4MtXPTqHuaUjbLF+io6uobbpGj8Y21jksoO9vFVGVS7m4/i2TlnvR5Z2E8aA1XCEW+dPhCBe/ZJjUS550zaTQLJFNZV95qnqDjOB17OhpYoTjZV4oPaK2ZCzayhqMGi5KT8dr7l/p5ppKNmkjHPmdL0SrggSvut8sL1IAACAASURBVMJRafckp/Ap8grWDDMVJOlbJPPWXSTmxAcUp1nyPref/fp9VNSIIOtgy/uzeXyMmMP8s/s4liOCZ+1dTHQ7GPxhNb1jLVB+4471g5Vk7L5M6+zBvEgVwK1xKtvvHvkfw/l/IQYWdgNXvVIIiwpD0ayAVttUlMTtGdTRzOW+XLY5H6Ek+x3qLz8yT2Minko7GNL5DNHsV2SYyBO8IIqk09K4GgvzudSI2wgg7TEene/PEfOQYOChCKqd/SwK3ssq/yUkymgzrykTmbyvtHtHkvztPuWOQphH93H50jEWHhdCIHI+BgvFmDXvGJIrjvLAwAc59U1s6UmjQKcUrYEabK8eodPiF/4G02hzmoX37Z2YOwqyXUOfxHwthjQrMqPgDce2yGFYbsqsyz/4/v+w91ZfWUDf/u7z0inSLSVKiqAIKAKiiIKioqIYWNgFdgJ2YGN3F3YgIqCIEooikkoo3Sld77nYZ5zbs6/Od5/x289fsMa8eNaYY67PXNq9JOd3IKGzm8yaXQiGpOPQMQnNT1pktZijd6aUfgk7yC7LRTpXgoqedpRn/yHq8R2WVNVzxvE4p+3LuX/mEcZ2f9m5tZjN45ehsGE6dq7T+f7nHE67QhhoO5XeuEEkht9l2BYbBse6c0Emmc/vxfk9ZzONivl02RnRZ3UlAuNkhnUrkN3HmxnBEmRki9EkooanjBov5M7R6L2VqguuXBwMW55sRrq1GZdjfVjhfJJdF715tNwPh3X1yDV1kJgVw+VDNxgi2IrxiT6ssBpF2pVt1Mv7czeuCdnJd9ksM57+emoMaRZhQXMDJ1em83qKPItFT7Jo4iUcAy4T9LaA+R0BFLu/oLEqGe1HgXzTHUdU+gHOb13G6KI/XNFbzbiIZTz7ksxA4RbKf2cgFbSVF6bqOG/cwKHlQ3Bb7stq02DGOoUg/aYfS74YUeAcwASj1wwQ1aeysYzB6psx8DjJd/fJ/P3pSIhAwJpEf+QEfsj+XcnNorO4Nj4mNcMdq5Fx3Ms/wPKhS1m5aCJCYQxBbcd5HebFaceZ3I59RPNYHwzWZiK9TRfTEIj5lsEp989orzhEekImrffiOLtYhy6p79QODuXqxdP0Su1nl+xF0JBDXFIPM7e1vFt3DZWegTjWDKBz2jp2djjQZv6MBzrOvF99GNH1k1k9Aqibz6aLz3lp5oSgIY1egxqS2yI592snX2U+IfnpPFdvFjP6uQ/93/fHb8Zy1i+YSW3qTQ78vM6keW85Fb6FHXr6CG9cZonMeC4enolAIGBCyFUysgMYqRKGdOZfDEdHoSgxm6zyGpr72HLv9HgOBN5ku3Yl22tmUtQwlh+/XuLsncI0tRKGNKgTKT6HAy8ms3lkLrqp7lj2O42n2Vc8P8eS9seB/j5DOHTEhsNxfkh0bUQqLhXblDBOdxvy8UMqyYufQI+Q8I4HdEw15cv8ERyeOZ8tzcc5uUeXR6vdWH1dmY6Zp7D/uJw1c1S5LpWPvsxuAif8wVVKgj0h/iRtXUOJ8Bm+HODWc0nMP58l6Jguuw70Uhf5ECOzzYiZdnD/cxGzDW9w4+RUfPcPQ6FnExJYMS1hKUe0jXmad5x1nn0BCaJbVAgY9IJ74RP4pRGNvqwidfnKiFSLoW03ABVxGdo+JpBg0Iq9tRlVi7M5JnGP743JWO9ehl3jJvxcHmA4w4m3sVb8vrObqb8D+TE+gtMZJQxWPsWGPgnMCZr637qj/l9X7f5/ga6hHnoxMwn/LI/mIE++Wpmj0DebmjGdiBvIYLF+KNPTVqKs2oSL9ia0NasI9huLk6YATa1+nJx9g5UqNqjabGa7k4CHBxazXfclaLzjl3gfmrM0iBUxJLJlEx3Tf6F4cSSxfKKjsoXLxTKEm8OZ3Q8RrbLAoVUSMYGALwu2saNgPz/y1WmI/8RCMXu8E6/Tr+sdUgdG4X3iJdMWrGb5ZmfWTHhL3agRXMjZhM5CKySfOCGhYcnBlNUo6uuRr2OLoqwUNgXXUL+rxxizkfzqp8eaEQrINM9ANccCo7vSBBzRYrDLaECNfgIBxc4h7JwdDBdfoayYwjo/c5TzIGzMDcLu5uLfsYxPumOZPfgX1XmT6ZsxFKUUB8IdBUxfEohW3FN8VjmhObWaS64CFgwdg/DiEQRaHkAId9uF+LrC9iFC7CcsJdVsDdtPO/HoyUey0WEAlrg22ONknMQEgYAL096xzLwFYfc0InQWMXxuB7lH1nJU9RRCNUkig2MIyTyA8LGQyIUhjC9pJTlzLI2xN/h9vJ2s3z5oTZdCuo8Kb2zsqJnZSZhUOWGl6sxPeEqt20/OB+Sg8TaKzAgFhBGdDK7tj1JIK1k5U1GxrCG3roxhoZ95MLg/ero7SennwOJvzzB6dIL83U8Rxq9EcLuUy3306Rw0jMmnDVmqOIcM5x903chln8dhpKxH8fmuGAmW3pwL/cuLOy9Jb3rNbPEc/q4bTcjWy4jef421FyzAlxBvSeQNgwnxT6WbU2wHNG9OZZ6mA8Ms9/Bv+iGOXSvgyMrdjLzVzdmui6gPUiNZZDuTA4vofd1B4anR3N5ygWbT9chGPeG790qav4ihaeHDoz2PMNr6mHzjfUwdYs6TzAYWV/9h98FznGjIZL2vDD+WPuPpKCmuK8TDobPI0srRzkvo2Zkj+a2WdStnIS4vRoDpdK7fDeeCQinu4uo4mjRD42MKorajatOfl6sG0C7awwZH2FjUQHxbJ4ckV/KhyYXhy3q5pSfBIK35WM4+yJVOC56pPSBK245GFyHBdQex7d6OjPNISroa2Ht2D45GQdw/68j9Yn2qhU34pX+FwmAwAhqBfE0+9T7B8dMUEtaZESp05d/ZMSibi+DwwpAB7lG4W9WxPWgwxdZDuSVoRerQAWIqP/M5Qw+6PrF/ywt2uh3A8+wJlpyIg3kyyIZs5damZ8S6hHFkQgELt2hjVTSDLunZLJy/nbVGWzi3xYS4uN3I37zGLfcXfN0bjN0kcS4FiqHBWxoSvHGqmYTt1Y/8swkkOzWM0kvd6B+7x/ee75iqD0bCZCKFjbuRqKvGqOY06X9T8B/2nNp3nWiOTaVBP4LpJf1wS3tOmtJGnis1kWIgxL/FlVtBexjqNRWHqkIEqT+4ZeGA7tcERDr3MHG+FPMDrvBduxqLkiWMN5Sn+8oWgub6INHmT3JQN8OeXaF82Vkuvy5n3URDwt+u/496+n+BnvYSxIdYYpZRx790dfrO70PS+0Jka34gZtxIdcQzxvrJM/aHI7vyvXnXdzbDrfqxtd6SpbNdeXB1F91ye1moWkOzbB+UnivSTTYuTh48bHtAX+shrGsfwpXyWMoSyxh2/Br3nDRIfWpOeGQ5TbbraXmjgc+qJOZOGQ3Omqxx2kqkxBfyruzhY3QGmsFe9Gu2YUqyLJ2PHsIvSfrqajKmw46aglDGGa/nwG95Yj0kCKmvI2y0Lk+XvKVOqT9DCn7zUNYCVS1Iq12BVe8BusNvU7dMGpMuYxQbtdEb/ILBtks4+7cJrc5TNEfnsjtxK5cc6lj/fSO+9jcwH36bm4NPwbMOxBGnt+ARVl67kMptoktcmy/vN7JoFIQUGCJY+RDPFBjGLPrp/2C/3WLswq4jZDPB0mqIFzbyQUecR1PFWGt7gbd/pjBT5iC3Xf2xc3Ghr28SD4YK6A37gJjdcC4tm8HFxGr+3e3k36Rqcj824D0VcpPuYBZ/mwAtL3aMtUHHxAAAMZr5W/gTOe3hOBuPILChgn46dxgre4bHJ8JQLniKYUg4ftbGyEuLUckLmsf/Ie9PLSZdAmwSami37EaJCVyba4+KWSuSOdJQn88AZhJ5Yx+HdH1YUriYlHN3eTumDneldB5eCGXGprnYBYZyNuoQl3Z8RqHXmpsRe5HIrCFTXsid6WY43r3DyQ0P8ClLI6XfJXYubUNHX5asE74ULrZkpdU8+k8J5O+yPWy7cpkTa92ZsNCWkqDxOP1cRgct9GUiC56+4vrDawidj7LtpDLL1kTytCKB4iUHmaCym9zvGfS9k4WZ9EjGPSmiuSmclJnaUCFGTckUJBu2M+tULDHR/uzyExAc3MUU86HES3ji7TWBfZ8WMmOKJ+YKdZT//E6JwVwGbx3HLW1HxCqGwNMc2G5OZdBJ3MQuMqIzkHO6bWQMa0DR8DUTn5yhr9c1MudpYRQ4HWsjASsWzOVSxQ+c+41kTvMztu49zsTyNq5O+4jAsw1hVQfwX/u1X4U8Is+xkZD2HfS37cDRbTaaHS0kbL2Cx/T5XK4V8lzQB7swQ8bKeDIlfxg/5/2m7/QgvD94EvQ5hnkDp2H6cwXxtpf5at1LtMwBfu6NRcu1hE0V3zj1YCMB8S9ZJDDg7oZJ/KqvJ0knkckhBhTFfYbvs9ih/pSq5CL22MxA5pkKn3Hh5KAnfArfT969xzhNcCI78gSvn67Gd7CAt/3Az2oqdR4j6K+0l/lhQ3ixtgXnOB8OO1+mpu85NozuxPfrJfaV/MIouRXhERVcz1oxTv4+jXoyzLh/GHLfcp0RxBq3IKi0JUH8AT/V1Wmvq6T5Sz0tEwz48f0t0iNH4fnnDB+tM1m/eCyTtGYT4GdGy493aL13IXd4K5Pb7/DQ3puZTaP4uGYj4vYxdMtJEtOrQztuJCYaIG+XzKLfZQSlKCFmY4Te1Wr+ehT8t136P2LyISIvEM46vp3eYlHMEt+hoNOH3nIDjjW2U69ixiIjQzaum8qK4GmYBQxkroUJh1ef5drpEK5tOsDe63HcviHkevlZdDsXM3igOGf6PmRxHzWmVIxAUFgClQbUG0DAn0vcbIxGUbyE+uN5UFcL9ACwe+0sgrJ0qA6TxdHkKqM2jCBILZDg0Gc8EHvBUDUBDr4BrN28gCM7lxAyZg0Xb85EuHYWslKObLq2g/H1PQzQ3UD7i0ysDfWp0i/ln6UcRT2yTHtZQmV3HC9eLCRnWBcusqVkW4oybnEpfqYnKQDyA+DJS2NyJD0ZoKXAAh1pnjvCnFItBLIVSAUoMX5ZHoFaStx7nEQf1SryGnzZffczXy9MYcC4UaTcXsjjuy+ZgD+veMWAAYN4YOGM7AglMj+aMKV7FKO1rRDLTMP2swvmwr2YpuwiU+sEM7UMmbfPEx3Nh8i+H8bwG/m8FBUQYiRENg/EBUqoMYRj92Yz59QhTi0PoLZuKWWHt+F28DfT+69CbPgoeoDPXTcxyhmItaU7LxKuope4ho2pJykyf85j41JCY7ZR3dqDtvJfDli/xc/Ck1kHHxHhKmSQqDfpvRFM6DQmTiWVi2nvyPJYQFYM2FkM4vnUANZfSSW/4BQPBw5C7rspHkqVkFLGZZs2XJBl+ooYnvSJIvHqAm5erubQiuV8XLySVclvYJguuchhDFAIT0f7kN8hjsgQLTTn7iWpxo35Y7qx6Z+IuDT0b4Nsibsw6i+8vYWL4hpCkgy4MtAXgaobJtUmdIz4y/fPN7H2ayfzhpDg5e280H9DV9Y7FPyuEvu1kDE//1DhdASpaAeKg/4g8saM7qJSdNQ0SK1SRrfqFsvC1xG9+wfr37/gk81XDmoKmOQIn6Ov8K63GxfpSNIU69mu4cyyh3B29y9yIsTRsppE7sFpmI1R51tXN0blthQwFs2Hj9h+YA/X8v4QtdqCxjpLWs6+x/T4KCpSrrHypy7HF03COSUGs8cN+Ife4HXVOoYqOjMvCG6lmhB2bysKLWrUO41HWe0QDzq6MXNV4a1iAz/CtjCrUpZnhfdI0JrI3GBDzu4q4FergEUyQjr4r2+nV3a8BB8vzPz6IxfVj6yL7TTrDIESXRDdBD3iGJt38VtUyNHLi0jaooy1/F+6lwygjRL+NYZyJlAV3ampJATVcyM0iu+l7/EeGYDDzxIkffqgPFoMqThN3AdtZeMrW74nXqCoN5KfiQ/Q0/XhVEczz0fkcf3NTtTUR2AxfBzpjY70M6tGteAROYanUcm7hOyQGLoeleC4dC//tvtwdfJpTLtWUrthF0MuSiPfWkatsg7aUiJUJ6TQ9ac/m6KEoC3Kvyghk+fFo2tkiZSVKnLZNayZM5b1X2SQyI3jS4UcQfNM+ZZzCAfx9dToaTFs2FBSPkFMggC7Gc7cHmdH2tX1WKkdxfXbIETEb7G32B3pY+v+d/LxH2SgkZpwyYaBJMfG42axn668OJKGTcGkbymCs/GYHz5GwKUh+B9JoWZ+Kum9izk65gxWUXHsmWrJWHVJQrOS0ekQQ6doNiU9s/C1XsSMR9tYab0dfWVnrqscpC1VgfFRMqSvnktrvCPdsksY0NqC9BBpxoyyYUvKBLb0JnAm8TvOe5yovSVOeNp3Fpllkaprjp3dMMbE+RM92Jd+Ly34qSfEzGQHxVJ3eH10Eef0jhLfNZsYxSBWFX0mxWIYlWrO9FFIQi5WArNuSR7nq/Br4hacMlZRpVSI17S+GOuEIVmVyZlFNsjPHk+OUTT36lcR/kud53kfuPUijK3fywi0qWThHROOfo3CNDaLqSvqyD6szpy996j+dZcDwRMoWycgoNIMd8dMVq4ScMLBkoDMdGjahrB0HwIdASs8OznzVJzeJ3+xmWHAtsnD+f30Ha2es/GwGsevrnrEJbfStV1IhoyAc0Ihe4GNgmGEGvXF4s5MSvN6mGvezv6AE5T5v+bS3H0E+KpyorkK4YvbxM8Fp9sCwjlKpFkpo5wrqS2xpnsqFIi858xiG4J90xAx7iFo2wYuOTxlif0XEGul2O4TD3JMCdTIw+KBGdkN4gi/vobDRyApg8XHPvHYT8Ad6aW80pbkzBE/BMozOHfIjeWtugixYJ52BkcM5qN225hlOs95t28hVxePw2nHcii2ZviIRNa924DSrqmsOTsS49CJWCQFsOfXGqa5LKBi2zqaxtsj432ZpAF5vPYqpTHLiOmukuRVO5Nhd5SWwtUs0HNBmHeOy2emI7E8lrTf01kTIODgzJdMrZjIR30he266EWrRg3azP+n2lshlbaOyZyQKxl+Q27eQBvGH9M3Vo/jpeE5vUidw51UURJT4Zn+VPgXd6PYsxjDHEY+pZRxPj2dVdA5Ls3SQmj2UrEdF5Gbt4Z6hgLN9t5OuuZmLdmn0jNuL7cdSnhje5vcHT2q0NiFvNhLV+9tZPnwip46GojvrIM0fjuGwdj55Ee0caJdisHIFrkqNuOw5jq1AwPMj3UTeOorEuA5EVSLIkS/EVTCRmOeWRJwdi7rNQCpfXYDPtWw9kY17yU2uHx2O8Ya79FYcR/ZlBxezjMh5/ombHkfpuz2bB5otVBwWYe3vTBq60vg9Uol9TwoQzv+A4MhD5in44LZWQEbbZLxz/RCrXs1RnbVsd3PgX0c5P2PCeZIZzgDPOKJmxvH4fgRfPwxF5lwY02/L4B83ANXYH5SUSWFyfwRZ0kvwfy/P4/x3bDNfSNUyU1J7LtIU2s4/C1PS74ew0HcGkypnsVftKJovw4j0+YbMDgt8DwtJSpmBlI0On0KHkWysh6v/XdY87YdkpwKfykt5OL4XkRECZpzTY8v5BoymJmM6ZCWr25wp7beFp0+WoWr9jDb9UMSb/Eg6+hPtA35IJabiab+bwMAAvt4s5bjpRDbsHMZZ3fUIByUgfTiGBq80/O3iMfG6/f+fyYemsi4ar7PIU7qO6lBlynuEmFr8wb3UhtKen2zdNQFXBQV2qh6hLqmHngnXEFOQZW2PJHpb9+Ds8Bf5pDvIl6ew2iKBmgwlnN++pkXJC3+5g5gN8qCsMYNjpYosbSkgf3wghpatsMMKKKOcbj7/SECiooMjqn/wnBPOnLuvMO5tRnP2UC6uGspFaT34bcrb9L/cvTAb4x5L3pVFIKoTRt62R/R2vqJ+1UeS31URcF4d91EwyGc5FL+lRmhKr8QoXN7txN9mEH3DpbkyuBULjU1IlOeSlrmLIbvsOdsuQqC6Juo7LjE3L45/pZHcUD2NZrUoiYlHsHBUovnSd2qF7viV5XJp6hlqq2PJ6JGn9cF6lCRe897fh6jq3QgjD0NLO3NC4yletJqr/hMIu7gHSmw5/i2VlqBQzC4E4W00lbkCR9ISqsl38eH7lkE4vV9LfYwym73n8FfkMMeFQgwnzcB68HKSL3sya99T5pyPRnj2Bfr7vLDqv5QidVsuzpVm+BZdGoW3mba+jhGDkphFOiHvzuK29hpzhz3kxm13lq4YQWVAB74d83hcIAfd7Xg0ivLy42NcvK8j/fwgc3/nsiRFkm1KpUTcjEImbwZBn2xpl7Wkv7IjKTmQf/0M+jarCS7+gl7mR1qkRDml30m+Ul/8e8R5lB6FzZgT1PMFoTATakDE35OZgp2s2tLD7tIEjPTWULfHm9E+Aur2z+ePfjLPusNpbx7GqUA1hMIE7jWARF8wBm6dryc0ciu+nvW8G6iIW7Anfh9LIO4hM/9o888a/jTZYTvCn4OfZZFnHIVTk3B7/ZdXur8pMH3L6b+zmHvWlbqYXsaVtmBe/5KfbZOYLLGZRcmK0GzPIEcvenr3ESrwYXTpOsIvS1B25S8u8xspka0jv94U33+ueOweTEXAEZTafnF55TRs9y4iN7+Dqb723PlSS0biWWYfeo2tahE1qSbsOZaEg9x+OqUdiDj8j7n2C/nybxdjj8A3eR3SHb+y29yXgw7HGd2yHg3ZUm5Le9AxIp6Wn1fIGt+XEYEZvDDQhv7wVSBg4649jJDdwT3tdQzGi8X7D/Dg0HuUPw/jyeSlLI7JoG9LDjyXZ/RQL9KfKyL3U5LmYY8RqiSAlR3nleVx+z2dmid/2fBZQFE+hD9cTX3ucu7kxiAwE2Dv/pXTvqq8GbOVAxoxrPsdhXxALGMT43AT78I1ZR/NHzS4tGcrFeMHYb1emowuUXZ+e4WNTT/GNFbxximQvIRA1nvu4VzQTvTS3bG9/J57XU2Ip8zmp3QfpitoYa6bSYe9FjmPlflpEslb9W4iW8/zylKevNHdyHWm0RuRQaHhXCSGbCfT2xyfcbCvCzauOY/ziVg6RIpRu/qaFut5XA5toVvVlxljd2Odos/Dsqcs1VMhK6Obl69+c/ET9Fc4xqN9ScQfzGTDq0pSPqrT8GI9ZRrJNPZK81O77j+t6v/jkVdUwTDhE4k90NYczyrDSdTL92Jd0YSJnT3xrXqoV5nR9FScVc6KvFDJ5maBKYGDLmA+9A/xQ1qZWOpJnYIC6r2xmI7J5MuNjUyb1YlBXiOpf1IYqbyaPKs2xHMPUpSewod0UU6cH4farVaqTVx4k7CJfuJG1Krb09q8i49vjEh/b8/4actwKJakJSAEnb1apMinodlkT0Z0AmKT50PFJwrNshg1ZDU57SZMGtKX6Pd9+f3tJxHbD+H+OhqlX9+Zox+AWf0FTLLKsfx1mRETk/BwsuF1kQ73C+pouhrFxMyHnBaYM9ZagtSoNhrU8nBzKiJnkzE7XQuJuJNKhPwDXmufgKn9qP3pxMSJpxGIXESnXwSSqRP42iyOprIdKZ+jmbZblLW1xylL9GPM8n3Ya+nw9h1ULn3KUX1ZxpV4MttLyH1pAQ6hRdR16PG6dBsmXXWU1gvZJuPCKJ9FrACezhlK4MoM2gYtoadehE9lolhrKNFz3BbxvcuAOJ7mq1IwvgroJbrQDwBbgzEkjnUns8AAlVmaDCvUYYf8HOxl0qHblN7OdCh/ys/RDljJ6SCuXsCDH+OYN8uPzefTydqUguUtbxYGbmRzuwxSam606pdRL+mFlTCbzGI1SAL8F5L8KoIcV2Wwk0el/2TK3+YhFrUQtemX2bbCFaczFzl+6iu5X6eiFq+H1r0EutvkOeXTTEFoFq+Eyrj1+0izhRFJ44/C0MlMGxfPb2MV/Id/YdTFAv48MqfFrT/LPoWhdvcxzi0LWCG7nEFho4i+6MO4diFfVgiZtc6RD8IWas/7M8tkFWmGcajJCjH4foRsy308GhxIVEgB9YMfsdvQm3FNR2kq+was4rIIrCOYgUljUDRXIiNWHq9TohzaGsH68lQ+tA2mYZYOg3f/RPaxIgCtZjCo6DGfOrz53NiOW9F4KiMDeLrsA8etHvAuM5c/nfFoWsrxIfsYw7Ss2DdJjbGVyYjGD+XlhNO4t2agl3YEi0XW3K1cBMCa4IusOreJMdazUdsnRfPRYG5cmoWbeAMC/X5EPNsIA5cQ5gAHEt4xu17AkWlCzDZc4vbLD+zK7s9dvcGM8/vLmO/P+HD0CecnHmFM1AZKHYSotj0gSXsWIyZaw/QqHHR8aD3zDfP3GxjspsPyM0v5XFrN1IVpXKtMYcLqi9w+7MPMz1H8iLjCK8NNvLueyLLIFmbadGBj34dLw30RXBYgDBFiWy+J7H03kiWUUBg3AJOvO2i88pSnZgfor74DpbPhmI5yovBjGBqKNXy8JoWz1Uy0Iw4yb0cyFxcLWHQ6lQtKf5muVozG52hiJsch6zOZgUcbuTpwAB7GYpwqUabCIJol/WSoaH6Fh9tWUl4Z0FumwaCerzyvSsPPtpSigjISlb2Y9d0P33WzSQnvx5pB5Xh5CUg9P4Qlfmo0VK+ksq4VM508JKTlydDp99926f+IwLmGpADnL2r4F94gveI97ab57JF2oL/JAAKdNjNtwxgmX3chfcgr6l2TCRy4HuE3W7TOVNF0/hpbvDuZ0uSPXN8WOgJecW7bO7YMsaCqbynuJsUcaUxBxkkBoU8i3oFK5LRdZdKLWBYamXH1+AEuzh3Kj+dXOZ3wAQf3Nyy4MYrhvh04z/ivQsaJRPPpkB43otrpVavHKNOU2t7XJPdU0hSQSMtqfUyNxhNWmErtdHm4dIhEvZkkllXydfQset9O4fXDMty3mzN0/3y2GxQy1GAm9WkHCT74lobLltzqmU+84TQqhjoSU5fKpfvN9A82w0zEB2PpYF5OUmNdcRX7FmoSYJeNiOdr9nnqe1BNaAAAIABJREFU0TbjGe9VJ/H73zvi0kwZd11IdsRM7P0CmRlqjZyYL3FqCYSNEnDj+XriDv1k7ONOtv/yp0JwmTaZbG6JS3D77CmU7d+jWdXOmehJ/BNqIfL+LnrXNrFi8A6W9xmFvFcpdrlazAp5hH7cRATWXvy+0orgtzbBJ6ewNXsQVw+uxCHnBbXx1yi1/8m2tSYs/LeE+Fn6hJ0ZTj+3fzjVj8RkmhEaP1aCdBMIdWgzOoBNRRHPE53Jcz/MRefDmE4xxkPzHxvDBHzUaGN0fzm8a99SesqVxI0uzP9xBYW7Q6hqESdSMZoFcnuZrz2MLdUT+bXaiy1HDyKmKPlfjcdvKcpUjBjnMZyuoHhO/DlA1M0PnJvuzT+BGSIjp3HAbCz3C3dy3qgI4o7i06MCgG/fa8gGP8BDsI4N3opYLzvAvdeKWJdtZfZoZ4wSSnAcPoKi0k3MGHOFGLVRlDo/wUnqMurEorH8FVlJ45kjdZMggwxWf5lD6amptOlvJtJ8Ou909Dm84zvyEvocPtCXEHk1IqPHIUI7+r19eYMFFvpOKAYmItHdn0UdXsyrHo9buRNS82fg92w3z+eMAg6SLrzAi4aZLN9Yy2gRG5TKTQmoMOX66w28KHLkWdkH9jZbklcSzMeq+/hFbuLHpkOIFDbReiaEzvh7/M3eTnP5c4pm6hHdXYHoD13ctPIYsH0TqZU78Rs7D8HRIawcOJ+YVxnMbXbl6K2hKF95zNnIh0Scv8swdVl2vf7C4egSaj2W8eatAyeWT8Vq8HNu2l3n2ahpPHLfxAqvG5wfuxF1m1AmDRhD4t0YJhkJebhBiLbKFYJcDlC0eB8Z1wKpW36ekAMHGRv9nD0vTnDhkRGqtocYdLyThAU7eVF8nUsuAnRLnnLl5QoefgtjQ0AnUStjMbyWiVZiFv1WN/LnhSym3fNwDpvPmgHvWZX2kAEjypnTPYlvEi+ovraAuI/3KE63onV6EWZGpfRXdmfDJ2ssXc0xzHyP2R8zFj7ZRF+zCu441mKdtxSZsufsuHcPA/0/GL0fjt7HBjJ1yzBWOUuHvQ1LbTbRnL6U+aNCGX3vHEmlv3msdow64SF0FX7xuCILzZcHGXnQhjkPr3CyOJZzyg10aSyja/RG8jP2/scc/b/8F0V/askfkYC49C7+jdDlNSUUvldCXbGFyyVhhIxbiYnaENrtFIjpk8KLx5+p2ueDavk7sqsWcnBVD901sgTEXWejkjkirQu5xjTe5FtzYUAQG6106HL9jUXuEBzE3GhTkSfYPZE0MVPCO0r5UNBGnZwhs7VesaXUlUnr/uJVIs+zvaPJCB3K4ce+FN01R9WukMwz96kqjEDK14FyzUa+xB/G6qEzSoOT+V1Zh26ogOkSdSy7tQVxo1KsEhx51amO3IlNzKr0wmPdHma75dH1qoHrR56iFLOJ1F4JPFXbGbXoIM3mq6mOE2K90ojXj6fRJLWT+KQu5MaHojFblJ0nHfBXP83lEU5MKOpDt3QE5UlHifiSAy1AjT/6HX9x01jEo/geODGaw5SR7NjAipwQGryzMPQNQWxBPfedYWPnBmYLhZhL+zL65gmujupiYEkF2yJ9EAo/UO8swRODNiQEm7lm3IHs0wbO35rPL7P32IyeytuJ1biaPObQFx8KvzSQENLN7rlJ7Lp+G3knSBqTjGR2KAdeJNKUI4vj5rVsdv7F13ppftRfYbjrdLrkdAgbNJOqwiy+RrfwryEB1VWxfK/NoLD+EFvW2JMm4sfAvx/Qs5zGXYE2wXOmccXpDZOdviB44EKCzHf8x+7myQRjHlhV8WuwHTu/OdEVo0FPpx6iZlPoE+LPr8ThqEwLYMKUY0TVj8RZ8y2Pn9myb7c1WxRP454cTONYBVq/PkT41Z2qPrk8W6HGrXXX+VY+APMAT6YFZBN8wQ3z8jDc9X+zSCikTT6ZBxei6Ng0l9HljkRN+cRzxTmMv7yFap3r3NK9z/XibrYNvcGbrRmE+XSTuOYv3YNmstm1nX+/VTCwsWMqYvSk3WdXWzLSKCNfNAZtEwXOLtjNyJHpHFeYSJnhTJZqB2Fy5zt3M+0RCORQfmWJacAnVg+6gbFBPwpOv0f5yhBkOuZSefkEEgUD+TW4glVtItyX3Iec0yo8wq4w1H8tz/vkIlL2HrX4v8Qcu8G38yX4qV9BmiBORFWSVR9O7S8P4uQPI6dZz75h8uwpPcG4WSM5fN2b4BsRzDz6jGd9L3FU8QHWKwXU311M08h4vh57wpAvH1DtTUNzljQGDm6EqO4gedg0rDqDOCl9g4/3F+Gw4DSQz/7Uq4RrdTNIJJTmfsb8OxrNpLvbMDPSQsbiAXeGqGH0Ipvy5XPI0crg+pBuAtIsSVUfh9ILY97KJxLdsRnspuBzWkC80Ild0mNAyptk7UTeWM1h6xcbCiZexHHuN6TnGyBeUELpqD+UCwqwvDsdlT619Pe9Sc67DuIPXuLQlWe0f0iiWfMoLp7O2I7w5kyWISvGGjOsSxaH8Gx2vCmlVbMWu5vmeAUWU3x2HkbmS4k/sY/u5d1oqlphITGXvxX6TJpfRvq3UO5tqsHIr5zYVgOiz97lqtY0dOQCqf4zidJSE3xHmFA4MB2P6P3/bZf+j2g+SisaePLpOd/XJTFFzhr1B00cIZwJ0olkPN7BSdVHqI+4yNuphxE6DuRe6xPGD41hc1AuDXd0MFimzMg+t/nZNYWDB2oo9ruLQHwjt/o5Epq1jXkyC+j3tpa4u6d5rXORvTGGfFsfTuqsQ1y594AOh0TEirdi+TOEPykFnDU3pu+VWDzGzmHLqCn0vlHkdPoubn+9w5unhVyUGUCnzXi0FQdiua+SRe9VqDd6QkNpHWJlAVzSmM0Gh3vIZAxE+bU2z3LPUFNcyQ6zA0yoV0D2ay9vJnix83o5KRIhJKzrw1qNLHqzmwhpLMErZB25W3OxueOA56FcpMW3MCb+DTs6JyNm/oDmJWUs2PGTwKVKGCYaMO7rFmY53GeVyyv6JZhiIjxN4P5IHgxeyfk3Ypz30uC2SwhalTWIW1bSPEGJl/dHsTZ5AlL3z7Ho4FzcVJI5fw+Obu1lofYlary+0vMnl6uPwHfMFOQ8xdDXfEHeIVe6juTj8F6eKx83sVRBBruQYi5fUkfyziQ+6kZiMVcDYyT4tGEXr70S0Lq3hnOtYgQHZ7Jg+G/iFf/yQU2fG+kySLUvRkQsnrmVktx3TyFP1YCCQSfYY3MPhfULyXHQwWXvdy73GhN96RVuemrYrogje/4C9KbPZfKmFRyUaiRWXRJH/WvItOmjICrH/RvLyY1Ygv3KPARhz2ge0M6R+SLcV1+IlutgLAJaSR6zE5vRXkjuGMFiDx8+HHnGCaEQ+Q0bEQqFqK/+TJy2FLOHLGT87png0cQwXQEjWh8iEAhYNfAYottT6FWdwCdVNzTcx9Oaa8EtxdGcWvmYZcMz2C04xvXz+Vw5/pM/RsbMZDSZznvZFO/M2LV9GZKyA8WmBpYbDmDjuiMUWDtSP3cv3e6STA2ZQr3gElYKn7FFkqmiEzAc9o+CfguoMoFlbiPwOfEMd4EiE42l+ba4EomUj3h0S7F9gBJx+okYF7lw/s9xXE9aYPUohC/v4rh1IpSO6Vep7WOG17kE2k59JC7aEZl7loheVsc2KQOn4+douPqEqy/u4rtYlAejJlGj7824WQrEJR7hVc4F1i09wsn0Moa3jCDwzjdWqriS/1SdFTk+pJfc5Kj8EtZ1ypHeno36wtesdbHhqIkPh94KeCN9BBXvTQwt6M+HNUZQH8fskN+wQZkbbj6sLV3K6eMFfBH9iFPKSUxlVvHWygph7DaWBe5k/bujyKlcoT4gmtlvNZGda8AW7SEIhUJmndNk0PI8Juzww0tvJ1fO5aBw9ilPD83katASOn7ksO2HLyNajnF/bCwy6/azS76G8N3vEWnMYMmeIpb9KObGjbUccFhJ6MZFhO45T/iGTlKVr9Dj4c68shrOpqxiX8EpdrWV4K4q4Kr0B/ZbldOVd4gfFq9ourAVb6PJJNx1Rbq6lScF/sjZHuPkrSNkbFtKU+RULr8czdFD8awYlEDZ/g5eVprRfPoSI8xD6Zw+lw8To/jQ/p3O70lIL/7PP5H9Px05CTG621ZgvseTvIZPiOjIo2BjSv4fAYbqLhyfMAmzc9b82PWSpvo8FgZNQCoetg6y4lX+AU7pGCNX8oPk1g/4y/QlKe86yjpGHA7dR0vEHQJPJ9D+4SMqAyuIERND2Wc2Gl0PSBd206p6kYK+XzAcYMCrD0vJ2FrOiZwG/HVGsuXmWQRy1rwry8BhoCVvfu5HS1+cQX0WUKbzgeY0HXSUx6OsFMtA8dF8MbLj+JJQRJ1LOVTWi/rTeDa9zSNaXY0zaTeQl63D7GcyVb7ZTMrqpkzWl+NfVZnWqcy6thOwDD5m9fLqgTlwB9s+GUSZDSfJU8iz45PIuN0H0bR04vyceHL4I7ayrziaV4dGiTimJhOIeQ8Jsef459uIr3EVmC/9f2q8s/s4Nr4DsBqmypdUJZYf7MNem630eZTNza8teGgnUL7QmmZHBxziNZh75yZ62yUJLD9L4V9DzM2mo/cLFJuLcLGew43m67zkMHNLY6nvr0xrQincVWaO6BzElw9n7da+HDj3mPZf8byukPy/TyEGRbDW3IpJ7ELYaEaN3GzE5TfCvrfMUFKjeboNa+eBl8kyphxdwJbVh8mVkeT7se0QtA6emqN+4z5NNdb0CH/xXryVawYzKKaLVbSwTeUVTYcKeNXyj+fXHhG+/iaR51VZ1xiAbYMxs6Ln8uZJBsXVn2DTUIKDG/lmtZS/OUWEcJHE3/+YuW4Zwu6rCKSyiXAbSFmPBFn9NFim7kJByB007g3n3y1J3rr74hIrz1D3c4hVCojSbWX/40gESz8w1nABj4INOfntNv4GdvQpWEyQZjE1A49gfU8avR3SLH4ehov5HS7deEFL2CRQXI9w5QRUmk5B4z7MJMIokCihrfQvOmPdePBKBQVFG/IHZ2LoaoBxzmcmyl5mJy2s+uJBdeQB+tuIUxophZH/aI6v/InG7MtMMtXGvFSASP5+PglKiRlaT6ZqOcJPjdjan0C2JhqRCjk6FMpYaLOHOQfXI8Utpt2EB1vBoC6d2n/evHV4T7FgC+F3bjPa15rB2ukY69uh3V7BVeskBk604AZp2FfacC75A741fTh5tRXePkC25R2z7SdxbdJu5l19z1WRJRyyuMbMwjp2qMWTJ3mcrC3veCuUxfnfI772yyGiScBeg5e41L1EYFEPbV/RD4pBMsGEXw4u6DlsRVxGgE/nCUqtrnLmZRGz319D7aMX/e0SGRmgwOvQdF5eq6XcMJYpCQOI/6GMnJgyv4P3klNTykQpESYOtYNsFWz6/KFJLBI/jXaWz1rDB6tK5idmY5rnxPx33SzTK0Sgup7imQ6I5eXQr68Y4a4RqGul8WeoPG7/erkQnId0bQSPk6QIjEjjwPQBFAr6UlFmiJbmLZ5dh+fd0P35Og5KeszZp4561zTC3+zlzL+/nH9ZiI1YDA59h3K/7iWiNQUcLTf9b7v0f0TmQ7WfrlB27nrU+4hjoizOGYUlXCtPpK7YCc9pYXSpy/G3JYrsmzIoNTsxY6g9qY/dST1iiFa4Fe2/wvhnOIS3WluIrcmH9DomDHalq7UZrRIJKqp+Mth8DF/ORLJqzkpudAYyrsWJC01n2OTjiFrkLGoaMkgZ7MfQ+jT2xF/FQrIvTo4DSCj9hr64IYdFEtnWVx9LB1l6C8uI/DAALRk5JmmV0KQcjYm5PfeE+YilyTFzjDUPfgXwvSmW2pIg9DWXccpmDi/f/yHu5yt+K4uxM6+U8/YqyKmOQqJDDJWqW0xZehC3zVPoU1bEjwthXCwejsz1X8R9sCVaRhe/Ce2MmC1O7Y1jHCw/gIfoCpy/TkDn5RNmHPrEldFK/LT8xMqMZM4ee0p0eyK3ng/iUNxOQrakwsrH4DmP11rFaNmLsMtvOl43lXHPG0nuG1E+D+zLu7HVxAnOIRQWkkE1wiMx3C+KZffhb5iYFjFnwzXcs3KIu21JpEYju4Omk5Z6jRjxavSD51C9W5vhKtBcm4y0wgbygw0Zm3CDQyeNuHXdnqu7G4mSek1RPFhPf87kv15US4Jvmj4pdYWszRSlIDeGcxMX0jxpBztWB3Hh5U4On8xnxVRHXNQnMiQWUnThZdlyGDEMxYyvrO1UAwkDPIba0RHjTsyJQnBIQSzpOt2Od1n5YwrizQ85EXSfi6aXya54RlVAAe0lz3miGwhoERT3ieo3kUhtX47SszZ+O/Ry/7gv0mfGs/epLAt0PDmScZXskm7sByqwzuc7QuFl+o1MZblEDKv0PNiQdgenzarciQ9n+tgT9GuUZN/hJRR6y1BwYRhLAuTIaFBEs1gDZaWfSHQUYVHZi+7vpXhmjwBg17L1aKUfwd8zCRbYMznIm1SpzxT23OZJ20KKLFqQF9HkU50bU0cICPTIQHT+P1xctAnWX8SzrBx+jjKm6FsLEWIzGdsHGtuOoK6ryNxjjRyryyCtxZvW+C+w3goPn6lErLHETmEDdlU1FPi1UZWbhpqKO8ZPuulj2IRKRQ0ivb1ET0mnsvAzGr0iDFebgszgQdS06qFSehndquX0ypSxWtcW/wRFpqzT4djmBEyX5NGSvxjRVXkMf2fOaJVg1jhtx+OgBQc9xrMiPIqE3UdQ/rcP9d1p6Ko78GCDGxWHX+M23Zu/Hpd5rr6IxTmJtCrqYWk9kLToCHTD9tI0M4sfQT+5nGzDKo+XWNsPINw/Cd/9f/gbXsDR6a/JEPzCcmkC/0rOkcp7ZEaPIqzyHTf6Lcduih/JS4K5urSLhflhFN0I5GLyWt71GYvuXwXCRStoGmnGpceZnH4xF4NPC/HJNiarNhDxOnjp1cWSsFOMmTSOF3JqlNaocvHhXPZNu0Vp3BGSjjWx4XQ5lz+/ZL9kGW8T9ejNs0bfdyxpGaK0T9JF5kkYG4c6oFZbwtmFvXg9FZKlsB536TAEzYV8KZRBpNSYobc7EIif/N/Mx38QHcNBwttTPMjVFsW8eyBRSgcRFEzESn4WP7PXke7mglh7Lx4R0O7Vh4vv11O1JpRpe/SxnBLCsZYIjHNl2WV0hsh3lcSaumInWoiaRRSp0bqMc3Qm9k4kcydr468Rz/gnW1CY9IfB+q8x/HUNzxdTsXW/xRaRKAKi89mwYxQ1F6sRVUsFpUXMWzmY9oImwqJ8+RXmhdSYH5i6iiN450+efAw6bnW8jE3DXWQ8s8fA9Nt+DJUeRoVtJr57jtF5IJD7bUsIqS/lW25/GhWaiZS2wbxTDeN+shQnieDp2sGXhGWsP6mKMC2CcMU/5FZe5tn8p3y1W4nwyk7WfpRFK0TILlkBz6SuM7Z/F4IDEqxat4WdI17hphpB/1m7edx/JYra7dTfGc1epvFLoMIoYQ3vV9sxynUxzVqO3LI35avQn7VjFHljsJh35h3kCGrx3BJKd9srCJlHetlcrhjlM3zlambI30No+Zk9tsfJjl/Mau/FOOx6w84kRW6s/szUn5c4ITgCI1ezY8tXxg/3oL5VyNWgs4yWUuL2xCzGJh8kcNoJdieuJC9UyN/yYp42vsHwWQiP/91nmp8zP7ZpEf1AhT7T37Dk4Axigk2Y/LELb0tpOr6ep2yLkOKLM/CzqUepXYP+1Wp46MqxVmQqIoMsGeicyDJlB0avCiY/S5UnsU94J4hF78si/ileIfKUCraWT8iudOL5TgGNG2z4uGwjBkYz2f/Ih+ob45ivasydKCe0SvvTdsEcV5v+fH6swLfk5yyxXs6rygvcu/2F/aICtu+A0OVCxmgupTnwBx82zaA+qJh3JhYEhr/ny3pDlK7XMX+KAgPljBib6cvRKl/+mS3G4WsYwluR+LpaMtDCiBCPieytzuP/Yuctn7IA33bd46FLQOkWBERKCaVERGxRsLBFsTGxsQC7FQM7UEFREAsDQQQlLARBQhoBKQVBOp71Yc2s2d/2+2XP793zruMvuL9cxznn3NdcuzcdpqdWiLGGM0UrGohRusTEUlk2KMXTIBBDtC2X63v9AT3OXX6AQH0LSanRRNj1MqHqDLnZ3mRIl1Fn44PxW1muD7qCxuNfeJrlMk/ZldJ3K5kyLQCbhiE8ChlNjaUv/bVleTnFmkXNqagb9zJ92FkEM91pdrfi0R97ZL43UzqrgIHduUySDkfgMYPTxRJsUI5jp00AwommOGq5YlK0iTbbf6zdq0DUtb+8CPJgvsxtDkvdQ3VOHqLP1rPQ9iX+LwPoPHIV51Qb4i4nIsjVZW7mN85EN6AacZiBhlk4SV1jo3o0Z12kiXw8jGbDBoqf++CjWUDEGwtu3wJbx2z8kr8w11EOgcCeDGEqzgITbhZdJ7n6GyedTiMUCpExEeBqv4osy1dYdYqjP+kMJRf28TjkHQ3x8L7xNs9St5OsuYmRp5JwXu9I1GZzrA/K8rr8CLqDW9FJVSJFR4pIrcGk9hYTlzONSfueUTHxPFftS1jiUsToaa7kbvJiS9pMbC0kMPl5BPchK9kol8TKz3/x+TeM+tg2hAVuHIyNxt5Yj4rQ/fTzc6P11CSkAufRnbSAB0V6yBfqY2T4kc37dyAQ9P8vZdR/i58PPWURLijr4BgoSqdkE312CmBoPI1fnnLB/icBHjdQPNrDjD9K1PacJG6ODJGzb2BXOgmjEUJ+uh2gp8WN+I5ookpzWHH/HcED+uI/IJWXfc5jtlyHyJxAlG/8QVJ2HRe8nJASnGCHoTu/K2x5XHecAJvz1Os5MPvqEETOquFzW5WXCsn0V5KjTf8zSRs3Y2SryjRNVWZYrMZt2Vtk3JbyZa48t8Q0kRzxi41uw5g1R5m9D7JpnerEkP5lbB7tx8f1v3mkNZNfebMQnZWBi3Ua3/cMJHDpNNqv+1D2qYQbVVEMdBBQuj+arPHL2LR3LsFRg9i1RAWN86poTVbml5kozW9WsVo1DJfQyRzbPpcN7a8JyvqHneUVrqkYkhDqQq9hN43D5Yia18U519l0iP9ih91ewq8ZEP3NidSQCVgLtKm4p0PFWBsMpp3g7ZHbfFyxnsu1TQi3lcHdcByPHeG01SfsSzUI9zRjxchkisRDUR/dj/eZdrjPGUu8ahjhqcUItccyO/U+3BJQqPeCdfP7cv9oLqfbbhK5dDqJzve5szeTQM9npAwuxW/pAszHTOFlrx92ri94L+XDD+tLhJzspsO1msay86x3EOF39giWjyzjuFEYRSUlXLp7A0eFQySLXcRMTY76fEl+SMxmnfFYBAObUZE34fjp7yx2WgdHEti88zx73g9F/eRR1CcrcGxtLI+DHqHpB+GbJ6PyWAyhUIiwpxLL+1qEHHJBJHQ8NY9l+LdSmS6lRmZlmfLkjB1rjn8kcH4Dw1d74Z7ui1Doz4YJexnzThbrR5s5YamMxu0DFI3bwPOz4bQXl+A235LYJ2kkBb5h8qFaLmUIEXlbR5thFph18UdHig/BZsTsiEIoFBIpFNJ74TjLVvmAvz2IQoCzK2XaIQgMc5keU4Hnxj+8cQhF7IsGbyamUDjQC6uBJji+ecmB9M8cUW+g/WsMFi2HWJgdyoeSC4x+/QO/vku4YG7FyL1qHPKp52/1XhrmTUUtfRi1S0I5ZZeHzqYmZpZEMFzfnryKd9Tvb0VGW4JPM0YgcbAblS5rvPp+Y8xtayqai3GX12NKSzWCz2ok23+gbo4cgenbORtmyKl1SsjajuX1my8EHZpF3cE8HpoZ41geTbn3YdK+DsGqMpyu0Zn0Sl2g74TzfJN6R8i7GfReLWeYTDHCeHPKbZ4gTPSlc990FEQOUOuzANnE27TU6/BstQYa53J44nyH0caH0Ko1JeXmMJorqzipdIIzf4bx/rYazr069BER4PzXjKyYI4RqnEK47wenk8fx5MNJLtunEOPaxqYz2ki7rmaDXjZWYXuZERNDUlQMnY1p2I7oxW17OqIl6dwJW8yTw5YUDJpNZP+JWK2I4FNQJ33OX+WquToPQ+35UdyF+3g5xN7mY9umiEimKL0FC7ldPAq5Nl0WDNFBJvUDLquDuDekPy+OHcBCwop011SWvJhLgoUul9YPJM18OPfDOhAMCP5Pq/p/PCoyYpjPnsbhjC7eilbi8n4ItsJItAqKyXPWpKzrFwbfvlMzdizteV+47j0SO1sZtHUnsth4Nid/T0RVooBdL5VQH9WD3Zd0xhr4Uf5bh2zzfpSm7MTUvC9XfmvS9SKFgcHOaBYlMam9nQPfhdhJGSKzQIHjr04R9MqIanMnZi0u4Ed2IynFr7B3WU5AwgUU2zVRGF9CZ2U7kv+kEP4+h461HFVnQtmlfZ4/in0IdxbDSPkT/3p10P+kioqOH1vLYK5IFw+k2nkqrcPnxD8MrYnFQmkmr2pXc2jWeObcjqeqTILgPSs4YqlNwp7B/Ft5i6LQZczRFuXU/oUEj0jj5UclpuZupKW7hKFeG+HdQjTMN6D6+hm/t57n4Vhr3kio8lohllP/ZlBQ9wqz3dtJvf0Mp5rftORaIl84iEvCQqYqvuHkzKPUia5E1VuTI9euMsTGAoAPKi6ISt0lUbqKWUFdwE8ejk8k67Mti3QX4CD9DSR9We2yllU+yqhliOI0QJH5OoE0/5pA/44cJmnCRpMaos/H8K3hK+IBjiioyGNs/I2pGgKCN+lQ++gr3NTlx5VT4CNKZqMlviq6LH+8iH1h7ziR84XmRYEMH3SRe3mjmVUMxx3nkqrsjLbYNwbrzCFbUM18zStE7dlFr5IebF3A8xw/llUm8NBPhyK20zFUhfZ5q1EYf5vBI4y4tVvAaP+9bJByw3mADjoCa0Jm3GfXtI+430jkbiUEPHDCddh6LkUQkq7lAAAgAElEQVRaUF30mX0TdiNqs4R7g1TozAhjnf80DlR6cyfIhCFWxvh57qX72F5OOJxGSVGSfaNcuHK9gFcHtTC6V4VQrYKWl68plJvI744ShsuYANAn3ZJlCY+IeteH7j690AXBr8KYNt0N8MHWo4wfDQeRL89glc1IPkXkIph7kHvDLlCWXcKalWW4W+4jOPwpgcpuBAwS4YpUDzlPk1hvM4/ivv/4bhyDtowuCgP0GO3oQPjXNF5mxXDC2Qaz6VYMtatDvViNlPpCOrKGkfrsBpulBhNfk8e18keIiVmjpxCDwoRwGNPO6InxbMj5wBSu0cc5i6IVplQeeoLW0RNEH8/k0RY/1ikmULtCFm97FVyVTyIrHk6+fhUIIGPFcjJfHKH1Uy2MNCWxjwbXZc7g3J5DiM5RyrcaEmZYzUzBJV6/zqFbzpzug5/pf6aA3JjzmH0OAsdsVCWGkZzyhogIERYOuEcZUjgM06Hl3EUyLVagKAoviu+xbfgSlixYgKvST9w+O3A6TxvTQU+hUo0CEXjckIS/4kSc243QvufE1/NxCFe/JL/zNGf0dYh+V0+mrB1xM47R9+IHRk5ypE97HZ2VU2j36MVdSZeOd2nkF8wjT1yVhdPT0NKQwMbehFViaQyVmElJ4y90ZGPYcnY489028K0xB6GRBHV3ZDn0SQ7phnno5mcxTakBd1kzfksUIvK1D9T1/Jdd+t+ifPwoF/KssYjmMVkckhBD+FnIgzmBGE1OZMm7H1jsPEet3CZWWlmjNvAsMY5GpJx/wvuz6/C64sC/LwLMpIZisPMv+U3p9N6pIqv4NVGhF7g4TkD/5PXcfXYZ+x4Vgp2a2duVivCAKzXOGawpEuXMkATGMJHyuDrmPgrmWKIYZx9HYmbciElPLqtyhdg1VCNuXYnyx1qkMh6RohaNov08pIot6V82gC+xikw+IE50yhLOWWxH1sOLtsLjFHSlsydOnDUX+3N95TZqP/XwW6ON+NcLGT5fD0f/max7UEFFbCVHTkWi8uIIU8/OZLjHIY6r7GD801R+nLqOt9hu+qdPY1ZSNn/nhXH+oC/+6qeRmPgYS0Mz9DFlscIC5i47yNbUU1ycMIo/utL8DdTEYWcpfb4do7a6m1ejRnKzoZulV1IJONJK7ypt0vTnsLEijCf2Gnx9PAoOg9+XLBZH5eL4QZaafXWs0PxF59J4nooqoV93jWdzh/BM3x7OXqTRtpTHP9aQmviahEqQK5uAos9AlkRNYNAwC9ZMC+DI8Uh2TM4mteco5jlCdN4v5oePOcdXF6Px5hxXnDXZ0WlIo0YqKmmzOXLzGbEXL3DQPJzBfRKZamfJPIUc3vd5y1nzd3wovI/YsGOMc65k1M4RZA56gVRpPEtdwCpmO75jahnz6hEK+yNIuh5Ljrg1/qITOOHdy3OfmWwdJWROZ18url7D0JsWCFQNmK6xjb8Tejix5hXu5t6sEmlHGGRBgNwPok4XcfXeVS4lObMoqhgp/ZPEfIvG0iMd0dmr+XQllDL9E/QWbuR2iDeWS2fg4OPMfMDgoCwJL8N48uw6gyVbuOWihYf6ZbTKe1HuHILp0j6MEu/l5PrxXOnrRnXEJ7QsxBE4ChCcnUKItgiCulsI/RZQ51/LyckTWHX5FGu3DWNL+QcWaizHs+cauU0T2BNxl7vHg/E6l0PtMkW6WcSgW770Blxm/S4B1ywKsO02ZvrYKoLOzuSsVTrHVz5DZZkF33W1+Jc4jAKj9ag3t2ARc4vimmy2LXtPatYKXgRuYFR+OMLPI/krnUui4CcXU7p5JzRjbHQoh8VnUF0QwuKrm/hV48i195+o1luEcfsAFnmVcqZ/LX032hH66jiPhm4jfaM77pdOIOOWjdAkijf/QmmOSWbtuzy8TuYhFdOfN5EjcNLTZrvwHP3+3KNJ4xRFfxv4OcSN9AsjMTk/lIbf01nwMJxPR9Ywa+U4BG82sbPPcrY1HsVr4XS0RguR2L4Tu0EjUDW35qDldv6tP47a3lk4PJxCR5gh695ZYdxXnPujK/gzbSIth//x1cGeLh9tJmdd5t+oRnT0g+nffJn3B+Zi9f4mh69d47KTNvPO76Xvy32MFz+NfO9lwk5ksS/GldflncyOe84nnUA0/kgROtOfoPqDyBzZiryzLqkKVZw4vIEpQ+35eOQYaZs/k71BSOaJ/kwwVOHaXm8yf0/kT95Achf4MFrj+n9a1f/jqalt58mELzy85Ujcp0hOZ7fSKe6FfvENhH12cuOvItIfH0BPNtXtAk4+G8vEYDNua5lw7qcQyoJpit5H4B5rLiZ+YWLMfm5GO5OhUoCz2ACU/U7xrz2VqXL9+Hz9MX8+Xqbmmy5zP+/EOd0bo5GjKfd24+UTZRwbtHjLK94m/2G+xj+shL9xXOHKkV9BKE90weTPcMTHJyDbpMzARZdQzP/EsdttJH5Mx1LBGtEZAm7rHUevtYWxgxeSMrmEn5fCKY5X4EeKFYNNmxCZqod40UDSmz/Tb+tplEcJ8LD8w0/j96ybXY/I2mcMUFtL2ARPFvoqIOhUwV27gyTs2CW+nVGKUlQs60XkYwDjP0Bn3VZYOpxZGULiFKVxm7sDA5FZ/JIDQ/d71Elspt13MjV/eli3czSJdQ48iTqGlrc/Ua+yuHoxABlXJd58u82o4X3Zc2I/9ndvYnrZEI+idigCnZ0baDAtY1U/HTZLivKw7gfwG40JB0mca82SR69JLtqI/rJzKGYL0HznCb0Qd8kUabX7DJo9k83z5+N9V5XvK3w4L63O+RAIm3qF2uAg/M96INKwlsFjbFnsN5UZNyV5O28O9V970H19lan2V5jtPRJl2St8q3jL5RW70RmaQcGIeO4V92KfHEpy9n5W2w2H34O4XaqI/I43jD4Vyg9eMgd3UtwUuLHTm4mDNTkYMIWQg/MIi/9H8OJ8hMHuiI0TsC+yEzueESuMYUViKMHDiplrOgMjk6EYWPzlzcEVbBaJ4XV6BnGtzRA0lwGfFYj4dwP9zCQO6U7nQ8Q0pnerMefgOtY/v0v3QlFO9FbhnXaX2o0VlLRepsFcmuYkKQDkzlhz0+UmzVbG5Mw/ylTvtYj6XWeN9gMoNuGfSBvG109iOuUmmSv3k/XhFgYFncz5GYmObQt+fllIZLozdIyAOAlpkiLTyLY6zqlsYza+VqH950e+jCrHQOcvFQ7pjHU4i/yBufR5eor3xvdJDT/A/UZjmhvj0FQeTPI/FaQnjSC+oQWFvjb49gni58gDKJYuQcx/O5ffi7PPz48dMTN50v8B289e4Z7WOZZHq+IhGEu/j2lMOXuIPZqLiXhwmbNq9nhf3Ihl8TIOdpQTaHGYMZtmYvxGnlN6IcgOzUQ20IjhT+5SvSmWfS5NZEkKKUmzpmB4OnKTf9CydxvaP/2o2ToM3IcQe3kvyy4MIb3zI+ddN/NRtgGnow1U3NSj7KMFC04qkGmhSWMPXAi8j5qLLDtPzUav4BoGhbqoeZ0gYNR1jn7axDA70FY9yZ43V3FXkOLu8Q4qRhxDrOAbsgaSpJSmET/jMxnyMlx9vJYdO+/iNvcAk6fYMsFtDB6zM+jXkUnmr07kX/9DUd4FwccktN3f8evDDfx6xnEs4jdlC7w5OPMFvfWfuHM4gIme24gOWMm4wizCOhUobPLFO/oLhYn51IbGM2rsdB6NBMdJA/7LLv1vsXZlojlE+OVdAL011ci/roVnjSQO+IPLh/eMERRTWgJtE+CnynlMbl1h8eWF9PZtRmG4GDWRi4iMuo3ayFYSygsIn7WW+2r7scsyJe9WPDcXGZH5xYkP70JYPmMFE8qLqdL24lvWXXTjv6E2vD8LPRfRVazKmroBPBoTgmqKCB+SHmC1/B+ZauboJbdQ1WbF5GpN/LSeoT53FQEVykQWv6ZhSRfP3hUxo3EWhd2V6Nb34Pb6JoVahjxGl61G4wlKlEN0YDIaJTb0thbTIVWMmE8QdnKhSLzMZWW9CBX+0nzf84sCsZn8ED2JhtJCrHNEeTqqgVdixZj8FWevYyA1Y1v5NHoIDdenc1j3EACuJoaU5RdRIgNCvy1QlUextD7P7CYzxnsRNwY4Yb45gl1V1hwYf57PsSm4h9ylYY0KwkoDcovrCK/+yd3IBZyIz+Lu6ouoMROzaYbs9xAjsF2U2FYLHP1W40I1P6c54OI9kYL8tbSlJXBmhScog8BGneojAZzrrmf8rz04jQSmCxEIBCgWu9OQ+h3LwFayTM5h9XQ9aiylCzvcTWtxi7Ji8eIhLJpykDW6UehXDsMry5rIH73s9qnkh2w7bg3GSHbnsyMxjXGGWrTe0cM3+jBuG7exff5srHqrsFnnQ3BOJ99KDtBxTJeitlgkr54h/owr0i0+tEVWcHxufywas3g3+CAJLzwZvUsHiWRl5hy2YUv8WyRuKPBxihN+Ut2cuPgeRRE7HnhpcKqunKjbseyzCGHZ/aHojVLh78sPNPK/rxAVCoeRcGccnw7tQ6rXiLFr51Ar38q/8nb+5imTLhFH3sQTTP54j0JDASpl3RzsXkW/zZ3MVB5MMvDr/zEXa/TgXNkQQBKhMI0+p5UJeuJF0twe1i/tR+fTQ5hIJqI31oXGr6VMCL1J0c9UFndO4sjddfAC3N8HIPytxK+4NqycxFGVTEainyE1g6Zyac0JoBGQBtGnRBcW4KkgwfngN3xT/YN7nwFYxA7io/UDnn4dj+3Q65QZOpDXpUDXibMUdflSUvYSfBtZZzqGLTJJaDeHkngfXj+4ywHuczz2Opv9QuhrfhNDD3kCNZZgo+DLQzkYcB4i/33HPKuAuh1TUVc8hdbIk9w17mbgaBvGnX+K4BWs37OSXx8v0b55C7+LbuG4dTnt9kLS++zH7h+ktQpxkTEn8cZXFudns2m3FZdVHnFWfhvGS4YyR9oKQWUcootXsu9KO8dvVeCe602XvjLhspIEtU7EP3kDh84thbuFBMotQyd4I0s+OjHe3Ynka8lMNbFCZ/AMYknE8m4PJQOkGaAlw7FNFzAcrEz0QX9qeqeioG/A25J5/M6uosmkC1PNqVSfTeBF9QusJk3hn58L82M1kZZvo7pRlrr0ewi73uA79AM79p3EaEMPeZYOLEmqp/yvPlcyC1gh8hcDyxweyzpycLX//127+g+ipqYnDNg2jWdtyRTEqtKRlES58D1blt9FwsCHmoETuDr1KYJRp3CaacbCX9WUlpoi7vKL7so/jHILIOjFGAzik7EOn0LURBvW2DXhWX6FMdZB7PVMRmWvA9H9vhI7bwo6b97QL6cdi3V9uRclwvzJ5rRLSLLKfS5/Q49QpOaJYttOYr/m8Cv/Cg901VFIG8CqpGA03s+nNn8Sbx9rULlmMzMCC8lx/oDMyAjSEwvZUfucleoVLO+QQUu0gzuFRcxbNZ7KtXcR9ZUhrFMH654wdnQ78aB7Cg8/1OO57jeiITKsEazH/0AUWrltlG59wZtt6ZRs0MFbWYpVFU8YyCEql6YQMG07HgVGRH1sITTcgCiRWcSujOCiSgBJCFmspkRR8zoOqRixT0+UtWGXmLXBhQwLKBg1nkEJr4jLCqDlgDTiw6WIcNFGaD6dya+fIRw7mVtCIXF/X1I87xmZy9S4F1TJ5K8PsQ2oQ0buAU0t11D/FY1vuyncLObwl+EcWZxMY5Y97BhBzGcDJm5OQOA3mWffLzEp7R0O8zbQV8mU4g5DRF3KeKjXzpYmOdZ4WBP38SVzlKQ4/t0XsxJDzqFD49eLTDVsgyFH6PwwGO8ZCkx+d5pNOgtZrryTJ8O30NAiIMarlfFVVcRMK0PRUYwQ6fecjj+En+AgSwZ2oNxyHsWXq9mVEI2p5jf2r1Uh+8wq7r4Hl8JpeIiA4ra+iCYZErI5mK6MBaSXHcNrky/iA5RZJz6GZ9f3k7dvLhVj7jJ6TTb7z6lzL2og5xvq+LvUlUzhdkDAQeNZVM25z0i3v6Q8VaDzizvnW62ZO0WAtIIUDkrqvG78iZ65PHYPTJkWbM4UEQMO73zDNKM4PNQbyb+3Aa0qIZNm6lH7Php5hzCmr4hE4XIAaYaNqLsGEB2Zw4+DVzicF8oi20B0m84h8FyNLLIsK+nP26I/eK08wdHsUwzeMBGjB6Fct23GNu4nu+0UGRBSgYTnJ1bLreVCcgTrT43n2Pht/Hi1g2inXPrPscCnLpHr/76y2NSfEcWv6NdoyxeVXPrnS6O98CSXd5/H/pUFnbmNtDxsp7PIju5/tpRm+lO+r4X7c9yYaHaar+utsFCQJvjnHxJUQ3gonkZxwiZCRvSn7yM/yidHU9p5GBvzHgqyNyH6M473ppMZ2dBNxbG7iA+2pHSYE0MVjHgXc4blt/y5NWAr0feX82hsLm+cL7N82w8Wfwxnh2QXYb/TkLnmxf2vjmy6fp3xHZGMtwpimmssmrH/OLs8muBdt8l4v5Bct3mkHbjDfGMBOr7z2LHhKI6pqUyLLmWGoTMbgw/y1VqZ1dvCiFhTzOj2lxT6RWD42I5FxuoMDfKlXFjLm9Ao6kSG0Tf/MOnO/Ult6yUm15LL54uRldfmg2MFkdPnc+v9S5S8+yBVXcmrlB3o/h3KycOfsVHVRbjcHDnlRywrGsEhk5uI161jcvYk6lcXYGBxlhnKgv9SRv23KB82tuZCTWOw9DiOX19t9t87wSRnDzIrC9DVcOfxkrNs7F2DrYYn6+5Pp+vqW1QUN+DracC9mDtM2bWUXX4rkMYS9bbBZD8oYuWhBzyrHsabx6506SrSVf2AskGuTP+eQ3dPEh0lAzi0cioVRj/5o7GEsk9f6RRzxV+hC1+zQEZcMKW9v4BCyxV8sR2L1+pvjFJfh4RxAbrufnhNe4rKRFPqan+gL5KJj+FaNnTex1JXmtK6UhaJFmHR8IFIUdgyXIhHsAerR42kUD0O02gB0ou8OJmswCTxRFQyP/Nm6mIWjRfFXP4TSg+/IveiCCfbKQwtbcPbeQqSU2cgu0XAxvpBpDsvZ/TFGkoHXCdpRy0jrhZzdbABSBmS0bGfWAaypSaMzS3yhDa28PXtGkaMDmJehS1igpV86g81dvtZmiqgzrwf9bUpnO47kKDM8/R/psDUmjUoqK0BYIXrFVas6MJrti827jDESUi7OPzMSEDQNgpdl3yyrg/EziKOpyKfGT5BwPSu2RiO6WbPUQOCT0qSV9fCh9dHOHbzJ9mxl1F3EFLdvRpaz/MqW0gdvYTOWst06/7YPnyArUINTrGPUN8ri+7nZkRl/LlQbsrnd6cxEYWsDU3cUdzM4tkGPHzxGSfDGWQP0MSj0Bpt8w/8CO9iYOp99on+4NMfIU2SfTH+UoPF8hOM6yukpOcj1RJ9+DOpnp6rBQxNCiVS5zbZhpEsHGhAYZsGcetz8U2fhI51FIl7DLmZkUWPvgnKry6QkdeElB60l4GoGliqLsJ5ogFxwTEs912OYx8fenUhvfod/+T6s2WdDuQ9YlvmK9S/i+C29zyNcr6MCNxI9+ZoLndEsTpsGpj+YPnVmXx/3IOcyB4ESqPwnHiLN/VbcZYYxBqjLLbl7uf5DVWyKcFzcC8qHtJcHhYHkxz/zzw1rLnFZo0yxPsb0iyQprgnDBd9UY7/imCn6GJqgm9wayio28vgKzxG9ryTSK4R8CKkkEu7Q7DYuZgL619wQEWGiWI3uP5nNCcnXEG+fTMVn0Mx8VzFo2t96C9MYpW9OT1nv/KzPhylhkJatGDShS/8dlBFDW0enPnBzHXG//th4VB2oYmahsFoLC8lXE6Ep/1Pot66AbV8uKq+nnXzrtNKEpo/4ynQ3Uwr8G7kdOaK6NLxdjy31M9Q/3w1t4N+4fVwBNPTVvDa4Q1lBVkc+nMC82XX+DuogAX3BpJ37CTHCv8yynQkkya4YiH4zLOUz2RnvMT/1mOyw+Yyb8InHgrP4TdvEsd3PkaYmsjApUfxne6KZ782vkXJ0u+oJ459Gqi9OJX0QSHUrrfj7/k4XsdroT5EgmEOBjQo1iObbImg8AGtg02Qyv7KEftvLGnIYvqP+8x48YpH5VeRiPBlgvJJdsfOJ6/Th/7uk6iQH8Vm01kYmcziZZkCbVH1aL8fR3tdOrHqYLzwENnzuqlr3k0U/N/y8R9ERiApjC1sJTPlJGsWbuXMxlO0OskhkzmI/WKPSTDSojhNhfu7lRj1oQX5PCd+1EZxI/8id1cc59S9N9ToNBHrZcKyu1eYPmkKd/cNJrS5DKdZKiz7HYxU43Wyq6so2x5Oh48Vzion+X3OiXf3XBjR/gPlSUdY5ypDT403BI8hz/Mx90se8KlIiOj7NMwlVuC6dTw6tkd4sHg+bfb9qZHqx5XkfuQOted1qgs1tj/pSTqGqlE53Vm/+Gbqxiqx9QxPFyD0jmHDn/m03lrHnOSxvJh4HwN7UxIufcLyqDXrm3qIMXRBmBpB3K+3rDB5xNVftqh+DaDT2J6CQTN56HuWZdaXcdOegaZvNYu0eyixKIElp3mnsI3hnYXsUprGfurYy3qeiRzjTrc0Aw1K8VSSJnryPgQCAUdN9ShYv5y8WhVcmuJRX7+L27J9GLryJ3sNA+lXLM/Ov/7sPxdBor4MIwX7CAgwQk32B38kBcQ+tGLM0HR26RrC2kISumDUTBUG1Blhdvc1xx/cJDX3G+WnLuH98B6pi+Zw7+N4shZNxHVWPn063eiR8mfXrjzeXzmP3/JwSo6OwCtlHpqeX9B2zmbgjMOk/E6hxOsiExZb4i26jY2b3DgV8xFhVyNv3z3iyNsuZkhWUp6RR08+7M+8gpPlTTJnVPG16yHG77/gJpBgdJky+X7XsE9/S8kCbfq4rmFnSypz7l/DM+UEDaKKVIy/x4E1mzlqsRrfx9s5P2AcsRWSLNg3gttXkxnvqcDR4AR8dyohojCFBXIJrI0cj5jeSB6es0dEJp74LQY8ThtL36VC/hSp0vChhNzYGtK91nN2zloan/ix+8ZTUobvQfbdNuLbl6Gx4RpzLtlw/fpFIr9Eodr8jsmO00hNaeVBuyifHQM5r2NE4PRiQnYGIbZ/EqM+h/E4pgsxH22uzdrOY99f6Hs78Cr9PFv3JHOgzyjcZWawRmE8zXW+KGqswWySPlH+9rjN8CfsfjghSzT4W5vAw5lfmFI5CxdRHU5pjKRkwAJCx5fALhFOXtXlvY40c1vnUJ9xFoUTa/n1LAF3GyuMnnSwxKoad8VePsSV0yw2B7ORMdzvUka/SYbfVz8zdIkb+a0PmdpkgXjHLiJl5tI9ejCrrsXxRFWTiUmfqdLZjLd7LmEWJrxPXUz/NgH1TfV0OYRzuWAcT6KDGTPGGA2Vh2x2MODF4RG88Ldkwnpb7G9WsLy1AN/nkYjmr2SU7SH+pk0lXHErQ7VPM1VUlHNh7gxY4IHs2y7MXJ+TzV70tHazzl1AnjCF5IWH8PFUZnvEa8ovP+emggUnrCwoUNGhdb0XnfciUFVQpKdkN3/nC3Fq/0jI9m4s7SvIKN5PVFY2L60yEJ2ShbQMFIl/ws0gh/MpomwLPo5L5wOEwgcItD0Qbn7MVom+6BvnMTbbhSut3rTfTEFWZxBKZ47ycdUx7hnt5olQwFw7H5obLFh0NhM7ryRWn875/0/5MNYWF/oGPKI2di4ux+8xTm8wnsunMXqqHX1+9COi5Csv5Do5PVCK8vp4fobY0mI9kake39D6rUl+viI6cY/QM5hGjs0rPta2cE75AA/L+tHuV0xk5TV+3fnHvEHG9PvUj5F+GqiMgTNf7iBrM4uqK130keylMascAx24UT6c0fn32Wo9DceWb3QpZbPSVoffHb+Y8UmbGKlrPO/Zwq6sdk7/nIDtyhh6dBTIOjmfoWMzaXieivIwX3525/A89wDJPTpo7Y5AtCSAntYHXFJTIjHdEWNhDss/FRDsOJa8f8MYHfaQ1eP+Yfc3kc/h4KwP4/rAzmdCEPdDoHEaUAXHoajcOYNbRDGTDA15cVyfsDQhCXRQgiRNtVAdcoerGDGOfAQkYqNxjVvZN5jx9jQKcxfzuaOLRfb/ePJCnNxxXWhUSvBGZj/bVRUpVl6Ga5QulfXLWHpRiNdaAcO0AvjXFUTSS6iThYlDYIjaOWo2PUQKXTKP3OTzlH+YqVbgcuwMj/O+MmtmNHpZ9VyK+YDOUR8O/IVducvxkdtMnU08f+0gNNAeB7vfzM5vIbnqI6/mtyIVlo9NsRcDhYvJ7gum8w8xNOYf4iHHWLd3E/pjlFhjvwmfdd7Ijw1g4PrdJGtW0mYozcObLwkF5J3gvqMNUv3H0ZVegI3COGZ6VBPZvI8Vsj94pfGIV/vuY+Egi2SGISPWnSfuqieiVQ68KhSgUPaJdzLNaCm9or8VPAtdiASZRGw9zW1hMQKBGqP6RpNf4kyHQitOg3RQldKnxb4NFwMfDJ6oU/tiNd0XprJ51Q32hXgzSSAg5n4ZmMSC0Qh+VBjjP28KtrbBSHZtw3bAQkaoJPFJ5CcRhVMRkY3nT4cuVzMkka7Ywq5rQtbKwKbpKQwY4oh/6iyOjI+gRjCb3sn2DE6S42PZLSSMm1k105FCdVkywy6wwzca6dFTGeJ3hemT0tm1TpFN+WrsuvOYp6HVTBtszZNrf5jmMYEBj/ZRnFvC+6GuDK+SZsmenWj+qmeAZRqtgi5sGE1XRh3m34aj0PcSZ0eI8Of5RIQdTrR4n0db8hEt6d3IeiSQ/eAqmi2H0asr4vcWSLtjg9KbL6gYeWKz+RBO4iao/R8LXKT9vhchx26iIT2Z1e/0abj2B7aogecKsArgYtFVpJWUSRN5j+e3dmQXD0YQN5JuvWi61Z3okrIh7PFKtAXqWBgpUJTTxqbNG/FZmkT5EB02LJ/IrT1pPH1qD8C2pUd4au9K7ubRvM24zoWjnzD3fIfp2EMor4vjQ2EmjjNtubnkNiVdVlsAACAASURBVCZ20QjHmLMlYhj3t8wn/OIwZC3KkZp5B7e0U3Q29SNp4Dwq6/uheF2OS3+XMdtiK7kDvSmznkXnSkkWH57M8cnb8A49wWibhVTXNqFf04P43dckTRuPdMRy/vq/xAlFHFdZoXN9KxoKR5kcH8SAEFta53txJaz1/5aP/yDqcrbCqZO/IDrxIcfqdTn07hDiC6yRfNsXqR5FfpldoK3anYNBcGGDIxYThiOlcZfIY+8Q/TUJt5kHSZE+xDORg0yq/cq/6glk5EnSt/ELUlZDGSeYz0C591z+p4PexHocKhRIqS3DZZAO13pUude0geFPFtOytgmV76rEvjHk6fIEfoiepvbhBvrjhXWyIx4hE+mp7uJy7jkGVUYxy/gns8RyqHi7kbqMQsb4KGIqqKRWT5kPDRJUZm/Cr+AYn94s46OrMcZ2i/mta0xLylc8aiDCTJQbQx2I+ZtH8uqT5IUO5WHbIBYom3Dr8le+TTcgr/gfSaE3GKw+jwTFZKoTXvK66B/+ERqcTXDDPu41CcImHodm0tR3EDEeJtxbvx8txb5MQII6nnAp8BzqK1fhP66HM9PySBydR9LVNZy4KWCHXh25SSrMvy7D+UkeJHcbob3SmeWFlczfUsSsBG/uj7rFph0ByInLotzXg1PvT3LD1IsRQWq8WnWMkM5frJh/lMruDkRLFUg6sBazcVsJLC/g7os2nHIKUTa7wMhFa9ljv4/EyoGMDNrPtYClhNx35oCpNxrax5GbuYn8u8l8dnTk5P6N5MgvYl74GIh2ZdXrRpwuxyCeb0t0+GFmDbPjlGI/Dt3YzN6py5gsKo5lsBTOz/cw9eoC6r89Q3KwK3O7h2LysIZbo7S5tHwbZz+E8bo8lY3uwWwbs5JLDgsIN13EW3FnPqa24qCVgVn9LM74j8X0zXDaLm/DZmEPDvo9SFbbki76FTtFXXp7yklqbsOoZxzHFkwGs5EIdLMRnl1KpP5hVnhoMsdZnq63u9GMmM/ZyN3U795LvuJzFALVOXJvBoVPgpG4/wafL0a4P9rOjkkbidV2Y67GOjR7BqNgO4DxU3ZjIzmHGXF3eTNHnz1JJWQbBDHF34ol2oHodHjQr38ghfE+PPvwgJgjgVxP70JTaMlcZVU64yLZum0YvjWnqf16hPqnh4hW62Rurh7jNvbj5gMp1hrJYqyeilWZNnP1ohHdfRHTj39oitjKmzJjPindYM7kHGQuvcHV1xCJ+HCiLbfT3F4KOXZgHk2LywtEnw2mTeY69eET8XJ6Qc4UU6qfHELWegqbzwmI9DpHy7evtM65SMP+YFrNm2iW3svbt9cxF8/FTXIBp2tdqTh/DMEjH/w9ghAr3YPuIQFu5z7zyN8Wn/MpnCk9i3qTMxk/szBT1WLPzc/0aBtw33Yew7Tu8b3JiEhFDSJ2ZfPgrTY/3utw/PkBsq+rsPGgEg/OJFOc8QUlPQFVwlg0HcaS4bmd675dyC/4xNHHidx75sK8m5ocdrhIh/1nbrrsxDViCR+PhhJ79j4W13fycMcctnn8Y/feUtL+9pA7v57txw8yWW0oIU+/sm14FjtMjZi80ZjYCYX8yV1P6IKDaL3wY721BI9KF5K36iOLH9WR+toew8ujydq4EbH8S9RuFFL05TCnR67CxPAmDNnwX8oo0cDAwP/Phf3/xskzjwOddbP51seC4jwZMupqaBsoh3lJO2+qPrNzeji/jh9m7cVk9E7tYvARXxQlK8gIrSRE5A8yMzTwr9jEd+kBNIrv5m1iK6Vd43mTrIiOLHj1l2SQwRZ6LA1x1HbmssNwomr0EeSUoSApyigJXa7XjGKtUIslPwtxvSfAYch04rreYupyhr4CXWq+6nNL3YISWx2m9TNFMe4Joho2DJz9AZU5P6j6m4/i8GI8lKwptskiov89+uRXs9t2NytX9cHw/SuqmqbzW8yJ1OJaIjvGM82hDyl91yMpbkp5+WX0K99x+YgvKj+mErp6JIMCFlA+ehKrbG1YdziNr0Ihg9w0efkyitatgWTH3yb6WTBiqqNoWebNXMSYukPA473RJA4fhaKDCKlNtuxsraXJpALPrJdEvXoLbou4Mq+Kjip57CqkcLsjSkeDGLZlU1lVZcsEbz+W3d/E0CnJDDFWxMpwLsdPzaD1tyfaQ7TZp6fCtJ0JPHRUY9SB78iMUKF6eDK+BRJsEEvA+vRWFGZXEPH2OA/1QsiLfoqXTAU1xWH08biFaOlicnNOkjRpB3IpN/lS34CYSQw/xysh+GLBQFMrHrc+Z4vLa77KtdOa8o6jSnqUSL8lNfk9L5IyeNITT2WnJP0KvvNotyvNbdkcGGfGcjM3RgzfT3aXMZ86tLAeNJFZXtNIFH1ETr4WK0aEs9r6MSmvh2CvuoKkF2nkqBUSEryY2otmnMy2IEd2HZ2e3lyMPsMj33CkJvlhMKSRfis3M7otjrqaLGSlYikRWjK6qxSZ4e/ozG/l3uvjrF/dF8VL3/jlm8J42R7MRzVz+1Ioh4OCmDHkOMnzRXnacY5PYfUU541CtCCHlvpS/r4U5c7LeByMx7CzvZ7ReuXId/dFRaGWyEvPMC4/y1iRr2i26qJvV8os+5dsXLAft4SVWOeYcsbRmSFiHynTNcTKzIfwD3m056uhpTyai0ltuJn5kdH9Bc2wCVhapvHPVgfdqEL0pK8Snm+E8WFtIrZvws9Jncl70rio9Id9LgY8iZfGSOwJOvlBPEvxwDzjI2mfMvCXLyfrSTpkr6TL4juinyKQObGLt7uLac0SQfBqA3o6TyltteTX4KVUjRoJomOZeSudmjwRqvTbqFo1n/iqTYxwCeKt2GrOVZlza5UT4pqxfFZaz1DJSu64zsRw83yOV13jdcc+Fk0bxbzUb/TZuo+I636UuH2jfIgjYYdvoSd0YY2jPG5j3LC9L4JplREpr18x/1w/0p/OQ03iJVMm2GBgaELci3vc+/YNBZeTBFgeIejQaiY6b+VP4RCUMjZjp3KU3i2F7DnVgsj7GQx8FMBX+VC2+Owg7MgZNJ2zaR6vwPArafwSyWKKWQ5Vt8ZiqrmHfcefE/5zOaOdGtDp85whWk85u+QUW29N5n1XFsWCPiweK4qO9grm+D2hzMKR+OIr7N07iJo3IQQO1eLuzL08dtnKl6Z9uOgs5OV+cVpsdCiMi/8VGBh4+T9r7P+5hFw4Hrh+0SJE+vUlKC8W/T6VNNV2YNwmz1fHWkx8NlK8+jl60zbzJy+bCqCw9iOW3ZJ8lnnPdDlrNie/pv84FeZl2hB9VJaRiS30t9ZB9ctIEuSrEJYpID9JGaWKL/zpaaBIzIDExl90l8sxu+gJH51ncqiskWeXJHE4mkFzoifZA+oYKTKI2qJGino0eaSTh3zVMg6YunFM+JbxvwWk7dRn/GxFNOaZ8dJqOWrvfUh8lk+eTSEC7SZ0TUdzKv4s2wz+0KakSf7f/kj21aMw7QnuJu/5aaaPYa0RUdJGrCj7gK+pEtt/66OomINIgwJ9DznhPskLt/MrmLX6OzmRd4gw+M6EBmc637xl+i5DrH+NYVquF5d/SxBd05fpKuXclf6GpGgzP1Dl90hHdNxL+PT8LaKxDfh3TeOGpAQoAibSZHwcyLfp1Zyqnsru0CDu3bZH8gQYzPLBa8I01H+vR9IyFJ0+g+kncEFO8jG9feoZNnwQX64VoPghl5Av7UgKRNhdrMxGmyoenLlN/WR7hG0puPW9jtbyJpL2z0MrfC+dz7dQmuHF8F2WHD4iSu+wAMyHKjA+V4T2lrl8tUpma39xkpN6aOhw4md+Hqd32LPl7jqs2ycQdWcf8ta+6LZ6MdjMn63PlXkeeo0NO0fyo8qJQGERcgVFiJfXs0HFAznRGjaoP6Dt4wja79wmc/YUfv09TmePBK5jrOl6doxflk/IFzTyQrOIE1rhiGCIpasXep9VkWlNQMrEgdKkeIy1VSjtFfJFWY7Rf0ToqHTFZoUyUphT984B90A/IpZ7IT/QE5fBE5BYMRmr5CDehiSxZrcYB/9EUTpzNH92H+J7dSASIkMJa/pMw9c0/JbNJqtXBH0xIT9FmjBQd8WgYhDRY+1IXBnI3CWeaDQnoD1kM6aSbyiaYcddx0jqxeYze4AmjxKfM3qgEfVNYkj/zqCmORtDFTHiO7TIFM1k6B07vEyk+HrHgXmZSXS1beB3pwhmQaV86i5Cc9Ja9jTI4z+7kSPbC8kcroK/hR7N0p709AvATMePhPq3JA9yQ1L8Cd2V6lDeD0GjIjJxwbR+MqD7ixYKXTv4N7Y/XSYaSJlKIvrLleyRgRgq2pG7UZS6E+6cW7GJwDsP2D0ziBlmo9CYu4t9Q/biX7mDlQ0zaZDKIPnnFFzED7FymQNqSec5Z+nO3mtn2bcwlmv7r2FgooKVVQD/+v5kinMD2UWVGA5UZ3FTNDbNjVSXmuKhosrFyx+YPX4IxfmSxKU6c8hLgNWkVRgnNKP6wAWTXak0YkCLcgX94sowUa2g0kgRwyp5ego9mTqgiYq7p5ELNKes2xiJq1fQOj6dP69+Y64gTvHfcopeJCNjO4TPtVcQKqaTK7kVD3l9Xgpucn27DRkxv9CYIIEgt4lKhfXod7eRUBLH3t4KfEWEBM7XYbj3W0YW70fEpgnptlSigxOYvuB/sfeWQV1A77fv50uHdHeD0gooCIgKggoqYiB2d3eLHdgd2IHdKAYpCigiiHQqISDd/b0v7nlx5s7cOb9X9/+/c86a2W/2PHueN8+sNWv22rNP0Lo8l/Sqsv9Io/5bmI+Qo/uCJf0GEeAzjrNB6zEruYtkvwCiPsC2lcfYd+A2a+3vsffFR0IMtyHTmEZtXicjXJehkGuKcY8c3X+/0Lf4JWlPxxF0zJPeQ7X4rdPCJN+Fvq/dyBepQF5Omyt6LdRF6mH5Kg85Ax9sN3Tyfv90ZFMvsKE5jUDTSZwNL+Sv/ytSZ42k/ek+zIuUCNfuoSmxnAP1x2mtK+LmL39qWzKZFT+GQqE6xt6LKX+zlGs1ikRbhLOm05akRgnKXeyxjbjEgkQJHqpNJyA3Chm7sdjLfeZhtjo2rZ20PNfBSLsSe6Px/OsjgTpqZDV7YHK6gba6LCbOu0ClhARb1p0g4mQ+OEtz+VwYA3zX82LTGdyGjuZUYABvtKz5MuUi21ctoefJRjxfSLJc8xgHlczof3Mh0o+nYeLnSsJMWZbU1zOis5m2wgZsHTXIUNdB8ro/G9OeYZH+m16v2ZQpveHnl0H8VtbmJds46HkJx8/Dmd7dhFtLI2NNw7nSO4JbDzvpExVNh0oR+89089r8GXMPrEX642Be/nhAwcaXxGTcY0CWBvt2jaYmLh7RV9cRW+jPc7frWPfrwLj8O2fOJvEky5nyP7ksq+1HXfZCNsxwo2JZAC9yZjN/YDALdNci0reE2DUPabX7xY+mRBzWPyHgqwR/nh/gec8UnnREITnCjg6z7yyTqsZargjtIXrc1HRDMW0cUg+m0hVUQ3vTPaKrRVE1cWHeowhClD4za10cJTskWbu3gC0zDyOzxh/LRwVMWePJmOIdHLPshzFSaPnMJ1ckh4R/oqiNUuD4hQLevYvH2fYdeY7GBD8u4eXqGB7El3JZ+JDa4NlcF/2CS1kJVbFbEDe9xm+ln/z0VSexQhaHMStITj6E8r4dWN4fQZ7pbTp61jB17mweCI4z6kgH+TJNrL8xEyu9Z/yafYgNY9V5fyqcEztl2Jo3kc1K/bgzbBOSpa2IKw1FrluO0PI2Fpk4EpOxkyflbbSJ1pEkJ4qbjRifMsewvV2ctlEFXL8Qxah77rzsVeTJ9Uu8L+hA7IYkmn3fk5I3ngR9Xxb2jeDuPwse9VPHN1QZ9T9t9GSdRPDsLiKZX8iuFWXi2Z3UDbGjz8i55Ddb4Z45iIYVjaj2h2faBlwuMcNZwYTUcz20/3ZENeoQRuP8eJITTuWY66h+2MjXsgxmTVmPl9R0PhtOw/3QY7SiWvjyVIeszlayv7QR2P2N7Ys88bQYgb6hLua2HcSVzubTx2YECkb0+5SA0zJontLAs4wwVrYpIBGmysnYA1zcu4O3VU+xCqrmrc4hLqy+z46VBRzoCaSj4DmGb2aSWN3InCXHkV17iO/DywjYOZy265Hk1STw114M3wF/6aptRkIrHwuLKhZdeY6zXxpGStVYz3pF1UEf1BRGYynhws2nsYzYoEiqoQ+1g5dzdVAhq681kRS6m9/r+iCvEUPGnhW8/zuNiC3FpKWUIBLhg8Mtf7rEnpOo8h6jwbp05JWREf/u/5iP/0Ic3rE5ePbmEN6//MW8oxloZSzEfMdHBl1ewtx9J3Bq9EBWYisZN4/SbjWGvJfVKIqUcLGlhtkeA0jaIoWnZCRx4+KZHjAWE6tguppkqPkQybymXo4FOWAtV41TppC6ij7oFJ3lysxBLAzNYPmjPI4PN0dEwoaf+vLM/nSFkP3WfIu2xlAkE93v9dxvGM4L0XB29v+CZZUDH8pUcTEXwf1YG0tvXGTAlS1oxjrTMr+OiX/XUf3HjfBCGa6dvIDnCQckb3kw0u0R13POYeusREXyZRaId3CodiHN1foYjo9j4p5a8lcNwWRXHww8jKiv7SBKJwHJ0AKmft3EhtMN3PObTvtqBRbm1pOv5MwRT2/O5Epyt2Uzcdt/sv6ZEyZSG3HtX0NRjxctOUkMlpVhpsMnwv5tpHjjNF5XWRIY+xIdRQfq93Wz0FyNbWIGBA0zxLdAkd1j/lBSVMAu5xUomY3lxPNDRERe4mdOGR7Ocjg0HGLxElcM/DYROtqUSK/xSPVfzJLR/ch7dImOgavY1fEejU+NDL19lAEto5Aelsn5523kYYz0cR8MU6R4nZ+FIDCIRUNqqVxyETOvTF4V6+MYW8E3nwSulIZzeoASp0vaGGc9nDfJFzgtW8gqNT3i96zm8f3zqKp/x1GgzdDowfRxmMnCoJUkxZ7igf8EtMp3EC1zm+PCd2Q2FtO3/TMO3hYsltxBfdkl+liPwKbZDymfMOLlf3Nv2yXizLtxuhrFSYEG6jeMeRV+Gefxrhiod/MkTZqFLmLUuUjT1mjLmerRxMmmY7+7nirPMoaJdzDceReRV1cjYrCO1+tCGPbOiN8XBhH5bxEp30ZzPHcJ4klb0chfwQEjFxxKCrDsI4Fojge3xgWwOvQTzf5f0H4rh47ncepOzMbpfTIf/xgiESxGidMHNo08yb+3trx1u0qq8g4mBf+kQLERI9XpaIpm8jGrBjtVQ54KK9mgMZrr39OxsWxFPMWOgfqPCJWwxMlOkoxfG0hRfYqGzDNe50dTIaVD5CgpRo9TJDDjAuOGDsZVpJNyPRW66yoRETnHtbYAKn+2MCvhCfWaijTmv6G3ppuGmx3onh5EZ7sehma9/J4wHssYbRo09OCVKHniQ9CIucnsCZFEe07C4UIGw5IfskD5LyNULJg3xJrV8zzZrieCtNtnpkUfRt5xJTniqwi7eZy7Hl8wn/mGK8LhPNo8mPkzjDEJDCYqPYH6sx30MW4l5+te5m6LwmFZLqmHuskoNqTJvgqNonnIFl2gtnQB85YdYf+flYiVGnLTZyV/e+rRdZxPVuFxTJIVMBgmj2XGR9IkFvNr5nUkE+NQv/OFr5LhHNj2GM+XAcz23MivLktM88OR6fUlJ8KX7fnHmT1xJb+Gzqe7N5Pb1Yt4VCvkcXoLnia9/F3kibThScYNC6fe5CKxYRvYfXkjx/1n0WIcivWAzbya/ZmdKduwm/mc3IYMEDtMRG4+SooC/gnTyMpu/Y806r9F7ErdREu42yeEP8UPOfjmCOyWJq5+AhXS9jz7m0aNXX8+FOrgJlOJDaL41HRgZ/yNAsVADAS5qEn2oKB5i+h+TsS+/EhCZiOLh1dQ0fyWSkkH+vyToq7zKn11dWhI0EBGqYIBuprsXXSQNo++mHi6Ixd3h8Wnt+AVLCRu5B8GyP9A+k8Ls3vkqOvtoaotGxfnUpzi17FI4IUC0vS7F0D9gQY0GofhpL6A6S/0GVx8ixDTdTy82k6LXzid+RtRW5hO+6Awhpz/TmZbLwvbpfmi5ouG1Ge+DWqkISaDT6YHKekwYl9uNn99Yyk1KSP08Bumy07n7D03BCxm+TZQ7BiJioYHmT9luNt8GvckOZK61aiv+sCsdcPpe3QZL+MmsPfrOjxrS9np7ENV+Q9E494hPtIUp5n3EAn5jFzCI5Ra9Kj2UuOqXTeOJY2Uf/2Fzp4mRpb5M8rhCw76hbh4r+ZrxG3MBk6mKOE8UuW5lJY4M/FpDh2Fekx1GsF3YTyX0rvx+uiH4qopfP+jj2bPCyCS07s9GUIIIwwHUOw2n4phsvQ3aqUy3wd3JPBbkMmUJxNpL1DENaGMzyKZPFcew/Ksd8gFHiJk5Su+3z9E1B8R1PUfotZbxKsvYozub0XHr0Z+ZZohZ51Ls1Ul/0p6EeloRRggzr+sH9yZ/pFtr9IJDLnE0bUeND7OR0J5N50F3swzGMsusStoi3fw+lgqpMCC+LXYiAWSv2wQosEvWbNrAMIUEZoVM7D6PBRaxGHxLmD3/zS9ObzYlEdcRgZrp22iOfoNdsabsd38k3PALNMLZBUs5cLmSQQu38BJGSfqlEBWMI+xF1r58CgNg7MhRG+ajK+lLWUZRpitbCDypyXHlZuY2CFkdl8Tpl5zoenrWZgm4L3zFPLD9PlU48Ch8bc4/TUbKVEnDMclcOlBBT1mooxtzWSYuhdVrUJUjSG7+wfFcVIEyeiT1W2FrOZrfqm/pJ8IGETo8rFHSMi6VXyub+bTUwE2w5opFddgpJQtCQ5ReMqP4WJ8J4YyGmR9zsDM0YIK7XLE+7rTy1FyChww2P2N9PtNmErYU6Emg+nSYpKF/7Co1eSvlwzSXz7yMbaeIvsNVMp5wpR8nBaPw9vIE9Pt+vTrbOCX0WS6n5ymRChEOuYq6ZIv8J32kJsL3rH3bjgqtxowl/nIX3MJ3kfncLhcl5Hvz6C44QjmziOQ7rLn7NNrfEybx2aX8VRWq1DWOBs1y0xS3IdQceYSSzVdGSzqSX55Iv8celFTNSRX/CoBbsf49+spYbn/eK3Yn3mpXSi9mEU/z0E0Fn+la/88EsoOYDxZCfuMUOrPPKKrew0b3+1EZEwmfoJAJqzcxYHKZAKieghOfcOs7mLMtt8gbIsNXq5SyOttIrW4iKTMDg6NX0ymhyo1y+bRL0CPFuEoWr/1EiJ7jmP+5nR5DORr7Ec29Ao4cSEUyaEp3D5o/H9iV/+FUDdQEF6/msbjVzfJFg0mYd0ZIm3V0H9qzfmiNzjnVVJo1Uvi7W6U9T/hKr0FfUET97VEKL3zmEBHbxoHPqbLdBDSaWO4Y9fNzFSw+pxLrk8aVzN8cJD7hlG1AFEjEYS5kLxLGW2j28yS6odnlCLXqqcyJuM7gtBD/OhNobZQk+UdfRg/5x4prbEU1x4nYaAn2cp9Cddaxu9/StzaFIpZxVLys1T4oW5F/3u3md6YxGzVUDzVknlWJOCFhxvTC3aTcPQUd4PdaU9IYfsIFXRWelAxW8jG8QK2z/yE03xVjpeJ4a6WT7zcKhZbn+PW6zAW6n3F1a8vAm8BqxeZIyvcjomeNvKt+7hRPpShUzMwf/SelO27MHquxKzAOfQ8ukZp/DcMfo9ltOohHp+NwX2hgBGv40jX2MbpFRNJqivn2tcmRNWM+KTSyr01OxH4BzHOJRvhwQNs27QVnccKaB2SJmByBMIf2dyN60O08ng8I/fS8aGIAwO2MtR1FXaqAbScPURO63qu1a9iypJPWHCSWc3uNHoMQFRzMn5bG5lprIt4fgfue5W48suFFa5B/H2hzMeIOjQm70U8wR9DEX0GmSlyy3Uoapp9WT7jOzuGXUTEKIDRZRbkqE6noOA8TosW8vbKZ6zMRdE6n8zDoIvc2+jIkYPH+PoliQ3n/Bgk78GZyZEYSMUzziCBZ0HHKVjwhw53SVJXRFGwp5T5I5awdMsOzi4cwt/jl9lXfoZr4+0o8Y2hQLyV9Ip47IonoWCzlPj969Hc8Y95VXoEXAyEsDquB71gnZqQ/ksdGNPexsIfP5E9aouNXTg/hRaM9x7N/qlfMBOL4trXBqJ29icjTYusB4ZsdnxF1KIojK8VULpHg+G+z9CafJaf12Zx9sYM/EZuwla9gWc6F1likUyOxlzOGSaT2S+FRSGTCGiYyxXNOHY1eZJfFY6Nbl+uRVmjpnCF0oEBWLVrYaPajVFrK9uyE1iquok8yd2km7Rj8MSVhkFaGP/8jsnAZAKtprAkW4KFe8R4p9jF5ml9eK5kTNTH96z1b2XTT11a5ijh8cwS27Q4soVZ9Iy2ZthvUHCCEqlmcj6oUCzewbcGNUaW/OX78VKcLvYj3MCN8aezEZswn4ctu5h5BmYVeqO85CJHfVrZHD0VRbs97Oh2IE1kAg2Ombh0/EAk4zizVl8novMiO8bvZfWtGRjFeFOhshJ9pc/IDTdmvrMIzu7/iEvRIXTGdCI0Bey9JOCMbzaJnaq4lm8k+uA12gKDadMJ5nWiGPOm30Mt+xmauhNIVrTHMHsyNqN3UfD6DQY/qkkbWkdaxSVU629jrGZO6enpjF+YgkRNC5sXvWGisg+iMTHEFgoZO7iOVLG7tGwvxGukAsn7drHNFnxXGzJ++FnoXIukkwRMN0FkYQnWZaKMmLoGf9lNuKbNQ9FgCo2u77hQt4anW7bysXkBdelF5LsuRhCjgMyvLSh+q+eo4Pf/f2JXB08fCR4x2p0m+TbUU/ryZOI8yp9t4621OvNN09Fpfc40cVlU5YZi5urDzbJ0kuz3Y5hcT5FEOU2DXZg8ZB9TFZfwROkqs/xbSTl8E/ufPnxXi6fZMIEee13O/bLGUKQUsUZRjkdGYlbkgrS3O71+3/k9cyKLZ+1nuVYBxQAAIABJREFUybLHfHzSi7ZdHYatprRmHEDQKY1+jyNV6Q5kaE5i1UcdJJu30O4/i1Em7oQWNTMvYxiuOeKciVzIsilTkDjXRXpGNb2LXzPS2JHSsCT+hC6nsN4KiXVuvChXINZ7Mp2d8tz/YcyTxnf07xfOQ5U8CsUkabkVyxn59SxynYhAOJEFM7bjJT0XN73tOLuboW+eSMuDMvDpRb/iK443Dbi9/Acew8+wuY8Pbb0KGFWMYvz3WwTWq/Ihs4y48+vwn29HTchMqma6YPijE+3YZoZZpnKxSI5BWV/ZkhVPybJQNg7T5bPjNDIqt3EkYDFzlslz6VIXbzYHYfFYC7teNeSua7PvTDmdBk4EtUVwsXwsiWcKcHJ9gB7DaBVIImfiSPKELjRmqvMj9g9tV4vxzxvGN4VIPrZ+JnNfCvPcgujJ68czN1eepeRzpE8gFqECpIbv4M25aXRUZ0HPZnISDMl9m46MphSxX3NIKJQi0/EIqrn2/JAbgFRXDP5b3OmpEWG7zwFuPn+EWnoNv82csD6fjYKEPEkKhTis+8SzquOsS3PGVKWWFulO1v/ainNgAs5DjvD1ui5Omh3sGPOGxrYkilI86PBpIbulDUnrcSgKhqGiLccTRytsg/fg6G2PXdBpZr1Up9r9Hh/WXWS1wBJJi+/0HzuDuHsXUfEag7fCBh4dnszX3buZIfyI7q8Z3Dl5Fclz8XgvHUODnimLDy+gLUWNWfM3ELx3D+dDbtNn3gOCHWOwP9TBOMFT3l4W5ejEFcjtEbJGPRCD4jzmZPty5XIW4866kJd0CD9rPUokHbCXaqM4oz/FrkrIe0tzvUaeOj1tch8JUM4djnLMULqb5ZllF8TG9DK+1k/i5eEcbLt1+JOUg0v/IRjvK4I+BjjqF9MgSCHAew6qqadxrWtEvk4V0exAGsosqKwdhsBtJJ8skqnpO4PrD13p0ZFH2SAPjV+NdEhcpl2qh87mYoT1PbTEJNHZuwd1rTzMu234K9BE/csrjieZoKOpgYK4H3tGiPC3xInOQFOeLX9M51ZtfpfU0pyqwoN7bbjJ/0PZdjbV1rZcS4hn/lY3bAf8ZIC2GLUVroTr62JtcQvx/vGYdLXiZzOcv32ek++gx9+lvbR3VhPdkEl/YT9yY48gqyQkxXQtcwwl6P+7De1b5kgbDSWqpYXs/HAmTcvg2IunVF1qIUu9P0/Mw0mNXMnMehekJPug/VeBSqkOTr/OZ/DEmdR6F1G5bQf9V35jwCJZqlU80UYJL81PLBzmxf2ym+iLbaNSSgVxc0dWH9bDUDmXrrLZJI1wYK/WMFadOIy2STBPs6CjNOz/3Hz8F+LC7uPBCobFnLE/w4Kzuxknq836/LcsdHvH+N9WaHybi2h7MjYGdkT+eMmmddt4eeEHkt5PkLKYRmvNeybu9uPuPFPcVIyx7A3DsNSZCK0kTHQqkLNWIDnWmZGVnaT3baYuq5DXz0+hO2QRKfXKpBinI9tcg29lFUFpotjvamVJ/8GMzjcm9VssxioTEFG/zpTse4RmmvOqPoJ+rUmM1DFg1c9KXluswuPSEfJOneLtufu0jNJl56UUvP1lCDYfgkbcXNYt2Mz2/TeJnWPKnd47uLa5cefyJpJThDyYoMltZzVq44X0ro3C7moBnzqtCB18AINwWyQre5nlOAdtuRUomfzl5bt3NJfLYaiiSXuBFZar/Bn5xxTXLZ7s3nGIhbbNLAnXwmCeIvbpkXj292bkzPX8/ZlDu8wDPAL6I5ehyCsVAXO2yvDwiAJ+XzuYdGktu/2fo2k2l1CLKPR+RLHc0oM4A2u2zj2LUmUCJn66LCwTZayHDgcvpGHq5kja3NfEl99E20GL/m6fsRV7QzOzMRkYjbrsLOZenMpA53xGZ5cyZ8xQ/H6vxFm2ixbpIK7N2oyrpzvu/1aim2ZPsv9j0nT7U73Aht2GTiwYuAGVtuV0t61HXEKJ/OYaAmTaCSnQY5qbLbVPs2gZ05fSvBQCJo0m8v5u3Abu5NC3K4zoXsSTD2+pclLGe9ZDFif+w0b8JI/aPzBTIYaLaz4j7qlPyvb1hAbpMEw7i2HDwqmaJouiQj71be5MMRtLYtoTyoZ1Ur/wH58XXqbaUwby8hm5/hznF4exdKwS9xphZUBf3g9tJ2KQL2/iE8gtMqNl8hdEIqxYePgkoXIV/LLawFKPVQSPncOnyDgGBk6g+ORhLqw/z5VnieSGvCO3+CO9m8sYG1zOk5hI6lYEkVb3Fa8PmpQPrWLIs1GkmK1DuCiWJ4uLWHRlN318tNGwETK7dT3Nok60/U6kSWkkZcrH0f6hTZeuPVON+rE/vRNtk2wmx/ejJqsT61EW7C4SRazfTH5rnuDgjUVoqD9iTIAOryP2EdTPjBOtv+jWsMZQypdfL7cx2ESVdhcrcop1sBW9x8bP3Yjn9+LafJovpi9QrxVBzG02Y/5KUqluy+83GUgbrCPZKY1hTWY8EXvH0oIgJva/T2yyAj0KmriYzuDytp2UOm5l8FNbRq+L5smmd2SXvEQqfhJahkF8ij1CXz0hG29KIBgn4N/+7aQOXE63RDtLxs3hxE5Ruoq+4y7uzfepE7Epkqe93J9I0Y1M0NpHYcJu1EwfsHh5HKnRBXinadBoX0DVNxFsB7QR2iWGddtZelRKKCm/j/OwrZRHruJvQDtLeq3YKbGc2t/T0CoqJ/ZUG0HOOjR63GNf2iBuex7Au18S1pu/0CyfyAD3J+Rcuke22Uvmf3vL3b1jaJJ/yrCiW2wPSCdsoxj2wy7zs0EHNYkFKAfZMGerCREHUgivX4qH91EidzmQlD2SjEdqNP3L/I806r/FPx91LY3kPEtHRHUufkZHqEtYwx/p57R65nCqwArVX3eRbfHF8ftAjB9e5spwHyTbXhF1Ko/HKwcSHyNHnfYyvq9IRnJ3InPHzyXDdgnXDCMQyI+gIruJprs6jKv6gah5MgZq1Wh6GeD2QpOd2us5hR97b23khfE82t9up9+9+dRUb6Gtu4kllucxLn+FHn3Q1D3AwZ6/XMjaxxb7V+hojmKimwaWTmoc0S5E1qmImbJhWG1TgSf7uR/uiYTDGQJ0HxMQMQjZI2WUe7ayP7CUOqEzvcmPqLW1ZND9LUw3eIqqzxpyEpfyfF8pDx3+4Fr+FMWPc7hsG4l/rRPBmXfYFrWWQ4+GcffUCr6OUqJmzAheZHXy3bcIUzV/ujJjkar3YvvplQh7kmnW8+KzCjhZ+DHjxT3OBs2ha+MMcqPluTdYSExgL9fWmBLZoMWRd43MUK1mb3Et1X3EkKgdiZXmVZx+J9H3ZAUvLh9HY44akS3v2WSylAW4Eq1rxeuEA/jNPI5aeiCpklPZZSpkhNIxFu9aST8vX15vi6P1ewhbFi5k56PZnHcUIDJtDYZKJzCcdpTCb2Ko3jlOwskwjo1poMftPH9CTNmXGssqyUREk19wqGoUewPUWL1HFG+5RiZO1cZ+7Td+j5BHPqAIw1FvEPUdwogTSXxzsOOx4Da/TkmSHDyVvJxNLDbOJ1gnjpV1Szn8LYePFnfIdx/LT7UjuF13pH/CEFqfTaTbaz4P/pQx7/onPqk/IM63iVST3dyNjyFjqC7v9hsjFEYjH5bGkV9C0hMf8vm+EBHDfmxfux3rSi9U68ypjX7A2amvebFuAAKBgCmCkYQIZ2N0w4uPMSfZKYhHLlSCmc0pTAq7Tcvsg/w1VWZjrBIJk7S5LRBwyE2Kez870ClpZEjpRVKb9BDeXI+hnSJDXoLozmhOPn7O/XsZOOl3czL1FKkBfxhUcQbJXl9Enr9mx9NA8roqUbjthPKhlXRlSdLSnELBdmMm67hTc8KXF2sHc8l2GQfXhLB9cX8Cbayoq1qGpbwkB01nMz+wg5Dr+yg9JODZ383cjj3L9a7fbO2KJ/SuHe2dc5EoNsV4qhYSQZOYfLCK4cOcuZt7kaT8wRT76xL+TYaDj+fzNPoPp9ecZHTtQy6ZxzNjnBaOzh3sSX2MRq6Q/DZNbs/egNXHOO6kaWDu18uRBCFH10gxdOZYCv6+Z6qqJluNrYn9OQknuylcrHmFyp8kAiW38PtwH5zSUxkqswZdqwJC1N8y3OgdM5LU0It5iV2iD2LG9lxVCCRQdB/OHWcJ3j0Edcm/bAkYiFBaFsefu2lOMKd3oAjhBxMxa29CrDeCg1o+dO/YzI0fT1k+aB6z+tzFX7Ifc1MG8KA0h6zy9+QomDPonZAt4x+w6EsMM1bnEuThR8+dyXCknOjzExnX/pXLH/rh1vUY/aIlVOZKo6wghfjbBTyK+8MMuQw2BJ/DxkTI7un7kZ/7k5bKVSR2KfxXU/X/9uiW6GRYniGra29hs9KXkJ5cOjLiua+3j6V5D9Hu8w4xf32ywrUw9B4LhOMzaz1uNPIm+TZjlt3AevhZJkwXJ/ztHoq+tNOskUS6vCwZLV6oxpYz0uQLFdKSFFdro1GfjnixOsqOFnSKfmKoYRbOrdMo/ylGZz9XpnoVoQtUVfZgbi/gV8kDFmhCYEkM02ZqYZ6bh4nZYLqT3sIqT879dOXVqkJKZF/zVgeyhnWRa9AH+7pLaN14yixPEzas3cX2Iy/5kemO+G8zUgfZkkQdDxcacXgtPNsYyNHxjjQ+E6OyppK+UgYIrqxnvd5S+ihqotqZyOe2U5S/rsN64Q+OjbpORuERagqLUZCeRlLWYlAygXego/wRdZFTtGZd57TJZEqMTNjCAwxsTEgpF8PgWxN5tCMqIs0F8RoQCvgR4sbcFRNQUwnAeJQB+bvfIyEH2E5n0PiTlAt+MjhlPkgHkhp/HtVtX0gdvZMHUyeTdDQCI5+fjFB6QlOWKfIqmjS2lmG+qI0lmZLESQxmRBt8DvjLixEFuJwCW+/+GKpBfC8MMXch81k2AwzjQDYfrPpzrk8WdvWqOLe8RaOPK405hbQ116DV3U6OqCTGPVoc/ZZK3lgLmiW1EGooA5Cs35/2GnlG+e9CpyuCLyJPmfd1G19POrO70oVXivNYNnsQqu7JIJrJyRvTcBICz3oITNbm02RL6rT6c/mXNM9/xdIploW4RBcmA1LYUHQMAPfLjtQ0S5N48j0Ahv4XCHJ2o/LERLJ2drEjRJZXH8N4+daeO2s1WXh1Nidunea4yFJm35MD4LXXImTaYHRLGFbjg1j1opfNAyPZJ7zGwIZKnDiBynhN3hx5QM6BUA7XFHOkrpzo3CCYr0K1lCT7bgqZkrsTra9RnNN/gE/NSK59zEDZrhgHC2eK8mSx/23Le8NmbFREePowjOXGGqRqtlCtn0FvgDm/au6TrqjCsdirWIlcZfSi7RSPnEl+pTQ+StsosskjS0ccPyUFXieIskLRkwIVER62PEUiW5QKUSW6VOcjq9BFjPhA6rrikRaPo4/WLPrpONP0T5tBd5wotfjI47jnHPsoRv+Ru+joOIiyjA3WXs7MGduXzj+fqBlaj6d1ComKv/iW+p4OVwW8/W/Qq1KPUr08YdN0ufWzD8cqfhKBHqPnvaFLMhQndSu0kqMQ2gvJU3enPE+TYXrpfHk/C5eBmzk8R5H29vvIVIkQbTWREZHN5EpI09Jfje0Ke3DT6UN05Sf6pqUhKfeehFYjxN0FBB/axIiOoWhpDkHK2AJRwThkfPMZ5FdK2+R7yB/WIl+iEbFfXlxrkYDuUrTc/CiaMA3RTGt+DYzn+QMTdodXE6hsSR8VazrV/HlVdY8lWl3UZ1ujRiW1A4fiVH8Oox3d2FuY0UeujfcmG2gtgVGZ6vT1CfiPufS/hfno26uN/nINVG3imVSVimzMNKR1+zMzMoIJKhK06V0C1e/cGuhN73ghyRkPsPomg7nPRWpN92L9ry8hU7dQWHIevToBUQvLmKL5CAUXFfqWL2OtvDYBRrfxVpDDsliNCbvikH6Wi/kfB665WvL37DSs9bYzduJujjrtZYnAHde0T4gUDeFZWhMalj/ItntBXqoVXxaWcuHseDTFPnB66Qae14/APTedx+v0mdyTzp0cMTJO3ueouzkTTPYis3cvq13yONJ2h4ABIwhYVsnaVT0s7XxBQPY/Hib9wmrsOH7LNeE3djhpySMZaDqcWN8xeKkqM8kkADnJJkTlfpA+aSWvjcZzcl4h518qk3MwglifwwiU3lC15TW2S13Yu2Qyx3/FI77DDlnpPiTSzHe24FO8jtaPe7GV8EEkQwTLqmpiEec8muivl0Tg1oSN8AtTNVXpYyjJDKcO5FyOoDc6jqMWAxktJyCkpZ45YzvIvTAX8977MPIRQqE8eYGxDO03GC+L2ZzZKySoUxr9pLV8fCxAseUPa01keB4ZhIjRLLJkIagWNrZ1cmn5au48DmPK7oX0XljG2KgPMCYJHGXILVHg+q97uMi6MdzenemKAg48SeTqQTOi/Ax5VVzNqNrhfCnayN9CEYY4qNJv0F9mlOcwO8eM9P1tDHqeyr+lRxkzZAkH+l0kfOBKfngXkvyniWULTEkMHcDxrROJ0uhAQyOMhi0unO/3j5nrirky0ZocnXgaFv1E/OobVnY8R2/oEsS2b+LUPWv0o+W50SHg0Y0SXK8L8N2ti0WMFZkWoUR4r0VpcxiX9+9CLg+EQiEnb/Xhsu0BJuVu4MKSqZQLPen7+Rwqv6BrihmBLbJsDSwn5GkBv7vcmbHQn5zcJhzObqLp0nASL/bwZZ0VT7py6G84gK0up+hQPs00aR2UHV0QWPvhPlqA11QJ5BHQJJvBx6FHMfV+zxjTEEzlchCve8fJRmOcMnsZea2BGXNT+P67D+cxYlHTAgbF7ed7/X7a7n/jy/G1/HntwIvPa3itI8Vps1GETppFZJwGXwt3kuvvS7vOYBwX9eXrq0JEovuiHKWM2G1RDLfGYF7uwDnzJSww3Ii8SRKxA74x3zmbWXqKDLWxZsy1cOSxwzrfhMeRKgz91szEHXOQSP+N+dNrTNn8ku9OV5i7tC8O9XIslJYhZHUpsefiYGE6sqFhBL/M4NqUCooGbsNTKxxJl8WcvdnCWTMnlj+8h1X+BHwHjKAkewM/O9UZ8NKa4o6pbBQr5nNtJE8cBjC28zMLte1ZMHMecmOWoZQkS/bxVK49vM0/YyXmhslScLMFM/MTLJFTp8ZZi+ihj9jfs4yp34dyIHYQJQtC2X+/kuJNe0lpl+Cj3zBCC29wXryWd8FrsFWQIHPLKP4ptLDWXYOK3lROOymgfSQL2cEh2HcBa00QDheh+mMVxyeVIvznCnNn0ztVgPnUJ8gMsuX0ttD/aqr+3x7qknrIzV6BuM41dscM4l+fC4z46EnbTWuGaZuiaqLGd0EhC5yLUbDVYk6aFFVtZzmRPIBH3sd4nXKCsXbqOJXnIOY1gy6zcuIaInCtFGDcU0ihvh1SnSaoOIuhlKeBzAAZSsOC+YcSU1ZYsvy5ASmNVWjXyPFEL5jbeg8oBgIS2imvOkepvhdFMX70HzuYI4uSyPSWZOW9leRE7GTifhMO+D3lQsxrUrHhoqCIkwr/sLYW41z3HnyL/bk/ToOiQ7s5m7SN7s9iGGJGQ9kqZhoEMDm5l/zDQzgo+gz90KE41w+jeqkM327eYbutJTP6+GPT8okaJxXGxhnwO3g1ewZtZKfiTmz3fOX8hYM8v2aP25VA3k4Yg9BbiGCRgGvCFQhZig3umLwDq1m2XFQLY8e5qZSvKWcLHZgiz5ejrZw5pMuvpDqu+TzhX81qzB4AUnMpz4TXv6NJGb6I76uvsPfFI+QenuXv6hBW08aMQFvqvl/Bbkc46zxsOT14FTPVPzHvXgkS464jczGD3FW7eRQ8jpEjrpOceI7LQf+4cqeKAf8m4zs1hCO6k9AaegT15Wc5WpMKSYXknn/BtGAHfFOfofkllW8tAlTMu2m3kqNKWRRJKUts1NKwjO4gRUaUNoev/J7YF0rAZHsVP4efIdT1PIjHMDSvHvuOq9gUtWKh1YZ2y1XWaq/lTHoIKLayw+sF54HYrYGkpBhx5F87Wt+fUy2bSP4CC/qaTSTAfRDy1xxpf2PL6VmatIyTxGXzBExXzQMpPdTaX9Cy1IM5U7czK24v+4ZfRcQ0mJTEEZC5i7G37UlJsSJO/jeRv9oA8P2Qy7UEIakym/GQiSbmsxFeN3QQFYyhIlLIhMunKSwpZPvXRKi+yPpzZ7m1bijfcw6gMduP+wYfEBzzRunsfu5mNLCk+jz7zsZQb32XK2etyS4r44ztJC5lraErsx/ZlfuxGvSUBx1STJN2o2LoVETqmzAtMWZaTSldw/UwX7EFfcFwHqPBNcKJzLOl7uJqPnhG0VD7mmkaweQN6k97Tg6mX3LwlJ9GWroWcisu8qhqCe8bh1BVkUnf9u+s212GbsZMgjPS6bqthbn4NcYY7KL82yocEj0p0lMlpqMN3bJc7LvlmGblxHOvm+SIFqGkFcqSiz14LD7Hk5tZZNSY0LBqCnvuthHmak57iDTjPnSgeu4l3/tFEKX9kvDLs9n8QEjU1USGSLdh/O0LT37epydOntor+Vwdu59b9i686QbB/hss89iD9/lZVLkUIj1AyNSRH0DwjSTTejYPDqQ+XJz5d0NR9dfH0tCFq8dLETc8RGPEe26/eUWCXyY/1/tSfuc8S2wWMKAsDZvqITxsuE394+8oiDbhP6qQieoFeI7fQ/iZNZwwmkv2pPU8mJOJkWQNHT7H4IglglWKFPazZWaYGP5T+6EYIOQRYYw7PpPeid9Q79v1H3Pp/9J8CAQCPYFAEC0QCLIEAkGGQCBY9T/2gwUCQZlAIEj9H2v0/3Rmi0AgyBcIBDkCgcDnf9VDUl6RpA9/WfXyIXLN7/jn7oCv6WoCM35QsMeR3TIXeD85lCa1dbxp3sTFGA+Kf4rwb1E9G1a8otfyAVnmhWQYCmnwaiCqcCa1nz8w9KsmthW+hF1to6XQkcyoP5w+Yce+Yy+QxJgxPy8xesx9LkWMYNKafxgMUyCowItL+aHID1RA5PtT8gVmHL8tilvCXVJTbhFTvATlpGhCtozm7LH5jJv4h4jPx3HYW8FWBRnUD8zGqyqSVY6H2OEwh10PStCaMoaBu7vwTQjhm/QfKpJeo2OYTmLmHf48uI/NmDm80LzMh+1BCJ+dJMlDgv4P9/Dh+FCOLrWkgZvcEMnibqIZc5x7qSzMJHmaAQKBAIFAgLCuF/uDfjzdvQnhm+uYnF+F38uPtJpX8kaslo6B67EaaITFKXMmGu1B4aY2Hvo1nESMdFtP7FZs4LWBGne+LCdPUYaqS77siE5iuSCWuZ4SSKhN57iNE/6Pz3BQZjKlTSLYHdlA3Jkh3PwWgc10V4ZNtWDVKmipsOFrwxB2bayBQ/e4MX8ipwseMMcxhPpjGdSt30iK7BjMGvvzfN085mnF07p7IJLL1zFjwWF+tl3ncMEU2rxbcYpUI9BCnEJpe+7sHM7JLfqsO+TJQbe93Bq3Dne7pRSkfmfGio00bRmM1eiVqDm1Mu6uIeFbL/L5/Vv+SEWwtCiRiP0CHuhKUPb9NIkWTlj9KqVrgC4tj52p7ruEuQM8eO3iz7nBL/AZdZY/y85i4b+E66ErWOR8F7sNbwjMvkDu6BusnpbBCMEG5hw144/SHypHLMX1sDEp3gV8Tz/KwZh3CNaNQL4DvggfsWqnG9tqDhLcN4bsiMtYL5nN/hRbSqulueVsjqNAhISH57i41of5v715Ib6f0SYzUL24GJ2Dl1g2YxEXBjtQ8NWW/k2HyHLt5ujYuyguMEZtixbDsvqTKOnAUq8bvM/ZjK/3AIzu9WVy9FuC+jwgsmwOLWrpzLYNo/DKPSb2+8IsnxQeVd3mktQhrhfPYq2iGy1HBnBofALxG0URGyxOi+5dto+KocLfkw86I9CbLMDl82oG3fXGy6KbPm7jmGpXRvnFGC6fOkBAwkB+48rItw7471SmyyCBpNMdFM1XwEHYhX77UDYfPsCmyirUX28nu3cPmZ07sahvQ85nFnPPvSD44UX88q4wqywAcdHr+LkGoSkWh8VyY4KWSmDqexzhS13mTP5LRPouCg+sQJA/ijnJuty8pciHrCm8OTaNi4pV3G6uYOCaSjpThtDh0cTea/VkWppzzeAYfzMuE3DOhricpWxeV0vAi00ECx+wZaQcQY91uDVCG5FkKa4MfUZm0CcuZD5HMv4PjR365F2/T/ypErru5XHWbQdnbuqT+cCG4KNbGCMjy9STV7iRcxGtHad5XjOQU3MG0/r2DtWWVfwotOGIgwJzL4iiY5TBu8T1VPgKYcwLiJeHynp4GU3wuH0c+nkLlxvXaA8/x2E/K2aqp//HxP6/I/6/0CqaJbHMzuWqkz7Kiisprmtimm4mrZ7f0SvT5Ep8IZ4Gw/lTFUXfdDkSYiM5VPoRUwdL3uel01ani26GH+I7FlDfHk5bgxDlv5a0mYojKS3APk8Lm9+3aFOMIexJDUlFlnQbGXM7MxgfmQm0WXcg0qHCrBElJP6RQvaeCoPpgMh2xPpGInt7PKoTphARfYG5W2aScecVYcm53L/kwgzfMPYvEiXJLh6FcXc4f+ktFZUDCLKVQ019Cm8fp7CzIJ6Z0ZeodVfmk1gtooWfGWW6m7Jh1pwu/EGHUiIPe5MZ0zQMcwtZHl5PxVBdAetaL5LrE0jpqCDlRzeGVlVIvf3ItHET2OM5gshfUcAYbHSXIfKrBY3V17gImAF1MtexIh0d+sPXWgpitsPsUNreWhE2tJf5VLMKZUbqDubd58d0DFEi7WwLqya0kZsPdr0DSGuHzhQbZvgM5+nqSrKv3WRQ1Ch2bF5G6nkJkh+dwGXXAkK1znD/L7hM98DCIoQd1rtwbbZAdYEmZpINKIQc5f7vubxt8SBKzoNnXT8xa7dk3gIZFCVq0bILpkN1INVO8cSfv0JY/++k5fykpEqDjoEf6dMMejbrQ2n2AAAgAElEQVTQ+6GXUnMRJFq+UlzxB9GZWki+ek9NgZBR+dnssbiKTd/+eJQVYhy8hwavaDrUVWjrbMPwjBiB9rtpjIFNZj24fCgDCUfCumNAEhyzZuKOJTNNOvmqOZrurrUMbozi3TZTem714Hx8OpOuTkO93oOQa/NYtSwKSa+f0D6IP0MkeEER18J+oYs4PITo/P97tNcPUOTljFTe3h2Oi104K2NsoOINI1pOcXHmY57fy2aTTi+TVzsRwVKaV4CS9woElR7oO6gw+PwaUJcltWof/h4DmfS+H22LP4ORO/z9wZx968lPj8aiwJs0bXFc3EczelUfGsrSEdU3YK3oJT6ITmeSWRVize6Y2aghnjGaTy138TX4QIfmRNJ0T9Prqo7R2kRaRPWwO6eKoaqQWr1L1M6TIlUnj6pPP6lTcuZx8wBcFLLod1WJ0Y8CyXz0AqdeS5q0L1GfnsjC+nbmNnswZMwopG5dZpeKM01/rlKWZ8tLRIB4YhTAz1oeRQNb4msUCH8Th5mRIVG9mYz6J4dudRONPiP5urcd6bZIhuhqoxn1j09qRhSX1mAVa4L1UEUOv2ngS60cfoX55IydSCGA5mKs+tii+fMbnDLkVJ0traaBbNB24azxZ66PBj/TbUTkRpDx5hN36tuZurOLsFejUFAJRIOXvG+6yquDd6i4sYBudX1K6j/QO2gqljVpKHd8wePLUMxG+RI8YBzFvyXQdgBLRyA2lhmaTgwWriR590JEz0vjM1mJ1p+p+J3RZWPle2asCSfDdi0th3zAeRQoLITjQujeBCOWwLKXSO49QeuQCYiH6KOk95uwiwn/OV//rx6cCwQCLUBLKBSmCAQCOeA74A9MBpqFQuHR/0e9JRAGDAS0gY+AuVAo7Pl/66GtrCl0Wb+NaUrWKCXu54rXSgRtQvR7Ipg7wIzrzzoo6niJ2pT5ZJYVMLRJyAbf5Rzc25cG4Sz+mZWio6SN3dittM55TbtOB36yRZTlRvNLZjxlnnLkxtxHu/UUJ4q+Yz0jGb+AwdxZ8gpbUyUoisPE9w/to78h9u40w8Re8zpTgG/zCtqGeJGef4R/IuBRPZDgH82UZBwlLmIxNx4/RX1mOan9TnBFqYiDSrEc+ried0s7CWl9hYGfDm9fPMZx4wVitoVyZMIGun/roOGgxKlP67EZdZDGQnFSex+ws8uCvwJLBvme44uhC4KK/hic1iVVNYo3k2o41utATPRmjvW7yqQJ2tSfccbw6QfW6R/EyDONC7lCVu89gp2dCWlb1rJkyltGhi0nV8eN7OeHuCrbw95DIvwZ3sJE5+eEdwZxRsqO6XIP+dUnFLe3dvR9roaWigdy/xd7ZxWW5det+99LdzdICygoSCMqoYKFjd0gdoOFCNiJ3YHdigUqiooJEgIKCIJId4s07z5Y38F37etaa//3wdone91Hc475jPEcPfd9zWfMMeaYAXz7qMnaMZIc0WzleX0ciU+/sywzhprlm0jfsIuHd+5yOqmNxK+j8Fpbxyopb7ZXjCREZiMXHf6S90WcX22xuOs70nHYF9uW/lxSiyKm5wF3N4rTMrKR6oJ4mvq/RsIPfu9z4O+XKcg1BrDt3Af2OE9kg+ZjyMmDNXZUvYhBY84W6HwO4j5AG+ml7xHGdRP37RO65XNIT36Iy6JWTkWeY1W/8xgrN5B2qYMGhedMLhvAqyPxdK6WJDWwndy6SvYvS8TCFgwFzuwcMpCZXX3Y+zuA+H4LCXaVxHX8eny33Ufp7jgUQ64TsfsDwu4XGNrM5LevI18DDRm9Phb9JyewUhtJZms6GtLjePTlFEYHLuMbp4r0utGEDxMAMGq9FNF7/wITqPOX4GJfNZZfUkXKvp6FjVEotJQhbxXAioNnWe/0Bk2dBIx+vqTeP45fpUlodqSzcIY/l94sxklBn+EvLJj7eBsr5+7j0Jez/Mxq5kN7LOJX+0K3N+fuv2JE/xWkeD7B2tKez026WDT5kfBnM6MNR3H+VSUSTvqIKID8uUNk7ougMDcQ++xxGJam8LtLDrc6J94lP0ZmiRq1v+SQkpFFvk4TJZEMUsaJoX9oKE/7xtBV04hIwTxKMquRsHxModZYiF+JrNtg/BslkRcKKBZRpV9xAybBLox3HsynonncuzcLqT+KdNW+Q6ZPF+E7kqE0A5dBoQgVMpBKfciKIwP5W/cCKeU51Ob34/XIGkTvXiMqxZm29BXUb81HqT2fz/U92CnakPw9ndR+bYgKasFAjTGKcnw5+wNTGz2u3P8GrnoU041F6hfEdh3nj1IjuouWIOewFT/jOD7ERdFr0DLKhYVY9FOmOu0jb5Vms7D5NytjLjPxWAQRLo14LfjOspqZzK69xfBVKhQ41JCyM4OBIZfJjf3FpqYezCpEuCsVhZvmUYaVP8Hdqw+ub98x7cIWZi61xNbqKfk3L6F+IgxeC6D9Jlz1h6YWRBTu8/JjGncs+hCsmI7hUnVCvQL/p+D8P8H/C63SVdUUZkTe4LrwHR8kB6J+6ROSapXUGckhRhetYmLodTth++QnQs0yuoaP42ruYbwk1vLD9BEmdS4U/dREU7qZRrk3aLXGUpW9hTr3T+QaKDJpXi5mqsGEhV1ggZk3NeFLUbYZhu6gIMrCJ6B7qIKitAZkLazxix6LoLQF4b0Iine8ZWS8BxMnpNF8rYI3jntJP/KG/IBTbOQxS9zHMmLFLb4v7MOd3a0M6dTmhqs2cY7XiJ/kyMERSRyM/MDeKk9E02eS5lbDCKEED/8exKMwFZGqPRyzrmPkU0Uy26qwVxEnr58fytovUXz6gBd2fWj7qsFjB01OdXZycM4yDoyayZXAA5iI3GVhVTaNY2LIuS4gLOII5zxdyX1uh5HGCW5KLkfKZywe3WpMUNTjtpYyMfXvsJPujwRC6gVSlLnq8FdcgNesI4jMNUFV7QTKg9rxLdQj2+QyN4wyONMngroXjTzt18WdhWZk91qBfGsUQ+TH8+p4GTPW27GsyYqDXa/JG9mL/JaPrIzyokCknF4TP3D1vDv7vSVZlRrI00dLuZRqzFxlD5J81rP16UcCwhfwzV2aK4lJbFt8kPCFW1kh2oan8WJipB8SOMOeyCo3Gr+Ekt02BtPAaRQIBFhVP2eZ+mIOCT+QOLIEvc6TnNutx7tfw+jv44Yg4hPd+TeIs09gSoYpZbIPeXdoHiOypHDcaU5i+DKGXzpE2461LPGzZ+aFZPbcvU93YzLDDcfw68gypvW/i3DHd85NHk/AfQG+ojCndCXvguy5/ykJn/xjpJtY4Zf3lRdTd3LJZCtiu0W4d+Qxk1pPMTVwOHfmXmKCxio6fu1i/57vdNRcxrpxIMvH9OeEVTiX1qowr+IjyW430RsImksFfNj6nbjFXjRePkCHmSIn5omxt08CEr9v0lLkgalHETFxk7ksPYN2SzceT9dCVzyK0rRJfF4jyiHtIewJUiRX/jx+IbPYLRxF95hA/D/cIqN7Cz8FuvRZJ0vn10g4e4AqwzvULt6L7f7biP74gdTCkeQa70X2piW9M3+Qa30HofsGXOliz+OpOE4+hdqnIhLnHyC/aR8BTXdoSttIyYk4WhdHkityALcuZdoVjFER/4OOWDuvBT2cCN5K31kjqbr7nMGGY3Gf7clfeRXk9DvBSgzpB9VomeixzHcRd8KD+W0fREPGDAx+9iPBFmSjPJGPc8Yo8Dj3ZQ2ZYf0KifZuLiXL0V9endDQBKwDbmNaUIHvSm0yTQOp3WOBooMNNbsX475Jik9FY/FoaOX56jm8uL6S4RlP6O1bS9cHTUI2ahB9ZzcbN+riOv8w0g7zONFZSEfdHYaJS7No8BsmtRkRdWcuf7usSVv1DOM/43jidpT+c2KRGCmk9vZ6XCPFqL8hTYPeYPq+S+RMawGLFWv5rK3Fi8g3pEV/ZVfaVbRmbYWbgZTcWgNNb8E5ALKkCX+WjoPvGyJyTnPPIwstn91sshrz33PJoEAgeAQcB1z/E0LfBCAUCnf/a/4CCBMKhf/plkhboC0s/1qO7tMgyj5sYrH5a+RzPtHgno6BYAjiMhZ8rchGRuwb2miwE02m/r2GevIcZAar8DdlDXpl/jSOHont1QQURllw8NtJrEbpcv0iDGiu5ouLDPz9wtLn19BboI16f1WU/PS5/GI0+kPsyBd1wv1TASaZhlR0yNGgmERG+EumjptGaZkEp6QH8qU7EafGr6ye1oxv/0C834fglN2FylAHimNjmVZcQ4xdGrpFT1AyLCcrKZ8/DXYsCGnn4LNU/lg2MPhTBQ0OjSgXeWKn38OsqHyWeHkQ930gr0SmMtt7C/K1r1EUFaXBNgmZOHd27o3AyELAq6PPkNg7lMW+R1Du1sHhRgMTT5gxvvchUv1j4JYqYStq2HNMQKutCiwwZN+PheQZdHFO2o1Qi340vg+jFzLooE6K83yUfDJRWfyCOzP0sC9WQCrtKd+zU3hY9gVfIzG++JtS659Fgu96ln6TYYnMOqZHBYLYR3Ds4duGTPZ17KfVowB/hXCMH3fSWzQbeuuxTWQ71n1mcNZlH1YGWdRL96V39WuCREXYejYbkSgBAgpoXyxKdW46hY0qvHRUZVFCDY61/kQYvydCuRfvtIyQcthHa/0N9AdNpDxuN1yr4OEaRQokVpDrpcD86xeZOscdpURxCoYpsEa5A8XgbwQuC0NeJ5Gyj1tZP/8BNk+zmD9OwJ+HyylP2knhQE++156n+dosQqNkwHM4StRiiirfgI7Luzi1MpgGK6i+30h3ajCrNlcz94IXCpfbCO54QWuvgVgI/qJpZcyO9Hlsb75IcXUhZxPDsRkB2yuOckXxCuvHPmOS9VAcNmRw76qQawIBh4WSpDAG++3jUBRRoi/vUXyxnx55G37GaKAhW8zW6YvRsHqH7uUOLrZJUTsuHJ2WPlzpt4w6pzsMvVdNcUcQ8lKybA0Px8AK3I6dpuX3eUY3RyIa8wwV1X5cNE5hXo0SSW3uqGr8paXsE50ZJbQPsKfYSQT5N/foLj3Ajo8/ODmvivDB1piUWjGvL0jrvET2vBOlhjcxyLhF5fIk4us8EXlWRt45PfJ5xFxLPX7++ssn42UEj+li3aaZKN8tZ37OOXqr2NLeMZ6OrqHsuVbK+gUwWhQyBYeokFBC0JmHRG0t/Wf40Pt0BjuaN7JsETT/jqKxLJtPBWL8te3FoFHmVBy9hGZeK6U7ksnonM2NW5IcXNmI3G0pFs1uJTwqmI74OZh4xXC0PZx3Us9xm6vOmQuTcPLzZ9JoT4KmruGd7XVUIn4yZq4ltZd0cKg6irneNerGZ2NslcnHuzI4zbPk770/DLudyTdnVVZYSfL0aAMDZ42mrC2NaK86FFfZI9/rM/4NhrzV+UbbzDzGlgq48iWQ00qfufTSlBuKQcz+NgW5jetI/p3Ejs0u2A3bxNBQZ8SKfWjxXohyoD8kjQLXkfxpW4DWoA/Uq9eTZ9AXd+n3vF0+ljtfFv3P5uMf4r9DqyztLIRZqUYkZf4ls0OcxIOP0TV5iry0JKkih5Fqmsiwrk/cq5yJxpw37PMrIbO/DGEhnUxN/YFURQAPHAI4nB9Bcok6H13X0lHyCPUnb0kfUk9jkyMjIuMp99vEjZSh7JnrxpUXWbwxV+fv34VslHiKvOVPpN8MZ3K9Nma1FxiRN5KHP9ZjNceWc8se8253IRGPBpHw6BNGY13Qit/N0g2/0HiznbYZq1k9yZqNT1w40hbK0kc/ULk3Dc00B9pPdGF15ytxD7zRV37J3T7ijBIXkvVjIPKPrjP7vjU2wUuRF1djS5+jlDSoIlWRgv0VaV6vT+N4yFr05YcSk/4GN+vXRL5Pp/XqDXK7FtP7QhvL7etZPsaGpWFbOSWoJF7RjHePwjh8cRNrjAMIlvNgh3gqWx8d5dfgDQxAiXY0+NZrAarBEWQcfYX4zhfEXXzDuMFuuB6+ivk2Q8aVDEIgEDDHKwDZvvJcshSS88sQ/d2h7D41h8JuXQ7pBuGMCtsVFzH42G7Wd9vS9+Qu2m4kkZcsZNLgJgI3HcRr0GK+u/RhQoINfjP12Sm4i1jqXxoOivBC+xXL9y+m23s6K9RV+XbpMBlps5khfp5rr69hmvyW0xttsHleT8PAvnDyGc+n+KGXcZHFx6M4mnQZl9YGBqgqU/hVkeI16pwOEqdgxk42xQwm3rmLIBV/Enb6805LG9sd05jRLwL5fTDoiIDd81XZaH6e+OXjebNIQE+1KuJtK+juGEDX3Ep2Lg1gxKBlaK7Ppv9lY5b7hHPJXoR9/bSZ6W/JzscbCNw8B23tn+gJv1KQ04qCmgPVfwKpFz/P6ZN2nJi8grnDNiGQNSCqvJDxX3+wWXEXu+8UEa5zET3jQrIE1Qyums64Nd3EP37PxuRgds+xo6d4IEP/tDNMfji2kX1QljDjunwD2tlfCZokTfFfAX6VEGYVykCgd1hftg8dxNfpsTjmHsRYaj3xanlMqVZEvOwdJZa+xKU3M0QYSG3HHOq6rCjq28ZaJWuy8i8RudiO0bvNsBrwgT4i0kSvm8YfgTtVEYPRaMmjcss01JdKEt77G31uDmND1VeSFojTU1xLe602/fp3o+UyHYfI+9hnnmbf/GAquvQwdTvMEicrKjnIOVtvotKX4bhaHM3WC0gMKMdIeRK2x2yZ4fYH/VZtpkgOIc9+HPXxFoysEFK8zprKyAdInPyCekwuv4sesvX5S7wjavG30SEjtAfTtYeYJ9hN6AZdqvveJqCwEbvwBUQlSDLWYRY2D/3Z3eccH/sL+OgYzorj7pSdP8YWg6F41kkz22M3hpGuTBD0YssA+NymjlxXMnptbjyXTCTuWH9OL7xJU6kD+draDLSW5En5VnzShpGySpKrBxzYMs0V8znu7J8xG5VXC0g/48jo62GYLNSk+u0Nhvf4cMThNu5Vu5FuHI503SJq9c5Q7yeA8xa8THqO16Cx3L7sjs+kCeg0e1EQM4Qjb+P+kUb9X9V8CAQCQ2AAkPgv03KBQJAhEAguCgQC5X/ZdIHif3Mr+Zftf48VIBAIkgUCQbKYjpDA98co3bKf96NbEBQbY26kj2OuK5+y5bhUdxgZq1TkY+9jGHmK9UkNGLQEUjYe3ktr0mz7AO0hPkg8PMphpRT8bq8hT/YzETutEOk0pkpNlw0dMzkXW4H18U+IyL6jpjIBpYSZXFbxo+3pECbusKTxSiZD2614qidHaF4pWxpu49OVxm3rbAS9vqLbeYN13mpc+CXDwvxSSk7lUGegjGH7QEyHribadD5htQrkR2VxRbKAkSsHUT4kg++VxShqaXPr5Q8ejlUmRaeUwvIsEl/kMXutOIrR7ayfM58PzrbIRV/kztE7qEtVMPSGLxP6Dib6nB3b/QJ4EPcLfXkJJjsbIdXRg8rODB7XlJA1r4Id5kKEwhqi3hjwa+lJDq/7RVitMuu76ygoj4ScozB0DvZhAWSMWc+WsAnI5wior7tGQuseHF88ofrSRX7a7EDeaRYed+z46D6N38NeknL3KdMaj6PQEcb0KHlwPkPAGnMSww+SecyHrZOXsbJyBVI2vZDoSWRv4E1qj6aytfMa4xhFjMxm9r07jXPOW4K6RPh7PhLOJoK6GfKjSmmbms6M2zr4Pu3i1uOBnMaf2q5ViMYF0FG4mg+oUND3ARpyo0nQVyO7ezTP6r+Q3pLChGYzPnZbE+1QgXTVMAosTJmZ1EiZ2GFm+8gSo7mDO84l/FAbzYigSMLmz+XZnla8PL2p9tuHaM4IEt0yyXccz7ahXtwQCEi6fpn4S67kbhUQNi+YyiZR2j5epiv9GrLlx7gcegtxbz+eL4lieM9wrA0LqB3XSu6zCzyf+gI9h/5ELHZly67dGNhdYuGARIYPWIKYrhire2dg6yFETCDGbPtIUq5tx4sjJJe+YVn7YOJk29GZ/YhdNa7c3RxL7rc2rlSv4lCtI+n2efQf/p2HTf5EK33EqMUW34K1rJB2QEXKBiHu+IeGUvg9GffOHwjEXYnt9EHkoiFxDdfZXKtDxq0O8l6/Ivfqc7bHXOZthw7xKW1EvRFlcdtEBB+9cdK1425eDoOz99AWfZgbYXZkDfTi3UUTZI4doeGeBVvcQ3m5xo70z0fJX5yF2bwQLuv68GniMoTfN7Fjzy6UFS0RnN9JL1EtBCWdDAi9ye7ts1D8vY69BsEM2dhNZWcmIiKPof4Vwu5Gelc386PfJkI2S1MtEFCtOJEeh99Iah7GpquKd/ufM3PASs7MGo+0zAYshPV4uFWRptvN4uHtXIm2hkXLkXH3w/z3TJQrz2Dy2BWHC8o4xa5GKCxnmct1oj49pymmiC77fI48tGSIjAjeIkbYe71BN/sdNt+kOIQoy5ZG4nVzC0bD3fEw6SDoTxved325k/eH5NFdzMuJYdnMh0y2UKLT4wm+7WJsUVlA4g9fZtnv55CzNefjr2O/YS1jYlP4kfmQlwlihMgnscmzG9USIVHvbJD+EALKo2DQJbBaTlL9SjKqs5GplaFpvyhLB19h3jrV/xu6/v8a/11alZVdyKLDa8h5KeDGmyrqxN/SIWFGlFAJ817WOJupU15pjlWpDMeaI8DIgpPxOZT/tcA6Xh79Jz/4FRfDrYJfFJYkoak4glsfQhFTbURbvoQ+UmCaPRXP6gy+uYQjlNXCO9CUANFJTMi9j+O33nyoKWatVRDFTrMws37Lw3vrmes7G08deWx09Cj5GclcHxHO1lVykU4qQzYza/pMVK/FUCHWwOU31eztDGG9wyy+j2lgpLkdR98moKgiT2KpGHnGb/EeqIa84AOTS6ag0yBKzaBW9PY5sCIkBdk86IzP4E5SEgZWJnyV+YmWpzkCuR7qUcSmvztNbEfRaBAf37TiP8KIJpxpzGtEyAF0UCCPxzwbsoC7bvY0/JyEfd5ldkrfJ+T3e0Q2jsfkeg5db0x4P7wYrchoGhesJbP7KNp63vD5F23zjtBcnszPuckAjJ0CH2K/Ui/bjkF+LqEtbwgNXYnnn98s6LRna8RxjoyvojyrCKXSPAzDbvB07yP8PYei6tvJrhUHyTLaySG7KmbUrmSp0JNNMx3pzm9BtO9clCba4mQqi5q0IgU7q8F6EefiUkmzOsPZn+uZ5WDOpsgYqhQqGZY+A4NGL7KLzuFZeZH7OYvodApjss5JBku+5a7qeZpl9Ngp94Vjg3byRgEULSQRzdqASvIqvBd4YTRkLCTIMEIFBn0+iY8jGPU6x5Hc8ZzIL8TyfBrSWpo4HJ7D598xPJn/g23Bo3kW64R+lid1CiPZ88UYyfM3yP+rQJ2BNN2GSViLTyLr8Cqk8v9S0NKLXTfWI/CYxsGg7WxT8qC4vYxbL+YzIryIjKd3iLlwmd1JV7l2bQyq3405ftSTgwZ3GSc3lKMHcuhSHULzgDk4lQ9Acd8RNJbL4rDlBPvO1GGgug9LT3WMjCvRiuvNj7+AqA8d1FDBdwzZT0wVOMl3MXraPsreRzHDwJNPz3UQcbHnW2YKFrPtcVafzTD9gaT3kWd9Ty4nr4eT4rILBZ2LdEk18WP/IOT/XEUyOoC/umP4U5GBeK4bN+fI0zPlL9OihdhqvSLK9Dr6CiKYq6tz/8E4dAbL8lk3G/vbm1kxfDDSzWoM6iWF/2dbBIIuNAnDadY2UnsWgkItQrmxiHUaUTtmGdm5E9F1rkBJPo6mOiPKBDP5Gv+U4WaBpOa04OtsR6BoLtZOfWhsyORUgTozdYw40iTG9moRCizj4PhK3MsfcFJOnO/p05npU8/xF08QrvpM2vjzPC9ZSs8mZ/Tks+j4NISzHY1MbixHfb4n31Nzsemjy1BTEDZCelU6re0GNGuksDzsFFNH+qA0oxdSi7IZ215D+9dUWqosyC0rROebKVZhtpy/94fRQ6eQpLYcvUXfGfLSg9AtpSS4GvPwmwEZi3Wxnp7CL7nPXHobgVj2QqTiDcD/FFip8ansNsrqNZhrmHAz5h3bXs3Hpr/rP+fof5r5EAgEckA8sFMoFD4QCASaQA0gBLbzH+nuBQKB4ATwWSgUXvuX3wUgRigU3v/PYouoqwrvrBpBl0gp62++46zEIp6ZP6FW3ZmhDmY8zSlAS0SK9r7P6TGtwOTjRUSSXlE/vJlBqR00GPfjraCQ0Rq1BCSl89e/iUPlw+n6qckbKUskxM2ZeOIyKur6VHnKodd5gl+D3Lnz/TIF5+XIGlnL4BgTPjQ+hNpVIEin8eRH5s/fz3in/ryT+cD5/NeMUtbB++ZM6m4ZszNwCUcOTKHr8lMae/2ll60v8ytbOD/CnatGj7F81pu8zGeIygbwTLqQqRodLHE9S9HTGXTMjOXHLQ/SDWuxkbCiODkVyywHxNzseVtaxL7RfmxKOsESAwtSlB5xdKcSc33C+ay1h2VLzhEyzoAAveHc1w1DbGwbCW2qROw7gKjuTwYdn86czCsUrfJFfUA8hwwG8j31Kb/7tZNaUY7b3jcU3B9Kry+SHMtRZcbDLaw73sHG/OMIK/vT5dTDaik5VuwsYJjhD048CuVv8nekfsgzT+4lm7W386X+IDIGBjRpdGIa3Rfvk7F8c+zHqtL9hHzrz8rrEnz3vvIfB3prQHhwBnyYi45wBZOP9EV/bTx/5MaC7BD2KDfAje0s6T2YvLZWDBVe0XhfkyvTx3Ghspyv07fTb9gHnizNoDy3AdnAiRjky7Jg2xE8wnW43qrCpy8/cRo+GK+iOh52a7D4vQzVna9QmmDB1b4pONY+53FgHs6uApa4h/NqhwvrRuZTmtiEuIEo14d18apSlnsGWYiL67Fh60U85S4idL1EYEot1vMziT/wk9qPHdQtnszi1dmkLQAzdlF0ZBSSLolUfbnFruVvAJgjEHBFKIRNGwjurOHxO1l8hmiy+UAwcrtBtvoilREL+NA4pHkAACAASURBVJALI8z+9RFIRnFXbwW1y0ag/KMO6aoKql0ENA2xI+jCMUQDhITHfyd20AZk++igtOYbA5oSaFeYwofek3Cwm0pY7W9CCwIIc4xl5zMwdQGdqmDSyx7Ry2ImOd/2ImITRX6JJzoqnswUEeFd1m/ivkoRbgwzVOXAxgEz12NI5ArYHG3Gz985mMtM4aCIPZHx65m1qZyzRWc5lOdKe3ZvOn3OoSiAZnMZuiracWsbgXuoCwAms9+Td0WVM8c/UVnziLVZbTzIGIjRBVjwdSg61fIsEI2luUgHTbVEfPes4tnKWJIVXmNpMRb5xCfE1muR7+6A3dQ5dF88iGdNNZKm8ShLNXP/sjHj+83H4kAkVzdYMMh3DT+KVyDl8YOeE4sIjq/h7JI2fhkuZNmZHfxeb8zopJfMHmmN5kdZXj9JZncfX0KEf3HSeEvW5SrWi1Vz7/ECVLQ6ybUE0YnQTwi/OqCzAupFHtPqoY3VuRjSLNeg9/o5poHTCbpigKqbHFpyHTSeSKRgQiJnhIMo+aNIp94wRBveUvVHgsJlIlhm/0Hp5h1yI3vxedUaVEUbkDU5jXDCBmTvr+R590SSP1dzRGcAQ1oUsQkaxo5v6hQ3T+Ls3Af/k/n4P+C/U6tsjIyF2hO9kO42YcUUd/7EHKRdegxZpmKk1X1m87ciutWnsEfuImrVm7Hc/56awtH8SfDHJmUy8XUamKslEFffjm//fsgUDCVeohEJhTgcK1uJe9/A+CQPPs99gXPmHV6v2ohGuifptZ7495vN6D2fKKh6hJHACh0FDaTfBDB8nwKy7tWc/ZPLF5fzqBx6wyfXzxj27ODk/fWoRXoy5sQFqnXHkqe9iNrWswySGUxZ3W0GyWpwLiEeectruMV9JnfyEao1IpBNMqQ71JtE8eeoBj2np2k7A94vQP2UL747tFkjvhZ908G8+LuMRSrDyJRS5+7WkyxdWUqg+lCaFn5AbKeAfSdfkrLUlA/9jSgcko39+HaSs55yrv8OBF0XiJv1Bx+tlYT5zafQR5fN1i8J3R7Pl88JvH97BKXIauIHTMJqajMbJm1gxEMrnA9uQdohE52+pki9+kzXVF36jPEk1m4dRfa6HDfry8diS15LnkJ55Uj6PbXBLM+F17YLOa+txISlKuiOsSau5AmNd1OQWzeZ09+r4ZAj1q/7sWyZI13rZ2Dz6B6xKyZBv5u8Ef3EzR3yLAwTohC7BNXKcRx3MWJLXSbVXvnUPD1Ix5fdPLH9yrwJnliP96A7u5CgwAgSysJYmtCPUD9nDpSN4d3cYQw8lkujyXdmiC7h2s+HPLoymrmCsQzxm0G5ihQVtV+I3WFDwJ1N5Kxexa3QFiJNvTBLUcQ3bA7Cw/LsytlO8M1MPoff5U1+N14Rx3lkcY8K1wZ6Bg9nqHcYpa/OELRmHYNswcivjbTqd2SEDiesl4A0+/E4DrhFcKgUxddD2V8nxpHlHtxFk4/B23mv3puVv4QMOmSPydUnCOZXEauXyO/iKVz0NKDfEmluPCrl0TVRbnjU4xaoj2HoGjKThewWOHCYExTvUyO3yQTDPx+xKPzDBksvsgsd8DfVYebW9dwIPoDa+Yd0entiOmwZRUkbER2cS27MCPr2t0PkfTkpXa4kCh/zWl+BGdqWOKqOpvTbOXbJJfAp2ZmGYVoMy/rLhc1hnFojzpPlQk6/12GFhD/hZqMYlv8FFckmurraKcnpYaHrWDZmB2Gr5omZUhgzVx9CIFhL1LIFjB9iwLqWb/w6Y4nRQkj9I8/b5mvIFS4hQL+M7evMmKw5H6u9qzAql6HOqIGW3A0ceOLCo0tHuDktiNUTzxAb60nFQXuODp+F8JIKd06cI9i7F8cGXMdhszelPyYx0VweJ617LD5Vz9IVG5jUsw3vG4ocu7ka47Rafnur4lixi94qjwlL+I6tjCuOty8wf20GK9slmZMixtbB4riJw8A+cKl8Fi4TriG1W5t4VXf0Gc85l4OsWLmVoEVjEN9dTeA1a4a++830dzIMETFjhnYR92ZNx2K3FiM2jGT+CXH69zuPpe4mQgyEBDz2p9LsMS7146n8GYFiHw+6Dy1nR5UAFo/hWfsUvvRtIt3LmroN9Uy0KmPlgcX/SKPE/iGZiwP3getCofABgFAorPy39XPA039NS4Be/+auB5T9V/E1xUU4PF+dQZk2rH9ehZ3+UxzcLHHIrkO5xpyh7dbk9/nFyWODkOgny8g+P4kzTWBs81juiBkhxQNmGM0m7+pP9jtuoqWugM5XfTFpL0HHtpABJgNIt96LRS9DRmqr4nd/JoLvMnzOkMPPeg1OLzdw4fNSRt9cyZABa7AuqWBp2EgUrZJIr2xCUcKdszMXMW7jQD4W61G/EH5OGc7XlgRqO5WJTf5DRMdQAvlC2bNHLNXWprlUQN3CaAqPX+CW/0xkor/yaPRKMmRK6K0fQnZFOhVF/XncZww51dI8myRDbVEMOw3dOZ26gT27fQhblIiqiTWzjS2ZuXInLqJdzFtuxQClKfRVS0XDMxDL9a1UnnmEm78+PgvjuaD1m4NbNvNqqiNK9aMZV9MXD/02iqXv8T7dj4sCefasmMjQYw+IFQYzADtumIfy+nEyc5fMISntNDvHe1N8/C2RjCf61C/8e7yxmBjFXYtzvLzohq6EN/rNfUiKLKJOyZhVyhHois8n9vVlnpOAxPFmjl68g2FZImMvNRBiZo9dTwFBz8fSOG8Ef8Y70PJEjF9V0ijG19HS/yKHP4+ht/oFOs8rISPRF7MvLbgGpHApo5RdV+5TYTqY1IAtcPkIYT9/cHi+NVtaJ0NKDIqOPtjmq5LbM4clv5ag01uLD36WWJZ+4toWSZ5UCFBohuAWeFa6lKO3LFGpcmZM6Evq+3cR/i4Ba1dnFCcUYT3Bh3N3DrB+RBC2/U7xddwHEtVWI365CfXEdsZm6WLt+QU/gROuSpY8qt/MnuIQ7DxsmHXsAmXX15GolkXv7e1079mHcsF4vh04zCriiRJMYLYwiuOxC5C794hZkx9QK7gCMyG0fRM5lLAYKALmhUXQ6pvP7doczgXXURY6n8RoE7Qi1vB3oA+eJ+8gaXCKlG9G2CVX8TR/MOGG06ibfoZVIjP4LWxH/nZ/Mu7tRCR5JyWbt5M0JpVKxaP4MJE5tR6s3JPLahNQ7OXEobJOgo7WYqw7giMW+qT9yCRKs4cguzOg387+zEdcXpVISWYQ08VseGXyhP2Df3J4ZRJbhFXAcxTHRJJZdpSb+45RJ6yj6u8rBIJwUr9mIW+jzIZ9r8kynQavFBiRlcTM31eQ2BOCoYot24TOBI6dyqxZh8lr8EFrjwhpIxQZPk6EorpjTDj6lUQncfyay9lh2Ic7DsrkdsugLPmK+SceE3v/Mmo/9xOi0BdBbTCdA9dhWZ5B2cKHjNsGTYIqUpNH0vTVg4fvX+CkJU3+oLmo1R/luasLF44Zc9DFhTy3LDaN7yRPAkwPwrrh04jPiSYzz5m0bie0VMbR/3caN+dZ8f5VFYv8ppH+3YJQn0Yed59nyLxDpEfkYOAxghVRNYzob0CUewEdN00IDAxG7ZEM5hYOnBu7iGUjHTmVuId+3nsZUXKQqp1TCKkWZ4SXJvd1nan69p7Udi0+nI2gNr2OM7Mf8D8XfPzX+O/Wqta2HozeZeGyIAjPtktEv8pHV17IYdHXjJ6pzfOHpbyNd0J+azHn358k99oOLt2YglW+PZ903HE2v4d6tw3n486joLOdJMEqPkkF8PBvLVGCH3QqBvDqRCzrZQezZqEs6o9eEmo/hIGvHAjvusqy6nCufPzNWkxItFagf0kYdVKTMVIwYmuTPFlvxRHryOdTpxR3dq+CAZ+Z419HnE43P/ocYMjtPcT9nUCFjiFSJl+p6TJhmrCBRJ0ijknFMaG3IZ0l3zBVe83lWSsYseoS3XIDWPnpExMmLyf01l18FFYgqSXFNbmH9H5Uj8qCTi5/VWfmklq630mS4jSTFx+38GzaI8bK/WV1sSG5/kJaYuJx8RYSHfeUHrd1xP0xwrPaCGHFLn4qnAbbCeRKxQNwSzQO3wO3SNn5hKtLHiO8PYBzez6hIXmVMSMKuHm9mWyRr0RWSWD14zND9bWJU7GhQMyRxZqvibr6B++Avrz8VcdQTQGFy9/QtvgXb6bUoXzgMgFjByEqmE6dbzAjemvgc7mWJy9+YbLaj5tL4MIPQ65Ee1EjOIhJ6hJUiszYOdCOvg35NBTb41l9kUQjSXacNiQ6fRZZ/vpYKm6jd4wOPxOTGNZXmQhEyLKdyOqWa/iEd5DzxI+QWUXc/FHD0nc5xCmuRkPtPks0nTA/lsjoWZrU/bUk10yMKDkjft/dwMNYO4T8Za2XKeMu9eVOcCw7u4op0OvLZJEZRBsv596TLYgGRXPsaDajrP3pc3sL5fdMmG4ly4botdixjuhQIYrjBOTmxsO1U9Sts8NmdCYyvSUJbRbSy1/AhKCV/ImsZtHmUEbKFNP44DKGRfOYE12P2s8+jJ0kT8j9+yRiwIXXAq4KfJkU4sKw8RvZv8gV6c+PGRK+m8zFO+jlLo7u2800pp3l+x9RNGw9GSu5l4QqfZxz2uhjE0jzuxTUvToovKCL9Ywf7EmfyIVZc4jcehMfi1FcDe/kdIgVIWeUeX23gKgn8qhJDOPi7J3syRiF8lYhPop9mWKUisgVHR4qZYDQirTjsQy08OWaYhK9jLYx3PozprkOOE0dQdHbcHw/ixD3MRPbcZ4UN4QBYQhbLCj1/chZRyFj2vV4PUaPXtGfkd42kx9XP9FSuIAWhZHE1Sli4OGBcWkKv3tqGSJyktNp47kz8Az3sq/Tb/V4rGK2MyAkDeU3kZze3Mr4421MmDaYRQPM2bb8LEddNHjsdx+l4NkMVZ1BxMHJTGkIRcTWjYPzIvGfUsp5qyOIXJhKaZ0UhYqvUPfuop+rG4Xf5elIEuHPAgmaT4oyazM0iYFSIbh8+4pp93Oi6v2pKkthaI4yno5NrBwjjXyqIeZbA1FPesLXp7uxnJzLtFOHyD+sS2xgIkMbPPnGezwCDhNX3Ij7x5tse7WGeoUu1hgM5kXUbJqWBGA0tYncF0mw1IXp4l5EmzykUsuQosrRHDPXYEFOzj/m6n/S7UoAXACyhUJhxL/Ztf/tsQnA93+NHwPTBAKBpEAgMOI//n9/+a/e0dkszsgVNsytH4eU10aijHLpHRDJPbHFdJh+4U5zPllPNvGipy/XTmex0EydeQ19mGNYgqpCGbrZb1m9XIyrMro0fbcj62QvfJxcGbW6D6MVh3PfQImGEzbcsP7FrVcwzFsEV+3LHJvfQ79BvfHb04vR2ysonLGYKaGRvHn4g85ljehHBxOQXsjP0eWoKUxh7tnxKGTEEiyAmvem2FrMJLRZDeVcIUtiRBEKdZDV8uN4xF0EXe6M3HWUbWbm3Nn5mruKbsx/eQyfQ/cx/m5NsG8I00S/khsTQrduHFfz7oOZAUt60tk+ej4rddYjK3Ak9cZ9Dj9ypGXtdT75SjC6Zyh/VGwQ6XMRVSV5tl16TIbxH8JnvcZdvT+rau1Ic33JgCw/zCobECqLoGC8CMvFi8ly60uStTlDj2VhMGghwowdTJQJYuCDJqoW3GJeZjI3kh2ZfqqAxE8HKdHbioN9A6pbPjG77wqORfoj39EPA9EO+rx9iaKyApfemJF2YAfFXmWsMHfAR7CChDcVrPiazane0zjxfB2lOysI0XanfuRRmL4Lyh6gNuMjg1c3U6V6joM+A1lpEsY016VIHpJAyUWJyQuGEP9zKoG639lsP51AV2NMz0SyyVCTxbJv6bvEBdHG29g698fS1BTS4/gaNQ3h2YfsmaaFbmIzKq/m4rjgD39st9Kj5MOSZRUUq0vT1HiZLg035vR8put6Dpr1M3n1XgaVWCeeqjWyzL6Luz07CVDWpk7eAK3YOhoSxPBqU+bCIBMO7S1j4iroM2UGx/zCWF7gziQtBTa89mf6KEtmWPflR7YZZcDTdQ959w6OMIY5PEQgELDAW4ChbyBygiusD/HG71d/wpiB/bc8vKyG0nR+LEfFtGCmM1OvBdHqp4JM9yUUf+XSO/UeSzJSWJciRWq8L/0y16OeaMQolRUcLz6Ccs9uxpQ0MfWnJ6339dDbWoOcRg7SrCRl1Xsyx0zBPHsvC+NMmLVKBs8tjmiEZNPPRRLpMYq0ZH9ksLwRIxebEjRGA+sPRyEjiqCCdKJKghDaWTBOP4sIDRPaj9agdWMPr+acoIcReBj3Ic9nMYnCXxR0yiMi0OdmaCS204K47vuJKdereeNwnB0FEzgmep5iI2dSI8JJtq4hrnEhsmu3ceP4ZZyLPlO85xPmJe/41JqCueIUNg2zpfyhNLdtBvH0MIR3aXGxw4CJ6ycyrBHsLbNwndvCMuWfDGy7R+jf2VzP3kLKBSV6CVfxZKoTIentDGjryxCz97T0iqd2fxPf+yfzalc2x6ZrcrE6g95P+nF4Fkx6Df4pFtSYlrGndh19y7oZq52Afl0Bxj+EiCbN5oi+GyE1Ag47HKT0TAQGb59xXmCBzeUyZDP38/mhCqvTTrJk7ilejS9HZ9Z0XH8NR+HcQlyyJHlcdofN9qOZnRXMpwXWtG5I5PCUb5h9MOa+6AG+rzEn84oWEycqs3D7WTpcNf8xsf//iP8XWiUhLUmEpS+tl8ejrjmCR7Pn80e5mpKZhhiNEKVcZSnmupYopMTz/vEygm76Yi4/gpi7Q5GVfU9Nsh3VJV8ZoJpBqv0tZo1/TLzlY/xaCnk/LZxpkUsxSjXgSe0URjca0F6SyPA5zcxzXobgzyKMb7Ti8LsP5W469LTrMujVDaxHGjL4syJBG2rQdCikas8mcpMWoTQ2FOdZmZSsuIFAZjaT5CIpMt6HzxNDLDTq6TYdyiTn+xQpDaAzIY3OYVKo/9FncmUrz+MvY+BmQZZaKg+uHqdy0laypA6TVBqOk4I5bkquTHLXo7N3LUPmnqeq+jShwUI+/tQjvPw9Iy79xOvwbVqnSfMkfT4f0p7So5GLy80YZuxIoNEyn6CV0CbYgXrjAPybVnNeWY8bG30ZqisgLlWLN1O80Hj7nlrhWdh/gaUbS7mSIEl06U3yss4iqmjBayVtwqZEkdCUzs354wmVlCDlyDMWOGshpj6TYCkt9F98YQ+xvJ3SiIeCLRt78hhhL+Coy3YemDtxR1afSI98opNhKEeoeZ6BSvouMI2lOy+Z0qpFPAxcT87ftxTF66Nh30X+NB2c9mkw0U6EhVPTyJLaxq3aDvLEbuLWchHzXt/ov3MXRcfy+GoyGNWsIkS3jEL9jChauSr0eXQDz/yX5Mwcw+/UIVAUyqqrRTxPDCLhngIdWxXIrJTHQPM91ctdMUzsy3WJSuTfdpGV9gWjibeQCDEh/9dzjB1eIdb4DefOsZwVuYfOPA+iUq+iM82bgFwbAoSJKI4T4PXmFS9GllOV+4p2bT3O7AqgfGsChgc8efNcSH+RXWxeMAEr0/78Gu1D3kcYr25B4chSHEe0sbFiIwnCTBCsYgGzsKSDTR1yCLW/EllayCXRapAajsnQIcy6/YibRGAsPED7y27aunvBngbepDzi5dw5rF1pxYuPDphFOKJ+fBt/4suo39jJjC+yXF1VSu6P2/ScH4/RvW2kTMxla95EdM6b46JbiE38dDZNa0SvNo5l+vV8/NuLnJGKfAg5xILt86nvLKSkSYG/3dMJexjOkF/1aKslUyQU4ugeimVbNMeDd9HUKUdtSz23tm9jy+nb6J6AQT7eeNTJE/k8B5kxlnTmixFdrAqx+URd1+VeSBWGvwsY1T8AqYbBZGpdRzNUkQsiiUyQs+XYjXdkLIT5fmEsUBjIZzNZRo1RIlXUh7L0V7ycHM9ZMRfS56xlUMFsgse8x3ilkOqAQ8QaSKHVGcb5ml90KYGZuRKVhb74h7vxNXgqSu3eeIp9wNZLEjnvJqaWQUmGBfm/BxMS1E13kwwfOl1Qti9grvxbOmy8UU2OZu5fKWoNS8g/W0y67G/aRZeh0C+V9IU3WD+gE+MtGbzRrmSE2zTqbl+i388Cxsnv47P3HMZJKvElfwiDbDzx+9KKePNmThxzZEh7NWMSopHs3Ia8iSFBsu9J/ebE2gnv/zFf/5OaD1dgNuD5v7Uq3CcQCL4JBIIMwANYAyAUCjOBO0AW8BxY9l91DwEQNVYnL2o/YS6f2dz9gEWRMjw/k8ctuyIGNZjjUZeCg9dsjl54Ss6eTuJTflFZ1cLZ94N5t3cvDR56CMKvYrFQk4EzYinUTWNr9xY8Im+yLzYVhx2BvIn/jV30LEpELYmpasN9dgjJm314uXk5qmWhrP1VwbyTiWxWt2SZ1h7mTIaxSjPJ+CTHpcUzaBaZgmF2GblfJDE99wCBo5Bzsx8xT+se5hOuk7NhN95rJ3Ki7AMaywNYZXGAtskhlI3oZML03eT+rKR/8Siiq7ei5JBPdUsSjSbS9B8YjN1bA+76aODcIcb5TZN4+ToFq/Gh9O7Tj33PHiMpCGR/348cGhfKgZWHuLyoBZ/Zijx+KsfB/K+c1VjHyfrbKJoY4CD7kwmGiXwQt6VG7Qy6o84wefh7Vgu2s/dTDF3xP4he8Yrf7w/hdVXA0gJbKqfYEXV2LIOtjNkbLYqMWCe1yjak5iVzqqycuom5jBGcI9r/ClJFzgxL+MkVranUdN1k2NbRLEnL4frzF1wZvxvhkh3c0bRksboa+mWNvH68BLmhk/CIa8JB4EO9nieNo+ejUmnO2wmL2LrmEYd+x9Lj8I2C1sdYiSrwoiOE3VXnKChYz4EDG2BnANO/PEO11oBbNapMNA6kl+tSxi414HjDDfRSAolwkmC1vwVCMy/i3t8muuAQKVo9VLss4dVSBa5lB9B6T5OYVdUo+n3mroUyA/KtURrhgkz9/2LvLJ+y8N59fT10d3e3goJggIgoWNjdXahfC1uxExW7uyVMxBYkFZBGEUG6u/s5L/aZM3PenNln5uz57TN7XzPrP1j3tWbNZ933SkG3q50Fon94vU4M/0OVTNIUsPKXGh0xIxEbqIqpRimdlpZ8aJnNGRk39kz9hfL1nRT5aXLqowchn9q5abeGQsU+JJkMwfDobL47LCO6BYYOlYecGoSBQoTCf1v5wgDyhUIWWqxjvnMq2wxD2Fy0ho6MB/j2fYjGrlYEZyQRyEtQNuAKU01O8mdVKNaLdjGqry2+YV5orsnluvQ97gh9WGiXjK++CYLP65g1u5EOVyHs7qT2yAvKS3RxfB/HX+F0bgkHs7j4EB9fvGL91hZuKWSgcteW4IedjPtcwFCvwyz7PJXlsvUIPWW5tekQiW7b2Pp6Ox6OCxhg6ERNswq71QeyactsNugtJuTeGtKXp+GgDRPE3ZBGHjXRcpoopFwsh/2jjNHQb0R72TEGZY/i1P71zLJuIHKGKsta23iq6cC4Jxn8I2zk1CRrjt6ewTjLJpbslkS1pQXHS8vYODcM7QtjuDXREAuLyTjv1SL5vDjCcyMYJeqGqXcQJ51GsehkL9fvfeP56EKqetw5+KkPG1aGkeL9jCGJ0bjaWOPd7IFKkQoXE4uZfsuD3g5T4oe3kSsryQo1EZ54wpVAVXS2muLkf5a6L0vQ6NcKEiBS9ImMHmM8dROY96ma94PUWX3FgyGvHWkc5o+n0J9P/fSwWrSJCSfh0NyJWBj1oeldNxnhsEn4miv+H1FpW0pTy3H8ryfzOGohHUeO0rX/Hn3qYeSysZgf2EL721skNysRfVqW9J3vyLpa8O8W+39R/sPPqqKGOqR8DEiXy6AqU4KrT3dgPKwD+Rt5BLvOps/vt9goXMTr+A6+X7yNmsF6Fqzp5WD9bFw97BFxyuTm7Rz2bTpGUboKJU6QFFuNS+0AtN5C0RZf1Ocf4Hu9ErlWAwixUuDj4ndIn+3GM78Q3b9v0JcwQXypJHFajUww1cag31FefbuMChJYOVshkQCf0hJwGhRBaxQE/nhBxhc5wj64YZk6jeLvJ3HeKMvQ08Yc2D2PnMRwfgXV8jpnB6YKC+hTY4jAaTBlel+plarm8JiDXP0VzAbnPRBahLbsT2KK8jDwsMVYayrbd34iYO1m0L1Pe48DD0xkeOjky0EVU/b6HWbP6X3sfPia0GRNTlWc4NKXFrIrJTj5Ig6pgWFk1eSwYMBKcjqqwPc1bv23M6miEPHLOQTtc6SoHvBPQmlZEis9TnO9LI1dEUfoKG4h3DiSmGWXOTfkLDcF9ZT3fUFCSg1NQ8zpl5XIqdUPmVBTQOBcV0a+N0RNrxSX2EUw1B/khEhV5rMnx49Hyu0YrVNC8vYHNKb15c783VB9gR8Xj+NR9AEOjqC7sJ7RFmKkFg+nsvk8vX9/sdl4KYc1XehKKEBCYQJP568me/BVVnos59MDbRTNKnEOKiZQwZ5vNVXIi9gSVl5MgMpFZgx+ypD3aXRY1UHhVUTDcigKuIdgwRQq3VezXmYUwp730NRJlaosQtMmen8PI6/LHZRlUM3NoQIQypthqf2OniJxek1nYj7yNVF79vN12gam30ghS+DCEeFRGh88x2ChLJfElNEqNeKWoQ01AyNRqLHAY7EEkwfLcrZoIrPGiBN/7jQ8h8aY/rQds0bEUoq1xsrAavon/+VtQTgOnMC6JpFCnUiyouRoEQun80EoXtOG8nx8PjHLsjHu/YvP5zlcPJCLksEnKjyHYNacA2LKlBg1I+KQg0VcPV2ZwzEcU4XLJ1vmr4xg34rtzMybxIyAGezLlkQurA6roTo0l7eiMn4CvXprUVKup7bzL1+yetCXbuWewIAfPzuYsHIw+gPcGFqWyMcRxvgaKUDRTgB8teaDsSj1hXKoiddjIKNEZ4sK3S1j+R41BBvlgZxOHcwCu26im38j9/Yj9h75uQAAIABJREFU2d2asPI2mxXzoFsGkYjVGMrPYGhXI1Wt48mWnEOYxyvqvz/EqU6B52Ur+Gb0nOO2PzBzqaFMfBqtnyUZ1Z5JRH8pdGLjWXg3ijM3oO/wetxTS7hdXIiD2lfye0RwrP9Or2wqapLK6Gm4EKm5HeOF/jRebiPbwIa5XR2cE0SQ+UOMmopa4r4Yo+hXSptsLDM6A/nUYk5yezeapdLM7KhDWmwYYmr1aMV948cEN5S/qiG/L4vKJSsJbYTE5+L8tHrDi0AVjH7XsntiFXefViIXrkSRSX8MnK8Qr5qLjqEqEftvwUdFTgfepzHtGH91Eyj6nIa9TgUdIzR499303y3r/+tpV/8R2CkJhDX/CCnf349tszZzpFuUQ6klBLtOZoqcAKvOND5qHcbQeSKXTp9Ds89jkuJeceqQIfeeJzJTQhqnAGvE1oux2uAVM4p1yBKTREGxH9739vDT0RllWzuuVMthEJlG5N/xNNZ+JUToyYuAZLqOtJChaUeW7zYKfb+gQw2nngSTtu4lq65+IUvyAlVvJDETmUlAzzPWuo1h5ftMRjcp06rzAncUKZJqpqdNgYryUuTKJnEzYDqT/eYxKuIyy5N+g2oJS0NDGGm2BsHNs0gxBhF3HXwsX7JC2x6VdYr0Qx21GTWsPz0O2xF+PLziif+ukyiPe4tG2R/S1Q5i2tCKhNCSGwHBjHLcSUpyF73HxhJ+bCUtSV/ZHHSK8H3Hedy+BYnuKG66u5KtX83yxfY0FaWik7Wd8NEWJAX85M7dlzzwbKRdo4d8VXFSS5wx2j8TN+FDYo+8x9bGiF1HxjHfUZa2K2KY3IrAwncyanNCmaCxizXjTRje4c3kxnEQEsBeFyUGlojQ9buRr8q9CAY1s9FegSuaX7F/nsmHj4HUTrRHMrwWs3sZ7Ho7k1nRu1DrEENC+gviImvREhygoHESGZpN+PS4I1QXQam9ir9FDcR25WFVL8kLd0008wWsNBrIPbM0Gg85kjTjJ1tS4jneLMHImbZ01i/HQXYfPztvMbBwFUq5P0mMFUM6pZcbE0og05TLM7WJe9/IYO8m+KvI8m1z4elV9sYOQVn0OqUDZfgur4nOn354dSdTaNKI3JefbHylytnxCvRee07g4kts1lLga68Nykp3aDUw4V6CHugJ2biiPydNm7EV9GVgIBhKb8a/8DuIRbJbZR5P5h4jW0UHgNR8sDMCMYGAxeZwcwqsPvKDtCP3iBotz+IQV7Yd8KS/ui6a1TboKzYQ2ZAE/NsnhgHZkayydKdl5yVuHV5NQpokepIdyCqcx2LqK6RDvSlDnH801vyvuhMsOAp328DKG65JMCX0Nb7FkDZLgEavEw2ZBRxLG46W0mc0NLuY0fKd9DpLfneFoWm/i4bEHAaIpfNDppsJXodJT7lBb68WWtrpFBY5YSkHeRQx1MiMnO4WkkcvQzN8E3Ft4/is9AdCsrk62BIldSckWj7TlVtAvVETqkbDmPX0MKum+vBL8iNLIwyos9Bh2RJLbJ7JsKt8EdeyMrCsDiVD5CDbfPw5+WsfWm+Os3S0IV++GuFwxoJtDXWEKZiwvr89C46nkpy/h+EzDnIrag/WAngi8wwZtUkoFDjQz8KW3h8/SJSIJ2LfQs580cMkIoX+RBGi3oW8si/lQh12Wm3h5YcEgg32oOw0lPLdN3iqUQkdAzjQrw0xFVD+6crdR9FYXvqDr/JsrhRqoJ37iTNF3kzvD79a7mNWY43L38N8cA1Bvh0idlrS5X2ANcoHqb80lLAjAp46K2N9/x2PxHywtvuH44VBPDq55L97Pv6FyGjaCScPk+L+kw0sHR6FXbspDZ9isQvUJviyJe1r/dBvCEIi8wEeF8Yz12gboWHFXD6+hemHQzl9wB7nqdM4ZVmNw4IefiSsJXjpfmwbInjoewDqb6Mf1kYf4Um2WxzH4EAgA4QTCN2RzvbWNTwp0GWM/Sam7S0i8HIeshVhNLcGoq6ylqM3XxD8ZCWvgzfzoLeIJb6/sYrZgt+MRpz8I1nhmsKxZycJ8vFF+dZcvhz8So2ggL7Xakk8ZovFW1HiC48zd8UFHnx6j8y94dybcwDvBAeyJY5SJ+3ITb2lPLrUhO78Htx3DOXmzCyalVcTmtqKlWp/vOyGkKz8BCXZw4ypasLvwW6abXVwVVpKh2w5nWWfeBT6kYq/eWgIBGR+u8yv0xfpDAngR0gZW8Yv4MTtTKQkZHGUPk2uZDmu9zexfvlrzper4POsjTXO33ma+YyHJ4VsPjkTFyVn1mZsYrdFMXfPzKFYLpL28fsZN3cjYQoLEZoFMUkg4PkJ2JgZhJluJKuSVcBBjRDNLj4aijLYpi+ZgbcRs9lCWWYUdRdtcHn2k7+T33CFl2ydfAttlxpkpPeTV9kXcb3h9Cb3UFkuh651CwpS8lwS7cSjswJFkW2sd3hJwpvvfFIxRvXoOlQtfWnYsZbLTT8JffyUAYcm8fFIPjtWKZMQVMH0ibbsjIij7/mruCkaMayxgPCvkFjkD7FiLJerQmqwPjNLvbBZqc2ONf2wOl/MPwIBh7OOkXP0JpZ1vghlK+ga54jUiR34pWRxZFw1OwbOo6hchu2zL3N8/iMG3w3AI2wsse+9+DX5L0bjWvFavZRteoF8HjeHUsdn+IdcQNg9j30Z35jmsAxvsy6KF9iw6qA7z/26Kc28xIVb+8m2Wkl6ijrOyQ0cu3mOPJkjmOjNRyAQ0H3rPckL3VlsKEXayJOsthvC11cPUQqfy7Y+u2jLfkvvLlfeDo7G5pkPNeulaT/kQK6nOwOWfuLaEn+UGyDdeiNXpfpgYPKH0KC31A0bh6IaeCg5InifzOzK+VwyX0uXlxHaZxP5ruLNp8qnLFgwnb+xUWwfvJlHjzex6GIWikoC/K+lItE8n/rUrejrvUOqVwVRHW105XTxdh5NrI0KF988Z0RxOEWPFvNmtDl6sefRMYhGxtidtrIatKsH8mhFG65vf+OkkUuFggNaeSO4JhbPSAkFpFoTeC2SxNz4RVSJTURr8TdSnhzHbpAhgsva9IxRwV0tFz8ZF67KV2BwK4uda/rS+fI5YzfFcfPiRlQk5EnOgFggdaQIVtfcOLbxJl+1ZNg0xY3heUqUVNzBM38oM6vyKDY8jVuyK980lfhyx4kFJrJ8Vq9hvspNIqqvMNhKmsT+J3F9NgQxt0XczJvJW/Up+EqMR1O2k5Hle7kwaTtrz4SR3FKDVZ41UeYdRFrb4l/zmWMPjmC2UkjVKXFmuXfRPuEzBtVBfKmyx93Undr6IsYY/0AwdPv/+2lX/1HUqiiQbx5A/DFrCpoMcDYpoX5LBV0Fq2iq9+ZTw3MMrqmTGtjJjj3PuHVzPyXLovlzNhcfoxJcVOsRJYnCwJNs/e1K+vm7LJVS4k/5XRRW3KVKpZGoNH1O9CQzZOdL/DZu4fgDfaR/ixGw8SCnauaTljWTtuGnuTp4Koe91yAI/8ntg058mbCfHpt4WvtIU8RfrH8rMOvkSfbWPEHW7gMvpwxi0+nT+O/ayrnAkzzdcZOrR4eRHD+C0IjZNKefZN2naTyNPcQ2K2k6gzbirllGUv9r6FONV/A9imYe5HCUD8ufKeMjNZYb34XMH9GX35dM2ecdgNj7ucxu/o1I0ih+5o7k1rVIBvnMIV/gh8+gHwx+M5gtPgkknFlI0ZuPbJ/znjybhZzd6U2FXTiK2iMYnenFdEM/XB1qwVuHYV+eo5U+nk0qC9m55zpm4l1knTRDZmci99ycmU4KCWeW8s71GEPat+Mz6xbTM07yfLsvEyy3ELLvCMZOlUxuHsfFhC1cNILqskZaq+vx3ZhH73BdFheK4T/mPrOG9ENm+SRM/vnC+vQVKEQfYtekmfhmBmHRbAjZtrxJO0RtxXjqVE0Y71rKVO1nmNbeJu7scWKqimiWKsdRXoc++kLmZZaxoL2QtOx7tKxdhYlZMHM+ZzDpwEWEVwN5n5pAxouDHGyWIz5lK3sVGtnYlcaHDdfp3K5LmOdi4ja48bm5jeLRImgv74fS9UHMPRPLiMZn7Kv6Ree8boy26+NdYM2omFaSBE7ot/7A3DyVm3t9qR79gkYtZX7ofsDXsAqJmtmUpXZje7EK4W1HhA2tjHpvzIl/9rChSoip+idUXE7wNO8GlS1w4J/7lGzURXulgMm6AiTj5yEKpHcnMdzqNqfmCzn4uR9R208R7dDEzQPgO3gpy4xUyX27j4j6RMptdyMUCsn4fpm0C4sZuOs9Cw8nIz/9Is/2z+OMNxzWXkPECQ8CNJ+TrLkdT4EqswS+9L5JRcV/EELhPu59l2TLkwi+qEPGvH2sv+nPxy9jETFYzbl//Bklkc3yphmkjtFB2O8sstprsS9+yRrDaqKS4qidsI/Hdw1AShYRnXzqKywQpZ50QQGTBYNwcZBm9SJT2qIsaUppx+UfL/bmdnGgfxsRYVdw/PmIzxlqaKnNo1l/LhlfB3DJaj0v3nym8o4pCidekjZiHyumHOa5RiWZOX0xsLxP7rxubNyPE6rcl6anO5HtKKVB5gfa4wdy0GEbY01SaWnL4eIpN3BayquxVXgL9pGgDOGNsLBmCufVu/jUG8ThkF202m1ng68hS77tpER+H7lK0SwPyWBD7geWjNHmUIoU/KzCVKkPfVKGM/zLEOYt7mHH6iJiJlUj8ygNicgIFI0vsHl8HKkXE4h7dAzvV6eY3f6LD5ruqHyW5lh7GBLN58jNqmLPjGksmD+bfjEzmfWnF0nDQUj+vsLh70ZcXv0Jz0hzYs944yHWjySn+/9qVf+Xp602l2Ojz/GirhdXPV0kvB0oPLmeyxaumGvGoivvRtb3YGS7bVl9Sp2nb7/x4ZYi04qmc+/HBULWGiDsNeTK33BmOxWy3PcZOlaSjG2OZOs4HbqLslhlp83dT3NpaBuI17qltN6tQL9qLF01lqT4VVI6eBFJL64imyRK2vwCxIwG0dXQzTiDg0grpNGwsBiVrrcEvRbjwR8TqviCYZEaHfUdOLloktsQzZM9odTU5ZJ8/xdh3z4wnATkpD7wMUGI/IErXLlVxsRB1hi8VGSFdi0qacsRxp9lz4RIZoUPZvrsiUjkww+VbjwKlRnp5oHhFD8u/znBiB+mdHa/IbJNH2v5/gwRdlMg3IV0hzriIg54DOrDsm1erNp1jvcZloh5zKFhcjNSPz+AKEySsSH86SPeFGSRmFeF0bIB7Jz0ia9HZUiR1aBt+mB01N0piYP9XuZodWVh0qnCuFG6eFtXoC6UBAVXFLpLORDiQRLwHDiROxMD3VJWZckzf1wfsCzjXUMHlw0mss/vOerDB2B06AUOU1bSfj0E949t/OUlTAcpGXva8ouxVHenqnQHIsUWONpVo2+/hL5mmvS1jsW8vQhdEU3ESEbxbwPBJtroqBsj9PVDbmoSjS/9cJLLZ4A0CI7+YIThG4Yr2CJr3Il1YydhgunQ9gkpzwJKdx8gaZ0GtIvwVSiCvbQmbTdSaZ9tgv+HmXRWalMpEOC46h7i4sa02MvTmtdF8wgDemOz8dtpwpY3m6gOvYbb0Xd8GKABe3eywfIF46PXkTDhDLMTPGkpFeXe/ecoRutg8ugYChk3UH9cioXNZRJl5fCXO8fooC28lV3K5EeKZCp3o40XV2y8mTOwlNtZk+g/MAhtqW2w9wy/gqLZw0XmWAxkxd0E0qjDplAIfhuQ1vzI9k9L8ZC8ROaTbvxn3qZxWTQVemr4lGoyLPcpCiPNUSu9S8c/YpxTuIWr8XA48JrlVZkk/2xGx9CVSQ170cyKZk55JyUpr5gz+A+yfTUpzX6GjUgcVmop7D82iPTPStj0ihL56xn3C3/S0Pg/i7i8HqGSAgoyJXS0arFA3Iwqm3baJSWgR5kZgINFNp8+fWFPZy0zpuYgWvmO4Yr2FCqVoDneHqdVEThGp3F+z0rKM+RY09KFqHMewweOJffaNYo/FzM0p5Y2WwfyFuVz/3cEuv2WEiQ0JlXfCZvWeWS1BqGWcY5+twvxGbGTipgy5hkMoiwavnjKIyoNUqvgiJsEUlEtzF+uyJT4QegciGcYc5lwcyO7o0Tps00G/0p5ClXOIaG4jZ5CTQ7bpdFHYRyNVflcaRzEAMc03mV8ZrREAvH1ery/9wxncR+ocEFbNpqu0hZe/eog99tjrrcMpFL2I9UCZ5Tr89H2u8W31Hyc8+CMhABJurl5+TtREaE4e/gzYmg6e8+X0y2mxc2kn/9ul/6nSD6UFKWE8Ynneb6/Bo22OvIGOFMtvQHt/BMc6o5meu9T6kQX4pm9iZhf4wiJHYWG7iMm3A4k7Pk9+s+dyrdHbzF2+olUSAeW/fpjmd5Du/E4dneF46A5nJGFecS+UmbhRU8aPuXTJHkJG9MN1Kqf4IdyX8xqqmm9Bh8VlZDV1MXHQozzskY4fexmUN8GVFqu8UxyD4O0kunWiiE/bwIm6+04s+s9g2SjGWe+HhE1AZ2S6UQm/8BBcR7q/c2QkQnjHDU4yCtROCeWYafGs/bBfibrBPLh7g3WGHZh7TGCiRMySHoNwzZ5cLF+Aqt1ZZnBT1refWasfBvfGr7R/6sY6VketNhLkl73kRktpjwqK2JSzXc0rc0431rMo8l+PEi6i/WwVurDROnqliWr4gUh2rNZP/s0n58aY2Dsi2rcO5z67qTw3Bsk/CZhLF+HT99y7rqp0K75hs2CF7B5Fi0TH+Jr74n5CDt22MzisPUNpsy7hpVgIgx8wQZ9P1A7jq/wDCVqQkoPiHNgxlK25cXRLOKB3j/LUNHq4eXw+Wi/e03uqyRsLmjgf8OQbfXr6W5cTavvZDQLC2lLiMVMYw755VJ4KDZQIFvI0ImDWTX7AeYWRigTS5dgJDO1O9HLM2aNZDZSkgIaagpw620ifM9F+rZqoK4Lxz2NyFkXyOT4a4ROOEbY3gCe/tam71hzFLUyOPNhM3neT9jysxNvo608SrmC0P4H5QevclB4j8mbulhrII5i0k+CBliiqnsPYco2GgrmYn0nksa+LxChk7/n6vnSo4tGiiXeN17g8W0mKf7NzM7/zQrBT7xCnjHhYBye+y0oH22AjZI4/Z/lImLrTK1qJi2OLuj1bsKrV5IkEVumSGnhugMIMgC1QsLOw54pETRvmcn+c6/Iz8pl0M6RxKHKaqopSVLDPVWZiktVVOg85OALY867TiTjcA2CL4XYmEYimDefPvtuU1HYwaK2CNQt1ChojmLDzmQW7fcg4tRVQI7re68gNgz+xIdTL6mONH+ZqNiXfOlG3tR08D1envkWj8j5YMI//YyZMdAek6/p6NV1UFUth6O7I2JyHfzNVkJOrYxN4laErDFlQ24hZ3IEFM07jKOTFit7pzKqp4gb075ypsCDNnt9rEp+8ei3CYemT+PxlT0cfbmfQSaJnBg+j22RA4kp+0bOmYmsP/kcp8VjMOungaVEFuExmnSfq2WS3zquG5ewqXQjPwSzefK1kJYxjvwOucaQIdNwF1Gk5GcBotrHkCx8hIOdDC2flCiN6eLcNiemfnWh2esWLrbmdEZkoSofjHHGRKyGrCbgdxiPjV6ROHwiuifArK2erYc1cJNL5/a9vZg01XPiDEyLO4iEhQjSIWIcXH6L661Z5H9bj+WHYoxmLybg5Q2OdfqwK0tAu3gYrlumMcl+Okefy1NQc5JJi5dxJ/869moNbD3ziXc7N7D3+AtG1XTivVifnAdBzD2Q/t/Jx78QWWkD4SzhUsS3x9HgNoTa1F6GNezh1IhR/PVv5YDeBNRLp9G2wBGj6xOYZ6VP8fwBBE48ScsmJQbVTSSt3xc8DNcRunQqS0/FsiMrmCNG3WhPVOGU7wueR9cwa54zv15oMm52FrUu62lM3YlR3DLK3BtJLKzj840rjNSdT0BII+8a5EjyTWGhYAAJXRsI3fwQK1lJtud9ZlWQN9/jTvBiUTet9jv50dyOVskfGo1LeHGnL+P0P1M0sgfJUVkoH1HG03Aa+8qe0NGzlaU/x1PtLMQ35QndK+5wNlSCyrOLUbz9lsaMMfz5UcXKiy8JedmId0E5z2pNcRokTrzIUVqFhSjlH8BA0wWNJiFBXb34aTXwQcqBqPRWLjvGcO/POpqqM5D3OI/o63202uVh3viXtjRI7PqG9C1vnA+3IJTsy4bZ9Qj8j5P5pYDDx7Tp2raf9jU/EGo20eA4kIGp7dzdfpN7AVuxyNmFgUcIDjNG4CFYhfyhCHplZyJW5c8ugRSf27aSGNCfum16nPj2iQ1uNXQZD2VefgnRizsQ3LEiMS6MoW9HsMp/Kp6i3/Hq8aP1w3L0/Rch2rCWggQdRk5pJvJgLWLuIqiO7GXJ4OPs9buCIr+pEZHngEkHIn/yEceCI10C5ER60VIUMH7zXrYCqQIB7/7ZAh8TebDIgov7LLl39hpHWg2YEj8Vcet2otx2MOTeCtzHyfGuZg8/59YQP0aNdv8SXLuPMqBCHNEdwSxbdIPAsO9sfJzI8xO30JediZedJ2Pm+aFXNY2QPfYMs9NjqHoka6b3Eim8zbF1jynTyyZHYMSYr1nc6ziO/twHnCqXYd6bU/RERjGr5jh/tphiNmModv1UqBKOZ55GGL3CPJo8zxE5wJwck1EUKQ/hautwjju28nhMKNPGzuC6yBMGfRbQJleAdH9DAuyERES9JfhJOJUiF1hy7ilhvw7hcF7IzVUuzM+Zitj8MOR+v0Xb6QGVRc0kmunTO8YJn+vbGbMiBOeeAXyrU0ZgtZe9uwoRyVuKmsCPt1WvsZKOo0pyM5OEAqIUVqFe+4wL4+N4mZyJ0Y47jDo0lbXFHuQ6q9Kd2IG4fCrKUpqsXuvLhfDlhMguZmFqPr8j+3LINAR3B0M8hW8Jzh3DvX6iPI7I4o+LF1q27VStmMLRrFKMJh5DOPE0p8/N4Lj6U8reJuNhvR8lAwP6r9HGQUaPI7cn0qfwBFO0LZm++CXviqsJ+bkDlcnSyCQXIf31DvRfT07xA2Ly2nAZ+IWiYFWWzVjIuee9LBvdwIWyWha/v8Lzx8M49usAC0cq4vTAnpElK3irvYB4ZS9EZI7g/mo7FdoLMcovYPH4sbyuSiVFvJYBKiYELBJnVHk9JOvjUu7E/EVzuJn9Cv34oXRmNiO16w0Z+5+RfPEI0bsCWVhowyTnXky7cxEus2ZxuC2ldUKeTE7Af+EHYmRM2e+UxurD1VzefIMizfVkGpQT6Jr6/0/yISXSwZWNksgsXkmxgyL575Jwi1jIbi91zlQ34mIzCh+jZJ54aRDi+w1TbylevDqLaGI7CkrzWJaiyyDPSvouXseUgHi6jb1omS2OseQ27vaRQvLRU7Sqinm6N5xzIf9gv6wVu8UyXB0ZQ23lSyrCbajPuszUHbMJOH2UCUFr2fP9EANq86nVGU9i00Nu6K+lc9ZxKsfKYt04FOvidTQELeKszCuO7DhKrs0tSkfBYb03DHfbSkmpCrcyvOi+3MzIV9G0H3chUt+W6IfzGFF5mqu9mjRahLLV8xVNQ6fy+I0+Tcom3N79Frnbe/BQEKC70oZ/kteQdiePuDdCzsrk4DT9G/0yNjNdJA2pITJMG6FDuPkcWkZOZ6PzYi49zsZj2Do+1/7EZfcq8nrVMe/+wCXPyUjPiyf+eAAT7OZRab2GJeL9ULo3jtSgZD76XmPhhe1sdr+F64mTNNz7znMRU77Ev8R23VgiHFPZLHzIjnY7rAQCFi4y44aPP242xpwSDmO8hhqpPzPwELQT2qCMwqjbvBw8AVkLZ0LbzmGy8BGdISd4deEeq+dl4/T6GPm/X6ErkQ9rwyk7tIvGqMGkfI2kMySTFceiWPvRhkUTKjC3MEITfcrwRkqniyGZYvw5OgA3+WK622roK29OntASzYI20oqvUzrvPP6vXoH/ZZwkluH7bhPJPhIMORJLolkZZ7ISMLZbT7lqCr8cW6jbB3/MExhc5sYy32XsXPaIa3MS6HqXQJOsKB0FuVSXTsJbwxh913gsDszCOtiA0LRiIm71smfgbAr2/cPgzMukLP9FWd04Loe+ZmqXFTu2iDIlzY9hzat5Pe4A3jpS3IsNJk1VmoHW+7ng0Z/0dzv4k1dE7v3dxFWdwmaWPcEVq1h7WkDSLFsmzf6AYfF84icPYNvrVIJrX5C75xYKgu1YOwmQ7DpDP4krXPEMxSjoHcLoYE4dzESj8T2CeXUIhUIsH4Zw/Lo6Ry+f4EnyX0aPvkrukevMiL+KwGgYupF+DN+7l4XD9tKVKsPwvxPpSjFEOf8ODwuMsQp2YnJbX1TkJ7LQaQhXx45heqMyh1dEUbLDCIlPmdAsg2uLHTf6mTLQcjBBDkGIrZ/EkcMyGETP5A8LmT1zA8NDWpjs2cONnImIKolTmtmCRY0MYcECHl+yYbu7GrP6LmbbzUzW129Gaos6QmEq1zqfEaZXyd5p3ky/N4or0WJMaDBAvl8SOjun0zdnFJeKA/mjNh590X7MfadIoOUHdOL0aK+aRaGiGstlXiJxQoSowY04Lstk5a/fTFimS2gU2E2ZxZ2xrpxsEeFK8CnmNDbjpnGMeisjFiRM4OHTVnycenFuElBX6MqiRctYlxtB3CwhMx8KaciRoTJUk6CCbvZU3CYxNoEaAllhI0q77Rg+v3VFcOAZVXLqGI4zY9KOc2SkCtk20Y8hXktYKLBl5OtuRn80IW7nNZYcK6HZRY+K3za8PGrLj9sS/2pV/5dHXF4DzSWeDNO1peVHBBNtTLkWN5nedz1c082nrFJIRp08u/pUEaBaivDcHJxufkJt7k4stbfwQMMPi6IpnAitRH96JYm+6xhrZcfFy6+YNu4F7r0HmONYR0NeGbumTuR613dORP1lWdMMfoueRymjhf7VGixd7sWSMRNw3vUMo88KyLn84L2hHFa3fzGwcRst9PqoAAAgAElEQVQ/U7VIfCuPULWCgWX7EH9awveOWK43xtAe/4wfjfcxSltKneAhGeItuOzfRKmJE28zS3idGoNMqQoxAl++HxVw/dNQVoYfZcP1AMxCM8jOU0W7KYFJ/Vvx3PocPZv12HRvQUpzMZfSJJErfc/M3jzM9S2pV9SiVnYA/fuokNxhQIeED5sHrOBqWjz7j7TQLReEl8p65KXq6Zc7lRtqSRRPMSMg2hS/yUZkZFQwaIM4+r8USLhQxyCBItZF3ZSMyWXj9MOc33WHw8Ui/Nx+gHJKONayGRkjJzanzSZ18nqMd7ykTXwWg+p12Su6lM09V0iT04fzr5n08TW/56iR0XAd5V4pwhctRzbrApV2lryTCGQVq/Cv/cqYnhMoS68nYfk/BD0ei3SnGsMlYgg4WEmnfDXFhh+JSTHB3w9ayUMEY+zG7OBgzlNc9sXQKV6Fm3g930QluNzcCUD1x4Ws/CaEM8e5OLo/tUXj2K+VhnuRA+q/TfmhV0lKRQH6TxegtUia7d/tGGcG088dpukDWD47imazKkkSetx1+0phyTO+zM5n9a2ZCHQCiVm1Aad5KQiFU9hlWMbmsTUUl9UQ+FiB78LbbB0wAA07HeQ+g1lPIdrteWi/P0VO7GgSPu5jqvcYpn74xsfVgUxyeMfNkEt4K4gw928AEftGIalggsKnSfjdU6ag0ITZGrGs6nlA065QrhbBukvmjLgYhMzYcM5+M+TKFSHyVXFcCGrkz9sIwoW3eLa2Cq+pp9i5VYXUS5eI3phPxph0lgyZy8ZJ/QmXrEO3Ix9Jn+MsDNxNZe0lgn+fwUP5M1QIEMp0sGhUf8KUahnsepY+3v2QVjBBxSoJdalZlEk8wiS1F7vCk+xJP4LVuoHMvryToSK69Gr95oeyAz0G5QhX30dUxQolHRmefj/OoVny3JRu5tC+P3yvHIyekR4vipPZYDuVmaN+Ibn+Oxs++WLkupINy83RCTiG2ZZ6vo66hcD0B2fnafLsIYx8q0JudDDPchvQdorgh7sxrh/laOq7AgMTa3Y/ekRP5heKehfyti0ERePZtIg+YcKwSj7MnkJSchM3q5RJHyBOzipnNmyEgNYLPB8tSt49e1qzx5NfJImthTonmtczJcIWwdGn/NWxQylWkTtvLvAg7C3d6sYoBsmw6FweSsvPYp1gi5zCQV6nJPI+pIVjH6fwpdcIjVQ5VlYmotf/NVeWTkA+9hFTl51gfe1ArFRCaf8dgemBH5w/+IZZY0bgOFeF0HhDdpxcS0tMK0pjWqnv+38cFvi/8Z/i8iEt4UiB4QOqtNfwSv4MRotEuaDzhviUddQZ3OHVby/OffdmpmgWaknr+fN0MmdPPWKWqj2nal+TcPUJC9WdsEwt4l7ZcuqVI9gbE8f+Zi/m+1chMmAw7U0NvBgxkF2znPlw+S1lxYNRm7CXmvDx2PV+RU9pM+9jbrDB343z0wXU9ehzP+0s3qVb2NpvAWbTjyEr6ozMu0oq32cy2Po2c4L0kZpny+aj02jKTef7rZN43XGlu/g3OYrb2dFmR5liKb1iDYhWN7MjVYGc8AV8e53HmuY+GCw8whL/FHLr3/FXqod4yXI0hUr0VAUTHAyn2qTRiwbtn5fItqvlqEkZlWk3Mezng6bCSIY5CSj91o+3D7axba4eIhOzuXbMhwOnx5EVIWDo+iN49ltL1YRoHKa3M7lQgcP2M6nI/stGwyZOWY9jhtNYglL24RYzDRHdO7x/HsfvMVGc+XuRC/XptC+/yyjJRKZ1e3NdQRqBSiMPzvtjaaBEYY8Kza2/mKm8mJWCEgLtJFjrK0/6oIW8GT0Cj/7Hqbtrhmnwa9qnl9CWpECB7GjmmQTg3PcCUlqStJFCkVEnIrZvocmNxMIp+P18jPvIGSyP00O1Ww+AfEroTybNOUL2Spmzx82c1FIrVEWX0UQ7w03VmfQtC/V/SqmN1MP0SyN7cCerKYaWli1UNylTkTsN/UYptntsZZrJYuSzNnBQyoBTx5dj4/2bQmtlSodMZmn7ck4fU6da7we2/VIZKmjAOkaZ1+1Qq3SZvxlVfLCUZLWHHU533tD/8XKGN5qR6J9Idrs+JTn3aNK24ZjMZpzLpdm2dzQbdQbjfDkYF+XFnM9s5aLbFjZlyhIWKM1k+1mkN33ipY8iRXpnOORmyfSsaPTEhOyZmsnwpEP4zTvOaeEjrmru5M31BK69kSd5SzYrdc5wvmksbaeNiMp1IvVqIK1VHugr3cA/8z36Sx4hkBXgMVSBWr0zHKrX5fvzV5jIFSEjsZeK02sIDfhD32E93N27l3C+cuzRF0Ii3yKQ788+PUuUyo+ju3URJubZRFaMInJAP6piJNGut+D6bfgzTxmbtxUgKWBPQQTbsuKIiD7NptLxzO9cQqN0JXeuaOI8PphNv+rR81dDSlSFVHslKmXF8Jz6lz/Zxdzf9YqDXzZz5qwmA54s5+KJPPy2i7O4YQ0yDjmIW/viNSucgAW/OCEsZI7iFrxFwWxuBMfThqE+9gHKI81Ir8mkU66NkumDiDZ8h8wcER5tHcS6aUnciOymsamJdocOTk4azeSoPXg+fU/6FmU0slfgeaIBYeIPTnoVoHXqG5IF7cQXLSQ/1JrdTuuZLvxI17JgXsVo4jopmnXi6WQm+pGYe5FXf1X5s9GHueJJBPsPYtzwAQzRLODHnMt0nr9O19dGVjgNYqBoJLEN3Vw9p8377QP4dfEXd++7Y/2tAmP7VOxsryK27hIzZXtJ/9pI5lRZcl/eJXXzrH+pp/8b6BUpxdZXnfQ/1YxSXU5LVhcXZKypqU9HVGUpJeXljL6UwOO4YM6Oc2ON72Gq2pVpST7JpZxg6pfr8EdRltyBxhjUy3D5jxwTe4ZSWy/OyO2OZF3ORvmqHcYd8uQevYzeJDv06hPQNfXCdvBE9PTlsDKTJigvj1tJQXR3juGjWD0TyjrxntKfZ+KPaKofjXlaIXoRRQyycUfu/XUarxlTkaeEsY04Q2ZcQyN3IFM94VVRKCv1vpJRqk9cjQ3liYfR/ADjWgeRk+TEjHcjiHE+SR8Xc2hLQkmpDRGFegwFFZQLXzLwsiMxD3cQUZPB99JSjs49QoOCCL/S31OZbkpDpwrTXEW4dtAUD7dFjMpK5MCvUK5svI3t6vGExkWT3KTL3GHziRF/hqJTCpVndInbq8pXg2JcpP+w79cyZgzx5dWYmbybvIo3w86wbtdFCgquEXEznqiSWgbzb42uhr2b8ZOq56TnLEaV91Ld64l4jR4Gkl/52DSaIRIVnKINx2p79vo08mHkYCbWDUNBy4Oc3+HU3QZBbiS12mVorDsFKoq0IkeW6H32y0/mXYcqw+ZocupoPoscN+FhoYtq1gz+anYAfdHBEm3eMcNJnEsXCvCmEKxjyOY1NnQwWVUFAKnfMWxw1sbLeSzSFhZk6sdTpvwZa0EXf+rb0JIXwU7KlNfqOVxNckN9z2S2bAhBRs0GOUCkpw7Nd3LIdXWht8UQmy8JiLVYUFungvKIMhpyXqNz5Rbh2k24BerjNPYfvLXe8jBkNs+/w4jV03i27wr6h8W5+W4PhgMHYzNJjt9mr0hycKPBUpTc0J9M7+9KmqIIWW1FEAPFJhKMfv6dYdLKXCGWU8FedByKZUvhdRQej2fbcwj1OkECJ3iOJ4EnN3H13hQAlqaHY75amSh0WV61loc8paqpGGfjwfzI3c2zMUvoeFMMo3cwU9cf68E/uDTJg/c+tqx3KeOq/WLyzb/h4j8cy6Vz2bfEgjHex1C2A9Gsz0hJBtEV6c+X1uFY921Fjh48ZDRILqwhLXYHle2yQAOfy36zX9yUuvZYfPTseB/xl+YQIb/u7kZ37HKwsKazs4eBnvBaWxWPnhIc3cpIoBihURkjTipxREeW86t8yAsQZcl+U+7EL2DJs2Z2rogn4a8LfwLa6DAyplXLjVllH3D6c4J2sYkM6XTl66UnZGZ/Q45yDMdMQV2+DVkNIZkWF7iYPYiUoMWoLCmkSUuAnEw5+UHOyD6MYXr1fW5gS3bGYAr2KXPefRyxo8/TENmAsPY1dtZuPHs4BoX3K3ipX0xFxy8GajxBvqiYPsJWVOuMOfcBrE6MJrHtE4/KnrFqqBt1v36xYpwPv/aFMuCFFk5eNrio15J5YDWeXvl4bVVizDgLep2M6PH8iU+vLS5a+Wilz+PtyEkE1P6hsU0U7XVR3Bap/3e79D/FsytrI2PhrxYdcFYkauwa4vyPkeo8nQd2VZzzWoPkNTUGHUxCKmQnOGmy/uw3pN1GMLT5PhN2WPNgbyGSEndQa9bnUOlGbvVfiYmZApcDTiCqZIHmVCVkh45n04xzWFrMIaFrOFJB75g5OZypoxRQkvIiRaKcvyU5LOnnyOoNb5jZLIuMwxTEVN/xt70JFQdjOmvkiC6TocF+AlGhlznls5IglTNs159B/DE/Vn9YS1egM6e/7aPTsB+/I/SxdS/mfVk2cQl+nFo3gwaxJ9g62VKS7syNllBGFbkzRaUfbxoi6COiT+Z+c9juj4jsGU4W+OBgLouClIAAqbv8cdzIOLe1hCWup/yPGSK7LrMwbCU6+gJKazto7LpK1qVOLnV2cmf2NpprY1BY6Mm8F+uQPFnMF2kjJlw0YuK+NsrCzPkwYAwdoXBnLbhFLSVqx0GuPPYjt2YQWkra5AbHcnHFO1DrZk+zKzkNFhgVJiMws0ddvBgjQS9vu3WZLJbHOXQwsROhwrCBzTEuhNQVcURDmhtysQiF9pS/vM3vz0XYnGiiPVOA+Zg9ZGSaM8DEHKmcq6S2V9PRLU4++ZyatYHoxPXElKnhKFCgoW07Od032IQjSSVgay6DUXcDuzuHYUgM+Uih/Pcqu+7+pe1/7qkxS25jMaiasribjGvWpFG3idZGH7qEDejbGCNseMCbSG0eR/7kft91PNFXJf1gLep3RNmh2srPcm0yxXowbYvF+MJS+lbmcuFKAdKezugrBRP/tZTZNfMxbvrFofH1uLscYPFFuL7RF4/mCi6eC8FN5wb1EhL039FLtXoloiv6MDPrA0vafiCnNRtDEWNutKjzO/Mz6wPNeLTzEQunVSNsgh1lKgi2Pcayjw2/4pNA5goCwVauB/Sns/wl7uueYqO/mqwVhVxW3ESfowbIfdKiy0uTnNEL6Bo1kvnqK5HVlMZweAMmglkkfhVyMlBASTG4f2ujvHQiWkWtDNTJoCjWjuL17ZSVp1E6z4fJd8+gwjv64QpcAnbz5NJcLuns4UnkBzSn/uJotCh+9mGIeleTMC+ebJ9m5lpZof7hEuvspLH/YMlne2fOKBYyRWkQG964IJ7mwF+XCCRNLrG35A0p2wIRmO0nOayXd3XjcPKKQ/7PaDauu4qnpx79K4fSWjCVWYuVOTboDNrB4Ty68pm3tsMRTgnkzYtpRKnropsLNy3GM6qhGxXxXkpH51Nx+SOjNWVpkTDjVfIT8ius2OjTl+LiFjyM2ohqdkdDQoVevYPIKlqSnRSNhnUfdGrTqF+rQPs6VTqcLfkuU8ek/8HeW3Zloajf1/Omu0u6QRARFTEQUUAxUbC7MDC32NsAu1uxNiJuFQwMRBFFVAwklG4E6e6u+/8VzptnnN8zxpkfYq0Xc13jumTOLrEd+K9xpjG6HqPNU2isreNRVDblAgl6jNUZfrMBVfPX9LkkM1vMgkUlmzm7yZT023dp7F5MQ0k85yeuoNEhF6ODsaxzu4z4p2gu37pKxsdtbF0Syrprcly4G4iEFDz7fo1+xxQeC7UJMIjBc2QM+8YL/je7+i8iUFUShh89ROSrENqU+5BTCEAj8RSePx7iu2gfi2Z3cMbzPOu+/OT8kkPozDAjxvMEZe0H2HltIZVLfjG9rpvPV94RtNWD3watOEzcjc+lQ/TKRCCb7EOLzE+mOpXx86kBnQ2fGeSVhkjqTSJKaqnziMbhzxrsMmKZ/jaGjPO2/AxYjvo6PzTbl7BqyADGpEfSnSjPsd8zWd54AYfv0/B4dot9z10Yp3wBk2UzKRy/nm8zNmBvbcKA7MH8kppMi741N0/VItwlxGKvgMCSBqS2TOPJgGKkdxVzQBo2bx+Bg+wQ2q7KUr7zAmpT5pJhMIDxIV2sVnLhfnYmnqa2uN6dyeotv4mKrsZXPwUD/xV4btpIY0YVI8aKsrVsLVOSfMg/cgCX0ztwtp7NqL3G3DtUgbvsWipV/ajp/Yvs/nMstXrAlf2hRCgMQjBnEMc2hvNoaxR+JR0UbF5I7MYXTFtyn0VdC9kkBrc1hnIxN5NkaQ0Gy1ZTqzuQsQnvURswiGgUkR8kSqNUFePUazn4zY4FsRr83PyFdhFjRMeFoZv4Gr9w2Jbrz9A59Xz8VICTjxW3Z6SxdOMbSmoFjCEUlYeneXLAlUKFVAzjvTGTEGLfF4EEOhT6+TNFIODR3iM8EjXCSLwQsR4hf4vKI6VayRY7awacCsJy0wayO1opn32eHyfGMSVLhm75PMQK1XEYO5DnjW1ItTdxVWE27B/Kk+69+A79xFrR6WivzEVGVBZPB10mv9NhwLO3ZOkuJeSwGnnXz3Pryz1iNi6k4ZI50rV+tJfvJOhUOR8TljPAYTVaw0/zZPwbGmIHke7VxKrrhcS76uMwu5Fq5xqMisYxRUGSs0ND+Z3yD5/Vk9H2ncb2ZbvRl9JAYGTF5pYaxvXpMeH2FQ7UPkEgEFAVIURzqh4/r7wlYmM2Nr7FbFkbh0W0KFa1i1gyXJEVV54wd1wyScJSwrPmMiTqJonldgiFUQgEAvbuiKYiyhVX75d8iz1Jt9U9mgICcD6YQsZwf2rLHNDc7kTjwbGk3JuO6phKVo+byUJnAYzx5rBSN83XhhA08gvVfo5ctNjK0W+VyF/zo2DtVMZGphH7ZRVsWYXv1L/oKP3I5mYZmm9k4XDakj3pudQoO1BT+I0pVStYG+TC3Q2LENmzlewMBUL6PCgt3I+3SCzOlXu51e/Mnn3mJP0dhJL5Y74bG3H76k2EYRIEfHNn6D/dvJrSwyD5GdTevkWD6h+Kzqmgvl4VzYFhpDT683P4HB6t2MIHf3V83PIJ+6qB4TpT4hZZ4j55M+/FxhKZWInX6Qc8acilO9mL27W7iChMQVBvy+RmEbSHryNM4wHJS4PQmHeSTzUn+Or0lFiDBvb8u4dQcyXOBDsxNCST3yo9HPdRY5ZlAmuEJzkq+4j2tHcoitzC8fAGVp64ScneSvQOqCL1+gM+J+KILo/lme0RtmRsZspLLTbmT8BprzaTjRVIHbSIT1U2FHu8+//P7KqmtYuUPRv46uXDq8g4VAzm0mJWw4RlG9izcwnzT8eyxHMSJ4pusmSNDo4Tj/F40iR+V4RiYelA9tRL1FflEHzJi5wDB1ERlnHzVTrzA3aTvriE2hYT3u64y2QNDcZbJ5J55zH1MZOY7elE2VQnLsbvQL+/AYs6PYJelfL6xXBqpw3nx5B6xHRFMbP+g2l9GRHRd1jmqYPgzwV27V1H4IdIXL6s51LQLYw8/dkiMGTZjfUYDu9Hoy4cOccy0jLqmdlsSMDWbLb5hOI3+wWr72bwXS2D42O9OT1Ek9yVk9HoLSbipzHFogfpPz4Y5Y8bmNNohP0kDYQCSdyLfWk9do2o+1c4ea+cP++PUeRjikBPQPmvKuYP+cLHYAdGpfnitXw3Xn/pkZ4xEJNRXcju60ZS/AZr21Q4u/lv3E0CSTl3FpMVh9A8HYyJgT5XF41k6i5TtOTqGW9WiBY/MDqYwRTllahV2XGoZynDxEwQMRiMvkQC7QITQlTHY2XczinGYybaxCU3B0LtV+CR0ol+shh+N7eTHfWA7uCXKFmP5N4iT2qy7EHlIgmneljZmMvFJRsJTdDjR+VL4Ckmet84+zmGxd2bqVTbyJkGFdK7olDEjaPAp+5sIv/U8a+mB4bkkiWqiwgy+Op70HFsP9u2HOL0sakMGp6Ii6oMRmqjWHjjA7l/hpPWnoD54bOE9zfzXcGDWTPW8U/AJRan+vAqwhvT3Q0Mt7TgRrE4GiVf0c8pI89pOc8MflJxMQXFjBPohwUSvSyJWctn4NvZg73kFSZIH0bhwh0iNK5i4H6Nr+efInt9E0OuDqKh4zMHvt3l0pN+PhiIEO5myThlA4rVA/jccZNjG+sJmSjO3b+vswgzau/uJitiPXofs/lsCXut2xFMkUYg2IqlfRert3+n/cwBXG0DeZD5A/MbooQkPaFtsjkGiXLM9n7BYd8ECoU/6V8wDa8NP4ikiUIbUHUScOw9dOwQ0nY1gQizGSR9/szTS5eo1/fmTMUh7l3sYkLeE+yX6FCcsILzsXeojvrNGIEKppmdfBx7Bc1z+3hsZcvuna68D1sCTMfet5PRdv0orZ9GzfCjHDj4m/dOxQw6Pxdm5PHUfCJBho406k6lJteN2xFv2F0oiqxAwIFRRaRaGFH+ZS8v9udRdSULU9NeVKsX8T7mBNNHBZF9KRqpuS30ZR9GeNcf4fnDXMwL5oyNCQoSIjSvkEdEJhb1AU6Yui1haEAU9mOGUjBFk7Sij2zOHsSSMZpcqKllbrcYZS/V0FgURMmSQxhU/EWnjCsN2q8o7LSl80g/w0as486rqaz4M4Zpreq0jtlN4rxMLu8yxNTwDfeCbPn2zg5tdVGSJlmR0GKMRps6xkt2Uhz5N6JdCVyrXEbt181IBCdRJyWFWNtgcgQdeP1dS9a1UNoktuC+ZQF9bQfYc7KfUUNn4L/jNWONTrFC7y1rVqUzxukjgtZWlhdO4e8D6/+7Qf0/kBSTolVJgaxeVUwrNGmZUkW0WA3x65YSef8oSz3P89ft92REXWfBLh0Cx4ozdutzrr+LZL7vAGyrhhJ3O4CR4zSZHZ7EP7nxzHUawoWb17Ctnkli+jtEO6U4H/YCA6cEhiW8Rty5lLLxCiiNSWPJ6JU01Qkp6OxGGLKUU75SVKgpICHyCCRTMMms5vL77zg62nL5w1cs3Hx5FXqaihc+dIs8ZZ7jQkJOCKlxOojz+uE0ZpcjqlJMc3Ydql2KSEyxxmmPgM8HKnD0d6fKEpYYOLE4o4hfqmAh1UNixlIseYFuNWzOnM31n2NR2i1gh8EEMqvm41/0BZXFu6gqacC57QsBYdc5cEiJFQJHpg1dz8JN8vh1nePNiXwMA+6wwzOFb/3XGNu6FQ2RQSC6lc1yWjx95MlWQ3Wqvv5DXZ4hpePKcOy3JTYmD29HZ/oU3VHtkaV8UCr2Mn8RXxvP7W5vHnbX8ExMBX3ZRBoxQLXmGRaGhuRWKWMrGs9eXSlc9HVI293BvvoGWqVfgNlmjD7XcZvX+NUPZHTQJ+SVltJYeAkfRzGO+Z/mzrx5rP4gAISI6t3njZIXBS1J2LdZkpUfTkZtHEoCM3KEcaTjhxn+vP/zD2NFbOjqLkdFRJGLgjz+lpWj5UMO2sOmkvw7i+dHDqLuNp2kXRforxVBZ5ofw50XkCUuZGRPBdKS0/BuD6Jv6wGMJRZjtFeXCeJxBMbJ0hYSw3OrQQyxU2fywmHoiT5FU8YOgYoGjqtnYLhsAFSe5vjGbGi2p8p6BYZNV5AbuZQ8sWRcHVwx+D2ApGHq0DuJYU8NWKYTTV2xKU3WBXxoakN57HRm2eii3anGOp7xuq6EGskmEot+UmW2hE/vxqM5yhJ/VuN7dCunt2zDsf44Z7TjsXD3xGOFLn+Gx7DJ6RzyX2V5NeMb+y62s+9SJft35mEedIwjZQOAFn52JSKsEeL824QJMf38SHzGEL9NaHeEY+opJKYiBuHJkTiGwMCZY9FJz8XTZQWLOjL597MjU05tZ8k2D4SBQTy+GcIFWy1Or33H7HFx7LDXom98F7f37yT2y14Ef2tx01ASm4OPCVhZRMLHfxnRogFlpuS2vUWhpp9FiiKsmTAB8YibLFXUI+nIA56lmjOxxY2u3z3IyC8lL2YD28cMpfTdYGKi9iI/eBLPfv7FFf2J8FWMF2KZpOX10NkoJOyXGPp6xRgN0UPK9zu1Y4ZhYG9H7e2fGNf38muqKg8S69neL01KliRdHqmUSLXyO8WAsqE9eNbbIQEIZm7B2nAPI1bE0y4jIELvB12fI5lkkEbt0wL81x6lomU7alINqI80p11Hgbyf8pxNlcfgnCYJZyRYPXAtI78PpmSKI+Jy8mQuP0xPYysetrb4PP9J3Opujt1UJeD0UdzdLtDfYoutZDd/rxrGtGm+lEt8RdxIhHTNaLZf16ddRp3ZPwz+4ywV9fPz+/8opv9zrt+97qc3VJu7zfepVr+Hs5U9/W2eSMWu4PXfU2k2GIJ4w0oylufz42g+RnUfiL1SQb2iG3OGOTDtayvXYlYRXh3FkQPXMLceT3RPLmcLY7m87gBdDXFoOlXg6CagWqqef/U7+WV5j181G2mufck+uw0M/qjFwQYfPpYU05hkTrqDNDE197EapUCwtDEyhZ3Yu69lt/dJ+u5LUvSwFiP9ewQWneeG12zW7xnNjNlONEwyRSUnn3KjRdS0xGAgH07bszhePYsm8GU+N5dfZU7fVHK/OVAjOZSKCeNocYikITUJM4NNCP72wXLpJLzav9Mn9GJSVRwqGrlUNhxF0VuEpNR72A15hdc4C6LSduMTdJ2s9wp8+OaKgfIE5tfORflNODNHqhL9p4RFo98i1KpEoa6A5CINmkOf8O/FYrx/WfPmdS7JUi+xafNBPMuJGVc2UyzXzZPXBzniKUX68SgiLWZw6E0NLeZufBBqspvXvBZTRlvBgdxSMWqK49kik4n8KHi6/yx7Hkejqe5MhbgMIwNP0P1Ln+Kne9g/fzk783KQNVQB8S9IWd4l1bqeJd4dFHdMYYLzMCb1zedZvBv5beuZmNKFfJE6Vl+tkBiWRFuPIeJ845ehgNGHh7Egr4Kw5hYauk1par/Dz+MP2Iv9ed0AACAASURBVDl2PDH5JUwTOcGywikkTy1BokiTvXPm4b5oJJsXnuXaw4ss0hcwtFCJxOYOGpsycXZxpMt5NCr6DgwxzkNsbB/nlfMwHydFj3gctw4N5cWfNIzV52KZpoDi53esv3mH5qpJCB8H8fLtKr4cL+TpWTXSRvyLzgNzjC+PQEM/mx/po1FTG4zhT1HcG2cT8/YDGweYEqIlCptsUJvUgFRKItdcpRhnd57uTcbc63/L9SGmdEn3c6VWlcLI3/hNXs5G/ws0hH3nXG0oNvMq0Ph5Gyezs+z09afv+mv2LdclJ6mOPWWZyM49SMO3h8jklZP7YzcBW5WYPNGA0Ie6CB8v5H5xD+5lEXx+58NXz2cEX3xGQKwGil/i6JwcjUv2X4QkrkS6xBX/p8VYXvTnt542K6pPsMNlETt6zqK7+gdGM+aRNGIG6gFDSHmhTvCr8ZSIB3BumzklFjvpWOvNm/Bw/KKUUdxgiKHzZSZfLWbzkSUEPzxC3tTlmMnL8dWgg+5GPcotOukW9LL6+yjUDK8hGFHPilgfxq8+yTXBSzbNSyLHzIfReW84X2HA2z5J7IS7aXxTxX5pUW7YPkEy1oBq6X7UM4yo+/cKA+XyaTRbi9C0Eu0pg7EJXUjNwBw2ta6n/b6QnjWziW03oevZedY9LSNlrjLv2nSQ9FEiObeMe3WtdO8fwZv7M7l+bxPHku/SlnYFD+kErv/WZNsnE5q1lWkY107Owy84jtnIm9QoHsoacfPxRMzOhBL+TwDBc1diM2slIi3d9OU9RrFkAM/C51HWJIPcmA+MmKCI2SAnvAs+M7E6BZVZA+mIGIH6wR7eOZfSMNKWt7cjKvz8/P736Py/xP6dO/zE7fVZbGaOx+z9qFQLMdPfg73ZIKbs/EgFjby4e56hG8biecsNY7FC1ndE4DjSDa2ufB40/svboC0UIWCf3GL8k16jN2M6W2daUBn+me+yIhzbtRN17XLi0iMp7ZnLncoKfpVF4dVqg1h4Er5mV4k895BWeU287Jy50X2IhT7eRMY1ERttwQYLVfJ+PUY/ZgIiZ2Ow/ZHKbsdEtttMY9ZOX4xdrDF1usKE7be54bgNc0EInYZ3kE7dSIXOYBIPzSW0+DZKk9agkCFOg2ELTnOWcWP4W5pVziGvq8mdAy0M7d5Cg8xIGntHc1w+kz3f22iw/o51dQmqv5v53NeIjYkGOQ+vM1HKHhPxcr6GhKNn6MLb0iZaEtIYM72P1y2NWLdbcqntE0P7+jmt8IE9Kclsel+C/ZZw1qinIDvoIelp+SwdGYNIwQGa/jyg85GADRHrWb72OLGnP7HhczWFbkMZ232FQtkvjJfwZLHIAGTE8vhZ386s4h4eHFvAmC9Z9L8UUG0+mMTf1Rg9mEfNrp/8OP2aQuM1eAnLGKX3BZT/oV0umhztVWzQCOVi0WLmzDtAanI2gs50WkRlGcAEBKolSP5ZgbJpCTPaa4kT6eTJBC2ShHLM6B+O3r1cNMya6eYt+w+G4Wonyhf9avqvKDAh4TQhaio8i3hE0OPH+L5+wpvjOxnt6MwgrWoeNhkjKfyFtmw7H4WtDIldwY5NaTzwi+HcFjfmSWlQIu7E+knqBKydh739VvQHy5NW08vjhCzi9gSS9nEwPWVZRIhUcHPSJtasWcEr76OYqizESCmTz5rljNLow+TBL1ommxP9wIYpA1M4bqjAqOYEhjeOIGT5V3qiGxkQvIDc4r28rA9jqIwM85bO4pa0Mw8urMb6mQaya1aTuMWdFx3FnFsGMpp1GCloUGbfxbv1n7H8sZGt+y2IlV7Or12baC8Nw07eiAsPGli9NYGlB1Io/3KTrSFx3F06h8UnUshZu4mPSyYjm/qZ6yXfCJU6zdwHyaw45kTVr994v4yhIz4dlbW2DFNu4UjceiyeKfFOx5nBge+J6jvG43Nl9IoOJvzSEhb5OnP5r3KmPT7LwrttvJmnydiLubRrzuRDcR2GiTY8fNrHvM2WSIZnsz/yI5m2Muwa2ID2FwXasiUYG19F6ygDpsnnUKD6k/BIHXar3ECxdBC1xj1or1nNitDbLNF04Z20GcF1odReaOaTtjn5e4IYWNyAaO1QpNv7qCw6hUdlC00zl5OnWoPP4HJalzmgtPEgYS5ujEtSp6R3BYl9VYgMECd3z2bsV7oT+lGRvZMMkc+OQId1CAxlOP9sBo+evETg5suhEbBhhSu3sg3IzyjAun4S2lUNhGgnsr5qFScTp/Hh0VbMr35GraiS1M/ZXGhLIku6ms2XfxPxUJG6/qF0LnnE2chIZn7aw88Vs5D5kcZ8X1/OqmynzzCS3hh5fGfqItaiQaOGAZ/CXv1HHfV/wny0NzUTdzcME5HV2OveZXMiaOiXEa8zEvuj77j0tYr8/nSybNx5/rAXkX5n8gZpslT6OU193vQfqEX31F0u3HvHowIBdhP/onx8DgXmKrQvM6DkbQ0RhbMIzLQizqcRdkzj9e4BNN44Sv+uZvTG2CN+ezSbFEZwdkQPI2SzOT5sNkdGzac++D3HiqWo+fad7LwAZr1ag4z1NXS0mtDQ3M4Dk0dcX3OPE7KDmWDWjsiPDWx+e5aCtzf5E1bNs2fuWAePwnXPv0w8FsTQWdUMNDnOs5G+nLY6S+7bemq219Bfak91RBxitx+SHfIC2xJddETj2KBxn63n21A8fAHVQ140ldZx42QqUl92YBkVRP85ETJDjuMzexOhVRdI/dJFtnYNd3uTsTSMIuy8ABuZGCzqxBlnWMKlZePIXKTKqBg/UjUT8JC7iWKNGObnu/me/pCWcz0Ye2xk1cMMDiy4zmxhNcLpQnaOiCZHcQzJ1hY0yJryuuk1S+Q+stF4CXGjH9J/r4+bTmfZ1bGVios/EbkdwzcDS5ID+zmTbYJp0Vs63+xEWeBCY5uQSTIrMD8WStk/Q3Do+IbcACVeRyYjPmAfY0RrEbXVotd4NG3LJBDoeeMpTMbnTw3J255yTLCeOSU7WbS9jcq6SG57LmN3jYCH70L5sSmCDJOZrExV4uMYf2TrviE/bD4KljO4/OU12/v6uZ+TSnxvEwLVdATypaiJyuJq0olD1x62+x2hMEqX2rvWSE+VoE/DEiXDe8z6JYut1TomhsezW34CKfe/IXzWyM0Z/uxQ0+bQByH2HqJ0HKnkyYT3pBY9Jv3Nd8zf7kXl7lXUtUfRxFlExh/gmfxLDL+6MWqpL5Xrx3Ll8h28d9hx6NIZDu+1Y29lIr6+++i/5EbM8/cIBHLsfDuCydPXILfSnyW2EBmkR9SeYgbJ3uFxqZB3DVNZtHwiFy9FYOQRxOnn8fx65E/ghGZunEtiyM3tBB8QRyg8Syaj6UufSPsGF553NmEmpkZf2C68fAfx9FoSPztKOKP+hkKDGJ5JvyVskANqeUeQ6LmIXoEid642Y9BpQF9eF71Jm3gnOEtCFHiNaeTsnDvovbpL9fkX5K5eyrs1uwj+YYlfcQny57cQfNqImd8W03Eui09NdVhIanB7jAKfzoPUznKO+yqTvvsw/he1ufkhnJL7Hny+k0yYpgXe/Y6kH27BzOskznKTsZ67mVnR7VgbfsBr2iV+zQphVKc4AsNoKu/u42XFbNR3Lcd7XiCrFxQj36jN1EnnOd9VhKqIPSHCPoKPVeN2cS0Tfi5l+1BjSsaeQ0l/BIZ6P1iY/5DzIatYNraF9G2v0Yhaztd/BCzLe86AlnE8EXFgmnw7l53aqVb6wglvGa4tWkfjVQ22uh7ji4MhQsds/h2dyUzNQ0iWf0OYG84gh0+IagxGWsqBkTPGIvusldOJb5g58jD9c31x6tZggbUrLovkue4ox+eUI2zpHs4/Nnn/7aj+HwIxCr4/4lJEHkin01hTyzibUeRJTmVIpwxjo+6iN8yd53MvUr2mlX9l8liq4s4s+wP8vNzBDLcCwuJ/M6FdHY8Xp9g1/jd2Eh/ZaWnNcKshzBvXw6lPiQyMu0z+jxrsecWQ0a4s0RjL91ZNgjPyoWQgVw4/oFWvlo76QB6vukF34yQ+Kv6Dj0crLmUZ6DaPQ1l+HAF/zqM9YRDCj/o0lYxgdukA2hTEUdGWIGG0BVmf7Rk+2wMVk6HcrGtho2IRR56tIqHmFwo/lGiuL2KGigB+PIdaecTMztJXFkRHbheC7L/wdpWjqfA5ah/bsRk9gz7pfOQG2DHdzZqxxu6cDl5J/G4PIm8kkZAry6CNC/n9qYBjw2wo1VUms2g19wwH8m1cEU2XThJR/gwf11XE9EylPQfudRegp2BHa1Uh4wdYEPnjX9ILNVjQrs7lkZas85yDvOxXvMtHwEFrDElnmWsik6QLuS0tjWJHHuLtRvjKzCJopQXyMX70VtchOkQele9h6J8Q4febSP5cv43rwQkwaj3aE0H0Qy0TBuhRnLcb5cnjKbj9gFXD/EiJiMKpLY/SQlfEpAaioh5PXYYYMhaf6GkrokGqDU0NI+peNLH4giO2V3eQsVaGL1U6gCTFwABLR84a2dHp3MbrJfspjUhh4vIjGNWqYq2iw/ErVxARtBKYJ4JpUw7q/db0d4jTKybCvqE1bNrYQF6KIRNW5ZBgVEtj1mfCwoq5ez6Mhs5QqrX1aY8t5dPdZ8xeN51Bxolk3Wnk/tFOVv8pxa1/FwtclpGiMJPKzFw8xs5Eoj2XFPEuUhOuMMfTChEDa/jwBy3r6Uxp62PPxUnUFXjQvhRWK17FUvof1nY5IVgow1afYbQBynn61MVBINBxMZFlZwOoDIjgYUIIzU4+DNZZx67Z35BlB+t+5HLo5F6u7V1Nv0w7y8XtOL7Jjft/+SPZLsnFc+6cfXyPHS9d2V45lKiMQUyuPsrN12/oPrWAAqGQqSfqsFY6zgt7P6Yp5GNaU0y0TgWbS++T3HUA3QZzhLN2AM5ITvAlb5sDCtsD8BwzAv2ZopTudWLAMmN6NSZiNq6YwSWWMFWJHTPTOEoFW+cuxq5NgSc2V7hf3Yx0pwLN17SxTQqiL2QTH2O/I1onRKpRl+JCXaTXyeC1aQDWfZkoP7zN1wQHmqxmMGjsfAoRIPG3JzvF4xmaP4Tv8hK06failFVH7XswlNzM2TothsmokW94mEqp52i5LqM4XI2+rmlMH6LD2vrHVDVsRnPadzJPl6CpbUp0xTV+io/F5/kelub/zaeWcCSOLmb7+GG86niJb2wWtg2O5BXu4uzJQkYPTcN9+xYGb27h/Ol3CMSWoK7dj+bE4YhrG3BHqwDpn78oTR6LjGQP8o2KJI4fwvXZTvw8/pxlG45ib+WNRshaIlrmIVPyCzkpWz4+sOVA4U28PNf8x1H6f8J87Pbd6bdH7gmmraJE9P6ia4AOsiN1UWnQQkRPk9cKQqqvJpM66jPmES6UPz3NuM5ZDLy8gE/PRDn0/BbeKpLId4ozVduAQXPNcUecy8ITbIsfg6aTFX1NgbQfuYGaYRdVfcf5Zv+ZjuI2qt5V4j5ZjEFDnCg11aHgawc6RjZUFp3j+YknbJjsSnbUXZQneNDU9g2Z96VYrnDCzbKaIUqiJAtjyZN9TePHu6yPeICG3kAC5lxh9vxJJLddQKy/l9EyZvxwPcQr21pCq2YRL7KbSIXlhL01Jav8Fr/jCjEOWU6qiw2jTHvoHbkfT+s6Aofc4e1fLni/D0FtXBxTWo7RWroVrSXeHBy4iOVd6nzJ68bUrYhXpY/YvtWfHRuSaVRUI3KMKZ0/Sgn+EERqiS0v5qtQuz2ES3P76V9xEr2ZWbz8FYZeUyZ9OZWs37OBd4YTKdTJwfpzJ8qae7jDN9R3ZxDYoolB0RgM/8TxVGkCHj05yAiVMBCK4GRlzpkuM7KX2dFwUAm5nXX0m/5CaBZMb+0wYobVY1+SzUyn44jrPUViZCLNij28irCjomcgH1Su0pW6lIxTWihMGk+huDWqjWLI12Yh1XcNbdk8OkRHkdp0mIn2WzCYpI7CzDcMkxqPe4cYC3wK8G3u4oyLD7FWsgwxHE9pZAY/v6VzIWE5jz5LsXihOV1NX1nkNYbRSU9xkTKkU7wDKTEFJDs0KNeSo6RGAyv5MSwdI0lcWxGrfr3H+aIsKt9h0y4fRh1dSGNxOCbFEWQ9PYVNWTkJP/cQ2nMX6cZYvtw3J2roHl4MEmOI4QJERW0Q2s1nvqIFn4YdIC3jKSPkU1AtO8hzselc083EQSeY1OcabBwmx8r4Oyj1Pue+6Vl6S1V56+bHCN13bJp9kgOhY3h0+hux6jMQTfiHjN+f2Dr3CYWHDuFo9JyKIHg1fx53lluhOFLAkE4hC7XC6Bdz425tFw9TtPjHYzu3nL0JW5zHpbTZOHpPR2HVcNxn2rFs5jH8Hrjid9IOl91TGB/7DedBD9CsPMjligyqdcswmSDDn9c7oG0WQWWSHDy+i5CUUmS1ckjwCuf8ufccLt/MUsMU/u04wWkRFxYVV7F2lwGxBxPo9Swib8sIJOVfMuqyKuve+OO31o8N0h+ZkARzTisS7i1Bu8EOXObOZtD2O0yzCcDYs5q3Q0tZF25BS64WueijpZ7NDkcJNAP8WCsQEDsgjZyOHYhkByGhVEP9yyzkFkjTpKOAY60Be9Ui2LvaHLmav+iyrWN1VBeRTW9wlunh7Ys8KiZdp6Zak8LqBHrH6vOESGw1ZQkp7SRwoTHf7mWzx3gvq27/JGxyPG1aB+mXtWG4wgDGVCcxWu8O9QkqFMiI8Us+ia6EVbxdPQqvynzqnC5x2D+WzHox1izUInTtE24t+klh+GpoE6E3YS15B6bw62QuQdkyHLFax6Dxu5F6WYjVhzx8vpfiW/YOj9R4ehacIqqt93/m47/Iw/CXfqYX5hMVp0Bnpw7brL9z8NdzunJFkHezo3JlCpNPlnDGcQeXQ8Mx0plPSu1NTjwuxN5SlJo2BWYpS9MdI0G+6j0Uq8agZ/UP0fMu4x6QSEv4dP7cv8on07vYeD7j0cBw5pzsxfRfNcRHFpExeSlT+yWRTk5ATFSBrkHm3L0SxezhWcTn5ZPd64OGzg1OpQ5BdeJbbNPKcdbeQa5eD2f/juR68mLCossQiKbT87YPn2ku3HtVQnmzC0tcI7kotMdDkMQgvR1U/alj+JAoZA2GMjtQDqOPrxG6mpFaIYmVizR9IvOwNphBYbM13tcnYGHoj/vtdOYL8thqLU9YQzEHXCSQah3Ph85GhooGsj5fhhnuizjyxIcNTnoUZGRimdeLRV8rNWtl8bH8m9Ky13yQbsFTQZH7XUEkiKbwpiOdMx0hPFx0nYzmc7ieu4T7Gh06Qhop2eWOUOQ9Jz9LcXn4RBoSvNDW80c7I4tXDfV4mB1BW6eNKzI/sc9Zi+k3KFr2h7YdCxBdspAWiWhumSjwbeFBep/uw+aDKta3tpKR8Agrpe1UxqfRY2zO17o0po2fz7uS05TovsRMIZj2LilE85rQcC2nc/BmJoutxSxnP/0NWpxMrSfgugu+iSnU+R3klMsJOnsLWaW7FP+o+fy8k0gfVmibjyYq5BBqf5uxv30mQS92USaiiVV5LY0DzVHrKKNVWRkpgShK2fnoacli17weCetcZuUHIBjpze4odeSixjF5pC2BxsN4qDgK796H3FpsSnKGEgK9U8zqc+B4rhh9zeupGp9AUU85ixqG8Ts7m2eyLYhpW2EkpUfU8C6mhNQj5rAKmfc3KDPRoqDFmYtRrrxaHobAMgRHhek8vnWLj4f38e7wKSrnLmDrWAWk/tLjy7WXyKfY8Mw8nwCvqVjNPgZfj3KuazV5wvFEzYxn0xshL5/rsjF8Nco5LjQ9OcFE8aVM0rAjr34oySsLeX34FTaasjy9E4TsLxEsHmgx1+cj7aUXkInywuSmOLVzyimwscZA3IDYnR/59mIOpt1v0NCywuiHkJhXziQ5lZGzN49/Wz/gte8Q+qFzyeAG57qG4Vxzj7Jl3qQnptM36gF9mmVUVRtSLfDEbf48Mqb1EnvgPVvGOzPCG+psSllcr86J5aGUrD/EqmpnOr32MLCpFzXdBA7/24qsajXieXIUmaui/m4m5ZnJDFN9QpiwB03VQxRX5lD19BXm7b7EDtBhj9VSzonJMy1Hl2xxb6pjjpE+Pxi7+gNIFA6lQ+0lJbLP+NU2gqmjSins/ECslRYT021oaWhm4+zffJLYSMFUZ6Z0FqD85A4HFxhwCyecZ9djFPqNaGkNmpQOobVkLn+OWUL2TwLnjSA0toKYdiUS5Qx4IkjEyyWB0VdlSfWQIfXbYeREn6AxqASJHnGODgjEu9eYQw9kSC78jLm4AjvWTOKfGjWWGfdzaLACc4cso8S//j/qqP8TB+eqOjrCo2WX+XRmP86XdSk2E6I4UpddR+8wETixfRk/5sqRlxxEoZ0cz/w6GC7fz5Up2RS+EGW0aw9iImHst9mCe30iUgmKzPefgiPuOPCVDzpNfG3uZ+SWDYypMcd+YA2iWaMJqSqgYsYHtHrXot5TTEe7PPpZlmib/YtE6j0GmHjhrbURTK4wIuE3Bi2L0VT0QSfHjepuTUr0qlk1OoWguFGIjB5Ie0wYATnGbC1Von2hMgMyxRhsnENtvQkmJRV8siiko0cNefc2ylmMMEQE7dQR9Lkc47v/Zt4nhMHk1bwvSsHTaBepgWsw0FJm28KV6E7WJ93DmDsS5oQf+4HXx+e8+hzKt6p0BLVzubt+J795w59H/hQkGSLzw5q8/ibyF2ozoDaGrs5PGFlY0Zgpy6OP21mbc4XuiReJFeRhP/oE0WcNiF2xBYHhThzsFyNz4xvK8l2IaNyjul8WjaZOzJmAyjAVLqn1sEK0mS5lR/406mMfX8DX77qoznpJS9ID+gquIBpSgX9YCTs7BBTHBDCgbAkC9e00280hvv4da4r2omZsQhaeWJGFIb+JZS/DGE4ag+iRq0KhrZa2hgweDM5ilcxRno/3RD+hDfPGT0jr/KZ9gjXbWYTvQyGSv/7m2PljuNwoIjpdnxNnX/JC8JZ3qWs4emMK4loreKBymTzDbTDFj7PbndGT/Ivnwjj+Onacz8RTfCwEO4ED0YGBVO9eSlRVBdl7t1N96zAvPfejpPqJqmBnXAaOZea1WAb3iKLf3cerx1OADEQNLRnTkYOFjjV2rvNJMxCSahXO2sKB/CxQYEixOolrf6Jb349KthdynYGUG/wmsKyQ+W3DsJu+jFf7s1kzzZ0LJW8YsHgkpzauwj37D8orbLi4PxX5bpCqhiliAjzMEjDiFJMEj9m21YfbH13Y/CsSlz5Hxm+IxszZDkOJKj4cPsGxmK2ovtVlWvRzriWWYyE7njxjbfKH69H9/SO1FnPolY3gs2o4Bwdfon+YHI1f22kcMxuRCsg+9AcP49d4rhXDQsEb/4G7UDRWo3qhNrHTglFLNuL53NFMGTQK5Q8/WOpowbdYe2QWCti1YAgSB5LpzgSeBxBKPmVv6mgacQdOb4WjF/CblotA0Mo6YRf5pNE1KgL3v7sJj2via6MyTyPk+XLcCXOJMeR3KKG0M40+I3XUenQJbu9HeVIuZs3fqO4fS2WfIgxPZm5bJ40FbZgoDaZEJId2mUgaPd2JPzAYd88h9JasB4E8Et17MPmRRV61Aay1ovTyA3pM01gev4aLUg8xtegh8NUJxrlMxXHMAmQcFIiSW4xe2XZaa9sYlHcJTcEWHlzK4cDXB4TcMmL1VG9+5D6lfrA9d09+4MboqYz2lGBfsjGjJq5FrewSKhITeXFsJg2jcjiqf4TvN5QoGqxJcXkx0jN6eZRfTcShQgIu61DSaMjK+doMN1/wv4Pz/yICgUDoIwwj9Mw9zjwzJH10HANU/WivCyIxrZoBX2S53upP4MwddK64Q8vF9zS07KA34Aq1+5vRGNLGKc8GiGnhTrcpRSYyFCyMQmnFQoQui3ESKefNwmE4BK3CVHc791r2sP2lKzUyKcyTS2Dkor+YH3SJYjM3RL5mojVOjarqSmwUEnhSMh+huB4T4q+TaL2J99OWsO7COYy0ipgjl8QTrdGEvYpFZN5UlPq+kXazBkv1GYjYJaElZkmf8hme6wdzpXIPtToeZNypR2HkM9YcOsK23UsxuymkVnMj7QeH8fLOXSam/OJ8ZSZic3Swqn2K+4wxSEcvY7KpMjW2mty12saTLx6EJuzl1RhNeie48z33JrOLlqBSvIAajQRs/6xDrU9AjkID7xbrofu+AmvXdm4kyxI57CBb1kqz+GQoTbuFiE/5SPHOCqbMNuRf8Vii/7RzXmkUa3WHs2RuIXpn5Gkc0UiU6n2GdvkxXLSIxvIOLC6fYsyPvQxe8Jp/7/ryMcKK1h3QpjoPGcFL9pk+gg1+vDWJJ/RGBwXTxuEZORo3y9lcjy+mWnsgFqbH8dO/jODLDK7r3+It5YxEnw7eo4cY0frzWSfmztjSX2zQOo5X4miuWqqyNvo5Q18EoC7Q5LnndmaGRVIgTMP4oypjxs9mi9CHeYIhgC/In2WBwwqyF79jVPoMdKXVKe6tolxCDm2xfHY6WnMrfyOKai9Iet6I7B5VthyKYsj9Gwh2bESoVUN4kSedO6yYO0OfY+Mus+eiF/GB/YyQGYvf4wPkN5YhYeHCb6VlOAwNhCuFrJ7mSqXsD5p7OkjrLiRkhBWbIkvJkJZFV9wDMeuDJBQ8JfPfOVj3FLL8kQs+m0v46rqF4g5PFma9IuuhA1v8t3Hp0HmEfUIEAgHCA8VM+KFPZ76Q4HzIXW7GZqEEZcHdWNNLYt1xtpwsRiaslWshlQiGP+Cc23IaFPbw3uMObiIt0PKHbetCCKysonFUICdX3OJs0W8K2lQRqtSDqZB/I/KY0bMfqWst8FAZg7fbSdAsY/RrF+LuO+GYLEZacDCm1WZMS9qJ7upHRBtOZUtRFz6BtygcUomyyR+6b7xCmPwNiaIPPDkiJODwKr7KBdLiWYnwuBb+jRl2KQAAIABJREFUoQcRlsEUc7BvXcMhwd+cUoljsJ8/rz8l4uT0Hq9yN25aJ9GvsYp2u7EUn1GhyS4fG5Ugsk/6UHPJDLdEa968vs6hnhzO2WZg1CNDhZwQxfyJjO5IRNK4nT0j9hPzVcBlg6+Q9wQ2JaMaPJg6mSKIfMH57bdw26aDh+NFopQHUNMggvPnFB64DUU/PhzrP3JUHhqEeFo36xPtCTg0CLuRihh9PkhC2g5E8yoJKb/CvaETuZkuQH/zePamhTFsphyJG9bjZrATkb42honuwsG+l76eaewLFpC5bDPD1snQeN6S0e1ZCOpaKBMs5UFXLZcnB+I4xYrJgiP/UUf9nzAfB/7e6fe5tA6PJwJGrnTE1dyQ3nRJ9sbvYtLRKZRHqtJT1sWvMlOMrDezZtgsbBQ8uS26m1rbENK0fyI+9ySCpxvZvOYT96K9uRS4nTnP1/FXeQ7uMrdZ6mWLx8ox+Me+pLBwJ/EfolAITCKyeTJjGjORfFePseUH7nML8Vxt1s7xJTHsDhLdWZjLDMZAuY9JKkLa30uQImFFhV4N5SUp6Kia0/xTBsU0VSyDG9h03IECnRdUtAYjrjeVjM/itA7+RvgAW/YPM2LW6Ca6C8Rxk85mRV0Cn8igojyQJ5f2YB/4kthYIQ/9rzLUrBspkb2kq9aj0RaJqIMbxZ3NzPfqRumFMvPmrubuiygyr11BMq+SPS9XEnTzDGnzt6CnJ0q9VTIizm/RXv2JbtlaqqniSY0Q+QdOuDR+Jf1oF5kJ17Bd6MOxwGKEPVr0l+sxuLkTd/Mqpn4JZrbCZ2KKvZmlKM3X5mwmd6mSqqPOpquyjBAY4KA9lvfN9xk69yUjlDNJMOpA0aCYTL8QxAf+YDRXOVNjzwKNdYzd2o5iRxe9dSCmNB0Z6RpaBXdRZR05jEeeBnpZRCdfaKGHyp7hFBrYId/YyWb3eUxzseLZ6RVoSfXjGf6dAm1ftj3bD1r15H/5hx1TpnJ3yTFeL17MSuUidj5PpVQ3nAU/nvImqA4TTw8G5w3BcZMfgm0KtCivpUYykY3qBogEP2HZ1GdMm2JAVq84Sz0mUv39IYvVhhLseBGFu54Ej1/NqT8BJAfLMSBjArcdb7BFYQ1vGu+x+6IfmzZNYIF1JVLe4nyMb+aLTSE5RR+4KbaMpA9bUNUxxGfZSvZtmIr1WTFuyp+nx+sy8RtNMaiRQ0Q2hTL5OJwVimkfNYn22hSEDe/ZN9cKYb8tnZ653LfYSpnIdbIidpBYc41upzTMUkbiEheGRv1pTtom42l/iuOOwxi7rAWFLBt2pCtxe2EkfpMMEJam8nzedBxsVyNrNZyU8Xk4bg7gsq84AxPDGT/nHIfUM4m7kobCs+Foqwzh0IiJPO39w3FbV2T7HFnzfhgX5r9jzD5TilwGI9bSy5LIDvbJXWSstC2DLAVMXl6Khkc4ScuP029czKhLf/HW6zaTjWG/uBFfunX5kavKp8fzqO+dQlmwIrF7LPjJSbYccqGjWo9ZDklIW+Xiq/MHzSxttNZOZskIEf6ST6LXbDqBjc/xcv6FrNR+XKf40Sz+lFFNmzCMqsJ+3G1kTGyoaRTiletMqOodzhjEMV7vFZHPzhCw+jKhN3MRlM5EUXEh3cKfvBskgnb5T0KT9Jgh349T1WeuHvyIWFMn3YoG2JraUTSqA5XZX5igdp/GayvJG/ge2bsmSK8yoa/zO2ujFvHm6gtqlN34VvuE6Lr92BTXs8FjJgmRFTjUj6Cq4jIRX/xxkriHttQ9Jvz9kPI6eaKDnciTKqfNsZKG1J3sy7ei+IYnUvktvA7SoU8yk2MLH9FA1f/Mx3+RWwE3/RZ9jWbSsr1QXEarGAwQs8RF4ROfqvYR4/aGbTXvSFw2hzcH05haIcln13Qkyx3xcGlltdlsljz1hqRqTsVnMdqkA49Dd3h74wkW04fx6lENG07Pw//qEobb+FH0jwfqK/SYKpQhKjqTP0Jd2pTT+RI7EFPrXCTNXvP5biOV6w7TbxlBYUoh53J9ePfkGL++L2R32Tg+iZ1j/rKZSH5WpdTEHIV9/3DjzTa+t4/h2sA41NpMGFL0lSJ5VXJbl+OuP5C4LFWSBKl8aBDFW2DBP0kCRFVC+aBnjm1OD7P7dVnjac76vYmsUCtivtMfYsYWYOE6kcKGH6T4uPJN7xSxeglM1tjJ39FbmSY2hYbMQPSKq0iNMUZq3GAac5KpN66jWUSPuvSM/8e+WbVlAXBpdz10d4e0RUipgAiiGNgBKirY2N2N3d2KnSiiYmKBiJSENEgjIN1dz3fy/YA5e2eumfUf9jq51yYuG3SKtmBS8A7pTztRGBPPI4NwDPU/s+nmJDT+apD4ezu2M8+hN8ka+fcBtI4aQIqBPAv+VrKt7Ad3tdfwWbcD4dM2Bq8R5VPKOb64BSCyYQXKKw5S3V6GqNQmivTAvO94Xu+3YmdsMVVGMQh/3uWnRDMWDtdIqrNAzsAMKfVStAnha4MPpxWNEUeHSmpoEpFFQkYbIwUJauKDeNLvM80GGVTtNGT19ygyJwjxVR6GwuylfPxaxYTgFchMz+JW3RyOvurk9K0ApoqY8SZHniPjXYn08kPLTo17EYuYeXkNLr4rWDdmPrmVrQweNoKxo6VI2BrO1CMTif56CoXSoZTKGVH0O4GX+45TOqeHQ/0zifD+QJF7GIfsj1Bpc47D107RLHxNPxF4q9DL8UFZRJcNIjMmD2ujAfytKqOo5ibVeo/5rGLJ8UOzqNpxgYIRJny4eJOh1HP9ixQW/2SY6/QE7c95aB78xYZTY9nr6clu1VK2ZJfS+ruBR5LfWVMjYEIxuM2Zh29jBZN8hpCrdQXR4Se5tmAX3la53BWuRyIpHFONQyhaO5P+V4wyKX1Olnznmkc7Hb3xyGZMxMTgG4OsFhAsJ4fDBlEcNnnzp2I20umzKJ+wAzvFm+gkf+KY1QycL31lqKMq1bISaFdlML6oiZApNZjf0CVdYwHWBeD/dxz77Mvp7g5mwDhdPG6qkhtvTWfuLPxNsnkV8YiD9x5QJ2ZFV0QDXyWqGCReQqvbT7YLQzFdfJicRSosHq5Fb7s9wd8KSTkmipvGAGRkTdD2X86E+CSWqh7ntmUcGxY1UJXVSJ6KP8b5zYxSNuR4eAQ9s41RyG7HvqyU/jKLOD3iEvcyBpCitYg9ajcZLTGDzp8/UXBQQFzJnqDv2hjlxTJC8JZjo/cz7MUj+vSE0NUMefrSmPe1QMxUQMUJa04NUmdQcRxrhwzGSLEf8tebqb1ag2OKL5qzxJF6OYdbnwKY41JJQr0jTn0a0G+WRDxbyHkfM+b7RtO4ZSJ2X5SxG1LHMLNehty2wa5xM1UJf3DzgjtP/CmwOUvcQQMcFO7huGwLUivu0O+fDM8Dfv3P+fmwtLBhrXUr7zaPI2i3L9cmDOK1hSUiae1Il1Xyd7AEVdbj6NX6h068Iu1329CPLUO/1BgFkb6UPNLgla896QdfYH/BjH1+UYgIrLi94Am2L9ajrR2Fge5UtlqdZzAr6f/uMM5WcbS6JSDsb8FqG3la7D4jY7uCbfpjcfWrwiZmPqqvdlBbUo3lbiFuyfl8Px1HkZsiQj1R0vIfMt5hBHvz/PjQeolH0s8peLaTqLC+xJeqYql9gQEigfQfIE5iuwkZSR5ci1Pme6s+BXWWtD6v53z/MXRk70J+VByJ+ltZ7xeB79yRRFRoY/DwCmUViXgm2RE72oml254j+dSZMOmLSM2ays2Xj5k4tj8i6wIQLrnDj9sfOHW5kgOBmwl1HEab42wSPs9BO8mF8VojcAmYiWm8M+0e07jva8rRkCKsnbWIvh3EqjmxuKpO4qRhFJaTZJkquxiV/gORbK0jfkgORwLLuV39Dak501nfG86AZRLs+Z3LniWTUHY7xGY3RZZumYVomhFNB5XIPj2Ds+tGIa8hycGOFZiIC9jnkU7kC3Nmrn2G9Qh/wkRqaMKNfshxmCz+TOrmx5xPvJH/QEt9FyqSUuRkXGbbMStsy21haRyXhHqcmFDOYy83YoOd2LFlGkVvbuF6aDnBHapYPLZijzCOTe+PsnmEMsJQGyy0ClhybA3r0o6wzXgHWjstOGt8mZftKri1C8mvymFgvi7lvvNICU7Cuj6dnx1ZSGpqU9VURv4uZfxUJpCp+Ym7W8s4kVJOj9d6FLZ7oxx1BemGR7jKKjJ/YihSbRuJiLqEQ489wqRW7jmOp8XUnkbVjbTIavPdWpfMAQ+YcnM8SrZ9OD12O1pq97Ea7kiFaCei+YMIbpblSsxh6oWfWPjEg7aXu/k5fyET2p+g92chaR98UX0gZG6lDtf2zGOURjM2R51YvHwExitb6SOwo2VCD2crhvNk/hpi3VdQaNTJ5xoV9j0dhbqXCVecwrnibINFiT9Zkx/S9GEp4nNe0nV4Ob4ObxEzFGGJjzKGvq+pjHfisUgTCToKJD/w4oytgHnBC3hv/h4xzy9kfDHku78rmz2hdKknYiWufF04g5pRK1BTvAU1f/Ddvo2jw0YxYOYaXs9woT3eAItlkhgPncW3zLV8Pj2AG7VwzTIOH487eOv3o3K7GM4ZyZgceUrr5ff0MVMi4IYarz87ITJrLUFzt3Fl9XFulmag6dOJbVsj8SOTeXE6HBURAZYzspA0TSV0UhTW1ab8nDCcBcobsbWfT/ZWDawtVqHZPwqraU+Y/GEvFimniB7ygLmTt9Da9yd0+aClZk7o36sM62ikyOs2Q0bKEhn+AcfhQ1BulmP6+8mM8eqi5LkK2YXnSfK9hVDkLYXCMlqaPHESm4yUghTPIl7iPC0Hp3MXiKowwU36Jnd/zuTxb0tMBAJeXNjMsDH1NG5ORTjdBLEPVxButmXIxqvcP6XHAIsSNOIMefDo939a1f/rkdNXJGuOI9XHsikyUaFlug5va9/xoXACtoNbKf0cQarmWaKiLuA2Xo3Y0YUs6nXjX/1EuhsL6P9oPUJbd3TqbZlxTI/iqls8jThHl/llCr+HMtlVFAktYww8B5LQ8BcVtQvE/3qM0ufPOB86jlR0FTIqJkwdaEFNqg4+MrV0KzbhM8ic2MoORH5YcFsniswDXxml4YC1vAadonkMaKvnYsMwLtVtZoGHAKeQZh7m11PYXouMlAh5E4wZry7JRLMN3K/oYatiA+ZD/2FrI0nntD2IixmQGPEam/IUVAJzuKAziHfFXYgqy+L50A+9byUMH/IbxSoVVMet4s+IdL4tP8H0gAgcOr9T7jKGlqSvuOleRaw1mdmj5akaEY85esz168utTgXcBIsYGZnAnXoB4/u4UOqhye+kP/hKgWyLK2vHj6LPyA58LNezdPl0sjYsIqZ7DA8Ti/F42cBxKx2i+x1lYKMBtyN60BnWgqO2Co0KskjsvIEaUP/8MtJyZ2krGkAf3Cny+skYCUvald9SryeOqGIXw2Sk6RZsRqxrCPptSfQjjXdEMoQmrg6MJKM5Czm0UKvopot4VNQOIv3Pnoxv81ktY4j5y+Xw3hmbXHUkkp6AqyYPhwDp+WiqtbNJ6TFmKpa42urwS7OH8aMvMvJyPkM6Jen8rofmrXoKAUqy0C+uwFyumr66Ctz0TyF2hyy3gn5jE+nLw5qHZNd8wqB1DS+ODkR4YSbz3SpYdG8/PJrDm2X+2L69xmRrA4J+HyWsWYqGph6OxX9Fo80UW9/VFIoU81esirFT9tKUGoBu6Sr0x7tw220FAT72hLy15NuB7+gpB9O+fSYT+g8kUmwDkades2L0BR6WD0C0tYMCme8o58lT4QGeDScgeyZ42rHz0isav4ugsNqMWSa+UAODDvYj+FAWB3VfkJcRxcId4ogUB2KjOBMRy414ye+maKAfpilVJMqI0NbdxlTgnFgKEZN86C1bT/bmgYxOTyDichcGWrcRn6OD1ypFPqme5mV1J9Ns16AuYouYvh5inzUJLnXGfnEo9St3E+Nmh4G7KDcHNmHwogGpc2Zws5LMdjHaX4IIDUiKQY6nL/bl0hRqPqPvcGXGqh+jOVwIHrsp8J7Et7MjOb7uDZ7bhmL35wOO22cSfOonvr2WqMus5hvfaPBazdXSTFzyPJi7eRkHA4PZOGYeA2o6MRgsikB7Jm9qm1kceZJD4nfp33ufyTpTqBK8RL5Kh6/J5oQeCGOJfyRTNfTpHbCbtqw8FA0cmOumRsrXociOaCG8fR/tMj3UHS2js6GJJJV6tlXoYfuvG3dlBb5miZKX0kN13U9qn0dx3WIUP+pVaRu5lIjCT3S0CnHrssTy1EqWHLdCpDEG96kTeRMWScXYBZyftJPQ8p/4Wb4iN+oOaweORkMgIK5tAINn3WH2HFCPseJO3OX/skv/W2RXKjIiwknzlyEiL0dafjuu47SJ+/eTvZEdSKss4LrxLw74+rBtzykkNjbimj6a+toORHTnYV+7nGipAxQXdaEuIUF9ezDyti6k/piNmYcOeRKGdKaup33rA8a7WVIX/Rhpyz0IsttYEeVNalEdZ4+fYuXOCXw7tJWs/sqYxLWhd2YNZbk6GLUoUl97GuuSrfzyFOfbnyoc825yxOgMT7oaKMguJKXrBHenj8Zk/ypOG5qT7LWI4mIRJCp6KRdUsvHIEUaLbSSy14/95w4RqGPJ9FpVLMUUaJaYRqp7IC2Tn7F9lAq50qokpXVxZ98iXFLt2DTwK7svJXBptj5F+73ZNOM3A09twUJXwAx1XXp0cunoq46xph2v4g35t0KZ4HdlLFWwQUaqgH6NM3iWmompUQhWOn/51+uAbP++qPYZwnU9AVYjx/HPLJKcymbaJz1gxr8WlOfKk3NBk9nbdbh+M4DzO03ZTyQGh534ICGGhrYkSvOmITvBjRLLsThsPICURglNm4N5I6vLb4EZThqfMYvcjuIxA2Y1T+Br+Atc3bewesIWlhnBMI8fxDfcxhxpMnmE0uRV/LpWxNgJLykW+4BxxDScBbPYbT8SUU9Psv6Cmb0AidFRdMSlE6YUzLotwzAqfcMQza08/LuSzj4WHPH+ys2CC7gdq4br0lwd3s4KB5DcIImM9k7qBVD8U0jv60wqpQPJ89bj0um7/FY2Zm9nGxerpfER0UDaTBLJ7gbqJHsISQ0m40kljh5gpOVNXWM3hb2NlBQ60pRRChKd3D03kaJmKbLjTuGmvAMxEzn2P7/JycL5pNkXIrG0i10+y1hedxO913LsubqDoYqdeLjNY1CXKJ+lxiOrFoveFx3URogT9bgP4eLvGO6WiXpIDn96FKkVL8HxUB7XbeFfWDvay6dRfrWc/Q8b8b6Uy96zxjxNKmCO9VgKag8yq2kwiZ0CfmdBSizoz4SP85cTFyuO9M9w7rc2876rgH3x8AJQnviLPbftidk0nTwnC7yWi5DLL5To4lX8VNrsxUgZVom+yx7WbThKXlw3iRcTEHx6h5XaavSvnKHGJI/idX40zv5KlHIvlnNEqEv+TXWMDEs7plO3J53MRnj2/2+/9XwE3w2H4/FODKvr9niwkhbuccT6HOPGHKRiiASf+jxk9chL3Dm+msOrsxk205julALCT0zCVncsnctUaFrVQHZeM6biMohXK1FysRa5uU2YDe2kc5AlZzY4MPOuG5JXNvFW/DQTDdKRu7IEsSXjUNAZyckjG9hzypzo4EY0RybzzL0A13UXcDiYQ1hwIPLic9AuqCD2nRSNM0yYrlZCc7qAVe0anB4EcspW1KRkIdpeh0zLCn51f2WImgbio26T0bWLnILXPPU/zKwtZ3AXxBCj/Y+azBYWDVyHzveDqPY0EFPXhr32QlbHatJs/4L8mN+8PCkkMM+CUXt6iJ2SRUAI/5dd/QcRaCoJbe8FsOeiHDpzvnMiV4a7flN5/FKEPclv8bROIv5MICtyTtCgP4Yu96sUOyvSt6qYScqrWJJxjL5VnVRr/mX3mFfIy3ayK+UJetITMO99g8InazIHJXKn9yR3C/7wU+0hfza8YLXIDcr8n/LWUJPyxOuYmu6i9Mcfen3OsEG2kCPpO1BsycDe5BhX4wtJPf2Lc1c1EPvqTOOCT4h2qBJxLQOHAGdag3JYrOXJbv37tBSNYZhUKPm17lgOUsMxYz13zK6T9Xo4YUb7mFpXQkCaNI/vXeBAvCfhYsZovpvCkQktCO70orbMkMd3ZrHEzo7CihYmSHsQkL2QCb23qf1WT86FMBpTZBgqq4OZ1G/qFBZxOnQQuxY/4v7zWKbrNyKrokGb3DWCY++T/+oFGguuYKP3iLlubczdcxvNt0qMC/Ej7O47OpoysBK+5dTnCeycs5HcJ4UYfamgMlaD4wOn8qToEzZvi3BVaibE/Qaff0ehatZLaU8dkyrHUn6ynL+CObzdakrSiQOcCwggKekj0+b3QfDuEzJhecSEFCF4G4Lm79/Yyy4mreEYiopPyDN+zG1fBZJWDmZ6HyUmFv6khvdUVhtjFxROtOxybKyuM22dFpevXqfK5Q3X1IwwM7Em410LQs8SAjrCKCmcwNNtyxlx8CRXjSuY7F+Lq2gxO2cMQ3qQFJMnKmDo9ZrVPqHYRQt4KraPl78qmaGiRolfJ96hBZxlO9FZO+ljYoq0tA7qKpLEZhVz/9xZBJKaLBujiMttQx5PceL4TD3CBmhgmXqJDJul/P0eh4nKckQTgqmzT6Pwqza1vUUMeTWRwyd3U777JX6iNmifHsOpWC0SYz4RvukYZ5qCOaBuR/w5I0pO6CLY/YChE2fyrTGeZT6ZZCzppmL8c+YPVuKtTj07nAbhamSN3U9jRqxVIn71OuIunsJD+Ilk188YlO1nhtleLnsIKNt5iF/aZ7jc+ZxIT3HCb3ylLEUZaa9KFtnJE9BvO3FBZZRusyHm8XnWznxM9bkQTnW7IOIxhncHXPFW+slX8ftUvBHlUpA5tsFPWRN8AceZdSyMfoHQ0Iprvh6kPd5O87UShM+EWB4WYHRgG60hx4mVe8zk83OI3riFtJaBNJxawK9FAl6J1fLF7goftf/yXMaQzCOL+O19Bem9MYgkWbAuuhSh3RNU5wk4J72F8MQWlP/lUT3DGvNxp0nwCuKw/2XyTZwYk6LEDsfPzEobzN4iG5xGVbNNZjdH/8ZyRaeR6wvNkT0hx2W3KLaWxvLi0l0kn9yA35s4vecTi2cs4u1nFdxM5XGz+kVMJZSpvcUqfR9Zm0D7wyCahdOpe3ecGbOrKU+WYZ0l9NsE+4P9+XNwBsITM5mT84+pRYeYo+WFkmk9JYkzmF/3hCWeYxi2pJUR55V4dD0VpQ5X3rR58WnGAIaurmbOnmNUir7kxfN2wnYq4jb3KcK7saxJLMZgTRalT2M5t+ft/5zs6qr/Rf/TGk+ZVaHEwsxCihVsQFlI46xtvBuggtSv8+iX6XB8ZjNW+3qQVhDS276I+MPDEIgHMKB6J+sPa2OqLMINHz989YvJ9Mjk5rUWnrZk0h2TSsukZCpsS5h/aDVvK8JpmqLH8qhQPofs5ckaE3prBRR13+LtpXRu391F1ZGrmCdE8cdkPVo6CWzt+Y5joxX/pBqx15Bg0mlnPLwMuJvqyYY5nygrE2Oclw+N9f3ZvG4VM5wcEHvewu7RfpwP3k1emBUnV31mgMZUjojaM0Ull8rsPGxtergpVsW5uHOYLG5E3mcaF3UtCZ7nyehWfcYlPcZJQ5aApDjclR7immlP+A4/jJ1UuKIvh7nBQir+5vK3eyJ9y28xTKSbqXlZKGXG0X7/AOKtsSxY0E1QSy6aZuqoxqTTUaJJ1/sQ7nZ+Qf3AeGxzPejunoSsfDjOT7oQxCQibvOZDjUpdL+8pP65gPK/KZT2vkHfsC9D6uOwuv+C6pZcHNdupGO9BV3f/7D/5jWGtjbj1WyAiWk6nZaFXApx5nhOMQ0D9ahOL2ZyfzXqrDwIb+hGUr2OoJZShvGUKdmZuMtNJ3Z3AHNNmujcH4Hovlr6xRixJ3APrip/6SOMRP7TVfRvn+Sc6ES2JV+iN/cQdZaXGK3pxAivQFLqVyOh5YrapI3M311OrqUxK/2H8/ejHPt1RvH52BsenA3ESz6ci5ZDOPg1m6ZToxhdlYZZcRN9pQ0w01ZDS0SLPmih5iiJeMdCYi28OaYxl689dxhtPJJ/jT0MGJDBJYPn2CxZwdOiF1ByFRM1TapUq7HWjaFHQ4n4Wd+40niU2r56/Lv1i0jBU1oLbzBGqQOr6UIW1K8htUcK7ZyvtN2oR7SqENWPoQzwXsM/yaNYIYtYwVdKPr1j6+NInh7qIikzgnmGZ4kOe8CCeGNs/HXoul2Ntctkdm8OYqH6PAQF8VhMl+SLfigxp6vp/abN0VcNjD82CWs3Dzp9fUhf3MG0yDAWhrxEV7eDvKxcms734ZukHutmmRIzIwYmqfPk4QXmTRnBtJumHOnbykq3VezYsIumky+ZfSyTQwINwn85I1OwnXP+yxhTlI/0AgHq1w/w+cB+Mq9VUfXWiI+jz6Ky/wsu7t5c9rZBd4IU0oOGMqJDjfm7Wlny4jIVIcUY3nrEdRMDzq5yR07yKUlSQ9G6MJntQ0By4hvSDS/Q6f4GA53RnNyzCw0PkIzqpLd9Bp+Lx/K6sJGf98qxrFYjtG472e29bB+jwY88Ica9ethUnkZXOgP5ieMp6C2jTEKbUjEtFIMmEFUxhipVBYZX/mLXHi38daZhM/Aqnw/1ZbW8IaPEG0juUkJhcDsLTZXR0IcxpSlsltTh40AZxOpSUBl+Ex3VRhT6pfC8yQyN9DC8lBR4nHYDj5xnjBWLITtbjkkDB5MVsw+hvpCiNbv5pGnBLsNuVlzpyzrda9jptmHUHEhJXRp//y4ieYQ3JY/f/F929R/k8v07/qbt0mT2niRnojuV4i0Yt8WQFxdDScRDhptvI0jhDYb5Rowc5UWFKMjcuovk2s+MFn6iXlGfgxFPUFeYiIcziCuZAAAgAElEQVSsMQ+0PiJ24RQurZ+YbabLKCUzfv96ytltr+nJjuJHSQvmC7YjnHcYSy8LPn3NQ9Wml/i3Iiw+tIyhMeWI55lTu3EMnup5GOSOJK5Zgfpn5/CVaWHl8F8c9j7Hjy1HEPTfigEZeNkex9vHFV0PSdz01vP+/ReWDzOgUXYaBhUlvNo7n0+RgTwMes9cSxWMHqZh5eSLp1M+dU5fyF3awTsXO55eq+Ch5k4cJDczLUmBOgNz9uj/4GTBRtbI+nN94VQSJQYy0FkVjQ5jxD508qa9D1MsLvCnNAa1tjXo6GlTn1bOmcvHOPA4gOBPwRhrNqLcJcL3tjhmNApRnqaGnc8a3hxMpkZDjzyZ16yctYK43ZcpcFhEfW8OlnOVcdOcieed5ezMWcrpjFROOQzhxftCzJZbExeTSsaZzbiUyLE8qQuv6ZOxd6tFsY8NbcqvUWruYVXYMfTTHImu6E+bSDKlgk3k5f3DTbWLnNY0JrSK8ixcisuKYsjmvcLs5k1KVZp5e28XqckhbHL2ZLh+AQFSSaRceYLWqClUzRTyslgW1fyT2K7sy+9gbbZuOUBmy04K9+TRczSb5lVj2evcgrO8OIar9bnXPINxk5bhOVDAngn7GOvkytjdq9mVOYm936rYYbCOhIZbLBvgRKeEKH/6gp54O3P7beR88geslT/z7Z8x18eeZO7bsxxSMiJKuZYU5SlUz9vG+tNHiWhMpb9qH/4kJ9Ix+xiKqm94XiZOgvYKToU/RnTNJY4U9adrwHd8dDcyRV1Ajbo+2e9rOTZ1Mz8k7zPGx40Xye+ZqW5NongPbhJjkbIeQ9asSkzSM5GbVsPLGzcYISHLSvELuK0MZre/A5siFjAwRpMFe3Zw2W8iPL/JGRtFZqkmsuCRBIf0anC078TTspN5UjUkzzxEjZILxZenMcrhMlvsijD3n8YSLSPcPBcSXhNM0XMvJoxW5Pu/o4wc0YFdnicLmvMJPGhHvrc3JdUrOXGsDskJdphuTeHEEm92J48nX7Ee6b3BTFFLpjAkiFeLriLq8ZmljQ6IxiQzb7Q48xJFGdZtj92dlTw0O8iXITtRl8jDIz+U/D5rsfCZz/JV3zmz9xgP8x+yQRhKXZQ1v1QDcTq5D5XFA1EeLEePbAP3aWXX0tdEvWnhkpgGIaqKqLbv4XpjMatFddhRnINkv8t0J5ijoKRCYK46+vIiyP7txkitkbaiGjrUjXAwz6TdLAQFxUhefbFj7n5V/gR14rihi9pzb6me7IuSdjsGIzr52S3CgVh42jKCp6SxOnktcSbWKJ4X5Z+NFV86Denb1UJtZwnPuiQ40mcGKR/Ooz3MkVRtefR//8RMZh+12hXMGt4H5XZFErabcsl9J1I193iy8y6V4k3I1a1koFIvr0I//c/JrqQHGXKo1BYvywtsDBzHWd0g5oydypNR5iSFLEbbcitLHr3h8G99FnlrUf63hDrzEzjf2Y+Y+n02aQzEctJ5VHJ0EN7aRlp/P0SKjFjQ9gVPua0oufZjcNgQVknfYNh2HSxrbOnb3MGGl1vJWbGdEXeecPlTOR0Omwh+k8/2jTB05Xb+qfbBIzmJ7N1nOdOzGtnqYM78jiAjKYMvhxJIzUll9diLVH+7gMvsERzZNwmDoxbcvRTEmp2HyZh7g2UNg/EaqMe3qDSuxR7HpjuYU32jaQ9XYlu3IVJrJ1BouZJTV3IZ4lzAFPvfjH71gj3TA3k/8CT7HObytt0Ux76DmeK0i1khMRjGp/OraAAOA0XIbdmHzvYFWNYtQMljMA9+NRHTEEuTvhohU2Yy8OVkwr6WsVpUh+qX2URJiROXc5Hb2V+5di8Ds2+TeFJxhFmeJZi1TGRNShyd3fZImg4l41UWW79tZd4LUS6vC8HgsT26D3MovdtM4+RtKLWoIFj3kONvOtj76SdHjosyfdYPlMZW0iBijbqCJkYRnXgmH6Nhvw3LRcVQqHSjMlIHlT965FUmo69znGUmxTxGB5faBiZnCYk2C+LVB1mOmf4mZHc72t72OIf/oWzsR1ZdDGKvbCNz9ENIelrJ0NPLeTXwHra3NGmISafjnjdJK79T/qCOO+0GzDVrZ4nBDoS9bmxV9uOqwJRLtU00qBuQEKlC4OyNNJwJ40VYIWGykkiIuxPdUUxqTxev28uJPpDF5L/tbP2xmLnHqlk2ayNGQ8BrkCeDlUYTZ+vH59jPfD8sS0f4IQwTNvFt9zOSQh1RTQ3F6l0760wfccF2NPLmGrDWjYGis/ghb8uXonMcUyxl0UAvpIYtxemkPZOvzaRP1huWp8rT11acIksTvv6UYHjJWAR/4P2mw3jqzuGFIIBArQpmK4QxM06Tx6K/uTbiBWcXr2HEANj3eyqu+4xZp5fC5bWVyI60wV9kJmdPOCMQCLATyHNXsIcD+tfYaziNNIu7WH1ZR8rPVBJNspnoOJYrzuXsGDiaMSvsmK8lYNgUJWRfRSGYm0I/K2+euM9i7bZN9BxZRq6wmPFXcvgirCKuugndKVfZIBTyrDebFcJVuAhFUZSrZM02By5t7kBm2kXSBd/x6x1HoFwXS3alsm+LFj1OlWg0uHDL4iUmr14gvJlEzY8eSrZv5ygVjLRfiMzxRp59fIvWjYksq8pFJ/En35XeMFfwnV39F3Bg4R/2905m52ADEipTOKr0jrKi+3h0/eTv19VIGhbw8VMhH98EcDf8OGbhlxk/oh+SxqnckAzHJuEIWxM3oup3Hef8fuS+e0nsnZeIfNtP5ihokW3GtEoSJ0UVnFQh0mIDmxtuopu1my75wVQcUUFMXYGMEBusSmoR1dPjX102B7jO3buqnGtYQ6XsSoo6+mOg9JX4GF9kAjuxCNXA4L0cQ9bbUBAId2ptWTNkOX87PyIh1sOWkP/Lrv7TVJf+oXH0EPztxhD++RZitQO4tuMe3UtG4eedxuNrj5g/yBsbmTKqPh5AYrQaio7e9H4qYF5RC60f3xC49C7hCW+xFFrioF6G2I++mFp4MU88AqlOBRyW7GDpGRF0Um5CfwH3Z1xhysnXHM3/yAh5P2JrIpgqv5A354KRlCsg4mEIxVcreBYeRlCtGv96Y5nmtoKLNDM23Y8nRdeoa6/l/sO1zF7tz9EANw5Ff0PsszQJZVm4OtmTWVJAQ/g+zl4r4FJKBA++vMFs7GBe/yhnnEIbJrHLcQ+UoqByO7KDrLDwyWajWyN/xfuTq/oCGfXv3K22QltfwLeGO+xYe4gZEYc4pDqGyzvf05MUhvvG5fT7+4XuwUuQF7dCt+MLCuVmPGgtR9HImsSdWUzsMxDroSsIzX6OWqcx2WViBL9sJvVZKdXlL/D3XoZ8lyPcuElHpy1tNS8oi29kcJwzXWVh3AsTsmP3XhQNZtNc0Yy08l16LEejnVjLS/Pp7Fmuw4mzy9AViqOp5keJiDRKde6YdQxjp2EfZvhIMlw2BqsOWyLLRRGY9KfCJAYxiYUYSsvimZbO6lF1THLeiFnVHu45badSJwiN2VMo/LOMDwa/UTLw5RxwFnuudffydOt9AlzW8/OdF6fOXOSYlSe/EtX4J9kXV4ultKcbs2uxkIYAL15XLuHqqMkEX3pGKP0IMjSET1IAPAxowcRzNibfIlCV/0VCewVtkmqYd5uSH/uJMa/3skm7lovhK+gQDeNieDa+avO4VVZBlZod2d8/M2ayFjoFA3h08g4NKmFYGFsT82chqUYNzJglhdgpE9aqjKTXxhfqLmBhu54hg6KgfCD7o56hajGEyZEO1A8ewpz1G1g+3Z/2pkj0Xkfx5UkUMnP/0n/dUW4kRfLOMwG1waDYIcuRzwJgEkW9b5BDFZnfIoA0yY32TGg/SMODeuQjrZA5Jo1Bk4BL/s959bKSLlFFzn1sYNXskWiU5HEydApJWYl0Pb/Fl+PJ8Ace3bbk2rfVlIck0jBtEW9+9OV1iwGhp2t5kF9A0u1Q/EQh6KYr0cGx6EiGMqP1IhOeQszcZ1StvcPHh0NIz4DXMSe5LIDlf3owHruKbVsjmDclmx1dkaSZO9PPKIC58sEYv4xhZAXkOM/gNJdpmruextIgXD1G8ddyOT+ePyRyUC32B03ITxJFpTkHjWniTOqayJk6O4r7OXJFvpTC+jxyW15x1z0Exf7liMvZk3j7ImOXXub7J2m2Hl7CF56xrKuT0nErcWhSZXpxF+mFv9lgMJotQZ/xnWpIvns8/QQfWTTtFf9aPXldJMHwzG6299NgaGgLSiZe9M27zrIN8pQL1vL1cTjqO4yQvCmkZ0A+H5RbMavK4qTRUFIl39F/rQtxypkMNGyhs1ebX6mvULMMJLdQkkfxSmiqzYQf6iS4D6XGyoR+Q1zQtAvCQln1v+zS/xbLx66dy/w1z7RAuzk6X5rJF+qj8jqDYL1hjNe0oa/IKqb4neVfugR54jZUKqSh6TcNucZqRO1UEMtNw0dmGh+7iqgvjmG530zknX8j26rItIkqPPpag4v7WLp4y+zEFgZojqCnQwO5oWNIl5Akrusirn7NDGydhu/DDdibjyNa9CtC1SJON/3D3sKA+0/T2TDFAsuLV/D0HMbOrIv4yc4jvtKV685CSo/54DznKik/azlb8oYZBnr8jCvHtHsH196/YqaaMwkGSsjIi/HmRQrq2gpkq5ej/sGb4LO7aL3/iMabcUzPOsHjK28YlQXWYd8Il7VjvLkoj/dt4byPAVXni9imaMz75ExUkvNRzddDmOrMrcyLvO/0oUpEgr5p4qQYOzLb0gIvlwBUB3TxXLQBo5JGzERMuPj4Hvs33iQ1sJspjg6sy1Uh51oeTv0UWTtdjy75ucgM7UV6qDV9Kl7y0c6Jl8NcOZSRy/dcMzolxbkVeZbwCBFWFXzhtt4Z5jteR7FqEIViF9lflol+xH1G/InFVfYbbZ23sVwhT21qGxHV9jSLD2GQ7RcMFbZSHDWft7tO4jBnCWM21CFzvZYr5WEEVczlj89ydP+UY1cRg51+BOJx5VyU2IXkT296d9/AesYKnrRN47BFJmu2VhOU/oh4xbnU6kQx6YY3k/1uMfuhEPXeXhaWn6XnXxh+mcbUfD9AqPYgFAyicJw2g4+rk6gsqyR8ggdz5TroU5VLuHIjLcqiXL66ENePXexwuEnLfmuGCe0RGTqCjy45PFT4g6WqK03TahivIoHU6L9o9ZYiZmNNRmA74tq7iF66gGvvhpJ77Rsu6wfQMbeE8pYPBGyczoLoqTQ4qBGWfIFtS+cSZ96Obp4CE1yG8+98Mm83CJH5psuazJckau6hZMdggs5ep/hcG8vWmRP3bRJDBZkUpHXgYLaCGd7z8L04k+It/qSdeoxVsSibTmzHrl2WzsALvN/7hZrQU3zbH8RcqX4EpFQzNd8eXbEmxI1kcDuTyLbaHrr8dVkk3I66qBXvr49nxGZ3lu2cj8WEOqz8ZxDuP4dx80dz1tcSsaqbpIoV8fx7NebCObyYf41JjlKkR4shd+ABdrUmGK+5ya6ax7iExmDufgr3Vd30fVGEaVYsdWURPM25TP8JB9DQd+dVlQjHcs7QFhDLQvGhZAw4S/+nK9B3aGKW3SLed0dz5NE2egNjkFarQGi1F8nB3igKBKSJytHHUZPwxm+cml5Ekd43RPppUCZZTWadLX9F5tEs1oOIrT0VSqKM99nAiMZ21BUymfzrPK9tP1C8IYcypXvE1hcTvWwUH44mUrhsO1kqGqQ8bmPv4HWcnvibXFkhIyPlmFzuSGjqIaSsdYnpkKN1RTlCnX3cOlPEgGZx1KIGUiDfTGiVEiY3nzF2xwXy1X2oSn2GqIk6LxMq6LbRokGjDwbDXXFYVcLdwQmoPBxFzaMgvBzKCVd6j3mNHk6dyVyPyf+/5eM/yPErwf7qnQfJlM9FaDEUj585nK4x4cwgGZL+/maV/CneFuxmmKMyd2jD2keSQfKFFDyQRVOnhpT0RiLFY1k5agGOOi28OSdHlYsKGhjz8J8eXqXtCEXuUPTPkGhva6b2y0VPx4Afr4v5a/URg9rZZHZ84e2vLHYt305Dz2n0c68gkO9m4VxjPqbG82SHD7P8x1OvO5LhtiJs+OhL06BGDr5fSciOa3h5DmdN4FNctHWwqjfit3EaUhUuZBecwkUXDCe60vHnA5Fhz7E3G4xSUBXq6c2s3DCa+rZEPO685UXSax5Nu83NlEi6ZHO4rerD2O4mHn+/zDw3YwrTh7Ht0Fl2HvRjoUQa0U7riFGqZLRWEZ/zNlFQ8IAZY/cStH8ZC4/N5mh+LyONdVH9cItz+eWsGbOOxEQJNLpHYTHhO4OX1mCfOoOg4A30WTUHobCXkh8JVKxSpedKAofadQiu9+V5zRQelFmiLCKCmkkOWcI6TJR0OD5vK/v3ebFHcxn1aYn8Up/DNZHzHLFowudrFQckU8mQnIX0U03G9pvP/vJ91Ghbs1J5OAoSxxglugKpJC8OZ5ozdlogyeeDqLAVJfj9DM5oDKQhshWdNX8ZvnMzzz2fo+gWh9lrZWqWxtB+8iYKq+ewPb8PddWVOBnLYWw+horwDwxvUKJmxE1GBijwSjKZNTHJaDb1sLs9jIgFPngviUFe/hMG87yZIqPBr42bCdhwhJHZDfRr0yPOKJHWNgPOLOhH9hcfon+PZeKSiZiaDadZZTXC1C046ragOEiVsgHPaYgehp3Fd+yrT/BP/QWCroGoDLCnpLWL0qgKjJzNubE3jiXnxLksWs1+CSGtW+MwmD+L/fWJ/P0Swq9F7znw7TbrNqrx7u4ZZAts2TLnKI0B00nec5Ban/nEX9pKjkIakxad56yKN4pcR2uaD0L3KaxUF1C3oI0tZ0KolnjNAMxRe5PCLsUIfkwZyVTzUWzcI0fP3G/4nbzK0fg4gkqHcst2M0v276fpsgUbvHRQ03hK/CYRxj+wo0juKvmz5Aj6qIyykwb7IxXYtncDew89Jb0qmBbDE7i/jUBt6Bx2vXnE5guxhN+DxfPMObp+Ia/WKVM9rYJ7E48hETKN6uHd/IlXZK6VDC9+dvC79SWOznZExwkR3/+YiKE9ONouR/3UOcR9zmAfFsfb7q9cXnyRsc9/cNJtPPsWByCncQUVwx2UjnFDt+UCoc80WTBSSE64HhPaJKlfX4JaxRH0ez3YKXKDPvXgXheCRHIbiYOEZCV/YGRGDD+ZjVhCKL/aoylTGoy9ny+he4PweW7Ejopc9hqIM/edJzWSAp75lZI/IILG5X1wuS1GU4snCorpfFz1B6mVOUx/coPiZEWeZD9FW9+IeRUC7rZWYdhRhFayNvk93tTJHqa22o6q+lo0i3rItO1LqakYQcdDyYqtQn7Hd3yftlJ1/AgK88bRZS5kdo4ppQ+iefIv4X/O8tGupEHCslzMTgxGcXASvrE7MO5ryiRHWTT+maLSG0z6Gy/EUpypyfrACvFJ7DjQjm5QCv8uBhH1Qp6fyR+Z30cT3U497rmcZlDxR5SiXXlRc5/xU9W51fuDc4UCUn58pWvVFkzOFrAg4yYL3u2gT8Iu/q3s5sfCZ0SMD+R3lQ89Mlt449TLqfU2nBk6n7sf1WmULGPCqI0EM5RlBdKcbzuKzZwecuuiSBs1hwdzJqETcYWp7WOpaKzDGRMGxB5i56nxXPyznPa5e0k49YD1/bV4NTicw8vFOFSaRZlcOFbKb+kYD/c2HKMmcQc1dfIsVb3KdH0VZq59xveZq+h35SkFV6xpLY/FWF8CBdNZNDmMQqHpEvaDNqCbuo2D08XRHtmDsYKQpYff0rLaEx9tcfRL1xGeuRDZHlg4/CANdSvRnZLPrZ8XOOSSSf2X2ZyRS2CP4A5++RvYtPU2JXm/ODPiFrPLFrL3NYwfO4vrKwsJPK3LqJaJdOUF4ZELp5Kv41yiSWiTC8KSHB556mPeZwpyfyVxdtcndkcg+/0FlClNQ2VoNdI25yjPdyAy+w1/e5PYdWgxHQFJXJt0F4H7KpSdN+EhtYYcp+n0U9mJSmQ7z4p/YuziRdqYdNpiFmJ47QFlf07RZqFL1JcFmJLAn0YRZM4bYZPwHZ09++iX/oQZg1TZOt2QrU/uM3jNQoQn5xGeKoRbp3AU60P9sVaSd86m6dpV3I9eJLP+L6fVxjDTtYXp5WKs3rwRyYgJlCik8Mx4BNIHTLGyhrVy41m4UYK5f++wtNmBnDxR8rJd0Nd35ZfGY0LXZBOsOJPBUxw5MGoNf0yiCKqP5UR4Gsf6biP0gAH+JYspiznEu+tnCEt4iL+RA8LmIMI33OfN8CkMd9Dh6Nwl3JcXUHZOH+nF/Why/0FBWBo5CR1cUw0lJKeF5sCpfFT7wJGcCHaUCkmKe82iF3+4P7ySG5P78eTnbvyFsQj/NDL/XQr5uaH4KmbjIipgpZ8Awe4lZIWUM3hfAsJBS+ij78VF6igc+ot7/i/Ylm/JvYkdLNi9g8GCOXgLBFzuN55nYY1ssl2NoCgfu+8XMb6Syh7FvlQkHUWnpQ3x43G8FhaT0VeWaAtPni5/ziUZM2KfruWxjoBgYIviZ+7bTOHf+TWIbA0hfOUeViisR+rVBTIco8ho8uKQjhhrtr3H3d4N42ufqMgO4NqD+5SPuEXhggDc+25gwscslhi386tewMTabcRuV2HY6qFU/BQnS8GXv6V1dP7xoNdRmpqcMAzysklfdYDiYGe+dluwzX4jze+2I1e8lqanIQw1ECHpEPg56XN43yO6XdVRfjWRxvi9nFoui+pfMyxf1qJZ/4D9ViK4v+/PJLczjLkpT/mdsyxbPJ9N2yOQ8T3DXAcxlAYXcfdDNA/kN9JkthDX+d0syFzDBItfjJe9SNCuGezwN0X5hA67PKYSHDgcSbGvjI224GRFJu3CMnbrWf+nVf2/HtUeMWb2Psd9dCuSD+5R3zKcM4rtmKT3pcmgmz+1mxiqfIcy3VfMt3TBGlFMBBb8qs4hrLAZBY1IdvnNJ1MrB741kji0mFjhcXIDJHDQmkpGxWV6qsQx8WlhwDMjJK0W8/n1d9wox6XFE+O2T0gpmbLuzDGWX7xPndFSEk2WUCP8wszfq2lKMKEuoYXGwauocO7Lr489fLKoJ6NRj+GRmxnbqc+lWWeYv2omv4wnoqaTyb9sGTRzo3FceZLdT/bRmQ/veyxZPsWND2rl5Oha0q6VicULDSbFfyLTZA7K41TAqAARpd30tFRy0M2bo1nRCH0GsigwhEO7nnPo215+yZpia+NAp3wPrs2d7PqWSFrCUwxVWsj/Es/YBUfwD/jGnumjCZeJQtzzOE+cyvkirKA0tJu+rg+RFGqS/PkfXTIimLtNprBQBIF8BFpdsghz4vDdMZwYETu8A3V55KSFcPwyyivfMcppOIKxsG7MVO4/KQRAUVGbAj1RhvQ0wQhjFgXkIJWkRsqmJ0S+iGPhijHItMAArT5UdMajW6dPviAFk8bzPBoqpKP7K0cfWNIimkbBl5Mo6LWx4rU8t673Q6bgBaFDrnOt3YK4gkC6gozoDl6Jwpq9xF05g7tkKN8/7+BWnhB3sWr8xk9FXFSK/hJ6JNYZYj/YG7MJXXz4EIpbpgeOa9fyz9CZQLER/OITJQfCSFs6HNfMn3S2dhAqYsLMBSpYWHTwKT6a6yfNqMqD8T6m+K4MJyoarrwNZ6J1O48apRk+ajKGDurk5RuTLDWSoCozFGUlIfMnq33H0yesljovXRzGK3Ayuhuaiyn42ReXcR9oLJuBguNA5q3bzMzAwxi1L2VwrgzaBmpUjFjL+2kqpE0ToCgzB92LQ5GbNIMxOoNQjSrncvwF/mgDU65Cf1DqWIbE+lqWPq9h+s6zsPgeCn29uDlNAsX/x95bfWUBeOvW86VLujtVQFBSQQQRQREBCxXFwMDuFru7sVuxsBtERAEVpSVEAWmQTmnec/Pdf79zccbeZ5w9/4i5xjPWWM+a9x7t5y+5yBZ2nHlCSmYavUUJBPpX8h14ETaCe6V3WOF3BLnYS/gTSLfFR7InbEOsI4Uxg2TJfXiacOVHrJl2gigF6NdWT9ybGJp81On+HIkysvzsCaQXkPA9QPWPOajnj6TvNF1eRRrRqXUBtR/iSHca8vtbNQG+0KgfQELiP+zqjXE74M2z6TMY7fQD9329PDrxCPnXHym7H4mvOxT8CeNNoSOvrkPaoBM0Dowg2OcpNRsCUA1y4kPmDwaV2xLfVoLEXwFPfxej5KTBn3JQaytG27IE9H8jndvGbLvlSHZ6g2ENdZpapMavwGaDJppPnzLRV515R0WwkJOg5agGQwaWIh+rzaGokQzIH4L0X2P6ninCSjmeRcrfKPs8mOJ+DsikhhN5MYZB43ew1vkxD1LL2Nupz+GWLFRGv6CjVRkxXXVsZdwZFrkfDWUdVGRSiA3xpuaQHhDKn2cmVDlmcgIhD6ZFkzfwN1Eyo+nUk/mPXfrf4uBcT99SuGXPDSLqLjFbW4qi6AaeOyvQ910349zN+RXbztA+LnwXeYmcbjLym7y4GVqMZ6MR/i59uZebgNtAUWJldTGp7yWhSYHssmsYm+hhEeWNoVQvqqGdXD+bRM6AGpq+ZtJHZxBfx1/gW/ZT6sJTGL1pDfNf78FHpQazf110Zk7DZmkWC06kIz56JaonDpPp24C3tivJv+rRrtdFQUsddZUv2Ky5SN72UYh63WTUxwTGl29jktUm0it7+P61HTnfkYySSmDZnBmYeYF9qyYD7cfQI3zF+MWvaC3r4VhYNxZqZfxNzaVGyYp7jbd4vuAitx5E8uDlUASiBqS1h9BSU8z7XF/kcafj8HUUHIeRlvEJlyAr9K1diFjrgby+PQe6flGkHMbNXx/RMbKi+Icfy67sI7pxPb8TtmJtso8bbxdj2y5Nu0M8OvbOOIhKMjnUncWXirFMnsjFS/sY5uiIcLU8Kwc24jEqk5kziphpNZHzO5/z7OdnfIf6s20AACAASURBVM8vZXNgJha20pwZIcHDN98ZJe9KkYYePxpFKU+TJfhKPuqVQoak3mRrbiBDO3IYJExGHlNuq/cje7EU+5fchd650D6SKTKPubd5AC+Th1KSlEhBchqKbrPY17OKf1MH4Hm4i3fHJ8NCSfpe6cTlnhFt08+w6PJYTug70Hp4H5HOPxmycR/VvzuR+nwcvU0pvPl6g8tLLqC9chblV7+gf7adoQJFFK+XscEzlO2poiyJGMEE5w10lKTjLzccjjkCIBBoM2rhaSJvbQC3Eka/1uRNQTyFmU8x/HmED44hROR30C0hyyXDDXwVe87vIX64I2SA/gKUGxuZd+QY6de3Eloyn4LtTtwum0uGhzi+2ZaUXHqF7ZAjvK88yWaVweTMhMKqvXT1FDLQWUhxwjXq6hLx1+6iOX4RTR8quOqtQJ6uCCLfXtARZ0JF22Vyr3+jrxvoPnUk3vo7Ssa/MXgxkka/Io5WfGC+5nASi+7iVJJHs7EBKtozebD0N5l/+vIzTMjT2Gf8m+2PzCJ/Zpx/zsM+35B0/cGYzV+ovXeZTaelefL9Mw+/vMHL4g/zPDbQXdDOo7srODlGki3yr9lrCod2w/q7M3ny6QbylbCl+DsitWIoatvgnh5NkrCUJrUXGL8qQr40GXHfSZytk6Dq4Cqa+EiwaDvTZS/zSLyQzmletDyR502sG9cyJbg5pYemTh2e7MjE4HMXS1QsUDE2wEniA0kNPyn/dJjSUaqMyAzHzreQ6686cTL3xkpYyUXuYiLmSMHTOHTv34NjK7iia8OctrcYGgzn3c9YtHXskCpT513DXyYodCOmJ07yPglGrCvhhL0aqf+aOfjiD6d7rLl4YzvjpswgxWoie75noqM4FOUBP/kaFctvuzp8cvIZFmhKSZoWqb2+dCuK46gqya39C9EaeZbiyiOsXnObyvgnOKnX8jNdCtMR0/BcfYDRlnbIp0mg7lHH296b9KmpJzUq/38Ozv8LEQgkhGEjjEj3cEJFczaPIw9wWsqDx+4adKSUMM/mN89UPbEwTKL2TS/ihwbhsX44SfqfmBU4G1UTF9Zvn4ZCeSmf4tJQHGDBaNcRRExowXbtU9TKpvLsSzJl+6MY8Hgn5ioirD8RSejtaHQOjkZ6zCAyDR4jlV+KQ8kDLjidYvuqWuIts9A4spJO8SauH+imJVqMbM+1nFv8FXGpIipyTyFTNwP51i+YWp+kT+E9XBWieSQ5nitiVUisl+HH0hrm7NnGPOW+FF67z6+yuTQ536Vs9yzeqViQlKjPqKnbsRCfR84XcbpKo9DX6UdJ22vk5TehL9rLxpKNCF9+IvOcPjm+H6j74UbF4yeIb1RHI/0V81yn4vhQhMGFG+nvupvoP4U8afBgSv0czDT9yImrokGpgqlLByE7cAA5S2XoychHcV4eub/kWeNli8PuAsTrMmgoXI2y1TjqMyv5vvAg9uckmHDiI9stVjHQyR7+fWJOWhP7bt5Cq1IOx5hvWByaSMiAbSwqdCdNxouaakX8RETI9n/Ppj8BPPacyqw7AZTfW4iYshYTDV5hzjnE1PcQe2MCd4w3Yz24mXYJP6YuHoueQEBc7DtKeudg+84Y5ejxmH/KZPTxNm5vfkCmMJmRxiUEiOSTsbKJBZa1THM/xnqBOodz1jCzcAO9u+2RXX+QrBeXOTDLka1vJemzLxHNomb0F2ow/fxMjAxGwCiw1jTHMnsiE7ab0HI+By1vO0Yt9mPWo+s8mDSfg4vf8HXgasQdjXG38ebl/A3kXdLn9c9wJjYfZvkORZRFejgwaSlHRFoQtu/h/OhnXNETsP/NecYbLOCky1RcFk9muuwRxqmH4rT1LkUeX0icvoW6V/dYpWnJ9EJN5jrG4Ks6GLFyCbwZxE0HH5KGCOh3o42dx9eyubuFtvUVjEsTRVxCF4u9r1E0LiWx4AABgk1IY4ekTzLpoUKmOw/H6MpH9sgIWXxxIW90krg/wQ2T8UfxfhuDtrcHPc2qXJerRjVEwLdQIY5GAsSEEDUrgrcmYVgbKjL7QjS6Cf0ILN9J9qECthzwwl5SHZf+9vzUbiRNtQa77rVM6zuB4wPGsr75JPsWDcFL8I0PC4NYN/suw94n054qJCO/HCP/u5h1wJGckTQoj6KKCpYq9yFrZzn9Jl/k98vFlCyt5IfyPQ7d3cGk35IIlv7lWVwf/NIPIhAI6KO9Ar+FEoz/W4mGhw7nkjrpF3sUwdwbqL/vIDnQF8uMcxRmaKMm6k2dxgUcxJqIzOzEJcCI4j2HoKuBspVe+OUbcVC1C99UVZR1xVg7q5UVWVKIy0niM02Fkhvl3DsCNrs6kdovT4qPJO6rNvE6SIcdm/Zy9aocNg5tpJ6po0oqi8+1rzmnEUDCxPFI/7uPn1N/gj8F8lRtNl5P+uM9eBzXq8M5PdqZyeduoyM1ifsH5pL7/iQH8x6Qm1nMrX9z2X68k37VrWw6qY1b9Ob/aEb9t9h8iCgL+JGezjjpFqqENwh2KuVOXBq+dULMq1rosRTBP/wEHfe+YPF7KuJTi5ly8Bf9RBQo7ihB18WWu+9zSV15GkflMsYVhnHH0BbZv3pkl8pTN10On7FFqBz8hNvdel6HpWLamMOs/ZKEv4wif9Vk+ix2x6GiB+PmYmIWLsev/ibOl7sZP2gCL05twHb7ISZIzeZPTzvagwYibVPNXOsqnjxq4uWVF7y3qWVL5kcUrofwUOM0/YZP4+uvmdRavGFvSSqJib9pFh5huMNTPE5+J7JeiJzWNB4+8KPx7TrubKzF0L6Fx7+2sq80k9c2PTg/sGaQ8jTUThiQfO06M1y0yMmbx4/ASLqjqmkSLybsznKWTtEmSdSZnUk9yBvFsbe2hEUZuhxp3cbAoVoIHJupHh/DtteFlBaIcqu/Nnsu3cStYiAdmp6MKxdh/kkXzp2XZHHEKJYcn8+20lVMsB9L+Ke5HBPEUX1pCtsO72Gw/k3u3ZDg7LfHaAZM4NbGWkZMb+S0rgy16w/i9F6D7vy5zJIopWedPDFBS/lzO4AT+4cQdP0cnQIVBA0OKEgdQ+Awjh/ZefRzKmdgzmHOaoXi23KScVr2jDd1Y/bHT6RJqKO3eQKFUgN43GuJqk8Wkmd34Xj/L1PuvuXuj3QMOoey9OZ+8t6kUPjKnH9jpfk6bAomoc48/xxPpsEo6sTLCJzzm/cCASlndjFPQ8A1zbVMXfaZQzbj2Bs6hdDPKgSF2HJekI+/+XpWyX7ifR54jM0kVngZN98WXublEam6ATVbH4yMfUjynYHW/mJE3YYQ7e5EYk0/hH+62D3SnhlBAmZtt8e35BJP/YvI+LCVgs/NTB6+nA7H2biEvETraRR/jUqx8oY+cpsZ3acTqebnLP7wEGPNKJbaCvmmtZ7ySQ95nVBC7L+x3Csdh8hCP87mveL0zWx2yY6mPLkBAqby5PNRlEQFXItbSeObcBSDxiEb0ImV/HjW6LgjryhgvGcUh4Y9pvR9f66N3s/00enMC17K3Ii1vI0sZNUII4Tzn6HvvJZd2wq44JjF8ZRurge3c2bofuzzRFHf8p3CkQJSg1P57juVAZeWMyFsDo4PhTQIhazbIkSYdYNSFXh/fxOiYxx5L3mcV+cFrF3jicusBjynn8d6zFqcF+9lx4YI0g+G49KhxuwVrmzpnUh8SxtudTDUQJphYwYyb9U3qlPlCZiagj7+bBQ7iPCqEWGdCTSYDuBmRTNl5cPQVy9nYclxmtXvENO3Gm2vTLKL7/N2UCnGjtb8UfyMnPMzmr6uY2xYfyY4hlNm3I+hTzvxb4P3XV10x/wkcbwG5f3baPXog9wbJeo1lHHN62VgiDxGzVZIfCgkNmQuD1yt2NtzBh/7VqIkD/Gn9hbuYxzRq3ak1WET78r86EltpP1fErMlcinOLsM3IhKZ+nmEb5jIHHcdAvpG8GL1dh5GbiC/KwRh0RkOOTfTufw0rqrKLC4VEnU+779a1f/Po6hjTfi3QbjbdnI3NhabjkY6HZ+jGVNPQ0IKD763kZR8ny6k8HauonBFLR25cRz9VEOAtBGbV4pgqypHdGwKHgH+aGslkH2iHf3dNqT8q6Wq4j3SrXNR0RuIfnwGU7Z6cKRZG7OvMxH3aKczwIBvUa08fOhGdtkjxo6x5vLqy0TIDqO5pYabt2IZH9IL1o9ZtHIWcXH5dDQNwlK5lszzWxGYTGJhzmaS9Xyxys2lM+ENyTXdqI58x5zyq0yauwZNlzWIeTzHwGoRlj0NxLTpMlpUElV7KTS1h6D/I5tpimaEq6riPdoaKQ1njIxt+Cz5Ef5C1alfeCfcpu2fPDEha2FYX3xf/MBh3HVGr9PBSPkKunIDWFq6nUB9A6ZFPERfeQjtMucYMqqQGIk/NJRV4hRawh5rHzwtPiGVno68zni2fV1D0dpVTJ78jlU1ClxotYL+/7i/vAnnQwu5LivLq3J7zGf1ZcG595jVaLFvRQeCmFhC3+6jojYKLTV4ZujJJSVXRF9Z0NEnnyATTdJPVTFqsjd9amv5alaDunou8obJ9A4ewc4STc7dfMHuj3W0FdXy3siFa+EhZN1ZQ05aGge+LaawxY8nK1biuSac8MsfaOu/FausckYMv4bP6EQC01SZ/tUev7vJiPQ3IfhJITc/POZWzD0u3N7FOkE/hg6bg4nbN7bNcuSiwRNCF/pS1+kEEz0oe/uCjMe9DLZXQqkxmdFWw2is+sczirk5KZig9Xe4fuc8l0O28dFGBMPnk1gYZEExDUifqyDI/ibTXp1DOLwHEa1qYgdFoDT/GR+9J3E48ySJS86hnPQV9RlduOx2wmLkSmQ+XaSgwgkFt0EMKYpngtokJKoUCLITI75pMC9Em7GaoIrZk2BWmYHEyp38uzeBkUOlEG+t5ZOZI9VShtTX/uPKiaWg3MmPG+tZLtyNKooMsOzL4+JP5MU/R+UpfH8axqeabzhlO/CmjzkqORmsMRvBW3VFPHfu59ekT/ivP8TsgFIKclfTDbivm8SHeGteJevh3/iPvG1J5K66wRCH/hxafgUoYNLNFMwDTzJFfCgG0oncrhnK+sdjUHpfSfjbc3icmU7W6VYOP1uMfK4MRRFHaHVRI90ESrpgocIkhstqcMQwFJ/DO5BMkmS/YT79BAfp6P1D77nF1AfLEHnlF9slpFknH82L+HOczNtOc/lJLnqHMylWkfSoEhRlLPgccJdfyWrUm1kiXtZDu6QXLgPUeFfZQUvDD4oVjVAzXE977GbSTwWyo7iThgKIqMrDXtYDKzN1HglFWW6mgalCL+U3c1FYX0NZXBcjZkvR5KCKwd5SXJ01ST95Ci8TbxI/V2NcV4C6aH88TOcgHG5Gza8ydkn9xbpPHr2tNmx3s6WxPAUNlR/YVV2COS78fXKegHEzoNac/GcjeblXBN0LO1hoEMakRj3sVnih/1edObrDORO9+T926X+L8NHVLIVn4zPefmzGoC2Xzw0uRAkd+TlOg1n34th7Yy1bnwsYemI0CcJ87lYF4Xg9gntK0by69ZDM/CQULIJpWz6JSwbDOd+9keDFYFKlRfToMOYPf0LTPQU+jiqjeZIRN/avZERvIM4un8kd18Xttae5sTyS78U1WK86hublg/hO12DJRRe8bwUy31WDnKSFxJWk4iliTMXyhwyUGsCN7Erw6EHkWDF6O18gdWIqCybMZuurZ9z1tMfb/xGSBY28bixErCeTbX2+MKOfD1/OROIpbEOprIl4jZV8kc9kjFYbl4V/8bx1F7sjW3ignIv3m3doiYVh+n4EGbIpbFxnTuKJQ+id9yRaYyrtAxS4cWMWnneuIib+GrVXa5ANmI7CqMV0TR6Fl9x4Nu46zay7sZzWr+Fs1T9eaAvImXeJaea1SE95SqJ6JzP+KmB9/hgyceboChbiFCnA55oCaj8ucW7EBoqqTVEbbMnHL7/xGybPk0VjeLv2DSYNouxe6ciaI438ipXEc6wQd6M7HN19jy/9l6E0dyfp/zIpCI/hos4//ItbcG6VZKORBOc9RrIkYz6+Z1RJ81QmYM4k1loHMWidJw8yUhn5TIXVzsb4jckhNq+Gc80LiUyrY9dQOw6rT2ferKXYnipj9vZwRM0KGVraD++HrTRd7cfMPA/ObQzHR/E2gzZeJ+hENLN3HOaezDju/XrBljdPCVs1Ho2cFF7cv0Je6QiEZ2yxsz7Mo3ghhgV/YZU9Je2JHD0tYJnFfMr3hDBK0ZP35waTPF8P27ClTFt8nqil15ARm0m4yS0u+Nbh01DOxm9H6eqng7n6EFo+BDHB/zlL+m9leKAe33mIvb4hNfdaSLd3Zr7HBCoSuvmpsJPqoIlIankSMGIxx/XmIvbCjPerlhOcOIasyBAcZfuzf+oWhujKU+pyntVuHcS6CkmuvoVvyDDeabWwcfNavJZWcKsqjB053zg28hnDfM/gHqrJ9PkrSCo6yMjVneyeIcH0mbNZELmZJ/fTaHgkjdG6PWS56PHUUJ+v/V6zKMGQ2qZUets6CV82icdHTjBtVhvh5a+Z3djNFaE+E7YNY212JiFH/emr9oXFhctYpLEegfhJxr2BeYGwt/9+4iJbiDpxC+EDIRN9hHisG4ffTTXmz5yK9LSN+MduwfDiWPyneBGo/Yv99+Zy3Bai7y3heaUtYZH7qMrM5UROMPqBx3kiFGJ+XQqXdW7EDjtB2vc9mM+bhJ9bCmYmqUSZJ6O80pu82/7kNi7H2FqB4geFnL1viEmTkPgP4DBHCQVJO/q9P49520GG7sjihLUR8lX1JBzSICRdCu1acSpPFXFKNJkqWVnKOwUsv91KaIKAKQIjZvqZ0t60gTm+m5DuVuSIrjNuptPororBoGgT85KEFLVYc3PeSvr9SaKgdgOdho9x6ipm1dRrlIovAfnJ6NkcZcDaZPY/zsLf8DDLb9zkXF0OIVGmfC7+QqnmZI4bPfj/l+n/8H+UprJkPut8wNpwDFNKExHN0OaznSx/MioZvuARygb9WWu8C7E+44mpnswGeVGOm73DsL2MiAmOxNxS4k3sVYbv9SJq9hu0SpxJCzTnt1EMSuXKrOz6zI3Agcx7Us+gEUM467wFk9k3+KCjTEpPX+oKigjJH0FG8kgSbB25/aqeKQc349ehT299M34X9pIV10zgrNc835uNjmMPHl3lrHuYyNGV8ZRr/cDogAYq+fm82/6Ar65WZEjc4WrsX2RfWTO34izTtQvQfVXKT/kf5GaeZ+kQbT4Imlmf/5CK8tWE3fjGU+N6QgziiGt8xe22mYhu28dz/3baV0dSLlKBld070r5VIufeSfsPe+RMSth/fg8jQvR5c/UdI1RFkEnIREXFiYlG7ZS0fuHNzKEkKy1CeOI26zPOsd1kNncbl2E+/AvnlA4ga27DkYMLiPnzm3mGEVzdc5v4z0/ZL+XA6w+/cK09yMeipczrBz+d5BEe3sWm6f05PecP3XvnIL00gBna00gIzyIrfTQf5P4QayZgUtM7NJ+a0vF9PLUPTEirLcBFJo+P3U0clg9gc1U38+q+sGSLKkNeXGO0jCHFJVuYUX6R42IxRKzIZpddPaPMB5FdNJyWYlOK8s+y62cRaCRyVzyI0XbHea/4iwEpmTz3+sbvASKo9ldi6LerLDpjyBPDEi645tB9KA25BBVq9bsQCoWMvHmR3utr8e6vzJADo9FS7kHq3S+sbloTbATjdURY9/AVq2ecQVbzL4tf5LPQNZLFdzVJTr9AodQxbiYu4fjJ21waqsSBm+/xXnOB97kNGPXz5aiIPVIbh1PwOB+xq8toHXWE6BYTDATaHJbJZeCS8dQFZCOWOYWi2z+ZaeGH/dN04nRFqZFQ4MnoY9z70czGA+sZrTSdfxL3qVTRwkXvAyPDh2J+T4Ag3oefw/J4q/qbA6slyNW5ySbrWHZNX4iMjSO/jP/yb3YMiybCzYxwhu4RMiVoNzuCc7l//w7HjoTyaPF54luiKZrkSkxQBorKdzCSOsrz+yAY8AjfhhjWxM9D7P5SBle68XeWB0nikQRpjgXsWDVYkfgHGiSMTUBMNYAInS08VeuDVnMq8t4OiFldpHNtH+xMVIlbv5N1UgJMA0ci8NuGaJYGXwTPqRdPYrdWKnGCWTg8WUCj3gN2+W7kupopnkkjSdkXzWCpcJpCdJlvFY2ZZDW6LbXAVQQajxjtI4+1ZR79/vxA/Wsg5opfaJrcQulPXVrbyhgs9ZB8dXWiuh0x6bzK63ODiGyHo9EhDLPcQo1wG71/xWiq+UlTfSMjG+TZrWzJvvA0mlVVOKatys1nEuSJCZCK6GDOKR2SUoo4tsSDuUZXeG+/jk9f1ThX/o07MndQbpGhS92LIwMSWCecikxpFB1nXqCml8fZrpnobXlA2TI/2g/+fx/rk9wYJrWDabfBa/MYIvWreZSUyPes82y45kxZxkM8G//zPx//LcKHSFsFu9BmkMJAZgZrErB+F5t11bmu0Y+lCyeybexyvj/vz2v3X5RZi9Np5MDUv4F0ff+GsooKivsyUBB+YkhZNgOjWxF7dw3p4HW8Neoit/YSydmHMV0cypsHD9GKAXF5I+64FuGTXEDwrUdMn69MTNFmynxOY7gqkVRRN3x//uLN0g2kLv5HwUZ96kXfk+4u4PG/Zsbvu0prjwllYkbYyf0kKvMO2Ws3MPXhISz357Nf4gRyRTLEHLhA7PEQDFRjGTqqDqfVo4iP8MWp/A51KlOIqJ/LdelGSmfu4o3lY+wuazC96wJHclbi3keRwW6fiJKVZG6zkEESQymKfcQUSwemifdhek4AXqN3E3bUhulj7jPzQyJuLZ+YVTUUv43fubByAQP32mF6ZSLWT96ydFAXtupDiLGD3OjV+Gy0IivLkdfjC3E+boiJnwkpd+8Q9+YRxYmQGbuMTyo+ZL5rp8zDlrMzvdkW6srJ8BB2Ft9kzJnHXNx0m487V3MjcB6J7kY4TtnLSZeR9L8YR03qHcwTHlMedh+x4LsEGB5FzWEfjdJnWf8zArtwKSZIzydQUIjo2mOUpmnT8kcNi281rDrgRWuRDcPfzmXQ+w+IVW/Fc5QFEwpHoVwbwsMxjhi9HsPn16n0H+bG2zWuFPr34jwjno1h0+gRRNDtY0uC0h3WrnLmrfAbCxfpIowq56jbLA6vuIjmzR8URd0gZNttmp7oIAhJ5/eHOTQ7HsXl6hIEgmQeHXmIpiTESH5lxKjrHD9+HXPp3Qzu9qZz+RNsNCrpp9TB8bMj0Jlkz5BV6gyQS6NVKohbtx4TZLwPh3PdxG+9zRQJM37cFTBtwExu732Icqs+mPZnx0xdojcdRUFhM0EW8izNjMEgN5zah97UiBylrNiYl2FhjOopQ0yykJ3Th3ItpS/F58dj1WRMrkESnc5hvLilRMavoTz99RWVf9MQH3QGHXV5rEqTmJweju6rIm5sOYEgcxreDf0wfJ6ES9FagqpgapUzz+w7GSCQZt2OifzN/I3BxQJ0b5mQ/1iZ+uF+lF6GezNnkOoKD5dtwPPUcqT2dPI05zJbkw9zzsmJprACLlw+w13pw9C9krnfXJlzTwHBdHEEo+Tw+yzO+gX3+TYxkNOrRMm4epnpr+FLo4BFu+9Q5OmNsDeXyRvH0hAvxcKwcCLiLdglmkJZ0T8yzmZj2TYIu8ghANzIqyIhdTxxY9YzSrWYoqOx5Ixx49Wy2dzJuczFBUuxKPqE169b7G/4QfOEkSxuv4zaNxW2LdnGOZt8Xs9RRjL+Pia9aRz4c4MxIsfZOHwXc6Ql2aHdy7u4bOYFDWRRaBfp6T14rZpOW9oMCrPuoLipgH8/v3Dy5i4en7RlXlo1vtvPk//mEHYyhqwJjqbSVQTHV6fx7byP/SgR6lZEopTtxrrbnhTECfCZJGBa6HgWn02ls+oKRz+sYODeAWgmP6Zfhy3fjnpT7mmE5GRXiiKS/mtF/T8gQBS8Z1Pf28nAbx7oeA/kwe9INF3qSMpdiv0gNWIf3SUmMZ8eDXG6yx/xoViC34kxLF1khohEF8t99iLRpczc887UJkgi/FNGWaEEJs7DWN11BPEf9ZQ5mNLh1473LyM2n5FkhJUUrvJC/pZ9ZvKjVl7cjUe1Kg3rKhUSV4qwZXQS86P06NqchVR1D4VN+WgEjqOrNIqa5n40MZBNV78jn1eCxL5Ekrv/gWYqmxfsI3nKB9Y+Cub7oF4MRjZQ+bqYt4VT8b++AwPJSXyP+0WGfjMzdOZw+8MDks3q8Y/sxM92Mtu0luAln4BwojhqHmZ8Sn/Oww5NPGvVaHm4hAFnd5Hhf44Lin6c9VlLTMR0bgaf5eSPHnQPn2f/3bdUfEvlonMc2tqeZES9AxVL1k8fy631JxggaCQsroufDTlsqvChqUqZW5HScGAzM6aMQNFtKLpfviDXcJ8n39eTp6dHsVQ5Dvk7MQYe/s3HVTIHy63RFDvY8z6nhWnaVqzXqEJNPJoHUmFI/auiS0qHUoVe5GRlmSdeyJdKLyRHqjLq2gT0zVLQ8R/ACa0/HLXroKW+hRbLk9x+Ck0N3QytrubdPZiZkstrj0jazMrI172HQpAiq1WHo26cwZoj15B0dkA0vp731/fiZS6Ba4UNtt7tJNvcwr3DkQD1QIxPzue470bcnb+y+HA2Wo8qGD81FrHHfVjc8RWF5UIM8vRInyLD28+neeVkw++AxYzsb4poZS1r0rpJWtKL9o8MKirVaX5+n+qPnux6uZH0zw1smOEK5pPRSdzJvYv78A0L5EjSV9ZEDKWtrzuiReX8av8H3jbEymlSeCOYiJ6+xGR948XVBOasD8XKZzItn+yxbrlP8yMQFZOguagXF9r5+TYbA9uN/P43hQunj2DQV50sBU2EBapEBB1k4/Q4lFsV6XJ/T0XZJWSkJxPzro7Q32VIXQnkzM8csrL9QTw28wAAIABJREFUUbZczooth1m88wETjUy5+3wlY/Ra6fmlg9OcyWjpy7LyxFAe1ALcZMCUTMpX/MTDQQxlV1OUxGt4GHCE9fEHaLwGvtpAtCd6z/byzXE057qsWL7lHF+MLWg5WkKumg1547bjUmLHqQEmFMzLZ4mzH43y8jh57EDlnwb/lvsjd+klPr8ryIs/RXveeBz1FdGUbcOoqZuKVjEUrTKpSO+m21OJHVO3M2Hgac4xB2m9H6iaTOZRtRRqg+PRGAGPne/z59UWzJXTaC8toU4ggU3bX9xtCqlQzcZjcitzj6xm28MoQlbvJEN5By4OkUiU/+RZeSqj1IVMfvKa+FWbGJyfT7tuPcOdcnn1QgIzq07m75rKnApjCm7dID9XiZdnnFHdIoW2tRr+rj9RFlTj412GmOUW8huEdKTqMVG9EocOEUaK6yBd+4ymYVfYceEDOlJONDAB96PrsAIwH0ffd28A2HujD/065WnpUkJZ3vQ/dul/i7arbbsP7ZD37EBnviz7Jyxi9igX3o4IJeiWDpFdmSwXHcP3X21I/puJR5Akym0fqHqQS9+e/njsv0mxTDab5h5EVUGU/fcOMShgCCJN0vz5mEfVvLM889zFmsgtbJn/kc0nT7Nm2xaWdM3is3ohLZq7GFlezOz+tvwunI/RvUFkfQ/A7Z89nn6LeG3iheZLFyTyblP7SRRWDeWB5GWOfryPjmwfelu8+XUhE4GpBur5f/h1Qpbf6iUoPldg2+YwrlhP48+ITmYoLiBeMZGGEw8YpNWN9X1xJKyOkGMqidT4S0TPSGfoDXnSx//DzmUYfZqeUz/sM14LGml7uJZMWwva2hV4bGtCfI0vjn39Wb1wPPr9hPSmzqVeQ5yaUhUupDUTohXBvbVJjBW6E3gmEKvAgZhdKsPV0oVlindJKDtPxLUopsqOQycxF9kdjXwLlSHudDgJjSo8PHKRuKdh/PCpQLjhKVM9xpIk/o757tYknLuLWdZ+Gq0z+blajcGdnmg860TqVwdFUpEMl/LB8rkciX3kqXAsIi/Bnrcm+rRbK7CtpplFCmNIE3VEVyoVi1OxOCevo6pwJ8MkTLiblYvT23zOnI+gabYnJ7+GkKVhzoKq77gUFCLsOoJIn0q+jXPj7avNaD0oorYklmmL4xkx+gMzL0yjM/wxCp6m2FWa8rmvFoFBZvj/9cOsshUsjbg2ejRGfjP58ek407z3QHEKi+JmsX9AOfq2ITi3vuOtmBytpX9YNCoK7RFzsFAM4eP6B1wzqMHZ7AfHA25woewyj6T3YfniEuXuU2lTlaDqdDof1ArYo3QMNaejDPMwpW/NUKTv96DfOpwK/+moqYvya+hwou6oU2I7mXtmPizduYsbi61Q0T6JSbYtaV4BdI0oJeVSEEK9EiqD9qPb+5a0c38QkWvEbexIDLrTkat9jUuqFuvQ4WlPMq6138m6n0evmT2NemLU5yvS/8NUpBWG0udBCLXRc8kpdaffW2lCzQ4itegRJocTOZ4nQYfzVMa02/DjnQ3rJZ9z8IU/heu2cP1FBr65vkRIfMdWMx2b6gnIpX5n049jRNtk8fNcJ8cTpzAvVIWtdU10TJzJh5K3NJfDu9hizA4ZUBdZTVDwOLavOcsSZ03sJjzHYENfdPvZYBK3nLLcGzw9vITPJ59x3f4Cd1+6EqzTh7a3rezZPx/Hj1Ox85jLO8uv9CybR+iME0jtXM7VK6LwxxhXpSkUSj7kiq0Wt/ftwC3iMgp/7TgTPpqHSodJr/3NbMEm6j5vxHRxOwUyDaiqDKa4qIPPrkrUL//EsOGhvE2yxtrTjIku0tgYOPFbegtOneN5qtdMxw5xHCybGafmRo++CduujMdteX+mx+czJSAexVwh8wdXExEZQLPRLrp+1XK7wJQKhVZsnGsoTSyktdYAkSXqZMebc3HvC9yXTkd48DR/EyNQMlRE7a+AZZN3sPNBNe+fNDMubDUyhW3IFYbh+K+b0PkHEML/tF39F7Jv98Ed2mEb0d6zhx4xM1J6lbC3Pol/XjgWYmbM6Oik20eCkcI+dGS/ISFmIBNFvjLsYBIJr88y3244Z7PDuHr2Ok7yJnSXeRFhtIIk6YNseP6YQe6DMBFpwsNlGRteDmGn4A7Rchdp6SxFdo0qvTME/Pzxih9rHGiR+4X5iHpeOSgzQCCHZEclDX812akcgqvFMWQrf5HcW8sGuVHIOV1EZ9QcbAoMoAuq7Ku4P+c1p+cs5OUlexaZdTBO6i83Ar2RKFJiYlsjnx3eMNXkEutUtvGgKZk3l/VJmziQg7sDkH66AeXdEehmTaP3UyRWW3MwvQzDju+n9PUlBK6qdLt9xSMzn02BszG08Gdg5RP0QnR4e/gr1XUdZL/Jpo+pCsvnWKBv9orcRAWiFJXpXreawaIu9ATroJZbzBDlE1wMSiDt7B923bREfuZzRlfnUyurgnKIAVqf1DE7W8Q1vzScVeeQY9XN3O+/WST3AOuOVaySs2V4YhJzZ+3ilWQRzYrNrP8oR87bDNoXTUdNRYkP3k3Etf4h/k07+0TjGNAjy1sxdxbKa/CvaCkbX0XjFqhO5zp/0oWT6KNswcksaY5uq6BwRDQ+K+2pVq0iNewd19M1WT7aEVfxR1RkHCRGcyReAxahkyBAY8ED6sYsQnijD/dr8xk/MJC5H1Ko3jmMkrqvhJ+6hODwEfLvjWHmg68on/Dn1u8qDt+WJ7mtCn8FSYzFXZlqp8q2tRW8P3OfwUeGMevOH6qrDGg2M+NroAaX/xQTIDECo22WHJ65iIH9rFGZ40Xinniidiwg/nED+p4dJJqNos8HNZ6+T8N4kCjt8R3cTNNEPfkOUQfuYyTah9c/+zDu00hOi+iyYrMrw+6b8u+sF4u05/P8moBmhVAEf22pdE3lw50UGt+m4rF7CLbFU6govEJ95Uj2+Dgy+JklGdFXGe0xiz3v+tM8YBcNL8/jcrcEEX0HoqRKsTZaxIOw/pT92cBO7XnIDvlNW4op773F2DDGBPcpi9khfhwLzzHERPfy6NRJdu7M4F7kcqZGvmeg2lCepG7l09j3bDyyjEb1O3hO3UHenp18ZTONQSG8/3yFwl/pvMr+hmPKCiS0R3I8fAbzNl1liqY/A22VWLdlFDOED5gwKYnb/iCum0fSXRU8ZGTZcSuLLMcAni87S1O2LlIO89GJiyVS15KUX+v4KpDH6s0hTr44h2B0P7xWz+XHYAvEbuawp20CLw01EC9P4/O9mzw4H8KO+Df0z02jRtaAv/9kWKW1h59Z7ThNnsWy438JmHmKoT+a0Q59QVvhRpS0LFmidJ3qv508T77L0lNNHIxxx/eyPD9udWDkPILyCeakfHKnwmMR0b9CKFE5SHfmBQwMXFBWMETHJhjT59m02CxESTGaZ91ySMukMblfEQ39llE9+zt2VxtZ7zEVb7tJlMgZIimTgnuSEYy1R0HLisjMrYi82UlC+30GOJTi2W8zdw5OIym5+v+etivJLhnOL/nIqf7PCX8gxucGBWbuNsZNuQ9jm9RJ1JLF31yco4IXvJ10EWe5CkIft9DctpaPD0r5SS0r1puzYUMRQQeW8fp7EmeHR+HeuZfok2MYePcgZ10+Yb5GHgsdT2InhZL9YQ3W3qtQyC0m3UqBCyWdSKYvRKRvJON2e/I1oIL4zzcxytvC48hKGtr9sB1lhH+VFNPql/HQ/ykNhc5MkVfljEUoS6/2w+r3FrT/XOd0wBPcvcM48ciHPSGbicq3Rn+BCb/1nHB/MgPpvQ5MnhbGl0mLqMw4jOHMn/xo3svIEZYkXT/M2XNprOqO4ZWGAkE7G1HePpKqmi/sjG4mKsIcv7mNnDxox/Ub6shrZrHx/lGi39mSsWAnI2dt4IfXSgKjx+NSd5RjpzbS3/koyUMGUydzkVjRJEy+xTBA0pgjKjH47rIh744VY82t+LJuEeFnTvFptQJ2phcQ2d7G7PdHeeo3h8VmTdRXTCRFvQb7NgN0265w9JcpEY11eB4ejvMUB5bUHsI2ZCWFIxTw8Tblw98YpBccZ3f5G05e7iFn+2lipbsRSx3Nd4OTzCpPY9X0SpoPdaD9SAMvqXE8dvDGzvMAmhcVqdKVQkEyl8sWB9kZvJUAv/UEj5HnolUDfxqGY/pmN3q7EviZ4c3zl09wuzYAy/47+Bd2l5pf+1jXt5S3696RN/kQlwfvw2CpI+YF0J20h7ZhBzAoy2NFTBFRme9Zt06dO/MnEuXmypZPs0gZu5veT/Ow7zCirLUC1flmbKqzZKX3EY5/fMWwl2u4E+xCpfMNrlRFYxgRyz2/7ShZbOKY4WkEi6czzEJAfm484xzNGT7tOHu8QxiroEukjRanSzJ44l2P418BTzL88dxcglCYRoVrBGlzL3DYdTJa6rH08xJF5NxUAqQdMLM1Z3vwRPYnn2LDjCJ+triibjOH5ZW7GJ+4kZAlr0nfKkdCezHRD8eyzu05B1yC2WKaQ5jXFw7lDOXKYyvKRz/ju+1yzhw6xazzUqTXxRD39hQV0dsIObeevqOPs3njdD6+XYav6j6+vnCn5d0zgiQribkVSVheKontDZzO8mDY9GBWq0aitG8ZvXsdUBWMYs68A+y45E51opDbK3MpFQrZcWEn6YcXITHCniylaMYajiLVx4wVykN5vEaJd/KvuW49j+BBsyjNt6Tt8UJ0G4I5LxCgtVeAEaksq1/Bs6DLCCaZ8Mc7mDHpcxEGTWfnnDTqcn2Ya++J4dIcHrUNZuymR7y6s42+8oXY2pzn8OPpeGjY87fcjxzRJUxdIIPXooPUyggxCJvGyS3pTBG5ScN9O5zs+mMsu4mCs57UZVljkGCAzqXB9PVfz9FVE/gw341VY1JZNfYpEaZOrBa9iN74n/xTKac0KJK4E2uZECek2OsjKXmqJF+zZYT2UnbpHSSwfCQaGVmYzVmORXAAa/KLccSaXx2K5M+yYqqdCRH6buy47YzS09kEDzhKtuwkUq420jM1+L9a1f/PIyLdy2Ob0SQ7eZNjV4B3rzIpWYcYpGBPe2M7hn0Gcl7JkqXjKpEZqUtzSTxyc9ZwY8kMmlozmbtqNSfHBlHtKkDWS4neuj+kzlnG6XpvBpoMRF79HsZv+pPRko60kSkNC/LwUjmOosNUPCVqsNODAhN1ZKxmMNDUHIn5D1GIm41N6Qj+eg2iJ+kWs68tIOFOKJLKLxHdepdjxlHk2Xkho7GSpFBRRtW1YJLUB9e5E0hfUca4gDaUSh6zwcCRVdvz0fBV5It5LMYFIYgPAOM/jcwd7onjRFUKB+ihlXAFEn052XUXO3EDlMWq+F79HZUDv2j/Iou2qgFfsk7yav5+7Pq5AJZcm97Jl6eP2ZcqSofSOCZJFnLYx5Pvh2OxGmLIsI1S7HVWwEfpCEqBWvTJ7sLZZTC/Td7wrPsbG1yiuKclQH5+LxZKgZTkDeWhSCz1md/5G5uBQlkrZ3/vY8KomcQ/uYiKkw+ppmo4Vz7l+c9sNBzCOVwehbqCG1L1dUS/OkD73EvIPzyHzf0/BD+Kg385/KGIYCWInNiPhwZHoD9YXWtm4LSTiF4rQWLIcIZOW0GXoJXQxespqNjI7rm6HMn4wrtXsexZ48b2UaUYa1qiKjcDp3RtlFIzEPSHmLKXnH9cQVxKLUOfi1EV8IyihmI6HPvTX3E40oxhxwFH+jksxCFhPLfmj6X51zi0XodTxG2ijbKQGV5A7O9k1KyHopoWQH5NCXnfankTLc9PjR4M1BtYdCYesZ/y9AtuxIGRfCyowmaZO3OV7XAw1qZStJVSd3FKHvVnzxBNFtWlk72jD7uVJhH4Igw+b2XRre0cvBWC6l8ztrsMxnlif+7Zn0IqQ4ltMePwmAe7L42gU1mHqqcvEM2qpC4ljsuhpwhab87fnGziH1yjxlsECRVpENHFucySC2PD2bl+CS251WQ9ikJB+IZ+gzMxHmbCuJ0LkOmoZ1+gIRI6sDDlISo9txCvtcXBZAFmNaWwYizf5D/QFlVL12BxQg/6I9x5g/OCuXxeH8aDKgnKfoshYwwtP3uJs/DAVA3+upgCL3jTIUD5pwob3QcztWcQTZp6fPjnQeikRYwAVNxWEJESzLaXr+hZfZfrY3VoOXIZkUroli4l9NkVlvzVY6xxIA5t8+ijMIjPWWf5VvSS4QIjilRdmGFznyEjXBi47Tx7F+6iStjNPHLg5AK2xFai2PmcFNVFVOt8YPT7OfzdEoydvizLxXQoGPaH6KsNOKlbEXdiOoUJAnwrzlI1XYmmfQ30Vt1BpsGUrA6IcRSSZfiePWatHI1yJvS3KHnrv2DUIUfa1DD6N+ezYoYTyY0r2RaUgJ92J2lGk2jYq0CnSCIHlD4ywUqUdzX/mPK9DMmqOiTUtPhYV4r74YX4XJJhENAa+IjmBePpk9WXwYJB6AIEQvWvcK5nJeHVO4jyDxkkdbwnZe28/9yl/wf8/L+NkZEKU32H8nDjXJT6J5GZ38JYw+3o5ogSKzeQlq4oNDpKyVjQgsHsMSza5cjr05epch7Lv3RvbGWS+V/svWdQVu2z5vt7yDnnnAREyYqAYhZUQAUDBsw5YsYcMCtmRUXMihERFcGEAUEERKIkJYkCSpacnvkw7zkf9tlnz3+q9sw5VXuuqlWr++6u7k/rqlp91V2t7LSIn8OiSV97iW3DR6L0+ALdtS3cmV+DeOwrdj5ciPn9ZtLDBnJLcAub/suJEI7hgPdnJMVnEKVhRodbA15vjVg0IgOjwhzqtSz4nlzMgQW9cN3xkF2+VdhEvCOg+AXi6oks3WLAj+GZHJDSJbv3OpqcMjBe549e+yXEHu1hRJ8zTLD+ibW4K8LGIZSsKqVU3JxMnZucvJKCc3IOhZmhnPt6nslfv9Pp2kTm3b3onjVksnghK0UzeDRzA7sPDyUvMZ7IHVIsNTpIpzAYVXU4k5rGN8l4HE+2MvD5LlTyaqmW/Mp7Jx3cI05SnVvKlFIfzkxxYo9iC97xFbS69ed1jQauwvHYqxji8dkYo91e6K/IJD1DnO4+MxlTGMaNcE8c3bQZnXKJo94+DLAazEelbIbaruO+ww7EqspJyG1ntX0hZT3R9PdpJUp1LWuDhuMQYE1++F0WrZzJfWkBuhYC6v48YLXIbHaH9aPl9wOqn1iTEW6LolgG7lKX6LfrNGF+69n/9hn9Dn9inFc7a8q2Mf1PFeFvGzBTV2eCKSxstMLl9S1G3FnJj9jn2CZeYtcoKUw/CbBau4h612dMuDuWKfeCuCZaim1VJe2bZZCP2cOmbzcpiA3mTXQyFrvEaFBIRN2zB85PRexJHa9nuqNwWpbcJ87g/ptdS1151+1IyYNf2P6SxSq4lXgDfwYO3kWK9zEiRsWxeLUqipsa6J6uS8KZXShUJlGedYKw7UbsqR/Mdz83hKWJOG16w9GTp0iUL6avhgfBggoqK5LZGbsQgcCOV+eP8XkPRDYWkCRxmQneMbyvXceCWWNJUR/JweEVjBUdwOLXsli+dODa1xP0Mh+D0L+drtG9OSbnjeLKUkouC3h5IJjSiN8kyKoyvY8nqhoWjNn+lYDm2zj6LEfexwqL4Xs4mD6CmiVFvA94SVGEBJsbDNn4SIBVbTf1PcMxUU0j/8hKxHpgub8D4rk2XBjWSf/Xr7B02U7QY2VyJJJgeQbumbE8VrhA8qaHFB13oTvlGs5fFjFi23zktAWEDP7I1owsfr3fi1vqbnryRrIAEN24lnTDRqzezCC0x47DzvMoThdiHeSKZzCEjZDGP2cS5cd+MLX+FMenR9NX35B6Zx2iJ9visa2MpmOwXLiHj16J1O67SP2ELZxau5tJ28UpDTXig9ooRh4YzZZXCVyvckdc/Q5DQ6s4ECxFSFcI8bI3mGO7iS+l3khapnHsTB4/XGMR83uDlepV/j5IJ58EGqqLeWOTRPtsX3zSR6HfbcP5Z9bMyq0nScqFp08ns2VGL26870Y4y5TyZ9r0VX5G760t3Ap5zj5jZQKu5XP6Wj2BS6JQqZ2MUtNhztonIFP1icytJ+gfeg0RsQpk8uVwmqPJQX8pvBT7ktdHht61Ff9fU/X/ryEQCKQEAkGyQCDIEAgEOQKBYPc/58YCgeCTQCAoFAgEdwUCgcQ/55L/+N/+iRv9j3q0trax/+AqDBUzEKhr02DyHr1fpkzUVeGnSW/GJH8n8k8x1nO/czdIliEhvajcOQU3Y1+cvAS8vSnk6HN93CsayPpWQ0xLB2+m7EZqw1vqDf8yyLgFkVEFSHyQxbpBhfBN13nkVcyNNU8w9lDiVH4R0r2e0XvbBdrEldi9qpCaMnFSPq0i7Wc2d4xKWTy0hlXHZ9JLtDf+j65y8Uke06o0mepegfVVMZY7PCQrRRmZ90V8udLBlcw1jK+WR+ZGPTsXP2HH6Qc46kkgZr+Ehy8K0c/qy6gX41HplgP/8ayV0+KrngYucckkZHigtiCO38mvSLoxmeKqd3y0NcGkCJYssMb/4BCuRk7mYsR3GkYMwsRlD+Xa0syNHUhm90NGHVxI8LoP/Na4g0B7Jpudg+koW0GIQi6mfh44uX3i2vy/yF0MZESFBeLrT2Mg+5g8u59YrRQl9qsx0inhZKZuQnFqOL+X1HD1cyC5Wo6EKbuyymQ+xS+/kLMIklqbseu24vjeYNTDJ/NTGIT88298uyVH+e8mhjbK85TV/JgsjqKEJMExdpSL3WJa6Rgaz1hRtMiISFVn3A9ux/zLa4Tfn9GWpMDp02mEuUnwefNmTp9YzmixKv7+VSLaLQeSdjCvzYuWQmc2miRya+gFumW1SNJtxqLakAYPXXQvOJFx8RrpNvVM0l3Fhi/RfD6TxHMtCb7cmUXB1Ml8WtGPgVO8KH0xGpHeVQiWbWCu0wuSL30hpWcytacU6SNeiYdJN8skFdE9oYy/ljEOVwTsvbOeKQ4v2BF9hTUns4gU3KNP+g2eWkjwathCon5/Z4WFGp6FN5hnORBhwS3uxRaw/e19gmbvZ+5iNz7JH0Vi3EoMtLwpPTGcG/NOMsPel6LqkejbZ9JgJUBXo5Aisbc0yRuTUNBJYPkTjG79JmijDTbfZ1IyWIPLyZcZpjWB8po9uNnPo1vfihX5I4n8cJyjCS04y7/nWOU7Fo8+j7enOkcaZ1D74QLOEx14lmnPw5PX6DroTqGuA6XjK+n+a8rxH2+R6Ooi6vEN3u2aTPHajxz5WonV9DIaH+hzV7aS20JddMii9somesmuJlPjIhbaBQwrG4SWyUA0BHV0PoaB887x7iyM8nREv0Wfa3Nb2L4yh9QMJfpK5JEdtZ/GV5ocvb2axY45lPQ854ecIevbxtD9pZvqXj1olR8g4qY0U8Wt+fvOmI79arAIajv3cajvHpbXLCMqIZObspfYGRaN5rxL5PtNYkfJQobFm6LkOY2KuioOvrbj7sTpGHnYU2X2HZ8+9kj0d6dU35lsswp2r3xGuf0sqnV3ca2ig549NQQa7uPIdFfcNe7wd2EPG60DiRwrYNfpbyQ5bkImK5vxhqlM93/O9lU38Xz8DOd4cawHNMODFpqdbrBBo5Shj1cx7NEn2mVl+HVzN/0ltekt6gJqf7h94gybbCywNVBiW/xOap930tE3gKKpl2neXPsv8/X/8Ofjfweh//j5mRGTLbkrpoDuizmcfPOFbOULHJ5pzERDATNG+rKtdzIvpfvTnpfJcq+9dOR9ZqLJW9bOfUDMyUH83PaOfXO7cF+4jaysBG6GfqVm+SFe3FHifLoYCr/akb1uSUiPEXkKHkR9CqN4ZyzTHmVwIkcf9bYLyJi6sWnJXU5PCKKp/R11Yk/QUkmj27ycGT+2MT3ZlPu2aVjGp5Ox7g4aZSf4cTwFP8k6lBrPEXz9OD/iDUiI6CHWpAW9juusHL+arkxF7sZ+wHn2X5LdWnhREUblwnv8Epqzxm8zrlvMmXHdgiAxGZZVuHLZYyDyVk/oeJPOxe2hDDmxnspBclyqvob1xHyeHfxAzpFsRqscobD6Hl/G1fM4wBL9/A/IlxugsaGUNz7m5D16x4RZx9m5IxADAzV+GC1FQ/kRiotmsttsN+5fm5mTpEzgYStqe86RMs2V+/svE/f5NAUbHtAVdQ9vnbV82NvBlSdhuH5LpbduJsrTtbl2CczE6ymQWElShh23byXyN1SacQ6r2Wt5G+26ReTfjGJUuT0Xsubw2WIiD2Kc8RsXxcjEaRxxvcfl55F09pmHqPAJiy6NRvriRk7tmM/TiLeItQ4na9Ao4oVS6N44iLBPILtjpBm8sJkbL+aidz8K67QxmHYfJj1+IPcM3jL0rAmKnXoMaLDj+DBt+p2cgNDblvUdStxsvsovyZUY9JZnsWcKLSdeUmYnSnXpQyIip+KXcJ9Vhz9guLmJ5RZu6K+qQrAsjZ1+JZyJWUxL8lT2fXHiz/xoBIKRFD4SMiZkMsPEFDDzUyE0S5FZ63+SnybJxhcjScmQYNgmXyqe7CPDTR7Z3pkMUYmiqGYbgoHGJLbEkrC5kV3CIEKO7sdthQqO8lWkfxVgt9KZwn5O/ClTZOxgA2REjPiyR5VshxjENb+y7sotHl5ZwovyYMLaI6hOukjjZhV0c4JYYVLDz03ROJn1Z7HicAQ7NnPeZTpF99+T5jmXAaqRbJ4bSUjZVyqXL+G58DweR0cjb5uI9FUB6+fN55DjT9TE1HnUsxuNICM+DRDQLebIQv3RBNgXc99KkdEKxgivGaNdegG9HCl2HpxESvoiVjzZyjTZLJQrVhKpthaPKik8RsxE5/F0rseXkfy6k90fxnH58EuEP4SMFC7hZq4haQO9kFu1A4HAhuBMf65dvcP1thOcOhpL+w8rPJdPQWx+JHVL9vHNKASTHUEIztZzqmQBNi8FfFB9hu4UX7othrBZCKOmWxO4ZR2UjaCliJUBAAAgAElEQVRf3EpspP4w88EiFiiFIXlhFRvyCrk88SAhj/thZurPvbLV/H3lT3L6Tp67Cui8P5GjiwsIDfxFwgdxPMeUElq2llWWcVQph5M5fDNb7ffTd8xVXKdHYPBQnAfalVgkluCxZTojLbOYWzcfHaE9WYlp1E5QQbP2BbVHd7IyWomqty+Z0lTKqEk7uLLhAydCw3k6/y8HPq/l0VxLoq+/wXt6K7kLbCj+up+eYfLkjR3/LxP7f1G0A8OFQqEtYAeMFggEzsAh4LhQKOwF1AHz/8mfD9QJhUIz4Pg/ef8hpLQMUBXpRK3Fj+lFVeR5dKGhIsZOSUtMPEWoFkZg6zmO7RUuVLV3kR0TygrHIJxslRCmd7LVKoyKxdsZVVHGr4gypr3w5qX2Anw3B3OzOZMvRU7kXu4mW/YXvlYbUbLNQvlpDxnXb4KJHsZqbjQqTKD52FfK8sI5OX0OJmI56KydioOFJIUDDlLS8IDg0qeEbP/I811hTN7TTNqvSJ6bZ1M39iua5caMGCaHrls18bUqDKg0wXfdWb6M/MkDuRz61iWy//0NSpJr6bp8EIUhK5BW82WefQEL78lx7el3ehkOY5OMGrIKjjSv6eL+FTj8ZyIvzoeRUVjLGtsgUj55k7/REdsnOdxOC2T9fFHSVnoxJmsIp8PGcf9dAosNftHc1Yuldqs5nnOG56eLCbd+wBifIbzRHIZM5ClGfejgbtlExDfJYOMRRssaRbqXmZAWNhjr9VP4nPuYxM9pbE7yYXZeKlXlAp6VeFO6+xhtZzzwXfeIE5/iGTVejCfioSwevZbej+uQuxeNzIITpHeZ8rWwGrcP3bRvisDxwS4sZLs47LqalXE/eVD9Db1ps8lzkaK8u5LlVr8pfvgCOdEJSIn3R26oJsfTzjDw2G0S506jsQEGH2zgfpUXvhbD0A8Xoq5ox7d2S4ITmnj5ag9Gu60YrzWSh1vayPJV5Ny1OI4p9UFv4gr0KlZgK/maQz8DyXb34dC3Km76HiRzbTqn21rxiGsg3TwRqb2jYSI0r9YiQBDAO6EWy/odJ+9RN7+H/UGrvoTEN0KcvnYxWK0RNbu3zFwlR5VOO37zNhKQX0iq8m4+9fuEWi8Fnq2bxeXeqghu9RDQto6IKdacyU7lzZ5lSOhocjh+M3eqY9BojyNqtAxqWgKUOi0pU8ggKh1sRoYi+PqVXmkZuIrvIGCEGqNtjjM3/DnLZRVYd2k3um3W5OgvpiXzBsXPbmK5dg9TpzQyXNmH020ZmC7ayKMhiqxfoUZUWCB232To/8qMtqHeSBepc51QXDcO4aN/MvOKlFnSZI1H/HTkE0VpkXImsC6QmMJ+pJ8+g2ShJLOd6uic087tfmOwt4tij6gCL7pHcWTBc25/VySu04ZLp0Po7gxAXP4gK3cLKQdiKGPK3oP4SpWiO0aJ6Xs3EcAYhoxcwkn9Y3x5Z4zswnv8lj+DzkcfZphHEORXjGDFBmqy6rHx/I6MvxLJadc4/KQ3poXxBIjrM/LXBnzOpzFx0jwQ/8tE+yoGNxcg9caXCWuEnJuhiKW9HG6fA5g3ZQqx5avgxybsVCFGtheNzzdS6LoOuQwB84IWcTjXifVzRbCMHczY1Rc5u+Imq9NLaXPewkjRJ3xRhyPh1jgP30P747Es0u1m2AxFnhxVQiSug4OFh1nlsIdKwUkUTi4mYX4SBWI/OCl5CJmURCZOCcFrSASrJwg5Y2pCd1AFvzbHMn9BDG2r5DAdn0f/7SL8Cg/h/eE1jNHV+5fJ+l9RPv6XE3qdjDqav/TY/OMX/Z0OES1lRXWv8ZTF7aC+KZbqDgP2mB3AfZo4WalKtFQn4zRsPYnipfiWvsTQXRnbJ9Yk7o2kNreT7a5bqRxwiCmprYRanqDwqAW3nr+hs7EO47PnsDYo5UBGKhdPdmKoNoq9OVuYWFJHXWwR4k1r0ZunySLZcaiP2cLj/LEk7tbjxGET+j7XxPSHA7s9LVC3nkzaTDNkGycRIv2LzX9SGDdkHxmK2sT3XYuM5CzOi+mRFBSDSmgJ5T9S+Njwk9dSvxCe66JM1hyZ+Cyqtk+i5lID2hu/cybzD9L9uik8Ph/htyHEHLFnkao89y5ew1HuG5bdxty6Mglj3VWsLLtJ56YaruWXYxAqx2TPCMYFeVJ4aT9rLxzCcGwTbsM38rhzDDI9jUTWLMF87QJu3IohM/MPM4fd45FDErekrmCuE8ovi9c0zWrgk+1BwudOoY+LN5HLF+J7N5OWt018qd6GSltfqtr28MhJgsFHo8kp1kazoIHm1Fhe3Splof1+aiO+MuuqNe/dZ7DiT280k6MxE91LxqjJjBttyF+dUuIzT3BduYNjrVG4+Z1l2V0JjFX8GKb0E8eeLv607mP3l1oK8yrxEVhydtd8jEUsOVSvjGxDX3ILypnZaE7LRWNeTnVmanUdPfK/SZl3k+sdaoTETkG+WwHZ7168yO9GuH0qUolteEVOpfxuCgE/XvFn/h6C3uYTM3Ipi2f5Urz5JIE3PrInqZyW4gMELdiA64gRLMu6zLzYbzQly5K4/xET2zwJyhLQO0LAtvdPGL7rPP2bmrhY8Z2SLB2kRrxjXeA3DLTSmN+3mL+jTAnI/oRm4koGfpiKxJ9gtk2bjvZ4AfNv1LAxI4yxa13J6lmJYI0WgT3g0ieDhuMDaJtgiabneXrd+sJZq06i+oxAzBxepcai4yBBXpMB40svoPRtJSmnshC8z+IJA+lfl8PyV6vZnvgcgzILbvyNQd19IE+ePGPTHAdmmPens7iIwgEveLw+noMnkrkz+Bkyed1cFQxFtBwW19mjVXqXOq1gntTCo6xcHC+tpUvtDMqvlWm7lob2kyYS4grR6hVOVM8T4i4tRij8zNq3pyhSFCE4digL5hxjyZjraJ5qYfaCk1wcCx5Glwlq3sqst+d57FBEp6MylQkwvfoMwgGa5AY9pu2KN4prAigIS0H/zSJOH1vIpShVXoVbMb57A0lOIxCKlnPz4B4WPRPS/Wc/SoLl9Hf5grbMOIp6RmPh+JDUKX8pENoRsK4AtZALKF+Sx71XDB9kO0jw7qBwpzQuCunYhUyCyRvQad4FwCWVVh5vDsZq1xako2o4PViEeVuuEbowmtIvk9kcIiBBbiQtqYP4edmLzQaBnPr5FtuUBnaXP2buzK00zU5FrG4YHcoHsVLXoqntGZMt+5EdvIozh2czaukg3txPY/1wAUKhkFVlx/G0ieVuTD43/HczcUoHnbWP0dacxIhWMY7Vv/uXif2/IoT/HU3/uOL/PEJgOPDgn/NrwIR/7PH/+PwTHyEQCAT/UQ/pHpha24p8hhJ5rxz4YdaGp5soAw1daJMToeZvOS3vK9nz+y0qv/LRcHKgoLcCX1pLiHtZi3xdBlXn1qGvsA1/c1WivsdiYKvE8ZrX3HRfwcfmCCznq5J/eQcH1GrxHHWY4pvfeH36Epd+3qDRLBFJLTfet/zE0y6Qh+0dnJvmQtzuCPp1NWPb8o3fhtexk2lFqCWNt6kzPfe/8FFcnysONRQ8jEXabiQFzQVUlTej0ZVNxwAZHD9Z49EwFYsUOdqd+jHNwIK+e2xwN3BHKuMt9rtiiRkxnoIdljxskQHNFBYrXeCvsixig/1ZfLIv49If4m3bwQ/FVFI/9WKY+UTMlCUhYwh9bIX0ul3CybtqjBjcgnS8KBuuP2LSotUMMreg5+d2ru9Oosl8A6VJaaw4tZWBw/0ZOHkBVloVGDYKqMmOxtlej6dyQcTEh7KtsB9FH5/ReMAEo2RjRtXmYi4xFznFX9jOmkr2g1x+TFKmpTEX+8E+JG3+QWVOM4pVw/nCMyaPGEHWzwssCPVCIO2AUPcz2b9vo3bMiC93qlF1hBrN4Vx+UUbNzwfIHc9nUrUVuU/yGL5jKRlfmzGgnk6zcA4rOWDZPpoI6S6GDjcied5rzMo0SMxP5+eSzyS+aqb8uyVuc0Twt1yE7Qwbsu/KIS3+kTazYN4opeIf/YK/FaU8v/WeqlQ13vhNpyTxPN+7v3I36jY7xk/FZfRvRgbcZYrJdt7uMcR5oCYwDBYUMjw0EBWETFktQcTbXGp3JnNGzpV3qe8pDdiP87YNlPWR52KSGFoTOkkJ16DicxttLnZEljYh0QMYqbFFxZ4Y0RhMCqyJUY3jjk4LNTK6qK/LYff8KiwE22m5/5TqblEEI41IlVZFs1KMiouGvFbO42GPE+a6pah01lAi2sX+qRdIfnySooGVSKv84HOUPHYtw+hdrU7Wu3z+SEqSHmJBTFccr9N/oDNFHyPHWLz6BKB+sZv0wG9oDxjJvWl3ucM2BPJTMfxUwc/ml3xTGs/bfr6ceOnNsBZb9ErLyTJuoOXmPlTdbBEZIORtZQXmS/7ipD+euQedsE54hcBoJh5+fbDwrWPr5FIKB0dCXjLnj81DTh3GdNaw7uRrhnjpgzRMaOuDRYAjdtMbUJoKT2w+scz4NOHmOcSJHkKpz14uKFWzzfcnDsbjMdH04/PtL3hra5Nq3sqjN4NpZT556hKouc6ld4MKlelFCPpDtaYSRrUu5KbtJGSkNJ/z82kcKUq+aRUWAwKIrhbBAlOaAkKZtNCGnopgeiJs0N++mdP7NxP0JY68Ie/oopRFBTtIuJWIyEZNdMR10VSFsuQ4rsef46TEfJ5WaWLS3Mq9yGoSHwxnt4EHUpFqTDPehHTjYxx6NzNEbRZRC48xbvMAfhR1oaz6gZqCYlymLMTFZSlK5qN4XfAM76sfOFQ7lsyun/QSONIj1oJqT9m/zNf/U0sGBQKBDPABWApEA1pCobBLIBC4ALuEQqGHQCB4/o/9USAQiAGVgLrwP2iko20gdLu6CvOPVRhNM+N5fC6hvidQiisjr02EnSeOE/v5PddXTUZsTBY5WhX4HtPm2Y8uZHrJI75Ql8YEaV5MSuLNqCz0lfuj1faTXo5GTN+5iUUzpqJjswShTD6uNh68/XIYl8JWvF3taZQ8T4j1dN4e0MVtQBfqqcV8MWxnjJw7omrFDOiQZcruHHavt0BonYray3FIT4unRC+ejFQzfP8+Il1lFZ0/5Oir5EeLWhhX/t5j9aSVGFz+zL7OXbgFzmHYIT8mnk9m/NZzWEo20nlLEXHlvlRb7mS0Yg8GZbc5L7BCwXUV7xJ68D2aycaXD1ikMp0xt/eRr+RHytcERjk5UP/emYEO2nQ0PyNSQ5ap36ey0tYBY/8LyL+y5sllV/JOhJB8bxnvG0OQLRFS5dZCsUong6v3oqz/kTm7JjPCw5Lf3zq4cieGEegz7uYRljrHYX06lE8n3fjGBRbHXidC5ztR1/So85fH/b0IzW+sKK7NZ23wdhLLxvFZTZNu+ZfoSp/gwPfb7E2zotClCtnbL+jn58q76+r0TRdna7ECdZO+8VRcwAOPX6z8I8utr6Lk6/hhUv2VnvclFHcFojT+Jd6SUmQkpdHRS4pj8qWcHpnKpd2Tsd8wiKsN/SGngGUdrxmg1kzyGlnMVvdBcOYS/f3NKOkfT26UMyr60gjtZHnzIJJQ2YP4ZO1AZF4hq/4OZGBpJ7fNlbjfLECuU4zRchPZd/wRCtrZnPXSJEfQl+GGo3i3NZhsj17YbNanNPwu7wdbIvqxDbNpkjxYIIOjfwfjHPqT3eDHvVwxjjUEoCn6ADmZD4xPGgSST/GN/o7JjkZGC7tI+tOEAAmW2AkY13qNOZxBUn4os1J3MlNSiKBGlJ1B+pSGlHHgxTQUpF6TKZGPRFMct4c851LFG7at9OHMh1psMmIoaX7LiyFz+JOfwprKAPSeHKR+706Obgtg9NT+dDfXctfRgjWNIZQ0ODPY0RipaHEimuJZvv4HdR9W8umJFXLafykWiUTWvhNRMzmaHqeRWmOCpe9mnM20Sbg/jqpvTygaN4XRA4aiWlFCc5UCW7rMWLx5KuosoY/wJDOCgrHfOZVhF04QrHuK39XPOfV4Ke/k+xB/NZehWfPoiLfHKe8i/d3HMEPuLj4Xs1HcfJ7msAgMVN3AbRFuQ+HskUBWulUzrq8LIesWYjPuOh2SCVwcVUPgHFs+dXdhFAqvg9OQsTZBucyUEsF3vle0YOakh8SzQqa6OVJvls2TKAfmdnjiXxSLjuwZYhSdSTZeideGUGYs0GL0dF0+nj6P0OY0WRof+amxGvWzkUwsPUZ/Bxe+G4qTXaBOy9Pn9Nw3oL98KxVFXkTkPKGl9RdJJa8xv56Kn5Q1/TrbUZF/xTaHmZj5BOJumEldtThtSmNZvf8Qot4+zFPzQOrlGcZdaePNpL+4ThiMtsVifklvwmvqFdxWTEE3fDIbhW/ocR/IU+tyekYfZU//P/9nyeB/AIFAIAp8BsyAs8ARIOmfYRgCgUAfiBEKhX0FAkE2MFooFJb/E/sODBAKhdX/puYiYBGAtoGi4zHdBYRskkE9Vpebrhl0Sq5mVtZTfAz82ZN3iLVDNBGIdfP8vTa3gkVYuTybtlHqiP55DtkC4tdNIdxwPFX7XyAtI0JpcgcmexyZNsyVOTMv42eQSGCSOvqtIuwfJCT93QseqMSjoNtFvcYgrj9ppk5uL0fkkhDo9CGl+jVn/bzpNXAe+a23iLx/iM9Pa3luJc35igO8ehmHyC4Xsn6+xiqknokLp/Ki5zapFb3ZNKadj7fyeS87DmFGHO0pg+k1fj4zlx5Ez3MlpYKhZN6dSkqzI7M0OnihNAjb0pf8cj2G7HEXPijZk/vpKLPGWfG3RxK7+sFUm/nQU5JKid5ZTunZ8HZoL05c+suP7J94dIpT1G8VAar5nDhryYgZZ5D49o7PrdZMNrPng70XSbsMOHhPn80BCURfEhB+aiDLT1zj8T1Thm0SkNNHSKiigNagOej53MJibAfHDgtwv51Kzfx0hp9tJkHFj2t7NOl9W8DiN5fJ14mjX8YSNuyv5OA0OdKydjFVU5yHOtW8KpzOpLr+BNsEsWn8eL6MSSVw1jr6Nlxk0NVPHHh8jnV1x3GoDGb2pX5EvnrD3fs2xPzaj/4Hc46bZWLq9pHGeFsqLZUoSUrh98JFmHnkElj4gKMXZiEm2slONXkOi4nRXfuc8gRt9Abo0F0jTeVkJf5kb8Ij3x8Zm1bEVdxQUq0k6YQYZ1Xy+Vl7lLVyC5mm7MC11aWcmeHNryU/eZbUSvywX7hGzmeR/wZCqnxJjRzPld5OBL7ax8pTx9C7LkqS5QCkory4v/Qxhn8SESlwpVyiDds/A/gp7IOceTIZngoc7+/N6fCTlOdnUdU5gCW1b/im34vXY60xuObB7Wh58u/Fsv9kOrOvgK9OLW6rm+htcpaes695OiqUuwNduZs+jiyFPBxyknh/L5rcOw44Cj+hf9ydFvWdaAxeTNqdUpZaevCpTxV/Ogz59b0AOeUa8vJ3slnvPZqFq1j9sp2pMRv54HMC1aH3UD+cT9qscIbuD+BPXF8EFZH05FahFRTGirsz8atNoo/tAt732cnKBH+aAqIpm72IW3PUMYzch4mcJ4r7jtKjNpBPpi6o5TSw+Mo49qRcQzB8KJfL7JjYXkHc7pUIhUJWDPNGa441L1Pu0rshgPPbFrHi9U6GVxdQ2ulJw4RsCPnOn2p5QmJvozIIhpXuRGHSaYYUzsTnbD3td41YNwnmfFpN6EJlFNeU0/jzPQKZNYQLqhBrqGKS0kPG5R5kk/RrIu4XsHNeOv7tYcyo2MHK/jdZLfaKtP37UX7XC6v6fEJFhpEcvR4TKzU+vv7CqKhyFgtLEWYYMGbwHpyWVdJ3pAOyzxfQqvGD7X92cUTWit6pGVzxArHGHVxdY8r6pb0ZNXM2sXHLOXGsL9WlhSSfyqJJtpwpYocISHHjeFkf5tfPR+/bJHyOaeG/KpucjoUE5vkw7WMM758HI+5QQ21gOtueXsS/9Q43J8f/5y0ZFAgEogKBIB34DbwEvgP1QqGw65+UckD3H1sX+AHwT7wBUP13ai4SCASpAoEgtY0GdO83ISU2kJ5rudxWamffPRueehpQ45SHlj0EHd9E6aiftDXX8CVrKLYq2rSZ2dBgoM5Stzj0E2zw8BnIqdxFuNhoMmvOHc6fOsrRhVZMGWjJrUIHFv1qZX9cBnN0vLCxmUBE2FhuiAlZUruOC30P88p4IWJy3ZzqF0dJcj0zXVSJOHELoTAKqyWmWDYsJearOJ1+i3gfPZD+pldJEwmgra4vpb0laP0aS1RSB5FfxTFZ3cTyz31Y0rIB/Zo2HItzeb1gA6Y3rtFn5gV23LDmo52AbqeRjFq9l5oxUTwq1OOJdAbJ4lbkHithddw1klbX8Mj4Hs9EhFhM/ExLjy9Ns3rTX6qeHJNO3C0eIN3nPqdE21n3uTeWQlfOBfQj/1MjDRv7s7vKmoaGdGR6FTJI2xaNvGbe3X1FQ1w+Q4tG8aZPH24NsqM24AeyjQnUPp/B25PePE18SvPOkzzQm0FAhCnD7I/TENxJ/N/XOD0KwXu2Ixb1bby+NhKnNEd8Xw5B960/10JLedn0A50/xqj29SOl8hzSgxVYljyBstspKOhU4jV4L1/i7HlWMgvz3x7M6G6mUTOeuSnT6Dt3MQu6O5E2DkVlrwlKo/+wVNaNzvj5hE0cxLxfkQRIaBMqZo3TJzUa0gXY+gQhYSDHkDWy5A1rQI+lJO+x5MFlDbS0fxEVMJG9BvIknd3Jx9cWCF73Y1NtPqbLD+Hv54/chDXUz23n3ruh+O18yFapYrrMaoj+fZ0n1q3kZFwnspcEsjVfcfR35lDnOjRWOCLcaMaA1AEMUJvJwoZ0GB/FWJnR2IvvR/l9NFc8ZhEcaEnHkgj01Tu4rVyDj6w8Rd6aGBTqU31kF1JrPiO7zhr5Bg1ulsvhIiPgz2kxjE1a+NLlwsQhJaxe8hufOVexPd8XD/VbjInvy16xfhQONadIvJVVciX4G8ig3PWV19X6vJUczYLFLxho/JdSkRy0U29wRSWQbvc6tgecJ3SKPrM2dnPg6mIyfj+n+Npr8jyn4rulDi3dEbTLyvF+7UJMdk3lQttWkrW3MthSjtjaCEbqtKHd0056goBCrymMbQ6i37VGlPzGsVQgiXDnVmYFmTKvsJF9B+aQ2VHDtOUL2evrD0uOIz6nN2dlL/IrpgqRu99JGTAIGtZjGH2BN38qkH1xBNHjW7h2dTW9ZYS8yGpg8xsb0qJ2ErFgFhI5P3D7LUl/yzZUbkD4KrgVk4Xw0RLS7oqRcrEdO1Vj3v+Wxxs7bm+9R8L0LOx7K/LGQBUtw5vECLN51SnBkU0P+c1ERjYL8NjiisBtHILOIszy5uP2NxHVtz70KrpH33Jzfo/dy5Qn49nXJ4a8IzHU3NtP1iNZtsscZGHbQJ6dqqLAbwlfT11hqfUMEkPMUNX3JyVuB+ZLl/HdQo6/KjVMOzCIAGsTxJ0vU3PElew7wVSc98fryk6e1Z7Fe1oiH2cth6b7jF/4iVv3VuC64C+K3a6ovpj0r9D1f2kIhcJuoVBoB+gBTkDvfy/tn/e/p3L8P4ZkQqEwVCgU9hMKhf1U5HVRMbJnkJY68xza2ZQVjcJkc5R/3GbfxQuY9HHl5c9odNq/oSE3mCddDVxWUEPU6C83z+Rww/8E5SXZ5Hse58OGCn6U/2TWQnEOPjiBrd0p+j1p4K3GJQ5pTeaB81ck6rr41vGWyoBFeFWvpyHBhe9jdDCMDcTXppg4dW927RGhKuolV75UcyJwFV3PVNC8dxivAjO6xkQzyiWIqQpFdBqKUm5QgWdRHY8kiphiWE553Uyi7O0IMk0heLg8U7c4oW33kYaccmxGAkJVfC4/xiavPxO9BrFUejzLTofQ88OIvHd+2GgW4swC4j6EU6KSQHH9A/RLTqJWeozEEYNY/a2EoftqMNJ9iY08WNpNR8S8gl+y9zGR12CMdTtlQQ9R6DuNeZXXEUjW0fJsDiayQxCxLODEwbG0NUixtHcJKkdzOOoYS7sEePU4kj/9HYWGFug/389wbXW23b/FSr3ZXIkJoEi+kCV7IdTrImGvLhCbK0JTeyc6HUlYTMhEPrYvQW1duNiZY9WuR9HdYEzmw/G+gQxMGYDnhyAae4dRNmcHtvLO/HpRxaPotUj7OHDiaRDaxXDw81aWzbJDbEQ3bVUZ1Cir0VCsgreDHvcOVyF/cD1lX6vpVRXDX9uZaMTPQ1twCteKcHLtH5Jg94u3Ykp0vX/A6ipbIvNdiCuTYnV7A8NPnWLT84Vo2PnTb/1UfksOILO6jISwVNZOsiI8YC+qi+JR/maJZzUkZZYwb8E4brkBz5Kp+FOJ17V85vWuwVmnh97TJehlNJl5AilsHL+j26JOudRX4o6JYvi2DOnGPNCVx7ryL0KhI+sqSnlYOoHOomdctptI3snVHLqwG4+onWyx6UAXQ2aurEHDeDML1orzY3o2cl8VGRp8nPzqCD7NHcMX8WbWbzhE2YuLKArnUSXUpa66gO/ukfyeaUhtZQwGKqtQLiiirLuZ8t9CBveKIu+VNTGeX1k18SG3sk/iopBGkZUWEvHHKMguIiNzAuJVr3hYIouiqy4vHq1gibs2F/TnE3B9EuP1o9HXGUbe2XkIVy9g8KtyVsps4qfueL56HqNI1YyBO6MZ1RhCdNYMBsx2p637MNVNJkhWu/7f3711VwA+Lm50mMtw4aYs2YJNnM3sxKuwilblyYjkG/LB2gKhRBGTfGWpjQO3rR5MDFzJQw8lVl02Qn01PNWFyz97mDXiIyWFcehk3GL4bANCtAw4mdpDhbkcCqLdDF6vQB1jUOEpKSMHES47m9m2y9maO5hJT18wsngS2jqxCKtNkJPZT499Fr2iquj7eD52ptEYX3Zm6PosRDK08ZV1w7jKj8/lGTiVWpCVvxvtLctoji0hcIQpa5wXsWrjPkY/3Mz3huPsC93AreO36aVsyseMNICUJ9YAACAASURBVJT+DmCAtir1CnZ4mV4kY2Q8j5ZM5krgIcit4ZClOFvvX0VLooOUkfM4RxDzEqz4q9P8L3P1/6zyoQREAjuAK/9mmvRMKBRaCwSCHMDj30yTnIRCYc3/W10zfSVhjNcYEjt90R6ig7t8Bh1i75H0vouLyUB8F/nQOqON1Lm69Kj3QrL2LMMMPSiRqMHctw9pxnfouTiFku536IgnM2bgLC4mHiGr7CvWT4fyu08n60Yqk6StjWOiCCFl12k0u8qyKRrc/BRJdvY9xv6po6vWk8STEdQemo7rDG3KhUNREivCJmIrKQqr6FOWjtT83RwcZEnO1H2EiX9kvo8OEv2taEx35IhmDCMrRlBbLomkcAyFv44jtV6CxCw55jR4YSQ5FrPCOq6cvcgwv4eUNcjTZpyJ3PcY8te2o/OikIldc5H785HUnLlIu6jgYOxFaGweqsYFZF3SxdpHhsbBCbzSCWBo2z1EM9OJ1emPd7sYy3uU+X5Bmi85I/mzzJDt3csJPL+UitijuJzQoiGxixSLHjaGmDFg7GZujH/NlktXsPFwINPuIxLZumz3tCfsQRNuU0zR0xWl5ls3hu/LeCmnT7r8frbUOFD1Vom/+0TRlTJDtSmTXSWV+PY5i7TUXj7FPeKkphE6GXa8tBZj/UADgpzE+BG9jZVGQawPkyJY0oeNqzrIfeTFjCGeyCgq0HX2BZbqfckanYpsohhvg5SQOapH0KuxOPYYs6+0gu7yMJoWilJTdIylGuA1pRntAcn0u7mLu80K/Bq7hbEbI2loeYNnf31UVJVpFlMCM2MshJdI/1pPe7I7T6WeM8qoBE+PcEpbYdflvejoltMp/5L2bhNW9FcjPAGURr7iwIcPnDEdQeubUPb1ZPBm4hBeHk5God824mJfcnf7KZKLznF9cAkq9mORDzyKyNHHRIw+gMb+HrqCHJFeG86Q9FFESOTR2+QPKz4uJqToOqID9BARiHBcMJHx9/YStM+C4HemGOT7Umexh+kaBTx7Jc1+a0++CMTJcx9A1Ho1Nq4ZgOOdTwRO/oP28UFYf5+H2OdSUnvuo7TWgXJBDWX9takstUXkhCyTmjfxJ300V185EOCezLmwbkzvtNJtOw2TgXH86vqMqocjQ9X7MqhQlatXLjDAcQvpFQsYN2ktD3YfRnP6G+qFbxlVKUNcsgw33lag1/sHbrnu3LP3ZLjtDnwGtRGWpYj+CV0ivf+S8nQniMDck+dwqRIh3UWErjdfCJmwiTXHOlAfpE/6+gvsnLKSaXfW8E3iJqZJV8h9mMuED3H0Ox/DHNNuvFdNo3zyNHq9zCCxCCgAKVuQGGVEw/RKVh0WYqSVyyzXVPK+iZBTPZWvecNpcixk2uMa5KZr4O9XSugBQ5zrPxHyWRR1mWTa1c7jMvsxh0Ml0VyvhNZZX5r6fKXwSwSP22X54fSUuz6RmO81ZXhFIMdad5JpMowrO7I5qmjMY7JZ4HiZUuMY9EfNRDS2nh7RYr45VVCvJop6sS3yJuakVO9hsNZyavMyKa3NxkdmAPFtz/GcKcPk+9nY95nLmLwuwhtU8Z4pR/GjbHorjOS+TQljq8XREdzmuoQzprfGExY74P8oH/8iBALBTqAFCOQ/SaU3VZEReju3YLE8gv6qC7iRc5RTKy1oOlVIpqonc3TC8LugQZXONSofaHLa8whxrcHkhwxEcHohbW9L0Xvwk/hyWzbv+cPF3O10GtxHfWgsX4WpOCW/QnawK63Pkshf4o9n+DgmLqtjevk6Jj7UwM3kIPsTBWRuOUjCqx4O2HaQ+c2Z2aK3EPz2Z527O5deOnAq5Qyu+keJaU3n/Y9prJF9ScpEF/xQQzNcmgOrK/Gc74LgYQ8f3+QwXe8Tv4p9sCzR5qfUdl7JjCay8wCHLD7QJRbMq2/t3M7vx/ER/RA6VSP2vgSjfEmOT3zP8FQHhuvbszR5O16zB5N6cznjkk9RvrCerIbZHNQ4ybnTYqiFn8b28XG0lfcgd3ML5w0dqaw/hUNEHr0OJBNV9xgHFQGDTYLwD9Pi3YlLzLt9Hp9jUjTYnCVMUpOnYTlIHi6hdUYnG6O+Mko8ivwBYxhxbRdPX43ji9N5+pvqMDD8Kcf2/OFSTS1xfX7iIdcOH1eQb2WK2KJ+RC+3xa9eC59J1Sx+OYEDEg/ZNu4VX/P78S46glyF9djLnqDFKg2R9Q4IhdfxntXODcXD9E5x58adU+SGLCb+kQdKe8J5lSmFdVo4KjMsuBImweZFvci4poPMlnGolcsh3pzM5Ie+BJscY/vQsVzxO4Tmp3WUBqvR7LyfJdUG6B4o4G6xFbFGyXgInOha0IG5/XBemPoS76fEptMz6PBZSqRdAof97yKaMY88B2nid0zih89pJhzW5NzR5+xU1uDoows4bEhklGY1mRryFO09yXRTe0oE9SjPrCLqjTemRUY8kPVh2bokFtop4PJzPEGH23grNwet1k/sK3zPhAm78am7jXv2Lca2C/hu8gbFV99JjphPaLIZwy2/4aoo4HbQTOwnnWPvmNOIfTajeHUJDyTCWPQghV3L9Xgjnsajvntw7utOeWgRjn9UGXdTyLFerZStNkalW5QC0ZX8uH+Kl7o5PPjozFZ3GY5Mv0jdzdm4vi3ievQRxuzfSKuYMrfLLjLC/xqt4k9QtfbkxpqhrFscQGS4LQLbKjoVP/PneSYqNoVI9HblWkIXl+3dEStuZE7cDUZ7LEYs/wvDvUxQrp2I/9oQtm5wQEtFi1VbXRD2CNk9ANL39WNEzmcsUz6xXPoI+Rfv86loB4fHNxIxRoPv2xaS0LOQFcpRrNjmi1iRHf4K3fgthtaj4Fmng8b0NB7kmGL99hEea1R4dt2SzqVLcTuxhkxxUfrWjMXo2hFuJfgyxdiPgswIsq7vZfvgnVgoGCEcfpJtp1TQX/WaxQxml+oynk/9jLJzJEvsnbi1vDcZ865hKbqFRwfnobFLwIsn/qRK32CHtAl9u7sImDue8PhOzItE+TsoGSslb+KH6aF2qYhbDcXc3G2HYe5KHlQfpazoFxntrZz/lYf5+CVILjiJ8OdnFp2YTa2MF5N1PrBvcTHnHz2m7FMAX5w+k+lvg/SAOQybEMSqVdH/ecrH/wWhUFgPvAWcAaV/CBv++5Tp1z92OaD/D/mLAYrAf3gFXlJJnRtDR/NUq5LwX8cZtLaGIyVHuBj8iHkzhrGhypLj+kmEOPdlg/g7Zs7vR+vKAjRazvE34CLaXtGYqz1i8/cvhITFUZvehz6dJuz23Yj/zcXsf72dMF1fBtod5VFCEptOPuXtelUOJ85nn9FOMubOJfJFBZdTI1ibuIAnAQPR+2/svFVUl2+3qH396A4J6ZKSFhURUQwwsFBRMf4WBtjdgYnYHRgoiomKgSgiIKKCiIR0SjdIp/AdrPc7XfvdB2uvPcba19Hz3M8zxn12zTHvOefdVcYI6wGoHg/BZNAGlvpnMqvJk6qUR8zy0SXDUZfiTf9wXVyG7SEhJEw8j3rEJSjeSEyuK5GbbvBqUgJKr/cxp7qCyL/KfGkYRNbLQOa86qbQbQDS3tIET3JH5XE3YpuWYKOeh26iG/GFC5By+Uux+FNSyp8yxtEbe61aVh+rplYxGBmzP+jdfIeymQaT5QYSox/Ld9PDeLtbslrMmw+JhhTUO7Amzp0Rt77jfX8qcXSzw/8cK8ZOY7TdBhJ2X2WCcSbFXwez93wVV8b6oZudxZJ7s5ngHUrWzxpC7uTQ4dVF8uWLPDfaxL6jczg6tgDxpdvpCqrkz+/XHIhRZ7G5PgqlhxEplSO0YiEfZyize+VevpmGsaTuOT0tf5AzzyX0yFN++dWwT3Uufu9ucCxKlCFtHrwJiKZHfiptdQb8jPGkz0iTCjdthOuaeeyYyM2p95jgcRrxAeNIaZPgvfhBJgr2Ijh8HIHac8SVtjPs52tuxw2hdUcJRquHMMRSgGTTDBY1faW6zYDNv6VIOq9B87BJOEzw4GzLchzv/cPI9hNMdlxJ4NFNHN5ux7xOZVT2v2SohICVEXW8tLRHWE2Pg+H3Gf2hjugGe4zmRtGmUcTuKyfx/byQUhM51JPVkdqSToi7N48iBBgts8d4cCOjK4KxeLaCvj/1lDTWovBCjWk+Qzi1XZryl6+JjfzE84HlrD6yidj8bfTuvMzCgCwidu3HcdlZMm+X8GZCEu+S9/NFZRNaK/0JU31M484+yuc+Y/G4a5zbnkj4wVZqr05jmf4gFtv/w425d7AWHcXmLa44BB1E2t+ZwYk6tAaNxlDXHJcV21j26Rllii2ccdzDtmIZrl72xVtiCxNujCBa9SA1q4OJeaaGcWgBqeU/mddvBzMidiBln4IYtzi40JXVjQtZ+Ocmze3yZJZtxXy1D0pXtTnjp0tfwXfume7g4TpvHh9ZRfrGC1zzXI+9ow4a9rcoMfbCem8VW4ccZHGzDUnBr1hxN5OggCbKLRLYaypA2+EMz64MonpMHmIxlawcAVdPQebytfzUW4SbSS6j2mW4dqiUw4EuPHj1ifD3CVQcPc0oh8982vSL95nv2X8knkb1OKQ/l9EzfxZL1uxmn+Aa34P7MEh5zZfxG+gOukn1klE8NnBmgu5bVuSvxetkAUm/D+P1jzBnwy0YohRHSpQP8dOGsdZ7B+lz6pCwWM/5mnj6nkaQtjKQ0Am9DFCPRqk6CjfBB3RNj+ErvpEE57EYV3YT+FeYCz0TGf19Nmf09rDijizquoWM7kmnKLaEFIEQqVGBmH4XorrBmRFFM2mq1sZmVMt/ptH/8QgEApV/HZAhEAgkAWcgE4gC/v+y0WLg5b+eX/3rnX99j/zPEg8AgZIyQlY9PNUJwdDakvJIGUYeLOX876uEf/SlPWcXleIDmWUyiKnOc3nx9jlmjRLkhV5ktPl4qj6fYM/jSM5ILubDjRtMmn+RWrXF/In9xf5MU4ommhB46hYqg6aR3TuO3tZNRL7tR1xoAGpXjqP98BHqU2zZab+GzQ/fo9czARXNB1zo0OSe6lFm7vvIh+DhbFikRIHMUSSM9uJZIsOc2f4oizvgd8cKibftdHRrMn6EFDELVlIvF8GsNgkiRjbRMGAKIpICbLXHYWjexwLr42QUTcJyqx63I0JIasrn9vqrmKrdIaO3DZsRNnQo2ZBedZMlX1czrPMNLXLldG6ZSLu4K8M0Ndh5NwYLv1moiMkjZtNBUN4GehxvMrzcD/XDbohLBaH4bCCzQ9tZc/ge45ZApaYR3YYKKGlUcmeOMvK6KRwbPIrHwfv4bBLJENtG3HSUqEmLx+lHItZ2gxk4so9t25R58FALs0f2/F0E3zYvoT2mgvP533hR1Mie0MekbU/FzcqM3PwG0A2gvduWKDkFoqPr8VZrZM3TGrqj/yB9yxunbbb0nSlBoO+H46DhLP+cy/grY3nluYNhdkWscP9BnWA3hbNHM05lEQH6w3mwth/tH4ajeFeKzQdjSHDUoycQMpV34TJqHjOvrWf48hm0975A7sMPZhkP4s7H/hgPW8/fjCeUVncTTiuWg2Qp1Q5jTHYPvxvl+dqlQdiqL1hOL2KI6XFuJTxnvqY2Peum4udsjpKUOGEunqSn6zJm2gykDe8wqXwE0yWtcJTWJrruK8/Us+mq0eQ+zWDcTJJ2f3qCT4PJYbSOyRLfpI707xwqa6WwrB1FxOvjfPteRWI6/GNwjNiUWK75mCOxyh5Lu10YycsQMDmALdr2BEYt58/ldk5qbWS9mT3MkGLoqwAW+O5lfuMA3ve1UDJwAa7f/3JYuowDSxOZEDOAWbWDyC6A/oXbiPB8x8iPhizf9JcLb0zYL16JtddBfkhVoxi6HEW1DgKE4tEjFEn7NOruPEU/7DPDuwUUO42FpXeYN2AagaeCmDdNkxo/Sd5F9DDIfyvDT8yjRjSB4K37GCmynfoGUdZ/bmDhyItcOFBN31A3agf/QGGNOgAu9VlceetIaIgkokIXuKm5GJO3ngwzOITyqnwiRNT44ruC7Vte8tznABXS9hiu+EPPtUwMXGDOPWFsTyiyY/5Fsqr6GLfuAu+f5qE/rIHSewZUGNsi1z6c7WaV5C0VoCpjyqbgHKasr8RysCWx735zYkkj40LhiKoe38sz+duhiZF5AWq1iSgMu0G8wli2P7mMvdMc3v74Cv3glrIi7+drIGIVQ8XQRuZ4nES0VhL7MSMZM+YyfnNcKZdqJlXCmnSJWoaZLqI2JgjREAmG6jXjL9qDj3gXFjUb2dOaBxq/2XxpO+YlOzF3CSXjTQ6p7734UwsOsQJ0Gn8h074JZ9EGrNyq/m1f/zu3Xf2XC11SRATVX2F0C+0lQ18GLb0YklNTuPblLLH9TeDmb7TkIlhydCTxCfe4ezmJ3K9DyHWyQ6TzN/UdOtzfnEFeWTa6ju40GPciprCehChXrFplSDgyHTdDMc4/eMjQmxs5ZTGO6bbOfEi2p9/24TRUHuPN278kHpvPfLVB/Jn8EJ8ry1jka461qxwqT81om69N2MxsasuaUGmRIiXrLd4btiGmvgzZqBGU67hy7mI1uwOXsq3RkyQzGd49/8i6MGdMn4ex8/VJEgRSXH9gz6ENB6hqCCYoq5Cxb0cRrHoAnf7jEHWxQ3WaC8GbdvFJazH9tS14mtQPM4tN/OprpjatGrdf3Tz9vhUh7yTW7ntIabsUrTdt2ee5lFnuPuSfe0/ebQFTbmzh8/1FXDNLZ97M1wxPPkaMaRl+BS40X04n54sY+eWF7H0dwrbXdZytm8PHz0O5q+2J6PkB2E6yQLJJlGOSzpx1FWe11DyOf1BAtViHEVYZ9Mp3UFvnzo7iMWz6HoGSqQYDctNom11JavZPnlS6Eie+haISSRr3LGXYnT76N3ex8vBLHNJGQ3p/Frm5U1fVhXb4fooqm7i9swopoVBO3HNCo8SOlqI+Bh0sJVDgzvCJ25h3RpjY3ZpUTRiI915x9owZwPKDCxDaHMHsjEpSntqhmixC87dS3j6xZ9GJKZzuWcLbOy94fWsTtwMe8KlgFErN4kxu1GRqaB5d49dgP2oDXb0+GGgKiH6rzEGvXzRlayHy0YNQP2+8fAppyVOgpnQGxYE/UfAUIkFRn051cTzOTENJOB+FhDY6zBUo8F6Nvos2sSkTsXpuSOOxY5SPO4zmyAJudKvTb5Qsd7uKSRdex9MnMXw6vY1E6Xgm1qUiKxJHyxx5Tg9xwcHjFotPvKCjZTtbdPdw03Avpp8DUG43otV3C7cfqCLUYcqRLTKISA7ge/c/lH/XQ3C3Fr0vp7l1/jyuCb/R+NBO8M3pLB2wFcHnNpZrS1Ejm0Pe0kh27tjE1OZwTrhn8XmzJOtNfrGr/jPfXH5gXJ7Nj/MHqZxnwLFtoRhpjcNlhRuels/pbqvEarEdM3jCiXcnyN+tiVpLEtMXJ7HmThQrowxwfKjO9JjF2Kd3097ng8N+GDFZBsF8RxI2r6a3pxmpe7epTC5iidlUXvh+4atcCwt/7qFW8zqrBZaEhK6j0bGRg6NaMLIWcMtegpGtMHssrHj0grDqZIpthiP8uxO7BZFU9XkRIbmFTeYa/HP9AIEjytHPvYNYTy0FDQYI3uWRUCWJUVkir7sziKz4ib5UPHKSdRywGQaPH9HjeIjVHZUUF7xgoFwHfTp6PMkaxfJGYdyMC0gQE2Bz8Agv+9uzoVWZfsHTeNNbgEeHAQlTJKj+OIejLwUknZ+ESN0OUhJtyPW7xeIH3+nvv5BJC2bhl9CN1059Oqeo0fM1l2mbFXhd8ZVquyKmS7Sjml2I7dZ8BvW9wuDbHUIatDg0LZ684UH/ttj/h6IORAkEglQgAfjQ19f3hv+ofGwWCAR5/EcL8K1//X8LUPrX+mZg5/9qg+qqbi6lP2GShi+9g5SZJ1HC3NarWAy1wedlMht3DWdUXwEDJHPImdqPP4dH8bN/O9bTxanqNcJ7iR+3RWMYdUkepQeKPF2tTX99G/pmLuWL+greuO9CvmASw0wKmGUozTCHBAqln3Cpbjovty9i4pbhzC0y4NO+M0SVf0JdWZEyj5dIfxjDCplzKPT7SdeprewuOYls6RwmtHykUiae8PQHZG67itdAO4p2PGL0nBhuFT9kzfJSwuV6uPS1nazyCRTKZlCe3gc5NRi06tA4biRZf7UI+SqHu5kunlkj2Zh6l93PSjHSycL5qiLWg/xJf3mAuc/aiEh3YoCgiM/vHvG1xIq2d/GE2PRSW6FH677zvCoOpHr8Q+akPcFp/GHEalo5Ed5G5UwZan738oVXCOYIMNRwo2D6O1bPvIvbTA0qx3/DevR2Gv8OY3zfEBYdWYyxjTOfP/URff4jE44lUnjkK6f7f8Tt4Gl85ylTKL0EsW+rSY3z4m9OG5+lnhJtPJWp4tDZo4BHSH8u5V5Hw1aLr0MtOJr3nrgFMjjVOPBEbxtOpySJOevJ8KonHP+axpEjFgSHPicwRJ0Byj/pLuyhLFyHgrhs9r61YNwVBwIuPUZNwY/c9kpaXw8h1mIoHpOqaW9tRXN4N69FnqGyaB5py/OwvTUGZ7sqfpT/pP3ydqZk36O3zQznF5dQl28g0TqbwZrLibx8g76mItI/hRH2J5NmZV8ovcegsftY0jSRGDUlLuqYM6N9OqqzHNh5dxWtZoZM+JrM5MiThH4SkJuVipX8dPQGL+JnejwnO+MITpiPT+R8qoMaODVYl3n67cSkm9FXaM4Aw3Y23DNmvewifrcb80fyNApR/ZhgqcrR5CNckFpM8sHlRMZWkvZGjtFL78Pqh0hVlDAo8SJtkmWsHazHx2PvebjYlvHl29lhJcvTo440Skdh/NmODR6dPJhXRVrGJ75bhTI+uQvr/dd5dqEVp6hW0v95imRnK7oX3MnUbcVS4R3VVXMw8fxCf9trfPr8AUaEUnYvnyjrwbzfGoRcQAHe9wTsmDOCzGpjZjy7yKe9t/nlZE6YyjR0hTuQazJG7lAFTiuLkZtfw6fYdgwHa+G74AD9Wmqwa11LQE8op31NievZww+Ndti+ndt1PjwX2Q1vaol8IESw8w3aBg9ihthBki+J0zJcBcn0Vmq9TFANhzuL9qHaPR6HgbacVBDheG4OLh7tZLd95JjEPqqeHyB3xwl2jwil9ut2nmT04UgbW2ZGMGTVdtZNrqZzWB8b7CxAWZHSrhKEy7qJGfuGkA2zUEv5xJePjRR+Wknwmud0eZWz0P4RSs6KJCXVsfSxP8bKomiFybG3uoIGoddcDXPiS102pt0KiAX/4uiIayyd+ZedKZWkSIPY7z+4q6eR89yEE0uUOWlmxNCOepYJa/JmkD8ptZZoa8lx48lPAkvdECjrIFG1F6VxeRzb95ofOTr/tqz/l21XAoHAiv+4EUSY/0hWnvT19R0SCAQGwCOgH5AELOzr6+sUCAQSwD1gEP9R8fDo6+sr+E/3kNDte7R4NaVDP6BlvpFLFx5QZdNM7sNfbE2O5VzQFtZLVqPb6UqcZArdM4JYcmgVb3JF0FtoRo1iLvuHGrHKPwx3z900CTdRFfUMqYGWSIT/Rvpvf7SCc2h3a+HtWD0kokWQ61am2msjul0wRAwUH31klXYsaiH1LJiowYsbEtzS34DkcR+2bfenW1uVjEcpbBmxkPF6suwrbcO6XZPCfSp4tnWQ9HUobYVx/C1yZviVWYgFxuPWdAiH+AksCvxLyrGzKAt95fLdJRhtteNPWBtOA/MQsrNi3vdeguw7WZX9nns2K7l/fRNLOn/wzfwohkf3EX1dhWdv0gmu3UqNqjFvVa14VDCajzo+SA6ZRllENIo1UTQHHiLv0zE6HXIRrmjBbvQw2OrKQgkZ2vJyGLX1JGX3piEmNAZ5aQ2caxP42TyYuWn6jFWMRd7Dk6FeOkiJ7KDfxBrkdD4y7cpSpJ74EPw5BpXD6nStuIZAcIZ+E1URLKtG6PQWBtpZE5IcwBKXDdRXmRKf0YjREzm0Jn3jt5Ekuwrn0WkI+/pmUfO4D607L3j38wqi377QYy0g7/s9FnjFcGTSKHoC+rAcK8DWfQvNX+KwNJnLjeniVBy7SujqyxzNUWZzmSjapQkc0FAgzewavoNHsKPyJW7idmxqkiVVL5PjNdI4L1qC0pz5jNK8ydD8DcSraJE6eAsyHydh5AayBmAi+o2T50MJa/hG5PtLpEuakfDoIBcLBvBTOoYTrp3Uv5XGd+VleoCyiV/IG5FFx/hm0orKiM06SWraPvQ1lCmL2EiIQxTZ8sPZ+zcGzzMDachsZcPzSuSNKhBTcCVO6Di6rWqoK60nXTiT1uZGmgYIk7bfjq52ULOcjoGiE6abpKjt8+K6YDZnX9/kwbNRlPxVY13gO7SHbmEFJ/nbupH6nRI4iuQR/UWNzRPDEZ/qRgCamCTGMbJ9PMES9txIbKVf6CG2ehzh+76ZdPRNodm0B+U1vShE1TLCwo23wqYMMP+EwsFL/PT8QHR+HN5/ChiY9AAWTifowwlc5gezfOUjnuUqc2dzNJvGRXJw/WwqvWyJGS5KtH8SD8RUGJzwB5FZ5tSrKrHnywNm/FrIqgU63M9rJnBsB4LwOiytjrD0rytzGywRqaxD1fwnd+scWXLkHiNWr8fFqT/KaTX0ufTSerqa5RNVOfdVEqfRFZxPVWJsz3RcNA/RZNxN2NOPLHm0HEFCJnv2htO0bD9iInpsLhxDamoyUoP6eHdDhKkLJAnJ2YVsqy2lDfH8vTiXojudWPWAlFUkD7T+Idx5BZPOHODjws1ctXWnwa0AJf2VvO52piX6Bzc9v3D9QjyLrT8hnfANkeHqqCcepvCQJWM/WRCj/w8vwmcib6nD352feHAtm2u676n1/oboP9vY8Wgrex2VyR+agHhLOz5T93C/YhdjOm9x8PptXt49yoZbw9CX+oeCcF8s9VegV7mQmrX4WAAAIABJREFUbz1qmBj8Yslxx//XdvXfiKzCwL6jOzJplvBnZJkQRVYGZH50wzewmfi+Vxzemoq4TiPT1etpy9DkocAch5qXyFao871jBcP+eYBumRAvm6swcopnrOw3PnmeR2iKFbHCk/jgsJkUr2XUGYXjvqyE5VGN2JmN4KDuR8Ld7uN7PpuRSaM5o7+MXn9dJp59Tp4WmPw8TPE6bb7NzsPTuxlx/bGUHI5kyviB1NWXEnh/GuMNTmBlmkOUdBW6OW9R8FjE4gNFzB76jHbnsTT6q+Jz8iHXbimREurLKcdusi5+wC+2lJ1/FvL0WTSSefaITJPlmHsj0hEqrMt6hqC7F8OLxiQdyWH5HnkiDdbSuy2Pvp9/kcyWxmNTFMeaW+jI38Zp68m82TSV5mFi5Awq4c9uZ2b9U4qXnRx3YtpZ7D4JZvxC40gi5cI3edIxmF16WmhnDOd3szkLK3IpNkggt1KLb55OfLTr45zLT94k+VDW95hF1xsYfeo5SRMSmWVdS/rt12j4uCKVo8CF8DaWHS6ifbYrFYNUuV+wnssJt7kqbsB0f0UGp67ngu0SbOM1uGPhxt7KOJzfHOLqoy6ufTqH9JXNzFPaSO8cTT6+SGJ0zQO6Bs/F3PIsXzLT0O8+Q20/AZNbtnNW+hQu1pZkJhiwR12L2+0RZCmYoGeqg6r4C/IvynIyowftzgTWRm9H5ks1ObEGDL5wj2k3t7NrxT3s3h8n2+ADieapfP+0gNX527kaVM7oDxFsGDeQGV9b+VxcTMCJ6SRO2YeL7w1ORb2jr+Ux7ltvotNlQKHFHxb8OMEf/Wiy/krT25SFXHU0x17doqzwCapD5xDgEUHKmHQUAtr5qFJBsaCXc70SPK6WQnQizC/3oVRrI5o/pvFUZyyjpCX4/tkOI5kOhq86y3Cbn4SMX4fbNwHnm25zaWAWOT/9MI0R8PbDY8JOz6X58WFaDEUYuMuKD79WUya8mV0l8kie7KPbSJ1Xfy0pKliOSowGPoairFOsYaSNJfVqP8iKmodwbRxGWtkMeGpBRYAnTsvN2bcpB5m7v1kwup0bfW3Mn+DEZjMNXj9ei/22Rp5dbELi21iC5CpRLIwn6UYkX35fpGnhHrZPOYVkbBlfd6ri8UCPb2VTQeUQKh3NfBWSIMlNmAMz9tE235FIqwZm9l+Ii3o+bfmmjHbZitV2ASMXezBRqT8pTgrExYug5V+On40TrkqN6DVZMqC4jmoJaYblRtC1WojD22vZ4taA5Fhh2saVUaE9m4SAy3S+UGZzbgw9JndYZviBN7k7CDo0i2R/d5ojO8jVCuaWwJNYDXcCBpZjNt+T13UlGE4Q0HzyMQYr5rJ36CwqXxWT83EKlpf/cmDZQQTWw6jrO8XV6WnIz1VA5MRFuh44kvXkBFds3bDbm42S3UPc/fJZbuiOj+VOTCwK+dI8j2LJKi7MK+BQ6lCWZfcjTOoibzpL8J8bxYPifti9f0mkRTbHTMw4F/OStwV9SPh2MCw5iwse3/6tGPW/NfPxX4W+imLfzfWX8M39TWXRH9LFYXRzF7bzJBkgZ47xpjaKhooRraaAtV4nLsN76b23kQHbPiL3SoR3FU/51mvFvvVuPDgShVa7LcV675GXziSochQKP8cwxiwKcYt8KnpDSKkV0PlnIsU1ZSybPJIla1wBkBmfwZnAeD58aKOxZjLhReH0nVtBMw+4G62Ox2gLnl1MxSu5kJSJcewOv88UgxAU+kehV2fF5RELUXfYwsm7p9Df8YBtO4z5oJzG2X+U8N4Yj1lNEBJBxryMCmd0rw18GY++zlAGj2+l58pXxoz1ZM2ZC0xSvMv+onOMWrWIpe+K+anXjU2HKlH6jXT1eeP1y5t/vm9mtNF8ug2f8NPEC63RcxF58whZsXycEi1J15hET0IVG0e95p6yDbUNITjXRFI/+RFt7W3svO/P9CG7+Lu2iakf0/lx8TuRWtPpJ5TE1wuybNzZx9AddpgomqPOQGYvN0U4xph5B70p/fKMhcnZbD8whvevjjBj9nOu/qplonAsxiqdGIV70CJhyqwht4g99gTt7ef5bvwF5TPT6HduBIeGlWLufhblegsE4lFkfR/ELr9xhDwIZ8/rbXx7HMTd5GoYWMowiwAsm4Opf/ubnBkR/Ep9gNR3U1QiXXl9VBn7Seu4YbSV/BU2vJfS4lSUCJ0vvNjSN4an+zJwND1Le7cIYnGvWCsTxw+PySQMGUPKt1gae4SQGunMq4D3PG+LQGXocoJyq1i8YDll3lGMsRmDkgOU9E8kO+kPpsVGfJb7iV7pMyoOHeB5UzoWnim8tPjJ5CZF9IbIcqj+Iov6bWNGjxBN4saoWhlT8HAVKiPvsvPRUPwtNxJs6svLLzPxNPZly8tZCN9qwGY1bBkzjV9/fiPUZ0/aCmH2Rh6h4+B7Ui9PwLZ+A4YnB/OmJ5yna8YyvmMkFWfdGXlxMFIrLTFx3szy8rWoj3LBtukPhppleJSGoqVljU7ySzYd3Mqj1ZuYcbOPyN8+GMsOxl31E1tMkjnd/JHtk6FT3ZGW8QPJ8Upg+gZnsmqrme9hSc87IY7decCZrwZEXahGuHkNcupCCJfcJEu7m6qoRTiPkyLh7TUk3K+jn3OHtmJNNt6IZjJw9f5yVoXfgxOaVPRXQLJHn/atLynICSLQYwFZ2SKo0Ujz729c2rqOnOsybJCRwGW8GI3ji1BvBWVpO5aTTvy7bfyOvUW2zjtyMkfyTq6b4UWyjGvbw1GHZ3RkalA3wJ0RU7wIDRLw7PAq0jw/8W1jDOpHWvlr/pu8fnbMVTTlwYdcpPRdUP/wBEMheTy/lDBnngEnWlKp6VfIMbcxnCgdx4KMi+z7NIXoqoEUL5HgceRq+n54Iph1HpOxRugN6o9iQwaiVwJQmJHIxVUrCPy4hc4fMYzoZ8j9X/EIu1jSFiuByYahCHU2Yf/Yns9tKQSkncLB7RfG3R54T67irhqI9XPjmPIt7I68oFj+Kza/41iQ94MYGwEbFsT/v+TjvxFTHeO+7F072FSmw+tzvhy9b02gVB4GhesZ/vATrVs6SG2yw9LmCB2LvZHZ0cDBS528MDBghdBT7osO5LX2OM40xbF5XBbdYrko319IWokOzSJvENoxkHW5OmT+SmaXtx6LdpkynBg67mgiIe7JN+/zlGxbiU/PCMIeXuB+djXuW1OwTmhAZ/1Vlmgl4nM6ns3pw5BLT2GF/GbCK9ajrteEvVg+UvHHSXDzZoTRDQ5dCUVO/RuC+6ZIXMvF18sCa4E1fguribnwnPGzbRjuPZULYn6MtnBh3MXHhJk+YbHsZx7WuWH98hlmxp3oXw6iYMgBNkw2Q3O4MS7hD/FxWI32iaUMTkggfOljuq5Ox3CxNSNXjSch9DsHlQ9yWnQ8Cu3LsW6tx77djLtC5aSXBfJaIoTAu/lsMfajQfg8tv5RPOxXR9vOGvzeHGHn/euEDcxD4vAZRO4W8SN9C2UBXVwyGsCtL7a0aXwmRjoTi9pcjkrIsPD+cdQ9z1HS3oC/rDF9Lks5euMipnNvoiLuyyhRBZw7RFkqoU7zsiJGrl1G/C4JAhcIeKuznbuau/gt+YpxRsPpqvnJ7bg7rJntT+Ds7XSe0MAhT5dBo3RY0l5D/rBm+OZE4NHTSCx0Yrf3UAbezWFLqC8bPy/CcUIixtsWkzWvC3mHJiSuPcPoxiBEMq0ZVD6dg8smEHR2N7+PjyKwyJfKH5LEifZn7/4r6J8ZwX6zWF7lrUen5ym35z0i2WsyUouTWTa9kPvK+UTs24FGthaLrUvYl2aKRNspxmi8oYwgrisqIHPJn9DDR0n+O538wjbG7A1grHc3Z8UaUXB/wrs3OzAXc0VU/xgZz8fD+CLqWwJRS/RHSHg7E+aeQqG6kB85A3luehfXIZIo7Z6Hv+wkHrw4hqmsKn0FZghUF5P64jSb7n5h74uXZAkkyXohilmkHYkW02nPS2XC2krCDpuwSqOI1Qly7JgVTvFmOcxePsF/kwgyDyYR9jaDjOJxbJwsz2KjJgqXaYOnEr0fxUmXUaJky3guHBvHulExxIf1J2ffAjYOlGV08wqeL5nJe9ePTPy+i/dpyVxWWY3ChBF0UULvb38SJdfz0LWMVetfMdbMCm/VCEZPmMCgoiL2RA3gaLQtuop7UJ1pxpV8RezMS3iTORiZSdOR/7ULJf0ejud0srVXiefS+kQutUPfKhiVQbf50efDW/cRuFQUs67/L/YJmsg31WLTr0UE4MN7qXb+KXnGrcw1NO0RwmawPl1LNajRDUEQIYrKFDeq+kUiJTaT7Vdu80Q/iXavHeTd/MCV+/dZ6OHMxH8SuP8+kI7+DXwqDCaoSYcFVrJ8E5Mn+no0Y8IeYz/NnDO6++k//wLFKOEasZ/VV15TJHeRkr73mIY8ZFTiJP6pmkPwjXWcr7mIpa+AaNM7KK0QYLU3DY9ea9JsxehIPkeQ5Wh8PFIImtOEiJASZ3YcJDctFoX0Oj4Ni0HRMIcrlsX/VowS9vHx+T+g7P+cTft2+Yw8bkV3/hOOqlqjrJ7JkemWPHoaizfKyCiX0G6SjanoI2rLxLku30nFEGcCgqZTIVRNvW0wYc86qBMV0JJyHzNPadKzU8lVTqZXbQZVmgF8UQ1lsrohQfFveOhnQ2ncUxZL+/E67SA/vmowYIo7Hnq+/Lreh+f3DqZqSaP+sZivcZ5E+3Twu+gBSXU/EA7Ow2maHpnCYxhXrIJcsjhLzj6j3/BxXP6ZSU2aBDWr/zB6axVxao4sUDcm/+hyFuTl0OnlztbjAexd4kWn1BvOuH2hq58tOdHDCDNWQC79Bzry44io386MtxPZXtVGkN5tZH8/oSDoPUUrU2mQXcvv4pN4v9hEdukUdMqtmNPZTcvNP5j32OO9L4mF6+TJ/KKG425bBs6ehbeZGW8W3ubPcCki3JORKk7HWlSZNR9L0T7xCmmhSnLtFTloVoS7khQp+5MRHTSPiAXPmXl0BTv11nE3tIp7u924fyQL6Xk9hJx7yJsPR7FSWkrj+y/I5MxF3qgCqZcXcfK3JL7tNicbB6LnbEzEz6H8uT+a4MkrGBdUyZhiRZKrfZGeHYDjj16s8uQ4O6yEvHglcrxn4u7fx6i5Ykje+APq82j6GE2fYg/ySdMx1T/CqYtqzL49jJbXHTyb+Jqjo3dQd2kMeuOqkWl4y+j9GbgerULj6nVWHY6np+oXadY3uFY7D8HUBMJOJTAovwHDXeYYipXQEx3AlRPttEwx4KrYKtSlrSnOfI+G+XGSJ77gebgEXXWFiE6UJuJ7ME1tA0j4OZPlm9qYf+E652YXIcoPbv4YwBy7a0Q2hNBbbIXamm98PfaIYpeNpCvWMErdhYpnLpRs3YvHHDPC5z1G16CB7uZ6nNb8RWLMLQraNHj1t4HQfYHI2JjTkSvgW3QJUV+zyBtrjIz5L2JbopE2kaT/qkhaenS5OLKXhNYJTBKWpqm8l4Eiknw7MhBPeTWULPLRX/MGvdNxXPm9jZmiQii96aCoVZJrD22x89xD4L3HRITVojXAgbj3OvDXFvcVSQipTSLl1HBiy/ozZcAAXlxqwdjBCVkbHSJCq/gkuo7iylrOjRCnSmcxmY6L0H5QRJv6S4Sq/ZBdLsa2RTlcsZDBKfU9ltnlDNpqwdU1y/E6qcL2FaY8iZxL6qhRiNdooW03HnW1LLa1WtBfQpUlYb1If1zL7esi1EW2YvTjL9rpfvjQhU7vGd6IhlCppo2yqRhRlWHYZXow+kMKGonOCCnrM8DkHnl+G1DaLER5RhSdjT4MbetGX8eBx2fzsNssgV9iKn/PilG4XID+80Aa5sqyW6qSdiVV5kf8JCZaCJWkHAbaGDAr0QMN0wok7c2Jqatig5o+YwvFGfRIHA0jGwYdnUv0zUwSj67hycR4pvcuQe7zG5xXH+XDA38C6r5i+vAnnx/9gPUHqc6uoOePgJubRyD8ZiqTDsYiFTeO78fGUVf4l6WRf1Hd/wCTPmvyyzWoYTOfvt6o8PHx8f/vdvb/VE6dOeYjUScHzT3MMQ/lbvc0lK9aMnaSFtUZ9WQaxNP8R5cxh/ojezcRPfHZWGjFUlrgTOqs9+SGyJPn0MbYZhmcVC150f6Y0B4F9Lrb+Vn1DeN2a5aUa/PEOR3L3AiCjw9GavJU1iU44fc+lScTDWn/voO79+MZ8PoIElPaSPtQi5hFD29SFbg9ALIyLRFuEEPSrwzFLG1KKvKQFXNFIleDkVNU8RnzlprIVg7PkqCtRJb531Yxz7sJveir/Kwu48SiY7TuNmLHFn+kv2oyz38lmoUDGGDlztUXG4mes4wPH22o+eiJpeVpqn95c84gDiv/VOzjRFlrF0altj8+Sh8Q777N9vObiH5Vh31rI3erWugnkOaOTzCuOyW51TeZ3x1KNH27hvuMakR+v6FZPJqICncWr5mDXr0jPe/yOLRiEPf9vFDYc4wq1zmUBF9miNNmVh54hGxjKhOGChP/qoggTQmCx1ryLtmWfrYqmGoOx9GrDREVMzq+9pAsdJjy/gZsV3Jgd/ok8uMLeFLmT29lDd8PTWHQuBYm3rDFsNQBh/XTGHlxI4lP+mGhDXEatfRkROMkV4XSa1Nw+4aFoRmrKiSRXziWtoQeKj5l8yHcD+8sLU6lFrMvtocFzurc8byPWs0L5lS5s0N7Bvb5XZTOO08/rwi8NGdxcncTa6Sv4pWUirp+KRqDnCn8kIaUpjQ9j23Z0uHJOUc3Nv5TzKnVVhhYPGJGVjZqxb8p1fbj1O4ARp0uJ+3ebbo6dHkq3c6CbiUiru7hk+RZBi2wY+EveRRc13DycwMpPdmMSf+AZl0VGpJzYZU0lTZ15N4Nof9wUW4uK2Zv3HRMZOoIFEzBXjSVAXk3+Tv9ArEfEqm8G4nzYk82q8GmLAkmeFlzbPVwXBS2khe4D+OgPVSFT8LZ6i0lyaHolq5C/IsNz0700BZRhEa3GkrPZWmbJERTmyurFxpTm5BG1OxAht/KocvCF4lKT45ZPyX9ow3xydu5ebwBySQLeu/L4BEykkizEIbMy0BtUT+SmiYiMj6B/mk5rB26iEu575EcqkP9t05k+33mzwZXbhcsIitflan3bXixXgKDEyt4UezAZ3UV9o6awaboCoL62zGkRYuNmT8ICQB5OV02X+9h+jYBuk8DkPaeRPW6VqaatrB+21bOpAmxZJwviRpH+BonzFTXPdh1bUF+6WYM80SYmPwYcWMpLOvT0CiVRCZhPtUdJyhmBonz69EQr0DnYxyJM5IZISKg5uctHJze0pLXyNEbBWSby9P70I9ZY2x5XjsSdwUTrlu/x/xKC64TzpPzKx6tCkPsBkayoWY/WY1PCddsQnH4EMKbk7BS8GZjeSP3VYopnTmf72fOIZJgyEwTWR6fb6BDbxSLXh9lZ9dvUhrPIeXizOQh86n/tpsc4+t0B23meLo2x6caY14djLV3CLFxH8noN5iNyhLExbZT1SlJn24rut+lEPrrQszL8H8rRv1vDZz/VyEtKcOzPRYMVllGUsVpVLUX8Ssojc2BCwgUlFOkFEO1tiSZ7U6oOVlw+8sAJG++w/RrICWOnlxN2kP26myyEu4guhJWpsYQMyUOu6Fjkd0xkNvLZ9ORLM689KPsVR7IZrd+6M3bwXq7P8zOPsGVVe1UbThN8tBDGOjY0jZRjFeJVzDYWI1O2wim2SowSMWceOlyhJeHMPjuYjSVMhAykSUu9yNRayt427cCj+GzuKg6i1pjT966zka6N4BXOZrItx7E29CHfs/GMa8mm/VDP3Nc7ym+g49xauZTJjoGcmOwCX5pN+j6e531V8/zy+M5K4IbmVN4HbFqP6RDFvHixyrqGiWgrZnEy6eItBFlYaUBl/UtMHFoI9RMFr2BS5l64h8yBHIkn9nO+qyz7JdqIrl4OR23zzBL3ZuZCudRcHOg7eZ0LioJ6D5wjjf35TD2aGRJTSa9ao7UisoyfUEAe4ZPAPVQnt1yY/CzTQx5k0PzmFJerrXifN4HvDpNef09k+4aC6Y0qRBY9pZnnUmU69VhYi7L8so5NByWxk/0MHf/uc22Ke1EbelC+WY+s6NuEeMQy7s1CXTnvsLd9Qsr3C7h5HGS749e8tfGmLCZcaTWVlDyR4Zdmd/5+uMFRVm6FHam4Cb0hai3BdzusGdb6Fa6qgeS4KWMzTcBPT4LGN7Rx/0pc+CVEsLvgzCbKcon1Tb0f+ohqNmE6toP9B0ayvl5sVhEXKY9XIbI6S/59W0mQTtseTJclZfZIozeWYyLeQaln3ayc4UCyeKfiAsbTOSoVyxxn8z62HVsuajG9KJ5JIxNZG+iPRMafSlTKkXzuA2NW9+Sr3WaHJ2/7Bu2kpIAHR4sf8jJ0qWcVNxJwrjbaMSMo/ifJzSPf83iqP74VG1k/AgVtKZP4EjVWywKRrPV2ZTTXo/Q/rsMp8ZKUpyWUH99HnLXFHC42Ed1TBdLNST49KMAkf6fuKymg4m4AcVr9vDdZCc+5rkk3p9EqmIq3lmFXH0YzJo766nz+Uxj3wHKJ6jgvFePrtVFdMe/IuZlMf1mH6NnoiMVI98gekCLr+2BZJht49EIIabYn2O07FBWZoZy9oklTtlxnPW+yLuiSjZXGDG6/gf+RaakxwhI1nzFssINeI2LoCXIi1P+NkSuHIDTQDNGby6gz92Jb5+t6Tp1kIfvatjWHs+rrU7kV05n88Yaxo4pRaqyi/kq57jZN4wWzQlIeydzXi2FfiUF5M/x5/eBX4TUn0F0fQYN3bqIhF+hunglz3MX05njzS/tMI7qdRAV8wDVTdL063eDiNn1cGQDSkfrsZQagJekAAsFJ14+aiBpgi/9h7xk6fWBbNj6Do/ag7yfMRaNpi70YmoY8CCZsrH3aJxTTNiz6zRedqYnX4G2RhcsXk9kZe8+5inOw/WcHzO2xTI3zpg/47dy1MefikMCYmNcWW1iAEn5rH09k+Srgcges6Frsw8TdMYQoQi/kzXxXPiLdY37qBg89L9b1f/jkREXZ7ZTMY71z2gaEcjSzhSk7scQfiqRFbKNFJvaIazajubQQXRez+Bq7V16pRvp9FVFXNmGcf9MwvWdJlMmDyYoQ41eETmWik+m5M4xdn/6zrnbm/lj8JnKJzl0CNsyeqMhyc8fcvA0dI+XpS3/K6IGJ1EP/sOiuTX01fWhO9OU6jB1DKVk2Ng8kMdDi3HnDQNvZNK7bC3yafXYaUviuN6IVQrtDDE7yJJOMyR2mnLTMI9ewSfqsvZjW7aKw1K9zGg/TozNGy5lbEVtCJwcncMW/z4yaq0I8diEh58bA5xEGbr3ImHffNmocZUDhjPxqBxN1+p8UuYdZZhOF08ir6Ni1U5nfwVCLv2hR1uCWSjwWHEUVXqZVAjpMrCng1FCV9hqN56jT3PR1nZAPO4lRm2TEW0U453rR0ymjWPfqiN4Z3xl86bzJJ4NYMKeDsSSlNi2zxTnfAeEtJQ5u30BDk5rWHpgMSPMn5Mt1sTIEhXejc9H6Fky2f3TMFVajprcGDYE67FyMpRnl/PA0o3JChIMm9NLc8EvOpJO0aygj2u5M4umpTCv5QYThzQjbSvCr9hsFDU+8Xa4CjJmNmTcaaLynQnx3+L41e2CRUktW9pkef1pOrrVeZwMy8WzpxUUN7P1wkqeO8KdTx3s4RWFrudx0D7N9bNrMfAX0FWoQ6aDNrZPLnPndAnZ1Y10l+zGcJg0yyZMZeXsOLY5a3Jk9WceW7hTui6ck8OWUH7jHjIL5iP24BTqjvN5KJ7MTY/VpI09Tx9e6P2woNp4G8lloZyyW0BB+29meT9Dvf94Cs+spk05hrCuX5Sey6D6QQ5hN87TJC+Fye1XpL3qwD69isinYsRMCCckoRrdRSaYrxuJuYQ+mKuy5IQ5iS8msqJmNjEznhMyx496b2cWGtYRXX4B44kaZInGMWVSL30He5msrYZgkAxlMi5ETRqMg4UvMa9r0VMZg06KL5Et0VQXn0XcYx9H8vJIbermue4OqlZl8zoLrHStkXNJ5eA8NQQpO9HWuEVy60b0+2+gQmcY3xOrUZ3RTVGCH6vkk4n/JYRxUC0vZj9DS1WEHSatzGlS5VRhOV5lF/klLknJ5HDMGqdx9484cRL13LbRoVXDGevmLpa6rCbjUCKpYxypPVvPVKWZFFc/YvzDo9hKiHDwyBrGWjohW1PBr4o8BunO4Nz1ocRanKN3tCZbvpqR3jSELPkX2KpUMUd8MEMOuFOnp4d0gSivczKoXufGH/FECqShXrgO1aYWGDOM7rpQdLrNCMmVxFDJh/CKJGwVDRHR0aTg1ifUNF3RSn7N0OmOeFa/p7+RI04WY5GOzsSyRhn7n2UkLsigp2gbYa+TuJ2yjRdvWjBy30CYbBx6DdUIhDZgPkIPX98A/vx4yYXiMFysTTn0OZH4P2IEuWoz3mkk58qy2XG8Hi378aRInyc7tB414SFIbPtIZpoUPefOcrj397/t0v8rKh8Pbj71OXt7EjEfVRnrOIIZ44Qx3jCZQ/O1GDrjCz7dA4h/18SDwE3Y2Auh4J7M6eW2rA1tRlNXFu/XOgSEeuLivxqnFb5UNJaypGkQTsghOHSVuydOMm7QDmrl2wlLGoLW2HFEv0tgYqQ/kxKdGf1gPw3Zyym8bYHVqGruGLXRoDuWjI/vWH7Ug5iILhSeC5CWHMnTdh1OxSlR5HKa8JJSXt5eSI28MQvt9/Pn1XYCZ9wkYtIpOmzGUh8UQpb/PkQknWiQrsb/6AHudu5gz4Q1DFYSAr12FM4PJ82tHsmnUiSnCmMSYs14qalktrxEMtSZIJNbaFdHIZxkxqn0ADRqs7hVeoDyLDOkX3qw3CYZ+SY5KiWek9YeTvAldRT6IrGbsxo91wL2qAa7AAAgAElEQVSO325HSsKOxtVJ7HbZx3v/SzTGatFv+U2WtVYjPNCP8Lhg3nX40RyQjaSMIr/zGvgud5Dg7mcIaY3i9Npwblf4kq0IcyXX83DLc8xveVPRcZdrFXp8OHqWBWIL8a02xlC+hfE7viMRqEXtpSNoab3EqPc8/RzUkZ5ZhpFDJ7Kybfx/7LvnPxcOwLZ9IHtmz8iWyAihoqShvZR2tPfWjtL+tUuTSintgaKkhbIS2XtmZxPh+/wL17vrvj/PffwR5/niOM9dE51QcjlHtVw6l78+It3eFfFSffLfGiDo8GT8oNHoPtZDxUmSum9fkZnpQlfsDZQbC1DT9uC+9E1ChIZzuLUIzXHTMTVRxtjTnweDjuA/azGqU+M47RDAe7uD1J/owFOmCYfo/cy20WdkRh6i+l8pC4vir/sNvCQsEF4bgZNtKfJx6ogFmKNfNYHsoAI6R51E+ftMdrjrU/1Rm28PCxAdGcOa+GqahSvxOh6A+68QlnmNI1/qBblimkjSxJ4pC2hf2M4bBwMW7pfmj9wk3uyLY7nlR1JjytHwOsmee10YrpiEmswmKgvcSdTUZunFbipLBrDbwIAVk/eSpX+W0C092K5pZuOzhRxL+MH75zmsHbOPBOOPfH+uj/4cSb50e1M6eyDpKUV0Lh2LpGgHwz3fYRYkQYW8Io9F9jBbz5T7/svIP/2CdHFRQnIycNNTYXxuOTLhWcw3GIpbmSoGOXkYKbsi9KeZyCcLaTzjTwDLSApTROvNHhr+qPNtx1B+h19hsrg5kdkfcdttS9mz9RiczOHV7x2E+qzFUjOLvwXquO6zY6BWJ8ecK2gdfxqZ2mTCo5oQrd3FxMNBPJOJQU1NlSERX5g8eAKmjp2YS7Tw+upW9q9/RUJaOskDVLgYtBSn1V0sqDqASNd8hr1Tx6lLjbqs4cT7zsC28Q0iv2o58dYVe9xITDrFb/E3DPkiyUJzc2bItdDaVM6vibd4+iSMl6ahJHuVUHIqlMFihpRPsESmLJ3unyK42nwjvbMKfcVW3sfk88xMi7zb0mgIqxFfpkPXrDqKzZfyud+JiuTvrDfYjVzPD0xbxxMT+5OHFTFMO/sYxd5PnL+ykXtBolS5ijI4I5Xwlx+5/GkzSl4ruRZzh6VLDHin8Zbv/vcQDDjCzuBHxL4LocTlFh1S8xgz0ZyTY/agd8Gfhzl//p/5+F/k0qUTfoN0TZD+Z8QioxLuytkRHpnFs8X1hFc78Pn9L4KMNBEVl8Fh0lV2lcsjLXudpPYfiAVkUZ21k+aIxXxduwO1gXOYZTyC1qoQHEc84fUydXTeNdGSco4RcuNJzdbFQCKOIcZfUPyayLEJ17k0S4GEpExMlwwkvfApv7WHI1SZhIlQPbr3FekULqXGeiQVve4U9aQy+MNc1EUK0fW14m1mAUV7BJTqnGaUnSL2niuIDBRnc14iIV1aNCYMo/aaHMl/d3M7Yya/n5/kdnE06ibSTMpT5twWG1SPvOR26n3mXt9IXM4YRt2P5peJOO1D7PGcV09TsSIxL9/xfU8IS2fN4eO/TH66lLL9rzcm6VGsKkxjYNdtdh/Zw9xZ81kR+JsekWp8Tg0nPriPvNyndLRY03lficGrTOhPLOFYpC+Bxp+Ys12buvOe1PTvIUDIC4esPYQlnkRonAuNQSmkCn1AMfEFVkoHKa0spS/jMiVrFKiJkOb3KE1ad/4he3k9898Loebwm5m1NoTJ/qKneBiyM38SVHWTu4aTCZ+7gW+XJUh6Y8rd0K18+5mEf8odVpSNZYuJErfei3NDTASZ9hQuthuRpuFCgkklbdMfszY4ly/lu+nSOkWcbAp66anUXF/H4tVriAzwZdqSQm7UPuLPmzl8/2LAsBFXGed9n0+vbHBt2454Tzcu8pvZlvWGIyc3I3PtCFl1lmgqzKNQtARds0pybtQzxXw6alOS+Jvai73qTh7p3ELYYhF/m5Px2eeHpp4HC2VtCRwnoL7nLb//e4vDiVu8+hJNTYMhthm2qNVE0S0kT5pNISbHpvJA/weH1Qu59Oo+vvfeo+FgzNC9e+n/lsm0sI3snzcTm6SHaEeF8Ee4huzY18QIbvAirAftNnWS+2VYqHEHNdlHtDpswzi9gBLVWF58HcpoH2me7XDEymcQDVbtlP/QZN3Hi0huyabwVgDlpt2UTy7izEtT3LVrODPxD6IBz1Cp3ovbxERSm5OZLa5F68h+Zh4eSHzuY+7P6yfL5x8MNUPCRAvbNdGUTxNH1SWOMM1BLA+6zffkHzytM+avSgIXtVs41tqC0WdXlr1t5lmwJfJj4gku8ERpTjNTYxrI+vGHPp3v9J1dQ+SbACaK9XLU9C+xkcU0I+DIoTNci82md00CSyeNoOdaCu5jg9nw2o2txktJjEln1ewO+swvIdzYwLHaLLpqJtPXE8pZt+HM03+Lru0dxg+RxUrVDblbQWiuvkZhjizbbwVSXvGanGUTsdb5RVjaRuKuLKAp4iz9SnNJ19rC6tYqXhVEMcJwIGOaP/D6yRfeHZiJZFY9d206yC4ej/ZnaSTlu8mw+4lw4WikRFLRcv6NYd8MHK3CmCPlR1CwLY1OyqywMeTau+sMGruKsBWBDPG0wqS0GcPMUh7/0WeHnyvqxaLoj0jDtbyGkq2prBK851fIT+r+jSCr7Qti1iOQ05/C8dQdPEuv53FV1v895kNCoQKbr36cHu/JcMNFPKmypafAiluvh6I87T80bEvJeD+LhKNPWLImiwWWwxhgN48l6d0Y2QaRaDuGqiQDVjZNxnPSKlLGTkKpLQapvo1c2R+A+6bxtKcux+yDOHl2LkTKNaLi2ErvfgHm1kvZtOoqNZO/sfDca7rKBfj8bmNM+0/G6JmQF5TJ6XoBYV4yiDXd58cWLVrDfUgoF5BQcZj1Z4by6M1YsmPs0Mrdxo0LAswvLabvUTDDp25g9aIWxM8d4J/IJD5sVqd4zSHUDl/i45JHPNKuovWOKMuqLzKxeD6b06/z0ayJkrt7iS3QYP5JfSziJGi2VqPY0YR1f3Xot92Alf1fpo31ZKPmLn7+TKRjnBbhLY1MOqLFhY03WCLnicSzRLJubEGpwJSYA5vpe+LLvHwPJAXW/N3ewWPLZfgcvkHLti+Yjl6DIPAjW4+oofHwG+vM97BoSyDLlUU5Ld7OgsNTmSqRyJH+NN7dCmfGfz955fMe8c4UVnTfYvWxUH4J5SBsuIJtcR9QVH+OiW8w3ZeNKJKvxlI+jgAhH95HnCTqwRAaE6MQ9B6nJ3kE1d/KCT10nA3tY/gh1Md4KxsEA98w90MKN+zc+XE1AqXdXjz9cIefOgqY+q9l9SlnlM97Y5/nilRzJZ7R4mw1KMdB6BmbJj1E6Io/hoslmb9qKkJK/iQ5dfCmtIdOeQWElmsQZnWNF78C8XiyksTHDrTKPqZ8/QnUtH4Sm5uAtog/Xb3PmYU9CkmXWKd4mA6L8UhMNkJa5TZOF6LYX7WbgdIFXFJby/ih8STULmSsRSDRnpP5s0yJUV+yEGtcyaIVvsy6YoXN47d8unQY2T+L+LBlFx8O7GbYiIHUuixhWKIkBsEuHDr/j8Urs5BVno+e6HZWxAzEzvQlM1VFkXlZRH2yASh/QGqDFJfNd/LPXA6No6/J669Ex8ALy/Sz6Al95WhDBJ9PKHKlsYCXJ77Sb1vEs94A1ijk4X7yKHWX/uO1ww72WR9HpECdQ/LtNOhv5nvRAC7/+k29RRHFatdQcxYw+tRrtjWvB6tELhwxo/tOHRMUJ+Kx5yJ3tbxISaxjXakSIqZB1NfH8LF0Dn/9+5gz6D62NdPZdtqYsbJ/6LPvIvK1JgVzKgjrTqRN7D7VTzvYt7uGcx42rFQaS6hpMQFGTqTKHOD9k90Yy1QSGnSAQZ31pOfsJu+6gNqUFVzVC8ZVeA1xr4cxpbUYhx2TUJZ0RX2HgCtCQsToRhFxaip9+bGUVQhjva6L0MIDvFIoYkK5EHulPiHw/Y3FumR6Hy9E1/cwOcWqlOcII/rRmaVWutyUm47lYin6SkTRtxhLUOgAjjX7MSglEiP9ZjSuGmJYEoFBoTCmyeGkNs7AxOsFCd7SvPDL4dH5PdiE7Of0jkk8tD3AZq/LDKvKIMxenfYR0zj+oBe9L0Fo6KmgYTyHNyukuGwjSkoVJK/2xVGkmw/DTvJS2JZlK8KIvaiNho/7/3ZU//+eqrpOtmnk8HfjRUL7y5DVy+Gm3VR09t7lwf1c3t1cjqrkeq41yrE19iJey0fhHb0D5V/xrPHoZNe6LM4xm3sxC9il/pvfYXfxsArntVYU2496kHfuLJXtY/ivTBz9MQWMFnnPKBMpzp/6w8n3a3n+QglXA1N64iVQVrOlTrKQvsZWTAdMZO1PMVSX9WBX855J+9Up+6OH0ucI/qbtJ0t0DFI9JjjprmO2dxYj9A+SrPaISrF6Uo7oopRchIRlN9/PXiWsYh4qcRZsmH2Ze6sXkx83BEaMxKZuC5tfBhBZ9I9nh/PZs0wEISkBQ41m8PjUTaJzHtMmu4D6t59YMWcBpYXC2Ek5IFa8nJsdO8g3H4mYXDmet4dzW2UF610vUjJemiqSCBBP4suL4zw64ESPgQlegaYod1sQ29vJ6UeBPPnry/olGzlrmcGnlwV4y/tw/7MdzIik6fIJtrt6ES5mgLlHIDJiCsxZsQOzQ9F0V+bS1O+ORnEKDbxko60ci59Hs/asIz6JC2mb3cSGphp6Uk04sieaecfDGR0UitWuUJzMHjPFQYWtTld4Ufkc/7Yv8HM2smOXI1eQTLukOjffSVGeeAslFzc6X8HUhQ/YnrKSriphQm4c4M/2UsSfDcXupRoptrBgVigiYk7YSBcT6T2f6wXD2X7xA27j1Zg3X5o+3Rb2PHjB7ftHSCs5xYKdb1BTlcPVdzDm2m2M+jsNbXUxSqIE2MkFEjW4Dl/DTiZZlSLSV4D2Kkd8hobxeN18BL9S+TzvNJGf5bHY18ZKbQGlLQ9YMOwcrZr5nBIv4dekZsyaVtHS+ZSQnY9QkzyO7/qtDJ2gSGKpCbPGrmD3cB/i3a15te0jNY7fyM7+zf5FgagaJKGtLMtGMy3ulTZwcPkkeh99RVhNhNS6DWiP6uXzKWta2+sZ8t8SlGZII3UpCh1nDxrvR7Bt8F8aflzDLWUuIrGGmF5UZt6Qx6w20yW0ZwrtyycxOmcbFRXRfJIxo0ViEuoxYlRX9qO98DhC3x0Z5D6QZw/3YGGSyuZ3QZjZTSW7dgd2CfLMPzgRYYsh6M+wRsWqESJ+omywlKSiYNZGWPNONgehInGU/DvRPCvHM3EZKt1cmdmljOj6SoZV9WJ09TIWbY+4fkAZjRff8Lq8hcPuaVhLDOD0yjMMnapBA+DiMpyrbUG4dfsTkvqSoKvq1DWOJFyygWrPr4xXsCFnfAguU8KYm69I75MmZMS0UJUeTmHwQU4smcP3zL9ouzlTldHEk7+vsZLVZNG2CBbfnEJ8TCSl4+OQ/KGG56dWGlUN2dFZhZbHYgwCyhAY2NLhE83VAf8x1DqOeJPRqLQNRNO2gBYlSf7800d+uA5lH+9xtuA/qreM57nVepqzTYi+uZoLq2/jFTyUqpw3yBX1oVZixAE1HVIuRpPfJIHz5lvs1fBi1xYxEg6vZ7nVYjofvuLoGmUq/+iT+TqDOGkVKm7q/4+z9P+Iw7nxEHWB583bKBVsomSZMMVxexh5fDkNlfZMlnZnl8ZRzOSVcFuXT3vBHGymXOHoGj8cqnLQs1SnefxI9jW95IrEcwouRMH0d+i2b2PXDBcefM8m+rcjZ02vc0XtB+0ad9HaIMWMte+YknAa898y7F87m6Sdz4lun4KR+jcSRozHR/4jEff+Mth4HJPVHnPV2Az97a6M8JpHsfNw0rN7GVAchPacEEo3L+GELZiihuKIM6x4m8O/ZY3kGkog/iMNC5E2XDrNWfl0KFGDOxnwHjILNDExWoXzLnsSMpK5Pek+08uPULwwl4rw8/ySHIju1ntMFpVD6ak1eU2LML33gS2TG9BqSObl9wqkR01jRH8t/2Lm4STmS8uRPyh+u8bdB65MFl+CnvsozPRGEdv8lcpzxlw9KYNeUSGWw8NxS6gl/78xyMh6Un/uMns/nufOpacYpstxLyOS9eN0+L51B0Ymy8moz6U0T52nS+MZInODrBX3mOW9kqjCdNbtsed7gg/WzrupTFhJhT9onfrEurmafInPYVjkYxytBVzw7UDmiBb34hVZkT2bNMNoDF2n8u/gTZyDhuJy+zCrbu7h26knzG75yLYT95l0HUZO+kdzSTHDjI1Z8qULr5a/OE5N5W3eZxZKB2LzoI/2JTK4Hh1DV40FxkvaWSA/hjF2s4l/fIVcgRhaJhYMb5AgIzKckxuMOSHyG3XxcUSJFjCqu51vZ+34ZpXI1oARjNEbw65l4/ggZcOarhhEfuugW13Byl1fEEpfRLLJNJ49K+D4W0MK7wt4lNnLHcUs/jOfzpUDHxg0QB71BUakFbRwsOkQ1/XicN89g3HCsEzxJPnTBjOxvRz0mrgce4zVd7fS+/oJO49XsOuqDiMKthBS+4TJm0aywfA/lJQkWLjeiQ0jxvOu+DDiCi64aO/jv+9luIlJUNJbzrRuATmeSqiKqFJT1MLX3Fr2Oinzq0WCY35zuH5uD/Exn/Fu9+CVuwIvE7Ton6jMlPp+dGqGIpV9mRaDfurmN/DxmRoBP5WJ+NNNeX89ozyPElzznYqCNKaPiGHo6k4+3v5GVcl2LH4eZENgLpdCG9DdqcvO1adpnWXHY9vx1PqrolNXxu90Gf7OnY9QdyuyPfJIuAyl++NvUheHETpCiGnvBbzbdogRdUXM9x3BJ72nnBAZiJbtCxYcn8FDly3MTg9G5a81bfYixA8aw4Hllky4txqvoOt8LZTl9IlIQuzu4ettjXTuX5Lj45jzbBo2s7ciGPuHb5rBbH8disR+Q9xqdZDw2Isg5AAGC4/xKqKIYzbNPI0KJF2gyOMAfeZeLqfpwhLcJA9R7adBwc9BbKqHz7ObSUhpY4lsB3cnn2Vp7GvkPAcS8SeCRY7pbJCawfWpp/hjHcv1XFds4xt52ATXOgREX3TGLtMNbfl/CNRzeKCihoqCFnoeAuaJLODt2k1MuvaWtb8jGNctS3bFCr489WJvzj+GjBHhoUo678pP8jbA/P8dzv8XURLSESzeOZCKKcOYHLUa1UmjCIvzw9aqkW0LOvlxdAlZCosR81uAX0EmOaaGDJt9nvEmG/Gp6MX0VycLt9lheryZ/a2NvIqZxe5zm5lReB37NjsS/23k5LAY8iWHcc1vKfuS5Dh0ZTcN3CHUOIEzYZ9pHDYKZxclwovOIO4rwQ/76XgbtCNrtwO/0oMEuLnwLD8VSeNfWN3WoblLkcroFxS/iuf1jP+QnJpD++tbRPx3g5xZYUg/3cSggrP8cLcmpquF+cdMsJ/RTL2HJHdc4WCRH9sMjqLlvZ8zpvd4cG4lA8/+oiXVncLKUH4YZrPCSAiJL0YUOFgTsE+JzserkUxYwOZDJvRcGMoItR0YFU5nxu1AApZIU9G/lab/jGgOdkW8zZbFqXs41HCNLwWR/HRdTE5JOq+WDcRZTIEXnu3MWKJHwaKZXHoijmB6Icy9ydZqH565zOBidRY9PVaEX9iD8IEq7E+WsN0jgmuT/Pm2Yh9mA4cxMOU8y0r1KPLs41lFKaXbJ7LB4hOvY66ze+1P7o0fS8zJagxuHmNo5EjCopqQMByBubEMlUVOqPVdJs8pkrxQeZb5neTcykLyo715HfqAyusuFJZlc7Z7HEISYaTnVdD4zZSKtkhi/+3DvruLq0aP0H+rywqz7VyvDKfd6SPu7TNRHHaD5ntPcMqq4pZsNkYbvjNQdQIPAwOYppdGhYwu1YmB6Cm1UxBYiNmORrQtOnh6+z6bnJ3Ja59MaN8mdg5dxPM/q9ivXoNDymeWX+1Ep0OFtxUruel6hh+3j7IjfCZbMwbRqCug+JsNckFXGPXtCboRf2hetwGNbZtwLdvAFcECXloM5/CKHhwfJ1K9bTFvlVSpiPzLolnv8L14Hf3J9ixXncv5NavQixGQolLMNL19lK8ZSd7ADfQpx/MwZDUPxmynZFgocg0KKP9yQclElJ+Fv1CcPoT7EW4095ny84wRv1LyufPwKVJiqtxUzUUpSg73mZrsyPcgsns120JeMuehGX0ikXzdv4WgUUocaktnoowbwjWvkTBfTkJ/HOM8vsKo3XywCYXxubjVq7E28Cst187x5ek3ppqaIBhsxtmRr/kZ4c80vQfE3lqGsVwrfyeHoPaniwrNfsRSdBE3EeP29u1MzBDQ7mfPJOEZdG3J5kvnQQ7V3uJYuAxrba8yVbOaVbnj6LFfSm7EORRnjCfggYALk1bxXMwQwf1HpG6wgRxDNBxV2SGcSOPw69gIZJkrlEO7wB/tV4Z4vjtNxr0i1gxWoWNqAhdSnNEceZPu85eYvXUOOxqkEfzwYP/dsdg8LkPW5Di/Hy3kq6EYIrGjsF5byLsuEdTuCTNgpSGXnZx472eOTKAPN5fGYuW5G6U/wxg/MgrH6c84tWoiG/wD+P13FGmCL0wUEkJswFBa1jxn6Y8UhCwtcJ9+EkvX6ZTEG+Mil43V4z8sWqSHS0gtqW1t/KwuwltDlBWTdiAlcpmNbaosWrT5/57D+ZHzQX7KujHIipTicPgCxskl1A+LRX3gN47/iGPH6peYVyyn4r8bZPf08WHeCDI/nmbD3nCmDNuK47IEfKfN4F30Ow4oq1Cl4c+H5Md41p1Hp/wYuYWP+TzSCccLm/mdMpdat7Es9J3MUj036oUyaBORInmeDIM2iRNSXIehjxbvSEBktgd21ncI6p9FYUsrovv06Brpi4hCBLuGCvN1agqRPVbcsXbDr0EFuYbBTG0qoXjOJNI/2NNQ8hqB7lj+HPvFy7LpHBnnSL7qIqZt+8KAWctRyu2nObKKLcFfaW1w4M+2Gi6cO0qKyizWOJRS2uhLxa002kec5OIXQ9K8TZF78JliZRGkLERQUV1PfFcpiorhzLdx4EuTBd7yBWTd/sLnvH3kDIiiJPYJW4wtUZ6TwpeKweSJdbHF2oVP7fZYO0tTdeETt8en4+Z0i8yf8RysXUWP3G9ml1mQJn6ZD/PMye8qYqa/HbIdTuyZ38W+1zUIJGLZ5jKUhqBQSka78Pr7E0qMyrBssuX+mAc8/TAHb11TykpnY75ZhPptVpz7VsQ67z4KhyaQOkMDXaM7FF+yRqehg7YrT3AIuIzhtflsH/eUM6FPUB2vi2pxJCfWtfNBeA1XIrxRtDjOv/dReLWP4Pv797xcbskGj4n0jEjn+XlN2vrPYjnemDdxYhQ8zkbyUjXp4fdJrbfkh1gFAXqDaHhnReH7HywSH03eu2/Mi7RD+ZQG3VJzOO64ixnTT9NQd5xWmUikfg5lf7YDJ2pb8I0ZxZJNvYzfo8FHdVXSOkFezwjfAYrcqz3ExcVSZDV+pDxaHVOrUUTtP848O0W+PV7LdxU/bBevw0PSEnmpagynyjF/2lBiN1+k/2kT2/39iRi7juyEFCx2zSc7bi/iKQbYSzzhVaUiL/R9qJLYh2qrLFLVbzj7wR6tcZ0YptWjLFvJrGgxtJX0GBCrwcTENnKqMwlTNcVB3IDk9AAU55SSJJ+G+ecQDuvE4JPxhc8pmRT81kAg84wBZcnkJKoxepQK7/vfsdlBEQm5HH7m99Eh/B4z/SUMe6NG1LoAhtXq0/AxlTvJnVwwPcZH2SA0BVX8MxMw4oMKOcp3EFMupmqIENoTgtBsPYLMPxmKRaSQyf1MjYYZFTfjOP1kDYGjZmFUV8nqdY7kS4myt0sJ/6/Dsaq2JVAwkNXl+2hyNKK+/Crz49+hf/QlV0SaaesyobB4JsedZxHy4QGj/DVR27GZN193cmbtO7YEW7M7ag1W27yxHWJN74MuvMZuI1pJmor8J5SW56HQP4S1W0JZWfqJddv2cdNYHuEHRQT6+fLpZSIld2fT1dGA2cIBXNOWo1tOGInV4nTaGnJn+3LqT18nasVeZi85yo5VNSR7HiRAsARNq38MVJrIrrZZfLpugq3eGBz3RZCk40RUeSKvLD0wFSlkwKs2dp8q5XtcHz/NXYiIzyeoaBoDWoWQyIXq4RpcmKTPjR/axAupMLImhMSU7P83u/pfJND/iN/0cQ1UxaagqhRDlaQw8qHXCG2XQEH8EIsaBGSo1VAr0szjCcPh1VF0nNaxVMmdizWXCAvczLlb39lQAfWm8/j0Jx/pQSr8U4sj55M123fNJGzXOYwcPFCwcePTqzUYe8zhXcFfNAZAnGghFY1iZL9rZIhXHbPuR5DScBcXoXE8bx+Hrnwws3J1+NUZgaT9Va73jUSj+wA93T48/X6Iswk9fN6qypHhI4nTLSDzfQn6ob/QnmhNyn9P+TnzGMkXF5KiuJTVnTto/eyLV5ERQu2JyNwOZ3LrRgqnF/OzWhEpyWrs/gRRP2wTQ6NXojDiHH5PJBg/rR/p5z00azsQ9UsVpZUvqSotwanmF2YKHwjWtaTOwxJlc0smS/QgwV+kbnewedkiHoqqMa9FmRSZMBb57Of+8wx8ojeTcuoF0c4fkIvfR+2NZyjlXWK5ViRRvy7z8Ecnqru28yvzIrJzfCnIS+C24w4kgt/TeLuHY/lTuPDkFme09XHoc+ZFy3bmVC4jVnE7yl6hGCarYzLABwPviaw7E8xB/Z0UFkXxq0YBpVnpjCvp452NAQ2txny9ro9EWhu6BRN5K+OKptVi9FtbaZ39AJVT1pyxfIPWzrfcGa9OWOZHcjwOIb7/Gppyyiza7UZVwwMmummTH2OA6IQ2KvfWceLeGnYd9mWi8xFedSbxMOc5BUcukRf4hiZ5RckpoPoAACAASURBVJxH7yaouZ+HS7XIGZNMb+9yvI1ukKx+jZa2PIQEPhgmfua/M9rsD3xGhHsKPoMG8/PadBoyUrAb3M0f3SkIOrajofGSXXVnKTx5j4f2W3D0M6S/SZxveU1Y6IpjbbmE69W/ODl8NpkDPtIgr0HVx0wMxSRZU5+NmsIhhvelI7F5HNkpDnjd0iDpgivBEuNQS5tA24EA1HdNoLxYguWBOoQs+418XBxmEZ5c09XnQUsrlvrZGIW7ojR9ACHqhURLpSFk5YOSTDtlqVcYLSxGfbkck+Yok9g4irb2aZhZtyKqlImL+mIaJfSxEMrGXbmQ4qYAJAYpkK+cgqbFYiYk+NMU64fsFDFkRQYRcD2eWz+SOXNgMo7bvQm5/R9+NdsI2tNC/4OZGLc9QrimjQ49bf5JnqZF3gRJoW5+C02mo8aWsWMvcWDhZ2YbyCC9SpFcbRDpE+HdZwkch+nxq+Q2airDea/Tgca3TzjrTuFLphQ/lYtZtLyJ/eliHFUwJfPlb4at/Eus1WCkhj/kZPsVQl1cELLJoOTCKi4zjpCTNYw77Etm1lNUpGX5HvebWatUMVfR5E1SHnOWHUREyoyUghR2ujux0LWJmcu/86y4ASczC8x+fafOQBw9WwXk2gfz/ncTV/3v8CtED5N5Svguv8nJuYas6QljwzhfanKT+SkygxUjn1Nc1oGXxUrC4hRQNoxGbcgC1ESuYZDTxevuOpbZLONW+Ggy29zJ1RBHrTWHYQ21fJi4nb+dBcT1ySJp9BGzpFe8+dzwP+qo/yPMh5aiqGDrsjIiFTKQHliJ6wD4FNNEUe97Ro/2R/JPCrWGjwiLXQKbxBE98ZKL7dYoTOkkvqyAy1+fk5XoxRBOIDRiIgd3hVH+Vxjte7c5su0MQrM3c+puBI3xi3EtgO+uRci7/+R0gCrHlw5HKqsCsW4BbcpP0ZZZgkyBHfHDy6iLliXl6D5sSq7RuluPoe3xtOxqInaDMHrlBXisG4ZxsTjH9MBiPgRffs9jn+mEiO9hS9J6en9e4ozBFtTqylldPYGnwn9YcHogn9USSbu9lLl2U7iluAUZtQBat/di/G8sf5RWoaF6jUaDEoZnBSNs1ohY8Uhq246SkBmKtc9HNgXZMT1yKUumaFEi3kvsWAkG1izAW9qE7iGV6OlNpHrtOLrXyTPWJxPTamOanEUIsCnG3XcdC7wuoD/RnAdt39EJSSH7cxXMWQa2D/HIv8pxz7XcdYC0iGNIOXYzOO0DmoOW0Hj3ByYN7ZRfqEIh8wfCNavYfuUcJ/aIMNi9h1mXR9OkVsWXQGlupmagED0Du+KX6Cn/5tadM7z1cSZVyoTA+OVMDtrE5eYglihvw74kjoB/dxErX4Cc8ir+frdngLYbS2Yv457LQ64cfMi8y1pM+HCTJxd3M6RgEjLNQ0i5H8W3Id1c8z3MvKWrkVvmTGt3C8h+AjddbAeMod/4Ls51I0g7/pv4kmw2SO5HRVkCS2dhbrZks2LoCF5KPaCiu4PF6q44K4jz8qoH0l8EfBtdw979AoR/3KBX05WdY5/he/AtH5+msibLlkhmoz13FFsTthI2Q8CczPsIOS1iVPtoxuk+p+hrF4nXG+mN1sLh/SYWXp7GyqMpjJsjh8jOeZhrXmXYaS/GWL1gxw1xYsK/MUV+PCWf4rAf+YqCrQfJOnkEaZsz6OT38/p1Cs5Os+hUTiI78QzhdttweH6WjFmOVHT18p+EMXbuQlx/dB+9CWnYnTrPslW7kXN9gq/lQSR114KLOyw1IfLDBa7u8qJL8jOp3Yew6QHFDk8GtmziWcRtpKtUSFApo0ktjCPdp9m3YCfuMauQ4Rjxpf/hPVMdM9MlRG2+Q2r+VRrw4PC8rWRVBfFs/2FmzXhN8LxQBurUId6/GUWlsxz/+xWRjXHMG/8bIbcsRo7sIumMFA4dm0ls28/CDSXEvKgkU2EUFurBKP0RQkpBg5xzMzEUEWXZiuNUDD+Ex2A/Chc1MFF+EzL1hjgbWfIrIp06sTpSgm6grjaMdVee8OLwD4JPaxCU8QkppcVoTL/I+yep3Lj8hdsvQHNADuFPHtFbpIqeRAet8ztp/ByFlswZ/O3PE2OvQN2xAZTKGyM8dzYNzy8ikDFlTT8EdK+g5xIYBdSj2bMBnUYXyrqSEDLSIHWsKOblEshI/6Uw2oGTw024nmCL0L89VInooytIw2FAMRIzVmHgPhZ6QEjchXmW01BW8mXc0JN0GjRy528n5jMlWbhCjlh9E1qW9nBsrNf/Mx//i2hKawp0x2TTc+AlwZEZWF3K4MKxdbQk36BsyTmMk7YgXL+JjDV+OBy5jO7dKXwQuBCzeChNKv5ca7di1NEXKFhNRxCTztHIo/xuvYfE4TwurYd/gbac3l+HbNkCJESl6XZp4/5GReYGd/AmdBDeLgPoMHXhX8k3NJSecDZGlq5GORYMNkHW4BPpsXcYW3+C92ff0LinAefFMygXmsXSgdcZsnUUn40bcKkDwXc/lh3ewljpwfTEZONre4o92auRs7mIbu8ZUlyk+edwBDZvo004DYHMH0TnpiJ6+SbiXbeRtJ9Gj/4JiqLSkalLw8A6hvy2X8hNWs3xCQuoe9zJ4Vgpdq0ScNVmNg1vhHDOnsBb4SYGd5VRKLQc72uzyCqvYGeuD94WJvhEmOH84hg33E6y0kUDS/MSZq1qJL4vH4mvMYSfeMO89zu4pO7MqPp1fJjXhs6xleiE3GLzUjDdrMxPnzC+zBNjyu4wmvd1Imp+hy8K4cR6eFIlOE5gRDGTcmWZXqzAHa9aDrzMINN0AynqYQQGF2Fz9zvFDsvRO+GCz4zlPN8mYG+JJZs8A3iRW0ryx7dk6RzkaIsjC88cYuPVr6ztciQu9Bg9WsvYdPcmPpK7yIraztN6K+bWu7G8bQTGCnJkyTTSK7hCl5UfryLViN/8nuPhDfT29OG5zgr1LWVcVNDF2kkD8bjnjBgpx+VeJXIFg9n62ID70xaxS2UvWa8SENSbk235m4kzPiOmG0veiaW8/beET55TGa/0mpdtg7i9oJ2yNkPmL88hxbWBzbqfuL87B1mjMfwqyaRtfzcS4wcxz2QTt9yPMq0wnMVR4dSpniRgjzU/DjQx//xlymb9JHeXPxJNk1n1wpIZix9zdP5N/nQl0T/HnlohIT6uCWKYqTeCr2m8GWzDarMwjNKSEPic4eQRIRLmJSBk78iSHZacuJxB2PVFCLVsJ+nNXbadzuNwrxF5Gw3wLppF16086HgNlnYMyJnI94JJRF7tZNbEHJwNpCkV+YZ07T06BhdA2nrejXYnVyDg2PUJWLmeZrXSeTS+PkMtVQP/pCS++U+kI+IUOUJ7OXh7Nsm1HcRaR+HWt5Fw2SRGPzegrvUtMt4xfL+1FFedvczSuI6vaQvSnupcqj+B4EcrB6t1mDtmEfnJA4lOEmfozh6MTkawfqQH9jG9HGyWQtOohIv2UxA7e5oW+1ROfF7LZa2n1Gp5UhtbxI2AgZyoXMH02QOpvvaCDRfl8MvyIWfTXCxf+uNY/h0z65XMXn2UzKRK9PO/IN/UT/lcL8ZJX+NExDzWrjZg2MMzzJitgriwL1faW8morQaTJv6+e4u76kyaS41Z1beMbcUXsbObgM64JD7m9DFA+imail4sHbCRB+VuRL1/TYbVQtIqDrFjyDpKClZSPOQCfmnmlM2vwFLMB1f5iezI2Mwq6V5oP8Ndi6e4Vf0j6dIroh3WMmZCPH8LB/BmtinzpKLZqxb8P+qo/yM+Hz2iw8iQvI53dRozI4t42ZPHgpm7cLXrw1p7AKpp+QwOcSV2nTV346Rp/5CF+soiohMXUBdVhq+DOEPePMBVyIHi79l0ZESQGLkLm/WLMPI7Q8SLC7TYhpPVIoW/sxN3WnW4ccAN98lGXAuC5zfhvmAFvz8vJveJFi9T1Kn262DEKC2cdsciPm4143U6kAkVoeC9NEPL56OrMgKFmlS2pZtROHosuz9D3pNnvPSPgAwfbpTF8WTeEtbMOI/EiDL2HCkgZ4MR5292kLxyCX2dolxNa0OkQgml+wI6lNdQ5XWEbxWlJFS/pz/Em5TyLEqONBL8sI37ZTKMv/yZVzlPMdsiIGmfJvvH9/BbJoVRrn/Z5jiNPNP3/N6qg5plJtHD5BjSJcSu6qVYvVLn4uxuLEpOoHpqByO91mEzPJTA9mgWbvJj3yMHSo5Kc1Dbk9ghfWxJ+YB3yn3CF5WzqXkI3RUjyEpcwRDlfTx5NYWIGSfY6ZzP8wNLGbt8JM3fuvF8kcPj9iE8LnRn9E593o6B0wmVSEotpaeti9PuXswY2kHY+yQmLh+P/wxvTn86xMN9AVyXcEJy5ju29i9A6KQplp7VyEnM4tK6EK6kPWSC7iUefajikKwPO78tp/2TFoXttVjtt6Qpu5rkzxt5dP4iJdbz0Hr0klONbuR+WsWe7zKcPJOK0mVPllzZze6TOjgc0eeCwTBmDlTh6nZf3id/4I75Y9zeTkWh3odHBjbkh76iYdE9lPT9ORB0nvs5XpxIDGLahwaCq4cgei2UHUuEuH1MhnvO+az98hZ3kdHMNP6Jo5I1XTIrqVbchPTR59Q8vcaiZwswmTqBtaOfcaRFm6LWbt4pbULMeAI7154gpNGfzJ0lWFqO502oNXUZBWzv/Y7/hDaC48pxGhZBdedexmyYSadkJuaqe9DVP8FQfSeuVvSgMiuaqJahzLAfzveLkRyc7EtptjUKv8w5IxiAUOABTp2eBGOuwvxCyF5IxOospiiMIWV9OVuHCZG9N4mlV4Zy4O0WOqpn8vHyCQoGT+BqfTJyKpa0X4xnw/I8li1Q5uaV16SE2XB55iN6ts+jbuIJgsPPcy7qHOnhizDaH0FMeyCacR5IHffF8OMOcnbUsNtPnsi9I7nTVInCKHGKpxowq8sE70MDmet2gLuLj2EyaCzzqk2okJ3J8PRPfNrpzu4FiUxfN4CI0ndkePpzq1KIkuMBrDDr44CvLrLBSwm3EWL0kizmn1vI2JwVrKoNwcXvI1fzXbAWL6bj1Gp8Xp9n+5rdrPRYRPZyR1pibjBsrBlmh/pon6mMVXEvi201maqSwUfhi+QG6iI/rJpJkz6Ttm4Losa9DO87Sc6Ih6zZcIRDzYXYTXemW2IQzRrvaJ6tRK6gCKu8SlKj65jSY88U1Z+I7Tfk0tsW6L+OmHgUtV7/2L9gHC/dh3M8VpEHqWsob/6MyRd7pkyeTZT6He59LGWwviQaq0tICXyEjOMjjA92/G9H9f/v6euvwf2PBFvqegie2IdD8wd8LaSwND7B8Df3aOuUR+R0LZXfSyl/mEjkSEdqd/TQmeZPVT9MWZyGptVgxDXc6D54iP1xJzjqdZTc+3M5f3g/u3x7SQ+o4MVvI2qNWpBK62T1mX7yXsmzzdKC3sXL+Vt2EblObRK+GTKuTgHnnbZ0VN6Dp94IXB/Ruj+DzLvmKERPR35RHs6j4ogUaHEuuJw5BR/ZNN0FCq6jZaCGVG4KTUtM8NA+RXxTAbk2qsjKjKPzrzi9ZTYIutbRJDcF1aaraOe+oEZvEKouSyiMG4ya5g7+adXQr6mDSpIosoqrabII5uuTzdx68JiO6U9p+vSCZ7xhUuoP6uVGMDIvh8Sey5Sl2zGpqJxmQN9EGBEPRcpeFTOl1gvHOTqMVnfCY7E7avqrERn1lXKdRxScUWGF1XDm/80gv2Y0E+YfIHP6Ngbbw6DpB3n6pQFJTTfGNlRRdGUYdfaDadcby9sZZ5hDF7ciXDlg+YOsE078AZpSfqJ8/R0zi3roq/qDivp5LiRFUu9nwPcP2QiJgm9iMMHL3hFeuQz98F6avxgRHvqS+v5ALkeeZIaGMXUNydwJe8CMQbcJuSVG9qhcyJHleF8HJpbL+dF/C9/r3sgkPcBGfxbjhATw/DRODpupKslEcq4PooECsi07GOelxIA8BUzMt3IjKhMfOW0OumsjYivKRfsnpDb8YIn3CS4MLGO6QQ3Wt5zRLJMlWU0YD/UzXO3oRO6jFtF1+aRoX0Gq4CMDB/Xz0rqVA5qLWZhvQ63OAHInbMXwgRArvjoQtA3mDnBCeOFcKo5Vc3L+NDrKGvjQ9RuDmN8ERs5GXGMiMoWfuOkQyvkz61DO+sqKOfao7HIl0208iw6OwWPQRGa6W1NQsQD19k8kZ94ivRVCbmrxaN4k/DYJUWv6hoLUDDIyY8gSLWLl8fN8ya4jPMeGl8438c32BdnhICIDBTdoGvgP38CV+JzM5pm7gPdCJ7BUfoVJnyof165F1V4VtbfwzwDuK/zj7b29qOQVYmucSNCXXFrSs/ktL8/97UvR9FhDyM801MvWIej8Su23y+gJ3iJUqoaprinx34VRUxOjoTUElXdHsD9rh9RBRdYZe1Lha8FhjwoeHqtBT/kuA/IGg1wPqvU6lEQGM1uzjR7FMYx5povwkwkcN7vIIHay32IhRo7jGRr4ELG9D5huE0vhl0yEjPVIbK7nvkY9+VGHcRB4kzXIl2OxD5Aum0BGTTQDE4rQbtam2cqR9Jp+6vOlcbcSRks1mxlD9oPBDJYKbrIpLBQNMyFav7ihv24QSUW3kXbU4cazVwxUW4C740nM/hqg3F+IjlwLtekVzB82lcQvckxau4jCrHSaLWowFhJHJeEtauMWIDxTETPxg4xyn8ip6CXoSnpyviyQm1zF5VYWL7qzKNX+AxZdGFwtxkbDBEG8CQ53Zv+Ps/T/iNnV+XOH/RbYj+WIeQi/dwmhfzmNIudN3FqpQ+/ThUw8ncgfkV8sSxpJ9pli/G+qQlUDFz4HIbzZkoiYTkQ1WvB+9ophO0Zg3qWMptU9ZK/XU6kkynbZYJL2qbNj/2jc5tmTlVWL48CdLKztoEE2kUW1XeT8a+HPmuH8XToOiWxZXBZIo1snQ3vzS1pnfqS6uowm6hmzazSvUkQ5fFuEShdLtHbd4KquH5s1Mzm2ORbjSfG8+fSBkEhF9O86027oygDVIQz5aklhRzrqCkMQdGTRlyfF1C81/LaUp/prEhkW8rj8uoGjqDKja6rpMuogf04X+4eO4pJ5KzuVxTmyOwJvj/EM8FvJ6tXOaCYWw5wQGkLeUK4whRU1Lynw0yfmVxgltT9Qlx5O3v06LCrjWXA2iKW9m7i2MZJfh2+h3DaFiaM20SemwHGtfsKVhBnrkc9CqWZOHUtG7ZI8v2pUiS3PR65aCI2nE9BKF0M+S5hp50vxtFXg5govQsek0HfTD6uzFYjIrEVolhdVkV3sHTeNt9odxNmGoNjszKfRr1Gt68HYS5LYR0qcGP2AU0924GZlR2ZiFd5eg7m67CCm7oMZqLGC8plN7DaajMHx5axKaKI68xs+Czcg3NbBraKXvO8PYZKjHoe9Tbn/JJR66WBytl3DQ/Qs3Uo9fGqqw6xBCrN1iQzQHExS3W1GTxtNQVEryxynE3cqGZ3+VFwPOWGzcjUK0kLkVnmSGBHELPnZvH30A1MhAS4yRoTWy+Dqq0bLV2VMS+TJbmlBNESM5UPWkzmwjn+ed/iQY4FfZQOvZkTxqu0e5d8MKD5xDe+R7zHxy+Jr5EDuhK1BfGo82hmudPjNYk/VUu4UvOL60sXMOuWESowpB6M1qatJ487t/+jW/MitK7bIueQw7XguYQ860FXy5IfQEh7niLPIyYn9m3YxtHEq/mbFZJ1/SK5FFp1RFYzWD+efWzD9U38RqP6Ci45OuK4/yB7jSRjm/SJFWZkpt14x/uc31Mw/EqgaxDJrf2a7qeO1cAd6f2/gqZ2O6/MI6pJ7KXddycFdvqjW2JC2OJOCZzF8K2qn/m87aQpveBDrSZj6FHrv3mV9njKtfUYkRa5CXb8M9XHnGRRQi8T/x95ZdmUBdut2PnQjDQKCghKKtIpigCKiCNiNASZ2t9gtdmBgJyiIohKCQQhKlyhKd6fkc76cH7C/nPHuM/aev+Ee8x5rXGtca486s9uFnEjKQXekKldcWxCZ483yfl54n7JgqkUacS9FUZROQzrfgx7teGbILmLPY2XEVdV4FpdA+Wp3ih7q02PZhHjsD6LL6hBbepBh6r9Q0juLTvlk/p0bzzOfT5S7dbFTQxltXS1+PI4nZUY1p3NuM9K6he+E4WG/D2HTV2yM/xAdl83y/BW0iwmwvWDMc8EnhKHbkAjNIeVAM3NHqWH8XIXjg6aTkCnJAMkRJKUOQ7NxPifkErlRrcgNK1fyfylgsfkzFhMs2XK/iUoLZQ4924PsRi3+pnjRKauF5gcDdg6IpH9BJ3dibCiJFzJYI4kvr7P52roZPdu93HlziB+iFlQPleP2jEZWDg9BZfxmHrwq/N+1q/8g2/cd8k22iWL08n4YTP5OoYYqwd8lGV5riMVtT+7tuYF+oDVXzR+hZq/Mvuc6VB5dT+3AMjZxkoyXdwj6Fkvg28c0uYsTNf00kWcmYBivhPyAHiI1FrJW+iTfrCJJ6XmKd64lmf/uUd4jhV5tNwHOdowdpUztrVCqs2Tok2WGud5ZamRraZIahkS5FnWTwikO06b0jznnT1ziYl9TfozOQfP4beJijtJalEVG7EWGNQo5+taCVTIdZOmJ8sj9CSH3q3g2WQLjxAHU5Leh3NROW5cpjpbBpLyUJmfcHyxazvPLcCLDY1XJlo+hU8SQi2pdDE/KZfU/aTZ5VzM7Q5qPS1Kx37YJ3edfaQ0dT+qTXUxaKcmBAylsXRNPWtoPYl/5c9f3MdfbdblzczsZT+8xy1GJOfdaSfGdjceC4aiIyTJw9XjyfYTkjI+mRbI/XjZePHT0RGNPFZuvPGDvelXuCYdy6VEyk+Zv4Id5JQrNCcw3l6f4QghzZrezYsJsKgfPJMLXCbtVKoRH7EJiVAy1RTksUFlOQaiARsf7WKhpIp1qSnR6LxmbPdn7aQTqHofQyJfi5NK+iJhtR3H2agpmHkDEoZahViXsmaDA0Q/72LhiOvo5zhRIaZNefJIldt54DjXC1jaAJ8l3yOyazrVOA2bJfOFeXhtRjhf59LMZddN7pPyJpUdbC6M0ET5XpTPJaDXDxfI42bEKQXsf7hzuYsuBSky2evP69VfuvM+n4W0rBmvfkRu9lSnyf/nmPZzcxecZNkzA+0cSbK3qZFHfZPTeiHM1Pw2Bawfaaru5U+SH38goyjJ9yK0+S5ixGC/3axB+zovs88sRjFOkQF2bi4rXKFyyiuR12/F6lI3A2gz/I1/Yfcmbg6ETeBqoQJtfLzYKpjxcvgufjwfQVU1nntIbvjitQyfmL19E/7Lg+kLWl9mS+C+E957mOL48hILmGVp8/JCO+IxN8QtmSKUgl74Utkcw5PwheJ5HV24HNT57qJSqZZrZBN6LrWfKHi8Mt5hTMK4EhbBE5j/1RGKMLddZgp5EMAqGutxdPJ1Xxjt5lHyH7xOdWaoZjVRiIuny6+m38C3zR0US+tASu9g/pGwVIFbUQfuOTMpnfUfnwDuKYm2YWvEPNw1XQoJesjz/E3pmWmydeJgveWvpdhRhlOt5aDlFcedGNF99oJ9SEwM0BmPoYYj8r28M6S1EsugN2/6pod6VxeuK2/TJWIGFVgOZyfZkHdlOcusLxJZ643FWhAclqdzaGcox/d/4KGtQ9Pwe8y7uJCY7j8nT+vEtzYj94R1EOxfzNns1CGZRHVHElnt3uT9iLq9nqbPsmiIfJ+ynoX8GJPxiaMUm/g4dzmHbdpR8fuHzZCxXCzSYvvcNfwJ2Izgfg92JVfj//UpZYSODBwygOKkB5YwopqzfSLKoKDHRp+gJN8DaQYtjAUNp7q/KCMzxNY4gNeErBp5tXE9NIWzGaPqaHObIhYL/0h/132L4OHZxi6/KugXsMR3H3P4TqTV6gIH0NxZMr8Jm+zVOT7rAwy+H0Ug8DXuGoOKnysVBg4kYvgs9rQpazj1k0ZId3IgLx6ekmmkqI/iwYzhn11zhVXsI0UP0EBk2lnPHLuPjNZkV4mZIhI/H26CUWMnxJJ40Q973NA7RrYz4KaThTSm/lbT40epKqdgPwtSnYaKvDHVTiNn2DdvdkpSvKKfosRP7OpxpS7/N09RXrNIRZen7WNIK9vHj7GAmNsA/YT0pFi9IGWqC+mcxahepI/EwCA2TtxgoPeBapQ5NJi8YXt6AZupEHn69RNpASUJbi6iPCWeekgvS4vsZ2mLP68X2eFfuRCPgFnlfbjNj12zO5b0i/L0STsK1HE24wYBuBzIiNNin70Do3bWUrA2nz0YjHsSW4624hsgQO8zkdVF0G8y75kQs2x6gqvaUjtqf9Ps5EnnR4bQH2LBNvwvnP8+ZsOscu3J6WCI7A83JEixxyublCGPsu5SpTROye38sR7WkOXJCg4UBo9FXvotagwwe+/fxJO4XpYPc0VrsgGJHHobvDPnmn8ulwQM42LoNzKURaq9n1ABdsk1i0F08luI8ez6dzcE9PIHXDETSyJT5o7u5cP8+shZGHJNNwmmWCLq14rhozsXR/yMHVtxheLAThcWO7DATEK4mw3vRG7ROmMvm7yHU9Ysn7XMBct/V6SyroyfnBUOG9uf5qWe4qA5Bz/EC2mP6E1ipxdStUxmWZ0nZWQPqDASUqTlgMLCdV8dLsdQSwcVqEH9DFdny+TzDja2oe36JH4OWM/fIGIoubKTk22cKrdqRk37Fi73VPFljjv4vaUq0tvDqwl6SdfcgG/2TtB0uzNSLYLbWLsoz2lG3LyKqq4BFMx7ysuAyzzpe8E2yjumXfJA9HsWrcC82zRtJWZkiye93s1piPh0z/qF/3wDlgafp6j6LtZ04pSIjSH8ogYK/CpEGwXw894y5OTPZMOU0ESo1DPFZS5SVLVYffWhX9iOlLISOMdYsfPCWwonHOJ3+DL8NyYi2jcUzXxqPe6Z8scvHbdtpZANL8dY89AAAIABJREFUab/sRfCl6bzWk6efuylF5bf5NPQSekmDOWP1AtVPclTlT0J6rj8T5H7y8fdIZGV28lJsGVKNtmhJHmGSnCc58xr525LBVekyFrS8JTlbE+fbu0hrLKde/Q6mhdvp1h9NQ30uoh7a9A6KwGJCG8PeivLzh5CUimVEz9Tl3/1Y9g1s5GyqEQONA2jW6+XPPxES//6g9Eg4K9xzsFUeTcS6NsouS9L31VkGue5EznUU6WV2fO9S4EdZEjX/xtG8QAlNz1m4zd3K/Y9ivPebzJ/Tb5Aq/Y3+vHWkpPXB3WEwhS2JqNYvpynJF3P9alTDZvNhgDNPXtykQ0qZJyun8Kumk5SOcqTCniHak0Jtkw5zNK341ByI1h9NCiZm8vVWOuPkliLmacLcGz2kK/QyZvtoTpqoUXF8EaOy5dnmIYnBP3XiL85D9aUXQQf/awec/pf/N1w4dcjXuMYYU+0/aLacIDdnMNkGR8mScyb/ZTgrQzbC4XGouZYRkBfBr0oPJkbm8npsEYczbfgrVkvc7WhMlk2hrO4zN/of5Y2PCHOOmqKVvpaivGE0jlUmv2MW8vE70ZXWpHxgKwOUmlkUok7BznHoEkTyoxKEjSeptXiEirCdGh0fEJ5FqGoFjQNRzZqCRnoYb871x/jzR/pppVDw9SA75xhQVf2YnS4lJG1/wP3pA1k3FZY7KfEn1Rr5ogeId3ZS3l7HMHdxglqVUJaUYPGfDL50O2EnfEutpB5dJ5XJ1T9J8VCwTihhlEElRbXn0LjohoSvD6VNnSzzO0nxRS/8tw+kYU8bo7V1eLtBBPN7g5BN0sCwVArrgRVUta+mT5AY3QIDMmvNCTDfxceYJhpPaVA9/xaTu3Q5Xj6AXYskSCufCg4FyKYswvP3RxzrpvPbRZn0lED29Dqz7p0i7rpFHB1VSdC5B/TdGM7Ok0ewzwrB8tps5HoW4XCgCOdX4ZjPmYq06zx+qa3j54CtODRO5/6+eIZFVSJWvZEP3Z5sdFJHPF2WtEJRsgcvJuf5GAaqZ5NTNBW+feVPxQ+MPHo5Nf0YEu9KeTMgAstCR3YlpWKa/5bAri4a+sYikulCROslyvXFmaLswN2YKUhqdbFMXplOsXL0LGUo16nA6vUsDnzqw+PN2YRkfCNy6XyOvIvjpakpW0z2EhcwnQ/mQk6VrOB1nwaOvdMha9NNPiZYY1c7lSNRXsz3tORF2TeCvrxk36w1TBj/gmkVebRIPSLhwFxybZoYOc+aEQ211Pw1YqpwMkdrM5ikqstkb3liVlWgMVODRklRDHqHM8frFlGBVRgtT6W/hj7vrgxilOdCpvTTxvO+Gwo6NUjV2SImfMasHTeJCMhgrF0wlRmXSEzczDqvp/iWX8cwvoGSigOsdvLl6vculHdvJbLsJ2lXNnFtTQs1w534MEeRr5M2ME7yMqKHk+gsUaN75Cy6/MKxDH5F7OKnDFV245LGUdbm2DM4dAm/11bTXGNB+MTPKLcIWSJuScXaTn6deMo0zUt8u3maksgUBm/v4UvlBJoEQuwG1/F9pR3KQ2PosPuEtFoQ4febWbXyHlP+BiKb4c1t7Mh0+8D+pQO5fC+EuY3WpCi1M99diSj/2xRre1DU+ITcEz+w3nmTTRXdlBjlYRCeQ/egFpSa/qLQ60z9EW9oPYCR/AW81B/ScmEUrQ9uUvVkGSNV5dm9cj0de4u4fTmQ3fKTUVrjw+/u46xp3k+TajUlDrXofRCh27yc1vqJFPQ5RP/C/fT9fIRBjq70OG2iUb6UpKQighZ5sDDiC8M9PbG9thBNA1eyZW1R1unHjbfnUGlsJzNRjAH5D8ke7soUx4V8ef+EY3t2s1TGjK2+22luMcbBx4Lf7fbUS34jzqgZlzs/mKdsTHzdQoZ0ZqM5TJo7DVUYq7RwL2AC/qsnczXBiLq3wTxO+K8NH/8t1q6a6pUo83nL741jSW20Z6BJEdsa/1J9dBmiR5Nx9TXBwHozeQYCTs8WsI2TVG6P4YDTIdQ/mbEgpZgFf32Za95FqdwwnnS1skQoxFZZwD4nQ/q9cmLIoq+cnGuLRoqAY6eS0c2Owk5zFq2lDQzxG0qpUywd5s5cbflOwnNxxMXkeFNzljYdOZwUzLm2bCiVW95hvr2cX63VZJ6qJ26LDGdupzKkaCSfwpw4WWdBsXkwK/tLcWyOAP1wT8baLGSUbyKrPpvR3CuNyoXDSKzwoF7yCcsWHWKaSRmeGkuxqrPirUEqW85GI+iOZ5WlFHslDzLoy0qSc1UQrrTD4r4rm0XPcmHiCmQllzFYcJppa62IHa1K4LQ5HBr1kcjglUh1NHDlVzHq5j/559ofPlYwMfUmd94KMF8aj2IfVfqn/aXVYR5h/awZNS+NUT6+CPvV0PdvM8cHDSVY4gMhOkvx9E7g1TYFStwluD59DqdshPyOquClvD4Pw34yZZKA5eJDSFT8hEAwH4k+hVhsvo6T9SpmHLDBcKcIv67fp+S1gCvSFXT7j+L54FICumqQ1Q3kb+Z5voSfw1tzO2ddd6I0NofFG67x8IkAmyXB9GmaTFh8O36eHvwq7yHrZxEajd5Y2I4nvzEV/+UruNrlxZPRtbin9MHg40uG6q1l4fV41CIa6BemyornDqyrMKTwlwivg3rQUbtF7rebrDu6hs+VM2g74Y1K/SCsPZ6gut8FZF3QnzSLivtmxD0p4IzbYb5YtqDXVkdc4h9Wrelg/DAvjubAiruTkNmaSd34keh7y3N27Hv+nG/gl+IQ1koN5sbO9ahJBGNqOBPTRdUMCutCINLGg5huqj5Z8W37KfqkevBc4xqBzQFEtO1CO2YcB4QmPD5zFceHw7kmIeCo4RrOr7dkp7ICjrPG88pShXmihbQm/cW5UpJ8xZEoSqiRteAwmw6OptixCXv/cvTXCZlUJUD1y2NMf0tBTwA7N/myeclY2sTnknM8EAPRCorXraA+cAK3TLzZtlyF6Ce2jB+QA3e1GJmWQJn/bX47CJC+/hp9wzmsknhN2y91OvJrcdAoZ+L6hdw894SD/pP4mWtM5RV7rrjaM1z0Dg9yyhkwxZYF/64RHP+Q6dYfmVothUK1EW7TEhDZCKnRzrguWYem1Qo0hY84O2UT76KeorZnFreOvMe4rpmnkVV0jM3HcMUZeqRy6VL6SIy7Bc+8ynk77DPBFz+in5ePy8iL3OY1L53rSfdfSWKHG96+22k8q0hOcDuF5VbIFnsQJZxJVXIINR3+OLdo8Cr4PjYSAh4Zy2L5Yhw1r9Jw6itJsVE5nXo9bNMOpU4sksOWXSiqGzJU+STXLrcQO/QFHqUV1FfW0q7cgfbvdEz+hbB/UASh6dHMj/2L5KID2Lva4LBpHIUvxxHqfxLzoevI6HakoekkzrN7MK6MZ7a0IiUezUjfDcasjwP1dSu5W56AUDiMU6ua/9Oq/h+PTPsQHo26TK3sUsK6VxFjc57zMxeiP3MiAQbaOH4zJM/8PJ/sVrPz/ASS61SJvirBqKBK7l8upNfREEsDG/YHfcVM24MVurMYvqydP6lSRDVPQb56AcWDbzMxaTQvXHeg2LyJvREFnMjXB+UMxEdE4iIIpXSoBB3WYSiqaXOrYRy6oV58H/6OoRWpTBfJoODnLNjnhGnoV9J/fkA2MpV+BgbMifTj+pgFGHz8zqYdq4jp6sTC1ZOQDb/xj3oOK0ahVFvLv2FNqD0BR90qyu2CeK0nzgCremp/ypCersr4BYY8r89h6uKXDJ/szABzLSqkFtK2MY6OycmUDBjAJc+VzPjdTpzWJVS3TCTQfBMD7MYz8VpfBk1/xL3GfQTlRpBx6wplIUqsiPcgR/0SFTIT6D80grZXYZjMcuKuihazImfxaFUsN/v1MEiykilZs1jc9JlFWomQ0I5EUz2pQ1NxnJ1MqetgLv+WZtqMhfzqI8GSnxDJJBAbw9VpCYww+YDC626qW0TR9i3G8nMqW3X2kHYgCqcHXQRWD6fw928uaAmwF7nB0knPmBA4kPm2mSiuX4zMa0fGnbNFYuxoBG4pLFv2mFNn9NGzHMNgzfuEjm1gj/xj3Ge8xHKYOqMrFnNwQwZ51X1ZfXo1FqpCAieNoDXxKwGbnpCVfwu/hFjiXiqB1F2spgzixpFUZljZU7F/Hzqzr2LSdYsruWNpkLnEgoQvVBgP4Fq7LxXWFvzM0katWwnp9hDu2a2mO+0Ok22PoD3Gg/PCX8hFqrEsWIpXKrY03t/JRodcGpNDuPVjEO267SQsjGdybSUZm2IJPZ6NyzI/sn9VMFNRj6GGcOKmP9tUWzktaUbeaEMKM0OYqy4kSvCX5Qt2Y5b/Hn+R1yxJeEWZQi3STUuYN8caie5A7M3i2X2yDrHSKiJF6pg78gSC68mcvrySWnMnSia5oLB4Kgcf6XHbOwKLZZUoTIHbyirMdjuBV34FP1TfUHm1P/3vPUdROhsFxzZEs/qh8ceJYqnxKO7MZXHMbLo9olG5IYfemAKEx/yRcP1Gtd0CKspGEXJ/Ft76n/nakcGt0mQMAitR8B5BhNpFsif1ZayjIbX0Y5KHFvddRrLBLRp3jR/0/+rP0w8lbATm9dVgfLsI3W0WqPyaSGNXBkpyU9nk48ftfV48vXiXyjpTFmcM4FX7ZIJUeoldVEaj43tGbBpFnkweh5vCKdaBnBhtfnw+glRiJfNchjBqciB+F01R9pxGy9JNJDd9I0cxn0M3QmkMVMCwrzxm8V1MGW5PwOZuxGN9mXB3L8FWLcSNO0H2p0hmbdqHYN5Bfh/QpShvDX9GrKMjeBHhGSkk3D3Dkf4LEJ59Sflyb9yuejAiPpDrN09wa9Yujtx151JNMEGmd0nKOYps6WaGyMUwPjILr/dnyXswnvkPlAnJ9Wd+qCebVnhwZ/RMBFJrMHxkwO4dqUiIBxAo/fO/7NL/FsnHgVP7fBdsSqOj6BOflhlxNfEgKTu+IDLSh5vxZzloOhDx7mQ0vc9S0DgUsy0rSLDN4+qGBO49iaDiuwMpHoEo/WzDqVEUu7zp+P1Ioi5ZCaURjpR+PY1RbzbPuxLo7DAmxngHj3NleBbtxZkxoWiMzqanz3XkpTxoe3OT2f3lEUl6jWtxIlVyaxj2p4R1nnsRG2bMoCotXkpeQ+R+Pj+8WliifYKHisqIalzDvUOe4zHn8fg6kEG+R8nafAklkQ7uEsLU321U9h4nPs+dl4O/Mzowk0HKknz1f0j7QEO2Dgzm74fvpMaeYIVeF+P/Lubb2D8kxlljoy3Nmevz0JhxHPHyBD4tWsDbfT6Y30nn2tMBpN9ay9hbv1m/PREbvUHUzLhMpedyMtWWEB6+jKiIj5gMGUFZSSGafZZQnZVAZm86hpJiaNTMJddJhSaPJlrljpDy7SM6SvqUTppCSk4+6QorkTAuZdFVU5xX2mGs4co/PUMubHlAk9clZliNJXeLBiNHdNK9RQnGj+PdOxVqdTPYdbKA2r4ByMj1Ryb7PfN8FCkLVURkej35jfm46hnjkP4Yu1YtZEqr8ZZcTbORBNnaoqwdWMTH4y9wPH+S9W/+cjGskuVmmnhKj2KpayTXCkWRmizkXaYoM8QU2LBoGaduhNCnjxQVbTM59moL8gUvkC5uwX7WZIpeP2Hk0gjWbDIlPMmVy+cP8jLtPX2d5Ei5eB7TzkMYFJ/mZ8gu2kr2sGX/QuYPHELqniLGDZtGWsNPvp9RRuFRD7FlZczvZ0nb0suMlD1N+oQ17Bk8nRPHU8hxDWCOzyG2T6wkbEEk53/1kDhCgKBUjTpBHXpVN5AdIc7ttMu46izl76GPXJcchLGsIa51/fm7cyVnrMcQanycCUv84cEqIi60cvb3Z+TfRdL87gwHo11pyX9CYmMzyik5lMjP4WiPCjcXrKRPsBEBn4pZaA8+a16wTNeXlVNtuHr5PCN2/eDvth66V7zA8cwUCs/vxqPhNSHX7Ol8lI6UWjPPnftxWdWag9O+oGSwjGd+F5mVP56qTFtqrFtxHqVEnZgjSglNjF8oSofOA6QjD7A2eitXTOMJOnqa2fsHYLlmEqIZBbxTUmfRYGva1/XSX/wYh9SmMravFt/nPyE67C76wi2cr9nFCZ3xuFg6Mkwzll8/A5gRZsaoQcUoFvZivzCU7vp/dIkacjvMlwUuWUS37GD1SzvuiKVwX0+VzAH7MTrqzvEfL+hqf8sT9WsY6xsiFKtHK+goSeU/KNPOpqCvNbua1bHI72Ji6ltWKo9jltdtBqjsYdUKN3xGL+fqZ0MKdpcjp7ycfY4ZNMQewOXvX3ws/pKgFcTXeC8UlfsxwK0LObdSPDKsyBv1lDNaG7m/UZtdnzuZEfOGHE0jXj3+yeoXQYzKfcF4p8GEzNmA8+W9DCp+xgvtMnoG3UdY2cij+T48/57NYadpXOrdjle2Pjcj5rL36Tnm/T2G2hlVJse28arw8/8mH/9Bblzz8zUvkSNdRpZcp8tkdVvxK34Pf/7dYM6oXm6++IlG30d4yL3Atm8JHfJK3Fy/kZSs/bS80uVT5z0mxdhz9pIXd92teLhPjvUrpPgi14zevUikh3sRojmFz+LqbDX15mOUFUY7RuLevp30Pjn86XsKZz03MpI+0NeuiqLCILx1x/Gh7DWLjW5xqE8mnY/nkLi2B4PqlxSWGBI6uJFncsFkTP7N3t/t3Ppnh7NuLWMSBXjY7sE3PY6+DhMoihGlhS90qcfxd7gRQtEUXItsqJEzQSYznGrtdo4YJjH3w3e+lGWwe+th8k4FobPXjSg3P1rNjDgatgzxTksm2VpzREIGVZ8THP/TSeGlrUSNTMbufX+Gny9GUt+T9sQh/EiTZLr/QiwcllLkFw2jp5JW74fFv6mU5wgRjPyHlXIt3/ttQW7cAmyebmdfVTpeetIc6muArZUELc0RxF//xTW1VZjuMyfk5Dg8DeSxkJzFsrWNBMcN5sQCVQqDDHk+cActytrcDBHnwol2EoKzkD3YTWSZEoMOWhP/p5sXK9byOaMXm4Fnqa4+SFb+XyYdUudyexgTc61IVo4h9Y092r3r0VNv5Gr8JUo2h5G2WAblej/WOLQQFLmKkxfOsDZlEdbGxfifTcf/sA+3659w8+Zfmsv9Gb/oM9O9JQkuqme2wUq6apPok9eLcPVk1MbY8CNoDx+XGCG+U47ir6YMcR1Ik6AcVTcBi37mEhh3k45/fflUWENqvAJXtK/xRFQfmXEiJPmLMXppfxrWyjC+0hi3j+ZoSJriNCuAluIP7F8gip+5B8lLqol0COKE5yHefPjKGOFm1E/PourEYBJyErCsi8Fn+3PcJBQIkO0iNVaVIcVL2OS5lZMDXBgTtYwYmWAe39hLv3NbeNf4kuYJJxj2soAGQS79B+ii5erD503mbJy9GYeAAO5Nnob57n1E+BmybF0+aaITGTD7JpdibLAzvILFksPoFfjz9YMb5zUWsXbvScSDxqJRfZy+5y/SPmEwXw6eQm6TPUq9L5lQH4P/n9k8WJZHkEIW1nu8aT54FqGNHx/ktxL5JAlX1V8c67yLdYgi+9fuQmryMGLiSvjmbEy8k5BaMXtiXrRzwHc8ImMM6L/CnWbjEsKvnGH1sfdcWeBJpc8kzgY/QOTveZ58/Erxr4dIOH6gvLyJl9GvaZWwxVCqE+m8DnD5TlxDOGaBA5m9rZTjd05xYVMVGX3FiZKs4X5aApv13bg/rAyjN6asNMnnaPtYRrZFU+vszOysfPJTnvJaoZp1453RetNIqNc+emuVmeRswaTzNUxfKsDFbhYTJS6gzDGUPu6AgyfYmNTNkU/VjBzqxkQ9JX7Jh9HxO5x/c4uJb7XHV6wPRts+8HTuO6682IB3y0iW6y3Bf9B71Pf358K7aKSj7lBWmc1nGytczS2I2/wSybGKHBdbTJObKssNjzHP8jeWC55wqesn818tprcxg6rcD3yP4/+f5MNafChez8t5a3mDNBMNyjWmkPB+HScWltF2ewKOglsMn9UfjTZXCr8psPXFMzYs+UNDyB4W9zuMiHY+8c3H2OnylMRIaUo8Fdl5/DRjdN7if/IgcV2RHNsRiaxMMTmfw1l/fRKHK2UJFL/P7ofHaft+lIr9gylSHY/nITNeX1BC3d2UH/PXMHR8CB8Fcwkudydn+iTKW8qYEyrkl7Ud8+4JyB2+gWeZQayQ8abYei727T5cfG3N9L2rOXxuKQsc8xEfGc6PNUJUJp1jxUobbpTZYbxSEZO4DmaazceytYPkBHO+uhTjkAC/bgipf2xAc3QUM+Nb2PLVjHvO8UhLg5JUPeoRa4m1O4lMvSJHbohz+EoY/RuP4edlxavmcJp2qfAER5zuvcC46AHquQ/prU9nhWA3Oc/mkeuszbd8LQRnf5J6/QYtLar865pGRPp6OsI76aOYSe2y3chXh9B4qIQW05Ec18pk/Rh9LuYJyCgLZ4rvSARnt8Iva9T6BiCrsZc/K9eQH+CL3QEzhEku+C4cgPLInUSfukK/+57EnRGyL8eLtpxmjikb0RAQTKaOIxv8yxlZ/5Mw91+ItocgG7mLvhEvSNi3kWvTX5M/Q8CzPUIawjr5/M0So4cSPM7ZTdv57fSGKTJ96hXWf/JhatZIxlnNpWlYNaICddyKlvFuQDDHk2/z17ibfKUwJh+6htaIDExNz/PHJZcb17zoHWZP8fAw/gYXoL9hE7m+9xnvcY4t3+Owzu6kYehK/m5XJ21VA4WzBqNfo8puiTOI33yBUCgkOMcO5c6TXFTwY0jVWToNzIkKkGadz3cUtCTYpmbFbK0WynbuoqvGiMTcan5WvWLV5yDS3+xHcOkCI9RuozXlKaI2ymy8bEKaeySHoi9QPzeGY0vGUyk7jzNZj8h4/B3j40b0RMhhLdtJ//oEjGubeEU7i/c3s23HP3x7vfky+xLH7U+RJzER36AzBHrYontSHrtxtkyYsp8pI/vill3Cw7AWJqt5YXreGM0FWsgeekqB/E+2SQmxWQAjLUeiVrmB+4Iozjgs4VHEb/JKBxG08RrPf8xBMnEx1886kClliPl1b0yHKIL3CU54bSEi5RHrBldR+3MCPcIKYsz6s+LZTFpb7uLY/YpkYS++4ss4dE+U7J2elOqmkNqrjtiwe6TPKiJi7ASCrCpxiZhLbbgFG8vk8R94hU97z3Gu/jIyWjvYeNsHo0uxiCW+Q3DIjE2Fs2nKHkt45DeKv+aj2J3D+6lzWTBKE8GvxxyUdiQ6OQeZFCsK17/h/dChBL0/S45BDWkVcP7LOjw2m2A1axDyASkczRLQLBlIplY6AQU1XDqhg4FWHhXZOlQrGCF57x/HF6RxL0OT7DGqaIg1ck8jnPRlN1nTPIXrnscI2CbkqaEveZt/c3jObWa9SCVqrBdmJlO4vXA3QW86mRYtZNQEP/Yu8uOtYw4HS/Ywpv0gQuFeNLYqMGeHFMzo+U+r+n88tc1/mT1xG1I/1chq1ua08yge901CJDsak9orzBw0kbQS2HpnAw6HzvH7SQmYwZrtIDetiRvr36AzeQJbdpxl8NYVaPb9xKNVNSgarEVCLI7vZjcYHmeCdk0C47OmoDNiMkGJ1ZTlTaFPf3h+ZSEN3VFMl1rJ/RBDnKbbk9Buit7wf3wNe4D8cDf+zRnHdNs8GvK/k5nVwqQ2dZJGr0VhUD/u6rnROUmKi8d+kHNQjOvfYlBbrYHY/RtMHBiC44BFaC0uQGdfBt0FpbTWp9AVJkuJhhICE1EGJrYj1L+KRmM0SmWutO404dmujZg/HUvX7b5UzZPA6txlRhSlYMZJ9OtWcv3oSgZJveHC+Hn0H3OFz41lDBNXpunFJw6cPcv+hTOIeHCX2u40RJN3IZBqJKQpDN1pByi9l0X6nc9kNwswVtDhn9I+eitzsLx6Bi11OzJuvQRLcQ54zWaC9DVuvY2iZtBzNFSb6NQ7i8HEbSjYajPF0ADJwEMoz3yNfNg4/g6Zzgv3SryaOuC1IhXdH3i+4RUGi4REUMcQLW2uLHmE6Z8m8l6rEH73Fh6F07h2J4/ilI+YmQv42VrIXvlEqoxVYOtHRllnotGsxY9baozZGczNz/WEGiVSkGbD2aPLUNbuYc3c2yzYtgyD7PXo3rHlzCN5Npm9J+zbYexjBfztl87v5Ou8zdqJoWUC82UHkRdagPsEE7LvafIppxnriBRuZMQyZv0SEj6/QHekHXJaoTTa+qFskslPHVu8rXrJF/3Hc6VnpNkZcW9lJt1iEtz03YnT3kVc9ZDi8LjRKG32QuVaJX1S36PRUUP0t04sjq9AIrAaukeSLelLwM7dhFiNI8nHkX4mMjRk9VBc3IXKwEDa2s8jambFswA31uv158baKl5lRKCo7Mu+bw/JGyJK669gxLqgUPMvlp1gfMICkuwJ6H8FYjfR05xJnugYhnSI8DuqijsJBoyt+Evt5pm8LKkh4LUkb2zjaFsaS1JmAAnP3qLuYELTZw3+egZgNesjU+Ltedr3AN93bCboyjrkxGbSlZxE3+8JkPcScRtR9k4fS/mAFdzM8+ShMAxo4mTLLfa6zUQpN4k2vsCN0dxx2M7nQx+oCxZj96AprHyxjlhbABVoyIaqUqpFJ9E2FOQSQdCnms0tNTwoLcTYeDklIkLUO2yY67ACx5H6fHgYie22B7x86IOI0kryXvkS01hFTHMEdZq1yHRK8EvXHIvKBs5lP2CerAPFSzo435nKroETaU3LRONTLFqOIkj1S8NIP4OV0xxYsrab8GtKmBzrg0SSEm1DLPlUlcvRain2u49BkCyB8gZR1Ou6qJkoy7ugTtJUtNC3+Mmule58EPPhn5gtfWbZ4XBGnV0XVLFJjEJKtpv0+mhUty5FVkeODXM/oLp3KB8KhRBhCVpVXCq/Qq+mF66T33F5GLwO1cXt3B9+JRr8l13636JqV1kgEN5DjVXYsWyJ9ZXLAAAgAElEQVSuM/VpZew4MITXFw8z5K4dFV23iMtI4sIcW5QFCtTTzMu2QP7IPGaLMIg3J8eSneLCLyMRhI3pKI8YRrJtLVN2J7BpihFrS5MZsDSMcqlEtK6twq7VmRrjVzzvY4+njR03Ztwmy2g3cm2RLOuaTWj1F+p+HMO9VchLv0DWNAv5VlyFzk5rCm9cY3SrAxKNItxWSCCpQo5Gn1OsGwRFt08wV9cKl0wbThTuYZOREHXjVbwufMX0R4cICxdywd8fTUEV9uX/0JNx5toNNwK7GtBS86a8+haRL/N5l9FB9fff1Bh8RtGuBxcrPx6cVyG8PZdVTZPx3pjIp7oCZLT00eyGZ/eOsHnaCB48siPwoz4HT19ghcp8cIL7+6O4EHgNw7mBNAjLyY7XYmS2GC0aXozYc4FS3UlcN9kJMkVssuimz7hQuDAHVYftDOzrgk+WDrYKPRS2QGtZIYNUopExmMW915cR5h9AzOAmF6tfsvOlG0kik3lfLMOgqS5E2KYTMiKET1u/ITITRvAUr+MlFJqG456wgq7IBi7P9sZE5ACSyhIMWaLEu/sv6Gl3J6ugimV98vjw2pDYovHcc48lQ0wVyYpIIlTz8R48g9kj2nhz9TuKMutRtjzNflE1rENHYD1OjbCXD0HJlJffi6l7/5qXTvd5syibqDcnOe/6jFSR27TnZqOcW8LOjEWsaP2I3c9SXj5XRnemBc33K+nVG0FVI4wpeceyNdP5cHM8Q5ry+Rpzj/DFS1F4V4uay3HexxujbpzNeHXQPnyMnck5rCzJRtgcTMjC66w8I0t/e0UGlg9g6DSX//vi1UClkannN7N24XFMuMD8TRt5MPcTFn8WIp+2FO2rAegfLEbWUo/A9yv4mpOH9bwYJsYUMlXHBm2RcQibRiGnu4R2WSFTll7BwvEbO66uY952Ew47b6R5xznqjh3kpp4dNjEPGF7tSqXzd95GVTPZxgyr8eFYp7zn4o67lE/PYKS5GtJ13fzrLMQ8pQpBv2+8VxvK1MZ1vKs5wcTjcdT0g5wkE+6a9mHgpSH4j2mjKrES8eUnkb4mw6TVxjyb84aC4iPMtfLjqbAPhQIf7Oe/pzzTjUEvzBGbMpQS8buYrVqKnf0ChPH38R2VxsG4s6xdLaQyJQT1I9AZnoZR9R2arxdw8OBHzil3odoUz7c2SYbJ5VPQ3ISkxRBkBq/h0WZ7do9zoCJOgiojeYpEU/Hqv4A8mXICLkRhaNmG7pBxBIefQVLuIibj6/AytGV5gy4/zx6ij40Oa3WCObivm4t+J1k3zI2HqQ9ZVCVg8K9YGq1CuWbtx6uNV9FftpzBM+H0gvmssdpBbOl3rj4NwkS2D4vcTxOYe5r9t64xY98Mot924yAQY8uZSwRtPciuewXcaPHDVbyXUcEFVEzrYPGVx/yudcPg/AkEi03Bbhvx/paM0J/HrhuzGfBOkRVDxmEbXEZS1vb/rdr9D6Kroy3UjLuHccov3PwiiZkzihUrN5Pot5t4dQuGN12npnEt+n9mcG1eBZcUmrHscadtRATrppqz71U1m9PuoXLnMbqqdhQcOki/h9Pp/6mbqzbW3Fvxk0aPzcQaxbJH5gnHhZ3Imv+g9+QxYpz3kLHmKcc2zsVtqpBNv/8xb/MSIpsO89HcieWrthKrdYXhyjNIfPYeW5dZJIl94Ha+gHXXBORumMIE+1gMPiYiV3sRNH/zXHIEtmKjMBKcQ1NkC4ZZArZfEVIyYQTj520gfPN89FwfIMgN4ohJIXq9KdxI1OfKtmAOfQri4Lhs5iibE1SqyJDODbzxX01VyQ+mxfXF1+YVC2POscaqEJHQCwSLRnHdvJNDSxez4PowZL68YeIWN5zvHybkx2oW6G9Bz1GRZX5JfDPuoTBoH7luQlz2NmErUOTCflg/wJd936sZbDyNU9t68G3rIOttLc/FRDl6/g/HnPow5lMnM0R/oXJkHv0HO9JQfJjPjqOY6toX8Y6NBE4x5sxdRb48dWXzwO+UrrWjfrMFs6lijlCNkUdmM1VyOhG/O/FtCWST5Q6OJAayX12L0PkCSnN18IsKwXmqHF4iSWyPn0tmkRm7y78TpFdDx0BdWvvlYqszHPN0GQ4cb0D34FbUrwopn7qdSblHaZ9wim6VsfgeGEGDUTipLzShLpqHodlkxZnwzkeCdd5vkJzaHxWlb+y+Xofv7dMERCewW1ya5nIBrdrmZJRPQ1l8BpNnK3NxVDdOLwfTGRRBgmYbVs1fUBVZSrrGcJwV/tDalUalzXDOeR2ievxCLrlroew+iJAPb/GQCKelZRor70+mc/okThW4MLb3BaOf67Oq72acwrZQJq3NuoXbsetTipLsWa6X2OO/YzsPn5VwPO8L4ep+aNQ94JdlDf1DvZi4TYoNJw+xzimNgvKbiMlGc+zOdYQ7G9j0bwx+jVY87DSjvHYMjcPuEHMyiocBYfzI38yXuDw0lpZTXDmC3mZ71M6vp2a5FmM8y7jgKom7wg7+yfyh3NSIeTYK+B/So09iGX0Wbsbwwj/KxI9TvqEZ2/sunOodg/p9Ly6prWO/y1g0IhLxdzJiyWK4cSyGY9VjKfdZg6O3MSP2buCA+UnkF7YifV4NqftK1DzZxNVd+QikFMjJrMB4sAbX3gq4fHgSPs4aqM/0JGPoOQ4tSmbR0AmM7l/GsyR3pug30/CsGaGHCVVLRrPMtR9/jp5B67scj3JjmTbDkjztLEw6DlD06R9FOX5ENPyjNjCUemU7dn34TbSnKrK3r2OeGYV8tQFrLd0RTIaH58RZoF6NwedIxhyYyRjtOeh9ckZGRZubYUFobLuBMzXEqW1GVzeZRfpSHHgZgce5YGSK3mGULaRonhtDhGc5vCwVRR1zZuy/jfxKL94Ix1KUoc5qs82EGiylY99EtmasI6nUn9SnC5moK+DKrUOs0XIlMeo1w98f5N6ALNJ7D9Bn3Av2zef/n6pd2f5yxAjDsI/Vp6MzlL5HaogxcWThXGOG/L3FnLgSTHs6qDp+BP37t4gSFpNV2I16/hqekMvrUVup3f2D4YXR1B9LJt76DQZfA2gcNQF/7zRGeyriICqH8PYBSpTsSP7gRtLDMux+NRAdl47roQCOW8kxz3ItvyRdcDnVxdjXSQxxk2OFygDWfVVDkFNAzcNMZssMRFpelHmPFvOvrJ3w2XrUhx7l4sd9OKbVo5t6lM+dDvQXraPd7Tov3DVxv3kI50vdbNlqQD+lo/yTO8qyog4eaQSSOaYBz4ejGbFxMO4/hNQF3SW/KhGTARKUiduxvEcab/tpaDZd4f3KVI7NmMW6LQJmPlpN9fd3ZPkIWDnFkAVHFpFhfh3DhzlsCFlAuvw1tj8QUHtoArnpQYyQ2IKV1A22t66kc2U3G5+fYv80aa5bx3BOdhICwQqGz1NBOLkvM4TfmRPTF+G3+0zXTuFJywdqtuawNeUUbkZrqGzOZdudA6CgyKkvk8gbe5fqwnuEXtbD9JAJ7+MCkLXOJz/en/ffHnNmdTyZj0SYbqSBflAyNUvPEbnsBw6KK5H9JkfPYxN63Aowa3PF5JsGoordRGRqEKMvoP7KKZ68EUM2LRcTFxc2anuwdGEn+9f3MsrOme9Z0xhmFotNoi2ik8L5mZvIcflp3Nq+izqjYSxYe4EJyfbUfv/KhTEfED8mRDxzF8WOC3HZsJ6thg9QXadEVfxoruoOY9nZ0djfHIhUyW08278zPrgK7e31GCV8JVK1g/m7liMTEo/8rftUZB1l2y197Fcsp+mbHy7zD9PotAS9d+v4+ngvBx7L0CH6jIjfXZy+ZYAwsITlDq4IF91AWFSCek8VzgIB/QQb2dMJq0bMZe+IgRSf+MPIp8Xctt2BtUMhGrfeMUsgwuIcB0zsz/MlwpVlJ/9gMdmDmxWlPKhfyKgTLzl5w57VDxz44x7BCek2TjnFsTLSmC3LH1GmNpuQCim6fmuzWPI2NfeCqJjQxInHR7AdmMAAybFEnXyA9Dcz6l7MYtoRHXTFKwg6oom6mT2S7hvRUBDyus2XiMEL2Siw4+CZYnR2mtK+fCqrLm/EJcSEKR6LcTFSZnznEeZ+fsqFq464qFsSt1GKtl4QRp7kh/8oqn4uwc/8E1/cn2EmGMuGd94Y7RZl2IKtmCyKZ35vHYNmyfGuUIm84BCu9hFHUuoXD7/Js22wEE2REGZouNAc+ZNRUZWsnelBjpEi3079w3rJPPaJ36O39DtZH81YOGMTN3838vmiL1MMlJgoe4ie5B882TKWBY+Xknncm69fezi4L5NnZ52ZbF3F/i5Ia8/m7IOnWAx/zP7SQXw68QTNLetIbj9P1+iPbJuwnoDHJ9n09DovJu0h9EwlijaurNq9i+NJ9UQY76XghjKvzocj9bUFpUs7KCk4QXJiFE99M3B+H0Bw6itI3cGAohCa2w/xIuEIzhcnEfpmHkcXfWLvgy2Ehdxi4Z/N2I/R+k+r+n88ok1alKx/gN2zXhQX6CH6r4h+B3fTY3QM1etebJUpwC4jkYU64sRu3E/s3VjGbSxHjL4MMa8hrSCLgX6tYGdLVakOPyVAq3EdxireKKyQJe+gIQ9D7OljYo1t/2NUqCxESXIXJf10GN07mj65ZZQ2jYW0h4zaL8UcvzLsL4zj9+r5bHrUxEVtBaScjHBoEKehQ5+Lp6OZuv8w/4d9+/7qAfD7Pv78tPekJNEmTVJkJCnJluxZCNkjI1lZ2XuGlJ0R0jJasqI0aChp77335/7h/geu377Xda7r+Uc8znmf13kvU56AeOIAdIa1k9pUjKXWV+I7uiirNGKal4BEozwmyCWx4nYDxd52mG/zpam7il0nL3FkfAcPB1ZgtOI6Q8v70uu0nf4fzHmopItMuSr++YWcG5bM0VpP5k2aT3ZRNFZ5P3lb/oon2tvIKJmIiP9y2lzl8ZodgOYsL7wPVPPjuiu2y0dyP9cVyfMuHIpvxU3FAR/RXm69quDPZE/WL0rBSqCIUChkm+8AvERTEX2eTNawKEz2qDDjWRpJup+ozO9hfXkZzlq/2DfBC0vNXTyoSwDAYlMNE9eVEfW7AL9ZM/F604TZhz5c+BqDQp4SJq31FANNKn0xfrmKcJ9g9H5fYICPIZX9GtEQ7WWe7kQGDv+C/G8BXX+/0FszB0/HsYScT+NC3w4S5//gxxcRZj86w6/nQcxyEUVHo4v+48uZr2eKkd0WFjvnsmiAHiLD4khOEye3JZ1ZR9cSs82Oo6eSOBOSTsqzG5wa1Ad/zxy6PLsY0r+XWZUd2O5ZSsLnNKzbZDCQElB+C0r6pFLQ/YKp/drIOpqHvu1k/F1SWWlnibDkDxHzY1k4RY3KailivjqjNTQdqW5p1mn44Wb+k7Yhqli6TMOm6Q+tyVNoeTSFA2cVOHp2FcckxDjn3chSMWduRZwl2OQcNiEjCJnQyxf7GE4ZqrMtrgydhuG4D5xDVNB9Mi/GYD6yDYUOKSolunibkoqzqzi73hXRnbiPz4k5qFrIsfBtIL0lMwmuK+NP6DK8Rinw8Es4q2PfIBLdi721KCoV9UiyD5NsN9KGTWHi8tH8spzCxMZa5pofZ/BYK/70PuDmQXmcj2zh0c1ZDHoWwjzHLayaf4J8BXFadF9hbZ2BLSOI13qN6WRzxAa14SppyJt5T8kOh+1/q8nfHsLphCtEhZ5D4esRWmpUSRsahJzWX9Ki4+jbeJvLS+S5tckOGz8TAHJSRFhtbktcSSBRu4IYN+kgGvkqNEk84uXrTtYadZFddI5prjo8+v2e2bI6+I8To1QV7DJjce3eRMytexQJApFr1cHQQITDt/yZUZjE6p3r0dYcQ77EKF5fW4WGvhm6Y9bD3jBkPSA5L59BZkF87VUkb2Q6lkX9+DnuI/a2bvz5rYh22Uwy6r7S/CoccXdZavp2wGZfUjad513gY6Laa1gwQZNbNQpYSplQzEdiSrr5EZsECjZMZSieeZ/gbxyxZcHEarVRcHYTsl5LaD44A5XrDtwSteGb5DB0Fcdg9k+auDfLkVu2i+Kvov9lS/9bLB9q5spC+wteqNvpo/s6hmYJGcbXrON+1BP6rd/LvYUCbOze8VfwmcHKqQRaFSKc/5DlG9fxsSaBE71bOL/7BP4Vr/n1dQZS0iu50vCLcpEJrDSbRu/F93g63eV8vS7y1WepmBpO418hbdWiSNa0omI8DM3CNPwLTZk3sYPiNDMSuhawdLMb8v/+MKj7OhG1w6nO9yBvVjQpeb9xbr7P74TdzPRyIOFEAiM7dyEjiOR4sDYn/d14r55NjlQuZvv2YhH9jjkhfdmbuZ2Uk04M+qLJibsW7B4/indhEgR21/D1Zyo2Sgd4sDGKePmvtDbsZGP2bQSpR9C2bODe97vMrNpGZrQ52z+1sWx3IEcDtiGb2ktkmx8d+eYkK7TzrcqWI1HfOFZ1EB1DCxbHu9FPJYBBk7+hZf4aQ9npCDYIAAgKP0NU7AcmLx5N8d3hJKk14thmRIGhB5tEvqK+EHQFAv4BQmEaNJkydrmAtELY77+HRycUsOxowF1TFq3aNBrHjuZIZTHC3EMou9zCSm4z4f5uBG/1RpA4A5cHVtgPCeLNr4tIfohlVWwPFf/s6H5VyBQ1DwKqN+IY+4d12W0ERB2kOrCKFqc+qNiMourrVUoH7EG2NZWMaGVML6bT7TOYji/ihHz+hvy6XCQGfmbovpEccArl8oYHhPx4gLXNWLr/vuRAoSSdd4t4+8uS6FpxXBVNyT55COvV6+g3B2RrYI+mkLaTEvQU9mXuw+/UiPbBbK4F90TUuTsqkqbSOez4GU2OZjc7KqwIevqE07M0Oes/lK5eDW5M8mFsvS9F0S+wX+JBztQulgtk8Au/hV33ObSte7F8sZrcuH3kyt4kx/AwvSlmVCFG5tMZzNZYhvzYOxj7qiI6P4OfWxsQU5qCh9lStHd747tAC+dKXXIba7Hb+weNC0+ZvHQDYkJJxkaVkzZ0Hw/bxZmjYsqQ6uFIDVDnx+aLnK/8we69J5jhMpScO4+YtWAvt1U+Yl9bxl+LnQiUhRQ2/EPqaTrWlqW0N/XicbeAyx5OTEidz6E957mjk8InT1nGnlGn8ZkTaw6nYdHbRvXwcWQWXWCu0xoEWgZoF7ciqi6CbLAWj+onU9n5m4bQiVivm0Ja6wiGzYwmTucfARtluDW2jQTAra2MP3ZRuIqA4UwBt84nkPezh/bJT6mTXIjh6zFoeyzj/g91tqiL8jOgDxqS21na04vTIRE2XzhGQ2cOLQP1uJUWjq3NUjIi9tE7aRm/3xZTUCfCy9tHePjwOSUPFNiiG0qJiRs1e+eS6L2Yfz8eImLtRHJAL+eyDjKoRpknDzOwXalC3al3iJrORNduJH9zeqlL2c1q21lYLBvDvX7HSY6YSe6HzbwWarEqNZ85WmM5VPcGI501/LvpxYZpxaxojEE5+yRjxgh4vPwjp/NCsTeYzpeYfTSfKGTXySXMuFDCB91slLo7KH2cjsPmAaRdCGLrOgfWXn2ArlcRpzcPY6WW4P+Wj/9gakMHCos2X8TY9zsXNkXRxyGaF1nH0SkwolzgjviebgxdVrHPWpHso2cRhuSD+SAEcwfiV/6CIs8oFrxZzrZ7x8m1t0Ff/QhvNLNQcX3EuuYoxkkakR+sSbekHeoH3pL6Uxyj6lDS4jXYZK/LnNfRTJgzBF+9TK5FrsPb6isnv4tROUYG6zQRCsLLKfbaROKtAaTO2orVByN+WCmxU+oO8g9bKZjuxJLmFlbm9+fvYFkqlkxEffJAhGsnYP6rii0WZ+l934Syojahvve5pnUdaUNF9r8pQ3LRCYrn/WV27U1cPPSY5r+AR3mHuGY2lZwYcy7NOUX3OmjWF7Iix4mOO+lsqztMseIyPl7bQcC+Ji68GkZVn1Ys2lOJiJpM6ejl7PD05cqKetrSPqF/bzjTrYah9301M90HkDOuhHtHw4k89JlNMw8Tl13A8w5HDmpf5HnvNgb0fKUwWhGJhTdJuFjF0UwDfuuPx0auFCepYbRfW8v+U7W4TmrjT1obPe9s+LEbDh2KBYfXnB+fR/r84Yy9WY/PGmWKc99wY6UEn/Y7EhouwMu1LwoOy9lon8ubFe4ERoXQRzOT1rHjKXj2iJiixTjlgfDRADzvDuVb+Q3aJvszqnwngQF5NGoPYVXJZN5d2Mzn9iPM9J/HzzYfhmxtxDp1JLOiFVmtNg39RWnoHR3NWp2ruM43RtXyGynKrWiuecjlQX34WGlDgyRsixFl66tOqv2GItqRRPMASVY41PL37VgimnPxUluCS/+ZjJRoQeLbMSbUDETCI4nfWR2I56kQP2olGl/dcRycyeMubWyvqSE5aiYBmXvZZrcJRdccqg7uJ/35JW6vuAgfehGO28Az1XbOeYSh0aPG7q+mPJD9hUWQB/7vMlD8HcKaUAeqUwbj/ugPh/bLkdSjjsWyQDI/b6Y09SWzJt9E+rwL2mJjueG+DdFbNrzc/5j1IUWEaRQRuMWEWaK+1O3U45CfP+UNoZguv41QP5avCr/JuDCFZPkYjiy0YaCGDbe8z5Kr1Q+Tue+Znp7Iy/wkZkWK06FWS9bSQM7cFLDqsgQ3dwbwcvk33IYFUbm5jn8nIKwtEk+HRPx8KjjvkME7RTNcrjczYf5HmsoWs+G6CTG+rtSFuVEdVovNTQuGXSxn5vspYDITpEHYKmTlhz2M/iDDi3rwjdLh8sqrfA/X5bqrBifl1zOosY7KMgG6+v6YXVQgYHEI+YPHcjHvBM1ZcTgqijDK4CaHmgQkKznit3oLtgbDCV12npdan1DSk0LnkiaXtGSQvDOdoolbuPSsnBfJj5hRCr696xj+Xp2yyCP08ZxOfsE0XrbHEes1gF95x/C6J+CP5Q8ud+zkttpGPvzN4ZBgEFZm1WSmJGFQ50aOwI+/K9/w/Iw30+4fZ33oQRzXifO8aCAi3uJUtOaTtSyYcyZJ/JJ0ZfXbDpryl2PsX8W0BWdZ9nkCXnpObJ6lhOvKXMK+WKOqNPx/zvLRUqXA4xk15Al+IbpzCAutLAnrOYH5mB56Dw7lQp6QqcPT2GoiT+CZF6SazaD6ogHaPe8x6ttO5kEF7CYICQutpfXgWqacU2a68CvSmc1k3xuDbZcke0Pcqc7cQfLwazQWzGd8mSQyaoaES3fTLiOLml0Xz0+Fkdf0kq2d2xja4kNFsjFvwrex9eEnUrLtKY/KI3bYE3aWv2ZmqiwnJh+h8k4165PHESrVxoGGQyyMc0NBM5BFfvvIkP1JVUAKioaJfD5rz+b44Vx8BbPn38Oyrhujp93clfuDdsYixA38WDbgEDo7R9O2uB9j4k7imDkRVQM74m7v5oWjJofCPZC//xm9r+PZO9KLkImmOD81Z5zLQ9LSqtj2KJMA42hebRfFJTUC6T4lNGfo4b4jjKdR/nSpdeGcs4HEsGMIhSXUbNqOcWk4tzbF86ZnCk5jq4iV2khu0UPWvRzHvhDwFwoRCoXMWyBJ6OdcjBw8WD3Rh6TMVjRVVehbNpgPms3scZvNQaExE5HBaP933EulKD1vTlvtXUpGK7EqaSgbnXy4HGvHrNNmlB3J5bOpG9odDeTtnMct/SG0xvmzO2YSy/ZVU5twiJCHI6nsJ0cXVuhLFmCUH0JrvCHrJnTyZ3gGmpLjee6yiiWvdNBdMJGzl0L5pq7DMzYSorKO9Lj3yF5so+q1BFqDermVP5OF8QoIv1VxIyWeezMuEZQhgZW1gP0PBVhHqtO3egja+h4EBM7g/uJKFie1Uiz+EjXtvQgW3SXKdiRLhygQK36S+QZ2bL9UjpzFYY4vz2e9vjZunr7cPiGLZu4ZjP7FMidoP0cM16Cflcf38LM8iPtL1L6VLDkykvsT72A51QCrO/cx2H+QqZcj0JopgkI/UZ4NvoXA0Jxvsm5YldnRFqFO/Ns4zs4dgvyoU5wJ7mRRrwP7nhggeimJM/JCRqkIsei4zEnPJKbVtKMxx56xs7WpUXuBQ8kdLnvvoE+wNn+lhYx9aMeFIjlkQ4fzadlO3Mut6Xx3gxfXG4lPaGPWjwyc8ux4YT8dxxtd+B/w4fDH11RO1qfwRieecw7T97Y8ifZ19GqOQ7pYjtK+Eyka1kOZkwPfd/zG79lD9A+84wUfuHKtH+oaBUSaHeKzfhA7fWMZXVKB9p3VHHU+xf6BEhjMyABUKdqyiP4d4TS/m4l18h2a1ED+hzqan/vw+GM3odNVOLNwEmtCAzDOOsDzDdPZazyRyE59tg6fztEhSnTtGsW+xQoYdf9CpuYJF9xGcVrHkQIXNYoPHUW7RYt2N1uU6vLR6h9EwtwjuB99y6uTUvSUPGZjeg6VD3woNU6jVnYkbq4CjmwWRdf7JBbb5iEUChl4fjfB17M4oXYNo9p2bFbK87vqF4tLApijUckgV2e291Wg36uZrPMy5sfdeUQOuYzigZM83eTFBs8j7Hp/jbuP4inyWIHQbjljfkRwe60d8Zca2e1mwihzHzxmRDPy8xlqF67+T1P9v77+Xe0Mj5Blh98ipuxSI6MgBdeiUZS9ekyZRyvNPpu4W/WK7D1n8XJ9Ae8GkXx8AIjLsuvbCEZe34vSkhsYny5nqYU8ithz/89OxJp7WFkZSFF2PgL1ETxUTiLzXw7ji0IoVPzDm/wfVFYsZJR6HENMCjn+tBmLUmkKPh1igLMicSd/kFtdjdEcKcRjRVjuMowlV86xxX41jGnARX04neUDuTViODUVWqz3HsbPTSuQPhyG94ZVWCsf4YXCJmTUT6Os3EjlgMP82xhNyfb+bO9aQMGDBej82EiS3QdOSVihVlPD48YHcNYej4oofmr70p0Ljo7WKOndZEtEFKcnLyS8LI0lOne5adNI8qw73PwezabCYtpNa7kuUKCwErLW7afm3WPnxYwAACAASURBVHROvhzGV5sCBqhoMz8mAWXdEtZ7zyHFYQptI7vYcH4l3WYXcPGZS/THZxSb7UK2ugPjBfvYsG41r1d688O7hqVuAkZMCEahAOZ0XGeXujkPwzPpM86M7BHmWIqNgqqP9FknzZS32Zivnoa6gRKHPKCxSoU1lo58tJ/G1tN7yGmXZOI6X7KkllGxoZpBC6Yhvf8r0fbRrDc4S+efeKxejWPY2wrM+0+haORxpKNjyezfxXb9Sqw9upConsyGz8E0OOcw8YUT27ZbkzZJhxBPVcr7nCPAoICfJQexXjiP+61PeNUqzae4SmZfFcclR8gFUxe0gzo5pSzAeG8PUq816JbXQ07eEh0jeWKC+mFgoI+ylhJxI/XYH6mMSboDb34VY9CYzMfcTtTFulAYkcgVKx0GZdxEBWfk/9ZhOdWOMLFafs2PpOCFBDWSAYw4lU+WxW8cRi9Bq0iV2b8uMlpcnz/HdKmRUOd7lBDNKarc/r6GXdVLCd86n5aIoeS5GLFgaTyqAnHkfsYRUzOFZ59esV5qBm0mlxjg0IcndRJU38nmtV0iny7t5LfvWNQf3WT8T1P8y9fypKyJ/cJC0o8k4mh0gcw2UeYLbjL48F767T3NxxtfqFS0ZenR6ZQWL0Hl00oyBe5opNjSpdbBsxBHRsyqpLJXkxyJbnKcFhEWHAvf6lEEMtqXMEAwmczQA9THXsFu4niORFZyLMONI7IvOHVNl4S9N0hWl+Ok/gRWBGgzQMeTL0HyHK2dyfsdAdD2/y3o+/smVbbjGdb/MX/zxBg9YA9DzOvpUa1ijpY2TaqP6D+0HtlbhWiNW011bSXDasV5/G4WLubaeODPQaMRFFwSEpd/nUNvbhLoEcWTCluUNUrQCWzn87B4CqV6uFIPT7YYcerkKv79jeFfbhAal6VREjxk1R09ZOVDsJy9iOK2OOqrZjPLUZU/gUKe3iqlQ9KMaT3VzM7cj/TgV9Q9EKWh2Rydua+4X59HrHwAcm1duE8Zxn7vT/gdHUDRjGDuWGuw9sJujg54RKDXBQ7Pe0Z2VxS2TS+Z7LCEVoNczi4bwrCRc3iemkxTTSiXD7r9ly39b3F8yIr2cmm+EhNMBlF9bzMfIxYhNVGOgNg/1DTM5qp0FAsXbWN+2lu8/53jYNlAZit54m9ykDz5KOpvdZHh/JcrI4cQ7pHI3uPNpGRtZt3sXmRHTmX/UjnUHcTQ1MjGpp8OSlXR/C6vQD9gHfu64hglOYno0tckP+pF7Y0lhySCmGhzjqn6j2kv/Mc10yS2xHxjw1ARZnuP516FHvVfb/L17nVuWPjx48t2bgbp4Xf3OZNPr0H+jiS1Sg4c/7SQsWHORCr6UbU6mrHXPuMc/4n16olsyQqnyHIBXXYJNCq+R8xsF98chZhly/KlfjezXgfyU38UB08ZsXRFMIWFNkTm3eCWezDG1wQsm/gPwzcWaNb1Z440ZIWe4VjKExrdipmzYice6vb0l5nL0HwNdnUs4UbHCdz+JLEqvYT8KRP5sLcP9fOCOVfRzJX7osw09ebftRT6jxiO8dmFmMspcCpqEs9+uhOw8hjXZ7zgW7MVSet7GdEowqzNTzg4oJSmceHI1JegkVVH8KUNaB/0pd17La/GrKXG1YWRLkW83ZFG559GcvUeIT9sELFn37Hmmy/vDSPZ8t4XzemjeZ4mgkBWl/n2pTQPOEN78HsG3PhBab8OfjzZx503k/imPpxMS39eOhUhYjMA6RdTMDE9RKZaNGfyjqE5zB23ZTakNtUzSXo5G92NmLsKTgyZQOis3TTnzsK+Wg4DL2MUI4ai02OOmqgXmpuNeF7xlXifMST3FCJv4YNTeTimC7dhkjSOvncP06U3hNA9UmzLf0nM+Rb6DJjL47BelCKGULp9E9u1s/E1lESjxJLJu05gOKOUT0WH2e6jTGj5T07O20+z41aMV2zC4HgLUnLRuHcfJXa5DeV68ThZDkFBMQL5mQuIShdh2ishQs8Clm48wFG9AQyWOsjeqqPozRVnTfZV0r+m8uFRD2PUb3ByVxprb4uT//wIP/dpcXqfG+WqGuhWe1Kw04J9yT4kzxnG52QhG+aXI12jRXBXNQblG3jQ3wrX0M2YocW5d41sEDjzNXMwTukPsPLdR0PudSTqf5PcEUf/LW8I8ArjaPFjqht8eS86CekvVxiVWI5uty6FW/1YbDaergsL0Nf4ypuPX2jVKKAwpYT56e+xm9mIwavNeO0PRzNoMS8S6vh3YRs/Q83xfxqI1eoIhKkpxF9fzhv5aQzUP0p5kTntJPA00gsNW3t+VXiwXtqE2c0maNz+weD3qXRmzGaCw3EUa3LJfJvI5bgkGq4q8uJAKS6/vtBiLWTzwitsimnkx9FPfPlpyzNvH5QqfVAct4Q6zXL6+D/liMxM9s9zQXnyJ96tE6WftA8SL7RI9JmMsDGN7cePMbf5IcKqYgRDBdiOzEP+hgvXEyOxl1uOen5/hGsc2ZRmzt/rGrQmm6PoA3ojjvHjgiXhb6yI3faaPbV/YXw/ehKFuO14ygczETyUp1Iyroa9g40YuX4LLitLWDrdjftdPqT8no+/93Ymhvr/p6n+X19px0C8alVYu7SJQ7+kKJDoYN9jVVZ56WB4dDnGRy4SnlBNcEY4Sxc/pzbvMcP7ZvBmvS6CgdnYWKxhxd9PjB9giKzRZWpjpMib/Jc7r0qRkoqhYcYB/up8YKT5fV6+y8RCdynyyt6sCgwjtW0a34vd6RM/m2FBO4hzskNniS43JESYuOkQ7vJVhAz0RSNFie2tPxmzDe77zKdf+lEU1E1R0ymjuFWev2vWwspE3t7ewwlBPqkNW6jpWML171to6ePP6r9u/JZqJ6vMjYEKY4jdEsxdaXvECEIp9TljjVdR0q+BzZ9WsnRFG94TJ/P5tzQRDgYEjdDDP8yDgHeW3I8WcOpHJhVbXqDiNJ60jEek/H7JrWEtJNlFclZkEH/DnVmdJOT642/cexrC5/nhSP5+ycpDShzfnE59hAlaj4uJ+BjG04h/fIs/S0TNFaw9uxhzeDPvZkWxofgwkXwgzGsHsb9dOPVFjbkfn3DhNmxxN+dWZV/0lHYQ+0aDpKndzDg6lYySejost9K45yFZJjZ8m7iF94NiqY+zIa0hgnkNNZSl1GByzpIJz28wR8UFrft59P12m4x7o7i10gTXtn+kvdhHjU8k/cTyCcmRQ2SzNtavviBz8xbfH21CZ58jnWUH2VSwkpZ6J87LVPDn+VUOOchTdv8If4eY8OK0D5MHPmdmfDxDn2rgGHkGt39yOH5MoJ/Uev4oDMTvdi4Pyv6wXjCPV83TGVJ+gpXb+iDMi0A0sZYTK2bwYtE//JPsUTc8hqjVJ6rOWrHu4136Nf+ku6gd0QoF3nbZIa/tS9sdWxbnzOD8V0lmWv3gVNJJko0z6B/iSNBMHXx2bcCk5zf3zz1H8chmtpYksz1jEy4ZZig0iPIuRIYlpss4uagvN7y0uK/6Cp8f3eyeEEZQv+dkN7rjdOMoAw5vpqr3BMtTZMmx9ufjcBU+3tDDR2c1LRqjSTAToO0dg8Hzubx6Uo+R9DhEBHIk6Ezjj+JWVjaM5LHBD/pn/+ZS/XaiamH7spmkSIQyfqg6i8+5czUe5s4dQEWsCaNs7Rly5yYxif6otM4hezs8GyXK04aVSCR856b7E2ZWFaB94gMbYk4xqTaHuNZvbLCMZpnJSUYIduG2aTvyjcG8C1NkQrotW2t2kZbRy6a8fAo13/Fg1VkEtgI2bqpBp6sJyeg5eM9+TN3yu0yziMXurghSk46gLmeMfNU1tLUWEZQ0hgk6J8jfrou5nhdzKo5yYKMVfx9k4Fm0lVcT9OhobmVX61N0BoRTtnowmUc6+XtBDKejU7FSHsqqN73smyyNQtVB8n/n8155AjF5FmSubWOd/xS2HTyCcfVRKpSz6BDW4LhBiblp09mxOZLyEauYaLwG7VYF2g3ec7slkSzBRkYvHYvToYtoNttxv2sMq/evwklNlZ9Gm1jdUI5QKOTfCT/uaogx0mcNtYadBOfJUXNMHvU9s/mZEMDhTw9wlFLEZ7Ub0iPd/8uW/rc4PtpkpWgZb4PKESXqR/whQSUTmWuSqEhkcPXXeUrOrEWgKuDv1Av4HDzHvuAIzrgs4KznaMzHN3DW9jQvi/TZmrWLyv7HOPnIGpMhA8n+oc6pieF8W78Rc8N3TFMvZ+Xy3STTyY/WNvqEPKCp8xMiiUmUP1/Go5/SFHs9oBR19uv95eD3b6wY7ERW5ReSVorgNdOP6yInmHtRSGjSaVY8O8qwzdNZ+Sud3+/qqD5xmdzRylzY+I3shVE8/niE0W0HmeT3Ea/qQL5kXuHkix6ETlu4pj4DPbXNhO+vYsvtc4gtP8buSVfRbNJEVDgIrSJlDqpnoZh9E8WOOHa395DddIEruUepKIGC4o/0WP0kOq6E/la7qVVfRZq7L9bBC9knCd0pQrQbXrHhpi1BXWLcbLqMlZMZrtFC5pltJOOWFETM45FFEffWhCN/7hiOxqNp9H7P39BvtEmFYFtsRb99c1Ef5s367ItIta9jz+xlbOky5MqNNVgO3stvFUvKlqxjkKYqBdezsc8oxVnaBeVdT+D3cLrD51OfnM4o203ENUaiMLmMxn7/UJes5PM4MXzFTckWnqdl7wrMAqypsJ5KHxN37u0yo03MiobuQZzDGLHzuxDLuI7E+Ne0ZD3gp9psrjeep+32NVrjihDO2sL7rSu4mB/Az4Pv+T05gfQhPSyvD8LH8RwNCTGMfLSKAzU5ZPhVo/v3B4q7hxCYNR8z3bF0jNxPg8VOJqXI0B0sRXNPLXInnjAg/xWfgnSItz+GBp0kVzUhsVWdJmlzBpyUpP1bGH0f56NT0MymcHFqBVOR3mFAWKQMU8qmMPFrCzL9VrBeoMqznQLu900haZsMEwVOaJn/pfdbNYNDzCn6NIkxqc3cMDeitP90jty/xMndZ3m2xYJEY2mKPtsSfHwSVQ8Subx/J+t8rVi5RYVrUwbiXSWGb/RuTC860dfvJjMP2DI7+hR/+mYxxOsdZ2JLUIzJ48JSAQXRFhzfsh6pwWbki5kz9esrFonfRmAj4LSwDpvgFXR2ymP5bRQyW60o/TiKjw82UPg2E8nlJ6jLWoDGYFWuhCShYOzLiIaNZFh+Y+dtW1bdt8Hz2DWULRaSeiidgJdRuE64x3aJOeT0CeBM9Qv0ZDbRWVZBteRYLgxt4mPGGEYGj6f930LUv84jX3MA55dORStnFU3h5TBHFjPRo2gGivE7wo+V5w5AVSwSH3uwKLOiZagiy/UC+Wc7DP01ebT0X8Yi0xloV31lzdqtPN5tiOHqzZyUaKZRuZeWAANU1EQxPV6OxW8jFO9sJqlfPcLpv3ii0Eq6xlN+ndVArDsU5ZYSbuaE08dIiiBBNfoJ0lS02XPqlB/ei98h0NEj89l7trba4yyoZ1l+PTtXPGf2GheyHY9RlBVOutE29EcV8ml2Jjmt+Yy0vcLvnXs5vteNPZcksL04jOOebpiUzGPp4LGYW85jvp0ck43Oc7Cljr7WOqj0HcS8kx7MdrL5T1P9v756sTb6KdRzkg20RPTwNjGVP12FuL/IRrEumBfuHwDQCJtCyvdFdJ1egJdEIy/D6jhuFYJZVD8ujo+mU2w0fp9KUSeRytYWxJamM3BdJgMuVqGbZ0206DP2jtmPSEYW5s53qPIbRqVmFLl/YsioL0B+2Sc0/7SSpfQPw9y+RM+5T2PnEgxU+vJYPYKVz29z+TIEZuhypfofZRE/2BAZSUxbDw3hNaiojeTLGH2GFRWRezyVk1ZrGTrlCxtSL5Ds4Mn725EcXmSNhLw5G+eWcP5WKWkPpdB9Ws13cSWuLof+49Wp6z+Ut16NaDiLMDm5nP1NCuh9CkRU4xKHl55BGPuWFN/bLH57k8O7fgEgmDaQfzcOsi1rOAtSr9D+4BEfv59AZuhoTvrvJmfSVZ6/NuG5pAnXy79T9OIM4xzSOXFXBe0CAZpCGx6GBMHyekzKG+gqAFNMsRq7DFQOYDx4OjYZC9mgAqf7V7PqYRchMy7CUiN0cgo5H1uNkUw+D0eo8b3Cgm7JTrDzRikih9q0JjKnfOJT6CSkklRZrTAep+40hlgtJ6HiGY1Z+sjmGJP00pLytjHUFVagMVCSZgt4nV5F4pWdTLMOQcTJA8V7Z/BY95fHXtqIT5uEWNk3otc8ZleSCK12SdisOU925gUsFW15kfCdoHNbeXGmFQf7+XxLTOKxQSJZRwYgu/oMzj0zadnoiLCwkPGZtykbnoJasyZNEuI0bLVDhEp2HZrP+BZrxEsC8NdeQGLed2bKLaVvPwHtdRPQLW6hNUuVQoVybnQcJKWyFBX3Sci9/MwbXRn6Sazlm7E8N4KEXEt1w7o6mKDRb6l76sNfk0rCUg9g3joEi2mj6PO2EBo+4bThLBcuJHFthTSHNJ/Qdb2bDR5Z9PZcR1AwEcVTF9Ga00JzZTvOj/PZdcSAnPKl3PwwhJvR30h5JeTFkZnYz34Kc13px1lWegg5LrmXPq0zOWu0EfvWFKI7ahD98obsf5kIBAL2t+bj3Oc500w90Dc2QebzZYxNlRmYWIqUthyd7xKZLm+AfgZYzB7PbP0OEv5eIVX+NrfufCIdbbJeXqfDQIExsbXEuS2lS/keM7RT0X3XRWtcPN12M9hStJ7l6yUYei0ZueWDePMrD+uibSANmoDB9KlI7tzJkm5pcmY+4ENMCzbXJCnLsudfRjEyVnrkScXjvO4ACdfTcYyWQO+JHDucVTG+eg/v889wYiZFZguIbluJVKYOvzO3cU4yE533Axg66jyxk/SRdRBD3DAFNYnRiIw4xqCOOpr0C9DpN4O+bUVMc7WgNtqM1hkpaOs5EF0F75MbMF7tQNXJ/aip/+KXfjppCHj1rIqhDkaod6RQv+wzz+SsmWN3j2eqUhiYanDM+Qw2Fe/wWy7NJYE5xkfu4CgpT0lyO5cN5rPz5z+8Ik7QXFGF8iZX5A3z+XX8LSKSfSnv1f8vW/rf4udjhFF/4XUbC16NM+TfT336VEZSIqvKRScRUn6LE9Wjj5PSEF4kvsfQy5Kz035gOE+VnbXyfEyOIsj0HaX+Qv667OJaxjv0NSYzUHQ0U/ILCDWKJL29gJd7C1mtZMWdZ7v5mp3OxM4eRhnF01Y5ge5BkRQbXMZO9Ai5OeqMaKpCpnoI217qYDbtFIIRA8l+Isuf4lSob6Em5zoq/yaCIzS3Q7xvIHEyY8jqPkF+X5gxsIq+jeZc/qBDae4bNuxYyk5BMir3fPEZ5cawNcfpLFdHMQfOZ5Ywqk4eg7a1OBxJ4/QZJ9pL1tNuEsaESiHX+tRisb6T+fe0GR/yCcQ1OPZkML4Cd3wC5qPl9oTyczsZvKSFd96XuSIl4OpEIevaszneVIjeqiTmte/G9qwd2ZVu5JoHIu92jwuFYQS+vsDPNwaMjQplxo5VNBno0rhmNzeBVm6BcCXTtx+jt8oID1E/wlXH8yX9NmnzV6HYJc1jrW1cTU7mVlkwN46X0VXyl5uH9dkhVUKHVAfZ1xzR819A/OoRSJkK0TzuQsiFEAI0pPhh1B9BszaFaRrc+aNLc7YF5a9r0OzW42zVK9T76ZC+X5tqLWPs7bfyWSSFULXFLBJYIFJQwILYqdQEHmLPp2dsLt/Ooz/fGHG0it+LFxP/UofnzeZcq4hE0D8Vk+qBuJyUQcFIl5dND+mx1GbBRCHRrxUZ/V0RHT0z2rVUOZhnT79ENwoH7eRvfBCzBoqgdcuIQR1l+Dy7ieXUA8TO/Mm9uD3YO+4k/JoNq8alMWqdKp/7imAtTEFkwn0eHR+Hlu4NHGuMSLofzJCdkoxfYo7Ptgn8fOZMJ/Es9fzFItXjLA38iMI4VdrtpBiUWM1C5y9k5xmyvqeVb99s8CGZs3qLaZh+jaBjf0nIauPPzk6+ZR5jyZwMmhQ6uRtlwNPyQlTTlDC28uVeVBhvHdwYkShg4EIhPrnHyf1zjj4jnvHugCe7Cto56rGe0N5KTu3vQ4xtInrK8dhvGE+qrDfu5aV8K8ig7k8oqw8q8qDei9R/Avqnwt3OJ/ST/U3+S1Fskh3wdrhA/ZNi/HtSyet+BRIDmdGmTfcMO7ZryXJtUQvr4tTpda2gqGAt10vLMVumxXety6xvv8+nHGtyHtYTIvICzSvJGJaq4/cohohbkyl9so6ar4qIqafw8Zor9s87qSqqp3fnfea1NPFmRALdfTbTqiCNisoXfp++ivFGeSIzDbm+7TwDn2fi0uBH7PVi7k+XR2yMBb0KmjT3iPCqPY30Ryp4TpJn96Jl8FOEebFZWDTdI6h+Jy0Vx9AzUcLtyHMyPv7GZHkY8ukJXJZaygqDKpb6HEV4pASBy30mKZ9FMDyfVVp3yLAuQHqPJ6Wc4OciA36WVDJNvIpZXf+IPq9NsXUx5zXUMYsORWtVD+Pruvnyxpb+6o04u+5gh99HQlbZEv0qAXPnDr6nZuA5QsC+P62skxHH8fCT//v5+A+m2ldXqHr5BHsUkxkicOZ2dCW5fY8wOs8H0dJKggs+c2/CJTw9Z5FQeYaIeh0yRa6R8TidqonBeI0q4orhPExS+xH+wYP5+7Nw1w6g3x0rJlQKWVmqwII78ghWbsHZ2p7wkDjCZomTEJyIVsMLLtk6YnU2gtOz8lhbH4i15W4uRQZQcjuBOVq3OFnkwbidapSdMuBDpw2BBz4TuP8KF96vx066iDm3vHk0ciOHKtazSWYSIl2uJGkpEv1Xj4V6ZlTUO7Ps1Qm+bBOiWRPBa7EmJORuEV94mBESaSxPWsfzta7kRedgGKlGuL0frYcssLrsTUx7G9k2q1hz8BHFXfWMOKZBvnMZng2viGcN8nctuRjozI/bddxxCaF1kBuGN+HZ88fMk1rItJyHjPnixZcBvmw1eMu7eaZUjsqizH0AU8SOExVuxNNfmTRuH8/nsni687wJ1FmO+yMh2ucikao+SXS/UgTRT2np8CD+iT/2KenEHBdFOL6T459KeX4mjWHbJpGSK8ZizW+Uj9tPo9xo1OZArI6AJ0InFtyfRU5xMh9+ziZdYjtBMxWZfs6NmM2/mXZKwOMJcpwfPhoNMWl294lharCAwGkijLidR7WMHjXuS3nrvo7VPZ8RcT9JzEE19j2cw9g0OKPpjJShPOu3m3IlOZa2Z9sQv5XKjIUtbMmUwHlgPNfeyLPDYxiqNgb4+zsySHYYLYV7aHZTYE/xT248CqZCTR9zK032v57AIS8tBM+cCKl9jrpePhu/VzJFtJxDrgMRCdNFJOo3NrsciMx/yxgpJZ6rmvPdXg7x1FxSV89h/Y02Yppuc0DTgoImBQ6vsWfvhjfUrpBE9ZsMYYf3M9d9MZOO6uG50IHADTGsvpDCaicRzl7tj4vuV6o3tJP/Sp151bvxKxHge11Im7cAofY4slaok+j5jDGmQn64rsCkbj/VdfMpl7jNjAJzFtoJCdsroEfsPV0TJ4LVMFxPChHe9UFg+JiPT9R5cu8t2jeMuBA1liK/DwhnXoYDBngJZHA870Gkw3nODRdw2vQIY0bJ8RZvLF0CeW8/hXYbH6598eDPpyEMFlvPoYDF1P6pRcN2H5rlyiwZfIXlbfkEejujoS3gSOpP7pz8w37j77QEtZH8WQGT6WbseBjPY+ExIuMHsGVuC7/MI5B9O5l3SxbQYaLEo0uqDDzgyZq226ikCfnn2M3p9GeskIniZ/8QRI/HMaknAeWEabjvmsBh9SgECiasUyiGLbH0WzScEas3MtLbmv36QvL1grjWtQyf2QtZ4KuOo7sVS0ZpcT8pmsiDD9HRduBX6zUG9Zgwb54j6y+dY9LCh+hWCPlhe4AVOTno9s3ksvs2+rtH8l1lB6VZnaQmXqS7VMgxl0ukaoiS0xmOjVIxdqO3cVjkF64dTUjeLca3UZM3U+SI7SfH48BGfJapI5unRU+iJNLvdJD3vsv6eY30eSbGWZ0OLN9N42rXTTxfRP/P+fko7RDlvf5K1pp601BWzjhTTSqHGHHviwXv5ZxIqu3CtcOPbcEX8dJYQsSBQ0waq8SYiIdoiZjQEbqWBONg4pOqUTnvTPfRGewf/AXdpRI0bpGic8Yx1lVcI/X6UuzrxfBulMBpqT1UK9O7oZJQQSXpAQa05VkyLHsmYnIDuSHrh+z7MN40TqSwQIxpOv2gvpY3gfNRPbaGZzW7iL9dyuWg7TyNjaI9/AGWX3UYVROHRMtAEkP30yfgMC0fM0jMuYbqCV9+qS9EUn8GDsfr+Kl3lcirG5gs0QjNp5DtUcXd6zDlQ814GLwChUEbWTt9GJPlW1FdbMiDj9VEP9mHrlY3ETPcaRNWM2TFY1LLW/gT/4OZASbciRSA40M8Hy5jwMvrqH+5xguHHDYeT0ajxYq5NQd4LOfMpu3ONPopE/wvnmfrt5Fg2cuDoSIcHqRLVsQnro0Q4nmygpQTN8m938UO/U9MmrWdUeECIiedgrEnOJ54EKnfCsjnfuS82A1kwkJYci2O6QrWvNO8ClllDLqiQ4+SJTF+p2he8xrvghcsmhBEZsMd7H2fcPCZC/eW9ccxywh5YQIGIxs49KWDBmlr0s5XUzT7B8YjnlIbcQ2dH8Z8LvvKhB8/cckZyMgLYYQvE+fWo4MYLvLjhtZqJAr2cnjBEaYvOsQ3Zz/UE/7gdsyJ2qTlHHcXcF20is25ozkWYcbRYzsQiTtO4OjZRMRmsT3hLC88HhOS4YlhUQejeosxNuoiULKTz/JSPL67DeXNjxh1vpvL/mcIFn5kiflldgY3UC/SwWal72TpqhD94z4nxFcwzfcttlPKcfmSzMmcWTgOPMCJmbfotMilLXEGE65OZHJzCVNd9HgwzZ0VpOBoMwAAIABJREFUWW6Mn+VPePw5aqu2ctPgBzkPNrL0eQhKIq1oFd3nxLpFbBSfT1qxPTemPsFK3RR3ubFM2D2f4iHS9J1RSU+hLmdTIkmasQwny3LGFx3ltak98edukfHrCOqm21G/+4rXa7YSpJhCpW0Ryree8HzqI962DWWl5isCAjzIVTPigYU1QenXOLB6Kc1fj7FldDOV1hlMMM3Az0+bvCk7+XrmGE9uChiYNQuPriIWvU+neekEto8x5WzRZqrrgqm7UYG8/y7uu01H3dMBueINiC/25f7oaFJ/z6aPchS/4o4zSC+KpPkNnHB+iOtUA2rTz0C4C92f+jJ0XDrGRx8gPus2ZX0TuOTqAubFjFXbyOSm61QMlcR+61IMhcN5P+M+EWPyefStg8LcCUzZ4ImOtA+1u6UIe7mDnRsrebvvEh9XzoVPQl43baF95CMG5T2k3dYAzcEODBeJ5uWGFfSeO0fYy9UUB/alo/08rr0qhJnf54vuPg5rCPi4OBCx7U84v74WgbA/le5BtBccwzBTBPHgnzxW6YvWvVZyQs2YOEyG1UMvorDXjxUOzpwoX0ZLqBoLBzUxQXIUuV4HeevUTfq5CSjNMSMpURlFFVduijtjWDKfB78+/qep/l+fjoIyCtL/sK5bxYefoegYC8l+morZ+L4UOQxnQIotQTdUOJ8yGSnlEag35LB3iDS33w7h7dS9LDPU4q6eNtLuITQ3tnO0+S99+qmhkdWJjXQd1jk63N8lBbazUKj3gqYRyJrL0PM9AHSUEP1ciYdYH9Z3LMBQ+TJZOTJEpE5iHj0Yuh7Dc/Uh/Ic20OJ9jrLCHVjv8efWxRCiL6whIPsSs9QqCO9IwKJsDjGVSsQ6PeN79BNGyq7i0uc5zH4iRCNBSPJHTTzV09Cu66LPXHtm561HsV6HQLXtrE0PI1MshSY5b9oPTWPMYsgV6jKrKp1MxSrS51gS5nmJ/PwIghoC8UWAV+9ebnX5khkjh4yOEttS3BnicR4Ftd80pSzAsjGMycknebAuhuL75+gxrWTgC3Uidncjds6PFQvCwcKdEwJ7+nTY8cT0KIqutsSapzCYn+xv20z1MT2iwmFlSBUZEXVUf6vF7n0EMeuW8LaiE9WM0cw16OJ7H0l8Gw/TL/ktUnNk6ZsTDWchDogIy6Dz114eJOYSUfeXnFlrCFUdgV7nEJw72uhabYrkvCIi1PQ4f7scC7tXSOtM4u9dLSipwPvYOm5sesYqiaH0iDZRfnAkOUUj4J8CPPYg4vt9Jh7sYfwqHbgsSbhmA9mmhbzOe8vEdheKFPW46WqF1EhllO420DCihKPVsxmo5YpAVI3HUtUY99mDmLUuXz+IIePQn/LPfrgMMaK4q5NqMVGaatup7LZhyqy1iGmMxlCpHHGRMwwRKyKm3/9j572Csvyabd/fS86ggogkQSUJiIKYQFREBVQMmBMmTCComBWMmLOoGDCgCAIKmEERCYogUVCJgihIzqCkd198/4tTu9ZZ67vYp86uWmtU9cXs2bP76hldPcesZyAWXEUiOgsZxX68MdjOhA5ZlslLEFt2iR3bJ5KfYIO5uiN6knkMHniHJN1eVCn8Imh3OnZ9p3Hi+ROGDpci4qEvQyVqaFPWokRHlzZpJS72WcDp/HS8Xp+homIxVWMNyHrUydTDV9m68RdLXnmisn03opMu0pP8moWmkFnxlYI5VrxvcOPOeBheoA1llqy+ms2zlntUVgWiE2FM4v51NKb+AolMWNdDMXCKdmJzK7D8GM0Mrxl49Ytnc+wBBpfbczE0FXt6uJdcCRjgma3HvIIpZL/fiSBLhMevMxHduoxPmw25u8cBOIJe+zTSo8yYsOcuurF9+fnKgeoUO97//s2yCf6YcJPcwrPEWQTyfKEiI7hE5996BOlxqC+IpyWiCxURefwqr/Emuwhbo68ovryNnPoTMgtE+GzWRvltS0bJfGfD0Wu8K/5AreN0iq1msHfjTobpyzDgyjus25MJzruASEc7H86aMM6ildSBl/itOg7Ny69w97bkyq8UZPK7CXyxkVw5KUb1MsFuszzXL19itEwCHl6GTBc0cHvESxZaBmGhqUbcvJmY7r7MAhUB65a4k/bxBkOz/vBqpwPFnzJY/1OfsgQdDMaaYiCmw483j5HU+0PHisH0NNagPF2fkzonMbn2lm+hkoz9pYWjSS4rXiuy1c4QzVUL/m0u/b9C+TDVsRDO/T6Y9neh7FPfwvk4DXQbqojV34Xo4xv0f+NJYos4SzunUn/Xm0+pV9AUhfR13fyRkOfpp0b8ngSit62E6GgFSDmJ3MnhxNZ9RTdiCGY1IdzpHMhQiQGYaekyxfA41wI6mbLHhNNHRNDvU871zW4MWfWRxcse4R3pwLfhjcT8TCRUSo2td4RMq41Cfgw0z4W7rW+Je1GOIKmZv8c6GSM7kG8azWR3BGG53pyHNm9of/wOUUs7Vp35gEaOG2PXzsfYeAJYN8BV2ONxhX4WurTf+kPt8BZkpTKQ7lIn84UMuaIPmTg2ifKu+QyRcqHT+jOlZc189QgiRSOXffZhjFzQSkHVfXwXRhN64CanvLdjvf4Aqz+40XurJw3Pl/Do4TcSD9QwbHAGV/dJcP7WNfZ/fcTGw44klJ7G+0s1iqKuSFeIs6j6JVMOdiE/TRp1Z0Pk2iLYUiPBGtk/zFswHm29S/ymgvbAXJYOFjBwxR42LFFGJrOViV1/cNjuTcE5CcaNPUv0rEBUR7ihlbOSpELoEn7B694xEpbe5Y/lQAbuc6D+jgKSjQJ6bMU5rzWVeS8E2Fjl8nSIHPF+AxmtZsKgUQWIdflw6Mw6rnw0I+/gYca11tDQbcTQ8TvJlNhJfUMuMR1pNFUvwyJYiWkjWrG8IaB2+jCS687xySgDj8FnuPZMkdcDhdx6Pg/fA88QNDhyJdcLhvfDQnAfg7bx5Kg2kPnuPU69etDWOs/X69fRk56JZosA9aYUek8wYVHddhqeNNJ//SKOrAjixuE1mByp5cW3Iews1KGpsg0PyUbe9ZdjqqcnANYuaUTeukcz27j29g1Fypaky2mS73qGV1O08X/5A0c1PVK6spg0yBIU+tMsG43CXHVUHrRS/7qD+XW7sJwayPRpzXy7Fsly0S3EacWgnyOHidN27nlN5EWDKJ56s3hHOQuNyvigYEJSjg5/OiMYd0gVsduZNA8Yx+GeBl6qxiHDRX5MTcFj4hZWXggjvTCT1vxJtE1uoSDUA52pFzhd7EjrzAO0ZY9CSyBHvHwesRNX0BqYjHp2IvLdXoyoaCF3nBwGRc9wsnBkRqCA9wk9zH5wkcZFHmhf1ERZ1ZUfPeUY5LWRe2koQ65M40vFPVy/qvBh/AcuThjDnLtujBDx5N6r69xcOIgXIU1w9TpducFMqpMg63Eeyl69eZc7nNI/4pjXdiM1LBdRv7t8eqmMw2x3altn0bEpmEbFIqqisxhvvJcQ7RKaf3ewUKGO7PBLeJ+NJ0fsDTtMD7Hg0mk68wahkG2Cp+hKvLNmcCTuEYdn26C62pD28sMc/tLAmRW/KBjhw7QbksT+HcGOVZMJuDiUFS5PwPYMASn3EX0XxPIR/fB/V8G6E5MRro/Cd14RgqJCKjsmEJfVzODm74yVl6fjQxWlDcbYiMyirf0zNu1vaZQWJ6JBi0DrLIxUgjHqPkR+TBljHIW01lxD56c5Lhcf/Y/y8f8jjDWkhUuWrscxfRh3h+WTLCvF0lIlOuTLqFjZG1/TIi6ddsR15QxSMjvI6fLkaswVVsenUHAlA7W9omyYsIqRhiORuTeLwa0drOmIJmmjJpKfBNhl+ZA96CMRA6qZtPErWjtamb58B4LloQx07KJwkA06fje4v2co2x7kcGXmEG6lf6PY1IHIM7p8SB3F/QebyUyUQ0//KC21g1jy7hBJB2VoexBDjW4LfSTVMdrynXtuE+n97BLPCqtYH9iPqmG+6J9/h+DaH8Q+1tNnoh0u1xcTVXSAwnmKlEW/p7l0HKMHfCEkKIVPH74QEHCVrwYH2Bhympf1ZxFds5jnXwbwKfgqUctqUHWJp+BTHndHWGO07AGF8gnsmrmf2N7DUBdmMF69kf3D/1K+P4DB8mVEPS5kqZQk6b3SUShPZJjkYeqm6dC0wB1RgSInpnvzZEojj6zH8MtiHmt2iHL30Fs8nEdxIXwHY7rjuCPoT8LYZ6z6IKBggSGtUsq8lJrBptBtnK4RsuaIKRapvfFQP0fFqpeEhemw/+gCAoUfiZszku3ZXhyZKoeHtgsJ93J5cusApbKeOKcGMPPxJLpsWjmp7sXVPmkcPbWE5QfCuHt4OaGP7+Mybjbidi+5vM+YHyfCiJsWyvu32rysyUXGUo93W34SJlDAXOYRXjdfMqkoCavIENxNrJnLNDYmevFlzltCnoYQ5/aIYaniqKToMWnucl7fd+ab3k5+xQvwO/Oah49kkeslg4XWLnasVmHBpQACCz4yV1dIwvMvPIvooGC9I/5DRbGSiaZEOJjSnyMZ2dZMd/p3elxm0dg7Gje7DM4Lp9GU8ZyB7uuIJIC3Gr9xyhSjeY8b/dv0+PJtBXOVF5OS2MOAixYI4sXY1iHkVNIHmn0LMLjTxtl9/Tk0YibftxehJXecz0l+PJNMYnVuOXl1NhyLPk7eqv7oeVzHzWkNo6ylqes1Go9Fg9leeRCVaZnUihygLtmba6MsEArvItgQgcvHPUydHcLH8Xpo9WQTsy+a480/ME6LRyAQIHzxDe39wezcPZm/RZ1sbtvO514H2X7kGepbUzA8tp4tVUqYCfYwaM0QbBO2cuObJeOFQuK8B+NzaB2fhSVoBdQyynQwUYe9+d07gXXjT3BFpYHeMaakNU9hpq4yyzrHkbj7Mul3NPmyejrzc4OIS1CE9re0l5bAA310b8oRLNJCV/1qDjy7h7p8KsdFYrn3aCI/d6jiUTyfsWfFaVzlSmr7b9xOJdJ85yLRP9sY166Lv/FBzH7n82PoOcZF9WWInQM3vm1g0dSNOEnNxzIiitTzC7jmMZnoLV7MdzvO3L7ZCA2rCErTY0BQPpNk/WlvW8fnmNNoC1cy6Mh8DudrYFF+k8srVVn3dTNT2p7y+5IXw8bdQa/eAdOzpogaFCIWXc/O62IoWl3llehw8oaM5u6sSh6sWMij3ZHcaIugX2cCjuJJPEiMwmL4Gxpa1OmVNQDZfnUcCgr8t3rU/xXDh4G8qlB29B4sTY2RlI9mZv1uqkX2ECymxKOTmZwpdmedjgmXb6jhtrqF8BNemE2dQqtEPId+DEUyOJ/Jl9fitW48DpqzkS1soXPCPLwXaRCdmY2L1Vyqq1vZej+EcRKGdH+PQld5Lt/bZFAd1kmdcCkPErTwOBNF3J0nSPwqY/EiSfwDxBkiHktxxhRU5vRHKJqAlf0Ojt3Yg1pSBGb7lnHe/zwbtvsj+FiIj98O2mQhQTWON2vGIiJyBG+rbITCcDycgjkfaQStplTEzmPKs1AGjRiOQ6UPrrYShHyKJjWsiecGw7E39+eUqQErZ3cT8Cuc2z8TyTnrwnijHTguNMQttoCI4+9RnNPCa9etbJBJonVUE5o3pOj/0YuhxT+Jq9JgzrqX1AZeIHr+H/Yn2WJwp4CtuePJfBLGJodndKSc4meaHKtumsMnC37chODPoO2yl2PeI5g3wYll386iPk6XiYv1mbbRlS2pR/BJsUb7ogJPkrR4dzKHBfeV+L2zAt1xvujc+UPmxs+075zGn8ovhGc9R1gkRXCfPP7UW1KqPYdJPX+wOlSKZIkG4T6i9P4xAGf7ZZRH5rF3jA+Hxt7mZKMZlmJ5dJXB9i+2zBu6hp3xz7Ba2UFDJigfXsuus/v51X88f3XmUtjeil21DtcachhV9ItvOvGUj+uHs+9Xem/aRkXXSNQax5GTX4WOzka6g/tyr8IPOeNWPLsuUOB4iNUu2mQGg2nhY96OfMf52EyOSDvjsOMbTieiySvWZsdAK9z8lrFh9nd8a8Xxln5Da8hXpnv25rXZcT5IjkG58DsjrR34/vgdauWfaIw9R1a6P5P3XaHqzWT6rjhO1KOlTD22jJKPMLFfA/VFI5Gbmoh1D7y4LcJ9vc/YRSkjuayVDdKX+TFKlvY+d2mNn0vtQQ9OpAZweuBOkjKOEXgqmT7qZ3gwbCkTwuu59KuZYvEupm9YzdqDsijtWMH+9EGMO7sHC38hSjcuc+zVRtYNcmPEZw1all+ip7sOvVpH8pRNyCwXwdBiBKMLbhIto8ZXHSkmDrCmMGgz69ROcMloFn6uIHwnpK40nxEdejSGGfOy9xbOS90k8KQHZ+dcpSFJkY7r7YxqqUW+LpWdlc7c89lJuYoFjw9uYaTCWt52vaVveRUpjpnE2D5CR9CPh5FbeBqWz+uSmwwq10LnpSOsK0Nh+2q2LrzLgcPTGZydjoFMM7N/KHK6QxItJy8iXs7FwrCO5Iel1F2eRf/3+1DrckeklyJ3GvURqEoi2laPc1c34ypqKP47jfA+wcyZ7EXh09dsO+ZHv4HO2LcqcRJFvp0K5n29P7Pci9is+xLLOaN4EP0aJ5cwNB6m8KNNA1VnX75t1+D76vdIBerzROiBrIIHXgHreb8vnJhdu7j2yp0r9zu4XDOY/TnGzPmVi2R0DR8MFVA20yf7RRsKtjJ0qreRHVnKMNNeCOO10NLrIDbOE/M8X8bPm83ChIUQk/E/w8f/CwQCgRQQD0gCYkCYUCj0EQgEtwEboPGfUBehUJgpEAgEwHnAAWj7x5/+n9Xo37efMHX1OwIXxtE3q5H0qD+MN+zD3INuMFkX4dMifNvBMOAWv4miplkb7/pLjMhKx9h4KJiEUHPNh3O+gdjMicTohgRhGfcY9WwOD/1SuHtKi+xfQlyubWXfwd0sGZxNQ70rhaIyaBjqI33vJfGn9vNpfG9+Rabi99yXqIxxKOl0sqdfB/6N7ViMrmOTqzdR977xvv03npbD8PC35v601Wx5Xkj8/cdIaGsyyjKI27VbsKhS5lNiO0JhDONnQptVCGaHFnBdBSjahlB4grTUL1i0+5Pj0Z81/cz58NKOYH1YcCUY4cc4BE0idPnOZcrYY9wyHUOchxZHwnZje3MHIl6eePxIYfmJh6QSw5gAe9bJrmDEd2UqvCqovJ3FVQ1/lBrHUH+kkBuzw6jqEhL4ygTj1BxICOXFDSHL75zkpTAJ93s5nMuLoFFlM9aCvVTI7GbXe3/6BEijkHiCWk1lsElG8L03wcELyBkQjOj4i+yb85SA+8/4KBDH/IgQpjTyqmkdH/YHM0QVYqcL6afowutaA4bIKBG2qRTXuG9s+xJP+oYgAir7Y3twEJfM1BkwI5F9jkOQWafGHOkKEop3sGdtNp2Z+miO/8Rvp53cTfbl+9bjLHzhhuvs2cwKPkWJRy1G3pfwGXaaRqu5OKU6cF8pkKX7qzA6E8I3XWksJ0vwyUSA3tGp7JZ8gWeXAJUmaXavaOPdylV8eHWDmmXGKJRIM2/Scy6fucM3w+2cCBpPyKyBWAwRo9XYlGV9N3Bg6k589h/meqwe/RWNOawsy/RlSxh54xgXbDaQ8TyQ45PcyZ3VTcinJJ68cuWsfSBKEaYoSphzcJQWbss9mHhUwNKqPH4qPGd9cRjDfvehck0iY+I+8lJ6Chk/f5NiMheLcweZk7yYNAdnvrgvIsRtOV+0z5MR+4zakmXITarnJE8RCoXcGDOQa327+RKpQrjQjE79nQxyek3xpDruZO1DfvsybggDSAq5wt3U94ybGEjF+gkcmG3HzXM3kE8qxuG4gGeDnpB36hs1u/ZyQms7gfUdfK05jarUTpYdPcTOOVOZEryTGRITSK0QcmePM5N9svHwm0BaX38OqC9BOd+F6VWTMHjTzYnF7xAul6ZVaxXal5YS0/aZyVkqRBhswDl+Gq4/Cvn55BjbZ4XgVr6L4o9z6VJYy3fLMrqnbOHwdTuqznxg5O8rWDqoUHJFkpF9vjNzqxjeY4K4vuY5U9+eY5nzLKbIyVMx7iSxT43QbJxH8a+jKCseZGTaBeqVOikcOohiRWO62+Pp0xLEx6ZwXk3rS+DuALZ4r0XP+jR9Rkjzp+QHjn4TsDXyJiLaguSCfMzaTyEcaM4Bsya2tj6nK16N/dvnUS/tjZVxKRd7wqgZsBvPNT64l+/mnpcChVEWmBcP4P3ffRg/LyXpsjtHOiwoSozm2Nf+9N3Ul7Fxb3FfZEjncSMsJ/0m/edhHKOX0uwjwrmMHyTt9Ps/8+xKIBBICQSCFIFAkCUQCHIFAsGBf/y3BQLBd4FAkPmPmf3jFwgEggsCgaBQIBBkCwSC4f9VDTkJZXztVyDo+oaKyhjeqN9mgNFIHp08zKANVWyWm0zw2xt8TXfHwz+SNTnLGLbtF3tPjWFhRR7LdfbzyTmRH1Puopsfzz6HbszWLqQgTBvZwm+4X9pMTNhb9sh28SnYFemOePIa25Ds9ZO2d1uw8Ldg6yBnRBba4GUhTkZOBGt2lXD9fRPlvYfQavyVIR+LWCGymoNKkrQeDuKAxxlur8zg06ZM3pi4EbTiOG0/zuDQk0CotD8aPz05dGYQgXlyGOaHojHuMO17/Jl/NQG1kQ9Ju/qLOVG+1MXdovjKES53jUTdQp87Nv0pvbqaM7WuBES7sOv5NcLODuaoTQHGequ5GV6O61s13h/ZwvwaP1Yf1cZAOJykZ3BziBdjVwbw+rAyfgqqvHi2D+Ve4zh3qx8v1K/QdMqM/eIriHB8xsTmVEJHliLm3Yuznyz4yWue2t5Df8h8ip6FYWqgzrxMTzSu/UFQeZrYnn1ITl2KZ0ox/bYMJbZ3E0MWZRDw3YUc2+0snaqI+bpD+OnGsdFgHveS3QhfEcSs74MZJboAU1chYiNfsd/8OlbHipnwQ4Gglpmk9hig41yGS89l3qRI8jwliX0i0mxfU0lyx00mee8lddlDmv0y+OHigfQrE/4cXsuHE1aEVp4lp9mZ9enVrI2J4K9zLOF2vTipl0l375Gs/eZI0HYjxNRHUjMYfk05iZGdL89zfNk0bwEbZBURljSy96INXY7VnF0zkOBFX3D++p5CpXo6SmtZ9X44hx6tpV9/FTz/XORU7kSufYymaKUHv8d5It93Hv3OvWP9t/1IJwrZUPSDdyVj8d4Ryao+E8kZtpsfR6sxf22Boe4mmk2nYfliH8EjZ+Cy/xGND8L4UGnObdVzHN07mdV+YznqIIGE0I4fW1Zy5kwF+zuncTfiJDIfY7m3Up56v5ucOmdIrIcZD5ZnM6ZiFO4hzrifDuZvcRDGUXuYN6OVVZXiiHvtI9hUhPIhu/nkZMWVkM84uTYwfKgreacusn6hgE2nCvBcmsOHByMZEJbL+xN1mJW/Y3d+LoOXnqND0AdpqRyWCkcw79wL4vd8427VKYJfnsBirw1F5i9oEY0iysGezTMvMtP4Lpv3xnJ2lCqeji9JPZXH2EGRLKvdQb84Wb73dqC1S59n+VGIpWXRaeDMcr9itoZGsW66H5abexCscUBsYBSBwx1gykA6R+zC+9p5pofM4FDESrrGabCruYaSiih6rXMiotQIF/1B3I+WZ63zVX52yJClfZykEjd+VM7AqkAE9+12qHSPJe5rEwf6GvDFJIi+faXYt2AMKTmPiD1eyW+1apTC53IpUo03n/UoEo0hRXgPmcW98T61DkkreQS5MVT5htPrnh9NwiH8XpSGcnQPZGqgdcqT101TeFPlwdQIXY4Mu8TTiZPJcDHhhYotrz7a4Gp1DlnZKejXbeG7XxpLTcuxfdSDztEe5g+dC6G9sbHwQeNNEX3/uCF/RIuDKWcRnn75X1Hpf3f8BSYKhcKhgBkwVSAQjPpnb5tQKDT7xzL/8dkDg/8xV+DKf1WgoquSyNJA5DJEaG8rIU7pCS+zi5B7ZYmFwyoKcg5glJeN+NBRyHyWoasqjXCPiaywSmNIr0j0U0U5sUQT1+wIXKcNJb5QBXnfPOQ1y0loqaNZTAlz0XLen13LgkVraZQ9QVv3TyRKj6Pd2kL5jHEsfByMjupIQiMe4GJtR061KtqupdRWNjN74ndGWDugIFCjc0l/DNX/MsPzFld2JOCz7SiVPR30X7+I5fojaPvzFWWb3nz67ctB39ccvPaBlU9uc7JvKeqjT/B02V7gJHkX4NPh84xVr+GT+nxm/SoD5nM8D/wmqHFIIRnnkt/4BP3gsY0V31SleF16Cx/NchRnaHPpMnwUdLHnkAODIucS53KU43n6RKr25rKPK3MUtRhyYiyW5Vmolzui8x6mHZtNwt5m1qbG0mP9FOubY5ELC2Ru8wP809NRf/2EhrCrBGXq4v37HuYBvXFa+5LSsZrcejYZzlaiWRTOgLrT1JxYhL2vOzO6YQ37GdAPDu1ZTqWCImZqB6mKB3ffFxzd44b1kUdcGKuOSMx6zIKtqE89wMbGpQw01GMpJozMvMCTRfXE12zg5q9eFM7oh3XVCPas/Y7w8XMumuSQ63IAhgxhaYwi3s+CCR89hLgAaT4va8Za5iZfPwxl9JVfzGiZTrdaEVVyiZjRzJPoXWhMl+BnwhksOxeTuPclm/IK0Uz0YLjBTsr3RjHU8CbJ8+fx3NqfQRoSVJk2YduQQUwPxPwYi6GyCSmKo0hueoLJsoH4HDNjSUwUvZhD/cB69o5ToqS4g+WTpiN3o4b8cfuolM/lT2IfdEV/8FYjlRs6ivQLUYFqXyZO8uK0ljLKz6pQ1W4lqiaKgVbOFMxNQzZ3ANnCHpz0hrNvwxK65G6xdYU+1Z8e42IWhf77IDxz3vHohxGlPQtx1jpCI4Ox2jWaMRGHUHbUY5H4VloR44mggn6ZdRhGrcNBZBcPc7tZuUwZciDx9hH8R4vTklhBancrw9VSkX0xGvuacxAFDqenEZ60DYv1HuicAAAgAElEQVTffRl2M5vlyb1g9BZmrhVHPBeG7DNhuvh49Lz0ybkeykzFct7dKaD4uD/KXrc5t+AeQT52DPWFlYpnsdvbQfhaL5pOW9Fe24TD3wIUJrgzsGIN6QsPsm8CeOQUMrF/CZn11SS0ZbBcdQed80fTbi+EFTokb2kjYMUx/hwfzMevOSz6s4bbd++xvNsJ11kSnLBzp1YuH4nqTsSuuaDdvwkjJz2GRzkgrdRIjbEJQr2BTBleQG2XDGZ9lmA4bC6n5jyg7xMrttjNBrHPiHbGkDVWg3E270kJTSLw0EoGj1rI1L+LURvdjLSyGGqa4qzQ00A2sR0jCxWyohOYNt4AryPn0dypw5+OGlq1dPmSUMrLDAOuXejm9/y3CH9+p0x2FOpPa5g5SxETpTbyWlpRn25G/sN0JI2OYvCtlpedQlonjeHp9QQCRk77t8n6v1Q+/rkdkhUKhS0CgUAcSAQ8gHXAU6FQGPa/xTsA7vzrNmkkcF4oFI78z2roapkK683seHBKEXvPKJD8TJ+vVtRkhWEwwpnzx1cgozwE73vDifsaQVm4E5++pvJr/0G+9jQxapAs0o4rKM/xIabPOsKa/HkhdYWC0F/U9g9Hss8oik2VkfkZxbJNS3k0/Sd5g+MwWDQdERSRu2xKstlZ1o4yo3XKGjI9+nC8exntWn+pKp2E9s0Cblg3Ei60xfOWAh5LfVlhtZw2dSEZd4JR2tVNU0kHHrZi9KsTJSNBlMW3gvG3DSO5IZyZesOJEalkxn4hDz1V0B/0h8/ukmhda2O6cm+8Z99DetZats9P5tjRN0QmvmfVzTvc7Nbm88MH/FSs54u8LEPmzifvyTuiygRYzpHANteWrO+/8YrZR3L1OwKCA3CPdMBY7wMTU0X4ZSQkP2c0DgNbWTO+Fwdyb5PhdhcMgKQS3r25RonoTiSHWKCZYI2UgQvxooeQULhKi/hurF8c5p6/AZ4vu7mYnMqqHSvIkbpMfUgESUFncGyDpTHPqQ+7xr0iC/50S7DBfBkhjzK5lL0F3eSpiKPCtngPfE87Y2Ghy86OI3DoMhELK1E2U0Jh7x/CXcZw8PoMYDnCt9PZHfCXzZP1MS88ze79B1g/VY8zL+fSJChiwufb3PdwY9bgo5wYmYyeXAVXn2gyX7kOtbORhJv1oyzjBSUrw/iifJeSmG5EMp/ze9dB9LO9SRtqxIYLoXRf1EGkOhKnP+V0GolgN6cvY49epExJFgnhQorcDjFl42ES+4sh+PYGmzs2HI/ez46X09Cpi+WToIK04vu8GbMJU4eFWKvo4ndDnimaU1F6tBVXt5MsvLyZEVIV1M/6guZfKaS7V/BhwleaFeZTEtiGTcZkZtsOJ8hMicH+4lhMXs79aF1MfyTxw8SV7vGhDIn8yVxhLSp7Pbi4awfOZ0cwf2kQ2mFvyb/Qn7azAjwDvIk7aYDHrR+kdM+i1XMAPf120vGyjgMLBrKhvxH1d78w5PwkjjjNYbKvLF9929hm7kS3fSVDRx7iUsYQ7gaC8JOQb3azyHSQpLW4BBvbHB6U/SVEvQRlsWasCwwxMs1gtqIZa6KWMnmOHK0JhUTmZKCnM5bvT4YR6vMFSTc5hIvK8TrYxgA3K16VNLIpv4J3TmKITXnOoIweOnTmolGQjFFVO3c19WhoGIiqVgELRs7kdro/+TbG1CXEMTS9ChFDZ5aWqlNd8pQx5gZM3/KMiaHzKKrpwkBVjVEd07iz9z5Vk8xpsZRmt3YD8eviuWyykO5B0lTY5sGEHoR3L7Di91Ii0jqxX7+GB/PHc/v8WAZ3yeL3dxhyfb7QnWfP4Cnv8TlZSfFCa7Y/DCDy5gysD6rS8iie0Ufnkuf7laHuc7mWqMcBrzOoh2hQqpuDVEg1ambdCPv78zxsAsPXuqCtZM36YycZG96L/m/VaRRtYZF4OSHVMWit2M0w0RbyesrpdLOAnAzsNnpjWC4g2PAxItJxvG9VRTLjFyc6RMD70v8oH/8GBAKBDP/qVev/sf+oV/kDcUKh8ME/6zxgvFAorPhP8gpbay8SdkHIpZebeD28mu3FyvjnCxAmC8nvCcXhwE6KIosIrRBQsHsXWsIcqromIJknisRsGdrU1lDtMYuFKQ1EPo1FI+smv02SKWscT01nEUqHfVBMq6d1cT4C0w3s8ftEyGkv8BPy4NJ3jrway74LXjQtseemkx2X+6sy+MBLfpaY8vFaOkqiImyqWc7WqGscMKyk3mwdN5YsxvmRkJPeAhpPNrFv6XDGG2QwqVgeOkGY8JziigZObajC5O0lcrXWM3t/E7araxEKL+I13J3TGWkE3QhkrtpK7pacQqw6k4P7e7P87lL6t7vSMXY25nllqJSrs6O9BJV9kegkLqJgxy9ahnURdLUT4ftd3O++Q9OJR9ycZYh+74NoFXrCbx/W6D0iP1ydqeHKhC6YwasDNQzbIcDIL4OICz/Y2RnJelM3Nume4OV1fTTL99NicZTc5A3cj1em/9kuEhzPIHLkFRW3H5Op34c6y3ZKJgmxvONFxjB5Vp6eyFPZeeSoLMZwRi1BirMpPv0cYdsVBCICum4JEXMRIOx+jJpoOM37s7hm78miOY+Y9f4IEa6FvHd/Sa9mU2p15AhzPMLyr5uQPjSDSbEHeex8lsP7JxJ50okd5Y/J3ToQy1N9aO/VQervQ8Rs1mCL/xqS8tSYmV7FjRXDuHpvMG+9bZHSukujwmzEKmSJzrdje+pGwpI2YD/fnDzxX3S3vkKmTBPrCRm87bxNdr9eGPqGMqhWwN1TXpgtW8iiyHwy7z9AfEYayksicBOIcOL8JQpS07GYps71O1V0mRszS+YexrdvE67pRPNmCyK+FnFMfjGfnk4jZNE6pj+yoOaNkMXvOml9bEJ2xA4GLtmIyMNEaq9bMy2uiMw7ehQ7lSNfVEpAVRSqVbbo1ZizYWEaS/+cZ/zZQUSvTqB/kB2X2n4wzHEbza3BeH8cjObWSxirTSU5MwK5bTps9jPl0u57rFYdhoX+OnI+JyCYJEAYL+Tl/Y00f3tD6EtHhhj44nNVkmPTFdA/08TsYQKe/TzA4fW3OLL4Ba8fVeH71IYX+4R4xAtI2xbN8QJnpomn4iO/nKAJkXTs7eL9SkfmfHUg0/QIaYk2ZH0fxbhkRTov7ubMeQGOp+Zg/ziMe99uUd7LjonRDghnGRL/fhF2JdvZ3yrCz6/RaDraM1LnLNUDDuI1IYG0keEcOHQHt46JPDpUzvS9VxETzMLh00Q+j/DFx8eQRWWzUX0jpOltJm/vpHPm/UHWXtnAsDoPLOeWIRwsJPLdaEamFWO9oQ83V25CefkInJsHUFWpSsYyXTbH61CDN1YFcdz79Z1NiofYtliIRLICo5uHM2COOEePudGeVImyiwdrD21ms20+OtYXMVuei1KzB043MsnYXou3iyfKvfZwq/ATV1yvMmTSSD6GRrPt6g6OLltP/9pppC2t4+DXMgIdDPn2IgMH+350r2xnhvwx5s2Zi5KKIdNCBJwf9J43F4P+zygfwn+h5Z+l+D/2n00sTsDdf84lA0oCgUDtP6vR3tRB/e4RpPj4wIs0GqTOETn3DYJhvRna3xm1PmqEvXlN1wVzHkvPZJSOAL0rQTQ7PUHLM5JHEl28ldpGo+4tROX24RpxEVE7eVqnpWM+wR5dZWUcCipRrJLg+aIaTN0X8iQgnLqkYDpvRxPvmoRBn+Fcn+LGw2vjOX4umW8Xz7DI7CRPHYI5V/OJ4At/WXmlkiO+Kwk4YE+neSmilffYkBRMoNM3XHtOErHkNhFLHrJ4SAqRy85jtNGPW6/DSGrQwtTamnuzWjF1vMe2Z50szFZFWNRE2O3v7Eu8ibXMLsYcDyCqsYeFUzQIsNvNxqG/CNhmTvuWWbxQjuaFYAoVPU/wuaeDueQPMupVkR6ejOkVJ9pFRJEYIs55rTge9hrJZq/zbM5OQbirmE0K/hSnfMZrwh5eGRSyTVPA8m23eS7Xh4RUFfJ77UegYcDryn78Dl/Lle4g2u93UbD2NmFLuqmoP8KIM314eyqWsmNJfJ8/Cs0dQtJ1pzDJP5f3iid4H3SHBq1MHrydjrT/FPafyyW8NJ/Twp3cuHMOEYN5LNK2Ydahdh4Ld9J3gALXKjOx6ejNQZEKzn8OYV61I4Jrf6gWeUrfgGoyDgQRPm8ikw3D6b31IX+EVvx83o/NhqIcuTqb1aJNdBYN4GS7DDK3Iui20CDTdjX3dFegeH007nUa1KeeofjpG3p93U/dyB5Uml1IWHOSCwny+OfHkXNRikEhj0mem0ly0nLaqq8SUqyD5+WPxHxejvLMUF7fyeWGxVucdj1Ca+45sqV2806mGqXJSpikj0QzvIf1DzpwnmCIeOEerPaP5aTPQKaWDONFgS2jNG0IaJSgerMY0+KnoOm+EzezUDavceOd5nmy+1yjZpE55RcjWDUtlGGWdSgO/sr0NsiViUNq3WCm+r9i4GNHZk6tZW6CH4OeN2N5ToL7xdmIRobzzWQRM4buRKWmgLO7zBi+/AwNTybQtXE5d2Oc2V3kRqTbGIb7uTO2wIPQuU5EO+XxTW0bh0Ji+CL+iKemT7jedJfvMe6caclEfdkW4qtnMurtEiaVXGdhzxtu7BVydP50DgfuZKjxbsSHOfE7xZ4NKkl0jHdE69B3vg96iJ/vYLbNfkXqIR8CfhdisHo5/lqVLFm+F4O/uWR+W0lmng2JKcvxXZPM3huFOI/rpuTrfILKnuH/qhyzkC6mxWvw6/ZXcl2r0es7jrqfxggE41Guf0rpEydaXoXzy/09W/KFaMfModDQkLxqcU5sC8I1z5hpbfcpVjBD+CcB4bZ9WF3fhk1NHJfNaniwZBabfHZyp9SRNS0HUBkygpnvNRkqvE9Bfhd/P3zj/AslblieJvSCOPYNzpx6MYJvMcdQWZFIW8pWPJR3oLS8F1XqarSWPyR0ohh3evdDPPs9I2fYMnCKPJNODyTxsBEDVnVh80wKnYuNBKSeJ+vrF9Iz0zkdHcKAHj3UL1RhHdhK8ZwNLHDPQSV5FuGnujEvg07taq62TPqv6Pq/PQQCgahAIMgEqoAYoVD48Z+tI/8o8WcFAoHkPz51oOz/cfznP77/PaerQCD4JBAIPgGsPO9OaK4mKclevBsvRCuoHrQdoS/cjM2g+GoxW12+ErJ0GLum+3LSaDci2kU8bW6mt0oyqnVnycp7jKLfY7QXwqeoIMQTJWirEMWspor0ZijxF6WhopqtGxbh9nEJioU6FEwchmlRJ4cv7EBstQZbA7U57FbG7ZO5JMYG0jlOSLpvP9aJWRD/8C3HFrgzeuQlerLisQ86zT4rcUrOe3IyUgHVulu0TBnD6OxlLOu3EKOJiegaLWTQDCkUmn8jdy6bpWecGE40mR31tGeUM8Z9JSJn5rLlSwmrzh9hWf8yinBmX9t5JBpa6Www5fLjJYTI1iMVsQLVpfZUP/9AS2wJsrr+0HqE3yYQlnUbtfI+2KnZ8GaLKr+OjcJZIpQLLyOQ66tO+bvD2Afvp9pAQJXmacKfOFCbsJtQOVPsMsoJLZ9I7Ydszps0YvT3Djo7FYEuQh3N+FmRRY2XLJUxFVQfHkgDQXRqwjcVLQbtM+beWwc0xMXQLS3k0IVbyN6aAZ5p2FyU5oZTCOMv2/PzkhSsGsoyoTmtOz9z9W8W9RtUsVLuz/QFbYRGN5L9TJotn5WQVJyOyehZLJyvg/wcL17HKqE5YizcuMjUyZms8NFCSteBvzXqmJbWELfsGnKDNFjT3I9WpzrmH15LQbcDviuFiMp8oN8FH+7/2cjMZlPK/jRiOOk6pW9S+OIyn7Mls5g/5A0/01ZzsUcGveZvLLCy4/eCyViOl8Ho6Cxagp8jWPCLSVbH+UUWlcxBVaeWEVOXUFmShO8APTr69sW6vzmyo8DKdAn5KYN47jWeQ2mZuBjoMEJeitbQ3vztp4aliRLPfD5Re/sUctcfMNzuDxUmE+hKk6JbYyytTy9it3QPlj2SzFo+gj/ieYw4akaN9mXqnkoi+nAJq4sS+HIqjhL3g8i/e4jZxr24zJ6CZe0RXsgdpO+Ps0hdsMEy1IJVAmPG7BxC0lZ77i+89a+PtI8v0bZCKoZo8VfhGchC1W9tsq+VAlC4Jx2t5+L8NZbEUEcF2o1RkQlh8pNnyFnZoZnWRMSpxxh+0GdblA+i7UkEn82EdF/6SkDKk3eM7t2K7dJtpNVVo7dzElklCuSXnKRD1xz7pqNU+7oQHvOQkAt2pNl0oGn7jZqsIOKuZiNTWMI8kQCaHtyj++YIXjx+gnTVHC65GNJHeQyNTXt5+nQvCqYKzFjwl6TQVBrbf7Gl1oKA6KlcuXufs1aX2GVTQ+qbABL81HASS0Gilz7er5rw3+2KrbIOlYoTGDR2EFMexfKxnzWZAzVBajTmtgM4YKhObH0tBSWu3DUvxPfkA8pV9VESimFsq0sf8b+0rBnEyL9xvBW348KVRyzKn0qRSSdaK4yYuN6QUG97oiUaqJXt4fOPIsCXwvYqFDXe0FibTp6+CO1Nf5ArkaNiRDN5myez+OdkHqZdxr6+LxmyGYgoO/7bXP1v/e3q/2tCrxOUI1h+CX2NhyR8vMaZ83J0Tb/Cq80RjHOcT2V0J2Lf9Xhedp2ZFxJY7diXxnEv2bVuPofslXh8K47xF8+xz3kUVeIxGOpOpOB7Gc69dSnJykRSXY2XX37TWGXFZuU16LWGcO6EAMX9r/jypgW9oC80BoTx5Pcf9HRfccJyKuPm9IbLMxGNX0E/PTsu5/3AoV8aRlcakHlSSLyvN0aJTdiLChDmf8bIXQ+rVVP5bGeB2eLFvBPLZ/iFt7xNnUFciCsyqW8ZNtYYQVIqpZOMuZzWQ5lhJyejQrFaZYZ4v8nYLJyB8PsPHiT10Fx+mAlGxcRHJVETt5X4pvGcFNQT+6CeV4fG8agmnEfqL3ieNZihykNYNWMolyfe40bACfLWfWXG4gbkbkxn5bCNPBYfTO7EfFS+HyexbxYt5/axYoYVkx2dGL70OlKxsbwwqEBjwFdap8hyUG4s9fJ1uNi+59aCbYg0/kUlsoXUjnWUNzZQNrcM+U85SA82ZH5rO/FPHnH8YT6vs4I4t70WT4GA86MEOP80Qb23gOoTEwk97km0iTPqzb+ZLS5ApsMU0dgIyqdF8+GPN5PO5bLwwzyEQUpoOiVSFfsG/10wvvdNrH+9w2A3RK0fQbjVPD72nYP5QWP8xPbzJ2AC134GwJ1gLp08yC0XBX4vs+XULG1OTFHn25kYYmyOEOFykTj51dQbdPHUs4I/J0zRUnTnyiYxni3QxFFMhXq7r1QmbiNZ2pYFBTPwr1+MSqQvNhYbCPl0E88AU6b5y1G1twTXtFUs6k7BRl4GJ3MPys2bMPC5RvVgYw6EF9Dmd4C3my8RFfuXlMAKel5kUNvrCOc2ebNiQQypfg3EpsyhKieJnT6hqB3QZLD3Ex7UXufCxHoyGucSsO0Ek07oc1ZgyOGzkzhzyY7h3zzZmbuf/LSbXFzyBk81U5o2nCT1ci1Lbq3jqYQbh/rZ0vF2NY6tfRjl5kRJopDnU+YwwPYmnTfiMT9Wjpt5NMdtrlDyeiHaS5cxuaMJR58mBO9UKVulQ7GyDTEJqpQGLiZA1Za1daKseTKSnUnW+NfN56PSNbyijNjTNBUDr028+fGAL9t+InXVkKB3BWyw3oVFdBbGPGN6gxU1V07wyE0MydftCHxv0p/pNMYfo/XRYkpcvfDWnUHOhloqT6cgffEuhhqLiSoNJr32Fa/qH/NyrTtOmQ20zLJnR5o0Sv6nUer7lN4yo/no3Z/lz2fRZ6UCx1ObKOw3Eb07jszf1YLRvKFI2TczPXIDniJCTOYnkt8RSECbDq8XGSGaUsImfTksJY4wvLIP9rUmzJOYyg0Jd/y3HUXv2mekTSfQNV2SQofxaFepsGLyJna7PWeM62IGa/fCYakLKdGPMd+xmdDp/WH8BdLd/nDl4AimGzhzzM6HxddLWaDTF5nGYrZ6TGHN7yR2lB7F+LEy5j1juNfwF/eHN/j9SY+qo38R2T4D/7V+WD72Jeu6JBUDtpNfuuffJvb/rhAKhd1CodAM0AAsBQKBMbCLf+m9I4DewI5/wgX/UYr/IOc1oVBoIRQKLforKmB/8BFHQl+wPtMTj/mv2NOnN8K3T7nxvYXTrhcJ2HKIlOiHhAVmkJp7FOvUz/S1WcOG2JmUad9gwdrNRLVHkrKrhPkkM8S4mpieZBaa6nL16DV+DpViykh5VGPtSQ/ayt/1ohRtn0rzDkPSP5XyJmAl7w+XsXRxNIpmIzk91xjP9wdRz2sg+lUXg38W8/ljf3RsBrAxyou+cmOJFFuEUPgW+wuf0DA6xb2DMtT1d8Q8Q4ZlEyWxeVkGZLFly1rKV+1Dv2cAa+ZGkk4+ZdEnuSxVxJ56IWpaB3lXMxlhXgRONw+BjgRD1pXR+lAfq8Ia9LMOMeBrPVW6oGDsSoC4OVFUM0YPZm5NYMGMMVh1XEV6TwBHre1JTk5jWaIll7KuIhSbQ6SkPqvGyyGHG1Nqn6Mio8eadSUIJ5uhodZEpaEiskG/+L5kDDPrI3hZK8JhBwF+P19RH2rIOI3bjHj5iNI7y/mcbcegfsMY8D4eyQH6CAtH0D6hmTVqP+notQWKPjDsF/j/SuGdezunP8wn4eND9oa/YFzBfBom+HNh3Hla0wLoVfqD2is16A5uYp7SCJrnjMM5vgydkxN5UrGdDXLBfP1iRGc8mNySxNxpDGNEN9EyxhaTlU6ctc2l//NhtNt+5bjCGGT/NqJ1eTV99gnYOOoSf/YeQOP7DvLFxbFT0KJ/aTXSW1OIKnuMZ3YATaU7ufl0OnGyUcwLvMpwlbF0fesiouQCdoZZ/HoWySbdFuwnjiL8xjX2HgjHU3sJ4WsMOLCunr+XojHpGY57ooCKsETOqaiSlJmKqqQfkjUGODvIYqNcxnS/k0z9LIbOTiGmcj94d+wJTavW8sA/DIXDq4gM82LoVW+i+v7ioUUo0gPTGJ7axINXGujraxFbeIbgyMfIq3UQN92UgsO/WTHVmkXOS1l2djZ5zfYsP11Kr+HQ82gkE0adY2v5bVZ2HCbQfTvmwus80Zei3/9i772Csmzabd3rJecoCAISBBEQUAFRopIMmAUD5pwzBgwYEXPOEQUFMYtIUgEVFUUkSlQBQTKC5Piug+/ftdfJmuvfu/aqWbvmHEddXd19HzxV46lRfY/RuRuobl4AVHHvgRKm5WNYPPQ5gRemopf2m7Ero+np9484GeYqQuykAj6qrOBXzj0YlcX2iRlcGZkB4qB+tJXA/pY0S1pjKmqMmrM3iiN8uMUtVp+342OdDxE2jihJlRGtOJmzGXNQTWxBsrmbv/mrqfQ4T8pyfxw8gHt1GHuHoC9uzfCf2xDTKUZDYE7dl3B69TpDxa0vjH60hG8VmWTqx/A5eiE9JZFcVFvO3ZcSzN0chW7GI2zLv/OyxYaPnz/RfleNSaMcuFpSg5L6LmKeNTI09ivFk3YxyVycQ9kd9JNq40aCCrZlKui1ijBmTS3yGjBlwRVCc+LJfxOH9JpeFKnvY9RsS3o36LB83ztiPBcwq6IbBTcBypJL+XPHhMarv2h0b+XCill4yX9GYbEevRLFSP5hwtz2XojsSMF330BsJ/txveoRL3WEOGgaU6A4kIibIZhbFvJq81e+ax7nw4H9nPFI4d6ZObw5upHulPh/m6v/LfHxf5rQO9QbMZ0sh85madp9zVk1OIBC2zN4KJ9A33UiG5Nm4TTOgk2rH+N/9jwTbxXzVDmVxf2Xo0BvZOo7EdcNJirYi52tymS5JlL1MJbzhhVUO2lx8dV2QkQvozhKlau39xJT8ZI669VoVAsZG36RkKp8WpZ54/7zLXKhkkTuf8zxLfasMe3FcqNadA6/Yc3JYtTkX7Lu4hZC87ow7N/Agj5xCIXPCIgQUP/CE+/Vs7mxUJJ1Z7opsl/BhNEhPFrggby+InHpt+mSqCd573osfA+zsq0V6WeXEFYHc8UvFb3tzSRsfsoIfQHXl4kh5zYEFydPjggnsOzHdDbOmcr0n/a4rDjNo6xUiiN9UTspRWRCEmMD3Ahf6YXiqMnM8xYw5ed4hnRNYY7VZmpO3yTVeTR/ZZtQWn2DsOFPKA0zIb3XEh7eX01xUBCFufPouKvEx8/PcBBr4FTYS87cyMSrbx+KvLW4q6zLpQdr2KW2lm75SvqJKVJaNgElHXN+Z74iTy8WmdObSND/yHTHH9gpGTJRmIz+1L8ID8/mvootQuEflgwWYHgmDGFiNh0KteiMCmDGJE0y+tlzUS4QySvjCBmailNKCXEFntxZso+GnfU4hDez4oQOTsvzWf1hA123VJFfuRPrWQIMO+bj+2EvGxRyEEbdoe/3TOrnHUPmayUimQNwjlbmosdT9IIN8e7bRcuWTyz6asKwNcv5dvw9KlrqzFuSgk9gIR/X/8EkzpxdB8BrmwPTIhqY98OYuZsGc1igz5KnHuwJysAvYDcl/r142lFMg1Qej5sSsU5u5vqRYuwlMxg/SATNe1VoDdvNipQxOGq5MUw4k593dNgm3MK82L60244lQisBt15nkB3yE5ENn/jRvha5e/3o+9SCPcaxlK1qJOhUJvW7jvBpXQwvdQ25su0++jHSLLQP471SLq9eC7nhFIHhRhVurlOm7csOMm3c+SVugMjlUzSmDuasqi/fzW1oykmn/EsCz3YE8shyCFt3D8THypRbQeOJenGasTEWtFxVQ9/4A18vCfB7/hUUwlnvLM+Yn/4EH7jN+oNaLB/5iljfOggdysPdP0kPNOTAIUvmFf+mW8GP413+CCIO8ePTQ+RES3mh/ZfwQ7FEGcwjJ/sa0iM8SVSNwmDlxVoAACAASURBVM11JGKBY7EaG4+NoyMZszoQi3zEhxPTUJfqy22l25h3qTEjLgt7xfds3SHPi9ZLGDu2Ypvjy+uCeTTJzOBsv8k4fWijq34XbpO8adP5y6TPluj07o+J+XZcJ+/hustqTo20xrPUHVbO4kiPBY81XFEauoc6nxj2BBmiXrqO87aZXOyQxvH4a6QMZImYXYLdMTty933F7NEgDu68wulTftwZaMvjV2e43FHBuScTWakezEfBCRb0z+PK5T+EjpjM+zohfyt62Oq9mNAFQ3BqiWfXShmkIgJpWRzDmDB7nL01WO8WgMnCXJ79tKPXlaeof5FHWmEl+8J6EAiGY3/Rh+2XnmBtPe3fJvb/6hAKhfVAAjBaKBSW/+smvh24CQz917JSQOd/2qYN/P6PzlVt12HejMmk8QPzwvlYB8/+px4glS7HgRVfyPgSi6ZRATpjzbj3uIdz50p5kTaTnDGHWSdVBFYQlDeMu5Qh+qub+tmqDPziQPKTcPpOu8Qmz+uIdvtRfvk+3bagq+qGVE4qkSet8Zwygd2C+ThdM8JhxHdMegei/UyeV1MKCZOM5txcLd6e6eDMFAuCIn+SJBNHsJgCW7MjWF0WiqfRTL6lfCI9LoR78ZZEGM3i8VUtTM81E3ZxJQCK0yRoOadKhFgyW3cH8CQukFfZaWyNVKVkzBUWD7vKMj9x5rjLsWr+PP5KZvB1+E+sDP3xm++I3dAp9C9aRPaLJ6gIC2ka9pRd7oU0H9chIcIW32BVfjf7AnDOLIwX6tG4Tzalwl4Fq9H7ieY+AJ+2bMNt1HbqjtxHLrmDaKlBKEskUKX4HDtjd3KkLLkUksMoQynypiSTvV2eG/Nms9d4E1mdn1HIcqBDaxA97Y20xz/m07FCLjfAjN9w+o8zT97l4rJ4MyGJ/3zbTVvvssSoD0Hxroz28uXwiDlkCYtIbWniiVwjgiZp0jvC0VOU50tBfxpr1tE4eTy9QpfjKT4A8/MdPEKepWb6FBx5huigyTxfE4/TrjiE9u0Mub2LMZtGMDHqHb9n9uPzMw+cLuti8KCb6IVdPKx5SK2FJmKB80nIDaajo5Gmmpvk4ck7USdE9m/jwgUhUY7t9KSHUmdZgMzSAsRW7uRB38Vs73BgwtJhGJoMRHOOJb6eC0BEkb86LYiPkuR381KM+v1FUvcHFUV6SPX7i3G1MnFb1VmqXMezpBQsNvajj9J0PP3V6CflhpKmB5nZ5wgcIMnr3d/RDNahOKmKdtNlrOtVQrhuM0mx4xFzbUJphwZzbR7QRzYHiekCTpNH3et2bEbPQOPuKHz9s9hb956mIn+2lxxlr4kyA769prJuAzPEVbE6pkHp5zYUe0SQNDJl7BLY/PIr3q4qiBQ5YfizGTNg1PMOXlrHUnL+n+7I1wM+M2a2C3uPiOB3dAfzqwKZW5vHlxvbEAh6M0mhjvmRz1k35DjBG9PB4xVOUy6RLrDkVo0h9UNy8NCcQOP8I4Rd8yfN9yOd6kuonnUYL7VWPrx5jKSgGflOO6rbggh6swOBgQWFW0AhXZ8nWW5ojhlDVpwGKq79eHr/KkZdx5B+b4qUdiwD1HJZZ/uHdwrWJFqZEbuqjHiVTD7ZhHOtvpLx/rMInDoXvUGiaCmqobnKAknVd3jf2srhrkLUpX2oTIvGxbo/dTsy+S7myCl+8CqylY6Hb7nwwIWqumLUN51FQQbuvQ4ko6OInsRw8heIU/K3k+XZf/Ec8IHU2zv4+ng4DhYCHB+O4MhOcZzrVpPWx5284rfcrGnFIWgg78PecWjQb9xtXGmfuoQBkxeSXFDL6KHdmGQKqJI2oMa0BZPiciLXXOLsxJ0Ynj6P4/z+/zZH/z9OuxIIBLuBZqFQeOx/mhsB+AqFwnH/b/poDYYMFi5Xu4WarhbR+gF0J5ry3Pwte6TH0CM7g06rBWiFPiFs8WGaSixIEpPno2YnWsbf0VOdCm9AcFhAbytz2o9ncTTckqL6tdzdvZCfzRC1ez9yY2yQzcvka+Qn4ipFyP+tyfYr/Xhx9RrXlbfzXqKemNhIjIecAsP75N8opHSFG1LlOVyJKGZb0g5UkeTFmZ3YTZXARlaOsUon2OEioP8cD8TbaricOoRlk/RQilFgrKEXV7dqUqoaipHDTL7Ni6Zg7Fhm3YvislQfoiTNmeSzHVsXMXy3iDBpbzhrDYeSLLqe1BMZ2B+ai9hW2FMPNxdlMvXRQkZXH6WncATRw5qo7Qlnv/5DDjovJMS/gqiTrZRpd+HW+y+xH54TsuQ5JkP7ItAWsLjMm2sW0yHDC6FQSPL7TUT9GID6xIE0nHyNXlMTHVpqFAu6Ue0uR31oCtNGviXzlgPZrZPQV9pEpvE9ZK9/I9NSEkm1QmQejcS8Vw+bRCzwNH6DWLMGWXvnMW1jOk8jXnDfORrfJY4crd6JYKgVuKeiGTuXPcv6M8JrB8bHerP26U0uxlrSeSOByaNdkHj9lFjp5fwRG0i6ZhaXfgynR2kVOnmvmbRBnNDx3wg4cwMf7TTKCr+TqPwRXa0OlOL2Eq2TzZ+M1YTe3IWNy2uylDz4/koCrYh+lJ69jGmgMpWflBnw4zNfdt1hma4On5ZcJke8ACc5OaSvGSG+WMBS2VpmPbDHeKwhHjURPB34kF5vW2kxsCA9XxrRX3EMcl/A24IvyJjtRkSuD7JXxrJDzpbLu/VYJPRgQbAHs+zcyKpVwriuhsJUA1Y/2sBsB1+Gt3siPW0k91OX0Sovh3m/ToRmelhLLkD+bzkvQgNwdHLHJGA+p5aEoFH3nbPiEiSe+cifAx50DtTlw7HvDCpJIfOgD5/vHkSv4x21yxWJcE2AjFvMur2Fge2amPYOxaWni0TdQtL7jsDBvA/rdozA7PAZtH5con1pFWOSLiP15jI3dkbg2ssE1TFGhH7JZvj7wdRt16Vf5E7im45w7f0A/O68pr6hjs2ut7gYPZ7Mqv6MTxahZFQH5s5SZAeV45VzG6t42Bhrwsv0XNaJPUTqfi4S/QzIbl2MsJcdbVEpfDs6mcZ7NXSYGaNcYYKO4wneP7LB2jETd4WznP00nJkt5uRPGo1z7nVqVLaw9VoWHtN9qan6i6ZsKOIdUtQlfGeV3TxyzqzmV9xypC2yeDZYDa3l+gTprmP0JnUcRh6iOf4L/YOvMLDhHePfOhCy5RE9J0VpSc0mWyEBrf5DiA6RR1zbnFGHJ6CT85vpE/pwbfxiWuZsYo6NCa5h5/DduRpdkRQ+rfhB7119ObdzNx9CLZh4oxId6XDGis0i7qoVv7jHnXeJSCoIMPDbgaShETZudbQ/PYe1kguq0SpsvX4EV0UT5qtKoC+7h2+ZzzmVdZrEG3qEyVQjKf0Kt9muDPNxRtNrDKrFOtw7Nfu/PR//CwgEAjWgUygU1gsEAmkgFjgMfBEKheX/8i+eBNqEQuE2gUDgCazm//YnnhEKhUP/V+cD6Mn2E845eo75o87xMX8N98fqMtn/J+5jBnBpmAGVfe2pSrHky5Jh/Ho2j7iqUPaufs1Qu7E0nrrEk6IYzgmFTAOm99LiXlUZ/VUEtExZwL2BAq4Nmo5OWjpy+RW05RgT5yjH5VlubLg9g5WYkNa9iXoDM6S6i9AVRJEf9IX382zZIchjws0SNmm4syKwH/0utRM28AHqy+Xo+niIjzq3UEn8TmtJBVUd8xHrXc/D5Z4Yzwnh0NNuDt2Zx68uIb01VxK2fR47Xn3iHcsoEZHk3pQkFnVHM/GxLk2CAAI+pXDhhitvKhRoPuOF7PA2Ilta4U8rmtu/8zognDDBPpxOWfBcWpXabAVO9E6hrmYRR8xGcW11DM93d+K5TZzYQ8ns73cc2bBSDpe+xyF2Jo2Khwjap8tv3ZN0vH8II87RfesuXauPIJ3xl795x/npWMe4iiEs3LWQ3D07eC4+CtvtjtQcccbN8TQbP71EWroDpbSpRA7WxPrrSuo3imOUsYS2qfZIqdxEZEYLgab7CHSPwtd4Jcevf0CmYzNBrhX4Pp/KCFkRRj0/QssYb86JJ/BwziFMjl/md1kBSn0jGDdoD2fWfmDgvB0klofQR1qcLb2smPG2hnfyF7nR/wbmoiNZ9M4Rnzn7Ccm4TNZcNXZ8UKBaQYISBX8OblLg5ZMTyIYpIqkZSpylNa22jThl5KN2bS6VirI8k16MyWgbFj3Q5p3WMLSeZpO9zpEDl25jFOrFkovheKlVE9mtTmbVcrKyVjNspzk2AR9Jl3lNvUY4DYeC+XvIjZ/NPnwz9UD/9CCMXA+zR9GJdUP2Yy9dRcHHVuIPJHAhbi3VL2qIf3kUB+uNNP2B1j6pWM/xxzpNhiW7j3BuSzUZVzSI9u5CYKmIufgnRPaNxNBpL7sU/fAcIeDQF0PCv1jzVsGfNy2xiOjOw+OzIu/fJGN0xYf2KzsQdFSzViydkf2mMe6bAz02CUR+O4tHzWHcfBzoXjIEyRod1JyLuT19ERk3T3D+xxTizhzl9402wtNP8jzEjrn5SWwd6snWKV/x2eaBc6cRN04W0munDF6CSjwMPHD/8pCBF2TZ7nmIG+GfOCOYgzB/M/rremG3egtz5BYQlh9EUKOQdZf20ivPFQXDTmQuf0b693Cispx4cmsmdafUSCw6TbK6BVJfI9CpeEm8xG2cXLxJ/+aJuesTZN6X8inTkL+mEZBWT28rb/Z+OY8w6gX3Dvbn9rW7ZL9o59kjER4oaTGz1Q3XHAf6XDuKQGCH7PhBpK3RxnLHWAytnmDnMY/arwYsVMuiWHsxV4vL0Q2ZyuKU9xxNPo1MXjsP3uUhUZbEzsgV9MQV0xNvwfWDb3FQesH4bacR6ewguisG58OLGNY/hgU7tPmePBjpn/ZsC3zPwx3nULQMQF/3MuZK4/m06TFJS3/x/FYHZf7b2XZhLMYWQzAbFsjiQ0MIPJLCo7pTeHUPYsc8Xyxmz0ZBvw+39h/5/yztSk0gECj9aywNuAG5/5eP41+EPgnI+teWZ8Dcf6VeDQMa/iPhAaBS14WSaDDaQw5SI+vK2+TfCAbdQrKmEMyP4Tw7lj6uW9Gpf4LJ8424/0rmyt537L9SjONxV6Y5ORMQeZLh1a2McxrOYs+vxKxfSEcfQ/Y7XWW0yXrqN2vxvc6X/FAHPFU02TJZmxeXZmErOMpk6RJCP6aT6vKULxkX6eo0p2S+KG0R6UhX9iI1UJu6qFzWCybyWlQNkRQvLkysY6HhCVQsbvHp1wnyX95FO8EI0duK1HWIEPh9ByIlW1C4kUPe2IE0B59H4dQqcp8E8GqiBVcqD5JlFssQ/0vMPnMPR7NhdFpvoLH4E5Yn5/H11SGkLzzF3kFA09a1CIXORKuNJPaVABGlOaiJLsJ2finTc44QKF/Ah0++JJkXE7isDjNLZe7PfsowwT3un2/jsnA/wltnCQp2Ys0yeV48nwPHM6k6XknXt4cUuTZQP7CL2gINNPM9+fFoI7sHDaWv8D7bF27iy69RDE9pJr/vc5TNvyN3T0BijgPxFYqEyKbwVrSQNpE+jJn/hUsGJ5gY48za1RYYXO5BsFAAnl0ct1mMs0EqNosseaR0Cq+hTeh/2snDKyP5tLOcFolrmLsrMH/VezRk1KlzLMFoyniEOnOolTLhY+IQJi/fiDD/IZ2P73G9+Q9C+5vMu9iLu+5R3HTpx7URG0mZlESBnzzfdrzEIOER1ZuC0NPQ59teZZLfnGKpdh0Z21sJO/yWCvknuNrKclLUgGNP6/Aa8QPpIfcIPr+amcUxXHGq4H3cSD52bCUr2JPOQbbc4w9jHqgh0usErwpVaZJwRSN+DvERGtQF3GPBpdWoKDtwVc2LtBJLKspGUG/ViwvXnzDZbyb224r5+dOJrVM1MFF6TW2cFK5iNtTvtSfnZSLis/vRbtPGjRcuBP5cgpuULFfHd3AhqS9PZ9fyubyLEUtHcXD9GCR6sihxE3KrDc6of8Uv0o2sSHkuek/lmLaAhy41mHmU0GfKXnZOceFesTGS4VMZ9s2E939vc0dKjacLnuL7yYPM3us4rj+Y8btu8bIhhXf5V3hQcoOz/YUIxkvwbvUPAoefJPvUbRIC+zG9rJvrI+aRlnsM0x+T2P7kHG0efjxs3ErYmpcMSPClvTiZzFpRxBwa6ay9SuvLAhr7HkR85xP6FvhT7TwQDdUBCBtOUffEin0eNRiHnOH8skw+fNjC2l6ZnIuQRk/6Bx+Pp7LfZgL9asOxqYrEzbGVwcYnMTN6z8KvsxnbtZmTZ3cT8cIGDftfzJDNZv8HCbbtu0i54Bh6abXMDR/G+63nWO8tQOx6ApFTPiI4NhDb1TboudoxaFQUfb0PUlLrQa37Ko69XEBaeH/KfaoIKFrHtj7yzPo1gctbztMk04HFrkr6T5nI0f3TGfbNkMHLM9G8a4bbHm3GDFGioQi6WoTYvBOyOqo/3erR7C/Lo8dbgwUOK4hbnUto9SNOaZuzY00Tr3+vIPGJHOyF6FY3etrXIr9QQPv4ZmYMlCct7/H/jq7/q0MTiBcIBBnAZ/5pEX4O3BEIBJlAJtALOPCv9S+AH0AhcBVY+b8rUC9sQ29CDzml+mTv/kK541jm9fGjdZgUWY3QcnINlbK58KweLRlXdKJaCFkwj3zxSLzOenJq0hg+L74AwL2aVE5eWEy5yVG0WyyxXzkTtYR42qYspvXWCdTcxRE30qb9wzmGiqXQqVGIdlIRwfo7MH4zgSpjI7IKztERp0zfJiOmTK7EK2Iah8134TH8MzO03dGhEvUymH43jyYTedTSxJC1/sXEITJsnWXKL6kvVC36w5+vYCG5kb8x+gg8tDjYVMjbo1JcifrF4YePsNrxi+sXVJlLJHLmKvxsrEbW4Q2Jnkn0NuyCP7spF9rwe5wVJoIg9IWSNGRo03n6IHX5g7gTKU3/nBiu+bWSunsOnn3ESaUTD79Y3m1RZ8/RKVhuKuNIRjM3D3xnvk8DU602IIhKAoM6RHOPYi+3ABkxBTrEVuHcpYW4nAoAxusPsH29Iw6HtzBJvT8P684jqqaB4DTklGcw4UoQMkfG8TZTneFNdkxEmbs2Z9h7fiWdfyuR/BRJ8JEPbDtzij4iViTfDUFJoEysoBDkG7B3N8Y1NYuL1zoQsenmsFYFi3uBzqcBHNnnR0rSIXILr9KWJ8P5L/FcM57OklIVMuXjOJz1jKf73iIx/BOTB/mAxmZq0+IouDyE7CRoG99I/Zvf/A0ZTJOjL7XPTlCx+gCFETF09EhTJ7MA7R9GXL9URc2I4YjHRZI8zIHtBbrssylCtUYMiby75KbNp7VxLrJ5wXjq5HHyYgifno7h2Zkupn89jfi0PZhuLMV+5w/SHtWy1+UIcfnH+dQ2hhFvbqMRpYWqpjzylZUoleejqtTOhNFeNLfXYt+3Hy+eGtAh8ojyvbHozmrGoncfMt52kiMU4fbJ9zQUNNG+XkBxfjgmLoEc9gzmZU0qysMLuFvzjHc6MXwdfJjDXodwstmDzvVq2kTN8HT8wmvN3zw1u0JRz03u7ykkfnI7Y69Jo2I6Dwy3M03VhtfLOuj5+JcjvWSZ7rkK18UetNVogpw0hhrDMdf6jKnKC9i0kW8ht1lgZ8iQvQ5ougWy6dRD3mqZsn3wAwYuP4bM/SC+Ug1AV6M4YxTLuSwC3ZmfSK5WIntqOzJqraR8diRe/gra6d20JZ1BpdIOH9c6ospkiMjIwj8sk80ndJGsW8RiS0fOXVhDh24h5Ypp5LfUMWBuJSZ9flLxqi/vMxNwnjmLg70XMN2xnt4/39A8YAGfC64ipjSc+wkH2Lx0JIuv/2bu3LdIHVJn2OvNOE9SxEV6ON9/+yHWbcv3+Os0moGpdR4DLGUJ2udN3YtXyCvYopFpydBfQ5ja/RevC70o0OphCvL42yqhp6BEV0MUg2zWMmRmBGut+lMRcZ1DJ+xQNZMhQSkGx96efNx9jFnzAnnsuw/vZfP48qWNi4EzcZ4RQP7pRDTHm+A+ewgD1skwTgy+SjYyIykeoyVr2Wa1kLBbuf82Wf87aVcWwC1AlH/ESrhQKNwnEAheA2r802aVBiz/VyKWADgHjOaf7PQFQqEw5T+qoWEtEK5K0eTlpmDcM4/x3T6aBaU3qZ09n4LuOoISNuNZ6kphr03cmpyO6tjeeK88QKrkRe6JlZIhMEbSLA/Dd9lcVQ/jyuYL9FDHm9Q3fHw1lHbTY1SHp5KWvJ7RG1+QPdqV8XFXETHfylWnW1i+PU15+URsujooZicn45J4u7uNiPcdFDsbY5aXx9iEYcRrj+KtnRX+j+W4oLKEE9NOo6STzopnz1AQWCB1ZjPi/cKpH/wcR40M7JwmcEV4h22CgYiO3Id/n0GM6/LHaFkvVnycieRyMXq/M8BqghUFx0/QIxqCVe+tfBb+xHtAOLExt1nv54ODfzpmdbr4zDjF4M5pjHBZibjfOiJ8KlDXDeLmtTO8/XCaXv2s0e8ZxMqYZBTy2mk4ew7iJJCLMKYkrpQg5UfYmdZy0usaOtJbkL37GsG+S2R2fCRv33rmb9jLnZNVpLGGRZHVVP4IJ6JNwCMLVVJeNCP/py/qo7QQjvVHTTcGC5slBK8ehJTSRWbvGc1kb39GyYdTdHkJA0tvcVvvHBeMVbAp0+fsi048p6sRdC8Rlfl22CrqUisWwh8nbX5ImjPq5W+CNCupiQ9mrZ4HZ1N1UPytR5fBF+yHjyO/+zO59VLo2+9CLes0ynkdfNAWIuPujVXsNl76aDJfZSTfjzdSYSXGvZ+eaIlZMlhvDpJLz3NibTgBt1vYe0qZdd+X8VnWGd+f3aQ26WLYT5nnrerMWtTJZON2BOdz2NEch1mRCdWDPBGptuGXwU1uV4Zyv3c9jldsSYg/QFBBHCXuyazJXY2MlDc/jgag5PWKyItu1PSko141BXGbJPpp2CMbr8LX2cW8Tl2Lc7QvXq6GvIhJYGv0DcqvxhA0aClNxn1wuDSaHA0wG21H5+XVDNaz5sXLrdjLltB1YTbf3rThl7qYIMn5fBfdy+yPEzA2XMJj65c82x2MuocCw7GgsLEJt6WmXLk0llt54ymZ+oDw5ousEEhjuC6AYz6KaLVM5Xd1BpOSnjNfwwftscOIvRGM7hRbXKPLqPfNY9u+f5492DvyGuLVEahsPYy00xtujMng51odjg3cROat3cQp5rN413iuXZzLCNkbTLEbztALyzi03o0XuR20fy4gYJUcOzcHM1ZWAkOhJXnDijB4V0a2vQOKn6HaZRmh+yKpyAvlza5snELjuWxyjNLnOuw/dpc1ysux0bblWWkY2qFD+NAgwjr/WXz5eAG1ADXcpgzE5tEGhD1CfIzXIbPSmubMp4Ref0BJShs3rKUYdPcFauFGNF1u4nNWLDM0CrjzVZafCWKYrd1A7fNMuh5cwczYhd9yGsjLHkA/YDUFhx9iaXQP1+Q8Ov/Icv5nCEUSjZRbyqKcJYGV3xyWhu3HzWgLM7ovUZtZDQc3s3VFAHeCllLyKhpJrU5knvUjsTCFC9P7sy/LkJ+pYjypOATF+dCgRFV4Bfv2HOLcNz8W+Jiw3W04MhdAcfcs5E66wmv+++bjPxFaGr2EE/rPoNbpAm/vu1EhmculjEBOHLrJrss6/JrhSaLqewY93UJjuh9HXvpwK+45RS2nca5xxNPoLYLNAnYLr7Pn4UIEXgKEQiEEwZ6O4XQWWfI18Dd2JRvo2JKCtU4D8fmj+PB0PXXblHA49hrtkDU0HDjDmeY5nP+RRL3kJSR2VSFTc5GcZHPeTRrC2K2NmPqV8Z29NKtuoXeVLy57dXh/+hB/r++meZqQ2CoBC9UXY/jKgbVu8+nKkqc+yZjKNVsYlVnPnvtDmLhkOlVXCwnDkxf+ozhaqkfvXRN4vkiW6KX2ND+I5VulLtGnF7Nj1WQuzL9KP5nX6OfOZNBBeYpninL/rheRP7OoD3Vh9i8V0i4twnLzUQRHtAldNxJpg3WcEb/GxH5unHrqzR6P9eRsK0PXy5Ou8qc0l2fQttmPbyv3UlD2i82z8llTv53apAf4bRyAj/IeMp2HYnatgdjcwQj1FyAq9olWmdHg35t24QjEAo8R7abD/q3TOBZvS7T1W2IHZ5JX/Iw3AXb037aeWbbXmDPuAm5i0xEbOhX9qOk8Ll5GpNFnskL6MtDcnPeDq7i50RdNz49MIZ5X+WaEhS+l0FeB8YziWLMzYdptrA2aTdIDV6TLr7LoxkNc1GfQ3T2QZaMMCKoJpvrJGyboLWBusTRvvH/wZ+4KFhS2Ydcvgb77lxK4qw+zpiky1mYj3WnrSWxczkfPYK4ra/Gu+R5bQkfyW3sM2ze+IKXyA6eueXGr7gO/jhpgfTuFPv23s+7PUEY7eKN0RI/Fa49y50oMt4SjEfa3pZemHp4RYZj3P0vnJHE8ip6yu+YHSSLqBMt5kSZbhWXmHM58NEFVewWT717gju8aQr9703/6ds6sr8Vw9gu2Kk3EN3sgtwfOJ/fHM9I3rGdm0QR85u+FBG9O3Rdg13qN82YphKdZcfFkJV7tfdGQKuNOsB9Xg48wtPAlWnYxrPZZwaYDl/gjBoW2/pzukcTU3IeGeXqUW8xF7kAwvxOf0hBkQPWHm/gYn8B90BruRE1GuGULqldMqE8zwTCzk9zU3TyKmYdE6BgWH21igGEJOn1vI3T+yaGBLswUvmaQ7ULWi95k7b4L7A+bhFjLWAZ7jWNQ1mgaPOy5cl+AiqUFI49N4kWbNj16v9DfOYjEN7+I3LkOgUCKLeE2NKVPYn6eKHKTxmI625jFj72ZlDSDrymSiFmkM6BxCKoXnrN3xAjWhHykMc+L2X2H4f3Aic2b1LkfN5GjU+eQ6T+Lyp13eCEp4MJQuJF8mt/z41BpfY6+1wUmLXJgxYJ9OKtY0+Veg2iOC+ElVXRMzmSghhFGX70cEwAAIABJREFUo5Zxy12XPLNR+B0TpV2/kcsHxTgjuZNHQ2PZ8Gowly/eoc1oG2sKRjP7STZ9o68QE5CFtXUldlGNSCiOI1slBSdJAzy6ihgz25O88i/s+1nA3+AGoiyMmW61n/fpBngPm0pDgyTKfbpRTnDn3iwDRA7N4E7St///PDKoLactdBDsIUusDL+Vy/By0yC84SYvQgczQK6eosnJpLYasrtdi8Ibp5hYEsaz74fZLNxKV/pBnsgpcXVZCmssQxlnmozlmbnsvHuK+BvXURvQRObbVQj13PGcuZknfnuZc+gkrSnSFM7pweDqJp4tWYwcjnSOlCe8Zi51az7QFtvBvd7mvFE9wnCJSby9/ZEHeSaYzC9k3eOZ6IlVElx3g7dEUcl41kzeiZS5kI7nAoa7L6S81ZP5ta60NQZzXjwLNS7RtPgeZRFJjJl3ll5iC0lqkEHmwFkGvN5O9pkOdl7OwX/1DqSwo7p0LjJKt6ntgZJ2AcqS4LdFyHZpV2rbHDHc2c6KPYEcluvPgMeBRK0oYH38FFqCa9juP5ZZPjMZVdzEhCQhk5ZKc8UpgMreubisjsJurjrGImV0iMogq96F3rZAfMavINTIhL2paxnb9y43ZUV5PVOX5vKX/Dmljb+pL5mZo2lTkYCgdB7sTyGhJx/T0cbodArZG72AswducnKAAMlFQiyzNPC10uOayHgSz17m1yAzHKb4UDStkLYME9yrG9D+NQjPz8eRHSDDnKZxNH1LRXXKFNLujkdinC7285JZJxCn3cuGJJXhFLUJEHHXZ73eSsJSwkgV6cJG/R1pqSFoWb3ns0QBEpvSMF/hwqZoF8LZyLXVCTyPH4dQxgEy3Rkg6UZ/J1UMv4bjfm86qgvC8Cvfw2WzPcwPENB2RQaV5IFMGx7Mt/sHaG29zYvxqZRsmcixu+epXXqMgw924nqxgKan2ezbOIF7jdcZ5WtNaZ4BVuXebHs/n6sj1lO79xjPlXVRuCFO7sEK/E28GDDVHN+QhdyJfIGCmg7zD9jSp1uX2PjFKP7sQLx+LyMMxuAqKMFX2M5w8wVU/4pC0XADGa83sG2OJitEjOkfWUeX2DeGSdbQ1dxBZ28jPooWI6tqQ8oxRZL65hJVPxWx8UEwLgV/e0cWBAfSxykG13pJzm/8g9xqESx2+yNcs4f8qhbKSSS+o5Rtb6M4t+oyCs8EbOyIp8ggBzERd2LuP0BS34huN1cmHOrAYHQXufkv6TPhHGpjB/Fy/3eSXY5Q+7KdevUv1PRRZYzcRPbc2sjK0QVI1InyQzaSZx8z2FYijp52GioGVfQaNIQikQaU+llx0LSOIX3kUQjpQfehNcVqL5A2tCU4I4N73W5k/cggzfwb9R5abPJeyJZ1WzGwtmXHqJFcS3+G7NoYVoz9S1fnOn5lvuat2lDeNglZ+mk/ZcrwQxbG+gp5EG/IROfrdFc1c629AvWGtxxxXUNiUwEjIp/hMfUOkz/t5eWIAXx8EkBAQQZZ8fORzJCgj9E4knMk+Rr4iZ2qZoR+W4vMdDNMCpYj8XobMb/zkWuG2j8q9OCE1vBJ6DuXMUjMnACTaPoomWH0Rwznsnoc3rdQYzSBMsnbmNuoc3NqFOsPbCRHIo2W2v585w4PAtTwT2riUNpoIlYv/W/x8Z+IAZrKQsW+OkQl3yRFcwGdxzbxvEiTvgap7Bfzw+vLQ6Y1PeZ9uR2nNUOZHCMgpGA+W/0CGDp6C0ER3TT8/sjb0NtsnfiOfU9kObtpLKJZw3kjcEZs3DpmVwm5kj4Dg2kFmJd4ke7/jf6dwRwRCBg61JS0ZAH9BRXsj9pBpW4uUecvc8FoOo+aJiCW30LNrXYWOjYg1NiBh58fua8O4XppCQ5zzVm025XrL01J3y2AFbEMUnMmO18cFQUVds6v59iBWqzsvDl6yYXAezvJrxJyZ8sevBX0SD+7jZ7fe8je3MXkJQOQffmHFit3SgUXKIrdDyru9O5VTcm6Dxgb9GOM0RxmHIUM8z3M9RVgrLqaB23VWJoMJfJjM23mu9hQNIbR12uYU7GSRGECJtzC1ESPxfsOod9iy6fsowT6KTNnVTlD22+y9pEF7w9/wE5BFvPn62la3sMPl/5kSHfz1W0e9ySVGDtCyJ44fRReKvLiQBi5f27yVisKwYYM/ljvwEY7mxVyTxFaZcI7CwarJDLOLJXIz/ncmKnP75921Dw0ppfDNe7XSaMwciJfTurRe/1hXCJSMBHa4vpiLy47L/BIUYWttZ6obC1AMsGfRQPvYjRFi5+JT1mnPJek1Qcp3T6JcX532N4ahPWKOBw++DFjZTXdu5TZcvkYGyvNyHefi8qBSG6NTWTe2XhSxMYjkyOGb9NNZG99ZX//Uj6dnkFIx0KSU0Q4K3OTxYtjMNs7mEbtN3DCnwVZE9ERZCNNF0vtJ/DZXBs1XRs2uPhQHBBFxNo7TCw25N3+Ej4OfIfoJBeMjiihfyUfOxl3TpX+ZOEDdU4qpfJ52zYU+m4h62MVaxNOE7ZZgrP7HpLx5QhZp924FNqXvDO6PDsvzbij5TROuUF7/RMi2xLQOHkVp8CldN4t5vAVEfa3arDohy0BX4VIiZZxY/h79tzZypRD5oi+ikP2pgcT7f2pq5rGqfumfDs5iDRhDPvsg5Cv9KPpKggjhCw8qoj6i+M0qEzn4jwvdJ5tplQ/hdulf7igX4auUAm/13U8OLIb6QeXaLrxAYvSY1zzcMRD9idW2/M4n3qRNXJPOfbnKhpDDNlR8Ypr2QlkvbzH0glxmPSZR9i3Hdx9o0DCNglCK7PZvzCF9CMrWXUujid7JRCOukOJVRQ7PU7R6bwIA9WLyH7YiRzfGeA4EOKuU+T8heD2mbyd7oOX4CLaB2Besy5Dzi/HY/gZrh6q40KFBU0fXzNolxMP1PZg9nQPEq8saWvMIDt1C8reRYwUjWHAt32kWb2iOWcDCmIbiHQM5FnjISSk1XjLN86PDaTWX8Ck27fpmW9Az8CFyCSK8PzcDqomR6K4/T5L57vhnuXB+jnvkHk9hWnbLnD7TyoNBToQ3oHH44EU5cTQN9OUDIsLzLU/wvew74gJcmly8iCkuB+HSx4jozyMb4FNjL5vRO8Dw/jispBT9dV4rLcj+SRIWKhyYPm/94/6twzn/6fR1v0HfdNgvpzZzazCh+SPvI1m43P6jZyOvvZbDNKUCH+XwceQMioXifNuznp8hFvZf07A0Z+vuX+1DjElU9Isl8Ki7xyeWEHW/QAoqKVH6ISNnDuzmoR8PPKVZUtmEHi4nA+fs/gb/YLEQAGmpx9RFWmO6Lo7eO9K5o4tRA+yQlx1FcvH+9DZMopReXrMevkSkxm/ET/3gLC8ZcwUKlAhnIFQuIyzj+H5AwG5xuspaBtOy68DlDbsIF7kPfsD1yNRNIQhIduxNy+k6MhVDli9I26+OroLrjJS6EVc9FvcHJPpWGFP3U4Bwl46tG0WkLRVQIzVZ3RL93FRTkCA+msOC0HVSQt90WeIBORz63hvlu3UQy+lBR9jM3x19YlovkThp1e8yQphsvV2dr/qTWgtHJkcyMzKHsK/6CKjsIEt8wOZ5mDGwn4ieMtnMUDqCBUdYVRNdKYjzBbjKneKN9qzZ1U1KbNcaJlswZjUYpJyT5LzsZw/G52IjDaiRzyChO8CVCxvMixwGb5ZpTiPCeTot0Z6DNSYYmWBY++/rIgNYZ74BizURfnQtZHV1tJox4RyUSWXMwWBHBTL51ORDcYfJPlm+wmfZ5M5NmwPBXJ/2Xj0KOt7L+bZMk0M3+Qhfmo3wghpLL+t4nd8LAoLbhCv8ZCXWzzZ5vQc0QBVVp20oWn4AeQW6SGInEnu+Feoxr/jj+JDgv1luCo7kgCrYRwYI0DS0xPDG/O5ahFHT8YV5A0Xc8PFlhn9znLo0hV850xk2dOl3Plxm+PeIgTFreHH0RwmLVPk53NNugWNPHxnzL4R1aRmHEZqmjm9Z7dRc6YBne4Q6rKS2WQ/gBk19STcqacyURqDMG1GFDowUPAAEeFCBBZWXGso5voIJVB4guHAzSQpX+XkiDGI9tHk5DVttE59wUfwlyHdmQhHTeBNow1RA+TIrTfmc4MFA/aXIFysznLbYawZ0437dTkOFbix2Uuc26duMMn/AeeWJ6Jd9QARi1HYFY0kPSyGNDUzZisMon7fbRYmhDOMBvb2LuWlmCnfK/si4z6O5rslDHqTTVfkbC4p93DnVDBL5KLo7SAga8YwWpqysCiUY3C+E94Ju2hMM8VAYRjflL/y4VsYfYb4kDF1Jnn9dFj+xYZa0wdsTtGlVmsZk20CiF95kYYsOxr+qGJiE8kIjelMq0xjW58yaq0raLD4Tb8KI7bpTMFJcgexb/K4OGcfOuajcG4awJgcL74bRaKmW0Hoq2mssvnG6rtHcak9z9PCAxyRhWOBfXCbchO59EIUH/1BLecI3sUvuXzHFn6F4DQ3mVJJASlrrjPs4wjUWiLZuNSIy1sdePOpiF8v7uDZXcbA8U8YofwAhyUL+LVZl8X5Z1DI96FKfwlRg03or2DK9iRTLtmk4mY2nGipc1wcOZ/PhYX8rWvlT/0vtJ/uxe6YFS1F0DfgETF7inB/PIWcjVOYL/OXVwErSB/nzKF3q7E7WfmfTdX/5dEqrsKeNRmkc5vESHly5ZIwLIynf/kcWltgWnU5J37Z0zPnCS3tlowuiOfquiLEtFcS/sEQcRENhk/4Jwzy8dPlZDKI6lRxFDzvI522DqPW00T/CcO6qzefvlhgK9+MnlE1DVcFTIqtJMUwDhWy6WIfkn/70HWhjNaO5dChR8rj2Wg3OpDQ8J6nq/pCDbjXBzJ1Vi9uLlLjx6v+CARmLHIroa/lOdzEOxEpucCMNxFkitUD+uzf6cPX4A/ot7pRl+JK36QoOr1mU+kyiPVnrZBcmYK4biFLQmrpHPCH0gvJsDGU2AvwUDaGrdEfaX+5h+Wqc9hoAEXhvZAXwJMTFnQE/8FFLAETxQE4B+3De8t4Ys0aGFWhw8pnC/j58TZQi3KzCKqFjkgrDeBQmQQ3wjUICb1J4l8HRJVH8FbUF/lkMC005ce4KaQ+0yRLUEuYhiLjvfvS990drGWr+Ek6oqIDsJn4C/ngTEp3fOCTeB167U/hTgIxZ7Tx1z9Py/SjJJdEslIoQZqYgLFD+iKp/5Q6nRZ0pqgRmRyEsclNdjr+ZJXidSaadhI2+RGvlmixJ/Ub23/o0rjqOLqldhSEbOVxYhk7/YNQaczBp38/Dod/pf1PFZICHX5nvqIxNB50X6M9qhORMX15MTqbiZYe/CpLZN+kDwQs9OCEmCelMQ0Mr1YmwTwYJcMbuFT5UfjBE6kEZ5wa4hg95QgzLWciWZbD3sHXWXgsldMzD/FSKgaj7QrM0R1K6h8JfJtSKSqJw65Wi9QSdazcy7CZsQWNqsdM0Kshr8yVxISVyPy9StNvI6bKN+Pz6BIn42/gf1wD/606SKcOZOuVdKaPBZH3rykobUV1izMRjxIx0svlReV6cn6sY5SdAj0hQ+gU+cbS/qqUN9xAzGUZ6ftMqLNrRVRVDEOxkeQrdRCfFEXgW1981jwnNKiAaDczJs4azZQN6dR8CQOlNfAVlN5C0y24+ewv5T0j8WysJL44ltLrs+lv50qngg19FPtSKzeGMql8JKKqGbZ0KJreM3kdcgPPybPJ/rEGiTp7Tk85RfUPDRwq1Gjs1YOG+WLKtTqpH6ZMX8959Ouqp+F7DSsr0lm/N56g9xV4zJLlWsZdNH2KSHecg6DPBhYFPERBfwDu2ddoa5tBZk40SaLl1GvLkJubhvTfXrhPH0XM4wwye2dzasdBKt+/5P2lbVjP0KR7/1QmpDSyef1ADnw1JXrJN05s8SCw5w+nRrSxwWYOi1eGYVqTCx+k+B/svPc3F4DbuH/JHmWPUGSPQkYkVBQqIZVCSkt7D+00tPfeO02iIqWUUVRGSSQz2ZtC5uv7w+cfeH9/+Jzn+Zznuf6E+z7nun+4zrnrxn3Dt/svX7UrMLJyZ9Dj9fzt/UL8wEgCT8zkdVE3SsOh68smTKepMkpenkLEScs7i2eXLKvrs4h+XcN4FxuiK0eTFN/MT9EUjFJ78HnhhNKnr2TYeFMol8Ao/b14Pg5neIMODlWFnHpyhXV9dej16TLlZwrFRp/peX4Ps/knic41YdR2TWIOWWLtOITYJyX/sUv/W5QPa2trwbglGfR0e9ItrIX87yh+SV/F6Y8rA2QPkaPTRddzUTrdlBC9q8i8QCHismNoFtNGum4L/4zf8u3OWDRUBJhMgPfiozB+PglZy0LqsxQw8VfhS1sIAoU2FLb9ojirAdnTo9itb8jyuy6URoOmsCrBXl9omdfA/sdxqP+WYUuAB9naUynMGcHTYFM+iCxh3VohEr5f5P7hGjb/GU0RSxC/uZxRg8/QMcAUzdaxNDy2ZoD/Xiq9XqGVMJrWj8/YfXYDF4+mEfPnKY1fbFm1TJkLV+6gMe8NLf9ApiYYk5TnNMisJKetEkOLk/z6JkSllidKrVbcOrqCFfvkedXSw/GDd/HqdMBLQg9zUV3Odl1kwc6vuIu3kBKyCO9Sdcbr/p/ZvmuIQknem/7toG0xED1HA9RqDKl+cRnnwEksWvicxUoG3Cg7SlHsDwZEmjH6rBvW76awxteLjjdv+J2YjPLgyfw1eIz2tQ3ISkpx1SkB4VMvcNlag9dIXSrLDCm3b0Fuw3iiA0VJWihE6M5HDK4xJ/pkGVtzh3Mh9i+Jn6YzVvsSBVG+2Fg6sU04ip93HiO08j0HXx/AwKqVS1qiyNfPQCzxE65Li/iqZ8TnC/V0vNnNvO3H6J49m5K1jmi526Df0cOPFkOsuuR4lLObplkDsRzfyq6IdvQPHmPKyYmMPr+EQRVVZHnKsvj+CI5bqSFW84d11rcxTCtnWu9W3g4UYrW5JoK1YwkT8qZWrZ3Ai97s8znMs0lLsag4xaEIFZp/RvOnI5LiTHXc5Wwoq97FMIE2olo7GP7ejAHn1hDa/QZjpQ4u5dpQv1ITOSsDAl0UeDnuMEWXZ+DXIknzxnYqgvYx2e8YudszeGQWzeXVa9nm44n48IHIJlxFyV0VQ8NfRDacw2ZeM30HZLnSGolvlitBnn/J+WeI0dhZdIa38fPgFtp0vrNuzmAGLrDD/PV7ghxnoXw+nU+jhGn53sLKae84cnI/pSKy/LAUYtZnKRK7ejAcd4Jy2VEsFWygeFwsRXO+s7j+EONmzOb0mg0cf36Tp0tuUXvmBr2rglgX8oif5bc4LbmasMYIzNbDtL5prH52iQkqM2kov8QeeVN2PtemSPIaVy75sePuSTSyFGkSyWOIujL63StQ17KkuzCetg5/rmX9o1jxJaWpoQA8LyrG+VwMEeJldIUd4mTVHQbeELBhsjePP1xkZJ4a3UYXGCc3hqszyxmbdoN7z7bTFTMBwQwZlNrDkTIfyMDY13TV+tPPw4/UK9ko2fZD3/kPoi27sJN3oSZtKDnasiT0JdGuO5Te5Bz2rsjE2+sRCh9BYUkIE6e+wTtjCLHnDZlYu4jYjltMX2BOwp8YVHNkKBrujcmERtZtXo+Bw1i6B9dh3e5Nnd03nDtU+JgqSVPdd36OySR5cSp0Q+m9WLS9JuIp0ohZSjg/OqezfkI161TD8Lt9nGtTp3JckIZ1CsROy0RirCXPBFO5/CDyf8vHfyGK8kICRRMBayzWIrVbhLRzpoxo/cRVFU9ku5IYe1iLYStNOdu9mRnrhCncroqcyANEZJV5a1CLW3M26g1mNBoLsbRKmX0eHxmdspSIzLF0u2YgdvchrBPQNiKK9NhHTBseQkqQOa2/3Fi2x5D39ePRbNmOyKTNKD30Rb1RiI3zbjBCQxs1nyL+yc1j2xhbvjipkzhJmMako2QlPyX6Uwq33x1lwOtCDtwaS+rOFexZcwppp6PkpE9Hv+U2B3reskxhLBL7k9gp+oEbbrOZo6zFXCVLpAS7UFF259yNrVydIYTHNgHhlzZjN3U/Jz2FuKgdi67+NZL2jGXZGAtaEpcwY1U/vhy4yV25tdT9e83qDVtx1O6gVfMPdVauDAoEM69qsksHsaE1nCuq3ux5GYDNWgEnvnnjfyiae4CgS8DuK+sJsnan8bAxZaZF5GePY0NEF6qr3Kg56UNoSDKN8aPQOViAQnMRD3bZEGu0HYFzPO8dvnH+4h8m6/+lum8xn9fNw1JbmKOPx2B9QBX9rC/4r9zB5Y5Y3M2s8JrwgcWLmtDRSeJv3DV+lB7k4RgRhpsdJS9qDp2if1jo0838/o+IPZyBqPEZ7Ld/Jvj7G05OkaLKVIW89Wux6/FliaMt3zufstU3kNlTIrF3dkR0tzg3j2xgZ3IOdr1B3NvUzeBXFuw7142t2l5Gj1uCScpgdut+4Up8A6dX3Gfh+Zm8H/aHiR2DOD/dF2EHHY7yi47bR8nY8ZDGmWps3jYW13Vd2AkLODh7JMkhD4nbaIZURAyjB7ohLfeXpsyNdEguZXC7GWle13HISmPLM33W5KgwL9+T/cEimLr9JduggvajKfwsUML+QhKz1J9y+t4Bfucs59jUNWTqn0PecipfbjjTbn+U2wemMtl+IipNpthZNdE2aSvTblohf+ksJdm2+JrYoL9MilyvwUxcHYhShBL3OEH49j0Y9uZyZegTzNf28vzxVYSkJvDu8xaiFjlhvFCBILVBLFtihNQ2e6bf0MN09U1khtsSqyvMukWrmPghHJe58awPuY5i8xnKP1cz9dkLAvuP5tSpSayLckenOZhtlWfZKuPEJnUnZL8KqNwYRO6zY8yqv8BYzx76X5uM9vdS6gPGM0C9mTHdQykVteRZ8Up8XwaSNq2I/nIz6ejMo/lHGUGT3YjYeopny6YRFhTI180i3D12hiN+T/llPoCGP0HoDb6C1i8/vv3upvJ8MVW7bqD5YS+Ddw0k9/hurCO3MPpwJU9O/+BOmRzjVw7kRVY1Bm+UMQ2cTWHWQLr7tmPs2Uyo6DFuOk9D5t0idv8ayyqlfHo7T3Dwz3ruhm+jbeJ5YpJvUKSTzirXFNJkbhISaskmFwmsnZ5xuKYErX6vsFLt4mNPf1ZKuCNQLMdKaxgxr8FP/wjBIrXoH1RlbsRGDr/9xEqJYXi9KKQtL54ti+Yj66rJ0+OeTNZbyL1bv7iecwcfISHcR5+gxMsSye4j7A95+v9O+ShrbuPy83I2LVZi6ChvfhXaI6KYS2bxB9TrxiG1tALXwLmIWs3HYdUzDkefoEnlEi1SkRRI/qTu8mO0ra5SMEmIj4ox6OQ/5qvQLSqyvtIzeyw/nhQhyBLAh5tc2ToNhfQv5Gb3Q+PieiTHrOB2ywrsP9vzUduZjkdjaBGXwSuylWbBXrIuukFSFXryl5mSpE0/8wV4pvYwYFk+3hmb6ffgImvGJNC5NoMdXm/ZttaFfp0jaPorjcU1URTKI1jof5Q1p9/wqu8H1xO3MXtZIZ9PRLAo5hN6K1u4UvEet+w0NlyuYPDHTajLGvH4khGvjAsYoeLM29DpJEu8oHKrLhcKrlH7cQ7n7Bfy8cBV9LuLsJo2DpnraVRvjUAsawshZZu5+iOczckejDlxG9ULk9ESB8G7KhJ37ufdaS8Smq9z9V0MiWEL+JSWwZAvSvT0F+J2fRF4lhExVgIhgQXluU0o5v4ib9wbOiOC6J9fwsPGTCKCmlg38i91hcOZ8zmJq1Olue/hy7VX9mjdzWDa0QdUvBiJSrw4Rcf7iK3sj1dqEIMHFiAh7I7Fz3oe/lrAplF2+FxK4d2yRNL9CokYdZJNGk70twpH9EI+aa21DM5IYOnWXqTrnzI2sJvmT6NQ2Hebt0HJ/Gw0I2X6c+703KZasRN375+sdsnCOdsXo2sivCxcR15PJQctLWlSXM9yk4fkqIeQ4q9LY/gY/ujvR2yNI90umZyxvsxBOyF2Jl7C9ew8Lqgs48r3C7zu7qGmuY+s4k3EZ8zh92sIk6lBJH8aGlV/IeY7EmILKJ4yi12jE1D1bEXn4Taso7VYez6BnxNUkXa7y6iNRgQrqPEhM5MZq9UYO3IZ2kmDafi7lqBVvSQfu8WlpAT8zh3D+Ml0nOzm8eTkfAZdAp3dznSKD8LlziDORF9hSlgs9w+bsLxQnuwZV7FceB9Tp1eEzxxG6IYa1gTdYq2fCKM0DrFypz0V/sFsK5Nk8axlxL0bRfPxtczbMBLnyYY8LjzDZM0RNJvGUb5HAs+bE/kUp0rNTGcybiaSM/wUFfcSGXkonmdXMphok4XbKj9mJYTi8280C8e/oP/CTaz5qEhSUTeLR3gQ8u4xbVrL8V8RSMWTDVyWLcex7ivnNsznkOdvxO1/U1kRT6/qL3wWZPGpfBGlqbvoEnRyvSEPy3vveK0mSXWFMjeneKJpd5srHmoUdL7BVryZqqpn1L+cwbb77UguMeTzinUM/KiD1uEGTCPPUXang4AP13G9Jsnkwc/4V72dKfP3cNjmO0dCp1B4bQjPN1UwskwUse/fCWrvx6orirhFtzBk6TqaPy7iyoa7TH54EL3740mXVkIxRZSTd1JJ22NCQPsdSnetJHHtZuplHtKrbUrelz0st53IMYEcnenhlEqocPR3Mjd/tTJn6h5kzspR9SISm+lD0bYzBRUYsGwjut9+Ym73lQuXjhJ0cTY37m7hePxFwoSEiD2QS1vRY+JHeLFo7s3/alX/j6exGfKjk7D038OHvRepHPwS9Q/i+A+SIPPzPkbcD+Zo2RUcfgWTvHYAmrZR1Kltpq0uHN7mUyQZQe6wDpTVvjI4w5LijlR0kULIcyPij56B7VuQvEHWPjNGX9zHt0szuf8e/Cc7MrFGlGcF5Ug3SaDVUMum2u1oKyuhcb+IthOfkOnJ5VDsRtjQSGX8IaQa1vGReWwLAAAgAElEQVTs5wVe+ZZQK91LoKsmKZ63Sdvpj4PJe3zEoGeKHyp6BQiJVDHLch/Hsu4igSFCw/WI2HKe+oxurBLfMcn/LktX5SDUlYbHTgGztaX4Mmo/OhsVeDBSlhDpGK66m7JlRRYPyopYPiUEeWcXupeOZEFnG74hcUQe3oX9g3vo/c7lrfgnElsHot3zl8K6u4QELiNZvYvxbnm0pPxkn50AfXFAFrymCrHD9Agb7nizti6HOuVYnkdKAXBKciOwCAruopB1iYT+Z1E54YV1M2AOxweu5oHZGhSfXeOkjjt6y1azA1fESow4KZROx+QiDDRVWDavi/7CJSx5KwvVtkwWa8VUSgyJ341EucXCel/6ZQuxfOVJVGTlaRR/zp7avehq2KOgX4FkSiPSYlpEZ8ZQHbSAtLs/+GXwgYbkJCpdw0mVbaV7QiHCP8WgFrq2HyZhZSwRWanQHcnvHGmkxBX4oDSEqvsvWKMaw9uyJ1g0lrHyVBF2y60xK+rj/BJXDry4xSESaN4Jo2YtY759MoY/5vPE7xrHQ9tJ+/sEpe+1rK82QMa6GI/tUfQ+FkdJp4tmgRMq8r9Q8fwL8T0sLtrKNAJx2KlIgPENhA8bk20Sh43MMT58zCN+8w2ONMLVv0PotyEITasCgo7U8l60mnWaXjhKRtOQuo7tjq5cf1mPqq0pCXkJtLyey2idASjmhVD/EDYat/Msw5b9nzwRN79K1bC/NE8cSNCeBKbIltJ79Bnft5ohWriWFcNfIDa+ANMVkBNWyc3sWua8ms5tryI6rN6QfVWc1tYGZr4fzFEnT7JzRHgzbi0frm3HNuQSTjttkZTNZO1ZP87MuICOaAB/Zc7h+ryOEb6buGQyhhFDujl1+QhjbfVwnrEGdZNCrEQHI6u9jHZxaTRaz+IffYWuT5JIDa5m4jh3LrRLcSUtm0ALa+7eLMGuS5xhc2aS/60ROaB/Ri9zPBfzrKuNmIcPqIkrxELHhZy+Ugy0rDEWlaFNdiiKKn+Q2hKDlPVUZpHIn6/KsEkUYVTQVs1jafs4yoUbmS5rz8LVrqy09KDreClqWk0odWmT++MpHqkyBJ2+juPWffwsnc8QzlFqAX1T1FCbrEZDrhg5pgmwaidOQnPJ+b6PUYHzaGt6wIlbEkyUkOV6chXZY8xo+prEp1ZVQmct5NKbZobNnornxR34u07n7QJPWjPW4WIqxcnv2RQ+vIOyiCKXoh5hJG+ExNxyFv6rZf34Q2Re+s7uwZv/Y5f+tygfGgrygm9PMrl4KIK8YW0Mk/Igse4tXg4OrN81A928MvTR5pVdKUZp0HLcgtEZEQzR0yGppRCPyBAWm/qz5d9GxPSDKRCdhOLfLAQqZhR/34y9vxPbAjYDN8iLaWDSpHXMNw9k1ZdxbCkwJTPuHhvVpXhyfQJSwiORLYsj0zWVb6272SXlwIJjjVy9coTHqx+yymcL59U2seigFQpzmuhS8+DhvTH0bYuk6Ugtz2qdGa2RwpD6Iwif2I6+lDfOvpJU7Ugmz68cs9eZXP4shLueGFebqzAYWkLcrDIOrd+H7+VywjfU4r/tNgfnRKBVfYcZC9v4qnOdUoU9/NbSpOl5L2VxSYhZ7+bNwOUMGGrG6zRDMgOf0qCmwcbfWUzfJcJ7QQ2a77UQGj6AIb1GiCn9QkhOj75OUWgpIb/pAvcO/5/d73NbQrRUAat2eCI3fCgnH59gpBBQN47ent/srcoiytSflwHXGSJwpENHlTEPOxj9KIonmruYsnIa+xX0aZy1DLFTKvxMzUB5j4CUZ/9QDzGg1PA25hFzSNhzkBEpAfBDi+Zhc0gT9ue+sBm/fzrR3WiP5fSFFD7/R4TsX2wGPGHYKgEnn8BloUO0mS5mRYwvDb0DKb7ziViDOP5GP6f98jI6avrYfkCIc0HTqVew4POeWK6/SCFthxAvfYegr5zB8mX6+JxfwcC5g1He6k+PpRd3lSbjpxqE2tULyAwyRVcIPpbeReBowCnxx8idcaSt/0Dqm8QId7AjTOw9fu6beBY0nvnfxajtiOWXIIN0GSs2r43lQMlMNto4072rk4uNlYTfaKDhSTknPlkgl7SYEfqviJ/+DS0dV65+TcdCcQkSSi8QCCnjIzaeE4deYIk8fQtFKMuIYIizPxbDqmnJWorR+06ytcuQ0K7k098UNpl3sT1ZnS7b/ZgMPMzGY13stRJHoSKF6gQ7NMscyd6/A3mvelSFt3Kl/0+Cu+HslcPMSwxh8Uw5XE3Xs1ovhuY54ph+rsDuUy4ZdWXsniPLzJ9GnN5djd8yPVa6LOHkzScst5qE8DRt4vxmUvhJHNH5+th9sCVjxQN0azSI2vCaBS2/uIc60tI6VGk4oJ6riLK1DT/ur6PG2ITMliys5jYSPecdbnFmPEh8wxTtCcxVcKT2kBTCyjXU9BWT7KyEXYACYkmVjEi+QrMuVESGMspnLPMT/PFy8KPjmz2iCj9w8Ggl/lUZQxwOUay+mXpbV45TROBWiO8PMsK9LM/LZbdINCofe3k5OZFTjW1YK5tQ6z4D5UsfEXWciJjubJL+fqflvgFHtl/Ce+tehlvdomPzVYb4CygPv4PEtZdctc7GN+0IK4bm821QLwYxQpDXzMxfO6n3gYCKR0wcJErj5W5S/Uah/UKdsb/fkSB0G8tXC/iw+gTlctYMe6ZMk91DSow9MR+0DQMnB6x6zvM6bR6ZJ4dx9GwIwY/qkWt9jJO4FIOUhRmza+X/lo//QoREFAVzLdURdz/OJKlHvC7OwHxCOvP8hQj3WkGDox3Hdd+ydKsObx27iDyxkwVj5pItbsp2iwFMO7QIqZlbabu3hABfUwa2jEfKwo5fjdnYu0xlid8u9r4XR7T1AxlxszE66YHs3HRuOAaxrX4hEqU+ZAz4QG38Rgw9V6G4o507mTEctPqHBQZENh3gytG9dCW/JV7lK2EXv1B3PAAbzR18/XmCjWEmTBskRP6cXrIsp6HmN42hPolcGzaAaNHXZGDHmaYylugNYqicOkfO/WGlkxip6VosGHSXIW7nmPhuH1XdSxn4fhVR197w9Y4iAYY2vLqehvSTbewPHYmntS7NRRH81jVkxPOFCCmPJfSTDYIVm2ApjE7cxfiBsnTPLUDfeCy1czPQkFxNwQYbcgxX4a0rxY+eNHYduUXZNwETBuqSk3AdIe8b7Dtqx9BqkA1ww/jkUs46xyD0yYDQsAISvgroMBfi4yMBu886ELZSlS2TQpncO4x0qaUE3YMvHbP5Pi8U+y8fkV2/iuBuLSrP27O2eC8F+rfZUjOD+pwJ9Mt9Rt+fSC7Puotg3DBm7ryBQsIxBgj+kG/oz6IDGuT77SEwWIkdv1UoV7zKv6GJ6K+5x9T973m3wYVdjsUIJgiYeDoW37j+zJ09FWcZcY78c2D9rV4WtwZzcPlD4hesQWG4HVsXDWPlijAOD9XFpnQxRVvNSZwxnFvPb/JSP4jYsmk4GcTRtGIUolP6czfUhYWz53Da4zQ5ez8RMW0tj0abceJHK+15lfRTE8E6fC+Lhd7yebkzFSMu4VASyrusSvKGCBBVKWKOjh5vegQctrhHt+kLkn5vQaosCqlRdZw8c5uzNkn8PjiZUHUTvqkJGCq+n7Xr1/Mzsh/WU30YXvsd71wHBCHlOLlUsSVMjl1DD3AiwoelFw3JWLmR4PdCXJ4lT8aXbxxcNwmv8Y489TfFZMkDtLs2Iz6rgvuGVRyeKU7pvh9k+2lRbiqFzP4ypkmdYP6xjTS+yWbI74nYf2xCPkOBqhljqDvvxPVT9/Aak4BweT8Oph9gkb8KCfMuMqhiJ+cWXSXf2ZPPi2fjaqzBxxFPkJlmSOnCzfjceM4vl4/0dsymRTmWzJnHGXk6jE/rMphy2pzK3cb0lX9Eep8Gb+PFMfuii5rlMwq2jkT+hwQaX09i0DyO5R2/WR4yHvUvg2mO2kv9/NE0BG9kzMdGWs8+QF2pDxfvWRhqZHJNpItLI9xo8T6NTtx6LgptIrQXxKbqk4QcUstT+O37io0ikRwNCMY6fwyaY8Oo2+ZJ8+dQsrfdZXKbNOvvPUWQ0Ejg41jGSkZilylD+b1gdljuwks4lJwp0qg33SHI1ZgnFydjGFdA+uRQmmaeRVEqhIT5b1gy1406dWu+jy3n+t5H9Bgtw7rzNFqt5uyy6iOt0IaKrjSG71tKs+8i9Fx7uZQkzOARe6h6sJeYNBnubNuGv/oh8o2Nue4/iS+7nxA+d9P/O+VDWkGXuxl6DI5NxFJBmvz650wMXE/wzJEYFVaR5Q4PKcWjay1OYbbkvZ1Gc78j/BAPJ+bHV5b8iuSe43D6W7eB1SPUS5ORKUmh7dA14nResO3iZgQVeQgq51Dw6TGm6md5L9iM87bZDHE9gmeVNB++qiJe+YHRDdfIX+PEMJ+dVFyYzYKmBm7kBvK19gHR5+T4IuxPdLU/Nx9s4fUqAw6JdjNizQQUP/sw+eJCso4YsClkC2e0DQheF0yX92wur23jym8H1H+Y0/19BPImQqT9nEjjT0UmCK3APXIzsRq/qE5uxT8hnen+y1nuaM635H+EPb7G4ekCNq7dRm/KUOySNPEyPYlD7k5s224z5WMY4sq6uF6MYFrJKQ5eH4NzRTv67TqkG9iirmdMRye0SVjQV3QS2s9DpTUBTvXor/TDeoI3Z40L+SSZy/xV1TyIymKGvB+k+kLTU9ojdMnpdeDqlmckuO4h6dl+8q6vJsmkC8GK50xZ6U56wHucL89mQPQX/JrVkAsHl8OGeLvVctzTijm7tZizcC6LdyYjf7uOzDePcLP9Ro/ecBqCJ6F3JIfaUgGpn06zp3MkeyXPcC8ehCsiONLwgNii8UyYrsMarzgWqUjiNCWBavd0sk7WERbcR5ZENOv1wahakWqBPn+merFsxkGu2u8gtaCFNSsUGOW7iNCsL/zumcfhQfbwso0T+RUoJzjzOaeX9+pbqaseRdEXWco9BNi4rsDB7TeZq8U5bNhH+OYwHixbQ9Ujb1Y8ciT00xX2Bh3itJAV0bkCeiRVMAiYzBWhVjzkKjjz+QuT+i9GqUeAze8g3i29QmntAjIUzMn/1sYc++OcLwhikGIIfrYivJFX4NuRF+jdLKKj5i+NQ2eiUtRM3T8rYuV2E20SQcWjZNSPZRCyYRmT2naiU/qVfv/mcmDxNxaph7H1zA6WqorwsLkQzdQPmA2fR+EfDyKSP5IR7MHipDDGp6jjYGKJ5MtpjMwQMP9jDFGucex72EHIzRL2BQdjdtkPj1Wp3GjP4e+TTnRlrLFRmYTwIBHi3a7jnqJKvmQ8308HomWiRaJuNtvvvsHFxJpIvSVouthxa4IebSbJKOqvJCXxDPb7ThKu9YScta6ktxzHbdYOxt0JgfCd6KZpc2tLCbcCVNDxNWCgXyyePapELaijrnwcDep7KI4VIv9oKGffhXN6azg/aj8h6rCTjJEnCUu/iuukY/yaBxN2NFE88Tl1hKLmCP3/gYNzMbW21QTiy/jsySQp7GBM8GoOD/PlzpdFLN8awqJNxcTdnsn78j20f7/K+X1HmKP5hXFG43j4ejrmd32RWRDIAp90BNrrcLt8i4L1z/h5/DlnEr+yI7ucrRMFzLrXwmhbYW5Tho39C3yujKRoyyt83UKZuaoBs0wDkqV6sNcyYsjqkZTfj8UnvYlnyzaifXAnRttN2ZRWxOzMgyhFL+ddTxjjPKRInfqIxEH/+RvD/+X/DoNUGlH4PJEcH01yytuwstKkpVCIlYvD2Gr0knNHn/DQfTDqUz8QU7YMMcDWYApfE8VxEduOPNDeFAZo0PZ0PpJhA6l5IonEZS9Cvl6l708GQUIHeZfiT15kGb04M/PaOPzumqLl40GTiDJPq+txkZLnvkCUS+Ty0mouXXO/c2HdLDgpx1ENeaT7PeBK73zqe09jpr+cEy/EEO63msJcGLF3HiOKhYn9GoznA1tm97/Eywx3NDQ3U92kRF2nPRLZH4nW/kxyii5JzU9Im7qQF5JN6GsPYvO4NnbkvGaZmjNetrKkFvci5ZWK2iAVPg3WwkboK8bPGtCoMuBvyWqcZgZhxAROH9XGMVyR8MOn2a+xkwEf4hGyCyClzRqVDgF92dBh7IGRzlR+NMdBpQ0DXEH/SgiydDCn9y20fic9Jo7JaxWpLfvGZT0RUHxOZ7Y1zmbw4GkRPwmgJtUPBoijNaUPIfExzKk/wVVs6JmSgM6uRLyjZ+NYMRz3xQYkWibw/VgahelpTF+7hX2mS2mo+czr1hJ8bhzhQHMcncskUPNr4r7xOGp+uxOFL6l/oziRHkj1MFB/0csokQasBk1h/slkUgKm0bK3COkqOV4o5bEuMpIMozhGZNQxQXctGcUSTMrbzOdHe1m+bwcha0wwmXSQ1m5JXtQLeD8vkp9FcciPlUD3jw1F/9bRGl+CSIsO40vcqNiyhSEGVpied8b0qxan1wxCBLgzoZ6mvLncjxpMztth3BpvxzT8sJ3dwpvlAvJ/BdPkNZYNxvMYcTyQAKkPKP6AI/7BXJ54h3++8jy885q+/DL2TNTl30sX5o0wokXSiGGdTmysVIKhenwp06CvxZnli9op2tGD5K9a4kWzmK0/jfsXbyP1OAWPJ7KYH6+hRBuqni9nhEcTNWef8eD8YZY3GjH9mx7lreq0/SugOucfIxus0ZvhxrnkKvSKJmHt78KerJNc1J7AXjk5hCx0uVmjR2HUEsRsVjDzoS+DRUZDx0ieJt7Be58RFqOMmGqyFr3cLF50CuhXfx9/r2hSXkvw+OEqlrodJ6tOheyMs5ySH8DQyoGM/JpAU44hnpMfUfZoF9pN8kz8ZcvYqlDGYcTT94/Z61zL7AQ3apeNwTovk1KhlVioLqJQVIUxzmNwGiXCr7dT+N3fiY6wjURNU+fMwx38VGggXbaW3i1pLIu04fXUXqx3nCJaWBqDeUG0f9uFwRFwmFzAwVFPacsYjHziCISGpSHnXE1zvTfC9YvZvTiB+qlPmbTpH1nWr7CoCQOTg/wan8ir8BZM+z/B9f0nPmaF4hu8G89+m2n4VEY/kVqyGgooEbWltloEPfcAMqKaCNtkQkRFOZJdzZS/esOn5WN4FbUP6V2D0O8ex6zHu+le7IGSiD4u9zSYvPcxR77ZcTpwHAeK7JBTVOJ0+ELmlZ9HoV2M/G8ZJA14iMsgtf/YpcKhoaH/Nxz9/4sdZ7aHGnf58n5DHWfTbjHCazC7nLdzIGoO+kFJbNYOZefCpTw5MhXXUQMZVe/B3YP32aAXxZnZrzAf7kCbQzYp5iOR3yJKu+x+blpG0T3/Dt+2XGFF/0iO37hIpGcc6am/+Lbfg2NbxjJcQxRL02Iiv6Wh7ahDp3IHxzev51BmGAu/KWA7Np+gnJWo9lWSZtGfhm4JUh/qctBdnq3Ll/JTPpZzh06j5VyAik0g7+JX4DdpORtfvmC5QJwP2/pRO12M9uwn9G55ROzRp4w/PInmohGU5pSg1rMAG8s8BJ9LGO1UQOYrYRxs23G2EiE8sYRle6X5G1hCX9NzTiu8QDvpDMXnjTB2ukV//Uw8M/WZ0HyJi1/SGG05nrijKZy/VUybvC60xaMvFUHpowmIR9xEgy66y0xhoB/N4g00nrtE5K4dDDE8iHu7J5Pa5QgZ0x+rBDnaMt5TaviPfp1aqNU+oGCYH/9qRVGW+oLu3w9Up9txjk4u2G0nbN0v0pmE0JtSFKbaE2N+BUtpKaaP6sFFYw2eWidZsleBitNvyNFYxOmo+yQNkaYgooph6gJitpxBZshxJm2O5mbyAT7MnMapukdMyFWhbLkUUcOjSdLdwBqNcMq9N1EeeQfnrUlc9q9BwvEUA8buwtmhm1W1I7hQepVWF1WapheT3WXDj8kyjMySxP+5JaZZ8ow5VsaYvK1UHzhPUoUFD1ujcHvZgaqSAZIGo/jRcgOzJX9ZfqCGt+LDmf5FjhFKEfyUcSTmiB4dt3OQuL+RZ+K3OCY/ld0FXiSld7JoSn9UNt8jObkNC7nh6FvF0OFxBOF0dxTz/qIUnYy9+VxKIj8TYG6IUH4/ZPPv88jgOGd6TvH6ux2yA1LZZ+xPUvRHNLzn4Km0jLduayj2+UNlrg3qXrZUq65F5YofI85W0j1ThQ3tfYi+b0Yt+y+Sbg4MnvWEKzoKNE2OJ8B8PDIbPjJUTAvrXoiLqWV/6yms/g7l2d7RLLaawIAvlawq64e8bDCub82J2fEVKT0RTA/2EjtIi7hWMV609nJ4zlIGnq7kbOIxdP0NeboijrS4dyTOnkp25y+0J8ti3mbLR68ZSNhosSMrkand3hjmf+cDqqy4torid1sw+WZA8rvFGI15zMxti5CcoEt37isk2lORMvNDq3Ifz+eb8fNjFdK/zBhp1w9X02zq+47xzcSJM4EbSNs3nGMRf5ghHYS+kxEa1Sc4+UcB1SB/otSVMVogxeSdPbgsT2SIyhDk8rNZ98KYoUmDWCsbhsxmTU6dfoxQ0TbGRD9DqbudtWUL0fHbz8XmnWhtNWHGWQeyBO8oVVWi40cSmg0rifI9QnvSGbYdGMLROAseZ6wh+rcEewPSeFukiHyPMDtmtyIYYIHMWQsGi/fxVqGDX4OlUErS4o1qKnEZxnwsn8kQ5ftYBTwhpbw/ch5/WHlkIRnS0/iQe5Hty0KQWeHDiuGe7A2fyYKaHHwmnqS5ZjjaA8Zz7/aJqtDQ0Ev/1c7+n8qx9YdC+6KbaIjr5e6RTQz4fp1HteXIq6xiZ8IoKlfYMDA7lT5RZWJ7Nah4eoFgpwjaxGV4keFGqtwrcIfQ0aEkF/YS8SwZ9S1ddKwIRXt5NiIRZykZ/pJ/B74Snf+HREElz2YIEFIezeWfFYgOnIyORwERuT+5b9vK6uR2ctQCEXvXSZ9PAsfkjVn02pq/B/qx5IwEjn/zaHDvwGhDEp93u2B4vojYF/eJcV1LmNgbZvfYobw/mfwPFaQ/V8d4pSX3/sSiJuyMRlYH69PqiZhgzo2fxzAY6krFl0ry/kkx76ALTkaTOH8zgjmr1vDk4CWeNJgSLF/EiDGr+Rbym1+vnmOgMpFHW+XQVhFwWu8CWS+kWSC6COXRj6kRS0CrcSyi4jEkF7ZgWHCfpo9Z9CxSRUzOjp6uH/i+TWHOxmUImdWRPFiOoYVJuL7Uxy5sO0/qImhq3s+A8q/onH6F9KWDZOUe5p6nO54R+xFTDcUy9h23v/zEzm8LphQwKcMS8UZ/YtyCERu+Bfml99HUX4VH2RdyA4YydrMIQVNukLx9Aa9/1NLVd4zpxiZc0tnPl8QApg//wNC2kyx0M6X3/Vm8hvYQm9ND9XRjPpyTxKk4B4X3y3makI/c7F2UPe/A4OkuFD45Uz/Cnn7DjClvr2BK+zzKFTPYkT2O9ZdsERcsQU4wA+8RQ/ih+gjzIdLkPnmNuOlWSg+m0uixAA2fQv7c+MafDf3YuXE7xs6GmBY9oH6HHEN/tvFo63fe561nxuxwimx/wMcTqLtV0iYqwOWxJjE3L9M+3xfh2GrGyFgjrV2MQ6kuDR7jSUw5TuyMO7hsmceCs4a4P7Cl1CQR+/62jGjaQOrSeNpS/3Cu8zHK6w6Tt3ERGvFXiDMIwFrKmM9RmQRcceZfcDylm+YjeTcNo6ZSVHXyuPXUCpWyDK7Fd2IRbs/U+AeMNRuBRnMVKa0JHB2tzaxJJngMcCMzZwcSbuFUvT9Mk6EAPTVhnPrkKDk0jkGD4vkg9JZ1PWrE94VR2v2HZ8raPK/6SsyMbcQ8Xo/33a1Erl5H5rY3BHXFcc9BGNMzqtjWSLJuiRuWuwKYbTediIR37Bk3kjabB3zLNsSls43kius0m9rRUzyLZO0wRq6L4NDg2XTo5KLjMg+rA/VUlfrxQu8qMxqkKejMpsrcEOXnhZS5DSGyrI3EqBJii6J5nd6fBeUNhGtOJl6hFi+XciY2XeT0GFeMyn0o89dA0uMH24OciM8fSGL5H0T/KmKStZphIcOJHBnNvJKHKG54zuert8nrus/xLb68Eh7Hhk+DWWDiTdyhucTJzmOIQxxerl6Uv8rmSeBr1Jc/QOqBPn2XV/KyRZ+pj2J41TuVHYJCTH/58KLjBF8KLOlVK8C6JJmBFpoc+WqJwF+CmYs0+fPzFRKDxyJj2oJkig+jeyx52hbPp5v3+Zw1CImTjjyWV2HW2Qj0I8wx29WPAJVTnBRPRGh4FZ9vpvxHN+q/RfkQ6RAnTcOSkoXBrHdK59zkSF7vKUBM4QVbx89k3JTF6P65xGWqkex3hoRtHTgvuo6K3B3eir8gtc2HzafjmBG4njL7sZyWFELe9CuekQHcFAlE4l0h6eNaEI2IJi9diRWZRdz3iKYyM4Xn9/phY5/N2TgFHhk/ZnWJgNEZjgiaurn8QQWHUYac/OuP7iI1Vh5qQVTxN0PHxuKwK41xldJEdXcTlWqE8x49jmW1YyM6CTvNGJZpjUYs/DNKuc9YtXsEW05/QHfkOR6OKWDzhkDuRmRzK2wmFQbNPDf6gJ/EMNZK9aFisJFL91Wp6R7Erpt/KXqsgIm0G7FW89ih0sULwRV2u6ziTKki55VvEpoii6v1Iez+vaOfcgmG5Z8pyfxN+tcHdNW1kV8QwiMcaDu8g39HQ2i8thiFjAK6DWcwMvoSl3YtoLW4mTL1CvIdvPg6bgBvvv9FrVYWCYkbtITv4kHTVD6O/MxunxvYK13nQm81XPeixcwb8wVQqe7Cw7YwNi8u5AOrebLFiOUCVaa6Tmehmy+v5k3inEQls203MfWwA07pcbiPE2HFEwc65CYQJjyXeUtmUmMVRljPcdyf6yDV7IbqRT2KLA5y9F0SP7U28XTdTvotFmA2IZNQwQZex+WgPvwHOXcH0zCqi4Th95D8lsZWyyNsXPmLp9aO9Gtdi2z0BPLvNfIAACAASURBVNJjd3Hmoit9A/NYuVkIU1Mrzkgacnj0TsoNc+lJzUQzy4TGlCDuay1iX1I+ucWWzDTfzJvIT2wKraQjuALFgCpOXg3mStRgXq84Q9uc5xjNU6IkPox5t9wx+G1Bc/lUCl9MQ+60NwaOMSwbZsvde1Mw0N/Gyagsfp4dx4zgTrK3SFFYEYmFz2/+Jd/lefprdoWZsuTPK8INSpC7fRL7twtRzDhGmWYC//6t4scrDVJUfrD70R2WmT6EcXfYtsWFhakG3I9M5ez1MoxvPWRPyku2HzanZKQjH8RMqKmrpuvSE+K+NJA+4gZDWh0ZgzCOXfqkFvjSOPcX+ge1SPmawpH06WRrgarzCJbUbSfvzgNMrwqxYbQpgioBi6+MYe7OTRSWr+d7bjE9CcUUJcWRtmEfbw6d5c5oMZp/nUdbTYG702fgHhrC/q3X2BexitsKcyh6qYrZwDE0p3/i6F5vlBdLY4AfA6d9w/aWKRolk+iRGEZdtwRfBBbEdwXiJeaBoDWKmPACFE3sKbOtovV6OlNFUvn1I50jMtFIDVmFQs58BsmrUVaZxb3AHM6UKiLn85dOZQUSLI+wp+kyZ5c9xOvuacQNreiv6UZ0cDAu8nFMrxZmoNMWjubcIEVYG9/O+2xyGk9OXByblbegxXkUxtXQkbWHOLG51IS5I4j14pXCVBasfoXj3vukNa2h/25vartLMBvuSs1XexYoXia2VYbSUkkmDjVmtkUiRQ8jES9fh3i5GMdXp7Moo5rwGwKUDz0mwDuZB4d388poOV7bn+GTGkTl793IfMr+r1b1/3jK6aXlrBKa++9x0dOB1481+KO6gP1rTXnyuomPsRrEaIvxMbOU9uM+DAiwQGOwBRVuKmiOnY1SDgRkxwFwrWESbWJRzH5gTcZ3VwJt4J1oEgbtMZzLrKb/6Fx4aYjLMTGaH4igYOPC0VRHov4Vs1rmM4PmPEcux4Y9PwuQH+THTsSpb8rlgGIBp/aWs/lYD2NHjEc334jdHr8QfvWGbYVLKcjshpCtiJ4OZ93vaIa+/obQMHEsZrQhdOQsP2ofYN/TR3jZTWZsHEBRbCaOS9ZzJ+sJv7tEEKSU09aUxMOz76gRdJL3phytEfbof5Og1ViWjI8+iJ+TofWZLBJxG9FvdEDk1TD8LZxYsbWbCVPteL/ZkpweCd6VV2Kqp0fkuTNU50WAzVREs17QEjoVycI6OqUgous8mjHy2F2WpEy9BlVNM8iBX4+LkflVwD/FsxQfXkPMkQDeDjXkiWEiozcvwrZACF/FYNqBU9fOEicqSWG8Nx9uzUGlezLeBhcZmC7GFUE9l6PHM7H0NYOoZPnESUxaGcvYP7Z0FYwhOsIUO1kBU4ZaknL8L/Pl1XgruZs6gyY6UvXom2XP5bMGbAn6TcH7Puq6HmIdEo6BRSfDXdJYVeCIxd9L/EgUsMEqha9ZnmgWXif+RSp05fMjAtI+yGGc18DFlz6UiIvSlVXG0H/R6KqlMKbzI9JWj5iY34nyv0H8a5ACa1BIG4B8+Q2iZpixRzwFYSkjZh1t4MvOP0yzn8O3GZd43pmMY183p/L3M2RZJX2Jq7DJO8zlgXGoFAqjWO5Lx1UTdtq6M+PmFOQN9tKa95SYqme8L04gsfY9cjMlOKO0mqfZ55BPlKDeYSiaL4QZ1eDB4Ju76RDKpZ+RBnE7fPhYJMnI9Eb6dVzjW3c/yqU6WfL7Bk7JO9lWK4udQi3ru3v53a+C/Fs5FI9zYGh3LoX5hZTK5ZP1+yLdsve5J6HLqNJWfGoOM6FdixLzPLwDLpJf38QT6a9oB/ignXcO6SgV9Ft/0t5jz0QfX9Y6z8FsRyRC9sa41qvi3jcE79j9eA1ppTOjg79bA5FWEmeF6T+iHjbRmNjIXAc7YuVKkE38jMveScwXteKfrC9Oz29wb/sh+iWJc6VgK5LdxXR7/EFl2Waqy1No0jTgj3IZGsPkqHt1GX/JYXir2XFyYRjfO5QZpFlFSEoxYxxdETZohiUPcbezYd4GASZ7bjJgrgo6N4ai2dvIH39xZhfIIurSQNCLZ4ytEaNp0QLSA/5SLTcBpbofWPZNoz3vIyFlc/mh50+eeCAlDXXYmQ9m38FCBr74TcbJLLQDoOXVD278HoVlqDwxsy/z9ctgoqeM4uaxfJxtFentlOXT0/vMGOfI61O52Du8oQgxsiV6CNknzVDFRVR/3Eltmxh7w78yoMWdEd7+rDwzB5t7BSDhwA8fU67u2c62TjHCW65gV9+ESZrRf+zS/xblI+zMgVD3OUXM/R7FEs1opGp6+TbNjDePg5i3JQyrKwmUzXqI/8qrCNfW4LEwmoQH38nvFJAcm8ImC1M6eiUYktZBa7IZ4bZ72K4/GKfFy/mpHUOLzT3Cj2jz/Pctntb/olh3DEP/2NEtP5sNho8JNPJkeY09D1+34X4vkuBd7/liPZp8D3PkFF8TNN2EXutONm0wJi7fgz3R38g9ncYAt7cYlD1n1uYEdoYG0/P3DcWf9Rk++CAxRUUUkk654SVm1V7jwwEJinrjmOcWwfq7ptw8mMyJB+ao2WgTUD6BA/pmdGyejYWIFEINRQzsX4n5ayk+Fddx7sJ1pO4XMVrLC52qWwwonEB7aRAFuWUsvazEhr/38cg/w8rZIRi53uT/Y+cst4IA2KW76Q7p7hQUQZRQwMLARhEbbOzuQkVF7C7sLhQDsZAWBGkkFKS7u/lu4fx7z1nfuy9injVr5hnHhTOxMDzAOkN5AlKGUVk1mjGqzsRcT2ZD1ka+/X2BaedTDCZ7U6+eScfgeXzL2EWh+Hd620voF3SkcUA/grhTGFpLu8EBnn1IJTZdkjmB20jqjuG+rCQnogbxMfcHfePW4bhBAcWvA1DaoozS0UqMyq5yQlCNkoJsLm+4xeHxqvy+WIdX+XPy7hahIiVKcVUrbmvucnGsBRtEpjP3QgXy6lZ8Ug3mkaMMbr1lLO67TkJ/EQs+uuAg95kzpkt4NiqCG0fjsVdr4G9ePQtVXFktqswoYQ8EKidRWVqP6bM4OmLqEMhRQ1DXjCfuWgRNjUHFVBaxwkm4uA7gn5AVSjK/yO2fS9mgdhraKxnyOory1m30bVcntPcmhwdoUyd7jx/zLmHFDO6pqfJWLJ36eYv4kzmHJ35zMLlUgUrDAkrfCmFruIQzF7/gKrGHrA95EK/JqpStuPj5Etf1hYm1/ygljW79JQQU3WT0pLsUv/nGUFlPPp9LY7G5MeZiPbjP0qfygTFa0o8wHqPB+7gS9D2V6f0cw8B4VQYKNfGzt4ENiw+wXS8DdfE49tvpMGhsNv907yGiMoo3ya9Y7y1I+dW19LdsZ8IdK/7daOb8EQd2Xxdh9iQ/RptXonY7DuG5//AQP4GXYTfhMU/Rd/VhRPVfBqxuJKXLh+Wvd2Omso3u06dZe6SSN8n1CJZMZt6ZENpOj0W9Rpwj11RYqZbJ5lwF1upIkNZQzAzDvzh4GmC7NZjSa4FsdhZFPmU7HrMMOXj8G7vNV9Ci74P56RISz5QyeYMHmrkXUFynQlupEbc+bGOFeCxlgQHMrmjGxssMeZPfvIqR4Y25M+09HbxPCmdThw9qMv7cXrWGRll7YsV9CV9pQfrAbmLGbGXT2xN0Of1jzuuBPEuN4+s2QwwdJiMZPZphnb/YvuITYxOXktWWibGAPDbyb5hfNYi8ljXsvzGK4tUCHF1TwIjVFUy1bML85QyONkmzfJ0BKvpVyEx2IaemGuVeU05FeDHNupHixKF0qNShotqM1ZDXjP/wEOvxQSQG1qMo6oi64nrEmybR+NWW35I95CZIU/hblqFDDMh5nY635zkcFcQ5rmfI5iwV1Dz1CL304L/Jx3+QQ4cO+Lo0bUJZbg3pr3IZ7y1NtqwbrfUSbFpTTu6mB0T9PMOkUn2+lL3CMmEzAs7/SHmTQJ6oOJU/XvFDNRCcvlKk2k9UWCr3l/tjIK9Ike5R/JM8iCo9wQ/FLE68z8Dz2lrevnSkmskM6nBB28iW7uzNzAuXZ6b/c9ZuceHrb3Msau5w+qc4Cy/WMi7ZlX/zW2kJi6Fj033yZU8R99wXFXlXXJ8U8ijDidZQda6b6KC3+iCp9qMZ1OVITP0GxO5bMkIzF/vCPej5dVMl1krah2KyV3eiXLOBy4K9CHw7QcQvdRQ7NBAd3EnHoD8kafyhZYYZ4+88JCZ3Avkpn2gxNOWD1CQc3zxl4h4Lgl894luSKHt8N/I6czz/OjahpC6OTGYGTL3KiRN3Ge3xhnmjhrD7fjSnt8xgRCE0jThM8V9damW2klBXhssQEYYpV9Mm4oBK11t6FPZRlm5PtEk47lH/yDOKZ+Xi6Wydq87TYfMRU5pCt2o3ch2JGPUeZJ/BC0pninP10WnSl8nyYs98xnZu5M7Q0ax4uZOUv1fYbG1EevQvftst5U9jNmNnfyRMZyQKE+x4GJ+NsMtolFM66U3v54tqEceJ5ZeKN7JuiZz7VUK2xxBuVP1BStYANfkikvde4PAWEwyFS/AztKTepZfCT2YcMGym0HkdSt+eELUtDcVZ2aTfW802NUUeqLQy6lswzgvf8XV7G989y5iitYju3DLeN/fgdEQO+fIWasxNaEvTxtvUnoLHaoi6RtP3eST9c1KpFPBC2bkVYzEVPi/PYkaqI6rSZvxL7OW342/2tSxl53EDEiOyqD+hi+eKQAbKFNEqpEGftSM9DbLEfLzDBsltTHm4C3sVZ3rO/aU6cggbXeQo/SzC3/BEzrmr80DamKFOozBbOQU5yXjqM8sRltvEIpfhHHtXiopwGhsNBJFb6khf0BcszddzKCqT0+qyGBebUJJvQs65h6Q2fsZLdwT1eZqs+XyQAz9f4FbajOTsUkzqrciVyeH2FWG0pE6gJaNIv4wM755Opso3jHlqy7Hd0IF+gRT5IZmMiL6NnUw6GiM6OLpuEQ47/Um+noF471qeCNsz3OkgphMv4dEfw/77UdiVjmV+1AHert3Ps8iFNEVK47RrAYNE1nHx2SQ+1P/l+AYPzlw4isL4QRAnTUPRG5Y6nyMs9DDy07ayosCH9N8thMTVoaKrQp7KeVpf6xLxUxqZHar4TpuO/E9P9H70U3a4jZXSylQ3r8D/TzF+o6L4a7oF/bQSDEbV0mY0DNvbLUTHjiYkO4Gja+bzuiyNhxPPsUrAlSknuum0bcX1cB51e5WYr1vH+jdzWVz+E4trfsweU0nCr6981R+O7KCRqMkPwKp9KJOcT7L7YBB7vVtxd29DvNKYPpPNVH44QFfRLPqLbpItMgtZoUQOr2xjf0EpTRV9lCTF8rswl9k1UlR8bEDXzYI1ymp42evjZq7FiNwGwn68/7+TfAj9q6J9/i9ily1huaQ+heueUmI6F2ejf3iJSHJg5zyurvzLoeYzuDbNQvlhKgeuyrDjRjafovaQEfuHljJ5iq51077wFsfueID5LH4mSyKvFcath1FcMblLf5MaU4b4U7EymcB/HjQWWTPjlxaXz0tg80yAgnXVjPz6isniD5B9Z4l8dhjHIpoplExhnosDXjNOcWV9AU4BBiwJaedG3gtO3z8JVsDDZhZ39vBzVj5Wet7sLZ1K0z1x7vt8od9zKUkz/PiwfjF19uf4erSRqzdzkCpKoS3Mmg9PCkjbsZvBY4eTFHyRCWMlyPONJSj3DIU/h1O25Qtfuy7xPLYGcaV35MiUYOt5h0v+OjQ+PMpLozm8ibtLvI005fZP+bbmC6e2NaMWGcVqjUwcRD7Q3/+F9IJGzmi+w6Y1n/a7fkhVLqOt4Tbi01yoSBInSHMGwQeDUVeVICZvDfdCO9G27mTugcuc8+ukf+lABvVHoGRnh9rhdAz95jFx2zHGBrqR/bgYr2lnuDJsH42HGlk4YhW3io2ZoSRH6nRPBoyS4rORAxdKS+n0G0az/R8uxs4mJusqB1xtWKNyg6enPhEtGchgeymmdnXyScmIk7PcKLPSJrlvHZ2aZqxOE0bjSCU/F83A/Usgh9v/8npQLIckgzBQnomE9DHEyv5RoKpB26Bw1OI/YjHxA9ZFVbRdGcbI42JsDtVkatQNvqT4kxOURZH2ZQbm2FCjLUO4DpQF2PNSfxbn+9Zw5vBOxBJXIKn/jHYjEE17S4dHAwqFN8hYpcVc248czXzNBg99/mkbc+MJHNo/g1vPzhIo8YkLhzdR3LaIXgVvTpeLYKmWyvp8JY5ckaCl7h69F1I4vHEbxbmfUXo1jzkHlLmy2hvXWVHIjj9Ls7whbZMMiRAw4eqh3Ux32cSc3lpeq9uiGGfH1p3/MI+3Z0HIIJYKb2DYQxXOzdlA1cxCDnaGsEttCuY2MYTE3yBrmgt/i+cQbtvDBf/fFMUIsDvnPpHb3RAdY0fIn0n8S6/h5M9BjEhy5XmcC2nXSynt/YDDxaEUTzhFpFkT59YXoW7swYmeBYhmj+GOfzwPy39w4WMDy4dupGlBPtUrdJAx6MSz0BF3v+FcPgsRqW00LjLi8eJDNHXYYGNZTf40RUrELxFz9zC/IhqIzXTD6Jo88edWkp34Ab/zwVztEkDwbgC7Xh6lwfwPbwP8eGMljlXtI0YOaiB2hAEf8gbw+pMAiv1pJE99jsatEO77nOO9gRDDX19in54hgbqTqTD6TMZqXQpnGlFeW4+ITSzxLSLM9HWAbUo0Xelj/YeBTFjlTP+I3+ipevH2/gNyb6cyxV+FrBOTKHtugWFuI5UXVZGaZcU1gYuYnXTAT+kOPUVPiRt9nxifLnLEjVnrbEDt0kN8eb8bxUUWPL/xCZONU/guuoICmdsIKnSjdP46BeZjaJhznh3rkzjsZs1WmzfsyvvFtRdNiJ6pY+DZ92hGNv2npfr/eyS0TBhwazNN4emIS+0gIPMdvTeyKX5+Hix9MLjkirKMGhKjBZigUUn/9sF0p3/Efdopvp4+RzXAHDEwFKbrTgZcsyK3SJyr9z/x4GkkWm/vcq6onCsL2ri48S2p49/jNVuH+AYXbs0WJSytjjbtUp5YWOBqKcbXz1rUD7pJqLYuew5YExaZyLhDXmg8EeFpXxa+MS9p1/XGZuI1VlY1ITflLXiGM2LCILI6Bfnk30NfqxPH4y7jn2GHzEZb2rxFiGzVpfbjDQpOhTJa3Zm5lSa0hCYQrNxOgUENEv01jF7nRdPgCrrfx1MleQabwp2YSDmTlbKJJevsURJoY4RuAQp7RvLvzQHGT3lJQbAA7bQB+rRuvUnm7T/kuLuinX2A7HBZzCeWUpK0GRIeIrhrHAKX7pPxfQVRedpoTJ1M11sN1qweh/1CVxoGDyEsZSrVhUfQN0pDxy+J+vkJ2BQo4Rm+jacX1NDsggkamsiOqkdRLY8UwzPMn/mdmdv6udxuz8wfMgwXKqO9bAxiF9KJn6HGV9F+rkWroT+zCsnTlbjvPcPX5RaIBw+hSeQMPapPaEt3JPmPIqJdcvRL9NEoUY2tpBC5OSXY+nxktbAL8x8Mp/7dHfK+/qRvohLP2nqYJD8I4642MhMEkJGOhVpV2jpfIrbpEIVvxQmWgtvDoO5BIwd6Fajc2M/qHfuwOuNMWPZnvpoIolYbw7giOWIsftGkZUxx1wlepd1FeoQMuXe7SW+rpjgrlGplcWzzg3B1hzviD7jSn8/LYm2CL31Ffk4cg7VLOaZwmuk1Fyn9Lc3+YzswPHsC8aBG/ox9wctbogxyHsvqsw/QNbAne5sv8Y3dVH9z5mCjCxHj06nWaueM2Vt2NbVwbK8YB/NEOFm5nJzrYax1skZ+SDTN8hLMHymFeGEdh378I+fyKix1TYn5HcmQvX6ktl5iwu4mKof+YKrLQVpiWlisPZp+pftInx+I4+Balq3xZtJURUbvUsNUbBpjZYvosu0iX3IOZsq6JClepFqqkP7zEbTJllGbmICxxwNCv/yg7edTDpWPo/jZQbTHylH4YzwDaqLByoSk69nUSrihcSMDhE2Y/Hw4c/1PI3kIln9RJv33B3zGTuZb4B6eSTiQ3fyIkobruJ2ARnVAeDQzA7y5fvYE82Ymkpg6laaHR8hvnM4LzyAKRymwyskRZY0R7NLu53dTLwfdWujp08dwzBJOfYkk97ktGzMaSKlrgeHa3E+IILltEu2OMogHhaFRJ0ujYxmgDbhT9zGZ2Z8g+W0iA1lBSAV09U1Fq2EsfzPzEUCQnl/JPPYpZUG1NskrBjOpy46G3DoKO/cTHOuCuagOhxZvw8JYkVnD8xAdNoO+HfcQHHmSxM77OG69S0jjaowMV0HDInrt3mIgOYVBE1eRNm4Sx7VXke1QQ+DHRFqexjL5Wgiz0r9jfWrc/1hL/1esXQkoC/Qv+RhDoZcpmxeE803wF9Ol3ait2cAst6lc8ztOy1I98i/cwCAhAbG0oVQm3iM1xJn3f0y4Y3uNiiQvOn0biKsoIPTFAb6vXc2nkPfckd3JRqVubCuTGP8zkNsTHrLE4AXvGjdjNl6M8zMcuBTfz9rqbri6HhvvStLutSHbIMgLvTJySsZDpzg7n8lgYteKlO4bPLvkmHN1Iy8EZvMjDcoXgoqEDlYrDyI4pJGNE3Mxc13N/n4rRm57wviQk0hm6bFSeijzHv8mdoEpT3p7uV0dhKzhARQNysmLWQ9PkmjYasO707ksXHqChwLjoKyZ5+KmHI4M40vuYdSrFrA1fxgaQYLYNPfxYNkwYg/f4O4WPezcfZnuupJei6F4hykw28eDXc0HqLMtwPWrDVl12phff8CHY3co0YqjcEMqdo4VyE2w4/IaN/avqMRgdD6C8U8oLhTmr+AApgz+hpSCLEKu+ylmHG9eHSZpUB/X1loz9YMpXzvjyLzbTlylOXJmX3nj3kGU/UQWU4+vrxKHjUcjVRWLW40jza6bGC3uTaPaQz5VlqAquJrpafORLu1D6sMhvjk84OOqQzRlB1PhEMX+kJVk5MvyUakUa+ur9Mg95Lf7XK7kTyRpXTgZQmPpbDMldnc83b/EWOlxlwS/26g0CyI57zy9pULs718E97+Rt3sCCm+lydP1YmOgKQti5lJorcPXaTd5PkaBR09baZRTRD+6g7XyuxhiUIicRg2vNXU4WpLCgM7fBPcGYW8py98saUZOmkVFoAwcVudBSCQSDkI41j/FVh5SrqthOfIAXsf70bFaA10rwdKWLWGhfNy7iuw73QStLkP09kceSL5hLAGEnZOFvqGolNgyIekRaukLmdixjRV9WqRIZaNkq49wYjvKQwYScukrM85soeDbOXoSpRkoEMU+p5XcsPzCjkxvzC3es7nqNXcflPJ+9TO2+TSy4uh5lr52wnJTDvXDTPH4o8S5XD1sZH5harCGTY8vMa3elNnJAxlUVcG7RRU0R4ojP8Ka9swkEt9l0LXSEIuuWVzo38VaiVF8y95IxRAvupPvod+2nH2jx1PxRoOm7SMwF/7D0R8b2KWggGe5DwJ3nXlaBZr+znTU3Sa8+QnV+3O4ofMQpKGw+SIh366R9HcgTZkTKBZbxoo9x4m8/Ynq1j8MySpj/5M4Hi68iYn9CmJ0VmC32Z0OCRANUEWtYxCTZl0jaHwpTfLhqPSZYtEYDwptvLqsjsWtDBrzpPC7f5kAGzCQmM+vxlkECgqyYqY3z021yfnlz0BJHTy053IxYRKtY9Qp336SOJ2x3K0LRmCiBTf8v6Aw/iAmSwIRbzmEmfJRsr4VstHEgz3vA9F2Au1Z3riEOLH43izMJm0nryCOyPpK+l33o1R9grvtI4nMe0nAhC68wqSpLk7nY1gMLFrKYHVp5kqLECZZi5pKM9KVmdiaKJAdmM5JlwYE/CT/u3b1H8TW1ra/UWAqF/Q/sldfjGH/1iNhcRKRubM52bYd34ShDFeOp+zjOPRuhHPkeRh7MhRZnddC0eORnB+8D6fU2ajeesDBB7Hc0dfhdNBTpN/vQCBxFJa1etgeteDH637sZ0ixc3cr8YWmyIfn80XgDnZlRcyJn4tY9nSKeochlTqS7VmBuIqa0+CqzbS8bezTWYRPjjxmBybx2SeUpR9S0an7RUhgEHo3r/Ii9wshHy5w+f1GZOQ92FE8jPKH0zi8ahdfa86ywPAxWRf3cv5xAMcq+tF4dItS230MEHvNs/T7eDl6sTT2FbVbBLjh7UG15nKW/NzGjRd+RAQdJvWxJwI/t7NlP/Q0XKBTWYqG6wUMnFGDhJA9Y7/1cttpGcqHv+Be+Yj7BiuYkXmY09kjsd/wjZ2msfT3d3I8C1LuSRD7TZYhixbQLhjO16VJPE6CIdKvCD9bglDtH8RmFvGt5R1D0zKQ8SrCMs2S0xkzGaO5h78axxlc9oHh+1XYOW4YHVpTGNlcwNXV6viM3YjrgT10chF9PyMe9z/FrM6HrN5hDD9zj+z5biSGWXA+7yS3l5STtuEjs21deFd6iFHPPfk4cBs2e9wJd8pB924XldbL6esbQ4S+DneVtjOxUIsr/5bzdeF2SkOzmSE3jMLWBK4pvuT2Y1Bs3kyIYiqpGkq43LRHxvIEo4zcuSU8kvkVDowvN+Ty6eFUvfFF/PldVD9uY+msv/TvHMjjrNW0tvpTPO0Xi1ds4cTyIOQdY7A+IU7fLEVCJUtxjxNC5k8zmQVqXD7hjNCm0WTHvEdAIJGXj825dW0tW5ODCVyyG8dhVszMW8LCv8Woz0hmQEcmAeILyCtt41q9IKl/5rJhxlFOlKoRe7uWnFP+XD/hSIvnOx4+EeD1+fd83nyb4Z4m3NFpYsxEa8b6vcNJ5Bu3E49B30gKFQYj5TWWqmhNxLVPs1FPnTnnF7DKeSyfb6RyIK4AGxdVTjhVUG17lHXzVJm31h3DBmuSjAtIz3lPw5p+2m7lccBqJ00mD0hOHsXJoUdIKKmgvr+Ispb9NEUMo2R6FZuE3hH9bwQFNws5cLCarS1L2VAlSHT9Wpj/PrLqogAAIABJREFUixNi6sRuK6Nq8QQujytnxsnFbL/aj7bAGW782MJ1B22Gx6xmxKUeno3XwLCilrQycZR/lNKtV4mw4j0ODjFmSX8Pd5ed4fGyKkT2eyO+ToaiCYVUftbAvWo58wI1mfQljeI2VT5df8t7wxW0noGs81DuN5Nr3bksiTVmhdkVPt41QfNmD0H9bhwd+5QEH2GyfpZwdOBMZk9IQGv6BcbqRXFr2QymfjhOjsEcxAaWcL7yPH4bjcjcWkbC91jWbT5G1lYf+uZPxVrbmog7n7DTd8Nqygd+dAzENfkJKd3HyIi/BbNzyP1rQla9Itd6AplsaEj/sDQmzvMmdJ8CC9THomZ6mt85YVjcsyPZywHL199R87YjS1gM2xRxRJuLyVAah6RUBSd37P8f3aj/FbWrhw/e+xoGr2eL+xk6RLTRyhnM45qvJI51IPBQGIKDtImJecJvpaG0WNci92YkDWf2YXtgM5PXn2DMT1seO6fhWKbHGPnlKIzYh9pzAbRHXqD8vQh5uReRWbUB8xHGXF25lfQka+IyIrlU0cKu+3u4u9yIkXZaNOf9oCIsCK23r3BWnkdycBE3dk9noI01RtN1kZRaxJGDYWiPfIS1dhsXP+uw8lM4ssrvyQ48SHyXNbHRX5h0+SsFeS9ZJ7+M4EUh2PaGIlC/hZsTvFCT/szA0HtkHN1IosRC9BRlKVXo4cFDa8zr9lDqc5LoM80IhPcw9NxQxka742R2h9HmHzjzoZLP24U5GDiHAvE+RCcmczj7J9HOAfxL38OswH0cCRTgiLsS5ZKqXIr5idlAO5SFZVkSkU245kSmP3JgjO5VHLqrSdRIZOej7dz5d46V3b/REgijqkWSN3PceJO7nplblrGv1ZnYOjgzdw0tBuV8Sg1jQGM/02as4Vl8H4V5j5AcUEinYxWvxtQz6M9mpj30YmxGM04lsTTIzcdl/QSujJ3A8DtCzHlyDYNn7/Fqa0U0r50Hf3y4c1KPUwIHsdYwRMi6l3nHvelP1WRfUwz2Ij6kvIkkS8+UndpPaI1KxixoKgc+S6K3J4nxq61ZUDWY+oq9VN7IRME/FWUDXRy31GCVrMOR7rHsXjqOl0oP6P/+jnvq53m0eA1dWpdYPtOa80WqtEpr46gziNxsWw7mB9A9uguxrhvYDCnni3gt8iZpjKxIIH/kNJbdd2FW+3yUq2dSNsGYnNHrSV0aS+eFTiT6d3Ek+Abj88fTKTYFT6tyglTP0P4rhbcv/1F2bTVXf4hw8ZA40wuj6ZfvYMySC0g7jeTAyzS68s8yuvcJZHfwsVebrc9v4d6UgXngM25fXc42U3fUbmxlxOjHDAj5jLj1QXR0bIg2/ES6lA5mUiO4W/qS6eFiFL7z5N/wI+SGBmCwehr7ruixyP0Qik9fYvvBiflLd2Ly+C3OOSIYKTfyMN+CzJI0RCdW8mlUJwXOn7CyHo2qjAEZ6dUY3DUmPj4BC28nxMpM2DIiiflyw7hyu4O2Kb9RKZdHxHUR3r5K5M0bgG3aejqNenHuK6UoPQTbq9vxaryErsdqbm98zcvKbG54LmH9FT/eTvNl5qNuktqnMTQ7lfre37zbL4r/85X8VbvAfreh3JROIsOykPpbKeSuSSVZ05ov3eu4Zj2NAe7xhMXbE3atA9seJQT3j2LdImFeVMTztKSRH1uXcFOtn7+Tw/FYegx/XTXu985E+mghZc6WHE+rQ8VgMpEj9lLX5Y520BOkK3upb7YkYcEg9ulGIjCsgeqoNN4stWd+x07WPs+DX4NYJHCTfC1BSiR+8n1pPK32VUj8akK0fi5r/M5hYm9Jcu59unTMCBZ7zHXZKxhLmpHv6c5xe31mDJxIWbkBaRFXwMyGN0rCZCU1sFpzE2WCHpx8b09q+Qja7UfxqfwO0Um//lu7+g+yeds233sH66kUkyNUbAnLHNtoFZAl8XgqknN0qMuU4U7Jeex7AxB6GEBL3mZybl5iVeAENt94SfUMV9ymzOF6kBbjrYX5ceIcfft8eflsAeMKLvNl9EJiY85ibpmHo30id1M9mHe8nb6d7YQ1HUJzoxlJn/fSlbEQwbjfdDVFE64qyBT5hUgXiLJzQQFnTw9B410NigH9uKXrkpRXSIjfYgYPDyZv9SCyAnooKfnJun49YvJbqFK+w9MCcRwS4lCtlOSTkDC2zT3oJD9lrb4V8TnLGWsYz/btTlzcM5zW956MvXAdJS0vLo1RZ5foB+7+dEBuiDp2E87ieHMem+PaOHYlGqHI57S2jcRbp5A3CdYILg/jRQZo/nBGbaAU+oVyuJS7c2KVBsNUUtjxdBnE3MXX14W5lTV43hZgYIc4044uJWH7acw61bn+/QQS+tPQsetFKrYdz5PXKHrbQ1BcOHcUt3ClRJc5s01YGajMeq8EUnq1uPcjDqOgOmrFHBkodoCFQq5Eb1rAMpO9rBF6govrHBLLYsg+ZMGamQls3p5Nfd4lGpWn4jRdhMBxW9Hd+oe7XwvZPFKJVS0tbPw9mJicPMwtBzDqjRufT9xCz+s144ZX4TDgDD+Vf6PSMRf1LW3Y98znb0oERgr7efwkmJoV1YyIe410XCti7dL83eTBaLV6ftbe45vFPkK2H+He73Ri9qRxcoobmVmt6N1qYPKOXhJk1VgscJ5v5/LYv9OW9uQ1LMvKQFYok5ehN1iZEYpg2iE07w3nbJwZo5VKadGYzkvhftYeS2L4QXdUXm1Fcuw34jtf0j5UiTXpfYx79YmSJ9B77jdnOzexMjSATkcX1ByScf77jtqX7ozdMwqttx6s+nmDF11eWEVt46DGdE453+Ki3DcU311joJQrWTNmo9MZx6PMVEQU+qmXCGbt2lvcvHmCyIAH7D00hbO1ytSIvqHx5h6ctp3klN9njjx4xPyl85lhsZ66GA/axm3BX6+Tmjnm6L6V5dXNp6gaaVCgG0y5eh5q+FAUE4D2XiGKlmrz0KeMA7sX0yy3jPx7buyz9Keo5Bt/3QewrDaY422b2LZ9HjeXbqTDdgfhdWpINjyjKK4fm3o3noSM5NPjyzwUn8Crk4ORVynk9/yLjFlwHdH6Il72DGWeXRWZQqFomK7E0sCMp7Ejych4QXBdN67tcmhEpZNkqsejAckIFx/H4P5fLj6/QkzLVH5Zu7C6vgYBZVXEUuDzgxpKLt/HR2gA10eXY5gWzZq4Myy22MnyqVIEHYuiQyeXngwLJhYf549XMmKxL3HTWkFmZD0JV5J5O/UUS1OiYNBIVKNb8HTS58fVaNJdjfDrSSZHS4jPretYN/Qvivnnua3qimxYB+UtXbwxvsM04wAc+qcyxGc2z4QO41p/gKxtglSH5VCVXEpJVR/ic1SoProNzxnraE6rwUpEni4TC3S0F2GWEE952ywi5WMRjMgmLT/7f3Sj/leYjwsXzvueuVtDQpcgf+rKmWzRQ7y5DEp10mRb27In7BQyLhfRSghG+9U54rOOE95VR2Xmd649VqdLaRQn/qmT9sqAzzJltPvZUzi0hpQjO5EeIYSK1TfSz3thJT6PFbee8nGMB7azpvAqwB+dtbYoCGwjJUMKlbYFVFZW0zRDGJ/EceTHPKHbTA8vdzlyfrTRtzST8h0DeHXODksTSXafm8os0woOqVihWTmNdwaPONh1hM8O62HMOlaWu9I9aAa1Q2XoctiBxwp/vCVjGLE+gKOHTRFMjSJIKAKzPU+YXxdG8dXrzF1Zh2awMbpSUrxri4cTU3niEErqJH+an/xjoLMeJYerUZ9ohuQxey7/UWZwxxYcPEMwVR9IeKUMLzKn80qthley12h/J0fRy1qyO/NZMG01OevF0bh3k4SmLwwxmIrHvucEFS7BVXIYqo0FCPx5iP4+DZyHebF/+TnE3gpRFN7Kg8M7kWk351YkGE/6xHSTMRRcy6JPRhb1eieM/TQw6NqK1evvTLC/yKeaDhqNjlC0tgkppQmonu3h7OX71D96T/ryHhZqdCMZn4uBsCqKb0R51HiHmLgOHH9NpuhPH826ckz++ItG/Z9M8PPi9ZcXSHwLYZ11KgVZkZQukeHhz8H079ZEWkiZv/nRWN/SpHZJEsNN8hlVKkyhlwnGXcL0xTbyrWYJq4c7sW7yIKbvzEF92E2cS17jq+DO65FmoJFNHDoEzsnHP/8ZT3qX8eikNalKHdhcmUPNiAu4G84iYb4lwweq8lT/DvqRWgiI70bkbD65jS4UPdZhdRWUqEaT+XY3PvrSGP61IG6cAguvz+TDg68sHC/CojNRCDSa0jvAk/Crl2hv+8fMnIGcPjsY/7Pp7FbdSIfEQ8Kct/FZdhyZBwKZa7CE40lxuK7fSXT5FaQcTfl69A4bLGWY/SGGwGAfEuT+Yfuglk8CkynrHY5lzSXynQWpP9KE9dkeXng5s3Tyba5/+cjOgCnkao7iQr0KxzPNqKxw4eBPb6oP7UPD9w3D83dSa1DPc7cQut3qkLosj6dNBFIJ4xF/fIlhxctJqhJDM9CWqU8syV/shbrtAJbPWYfb1Vxe1Y/nn1wJ6dMmsvNtK8Ui2jy8p0ZBthLvEmXpF6hBbnMfCi1ZiEV/Qbk0F9FBmsgoeeI9zZqjj+6yPLWFiT+aub5ekBOGezl4TZRQDRmcflhifMobWYOH6Hc8I7rmLVZ1Roy7Xcc1i+XUhdeTGGaJln04ZaOUSN0/BYOeryj2lXPkchdGH74gYWdGdMliHMUCEP56nsQePY4FvcGp4SW/jNrQmKiGSK0N9nkR9LbIMVjGhKjx2dh1+nF7rBUKjSI8c7Lm58vRjLDS597PXKYEOyP+birTGtoh3Y10jZWkXXyKzfC1WJw6h+3Ug+Tl+PLHdxxLY2ZjK5lMh4QZzZEvOes3nc4BqRi5zkN9oDpjxJ6hXTqfKwMfIuDtg0mdJtsMX3EoOP+/5uM/yL49e3z1jHSQmj0R49bB5Az4ScJTf6J6hrBYeDBvXHsYnnsMbX8v/l5ZTnRRJm4vg/nUrIXb9RtkBNjT3zgGuRZhuDeOSZHfid18jx/mcSxQTidTbSiJEsZ8OjaTH9ttEVbN5M+OVfifeQwKOoTVtUC4HH/+dGA4VIXQL838C/tIXOJzTOzbCBsTTJ+PNh9Ov2BA0Vnm/BJktYsSxuUyPPu5m32zzlIXfRK3rDN8cTiFbN5gnIobuSkcwsmZv8hJj6fu7SEiPUSQm7CV1r+69FQlMqezlg0vS7GabIe9TDD+wRvZlXKWjreXCNg7lhJRQVzdbGgQPEzc+yU8Np/Lya4ejOcKoBwwgr/T3/Lr+RuG6ljzti4CC5FIZDQvkl3jRWOpAI21A1BtTuV3QQ0NCVH4+s6iP7KTH79fM19EAVcxMRwzKlHdMIkUeTnky9Jp/mTK9+5wqmxg55y1lApZss5bl2F/zWjrCKNibB7jXD14PlgXsWvhdLUI0x6jzuhxnsjkHUI++Dof3tzBecFN5G1T6TafhvTnTALTHzPM9jICUroYHhRGMiCNTvFgelS3s6jrA5+Espk/1ZToBznoz+4kvikCPeNpvHWJQVm9iSMiWYSc/YTeNDHeypcjV6hEed9v5q5yoDHPBo1twgy69IMI/+/sTtFhyzJHJMKFuGZ6gJGSdzD9fg8lO0+a62IQEQsnvLiDsvR4auetIOnJc0xXr0Bs5U6OWgWgp9XARMsQopJHkyF/m/FT9vOiMI65EoNYOzwZw5IG4oL/4FA6glGmifwNkeCukQmb4tXYv34u2zU2EFVfzjizclTVbXnxr5TIiasRiA+h0VaD91WyiCtFY/pQkJEWUxhddwlduRVcWaWMT8QH1NXO0N16BUupOZT2PaFTTYtPzz5yS9ycPbYKXIx+QuiOTdz3T6Fg3Q2++1dzuMoIQ8tjSNgVUVDUg5S1GQaXPMgM1KPEYw9Obw25vP0is8bMJUegivCsFg40dBCUackjmdP4Pn2C7q1V1Os/o2ZgDXWL72Gz3YqODgU6JG/gaf+RF0ErERWVQtFEkozSaIr/KtHYkMLwuU58XneH7YbaPEtfRvuU+1hZCqORVYSVizN+epUsEwzhwcwMnm1KRPzZGNyDxzHm+R1SVqvz2G0RLSfOM3dhLjUdJjh8SuVly3T6J8izcqEls5MKMa98xJZxJ3nwuhErg2qqigp41XSaqe1nufLrLe75LYTqFiCrvgpp5RMsHe7JH0VDRORjMc6vYnGoBv5Hc9AYKk1BVwdj1aORLNzM7wmzqG4/iHqlDDm6tRj2TSGtIQmzqyORiRQipzuLGMlE9mgKsbbrOP09B2kcNIrOwj7GjbbgW9RDvJr6uGSwAL/oXr7XDqDy0gyUT1ziZ1cR4u+fIqmjTeOHC2guuYH9QF+yujUxVGvhy7unGMmO41vTX87OOcPvJ1eJdltF5pVB6A0YiZC1AwJfs7nk8RS/d93/d8zH/vXrfMWOPUGyqZnPZqqkR0QxQVQByjLRk1KhvjWbJmEhfFeORnOfGFP0nlKjuZyVWqto+fIMH7V1BKTGozWxA4myA8TZmDC9y4p5mkfQT8lgvMgwSgIe4NsUyJnjvtS0SVFoPIzou4qMaR3Fln2n4WkIZ4t30yNwBbrMUHn0gsK9JpTtq8C4uIS/P3ORe/4ZeQ0H0LxKREA2qwTOYiL9E1WRFH75zGRFUAWT7m9lf8I+khYc4kNtLEN7gtjy/iDhgRsx3b6VUxaTSIqQ5vXMSbQW+PPt8gL6xuogGRFKl+1ugo5uZJC0CoM26qOTvhSHSX+oCCun8M1bZp9bzBfti2wZ/IAcj4fMCzHG5uplZGZFoPN0FlWpxcxstccipg9/QV3s6p+hnlLOOpObPP9RS3uZB1auEgz9Jo+yrDOSsnp8v+ZLyodQyqdKIFj3lYzf03GdG0ZJTw0bJlqT3XSKGVN9CGuuQlOjmJBBOxgpuIcXT9cgXXKJ7wPlaOpM4Z5VJAcFU7jlWMHNz+OwedHPBvXRuI/wZWNEDj0auzl3pZvr1/fgOnEZ1RsekGbynfBcYeJ1fiMhOpopKl85eWEPQzYZ8b6mllUayeR++sr4onqGdWqg116DQ/d43gS9omBEMl8UxxL2KA1D8XfkeR/A1+MSsvpqnLjZwNYAZyRfl+Er/gzZmSDhrYJZthKVd/oZv8WcoFxJXrZ4kn47Fs+BUeRviuf5XnXS8/YR0drLqCOf0dt5ECvBbDLt76L17gMiN/NZbHGS1qQozHIH0KBYQdWPqWhv/ItA21LmZPYxbNMKhDMlGD9tJvNjbyMffB7x0HDW+9ziQuN8bkQdY8CFF9j6nebiwBL2Z9/GrliDrhd7ie79TtfDJZifXoznggNoStegIquI+MRSzktm8fDIKe7lP0W2xYbsnBKmiQVy59ddfuh6MnVqL0rXHPm8XZvO3t30+1TTvN2SHzvzaIj+xw2zQErnq1NkGIXNclWE1IXQ+Qg+cz7jpPiPC/I/+SxzmqFrJvJS3Bjr0GZOPs9FTfwy/Xdi6Rg5nu9r9RDWkcR00XCmHvuAzL0GxLZFsnT9RFwH27Nt/FkOvlbj2vRxyA28i06vDf9+P8LlznGErv5gTtlorJfLISz6iev3tDgjIo+P2TZmPkhnhVMMr6JvYaPdioXrOYpq3LmfUkmd7UqOK0RzKuYTExqr2aUQhZxECRJDzrKmyI3Z19KYsH09s4vv4bN5HJJtuYw4vIHtUjpobM/BSK+Y3a8a8XwynZarRmx6/5JV4iuYnj2eJVXrWXdiEdOm6fOi4QsCd+QZJaLJp2ghNOf94c/dSk7P00fhyS20TCtp+qvAldByipR0WbvoDCNEXfihUIO0hDhmvdM590iR1sYSCsUXszkmGrHWIizn/2RFwU+aJo6i5FcYDp07eamzlWPPwwgt70akoQuFbn1unLvK8HJ77udJcSxvJxOXrcCy3ZY5Np5UpD2nqDYaH7XDNL28/l/z8R/kxLlgXzWZRvQa7Kh6G81D6ygG/x6Eyqw1TLD/xqKg38z8MprLZ8Pp+anFx3oXhiTd5qj3HRr7hzFg+UXm2lXxrimSPNU5dDQ9Q7I9lYigdoSFj5Bnv4nCfWXMzX3HFdtWgmvS2SxZzp1V6rzzyEHpyUW22FxmkoQkZSljGCKog98fK/rutZNnLUXuxO80PisiV7uHl78FcBn5GuW8BLY6qSHc2UnGyy3M3hWJwlVhsr47Ij5PkN5F9owQ92TepVGMX23HhUUb8XmcxvauFlSmreZk/HnCbcfye68C8bNCaHs0C6HtaQx/78KOHB8SduXw/sEDdEbc5nqJApeXLSbp2khWEMC6r4oMNQ8lO/QKfc9f4/Iwno89C5gyuJOu8GJMZ1pwLnIXHSoz8Po7DGMzBxo7s3Cx0ybk/gvm9qjwcVIdkqbj2Clwkv4xxSTcacMwWRj9llb0XunyeeE3NExmsGnvOtp/BWMdpc36JUsInfiLL++TWD2jnSMrxektb+a0lxru75WI8MwjoqyJtTcKcWtYhugNIaq+ROAX9Aev2QfpWf2Ug0pzMDwvwdnIKTzY9QV/CzlCXwjRVtHBiYTHSI0URDJOgHttX1kYNBqxPcrMLbbl17EHGEU6kVg7n7s73nJo1xBk7Jwp276C+B+5JB7IQPJ9G5vXj6DacikCL0p4sfI5V7JXEro+hoJhTpiu8CcsdwdZp7dwetoSHpflIXU0HHeFRegkZHG21pYZD34gq2NPRG0g8j7jWXrNBjXbBm51VfBv8B/mWdgRUVSJgZgdc7TcSPvmzQvHYRycZ8X5kzvZ3hPPP0V7JJ+t59HeA7RPH4G84krUeuJIGvWGlpxQmgZkEnXBhwhxUXpV3tAw0YiJGgoMzt2HuZYv92svYbJuOsYxEUgP3kbFP1H2P/Pnj88JljnWceiqLKr6K/Cy3U2JRSx7VgwgMW4CPXbxfJOfCsqalM2KRmmyGALCbxmu1Ut6RwWDbZwY1DgEuWNz0J6+h0enAsD5H3HOk/C74s7ia008TTbC2/YaJT9v0B6zgxr/Rqb4BFE6WIkB69YR/OYFARcGki0+hfReZc6PcuXYh3hebtVi/6fPWCy6TX90LhqJaxk3cTRDH93haZQ6tQLTsVqth++adpZETyak7ByuuHGjVJ74r4VcXj8R7exInvVsJVTcn5kdrphVvGauVSXbKi3w7Q9k72xJSt7Lc0DjFZr/1Dh+YC1mX0KwMvalpbMO86yfNM5VZkCyIZmnuxF5L0xklTQKERq0O6tT1F2J6SkByoyV2HE+moNaM9i/9C3vy8w5+yudZgMLVtLM1kGfmaT+g69bLUi3uYCdWBF+cgNYN3Age2qcCW1oQtfgH63vurCTN+XeNzG61rRgVppPtIg2bomG6O+s5VYFLNHx5M23MPQdF/Nx+EOsU3uQ7Z/AgNpO1Mq96U6IxNFtLvPSUjF0zqE4OJyM1uEsnqdL0otMdJzMiK6v5Gt44f+dh/Mm1HCI0kG/YxzT0sXRddtL5KhfuDaqstI6n0CNbL7cvk1g/kYc3xsSnx3OKa2V+IzKw+XKI35apnAoqoc+vyB0nnsz0mMZPwYrslZqD2n3ikg9qYDLfRH6PcYT99GE+3MkOC2YzdpnU7lgLI7KvD6+xtozs2wkf0NHU1elzqd+U/AbwswmN3L9OqgZJkxswAR6xG2ZPMUWn8iPOIwSJbotBsXqOJY6C+BYm4rRwsX82PaDzPZ+ZsT7E/H4I/0x5wjo388OvHi2IB2rvWc4JZfL2gAVaoM/knVRjQORG3BYpcIwp0gmzOkgcGgEvU0f6K32wSFDkgCTNej73mJD8FimVozB++9cfM89ZEtwMs9zpxPdGo/3qFdU1rXja3iLyVts0C1chMJJb/xXHGTHLV/M1zmQdrgJfwcVbAZIUttXzt1NewiTq+PtbWHUqiww0u8kcEkzR3RjqV68lM3rT2HevhJDO1DKgrW5iVQ8iaTiWDRLSgWZduECaR06JJ0dzNb5UcivM6ByvAB+n6wo0BvB/AGTqF3xnFcr2xFOhCf5ExiwuxSN5RHMXfKcCdtcOLhAnq+qhzim6si6le5U5+vSGVDO4N19xH3zwPeSKh+mqvLzmjNbp0fh4ttO4KhshoYLM+rOPoJnmDOz7wH9g314P0yR5h/2eEutZuqOco6JD0Xvswr6+VnY3L6A3JS/OOTLsqXGBXOF6QxbcQzLp2pMHj6BI7M2kFA8AvEzXyn88JuZ7wXwGLOfLVpvqJ4kTYqeDJ/Pbqb32gvSE6fTLjMch6BnpK9Woe3Fc77TS1JXHq3+RoRqqnD81Sgy+1/zT88YKztZojx7qWyIoG3uJEY6XWLHUTtixIJpV6xl+TU/RD/PxnvHKay2TaFtQzeTg3ayMF6d3zEd7C7w5ozTAjwSRyB5PpK7n+P45XacEun/196dh/WU9g8cf9+lolIqlUokEhIxocJQKCZLyL6NZbKN5bGNZSwxzxiGGWPsa4yyb8lWylq2olLRgpI1oSalRc7vj76ex/P85vr9nuv3o/C9X9d1rvM99/d2Xffnczrnc93fs5iB5upgBvwxEN32MWR6/chugyFc6BRKRNtjzP9jF12OdWb5nq6c87RjRs01uK3uT1wrhXs+JmSvnspPHkZs6FOXVK+tPFMSCHOuyjBRjNfODSQteswtZTGDA8fjNb+Qh0tXsvHgSQIu+PBqoz2d4+rjF/IAXYfh5HTsikeNH5nq6U4705tMHtyDw69XY+0xir3OHuQ3WcrCtfMZ98UIYq404NyRBJ7MWczwnN60XZ+Lv6nCy9gv8Z72GzXGRmNl60ei3jls57ryZsV82vXqw51GkRi120+U7gHCX2TjpGyh2/kYVusdpsrf3ch42Ye4hKfYde3KoMIJzNTwoPF4O5xmt2LWhh50TZ3E35JOsOPoE4ZkFfPTqTlUmr6KlN732BAVzfmDMdQZr8WePiY470xkynJn9mrPJzouktpnZhATSs2hAAAS5klEQVRXaRx+Vy1ZOz2cndsv4lO9GdqJYew59z1OqxW0ujYl2X4NnW9OI639n/y6pBmPEr+mbdQh2lj9nbUXz1Hn66r0NUqmfdpUTge+4cTjInr0+wOdkgwWlJ7lQnwHdgRcZdFFV5pNv4vJsxZULTBm3QPtij5Vqz2HutkIJZW0qCJe1muGjW4MCi4YDy6mTZAB1TbtxWH7Nopzd5B0vw8TzHeRZe/BiFJHXlGIZotjwGUKbr3kxgkr9F85knoVqo4Lw8oyhs4BUHMe3NE7RUTeXC6/LsLC+FuaZFQlpGgK5/rWo19aXU4lavGqZRPaV1+OcWVblkx2JzrvFCnxbcjpZ03D/EHU8TiNof0DXE1rkZthgeZQN/QvPuaOJfQcsIychh3o7zKaXq53iErxw/+CHTOtrrCmZhhKh0MEZp8m+4kZExzPEF5QCofaEs5ztiXaUitzGpZNV7H93Ch0xmZhb3SOB3lVaDJ8AMl5h7ExAx9TK0J6WRCcAhkTFTKz9nP6yBW+9igib8YjlpqcoMHG8yydOZ7DlYw5XZTMiei59Gw5hznf7+eS/RQqPdMkx6sEpwdZbO4cQFJRa5pXv01CfhUiuj5mZ+hDAr03oZepRXbYKMxT1mEe9ZJuL85Bqg7aJcHsSypF79U8QvcUET39Ddv1XmLuPJKwXrcodk1g3UYwizam9aDG6Gjs58z2oVgXhpBq58G+YdXRbfGM7iH1mB2hR3Ne0eB0HG0tsok0+YOs7+w4mQmbtW2pm9WLjFRj3Pw6ceT3hdi1DME0uye2boJzWxwp6VaVTNea+DaI5IX2Yo4d2UKd9DSelhpReKCYfMOj6A7sR3biSV6mzMDI9TFdZ4zkddDPfJlhQRbdyXuwkYQnNzFR6rNjySYsCOR4rzZou1YlULMjGncbM8zkHolZbpystJceDayxdirgbqV1aHjZ0LNuaw5dXoN35zQC7qaiXx00mt0k/ctY4r0HMvyJE68bpJI8tg+vbbswymggKywVbtnoYWtmgF4DR/Tnn+LGqDaUmDajdAq0jxtBzLN4dtSPIzO9FB4f4n7mPY4ZPGWbtyvxezO41X82x9vNJyWvDcZNfmFXL0PehMVQ3Lwuo0aOJemiP7+Pe4Xz6xxcrgXiVuTHmcM/E9H8Ao6/m1HYpYiqmvZM2r4KzZ++x33QH/j09WWf4S2qeY+hUp+tuARfIK7nN0QWPyO9WVP6OXxLyPo0ml65wai+7Xj55g4NPZuS0bwdvn8mkF/LE+uke7Tank6DLbuY0X0RB6yrYqxzk465lZma35cgkcCtk4cIn3YL39LHuHkacSYpiLWKKe5bL/HCrxWPzK/zoqoWbk+cqRziSLKOPs46jYi9nU7jaw7Mi5jAF2tisUrvQVrsJYKMU6n1VR8qt5+DQ68M7oRmE95FE8eRY3DK16Pbt13Y/PtZWhzpTlC1VIb/8BDDdr50v96CR9ur4lvPmBvCkuNLimnfURONXad5qglVhmugdfcwWzIXMqrGE6Y8TeaFrgUaVvnUalzK4dgMLD27URKtTy2zQgxnDiE0K56CoCpMtdBkGd/T3ngbRcaZNNaM4pCLFyWPTqL7Mo+qNsZY1ehCSewqBtvXQzOuAM8vhnF2dGfCLx8jz8mYZznJuFp2+Y/PpR/FA+eNnJ0Vb7doNKL+xrDoUZj+6cB9A7DkEZUx5sjjYM7MP0CHljspzr9Hpa4FDJ6ZyOY9vTE8f5eG+eHU7uzCuJzGbNa9QE5OPe6Y1eAikJW2Gft6t9Hb8yPXd7ni3EiLZ10qo9E6lIs3t5K/NxhHo3pcMF+C+dlRaGhXZoydHzV9nDDXOUGwSWdSD+fiahdKRoNnDNAYQ9Hlh4S3SiD7wAvmOcRzyv5LtvyWxHeaf8PgWzh+/wlKYDLnjgyhYPp+9LPXsbieF2uPZ5ObcpT0q6X4LBtEl6AhrFwbT/3bpXQ+vQZlnjfQA4D5uYPwNwxkXlBTXO/5cXyjH8H6qxg4Yju6zTsy12sTkz03YJSdSHxBBu7drGnsv5ChC9uzfvwZHExg/x2wuQk+3v/Mdeqlp9i5xLPtvhFr+8+kMLIL2oozV8WXzHbZhPvc+9RTfmR7hA+XzI7ScFh/Gmqbkn7Qgqs7JlFQ3xj3pm1ZOG4BkELg0b4M8i6BIi0m7ehPk0pDuVIQQLFGdbwNLDAY4IsncRyJ7M/5M4IZfW6R/9SeiJYR9Hhui7G5DaFPCtk7vwpfTIPrqQquuZFsueKH+S9zCX52i9uhVjQ4EMvqPS1ID3yA5lYbVoyZQNyVJpyxr0P1vOp4jWgB8X25rFFM49bz0DsFdHxE6e0t+Ict4c1gA4r1J9Bi6c80r+FJ4stkvMbV5trZZeS/8ubMolbEdghBM/MN5nWPYtp0HJu7HeHJynCCJ/aj5sFfOHhPA62nh3mcrotLy1wO/pmO1ZDL1Nvqy90ax1kxJgAoAlKBNOKwxQQ9fh2wCkVXh6Gbf2J5L0G7aRbcO/gFb7JsGTd2EUZ/+hPraYfAmc0Td/Cl8xlMBv9B8ABfBt9bxqasboyosRp9jztEPC3ipY4Bba3NWed/iC+j3NnVJ5exJ6swZPBhfj7vQdPoNjSJfcoym7n87PCMdIt7HA6oTWlhNabuy6H3w+FMSHIlcmEOLvOm4wwUHLmEe9w23KsYUfV6NZYEzoBlQGQUua9g0/E0TOnE0J06PDQ4h6W3Dy+BldyloPV2ao9zQvfBfkq9DRnqMAtCBXc9LdizcxkOJl4kJy1ijNUO9F7F8dy+GsY6+wgvPswNWxusi1rSNTieJ/UDqNUBliYVkOjqi2XDnqR5K1hav2R2/SmYu/31+WPptz+imzCbZj/Ade2nFBmYMvXt68bTocgGtgFtgB1rFqCdVhf/X4cycdhv9A8YgitGpABBbMFh91PO6dhSaGSNzp6trFrdnEhG03pfGHRqCIY1ySSay1uuknYxnZCLS5kz/RjGMftptrIpxzceJ6Pxemo+06Xezu7Y9nRG39cfboeSeP82GrmxHA6qR17tQl4Zu1E93QUdx5+5eXEbGt59KUmxx+alLp5uYzi98zusW7uzJeQbFrTzI7+2Ed5DJsgHzitQDSGUb0a2wHrTBVZ1PsoIvw2Y9J7G73um4b7/a/rd+44J5ilsst3JnAFnONjCjTTFgbY6vdmw3ptuq44y8YKC+1SBbt5xojrqsevqQCx236fLb2HUjnCgv7ElgSF1GDR+ExsMm6Gxcw5LUsNI9W/K8vBNpHbyoVWkGfts/2BM9xo8nfIN4mAH0qeNh1fLyLbryfDJCsGOBqyYU4spgTqs99zIwwl5LKofyNwWGwgKfEXMeV2WT26H3rWz/DJKYYymYO3YMAqdlpIwPIwLGn70MSwkqGA7v/Yvpfut69Tc58P9+XXw1LnDrAZLCFm/gGTdr+gRugYHr+/pO2M+DbPacaLKOeqP3YfBursM6WmNfsfO7NlTjVb7FbqcXMjuA7b8vm8wSxcsoBDIW7CA0acWscPwPiVTN3D+RQ9GbD7Mb1PXElswnQ3XfHmsfEOmaI3+EIWv1o0lZtxuaqf8xslpO/HxP85zq+E0KKmN66kFDDypEPSqN4FxBxg0ReGHZS94rWyEXHsCMjSwGhtBae9s5tS4zDBNDyZNvkbxi1y0tmngn3yLjg1g8rgNXLVygqgiirvsZ7HnCq4EWNNs/j2G14GBflvwSRlJ0dzRfCdCaeN4h247LzO7pBV1LkNMoGDdOYWN1e1wuT2dNSZt0d+5A8vS5hQlLKHr6z6MOluCh9NsktcJPJQIjqcu5psBu6kR05xgpRKRcyezpfoOUqpEM2P0AzomfkWf5Eh2iGc4TrfC5UEOnWpqkr16CpM9hzNqmysNkppQKW05C7064V3XiNjXddFKUbCovpnEZU5Yb4zgxoHvUbTW0spzL5d37+b3JweY5ZmBVXIxp6pNJPfhA9wy+/HIxB2zKYYMi+zP19X+xsPQFbTe+AWrFRN6tl1OK5dDpPR4xPg2q3EWgktL+6OtW0rQzWwaedkQeb0ubsM9cdrcjtrF25j6RS4BdoL6eUWkXnXG/0kreuS9ZnFDGxo99+D1nfaYlYZS13Yx8cfucutqJr5JQ0lnDzFzLmM1yIDKq/6O5YibbB8aQPiUYdQ3tcPzRCalZ+ZgcKkZ4/ZM5mzRcurY6hP1dAyPbYfgvcKKnAa2nLfwosmkVfyY0Y6ts0zY1ukRZhm9KPVSqB3gwgDvhYxK8yKp5Wi+vb6OOZUbsnJgCWFTejLRpoixg1di69eBsKz7DGvVhNYFe1kS9SfXr23h9q6xBHbI442ZKdc2RlEj6yTOrpGMnnYKf0c3Kmv/QN1oD3qiEPZVB7yOhbNgQVVe+rejhjKe54vicTctxPPYSeYHR+EvpqG8WEZ0tVyMA6qx69okil82YWv+NQYvcuT5wu7Mmu5LraZRLFig4O8/kdCohqRcm8a3V+3wtN2DZloKbZVuaNVbhs21J+y2vEf4OgMcvTtQyzyJUSbHqGnSgTk/+WM+3Z2UeX60/nkXycke5Ld9Rt/sLILuDORL3bk8b6+Flslkin45i7e9GQ8m3OekzxUq/7SWC7sCuWJoxlmf7+niLP6jGvVRTD6EEE+BfCC7osdSgaqj3vGDzIG6xw8yB/9b/LUVRTEtr8FI/0oIkQckV/Q4Kpg8RmX86hw/yBz8T/H/RzXqo5h8AAghotX5Fz11jx9kDtQ9fpA5UPf4P3Zy/8gcyPjVO36QOXgf8X8Uz3xIkiRJkiRJkvT5k5MPSZIkSZIkSZLKxcc0+VD310eqe/wgc6Du8YPMgbrH/7GT+0fmQMYvqXsO/t/xfzTPfEiSJEmSJEmS9Hn7mK58SJIkSZIkSZL0GZOTD0mSJEmSJEmSykWFTz6EEJ2FEMlCiDQhxMyKHs+HIoTYIoTIEkIkvNNmLIQIE0KkqtZGqnYhhFipykm8EKJ5xY38/RBCWAshTgshbgohEoUQk1TtapEDIURlIcQVIUScKn5/VXsdIcRlVfy7hRDaqnYd1Xaa6nubihz/+ySE0BRCXBdChKi21SYHQoh0IcQNIUSsECJa1aYWx8CnTh1qlaxT6l2nQNaqt2Sd+rB1qkInH0IITWA10AVoBAwQQjSqyDF9QAFA539rmwmEK4piB4SrtqEsH3aqxQ9YW05j/JBeA1MVRWkIuADjVftaXXJQBHgoitIUcAI6CyFcgCXAr6r4XwAjVf1HAi8URakH/Krq97mYBNx8Z1vdcuCuKIrTO+9JV5dj4JOlRrUqAFmn1LlOgaxVb8k69SHrlKIoFbYArsDJd7ZnAbMqckwfOF4bIOGd7WTAQvXZAkhWfV4PDPirfp/LAhwGOqljDgBd4BrQirL/JbSSqv0fxwNwEnBVfa6k6icqeuzvIfaaqhOXBxACCHXKAZAOVP+3NrU7Bj61RZ1qlaxT/5ILta1TqnjUslbJOvXh61RF33ZlBWS+s31f1aYuzBVFeQSgWpup2j/rvKguSzYDLqNGOVBdxo0FsoAw4DaQoyjKa1WXd2P8R/yq73MBk/Id8QexApgBvFFtm6BeOVCAUCFEjBDCT9WmNsfAJ0yd94Va/n2qa50CWauQdeqD16lK73Gw/xfiL9rku38/47wIIfSB/cBkRVH+FOKvQi3r+hdtn3QOFEUpBZyEENWAg0DDv+qmWn928QshugJZiqLECCHav23+i66fbQ6A1oqiPBRCmAFhQohb/0PfzzH+T5XcF//dZ5sTda5ToN61StYpoBzqVEVf+bgPWL+zXRN4WEFjqQhPhBAWAKp1lqr9s8yLEEKLshN6oKIoB1TNapUDAEVRcoAzlN1TXE0I8fZHgHdj/Ef8qu8NgeflO9L3rjXQXQiRDuyi7JL2CtQoB4qiPFStsygr6i1Rw2PgE6TO+0Kt/j5lnfonNa1Vsk6VQ52q6MnHVcBO9RYBbaA/EFzBYypPwcAw1edhlN1f+rZ9qOotAi5A7tvLXZ8qUfbT0WbgpqIov7zzlVrkQAhhqvoVCSFEFaAjZQ+znQZ8Vd3+Pf63efEFIhTVDZWfKkVRZimKUlNRFBvKjvUIRVEGoSY5EELoCSGqvv0MeAIJqMkx8IlT51qlNn+f6l6nQNYqWafKqU59BA+2fAWkUHZP4ZyKHs8HjHMn8AgooWymOJKy+wLDgVTV2ljVV1D2ZpXbwA3AuaLH/x7ib0PZpbh4IFa1fKUuOQCaANdV8ScA81TttsAVIA3YC+io2iurttNU39tWdAzvOR/tgRB1yoEqzjjVkvj2fKcux8CnvqhDrZJ1Sr3rlComWav+mQtZpz5QnRKqfyxJkiRJkiRJkvRBVfRtV5IkSZIkSZIkqQk5+ZAkSZIkSZIkqVzIyYckSZIkSZIkSeVCTj4kSZIkSZIkSSoXcvIhSZIkSZIkSVK5kJMPSZIkSZIkSZLKhZx8SJIkSZIkSZJULv4L5oec25uK170AAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x_deltas, y_deltas = high_pass_x_y(content_image)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(14,10))\\n\",\n \"plt.subplot(2,2,1)\\n\",\n \"imshow(clip_0_1(2*y_deltas+0.5), \\\"Horizontal Deltas: Original\\\")\\n\",\n \"\\n\",\n \"plt.subplot(2,2,2)\\n\",\n \"imshow(clip_0_1(2*x_deltas+0.5), \\\"Vertical Deltas: Original\\\")\\n\",\n \"\\n\",\n \"x_deltas, y_deltas = high_pass_x_y(image)\\n\",\n \"\\n\",\n \"plt.subplot(2,2,3)\\n\",\n \"imshow(clip_0_1(2*y_deltas+0.5), \\\"Horizontal Deltas: Styled\\\")\\n\",\n \"\\n\",\n \"plt.subplot(2,2,4)\\n\",\n \"imshow(clip_0_1(2*x_deltas+0.5), \\\"Vertical Deltas: Styled\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"这显示了高频分量如何增加。\\n\",\n \"\\n\",\n \"而且，该高频分量基本上是边缘检测器。您可以从Sobel边缘检测器获得类似的输出，例如：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(14,10))\\n\",\n \"\\n\",\n \"sobel = tf.image.sobel_edges(content_image)\\n\",\n \"plt.subplot(1,2,1)\\n\",\n \"imshow(clip_0_1(sobel[...,0]/4+0.5), \\\"Horizontal Sobel-edges\\\")\\n\",\n \"plt.subplot(1,2,2)\\n\",\n \"imshow(clip_0_1(sobel[...,1]/4+0.5), \\\"Vertical Sobel-edges\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"与此相关的正则化损失是值的平方和：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def total_variation_loss(image):\\n\",\n \" x_deltas, y_deltas = high_pass_x_y(image)\\n\",\n \" return tf.reduce_mean(x_deltas**2) + tf.reduce_mean(y_deltas**2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 重新运行优化\\n\",\n \"设定total_variation_loss的权重：\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"total_variation_weight=1e8\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"现在将它包含在train_step函数中：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"@tf.function()\\n\",\n \"def train_step(image):\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" outputs = extractor(image)\\n\",\n \" loss = style_content_loss(outputs)\\n\",\n \" loss += total_variation_weight*total_variation_loss(image)\\n\",\n \"\\n\",\n \" grad = tape.gradient(loss, image)\\n\",\n \" opt.apply_gradients([(grad, image)])\\n\",\n \" image.assign(clip_0_1(image))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"重新初始化变量\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"image = tf.Variable(content_image)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"并运行优化\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"................................\"\n ]\n }\n ],\n \"source\": [\n \"import time\\n\",\n \"start = time.time()\\n\",\n \"\\n\",\n \"epochs = 10\\n\",\n \"steps_per_epoch = 100\\n\",\n \"\\n\",\n \"step = 0\\n\",\n \"for n in range(epochs):\\n\",\n \" for m in range(steps_per_epoch):\\n\",\n \" step += 1\\n\",\n \" train_step(image)\\n\",\n \" print(\\\".\\\", end='')\\n\",\n \" display.clear_output(wait=True)\\n\",\n \" imshow(image.read_value())\\n\",\n \" plt.title(\\\"Train step: {}\\\".format(step))\\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"end = time.time()\\n\",\n \"print(\\\"Total time: {:.1f}\\\".format(end-start))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "040-App/403-image_caption_with_attention.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2教程-基于注意力的图片描述生成\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"图片生成描述，就是给定一张图片，生成相应的文字描述。如下图的生成描述为“冲浪者在波浪上滑行”。\\n\",\n \"![](https://tensorflow.org/images/surf.jpg)\\n\",\n \"下面，我们将基于注意力机制来构建描述生成模型。\\n\",\n \"![](https://tensorflow.org/images/imcap_prediction.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"该模型来自论文：The model architecture is similar to Show, Attend and Tell: Neural Image Caption Generation with Visual Attention。\\n\",\n \"下面，我们将会使用MS-COCO数据，预处理和缓存图像子集，训练编码器-解码器模型，并使用训练模型生成新的图像描述。\\n\",\n \"在教程中我们将使用大约20000个图像的30000个字幕\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/doit/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\\n\",\n \" from ._conv import register_converters as _register_converters\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-beta1\\n\"\n ]\n }\n ],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"print(tf.__version__)\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn.utils import shuffle\\n\",\n \"\\n\",\n \"import re\\n\",\n \"import numpy as np\\n\",\n \"import os\\n\",\n \"import time\\n\",\n \"import json\\n\",\n \"from glob import glob\\n\",\n \"from PIL import Image\\n\",\n \"import pickle\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 下载并准备MS-COCO数据集\\n\",\n \"MS-COCO数据集包含82000个图像，每个图像至少有5个不同的标题注释。（ps，文件比较大有13G最好提前下载）\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"annotation_zip = tf.keras.utils.get_file('captions.zip',\\n\",\n \" cache_subdir=os.path.abspath('.'),\\n\",\n \" origin = 'http://images.cocodataset.org/annotations/annotations_trainval2014.zip',\\n\",\n \" extract = True)\\n\",\n \"annotation_file = os.path.dirname(annotation_zip)+'/annotations/captions_train2014.json'\\n\",\n \"\\n\",\n \"name_of_zip = 'train2014.zip'\\n\",\n \"if not os.path.exists(os.path.abspath('.') + '/' + name_of_zip):\\n\",\n \" image_zip = tf.keras.utils.get_file(name_of_zip,\\n\",\n \" cache_subdir=os.path.abspath('.'),\\n\",\n \" origin = 'http://images.cocodataset.org/zips/train2014.zip',\\n\",\n \" extract = True)\\n\",\n \" PATH = os.path.dirname(image_zip)+'/train2014/'\\n\",\n \"else:\\n\",\n \" PATH = os.path.abspath('.')+'/train2014/'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 选择训练集\\n\",\n \"为了加快训练，我们这边选择30000个字幕及其对应的图片作为训练集。如果想提高模型能力可以使用更多的数据。\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 读取字幕文件\\n\",\n \"with open(annotation_file, 'r') as f:\\n\",\n \" annotations = json.load(f)\\n\",\n \" \\n\",\n \"# 读字幕及对应图片\\n\",\n \"all_captions = []\\n\",\n \"all_img_name_vector = []\\n\",\n \"\\n\",\n \"for annot in annotations['annotations']:\\n\",\n \" caption = ' ' + annot['caption'] +' '\\n\",\n \" image_id = annot['image_id']\\n\",\n \" \\n\",\n \" full_coco_image_path = PATH + 'COCO_train2014_' + '%012d.jpg' % (image_id)\\n\",\n \" \\n\",\n \" all_img_name_vector.append(full_coco_image_path)\\n\",\n \" all_captions.append(caption)\\n\",\n \"# 打乱数据\\n\",\n \"train_captions, img_name_vector = shuffle(all_captions, all_img_name_vector,\\n\",\n \" random_state=1)\\n\",\n \"# 选取30000个字幕\\n\",\n \"num_examples = 30000\\n\",\n \"train_captions = train_captions[:30000]\\n\",\n \"img_name_vector = img_name_vector[:30000]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(30000, 414113)\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"len(train_captions), len(all_captions)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 使用InceptionV3预处理图像\\n\",\n \"接下来，您将使用InceptionV3（在Imagenet上预先训练）来对每个图像进行分类。\\n\",\n \"\\n\",\n \"首先，通过以下方式将图像转换为InceptionV3的预期格式：*将图像大小调整为299px×299px * 使用preprocess_input方法对图像进行预处理以对图像进行标准化，使其包含-1到1范围内的像素，这是用于训练InceptionV3的图像格式。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def load_image(image_path):\\n\",\n \" img = tf.io.read_file(image_path)\\n\",\n \" img = tf.image.decode_jpeg(img, channels=3)\\n\",\n \" img = tf.image.resize(img, (299, 299))\\n\",\n \" img = tf.keras.applications.inception_v3.preprocess_input(img)\\n\",\n \" return img, image_path\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 加载预训练的InceptionV3模型\\n\",\n \"现在，我们将创建一个tf.keras模型，其中输出层是InceptionV3体系结构中的最后一个卷积层。这层的输出形状是8x8x2048。使用最后一个卷积层，因为我们在此示例中使用了注意力。\\n\",\n \"- 通过网络转发每个图像并将结果向量存储在字典中（image_name - > feature_vector）。\\n\",\n \"- 在所有图像通过网络传递之后，挑选字典并将其保存到磁盘。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\\n\",\n \"87916544/87910968 [==============================] - 30s 0us/step\\n\"\n ]\n }\n ],\n \"source\": [\n \"image_model = tf.keras.applications.InceptionV3(include_top=False,\\n\",\n \" weights='imagenet')\\n\",\n \"new_input = image_model.input\\n\",\n \"hidden_layer = image_model.layers[-1].output\\n\",\n \"image_features_extract_model = tf.keras.Model(new_input, hidden_layer)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 缓存从InceptionV3中提取的功能\\n\",\n \"将使用InceptionV3预处理每个映像并将输出缓存到磁盘。缓存RAM中的输出会更快但内存密集，每个映像需要8 * 8 * 2048个浮点数。在撰写本文时，这超出了Colab的内存限制（目前为12GB内存）。\\n\",\n \"\\n\",\n \"可以通过更复杂的缓存策略（例如，通过分割图像以减少随机访问磁盘I / O）来提高性能，但这需要更多代码。\\n\",\n \"\\n\",\n \"使用GPU在Colab中运行大约需要10分钟。如果您想查看进度条，可以：\\n\",\n \"\\n\",\n \"安装tqdm：\\n\",\n \"\\n\",\n \"!pip install -q tqdm\\n\",\n \"\\n\",\n \"导入tqdm：\\n\",\n \"\\n\",\n \"from tqdm import tqdm\\n\",\n \"\\n\",\n \"更改以下行：\\n\",\n \"\\n\",\n \"for img, path in image_dataset:\\n\",\n \"\\n\",\n \"至：\\n\",\n \"\\n\",\n \"for img, path in tqdm(image_dataset):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"!pip install -q tqdm\\n\",\n \"from tqdm import tqdm\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1622it [30:52, 6.12s/it]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 获取无重复图片\\n\",\n \"encode_train = sorted(set(img_name_vector))\\n\",\n \"\\n\",\n \"# 获取训练图片特征，用于输入\\n\",\n \"image_dataset = tf.data.Dataset.from_tensor_slices(encode_train)\\n\",\n \"image_dataset = image_dataset.map(\\n\",\n \"load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE).batch(16)\\n\",\n \"\\n\",\n \"# 读取图片，获取特征\\n\",\n \"for img, path in tqdm(image_dataset):\\n\",\n \" batch_features = image_features_extract_model(img)\\n\",\n \" batch_features = tf.reshape(batch_features, \\n\",\n \" (batch_features.shape[0],-1, batch_features.shape[3]))\\n\",\n \" \\n\",\n \" for bf, p in zip(batch_features, path):\\n\",\n \" path_of_feature = p.numpy().decode('utf-8')\\n\",\n \" np.save(path_of_feature, bf.numpy())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 预处理并标记字幕\\n\",\n \"- 首先，将对标题进行标记（例如，通过拆分空格）。这为我们提供了数据中所有独特单词的词汇表（例如，“冲浪”，“足球”等）。\\n\",\n \"- 接下来，将词汇量限制为前5,000个单词（以节省内存）。将使用令牌“UNK”（未知）替换所有其他单词。\\n\",\n \"- 然后，创建单词到索引和索引到单词的映射。\\n\",\n \"- 最后，将所有序列填充为与最长序列相同的长度。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 寻找字幕最大长度\\n\",\n \"def calc_max_length(tensor):\\n\",\n \" return max(len(t) for t in tensor)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 使用词频前5000的词构造词典\\n\",\n \"top_k = 5000\\n\",\n \"tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=top_k,\\n\",\n \" oov_token='',\\n\",\n \" filters='!\\\"#$%&()*+.,-/:;=?@[\\\\]^_`{|}~ ')\\n\",\n \"# 获取tokenizer解析器\\n\",\n \"tokenizer.fit_on_texts(train_captions)\\n\",\n \"# 文本转token\\n\",\n \"train_seqs = tokenizer.texts_to_sequences(train_captions)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# padding\\n\",\n \"tokenizer.word_index[''] = 0\\n\",\n \"tokenizer.index_word[0] = ''\\n\",\n \"\\n\",\n \"cap_vector = tf.keras.preprocessing.sequence.pad_sequences(train_seqs, padding='post')\\n\",\n \"# 最大长度\\n\",\n \"max_length = calc_max_length(train_seqs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 拆分训练集和测试集\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(24000, 24000, 6000, 6000)\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"img_name_train, img_name_val, cap_train, cap_val = train_test_split(img_name_vector,\\n\",\n \" cap_vector,\\n\",\n \" test_size=0.2,\\n\",\n \" random_state=0)\\n\",\n \"\\n\",\n \"len(img_name_train), len(cap_train), len(img_name_val), len(cap_val)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 创建用于培训的tf.data数据集\\n\",\n \"图片和标题已准备就绪！接下来，让我们创建一个tf.data数据集来用于训练模型。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 相关超参\\n\",\n \"\\n\",\n \"BATCH_SIZE = 64\\n\",\n \"BUFFER_SIZE = 1000\\n\",\n \"embedding_dim = 256\\n\",\n \"units = 512\\n\",\n \"vocab_size = len(tokenizer.word_index) + 1\\n\",\n \"num_steps = len(img_name_train) // BATCH_SIZE\\n\",\n \"features_shape = 2048\\n\",\n \"attention_features_shape = 64\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 导入图片特征\\n\",\n \"def map_func(img_name, cap):\\n\",\n \" img_tensor = np.load(img_name.decode('utf-8')+'.npy')\\n\",\n \" return img_tensor, cap\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 构建tf.data训练数据\\n\",\n \"dataset = tf.data.Dataset.from_tensor_slices((img_name_train, cap_train))\\n\",\n \"\\n\",\n \"# 获取输入特征\\n\",\n \"dataset = dataset.map(lambda item1, item2: tf.numpy_function(\\n\",\n \" map_func, [item1, item2], [tf.float32, tf.int32]),\\n\",\n \" num_parallel_calls=tf.data.experimental.AUTOTUNE)\\n\",\n \"\\n\",\n \"# 打乱分批次\\n\",\n \"dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\\n\",\n \"dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 模型\\n\",\n \"\\n\",\n \"模型架构的灵感来自Show，Attend和Tell论文。\\n\",\n \"\\n\",\n \"在这个例子中，从InceptionV3的下卷积层中提取特征，获得一个形状向量（8,8,2048）。\\n\",\n \"将它压成（64,2048）的形状。\\n\",\n \"然后，该向量通过CNN编码器（由单个完全连接的层组成）。\\n\",\n \"RNN（此处为GRU）参与图像以预测下一个单词。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 注意力机制\\n\",\n \"\\n\",\n \"class BahdanauAttention(tf.keras.Model):\\n\",\n \" def __init__(self, units):\\n\",\n \" super(BahdanauAttention, self).__init__()\\n\",\n \" self.W1 = tf.keras.layers.Dense(units)\\n\",\n \" self.W2 = tf.keras.layers.Dense(units)\\n\",\n \" self.V = tf.keras.layers.Dense(1)\\n\",\n \"\\n\",\n \" def call(self, features, hidden):\\n\",\n \" # features(CNN_encoder output) shape == (batch_size, 64, embedding_dim)\\n\",\n \"\\n\",\n \" # hidden shape == (batch_size, hidden_size)\\n\",\n \" # 对hidden扩维 hidden_with_time_axis shape == (batch_size, 1, hidden_size)\\n\",\n \" hidden_with_time_axis = tf.expand_dims(hidden, 1)\\n\",\n \"\\n\",\n \" # 获取匹配得分score shape == (batch_size, 64, unit)\\n\",\n \" score = tf.nn.tanh(self.W1(features) + self.W2(hidden_with_time_axis))\\n\",\n \"\\n\",\n \" # attention_weights shape == (batch_size, 64, 1)\\n\",\n \" # 获取注意力权重\\n\",\n \" attention_weights = tf.nn.softmax(self.V(score), axis=1)\\n\",\n \"\\n\",\n \" # context_vector shape after sum == (batch_size, embedding_dim)\\n\",\n \" context_vector = attention_weights * features\\n\",\n \" context_vector = tf.reduce_sum(context_vector, axis=1)\\n\",\n \"\\n\",\n \" return context_vector, attention_weights\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 编码cnn获得的图像特征\\n\",\n \"class CNN_Encoder(tf.keras.Model):\\n\",\n \"\\n\",\n \" def __init__(self, embedding_dim):\\n\",\n \" super(CNN_Encoder, self).__init__()\\n\",\n \" # shape after fc == (batch_size, 64, embedding_dim)\\n\",\n \" self.fc = tf.keras.layers.Dense(embedding_dim)\\n\",\n \"\\n\",\n \" def call(self, x):\\n\",\n \" x = self.fc(x)\\n\",\n \" x = tf.nn.relu(x)\\n\",\n \" return x\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# RNN解码器，解码出下一个词\\n\",\n \"class RNN_Decoder(tf.keras.Model):\\n\",\n \" def __init__(self, embedding_dim, units, vocab_size):\\n\",\n \" super(RNN_Decoder, self).__init__()\\n\",\n \" self.units = units\\n\",\n \"\\n\",\n \" self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\\n\",\n \" self.gru = tf.keras.layers.GRU(self.units,\\n\",\n \" return_sequences=True,\\n\",\n \" return_state=True,\\n\",\n \" recurrent_initializer='glorot_uniform')\\n\",\n \" self.fc1 = tf.keras.layers.Dense(self.units)\\n\",\n \" self.fc2 = tf.keras.layers.Dense(vocab_size)\\n\",\n \"\\n\",\n \" self.attention = BahdanauAttention(self.units)\\n\",\n \"\\n\",\n \" def call(self, x, features, hidden):\\n\",\n \" # defining attention as a separate model\\n\",\n \" context_vector, attention_weights = self.attention(features, hidden)\\n\",\n \"\\n\",\n \" # x shape after passing through embedding == (batch_size, 1, embedding_dim)\\n\",\n \" x = self.embedding(x)\\n\",\n \"\\n\",\n \" # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)\\n\",\n \" x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)\\n\",\n \"\\n\",\n \" # passing the concatenated vector to the GRU\\n\",\n \" output, state = self.gru(x)\\n\",\n \"\\n\",\n \" # shape == (batch_size, max_length, hidden_size)\\n\",\n \" x = self.fc1(output)\\n\",\n \"\\n\",\n \" # x shape == (batch_size * max_length, hidden_size)\\n\",\n \" x = tf.reshape(x, (-1, x.shape[2]))\\n\",\n \"\\n\",\n \" # output shape == (batch_size * max_length, vocab)\\n\",\n \" x = self.fc2(x)\\n\",\n \"\\n\",\n \" return x, state, attention_weights\\n\",\n \"\\n\",\n \" def reset_state(self, batch_size):\\n\",\n \" return tf.zeros((batch_size, self.units))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"# 网络模块\\n\",\n \"\\n\",\n \"\\n\",\n \"encoder = CNN_Encoder(embedding_dim)\\n\",\n \"decoder = RNN_Decoder(embedding_dim, units, vocab_size)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 优化器和损失函数\\n\",\n \"optimizer = tf.keras.optimizers.Adam()\\n\",\n \"loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\\n\",\n \" from_logits=True, reduction='none')\\n\",\n \"\\n\",\n \"def loss_function(real, pred):\\n\",\n \" mask = tf.math.logical_not(tf.math.equal(real, 0))\\n\",\n \" loss_ = loss_object(real, pred)\\n\",\n \"\\n\",\n \" mask = tf.cast(mask, dtype=loss_.dtype)\\n\",\n \" loss_ *= mask\\n\",\n \"\\n\",\n \" return tf.reduce_mean(loss_)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 模型保存\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"checkpoint_path = \\\"./checkpoints/train\\\"\\n\",\n \"ckpt = tf.train.Checkpoint(encoder=encoder,\\n\",\n \" decoder=decoder,\\n\",\n \" optimizer = optimizer)\\n\",\n \"ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"start_epoch = 0\\n\",\n \"if ckpt_manager.latest_checkpoint:\\n\",\n \" start_epoch = int(ckpt_manager.latest_checkpoint.split('-')[-1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 训练\\n\",\n \"您提取存储在相应.npy文件中的功能，然后通过编码器传递这些功能。\\n\",\n \"编码器输出，隐藏状态（初始化为0）和解码器输入（它是开始标记）被传递给解码器。\\n\",\n \"解码器返回预测和解码器隐藏状态。\\n\",\n \"然后将解码器隐藏状态传递回模型，并使用预测来计算损失。\\n\",\n \"使用教师强制决定解码器的下一个输入。\\n\",\n \"教师强制是将目标字作为下一个输入传递给解码器的技术。\\n\",\n \"最后一步是计算渐变并将其应用于优化器并反向传播。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# adding this in a separate cell because if you run the training cell\\n\",\n \"# many times, the loss_plot array will be reset\\n\",\n \"loss_plot = []\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"@tf.function\\n\",\n \"def train_step(img_tensor, target):\\n\",\n \" loss = 0\\n\",\n \"\\n\",\n \" # initializing the hidden state for each batch\\n\",\n \" # because the captions are not related from image to image\\n\",\n \" hidden = decoder.reset_state(batch_size=target.shape[0])\\n\",\n \"\\n\",\n \" dec_input = tf.expand_dims([tokenizer.word_index['']] * BATCH_SIZE, 1)\\n\",\n \"\\n\",\n \" with tf.GradientTape() as tape:\\n\",\n \" features = encoder(img_tensor)\\n\",\n \"\\n\",\n \" for i in range(1, target.shape[1]):\\n\",\n \" # passing the features through the decoder\\n\",\n \" predictions, hidden, _ = decoder(dec_input, features, hidden)\\n\",\n \"\\n\",\n \" loss += loss_function(target[:, i], predictions)\\n\",\n \"\\n\",\n \" # using teacher forcing\\n\",\n \" dec_input = tf.expand_dims(target[:, i], 1)\\n\",\n \"\\n\",\n \" total_loss = (loss / int(target.shape[1]))\\n\",\n \"\\n\",\n \" trainable_variables = encoder.trainable_variables + decoder.trainable_variables\\n\",\n \"\\n\",\n \" gradients = tape.gradient(loss, trainable_variables)\\n\",\n \"\\n\",\n \" optimizer.apply_gradients(zip(gradients, trainable_variables))\\n\",\n \"\\n\",\n \" return loss, total_loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1 Batch 0 Loss 2.0568\\n\",\n \"Epoch 1 Batch 100 Loss 1.0937\\n\",\n \"Epoch 1 Batch 200 Loss 0.9828\\n\",\n \"Epoch 1 Batch 300 Loss 0.8723\\n\",\n \"Epoch 1 Loss 1.069455\\n\",\n \"Time taken for 1 epoch 1820.9269726276398 sec\\n\",\n \"\\n\",\n \"Epoch 2 Batch 0 Loss 0.8249\\n\",\n \"Epoch 2 Batch 100 Loss 0.7593\\n\",\n \"Epoch 2 Batch 200 Loss 0.7707\\n\",\n \"Epoch 2 Batch 300 Loss 0.7423\\n\",\n \"Epoch 2 Loss 0.807992\\n\",\n \"Time taken for 1 epoch 937.0156309604645 sec\\n\",\n \"\\n\",\n \"Epoch 3 Batch 0 Loss 0.7363\\n\",\n \"Epoch 3 Batch 100 Loss 0.6849\\n\",\n \"Epoch 3 Batch 200 Loss 0.7081\\n\",\n \"Epoch 3 Batch 300 Loss 0.6885\\n\",\n \"Epoch 3 Loss 0.732593\\n\",\n \"Time taken for 1 epoch 919.8678886890411 sec\\n\",\n \"\\n\",\n \"Epoch 4 Batch 0 Loss 0.6817\\n\",\n \"Epoch 4 Batch 100 Loss 0.6426\\n\",\n \"Epoch 4 Batch 200 Loss 0.6681\\n\",\n \"Epoch 4 Batch 300 Loss 0.6524\\n\",\n \"Epoch 4 Loss 0.686184\\n\",\n \"Time taken for 1 epoch 947.0094523429871 sec\\n\",\n \"\\n\",\n \"Epoch 5 Batch 0 Loss 0.6423\\n\",\n \"Epoch 5 Batch 100 Loss 0.6101\\n\",\n \"Epoch 5 Batch 200 Loss 0.6388\\n\",\n \"Epoch 5 Batch 300 Loss 0.6257\\n\",\n \"Epoch 5 Loss 0.651183\\n\",\n \"Time taken for 1 epoch 942.7549574375153 sec\\n\",\n \"\\n\",\n \"Epoch 6 Batch 0 Loss 0.6213\\n\",\n \"Epoch 6 Batch 100 Loss 0.5819\\n\",\n \"Epoch 6 Batch 200 Loss 0.6204\\n\",\n \"Epoch 6 Batch 300 Loss 0.5905\\n\",\n \"Epoch 6 Loss 0.621135\\n\",\n \"Time taken for 1 epoch 889.1938171386719 sec\\n\",\n \"\\n\",\n \"Epoch 7 Batch 0 Loss 0.5910\\n\",\n \"Epoch 7 Batch 100 Loss 0.5558\\n\",\n \"Epoch 7 Batch 200 Loss 0.5868\\n\",\n \"Epoch 7 Batch 300 Loss 0.5665\\n\",\n \"Epoch 7 Loss 0.595972\\n\",\n \"Time taken for 1 epoch 787.7603008747101 sec\\n\",\n \"\\n\",\n \"Epoch 8 Batch 0 Loss 0.5588\\n\",\n \"Epoch 8 Batch 100 Loss 0.5361\\n\",\n \"Epoch 8 Batch 200 Loss 0.5543\\n\",\n \"Epoch 8 Batch 300 Loss 0.5484\\n\",\n \"Epoch 8 Loss 0.572979\\n\",\n \"Time taken for 1 epoch 767.9682667255402 sec\\n\",\n \"\\n\",\n \"Epoch 9 Batch 0 Loss 0.5355\\n\",\n \"Epoch 9 Batch 100 Loss 0.5197\\n\",\n \"Epoch 9 Batch 200 Loss 0.5230\\n\",\n \"Epoch 9 Batch 300 Loss 0.5263\\n\",\n \"Epoch 9 Loss 0.549804\\n\",\n \"Time taken for 1 epoch 770.8820085525513 sec\\n\",\n \"\\n\",\n \"Epoch 10 Batch 0 Loss 0.5266\\n\",\n \"Epoch 10 Batch 100 Loss 0.5051\\n\",\n \"Epoch 10 Batch 200 Loss 0.4956\\n\",\n \"Epoch 10 Batch 300 Loss 0.5217\\n\",\n \"Epoch 10 Loss 0.529101\\n\",\n \"Time taken for 1 epoch 802.3947956562042 sec\\n\",\n \"\\n\",\n \"Epoch 11 Batch 0 Loss 0.5078\\n\"\n ]\n },\n {\n \"ename\": \"KeyboardInterrupt\",\n \"evalue\": \"\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 5\\u001b[0m \\u001b[0mtotal_loss\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m0\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 6\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 7\\u001b[0;31m \\u001b[0;32mfor\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0mbatch\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0mimg_tensor\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mtarget\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0menumerate\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdataset\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 8\\u001b[0m \\u001b[0mbatch_loss\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mt_loss\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mtrain_step\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mimg_tensor\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mtarget\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 9\\u001b[0m \\u001b[0mtotal_loss\\u001b[0m \\u001b[0;34m+=\\u001b[0m \\u001b[0mt_loss\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/data/ops/iterator_ops.py\\u001b[0m in \\u001b[0;36m__next__\\u001b[0;34m(self)\\u001b[0m\\n\\u001b[1;32m 584\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 585\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0m__next__\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0;31m# For Python 3 compatibility\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 586\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mnext\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 587\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 588\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0m_next_internal\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/data/ops/iterator_ops.py\\u001b[0m in \\u001b[0;36mnext\\u001b[0;34m(self)\\u001b[0m\\n\\u001b[1;32m 621\\u001b[0m \\\"\\\"\\\"\\n\\u001b[1;32m 622\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 623\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_next_internal\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 624\\u001b[0m \\u001b[0;32mexcept\\u001b[0m \\u001b[0merrors\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mOutOfRangeError\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 625\\u001b[0m \\u001b[0;32mraise\\u001b[0m \\u001b[0mStopIteration\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/data/ops/iterator_ops.py\\u001b[0m in \\u001b[0;36m_next_internal\\u001b[0;34m(self)\\u001b[0m\\n\\u001b[1;32m 613\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_iterator_resource\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 614\\u001b[0m \\u001b[0moutput_types\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_flat_output_types\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 615\\u001b[0;31m output_shapes=self._flat_output_shapes)\\n\\u001b[0m\\u001b[1;32m 616\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 617\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_structure\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_from_compatible_tensor_list\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mret\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;31m# pylint: disable=protected-access\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/gen_dataset_ops.py\\u001b[0m in \\u001b[0;36miterator_get_next_sync\\u001b[0;34m(iterator, output_types, output_shapes, name)\\u001b[0m\\n\\u001b[1;32m 2104\\u001b[0m \\u001b[0m_ctx\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_context_handle\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0m_ctx\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_thread_local_data\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdevice_name\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 2105\\u001b[0m \\u001b[0;34m\\\"IteratorGetNextSync\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mname\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0m_ctx\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_post_execution_callbacks\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0miterator\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 2106\\u001b[0;31m \\\"output_types\\\", output_types, \\\"output_shapes\\\", output_shapes)\\n\\u001b[0m\\u001b[1;32m 2107\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0m_result\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 2108\\u001b[0m \\u001b[0;32mexcept\\u001b[0m \\u001b[0m_core\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_FallbackException\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m: \"\n ]\n }\n ],\n \"source\": [\n \"EPOCHS = 20\\n\",\n \"\\n\",\n \"for epoch in range(start_epoch, EPOCHS):\\n\",\n \" start = time.time()\\n\",\n \" total_loss = 0\\n\",\n \"\\n\",\n \" for (batch, (img_tensor, target)) in enumerate(dataset):\\n\",\n \" batch_loss, t_loss = train_step(img_tensor, target)\\n\",\n \" total_loss += t_loss\\n\",\n \"\\n\",\n \" if batch % 100 == 0:\\n\",\n \" print ('Epoch {} Batch {} Loss {:.4f}'.format(\\n\",\n \" epoch + 1, batch, batch_loss.numpy() / int(target.shape[1])))\\n\",\n \" # storing the epoch end loss value to plot later\\n\",\n \" loss_plot.append(total_loss / num_steps)\\n\",\n \"\\n\",\n \" if epoch % 5 == 0:\\n\",\n \" ckpt_manager.save()\\n\",\n \"\\n\",\n \" print ('Epoch {} Loss {:.6f}'.format(epoch + 1,\\n\",\n \" total_loss/num_steps))\\n\",\n \" print ('Time taken for 1 epoch {} sec\\\\n'.format(time.time() - start))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(loss_plot)\\n\",\n \"plt.xlabel('Epochs')\\n\",\n \"plt.ylabel('Loss')\\n\",\n \"plt.title('Loss Plot')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 字幕！\\n\",\n \"evaluate函数类似于训练循环，除了你不在这里使用教师强制。在每个时间步骤对解码器的输入是其先前的预测以及隐藏状态和编码器输出。\\n\",\n \"停止预测模型何时预测结束标记。\\n\",\n \"并存储每个时间步的注意力。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def evaluate(image):\\n\",\n \" attention_plot = np.zeros((max_length, attention_features_shape))\\n\",\n \"\\n\",\n \" hidden = decoder.reset_state(batch_size=1)\\n\",\n \"\\n\",\n \" temp_input = tf.expand_dims(load_image(image)[0], 0)\\n\",\n \" img_tensor_val = image_features_extract_model(temp_input)\\n\",\n \" img_tensor_val = tf.reshape(img_tensor_val, (img_tensor_val.shape[0], -1, img_tensor_val.shape[3]))\\n\",\n \"\\n\",\n \" features = encoder(img_tensor_val)\\n\",\n \"\\n\",\n \" dec_input = tf.expand_dims([tokenizer.word_index['']], 0)\\n\",\n \" result = []\\n\",\n \"\\n\",\n \" for i in range(max_length):\\n\",\n \" predictions, hidden, attention_weights = decoder(dec_input, features, hidden)\\n\",\n \"\\n\",\n \" attention_plot[i] = tf.reshape(attention_weights, (-1, )).numpy()\\n\",\n \"\\n\",\n \" predicted_id = tf.argmax(predictions[0]).numpy()\\n\",\n \" result.append(tokenizer.index_word[predicted_id])\\n\",\n \"\\n\",\n \" if tokenizer.index_word[predicted_id] == '':\\n\",\n \" return result, attention_plot\\n\",\n \"\\n\",\n \" dec_input = tf.expand_dims([predicted_id], 0)\\n\",\n \"\\n\",\n \" attention_plot = attention_plot[:len(result), :]\\n\",\n \" return result, attention_plot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def plot_attention(image, result, attention_plot):\\n\",\n \" temp_image = np.array(Image.open(image))\\n\",\n \"\\n\",\n \" fig = plt.figure(figsize=(10, 10))\\n\",\n \"\\n\",\n \" len_result = len(result)\\n\",\n \" for l in range(len_result):\\n\",\n \" temp_att = np.resize(attention_plot[l], (8, 8))\\n\",\n \" ax = fig.add_subplot(len_result//2, len_result//2, l+1)\\n\",\n \" ax.set_title(result[l])\\n\",\n \" img = ax.imshow(temp_image)\\n\",\n \" ax.imshow(temp_att, cmap='gray', alpha=0.6, extent=img.get_extent())\\n\",\n \"\\n\",\n \" plt.tight_layout()\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Real Caption: a black and white photo of two old trucks next to each other \\n\",\n \"Prediction Caption: a truck driving down the back of a city street \\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 49,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# captions on the validation set\\n\",\n \"rid = np.random.randint(0, len(img_name_val))\\n\",\n \"image = img_name_val[rid]\\n\",\n \"real_caption = ' '.join([tokenizer.index_word[i] for i in cap_val[rid] if i not in [0]])\\n\",\n \"result, attention_plot = evaluate(image)\\n\",\n \"\\n\",\n \"print ('Real Caption:', real_caption)\\n\",\n \"print ('Prediction Caption:', ' '.join(result))\\n\",\n \"plot_attention(image, result, attention_plot)\\n\",\n \"# opening the image\\n\",\n \"Image.open(img_name_val[rid])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 尝试使用自己的图像\\n\",\n \"为了好玩，下面我们提供了一种方法，您可以使用我们刚刚训练过的模型为您自己的图像添加标题。请记住，它是在相对少量的数据上训练的，您的图像可能与训练数据不同（因此请为奇怪的结果做好准备！）\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Downloading data from https://tensorflow.org/images/surf.jpg\\n\",\n \"65536/64400 [==============================] - 0s 5us/step\\n\",\n \"Prediction Caption: a man is surfing on a surfboard \\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"image_url = 'https://tensorflow.org/images/surf.jpg'\\n\",\n \"image_extension = image_url[-4:]\\n\",\n \"image_path = tf.keras.utils.get_file('image'+image_extension,\\n\",\n \" origin=image_url)\\n\",\n \"\\n\",\n \"result, attention_plot = evaluate(image_path)\\n\",\n \"print ('Prediction Caption:', ' '.join(result))\\n\",\n \"plot_attention(image_path, result, attention_plot)\\n\",\n \"# opening the image\\n\",\n \"Image.open(image_path)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "101-example_image_classification.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow 教程-图像分类\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"import tensorflow as tf\\n\",\n \"from tensorflow import keras\\n\",\n \"from tensorflow.keras import layers\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"print(tf.__version__)\\n\",\n \"\\n\"\n ]\n },\n {\n \"attachments\": {\n \"image.png\": {\n \"image/png\": 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\"\n }\n },\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.获取Fashion MNIST数据集\\n\",\n \"本指南使用Fashion MNIST数据集，该数据集包含10个类别中的70,000个灰度图像。 图像显示了低分辨率（28 x 28像素）的单件服装，如下所示：\\n\",\n \"![image.png](attachment:image.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Fashion MNIST旨在替代经典的MNIST数据集，通常用作计算机视觉机器学习计划的“Hello，World”。\\n\",\n \"\\n\",\n \"我们将使用60,000张图像来训练网络和10,000张图像，以评估网络学习图像分类的准确程度。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(train_images, train_labels), (test_images, test_labels) = keras.datasets.fashion_mnist.load_data()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"图像是28x28 NumPy数组，像素值介于0到255之间。标签是一个整数数组，范围从0到9.这些对应于图像所代表的服装类别：\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
LabelClass
0T-shirt/top
1Trouser
2Pullover
3Dress
4Coat
5Sandal
6Shirt
7Sneaker
8Bag
9Ankle boot
\\n\",\n \"\\n\",\n \"每个图像都映射到一个标签。 由于类名不包含在数据集中，因此将它们存储在此处以便在绘制图像时使用：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \\n\",\n \" 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.探索数据\\n\",\n \"让我们在训练模型之前探索数据集的格式。 以下显示训练集中有60,000个图像，每个图像表示为28 x 28像素：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(60000, 28, 28)\\n\",\n \"(60000,)\\n\",\n \"(10000, 28, 28)\\n\",\n \"(10000,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(train_images.shape)\\n\",\n \"print(train_labels.shape)\\n\",\n \"print(test_images.shape)\\n\",\n \"print(test_labels.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.处理数据\\n\",\n \"图片展示\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure()\\n\",\n \"plt.imshow(train_images[0])\\n\",\n \"plt.colorbar()\\n\",\n \"plt.grid(False)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"train_images = train_images / 255.0\\n\",\n \"\\n\",\n \"test_images = test_images / 255.0\\n\",\n \"plt.figure(figsize=(10,10))\\n\",\n \"for i in range(25):\\n\",\n \" plt.subplot(5,5,i+1)\\n\",\n \" plt.xticks([])\\n\",\n \" plt.yticks([])\\n\",\n \" plt.grid(False)\\n\",\n \" plt.imshow(train_images[i], cmap=plt.cm.binary)\\n\",\n \" plt.xlabel(class_names[train_labels[i]])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.构造网络\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model = keras.Sequential(\\n\",\n \"[\\n\",\n \" layers.Flatten(input_shape=[28, 28]),\\n\",\n \" layers.Dense(128, activation='relu'),\\n\",\n \" layers.Dense(10, activation='softmax')\\n\",\n \"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model.compile(optimizer='adam',\\n\",\n \" loss='sparse_categorical_crossentropy',\\n\",\n \" metrics=['accuracy'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5.训练与验证\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1/5\\n\",\n \"60000/60000 [==============================] - 3s 58us/sample - loss: 0.4970 - accuracy: 0.8264\\n\",\n \"Epoch 2/5\\n\",\n \"60000/60000 [==============================] - 3s 43us/sample - loss: 0.3766 - accuracy: 0.8651\\n\",\n \"Epoch 3/5\\n\",\n \"60000/60000 [==============================] - 3s 42us/sample - loss: 0.3370 - accuracy: 0.8777\\n\",\n \"Epoch 4/5\\n\",\n \"60000/60000 [==============================] - 3s 42us/sample - loss: 0.3122 - accuracy: 0.8859\\n\",\n \"Epoch 5/5\\n\",\n \"60000/60000 [==============================] - 3s 42us/sample - loss: 0.2949 - accuracy: 0.8921\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.fit(train_images, train_labels, epochs=5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10000/10000 [==============================] - 0s 26us/sample - loss: 0.3623 - accuracy: 0.8737\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[0.3623474566936493, 0.8737]\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.evaluate(test_images, test_labels)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6.预测\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2.1831402e-05 1.0357383e-06 1.0550731e-06 1.3231372e-06 8.0873624e-06\\n\",\n \" 2.6805745e-02 1.2466960e-05 1.6174167e-01 1.4259206e-04 8.1126428e-01]\\n\",\n \"9\\n\",\n \"9\\n\"\n ]\n }\n ],\n \"source\": [\n \"predictions = model.predict(test_images)\\n\",\n \"print(predictions[0])\\n\",\n \"print(np.argmax(predictions[0]))\\n\",\n \"print(test_labels[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def plot_image(i, predictions_array, true_label, img):\\n\",\n \" predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\\n\",\n \" plt.grid(False)\\n\",\n \" plt.xticks([])\\n\",\n \" plt.yticks([])\\n\",\n \" \\n\",\n \" plt.imshow(img, cmap=plt.cm.binary)\\n\",\n \"\\n\",\n \" predicted_label = np.argmax(predictions_array)\\n\",\n \" if predicted_label == true_label:\\n\",\n \" color = 'blue'\\n\",\n \" else:\\n\",\n \" color = 'red'\\n\",\n \" \\n\",\n \" plt.xlabel(\\\"{} {:2.0f}% ({})\\\".format(class_names[predicted_label],\\n\",\n \" 100*np.max(predictions_array),\\n\",\n \" class_names[true_label]),\\n\",\n \" color=color)\\n\",\n \"\\n\",\n \"def plot_value_array(i, predictions_array, true_label):\\n\",\n \" predictions_array, true_label = predictions_array[i], true_label[i]\\n\",\n \" plt.grid(False)\\n\",\n \" plt.xticks([])\\n\",\n \" plt.yticks([])\\n\",\n \" thisplot = plt.bar(range(10), predictions_array, color=\\\"#777777\\\")\\n\",\n \" plt.ylim([0, 1]) \\n\",\n \" predicted_label = np.argmax(predictions_array)\\n\",\n \" \\n\",\n \" thisplot[predicted_label].set_color('red')\\n\",\n \" thisplot[true_label].set_color('blue')\\n\",\n \"i = 0\\n\",\n \"plt.figure(figsize=(6,3))\\n\",\n \"plt.subplot(1,2,1)\\n\",\n \"plot_image(i, predictions, test_labels, test_images)\\n\",\n \"plt.subplot(1,2,2)\\n\",\n \"plot_value_array(i, predictions, test_labels)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Plot the first X test images, their predicted label, and the true label\\n\",\n \"# Color correct predictions in blue, incorrect predictions in red\\n\",\n \"num_rows = 5\\n\",\n \"num_cols = 3\\n\",\n \"num_images = num_rows*num_cols\\n\",\n \"plt.figure(figsize=(2*2*num_cols, 2*num_rows))\\n\",\n \"for i in range(num_images):\\n\",\n \" plt.subplot(num_rows, 2*num_cols, 2*i+1)\\n\",\n \" plot_image(i, predictions, test_labels, test_images)\\n\",\n \" plt.subplot(num_rows, 2*num_cols, 2*i+2)\\n\",\n \" plot_value_array(i, predictions, test_labels)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1, 28, 28)\\n\",\n \"[[2.1831380e-05 1.0357381e-06 1.0550700e-06 1.3231397e-06 8.0873460e-06\\n\",\n \" 2.6805779e-02 1.2466959e-05 1.6174166e-01 1.4259205e-04 8.1126422e-01]]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"img = test_images[0]\\n\",\n \"\\n\",\n \"img = (np.expand_dims(img,0))\\n\",\n \"\\n\",\n \"print(img.shape)\\n\",\n \"predictions_single = model.predict(img)\\n\",\n \"\\n\",\n \"print(predictions_single)\\n\",\n \"plot_value_array(0, predictions_single, test_labels)\\n\",\n \"_ = plt.xticks(range(10), class_names, rotation=45)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "102-example_text_classification.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# tensorflow2.0教程-文本分类\\n\",\n \"\\n\",\n \"我们将构建一个简单的文本分类器，并使用IMDB进行训练和测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"import tensorflow as tf\\n\",\n \"from tensorflow import keras\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.IMDB数据集\\n\",\n \"\\n\",\n \"下载\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"imdb=keras.datasets.imdb\\n\",\n \"(train_x, train_y), (test_x, text_y)=keras.datasets.imdb.load_data(num_words=10000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"了解IMDB数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training entries: 25000, labels: 25000\\n\",\n \"[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]\\n\",\n \"len: 218 189\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Training entries: {}, labels: {}\\\".format(len(train_x), len(train_y)))\\n\",\n \"\\n\",\n \"print(train_x[0])\\n\",\n \"print('len: ',len(train_x[0]), len(train_x[1]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"创建id和词的匹配字典\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert is an amazing actor and now the same being director father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also to the two little boy's that played the of norman and paul they were just brilliant children are often left out of the list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\\n\"\n ]\n }\n ],\n \"source\": [\n \"word_index = imdb.get_word_index()\\n\",\n \"\\n\",\n \"word2id = {k:(v+3) for k, v in word_index.items()}\\n\",\n \"word2id[''] = 0\\n\",\n \"word2id[''] = 1\\n\",\n \"word2id[''] = 2\\n\",\n \"word2id[''] = 3\\n\",\n \"\\n\",\n \"id2word = {v:k for k, v in word2id.items()}\\n\",\n \"def get_words(sent_ids):\\n\",\n \" return ' '.join([id2word.get(i, '?') for i in sent_ids])\\n\",\n \"\\n\",\n \"sent = get_words(train_x[0])\\n\",\n \"print(sent)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.准备数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 1 14 22 16 43 530 973 1622 1385 65 458 4468 66 3941\\n\",\n \" 4 173 36 256 5 25 100 43 838 112 50 670 2 9\\n\",\n \" 35 480 284 5 150 4 172 112 167 2 336 385 39 4\\n\",\n \" 172 4536 1111 17 546 38 13 447 4 192 50 16 6 147\\n\",\n \" 2025 19 14 22 4 1920 4613 469 4 22 71 87 12 16\\n\",\n \" 43 530 38 76 15 13 1247 4 22 17 515 17 12 16\\n\",\n \" 626 18 2 5 62 386 12 8 316 8 106 5 4 2223\\n\",\n \" 5244 16 480 66 3785 33 4 130 12 16 38 619 5 25\\n\",\n \" 124 51 36 135 48 25 1415 33 6 22 12 215 28 77\\n\",\n \" 52 5 14 407 16 82 2 8 4 107 117 5952 15 256\\n\",\n \" 4 2 7 3766 5 723 36 71 43 530 476 26 400 317\\n\",\n \" 46 7 4 2 1029 13 104 88 4 381 15 297 98 32\\n\",\n \" 2071 56 26 141 6 194 7486 18 4 226 22 21 134 476\\n\",\n \" 26 480 5 144 30 5535 18 51 36 28 224 92 25 104\\n\",\n \" 4 226 65 16 38 1334 88 12 16 283 5 16 4472 113\\n\",\n \" 103 32 15 16 5345 19 178 32 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0]\\n\",\n \"len: 256 256\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 句子末尾padding\\n\",\n \"train_x = keras.preprocessing.sequence.pad_sequences(\\n\",\n \" train_x, value=word2id[''],\\n\",\n \" padding='post', maxlen=256\\n\",\n \")\\n\",\n \"test_x = keras.preprocessing.sequence.pad_sequences(\\n\",\n \" test_x, value=word2id[''],\\n\",\n \" padding='post', maxlen=256\\n\",\n \")\\n\",\n \"print(train_x[0])\\n\",\n \"print('len: ',len(train_x[0]), len(train_x[1]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.构建模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"embedding (Embedding) (None, None, 16) 160000 \\n\",\n \"_________________________________________________________________\\n\",\n \"global_average_pooling1d (Gl (None, 16) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense (Dense) (None, 16) 272 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_1 (Dense) (None, 1) 17 \\n\",\n \"=================================================================\\n\",\n \"Total params: 160,289\\n\",\n \"Trainable params: 160,289\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"import tensorflow.keras.layers as layers\\n\",\n \"vocab_size = 10000\\n\",\n \"model = keras.Sequential()\\n\",\n \"model.add(layers.Embedding(vocab_size, 16))\\n\",\n \"model.add(layers.GlobalAveragePooling1D())\\n\",\n \"model.add(layers.Dense(16, activation='relu'))\\n\",\n \"model.add(layers.Dense(1, activation='sigmoid'))\\n\",\n \"model.summary()\\n\",\n \"model.compile(optimizer='adam',\\n\",\n \" loss='binary_crossentropy',\\n\",\n \" metrics=['accuracy'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.模型训练与验证\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 15000 samples, validate on 10000 samples\\n\",\n \"Epoch 1/40\\n\",\n \"15000/15000 [==============================] - 1s 73us/sample - loss: 0.6919 - accuracy: 0.5071 - val_loss: 0.6901 - val_accuracy: 0.5101\\n\",\n \"Epoch 2/40\\n\",\n \"15000/15000 [==============================] - 1s 44us/sample - loss: 0.6864 - accuracy: 0.6242 - val_loss: 0.6829 - val_accuracy: 0.6380\\n\",\n \"Epoch 3/40\\n\",\n \"15000/15000 [==============================] - 1s 42us/sample - loss: 0.6752 - accuracy: 0.6881 - val_loss: 0.6691 - val_accuracy: 0.7091\\n\",\n \"Epoch 4/40\\n\",\n \"15000/15000 [==============================] - 1s 45us/sample - loss: 0.6559 - accuracy: 0.7162 - val_loss: 0.6471 - val_accuracy: 0.7509\\n\",\n \"Epoch 5/40\\n\",\n \"15000/15000 [==============================] - 1s 44us/sample - loss: 0.6274 - accuracy: 0.7697 - val_loss: 0.6175 - val_accuracy: 0.7724\\n\",\n \"Epoch 6/40\\n\",\n \"15000/15000 [==============================] - 1s 43us/sample - loss: 0.5909 - accuracy: 0.8049 - val_loss: 0.5821 - val_accuracy: 0.7869\\n\",\n \"Epoch 7/40\\n\",\n \"15000/15000 [==============================] - 1s 45us/sample - loss: 0.5490 - accuracy: 0.8208 - val_loss: 0.5418 - val_accuracy: 0.8158\\n\",\n \"Epoch 8/40\\n\",\n \"15000/15000 [==============================] - 1s 42us/sample - loss: 0.5054 - accuracy: 0.8437 - val_loss: 0.5030 - val_accuracy: 0.8285\\n\",\n \"Epoch 9/40\\n\",\n \"15000/15000 [==============================] - 1s 45us/sample - loss: 0.4630 - accuracy: 0.8557 - val_loss: 0.4662 - val_accuracy: 0.8400\\n\",\n \"Epoch 10/40\\n\",\n \"15000/15000 [==============================] - 1s 49us/sample - loss: 0.4239 - accuracy: 0.8707 - val_loss: 0.4345 - val_accuracy: 0.8470\\n\",\n \"Epoch 11/40\\n\",\n \"15000/15000 [==============================] - 1s 46us/sample - loss: 0.3896 - accuracy: 0.8772 - val_loss: 0.4070 - val_accuracy: 0.8563\\n\",\n \"Epoch 12/40\\n\",\n \"15000/15000 [==============================] - 1s 47us/sample - loss: 0.3599 - accuracy: 0.8867 - val_loss: 0.3856 - val_accuracy: 0.8594\\n\",\n \"Epoch 13/40\\n\",\n \"15000/15000 [==============================] - 1s 44us/sample - loss: 0.3352 - accuracy: 0.8925 - val_loss: 0.3660 - val_accuracy: 0.8646\\n\",\n \"Epoch 14/40\\n\",\n \"15000/15000 [==============================] - 1s 44us/sample - loss: 0.3131 - accuracy: 0.8978 - val_loss: 0.3517 - val_accuracy: 0.8697\\n\",\n \"Epoch 15/40\\n\",\n \"15000/15000 [==============================] - 1s 48us/sample - loss: 0.2947 - accuracy: 0.9013 - val_loss: 0.3392 - val_accuracy: 0.8716\\n\",\n \"Epoch 16/40\\n\",\n \"15000/15000 [==============================] - 1s 44us/sample - loss: 0.2782 - accuracy: 0.9077 - val_loss: 0.3293 - val_accuracy: 0.8747\\n\",\n \"Epoch 17/40\\n\",\n \"15000/15000 [==============================] - 1s 45us/sample - loss: 0.2632 - accuracy: 0.9126 - val_loss: 0.3208 - val_accuracy: 0.8757\\n\",\n \"Epoch 18/40\\n\",\n \"15000/15000 [==============================] - 1s 43us/sample - loss: 0.2500 - accuracy: 0.9159 - val_loss: 0.3132 - val_accuracy: 0.8800\\n\",\n \"Epoch 19/40\\n\",\n \"15000/15000 [==============================] - 1s 46us/sample - loss: 0.2381 - accuracy: 0.9197 - val_loss: 0.3073 - val_accuracy: 0.8792\\n\",\n \"Epoch 20/40\\n\",\n \"15000/15000 [==============================] - 1s 44us/sample - loss: 0.2274 - accuracy: 0.9229 - val_loss: 0.3029 - val_accuracy: 0.8801\\n\",\n \"Epoch 21/40\\n\",\n \"15000/15000 [==============================] - 1s 44us/sample - loss: 0.2167 - accuracy: 0.9277 - val_loss: 0.2992 - val_accuracy: 0.8811\\n\",\n \"Epoch 22/40\\n\",\n \"15000/15000 [==============================] - 1s 43us/sample - loss: 0.2077 - accuracy: 0.9299 - val_loss: 0.2951 - val_accuracy: 0.8835\\n\",\n \"Epoch 23/40\\n\",\n \"15000/15000 [==============================] - 1s 42us/sample - loss: 0.1986 - accuracy: 0.9335 - val_loss: 0.2931 - val_accuracy: 0.8827\\n\",\n \"Epoch 24/40\\n\",\n \"15000/15000 [==============================] - 1s 42us/sample - loss: 0.1907 - accuracy: 0.9371 - val_loss: 0.2911 - val_accuracy: 0.8835\\n\",\n \"Epoch 25/40\\n\",\n \"15000/15000 [==============================] - 1s 42us/sample - loss: 0.1828 - accuracy: 0.9415 - val_loss: 0.2885 - val_accuracy: 0.8841\\n\",\n \"Epoch 26/40\\n\",\n \"15000/15000 [==============================] - 1s 43us/sample - loss: 0.1756 - accuracy: 0.9436 - val_loss: 0.2884 - val_accuracy: 0.8840\\n\",\n \"Epoch 27/40\\n\",\n \"15000/15000 [==============================] - 1s 42us/sample - loss: 0.1689 - accuracy: 0.9463 - val_loss: 0.2870 - val_accuracy: 0.8836\\n\",\n \"Epoch 28/40\\n\",\n \"15000/15000 [==============================] - 1s 41us/sample - loss: 0.1624 - accuracy: 0.9497 - val_loss: 0.2870 - val_accuracy: 0.8853\\n\",\n \"Epoch 29/40\\n\",\n \"15000/15000 [==============================] - 1s 46us/sample - loss: 0.1568 - accuracy: 0.9523 - val_loss: 0.2872 - val_accuracy: 0.8840\\n\",\n \"Epoch 30/40\\n\",\n \"15000/15000 [==============================] - 1s 43us/sample - loss: 0.1509 - accuracy: 0.9534 - val_loss: 0.2864 - val_accuracy: 0.8858\\n\",\n \"Epoch 31/40\\n\",\n \"15000/15000 [==============================] - 1s 43us/sample - loss: 0.1449 - accuracy: 0.9567 - val_loss: 0.2866 - val_accuracy: 0.8858\\n\",\n \"Epoch 32/40\\n\",\n \"15000/15000 [==============================] - 1s 45us/sample - loss: 0.1395 - accuracy: 0.9595 - val_loss: 0.2874 - val_accuracy: 0.8856\\n\",\n \"Epoch 33/40\\n\",\n \"15000/15000 [==============================] - 1s 43us/sample - loss: 0.1343 - accuracy: 0.9600 - val_loss: 0.2888 - val_accuracy: 0.8863\\n\",\n \"Epoch 34/40\\n\",\n \"15000/15000 [==============================] - 1s 44us/sample - loss: 0.1297 - accuracy: 0.9623 - val_loss: 0.2903 - val_accuracy: 0.8843\\n\",\n \"Epoch 35/40\\n\",\n \"15000/15000 [==============================] - 1s 43us/sample - loss: 0.1255 - accuracy: 0.9630 - val_loss: 0.2915 - val_accuracy: 0.8870\\n\",\n \"Epoch 36/40\\n\",\n \"15000/15000 [==============================] - 1s 42us/sample - loss: 0.1208 - accuracy: 0.9659 - val_loss: 0.2928 - val_accuracy: 0.8862\\n\",\n \"Epoch 37/40\\n\",\n \"15000/15000 [==============================] - 1s 48us/sample - loss: 0.1162 - accuracy: 0.9679 - val_loss: 0.2949 - val_accuracy: 0.8851\\n\",\n \"Epoch 38/40\\n\",\n \"15000/15000 [==============================] - 1s 49us/sample - loss: 0.1121 - accuracy: 0.9691 - val_loss: 0.2975 - val_accuracy: 0.8848\\n\",\n \"Epoch 39/40\\n\",\n \"15000/15000 [==============================] - 1s 49us/sample - loss: 0.1088 - accuracy: 0.9697 - val_loss: 0.3003 - val_accuracy: 0.8840\\n\",\n \"Epoch 40/40\\n\",\n \"15000/15000 [==============================] - 1s 45us/sample - loss: 0.1046 - accuracy: 0.9721 - val_loss: 0.3022 - val_accuracy: 0.8843\\n\",\n \"25000/25000 [==============================] - 1s 22us/sample - loss: 0.3216 - accuracy: 0.8729\\n\",\n \"[0.32155542838573453, 0.87292]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x_val = train_x[:10000]\\n\",\n \"x_train = train_x[10000:]\\n\",\n \"\\n\",\n \"y_val = train_y[:10000]\\n\",\n \"y_train = train_y[10000:]\\n\",\n \"\\n\",\n \"history = model.fit(x_train,y_train,\\n\",\n \" epochs=40, batch_size=512,\\n\",\n \" validation_data=(x_val, y_val),\\n\",\n \" verbose=1)\\n\",\n \"\\n\",\n \"result = model.evaluate(test_x, text_y)\\n\",\n \"print(result)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"## 5.查看准确率时序图\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"history_dict = history.history\\n\",\n \"history_dict.keys()\\n\",\n \"acc = history_dict['accuracy']\\n\",\n \"val_acc = history_dict['val_accuracy']\\n\",\n \"loss = history_dict['loss']\\n\",\n \"val_loss = history_dict['val_loss']\\n\",\n \"epochs = range(1, len(acc)+1)\\n\",\n \"\\n\",\n \"plt.plot(epochs, loss, 'bo', label='train loss')\\n\",\n \"plt.plot(epochs, val_loss, 'b', label='val loss')\\n\",\n \"plt.title('Train and val loss')\\n\",\n \"plt.xlabel('Epochs')\\n\",\n \"plt.xlabel('loss')\\n\",\n \"plt.legend()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.clf() # clear figure\\n\",\n \"\\n\",\n \"plt.plot(epochs, acc, 'bo', label='Training acc')\\n\",\n \"plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n\",\n \"plt.title('Training and validation accuracy')\\n\",\n \"plt.xlabel('Epochs')\\n\",\n \"plt.ylabel('Accuracy')\\n\",\n \"plt.legend()\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "103-example_overfitting_and_underfitting.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2.0教程-过拟合和欠拟合\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"from tensorflow import keras\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"NUM_WORDS = 10000\\n\",\n \"(train_data, train_labels), (test_data, test_labels) = keras.datasets.imdb.load_data(num_words=NUM_WORDS)\\n\",\n \"\\n\",\n \"def multi_hot_sequences(sequences, dimension):\\n\",\n \" results = np.zeros((len(sequences), dimension))\\n\",\n \" for i, word_indices in enumerate(sequences):\\n\",\n \" results[i, word_indices] = 1.0\\n\",\n \" return results\\n\",\n \"\\n\",\n \"train_data = multi_hot_sequences(train_data, dimension=NUM_WORDS)\\n\",\n \"test_data = multi_hot_sequences(test_data, dimension=NUM_WORDS)\\n\",\n \"plt.plot(train_data[0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"防止过度拟合的最简单方法是减小模型的大小，即模型中可学习参数的数量。\\n\",\n \"\\n\",\n \"深度学习模型往往善于适应训练数据，但真正的挑战是概括，而不是适合。\\n\",\n \"\\n\",\n \"另一方面，如果网络具有有限的记忆资源，则将不能容易地学习映射。为了最大限度地减少损失，它必须学习具有更强预测能力的压缩表示。同时，如果您使模型太小，则难以适应训练数据。 “太多容量”和“容量不足”之间存在平衡。\\n\",\n \"\\n\",\n \"要找到合适的模型大小，最好从相对较少的图层和参数开始，然后开始增加图层的大小或添加新图层，直到看到验证损失的收益递减为止。\\n\",\n \"\\n\",\n \"我们将在电影评论分类网络上使用Dense图层作为基线创建一个简单模型，然后创建更小和更大的版本，并进行比较。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.创建一个baseline模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_5\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_15 (Dense) (None, 16) 160016 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_16 (Dense) (None, 16) 272 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_17 (Dense) (None, 1) 17 \\n\",\n \"=================================================================\\n\",\n \"Total params: 160,305\\n\",\n \"Trainable params: 160,305\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"import tensorflow.keras.layers as layers\\n\",\n \"baseline_model = keras.Sequential(\\n\",\n \"[\\n\",\n \" layers.Dense(16, activation='relu', input_shape=(NUM_WORDS,)),\\n\",\n \" layers.Dense(16, activation='relu'),\\n\",\n \" layers.Dense(1, activation='sigmoid')\\n\",\n \"]\\n\",\n \")\\n\",\n \"baseline_model.compile(optimizer='adam',\\n\",\n \" loss='binary_crossentropy',\\n\",\n \" metrics=['accuracy', 'binary_crossentropy'])\\n\",\n \"baseline_model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 25000 samples, validate on 25000 samples\\n\",\n \"Epoch 1/20\\n\",\n \"25000/25000 - 3s - loss: 0.4808 - accuracy: 0.8096 - binary_crossentropy: 0.4808 - val_loss: 0.3333 - val_accuracy: 0.8762 - val_binary_crossentropy: 0.3333\\n\",\n \"Epoch 2/20\\n\",\n \"25000/25000 - 2s - loss: 0.2450 - accuracy: 0.9126 - binary_crossentropy: 0.2450 - val_loss: 0.2831 - val_accuracy: 0.8882 - val_binary_crossentropy: 0.2831\\n\",\n \"Epoch 3/20\\n\",\n \"25000/25000 - 2s - loss: 0.1806 - accuracy: 0.9374 - binary_crossentropy: 0.1806 - val_loss: 0.2921 - val_accuracy: 0.8832 - val_binary_crossentropy: 0.2921\\n\",\n \"Epoch 4/20\\n\",\n \"25000/25000 - 2s - loss: 0.1450 - accuracy: 0.9511 - binary_crossentropy: 0.1450 - val_loss: 0.3135 - val_accuracy: 0.8788 - val_binary_crossentropy: 0.3135\\n\",\n \"Epoch 5/20\\n\",\n \"25000/25000 - 2s - loss: 0.1187 - accuracy: 0.9614 - binary_crossentropy: 0.1187 - val_loss: 0.3406 - val_accuracy: 0.8752 - val_binary_crossentropy: 0.3406\\n\",\n \"Epoch 6/20\\n\",\n \"25000/25000 - 2s - loss: 0.1000 - accuracy: 0.9676 - binary_crossentropy: 0.1000 - val_loss: 0.3765 - val_accuracy: 0.8692 - val_binary_crossentropy: 0.3765\\n\",\n \"Epoch 7/20\\n\",\n \"25000/25000 - 2s - loss: 0.0827 - accuracy: 0.9750 - binary_crossentropy: 0.0827 - val_loss: 0.4124 - val_accuracy: 0.8659 - val_binary_crossentropy: 0.4124\\n\",\n \"Epoch 8/20\\n\",\n \"25000/25000 - 2s - loss: 0.0685 - accuracy: 0.9815 - binary_crossentropy: 0.0685 - val_loss: 0.4567 - val_accuracy: 0.8610 - val_binary_crossentropy: 0.4567\\n\",\n \"Epoch 9/20\\n\",\n \"25000/25000 - 2s - loss: 0.0581 - accuracy: 0.9857 - binary_crossentropy: 0.0581 - val_loss: 0.4987 - val_accuracy: 0.8597 - val_binary_crossentropy: 0.4987\\n\",\n \"Epoch 10/20\\n\",\n \"25000/25000 - 2s - loss: 0.0481 - accuracy: 0.9889 - binary_crossentropy: 0.0481 - val_loss: 0.5402 - val_accuracy: 0.8569 - val_binary_crossentropy: 0.5402\\n\",\n \"Epoch 11/20\\n\",\n \"25000/25000 - 2s - loss: 0.0392 - accuracy: 0.9923 - binary_crossentropy: 0.0392 - val_loss: 0.5883 - val_accuracy: 0.8540 - val_binary_crossentropy: 0.5883\\n\",\n \"Epoch 12/20\\n\",\n \"25000/25000 - 2s - loss: 0.0310 - accuracy: 0.9946 - binary_crossentropy: 0.0310 - val_loss: 0.6316 - val_accuracy: 0.8534 - val_binary_crossentropy: 0.6316\\n\",\n \"Epoch 13/20\\n\",\n \"25000/25000 - 2s - loss: 0.0242 - accuracy: 0.9964 - binary_crossentropy: 0.0242 - val_loss: 0.6779 - val_accuracy: 0.8515 - val_binary_crossentropy: 0.6779\\n\",\n \"Epoch 14/20\\n\",\n \"25000/25000 - 2s - loss: 0.0185 - accuracy: 0.9978 - binary_crossentropy: 0.0185 - val_loss: 0.7149 - val_accuracy: 0.8510 - val_binary_crossentropy: 0.7149\\n\",\n \"Epoch 15/20\\n\",\n \"25000/25000 - 2s - loss: 0.0143 - accuracy: 0.9989 - binary_crossentropy: 0.0143 - val_loss: 0.7571 - val_accuracy: 0.8496 - val_binary_crossentropy: 0.7571\\n\",\n \"Epoch 16/20\\n\",\n \"25000/25000 - 2s - loss: 0.0111 - accuracy: 0.9994 - binary_crossentropy: 0.0111 - val_loss: 0.7954 - val_accuracy: 0.8492 - val_binary_crossentropy: 0.7954\\n\",\n \"Epoch 17/20\\n\",\n \"25000/25000 - 2s - loss: 0.0087 - accuracy: 0.9997 - binary_crossentropy: 0.0087 - val_loss: 0.8301 - val_accuracy: 0.8497 - val_binary_crossentropy: 0.8301\\n\",\n \"Epoch 18/20\\n\",\n \"25000/25000 - 3s - loss: 0.0068 - accuracy: 0.9998 - binary_crossentropy: 0.0068 - val_loss: 0.8629 - val_accuracy: 0.8490 - val_binary_crossentropy: 0.8629\\n\",\n \"Epoch 19/20\\n\",\n \"25000/25000 - 3s - loss: 0.0055 - accuracy: 0.9999 - binary_crossentropy: 0.0055 - val_loss: 0.8937 - val_accuracy: 0.8492 - val_binary_crossentropy: 0.8937\\n\",\n \"Epoch 20/20\\n\",\n \"25000/25000 - 3s - loss: 0.0044 - accuracy: 0.9999 - binary_crossentropy: 0.0044 - val_loss: 0.9217 - val_accuracy: 0.8488 - val_binary_crossentropy: 0.9217\\n\"\n ]\n }\n ],\n \"source\": [\n \"baseline_history = baseline_model.fit(train_data, train_labels,\\n\",\n \" epochs=20, batch_size=512,\\n\",\n \" validation_data=(test_data, test_labels),\\n\",\n \" verbose=2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.创建一个小模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_6\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_18 (Dense) (None, 4) 40004 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_19 (Dense) (None, 4) 20 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_20 (Dense) (None, 1) 5 \\n\",\n \"=================================================================\\n\",\n \"Total params: 40,029\\n\",\n \"Trainable params: 40,029\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"small_model = keras.Sequential(\\n\",\n \"[\\n\",\n \" layers.Dense(4, activation='relu', input_shape=(NUM_WORDS,)),\\n\",\n \" layers.Dense(4, activation='relu'),\\n\",\n \" layers.Dense(1, activation='sigmoid')\\n\",\n \"]\\n\",\n \")\\n\",\n \"small_model.compile(optimizer='adam',\\n\",\n \" loss='binary_crossentropy',\\n\",\n \" metrics=['accuracy', 'binary_crossentropy'])\\n\",\n \"small_model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 25000 samples, validate on 25000 samples\\n\",\n \"Epoch 1/20\\n\",\n \"25000/25000 - 3s - loss: 0.6170 - accuracy: 0.6609 - binary_crossentropy: 0.6170 - val_loss: 0.5217 - val_accuracy: 0.8034 - val_binary_crossentropy: 0.5217\\n\",\n \"Epoch 2/20\\n\",\n \"25000/25000 - 2s - loss: 0.4356 - accuracy: 0.8661 - binary_crossentropy: 0.4356 - val_loss: 0.3979 - val_accuracy: 0.8781 - val_binary_crossentropy: 0.3979\\n\",\n \"Epoch 3/20\\n\",\n \"25000/25000 - 2s - loss: 0.3002 - accuracy: 0.9146 - binary_crossentropy: 0.3002 - val_loss: 0.3160 - val_accuracy: 0.8866 - val_binary_crossentropy: 0.3160\\n\",\n \"Epoch 4/20\\n\",\n \"25000/25000 - 2s - loss: 0.2255 - accuracy: 0.9322 - binary_crossentropy: 0.2255 - val_loss: 0.2930 - val_accuracy: 0.8880 - val_binary_crossentropy: 0.2930\\n\",\n \"Epoch 5/20\\n\",\n \"25000/25000 - 2s - loss: 0.1884 - accuracy: 0.9416 - binary_crossentropy: 0.1884 - val_loss: 0.2901 - val_accuracy: 0.8858 - val_binary_crossentropy: 0.2901\\n\",\n \"Epoch 6/20\\n\",\n \"25000/25000 - 2s - loss: 0.1632 - accuracy: 0.9507 - binary_crossentropy: 0.1632 - val_loss: 0.2918 - val_accuracy: 0.8848 - val_binary_crossentropy: 0.2918\\n\",\n \"Epoch 7/20\\n\",\n \"25000/25000 - 2s - loss: 0.1449 - accuracy: 0.9560 - binary_crossentropy: 0.1449 - val_loss: 0.2991 - val_accuracy: 0.8817 - val_binary_crossentropy: 0.2991\\n\",\n \"Epoch 8/20\\n\",\n \"25000/25000 - 2s - loss: 0.1295 - accuracy: 0.9618 - binary_crossentropy: 0.1295 - val_loss: 0.3087 - val_accuracy: 0.8796 - val_binary_crossentropy: 0.3087\\n\",\n \"Epoch 9/20\\n\",\n \"25000/25000 - 2s - loss: 0.1167 - accuracy: 0.9669 - binary_crossentropy: 0.1167 - val_loss: 0.3210 - val_accuracy: 0.8772 - val_binary_crossentropy: 0.3210\\n\",\n \"Epoch 10/20\\n\",\n \"25000/25000 - 2s - loss: 0.1059 - accuracy: 0.9709 - binary_crossentropy: 0.1059 - val_loss: 0.3325 - val_accuracy: 0.8748 - val_binary_crossentropy: 0.3325\\n\",\n \"Epoch 11/20\\n\",\n \"25000/25000 - 2s - loss: 0.0961 - accuracy: 0.9742 - binary_crossentropy: 0.0961 - val_loss: 0.3468 - val_accuracy: 0.8727 - val_binary_crossentropy: 0.3468\\n\",\n \"Epoch 12/20\\n\",\n \"25000/25000 - 2s - loss: 0.0877 - accuracy: 0.9780 - binary_crossentropy: 0.0877 - val_loss: 0.3602 - val_accuracy: 0.8715 - val_binary_crossentropy: 0.3602\\n\",\n \"Epoch 13/20\\n\",\n \"25000/25000 - 2s - loss: 0.0800 - accuracy: 0.9807 - binary_crossentropy: 0.0800 - val_loss: 0.3759 - val_accuracy: 0.8693 - val_binary_crossentropy: 0.3759\\n\",\n \"Epoch 14/20\\n\",\n \"25000/25000 - 2s - loss: 0.0727 - accuracy: 0.9837 - binary_crossentropy: 0.0727 - val_loss: 0.3923 - val_accuracy: 0.8690 - val_binary_crossentropy: 0.3923\\n\",\n \"Epoch 15/20\\n\",\n \"25000/25000 - 2s - loss: 0.0667 - accuracy: 0.9858 - binary_crossentropy: 0.0667 - val_loss: 0.4089 - val_accuracy: 0.8672 - val_binary_crossentropy: 0.4089\\n\",\n \"Epoch 16/20\\n\",\n \"25000/25000 - 2s - loss: 0.0610 - accuracy: 0.9876 - binary_crossentropy: 0.0610 - val_loss: 0.4255 - val_accuracy: 0.8646 - val_binary_crossentropy: 0.4255\\n\",\n \"Epoch 17/20\\n\",\n \"25000/25000 - 2s - loss: 0.0555 - accuracy: 0.9899 - binary_crossentropy: 0.0555 - val_loss: 0.4422 - val_accuracy: 0.8651 - val_binary_crossentropy: 0.4422\\n\",\n \"Epoch 18/20\\n\",\n \"25000/25000 - 2s - loss: 0.0509 - accuracy: 0.9912 - binary_crossentropy: 0.0509 - val_loss: 0.4614 - val_accuracy: 0.8626 - val_binary_crossentropy: 0.4614\\n\",\n \"Epoch 19/20\\n\",\n \"25000/25000 - 2s - loss: 0.0466 - accuracy: 0.9925 - binary_crossentropy: 0.0466 - val_loss: 0.4780 - val_accuracy: 0.8622 - val_binary_crossentropy: 0.4780\\n\",\n \"Epoch 20/20\\n\",\n \"25000/25000 - 2s - loss: 0.0426 - accuracy: 0.9936 - binary_crossentropy: 0.0426 - val_loss: 0.4976 - val_accuracy: 0.8608 - val_binary_crossentropy: 0.4976\\n\"\n ]\n }\n ],\n \"source\": [\n \"small_history = small_model.fit(train_data, train_labels,\\n\",\n \" epochs=20, batch_size=512,\\n\",\n \" validation_data=(test_data, test_labels),\\n\",\n \" verbose=2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.创建一个大模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_7\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_21 (Dense) (None, 512) 5120512 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_22 (Dense) (None, 512) 262656 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_23 (Dense) (None, 1) 513 \\n\",\n \"=================================================================\\n\",\n \"Total params: 5,383,681\\n\",\n \"Trainable params: 5,383,681\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"big_model = keras.Sequential(\\n\",\n \"[\\n\",\n \" layers.Dense(512, activation='relu', input_shape=(NUM_WORDS,)),\\n\",\n \" layers.Dense(512, activation='relu'),\\n\",\n \" layers.Dense(1, activation='sigmoid')\\n\",\n \"]\\n\",\n \")\\n\",\n \"big_model.compile(optimizer='adam',\\n\",\n \" loss='binary_crossentropy',\\n\",\n \" metrics=['accuracy', 'binary_crossentropy'])\\n\",\n \"big_model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 25000 samples, validate on 25000 samples\\n\",\n \"Epoch 1/20\\n\",\n \"25000/25000 - 7s - loss: 0.3523 - accuracy: 0.8466 - binary_crossentropy: 0.3523 - val_loss: 0.2936 - val_accuracy: 0.8808 - val_binary_crossentropy: 0.2936\\n\",\n \"Epoch 2/20\\n\",\n \"25000/25000 - 7s - loss: 0.1419 - accuracy: 0.9489 - binary_crossentropy: 0.1419 - val_loss: 0.3197 - val_accuracy: 0.8742 - val_binary_crossentropy: 0.3197\\n\",\n \"Epoch 3/20\\n\",\n \"25000/25000 - 7s - loss: 0.0421 - accuracy: 0.9892 - binary_crossentropy: 0.0421 - val_loss: 0.4467 - val_accuracy: 0.8694 - val_binary_crossentropy: 0.4467\\n\",\n \"Epoch 4/20\\n\",\n \"25000/25000 - 7s - loss: 0.0054 - accuracy: 0.9994 - binary_crossentropy: 0.0054 - val_loss: 0.5972 - val_accuracy: 0.8704 - val_binary_crossentropy: 0.5972\\n\",\n \"Epoch 5/20\\n\",\n \"25000/25000 - 7s - loss: 0.0010 - accuracy: 1.0000 - binary_crossentropy: 0.0010 - val_loss: 0.6727 - val_accuracy: 0.8707 - val_binary_crossentropy: 0.6727\\n\",\n \"Epoch 6/20\\n\",\n \"25000/25000 - 6s - loss: 2.6156e-04 - accuracy: 1.0000 - binary_crossentropy: 2.6156e-04 - val_loss: 0.7189 - val_accuracy: 0.8710 - val_binary_crossentropy: 0.7189\\n\",\n \"Epoch 7/20\\n\",\n \"25000/25000 - 7s - loss: 5.5455e-04 - accuracy: 1.0000 - binary_crossentropy: 5.5455e-04 - val_loss: 0.7534 - val_accuracy: 0.8698 - val_binary_crossentropy: 0.7534\\n\",\n \"Epoch 8/20\\n\",\n \"25000/25000 - 6s - loss: 1.0939e-04 - accuracy: 1.0000 - binary_crossentropy: 1.0939e-04 - val_loss: 0.7652 - val_accuracy: 0.8708 - val_binary_crossentropy: 0.7653\\n\",\n \"Epoch 9/20\\n\",\n \"25000/25000 - 6s - loss: 7.9712e-05 - accuracy: 1.0000 - binary_crossentropy: 7.9712e-05 - val_loss: 0.7890 - val_accuracy: 0.8711 - val_binary_crossentropy: 0.7890\\n\",\n \"Epoch 10/20\\n\",\n \"25000/25000 - 6s - loss: 6.1083e-05 - accuracy: 1.0000 - binary_crossentropy: 6.1083e-05 - val_loss: 0.8069 - val_accuracy: 0.8706 - val_binary_crossentropy: 0.8069\\n\",\n \"Epoch 11/20\\n\",\n \"25000/25000 - 6s - loss: 4.8950e-05 - accuracy: 1.0000 - binary_crossentropy: 4.8950e-05 - val_loss: 0.8233 - val_accuracy: 0.8704 - val_binary_crossentropy: 0.8233\\n\",\n \"Epoch 12/20\\n\",\n \"25000/25000 - 6s - loss: 4.0149e-05 - accuracy: 1.0000 - binary_crossentropy: 4.0149e-05 - val_loss: 0.8387 - val_accuracy: 0.8706 - val_binary_crossentropy: 0.8387\\n\",\n \"Epoch 13/20\\n\",\n \"25000/25000 - 6s - loss: 3.3564e-05 - accuracy: 1.0000 - binary_crossentropy: 3.3564e-05 - val_loss: 0.8517 - val_accuracy: 0.8704 - val_binary_crossentropy: 0.8517\\n\",\n \"Epoch 14/20\\n\",\n \"25000/25000 - 7s - loss: 2.8433e-05 - accuracy: 1.0000 - binary_crossentropy: 2.8433e-05 - val_loss: 0.8650 - val_accuracy: 0.8706 - val_binary_crossentropy: 0.8650\\n\",\n \"Epoch 15/20\\n\",\n \"25000/25000 - 7s - loss: 2.4336e-05 - accuracy: 1.0000 - binary_crossentropy: 2.4336e-05 - val_loss: 0.8768 - val_accuracy: 0.8704 - val_binary_crossentropy: 0.8768\\n\",\n \"Epoch 16/20\\n\",\n \"25000/25000 - 7s - loss: 2.1041e-05 - accuracy: 1.0000 - binary_crossentropy: 2.1041e-05 - val_loss: 0.8884 - val_accuracy: 0.8704 - val_binary_crossentropy: 0.8884\\n\",\n \"Epoch 17/20\\n\",\n \"25000/25000 - 7s - loss: 1.8337e-05 - accuracy: 1.0000 - binary_crossentropy: 1.8337e-05 - val_loss: 0.8994 - val_accuracy: 0.8703 - val_binary_crossentropy: 0.8994\\n\",\n \"Epoch 18/20\\n\",\n \"25000/25000 - 6s - loss: 1.6106e-05 - accuracy: 1.0000 - binary_crossentropy: 1.6106e-05 - val_loss: 0.9094 - val_accuracy: 0.8703 - val_binary_crossentropy: 0.9094\\n\",\n \"Epoch 19/20\\n\",\n \"25000/25000 - 6s - loss: 1.4224e-05 - accuracy: 1.0000 - binary_crossentropy: 1.4224e-05 - val_loss: 0.9193 - val_accuracy: 0.8703 - val_binary_crossentropy: 0.9193\\n\",\n \"Epoch 20/20\\n\",\n \"25000/25000 - 6s - loss: 1.2638e-05 - accuracy: 1.0000 - binary_crossentropy: 1.2638e-05 - val_loss: 0.9282 - val_accuracy: 0.8704 - val_binary_crossentropy: 0.9282\\n\"\n ]\n }\n ],\n \"source\": [\n \"big_history = big_model.fit(train_data, train_labels,\\n\",\n \" epochs=20, batch_size=512,\\n\",\n \" validation_data=(test_data, test_labels),\\n\",\n \" verbose=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def plot_history(histories, key='binary_crossentropy'):\\n\",\n \" plt.figure(figsize=(16,10))\\n\",\n \" \\n\",\n \" for name, history in histories:\\n\",\n \" val = plt.plot(history.epoch, history.history['val_'+key],\\n\",\n \" '--', label=name.title()+' Val')\\n\",\n \" plt.plot(history.epoch, history.history[key], color=val[0].get_color(),\\n\",\n \" label=name.title()+' Train')\\n\",\n \"\\n\",\n \" plt.xlabel('Epochs')\\n\",\n \" plt.ylabel(key.replace('_',' ').title())\\n\",\n \" plt.legend()\\n\",\n \"\\n\",\n \" plt.xlim([0,max(history.epoch)])\\n\",\n \"\\n\",\n \"\\n\",\n \"plot_history([('baseline', baseline_history),\\n\",\n \" ('small', small_history),\\n\",\n \" ('big', big_history)])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"请注意，较大的网络在仅仅一个时期之后几乎立即开始过度拟合，并且更过拟合更严重。 网络容量越大，能够越快地对训练数据进行建模（导致训练损失低），但过度拟合的可能性越大（导致训练和验证损失之间的差异很大）。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.添加l2正则\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_9\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_27 (Dense) (None, 16) 160016 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_28 (Dense) (None, 16) 272 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_29 (Dense) (None, 1) 17 \\n\",\n \"=================================================================\\n\",\n \"Total params: 160,305\\n\",\n \"Trainable params: 160,305\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\",\n \"Train on 25000 samples, validate on 25000 samples\\n\",\n \"Epoch 1/20\\n\",\n \"25000/25000 - 3s - loss: 0.5264 - accuracy: 0.8019 - binary_crossentropy: 0.4874 - val_loss: 0.3828 - val_accuracy: 0.8769 - val_binary_crossentropy: 0.3415\\n\",\n \"Epoch 2/20\\n\",\n \"25000/25000 - 3s - loss: 0.3070 - accuracy: 0.9073 - binary_crossentropy: 0.2607 - val_loss: 0.3356 - val_accuracy: 0.8876 - val_binary_crossentropy: 0.2855\\n\",\n \"Epoch 3/20\\n\",\n \"25000/25000 - 3s - loss: 0.2552 - accuracy: 0.9295 - binary_crossentropy: 0.2023 - val_loss: 0.3374 - val_accuracy: 0.8860 - val_binary_crossentropy: 0.2825\\n\",\n \"Epoch 4/20\\n\",\n \"25000/25000 - 3s - loss: 0.2346 - accuracy: 0.9377 - binary_crossentropy: 0.1777 - val_loss: 0.3500 - val_accuracy: 0.8826 - val_binary_crossentropy: 0.2916\\n\",\n \"Epoch 5/20\\n\",\n \"25000/25000 - 3s - loss: 0.2190 - accuracy: 0.9456 - binary_crossentropy: 0.1591 - val_loss: 0.3635 - val_accuracy: 0.8796 - val_binary_crossentropy: 0.3027\\n\",\n \"Epoch 6/20\\n\",\n \"25000/25000 - 3s - loss: 0.2041 - accuracy: 0.9526 - binary_crossentropy: 0.1426 - val_loss: 0.3764 - val_accuracy: 0.8769 - val_binary_crossentropy: 0.3144\\n\",\n \"Epoch 7/20\\n\",\n \"25000/25000 - 3s - loss: 0.2001 - accuracy: 0.9542 - binary_crossentropy: 0.1368 - val_loss: 0.3963 - val_accuracy: 0.8728 - val_binary_crossentropy: 0.3322\\n\",\n \"Epoch 8/20\\n\",\n \"25000/25000 - 3s - loss: 0.1891 - accuracy: 0.9577 - binary_crossentropy: 0.1240 - val_loss: 0.4038 - val_accuracy: 0.8714 - val_binary_crossentropy: 0.3383\\n\",\n \"Epoch 9/20\\n\",\n \"25000/25000 - 3s - loss: 0.1804 - accuracy: 0.9632 - binary_crossentropy: 0.1143 - val_loss: 0.4181 - val_accuracy: 0.8702 - val_binary_crossentropy: 0.3515\\n\",\n \"Epoch 10/20\\n\",\n \"25000/25000 - 3s - loss: 0.1729 - accuracy: 0.9662 - binary_crossentropy: 0.1057 - val_loss: 0.4310 - val_accuracy: 0.8684 - val_binary_crossentropy: 0.3635\\n\",\n \"Epoch 11/20\\n\",\n \"25000/25000 - 3s - loss: 0.1717 - accuracy: 0.9672 - binary_crossentropy: 0.1029 - val_loss: 0.4478 - val_accuracy: 0.8651 - val_binary_crossentropy: 0.3781\\n\",\n \"Epoch 12/20\\n\",\n \"25000/25000 - 3s - loss: 0.1612 - accuracy: 0.9714 - binary_crossentropy: 0.0910 - val_loss: 0.4653 - val_accuracy: 0.8650 - val_binary_crossentropy: 0.3949\\n\",\n \"Epoch 13/20\\n\",\n \"25000/25000 - 3s - loss: 0.1552 - accuracy: 0.9749 - binary_crossentropy: 0.0844 - val_loss: 0.4799 - val_accuracy: 0.8628 - val_binary_crossentropy: 0.4088\\n\",\n \"Epoch 14/20\\n\",\n \"25000/25000 - 3s - loss: 0.1500 - accuracy: 0.9770 - binary_crossentropy: 0.0786 - val_loss: 0.4904 - val_accuracy: 0.8616 - val_binary_crossentropy: 0.4186\\n\",\n \"Epoch 15/20\\n\",\n \"25000/25000 - 3s - loss: 0.1436 - accuracy: 0.9801 - binary_crossentropy: 0.0716 - val_loss: 0.5095 - val_accuracy: 0.8602 - val_binary_crossentropy: 0.4373\\n\",\n \"Epoch 16/20\\n\",\n \"25000/25000 - 3s - loss: 0.1401 - accuracy: 0.9820 - binary_crossentropy: 0.0676 - val_loss: 0.5226 - val_accuracy: 0.8580 - val_binary_crossentropy: 0.4496\\n\",\n \"Epoch 17/20\\n\",\n \"25000/25000 - 3s - loss: 0.1368 - accuracy: 0.9820 - binary_crossentropy: 0.0634 - val_loss: 0.5375 - val_accuracy: 0.8596 - val_binary_crossentropy: 0.4639\\n\",\n \"Epoch 18/20\\n\",\n \"25000/25000 - 3s - loss: 0.1315 - accuracy: 0.9844 - binary_crossentropy: 0.0580 - val_loss: 0.5472 - val_accuracy: 0.8600 - val_binary_crossentropy: 0.4735\\n\",\n \"Epoch 19/20\\n\",\n \"25000/25000 - 3s - loss: 0.1314 - accuracy: 0.9842 - binary_crossentropy: 0.0572 - val_loss: 0.5676 - val_accuracy: 0.8578 - val_binary_crossentropy: 0.4927\\n\",\n \"Epoch 20/20\\n\",\n \"25000/25000 - 3s - loss: 0.1278 - accuracy: 0.9856 - binary_crossentropy: 0.0530 - val_loss: 0.5750 - val_accuracy: 0.8580 - val_binary_crossentropy: 0.5001\\n\"\n ]\n }\n ],\n \"source\": [\n \"l2_model = keras.Sequential(\\n\",\n \"[\\n\",\n \" layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001), \\n\",\n \" activation='relu', input_shape=(NUM_WORDS,)),\\n\",\n \" layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001), \\n\",\n \" activation='relu'),\\n\",\n \" layers.Dense(1, activation='sigmoid')\\n\",\n \"]\\n\",\n \")\\n\",\n \"l2_model.compile(optimizer='adam',\\n\",\n \" loss='binary_crossentropy',\\n\",\n \" metrics=['accuracy', 'binary_crossentropy'])\\n\",\n \"l2_model.summary()\\n\",\n \"l2_history = l2_model.fit(train_data, train_labels,\\n\",\n \" epochs=20, batch_size=512,\\n\",\n \" validation_data=(test_data, test_labels),\\n\",\n \" verbose=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_history([('baseline', baseline_history),\\n\",\n \" ('l2', l2_history)])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5.添加dropout\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_10\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_30 (Dense) (None, 16) 160016 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout (Dropout) (None, 16) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_31 (Dense) (None, 16) 272 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout_1 (Dropout) (None, 16) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_32 (Dense) (None, 1) 17 \\n\",\n \"=================================================================\\n\",\n \"Total params: 160,305\\n\",\n \"Trainable params: 160,305\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\",\n \"Train on 25000 samples, validate on 25000 samples\\n\",\n \"Epoch 1/20\\n\",\n \"25000/25000 - 4s - loss: 0.6364 - accuracy: 0.6512 - binary_crossentropy: 0.6364 - val_loss: 0.5510 - val_accuracy: 0.8113 - val_binary_crossentropy: 0.5510\\n\",\n \"Epoch 2/20\\n\",\n \"25000/25000 - 3s - loss: 0.5050 - accuracy: 0.8296 - binary_crossentropy: 0.5050 - val_loss: 0.4348 - val_accuracy: 0.8736 - val_binary_crossentropy: 0.4348\\n\",\n \"Epoch 3/20\\n\",\n \"25000/25000 - 3s - loss: 0.4169 - accuracy: 0.8725 - binary_crossentropy: 0.4169 - val_loss: 0.3729 - val_accuracy: 0.8830 - val_binary_crossentropy: 0.3729\\n\",\n \"Epoch 4/20\\n\",\n \"25000/25000 - 3s - loss: 0.3538 - accuracy: 0.8985 - binary_crossentropy: 0.3538 - val_loss: 0.3368 - val_accuracy: 0.8841 - val_binary_crossentropy: 0.3368\\n\",\n \"Epoch 5/20\\n\",\n \"25000/25000 - 3s - loss: 0.3099 - accuracy: 0.9104 - binary_crossentropy: 0.3099 - val_loss: 0.3291 - val_accuracy: 0.8842 - val_binary_crossentropy: 0.3291\\n\",\n \"Epoch 6/20\\n\",\n \"25000/25000 - 3s - loss: 0.2699 - accuracy: 0.9236 - binary_crossentropy: 0.2699 - val_loss: 0.3193 - val_accuracy: 0.8820 - val_binary_crossentropy: 0.3193\\n\",\n \"Epoch 7/20\\n\",\n \"25000/25000 - 3s - loss: 0.2419 - accuracy: 0.9328 - binary_crossentropy: 0.2419 - val_loss: 0.3168 - val_accuracy: 0.8808 - val_binary_crossentropy: 0.3168\\n\",\n \"Epoch 8/20\\n\",\n \"25000/25000 - 3s - loss: 0.2203 - accuracy: 0.9379 - binary_crossentropy: 0.2203 - val_loss: 0.3318 - val_accuracy: 0.8807 - val_binary_crossentropy: 0.3318\\n\",\n \"Epoch 9/20\\n\",\n \"25000/25000 - 3s - loss: 0.1985 - accuracy: 0.9446 - binary_crossentropy: 0.1985 - val_loss: 0.3471 - val_accuracy: 0.8792 - val_binary_crossentropy: 0.3471\\n\",\n \"Epoch 10/20\\n\",\n \"25000/25000 - 3s - loss: 0.1839 - accuracy: 0.9478 - binary_crossentropy: 0.1839 - val_loss: 0.3624 - val_accuracy: 0.8792 - val_binary_crossentropy: 0.3624\\n\",\n \"Epoch 11/20\\n\",\n \"25000/25000 - 3s - loss: 0.1620 - accuracy: 0.9548 - binary_crossentropy: 0.1620 - val_loss: 0.3806 - val_accuracy: 0.8781 - val_binary_crossentropy: 0.3806\\n\",\n \"Epoch 12/20\\n\",\n \"25000/25000 - 3s - loss: 0.1503 - accuracy: 0.9580 - binary_crossentropy: 0.1503 - val_loss: 0.3941 - val_accuracy: 0.8764 - val_binary_crossentropy: 0.3941\\n\",\n \"Epoch 13/20\\n\",\n \"25000/25000 - 3s - loss: 0.1440 - accuracy: 0.9598 - binary_crossentropy: 0.1440 - val_loss: 0.4231 - val_accuracy: 0.8754 - val_binary_crossentropy: 0.4231\\n\",\n \"Epoch 14/20\\n\",\n \"25000/25000 - 3s - loss: 0.1345 - accuracy: 0.9643 - binary_crossentropy: 0.1345 - val_loss: 0.4272 - val_accuracy: 0.8758 - val_binary_crossentropy: 0.4272\\n\",\n \"Epoch 15/20\\n\",\n \"25000/25000 - 3s - loss: 0.1307 - accuracy: 0.9640 - binary_crossentropy: 0.1307 - val_loss: 0.4550 - val_accuracy: 0.8746 - val_binary_crossentropy: 0.4550\\n\",\n \"Epoch 16/20\\n\",\n \"25000/25000 - 3s - loss: 0.1217 - accuracy: 0.9668 - binary_crossentropy: 0.1217 - val_loss: 0.5008 - val_accuracy: 0.8740 - val_binary_crossentropy: 0.5008\\n\",\n \"Epoch 17/20\\n\",\n \"25000/25000 - 3s - loss: 0.1161 - accuracy: 0.9686 - binary_crossentropy: 0.1161 - val_loss: 0.5079 - val_accuracy: 0.8738 - val_binary_crossentropy: 0.5079\\n\",\n \"Epoch 18/20\\n\",\n \"25000/25000 - 3s - loss: 0.1125 - accuracy: 0.9698 - binary_crossentropy: 0.1125 - val_loss: 0.5297 - val_accuracy: 0.8718 - val_binary_crossentropy: 0.5297\\n\",\n \"Epoch 19/20\\n\",\n \"25000/25000 - 3s - loss: 0.1069 - accuracy: 0.9705 - binary_crossentropy: 0.1069 - val_loss: 0.5379 - val_accuracy: 0.8740 - val_binary_crossentropy: 0.5379\\n\",\n \"Epoch 20/20\\n\",\n \"25000/25000 - 3s - loss: 0.1068 - accuracy: 0.9720 - binary_crossentropy: 0.1068 - val_loss: 0.5721 - val_accuracy: 0.8732 - val_binary_crossentropy: 0.5721\\n\"\n ]\n }\n ],\n \"source\": [\n \"dpt_model = keras.Sequential(\\n\",\n \"[\\n\",\n \" layers.Dense(16, activation='relu', input_shape=(NUM_WORDS,)),\\n\",\n \" layers.Dropout(0.5),\\n\",\n \" layers.Dense(16, activation='relu'),\\n\",\n \" layers.Dropout(0.5),\\n\",\n \" layers.Dense(1, activation='sigmoid')\\n\",\n \"]\\n\",\n \")\\n\",\n \"dpt_model.compile(optimizer='adam',\\n\",\n \" loss='binary_crossentropy',\\n\",\n \" metrics=['accuracy', 'binary_crossentropy'])\\n\",\n \"dpt_model.summary()\\n\",\n \"dpt_history = dpt_model.fit(train_data, train_labels,\\n\",\n \" epochs=20, batch_size=512,\\n\",\n \" validation_data=(test_data, test_labels),\\n\",\n \" verbose=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_history([('baseline', baseline_history),\\n\",\n \" ('dropout', dpt_history)])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"防止神经网络中过度拟合的最常用方法：\\n\",\n \"\\n\",\n \"- 获取更多训练数据。\\n\",\n \"- 减少网络容量。\\n\",\n \"- 添加权重正规化。\\n\",\n \"- 添加dropout。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "104-example_classify_structured_data.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2.0教程-结构化数据分类\\n\",\n \"\\n\",\n \"tensorflow2教程知乎专栏：https://zhuanlan.zhihu.com/c_1091021863043624960\\n\",\n \"\\n\",\n \"本教程展示了如何对结构化数据进行分类（例如CSV中的表格数据）。我们使用Keras定义模型，并将csv中各列的特征转化为训练的输入。 本教程包含一下功能代码：\\n\",\n \"\\n\",\n \"- 使用Pandas加载CSV文件。\\n\",\n \"- 构建一个输入的pipeline，使用tf.data批处理和打乱数据。\\n\",\n \"- 从CSV中的列映射到用于训练模型的输入要素。\\n\",\n \"- 使用Keras构建，训练和评估模型。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"from tensorflow import feature_column\\n\",\n \"from tensorflow.keras import layers\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.数据集\\n\",\n \"我们将使用克利夫兰诊所心脏病基金会提供的一个小数据集。 CSV中有几百行。 每行描述一个患者，每列描述一个属性。 我们将使用此信息来预测患者是否患有心脏病，该疾病在该数据集中是二元分类任务。\\n\",\n \"\\n\",\n \">Column| Description| Feature Type | Data Type\\n\",\n \">------------|--------------------|----------------------|-----------------\\n\",\n \">Age | Age in years | Numerical | integer\\n\",\n \">Sex | (1 = male; 0 = female) | Categorical | integer\\n\",\n \">CP | Chest pain type (0, 1, 2, 3, 4) | Categorical | integer\\n\",\n \">Trestbpd | Resting blood pressure (in mm Hg on admission to the hospital) | Numerical | integer\\n\",\n \">Chol | Serum cholestoral in mg/dl | Numerical | integer\\n\",\n \">FBS | (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) | Categorical | integer\\n\",\n \">RestECG | Resting electrocardiographic results (0, 1, 2) | Categorical | integer\\n\",\n \">Thalach | Maximum heart rate achieved | Numerical | integer\\n\",\n \">Exang | Exercise induced angina (1 = yes; 0 = no) | Categorical | integer\\n\",\n \">Oldpeak | ST depression induced by exercise relative to rest | Numerical | integer\\n\",\n \">Slope | The slope of the peak exercise ST segment | Numerical | float\\n\",\n \">CA | Number of major vessels (0-3) colored by flourosopy | Numerical | integer\\n\",\n \">Thal | 3 = normal; 6 = fixed defect; 7 = reversable defect | Categorical | string\\n\",\n \">Target | Diagnosis of heart disease (1 = true; 0 = false) | Classification | integer\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.准备数据\\n\",\n \"使用pandas读取数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
063111452331215002.330fixed0
167141602860210811.523normal1
267141202290212912.622reversible0
337131302500018703.530normal0
441021302040217201.410normal0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\\\\n\",\n \"0 63 1 1 145 233 1 2 150 0 2.3 3 \\n\",\n \"1 67 1 4 160 286 0 2 108 1 1.5 2 \\n\",\n \"2 67 1 4 120 229 0 2 129 1 2.6 2 \\n\",\n \"3 37 1 3 130 250 0 0 187 0 3.5 3 \\n\",\n \"4 41 0 2 130 204 0 2 172 0 1.4 1 \\n\",\n \"\\n\",\n \" ca thal target \\n\",\n \"0 0 fixed 0 \\n\",\n \"1 3 normal 1 \\n\",\n \"2 2 reversible 0 \\n\",\n \"3 0 normal 0 \\n\",\n \"4 0 normal 0 \"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"URL = 'https://storage.googleapis.com/applied-dl/heart.csv'\\n\",\n \"dataframe = pd.read_csv(URL)\\n\",\n \"dataframe.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"划分训练集验证集和测试集\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"193 train examples\\n\",\n \"49 validation examples\\n\",\n \"61 test examples\\n\"\n ]\n }\n ],\n \"source\": [\n \"train, test = train_test_split(dataframe, test_size=0.2)\\n\",\n \"train, val = train_test_split(train, test_size=0.2)\\n\",\n \"print(len(train), 'train examples')\\n\",\n \"print(len(val), 'validation examples')\\n\",\n \"print(len(test), 'test examples')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"使用tf.data构造输入pipeline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def df_to_dataset(dataframe, shuffle=True, batch_size=32):\\n\",\n \" dataframe = dataframe.copy()\\n\",\n \" labels = dataframe.pop('target')\\n\",\n \" ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))\\n\",\n \" if shuffle:\\n\",\n \" ds = ds.shuffle(buffer_size=len(dataframe))\\n\",\n \" ds = ds.batch(batch_size)\\n\",\n \" return ds\\n\",\n \"\\n\",\n \"batch_size = 5\\n\",\n \"train_ds = df_to_dataset(train, batch_size=batch_size)\\n\",\n \"val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)\\n\",\n \"test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Every feature: ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal']\\n\",\n \"A batch of ages: tf.Tensor([61 51 57 51 44], shape=(5,), dtype=int32)\\n\",\n \"A batch of targets: tf.Tensor([0 0 0 1 0], shape=(5,), dtype=int32)\\n\"\n ]\n }\n ],\n \"source\": [\n \"for feature_batch, label_batch in train_ds.take(1):\\n\",\n \" print('Every feature:', list(feature_batch.keys()))\\n\",\n \" print('A batch of ages:', feature_batch['age'])\\n\",\n \" print('A batch of targets:', label_batch )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.tensorflow的feature column\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"example_batch = next(iter(train_ds))[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def demo(feature_column):\\n\",\n \" feature_layer = layers.DenseFeatures(feature_column)\\n\",\n \" print(feature_layer(example_batch).numpy())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 数字列\\n\",\n \"特征列的输出成为模型的输入。 数字列是最简单的列类型。 它用于表示真正有价值的特征。 使用此列时，模型将从数据框中接收未更改的列值。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING: Logging before flag parsing goes to stderr.\\n\",\n \"W0324 13:43:10.728773 140513109178112 deprecation.py:323] From /home/czy/anaconda3/envs/tf2_0/lib/python3.6/site-packages/tensorflow/python/feature_column/feature_column_v2.py:2758: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"Use `tf.cast` instead.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[61.]\\n\",\n \" [51.]\\n\",\n \" [57.]\\n\",\n \" [51.]\\n\",\n \" [44.]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"age = feature_column.numeric_column(\\\"age\\\")\\n\",\n \"demo(age)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Bucketized列（桶列）\\n\",\n \"通常，您不希望将数字直接输入模型，而是根据数值范围将其值分成不同的类别。 考虑代表一个人年龄的原始数据。 我们可以使用bucketized列将年龄分成几个桶，而不是将年龄表示为数字列。 请注意，下面的one-hot描述了每行匹配的年龄范围。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"W0324 13:48:31.327955 140513109178112 deprecation.py:323] From /home/czy/anaconda3/envs/tf2_0/lib/python3.6/site-packages/tensorflow/python/feature_column/feature_column_v2.py:2902: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"Use `tf.cast` instead.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0. 0. 0. 0. 0. 0. 1.]\\n\",\n \" [0. 0. 0. 0. 0. 0. 1.]\\n\",\n \" [0. 0. 0. 0. 0. 0. 1.]\\n\",\n \" [0. 0. 0. 0. 0. 0. 1.]\\n\",\n \" [0. 0. 0. 0. 0. 1. 0.]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"age_buckets = feature_column.bucketized_column(age, boundaries=[\\n\",\n \" 18, 25, 30, 35, 40, 50\\n\",\n \"])\\n\",\n \"demo(age_buckets)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 类别列\\n\",\n \"在该数据集中，thal表示为字符串（例如“固定”，“正常”或“可逆”）。 我们无法直接将字符串提供给模型。 相反，我们必须首先将它们映射到数值。 类别列提供了一种将字符串表示为单热矢量的方法（就像上面用年龄段看到的那样）。 类别表可以使用categorical_column_with_vocabulary_list作为列表传递，或者使用categorical_column_with_vocabulary_file从文件加载。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"W0324 13:55:01.628555 140513109178112 deprecation.py:323] From /home/czy/anaconda3/envs/tf2_0/lib/python3.6/site-packages/tensorflow/python/feature_column/feature_column_v2.py:4307: IndicatorColumn._variable_shape (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\\n\",\n \"W0324 13:55:01.629235 140513109178112 deprecation.py:323] From /home/czy/anaconda3/envs/tf2_0/lib/python3.6/site-packages/tensorflow/python/feature_column/feature_column_v2.py:4362: VocabularyListCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0. 0. 1.]\\n\",\n \" [0. 1. 0.]\\n\",\n \" [0. 0. 1.]\\n\",\n \" [0. 0. 1.]\\n\",\n \" [0. 1. 0.]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"thal = feature_column.categorical_column_with_vocabulary_list('thal', ['fixed', 'normal', 'reversible'])\\n\",\n \"thal_one_hot = feature_column.indicator_column(thal)\\n\",\n \"demo(thal_one_hot)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 嵌入列\\n\",\n \"假设我们不是只有几个可能的字符串，而是每个类别有数千（或更多）值。 由于多种原因，随着类别数量的增加，使用单热编码训练神经网络变得不可行。 我们可以使用嵌入列来克服此限制。 嵌入列不是将数据表示为多维度的单热矢量，而是将数据表示为低维密集向量，其中每个单元格可以包含任意数字，而不仅仅是0或1.嵌入的大小是必须训练调整的参数。\\n\",\n \"\\n\",\n \"注：当分类列具有许多可能的值时，最好使用嵌入列。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 0.21029451 0.28502795 0.27186757 -0.13927 0.44176006 0.18506278\\n\",\n \" -0.14189719 0.2901029 ]\\n\",\n \" [-0.02674027 -0.21359333 -0.26675928 0.6544374 0.12530805 -0.5243998\\n\",\n \" -0.23030454 -0.10796055]\\n\",\n \" [ 0.21029451 0.28502795 0.27186757 -0.13927 0.44176006 0.18506278\\n\",\n \" -0.14189719 0.2901029 ]\\n\",\n \" [ 0.21029451 0.28502795 0.27186757 -0.13927 0.44176006 0.18506278\\n\",\n \" -0.14189719 0.2901029 ]\\n\",\n \" [-0.02674027 -0.21359333 -0.26675928 0.6544374 0.12530805 -0.5243998\\n\",\n \" -0.23030454 -0.10796055]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"thal_embedding = feature_column.embedding_column(thal, dimension=8)\\n\",\n \"demo(thal_embedding)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 哈希特征列\\n\",\n \"表示具有大量值的分类列的另一种方法是使用categorical_column_with_hash_bucket。 此功能列计算输入的哈希值，然后选择一个hash_bucket_size存储桶来编码字符串。 使用此列时，您不需要提供词汇表，并且可以选择使hash_buckets的数量远远小于实际类别的数量以节省空间。\\n\",\n \"\\n\",\n \"注：该技术的一个重要缺点是可能存在冲突，其中不同的字符串被映射到同一个桶。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"W0324 14:03:23.451644 140513109178112 deprecation.py:323] From /home/czy/anaconda3/envs/tf2_0/lib/python3.6/site-packages/tensorflow/python/feature_column/feature_column_v2.py:4362: HashedCategoricalColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0. 0. 0. ... 0. 0. 0.]\\n\",\n \" [0. 0. 0. ... 0. 0. 0.]\\n\",\n \" [0. 0. 0. ... 0. 0. 0.]\\n\",\n \" [0. 0. 0. ... 0. 0. 0.]\\n\",\n \" [0. 0. 0. ... 0. 0. 0.]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"thal_hashed = feature_column.categorical_column_with_hash_bucket('thal', hash_bucket_size=1000)\\n\",\n \"demo(feature_column.indicator_column(thal_hashed))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 交叉功能列\\n\",\n \"将特征组合成单个特征（更好地称为特征交叉），使模型能够为每个特征组合学习单独的权重。 在这里，我们将创建一个与age和thal交叉的新功能。 请注意，crossed_column不会构建所有可能组合的完整表（可能非常大）。 相反，它由hashed_column支持，因此您可以选择表的大小。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"W0324 14:09:05.265740 140513109178112 deprecation.py:323] From /home/czy/anaconda3/envs/tf2_0/lib/python3.6/site-packages/tensorflow/python/feature_column/feature_column_v2.py:4362: CrossedColumn._num_buckets (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"The old _FeatureColumn APIs are being deprecated. Please use the new FeatureColumn APIs instead.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0. 0. 0. ... 0. 0. 0.]\\n\",\n \" [0. 0. 0. ... 0. 0. 0.]\\n\",\n \" [0. 0. 0. ... 0. 0. 0.]\\n\",\n \" [0. 0. 0. ... 0. 0. 0.]\\n\",\n \" [0. 0. 0. ... 0. 0. 0.]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"crossed_feature = feature_column.crossed_column([age_buckets, thal], hash_bucket_size=1000)\\n\",\n \"demo(feature_column.indicator_column(crossed_feature))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.选择使用feature column\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"feature_columns = []\\n\",\n \"\\n\",\n \"# numeric cols\\n\",\n \"for header in ['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'slope', 'ca']:\\n\",\n \" feature_columns.append(feature_column.numeric_column(header))\\n\",\n \"\\n\",\n \"# bucketized cols\\n\",\n \"age_buckets = feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])\\n\",\n \"feature_columns.append(age_buckets)\\n\",\n \"\\n\",\n \"# indicator cols\\n\",\n \"thal = feature_column.categorical_column_with_vocabulary_list(\\n\",\n \" 'thal', ['fixed', 'normal', 'reversible'])\\n\",\n \"thal_one_hot = feature_column.indicator_column(thal)\\n\",\n \"feature_columns.append(thal_one_hot)\\n\",\n \"\\n\",\n \"# embedding cols\\n\",\n \"thal_embedding = feature_column.embedding_column(thal, dimension=8)\\n\",\n \"feature_columns.append(thal_embedding)\\n\",\n \"\\n\",\n \"# crossed cols\\n\",\n \"crossed_feature = feature_column.crossed_column([age_buckets, thal], hash_bucket_size=1000)\\n\",\n \"crossed_feature = feature_column.indicator_column(crossed_feature)\\n\",\n \"feature_columns.append(crossed_feature)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"构建特征层\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"feature_layer = tf.keras.layers.DenseFeatures(feature_columns)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"batch_size = 32\\n\",\n \"train_ds = df_to_dataset(train, batch_size=batch_size)\\n\",\n \"val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)\\n\",\n \"test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5.构建模型并训练\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Epoch 1/5\\n\",\n \"7/7 [==============================] - 1s 133ms/step - loss: 1.1864 - accuracy: 0.6357 - val_loss: 0.6905 - val_accuracy: 0.5714\\n\",\n \"Epoch 2/5\\n\",\n \"7/7 [==============================] - 0s 24ms/step - loss: 0.9603 - accuracy: 0.6804 - val_loss: 0.4047 - val_accuracy: 0.8163\\n\",\n \"Epoch 3/5\\n\",\n \"7/7 [==============================] - 0s 24ms/step - loss: 0.5744 - accuracy: 0.7389 - val_loss: 0.6673 - val_accuracy: 0.7755\\n\",\n \"Epoch 4/5\\n\",\n \"7/7 [==============================] - 0s 24ms/step - loss: 0.4890 - accuracy: 0.8092 - val_loss: 0.6298 - val_accuracy: 0.6122\\n\",\n \"Epoch 5/5\\n\",\n \"7/7 [==============================] - 0s 24ms/step - loss: 0.5618 - accuracy: 0.6795 - val_loss: 0.3861 - val_accuracy: 0.8367\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model = tf.keras.Sequential([\\n\",\n \" feature_layer,\\n\",\n \" layers.Dense(128, activation='relu'),\\n\",\n \" layers.Dense(128, activation='relu'),\\n\",\n \" layers.Dense(1, activation='sigmoid')\\n\",\n \"])\\n\",\n \"\\n\",\n \"model.compile(optimizer='adam',\\n\",\n \" loss='binary_crossentropy',\\n\",\n \" metrics=['accuracy'])\\n\",\n \"model.fit(train_ds, validation_data=val_ds,epochs=5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2/2 [==============================] - 0s 16ms/step - loss: 0.8278 - accuracy: 0.6066\\n\",\n \"Accuracy 0.60655737\\n\"\n ]\n }\n ],\n \"source\": [\n \"loss, accuracy = model.evaluate(test_ds)\\n\",\n \"print(\\\"Accuracy\\\", accuracy)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "105-example_regression.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2.0教程-回归\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"tensorflow2教程知乎专栏：https://zhuanlan.zhihu.com/c_1091021863043624960\\n\",\n \"\\n\",\n \"在回归问题中，我们的目标是预测连续值的输出，如价格或概率。 \\n\",\n \"我们采用了经典的Auto MPG数据集，并建立了一个模型来预测20世纪70年代末和80年代初汽车的燃油效率。 为此，我们将为该模型提供该时段内许多汽车的描述。 此描述包括以下属性：气缸，排量，马力和重量。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0.0-alpha0\\n\"\n ]\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"\\n\",\n \"import pathlib\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"from tensorflow import keras\\n\",\n \"from tensorflow.keras import layers\\n\",\n \"\\n\",\n \"print(tf.__version__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.Auto MPG数据集\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"获取数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Downloading data from https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\\n\",\n \"32768/30286 [================================] - 1s 25us/step\\n\",\n \"/home/czy/.keras/datasets/auto-mpg.data\\n\"\n ]\n }\n ],\n \"source\": [\n \"dataset_path = keras.utils.get_file('auto-mpg.data',\\n\",\n \" 'https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data')\\n\",\n \"\\n\",\n \"print(dataset_path)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"使用pandas读取数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
MPGCylindersDisplacementHorsepowerWeightAccelerationModel YearOrigin
39327.04140.086.02790.015.6821
39444.0497.052.02130.024.6822
39532.04135.084.02295.011.6821
39628.04120.079.02625.018.6821
39731.04119.082.02720.019.4821
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" MPG Cylinders Displacement Horsepower Weight Acceleration \\\\\\n\",\n \"393 27.0 4 140.0 86.0 2790.0 15.6 \\n\",\n \"394 44.0 4 97.0 52.0 2130.0 24.6 \\n\",\n \"395 32.0 4 135.0 84.0 2295.0 11.6 \\n\",\n \"396 28.0 4 120.0 79.0 2625.0 18.6 \\n\",\n \"397 31.0 4 119.0 82.0 2720.0 19.4 \\n\",\n \"\\n\",\n \" Model Year Origin \\n\",\n \"393 82 1 \\n\",\n \"394 82 2 \\n\",\n \"395 82 1 \\n\",\n \"396 82 1 \\n\",\n \"397 82 1 \"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',\\n\",\n \" 'Acceleration', 'Model Year', 'Origin'] \\n\",\n \"raw_dataset = pd.read_csv(dataset_path, names=column_names,\\n\",\n \" na_values='?', comment='\\\\t',\\n\",\n \" sep=' ', skipinitialspace=True)\\n\",\n \"dataset = raw_dataset.copy()\\n\",\n \"dataset.tail()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.数据预处理\\n\",\n \"### 清洗数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MPG 0\\n\",\n \"Cylinders 0\\n\",\n \"Displacement 0\\n\",\n \"Horsepower 6\\n\",\n \"Weight 0\\n\",\n \"Acceleration 0\\n\",\n \"Model Year 0\\n\",\n \"Origin 0\\n\",\n \"dtype: int64\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
MPGCylindersDisplacementHorsepowerWeightAccelerationModel YearUSAEuropeJapan
39327.04140.086.02790.015.6821.00.00.0
39444.0497.052.02130.024.6820.01.00.0
39532.04135.084.02295.011.6821.00.00.0
39628.04120.079.02625.018.6821.00.00.0
39731.04119.082.02720.019.4821.00.00.0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" MPG Cylinders Displacement Horsepower Weight Acceleration \\\\\\n\",\n \"393 27.0 4 140.0 86.0 2790.0 15.6 \\n\",\n \"394 44.0 4 97.0 52.0 2130.0 24.6 \\n\",\n \"395 32.0 4 135.0 84.0 2295.0 11.6 \\n\",\n \"396 28.0 4 120.0 79.0 2625.0 18.6 \\n\",\n \"397 31.0 4 119.0 82.0 2720.0 19.4 \\n\",\n \"\\n\",\n \" Model Year USA Europe Japan \\n\",\n \"393 82 1.0 0.0 0.0 \\n\",\n \"394 82 0.0 1.0 0.0 \\n\",\n \"395 82 1.0 0.0 0.0 \\n\",\n \"396 82 1.0 0.0 0.0 \\n\",\n \"397 82 1.0 0.0 0.0 \"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"print(dataset.isna().sum())\\n\",\n \"dataset = dataset.dropna()\\n\",\n \"origin = dataset.pop('Origin')\\n\",\n \"dataset['USA'] = (origin == 1)*1.0\\n\",\n \"dataset['Europe'] = (origin == 2)*1.0\\n\",\n \"dataset['Japan'] = (origin == 3)*1.0\\n\",\n \"dataset.tail()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 划分训练集和测试集\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"train_dataset = dataset.sample(frac=0.8,random_state=0)\\n\",\n \"test_dataset = dataset.drop(train_dataset.index)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 检测数据\\n\",\n \"\\n\",\n \"观察训练集中几对列的联合分布。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.pairplot(train_dataset[[\\\"MPG\\\", \\\"Cylinders\\\", \\\"Displacement\\\", \\\"Weight\\\"]], diag_kind=\\\"kde\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"整体统计数据：\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
countmeanstdmin25%50%75%max
Cylinders314.05.4777071.6997883.04.004.08.008.0
Displacement314.0195.318471104.33158968.0105.50151.0265.75455.0
Horsepower314.0104.86942738.09621446.076.2594.5128.00225.0
Weight314.02990.251592843.8985961649.02256.502822.53608.005140.0
Acceleration314.015.5592362.7892308.013.8015.517.2024.8
Model Year314.075.8980893.67564270.073.0076.079.0082.0
USA314.00.6242040.4851010.00.001.01.001.0
Europe314.00.1783440.3834130.00.000.00.001.0
Japan314.00.1974520.3987120.00.000.00.001.0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" count mean std min 25% 50% \\\\\\n\",\n \"Cylinders 314.0 5.477707 1.699788 3.0 4.00 4.0 \\n\",\n \"Displacement 314.0 195.318471 104.331589 68.0 105.50 151.0 \\n\",\n \"Horsepower 314.0 104.869427 38.096214 46.0 76.25 94.5 \\n\",\n \"Weight 314.0 2990.251592 843.898596 1649.0 2256.50 2822.5 \\n\",\n \"Acceleration 314.0 15.559236 2.789230 8.0 13.80 15.5 \\n\",\n \"Model Year 314.0 75.898089 3.675642 70.0 73.00 76.0 \\n\",\n \"USA 314.0 0.624204 0.485101 0.0 0.00 1.0 \\n\",\n \"Europe 314.0 0.178344 0.383413 0.0 0.00 0.0 \\n\",\n \"Japan 314.0 0.197452 0.398712 0.0 0.00 0.0 \\n\",\n \"\\n\",\n \" 75% max \\n\",\n \"Cylinders 8.00 8.0 \\n\",\n \"Displacement 265.75 455.0 \\n\",\n \"Horsepower 128.00 225.0 \\n\",\n \"Weight 3608.00 5140.0 \\n\",\n \"Acceleration 17.20 24.8 \\n\",\n \"Model Year 79.00 82.0 \\n\",\n \"USA 1.00 1.0 \\n\",\n \"Europe 0.00 1.0 \\n\",\n \"Japan 0.00 1.0 \"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train_stats = train_dataset.describe()\\n\",\n \"train_stats.pop(\\\"MPG\\\")\\n\",\n \"train_stats = train_stats.transpose()\\n\",\n \"train_stats\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 取出标签\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"train_labels = train_dataset.pop('MPG')\\n\",\n \"test_labels = test_dataset.pop('MPG')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 标准化数据\\n\",\n \"最好使用不同比例和范围的特征进行标准化。 虽然模型可能在没有特征归一化的情况下收敛，但它使训练更加困难，并且它使得结果模型依赖于输入中使用的单位的选择。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def norm(x):\\n\",\n \" return (x - train_stats['mean']) / train_stats['std']\\n\",\n \"normed_train_data = norm(train_dataset)\\n\",\n \"normed_test_data = norm(test_dataset)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.构建模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def build_model():\\n\",\n \" model = keras.Sequential([\\n\",\n \" layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\\n\",\n \" layers.Dense(64, activation='relu'),\\n\",\n \" layers.Dense(1)\\n\",\n \" ])\\n\",\n \" \\n\",\n \" optimizer = tf.keras.optimizers.RMSprop(0.001)\\n\",\n \" model.compile(loss='mse',\\n\",\n \" optimizer=optimizer,\\n\",\n \" metrics=['mae', 'mse'])\\n\",\n \" return model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense (Dense) (None, 64) 640 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_1 (Dense) (None, 64) 4160 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_2 (Dense) (None, 1) 65 \\n\",\n \"=================================================================\\n\",\n \"Total params: 4,865\\n\",\n \"Trainable params: 4,865\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = build_model()\\n\",\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0.18062565],\\n\",\n \" [0.1714489 ],\\n\",\n \" [0.22555563],\\n\",\n \" [0.29366603],\\n\",\n \" [0.69764495],\\n\",\n \" [0.08851457],\\n\",\n \" [0.6851174 ],\\n\",\n \" [0.32245407],\\n\",\n \" [0.02959149],\\n\",\n \" [0.38945067]], dtype=float32)\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"example_batch = normed_train_data[:10]\\n\",\n \"example_result = model.predict(example_batch)\\n\",\n \"example_result\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4.训练模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"....................................................................................................\\n\",\n \"....................................................................................................\\n\",\n \"....................................................................................................\\n\",\n \"....................................................................................................\\n\",\n \"....................................................................................................\\n\",\n \"....................................................................................................\\n\",\n \"....................................................................................................\\n\",\n \"....................................................................................................\\n\",\n \"....................................................................................................\\n\",\n \"....................................................................................................\"\n ]\n }\n ],\n \"source\": [\n \"class PrintDot(keras.callbacks.Callback):\\n\",\n \" def on_epoch_end(self, epoch, logs):\\n\",\n \" if epoch % 100 == 0: print('')\\n\",\n \" print('.', end='')\\n\",\n \"\\n\",\n \"EPOCHS = 1000\\n\",\n \"\\n\",\n \"history = model.fit(\\n\",\n \" normed_train_data, train_labels,\\n\",\n \" epochs=EPOCHS, validation_split = 0.2, verbose=0,\\n\",\n \" callbacks=[PrintDot()])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"查看训练记录\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
lossmaemseval_lossval_maeval_mseepoch
9952.1911270.9407552.19112710.4228182.59411710.422818995
9962.1136790.9036802.11367910.7239252.63132010.723926996
9972.5172610.9895572.5172619.4978682.3791989.497869997
9982.2502720.9316182.25027211.0170412.65853811.017041998
9991.9763930.8535471.9763939.8909772.4917399.890977999
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" loss mae mse val_loss val_mae val_mse epoch\\n\",\n \"995 2.191127 0.940755 2.191127 10.422818 2.594117 10.422818 995\\n\",\n \"996 2.113679 0.903680 2.113679 10.723925 2.631320 10.723926 996\\n\",\n \"997 2.517261 0.989557 2.517261 9.497868 2.379198 9.497869 997\\n\",\n \"998 2.250272 0.931618 2.250272 11.017041 2.658538 11.017041 998\\n\",\n \"999 1.976393 0.853547 1.976393 9.890977 2.491739 9.890977 999\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"hist = pd.DataFrame(history.history)\\n\",\n \"hist['epoch'] = history.epoch\\n\",\n \"hist.tail()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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8FuZU7D0VRlCpAuOqsfcYYb9Gpe0RkVnlNqKqjKxFFURQf4QqRFk4dkZVYz6gyu/VWV4yuRBRFUYoIV4jcB/QGLnY+6zqut4uAxcaYd8tpflUOUSGiKIpSRLguvhPc+yLSGitM+mC9tWqMEPHZRFSdpSiKEq531nDsimMHgDEmHZsA8etynFuVROJ1JaIoiuIlXHXWVGC7iBRiAw2XAIudz2XGmEPlNL8qh8R5DesqRBRFUcIVIn8HrgTeB34FumIz+14GdAeal8fkqiK+lYiqsxRFUcKKEzHGPAccCxjgSSAPuN4YM8wYU2MECKg6S1EUxU24wYYYY7KNMf/BJkjsDMwWkaPLbWZVFE98kt1QIaIoihK2Yf14bLR6N6z6qimQBTQuv6lVTeISVJ2lKIriJVybyI/AQmxd86eNMevLa0JVnvhk+5mfXbnzUBRFqQKEK0SuAXoBpwE3i8gurGfWEmCpMebTcppflcOTWMtu5KkQURRFCTfY8CX3fkCw4TlAjREi8fEJHDIJJOYeRCp7MoqiKJVMRFUJvdTkYMPEOOEgScTnHiCusiejKIpSyYTlnSUi82PR53AgMd5DNokU5h6s7KkoiqJUOuGuRLo79c9DIUD9cAYSkYnA6cB2Y0wvp+2/wBlALrAWuNwYszfIueuxXmEFQL4xJi3M+ceMhDgP2SYJo0JEURQlbCHSLYw+BWGO9TrwLPCmq20qcIcxJl9E/gPcAdwW4vxhxpidYV4r5tiVSBIm90BlTUFRlMORV0+GI0bCkJsqeyYREa5hfUOsLmiMmS4i7QPavnXt/gacG6vrxZqEOKvOMrnqnaUoSgzZNMv+VDMhEnbEegVyBaEN9gb4VkTmOUWygiIi40RkrojM3bFjR0wnlxRv1VnkqTpLURQlbCEiljblORkRuQvIB94O0eU4Y0x/YBRwrRNJXwxjzARjTJoxJi01NTWmc0yKjyMbFSKKoigQWe4sA0wur4mIyGVYg/vFzrWCzWGz87kd+AQYWF7zCUVyglVnabChoihVhsx0+PLGSknHFKk6a76IHBXrSYjISOBW4ExjTNBXfBGpIyIp3m3gZGxtkwolKT6OgyYJT76uRBRFqWSWfQI5++Dzf8DcibBueoVPIdJgw6OBi0VkA3AA69prjDF9wh1ARN4FhgJNRCQdW7/9DiAJmCoiAL8ZY64WkZbAK8aYU4FmwCfO8XjgHWPMNxHOv8wkJXjIIQmP5s5SFKUyyVgGH1wGPf/kawuuxClXIhUip5T1gsaYMUGaXw3Rdwu2hjvGmD+AvmW9fllJjo8jm0Q8+TmVPRVFUQ5X5k6Ehe/CVVND98l2QumytkJiXbttCst/bgFEJESMMRtEpC8wxGmaYYxZFPtpVV2SEjwcNMl4TD7k54K3SJWiKEqs+PLG0vt4axrFJYA4mfwqQYhEZBMRkeuxnlNNnZ+3ROTv5TGxqkpSvIccHMGhHlqKolQWhfn20xhrF7E7FT6NSA3rVwJHG2PuNcbcCwwC/hL7aVVdkhMcF19QDy1FOZw5sBO+usVqHKoi3pXI+hmw6Te7bQph3xY4tL/CphGpEBH805sUOG01hqR4DweNV4joSkRRDlum3AVzXoYVn5f/tUIZxAtLUE8Fc+c1hfB4d3htVGzmFQaRGtZfA2aJyCfO/lmEMIofriQ5hnVAhYiiHA7kHrAP36QU//ZC5yFdER5Poa5hCgj6rv/9A5CVEXqcbSXly40tYQsRsb61H2BL5R7nNF9ujFlQDvOqsiTECYdUnaUohw+PdrLlru/P9G/3PpClApQtJkT+2sICazgPZMZjkY1TjoQtRIwxRkQmG2N6AzWidkgwRIT8OKdErmbyVZTqT8iYrxCrg/vrQ9tj4IoY1uQL5VXlNZ6HyweXlXkqkVIlItarGwXxyXZDVyKKcvhS0kpk46+RjZV/yI5XWAC/TymuvgolRBa/BwtCpRKsGkQqRI4GZorIWhFZLCJLSilWdViSH+8E9mSmV+5EFKW6U1hg3+y/f7CyZxIE50G/7FN44djobSPf3g0PNbV2jF+ehHfOt4LEy6qvYf/24Od+dRN89jf/tpKM7ZVARFl8gXFAJ+BEbCXC053PGsX2hFbsiU+FDb9U9lQUpXrjdVOd+WzlziMY3tXBis8hY6ldTQSSexDycoq3Hdjl2//1Gfs573XY9YfdPuCUqDi0H969EN76E2FTGGaSxa9vg93rwh83SiLN4vucMWZD4E85zq9KkpQQxz5PA1VnKUpZqYRcT0XkZcOmOaGPB84tmDfmv1vA0/382969AP7bsfj5hQU+w7d4fHMA2LXG129rEOXOrAm+7XAz9c56ET68PLy+ZUBtIlGQnBDHIUkswSCnKEpYVII3URFvnwevjgi/f162FQSBZG313/dm0s1M9+9vCnyrm1WT4fljIC+Ic0727uJtX//T5spa8LZv9RYOBREa5qMgmiy+l4jIeqLM4ns4kJwQR45JLL6MVRQlMoI9lGPBnFcgJxOG3By6z/oZ/vs5mZC9Bxq2D94/Pwem3lv6tWs3hoO7IHMT1HEVxSss8N3vyi/tp1vt5cXruBPIf9rZz0s+Ln0OXiogl1aFZ/E9HGhcJ5H9OxJUnaUoZaW8HnJfOcKjJCESyCsnwc5VvniRQHXUwV0w+2Xf/u/fBh8nKcX2Pbjbf9WQnw1LP/TvmxskPUkw24ubSFSAFSBEwlJnicitYLP4AgMD7CF/Lc8JVkWa1E1iX368qrMUpayUx0okHO+lYH12rrKf3od04AP41ZOgwPWAX/dT8LG9ke/Zu0uP8zi0r3hbaUIkojIU5W9zCtcmcqFr+46AYyNjNJdqQ5O6SWTlx1tjWGUaBhWlulMeNpFwxpw9IfSxood0Kf/b7of5by/6DOJJ9exn9p7SjeA5mcXbShMS711c8nE3FfB8CleISIjtYPuHPQ1qJ3B6nJM1c1W5lZ1XlKrHwd02rsMd51AWymUlUsqY+3fAN7eFPp4TQp0ViDtjxTe3wUtOmSVvmpLsvdY2UxKHsoq3bVtS8jmRUFXUWfiL5MBvtsa9iqckx3MI5w9l5+qqmypaUWLN9hX28+cnYjOee9WwYaYVUKu/g8zN4Y9xYCdsWRh8zGAE835yk7PPChCveisUi94N3u71iFr2CUx/tJRrBVmJlHZOJFQhIdJXRPaJSBbQx9n27veO5IIiMlFEtovIUldbIxGZKiKrnc+GIc4d6/RZLSJjI7luLElJTuCK3H/ane/uKx5RqiiHK7GuoOceZ/mn9vPtc+CJHuGPMWEYTDjBtx+4Evl9CuxZH/p4IDmZ8MtT/udEgjcYMGtb6X1/fCS6a4RLVREixpg4Y0w9Y0yKMSbe2fbuB0kxWSKvU9yOcjvwvTGmC/C9s++HiDQC7sO6GQ8E7gslbMqblOR40o3LdW/FF5UxDUWpeLxBcrHStbsf6KXZD/JyYHmQ2h6ZG52xnAdm4ErknfPh2YGua5Zm7M6E378puU9JeO+jSjjeVB2bSMwwxkwHAteTo4E3nO03sHVKAjkFmGqM2W2M2QNMpZKM+inJ8b7qhuD7x1KUw4W8bOsmm73Xv71IiAS84c5/E7YtJWLc45SWzuOb2+H9SyF9bvDjeQdgx+/B7RAFh5wVyYbSr+NVZ0WKx4mYCDctSSQcEWWRqaqyEqkAmhljvGGf24BmQfq0Aja59tOdtmKIyDgRmSsic3fs2BHbmQIpSQkcdAsRr2/B2h9gYQg9qaJUB/Zvh91/wIK37MP4p//4Hy96YQp4yH7+d3jx2OBjrvnOPryD4V6JlKZmynCElHclkZPpf86hLJgwFH54yNfmFgbvnA9P9YE135d8nbWlHA+Fx1HKxCpKvNe5vu2u0QqRw3AlUhpOjq4y3bkxZoIxJs0Yk5aamlr6CRGSkhxPAXG+Bu8/1v+dDZ9eHfPrKUqF8Xh3ePpI3xtssZiFKGwib51jH97BMjyYMNRZG2balZHXk2n3HzZx4fi28OZoX79D+4unEdkRxDg+7eGS57vgrZKPhyI/2zoG7FgR3fluxrzn/x2ntIhunPLKCOCiqgiRDBFpAeB8BsuLvBlo49pv7bRVOHWTAwL9K6LymaKAzco69b7ye8P0vuWHUlt58VNDhSlQvKk+/K7nXokEeYPftRZeGwmT/+nzZPr0Gl/dc3fqkmDuss8fHd7cilGJTqd9LoCuI/2/44Rk+Ov0yMe6+P3YzSsEEQkRsVwiIvc6+21FZGBp54XB54DX22os8FmQPlOAk0WkoWNQP9lpq3AS4jzUSohjY92+lXF5pSbz3iW2JsXO1bEdN3svvHS8b9/jrLRNIUwcCe9c4Ow7D323EAvXBlBkcD4Ej/eAlZN94+XnBK/P402ZnrHUP9HhBqcoVH3Xe2VuECESDXWaRpbkMBi1G0d/rvfeTnrA1+ZJgIYdivfte5H9bNwl+FhVUJ31PDAYGOPsZwHPRTKAiLwLzAS6iki6iFwJjAdOEpHVwAhnHxFJE5FXAIwxu4EHgTnOzwNOW6WQnVfAH14X78DUBaGW5VkZFZJVUzkM2LXWqkY2zfZvjyjlRQTsWAVbF/n23SuRjTN93kqFQYRISWk63PnlvAJj9x+wb7Otd+FexaQH3Cv47jdjmX+7V4i6rx1sJRINnnhbE6QsJNaJ/ty6Te1nw3bQKs03J286FTdewdm68pKrR1zZ0BhzLZAD4HhJJUYygDFmjDGmhTEmwRjT2hjzqjFmlzFmuDGmizFmhFc4GGPmGmOucp070RjT2fl5LcK5x5z1xmX/dwccfnVT8c7Ze+GxI2DqPeU/sdLI3msryYVbl0CpeLzG38UB6oiSSraWicA3Vmf8Bf8X0M0rRFwP/5L+jt46x7ftFSjPDyo+XtApGZhyt3ONgJVB9h7n0/UeeSgrNp6SWVvsCqhJ1+LHjr0+vDGSG0R2zWa9fNsXupxzvKs8T1zw33kzJ0yv8/Dg49ZuFNk8oiDSbzxPROJw/uJEJBWoWrUaK4h/nNiZR/Iv8jW4k7Et+cimh9j9h6/NG3i0emrFTLAkfngQZvzPRtQqlsJCa5itKngf0kXBfd6HvPcz4IGyZ711TY2WwBVOsAJM4FqJuIVICSsRd/XPnAB3YUzJht/8HMgIkQLEK0TcdpRD+2Pnbp+9G1r19+3XckLSOhwfvH8gKc0ju95fZ8CZz8DdOyDF9XLq/X7iAsLx2g+B4ffC8bfAbeuh97n+xz0JcO0cqN86snlEQaTf+NPAJ0BTEXkY+Bn4d8xnVQ1ISU7gEInkdT/bNrzt+iXmHYCXTrBeLmun2aWx940puX7FTzYQrwogN0hBnJrK1HvgkVYVk95/ezjeO94Vhwee6A3PDHCaQ2SYfaovvBalGygUV0kFS8cBroe2sYIrL9t/lbBtqXUTzlhe/NzAmBMo2csrUIXlN78gY637qeRAwl7n+N7cw8GtPjryEvug7xxmESuvSsrLOa/6tv/yQ/H+Hg/0/zPEByh24px9r/vwX6ZBUn0Y9R+b5t4T5xNwbsZ+AalHhDfXMhJpjfXpwK3AI8BW4CxjzAflNLcqTXKC/er2jnoxeAdvFO3/nWXVW/sznBPrVcDsSsH7h6nqLB9et85QQiT3QOkpusNhyYdWnbPyq5L7FT1cxf4t7V7rPWA/gj0sM6II9vMSuBIJ9sB/fywsdQoiFRZYwTXpIv+/oxePhf91gRcGFz9/5rP+Ly7GlKzOeiWEigaCG76DeX+56fkn+7AOlyTX/2phAbSIoPaeW73d7XT/lUKrAXDWi3De66WPc/4bMPQOSHVUa636wx0boVnP0Ofcnwntgnz/5USkNdYnG2NWGmOeM8Y8a4yJgUN09SQpwXqv5OQXwvD7oPd5hExonLHMF81br6X9DGYEXPKhzQNU3h4VRUIkyD/iT4/Ca6eVz3UXvguLK+idw5jgD8JSzwvxZvzvlvDikLLNCXwP+tJWI6FsH975uYVILP5eiq1Egnx3yz+FhY6w3eUYttf+EJlw3e/y3i/Mg99eiGyeoXB7aYWi84jIviv3SsT9v3LaY6Wf29ax+zTrBRe+Xfx4vzHQ8+zSx2nQFobeHp4NbNC19qeC0RrrUZLsCJFD+QUw5CY45xUY/Wzwztv/DaqRAAAgAElEQVQW+5bmxti3uUda++oPePnoStgy32YlnfaITQK3byt8cX1s3oK9ePWrwVwzpz0MG36ObtysbSXP89Or4eOrircX5Mc+E/L8N2w50R2/R3ZeSauzUFld92/3uWWWivdhUNrDzKXOCob7d+ee8+M9/D2LjIFln5YedFZsJbKnlPm5COUO+0mQwNtfn/Ft78+ITSmFpHrQohR3+5MesLEWkQiR5PpwtHMP7hXqUUH+ht3cvMonRCqSkf+2PxVMxN5ZwEwRWSsii0VkiYgsLvWsw5Ba3pVInuvNtd/FoXWmO1baz4Vvw4eX2+3N86xr5eR/wo//8ek2tyyAn8bbes5T7oR5r4f+ZyssDD/Yy0t5qLOMgce6wkel/IMFY8IJ8FCMMwt4S5d6H/yFBfDptSXo2Z2HSzTxAS8PD98eUWQoL6VfsBVRXo7vPL/EhS7BvW+zf/bZxe/BB2Nh1kslXy9Q+EeSgDDU31GwVOlzXy3eVlbqpEJqt5L7FH1fEQiRxLrQxglWDNd+eOSl1qjuFf5xETmvVksiFSKnAJ2AE4EzgNOdzxqH1yaSnef6ZxbxpScIDAzas674IF/eAM8NtFXWfvy37+3PHYV7cJf93LfFqsC2LIA/fvQdf36Q1UG72b7C6viDvXWlz7UVGSG0Bw7Yeg7GwMbfwnt7876prQiSZbU0vCqep/rBx+MiP3/3OhtT4S7mE+jVtPsPq4p579KSx1ruinNd/4szrsvW8EhbePE4/3MCs8iGy9ZF8POTwY8FU2flZwdXZwWu4tzneNVH+0pJ7lCW+JNQHlRuOp0Y/filUScVmnYvuU+gV1nHYXD2BJf6J4i6qHZDX7xHoBAZMyn4dY50/r5Su8FxN5Vu9zjmH9DuuJL7VHEiEiJOTfV92ASJ7Vw/NY5GdewbxrodAX9cpzwMl30F1y+E2zdGN/ivT/u2va7DU+6EZ4+yCebeHO17yOxcBQd3WsHhTUn/8onw2bXwrwbFq6S9MtxXt+HQfpsYzzuWOzHda6NsjMDEU4Knut80x181EUogPXsUvH2ef9uHVwTvu2edfXMuDWP8BZvXoLrApXsuUhU6/bwZVr1vzbNftgIi0AvJHcfjHfePab62Q5n+36l7Ho8dAS8cGzxHVPGbsBHi390X4rDXxdf1L5p/yHc/3vvI3gO/f+1/bkGuPb7+F9/57pXLrrUw6WKf4P/5Cf+iTpHy1c2l94mvFf34pdG0GzQvxeuqdYB3W9rl0PcCX66tYPEUtRtDQm27Hfj3XeTq6xI+fS+Clv2cZoER99mAwaJ59ihuBzn5Qbi8FCeLKk6kaU+uwnpoTQH+5XzeH/tpVX16tKhH4zqJzN0QEDSfXB/aH+fbvm198AESU6DzSZFd1J32YeNv1gjv5flBNiUG+P/BzylBfbBxpk2M99sL8MHl8NaffMf2bvA9iIO9xb46Ar692/5T5mX71HUA897wbe/8HVZ/63/u0o982+4EesHIz7VZYN18/y8rIAsLbDyO9wE573XfasC7Otj4m/+5hXkwawJMvsXuZzlec4GLrawM+O1555wg9oT768PejdYRwcuBHU56ji0l3FCQN96g6qAg8SB52b6HoHcl8t6l9oXBTV42TLkLXj8VVjuZgbxCaf3P8Ex/KyDXTbcC77v7YemHJcw5BgTGOYRLaWoqsNHajTtD9wClSFFsxwm+lZD3e4hzsnB77Ue1ggiR5AY+r6he5/gf87rcutVVo5+F+CRC8reZ4XlkVTMiVWddDxwFbDDGDAOOBKJwgan+iAgtG9QiY18pBu9aDa2LXj1X0M/9mXBnuvXQAEh1luIt+sGIf4U3gddGWiN8IIEuqvNesw8OY4ob8r3BkFPugGUfFx9rzsv2c/92a09Y+jEses/fo+bQPiuAXnd5dH3xj+KBb1/d4r8/03lAu1Vzbha+Y1dA39xmo563LbUOB19c7yvNOvtleLSD720+P9uq09z6fa8g8D6oC/Lg638Gv6aXxR/4R2rPnRi835O9rRoykPxcO7fJJVzHnUn23QuLHw8MNgT/lYhXsAWrxz3xFJjt2EDWOUn7TIFN3Ph6gOddYMqe8qJWCRHc57u+62vnQB/X93Gxy5vvljXBz09uYOMlLgjIvnvq/+wKdNhdrkbn+/MKtaG3W7tHME+pOqk23uPe3ZAWsHqOS7DqqitdL0ieOGoi8aV38SPHGJMjIohIkjFmpYgEyQ1QM2hWL4n0PWEEpw293f788aP/W22302HgX+H4f1q9cssjrTGvUUfochI87ES99jw7/Ojyh4NEyr5+mr1WoB99STYRNz8/bj8XBkmRnT63uDoFrA7ZvYLxCiQvU+4I7eWy5EObqRV8ieUKDtnvYN7rvn7f3BbkuvutN1IgXm+mQPVVMEP6x1dZ10ove0PUwghF3gHfSufkh+yDzPuAKYr3cLHmO3vPnYfbVVrrgT47jJ86K8e3EsnPsU4CwVxxgxGsUNO+zfYNviLodgY07VlcgA++DnqcaQPoDmVCYm1/93f3CiEh2X72HWOdBzbOtPvu2Kvmfaw3JNgH/b27/K/nVU95fx+NO1lBMDvg7/OiDyCprn9fN151lRKxEEkXkQbAp8BUEdkDRPgfdvjQrnEdZqzeSV5BIQlxYSzqOg71349PglMddUhdl+Gxx5n2c8x7Nq6kRR9/IRJfK/LSmyUFYnUc5q/3jwS3CszNd/eVbt8I5ZH10ZW+ba9hePN834O5JCQOPgkwzufs861EAt2aV39r39YD2/dGac8C/9XgQ01tiop+F1sniVAvAx9d6fs9eOJ96iq3rcJdR/z9UhwEwuHLG6H/2NL7eelzAaz4snjNjmBcNw+edewQNyyxQjk/1+c9tvE32L/Nl8HBK8wTavu7F7sTGSalwPWLIKWljezescqqE70eVABXz4Afx9va5fHJxefVrCdsXVg8hqjn2Vb1e8ZT1g4ZbmS6EpkQMcZ413z3i8g0oD4Q5DW0ZtCvTQNe/Xkdv2dk0bNlOaQz6eqq/jtmkk/tcc0vVq8NcM8u6831f05F4aY9YPtyOOG24lXpep8PS4LUF/jTBMjcZA3ysSIcA3k4HHJWDuEIEIDfgiSVHt8GGoTw//g+TPVhJASqFNfP8Pe4C4VXkLs9r6Ktshcu898ovY+XwgJIqFVciDTrXdxDq0lnK0hEfKu6+EQ4yfm+v7rFrk69NgSvEE+o7VtdnXBb8SC7hu1926ld4dwgNr8ht9ggvy4nFz92yr+tET2wUmCdJnCtYz9rG20NkppJRELEW0ckgH7AA0HaD3s6pdrl7ofz0stHiLjpOsoGMWVts0vw0x6zD8a4eOg0DMb9ZAOuCnKtx1XqEVZVsHmefQNuNQAadYDjboAXjvEfOynFHv/bLOt2GvgmH8ip/7PXyc+B712/+mF3WfWINw6mMghmI4DIVVJlIVhdjEhITLEqlHBVVbFC4vzTkMQn+7v+tuhTPDU9hM5X1aQEVVmHIVaIeFOLXPqprdMen2TVutuXw0Dn7/CcVyNLrR4XD91PD36sVgOrYowlf58fvIJiDUFMBBGcIuL25UvGxomsMMaE8NmsfNLS0szcuXPLZewDh/LpeZ/1fplx6zDaNKpdLteJOenz7JvX+hlWzZMW8NB/cYhPr3zpp9bIvXut1UW36AttnDpkOZm2RKmXe/dA+hyYGOQNMFzEU3JSvvptfZ5XFUWzXmXLS1UZnPq/8FdvXhp38aUzAesUkr0HrvzO2hea97GlX2e95L+CadHPqojc3B8igaOb9Ln27ynQcysv28b+NOsR2fyVmCIi84wxaaX1izRO5DHXz8PAUKBjlHOs9tRJiuepC61f+DdLt1XybCKg9QDrv37kJcUFCPjStzTrZVc557xsM48O/ItPgEDxjMQej/XZT+0GV0yxGUc9rgdEv4tt+mqwdRkadbLbQ+90xmtQeu3uUEFlJ95d8nlehkdhDL36Z/uWXlWo3Th4rQsvZz5jf7fBqt0l1bNlVi8J8MbrfZ51P+19Hox+zq56vbE1Cck2/sHjsTaFM5+GsV9C2pVw7A32PHcwYVKYq/LWacFdfxNqqQCpRkRqWA+kNrbWeY1ldL9W3PvZMpZsDuPNq7rgTWZXWo4gsO6ZWxfZhxZYwXLtLN/xO7fA5Jth5xqrj/bEWdfi426y42/8DfqcD0Nvg2/u8LnkhpxbK//9+GS4bLJ9yP0QTE0h+AWBdBwa2g5y3hv2jXrzfP/6MCLWx/+z64JX36soGneB05+wcUj5h+BhV92JlJY2PsUTb1OKg1V5vuk4aVw22caNNOpg3/4DNRDnvOL/CXDKI7BmqvWqCqTDEPvj5ZKP7ZiHMn3CR6kRRBpsuMTJmbVYRJYBq4AQeRtqDr1b1Wfykq3syzlMUqvXbmTVEcFWKYH0OBOG32MfTsGIT7Rvxld8bfXRSSn2TbdWA2tw7XO+r2/HYcXPd/vvdzgejr8Vblph00v8YwHcsdmurDxxcFUQI/TYgDQsJQW9JdeDEfcXPwesEbfvBaHPPWJk8PaEELr8rqcWbytpldR2MPx9rn1wi/jcXQGu+RX+7Lg1u1dMLY/0bTdxakv0c4S922D9t4CATC99zrNOF+GkTxex/Wo1DF7GVTlsiTTY0Jsr6wzgZKClMSZE6trIEJGuIrLQ9bNPRG4I6DNURDJdfYIZ+iucccd3JL/Q8P6cTZU9lerNESdbZwCAP39m1VynP2FzHP3pZVtop14L6/Y8+lkbTxPneutt7VLf/m2WVbl0ON66hdZzVjCeBDv2GU/5+nozB8S5oo2DGV8DI9e9D+ZWaVYwBuO29XbunVy1MdoMsmqgQIbcZB0kvLR1OUCUVD+8WU+7SmnRz38l4Y6fqJtqg+YG/sXXduazcPFHpeedUpQSiNTFt9xcXIwxq7CeXjgleDdjqygGMsMYE8L1onIY0qUJHVPr8NPvOzjhiFREoHNTfRuLipMetMWDWg/wxdWUtAIIRdNugJMyo2F7G8QJ1ubiHfeL662h/oynYNYL/um7j/m7L7bDy4DLbIbW98cCxufKe+w/rJ1iyM02BYc7Aj0+0a62vCuurAxf1bs2R8Mml+oPfLmXAM57zcZyZCyFkeNLvl+PB/76U/H2oXfADCdYNDBorn8MYk2UGk+kLr43lXTcGPN42aZTxHBgbXkKrVgiIvRt3YBPFmzmpCdsmon148upsNPhjsfjS5YXDTetDB5h3KynTVbpjgC/I92qfxJrB1951G7kn5gvPgl6jLaqG2OsMX/nahuNLeJzGrg/E2Y8Bh2GFh/TXT/7iik2tcuu1TYHmJe/TrdusynNYUyQdOqR4M2WoCjlRKQWsDRs7iyv0vgMYDawOuQZ0XEhEOq/Z7CILAK2ALcYY4oViBCRccA4gLZt2wYeLheO6dSYTxaUkm5bKX/qtQjePvpZ6HmWs0JxiFZ3n1zfur72Oie0jWVIGJltRXzZCdyUVmDJizdViKJUIpEKkdZAf2NMFoCI3A98ZYy5JFYTEpFE4EzgjiCH5wPtjDH7ReRUbPqVYn6MxpgJwASwcSKxmltJ9G/XsCIuo0RLYh27iogFV061+a6izUwbK25cGjrQT1EqiEgN680Ad8a6XKctlowC5htjMgIPGGP2GWP2O9uTgQQRaRLj60dFu4BAw537D3HvZ0vJyQuSRlyp3jTpAoOuqexZWMN5sDoYilKBRLoSeROYLSKfYB3wzwJej/GcxhBClSUizYEMY4wRkYFYIbgrWN+KJj7OQ1q7hszdYJPHpT1ka2D0bd2AcwbU6FAaRVEOYyKNWH8YuBzYg314X2aMeSRWkxGROsBJwMeutqtF5Gpn91xgqWMTeRq40ESSt6WceWrMkZzcw39hdjBX1Q2Kohy+hCVEROQoZxWAMWY+thDVCOByEYnZetoYc8AY09gYk+lqe9EY86Kz/awxpqcxpq8xZpAx5tdYXTsWtGpQiwl/TuO6Yb7Ec2HVG1EURammhLsSeQnHFiIixwOPAG8AmTgGbMXHpYN9ace/XrqNgsIqs1hSFEWJKeEKkThjjNeR/QJggjHmI2PMPUAFlUarPjSrl8z68acx/k+92bj7IJ3unMynCzazZnsWj3y9gkIVKoqiHCaEa1iPE5F4Y0w+NhDQXXBCs62FoEMTX96kG97zpcoec1Rb2jeJoD6CoihKFSXclci7wE8i8hmQDcwAEJHOWJWWEoRWDWsFbVc7iaIohwthF6USkUFAC+BbY8wBp+0IoK5jbK+SlGdRqnCYu343nVLrcuSDU/3aX7xkABN/WceRbRrQrUUKufmFHH9EKi3qBxc8iqIoFUm4RakiqmxYHalsIeLlwKF8DuTmc8Okhfy6NnhoS48W9Zh8/ZCgxxRFUSqScqlsqERPnaR4mqYk88YVA0P2Wb51Hzv3H+JQvi/KPX3PwYqYnqIoSlSoEKlgEuI8fH7dsX41gdykPfQdXe/+BoDvlmdw3H+m8cPKYhlgFEVRqgQqRCqBPq0bsO6R07jlZFvUqEeLesX6dLlrMjNW7wBg0Sb1XVAUpWoSaT2RJOAcoL37XGPMA7GdVs3guhO78NcTOnHgUD79HvA3vOcVGN6YacupiMDKbfv435Tfue+MHrQJSPaoKIpSWUS6EvkMGA3kAwdcP0qUJMR5aFA7kRtH2FXJ8G5Ni/V5d/ZGRj45g+9WZDDk0WkAGGP46fcdGGPI2JfD4e4goShK1SQi7ywRWWqM6VWO84k5VcU7qzQKCw2Z2Xk0rJPIGc/8zJLNoVVYI7o3paDQMG3VDm4+6Qgem/o7/zylK9cO0+QBiqLEhvLyzvpVRHpHOSelBDweoWGdRADe+cvRRe0XHtWmWN/vVmxn2iprL3ls6u8A/HfKKpakq+1EUZSKJVIhchwwT0RWichiEVkiIovLY2I1mZTkBFY9NJI5d41gSJdUAO4+rXup553x7M+c/fwvrN95gO9XZFBYaNhzIJftWTn8npHFnPU2/VnmwTxy8wsB+HXtTk0QqShK1ESqzmoXrN0YsyFmM4ox1UWdFQpjDPty8qlfK4E12/fTsUkddu4/xPDHfyIrJ/JaJeseOZUOd0xmVK/mXHR0Wy59dTY3jjiCjxekc+/pPRjePdaFKhVFqY6Eq86KyDvLGLNBRBpi65onuw5VWSFS3RER6teytbw7N60LQNN6ySy+72Q27c7m5zU7qZMUx/WTFpY0TBF/edMK1K+XbqNtY+vl9cPKDDbsOsiDXy5XIaIoSkRE6uJ7FXA90BpYCAwCZgInxn5qSkmICG0b1+aixm0BGNatKWu376dT07o88MVymtVL4oQjmvLdigwmTP+j6LzvVmwv2n7pJ9u+yLGl1K+dWHTsqe9Wk5zgITUlifW7DnLTSUdUxG0pilLNiFSdtQQ4CvjNGNNPRLoB/zbG/Km8JlhWqrs6Kxa0v/0rAIZ0acKM1TtL7Z8U7+GQYzPx8txF/TmtT4tymZ+iKFWPclFnATnGmBwRQUSSjDErRaRrlHMshoisB7KAAiA/8AZERICngFOBg9ga71U2g3BVYdadw9mamUO/Ng3YlpnDOS/8yua9odPRBwoQgGvfmc/DXyWzJTOHj645hiZ1E/k9Yz8n9VD1l6LUZCIVIuki0gD4FJgqInuIvT1kmDEm1OvyKKw9pgtwNPCC86mUQLN6yTSrZ01YzesnM+HPA5iyLIPm9ZL5YN4mFmzcW9T3imM7MPGXdUHH2ZKZA8A5L/hK20+8LI1Nu7O57/NlvHjJAEb2al6Od6IoSlUj6lTwInICUB/4xhiTG5PJ2JVIWighIiIvAT8aY9519lcBQ40xW0ONqeqs0snMzuOFH9dSv1YC1wztxNdLtnLN2/NJa9eQuRv2RDTWqodGMuLxn2hRvxa3jexGn9b1SYgLz5N80+6DLNuSycheqjZTlMqmXIINxXKJiNxrjPkJa1zvF+0kg2CAb0VknoiMC3K8FbDJtZ/utAXOc5yIzBWRuTt27Ijh9A5P6tdK4PZR3bhmaCcARvVuwZqHR3F+QKBjt+YppY41c+0uNu3OZva63Zzzwq90uetrMrPzeHn6H9z32VK278th+76coOee9vQMrn5rPsYYlqRn8sjXKzSdi6JUcSJVZz0PFGK9sR7A2i8+whrbY8FxxpjNItIUqy5baYyZHukgxpgJwASwK5EYza1GER/n4dz+rWlSN5GMfYe44+MlvHb5UQx+5AfACp7M7Lxi51322pxibX3/9W3Rtjep5GuXH8XQI1IRV078fU7cy6H8Qs576Vdy8gq5YfgR5BcWMvrZX3jign70bdMgpvepKErZiFSIHG2M6S8iCwCMMXtEJLG0k8LFGLPZ+dwuIp8AAwG3ENkMuF+PWzttSjng8QgndrOG8zEDrSvxXad254SuqRzRLIXMg3n0fcAKiLaNarNxd/gFtC53hM2Do3sysENjOqXWKTq2/1B+UUR91qE8VmzN4o+dB/jvlFW8dZWawBSlKhGpEMkTkTis2gkRScWuTMqMiNQBPMaYLGf7ZOxqx83nwHUiMglrUM8syR6ixJ6/HN+xaLt+7QQmjRtETl4BJxyRSoc7JgNWMNzz2TIA3rhiIN8u28bbszYGHc/bz83KrVl4M7Fk5eQT56xWClW1pShVjkiFyNPAJ0AzEXkYOBe4J0ZzaQZ84qg34oF3jDHfiMjVAMaYF4HJWPfeNVgX38tjdG0lSgZ1bFy0PevO4eTkFdCucR2Gdm1Kywa1iPMIJxyRSkKch9d/XR/WmJe8Oqtoe3VGFnkFVnj8nrGfH1dt59jOTUIa63PzC3nhx7Vcdkx76tdOiP7GFEUJi4i9s5wAw+HO7vfGmJUxn1UMUe+sqoc3+BGgcZ1Edh2IzLkvNSWJvx7fkdYNa3H1W/N58oJ+PDx5BR2b1KFRnUS+XrqN64d34UaNsleUqAnXOyssISIinwc2OZ8GwBhzZsQzrCBUiFQ9tmflUC85gdyCQuJEWLtjP1k5+TSrl8yIx3+KyTXuPLUbfxnSkdd+Wc8ZfVvSqE4icZ4Qhe0VRSlGrIXIDqxr7bvALHxCBADH3bdKokKkelFQaHhu2hoGd2rMEc1S/Dy7IqFTah0ePbcP57wwE4DEeA9J8R7GDGzLnaeGTqs/dXkGf+zYT8sGtRjWrSl1kyLV+CrK4UGshUgccBIwBugDfAW8a4wpbhWtYqgQqd5s2ZtNozqJJMV72Lj7INl5BTw3bS2/rNnJ7gjVYF686rBBHRvTq1V9v2NuVduf+rfi8fNjGQalKNWHmObOMsYUAN8A34hIElaY/Cgi/zLGPFu2qSpKaFo2qFW03a6xdQN+ZsyRFBYaHvpqBV8s3sLgjo35fNEWvvz7cZz+zM+ljrkj6xAPfbXCr+2kHs146Cz/ys/bMoMHRSqK4iNsw7ojPE7DCpD2WHfbid7YjqqKrkRqBsYYCg08+OVyLhnUjsXpe7np/UUAtKifzMhezXntl/URj3ts58aM6N6My45pX9R2MLeA7VmHuGHSAhalZ/LQWb24ZFDQem1FHMovIMHjwaN2GaWaEGt11ptAL6yL7SRjzNKyT7FiUCFSc8nJKwAgOSEOgKycPK56Yy792jTgJVeNlUiomxTP/kP+FSU7pdbh+5uHUlhoggqJgkJDpzsn85chHbjrtB5RXVdRKppYC5FC4ICz6z5BAGOMqRfVLCsAFSJKIMYYXv15HfVqJVAnMZ4Dufnc+uHiouPHdW7Cz2tKr7vipUX9ZLY6qq+/De3ErSO7sWn3QT5dsJl6tRIY2as5R//7e5LiPax6aBRfLt7CCz+upWWDWrz851L/RxWlUoi1TSSiRI2KUpUREa4a4ou8Lyw0rNm+nwuOakNmdh5HtmlQFH3fplEt9h7MK7Ge/VaX7eT5H9fy1m8bivKAASzfsg+wOcHchvtlTru3zzdLt3LJoHY0reeuPO3jp993sH7nAca6VGtejDEYg6rLlAon6lTw1QVdiSjRkLEvh3iP0LhuEvtybKLJOBE+nJdO3zYNmLJsG/Ee4Zkf1pTpOlNuOJ4te7O5/HVf4srVD4/isW9/58+D2xU5FhQWGjreaQXb+vGnMW/DHprXT6aVc/yK1+fww8rtrB9/WpnmoyheyquyoaLUCJq5VgP1kn3pU7yrgH5ONuFzB7Rm+GM/ke8k+1o//jS63DW5KFVLaZzyZPEk1Y9MXsnEX9bxzdKtvHRpGmc99wvZjn0HrK3HWxhs3SOnMn/jHn5Yud1ef+cBfl6zs1RDv6LECl2JKEoZMcaw60Auew/m0blpXXLyCvh4/mbOT2uNR6RoBeHlyLYNePKCfpzy5HRy8iLPXzqoYyN++2M3ALeO7Mqj36wqOta/bQPmb9zL9zefQKfUuoB1ab7yjTmMO74jp/dpWYY7VWoSMTWsV2dUiCiVzcHcfBLjPMQHJI1cuGkv783ZyOqM/RzXpQlPfre6zNfq1jyFlduyAGvkP65LEy562ZfQcnS/ljx+fr8ypYDJLyhk4i/ruHRQe2olxpV5zkrVRIWIgwoRpbrw/YoM3pm1kVtO6Upmdh6fzN/MvI17KCw0/O/8vvzp+V9LHyQMhnRpwozVO3nygn6cdaQtDPrl4i08MfV3vr3xBAD2HMylVkIcifGeYhmT3529kTs+XsJNJx3BP4Z3icmclKqHChEHFSJKdaag0CBYr6tPF2zmhvcWArDo3pM558VfWbN9f7nP4cRuTdlzMJeTezTnP9+sZMzAtrw7eyN/P7EzN5/clZy8gqJYnMrGGMOUZdsY0b1ZsZWfEhkqRBxUiCiHE7/9sYvuLepRv5Y19m/PygEDIx7/ibwCw4fXDCY7t4AlmzMZM7AtN7+/iIx9OSzZnMmh/JjUjysi3iO0aVSbdTsP8LehnejRsh5z1u1myeZM3v/rYH5es5N4j4fjujQBYM763Xy6YDMPndXLryxyLPly8cl7+gYAAA21SURBVBaue2cBt4/qxtUndCqXa9QU1DtLUQ5D3EXAAJqmWC+yxfef4tee1r4RAM9d3B+wecCuenMOV5/Qib6tG7Bh10G/4l/RkF9oWLfTxiA//+Nav2Od7/q6aPulSwcweclWPlu4BYDGdZM4uUezYskvwZZGTo73sHlvNovSMzmzb2SOADuyDgE2cadSMagQUZQaQPP6yXz59yFF+20a1ebz645lxuqdtG5Yi+snLaRP6/p8ft1xrNmexU+/72Tu+t18vXRbma/91/+b57f/9Perefp760Tw6Dl9OLNfS7rd803Qc0/t1bxILbU9K4emKclMW7mdhnUSi9yslcpFhYii1FD6tG5An9b2QZwQ51M7dW6aQuemKVx5XAe+XLyFzxdu4aohHdmXncfmvdm8N2cTy7f6ou2PbNuABRv3RjWHWz9azK0fLQ55/PhHpzGqdwty8gp4e9ZGUpLiyXJyl825awRHPfwd/z67N2MGtuHd2Zt0BVIJVBmbiIi0Ad7E1lo3wARjzFMBfYYCnwHrnKaPjTEPlDSu2kQUJfbsPZhLYryHtdsPcETzumTnFtDvgal+fU7v04IvF2+tkPn899w+/NOV/6xF/WSMgab1kkjfk81R7RtyZt9WDO2aSp2keOZv3MNbv23g3AGt6dikLs3r+6ea+T0ji86pdfF4hI27DnL8f6fx9xM7065xHc4d0LpC7qmyqXaGdRFpAbQwxswXkRRgHnCWMWa5q89Q4BZjzOnhjqtCRFEqht8zsmjTsDYLNu2hVYNatGtch6WbM5m3YQ9/HtwOEWHF1n28+vM6fl2zk/vO7FlM1VUZpCTFc/lxHWhZP5mzjmzFj6u2c/Vb8zmxW1M27j5IozqJzF63u6j/Q2f14uQezULmODtcqHZCJBAR+Qx41hgz1dU2FBUiinLYsPtALo3qJALWe2vu+j10bV6XXftz6dIshZlrd/Gfb1YW9U9Jiqdlg1qsysiqrCkXUS85nttHdWfMwDbMWb+Ho9o3REQ45YnprMrI4v4zenDZsR38zsnYl8N9ny1j/sY9eESI8wiTxg2iTaPaxcafvW43m3Yf5JwoVj65+YWs3bGf7i2iT7BerYWIiLQHpgO9jDH7XO1DgY+AdGALVqCUWKJXhYiiVF/yCwq565OlHNWhEYM6NqKg0BRVuFyxdR9jXv6Nni3r8dplAzn9mRn8nrGfu0/rzheLtrAoPbNC5njfGT341xfLObNvSxLiPHw0P73o2LGdG5OVk8/i9Eya10smziNsDmK3efnPaQztmloU2Llu5wGG/e9HgKiSaj7wxXIm/rKOGbcOCyqgwqHaChERqQv8BDxsjPk44Fg9oNAYs19ETgWeMsYUC5kVkXHAOIC2bdsO2LBhQwXMXFGUyuTVn9fx4JfLWXTvydSvbeNofliZwb2fLeNvQzvz+aLNjOjejHdmb+R/5/Xl5vcXkdauIS0b1OKp78ueciYWDO7YmHtO78GpT88oalv3yKmICNm5Bdz1yRIuP7YDvVvX54eVGcxYvZN7TuvBwbwC6ib5/KTOeu4XFm7aywdXD+Yox907UqqlEBGRBOBLYIox5vEw+q8H0owxISsI6UpEUWoGxhjyC02xNC3hUFho+GLxFto2qs25L86kQ5M6RdkARvVqXuTqPKJ7M64Z2onvV2T4xcYMaNeQeRv2FO3He6Qos3NZ6dosBY/H2pOC4REoNNCxSR2Gd2/Ksi37+HXtLgBevGQAI3s1j+q61U6IiA1hfQPYbYy5IUSf5kCGMcaIyEDgQ6CdKeEmVIgoihIJBYWGOI+Qm1/It8u3cVrvFuzcn0vtxDjqJPlHRXy6YDM/rNzOkxf0I7egkI/mp5OZncffhnbmo3npvD93E2MGtuWsI1tx8/uL/FRdwWhSN5Gd+3Njdi8PjO7Jnwe3j+rc6ihEjgNmAEsAb36GO4G2AMaYF0XkOuAaIB/IBm4yxpSYlU6FiKIoVYX7PlvKGzM30KB2Ai9eMoCXp//B8O7NuOjotmTsy6Fh7UQ+nJfOym37eHOmvxq+Wb0kerWsz/dO7Zhw8JZrjoZqJ0TKCxUiiqJUR7bvy+HVn9eRk1fA4E6NGdmrBdsycxj0yPcA3HVqdxZs2sPZR7ZmQLuGpCTH879vV/HRvHQKCg17DuZxTv/WPHZ+36iur0LEQYWIoig1kbOe+4W6SfG8ddXRUZ2vCRgVRVFqMK9ddhR1k8v/Ea9CRFEU5TCkoRPEWd5o1RZFURQlalSIKIqiKFGjQkRRFEWJGhUiiqIoStSoEFEURVGiRoWIoiiKEjUqRBRFUZSoUSGiKIqiRI0KEUVRFCVqVIgoiqIoUaNCRFEURYkaFSKKoihK1KgQURRFUaJGhYiiKIoSNSpEFEVRlKhRIaIoiqJETZUSIiIyUkRWicgaEbk9yPEkEXnPOT5LRNpX/CwVRVEUL1VGiIhIHPAcMAroAYwRkR4B3a4E9hhjOgNPAP+p2FkqiqIobqqMEAEGAmuMMX8YY3KBScDogD6jgTec7Q+B4SIiFThHRVEUxUVVqrHeCtjk2k8Hjg7VxxiTLyKZQGNgp7uTiIwDxjm7+0VkVRnm1SRw/BpATbvnmna/oPdcUyjLPbcLp1NVEiIxwxgzAZgQi7FEZK4xJi0WY1UXato917T7Bb3nmkJF3HNVUmdtBtq49ls7bUH7iEg8UB/YVSGzUxRFUYpRlYTIHKCLiHQQkUTgQuDzgD6fA2Od7XOBH4wxpgLnqCiKorioMuosx8ZxHTAFiAMmGmOWicgDwFxjzOfAq8D/icgaYDdW0JQ3MVGLVTNq2j3XtPsFveeaQrnfs+iLvKIoihItVUmdpSiKolQzVIgoiqIoUaNCJASlpWCprohIGxGZJiLLRWSZiFzvtDcSkakistr5bOi0i4g87XwPi0Wkf+XeQfSISJyILBCRL539Dk76nDVOOp1Ep/2wSK8jIg1E5EMRWSkiK0Rk8OH+exaRG52/66Ui8q6IJB9uv2cRmSgi20Vkqast4t+riIx1+q8WkbHBrhUOKkSCEGYKlupKPnCzMaYHMAi41rm324HvjTFdgO+dfbDfQRfnZxzwQsVPOWZcD6xw7f8H+P/27i3EqiqO4/j3RyM2KdiMgUxKjFIUdFFDQquHsLKQ6KUHG4TCfPKhy0vF0IMEPUV0sSKsoCKkh8oKfNBqjAgKJWFSu1hjShkzqZBGETLZv4e1jm7HKebszpkzZ/f7wGb2XntzWOv8B/5n7ct/P5XL6PxCKqsD1Smv8wywNSIuAxaSxl7ZOEuaC9wHLImIK0g36NxJ9eL8KnDrmLa64iqpG1hPeqD7GmB9LfHULSK8jFmAZcC2wnY/0N/qfjVprO8BNwP7gJ7c1gPsy+sbgb7C8aeOa6eF9NzRALAc2AKI9CRvx9iYk+4QXJbXO/JxavUY6hzvLODA2H5XOc6crmjRneO2BbilinEGeoG9ZeMK9AEbC+1nHFfP4pnI+MYrwTK3RX1pmjx9XwzsAOZExHDeNQLMyetV+S6eBh4C/srbs4FjEfFn3i6O64zyOkCtvE47mQ8cAV7Jp/BeljSDCsc5In4CngB+AIZJcdtFteNcU29cGxZvJ5H/KUkzgbeBByLi1+K+SD9NKnPvt6TbgMMRsavVfZlEHcDVwAsRsRj4ndOnOIBKxrmLVKR1PnAhMIOzT/tU3mTH1UlkfBMpwdK2JE0jJZBNEbE5N/8sqSfv7wEO5/YqfBfXAbdLOkiqDr2cdL3g/Fw+B84cVxXK6xwCDkXEjrz9FimpVDnONwEHIuJIRIwCm0mxr3Kca+qNa8Pi7SQyvomUYGlLkkR68v/riHiysKtYUuZu0rWSWvtd+S6PpcDxwrS5LUREf0TMi4heUiy3R8Rq4CNS+Rw4e8xtXV4nIkaAHyVdmptuBL6iwnEmncZaKum8/H9eG3Nl41xQb1y3ASskdeUZ3IrcVr9WXyCaqguwEvgW2A880ur+NHBc15OmuruBwbysJJ0LHgC+Az4EuvPxIt2pth/YQ7rzpeXj+A/jvwHYktcXADuBIeBNYHpuPzdvD+X9C1rd75JjXQR8nmP9LtBV9TgDjwLfAHuB14HpVYsz8Abpms8oaca5tkxcgXvy2IeANWX747InZmZWmk9nmZlZaU4iZmZWmpOImZmV5iRiZmalOYmYmVlpTiJmDSDppKTBwtKwys+SeosVW82mkinzelyzNvdHRCxqdSfMJptnImZNJOmgpMcl7ZG0U9LFub1X0vb8jocBSRfl9jmS3pH0RV6uzR91jqSX8rsy3pfU2bJBmRU4iZg1RueY01mrCvuOR8SVwHOkasIAzwKvRcRVwCZgQ27fAHwcEQtJta6+zO2XAM9HxOXAMeCOJo/HbEL8xLpZA0j6LSJmjtN+EFgeEd/nwpcjETFb0lHS+x9Gc/twRFwg6QgwLyJOFD6jF/gg0guHkPQwMC0iHmv+yMz+nWciZs0X/7BejxOF9ZP4eqZNEU4iZs23qvD3s7z+KamiMMBq4JO8PgCsg1PvhJ81WZ00K8O/Zswao1PSYGF7a0TUbvPtkrSbNJvoy233kt46+CDpDYRrcvv9wIuS1pJmHOtIFVvNpiRfEzFronxNZElEHG11X8yawaezzMysNM9EzMysNM9EzMysNCcRMzMrzUnEzMxKcxIxM7PSnETMzKy0vwFGPDqhj5eURAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def plot_history(history):\\n\",\n \" hist = pd.DataFrame(history.history)\\n\",\n \" hist['epoch'] = history.epoch\\n\",\n \"\\n\",\n \" plt.figure()\\n\",\n \" plt.xlabel('Epoch')\\n\",\n \" plt.ylabel('Mean Abs Error [MPG]')\\n\",\n \" plt.plot(hist['epoch'], hist['mae'],\\n\",\n \" label='Train Error')\\n\",\n \" plt.plot(hist['epoch'], hist['val_mae'],\\n\",\n \" label = 'Val Error')\\n\",\n \" plt.ylim([0,5])\\n\",\n \" plt.legend()\\n\",\n \"\\n\",\n \" plt.figure()\\n\",\n \" plt.xlabel('Epoch')\\n\",\n \" plt.ylabel('Mean Square Error [$MPG^2$]')\\n\",\n \" plt.plot(hist['epoch'], hist['mse'],\\n\",\n \" label='Train Error')\\n\",\n \" plt.plot(hist['epoch'], hist['val_mse'],\\n\",\n \" label = 'Val Error')\\n\",\n \" plt.ylim([0,20])\\n\",\n \" plt.legend()\\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \"\\n\",\n \"plot_history(history)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"使用early stop\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"..........................................................\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"model = build_model()\\n\",\n \"\\n\",\n \"\\n\",\n \"early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\\n\",\n \"\\n\",\n \"history = model.fit(normed_train_data, train_labels, epochs=EPOCHS,\\n\",\n \" validation_split = 0.2, verbose=0, callbacks=[early_stop, PrintDot()])\\n\",\n \"\\n\",\n \"plot_history(history)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"测试\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Testing set Mean Abs Error: 1.85 MPG\\n\"\n ]\n }\n ],\n \"source\": [\n \"loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=0)\\n\",\n \"\\n\",\n \"print(\\\"Testing set Mean Abs Error: {:5.2f} MPG\\\".format(mae))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 5.预测\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"test_predictions = model.predict(normed_test_data).flatten()\\n\",\n \"plt.scatter(test_labels, test_predictions)\\n\",\n \"plt.xlabel('True Values [MPG]')\\n\",\n \"plt.ylabel('Predictions [MPG]')\\n\",\n \"plt.axis('equal')\\n\",\n \"plt.axis('square')\\n\",\n \"plt.xlim([0,plt.xlim()[1]])\\n\",\n \"plt.ylim([0,plt.ylim()[1]])\\n\",\n \"_ = plt.plot([-100, 100], [-100, 100])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"error = test_predictions - test_labels\\n\",\n \"plt.hist(error, bins = 25)\\n\",\n \"plt.xlabel(\\\"Prediction Error [MPG]\\\")\\n\",\n \"_ = plt.ylabel(\\\"Count\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "106-example_save_and_restore_models.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# TensorFlow2.0教程-保持和读取模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'2.0.0-alpha0'\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from __future__ import absolute_import, division, print_function\\n\",\n \"\\n\",\n \"import os\\n\",\n \"\\n\",\n \"import tensorflow as tf\\n\",\n \"from tensorflow import keras\\n\",\n \"\\n\",\n \"tf.__version__\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"导入数据\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()\\n\",\n \"\\n\",\n \"train_labels = train_labels[:1000]\\n\",\n \"test_labels = test_labels[:1000]\\n\",\n \"\\n\",\n \"train_images = train_images[:1000].reshape(-1, 28 * 28) / 255.0\\n\",\n \"test_images = test_images[:1000].reshape(-1, 28 * 28) / 255.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.定义一个模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_2\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_4 (Dense) (None, 128) 100480 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout_2 (Dropout) (None, 128) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_5 (Dense) (None, 10) 1290 \\n\",\n \"=================================================================\\n\",\n \"Total params: 101,770\\n\",\n \"Trainable params: 101,770\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"def create_model():\\n\",\n \" model = keras.Sequential([\\n\",\n \" keras.layers.Dense(128, activation='relu', input_shape=(784,)),\\n\",\n \" keras.layers.Dropout(0.5),\\n\",\n \" keras.layers.Dense(10, activation='softmax')\\n\",\n \" ])\\n\",\n \"\\n\",\n \" model.compile(optimizer='adam',\\n\",\n \" loss=keras.losses.sparse_categorical_crossentropy,\\n\",\n \" metrics=['accuracy'])\\n\",\n \" return model\\n\",\n \"model = create_model()\\n\",\n \"model.summary()\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2.checkpoint回调\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 1000 samples, validate on 1000 samples\\n\",\n \"Epoch 1/10\\n\",\n \" 544/1000 [===============>..............] - ETA: 0s - loss: 2.0658 - accuracy: 0.2831 \\n\",\n \"Epoch 00001: saving model to 106save/model.ckpt\\n\",\n \"1000/1000 [==============================] - 1s 855us/sample - loss: 1.8036 - accuracy: 0.4190 - val_loss: 1.3101 - val_accuracy: 0.6700\\n\",\n \"Epoch 2/10\\n\",\n \" 800/1000 [=======================>......] - ETA: 0s - loss: 1.0327 - accuracy: 0.7125\\n\",\n \"Epoch 00002: saving model to 106save/model.ckpt\\n\",\n \"1000/1000 [==============================] - 0s 132us/sample - loss: 1.0101 - accuracy: 0.7190 - val_loss: 0.8742 - val_accuracy: 0.7650\\n\",\n \"Epoch 3/10\\n\",\n \" 768/1000 [======================>.......] - ETA: 0s - loss: 0.7168 - accuracy: 0.7865\\n\",\n \"Epoch 00003: saving model to 106save/model.ckpt\\n\",\n \"1000/1000 [==============================] - 0s 113us/sample - loss: 0.7214 - accuracy: 0.7900 - val_loss: 0.7212 - val_accuracy: 0.7950\\n\",\n \"Epoch 4/10\\n\",\n \" 992/1000 [============================>.] - ETA: 0s - loss: 0.5918 - accuracy: 0.8367\\n\",\n \"Epoch 00004: saving model to 106save/model.ckpt\\n\",\n \"1000/1000 [==============================] - 0s 90us/sample - loss: 0.5904 - accuracy: 0.8380 - val_loss: 0.6292 - val_accuracy: 0.8140\\n\",\n \"Epoch 5/10\\n\",\n \" 864/1000 [========================>.....] - ETA: 0s - loss: 0.4970 - accuracy: 0.8600\\n\",\n \"Epoch 00005: saving model to 106save/model.ckpt\\n\",\n \"1000/1000 [==============================] - 0s 105us/sample - loss: 0.4997 - accuracy: 0.8600 - val_loss: 0.5710 - val_accuracy: 0.8410\\n\",\n \"Epoch 6/10\\n\",\n \" 896/1000 [=========================>....] - ETA: 0s - loss: 0.4247 - accuracy: 0.8839\\n\",\n \"Epoch 00006: saving model to 106save/model.ckpt\\n\",\n \"1000/1000 [==============================] - 0s 97us/sample - loss: 0.4316 - accuracy: 0.8810 - val_loss: 0.5430 - val_accuracy: 0.8420\\n\",\n \"Epoch 7/10\\n\",\n \" 32/1000 [..............................] - ETA: 0s - loss: 0.2628 - accuracy: 0.9688\\n\",\n \"Epoch 00007: saving model to 106save/model.ckpt\\n\",\n \"1000/1000 [==============================] - 0s 81us/sample - loss: 0.3724 - accuracy: 0.8930 - val_loss: 0.5041 - val_accuracy: 0.8480\\n\",\n \"Epoch 8/10\\n\",\n \" 32/1000 [..............................] - ETA: 0s - loss: 0.2136 - accuracy: 0.9375\\n\",\n \"Epoch 00008: saving model to 106save/model.ckpt\\n\",\n \"1000/1000 [==============================] - 0s 75us/sample - loss: 0.3221 - accuracy: 0.9030 - val_loss: 0.4861 - val_accuracy: 0.8510\\n\",\n \"Epoch 9/10\\n\",\n \" 960/1000 [===========================>..] - ETA: 0s - loss: 0.3195 - accuracy: 0.9177\\n\",\n \"Epoch 00009: saving model to 106save/model.ckpt\\n\",\n \"1000/1000 [==============================] - 0s 108us/sample - loss: 0.3230 - accuracy: 0.9150 - val_loss: 0.4580 - val_accuracy: 0.8600\\n\",\n \"Epoch 10/10\\n\",\n \" 704/1000 [====================>.........] - ETA: 0s - loss: 0.2577 - accuracy: 0.9219\\n\",\n \"Epoch 00010: saving model to 106save/model.ckpt\\n\",\n \"1000/1000 [==============================] - 0s 128us/sample - loss: 0.2701 - accuracy: 0.9170 - val_loss: 0.4465 - val_accuracy: 0.8620\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"check_path = '106save/model.ckpt'\\n\",\n \"check_dir = os.path.dirname(check_path)\\n\",\n \"\\n\",\n \"cp_callback = tf.keras.callbacks.ModelCheckpoint(check_path, \\n\",\n \" save_weights_only=True, verbose=1)\\n\",\n \"model = create_model()\\n\",\n \"model.fit(train_images, train_labels, epochs=10,\\n\",\n \" validation_data=(test_images, test_labels),\\n\",\n \" callbacks=[cp_callback])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"checkpoint model.ckpt.data-00000-of-00001 model.ckpt.index\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"!ls {check_dir}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1000/1000 [==============================] - 0s 69us/sample - loss: 2.4036 - accuracy: 0.0890\\n\",\n \"Untrained model, accuracy: 8.90%\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = create_model()\\n\",\n \"\\n\",\n \"loss, acc = model.evaluate(test_images, test_labels)\\n\",\n \"print(\\\"Untrained model, accuracy: {:5.2f}%\\\".format(100*acc))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1000/1000 [==============================] - 0s 47us/sample - loss: 0.4465 - accuracy: 0.8620\\n\",\n \"Untrained model, accuracy: 86.20%\\n\"\n ]\n }\n ],\n \"source\": [\n \"model.load_weights(check_path)\\n\",\n \"loss, acc = model.evaluate(test_images, test_labels)\\n\",\n \"print(\\\"Untrained model, accuracy: {:5.2f}%\\\".format(100*acc))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3.设置checkpoint回调\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 1000 samples, validate on 1000 samples\\n\",\n \"Epoch 1/10\\n\",\n \"1000/1000 [==============================] - 1s 1ms/sample - loss: 1.7242 - accuracy: 0.4490 - val_loss: 1.2205 - val_accuracy: 0.6890\\n\",\n \"Epoch 2/10\\n\",\n \"1000/1000 [==============================] - 0s 102us/sample - loss: 0.9133 - accuracy: 0.7450 - val_loss: 0.8194 - val_accuracy: 0.7800\\n\",\n \"Epoch 3/10\\n\",\n \"1000/1000 [==============================] - 0s 88us/sample - loss: 0.6489 - accuracy: 0.8360 - val_loss: 0.6748 - val_accuracy: 0.8050\\n\",\n \"Epoch 4/10\\n\",\n \"1000/1000 [==============================] - 0s 78us/sample - loss: 0.5492 - accuracy: 0.8360 - val_loss: 0.6144 - val_accuracy: 0.8150\\n\",\n \"Epoch 5/10\\n\",\n \" 32/1000 [..............................] - ETA: 0s - loss: 0.4468 - accuracy: 0.9062\\n\",\n \"Epoch 00005: saving model to 106save02/cp-0005.ckpt\\n\",\n \"1000/1000 [==============================] - 0s 130us/sample - loss: 0.4755 - accuracy: 0.8750 - val_loss: 0.5483 - val_accuracy: 0.8330\\n\",\n \"Epoch 6/10\\n\",\n \"1000/1000 [==============================] - 0s 94us/sample - loss: 0.4191 - accuracy: 0.8790 - val_loss: 0.5164 - val_accuracy: 0.8500\\n\",\n \"Epoch 7/10\\n\",\n \"1000/1000 [==============================] - 0s 107us/sample - loss: 0.3699 - accuracy: 0.8980 - val_loss: 0.4935 - val_accuracy: 0.8420\\n\",\n \"Epoch 8/10\\n\",\n \"1000/1000 [==============================] - 0s 87us/sample - loss: 0.3404 - accuracy: 0.9070 - val_loss: 0.4559 - val_accuracy: 0.8600\\n\",\n \"Epoch 9/10\\n\",\n \"1000/1000 [==============================] - 0s 85us/sample - loss: 0.3060 - accuracy: 0.9250 - val_loss: 0.4513 - val_accuracy: 0.8630\\n\",\n \"Epoch 10/10\\n\",\n \" 800/1000 [=======================>......] - ETA: 0s - loss: 0.3016 - accuracy: 0.9150\\n\",\n \"Epoch 00010: saving model to 106save02/cp-0010.ckpt\\n\",\n \"1000/1000 [==============================] - 0s 120us/sample - loss: 0.2845 - accuracy: 0.9220 - val_loss: 0.4402 - val_accuracy: 0.8580\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"check_path = '106save02/cp-{epoch:04d}.ckpt'\\n\",\n \"check_dir = os.path.dirname(check_path)\\n\",\n \"\\n\",\n \"cp_callback = tf.keras.callbacks.ModelCheckpoint(check_path,save_weights_only=True, \\n\",\n \" verbose=1, period=5) # 每5\\n\",\n \"model = create_model()\\n\",\n \"model.fit(train_images, train_labels, epochs=10,\\n\",\n \" validation_data=(test_images, test_labels),\\n\",\n \" callbacks=[cp_callback])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"checkpoint\\t\\t\\t cp-0010.ckpt.data-00000-of-00001\\r\\n\",\n \"cp-0005.ckpt.data-00000-of-00001 cp-0010.ckpt.index\\r\\n\",\n \"cp-0005.ckpt.index\\r\\n\"\n ]\n }\n ],\n \"source\": [\n \"!ls {check_dir}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"载入最新版模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"106save02/cp-0010.ckpt\\n\"\n ]\n }\n ],\n \"source\": [\n \"latest = tf.train.latest_checkpoint(check_dir)\\n\",\n \"print(latest)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1000/1000 [==============================] - 0s 78us/sample - loss: 0.4402 - accuracy: 0.8580\\n\",\n \"restored model accuracy: 85.80%\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = create_model()\\n\",\n \"model.load_weights(latest)\\n\",\n \"loss, acc = model.evaluate(test_images, test_labels)\\n\",\n \"print('restored model accuracy: {:5.2f}%'.format(acc*100))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5.手动保持权重\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1000/1000 [==============================] - 0s 69us/sample - loss: 0.4402 - accuracy: 0.8580\\n\",\n \"restored model accuracy: 85.80%\\n\"\n ]\n }\n ],\n \"source\": [\n \"model.save_weights('106save03/manually_model.ckpt')\\n\",\n \"model = create_model()\\n\",\n \"model.load_weights('106save03/manually_model.ckpt')\\n\",\n \"loss, acc = model.evaluate(test_images, test_labels)\\n\",\n \"print('restored model accuracy: {:5.2f}%'.format(acc*100))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6.保持整个模型\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Train on 1000 samples, validate on 1000 samples\\n\",\n \"Epoch 1/10\\n\",\n \"1000/1000 [==============================] - 0s 240us/sample - loss: 1.7636 - accuracy: 0.4460 - val_loss: 1.2041 - val_accuracy: 0.7230\\n\",\n \"Epoch 2/10\\n\",\n \"1000/1000 [==============================] - 0s 82us/sample - loss: 0.9278 - accuracy: 0.7410 - val_loss: 0.7989 - val_accuracy: 0.7880\\n\",\n \"Epoch 3/10\\n\",\n \"1000/1000 [==============================] - 0s 97us/sample - loss: 0.6722 - accuracy: 0.7970 - val_loss: 0.6739 - val_accuracy: 0.8110\\n\",\n \"Epoch 4/10\\n\",\n \"1000/1000 [==============================] - 0s 110us/sample - loss: 0.5326 - accuracy: 0.8530 - val_loss: 0.6027 - val_accuracy: 0.8170\\n\",\n \"Epoch 5/10\\n\",\n \"1000/1000 [==============================] - 0s 88us/sample - loss: 0.4674 - accuracy: 0.8640 - val_loss: 0.5623 - val_accuracy: 0.8270\\n\",\n \"Epoch 6/10\\n\",\n \"1000/1000 [==============================] - 0s 91us/sample - loss: 0.3986 - accuracy: 0.8900 - val_loss: 0.5429 - val_accuracy: 0.8370\\n\",\n \"Epoch 7/10\\n\",\n \"1000/1000 [==============================] - 0s 87us/sample - loss: 0.3717 - accuracy: 0.8830 - val_loss: 0.5205 - val_accuracy: 0.8340\\n\",\n \"Epoch 8/10\\n\",\n \"1000/1000 [==============================] - 0s 100us/sample - loss: 0.3492 - accuracy: 0.8980 - val_loss: 0.4844 - val_accuracy: 0.8480\\n\",\n \"Epoch 9/10\\n\",\n \"1000/1000 [==============================] - 0s 90us/sample - loss: 0.3048 - accuracy: 0.9200 - val_loss: 0.4603 - val_accuracy: 0.8550\\n\",\n \"Epoch 10/10\\n\",\n \"1000/1000 [==============================] - 0s 90us/sample - loss: 0.2574 - accuracy: 0.9290 - val_loss: 0.4674 - val_accuracy: 0.8540\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = create_model()\\n\",\n \"model.fit(train_images, train_labels, epochs=10,\\n\",\n \" validation_data=(test_images, test_labels),\\n\",\n \" )\\n\",\n \"model.save('106save03.h5')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_11\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_22 (Dense) (None, 128) 100480 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout_11 (Dropout) (None, 128) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_23 (Dense) (None, 10) 1290 \\n\",\n \"=================================================================\\n\",\n \"Total params: 101,770\\n\",\n \"Trainable params: 101,770\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"new_model = keras.models.load_model('106save03.h5')\\n\",\n \"new_model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1000/1000 [==============================] - 1s 810us/sample - loss: 0.4674 - accuracy: 0.8540\\n\",\n \"restored model accuracy: 85.40%\\n\"\n ]\n }\n ],\n \"source\": [\n \"loss, acc = model.evaluate(test_images, test_labels)\\n\",\n \"print('restored model accuracy: {:5.2f}%'.format(acc*100))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 7.其他导出模型的方法\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING: Logging before flag parsing goes to stderr.\\n\",\n \"W0326 20:00:41.243743 140450529666816 deprecation.py:323] From /home/czy/anaconda3/envs/tf2_0/lib/python3.6/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:253: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\\n\",\n \"Instructions for updating:\\n\",\n \"This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\\n\",\n \"W0326 20:00:41.244926 140450529666816 tf_logging.py:161] Export includes no default signature!\\n\",\n \"W0326 20:00:41.639915 140450529666816 tf_logging.py:161] Export includes no default signature!\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"'./saved_models/1553601639'\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import time\\n\",\n \"saved_model_path = \\\"./saved_models/{}\\\".format(int(time.time()))\\n\",\n \"\\n\",\n \"tf.keras.experimental.export_saved_model(model, saved_model_path)\\n\",\n \"saved_model_path\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential_11\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense_22 (Dense) (None, 128) 100480 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout_11 (Dropout) (None, 128) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_23 (Dense) (None, 10) 1290 \\n\",\n \"=================================================================\\n\",\n \"Total params: 101,770\\n\",\n \"Trainable params: 101,770\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"new_model = tf.keras.experimental.load_from_saved_model(saved_model_path)\\n\",\n \"new_model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1000/1000 [==============================] - 0s 131us/sample - loss: 0.4674 - accuracy: 0.8540\\n\",\n \"Restored model, accuracy: 85.40%\\n\"\n ]\n }\n ],\n \"source\": [\n \"# 该方法必须先运行compile函数\\n\",\n \"new_model.compile(optimizer=model.optimizer, # keep the optimizer that was loaded\\n\",\n \" loss='sparse_categorical_crossentropy',\\n\",\n \" metrics=['accuracy'])\\n\",\n \"\\n\",\n \"# Evaluate the restored model.\\n\",\n \"loss, acc = new_model.evaluate(test_images, test_labels)\\n\",\n \"print(\\\"Restored model, accuracy: {:5.2f}%\\\".format(100*acc))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "README.md", "content": "# tensorflow2_tutorials_chinese\n\ntensorflow2中文教程，持续更新（不定期更新）\n\n\n\ntensorflow 2.0 正式版已上线， 后面将持续根据TensorFlow2的相关教程和学习资料。\n\n最新tensorflow教程和相关资源，请关注微信公众号：DoitNLP，\n后面我会在DoitNLP上，持续更新深度学习、NLP、Tensorflow的相关教程和前沿资讯，它将成为我们一起学习tensorflow的大本营。\n\n\n当前tensorflow版本：tensorflow2.0\n\n\n\n\n**最全Tensorflow 2.0 教程持续更新：**\nhttps://zhuanlan.zhihu.com/p/59507137\n\n\n本教程主要由tensorflow2.0官方教程的个人学习复现笔记整理而来，并借鉴了一些keras构造神经网络的方法，中文讲解，方便喜欢阅读中文教程的朋友，tensorflow官方教程：https://www.tensorflow.org\n\n\n[TensorFlow 2.0 教程- Keras 快速入门](https://zhuanlan.zhihu.com/p/58825020)\n\n[TensorFlow 2.0 教程-keras 函数api](https://zhuanlan.zhihu.com/p/58825710)\n\n[TensorFlow 2.0 教程-使用keras训练模型](https://zhuanlan.zhihu.com/p/58826227)\n\n[TensorFlow 2.0 教程-用keras构建自己的网络层](https://zhuanlan.zhihu.com/p/59481536)\n\n[TensorFlow 2.0 教程-keras模型保存和序列化](https://zhuanlan.zhihu.com/p/59481985)\n\n[TensorFlow 2.0 教程-eager模式](https://zhuanlan.zhihu.com/p/59482373)\n\n[TensorFlow 2.0 教程-Variables](https://zhuanlan.zhihu.com/p/59482589)\n\n[TensorFlow 2.0 教程--AutoGraph](https://zhuanlan.zhihu.com/p/59482934)\n\nTensorFlow 2.0 深度学习实践\n\n[TensorFlow2.0 教程-图像分类](https://zhuanlan.zhihu.com/p/59506238)\n\n[TensorFlow2.0 教程-文本分类](https://zhuanlan.zhihu.com/p/59506402)\n\n[TensorFlow2.0 教程-过拟合和欠拟合](https://zhuanlan.zhihu.com/p/59506543)\n\n[TensorFlow2.0教程-结构化数据分类](https://zhuanlan.zhihu.com/p/60232704)\n\n[TensorFlow2.0教程-回归](https://zhuanlan.zhihu.com/p/60238056)\n\n[TensorFlow2.0教程-保持和读取模型](https://zhuanlan.zhihu.com/p/60485936)\n\nTensorFlow 2.0 基础网络结构\n\n[TensorFlow2教程-基础MLP网络](https://zhuanlan.zhihu.com/p/60899040)\n\n[TensorFlow2教程-MLP及深度学习常见技巧](https://zhuanlan.zhihu.com/p/60900318)\n\n[TensorFlow2教程-基础CNN网络](https://zhuanlan.zhihu.com/p/60900649)\n\n[TensorFlow2教程-CNN变体网络](https://zhuanlan.zhihu.com/p/60900902)\n\n[TensorFlow2教程-文本卷积](https://zhuanlan.zhihu.com/p/60901179)\n\n[TensorFlow2教程-LSTM和GRU](https://zhuanlan.zhihu.com/p/60966714)\n\n[TensorFlow2教程-自编码器](https://zhuanlan.zhihu.com/p/61077346)\n\n[TensorFlow2教程-卷积自编码器](https://zhuanlan.zhihu.com/p/61080045)\n\n[TensorFlow2教程-词嵌入](https://zhuanlan.zhihu.com/p/61224215)\n\n[TensorFlow2教程-DCGAN](https://zhuanlan.zhihu.com/p/61280722)\n\n[TensorFlow2教程-使用Estimator构建Boosted trees](https://zhuanlan.zhihu.com/p/61400276)\n\nTensorFlow 2.0 安装\n\n[TensorFlow2教程-Ubuntu安装TensorFlow 2.0](https://zhuanlan.zhihu.com/p/61472293)\n\n[TensorFlow2教程-Windows安装tensorflow2.0](https://zhuanlan.zhihu.com/p/62036280)\n\n\n完整tensorflow2.0教程代码请看[tensorflow2.0：中文教程tensorflow2_tutorials_chinese(欢迎star)](https://github.com/czy36mengfei/tensorflow2_tutorials_chinese)\n\n更多TensorFlow 2.0 入门教程请持续关注专栏：[Tensorflow2教程](https://zhuanlan.zhihu.com/c_1091021863043624960)\n\n深度学习入门书籍和资源推荐：https://zhuanlan.zhihu.com/p/65371424" } ]