{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "datebase" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-12-06 02:29:55.975880: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", "2024-12-06 02:29:55.987842: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", "2024-12-06 02:29:56.103266: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", "2024-12-06 02:29:56.210334: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1733423396.294028 13437 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1733423396.318467 13437 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "2024-12-06 02:29:56.510180: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] } ], "source": [ "import numpy as np\n", "import seaborn as sns\n", "import scipy.io\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import confusion_matrix\n", "from tensorflow.keras import Sequential\n", "from tensorflow.keras.utils import to_categorical\n", "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout,BatchNormalization\n", "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout,BatchNormalization\n", "from tensorflow.keras.callbacks import TensorBoard\n", "\n", "log_dir = \"board/model\"\n", "tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=0) # 设置TensorBoard回调\n", "train_data = scipy.io.loadmat('data/train/train_32x32.mat')\n", "test_data=scipy.io.loadmat('data/test/test_32x32.mat')\n", "train_images = train_data['X'].transpose(3, 0, 1, 2).astype('float32') / 255.0 # 归一化图像数据\n", "train_labels = train_data['y'].flatten() - 1 # 假设标签是从1开始的，转换为从0开始\n", "test_images = test_data['X'].transpose(3, 0, 1, 2).astype('float32') / 255.0\n", "test_labels = test_data['y'].flatten() - 1\n", "\n", "# 将标签转换为分类格式\n", "num_classes = 10 # SVHN 数据集有10个类（0-9的数字）\n", "train_labels = to_categorical(train_labels, num_classes=num_classes)\n", "test_labels = to_categorical(test_labels, num_classes=num_classes)\n", " \n", "# 划分训练集和验证集（如果需要的话，可以从训练集中划分出一部分作为验证集）\n", "X_train, X_val, y_train, y_val = train_test_split(train_images, train_labels, test_size=0.2, random_state=42)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "model" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/robot/miniconda3/envs/env-test/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n", "W0000 00:00:1733423401.042268 13437 gpu_device.cc:2344] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n", "Skipping registering GPU devices...\n" ] }, { "data": { "text/html": [ "
Model: \"sequential\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (Conv2D)                 │ (None, 32, 32, 32)     │           896 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization             │ (None, 32, 32, 32)     │           128 │\n",
       "│ (BatchNormalization)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (Conv2D)               │ (None, 32, 32, 32)     │         9,248 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (MaxPooling2D)    │ (None, 16, 16, 32)     │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (Dropout)               │ (None, 16, 16, 32)     │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (Conv2D)               │ (None, 16, 16, 64)     │        18,496 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_1           │ (None, 16, 16, 64)     │           256 │\n",
       "│ (BatchNormalization)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (Conv2D)               │ (None, 16, 16, 64)     │        36,928 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (MaxPooling2D)  │ (None, 8, 8, 64)       │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (Dropout)             │ (None, 8, 8, 64)       │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_4 (Conv2D)               │ (None, 8, 8, 128)      │        73,856 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ batch_normalization_2           │ (None, 8, 8, 128)      │           512 │\n",
       "│ (BatchNormalization)            │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_5 (Conv2D)               │ (None, 8, 8, 128)      │       147,584 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (MaxPooling2D)  │ (None, 4, 4, 128)      │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (Dropout)             │ (None, 4, 4, 128)      │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (Flatten)               │ (None, 2048)           │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (Dense)                   │ (None, 128)            │       262,272 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_3 (Dropout)             │ (None, 128)            │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (Dense)                 │ (None, 10)             │         1,290 │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "
\n" ], "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n", "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m262,272\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m1,290\u001b[0m │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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\n" ], "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m448\u001b[0m (1.75 KB)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "CNN = Sequential([\n", " Conv2D(32, (3, 3), activation='relu', padding='same',input_shape=(32, 32, 3)),\n", " BatchNormalization(),\n", " Conv2D(32, (3, 3), activation='relu',padding='same'),\n", " MaxPooling2D((2, 2)),\n", " Dropout(0.25),\n", " Conv2D(64, (3, 3), activation='relu', padding='same'),\n", " BatchNormalization(), # batch_normalization_2\n", " Conv2D(64, (3, 3), activation='relu', padding='same'), # conv2d_4\n", " MaxPooling2D((2, 2)), # max_pooling2d_2\n", " Dropout(0.25), # dropout_2\n", " Conv2D(128, (3, 3), activation='relu', padding='same'), # conv2d_5\n", " BatchNormalization(), # batch_normalization_3\n", " Conv2D(128, (3, 3), activation='relu', padding='same'), # conv2d_6\n", " MaxPooling2D((2, 2)), # max_pooling2d_3\n", " Dropout(0.25), # dropout_3\n", " Flatten(), # flatten_1\n", " Dense(128, activation='relu'), # dense_1\n", " Dropout(0.5), # dropout_4\n", " Dense(10, activation='softmax') # dense_2\n", "])\n", "CNN.summary()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "train" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/8\n", "\u001b[1m916/916\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 43ms/step - accuracy: 0.1829 - loss: 2.2818 - val_accuracy: 0.3392 - val_loss: 1.8202\n", "Epoch 2/8\n", "\u001b[1m916/916\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 41ms/step - accuracy: 0.3494 - loss: 1.7242 - val_accuracy: 0.8403 - val_loss: 0.5841\n", "Epoch 3/8\n", "\u001b[1m916/916\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 44ms/step - accuracy: 0.7911 - loss: 0.6765 - val_accuracy: 0.8899 - val_loss: 0.3787\n", "Epoch 4/8\n", "\u001b[1m916/916\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 44ms/step - accuracy: 0.8813 - loss: 0.4180 - val_accuracy: 0.8890 - val_loss: 0.3766\n", "Epoch 5/8\n", "\u001b[1m916/916\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 44ms/step - accuracy: 0.8972 - loss: 0.3623 - val_accuracy: 0.9175 - val_loss: 0.3073\n", "Epoch 6/8\n", "\u001b[1m916/916\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 44ms/step - accuracy: 0.9105 - loss: 0.3191 - val_accuracy: 0.9195 - val_loss: 0.2880\n", "Epoch 7/8\n", "\u001b[1m916/916\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 45ms/step - accuracy: 0.9199 - loss: 0.2909 - val_accuracy: 0.9286 - val_loss: 0.2713\n", "Epoch 8/8\n", "\u001b[1m916/916\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 45ms/step - accuracy: 0.9223 - loss: 0.2725 - val_accuracy: 0.9339 - val_loss: 0.2487\n", "\u001b[1m814/814\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.9352 - loss: 0.2530\n", "loss: 0.2482585459947586\n", "Accuracy: 0.9357329607009888\n" ] } ], "source": [ "CNN.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", "history = CNN.fit(X_train,y_train,batch_size=64,epochs=8,shuffle=True,validation_data=(X_val,y_val),callbacks=[tensorboard_callback])\n", "loss,accuracy = CNN.evaluate(test_images,test_labels)\n", "print(\"loss:\",loss)\n", "print(\"Accuracy:\",accuracy)\n", "CNN.save('model/CNN_model.keras')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "print" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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cMmWKGBgYKCqVSrFr167iI488Iubl5Zm8VnZ2tjh9+nTRx8dHdHd3F4cOHSpu3rxZ/OKLL1rsfapXVFQkPv/882Lfvn1FNzc30cPDQ+zZs6c4ffp08euvvxbVarWhbXv3Pj1x4oQIQAwODhabmpqu2nbXrl0iAHHw4MFGx1esWCHedNNNore3t+ji4iJGRkaK99xzj3jgwIEW12hv24qKCnH27NliUFCQqFKpxL59+4off/xxm/uonzlzptX4N23aJCYmJoqenp6ij4+POG7cOHHXrl1X/Ts/e/as+Oijj4qRkZGiUqkU/f39xYSEBPG1115r9T6DBg0SAYipqamttiEiIufR2T9LXNn+ar8u3wu9vf3NysoSn3rqKXHIkCFiYGCgqFKpxO7du4vTp0832oe9qalJXLx4sXjzzTeLXbt2FV1cXMTAwEAxMTFR/N///ic2Nja2qx9EUhBE8Yq5IEREZDFVVVXo0qULAgICcPr06Vb3zCUiIiIi0uMnRiIiK/rwww9RU1ODOXPmMEknIiIionbhiDoRkYVVVlbivffeQ15eHj7//HMEBQXhr7/+gqenp9ShEREREZEDYKJORGRhOTk5iIqKgqurK6677jq89957GDhwoNRhEREREZGDYKJOREREREREZEe4YJKIiIiIiIjIjjBRJyIiIiIiIrIjTNSJiIiIiIiI7AgTdSIiIiIiIiI7opA6ACmVl5dDo9FY5FpBQUG4cOGCRa5lb5y1b87aL4B9c1Tsm2OyVN8UCgX8/PwsEBFdyVLPe/537JjYN8fjrP0C2DdHJcWzvlMn6hqNBmq1usPXEQTBcD1nK6LvrH1z1n4B7JujYt8ckzP3zZlY4nnvzD9r9s0xOWvfnLVfAPvmqKTqG6e+ExEREREREdkRJupEREREREREdoSJOhEREREREZEdYaJOREREREREZEc6dTG5q2lsbERjY2O729fX16OpqcmKEUmnI31zcXGBi4uLhSMiIiLqOFEUUVNT0+7iQHzWm8ZnPRGR5TFRN6G2thaCIMDLy8tQ5a8tSqXSIhXk7dG19k0URdTX16O2thYeHh5WiIyIiOja1dTUwMXFBSqVql3t+axvic96IiLr4NR3EzQaDdzd3dudpJNpgiDA3d3dYnvVExERWZIoiu1O0sk0PuuJiKyDiboJTNAti3+fREREzo3PeiIiy2KiTkRERERERGRHmKgTERERERER2REm6lak1WmRnp+O9SfXIz0/HVqdVuqQzDZjxgy8/PLLUodBRERkl/isJyIia2DVdytJPZOK+Xvmo6C2wHAszCMMi4YtQnJUssXv16VLl6uev/3227F06VKzr/vJJ5/A3d39GqMiIiJyXnzWExGRtTBRt4LUM6mYvX02RBjvy1pYW4jZ22dj2dhlFn+A//HHH4Y/b9y4Ef/5z3/wyy+/GI65uroatVer1VAqlW1e18/Pz6m3oyEiIroWfNYTEZE1cep7O4iiiDp13VV/1aprUaeuQ3VjNV5Jf6XFgxuA4dj89Pmobqxu85p16jqIYsvrmBIcHGz4pd//Xf+6sbERffr0wcaNGzFjxgxER0dj7dq1KCsrw5w5czBkyBD06NEDY8aMwfr1642ue+V0uMTERLz77rt4+umnERMTg+uvvx4rVqy49r9cIiJHpdVClZ4OfPNN8+9ax5vyTJdc7Vlf21SL4tpinK86j+LaYlQ1VPFZT0REVsUR9Xao19Sj1/JeFrmWCBEFdQXo/VXvdrXPnpkNd6VlpqO98cYbmD9/Pt555x2oVCo0NjYiPj4ec+bMgZeXF37++Wc88cQT6NatGwYPHtzqdT7++GM8++yzePzxx7Flyxa8+OKLGDp0KHr27GmROImI7J1raip85s+HvKB5ynMAAG1YGCoXLUJDsuWnPJP18VlvjM96IiJpcUS9E5k1axaSk5PRrVs3hIaGIiwsDI888gj69euH7t2748EHH8SIESOwefPmq15n9OjRmDlzJqKiojB37lz4+/sjPT3dRr0gIpKWa2oq/GbPhqygwOi4rLAQfrNnwzU1VaLIiPisJyJyFhxRbwc3hRuyZ2ZftY1CqYBGrUFGQQb+tvVvbV5zxYQVSAxLbNe9LWXAgAFGr7VaLd5//31s2rQJBQUFaGpqQlNTU5sFZeLi4gx/FgQBQUFBKC0ttVicROREtFqo9u0DmpqaR/cSEgC5XOqoro1aDaGiAj4vvQSIIoQrTguiCFEQ4L1gARomTHDcfnZSVz7rRVHEX+V/QaPTtGh7tOQo5u+d3+Y1+awnIqJrxUS9HQRBaHNKmlKphBpq3BRxE8I8wlBYW2hy7ZoAAWEeYbgp4ibIZbb9EOfmZvxB4OOPP8Ynn3yClJQU9O7dG+7u7liwYEGbxWQUCuP/bARBgE6ns3i8ROTY7GZ6uEYDoboaspqa5t+rqy+9rqoyHDecq6mBrKqq+Xf96+pqCA0Nbd5KEEUo8vOhyshAU1KSDTpHlnLls76mqQYKmQIKWcuPSoNCBiHQNRAlDSWmr8VnPRERdRATdQuTy+RYNGwRZm+fDQGCUbIuXBx/SRmWYvMHtykZGRmYMGECpk+fDgDQ6XQ4c+YMevWyzBo9Iuq89NPDcUWRLP308PJly9pO1rXa5iS5vQm1ieNCdTVk9fVW7Klp8uJim9+TLEutaz2RlQty/CP+H3h93+t81hMRkVUwUbeC5KhkLBu7zOTeqinDUqyyt+q1iIyMRGpqKvbv3w9fX18sW7YMFy5c4MObiDpGq4XP/PmtTw8H4PvUU6jbswey2trm5PvyZFufmNfVWTQsnasrRC8viF5e0Hl5QfT0hM7bu/n3K44b/uzlBZ2nJ0Rvb+g8PaE8cgSBd93V9l9BcLBFYyfbU8quvq3ZDeE34KWEl/DZ0c9QWFdoOM5nPRERWQITdStJjkrGhO4TkFGYgeK6YgS7ByMxNNEuvl3XmzdvHnJzc3HvvffCzc0N9957LyZMmIDq6mqpQyMiOybU10NWVAR5cfGl34uLIS8qgqy4GIozZwzT3U2+H4BQUwPPzz9v1/1EF5fmZNlEgt1aQt3iuJcX0I79pNvSlJQEbVgYZAUFLb6EAAARgDY8HE2Jba9LJvvmofSAUqa86sj6yIiRmN1vNvYV7eOznoiILEoQ27t5pxO6cOGCyTVaVVVV8Pb2NutaSqWyzfVejqqjfbuWv09rEwQBYWFhKCgoaPf+tY6CfXNAWi1c9u1DQFMTSqUquCaKECorW02+Da+LiyGz0Af8+vHjoR48uMVotlES7ukJuLhY5H6WkrX8NYx+6X8AjLdO0a/e3fH6o4ib+XKL97VFqVQiKCio4wFSC9f6vK9srEROVU6r5yO9I+Hj4mOJECXnjM96wImfG3DevjlrvwD2zVFZsm/mPOs5ok5EjsGZqodfxuoF17RayEpLDUm3USJ+4YJRIi40Nrb7sjpXV+hCQqANDoYuOBjakJDm34ODISsthc/rr7d5jdqHH3a4gmtanRZ/V63H0DuA//4IdK26dO68N/DUzcBe1Qbs1b1oV6OqdG18XHwQ6R2JvJo8o5F1mSBDN69uTpOkExGR/WGiTkR2z26qh1tYhwquNTZCfuGC6Snol/9+4QIEMyo163x8TCbfRkl5cHDzVHLB1ORvAFotPD//3C6nh4uiiFp1LarV1ahpqkFVUxVq1Bd/b6pBtboa1U2X/VJf+r2mqQYl9SUobyzHujhgQ2/gxrNAWA1Q4An82h3QyQDU5iOjMANJ4Y71JQSZ5uPiA2+VN2rVtWgSm5BblQudqIO74uq7wRAREXUEE3UismsWqR5uj9pRcM3nuecgP3cO8pIS4wS8qAiyiop230oUBOgCAw3Jd4tEPCioOREPCgLcLLCfs1yOtMemYPRL/4MI09PDd86djDgzZkSIoogGbYPJ5LpKfTHJvjyxbqpGjbqmRcJdo66BTrTMFlM6GbAryvS54jpWfXcmgiDAU+UJpVKJsvoy1KprUd5YjmB3Fg0kIiLrYKJORParrWRWEOC9YAEaJkxofRq8Vgs0NUHQaAC1GoJa3fznK44Z/a7RmDzW4j0aDYRWjuGKaxjOXTwmq6hos+CavLwcPq++2mobUaVqTrJNjX7rk+/gYOgCAwGF7f65b2t6+LybgV2yVZh/Iha1mtrmBPqyBLzFaPbFBPtqRb3MJRfk8FJ5wUvpBU+VJ7xV3vBUXvxd5QkvpVfzef2vi+3OVJ7B87ufb/P6TOCcl5+LH2rVtahorODPmYiIrIaJOhHZLVVGxtWTWVGEIj8fIddfD8hkxsmxPrF28IImTYMHo2nw4BbJtzY4GKKfX+vTz61IJ+pQ0ViB0vpSlDSUGP9eX4IT5SdQUFtw9enhjWWYt2ue2fcWIMBT6Wkyuda/vjy59lJ5tUi8vVXecJW7QriGv7uhoUOx9I+lKKwtNNo7+/L4wjzCkBjKqu/OysfFB3k1eajX1KNeUw83hQVmoRAREV2BiToR2SVZWRlcN25sV1t5UVG7rysKAqBSQVQoAKUSolIJKBQQWzkGhaL59cXjVzsHpdLoGkbnrmiryM6Gz2uvtRlv1YsvWr3gmiiKqFZXo6S+BKUNpYaE2/DnhhKU1JegrKHM8LtW1Lbr2lebHh7rF4sevj2MkmhD4t3K6LaH0gMyQWb6gjYgl8mxaNgizN4+GwIEo2RduDjvI2VYCgvJOTGFTAFvlTcqmypR3lAON08m6kREZHlM1InIPogiFKdOwXXbNrhs2wbVgQPtLoJW8dprUA8ebJwsX0y8rzxmL5XiG0eNgudnn1mt4Fq9ph4l9SWGX/okWz/yXdpgnIw36ZrMvoeviy/8Xf0R6BqIQLfA5j+7BaKqqQqfH2t7j/TXkl5zyIJryVHJWDZ2GebvmY+C2kszPsI8wpAyLAXJUQ5YM4HM4ufqh8qmSlQ0ViDMI+yaZmcQERFdDRN1IpKORgPVvn1w/eknuG7bBkVOjtFpdVwc5OfOQaipMZ3MCgK0YWGo+/vf7SYBbzczC641ahtRWl/aasJ9ZTJer6k3OyQPpQcCXQMR4BaAANcABLoZ/znQNRD+bs2Jub+rP1RylcnraHVa/JDzg1NPD0+OSsaE7hOwr2gfmlRNUDWpkBCSwJH0TsJL5QW5IIdap0aNugZeKi+pQyIiIifDRJ2IbEqoqoJLWlpzcp6WZlS9XFSp0JiUhIZx49A4bhy0XboYqr6LgNF6c/HiCFZVSorjJekANFoN/qZci6SrFFzbrP0U4d+moryxHFVNVa1frBUucpdLSbZbIAJcAxDgFtAiGdePhltqrW1nmR4ul8mRFJ6EsLAwFBQUQHTwegjUfjJBBl8XX5Q2lKK8sZyJOhERWRwTdWvSapuLYRUXQxsc3DyF1Y4TisTERMyaNQsPP/yw1KGQk5GfPducmP/0E1R79zYXertI6+eHxrFjm5PzESMgenoavbchORnly5YZ7aMONO+jXpWSYldbs4miiPLGcpTUl+BC/QVcqLuAC/UXLr2+/FfdBWhF7dULrunUOFt91nB9haAwJNiXJ9z6JPzKZNxD6SHZlFxODydn5+fqh9KGUlTWlUGZmQvFhQt81hMRkcUwUbcS19RUk4lF5aJFdpVYEFmFTgflH3/Adds2uP70E5THjxudVvfsiYbx49E4fjyaBg9u80NtQ3IyaseNxekfv4SyogRq30BE33w/5ErTU68tSV/hvLWk+/KkvKS+BBpR0/ZFr7zHVQquPTXoKUzpOQWBboHwUfk41FpYTg8nZ+aucEeXHRnotfgDuBWVGI7zWU9ERJbARN0K9FN1ccU0SFlhIfxmz0b5smV8gJPTEerq4PLLL3D56Se4bt8OecmlD66iXI6mhAQ0jBuHhnHjoI2ONuvaqWdSjUdmC4Gw7z/GomGLrmlkVp98F9cVGyXbJfUlKK4rNnp9Lcm3r4svAt0CEeQWZPilfx3oFohg92CcrTqLR3c82ua1ksKT0NO3p9l9tBecHk7Oyu2HH9D/nym4sgwDn/VERGQJdpGob926FRs3bkRFRQUiIiIwc+ZM9OnTp9X2P/74I7Zu3Yri4mIEBgZi2rRpGDFihPUCFEUI9W0UZlIomqfzarXweeWV5vdc0UQQRYiCAJ/589F4443tmhonurm1a5/kr7/+GkuXLsX+/fshk10qSzVz5kz4+PjgqaeeQkpKCg4ePIi6ujr06tULL7zwAm666aY2r03UGllBAVy3b2+u1P7bbxAaGw3ndF5eaBw1Cg3jx6Nh1CiIvr7XdI/UM6mYvX12i6JkhbWFmL19NpaNXYbkqGToRB3KG8pxof4CiusvJtt1pkfAS+pL2r29mJ6vi69Rwh3k3jIJ1/+5tSJrl+sX0A9hHmFOXXCNyKFc07MefNYTEZFVSJ6op6enY/ny5Zg1axZiY2Oxfft2vPHGG1iyZAkCAwNbtN+2bRu++eYb/OMf/0CPHj1w8uRJfPzxx/Dw8MB1111nlRiF+nqE9eplmWuJIuQFBQjr3btd7QuysyG6u7fZbuLEiZg/fz5+++033HjjjQCAiooK7Nq1C8uXL0dtbS1Gjx6N5557Di4uLvj+++/xwAMP4JdffkGXLl061CfqREQRimPHDFPaVZmZRqc13boZRs2bEhObt0PrAK1Oi/l75ptMZPXHHv35Ufi5+KGssf17e+v5uvgi2C24OdF2v5RwG45dTLzbm3ybo7MUXCNyFHzWExGRPZE8Ud+8eTNGjx6NMWPGAGj+Vvjw4cPYtm0b7rnnnhbtf/nlF4wdOxZJSc1774aEhCA7OxsbNmywWqLuCPz8/DBy5EisX7/e8PDevHkzfH19MXz4cMjlcvTt29fQ/vnnn8ePP/6Ibdu24YEHHpAqbHIEDQ1wSU83FIO7vO6CKAhQDx7cnJyPHw9NTEy7RoWuRifqcLbqLI6WHsWPOT8aFSIzRSNqcKHhguG1n4uf0RTzK0e79ccCXAMsnnybiwXXiMgcfNYTEXUekibqGo0Gp0+fxpQpU4yOx8fH4/gVxaf01Go1lEql0TGVSoWTJ09Co9FAoWjZJbVaDbVabXgtCALc3NwMf26L6OaGguzsq7ZRKBTQaDRQZWQg4G9/a/OapStWNI84tuPe7TV16lQ8//zzeOONN+Di4oJ169Zh0qRJkMvlqKurwzvvvIPt27ejqKgIGo0GDQ0NyMvLa/f1O8LeCmDp47G3uCzBEn2TlZbCZft2uPz0E1x27oSsrs5wTufmhqYRI5qrtI8dC11Q0KV7m3kfjU6DkxUncbTkKI6UHMGR0iM4VnIM1epqs67z/PXP486YOxHgFgClTNn2G+zIrdG34ubIm7GvaB8alY1wUbs4XcE1/v9GjoDPeiIisieSJupVVVXQ6XTw8fExOu7j44OKy/ZWvtyAAQOwY8cOJCQkICoqCqdPn0ZaWhq0Wi2qq6vh5+fX4j3r1q3D6tWrDa+joqKwePFiBF2WYFyuvr6+xZcB7ZnCqwCgGzMG2vBwyAoKjPZ81hMFAbqwMOjGjIHCwtu3JCcn49lnn8XOnTsxaNAgZGRk4NVXX4VSqcTrr7+OtLQ0LFy4EFFRUXB1dcVDDz0ErVZr6KsgCJDL5S37Dpg81l4qlQphYWHX/H6L02qBX38F0tIQGhYGtHMNoaMJDQ1tf2NRBP78E9i0Cdi4Edizx7gYYng4cNttwKRJkI0eDVdXV7iaGU+DpgFHi4/iYMFB/FHwBw4WHkRmUSYaNA0t2qrkKsSHxCPEIwRbsre0ee2b427GoMhBZkZkXyK6REgdgtWZ9d+kg3HmvnUagtD29HOlEqJajcabboI2LAyywsJWn/XasDA03nSTxZ8v48aNw7PPPouff/4ZAwYMQEZGBhYsWAAAePXVV7Fr1y688soriIyMhKurK2bPno2mpiaLxkBERNYn+dR3wPRIRGujEzNmzEBFRQVeeukliKIIHx8fjBgxAhs3bjQqrHK5qVOnYuLEiS2ufeHCBWg0Las5NzU1GY3At4dSqTS8pzIlBX6zZ0MUBKMHuHjxvpUpKVDrdIBOZ9Y92qJQKHDLLbdg9erVOHXqFKKjoxEXFwe1Wo29e/fi9ttvx/jx4wEAtbW1yM3NhVarNcQtiqLRa1N9uxZNTU0oKLj69GVbcU1Nhfcrr7Tcj/vVV52jOq9WC5d9++Df2IgyFxc0JiS0/iFRrYZq3z64XFxvrsjJMT7dr19zIbjx46Hp3//SlPby8jbDqGmqwbGyYzhScgRHS47iaMlRnCg/YbJ6uofSA30D+qJ/YH/0C+iH/oH90cuvF5QyJbQ6LRLyE9osuNZT2dNu/hu7VoIgIDQ0FIWFhU5XGZ19ax+FQtHqF8hkZ+RyVC5adNVnfVVKilW+BHZzc8Mtt9yCdevWIScnB9HR0YiPjwcA7Nu3D7fffjtuueUWAM3P+vPnz1s8BiIisj5JE3Vvb2/IZLIWo+eVlZUtRtn1VCoV5syZg9mzZ6OyshJ+fn7Yvn073Nzc4OXlZfI9SqWy1RFha3xobEhORvmyZSb3Ua9KSbFqQjht2jTMnDkTx48fx7Rp0wzHIyMj8cMPP2DcuHEQBAH//ve/obPwFwVXYw8fzl1TU+HbyrZ5vg8/7PBb6bimphr9N+ePlvv5CpWVcNm5s7kYXFoaZJWVhveLKhUahw9Hw9ixaBg7FrorCw+18jMsayjD0dKjhunrR0uP4kzlGZOJtZ+LH/oF9kP/gP7oF9gP/QL6IconCjKh5ZdsoihCJsjaVXBNJsjs4r8xSxBF0Wn6ciX2jZxJa896TWgIqhdZ98tfe33WExGR5UiaqCsUCkRHRyMzMxMJCQmG45mZmbj++uvbfG9AQAAA4LfffsPgwYNbHVGXQkNyMhomTIAqIwPy4mJog4Ob16lZeYr1DTfcAF9fX5w6dQpTp041HF+4cCGefvppTJ48Gf7+/pg7dy5qamqsGotd0WrhM3/+VbfN816wAA0TJjjkNHjX1FT4tfIlhN/DD6PujjugyMuDKiOjeWuhi7QBAWgcMwYN48ej8aabIHp4tHoPURRRWFeIIyVHcKz0mCEpz6sxvfYx1CPUkJDrfw/3CDd7LS8LrhGRvbr8WV9x9k+U+qiApBvR1TfSqvfls56IyPlJPvV94sSJeO+99xAdHY2YmBhs374dJSUlGDduHABg5cqVKCsrw2OPPQYAyM/Px8mTJ9GrVy/U1tZi8+bNyM3Nxdy5c6XshmlyOZouVqe33S3lOHjwYIvjXbt2xffff290bObMmUavMzIyrBmapFQZGUYjHlcSRBGK/Hz4PfggdOHhgEwGUS5vnu4tlxu/lskAuRyiTNb858tfX97+snNGr/XtL2treG2qvf61qfZyOSCK8PnXv1r9EgIAPL77znBMHRPTPKV97FioBw82+cWEKIo4W33WkIzrR8tLG0pN/v1FekeiX0A/o6Q80K3l9orXKjkqGRO6T8C+on1oUjVB1aRyuoJrRI5q3bp12LdvH/Ly8qBSqRATE4O//e1vCA8Pv+r7srKy8OWXX+L8+fPw8/PDpEmTDMuz9Pbu3YtVq1ahqKgIISEhuPvuu42+2LcLF5/1uuviUVZ5CnJNDbqIOpMzhSx3Sz7riYicneSJelJSEqqrq7FmzRqUl5eja9euePHFFw3r9MrLy1FSUmJor9PpsHnzZuTn5xu2IXnttdcQHBwsVRfIAciLi9vVzm37ditHIp2a++9H7ezZ0EZGGh03VF4vPWoYLT9actRk5XWZIEOMb8ylNeWB/dA3oC+8Vd5Wj18ukyMpPAlhYWEoKCjgNGMiO5GVlYUJEyagR48e0Gq1+Pbbb/Haa6/hnXfegaur6bKTxcXFePPNNzFmzBg8/vjjOH78OD799FN4e3tj6NChAIATJ05g6dKluPPOO5GQkIB9+/ZhyZIlWLRoEXpZaL9zS/JQekApU0KtU6OqqQq+Lr5Sh0RERA5M8kQdACZMmIAJEyaYPHflSHlERATeeustW4RFzqS2tn3N7rgDuoiI5kJ/Wm3zVHKdDoJW23zs4i/Da1EEtFoI1mh/8ZyhvanzWi2E+nrI2jG1UZ2QgNqIUBy/cNiQlB8tPYo/S/9Eg9ZE5XWZCn38+xjWkvcL7Ic+/n3gpmj/NkJE5Pxeeuklo9dz5szBrFmzcPr0acTFxZl8z7Zt2xAYGGgY7Y2IiMCpU6ewadMmQ6K+ZcsWxMfHG6Z2T506FVlZWdiyZQvmzZtntf5cK0EQ4Ofqh+K6YpQ3lDNRJyKiDrGLRJ3IWuSnT8P73/+G28aNAAARpvf61m+lU/mf/zjcGnXFb7sRfMedbbZ78s//wzclT7Zeed2/eZS8b2Bf9A+4VHmdiMgcdXV1AABPT89W22RnZxsqlesNHDgQaWlp0Gg0UCgUOHHiBG699VajNgMGDEBqamqr11Wr1Ua7lAiCALeLe5TbYq97P5fmRL26qRoanQYKWef6mGWLv2Nz6WOyx9g6yln75qz9Atg3RyVV3zrXE4Q6DVl+PryWLoX7t982jz4DaExIgGrfPptvpWNtv3QTkegNdKkCTK2I1AE47w38P/9c6ETzKq8TEZlDFEV8+eWX6N27N7p169Zqu4qKiha7u/j4+ECr1aK6uhp+fn6oqKiAr6+vURtfX98WO8Vcbt26dVi9erXhdVRUFBYvXtzqtnf19fWt7grTmqu1VyqVcK9xR526DtWaagR7ONayPHP/Li6nUqkQFhZmwWgsKzQ0VOoQrMZZ++as/QLYN0dl674xUSenIpSVweuDD+DxxRcQGhsBAA1jxqDq+eeh6du3xRZmgG22zbOm4sZSPHkzsPq75qT88nRbvynPvJuBWfGzMav/rGuqvE5E1B6fffYZzp07h0WLFrXZ9sp/h/R1J67275Moilc9P3XqVEycOLHFPS5cuACNpuVsoqamJqMR+LYolco22/uqfFGnrkNJXQn8VH7tvrbU2tO3q2lqakLBVYq2SkUQBISGhqKwsNDpaps4a9+ctV8A++aoLNk3hULR6pfHLdp26E5OTKfT2dV2b47KVvu3CrW18Fi2DJ4ffwxZdXMRtMbERFS/8AKaLqsQrN9Kx2XfPgQ0NaFUpUJjQoJDjqTraUUt1sUBM+4A/vsj0LXq0rnz3s1J+ro44Pvu49DFs0vrFyIi6oDPP/8cBw4cQEpKimH71NaYGhmvqqqCXC43TJk31aaysrLFSPzllEplq6PCrX24aiv5N5eviy8KagtQp65Do6YRLgoXi13bXumf9fb84VwURbuOryOctW/O2i+AfXNUtu4bE3UT3N3dUV1dDS8vLybrHaDT6VBdXQ2Pq+zN3WGNjfD4+mt4vvsu5KXNW4ep4+JQ9eKLaBw1qnm7tCvpt80LC0NTQUGLvccdRa26Fu8eehcfH/4YQHMyvqE3cONZIKwGKPAEfu0OiDIB4R5hSAxNlDhiInJGoiji888/x759+7Bw4cJ27cLSq1cvHDhwwOjY4cOHER0dDYWi+aNJTEwMjhw5YjRCnpmZiZiYGIvF7uLigvr6eri7u1vsmkq5Ep4qT1Q3VaO8sRyhCuedBgrY6FlPRNQJMVE3QaFQwMPDAzXtqKStp1Kp0NTUZMWopNORvnl4eBg+dFmUVgu31avh9fbbUOTlAQA0kZGoeu45NNx2W/Ne405KFEVsPL0RizIWobC2EADQN6AvjpUegygTsCvq0hcPwsXSeSnDUrjnOBFZxWeffYbdu3fjueeeg5ubm2EU3N3dHSqVCgCwcuVKlJWV4bHHHgMAjB8/Hlu3bsWXX36JMWPG4MSJE9ixYweefPJJw3WTk5OxYMECrF+/Htdffz3279+PI0eOtGtafXu5uLigtrYWlZWV7RpVb+/zUFuvRW55LkrkJXALcXOI5UZ2+awnIurE+K9qKxQKBby927c3tCAITru3s931TRTh+sMP8HrrLSizswEA2tBQVD/1FOruvBPoQCEcR5BVmoX5e+ZjT8EeAEA3r25YOHQhxncfjx9yfsD8PfNRUHtpjWCYRxhShqUgOcox198Tkf3btm0bAGDhwoVGx+fMmYORI0cCAMrLy1FSUmI4FxwcjBdffBFffvkltm7dCj8/PzzwwAOGrdkAIDY2FvPmzcO3336LVatWITQ0FPPmzbP4HurtHQk253mocFMg+Ydk1GnqsH7Selwfcr0lQrUau3vWExERE3VyHKpffoH34sVQHToEAND5+qL6scdQO3Mm4Obce3tXNFbg7QNvY3nWcuhEHVzlrnhs4GN4JP4Rw77myVHJmNB9AvYV7UOTqgmqJhUSQhI4kk5EVvXdd9+12Wbu3LktjsXFxWHx4sVXfd/QoUONkndH4a50xy2Rt2DNyTVYm73W7hN1IiKyP0zUye4p//gD3m++CZfffgMA6NzdUfvww6h55BGI7Zz14Kh0og7fHv8Wb+5/E2UNZQCaE/IFiQsQ4RXRor1cJkdSeBJHRoiIJDaj1wysObkGG09vRMqwFKjkKqlDIiIiB8JEneyW4sQJeL31Ftx++AEAICqVqP3731Hz+OPQtXNbA0f2R/EfeDn9ZRy6cAgA0Mu3FxYlLcJNXW6SNjAiImrTDeE3IMQ9BEV1RdiRuwM3R94sdUhERORAmKiT3ZHn5sLr7bfhtmYNBJ0OokyG+unTUf3Pf0LbtavU4VndhboLeHP/m1h1YhUAwFPpiacHP40H+z0Ipcy51+ATETkLuUyOKT2m4OMjH2PNyTVM1ImIyCxM1MluyC5cgOe778Lj668hqNUAgPpbbkH1c89BY8HteOyVWqfGl1lf4j+//wfV6ua94G/vdTv+lfAvBLu3vd0RERHZl+m9puPjIx9j+9ntqGisgK+Lr9QhERGRg2CiTpITqqrg+dFH8PjkE8jq6gAAjcOHo+qFF6AeNEji6Gzjt/zf8Er6KzhefhwA0D+wP15Leg3XhVwncWRERHSt4vzj0NuvN/4q/wubT2/G3/r8TeqQiIjIQTjvZtNk/+rr4fG//yFk2DB4/fe/kNXVoWngQJR88w1KV63qFEl6Xk0eHvn5Edyx5Q4cLz8OPxc/LB6+GFsmb2GSTkTk4ARBwPRe0wEAa0+ulTgaIiJyJBxRJ9tTq+G+ahW8liyBvLCw+VCvXqh+7jk03HILIAgSB2h9DZoGfHzkY7x36D3Ua+ohE2S4r899eHbIs/Bz9ZM6PCIispApPabgjX1vIKMwA7nVuejq5fy1VoiIqOOYqJPt6HRw3bQJ3m+9BUVODgBA06ULqv/5T9RPnw4oOsd/jj+d/QkL9y5ETlUOACAhJAGv3vAq+gX0kzYwIiKyuHDPcCSFJ+G3/N+wJnsN5g2eJ3VIRETkADpHZkTSEkW47NgB78WLoTx2DACgDQhAzRNPoPa++wAXF4kDtI3TlaexYM8C7MjdAQAIcQ/By4kvY2qPqRA6wSwCIqLOanqv6fgt/zesPbkWTw56kv/mExFRm5iok1Wp9u2D15tvwmXfPgCAzssLNf/4B2offhiip6fE0dlGrboW7x56F8syl6FJ1wSlTImH+z2MJwc9CU9V5/g7ICLqzJIjk/Gv3f/CqcpTOFxyGAODBkodEhER2Tkm6mQVimPH4L14MVx//hkAILq4oPaBB1A9dy5Ef3+Jo7MNURSx8fRGLMpYhMLa5rX4IyNGImVYCnr69pQ4OiIishUvlRcmRE7AhlMbsCZ7DRN1IiJqExN1sij5mTPw+s9/4L5+PQBAlMtRd9ddqJ43D7rwcGmDs6Gs0izM3zMfewr2AAC6eXXDwqELMb77eE55JCLqhKb3nI4NpzZgw6kNmD90PpQypdQhERGRHWOiThYhKyyE19KlcP/mGwgaDQCgftIkVD3zDLQ9ekgcne1UNFbg7QNvY3nWcuhEHVzlrnhs4GN4JP4RuCncpA6PiIgkMiJiBAJcA1DaUIpd53dhbLexUodERER2jIk6dYhQXg7PDz+Ex+efQ9bQAABoGDUKVS+8AE2/zlPFXCfq8O3xb/Hm/jdR1lAGAEiOSsaCxAWI8IqQODoiIpKaQqbAlB5T8Nmxz7Amew0TdSIiuiom6tQ6rRaqffuApiaoVCo0JiQAcjkAQKithcenn8Lzo48gq6oCADRddx2qXnwRTUOHShm1zf1R/AdeTn8Zhy4cAgD08u2FRUmLcFOXm6QNjIiI7Mr0XtPx2bHPsO3sNlQ3VcNL5SV1SEREZKeYqJNJrqmp8Jk/H/KCAgBAAABtWBgqX3kF8tJSeP73v5CXlAAA1H36oOr559E4dizQidZfl9SX4M19b+LbE98CADyVnnh68NN4sN+DXHtIREQtxAfGo6dvT5ysOInUM6m4M/ZOqUMiIiI7xUSdWnBNTYXf7NmAKBodlxUUwG/OHOhTcU337qh+5hnUT5kCyGQ2j1Mqap0aX2Z9ibcPvI2qpubZBDN6zcBLCS8h2D1Y4uiIiMheCYKAaT2n4a3f38Lq7NVM1ImIqFVM1MmYVguf+fMBUcSVY+P616JMhspFi1B3772ASmXrCCX1W/5veCX9FRwvPw4A6B/YH68mvYrrQ66XODIiInIE+kR9T8Ee5NXkoYtnF6lDIiIiO9R5hkGpXVQZGZAXFLRI0i8n6HTQxMZ2qiQ9ryYPj/z8CO7YcgeOlx+Hn4sfFg9fjC2TtzBJJyKiduvq1RVDQ4dChIj1J9dLHQ4REdkpJupkRF5cbNF2jq5B04D//vFfjPh+BDad3gSZIMP9cffj1zt+xd/6/A1ymVzqEImIyMFM6zUNALDm5BqIVywzIyIiApio0xW0we1bY93edo7sp7M/YcyaMXjr97dQr6lHQkgCfpj6A9644Q34ufpJHR4RETmoiVET4SJ3wfHy4zhWdkzqcIiIyA4xUScjTYmJ0IaFQWylersoCNCEh6MpMdHGkdnO6crTuO/H+zBz20zkVOUgxD0E7416D2tvW4t+AZ1nb3giIrIOHxcfwz7qa7LXSBwNERHZIybqZEwuR+WiRS0qvgMwJO9VKSmG/dQdkVanRXp+Or458g3S89Oh1WkBALXqWry5/02MWT0GO3J3QClTYk78HPxy+y+Y1nMahE609RwREVnX9J7TAQDrT66HRqeROBoiIrI3rPpOLTQkJ6Pm0Ufh9b//GR3XhoWhKiUFDcnJEkXWcalnUjF/z3wU1BYYjoV5hGFi1ERsOrMJhbWFAICRESORMiwFPX17ShUqERE5sVFdR8HPxQ/F9cX4Lf83jIgYIXVIRERkR5iok2murgCAhlGj4PrwwyhVqdCYkODQI+mpZ1Ixe/tsiDCeLVBQW4BPjn4CAOjm1Q0Lhy7E+O7jOYJORERWo5KrMKnHJHyZ9SVWZ69mok5EREY49Z1MUhxrLm7TOGoUcPfdaEpKcugkXavTYv6e+S2S9Mt5Kb3w07SfMCFyApN0IiKyumk9m6u//5DzA2rVtRJHQ0RE9oSJOpmkvJioa/o5R/G0jMIMo+nuplSrq5FZkmmjiIiIqLMbEjwEkd6RqNfU48ecH6UOh4iI7AgTdWpBKC+HIi8PAKCOi5M4Gssormvfvu/tbUdERNRRgiAYisqx+jsREV2OiTq1oMzKAgBounWD6O0tcTSWEezevn3f29uOiIg6Ea0WqvR04Jtvmn/Xai126ak9pwIAfs3/FUV1RRa7LhEROTYm6tSCftq7um9fiSOxnMTQRIR5hEGA6bXnAgSEe4QjMdR594cnIiLzuaamIiQxEQEzZgD33IOAGTMQkpgI19RUi1w/yicKQ4KHQCfqsP7keotck4iIHB8TdWrBGRN1uUyORcMWmTynT95ThqVALnPcgnlERGRZrqmp8Js9G7IC4xonssJC+M2ebbFkfXqvi9PfT3L6OxERNWOiTi04Y6IOAMlRyfh4zMeQCcb/2Yd5hGHZ2GVIjnLc/eGJiMjCtFr4zJ8PiGKLuViC2LyDiPeCBRaZBn9b9G1QypQ4VnoMf5X91eHrERGR42OiTsYaG6HIzgYAaJwsUQeAWP9Y6EQdlDIllk9ejtUTV2PvXXuZpBMRkRFVRgbkBQWtLJhqTtYV+flQZWR0+F7+rv4Y3XU0AGDtybUdvh4RETk+JupkRJGdDUGjgc7XF9rwcKnDsbjf8n8DACSEJuD+gfcjKTyJ092JiKgFeXH7dgFpb7u26Ke/rz25FjpRZ5FrEhGR42KiTkYM097j4gChtXEEx6VP1JPCkySOhIiI7Jk2uH27gLS3XVvGdB0Db5U3CmoLkJ6fbpFrEhGR42KiTkacdX06AOhEneHDzw3hN0gcDRER2bOmxERow8IgtvKltSgI0ISHoynRMruFuCpccVv0bQA4/Z2IiJio0xWcOVH/s+xPlDeWw13hjoFBA6UOh4iI7JlcjspFzbuFXJms619XpaQAcsstn5res3n6+5YzW1CvqbfYdYmIyPEwUadLRNGpE3X9tPfE0ESo5CqJoyEiInvXkJyM8mXLoAsNNTquDQtD+bJlaEi2bCHS60OvR4RnBGrUNdh2dptFr01ERI6FiToZyHNzIauuhqhSQdOzp9ThWBynvRMRkbkakpNRlJGBirffBgDoXF1RnJ5u8SQdAGSCDNN6TgMArMnmnupERJ2ZQuoAAGDr1q3YuHEjKioqEBERgZkzZ6JPnz6ttv/111+xceNGFBQUwN3dHQMHDsR9990HLy8vG0btfAyj6TExgMq5Rpw1Og32FuwFwEJyRERkJrkc9bffDt+XXoKsoQHys2ehtdIX2tN7Tce7h97FzvM7UVJfgkC3QKvch4iI7JvkI+rp6elYvnw5pk2bhsWLF6NPnz544403UFJSYrL9X3/9hffffx+jRo3CO++8g6effhqnTp3CRx99ZOPInY8+UXfG/dOPlBxBtboa3ipv9AvoJ3U4RETkaBQKYMAAAIDqyBGr3aanb08MCBwArajFhlMbrHYfIiKyb5In6ps3b8bo0aMxZswYw2h6YGAgtm0zvTbrxIkTCA4ORnJyMoKDg9G7d2+MHTsWp0+ftnHkzkfRCdanDwsbxn3TiYjo2gwZAgBQZmZa9TaX76lORESdk6RT3zUaDU6fPo0pU6YYHY+Pj8fx48dNvic2NhbffvstDh48iEGDBqGyshJ79+7FoEGDWr2PWq2GWq02vBYEAW5uboY/d5T+Gpa4lpQMI+r9+rXok6P3TZ+o39DlBgiC4DT9MoV9c0zsm2Ny5r6RCfpE3Yoj6gAwucdkpOxNwaELh3Cy4iR6+jpf3RgiIro6SRP1qqoq6HQ6+Pj4GB338fFBRUWFyffExsbiiSeewNKlS6FWq6HVanHdddfhwQcfbPU+69atw+rVqw2vo6KisHjxYgQFBVmkH3qhV1SFdShlZUBeHgAgYPRo4IqfiSP3rUnbhP1F+wEAU+KnICwkzHDOkfvVFvbNMbFvjsmZ+0aX0SfqR48COh0gs87ExEC3QIyIGIEduTuwJnsNnr/+eavch4iI7JddFJMzNRLR2ujE+fPn8cUXX2DGjBkYMGAAysvLsWLFCnzyySd49NFHTb5n6tSpmDhxYotrX7hwARqNxiLxh4aGorCwEKIodvh6UlD99hsCAGi6dcOFujqgrg6Ac/QtoyAD9Zp6BLgGwF/rj4KCAqfoV2vYN8fEvjkmS/ZNoVBY/AtksrC4OIguLpBVV0OekwNtdLTVbjWj1wzsyN2BdSfX4dnrnoVMkHy1IhER2ZCkibq3tzdkMlmL0fPKysoWo+x669atQ2xsLCZNmgQA6N69O1xdXTF//nzcdddd8PPza/EepVIJpVJp8nqW/NAoiqLDfghVHD0KoHl9uqk+OHLfduftBtBc7V2AYNQPR+5XW9g3x8S+OSZn7lt7ZGVlYePGjThz5gzKy8vxzDPPICEhodX2H3zwAXbt2tXieEREBN555x0AwM6dO/Hhhx+2aLNixQqopNqZRKmEOi4Oqj/+gPLIEasm6uO7j4en0hO5NbnYX7gfiWGJVrsXERHZH0kTdYVCgejoaGRmZho90DMzM3H99debfE9jYyPkcuNiYLKLU88684ekjlJ2gkJy3D+diMg6GhsbERkZiVGjRuHti/uNX80DDzyAe++91/Baq9Xi2WefxdChQ43aubm54b///a/RMcmS9IvU8fFQ/fEHVJmZaJg82Wr3cVO4ITkqGd+d+A5rTq5hok5E1MlIPo9q4sSJ+Pnnn7Fjxw6cP38ey5cvR0lJCcaNGwcAWLlyJd5//31D++uuuw779u3Dtm3bUFRUhL/++gtffPEFevbsCX9/f6m64fCcNVGv19TjYPFBAEzUiYisZdCgQbjrrruQmNi+ZNLd3R2+vr6GX6dOnUJtbS1GjRpl1E4QBKN2vr6+VojePOp+zVt8WrvyOwBM79lc/X3T6U1o0DRY/X5ERGQ/JF+jnpSUhOrqaqxZswbl5eXo2rUrXnzxRcM6vfLycqM91UeOHIn6+nr8+OOP+Oqrr+Dh4YG+ffvib3/7m1RdcHyNjVBkZwNwvj3U9xftR5OuCaEeoYjyjpI6HCIiMmHHjh3o379/izX6DQ0NmDNnDnQ6HSIjI3HnnXciKqr1f8utucuL/v2ai3upK48ehdB8okPXvZobutyAMI8wFNQWYEfuDtwafatV7uPMuxewb47HWfsFsG+OSqq+SZ6oA8CECRMwYcIEk+fmzp3b4tgtt9yCW265xdphdRqK7GwIGg10vr7QhodLHY5FGaa9h93glP9wEBE5uvLychw6dAhPPPGE0fHw8HDMmTMH3bp1Q319PVJTU/HKK6/g3//+N8LCwkxeyxa7vASOGAGoVJBVVSGsrg7oad2t0+4bcB/eSn8Lm3M3Y9YNs6x6L2fevYB9czzO2i+AfXNUtu6bXSTqJC3DtPe4OKuODEjh8v3TiYjI/uzcuRMeHh4tis/FxMQgJibG8Do2NhbPP/88fvjhh1a3ZLXmLi+GCv9lZfDv0weqw4dR/vPPaPDw6NB123Jz+M14C28hNTsVx84cg7+r5Zf5cWcGx+SsfXPWfgHsm6OSaocXJurktOvTq5uqkXmheQ3hDWFM1ImI7I0oikhLS8ONN94IheLqH0lkMhl69OiBwsLCVtvYYpcXURSh7t8fqsOHocjMhHjbbRa5bmti/WLRN6AvjpUew8ZTG3F/3P1Wu5cz717AvjkeZ+0XwL45Klv3TfJiciQ9Z03UMwozoBW1iPSORIRXhNThEBHRFbKyslBYWIjRo0e32VYURZw9e9Y+CsrFxwMAVDYoKAcA03pOAwCsyV5jk/sREZH0mKh3dqLotIm6ftp7UliSxJEQETm3hoYG5OTkICcnBwBQXFyMnJwcQzHYK3dw0duxYwd69eqFbt26tTj3/fff49ChQygqKkJOTg7+97//IScnB+PHj7dqX9pDn6grjx4FbDC6MrXnVMgEGQ4UH0BOVY7V70dERNLj1PdOTp6bC1l1NUSVChorF8SxNe6fTkRkG6dOnUJKSorh9VdffQUAGDFiBObOndtiBxcAqKurQ0ZGBmbOnGnymrW1tVi2bBkqKirg7u6OqKgopKSkoKcdPKvUsbEQlUrIKiogP3cO2u7drXq/EPcQ3Bh+I3bl7cLa7LV4esjTVr0fERFJj4l6J2cYTY+JAVQqiaOxnLKGMhwrbe5bUjhH1ImIrKlv37747rvvWj1vagcXd3d3rFixotX3zJw5s9UkXnIqFdS9e0N15AiUmZlWT9QBYFqvadiVtwtrTq7BU4Of4k4mREROjlPfOzl9ou5s+6fvLdgLAIjxjUGwe7DE0RARkbMxTH8/csQm97sl8ha4KdyQU5WDg8UHbXJPIiKSDhP1Tk7h5OvTOe2diIisQd2/PwDbFZTzUHrglshbAABrTrKoHBGRs2Oi3sk5fSE5TnsnIiIrMBpRt9F2PdN7TgcAbDi1AU3aJpvck4iIpMFEvRMTysuhyMsDAKjj4iSOxnKK6oqQXZENAQKGhg2VOhwiInJC6t69LxWUO3/eJvcc3mU4gt2CUdFYgZ3nd9rknkREJA0m6p2YMisLAKDp1g2it7fE0VhOen46AKBvQF/4u/pLHA0RETklFxeoY2MBAEobTX9XyBSY0nMKAGB19mqb3JOIiKTBRL0Tc9Zp7/pEnevTiYjImgzT322UqAOXpr9vP7cdlY2VNrsvERHZFhP1TsxZE3WuTyciIltQ9+sHwHaV34Hm2WKxfrFo1DZiy5ktNrsvERHZFhP1TswZE/Xc6lycrT4LuSBHYmii1OEQEZETMxpRt1FBOUEQDKPqa7JZ/Z2IyFkxUe+sGhuhyM4G4Fx7qOunvQ8IGgAvlZfE0RARkTNT9+kDUaGAvLwc8ovFWW1hSs8pECBgb+Fe5Fbn2uy+RERkO0zUOylFdjYEjQY6X19ow8OlDsdidufvBsD16UREZAOurtDExACw7fT3Lp5dMCxsGABg3cl1NrsvERHZDhP1Tsow7T0uDhAEiaOxDFEUkV7AQnJERGQ7TRIUlAOAGb1mAADWnFwD0UbT7omIyHaYqHdSzrg+/XTlaRTWFkIlU+G6kOukDoeIiDoBdf/+AGw7og4AyVHJcJW74mTFSWSW2PZLAiIisj4m6p2UMybq+mrvQ0KGwE3hJnE0RETUGUhRUA4AvFReGN99PIDmUXUiInIuTNQ7I1F06kSd096JiMhW1H36QJTLIS8thSw/36b3nt6rufr7hlMboNFpbHpvIiKyLibqnZA8Nxey6mqIKhU0PXtKHY5F6EQd9hTsAcBEnYiIbMjNzVBQTmXj6e8jIkYgwDUAJfUl2HV+l03vTURE1sVEvRMyjKbHxAAqlcTRWMbx8uMobSiFm8INA4MGSh0OERF1IoZ16jYuKKeUKTG5x2QAwNqTa216byIisi4m6p2QPlF3pv3T9dPeE0ISoJI7x5cPRETkGAyV3208og5cmv7+Y86PqGmqsfn9iYjIOpiod0IKrk8nIiKyGKMRdRtvlTYgcAB6+PRAg7YBW3K22PTeRERkPUzUOyFnKySn1Wmxt2AvAOCGLkzUiYjItjR9+0KUySAvKYGssNCm9xYEAdN6TgMArM3m9HciImfBRL2TEcrLocjLAwCo4+IkjsYyjpYeRVVTFbxV3ugX0E/qcIiIqJMRLysoJ+X099/yf0N+jW0rzxMRkXUwUe9klFlZAABNt24Qvb0ljsYy9NPeE0MToZApJI6GiIg6I/30d5WNC8oBQFevrkgMTYQIEetPrbf5/YmIyPKYqHcyzjbtHeD6dCIikp5aX1BOgkQdwKXp76z+TkTkFJiodzLOlqg3aZuQUZgBgIk6ERFJx1BQToKp7wAwMXoiVDIV/iz7E8dKj0kSAxERWQ4T9U7G2RL1QxcOoV5TD39Xf/T27y11OERE1Emp9QXliottXlAOAHxdfDG2+1gAwJrsNTa/PxERWRYT9c6ksRGK7GwAzrOHun7ae1JYEmQC/3MmIiJpiO7u0PTsCUC6UfXpPZuLyq0/tR5anVaSGIiIyDKY2XQiiuxsCBoNdL6+0IaHSx2ORRgS9fAkiSMhIqLOTurp76O7joaviy+K6ooMz0ciInJMTNQ7EcO097g4QBAkjqbj6jX1OFB0AADXpxMRkfT0BeWkqPwOACq5CpOiJwEAVmevliQGIiKyDCbqnYizrU//veh3NOmaEOoeih4+PaQOh4iIOjlD5XeJRtQBYFqv5urvP+T8gDp1nWRxEBFRxzBR70ScLVFPz08H0DztXXCCGQJEROTY1H37QhQEyAsLISsuliSG64KvQ6R3JOo0dfjx7I+SxEBERB3HRL2zEEWnS9T16++Ghw+XOBIiIiJA9PC4VFBOounvgiAY9lRn9XciIsfFRL2TkOfmQlZdDVGlMnyIcGQ1TTU4dOEQABaSIyIi+yF1QTkAmNpzKgDgl7xfUFwnzcg+ERF1DBP1TsIwmh4TA6hUEkfTcRmFGdCKWnTz6oauXl2lDoeIiAiAfSTq0T7RGBw8GDpRh/Wn1ksWBxERXTsm6p2EPlF3tv3TWe2diIjsidSV3/Wm92reU53T34mIHBMT9U5C4WTr09MLmgvJMVEnIiJ7ou7Xr7mgXEEBZCUlksUxKXoSFIICR0uP4njZccniICKia8NEvZNwpkJy5Q3lOFpyFADXpxMRkX0RPT2hiY4GIF1BOQDwd/XH6G6jAQBrT66VLA4iIro2TNQ7AaG8HIq8PACAOi5O4mg6bm/BXogQ0dO3J0LcQ6QOh4iIyIhhP3Wpp7/3bJ7+vvbkWuhEnaSxEBGReZiodwLKrCwAgKZbN4je3hJH03Fcn05ERPbMHgrKAcDYbmPhrfJGfm0+9hbslTQWIiIyDxP1TsCZpr0DXJ9ORET2zV5G1F0VrpgYNREAi8oRETkaJuqdgDMl6hfqLuB4eXNRnGFhwySOhoiIqCX981aRnw9Zaamkseirv285swX1mnpJYyEiovZjot4JOFOirh9Nj/OPg7+rv8TREBERtSR6e0MTFQVA+unvCaEJ6OLZBdXqavx09idJYyEiovZjou7sGhuhyM4G4Bx7qHN9OhEROYImO5n+LhNkmNZzGgBgzUlOfycichQKqQMAgK1bt2Ljxo2oqKhAREQEZs6ciT59+phs+8EHH2DXrl0tjkdEROCdd96xdqgOR5GdDUGjgc7XF9rwcKnD6TAm6kRE5AjU8fHAhg2Sj6gDzdXf3zv0Hnbm7kRpfSkC3AKkDomIiNogeaKenp6O5cuXY9asWYiNjcX27dvxxhtvYMmSJQgMDGzR/oEHHsC9995reK3VavHss89i6NChtgzbYRimvcfFAYIgcTQdk1eTh5yqHMgFOYaG8edNRET2y1D5XeIRdQDo5dcL8YHxyCzJxMbTG/FA3wekDomIiNog+dT3zZs3Y/To0RgzZoxhND0wMBDbtm0z2d7d3R2+vr6GX6dOnUJtbS1GjRpl48gdgzOtT9ePpscHxcNL5SVxNERERK3TJ+qK8+chlJVJHM2lonKs/k5E5BgkHVHXaDQ4ffo0pkyZYnQ8Pj4ex48fb9c1duzYgf79+yMoKKjVNmq1Gmq12vBaEAS4ubkZ/txR+mtY4lqWpk/UNf36XVN89tS39PxL27J1NB576pelsW+OiX1zTM7cN+oY0dsbmshIKHJyoDpyBI0jRkgaz+ToyVi0dxH+uPAHTlacRE/fnpLGQ0REVydpol5VVQWdTgcfHx+j4z4+PqioqGjz/eXl5Th06BCeeOKJq7Zbt24dVq9ebXgdFRWFxYsXXzW5vxahoaEWvV6HiSKQlQUA8B05Er5hYdd8Kan7Jooi9hTtAQBM6jcJYR3oy+Wk7pc1sW+OiX1zTM7ct/bIysrCxo0bcebMGZSXl+OZZ55BQkJCq+2PHTuGlJSUFseXLFmCLl26GF7v3bsXq1atQlFREUJCQnD33Xdf9br2Rh0fD0VODpSZmZIn6kHuQRgRMQI7cndg3cl1ePa6ZyWNh4iIrk7yNeqA6ZGI9oxO7Ny5Ex4eHm0+tKdOnYqJEye2uPaFCxeg0WjMjLYlQRAQGhqKwsJCiKLY4etZivzcOQRXVUFUqVDo4wMUFJh9DXvp2+nK0zhfdR5KmRLRqmgUXENfLmcv/bIG9s0xsW+OyZJ9UygUFv8C2VYaGxsRGRmJUaNG4e233273+5YuXQp3d3fDa29vb8OfT5w4gaVLl+LOO+9EQkIC9u3bhyVLlmDRokXo1auXReO3FnX//nDbuNEuCsoBzUXlduTuwNqTa/HMkGc4E4SIyI5Jmqh7e3tDJpO1GD2vrKxsMcp+JVEUkZaWhhtvvBEKxdW7oVQqoVQqW72OpYiiaFcfQhVHjwIA1DExEJXK5hH2ayR1337La16fPiR4CNzkbhaLRep+WRP75pjYN8fkzH1rj0GDBmHQoEFmv8/HxwceHh4mz23ZsgXx8fGYOnUqgOYv3bOysrBlyxbMmzevI+HaTJO+oJydJOoTIifAQ+mBc9XnsL9oPxJCHWd2AhFRZyNpoq5QKBAdHY3MzEyjUfHMzExcf/31V31vVlYWCgsLMXr0aGuH6bAM69OdqJAct2UjInIezz33HNRqNSIiIjBt2jT069fPcO7EiRO49dZbjdoPGDAAqamprV7PmjVprqUegebiXuqKc+cgq6iA6OfXoRg6yl3pjlujbsV3J77D2pNrkRiWCMC5ay2wb47HWfsFsG+OSqq+ST71feLEiXjvvfcQHR2NmJgYbN++HSUlJRg3bhwAYOXKlSgrK8Njjz1m9L4dO3agV69e6NatmxRhOwSFk1R8F0XRkKgnhSdJHA0REXWUn58fZs+ejejoaGg0Gvzyyy949dVXsWDBAsTFxQEAKioq4Ovra/Q+X1/fq9awsUVNGrPqEYSFAdHRwOnTCM3PBy72TUqzE2fjuxPfYfOZzfhk2idwUbgYzjlzrQX2zfE4a78A9s1R2bpvkifqSUlJqK6uxpo1a1BeXo6uXbvixRdfNDxUy8vLUVJSYvSeuro6ZGRkYObMmRJE7DicZWu24+XHUdpQCle5KwYFmz+1koiI7Et4eDjCw8MNr2NiYlBSUoJNmzYZEnVTRFG86oiGNWvSXGs9At++feF2+jSq0tJQawfP41iXWIR5hKGgtgAr9q1AclQy60g4KGftm7P2C2DfHJVU9WgkT9QBYMKECZgwYYLJc3Pnzm1xzN3dHStWrLB2WA5NKC+HIi8PAKC2g2/wO0K/LVtCaAJc5C5ttCYiIkcUExODX3/91fDa1Oh5WzVsbFGTxtx6BOr+/eG2aROUmZl28eFVJsgwpccU/C/zf1iTvQa3RN5iOOfMtRbYN8fjrP0C2DdHZeu+yWx2J7Ip5cVt2TTdukG8rIquI+L6dCIi53fmzBmjqe4xMTE4ckURtszMTMTExNg4so6xt4JyADC913QAwPZz21HeUC5xNEREZAoTdSflLNPetTot9hQ075/O9elERPapoaEBOTk5yMnJAQAUFxcjJyfHsHRt5cqVeP/99w3tt2zZgn379qGgoAC5ublYuXIlMjIycPPNNxvaJCcn4/Dhw1i/fj3y8vKwfv16HDlypEWBOXunvpioK86ehXCV9fW21Me/D+L846DWqbHp9CapwyEiIhPsYuo7WZ6zJOrHSo+hsqkSnkpPxAfGSx0OERGZcOrUKaSkpBhef/XVVwCAESNGYO7cuS3qzWg0Gnz99dcoKyuDSqVC165d8cILL2Dw4MGGNrGxsZg3bx6+/fZbrFq1CqGhoZg3b57D7KGuJ/r5QdO1KxS5uVAePYqm4cOlDglA86h6VkYW1p5ci/v73i91OEREdAUm6k7KWRJ1/bT3oWFDoZDxP1ciInvUt29ffPfdd62ev7LezOTJkzF58uQ2rzt06FAMHTq0w/FJTd2/f3OifuSI3STqU3pMwev7Xsf+ov1Yl70OfiV+UDWpkBCSALlMLnV4RESdHjMfZ9TYCEV2NgDH30M9vaC5kBzXpxMRkaNSx8fDLTUVqsxM1EodzEWhHqHo7dcbWWVZmJt26YuUMI8wLBq2CMlRyRJGR0REXKPuhBTZ2RA0Guh8faG9bPsbR6PWqbG3YC8AJupEROS41PHNS7eUmZkSR3JJ6plUZJVltTheWFuI2dtnI/VMqgRRERGRHhN1J2SY9h4XB1xlv1l7d+jCIdRp6uDn4oc+/n2kDoeIiOia6Cu/K3JyIFRVSRxNc6HW+XvmmzwnonnroQV7FkCr09oyLCIiugwTdSfkNOvT85rXpw8LHwaZwP9UiYjIMYn+/tBERACwj23aMgozUFBb0Op5ESLya/ORUZhhw6iIiOhyzH6ckNMk6tw/nYiInIRh+rsdJOrFdcUWbUdERJbHRN3ZiKJTJOoNmgYcKD4AABgebh8VcomIiK6Vul8/APaRqAe7B1u0HRERWR4TdScjz82FrLoaokoFTc+eUodzzQ4UH0CjthEh7iHo4dND6nCIiIg6RD+irrKDgnKJoYkI8wiDANN1bAQICPcIR2Jooo0jIyIiPSbqTsYwmh4TA6hUEkdz7fTT3pPCkiA4cEE8IiIi4FKirjh9GkJ1taSxyGVyLBq2CABaJOv61ynDUrifOhGRhJioOxl9ou7o+6dzfToRETkTXUAANBe3TFUePSpxNEByVDKWjV2GUI9Qo+Mh7iFYNnYZ91EnIpIYE3Uno3CC9em16locKj4EgIk6ERE5D3vbTz05KhkZd2Vg9cTVCPMMAwAsHLaQSToRkR1gou5knKGQ3L7CfdCIGnT17Ipu3t2kDoeIiMgi1Bf3U7eHgnJ6cpkcSeFJuLPvnQCAXed3SRwREREBTNSdilBeDkVeHgBAHRcncTTXjtPeiYjIGdnbiPrlbu55MwAg7XwaRFGUOBoiImKi7kSUWVkAAE23bhC9vSWO5toZCsmFJ0kcCRERkeXoR9QVp09DqKmROBpjIyJHwFXuisLaQvxV/pfU4RARdXpM1J2IM0x7r2iswJGS5imBTNSJiMiZ6IKCoA0NhSCKhme2vXBVuBpmsqXlpkkcDRERMVF3Is6QqGcUZECEiB4+PRDmESZ1OERERBbVZMfT30d1HQUA2JG7Q+JIiIiIiboTcYZEnevTiYjImdnzOnV9or6/cD+qm6Td652IqLNjou4sGhuhyM4G4Nh7qHN9OhEROTN7rPyuF+UThUjvSGhEjeF5TERE0mCi7iQU2dkQNBrofH2hDQ+XOpxrcqHugqGATVIYE3UiInI++hF1xcmTEGprJY6mpdFdRwPg9HciIqkxUXcShmnvcXGAIEgczbVJL0gHAPTx74MAtwCJoyEiIrI8XXCw3RaUA4CRESMBNBeU4zZtRETSYaLuJJxhfXp6fnOizvXpRETkzAzT3+1wnXpSeBJc5C7Ir81HdkW21OEQEXVaTNSdhDMk6iwkR0REnYGh8rsdrlN3U7hhWNgwAJz+TkQkJSbqzuCy6XOOmqjn1eThTNUZyAQZhoYNlTocIiIiq1H36wfAPhN14FL1d+6nTkQkHSbqTkCemwtZdTVElQqaXr2kDuea6Ke9xwfGw1vlLXE0RERE1mMoKJedDaGuTuJoWtKvU99XuA+1avsreEdE1BkwUXcChtH02FhAqZQ4mmujLyTHae9EROTsdKGh0AYHQ9DpoLDDgnI9fHqgm1c3NOmauE0bEZFEmKg7AUef9i6KItenExFRp6IvKKeyw+nvgiBw+jsRkcSYqDsB/bfxGgdN1M9Wn0VeTR6UMiWuD7le6nCIiIisTj/93R4rvwPAqIhLiTq3aSMisj0m6k7A0UfU9aPpg4IGwV3pLnE0RERE1mfPld+B5hluKpkKuTW5OFV5SupwiIg6HbMT9b1790Kn01kjFroGQnk5FHl5AAB1nz4SR3NtDNPeu3DaOxERdQ76qe+KEycg1NdLHE1L7kp3JIYlAuD0dyIiKZidqC9ZsgRz587F2rVrUVlZaY2YyAzKrCwAgKZ7d4jejlctXRRFQ8V3rk8nIqLOQhcaCm1QUHNBuYvPcntz+fR3IiKyLbMT9QULFqBXr174/vvvMWfOHLz33ns4ceKENWKjdnD0ae/ZFdm4UH8BrnJXDA4eLHU4REREtiEIhlF1e53+ri8ot7dwL+o19jfqT0TkzBTmviEuLg5xcXEoLy/Htm3bsGPHDuzevRuRkZG45ZZbcMMNN0DpoFuEOSJDoh4XJ3Ek10Y/7f360OvhIneROBoiIiLbUffvD9cdO6DKzIT97aYO9PLthS6eXZBXk4f0/HSM6TZG6pCIiDqNay4m5+fnhzvvvBMffvghHn/8cchkMvzvf//DI488gpUrV6K8vNyScVIrHH1EXZ+oJ4UlSRwJERGRbdl75XdBEDj9nYhIIh2u+l5cXIyTJ0+ioKAAMpkM3bp1Q2pqKp588kn8/vvvloiRWtPYCEV2NgDHTNR1og57CvYA4Pp0IiLqfJouKygHOywoBwCju44GAKSdZ6JORGRLZk99B5oLgB04cABbt27FkSNH4OnpiZtvvhnjx4+Hv78/Kisr8b///Q9ffvklrrvuOkvHTBcpsrMhaDTQ+fpCFx4udThmyyrNQkVjBTyVnhgQNEDqcIiIiGxKFx4ObUAA5KWlUP75J9SD7a9Wyw3hN0ApUyKnKgenK08j2ida6pCIiDoFsxP19evX46effkJJSQm6d++Of/zjHxg+fLjRunQfHx9MmjQJKSkpFg2WjBlNexcEiaMx3+783QCAxNBEKGTX9J0RERGR4xIEqOPjIU9LgzIz0y4TdU+VJ64PuR7pBenYmbuTiToRkY2YPfV91apViIqKwoIFC/DWW29h1KhRJovHhYaGYsaMGRYJkkxzmvXp4VyfTkREnZOh8vvRoxJH0jpOfycisj2zhzHfffddBAUFtdnO398ft99++zUFRe3jyIm6WqdGRmEGAGB4+HCJoyEiIpKGvqCcyk4LygHAyK4j8dq+15Cen456TT3cFG5Sh0RE5PTMHlH38/NDQ0ODyXMNDQ3QaDQdDoraQRQdOlHPvJCJWnUtfF18ERfgmFvLERERdZR+RF1x/DjQyucrqfX2641Qj1A0aBuQUZAhdThERJ2C2Yn6xx9/jI8++sjkuWXLluHTTz/tcFDUNnluLmTV1RBVKmh69pQ6HLNdvi2bTOjw5gNEREQOSdulC7R+fhA0Gij/+kvqcEwSBAGjI5qnv+84v0PiaIiIOgezM6Rjx461Wsl9yJAhOHLkSIeDorYZRtNjYwETNQLsnT5R57ZsRETUqV0sKAfY737qADCqK/dTJyKyJbMT9crKSvj5+Zk85+vri4qKio7GRO3gyNPeGzQN+L3odwAsJEdERGQoKGfHgx3DuwyHQlDgdOVpnK06K3U4REROz+xE3d3dHYWFhSbPFRYWws2NBUZsQXExUdc4YKJ+sPggGrQNCHILQi/fXlKHQ0REJClHGFH3VnnjupDmGZWs/k5EZH1mJ+p9+/bF+vXrUVNTY3S8pqYG69evR79+/SwWHLXOkUfU0wvSATRPexcccP93IiIiSzIk6sePA42NEkfTOk5/JyKyHbO3Z7vjjjvw4osv4oknnkBSUhL8/f1RWlqKvXv3QqPR4I477rBGnHQZobwcirw8AIC6Tx+JozHfb3lcn05ERKSnjYiAztcXsooKKI8fNyTu9mZU11F4c/+b+C3/NzRoGuCqcJU6JCIip2V2oh4eHo6UlBR89dVX+Pnnn6HT6SCTyRAXF4e///3vCA8PNzuIrVu3YuPGjaioqEBERARmzpyJPldJQNVqNVavXo1ff/0VFRUVCAgIwNSpUzF69Giz7+2IlFlZAABN9+4Qvb0ljsY8deo6HCw+CICJOhEREQBAENAUHw/XX36BMjPTbhP1OP84hLiHoKiuCPsK9+GmiJukDomIyGmZnagDQGRkJObPn4+mpibU1NTA09MTKpXqmgJIT0/H8uXLMWvWLMTGxmL79u144403sGTJEgQGBpp8z5IlS1BZWYlHHnkEoaGhqKqqglarvab7OyJHnva+r3AfNKIGXTy7oJtXN6nDISIiC8jKysLGjRtx5swZlJeX45lnnkFCQkKr7TMyMrBt2zbk5ORAo9EgIiICt99+OwYOHGhos3PnTnz44Yct3rtixYpr/sxhz9T9+xsSdXslCAJGRozEqhOrkHY+jYk6EZEVXVOirqdSqeDv79+hADZv3ozRo0djzJgxAICZM2fi8OHD2LZtG+65554W7Q8dOoSsrCy8//778PT0BAAEBwd3KAZHY0jU4+IkjsR8XJ9OROR8GhsbERkZiVGjRuHtt99us/2ff/6J+Ph43H333fDw8EBaWhoWL16MN954A1FRUYZ2bm5u+O9//2v0XmdM0gHHqPwONE9/X3ViFdJy07Bg6AKpwyEiclrXlKjrdDr88ccfyMvLQ1NTU4vzM2bMaNd1NBoNTp8+jSlTphgdj4+Px/Hjx02+5/fff0ePHj2wYcMG/PLLL3B1dcWQIUNw1113tfrwVqvVUKvVhteCIBiq01siWdRfw1aJp2Hqe79+Vr+npfum3z99ePhwSRN1W//MbIl9c0zsm2Ny5r6ZY9CgQRg0aFC728+cOdPo9T333IPff/8dBw4cMErUBUGAr6+vhaK0b4aCcn/9BTQ1AXb6hcSNXW6ETJAhuyIb56vPI8IrQuqQiIicktmJenV1NebPn4/8/PxW27Q3Ua+qqoJOp4OPj4/RcR8fn1b3Yy8qKsJff/0FpVKJZ599FlVVVfjss89QU1ODOXPmmHzPunXrsHr1asPrqKgoLF68GEFBQe2Ks71CQ0Mtej2TGhuBEycAAP6jRwNhYda/JyzTt4qGCmSWNE/pmzpoKsK8bRP71djkZyYR9s0xsW+OyZn7Zgs6nQ719fWGmXJ6DQ0NmDNnDnQ6HSIjI3HnnXcaJfJXsuYX89b+UkbXvbtRQTmNDdepm9M3P1c/DAkegv1F+7Hz/E7cF3eftcPrEGf+Ms1Z++as/QLYN0clVd/MTtS/+eYbqFQqfPDBB5g7dy5ef/11eHp64qeffsLBgwfxyiuvmB2EqU639hchiiIA4IknnoC7uzuA5gfzO++8g1mzZpkcVZ86dSomTpzY4toXLlyARqMxO15TsYaGhqKwsNAQn7UojhxBkEYDnZ8fiuRyoKDAqvezZN+25myFTtQh2ica8lo5CmqtG/vV2PJnZmvsm2Ni3xyTJfumUCgs/gXy1Zw9exa1tbWIu7iMqqGhAStWrMCZM2cQHx+PO+64w2YfSjZv3ozGxkYMGzbMcCw8PBxz5sxBt27dUF9fj9TUVLzyyiv497//jbBWvqS2xRfzVv1SZsgQ4OefEXTuHDBhgvXu04r29m1y3GTsL9qP9AvpeC7sOStHZRnO/GWas/bNWfsFsG+OytZ9MztRP3r0KGbMmGFYmy6TyRAaGor77rsParUaX331FebNm9eua3l7e0Mmk7UYPa+srGwxyq7n6+sLf39/Q5IOAF26dIEoiigtLTX58FYqlVAqlSavZ8kPjaIoWj9Rv2x9uth8U6veT88SfdudvxsAkBSWZDcf1m3xM5MK++aY2DfH5Ih9++qrrxAVFWVI1L/55hv8/PPP6NatG9avXw9vb2/ccsstVo9j9+7d+P777/Hss88aPftjYmIQExNjeB0bG4vnn38eP/zwAx588EGT17LmF/O2+MLJKzYWnj//jNrdu1F1WT+szdy+Xed3HQBg++ntOHv+LFRy+5ymD/CLQkfkrP0C2DdHJdWX8mYn6qWlpQgODoZMJoMgCGhoaDCcGzJkCN59912zAo2OjkZmZqZRddjMzExcf/31Jt/Tu3dv7N27Fw0NDXB1bd6/s6CgAIIgICAgwNzuOBxHrvienn+pkBwREUnr3LlzmHBx1FYURezevRu33347pk2bhm+//RZpaWlWT9TT09Px0Ucf4emnn0Z8G1O9ZTIZevTogcLCwlbb2OKLeWt+KdOkLyiXmSnJB9329q2vf18EugWipL4EGQUZGN5luA2i6xhH/DKtvZy1b87aL4B9c1S27pvM3Dd4e3ujrq4OAODn54fc3FzDuZqaGrO3SZs4cSJ+/vln7NixA+fPn8fy5ctRUlKCcePGAQBWrlyJ999/39B++PDh8PLywocffojz588jKysLK1aswKhRo5y2EuzlHDVRL60vxZ9lfwIAksKTJI6GiIjq6urg7e0NoHkafE1NDZKSmv997tevH4qKiqx6/927d+ODDz7AE088gcGDB7fZXhRFnD171qmLyxkqv//5J3DZWnt7IxNkGBkxEgCw8/xOSWMhInJWZo+oR0VFITc3F4MHD8agQYOwevVquLm5QaFQ4JtvvkGvXr3Mul5SUhKqq6uxZs0alJeXo2vXrnjxxRcNUwLKy8tRUlJiaO/q6oqXX34Zn3/+OV544QV4eXlh2LBhuOuuu8ztiuMRRYdN1PXbsvXx74NAt0CJoyEiIk9PT8Pz9ejRo/D19TWsvzN3mnhDQ4PRSHdxcTFycnLg6emJwMBArFy5EmVlZXjssccAXErSZ86ciZiYGMMSOJVKZVja9v3336NXr14ICwszrFHPycnBQw891NGu2y1tZCR03t6QVVVBcfw4NP36SR1Sq0Z3HY3V2auRlpuGlxNfljocIiKnY3aifvPNNxu+Zb/rrruQnZ2NDz74AAAQEhKCBx54wOwgJkyYYJh+d6W5c+e2ONalS5drKlrn6OTnzkFWXQ1RpYKmZ0+pwzGLflu2pDCOphMR2YM+ffrg+++/R3V1NbZs2WK0vVphYaFZy8lOnTqFlJQUw+uvvvoKADBixAjMnTu3xZfu27dvh1arxWeffYbPPvvMcFzfHgBqa2uxbNkyVFRUwN3dHVFRUUhJSUFPB3v+mUUQoO7XDy7p6VAdOWLXibp+m7a/yv9CXk0eunh2kTokIiKnYnaifvkaMm9vb7z11luG6e9dunSBXC63XHRkxDCaHhsLtLIGz17pE3WuTycisg/33HMP3njjDSxfvhwhISFGW6vu2bPHrBlyffv2xXfffdfq+Su/dF+4cGGb15w5c2aL/dY7A3V8PFzS06HMzATuvlvqcFrl7+qPgUEDcbD4IHad34V7et8jdUhERE7FrES9qakJr776Km6//XZDwi4IArp162aV4MiYo057L6gtwOnK05AJMgwNGyp1OEREBCA4OBhLly5FTU1Ni/3LH3roIadeC27Pmi5+vlIeOSJxJG0b3XU0DhYfRFpuGhN1IiILM6uYnEqlwrlz5zhqLhF9oq5xsERdX+29f0B/+LiY3naPiIikcWWS3tTUhG7duhkKzZFtGQrKZWXZdUE5ABjVdRQA4Ne8X6HW2XesRESOxuyq7zExMTh58qQ1YqE2KBx0RJ3T3omI7E96ejq2bt1qeF1YWIinnnoK9913H+bPn4+amhoJo+u8tJGR0Hl5QWhshCI7W+pwrio+MB7+rv6oVlfj96LfpQ6HiMipmJ2o33fffdi+fTt27dpltIc6WZdQVgZFfj4AQN2nj8TRmMdQSI7bshER2Y1NmzahsbHR8Prrr79GbW0tkpOTkZeXh3Xr1kkYXScmk0F9sYicvU9/N9qmLXenlKEQETkds4vJvfzyy9BoNPjwww/x4YcfwsXFBYIgGLX58ssvLRYgNVNmZQEANN27Q3Sg6Yjnqs7hfM15KAQFEkITpA6HiIguKioqQteuXQE0T3c/fPgwHn74YYwYMQLh4eHYtGkT7rvvPomj7JzU/fvDZc8eqDIzUX/nnVKHc1Wjuo7C2pNrsSN3B15MeFHqcIiInIbZiXpiYmKLxJysz1ELyelH0wcFD4KH0kPiaIiISK+xsREuLi4AgJMnT0KtVhu2aIuIiEBZWZmU4XVqan1BucxMiSNp24guIyBAQFZZFgprCxHqESp1SERETsHsRN3UvuZkfYZEPS5O4kjMw/XpRET2yc/PDzk5OYiLi8OhQ4cQHh5uKCBXW1trSOLJ9pouFpRTZGUBGg2gMPvjms0EuAVgQNAAHLpwCDvP78RdsXdJHRIRkVMwe406SUM/9d2RRtRFUWSiTkRkpxISEvDtt9/iP//5D1JTU5GUdKmOyNmzZxESEiJhdJ2bNjoaOk9PyBoa7L6gHHCp+ntabprEkRAROQ+zv6LdtWtXm21GjBhxTcFQKy57UDtSon6y4iSK64vhInfB4ODBUodDRESXueuuu9DQ0IATJ05g+PDhmDx5suHcwYMH0f/iqC5J4GJBOZe9e6HMzITGzovIjooYhSUHl+DXvF+h0WmgkNnvDAAiIkdh9r+kH374YZttmKhbljI7G4JGA52vL3Th4VKH026/FTSPpl8Xch1cFa4SR0NERJdTqVSYPXu2yXOvv/66jaOhK6n7929O1I8etfuCcgODBsLXxRcVjRU4WHyQxWOJiCzA7ET9/fffb3Gsuroa+/fvR3p6OubNm2eJuOgyRvunO1AhP057JyJyDPn5+aipqYGXlxfCwsKkDodwqaCcygEKysllcoyIGIENpzYgLTeNiToRkQWYnagHBQWZPBYdHQ2tVovU1FQWnLMwR6z4rhN1SM9PB8BEnYjIXu3Zswdff/01SktLDccCAgLw97//HUOHDpUwMtIn6opjxwCtFpDLJY7o6kZFjGpO1M+n4fnrn5c6HCIih2fRYnL9+vXD77//bslLEhwzUc8qy0JFYwXcFe4YEDRA6nCIiOgKBw8exNKlS+Hu7o57770Xjz32GO655x64u7tj6dKl+OOPP6QOsVPTREVB5+4OWX09FCdPSh1Om0ZGjAQAHCk5guK6YmmDISJyAhZN1EtKSiCTsZC8Rel0Dpmo60fTh4YNhVKmlDgaIiK60rp16zBgwAC89dZbmDRpEm688UZMnjwZ//73v9G/f3+sXbtW6hA7N7kc6n79ADjGfupB7kHoH9hcgHDn+Z3SBkNE5ATMzqqzsrJa/MrMzMSmTZvw//7f/0O/iw8Vsgx5bi5kNTUQVSpoevaUOpx24/p0IiL7lpOTg/Hjx7f4gl0QBEyYMAE5OTnSBEYG6ouV95VHjkgcSfuMimjepo2JOhFRx5m9Rj0lJaXVc/3798eDDz7YoYDImGE0PTYWUDrGyLRGp8Hegr0AmKgTEdkrmUwGjUZj8pxGo+EMOTugX6fuCCPqADC662i8e+hd7Dq/C1qdFnKZfa+rJyKyZ2Yn6gsWLGhxTKlUIigoCL6+vpaIiS7jiNPeM0syUaOuga+LL+L846QOh4iITOjRowc2btyIwYMHQ6VSGY6r1Wps2rQJPR1oFpezMiTqR486REG5QcGD4KPyQUVjBf648AeuC7lO6pCIiByW2Yl6XBwTL1vSJ+oaB0rU9dPeh4YO5bfpRER26o477sCiRYvw2GOPYejQofD19UVFRQUyMjJQU1OD+fPnSx1ip6fp0aO5oFxdHRSnT0PTq5fUIV2VQqbAjV1uxOYzm7Hz/E4m6kREHWD2vLb8/HxkZWWZPJeVlYWCgoIOB0WXKBxwRJ3bshER2b/evXvj5ZdfRlBQELZu3YpVq1Zh27ZtCA4OxksvvYSAgACpQyS53PD8d6Tp7wCQlpsmcSRERI7N7BH1r776CmFhYSZH1n///XcUFBTg+ee5f6YlCGVlUOTnAwDUffpIHE37NGobsa9wHwAm6kRE9i4uLg6vv/46GhsbUVtbCw8PD7i4uGDv3r1ISUnBqlWrpA6x01PHx8Nl/34oMzNRP3261OG0aWTXkQCAwxcOo7S+FAFu/MKHiOhamD2ifurUKfRpJWmMi4vDqVOnOhwUNVNenLmg6d4dore3xNG0zx/Ff6BB24BAt0DE+MVIHQ4REbWDi4sL/P394eLiInUodAXDFm0OUvk9xD0Ecf5xECGy+jsRUQeYnajX1dXB1dXV5DmVSoXa2toOB0XNHLGQ3OXbsgmCIHE0REREjs2ooJxOJ3E07aOf/s5EnYjo2pmdqPv7++PkyZMmz508eZKV3y3IkKg7UAE/faKeFJYkcSRERESOT9OzJ3SurpDV1kJx+rTU4bTLqK6X9lPXiY7x5QIRkb0xO1G//vrrsWHDBhw9etTo+LFjx7BhwwYkJCRYLLjOTj/13VFG1Os19ThYfBAA16cTERFZhEJh2PnFUQrKDQkZAi+lF8oaynD4wmGpwyEickhmF5ObMWMGDh8+jFdffRXh4eHw9/dHWVkZ8vPzERERgdtvv90acXY+DQ1QZGcDcJxEfX/hfqh1aoR7hCPSO1LqcIiI6Aqn2zkiW1xcbOVIyBxN8fFQHTgA5ZEjqJ82Tepw2qSUKXFjlxuRmpOKtNw0DAoeJHVIREQOx+xE3d3dHa+//jo2b96Mw4cPo6SkBN7e3rjjjjtw6623trp+ncyjzM6GoNFA5+sLXXi41OG0C9enExHZtxdffFHqEOgaqPv3B+A4BeWA5unvqTmpSDufhqeHPC11OEREDsfsRB0AXF1dMWPGDMyYMcPS8dBFRvunO0jSa1ifHs716URE9ujRRx+VOgS6BoaCckeONBeUk5m9ctHmRkaMBNC8G0xZQxn8Xf2lDYiIyMGYnahXVVWhpqYG4SZGefPz8+Hp6QlvB9lKzJ45WsX3qqYqHC5pXofG9elERPZp5MiRUodA10DTqxdEV1fIamogP3MG2h49pA6pTeGe4ejt1xt/lf+FX87/gik9p0gdEhGRQzH7K9lPP/0UGzduNHlu8+bN+PzzzzscFDleop5RkAGdqEOkdyS6eHaROhwiIiLnoVBA3acPAEDlYNPfASDtfJrEkRAROR6zE/Xjx49j4MCBJs8NGDAAx48f72hMpNM5XKJ++fp0IiIisizD9HcHqfwOcJs2IqKOMDtRr66uhqenp8lzHh4eqKqq6nBQnZ08NxeymhqIKhU0PXtKHU67MFEnIiKyniYHTNSvD7keHkoPlNSX4GjJ0bbfQEREBmYn6j4+Pjh37pzJc+fOnWs1iaf2M4ymx8YCSqXE0bStrKEMWWXNe74nhbGQHBERkaUZKr8fPdpcUM4BqOQqDA8fDgDYkbtD4miIiByL2Yn6wIEDsW7dOuTn5xsdLygowPr16zFoEPfK7ChHm/a+p2APACDWLxZB7kESR0NEROR8NDExEF1cIKuuhvzsWanDabfLp78TEVH7mV31/fbbb8fBgwfx7LPPom/fvvD390dZWRmOHTsGT09P3HHHHdaIs1PRJ+oaB0nUOe2diIjIypRKqOPioPrjDygzM6GNipI6onYZFdGcqB8oPoCKxgr4uvhKGxARkYMwe0Td398fb775JoYPH46zZ89i165dOHv2LG688Ub83//9HxSKa9qanS6jcLARdSbqRERE1qef/u5Ild8jvCLQy7cXdKIOv5z/RepwiIgcxjVl1f7+/nj00UcNr3U6HQ4dOoTPPvsMBw8exMqVKy0WYGcjlJVBcXFZgX4rFntWWFuIkxUnIUDA0LChUodDRETktByx8jvQPP09uyIbO8/vxKQek6QOh4jIIXRo+LuwsBBpaWnYtWsXysvLoVAokJiYaKnYOiVlVnNRNk337hC9vSWOpm3pBekAgH6B/TidjYiIyIqaLi8oJ4qAIEgcUfuM6joKy44sw87zOyGKIgQHiZuISEpmJ+pNTU3Yu3cvduzYgT///NNwfOLEiZgyZQq8vLwsGmBn42iF5NLzmxN1TnsnIiKyLk1MDESVCrLKSsjPnoU2MlLqkNolMTQRbgo3FNUV4VjZMfQL6Cd1SEREdq/difrJkyexY8cOpKeno76+Hq6urhg5ciQSExOxePFiDBkyhEm6BRgS9bg4iSNpH65PJyIishGVCuo+faA6fLi5oJyDJOouchfcEH4Dtp/bjrTcNCbqRETt0K5E/ZlnnkFubi4AICYmBqNGjUJSUhJcXV1RV1dn1QA7G/3Ud0cYUT9XdQ7nqs9BISiQEJIgdThEREROT92/f3OifvQoGiY5znrvUV1HYfu57diZuxOPD3xc6nCIiOxeuxJ1fZI+ePBg3HvvvYiIiLBqUJ1WQwMU2dkAHCNR169PHxg8EJ4qT4mjISIicn76gnIqRysod3Gbtv1F+1HVVAVvlf3X4SEiklK7tme7//770b17dxw8eBD//Oc/8dJLL+Hnn39GfX29tePrVJTZ2RA0Guh8faELD5c6nDbpp70nhSVJHAkREVHnYKj8fuRIc0E5B9HduzuifaKhFbX4Ne9XqcMhIrJ77RpRT05ORnJyMk6dOmVYp75s2TIsX74cgwcPBgBW8LQAo/3T7fzvUxRFFpIjIiKyMXVsbHNBuYoKyHNzoe3WTeqQ2m1U11E4XXkaablpuDXqVqnDISKya+0aUdfr0aMHHn74YXz88ceYO3cuevTogb179wIAPvroI2zatAnV1dVWCbQzcKSK76cqT6GwrhAuchcMCRkidThERESdg0oFde/eABxvP/XREaMBAGnn0yA60GwAIiIpmJWo66lUKtx0001YuHAh/vvf/2Ly5MlobGzEihUr8Oijj1o6xk7DkRJ1/bT3IcFD4KZwkzgaIiKizkOt30/9yBGJIzHP0LChcJW7orC2EH+V/yV1OEREds3sfdSvFBoainvuuQd33XUX/vjjD6SlpVkirs5Hp3PIRD0pnOvTiYg6u6ysLGzcuBFnzpxBeXk5nnnmGSQkXH03kKysLHz55Zc4f/48/Pz8MGnSJIwfP96ozd69e7Fq1SoUFRUhJCQEd999d5vX7QwMibqDjai7KlyRFJ6EHbk7kJabhj7+faQOiYjIbnU4UdeTyWQYMmQIhgwxfxr01q1bsXHjRlRUVCAiIgIzZ85Enz6m//E+duwYUlJSWhxfsmQJunTpYva97YU8NxeymhqIKhU0PXtKHc5V6UQd9hTsAQAMDx8ucTRERCS1xsZGREZGYtSoUXj77bfbbF9cXIw333wTY8aMweOPP47jx4/j008/hbe3N4YOHQoAOHHiBJYuXYo777wTCQkJ2LdvH5YsWYJFixahV69e1u6SXTOq/C6Kdl/X5nKjIkYZEvU5A+ZIHQ4Rkd2yWKJ+rdLT07F8+XLMmjULsbGx2L59O9544w0sWbIEgYGBrb5v6dKlcHd3N7z29nbsbT4Mo+mxsYBSKXE0V/dX2V8oayiDu8IdA4IGSB0OERFJbNCgQRg0aFC722/btg2BgYGYOXMmACAiIgKnTp3Cpk2bDIn6li1bEB8fj6lTpwIApk6diqysLGzZsgXz5s2zdBccirp3b4hKZXNBubw8aB1o29xRXUcBe5q3aatpquH2rkRErZA8Ud+8eTNGjx6NMWPGAABmzpyJw4cPY9u2bbjnnntafZ+Pjw88PDxsFabVOeK098TQRKjkKomjISIiR5OdnY34i6PCegMHDkRaWho0Gg0UCgVOnDiBW281rgw+YMAApKamtnpdtVoNtVpteC0IAtzc3Ax/7gj9++1ilxtXV2hiY6E8ehSqI0fQ0LVrhy5ny75F+0YjyjsKZ6rOYHfBbtwSeYtV72dXPzcLc9a+OWu/APbNUUnVN0kTdY1Gg9OnT2PKlClGx+Pj43H8+PGrvve5556DWq1GREQEpk2bhn79+rXa1poP7suv0ZFr6RN1Td++dvUfuKm+6RP1G7rcYFexmoP/mDgm9s0xsW90pYqKCvj4+Bgd8/HxgVarRXV1Nfz8/FBRUQFfX1+jNr6+vqioqGj1uuvWrcPq1asNr6OiorB48WIEBQVZLPbQ0FCLXatDhg4Fjh6F3+nTQFiYRS5pq77dGnsr3t//PjJKMvDgsAdtck+7+blZgbP2zVn7BbBvjsrWfZM0Ua+qqoJOpzP5sG7tQezn54fZs2cjOjoaGo0Gv/zyC1599VUsWLAAcXFxJt9jiwc30MEf3l/N1U99RoyAj4UeuJak75tGp0FGUQYAYHL8ZITZYazm4D8mjol9c0zsG13uyi839Nt1Xe1LD1EUr3p+6tSpmDhxYot7XLhwARqNpiPhQhAEhIaGorCw0C62FnPv2RM+ABrS01FeUNCha9m6b4kBiXgf72PLiS3Iz8+36hdd9vZzsyRn7Zuz9gtg3xyVJfumUCjanYNKPvUdMP1Qbu0f7fDwcISHhxtex8TEoKSkBJs2bWo1Ubfmg1t/vY788ISyMoTm5gIACoODIXbwgWtJV/btUPEhVDVWwVvljVAxFAV2FKs5+I+JY2LfHBP71j7mPLwdnamR8aqqKsjlcnh6erbaprKyssWX+5dTKpVQtlLnxVL/7YmiaBf/HTddnEmozMyEqNNZpKCcrfo2LGwYXOQuyKvJw4nyE4jxi7H6Pe3l52YNzto3Z+0XwL45Klv3TdJE3dvbGzKZzOwH8ZViYmLw66+/tnreFg9u/bWu5XqGae/du0Pn5dVcwdXO6Pu2O283gOaHrEyQOfz/iPzHxDGxb46JfSO9Xr164cCBA0bHDh8+jOjoaCgUzR9NYmJicOTIEaMv2jMzMxETY/2kzhGo+/SBqFBAXlYGeX4+tA60842bwg1DQ4diV94u7MjdYZNEnYjI0cikvLlCoUB0dDQyr9gHNDMzE7Gxse2+zpkzZ1qsY3MkjlhI7obwGySOhIiI7EVDQwNycnKQk5MDoHn7tZycHJSUlAAAVq5ciffff9/Qfvz48SgpKTHso75jxw7s2LEDt912m6FNcnIyDh8+jPXr1yMvLw/r16/HkSNHWhSY67RcXaG5+KWFo+2nDlys/g5g5/md0gZCRGSnJJ/6PnHiRLz33nuIjo5GTEwMtm/fjpKSEowbNw5A88O9rKwMjz32GIDm7VqCgoLQtWtXaDQa/Prrr8jIyMA///lPKbvRIYZEvZWp+/aiSduEfUX7ADBRJyKiS06dOoWUlBTD66+++goAMGLECMydOxfl5eWGpB0AgoOD8eKLL+LLL7/E1q1b4efnhwceeMCwNRsAxMbGYt68efj222+xatUqhIaGYt68eZ1+D/XLNcXHQ5mVBeWRI2i4xbrV0y1tVNdRWLh3ITIKMlCrroWH0nl28iEisgTJE/WkpCRUV1djzZo1KC8vR9euXfHiiy8a1uld+XDXaDT4+uuvUVZWBpVKha5du+KFF17A4MGDpepChymzsgDY/4j6oQuHUK+pR4BrAGL92j/jgYiInFvfvn3x3XfftXp+7ty5LY7FxcVh8eLFV73u0KFDjZJ3Mqbu3x/49lsojxyROhSz9fDpgW5e3XCu+hx+y/8N47uPlzokIiK7InmiDgATJkzAhAkTTJ678uE+efJkTJ482RZh2UZDAxTZ2QDsP1HXT3tPCk/iVkREREQSU1/ci16Zmdlc38aBns2CIGBkxEh89edXSMtNY6JORHQFSdeoE6DMzoag0UDn6wvdZdXs7RHXpxMREdkPdZ8+EOVyyEtKIHPAXVj069TTctNYjJGI6ApM1CWmuLyQnB1/E16nqcOBouYKvUzUiYiI7ICbm6GgnMoBp7/fEH4DVDIVcmtycarylNThEBHZFSbqEnOUiu+/F/6OJl0TQj1CEeUdJXU4REREhIvr1OGYld89lB5ICE0A0DyqTkRElzBRl5ijJOrp+ekAgBvCbuD6dCIiIjvRdPk6dQfEbdqIiExjoi4lnc5hEnXD+vQunPZORERkLwwj6keONBeUczCju44GAOwp2IN6Tb3E0RAR2Q8m6hKS5+ZCVlMDUaWCpmdPqcNpVVVjFQ5dOASgeUSdiIiI7IOmb1+IMhnkFy5AVlQkdThm6+XbC108u6BR22iYvUdEREzUJWUYTY+NBZRKiaNp3a9nf4VW1CLSOxIRXhFSh0NEREQXiZcVlHPE6e/6bdoArlMnIrocE3UJOcq097Sc5gdnUliSxJEQERHRlfTT3x2x8jtwafp72nkm6kREekzUJaRP1DV2nqjvOLMDALdlIyIiskdqBy8oNzx8OBSCAjlVOThTeUbqcIiI7AITdQkpHGBEvayhDIcKDwEAksI5ok5ERGRvmi4vKOeAPFWeuD70egCc/k5EpMdEXSJCWRkU+fkAAHWfPhJHY5pWp8XyY8shQkSEZwQCXAOkDomIiIiuYCgoV1TkkAXlAE5/JyK6EhN1iSizsgAAmu7dIXp7SxxNS6lnUpH4bSL+c+A/AIDzNeeR+G0iUs+kShwZERERXU50dzfsHuOo09/1+6mn56dzmzYiIjBRl4w9F5JLPZOK2dtno6C2wOh4YW0hZm+fzWSdiIjIzhj2Uz96VOJIrk1vv94I9QhFg7YBGQUZUodDRCQ5JuoSMSTqcXESR2JMq9Ni/p75ECG2OKc/tmDPAmh1WluHRkRERK1w9IJygiBgVETzqPqO8zskjoaISHpM1CWin/pubyPqGYUZLUbSLydCRH5tPjIK+W03ERGRvdAn6ioHTdSBS9Pfd+bulDQOIiJ7wERdCg0NUGRnA7C/RL24rtii7YiIiMj61H37QhQEyAsLIbtwQepwrsmNXW6EQlDgVOUpnKs6J3U4RESSYqIuAWV2NgSNBjpfX+jCw6UOx0iwe7BF2xEREZH1iR4eDl9QzlvljetCrgPA6e9EREzUJWC0f7ogSByNscTQRIR5hLV6XoCAcI9wJIYm2jAqIiIiaouhoJyDJuoAMLLrSADcT52IiIm6BOy54rtcJseiYYtMnhPQ/KVCyrAUyGVyW4ZFREREbTAk6keOSBzJtdOvU/8t/zc0ahsljoaISDpM1CVgz4k6AHTz7mbyeJhHGJaNXYbkqGQbR0RERERtcYaCcn39+yLYLRj1mnoWriWiTk0hdQCdjk5n94n6d8e/AwDcGnkrHuj3AJpUTVA1qZAQksCRdCIiIjul7tevuaBcQQFkJSXQBQZKHZLZBEHAyK4j8d2J75CWm4abutwkdUhERJLgiLqNyXNzIaupgahSGYq+2JNGbSPWnlwLALir911ICk/C3f3vRlJ4EpN0IiIiOyZ6ekITHQ3Awae/R3CbNiIiJuo2ZhhNj40FlEqJo2npp7M/obyxHKHuoRjR5f+3d+fxUZVn/8c/s2TfF8hCAoQlEQUUVFaV3fwMVEABcXl80mqxRWvVqq2PVYS2+qht1dal5amWthZZRRFQEVkKgqC2gAiyCGFLAoSsZJ+Z8/sjZCCEQCCTOZnh+3698oI5OXPOdRHbk2vu+77uIWaHIyIiIhegfvq7LzeUuyHlBqwWK7uKd3H4xGGzwxERMYUKdS9r69Pe5+6aC8CE9AkaQRcREfEx/tBQLjoomqvbXw3AyoPapk1ELk0q1L2svlB3tMFCPb88n9WHVgMwqfskc4MRERGRC+YPI+oAQ1OGAtqmTUQuXSrUvczehkfUF+xegMtwcW3CtXSN7mp2OCIiInKBanv2BMB++DDWwkKTo7l4w1OHA7Audx01zhqToxER8T4V6l5kKSzEnpsLQG2PHiZH05BhGO5p75MzJpscjYiIiFwMIyICR1oa4Nuj6j3jexIfEk95bTlfHPnC7HBERLxOhboXBWzfDoCjUyeMyEiTo2noy6NfsrdkLyH2EMakjTE7HBEREblINfXT3314nbrVYnU3tdX0dxG5FKlQ96K23Ehu7s660fQxaWMIDww3ORoRERG5WP6yTr1++rsKdRG5FKlQ9yJ3oX755SZH0lBFbQWL9y4GNO1dRETE1/lD53c4tU3bt0Xfknsi1+xwRES8SoW6F9VPfW9rI+pL9y2lvLaczpGd6Z/Y3+xwREREpAXqC3X7wYNYfLihXGxwLFe1uwrAvSuNiMilQoW6t1RVYd+9G2h7hXp9E7mJ3SdisVhMjkZERERawoiMxNG5MwCB27aZG0wLDUsZBmj6u4hcelSoe0nA7t1YHA5c0dG4kpPNDsdtf+l+NuRtwIKFiekTzQ5HREREPMBf1qkPS60r1NceXkutq9bkaEREvEeFupc02D+9DY1az9s1D4AbOtxAh/AOJkcjIiIinlDjJ4X6le2uJDY4lrLaMr468pXZ4YiIeI0KdS9pix3fnS6nu1C/LeM2k6MRERERT6nt2RPw/YZyVouVoSlDAU1/F5FLiwp1L2mLhfpnuZ+RW55LVGAUmZ0yzQ5HREREPMTdUO7AASzFxeYG00L1hfrKgyvNDURExItUqHuDy9UmC/X6JnLjuo0j2B5scjQiIiLiKUZ0NI5OnQDfH1UfmjIUCxa2F24nvzzf7HBERLxChboX2A4exHriBEZgII5u3cwOB4Di6mI+zPkQgNvSNe1dRETE39SPqgf6eKEeFxLHle2uBGDNoTUmRyMi4h0q1L3APZqekQEBASZHU+f9796n2llNj9ge9I7vbXY4IiIi4mH+0vkdNP1dRC49KtS9oC1Oe69vIjcpfZL2ThcREfFDNSdH1H196js03KbN4XKYHI2ISOtToe4F9YW6o40U6t8WfsvmY5uxW+zc2u1Ws8MRERGRVuBuKJeTg6WkxORoWqZPuz5EB0VTUlPCf47+x+xwRERanQp1L7C3sRH1+iZyozqNIi4kzuRoREREpDUYMTE4UlMB3x9Vt1ltDEkZAmj6u4hcGlSotzJLYSH23FwAanv0MDkaqHXVsnD3QqBu2ruIiIj4r/pR9YBt20yOpOXc+6kf0n7qIuL/VKi3soDt2wFwdOqEERlpcjTw6YFPOV51nHYh7RieOtzscERERKQV+VNDuWEpdevUvy74mmMVx0yORkSkddnNDsDftbVGcvXT3id0n4Ddqh+/iIh4xscff8zixYspLi4mJSWF7OxsejQxk+y1115jzZrG22ylpKTw+9//HoDVq1fz+uuvNzrn7bffJjAw0LPB+7H6Qj3QDwr1dqHt6BXfi68Lvmb1odVMTJ9odkgiIq1GlVorcxfql19uciRwtOIonx74FNDe6SIi4jnr169n1qxZ3HvvvWRkZLBixQqeffZZXnrpJeLj4xud//3vf58777zT/drpdPLYY48xYMCABueFhITwyiuvNDimIv3C1Hd+t+/bh6W0tE3M7muJoSlD+brga1YdWqVCXUT8mqa+t7L6qe9tYUT93T3v4jSc9Gnfh+4x3c0OR0RE/MSSJUsYPnw4I0aMcI+mx8fHs3z58rOeHxoaSnR0tPvru+++o7y8nGHDhjU4z2KxNDgvOjraC9n4FyM2FkdKCuAf69Trl+2tObQGp8tpcjQiIq1HI+qtqaoK++7dgPmFumEYzN1ZN+19cvpkU2MRERH/4XA42Lt3L+PGjWtwvHfv3uzcubNZ11i5ciW9evWiXbt2DY5XVVUxdepUXC4XnTt35rbbbiMtLa3J69TW1lJbW+t+bbFYCAkJcf+9Jerf39LrmKG2d2/shw4R+PXX1A4e3Oj7vpTb1QlXExUYRXF1MVsKtnB1wtXnPN+XcrtQ/pqbv+YFys1XmZVbmyjUL2Rd2+m+/fZbnnnmGVJTU3nxxRe9EOmFCdi9G4vDgSs6GldysqmxbD62mV3Fuwi2BXNz15tNjUVERPxHaWkpLpeLqKioBsejoqIoLi4+7/uLiorYvHkzDz74YIPjycnJTJ06lY4dO1JZWcmyZct46qmnePHFF0lKSjrrtRYtWsSCBQvcr9PS0nj++ecbfQDQEomJiR67ltcMHgzLlhG5ezeRTfzbge/kdmO3G5m/fT5fFH3BmKvGNOs9vpLbxfDX3Pw1L1BuvsrbuZleqF/ourZ6FRUVvPbaa/Tq1atZvwiYocH+6SZ/ujRn5xwAstKyiAz07fVpIiLS9pxtpKE5ow+rV68mLCyMfv36NTienp5Oenq6+3VGRgY///nP+fDDD/nBD35w1muNHz+eMWNOFW719z927BgOh6NZeTTFYrGQmJhIfn4+hmG06FreFtS5M7GAY+NGjuXlNfq+r+U2IH4A85nP4h2L+dFlPzrnub6W24Xw19z8NS9Qbr7Kk7nZ7fZmf3hseqF++ro2gOzsbLZs2cLy5cu54447mnzfzJkzGTx4MFarlS+++MJb4V6QttLxvdJRyeK9iwE1kRMREc+KjIzEarU2+tC8pKSk0Sj7mQzDYNWqVVx//fXY7ef+lcRqtdK1a1fy8/ObPCcgIICAgIAm7+UJhmH43C+h7oZye/dCaSlGRMRZz/OV3Or3U99ybAsFFQXEhcSd9z2+ktvF8Nfc/DUvUG6+ytu5mVqoX+y6tlWrVnHkyBF+8pOfsHDhwvPepzXXrJ1+jTOvVV+oO3r2NHW9xkf7P6K0ppTUiFQGdxh8QbH463oTf80LlJuvUm6+yZ9zay673U6XLl3YunVrg1HxrVu3cu21157zvdu3byc/P5/hw4ef9z6GYbB//35SU1NbHPOlxhUXhyM5GXtuLgHffEPNGd31fU1iWCKXx17O9sLtrDm8hlu63WJ2SCIiHmdqoX4x69ry8vKYPXs206dPx2azNes+3lizBmesW3C54GTH9+ihQ4k+x5qw1rbok0UA/KDvD+iQ3OGiruGv6038NS9Qbr5Kufkmf86tOcaMGcMf//hHunTpQnp6OitWrKCgoIBRo0YBMHv2bAoLC3nggQcavG/lypV0796djh07Nrrm/Pnz6d69O0lJSe416jk5Odxzzz1eycnf1PbuXVeob93q84U61HV/3164nVUHV6lQFxG/ZPrUd2j+ujaXy8Uf/vAHJk6cSPIFNGdrzTVr9dc7c92Cbf9+2peVYQQGkh8VBWdZE+YNh8oOsXLfSgCykrPIu8A4/HW9ib/mBcrNVyk332TWurW2ZtCgQZSVlbFw4UKKiopITU3liSeecOdTVFREQUFBg/dUVFSwceNGsrOzz3rN8vJyZs6cSXFxMaGhoaSlpTF9+nS6devW2un4pdpevQj56CMCvv7a7FA8YmjqUF7d8iqrD63GZbiwWrTjsIj4F1ML9Qtd11ZZWcl3333Hvn37eOutt4BTawUmT57ML3/5S3r27Nnofd5Ys3Z6LAD2k3uV1mZkYNjtYNIvp/N2zcPAYHDyYFLCUy46X39db+KveYFy81XKzTf5c27NlZmZSWZm5lm/d//99zc6Fhoayttvv93k9bKzs5ss4uXC1fbuDUDA1q0mR+IZ1yRcQ0RABIVVhWwt2MpV7a4yOyQREY8ytVC/0HVtISEh/Pa3v21wbPny5Wzbto1HHnmE9u3bt3rMzdUWGsm5DJd773Q1kRMREbl01Rfq9u++w3LiBEZ4uMkRtUyANYDrO1zPspxlrDq4SoW6iPgd0+cJjRkzhk8//ZSVK1dy6NAhZs2a1Whd26uvvgrUdXzt2LFjg6/IyEgCAgLo2LEjwcHBZqbSgLuRnImF+oa8DRw8cZCIgAiy0rJMi0NERETM5YqPx5mUhMUw3L+j+LqhqUMBWHlwpbmBiIi0AtPXqF/MujZfYG8DI+r1o+k3d72ZEHuIaXGIiIiI+Wp69SIkL4+Ar7+mpn9/s8NpsWEpwwDYfGwzhVWFxAbHmhyRiIjnmF6ow4WvazvdpEmTmDRpUmuEddEshYXYc3MBqO3Rw5QYSmtKWbpvKaBp7yIiIlI3/T1k+XK/WaeeHJ7MZTGX8W3Rt6w9vJaxXceaHZKIiMeYPvXdHwWc3JbN0akTRmSkKTF8sPcDqpxVdI/uTt/2fU2JQURERNqO2l69APym8zvAsNS6UXVNfxcRf6NCvRW0hUZypzeRO9tWdyIiInJpcTeU27MHS0WFydF4xtCUoQDubdpERPyFCvVWYHahvrtoN18d/Qqbxcat3W81JQYRERFpW1zt2+NMTMTicvlNQ7l+if0ICwijoLKAb477R04iIqBCvVXUT303q1Cft2seAMNTh9M+tO1sWSciIiLmck9/95N16oG2QK5Lvg7Q9HcR8S8q1D2tqgr77t2AOYW6w+Vgwe4FgJrIiYiISEM1J6e/+0uhDqemv686uMrcQEREPEiFuocF7N6NxeHAGRODKynJ6/dfdXAVRyuPEhscy4iOI7x+fxEREWm7/LGh3PDU4QB8dfQriquLzQ1GRMRDVKh7WP3+6Y4rrgATmrjVT3u/tdutBNoCvX5/ERERabvqC3X77t1YKitNjsYzUiJS6B7dHZfhYu3htWaHIyLiESrUPczMRnLHK4/zyYFPALgtQ9PeRUREpCFXYiLO9u2xuFzuwQV/UL9Nm6a/i4i/UKHuYWYW6u/ueZdaVy2943vTI7aH1+8vIiIibZ8/Tn8fllJXqK8+tBrDMEyORkSk5VSoe9Jp2514u1A3DIO5u07una7RdBEREWlC/X7qgX7UUK5/Un9C7CEcqTjC9sLtZocjItJiKtQ9yHbwINYTJzCCgnB07erVe287vo0dhTsIsgUxrus4r95bREREfIe787sfjagH2YIYnDwY0PR3EfEPKtQ9yD2anpEBAQFevfecnXMAyOyUSXRQtFfvLSIiIr7D3VBu1y7wk4ZyoHXqIuJfVKh7kH3bNsD7096rHFW89917AEzOmOzVe4uIiIhvcSUm4mzXDovTScB2/5kmXr9O/YsjX1BaU2pyNCIiLaNC3YPMWp++fP9yiquLSQpL4rrk67x6bxEREfExFotfNpTrFNmJLlFdcBpO1h1eZ3Y4IiItokLdgwJO30Pdi+qbyE3sPhGb1ebVe4uIiIjv8cdCHTT9XUT8hwp1Tzl+HFtuLgC1Pby3NVruiVzWHFoDwKT0SV67r4iIiPguf+z8Dqemv688tFLbtImIT1Oh7ilbtgDg6NwZIyLCa7ddsHsBBgYDEgeQFpXmtfuKiIiI76o5vaFcVZXJ0XjOgKQBBNuCyS/PZ2fRTrPDERG5aCrUPWXzZgBqL7/ca7c8fe/0SRkaTRcREZHmcSUn44yLw+JwELBjh9nheEyIPYRByYMATX8XEd+mQt1T6gt1L65P35S/iZzSHMICwhiTNsZr9xUREREfZ7G4p78H+Ov094MrTY5EROTiqVD3FBMK9frR9O+lfY+wgDCv3VdERER8n7uhnJ8V6kNThwJ127SdqDlhbjAiIhdJhbonVFXByWlj3irUy2vL+WDvBwDclnGbV+4pIiIi/sM9ou5nnd+7RHWhc2Rnal21rMvVNm0i4ptUqHuAfdcucDhwxcTgSkryyj2X7F1ChaOCtMg0rk241iv3FBEREf9RX6jbd+yAv/+dwPXrwek0OSrPqJ/+/s637/DO1++wPnc9Tpd/5CYilwa72QH4g/r902uvuAIsFq/cs37a+20Zt2Hx0j1FRETEfwRs3oxhsWBxOuG//5s4wJmURMmMGVRlZZkdXouEB4YD8MmBT/jkwCcAJIUlMWPgDLLSfDs3Ebk0aES9pZxOglasAMAVE+OVT6L3luxlY/5GrBYrE7pPaPX7iYiIiH8JXraMmPvugzP2Grfm5xMzZQrBy5aZFFnLLdu3jFc3v9roeH55PlNWTGHZPt/NTUQuHSrUWyB42TIS+vcn5MMPAQj54AMS+vdv9YfbvF3zABiaMpSkMO9MtRcRERE/4XQS9fTTYBicOSfPcrJwj5w2zSenwTtdTp7e8DQGRqPv1R+btmGapsGLSJunQv0iBS9bRsyUKVjz8hocb+1Pop0uJ/N3zwdgUrr2ThcREZELE7hxI7a8vEZFej2LYWDPzSVw40avxuUJG/M3klee1+T3DQxyy3PZmO97uYnIpUWF+sUw8ZPofx3+F/nl+UQHRXNjpxs9fn0RERHxb7ajRz16XltytKJ5MTf3PBERs6hQvwhmfhJd30Tulm63EGQL8vj1RURExL8527f36HltSfvQ5sXc3PNERMyiQv0imPVJdFFVER/nfAzAbenaO11EREQuXE3//jiTkjDOsWuMKzjYvX2bL+mf2J+ksCQsTQ6n1Jm3ax5lNWVeikpE5MKpUL8IZn0S/d5371HjquHy2MvpGd/To9cWERGRS4TNRsmMGQCNinXj5Je1qoq4iRMb9eJp62xWGzMG1uV2ZrF++uv5u+czcuFI1ueu92p8IiLNpUL9Ipzvk2jDYsGRnExN//4evW/9tPfJGZM9el0RERG5tFRlZVE0cyauxMQGx53JyZQ9/jjO2FgCt26l3ZgxBGzdalKUFycrLYuZI2eSGNYwt6SwJP5v5P/x7ph36RjRkUMnDjFx6USe2fAMVY4qk6IVETk7u9kB+KSTn0THTJmCYbG4G8jBqU+mS6dPB5vNY7f85vg3fF3wNQHWAMZ3G++x64qIiMilqSori6rMTII2bSKupobjgYFU9+sHNhuV48cTm51NwM6dxI0fT/Err1A1ZozZITdbVloWmZ0y2XRkEzWBNQTWBNIvoR82a93vZp/c8gkzNs7gn9/+k//b9n+sObSGPwz7A73ie5kcuYhIHY2oX6QmP4lOSqJo5kyqsrI8er/60fRRnUYRGxzr0WuLiIjIJcpmo2bQILj99ro/Tw4yODt2pOD996kaPhxrVRWx991H+Msvg9F4f/K2yma1MSh5ELf3up1ByYPcRTpAeGA4L1z/ArNunEW7kHbsKt7FmPfG8PK/X8bhcpgYtYhIHRXqLVCVlcWRjRs5vmABzJ7N8QULOPr55x4v0mucNSzaswiAyema9i4iIiKtz4iIoHDWLE788IcARL74ItEPPACVlSZH5jmjOo1i5YSVZKVl4TAcvPjVi4xbPI49xXvMDk1ELnEq1FuqiU+iPWnFgRUUVhWSEJrAkJQhHr++iIiIyFnZbJQ+8wzFL7yAYbcT+t57xE+ciNUH91hvSmxwLDNHzOSPw/5IZGAk/zn2HzLfzeSv3/wVl+EyOzwRuUSpUPcBc3bOAWBC9wnYrWorICIiIt5VceedHH/nHVzR0QT+5z/Ejx6Nfds2s8PyGIvFwi3dbmHFrSu4vsP1VDmr+OX6X3LHh3dw+MRhs8MTkUuQCvU27kjFEVYdWgXApPRJJkcjIiIil6qaQYM4tmQJtd26Yc/NJX7cOII/+sjssDyqQ3gHZt80m18P+jXBtmDWHl7LyIUjeXfPuxg+tD5fRHyfCvU2buHuhbgMF9ckXEO36G5mhyMiIiKXMGdaGgWLF1M1ZAjWykpi77mH8Fdf9akmc+djtVj5/hXf5+NbPqZPuz6U1pTyk1U/4b5P76OwqtDs8ETkEqFCvQ0zDMM97f229NtMjkZEREQEjKgoCv/+d0784AcARD73HNE//SlUV5scmWd1i+7Geze/x2NXP4bdYmfpvqUMXzCcT/Z/YnZoInIJUKHehn119Cu+K/mOEHsI3+vyPbPDEREREaljt1P6q19R/OyzGDYboQsXEj9pEtaCArMj8yi71c5DfR9iybglpEenc6zyGNnLs3nsX49xouaE2eGJiB9Tod6Gzd1Zt3f66LTRRARGmByNiIiISEMV//3fHH/7bVxRUQR++WVdk7kdO8wOy+N6xffiw/EfMqXXFCxYmL1zNqPeHcXGvI1mhyYifkotxNuoitoKFu9dDGjau4iItH0ff/wxixcvpri4mJSUFLKzs+nRo8dZz/3mm2+YPn16o+MvvfQSHTp0cL/+/PPPmTt3LkeOHCEhIYHbb7+dfv36tVoOcnFqbriBY4sXE5edjX3fPuLHjqXotdeoHjXK7NA8KtgezLQB0xjVcRQPr3mYA2UHuHXJrfyo94949OpHCbYHmx2iiPgRjai3UctylnGi9gSdIjoxIGmA2eGIiIg0af369cyaNYtbbrmF559/nh49evDss89ScJ5p0C+//DIzZ850fyUlJbm/t2vXLl5++WVuuOEGXnzxRW644QZeeukldu/e3drpyEVwduvGsQ8+oHrwYKzl5cR+//uE/elPftVkrt6g5EGsuHUFk9MnY2DwxtY3GP3eaLYd95/t6kTEfCrU26j6JnIT0yditejHJCIibdeSJUsYPnw4I0aMcI+mx8fHs3z58nO+LyoqiujoaPeX1Xrqebd06VJ69+7N+PHj6dChA+PHj6dnz54sXbq0tdORi2TExHD8n/+k/K67sBgGUb/6FdE/+xnU1JgdmsdFBEbwuyG/4683/pX4kHi+LfqWMe+N4Y+b/4jD5TA7PBHxA5r63gbtL93PhrwNWLBo73QREWnTHA4He/fuZdy4cQ2O9+7dm507d57zvY8//ji1tbWkpKRwyy230LNnT/f3du3axejRoxucf+WVV7Js2bImr1dbW0ttba37tcViISQkxP33lqh/f0uv0xZ5NLfAQEqffx5HRgaR06YROncutpwciv7yF4y4uJZf/wK19s8ts3Mm1yRcw+NrH+fDnA/53y/+l08OfMIfhv6BtKi0VrlnPX/9b9Jf8wLl5qvMyk2Fehs0f/d8AK7vcD0dwjuc52wRERHzlJaW4nK5iIqKanA8KiqK4uLis74nJiaGKVOm0KVLFxwOB//617/41a9+xbRp07j88ssBKC4uJjo6usH7oqOjm7wmwKJFi1iwYIH7dVpaGs8//zzt2rW7qNzOJjEx0WPXams8mtsvfwnXXAO33UbQxo0k3nwzLFkCJ3++3taaP7ckkliatpR/bP0HP/nwJ3x15CtGvTuK3934O+67+r5W/+XeX/+b9Ne8QLn5Km/npkK9jXEZLubtmgeoiZyIiPiOsxUjTRUoycnJJCcnu1+np6dTUFDABx984C7Uz8YwjHMWPePHj2fMmDGN7n/s2DEcjpZNR7ZYLCQmJpKfn4/hZ+uuWy23K6/E/sEHxNx9N/Z9+3ANGEDxG29QPXy45+5xHt78uY1qP4oVt67gkdWPsC53HT9e+mPmbpnL74b8jqSwpPNf4AL563+T/poXKDdf5cnc7HZ7sz88bhOF+oV0iv3222/55z//yeHDh6murqZdu3aMHDmywYPZl63LXcfhE4eJDIwks3Om2eGIiIicU2RkJFartdFId0lJSaNR9nNJT09n7dq17tdnGz0/3zUDAgIICAg46/c89YujYRh+90tovdbIrbZbN44tWULslCkEbdhAzN13UzptGuX33ANenEbqrZ9bh7AOvJP1Dn/95q88u+lZVh9azYgFI3h28LOM7Tq2Ve7pr/9N+mteoNx8lbdzM71L2YV2ig0KCiIzM5Pp06fz0ksvccsttzB37lxWrFjh5chbx7yddaPp47qOI8QeYnI0IiIi52a32+nSpQtbt25tcHzr1q1kZGQ0+zr79u1rMNU9PT2dr7/+utE109PTWxSveJ8RG8vx2bMpv/12LC4XUdOmEfXzn8Np/QT8idVi5Z6e9/DR+I+4Mv5KiquLmbpyKj/+9McUVRWZHZ6I+AjTC/UL7RSblpbGddddR2pqKu3bt+eGG27gyiuvZMeOHV6O3PNKqkv4MOdDAG7L0LR3ERHxDWPGjOHTTz9l5cqVHDp0iFmzZlFQUMCok/toz549m1dffdV9/tKlS9m0aRN5eXkcPHiQ2bNns3HjRv7f//t/7nOysrLYsmUL7733HocPH+a9997j66+/btRgTnxEYCAlL75IybRpGBYLYf/8J3F33IGlsNDsyFpN95juvD/2fX7W92fYLDYW713MiIUjWHVwldmhiYgPMHXqe0s6xdbbt28fO3fuZPLkyU2e05pdYE+/RkuvtXjvYqqcVWTEZHBVu6vaRNdEf+3g6K95gXLzVcrNN/lzbhdi0KBBlJWVsXDhQoqKikhNTeWJJ55wr8MrKipqMFPO4XDwj3/8g8LCQgIDA0lNTeUXv/gFffv2dZ+TkZHBQw89xJw5c5g7dy6JiYk89NBDdO/e3ev5iYdYLJRPmYKjSxdipk4laP162n3vexT+7W84unUzO7pWEWAN4JGrH2FExxE8uPpB9hTv4a6P7uK/evwXT/V/irCAMLNDFJE2ytRC/WI6xdb70Y9+RGlpKU6nk4kTJzJixIgmz/VGF1hoeSfAd5e+C8CUa6c0aLLTFvhrB0d/zQuUm69Sbr7Jn3NrrszMTDIzz95b5f7772/weuzYsYwde/71ugMGDGDAgAEeiU/ajuqRIylYvJjY7GzsOTnEf+97FP35z1TfcIPZobWaK9tdyUfjP+J/v/hf/rLtL/xjxz9Ye3gtLw99mWsTrjU7PBFpg9pEM7kL6RRbb8aMGVRVVbFr1y5mz55NYmIi11133VnPbc0usPXXa2knwJ2FO9l0eBN2i51RCaPIy8trcVye4K8dHP01L1Buvkq5+SazOsGK+DrHZZdRsGQJMffeS9AXXxB7112UzJhBRXa22aG1mhB7CNMHTmdUx1E8vOZhckpzuOWDW5jaeyqPXP0IQbYgs0MUkTbE1EK9JZ1i27dvD0DHjh0pKSlh/vz5TRbq3ugCW3+ti73enJ1zABjRcQRxwXFt7pdZf+3g6K95gXLzVcrNN/lzbiKtxRUfz/G5c4l+/HFCFywg+sknCdi1i5IZM8DeJsaSWsV1Ha7j0wmf8vT6p5m/ez6vbnmVTw9+yh+H/ZEesWff9UhELj2mNpPzVKdYwzA8MjJullpXLQv3LARgckbTa+1FRERE/EpQEMUvv0zpk0/WNZn729+Iu+suLOdZAunrIgMjeXnoy/xl5F+IDY5lR+EOshZl8fqW13G6nGaHJyJtgOld3y+0U+xHH33El19+SV5eHnl5eaxatYoPPviA66+/3qwUWmzlgZUUVBYQHxLPsNRhZocjIiIi4j0WCyemTqXozTdxhYYStHYt8TffjG3vXrMja3U3pd3EyltXcmOnG6lx1fCbTb9hwpIJ7C/db3ZoImIy0+cVXWinWMMweOeddzh69ChWq5XExETuvPNORo4caVYKLTZ311wAbu12KwHWs0/RFxEREfFnVZmZFLz3HrHZ2QR8911dR/g//5maJpY2+ot2oe14a9RbzNs1j6c3PM2mI5sYuXAkzwx8hjsy7rjkd5UQuVSZXqjDhXWKvemmm7jpppu8EZZXHKs4xqcHPgXgtnTtnS4iIiKXLscVV1CwdCmx99xD4L//Tdydd1Ly619T8V//ZXZorcpisXBbxm0MSh7Ew2seZkPeBh5f+zgf5XzEb2/4LQmhCWaHKCJeZvrU90vdwj0LcRgO+rTrQ0Zs89fli4iIiPgjV/v2FMyfT8Utt2BxOIj+xS+IfPpp8OF+RM2VGpHKvNHzmDZgGkG2IFYeXMnwBcP5YO8HDc5zupysz13PO1+/w/rc9VrXLuKHVKibyDAM5u2aB8Ck9EkmRyMiIiLSRgQHU/yHP1D6+OMAhL/5JrHZ2VhKS00OrPVZLVam9JrCR+M/old8L4qri/nRpz/iJ6t+QnF1Mcv2LaP/nP5MWDKBO969gwlLJtB/Tn+W7Vtmdugi4kEq1E20pWALO4t2EmwLZmzXsWaHIyIiItJ2WCyc+OlPKZw5E1dwMMGrVtU1mcvJMTsyr0iPSeeDsR/wUJ+HsFlsvLvnXQbPHcwPV/yQvPK8Bufml+czZcUUFesifkSFuonq906/qfNNRAWde994ERERkUtR1ejRHH/vPZyJiQTs3k38mDEEfv652WF5RYA1gMeueYz3bn6PtMg0iquLz3qegQHAtA3TNA1exE+oUDdJpaOS9797H4BJGZr2LiIiItKU2l69OLZ0KTVXXYWtqIi4yZMJmTPH7LC8pm/7vvxq0K/OeY6BQW55LhvzN3opKhFpTSrUTfJxzseU1pTSIbwD1yX797YjIiIiIi3lSkykYMECKm++GUttLTE/+xmRM2aA89IYQS6pLmnWeZuPbcYwjFaORkRamwp1k8zZVfcp8KT0SVgt+jGIiIiInFdICEWvv07pz34GQPif/0zs97+PpazM5MBaX/vQ9s067zebfkPvt3vzwxU/5K/f/JUdhTtwGa5Wjk5EPK1N7KN+qTl84jDrDq8DYFJ3TXsXERERaTaLhROPPIKja1diHnmE4E8/JX7cOApnzcKZmmp2dK2mf2J/ksKSyC/Pd69JP1OQLQgMKKwqZNm+Ze7mcjFBMQxMGsiApAEMTBrIZbGXaaBIpI1ToW6CebvmYWAwMGkgHSM7mh2OiIiIiM+pGjuWgk6diP3BDwj49lviR4+m6M03qbn2WrNDaxU2q40ZA2cwZcUULFgaFOsWLAC8OuxVRnYcyZaCLWzI3cCGvA18ceQLiqqLWJazjGU5dYV7dFA0AxIHMDC5rni/PPZyFe4ibYwKdS9zGS733umTMyabHI2IiIiI76q96iqOLVlC7Pe/T+C2bcRNmkTxCy9QOXEiOJ0EbtoENTUEBgZS3a8f2Gxmh9wiWWlZzBw5k2c+e4pu2/NJOgF54fDd5YlMGzyDrLQsAK5NuJZrE67lwT4PUuuqZcuxLXye9zkb8jawKX8TxdXFfLT/Iz7a/xFQV7j3T+zPgKQBDEoaRI/YHtisvv1vJeLrVKh72ed5n3Og7ADhAeGMThttdjgiIiIiPs2VnMzxRYuI/ulPCVm2jJiHHiL4448J3LwZW17dfuNxgDMpiZIZM6jKyjI34Ba6ZQdkv2zBftpW6o4kKI2FqrTG5wdYA7gm4RquSbiGB656gFpXLV8XfO0ecd90pK5w/3j/x3y8/2MAIgMj6Z/Yn4FJAxmYNJAr4q5Q4S7iZSrUvWzurrkA3NzlZkLsISZHIyIiIuL7jNBQiv78ZxwvvkjEH/5AyIcfNlrFbc3PJ2bKFIpmzvTZYj142TJipkyBM7q62y4gtwBrAH3b96Vv+77cf9X9OFwOvi74ms/zPmd93no25W+itKaUTw58wicHPgHqCvd+if0aFO52q8oIkdak/4V5UVlNGUv2LgHgtozbTI5GRERExI9YrZQ9+ihhf/87luLik6u2T7EYdau6ox5/HGpqwG6vmwpvtWJYLGC11n3ZbA1fn/wyzvO6wXWauG6jY01cB8uZ0QNOJ1FPPw2GcfbcLBYip02jKjPzgqb42612+rTvQ5/2ffjxlT/G4XLwzfFv2JC3gfW5pwr3FQdWsOLACgDCA8Lpl9iPQUmDGJA0gF7xvVS4i3iY/hflRR/s/YAqZxVdo7pydfurzQ5HRERExK8EbtyItbi4ye9bAFtREbH33++1mC5Go2LeYgHDwFpd3eR7LIaBPTeXwI0bqRk06KLvbbfaubLdlVzZ7kp+1PtHOF1Ovjn+Devz1vN53udszN9IaU0pKw+uZOXBlcCpwr2+QV2v+F4EWAMuOgYRUaHuVfXT3m9Lvw3L2T4pFREREZGLZjt6tFnn1Xbvjis2FlwuLC4XuFx108ldLixOZ4PX7mOnvW7wviZe43JhMQw4eT2Lq/l7mbvf53TWvb6Af4OglSup7dMHI8QzSyxtVhu92/Wmd7ve7sJ9R+EO1uetZ0PeBjbmbaSkpqRB4R4WEMa1Cde6t4S7st2VzSrcnS4nm45soqaghsCaQPol9NPaeLlkqVD3kj3Fe/jyyJfYLDYmpE8wOxwRERERv+Ns375Z55U8+2yLRp0v2unFvtNZV5CfXtyf5Vj9BwABX35J7AMPnPcWEW+8Qfhbb1E9YADVQ4dSPWwYjm7dzj6d/iLYrDZ6xvekZ3xPpvSaUle4F+1gQ+4GPs/7nM/zP6e4upjVh1az+tBqAELtoVybcG3dPu7JA7ky/koCbYENrrts3zKe3vA0eeWnuuQlhSUxY+CpbvYilxIV6l5SvyXb0JShJIQmmByNiIiIiP+p6d8fZ1IS1vz8uoL3DIbFgjMpiZr+/U2Ijrpi2War+woIaNTwDjjrMQBncjLO3/ym6dyoa6rnio7GnptL8Jo1BK9ZA9On40hJcRft1YMHY0REeCwlm9VGz7ie9IzryQ97/RCX4eLbwm/ZkFfXVf7zvM8pqi5izeE1rDm8BoAQewjXJFzjbk6XX57P1JVTG+wND5Bfns+UFVOYOXKminW55KhQ9wKHy8GC3QsANZETERERaTU2GyUzZhAzZQqGxdKgoDVOjiiXTp/um/upNyO34ldeoeqmm7Dv2UPQqlUErV5N0OefYz90CPvbbxP29tsYdjs1115L9bBhVA0diuPyyz022g5gtVi5PO5yLo+7nHt63oPLcLGzaGfddnD5dYV7YVUhaw+vZe3htee8loGBBQvTNkwjs1OmpsHLJUWFuhesPrSaIxVHiA2OZVTHUWaHIyIiIuK3qrKyKJo5k6inn3bvow51+6iXTp/us1uzQfNzc3TvjqN7d8qnTMFSUUHghg0ErV5N8KpV2PftI2jDBoI2bCDy2WdxJiRQPXQoVUOHUn399RgxMR6N2Wqx0iO2Bz1ie/CDnj/AZbjYXbS7rqt83nrWHlpLaW1pk+83MMgtz+XPW/9MVpcsUsJT1GFeLgn6r9wL6pvIje82vtF6HBERERHxrKqsLKoyMwnatIm4mhqOBwZS3a+fb46kn+FCczNCQ6keMYLqESMoBWw5Oe6iPfCzz7AdOULo3LmEzp2LYbVS26cPVcOGUT1sGLW9e9d1nvcgq8VKRmwGGbEZZF+RzaI9i3hg1fnX3v/mi9/wmy9+g91iJyUihc6RnekU2YnOkZ3dX6kRqYTYPdNET8RsKtRbWWFVIZ/s/wSo6/YuIiIiIl5gs9U1jEtKoiYvr66Rm79oQW7Ozp2pyM6mIjsbqqsJ3LiR4NWrCVq9moCdOwn86isCv/oKfvtbnLGxVA8ZUre2fcgQXPHxHk+lub2bUsNTOVZ5jCpnFTmlOeSU5pz1vKSwpAbFe6fITqRFptEpshMRgZ5bmy/S2lSot7J397xLrauWXvG9uCLuCrPDERERERGpExREzQ03UHPDDfD001gPH3YX7UFr12IrLCR00SJCFy0CoKZ377qifdgwavr0AXvLS4n+if1JCksivzy/UTM5AAsWksKS+Oy2z7BYLBypOEJOaQ77S/ezr3QfOSU57C/bT05JDmW1ZeSV55FXnseGvA2NrhUXHNdoFL6+kI8NjtX2ydKmqFBvZXN3nto7XURERESkrXJ16EDFnXdSceedUFtL4L//TdDKlQStXk3gtm0Ebt1K4NatRLzyCq7ISKqvv56q4cPrRtuTki7qnjarjRkDZzBlxRQsWBoU65aTO8hPHzjd3UguKSyJpLAkBiYNbHAdwzAoqi5iX8k+d+G+r3Qf+0v3k1Oaw/Gq4+6vfx/9d6M4IgIizlrEd47sTGJYIlZLy5YAaI94uVAq1FvRtoJtbC/cTqA1kHFdx5kdjoiIiIhI8wQEUNO/PzX9+1P2xBNYjx4laM0aglatInjNGqzFxYQsXUrI0qUA1PboUdeUbtgwaq69FgKb35cpKy2LmSNnnnUf9ekDpzdrazaLxUJscCyxwbFcnXB1o++X1ZS5i/YGI/KlOeSV51FWW8a249vYdnxbo/cG24LpGNHxVCEf1ZnOEXV/dgjvQIA14JyxaY94uRgq1FvRnJ1zAMjsnElMsGc7aIqIiIiIeIurfXsqJ06kcuJEcDoJ2LKlrindypUEbN5MwI4dBOzYQfgbb+AKC6N68GD3NHlnaup5r5+VlkVmp8y6UedAz486RwRG0DO+Jz3jezb6XqWjkoNlBxsU8fV/P1h2kCpnFbuKd7GreFej99osNlIjUukU0YnOUZ3pFNGJtKg0OkV0omNkR1YdXMWUFVO0R7xcMBXqraTaWc2i7+rW82jau4iIiIj4DZuN2r59qe3blxOPPIKlsJCgtWsJXrmSoDVrsB07Rsjy5YQsXw5AbdeuVA8dSvXw4VT37w8hZ+/MbjNgaA7E1cDxQKhu7510QuwhpMekkx6T3uh7ta5aDp847B6Bry/i95fuZ3/p/gbN7dYcXtPo/VaL9axr77VHvJyPCvVWsnz/coqri0kMS+SGDjeYHY6IiIiISKswYmOpGjuWqrFjweXCvn07watW1a1t/+ILAr77joDvviP8zTcxgoOpHjjQPU3e2aULWCwEL1vWYH/4OOr2hy+ZMcO9P7wZAqwB7jXrQxjS4Hsuw0V+eb57TXxOWU6j5nYuw9Xktev3iL9h/g10iepCUlgSiaGJJIbVfSWEJpAUlkRMUIwa3V2CVKi3kvomchO7T9QnZCIiIiJyabBacfTsyYmePTnxk59gKS0laN069zR5W14ewatWEbxqFVHTpuHo2BFHWhpBa84yGp2fT8yUKRTNnGlqsd4Uq8VKcngyyeHJZ21u989v/8nP1/38vNc513ZzAEG2IBJCE0gITagr4uuL+TOKerP2kFejvNahQr0V5JXnuae+TEqfZHI0IiIiIiLmMCIjqcrKoiorixLDwL5rV11DutWrCdy4EfuBA9gPHDjrey1G3aTxqF/+kqohQyAszLvBt4DFYqFLVJdmnfvEtU8QExxDfnk+RyqOkFeeR355PvkV+RRWFVLtrOZA2QEOlJ3936ledFB0oyK+vrivH62PC4lrcQf706lRXutRod4KFuxegMtw0S+hX7P/ByoiIiIi4tcsFhwZGTgyMij/0Y+wVFQQ+pe/EPX8802/BbAdOUJyejqu0FBccXG4YmPrvmJiTv39bF8xMR7Z6/1inb5HvMVlcP1+SDoBeeGwthMY1ro94n/c+8dNjkBXO6s5WnGU/PJ88srzOFJxhPyK/LpC/mQxn1+eT5WziuLqYoqri/m26NsmY7Jb7LQPbe8eiU8KTTo1Un9agR8WcP4PRZbtW6ZGea1IhbqHGYbh7vY+OWOyydGIiIiIiLRNRmgoro4dm32+taICa0UFHDzY7Pe4oqKaLOSdZyn4jagosHpmxLl+j/gP//BDXv4IUktPfe9gJDz0/wxuenD6OaeJB9mCSI1IJTWi6c75hmFQUlPiLt7do/IVp17nl+dzrPIYDsNBbnkuueW554w9IiCiQfGeEJZAUmiS+1h8cDxPb3hajfJakQp1D/viyBfklOYQag9lTJcxZocjIiIiItJmOds3r7X78b/9DUe3blgLCxt+FRVhPX688fHiYiyGgbWkBGtJCezb16z7GFZrXeF+ASP3RlgYNNHs7ZYdcM88GpWzHUphwTwoGgVVac0KrUkWi4XooGiig6K5LPayJs+rddVytOKou3CvH5F3j9SffF1eW05ZbRllxWXsLt59UTHVN8qbt3sew1OHExsce9795qUhFeoeVt9EbkyXMc2aMiIiIiIicqmq6d8fZ1IS1vx8LMZZRmctFpxJSVQPGwY2G87OnZt3Yaezrkg/s4A/s6gvKjr197IyLC4XtuPHsR0/3uwcjMDAsxfx0dGEvfUWAGeO0VtP5hY5bRpVmZlga/1R5wBrAB3CO9AhvMM5zyurKWu0Vv5I+akp93kVeRwpP3LW0fQzPfqvR91/jwqMIjY4lriQuLo/g+OIC45zH3P/PTiOuJA405rjnc7MRnkq1D2ovLacxXsXAzA5XdPeRURERETOyWajZMYMYqZMwbBYGhTrxslR6tLp0y+8kLXZ3AVzs9XUNCzc6wv7M4+d/LIVFmKpqsJSU4MtPx9bfv4FhWgxDOy5ubQbORJnx4510/Sjo3FFR2PU//3kn8bJ466oKAho3ZHpiMAIIgIj6Bbdrclz1h1ex23LbgPA6qLR+nvXyU8mIgIiOFF7AoO66fklNSXsK23e7IYQe0iD4v1sBX1syKmCPzIw0qPb2JndKE+Fugct2buECkcFnSM70y+xn9nhiIiIiIi0eVVZWRTNnNlgH3Wo20e9dPp0723NFhiIKyEBV0JCs99iqaxscrQ+4KuvCF637rzXCNi1i4Bdu5p9T1dYGK6oqFPF/LmK+9OOGZGRHlt/PzBpIElhSQz8Iq+J9ffw+bXJfD75cwBKako4Xnmc41V1X4VVhRyvPPnnyWPHK49TWF1IYWUhNa4aKh2VHDpxiEMnDjUrJrvF3uSI/ekFff2xmOAY7Nazl8NtoVGeCnUPmrurbtr7pPRJHv00R0RERETEn1VlZVGVmUnQpk3E1dRwPDCQ6n79vDIlvCWMkBCcHTrg7NB4Onng+vXNKtRLH30UV0IClpISrMXFdV8n/+4+Vr/WHrCWl2MtL4fcczeEaxSrxdK4mI+KajCS31TBb4SENFiHb7Pa+HvNOIbPe6PRfTqUwvx5sPLKse5p4rHBscQGx9Kd7ueP0zA4UXviVPFeVdiooD9edZyiqiL3sfLachyGg6OVRzlaebRZ/x4WLEQFRZ0q4E8W+NFB0fxjxz9Mb5SnQt1D9hTu4fO8z7FgYWL3iWaHIyIiIiLiW2w2agYNgqQkavLy4Cxr1n1Jc9ffn3jwweZ9IOF0YiktdRfx1pISLPWF/ZnF/WlFv6W4GGtlJRbDcJ9/oYyAgFMj89HRuCIjGb5hAxbqttA7nZW65nlDX3ufo//1xAV/2GKxWNzT7ztHdm7We6ocVe6RendRX3m84bHTRvSLq4sxMNxb2n1X8l2z46tvlLcxfyODkgddUG4XQoW6h8zaPAuAISlDSA5PNjcYERERERExl6fX39tsGDExOGNicF5oLNXV7lF5yxmF/dmOnT6Sb6mtxVJbi+3YMWzHjjXrdhbAnptLwpVX4uzQAVe7drjatcN58k/339u3xxkfjxEd3WTn/OYItgc3q1FePYfLQVFVUcOR+qrjFFYW8sWRL/jX4X+d9xpHK5o3cn+xVKi3kNPl5PO8z3njy7ppHxO6TzA5IhERERERaQvazPr7oCBc7dvjauZ2eG6GgaWioq6QP614D1q1irB//vO8b7cVFWErKjr/bQICcMXH42zf/tSfZxT39X8aEREtKuoB7FY77ULb0S60XaPvrc9d7y7Uz9Uor33oBf5bXmiMrXp1P3e2ToC/3vhrgmxBXukEKCIi0lZ8/PHHLF68mOLiYlJSUsjOzqZHjx5nPXfjxo0sX76cnJwcHA4HKSkpTJw4kauuusp9zurVq3n99dcbvfftt98mMDCwtdIQEfE4X11/D4DFghEWhhEWhuu0dfiuqKhmFerFzz+PMzkZ68nReOvRo1gLCrDV/3nsWN0Ifm0ttry8Bh9mNMUIDsYZH19XvJ8s7OtH5l0nC3x3UR924dtl90/s36xGef0T+1/wtS+ECvWL1FQnwCMVR7zWCVBERKQtWL9+PbNmzeLee+8lIyODFStW8Oyzz/LSSy8RHx/f6PwdO3bQu3dvbr/9dsLCwli1ahXPP/88zz77LGlpae7zQkJCeOWVVxq8V0W6iPikS3T9fcXtt5//A4nq6lNFe31BX//3Mwp7a1kZlqoq7IcOwaHzd4N3hYbWFe9njtafUdw74+MhpG7f9gttlNdaVKhfBKfLydMbnja9E6CIiEhbsGTJEoYPH86IESMAyM7OZsuWLSxfvpw77rij0fnZ2dkNXt9xxx18+eWXfPXVVw0KdYvFQnR0dLPjqK2tpba2tsH7Q07+4tXS3Vjq3++Pu7ooN9/kr7n5a17gZ7nZ7ZT+6ldE//CHTa6/L5sxA4u9GeVmcDBGSgqOlJTzn1tZia2goG5k/ujRU3+vL+bri/yjR7FWVmKtqMCakwM5Oee9tCsiom40Pj6e4Vu3nrNR3rDX3ufo3f/TqrMiVKhfhI35GxtMdz+TtzoBioiImM3hcLB3717GjRvX4Hjv3r3ZuXNns67hcrmorKwkPDy8wfGqqiqmTp2Ky+Wic+fO3HbbbQ0K+TMtWrSIBQsWuF+npaXx/PPP065d4zWIFysxMdFj12prlJtv8tfc/DUv8KPc7rkHYmLgpz9tMLptSUmBl18m5pZbWue+Xbo077wTJyA/H44cafjn2Y5VV2MtK8NaVoZ9795zXtYC2HJzSdqzB4YObXE6TVGhfhGa2+GvtTsBioiImK20tBSXy0VUVFSD41FRURQ3cwugJUuWUF1dzcCBA93HkpOTmTp1Kh07dqSyspJly5bx1FNP8eKLL5KUlHTW64wfP54xY8a4X9ePWh07dgyHw3GBmTVksVhITEwkPz8fw8enrJ5Jufkmf83NX/MCP81t4EDYsIGgTZuIra6mMCjo1Pr7Zqw3b3VhYXWF/bmKe8PAUlbmHqEPWrqU8DffPO+li7Zvpyoj44LCsdvtzf7wWIX6RWhuh7/W7gQoIiLSVpxtKmdzpneuW7eO+fPn89hjjzUo9tPT00lPT3e/zsjI4Oc//zkffvghP/jBD856rYCAAAICAs76PU/9UmwYhv/8gn0G5eab/DU3f80L/DA3q5XqgQMhKYnqvLy63HwsPyMiAldEBI6uXTGczmYV6s727Vv152httSv7sfpOgJZGqxbqWLCQHNb6nQBFRETMFhkZidVqbTR6XlJS0miU/Uzr16/nT3/6Ew8//DC9e/c+57lWq5WuXbuSn5/f0pBFRESaVN8oz2jiw2bDYsGRnExN/9at9VSoXwSb1caMgTMAGhXr9a+nD5yuRnIiIuL37HY7Xbp0YevWrQ2Ob926lYxzTAlct24dr732Gg8++CB9+/Y9730Mw2D//v0X1FxORETkgtlslMyoq/XOLNbrX5dOn97q2+upUL9IWWlZzBw5k8Swhs0gksKStDWbiIhcUsaMGcOnn37KypUrOXToELNmzaKgoIBRo0YBMHv2bF599VX3+fVF+t133016ejrFxcUUFxdTUVHhPmf+/Pls3ryZI0eOkJOTwxtvvEFOTg433nij1/MTEZFLS1VWFkUzZ+I6o/GfMymJopkzqcpq/VqvTaxR//jjj1m8eDHFxcWkpKSQnZ1Njx49znruxo0bWb58OTk5OTgcDlJSUpg4cSJXXXWVd4OmrljP7JTJpiObqAmsIbAmkH4J/TSSLiIil5RBgwZRVlbGwoULKSoqIjU1lSeeeMLdMKeoqIiCggL3+StWrMDpdPLmm2/y5mnrAIcMGcL9998PQHl5OTNnzqS4uJjQ0FDS0tKYPn063bp1825yIiJySarKyqIqM5OgTZuIq6nheGDgqUZ5XmB6ob5+/XpmzZrFvffeS0ZGBitWrODZZ5/lpZdeIj4+vtH5O3bsoHfv3tx+++2EhYWxatUqnn/+eZ599tlzbtnSWmxWG4OSB5GUlEReffMEERGRS0xmZiaZmZln/V598V3vmWeeOe/1srOzG+23LiIi4lU2GzWDBkFSEjV5eV5tkmd6ob5kyRKGDx/OiBEjgLoH85YtW1i+fDl33HFHo/PPfGjfcccdfPnll3z11VdNFuq1tbXU1ta6X1ssFkJCQtx/b6n6a3jiWm2Nv+bmr3mBcvNVys03+XNuIiIiYh5TC3WHw8HevXsZN25cg+O9e/dm586dzbqGy+WisrKS8PDwJs9ZtGgRCxYscL9OS0vj+eefb/Yeds2VeMYaBn/ir7n5a16g3HyVcvNN/pybiIiIeJ+phXppaSkul6vR9i1RUVGNtnlpypIlS6iurmbgwIFNnjN+/HjGjBnjfl0/8nHs2DEcDseFB34Gi8VCYmIi+fn5fjf13V9z89e8QLn5KuXmmzyZm91u9/gHyCIiIuKbTJ/6DmefMticaYTr1q1j/vz5PPbYY+fcqzUgIICAgICzfs+TvzQahuF3v4TW89fc/DUvUG6+Srn5Jn/OTURERLzP1O3ZIiMjsVqtjUbPS0pKzll4Q10Tuj/96U88/PDD9O7duxWjFBEREREREfEeUwt1u91Oly5d2Lp1a4PjW7duJSMjo8n31e+/+uCDD9K3b9/WDlNERERERETEa0wt1AHGjBnDp59+ysqVKzl06BCzZs2ioKCAUaNGATB79mxeffVV9/n1Rfrdd99Neno6xcXFFBcXU1FRYVYKIiIiIiIiIh5j+hr1QYMGUVZWxsKFCykqKiI1NZUnnnjC3VCnqKiIgoIC9/krVqzA6XTy5ptv8uabb7qPDxkypNE+rSIiIiIiIiK+xvRCHSAzM5PMzMyzfu/M4vuZZ57xQkQiIiIiIiIi5jB96ruIiIiIiIiInKJCXURERERERKQNUaEuIiIiIiIi0oaoUBcRERERERFpQ9pEMzmz2O2eTd/T12tL/DU3f80LlJuvUm6+yRO5+fO/j9k8+W/rzz8n5eab/DU3f80LlJuv8vaz3mIYhtHiO4qIiIiIiIiIR2jquwdUVlby85//nMrKSrND8Th/zc1f8wLl5quUm2/y59ykIX/+WSs33+SvuflrXqDcfJVZualQ9wDDMNi3bx/+ODnBX3Pz17xAufkq5eab/Dk3aciff9bKzTf5a27+mhcoN19lVm4q1EVERERERETaEBXqIiIiIiIiIm2ICnUPCAgIYMKECQQEBJgdisf5a27+mhcoN1+l3HyTP+cmDfnzz1q5+SZ/zc1f8wLl5qvMyk1d30VERERERETaEI2oi4iIiIiIiLQhKtRFRERERERE2hAV6iIiIiIiIiJtiAp1ERERERERkTbEbnYAvmz79u0sXryYffv2UVRUxKOPPkq/fv3MDqvFFi1axKZNmzh8+DCBgYGkp6dz1113kZycbHZoLbZ8+XKWL1/OsWPHAEhJSWHChAn06dPH5Mg8b9GiRbzzzjtkZWWRnZ1tdjgtMm/ePBYsWNDgWFRUFP/3f/9nUkSeVVhYyNtvv83mzZupqakhKSmJH//4x3Tp0sXs0Frk/vvvd/9v7XQ33ngj9957rwkReYbT6WT+/PmsXbuW4uJiYmJiGDp0KLfccgtWqz7/9jd61vsePet9k571vslfn/Vg/vNehXoLVFdX07lzZ4YNG8bvfvc7s8PxmO3bt5OZmUnXrl1xOp3MmTOHX//61/z+978nODjY7PBaJDY2ljvuuIPExEQA1qxZwwsvvMALL7xAamqqydF5zp49e1ixYgWdOnUyOxSPSU1N5amnnnK/9peC6MSJEzz11FNcccUV/M///A+RkZEcOXKE0NBQs0Nrseeeew6Xy+V+feDAAX79618zcOBAE6Nquffff59PPvmE+++/n5SUFPbu3cvrr79OaGgoWVlZZocnHqZnve/Rs9536Vnve/z1WQ/mP+9VqLdAnz59/PLT2SeffLLB66lTp3Lvvfeyd+9eLr/8cpOi8oxrrrmmwevbb7+d5cuXs3v3br95eFdVVfHHP/6R++67j3fffdfscDzGarUSHR1tdhge9/777xMXF8fUqVPdx9q3b29iRJ4TGRnZ4PV7771HQkKCz///yK5du7jmmmvo27cvUPfzWrduHd99953JkUlr0LPe9+hZ77v0rPc9/vqsB/Of9/7xMZW0qoqKCgDCw8NNjsSzXC4Xn332GdXV1aSnp5sdjsf85S9/oU+fPvTu3dvsUDwqPz+f++67j/vvv5+XX36ZI0eOmB2SR3z55Zd06dKF3//+99x77708/vjjrFixwuywPM7hcLB27VqGDRuGxWIxO5wWueyyy9i2bRu5ubkA5OTksHPnTr8s5uTSoWe9b9Gz3rfoWe+bzH7ea0RdzskwDP72t79x2WWX0bFjR7PD8YgDBw7w5JNPUltbS3BwMI8++igpKSlmh+URn332Gfv27eO5554zOxSP6t69O/fffz/JyckUFxfz7rvv8stf/pLf//73REREmB1eixw9epRPPvmE0aNHM378ePbs2cNf//pXAgICGDJkiNnhecymTZsoLy9n6NChZofSYmPHjqWiooKHH34Yq9WKy+Vi8uTJXHfddWaHJnJR9Kz3LXrW+x49632T2c97FepyTm+++SYHDhxgxowZZofiMcnJybz44ouUl5ezceNGXnvtNaZPn+7zD/CCggJmzZrFk08+SWBgoNnheNTpn1x27NiR9PR0fvKTn7BmzRrGjBljYmQt53K56Nq1K3fccQcAaWlpHDx4kOXLl/vVw3vVqlVcddVVxMbGmh1Ki61fv561a9fy4IMPkpqaSk5ODrNmzXI3mRHxNXrW+w49632TnvW+yeznvQp1adJbb73FV199xfTp04mLizM7HI+x2+3uBjNdu3blu+++Y9myZUyZMsXkyFpm7969lJSU8Itf/MJ9zOVysWPHDj766CNmz57tN01ZgoOD6dixI3l5eWaH0mIxMTGNfnFMSUlh48aNJkXkeceOHWPr1q08+uijZofiEW+//TZjx45l8ODBQN0vlMeOHeO9995ToS4+R89636JnvW/Ss943mf28V6EujRiGwVtvvcWmTZt45pln/KbZRVMMw6C2ttbsMFqsV69e/Pa3v21w7I033iA5OZmxY8f6zYMboLa2lsOHD9OjRw+zQ2mxjIwM99qnerm5ubRr186kiDxv1apVREVFuZux+Lrq6upG/3uyWq0YhmFSRCIXTs9636RnvW/Ss943mf28V6HeAlVVVeTn57tfHz16lJycHMLDw4mPjzcxspZ58803WbduHY8//jghISEUFxcDEBoa6vPTrGbPnk2fPn2Ii4ujqqqKzz77jG+++aZR91tfFBIS0mhtYVBQEBERET6/5vDvf/8711xzDfHx8ZSUlLBw4UIqKyv9YrrY6NGjeeqpp3j33XcZNGgQe/bs4dNPP/X5UZ96LpeL1atXM2TIEGw2m9nheMTVV1/Nu+++S3x8PCkpKeTk5LBkyRKGDRtmdmjSCvSs9z161vsmPet9lz8+68H8573F0BDARfvmm2+YPn16o+NDhgzh/vvvNyEiz5g0adJZj0+dOtXnp3W+8cYbbNu2jaKiIkJDQ+nUqRNjx471u66p9Z555hk6d+5Mdna22aG0yMsvv8yOHTsoLS0lMjKS7t27M3nyZJ9fa1jvq6++Yvbs2eTn59O+fXtGjx7NyJEjzQ7LI7Zs2cJvfvMbXn75ZZKTk80OxyMqKyuZO3cumzZtoqSkhNjYWAYPHsyECROw2/X5t7/Rs9736Fnvm/Ss913++KwH85/3KtRFRERERERE2hD/WcgiIiIiIiIi4gdUqIuIiIiIiIi0ISrURURERERERNoQFeoiIiIiIiIibYgKdREREREREZE2RIW6iIiIiIiISBuiQl1ERERERESkDVGhLiIiIiIiItKG2M0OQEQ8b/Xq1bz++utNfn/atGlcccUVXozolKNHj/LAAw9w1113cfPNN5sSg4iIiK/Ts17Ev6lQF/FjU6dOJTk5udHxlJQUE6IRERERT9OzXsQ/qVAX8WOpqal07drV7DBERESklehZL+KfVKiLXMImTZpEZmYmHTt2ZMmSJRw7doyEhAQmTJjA4MGDG5x74MAB5syZw44dO6ipqSE5OZnRo0czdOjQBueVl5ezcOFCNm3aRGFhIaGhoXTt2pW7776bDh06NDh3yZIlfPjhh5SWltKxY0f++7//m/T0dPf3jxw5wjvvvMOOHTsoKysjLCyM1NRU7r77bjp37txa/ywiIiJ+Q896Ed+kQl3Ej7lcLpxOZ4NjFosFq/VUH8kvv/ySb775hkmTJhEUFMTy5ct55ZVXsNlsDBgwAIDc3FyeeuopIiMj+f73v094eDhr167l9ddfp6SkhLFjxwJQWVnJ008/zdGjRxk7dizdu3enqqqKHTt2UFRU1ODh/fHHH9OhQweys7MBmDt3Ls899xyvvfYaoaGhADz33HO4XC7uvPNO4uPjKSsrY+fOnZSXl7fmP5uIiIjP0LNexD+pUBfxY08++WSjY1arlTlz5rhfl5WV8dxzzxEdHQ1A3759+dnPfsbs2bPdD+958+bhcDiYNm0a8fHx7vMqKipYsGABo0aNIjQ0lKVLl3Lw4EF++ctf0rt3b/c9+vfv3yiOkJAQfvGLX7h/kYiJieF//ud/+M9//sPgwYMpKysjNzeX7OxsbrjhhnNeS0RE5FKlZ72If1KhLuLHHnjggUZT0CwWS4PXPXv2dD+4oe7hPnDgQBYsWMDx48eJi4vjm2++oWfPnu4Hd70hQ4bwn//8h127dnHVVVexefNmkpKSGjy4m9K3b98Gn/Z36tQJgGPHjgEQHh5OQkICixcvxuVyccUVV9CpU6cG7xEREbnU6Vkv4p9UqIv4sQ4dOpy3wczpD+4zj5WVlREXF0dZWRkxMTGNzouNjXWfB1BaWtroAd+U8PDwBq8DAgIAqKmpAep+yXj66adZsGAB77//Pn//+98JDw/nuuuu4/bbbyckJKRZ9xEREfFnetaL+CcV6iKXuOLi4iaPRUREuP8sKipqdF5hYWGD8yIjIzl+/LjHYmvXrh0//vGPgbq1cxs2bGD+/Pk4HA6mTJnisfuIiIj4Mz3rRXyP5pWIXOK2bdvW4AHucrnYsGEDCQkJxMXFAXVT5rZt2+Z+WNf717/+RVBQkLt761VXXUVeXh7btm3zeJzJycnceuutdOzYkX379nn8+iIiIv5Kz3oR36MRdRE/dvDgwUadYAESExOJjIwE6j4hnzFjBrfeequ7E+zhw4d56KGH3OdPnDiRf//730yfPp0JEya4O8H++9//5q677nJ3bh09ejQbNmzghRdeYNy4cXTr1o2amhq2b99O37596dmzZ7Nj379/P2+99RYDBgwgKSkJu93Otm3b2L9/P+PGjWvRv4uIiIi/0LNexD+pUBfxY6+//vpZj993332MGDECgGuuuYbU1FTmzJlDQUEBiYmJPPjggwwaNMh9fnJyMr/61a945513ePPNN6mpqaFDhw5MnTq1wd6qISEhzJgxg/nz57NixQrmz59PeHg4Xbt2ZeTIkRcUe3R0NAkJCSxfvpyCggIsFgsJCQncfffd3HTTTRf+jyEiIuKH9KwX8U8WwzAMs4MQEXNMmjSJzMxM7rnnHrNDERERkVagZ72Ib9IadREREREREZE2RIW6iIiIiIiISBuiqe8iIiIiIiIibYhG1EVERERERETaEBXqIiIiIiIiIm2ICnURERERERGRNkSFuoiIiIiIiEgbokJdREREREREpA1RoS4iIiIiIiLShqhQFxEREREREWlDVKiLiIiIiIiItCH/H7r+9VScJ/unAAAAAElFTkSuQmCC", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.style.use(\"ggplot\")\n", "fig = plt.figure(figsize=(12,6))\n", "epochs = range(1,9)\n", "plt.subplot(1,2,1)\n", "plt.plot(epochs,history.history[\"accuracy\"],\"go-\")\n", "plt.plot(epochs,history.history[\"val_accuracy\"],\"ro-\")\n", "plt.title(\"Model Accuracy\")\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Accuracy\")\n", "plt.legend([\"Train\",\"val\"],loc = \"upper left\")\n", "\n", "plt.subplot(1,2,2)\n", "plt.plot(epochs,history.history[\"loss\"],\"go-\")\n", "plt.plot(epochs,history.history[\"val_loss\"],\"ro-\")\n", "plt.title(\"Model Loss\")\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Loss\")\n", "plt.legend([\"Train\",\"val\"],loc = \"upper left\")\n", "plt.show()\n", "\n", "y_pred = CNN.predict(test_images).argmax(axis=1)\n", "y_true = test_labels.argmax(axis=1)\n", "conf_matrix = confusion_matrix(y_true, y_pred)\n", " \n", "# 绘制混淆矩阵\n", "plt.figure(figsize=(10, 8))\n", "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=np.arange(num_classes), yticklabels=np.arange(num_classes))\n", "plt.xlabel('Predicted Label')\n", "plt.ylabel('True Label')\n", "plt.title('Confusion Matrix')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "env-test", "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": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 2 }