ResNet_ImageNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import metrics\n",
    "from tensorflow.keras.layers import Dense, Conv2D, BatchNormalization, Activation, MaxPooling2D, UpSampling2D\n",
    "from tensorflow.keras.layers import AveragePooling2D, Input, Flatten\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.models import Model\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import cv2\n",
    "import h5py\n",
    "\n",
    "# 自動增長 GPU 記憶體用量\n",
    "physical_devices = tf.config.list_physical_devices('GPU')\n",
    "print(physical_devices)\n",
    "tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
    "from tensorflow.python.framework.ops import disable_eager_execution\n",
    "disable_eager_execution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "h5_base_dir = '../../../imagenet100/'\n",
    "with h5py.File(os.path.join(h5_base_dir, 'x_train.h5'), 'r') as h5f:\n",
    "    classes = list(h5f.keys())\n",
    "num_classes = 20\n",
    "classes = classes[:num_classes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = list()\n",
    "y_train = list()\n",
    "x_test = list()\n",
    "y_test = list()\n",
    "with h5py.File(os.path.join(h5_base_dir, 'x_train.h5'), 'r') as h5f:\n",
    "    index = 0\n",
    "    for cls in classes:\n",
    "        x_train.append(h5f[cls])\n",
    "        y = np.ndarray(x_train[-1].shape[0])\n",
    "        y[:] = index\n",
    "        y_train.append(y)\n",
    "        index +=1\n",
    "    x_train = np.concatenate(x_train)\n",
    "    y_train = np.concatenate(y_train)\n",
    "\n",
    "with h5py.File(os.path.join(h5_base_dir, 'x_test.h5'), 'r') as h5f:\n",
    "    index = 0\n",
    "    for cls in classes:\n",
    "        x_test.append(h5f[cls])\n",
    "        y = np.ndarray(x_test[-1].shape[0])\n",
    "        y[:] = index\n",
    "        y_test.append(y)\n",
    "        index +=1\n",
    "    x_test = np.concatenate(x_test)\n",
    "    y_test = np.concatenate(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train = shuffle(x_train, y_train)\n",
    "x_test, y_test = shuffle(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_shape = x_train.shape[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training parameters\n",
    "batch_size = 32  # orig paper trained all networks with batch_size=128\n",
    "epochs = 200\n",
    "\n",
    "n = 2\n",
    "\n",
    "# Model version\n",
    "# Orig paper: version = 1 (ResNet v1), Improved ResNet: version = 2 (ResNet v2)\n",
    "version = 1\n",
    "\n",
    "# Computed depth from supplied model parameter n\n",
    "if version == 1:\n",
    "    depth = n * 6 + 2\n",
    "elif version == 2:\n",
    "    depth = n * 9 + 2\n",
    "    \n",
    "# Model name, depth and version\n",
    "model_type = 'ResNet%dv%d' % (depth, version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resnet_layer(inputs,\n",
    "                 num_filters=16,\n",
    "                 kernel_size=3,\n",
    "                 strides=1,\n",
    "                 activation='relu',\n",
    "                 kernel_regularizer=l2(1e-4),\n",
    "                 batch_normalization=True,\n",
    "                 conv_first=True):\n",
    "    \"\"\"2D Convolution-Batch Normalization-Activation stack builder\n",
    "\n",
    "    # Arguments\n",
    "        inputs (tensor): input tensor from input image or previous layer\n",
    "        num_filters (int): Conv2D number of filters\n",
    "        kernel_size (int): Conv2D square kernel dimensions\n",
    "        strides (int): Conv2D square stride dimensions\n",
    "        activation (string): activation name\n",
    "        batch_normalization (bool): whether to include batch normalization\n",
    "        conv_first (bool): conv-bn-activation (True) or\n",
    "            bn-activation-conv (False)\n",
    "\n",
    "    # Returns\n",
    "        x (tensor): tensor as input to the next layer\n",
    "    \"\"\"\n",
    "    conv = Conv2D(num_filters,\n",
    "                  kernel_size=kernel_size,\n",
    "                  strides=strides,\n",
    "                  padding='same',\n",
    "                  kernel_initializer='he_normal',\n",
    "                  kernel_regularizer=kernel_regularizer)\n",
    "\n",
    "    x = inputs\n",
    "    if conv_first:\n",
    "        x = conv(x)\n",
    "        if batch_normalization:\n",
    "            x = BatchNormalization()(x)\n",
    "        if activation is not None:\n",
    "            x = Activation(activation)(x)\n",
    "    else:\n",
    "        if batch_normalization:\n",
    "            x = BatchNormalization()(x)\n",
    "        if activation is not None:\n",
    "            x = Activation(activation)(x)\n",
    "        x = conv(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resnet_v1(input_shape, depth, num_classes=10, kernel_regularizer=l2(1e-4)):\n",
    "    \"\"\"ResNet Version 1 Model builder [a]\n",
    "\n",
    "    Stacks of 2 x (3 x 3) Conv2D-BN-ReLU\n",
    "    Last ReLU is after the shortcut connection.\n",
    "    At the beginning of each stage, the feature map size is halved (downsampled)\n",
    "    by a convolutional layer with strides=2, while the number of filters is\n",
    "    doubled. Within each stage, the layers have the same number filters and the\n",
    "    same number of filters.\n",
    "    Features maps sizes:\n",
    "    stage 0: 32x32, 16\n",
    "    stage 1: 16x16, 32\n",
    "    stage 2:  8x8,  64\n",
    "    The Number of parameters is approx the same as Table 6 of [a]:\n",
    "    ResNet20 0.27M\n",
    "    ResNet32 0.46M\n",
    "    ResNet44 0.66M\n",
    "    ResNet56 0.85M\n",
    "    ResNet110 1.7M\n",
    "\n",
    "    # Arguments\n",
    "        input_shape (tensor): shape of input image tensor\n",
    "        depth (int): number of core convolutional layers\n",
    "        num_classes (int): number of classes (CIFAR10 has 10)\n",
    "\n",
    "    # Returns\n",
    "        model (Model): Keras model instance\n",
    "    \"\"\"\n",
    "    if (depth - 2) % 6 != 0:\n",
    "        raise ValueError('depth should be 6n+2 (eg 20, 32, 44 in [a])')\n",
    "    # Start model definition.\n",
    "    num_filters = 16\n",
    "    num_res_blocks = int((depth - 2) / 6)\n",
    "\n",
    "    inputs = Input(shape=input_shape)\n",
    "    x = resnet_layer(inputs=inputs, kernel_regularizer=kernel_regularizer)\n",
    "    # Instantiate the stack of residual units\n",
    "    for stack in range(3):\n",
    "        for res_block in range(num_res_blocks):\n",
    "            strides = 1\n",
    "            if stack > 0 and res_block == 0:  # first layer but not first stack\n",
    "                strides = 2  # downsample\n",
    "            y = resnet_layer(inputs=x,\n",
    "                             num_filters=num_filters,\n",
    "                             kernel_regularizer=kernel_regularizer,\n",
    "                             strides=strides)\n",
    "            y = resnet_layer(inputs=y,\n",
    "                             num_filters=num_filters,\n",
    "                             kernel_regularizer=kernel_regularizer,\n",
    "                             activation=None)\n",
    "            if stack > 0 and res_block == 0:  # first layer but not first stack\n",
    "                # linear projection residual shortcut connection to match\n",
    "                # changed dims\n",
    "                x = resnet_layer(inputs=x,\n",
    "                                 num_filters=num_filters,\n",
    "                                 kernel_size=1,\n",
    "                                 strides=strides,\n",
    "                                 activation=None,\n",
    "                                 kernel_regularizer=kernel_regularizer,\n",
    "                                 batch_normalization=False)\n",
    "            x = tf.keras.layers.add([x, y])\n",
    "            x = Activation('relu')(x)\n",
    "        num_filters *= 2\n",
    "\n",
    "    # Add classifier on top.\n",
    "    # v1 does not use BN after last shortcut connection-ReLU\n",
    "    x = AveragePooling2D(pool_size=8)(x)\n",
    "    y = Flatten()(x)\n",
    "    outputs = Dense(num_classes,\n",
    "                    activation='softmax',\n",
    "                    kernel_initializer='he_normal')(y)\n",
    "\n",
    "    # Instantiate model.\n",
    "    model = Model(inputs=inputs, outputs=outputs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resnet_v2(input_shape, depth, num_classes=10, kernel_regularizer=l2(1e-4)):\n",
    "    \"\"\"ResNet Version 2 Model builder [b]\n",
    "\n",
    "    Stacks of (1 x 1)-(3 x 3)-(1 x 1) BN-ReLU-Conv2D or also known as\n",
    "    bottleneck layer\n",
    "    First shortcut connection per layer is 1 x 1 Conv2D.\n",
    "    Second and onwards shortcut connection is identity.\n",
    "    At the beginning of each stage, the feature map size is halved (downsampled)\n",
    "    by a convolutional layer with strides=2, while the number of filter maps is\n",
    "    doubled. Within each stage, the layers have the same number filters and the\n",
    "    same filter map sizes.\n",
    "    Features maps sizes:\n",
    "    conv1  : 32x32,  16\n",
    "    stage 0: 32x32,  64\n",
    "    stage 1: 16x16, 128\n",
    "    stage 2:  8x8,  256\n",
    "\n",
    "    # Arguments\n",
    "        input_shape (tensor): shape of input image tensor\n",
    "        depth (int): number of core convolutional layers\n",
    "        num_classes (int): number of classes (CIFAR10 has 10)\n",
    "\n",
    "    # Returns\n",
    "        model (Model): Keras model instance\n",
    "    \"\"\"\n",
    "    if (depth - 2) % 9 != 0:\n",
    "        raise ValueError('depth should be 9n+2 (eg 56 or 110 in [b])')\n",
    "    # Start model definition.\n",
    "    num_filters_in = 16\n",
    "    num_res_blocks = int((depth - 2) / 9)\n",
    "\n",
    "    inputs = Input(shape=input_shape)\n",
    "    # v2 performs Conv2D with BN-ReLU on input before splitting into 2 paths\n",
    "    x = resnet_layer(inputs=inputs,\n",
    "                     num_filters=num_filters_in,\n",
    "                     kernel_regularizer=kernel_regularizer,\n",
    "                     conv_first=True)\n",
    "\n",
    "    # Instantiate the stack of residual units\n",
    "    for stage in range(3):\n",
    "        for res_block in range(num_res_blocks):\n",
    "            activation = 'relu'\n",
    "            batch_normalization = True\n",
    "            strides = 1\n",
    "            if stage == 0:\n",
    "                num_filters_out = num_filters_in * 4\n",
    "                if res_block == 0:  # first layer and first stage\n",
    "                    activation = None\n",
    "                    batch_normalization = False\n",
    "            else:\n",
    "                num_filters_out = num_filters_in * 2\n",
    "                if res_block == 0:  # first layer but not first stage\n",
    "                    strides = 2    # downsample\n",
    "\n",
    "            # bottleneck residual unit\n",
    "            y = resnet_layer(inputs=x,\n",
    "                             num_filters=num_filters_in,\n",
    "                             kernel_size=1,\n",
    "                             strides=strides,\n",
    "                             activation=activation,\n",
    "                             batch_normalization=batch_normalization,\n",
    "                             kernel_regularizer=kernel_regularizer,\n",
    "                             conv_first=False)\n",
    "            y = resnet_layer(inputs=y,\n",
    "                             num_filters=num_filters_in,\n",
    "                             kernel_regularizer=kernel_regularizer,\n",
    "                             conv_first=False)\n",
    "            y = resnet_layer(inputs=y,\n",
    "                             num_filters=num_filters_out,\n",
    "                             kernel_regularizer=kernel_regularizer,\n",
    "                             kernel_size=1,\n",
    "                             conv_first=False)\n",
    "            if res_block == 0:\n",
    "                # linear projection residual shortcut connection to match\n",
    "                # changed dims\n",
    "                x = resnet_layer(inputs=x,\n",
    "                                 num_filters=num_filters_out,\n",
    "                                 kernel_size=1,\n",
    "                                 strides=strides,\n",
    "                                 activation=None,\n",
    "                                 kernel_regularizer=kernel_regularizer,\n",
    "                                 batch_normalization=False)\n",
    "            x = tf.keras.layers.add([x, y])\n",
    "\n",
    "        num_filters_in = num_filters_out\n",
    "\n",
    "    # Add classifier on top.\n",
    "    # v2 has BN-ReLU before Pooling\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)\n",
    "    x = AveragePooling2D(pool_size=8)(x)\n",
    "    y = Flatten()(x)\n",
    "    outputs = Dense(num_classes,\n",
    "                    activation='softmax',\n",
    "                    kernel_initializer='he_normal')(y)\n",
    "\n",
    "    # Instantiate model.\n",
    "    model = Model(inputs=inputs, outputs=outputs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert class vectors to binary class matrices.\n",
    "y_train = tf.keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = tf.keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lr_schedule(epoch):\n",
    "    \"\"\"Learning Rate Schedule\n",
    "\n",
    "    Learning rate is scheduled to be reduced after 80, 120, 160, 180 epochs.\n",
    "    Called automatically every epoch as part of callbacks during training.\n",
    "\n",
    "    # Arguments\n",
    "        epoch (int): The number of epochs\n",
    "\n",
    "    # Returns\n",
    "        lr (float32): learning rate\n",
    "    \"\"\"\n",
    "    lr = 1e-3\n",
    "    if epoch > 180:\n",
    "        lr *= 0.5e-3\n",
    "    elif epoch > 160:\n",
    "        lr *= 1e-3\n",
    "    elif epoch > 120:\n",
    "        lr *= 1e-2\n",
    "    elif epoch > 80:\n",
    "        lr *= 1e-1\n",
    "    print('Learning rate: ', lr)\n",
    "    return lr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/z99792458/anaconda_env/tf2/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1635: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n"
     ]
    }
   ],
   "source": [
    "if version == 2:\n",
    "    model = resnet_v2(input_shape=input_shape, depth=depth, num_classes=num_classes, kernel_regularizer=l2(2e-4))\n",
    "else:\n",
    "    model = resnet_v1(input_shape=input_shape, depth=depth, num_classes=num_classes, kernel_regularizer=l2(2e-4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate:  0.001\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 224, 224, 16) 448         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 224, 224, 16) 64          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 224, 224, 16) 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 224, 224, 16) 2320        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 224, 224, 16) 64          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 224, 224, 16) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 224, 224, 16) 2320        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 224, 224, 16) 64          conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 224, 224, 16) 0           activation[0][0]                 \n",
      "                                                                 batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 224, 224, 16) 0           add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 224, 224, 16) 2320        activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 224, 224, 16) 64          conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 224, 224, 16) 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 224, 224, 16) 2320        activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 224, 224, 16) 64          conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 224, 224, 16) 0           activation_2[0][0]               \n",
      "                                                                 batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 224, 224, 16) 0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 112, 112, 32) 4640        activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 112, 112, 32) 128         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 112, 112, 32) 0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 112, 112, 32) 9248        activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 112, 112, 32) 544         activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 112, 112, 32) 128         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 112, 112, 32) 0           conv2d_7[0][0]                   \n",
      "                                                                 batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 112, 112, 32) 0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 112, 112, 32) 9248        activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 112, 112, 32) 128         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 112, 112, 32) 0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 112, 112, 32) 9248        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 112, 112, 32) 128         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 112, 112, 32) 0           activation_6[0][0]               \n",
      "                                                                 batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 112, 112, 32) 0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 56, 56, 64)   18496       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 56, 56, 64)   256         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 56, 56, 64)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 56, 56, 64)   36928       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 56, 56, 64)   2112        activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 56, 56, 64)   256         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 56, 56, 64)   0           conv2d_12[0][0]                  \n",
      "                                                                 batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 56, 56, 64)   0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 56, 56, 64)   36928       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 56, 56, 64)   256         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 56, 56, 64)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 56, 56, 64)   36928       activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 56, 56, 64)   256         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 56, 56, 64)   0           activation_10[0][0]              \n",
      "                                                                 batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 56, 56, 64)   0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, 7, 7, 64)     0           activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 3136)         0           average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 20)           62740       flatten[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 238,644\n",
      "Trainable params: 237,716\n",
      "Non-trainable params: 928\n",
      "__________________________________________________________________________________________________\n",
      "ResNet14v1\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=Adam(lr=lr_schedule(0)),\n",
    "              metrics=['accuracy'])\n",
    "model.summary()\n",
    "print(model_type)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_scheduler = LearningRateScheduler(lr_schedule)\n",
    "\n",
    "lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),\n",
    "                               cooldown=0,\n",
    "                               patience=5,\n",
    "                               min_lr=0.5e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare model model saving directory.\n",
    "save_dir = os.path.join(os.getcwd(), 'saved_models')\n",
    "import datetime as dt\n",
    "model_name = 'pmon_%s_%s_model.{epoch:03d}.h5' % (model_type, dt.datetime.now().strftime(\"%Y%m%d%H\"))\n",
    "if not os.path.isdir(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "filepath = os.path.join(save_dir, model_name)\n",
    "\n",
    "# Prepare callbacks for model saving and for learning rate adjustment.\n",
    "checkpoint = ModelCheckpoint(filepath=filepath,\n",
    "                             monitor='val_acc',\n",
    "                             verbose=1,\n",
    "                             save_best_only=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [checkpoint, lr_reducer, lr_scheduler]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16439 samples, validate on 4119 samples\n",
      "Learning rate:  0.001\n",
      "Epoch 1/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 2.5323 - accuracy: 0.3350\n",
      "Epoch 00001: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.001.h5\n",
      "16439/16439 [==============================] - 71s 4ms/sample - loss: 2.5316 - accuracy: 0.3351 - val_loss: 2.2068 - val_accuracy: 0.4173\n",
      "Learning rate:  0.001\n",
      "Epoch 2/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 1.8448 - accuracy: 0.5091\n",
      "Epoch 00002: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.002.h5\n",
      "16439/16439 [==============================] - 66s 4ms/sample - loss: 1.8444 - accuracy: 0.5092 - val_loss: 1.8059 - val_accuracy: 0.5251\n",
      "Learning rate:  0.001\n",
      "Epoch 3/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 1.5657 - accuracy: 0.5853\n",
      "Epoch 00003: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.003.h5\n",
      "16439/16439 [==============================] - 66s 4ms/sample - loss: 1.5653 - accuracy: 0.5853 - val_loss: 1.9234 - val_accuracy: 0.5013\n",
      "Learning rate:  0.001\n",
      "Epoch 4/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 1.3927 - accuracy: 0.6293\n",
      "Epoch 00004: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.004.h5\n",
      "16439/16439 [==============================] - 67s 4ms/sample - loss: 1.3930 - accuracy: 0.6292 - val_loss: 1.7972 - val_accuracy: 0.5210\n",
      "Learning rate:  0.001\n",
      "Epoch 5/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 1.2577 - accuracy: 0.6725\n",
      "Epoch 00005: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.005.h5\n",
      "16439/16439 [==============================] - 67s 4ms/sample - loss: 1.2575 - accuracy: 0.6724 - val_loss: 1.7590 - val_accuracy: 0.5751\n",
      "Learning rate:  0.001\n",
      "Epoch 6/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 1.1576 - accuracy: 0.7016\n",
      "Epoch 00006: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.006.h5\n",
      "16439/16439 [==============================] - 67s 4ms/sample - loss: 1.1577 - accuracy: 0.7017 - val_loss: 1.7981 - val_accuracy: 0.5402\n",
      "Learning rate:  0.001\n",
      "Epoch 7/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 1.0568 - accuracy: 0.7244\n",
      "Epoch 00007: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.007.h5\n",
      "16439/16439 [==============================] - 67s 4ms/sample - loss: 1.0566 - accuracy: 0.7245 - val_loss: 1.5822 - val_accuracy: 0.6094\n",
      "Learning rate:  0.001\n",
      "Epoch 8/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 0.9835 - accuracy: 0.7521\n",
      "Epoch 00008: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.008.h5\n",
      "16439/16439 [==============================] - 67s 4ms/sample - loss: 0.9833 - accuracy: 0.7521 - val_loss: 1.5899 - val_accuracy: 0.6094\n",
      "Learning rate:  0.001\n",
      "Epoch 9/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 0.8989 - accuracy: 0.7756\n",
      "Epoch 00009: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.009.h5\n",
      "16439/16439 [==============================] - 67s 4ms/sample - loss: 0.8990 - accuracy: 0.7757 - val_loss: 1.6162 - val_accuracy: 0.6227\n",
      "Learning rate:  0.001\n",
      "Epoch 10/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 0.8276 - accuracy: 0.7940\n",
      "Epoch 00010: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.010.h5\n",
      "16439/16439 [==============================] - 67s 4ms/sample - loss: 0.8277 - accuracy: 0.7939 - val_loss: 1.4654 - val_accuracy: 0.6198\n",
      "Learning rate:  0.001\n",
      "Epoch 11/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 0.7537 - accuracy: 0.8179\n",
      "Epoch 00011: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.011.h5\n",
      "16439/16439 [==============================] - 68s 4ms/sample - loss: 0.7538 - accuracy: 0.8180 - val_loss: 1.7018 - val_accuracy: 0.6113\n",
      "Learning rate:  0.001\n",
      "Epoch 12/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 0.7064 - accuracy: 0.8321\n",
      "Epoch 00012: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.012.h5\n",
      "16439/16439 [==============================] - 67s 4ms/sample - loss: 0.7065 - accuracy: 0.8320 - val_loss: 1.6861 - val_accuracy: 0.6324\n",
      "Learning rate:  0.001\n",
      "Epoch 13/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 0.6626 - accuracy: 0.8453\n",
      "Epoch 00013: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.013.h5\n",
      "16439/16439 [==============================] - 67s 4ms/sample - loss: 0.6624 - accuracy: 0.8454 - val_loss: 1.7746 - val_accuracy: 0.6145\n",
      "Learning rate:  0.001\n",
      "Epoch 14/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 0.6121 - accuracy: 0.8609\n",
      "Epoch 00014: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.014.h5\n",
      "16439/16439 [==============================] - 68s 4ms/sample - loss: 0.6121 - accuracy: 0.8609 - val_loss: 1.7605 - val_accuracy: 0.6133\n",
      "Learning rate:  0.001\n",
      "Epoch 15/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 0.5571 - accuracy: 0.8823\n",
      "Epoch 00015: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.015.h5\n",
      "16439/16439 [==============================] - 68s 4ms/sample - loss: 0.5569 - accuracy: 0.8824 - val_loss: 1.8183 - val_accuracy: 0.6086\n",
      "Learning rate:  0.001\n",
      "Epoch 16/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 0.5122 - accuracy: 0.8941\n",
      "Epoch 00016: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.016.h5\n",
      "16439/16439 [==============================] - 68s 4ms/sample - loss: 0.5121 - accuracy: 0.8941 - val_loss: 1.6158 - val_accuracy: 0.6404\n",
      "Learning rate:  0.001\n",
      "Epoch 17/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 0.4820 - accuracy: 0.9072\n",
      "Epoch 00017: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.017.h5\n",
      "16439/16439 [==============================] - 67s 4ms/sample - loss: 0.4823 - accuracy: 0.9070 - val_loss: 1.9041 - val_accuracy: 0.5972\n",
      "Learning rate:  0.001\n",
      "Epoch 18/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 0.4586 - accuracy: 0.9118\n",
      "Epoch 00018: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.018.h5\n",
      "16439/16439 [==============================] - 67s 4ms/sample - loss: 0.4588 - accuracy: 0.9117 - val_loss: 2.0504 - val_accuracy: 0.6184\n",
      "Learning rate:  0.001\n",
      "Epoch 19/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 0.4300 - accuracy: 0.9220\n",
      "Epoch 00019: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.019.h5\n",
      "16439/16439 [==============================] - 67s 4ms/sample - loss: 0.4304 - accuracy: 0.9218 - val_loss: 2.1377 - val_accuracy: 0.5781\n",
      "Learning rate:  0.001\n",
      "Epoch 20/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 0.4180 - accuracy: 0.9271\n",
      "Epoch 00020: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.020.h5\n",
      "16439/16439 [==============================] - 68s 4ms/sample - loss: 0.4181 - accuracy: 0.9271 - val_loss: 2.0857 - val_accuracy: 0.6033\n",
      "Learning rate:  0.001\n",
      "Epoch 21/200\n",
      "16416/16439 [============================>.] - ETA: 0s - loss: 0.4035 - accuracy: 0.9313\n",
      "Epoch 00021: saving model to /home/z99792458/nbs/detection_models_imagenet_100/resnet/saved_models/pmon_ResNet14v1_2020081507_model.021.h5\n",
      "16439/16439 [==============================] - 67s 4ms/sample - loss: 0.4036 - accuracy: 0.9313 - val_loss: 2.1671 - val_accuracy: 0.5989\n",
      "Learning rate:  0.001\n",
      "Epoch 22/200\n",
      " 3648/16439 [=====>........................] - ETA: 48s - loss: 0.3358 - accuracy: 0.9550"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-95fde601ecd9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m           \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\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[1;32m      6\u001b[0m           \u001b[0mshuffle\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\u001b[0;32m----> 7\u001b[0;31m           callbacks=callbacks)\n\u001b[0m",
      "\u001b[0;32m~/anaconda_env/tf2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m    817\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    818\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 819\u001b[0;31m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[1;32m    820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    821\u001b[0m   def evaluate(self,\n",
      "\u001b[0;32m~/anaconda_env/tf2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)\u001b[0m\n\u001b[1;32m    678\u001b[0m         \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m         \u001b[0mvalidation_freq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_freq\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 680\u001b[0;31m         steps_name='steps_per_epoch')\n\u001b[0m\u001b[1;32m    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m   def evaluate(self,\n",
      "\u001b[0;32m~/anaconda_env/tf2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[0;34m(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)\u001b[0m\n\u001b[1;32m    396\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    397\u001b[0m         \u001b[0;31m# Get outputs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m         \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\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    399\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\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[1;32m    400\u001b[0m           \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda_env/tf2/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3565\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3566\u001b[0m     fetched = self._callable_fn(*array_vals,\n\u001b[0;32m-> 3567\u001b[0;31m                                 run_metadata=self.run_metadata)\n\u001b[0m\u001b[1;32m   3568\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\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[1;32m   3569\u001b[0m     output_structure = nest.pack_sequence_as(\n",
      "\u001b[0;32m~/anaconda_env/tf2/lib/python3.7/site-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1472\u001b[0m         ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[1;32m   1473\u001b[0m                                                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\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[0;32m-> 1474\u001b[0;31m                                                run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1475\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1476\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Run training\n",
    "history = model.fit(x_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          epochs=epochs,\n",
    "          validation_data=(x_test, y_test),\n",
    "          shuffle=True,\n",
    "          callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
    "    featurewise_center=True,\n",
    "    featurewise_std_normalization=True,\n",
    "    rotation_range=20,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    horizontal_flip=True)\n",
    "train_datagen.fit(x_train)\n",
    "validation_datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
    "    featurewise_center=True,\n",
    "    featurewise_std_normalization=True,\n",
    "    rotation_range=20,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    horizontal_flip=True)\n",
    "validation_datagen.fit(x_test)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Run training with augmentation\n",
    "history = model.fit(\n",
    "          train_datagen.flow(x_train, y_train, batch_size=batch_size),\n",
    "          steps_per_epoch=int(len(x_train) / batch_size * 3),\n",
    "          epochs=epochs,\n",
    "          validation_data=validation_datagen.flow(x_test, y_test), \n",
    "          shuffle=True,\n",
    "          callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights('./saved_models/pmon_%s_%s_model.%s.final.h5' % (model_type, dt.datetime.now().strftime(\"%Y%m%d%H\"), '22.2e-4'))  # The third string parameter is the suffix string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.cm as cm \n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.cbook as cbook \n",
    "\n",
    "plt.plot(history.history['acc']) \n",
    "plt.plot(history.history['val_acc']) \n",
    "plt.title('model accuracy') \n",
    "plt.ylabel('accuracy') \n",
    "plt.xlabel('epoch') \n",
    "plt.legend(['train', 'validation'], loc='upper left') \n",
    "plt.show()\n",
    "\n",
    "# summarize history for loss \n",
    "plt.plot(history.history['loss']) \n",
    "plt.plot(history.history['val_loss']) \n",
    "plt.title('model loss') \n",
    "plt.ylabel('loss') \n",
    "plt.xlabel('epoch') \n",
    "plt.legend(['train', 'validation'], loc='upper left') \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4119/4119 [==============================] - 5s 1ms/sample - loss: 1.8263 - accuracy: 0.6594\n",
      "Test loss: 1.826332885537746\n",
      "Test accuracy: 0.65938336\n"
     ]
    }
   ],
   "source": [
    "# Score trained model.\n",
    "scores = model.evaluate(x_test, y_test, verbose=1)\n",
    "print('Test loss:', scores[0])\n",
    "print('Test accuracy:', scores[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.utils.multiclass import unique_labels \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "inferenced = model.predict(x_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred, classes, title=None, cmap=plt.cm.Blues): \n",
    "    \"\"\" \n",
    "    This function prints and plots the confusion matrix. \n",
    "    Normalization can be applied by setting `normalize=True`. \n",
    "    \"\"\" \n",
    "    title = 'Confusion matrix, without normalization' if title is None else Title\n",
    "    y_pred = np.argmax(y_pred.T, axis=0) \n",
    "    y_true = np.argmax(y_true.T, axis=0) \n",
    "    # Compute confusion matrix \n",
    "    cm = confusion_matrix(y_true, y_pred) \n",
    "    # Only use the labels that appear in the data \n",
    "    classes = classes[unique_labels(y_true, y_pred)] \n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(15,15)) \n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap) \n",
    "    ax.figure.colorbar(im, ax=ax) \n",
    "    # We want to show all ticks... \n",
    "    ax.set(xticks=np.arange(cm.shape[1]), \n",
    "           yticks=np.arange(cm.shape[0]), \n",
    "           # ... and label them with the respective list entries \n",
    "           xticklabels=classes, yticklabels=classes, \n",
    "           title=title, \n",
    "           ylabel='True answer', \n",
    "           xlabel='Inference label') \n",
    "    # Rotate the tick labels and set their alignment. \n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\") \n",
    "    # Loop over data dimensions and create text annotations. \n",
    "    fmt = 'd' \n",
    "    thresh = cm.max() / 2. \n",
    "    for i in range(cm.shape[0]): \n",
    "        for j in range(cm.shape[1]): \n",
    "            ax.text(j, i, format(cm[i, j], fmt), ha=\"center\", va=\"center\",  color=\"white\" if cm[i, j] > thresh else \"black\") \n",
    "    fig.tight_layout() \n",
    "\n",
    "    return ax "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5b806f06d0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3X6OghfVcSUvM7A0za7EV8eTFVC/l9x+3IZ4V7p4b/pyXHP6Usn5d3v5m1szMRpnZj2a2SkGPhEK7+adY5u7rS9jmEUltJN0TJnQlClv7VytI7A6SNErSYjNrrtIl9kXVWUnnPx225dhZCiZkzPNDIeUVPH9Fnc89zOw5M1sUns+nVfL5VLhvBQUTIj3j7s+lLD/NzGaE3f5XKjivW1WmCvy94c2MFSr9tQ0ASBASewBAaU2WtF5S/2K2WaygG3mevcNlpbFGUpWU3/dMXenub7v7YQparr9WkPCWFE9eTItKGdO2eEBBXE3dvbqkvyrozl0cL26lmVVV0F38MUnXh0MNttZ4BU8eqBi2Ro+XdJqkmipkGMPWxFOI4s5/vvNpZvnOZymOtTXHzlH+RH17jvGPcP+24fk8RSWfzzz3SPpNKcMSzGwfBdfshQqeBFFD0hcpZZYUa76/Nxyesrsyc20DACJGYg8AKBV3/1XBuPL7LJg0roqZVTCzvmaWN/76WUnXWPBM1lrh9k8XVWYJZkjqaWZ7WzAJ2l/yVphZHTM7JkxmNihojc4tpIzRkppZ8Ii+LDMbJKmVghbrslZN0ipJq8PeBOcVWP+TgrHg22K4pGnho9TeUDDBmqTNE7aNK2bf8QqSyLyJ+8ZJ+qOkCSm9EAra1hiLO/8zJbU2s/Zm9jttOT69NPVR8NiXmlmj8AZI3pwN6Zr9vpqC62ylmdWT9Ket2cnM/qCgV8QQd9+UsmoXBcn7snC7YQpa7PP8JKm+hRMuFuIZScPC+qyk4O+dEg77AIB4sR3sKwZI7AEApebud0q6TEHL4zIFXZsvVDB2WJJuljRV0meSPpf0abisNMd6V9LzYVnTlD8ZLyfpcgWtlj8rSJzOL6SMFQomELtcQTflKyX1c/flpYlpG12hYGK+3xS0zD5fYP31kp4Mu2GfWFJhZnasgkcLnhsuukzS/hY+DUDBZG4TiylivILkNC+xn6CgBf2DIvcIWqmvCWMsblLBPEWef3efo+CpCu8pGEte8PGIj0lqFR7rVW27xyU9peDvma+gd8kfS1FOUW6QtL+kXxXcVHllK/c7ScENi8X2v5nx/+rusyT9S0FPmJ8k7af852+MgjkbfjSzLa5Xd39f0t8UPCJpiaR9FTwfGQCwE7ACk94CAIAdgJnNkHRIeDMDAIDYKL97I696xI1Rh5E2q549bZq7d4oyBlrskRhmVsPMXjKzr83sKzM7wMyeDycammFmC8J/ZDMVz+/M7GMzm2lmX1rw3G2F3T6nWPCs4OeL6TaZqXgutOC5xm5FPI87A7E1MLOx4Xn70swujiKOAjE9bsGzrb+IOhYptnV0pJnNDq+fq2IQT/OU1/sMM1tlZpdEHFPkdVTYtRyOtV8m6SMze9fMakYRWxjLpeE1/YWZPRt2u49MjM9ZezP7KLy2p5rZ1s6GXxbxRV5HKbFE/t5YVAxmNjD8fZOZRfoPfWH/I0UYC5+vW8HMypvZdDPLxFAw7ATKLLE3sz0tmC12npnNMrPRFswI3DspF3CYKJYqETKzceGH4ozwjeSckvdKHwvGu7YqZv25ZnZaJmNKg+GS3nL3FgoepfSVuw9y9/bu3l5B98Ot7QqZDhsk9XH3dgpmlj7SzLpJuk3SXe7eVMHzic+MOJ6JCp7BXHAm8EzKkXS5u7dU8EzrC4q7PjNkhIJuzHERqzoys/KS7pPUV8EY9JOiPmfuPjvl9d5Rwaze/40qnhjV0QhteS1fJen98H3o/fD3jLNg7PlFkjq5extJ5RVh9/CYn7PbFTw2sL2CuQhuL7hTJsSojvLE4b2xqBi+kHS8ih++kilb/I8UYSwjxOfr1rhY0Z6nSJmif/Z8Or/ioEwSewv+uv9KGufu+7p7KwWz/9Ypfs8dzsnhB3QPSbdZmltuww/fovRX8IFc2H5Z7v6gu/8nnfEUPEaay6uu4FnLj0mSu29095Up603SiQomS8oID6wOf60QfrmkPgoeYyRJT6r4GcPLPB53nx715EnhM8U/DX/+TcEHWb3i9yrzmD5QMBY7FmJYR10kfRM+A3yjgme0HxthPAUdImmeu0d5wyoWdVTEtXysgvcfKYPvQ0XIklQ5/FyootI/FSEd4nzOXFL18OddFV09xaKO8sThvbGoGNz9K3efnclYClPS/0iZxudrycysvqSjJD0aZRzYsZRVi/3BkrLdffPsvO4+w90/DH+tmtJdaGSYlMnMOprZeDObZmZvm1ndcPlFYav/Z2b2XLisi5lNCruwTLLg2bsys6Fm9pqZvRW2mF+XF4OZnWJBV+UZZvZQXmJsZg9Y0O1tc/flVGZWOSzv7OLKKUZVBY/1yS3qeGZ2iJltbnkys8PMbIvWZwt6EVxrZhMkDTSzfcPYppnZh2bWwsy6SzpG0j/DGPe1oAfBLWY2XtLFFsyWfEVYZmFl7Boeq1y4TRUz+8GCGa+32D7cZoSZ3WlmYxW0WqdTYwXdSp8Iz/mjFsx+necgST+5+9w0H7dYFnSjmiFpqaR3Jc2TtDJl1uWFyuCHR8F43H1Kpo69tcysoaQOkmIXW1zEpI7qKf8zvjN6LW+FwcrgjbwixLmO6rj7Ein4p1bSHlEEET5G7w5J3yuY0O1Xd38nilhCcT5nlyj43P5BQZ39pYTty0ps6ygO741xiKEQJf2PhFCMzt/dCiZv3VTShsDWKqvEvo2CGYuL0kHBB1grBW9GPcysgoLnug5w944KZrP9e7j9VZI6uHtb/W/2368l9XT3Dgq6rN2SUn4XSScr6I480Mw6mVlLSYMk9Qhb0XPDbSTp6nCyg7aSeplZ25Syqkp6XdIz7v5ICeUUNNLMPpM0W9JNKY8PKux4YyS1NLPa4TbDJD1RRLnr3f1Ad39O0sOS/hjW2RWS7nf3SZL+T9Kfwm6r88L9arh7L3f/V4HyCivjVwWPIuoVbnO0pLfdPbuw7VPKaibpUHe/vIjYSytLwezDD4TnfI3ydy09SRH8k+/uueF1UF/BddeysM2iisfM2pS0TyZZ8MiplyVd4u6roo4njmJUR4X1K4vFbKth76djJL0YdSiFLItFHcWFBWP7j5XUSNJeknYxs1OiDKmQZXE5Z+dJutTdG0i6VGHrawRiWUdxeG+MQwxFKOl/JCg+58/M+kla6u7F5UrANiuTWfHN7CJJjdz90kLW9VaQ2B4W/v6AgjHAMyRNkvRtuGl5SUvc/XAze0vBs2JflfSqu682swaS/i2pqYIPnAru3sLMhioYZ3xaWP6NCroD5SgYDrA0LL+ypGfd/XozO1fSOQreGOsqSFqfM7MFCh5jc7u7jwzLu7Cocgr8neMkXeHuU8NkfZKChPe7Yo53tYIxo09Imi6pqRd43m4YU6+wnKoK7tCmdgOr5O4tzWyEpFHu/lJKPNe5+/jw9+vDOn2wmDKGKLh5cm7Ym+B+BY/hKe6YY939SRXCgnkGzpGkSpWrdKzfqElhmxUqJydbi+Z/o32aBnnzujVrtHLFUtXdu5HcXd/N+Ur1GzdRVoXSjXaonFVSp4uSLf1piczKafmyn9Si1X4yM61ds0ZLf1qiho23/m/Ns72vzKU/LVG5cuVUq3YwAmbOV1+qcdPmysoq3SiJilnbdx/Q3TXvm29UvXp17VEnHqNyNmzYoG/nfaOWrVpHHYqk9NfR9oz4Wr16tZYsWaKmTZtKkn78cYkkac8965a6zE1p+rj5deVKLV++VPs2abbdZZXbjkoqizrKLeVn8sYNGzT/23lq3jIYgfX1rC+1b9NmqlChgrKzszVv7hy1KMV1btv5cN6Vv/yiVat+1d77NJQk/bxihdasXaMGDfYudZlxO2c5pbywN27YoPnz56l5i+CcffHZDLXer53MTO6uLz+fqTZt229zuVnbU0Eqmzra3pd+ut8bc3K3PSJ314Jvv1HV6tVVu3b+GL79Zo723KueqlQpfSN5+fKlP2/Z2dmaM/trtW6znyRp9erftPTHH9W4SdNSl7m9I4Z39M/X7bF40SL9/PMKmZk2bdqk3Nxc1ahRUw0bNSp1md9/t0DLly+Px0DvrZS1e2Ov1vemqMNIm5UjT4l8Vvy0joNO8aWkAcWs35Dyc24Yh0n60t0Lm8XzKAVjh46R9Dczay3pJgVJ5HFht5pxKdsXfMf2sPwn3T1f1zYza6Sg1bmzu/8SJqepM/ZOlNTXzJ7x4C5IoeUUx92XmdmnkrqGXduLOt4TCnoHrJf0YsGkPsWa8Hs5Bd2+t/aTf00hy4or4/8k/cOC2ZU7KuhVsEsJxyzsGJIkd39YQWu/mrRu57c/89ZWhh24Zlh/nXfdHarXsImef+AOrV+3Vqdfdq2mTxyrVx6/Rzc9Vvp58/bbc9dt3ufn5cuUVaGCqu9aQ+vXrdOwwcfonAsu039fGKnD+x2rfv0H6torL1Lzlm108rBtnztxW/+/LxjPGYOP0dkXXqaDD+srSerTuaVGvDBKu+1euonx6+9WuVT7ScEH6tlnDlX3Hgfqn/+6u9TlpErHPCXfLVigE/ofrYkffbL9hWnbz1n+fdNfR+W24x/8nJwc7deqmUY+96L2qldPB3brrBFPPaNWrUv/T9qG7NySN9oKZ5w2RH0OPVynnDZ0u8uqVKH0N/XKoo7Wbijqbb9433+3QCcN6K8xHwa9S6+7+s+qudvuuuTyK3X3v27Xyl9+1vU337rN5W7PNSRJUz+eoj+ed7beeHesKleurPPPOUMd9u+oc867sNRl/i5m5+zn1RtLtd8P3y/Q6YOO05tjJ0uSenVtq2tvvk3dD+ylD8eP0d+v++vmddtit6rbN51PWdTRpu24q1cW740rtvGcubsuPf9MdTmgh66/5Y4t1p94zGG6+oZb1a5Dx1LHtGuVCqXeV5KOPKSX7nngYTVt1lz/uPkGrVmzRjf/o/TzL1bYjhsN0o7/+SpJm9LQOPrh+HEafve/9NJ/X9+ucnp2j+whGoiRsuqKP0ZSpbwx6ZJkZp3NrFcx+8yWVNvCx3OEY7lbh4lwA3cfq2AsSg0F3eN3lbQo3HdogbIOM7PdzKyyggmDJiqYFXiAme0Rlr+bme2jYKKaNZJ+NbM6CmaBTXWtpBX6X3fzosopkplVUTD8YF5xx3P3xQomy7lGwYyixQq7Ec03s4HhcczM2oWrf5NUbXvK8GAito8VzLQ6KuzmXdwxy9SZf75Zw/96oS4deIjmz/5SJ5x1kSRpwluv6cAjMz8v1NKlP+rUE/rq6IO76IQjD1KPnn108OF9dcXfbtITD96jQ7vtp5U//6yBQ07PWDynDeiro/t00YC+B6l7rz46+LC++s+j96vn/k3145JFOuaQrrr68vMzEk+qyZMm6tmRT2n8uLHq1rmDunXuoLfeHJ3xOFKdfsoQ9e7ZXXPmzFaTRg004omoer0G4lZHWVlZumv4vTr6qCPUfr+WOmHgidv1j326rF27VmPHvKejjz0u6lBiU0dnDz1FR/Y5SN/Mna02zRrq6Scf18WXXalxY95T53YtNW7Me7r4siszHpckderSVcf0P169u3dW987ttWnTJp1+xtkl71hG4nLOzj/zVB1zeC/N+2aOOrZurGefekL/vPsB3XjNn3XogZ10203X6va77y+5oDIQlzrKE4f3xk+mTNIrLzyjSR+O05G9uujIXl005t239Nao19Slzb769JMpGnbScTplQL+MxpXq9juH66xhp6p75/b6fOYMXX5lVFM08PkKRKVMuuJLkpntpWBiiI4KWqAXKBhXX09BF/V+4Xb3Sprq7iPMrL2C7vW7KmjFv1tBgjs2XGaSnnb3W8MbAE8q6BY+RtKp7t4w7Ir/ewUty00UjI3Pm6BukILJaMpJypZ0gbt/FLaad1UwDGCDpP8L41kgqZOCxP5xScvc/cqiyinw949T0M1+naRKkp5y91vCdYUeL1w3WMHYn25F1OsCBY8NWh7+3kjSA+GxKkh6zt1vNLMekh4Jyx+gYKzeFe4+Ndzvekmr3f2OosoItxugYBxr75Ru/EUdc4RSuv8XpzQt9mWpNC32Za2MXpqltj0t9mUhJk8WySdu52x7W1vTLV0t9um0PS32ZaG0LfZlJW7XkLR9LfZlobQt9mVle7Sg71MAACAASURBVFvsy8L2tNiXhW1tsc+E7W2xT7ftbbFPt7h9vkrpabFPl57du+jTaVPjddJKQFf89CuzxD4qYWLfyd1L388vQuGNjunuHu3tzTJGYl+yuL00SexLFrdzFrekjMS+ZCT2JSOxLx6JfclI7EtGYl8yEvvtk7V7Y6/++5ujDiNtfnn65MgT+7IaY49SMLNpCrrpp3tGeQAAAADADmqHS+zDLu0jIg6jVMLHxwEAAAAAsNXKavI8AAAAAACQATtciz0AAAAAIN4sjhMmJRgt9gAAAAAAJBiJPQAAAAAACUZiDwAAAABAgjHGHgAAAACQORZ+IW1osQcAAAAAIMFI7AEAAAAASDASewAAAAAAEowx9gAAAACAjOI59ulFiz0AAAAAAAlGiz0iUa1SBfVptkfUYWw2bOT0qEPYwvDj94s6hFjLyfWoQ4i9cuXidSc8q3z87iXn5G6KOoR84nZVZ8XsGoqjGlUqRB1CPu5xu4qknE3xiilu742SVKF8vGKKW0vqphhe17kxuq5jWD2IQPz+ywIAAAAAAFuNFnsAAAAAQMaYLHY9Q5KOFnsAAAAAABKMxB4AAAAAgAQjsQcAAAAAIMEYYw8AAAAAyCjG2KcXLfYAAAAAACQYiT0AAAAAAAlGYg8AAAAAQIIxxh4AAAAAkFkMsU8rWuwBAAAAAEgwEnsAAAAAABKMxB4AAAAAgAQjsUci3X/P3TqgY1sd0Kmdzjz9ZK1fvz4jx73woIYacXI7DT++9eZl3RvV1PATWuvlMztq31pVNi/PKme6sGdD3X18K915XCu1rlutTGNbvOgHDTr2cPXp1k6Hdu+gxx+6V5L09+v+oj5d2+qIgzrpnFNP1K+/rizTOAqz8Icf1PfwPtq/bSt1at9G990zPOMxFKZN88bq1qmdenTdX716dIk6nNjF887bb6lt6+Zq3aKJ/nn7rVGHo/Xr16tXj67q1qm9OrVvo5tvvC7qkCRJubm56tG1owYcd3Qkx7/ovLPUouFeOrBz+83LXnvlJfXo1E61q1XU9E+nRhJXnqjrp6A4XddxfG/8w9lnaJ96ddSp/X5Rh7LZypUrdepJA9WpXSt1bt9aH380OaPHv/zCc9SuaX0dckCHzctmff6Zjjm8pw7pvr+GDj5Ov61aldGYUsXxnMXpdRbHz465c2brwK4dN381qFNT998b/es/oyx4jv2O8hUHJPalYGa5ZjYj5athGR6rt5mN2ortxplZp20se3X4vaGZfZGucsva4kWL9ND992rMhCmaPHWmNuXm6pUXn8/IscfMXa4b35qbb9n3v6zTbe99o1k/rs63/LDmtSRJl7wySze8OUfDutYv0zlCypfP0jU33qYxH83Uq29/oP889qDmfP2VDurdR+9M/FRvfzhVjfZtqvvv+mcZRlFEbFlZuuW2O/TpZ7M09sPJevjB+/XVV7MyHkdh3njrfU2c8qnGT/w46lAkxSee3NxcXXLRBXrt9Tc1/bNZevG5Z/XVrGjPWaVKlfTG2+/ro6kzNPmT6Xrvnbf18ZSPIo1Jku6/999q3rxFZMcffPLpev7V/B8TLVu11ohnXtABPQ6KKKr/ibp+UsXtuo7je+Oppw3Vq6PejDSGgq664hIdevgRmjpzliZ+PF3NWrTM6PEHnnSqnn7p9XzL/nTxufrLdTfr/Umf6sh+x+rBe+7MaEyp4nbO4vY6i+NnR9NmzTVhyjRNmDJN4yd9rMqVq6jfMf0jjQnJR2JfOuvcvX3K14LUlWbG0wbKWE5OjtavW6ecnBytXbtWe9atm5HjzvpxtX7bkJNv2cKV67X41w1bbNugZmV9vji4g//r+hyt2ZCrJrV3KbPY6uxZV/u1C1oTqlarpiZNW+inJYvU8+DDlJUVXJIdOnXRkiULyyyGotStW1cdOuwvSapWrZqat2ipxYsWZTwObL1PPv5Y++7bRI0aN1bFihU1cNBgjXr9tUhjMjNVrVpVkpSdna3s7OzI75IvWrhQb785WqcPOzOyGLofeJBq1twt37JmLVqqabPmEUX0P3Gon1Rxu67j+N544EE9tVuB6ylKq1at0sQJH+q0ocE1VLFiRdWoUSOjMXTrcZBq1KyZb9m8b+aoW/fgxlnP3odo9Ov/zWhMqeJ2zuL2OovjZ0eq8WPfV6PGjbX33vtEHQoSjsQ+TcxsqJm9aGavS3onXPYnM/vEzD4zsxvCZQ3N7Csze8TMvjSzd8yscriuiZm9Z2YzzexTM9s3LL6qmb1kZl+b2Ugr+t3oFDObZGZfmFmXsMzrzeyKlDi/KK6HgZlVNrPnwpifl1Q5Zd1JZvZ5WMZtKcvPNLM5Yev+I2Z2b2nqcGvtVa+e/njJZdqveSO1aFxf1XfdVX0OPbwsD1kq81esVZe9a6icSXtUrah9a1XR7rtUyMixf/h+gb78fIbad8zfnfuFZ55U70OOyEgMRfluwQLNnDldnbt0jTQOKfiw73/0kerZvbOeeOzhqMOJVTyLFy9S/foNNv9er159LYrBzZjc3Fwd0LmDGtWvoz6HHBr5dfTnP12qm265VeXK8XFamLjVT1yvayle741xsmD+t6pVq7bOP+cMHditoy4872ytWbMm6rDUvEVrvfNm0Io/6rWXtXhR5m+ax1UcX2dx++xI9fKLL+iEgYOjDgM7gHh80iZP5ZRu+Km3aA+QdLq79zGzwyU1ldRFUntJHc2sZ7hdU0n3uXtrSSslnRAuHxkubyepu6Ql4fIOki6R1EpSY0k9iohrF3fvLul8SY+X8m87T9Jad28r6e+SOkqSme0l6TZJfcK/p7OZ9Q+X/01SN0mHSSqyv6WZnWNmU81s6vLly0oZnrTyl180etT/acasb/TVvB+0ds0aPf/syFKXV1ben7Ncy9dk647+rXTmAQ309dI12rSp7I+7ZvVqnTv0JF379ztUrXr1zcvv+detyiqfpeMGnlT2QRRh9erVGjJ4gG6/4y5VT4ktKu+M+VAfTp6ql199Q4889IAmTviAeELuvsWyOLRwlC9fXpM/ma7Z3/6gqVM/0ZdfFjqKKCPeHD1KtWvvoQ77d4wshjiLY/3E9bqO23tjnOTk5GjmjE915tnnasJH07RLlV101x23lbxjGfvXvQ/pyUcfVN/e3bR69WpVqFAx6pBiI46vszh9dqTauHGj3hz9uvofPyDqUCIR9bj4dH7FAYl96aR2xT8uZfm77v5z+PPh4dd0SZ8qSHibhuvmu/uM8OdpkhqaWTVJ9dz9v5Lk7uvdfW24zcfuvtDdN0maIalhEXE9G+77gaTqZlaavmo9JT0dlvOZpM/C5Z0ljXP3Ze6eo+AmRE8FNy7Gu/vP7p4t6cWiCnb3h929k7t3qlWrdilCC4wb+7722aeRatWurQoVKujoY4/L+EQ6W2OTS09M+UGX/XeW/vHuPO1SsbwWryrbSf6ys7N17tDB6j9gsPoe/b+xWi89+5Tef+dNDX9oRGRvPtnZ2RoyaIAGDR6iY/sfH0kMBdXday9JUu099lC/Y/pr2iefEE+oXr36Wrjwh82/L1q0UHuF8cVBjRo1dFDPXnrv7bcii+GjSZM0+o3X1bpZYw09bYg+GDdWZw09NbJ44iaO9RPH6zqO741xUq9efdWrV1+dwhbWY487QTNnfBpxVFKTZi30zCuj9ea4j9T/hBO1T6PGUYcUG3F8neWJw2dHqnfffkvt2nfQHnXqRB0KdgAk9umV2jfMJP0j5QZAE3d/LFyXOiA7V1JWuH1RCtu+MAVvkbqkHOU/z78r5jhFlSMVHV/Gs8T69Rto6idTtHbtWrm7xo8bo+Yt4jExU6qK5cupUlZQ9e3qVVfuJtfClWWX2Lu7rrzoD2rSrIXOPv/izcvHvf+OHvj3v/TYyJdUuUqVYkooO+6u8/5wlpq3aKGLLrkskhgKWrNmjX777bfNP4957121bN26hL12nng6de6sb76ZqwXz52vjxo168fnndFS/YyKLR5KWLVumlSuDpzqsW7dOY8e8r2YRTsp2w823aPa87/XlnG814j/PqGfvg/XoiKciiydu4lg/cbuu4/jeGDd19txT9eo30Nw5syUp/MxvFXFU0vJlSyVJmzZt0vA7btWpw86OOKL4iNvrLG6fHalefvE5uuEjbZjkrey8LekmMxvp7qvNrJ6k7KI2dvdVZrbQzPq7+6tmVklS+W085iBJY83sQEm/uvuvZrZAUj9JMrP9JTUqoYwPJJ0cltNGUttw+RRJw82slqRfJJ0k6R5JUyXdZWY1Jf2mYFjB59sY9zbp1KWrjul/vHp376zyWVlq2669Tj8jMx+olx3cSK3rVlP132XpkZPa6rlpi7V6Q47O6r63dv1dlq45oqnmr1irG9+aq10rZ+m6I5vJ5VqxJlvDx88v09imTpmkV154Ri1atVHfXsHY+j9dc6Ou/8tl2rhhg0454ShJwQR6t/yrTKdB2MLkSRP17Min1LrNfurWOZjg7/ob/64j+/4+o3GkWrr0J508KBgFk5OTo4GDTtJhhx9JPKGsrCzdNfxeHX3UEcrNzdXpQ89QqwhvNEjSTz8u0TlnDlVubq42bdqk4wcMVN+j+kUaUxycPfQUTfxwvH5esVz7NWuoP199rWrW3E1XXXGJVixfpiEnHKs2bdvpxddGRx1q5OJ2XcfxvfH0U4bogw/GacXy5WrSqIGuufZ6DY148sPb7xyus4adquyNG9WwYSPd93BpRxuWzgVnnqrJEz/QzyuWq1Prxrr8qr9pzZrVevLRByVJffv116CTT89oTKnids7i9jqL62fH2rVrNXbMe7rrngeiDgU7CCtsHAyKZ2ar3b1qgWVDJXVy9wtTll0s6azw19WSTlHQ4j7K3duE21whqaq7X29mTSU9JKmWgpsAAyXtLekKd89Lzu+VNNXdRxQ4/jhJkyX1klRd0hnu/nE4Md9rkvaQ9ImkAyX1dfcFeX9HOJneKHdvE27/hILx/DMkNZF0kbtPNbMhkv6ioJV+tLtfGR77HElXSFos6StJP7v71cXVYYf9O/nYiVOK2ySjho2cHnUIWxh+fHyeRytJtarGa/xg7ibeu0pSIStenbLieM7i9hm4IScDE3Fsg0oxu4YkKat8vGLaFLPrOiZDPfPJzo1XHf22PqfkjTJstwxNrru14jJmOE8cPz9ycuPzft27R1dN/3RqvE5aCSrU3tdrHXd71GGkzY+PDJjm7pE+IpwW+1IomNSHy0ZIGlFg2XBJwwspok3KNnek/DxXweR0qb6VNC5lmwtVCHfvXcTydQrG+he2rmr4fUFeTOH2hfYJcvdnJD1TyKpn3P1hCx7z91+FTwUAAAAAAJS9eN32RlJdb2YzJH0hab6kVyOOBwAAAAB2GrTYY7u5+xVRxwAAAAAAOysSewAAAABAxpji8/z3HQVd8QEAAAAASDASewAAAAAAEozEHgAAAACABGOMPQAAAAAgsxhin1a02AMAAAAAkGAk9gAAAAAAJBiJPQAAAAAACcYYewAAAABA5ph4jn2a0WIPAAAAAECC0WKPyMTpHt3dx7WJOoQtXD3666hDyOeRwe2iDiGfcuXidAUFNmTnRh1CPu4edQj5lI/hOYvXO5G0KV6nTOVoTSnRppi9zrLKxa/NpkL5qCPILyuW70UoTtw+zySpUowubC5pSLTYAwAAAACQaLTYAwAAAAAyijH26UWLPQAAAAAACUZiDwAAAABAgpHYAwAAAACQYIyxBwAAAABkFGPs04sWewAAAAAAEozEHgAAAACABCOxBwAAAAAgwRhjDwAAAADILIbYpxUt9gAAAAAAJBiJPQAAAAAACUZiDwAAAABAgpHYI5HmzpmtA7t23PzVoE5N3X/v8IzGsHjRDxp87BE65ID2OqzH/nr8oXvzrX/43rvUsFZl/bxieZnFcFa3BrpvQCv9o1+zzcu67L2r/tGvuZ48ua0a7VZ58/I2e1bVjX2b6pajmunGvk3Vqk7VMourMO+8/Zbatm6u1i2a6J+335rRYxclTjHF4Zou6A9nn6F96tVRp/b7RRpHqjidszjGs3LlSp160kB1atdKndu31scfTY40noU//KC+h/fR/m1bqVP7NrrvnmivaSl+50yScnNz1aNrRw047uioQ5EUrzqKw/vQJRecrdb71lOvbu3zLX/0ofvUo2Nr9ezaTjf+7aqIootHHRUUp2soT5xeZ3GsnyiY2Q7zFQc7bWJvZnXM7Bkz+9bMppnZZDM7Luq4tpaZjTCzAWkqa4GZ1UpHWWF5aYutKE2bNdeEKdM0Yco0jZ/0sSpXrqJ+x/Qvy0NuIat8lq658Va9P3mG/vvWeD312EOaO/srSUHS/+H4MapXv0GZxvDhtz/r9jHz8y1buHK9hn+wQLOXrsm3/LcNubpz3Hz99Y05enjSD/pDj73LNLZUubm5uuSiC/Ta629q+mez9OJzz+qrWbMydvwkxBSHa7qgU08bqldHvRlpDKnids7iFo8kXXXFJTr08CM0deYsTfx4upq1aBlpPOWzsnTLbXfo089maeyHk/Xwg/frq684ZwXdf++/1bx5i6jDkBS/OorD+9CgIafp2ZdH5Vs24YNxevuN1zVm0qf6YMpMnXfRZRFFF486ShW3ayhPXF5nca0fJN9OmdhbcFvlVUkfuHtjd+8oabCk+ttQRvmyii9JzCzyJyuMH/u+GjVurL333iejx91jz7pq066DJKlqtWrat1kL/bhksSTppmuu1F+u+7tUxnfwZi9dozUbcvItW7xqg35ctWGLbb/7ZZ1Wrgu2XfjrelUob8oql5k7jJ98/LH23beJGjVurIoVK2rgoMEa9fprGTl2kmLKE9U1XdCBB/XUbjV3izSGVHE7Z3GLZ9WqVZo44UOdNvRMSVLFihVVo0aNyOKRpLp166pDh/0lSdWqVVPzFi21eNGiyOKJ2zmTpEULF+rtN0fr9GFnRhpHnrjVURzehw7ocZBq1KyZb9mTjz2kP176J1WqVEmSVLv2HlGEJikedZQqbteQFK/XWRzrBzuGnTKxl9RH0kZ3fzBvgbt/5+73SEHSbmb/NLNPzOwzM/tDuLy3mY01s2ckfW5mDc3sazN71My+MLORZnaomU00s7lm1iXcr4uZTTKz6eH35uHyoWb2ipm9FW5/e2HBmtmtZjYrjOWOlFU9w/K+TW0hN7M/pcR+Q7gsL9Ynw+UvmVmVlLL+aGafmtnnZtYi3GcXM3s8LGu6mR2bEveLZva6pHcscG8Y4xuSMvrp9vKLL+iEgYMzecgt/PD9d5r1+Qy179hZ7745SnXq7qVWbdpGGlNxOu+9q777eZ1yNnlGjrd48SLVT+m9UK9efS2K8J97KZ4x5YnDNR1HcTtncYtnwfxvVatWbZ1/zhk6sFtHXXje2VqzZk3JO2bIdwsWaObM6ercpWtkMcTtnEnSn/90qW665VaVKxePf8niWEdx9O28ufpo8gT17dND/X9/iKZPmxp1SLERx2soTq+zONYPdgzRX93RaC3p02LWnynpV3fvLKmzpLPNrFG4roukq929Vfh7E0nDJbWV1ELSEEkHSrpC0l/Dbb6W1NPdO0i6VtItKcdqL2mQpP0kDTKzfH23zWw3ScdJau3ubSXdnLK6bnisfpJuDbc/XFLTMM72kjqaWc9w++aSHg7LWSXp/JSylrv7/pIeCGOXpKsljQnr4WBJ/zSzXcJ1B0g63d37hPE1D/+GsyV1L6xSzewcM5tqZlNXLF9W2CbbbOPGjXpz9Ovqf3yZ9vwv1prVq3Xe0JN07d//qazyWbr3rtt02VXXRhZPSertWkmDOtTVE1MWZuyY7lveQIh6PFIcY5LicU3HVdzOWdziycnJ0cwZn+rMs8/VhI+maZcqu+iuO26LLJ5Uq1ev1pDBA3T7HXepevXqkcURt3P25uhRql17D3XYv2NkMRQUtzqKq5ycHP26cqVGvz9B1950q84ZOqTQutsZxe0aitvrLG71E5Wox8Sn+ysOdtbEPh8zu8/MZprZJ+GiwyWdZmYzJE2RtLuCZFmSPnb31EHN8939c3ffJOlLSe978Ir9XFLDcJtdJb1oZl9IukvBjYU877v7r+6+XtIsSQX73q6StF7So2Z2vKS1KetedfdN7j5LUp2U2A+XNF3BzYsWKbH/4O4Tw5+fVnBTIM8r4fdpKXEfLumqsB7GSfqdpLyB2e+6+8/hzz0lPevuue6+WNIYFcLdH3b3Tu7eafdatQvbZJu9+/Zbate+g/aoU6fkjctAdna2zh12kvoPGKQj+/XXdwu+1cLvv1PfXl3Uo0Nz/bh4kfr1OUBLf/oxkvgKqlmlgi7u1UgPTfpeS1dvzNhx69Wrr4ULf9j8+6JFC7XXXntl7PiFiWNMUvTXdJzF7ZzFMZ569eqrU9gifuxxJ2jmjOLuYWdGdna2hgwaoEGDh+jY/sdHGkvcztlHkyZp9Buvq3Wzxhp62hB9MG6szhp6amTxSPGro7jaa6/6+v3R/WVm2r9jZ5UrV04rynCy3CSJ2zUUt9dZ3OoHO46dNbH/UtL+eb+4+wWSDpGUl22apD+6e/vwq5G7vxOuK9ivMXUw86aU3zdJyht/fpOkse7eRtLRChLkwvbPTdknL7YcBa3vL0vqL+mtIva1lO//SIm9ibs/lldcgdhTf88rKzUGk3RCSll7u/tX4bqC9RDJbeqXX3wusi7L7q4/X3yumjRrrrPOv1iS1KJVG037+ntNnD5bE6fP1p571dOoMZO1R509I4kxVZUK5XTFwY30wvQlmrtsbck7pFGnzp31zTdztWD+fG3cuFEvPv+cjup3TEZjSEJMUrTXdNzF7ZzFLZ46e+6pevUbaO6c2ZKk8ePGqHmLViXsVbbcXef94Sw1b9FCF10S3eRieeJ2zm64+RbNnve9vpzzrUb85xn17H2wHh3xVGTxSPGro7g68qhjNOGDsZKked/MUXb2Ru2+e9rmIU60uF1DcXudxa1+sOPYWRP7MZJ+Z2bnpSxLHW/+tqTzzKyCJJlZs5Qu6KWxq6S8wTNDt2VHM6sqaVd3Hy3pEgXd64vztqQzwv1kZvXMLG/M+95mdkD480mSJmxFWX+0sH+JmXUoYrsPJA22YG6Cugq67Ze5tWvXauyY93T0sdE8zGDqlEl65YVnNPnD8erbu6v69u6qse++VfKOaXT+gXvruiObas/qv9Pw41qq1767qWOD6hp+XEs1qVVFlx/cSH/q01iSdFjzWqpTraL671dHN/++mW7+fTNVr5SZuQ+zsrJ01/B7dfRRR6j9fi11wsAT1ap165J33MliivqaLuj0U4aod8/umjNntpo0aqARTzxW8k5lKG7nLG7xSNLtdw7XWcNOVffO7fX5zBm6/Mq/RBrP5EkT9ezIpzR+3Fh169xB3Tp30Ftvjo4snjies7iJWx3F4X3o3DNOUb/Demre3Dnq0LKRnvnPEzrp1KH6fsF89erWXucOO0X/fuCxyLrjxqGOUsXtGoob6gdlxXbW8UBhAnqXpK6SlilogX7Q3Z83s3IKxrIfraDVepmC1vIOkq5w935hGQ0ljQpb4mVmI8LfX0pdFybTT4bljJF0qrs3NLOhkjq5+4Xh/qMk3eHu4wrE+ZqCVn4L1z+Zeqxwu9XunpfMXyzprLCI1ZJOUdASP1pBEt5d0twwjrVmtiCMY7mZdQqP0dvMKku6O9zeJC1w936FxG2S7lEwKeGc8LhP58VWmA77d/JxE6cUfYIybOXa7KhD2MI1b86OOoR8HhncLuoQYm9Ddm7UIeRTMSte927jMgYtzjbmbIo6hHwy9eSMbVEuZjHl5MbsnJWP1+teKnxMcZRWrcspeaMMq1458ocM5RO39+u4vc6keL3WenTtpGnTpsbrpJWgUp2mXnfwXVGHkTbf/fvoae7eKcoYdtrEfmdT8CZE1EjsS0Zinzwk9sWL2z+KcURiXzIS++LFKdnIE7f/NUnsSxa39+u4vc6keL3WSOyjF4fEPj5XJAAAAAAA2Gbxuj2IMuPuCyTForUeAAAAAJA+JPYAAAAAgIyK25CPpKMrPgAAAAAACUZiDwAAAABAgpHYAwAAAACQYIyxBwAAAABkFkPs04oWewAAAAAAEozEHgAAAACABCOxBwAAAAAgwRhjDwAAAADIKJ5jn1602AMAAAAAkGAk9gAAAAAAJBhd8RGJciZVqlA+6jA2q7NrfGLJ8+CJbaMOIZ+aAx+JOoR8fn7hrKhD2ELFLO6VFid3k0cdQuxVKE+3xJK4x+s6yiofr9f9xpxNUYewhaxy8bqud6kUv8/8uNkUs/fruL3OJGnN+pyoQ9gsN2bvi4gGiT0AAAAAIHOMMfbpFr/bXwAAAAAAYKuR2AMAAAAAkGAk9gAAAAAAJBhj7AEAAAAAGWOSGGKfXrTYAwAAAACQYCT2AAAAAAAkGIk9AAAAAAAJxhh7AAAAAEAGGc+xTzNa7AEAAAAASDASewAAAAAAEozEHgAA4P/Zu+/wqKr8j+PvLwkBFRAVREhAmiT0AAm9CYiiIKggiFIUsYuu67r7W3dXrGthdVVExYZdRF0UpFgoCtJCVWApIi4EC6iIEErK+f0xQ0xCIIDJ3DP4eT3PPJm59869n5xz7p05c+6dERERKSFm9ryZfW9mX+SZNtLM0s1sWfh2bp55/2dm681sjZmdfTjbUMdeotIH06fRpGEiDZPq8tCD9wcdB/Ar0549e+jUrhWtU5JJSW7EPXfdEbFtP3VDR74edxlpj16UO61JzZOZff/5zH/4QuY81IeUMyrnzvvXsDZ8MeZiFj5yIcm1T4lYzquHX8Hp8VVISW4csW0eim95wL9MQbbraMgD/tWZ8hTNp9eO/bZv386gS/qR0rQBqckNWTh/XmBZNm/aRI/uXWjepAEpyY144vFHA8uSV3Z2Nu1ataDvBb2CjuJdu/axznzYz0ZceyVJp5eJ2gAAIABJREFUtarRvmVy7rQ7bv8zrZs3omPrZgy+pC8/b98eSLagmB07t8MwDjinkOmPOOeSw7cpoXKxBsAAoGH4OWPMLKaoDUR9x97MnJn9K8/jW81sZDGu/z9m1ifP4zVm9rc8j982swuPYH01835SU2DeLDNL+W2JwcyGmtnog8zbmef+Q2a2Mvz3GjMbfJTb22hmlY4275HKzs7m5hHX8+6kqSxdsYoJb7zO6lWrIrX5qMhUpkwZ3p/+MfPTljFv0VI++mA6CxfMj8i2X56xlt53Tc037d4hrbj3zSW0vuUd7n59MfcObgnA2c2rU6faiTS67k1ueHIOj13dPiIZAQYNHsrEyVOLXjBCfMsD/mUKsl1HQx7wr86U59B8e+3Y7y+33ky37meTtnwVcxcupV5S/cCyxMTGct8Do1iyYhUzP53H2KfGsHp18GU0ZvRjJCYmBR0D8K9d+1ZnvuxnAy4dwvj/TM43rXOXbsxZuIxP5i+lTt0z+Pe/Hoh4LokM59wnwI+HuXhv4A3n3F7n3FfAeqBlUU+K+o49sBe4sAQ7lp8BbQHM7BRgJ9Amz/w24WWKZGa+/QrB1UBz59yfnHNPOedeCjrQ4Vi0cCF16tSlVu3axMXF0a//ACZPeleZ8jAzypUrB0BmZiaZmZkR++bRuau+5cdf9uab5hxUOC4OgBOPj+ObHzMA6NnydF6buQ6AhWu/58QT4jjtpOMikrN9h46cfNLJEdnW4fAtD/iXKch2HQ15wL86U55D8+21A2DHjh3MnfMpg4cOAyAuLo6KFSsGlqdq1ao0a9YcgPLly5OYVJ8t6emB5QFI37yZ6VOnMOTyYYHm2M+3du1bnfmyn7Vt34GTCtTTmV3PIjY21D1ISW3Fli2bI55Lik0lM0vLc7vqMJ93g5mtCJ+qf1J4WjywKc8ym8PTDulY6NhnAWOBPxScYWaVwyPqi8K3duHpn5tZRQv5Yf9ItZm9bGbdCqxmLuGOffjvZKBy+Lm1gN3OuW/NrKyZvRBe91IzOzO8zqFmNsHMJgEfFMh3nJm9Ea7M8UChPRozO9fM/mtmc8zsMTObHJ5+splNDD9/vpk1KeS5tcxsXvj/vzvP9PeAE4AFZtY/fI3HreF5s8zsATNbaGZrzaxDeHqMmY0K/48rzOzGPJu60cyWhOeV6EfYW7akk5BQPfdxfHwC6QG/yPuYKTs7mzapzaiVUIUuXbuR2rJVYFn+9Pw87hvSinXPXMI/h7biH68sAqDaKSew+Yfck0hI/2EX1U4+IaiYEgV8atc+5pHo4uNrx8avNlCpUmWuu+oK2rduwQ3XDmfXrl2BZtrv640bWb58aeD72Z//9Afuvu9+SpU6Ft5Glywf6szH/awwr748jq5nFXamtkSJbc65lDy3sYfxnCeBOkAy8A2w/yz0wkYJXFErO1aOSE8Al5rZiQWmP0rouoVU4CLg2fD0uUA7QtctbAA6hKe3BgqeR7kYaGRmcYQ69vOANUD98OO54eWuB3DONQYuAV40s7LheW2AIc65LgXWfS2Q4ZxrAtwLtCj4j4XX8TTQwznXHqicZ/adwNLw8/8KFDbi/ijwZLgMvt0/0Tl3PqEPJZKdc+MLeV6sc64lcDOw/8LRq4BaQLPwNl/Ns/w251xzQg301kLWV2ycO7BdBz1K5mOmmJgY5i1aypoNm0hLW8TKlYVeARIRV51dn9uen8cZw1/ntufn8+T1HYGDHLWKPGzJ75lP7drHPBJdfHztyMrKYvmyJQwbfg1z5i/mhONP4JFRwZ8evHPnTgYO6MuDox6hQoUKgeWYOmUylSufSrPmB7xlkwJ8qTMf97OCHn7on8TGxtKv/8Cgo0SUmR0zt6PhnPvOOZftnMsBnuHX0+03A9XzLJoAbClqfcdEx945t4NQp3ZEgVndgNFmtgx4D6hgZuWBT4GO4duTQGMziwd+dM7tzLsC59xeYCXQnFDHfwGhzn3b8G3/afjtgZfDz/kv8DVQLzzvQ+dcYddUdAReCT9nBbCikGWSgA3h6ysAXs8zL+82ZwCnFPLhRrs8z3m5kPUfzDvhv4uBmuH73YCnnHNZ4W3+WMTy+ZjZVRY+PWXrtq1HECW/+PgENm/+9eyU9PTNVKtW7ajXVxx8zLRfxYoV6dCxEx9NnxZYhkvPrMfE+RsBePuzDblfnpf+wy4STimXu1z8KSfwzU9+jAyJ33xo13n5lkeig4+vHfHxCcTHJ5ASHmHtfcFFLF+2JNBMmZmZDOzfl/4DBtK7z2F/rVGJmP/ZZ0x5fxIN69Vm6OCBfDJrJlcOHRRoJh/5VGc+7md5vfHqS3ww9X2eeu4l7z5wkJJlZlXzPLwA2D868B4wwMzKhM8QPwNYWNT6jomOfdi/gWGETi/frxTQJs83DcY7534BPiE0St8BmAVsBfoS6vAX5jNCnfDyzrmfCI3q7+/Y7x+xP9SeeKieSlHjk4da7+GepnE0Y6D7L5LOBvZ/N4AdYl2FLZ8/hHNj95+eUrlS5cIWOSwpqamsX7+OjV99xb59+5gw/g3O63n+Ua+vOPiWaevWrWwPf7Pq7t27mTnjY+oF+CU/3/y0iw4NQ8euzo2rsf6bnwF4f9HXDDzzDABa1juVHRn7+Pan3YHlFL/51q59yyPRx7fXDoAqp51GfEJ11q1dA8DsWTNITGoQWB7nHNdefSWJSUmMuPmWwHLsd+c997Hmy/+xcu0Gxr30Gh07n8mz445k3OTY51ud+bif7ffxh9N57JFRvDL+Pxx//PFBx5ESZGavExocTjSzzWY2DHhw/yXOwJmELy13zq0E3gRWAdOA651z2UVt45jp2IdHj98k1Lnf7wPghv0PzCw5vOwmoBJwhnNuAzCH0OnjB+vYzyX0RXPLw49XEBq9r0FoNB9CHxZcGt5OvfC8NUXEzvucRsAB18gD/wVqm1nN8OP+B3l+Z0Knw+8oJPuA8P1Li8hTlA+Aayz8JYBmFsg3tcTGxvLIo6Ppdd7ZJDeuz0X9LqZBw4ZBRPE203fffsO53bvQqkVTOrZtSZeu3ehxXs+IbPvFW85k1v29qVetIuufuYQhXRO5fsyn3H95KxY8fCF3XZbKDWPmADBt8Sa++m4HK5/szxPXdeCmp+cWsfbiM+SygXTu2Ja1a9dQt1Z1xr3wXMS2HQ15fMwUZLuOhjzgX50pz6H59tqx34MPP8qVlw+ibWoyny9fxh9v+7/Assz7bC6vv/oys2fNpHVqM1qnNmPa1CmB5fGRb+3atzrzZT8bfvllnNO1A+vXraFxYk1eefF5/nLrTezc+Qt9e59D57Yt+ONN10U8l0SGc+4S51xV51xp51yCc+4559wg51xj51wT59z5zrlv8ix/r3OujnMu0Tl3WD97YYVddxJNzGync65c+H4V4CvgQefcSAt9U/4ThK6HjwU+cc5dE172ZSDGOTfQzNoS6txXds79UMg2TgW+A4Y7554NT5sF7HXOnR1+XBZ4itB18lnALc65mWY2FEhxzt0QXq4mMNk518jMjgNeABoAy4C6wAjnXFqB7fcCHgK2EToNo4pz7tJwx/oFQte9ZwBXOedW5N1m+PSN18L//9vA3/KUV96yGwnsdM6NCv9vtzrn0sJlmOacqxnu0D9I6PcUM4FnnHOjzWxjeHvbLPRzfaOcc50PVW8tWqS4uQvSDrXI7152jl/7ZqX+zxa9UAT9+OaVQUeQI+RZk/ZSKZ2FGXV8O3V2X1ZO0BEOEOtZw87x8L1vjGdl5FsRlfKsfAB27ckKOkKurh1bsWzJYv8K6RDKVq3nag55POgYxWbNA+csds795p8t/y2ivmP/e2Bm5ZxzOy307uEJYJ1z7pGgc/0W6tgXTR37Q1PHPvp41qS95OF7VymCOvZFU8e+aOrYH5o69oemjn3wfOjYHzOn4h/jhoe/AHAlcCKhb8kXERERERERKfxLzsQv4dH5qB6hFxERERERkZKhjr2IiIiIiIhEjOHnJRbRTKfii4iIiIiIiEQxdexFREREREREopg69iIiIiIiIiJRTNfYi4iIiIiISER59muhUU8j9iIiIiIiIiJRTB17ERERERERkSimjr2IiIiIiIhIFNM19iIiIiIiIhJRpovsi5VG7EVERERERESimDr2IiIiIiIiIlFMp+KLAM65oCMcIKaUX6cn/fjmlUFHyOfkLncEHeEAP828K+gI+eTk+NWufWvT4Oe+7xMfT5P0rc58y1M6xr86860dmV9VBvhXRuBhIXkm1qN9zfAniwRHHXsRERERERGJHNPv2Bc3nYovIiIiIiIiEsXUsRcRERERERGJYurYi4iIiIiIiEQxXWMvIiIiIiIiEWP4+KWR0U0j9iIiIiIiIiJRTB17ERERERERkSimjr2IiIiIiIhIFNM19iIiIiIiIhJBpmvsi5lG7EVERERERESimDr2IiIiIiIiIlFMHXsRERERERGRKKZr7EVERERERCSidIl98dKIvUSlD6ZPo0nDRBom1eWhB+8POg5XD7+C0+OrkJLcOOgouVRGIU/9pQ9fv3cbaS9enzutcZ0qzHpyOIvGXc9b919K+ePLAFDjtIr8+NHfmf/8tcx//loe+2OviGb1qc42b9pEj+5daN6kASnJjXji8UcDzbOfT2Xk437vYybV2aH5lsm3POBXGwL/ykjlUzTfymjd2jW0b9Ui91a9ykmMGe3H66xEL3Xs5aiYWYqZPRbEtrOzs7l5xPW8O2kqS1esYsIbr7N61aogouQaNHgoEydPDTRDXiqjX708dSm9b30537Qn/9yHvz39IalDn+C9T1bxh0va5c7bkP4jra94ktZXPMmIf02KWE7f6iwmNpb7HhjFkhWrmPnpPMY+NYbVq4NtQ76VkW/7PfiXSXVWNN8y+ZbHtzYEfpWRyqdoPpbRGfUSmbNgMXMWLGb2Zws57rjj6Xl+n0AzSfRTx16OmJnFOufSnHMjgtj+ooULqVOnLrVq1yYuLo5+/QcwedK7QUTJ1b5DR04+6eRAM+SlMvrV3OVf8+OO3fmmnVHjFOYs2wjAjLQv6dO5QcRzFeRbnVWtWpVmzZoDUL58eRKT6rMlPT2wPOBfGfm234N/mVRnRfMtk295fGtD4FcZqXyK5mMZ5TV75sfUql2bGjVODzqKRDl17H8HzGyimS02s5VmdlV42jAzW2tms8zsGTMbHZ7ey8wWmNlSM/vIzKqEp480s7Fm9gHwkpl1NrPJ4XnlzOwFM/vczFaY2UUl+f9s2ZJOQkL13Mfx8QmkB9zh8I3K6NBWbfienu2TALjwzEYknHpi7ryaVU9i3nPX8sHjV9CuSeReZH2us683bmT58qWktmwVaA6fy0gKpzqT30pt6NBUPkXzvYzenvAmF/UbEHSMQJjZMXPzgTr2vw9XOOdaACnACDOLB/4OtAbOApLyLDsHaO2cawa8AdyWZ14LoLdzbmCB9f8d+Nk519g51wSYUVgIM7vKzNLMLG3rtq1H/c845wpb91Gv71ikMjq0q++fyNUXtGTus9dQ7rg49mVmA/DtD79Qr++/aDPsSf78+FTG/aNv7vX3Jc3XOtu5cycDB/TlwVGPUKFChUCz+FpGcnCqM/mt1IYOTeVTNJ/LaN++fUydMok+F/YNOoocA/St+L8PI8zsgvD96sAgYLZz7kcAM5sA1AvPTwDGm1lVIA74Ks963nPO5T+nOaQbkPtRo3Pup8JCOOfGAmMBWrRIOfAoe5ji4xPYvHlT7uP09M1Uq1btaFd3TFIZHdra/22j1x9fAqBu9VPo0SbU/PdlZvNjZqiJL137DRu2/MgZ1U9hyZotJZ7JxzrLzMxkYP++9B8wkN59Lgw0C/hZRnJoqjP5rdSGDk3lUzSfy+jD6dNomtyMU6tUCTqKHAM0Yn+MM7POhDrebZxzTYGlwJpDPOVxYLRzrjFwNVA2z7xdB9sMcNQd9SOVkprK+vXr2PjVV+zbt48J49/gvJ7nR2rzUUFldGiVK54AhD6x/8vgTjzz7iIAKlU8nlKlQp/i16x6EnUTTuGrLYV+TlXsfKsz5xzXXn0liUlJjLj5lsBy5OVbGUnRVGfyW6kNHZrKp2g+l9HbE9743Z6GL8VPHftj34nAT865DDNLInT6/fFAJzM7ycxigYsKLL//wqMhh7mND4Ab9j8ws5N+e+yDi42N5ZFHR9PrvLNJblyfi/pdTIOGDUtyk0UactlAOndsy9q1a6hbqzrjXngu0Dwqo1+9eEdfZj01nHo1KrH+7T8y5LzmXNytMSteG8HyV2/km22/8NKUpQC0b1qTReOuZ8EL1/HaPf25cdQkfvqlsJNUip9vdTbvs7m8/urLzJ41k9apzWid2oxpU6cElgf8KyPf9nsfM6nOoi+Tb3l8a0PgVxmpfIrmYxkBZGRkMHPGR/TqfUHRCx+LLPQ79sfKzQdW2HUncuwwszLARCCe0Eh9ZWAkoVPvbwW2AKuBH51zt5tZb+ARQp37+UCqc66zmY0EdjrnRoXX2xm41TnX08zKAU8QugY/G7jTOffOoXK1aJHi5i5IK+b/9uj5uB/4cv3Xfr6V0cld7gg6wgF+mnlX0BHyycnxq872nw3hE9/atW98Ow6B6iwa+daOfGxDKqND8618APaGv5/HB53btWLpkjT/CukQjo9PdElXPxl0jGKz9I6ui51zKUFm0DX2xzjn3F6gR8HpZpbmnBsbHrH/D6FRd5xz7wIH/AaIc25kgcezgFnh+zs5/NF9ERERERERKUY6Ff/3a6SZLQO+IPQFeRMDziMiIiIiIiJHQSP2v1POuVuDziAiIiIiIr8/hp+XWEQzjdiLiIiIiIiIRDF17EVERERERESimDr2IiIiIiIiIlFM19iLiIiIiIhIROkS++KlEXsRERERERGRKKaOvYiIiIiIiEgUU8deREREREREJIrpGnsRERERERGJKP2OffHSiL2IiIiIiIhIFFPHXkRERERERCSK6VR8CYQDsnNc0DFylfLwTCDn/Ckf8Ku+AH6ccWfQEQ6QMvLDoCPkkzbyrKAjeM+z3cy7n/7Zk5kddIQDlC0dE3SEfHw7VkvRfDz917d25FsZbfohI+gIB6hasWzQEXJ5Vl0SEHXsRUREREREJKL0gUTx0qn4IiIiIiIiIlFMHXsRERERERGRKKaOvYiIiIiIiEgU0zX2IiIiIiIiEjnm35c0RjuN2IuIiIiIiIhEMXXsRURERERERKKYOvYiIiIiIiIiUUzX2IuIiIiIiEjEGPod++KmEXsRERERERGRKKaOvYiIiIiIiEgUU8deREREREREJIrpGnsRERERERGJINPv2BczjdhLVNqzZw+d2rWidUoyKcmNuOeuOwLNc/XwKzg9vgopyY0DzZGXj5mys7Np16oFfS/oFXSUwMrn7gsaMPsvnfjPjW0OmDe03el8cc9ZVDy+NACptU5i3t8689b1rXnr+tZcc2btiGb9YPo0mjRMpGFSXR568P6IbvtgfMq0edMmenTvQvMmDUhJbsQTjz8aaB7wc78f8/i/adOiCW1SmjJsyKXs2bMn0Dw+tSHwr858ywP+1ZlveVRnB/omfTODLuzBOR2ac27HFF585gkAVq9cwcXnnUnPzqlcPagvO3/ZEfFs+/n0nkiODerYe8jMaprZF0f4nHFm1jd8f5aZpZRMOj+UKVOG96d/zPy0ZcxbtJSPPpjOwgXzA8szaPBQJk6eGtj2C+NjpjGjHyMxMSnoGEBw5TNx6RaueXHJAdNPO7EMbeqezJbtu/NNX7JxO32fmE/fJ+bz1MwNkYpJdnY2N4+4nncnTWXpilVMeON1Vq9aFbHtR0OmmNhY7ntgFEtWrGLmp/MY+9QYVq8Otox82++3pKfz9JjRzJizgHlpy8nJzuadCeMDy+NbGwL/6sy3PL7VmW95QHVWmJjYGP4y8j6mfbqEN6fM5NUXxrJ+zWpuv+V6br39LibPWsRZPXrx7Jh/RzRXXj69J5Jjgzr2EpXMjHLlygGQmZlJZmZmoKfztO/QkZNPOjmw7RfGt0zpmzczfeoUhlw+LOgoQHDls3jjdn7enXnA9Nt6JPLw9HU4F/FIhVq0cCF16tSlVu3axMXF0a//ACZPeleZ8qhatSrNmjUHoHz58iQm1WdLenpgecC//R4gKyuLPbt3k5WVRUZGBqdVrRpYFt/aEPhXZ77l8a3OfMsDqrPCnFqlKg2bNAOgXLny1Dkjke++3cJXX64jtU17ANp16sr0ycHUnW/vieTYoI69v2LN7EUzW2Fmb5nZ8QBm9g8zW2RmX5jZWDuC3qyZnWtm/zWzOWb2mJlNDk9vaWafmdnS8N/E8PShZjbRzCaZ2VdmdoOZ3RJebr6ZnRxero6ZTTOzxWb2qZlF5OPH7Oxs2qQ2o1ZCFbp07UZqy1aR2KwcpT//6Q/cfd/9lCqlw05BnZMq8/2Ovaz5ducB85rWOJG3r2/Nk4ObUefUEyKWacuWdBISquc+jo9PID3gTquPmfb7euNGli9fquNQAdXi47nx5ltonFiLpNoJVDjxRLp06x5YHp/bkBTOtzrzLY+PfCujzf/7mlVfLKdp81TqJTXg4+nvAzB10jt8u2VzIJn0nijE7Ni5+eD33Zr8lgiMdc41AXYA14Wnj3bOpTrnGgHHAT0PZ2VmVhZ4GujhnGsPVM4z+79AR+dcM+AfwH155jUCBgItgXuBjPBy84DB4WXGAjc651oAtwJjDpLhKjNLM7O0bdu2Hk7sQ4qJiWHeoqWs2bCJtLRFrFx5RFcvSARNnTKZypVPpVnzFkFH8U7Z0qW4qlMtRn/85QHzVm3ZwVmj5nDRE/N5bf4mHhuYHLFcrpBTB4L+khsfMwHs3LmTgQP68uCoR6hQoULQcbyy/aefmDL5PZatWs/qLzeRsWsX419/NbA8vrYhOTjf6sy3PD7yqYx27drJjVcO5K93PUi58hW475EnefWFp7mgezt27dxJ6bi4iGfSeyIpKerY+2uTc25u+P4rQPvw/TPNbIGZfQ50ARoe5vqSgA3Oua/Cj1/PM+9EYEL4uv5HCqxzpnPuF+fcVuBnYFJ4+udATTMrB7QNP38ZoQ8PCj3P0jk31jmX4pxLqVSpcmGLHJWKFSvSoWMnPpo+rdjWKcVr/mefMeX9STSsV5uhgwfyyayZXDl0UNCxvFD95OOJP+k43r6hNdP/2J4qFcow4bpWnFIujl17s9m9LxuAT9duIzbGcr9Yr6TFxyewefOm3Mfp6ZupVq1aRLZ9MD5myszMZGD/vvQfMJDefS4MNIuPZs38mNNPr0WlypUpXbo0vXpfwML58wLL42MbkkPzrc58y+MjX8ooMzOTG4cNpNeF/Tn7vN4A1DkjkRfGT+I/H8yl5wX9qH56rYjn0nsiKSnq2Pur4MedLjzqPgbo65xrDDwDlD3M9R3qo9K7CXXgGwG9Cqxzb577OXke5xD6ucRSwHbnXHKeW/3DzHTUtm7dyvbt2wHYvXs3M2d8TD19AYm37rznPtZ8+T9Wrt3AuJdeo2PnM3l23MtBx/LCuu920un+2Zz9rzmc/a85fLdjL/3GLOCHnfs4pdyvIwmN4itQymB7xoHX55eElNRU1q9fx8avvmLfvn1MGP8G5/U8PyLbjpZMzjmuvfpKEpOSGHHzLYHl8FlCQnXSFi0gIyMD5xyzZ80gMSm4Y7VvbUiK5lud+ZbHRz6UkXOOv/7hWuqckcgV14zInf7D1u8ByMnJYcwjD3DJ4Mhf4673RFJS1LH3Vw0z2/97WJcAc/i1w70tPFLe9wjW91+gtpnVDD/un2feicD+i5+GHklI59wO4Csz6wdgIU2PZB1H47tvv+Hc7l1o1aIpHdu2pEvXbvQ477CuSigRQy4bSOeObVm7dg11a1Vn3AvPBZbF50w+Cap8Hry4Ma9e3ZKalY7noz914MIWBx/F6N6wChNvbMPb17fm/3om8qfxn0ckI0BsbCyPPDqaXuedTXLj+lzU72IaNDzcE4R+H5nmfTaX1199mdmzZtI6tRmtU5sxbeqUwPKAf/t9SstWnN/nQjq3TaVtajI5OTkMuWJ4YHl8a0PgX535lse3OvMtD6jOCrN44Tzefet15s+ZzfldW3N+19bM+mgakydOoHvbppzTvhmnnlaViy4ZXPTKpMSY2TFz84EVdh2MBCvc+Z4CfELoNPd1wCDnXIaZ3QMMADYCm4CvnXMjzWwcMNk595aZzQJudc6lFVhvL+AhYBuwEKjinLs0/AHCi8BWYEZ4WzXNbCiQ4py7Ifz8jeHH2/LOM7NawJOETsEvDbzhnLvrUP9j8xYp7tN5i46+kIpZKT/2R69l5/h1rIjxsNJS7/wo6Aj5pI08K+gI3svxrF178t4g196snKAjHKBs6ZigI+Sj91FF8+VNr898a0e+1dmmHzKCjnCAqhUP96TZktexbUuWLE7zq9KKUC4hyTW96ZmgYxSbz27ruNg5F+jPjccGuXEpnHNuI9DgIPP+BvytkOlD89zvfJBVz3TOJYW/Sf8JIC28/DygXp7l/h6ePg4Yl2e9NfPcz50Xvm7/nEP9TyIiIiIiIlIydCr+78vw8BfcrSR0+v3TAecRERERERGR30gj9r8jzrlHCH3rvYiIiIiISDA8+v33Y4VG7EVERERERESimDr2IiIiIiIiIlFMHXsRERERERGRKKaOvYiIiIiIiEgU05fniYiIiIiISMQYYPr2vGKlEXsRERERERGRKKaOvYiIiIiIiEgUU8deREREREREJIrpGnsRERERERGJKF1jX7w0Yi8iIiIiIiISxdSxFxEREREREYliOhVfAlPKo7NvMrNd0BEO4FP5AMT4FshDi+7oFnSEfPo9vyjoCPm8eXlK0BEOkOP82vd3780OOkI+Pu73zrM625uVE3SEfMqWjgk6wgG+/3lP0BHyOblcXNARDpDjV7OmlPkVyMf9wA9pAAAgAElEQVQ602nk4ht17EVERERERCSi9NlI8dKp+CIiIiIiIiJRTB17ERERERERkSimjr2IiIiIiIhIFNM19iIiIiIiIhJR+gLC4qURexEREREREZEopo69iIiIiIiISBRTx15EREREREQkiukaexEREREREYkc0+/YFzeN2IuIiIiIiIhEMXXsRURERERERKKYOvYiIiIiIiIiUUwde4lKVw+/gtPjq5CS3DjoKLm2b9/OoEv6kdK0AanJDVk4f17QkcjOzqZdqxb0vaBX0FG8qzPlCRnRqSYvD0pmdN+GudMub5XAkxc34rGLGvLXs+pyQlwMAOXLxHBvz0TevLw5V7erEdGc4F+d7Rf0fnbTdcNpUDuejq2Sc6fdf/cddGrTnDPbpdCv97l8+82WiOW58dorSaxZjXapv+b56ccfubDXOaQ2rc+Fvc5h+08/RSxPXj62oTGP/5s2LZrQJqUpw4Zcyp49e4KOxAfTp9GkYSINk+ry0IP3R3z7W9I30b/32XRpk0y3ds15/unRAIz6552c3TGVHp1bcVnfnnwXwXZdUND7fUF6D5LfiGuvJKlmNdrnOQ69+85btEtpSuXycSxdkhbxTPvt2bOHTu1a0TolmZTkRtxz1x2BZQmSYZgdOzcfqGMfIDOraWZf/IbnjzOzvsWZKbzejWZWqbjXW5wGDR7KxMlTg46Rz19uvZlu3c8mbfkq5i5cSr2k+kFHYszox0hMTAo6BuBfnSlPyMdrtjFyytp805Zt3sH1E75gxNsrSf95D32TqwKwL9vx6qJ0np+/KeI5wb862y/o/WzApYN5453J+aZdf9MfmT1vCTPnptH9nHMZ9cC9EctzyaVDeHNi/jyPPvwgHTt3YdHy1XTs3IV/P/xgxPLk5Vsb2pKeztNjRjNjzgLmpS0nJzubdyaMDzRTdnY2N4+4nncnTWXpilVMeON1Vq9aFdEMMTGx/O2u+5kxbxkTp83mpeeeZu2a1Vx9wx+Y/skips5aQNfuPXh01D8jmiuvoPf7gvQeJL8Blw5hfIHjUP0GDRn32pu0adchkEz7lSlThvenf8z8tGXMW7SUjz6YzsIF8wPNJMcGdeyjlJn9pl80MLOY4soShPYdOnLySScHHSPXjh07mDvnUwYPHQZAXFwcFStWDDRT+ubNTJ86hSGXDws0x36+1ZnyhKz8die/7M3KN21p+g5yXOj+mu93UumEOAD2ZuWw6rudZGbnRDom4F+dgR/7WZt2Hah40kn5ppWvUCH3fkbGroiOJrRt34GTCtTTlPcnMeDSQQAMuHQQUya/F7E8efnYhrKystizezdZWVlkZGRwWtWqgeZZtHAhderUpVbt2sTFxdGv/wAmT3o3ohmqnFaVxk2bAVCufHnq1kviu2+2UL583nadEdgomQ/7fV56D3Kgwo5D9ZLqc0a9xEDy5GVmlCtXDoDMzEwyMzO9GfGV6KaOffBizexFM1thZm+Z2fFm9g8zW2RmX5jZWAvv7WY2y8zuM7PZwE3h53czs0/NbK2Z9QwvF2NmD4XXscLMrg5P72xmM83sNeBzMzvBzN43s+XhbfXPk+tGM1tiZp+bWVL4+Seb2cTwOuebWZPw9JFm9rKZzTCzdWY2PFKF54uNX22gUqXKXHfVFbRv3YIbrh3Orl27As305z/9gbvvu59SpbSby9E7K7Eyizf9HHQMb/m8n913199Jrl+bt998nT/fHuypnlu//47TTgt1WE87rSrbtn4faB5fVIuP58abb6FxYi2SaidQ4cQT6dKte6CZtmxJJyGheu7j+PgE0tPTA8uz6X9fs/LzZSS3SAXgwXvvoHWTukx86w1u+cvfA8nk236v9yDRJzs7mzapzaiVUIUuXbuR2rJV0JHkGKC9LXiJwFjnXBNgB3AdMNo5l+qcawQcB/TMs3xF51wn59y/wo9rAp2A84CnzKwsMAz42TmXCqQCw82sVnj5lsDtzrkGwDnAFudc0/C2puXZzjbnXHPgSeDW8LQ7gaXhrH8FXsqzfJNwhjbAP8ys2m8rluiSlZXF8mVLGDb8GubMX8wJx5/AI6MeCCzP1CmTqVz5VJo1bxFYBol+FzerSnaOY9b6H4KO4iXf97O//uNulq3ewEUXX8JzT48JOo4UYvtPPzFl8nssW7We1V9uImPXLsa//mqgmZxzB0wLajRx186dXDP0Ev5x70O5o/W33X4n81esp0/fAbz47FMRz+Tjfq/3INEnJiaGeYuWsmbDJtLSFrFy5VFfmRvVzI6dmw/UsQ/eJufc3PD9V4D2wJlmtsDMPge6AA3zLF/w4rs3nXM5zrl1wAYgCegODDazZcAC4BTgjPDyC51zX4Xvf05oxP8BM+vgnMs7LPdO+O9iQh8eEM72MoBzbgZwipmdGJ73rnNut3NuGzCT0AcI+ZjZVWaWZmZp27ZtPYyiiR7x8QnExyeQEv7EtfcFF7F82ZLA8sz/7DOmvD+JhvVqM3TwQD6ZNZMrhw4KLI9Eny5nnEJqjYr8a8aGoKN4K1r2swv7DeD99/4TaIbKp1bh22+/AeDbb7+hUuVTA83ji1kzP+b002tRqXJlSpcuTa/eFwT+pWfx8Qls3vzr92ikp2+mWrXIf1afmZnJNZdfQp++/enRs88B83tfdDFTJ0+MeC4f93u9B4leFStWpEPHTnw0fVrRC4sUQR374BX8aNwBY4C+zrnGwDNA2TzzC55bVdjzDbjROZccvtVyzn1Q8PnOubVAC0Id/H+a2T/yrGdv+G82sP96/sI+j3IF/h4sF865sc65FOdcSqVKlQtZVfSqctppxCdUZ93aNQDMnjWDxKQGgeW58577WPPl/1i5dgPjXnqNjp3P5NlxLweWR6JL84QKXJRclbunr2NvQNfTRwOf97MN69fl3p8+ZTJ1A76utMe5PXnj1VDZvPHqy5x7nh/fJB60hITqpC1aQEZGBs658GtHsF/IlpKayvr169j41Vfs27ePCePf4Lye50c0g3OO2266hrr1Ehl+3U2507/6cn3u/Q+nvU+dM+pFNBf4ud/rPUh02bp1K9u3bwdg9+7dzJzxMfU8+iJGiV7q2Aevhpm1Cd+/BJgTvr/NzMoBRX3rfT8zK2VmdYDawBpgOnCtmZUGMLN6ZnZCwSeGT5fPcM69AowCmhexrU+AS8PP7UzodP0d4Xm9zaysmZ0CdAYWFbGu32TIZQPp3LEta9euoW6t6ox74bmS3NxhefDhR7ny8kG0TU3m8+XL+ONt/xd0JK/4VmfKE3Jrl9o81Kc+8RXL8sLAppyVWImr253OcaVjuPvcRB69sCHXtT89d/lnL2nCsNY16FqvEi8MbEr1imUPsfbi5Vud+eLqyy/j3G4dWb9uLU2TavHqSy9w98jb6dgqmU5tmjNrxofc+8DDEcszfOhlnNOlA+vXraFRvZq88uLz3HTLbcya8RGpTesza8ZH3HTLbRHLk5dvbSilZSvO73Mhndum0jY1mZycHIZcEezX1MTGxvLIo6Ppdd7ZJDeuz0X9LqZBw4ZFP7EYpS34jHfefI3PPp1Nj86t6NG5FTM+nMb9d/+Ns9q34OyOqXw682NG3jcqorl8pvcg+eU9DjUOH4fef28ijevVJG3hfAZe1Jt+vc8NJNt3337Dud270KpFUzq2bUmXrt3ocV7Pop8oUgQr7FoqiQwzqwlMIdRhbgusAwYRun59ALAR2AR87ZwbaWazgFudc2nh548DfgJSgCrALc65yWZWCrgH6EVolH0r0AdoFn7+/i/ZOxt4CMgBMoFrnXNpZrYRSHHObTOzFGCUc66zmZ0MvADUAjKAq5xzK8xsJFANqAPUAB50zj1zqP+9eYsUN3d+ifb9j0hmtn/7QSlPrtfZL8a3QFKki18I7nd6C/Pm5SlBRzhAdo5f+/7ufdlBR8jHx/3+uDi/ftRlb5ZfZ7WULe1X+QB8//OeoCPkc3K5uKAjHMCzQ5F370F828/Ar32tQ5tUlixO86zWDq1Cjfqu1W0vBB2j2Hx0Y5vFzrlA3+j8pp9Mk9/GObcRKOxcqb+FbwWX71zg8dCDrDeH0IcDfy0wa1b4tn+56YRG9ws+v2ae+2mERuBxzv0I9C5sm8Ba59xVB5knIiIiIiIiJUSn4ouIiIiIiIhEMY3Yy2/mnBsZdAYREREREZHfK3XsRUREREREJKJ8+f33Y4VOxRcRERERERGJYurYi4iIiIiIiEQxdexFRERERERESoiZPW9m35vZF3mmPWRm/zWzFWb2HzOrGJ5e08x2m9my8O2pw9mGOvYiIiIiIiISMWZgZsfM7TCMA84pMO1DoJFzrgmwFvi/PPO+dM4lh2/XHM4G1LEXERERERERKSHOuU+AHwtM+8A5lxV+OB9I+C3bUMdeREREREREJDhXAFPzPK5lZkvNbLaZdTicFejn7kRERERERESOXiUzS8vzeKxzbuzhPNHMbgeygFfDk74BajjnfjCzFsBEM2vonNtxqPWoYy8iIiIiIiIRVerY+h37bc65lCN9kpkNAXoCXZ1zDsA5txfYG76/2My+BOoBaQddEToVX0RERERERCSizOwc4M/A+c65jDzTK5tZTPh+beAMYENR69OIvYiIiIiIiEgJMbPXgc6ETtnfDNxB6FvwywAfhr9Zf374G/A7AneZWRaQDVzjnPux0BXnoY69BMLgcH8aIiJyXE7QEQrhT/kA5GS7oCPkExfr3wlHezOzg46Qz/ihR3xGWIm6ddLqoCMc4KGe9YOOkM9xcTFBR8jHp+P0fr5l8isN7Mvy7/Xs5HJxQUfIJzvHr9czgFKetevYGL9eY30rH4Ac5187En855y4pZPJzB1n2beDtI92GOvYiIiIiIiISUb59UBvt/Po4TkRERERERESOiDr2IiIiIiIiIlFMHXsRERERERGRKKZr7EVERERERCSidIl98dKIvYiIiIiIiEgUU8deREREREREJIqpYy8iIiIiIiISxXSNvYiIiIiIiESMAYYusi9OGrEXERERERERiWLq2IuIiIiIiIhEMXXsRURERERERKKYrrEXERERERGRiCqlS+yLlUbsJSp9MH0aTRom0jCpLg89eH/QcQAY8/i/adOiCW1SmjJsyKXs2bMn0Dzbt29n0CX9SGnagNTkhiycP095CvCpHa1bu4b2rVrk3qpXOYkxox8NLM/mTZvo0b0LzZs0ICW5EU88HrkslzWvyv3n1uP2rrVzpx1fuhQ3tKvBHd3rcEO7GhxXOvTy1aRqOf7atTb/16U2t51ZizqnHBexnEGW0aFkZ2fTrlUL+l7QK+go7Nmzh07tWtE6JZmU5Ebcc9cdQUfSfn8YfDxe+9Kufa2zRom1aZ3SlHatmtOpXcug43i1n+lYLb8XGrGPMmZWE5jsnGsUcJSDMrONQIpzbltJrD87O5ubR1zP+1M/JD4hgfatU+nZ83zqN2hQEps7LFvS03l6zGjmL/mc4447jssvG8A7E8YzcNCQwDL95dab6db9bF5+fQL79u0jIyMjsCw+5vGtHZ1RL5E5CxbnZqtfpwY9z+8TSBaAmNhY7ntgFM2aNeeXX36hfesUunQ7i/r1S7585n/9M7M3/MTgFtVyp3VPrMSarbv4cO4PnFXvFLrXq8S7K79nzfe7WPHNBgCqVSjDsFYJ3P3hlyWeEYIto0MZM/oxEhOT2PHLjkBzAJQpU4b3p39MuXLlyMzM5KwzO9D97B60bNU6kDza7w+Pb8dr8Kdd+1pnAO9P+5hTKlUKOoZ3+5mO1fJ7oRH73xkzi/oPcxYtXEidOnWpVbs2cXFx9Os/gMmT3g06FllZWezZvZusrCwyMjI4rWrVwLLs2LGDuXM+ZfDQYQDExcVRsWJF5cnD13YEMHvmx9SqXZsaNU4PLEPVqlVp1qw5AOXLlycxqT5b0tMjsu31P2Swa192vmlNqpZnwf9+BmDB/36mabXyAOzNdrnLlIktBY6ICbKMDiZ982amT53CkMuHBZpjPzOjXLlyAGRmZpKZmYlZcOdear8vmo/Ha9/a9X6+1JlvfNvPdKyW3wt17KNTrJm9aGYrzOwtMzsewMz+YWaLzOwLMxtr4XdPZjbLzO4zs9nATWY2zsweM7PPzGyDmfUtbCNmdkt4XV+Y2c3habeZ2Yjw/UfMbEb4flczeyUS//yWLekkJFTPfRwfn0B6wAfoavHx3HjzLTROrEVS7QQqnHgiXbp1DyzPxq82UKlSZa676grat27BDdcOZ9euXcqTh4/taL+3J7zJRf0GBB0j19cbN7J8+VJSW7YKLEP5MrHs2JMFwI49WZQv8+tnlE2rlefvZ9Xh2rY1eGXJlkDy+VBGAH/+0x+4+777KVXKn5f37Oxs2qQ2o1ZCFbp07RZoGWm/L5qPx2sf2zX4U2cQ+hCtT69z6Ng2lReeGxtoFp/3Mx2rPWKGHUM3H/yOW1NUSwTGOueaADuA68LTRzvnUsOn6R8H9MzznIrOuU7OuX+FH1cF2oeXOeDiJzNrAVwOtAJaA8PNrBnwCdAhvFgKUM7MSofX9emhQpvZVWaWZmZpW7dtPeJ/ej/nDhySC3qH2v7TT0yZ/B7LVq1n9ZebyNi1i/GvvxpYnqysLJYvW8Kw4dcwZ/5iTjj+BB4Z9YDy5OFjOwLYt28fU6dMos+FhX7eFnE7d+5k4IC+PDjqESpUqBB0nEIt3/ILd3/4JWPnbaJng8oR374vZTR1ymQqVz6VZs1bBJahMDExMcxbtJQ1GzaRlraIlSu/CCyL9vui+Xa89rVd+1RnAB/M+JRP56Xx9sT3eebpJ5k755PAsvi6n+lYLcc6deyj0ybn3Nzw/VcIdaoBzjSzBWb2OdAFaJjnOeMLrGOicy7HObcKqFLINtoD/3HO7XLO7QTeIdShXwy0MLPywF5gHqEOfgeK6Ng758Y651KccymVKx39m+/4+AQ2b96U+zg9fTPVqlU7xDNK3qyZH3P66bWoVLkypUuXplfvCwL9sqH4+ATi4xNICX8i3fuCi1i+bInyFMjkWzsC+HD6NJomN+PUKoXtlpGVmZnJwP596T9gIL37XBholl/2ZlGhbGiUvkLZWH7Zm3XAMut/yKDSCXGcEBcTsVw+ldH8zz5jyvuTaFivNkMHD+STWTO5cuigQDPlVbFiRTp07MRH06cFlkH7fdF8O1772q59qjOAquF2XPnUU+l5fh8WL1oUWBYf9zMdq+X3QB376FTwo1BnZmWBMUBf51xj4BmgbJ5lCp5HtzfP/cI+Ri30o1XnXCawkdBo/meEOvNnAnWA1YeZ/zdJSU1l/fp1bPzqK/bt28eE8W9wXs/zI7Hpg0pIqE7aogVkZGTgnGP2rBkkJiUFlqfKaacRn1CddWvXAITzBPclMb7lAT/bEcDbE97w4tRO5xzXXn0liUlJjLj5lqDj8Pk3v9CqxokAtKpxIiu++QWAyieUzl2mesWyxJayA67PLym+ldGd99zHmi//x8q1Gxj30mt07Hwmz457OdBMW7duZfv27QDs3r2bmTM+pl5icMdG7fdF8+147WO7Br/qbNeuXfzyyy+592d89CH1GzYs4lklx7f9TMdq+b2I+i9S+52qYWZtnHPzgEuAOfzaid9mZuWAvsBbv2EbnwDjzOx+Qp38C4BBeebdClwBfA48DCx2hZ17VQJiY2N55NHR9DrvbLKzsxky9AoaBPgCBpDSshXn97mQzm1TiYmNpUnTZIZcMTzQTA8+/ChXXj6IzH37qFmzFk+MfV558vCxHWVkZDBzxkc88viTgeYAmPfZXF5/9WUaNmpM69RmAIy8617O6XFuiW/78tR4zqh8POXiYrmnxxm8v2orH6z9gWEtE2hbsyI/ZWTx7ILQaFByfAVa1TiR7BzYl53D8ws3l3i+/YIso2jx3bffcNWwoWRnZ5OTk8OFffvR47yeRT6vpGi/Pzy+Ha9941udff/9d1za/yIgdClFv/6XcFb3cwLL49t+pmO1vzy4QuOYYhHqi0kxCf/c3RRCneu2wDpgkHMuw8zuAQYQGlHfBHztnBtpZrOAW51zaeF1jCP0k3lvhR/vdM6VK2RbtxDqvAM865z7d3h6V2Aaoev2d5nZWuAp59zD4fkbKeLn7lq0SHFzF6T9hpIoXnsyIzPCdyRK6Wh3SHGx/p1wtNezdlQ6xq8y+tPkiJzUc0Qe6lk/6Aj55Hj2muzDdbEFxZTyK5Nv+72PdeZZlZGd49d+Bv695pf27DU2x8M68+l43bFtS5YsTvOrERWhYs0GrvPfXgo6RrF5d3jqYudcSpAZNGIfZZxzG4FCz4lzzv0N+Fsh0zsXeDy0wOMDOvXh6Q8TGo0vOP1joHSex/UKzK9ZeHoREREREREpbn59HCciIiIiIiIiR0Qj9iIiIiIiIhIxhn+XoEQ7jdiLiIiIiIiIRDF17EVERERERESimDr2IiIiIiIiIlFM19iLiIiIiIhIROkS++KlEXsRERERERGRKKaOvYiIiIiIiEgUU8deREREREREJIrpGnsRERERERGJKNNF9sVKI/YiIiIiIiIiUUwdexEREREREZEoplPxJRAOcM4FHSNXXIx/n3Hp7CT5rfZl5wQdIZ9RveoHHeEAbe6dEXSEfD6+tVPQEfI5Li4m6AgH2LUnK+gI+Rxfxr8y8k3GvuygI+RzXGn/6iw7x5/3RODXezSAUqX8e1NkfhWRiDr2IiIiIiIiEjlmGsQqbv4NU4qIiIiIiIjIYVPHXkRERERERCSKqWMvIiIiIiIiEsV0jb2IiIiIiIhEVCldZF+sNGIvIiIiIiIiEsXUsRcRERERERGJYurYi4iIiIiIiEQxXWMvIiIiIiIiEaUr7IuXRuxFREREREREopg69iIiIiIiIiJRTB17ERERERERkSima+xFREREREQkoky/Y1+sNGIvUenq4VdwenwVUpIbBx0FgM2bNtGjexeaN2lASnIjnnj80aAjeVdGvuUB+GD6NJo0TKRhUl0eevD+QLOsW7uG9q1a5N6qVzmJMaODbUdjHv83bVo0oU1KU4YNuZQ9e/YEmieoNjSyd31m/KkDb13X6oB5g9vWYNnIrlQ8vjQA5zauwpvXtuTNa1vy4rAW1KtSrsTzjbj2SpJqVaN9y+TcaXfc/mdaN29Ex9bNGHxJX37evr3EcxTGh2Ojz+UD/h0bfcgz4torSapZjfapv9bZu++8RbuUplQuH8fSJWmBZfOhTRemUWJtWqc0pV2r5nRq1zLQLD60oYJ8er0HP8tIop869gEzs5pm9kUh02eZWcpRrG+omY0uZPo4M+t7tDmPYPsjzezWkt7OoMFDmTh5aklv5rDFxMZy3wOjWLJiFTM/ncfYp8awevWqQDP5Vka+5cnOzubmEdfz7qSpLF2xiglvvM7qVcHV2Rn1EpmzYDFzFixm9mcLOe644+l5fp/A8mxJT+fpMaOZMWcB89KWk5OdzTsTxgeWB4JrQ+8t+4brXll2wPQqFcrQuvbJbNm+O3da+vY9DHthCRc/uZCxszfy915JJZ5vwKVDGP+fyfmmde7SjTkLl/HJ/KXUqXsG//7XAyWeozA+HBt9Lh/w79joQ54Blw5h/MT8dVa/QUPGvfYmbdp1CChViA9t+mDen/YxcxcsYfbchYHm8KEN5eXb6z34V0ZybFDHXo6amcUEte32HTpy8kknB7X5A1StWpVmzZoDUL58eRKT6rMlPT3QTL6VkW95Fi1cSJ06dalVuzZxcXH06z+AyZPeDToWALNnfkyt2rWpUeP0QHNkZWWxZ/dusrKyyMjI4LSqVQPNE1QbWvL1dnbszjxg+q3n1OPfH67PN235pp/5ZU8WACs2/0yVCmVKPF/b9h04qUC5nNn1LGJjQ1fbpaS2YsuWzSWeozA+HBt9Lh/w79joQ57C6qxeUn3OqJcYUKJf+dCmfedDG8rLx9d738pIjg3q2Psh1sxeNLMVZvaWmR2fd6aZPWlmaWa20szuzDM91cw+M7PlZrbQzMoXeN55ZjbPzCqFJ3Uzs0/NbK2Z9QwvU9bMXjCzz81sqZmdGZ5eM7zskvCtbXh6ZzObaWavAZ+Hp91uZmvM7CMg+FfdgH29cSPLly8lteWBp+2KP7ZsSSchoXru4/j4BNI9eXP29oQ3uajfgEAzVIuP58abb6FxYi2SaidQ4cQT6dKte6CZfNIpsRJbd+xl7Xc7D7rMBc2rMWf9DxFMVbhXXx5H17POCTqGt8dGX8pHoo9PbdrM6NPrHDq2TeWF58YGHccrPr/e/54ZUMqOnZsP1LH3QyIw1jnXBNgBXFdg/u3OuRSgCdDJzJqYWRwwHrjJOdcU6Abkng9qZhcAfwHOdc5tC0+uCXQCzgOeMrOywPUAzrnGwCXAi+Hp3wNnOeeaA/2Bx/LkaRnO1MDMWgADgGbAhUDqwf5JM7sq/AFF2rZtW4+shKLEzp07GTigLw+OeoQKFSoEHUcOwTl3wDQfvsRl3759TJ0yiT4XlviVM4e0/aefmDL5PZatWs/qLzeRsWsX419/NdBMvihbuhRXdqjJmJlfHnSZlJon0adZNR4tMKIfaQ8/9E9iY2Pp139goDl8PTb6Uj4SfXxr0x/M+JRP56Xx9sT3eebpJ5k755OgI3nD19d7keKmjr0fNjnn5obvvwK0LzD/YjNbwv+zd9/xVVT5/8dfHxJQEBCUnqD0BEJJIKGDgAUREERYiiJYEN111fVn212/rLrKWtjFwuqK64oV7I2O9N4ERFGKglJFVKSTkJzfH7nBAIEEktw5F97Px+M+cu9k7tz3nTkzc8/MnDmwDEgA6pF5MGCrc24xgHNul3PuUGj89sB9QGfn3C/ZpvO2cy7DObcW+BaID33Wa6FpfA18B9QBigIvmtlK4J3QZ2ZZ5JxbH3reBvjAObfPObcL+Ph4X9I5N9I5l+ycSy5Xrnze5kwESUtLo1/vnvTu0was2iYAACAASURBVI9u3XsEHUdyERMTy6ZNGw+/3rx5E1WqVAkwUaYpkybSKDGJChUrBppjxvSpXHhhdcqVL0/RokXp2u0qFi2YH2gmX8SWLU5M2eK8fWszxt/Zkgqlz2L04KacX7IYALUrluRvV8Zz55gV/Lr/UC5TKzxj3niVyRPG8Z+XXg30R6yv20Zf5o9EHh/LdOXQ/qt8hQp0ubI7SxcvDjiRP3zd34sUNFXs/XD0ocTDr82sOnA3cHHojP444Gwyr2A59hBkpm+BUmRW0HP7nOP9mvkT8APQCEgGimX7395cpnvGcc5x6+CbiIuP5/Y77wo6juRBckoK69atZcP69aSmpvLOW2Po3OXKoGPx3jtjAr8MHyA2tipLFi9k3759OOeYOWMacfGFfyO4SLBu+146PDmbK56axxVPzWP7roP0fWERP+1JpdK5Z/HP3g144INVfP/T/twnVkimTpnEM8OH8fpbH1CiRInc31BIfN02+jJ/JPL4WKb37t3L7t27Dz+f9ukU6iYkBJzKH77u70UKmir2frjAzFqEnvcF5mT7X2kyK9K/mllFoFNo+NdAFTNLATCzUmYWHfrfd2ReFv+qmWXfsvcysyJmVhOoAawGZgHXhKZRB7ggNPxcMq8IyAD6A8e7Ud4s4CozKx5q49/1lObASRpwbT/atW3JmjWrqVW9KqNefikcH3tc8+fNZfQbrzFzxnSapyTRPCWJiRPGB5rJt3nkW57o6GiGPz2Crp07ktigLlf3+h31Av4htG/fPqZP+5Su3a4KNAdActNmXNm9B+1aptAyJZGMjAwG3DAo0ExBlaF/XJ3AKzcmc+H5JZh0Vyu6Jx3/JoI3X1SdMsWL8pfOcbx1S1PeuPm4rZMKzKDrr+Xyi9uwbu1qGsRV4/VX/sf9d9/Bnj276dntctq1bML/u+PoFl7h4cO20ef5A/5tG33IM2jgtVzeIbTM6mQus3Eff0iDOtVYsmgB/a7uRq9uV4Q9F/hRpo+2ffsPdLy4LS2bJtG+TXM6drqCSy8L7r4RPpSh7Hzc3/s2jwJhhp1GDx9YTu1OJHzMrBownswKcktgLZkV6fHA3c65JWY2CmhG5pn4g8DHzrlRoUr9s0BxMtvXXwL0BJKdc7eZWRLwBpmV7f8DfiHz7HtF4C7n3NhQe/r/AE2AQ6Hh082sNvAesA+YDvzROVfSzNqFcnXJ9h3+ClxH5gGFTcAq59ywE33vxk2S3dwF/lwm5uNq4Mk2wlu+bESzO5iWHnSEI/hWrM+K9u9YcotHpwUd4QhT774o6AhHKF4ssM5Pjmt/ql/rWYmz/JtHvtnn2TIrXtS/ZZae4dcWOzrKr32sj/t8n+pQrZqn8NnSJf7NpBM4v0aC6/Twm0HHKDBv9E9cGronWmCicx9FCpNzbgNHtl/P0i7bOAOP897FQPOjBo8KPXDOLcs27eNN40BO/wu1w2+YbdCfQ8NnADOOGvdR4NGcpi8iIiIiIiKFy7/TJyIiIiIiIiKSZzpjLyIiIiIiImHlYQuLiKYz9iIiIiIiIiIRTBV7ERERERERkQimir2IiIiIiIhIBFMbexEREREREQkrH7sxjGQ6Yy8iIiIiIiISwVSxFxEREREREYlgqtiLiIiIiIiIRDC1sRcREREREZGwMaCImtgXKJ2xFxEREREREYlgqtiLiIiIiIiIRDBV7EVEREREREQimNrYS2AyXNAJfnMoPSPoCMeI8qzhkW95nPOoAIUUjfLrWOkhn1Yy/Frnsyx44OKgIxzhtve/CDrCEZ7qVi/oCMcoXiwq6AhH2JeaHnSEI5xd1K/5A7DvoF/zyLdtNUCGZ/u0Is6zfX6Gf7/T1Ad7/mkeFiz/tmwiIiIiIiIikmeq2IuIiIiIiIhEMFXsRURERERERCKY2tiLiIiIiIhIWKmFfcHSGXsRERERERGRCHbCir2ZRZnZpHCFEREREREREZGTc8KKvXMuHUg1s9JhyiMiIiIiIiIiJyEvbez3ACvMbDKwN2ugc+6uQkslIiIiIiIipyUzKKJ+7AtUXir2n4YeIiIiIiIiIuKZXCv2zrmXzKwYcIFzbl0YMomIiIiIiIhIHuV6V3wz6wysBKaEXiea2QeFHUxEREREREREcpeXS/EfBpoB0wGcc8vNrFahphIREREREZHTlprYF6y89GOf5pzbedQwVxhhRPLqwIEDXNSqGc2TE0lOrM8jD/8t0Dxr16ymdbMmhx9VK5bluRFPB5oJID09nVbNmtDzqq5BR2HwoBu4MKYiyYkNgo4C+Jdn08aNdLqsA40b1iM5sT7/fjb48rNz50769+1FcqN6pCQmsGjB/EDz+LbeA0yeNJGGCXEkxNfiySceC8tnDkyJ4V9XxvNQx9+OsZ9TLIq72lbj0U61uattNUoUzdy9d4wrx5BLazLk0po81LEWI3smcE6xqLDkzOLTdsiH9ez2W28ivloVWqckHh720fvv0iq5EeVLFWPZZ0vCnik7H9azLZs30rvbZXRo3oiLWybx0gsjABj70Xtc3DKJC8sVZ8WypWHPlcW3beNzzz5FiyYNaZHciBsHXMOBAwcCzeNDGcqJL9siX+ePRL68VOy/MrPfAUXMrLqZPQUsKORcUgDMrJqZfVEA0xloZlUKIlNBOeussxg3aSoLlixn/uJlfDp5EosWBlcsa9eJY87CpcxZuJSZ8xZRvHgJulzZPbA8WZ4b8QxxcfFBxwCg/3UD+XDshKBjHOZbnqjoaIY+PozPPl/F9NnzGfmf5/jqq1WBZrr/7ju55LKOLFmxirmLllEnvm6geXxb79PT07nz9j/w0ScTWPb5Kt4ZM5qvVhX+Mpu7/heemrXhiGGd4svx1fY9/HXCWr7avodOdcsDMGn1Dh6e8g0PT/mG9z//gdU/7mVvanqhZ8zOp+2QD+tZn2sG8NaHY48YVrdeAqPefJsWrdqENUtOfFjPoqKieeDhx5m2YAUfTZrFqy/9hzVff0VcfAIjX3mLZi1bhzXP0XzaNm7ZvJkXnhvBtDkLmb9kBRnp6bz/zluB5QE/ylBOfNkW+Tp/JPLlpWJ/G9AEyAA+AA4AdxZmKPHOQOCkKvZmlpdmHqfMzChZsiQAaWlppKWlYZ5czzNz+lSq16jBBRdcGGiOzZs2MWnCeAZcf2OgObK0btOW88qeF3SMw3zLU7lyZZKSGgNQqlQp4uLrsmXz5sDy7Nq1i7lzZnPdwMzyU6xYMcqUKRNYHvBvvV+8aBE1a9aieo0aFCtWjF69+zD2k48K/XPX7th3TOU8sUpp5m3IvLhu3oadJFUpfcz7ml5wLos2/lro+bLzbTvkw3rWsnUbyh617akTX5fadeLCmuN4fFjPKlaqTINGSQCULFWKWrXj2bZ1M7Xj4qlZu05YsxzNx23joUOHOLB/P4cOHWLfvn1Uqlw50Dw+lKGj+bQt8nH+yOkhLxX7851z9znnkpxzic65+51z+wo9mRSUaDN7xcw+N7N3zayEmV1sZsvMbKWZ/c/MzgIwsyFmttjMvjCzkZapJ5AMvGFmy82suJk1MbOZZrbUzCaZWeXQ+2eY2VAzmwncUdhfLD09nRYpSVSPrUiHiy8hpWmzwv7IPHnvnbe5ulefoGNw3z1/4u9DH6NIkbys5uKT7zZsYMWKZYGW6Q3rv6VcufL8/uYbaN28CbfdOoi9e/cGlieLT+v9li2biY2tevh1TEwsmwM6GFP67Gh+PXAIgF8PHKLU2UceWy0WZdSvVJLPNu0Kay6ft0M+rGe+8mk92/j9Br5cuZykJk0Dy5Cdb9vGKjEx/PHOu2gQV534GrGUPvdcOlxyWWB5svhUhsC/bZFv8ycoZnbaPHyQl9I9xszWmtnrZnazmQV7LaacrDhgpHOuIbALuAsYBfR2zjUg8waKt4bGHeGcS3HO1QeKA12cc+8CS4BrnHOJwCHgWaCnc64J8D/g0WyfV8Y5d5Fz7p9HBwmVnyVmtmTHjh/z/cWioqKYv3gZq7/dyJIli/nyy3y3Osi31NRUJoz/hO49egaaY8L4sZQvX4Gkxk0CzSEnb8+ePfTr05Mnhg2ndOljz7qGy6FDh1ix/DNuHHQLcxYs5ZwS5zB82OOB5cni03rv3LG3m/Fl5360RlVKse6nY8/0Fyaft0O+rGe+8mU927tnD4MH9uVvjw6jlCfLybdt485ffmH82I9ZvmodX32zkX179/LW6DcCy5PFlzIEfm6LfJo/cvrItWLvnGsJNABeBCoCk8ws/7UyCZeNzrm5oeevAxcD651za0LDXgHahp63N7OFZrYS6AAk5DC9OKA+MMXMlgMPALHZ/n/chl3OuZHOuWTnXHK5cuVP/RsdpUyZMrRpexGfTppYYNM8VVMmTaRRYhIVKlYMNMeCefMYP+4TEurUYOB1/Zg1Yzo3DewfaCbJXVpaGv1696R3n350694j0CwxMbHExMSSHDqL0O2qq1mx/LNAM2Xnw3ofExPLpk0bD7/evHkTVaoEczuSXQcOcW7oLP25Z0ezO3T2PktK1TIs/D68l+H7uh3yaT3zXZDrWVpaGoMH9uGqnn3o1DX4e9Zk8W3bOGP6VC68sDrlypenaNGidO12VeA388vOh221r9si8GP+yOkjL/3YNwf+CNwNXAFMJPOsr0SGPPVgYGZnA8+ReSY+60DO2TmNCnwZapaR6Jxr4JzLfs1XWK5H+/HHH9m5M7M96f79+5k+bSp1PLghynvvjPHiMvyHHhnK6m++58s13zLq1Tdp2649/x31WtCx5AScc9w6+Cbi4uO5/c7gN7EVK1UiJrYqa9esBmDmjGnExdcLNJNv631ySgrr1q1lw/r1pKam8s5bY+jc5cpAsizfsouW1TLb+basVoblW3675L540SLElS/B8s3hvQzfx+2Qb+uZj3xYz5xz3HP7YGrViWfQ7wu9Zd9J8W3bGBtblSWLF7Jv3z6cc6E8wf4e8qEMZefbtsi3+SOnj7zc4GwumZdi/wMY65w7lMv44pcLzKyFc24+0Bf4FBhsZrWcc+uA/sBMfqvE7zCzkkBP4N3QsN1AqdDz1UD5rGmaWVGgjnPuy3B9IYAftm3l5hsHkp6eTkZGBj169qJT5y7hjHCMffv2MX3apwx/9vlAc/hqwLX9mDVrBj/t2EGt6lV5YMiDDAzwJja+5Zk/by6j33iNhPoNaJ6SedOoBx9+lMs7XRFYpif+9TQ3Xd+ftNRUqlWrzr9H/i+wLODfeh8dHc3wp0fQtXNH0tPTGTDwBuol5HShU8Ea1DyWuPLnUPKsaJ7oEsfHX25nwtc7uKVFVVpXL8vP+9L4z/zfriRIiinNlz/sITVdPdX6sJ4NGngtc2fP5OefdtCgTjXu++sQypY9j/vvvpOfdvxIv6u7Ub9hI975aHzYMmXnw3q2eOE83n/7TeLr1efyizLb1t/7wMOkHjzIkPvv4ueffuT6vldRr35DXn93bC5TK3g+bRuTmzbjyu49aNcyhajoaBo2SmTADYMCywN+lCGfaf78xtPWaxHLcmojeMQIZuWAVmRert0ESAXmOuceKvx4kh9mVg0YD8wCWgJryazItwCGkXlgZzFwq3PuoJk9AvQBNgAbge+ccw+a2dXAUGB/6L1xwDPAuaFpPOWce9HMZgB3O+dy7YS3cZNkN3v+4gL7rvl1KD0j6AjHiCri19bOtzw+ymVzGnaHMvwK5GMZ8i3Tbe/71c7yqW7BXqWRkyKe/RLcnxbe7gNzc3bRqKAjHOPnPalBRzjCuSWKBh3hGBme7UCKRvlxk7ksudVXguDTPVXatEjhs6VL/AmUB+VrJrgej78ddIwCM7JX/aXOueQgM+R6xt45t8PMVgHlgXLARcA5gCr2nnPObQBy+lU2FUjKYfwHyGwzf/Tw94D3sg1azm/t8rOP1+4Uo4qIiIiIiMgpyrVib2bfAN8As8m8m/pg59yBQs4lIiIiIiIiInmQlzb2dZxzfl1nJiIiIiIiIhHJMO+aVkW6vDSgecTMSptZtJlNMrMfzKxfoScTERERERERkVzlpWLfyTm3C+gCbCezb/P7CjWViIiIiIiIiORJXir2WZfrXwGMds7tII99o4uIiIiIiIhI4cpLG/sJZvYFkA78IdT93cHCjSUiIiIiIiKnJVM/9gUt1zP2zrl7gA5AE+dcGpl9mfco7GAiIiIiIiIikru8nLEHqAa0M7Ps479Z8HFERERERERE5GTkpR/7UUA9YDmZl+NDZht7VexFREREREREApaXM/bNgXrOuYzCDiMiIiIiIiKnP1Mj+wKVl7vifwmUK+wgIiIiIiIiInLy8nLG/lzgKzNbQLa74TvndAM9ERERERERkYDlpWL/j0JPISIiIiIiIiKnJNeKvXNuajiCyJnFOTiU7s9tG6KK+NfGx7mgExzpULpfgaKj/FtmhzL8mke+zSEPVzPSPVtmQy6pHXSEI3R/cWHQEY7x8c3Ng45whOJFo4KOcAQfm6yWLp7XTpjCo1h0XlqihpdPv4nAv/3HvtT03EcKs7M8Wvf92pPlnX9rYmTLdX6aWYqZLTCzX83sgJkdNLNd4QgnIiIiIiIiIieWl0OozwHXAmOApsBAoGohZhIRERERERGRPMrLFRBFnHOrgWjnXJpz7kXgkkLOJSIiIiIiIiJ5kJcz9nvNrBiwwsyGAluBkoUbS0RERERERE5HhvqxL2h5OWM/MDTebUA6UBvoWYiZRERERERERCSP8nJX/G9DTw8A/1e4cURERERERETkZKiXAREREREREZEI5lfHoiIiIiIiInLaK6Im9gUqz2fszeyswgwiIiIiIiIicroxs/+Z2XYz+yLbsPPMbIqZrQ39LRsabmb2jJmtM7PPzaxxXj4j14q9mTU1s5XA2tDrRmb27Cl+JxEREREREZEzySjg8qOG3Q9Mdc7VBqaGXgN0IvOG9bWBm4Hn8/IBeTlj/wzQBfgJwDm3Amifl4mLiIiIiIiInMmcc7OAn48a3A14JfT8FaB7tuGvukwLgDJmVjm3z8hLG/sizrnvjupnMD0P7xMRERERERE5xmnWxr6cmS3J9nqkc25kLu+p6JzbCuCc22pmFULDY4CN2cbbFBq29UQTy0vFfqOZNQWcmUUBfwTW5OF9IiIiIiIiIqe7Hc655AKaVk6HPFxub8rLpfi3AncBFwA/AM1Dw0QCs3bNalo3a3L4UbViWZ4b8XTQsUhPT6dVsyb0vKpr0FGoH1eD5smNaNWsMRe1ahp0HMCvTIMH3cCFMRVJTmwQaI7sdu7cSf++vUhuVI+UxAQWLZgfaB6flhf4t8wOHDjARa2a0Tw5keTE+jzy8N/CnmHL5o307nYZHZo34pKWSfzvhREAPPq3P9OhWUM6tknm5v6/49dfdxZqjrs71OSdG5J5sW+jw8Nubnkh/7smkZF9GvJgpzjOKRYFQIc65fhP74aHH5P/0Jya5UoUar4smzZupNNlHWjcsB7JifX597PB7jd8ywP+rWc+7u8nT5pIw4Q4EuJr8eQTjwWaJYtPvz98KNd3/H4Q9WrE0LZZ4uFhj/39b1zUojHtWyXTq9sVbNu6Jey5svi2v5fA/JB1iX3o7/bQ8E1A1WzjxQK5FlhzLtfKv3jCzKoBY51z9fM5nYHAZOdcYFu0pMbJbsbchQUyrfT0dOrWvIBPZ83jggsuPKVpRBXQtUDPPj2cZUuXsGv3Lt794JN8TSu/q2b9uBrMnLuI88uVy9+EClBBZoqOyt8ymzN7FueULMmg6wewZPnKfOcBSEvP30K75aaBtGjVmgHX30Rqair79u2jTJkypzy9/Jbqgi5DPi6zjHwsMucce/fupWTJkqSlpXFp+zY88c+naNqs+SlP86c9qSc1/g/btrL9h200aJTEnt276XJxC0a++g7btm6iZZv2REdH848H/wrAnx989KTz3PDmZ3kar0GVUuxPy+C+S2oxaPQKAJpUPZdlm34lw8FNLS4A4L/zvz/ifdXPL8HDV8TR/7Vlec708c2nPn+3bt3Ktm1bSUpqzO7du2ndPJkx735A3br1Tnma+VEYeSyfK35hrGephzIKZDoFsb8HOKtoVL4yNKhXh3ETphATG0vr5im88vpo6tbLXxk6lJ6/eVSQvz8AiuSjIBVGud578NBJjT9/7mzOOacktw2+nlkLlwOwe9cuSpUuDcCLz49g9eqvGPbUv085U37KUUHv7y9q1ZRlS5dE1IXtFWvVd9f8692gYxSY4d3qLs3tjP3RdTkzexL4yTn3mJndD5znnLvXzDoDtwFXAM2AZ5xzuZ5hyctd8V80s5FHP3L9duKzgUCVoEMUlJnTp1K9Ro187eQLwuZNm5g0YTwDrr8x0BySN63btOW8sucFHeOwXbt2MXfObK4bmFl+ihUrlq+d/OnIt2VmZpQsWRKAtLQ00tLSsPzWqk5SxUqVadAoCYCSpUpRq3Y8P2zdTNv2lxIdndnaLim5KVu3birUHCu37Gb3gSN/eC/d+OvhAydf/bCH8iWLHfO+9rXPZ9raHYWaLbvKlSuTlJTZa1CpUqWIi6/Lls2bw/b5vucB/9az7HzY3y9etIiaNWtRvUYNihUrRq/efRj7yUeB5QH/fn/4UK5btGpDmbJljxiWVakH2Ldvb9i311m0v89klrkfPV0euX9fGw3MB+LMbJOZ3Qg8BlxqZmuBS0OvAcYD3wLrgBeB3+dlnublUvxPybz9/lRgLlABOJiXiUuhiDazV0J9Gr5rZiXM7GIzW2ZmK0N9JJ4FYGZDzGyxmX0ROiBjZtYTSAbeMLPlZlbczDaY2VAzm29mS8yssZlNMrNvzOyW0LRKmtlUM/ss9DndQsOrmdlXoQNAX5rZZDMrHs4Z8t47b3N1rz7h/Mgc3XfPn/j70McoUiQvq1XhMzO6d72cti1TePklP47F+ZjJFxvWf0u5cuX5/c030Lp5E267dRB79+4NNJOWV+7S09NpkZJE9diKdLj4ElKaNgssy8bvN/DlyuUkNjnyoP7bb75Cu4s7BpQq0+V1y7Pou2ObA7SrXY7pa8JXsc/uuw0bWLFiWaDLLDvf8vjIh/39li2biY397QrZmJhYNgd8MMa33x/Z+Vauhz78fyTWrcF7b4/mvr+Gv/kU+Lm/l8LnnOvrnKvsnCvqnIt1zr3knPvJOXexc6526O/PoXGdc+4PzrmazrkGzrkluU0f8lCxd869le3xCtADCOaaNQGII/Muiw2BXWTe/2AU0Ns514DMGyJm3QNhhHMuJXS5R3Ggi3PuXWAJcI1zLtE5tz807kbnXAtgdmh6Pcm8n8LDof8fAK5yzjUms7vDf9pvh6dqA/92ziUAO4GrcwpuZjeHDhws+WnHjwUxL0hNTWXC+E/o3qNngUzvVE0YP5by5SuQ1LhJoDmymzxtNrPnL+G9D8fx4gvPM3fOrKAjeZnJF4cOHWLF8s+4cdAtzFmwlHNKnMPwYY8HmknLK3dRUVHMX7yM1d9uZMmSxXz55ReB5Ni7Zw+3DOzLkEeHHXFW6tl/PkZ0VDRX9eobSC6Afk1iSM+AqUdV4OMrluTgoQw2/Lz/OO8sPHv27KFfn548MWw4pbPNr6D4lsdHvuzvc2rCGtSZX/Dz90cWH8v1X4b8neVffcvVv+vLSy88F0gGH/f3cno4lUN71YFgr3k+s210zs0NPX8duBhY75zL6qngFaBt6Hl7M1toZiuBDkDCCab7cejvSmChc263c+5H4ICZlSGzue5QM/uczKs4YoCKofesd84tDz1fClTL6QOccyOdc8nOueTzy5U/ia98fFMmTaRRYhIVKlbMfeRCtGDePMaP+4SEOjUYeF0/Zs2Yzk0D+weaqXKVzNYW5StUoMuV3Vm6eHGgecDPTL6IiYklJiaW5NBZjW5XXc2K5Xlr31xYtLzyrkyZMrRpexGfTpoY9s9OS0vjloF96N6zD526dj88/N3RrzF18gSefmFUYBWPS+PL07x6Wf4xZe0x/2tf+3ymBXC2Pi0tjX69e9K7Tz+6de8R9s/3PY+vfNnfx8TEsmnTb71Qbd68iSpVgmvd6OPvD/C/XPfo1YdxH38QyGf7uL+X00Ne2tj/YmY/hx47gSnAXwo/mhxHnm71ZGZnA88BPUNn8l8Ezj7BW7KaV2RwZFOLDDKvArgGKA80cc4lktlDwtlHvRcgnbx1o1gg3ntnTOCX5QE89MhQVn/zPV+u+ZZRr75J23bt+e+o1wLLs3fvXnbv3n34+bRPp1A34UTHdc7MTD6pWKkSMbFVWbtmNQAzZ0wjLj64i6O0vHL3448/snNn5uXl+/fvZ/q0qdSJiw9rBucc994+mFp14hn0+zsOD58xdTLPP/NPXnrjXYqXCM8d54+WckEZ+jSuwv+N/ZqDR908zYC2tc5nRhjb10Pm/Lp18E3Excdz+513hfWzIyGPz3zZ3yenpLBu3Vo2rF9Pamoq77w1hs5drgwsj2+/P8Dfcv3tut8OME4aP5ZadeICyeHb/j5IRez0efjghBWw0KXWjYCsxkMZTrfRD9oFZtbCOTcf6Evm2fPBZlbLObcO6A/M5LdK9w4zK0nmpfVZt57cDZQ6yc89F9junEszs/Z4cNXGvn37mD7tU4Y/+3zQUbyzffsPXNM7s0XEoUOH6NW7L5dedrkyZTPg2n7MmjWDn3bsoFb1qjww5EEGBnzjoSf+9TQ3Xd+ftNRUqlWrzr9H/i+wLL4tL/Bvmf2wbSs33ziQ9PR0MjIy6NGzF506dwlrhiUL5/H+228SX68+nS7KbFt/zwMP8+Cf7yL14EGuvbozkHkDvaH/HFFoOf5yWW0axZTm3LOjGT2wMa8s3ETfJjEUjTIe75b5g/WrH3bz9Iz1ADSMKc2OPals3RXeW/bMnzeX0W+8RkL9BjRPybzp4IMPP8rlna4Iaw5f84B/6xn4tb+Pjo5m+NMj6Nq5I+np6QwYeAP1/L09gQAAIABJREFUdNDzCD6U68HXX8vcObP4+acdNIqvzr1/GcKnkyfwzdo1WJEiVK16AU/m4474+eXT/l5OH7l2d2dmS51z/jXcOQNZZhcJ44FZQEtgLZkV+RbAMDIP1CwGbnXOHTSzR4A+wAZgI/Cdc+5BM7saGArsD733KyDZObfDMrvCS3bO3Rb6zA1k3mwP4BOgKLAcaAV0Cg3P3m3D3UBJ59yDJ/ouBdndXUEoqO7uCpIOoZ1YfrtOKwz57e6uoPk2h3xcZvnp7q4wnGx3d4Utr93dhVN+urs7EwTY3Pu4Cqq7u4KSn27KCkt+u7sraPnp7q4wnGx3d+HgUzmKxO7uKtWu7/oPfy/oGAVmWNf4XLu7K2x5uWR6kZk1ds75t3c/wzjnNpDzjQunAkk5jP8A8EAOw98Dsq9J1bL9bxSZN8/Lel0t23gtjhOtfrbxhx1nHBERERERESkEx63Ym1m0c+4Q0BoYZGbfAHvJPAnkQndHFxERERERETkpnl0YEvFOdMZ+EdAY6H6CcUREREREREQkQCeq2BuAc+6bMGURERERERERkZN0oop9eTM7bh8Vzrl/FUIeERERERERETkJJ6rYRwEl8e/GyiIiIiIiIhKhDP96X4h0J6rYb3XOPRy2JCIiIiIiIiJy0oqc4H86hCIiIiIiIiLiuRNV7C8OWwoREREREREROSXHvRTfOfdzOIOIiIiIiIjImeFEZ5jl5Gl+ioiIiIiIiEQwVexFREREREREIpgq9iIiIiIiIiIR7ETd3YkUGjMoFu3PcaX0DBd0hGNEFfGrYwrfuhpNS/dvmXk2i7xbZs6/RcbBtPSgIxyh1Nl+7ZbfuaFp0BGOkfjApKAjHGHFox2DjiAnKcPDfb5vkTz7CUJJz7aNAObRTtafX9Qnx6NZeFqI1HIgIiIiIiIiIqhiLyIiIiIiIhLRVLEXERERERERiWD+NVgRERERERGR05aZUUSN7AuUztiLiIiIiIiIRDBV7EVEREREREQimCr2IiIiIiIiIhFMbexFREREREQkrNTEvmDpjL2IiIiIiIhIBFPFXkRERERERCSCqWIvIiIiIiIiEsHUxl5ERERERETCqoja2BconbEXERERERERiWCq2EtEGjzoBi6MqUhyYoOgoxwhPT2dVs2a0POqroHm2LRxI50u60DjhvVITqzPv599OtA84Ocy27lzJ/379iK5UT1SEhNYtGB+oHnqx9WgeXIjWjVrzEWtmgaaJYsvZRr8KNd/vPUm4qpVoVVK4uFhv/z8Mz26Xk5Ko7r06Ho5O3/5Jey5svzn38/QMrkRLZIb8vyI4Nd7CCbT0F71mT+kPWPvanV42B8vrcXsv7bjoztb8tGdLbkovtzh/w1uX4Mp97Zh4j1taF2nXE6TLDS+bRt9y7N2zWpaN2ty+FG1YlmeC7Bs+7AdyolP+zMf55Fv5XrypIk0TIgjIb4WTz7xWNBx5DShiv0ZyMyqmdkXQefIj/7XDeTDsROCjnGM50Y8Q1xcfNAxiIqOZujjw/js81VMnz2fkf95jq++WhVoJh+X2f1338kll3VkyYpVzF20jDrxdYOOxLiJU5m78DNmzl0UdBTAnzINfpTrvtcM4O0Pxx4x7Ol/PUHbdh1YvOIr2rbrwFP/eiKsmbKs+vILXn35JT6dNZ/ZCz5j8oRxfLNubSBZgs70/pLN3PjS0mOGvzx7A92emke3p+Yx8+sdANSscA6dG1Xiin/O4ab/LuHBq+qF9fJQ37aNvuWpXSeOOQuXMmfhUmbOW0Tx4iXocmX3wPL4sB3KiU/7Mx/nkU/lOj09nTtv/wMffTKBZZ+v4p0xo/lqVfBlSCKfKvaSIzOLCjrDibRu05bzyp4XdIwjbN60iUkTxjPg+huDjkLlypVJSmoMQKlSpYiLr8uWzZsDzeTbMtu1axdz58zmuoGZy6tYsWKUKVMm4FR+8alMgx/lumXrNpQ9qhyPH/cJfa7pD0Cfa/ozfuzHYc2UZc3qr0lu2owSJUoQHR1NyzZtGffxh4FkCTrTkvW/8Ou+tDyNe0lCRcat2EZaumPTL/v5bsc+GlYN37bAt22jb3mymzl9KtVr1OCCCy4MLIMP26Gj+bY/83Ee+VSuFy9aRM2ataheowbFihWjV+8+jP3ko6BjhZ0BRcxOm4cPVLE/c0Wb2Stm9rmZvWtmJcxsg5kNMbM5QC8zG2Rmi81shZm9Z2YlAMysl5l9ERo+KzQswcwWmdny0DRrB/rtAnDfPX/i70Mfo0gRv1ar7zZsYMWKZaQ0bRZ0FK9sWP8t5cqV5/c330Dr5k247dZB7N27N9BMZkb3rpfTtmUKL780MtAs4G+ZBr/K9Y/bf6BSpcoAVKpUmR0/bg8kR916CcyfO5uff/qJffv2MWXSBDZv3hRIFl8zXdvyQj7+UyuG9qpP6eKZ9w+uWPostu7cf3icbb8eoOK5ZwUVUU7gvXfe5upefYKOcZgv2yEf92dZfJlHPtmyZTOxsVUPv46JiWVzwAc+5PTg3681CZc4YKRzriGwC/h9aPgB51xr59wY4H3nXIpzrhHwFZB12m4I0DE0/MrQsFuAp51ziUAycMwvNzO72cyWmNmSHTt+LLxvFoAJ48dSvnwFkho3CTrKEfbs2UO/Pj15YthwSpcuHXQcrxw6dIgVyz/jxkG3MGfBUs4pcQ7Dhz0eaKbJ02Yze/4S3vtwHC++8Dxz58wKLIuvZRpUro8nLr4ut991Dz26Xk6v7ldQv0EjoqKCvfjKp0xvzv+eSx6fSben5vLjroPc3yWziUlOJ1qcC3M4yVVqaioTxn9C9x49g44C+LUd8nF/Bn7NI5+4HDYw5skZX4lsqtifuTY65+aGnr8OtA49fyvbOPXNbLaZrQSuARJCw+cCo8xsEJD1C20+8Bczuw+40Dm3n6M450Y655Kdc8nlypUv6O8TqAXz5jF+3Cck1KnBwOv6MWvGdG4a2D/QTGlpafTr3ZPeffrRrXuPQLP4KCYmlpiYWJJDZxG6XXU1K5Z/FmimylWqAFC+QgW6XNmdpYsXB5bFxzINfpbr8hUqsm3bVgC2bdtKufIVAsvSf8ANzJi3mHGTZ1C2bFlq1gr+4ilfMv20J5UMl1lpf3vRJhpWPReAbb8epHKZ4ofHq3Tu2WzfdTCQjHJ8UyZNpFFiEhUqVgw6infbIR/3Z77NI5/ExMSyadPGw683b95EldD+XyQ/VLE/cx19uDDrdfZrt0YBtznnGgAPAWcDOOduAR4AqgLLzex859ybZJ693w9MMrMOhZjdOw89MpTV33zPl2u+ZdSrb9K2XXv+O+q1wPI457h18E3Excdz+513BZbDZxUrVSImtipr16wGYOaMacTF1wssz969e9m9e/fh59M+nULdhIRc3lV4fCvT4G+57nRFF8a8kTlvxrzxGld0Dq4HgR+3ZzYD2LTxe8Z+/KEXly37kql8qd8ur7+0fgXWbtsDwNRV2+ncqBJFo4zYssWpVq4En2/cGUhGOb733hnjRXn2cTvk2/7Mx3nkk+SUFNatW8uG9etJTU3lnbfG0LnLlbm/8TRkdvo8fKCK/ZnrAjNrEXreF5iTwzilgK1mVpTMM/YAmFlN59xC59wQYAdQ1cxqAN86554BPgYaFmb4Adf2o13blqxZs5pa1asy6uWXCvPjIs78eXMZ/cZrzJwxneYpSTRPSWLihPGBZvJxmT3xr6e56fr+tExJZOWK5fy/e/8cWJbt23+g48Vtadk0ifZtmtOx0xVcetnlgeXxkQ/letDAa7m8QxvWrV1N/TrVeP2V/3HHXfcyY9qnpDSqy4xpn3LHXfeGNVN2A67pRfMmDejbsztP/OsZypQtG1iWIDP9q18j3vpDM6qXP4dZf2lHz5QY7r2iDp/8qRUf/6kVzWqez9BPvgZg3Q97GP/5Nibc3Yb/3pTMQx+uIiOMl+L7tm30LQ/Avn37mD7tU7p2uyroKF5sh3Li0/7Mx3nkU7mOjo5m+NMj6Nq5I4kN6nJ1r99RL8AD+XL6sJzaecjpzcyqAeOBWUBLYC3QH1gFJDvndoTGuxW4F/gOWAmUcs4NNLP3gdpk3tByKnAncD9wLZAGbAP6Oed+Pl6Gxk2S3dwFwV1mfLT0cP6KyyNf7rCZxbM4pKX7t8w8m0XeLTPfyjTAgbT0oCMcQe0sc9fswSlBRzjCikc7Bh3Be6mHMoKOcISiUf6d1zrk2e+Q6HD2+ZgHPm4afdpet2qWzNKlS/wJlAcxdRq4W577IOgYBWbIpbWXOueSg8wQHeSHSzCccxuAnK7RqnbUeM8Dz+fw/pwaS/0j9BAREREREZEwUsVeREREREREwsfAswtDIp5/1yKJiIiIiIiISJ6pYi8iIiIiIiISwVSxFxEREREREYlgamMvIiIiIiIiYWXe9ScU2XTGXkRERERERCSCqWIvIiIiIiIiEsFUsRcRERERERGJYGpjLyIiIiIiImFjqB/7gqYz9iIiIiIiIiIRTBV7ERERERERkQimir2IiIiIiIhIBFMbexEREREREQkrtbEvWKrYSyAMMPNnbY6O8ieLrzIyXNARjhDt4d6giIeZfOJbGQIocZZfu8FD6RlBRzhCdJR/F/Z9PvTyoCMcofGQyUFHOMLShy4NOsIxikX7VY7S0v3bFvm29/Btf+acf8ts+66DQUc4zMcyLeHn15ZWRERERERERE6KKvYiIiIiIiIiEcyvaxBFRERERETktOdTs9zTgc7Yi4iIiIiIiEQwVexFREREREREIpgq9iIiIiIiIiIRTG3sRUREREREJGwM9WNf0HTGXkRERERERCSCqWIvIiIiIiIiEsFUsRcRERERERGJYGpjLyIiIiIiIuFjoG7sC5bO2IuIiIiIiIhEMFXsJSJNnjSRhglxJMTX4sknHgs6DuBfJp/ybNq4kU6XdaBxw3okJ9bn388+HWgeXzP5tMx8zKNlljfp6em0ataEnld1DToK4N88CiLPIz0SmP2Xdnx0R8tj/nd96wtZNfQyypQoCsANbarx/m3Nef+25nx0R0tWPnIp5xYP3wWWgwfdwIUxFUlObBC2zzwR3/IA7Ny5k/59e5HcqB4piQksWjA/6EjUj6tB8+RGtGrWmItaNQ06jnfrfdDlaMvmjfTudhkdmjfikpZJ/O+FEQA8+rc/06FZQzq2Sebm/r/j1193BpJPTg+q2J9BzKyamX0Rhs/ZYGblCmv66enp3Hn7H/jokwks+3wV74wZzVerVhXWx0VkJt/yREVHM/TxYXz2+Sqmz57PyP88x1dfBbvMfMvk2zLzLQ9omeXVcyOeIS4uPugYgH/zKKg8H3y2hZtHLT1meKVzz6JFrfPZ8sv+w8P+N3sDPUYsoMeIBQyftJbF63/h1/2HCj1jlv7XDeTDsRPC9nm58S0PwP1338kll3VkyYpVzF20jDrxdYOOBMC4iVOZu/AzZs5dFGgO39Z7CL4cRUVF88DDjzNtwQo+nDSLV1/6D2u+/oo27Towee5nTJq9hOo1a/Pc8CcDyyiRTxV7OYKZRQWdITeLFy2iZs1aVK9Rg2LFitGrdx/GfvKRMnmcp3LlyiQlNQagVKlSxMXXZcvmzYHl8TGTb8vMtzygZZYXmzdtYtKE8Qy4/sZAc2TxbR4FlWfphl/4dV/aMcPv6xzPPyeuwR3nfVc0qsT4FVsLN9xRWrdpy3llzwvrZ56Ib3l27drF3DmzuW5g5jpWrFgxypQpE3Aqv/i23kPw5ahipco0aJQEQMlSpahVO54ftm6mbftLiY7OvCInKbkpW7duCiyjRD5V7M880Wb2ipl9bmbvmlmJ0Bn2IWY2B+hlZjXNbKKZLTWz2WYWD2BmXc1soZktM7NPzaxiaPj5ZjY5NPwFoFBvhbFly2ZiY6sefh0TE8vmgCuJvmXyLU92323YwIoVy0hp2izoKIf5kMm3ZeZbnqNpmeXsvnv+xN+HPkaRIn7s3n2bRz7laR9fnu27DrB6254c/3920SK0qV2OKV/+EOZkciIb1n9LuXLl+f3NN9C6eRNuu3UQe/fuDToWZkb3rpfTtmUKL780MtAsPq1nPtr4/Qa+XLmcxCZHNpl4+81XaHdxx4BSBaOI2Wnz8IEfe34JpzhgpHOuIbAL+H1o+AHnXGvn3BhgJPBH51wT4G7gudA4c4DmzrkkYAxwb2j434A5oeEfAxfk9MFmdrOZLTGzJT/u+PGUv4Bzx57bsIBXKN8y+ZYny549e+jXpydPDBtO6dKlg44D+JPJt2XmW57stMxyNmH8WMqXr0BS4yaBZTiab/PIlzxnFy3C4PY1eHbKN8cdp118eT77bmdYL8OX3B06dIgVyz/jxkG3MGfBUs4pcQ7Dhz0edCwmT5vN7PlLeO/Dcbz4wvPMnTMrsCy+rGc+2rtnD7cM7MuQR4dRKtv+69l/PkZ0VDRX9eobYDqJdKrYn3k2Oufmhp6/DrQOPX8LwMxKAi2Bd8xsOfACUDk0TiwwycxWAvcACaHhbUPTwjk3Dvglpw92zo10ziU755LLlyt/yl8gJiaWTZs2Hn69efMmqlSpcsrTKwi+ZfItD0BaWhr9evekd59+dOveI9AsWXzK5Nsy8y1PFi2z41swbx7jx31CQp0aDLyuH7NmTOemgf0DywP+zSNf8lQ9rwQxZYvzwe0tmHJPGyqWPov3bmtOuZLFDo9zRcNKjP88vJfhS+5iYmKJiYklOXS1ULerrmbF8s8CTgWVQ+W4fIUKdLmyO0sXLw4siy/rmW/S0tK4ZWAfuvfsQ6eu3Q8Pf3f0a0ydPIGnXxilAyCSL6rYn3mOPoya9TrrOrIiwE7nXGK2R9ZdYZ4FRjjnGgCDgbNPMN1Ck5ySwrp1a9mwfj2pqam889YYOne5MlwfHxGZfMvjnOPWwTcRFx/P7XfeFViO7HzL5Nsy8y0PaJnl5qFHhrL6m+/5cs23jHr1Tdq2a89/R70WWB7wbx75kmftD3toM3QGlz45m0ufnM0Puw5y9YgF7NiTCkDJs6JJqX4e01ad+tVtUjgqVqpETGxV1q5ZDcDMGdOIi68XaKa9e/eye/fuw8+nfTqFugkJubyr8PiynvnEOce9tw+mVp14Bv3+jsPDZ0ydzPPP/JOX3niX4iVKBJhQTgeq2J95LjCzFqHnfcm8vP4w59wuYL2Z9QKwTI1C/z4XyGokNSDb22YB14TG7wSULaTsAERHRzP86RF07dyRxAZ1ubrX76gX4A7Mx0y+5Zk/by6j33iNmTOm0zwlieYpSUycMD6wPD5m8m2Z+ZYHtMwikW/zKKg8T/ZuwOhbmlGtXAmm3deWHk1iTjj+JQkVmLtuB/vT0gs929EGXNuPdm1bsmbNampVr8qol18Kewaf8wA88a+nuen6/rRMSWTliuX8v3v/HGie7dt/oOPFbWnZNIn2bZrTsdMVXHrZ5YHl8W29h+DL0ZKF83j/7TeZN3sGnS5qSqeLmjJtykSG3Hcne/fs5tqrO9Ppoqb85f/dFtZcQTKgiJ0+Dx9YTu1g5PRkZtWA8WRWxFsCa4H+wCog2Tm3IzRedeB5Mi/BLwqMcc49bGbdgOFkVu4XACnOuXZmdj4wGigHzAR6AE2yppeTJk2S3dyFSwrja0ohycjQtiI3RXzZsnvKxzLk2zI7lJ4RdIQjREfp+H9uGg+ZHHSEIyx96NKgI3gvLd2/bZFfWyIoGu3Xuu9jfeXH3alBRzisS4eWfL58qW/F6IQuiG/g7v7vx0HHKDB3tKmx1DmXHGSG6CA/XMLLObcByOl6sWpHjbceOOZQr3PuI+CY/kqccz8Bl2Ub9Kf85BQREREREZG88+twnIiIiIiIiIicFJ2xFxERERERkbBSJwAFS2fsRURERERERCKYKvYiIiIiIiIiEUwVexEREREREZEIpjb2IiIiIiIiEkZGEe86eoxsOmMvIiIiIiIiEsFUsRcRERERERGJYKrYi4iIiIiIiEQwtbEXERERERGRsDHUj31B0xl7ERERERERkQimir2IiIiIiIhIBFPFXkRERERERCSCqY29BMIBGRku6BiH+djGx3wMJSKFKjpKx9sjzWcPXxZ0hCOUTbkt6AjH+Gnhs0FHOELRKO1fI42Pv4nOO6do0BEOi47EMm1QJAJj+0y/IEREREREREQimCr2IiIiIiIiIhFMFXsRERERERGRCKY29iIiIiIiIhJWRTy8d0Ik0xl7ERERERERkQimir2IiIiIiIhIBFPFXkRERERERCSCqY29iIiIiIiIhI0BamJfsHTGXkRERERERCSCqWIvIiIiIiIiEsFUsRcRERERERGJYGpjLyIiIiIiImGlfuwLls7YS0TatHEjnS7rQOOG9UhOrM+/n3060DyDB93AhTEVSU5sEGiO7CZPmkjDhDgS4mvx5BOPBZrFt+XlayaflpmPebTMIi8P+JdJeSC2YhkmjrydZe89wNJ3/8of+rYDoGzpEox9/jZWfjSEsc/fRplSxQFo06Q222Y9yYIx97NgzP38+ebLw5IT/Fzvfdvn+5YHtJ7lRXp6Oq2aNaHnVV2DjiKnCXPOBZ1BIpCZjQLGOufePZX3N26S7ObMX3zKn79161a2bdtKUlJjdu/eTevmyYx59wPq1q13StPL7wHDObNncU7Jkgy6fgBLlq/M38QOZzr1UOnp6TSoV4dxE6YQExtL6+YpvPL6aOrWO7X5A5CRcerbioJeXgWhMDIVKeLXMssP38oQaJlFWh4fM50Jecqm3JbrOJXKlaZSudIs/3oTJUucxbw37+N3d42kf9dm/LJrH8NensLd119KmVIleOCZj2jTpDZ3XncxV9/xn1PK9NPCZ0/pfVA4672P+3zf8vj2GyQ/CivPofSMfL3/2aeHs2zpEnbt3sW7H3ySr2m1bdmUz5YuiajT39XqNnT/98rYoGMUmJuaXbjUOZccZAadsZeIVLlyZZKSGgNQqlQp4uLrsmXz5sDytG7TlvPKnhfY5x9t8aJF1KxZi+o1alCsWDF69e7D2E8+CiyPb8vLx0y+LTPf8oCWWaTl8TGT8mTatmMXy7/eBMCefQf5ev02qpQvQ5d2DXn9k4UAvP7JQrq2b1joWXLj23oP/u3zfcuj9Sx3mzdtYtKE8Qy4/sZAc8jpRRV7D5jZdWb2uZmtMLPXzKyrmS00s2Vm9qmZVQyN96CZvWJmk81sg5n1MLMnzGylmU00s6Kh8TaY2eNmtij0qBUafqLp/s/MZpjZt2Z2e2h4NTP7IlvOu83swRzyDzGzxWb2hZmNtPwc5j0F323YwIoVy0hp2iycH+u1LVs2Extb9fDrmJhYNgf8QyiLj8vLh0y+LTPf8hxNy8z/POBfJuU51gWVzyMxLpbFX2ygwvml2LZjF5BZ+S9/XqnD4zVrWJ2Fb93PhyNupW6NSmHNmMWH9V5y50O59jkPwH33/Im/D32MIkXO7KqY2enz8MGZXZo8YGYJwF+BDs65RsAdwByguXMuCRgD3JvtLTWBzkA34HVgunOuAbA/NDzLLudcU2AE8FRo2ImmGw90BJoCf8s6SJBHI5xzKc65+kBxoMtJvDdf9uzZQ78+PXli2HBKly4dro/1Xk5NbMJ8vCVHPi4vXzL5tsx8y5OdllnOfMsD/mVSniOdU7wYo4fdxD3D3mP33gPHHW/51xuJu+L/aNb7MZ4fM5O3h98ctoxZfFnvJXdBl+uj+ZZnwvixlC9fgaTGTQLLIKcnVeyD1wF41zm3A8A59zMQC0wys5XAPUBCtvEnOOfSgJVAFDAxNHwlUC3beKOz/W0Ren6i6Y5zzh0M5dgOVDyJ79A+dCXAytD3SchpJDO72cyWmNmSHTt+PInJ5ywtLY1+vXvSu08/unXvke/pnU5iYmLZtGnj4debN2+iSpUqASbyc3n5lMm3ZeZbnixaZpGTB/zLpDy/iY4uwuhhg3hrwhI+mrYCgO0/7aZSucxKc6Vypfnx590A7N57gL37UwGYNGcVRaOjOL/MOWHJCX6t95I7rWcntmDePMaP+4SEOjUYeF0/Zs2Yzk0D+weWR04fqtgHz4CjDyU+S+ZZ8AbAYODsbP87COCcywDS3G+HITM4svtCl8PzXKcbkh6a1iGOLCPZx88Mb3Y28BzQMzTdF3MaL5R5pHMu2TmXXK5c+ZxGyTPnHLcOvom4+Hhuv/OufE3rdJScksK6dWvZsH49qampvPPWGDp3uTKwPD4uL98y+bbMfMsDWmaRlsfHTMrzm//87RpWr9/GM69POzxs3MyVXNs18zL3a7s2Y+yMzwGoeP5vl+QnJ1xIETN+2rk3LDl9W+8ld1rPTuyhR4ay+pvv+XLNt4x69U3atmvPf0e9FlgeOX2oYh+8qcDvzOx8ADM7DzgXyGr8M+AUp9s729/5oecnO90fgApmdr6ZnUXOl9hnVeJ3mFlJoOcp5j0p8+fNZfQbrzFzxnSapyTRPCWJiRPGh+OjczTg2n60a9uSNWtWU6t6VUa9/FJgWQCio6MZ/vQIunbuSGKDulzd63fUS8jxQoqw8G15+ZjJt2XmWx7QMou0PD5mUp5MLRNrcE2XZlyUUudwF3YdW9dj2MtT6NAsnpUfDaFDs3iGvTwFgKsuSWLpu39l4Vv38897e3Ldn18u9IxZfFvvwb99vm95tJ5JXhiZFdHT5eEDdXfnATMbQOal8enAMuADYDiZlfAFQIpzrl3oxnV7nHPDQu/b45wrGXp++H9mtgF4GbiCzLLW1zm3zsy65XG6XwBdnHMbQjfSux1YH3rfBufcg9m7uzOzR4A+wAZgI/Cdc+7BE33n/HZ3V9A8aTp8hKDbxh4tv12VnQny03XamcDHMqRlJqebvHR3F2756e6uMHi2e/WSb79BfJTf7u4KUiR2d1e9bkP3t1dPn+7urm8afHd30bmPIoXNOfcK8MpRg4/ph+PoynJWpT6n/wH/ds49dNR7f6AbAAAgAElEQVT4H+VxuvWzPX8GeCaH9wzM9vwB4IGjxxEREREREZHC58uVAyIiIiIiIiJyCnTG/jTknKsWdAYREREREZEcmZp8FDRV7EVEREREREQKiZnFAW9lG1Tj/7N35/FRVff/x1+fJAQXQEACQgKySQJhSWDCvosoIogKgqCAIlrrRv2qtdUfWrXWrVIVtdpaUeq+yybIvsiSsLlAxQ0LQWUT2SWZnN8fmdAEEBDC3DP4fvqYB5M7M/e+Pefce+fMvedeYCRQERgOFN0L/I/OuSO6Qqg69iIiIiIiIiLHiHPuMyADwMziKbwo+dvA5cCooouYHw2NsRcRERERERGJjjOBL51z35TmTNWxFxERERERkaiy4+jxCw0AXi7293Vm9pGZ/cvMKv3y2RVSx15ERERERETkyFUxs5xij6sO9CYzSwR6A69HJj0F1KPwNP1vgb8eaQCNsRcRERERERE5chudc6HDeF8PYIlz7nuAon8BzOwfwPgjDaAj9iIiIiIiIiLH3iUUOw3fzKoXe+0C4JMjnbGO2IuIiIiIiEjUGBD3K7uPvZmdBJwFXF1s8oNmlgE4YPU+r/0i6tiLiIiIiIiIHEPOuZ3AqftMu6y05q9T8UVERERERERimDr2IiIiIiIiIjFMp+JLYFzQAYrJyy8IOsJ+EuL9+t0tP+xXGcXH+TcuqyDsU6uGXXvCQUcoodwJ/u1ywgV+1dnWXXlBRyjhxMT4oCPsJ8Gzdd88GyO6YcFjQUfYz19nfRl0hBJu7lwv6Aj7yfNs/xFnvuXxaz2T0qFaLV1+9RxERERERERE5BdRx15EREREREQkhqljLyIiIiIiIhLD/BvwKCIiIiIiIsc1XTqhdOmIvYiIiIiIiEgMU8deREREREREJIapYy8iIiIiIiISwzTGXkRERERERKLIMA2yL1U6Yi8iIiIiIiISw9SxFxEREREREYlh6tiLiIiIiIiIxDCNsRcREREREZGoMXSEubSpPEVERERERERimDr2EpN2795Np3ataB3KIJTRmHvvvjPQPJ+v+oz2rVrsfdSsVoknRz8aWB7fygf8K6Mi4XCYdq1a0PeCXkFHAYLPc+Nvh9OobjIdW2XsnXb/PXfSqU1zurQL0e/8c/nu23WBZLt6+BWcnlyNUEaTQJa/L1/Ws99dexVN6qfQpU3m3mlXXz6Ibu2z6NY+i5ZNGtCtfVYg2QCefPxvtGnRlDahZgwbMojdu3cHlqVI0OtZEV/a0L58KJ/5b49h9PBzeXx4Dz586zkAPpk9iceH9+DOsxuQu+rjwLL5ti0C2LJlC5dd0o9Qs0ZkZaSzaMH8oCN50Y4A1q5ZQ4/uXWnetBGhjMY88Xjw3z3An/KR44c69jHOzEaY2UnF/v7j0c4jFpQtW5YJk6exIGcZ87OXMnXKZBYtXBBYnjMapDJ34WLmLlzMrA8XceKJJ3Fe7z6B5fGtfMC/Miry5OjHSE1NCzrGXkHnGTBoMK+8Nb7EtGtv/D9mzV/CjHk5dD/nXB5+4M+BZLts8FDeGT8pkGUfiC/rWf+Bl/HiG+NKTHv6uReZOjebqXOz6dm7D+f2CmZdW5eby9NPjmb63IXMz1lOQTjMW6+/GkiW4oJez4r40ob2FXT5fP/1KhZPfI2rHn+T3/59HKsWzmRT7mqq1T6DS0Y+welNgvuhCvzbFgHcdvMIunU/m5zlK5i3aCkN0hoGHSnwdlQkPiGB+x54mCUfrWDGnPk88/cnWblyRdCxvCkfOX6oYx/7RgDFO+W/uGN/gHl4z8woV64cAHl5eeTl5XlzL8xZM6ZRp25datU6PbAMPpcP+FFGALlr1zJ50kSGXD4s0BxFfMjTpl0HKlaqVGJa+QoV9j7fuXNHYG2pfYeOVK5UOZBlH4gv61nrdh2otE+dFXHO8d47b9Kn78VRTvU/+fn57N61i/z8fHbu3Mlp1asHlgX8WM+K+NKGivOhfDas+ZKUhhkknnAi8fEJ1G6SxYp5U0iqVZ8qNesGlquIb9uirVu3Mm/uHAYPLayzxMREKlasGGgmH9pRkerVq5OZ2RyA8uXLk5rWkHW5uYFm8ql8gmRmx83DB+rYlzIzG2xmH5nZcjMbG5nWy8wWmtlSM5tqZtUi0zuZ2bLIY6mZlTez6mY2OzLtEzPrEHlvdzObb2ZLzOx1MytnZjcANYAZZjbDzO4HTox89sXI5y41s0WRaU+bWfw+eUvMIzLtKTPLMbNPzexPxd672sweiMxvkZnVj0xPMrM3zSw78mh3zAuawlOY2mRlUielGl3P7EZWy1bRWOwhvfn6a1zUb0DQMbwtH/CnjH5/y++45777iYvzY1PoW57i7rv7/5HRsC5vvvYyv7/dj9OFfeDzegaw8MO5JCVVpW69MwJZfo3kZK4fcRNNUuuQVjeFCqecQtdu3QPJUsS39cy3NuRD+VSrfQbffJzNzq0/sGf3LlZlz2Lrhu8Cy+O71V9/RZUqSfz2qito37oF110znB07dgSayYd2dCDfrF7N8uVLtZ7JcUmtqRSZWTpwO9DVOdcMuDHy0lygtXMuE3gFuDUy/WbgWudcBtAB2AUMBCZHpjUDlplZFeAOoJtzrjmQA9zknHsMWAd0cc51cc7dBuxyzmU45waZWUOgP9AuMr8wMKh45n3nEZl8u3MuBDQFOplZ02If2eqcawmMBv4WmfYoMMo5lwVcBPzzZ8rnqsgPBjkbN2443GL9WfHx8czPXspnX60hJyebTz/95KjnebT27NnDpInj6HNh36CjeFk+4E8ZTZo4nqSkqmQ2bxFojiK+5dnXH0few7KVX3HRxZfw7NNPBh3HG76uZ0XeefNV+lwU3NH6LT/8wMTx77FsxRes/HINO3fs4NWXXwwsj4/rmU9tyJfySapVn/YXX8Xztw1l7B+v4LS6acTFxR/6g79S+fn5LF+2hGHDf8PcBYs5+aSTGfXwA4Hl8aUd7Wv79u0MHNCXBx8eRYViZ6JFm6/lI7FPHfvS1RV4wzm3EcA5tzkyPQWYbGYfA7cA6ZHp84BHIkfNKzrn8oFs4HIzuwto4pzbBrQGGgHzzGwZMAQ4nHOYzwRaANmRz50JHM45bBeb2RJgaSRro2KvvVzs3zaR592A0ZFlvAdUMLPy+87UOfeMcy7knAtVqZJ0GDEOT8WKFenQsRNTJ79favM8Uh9Mfp9mGZlUrVYt6Ch7+VQ+4E8ZLfjwQyZOGEd6g7oMHTyQ2TNncOXQy5TnEC7sN4AJ770ddAzv+LaeQeGX/Ynj3qX3hf0CyzBzxjROP70OVZKSKFOmDL3OvyDQi3r5vJ750IZ8Kp8WPfpxzZPvMuyRlzmxfEVOTa4dSI5YkJycQnJyCqHIUejzL7iI5cuWBJbHp3ZUJC8vj4H9+9J/wEDO73NhoFl8LB85PqhjX7oMcAeY/jgw2jnXBLgaOAHAOXc/cCVwIrDAzNKcc7OBjkAuMNbMBkfm+0HkSHyGc66Rc+5wBuUY8Hyxz6U65+466AfM6lB4JsGZzrmmwISivBHuAM/jgDbFlpMc+UHimNmwYQNbtmwBYNeuXcyYPo0GHlyA5M3XX/HiFHNfywf8KaM/3Xsfn335Xz5d9RVjXniJjp278M8xY5XnAL764vO9zydPHE/9BqkBpvGHz+sZwJyZ06h/Rio1klMCy5CSUpOc7IXs3LkT5xyzZk4nNS24MvJtPfOtDflUPtt/2ATAlvXrWDl3Ck26nBdIjlhQ7bTTSE6pyeerPgOIrGeNDvGpY8endgSF1xq55uorSU1L44YRNwWWo4hv5RMkO44ePkgIOsBxZhrwtpmNcs5tMrPKkaP2p1DYUYfCo+0AmFk959zHwMdm1gZIM7NdQK5z7h9mdjLQHPgz8ISZ1XfOfWGFV7BPcc6tArYB5YGNkdnmmVkZ51xeJM+7kTzrzawyUN45980+uYvPowKwA/gxci2AHsDMYu/tD9wf+bfosMsU4Drgocj/V4ZzbtmRFuLh+P67b7lq2FDC4TAFBQVc2LcfPXoGu9PfuXMnM6ZPZdTjTwWaA/wsH/CrjOTArr78UubNnc3mTRtpllaHW/84kqlTJvHl56uwuDhq1qzFQ397IpBsQy4dyOzZM9m0cSP169TkjpF3MTTACw/5sp5dM+wy5kfqrEWjuvzfbf+PgYMv5903Xw/0onkAoZat6N3nQjq3zSI+IYGmzTIYcsXwQDP5xJc25KNX7rmOXVt/IC6hDD2vv5MTy5/CirlTmPjk3ez4cTP/vmM4p9VryJC/PBf1bL5tiwAefORRrrz8MvL27KF27To88cy/As3jk/kfzuPlF8eS3rgJrbMKbwt6191/5pwe5wacTKR0mXMHOsAsR8rMhlB4un0YWOqcG2pm5wOjKOzcLwCynHOdzexxoEvkvSuAocCAyOfzgO3AYOfc12bWFXgAKBtZ1B3OuffM7HrgWuBb51wXM3sA6A0siYyz7w/8gcKj6nkUjukvcS+dA8xjDNAK+Ar4CXjPOTfGzFYDzwHnRuZ3SeSHhirAE0BDCn8smu2c+83Byql5i5CbMz/7lxXuMZQfLgg6wn4S4v06oca3MoqP8+X3UX/t2hMOOkIJ5U7w77fkAs92gVt35QUdoYQTE/0b15zg2brvy9WQi/j4vW7U7K+CjlDCzZ3rBR1hP3lhv+rNs9WMOM/WM4ACj9a1jm1bsmRxjn+FdBD1GjVz97/k120jj8bFmcmLI9coC4w69nLYIh37UNE1BI6GOvaHpo79waljf2jq2B+aOvYHp479oaljf2jq2B+aOvYHp479waljHzwfOvb+fcsSERERERGR45f598NorFPHXg6bc6520BlERERERESkJL/O9RURERERERGRX0QdexEREREREZEYplPxRUREREREJGoMHWEubSpPERERERERkRimjr2IiIiIiIhIDFPHXkRERERERCSGaYy9iIiIiIiIRJXuY1+6dMReREREREREJIapYy8iIiIiIiISw9SxFxEREREREYlhGmMvIiIiIiIiUaUR9qVLHXsJjE8r8+68gqAj7OdEzy4oUrZMfNARSigocEFH2I9nVcbJZf3axOeF/auzMvF+VdqJiX6tZ4nx/p3YFxfnV51t3ZUXdIQSynm23gPc3Lle0BFKaPrH94OOsJ+P/9Ij6Ahec86//Yfz76uj/Mr5t8cWERERERERkcOmjr2IiIiIiIhIDPPvfC0RERERERE5rvk2hDHW6Yi9iIiIiIiISAxTx15EREREREQkhqljLyIiIiIiIhLDNMZeREREREREosaAOK9ufh37dMReREREREREJIapYy8iIiIiIiISw9SxFxEREREREYlhGmMvIiIiIiIiUaX72JcuHbEXERERERERiWHq2EtMWrtmDT26d6V500aEMhrzxOOPRj3DiGuHk14vmU6tM/ZOe+gvd5ORVpsz24c4s32IqVMmRT1XkS1btnDZJf0INWtEVkY6ixbMDywLwJTJ79M0PZX0tPo89OD9gWYBP9pQcVcPv4LTk6sRymgSaI4ivpVPEZ/atW91BvDk43+jTYumtAk1Y9iQQezevTvQPD62Ix+2RTf+djiN6ibTsVXGfq89+dgjVKuQyKZNGwNI5l+dBbWe/aVfExbc2ZUJ/9d+77Trz6rPnDu68N7v2vHe79rRKS0JgIonlWHs1S1Zdu9ZjOzTKKo5wY82vS/fMvm4vW6cWpfWoWa0a9WcTu1aBh1HjgPq2EtMik9I4L4HHmbJRyuYMWc+z/z9SVauXBHVDP0HDublN8fvN/2q397AtLk5TJubQ7fuPaKaqbjbbh5Bt+5nk7N8BfMWLaVBWsPAsoTDYUbccC3vjpvE0o9W8PorL7NyRXTra18+tKHiLhs8lHfGB/dD0L58K58iPrVr3+psXW4uTz85mulzFzI/ZzkF4TBvvf5qoJl8a0e+bIsGDBrMK2/tv//IXbuGWdOnkVKzVtQzFfGtzoJaz97KWcsV/8zZb/qYOavpPWoevUfNY9Z/NgDwU14Bf5v8OQ+M/0+0Y3rTpn3P5Nv2usiE96cxb+ESZs1bFHQUOQ6oYy8xqXr16mRmNgegfPnypKY1ZF1ublQztGnXgYqVKkV1mYdr69atzJs7h8FDhwGQmJhIxYoVA8uTvWgR9erVp07duiQmJtKv/wDGj3s3sDzgRxsqrn2HjlSuVDmw5e/Lt/IB/9q1b3UGkJ+fz+5du8jPz2fnzp2cVr16oHl8a0e+bIt+bv8x8g83M/Ke+7AAB576VmdBrWfZX//AjzvzDuu9u/LCLF79Az/lFxzjVPvzpU37nsnH7bXYcfWfD9SxF8xssJl9ZGbLzWysmfUys4VmttTMpppZNTOLM7PVZlax2Oe+MLPTzexrMysTmVYh8r4y0cr/zerVLF++lKyWraK1yIP61z+eokvb5oy4djhbfvghkAyrv/6KKlWS+O1VV9C+dQuuu2Y4O3bsCCQLwLp1uaSk1Nz7d3JyCrkBdxKL860N+caX8vGtXfumRnIy14+4iSapdUirm0KFU06ha7fuQcfay4d25PO26P2J4zitejLpTZoFHWUvH+rMN5e2rcW4m9rxl35NqHBi8Neg9rFN+5jJR2ZGn17n0LFtFs89+0zQceQ4oI79r5yZpQO3A12dc82AG4G5QGvnXCbwCnCrc64AeBe4IPK5VsBq59w3wEygZ2SWA4A3nXP7/cxtZleZWY6Z5WzcuKFU8m/fvp2BA/ry4MOjqFChQqnM82gMHXY1C5f9h2lzc6hW7TTuuuPWQHLk5+ezfNkShg3/DXMXLObkk05m1MMPBJIFwDm337Qgj0gV51sb8o1P5eNbu/bNlh9+YOL491i24gtWfrmGnTt28OrLLwYdC/CnHfm6Ldq5cyd/e+h+fn/7nUFH2cuXOvPJS/P/y5n3z6L3qHms37abP5wX3FCgIj62aR8z+WjK9DnMmZ/Dm+9M4B9PP8W8ubODjiQxTh176Qq84ZzbCOCc2wykAJPN7GPgFiA98t5Xgf6R5wMifwP8E7g88vxy4LkDLcg594xzLuScC1WpknTUwfPy8hjYvy/9Bwzk/D4XHvX8SkNS1WrEx8cTFxfHoCHDWLo4O5AcyckpJCenEIocZTn/gotYvmxJIFmK8qxdu2bv37m5a6lRo0ZgeYr42IZ84lv5+NaufTNzxjROP70OVZKSKFOmDL3OvyDwi2aCX+3I123R6q+/5L/frKZruxChxmewLnctZ3Voxfrvvwskj0915pNN2/dQ4MA5eG3hWprWOiXoSF62aR8z+ah6pEySqlblvN59WJwdzHdGOX6oYy8G7PvT6uPAaOdcE+Bq4ITI9PlAfTNLAvoAbwE45+YBtc2sExDvnPvkWId2znHN1VeSmpbGDSNuOtaLO2zff/ft3ueTxr9LWsP0g7z72Kl22mkkp9Tk81WfATBr5nRS06J/pd4ioawsvvjic1Z//TV79uzh9Vdfoed5vQPLA/62IV/4WD6+tWvfpKTUJCd7ITt37sQ5FymftEAz+daOfNwWATRKb8KKr3LJ+eRzcj75nBrJKXwwZyFVq50W9Sy+1ZlPksqX3fv8rMbVWPXdtgDTFPKxTfuYyTc7duxg27Zte59Pn/oBDdOD+c4YJLPj5+EDdexlGnCxmZ0KYGaVgVOAosFQQ4re6ArPrXobeARY6ZzbVGw+LwAv8zNH60vb/A/n8fKLY5k1cwatszJpnZXJ+5MmRmPRe/3miks576yOfPn5KjIb1uGlF57jnpF/oHObTLq0bc68OTO5+y8PRzVTcQ8+8ihXXn4ZbbMy+Hj5Mv7v1j8EliUhIYFRj46mV8+zyWjSkIv6XUyjgHdgPrSh4oZcOpDOHduyatVn1K9TkzHPPRtYFvCvfIr41K59q7NQy1b07nMhndtm0TYrg4KCAoZcMTzQTL61I1+2RVdffik9uxXuPzLS6vDiC1HZdR4W3+osqPVs1MBmvHZda+okncyc27vQNyuFW3umMv6m9oy7qR2t61Xmvvf+dxX8GX/oxB96pXFhKJk5t3ehftVyUcnpS5v2PZNv2+v167/n7DM70rZlJl06tObsHudyVvdzAs0ksc8ONA5Gfl3MbAiFp9yHgaUUdt5HUdi5XwBkOec6R94bArKBoc6554vN4zTga6C6c27LoZbZvEXIzZ3vzylH23bnBx1hPycmxgcdoYTEBL9+Bywo8G/b5csvtkV827zne1hnZeL9qrQgrqp9MInxfq33AHFxftXZ1l2Hd+X0aClXNvgLuu3Lt21j0z++H3SE/Xz8l+BujxsLfOyv5If9ydSpXUuWLM7xbE07uDPSM9yjr00JOkap6dm42mLnXCjIDP5t/SXqIh305/eZfMD7kjjncuCA93RoT+FY/UN26kVERERERKT0qGMvR83MHgd6AOcGnUVERERERPxmQJwn938/XqhjL0fNOXd90BlERERERER+rfwbPCciIiIiIiIih00dexEREREREZEYplPxRUREREREJHo8uv/78UJH7EVERERERERimDr2IiIiIiIiIjFMHXsRERERERGRGKYx9iIiIiIiIhJVGmNfunTEXkRERERERCSGqWMvIiIiIiIiEsPUsRcRERERERGJYRpjLyIiIiIiIlFlaJB9aVLHXgLj0wUzTi4bH3SE/cTHeVRAwJ78gqAjlOBX6RQqk+DXSVD5Yb/qzLc27aNw2AUdoQTn36aRcIFfZZTgWbsucH6VD8BPeX5ti5b9+ZygI+yny19nBR2hhInXtw86Qgll4v1az0D7NPGPX99CRUREREREROQXUcdeREREREREJIbpVHwRERERERGJGgM0mqF06Yi9iIiIiIiISAxTx15EREREREQkhqljLyIiIiIiIhLDNMZeREREREREokr3sS9dOmIvIiIiIiIiEsPUsRcRERERERGJYerYi4iIiIiIiMQwjbEXERERERGRqDINsS9VOmIvIiIiIiIiEsPUsZeYdPXwKzg9uRqhjCZBRykhHA7TrlUL+l7QK+go3pXRli1buOySfoSaNSIrI51FC+YHHYnGqXVpHWpGu1bN6dSuZdBxmDL5fZqmp5KeVp+HHrw/6Djelc/u3bvp1K4VrUMZhDIac+/ddwaax5d17IZrriStTg3at8zYO+3O239P6+aN6dg6k8GX9OXHLVsCyeZbnfmS5/prriS1dg3aZf2vzn7YvJkLe51DVrOGXNjrHLb88EMg2SD4fdkN11xJWu0atC9WPu++9QbtQs1IKp/I0iU5geSCYNvQ7T0aMOG6Nvz7itDeaVd1qM3Yy1vw/NAW/O3iJlQpl7j3tcyap/D80Ba8OCzEk5c0i1pOgL8/8RhtQ81oE2rKU6Mfjeqyf07Q7brI2jVr6NG9K82bNiKU0ZgnHvejfCT2qWMvmFlvM7st8vwuM7s58vxuM+sWbLoDu2zwUN4ZPynoGPt5cvRjpKamBR0D8K+Mbrt5BN26n03O8hXMW7SUBmkNg44EwIT3pzFv4RJmzVsUaI5wOMyIG67l3XGTWPrRCl5/5WVWrlgRaCbwp3wAypYty4TJ01iQs4z52UuZOmUyixYuCCyPL+vYgEFDePXt8SWmde7ajbmLljF7wVLq1T+Dv/31gUCy+VZnvuS5ZNAQXnunZJ09+siDdOzclezlK+nYuSt/e+TBqOcqEvS+bMCgIby6T/k0bJTOmJdeo027DgGlKhRkG5rw8ff87vWPS0z798I1XPbcYoaMWcy8LzdzRdvTAShXNp5bup/BrW9+wqBnc7j93ejtT1Z8+gkvPPcsU2fPZ86CJUyZNIEvv/g8asv/OUG36yLxCQnc98DDLPloBTPmzOeZvz/JypXB7+8l9qljLzjn3nPO7Xd40Dk30jk3NYhMh9K+Q0cqV6ocdIwScteuZfKkiQy5fFjQUQC/ymjr1q3MmzuHwUMLyyYxMZGKFSsGnMov2YsWUa9eferUrUtiYiL9+g9g/Lh3g47lFTOjXLlyAOTl5ZGXl4cFOEDPl3WsbfsOVNonR5czzyIhofAyOqGsVqxbtzaIaN7VmS95DlRnEyeMY8CgywAYMOgyJo5/L+q5wI992YHKp0FaQ85okBpQov8Jsg0tW/sjW3fllZi2c0947/MTy8ThIs+7N6rGzFUb+X7bTwD8sLPk546lVZ/9h1DLVpx00kkkJCTQtkNHJrz3TtSWfyA+tOsi1atXJzOzOQDly5cnNa0h63JzA04VDDuO/vOBOvbHMTMbbGYfmdlyMxtrZklm9qaZZUce7SLvG2pmow/w+TFm1jfyfLWZ/cnMlpjZx2aWFpmeZGYfRKY/bWbfmFmV6P6f+uH3t/yOe+67n7g4rVb7Wv31V1SpksRvr7qC9q1bcN01w9mxY0fQsTAz+vQ6h45ts3ju2WcCzbJuXS4pKTX3/p2cnEJuwDt6n8qnSDgcpk1WJnVSqtH1zG5ktWwVdCTvvTh2DGeedU5gy/etznzLU2TD+u857bTqAJx2WnU2blgfSA7tyw7NtzZ0dYfavHNNK7o3qsY/5qwGoFblE6lwQgJPXNKM54Y0p0d6tajladgonfnz5rB50yZ27tzJB5MnkZsbzI+LRXxt19+sXs3y5UsDb0NyfPCrdUupMbN04Hagq3OuGXAj8CgwyjmXBVwE/PMXznajc6458BRwc2TancD0yPS3gVqlkT/WTJo4nqSkqmQ2bxF0FC/l5+ezfNkShg3/DXMXLObkk05m1MPBnBpc3JTpc5gzP4c335nAP55+inlzZweWxTm337Qgj2yCX+VTJD4+nvnZS/nsqzXk5GTz6aefBB3Ja4889BcSEhLo139gYBl8qzPf8vhE+7LD41sbenrOavo8tZApK76nb4sahRnNSD2tPP/3xseMeO0jLm9bi5qVToxKntS0htxw0y1c2Osc+vU5l8ZNmhEfHx+VZR+Ir+16+/btDBzQlwcfHrYeiO4AACAASURBVEWFChWCjiPHAXXsj19dgTeccxsBnHObgW7AaDNbBrwHVDCz8r9gnm9F/l0M1I48bw+8ElnG+8DPXu3HzK4ysxwzy9m4ccMv+X/x3oIPP2TihHGkN6jL0MEDmT1zBlcOvSzoWN5ITk4hOTmFUOQX6fMvuIjly5YEnAqq1yj8ApRUtSrn9e7D4uzswLIkJ6ewdu2avX/n5q6lRiRfUHwqn31VrFiRDh07MXXy+0FH8dYrL77AlEkT+PuzLwT+IxH4V2e+5UmqWo3vvvsWgO+++5YqSVWjnkH7sl/GtzY0ZcV6OjdIAmD9tp9Y8NVmducV8OOufJat/ZEzqp4ctSyXDbmCmR9mM2HKTCpVqkS9+mdEbdn78rFd5+XlMbB/X/oPGMj5fS4MNIscP9SxP34ZsO8hwDigjXMuI/JIds5t+wXz/CnybxhIKLacw+Kce8Y5F3LOhapUSfoFi/Xfn+69j8++/C+frvqKMS+8RMfOXfjnmLFBx/JGtdNOIzmlJp+v+gyAWTOnk5rWKNBMO3bsYNu2bXufT5/6AQ3T0wPLE8rK4osvPmf111+zZ88eXn/1FXqe1zuwPL6VD8CGDRvYErm6+65du5gxfRoNPLgQko+mfTCZx0Y9zL9ffZuTTjopsBy+1ZlveYrrce55vPJi4X7jlRfHcm7P6F+5W/uyQ/OtDaUUOwrfvv6pfLN5JwCzv9hERsopxBuUTYijUfUKrN60M2q5NqwvHEqyds1/Gf/eO1zUb0DUlr0v39q1c45rrr6S1LQ0bhhxU2A5gmZAnB0/Dx8kHPotEqOmAW+b2Sjn3CYzqwxMAa4DHgIwswzn3LKjXM5c4GLgATPrDlQ6yvkdliGXDmT27Jls2riR+nVqcsfIuxjqwQVRfOJbGT34yKNcefll5O3ZQ+3adXjimX8FlgVg/frvGdT/IqBwqEC//pdwVvfgxiEnJCQw6tHR9Op5NuFwmCFDr6BRgB1p38oH4PvvvuWqYUMJh8MUFBRwYd9+9Oh5XmB5fFnHhl9+KfPmzGLzpo00Sa3N7/84kkcfeZCffvqJvucX1lmLrFb89dEno57NtzrzJc/woYV1tmnTRho3qM1tt4/kxptu5YrBl/DiC8+RnFKT58a+EvVcvigqn82bNtKkQW1+f/tIKlWqzG03j2DTxg0MvOh8GjdtxuvvTox6tiDb0J96NaR5rVOoeGIZ3v1ta/45dzVt6lamVuWTcM7x3dafeHDyKgC+2bSTBV9vZuwVIQocjPvoW77aGL2O/ZBB/di8eTNlEsrw4COPUbFSVL4exoT5H87j5RfHkt64Ca2zMgG46+4/c06PcwNOJrHODjSuU44PZjYEuIXCI+xLKRwX/wTQkMIfdWY7535jZkOBkHPuOjO7C9junHvYzMYA451zb5jZ6sh7NppZCHjYOdfZzKoCL1PYoZ8F9AfqOOd+4iCatwi5eQv8Oa03XODfehDvy89/EXlhv8rIr9IpVCbBr5Og8vILgo5QQpxnbRr8+ZW9yM6fwod+UxSdkBjcuNhY8VOeX3WW6Nl2COAnz7ZFJ5Txr113GxX8NUyKm3h9+6AjlFAm3rONNRDnwRCnIu3bZLFkcY4/gQ5DWuMM98xb04OOUWo6pZ662DkXCjKDjtgfx5xzzwPP7zO5/wHeNwYYE3l+V7HpQ4s9r13seQ7QOfLnj8DZzrl8M2sDdDlUp15ERERERERKjzr2crRqAa+ZWRywBxgecB4REREREfGaP/d/P16oYy9HxTn3OZAZdA4REREREZFfK/8GYomIiIiIiIjIYVPHXkRERERERCSG6VR8ERERERERiR4Dj24scFzQEXsRERERERGRGKaOvYiIiIiIiEgMU8deREREREREJIZpjL2IiIiIiIhElYbYly4dsRcRERERERGJYerYi4iIiIiIiMQwdexFREREREREYpjG2IuIiIiIiEjUGBCnG9mXKnXsJTDOBZ3gf+Lj/NuwmGcbu8QEv/LkhwuCjrAf51OjlpiUmODXiXR+rfWF4jzbXvu21vv4RfmkxPigI5Tg2/4V4J1r2gYdoYQ3PlobdIQSBjavFXSE/RRony+e8esbhIiIiIiIiIj8IurYi4iIiIiIiMQwnYovIiIiIiIiUeXfoJjYpiP2IiIiIiIiIjFMHXsRERERERGRGKZT8UVERERERESOITNbDWwDwkC+cy5kZpWBV4HawGrgYufcD0cyfx2xFxERERERkeiy4+hx+Lo45zKcc6HI37cB05xzZwDTIn8fEXXsRURERERERKLvfOD5yPPngT5HOiN17EVERERERESOXBUzyyn2uOoA73HAFDNbXOz1as65bwEi/1Y90gAaYy8iIiIiIiJy5DYWO73+57Rzzq0zs6rAB2b2n9IMoI69iIiIiIiIRJX9yu5k75xbF/l3vZm9DbQEvjez6s65b82sOrD+SOevU/FFREREREREjhEzO9nMyhc9B7oDnwDvAUMibxsCvHuky9ARexEREREREZFjpxrwtplBYR/8Jefc+2aWDbxmZsOA/wL9jnQBOmIvMWntmjX06N6V5k0bEcpozBOPPxponquHX8HpydUIZTQJNEdxUya/T9P0VNLT6vPQg/cHHce7PEXC4TDtWrWg7wW9As3hYxtqnFqX1qFmtGvVnE7tWgYdh927d9OpXStahzIIZTTm3rvvDDSP6uzQfNtWQ/DbohuuuZK02jVon5Wxd9q7b71Bu1AzksonsnRJTtQzFedbnfm4ngXdhgBGXDuc9HrJdGqdUWL6P59+gnYt0unYqhl3/78jvmvWL/bdN19y9+Aeex83nNmYqa88y5rPV3D/8Au4a9DZjL55GLt2bItapiK+7TuK+PL9Q6LDOfeVc65Z5JHunPtzZPom59yZzrkzIv9uPtJlqGPvMTMbY2Z9j8F8a5vZJ4f7HjMLmdljpZ3jaMQnJHDfAw+z5KMVzJgzn2f+/iQrV64ILM9lg4fyzvhJgS1/X+FwmBE3XMu74yax9KMVvP7Ky6xcEVz5+JanuCdHP0ZqalrQMbxrQ0UmvD+NeQuXMGveoqCjULZsWSZMnsaCnGXMz17K1CmTWbRwQWB5VGeH5tu22odt0YBBQ3j1nfElpjVslM6Yl16jTbsOUc1yIL7VmW/rmQ9tCKD/wMG8/GbJdjR39kwmTxjH9A+XMHvhcq654aao5Tnt9HqMfGESI1+YxB3PjSfxhBPI7HQ2L/zlNi645vfc9eJkMjqdzZR/PxO1TEV823cU8eX7R5DMjp+HD9Sxl0NyzuU452443Peb2TEf4lG9enUyM5sDUL58eVLTGrIuN/dYL/Znte/QkcqVKge2/H1lL1pEvXr1qVO3LomJifTrP4Dx4454yM5xl6dI7tq1TJ40kSGXDws6indtyEdmRrly5QDIy8sjLy8PC3Bvqjo7NN+21T5si9q270ClfdpNg7SGnNEgNao5fo5vdebbeuZDGwJo064DFStVKjHt+Wef5vrf3ULZsmUBSEo64rtmHZWVOfNISj6dU6un8P03X9EgsxUAjVq2Z8nM6P9I49u+A/z6/iHHD3XsPWJmg83sIzNbbmZjI5M7mtmHZvZV0dF7MytnZtPMbImZfWxm50em1zazlWb2DzP71MymmNmJkddaROY7H7i22DLjzewhM8uOLPvqA+TqbGbjI89PNrN/Rd6/tNiyh5rZ62Y2DphyTAtqH9+sXs3y5UvJatkqmov12rp1uaSk1Nz7d3JyCrkBfjHzLU+R39/yO+65737i4rQpPBAzo0+vc+jYNovnno3+UZYDCYfDtMnKpE5KNbqe2U3r/T58rLMiPmyrfd0W+cqHOvONz23oqy8/Z8H8ufTo2o4+557J0sXBDOvI/mAcWWf1BqBG3QYsn/MBAIunT2Tz+m8DyeTbvkPfP+RYUGvyhJmlA7cDXZ1zzYAbIy9VB9oD5wFFA7l2Axc455oDXYC/2v9+ejwDeMI5lw5sAS6KTH8OuME512afRQ8DfnTOZQFZwHAzq3OQqLcD0yPv7wI8FLmyI0AbYIhzruvP/D9eZWY5ZpazceOGg5bH4dq+fTsDB/TlwYdHUaFChVKZ5/HAObfftCB/nfYtD8CkieNJSqpKZvMWgebw2ZTpc5gzP4c335nAP55+inlzZwcdifj4eOZnL+Wzr9aQk5PNp58edFTRr46PdQb+bKt93Bb5ypc6843PbSg/P58ft2xh4rS5jLznfq4aOvCAeY9phrw9LJ87ldCZ5wIw5PYHmfHmWO4deh67d24nIaFMVPMU8Wnfoe8fcqyoY++PrsAbzrmNAMUunPCOc67AObeCwqspAhhwn5l9BEwFkou99rVzblnk+WKgtpmdAlR0zs2KTC86GwAKb7Uw2MyWAQuBUyn8ceDndAdui7x/JnACUCvy2gcHu+CDc+4Z51zIOReqUiXpIIs4PHl5eQzs35f+AwZyfp8Lj3p+x5Pk5BTWrl2z9+/c3LXUqFFDeYpZ8OGHTJwwjvQGdRk6eCCzZ87gyqGXBZrJN9UjdZRUtSrn9e7D4uzsgBP9T8WKFenQsRNTJ78fdBSv+FhnPm2rfdwW+cinOvONz22oRo0Uzu3VBzOjeYss4uLi2LRpY1QzfDJ/JrVSG1OhcuH3vOq16/O7R8dyx5jxtDyrN0nJp0c1z7582Hfo+8f/2HH08IE69v4w4EA/q/60z3sABgFJQAvnXAbwPYUd7H3fH6bwdgo/N++ieV7vnMuIPOo45w52Kr0BFxV7fy3n3MrIazsO8rlS5ZzjmquvJDUtjRtGRO/iMLEilJXFF198zuqvv2bPnj28/uor9Dyvt/IU86d77+OzL//Lp6u+YswLL9Gxcxf+OWbsoT/4K7Fjxw62bdu29/n0qR/QMD090EwbNmxgy5YtAOzatYsZ06fR4Fd+4aHifKwz37bVPm6LfONbnfnG5zZ0Ts/ezJ09A4Avv1hFXt4eTj21SlQzLPrgPVqe9b+rvG/dXPjDQkFBAROeG03HCwZFNQ/4t+/Q9w85VtSx98c04GIzOxXAzA52pZhTgPXOuTwz6wIc9OdP59wW4Eczax+ZVHyrOhm4xszKRJbboNip9QcyGbi+6NR/M8s82LKPlfkfzuPlF8cya+YMWmdl0jork/cnTQwiCgBDLh1I545tWbXqM+rXqcmY554NLAtAQkICox4dTa+eZ5PRpCEX9buYRgF+wfctj498a0Pr13/P2Wd2pG3LTLp0aM3ZPc7lrO7nBJrp++++5dzuXWnVohkd27ak65nd6NHzvMDyqM4OzbdttQ/bouFDL+Wcrh344vPPaNKgNv9+/l9MeO8dmjSoTc6iBQy86Hz6nX9uVDMV51ud+bae+dCGAH5zxaWcd1ZHvvx8FZkN6/DSC89xyWVD+e/qr+nUOoPfXH4pjz31bFSHCfy0excrF80ls/P/tjvZH7zHHRd3YeSAM6mYVJV25x3xLbqPmG/7DpFjxaI99kZ+npkNAW6h8Ej70sjk8c65NyKvb3fOlTOzKsA4oAywDGgH9Cj2/saR998MlHPO3WVmLYB/ATsp7Jz3dc41NrM44F6gF4VH4zcAfYBKRfMys87Azc658yIX4/sb0Dby/tWR6UOBkHPuusP5f23eIuTmzg/+FNEingyPK8GXMXu+yg8XBB1hP/FxftVZftiv7XucZ+UD4Fsk3+rMtzYN/rWjHT/lBx2hhBPLxAcdYT++7c583L/+uDMv6AglvLdiXdARShjYvNah3xRlPvWhOrZtyZLFOf417INo2CTTvfDuzKBjlJqW9Souds6FgsxwzG9LJofPOfc88PxBXi8X+XcjhReqO5DGxd7/cLHni4Fmxd53V2R6AfDHyKO4H4vm5ZybSeF4epxzu4D9rpzvnBsDjPm57CIiIiIiInvF1E8R/tOp+CIiIiIiIiIxTB17ERERERERkRimjr2IiIiIiIhIDNMYexEREREREYmawvu/a5B9adIRexEREREREZEYpo69iIiIiIiISAxTx15EREREREQkhmmMvYiIiIiIiESPgWmIfanSEXsRERERERGRGKaOvYiIiIiIiEgMU8deREREREREJIZpjL2IiIiIiIhElYbYly517CUQBsTF+bM6FxS4oCPsxzm/MvlUXwBxHl5xZU9+QdARSkiI9+ukrHjP2pCPCpxfbahMnF9tyEeJnq1nvm2rwb/9mY8qnOjXV/JLW9QKOkIJlXs/GnSE/ax/+4agI4iU4NfeSERERERERER+EXXsRURERERERGKYX+f9iIiIiIiIyPHPv5FDMU1H7EVERERERERimDr2IiIiIiIiIjFMHXsRERERERGRGKYx9iIiIiIiIhJFhmmQfanSEXsRERERERGRGKaOvYiIiIiIiEgMU8deREREREREJIZpjL2IiIiIiIhElWmIfanSEXsRERERERGRGKaOvYiIiIiIiEgMU8deYtKUye/TND2V9LT6PPTg/UHHYe2aNfTo3pXmTRsRymjME48/qjz7UJ0d3OerPqN9qxZ7HzWrVeLJ0cFl2r17N53ataJ1KINQRmPuvfvOwLIU51s78imPb22oiE9l5GOexql1aR1qRrtWzenUrmXQcQC/yujq4VdwenI1QhlNAs1RnE/lA/6VUVB5/v67s/jm5avIeerSvdOa1k1i1qj+LBg9iLmPXkKoQTUAOjRJ4bs3rmHB6EEsGD2IPwxsFdWs4Oe6L7HNnHNBZ/hVM7MxwHjn3BvHYN4zgZudczlHOZ/aFGZsXAqxAGjRIuTmLTyyWOFwmCaNGjBh0gckp6TQvnUWz//7ZRo2anTEeQoKjm49+Pbbb/nuu2/JzGzOtm3baN86xCtvvE3Dhkeeybc8cXFHPhDq11JneeGCo8pUJBwO07BeLabO/pBatU4/4vkkxB/5b7fOOXbs2EG5cuXIy8vjrC4dePCvf6Nlq9ZHPM/4o2hDcGzakW95fsoLl1q20mhDZcvEH3WO473O8vKPbr1vnFqXWfMWcWqVKkc1nyJlEo7umM2xKKOj+a45d85sTi5XjuGXDyFn2cdHPJ/i7CgG9h6rNu1bGR2NY5Gncu9D/0jZrnEyO3bt4Z83n03omn8DMO7PF/D420uZkrOas7Nqc1PfEGf//g06NElhxEXNueiu94440/q3bzjiz0Lprvud2rVkyeKcmBqxnt60uXtp/KygY5SajNMrLHbOhYLMoCP2xykzO7pvYx4vO3vRIurVq0+dunVJTEykX/8BjB/37rFc5CFVr16dzMzmAJQvX57UtIasy81VngjV2S8za8Y06tSte1QdsqNlZpQrVw6AvLw88vLyjurLcGnwrR35lqc4H9oQ+FdGvuXxkW9l1L5DRypXqhzY8vflW/mAf2UUVJ55n+SyedtPJaY5BxVOSgTglJPK8u2m7VHPJRIt6thHmZkNNrOPzGy5mY2NTO5oZh+a2Vdm1rfYe28xs+zI+/9UbPqlZrbIzJaZ2dNFHWkz225md5vZQqDNPsvtbmbzzWyJmb1uZuUi00dGlvGJmT1jkW/uZtYiknE+cG2x+cSb2UPFcl0dmd7ZzGaY2UvAMf25eN26XFJSau79Ozk5hVxPOmQA36xezfLlS8lqGf3Tug7Ehzyqs1/mzddf46J+A4KOQTgcpk1WJnVSqtH1zG6Bl49v7ci3PMX50oZ8KyPf8kDhj2h9ep1Dx7ZZPPfsM4FmAT/LyCcqn9hyy9MzuW9Yez5/YRh/ubIDI8fM2/taq4bVWfjEIN65uw8Na0X/hwjf1n2JferYR5GZpQO3A12dc82AGyMvVQfaA+cB90fe2x04A2gJZAAtzKyjmTUE+gPtnHMZQBgYFJnPycAnzrlWzrm5xZZbBbgD6Oacaw7kADdFXh7tnMuKnGZ/YiQDwHPADc65Ej8QAMOAH51zWUAWMNzM6kReawnc7pw74PloZnaVmeWYWc6GjRsOt9j2c6DT1YI+klhk+/btDBzQlwcfHkWFChWCjuNNHtXZ4duzZw+TJo6jz4V9D/3mYyw+Pp752Uv57Ks15ORk8+mnnwSax7d25FueIj61Id/KyLc8AFOmz2HO/BzefGcC/3j6KebNnR1oHh/LyCcqn9hyVc+m3PrMbM4Y/Cy3PjOLp0acBcCyL9eTOuRftLr2RZ4at4zXRvaKejbf1n2JferYR1dX4A3n3EYA59zmyPR3nHMFzrkVQLXItO6Rx1JgCZBGYUf/TKAFkG1myyJ/1418Jgy8eYDltgYaAfMinxkCFJ2f2cXMFprZx5F86WZ2ClDROVc08GVssXl1BwZH5rMQODWSC2CRc+7rn/ufd84945wLOedCSVWSfr6UDiE5OYW1a9fs/Ts3dy01atQ44vmVlry8PAb270v/AQM5v8+FQcfxKo/q7PB9MPl9mmVkUrVatUO/OUoqVqxIh46dmDr5/UBz+NaOfMtTxKc25FsZ+ZYHoHpk+UlVq3Je7z4szs4ONI+PZeQTlU9sGdStEe/M+wKAN+d8Tii1cLu4beceduzOA2By9mrKJMRzaoUToprNt3U/EHYcPTygjn10GXCgq6P8tM97iv79i3MuI/Ko75x7NjL9+WLTU51zd0U+s9s5d6ArLxnwQbHPNHLODTOzE4Angb7OuSbAP4ATDpKzaF7XF5tXHefclMhrOw6vGI5OKCuLL774nNVff82ePXt4/dVX6Hle72gs+mc557jm6itJTUvjhhE3HfoDv7I8qrPD9+brr3hxCvWGDRvYsmULALt27WLG9Gk0SE0LNJNv7ci3PEV8aUPgXxn5lmfHjh1s27Zt7/PpUz+gYXp6YHnAvzLyjcontny7aQcdmqQA0DmjJl/kFu7XqlU6ae97Qg2qEWewaevuqOXycd2X2JcQdIBfmWnA22Y2yjm3ycwONqBnMnCPmb3onNtuZslAXmQe70bmsT4yj/LOuW8OMq8FwBNmVt8594WZnQSkAOsjr2+MjLnvS+EZBVvM7Eczax85pX9QsXlNBq4xs+nOuTwzawBEdXBZQkICox4dTa+eZxMOhxky9AoaBbwxnP/hPF5+cSzpjZvQOisTgLvu/jPn9DhXeVCdHa6dO3cyY/pURj3+VGAZinz/3bdcNWwo4XCYgoICLuzbjx49zzvk544l39qRb3nArzYE/pWRb3nWr/+eQf0vAiA/P59+/S/hrO7nBJYH/CujIZcOZPbsmWzauJH6dWpyx8i7GHr5sMDy+FY+4F8ZBZXn+d/3oEPTFKpUOIEvxg7jnrELuPaxqTx0dScS4uP4aU+Y6x6bBsAF7c9geM+m5IcL2L0nn8H3Tzrm+Yrzcd2X2Kfb3UWZmQ0BbqHwtPmlkcl7b3dnZtudc0UXtrsRuDLynu3Apc65L82sP/AHCs+4yAOudc4tKP7ZyOdnErndnZl1BR4AykZevsM5956Z3QsMAFYDa4BvnHN3mVkL4F/ATgo7832dc43NLA64F+hF4dH7DUAfIDOyrMP65n80t7s7Fo721mm/Bkdzu7tjwcc6K63b3ZWWo7nd3bFwtLe7+zUordvdlZajvd3dr8HR3u6utB3t7e6OBd++a/o4Jt63MvLN4dzuLtqO9nZ3pSlmb3c34Ti63V2t4G93pyP2Ueacex54/iCvlyv2/FFgvy2Zc+5V4NWDfTbyd+diz6dTeLG7fT9zB4UX1tt3+mKgWbFJd0WmFwB/jDyKmxl5iIiIiIiIHJT5Mjj9OOHfz7oiIiIiIiIictjUsRcRERERERGJYerYi4iIiIiIiMQwjbEXERERERGRqPLwOpYxTUfsRURERERERGKYOvYiIiIiIiIiMUwdexEREREREZEYpjH2IiIiIiIiElUaYl+6dMReREREREREJIapYy8iIiIiIiISw9SxFxEREREREYlh6tiLiIiIiIiIxDBdPE9ERERERESix9DV80qZOvYinoqL82trlx8uCDpCCQUu6AT7K1smPugIJTjnVyH5lgfAzK/1LDHBrxPpVGeHtisvHHQE7/m2Pwt7tj8DKBPvVxn5ZvN7NwYdYT+Nbp0YdIS91q39MegI4gG/vkGIiIiIiIiIyC+ijr2IiIiIiIhIDNOp+CIiIiIiIhJVpkH2pUpH7EVERERERERimDr2IiIiIiIiIjFMHXsRERERERGRGKYx9iIiIiIiIhI1Bnh299KYpyP2IiIiIiIiIjFMHXsRERERERGRGKaOvYiIiIiIiEgM0xh7ERERERERiSoNsS9dOmIvIiIiIiIiEsPUsZeYNGXy+zRNTyU9rT4PPXh/0HFYu2YNPbp3pXnTRoQyGvPE448GHcm7MgIIh8O0a9WCvhf0CjoKAFu2bOGyS/oRataIrIx0Fi2YH2ge3+rs6uFXcHpyNUIZTYKOAviXB1Rnh+JbHvCjzm787XAa1U2mY6uM/V578rFHqFYhkU2bNgaQrFDj1Lq0DjWjXavmdGrXMrAcALt376ZTu1a0DmUQymjMvXffGWge8G/f4dt65lseCC7TAwOakn13N96/tePeaTeefQbz7zyTCTe3Z8LN7encMKnEZ2pUPIFP7j+b4Z3rRjWrxD517H/lzOw3ZjY48nyomdUIOtOhhMNhRtxwLe+Om8TSj1bw+isvs3LFikAzxSckcN8DD7PkoxXMmDOfZ/7+JCtXBpfJxzICeHL0Y6SmpgUdY6/bbh5Bt+5nk7N8BfMWLaVBWsPAsvhYZ5cNHso74ycFmqE43/Kozg7Ntzy+1NmAQYN55a3x+03PXbuGWdOnkVKzVtQz7WvC+9OYt3AJs+YtCjRH2bJlmTB5GgtyljE/eylTp0xm0cIFgWbyad8B/q1nvuWB4DK9uWgtQ5/Zfx3616yv6fnwXHo+PJeZKzeUeO2OPo2Ytc80kcOhjv2vnHPu7865FyJ/DgUO2LE3s/iohTqE7EWLqFevPnXq1iUxMZF+/Qcwfty7gWaqXr06mZnNAShfvjypaQ1Zl5sbbHU7EwAAIABJREFUWB4fyyh37VomT5rIkMuHBZqjyNatW5k3dw6DhxbmSUxMpGLFioHl8bHO2nfoSOVKlQPNUJxveVRnh+ZbHl/qrE27DlSsVGm/6SP/cDMj77kP082d9zIzypUrB0BeXh55eXmBlo9v+w7wbz3zLQ8El2nRV5vZsiPvsN9/VuNqrNm0k1XfbTuGqTxix9HDA+rY/8qY2WAz+8jMlpvZWDO7y8xuNrO+QAh40cyWmdmJZrbazEaa2Vygn5nNNLNQZD5VzGx15Pn/Z++sw+Uqry7+W8EtRYK7u0OhUIoXC1rcNbhbW6C4p2iR4u5OcLcWaylQoBSKFC/uGtb3x3onmd4GaPlyzgzJWTw8uffM3Dv7HnnfLWuvPaukB8vPPSZp+ir/hldffYXJJpt80PeTTjoZr3QwiO6JF194gUcffYT5f7xAx2zoxnO05+47c+Ahh9GrV3csOy88/xx9+ozPNv025acLzst2W2/Bxx9/3DF7uvGaNfh2NNfsh4duvmY3Xn8tE008KbPOPmenTUESq6y4LD9baH7OPP2UTpvDwIED+cn8czP1ZBOyxJJLdXR/7ba9o8EPExsuMiU37L4Ih689B71Hi5b5aCOPwFZLTsuxNz3TYesa/FDRHR52g1ogaVZgL2AJ23MCO7Zes30Z8DCwnu25bH9aXvrM9k9tX/Qtv3or4Fjbc5HkwMvV/AWDbP2PY91S3fjoo49Yd+3VOaL/0fTu3btjdnTbObrh+gGMP/4EzD3PvB2zoSe++uorHv3Ln9lsi6249/4/McboY3B0/8M7Zk+3XbMG343mmv3w0K3X7JNPPuGYIw9jz7063z8OcPPt93DPHx/m8quu49Tfn8R9997dUXtGGGEE/vjQIzz93Es8/PBDPPHEXztmS7ftHQ1+eDj/vhdZ9KA7WL7/Pbz5wefstfIsAOy87AyccdfzfPLFwA5b2OCHCg1pk2swbELS9sBEtvdqO7Yf8JHt/pLuBHaz/XB57QVgUdsvlu8HvS6pD/Cw7akkrUsSBucAV9geYqpRUj+gX/l2RuDp7/mnjEFaBp4B+jB4bOPr3/P3DS0ImAV4C3ijw7Z02zmaFBgPcJst7wHPd8geih0zA6+RazYmMBHwbIfs6bZr1sLIwEzAYx22o4Vusqd1zd4l99BE5Xhzzf4d3WRPN12zkYHpgSeA0cg5+qrttS+Ap9qO1Y0+5BxNAgyk8/tay56Jga/pnD2tvePxYtNndHbvaKGbnjPoPnugQzbNMMMMIw8YMGD6GWaY4Ymer0077bQT3XDDDePNMMMMTzz88MMzTjTRRCMD9O7de4Svv/6aww8//JVDDz30v2m4n9L2+N/9tu7BbHPO40tvvKfTZgw1zDLJmH+yPV8nbWjm2A9fEAms/he088u+YjDLY9TWQdsXSHoAWAG4SdLmtm/v+YtsnwL8vzl9kkYE/g6sAVxG7uN1bf/HglkXlJLP2cBktif/rvfXYE/XnaMWJD0NPGO7bxfYcg8wtu35SpJrDNu7d8iWrrxmkqYCnur0ZtVCN9nTds0+AhYCHqK5Zv+BbrKnm65ZOS8DWudF0sNtX78AzGe7dml8SWOQvf4OYFHgFuAA2zfWbUuxZ3zgS+BWYBHgZuBw2/+pPlifTfcAmwPnAwPo4N7RZtNUdMlzBt1nD3TUpqloe9ZJcuo1gH333fef008//dW21+7xM/sBHx1yyCH9DznkkNoMrRvqlub0YQQNFX/4wm3AmpLGA5DUU0XkQ2Csb/n5F4AWl3r11kFJ0wDP2T4OuAaYY2gZPCTY/grYDrgJmBW4pNOONLAwsAEwVtEa+Iuk5TtlTJeeo27E9sA0kh4D5gI6tnt24zWTdCHwR2AUSS9L6qjyYbfZ03bNZiCV1eaadbk93XLN2s7LjN1wXnpgQuBewkB7ELiuU0F9wcQkyTALScTc0smgvmB7EtTPQof3Dui+56zb7OmwTYOeddKquhlwBGF8PLbooov2BnauyZYGwzgaKv5wBkkbAbsTWt0jJFhvUfF/QTanT4GfEKdnUMVA0kzAJaTScTuwfqHi/wpYn2TUXyfVj3dq+nse7rJscFfZA91nU2PPd6PbbGrs+XZ0mz3QfTY19nw3us2mxp7vRrfZ1Njz3eg2m7rNnjox25zz+LIb7+20GUMNM08yRkPFb1AvbJ9NKONDeu1y4PK2Q1P1eP1v/Hs1fu9y/FDg0KFq6H+Pzsv1/ju6zR7oPpsae74b3WZTY8+3o9vsge6zqbHnu9FtNjX2fDe6zabGnu9Gt9nUbfY0+AGjqdg3aNCgQYMGDRo0aNCgQYPaMNuc8/jym4adiv1ME3e+Yt/02Ddo0KBBgwYNGjRo0KBBgwY/YDSBfYMGwygkjdxpGxo0GJqQNGr5t5HRbTDMobmvGzRo0KDB/wdNYN/gBw1JY3fahm6EpAmAPSQNl4IsDYYtKJgeuF/SNLbdLUGQpIkkNXtpg+8NSXNJGtlNb2SDYQQ91+dOr5HdZk+DBlWhubEb/GAhaSngUEk/6rQtLUiaUdKUnbaDPNvTACtImrvTxpTArCsCsRZa9kgatxs2+TZ7JpbUu9P2dBMcPAPcBVwkaepuCO4lTQH8Gli3G+6hdpRESKdt6KpnHrrTJmB/4HpJo0DX2tgxtK2NI3frOeo2ezoJSWolqSStK2ki2193iT0bSurdSXtaNnXy87sJGob+7wZ0lSPSoMF/C0nTARsBJ9l+v5OLZJvTMTtx8veQNGkH7RnB9utkwsFCwM6SZu6UPTAoMLOk2SVNJ2niTtrTsklSXzLRoWPXq4c9ywPXAPtKuqHTNnWL89EKmm3vSEZuXtElwf2bwDNkWsgvumEdKl9vBOwiacxO2tPmTC8saZJOM6x62LSWpJk7fM1GALC9cjl0eqty3y3PHnR+HSjnYyUyN/5CSet0A7tB0oKSToXY2AX2dHwfg8HnQtIqwKZ0ONZos6cvsDkwVift6bEOzS5ppE7a02DYQhPYN/hBQVKv4hweAMxEWaA7uam2OR0nAp8D0wK7SZqqQ/YMlLQssBdwNTA7sLGkueq2RdIUkg4pXy8O3ErGJJ7cCXt62LYQcBhwlu2XOmlLsWcucl+vS4LFyTvNRin39qKSdpC0hqTRO2TH15IWkHSq7f7AtcDlkqbqZBBk+1PgA7IWbQOs0qnKfZujuDQwL9Df9kedODc9HNd+JCD7PbBtJ5OMbTZtS9ahLzq8dwws9qwFPAcsDdzYqeC+LUk9r6QlJE3eupYdToAsCvwK2I0k03ZtVe47ZE/rXPwNmEDSLJ22RWHmPSTpV52ypR3Fnu2Bi2y/Kqmj47UlzUGS+OfafqWTwXTbOrQjcCYwXvvrnU6kNfhhownsG/wg0L7Q2X4P+A3wd+AnCh22YyjBzgbArrb7AQcBnwLbdzCDvhZwuu0Tgb7AuMCOkmar2Y73gDUknQIsCqxKNvvbgf6S5qzLkOKk/rzt0ALANbb/KGkEBb06uKl+ARxFgsRNgJUKG+WnHbIHSQsDJwNjEjbKrpJm7ZA5LwGfSZrD9m+A24CLJU3bqeBM0ibA1sARwPPkHl+9znuozbHvVZzVrYAlgKkl9erEuWlzXNcA5iLJxcOAUYD1JM1Ut00tKMyqTYFlbf9D0tKSlpI0SYfsWQrYjyRiZwA+AQZIGqnua9eWpD4dWJ3c12soLLBOBvcTkkTMvOReWt325526Zgz2nT8FXi52dSQgK9dlOWB34Arg15L2qNuOIfztXwIvkOd9Zttfddie14G7gR0kTW/7yw4nq1YnfuMytl+XNJWk8aE72B8NfrhoAvsGXY+2isEKwDmSTgImI32JcwArS5q6gyZ+SQKfRQBs3wv8g9Dgt1CN9FNJy5TK71+A2SSNa/sV4lSvAKxVVxVY0oi2PyBOz3TASsCrtj8EziKU85MlzVODLSJO8xuSximH32IwJa9VYZyXBPyVoy0gazkXIoH9UcBCtp+TtAihVNfuwEqaEdgW2Nf2IcCaxMFe+Vt/cOjbsaqkk4HXyv87AtjeHXiY0JdrqQYNwRGcHTi1PPM7EedxE2DNOir37ZVxYELbX5Kk3t3AKsBEVdvwLbaNQZIMi9r+0PZ9wE2AgS0lzVCTHT2v2cvk/OxT7qu9gB2ATiXQ3gHuA96y/b7tvmR/u7WOqrSk3q2AolSetwd+DvwB+DGwJNk3aksSDeGajUj2sG1IUP+CQqveXzW3m0iaFvhLCab7kMTnrpKmqzsgK8nosck9fIHt7cj+tYOkX9dpRztjqJybN4EDgTuArct+0gl7li1B9GjAwYQ9dFgJ7juZrPoCuJC0cO1D2IwHqoPsj46h043xw1iTfRPYN+h6lMV3SVLV6A+MDhxo+++E/r4osJrKKKyq0RaQTaMIVH0NHAJMWzYQSGD9NAkma3GuS4C8BzBy+fyRgUWKg/0VcC9wme33a7BFtr+S9GNgYhLUfwzsAlBsOAe4FKicElda/G8jFPezJK0J3AgsJGlzUt2cn2z6A6u2p2WT0lN/nqSNgTeAzUiy4eeS1gd+R1oFXq3Dph6YF5gS6CtpPEe87iTi5Nd1TwuYm1Q2fgU8SO7pXwLY3hZYs45qUA9ncddy7R4FllX6/T8AjgPGB2Yjyb5K0YNafqak48kzthMJOnaXNHnVdhQb/s2tsf0xsCHwcUnGUoL724C3gXfrsKntHO0g6RDb7wKPEAbIibYXA/4M1JVg7In3yD4xf9uxkwm7YdyK7RkDOJxU5ccF/gXsSlhDuwArAh+SxMcmVdrSjrI2LiGpn9JPfwFJln8KfFQCxyOAK2x/VJddJdE6AfFFFiR72MLAY8D05T0j1GVP2dfeK5//frnfnyTJz/3LvlKLHTBoHdqfnJvHia92DQnyf6mKBT17Pl+SdgD2Iayh24CpyTV7CDhJHWB7SdpAYek8Qto2VwTuB1Yjz/w43/LjDRp8Jzra89Kgwf+AaYAtSSVjetKHDHHIDiJ7y2d1GFKcjlWAPUnW9TEGV4F2kbQq8BNgeUKPm4X04lUGSZMRh+w52w+WY7MCyzLYyd/D9qNV2tFCOUd9ScJjO9tPS1oRuFnS0bZ3tv2epGNbfaZVoeXcl+TQjCR434AE0msSx3ZJYBLSTvFQlfa02TUtuWa3ArOS6lh/QhNeF/gM2NP2jT0qs1XZ0zpPkwDv275A0hvAL4C1JZ1NNCQ+J8msSqHBoxoPJoHPPMATwCvAhpJutf0wcRorR5vz2pdUxS4BRiBB0PqSriL30D+B35VAv3Io/dmrA+uRquaEtr+QtBmpCG0v6VdVPmc9AuhNiXP6vu3Tynp4uqQTbG9r+05JDzj6BJWizaadyX28dXnp3LbX1iWJx3WH+EuqsWcr8sy/BpwKHEvYS+cDY5Nk1qq2X6vYno8l3U7atb4ggfJjkrYELrb9pKQ/kuT0fVXaAv+2Bs1FkpqXAwuU4H4lSWcBxxPm0G626xYYnQz4JbCY7csVvYgdCXNwDuCGGvezyYEPSpL85WLHo0Tz4zngAlIB/pvt+yuyZdLCCGz5Gz8nbUAbAk+UJAOSPidteFWviZMAr5TzMxOwiO2FFZ2PF0tiEUnnkAT+5xXbMySMBJwNrGJ7G6Xl5suyr8wGdCKJ32AYQhPYN+hqSPoJqdL1IiIjbwOrOT1JKxC6+75VV+wUMaMvytcTkMr4poTOvRgJ5B8kNNh5gH1JZn8hQkerGl+RoGd1SSvavtb2iZImLHYMbG2ydUDRFtgF2MT2n8rm9Z4i7HWvpBNtb1O1EwSDkgyzkWu2M2FSfE0SM/1tr66wPfrYfrnKILrNKZuOBIeXl+s0G8nc7wocbXuDnn9DFfb0/IxShT4U+Eep5q1JGAT9yL39Pnne/jW0P79HcDg6sAwwM2GftJ63x0jwuh8J8GvtRywJtF2Aj1wEFyXdQp7zEwnNfFvbb1Rowwg9npuvif5BXxKArVCOj0uu37hVP2dt1207YB3ybN0haWLbB5Zg/3JJR9nepY6gvoVyL81JAouxShVzZUl7E1bFSsBGtitNvrbZswNpk9if7BOLEqXurck+Mj5JwlYa1CvU+q9tXyrpTfKMW9KlJBF9m6LZsA6wdR3np6xBi5KJN7+yfXWx9RpJv7e9sdLi0rtUqmuF7QsVjYafAVfbfqpcT0jyarkqkw1t+8fyhD11h6Q3bO8p6UTgXEkvk0T1iiTJWMmzX3yLPSTt5bAm/gn8EfgtKb78vLxvC+A84Igq/TRJ4wE3FN/iZMLIeVLSucQHatmzMWER9K957xgT+Nj2GZK+IPow/WzfWpKLOwKb2n6+LpsaDJtoqPgNuh07AEcCp5Gg/u0S1C9NNpC7awjq+wCnSJq3HBqB0Nzfsv0mcCehUM1h+6XijPyIiP2sZfuFCmxqtQMsVCpikxMa8BmEOr0sgO03bD9eZ1Bf8DERgfqkfN+q8H5CaHrn1mWIMp1ge7KpPlmCnJvJPbWPpPVtf2b7Zag2UCxO2VLl8zcjvZAj2v4rmWDwJnGWxlbN6uoKTfJAYCvbq5Eq0KXAAOAY4K/ArS3HtXUPDqXPbg/qZyHBzYkk0bEU0R2YD5jF9k3A4lUHPi27ehx6vdgypaTtAWzfbvsgEqytaPvxCu0Zj9ILLmkrpf3GpK1kbds/L9WfLUgCYqBrauMoz9lyJKCYi7T+rKUwdF4lVNNjarCj5zX7jPTXXlg+f0JC6d7J9gPA5lVesx62TURak/qSBPDnZP84jaxPx9jeqo712pk0MbPSi/0gcAqln972XSSxNgY5T3dXbU8bJiTsivbWiM2AMSWN7swfr7ydrAVJP5N0hKRZFJr9k4Q92MLAkvT/hFC9q7ChNe7TZX1cirRGHAmMJ+l429uQpOzdJFk1GUlmvVWBPeOW5OVewOyF4fERuWYLkXXwS0lrE52W8SoO6mX77fJZ/SSt57QCjUvo7ls404LWJXvKGFUH9e3rUClCHQVMAWD7PDIB5wRlOs8AwtCpZR3qJqQ1fdj5rxvQBPYNugpDcMoOJoH0qMRx7iPpYqKKv0tx8iuF7bcIbXtnSXOWgOIuYE9J45fg/hHSp93qr/srsLHtxyqyyZKWIYH8OMADhDlwLaHjrd8K7utAW6JhFEmjlGrK68AsksYom+pPCI2yl+0/1mUbcQKfBnqraCA4NOnbgBOouE2iHcUp2w5YwfYSpG/0wlKFfZIE0ofbfq84sHXiQ+Ap4rhiezMSAO1h+2ZCxZ1T0kYaykJabUH9HiTAuIpUM2cgweJtJCA6RhHK+2JoffY3oUeyYU1F4X0x2wNIRXopSdu0/Q1vu3r9iq/I6MpbyH30pu3LCG15oKRZJW1NHNzTHTG9StAz8VQSmBsSbYY1bS9OArIdJW1n+zXb/6zKnmJT+zVbtVQ2pyM0+yNJ5flwIt45QQkUK+vR7rmf2X6dtI7NSvazlYErSUXxYEmj1pHQa7NrGpIU3pb0+Z4C/Kzc1/cDv7Z9Sx22SOpTkpyXkFapDSQtpwgIzkySRb2hdtXwv5PrszVJwFxZ7N28vP51SbiNS/yCoQqlNapv2VvHIUm86UhC6G8kGTuKpPOAP9m+mLBxjwXWGdoV4JKcukfS8uXZmQJYg7Qebkv2jGOUtok9gfVbSfOq0HY/jEZ6+w9UWv/2J+0uB5bzsyc5J5WOt+2xDq1OtAbGpWielHv+CsIqOAH4vK4EbINhHw0Vv0FXoY2K96XtP5BesUmB7W0fBixeKE2jlYC6UhRH4yvSr3YycLakDYGLiVN2laQzSOa6lRXuZftzIkRUhU0ifZhbkIz8OCSR8HBhM1xCGAWvVPH5Q0K5bisTSunrkn5HnMR9gUUl/YvQK3d2VPErQ2tTlbQgWePetX2UpIHATyV9aftqZ5Tcla6hHaDY1aKXz0N66Z6yvYKka4BrJfW1/UQdthR7Rij368hkssNnpFq2KKEqQlgE4wLYPl/Sl8BdVSQdStJjRUJzHY84i6sCT9s+Selh71U1Q6eFNsdse9K//lvgSkmb2L5M0tdktNTntk+vyab3JV1OZsJfavslhS59KGkz2Z9cy/Wqvpda94AiZDYBcJXtt8v61Fp7JiCj066v0pY2mwaJG5LA+TYSbFxYqmSt1zYkAccn3/S7/r/o4dxvTtbk3rYPk/QZ8LKjhTAnYfAc5Zp0YoCpgOdtX1fu46WIFkv/YudmwLWl6lkpylq9EmkFeFfSxbavKUny35Ok9SuEmv961fa0oIwZ7U2u08YlqD6QiK9NAqwg6ZxSrX9b0sYVna9JiR80hu13JK1Hnqk1bV8EvCjpUOKDzEIS+88CS5SixFBF8TEOBw6S9Jnti5Ue+u0II2ZZ0mbWB9h/aCcWvglKH30/IrL6FEmg7UXW7rlJYvjXVScX4d/WofkIA2YN4NK25MLvSXvQA8AJxV9s0GCooAnsG3QjpiUU5asIRXA7UtUcYPuvtj+SVLnDAeAouy9FNokDyPimA0iP75GErtyb0DlvKz9TaaW1bBrvSnqABMuLERrX68pc7XuBY+usaiijq3YkfX99gMsInbI1U3syYDPbd7c7vFWgOIp9CdvjQmAJpT/zWKUHeIUS1F5RdVDf/rfa/kTSaaRtYxlJ79q+1RGFuolUpP5cpT3FpklIpffLkozZgvSuX0gUhE9VhJA+JNdv19bPlmpaZaaRysZ4tt8sAez5pGf8TNdAvf8PgyLitQqwNDlPfwXOL5Xec0qy6O8V29AeII4I3E4o04dIOogEhO9IOqxc0549+FXasykRE7sf2FdpkXqWKJdfTdbylW0/V5U9Q7Dpx8DCthcp5+dHZMrEqLZPI+yvdVwx3b1HYugXxKG/TtK7hDEwuaTLSIvJ8q5Ql6EdksYiQca1tve3fUMJorcjPuGRJEn8Tk32LEESwH1JO9kBkiaxfYqkT0mCeM8S7Peqen8tNq1E9vlzCVPvGtvHA9uVYG1+Bidm5GCo+iSKls+KpLL7PvGB7rZ9gkJ9P6N89MW2n1dYMZ8BlGTDUA3qe+xl55SE0LGSdrJ9VUnobQtMYvvMofnZ/yX6AIfavkXSrURz6Ejg4FZSr2q0r70lYXcLEcdsYfNi046kZWF1N5X6BkMZTWDfoONoq7DOR3qxbyNquEuTzfVREjzPRhzrSql4Sq/oTLZvLIeWBm6yfT1wvaT+JGO+ue2TqrKjh02t6upEwIfFiRgNWJ/0G/+jbCS7A886o8mqtKcPOUf3liCwP3Cf7UvL6x8TWvlWts9u/9mqEw5Kr/geJCBclmz420kajdDedqIG+n3bfb08YXe8Q6o9xxFa5+oKI+RG28tUbU8b+gNjS9ql2HEZuZeOJX3ZaxIne3rCsLil6mRMwTOEXrqGpMttvyHpTmoYG9fCEP7Ox0jF5+ckefYThaZ8lqQPbV9Zlz1K3/z8JIi+ksz0PhXYpgT8y5cgqbKkZw97+hDa7ZIOc+AgMilgVaIvMitRov5HVfYMwabFiV+zk6R1yCiyFUnlbjtJn9g+uEZ7Rib71nLkWfsjGV/5uaTFyDjJz6pOWrWtRbL9oTKW7GiFbXKY7QEKZXgGYLKqr1kPTEoSiPMSWveZwGYlEXNcWadOlfS67TuqNkYRyNya3DdLEMbS8pJ+ZPsgZxLHw+W9Va6Ls5P7dxRS4T0LWLdcs9MUEbjLSrLjQlfI9uhxT09I9CDOUxhcx0ja0faV5X5fVdKVZCpGpSK0PQ6PShILl5d7/X6SdN1W0rVkgkCVfmMfksh8QGF1Xk0YDGsqE0FetP2ZpJ0IK2a0upJnXQ3B0FPraQBNYN+gw2hzOJYjmcwTSaV1OYfuei3Z9Keh+lEpLcxFRE22tn0NSSbMq8zyftv2bpKeJHNZN3eF1HJJU5MM+H1Kz9hRwN2SnrF9gKQZgf3KBjsn8Evb91RlT7FpQhKo3qyoyT9N+unnlTQF8FLJ6I9IWhfmIXT4yinv5TNfJ47ZlKQKtQoREzoQGNH20VXbAf+mMn8goUlvS5IdGxGtgd3Ipv8QOT+19NTbXrdUCi8Ezi5O4phkrvhvgUNsH9nzb6nKnraK1xeS7ibicOdKuoco4deS9OjhvM5NxhL9xWHCjMrg/tm3CTPlqaptarNnK5Jg2I+sAT8hTuNmpPozKWkFqrJffFC1VGG+bEMUt68gkxL2TtGOu4ClbN9elS3taDtHPyU03JVtf1oC1VNsvyvpJeA6kjiqDD3uoZ+TdqyxSAJmZEKf/lxRUn/S9q1V2tNCWYsWJWv0X23fXK7hCcr0iytJr/2uVQb1par7C1LJnIdoVuwrqTdhBq3tVJ+XAhaSdFXxAyD9yJVCmVM/B1mrJyXr9sokwN+nPAMHtN5fxbpYkvdLOq1PY5L1bysS3H8BbC7pa0ddfS0inlkZetzTuxOa/ejKaMbLiQZSf0m/dmj511W8DrXbs2L5/D/b/o2kixWm5/qkIPMcKcBUqn2itNl9TfRERiXJvBts719eu1zSak4bwNfOVJDaJoM0GL7QiOc16AgUOmDL4ZiQVFj7EpG654DnlRFpn5MK53K2ry+OQVU2TSdpBttXEWf5YEWA7hZSyVhe0oxKL/BDhAJbab84ceCvLU7qciRQPQWYW9K+ttcj5+dSsoFdU/E5mokICD1IKoOHEErZFuTa/ZLQ7rF9BrCg7beqDOpbf68yU/hAYASnv3haIiD2AnGwr6ZeobxRyLlZj+ggTEyo5ecRMaijgANLsqgOemnrPMn26kQkb83ynH1EArTzSKJoAg0WghzqNpSvR4BBa8BI5eurCaviXEI/XcZ2pVT3FtqcxR1IdWwv4C5J8xOHejpJZ5bjh7q+8WhTADOR9XEm0iLxL7JmTmy7pe/xaJV2tAX1i5D7dx3C8hhfYRNge2/CZqq8DUhtInNlfTyIJKpaDvO7hK58KElCnOaKaa9t99DCZM76XwgDbS3ggJJwWJesl89WaUs7yjXUOBM8AAAgAElEQVQ7jaxDFyo6EX8mInWLkHP3u2JvZSjnZ3Tyt59Iki2Qe3p0YOuSVBuFjCP7Z1mvLrNd6fkqQeKxJOHyHGW9tv0iuZeuJMJ1VWNa4M+SfkS0Tq4i7JctSdLsVGAlZVTa7bbvqHLPb7unVwF+XvaO0Uiy43PbFxDxzn1UsRhlD3t2IWvgoiRBtTqZFPARWb93Jft/pVpMxSfaoFTfbyCtkee1Ptf2nmQNuFPS5FUmyRs0AFBzjzWoG5LGJQvw2bbfKg7+QYQ2tTmZff53SasBj1W9obfZtR0Jkmex/beSDd+HOGHvk41iXMIe+KUrnFfbw66NSMB6m+1NSlV6OtKX+Jbt7WuyYyTCpnieOO8rEpbAmISC+zARGBwR+I1rEKlps20JIog1P6GWH0cc1oNJdXUHsvk+UJM9U5fK0xikFeBCojHwlKKNMAEZj1h1YqhlT4sZ05cEiHvY/kDSADJyay1HT2J0YOyqAyCFCjwdoXTuXY6N5ApV3P9Lu2YlibMVnb71Pck9tSWxd2bgAduVVevbK1Jtx0YnrRFH2v65pNlIIuYy4CBXKwLX3jc6DWlPONP29pLGJ5WxnwJ/s31cVXZ8i30TkIB1ADDA9s5tr61KArTbq0zE6N/ZDKuTBOzpts8tx/Yg+8idZM3c1BlxWTkU/ZO9CUX56lK5Pw443mHrjERowR8M6d6rwJ5RyLWaDljA9r/K8TnJfjsNsJ/DlqsFpTJ+LplIcn85tipwBKmUb090Gf5Qoz39yWz4Q0kr0Mrk2TsNWJzopFSWiJG0ALC0M8oThV4+kOxdywArOSyryWy/LKm3M22mcpRE1S62V5W0Dxml+TBwXSnMIGmsOvbXsmf8iwgaz0JU+A8gQf7ZzoQgJO0MXO2K9UZ+aJh9rnl85c33ddqMoYbpJxz9T7bn66QNTcW+QSfwBVGVl6Q1itM4LdmwlilB/Y9J9XfUuoyy/TvSo36PpJmdsTEHkQz5uM74r82B1aoO6tsz8E6P+g5kvNYSjir434ttE5aNpXKUoOt+Qtd+hlQvjiez19cm1MqtybrSuw6bAEpF9TRynU4h1NedywZ/IEk8tGZWV2lHqyI+PXCKpD0cLYR3yfn6SJlZewehC9cS1MOgqvhShGFxRssBs92XzF++Tun3/6SGoH5lQrG/mQgZXlRs+bIkrWqD/nO02L8I82SiYtPhZL3ayfYDts+qK6hXxrXtWM7XGGRqwRTlrZMT1tAxFQf145GgvdUO8CWhJy8raf5SlbqBONVTSxq7KlvabFpImY/dShDdQtblvwDrlWMA2L7S9olVsyvagvoJyLitSWmbw277CKJdcRjRaqg8qG+7txciKvhLKX3id5HEw68lbWn7y7b1oI5KzxdE++QY4A5Jc5TjH5Vq8LKumHk2BJgkYMeCQYmaK0kC+03Chqs0qO/x935KGAKTkeft5vL9AmRk4y1VsyuKDWuU5CZkbdyGJBn6lqB+NyLiOVKVQf0Q7oVnCe19HVKxX5SMAt2rJCCoen9tPV8OO3BG0hY1P2nR2o0kYtaUtG9JoB/XBPVDhoah/7sBTY99g1pRNsyPJE1Meo2nkPQOqeCPDZwj6UFCXdyvrqpGsW1U278tDIK7Jf3M9kWK+uu5kna1fQXpsa0UJRBbnFTDHgNuIn12p0na1Padkp4i7IZKJwQoKurnE+rdVYS98CNgTEfg7FxCN9+UZKg3rNKeIWBSUpG7T9IfGNwPuQ+ZDvBB+TvqUONfidzLH5DgZ2TbB0n6irAHliJO4mNV2dGOHn/zTwnV/qni/CxNaKcbSLqO9JYOdVX+HsHqYkRc7dCSHLtB0p8lnW97Pdc0yq7Y0hpl2dKNGMH2q5LeI73I7zjjte6lJgG/tvO0E6G638Vg2ns/si49RKiwa7WqnRXiK2BjSXuT5+w626eWquv5kjaw/YCkK4DLaqrYjQMcqrTeTEXup+lJpXdCYFdJE9nep2pDSqJuirJPbEuu0c0MTjI8a/uE8va/1BE4tz1v4wBv2z6r7LHLAL+QdKnte5QJKrXoerRs0mD2x0CiqD4acIGk40gL0DK2H4d6Z9Xb/lgZE7uwpJcdZtVPSFJkG1c8h73t/Pyc3Mu9bB+vTN1YlbQGHkv6yWuZwW77MUkHkJbEd0nifF3CIuirMC83ANZ1hUyrHvvH4kRD5yvbzyhtLVc7bJPHyX1VR7tEezJvazKi9RLSOrkbKXhsT3y2SYF9XNNY3QYNmsC+Qa2w/bXSf7glEctbjGQ2RyQ95FuRCuJOtu+qOhhrQemTOlmZRXtESRDfLWkR25eU7GylvVo97JmPUPDuJAHZ+kTQZyfgYklrOwrBdcwZflXSB8A9tn8KTFw2syclLV4cgAsJFb5SkRr4NydoWuLo/JFk6pe2fQtwm9JGMSlRxr9QNYxJKtXKX5N7+ymyyW+nCCxuJmlSQvWsZVZ923lakVRZBhCnY/Xy9R+B+SSNYXuFKm0oXy9N2iMmBBaU9IDtl4ga9nOSTndYMZVDYblMQRILOxLGyTiSjgKuJUnHRUpCZpHyepX29CprowjjZT4itPaiIqa1DQkw+ikMlZdcw0xv2+8rYwd/D1zqqN+PaPt3ZY28TtJyth+q2pY2m66T9AVwNPCo7eckvUICjqlIUL2NpD6uYI53D/zXSYa6AtXyzC9HKvJ3Ecr2sSUZswgwiqTzSuW+0oRn2+8ei6iSt1o6RrT9le3DJL1ONGw2bgX1HcIVFJE6SfcRhsX2VQf1MOiaLQMcTvb5GxQdoiOJb7QWETbsX4ctMCi5ODdh6e1M9tqtin0rlO/XrXo/62HPqsQnWrD4II8A1ygiwosTJlzVyc5BKIn8bYAVHD2IV8n+ug1wjtOuNKornFjQoEFPNIF9g1qhKKT3Ba6w/biiVrwJCepHbqtuAPVl7Z2e+mcIhXrzEtx/DfxF0jy2Lyr219GDOCfJju9dnNjxyYb2K9tblepiLedFg/ue+wH3Sbrd9hK2T1LEfW4qjv1fJB3piB1WirZg9SDgBZJMuJyM2ZmITDGYHniAUBcvrDqoL2hRXz90aIp/JqMat5I0Srm3X6nBDmDQeZqHOIXH2H5Y0tPltRdK8mhr0jP5fFU2wKBKy062VygB/gaEGnyz7VcIjXvqKmz4BiwPzKGMtlqcJD6mJvfUVcR5nYP0Af/WFY//ars/pySMoFkI8+RMR5X/sWIjVQfRPZIxIwK3A0sSyu2BpBr1rxLcv0FaTWqFM4JxLzJ2cC2nbeqpEmBfZ3uRmuzopiQDMIgVcwR5xjYCVivJhV8p/fQ/I8mrD8vfUDWLaVlgC0l/Al4FznX0PHrZ/tphFHRcX8P2K5IOI0JnEwJXuSZNloJVSHJ8QjKD/bxyTm4svkht885LEnoDoqPzBhHvO4OMZjyyvKfSgFVhu31Rvp6dCCgvqrA7PgT+WZ63xUgS/TjXpMfUhkmIf/HPkqx6oFyr9Qg75vgmqG9QN5rAvkGlUOaajupQpcYkm9dqwEtl4X5P0ukkwFha0kN1VKLa7JueKEvfbXsLSccC50la33b/4thOShTE60o0fESo7v2Ik/qmQjFfVhGoObXYXnmSwel7bmWljwU2lPSI7blLtWVkohw+KTWNb5G0IPAbQiVfutj1KXA3CcjeJpoEkwKbKHTPz4bmuVKboFgLjtjazcBvJW1XHMVHSVVvXklTOQr9tUARXFuHnKOdyuEXgREkLUlEBXexXUlQ32bHiqRN40YYFJSNQdaCUZWRVq9VbUexpRVMHKmIZa5AWuM+d5gnewC3As+7BnFM/SeVe3NCv38L2FLSW7avJSrh45fz9kkNFVYUpfv5ScXuSrIGnAr0KwHissAiLee7bjhCcBsAxymTSh4k2gO1BUDFjq5IMkgS8ekmJAyTSQnba3vCaDrIGUl4e117bLm/f0so3AeRJOzFwKcuDBUHHQ3qW3Da2u6s47Naf3sJTMchSd+NSZV84xIsbkCYDlfXYUvboa9J8Py57YGSniAJz6OKD3IsEV2typ45yVi/wx3GxEDgH0q//7TAL0pyqC9wi+0/VmXLd+BFYGVJM9p+uhybhPhwZ3dqbfzBoVua04cRNOJ5DSqD0qu+OLBYcRJPI3OYzycVoBlKgPQBUVM/qkaHQ8VJ3phUNBYBsL0jCQzvkDSl7cOK41blCLlBomvFQf0nOT/jSWrNXO9F6Irjt36uQud+AmXkUMu2tYCLbB9ve37gWUn3FhsOAOa2/VHPQLdCvEyCjLlIwLoAqbauB+xCKJQTk3FyB9n+dCgH9RMBh0uasu1Yay09jlTpb5O0KxGIupqMcqpcULDtXhrZEVX7Ham4Hi1pzHIeRiOBYj/bA6qyoQ3PEyruXEpfJo6w4Q1EIbwy8bee8OC+yJWAi4oNIwJLKsJifyNK86PXZFKLyr0/8GMy4/s6klwYiQSLp5NE1t62P666wgq0hPI2JEHYjqQCPB4RiBqNMAu27LTjWu7fXYjq+wZEmK7uql1rTOMGlGspaQVqSjK0PW9yhPAuJgHHFsB2tm8ja+aiyjjX2hLn5D75DVlvJgL2ckb+TQX19tF3G0pQ35ewPV4jvfPbAL+2/bTCttoTeK9KO3ok82Yqyc/XCM39CoVNMbDYdwRhe1R97f5B7p2dFT2mvxEGzGZEuO+z4lPuSn1r9ZBwH/AnYCNJfSWtR8ahnuGaGDoNGvREM+6uQaXQ4PFRU5MRW+eV44eThfowMtKuVmERhRr9uaQ+pKIxMnCj09e/JBk1s6ntR2qyZxUyBeAZki0/klShry///pGMK7q1YjtGIuyJHxPhuYdKcuEJ26eV98wA/AH4h+0FWtXrOhgEPWw9mFCCjy2Vje1IcP8c6SW/ui2LPrQ+cwSSZLmYOO2HO33iPd+3PnFmHySOx4mk/6/yfs0SVCxLqgYXkPtpc6KsvqvtD4fEOBhKn93uJC4IvO7Q/qcg1d7bgVNsv1veM6YrnntcPmdWYE7bF5RAaACwhu1PFGXn2Yjg4ePEmV62rgBRaU1oUbnXU1gwU1PE8khrySfF2a7DnilIsLwv0fZoifhNRpKvj6oLqNPtUEa4veDMHO+kHauQtqBLyUjUF2r63BXItXoDuNX2AEnXAGcSsbE9gd1tP1OhDb2ARZ256guSdVJEWf5LYAmHfdYXWJAkXYdbmrIGj9g7olVxVgRfVyUipvNQ8di/Huv1diRR/gAR672csCyWImyrFckc+0rH2Gqw3siMRHD2NaI3NDvpX+9NpnCsB6zvGgWWh4SSeFgZWIkwUg51TeK4wwJmn2seX3XLsDPubroJmnF3DYZRtKoIjrDKzaRnbCSF+o7tPcmC/RtqyLhKmkgRW6M4FtcqI0gWJ5TkL0jW9Qji0G5RY1A/DQlKFyeB4MzAK05f7/IkU96rFdRXyR4ozvqtpOq8WQniLweOkPSz8rYfEZGfXcvPDCz/1p0lfBxYpVTGNyM93M+Wqmz/CoL6CUnyZWLCYhiLCFRN3vO9ts+zfTpRU28lieoI6hchTtDxxNHYhowGOpEE+L8ryYlKNAfanMRdSXXnAKVvdSAJVH8G7KQyFq2moH5EkqhaThmvaVINn6TY0J/QzRciVdZl6qz6OoKPewHLK1TuL8q9Oy0wou1/VBnU91xPiuP+a5J4XdkRzLyQ9LGuJWn0bgrqAWzf1emgvthxFdFG2LPGoH4G0np0A0kCnV2SRccQsbNjSV97ZUF9yxRSYf0DYS59RCqtt5HAcCxlNvrBwP3Dc1Bf0BqxNyYMCrIPJOv3ASRorXTsX9t6vTIJnJck/tpcZOLOziRJfi+ZW19ZUN/mM35dEvWbk/t3ApJofJb4i38hLIa1Ox3UAzhtZCeT1rKNmqC+QafRVOwbDHW0ssCFSvYlGZn0HglwHiQiLOMRVdURh3YA9g02HUGEsI4nG9XRpEp/HGkPuISMA1oWuNZ2LSNTim3Tkg3sRdKLuIHtf5Sqx0NEvfvKYldlI5x6ZO+nJ5T2KYF9iHL50aT/cDnSA3h73VX6Hvb2JtWNlYDTbV9fjldiU6lkHkYcjN+Tub6nESr5Ia3KfY/zOB2ptlY9G771zO0O/J20k/yWwcrqY5De21FtP1mxLSsD29r+uaQziMP4B5IMGo0E/P1sVz42sq36MwGlJ7zYMidwIIOFDscifckD6qqMD8HWvmQ9OpeskwcSVkFlwn097tVVyVrzAjlH4xLWy0yKyvr6wM6uUXW6wbej7LGnk4kFh5Rji5H16adk/5Xtd+tYqyXNS9Tln7b983JsCdIutQpZl37v6CN0bO/oFkjanvhCFzsj9hYiweuGdT1npeJ8H0m2rFsSv6uS++dV4CRXPBO+hz2bkOLGmeWcjEX22VeAw5r1Z9jC7HPN66uHoYr9tBOM1vGKfRPYN6gEkpYndPKTgP0J9W4CEsC+S8S0lrV9b402HUsot8/a3rIcm5vQ3VdzmwBLTU7QSCTp0Ys4Z/MC69j+a6m4HFHsel7pSXRVVam2wHBmwl54v9i2LQnu9yUVhnFIcFgLm+G/gcropCqvWdv5mRr4FRl9eCKDg/sPCaWyUpriEOz6tzF+ktYgStgTAquXoH59Un09uKKEx6BRbeUcLUaSVMsTiuKvSFLv9fL1y65hXn2PoHVqQlNeg0zlWAF4jNznXxIGwxquYarDt0Gdo3LvxGDKfW9gbMKw6E/EvEYD1nJNoxobfDN63Ncimgw/ItXWz8szeBpwdNXXq3z+qmQPXZAwpw4nOjp/tb1eed9YTgtQ69/hPqiHQerzWwGLkuB6TWAH29dV+Jn/ce4VgdOTiY7HmeXYekQNv7/td6q2p+3fs4i+x6y2nyrvGZMUXx4nGgTNTPhhBE1gP/TRqOI3GOpQxrP9kjjP8xIxuA9sP6PM+ZyJZKhreZpV+olt76j09i8sqRXgPyLpPHq0A1QUAI0LjGv72VKdW41QlLcjjhCE/v40Eaza3UUpvGoHv2yoyxLa5n2kLWBN0he9OalU/841zqv+H1BpK0Cbw9GrJFkOItTp7Yg43ebk+u0tafs6gkNF6f8rZ2rBgoTS+QpxfEalMAlKRW8PMiqxkvPTlliYStILwL0l0TIXmb3+rKS/kkTRJ3UE9cWu9t7RTYjz3HKYPyDMhhNIUD9Op4N6CJW7VDgr7RdvT8aQxOJ8DGZ3TERaOLax3U/S/MBLrld0rcE3oKxFCxIh1VttLy/pWuBsIpJpMhrxxJpsGY+wPN4hVOS/Svox8GBJMJwAHCxpQ1KxH65F89rhmkfsDYGhMxrwpO1rJX0GHCkJ22faPr+ViKnDHsIWetH2xpI+Aq6WNJvTmvRRSVqP3QT1DRp8O5oe+wZDFYpS+MhEJOtnpA97LdtvlI3kS9s3tIL64lhWCkfYbYTy9Z6kf/yXwDrKfO01qXhUmyKItR+wfqG17k/UZUch/b13kgr9q4Sat60jgFT5+VEwMaEAbmF7U9LjdyFxHk8HnqamcXb/K6p0EtuC+iWJEv6qpHf0N4S9sDVRe14fOKGmoH48wqCYrwSBF5Fq3TWEkXImqbKeQ1gzezvztofqvSRpIUlrl6+3JWry55B5571JNfxySVuTwPHoummUiqjYJmQ80keOUvEA8rzNTVhDn9h+pU67vg2uoV+8LRkzJUlqzkJ6wykB/GPANOX7WkeQNvh2KDoaZxPR199KWsT2isSfO58E9evZ/nPFdrTWk3NIkmzU8i9lHZyfjNw7mtDv32oC+v+EM+niTtsXVxnUl89qBfXbEh/oR8A9klZwtD52AfYrLC+qDOp72LMdcIKkoyVtY3s7UmB4UNIo5b0fd9M63aBBt6IJ7Bv8v9Ha4EuW/nekgroAUcNfyvbfy2utjWQQKqqMq+f3JbgfsXzmtiSA/hVxgja0/YehbUc7nLFQZxFH5xckM3+l7Q1JouF+Mjv7SKKqenv5ucodIQevEUX+fxWGw5lEC2FX228Ax7kLhGrqRhuT4TgS7OxBep/HJtoDk5PKvWw/WpNZH5Lk2ZqEPr2FI3K0LqFPv297K9IzPkiAqYJ7qeeotjVIUuELkqTam4z6m6fYWGubQsEowCWOMv9o5Ty8TSr3VxPncbjBEJIxV5L7+S1gy0LJhZy38SWNUUdyscG3o22PHZ2w4DZ0etifA9Yowf0aZPTWHLbvaf+5Kuwpa+PkxI9cjSQR71XUzAHGtL0csIpLT30VtjT431AYOGsRtftepG3qeElrFr9jQ6KxUZc9KzJ4L5uDCPdhexPgKdIe1GAYhjTs/N8NaAL7Bv9vlA1+ESKOc3qp7qxN1EtPlbQHEfM5xNX3/PVqywLPrAjT9Sp2ftUW3O9BlHovs13pxqEy47xUUA4o9syujODC9sak0vF4eW9ty4OkeRQBH8rnrtNGdXuCMmPcNaiXdyMU0bVfkF7xV0li6nMyFmhMYHeiOF0LjbskXVpJohFID+T8kkYrzvzvgL0KhfL9krCpJEHk9IH2I+dnRNvPEfXks8m5mcH2bwj7pPKk0DcEDp8DW0ia0fanZa3qByxo+0J3SCivg+iZjPkFSXLcSiYFnCXpdMJI2btUyZoqa4dR7tuVCTtnE8LMgVTLXwA2lLS47TVJS8wprZ+r0J7lydjPvchYtpOIPsRFkjYFHitU6veqtKXBt6Pnuui0061Fihq/sD0baZe4SNJShTH0XI129SZaTKszuDURSVPZXodoODRo0OC/RNNj32BoYQGyIO8MYPt9ZbbwTqSvbmfbd1ZUOQQG9fbvA+wgaWFCIf+Q0IGvtf1ECe5Hsv2l7R2qsKOHTXJ6WZchG9c2RJBqN2AlSV/bfsr2OpJmb6PIVm2TldFD6wNLKtoH25IxgKcQUbi+pOo6XEFtM95t/0vSXsT5OBBYmFTpryaia7+qi8nQxjxZiDxXe5FKx6RkVNttwMvknq+lD9H2LeX8nKWMarsYeKZUFmcGHiuJiErRvq5I+gU5J3eR5N3RwEmSfktGFW5D2A3DHZyWjC/IOXnU9nOSXiY6KBMCd5ORaZ8Mh0mProWiCdMSM5wDOEbS07bvVTRiNmZw//r8ygSPqu05lCQ8twVmUcYg7ifpn+TZ33x4ZHl1EySN6jJWUBEK7mX7T7ZfK0nrx8tbXyRJmUrHfPbw/0QEeZ8lk0Desr1Qed/2wAySdm7WoQYN/jc0gX2D/xdaC7Xt/pK+JnNs77P9t1LFPLz9/RVn7XsDY0o6h/SNLk/6/rYBVlVEYZ5wjTOYSwC9FKmkblo++0lJvyF90utIurgwGWpxgopNS5Is+T6kF3ojQu9ejjhrE5K58JUmY7oJpcr9YQmefwrMSNoTHiH300jO2KjxgT8DJ9r+uC77ynWbj1yfqxwxyv7kGu4maXMyouz3tj+p0a6rlbnDx0mahYxqmwp4uEYbWkH99oQtdA1wHhGDvBH4mAQ/n5H+40pH/nUzviEZ83RhN/3BFY7Xa/C/owTpOxOxzDuAOxRxsSsLffoOSUc7Qpoj2v7K1be9jEJYQ1MRLZ11bH8iaW7bZ6iGSSUNvh2SZgcWLImfTYkg72uS3rW9CmkBXFTSxcDUZCpI1doerXV6A2BGSY8S0dfLgFEU/aEJyFq9kWsSWm3QoC6U9qVziDbT18Apto+VtB+wBZm4BJn+cP33+YwmsG/wvdFW+W1VwI8qlboLJW1Qd7bemf1+GNnE1gTesf1eoZZuAqwt6RLbj3/rLxqKUEbatSrf90pap9h2Jam4HkmCjbqpijMQMbOLJd1IRhXtS3rFL2h/4/DgmJX79jplJOLjRFH6z2TuuYhI3vOSHiB05p1t/61G+1oO8sKEmvh+od9/WmjV+wDTATva/lvdDrUj9Dgig0e1reYaRrVJmg54z/ZbygSAJchEh37lLYuRfe4U22epx3jA4RXfkIyZgrRPNeguvAk8QBhe6xPNiNMVQdYBkiYjUx6oKhBq2+unIXoMr5ExbSMDc5d9dimgnyJ+9laxZ5jfO7oYU5JE/ejAT4Afl+v0gKSLbK8t6UMyLWTfqoP6FiRtCWxAij4nED/oIqLFshVhnmzcsD2GD4gae0+7A18R7ao/SxoL+JOkW8prR9vu///9gCawb/C9oMw730bSTqVS0Mv217YPKg7HpZLmq6Oi2eZ0zEGcjlNJBvoYZfzYI0rv+ubUrOxezs0fSKC4IXGcrye92ZeRkVK1VX3bIOKEXeC0TTwAvAT8QtKHtq/tgE0dQ6k2HU0EHj8E+tm+X9JUwJakYtbqS3zN9mN12NUWoPcB3iyZ3feA9YC7JD1k+wtlBN9Utluq1LU71K5pVBsM6s8cmVTkn5B0cNkotyYTAlaxPbukXYnj+Kmk85sK0GB0KhnT4NvRtp/9lGhVvG37lMKIWwAYKOky2ycpLWbvVm1TsacvcDAR5Hy8sIWWJGy4V4hg5r6toL5BZ9Dmiw1QhAznJMnoPiQJuoCk+yXd4Igb1lLoKGt2b6IPsQpJOjwDnF+SrY9JOpcUF5p1usEwCae1pKV99KGkp0jr4FBDI57X4H9CWZwhFPeRGHwPWYNF4n4DrFhXwFqcjmVI3/PERExob+Bd4ChJvW3/CdjDdqU9ZN9g3yVkI9vO9l7ALSR4HKvqcySpT6GOt75XsenEYsdJksYhfeO9gOfL18MdbF9J7pv5iWIwpGf9HmBaBzfVFdQXm1oO9XmS+kta1fbZJBDbC1hY0sjOrN+/12XXN8E1jGor6OW0+mwDTAv8UlIfR7hzIqIRAbmf7wFubJzF/4TtqwjLYc8mqO8OlGd+JdK+NSsRO9zc9mlE9X5JkmSE4iC27cuVQJlqcyhpY3lc0tgkSX0CYRCtTMQWG/X7DqPFSJK0FamC30oYHYsUGjC2FwRGbn1fFdrvhbJ/vk/W5mtIgmhJR4Noe0W4b2CzTjf4gaOPpIfb/u/3TW8shaO5CSMLYDtJj0k6o/jl3wtNYN/gf2YInNEAAB9xSURBVMUY5d9nyJzj3WDQov11K7gHauvTVHoQDyY9WX8qm8MzwMmE9vI7ZY59rZVxFQDYvtf284qy8XXAkc4YuSo/fwTgt8CBkiYqdrhtsz2aMBxuJOI1e5PrOrukXsOjg+bM8t0Y2FjSOsXJ+AiYVdKEdZ8TSYuRe3tbwkLZVdIOtk8lzI+9GfxMDjfw4MkNb5M1aD5gk7IZ3gpMK2kAaS/Zr+pn7YeMGpMxDf4LFGr91sCKZH0eh4y02972WcQJfAQGPwdVMHR6rHUGbgbmlLQbcBMR8PyCwUnrAW0MowYdREkMbUvEXS8gk0qWAJaWNDVACapfqtCGUVv3gqS5JM1bXnqE3De/K6+tTVqnOjEOtUGDoY23bM/X9v8pQ3qTpDEJW24n2x8QzatpybjH14jv/r3QUPEb/NdQ+uuOlnS27SuUOci7SJrU9iswOFtcx+be5kR8Bjxl++5yvKUE+zpwFBE9q1QlvI0+ORfpVb2+Z+a5BNojkP6aG6t0gsrvHqiMHboc2ErS8bbfbn2m7VfJBIGJyFi7eYFdgFU9HPch275S0lfA2ZLWAN4DDqwrOFSbKj8wExGDm5H0TJ4OrFKu77GSrq6DitstUCYCTGH7orL+9CPTAN5i8Likc8kYt6WAe9yIwTXocrTtH4sQ1fttCT1zZ1INXwLYT9GzOapGe5Yi6997hGW2LWl/2RFYFpi4fa9ogvquwSTAhbb/qQgZDpA0kOgPfSrpJWBghf7HkIT7Xpf0su11iy+5niJ2+iMivthxxlmDDmE4KyEp2luXkzaUKwDa/UtJpwIDvu/vbyr2Df4XvAmcT3qzjyFU4D4k4KicDthC2+eMUv79GJhD0u4Atj9TRu39Bvina1DAbnOCLgH2ICP25irBfOs9A21fYfvG1s9UaU/5ch4ylm0HkpSZoPUeDW6deJ0I7CxF+myHW8XwFhyNgc2JIN1JrWpUlZ+pCKlQEjKLSFoTeI7QKFcgM4dPIkqq8ypzfiuruHQpes5hX5UwTv5AAo++pIL/ju2zmqC+wQ8BZf9YkQTNTzpzxCcmjt+LpK3sMuC+Gu1ZgVDtxykMuAOBJZ0pCp+QZ2+4SSr+wPAiod7P2FZg6EUYTnc4kxOqTMK0hPv6EQHaH9v+GTC9pHNsH0dG7e4ErOBGKK/BcILiR55OipFHtR2fuO1tq/L/mJLVVOwbfCPasvYLkyrCg7YvURQcxwH2JGqrB0lazfZ7ddhVbFoO2KnYcg2hAw5QesYeA7YjFNzP67BJERPciWgLPK0IC+1AVKcfr5ox0GbHINVvRUzwTGANQtk+GviVpANsv9uj0vK6pAML06EBUFgpd9p+p3xfmSOk/1TlP4FQFr8mFY15gD8rQowjAv09HPZEe8hz2F8hwo9TkcBnNSI6VtvIvwYN/j8otMxNiZjq/W0v9ZNkYHtS1XxgiL9g6NvThyTG17X9J2UG+oRE4Gwu4DjgN7bvqsOeBv8z7iMTVDYqe8bYxB9ZuyTyK4G+W7hvfkl/lHSr7aWA4b6I0GC4w8JkKsTjklpTaH5NRl/PRdqeXiCizd8LTcW+wRDRFtQvB5xG+njPk7QjMKbt52xvSWjCT5LqQl22zU1Es64kdMVtiGDWEkT1flIilHdVHSyCEpQtS5IfcwHY3o3Qg3/ZOlaDHX2Aawq1HsJoeA74h+2nSYZ8BeAESZP0/PkmqP9PtIL6Gj7nExKs7kEmKPSzvRGpkt1BZsJvA5wBnOgaBfy6DY4Owl7A8soc9s9tP0XYFe/ZXt/2v779tzRo0FVoTb4YCwYFSFeSys6bwOa2/1CzPU8DS0k6iwT5vyWsrmeBDd0I5XUtnJ7dE0jf+jZk39+8MEGq/Nz/RrjvJ+U9w6VIb4PhG47elmzPYXuu8v/1tjewPXs5vpKjnv+90FTsGwwRJaifhvTUrUCEu0Yh885HUObBv+yMkpuSzIt+qmq7ik2XAsfYPlnStKQHcS3gCtt79vw7KrJjUH+8MyrtNDIpYGlJb9m+zfYeygi1L6uwYQh4l2zkFymCNE+TquU8hTXwTmmh2KzY2qCL4PT2f0Qot0sB95NK9D/JtdwYGN32v6rUZ/ghwN88h70RgWvwg4PtjyVdQqZcvGz7KUk/ARYiVfyXq/z8tkT+NMAbtt+WdDkZr3eB7ZslbUiS5+fZfrPYPdyuQd2OEhicLOmM8v0XdXyuBgv3reD0+H9A/DNJusP286Va36ABABremuwrRhPYNxgESTOQIP5T2ycXmuvOhNZ6GKlIL0sqigMlnUlkL/oAd9Zk5vOkgrmbpItt/6M4IOsR5eC/VV2ta3OClidJhXeAc0h/5NbFjpFs32h75ypt6WHTQEl7k8D9YtJvfBPpOb5X0jvASsBWVWfuG3w/2L5F0sbAkZL+YftCSe8CywBHt+7txqEe4hz2VYfH9oQGwwyuALYCfi/pPmBNYPuqg3oYlMhfnkzguEGZLrG37asBFFG/3YHdPBwLq/4QUVdA34aOCvc1aDC8o6HiNwCg9ENdDowG7C/pIABHNGcS4E3bH5HK4YPALbY/cOaSLlGosFXYpfLvPMqouClJL/sFRKBuAkdc6ALgqDoouG1O0IFETLDFIpgJOJ5UWNeUNF5dVMVi09JEEfxUMu/4ShLg/5702PUFjrP9UB02Nfh+KBTcnUnLxBWkUr+f7bc6algXws0c9gbDCJzJMocR2vtfSH/79XV8tqQFymevTqbMLEqmgkwuaTxgVzI67aY67Gnwg0anhfsaNBiuoeb5alDod1cAJ9j/197dx+tez/kef71r2sWuKE2YxDYoMqVoClEyKSI3UXTzcBoNlWnmOMhtTNGEwyGlJrnJYCYapUmzm4ghN2FX7OggUaZimkql+529P/PH97uOq33Krum6Wddar2eP9bDW7/qt6/dd60rr+vy+n5v6SJKtac0dFtN24hfS0oNvogWxr6mqswd2rkeaFtxTu46gdb9+Ou0NyGLgcFoGwbPHWVOb5H602vmTaGPIXgecCbyc1nPgJ7QxQJeOcU2PA95FCwC/nzZO413A5rQ3iNemjwGc72nc0yLJ7sBhwP5VtcTXTdIoJHkybarEQ4H30HZX30hrfLYvsKKqrvO/QVqVJOvSesWsRptWMti4z0xB3ckWWz6pvvDlcbYPGa1FG6x1flVtPck1mIovgO1p/y6clmQB8CHgKlpQsZTWhGVvYDfaKJ5vwu/SgUcc1D+ItmO5A62+fxfgW1V1R9rIqwXAo4CxpN8DVNWtaV3v16e9+dm/10S+EDgN2GLMQf3atJKAbYEN+xrvSPImWsOjM9LG/93RH/ON2RSoMXbllzR/DNyU34E2zu60tNGsrwDeWlVLk1xKG4O6qKrOB/8bpFWrqt8kOZb2nuTVwA2MoXGfppctOIfLwF5U1Sd6l/RPAOsC36iq1/ed6U/SdnzfTEvxHpskm1TVxUl+AvwNLXB9YVVdnWRn4IfVus+PXH8TtAst3f7aqvp02hiinwI3JXkqrfb/H6vqxlGvZ+CN2WpVdVOS42kdlfdIcl1VLamq3yZ5PfDHE6iz0xDUmLryS5o/+t+O59HKyd4wc5i2u/rMXkH2dFo/FkeS6V6ZVOM+SdbYz3tJVgOoqiNp6e33o80+p6pupaWbPzDJmuOqF+/r2o7W3f3xtHEpOwBvqqpLkzyNVsv+kN/3HENezybA8bQbH3smOa73HLgdOJJWqnBOjWEMWQ/mK8luwMeSLAYeT6un/yltdu220Hbuq426kyRpJstrf+Cg3rBz9d4U7whaT50DgA8a1Ou+qKplBvXSeLljP89V1YoeKK6oqmOTPAB4d5LX0urt3gEcUlW3j3IdabPXb66qG5M8mraL8MaquijJQlrTvFcmeQltFNjrquqCEa9pZlf8UcCifs1TkzwUODHJ31bVq5I8GFh/VA0EB9azELi978RvQ3sT9lfAlrSMhg/SmvkdAOyf5EfV5tlKkjSjaNNs1ulfz9y0/3VV7ZtkQVUts6ZekqaLO/aaCe7X6J8fCXyT33VXf0NVnTnK3fokjwXOAbbq19kc2AjYu7+x+C7wvr6ebwEvrzZCZZRrmgnqd6J1lz8KeFGSh/U0s/1onV9PqKqrxhDUP4iWGbBxP/Q44LtVdU5VHU3LJji6P/Yp4D0G9ZKklVXVzcDJwHZJHtdvFm8HnNz/xi3r5xnUSxqpzKGP2cAd+3lmIGDdktZ47tqq+llvtjazc//uJDfSGuX9G4zuD3ySzWjN+f53VZ3TD38+yc3A7rSxX++vqkuASwa/d5RvOvrvaDvgpbR5wg+hNRbaNckZVfXLJC8FHjaqNazkemAz2s78fsDPgD9L8nDgyp5JsAuwUb8RIknS3TkVOBD4cJJvAnsAf11VV0x2WZKk/y537OeZHrC+EDgB2Av4Pz0gnNm5X71/fmxV/duId8XXAE4Hbqqqjyb5gySf76N3vk4bb7eod3cfi5mft6e970a7uXBZVX0V+AywHa1B3UZV9asa8Uz4JOsnWVRVy4EXAZslOQBYQrtB+BfALr3vwE60mn9Jku5WVV1JGx37duD7wD5VtXiyq5Ik3RcG9vNMrwc/ENgR+L+0nejzBgL65YPnj3hX/A7azYVtk/wlLYX851X17d64719pKfqLkiwa1TpmDGQz7EJrhvc+WgB9Ul/vYuAUWnf+1cewnvWAs4Fjk+zcm/UdATyBVh/5OlrWze60nfy/rqqlo16XJGn6VdXNVfXVqvpsVX1n0uuRJN03puLPAwMB6xrArcDltPmiLwD2raprk2yf5OKq+o9xrq2qliTZFfgS8KOq2quveUFV3ZbkDFq3+ZHOqe9rqSRbAX8OHF5V1yR5GS2wPrmq9qyq05OcW1VXj2E91yU5lZY58O4kDwSupZVQPKvamMJ3VtWtSTbo67XZkSRJkma3OMd+2Nyxnwd6wPo04FXAjbTxcQcAr6mqS5I8k1bn/oAJre884BnA45Ls348t6yN4bhtHUA/tZgItnX23gbVdT7sJskaS0/rha0e8jvV7J36A9wIfozXHewKwKfBg4LAkT+qZDf9vTQb1kiRJ0vzjjv0cleRhtNq5g3p6/TbAHT3IP5NWn31oknOAV9JG2k1s3nlVLU3yLGBxkoVVdfTKZQGjMJDNsBC4BfgwsD7td/P6qvpxVd2Q5M9pI+/o835HtZ4FwPuB25J8r6o+3BsZ/iFt9OCmfX3PBY5I8uKqusWAXpIkSZq/DOznqKq6IsnmtLr1vYG1gev6Y2cnuRB4Di2t+8Cq+tqk07ir6rwkuwFn993xK0YZRPdrVpIX0DoCLwP+kbZD/hLgnUkOq6qL+s7990e5lr6eZUneQbsRs0+SpwOvBT7RHz8auDDJCuDrVXXLqNckSZIkaXaLG31zT09hX94//yLwU1qjvDWA82mp+MuBG6vqFxNb6N1Isu64ZrD3DvzH0nbAPwncAOxL2xV/FfAkWoO/seyKD2QQBFiL1qzvUuAntNF7bx4YC8ikb8ZIkiRJ99YWWz2pFn/l3EkvY2g2Xn/N86tq60muwR37OaYHest7OvvNVbVzb8B2EG1c2xNou/drAYcBsy6wp/UBGEnQmmQdYI2q+nU/tCnwQWALYB3ggKq6PcmtwDHAelV18zDX8PsM/Lzp9fO7JjkYeBjwFGDPJN+uqmUrnS9JkiRpnjKwn0NWGte2a5KfAp+qqt2TfBpYs6r27ueuV1XXTXTBd2MmWB1BUL8prRndd5KcUlU/Bn5JS3V/ELBnVV3WO+HvCBw8cANg5JI8AlhWVb+qqhVJVquqFVX1oSR/SCuluGgmqJckSZIksCv+nNKD+p1pzdc+DxwMfDDJ5lW1L7Bh372Hlo4/byTZDPg08M/AcT2oB1hKa5r3L8ADkzwVeCvwhaq6Y4zruz9wJK0r/8xNmhU9JZ+qurqq3lNVZ8wckyRJkiRwx37OSLIaLZV8D1ot9kOA22kN4d7Sm8DtkGRrgHF0nJ8tevr9McDxVfWxgeMvB66ilSkcAryN1ofgLTMB9Jjq6jeuqsuTHA18MsnPq+qbcNdZC6bfS5IkaZoF59gPm4H9lJsJPnv3+BuS/C9gPVpDuG2BAn4OHJTkbX1m/HxzK3AF8LmZA0n2A94A3B84sqoO6Tvh61XVr8cR1CdZHXgE8L0kp9PKBA4G9kpycVVdPcrrS5IkSZobTMWfYgM19dsneXOS5wAbAHcAD6B1dv8jWif8E6vqxgkudyJ6sL428ERgu4FjC/vXTwH2S/LwfoPk1zCeXfGqWl5VPweOot1g+AjwDGBdYJNRX1+SJEnS3GBgP8V6UP9c4EPAr2mp5HtX1X/QauxPAxbTUtCXTm6lk9OD9etpv6OXJHliD9qP780DH0773d06znUleUKS05JsAXyVVv//P2k3ZXYEThjneiRJkiRNL1Pxp1iStYBnArsCj6KNsPtEf/go2gz0VNUPJrLA2eVUWhD/qiQnA+ckeRpt1N2h40h7H0zvr6qlSb4FvIyWUbAJLavisD7B4CGjXo8kSZI0KZbYD5eB/RSrqtuS3AJ8lJa+/cKq+mWSXYHbquork13h7FFVV/fmdHvSdu+XAo8E3llVZ476+gNlE88Ftqdly7wT2BDYBtgNOD7J8qr6HHDJuJr3SZIkSZpuBvZTZCA4fBRtl/dC4GvAU4GPV9W/J3kKbbf+lRNc6qxUVVcBx/Qd++XAmlV15TgC6P66PQs4DNgPOJkW1O9XVZckuQp4NXDp4PeMck2SJEmS5gYD+ynSg8Nn07qn3wacC/wIOAN4bpLdaCn5r62qr01upbNbD/AHvx5JAJ1kA+CPqurCfujptOB9EXA98Pb+mq5RVV9OsqSqfjOKtUiSJEmauwzsp0iSzWnj0F4IXAnsT9v1PYvWfO2RwPVVdbFp3JOVZAHt9dk4yYlVdT5wO/A64EG0nfpfJHkp8Igk7wXm3dQCSZIkzU/OsR8uu+JPiSQPoNWHbw2sU1W3AScBjwWeWVVXV9V3q+piMI170qpqGa3b/dXAS5M8mjap4M+AU6rqp0meDPwNcH7v3u9rJkmSJOleM7Cfxfq8dQCq6gbgROB0Wmf3TfvM9X8BNkqyYPB8TUaSjZPsDFBV3wH+njbC7gDgBuAFwIFJPgV8AHhDVX15UuuVJEmSNP1MxZ+lBhrlPZu2S38LcDxwJHAg8OkeHO4NvKPvEGuCevr9N2jp9x8B/pM2Zu8Mfldf/wHgGbT/761TVZdaNiFJkiTpvnDHfpbqQf3zgL8Fvgc8D/gMcA0tOPwBLVh8T1UtTuJrOWH95srzgcuBBwNLaDdj9gZ2AR5Da3y4YVVdU1WX9u8zqJckSdK8kjn0z2xgMDhL9Zr6FwEvA9ai7fDeAPwzcDPwDtq4u+ckeWRVrZjUWvU7VbWUFtzvAKSqtqWNH7wEWA/4H8CCya1QkiRJ0lxjKv4sMpB+v2FV/WeSQ2nz6t9Cq81eBpwHLK6q7ZOcTUvrvmVii9b/p6qWJtkF+GKSN1fV3yU5qKpW9Jswl67ySSRJkiTpHjKwnyVWqql/TZL9q+rKJJvQgvlfAk+hNdBbDFBV5ya5oKpun9zKdVeq6rtJdgIWJ1mzqo7qD10Gv3u9J7ZASZIkSXOGgf0s0YP6HYBjaDPOr0yyJvArYBFwHG1+/T5V9Z0kq1XVCoP62auqzkuyG3B2klOBy2eCeYN6SZIkzWuzozR9zjCwnwX6mLrVgCfSmqv9MMn+tBFpJwP70oL7j1XVEgBr6qdDvwmzUVX9ZtJrkSRJkjQ32TxvggbmzqeqlgNfB94FnEJrtPZ2Wkf1B1fVkpmgXlPnRrjT6y1JkiRJQ+OO/YSsVFO/V5LLgC8AjwdW9OZ5GwMrgFsnuFTdR6bfS5IkSRold+wnpAf1z6LNqT8ReDTwNuCaHtTvAXwJOKKqfjbBpUqSJEnSUGUOfcwG7thP1iNpc80fSgvs96iq3yZZH7gYOLiqzraDuiRJkiTp7hjYj0mvr85KTe/WBf4JuBZ4flVd1VPztwDeN3OuQb0kSZIk6e6Yij8GSdaqZkWSrZPs2kfZHQ/8CLikB/U7AkcBS+16L0mSJEm6J9yxH7EkDwSOS/IuYA3g08A1wIuAk4BDgSOSnAWsA7yuqs6a1HolSZIkaZSS9qHhMbAfoSQLaL/j84DDaYH9c6vqZ0kOB3YFPl9VuydZG7h/b5xnTb0kSZIk6R4xFX9EkmwKHANsCZwJnAVsC2zaT3k/bb75fkl2qqqbgKvBmnpJkiRJ0j3njv0IJNkM+CzwMeDCvgt/BfAg4C+TXFdV5yb5AHAIcCUY0EuSJEmS7j0D+yFLsi5tp/4DVfXxmeNVdWOSD9N26d+Y5P1VdU6Sw6pq+aTWK0mSJEnjllkzAX5uMLAfvltpO/CnACRZHVjRu+Jfm+Rf+3lvSnIh8JsJrVOSJEmSNAdYYz98C4GtgKcB9N34dOsBi2iz6w+qqusdaydJkiRJui8M7Iesqq6npeK/OMmW/fBMl/sdgH2Am6rqF5NaoyRJkiRp7jCwH43PA78CDkzyTGBFku2AI4HP9g74kiRJkjQ/ZQ59zALW2I9AVV2d5GhgT+BY4ALgUcBbqupM59RLkiRJkobFwH5Equoq4JgkJwMrgDWr6gqDekmSJEnSMBnYj1gP8Ae/NqiXJEmSJA2Ngb0kSZIkaaxmSWn6nGHzPEmSJEmSppiBvSRJkiRJU8zAXpIkSZKkKWaNvSRJkiRprGKR/VC5Yy9J0iyR5KZ7cM7Tk1yU5PtJ7jeOdd1bST6R5CWrOOerSba+F8/5jCRn3PfVSZI09xjYS5I0XfYB3ldVW1bVras6OY1/7yVJmsP8Qy9J0izTd6e/muRzSX6c5B96gP4XwJ7A25P8Qz/3kCRLklyY5PB+bFGSHyU5DrgA2DjJzknOTXJBkn9KsnY/97Ikh/fjP0jy2H587SQn9mMXJnlxP36Xz/N7fpa39/X9MMkJyZ2SL/dN8q3+2Db9/IVJPt6/53tJXjDkX68kSXOOgb0kSbPTVsBrgM2APwa2q6qPAqcDh1TVPkl2Bh4DbANsCTwpyfb9+zcFPllVWwE3A4cCO1XVE4HzgNcOXOuafvzvgNf3Y28DbqiqzatqC+ArSTZYxfPclQ9V1Z9W1Z8A9wOeN/DYwqp6KvBq4OP92FuBr1TVnwI7Au9NsvAe/cYkSVMic+qf2cDmeZIkzU7fraorAJJ8H1gEfGOlc3buH9/rX69NC/T/HfhFVX27H38y7QbBN/uG+QLg3IHnObX/7/nA7v3znYCXzZxQVdcled4qnueu7JjkDcD9gfWBi4Av9MdO6s99TpJ1kzyw/zzPTzJzg2Et4OGruIYkSfOagb0kSbPT7QOfL+eu/2YHeFdVffhOB5NFtF36wfO+VFV7reJag9cJUHdxvd/3PHc+OVkLOA7YuqouT3IYLVCfsfLzV7/Gi6vqJys914PvyTUlSZqPTMWXJGl6nQW8YqBefqMkG97Fed8Gtkvy6H7e/ZNssorn/iJw8MwXSdb7bzzPTBB/TV/jyp3yX9qf52m0tP8b+s/0VzO1+Em2WsU6JUma99yxlyRpSlXVF5M8Dji3x8E3AfvSdt4Hz7s6yX7ASUnW7IcPBS7+PU9/BHBskh/25zu8qk69N89TVdcn+QjwA+AyYMlKp1yX5FvAusAr+rF3AkcBF/bg/jLuXJcvSZpywTn2w5aqlbPgJEmSJEkaja2euHV95RvfmfQyhmb9hX9wflVtPck1mIovSZIkSdIUM7CXJEmSJGmKGdhLkiRJkjTFDOwlSZIkSZpiBvaSJEmSJE0xA3tJkiRJkqaYc+wlSZIkSWPlHPvhcsdekiRJkqQpZmAvSZIkSdIUM7CXJEmSJGmKWWMvSZIkSRqrYJH9MLljL0mSJEnSFDOwlyRJkiRpihnYS5IkSZI0xayxlyRJkiSNT5xjP2zu2EuSJEmSNMUM7CVJkiRJmmIG9pIkSZIkTTFr7CVJkiRJY5P+oeFxx16SJEmSpClmYC9JkiRJ0hQzsJckSZIkaYpZYy9JkiRJGi+L7IfKHXtJkiRJkqaYgb0kSZIkSVPMwF6SJEmSpClmjb0kSZIkaaxikf1QuWMvSZIkSdIUM7CXJEmSJGmKGdhLkiRJkjTFrLGXJEmSJI1VLLEfKnfsJUmSJEmaYgb2kiRJkiRNMQN7SZIkSZKmmDX2kiRJkqSxssR+uNyxlyRJkiRpihnYS5IkSZI0xQzsJUmSJEmaYtbYS5IkSZLGyyL7oXLHXpIkSZKkKWZgL0mSJEnSFDOwlyRJkiRpilljL0mSJEkaq1hkP1Tu2EuSJEmSNMUM7CVJkiRJmmIG9pIkSZIkTTFr7CVJkiRJYxMgltgPlTv2kiRJkiSNSJJnJ/lJkkuSvGkU1zCwlyRJkiRpBJKsDhwLPAfYDNgryWbDvo6BvSRJkiRJo7ENcElV/byqlgGfAV4w7ItYYy9JkiRJGpsLLjj/rPutkQ0mvY4hWivJeQNfn1BVJ/TPNwIuH3jsCmDbYS/AwF6SJEmSNDZV9exJr2GM7qpNYA37IqbiS5IkSZI0GlcAGw98/TDgl8O+iIG9JEmSJEmjsQR4TJJHJlkAvAw4fdgXMRVfkiRJkqQRqKrfJjkYOAtYHfh4VV007Oukaujp/ZIkSZIkaUxMxZckSZIkaYoZ2EuSJEmSNMUM7CVJkiRJmmIG9pIkSZIkTTEDe0mSJEmSppiBvSRJkiRJU8zAXpIkSZKkKfZfXWNC8UlopNEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test , inferenced, np.array(classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5b8067b050>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "inferenced = model.predict(x_train) \n",
    "plot_confusion_matrix(y_train, inferenced, np.array(classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}