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"accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## Задание 1" + ], + "metadata": { + "id": "oZs0KGcz01BY" + } + }, + { + "cell_type": "markdown", + "source": [ + "### 1) В среде Google Colab создали новый блокнот (notebook). Импортировали необходимые для работы библиотеки и модули." + ], + "metadata": { + "id": "gz18QPRz03Ec" + } + }, + { + "cell_type": "code", + "source": [ + "# импорт модулей\n", + "import os\n", + "os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab3')\n", + "\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "from tensorflow.keras.models import Sequential\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "from sklearn.metrics import ConfusionMatrixDisplay" + ], + "metadata": { + "id": "mr9IszuQ1ANG" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 2) Загрузили набор данных MNIST, содержащий размеченные изображения рукописных цифр. " + ], + "metadata": { + "id": "FFRtE0TN1AiA" + } + }, + { + "cell_type": "code", + "source": [ + "# загрузка датасета\n", + "from keras.datasets import mnist\n", + "(X_train, y_train), (X_test, y_test) = mnist.load_data()" + ], + "metadata": { + "id": "Ixw5Sp0_1A-w" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 3) Разбили набор данных на обучающие и тестовые данные в соотношении 60 000:10 000 элементов. Параметр random_state выбрали равным (4k – 1)=23, где k=6 –номер бригады. Вывели размерности полученных обучающих и тестовых массивов данных." + ], + "metadata": { + "id": "aCo_lUXl1BPV" + } + }, + { + "cell_type": "code", + "source": [ + "# создание своего разбиения датасета\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# объединяем в один набор\n", + "X = np.concatenate((X_train, X_test))\n", + "y = np.concatenate((y_train, y_test))\n", + "\n", + "# разбиваем по вариантам\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y,\n", + " test_size = 10000,\n", + " train_size = 60000,\n", + " random_state = 23)\n", + "# вывод размерностей\n", + "print('Shape of X train:', X_train.shape)\n", + "print('Shape of y train:', y_train.shape)\n", + "print('Shape of X test:', X_test.shape)\n", + "print('Shape of y test:', y_test.shape)" + ], + "metadata": { + "id": "BrSjcpEe1BeV" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 4) Провели предобработку данных: привели обучающие и тестовые данные к формату, пригодному для обучения сверточной нейронной сети. Входные данные принимают значения от 0 до 1, метки цифр закодированы по принципу «one-hot encoding». Вывели размерности предобработанных обучающих и тестовых массивов данных." + ], + "metadata": { + "id": "4hclnNaD1BuB" + } + }, + { + "cell_type": "code", + "source": [ + "# Зададим параметры данных и модели\n", + "num_classes = 10\n", + "input_shape = (28, 28, 1)\n", + "\n", + "# Приведение входных данных к диапазону [0, 1]\n", + "X_train = X_train / 255\n", + "X_test = X_test / 255\n", + "\n", + "# Расширяем размерность входных данных, чтобы каждое изображение имело\n", + "# размерность (высота, ширина, количество каналов)\n", + "\n", + "X_train = np.expand_dims(X_train, -1)\n", + "X_test = np.expand_dims(X_test, -1)\n", + "print('Shape of transformed X train:', X_train.shape)\n", + "print('Shape of transformed X test:', X_test.shape)\n", + "\n", + "# переведем метки в one-hot\n", + "y_train = keras.utils.to_categorical(y_train, num_classes)\n", + "y_test = keras.utils.to_categorical(y_test, num_classes)\n", + "print('Shape of transformed y train:', y_train.shape)\n", + "print('Shape of transformed y test:', y_test.shape)" + ], + "metadata": { + "id": "xJH87ISq1B9h" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 5) Реализовали модель сверточной нейронной сети и обучили ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывели информацию об архитектуре нейронной сети." + ], + "metadata": { + "id": "7x99O8ig1CLh" + } + }, + { + "cell_type": "code", + "source": [ + "# создаем модель\n", + "model = Sequential()\n", + "model.add(layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\", input_shape=input_shape))\n", + "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"))\n", + "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(layers.Dropout(0.5))\n", + "model.add(layers.Flatten())\n", + "model.add(layers.Dense(num_classes, activation=\"softmax\"))\n", + "\n", + "model.summary()" + ], + "metadata": { + "id": "Un561zSH1Cmv" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# компилируем и обучаем модель\n", + "batch_size = 512\n", + "epochs = 15\n", + "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n", + "model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)" + ], + "metadata": { + "id": "q_h8PxkN9m0v" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 6) Оценили качество обучения на тестовых данных. Вывели значение функции ошибки и значение метрики качества классификации на тестовых данных." + ], + "metadata": { + "id": "HL2_LVga1C3l" + } + }, + { + "cell_type": "code", + "source": [ + "# Оценка качества работы модели на тестовых данных\n", + "scores = model.evaluate(X_test, y_test)\n", + "print('Loss on test data:', scores[0])\n", + "print('Accuracy on test data:', scores[1])" + ], + "metadata": { + "id": "81Cgq8dn9uL6" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 7) Подали на вход обученной модели два тестовых изображения. Вывели изображения, истинные метки и результаты распознавания." + ], + "metadata": { + "id": "KzrVY1SR1DZh" + } + }, + { + "cell_type": "code", + "source": [ + "# вывод двух тестовых изображений и результатов распознавания\n", + "\n", + "for n in [11,43]:\n", + " result = model.predict(X_test[n:n+1])\n", + " print('NN output:', result)\n", + "\n", + " plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n", + " plt.show()\n", + " print('Real mark: ', np.argmax(y_test[n]))\n", + " print('NN answer: ', np.argmax(result))" + ], + "metadata": { + "id": "dbfkWjDI1Dp7" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 8) Вывели отчет о качестве классификации тестовой выборки и матрицу ошибок для тестовой выборки." + ], + "metadata": { + "id": "YgiVGr5_1D3u" + } + }, + { + "cell_type": "code", + "source": [ + "# истинные метки классов\n", + "true_labels = np.argmax(y_test, axis=1)\n", + "# предсказанные метки классов\n", + "predicted_labels = np.argmax(model.predict(X_test), axis=1)\n", + "\n", + "# отчет о качестве классификации\n", + "print(classification_report(true_labels, predicted_labels))\n", + "# вычисление матрицы ошибок\n", + "conf_matrix = confusion_matrix(true_labels, predicted_labels)\n", + "# отрисовка матрицы ошибок в виде \"тепловой карты\"\n", + "display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)\n", + "display.plot()\n", + "plt.show()" + ], + "metadata": { + "id": "7MqcG_wl1EHI" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 9) Загрузили, предобработали и подали на вход обученной нейронной сети собственное изображение, созданное при выполнении лабораторной работы №1. Вывели изображение и результат распознавания." + ], + "metadata": { + "id": "amaspXGW1EVy" + } + }, + { + "cell_type": "code", + "source": [ + "# загрузка собственного изображения\n", + "from PIL import Image\n", + "\n", + "for name_image in ['190 (2).png', '690 (1).png']:\n", + " file_data = Image.open(name_image)\n", + " file_data = file_data.convert('L') # перевод в градации серого\n", + " test_img = np.array(file_data)\n", + "\n", + " # вывод собственного изображения\n", + " plt.imshow(test_img, cmap=plt.get_cmap('gray'))\n", + " plt.show()\n", + "\n", + " # предобработка\n", + " test_img = test_img / 255\n", + " test_img = np.reshape(test_img, (1,28,28,1))\n", + "\n", + " # распознавание\n", + " result = model.predict(test_img)\n", + " print('I think it\\'s', np.argmax(result))" + ], + "metadata": { + "id": "ktWEeqWd1EyF" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 10) Загрузили с диска модель, сохраненную при выполнении лабораторной работы №1. Вывели информацию об архитектуре модели. Повторили для этой модели п. 6." + ], + "metadata": { + "id": "mgrihPd61E8w" + } + }, + { + "cell_type": "code", + "source": [ + "model_lr1 = keras.models.load_model(\"best_model.keras\")\n", + "\n", + "model_lr1.summary()" + ], + "metadata": { + "id": "DblXqn3l1FL2" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# развернем каждое изображение 28*28 в вектор 784\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y,\n", + " test_size = 10000,\n", + " train_size = 60000,\n", + " random_state = 23)\n", + "num_pixels = X_train.shape[1] * X_train.shape[2]\n", + "X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255\n", + "X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255\n", + "print('Shape of transformed X train:', X_train.shape)\n", + "print('Shape of transformed X train:', X_test.shape)\n", + "\n", + "# переведем метки в one-hot\n", + "y_train = keras.utils.to_categorical(y_train, num_classes)\n", + "y_test = keras.utils.to_categorical(y_test, num_classes)\n", + "print('Shape of transformed y train:', y_train.shape)\n", + "print('Shape of transformed y test:', y_test.shape)" + ], + "metadata": { + "id": "0ki8fhJrEyEt" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Оценка качества работы модели на тестовых данных\n", + "scores = model_lr1.evaluate(X_test, y_test)\n", + "print('Loss on test data:', scores[0])\n", + "print('Accuracy on test data:', scores[1])" + ], + "metadata": { + "id": "0Yj0fzLNE12k" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 11) Сравнили обученную модель сверточной сети и наилучшую модель полносвязной сети из лабораторной работы №1 по следующим показателям:\n", + "### - количество настраиваемых параметров в сети\n", + "### - количество эпох обучения\n", + "### - качество классификации тестовой выборки.\n", + "### Сделали выводы по результатам применения сверточной нейронной сети для распознавания изображений. " + ], + "metadata": { + "id": "MsM3ew3d1FYq" + } + }, + { + "cell_type": "markdown", + "source": [ + "Таблица1:" + ], + "metadata": { + "id": "xxFO4CXbIG88" + } + }, + { + "cell_type": "markdown", + "source": [ + "| Модель | Количество настраиваемых параметров | Количество эпох обучения | Качество классификации тестовой выборки |\n", + "|----------|-------------------------------------|---------------------------|-----------------------------------------|\n", + "| Сверточная | 34 826 | 15 | accuracy:0.987 ; loss:0.037 |\n", + "| Полносвязная | 79512 | 50 | accuracy:0.946 ; loss:0.185 |\n" + ], + "metadata": { + "id": "xvoivjuNFlEf" + } + }, + { + "cell_type": "markdown", + "source": [ + "По результатам применения сверточной НС, а также по результатам таблицы 1 делаем выводы, что сверточная НС намного лучше справляется с задачами распознования изображений, чем полносвязная - имеет меньше настраиваемых параметров, быстрее обучается, имеет лучшие показатели качества." + ], + "metadata": { + "id": "YctF8h_sIB-P" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Задание 2" + ], + "metadata": { + "id": "wCLHZPGB1F1y" + } + }, + { + "cell_type": "markdown", + "source": [ + "### В новом блокноте выполнили п. 2–8 задания 1, изменив набор данных MNIST на CIFAR-10, содержащий размеченные цветные изображения объектов, разделенные на 10 классов. \n", + "### При этом:\n", + "### - в п. 3 разбиение данных на обучающие и тестовые произвели в соотношении 50 000:10 000\n", + "### - после разбиения данных (между п. 3 и 4) вывели 25 изображений из обучающей выборки с подписями классов\n", + "### - в п. 7 одно из тестовых изображений должно распознаваться корректно, а другое – ошибочно. " + ], + "metadata": { + "id": "DUOYls124TT8" + } + }, + { + "cell_type": "markdown", + "source": [ + "### 1) Загрузили набор данных CIFAR-10, содержащий цветные изображения размеченные на 10 классов: самолет, автомобиль, птица, кошка, олень, собака, лягушка, лошадь, корабль, грузовик." + ], + "metadata": { + "id": "XDStuSpEJa8o" + } + }, + { + "cell_type": "code", + "source": [ + "# загрузка датасета\n", + "from keras.datasets import cifar10\n", + "\n", + "(X_train, y_train), (X_test, y_test) = cifar10.load_data()" + ], + "metadata": { + "id": "y0qK7eKL4Tjy" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 2) Разбили набор данных на обучающие и тестовые данные в соотношении 50 000:10 000 элементов. Параметр random_state выбрали равным (4k – 1)=23, где k=6 –номер бригады. Вывели размерности полученных обучающих и тестовых массивов данных." + ], + "metadata": { + "id": "wTHiBy-ZJ5oh" + } + }, + { + "cell_type": "code", + "source": [ + "# создание своего разбиения датасета\n", + "\n", + "# объединяем в один набор\n", + "X = np.concatenate((X_train, X_test))\n", + "y = np.concatenate((y_train, y_test))\n", + "\n", + "# разбиваем по вариантам\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y,\n", + " test_size = 10000,\n", + " train_size = 50000,\n", + " random_state = 23)\n", + "# вывод размерностей\n", + "print('Shape of X train:', X_train.shape)\n", + "print('Shape of y train:', y_train.shape)\n", + "print('Shape of X test:', X_test.shape)\n", + "print('Shape of y test:', y_test.shape)" + ], + "metadata": { + "id": "DlnFbQogKD2v" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Вывели 25 изображений из обучающей выборки с подписью классов." + ], + "metadata": { + "id": "pj3bMaz1KZ3a" + } + }, + { + "cell_type": "code", + "source": [ + "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n", + " 'dog', 'frog', 'horse', 'ship', 'truck']\n", + "\n", + "plt.figure(figsize=(10,10))\n", + "for i in range(25):\n", + " plt.subplot(5,5,i+1)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.grid(False)\n", + " plt.imshow(X_train[i])\n", + " plt.xlabel(class_names[y_train[i][0]])\n", + "plt.show()" + ], + "metadata": { + "id": "TW8D67KEKhVE" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 3) Провели предобработку данных: привели обучающие и тестовые данные к формату, пригодному для обучения сверточной нейронной сети. Входные данные принимают значения от 0 до 1, метки цифр закодированы по принципу «one-hot encoding». Вывели размерности предобработанных обучающих и тестовых массивов данных." + ], + "metadata": { + "id": "d3TPr2w1KQTK" + } + }, + { + "cell_type": "code", + "source": [ + "# Зададим параметры данных и модели\n", + "num_classes = 10\n", + "input_shape = (32, 32, 3)\n", + "\n", + "# Приведение входных данных к диапазону [0, 1]\n", + "X_train = X_train / 255\n", + "X_test = X_test / 255\n", + "\n", + "print('Shape of transformed X train:', X_train.shape)\n", + "print('Shape of transformed X test:', X_test.shape)\n", + "\n", + "# переведем метки в one-hot\n", + "y_train = keras.utils.to_categorical(y_train, num_classes)\n", + "y_test = keras.utils.to_categorical(y_test, num_classes)\n", + "print('Shape of transformed y train:', y_train.shape)\n", + "print('Shape of transformed y test:', y_test.shape)" + ], + "metadata": { + "id": "iFDpxEauLZ8j" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 4) Реализовали модель сверточной нейронной сети и обучили ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывели информацию об архитектуре нейронной сети." + ], + "metadata": { + "id": "ydNITXptLeGT" + } + }, + { + "cell_type": "code", + "source": [ + "# создаем модель\n", + "model = Sequential()\n", + "model.add(layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\", input_shape=input_shape))\n", + "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"))\n", + "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(layers.Conv2D(128, kernel_size=(3, 3), activation=\"relu\"))\n", + "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(layers.Flatten())\n", + "model.add(layers.Dense(128, activation='relu'))\n", + "model.add(layers.Dropout(0.5))\n", + "model.add(layers.Dense(num_classes, activation=\"softmax\"))\n", + "model.summary()" + ], + "metadata": { + "id": "YhAD5CllLlv7" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# компилируем и обучаем модель\n", + "batch_size = 64\n", + "epochs = 50\n", + "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n", + "model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)" + ], + "metadata": { + "id": "3otvqMjjOdq5" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 5) Оценили качество обучения на тестовых данных. Вывели значение функции ошибки и значение метрики качества классификации на тестовых данных." + ], + "metadata": { + "id": "Vv1kUHWTLl9B" + } + }, + { + "cell_type": "code", + "source": [ + "# Оценка качества работы модели на тестовых данных\n", + "scores = model.evaluate(X_test, y_test)\n", + "print('Loss on test data:', scores[0])\n", + "print('Accuracy on test data:', scores[1])" + ], + "metadata": { + "id": "SaDxydiyLmRX" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 6) Подали на вход обученной модели два тестовых изображения. Вывели изображения, истинные метки и результаты распознавания." + ], + "metadata": { + "id": "OdgEiyUGLmhP" + } + }, + { + "cell_type": "code", + "source": [ + "# вывод двух тестовых изображений и результатов распознавания\n", + "\n", + "for n in [3,16]:\n", + " result = model.predict(X_test[n:n+1])\n", + " print('NN output:', result)\n", + "\n", + " plt.imshow(X_test[n].reshape(32,32,3), cmap=plt.get_cmap('gray'))\n", + " plt.show()\n", + " print('Real mark: ', np.argmax(y_test[n]))\n", + " print('NN answer: ', np.argmax(result))" + ], + "metadata": { + "id": "t3yGj1MlLm9H" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 7) Вывели отчет о качестве классификации тестовой выборки и матрицу ошибок для тестовой выборки." + ], + "metadata": { + "id": "3h6VGDRrLnNC" + } + }, + { + "cell_type": "code", + "source": [ + "# истинные метки классов\n", + "true_labels = np.argmax(y_test, axis=1)\n", + "# предсказанные метки классов\n", + "predicted_labels = np.argmax(model.predict(X_test), axis=1)\n", + "\n", + "# отчет о качестве классификации\n", + "print(classification_report(true_labels, predicted_labels, target_names=class_names))\n", + "# вычисление матрицы ошибок\n", + "conf_matrix = confusion_matrix(true_labels, predicted_labels)\n", + "# отрисовка матрицы ошибок в виде \"тепловой карты\"\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=class_names)\n", + "disp.plot(ax=ax, xticks_rotation=45) # поворот подписей по X и приятная палитра\n", + "plt.tight_layout() # чтобы всё влезло\n", + "plt.show()" + ], + "metadata": { + "id": "od56oyyzM0nw" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "По результатам классификации датасета CIFAR-10 сверточной моделью делаем вывод, что она удовлетворительно справилась с задачей. Полученные метрики оценки качества имеют показатели в районе 0.71." + ], + "metadata": { + "id": "RF4xK1cxamBc" + } + } + ] +} \ No newline at end of file diff --git a/labworks/LW3/lab3_full.ipynb b/labworks/LW3/lab3_full.ipynb new file mode 100644 index 0000000..11332e7 --- /dev/null +++ b/labworks/LW3/lab3_full.ipynb @@ -0,0 +1,1608 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## Задание 1" + ], + "metadata": { + "id": "oZs0KGcz01BY" + } + }, + { + "cell_type": "markdown", + "source": [ + "### 1) В среде Google Colab создали новый блокнот (notebook). Импортировали необходимые для работы библиотеки и модули." + ], + "metadata": { + "id": "gz18QPRz03Ec" + } + }, + { + "cell_type": "code", + "source": [ + "# импорт модулей\n", + "import os\n", + "os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab3')\n", + "\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "from tensorflow.keras.models import Sequential\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "from sklearn.metrics import ConfusionMatrixDisplay" + ], + "metadata": { + "id": "mr9IszuQ1ANG" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### 2) Загрузили набор данных MNIST, содержащий размеченные изображения рукописных цифр. " + ], + "metadata": { + "id": "FFRtE0TN1AiA" + } + }, + { + "cell_type": "code", + "source": [ + "# загрузка датасета\n", + "from keras.datasets import mnist\n", + "(X_train, y_train), (X_test, y_test) = mnist.load_data()" + ], + "metadata": { + "id": "Ixw5Sp0_1A-w", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ab0db71c-14bd-4d90-b103-de0f680bb148" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n", + "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 3) Разбили набор данных на обучающие и тестовые данные в соотношении 60 000:10 000 элементов. Параметр random_state выбрали равным (4k – 1)=23, где k=6 –номер бригады. Вывели размерности полученных обучающих и тестовых массивов данных." + ], + "metadata": { + "id": "aCo_lUXl1BPV" + } + }, + { + "cell_type": "code", + "source": [ + "# создание своего разбиения датасета\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# объединяем в один набор\n", + "X = np.concatenate((X_train, X_test))\n", + "y = np.concatenate((y_train, y_test))\n", + "\n", + "# разбиваем по вариантам\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y,\n", + " test_size = 10000,\n", + " train_size = 60000,\n", + " random_state = 23)\n", + "# вывод размерностей\n", + "print('Shape of X train:', X_train.shape)\n", + "print('Shape of y train:', y_train.shape)\n", + "print('Shape of X test:', X_test.shape)\n", + "print('Shape of y test:', y_test.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BrSjcpEe1BeV", + "outputId": "297e8485-c5bd-473a-96fd-a0459a264bd4" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Shape of X train: (60000, 28, 28)\n", + "Shape of y train: (60000,)\n", + "Shape of X test: (10000, 28, 28)\n", + "Shape of y test: (10000,)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 4) Провели предобработку данных: привели обучающие и тестовые данные к формату, пригодному для обучения сверточной нейронной сети. Входные данные принимают значения от 0 до 1, метки цифр закодированы по принципу «one-hot encoding». Вывели размерности предобработанных обучающих и тестовых массивов данных." + ], + "metadata": { + "id": "4hclnNaD1BuB" + } + }, + { + "cell_type": "code", + "source": [ + "# Зададим параметры данных и модели\n", + "num_classes = 10\n", + "input_shape = (28, 28, 1)\n", + "\n", + "# Приведение входных данных к диапазону [0, 1]\n", + "X_train = X_train / 255\n", + "X_test = X_test / 255\n", + "\n", + "# Расширяем размерность входных данных, чтобы каждое изображение имело\n", + "# размерность (высота, ширина, количество каналов)\n", + "\n", + "X_train = np.expand_dims(X_train, -1)\n", + "X_test = np.expand_dims(X_test, -1)\n", + "print('Shape of transformed X train:', X_train.shape)\n", + "print('Shape of transformed X test:', X_test.shape)\n", + "\n", + "# переведем метки в one-hot\n", + "y_train = keras.utils.to_categorical(y_train, num_classes)\n", + "y_test = keras.utils.to_categorical(y_test, num_classes)\n", + "print('Shape of transformed y train:', y_train.shape)\n", + "print('Shape of transformed y test:', y_test.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xJH87ISq1B9h", + "outputId": "e01d8833-3b5d-4e63-a680-37a437ee81cc" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Shape of transformed X train: (60000, 28, 28, 1)\n", + "Shape of transformed X test: (10000, 28, 28, 1)\n", + "Shape of transformed y train: (60000, 10)\n", + "Shape of transformed y test: (10000, 10)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 5) Реализовали модель сверточной нейронной сети и обучили ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывели информацию об архитектуре нейронной сети." + ], + "metadata": { + "id": "7x99O8ig1CLh" + } + }, + { + "cell_type": "code", + "source": [ + "# создаем модель\n", + "model = Sequential()\n", + "model.add(layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\", input_shape=input_shape))\n", + "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"))\n", + "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(layers.Dropout(0.5))\n", + "model.add(layers.Flatten())\n", + "model.add(layers.Dense(num_classes, activation=\"softmax\"))\n", + "\n", + "model.summary()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 + }, + "id": "Un561zSH1Cmv", + "outputId": "fe8a1667-aa09-4b2c-ec6d-1c8606d80d1e" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: 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" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1mModel: \"sequential\"\u001b[0m\n" + ], + "text/html": [ + "

Model: \"sequential\"\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "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;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m320\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m64\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;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m64\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;34m1600\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;34m10\u001b[0m) │ \u001b[38;5;34m16,010\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ], + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ conv2d (Conv2D)                 │ (None, 26, 26, 32)     │           320 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d (MaxPooling2D)    │ (None, 13, 13, 32)     │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_1 (Conv2D)               │ (None, 11, 11, 64)     │        18,496 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_1 (MaxPooling2D)  │ (None, 5, 5, 64)       │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout (Dropout)               │ (None, 5, 5, 64)       │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ flatten (Flatten)               │ (None, 1600)           │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense (Dense)                   │ (None, 10)             │        16,010 │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "
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 Total params: 34,826 (136.04 KB)\n",
+              "
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 Trainable params: 34,826 (136.04 KB)\n",
+              "
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+              "
\n" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# компилируем и обучаем модель\n", + "batch_size = 512\n", + "epochs = 15\n", + "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n", + "model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "q_h8PxkN9m0v", + "outputId": "6dc60b63-4778-4097-946b-d3e43c78ec73" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/15\n", + "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 41ms/step - accuracy: 0.5999 - loss: 1.2914 - val_accuracy: 0.9450 - val_loss: 0.1909\n", + "Epoch 2/15\n", + "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9346 - loss: 0.2144 - val_accuracy: 0.9672 - val_loss: 0.1132\n", + "Epoch 3/15\n", + "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9569 - loss: 0.1385 - val_accuracy: 0.9738 - val_loss: 0.0877\n", + "Epoch 4/15\n", + "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9657 - loss: 0.1122 - val_accuracy: 0.9763 - val_loss: 0.0765\n", + "Epoch 5/15\n", + "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9699 - loss: 0.0973 - val_accuracy: 0.9795 - val_loss: 0.0701\n", + "Epoch 6/15\n", + "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9744 - loss: 0.0823 - val_accuracy: 0.9833 - val_loss: 0.0626\n", + "Epoch 7/15\n", + "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9775 - loss: 0.0757 - val_accuracy: 0.9832 - val_loss: 0.0588\n", + "Epoch 8/15\n", + "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9782 - loss: 0.0701 - val_accuracy: 0.9830 - val_loss: 0.0578\n", + "Epoch 9/15\n", + "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9798 - loss: 0.0651 - val_accuracy: 0.9848 - val_loss: 0.0537\n", + "Epoch 10/15\n", + "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9814 - loss: 0.0598 - val_accuracy: 0.9858 - val_loss: 0.0534\n", + "Epoch 11/15\n", + "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9832 - loss: 0.0567 - val_accuracy: 0.9858 - val_loss: 0.0526\n", + "Epoch 12/15\n", + "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9826 - loss: 0.0554 - val_accuracy: 0.9863 - val_loss: 0.0509\n", + "Epoch 13/15\n", + "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9844 - loss: 0.0490 - val_accuracy: 0.9862 - val_loss: 0.0486\n", + "Epoch 14/15\n", + "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9843 - loss: 0.0475 - val_accuracy: 0.9870 - val_loss: 0.0469\n", + "Epoch 15/15\n", + "\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9850 - loss: 0.0491 - val_accuracy: 0.9875 - val_loss: 0.0458\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 6) Оценили качество обучения на тестовых данных. Вывели значение функции ошибки и значение метрики качества классификации на тестовых данных." + ], + "metadata": { + "id": "HL2_LVga1C3l" + } + }, + { + "cell_type": "code", + "source": [ + "# Оценка качества работы модели на тестовых данных\n", + "scores = model.evaluate(X_test, y_test)\n", + "print('Loss on test data:', scores[0])\n", + "print('Accuracy on test data:', scores[1])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "81Cgq8dn9uL6", + "outputId": "697e0828-3818-47e8-a928-5025affd02c5" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.9891 - loss: 0.0327\n", + "Loss on test data: 0.03766785189509392\n", + "Accuracy on test data: 0.9879000186920166\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 7) Подали на вход обученной модели два тестовых изображения. Вывели изображения, истинные метки и результаты распознавания." + ], + "metadata": { + "id": "KzrVY1SR1DZh" + } + }, + { + "cell_type": "code", + "source": [ + "# вывод двух тестовых изображений и результатов распознавания\n", + "\n", + "for n in [11,43]:\n", + " result = model.predict(X_test[n:n+1])\n", + " print('NN output:', result)\n", + "\n", + " plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n", + " plt.show()\n", + " print('Real mark: ', np.argmax(y_test[n]))\n", + " print('NN answer: ', np.argmax(result))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "dbfkWjDI1Dp7", + "outputId": "37cfdc3f-fc1d-4232-96d1-14bca6515589" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 975ms/step\n", + "NN output: [[5.5381900e-11 1.1135461e-07 9.9999964e-01 2.1214625e-07 8.5678256e-11\n", + " 6.3683562e-14 3.7713298e-13 1.9314649e-08 9.5836794e-10 7.0249643e-15]]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Real mark: 2\n", + "NN answer: 2\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n", + "NN output: [[3.5015901e-09 3.6648150e-13 1.8645321e-09 5.8098647e-07 6.5824447e-06\n", + " 5.3449198e-06 7.8439746e-12 3.8253744e-05 7.2931616e-05 9.9987626e-01]]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Real mark: 9\n", + "NN answer: 9\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 8) Вывели отчет о качестве классификации тестовой выборки и матрицу ошибок для тестовой выборки." + ], + "metadata": { + "id": "YgiVGr5_1D3u" + } + }, + { + "cell_type": "code", + "source": [ + "# истинные метки классов\n", + "true_labels = np.argmax(y_test, axis=1)\n", + "# предсказанные метки классов\n", + "predicted_labels = np.argmax(model.predict(X_test), axis=1)\n", + "\n", + "# отчет о качестве классификации\n", + "print(classification_report(true_labels, predicted_labels))\n", + "# вычисление матрицы ошибок\n", + "conf_matrix = confusion_matrix(true_labels, predicted_labels)\n", + "# отрисовка матрицы ошибок в виде \"тепловой карты\"\n", + "display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)\n", + "display.plot()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 762 + }, + "id": "7MqcG_wl1EHI", + "outputId": "db39223c-5f28-4cf0-9fb2-2cad1c92b197" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 0.99 0.99 997\n", + " 1 1.00 0.99 1.00 1164\n", + " 2 0.99 0.98 0.98 1030\n", + " 3 1.00 0.98 0.99 1031\n", + " 4 0.99 0.99 0.99 967\n", + " 5 0.97 0.99 0.98 860\n", + " 6 0.99 0.99 0.99 977\n", + " 7 0.99 0.99 0.99 1072\n", + " 8 0.98 0.98 0.98 939\n", + " 9 0.99 0.98 0.99 963\n", + "\n", + " accuracy 0.99 10000\n", + " macro avg 0.99 0.99 0.99 10000\n", + "weighted avg 0.99 0.99 0.99 10000\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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1q4zSiw7ZMwF4e3vbTNfqIrjZqFoMXG9wRXVzcbXQtF0e+7d7WecpioYD271o1cmxmr1+XePHo63bMKpXc76YHUpBXvnnu/78P3/07gp39M+o0gxjZsfzxxZvDlxxvNTiiK+dZLp5M13Nw7ukRSA7Q/2i15HcDK/dzSIkJASApKQkm/lJSUnW50JCQkhOTrZ53mQykZaWZrPMtbZx5T4qQtUBhP80uKImePub0blARortYUhPdSG8SaFKqcrq9VA6QfWKCAgu5sxxdz6fFcqF03re/PzsNZff+E0AvR5KR+9edefE9hyYTpO2+bx4f9Mq26Y9HPG1k0w3b6YraTQKz8+I58gfRs7FlB3D48wc/bW7HvvvTVB1v58jIiIICQlhy5YtdOjQASgZmLhnzx5Gjx4NQNeuXcnIyCA6OppOnToB8Msvv2CxWOjSpYt1mSlTplBcXIyrqysAmzdvpnnz5vj5+VU4z011NkFhYSGFhZffcFeP6Kyt7n/qcnN/RMsC/IOKmfRYExLOuhHWsMhm2WP7jJw/aeDVj89V2f4Dw4oYPTOByYMbUVzo9MNMRC03ZnY8DVoU8MqgJmpHEVXMggYLN34Vwcqum5OTw6lTp6yPz5w5w8GDB/H396d+/fqMHTuW//znPzRt2pSIiAimTp1KWFgYgwYNAqBly5bcd999jBw5koULF1JcXMyYMWMYPHgwYWFhADz55JPMmDGDZ599lkmTJnHkyBE++ugjPvzww0plvamKgaioKGbMmFFl28tK02E2gW+g7SA7vzom0lMc99C0uKWkOS7hrL5MMbBhRQCNW+dVaV9nk3b5+AWamL/xhHWezgXa3p7Lg8NTeaBhOyyWmr1MpyO+dpLp5s1UKnLWBbrcm8UrDzUm9aLb9VdwMo782lVETbcM7Nu3j169elkfjx8/HoChQ4eydOlSXn31VXJzcxk1ahQZGRn06NGDDRs2YDBcHqC9fPlyxowZwz333INWq+WRRx5h3rx51ud9fHzYtGkTkZGRdOrUiTp16vDmm29W6rRCuMmKgcmTJ1sPJpS0DISHh9/w9kzFWk7+aaRjj2x2/T1IR6NR6NAjhx+WBtidt7qUnn7oH2Q7ejc/V8u2db4Mn3yxSvd3cLsno3o1s5n3yodxxJ0ysHp+YI0XAuCYr51kunkzgULkrHi63ZfJxH81ISnu5h8QVh0c87VzXHfddRfKP9z2WKPRMHPmTGbOnFnuMv7+/qxYseIf99OuXTu2b99+wznhJisGyruwgz3WLKrDhLlxnDhkJOaAkYdGpmAwWti00r9Ktp+fqyXhzOXMiXFunD7ijpeviaB6xWSl60iJd+NSUslLEXe6ZFm/oGL8g0wknHXj17V+3HZPFl5+Zs4cM/DZ9Lq0vT2HRq1sz4Pe+r0vZrOGex6p+LmlFfs/6Mr0nRbkaclOLzu/JlX3ayeZnCfTmNnx9HoonenDI8jP0eIXWFJo52brKCpQp2vMYDQTFnG55S8kvIhGrfPJzij5zFCLo712lWH/RYdqbzfpTVUMVIetP/jhE2DmmYmJ+AWaiD3qzpQhEWSkulbJ9k8cMvLqvy73PX42vS4A9z6WxoS559m9yYf3x9W3Ph81uiEAT41P5OkJibi4KhzY7sXa/wZSkKclMKyYHvdn8MRY29GjABu+CaB7vww8fdQ7N7omVfdrJ5mcJ9OAYSXjct5bc9pm/ntjw9m8Wp0vuWbt83n328t5np9Rcm2RTav8bD4zapqjvXaVYVE0WOy486A96zo6jfJPbRjV7MrBFR07duSDDz6gV69e1sEV15OVlYWPjw93MRAXjeO8ETcmHFQ7Qhl9wzqoHUEIIcowKcX8xvdkZmbi7e1dLfso/a6Ys/cO3D1v/Ddwfo6JV2/dXq1Z1aJqy8D1BlcIIYQQVcViZzeBRboJqsf1BlcIIYQQVeVG7jx49fq1Ve39nwkhhBCiQpx+AKEQQgjnYEaD2Y6LDtmzrqOTYkAIIYRTkG6C8tXe/5kQQgghKkRaBoQQQjgFM/Y19dfmK7hIMSCEEMIpSDdB+aQYEEII4RQc6RbGjqb2/s+EEEIIUSHSMiCEEMIpKGiw2DFmQJFTC4UQQoibm3QTlK/2/s+EEEIIUSHSMlANHPEOgaNOxKodoYxFzRqpHUHUJhoHbMKVe684FLmFcfmkGBBCCOEUzHbetdCedR1d7f2fCSGEEKJCpGVACCGEU5BugvJJMSCEEMIpWNBisaNB3J51HV3t/Z8JIYQQokKkZUAIIYRTMCsazHY09duzrqOTYkAIIYRTkDED5ZNiQAghhFNQ7LxroSJXIBRCCCFEbSUtA0IIIZyCGQ1mO242ZM+6jk6KASGEEE7BotjX72+pxVeXlm4CIYQQwslJywAwYFgq/xqdjH+gidhj7nz6Rl1iDhprZaaLew0c+q8PqUf15CW70Gd+Ig3vzbM+rygQPc+P46u9KMrSEnJLAT1mpOLT0GRdpiBDy863Ajj3iwcarUJE31y6TbmEq8flsvn0jx4cXOhLxllX3P3NtH4qi/bPZdqd/0ptuuTw6AspNG2bR0CIiekjGrJrg0+V7qMyHngmlf7PXCI4vAiAczEGln8YzL5fvVXLVMqZ3uM3yt3DzNBXL9Ltvkx8A0ycPurOgjfrceKQusep1GNjknj29UTWLq7Dwml11Y4DOGamf2KxcwChPes6utr7P6ugng+mM2paAss/CCGybzNijxmYtSIWn4DiWpmpOE9DQIsiur+Zes3nDy324ciX3twxI5VB/0vAxajw44hQTIWXm9Z+fSWI9JNu9F96kfs+S+TiXne2TQ20Pn9+qzu/TAii5RNZPLr+Aj2mpXJ4qQ9HvqraL0WD0ULsUQOfvF6vSrd7o1IuuvLF7FDG3NeMF/s149DvnkxfcpYGzQpUzeVs7/EbNe69OG65I4c5LzXg+d4tiN7qxdsrTxEQUqRaplLN2ufR/6k0Yo8a1I5i5YiZrseCxu6ptlK1GIiKiuLWW2/Fy8uLoKAgBg0aRExMTI1meHhUKhtW+LNplT/nTxqYN6kehfka+j6RVqM5aipT/Z753DounYg+eWWeUxQ4vMyHji9k0LB3HgEtiug1J5m8ZB1nN5f8Oko/5UrcdiN3zkohqH0hIZ0L6T41ldP/z4PcJB0AJ7/3omHvXFo9kY13fRP1e+XTYVQGhxb7VukdXff96s2yOaHsVLE14Ep7Nvuw9xdvEs7oiY/Vs/SdUApytbTolKtqLmd7j98IN4OFHvdn8N9ZoRzZ40nCWT1ffxBKwlk9DzxzSZVMpQxGM5M+OcfcifXIztSpmqWUI2YS9lG1GNi6dSuRkZHs3r2bzZs3U1xcTJ8+fcjNrZkPTxdXC03b5bF/u5d1nqJoOLDdi1adyn5Z1vZM2XEu5Ke4ULdrvnWem5dCUPtCkg+WVP9JBw24eZsJbHv511LdbvlotJB8SA+AuUiDzs32W9/FoJCb6EJOvHP0TGm1Cj0HpqM3Wji+z0O1HPIerxidTkHnAkWFth+JhQVaWt+ao0qmUmNmx/PHFm8OXHG81OaImSqi9AqE9ky1laqfzBs2bLB5vHTpUoKCgoiOjubOO++s9v17+5vRuUBGiu1hSE91IbxJYbXv39Ey5aWWVPjGOmab+e51zOSllDyXn6LDPcD2ea0L6H0s5P+9fniPPHZFBRC/M5uw2wvIPOfKn1+U/HrPS9HhVc9EbdWwRT5z153CTW8hP1fLzGcbcv6kes2o8h6vmPxcHcf2GXny5UTOnzSQkeLCXYPSadkpl4SzelUyAfQcmE6Ttvm8eH9T1TJczREzVZSMGSifQ/1My8wsGWDm7+9/zecLCwspLLz8YZGVlVUjuUTltHg8m6w4Vzb8OwSLSYObp4U2z2QS/bE/mtr7twTAhdN6Xri3GUYvM3c8kMmEj84z8eEmqhYEomLmvNSA8e+f55v9RzGb4NRhI79950fTduq0VgSGFTF6ZgKTBzeiuNAx/nAcMZOoGg5TDFgsFsaOHUv37t1p06bNNZeJiopixowZVbbPrDQdZhP4Btr+UvWrYyI9RZ1Do2am0haBvFQdxqDLv/7zU3UEtCzpFnAPNJN/ybaP0GKCwkwt7n+vr9FAl4lp3Do+jfwUHQZ/M/G73AHwCldvgFhNMBVrrb8kTx020rxDHoOeS2HepHBV8sh7vOIuntMz8V9N0bub8fCykJbsyusLznLxvDotA03a5eMXaGL+xhPWeToXaHt7Lg8OT+WBhu2wWGq22doRM1WGBTvvTSADCKtfZGQkR44cYeXKleUuM3nyZDIzM61TXFycXfs0FWs5+aeRjj2yrfM0GoUOPXI4Fq3O6URqZvIKN+EeaCLh7y9ugKIcDcmH9AR1KBkRH9yhgKIsHSlH3KzLJOx2R7FAUHvbJl6tDjxCzOjc4PR6T4I7FuDub6nW/4Oj0WjA1U29K5XIe7zyCvN1pCW74uljolPPLHZtVOfU0IPbPRnVqxmj7708xRx055c1foy+t5kqX7qOmKkyFDvPJFBqcTHgEC0DY8aMYf369Wzbto169co/TUyv16PXV22VvmZRHSbMjePEISMxB4w8NDIFg9HCppXX7qqoCdWZqThXQ+Y5V+vjrAuupB5zw+BrxjPMTNuhmexf4It3w2K86xWzd64/xiCz9VoEfk2KCb8jj21vBHLHjFQsJvh9ZgCN++fiEVzSMlCQpiV2owdhtxVgKtRwYo0XsRs8GPD1RbvzX8lgNBMWcXkgY0h4EY1a55OdoSMl3u0f1qwewydfZO8vXqTEu+HuaabXQxm065bDlCcb1XiWKznbe/xGdeqZhUYDcaf11G1YxHNT44k7bWDTqgBV8uTn6jgX424zryBPS3Z62fnOnKky5K6F5VO1GFAUhRdffJG1a9fy22+/ERERUeMZtv7gh0+AmWcmJuIXaCL2qDtThkSQkep6/ZVvwkwpR/SsfzrM+nh3VMkHXbOHsrnrnRTaj8zElK9l+9Q6JRcd6lRAv88TcdFf/nXb6/1kfp9Zh/83LBQ0ENE3l+5v2F634MRaL3a/EwBKSWvCgK8vlmk5sFez9vm8++1p6+PnZyQAsGmVH++Pq1+l+6oI3zomJs47j3+QibxsHWeOG5jyZCP2b1N3xLWzvcdvlIe3meGvXaROaDHZGTp+/9GXJe+EYjbV3i8AIUppFKUqz/yunBdeeIEVK1bw/fff07x5c+t8Hx8f3N2vX2VmZWXh4+PDXQzERaPeh8jNYNSJWLUjlLGombq/mEUto3HAL231Pl5vGialmN/4nszMTLy9q6dLpvS74qHNw3H1uPFWw+LcItbeu6Ras6pF1ZaBBQsWAHDXXXfZzF+yZAnDhg2r+UBCCCFqLekmKJ/q3QRCCCGEUJdDDCAUQgghqpu99xeozacWSjEghBDCKUg3Qfkc5joDQgghhFCHtAwIIYRwCtIyUD4pBoQQQjgFKQbKJ90EQgghhJOTlgEhhBBOQVoGyifFgBBCCKegYN/pgbX5yjhSDAghhHAK0jJQPhkzIIQQQjg5aRkQQgjhFKRloHxSDDgJR7xDoNxJsYK0OrUTlGUxq52gLLnXibiOmi4GzGYz06dP5+uvvyYxMZGwsDCGDRvGG2+8gebvu2wqisK0adNYvHgxGRkZdO/enQULFtC0aVPrdtLS0njxxRdZt24dWq2WRx55hI8++ghPT88b/r9cTboJhBBCiGrwzjvvsGDBAj755BOOHz/OO++8w5w5c/j444+ty8yZM4d58+axcOFC9uzZg4eHB3379qWgoMC6zJAhQzh69CibN29m/fr1bNu2jVGjRlVpVmkZEEII4RSqqmUgKyvLZr5er0ev15dZfufOnQwcOJD+/fsD0LBhQ7755hv++OMPoKRVYO7cubzxxhsMHDgQgC+//JLg4GC+++47Bg8ezPHjx9mwYQN79+6lc+fOAHz88cfcf//9vPfee4SFhd3w/+dK0jIghBDCKSiKxu4JIDw8HB8fH+sUFRV1zf1169aNLVu2cOLECQAOHTrEjh076NevHwBnzpwhMTGR3r17W9fx8fGhS5cu7Nq1C4Bdu3bh6+trLQQAevfujVarZc+ePVV2bKRlQAghhKiEuLg4vL29rY+v1SoA8Nprr5GVlUWLFi3Q6XSYzWZmzZrFkCFDAEhMTAQgODjYZr3g4GDrc4mJiQQFBdk87+Ligr+/v3WZqiDFgBBCCKdgQWPXRYdK1/X29rYpBsqzevVqli9fzooVK2jdujUHDx5k7NixhIWFMXTo0BvOUR2kGBBCCOEUavpsgokTJ/Laa68xePBgANq2bcu5c+eIiopi6NChhISEAJCUlERoaKh1vaSkJDp06ABASEgIycnJNts1mUykpaVZ168KMmZACCGEqAZ5eXlotbZfszqdDovFAkBERAQhISFs2bLF+nxWVhZ79uyha9euAHTt2pWMjAyio6Oty/zyyy9YLBa6dOlSZVmlZUAIIYRTuHIQ4I2uXxkDBgxg1qxZ1K9fn9atW3PgwAE++OADRowYAYBGo2Hs2LH85z//oWnTpkRERDB16lTCwsIYNGgQAC1btuS+++5j5MiRLFy4kOLiYsaMGcPgwYOr7EwCkGJACCGEk6jpboKPP/6YqVOn8sILL5CcnExYWBj//ve/efPNN63LvPrqq+Tm5jJq1CgyMjLo0aMHGzZswGAwWJdZvnw5Y8aM4Z577rFedGjevHk3/P+4Fo2i3LyX7crKysLHx4e7GIiLxlXtOKKS5AqEFSRXIBS1mEkp5je+JzMzs0KD8m5E6XdFp2/H4eJx7ZH/FWHKLST6kQ+rNataZMyAEEII4eSkm0AIIYRTUOzsJrBnvIGjk2IAGDAslX+NTsY/0ETsMXc+faMuMQeNkqkcj41J4tnXE1m7uA4Lp9W1e3sX9xo49F8fUo/qyUt2oc/8RBrem2d9XlEgep4fx1d7UZSlJeSWAnrMSMWnocm6zP4FvsT9ZiT1uBs6V4Vh0efK7Of3twJI2m8g7YQbfo2LeOSHeLuzX4uar12bLtk8+nwSTdvmExBSzPRnG7Fro+8VSyg8M+Ei9z2RiqePmWN7PZn3ejgJZwzlbbLaONJ7vE2XHB59IYWmbfMICDExfURDdm3wUSVLqQeeSaX/M5cIDi8C4FyMgeUfBrPvV/Wapx8fk0T3+zMJb1JIUYGWY/uMfD4rlAuna/79cyMU7Luf1U3bp14BTt9N0PPBdEZNS2D5ByFE9m1G7DEDs1bE4hNQLJmuoVn7PPo/lUbs0ar74y/O0xDQoojub6Ze8/lDi3048qU3d8xIZdD/EnAxKvw4IhRT4eUq3VKkIeK+XFo9kXXNbZRq/kg2je/PqbLsV1P7tTMYLcQeM/LJG+HXfP6xF5IYODyFjyfX5+UBzSnI0zL761O46i01kq+U2sfpagajhdijBj55vZ4q+7+WlIuufDE7lDH3NePFfs049Lsn05ecpUGzguuvXE3adc1l3dI6jH2gKZMHN0LnojD7m1j07jKG5GanajGwYMEC2rVrZ72aU9euXfnpp59qNMPDo1LZsMKfTav8OX/SwLxJ9SjM19D3ibQazeHomQAMRjOTPjnH3In1yM6sukFt9Xvmc+u4dCL65JV5TlHg8DIfOr6QQcPeeQS0KKLXnGTyknWc3Xz5V2Tnl9NpNzwT/+ZF5e6n+9RLtH4qC69wU7nL2Evt127frz4sezeMnRt8r/GswqBnk/lmXgi7Nvly5riROWMbEhBcTLe+GTWSr5Tax+lq+371ZtmcUHaq3BpwpT2bfdj7izcJZ/TEx+pZ+k4oBblaWnTKVS3TlCGN2Lzan3MnDMQec+f9sfUJrldM03b5qmWqjNIrENoz1VaqFgP16tXj7bffJjo6mn379nH33XczcOBAjh49WiP7d3G10LRdHvu3e1nnKYqGA9u9aNWp7BeTs2YqNWZ2PH9s8ebAFdmqW3acC/kpLtTtevnDxs1LIah9IckHHatp0pFfO4CQ+kUEBJts8uVl6/jroActa/ALxtGPkyPSahV6DkxHb7RwfJ+H2nGsPLxLWgSyMxzwjJdrqKobFdVGqo4ZGDBggM3jWbNmsWDBAnbv3k3r1q3LLF9YWEhhYaH18dW3kawsb38zOhfISLE9DOmpLoQ3KSxnrerliJkAeg5Mp0nbfF68v2mN7jcvteRDxljHthnSvY6ZvBTH+gBy1NeulH9gSRN8RqrtabgZKS7W52qCox8nR9KwRT5z153CTW8hP1fLzGcbcv6kYxTBGo3C8zPiOfKHkXMx7mrHEXZymDEDZrOZlStXkpuba70M49WioqJsbhsZHn7tflFRtQLDihg9M4F3xtSnuNBh3jJC1HoXTut54d5mvNS/Keu/rMOEj85Tv6l6YwauNGZ2PA1aFBA1uoHaUSqs9KJD9ky1lepnExw+fJiuXbtSUFCAp6cna9eupVWrVtdcdvLkyYwfP976OCsry66CICtNh9kEvoG2fch+dUykp6hzaBwxU5N2+fgFmpi/8YR1ns4F2t6ey4PDU3mgYTsslur5IyltEchL1WEMutw6kJ+qI6Bl+eMD1OCIr92V0lJKWgR86xSTlny5dcA30MTpozX3y87Rj5MjMRVrSThbcpGcU4eNNO+Qx6DnUpg3Sd0fQpGzLtDl3ixeeagxqRfdVM1SGYpi59kEtfh0AtV/5jVv3pyDBw+yZ88eRo8ezdChQzl27Ng1l9Xr9dbBhhW9heQ/MRVrOfmnkY49sq3zNBqFDj1yOBatzilOjpjp4HZPRvVqxuh7L08xB935ZY0fo+9tVm2FAIBXuAn3QBMJuy5/WRXlaEg+pCeog2P8QirliK/dlRLPu3EpycUmn9HTTIsOuRyPrrl+aEc/To5MowFXNzW/kRQiZ12g232ZvPpoY5LibvxqfsKxqF6Gu7m50aRJEwA6derE3r17+eijj/jss89qZP9rFtVhwtw4ThwyEnPAyEMjUzAYLWxa6V8j+78ZMuXn6sr0CRbkaclOLzv/RhTnasg8d/mXatYFV1KPuWHwNeMZZqbt0Ez2L/DFu2Ex3vWK2TvXH2OQ2eZaBDkJOgoydOQkuKBYNKQeK/m14tOgGFePkg/PzHMuFOdqyU/VYSq8vIxfkyJ0VfTjRu3XzmA0E9bwcr97SHghjVrlkZ3hQkqCG999HsQTLyUSf0ZPYpyeoRMSuJTkyk6baxFUP7WP09UMRjNhEZdbmkLCi2jUOp/sDB0p8er88h0++SJ7f/EiJd4Nd08zvR7KoF23HKY8qd4ls8fMjqfXQ+lMHx5Bfo4Wv7/HmuRm6ygqUP235XXV9I2KbiaqFwNXs1gsNoMEq9vWH/zwCTDzzMRE/AJNxB51Z8qQiDKDrGqSI2aqTilH9Kx/+vLdt3ZHBQDQ7KFs7nonhfYjMzHla9k+tU7JRYc6FdDv80Rc9Jd/Ie37yJ8Tay+PTl8zqOR88Qe+SiCsS0kLwrYpgVz8w73MMk/8ch6velVzuqHar12z9nm8+7+T1sfPTy+5sNKm1f68P74hqz8NxmC08PI75/H0NnN0rydTnmpS42NB1D5OV2vWPp93vz1tffz8jAQANq3y4/1x9VXJ5FvHxMR55/EPMpGXrePMcQNTnmzE/m01dzbP1QYMuwTAe2tO28x/b2w4m1er9wOqoqQYKJ+qNyqaPHky/fr1o379+mRnZ7NixQreeecdNm7cyL333nvd9eVGRTc3uVFRBcmNikQtVpM3Kmq+4jV0xhvv2jDnFRLz5Nu18kZFqrYMJCcn88wzz3Dx4kV8fHxo165dhQsBIYQQQlQNVYuBzz//XM3dCyGEcCJyNkH5HG7MgBBCCFEdSooBe8YMVGEYB+P4wz+FEEIIUa2kZUAIIYRTkLMJyifFgBBCCKeg/D3Zs35tJd0EQgghhJOTlgEhhBBOQboJyifFgBBCCOcg/QTlkmJACCGEc7CzZYBa3DIgYwaEEEIIJyctA0IIIZyCXIGwfFIMCCGEcAoygLB8UgwI1TjiHQL7Hc1QO0IZP7X2VTuCEKKWk2JACCGEc1A09g0ClJYBIYQQ4uYmYwbKJ2cTCCGEEE5OWgaEEEI4B7noULmkGBBCCOEU5GyC8lWoGPjhhx8qvMEHH3zwhsMIIYQQouZVqBgYNGhQhTam0Wgwm8325BFCCCGqTy1u6rdHhYoBi8VS3TmEEEKIaiXdBOWz62yCgoKCqsohhBBCVC+lCqZaqtLFgNls5q233qJu3bp4enoSGxsLwNSpU/n888+rPKAQQgghqleli4FZs2axdOlS5syZg5ubm3V+mzZt+O9//1ul4YQQQoiqo6mCqXaqdDHw5ZdfsmjRIoYMGYJOp7POb9++PX/99VeVhhNCCCGqjHQTlKvS1xmIj4+nSZMmZeZbLBaKi4urJFRNGzAslX+NTsY/0ETsMXc+faMuMQeNquVp0yWHR19IoWnbPAJCTEwf0ZBdG3xUy1PK0Y5TTWcy5cKJee4kbXGlKE2Dd0szLV/Lx7ft5TNock5rifnAnbR9Lihm8GxkpuPcXNzDSj5Fzq924+KPbmQe02HO1dB7Vyau3tX/CeNor52jvccdLQ/A42OS6H5/JuFNCikq0HJsn5HPZ4Vy4bRBtUyOeJxE1ah0y0CrVq3Yvn17mfn/93//R8eOHaskVE3q+WA6o6YlsPyDECL7NiP2mIFZK2LxCVCvsDEYLcQeNfDJ6/VUy3A1RzxONZ3p8JtGLu1yof3bufRYm02dbib2PudJQVJJ02HueS27n/bEI8LMbUtz6L4mm8bPF6DVX96GuUBDne7FNB5Zc4NvHfG1c7T3uKPlAWjXNZd1S+sw9oGmTB7cCJ2LwuxvYtG7q3f6tiMep0qRloFyVbpl4M0332To0KHEx8djsVhYs2YNMTExfPnll6xfv/6Gg7z99ttMnjyZl19+mblz597wdirr4VGpbFjhz6ZV/gDMm1SP2+7Jou8Taaz+JLjGclxp36/e7PvVW5V9l8cRj1NNZjIXQNJmV275OBf/ziUfxk0jC0j+zYXzK/U0e7mAk/MMBN5posWEy1/0HvVtT8uNeKYQgEt/1NzFPx3xtXO097ij5QGYMsT2Ft/vj63P6iNHadounyN7PFXJ5IjHqVLkroXlqnTLwMCBA1m3bh0///wzHh4evPnmmxw/fpx169Zx77333lCIvXv38tlnn9GuXbsbWv9GubhaaNouj/3bvazzFEXDge1etOqUV6NZHJkjHqeazqSYQTFrbH7lA+j0kH7ABcUCyVtd8WhgZu9ID7bc4c3OwZ4kbXGt8iyV4YivnbgxHt4lRWh2hu46SwpReTd0nYE77riDzZs3k5ycTF5eHjt27KBPnz43FCAnJ4chQ4awePFi/Pz8/nHZwsJCsrKybCZ7ePub0blARortr7T0VBf8Ak12bbs2ccTjVNOZXDzAt4OJ0wsNFCRrUMwQv86V9EM6ClM0FF3SYM7TEPu5gTo9TNy6KJfge4rZ/7KRS3vV+/B2xNdOVJ5Go/D8jHiO/GHkXIy72nFuWqW3MLZnqq1u+KJD+/bt46uvvuKrr74iOjr6hgNERkbSv39/evfufd1lo6Ki8PHxsU7h4eE3vF8hKqtdVB6KAr/28mFjRx/Ofa0n7P5i0F7+kAjqVUzE0EK8W5ppPLKQoJ4m4lbp/3nDQlzHmNnxNGhRQNToBmpHubnJmIFyVbrj8sKFCzzxxBP8/vvv+Pr6ApCRkUG3bt1YuXIl9epVfGDJypUr2b9/P3v37q3Q8pMnT2b8+PHWx1lZWXYVBFlpOswm8L3qF5JfHRPpKXJDx1KOeJzUyORR38Lty3Iw5YEpV4MhUOHAK0aM9Sy4+SpoXBQ8G9sO7vJoZCZ9v3rvJUd87UTlRM66QJd7s3jlocakXnS7/gpC3IBKtww899xzFBcXc/z4cdLS0khLS+P48eNYLBaee+65Cm8nLi6Ol19+meXLl2MwVOxUGb1ej7e3t81kD1OxlpN/GunYI9s6T6NR6NAjh2PR6p4y50gc8TipmcnFCIZAheJMDam/uxLUqxitG/i0MZN71rZLIO+cFvcw9e7t4YivnagohchZF+h2XyavPtqYpDhpYbJb6QBCe6ZaqtI/DbZu3crOnTtp3ry5dV7z5s35+OOPueOOOyq8nejoaJKTk7nlllus88xmM9u2beOTTz6hsLDQ5qJG1WXNojpMmBvHiUNGYg4YeWhkCgajhU0r/at93+UxGM2ERRRZH4eEF9GodT7ZGTpS4tX5ZeCIx6mmM6XscAEFPCIs5J3X8td77nhEmKn3UMlrFTG8kIOvGPHv5Ib/bSZSd7iQ/Jsrty3JsW6jMEVDYaqWvPMldXj2SW1JcRFa0rpQHRzxtXO097ij5YGSroFeD6UzfXgE+Tla/AJLTgXNzdZRVGDXbWVumCMep8rQKCWTPevXVpUuBsLDw695cSGz2UxYWFiFt3PPPfdw+PBhm3nDhw+nRYsWTJo0qUYKAYCtP/jhE2DmmYmJ+AWaiD3qzpQhEWSkqjcKvFn7fN799rT18fMzEgDYtMqP98fVVyWTIx6nms5kytEQM9dAQaIWNx+F4HuLafZyPtq/dxfSu5jW0/KJXaznWJQ7Hg0tdJybi3+ny10H51frOfXp5ZawPc+UjPJv+588a1FR1RzxtXO097ij5QEYMOwSAO+tOW0z/72x4WxerU4h54jHqVLs7fevxcWARlEqNz7y+++/Z/bs2cyfP5/OnTsDJYMJX3zxRSZNmsSgQYNuOMxdd91Fhw4dKnydgaysLHx8fLiLgbho1D2FS9QO/Y5mqB2hjJ9a+6odQYhqY1KK+Y3vyczMtLvrtzyl3xXhc2eidb/xKzha8guIG/tmtWZVS4VaBvz8/NBoLveV5Obm0qVLF1xcSlY3mUy4uLgwYsQIu4oBIYQQotrIRYfKVaFioKauCPjbb7/VyH6EEEI4IekmKFeFioGhQ4dWdw4hhBCi1omPj2fSpEn89NNP5OXl0aRJE5YsWWLtZlcUhWnTprF48WIyMjLo3r07CxYsoGnTptZtpKWl8eKLL7Ju3Tq0Wi2PPPIIH330EZ6eVXdZaruGpBYUFFTpFQGFEEKIalPDFx1KT0+ne/fuuLq68tNPP3Hs2DHef/99m6vtzpkzh3nz5rFw4UL27NmDh4cHffv2paDg8j1OhgwZwtGjR9m8eTPr169n27ZtjBo16kaPwjVV+myC3NxcJk2axOrVq7l06VKZ581m9e6oJYQQQpSrhrsJ3nnnHcLDw1myZIl1XkRExOXNKQpz587ljTfeYODAgQB8+eWXBAcH89133zF48GCOHz/Ohg0b2Lt3r7U14eOPP+b+++/nvffeq9RZfP+k0i0Dr776Kr/88gsLFixAr9fz3//+lxkzZhAWFsaXX35ZJaGEEEIIR3V1i3hhYeE1l/vhhx/o3Lkzjz76KEFBQXTs2JHFixdbnz9z5gyJiYk2l+P38fGhS5cu7Nq1C4Bdu3bh6+trLQQAevfujVarZc+ePVX2f6p0MbBu3To+/fRTHnnkEVxcXLjjjjt44403mD17NsuXL6+yYEIIIUSVqqIrEIaHh9vcJycqKuqau4uNjbX2/2/cuJHRo0fz0ksvsWzZMgASExMBCA62vZV4cHCw9bnExESCgoJsnndxccHf39+6TFWodDdBWloajRqV3Gfb29ubtLQ0AHr06MHo0aOrLJgQQghRlarqCoRxcXE21xnQ6699qWiLxULnzp2ZPXs2AB07duTIkSMsXLjQ4QbmV7ploFGjRpw5cwaAFi1asHr1aqCkxaD0xkVCCCFEbXX1PXLKKwZCQ0Np1aqVzbyWLVty/vx5AEJCQgBISkqyWSYpKcn6XEhICMnJyTbPm0wm0tLSrMtUhUoXA8OHD+fQoUMAvPbaa8yfPx+DwcC4ceOYOHFilQUTQgghqlQNn03QvXt3YmJibOadOHGCBg1KbkUdERFBSEgIW7ZssT6flZXFnj176Nq1KwBdu3YlIyOD6Oho6zK//PILFouFLl26VC7QP6h0N8G4ceOs/+7duzd//fUX0dHRNGnShHbt2lVZMCGEEOJmNm7cOLp168bs2bN57LHH+OOPP1i0aBGLFi0CQKPRMHbsWP7zn//QtGlTIiIimDp1KmFhYdar+bZs2ZL77ruPkSNHsnDhQoqLixkzZgyDBw+usjMJ4AaKgas1aNDAWuUIIYQQjkqDnWMGKrn8rbfeytq1a5k8eTIzZ84kIiKCuXPnMmTIEOsyr776Krm5uYwaNYqMjAx69OjBhg0bMBgu30Nh+fLljBkzhnvuucd60aF58+bd+H/kGip0o6LK7PSll16yK1BlyI2KRFWTGxUJUbNq8kZFDd75D1qDHTcqKijg3KQ3nPdGRR9++GGFNqbRaGq0GBCiqjniF6/f7+rcrvafpHdPUzuCEJUnNyoqV4WKgdKzB4QQQoibltyoqFx23ZtACCGEEDc/uwcQCiGEEDcFaRkolxQDQgghnEJVXYGwNpJuAiGEEMLJScuAEEII5yDdBOW6oZaB7du389RTT9G1a1fi4+MB+Oqrr9ixY0eVhhNCCCGqTA1fjvhmUuli4Ntvv6Vv3764u7tz4MAB632cMzMzrXdmEkIIIcTNo9LFwH/+8x8WLlzI4sWLcXW9fNW/7t27s3///ioNJ4QQQlSV0gGE9ky1VaXHDMTExHDnnXeWme/j40NGRkZVZBJCCCGqnlyBsFyVbhkICQnh1KlTZebv2LGDRo0aVUkoIYQQosrJmIFyVboYGDlyJC+//DJ79uxBo9GQkJDA8uXLmTBhAqNHj66OjEIIIYSoRpXuJnjttdewWCzcc8895OXlceedd6LX65kwYQIvvvhidWSsVm265PDoCyk0bZtHQIiJ6SMasmuDj9qxGDAslX+NTsY/0ETsMXc+faMuMQeNkulvj49Jovv9mYQ3KaSoQMuxfUY+nxXKhdM3fkeyqlJTx0kxKxR8nk/RpiIslyxo62hxu1+PYZgBjaakOTP3PzkU/VRks55LF1e8PvCyPs55NRvTKTNKugWNlwbXzq64jzaiDay+y5DI3931PfBMKv2fuURweMnrdy7GwPIPg9n3q/p3y3Ok41QZctGh8lX6r12j0TBlyhTS0tI4cuQIu3fvJiUlhbfeeqs68lU7g9FC7FEDn7xeT+0oVj0fTGfUtASWfxBCZN9mxB4zMGtFLD4BxZLpb+265rJuaR3GPtCUyYMboXNRmP1NLHp3syp5StXkcSr4uoDC7woxjjfivcIH9xeMFCzPp/D/Cm2Wc7ndFZ8ffK2Tx3QP2+dvccVzpife3/jgOcsTc7yFnDdyqjzvleTv7vpSLrryxexQxtzXjBf7NePQ755MX3KWBs0KVMlTytGOU6VIN0G5brj0d3Nzo1WrVtx22214enre0DamT5+ORqOxmVq0aHGjkW7Ivl+9WTYnlJ0O8Kuk1MOjUtmwwp9Nq/w5f9LAvEn1KMzX0PcJ9W4b62iZpgxpxObV/pw7YSD2mDvvj61PcL1imrbLVyVPqZo8TuYjJlzvcMW1mxu6UB1uvdxwvc0V8zGTzXIaV9AGaC9P3rZ/9obBBlzauKAL0eHS1hXDUwbMR00opur75JO/u+vbs9mHvb94k3BGT3ysnqXvhFKQq6VFp1xV8pRytOMkqkaluwl69eplbYK8ll9++aVS22vdujU///zz5UAuzn1RRBdXC03b5bHykyDrPEXRcGC7F6065Ummcnh4l7QIZGfoVMtQ08dJ18aFoh8KMZ83o6uvw3TShOlPE+4v2jbXmg6YyOifjsZLg0snV9xHuaP1ufbvAEuWhaJNRejauqBxqb0jp6/m6O9xrVbhjgEZ6I0Wju/zuP4K1cTRj9N12Xt6YC1uGaj0N2+HDh1sHhcXF3Pw4EGOHDnC0KFDKx/AxYWQkJAKLVtYWGi9yBFAVlZWpffn6Lz9zehcICPF9qVJT3UhvElhOWs5X6YraTQKz8+I58gfRs7FuKuWo6aPk+FpA0qeQtaTmSVtfBYwjHJH31dvXcb1dldce7qhC9NijreQ/1keOa+Y8PrMG43u8pd93qd5FH5bAAWga63D812va+yx9nLU93jDFvnMXXcKN72F/FwtM59tyPmT6o2LcdTjVGFyOeJyVboY+PDDD685f/r06eTkVL6f8eTJk4SFhWEwGOjatStRUVHUr1//mstGRUUxY8aMSu9D1G5jZsfToEUBrwxqonaUGlX8SxFFm4rwmO6BLkKH6aSZ/I/y0NbRor+/pCBw6325MNA1Bl1jHVmPZWI6YMK18+WLhhmeNKB/QI8l0UL+knxy38rF813Pf2wFFNXvwmk9L9zbDKOXmTseyGTCR+eZ+HATVQsCUTtV2XDhp556ii+++KJS63Tp0oWlS5eyYcMGFixYwJkzZ7jjjjvIzs6+5vKTJ08mMzPTOsXFxVVFdIeSlabDbALfQNt+X786JtJT1OlCccRMpSJnXaDLvVm8+q/GpF50UzVLTR+nvPn5GJ4y4NZbj66xC/r79OgfN1DwVfnjJnR1dWh8NVgu2A601Ppq0dXX4XqbK54zPDHtKsZ81FTOVmofR32Pm4q1JJzVc+qwkSVRoZw55s6g51JUy+Oox6nCZABhuaqsGNi1axcGQ+Wq1X79+vHoo4/Srl07+vbty48//khGRgarV6++5vJ6vR5vb2+bqbYxFWs5+aeRjj0uF0QajUKHHjkci1bn1B1HzAQKkbMu0O2+TF59tDFJcfrrr1LNavw4FShorv4L1vKPH1iWZAtKpoIm4B/+9C0lG1CKyl+ktnHM93hZGg24uqn3jXSzHKfyyOWIy1fpUu7hhx+2eawoChcvXmTfvn1MnTrVrjC+vr40a9bsmlc4rC4Go5mwiMufeiHhRTRqnU92ho6UeHV+aa5ZVIcJc+M4cchIzAEjD41MwWC0sGmlvyp5HDHTmNnx9HoonenDI8jP0eIXWHJaU262jqKC6js//npq8ji5dnclf1k+2mAt2ggd5hNmClcV4Na/pDBS8hTyv8jH7S43NAEaLPEW8j/NQ1tPi2uXki4C01ETpuMmXNq5oPH+e5nF+WjranFpU32/9OTv7vqGT77I3l+8SIl3w93TTK+HMmjXLYcpT6p7pVdHO06ialT6r93Hx/ZUIK1WS/PmzZk5cyZ9+vSxK0xOTg6nT5/m6aeftms7ldGsfT7vfnva+vj5GQkAbFrlx/vjrj12obpt/cEPnwAzz0xMxC/QROxRd6YMiSAj1fX6KztJpgHDLgHw3prTNvPfGxvO5tXqfSjV5HEyjvMgf3Eeee/lYUkvueiQfqAew/C/B1HqwHzaRM5PhSg5Cto6Wlxuc8V9pDsat5KxABoDFG8touDzfJQCBW2AFpcurri/5WldpjrI3931+dYxMXHeefyDTORl6zhz3MCUJxuxf5u6gzsd7TiJqqFRFKXCDR9ms5nff/+dtm3b4ufnZ/fOJ0yYwIABA2jQoAEJCQlMmzaNgwcPcuzYMQIDA6+7flZWFj4+PtzFQFw08kYUtZPf7473iyu9u5xTLqqGSSnmN74nMzOz2rp+S78rGk+eja6S3dlXMhcUcDrq9WrNqpZKtQzodDr69OnD8ePHq6QYuHDhAk888QSXLl0iMDCQHj16sHv37goVAkIIIURlyOWIy1fpboI2bdoQGxtLRESE3TtfuXKl3dsQQgghhH0qPdLqP//5DxMmTGD9+vVcvHiRrKwsm0kIIYRwWHJa4TVVuGVg5syZvPLKK9x///0APPjggzYXJFEUBY1Gg9ms7o1ihBBCiGuSKxCWq8LFwIwZM3j++ef59ddfqzOPEEIIIWpYhYuB0pMOevbsWW1hhBBCiOoiAwjLV6kBhHKdciGEEDct6SYoV6WKgWbNml23IEhLk/OPhRBCiJtJpYqBGTNmlLkCoRBCCHEzkG6C8lWqGBg8eDBBQUHVlUUIIYSoPtJNUK4KX2dAxgsIIYQQtVOlzyYQQgghbkrSMlCuChcDFoulOnMIIYQQ1UrGDJSv+m5YLoSoEuk90tWOUMaDxy6pHaGMH1oFqB1BODppGShXpe9NIIQQQojaRVoGhBBCOAdpGSiXFANCCCGcgowZKJ90EwghhBBOTloGhBBCOAfpJiiXFANCCCGcgnQTlE+6CYQQQggnJy0DQgghnIN0E5RLigEhhBDOQYqBckk3gRBCCOHkpGVACCGEU9D8Pdmzfm0lxYAQQgjnIN0E5XL6YqBNlxwefSGFpm3zCAgxMX1EQ3Zt8FE7FgOGpfKv0cn4B5qIPebOp2/UJeagUTI5eCZHfD8FhBTx7OsXufXuLPQGCwln9bw/vj4n/6ye42TKhb/mGbn4sxuFaVp8WppoMzkXv7Zm6zLZp3Uc+8DIpb0uKGYNXo3NdJ6bjTGs5O6ouee1HH3Xg7T9LliKIKhHMW2m5GKoU72fxo72fpJMVUtOLSyf048ZMBgtxB418Mnr9dSOYtXzwXRGTUtg+QchRPZtRuwxA7NWxOITUCyZHDyTo72fPH1MfPDdScwmDW881YiRvVqwaGYYOZm6atvnwamepOx05ZZ3crjruwwCuxWz61lv8pNKPm5yz2vZ8ZQ3nhFmui/N4q61GTR7Pg+dvuST1pQHu0Z6g0ah25IseizPwlKs4Y9Ib5RqvJO6I76fJJOoKaoXA/Hx8Tz11FMEBATg7u5O27Zt2bdvX43tf9+v3iybE8pOB2gNKPXwqFQ2rPBn0yp/zp80MG9SPQrzNfR9Ik0yOXgmR3s/PfZCMqkJbrw/vj4xBz1IitOzf5s3F8/pq2V/5gK4uNmNVhPyCOhswrOBhRZj8vGob+HsypJ9Hv/ISPCdxbSekIdPKzMe9S2E3F2MPqCkGEg74EpevJaOs3PxbmbGu5mZjlE5ZBzRkbrbtVpyg2O+nyRTFVOqYKqlVC0G0tPT6d69O66urvz0008cO3aM999/Hz8/PzVjqcrF1ULTdnns3+5lnacoGg5s96JVpzzJ5MCZHNHtfTI58aeRKZ+dYdWhI8zfGEO/Jy9V2/4UMyhmDVo3209NnUEhbb8rigWStrrh0dDMrpFebOjhx7bHvbn48+UveUsRaDTYbEOrV9Bo4dL+6unZdMT3k2SqJioVAm+//TYajYaxY8da5xUUFBAZGUlAQACenp488sgjJCUl2ax3/vx5+vfvj9FoJCgoiIkTJ2IymewLcw2qFgPvvPMO4eHhLFmyhNtuu42IiAj69OlD48aNr7l8YWEhWVlZNlNt4+1vRucCGSm2H3rpqS74BVb9G0Ay1W6h9Yt44OlUEs7oef3JRqz/MoDRMy/Q+9Hq+RXn4gF+HYo5sdBIQbIGxQxxP7iRdtCFghQthZc0mPM0nPqvO0E9ium6OIvQ3kXsfdmL1L0lr6VfexM6d4Xj7xsx5Zd0GxybY0QxayhMqZ6PLEd8P0mm2mPv3r189tlntGvXzmb+uHHjWLduHf/73//YunUrCQkJPPzww9bnzWYz/fv3p6ioiJ07d7Js2TKWLl3Km2++WeUZVS0GfvjhBzp37syjjz5KUFAQHTt2ZPHixeUuHxUVhY+Pj3UKDw+vwbRC3Hw0Wjh1xJ0lb4dx+qiRn5bX4acVAfR/OrXa9nnL2zmgwKa7/FnfwZ8zy92pe38RGq1i/XUVcncRjYcW4NPSTNORBQTfVcy5VQYA9P4KnT/MIfE3N37s7M9PXfwpztbi08rkAB2b4mZWOoDQnqmycnJyGDJkCIsXL7Zp9c7MzOTzzz/ngw8+4O6776ZTp04sWbKEnTt3snv3bgA2bdrEsWPH+Prrr+nQoQP9+vXjrbfeYv78+RQVFVXVYQFU/tOKjY1lwYIFNG3alI0bNzJ69Gheeuklli1bds3lJ0+eTGZmpnWKi4ur4cTVLytNh9kEvldV2X51TKSnqHPyh2S6eaUlu3DuhMFmXtwpA0Fh1TfYy6O+he5fZnH/vkvc+0s6d67KRDGBsZ4FN18FjYuCV2OzzTpejczkXbz8cRTUvZjeGzPouyOd+35P55Z3cihI0uJRz3z17qqEI76fJFM1qKIxA1e3UBcWFpa7y8jISPr370/v3r1t5kdHR1NcXGwzv0WLFtSvX59du3YBsGvXLtq2bUtwcLB1mb59+5KVlcXRo0ftOBBlqVoMWCwWbrnlFmbPnk3Hjh0ZNWoUI0eOZOHChddcXq/X4+3tbTPVNqZiLSf/NNKxR7Z1nkaj0KFHDsei1Tl1RzLdvI7t9SC8se0HVd1GhSTHV99AvFIuRjAEKhRlakj+3ZWQu4vQuoFvGxM5Z2zPZsg5q7OeVnglvZ+Cq7dCym4XCtM0hNxdtb+GSjni+0kyOa7w8HCbVuqoqKhrLrdy5Ur2799/zecTExNxc3PD19fXZn5wcDCJiYnWZa4sBEqfL32uKqlayoWGhtKqVSubeS1btuTbb7+tsQwGo5mwiMsfMCHhRTRqnU92ho6UeLcay3GlNYvqMGFuHCcOGYk5YOShkSkYjBY2rfRXJY9kqjhHez+tWRzEh9+fYPCLSWxb50vzDnncP+QSc1+tvlMfk3e4ggIeEWZyz+s49q4Rrwgz9R8qKUqajChg33hPAjrrCbitmJQdbiT95kq3pZfHAJ1fo8ezsRm9n4W0gy4cifKg0TMFeEZU37mFjvh+kkxVq6quMxAXF2fzY1SvL3t2TlxcHC+//DKbN2/GYDCUed7RqFoMdO/enZiYGJt5J06coEGDBjWWoVn7fN799rT18fMzEgDYtMqP98fVr7EcV9r6gx8+AWaemZiIX6CJ2KPuTBkSQUZq9f+ak0z2cbT304lDRmY+F8Hw1y4yZGwiiXFuLJxWl1/XVt8Hd3G2huNzjRQkanH1UQjtU0TLl/PQ/v2yhPYuov20XE4udufwbA88G5ZccCig0+Wm55yzOo5/aKQoU4OxroVm/86n0dCCassMjvl+kkxVrIquQFiRluno6GiSk5O55ZZbrPPMZjPbtm3jk08+YePGjRQVFZGRkWHTOpCUlERISAgAISEh/PHHHzbbLT3boHSZqqJRFEW1Myf37t1Lt27dmDFjBo899hh//PEHI0eOZNGiRQwZMuS662dlZeHj48NdDMRFcxO8EYW4ERrHuyL6g0erbwDijfqhVYDaEcQNMCnF/Mb3ZGZmVlvXb+l3RdtnZ6Nzu/Ff6eaiAg5//nqFsmZnZ3Pu3DmbecOHD6dFixZMmjSJ8PBwAgMD+eabb3jkkUcAiImJoUWLFuzatYvbb7+dn376iQceeICLFy8SFBQEwKJFi5g4cSLJycnXbJG4Uaq2DNx6662sXbuWyZMnM3PmTCIiIpg7d26FCgEhhBCiMmrycsReXl60adPGZp6HhwcBAQHW+c8++yzjx4/H398fb29vXnzxRbp27crtt98OQJ8+fWjVqhVPP/00c+bMITExkTfeeIPIyMgqLQTAAe5N8MADD/DAAw+oHUMIIURt52A3Kvrwww/RarU88sgjFBYW0rdvXz799FPr8zqdjvXr1zN69Gi6du2Kh4cHQ4cOZebMmVUbBAcoBoQQQogaoXIx8Ntvv9k8NhgMzJ8/n/nz55e7ToMGDfjxxx/t23EFyCU8hBBCCCcnLQNCCCGcgtzCuHxSDAghhHAODjZmwJFIN4EQQgjh5KRlQAghhFPQKAoaOy6tY8+6jk6KASGEEM5BugnKJd0EQgghhJOTlgEhhBBOQc4mKJ8UA0IIIZyDdBOUS7oJhBBCCCcnLQPOwgHvfEctHplbpRzwODniHQJnxEarHaGMaY06qR1BXEG6CconxYAQQgjnIN0E5ZJiQAghhFOQloHyyZgBIYQQwslJy4AQQgjnIN0E5ZJiQAghhNOozU399pBuAiGEEMLJScuAEEII56Ao9p2q64Cn+VYVKQaEEEI4BTmboHzSTSCEEEI4OWkZEEII4RzkbIJySTEghBDCKWgsJZM969dW0k0ghBBCODlpGQAGDEvlX6OT8Q80EXvMnU/fqEvMQaMqWR4fk0T3+zMJb1JIUYGWY/uMfD4rlAunDarkAXhq/EWefiXJZl7cKT3P9WypUiLHPE6OmKmUI73HqzvT2T88+X1RMBePGMlOdmPwwlO07JNpfV5R4Ne5oUSvDKQgS0f9Tjk88NZ5AiIKrct8eEcbMuL1NtvtPfECd4wu+Tv4dW4ov80LK7NvV3czbxw9aPf/ARzz/eSImSpFugnK5fTFQM8H0xk1LYGPX6vHX/uNPDQyhVkrYnn2juZkXnKt8TztuuaybmkdThw0onNRGPbaRWZ/E8vIns0pzNfVeJ5SZ/8y8NrgxtbHZpO6d0F0xOPkiJnA8d7j1Z2pOE9LSMt8bnn0EitHNy7z/I7PgtmzNIiH3juLb70ifvkwjK+GNSVy01Fc9Zc/7XuNi6fT4FTrY73H5TbibiOT6DwkxWa7y55qRt12uXZlv5Ijvp8cMVNlyNkE5VO1m6Bhw4ZoNJoyU2RkZI1leHhUKhtW+LNplT/nTxqYN6kehfka+j6RVmMZrjRlSCM2r/bn3AkDscfceX9sfYLrFdO0Xb4qeUqZzZCe4mqdstLVrSMd8Tg5YiZwvPd4dWdqelcW97ySQMu+GWWeUxTYvSSYO8ck0uLeTEJa5vPwe2fITnLlr02+NsvqPSx4BZqsk5vRUu5zuamupJx055ZHL9mdv5Qjvp8cMVOllF5nwJ6pllK1GNi7dy8XL160Tps3bwbg0UcfrZH9u7haaNouj/3bvazzFEXDge1etOqUVyMZrsfD2wxAdoa6VXfdiCJWRB9h6c5jTPr4HIFhRarmuZqjHKcrOUImR3yPq5kpPc6NnBRXGnXPss4zeFuo2yGXuAMeNsvuWBjC27e0Z8EDLdmxKBizqfztRq+qQ0BEAQ1uy6mu6A7xfrqaI2YSN0bVn3eBgYE2j99++20aN25Mz549r7l8YWEhhYWX+/WysrKuuVxFefub0blARortYUhPdSG8SWE5a9UcjUbh+RnxHPnDyLkYd9Vy/HXAg/fGuXPhtB7/oGKeGp/I+2tP8u+7W5Cfq/6HgKMcpys5SiZHfI+rmSknpaQLwrNOsc18zzrF1ucAugxNJrRNHu4+ZuL2e/Dzu3XJSXblvjculNlmcaGGw9/70+P5xGrL7Sjvpys5YqbrkW6C8jnMmIGioiK+/vprxo8fj0Zz7f7oqKgoZsyYUcPJ1DNmdjwNWhTwyqAmqubY96u39d9njrvz1wEjX+05xp0DMti4MkDFZCUc5ThdyREziYrr9lyy9d8hLfPRuSqse6MBvSfG46K3/Ub4a6Mvhbk6OjxcdV0EV3PE95MjZrouGUBYLoc5tfC7774jIyODYcOGlbvM5MmTyczMtE5xcXF27TMrTYfZBL6Btu1/fnVMpKeoWydFzrpAl3uzePVfjUm96KZqlqvlZrlwIVZPWEP1W08c8Tg5UiZHfI+rmckzsKRFICfVdpBiTqqr9blrqdchF4tJQ0Z82dczelUdmt2dgWfgP/Qj2MGR3k+lHDGTsI/DFAOff/45/fr1Iyys7Ok6pfR6Pd7e3jaTPUzFWk7+aaRjj2zrPI1GoUOPHI5Fq3XalULkrAt0uy+TVx9tTFKc/vqr1DCD0UxYgyLSktUZiV7CEY+T42VyxPe4mpn8wovwDCwmdufl8QoF2VriD3oQ3rH8MwESjxnRaBU8Amy/8NPj3Di726tKBw5e5njvJ8fMVHGl3QT2TLWVQ3QTnDt3jp9//pk1a9bU+L7XLKrDhLlxnDhkJOZAySlOBqOFTSv9azwLlDS99XoonenDI8jP0eL396+V3GwdRQXq1G4jp8aze7MPyRdcCQgx8fQrFzFb4Lfv/FTJA455nBwxEzjee7y6MxXmakk7d/lLKj1Oz8Vj7rj7mPCtW8ztw5PY9kkoAQ0L8atXyC8f1sUruJgWfTIAiNvvwYWDHkR0zcbNw8yF/Z5smFWPdoPScPcx2+xr///q4BlUTNO7Mqlqjvh+csRMlSJ3LSyXQxQDS5YsISgoiP79+9f4vrf+4IdPgJlnJibiF2gi9qg7U4ZEkJGqzq/eAcNKfmG8t+a0zfz3xoazebU6H951QouZPP8sXn5mMtNcOPqHB2MHNCMzTb23jyMeJ0fMBI73Hq/uTAmHjSx9srn18cZZ4QB0eCSVh949R49/J1Gcr2Xd6w1KLjrUOYenlpy0XmNA52bhyHo/fvsoFFORFr/wQm4fnky3Z20vvGWxwMFvA+jwyCW01TCO1hHfT46YSVQNjaKoW+pYLBYiIiJ44oknePvttyu1blZWFj4+PtzFQFw0ajZZ3wTKGZSpqlpcZYuaNyM2Wu0IZUxr1EntCA7PpBTzG9+TmZlpd9dveUq/K7r2m4mL641fLdFUXMCun96s1qxqUb1l4Oeff+b8+fOMGDFC7ShCCCFqMzmboFyqFwN9+vRB5cYJIYQQwqmpXgwIIYQQNUEuOlQ+KQaEEEI4B4tSMtmzfi0lxYAQQgjnIGMGynUTnBgqhBBCiOokLQNCCCGcggY7xwxUWRLHI8WAEEII5yBXICyXdBMIIYQQTk5aBoQQQjgFObWwfFIMCCGEcA5yNkG5pJtACCGEcHLSMiCEEMIpaBQFjR2DAO1Z19FJMeAsavGbuErJ3R1vWo54h8BbD5rVjlDG3g7VcL/lm4Xl78me9Wsp6SYQQgghnJy0DAghhHAK0k1QPikGhBBCOAc5m6BcUgwIIYRwDnIFwnLJmAEhhBDCyUkxIIQQwimUXoHQnqkyoqKiuPXWW/Hy8iIoKIhBgwYRExNjs0xBQQGRkZEEBATg6enJI488QlJSks0y58+fp3///hiNRoKCgpg4cSImk8new2FDigEhhBDOobSbwJ6pErZu3UpkZCS7d+9m8+bNFBcX06dPH3Jzc63LjBs3jnXr1vG///2PrVu3kpCQwMMPP2x93mw2079/f4qKiti5cyfLli1j6dKlvPnmm1V2WEDGDAghhBDVYsOGDTaPly5dSlBQENHR0dx5551kZmby+eefs2LFCu6++24AlixZQsuWLdm9eze33347mzZt4tixY/z8888EBwfToUMH3nrrLSZNmsT06dNxc3OrkqzSMiCEEMIpaCz2TwBZWVk2U2FhYYX2n5mZCYC/vz8A0dHRFBcX07t3b+syLVq0oH79+uzatQuAXbt20bZtW4KDg63L9O3bl6ysLI4ePVoVhwWQYkAIIYSzqKJugvDwcHx8fKxTVFTUdXdtsVgYO3Ys3bt3p02bNgAkJibi5uaGr6+vzbLBwcEkJiZal7myECh9vvS5qiLdBEIIIUQlxMXF4e3tbX2s1+uvu05kZCRHjhxhx44d1RnthknLgBBCCOegVMEEeHt720zXKwbGjBnD+vXr+fXXX6lXr551fkhICEVFRWRkZNgsn5SUREhIiHWZq88uKH1cukxVcPqWgTZdcnj0hRSats0jIMTE9BEN2bXBR+1YDBiWyr9GJ+MfaCL2mDufvlGXmINGVbI8PiaJ7vdnEt6kkKICLcf2Gfl8VigXThtUyXMlRzpOT42/yNOv2P7Rxp3S81zPlqrkuZbHxiTx7OuJrF1ch4XT6qqWQ/7uwJwL8fM1pP+qoTgNjM2h/qsWPNuApbjkucwdGgovgM4LvLso1HtJwS3o8jZOvqwlLwaK08DF++9lXrZdpio98Ewq/Z+5RHB4EQDnYgws/zCYfb96X2dNx1DTlyNWFIUXX3yRtWvX8ttvvxEREWHzfKdOnXB1dWXLli088sgjAMTExHD+/Hm6du0KQNeuXZk1axbJyckEBZW8sJs3b8bb25tWrVrd8P/lak7fMmAwWog9auCT1+tdf+Ea0vPBdEZNS2D5ByFE9m1G7DEDs1bE4hNQrEqedl1zWbe0DmMfaMrkwY3QuSjM/iYWvbu6d2RztOMEcPYvA4M7tLZO4wc1VS3L1Zq1z6P/U2nEHlW/iJO/OzgzQ0Pmbg2N/mOhzf8s+HRVOPG8lqIksBRA3nENYSMVWq200OR9CwVnNZwca/uR7dVZofEcC22/s9D4PQsFcRpOTai+j/WUi658MTuUMfc148V+zTj0uyfTl5ylQbOCatvnzSwyMpKvv/6aFStW4OXlRWJiIomJieTn5wPg4+PDs88+y/jx4/n111+Jjo5m+PDhdO3aldtvvx2APn360KpVK55++mkOHTrExo0beeONN4iMjKxQ90RFqVoMmM1mpk6dSkREBO7u7jRu3Ji33noLpQYv+bjvV2+WzQllpwP8Kin18KhUNqzwZ9Mqf86fNDBvUj0K8zX0fSJNlTxThjRi82p/zp0wEHvMnffH1ie4XjFN2+WrkqeUox0nALMZ0lNcrVNWumM0vhmMZiZ9co65E+uRnan+LWyd/e/OUgDpWzSEj7Xg1QkM9aHuaAV9OCT/T4OLFzT/zIJ/XwX3huDZDuq/ZiHvmIbCi5e3E/K0gmc70IeBVwcIHWEh93BJy0J12LPZh72/eJNwRk98rJ6l74RSkKulRafc66/sCGr4OgMLFiwgMzOTu+66i9DQUOu0atUq6zIffvghDzzwAI888gh33nknISEhrFmzxvq8Tqdj/fr16HQ6unbtylNPPcUzzzzDzJkzq+ywgMrdBO+88w4LFixg2bJltG7dmn379jF8+HB8fHx46aWX1IymGhdXC03b5bHyk8vtfIqi4cB2L1p1ylMx2WUe3iUtAtkZ6n2pOOpxqhtRxIroIxQVajke7cEXUaGkJFTNecD2GDM7nj+2eHNguxdPvJx0/RWcTE2/nxQzYNagveqHnVYPOQc0XOuOOOYcQKPg4nXtbZoy4dKPGjzbg9a1qhOXpdUq3DEgA73RwvF9HtW/w6qgABY716/M4hUoHgwGA/Pnz2f+/PnlLtOgQQN+/PHHyu28klQtBnbu3MnAgQPp378/AA0bNuSbb77hjz/+uObyhYWFNudzZmVl1UjOmuTtb0bnAhkpti9NeqoL4U0qdi5rddJoFJ6fEc+RP4yci3FXLYcjHqe/Dnjw3jh3LpzW4x9UzFPjE3l/7Un+fXcL8nPVK5x6DkynSdt8XrzfcbosHE1Nv590HuDRTiFhkRZDhAXXALi0QUPOn2AIL7u8pRAufKTF/z4Fnaftc3FzNSSv1GAp0ODRTqHZPHu+7a6vYYt85q47hZveQn6ulpnPNuT8SfW7nipCbmFcPlW7Cbp168aWLVs4ceIEAIcOHWLHjh3069fvmstHRUXZnNsZHn6NvxpRrcbMjqdBiwKiRjdQO4rD2ferN9vX+3LmuDvRW7154+lGeHqbuXNAhmqZAsOKGD0zgXfG1Ke40OmHCDmURrNKvrQP9dGx7zYtySs0+N+nlPlUthTD6Ve1oEDDKWW/jEKGKrReZaHZAjMaLcS+oa3Wm+tdOK3nhXub8VL/pqz/sg4TPjpP/aYyZuBmp2rLwGuvvUZWVhYtWrRAp9NhNpuZNWsWQ4YMuebykydPZvz48dbHWVlZta4gyErTYTaBb6DtTSj86phIT1G3/zly1gW63JvFKw81JvWiuk3fjnycSuVmuXAhVk9YQ/VadJq0y8cv0MT8jSes83Qu0Pb2XB4cnsoDDdthsWhUy+co1Hg/GcKhxecWzPklXQBugXDqVQ36K07yKC0ECi9Ci0WWMq0CAK5+JZOhAbg3snCor47cP8GzfbXExlSsJeFsSf/GqcNGmnfIY9BzKcybdBN8FivYeQvjKkvicFT9qbB69WqWL1/OihUr2L9/P8uWLeO9995j2bJl11xer9eXOb+ztjEVazn5p5GOPbKt8zQahQ49cjgWrc4pc6AQOesC3e7L5NVHG5MUV3UjWG+UYx4nWwajmbAGRaQl10AHbjkObvdkVK9mjL738hRz0J1f1vgx+t5mUgj8Tc33k869pBAwZUHWTg2+d5V841gLgfPQfKEFF9/rb0v5u4fAUlR9ea+m0YCr203yLVnDAwhvJqr+hJo4cSKvvfYagwcPBqBt27acO3eOqKgohg4dWiMZDEYzYRGX/3JCwoto1Dqf7AwdKfHq/Ppds6gOE+bGceKQkZgDRh4amYLBaGHTSn9V8oyZHU+vh9KZPjyC/BwtfoElQ5Vzs3UUFahXTzracRo5NZ7dm31IvuBKQIiJp1+5iNkCv33np0oegPxcXZmxHQV5WrLTy86vSfJ3B5k7AQUMDaHgPMR9qMUQAXUGKiWFwEQtuccpGQNggeLUkvV0PiUDBHMOQ+5RDV4dFHTeUHgB4udr0Ycr1dYqMHzyRfb+4kVKvBvunmZ6PZRBu245THmyUfXsUNQYVYuBvLw8tFrbLxOdTofFUr0DYK7UrH0+73572vr4+RkJAGxa5cf74+rXWI4rbf3BD58AM89MTMQv0ETsUXemDIkgI1WdX5gDhl0C4L01p23mvzc2nM2r1fniBcc7TnVCi5k8/yxefmYy01w4+ocHYwc0IzPNMbotHIn83YE5W8OFjzUUJYGLD/jdo1B3jILWFQrjIeO3klabo4/bDj5tvtiM962gNZScnhi/QIMlH1zrgE93hcbPKWirqZ7yrWNi4rzz+AeZyMvWcea4gSlPNmL/tnJOcXA0FsCexrCa+2qqcRqlJk/qv8qwYcP4+eef+eyzz2jdujUHDhxg1KhRjBgxgnfeeee662dlZeHj48NdDMRFo15TrKhFNA7YbF6LmyZru1sPqnthrmvZ20H960xcyaQU8xvfk5mZWW1dv6XfFfe0eRUX3Y13c5rMhWw5Mqdas6pF1Z8sH3/8MVOnTuWFF14gOTmZsLAw/v3vf/Pmm2+qGUsIIYRwKqoWA15eXsydO5e5c+eqGUMIIYQzsHcQYC1upZPOTCGEEM5BioFyyVVIhBBCCCcnLQNCCCGcg7QMlEuKASGEEM5BTi0slxQDQgghnILcqKh8MmZACCGEcHLSMiCEEMI5yJiBckkxIIQQwjlYFNDY8YVuqb3FgHQTCCGEEE5OWgaEEEI4B+kmKJcUA0IIIZyEncUAUgwI4RwcsfKXOynetBztDoEAdx/OVTuCjYKcYn67Xe0UQooBIYQQzkG6CcolxYAQQgjnYFGwq6lfziYQQgghRG0lLQNCCCGcg2IpmexZv5aSYkAIIYRzkDED5ZJiQAghhHOQMQPlkjEDQgghhJOTlgEhhBDOQboJyiXFgBBCCOegYGcxUGVJHI50EwghhBBOTloGhBBCOAfpJiiXFANCCCGcg8UC2HGtAEvtvc6A03cTtOmSw4xlZ1ix/ygbEw7R9b5MtSM5ZCaAAcNSWbbnGOti/+Sj9Sdp3iFP7UgOl8kRX7uAkCJenXeO/x05zA+nDrHw579o2k5euys9PiaJeT+eYO2Jw6z68yjTvjhDvcYFquW5lsfGJLEx4RDPz4ivtn2YcuHEO2783sed3zob2feUgawj1/6a+GumG7+09SDuK9vflHlnNfz5op7tdxjZeruR6GcMpP/h9F81Ds/pXyGD0ULsUQOfvF5P7ShWjpip54PpjJqWwPIPQojs24zYYwZmrYjFJ6BYMl3B0V47Tx8TH3x3ErNJwxtPNWJkrxYsmhlGTqa6d9NztNeuXddc1i2tw9gHmjJ5cCN0Lgqzv4lF725WJc/VmrXPo/9TacQeNVTrfv6apid9l45Wswu5bU0+/t3MHBhpoDDJ9s6ZKVt0ZP2pxS2o7C/lQ2MMKGbo+N98bl2Vj2czC4fGGChMdYC7b5Z2E9gz1VKqFgPZ2dmMHTuWBg0a4O7uTrdu3di7d2+NZtj3qzfL5oSyc4NPje73nzhipodHpbJhhT+bVvlz/qSBeZPqUZivoe8TaZLpCo722j32QjKpCW68P74+MQc9SIrTs3+bNxfP6VXN5Wiv3ZQhjdi82p9zJwzEHnPn/bH1Ca5XTNN2+arkuZLBaGbSJ+eYO7Ee2dVYxJkLIOVnHY3HF+HX2YKxvkKjF4oxhlu4sOryr//CJA0nZrvR6u1CtFd1NBelQ/45LQ2eLcazuYKxgULjcUVY8jXknpRiwJGpWgw899xzbN68ma+++orDhw/Tp08fevfuTXx89TWDicpzcbXQtF0e+7d7WecpioYD271o1Umdpl1HzOSIbu+TyYk/jUz57AyrDh1h/sYY+j15SdVMN8Nr5+Fd0iKQnaFuCwrAmNnx/LHFmwNXHK/qoJhBMWvQutl+4WkNkHmg5DgoFjj6up76w4vxbFL2i9HVF4wNLVxc54I5DywmSPifC67+Cl6tam9/e22gWjGQn5/Pt99+y5w5c7jzzjtp0qQJ06dPp0mTJixYsOCa6xQWFpKVlWUziern7W9G5wIZKbY/A9JTXfALNEkmBxZav4gHnk4l4Yye159sxPovAxg98wK9H1Wv9cTRXzuNRuH5GfEc+cPIuRh3VbP0HJhOk7b5fBEVWu37cvEA7/Zmzn7mRmGyBsUMiet0ZB7SUvR3E/+5L1zR6KDekGu/ThoNdFhcQM5xLVtvN7K1s5HzX7rSYWEBro7QWGZR7J9qKdXOJjCZTJjNZgwG2z4wd3d3duzYcc11oqKimDFjRk3EE6JW0Gjh5J/uLHk7DIDTR400bF5A/6dT+fl//iqnc0xjZsfToEUBrwxqomqOwLAiRs9MYPLgRhQX1szvtlZRhfw1Vc/v9xjR6BQ8W1oI7mcm+5iWrKNaLnztwq2rC9CU0+KvKHBilhtu/gq3LCtAp4eENS78OUZP55UF6APV/TJVFAuKHXcetGddR6daMeDl5UXXrl156623aNmyJcHBwXzzzTfs2rWLJk2u/Uc4efJkxo8fb32clZVFeHh4TUV2WllpOswm8L3qV5tfHRPpKeq8hRwxkyNKS3bh3AnbgjvulIEe96t3loMjv3aRsy7Q5d4sXnmoMakX3VTN0qRdPn6BJuZvPGGdp3OBtrfn8uDwVB5o2A6LpWr74Y3hCrcsLcCcB6ZcDfpAhSMT9LjXs5C5X0tRmoadfS63lihmDSffcyPua1e6bcwnfY+W1G067vw9DxfPkmWatyoibZc7F793oeFz6g3uLQls56/7WjxmQNW/vK+++ooRI0ZQt25ddDodt9xyC0888QTR0dHXXF6v16PXqzvwyRmZirWc/NNIxx7Z7Pp7YJxGo9ChRw4/LA2QTA7s2F4PwhsX2syr26iQ5HhXlRI56munEDkrnm73ZTLxX01IilP/c+bgdk9G9WpmM++VD+OIO2Vg9fzAKi8ErqQzgs6oUJwJaTt1NB5XRNC9Jvxutz274uDzBkIeMBE6qKSwsxT8nemqhgyNllp9Kd/aQNVioHHjxmzdupXc3FyysrIIDQ3l8ccfp1GjRjWWwWA0ExZRZH0cEl5Eo9b5ZGfoSIlX55eBI2Zas6gOE+bGceKQkZgDRh4amYLBaGHTSvWamh0xk6O9dmsWB/Hh9ycY/GIS29b50rxDHvcPucTcV9U99dHRXrsxs+Pp9VA604dHkJ+jxS+w5BdsbraOogJ1hlbl5+rKjFkoyNOSnV52flW59LsOlJJBgPnnNZz6wA1jhIXQQSa0ruDqe9XgQhfQ11HwiCiZ793ejKs3HJ+ip+Hzxej0CgnfupB/QUPAnQ5wmqZi5y2MpWWgenl4eODh4UF6ejobN25kzpw5NbbvZu3zeffb09bHz89IAGDTKj/eH1e/xnI4eqatP/jhE2DmmYmJ+AWaiD3qzpQhEWSkqvcL0xEzOdprd+KQkZnPRTD8tYsMGZtIYpwbC6fV5de16o4XcLTXbsCwkjMs3ltz2mb+e2PD2bzaecZWmLLh9EduFCZpcPVRCOxtpvFLRWgr+LK4+UH7hQXEznPlwLMGFBN4NLbQbl4hXs0doL/dYgGNHTlq8ZgBjaKoV+ps3LgRRVFo3rw5p06dYuLEiRgMBrZv346r6/XffVlZWfj4+HAXA3HRqPcFIES1Km+0lppq8S+k2u7uw7lqR7BRkFPMzNt/JjMzE29v72rZR+l3xT1eQ3DR3HgLnUkpYkv28mrNqhZVWwYyMzOZPHkyFy5cwN/fn0ceeYRZs2ZVqBAQQgghKkW6CcqlajHw2GOP8dhjj6kZQQghhJNQLBYUO7oJavOphU5/bwIhhBDC2TnEAEIhhBCi2kk3QbmkGBBCCOEcLApopBi4FukmEEIIIZyctAwIIYRwDooC2HOdgdrbMiDFgBBCCKegWBQUO7oJVLwsT7WTYkAIIYRzUCzY1zIgpxYKIYQQ4gbMnz+fhg0bYjAY6NKlC3/88YfakcqQYkAIIYRTUCyK3VNlrVq1ivHjxzNt2jT2799P+/bt6du3L8nJydXwP7xxUgwIIYRwDorF/qmSPvjgA0aOHMnw4cNp1aoVCxcuxGg08sUXX1TDf/DG3dRjBkoHc5golntli1pMblQkqk5BTrHaEWwU5pqAmhmcZ+93hYmSY5eVlWUzX6/Xo9fryyxfVFREdHQ0kydPts7TarX07t2bXbt23XiQanBTFwPZ2dkA7OBHlZMIUY3ke1dUod9uVzvBtWVnZ+Pj41Mt23ZzcyMkJIQdifZ/V3h6ehIeHm4zb9q0aUyfPr3MsqmpqZjNZoKDg23mBwcH89dff9mdpSrd1MVAWFgYcXFxeHl5obHzNq9ZWVmEh4cTFxfnMLemlEwV42iZHC0PSKaKkkwVU5WZFEUhOzubsLCwKkpXlsFg4MyZMxQVFdm9LUVRynzfXKtV4GZzUxcDWq2WevXqVek2vb29HeYPrpRkqhhHy+RoeUAyVZRkqpiqylRdLQJXMhgMGAyGat/PlerUqYNOpyMpKclmflJSEiEhITWa5XpkAKEQQghRDdzc3OjUqRNbtmyxzrNYLGzZsoWuXbuqmKysm7plQAghhHBk48ePZ+jQoXTu3JnbbruNuXPnkpuby/Dhw9WOZkOKgb/p9XqmTZvmUH0/kqliHC2To+UByVRRkqliHDGTo3r88cdJSUnhzTffJDExkQ4dOrBhw4YygwrVplFq88WWhRBCCHFdMmZACCGEcHJSDAghhBBOTooBIYQQwslJMSCEEEI4OSkGcLzbS27bto0BAwYQFhaGRqPhu+++UzVPVFQUt956K15eXgQFBTFo0CBiYmJUzbRgwQLatWtnvehJ165d+emnn1TNdLW3334bjUbD2LFjVcswffp0NBqNzdSiRQvV8pSKj4/nqaeeIiAgAHd3d9q2bcu+fftUy9OwYcMyx0mj0RAZGalaJrPZzNSpU4mIiMDd3Z3GjRvz1ltv1cg1/P9JdnY2Y8eOpUGDBri7u9OtWzf27t2raiZhP6cvBhzx9pK5ubm0b9+e+fPnq5bhSlu3biUyMpLdu3ezefNmiouL6dOnD7m5uaplqlevHm+//TbR0dHs27ePu+++m4EDB3L06FHVMl1p7969fPbZZ7Rr107tKLRu3ZqLFy9apx07dqiaJz09ne7du+Pq6spPP/3EsWPHeP/99/Hz81Mt0969e22O0ebNmwF49NFHVcv0zjvvsGDBAj755BOOHz/OO++8w5w5c/j4449VywTw3HPPsXnzZr766isOHz5Mnz596N27N/Hx8armEnZSnNxtt92mREZGWh+bzWYlLCxMiYqKUjHVZYCydu1atWPYSE5OVgBl69atakex4efnp/z3v/9VO4aSnZ2tNG3aVNm8ebPSs2dP5eWXX1Yty7Rp05T27durtv9rmTRpktKjRw+1Y/yjl19+WWncuLFisVhUy9C/f39lxIgRNvMefvhhZciQISolUpS8vDxFp9Mp69evt5l/yy23KFOmTFEplagKTt0yUHp7yd69e1vnOertJR1JZmYmAP7+/ionKWE2m1m5ciW5ubkOcYnPyMhI+vfvb/O+UtPJkycJCwujUaNGDBkyhPPnz6ua54cffqBz5848+uijBAUF0bFjRxYvXqxqpisVFRXx9ddfM2LECLtvgGaPbt26sWXLFk6cOAHAoUOH2LFjB/369VMtk8lkwmw2l7nGv7u7u+otTsI+Tn0Fwpvp9pKOwmKxMHbsWLp3706bNm1UzXL48GG6du1KQUEBnp6erF27llatWqmaaeXKlezfv99h+lC7dOnC0qVLad68ORcvXmTGjBnccccdHDlyBC8vL1UyxcbGsmDBAsaPH8/rr7/O3r17eemll3Bzc2Po0KGqZLrSd999R0ZGBsOGDVM1x2uvvUZWVhYtWrRAp9NhNpuZNWsWQ4YMUS2Tl5cXXbt25a233qJly5YEBwfzzTffsGvXLpo0aaJaLmE/py4GROVFRkZy5MgRh/gV0Lx5cw4ePEhmZib/93//x9ChQ9m6datqBUFcXBwvv/wymzdvrvG7o5Xnyl+R7dq1o0uXLjRo0IDVq1fz7LPPqpLJYrHQuXNnZs+eDUDHjh05cuQICxcudIhi4PPPP6dfv37Vekvdili9ejXLly9nxYoVtG7dmoMHDzJ27FjCwsJUPU5fffUVI0aMoG7duuh0Om655RaeeOIJoqOjVcsk7OfUxcDNdHtJRzBmzBjWr1/Ptm3bqvzW0TfCzc3N+mukU6dO7N27l48++ojPPvtMlTzR0dEkJydzyy23WOeZzWa2bdvGJ598QmFhITqdTpVspXx9fWnWrBmnTp1SLUNoaGiZgq1ly5Z8++23KiW67Ny5c/z888+sWbNG7ShMnDiR1157jcGDBwPQtm1bzp07R1RUlKrFQOPGjdm6dSu5ublkZWURGhrK448/TqNGjVTLJOzn1GMGbqbbS6pJURTGjBnD2rVr+eWXX4iIiFA70jVZLBYKCwtV2/8999zD4cOHOXjwoHXq3LkzQ4YM4eDBg6oXAgA5OTmcPn2a0NBQ1TJ07969zKmpJ06coEGDBiolumzJkiUEBQXRv39/taOQl5eHVmv7Ea3T6bBYLColsuXh4UFoaCjp6els3LiRgQMHqh1J2MGpWwbAMW8vmZOTY/PL7cyZMxw8eBB/f3/q169f43kiIyNZsWIF33//PV5eXiQmJgLg4+ODu7t7jecBmDx5Mv369aN+/fpkZ2ezYsUKfvvtNzZu3KhKHijpT716HIWHhwcBAQGqja+YMGECAwYMoEGDBiQkJDBt2jR0Oh1PPPGEKnkAxo0bR7du3Zg9ezaPPfYYf/zxB4sWLWLRokWqZYKSYnLJkiUMHToUFxf1PxoHDBjArFmzqF+/Pq1bt+bAgQN88MEHjBgxQtVcGzduRFEUmjdvzqlTp5g4cSItWrRwuFvyikpS+3QGR/Dxxx8r9evXV9zc3JTbbrtN2b17t6p5fv31VwUoMw0dOlSVPNfKAihLlixRJY+iKMqIESOUBg0aKG5ubkpgYKByzz33KJs2bVItT3nUPrXw8ccfV0JDQxU3Nzelbt26yuOPP66cOnVKtTyl1q1bp7Rp00bR6/VKixYtlEWLFqkdSdm4caMCKDExMWpHURRFUbKyspSXX35ZqV+/vmIwGJRGjRopU6ZMUQoLC1XNtWrVKqVRo0aKm5ubEhISokRGRioZGRmqZhL2k1sYCyGEEE7OqccMCCGEEEKKASGEEMLpSTEghBBCODkpBoQQQggnJ8WAEEII4eSkGBBCCCGcnBQDQgghhJOTYkAIIYRwclIMCGGnYcOGMWjQIOvju+66i7Fjx9Z4jt9++w2NRkNGRka5y2g0Gr777rsKb3P69Ol06NDBrlxnz55Fo9Fw8OBBu7YjhKg+UgyIWmnYsGFoNBo0Go317oYzZ87EZDJV+77XrFnDW2+9VaFlK/IFLoQQ1U39u3EIUU3uu+8+lixZQmFhIT/++CORkZG4uroyefLkMssWFRXh5uZWJfv19/evku0IIURNkZYBUWvp9XpCQkJo0KABo0ePpnfv3vzwww/A5ab9WbNmERYWRvPmzQGIi4vjsccew9fXF39/fwYOHMjZs2et2zSbzYwfPx5fX18CAgJ49dVXufr2Hld3ExQWFjJp0iTCw8PR6/U0adKEzz//nLNnz9KrVy8A/Pz80Gg0DBs2DCi5g15UVBQRERG4u7vTvn17/u///s9mPz/++CPNmjXD3d2dXr162eSsqEmTJtGsWTOMRiONGjVi6tSpFBcXl1nus88+Izw8HKPRyGOPPUZmZqbN8//9739p2bIlBoOBFi1a8Omnn1Y6ixBCPVIMCKfh7u5OUVGR9fGWLVuIiYlh8+bNrF+/nuLiYvr27YuXlxfbt2/n999/x9PTk/vuu8+63vvvv8/SpUv54osv2LFjB2lpaaxdu/Yf9/vMM8/wzTffMG/ePI4fP85nn32Gp6cn4eHhfPvttwDExMRw8eJFPvroIwCioqL48ssvWbhwIUePHmXcuHE89dRTbN26FSgpWh5++GEGDBjAwYMHee6553jttdcqfUy8vLxYunQpx44d46OPPmLx4sV8+OGHNsucOnWK1atXs27dOjZs2MCBAwd44YUXrM8vX76cN998k1mzZnH8+HFmz57N1KlTWbZsWaXzCCFUovJdE4WoFkOHDlUGDhyoKIqiWCwWZfPmzYper1cmTJhgfT44ONjmdrBfffWV0rx5c8VisVjnFRYWKu7u7srGjRsVRVGU0NBQZc6cOdbni4uLlXr16ln3pSi2ty2OiYlRAGXz5s3XzFl6u+r09HTrvIKCAsVoNCo7d+60WfbZZ59VnnjiCUVRFGXy5MlKq1atbJ6fNGlSmW1dDVDWrl1b7vPvvvuu0qlTJ+vjadOmKTqdTrlw4YJ13k8//aRotVrl4sWLiqIoSuPGjZUVK1bYbOett95SunbtqiiKopw5c0YBlAMHDpS7XyGEumTMgKi11q9fj6enJ8XFxVgsFp588kmmT59ufb5t27Y24wQOHTrEqVOn8PLystlOQUEBp0+fJjMzk4sXL9KlSxfrcy4uLnTu3LlMV0GpgwcPotPp6NmzZ4Vznzp1iry8PO69916b+UVFRXTs2BGA48eP2+QA6Nq1a4X3UWrVqlXMmzeP06dPk5OTg8lkwtvb22aZ+vXrU7duXZv9WCwWYmJi8PLy4vTp0zz77LOMHDnSuozJZMLHx6fSeYQQ6pBiQNRavXr1YsGCBbi5uREWFoaLi+3b3cPDw+ZxTk4OnTp1Yvny5WW2FRgYeEMZ3N3dK71OTk4OAP/v//0/my9hKBkHUVV27drFkCFDmDFjBn379sXHx4eVK1fy/vvvVzrr4sWLyxQnOp2uyrIKIaqXFAOi1vLw8KBJkyYVXv6WW25h1apVBAUFlfl1XCo0NJQ9e/Zw5513AiW/gKOjo7nllluuuXzbtm2xWCxs3bqV3r17l3m+tGXCbDZb57Vq1Qq9Xs/58+fLbVFo2bKldTBkqd27d1//P3mFnTt30qBBA6ZMmWKdd+7cuTLLnT9/noSEBMLCwqz70Wq1NG/enODgYMLCwoiNjWXIkCGV2r8QwnHIAEIh/jZkyBDq1KnDwIED2b59O2fOnOG3337jpZde4sKFCwC8/PLLvP3223z33Xf89ddfvPDCC/94jYCGDRsydOhQRowYwXfffWfd5urVqwFo0KABGo2G9evXk5KSQk5ODl5eXkyYMIFx48axbNkyTp8+zf79+/n444+tg/Kef/55Tp48ycSJE4mJiWHFihUsXbq0Uv/fpk2bcv78eVauXMnp06eZN2/eNQdDGgwGhg4dyqFDh9i+fTsvvfQSjz32GCEhIQDMmDGDqKgo5s2bx4kTJzh8+DBLlizhgw8+qFQeIYR6pBgQ4m9Go5Ft27ZRv359Hn74YVq2bMmzzz5LQUGBtaXglVde4emnn2bo0KF07doVLy8vHnrooX/c7oIFC/jXv/7FCy+8QIsWLRg5ciS5ubkA1K1blxkzZvDaa68RHBzMmDFjAHjrrbeYOnUqUVFRtGzZkvvuu4//9//+HxEREUBJP/63337Ld999R/v27Vm4cCGzZ8+u1P/3wQcfZNy4cYwZM4YOHTqwc+dOpk6dWma5Jk2a8PDDD3P//ffTp08f2rVrZ3Pq4HPPPcd///tflixZQtu2benZsydLly61ZhVCOD6NUt7IJyGEEEI4BWkZEEIIIZycFANCCCGEk5NiQAghhHByUgwIIYQQTk6KASGEEMLJSTEghBBCODkpBoQQQggnJ8WAEEII4eSkGBBCCCGcnBQDQgghhJOTYkAIIYRwcv8fM7HJ/rOXg1AAAAAASUVORK5CYII=\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 9) Загрузили, предобработали и подали на вход обученной нейронной сети собственное изображение, созданное при выполнении лабораторной работы №1. Вывели изображение и результат распознавания." + ], + "metadata": { + "id": "amaspXGW1EVy" + } + }, + { + "cell_type": "code", + "source": [ + "# загрузка собственного изображения\n", + "from PIL import Image\n", + "\n", + "for name_image in ['190 (2).png', '690 (1).png']:\n", + " file_data = Image.open(name_image)\n", + " file_data = file_data.convert('L') # перевод в градации серого\n", + " test_img = np.array(file_data)\n", + "\n", + " # вывод собственного изображения\n", + " plt.imshow(test_img, cmap=plt.get_cmap('gray'))\n", + " plt.show()\n", + "\n", + " # предобработка\n", + " test_img = test_img / 255\n", + " test_img = np.reshape(test_img, (1,28,28,1))\n", + "\n", + " # распознавание\n", + " result = model.predict(test_img)\n", + " print('I think it\\'s', np.argmax(result))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 912 + }, + "id": "ktWEeqWd1EyF", + "outputId": "c935511d-f915-46e9-a358-ba32c277fb06" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n", + "I think it's 1\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n", + "I think it's 6\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 10) Загрузили с диска модель, сохраненную при выполнении лабораторной работы №1. Вывели информацию об архитектуре модели. Повторили для этой модели п. 6." + ], + "metadata": { + "id": "mgrihPd61E8w" + } + }, + { + "cell_type": "code", + "source": [ + "model_lr1 = keras.models.load_model(\"best_model.keras\")\n", + "\n", + "model_lr1.summary()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 212 + }, + "id": "DblXqn3l1FL2", + "outputId": "26bc1cd5-6efb-491d-b99c-7047210f28da" + }, + "execution_count": 24, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1mModel: \"sequential_1\"\u001b[0m\n" + ], + "text/html": [ + "
Model: \"sequential_1\"\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "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", + "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m78,500\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m1,010\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ], + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_1 (Dense)                 │ (None, 100)            │        78,500 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (Dense)                 │ (None, 10)             │         1,010 │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "
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\n" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# развернем каждое изображение 28*28 в вектор 784\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y,\n", + " test_size = 10000,\n", + " train_size = 60000,\n", + " random_state = 23)\n", + "num_pixels = X_train.shape[1] * X_train.shape[2]\n", + "X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255\n", + "X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255\n", + "print('Shape of transformed X train:', X_train.shape)\n", + "print('Shape of transformed X train:', X_test.shape)\n", + "\n", + "# переведем метки в one-hot\n", + "y_train = keras.utils.to_categorical(y_train, num_classes)\n", + "y_test = keras.utils.to_categorical(y_test, num_classes)\n", + "print('Shape of transformed y train:', y_train.shape)\n", + "print('Shape of transformed y test:', y_test.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0ki8fhJrEyEt", + "outputId": "aff9bef1-f9cd-4aa1-d424-cb909d07c692" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Shape of transformed X train: (60000, 784)\n", + "Shape of transformed X train: (10000, 784)\n", + "Shape of transformed y train: (60000, 10)\n", + "Shape of transformed y test: (10000, 10)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Оценка качества работы модели на тестовых данных\n", + "scores = model_lr1.evaluate(X_test, y_test)\n", + "print('Loss on test data:', scores[0])\n", + "print('Accuracy on test data:', scores[1])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0Yj0fzLNE12k", + "outputId": "ab3054a0-47de-4cfc-da39-2d5e2c1f579b" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9490 - loss: 0.1739\n", + "Loss on test data: 0.18475718796253204\n", + "Accuracy on test data: 0.9458000063896179\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 11) Сравнили обученную модель сверточной сети и наилучшую модель полносвязной сети из лабораторной работы №1 по следующим показателям:\n", + "### - количество настраиваемых параметров в сети\n", + "### - количество эпох обучения\n", + "### - качество классификации тестовой выборки.\n", + "### Сделали выводы по результатам применения сверточной нейронной сети для распознавания изображений. " + ], + "metadata": { + "id": "MsM3ew3d1FYq" + } + }, + { + "cell_type": "markdown", + "source": [ + "Таблица1:" + ], + "metadata": { + "id": "xxFO4CXbIG88" + } + }, + { + "cell_type": "markdown", + "source": [ + "| Модель | Количество настраиваемых параметров | Количество эпох обучения | Качество классификации тестовой выборки |\n", + "|----------|-------------------------------------|---------------------------|-----------------------------------------|\n", + "| Сверточная | 34 826 | 15 | accuracy:0.987 ; loss:0.037 |\n", + "| Полносвязная | 79512 | 50 | accuracy:0.946 ; loss:0.185 |\n" + ], + "metadata": { + "id": "xvoivjuNFlEf" + } + }, + { + "cell_type": "markdown", + "source": [ + "По результатам применения сверточной НС, а также по результатам таблицы 1 делаем выводы, что сверточная НС намного лучше справляется с задачами распознования изображений, чем полносвязная - имеет меньше настраиваемых параметров, быстрее обучается, имеет лучшие показатели качества." + ], + "metadata": { + "id": "YctF8h_sIB-P" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Задание 2" + ], + "metadata": { + "id": "wCLHZPGB1F1y" + } + }, + { + "cell_type": "markdown", + "source": [ + "### В новом блокноте выполнили п. 2–8 задания 1, изменив набор данных MNIST на CIFAR-10, содержащий размеченные цветные изображения объектов, разделенные на 10 классов. \n", + "### При этом:\n", + "### - в п. 3 разбиение данных на обучающие и тестовые произвели в соотношении 50 000:10 000\n", + "### - после разбиения данных (между п. 3 и 4) вывели 25 изображений из обучающей выборки с подписями классов\n", + "### - в п. 7 одно из тестовых изображений должно распознаваться корректно, а другое – ошибочно. " + ], + "metadata": { + "id": "DUOYls124TT8" + } + }, + { + "cell_type": "markdown", + "source": [ + "### 1) Загрузили набор данных CIFAR-10, содержащий цветные изображения размеченные на 10 классов: самолет, автомобиль, птица, кошка, олень, собака, лягушка, лошадь, корабль, грузовик." + ], + "metadata": { + "id": "XDStuSpEJa8o" + } + }, + { + "cell_type": "code", + "source": [ + "# загрузка датасета\n", + "from keras.datasets import cifar10\n", + "\n", + "(X_train, y_train), (X_test, y_test) = cifar10.load_data()" + ], + "metadata": { + "id": "y0qK7eKL4Tjy", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b9dbc3c1-08ad-4fc5-83b0-9b384b0d3759" + }, + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n", + "\u001b[1m170498071/170498071\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 0us/step\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 2) Разбили набор данных на обучающие и тестовые данные в соотношении 50 000:10 000 элементов. Параметр random_state выбрали равным (4k – 1)=23, где k=6 –номер бригады. Вывели размерности полученных обучающих и тестовых массивов данных." + ], + "metadata": { + "id": "wTHiBy-ZJ5oh" + } + }, + { + "cell_type": "code", + "source": [ + "# создание своего разбиения датасета\n", + "\n", + "# объединяем в один набор\n", + "X = np.concatenate((X_train, X_test))\n", + "y = np.concatenate((y_train, y_test))\n", + "\n", + "# разбиваем по вариантам\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y,\n", + " test_size = 10000,\n", + " train_size = 50000,\n", + " random_state = 23)\n", + "# вывод размерностей\n", + "print('Shape of X train:', X_train.shape)\n", + "print('Shape of y train:', y_train.shape)\n", + "print('Shape of X test:', X_test.shape)\n", + "print('Shape of y test:', y_test.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DlnFbQogKD2v", + "outputId": "9d7a6710-eb51-45e3-b3e2-e93e57daee95" + }, + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Shape of X train: (50000, 32, 32, 3)\n", + "Shape of y train: (50000, 1)\n", + "Shape of X test: (10000, 32, 32, 3)\n", + "Shape of y test: (10000, 1)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Вывели 25 изображений из обучающей выборки с подписью классов." + ], + "metadata": { + "id": "pj3bMaz1KZ3a" + } + }, + { + "cell_type": "code", + "source": [ + "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n", + " 'dog', 'frog', 'horse', 'ship', 'truck']\n", + "\n", + "plt.figure(figsize=(10,10))\n", + "for i in range(25):\n", + " plt.subplot(5,5,i+1)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.grid(False)\n", + " plt.imshow(X_train[i])\n", + " plt.xlabel(class_names[y_train[i][0]])\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 710 + }, + "id": "TW8D67KEKhVE", + "outputId": "0eaf0395-6883-4d49-979b-983d0a48ee21" + }, + "execution_count": 29, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+AvGBAzdAPLJjGGJeZuKk78q5XQ3/IP6B72VmZ2chPk+JptmzIWImD62Tz43vAziZHif4u3Ae7wlFRCoV9HHwGsqJBPl19vnanjn/se/QpbUk7vIQrZegEsra8DsVRVEURVEURVE2iD5oKIqiKIqiKIrSc/RBQ1EURVEURVGUnrMpj0ajFUmwomdr+agHq5L3oRqQ5lFEcjl8T6G/APHE0CjEP/DgGyF+6rmvQPz8afRsiIj4AertSqU+iFtN1FH6PmqDq7OovwtIP9o3jHUWEfFD1FXWZvHcx0nLVi1jHU6fRU3gwADq+URE0pkSxE3ad7lNOuoO7fHN3pMwYD2pSC5PPht+Do2vto0Tfys+o67jXdiAtYH3y2btZoH8Ofz+RdKwdijPhpegK2etOscR7z0erefZWB/+DOdlYb+F51GehARPRzaLGtJ0Gj/TvW98FEVSrZhzzPVmdHBitV4dyjNTqaJm2UnQs7t03rto7/lKGc+pUl/EA9g4N2Qz5hQeUL6JgPf7pznSj2l/f9I4Oy72uUIR/RUiIp0Ozptpyv+R97AtQgtfP3cZ58A4TshBQh6MFGmOOyHOaW0fj8G5PVizLCLi13FeDQI8r/yKF9Gyt8cjpPQIGpuzs+hT8v2EfEVGTiPsX5Exr1KRCb6PhIphSMewHSvx31tFs9lanR947ao3cO5qt9GzISJy6dIliDmfToV8b/fdfy/Ew8ODEPu+6ZfgVjGWbZrfmk3MB3H6NOYke+KJJyAuFMx7wIMHD0LMOaB88u/x63yfsGPHDqOMTgePwd4S9mhks9k1X7dT5trhkq/j+KsnID527Pg167MW34p3i4qiKIqiKIqibDP6oKEoiqIoiqIoSs/RBw1FURRFURRFUXrOpjwas7WWeCu6rudOzcFrqcFpiO0M5mQQEYlCVMuRrFcsFzWzw/2oKbttF+rz4hrqKkVEQvKGtEmP10mjriywsAyH9rpORxg7vumfyJAY022UIR6nPZQbbdQEvnwa90yemjA1gCRNlHIF2/fsPJ5XTbBx++pY5nzafMasDuHf2pTHJO7K8RB0zH3urzeWZXV5A1jL+hp8BvwH45DURhsogvf6X1igfCfNBsQDgwMQ96OlSGbncJyxtlPE1NA7Lva3YhEPmicvzsIC5mCpVMpGGUbegHXyYnAcBtgu7OEQEXFd+gztde93+YpYD71VNJu+hFfmMZob2FbgJ/igeI/3N9x1N8Tnz6GGea6CHo1CCueGoT5zrrh46hzEDvsJqOm5nS3ykWQ87HN8LUVEFhZwPhobxQnrnnvvgfjxr74AsU9t6XpmP48F35PLYb+uLWI/rtZw/soX8P3pjJmvyPNwDWIdfntFlxz4Wz//ifAceP3ZpnQ13zDrNRF7xMqL2H+bDfP65gucF2ftPrB+/qKkz0Rrxt3zv+ua4/B6Y9v2atulKX/YpYtnIWa/oQj67EREjr96EuKFefzM/DzOfw8+eD/Ee/buMsowmpm8DNylHVpTxycnscyHHoL40kWco0VEykvoLWleugxxvfEixKPkwdi7bx/EA4N0wydm3hTO1WH4QclLwp4aO5Xg96Q1mMt44fmr5xGGG+9/+ouGoiiKoiiKoig9Rx80FEVRFEVRFEXpOfqgoSiKoiiKoihKz9mUR6NupcW1lj/y+CuoPR8ZQN1asYB6WRGRORv1X8NDqEPLF0gzZqGez6V9o3d4poD0xhwe4/Qiaszu3X0TxLkR1MYVSfM8khmCeKjf3N84RfvSP/VXfw5xqXke4naEz3f1OplVEvShURr/5pCGuRnhMTIpLKO/iPq9ZhrrLCKSp95gp3Af5tnK1TLCztaLd9Pp1KrONSJdOe8pzbkjkmANralHpj9wXoSEJjD2UrcpD0aIcWWJ9+1H3WOK8k8k6XxLpRLEBw4ehvjuN3wHxLt3o671+eeeg/hv/uZvjDJmZ1Fzyu3Lmnqf9tgOSc8cBGYf5z3U+VzDrrbja7dV9JWGJZ1e1vG/8PLL8FpMPgPed1/EkAsbnoCQcj3E5CEr9I9AbCVM4e2A/DOGvQb/wH3OJm9SQE3dbpvaXM73EpHXbWiEPHzeGYgnd94IcSZr6oeDGvaPwR1jEC82sM+NjvVDzHkMJiamjDLSGZzzjr7wPL4hWvFoJPTfrWAtjwb/Pel9642bb1ZPhsnabcE+pFqV9O81019VKHIuoLU9GNyWG7o+HNMfur0lSbmIrjfLHovleqdo7mo2sQ05z42ISKvFuSDw9QYd4/GvPA7xmTOY4+K+++4zyjhyBO/xsjm696G8GfPzeK/66quvQux38D40SPAm8LVYWkL/MHsdsnm8z7RsnMfTtO6LiPQVi/gZ6j958gJzHg1eox2PPUciQt7Jvj70tRWKV+u9mTlQf9FQFEVRFEVRFKXn6IOGoiiKoiiKoig9Rx80FEVRFEVRFEXpOZvyaLSbloQr+t9KC187+iru3b5356jx+fIi6taOPY/61wP7hiFOZ7B69QrpvlumjnIwhaLGrId7pd904I0Qv+Hh74U4ItmaE2MdbMfUtXVob+ihGLVrsye+AnFzAduqNYdlVOdRzyciUlmkXAl96B3ZtwvjsX58hty1EzV/5YKpATxL+rwvHEcddTW++pmgvfXPqHfeMiquu1wuyySDgHMumB4N1pG329h//A4elI/h0yG5TBGRjs9eBDwme0k41wJ7PPr6UJfpJ+zfXyQd5T/+x/83xPdSDoN8HrWbb3oT7k1+5513GGX8j0/9D4j/4ctfgPiKb2EV1vW3cMLwfXPsmppmfsfVP2yXRyOI3a45AcdQFPCe96bPgHOzROzJiPF1x0F9cS6HvoPKkpmvKCSPhinlxjJt8oGwt4T9UJ0kjwYNjgb5zgIfKzEyjP6I2eMnIO4n35GISCGN/TaVxba4+zveBPGNN6Lv49TxYxBfmUu6iUnbX55HD5VtLbddUv/dbjbiAVg/GRCPK37/N57Dw6zX1o9lrkO7hWv44qI5rsYncS62nbXbwpyjenF9tpeFhQVJpZbnC9vC+W1xEXNeJM3R9Tq2K1kWxSL/xI5RvCdkX8CXvvRFo4xTp3AuGRzCXFWcX+LMGbzPqdWwjuwDGRjA4yUdk3NWNBqYP+vkScwf0mzhefcVzPxI+QL7OnD+4jWY4yvXbfXzCeuTzx7SatV4z2tBf9FQFEVRFEVRFKXn6IOGoiiKoiiKoig9Rx80FEVRFEVRFEXpOfqgoSiKoiiKoihKz9mUGdy2U2KvJBaxc/jRxSaaPesN0yxX6sMES+dPoQHmyccxGcvenfj+agXN5HYKjZIiItks/m14AE3ppeFJ/ECM5m7H4oRZ+CxmhaYBOOPiMQ7f+gaIL/aj6ebsMUxC0y6iyXHhMpqqREReOo3nfuEMGhvTNprZJgfxvHNZNAKdWMLkVyIi5zp4DeMUHrMwcvUYfmvrTWspz141cNppSo5Ez8xJtYtjbAPDqkfmtYhivvRJOQF9ym52aRZNYHOL2OcDNoOTObjjcBJL88xuvvkIxLfdfivEff1oIvPomF4K++/0tLmRQ6GAmyo06ngenRYnNUJTGZtnk7zcbJSPY467/709ZvB0piDpzPIcM0BjbIGSGiaZjW1KzLRIG2SEZCiPeTMB2gzACs12cMikaVFPD6kjs3FROGGfj8Zuv4PXfvkjMb0Hy6hV0GA5OoSJB1+hjSeOH0OzpIjI1CS2t5fFsTBByVQLRYxv2I/9vN4oG2Xksmj4/Z63fz/EiwszIiLSarXk03+OiVm3AstKnAJERMSmzTwSh8g6Pmwj6Rx/fgPjjuvHBntzro4oXjtRamyZJlaHNzgQ2rCANtmIHdqUw8fX52fM9TGKJrCeNo+btec43ughqSn5PRJzW1mJ/94qSqXSqsmYN1eZmBiH+MKFi8bnl+i+I6LL5FFSub37dkN8882YjPbixQtGGTMzMxDPzc9CPDqK8wibu2+77TaId+3CBLdJ8NrVblPiwcfxnu/YMbx/e/TRRyGuJtzbVsmYzUkBuUxO2Dc8gnNuvogJ/kRELFoLOBFh9xzD881a6C8aiqIoiqIoiqL0HH3QUBRFURRFURSl5+iDhqIoiqIoiqIoPWdTHo0oDlaljlYONWReEeM85hBb/lsaNX2FAUy4dKaCGrPjZ1DPR/I9mWuj/n25XNSYvelW1NcNkM6XJZGs9RTWj9pJwkrWvKOevTS0G+KgXoZ4iTSnlbLpb7FSqIuuUnK5GUqQVTgxh+8X1NA/XjE1qDN5SgDTRw1ud9XBNhPHXW+arUjclSqtlcxtmSQtMf+Nk6WtnWBpvddFRNo+/s0PWeOIcbOBvpi+fvSR3HIn6l5PHkddpojI4CiOPYcGiuvi676PfeGlF1+E+Itf/JJRRoc8F5aFZTSbqMEv9GGyoA52z0SdOXsaOCljd/vHsUiY4E+43hw/eUa8lcRHnTa2ScajhHKuWb+AkkSeOoW+tHodk3XGlKBKyOviJbSjTXphy6Wko6StZc+GmVgMP8965OVPcJI/8oWQh6dRxTjnYb8/Xzb7eYMSb+XJDzW3gBrm7DnUarsxedDEnGf9DvZj181SnFr5v5m0cGuI5VoJ7nhMWUlfI7Inw3jP2nPeRubAa3lIrh5jnSSAbH7jAyYkyjPqSccM2WtJXky2dCwleDRimje5GpbFnjJuq/U9GhH7UdZq7m3I7RcEoTjOcmOxZ2BsDNeqfN5MOnf2LCYrbvg4JosZ9A3Mz+N9DCe6KxRMnwEntc2n8X4sk8b1cGpqes242cB7r0bTvO9cWFhYs56XLl2CeIT8Ehcvop/l5KvHjTLOnD5Nf8EOMD2N9eaEfQ1KhphL8Gjkcvi3iXH0JU1NXU202ulsPGmp/qKhKIqiKIqiKErP0QcNRVEURVEURVF6jj5oKIqiKIqiKIrSczbn0bB8iVa0jTXKP1Hx0GcQiKnjbVVRx5bBQ8jYaAnic5fKEJ+/jPrliDTRIiLTWSzXdVB86UWoCbRYpxuT3p21nEn6WNJ/xqTVdItoWMmPoZZunvaCXlpAnbCISCbGeg4WsPFO00eOX0SNaWoM9c0NC/eLFxG5eGEe4t051PgNDV3VUXcScgRcb8qVtjjOSh4N3sN5A3J91uWyJ8ChU2I/RUybfods8BGRchXLaLUpNwft525R37mi/1+tk4t9aXq32eejGPWflTJexz1TeyCeXcQ8LZdOo550YQbzQYiInDp9AuK9N5YgrtewP5ItQFpNyqvRMTXu3DZr6cO3K49GHFkSR8t9Lys45x2Z2AvxyCBqg0VEvALqlqcPHYL46AnU5i6Wcc5skS46nTbLcGkv9JA07hFp4KMI55YwQEONS30yCZ88PAEZbPwY4xrlsAhpXk7SXpcp58jIMOZZOkV70595Ffu1FeN52Y7Zh654MK7AHqp0ZrltO7Rn/esBzoHB89fKu76hMjYy7vg9Zr6HtX0eNufZIE9QMaE79lMuqrkyLog1zh1j8dxP9zNlM1dMp4XX3Mun6R1rjzPO08TXa+VDEG7XPHctpqd2reYq8zxss4By/gTsLxORZ599BuLLl3GtOXjoBoiHhoYg5lwRtRreE4qYXpFWC8dwQN6u/r4SxJcuUh6OOfSJVCplo8xaHedl9q9MTKDXIZfDeZvzTKVz5jofUnc5cfxViAd3DEOcIh/1q6dwDe8rlowy2J8yMYF55x5++OHVfzebTfnjP/mkcYwk9BcNRVEURVEURVF6jj5oKIqiKIqiKIrSc/RBQ1EURVEURVGUnrMpj0amPyVuavkjUYc0ufTeypKpwd43WIK4L4eiMyeNWrolwTwbzQoes9nAfa1FRHZaqO3tNNCrUCuXIc71Y81jl/axZr+FsQe4iNDfbNJeciNbIerxGh0878WWeV4XKMdIx0Z9aGkQPRhejBrT/tIgxA/ccNgoo/7E30Pst1Gb6DpX9eURGxq2gJNn51d1rYYe2dAno05dRFb9HVfo70e9fIE0t0MD2KZ9fairXFg0vTSNGdwPOyZ9seeirjWbRS3m5NQ+iA/ufwPEhw7uNMocH5+CeGIUc8dIiH2nVUEPR5o0/ekE7XCB8hxMTh+A+JVXUB9//gzqXDnnRVIuhqS/dcN5NLaDXNaRVGq5vTIxXrv79qFHY2oM90oXEanTXFGltg/oxAJqk4D2Lk95ZptlSN/bIK10m3KeuJQbiIcOexksxxxbdoTnlcqiPrhBiVSGR1F7vXM/tt35i6ZP6PEvPQ5x3EZ9dn2hDHEQYJ3SedaUmxpyl8xFM7OzEJcGl+dRv2PO0a83Ej0A2wH1ac4/wTkvrAD7dJ7G+jvvRB2/iMiR/ajL/+Ljz0L8KnkWT1dxzW2EOL/Va2aOgKUGVmSgSB4Ni/2ea09SpndFjJwh7NHoniPXmy+vB8W+ouRW1qwbbsDrcOrUKYjZTyEi8tZH3wrxlyhnE+eXYO8Cj1l+XURkcBDvdbid+TN8mZaW0At2jLxfmQzObSLLOea6cRy666N5n/MMBT7e2ya13ciOHfgZaosm5cng/nHhAnqBjzfQwyYicubMWYgPHMB1PtXl12Pvy1roLxqKoiiKoiiKovQcfdBQFEVRFEVRFKXn6IOGoiiKoiiKoig9Z1MejdiLJF7RBEdt1IfZKdTtZobNfYB37sc9ehcu4V7DTz6P+/ufmGlA7Aeoi40SNI4LZdTtNluofevUsYygjdpNN4P13pAUnDWnAekKy6iNkyrq1zmfiGRNDeDJ2TN4iCbq7/oKqBc9vBc10Ht3owb6hvseNcqwqNwvHv1fEC8sXm0rv2Xqm6837a4yzT3jKXZM/arjUh/18ermbbz245Pol3jzm98E8de+jnuCi4g02k9SvfBZnjXgO3fuhvgd73wnxI8++p0QF3Nm/hOXzitu4RhYvHQa6ziL+vcG+ZZKCTkMvvPND0E8SL6QPdOY/+H/fP7zELPOlfdcF1lfU45a2u0xadhOe9WzEDuoo33ipacg/torSXuh42B/eh7nArsfr+89934HxAXKyVOrmPvIT+/dD3GljvWsVlCDPEe+otlLGHfaqMXlPixi9nPOM1Gr4FwfWzi3t30cr4cP3GiU0aG96Tsd0iR32DNFOWnI18Z+GBERh/pgNoOfsSSE/28nPFx4/LzOUjCsYtSbtOR3HUIf2r37UZu+dxi9myIio0Vcu37gPuw/lSqOk6+dxXH3zAnsO7M1M49GbR6PMTSKXgCbcnNYnMeEl6QNWGiMXEJdeTbiaOsvcCbtSWblhiUg79/IDmyPJA0/+7/mZtHnwetCh7xQ7F3I59FnuXwMvDc5dfoUxJOTmBsin8+vGbPXod02/VlcZkh5hBZDvM9sNnENZo9pLm/eA9pkbhobwzxCBcrRlE24j+zmxInTxt84/8fzzz8PcbdHg6/NWugvGoqiKIqiKIqi9Bx90FAURVEURVEUpefog4aiKIqiKIqiKD1nUx6NMIjFurLnOu0T3CKd+MuL6IUQEQlfegHi/UO49/XoFPoKmi7qY3N11JydvmTmMThPOsovP4Uas/EJ1DiXBochzmZRI+2mUa9nJTybRfSnMESdW33hOYjjBr5+5OaD+HoG8zWIiHz1xRMQX57F9k2nsf1zOdSCT5MucaSIsYjId9z8AMTH57HtXrl8dW/yYBv0oSLSlUcj+e+rcYKGnyWxnOOC48nJCYgnJrG/XrqMe+yLiHz/938/xCmP/BMk1B2kXB23UF/IZ1kjbu4bznv6n33xaYhPPv0ExIvzmB/lpeOvYhn9OCZERAb7SxDXywsQjw7jeezfj/6W2TlsK9czczEYetwW6nEtqzuPRixBsPX7yPcXSpJOL1+T4gC204Ex1JZHsTm9XjhzCeKFE+hteeTBN0L8zh/6XoiPvvwSxCdOoHdLROQf/aO3YD0E54LyIuZReeXFFyH+8hcxn07QRj9FXz/OoSKo3RUxfR058r6FNBptip0EAfv996Ff5dirWO+L589hGTRHeZRHx0+YIxYXsG0i4Tw4y3NzzJP+FmFZ1jW9TMafXzceDTYxYts5lIXrzoPo5bz/IN4X/Pnfmt64FOWCefv9N0O8cxf6PI4cwbF77Dj6lr52DOdIEZGwhe9JdzDnQ4PugSwb+05E/THp8vAaxP4AzCW09Rc4l0tLLrd8Hxb4OF/HEfoSUilz/hsfG4V4eBjb8Otf/zrEi3Qfyf6wHZRbQsTMg8G+g74+nL/YTzEzg/6d+XmcE4aGzPXR97EPz82Z/acbh3IR1WpYx6Eh9LuIiJQGsN62hWXmcnjfyOvjrl27Ie4rlowyLl/Gcz97FvNqdOf9Ssphci30Fw1FURRFURRFUXqOPmgoiqIoiqIoitJz9EFDURRFURRFUZSesymPhp2yxE4t6y190lXOtNEvcbli6kjPnULd7osWampnL6PuO53FfYFTpEFrdcwyvBC1woN9eIr+EmqkL73yd/j+IdSDRlncjzuKzDId0l46FsanT6G/Iqzjee6hHBC5EDWGIiL/16NvgPiJr70C8aUytn9+EM+jNIx762dT5v7TeWrfTBb9Kbab7fr31ufRWEufzHJVxzG7NusiWdTMetE3PvQgxKOj2KZ9fWa+iXHa23pkuARx4OP+7LzXei6D9a5VsS806mbeBNZAzyyWIT7xCnowFskvkR5B3WxmBM9BRKTto461TMdYrGCZ7RZqTvfsRf9Cu23qO8+dPQ/xPOlcu6/xskcDPRxbQdopSNpZ9mjs3Y9janoP+lJcjxPkiEzswXbJ7kE9+vg0eqfKlOPkxHGcSxzbLKNCn8nm0XeW8bCPHbgB671zAjXIJD1f9ahAPUg7XV3COrQa6PM4fwHn4R2j2AdrNdN/F6bwXB0aO6UBPM/BQfRYsafm4kXMFyIiMj+PJzswWII4nV1eX9ptM0fA1mDJlfFuTIW07iTNlHFsmNvoDfx+8hXw64lOA/LL0VvYj0PSfinXsK8MjB2C+I67DhslXjx9EuKzlAvGzWG+hj1TuK7fW0K/wfQOyn0lIqcuY96B2Tl8T429bZxbxiK/RUIuFrY+GtcL2m4DiTh6TLvZWs01E4fsyaB5IcFDYlEuiIFSCWLOR8HjzPNwjC+S30zE9Evs3Yvz2yD5cufm8H7smWfQU7uwUKbjm9eN83vUaZ3mOZPvY2o1fH+5bM5/JWqrQgHv15aW8BgDA5hvZnAAfR+ZBC/w4iLeb0QhedS61o7NeIT0Fw1FURRFURRFUXqOPmgoiqIoiqIoitJz9EFDURRFURRFUZSeow8aiqIoiqIoiqL0nE2ZwTP9KXHTy4Y8q4pmFo/MgFa/mZCrFaOJ5sISGmicDBqUZypobqnNogG1k5AvZP8oJg57+MEjEI/n0JTzxONPYh1SaAg+/EaMYxfNSCIiFiXEWiqjgemvP0sJ+1w02AyfrEB8cBpNPCIiD91zAOKpEWzvf3gOjWq33/NmiHdMo4Gu3EIzpojIEy/9LcRnZk5B3A6vmn+CcOuTBXWbwdnYbZOh3rbNZ+hMBhM+cpKxrGGGxyRjg4OYMOfADXuMMmpVMmuH2BdcC02HtoXn0ahigqJaFU1hS5QoT0SkTubJxVk0US+RaS/K43n0D6Nptp3w/UOLNntoNtDUXqtgvRt1NJXV6/j+hQVzw4NKBccBm826E15tR7IqEZH5+dnVfvO1BrbJl7/+NYgtw8gpUnKxD0ofxicuoKl1npJHseGyUDDnirMnMcnS0DCaAG0y8GYoeSJvZuEHPsXmRhCFAs7dASVzOkUm9g7NHy+8+DLEjRaOExGRiK75xCSeV5kM5K0OGoLTi9i/khJO5TLYvp6LY6HdWh5rbP7cFtbxdW/ILLyu+Xtz8XI9OHkqvc7jmubqLz+LSSxHSzgvP3S3aQY/MoV9Yf4s9rfmEs6bi0XsryNFLIPnehER28YNC2JKfFqhBK7pIm4e0qFEhVZozrOhrNfeVuK/t4pXXn5FMitjhA3Ohw/jdfESNsOIY7z3maLNLw7fhMb/UyfxunCyPU6uJyIyNoaJdfN5vNY87ptNXD95HeI46bwi2tEgS/cOLu2oYSYexPvMgQHsOyIiGUp62m7hmjo7i21x/jxuVpDP4+Y1MzNmUsFLl/C+MEPzodN1Hk608YS5+ouGoiiKoiiKoig9Rx80FEVRFEVRFEXpOfqgoSiKoiiKoihKz9mUR8MPfImdZc2g30Cdrl9H3Vsmh3oyEZHIRt1tdhDfY1fxuac5j3r3MEa9WNYzdbJZD3Vrjz/xPMRDlPTkuZcxgdZTZz4L8U/vuR3inXtvNsrk9C2diBJLpVGHODGM3oA906jTHh9N0DZ6WErfCH7mwTdhQr/pQ3dD3CCd9VdP/K1Rxuee/jTEc3VKmNalK0xO1HR9WSthn2Vh37Ed8xnaIZ3kECVnPHjwIMT5PPU3SqZ3+JDp0ZibRS1wq479q9XCcROEOCaWFlBXaTs4Rspz6IUQETl/Bv05LUr+Y3l4jCDEcXP+9BmIQ8dsY4va06fEg1GM55HLYh8PIvz82dOYrFPE9H2w7+Yal35LefOb75HcipenUkdtb5XGmB2a0+u5l1B//tWvfQXipQp6V+IGHvOO2++CeH4GdeEiIsdfRX16fwk1yv2U6Cnt4bXp70Mf2sAwJrgqUzI+EZFWA/scezSGhlBD/wrV0XKxv3CyVhGRNiWPOkEJHnfuxHm2kEU99+XzqD+emEBvkoiIS96vMMbx2gmW62AnJFv7ZuQb9WRsxKNhlCmk7baxzc/Oo9fmP33qixB/9etHjWM+etcuiCeL1Kc98t9R8ry5Wewbp0/hOBUR2bt/N8bkr5t5AT9TcUjfnilBHMfmGmVx2xjNG6/14nWnVCpJNrt878E+A/ZNJvUD9k4WCngf8/DDmCT3pVFMHvv88y9CXKngHCwiEgQ4L0xP4zjP5nCuaZDXbnISyxwbR//ECCX2TYL9K+wt4XuP/hJ67TJp8vKJ6QPhZL+XL12G+C//8i8h/trXnoI4lzPnWL4+nEAx6vJ7RuHG50D9RUNRFEVRFEVRlJ6jDxqKoiiKoiiKovQcfdBQFEVRFEVRFKXnbMqjIR1rVSgd0z7oJFeU0DL1W04en2tqtH+x38Bjtmm7difC6h7cXzLKuP0G3Cv4+LOoXz/mlbEOPla8Vcc9ky8toJ59ai/q+0REQsoPUhhAjfMP/uMfgdiqoiZeWq9AmE5hHURE2jXU5rdqqIkfmEJ9cpr2jj51Ccv86tHHjTJmuIwmXoBM9qp3xPa3XjAfRdE19b9OgieD4f2zI9oHenR0B8QDA6ibjCLOH2Du9Z/ysIx6gB6NmPwRjoP9KQzRp9Ch/BU2a3hFZIj24JYRPI8WjbMq7Qu+tIRllMtmjgu/jecRBfgZ18GxmyaNqdfGtvI8cxyxJ2OtXBnblUfj7KtX95HfuXsaXuM+mEqZOtvFPvRfLdBe57FQTgvqH+Uy+dYCMxfE+Qs45y2UUZ+epXwynAPp0CH0Ko1m8PNjWZxjRUTSHh4jCHGslEawjzZo7D34xjdCfPI05gIREYksbItLl7HtKlXsoxOTOyGuUR6N4UH0jYiILC6iLy2fxesVN5f7XWRvT/9bi434J/hPPAe+Fk/GuvXgcc3vp5j9Ok1aX585a85P9Spq9x88gDkvHrwbdfoO+SfOnz8G8VwZ+5KIyAj5UKM21rxAc1q0hPWMyG8XOKaPNebp/XXgS+tmYmJ81aPG15nX5sS+Qj4DvndyHJxHhofRy1Cp4HU5eRznOhGRB954H8SPvu2tXAmIauRpHBvH9bO/H+8DkvJosLdhvdjINUPX2bzXEGnTDTHnUeGcJG9605sg/tSn/pzKMO8l7rjjDoirVezDla6Y8xqthf6ioSiKoiiKoihKz9EHDUVRFEVRFEVReo4+aCiKoiiKoiiK0nM25dGImlcldo6FH7U81Gslacy8NGoYC3lTw9zNYB7L6FTKEI/3mRqzvSOovfR2o1bz2Bna75009bfQPvVTE6jzTZalYT28NOqs9990AOJOsBfi6jxqhRdOfM4oYeb0SYhTOdw3vDR4COIwwopenLsI8exC2SjDibDedkTCQT9K/vcW0a355D2labtn8Tyza3suaitD2gd6hnISzMygXjubxnwCrm0KaB2XdPppfI/vkw+pg56MmES6QRv7p98xx5VNY9GntwQR+kIi8pZYFno48lkzP02bPBahj+9Jp7Dv5HPYVqFgGQ7tnS9iakZt1vxu8x7yIiJ//w//sOppeOtbUfu75/AtEB8/jfu5i4i8cgrzh7Ra2I4Z0nlXKbfI3Dz2yVRCPw9C0pLTpLVn3z6I774bc+5MTqLWd2CgBLGXwjlWROTMOTyvi5dwLNUj1KNP7LkJ4stl7F/t2FwbXMoHMzS6G+LInoe4fxh1+fc9hHrvQs4sY34W97s/fw69bVdylHQ6pjdmK4jlas/ntYg18Uka7CjavOfiGwfHMS8rvH4aoU19PGP2P7YMWjaOo3q5DPGL85jv6JkXXoB41437jTJqLSxkcYZ8a2TZy9XRxxYI1iEcSMiXtY4no/uaJl3f601fqST5vOnRElnf77P8t3WuNcUpykeRIn9EKm3Of6Oj6Afj+nY6OOcOD+O80mq11oz5vkFk/Rwi7NFgnwfHSV5ULoPHsk9r8m7yEB6+Cb13X33iGaOMs2fxPtGj+5l67ep6xJ7XtdBfNBRFURRFURRF6Tn6oKEoiqIoiqIoSs/RBw1FURRFURRFUXrOpjwabb+9mh+DZMBiU14NO23qB8MA/1YYQh3avsNjEO/agd6G8hnUPC8896xRxunjuFd6h7T8bcF4fBK9Dvfedz/Ejoe6uGYTdZciIj5p/lIp1Ic6WdQAuinUDKYzAxA3fHN/bbt0I8QTBx+GuJ/aqhVi2w4O4d7QE8Oo3xMRiQTP7bKFotNOtyZwG/aRd11nVbto6j8xTtJQOy6eD+smz1/Affmfe/YoxGGbfDElUyvskVfJ8dIUY19pNLC/NhvkIfJZD5rkbcBr3SZfR7PO+WqwDJeuc+iY+UHS5K8Su4RhB8+7OIj9LZvG834pj7kZREQsm/cWJ49GtD2+jG4uzMxf1cqmUc8/vvsIxGcr5vS66yC27ejoOMSDRfQNRDH20TBCLa/rmjrvu+9Dn1lfCeeXG2/EuWTXLuzXrBdOUx6NywtmHoPy6csQNz1sm1YN+zHre+0q5vAJI1MH3WzhWGGtdEBGraeOvgzxjkGaV8MEvxN5C09fwPM6cfJiYllbhSVXHQ9mToyNeDT4L9d/TMVcBsnPbTImxJxjgCqds8zz+u43fwfEt+zAcRPX0JPB7dBfxFwJM7Po91n+EIYZi/x45Dsbo9urFOVrqBewTBGReTpmi+b27ku8HdNhvdkUWZmneQzzmpw0hsMA/8bXwaWcPnwvtWs3zlWXLuP4FBEpFHC+2myuGPZCGPdzjrkGM2aeDLqOVCfT75B0cdfOvcHnYTv4hvvuuwfixXkzX9uxYycgdugYO0bRe7lR9BcNRVEURVEURVF6jj5oKIqiKIqiKIrSc/RBQ1EURVEURVGUnrMpj0a+lBV3Zd/igDauDkPUHrueqWNrNFDXWq7hHvGVEurU0uRtOHAQc0Ucr5v7/b9w/FU8ZhX3ui4VShAfueN2iHN9qC1uk9a/Xsf94UVEnnn6aYh5b/y3vvU7IR4cwP3cRVBDXRq/1yhjx8RuiLNF9LNElAMjCrEONdpH3InMZ8wm6aitkHMjXNWP+9bW7yNv29aq1pG1nawDjni/bhGJ6UPzc6jDfYE8P2nSi9oxnvPkhKmxHRkqQux52M6szeQcB4bXxMc9vB2HvBIiwsM4jnEsOg6Wmc6hhtVxSxBnE8S/MXkFqlXUG/tL6O8ZGMDcMLGPbe05Cd9xcF4AYyvxdTaZ3wKW6o1Vb09oo5/i1TM4N7x0HD0/IiKTOzC3w5vuR93sDbtxXNcpj8b5i+hlaHZMHbTQ9XbJJ9QhPfBzx45DzHrhNiVmuThnejSW2njxGpTvxYqwH/PFtsgvVaua+uHz5zGnRb6AXjfOuyIksz9JaU0yttkH7QDP/ZUX0KfVbC6fV5jg79gK4ji6mouAPRohezSSPs9/2dyYsqz1jQHraeC51S3BOS0iDXyaPJC37UL/l4jIm+69GeLRApbSWMLOMHcR89G4M2WIqwm5EubK2CcH6EQCugfyA4xTZGzNNdE7JyJSdXFutmhdD7s8elGCX+96UyjkV/NSsE+J407H9Pq1aW3i+0aeexyH8maQX2JsDOdLEZFMBufldhv7zzzlImo2cY5lsnQfyscXMX1t7DVhXwf7Q10XX0/ygfBnmPW8Izt24Li5465bjfcs0jjx6Dx2d3lkuF3XQn/RUBRFURRFURSl5+iDhqIoiqIoiqIoPUcfNBRFURRFURRF6Tn6oKEoiqIoiqIoSs/ZlBncsW1xriSlIWNKo46m67BtHjplU8KkiBKLtTipE5YxtmsfxDseMZPO7TpwDuJTJ9AcvjSPBs0zlzEe3Y/GoPp5dBB+6pOfMsp8+ehLELPBt1HHY/7Ye/4/EKdyaJwd3VUyynDTaOj0ySlLuc7EIadgIVWAuD9lGurGhnZD3JrFtuu0rp6H39p6M2QYhqtmcCMhDpkak4xTnOBriQzy9ToamjudxyHOZdHw1V+8xSgj7WAZmQwlfGyhAZA3DvB9Tm6F5jcvjQZYERGLEkUJxU6KkiRR32m3cBwGgWn0b9TRCHnhIo6bQt8IxFkyzKU9TsRkzg98TU03+PYn7HPdlDgrRvYUmQQpR6YMDpqbBRSKOA5n5tHcfeMNeyB+8tknIf7U//wbiPuHR40yBnaQEZ/GxnrGRLPVKdlUwoYEsYd/S/FGChZvgkBJtDgpm2MaLndmp6hQMh0blaI5IcY6ZRJ80JdOnYJ4qYJzwpW242u9VcRxnJCsdBlO2Jf8vu2oOCXkIxOzTYndLNoA456bcUz8k//rLUYJ42O4oUp15hLE84u4BlsFfL/04Xp49IVjRhlLVZy7D5Vw44+RPCXbo7k8zuIYaZdxQw0RkaCfxtb2T3mA53mrxmfT0OyuGYuYRmresIANxrGRjBZfX1jARIwismpWv1Y9+vtxw58cbY7CJnZjXUqATex8jPU2SEjT/Z2TsFmKR2sotz+b8fl1m24SR0ZKRhnT07ieeLSRyNTU1c1Mmi3e4OPa6C8aiqIoiqIoiqL0HH3QUBRFURRFURSl5+iDhqIoiqIoiqIoPWdTHo2luaq4qSsJ+1Bjls2i9s6zzOQhzRrq1tKkFR7uH4Z4ZGAc4lwe9WPZkln94UH8zMEbDkB8afYUxJUaaXBJB7ewiBrqZ5593ihzaaEMsUdJZRYWUd/OyeU6EeoOXRd1cSIiVsS6eUrOZJHO2kbx4/TYbojtjKn1l5fwM2dmT2IZXR6HKCGh0fUmimK5IpdkvSHHToJHg3WRrL10XfRgLJZR//mVr6BefmwYNboiIplDkxBfSfB1hYj0yJ02JzCiOtIQ9RMShUWkB2UPhkWaes/GY0akXW8lJIFrtVDjzPLvwSEcmynSdhaLpIvNJvQ/1o+vmcxwe8TLmVRqVaf/6quvwGvjlHhsaQH9YiIiVhs9GudI913qR+34yOQuiPPDmKAqO2wmrMoOo0cjDHFcWzRX8HzE7c5+L/68iIhHhwjJgxGF6Gdhjwb7p5KWJtfGPsWf8SmxYBjxeWP/qlXMpIBnTp7FetIx4hWvSRhtT8K+KIqMOl3B1IEnvgsiU36+OV/Uhsqg7zNt+sxQFu8Dbj68E+Lvf+QuiN9w50GjRJeS4bUWcC63Xex/Xhb70q13oN/ucsfU5T/xNCZvPL2AY7dcxc90aE3yUnmK0RsgIhI75GfhNWubp8A4vnrN1/cEJXkb+PzWHqMueflKJVxHooRxmEqtnTwvk8G+kM9j/1vPk5H0+rXG5LU+s969yAZsIQacNJDnfa5jUoK//n70Fbourmk7Rq96mRqNtRMddqO/aCiKoiiKoiiK0nP0QUNRFEVRFEVRlJ6jDxqKoiiKoiiKovScTXk0soXUqmbOtVG7xfpYv2Vq1mLBv/XTXtZDpOO2BTVgfgf3nc6lUIssIpJKowaatW8jNmmei3jMdhPLzFMehDfcc4dR5t9/4UsQs1Zuz417Ia7UyhDH6/gvREQC1jvSeRm6fPIoFPuwrfMtzHsiIlKroR+Ft8uP2lfrFVnbo5G/CpbPHo2N7CHPWk7WRWYpT0Iuhz6ket1sw3oDPT8pEq/ztWb9spBmtdPBvapdMXW9PNIsC4c1yz1b5AsJfNa5mgLRVBbHZnGQ8mRQW+UL6F/pLwxBPDho5nGx6QIEpMHv9hJcK5fA9aavOLCq+X356MvwWqoPPWZnT54yPu+PoqfC7+B5vPDKCYjvuhfnm5tvw7gZJeSLEfI7uOtoqSlm/1VAvqI4SvBncV4MOmYYOfQ6j1ceB+Z5WeQlcrmb2tiPLfK+RR0cmxdOnzbKqC2hb8N1aCx5K3l8OHHRtwkbG3fstcK+kab++UPf/QjEb3sL9vFMhGty2ERvhIjI3NwsxHnK17BzN65/bhrrVJlBP96NU5gXSETEo2seVLFe7QDn1QrlVniVcj6MjeJ8ISJSJH9n2DHPdTuxLHvVo2V4vcxENsbn2TfA+SbYd8W5IQpFXP/2U94hERE/wHHfbuMaysdkr8J6OS+ScnQl/a0bvg9N8kfg+9cvg+vl0FzleXy/g33L5vxbIjI8jOvyjh3ovZycvJpHo14388BcC/1FQ1EURVEURVGUnqMPGoqiKIqiKIqi9Bx90FAURVEURVEUpedsyqMh4oms6L9bbcoHwHvvs+1ARNIZ0h+ShO/suQsQF2zcd3ogi/qxgT5T4+g4qLv3SA+ajlDjF0WoY/NJM9ghneVdb8D9tkVEBkdKVAfUvu3ZNwVxu4PazhS9PwzNxovomdAPSEdIr6czuE8460t3DJga1DsP3wvxXG0O4vKZ8tVge7aRX8Xcy5o13wmfoQ3cg4D8EhZrHtF3MDaOPoPdu0yfgUP9LaIy/DbpjclgwecVBbQXto39W0TEp2NErCmlvdl5y+8gwDqzjlZEZGYO9e21Jo6TqUkc24ViCeKBfvQmDI9MCMMa047hHbnKNlk0pNEK5MpwtcrYJs0a6qn9wJxez16Yx/fQWK808ZhDNLewh6yR1EYxeSos3k99ndwIHBt+CvPzvB8+HyQy9nQnXwh3Sj6eiEQ+nleL2qJO+7oHbbwerQrq+C+dMz0aEfvl3GRdc7xOG24Pm68TX8rN7t+fnHOAcxphfPNenAve9iCuqVOUn6i+iJW8fB6vo4hIg3yPdZrLG3S/MjKNc3eH+ufiHI5TEZFhyuHg5dEPWiV/zwit65xD6dLZi0YZlodtw17L7tPaDpukZdmrXgG+9ut5CDbCen4Jn+7HxsbMPEJ8jBrlSmM4zwZ/fr28GiLr59Fg1vN9JLWdkdNnnfZdr97NppkHo1QaoLh0zWNupF2uoL9oKIqiKIqiKIrSc/RBQ1EURVEURVGUnqMPGoqiKIqiKIqi9JxNeTQ6nUiilV37ffJkpDzUswcdU//F+7MvLmEegrCDeuOJIdz/2Kf9j9sd3C9ZRMTmM+K9hukNLnk60gU8D8/D94/sMJ/NDh64EeJCobBmLKS7532drdjUvvGfWrz/NOli2aNhkaa4P0N1EpG7D9wP8bnL5yGeXby8+u+Ol2DC2RKWz4P1ihbp0Fl3KWJqL22LtJi0r3REJqL+PPp7vASRbOBjv+e8GSF5NjodakfWPVqoEbeM5AFJGnqqE427VhvLbLSwL52/hPlURESeeuYoxBMTqI31SMuezaK/qo/yuBQK/UYZhj6XNedduv3tyqPhpB1x3OV6Zgu4V/+5S2cgXqyY+4yzH6YTYH+ZncXXHcE57szlMsTVtjkHOjQW7Ghtj4WxBzz1Qc7LkpBmxcBwUK1zuczLmZCHKcR5cr09+B0eKxG+P2kspfN4Tdk3ZF9ZD5zt+o7OkmtdAJ4HkjwbfO03o7O+Wv7a0Pb9cusBzHXwf7/jHoh370EPo03Xea6K12121vRoLC6in7C/H9c3N43r4fxlzGlx6uRljC+bc2CphH1yKI1zXkD5GpqUr6jI+RksM49BYx7nDKsf+6NYYfK/twjbvrZHYz3PhkhS7oe180m0aX7jdT1pHcjlcJ1O07VnuA4cc5lJY2azfonNxhs55np14rbk+VLEbKtWC/t0d+6MRmPjOV70Fw1FURRFURRFUXqOPmgoiqIoiqIoitJz9EFDURRFURRFUZSesymPRirliJta0a+x3pASKyTlgnA91H9xbggng7puL4NaO9aU1eumRszLo74uncZTDEP0ZORyGLOuMOXR666ZxyCTQV9HKkW6epLvNUnLyXtDp7J43ssHQX1o20f9Z531oDlsSyEvSlIigoKN53HLDbdC/PyrT18t3zG14dcbz/NWdYkeXZcMeVKyOdK2ikjG0B/iOTiUb2LPLvQhDPThdfWDBI8QySbDgD1CeIx8Hq9TQF6aMMb+7Aemdj0kDX6TtJMRHWOpivVeamCZJ8+Z/qrz58sQ7969C2I3he2dTmMfTqX5dfP6sB5+25JlrMHA6IC4K33v8E2H4TWLxpyVmzE+n8/iGLNpXI+NYG6gAfJ31cMXIM6F5lzhUL4YTg3BKSos7rRE/BpMGfwJm+Z6m8o0c3eY/dyy8ESM3rHJpBCdZsv4W0DzqMU5IdLL1z4gf8hWYVm2WFby94P8d/atJX9m7TYyX+cyzM/YlEuoMYfjYN8w5qNI0b3DS889jwekHBcDRdNfGNB6mCaP2PQ+9IkEDZwDv/KVlyGeXTRzL7BHKDOI+T5a1CPrPpbRpnXfixOu4zy+x6b7F7vrZsLehkQajuOsehh4DTZyQCXklmDfAPsf2K/KnoF9+/ZBPD4+bpTBa2qW5tz14DqaftDNezT4vvK1eDLWK5OPsZ4Xb2AAfZMbOWa1etVXzdd/LfQXDUVRFEVRFEVReo4+aCiKoiiKoiiK0nP0QUNRFEVRFEVRlJ6jDxqKoiiKoiiKovScTZnBIz+WaMWAZNNHfR9NTG4qIWFaBg2gk5PTEB/efRDimynuI4OXk5AQhg2BASUWNBO5kUmHjsmG9dA3k5x4HpqFOmQoZGdkTMmefJ9MO03TiBZYaIp66dRFPAYZzaaG0chscwKsBCOZRdd0/zgmIjyw88jqv5t10zB8vbFtZ9WclMlie4yOool2z65J4/PTkyMQWzGaSxfKSxBPjg3i5yewjEwGjd0iZp/sdNCYzcnybBf7dBRj/+L+5mYSNgoI2QSLhrqlCl6r0xfRpDhbwXEZBKZRO6a2SqfRYJemBJAOJfBjU5mXMtvOS5G5jPvo68AbbmVSYq2Y4ErU5wIyxw1EeB1ERDL8ngJe/yOHDkDs0gYFs5RAyU8wEdpkBg95mifTMBuvI9qQgL+OihKM2gnObIy4jxJsuIxjc54N4za9Z50OwVMeJy4MEhKe0WYLRtLIFRN7p7X1m2GILG+IkpRoS8Q00m6EzSe+5GufYIylNkvTWE+3cE4899KLENermHyPu2MuZSZg27FzJ8TDY6MQ9w0PYZknMRntsVMXIG42zY1mWpSktrKEbeF4RYrJhNzG69Osm32oTkmL25exjLBjJf57qwiCYLWfcTI3NmEnJXTjvmtsnEPzGZvBub9ycj4RkWIRr8N6ZfJ5cCJQPl7SmOEy1jPK8/v59aREhuuZ0teLeX6IEtan9epdKFxt71rNTEh7LfQXDUVRFEVRFEVReo4+aCiKoiiKoiiK0nP0QUNRFEVRFEVRlJ6zKY+GX40l9pb1ab6P+i5OXlYYMJPq7N69F+ID+w5BfHgaPQGDGUzsE5F2rtEyNYDNKurGhodQZ+8ayY5IW0faOE6yk2IduZhatzheR4/nYbM7pItr+aYG+nwF/zbXxPM4sgs1qjlKiBaTtjZMSOZkUWK3jIOay9sO3b7673rV9JFcbyYmRsVZ8bfs24sJ447chH1n327To5FJ4zk3a6gFbtT6IbZt0knSdfbbpsbWyXKyoBK+7qDOt9WhBEY29oVaDf0Vbsfs82LjtW60sW9cWMD+d24Gy6yQ36ZZQ/2yiJmQM5/HtuIEfQ6dB4+JVILO2iNfx+vBk8EUBoZW/SWRg/UNqb94CYmi2HPBvhTWhjdpXg09mlsSviuKeWzTW4wpkK6Nw7p7GgZ2Uh449orwtUtI3gVvp+RlUWzqz416rQMn27OoDJvF/yJi0d9Yjn2lWl5Csr+tIJ/PS2bF68ia+Ax5IDfvv0jQeRsXkj2M5jVJufieg0duhvgiWuFkcBjnkpEDmFyv1sb5yXZMf5dbQB19h+4dvvbCcYiPHzsDcTnA8xgaQ4+jiMj4CK0P1EdbPo7l0Mb7lTjiZMJGEeLV8DOLdbyfcSTT9e+t92hYlrXaR9jrwHB/FDGTE7NvgP0RnGSOvQtJdeBjcBns6+BjsheiTet8UiLCWg3vhzhJIHuDuY4FSsya5MNiLwm/h9ubj8llNhqmx4ITH6/VNnxvvBb6i4aiKIqiKIqiKD1HHzQURVEURVEURek5+qChKIqiKIqiKErP2ZRHQ1qyammIG7S/P2nlBkoDxsfHKbfD/vF9EA8X0U9hk7aOc1zYaVOfd+nSJYznZyE+fAPuUz84WIK4Wi1jGbQnveeZTca6VoY1gtxWuQzq4M4E5nl9+Sz6CSb7cV/wG/ehrtUl/aZNOQki29TvOpxThF4f6ruaNyAlCQLT68wHfuNXJJdb1j5OTeyA1wp50idHlMtERMpl7AszmIpEZukzS5UqxK6H5xwmbFtvka+F+4Yf4LWuLS1CzHpQn7TDjmkRkgp5lc5cQL3opXnav91H/ahDmme/Y3pPCoUSxMUC6pU9l7SdNvstyP+TMv0LKY/7/Xoa5K03ceSKJUmtiKsD9mKRmcFN8KG4Nu91jp+p1PBa1kLS5ZJmNjQMF2L4IdKk1bcizpuBsW2z34LyT2xA+89XzqZxwLkWIjpmkvY/tjgfyNr9g20eIa0f7MdYLpj019x2K8ewos0tnb3i0UcfXd3Xf3x8/Bs+3nprl8U5U3hv/oRbCF5rSrS+9ZFPKUX5iGLq4yXqK1bCxOvQMflaD+zGe4sjY+gbOXjPd0LM/VVEJE25wSz2q1CeFu7T5rgxy6CUWtKh/DM79171udZqNflX/89HjGNcT+I4Xj0P9giwByDJo2HmcsD+ZeY5Q9gzwB4OETMXRJ18LtUqruvrHZOPlwR7RTj/B7Ne3oykOZaPyfcK67Ud+0aS/cZ4Tdea65Pa/prv3fA7FUVRFEVRFEVRNog+aCiKoiiKoiiK0nM29Pvv6k9lXVvahvQTmNDPpX4btzETEWnTloAN+kmr7uFPOyydiv31f9bhtPcBbcPL25B5nrPm6xadVxCYZa73k1W7jT8puvSTo+9SHeqmdKpVx3o1qcxKpQJx5zVIp1ySfvA7ql1SotrK9ravZQvFzXKljGbz6jaH9Tpt8xpT34jM/lenbVwbTfzpsdnC69SiPuyGLClJOHcXj2nRVqN+G+vZor7RodinMp3I/G6g3aEy1tlCMCT5Fl/oKEGaYFlYb/7ZlscdjyOWTrVaeC1EzJ/R1+pbV17biv7XXU73lsbtJp4Dt3O7bZ6jkCypRefcJAlIK8R2NspMkE45EV8/lmuRxIMkRMYv4lSnOElyZK0ZCk835u63JOdKuK7GjreblE5FfGKvQTp1pXE6K1tNbnX/6x5XPOZeC1shnfJoW1eWOXVovokd3kKXpVPm1p98TO7EDdo2mtfkoENb0Ce0S0Dz5vWQTvHtBUunuq//FUnQVq7B3TKkIMB1hqVUQWBeJ96+O2mr2LXYiHTKdVESxOPEcdbeMrfVMqXD68HXgNM/MKZ0CuuUdElNqRrW03FwLLLkiz8fJqzzMd9HsaS163pduQfbkJQ23sC7zp07J9PT0+seTPn24+zZszI1NXVdy9D+p1yLreh/ItoHlWS0/ynbja7Bynaykf63oQeNKIrkwoULUiwW1/0GRPn2II5jqVarMjExsSlT0GtB+5/CbGX/E9E+qCDa/5TtRtdgZTvZTP/b0IOGoiiKoiiKoijKZlAzuKIoiqIoiqIoPUcfNBRFURRFURRF6Tn6oKEoiqIoiqIoSs/RB41r8PDDD8sv/MIvbHc1FEVRtpwf/dEfle/7vu9b8z27d++Wj33sY1tSH+VblziO5Sd+4idkcHBQLMuSp59+erurpHyLovd128OG8mgoivL65wMf+ID8j//xP3ShVraEJ598UvL5/HZXQ/km5zOf+Yw89thj8rd/+7eyd+9eGR4e3u4qKYrSQ/RBQ1EURdk0IyMj210F5VuA48ePy/j4uNx3332Jr3c6HUmlzCS2irLdaN/cGCqdkuVsl+9+97ulUCjI+Pi4fPjDH4bXFxcX5d3vfrcMDAxILpeTt73tbXLs2DF4z8c//nGZnp6WXC4n73znO+UjH/mIlEqlLTwL5VuBKIrkt3/7t2X//v2STqdl586d8q/+1b8SEZFf/dVflRtvvFFyuZzs3btXfuM3fmM1C/hjjz0mH/zgB+WZZ54Ry7LEsix57LHHtvFMlG8GPvnJT8qRI0ckm83K0NCQPPLII5D990Mf+pCMj4/L0NCQ/MzP/AxknWfplGVZ8vu///vytre9TbLZrOzdu1c++clPbuXpKN9k/OiP/qj83M/9nJw5c0Ysy5Ldu3fLww8/LD/7sz8rv/ALvyDDw8Py1re+VURE/u7v/k7uvvtuSafTMj4+Lr/2a78mQXA1u3G1WpV3vetdks/nZXx8XD760Y+qVEYxiKJI3vve98rg4KCMjY3JBz7wgdXXzpw5I+94xzukUChIX1+f/NAP/ZBcvnx59fUPfOADctttt8knPvEJ2bNnj2QyGRFZfx79xCc+IYcOHZJMJiMHDx6U3/u939uy831dECvxT/3UT8U7d+6MP/vZz8bPPvts/Pa3vz0uFovxz//8z8dxHMff+73fGx86dCj++7//+/jpp5+O3/rWt8b79++PO51OHMdx/A//8A+xbdvx7/zO78Qvv/xy/Lu/+7vx4OBg3N/fv30npXxT8t73vjceGBiIH3vssfjVV1+Nv/CFL8Qf//jH4ziO49/6rd+Kv/jFL8YnT56M/+f//J/x6Oho/K//9b+O4ziOG41G/Mu//MvxTTfdFF+8eDG+ePFi3Gg0tvNUlNc5Fy5ciF3XjT/ykY/EJ0+ejJ999tn4d3/3d+NqtRr/yI/8SNzX1xf/5E/+ZHz06NH4L/7iL+JcLhf/x//4H1c/v2vXrvijH/3oaiwi8dDQUPzxj388fvnll+P3ve99seM48YsvvrgNZ6d8M1Aul+Pf/M3fjKempuKLFy/GMzMz8UMPPRQXCoX4V37lV+KXXnopfumll+Jz587FuVwu/umf/un46NGj8ac+9al4eHg4fv/73796rB/7sR+Ld+3aFX/2s5+Nn3vuufid73wnrOOK8tBDD8V9fX3xBz7wgfiVV16J//AP/zC2LCv+67/+6zgMw/i2226LH3jggfirX/1q/JWvfCW+884744ceemj18+9///vjfD4fP/roo/FTTz0VP/PMM2vOo3Ecx//lv/yXeHx8PP6zP/uz+MSJE/Gf/dmfxYODg/Fjjz22Ta2w9XzbP2hUq9U4lUrF//2///fVv83Pz8fZbDb++Z//+fiVV16JRST+4he/uPr63NxcnM1mVz/zwz/8w/F3f/d3w3Hf9a536YOGsikqlUqcTqdXHyzW43d+53fiO++8czV+//vfH996663XqXbKtxpf+9rXYhGJT506Zbz2Iz/yI/GuXbviIAhW//aDP/iD8Q//8A+vxkkPGj/5kz8Jx7nnnnvin/qpn+p95ZVvGT760Y/Gu3btWo0feuih+Pbbb4f3/PN//s/jAwcOxFEUrf7td3/3d+NCoRCHYRhXKpXY87z4T//0T1dfL5fLcS6X0wcNZZWHHnoofuCBB+Bvb3jDG+Jf/dVfjf/6r/86dhwnPnPmzOprL7zwQiwi8RNPPBHH8fIa63lePDMzs/qetebROI7jffv2xf/tv/03+Ntv/dZvxffee2+vTut1z7e9dOr48ePS6XTknnvuWf3b4OCgHDhwQEREjh49Kq7rwutDQ0Ny4MABOXr0qIiIvPzyy3L33XfDcTlWlPU4evSotNttefOb35z4+p/8yZ/I/fffL2NjY1IoFOR973ufnDlzZotrqXyrcOutt8qb3/xmOXLkiPzgD/6gfPzjH5fFxcXV12+66SZxHGc1Hh8fl5mZmTWPee+99xrxlXlSUTbKnXfeCfHRo0fl3nvvFcuyVv92//33S61Wk3PnzsmJEyfE931Yd/v7+1fXcUW5wi233ALxlXnt6NGjMj09LdPT06uvHT58WEqlEsxhu3btAn/aWvNovV6X48ePy3ve8x4pFAqr//3Lf/kv5fjx49f5TF8/fNs/aCjK64VsNnvN17785S/Lu971Lvmu7/ou+cu//Ev5+te/Lr/+678unU5nC2uofCvhOI78zd/8jXz605+Ww4cPy7/9t/9WDhw4ICdPnhQREc/z4P2WZUkURdtRVeXbDN3NTLlefKPzGvfNtebRWq0mIsse3qeffnr1v+eff16+8pWvfOMn803Ct/2Dxr59+8TzPHn88cdX/7a4uCivvPKKiIgcOnRIgiCA1+fn5+Xll1+Ww4cPi4jIgQMH5Mknn4Tjcqwo63HDDTdINpuVz33uc8ZrX/rSl2TXrl3y67/+63LXXXfJDTfcIKdPn4b3pFIpCcNwq6qrfAtgWZbcf//98sEPflC+/vWvSyqVkk996lOv+Xi8eH7lK1+RQ4cOfaPVVL7NOXTokHz5y1+WOI5X//bFL35RisWiTE1Nyd69e8XzPFh3l5aWVtdxRVmPQ4cOydmzZ+Xs2bOrf3vxxRelXC6v3utdi2vNo6OjozIxMSEnTpyQ/fv3w3979uy53qf0uuHbfnvbQqEg73nPe+RXfuVXZGhoSHbs2CG//uu/Lra9/Ax2ww03yDve8Q758R//cfkP/+E/SLFYlF/7tV+TyclJecc73iEiIj/3cz8nb3zjG+UjH/mIfM/3fI98/vOfl09/+tPwM6+irEcmk5Ff/dVflfe+972SSqXk/vvvl9nZWXnhhRfkhhtukDNnzsgf//Efyxve8Ab5X//rfxk3hLt375aTJ0/K008/LVNTU1IsFiWdTm/T2Sivdx5//HH53Oc+J9/5nd8pO3bskMcff1xmZ2fl0KFD8uyzz76mY/7pn/6p3HXXXfLAAw/If/2v/1WeeOIJ+U//6T/1uObKtxs//dM/LR/72Mfk537u5+Rnf/Zn5eWXX5b3v//98ku/9Eti27YUi0X5kR/5EfmVX/kVGRwclB07dsj73/9+sW1b12FlQzzyyCNy5MgRede73iUf+9jHJAgC+emf/ml56KGH5K677rrm59aaR0VEPvjBD8o//af/VPr7++XRRx+VdrstX/3qV2VxcVF+6Zd+aatOb1v5tv9FQ0Tkd37nd+TBBx+U7/me75FHHnlEHnjgAdCI/sEf/IHceeed8va3v13uvfdeieNY/uqv/mr1J7j7779f/v2///fykY98RG699Vb5zGc+I7/4i7+4uvWZomyU3/iN35Bf/uVfln/xL/6FHDp0SH74h39YZmZm5Hu/93vlF3/xF+Vnf/Zn5bbbbpMvfelL8hu/8Rvw2R/4gR+QRx99VN70pjfJyMiI/NEf/dE2nYXyzUBfX5/8/d//vXzXd32X3HjjjfK+971PPvzhD8vb3va213zMD37wg/LHf/zHcsstt8h//s//Wf7oj/5o3W8DFWU9Jicn5a/+6q/kiSeekFtvvVV+8id/Ut7znvfI+973vtX3fOQjH5F7771X3v72t8sjjzwi999//+qWooqyHpZlyZ//+Z/LwMCAvPGNb5RHHnlE9u7dK3/yJ3+y5ufWm0d/7Md+TD7xiU/IH/zBH8iRI0fkoYcekscee+zb6hcNK+7+LVLpGT/+4z8uL730knzhC1/Y7qooiqJcdyzLkk996lPyfd/3fdtdFUWRer0uk5OT8uEPf1je8573bHd1FOXblm976VSv+NCHPiRvectbJJ/Py6c//Wn5wz/8w2+/pCyKoiiKsg18/etfl5deeknuvvtuWVpakt/8zd8UEVmVOCuKsj3og0aPeOKJJ+S3f/u3pVqtyt69e+Xf/Jt/Iz/2Yz+23dVSFEVRlG8LPvShD8nLL78sqVRK7rzzTvnCF74gw8PD210tRfm2RqVTiqIoiqIoiqL0HDWDK4qiKIqiKIrSc/RBQ1EURVEURVGUnqMPGoqiKIqiKIqi9Bx90FAURVEURVEUpefog4aiKIqiKIqiKD1nQ9vbRlEkFy5ckGKxKJZlXe86Kd8ExHEs1WpVJiYmxLav7/Oq9j+F2cr+J6J9UEG0/ynbja7Bynaymf63oQeNCxcuyPT0dE8qp3xrcfbsWZmamrquZWj/U67FVvQ/Ee2DSjLa/5TtRtdgZTvZSP/b0INGsVgUEZHb3vGj4ngpERGJowje46VSEIf0uohIJp3Fwl0sPgxDiAPfh7gTdCB2bLP6to1P25wmJIywjE4bj8nP6nxe6VTeKDOKsJ5RHOAbYmwLI3OJ42BsmalN3JjOiyrqU9vxefO3EHZsXh9L8DM2tUb3q4Hfka9+6rHVvnE9uVLGO3/wTvG8lWuewmsfp6gNPYpFRBzus9R/LIyXKm0sg4bL8PAOo4iMi20WBXhdFhYq+P5MAevkpSEO6Zq49LqIiBNjvZwI++zYME4CA/0DEAcx1jFK6BudThPiubkZiLM5HNv9g4P4/sVZiC/PXjDKuHAe/9ZqYT2OHLjjan3abfmDj3x0S/qfyNU++PCBAXGd5Ws82JeB90Q07IMA5wURkVoT/3a53MJjRHi9x+j0bj1Qgnj/nj6jjEKR5igLr83CEpY5svceiPM7DkIc+Pht1f4b8XURkf4S1iObxX7q0VzPcw3HIub4bbex3n6IDe5l8Hp4NFfzd25R1BGG15yLJ1+C+KuffUxERJrtQH7uY5/d8v73bz/8W5LNXjlPnGscB+OkL575m0dznRB6HeOI5go3aQ2m9cynNrXoSth0DPMbc6xEUuYvh8qMaJ2PorXX4Njof+vD90AWta1tcVvzvGpeIK5FHF17DW42m/Izv/irW7oGf+m/f1gKK3M9ny9fN8s2zy+0cE11Q5oj67jOtKpl/Dzda4X8eTHvI4uDo1hmFtvLSeManKJ5w4rpOiacl9DfQrrWYUB9OFj7FwCzr2wE/Az3eSNOKCMMqN4hxnbXNa41mvLQP/mFDfW/DT1oXOlAjpe65oOGm8KFxQrNk+D38IOGRR1EaKBGFk+sm3/Q4DK4mtyF+MaOz0FEJIqonjEtkvygwU3jfuMPGvFmHzRoIhZJeNCwrj3JXeu414MrZXiee/XhYL0HDY5FjAeNlPGg4UHopalNabikMnhDv1yttR80vDSWkaLY8+iBnR80UmaZTozH4AeNTBZvwLK5HMQBPRjzhCQiYlNzpjM4DriMDD14pFv4eiptnoebwvNwaXCmMubY26qf8a+U4zqWuM7yeE+5OO55RPECdeXz3Tg0XxnzDx0i7eEfsmmzn+f4b3Qj12zh6zl6KMjTtfPpQaNYML9s6SviYp2l/rD6BcEKFs9PPLtYCQ8aLTyG+aCB9fbSr+FBo4M3LrU8jpUcjdet7n/ZbEZy2Svnuc6DRsK9zDf8oEHrhpu0BhsPGvyQ+Tp80HgNeYvXfdAw2noDDxpcrzUeNFaPsoVrcCGXlWL+G3nQwM+4IfcfWu9C+uKN7rXC0OzkIa25xRzORS7Nbw59AZ5O45h/LQ8awXoPGv7r80EjoLZb60HjChvpf2oGVxRFURRFURSl52zoF40rxPHVJ26WRgVBsGYskvRTLkkPIv61gcrwSQaVIE1IpfEJ2OGnbndzT/8OfZUbi/kU6NDXjvTrnuTy+MScT+F5LzXqEHf4lx0RsfibAId/RcF6deibOZaAWQnSBJt+JuEnVfwGZ+ufUQ8dvEcyK99qN/wGvHa5fBnidoQyCxGzzdIOfjvJv5jl6RcNP+T+uGSUUac+yb+w5fsx7nQWMcZflsWiOraNb4BE8vRTsE/fgl2Yx7boCJYZ29SnE77dC+gbmcjF8wyoP12aO4d1uIyyqPkFlF6JiCxWFqgeOJa7uyf/ordVROJKtNL3W/SFuEVjkH8FEhEZ6sex3/Zp3Pp40Bv34i8FN96IkrT+XMJ85LAcC485VMA+xPraXTffDXGxhFI7N43nICJi8TfGxjfIXE/6FY2+Sbt46hmjjOef+N8QL83PQ9w3MgHx1L7bIJ7ccwTfnyB95F/Vpm7Az3Q67xIRkVq9IfKvP2N8/nrTaXdW1zSbOphPY9Tln8PE/Jadv+XkXwbM92MZPsuERcQ2rj3/WkVzyXrv3wBJv8KudczXUiZ/xvgmd51j8vtZ0SFifoNsnpbV9V7zPuF6U282xFq5fhn+BZqua8xaUhFpdVA6XJnBdaF26SLEzTKO8SDEBdLimy0RSblYr7liCT+TxTm1NIbzxtAQzofpVD/Edtr8ZZ1/qQlpbEb86zat0QErUtbpz8uQSmWdsc0SxqQxY46La98Tslx/LfQXDUVRFEVRFEVReo4+aCiKoiiKoiiK0nP0QUNRFEVRFEVRlJ6zKY9GFEerOuR2G7VyrBdkraeISL2OXoRWC7XE2SztBkD69MEh2pYsoQyWboe0M0mrjdun8c4JrE9P85aJHtZJRKTTIT+ARVptKmJ4ALeCrDZQtxgn7Ajlk4a5Q9rYkPSQgY+xFbMGMGH3ANIR8rl2t7edsCvM9WZ68ibJrewY0fKr8FppEPXWS3XUdoqYnh6W5bZ87BuSWvt1z9hnSMSm7hGTFt0i3bQh0yUdL1/HTsfcKSflYR8NSStbqaGXJHaw7TJZ2sErQa7Mu3tY1DiVeo3KxLE+M3Me4oVF8/rUajiO8tkhiFNdOxfFoan/3Qo6QSzRSgN1qAq80VnS/JSmnc52jqP+d3gQ2/XwfuzXO8bw/W6M7SwiEtPckO3bhcfY/0aIJw5gnOsfxuPxTkQJ2msxtjANKV57d63zJ78O8d/8yb8xinj1hRchbvs0J0bYtnb2LyDee/AOiB9883cZZew9gJ4MIV9X38Dy9bA8s923gjCMVnX87NHg/pa0vfJ6u07xOs7vX+/zIgnePnoP18um+YrL4PNKKnM9/wR/Zj3/xEZ2/VmvDHM3HtoCNdGL6VCMr3d/ZmM6/t4SBOGq/zZibx/d9zRpa1oRkfOnj0F86eRJiP0qriOtBq65lSZ6MyWhj5cKtFNcEeuR70ePRlhHb2CwiPHgjj0QD4yip0NEJHY5vcPa23Xz68a9iVFC0h5l3P9w/mM/1Xq7UCX9jT0a3WzGI6S/aCiKoiiKoiiK0nP0QUNRFEVRFEVRlJ6jDxqKoiiKoiiKovScTXk0PNcV94puf519qdmXICKSzqCA3aa9h/0AfR9t8lOEtB98jjIcL5dLBZPEjH0HrKPk/B/tNurGfd/UyEfGXuLUNqQnj308Zi6Nl6HBG/SLCGv8OD+IS36WTJrzZvCey+alN/R4dI1TXbk4Anvze51/o0yM7ZR8fjnbbxBh35iypiGuN80cF5Ul/Fu7hf2t3kKvTLWB72900NtQb2EsIuLTteX8Jpytl5OMcnbfLO3Z7VumrtKlMqKQcqg4pKWktgv9tbXEIiJsibAoi3rMRQSoYQ9a2JZhQtvZtIe8RfNByrna52Jn6/ufiEjQlUzIpZw8POX5HbOOrQZpjEl/vmsKPRj5PPpv2DNWLGCOCxERJ4Vet1xxEuKJvbdhGYPjEHOuIIv6U5KJx8hCTR4wn6734sUTEP/dX/xXiL/6OHo2RESqNSzkXIV8abQGhRHmavnic38F8ZNPPW2U8X3f//+CeGwMPTK1mWWfSKOZNEdff1zHXs0HFBkaadJXJyq96XhsEjM+Qr4DTomRsNAb7+H5ZJ01mvNSGXnCE3TjfC9hTmG8/q2dM4rzBokk5cUx0ovjMXlyN/KJGEWIsLeJ84B19/GEteB6E4bhqjY/DLD8ehPXz7MnXjI+f+EkejTq8+jJqFfx/M9cwpxPlTa+7iVYRUdLeO+zg6av0Mcy3QDX7LCJH2jWKWdUYI790sRO/INLOeLo/ewBingBTYB7JI8rHheml2n9+YCPyfcv3YdcL3dNN/qLhqIoiqIoiqIoPUcfNBRFURRFURRF6Tn6oKEoiqIoiqIoSs/RBw1FURRFURRFUXrOpszgqVRK3BVD8HomatMgbRqp10sWxEnoGmQ2qtXM5Hlsbstk8hA7Nppr10s6wsmCQjZGipkEkE3tfXk0FLZbaJStLKLhKYjM5z/D+EfG+DDieq2dIMswzYuIbbFBDt/T7lxtiyDBFH+9SWdTksktXz/qbhKGeJ1yJbzuIiLDxVH8DCUZ86lNG228TvU2msgWy5jYR0SkRobyVpuTVKIZuLyEieu8NPbpUhGTO8ZJeRIpqZhvY+MstMsQR5RpLiATdoeScYqYNjLefMDzcNzlXeyvA3l8f84pGWVEIW1okKKEfV1GSTYsbxWWdTWJlk/JFJfIRDhfoUSeIhLTSBwsYPKop5+fg7hZw/70jx7cD3FqgLJKiohF139p9lX8zOlnIc4OTOHnPfOY3SQlFG0tXYK4fOF5iBcuYpmvPvtViL/2lZchnq+YxkWXNkoI6HuyFGXL3E3JDU8v4fg9P08JOkXkqa8+CfHevZisy20tm9ib7e1JGOnYlrgrJuN1k9QZDn1zPePYpTY112QsM+0mJLCleZUTy7kp+ozNJlZKLkvHt6yE25Z1kufxfUFEfZjf76XMMiwyX3PbRFRTNpybG+Ykfc9LiQVpXbe6kuRZnDBvK7CiVRN6h6aBhUuYlHX2DBq/RURqtGYu1LGNTpzDMVptYP/MptFkXQtocw0Rac1hmy0s4bo+OcqJBvGesF9wXgjCCxDHoTlvRCGW0Te5D2I3i8mmfd5Mh5LrJXnDLRqbptUbzzviDZvoE2ZSQZEopM/wZjbd5W3AwH4F/UVDURRFURRFUZSeow8aiqIoiqIoiqL0HH3QUBRFURRFURSl52zKo+G4KXHcZf0uJwfhBHDNtqlj40Q8adLbBZQVjLXi7NnodEy/RJv+1miUIfYo645NGlX2JbC200nQhnPeHJuSVRWM80Rt22IddYYpFxNuiYg4pOkLHLp0pFtlvS7rQd0EfafD2lhqC6ur7Swze9F1x7IisazlOrouXbcIdb9ugo7XTVMb5Dh7HoadgBL1UPv4o2b/a5IHo9nERGVRjJ8JQ0pKR0ktHdJZdprmuOrwuCBd72IVk+V1KOFQo46vnz9/xiiDRaPZIur4c+QtsbOYTHMgj3GUoA+1Bft9FGDc6Uqg2GmZPpKtwJar3aTj4xiarVJyz4TvcW7dPQHxnuFhiM9fvAgxz4k8JisVU6PcJp+Ql0NvkpNF70vQoARW5NHgvGNRYGpzZ868APGpp/8c4uo8+kSqC9jn0uTpmW2a/WNHP9br0DT2uTPnsf1jMnIdGEM/THYY20VE5KabD0L88gt4XiPF5fHX6myPR8OyrNW5nZPPGheKYzE9GSz03piP4CrszRQRiWl981J43TiPWNIxujHXMvP9/B6L+yzNu2xa5ARqccL8xO/htuHY8HBQ2yYlPOPz6HRwru7+TJKX7noThcFqAuImrUUXz6FHo1U11yppkcdiHsdseQHnov03YiK8sR2UcDmNyUhFRI4e5cSBOKdOTuC49zK4ztjkO4rJb1xfMr2Z8x6eV2FwDOJUDr2W7Rj7SmThuIwThl1M3l3uP7GQT5q6V0j+C9835/GA/ubRfaLbNX+oR0NRFEVRFEVRlG1FHzQURVEURVEURek5+qChKIqiKIqiKErP2ZRHw3NdcVf0ay55BFi/yDpfEZGYBKGmRpE0jxksgzWQSRpH3g878FHHaNH+2Y7LXgd69trAvuESYJmZFO7LnE+jPj2gXBxeDrXDdpCw/zl5NGzyu0Qh76lOey4b+yGb+t2YfDY2XWPXu3pelrX1Hg2/05JOe7lc7n8RadlDFgKLSEQax3ZA+5yTptmn/eAdajNX8DqLiFw+P4NlNFAvOjyAetChEmr0vTz2hXyW8sAYJYrY5FdZIs8F6z1tykdQW0DNaXkaczmIiDQphwjnXCmyBpXGWaWBXpVqrWyUYVl4zebmsB7l8tW27bRNf8xWUMo74q3kLZmvsu4b2+TQOLaJiMh4EeePxiKe43AJX7/jdsybEVn4enkpQQcd49iYOHAnxCPjN+AxfdRJt5vUf2godRLy/HQi7NeNNtaz7WNcHMTcQjffisfzs1gHEZHKAnmLWtjHWgHOcZU6tk33/CUisnTO9CLFt2NFdpOn5uUXlvN/tBP0zVtBKuVJKpWc5yRm34E5xRt77zvs4zCnTcDjHBhJ72eDBMEexc1+3RkZOaM24tGI6fW1824knRZr3DcL+2O4zGv97Vqvr/fe64EdxWKv+FcWZ3GtW7yIc1mnZo6RNs1NS5xjpQ/9Y6cuYx6q4+dxPc3l0BshItIo47Xt78c1tFCg+7M8zl3ZHL7fEvJmdsz8SNUy5sMqz6IvZJDyaIiF9WbLLHs2RESCdfLOCXlJuHtEbG1KuH+mlFxi0X2n48SJ/14P/UVDURRFURRFUZSeow8aiqIoiqIoiqL0HH3QUBRFURRFURSl52zKo9HxOxJd0Tay5p88Aem06WVgzXtI+7GzPiybMfV33SR5NGzK7+CTNph9Hp6HTRD6pHMjYVs6ZeryLdKLT42j/nhkAPV5jRZqh4tF1L9LJ0HjavEe3LyHNtaT9aDtNr7eTNpDmZSptqAmOparesnQx9e2gvmZWWnmluuQzaKuMkN9JUkmbJPHgqw1YpNfp7yI3oX6/CLEjTJdNxGpNbAvZEjH2IgpNwztRW5lUJM6vAM14v2FfqPMFO0Dns/iebQpb0aK9t/PUf8cGcQyRUyPRky5YlwL61ClPd4dD7W12Qx6NkRELAvrOTCAe5FXq1eP0Wy2RQRzNWwFnh2Lt6JbLTewvvkUtns2QcNaX0LvQUj75N90EPeN7y9hu7aalHMnQ9pfEZk+8BDEOw8/AHE6Q3MYzRVRC+fIgPLJ2J0Eb5yNfSp0sAyX+mgmi+O1bweexyNj40YZzz2D+/SfOINtuW8S6+3XsG1HhgcgzubM84gC7Oc79+A+/S+/8LWVf63tQ7hedOfR4DU3ohxPVkIeDSMvBnXR2MJ1gSTaEjZp/mKdeEIZDvk6XFpzI/KM8Rou5N0KQjN/RESeMPZksD+Ubl/MnBjs1RQRKsI4d15zjZwlRBia+UBsumacK6X79SDY1O1bTwg7HQlX/KH1efQl1ObJ22UOL+l42GYhxQFd+9HRXRBzXofTJ44bZdQWcQ0d2ok+yIjW4EYT2zxHntl6A+dcR8w+77g4R9bK6F/J0rrtUi6jlMtjwry2hiUnpvuzDq2pfH9D3jo3IVkHp48x8qV1j0VLPRqKoiiKoiiKomwj+qChKIqiKIqiKErP0QcNRVEURVEURVF6zuY8Gu32qoaL8zIYWs2aqd/i/b95L37WlPmk82ZPBusXRUTSVIap1afcCQHqJP026vHSpCflWMTUd44Ooh7vwXtug/jVk7h/+/HLuP90nLCHcsx6OAdzc1gkrovo+kQu6vmc2NQZ8v7kQRv9A3aXBjjYJo3yFZZI687xwADqsUVEsjlsM4dzvfBe6xR/9ckn8XiW2RfS1CdfvXAa4rCNOspCH+pBJ/fsg7hDHg5n516jTD/AcdLqYNwhn1GD+ldI+QeaTfRTiIhw+pgc5fdI51CD74RYBvuUkjw0jSZqaz3OcTN29XrU6/jerWKuFoi7YiZrkclnpA/PeahkjuN2Bcddrh/fs2MU2zUmPXqW94138f0iIj7l4anR2IjIs5Mr4lixKFcHz+SRmP6sdL6P4hGIKxUcB5zTIqC2zOXN87rxIPol+iknzezFyxCfPYVjp91Bz9XkhDlHDAzieXgWjp1779otIiKNViDyF68an7/exJazuj5YZGq0yTdlJYjkea3yaU018mxcwDY79nf/AHFl0cy545FHLE3zbq4f18f0QIlinCuyg/h6huZMERE3i3N5RH6pmPqX79P9C33n6oXmd7ApD/8WctsZvhAc65wnSKyEXCw0MbJEfqOvXTfiWOKVe41WFf1MzRrnlzD9rC7l/+rvUDs72Cb3HUF/xcgwzk1f8Eyf5IlX8W+OS74jB68be2bPnL0AcRjifDfYZ3qH2SO6NDcL8cXzOE6yOZx7ioODEI/tQm+KiHn/vHgJ69mq4Vj1yTvsOvj5Ur85/7Ffz+mj93R78ZJMONdAf9FQFEVRFEVRFKXn6IOGoiiKoiiKoig9Rx80FEVRFEVRFEXpOZvyaNhOLPbK3vBhiHpDl/SLSVvscp4MVoQGlFeD95lmDWSzYerzqhUWf3OMx0i7+PrUKO25THXwk/bwbuHf4hZqou+66QaIWb/n11AT79qoaRURCencbdIE8vVgsa3rol6y08Q6iojYPvpTdu/AvZ6/467bV//dajbkyT/7/xnHuJ7kcnnJ55fbhjWRDOsZRUzNf8g+FfK5DA6glrhZx/524sRJo4xDe6fwDx28tuU51JG36qgzbzfQe2CR7jpO2Pt6xyjmm2DxLiuBO5QXoUPjzkrIT+O3UKdar6Iet13Ctm118PVsmq+HKTD2W1iPFuV6cTNXj9HCrrplXKr44qzsZZ+i/f89l3PXmHNFSPlvRkZRA+uRftiK8VqlU+hd6Bibq4vUlzDfxOVzqGkf3oE5KjzyS/iUS8jvcH8x9//v1MoQu4Kf4bQ9to39xSJtequFnxcR6S/hvOi3cWy1q7wfPtazUiMPUOkWs4xh9IGIj7kCsrlljXK0TV/RRXF8NUcEzVce770fmdcppgvhUd4Gl+aGY1/+EsSXvvw4HtBPWA/pmD61FaWEkpSQJ4j8FnYfXvf0CK7RIiLDO/G6De/GebhvDHMDpQdx3LUjvhcx50C2TiblKemGfZJCeU4cx/w8D2f2pcZdx4hl4xr5XuE6nngrhr065YxqtNb2PoiIpMnsV6NcRC3KXzJPXrxL5TLEds706ebIK5eycRyEdK1nad44fQznz71TJYjTaXPtiip4P2VRXrP5Obx3CKNzELvkeTx/9pRRxvgo1mPmMh7j0ix5fQOsZ4lyeQRDZk6uTAbbMzOI4yg3cnWc2U1z7F8L/UVDURRFURRFUZSeow8aiqIoiqIoiqL0HH3QUBRFURRFURSl5+iDhqIoiqIoiqIoPWdTZvBGoy6Ov2wA8n008bA5N83J0EQkCimJHCeZixIS2HTB3is7wXHOiQQ5QVFE5rX+IiYHevTheyFemEczYL1tGrBcSnJyaA8az4oe1nP/9CjFaOa9eME0arORPktJaHbvwjIDautLFy9CbKVMw/lgEU123/u2RyC+48jh1X9XKxX5/xpHuL6k0ylJp5eNq75vmkW7SUrmyE67iBLxuC724UIBDVo33oDJ9C4dNxN21cpo9naoj9ZraArLZtAMLjSu5i6iMS2Vw/4qIuJQH0/Te3KUTM2m7xdiMsW7CckYbfpbJ+Qkf+jObvlo1G2RmZyTwomIRJTkzxacQ1pdpr1GY3vc4HFsSRwv18P1sL4zFTJAm3ntZA/uryAlMjjzhhcLZTTVnzuJ/SuXMZNi3XkHFhLRtWg30QTYoU01eOOJkJzPtao5P5185asQ1+cxKWk2i4b0gI6Zz+PY6/jmPMubgxT6cOzkCxiX+nB523v4IMQ33vkmo4xUGo9Rq9PGHStJsIK2abTeClzHEXdl0wHDOExrX9KmDhEl8aotYJKvDF3bhePHIc7SXGEnbOoQ0gYW4dp5ecWm6xo1yLxLSUuDC4tGmXPH0Bg7k3kK3zCOa+7kG3GdHz28H+uQYNS2qeYhbaLBY/fKxjlXXzcOacCbIhhJALvjjRywxzieK85K0uIUrY812vyiUjEnQMfDcd7m5LFpnCcefxqv68LcJYjHx3cYZVy4iGP2wBRe+1od+9fsHM2PdI83MIDrZypjtnutgceIQpy3OZ91o0nrId+vzc0YZVTncQODkWFsq5EhrOfSAq6Rs5cpESFtpiEiUijg9XGaeK67Slfb2w/WvgfrRn/RUBRFURRFURSl5+iDhqIoiqIoiqIoPUcfNBRFURRFURRF6TmbS9gnzqom0yWNtU+a1chP0LDGqD+0bDxGRKnFXJsS3JAW3U2ZSdvYtlHIYhkH96FO9/5bboK4mEFdeNjEhDG3HMHkeyIilSXUtaZIm3l5BjWDhSzqqt/68AMQf+GLXzPLoGRxt5Om9P6774I4ncLzvnwZtY2ptNl2feRX2TFYwmN2NW47KSPjdcbzvFXvRauFGkjXXb8rN0iL7geoKS2mUC/KnoFMGtu0XjO16jM+Js0ZGByEOKBn+/OXSE+6ZxfWuVaFuJWQpNLvoHYzFmwbickPQW3lWCQgpWR7IiIRJV9if1WL9MrFQdS6ew7WISkhW6uD1yOgZHVhl9g78M1kUFuBH4pcyY3ZbuIcZ1mcmczUr0/QtcjmUOccRviZZ49hf/rsk+jRuHE36nJFRPZMoXbXtnG+KfSjJ6xJc1xMfpzIxrni0gXUTYuI/MX//grElXnUAx/eNwLx5AT22XFv7bEmItJq4FhwqE9lsljPbAaPOTCMZRb6zIRVRiJJWnOCTmfl/9vj0bAse7WfsacxIE+GzcJwEbEpEZ1NSTIvvYrXrc/D/lWO0bPo2GYZFiWSC6leEWXsCxw6BnsdaM502fQhItLGz1g2ruPNyziOvvKZz0N8Tz/OV8PT6HkUEbHW+V7WJn+FbbEXjpLvbcBjYdvs2eiKrbUTBl4PbM8Ve8Wjsfsg3gu9+NzLEM/Pm/NEm9qgTH6IeliGuFDE/pfPoP/slZfPGmXQrYF4FiXqpKSntTKO5V0T6FXNF7EvOY7pPWEvcKeN65NNSVGDJo1DOl6zY46r+SU8Zi6HxxwYxvNyLJwPz57FtWN41PS3CHlGffIqhV1xuE7Cym70Fw1FURRFURRFUXqOPmgoiqIoiqIoitJz9EFDURRFURRFUZSesymPhhVZYq3skZ1NoZbLIr2W45n6rYj0r5aQbruD+/6mPdqnmvYatjxzD/kM5U/IZ7AeQQN19ZVLuN97ijT1aRfr/PwztD+3iJw+cQLi8jzuTS6kUb3p8AGIH37L2yB+8J7bjDIGS6hVHB8uQTzMfgryYNy4ZwridmBqjNsd1B76rTa9fvV6dJI8ONcZy7bFWtFCcp4M1rtGCXvIG5pZ0jBHtJ97m3wJvLW63za9DC1BX8HNt90O8dA06kU/+7//BuKZWew70wXS4MfmeQWUsCEgDTNvd53OkX8iTZrqpukDOf/qMYgbpFFfJM/GLXfeCfG+vXshrliotxcRcS1szw6V0Ymu9sekXB9bQbXtr2qxec4bKWL/2rXDzFVTGsY5a47yZNQuYPz8CWynOml3T10w90I/ehTno4Mh6dcz6NHo0FyQzqDWt1rHPvnZz6O+XUTkr7/8IsQ8l1ycx7l95w485s5JnHf37iwZZaQF2yJD+9mXBtFzEfrjEOdpLGUy6GURMXM9CfWzK00VrJ3y6bphWdZqrgUj54KNa5WXMFccf+Y5iPPkv5EYr32URl+LPYjX0U7IfRXSvBg3aZ6McF1xbXzdprnbFcqr4ZjeJ/arWDRZx+T/jEnIf/YU+gk8yhEhIjKQx7+5adTuc+4N36I1kuYLKzLnMMeY18gj1DVWw8C8vtebbo/G4ATmpxjdOQnx+ZPoPxQRaXbw2jUpR0qDfHq7KefYG25Dj+3fP47vFxF57iXMQXF2Du/5clmcm7I+eYNpXHku+ZETcvxEIfs46DqSXy9fxLWhQzmjcrzui4hPc9PcPM6HnA8roDrNL+CNQN+86TXpp3oMDdIalup6ZAg2/vigv2goiqIoiqIoitJz9EFDURRFURRFUZSeow8aiqIoiqIoiqL0nE15NFzPFXdFn8d7QgekW2t2zH3yHRf1yRbpJvO0L/DYMOZ1YN19uWrq8zzS4TukW+s0UWP60suv4AFI69k3jPu/v3LsVaPM2hLqpPO0F3Ehhzo3m7R2Q5S/IoNSRxERKWbwUsWC+roG7YU/XyVtd4v2/I9NbW3awbbjehe79vxnj8RWYDuuOCs5INJZ8hlQfYIEETU/VcekR26xVp0+4FDukFSCT6BvGL0wt9//JognF1Av+uTXn4F4Zn4R4uld6G3IuqihFBEJqE/7MfaFdgf3zy6Q/jjjYn9szOH7RUQunj0OcYv0xecrWId8H2q7R8ewXSzHPI9MjvJ7eDiHZLv6o5s2/VlbQScMV7XxORovE0PYrrt24n7sIiKFAp7js8cxv80rZ1GzvOTj+7ndWm1znj15BnMdZCivT5TBXC0dymlSLOFe9RdnyhA/8fUXjDJrdcprQ+Px5ByWMV/HOfOlMzguho6a/eOufXjMW24sQTw4OQ1xJk85bGzS3dvm92wR+VmalCun1Vju5+329uTRsG17dc/+iPNNkEcjmCOvoIiEFzFPhl/Ea92hcWXvxT48uBvXKvHN62S3ydeyiGtRo4z1arZwzovIIxa08PNR25zb3RCvpRXhebR87J+pQdTAV5dw/jrx1PNGGaOk1d+xG+e07A7sb6k+8hGStyRO8GhEAeXWoPsZ17l6Xo6z9bmsYkskXpn/Ipr/7rznDRA350wP41NfQ69fljyw6RTl/EnhPZ7l43w5NmT64M724zhv1PEYi2Xsb8PD6O1Kkfd3kXx0ftu870zTuOG8USHljskV0Bu1NIdrdhiZ91cezf2chqVR43GBZXppbJe5iunRmNiP+WPGpnGt8Lvuj4MEf9a10F80FEVRFEVRFEXpOfqgoSiKoiiKoihKz9EHDUVRFEVRFEVRes6mPBph5K/u/Wzs9kz7BmcSNNgx7bWf8fA5Z4B0axOknWs0UEe5tFA2yujrx8/ceghzVrzpPtzfv5TCetbrpAelR7HMX5tNNlhCPfru6Z1YpwLu154jf4FQHoR62dTWpvNY7ouU16AwgnvjX1rA85ivos7QiUxt94P3YNvkR9Cfkupqq1TavL7Xm+48GuzR4P24k/JoZFPYv7JZ1Hf6Keyfjot92iVNato1dZT7DmHejP4x1I03aD/twR2ogT49w3uPY5nsQRIRiTp47s0mXvvLs6hr9ei8srRveLNSNssgPaaXRq12o4Ya0wsXz0McW/h5O0Efz3uNeynyJ3TvQ29upb8lpBxrNY/Grh3YBvt34Rgs9Jl5GgqU+6GP9lNP0ZyYomuTIh1+KsJ2FxFZWsL+cGkG46lDeIxOG4+xeBnn2cUFjCt1PJ6ISM7DY37HTTdB/OoCejIWF3Gv+2IRx2a7Zep/Mxmcc4r92L4pyu3E29375HdpJ2itW3X0ByzMYD8OV/IvdDrblEijCx5DKdL3v/o85jYREYk9fE9nAOewmoVr0cxlvE5pF8sslUwfkhNiX8hOoA9kMEYTot3AOoU+ec5aWKfWHHqQRETqc3MQV8gD5JMnMb8D17ZsEfXr8YWLRhlnjp6E+EIWdfl9O+g8aT4Y2YlrQWHSNGOGNB+0qd7duVM4j8pW4Eci/sp9XBxTHqFJzHlx8Pb9xudfOYltGJbJZ2Rhf1y6hH6K4yHetywummN4NI3jfGAM54XhAZxrLMoJdmEJ58M5ygszUjDvAQeKJYhr5JG1qE65PupvMZ7XxYt43iIiGVrzpiexD/OSmqFcVfe+8Q6IS/sOGWX00ZrFPpCwayiG5i3kNdFfNBRFURRFURRF6Tn6oKEoiqIoiqIoSs/RBw1FURRFURRFUXrOpjwagd+WWJZFW57LHyW9YGDuM54iDbZFuTZumERvwz13HIb47FnUyy5eRu25iMiBnagTfOT+uyGeHtsBcamI+2nHgvX2Q9Tn1ZdQ9yYiMjqCx0x7qCXutPAYGfI3xPS4Vy6XjTJKBSxjqYJa4pOXUI97/jL6PGYWUbP64H3YLiIikxOoGc1kUMtod+n17ASvwPVmdm5W6o1l/aRDe5KnyGuTYR9Mwns8j3KTWHjtberi2RzqF0uUY0VEZHwKdbi5PH6mXq1RjHrQXB61m+ks1rndMTX5nTZq5qvVMsazmBeDc+C4Dp1oaI7dIo2TJuUpabZRR3327FmqE46bsTHUL4uYvhrf34QIdIsYL2XEWfGKHNqN++YPD2EbeSnTw5NJ4zlOks+jWsZ+G7ZJE5/FMXm5jHOiiEiTNMW5wd0QT+w6iK9TH2vVyhCPxPj6nmnca11EpLOI+SbuncT37Bkfh/gFTMti1KGUM+fZ6Wkcb8UB1MS3fBQU11skMCYfYatmlrE0h/22toTa/+yV3E8JeYi2mpgE1Cdeehnic8fRxyciki/gvBm2sQ1qgh6Bi2Xe/x/ns6ZnzhWL89gn85SPySXzzL6xEsSZPN1LhPj62I24t7+ISNDA8/j6338e4qiBc0lE58FrQdvwyokM0brtkUdv8dQZiOdPnYL4QvEliDNT5jgavx99kn07aZ7sapor92JbSRjFEq54NGzyiPAtgZWQ52NsDOe7fkrLMjeD/pw25fi5cJ5ysgTmfYhF61mL1ibbK0HcpDW1QnmpBqn/5geo0iLSJo/FIvXHYgbXinZAfWcJ6zg7Z85NwyX0BDXJY+Y3MY48jMeKuLaUhnH+FBEJKN9MFLIP6GrMHp210F80FEVRFEVRFEXpOfqgoSiKoiiKoihKz9EHDUVRFEVRFEVRes6mPBqWfVWH1+6glov3JB/Im/rkXVNTEOdJF9nnos7N81GnNk4atXtuNvdp3j2NPoP6/AWIX5zB/bEP33wrxJksaljbPmrqbzx8o1HmAu3rfXkOvSOlPtRu+wG21clz+P6nXjxqlPEPj38Z4jOnz0F84SLud16lvAZvfPhhiB+67z6jjFQa27fTQd1gqkv7mJSn4npj286qNyMgD5BLniF+XUSEd9y2KS9GKo19tkXemomdeyD+wX/8T4wyBmlvdJ/asEHemoDbmK5By0f/xdwC+i1ERDLkPaktoV6+Sb4Qi8S0SyHqzYOOuTd5QIkrKm28/mfO4bgqtnEsv/rqqxDz9VquF2o+PQ+vR7cmvdnEa7NVTA244q30m0KaNch4zq6dFsaizc6zlBui0kRvVSZCLXkQ4bUtFBO8cOkSxPkB1HnXKEdFOoPXYnQK+/noNNb5/10wtb1PPfl1iOeOo9ehWEBd8+QoeunmKqjrL2bM+YW9RJ0OvqfWwn4euSWIU+SdazWwLUVEyjO4z3/axrHgOcvtHUTb79GIeNySt4rzjIiI7N2LfSEYwhxQlSb5tyjfUuSiRyhxHEbkQbRQG35+Fr0Mw0PYv05cwLVsdg6PN1wwc3cMD+BY69+L51U/SedBc2ZMnptOgk+ylcdzzw1h+w4WKQdEBdfgqIpz+dln0bMhItIcQI/ezeN4ri74vrY+j0Y3EXv9XLwGWfKTiYgU6b4wpDU2pHsjoTm0UsXXm6HpAwkFj9kJ6D6zgmtukXKXtMroy0qlsf/2DZr+z4juh4XuI0PK8fPiSZznXzyO94DplJmnLJPDvrFIfbRAt9wjlBsmojx2ccI9HHtf7TV+i3DpvWuhv2goiqIoiqIoitJz9EFDURRFURRFUZSeow8aiqIoiqIoiqL0HH3QUBRFURRFURSl52zKDN5ptcRZMaD15dHccvMeNHrvHCoZnx8bw4RLnofPOf0umlWCMpr1bEr4Nz5ollGroMnm/Gk0obaaaJibmcP3p9NYxsLiZYrNRCqXL+F7CmTaufc77oF4bAcm3zt55jTEX3n8CaOMChl8O5ScpU7m75CMgmMjaCrLeKZZv1nHRDUOGVedrufSMNx6M7jnuasG4Rwl0eHkgkm0m2jGa5PpOZXDPm2zcbfQj/EevM4iInEK+49PSSlt8q5lyKDaoE0VLl5Ck3WnbSYL6u8rQTy3sAhxs4N9wXXxvC7O4BiIAjNRnpvFci+Xqb9RdkMvjdeDzd+cbEzENN/XamjuTXcZ5VstvJZbRTFti7fSfraFYyAOyBAYm6bBMMJ2SNOGGPkMzk8nLuFmFgcK2K6phHHcCNBA2SB/5aUL2KeqC2h+bA5jcqkdY2jcvv2WW4wybzmCm2r80R/9d4g/9ed/CXHKwfPcg0VIHKBhU0Tk6FEaC5T4jY2khQEsI53B69Fp43wnIhK2cOxkUjQHrmxY4BiJrLaGOI5Xxw5vLNDfjwbo2dico1u0mUCRDKKlYdy4ZDclWozIgFyvmddpbh5N0tU2zokZ2mAlZWEdWjWM25Qc9HFKTCgi0iZj/6178X6kbwjn7pKFx7RpXY/a5iYLoYvt2VjAz2RcnMuHCiWIY9obIogSNnLo4/kdrzF41rdhP4IoDFfvLSy6brwhECdpFRFZoLVm5hImEm5QPlo/xjbqkGnf88xxmKVNI3jN9ek+wM7jdT2wF+/Ppiaw73ix2ee9HG000cH7EyuDY+KFY7jpxPk5PPE90zgORUTSlDA4bGF78+Y9zSb2Hd4gx0/YMMcjfzev092Jmq1NbEagv2goiqIoiqIoitJz9EFDURRFURRFUZSeow8aiqIoiqIoiqL0nE15NBznasK0tKCurUSaW0lIhvTKUUzEUyONbIaSILE+rO5T0p22qZWLSRPvkw6f9Z72F78EMWu/l8gbkeRNSJMe/fY7boe4XkfttuWgnu8Nd9wJ8fgoJlUSEZmbQx11RPq6mVl8vVLH89i9bx/ErOcTEcllUETKng2rS3aY9Pnrje8H4q/0gTIlqxkeRg9KoWAmq+qQ7tuxUWM4PzsLcR95H1IeXud2gsY2onYJye/AyfM6lPCqQf1vdhb7Lx9fRMSipEaTezGp5DD1pyrV4dxnPwux55nJgsYmd0Icpskz5KGOdXwKtd0jI+jPSqfNZHbN5tq+C79r/Pu+6SPZChzPXfW4uC4KWuOY5h7f9AB4LvoEPME5bnKQvCy+IZqF8PSc2R8GRtDwMLuI+t/5xecgDmhcDJfwWh46gHPHvhuw/4iI7KE+99BDD0D8xFf+AeLO0imId2TRa5LNmMmgFsmzM7+IevbpKfSWtJt43rQ0SLOBc6aIiEQ4Hm2aIyS+8t2c6THaCmzbXvWOsc3p4nn0+nkpM/Fmq41tuHT8GMSR4Lh0yFtVyOO8WhpEX4iIyI1TqHFPZXHsB4fwOlXr6IuZ3oMJI1s2zrvPvvSKUeaZk5gEsENtkyuRvn0R5/rWDPl7YvPWiKT8Yjco2VyI471NX+M65DEtFM01Kqpjn23TPU63F5ETsG0FURheTRRJHqCAxorfMddHR3BtSTvkZaC0upaLxxzdget8yjO/K3dj/Fub7mOKlJh3kBL2jY5iPNBPyYAbZaNM28E+OjyIffz5U5jU+fwMHiMSrFOlYq4d7TbWa5K8vqkI2y6bIj8LrVdhgkcjjmnepXucbl+GH258DdZfNBRFURRFURRF6Tn6oKEoiqIoiqIoSs/RBw1FURRFURRFUXrOpjwa+WJJ3NSyxq5eQV3lX/315yCu18vG523a9zxuo8Zr4uANEO/eNQ3x5eO49/DCLOreRERKRdTteinUvgW0fzbrwln7XaTjPfzwm4wy77vvPohHR1EjvYO0dOwfSKVQt7iP/BQiIgFp/XlP5A4JkGMbzzudJW14wiMm76HM+2R35z3hHChbgWVZYq3sY+9R/gBuj0oF9+cWEVmiv7kO6j8dC49pCzaIQx4NJzbbIKA94hcWcN/wM6dQR807UQ8Po555II+5OvoyqNMUESn0DUH80Fu+C+Idu3ZBzHlaws//H4izadOjsXc/+T6mcSy3nsW97SencOyyR2NgwNR2c94SzgXTPVb5+m8VruesejQcykcSkWje903PSYe6jOuiJn6C8hbUGucgfv4M7k3fDM12CATnxdPnMM9PO8BrZ9M4Hy/hnNeooB/Hb9Nm9yISdfBchyZQZ//IQzhHnj2K+vapCWwH1zbH1i4Px0K+D+dRm9pSPHzdctF7Ipbp0ei0sV5OiLpnb0UzHm+DPl4E50DbwTayKc+DmzYTLYSUfKE0gJr32Mf+5Ps4r4YtLOPyeexbIiJnz2Duqiu+zisUi+gZG5xA/1eR1pbhAtZp94MHjTLj+w5B7Hfw1sal9eHkc8chPnbiJYhTlnlr1KQ+bnGOgRDjkPIteCRpbyya9y9VF+s5fjvmrOm+V4iirc9lZcWhWCu5LGyaR1yH5qLIzLMws4DrY6OBx2j62D9T7BckH1wrMMuIqc/myaswRH7OgHIbVZrUd1z8fNShfEkiMlLC9WypifdbR19GL9QA+ULaNexbmQT/p1/DcjtZuj+xyR/K9760HlkJuYraMfmGYvKsdeWO6kQb9+nqLxqKoiiKoiiKovQcfdBQFEVRFEVRFKXn6IOGoiiKoiiKoig9Z1MejXqrI86KXC2bQb3s8ATqwMvPmfrXuIV7Azsx6usunkQ9cmsOfSA5qm22aOrVpYDaOJv26y/RXsJ33303xHv37oWY/RI33XTEKJJ9HAx7MK5obK/A/gLOH7L8N4x5D+Tu/bVFRIT0kqzmtB1zn3o35fCbIPS69lRP86b0W4DrOuKu1IE1+nz+i4tl4/NRiG3WZi1nlvMioDaTLB1X9xPvhvK0LFD+kzPnsI8fPoIa3NFJ1Oi3KXeAF5ua1JFp1DjnB9Cz4VvY/6od7A3FQfRPFDKmR6NvBH1GY/24T/iFMnoHirRHfDaH80Wjae7xH5Lu3aY+7HpR178T2n4LyKRTq3u3Oy6bmjjnhXmtfBo3tTadY4ifqTXwPFlPnEkYxyH5zBaq6Kmo+5yviHILCenEF/Baths414uIuDSnhaRj3nPgZojPnnoe4lQe+5znmUuTRY4mxyWdc0DHKGKfFQfXhnzB9AnVeN4kX2G0osOPk8b+FhBF0ao2nz1Bo1O7IX5pBv1gIiKNCo7TYAZ120ePYo4K9ozt3o1luGJqyUdH8Lr4LVzHOzT2y/OY06JWxzkvJi9C1jb7PHsQ3SzWIZ/D9WHXJOr067snsc5Lpr+qRfcvEY3lgM7Lb6AnsOPj64vm9CD9Y+jv5Gvc7VtjD9tWYMXL/4lcHQtX6NA8MjyGa5mISKa/BHErwDYpZfogTqXJL0F+i0zW9KjZMfbJqI3XslzF6zJXwzqkL1JeqiHsOzuGcS0TEbHm8Tv7r7+AY69RxT68exJ9SrMXMMecnXBtqXmlWqW8LYKxm8Z5u7aE47DUxLlARESovX3yYXT7dnktWwv9RUNRFEVRFEVRlJ6jDxqKoiiKoiiKovQcfdBQFEVRFEVRFKXnbMqjEQSBxNbys0lMWtYbDh6GeGLQ9C383ef/Ev9AWjqPcm+kac/vvkHUnOVypkdj1270VOw/jPWa2ov7+991110QDw+jvt2ybIpNfSh7LBjeR5w9GhuDtZp46RwH45D0zJ0Oau1qDVNf55Iumn0h1epVTV+tmqDvu874frCa58R1sa7tNmv+E/a5pxPqtFC72VfoW/P1Shm1nR7v8S0ibdI9vvDccxAPj6M288CNmJ9ibh49HRZ5UfpKJaPMVAE1o4sV1Dg7PvaFiQkcA4duuRXi+TnUi4qI1Ml7MlrCtrr1FtzHnrZYl4i+06jWzP4T0zXLZikvgnT32e35jiSXz0p6JeGMMa5t0hOzqUdEHMoP0aphn6qT12WRximXMTqckI/EwXar0FAP65wHg7xYlC9mYAi9DqPjU0aZfQP4HotyPHgZ7MczDYxf+TvMw3Jo34RRhm3jPDsxie/pH8I4pPNyeUJLyEPg2HjNLItyP0Xhyv+3PoeByPIUduU0QhqTzRDPt5Mmj4qIXDyH/W3mMuq2L1Xwuk3diXPD8GH0KD7++c8bZZy+fAGPMYxt6tG6PpjGMl3KXcLDyPHRjyEi0upQfoZFzO8RUF6CdIQesxMXzmIZYmr/B3P4mVIJ512X7hVq85cgXpzBebWTMctITaCvgT0a3et4x994HoPe4Yq1ctsY04XxaXgNjOJaJyJy023Yf5744tMQL5bJx5vG+8iUg2vuQJ95n2mTr6BF9ay1sP+5GTxGNo3jaHwCr/uOiZJR5rmL6DNarGDskMGiXcXcREXyGJXnMf+WiMj8PH6m3cL+Nr4D7495zbVpDMQtM9eYa2P7tilXWPes5/sbnwP1Fw1FURRFURRFUXqOPmgoiqIoiqIoitJz9EFDURRFURRFUZSesymPRtoVuWIFYN3kKOnxHn7Tg8bnb7iAusnBCxchzpBG3sgPMNiP8b49ZhlvfgjisUMHIHZoP22f9pznOAwx9jzWjYvYNvs47DVfX+/zSXk02IPhuaj5433nXdJyP/f8CxB/+vP/xygjsNb2kiwsXNUNdgxPxPWnWq1IuJILgz0ZXNds1tTxcjtzLpJLF1FbzNabTAY9QUOD6OcREXnuWfRkLJZRV/nwww9DfOzYMYgvX8Yxct896CHqp/wUIiKLVfSSzFHujqk9JYiP3HQTxC8+8yTEYYL+vEON0WhimROkLe6QScN1UY88PmFq8CsV1IzyPvFOly8nKQ/MVpDNZiS9km/G5kmQQs8xp1eXPDfpNPapch3bucw6Wjrm5DT6bUREOi0cG6cvY39IUb1T5Hcq5XDsFIo4Lw9QbgURkVZnbZ9aTPvlS4j9Y24e8248XTtpHGN0Cj0Hk9Svy1XKOUN77rsenldAnkARkaBD/hXSNae8K9fe+OiWEMe2xCu6acvC65bvQ8/A9D7MCSUi4uWxv3klnG/shTLE6UH0APWRP2f6NpyfREQ+/WfPQpzNY86KvjTq8FMNXFPnLp2HOOrgXOOlTV2+l0pTjG2TymPbtCnHTXkJvVGdJXN9mxXyrtGak6PcHWmL7iVoXA7sNr1OEc1rLZpn3a7X1/OGXg8CPxTfT87fEZG/yZgfRWSwRLlubOwL1RqulxcX8bqkyY945jK+X0Qk5eNcUsxgXygUsP8EdI+XyeE18EOsw1LHnNcP3YF5goIYz/OFpzA/TYNyYLiU46eQNf3HsWA9IxvXkmxfCY/pUZ6XDpZZvmTm2SngUJUgTTm5um5N252N53HRXzQURVEURVEURek5+qChKIqiKIqiKErP0QcNRVEURVEURVF6jj5oKIqiKIqiKIrSczZlBk+5jrjuslHmSuKiKzzzLBrAWpSsRkTk1sldEN/eIQPXNJlr70ODTf+NmIyvsNM0QrpjoxC3U2isTrGBiozXnAguJNNiGJkGGJvM357H5nD+BJmmyKQcRQnJ5jhhH/l1F+cx8dLRl9Fk/Jd/9WmInzt2wiwijYakVgtNVd0JgsLATPi3lbBxmJM3ep6ZDOmKifcKIzvQ1Folg1anjX2F2+PkSdOweuIUGqymprCPOi6axJbIwNomU21fCZMFddpYBxHTYOyT0TGfxQ0Qzp45BXGVki9OJRiM+/txIwY22/MxvBS+zuZ9M8GiuQkCb8zQPU7W22DheuF6tngrhmCL62B4w00zZExj23IwzpHZP5tBs6PjUjK9QTNhX2OpjMck4zLaS0UcSqAWBfiOTBr7LPc3ERHqcmLTZhS5Is7t2RyeZ4rGr502225qCjccaDSwnmxSHdqBxwwCTOTVbOCcKSISBTi+bIuv18q13yYzuG1b1+z7fX1o2jcTXooMDeF12L0XN1SZXcQ2uUiJyE6fwHXl+FmMRUSqEV67V87jOB7ux7nBps0sihlcwwsFSigppgk6ouR11ToZYSmBWi6HfXj4hoMQnz5rJi29sITH4PY+vYRjtZ/mrxwNkuGEPuTQefCmF92bnry2xL/fGI7tirMytmMqn5MLRgnVK+ax3ftpcuov4phtVPBGZ7aC45OTJIqIHJrATSM8C9t0YaYMcUym6oqF9wF1us7thJ0gJndivQ8euh3iRhn7wux57F/VKiXTEzMZcJruz/wA63n5Eo5daxjPq93E12s1vPcQERnoYHvbw/SIkLp6nmFn4/eA+ouGoiiKoiiKoig9Rx80FEVRFEVRFEXpOfqgoSiKoiiKoihKz9mURyMKRaKVRxPWBzZ81HZ9/fhZ4/MnOqgJu2ijTjszew7i+iuo+TtUQh3czoKZ1CQXoE53eAj1eiVKasJa8Spp5lkPmya9u4hIOoN/a9RQr86JdVwjkRcl7EsQNwaUAO35l1+G+KnnMFHcy68ch3ieEse5WTPxm0+J2tiNEnXVm/WYW8H09E4prCTscUi7ysndGg3UL4qIWJREJ5PC68Zem0IB28j30RfSapk+gzvuekNS1a/Wq4160d1790P81FNPQTxLCbSKObPP1+lcOXHZAOmy2Uywfx96n9IZUx9aLGKSowz1eR5HWUoMxtdnaclMtMTjhNs/03Xu7N/YKqIgktBePpeIRohts1HBHMc2J0MMsM+5KYxZZ98JeK5ISO5JY3OsD68Fj92Ui8cczOH1HyyiNti1zYSOUYjXP/JxfLap33NbNSj50223YKJVEZHhEfRUtZpYZpqTtnl4HiEl42uQbl/E9MdxMkO54j1JuLZbDXuaOE7S8LO3KkX+m/4SjvPxEfRLLMzjHDg+aiYtffv3fC/EYRV19eUl1IovNhcgnlvA6+TGeJ37TYuQZMg35KUwsVs2h21xmTTxL1yYh3ghwUN26N7vgPimm9FD+uSTmPj03FPPQNzn4DgrFcx7if5+9AO4a6xzERs1t4A4siVevQnE1yzyoCRaSChppuXimB3Ikb9V8BydGvY/PzTXgaEhPEaOfFZOgGVaLtapmMO5KGNT0k4rIVEdVWNochLiG+84DPHFyzj3zNRxPeyj+1QRkWI/JZ1kX0cTz3NuBl/3IxyHAwNmH1+q4b1EXx0v4viNV5P9etbG+5/+oqEoiqIoiqIoSs/RBw1FURRFURRFUXqOPmgoiqIoiqIoitJzNuXRSKdS4qaWNZ2dDmpuhbwMLmnxRESqDmrCnp1FL4NF3obwDGrhFiIs48Vjp4wy+kq4r/zUNObuSJGOl7X8C/Oo1QxJU53rQw2riEh/CXMMXL58GY+5gBpUroProE7WEnOf5g7lVzh+7jzE56nMVBZ12T75D6KEZ0zOGeJ5qCHt1ouGtN/3VtBut1bzuHC+E9Yn8/7jIiILi3gdGnXyZOTxOlrUZhMTExA/+MAbjTJmqP9wfhk/xHoODqHu/P4H8ZhHjqAOePaymZ+GFaMO6ZWnxlFnXV7i3Ax4nuzHEBFJp7HPNpvNNV8vL5YhdqnPs8dDxLym5TIeI+rSJyd5cLaCZrslUbTctwLygzlUf9a/i4hY5G+IYmx726J95innwOw8zqGdtjkOeX/0Fs3V7ENguxVLbxtV1EVXK2WjzDTNN1YaD7JI17JanoN4bAD73MTElFGGm8K2yQjF5J+7slZdIQpojnBNsX9Ac29A8/+KPUeCBJn2VhBFsUQrdWLfk7CePWEOjOh84hjjDLVxIYPXdXIU58Ab+D5ARALKsRSQl6ZG+YjmGrjuNyo4T1dp3q5UzLG/WKfcB2X0ecQh5Uhq4nraoE5v50yDwf4ufbqIyPAO7KN33Y1z3PlXz0A8eeNuiEd24rwsIlKifEU8J243nSgQL1xuO8MDRHHSt9jZPPoMBofR4zN7EdvMpclpMEf5ebKmZ3GkSN4s6hvFNH6GUkdIjd5faWGdJ6dKRpmRi8dcbOIY6BvH+9CpA3dA/OyZz0GcT/BiBlnKJ2PjfBeQFyqKcSzHIc6x1arpb7GobUb6B+j1q21rbcKnq79oKIqiKIqiKIrSc/RBQ1EURVEURVGUnqMPGoqiKIqiKIqi9JxNCQAXFxcM/fcVPA8PFToJmyh3UNc90yYdJekk26Tznim/gMezzer3lQYhnpxCzZ+QrKzZwjrxXv6877idNs+fcwJUKpRvgfbkZmmb69H+766p7XZsLLdFWtuANrVu1LFtxcK2siXhOlK9WM/rOFc/E/Gbt4BKpbJ6fUZoT332DCTtIW/FeG0HaF9qljx7lO+EfQheyrxO1Rq2e76AusgU7e0/TBpVIzeHjbrMiDcvF5FcHvNN7BgdgzikPn3xAvp7BkoliEsUL9cL9Z/skTDbBvuXT3Xg6yUiMkl7j/NYzHSVwZ6craLdCVYHcEyaeN5H3tTQy9VERCs45BNwqM+NDGMfDQLsD1bCOOS/NBvYp7w2+c5iLNNq4xHadK3m59FfISJSHMB+HFD/OHPqJMRl8q1NjGOfLRYwn4CIiJCfwKV8L56xNpFvMI3jZGDyFqOIVh41ya0aeqI6neW5vhNvTx4Ny7JW5zbO8bSe92YZ6n9O8np+rWN0fNKBJ/Rxj9Z+J4dl2GmsQ4bytMgIruERGWLagXliDeqjnNenVsU1ukXv98nrNEf9U0Tk0oWLEO+c2ollLOG6H1L+hb4d6L8YmTA9Gi6tDw75BLvnve3ogYuVirT9tlEXEXPN9ZI8Qh3sP26KPFEBvl6iXEqjfdiGuay5Bo+WcM6cb9Qopr5BftOOhe/PNrCMHbZZ5oU56l8W3X852FYnZjCXzLkl9Cm1Eu7Mp/aiXzMTo5/i+LOvQGzTmp2j3DLiJfiNxzCn1ui+GyEOu1YXK9A8GoqiKIqiKIqibCP6oKEoiqIoiqIoSs/RBw1FURRFURRFUXrOpjwaYezzVt2reBZq7WzSV4uIOE3UwoX0nBPQHvK+j5q/doz7/kaxWUY9RO1bpU37GRdR82eTBpJ19x69nkrwhWTzqHULSb+bK6A+z6McAiQ9ljghj0ZE+ReiDrWFjzpWn3JiGHklEjTGedrjWmx8j9+lyXScrX9GXVqqSGf1vLFu7LVJ0sfncnjtQ8ph0OqgdjOgU8ySF+JSQk6LmUv4N5/0n2xdapOOcpbyoQQ+1qlSQS2oiEgc4bnOzc1CnKK92CPS1g5QHhjOByFi6m+ztM934GN/rJNHKJvDvrVYRU2qiMjxY69CXChgGfWl8uq/tyuPRhTFEq5o4WlqEIs08zG/QURs8txwzMfIZfFa7N6JHgI7Ik+PiGToWtw5jlryrIua+DTlOIkj7LPWAl6rmZOnjTLn51DTvkS5PI6/ivrhoQEcSzcdOQhxkbTYIqZXJKA5MCTNsDH2yJfmpkyNcmEUcyWkitMQVy89s/xZMdeerSAMw2Tvj5j+niRMb9PaccSLE71uJ3gxfaqfQ32aV7c0eTMj8hOyJSNrmx6NNOUxsPtprh8qQbxeO3R8s43PX8IcSa8efw7iRgPnvO/67jdDXKI6ZRNyCXmcH4rX8a552E7wIV5vgiiQIFy+gpxjjFs0CM36BS2ct6s1XM/yNN/15XHMBhF+3rbMXBAtGppNyutSIf9Oy8ZrPbKjBPH46DDErmX2P7+F175Nt9atEOfDyMH4znuOQLxjBD1vIiL7bsB53Alwfqss4PxYOYtzsk/3wnZsrk+5YfTGtcn35XbfhyZ4pa6F/qKhKIqiKIqiKErP0QcNRVEURVEURVF6jj5oKIqiKIqiKIrSczbl0cik06t5NHiP+w7p26PI1M5l6D0h+QYCa509vkmbGYamRqxDf7J92ouftJkuaUxrNdQjl0lrPD0+YZQ5NID7fg/0lyC26LxC0lb6hrbY1Ie2aO/n2CL9sYPHaNC+4JxzhLWfy8fAtglirEenaw/sgPTPW0EQhBKs7Kl+/vwFeI3znTgJe3gL7bvfpv5TLKEWM59F7Xp5Af0/F85jPgoRkVq5jEWSDrddw3ZbIp1lkfYFT7vkU2rS/txinitronnDdfZgsJ8iiMy+0SFdq0/jIqbPhDTufOrzxRztnS/mnv0xeQ2aXfuh8z74W4VlLf8nIuJSDgyb5qs4Yaf79VStnBuBvVA2jXurYc6zh3dMQTwyMA5xhy5vmnxpjuAxq0uoTX/2RfTSiIjMNnDe5HMvDqJH5+DNN0CcLeBYc1xz/KYz2Gcs6jCdNuVGaFBbZVHH77vm1bBpbDhp9HGks8tzfZigDd8KuvNosM+A54GkXEI+jUszFwK+n/sjw/cBIiJu0ty7BpyvievNcVKd+D1mDqi168Sfz2TN9/f1Yf9hr0wYctviMTukkU/M9URxbF87V4Gb4KW73oRRtOrN4DbmvhRE5vm5nJuK5p6+AfRmRR1s45juSZyEnGOdEMd9QMbikNo0ojU2nSKPLd0jpj3TWxNm0vQXyiVDhs8b9mIOlXZA/Tfh3nbmAt7z5NJ47tN7d0N8iXI21Rfx/mXXQVwnREQGRtCjEdB9ZKorn9saXdNAf9FQFEVRFEVRFKXn6IOGoiiKoiiKoig9Rx80FEVRFEVRFEXpOfqgoSiKoiiKoihKz9mUmyiO41XDT4bML2xsajbNxGKLLUpaEpKhjow9LpuiybQTJzwnsce5SQasiMy6+QIm0WFTK9uZ6jUzUVhEZu7+fjLU0HlGlAgvlUaTY6OOSdxEEhJ5ZdCYFtXRCGmTEW1xsYwHNBIxiSxRAkSfEsG1u8y5UYIJ8HqTSqUkvWKASqU847Vu2IQtImKREa28hP0x5aHxaYiMaekUfv7CEpqrREQ4l0+OkzPS+zlBU54S4XF/7Oszk4yx0ZFNgmzS8+naGT3BSGZlmj6XlnB8Z9I4H9g0cqKAkyLtMMqoUxI+cyzmINoOLNtZTbLnUaI7Tr4XReamDhFNUJGzduIwmiqEcyxl8qap3hMyVtNGEXmPzN8uHtRv47U+PTcD8Uy1YpTpZHA8DgxiYsHpfWg8zObJmE2bDYRp02zNm2pwsjj2CBvGZ0rcleRlDOma8TzqB8vzLM/pW4Vt26tm6PVM0kmJ/dh/bB6Dy8PXAxrHG8kZx8eIorWN21wnns+SksVGlFgsyaTeTRyzkZleT9gsxaY3eTTeXapXh+rA87SdcB68fkSSnJwx6XhbgRXGYq0YlXkzBp7zk9YRz8a5hzdguXQZ72NadB1Kg/j+bJ+5zvuUFNCieo0M41zVoXllpA/n1DytbZcWzI1wFiNM1BtSItWI7n1rS7h5RqOJ93zpNJvLRQqUUDmihLa8WUFhEmN3ENuhb9xMCligpMYZus90usfyJrqf/qKhKIqiKIqiKErP0QcNRVEURVEURVF6jj5oKIqiKIqiKIrSczbl0fDDWKKVpHmcKyYk/avfNL0M4uGHkhLWdBOTBtJxUN9nJ2gUbdL0WaQPZdlqu4X1Zp0raz/PzswaZXLSmUwZNczsJzDqTRrBZidB/2txwj18uU3JzSxKeJUuoO6w0zZ9IKzNZpkqJFBcL/PYdaBeq6z6egYHB+lVSjyVUMEK+XM80qrH1DnqFbyOWfIlDZKHY/kzpA/l5FP0ftYns7aYryMnJtwIrRZea9aue+QjSdJ2D5LmvkDepvl5TOpmWeRXIL1uo44aVRERj8aFRwnWWp2r2thomzwajmMnasSXoSRhScnOOCGatXa/9ags9qUlJbarWliP+iLqh9Mh+elo7n7pIiaGulDGazs+PmKUOTWNiUz7h7G/ZHM41jjBq+NinVttc/3wXO6npLOn97vUNsbYC00df0C+wLBVhrhdX27Ldvva2vnrSb1RX/UX8Dhlzb7vm+uI51FSSdbZ07rOcweXEQYJPhBat5vkvfJoPeTbAGNOpPkqlzAHcluwh7TeIO0/nRffJ+Ro7hERyblYrk1j0SffDifw4/UmKfEg+9IkuvZCa1tb/z2xY9tX5zWq6nr3cyIiS5SwdqGFbbTo40GzBfQk2kVcc8M8JXUWEY98jqmA2tBDv0R/Go8xMobehfwwzneVlnnbXHKxTEsouWiI/oqxkTGsEo3LJI9pnjwauRy+x7gnN+4tsH9ymSIimTSeB/exbp8ce+bWQn/RUBRFURRFURSl5+iDhqIoiqIoiqIoPUcfNBRFURRFURRF6Tmb8mik0xlxVnSGvo8a20YVNddBCzWRIiIeafgcl7wL9H7LxtcjYx91s/quy5pm1Km5lAuBdWqcj6HTQU0hSyhFRBzaT98isVyjhW0VkHazQxrUMOH5zw9Jyx1zW2KZfpP0u6SLTdoAnbW1rnft51De338rsOJIrBVzytLiArzWpn34+TqKiMSk0h4k7WWd8gMszV3GY3rYPp2W6XNxqE9Wa6gH5WbjerNut1hCTaqV4EtiDxDDfZi1mxZpN5N0/5wjwnWxP2bSqBdNZ9DDkaZjzl44bZRR6sdzjWgcdKerCZKSIGwBtmOt5m+IySjFaTN4/38Rs3+YumbSE/PrnBOF9msXEWm6qInveDi/NCpliCu073yN3r9//yTEu3ZOG2V6RdQPC3lL2m1cD0LSs7vUJzu+eV4OrQec9MGhuZyvR5P2qm/6CR4N+kzUwTkhfaX9E3IEbAVzs7Or+u2LFy/Ca6zrZv+FiEizideBvVNpysvSJG8Dr10Sm3NFgXT1iws4V/sBzkfpDM7VpVIJ4tlZzOOScc35rtPGY+7ZswfikMbVydOnsA6Ut+Dmm242ykh5tAaTF8qnew3b4XpyfhDzPALDV4Nx9zVNur7XmzAKV3PNcE4Vrk+SZ6NK3iyfxuzkvn0QF4uYk2x0DPNoZIrmOh/SXHOc8pI16V41jLBPV+k88h72jVHK5SEi4tB6x/mxHL73tddeBzbidzGhez7y92SpjsllsEeZXu32XiaM/Wuhv2goiqIoiqIoitJz9EFDURRFURRFUZSesyHp1JWfxMKun/VC+omPt7eNAvNn6dDi7QgtihHeci6irSDthJ9+YouPyT/n8XaSCB+Tf8pMlE7RT/gsK+Jj8M/P4QakU2G0tnSKf8oPWTdg7O1oFGFcw7V+mr3y3q34+fZKGd1bErLEqE0/nSdt0cr9rUHbLrZIVsA/wQYBbR/ZMuUdluB7At5umd7fJlkTn5fTxJ+GWd62XK+E7ZC7aJLEi7eP9KmOLK1arhdva0lyHJJYhBGeR0jSKZZwiIikaPtHl36a7b5eVz6/VfKBK+V0urY/XW8uCSNzflpnZ2sJ6DM2TSaGPCFBwxiRLKnTwWvV9lm6iWOlQ5IIfn+zbfa3IIX9mGVNUcDjE4/p0vzmeHQ8EYlJJsb9OI5orqYyfKp3M2GNMqVTvK4tv6HVWf7/Vve/Vtecw3MeSyGS6sbzDUunhKTH/H6+bknyCZaDsnTTp/mKx4D5edqCPmHLV35Pi+Zmlk7x+7ntWGYnIuLyCF/nXoGblm9XDBmamPdVAUmNuq/plXl9K9fgZuvqtWTp1LonLCJN6rNtmpsCn7b3puvEn48989xDLsOY32jeoC7covfzfBckzE224HXyXJIxGdIpfN2S3kuneGw7tIYnlZG0nnTjdi1gVywBG+l/VryBd507d06mp01drqKcPXtWpqamrmsZ2v+Ua7EV/U9E+6CSjPY/ZbvRNVjZTjbS/zb0oBFFkVy4cEGKxeJrfNJSvtWI41iq1apMTEwkJh7qJdr/FGYr+5+I9kEF0f6nbDe6BivbyWb634YeNBRFURRFURRFUTaDmsEVRVEURVEURek5+qChKIqiKIqiKErP0QcNRVEURVEURVF6jj5oXIMf/dEfle/7vu9b8z27d++Wj33sY1tSH+Wbn4cfflh+4Rd+YburoSgbQvur8q3GY489ZmQeZz7wgQ/Ibbfdthpv5F5A+dZD57/eoQ8a3wBPPvmk/MRP/MR2V0NRFEVRXnds5Mb+9cY/+2f/TD73uc9tdzUU5VuGDSXsU5IZGRnZ7ioo38Z0Oh1JpVLrv1FRFEXZEIVCQQqFwnZXQ1G+Zfi2/0Xjk5/8pBw5ckSy2awMDQ3JI488IvV6ffX1D33oQzI+Pi5DQ0PyMz/zM+J3Ze5k6ZRlWfL7v//78ra3vU2y2azs3btXPvnJT27l6Sivc6Iokve+970yODgoY2Nj8oEPfGD1tTNnzsg73vEOKRQK0tfXJz/0Qz8kly9fXn39yk/6n/jEJ2TPnj2SyWREZP0+/IlPfEIOHTokmUxGDh48KL/3e7+3ZeerfHNQr9fl3e9+txQKBRkfH5cPf/jD8Pri4qK8+93vloGBAcnlcvK2t71Njh07Bu/5+Mc/LtPT05LL5eSd73ynfOQjH/mm+zZbQT7zmc/IAw88IKVSSYaGhuTtb3+7/P/Ze+8wSc7y3Pup0LlnpifvbJzN2tUqoZwQIMnIAixkw4cx/gBbxmBfJGEw+DMiGDjYgLGNr3MOmMMBycbGxgFxQOgAAsnKOa425zC7E3umezpW+P6Y2Zm577c0QeqdMej56dJ17TNdVe9bVW+o6r7v99m3b5+IiNx9991iWZbk8/mp7Z966imxLEsOHjwod999t/zO7/yOjI6OimVZYlnW1Hg3V3s69UvID37wA9m8ebOk02l505veJKVSSW699Vbp7e2V1tZWef/73y++P50ReT7tVETke9/7nmzcuFGSyaS89rWvlSNHjkx9xtIpJggC+fznPy9r166VVCol55xzjs7zv+Do+Hd6eVm/aPT19clb3/pW+d3f/V3ZsWOH3H333fLrv/7rUynVf/7zn8u+ffvk5z//udx6663yrW99S771rW/NesxbbrlFfuM3fkOefvppedvb3ia/+Zu/KTt27FiEs1F+Ebj11lslk8nIww8/LF/4whfkz/7sz+QnP/mJBEEgN9xwgwwPD8s999wjP/nJT2T//v3ylre8Bfbfu3ev/Nu//Zv8+7//uzz11FNztuFvf/vb8olPfEI+97nPyY4dO+S//bf/JrfccovceuutS3H6yn9RPvKRj8g999wjt99+u/z4xz+Wu+++W5544ompz9/5znfKY489Jt///vflwQcflDAM5frrr5/64uX++++X97znPfKBD3xAnnrqKbn22mvlc5/73FKdjtIgxsfH5UMf+pA89thjctddd4lt23LjjTdKEARz7nvZZZfJX//1X0tzc7P09fVJX1+ffPjDHxaRuduTiEipVJKvfOUr8p3vfEfuvPNOufvuu+XGG2+UO+64Q+644w75+7//e/na174GD/nzPe7nPvc5ue222+T++++XfD4vv/mbvznva/L5z39ebrvtNvnqV78q27dvl5tvvll++7d/W+655555H0P5r4WOf6eZ8GXM448/HopIePDgQeOzd7zjHeGaNWtCz/Om/vbmN785fMtb3jIVr1mzJvyrv/qrqVhEwve85z1wnIsvvjj8gz/4g8ZXXvmF46qrrgqvuOIK+NuFF14YfvSjHw1//OMfh47jhIcPH576bPv27aGIhI888kgYhmH4yU9+MozFYmF/f//UNrO14TAMw/Xr14f/+I//CH/7zGc+E1566aWNOi3lF5xCoRDG4/HwX/7lX6b+NjQ0FKZSqfADH/hAuHv37lBEwvvvv3/q88HBwTCVSk3t85a3vCV83eteB8d929veFra0tCzKOSiLw8DAQCgi4bPPPhv+/Oc/D0UkHBkZmfr8ySefDEUkPHDgQBiGYfjNb37TaAPzaU/f/OY3QxEJ9+7dO7XNu9/97jCdToeFQmHqb6997WvDd7/73Qs+7kMPPTS1zY4dO0IRCR9++OEwDCfG2XPOOWfq83e84x3hDTfcEIZhGFYqlTCdTocPPPAAnNNNN90UvvWtb53PJVT+i6Hj3+nnZf2LxjnnnCNXX321nHXWWfLmN79Zvv71r8vIyMjU52eeeaY4jjMV9/T0SH9//6zHvPTSS41Yf9FQTnH22WdDfKpN7dixQ1atWiWrVq2a+mzr1q2Sy+Wg/axZswa8QbO14fHxcdm3b5/cdNNNU7rjbDYrn/3sZ6fkD4qyb98+qdVqcvHFF0/9ra2tTTZv3iwiIjt27BDXdeHz9vZ22bx581Tb3LVrl1x00UVwXI6VXzz27Nkjb33rW2XdunXS3Nwsvb29IjIh83yxzKc9iYik02lZv379VNzd3S29vb3gn+ju7p6ak+d7XNd15cILL5yKzzjjDGOcfSH27t0rpVJJrr32WhhTb7vtNh1Tf0HR8e/087I2gzuOIz/5yU/kgQcekB//+Mfyt3/7t/Knf/qn8vDDD4uISCwWg+0ty5rXT8aK8kK81DaVyWQgnq0Np9NpEZnQjs4cJE/tpyiKMhtveMMbZM2aNfL1r39dli9fLkEQyLZt26RWq0098IeTMk0RAYnSSyVqrFzqOblYLIqIyA9/+ENZsWIFfJZIJBatHoryi8TL+hcNkYmB6vLLL5dPf/rT8uSTT0o8Hpf/+I//eNHHe+ihh4x4y5YtL7Wayi85W7ZskSNHjoAp8fnnn5d8Pi9bt26ddd8XasPd3d2yfPly2b9/v2zYsAH+X7t27ek+JeUXhPXr10ssFpv6gkVkwvy4e/duEZlom57nwedDQ0Oya9euqba5efNmefTRR+G4HCu/WJy6xx//+Mfl6quvli1btsAv/qd+We3r65v621NPPQXHiMfjYNYWmV97ejHM97ie58ljjz02Fe/atUvy+fy85umtW7dKIpGQw4cPG2PqzF+jlV8cdPw7/bysf9F4+OGH5a677pJf+ZVfka6uLnn44YdlYGBAtmzZIs8888yLOuZ3v/tdueCCC+SKK66Qb3/72/LII4/IN77xjQbXXPll45prrpGzzjpL3va2t8lf//Vfi+d58od/+Idy1VVXyQUXXPCC+83WhkVEPv3pT8v73/9+aWlpkeuuu06q1ao89thjMjIyIh/60IcW6/SU/8Jks1m56aab5CMf+Yi0t7dLV1eX/Omf/qnY9sT3UBs3bpQbbrhB3vWud8nXvvY1aWpqko997GOyYsUKueGGG0RE5H3ve5+88pWvlC9/+cvyhje8QX72s5/Jj370I7EsaylPTXkJtLa2Snt7u/zd3/2d9PT0yOHDh+VjH/vY1OenHq4/9alPyec+9znZvXu3sVpPb2+vFItFueuuu+Scc86RdDo9r/b0YpjvcWOxmLzvfe+Tr3zlK+K6rrz3ve+VSy65ZF5Sl6amJvnwhz8sN998swRBIFdccYWMjo7K/fffL83NzfKOd7zjRddfWRp0/Dv9vKx/0Whubpb//M//lOuvv142bdokH//4x+Uv//Iv5Vd/9Vdf9DE//elPy3e+8x05++yz5bbbbpN/+qd/eknf0igvDyzLkttvv11aW1vlla98pVxzzTWybt06+ed//udZ95urDf/e7/2e/K//9b/km9/8ppx11lly1VVXybe+9S39RUMBvvjFL8qVV14pb3jDG+Saa66RK664Qs4///ypz7/5zW/K+eefL69//evl0ksvlTAM5Y477piSslx++eXy1a9+Vb785S/LOeecI3feeafcfPPNU0swK7942LYt3/nOd+Txxx+Xbdu2yc033yxf/OIXpz6PxWLyT//0T7Jz5045++yz5S/+4i/ks5/9LBzjsssuk/e85z3ylre8RTo7O+ULX/iCiMzdnl4s8zluOp2Wj370o/Jbv/Vbcvnll0s2m51znJ3JZz7zGbnlllvk85//vGzZskWuu+46+eEPf6hj6i8wOv6dXqxwpsBSeUlYliX/8R//IW984xuXuiqKoihLyrve9S7ZuXOn3HvvvUtdFUVRlEVFx79pXtbSKUVRFKUxfOlLX5Jrr71WMpmM/OhHP5Jbb71Vk0MqivKyQMe/F0ZfNBRFUZSXzCOPPCJf+MIXpFAoyLp16+QrX/mK/N7v/d5SV0tRFOW0o+PfC6PSKUVRFEVRFEVRGs7L2gyuKIqiKIqiKMrpQV80FEVRFEVRFEVpOPqioSiKoiiKoihKw9EXDUVRFEVRFEVRGs68Vp0KgkCOHz8uTU1NmulQERGRMAylUCjI8uXLpzJoni60/SnMYrY/EW2DCqLtT1lqdA5WlpKFtL95vWgcP35cVq1a1ZDKKb9cHDlyRFauXHlay9D2p7wQi9H+RLQNKtFo+1OWGp2DlaVkPu1vXi8aTU1NIiKy/pq/E8dNi4iI44zCNgEtklv3W4zjxK0yxR5uYPsQlsIUxDGqritVowxPKhBXgiY6Rh3ilIux4+LbetlzIK4GcaNMRwKIsw6eVy3Et71iFctwbTyvlFszyqBNpFzHY1iC1y4ewzI9Og8rYlVjh76pKNXwvCw3OfXvoF6WAz/9/am2cTqZan8b3iSOExMRkXiyE7bx6niBRofGjePE3ATEAd1rz8bzbaZz62hphrg0PGyUMTqG/SKWzUCca2qDuIk+r1awj4yO5iG2HTwHEZFCidp8BWO+r2GI5xl62F6DENuSiEgqi+cez3VA7Lp4/RNJLDMoYz8b6hswyvAs7M+Wjefh2mPTxwvqcuTg9xal/YlMt8HLfuP/ETc2MQZsu+StsE1L20aI4zHscyIiQX0M4ice/AnEw0f3QZzuXA3xmee/FuLOrrVGGSF/48gh3n4JfPyD6+LYEdCXVRUxxw7bpvtNZfAudkDjl/EtqVmGxX8KcJy0HGyDVR93sOh+eD5XUsSv0z0Lqe9MjrPV0pj893f1Lnr7y+SyU9fKkjmuYcQYH6N+2tnZTsfAfcbGCrh9F467vWvWGGWcdeY5EL/61dfiPr24j+viNefxKRaLGWUwpVIJ4nIZx//xEsY8Rpbo83vvM7M5b9/xDMQDA/0Q1+s4xgV0HtUAx1U+bxGRdCoNcSaTpi2mx2qv7snddz66qHPwI/d9W7LZiTr51MnnbI8vAj7GfI5pzG889tgLrdfcdTCzRISzfh7QA7MxZFsv/RequTJXRH1u/jLB5zH972KxJBdf+bZ5tb95vWicurCOmxYndupFgwZ5qrNvcwcxH3gcCzsmv2g4IR7Doeo6YnbUkBqFHfAx+MWiRjHXEcuwA/NBj180HIfKCPkYVAa9RTgRA5DN9eLGL/iw6NCLRmhhGfN50XDCF37RmPrbIvyMOtX+nJg4Tnzy33gfwgDPz7bpJVZEbJvuHQ04ts33Ec/XpfPnOkwcIz7rNnwM18WXad/F+8L72455D2wH97Ftiud60bD54Sri2lE9HL4Wboxiap/Upo17IcbtEIvuh+2YDxyL9TP+qXLcWFzc+MQ9TqTwJTGZxgE3+kUD700sgff/1EvMVBzH65xIZalMfAEUEQmNC0khvUfO+aJhnEYDXjT80/+iYfGLRhxPpO69+BeNqWMucvuzLAv+PWtdzNMzHiQch76AsngsmX37qJeAZBLbbDaLbZYfTGIxvG8v5kWD68Vt2KL2yQ/5fOmSSXN84nrM9YLkU9tx6H5EzfN8TOPaGHss7hyczaalqWli3PNp3HgxLwXzLXchx9QXjReq09yfL+RF4xTzuSdqBlcURVEURVEUpeHM6xeNU3heXcLJXwR+7aIifBbQ2/utPzblF+96HW7T5ByBOE0/E37jx/jNx6Ye3P/8HtxfRCTWsgLiv/k+SlG2rMOfTP/f6/AbhP17j0J8/w78xvGZEz1GmddfgpKPy84gmUgxD/Gjz+N5Fkt4G978KvPXoH17jkF8YBiv7yvOXQ7x6AmUphw4gZKe889ZZ5Th1fGYh/vxm4HvPTQthwsDvK6LwbVvOFcSk98Ar1qNP/kP9OchPrhnl7F/VzfuU7fwGypuf+vXrYd4dQ/e+8M7njPK6Du0B4+xbSvEy1b1QtzRiRKkShnb58mjhyAeHTMlYT+6C3/mD2L0LayP7bHu4/cLAyRjSsVNeWBHTzfEr/i1ayBOtKAkLJuibxhLKDP7v//0D0YZoyMof4jRLwJxZ/pa1etVObTfOMRpJ/DrEngT1/fkse3wWa2MMpNMc87YP5vGNlco9kE8XsPrlA7xuhby2B56uruMMsKQv4HDz22LpVH0LT1JPR369SHmRHyjF8z+TbhH8jyffxmgMisV81e1mIvtMiZ4LctVHIfr/Msxfa/miDlH2fyVHf1SGkz+Cu+H9Gv8IlGr1aZ/XXP5V1z6FTtCGlaj8xsdxTZrfkuP+2foV7tzz73QKOPNv/EmiFeuZHkVf0uK96Faw3Eg5PvGOmIxf21wXKxnM8le+wew3wkpHc488yyjjCGSyhaLKINk+ZZfw1/c6jS/el5E+6NfZsIyteEZv17zfL0YhOF0m5jr14a5vlGfX3n8zb/x1b+5D+v4CePX1rlrsdAdIq7FXJ/jBr4fpcigX+nm+CXhxfyixPWY7R4u5P7qLxqKoiiKoiiKojQcfdFQFEVRFEVRFKXh6IuGoiiKoiiKoigNZ0EejUACObWURXMMhW7nnoPL2d5+325j/2svvwTig7twebhMiqrjoR49QyuwtGVMn0C2HTWONQ81qKk4riaxuhvjPc+hFyKsoQbaCkyPRnczXosTJ1Fn/+OHsA43vxN9JI88tAPr1IXeABGR5586DHFKUHO6qQev/317UT8eC/FadkWUcecdeO5vunEbxHc/fWLq356L57gYVCtjEoYTutc6aaebmlEv+MpXnWvsn+vAe8fLDjvkCfA91DgOFFGj27rB9Lm0b8LlRltb8b7EaTWTMmmLPVqCt+uMLVinYweNMnOd6CNq7WiF+MSx47gDrRA1lkdte64VjyciUiwegLhWQX9U1zr0cDi0AlBbB3qILrgUvSsiIgcefxBi28P+XZmxpHNNzCWgFwOvVBaJTehnB46SR6OI/i4ngfdBRGTFCuz7gZeHOJHEsSSoj0A8OoAeoGANHk9EJCBtLut9HfJwsH/C81H7zd9GWRHa8kqVxgOS71Zq+PloBcejahXrYIXmqj/LunGt9qCKno0S2SaaurDNJdPUrgNTB82rTLG4+pRHMVgij4bv+1Pa67k021Eaal5hrFbBvu/QilHJBN6HdetwzPuVa3C5ZRGR3l5ckrlWm/1aBSSa5/ZorELlmudVq5tL3c+kTp/n8ziWl8voOe3uRm+UiMiWrWdA3D90EusQnICY205AviPfi2h/5Osgm5HEEtNt3vNfugdioYRh+ILa/NOxyhSXZbYNsy7sXVpovRrhbXipx5zPylYL9Wy8mDJm22chSSL1Fw1FURRFURRFURqOvmgoiqIoiqIoitJw9EVDURRFURRFUZSGsyCPhm1bYk+uoX7sEGq0z9+Ah3rdWaaGujyAWvH8EG7jkdfBq6GmlpZJl4Giqf0MmvBvPmn5OYP0kYOozfzxE6gvHZZlEDenzEWYB47igv59Hr6/7T+AFS+Po0Y+Rxlrd+9HXbaIyJ3Poz55TRdqSPuO5CG+b3snxOkk5msYOszriIu8+kos40d37IW4ODatOY/Ul55mnn9qp7iT/gLLQv11Tw9qh/cfRr+JiIhzfAjiDOV+OHocNbfFIt63pibU3Ld3oS9BRCTOmZv7cK31NGXJ9ShrfDXAthFPo2Y602x6hK58w69DfPIA5vc4sAfvo1iYL8SJYZ3KdVO7WaP8HvseeRzidAbrZVObrlKm+mQKvSsiIhu2nQlxWMJ+MNI/reuv8mCwSHTlWiU2mWcknsXr1N6E18hOmF6GljiOeWefgTkGAvKAcWb4dBqzkcdDc6yok+bdCoxU4Pg5eThs8mBYfLyyee1d0uFXybPh13CfwMfPx/OYg2Bk2PTfVUdQA18epbE9RnlwqO+0dmL/jVpv36K06aHNHo2Ja+Vbiz/+iYjYrjulk3aMjNq0sWN+j2ixrpt8aS55yHKUf2Ljhg0Qr13LOTJMTwZ7MFgHXqW2USOfAmvuOSO1iNnebBvPq1hAn+R4Eed9TjtfrZrtr7kJx8lYivJdUczZnSldjdQiyqhQm6zXsJ3FZpyXH+GVOt2EMu3ReDG+hLnyNMzlOzByxUT5kKi9ccztaa5cHQvxIjQKJ6Lv8njF5zVXPeeT9+J0ZHcX0V80FEVRFEVRFEU5DeiLhqIoiqIoiqIoDUdfNBRFURRFURRFaTj6oqEoiqIoiqIoSsNZkBncsu0pw0m+gEaUx5/cB/G2jWjWFRHZuRfNfIVRNHAlyCC6gozdiQANXMWCaUpcsRrfnbrSgxDHfTS3SY2SVwWYBMonM2bWMo1onTms9/M70Hy7qQtNW0PDaEzbfQyvwzkZNHKLiIxW0SQc2GiUr9XRWHayiGa4ZWms4/bH7jXKKNiYcMx2sIwVLdP18uquYJrB049tV8W2J69/LQ+fDdNCA/2DaPwWEUnG0Eh7+CCe72Aejdv1OpmtrEMQHz+E5kARkfZ2XDwgTYbzeA4NqSn6PEaG89DF9pxINRllplvRlL4xh/UaHchDvP3Jp7FOlHyzUjP7Va4Z+015EBcT2Ps4tqe2TmzDqWWU0E+wn4mIZJZjEr8EJQ/rKE/3o3JpXET+0jjG6ea1r75aUukJ02dTKw6fXTm8V75tJuyzLEpEZ6EB3rPZ4If92LHRcBraZnJFi8yOrksGPzIF+pSgj02DMTIEJr0oEzVuU6/j2F2hhGkjFexrJ0+OQvzznz5slHF0Ny5yIHUaA21sUyvW4+IWlo+xY0ech/EnMmBOhu4SfUWXbWmemoPj8dis20ZZP11KEJrO4pjYQePT2ZuwT15++RW4f9pMrFitzm4GN7anpIF1D83gId+DCDN4pYJzqGVhvxkaxgR9VTJZx+PYdsolMotPHBRCN4b71DysV5XM2j4lHvQjrotfY4M3Xkt7Rr0Df/HN4K7jTpmp2bTP4wibrkXmNoPPhWF4jvAqh9SJfZrHA8OAjvU0FiOI0X23zUItSvRprKdic0j33sb26EbkuLRp4ZiAxnUJcKcYPd5bVAk/NNsP341QHIpnlEn1mQ39RUNRFEVRFEVRlIajLxqKoiiKoiiKojQcfdFQFEVRFEVRFKXhLMyjYVlTWtyTBdTH5p9HzW3iEGoiRUSS9FpTLKMirKUPdZabe/AYJwZx+2cGzKRfoX0Y4vO60aNRHcR6P7ETNarrW1Hbv28EvQ+DASaGExHZeRDrWanxuaOW87Y7MMlW3MHzaM6hl0VE5JLVeH0LedTn7TuCWv/L1mMCrP5hvC4l29TXHe1HbeIF5+G16R+bvjb1EO/VYnDmmVslnpjQBMeSqOmulFFT293ZbuxfGkdd5PGTmNTPtrA7xBy8RjUPtcRjedMHwvpNN4ueimQS25+dQB+CTRpqm8Tgvm8mChss4H1ry6AW/aLrXgdxXLBtHN+7E+Ja3by3tRr2A28cPx/dh/VyiisgzpAevrVjlVFGQP4DixKwNWem26M7jj6nxWLbGVskO5l0sSmL9ybr4jWybNPDE1JCRvZTeHGMAzYNkPbcD02dPlsoWNbM+mBDQ0/S3xgdwK1HCIipTD5m3cN23ubhmNgSx36xs9X0Iu16YjvEXo2Sm9G1TNqXQxy38f4EMrvHQUSMLHj25HdzdkRCrcVgxfpV4rgTbcChhHCOg+dvx8wxPp7C+9DUkoN4TTf22zdefR3E5557DsTspxARKVdwcOBEY5w0sE7jTa2OxwwCHFvKJTPRnUfjYr2O+vPhYRyrq1Wso0fj8Oio+fziUPta04PJffcfwPmkQD62SgXrXa+Z/Sj02C9FG8zom3N5X04Hnu+J9wLJetlvEbVdLDaPPrcQIiweFvlo4zSHhpyAVMgfQV2bE0pKHNuKiEiMvrM3RgcjiSp+XCfvXsw2r5PvYr84Noqe0fwotr9MBS9Oewp9kl3L1hpleJTQ2idfUjij4iH7TGZBf9FQFEVRFEVRFKXh6IuGoiiKoiiKoigNR180FEVRFEVRFEVpOAvyaISWLeGkLnQ4eS581pJCLddI1dRu2gFp5RKo8TpRRn1irIrauMBCbVyYMXVsdx9Brb5lr8c6pFD3feQg6ibHx1G3VgtRZ2055rrhj+7AMrtaMOfAoL8Jj5nCMhwftZqHnkQ/hoiIw0JrH/W3u57Ea2WTv8ClW33Omt1GGc+MoVfkyR0DEPd708fw6wtqOg0hmWiXRGJCy51oQw9GoortL6iih0NEpP8E5nqxQ7ymAemuPVr73yL/ReCbAtHCWB7iTBnrkaW1rus+rRlfwbbEHg3Wl4qI+AnsNx6tG768E3PaXPy6GyG+77vfxP37zbZhUf/2SAOdzmKbjVE+iMqBfoif3W7m0bCbMAdJ+0rsJ+0d05roiTwai08qHpdUfGIMyMYoh0UR7+WRw9jeRESG+tGftXnrNoizy3sgDniIY82sY67pXqdx1hLWJNNa9havK09jh8P646j18SlHgLFEOx6jiXJe+BmsQ9ox/QUe3fPQx7G7qQnHXVdQl+9IHuJAckYZQmvq83mdWsd/qTwam87aKLHExLUL2IxDYVQeg5YWHOOzTRivJd/B2rVrIHbovMcjvFKlqBwUs9TLozEwoPmwVMX7PDaSN45ZowZXGEcfWn4EPRdBgGN7oYDHHKNxXEQk5lIOEsq9Ead+URjCMbFK58F5J0REQsoRwr6Hmb6MheagaARBEE7VwaE+yrl0ojwk/DcjL8YCsSISaTge1ovTXvjk+QnJMEG3WSyXvCcWti0REQnwXtYGcd73CuhDilGl8lXye3aY8+NYDI+5+/jjEPtOHmLrCNazdhh9b697/R8aZbR19ELsBXSu9ox+Y5vP+C+E/qKhKIqiKIqiKErD0RcNRVEURVEURVEajr5oKIqiKIqiKIrScBaYR2NaIlwiTXy1hrq40DLXGmYdWxiyljhFMWrKQqH9eTFiEbFJd8sqxoA0fXZIvg/SPLMGMHRMXaSbQW15Jf8oHqMDfR4urXceBnjtAsofICISGgJcPHeHdNkeXVuJ4fZH86YGMEuaU7+GYsUxZ3qNdT8w1zI/3dSHjoo9qY/PxPGaDZfxPo6NRXg0htBzkslg+3JdzBviUlvwKX8A67dFRKoV1P6ODp7EMjtxLetMDusQ+Lh/tYIayXrE0tVODH1DWbq1J8fxmBu7eiG+5Mbfhvj7X//fRhmdOWyT9fxRiE/0o8a0OYvtI5Wi/CAlsx9VC7gO+PH+ZyE+mZr2L9R4bfNFwrVtcU/pismntO/54xB/77vfMfYvjqCm/U2/iTfrkmWoia8aOQgw9kNTo8w+DktIS81jiZGPgbYnPbEfoQ0PqR4hjZsW9ZU4HTOTwDrkmsw8GiH1vyBADXxLE46z1TLej6CGOZViKbOMgL57C0Kcc05NYUEYkUtkEcgm4hKf9GhUPNR9V8nTWB+PuE8OtrdO8m/1dnRCHGdrII1PlYqpV6/VULvN2n32JlQqlLeHfAqVMo4lo/m8UWbFw/tRJo8oX5vhERyXA6pTVL6iWJyfVygXTJZyIlGf8CpcRpRHg9s4ezaW1qMRj7kSj0+M5UEwe/lR/ovZPCciZlsxoI85R8tEGTjX+DT2BPQMF9h4zcc9bNOFCuZDGa5j2xERqRXIq7QHPYmVw9j+9u3C+bN3HbaF0jrTC1xsw+fjdBPOP24Wx79kL7bPvjHsZ8NlHA9FRJoszMHlU44RmXEtg4jnnxdCf9FQFEVRFEVRFKXh6IuGoiiKoiiKoigNR180FEVRFEVRFEVpOAvzaEz+JyIi4ex+CTs010FnT4awxouPyeWzfC9Kn8y6xTkkfwGVYhtrziNeRJlBeiPEA8cew88DXE8/tRq3d3mtYidCu8laRrp0tqHdxs/TCdQh2jFT497soKb5kLcVy4xNr1Nv2YufR+NQ37GptczXebhWey7bBrFjo15RRKTeuQLi0RL6OOq0zrkTQ62n42KbNlW8IrUAtcHjw6iDdOqYCyDm4lHGyxgn2H/RRLkbRKSpGf0TAWljwxC1mieHyUdyCDWnrT2o0xQR2XDWRRCTRUieeOhuiHftegbiyijmjwgi8iRk6TxSLtbTmbEWea2+NBp5y7bEnvTuWILt4+EH8Zy3P2fmI3Eo/8hj9z8C8aVXX4vbx7GMgHxqkcvQh9g3edzkXVg37ZBngz0cvjuPcdfQn+M+DmmrEwlso22UJ0dExKImE/o4bnp17M9HDj4HcbKlC+LVm9FbJyLiB6hjtijHyKncOYE/e66I08Wh7bvFjU3c31qF9Pz+7B5IEZGQxpcLzzwX4jXL0bPRTH2yRp6MWpX8FRLlb5jdo1EcQ69Nvcz5JnCwKbGnQ0RqHh6Tc3XUPaxTqUj3mebPeMwcn6p0rg5p+5d1Y5ttzuAcVMijjt+OsDj4PM9zvwpf4N+LRfjC5TbCMzLXMWzym3GeKRERz8Y2Wgnwuo+U0D8xMIbPPSeH0T8xksfPK3Hz2SmgHErNz5FnYx/2gYf/cy/E7sXY70Ytc/wr11sh3mivoi2wjFIcx8PMeuxXJ+voiRQR6bYxd5Ud4LhsS2LGv6OegKLRXzQURVEURVEURWk4+qKhKIqiKIqiKErD0RcNRVEURVEURVEazgI9GjNUYDZp5Yy12yPeYQxpL+3DayTPtXsU1qyh2HPUQUg7zPkrnCCiFg7qWHvWvwJi78Q9ECfr6CfwkhvwcGKuTzxcxzXf0xnUh7q0rnu5gvq59U2oM/Q98zz2l7ZAXI110Bb1F/j34lD1A/EnzSl7T+Ths85m1Ei2NZl5XJalMU9GNo7NP1/BazJeZQ00XXPycIiIOElsCz7pekf7UReZXYZtQSzU9cYC9GRkAlMbnqHkGpaD9bK8PMR9z2yHeP/OPRBv3oTtQESke8VqiMfywxBfcs31EG89/3KIn3jgPyHeueMpo4xjfVhPiwwIcXf6nnre0ng0bGfao+HydaYx8Q2//hvG/kER9cNjY3g/6wH2W9tBj05IngHLNscKHnst8lhYPLCSLtpmj4bNx5vbo8HHMOpAunuXcr8kUmaeH8ulsbmGx2iiPr+sqwXiw/ueh3jV2vONMuIxLDcgz9Wp7+ZcGZelYPDE8FSek/I4ecrIlxCPm2vx+23YvtpyOP6k0ngNKxU8T6+C1yOom37CGnko2NtWGkdPxsBxzFNQGEV9e64D56FEEsdxERGbPBqJJJ7HeBGvVUc7auIHh3BcrlTNPFHsPYnH8HrnWnDszmZxLuAcGVF+hLk8CnPmmTjdzHwIZDvsPOrG58fjgmGxpSfUWohtY4j8FSIix0rof+gfw/Z1cgj3KZTRP1jxsM075Df23Aj/cRkrfuwozo+ZIXx+6+7F+bTPJ3+Fbz6/xFP4t7yNXpEkjV2pGLbHkyNYp6GTO4wy1vRshrgthfUUf8acF6hHQ1EURVEURVGUJURfNBRFURRFURRFaTj6oqEoiqIoiqIoSsPRFw1FURRFURRFURrOgszgtuOIfSrRVlSyvBlEWZrYyMjbBIa5SOb4w8Jh0zoTkBGbzUlOxP5scCLPsbzm6m0Qj1TRlOOHT0J8ctg08Y2UMXlexhqCuDb4LMTrW9AcFy+jcbU/RNOPiEjZxoQwFiVQtGY46ZfCk9aSTkps0vA0WsZ35D19aDDsGKUkiCKSjOE23e1oFm3OoJlqiE5ypEyGrappxE0k0bxtJ7BtDPcPQOzuPwhxU1MOYm8IjWyVo+aFHyJvWiqLDdC38LyGh9Ds1r0Kzd9ujgxgItI/hkbGDCVYyySwzcZTaES75o1vgnjN5k1GGY/dexfEBw+gqa9UmjZomknBFgk7mPhfRAJK2PXKay6FeNlqTBApIuLWsN5HDh7EDSgfY536IPnNI83gIf+Nk6eGbMCkcZnN37SCRvS3U3OswuFQ0j9edMOhxHicEVJEqj4adGt13OeVV14F8YoN6yDevhuTZ+aSZhlj45i8sjiG+9SqEydWKZvjy2IwPDQq9uT9qdFiFWystc2VT8SiKT8Rx36cTuA8MV7M0/7UFiLKGC2guZZuvQyfwDHt+eexnwd1PGYsiWNJz6oeo0xeiMGmNuzaeJ5HjhyBeHAwTwc0kwLGafEQO0NmbzY28wMNdwpOWBy1zZJk5XtxzMcMbtPoEXIcw2syUEGT/tP774b4cB4XNhERGarjPF/18BpWizgGc3uTAO9zjMampjbTDN7Sjc8SJ8/EtrGnhM9rksXzbNuG+y/fjPuLiDTRMx0vSGGPoDl86BiOZSMHcCxb1n6GUYZdxH7ipmjxB3e6X1ju/MdA/UVDURRFURRFUZSGoy8aiqIoiqIoiqI0HH3RUBRFURRFURSl4SzIowEYmVVYf2hqC1nPGYYcU+IxI+nTHBn9RCIkjrP7Qow6crIqlh5HnZeDl/HoGGpIH9iBiaKuu7QX4n37MYFM1jaTBbWOPA5x/17U23V1YeKlKy7ChGlODXWzD+/nRFQidoA6e4u0iaE1rRFcisRBqXRyKqHWiRFK2Ea681LNrN9YmfaxMPlPJo7HyMZQi5lK4/U5MGgmjatVSR/qk8+FkmjZB49CHHSgNjjZksPjx03t5ji12bSP980nL4FHn1dJU52PSvQVdEJsNcXpc9S9uuQtSGcwYdG6My80ykilcZvkvf8X4l3bpxMLhvNL39lwAsuXYLIfeIIa1Q1b0ZNRjZPhQkQy5JfZ2tkMcSVJ984YE9lPYXpVQos1xDymkQ+NtOQBlWGzLj9iFOXEpsY3WDT2+1RFn48ZM/XrJY8SuTXjmHfVVddA3NSBuuc1vVjoyIipMS6PoJb68I5HIO4/OdG/6zVz/FwMRvNj02NvyPd19gS4IiLJBF7DwiiOVxZ5FB3yOozmRyH2fPM+sa9jeAiv6VOPPw3xwBDOd1s3oXY8oOcEl7M7ikgqlaFtcE5OUpK/chn17Fu3ngVxf/9ho4yRPM65YwXUyLsxHGd7lnVDfGD/AYjr9YinEe6b5hbTn/3i2Dem4UZp4zxxoojJY//zOZwDDg3vh7jmmONfidqkTx4NO6BEux75x8ieUw/weHbEdbdLuFMuh2NPijwbEmCbz67GsSkjpkcoO4rbOGM4Bg3s6Yc4fxQ9Glu2boT4kgvONMroyWK5tXH0T4kzI2lgxXxOfSH0Fw1FURRFURRFURqOvmgoiqIoiqIoitJw9EVDURRFURRFUZSGsyCPhiXTlgVee511aywfndiHjsdrcLMeOeAyeBH5iEpyPYw64F/YaxCxLDiVGbE2OenR4+nlEB+sXAvx/Xswl8K5q/G8mzu7jDJq4TMQP/vsY1gHQc3pzudRD7rzKOruB5MrjTKcBN00uh+BNd1cLOvF23teLN1dbZKYzNdw6DjWNZZADaQXon5WRMQiaebJMdQjsicol8J7vboD4zNRgisiIvkCHrNQwzZbrBcxPokaU7+K9zFHa3p3LkdduohINtcOsRtPQlylXACOkJ+CvCmWYHsWEamQHnMoQH1ognxKvB6/i1WK9Fh0r9wA8WVXkw8knM6zUatV5bHHfmYc47RjhRP/i0jo4r0KBK9JMUokT10sRXaYuov3xhZqtNQnZ/qmpusxe34F9pkF7K8gTTznTrCi1v835gPahr1v7hz5AmLmefkWnldzC7b75uYOiMtlbLM7ntsN8dOPP2qU0dSE9dj33MMQF0Yn7rHnLVEeF7Hl1MRn+GLYo2Gbk7DrUL/kJBd0G1zquNUa5oDqP3nCKGNkBOe3vuOYC+G5Z3ZC3LMKteNnveJ8iDs68L6e8ulBPR0890QSt0lQnp9163shbm1Df9jOXWauhGod73mlgnNMqYRxtYoeIDMHiXl/+J4ZzyszjhGGodRksdshPAXOse3cHpRCHX0Fj+3+OcT7hzE/WI2eO6pVmlhEpFrHbeo0p7L9y61hnxjrx3Fj7CjGsZrpzYwLbtNkY+6XZEj9LsBj1HeThyhjejGP1bBfjQ6i92llB3nWtqyF+BUX5SD2Uzi2iYgcPXofxPkRzK2WSk/n3Bov4XWdDf1FQ1EURVEURVGUhqMvGoqiKIqiKIqiNBx90VAURVEURVEUpeEsTGg/Q57H+sJQODY1tg5pSL0aasxsF3WSdgx1a6xPjtQAziEbNHwhxv4LX58/YG8JrUUuWdTK7T6BOtfLN5MOP2PW4entOyCOxXCfYuIciO8ffhUeoAnXdbZdU4MqlPNhqXIVvBCbt66TdGoiN8HJftRuDhVRq5nPm+vce1X8m2vzNcD7NlzE8w981Nx2NZF+XkSW57AeCdIOj3js2cB+c3IM28bwIdR+1kro8RAR6ehZBXF71zKI45RvoCm5GuJkEvXLtbqp+/VJk+6xV4DWQ6/UcPvxKmpSU3Gz/cWpTbb2rIH4FZdPe50q5XH5l3/4inGM004YTvkROO+PR2OLE9HF2MzmCXvGaPuA+qRhOovQeRsxjU+s+2ZfG+c34iIDc2xnLTnHPPbHOD8RHS8WMY4n6VRj5C2qcxulag4Po1dpdAjXmRcRWbsCx2rWUsetiTHEjvDGLAau6075Cg2fJF3jCCeNXHTRKyDevHk9HoPGxJDOv0i5I0aGMQdP1DZkYZTeNb0QbzsXPRlrevHzWAzHWfZGiIh4lPwgRrkR4jEc40oeaeRpzEunUWMvIpJJ4/NIQLmDjh/vg/jYUcyPxdfWifQwsD+KP5/+50T+m/nr5BtBGIZT7W42/4iI6Q0TESnV8xA/sw99AocHj+AOdO9DPt2yOVfVyzQu01xUL9KYOobxyDGMC4fxvJp8c97PNuG9TdCzK08FJQ/n9QHBeT8ew3w1IiKtMbye65qwjPNX5CDujuHzztP374N4x7CZqyPWgXNuayd6NLpXTPej8dL8E7noLxqKoiiKoiiKojQcfdFQFEVRFEVRFKXh6IuGoiiKoiiKoigNZ2F5NKxArMl1kH3SqFo+asyCwSeM/YeOos/Aq5AurbkH4lgrrq8dT6HW3HHNtYYtB/9m0zY+6ey9CmreWUtsrGttm5fMdVH/GVC+j1SA6x+ny7dDPDSAeTfasqibFRFpacYyDlkpiLNdl0JsJ/GYAWlYJTD9FxF/+i/F2pXLJHNqfekrUdf7k/sOQVyN0PEOFEnH66DG0CWxekia2sEi6nrzJVOnfWIUt1ndjnrOtd3kQ6L8E2va0eNBS3rLYBF1wCIig8dQuBrWcafW5ZifIk15NgLBthSLU3IHEbEN/w7ikebZD7BO5TLuMR4z+1GS1rpn70iiaTpvQuCYdVwMZqTRYEuT+DbWKRaZTIhy05Cvg6wuRl4fQwcdmNfR87AMx+W7RWUaSnCqN4n9q1VzHXnOR+SQQcWiY7p1PKhfwRNPR5xXzsV22tqGsUdmAPYatXWgT+3MMzcbZWxYixrl57pXQHwinMgJwZr+xcJxnBlz0uwa6YBNKiLixtjXUqUYz2toCH2UJ/vR12K7pl49kUR/QxNNLFu2ou571Wr0mLku3nv2h9Wqprac8/wMnsT8DC61x3IN9euVGuVaiPAwZpvwvKpVLLMwis8WPvlbupY1QxyVZsfi5yrD9zVdL98PZHTE9OydTmZY1MzPKEdGzTN9krv2Pw3x8SHM95XO5HCHAJ/fWsgD6Y2Z8/zhfvRi+Q7uMzKM12zPdswFUx3CcSIW4H0vScEos+bjRanZlHOFBtHQxX5oV/Dz8rjZd5tasd+MjGPfu/dJfM5c1U5jaAqvg9+MY5uISCyLfwtz+LzitE4fwzHT2bwg+ouGoiiKoiiKoigNR180FEVRFEVRFEVpOPqioSiKoiiKoihKw1mQR8MPQpFgQovWkkLt4P/7KtSxreu6yNj/f39zL8Q/+Snq6kslXEO5cuheiC0bRWG2beq0Y7RetkN684CE1X4NNX7xBF6SWBx1cJ5vrk6ezpBWmDSlgYvXKtsOoZRK5KeIEEGytv/AYSzDiqP+UwJTRz2T+a+A/F+Hhx98XJLJUxpB1CP6Ad4XN2Zqhzn3C+/j87rgpJc19o+4T/0F1A8Pl/A+HBlCXe+2FdhvVixDfWiOdMGrckaRsj+PmtPRftS9FgtYh9LqlVjmKswdkEpjWxMRSSVR31mpoqbZo2tp5LyhNefLJXP99/ESXrtEEvu760/3zUrV1P8uBoEXTGnfLco34bPHJ4jQ8ScoT4Hhl6DvfqiNBQG1uYhkCcUCaoibmnBscMgXFNKYFpLXwSG9OucDmPgbedk4rwbXk+LREVw3PqiZGuXO1k6IV67EcbO/H3X5hTK2sRRplDdtNj0ane3Y/2644UaIDx7cIyIi5UpFfvxTzAGwGMTiztS1Zh8M3ycnwgPw7NOokX9u+x6I01ns+wePHIS4WEIfwvIVvUYZ1BTEZ68I5Vdoa+uCmPNTjFKujuFBvM8iIoeP4bPD4BDuUypivS26OKvWYFty3YjvYKnvhfQsceZW9FauWdeNu3PfjZiFPZ/mbZ6TZtzzWq0u25/Gsf5044e2+JPeM4/Ox03gfTs5hs97IiJ9Q8cgHh/APpluxhxQYyfyELeuQp/CCrzEIiISHyJ/hINzbpWu+9re8yBedfGVuP04+pSGj5v+Y2+YPKJVfK4sk6erWKGcP1V+NjHbRpFybzguzpcB+SLvO4TPBVu2oP9sSy89iIpIOotzg0u5ihIzng28cP6vD/qLhqIoiqIoiqIoDUdfNBRFURRFURRFaTj6oqEoiqIoiqIoSsNZkEcjqE+vwH7GZtT9vuoV+M7S3JYz9n/rW14P8RNP7YR4jPIUBCTsLZPmNgzNNZRrNdS2xevo0bBt0u4b4mH8PJPGS2RHrU1uof4zlsZrUSzQ5zHUIRbGaT3ukqnPi9Ha5Kk2jJ0UxpwTw/L5Dy8taQavm78Y3PvYHolNrtvuxnA996YO1Fu7cXOtdUM3Thp7oynw/uzPibgEvP66TetrnxzD9jNIXoP0IdQfr2jDXDMbV5i6ym3d2BdZZ73nBOpHj+zHMiwf+9WWs01/VS6Xg/j4CVx7nD0arF8O6ti363XTY8GeGdatujPyfVTKZt9fDCyxp3JCcG4Ibk8SlUfD+G4HY69O1y0gvwTp8rlNi4iUyzjepFIZOiZV02j4dEyfyozoJx5737jelH+IPR018rWNV8z+a1HfSiRm99+lU+idq3vYZjJp/FxEpEpa6vUbNkG8bPnE2F0sLk37u+D8bRKbzEETIx8at4SYY3pplvegP2vlKsxhURxHXffgIOYksC30TUX5uTj/Dccu3cd4HO9DiebD4SGsw9Ej6McQEXn40UcgPnzsOMT9lFthxXIU93d2ofY/FjevHecY4Su+ajUeM57Ga12tUj6QmumjrFWpH3H+ohlDTLVi+txON9VSVWKTHq1R8iP6FvkT6blHRGT4CI4DD/2M5qLYGMQlehbam8X5sK3VHCe8Ct67tVvRg3H2Jswr5dpbID5JeamqRfT7cPsVERkcw3MdHcLzqga4T0WwH/nUlqLyuNTIv+PS2O845PNIkd9qFOfcbB/64kREzl+B40FPK/qnWmc8W8TErOMLob9oKIqiKIqiKIrScPRFQ1EURVEURVGUhqMvGoqiKIqiKIqiNBx90VAURVEURVEUpeEsyAxuT/4nItLeikaQp5/dDvGqXkxeIyLS3oTmlJAMMKGFZpeWZkzmwmYqnx3PEpGEjcxU8TiacNrbMQlUPo/JrtiA3tNOifFEZNwwe3MiJbzMqTjGbPj8l9sxUaGIyJ6DaL5N9f46xBkHr1XNMJEtvnm78SREJs2IATXdQh6T6pTHTbNmQG2DzbzGezclYLO4vUbUsE6G8YCSUzkOtr+gju1rpI73KX8EzW6D42YSuDNW4r1eQUkot3RjErLWETzm0f4dEBf7O4wy1q7EflJuxr58sh8NnOUqlhHSIgp+3TRC+pSoi/3i+RmmvFoFy1ssRkfHxJ+8x5kMmqybm2ls4MxlIhJ6lIDPpvHK4+R5tD2brv2IxSnIBMzXuh7QhaXElEGI27ORO/DMZJjsSefFImo0tnNyuQQZ1msRiVGP9eEYGNuF9fi1GzG5XjKL94ONy/mTeDwRkWxnGx6D9jmVpNPjxTUWiV997VVTiQd9ukbjJez3doQZfG0vGmF7erBf9/fjIhslSqKZImN3sWiacWtVTjqHYUsOr2mCkuryggg8Z/NCAiIiVUoguv/gQYjHCnhtlvVQkkBa4aBSiTJqk/mazou94hk3TdvTBqG5IEZIzwKWRfvMaHa+u/htcHxsVKxJU3LfYTRmHzncB/FARGLFnTtwAaATB/dBXKKLaMdxkZtjNo77516M5mURkVf/xqUQr16HZu9jh/AY/3LrNyA+uvsoxF4VzeDVEj5riIhYPt7LOo3LNYsWbjCSa2J7C8Uc10fpWdSh55VEDPsJe7XHuV9GJF6Nu1jPlIvHjM1ogLGo1XBeAP1FQ1EURVEURVGUhqMvGoqiKIqiKIqiNBx90VAURVEURVEUpeEszKMRd8WeTBZ0oh/1d48feRziZAp1lyIiadIbskY+TQmUEqRBzGTQh+D7pla4QFpMnxKmeV6NPsc6JZNYRrGACWRiPZjYR0QkTp6LKiXiCUmnb1mUsCiB+uTRgpmQqCKYDKij8wIsg5KFOaSfCy1ONmdq/V3yktRILz4z05clpob1dOOHIqck7baN2sFiAb01nHhKRMRiTSJdM1NxyMJzCiOSpfGfgpC8MqQFZj09Z0OrB3jAE0NmkqZ8AbWxq5ehNn3jMjxmTytqhze46DEq7H/AKGO4BetZdzDppJC2OKxhPT3qZ15E4ss6JUurlPCe1urT51GrLo1H42tf+7spzbhF/TpJCeTaO02vi+NgH0sksB3HSa/ukt+Lk5+lUmbSOU5kZ5FYN0aa90wGx9FkCrd3XTyeX8MxUsRs97UajrMV0rfHUngMl3T3QyOoixYRKVObOnkS/QScsMqx2R+F5xVE9PhYHOvVtWw5xKXSRN9K03izWCSSKUlMeTTworsxrLvjmOdXJ09YoYBJu7hNx+M852K/zedRpy8iRmNg/0RTE3rGQkpsyd4THkPjCbP99a7uhfhI3wDEx/vRj9PVhX2zRm1LAnN+rJVxzPFpDKvW8HqXK5SMk5IJc3LhiWPUaBueZ6fLqFZNj8fpxnZssZ1TCUspoXIR55HDe48Z+/cdxvuQTePYY1fwug+M4HNmkMRrPFYyk84NBM9C7FawrzqUfLZm78Y6ntgLcdrGMdZxI+Z9B8/DF47J58YDJs37lhPxG0DAHmXy9wXYVmIJvFatrfjs2tGB/VBEJJEkTwbNRzNjN27O4S+E/qKhKIqiKIqiKErD0RcNRVEURVEURVEajr5oKIqiKIqiKIrScBbk0ZDQn/hfRKoBatBuvB49AytWrTB2/96/3g5xvY5rcDflcA3z8jhq/tIZ1MqZ+kVT5x2PYz0zWdR3Dg6ilrO9Hb0QrPttaUE/hYhIyLrVUfSJlMaxnoeOo/543MPPj55EX4iISPfW38Y/ZFBjGtA6zjbVmyWBEUuRSzKJzSGs0Zr9M+LQjsoicXoJrZgEdmyyfGwL8RTqFTMRJ1ijtuGTHpbzDfgBn6ORLCCikrNfF/ZocC4YI9cHFeG4pi+pRl6TPcew/YyVUH980XpcO781i7rMXDholNH3JOZ2qWR7IC4lKC7WKEYtd6mEfVtExKf8DYk0ek0Ce7rvehH5IxaDJx9/ekrrX6NEH+OUu6WlJWfs78Yo/8sY3ivW7oZ0TbJZ9Nd0dJg+kEwW156vVfFadXXhGLesB+uZzqKXIT+CGufnnzXzT7AnI03ekZEx1FLXSN/dlMXcCl1t7UYZTS2oMXZoDGLNPPsJRoawDbIfRkTEIq11qYznFU6uhx8ucOpsFOVqfWr9+5DGDvagWBatqy8iEuI5swcjm81BvGI5zuP95M08edLU4XMejPES+uW6u3GsYM/iaB7bijlWmGPssmXYpjdtwHwhNuWrcShH0sgQPge4Uf6WGuefoVwx5H8ZGMBx1GPfGifeEJEqPRNxvwpmzEn82WIQus5UDojApbo5OP5ZMXOMz3ViP2fvVpy8fGuWY56MsRpenwN7zLGo5Xnsm2NVvE9nrMf4yl9ZC/HAfhwnxo7TnGybOX5C4ectmp/ovFyL+y75RdkfKyI2jXfJJPabXDPODWvXoY/y7G3rIN603sxBkmvD++OQ1zqc4TEMnfmPgfqLhqIoiqIoiqIoDUdfNBRFURRFURRFaTj6oqEoiqIoiqIoSsNZkNA04VriTOa26DtJ6/sfzUPcmd5n7F8dQ81iM+mNY7T++/AQehkSSdT91uumRnH1Glz3fMUK1IMeOXocyxgmvwTnX6D8E15g+kISKcqjMYL6vBrp7k+MoJax7OH73nL2Y4hIYvW5WAbpM2PkyYjR+vy1KtY7FbEWOeeViLu0vrmdmPHvJdDI2/GJ/0XEopwfvMZ0Immen5sgTXcN9dj1CutjUT/qk6Y2CE0d5UJhzwbDuU2aWtLGNiF5SQpFbMPHh3H99/+soR550wrUZa5rR2+EiEhTHHWrwXAe4qPj2K/6htB7MDqKOQ/KdK1FRCwbz7V9FeqsU5lpzWm9Zu6/GIRhKOGkj6JOHjGXNKuxmOmn4RwXfUXMgZLP43gUhNjm2Hp05IiZc4dzATmUcyZ1IY6jHZ3oOytS++nvx3t37Kipy2c9+vr16yHOkxfl0ImjECfIL5FNme28g9aBr1Eb+NfvfhdiN8E+LvSudLShJ1BE5OCB/RDf8aM7IP7Yx/5ERCLmiUWiXvPFdaLHXh5L2MMhIpKIY3tin2OC5oW2thxuTx6Cct2cD4cGsQ0fPXoY4lwLXveeHvSB+ORZrNI4PF4yc5gMD+OYViqiz4OfNcTHMgZP4vgVi1N+IzH9cU1N2J6qVfQkjBYwrpBXzsjdISI1j7chj9CMfEXsR10M/NAXf9J/YLvY3to6sb+tWWf6rFatRU+ZzdN4HftVzMGxa2QMPx8ZN3NBuAVs9ycPoo+jHOQh3rIFPRoXvhLb4zN3HYS4PWLcCKmeJ0+y35PyTAmOh1nKK2S0VxGx6NlzWTfWo5P6alsXjuurVuP2Ta1mGYkm/FuS/H7WjDnO4hxgs6C/aCiKoiiKoiiK0nD0RUNRFEVRFEVRlIajLxqKoiiKoiiKojScBXk03IQj7qR2sVhETe3tP0at8cHnfmrsn0ygxjZHejCPNM6ZDK0HT3pQXqtdRKS9MwcxpzU40YfrgLNmOpPBOo3R+u/HjqH+VESkvRU17bzGukO6WK+CcefmGyFuXvEao4xx0sayuNGN4YnGYrTGdw0/d+wIfR2t3RwaEt/p91JeN3oxsCQU69Qa6haWb9P1MNaxFhHXprWrbT4Gfm6TBrFOa37XI/TJAfklTB8HaTXnSEfCHg1ec36inhinUqjNLBRR01wsY1t67jDWcWjUPK+zyMfB6uADB1AjPVxALa3vo0/EiZt916FzGxlAL4Bfm/48yp+1GFTLJbEn202CcmJUKnzdzDbI98on3W1gtFvyFVH7Gi/hdRURKZXxb7aF9TxAPoRiCcfEWBzr0NqKuuqNm9A7M1FLbEPnnnc2xHv3Y5ljVWyTmTS22XrVvL8nT+Ic49jYCv/tX/8V4gsuuQziDRu3QBxE5GI5eBzb3FNPPQ3xQw8+JCIilYp53ReDWq06Y5yi8ZraStQInc2gzyAkDxDn8YnFUHuepfvU2hQxBtI8Mjqah/joYfTnjFKelibKBzBI/p/+4+inEBEZGhyCOEbj5rJlmFMgQfYpr45+Cs8zz8uycCe6VBKS34XzgfB5VCJ8anUP2z3XY+a8xnljFgPXCsWdzEESd3Ewy1Kes+Zmc4zPuDjHpl3ys3r4PMbjYXsajzk8bPolDvXjM9rYKN7bAo25XvkQxGu2YZ6pDavQw9HW3GWU6XN+j2dxTI3zeBHg59U4PkPGk/jsKyJSq6K3t6uzjT7HMsIA43gS+3I1qv1QHiGHYntG/g87Ko/YC6C/aCiKoiiKoiiK0nD0RUNRFEVRFEVRlIajLxqKoiiKoiiKojSchXk0QkvcSeG+baNAcdxGvdjBw+Za182tqPfidX9TGXzvCeuoSTsxiBr5jk7U0k0cE+vx9DO7IS6RpjmVRk1guczaW9QhBoH5bjZO62OzuSGTQY1z1xb0YKzY/FqIq56pT26yMI/BYIiavsDF84jTna2Qni4dM88jIM9ClTTMiRn7eBFegdPN8p42iU+ut18YR91lycf2aEXo44UkhezrcMj3EaNrWqV11K0ojS35OEKfTRhYiZBMGhbdJ87FwJ4NEZFMBnXT4+PYb1hPzvlAOA/HkWGz7xbKeD1jcdJh4+0QsbHeLU3o8WjrxrXKRUQyWey7IyOouy4WpjXP3hJ5NF7z6ldKPD6hdT1GWvF8Pg+xL6YBp6kZ+20qhW2sWCSfEJk6WLMc5fFhr5FFbe7oEcxrMDSM7T7XhjroahXLLI1hnUVEfMpLMDSEevRqBRtIlnIQsKY+nTDLODSIPqB6Ddt571rUUp979jkQF8axHwwMoE56op7Yp6+84kqIb7v170VExI/wdywGBw8clPhkjqQgZP0+9kmHkxSISJnmvxUr1kBMy+YbPgTHoXwnWTPnDnsUebxhb1t+FOe2sQLGpRL5J3ggF5EmyrGSbsGK+3StxMfxw/exn0VZGLkvVik3VSmPuREOHkDt/8gInle0x489M9zfgxmfzWHwOw3UvfqUj8SjPu+Q/8J1zYtYGEKflRVif6uQb6BObalAz1oHjuKYICKy4zi2l7KD93qd4PPYYBnr5K/ANr5t43kQB0PovRERkRLmGrpwA557j0/PVuPYb/b72H7rcTMPmOXgHFoiT4a42B5S5HEujeO1S6TMfhSP4bmHAT9XhZH/ngv9RUNRFEVRFEVRlIajLxqKoiiKoiiKojQcfdFQFEVRFEVRFKXh6IuGoiiKoiiKoigNZ0Fm8PaWhMQmTSqjNhpBymNoID1aNI1AHpn5YpQcL9d5FsSdFTTrjXv7IF61MmeUEVBiu1IBDTOxOCYtIa+VVGtoHIqTSXFFJxpyRERGyaCUakFT4vItb4B4Yy8mjkqn0Th7YNA0GbtJNMqmyWzrxCh5TgJji4xjPU2mEehkiZI1kTl6pvmnbi2+EW3T+lWSTE4YVWs+3pennsckUKWyaRa2ybzE5knbZuM1vYeTuZLN5CIiLhvqq3gvPTKH+2RiZ3Mvm78TCdMk1kwGYzaYj5BJmZMIemRstdgBKiIDRTTYBWSMdGmRhIDqYMfRmFb3ze84jhzBpG6VMTRP+v70/fE8Thm4OHR2tE0l+STPtWF4HhvHpIUiIoWxPMQ29aNUEs147P3nhGr1iOvA5luh+12j5FLUBKVYxHr392ECrJhjJsmqVHAMfOD+eyHesHEjHoOuXZIXPYhIBtWcxUUPLMFr1U4LgWTJDLmsBxcgcGxzDHviiScg3rBuHcQP3f+wiJgm3cXiue3bZ5hsqd/S+diUYE5EpFLCcfGVr+SEoLg9r2URS+KcnXFM034YYrkjeezH5Uoe4sDnBTJobKHEqamUOQZWKAkpJ5DkxJd1Mh1z8sZkxGIEPCx6ZOYu00ICw8O4mMXYGJrFoxYUsOaYV2fWYSnM4BL6U0kD6x4tgmNcc3MeYbO3BNj+BvJ4zICSOEuMDNEWr0Ii4tOCGdY4tq+B7TiedW3F9lRuxTr4AT5bdCfM+5aJ4/NvJ43jJ47gPs/uwTokVuJzKT8TiojUqL2kKEGiQwn56FFWSuPYXpctx74sYiYEtniSW4ABfCb6i4aiKIqiKIqiKA1HXzQURVEURVEURWk4+qKhKIqiKIqiKErDWZBH48wVaUkkJ3SyJ06if2LfrocgPk7Ja0REci1YXG/vhRB3rkEvw/K1qJVrO3oM4lWdpgbwwFEs9xVXYTK847vvhng0j5rcpu5zIV636VKIVyfRJyIicv/DD0J8xrk3QJxdjglfujKoWV3WgRrBwrj5/heLoT456aFWjhNeJcmzUackgJ0ZU2dY93CfJGX9q804Ri0iadLpJpmIS2pSh7i1dy19hnrFR5/aa+xfJt+GZeH5eQG2N058liLdbsw271OFk6WRxrRawSRjIqibDDhhURX19qwDFhEZGUW9Z0hJJsNw9nsVku7Sj/j+IQyxHqyhTzqz+z4KQ5jcboQSN03sg36qBGmz/XD6Hvv+0ng02jtaJZWc6K/dyzDx05at6EOI8miUSUs+OIiJ7dgfUSpx8kVso7WIxIWc4KxaoTYWsC+I2xjWYZwS3QURl96hBK5BgGWmUqQ5HsX2kaXEqU7E+LJl8waIW1pQr93UkoN4z65dEKebWqhMUwftkX/lZB+22+Skx2+pEvad6OsT25non2ShM7wMiTiOiSIi66/ZDHF7O7Zhx8GDplJ4jCDAMritiYiEgnOwbfMxab4roL6d8sAZc1tp1EyYVijQ38gi6sRpPiTvHCdGjUf4QLw6JQH0sKKdlEC4owPjfB7ryF66+bH48+5M3JgjsclraVmzezKW0fgoYiYSLuRxfIsF2N7iCfRZJajPdizvMspoasaEpM88ic8CYwN437ydGG9uoUTQ6/G8uldQVksRWe1QotWRPMQ/PYgJ/XYGeG2aqf2m6+Y837NsOcRBgGXG4/iM6NVxoLYFPRyckFhEJBGnxM/UL2YmFF5IS9RfNBRFURRFURRFaTj6oqEoiqIoiqIoSsPRFw1FURRFURRFURrOgjwaZ60KJZ2e0KvtpbXZRzOo2BoeNf0Tl1x2HcTbzv81iIslXNc3mySdL62L3tPK6ziLZLJYD09wnx/1PQ5xdw51b2dc8CaIW1ecC3Fi5EdGmbnM0xCv7kHtcDKH73Mbl6H+M5XGOm+qmTlI4oI66Vqd1kyu462MUx6NTtxcervNMla0oma0UCMddWL6/pTLoXzbOMLpxbJtsSbzN9i0ZvzGdasgTifNNaIfe3onxCMFbF8xygXhka+FRYluxFrrcYHaxTUAAOVLSURBVF5nmrwMrKt2aliHShnvs0drpbsu3UgRsej7glQatZpNlGdjqB91sRZpqB1j7WyRgPXJNfJPNWF7aqZqjnPuBss8j7KNGvoKXcrRwnR7XIIV5CcL9ib+FxGbhs9kCs8plW4396f2sGF9L8Ss/ec8Kuy3ifKq1Iw2hde+UqlRjG2uVMb2USxiPDSIniARkXHDF4JlsmcjGae+VqP96RxERJpp/K9VsQ2WS3ge23fsxu09bMNdHWZOpHQSx+bxcSyjvS03Ud8lyuPiB/6Up8oijTaPAx0d3cb+l1yCnsOmJhwbuD3Va3jNikW8r7WqqSUPQrx3Do0vnCeD/RF18giNjaOHI8oXUvewHhb1k2qVciiRvyDXgmOPGzfHdk7pUKL5I0HzwbJlyyDevx/zBEWNYi/Ot7F4xFxnyjNTr5EHgPJKdXaa/onBQfSn1snw1dmZgzgRw/7oODg+NjebPiS3jtd9+AT6iXfvx/YzegLrcPixPNZpNR5vc7d5XmOD+GyRHcU2vG0NjjX1Kj6fDFD7HCqb/r5a/wmIi8M4Li9rQ29JW3sPxHF6JmKPqoiIb+RmwX7kzPCBOY75DPlC6C8aiqIoiqIoiqI0HH3RUBRFURRFURSl4eiLhqIoiqIoiqIoDWdBHo0Vy0Ykm5nQrDbTutTeMdStrdy42tj/hte9Egt3UeM4SvpPl7Se7WnUy6aT5hrybbT8dSyOWsxHcvS5jTq2i85eD3GJNKjdHZi/QUTk+OEVEK9uRw3zGZvxfS6XIh12DLXFHRlT/+uRBrVGa3gPFfBaZBKo08+mscxU3NTW+t2o4SuSTjCVmD6P8ZKpoT7dWJP/iYj4pMENKTfAulW0FraIJGkt/8eexZwofSfyELNXwQrJExSaGkVeh95xaRubdJLUBwLS7bIWPEpXmUqRdp1zcdSwbbAKk3XBUTphi7wFfoj16BvDMkrUD+OU58Tjhe4nCuZSIYrN0Ota1tJo5B1n4v+JOlD+EUoAYMfMe8V+GN/H68aeDM5BwJfIts1cEKwVz2RwLAhZ20/3NiCdrsc+kND035l5JfAYo2OYW2FkDNeNr5C/ojSGunwREYcMTn6IsR1DjwzngxkroDY7ETdzYYSkGQ/pvLom807U64s//olMWHxO3S6+b3zNzzzzTGP/7m7Um3NfT5JHpVzGMW90DOc29smImNcmQceMU36P8XEcC8h+J46NbUPM5ich5SOKJ7EtcL9JUH4QJ479yI7Qn/t0XqxRr9U4TxPnOcAyKpWF52KZeUjj9i8C46NlkckuUi1hX+GcQIMDZi61Iu3T0ob5JLwqXuOqh+0rRbkfxovmNWxO5SB+zZXoS8qk0VO7Yy/m3eg7hHV46MdHIN6cbTPKdEawHn3H8TzTafRoXLcKj3GQch09eRDLFBEZo2vXs3wlxPUC1mFoGP0wOfIMSYQXk+dcn3xtMx9nIqaBF0R/0VAURVEURVEUpeHoi4aiKIqiKIqiKA1HXzQURVEURVEURWk4C/JotMTHJBuf0Imlc6gX68ihBm1la6+xf1sKNbK+YJxtRy1npYI63eY0lplwSQguIkEKNWbjPr5LNWdRWNa7Ej0Zq5ehVi4U1F22ps0yr7x0G8TFSh7i9cuHIK7SuuCxOApIY4GpP7dJMuqQHtm1UfOczuD9aEqQp6FuamuFvCKdbagp9f1pzV8qbq5lfrrxg0D8SS9GSBp/m/SGgZjazVXLURfJeQ8efGwXxCdP9GEZpElkL4SISJ3l7FxP8nDESIPPuTo4N0ChYK6vzV6SIuVBKFFuDsvifCF4HjY3NjG9Ba6F29TJrzJM2s4knVgYoQ+1HPob+XDsGdfSjhJqLwJ+4IsfTFwvyzgH9l9E+EiC2YXVIensbYt9K+RL4PYTAd9vMY5JJdD2LiUQYD18VD3Ym+S4mKegrT2Hx/TxfjrcEcTMjVCn7ufR52dQEymR38Cvmz6QkO5ZpYxxMDmfVKtV+eGPjd1POxP+mYn7Zzt4gvEE+r96e00/IV/WCnkQeRzNZvGYtuD1GC+a41HgY/vKpslDxvkXnBzEDuVjSLO/MIXjm4hIMoXzsu3wfED5iMjHZFMbN3IoiUhAfh2HchqxR4s9FPPpq3yDInrBjH8tfs4Ny7KnxpPWVpxP6zT5RXn9OtrRO5kl/06J2hN7t7JJfK6JcXITERkPKYcPtemt2zZB7KbQw7Zr7zGIy0M4Tjz00weMMqsZ9Px0ZzGv184D6F9J96EvZNsK7GfnZbHPiIg8lyePkODzWS2B+9jUxtl/lUyZOUjY38f3MJjhhQ0WYNLQXzQURVEURVEURWk4+qKhKIqiKIqiKErD0RcNRVEURVEURVEazoI8GoFXkcCb0LuFIemPXdR7JdPmob0q6u8sWjDbjaHuP0UaVCN3xMmTRhkJ0tv5Fq7lnCYfwtlbMP+HVA5AmM3gefgR3oaVPe0Q796HGr96fQDiVAb1y34Vr6VjR3g0eM140hIvb0dt3WgR1zsvk77U8k39pBvi9XdiqOmzZryXWkJrmy8Kgcik98LmvA60pR8hH7RY1E3XIEn6xIC6B9kOIpTqIqFDOl3SMToh1oG1wyHlXkhnsV/ViqY+eXgI74VPZfi0FjlLhTkHCddJRAyxsEVabdb1s3+hxnrdwPS32LQNe0/cme1xKRaRF5HQsiWcPNeA1xwnjbLlmN/jGF4XWoufZa++0bJfWDM7VYbF3haErzNfS9bIM2GUx4P+ZlF+EIs07wH5KRzKHxIh7zZapeFbo/wfvH02TbkSrJxRBuvwpYV8IZNjSKVi5iFaHKY9GtwDUknUeWfIpydiaq7LZfJN+jhWZJKUb4I8HbVR0+fSthLX9/co/0S1jH7CWAzH3eZWnHfYI9Tff8Io03Hx3lbHcZwslLCeIXnMsk2kV4/KJZSktkA5H4qjOOf6Hvs958ai3mrmSpnh0bAW36eWzWanfDvsOWF9fzUixwqP+xb5A5upzfI8X6b5r14zZ+HQpftEZTS1opdhy5no0+V8PCOF4xCvX2nm0RgbyEP87PZ+iAdq2D7r1D4PHcF+uGbjGqOMoyfwmM445slobkP/S1s71jOdxvGBPRsiIrEY1pPzOs1sj1aEg+iF0F80FEVRFEVRFEVpOPqioSiKoiiKoihKw9EXDUVRFEVRFEVRGo6+aCiKoiiKoiiK0nAWZAYfLVTEm0w4NV5CM8tgEc1WXSvRVCIiImTujDloj6pX0dQai6MpR8gkPV7OG0VUyfAbz6AB5hVnboB4wxpMJOXV8DzY8OVYVCcRyTXjMXqW4zHGxtCQ3pbLYRkBGYht0zYWkME3JDNlrYbGMDZRVckdPR6R+C1nNUNcqmC9nRlG5fHS4pshLScUa7LNuDa2LyN5WkQyGZuuWaWEZrVUHI/ZuwpNjYODaPYbLZpJC0PDNE1JtWgRhVoN3/UDygqYSKBhKx1Hw52ISLFAbZYSQrrsmuWYjNx2hG0xoMUH2IRssUGY/cbcpqN8ZDFKVhfnpG/T7c+qL36yKhGRMLAkmBwD2Qxpu3Mn02OPKfdjjk0z6MLP20wcSNeZ+o5PCUN9NllzYkURsak9hGQGD8kYKxb2tYAXAnDN+YNN6IFH5nvanpP+cZtlQ3AUPi9acMqAuwRGXBFMmMaJGM/YfCbEF154kbE/G0BL43hfONmezwtm0AIH4bhpBmcTcKmC83oxT2bwNBqxM004DyWTOOZ5VTOZXqWAzyN+HbeJk6E8TsZlNsFGLVZRo8SnVTIm1wo4X1ZLsy+YEpUYNU7G+DoZ6WfuE7UQxOkmM8MMHqfnMzYbR5nBeUzkPluv4X3z6PnLjWP7rUW0hRQ9V1Y9fFbhuSqfx2chj56VKlV8vluzyjSDD9mY3DcYx2eF1lQO4nQK7/NAP7adQ8cini1CNMp3WbjwkR3gteWFHFKUoM9o8xIxp/H9mjEfzS8B5eS2895SURRFURRFURRlnuiLhqIoiqIoiqIoDUdfNBRFURRFURRFaTgL8mjk84WpBCkn+wfhs6ZmTIIST5j6w2oNNXuekI43mF3HXauQJjcwE45UyDvASsu2FkrCVkUtXLnCCYzwXaweoQ2v1PBcmzN4LVyL/BWke61WKWFahL+gTAmifNInt5Cu1SEdokO6w+YM6vtEzORNLmv4/Okb4nNiq0Xg5MmCJBIT5x1z8XxYS2xbZv0oN5r4Ht7bzpYcxF4WjxkjX0hh/KhRRnGMNLWkoy7Ru71vZBbEz6lpScw2PRrxBPuG8N6WOCmXIdmnOoUR+mTuuz4nSuLrP3sCP9aXi4i4AZ5HEOK52t50f/e8ufX1pwPP98WbbPs2Nygav6JyCnLCNPZkmP6J2RN4mR4Ocx8jASPpz8M5kh+GnFQyQhoecCKucPbzYDMT+0g8TggrYswHnGgypA3CAO+PYavg+xdJdILEpdDHi0z0q+lrSZ7HGCXNTaBmXkQkCPi+UB8j34rt4OctHahPj9t7jTKGDh6GuEL3Nkb+G259Hs0t7BuJueZjC98Ph8ZETnzKCSL5ulSrZiK4Ks3B7Fvj1uTRs0SMvE0e+wplIiHeTHi8mHktJuYOTAZ8uom57pS2n8c/9r2kPbMPhzbPNezTxWdCn5LtpZrxOYd9WiIibgrblx+SZ4h8RbnWVoh7elZAnB+jZI9V05fU1Yt97cw2rFelzol7sU5Nbdg+R/JmGUnqi6k4ltnZicmnV/X2QNzR0QFxJuIZkOcTbn9R88180F80FEVRFEVRFEVpOPqioSiKoiiKoihKw9EXDUVRFEVRFEVRGs6CPBr1elVqtQmNlkNrQHe1t9PWpnbOJ113Iob6u+FhXM/YcrGMkRFct7peNf0SmTRqMytjwxCn4qwlJ60/aekGhlBn6UmEf4J8Hss6UGdpkwa+TOtvn8pNcgojJ4GI8J9YY+qS7rUe0jriSfzcccxbX/dQNxijvAAzq7AUb6j/8e8PieNMnEc8jnrDMESNoyXmGtGmupBF39Zsn7IEX8bLET6XCueoIM2jcBncnig/hbH93Jp8bj0emzKMJjy7LlPEzCdgbEMNNKqe8HnEx+yrsSzycNnTa40HgamhXgzCMJw6d3ONe/bfzO1j4rXMWQPrca6IeWhknbm8B9T3zbXTSadv03lEeHjYghF36Lyovfg1aj+UP8YPzftrOew1wbhSpfwf5H8KAz5vs53PlaXk1OV/kVLll4xlOzPuF/t9cNt63Zyr2BPkkQa+RnO0RfOjm8AxL5s2fZIn9x+EuJ/05t3rMJdVWMM523ZpvEpjnTPkYxARCaiv8bODQ3GphHO2RX0mljL9LVkey6nMSgHzg+Sa8RiX914A8b79R4wyRkYovxV52Rx3Rh0ifIinm0Cmpw+2l/E1tqLyLNA1dIX9NzR2cZfn/E1RlaRigwDvQzKeoc9n90l2UT6QIuVLERGpUHtq4VtDFfXreMxyBee68fGIPGV0wdMZzKvR0oJek47OHMTNzeThSpp9l+csM1/WdB2inhNeCP1FQ1EURVEURVGUhqMvGoqiKIqiKIqiNJx5SadO/URSKk//3FMu408/dfq103FN6UqR5DtBDHcap6Vp+efPEpcZIZ1iCUeVfvbin9+LVOZ4iZZCK/OSvObP0Ra9r42XSNYU45+f8Lzr85BO8Xnwz5aujT8/1+skO6F6s/RNxLz+Hi0rGM44z1PbLuTnsxfLqTJmymV8n86P5GmmJKnx0qkgqAoTBFgvUzrFNeC/LFw6ZUifjDrR77gvQjrFx+AlT1m38WKkUywFMKRTMn3/T7WFxWh/M8upVKbvuSk5mj2OgqUrc0mjGiGd4mrNJZ1iWUrU+rY2yak8WspzodKpYF7SKdynUsM61GkJZJ+lUxHSNkPISNuckhacageL3f5QyoBl12rYX4pFkuGIiOvifeF5IiC5WUBLTlvVMsSlsinxqNBcVaV68VLtnoX3JSS5kBvH54ZyGesgIlKpzP7swEuxGtvT53VjDXARr0LnRUux1khKWaeHIqeGn3PfFzGlKyy1nTlWn5IGLuYcXCxOS4R4GX6bxqYgsl48V9HyvTy2GF2e55kIDOkUjU3UHueSTnFahvHxiPZX5mcn2mAu6RS1JX6+FhHzoY+W24/FsF7FcZIkklSUUwKImBJpZuZz46m2MJ/2Z4Xz2Oro0aOyatWqOQ+mvPw4cuSIrFy58rSWoe1PeSEWo/2JaBtUotH2pyw1OgcrS8l82t+8XjSCIJDjx49LU1PTi07YofxyEYahFAoFWb58+by+uX0paPtTmMVsfyLaBhVE25+y1OgcrCwlC2l/83rRUBRFURRFURRFWQhqBlcURVEURVEUpeHoi4aiKIqiKIqiKA1HXzQURVEURVEURWk4+qIxg1e96lXywQ9+cKmroSinhW9961uSy+Vm3eZTn/qUnHvuuVPxO9/5TnnjG994Wuul/HJy8OBBsSxLnnrqqaWuivILiM7HymIQhqH8/u//vrS1tel4dZrQFw1FOU3M58H+vxof/vCH5a677lrqaiinEX2AUxRFmeDOO++Ub33rW/KDH/xA+vr6ZNu2bUtdpV865pWwT2kMtVpN4vH43BsqyhKRzWYlm80udTWUJSQMQ/F9X1xXpwdFUX652bdvn/T09Mhll10W+bk+t710Xra/aIyPj8vb3/52yWaz0tPTI3/5l38Jn1erVfnwhz8sK1askEwmIxdffLHcfffdsM19990nV155paRSKVm1apW8//3vl/Hx8anPe3t75TOf+Yy8/e1vl+bmZvn93//9xTg1pUHceeedcsUVV0gul5P29nZ5/etfL/v27RMRkbvvvlssy5J8Pj+1/VNPPSWWZcnBgwfl7rvvlt/5nd+R0dFRsSxLLMuST33qUyIiMjIyIm9/+9ultbVV0um0/Oqv/qrs2bNn6jinfgn5wQ9+IJs3b5Z0Oi1vetObpFQqya233iq9vb3S2toq73//+yGT7FzHPcX3vvc92bhxoySTSXnta18rR44cmfqMpVNMEATy+c9/XtauXSupVErOOecc+dd//dcXeYWVxead73yn3HPPPfI3f/M3U+3yW9/6lliWJT/60Y/k/PPPl0QiIffdd1+kbO6DH/ygvOpVr5qKgyCQL3zhC7JhwwZJJBKyevVq+dznPhdZtu/78ru/+7tyxhlnyOHDh0/jWSq/aMw1H89nbPv6178uq1atknQ6LTfeeKN8+ctf/oX7RVlZXN75znfK+973Pjl8+LBYliW9vb3yqle9St773vfKBz/4Qeno6JDXvva1IiJyzz33yEUXXSSJREJ6enrkYx/7mHjedPb3QqEgb3vb2ySTyUhPT4/81V/9lf56PMnL9kXjIx/5iNxzzz1y++23y49//GO5++675Yknnpj6/L3vfa88+OCD8p3vfEeeeeYZefOb3yzXXXfd1OC2b98+ue666+Q3fuM35JlnnpF//ud/lvvuu0/e+973Qjlf+tKX5JxzzpEnn3xSbrnllkU9R+WlMT4+Lh/60Ifksccek7vuukts25Ybb7xRgiCYc9/LLrtM/vqv/1qam5ulr69P+vr65MMf/rCITAxujz32mHz/+9+XBx98UMIwlOuvv17q9frU/qVSSb7yla/Id77zHbnzzjvl7rvvlhtvvFHuuOMOueOOO+Tv//7v5Wtf+xo85M/3uJ/73Ofktttuk/vvv1/y+bz85m/+5ryvyec//3m57bbb5Ktf/aps375dbr75Zvnt3/5tueeee+Z9DGXp+Ju/+Ru59NJL5V3vetdUuzyV8fdjH/uY/Pmf/7ns2LFDzj777Hkd70/+5E/kz//8z+WWW26R559/Xv7xH/9Ruru7je2q1aq8+c1vlqeeekruvfdeWb16dUPPS/nFZq75eK6x7f7775f3vOc98oEPfECeeuopufbaa1/whVdRTvE3f/M38md/9meycuVK6evrk0cffVRERG699VaJx+Ny//33y1e/+lU5duyYXH/99XLhhRfK008/Lf/zf/5P+cY3viGf/exnp471oQ99SO6//375/ve/Lz/5yU/k3nvvhTb8siZ8GVIoFMJ4PB7+y7/8y9TfhoaGwlQqFX7gAx8IDx06FDqOEx47dgz2u/rqq8M/+ZM/CcMwDG+66abw93//9+Hze++9N7RtOyyXy2EYhuGaNWvCN77xjaf5bJTFYmBgIBSR8Nlnnw1//vOfhyISjoyMTH3+5JNPhiISHjhwIAzDMPzmN78ZtrS0wDF2794dikh4//33T/1tcHAwTKVSU+3xm9/8Zigi4d69e6e2efe73x2m0+mwUChM/e21r31t+O53v3vBx33ooYemttmxY0coIuHDDz8chmEYfvKTnwzPOeecqc/f8Y53hDfccEMYhmFYqVTCdDodPvDAA3BON910U/jWt751PpdQ+S/AVVddFX7gAx+Yik+15e9973uw3cx7f4oPfOAD4VVXXRWGYRiOjY2FiUQi/PrXvx5ZzoEDB0IRCe+9997w6quvDq+44oown8838lSUXwLmmo/nM7a95S1vCV/3utfBcd/2trcZ46+iMH/1V38VrlmzZiq+6qqrwvPOOw+2+f/+v/8v3Lx5cxgEwdTf/vt//+9hNpsNfd8Px8bGwlgsFn73u9+d+jyfz4fpdBrG2pcrL0sR7r59+6RWq8nFF1889be2tjbZvHmziIg8++yz4vu+bNq0CfarVqvS3t4uIiJPP/20PPPMM/Ltb3976vMwDCUIAjlw4IBs2bJFREQuuOCC0306ymliz5498olPfEIefvhhGRwcnPol4/Dhw5JOp1/UMXfs2CGu60Lba29vl82bN8uOHTum/pZOp2X9+vVTcXd3t/T29oJ/oru7W/r7+xd0XNd15cILL5yKzzjjDMnlcrJjxw656KKLZq373r17pVQqybXXXgt/r9Vqct555833Eij/RVnoWLVjxw6pVqty9dVXz7rdW9/6Vlm5cqX87Gc/k1Qq9VKqqPwSMtd8PJ+xbdeuXXLjjTfCcS+66CL5wQ9+sAhnoPyycf7550O8Y8cOufTSS8WyrKm/XX755VIsFuXo0aMyMjIi9Xod5tCWlpapNvxy52X5ojEXxWJRHMeRxx9/XBzHgc9OPegVi0V597vfLe9///uN/WfKAjKZzOmtrHLaeMMb3iBr1qyRr3/967J8+XIJgkC2bdsmtVptqh2EYTi1/UyJ0kslFotBbFlW5N/mI+NqFMViUUREfvjDH8qKFSvgs0QisWj1UE4PPFbZtg3tWwTb+HxfGq6//nr5h3/4B3nwwQflNa95zUuvqKIoymlEn9say8vSo7F+/XqJxWLy8MMPT/1tZGREdu/eLSIi5513nvi+L/39/bJhwwb4f9myZSIi8opXvEKef/554/MNGzboCgW/BAwNDcmuXbvk4x//uFx99dWyZcsWGRkZmfq8s7NTRET6+vqm/sbrb8fjcTBri4hs2bJFPM+DtneqrK1bt77o+s73uJ7nyWOPPTYV79q1S/L5/NQvcLOxdetWSSQScvjwYaPNn9L5K//1iWqXUXR2dkL7FsE2vnHjRkmlUnMuh/wHf/AH8ud//ufya7/2a+rlUQzmmo/nM7Zt3rx5Sl9/Co4V5cWyZcuWKW/QKe6//35pamqSlStXyrp16yQWi0GbGx0dnWrDL3deli8a2WxWbrrpJvnIRz4iP/vZz+S5556Td77znWLbE5dj06ZN8ra3vU3e/va3y7//+7/LgQMH5JFHHpHPf/7z8sMf/lBERD760Y/KAw88IO9973vlqaeekj179sjtt99umMGVX0xaW1ulvb1d/u7v/k727t0rP/vZz+RDH/rQ1OenHq4/9alPyZ49e+SHP/yhsVJKb2+vFItFueuuu2RwcFBKpZJs3LhRbrjhBnnXu94l9913nzz99NPy27/927JixQq54YYbXnR953vcWCwm73vf++Thhx+Wxx9/XN75znfKJZdcMqdsSkSkqalJPvzhD8vNN98st956q+zbt0+eeOIJ+du//Vu59dZbX3TdlcWlt7dXHn74YTl48CBIApnXvOY18thjj8ltt90me/bskU9+8pPy3HPPTX2eTCblox/9qPzxH/+x3HbbbbJv3z556KGH5Bvf+IZxrPe9733y2c9+Vl7/+tfLfffdd9rOTfnFY675eD5j2/ve9z6544475Mtf/rLs2bNHvva1r8mPfvQjkLooyovlD//wD+XIkSPyvve9T3bu3Cm33367fPKTn5QPfehDYtu2NDU1yTve8Q75yEc+Ij//+c9l+/btctNNN4lt29oG5WX6oiEi8sUvflGuvPJKecMb3iDXXHONXHHFFaDL++Y3vylvf/vb5Y/+6I9k8+bN8sY3vlEeffTRKVnU2WefLffcc4/s3r1brrzySjnvvPPkE5/4hCxfvnypTklpILZty3e+8x15/PHHZdu2bXLzzTfLF7/4xanPY7GY/NM//ZPs3LlTzj77bPmLv/gLWIFCZGLlqfe85z3ylre8RTo7O+ULX/iCiEy0rfPPP19e//rXy6WXXiphGModd9xhSKMWynyOm06n5aMf/aj81m/9llx++eWSzWbln//5n+ddxmc+8xm55ZZb5POf/7xs2bJFrrvuOvnhD38oa9eufUl1VxaPD3/4w+I4jmzdulU6OztfcKnZ1772tXLLLbfIH//xH8uFF14ohUJB3v72t8M2t9xyi/zRH/2RfOITn5AtW7bIW97ylinfEPPBD35QPv3pT8v1118vDzzwQMPPS/nFZT7z8Wxj2+WXXy5f/epX5ctf/rKcc845cuedd8rNN98syWRyqU5J+SVixYoVcscdd8gjjzwi55xzjrznPe+Rm266ST7+8Y9PbfPlL39ZLr30Unn9618v11xzjVx++eWyZcsWbYMiYoUswlUURVEURfkF5l3vepfs3LlT7r333qWuivIyZHx8XFasWCF/+Zd/KTfddNNSV2dJUTO4oiiKoii/0HzpS1+Sa6+9VjKZjPzoRz+SW2+9Vf7H//gfS10t5WXCk08+KTt37pSLLrpIRkdH5c/+7M9ERF6SJPqXBX3RUBRFURTlF5pHHnlEvvCFL0ihUJB169bJV77yFfm93/u9pa6W8jLiS1/6kuzatUvi8bicf/75cu+990pHR8dSV2vJUemUoiiKoiiKoigN52VrBlcURVEURVEU5fShLxqKoiiKoiiKojQcfdFQFEVRFEVRFKXh6IuGoiiKoiiKoigNZ16rTgVBIMePH5empibNcqiIiEgYhlIoFGT58uVTGVxPF9r+FGYx25+ItkEF0fanLDU6BytLyULa37xeNI4fPy6rVq1qSOWUXy6OHDkiK1euPK1laPtTXojFaH8i2gaVaLT9KUuNzsHKUjKf9jevF42mpiYREfnd3/1NicfjEzu6+Fbb1twCcUu2yTiO42BsiQ9xIp6AOJtOQ5x0Y3g811yZ13Vmf9sOQyzToUq5NlXyRcArBnMchAHElUoR41rFOGYijtfTdeMQe54Hcb1eozJD+hy3FxGp1rBeVdqmXJs+ZqVSlU9+/n9MtY3TyakyHrjjnyWbmWgTlpuEbXxSAdbLeE1FRJwk7nNoaAji//W/bsNj0vVobmmGOPCxLYmIePU6xCHda/HxmtZqeJ/4PoYB7u9V8fiTG2G9AnOTmVghluH7XOeIFa/pT16A516h8/CpEjZ9C8b9bqII/NtsK2/7fiDP7d2/KO1PZLoNfuaLX5JkKiUiIuNjedimJYntw42Z7WOoVoWYu2Esi2OeZ+G9KdbHIa7VzPEuaeM4uXrNGohLYzTeFDFeu6IH4lwH1imommUWCjhmZeLY144cPQpxldpPSzfOHyMls//Wq9jG2nI5iP0Ax4Dh0QLEPPYHNbMvNeewHtzum9NZEREpl0ryR29/56K3v1f8wf8WJzFxP8KA2xfNO1x5ERGjT9FgYewy1+dRZcw+ABn1mj2cH3PuxNdm4Vi0E3+HO9cxLZnHLwG8CR90xud+rSxP/e8PLOoc/MwzB6bLC3DwsqnuYWieLz+X7Nj5HMS1egni3t5eiFspJ0UY0Ye9cTxGlcaSgf7jEBdHR/GYHvYrn+b0aohjuIhIUw7H/piLj9b8jb8bS0GcbeqEuG0ZjtkiIm4Gx2Huy9y+eP60LXpGquJ1EhH5yR3/AfFjTzwJceey5VP/rlar8ld/+1fzan/zetE49VNZPB6XRCL6RSOZxJeEFD3UiYg4VNpcLxrpFN6MVOyX80XDsanDRlQhmcDr6bp4rTwPO0Otho3KeNFwzRcN26GHQ4c6MY8kIovyM+qpMrKZtDRlMxN/m+NFo2abbcNJ4T6ZShniGLUvm+5TnD73I34uNOYJnnRtHgzwc94/4LcGf+6JPQhmn/KsgAco3H8+LxpcUcexZ/280S8aU8Us0s/4p8pJplKSmhyXfPpCIEXjVdSLRpLGJ+5iMTqGZ+GgWXfxmFbEeJegF40UfWETeNym8JhpmtAyk31uan/XLNMP8N6l6UWDr41FD8kpKrMs5rXjNsPnxS8ayTq/tNOLBk9IIpJM82SOIddzsdufk0iL+1/oRSO6jJf6ovEiXgNehi8aU39axDm4qalJmpsmH6pfxItGjV40MhkcW2L0xUk2m4W4qQkf6EP6gktExKMHqDjdqNI49uGQvxykFw2PynBCc97P0Lgx54tGnF40jPM0H95jNA4bLxL8okHzPNehVjHHPx6nT/2wcIpEwnyun0/7UzO4oiiKoiiKoigNZ16/aJzCtu2ptyKbvsG06dsmK+Lb3iDgb5gwdukt0PhlgL+ViNCI8Hc8jkv1srhe+DYW0Fs4f9s7H+b6RYNjfiOMx/AtUsT82Yu/HXHmMOMY3w7P4y2Ut7Bm+WwxGK8FYlUn7odFEiSP7ptXMn8WtKr4C0atQr/q0LfHHv2MG9CvRlFXPB7DY7jUTxIuxiWqZ7WKP8vytxKliDZfq+J5BCSF8vgbGvpWieOobygMsxd9fWV8Y0N9mb+NjvpFIwjxbz590z6zRG8RDLhRPHt4r8QTE78mPvrc4/DZsmwXxMtb8RsoEZG6T/eXPj9Ww3s5TBscH8Kf/et5U2bJ92LdurUQb1iDetoUfVU7XB+A2OrD43W3mlrtIMRfUfp24LXZ89QzEGcT7RC7XfgNnp80pya7jv2XL15/Cdv9wWPHcAM6ZK1ojhG59jbchvpGT8fEPfaq5jepi4HtOFNzbWj000b8osGfUz+bh3QqnOOY5iFe+q8N85J0vfjNRcSc817yCDSfXyJm+UUjiBhDTzeJmCWJ+EQl+FfMkK5QhABCnn8Wx4FvfeOrEA8cPwzxJVdcDPGrXnstxMUhlD2JiAwd74f4+OEjEJfKKKmslnEc8CrYt5P06+xA3pR1sgyF1REu3atWmhvO2HYGxGvGR4wi1m85B+JUGmWelszeHnheOHRgr7HNof34t672Voh379g+9e96PULG/UJlz3tLRVEURVEURVGUeaIvGoqiKIqiKIqiNBx90VAURVEURVEUpeEsyKMRdxyJT2rN4rTUrEtxpBPdkH+yz4A0fnMcIAjNlUks0uqzfp1XZ8omcbUAJ46XhFdrijYnsK9jdu2lTRLWOK1+4kZp5OlPFumqfdqAvQG80pUVuaKPUQiFVuS/F4vi2KiEk/4Di1bdqtaxLdRKpnbTo+Y+yitz0ao11Srp3wNckSFqBYYErbYWJ4+QTfrlVAL9OLzKVJ1WvCjw6k4iUhjDezkwiOdeKKAmtU5L6PJStDHqyyKm14k9GNxWWKNq+q1M5mpT4Qv8ezGJN2UlPnmPU8txqcUxG9tkiZa/FRFJ00p59TJ6NvqK+PngGC5n66bIz5XBMkVERgZQ37tn+/N4jDIec+UyXFpxeGwQy6AmN5hBD4eIyDC1IZdG77AdPRirl6NHY5TGs0LV1BvHaOnZw4exHid4FbkEXhsrwL5k9G8RKQ6PQcxLj8cm26gfsazmYmDJjFF6riE4aoznpZM4nGPVI+6ikUXw6jfG53MdlI84HwMF+yBn39xYMepFLAi10EHILCLK38Jz8AuHS+GTrA3tklptYoUkK4X6fUlhnx7O4/LxIiL/987vQdx3+CDEAfXJe3/yE4hPkIejrcNcBnZ4AMe3kJapT9GY6fm8eh2tGBXinN7Rg148EZEY+TjKJTyPJkr/YFk47g+R1ySW3G+UcZjO/eLLroa4vX0FxIFPS+OT3+zQQdOjsW/PLoibWrDe6cT0uFzjB9lZ0F80FEVRFEVRFEVpOPqioSiKoiiKoihKw9EXDUVRFEVRFEVRGs6CPBox153KeJikLN7sM4jUW3MuCGv2HBfsQ7Asf9bPRUTGS6jPGxpCvXHfMK6ZnM1ipslta3GN+XQateZehC7TNvShs6eC52vD2ciDKHvLHCJSPibHNVr3nXMrTDC7z2apPRrjpWkNtkUZkkdpTfzRIrYDEZECeTIKFbwvxXHOYYFeBvZbpJNmvpN0GrWanCHUr6N2M6CbHaeMoZUK5Z+I0PUmYrhNrYZa9fwIamUrtE44r8cfpbx0KAsx+zqMPBsGc2uoLfIZeeQlmdnmAj+q/Z5++kbHJFaZaCd9/Xn47Mz1qN0dHaM8LSLS338C47FhiMfQTiN+Ee9ltgXXX29fvcwoI7eWNMfcPmgQ238C65Ruo5wWNHasHjf7/uptGyCukodq5/g+iEdy1E8o/8y+p1ErLCKSH8axfJjG8nQLemY2dKNm+WQfapzPOPtso4w0+UBODpAfZXyi/3r2EuXRsK2pvmbmZ8Jto8Zo9gAYc5ORA2N2/0T0NMDGD+7ss/tEzGPOsb9EzbFR9Zrx+VzelCgMs8nsJg32qsxrxpxzoxkbLEEuoaOP/71k0xN9u6kd/RHZZVsgrpbN+i3P4pw5tgLHr5ECztuFYh7iShHH/WWXnGuU0bEc2/DR53dAnOlAL0lLF3pNavQM6dA8tPnMzUaZCRrvnn4C84UsW90LcVXITxbSOJ/G51IRkTvuvB23oSzpF12K2cVtykvVd/QgxM8/+7BRRqYJ55e6R5nc09PPJ25t/s+A+ouGoiiKoiiKoigNR180FEVRFEVRFEVpOPqioSiKoiiKoihKw9EXDUVRFEVRFEVRGs6CzOCu60yZW9kY65AvJCKvmDhsGCczU8zlBGpo/AnIjeV75JwUkUoJzXs1HxMrrVuHyamO9aPx55lDJyE+Z30PxAnXTCQVBpRI0DC+YmyzOW4++Yi4zDkSCbI5l5NTDefNhHbZJjRJ8TGW2gz+6O5jkpxMllakRGdDeUy0NVxAo6iISKGMda6W0Oh0/PAxiDeuRLNVkky1cdds5AlK+Og6uE+V8nyVSljPahlNYQ7tn4ybBvQEGc45GWNxDK9N8XgfxHXa3q+bdnBeqCFGfTMRw3qZCwnwEc0yaE2EWc3g/hKZwceGRsWdvAf9B/E6ru1EM3iMktSJiIxQMrzm1esg9k6gcT+2sg3itIMm6raUWUZHDk2BJwPsK61pXHBgvJKHuE5j3HAdF4no4+yhInLtOjSC2jHc5uCuZyF++GdoRBwfRVN8opuSgYmIU8A2Zg9iuy5Qsr2mDRshLo2j0Xv3sQNGGfGT2Bdam7uxzMLkmFFfooR9ljWjH8w1Bs89Rpv5+exZP+c/GPPQvJh9bHgxx+TxxjS5z1aD6AVYzELm/ANgzA7zOS2joi9sKLcj+uHp5gc/f3ZqjmuPPwSfve7V2yBuyuCiFCIirzsXx54zOnHBhn/76XaIBwZwfqzTQhaxJJqXRUSEkjLbNIdm2nGc7liFCwBVijiO+OP4jLhq8yajyHZKyHfoyBH8nMaz4/10XjTnNufM8W+orx/iO/7tuxAfPowLbrS34eIYgY/z6a49mMhVRKSrYznEA/04X1VmJP+tLWAM1F80FEVRFEVRFEVpOPqioSiKoiiKoihKw9EXDUVRFEVRFEVRGs6CPBqOHYpjT2jkOHmeUMxJ7EREXBc1hex2iFmoU/NJU1atYXU7u81kVa0tqJVLDKDmuWdZjuqJut8dh9G78NDzqLV7xTqzzOYUJ/UjjwZtb4UBxbP7LSaOgfsElGGIFe/skYmR7po9GyIi6QxpG228NuGMMwnnJThtLP/00yfFdieudd2n8sn/E0TofD0PL4pTRe16Tyvq4VetJM09XVT2T4iIuLRNja7z2Bi2r2IBfUblCm7vkE+mOYv6exGRZAJ1+xnS4K9cjrrLk8Ooh2cPR9R5zZWQz54jERj3ZdfFPiMiEnjYir067hOLTe/DCQMXi5aWjMQSE8mZulejvngkQM9PYJk+qLXnnAlxjK5rrjsH8QqKR0/iMU8exmR7IiJdKfQWNdEwH9p47RKUZLJOY8tyqkOTbfqEghq2W9fG+98cw3t5uIDa3xFKkpUYMYqQCxPnQrymC8f6p0tHId67+xDEdcGDjgdmIYUhvIfLMujR6Fg30ZfqNHYsFrN5NAyfQtT+PDcZHgBO2CezxtFevdk9ipHZOmc5Js998/I0zuEh5McTm+s8dxELLmM+tsY5vSJg0lh8j8Z9231xJw25rXX0OF1/KfoKnBCfvURE0nH0lG1ehR61pOD4dvQgltG9Yj3EQcQ8ME6+x3QSx7c47VOnOdip0zhOff3IHtPbNUhz5oGnMWGfFPG8TvSj36Iph9cle8E5RhlCz3CPPPgAxNuffwLizh70I//KdW+AeNXKXqOIXc9gvfv70bNcqUxfC8+f/xysv2goiqIoiqIoitJw9EVDURRFURRFUZSGoy8aiqIoiqIoiqI0nAV5NBZEpHzwhdeEFhFDu8l5NRxaaL+peZVRwpr1vRAf2v4ziPuH8hAXirQWMPkn9vahfr1WRa2xiMirzlqDhyANdEDiTJs0gixZDawo7RvrVmfX47IuNkU6xbZ2zJkhIuI4pm7+her5opZPf4kMjTtiTWohHScBn5FFw/CwiIjUaD3sziRe54tegeuAt6Xx8+JYEeJkCr0QIiKVGuo5h4cwr0uJPBpeDfWgQm2jXMbP/ZqpDY/H8L5lyceRa0H9Z1cXajcHRrGN1+tmGzf1yBjXPfRoZZKo49+wFvuIHSFY3nXwOMSUnkb8GaLnKG3uYtCUTUo8OdH2epah96WWptxCCbyuIiJbN62GeO++XRBnM9iQR/PoI7Cb8Lq63ejHEBHJrMW+3UFjWpx8CM/tPQhxd28vxBvOxHF2eAD9FSIi//Kj2yHOdWL/fOWFF0NsZ3D9+/07MIfN6BDmkxERCcuHIX7jlZdBXH0e+6fTjNcm3oXxsbw53jVT7pzONNbzFWduFhGRSqks/8fYexGwremkVXMJ+sOI7xHJOMA5nTiPRkQCp1k/jtgkog5z1ZsPwHWKKmBhuam4CpbhMZt9/8mdXtrnL2YXyGW1+N8T172ahJPjyfgY9VEf/WKuZc5VD+3G3BrFBJ5DtgXHVMc/CHE6iz5K3zPzKfl19pqSb3I8j/VsxmcjzpVWpHxOQ3uwTiIiIyfx3Hc88gjEheM4dlUoD1jvNsxDFE+YPriulZjTbcd29FNwHiGvjl6Vpx9/FOKEmzPKOLJ7Nx5zHMdUL5y+3j5P0LOgv2goiqIoiqIoitJw9EVDURRFURRFUZSGoy8aiqIoiqIoiqI0nAV5NCwJxTolfjQNFhRGaKhZ02V4EfC9h5eJ5jwbo4Oo6RYR2VPFtZt37kGNfKGEertSjeuAdWyiHBn7+sy18dubUbO8jTTSPq03zLpYMwtGlPZtrndC0trStXbIxNCcQV2iiMjYOOXWoBwjcE+j7u9pxpWY2DKZR6OIdS1XcS3sao28NyJieaiL3PbqsyBevxbzIhSHcA1pN4a681jM1FEWx1EnOU75ASqkeYyRv6KlBTXi5TLqYFlLLCLiB6hTHR3LQxyPYz2XLcNcMHsPY66YYtHUxxt6YAv7UY7y11x4wXkQX3HxBRBvf+5Zo4zn9mLeA/YheTNycSyVRyMRE4lP3rJsFb0OXhzvZdBs9rHdh1BX29ONuVrWdeK68o8/9hjErUn031z96lcYZezr2wvxGevPhXjZRtQg+3W8ls+fxHZ/9wDeF7HMHDxNDt7/M9fiOvBdzehN2bIS2+yhO7E9nOnimvwiIm9+9UaIs9U+iNefxDFgy6XXQNzXjm125FHcXkRk4xr0o3Q35yBuy0ycZ9ma3c92urBtR+xJr2IwlxEhymhgeCzmmosIzrsRUYaRB2MOw4PxueHhmE9fp32M3B3zOMRsVVgkorxryPTn9hJ4NNy4I+6khyGoYx8OfZw3Qs61JiJD4VaI+2o4frktD0OcyWI/S9BcFpRxPhURqY+jr61WzmMZKapXgHOuKzhu+x56HfID+EwpIjJCXsyKj/UaLuIzYpLybiTT+HyWSKCXRUSkicZ+h57xPMpDVRnH8W7wBD67rl7ZYZSRzeUgHqXnldCbkUttAUZd/UVDURRFURRFUZSGoy8aiqIoiqIoiqI0HH3RUBRFURRFURSl4SzMo2FNaxdNbSdpPyM9GhySxs9C3VoQoMYsoDwadc9c77/vAGrlxqr4LhU4qL9zY3iMLK2FX/NQYz+QN9eGfno/+kJW03rtqSTVwadrZdPa5hGC0rnUcPYcFhnWfjbF8TxFRIaGUbMcOnge4YwbGIqpvzzdDB85IpY90WRDWj/bJt24JZSfQkS62/F8Nq5Gr4JD621bFDc34f6xuPmeblMb5ZwWCfo8l0Ntu0temiR5NKLaQY1ya5RKqCmNUQ6VrhzmBli7BnNc7N27zyjDo+vd0YYa+tdd/6sQX3rxhRCPU/6Q/kHsMyIiNcopwh6NmQRLkchFRLwZPqFCDDtZnjw9oyfMPBpuBvdpz+C68OEu9EO0PoH3ot6fxzJWox9DRERyeP99GgOLWdT/jod4b4YG0PtWprFk01ozB8+brn8VxIVxbMd7j+6HeFkH9iWvhfKH9KPOWkQkGME2s+cQejRaqL2cePgpiEvnUr4jx2xDtTq2wXZat79jcgwo2eb4uRhYlj3ll+KxxrBJRmjkeRuL8g3N2atOQ7+by9Nh5ouImB8N74mRKGNWjLEm8jRnf+aZC6MKEX4M/tNsl3spfCSxeFxcd6Kv8pzAXlSxzfbX3NYNsZfcBPFA8TmIs204Lqxehf2xNWVehI5e9L1lN2NujqYOnLuScfReWvR8tqwb5+hKBfNZTPwNx9z1526AuDSOn3tVfK48/7IrIM7l8BxERDqbsN4ZeugrUX4Zx8ZnjwsvfjXEl1yOeYhERA4cfh7i4ycwv5HrT98PS/NoKIqiKIqiKIqylOiLhqIoiqIoiqIoDUdfNBRFURRFURRFaTgL8mjMhD0BDsnzWPs5AeV6IAGi75Omj0SIDuUtGCUtuojI4DDqjbOkq69QfgWfXrVCl9Y3Jn17c8bU/o8UsN4nC7jNhgRqAIOQvCnGkt+mtpHXTA/o1hlaW8OkQZ6GiDwEQZ1yjHjoD3BnrP3sRfhjTjueNyV0tSknhh2iHr45Y+oHV+RQ45iw8D5Vy9SeSBucTOJ9HC+ZGvxYDO9LC+WXiLfi2tXtbag5rVTxvJqpT9iuqQ33uN9QW1m5ciXEvEZ3cwtpP5PmGt5jBfTvnHfeuRBfeD6uhx6nvnq8gOtxHzyM2k8RkUqF/E/U/+0ZnpmFrOHdSDzbEXvSJ7T92e3wmUtVal5hehm848MQ79p9F8T7nz8IsZXH676C1lLv7zthlFG3sW8eemAXxB3d2AbXtuH9XpNKQWz3Yn6ZeKe5/ro3iJrjI0O4bvyabtRm7z+yG+LNl2yDeO/P8dqKiNz+n49DnEthf9wzgOe9rIR9sTOHfa25CfcXEalX0OuVjOFN7T95WEREyiUz18xiYFuO2JNeRsMxYM3tITDyXszRjcx0Wbz/3H5CLtPYw/BksG9kHufF575gOwV7OqJ2oBwicxx0riKjnpD43Hmbmcc0vSunn1g8LrFJj0aVniF8H/tfWDNzzbAtyo3hHJqK45y8dduZEP/a//MbEK/owbxDIqYHVlzykpC3idO5uXRdHbonnm/2fc5pEgQYhwEeI6Dnl8DGsSiZwHFeRGQd5b3YTF6UHQf68ZiUq6Mph+PhszvuNcqojuH8tL63F+LyDMOe5/uyc9gcp6PQXzQURVEURVEURWk4+qKhKIqiKIqiKErD0RcNRVEURVEURVEajr5oKIqiKIqiKIrScBZkBrdFxJ70tDhkUzIsqmFEsqCAzeBkJjISDpEpjEw7XmgakmtkSKpW0KTI3rWAks7EHExmlkZfpLRk0RwuIjIyjmWcGMZ4YyeaotjszcnHIhPxsGOJzp1zp4SUHTEkw/B42TS1e1SPMl27mDttWKpUzf1PN3YQiDWZhMry0LidcNGgleDskCKSjpPZvYDGJ8fCe++SaaxKRu0oQzwvHlCtorm0uRnN4U3NlNyR7lM8jqbqdLNpEoslcBs2Ca5evQrioX487842NC1ffOFFRhlsUly5EpMWJWJ8rfC8B4ewzJExNIeLiLgx0zw4kyBiAYPFZqz/xNT19qvYBpf1rIZ4BcUiIrt/iCbogQOYHK+bOnImgdcknsP2FW+hAUpE6kXstyOULGp872GIc0ksY0U3tofwBCbKqznY3kREjtMCAzbFO2rPQtx84RkQdzVj8sy79txhlDFcwvM6ey3u84q1aJbsbMVFDo4ePApx28omoww3gf2vpRnP9WRx4lpWvcUf/0QmFkQ4tShCEPC8MftiKxMb8Tb88VyG53m4rOdIJGca0mevNyffM83hZjWMOTTS3D3bMSNOwjjE7N/TWsacPbvRe3KnWasxM7Ttxf+e2Ik3izM5Ttc9esag6TDl4sI7IiLNKZwXqg72ycwKTHp6yUVvg3j1ZjSHW4G5OIodUuJnH+thrJNDyYsDi5+tcH/HN8fcUwuETJeBhcRtXrwHx546PWvl6dlERKS1GxOOrlyG88uhEzgmZZbhvO+U9kC8dbmZFLV6MZbhdmBSv5MD04stVatV+fkzagZXFEVRFEVRFGWJ0BcNRVEURVEURVEajr5oKIqiKIqiKIrScBbk0XAkFGdSqGjbrKOkOMI/wVpLE9ZukpiONKm51pxxhLKHerrhfkxoVa/i55xYJZnGuKUZdfueZ+rI+8dQ43foRB7ic9egrjoTo+QtpOdjvZ+Iee0sYX8L+T7oWgV13L7mmZrVWBzr6dZRTzk+Q+tdXQKPRhB6U+3MDtCTEXio+S+VTX3ocB51j/3UNrJp1CcG1MbLdM5t7ei3EBERC+9Difw76Sx6LNwEXvOWDLa3pibUcja1o+5cRCTbZPo2ZlKv47XYvv15iAtjmDSpLZczjpGhemXJvFQYzUM8XkY/y/ETeK05caaISIISWxqJu2Zot4MglNEiJfhbBBJxV2Lxif7ZtLwTPuugxHaFo5i0TkSkeAj9Dl2kMc4kse+v6cD73dmZg3g4wutyOI9tjscCl3xsy1rIq1DAvhUvYtLAtGOOT2GA42JgYd8YrGL7eXjHf0J8tI7JpkaPm4kIJY1+iXJA/jrqji2teN67j2OSyMNPmv3m/BvRnxTUcNzsmEy4WY6byWIXA8uypzxYPAezz8BIYicR/gdrjs9pnjHz4kV5GebKAkhz2Rwejbk8G5N/nLUOXE/DwhHh6TPLYK/I/JPrRez+Agn9ZvfIhODzmOt5qvHEQ1ti4UT7q1G242Id+1O+jOOGiEii+lOIO4o5iNuX4Ry8fhv2x7BIz06+WUathuOfT8+E4mC9YwnyXNgYc3JkxzGvO5fh0bNCLIuetZpP50HPb7sO7jfK2LEL/1av4j7nb1gO8cWXnw1xuvAoxO19ZsLhnp7rIH7uOM4VLekZPl3THvOC6C8aiqIoiqIoiqI0HH3RUBRFURRFURSl4eiLhqIoiqIoiqIoDWdBHg3XCsSd1KDbQh4MiwRbVoTPgLbhZaA5rwZrMz0fNdndLW1GGekW1AInY3jMnc/jWsKOhXq8agl1a80tqD1uz5rek1wS9zncj3q9vcfwGOetw/XefUogwnk1RERsw78yuw6WfR8e5WeIUncmXaxniq7d2Ni0dturm9fhdBOG/tRVcBw8H99DP0k5Io9L3wBq5rsGUBe+vhfXpQ5Iy+7GsE2zf0JEpFxBzXxbO+YkyLViHKPcET3LUeff1d0FcabV9IVYzuz5PnbsQE9GPp+H2Kd8IK5jii9ZFx1zcZtaFY8xQNd2aBjX7B4v4/0SEXHs2UWf9ozPo/rIYrB581pJpCZ8NfUc1red/DdP7zlk7F8NcGwoUUeskv63ezm2lw1rUIc7VDA1yhUaevuOozcpRnkzYkn0PjiUuyOT5Htt+rMCqndzEw7uTh7j55/YB3EpwH7TnjTXql9NfaF5w3qIKyux73g9mGdDBP1Q49vN+1MYHYV4525s18vbJupQLqGPZbGwbEvsyWRWYYjXNOQxzzK/RzRTWJAHw9iDjkEmDSMnhpjeqjkrMQdzHi+yCPZ1GFtQyNcqqo6ms2P2Tx2K8ZgJ2/SptaewvaUt7Gt2anrOqZVDQafT6ccJA3Emny0C8hmMkS9yz/Nm+/vpAzg3NcdwTr7sjVdB3GVjnqlEsQ/ifXsfNMo4fnQvxMUx9FNZMaxXexc+j61ctRXiVavOhdiNm/P+6GAe4koBfSLZjWsh9mzyzNZwHK+UcBwSERkawJxLhQKWSY8WsvfADyBensYxa2AIn5VFRH42sAviY0PURr3pvCd1LyJX3gugv2goiqIoiqIoitJw9EVDURRFURRFUZSGoy8aiqIoiqIoiqI0nAV5NKwwnNJkGnkzhPRahubR9BlEKEi5RIhYMx+LeE1as+EsiLOkPz52BLXiFulaq5RnY3SYdPyrzLX7h5oGID4xhPq7A8dQq3nOWtQEzrEEuIiI+Oxfoc/Zk+GzR4P296M0qKSFden6phPTf7CXYA3vdCI25fPZsmkTfFYqoP76wH5Tf805LeLkSbFJf8zXNJ1B3XgsZnoK6h52qc5OzLWQyeB62u3k4VizBn0iLTn0ZLgZU1dZKqP2skS6/dFBzN3Q3YV1yg+jhr/mmfrzoIL9pHICr6XLa4tT/6977OkyiuARRHzSgIYztmAN9mJh+4E4/kS7WJVD/fAW8vhUNuPYISKy4+7HIC5U8DoeqeF5jdMo2bUM20uq2czrE7rYbh26Vj7l1OF+79E685kctrlEaGrLHQfrUacyutvw842rsA2ePIF1yEXkhnnlxRdAnN7WC/Hukzi2JwPsi61nrIP4ig3o6RARWb0MPTClMuq7g0k/XeAvUfuzLbEnzY1GvgnyBIRBhI+OPRbW7D6CkPNLUBhaUfkn2P9AnZ33CWf3Osyvr7N35IVz8EQWwn6LiDKNv/C1oznWDtCHlvRwHF6WNcfZ5SnqexY+bySap9tjObb4PqFMa0bisYlnKj/AuiVjOC5sXGWOEwdX4jXZeDb6HV5xKfqqkjblMQuwP+7evdsoY+AkehnG8lgP30ffS3s/3pexEew3HW3oBYulzbHJobbgUi40o/nRHBuOo883ZZkejXiI9fQd3KfanIO4r4p5qZ6t4jzf1GJ67QbsByC2O/C5yp/hpQt5HpkF/UVDURRFURRFUZSGoy8aiqIoiqIoiqI0HH3RUBRFURRFURSl4SzIoxGGoYRBOPlv9gSgntoJIt5hQizOJm2wzbpJm3WTlNdhCNdgFhFZv+0yiNdtRo1fkdbvf+Teh7EMh+oYoiawM4V+DBGRc1ejJr7vJPpC8kXUYRfLqI3LxFnPN7c+lLdhP4ERs047MPV1/LeQ76kV/e/FYvXyjikt+KWXngGfefVVELMPQUQkYeN9OWPTBtyAzt+hkwxIm14q430VEcmQh2L5ctR8p1Po0eihz9vaWyF2Kc+GROW4YK02r29dw3qf0tieor0T89HU6uZ5Fcaw33hVaitUZt0nfwvlRXAcc3zwacyw2SQ0s7wwFBFTA3y6OTgwKPHkhPZ15zHMyTNC/TxoNrW8HWvWQHxy1w48BnlZdhzD8Wb9CdTpWrEI/XocdbUtHejrKIxiPat1vI4h+drqCTxeZ7OZy8WmcfPIScyLYZG/7tXnocdqYAS12aO+mWelYyXm0cjTfHCyhB6Nk4dxXO5didehNYkeGxGRviHSaxfxGOOxif5drZj1Wwws2xLrVB4N6mOcb8JKmLlI+D74PnsrZ8+rwXN0GJWrgy1AlHPL8BdSQi0ez8w8GlHzI1eM6zBHJdl8EpXTh70kAc7jTukkbl44BnHdy0M8XjGv3cF+9D2EZWzTTkvv1L9rVdMverqx47bY8Yl6B2y9obFrfZd5n5a/Cq9ZLY3bdLbjXHRqrD1FoY5xPGaORdkE3Zcc1mu8jN4G18JxujSO4+FYBbdvEs7PI5KheqdbcxB7dClqlE+pSnnJ0m7eKKOtBcft/gTWe+uZl+LnFvpU9wxjDpK6mO2nI4lt0qvjOB7MOI/aApqf/qKhKIqiKIqiKErD0RcNRVEURVEURVEajr5oKIqiKIqiKIrScBbu0ZjUT3qkx2NPgBOl3WTtJWszaS1izmsQI818dcz0S/Tt3wXxhnPRs3HBRVdDnCIx550/vhvifcdRi9tkLlsvrzwXL+O+Iyhe+/mzeYhPDKLubf0K1BkGhn5UxMgpMocng/0Wxud+hEeD9LrGOu0z6rUUeQw2buqR2KS/oL0D1992LdSRn3vmucb+LRnUZA/1n4D48IG9EGebsAyb2mutZq5DvYnye7S1oXaziY6ZSmG9WY7MXoZa3fQl1KkemTT6RFrbUJs+OJiHuKMbfSFHjuJ62yIiVSrDoqGjTJp1ztOSiKPuP5VAra2ISKFEZdD1nnn9lyqPhudZYnkTN2ngBOUfKZFvpWr2MbcH20PiEOroqyX0hA2XsE/uJM9GqtXMq9JGuVq6l6G3IZZA38HQKOrAKzFshGMuecjqnPFExKpguxws4fxgxfF+berthviMLWshPjyCencRkZWbcT37yjiuNZ8mf9QIXcsDxzGvyWjCzEPQlMH+OTBC93jy46XQx4uIWG5crMkxMCzgNfJPot/H6ew19neaeiC2qR8GAY35nPKCfGoW+8FExHY4F4fZ12fiCs87lAPDSN5h9n3O/xGwR8MwjrAHY3afiIiIlLH9hKM4X1gBzut+FcfEUgE9pf2HTY+pReN7qhW9h8m2M6f+7TnYvheDRCIp8cmxnC+pV2dPp/kMWB/EPndkEA/SdQn6H3y+9zTPtAeml6Y1huNfSOPZqJFzBY/hkYe2OoK+LVluzsFhyPWg9kN5d+wKtpWRJx+B2Eng5yIi3T29EB/N4jjuOuhLPXpkO8RFm/K8RPj7arRN3cZ7alvTD8AR08ALor9oKIqiKIqiKIrScPRFQ1EURVEURVGUhqMvGoqiKIqiKIqiNJyFeTQCfyq3AqVYMGLPNQ8dRqx/jcenNbzJk+HSuvqubR7vxMGnIG7tWgFx53LU+Z57Ca49nGpCbdw9d+Hxnj+M2mMRkbYmXN948xq8GI/tRk3fyeE8xL3LcT3kuqH3ExGLNagYe6yX5M/Jf8F5T0Tmzs0x8+OlkMi35ByJxyeuTTKObaGtBTXfq1esNvaPkd8hpLX6jx4mD1AV9aQ5ykewahXqZ0VMT0acvAnsuShTLo6WHK3tT/e9WjF1uTXSAsccNBL1rMB6Wg5qpkfyqD22bdOIlGvDNjoyiPr4qodtnD1CnEejpwvvl4iINYR6eEMvPqPR8WeLRTKWknhswldTLtL67EePQuzb5lixrBnb0LLVmFfj2B7UffucO6hKGnnLvFclC8fe5hSOJ03kA6rHsIwy5Qc4UUI/wv4irisvItJGmvdCCevVjrJpsWO4fVs7tvugyzyvMy7YCvGl3diuNz3zGMTDlPvl/95/L8RH+o4bZbzynFdAXC/gfJAfyE/8PcKftRhYEog1qVt3c6jJrgzjuvn9j3zH2D8ew/En1425hBLLzoTYaSLNvI1jRxiPmOdpLEiMo8a9lsAxMXCwcVghzk1mHg2TkLbhPE+hYHuyAuqbNdS7hyPYD0VEpIyePn5eKRfRc1HMo4emNo79pl43z6tjGXqVmrdeB3GQnX6eCStmvqPTTSwWm5rTOBVXrYbXtBbh36llcfxLNl8IsW+jXzUcx3Os1XD+6yL/mYhIvD2HxxAca8rkg+FHoSLV26G2I545rtvsJSFCysvi1HFMzT+JY1frWecYx8guPxePmdgH8eNPPgnxzsJ+iJPruI4R/ir67aHGudRm5PGqz/E8j8dVFEVRFEVRFEVpMPqioSiKoiiKoihKw9EXDUVRFEVRFEVRGo6+aCiKoiiKoiiK0nAWmLAvmEraxkl12LAVRiSdYzP43Em3+JhkVo54TwqraB46+MxPIa4U0dCVa0OjT5ONJp2Ny7EO8ZhpAN57ApOubVyOZfR2Y518C8uoUvK8MML7ZiTHIfe3TwlhPDI0eb5HsWkE4oR9HAczjEFBhJn8dFMaH5J6beJ+VStoGutatxFiNrKLiNhkIE8m0dgYJ2NjIon3tasLjWednWjGFJkwy80WByHeB05qFNLnAd1Xv44GVxGROpnBJY4NaPnqXojXbdoC8U9+8kOIvYhu2d3eAXEnLbJQpKRvJ05gH6gdwzbf2kSmdxEZI+OfaaSfNqL5fiD9A1jmYlC3PLGsCTPh2jPQnDxyFM+5UqJETyKSP9IHcVsC23GiOYfHKKMZmRMjOhGGPL+M17pGxuuEje3DpqSRUsXtD5zIQzw2bCZ0bHGw7xQoF975OTxmlu7/aB13iK1G06iISB9dz9YAk8+dv4UWPSADZncG42d37jTK6G7HxRxsMrju6j888Y8lMoPbli32qTGDFm1o3nA5xOmeM4z98wcehvjYwUfx+DsfgDiZwkUgmpfjONvUhYuriIj4zz4HsTNCiQQv/lWIY2tfBXHA8w4/W7CRW0RsMvjyHBrW0IhtFXDhBrtKxu2KueBBuUiJLUcxeWatiIbygJLJxTs2Q9x1xmuMMkJa7KHi4TFSM56JApnbJN9oEvG0JOKT8yYtOlKki37nfTh+i4jEW3Dxi5VNOG4884//AHGVjNg1Wqji5Ig5B5RqeA1dnufTWK8cJemMp3BxAqefnhvSZvtLpTDprR3H8S2WxmPWqYzExbgoUdcm0wy+/TE0e+/dj3PJ8WFc1MVfjQu2uNbszxYiIjQTiFfjRYSmj1GvqRlcURRFURRFUZQlRF80FEVRFEVRFEVpOPqioSiKoiiKoihKw1mgRyOc8kmwvWJeSXWMBHCUoI+2n+uQQYQPxKVkVSVKorPn8Z9jHRzUQFohaqDHa6jfW5Yzk+n1dGCSnbYcKt3WDB2D+IkjmHSmVEHtXDJhahs9unZ1ShrjG56N2f0WHItEJPUzkvxNlxHlgTjdVEol8WMT93dsJA+fOZQwjJPYiYhY9F7d1JyBeP16vI+Oi/eBk/E5jtkW+Lqwl8WhVp4g/ahHya5CSl4VeKY2vF7Dc00kUVeda0W9uxPD80qTRrW7B/0XIiIXX4z6b9fGYwwOYPKzkZ/+DGLu+10dpga/RP6TUgn7iedPX5vAX/z2JyIyVspLLJi4Z1UHPSUrN6P++PB92O9FRAZ3H4a4llwOcYn6cSqNiQ4z286C+KCPHg4REa8fvQwdlNwwTomaiiU8RqWEY8vRYbwvQ0OoTRcxEyi6LmqrX/PKCyCuxknfjU1Qyi5qnkVEjh3BsTzdhHF7Evu37eH9+ZWLMTnYylb0x4iIPLlrD8RH+1AHnc5M3Pt6bPH18SIijm2LY0+cZ0hjC3sgE02mh6zrbEwAl1uP2vDiEdSBj+xBz8aB5+6DOF272yjDPYRacSeHY0XLkWcgTtk4Z9upHMRWHMdp+xh6oURE4lXyDa3GMcyiz8fy5KeqoL+imjf9VaUCat5DqneyEz0xTb3nQ5zu2IT7x3HsFxEJauhVMnISz5wPwsX3ScbchMTciecbizxCx0ewT/zkQTO57PrV5J/oxLGk5VFsb7bg9i10PapFc55nv05AfbVMY1OF5vE6TetDKzG5bLDZ9OlWm/Fe2hlss6uT+OzQ1I3eppXn4LhudZjj39NPYFK/Z57DpJLlAK9F9wr2NrGPyXx+qdEztW/4o6Y/9xaQNFd/0VAURVEURVEUpeHoi4aiKIqiKIqiKA1HXzQURVEURVEURWk4C/Jo+GEg/qSGy6FdWa9uRxgsOA9GSBovy5pLd00a3IgyLItzdWC9BodQq5kfRx1hZytq69JZ1Lu7gak7rJM2bqyYg3jlOqzDkwcOQrx9L+pFz9qIum0RES+cPQ8Gr4lcr8/ut/AiNO512qZOuvqZ+7BnZDGIuwmJTeory+OoZR0bzUOcSJo+F59yieSa8N6uXYceDYs0jIkUCslTtDa2iKlVt21qszZr2bkLUt4Mn9e+Ntfw9uro2zD6IuU4YH9VthnX/O5NrTPKWLUa/QecR+fQIdSLDg2jxtmhnAbLulD3KiLSuxHLLRRQNz0wOK3Jr9c92bHniHGM003p6LC4k/k99h9+Hj4b7VkN8cln9xn7c5sKV+J4Uz+IXpeWVZSrI4P3aveBQ0YZy7tR32u1YBkhJUppor7sj+Uhruwlr4lFhgoRKZ3AcbW3E6/FyrPR49OXxTHzcBnXww8o74qISM3DsdcNnoZ4fRdem7Y49m+PxNe7Du03ynj+EF1P8mk15yauZa1q1m8xsGxbrKkxhTTW3LHDiHwTlLcnkc1hvPXVEGcoT8aJZ++EuNxntvFSG/ZbSaBncWj3sxC7+7dDnGrCOmUs3N/dZbb5OHkne1LY3hLNqJHvH0bvTX4IfSWWa47tqW7si5mVZ+PnnejBcNiDQVNmGOGTZF9gSM884MOxF98nlE67kkxOzCeZDM4rxwbxHpQtM1eSX0N/RH75BoirVcy5cnIY85t4bTmsj2kzEMvDa9ZCfsL2ccqpQnk34jS35QbR65Xdi2O0iIi4OAfXM9h+dpxzLsTN3gGIN7XhiRzxzfwgxwex3KHhPMRhiuYWyophcXMLzPZTpm3Yx2E50/uEjno0FEVRFEVRFEVZQvRFQ1EURVEURVGUhqMvGoqiKIqiKIqiNJwFeTQCP5hevz42hxbdmcc7TMgeDYr5c949wicQkCzVozWV+4dJ++bgUcfKqKtMxFG0lo7juvYiInXSvY4FeFlbaD3zS87D/f/9p6gV7lrWa5TR0ozaRr9GmmYPz8PMmzF7Ho6ov80WR+1/ulnes1wSkzlGetegjyXbjHrsVBKvl4hIPIF6Q5eaaCKG9y2TRS26ZXN3MTWOdogHdVwsk1NvxGKxWWOPr7Nl9qtajXNvYL/hXB6H9++GOE6+kHjSbOPFAupU47QW+YF92Ia9Gtabc+bweYqIdJNvY/NmXJe+PqMNl8sV+bf/81PjGKcbb2BcJDahxy3uxjwOpZOU0yJijffEcswfErLdga5bwUcvQN8oapbbM+Za/KkYXuv1W3Dd941reml7upcnsIz+u3Bt+9oOc9y1+1H3vHn1ZjxGCXX7zxTQw3O47yjEsSbz2jU3k/fER8/G7t1PQZyxUCfd3orty3VNjXxb9zKI0zn8PDF5rapls36LgW1bYk9q80P2aDAReaZ4bOAhjOfcVGsPxKsu/HWIS0OYF0ZEpNi3C+IKbVPoPwhxbRzHltIA+iVGy9gHYnXcXkTEDTEninUcy3QG0ZNRGMpDHKccGC3rLjbKSLb14jEpvwc/z4RkyjCHbvP+GbnEhI85vYW9BB6NpkRZUomJ8zrzLBxXdg7gGNCzEX0yIiJpesZLxfEa7KDcN+ky+Sp5fs2a13C0Ff/md2FOlX7KzzQwhON4nL5/bxH0eBQreJ4iImeO4Ni/bRz3SV34SojHduJ8eXAXep0O9Jk5mErkQ6XHTKnRM1+1itehMET5QjzzWcKl/Eacx6s+Y9zz6urRUBRFURRFURRlCdEXDUVRFEVRFEVRGo6+aCiKoiiKoiiK0nAW5tEIAgkmfRE++SMCIzb3N3wDlAeD7BK87LT49Hkm2yYML00dI71yrg3XO3YpB0E5wHcvznHRmUMtqIhIrBV1vaOUw+Lg0VEsk97vuttQS/zkbnMN5d6tGyFuCVGXn/CxDC+YPc+Gof2P+Jvp0fAj/71YXHjheZJOT/gH1vb2wmdx0pmHAd5XEZFEHJs7p2Fh/XKc9IqUCkIq5Yi19C3KL0N5MmLk2eB+Ylns8SAvg+ETERFe65p0vcMDqLl/6N6fQdyWQ6OAkyDtsYisXEk5VQJsf7aHn/M3GPE4alajPFy1KureOR9Ipjk3fbwE5lFZLKxg4n8REeqCYhWxPfAa5CIiPp138Rhqx5Muei5aV6OnI9eN/pmuHPq/RER6V6IXYe3yLoh5nXiXtN5jefRTxGi99eYus8xaM+qWywUcw57e+QzE/3lsD8R18rMs32IUIbEYtqER0k7HHFw3XmzsO4V+9IV05Mx2vnplB+5TRD33eGViTKxH+B8WA9tyxJ5sV6Exfvm8sbG/X8V+E+x6BOLkKvTW1Glui1ObdlLmfJjtxrmqPHIQ4sx+LPPk3iexTpTHJUjimBdksU+IiAjlTeofQN19rYLnbcWx3mvbMAdGNiLHQJ3Gbp5AjPlEeFyem7lU7zOPYfOEtAh8+x+/K87kGNZ/AvM6BDb2v2vXm/3r+d2YJyN/BO/92k04tqxctRJiux/z9QztxXwUIiJN9OTo9+G4nKzmIV4n2DYcmoNjKXw+a0ub/kI7xPHryXEaa+jup9ai9ylPHrZsjznPV6rYv0Py3bIPsjxAczSlFnOz5jNcNoX3sEhzWrU8fUxfPRqKoiiKoiiKoiwl+qKhKIqiKIqiKErD0RcNRVEURVEURVEazoI8Gn4QiD+p/Q88WqvYIV2ca3oAWMMYo9wbXow8HIK6t3IdY99YhF6m6jdVhoua5sDGeg6UcN3mSkjr2Huo3xsdMP0T/hCuAd8/gH6JsXFad9lFsdz6HtTrHdy11yhjjDR+61bhua9I4LUMfbpWnF8kwmNRI81flbapzvict10Mlvcsk0xmQveZTKKWkD0AoW82bfYAWZz7xYh5zXnSSEbU0avjheb8JRJjjwZ7m/CoLnk8DGG2mPXmLXZs3w7xkcO4xnxTBnXZ7ctyRhncd4cGByCu19ATk0yg18DNsF7XvHr1Cq5FXiKdvz2jErXy0ng0fL8+dS0C0n2HAXt6zDYYOngdQsGxoXMZauI7e9Bv0VdAjXJ7qzkG2qQlf/DJnRC/YtN6iFty2HfqZHQ7c/M6iI8dRs+PiMgJDz0XJ4YxF0JhN+qFi8ex/VRrpJN2zfwMTjIHcd4iX0c7XouWJjwvWsJfYo6Zg+TkKI3deYxta+KYtcpS5dGwp/s7ewIs8qnxCYuIVcY+VnzkLohbSuhJdK98K8T1kNp0lBezQp7ECvbjWAZzHjUv74U46eC9L43j/qFH5igR8dh7EuC9L5XwfgV5jI//5B8g7l6B/U5EJPurfwix1dyM9aIkXi8my4WRR4MG3pk+Qjsip9LpZteeQ1N1YvtgNovt7cHH/tnYv6UH80n4HVdD3LYuB3FzJz6/PX8Q28aP/uX7RhmxCl4zO9ULcVh4DOJuejjqpPGvvYrt7awus20k2nJYJs1347vQi7LzMD7jnX3lZVhmB/nNRMSjuZ+f4MjCJjXK9ROj9DN+ROcdob7FvpCZc8uE73d+z4H6i4aiKIqiKIqiKA1HXzQURVEURVEURWk4+qKhKIqiKIqiKErD0RcNRVEURVEURVEazsIS9vm++P7Eu4lHBhqXzLe+bxq22BvpJiiBWgL3CR021qLZaHBon1FGSAn36jWMC2NovqxScr2QHMNNZJSsVE0TaqmOf0um0BjrkYHuVNKnU5wwDIdmsrkje5+GuFZZDnGwDJ1Z3Sm8VnSaUo24P6UaJmcpUwK1SnW63rW6afY/3cTi8SnTN5uk2Qzu1UwrnkeGZcMdSgsBeGSot2y8iLWqeQ0Can8eXfiA2zy7rAk2rLuxubtsvY739uABTGqUL6AhNEHmTCeijP/z/e9BnIqjkTaVQtNeZycmXuJkiHaEUbU5iyZmNlcO9R+b+ne5EpEscRGwxJq6Z2ENr7NPpkGvatbR6kMTtEf9sNKM7fjAc/sh7ithIrLKEI4dIiK7dhyEuH8YXYDZLJqmk8fQAHxgPy5usawNF6tY1Wkm7Nu7ApOolQaxrzljmJCqmcbEwTyOy+EILaAhItQ8pFjBZHqJgBbI6MBEhQcPHYN45x68tiIimVZMzhUje25reqISdn1pEvZZjjWV7DLk7LQBtbcIs3CM7r27aRvE1f5dEHfeg4beExsvhLg4jgknRURqY0cgLg1ie6rkcaEAn+YSr0ILtIzjOOw25YwybQ+3GS/gMSifqDhVbJ/FAvar5rVrjTJa0q0Qh2SE5bG6EcxqBo9IyHi6SaTiU4typFJ4vukMJbrj1VdE5OgAXrM945g48emTeIwcPcf4ia0QN1+Bz0EiIhu7yPifwIFj6CQe48RJ7Dfb8zg/2iGOXd5Za4wyX3kxZhjNZMm4TYunjIxi+3u0D/vAlm5z0SHHwWe8ZA4N404Sz8Ol8w7p8SewzHm+RENIEOIxEsnp+2PVQxExnyOj0F80FEVRFEVRFEVpOPqioSiKoiiKoihKw9EXDUVRFEVRFEVRGs7CPBpBIMFkAjKfkrn55Keo181D51pJY4bScAkpkV0YoAatTkm+3IikX2MF9EuUxzGu+ShUq5AvgROmlUiXz/p3EZEyafWrNdzH81GvF3PwPAeHUWediKPeXUTEL6NO8ND+HRDXRnEfdyPqqmOkx6tUTR9Imf7G29Rn+A04Md1i4NiOOM6EZpA9GlXSwx/ch74EEZGRYUx2lsvl8PhkIgpJf5hKYfut18ykhyFpGmMuehkyGTwGJ+xjL4NQHOesPBHH8Em7fcEFqKte3bsa4jPPPhPLiJtl3P/A/RA//wx6hlYvXwlxqYT6+a4u1MuzH0ZEpKc7B3GV2lh9YGjq355Dgv1FwnZiYk9qZcOQEiXS2GHxvRSRkBINOim81kXyezkD2O/ro6gfPlQyx6NYGn0G7B25/Qf/CXFzCyaXWtGJSQMrRdx/vGz6Qno29UIcxvK4TwU9FwXSxP//7f17lF1Vne6Nf9da+36re6WqcquEJBAIgRgQItBR8W1oVMT2KMeX80aG2Ppq04Ci0A5E4efpHp5GWx326X5PwzjSfVDRt221++dRUVoQBCK3cEsg5FpJqpKqVGpX7ftlrfX+UZVd9TxzJZXoThWX72cMBpm1915rrrnmnGutvZ9nPj55IdLtqN0WEakVcfxWyC83MoyvT3ThHJhJYsDac1tfMPaxuB/11xkav9XKpPa/FuC/mQscZ3oOLJdG4bWJnY/je2vYd0REEj0roZw5rR/KhV88CeX8Q9+D8sFzXoSytQR9CyIitSPohSlTyKRN16LM4rVQjq28GN+/C+eaWJupy4+WMURy7zYMZavSHBmiuSURwnuNli4zlM2hkFi2yMzmtzMJinw9/ne/M/fBQa1zgWO708GpNN+5NTx+b8Jsj0R9O25vAq8TpTCO2YP2QihbFHh6Wp95n7l+DZ673j70JY0uxf7zv59GP88wnRc3gft4IGfOuU8/hfWKx8hD6uOcXPbw5rf1II7lwnOm9ykSxv6XasFtViwOPcXPuzU8Li8g/DeUwnGRTNE9uT89joKub8dCf9FQFEVRFEVRFKXp6IOGoiiKoiiKoihNRx80FEVRFEVRFEVpOifl0ai7dXGm/AYOPaJUKa8iHEc9mYhIPIm78ygXwxfOwEC9WJkWw/ZrpkbMIt2ZRWtd+6TVjFKuQaWMvgTXQi24ZZna8DD5OHj5co/06L6HdaxQroYXMjWAyQR6MA4dQY3zWATrMFHEtaST5H8pl02PRo28JuxBmHk+avPg0SiXS+JMdbyxMVxnenQUNY6HBgeNz9uGhhb7Qow0uL6Px0/yeolGUSMpIpLPoS66WCTdpI0a1ESS9PQk/E2nUV/q+eb6/Ratp36EvCgtmXYo969AnXYijXr4WNScFladjp+ZoH2UqHHY5xGJotY9ZGNZRMQi/wk1vziR6fPjzE+MgfjJmPjhKY8GiWBt9pClTZ9BuchrtOM4iq/sh3IyipkViR3YKAUxx6FbQ/+ATXPikf3oschlaS7wcRxMRHAuOXQAcxBERIo1fM/qNjz2ykHOQsB+vWAl6qr7151j7OOtl7wVygdHUMdsZXEfBw+hbn/DW94C5XjE7ESRLswD6WvF8vihyX5fLuG+5oqZOS7xJNYtetZlUC5SnoWISHb301Au7NkC5bqLmvkj3XjdCTs47mNZM1fKIp9a+2kXQLnzrEuhnOpebmxjJn3nvxfKhSEzPyu7YzOUV78b91mawL5SOoS5BuEIauxD6/7YrIiF/jrbphyNk/RoBEnczW0ce5vz4dFoiUXFmbreRKlqnDMVdkwfU0sK546Kj/1pNPcKlfE64Vs4N+3Pm9fgh+zToNzbg9c/O4LbOLgH6105xNcu9FMUbNNDmxM8DrrFE49uG+ulLJSXZdDX5AfcvxTomlsnj4yTxOOyI1gJzr7za2bfqhXoMzaew/iMjC2+fz8e+ouGoiiKoiiKoihNRx80FEVRFEVRFEVpOvqgoSiKoiiKoihK0zk5j0atLs5RDSFr0Ehb2NNq6tgipP2uV1HjWC2hHqxSY706bnOAfAoiIt1tqP0tlnEfEZuyEkjnZsjWyIzi1k1db4i02hblLzikoS+U8fU6rcefL6COW0QkFkKRX4i8ItUKtlWZ9sGS+HLN1E/Wa5THQFkp1eq0lrtWMzMkTjUHDgxKYsqrkiMvBGtbu7pRvywiUquirtrzOTcENYwWmW2sGpZDAUaBVCpDf8H+lS/gmt3VGtahStklnEdRD9BF8mfqdcqCKR6EcnsP6uH5TFYqpv7coXGwZMliKI8eorXybfZbYR2jKdREi4hMFLBPFum47BmeGNubn+9IOrvaJByd1PB2LkL/RGkMz5UfNX0ooTTqaNOLWqGcXIwZFm4V55ZoN/avehX3KSLikVa6ewGebz+P7Zwjr9vAftSvOyHq93nTQzZ+EP0Q+zvx9RTpnHtWooejfTlmu+wYMHX4bbtboZxpxT60Zs0KKFsTWSjHHaz3pRdhXoOISE8/+gVyZVrnf2reKeRxHM8VlmOJ5UzOdR55BhzyOKUi2B4iIvE2bOd8Jx5vcTHmDyUnsBzOYP+Md51h7CPSguMinMHOYFO9OTuIs6wsysyJd+HcM7lPzOkJJXCc2LRNY+Y+EXsF1dP+A7N8TsyjcWxs+2RzO/5wIrbVuJ9JxeleKkL3SqmAHKEEtplfIJ+Bi3NTguY3nvUrBfM+5MmDr2Kdw9hOyTTWe7yI48au4bztOeTNCaPnQ0TEEtyGw3499tPU8do2MZ6F8g7XnF/icfJrUr28GmeLYS+v16m/BORoWOQBjdHtjG1Nv+5Z6tFQFEVRFEVRFGUe0QcNRVEURVEURVGajj5oKIqiKIqiKIrSdE7Ko1Gruw1dIC2VLRZpuEkuKiIi8RjuruTReu+kOQyH8P2dSdTOTSRMDfRYNovboEcpP8yeC9LSke6yUCE/Rd3UBPJ6/7aQDpF9IOTzcOn1ctXUQNcqlMcQwraq0mfyRWxbh54pyxVzHz4dW63OHoZp7X5QO5xqCqVSQ1vrk6jWprLnmfVj+avrsieDchFsPI82+WKC2wD/FothH62Q7yBL/TUUxjqw/yccMb1PFvW3TKYVypyHwt6bIyOYQZLLYllEZN9eXJP/wMB+3Af5W/KkYef8mnPXnWvsIxbHY0vFUNcfmeHRYO/KXJFpSzcyQdKnoV7dOYjHnBtDH5GISHQp5qhYaZwocwfQ61AhHW04g7rcjrTpRXKp3/rkNUp1otb3LcvPgvKRUfTbbNv8PJRLWdMbJ5T/UqWF409fgfv4zUuYezC0/0ncnGPOT7/10LvWc9pCKL/tdPQLrFiyCMqRKs6JSxfjuRARCVFbFUjnHJ668IX5AjhHWPZ0TpNNc43pQzA11CHKt2lbfh6UW5eth7Jfwr7g0hzpRM2sGN/Dc8d5RMYFk+ppWA9IN+5EzIyuMP3NYxcGnVf+htUy6hQAB2T9wQRp3E/cd8H5SXOBG/IaJ6gcogyfOLZPybSBil2i7LQq3X8JXi9jNmcV4T6iAfeZ3Col8nnUyV+acShHKI/zG8e1ub6ZT+Oz34E+Y3P/o66UK+CYKTlmPojlY9vYCao3zZmRKHlMOffFdCqJHcFtxMI4z3kz2s6vq0dDURRFURRFUZR5RB80FEVRFEVRFEVpOvqgoSiKoiiKoihK0zkpj4brelJ3J3VdFum86xbq9WpVU6Dn1UhHSTr6MOnRfdL91snbkEmYWs1x0oZHSGNWKaLXwaP1+NlPIax7JV2/iEiN8ifqJOpzPfZk4HH5pEE16iAiJdLAc9uxHrdYptwCWnO5UjE1qV69Ru+hXIPy/Ho0ShVPZCojwHexrmHyCAVYNCQapvWwWeNKeSaWzOLZYKGliHikTy4WsWxRxsE4rZ+dSePC1ek06ukdDkQR81jZkxGN0rgjP8/eV3dAefduXDtfRGRsFP0GR8h/wP4VHoeJOGpOQzHTaxKjDBKH9LmWN32+QvZJTV1No1qqikzVY2wMvSwVqlKoy5yfapTLUNixF8p+DuenUBqzItrWoA8hlDTboZidwDpT/2jtxHXgFy7FDIJIG46TfTvaoJyImPtM5HGfoTBprXtwG9ExLI/uOwBlu2BmueT3DGE9+9GjUaGslmWnrcQ6ka/HqZl5RSN5/NuOIazXwgWT+3TDJ65Pbia27TTmIZ/mfMumTIKAoAa+1jBGjEMcz5PN4nPfvB76Fl3PSJ9u+ut4m6TjJz9M4BH4fOw8N1OduG14bg/8DtY/TmnuCbr+nGpqjsjRWyaXE5j4vsY2fUw1usdwOHuDsq3y5OP1y3SeA7JMHGqXKnlNo3QfGqd7RJv8Fhb3x4AeyH2BxyZ/p29sg+5tvYD7q3oZrw3RBG0jSn4y8q9wFla5SB4PEbFcek8Wt2nPuCdif/Px0F80FEVRFEVRFEVpOvqgoSiKoiiKoihK09EHDUVRFEVRFEVRmo4+aCiKoiiKoiiK0nROylFZKpfEdSc/Unc5vIxNsKaZr1jgUB0yyJD5u0LBKmE2nwQE6NhksqHcOqnV2NyN7+fgu2rt+IZ0ERE6dCOUrUJGbpfDrcjEyGURkRr9jbfBAUWFEhqHwg4anEr0uohIvYr1LJFxvlCYLrvu8U2Fp4JwJNoIZirlsT34vItrGpUs+lMiiUZbM/4I/8LHnM+PGZ+o17ENEwkyQYd5L9iHI2TcjrJpmoOBRMQiByf3P64317GQQwNspWSOXVrPQFpayCBMx7ls+XIod3Z2Qrm1HT8vIlITrBeHMNrH+Pdckh2ZkPBU6FnYRcNzmYIOy+Om2diiySLR0gplJ4XlKhm7s4MjUG6LYWigiEjEIRM6ucGLQ2jkf+xJDMuLxCkUcHkXlFszpxn7HH5lD5QLtBjI7tJBKEe7MYyxrYjBb7WI2c9TFH5ZPozHsfmFLVAO29jWS7rQ9L6mb6mxj8MFnPMeeWUblN82tQBJqTA/gZG2NR1o59O4t9iAaji7RXw2SRvGa94GFXn+CciM8zmw1nDK8h/YPG5sELADTO58rDzX80IfszN7GN7s7zi+WTbw1YD5/Vg7nY/AvnK51lhEhW3YUVokIhQ127xcx3mB751CNl3vKGSu4lLIsxfUx4/f7rkczYcWBfqxqZ03ENjnjx+WaZrFaZN0HL6YgaUlWryiWsTrZcjBevN9AV/DS8WA+2dayCESxW3EY9Of8QLusY6F/qKhKIqiKIqiKErT0QcNRVEURVEURVGajj5oKIqiKIqiKIrSdE7Ko+FE4uKEJz8SjqBCj2Vrnh8Wpubi7sKkR3e4NjUM/eLgKTtiBmJZtJEyeREqpE936+RdIH17tUZBhAEhJTUXt1EnMVxbK+rRQ3nyR1iYrBIKm2FmlTLq5tnHESZ9pGVj+6cyGIbmBzxjFj2sVyKF24glpnXUk4F9g8Y2TiU9Cxc1vAATRw7Da5Ui6he9mukzYDjYJxTCNjx06BCUixT4FeSliVGfjkTw3DrUPyMR1J17FNbI+Vq+IT4W4dEXFGI0E/b3xGJY57Y20z8RprBD9lxkqH+1tKDmntvWCwgOs0hHzT10cHA63C7IYzQX1F1XrKn2q5VRRxtJoO+gNhHgg6J5M7kEQ+c6+xdBuVjAfn14934ol8fQwyEiUqJQ0doozqNda/ugXKiSx6eKYydMwVATAWGfrSvQK3JkF4Y+7tiLoZBR6nPt5NnpXne6sY8OmkdfePFFKD/xxBNQ3rLtKSivP/dcKO87c72xj/EatYWF9dwxMBkayMGvc4UTcsQJTQX20RDikLkgqboRJDabR2O29wc5DfgjxuuzeDSMz/MkGBRESOGFs+4TmcUWEvi3k/VozN4OJ7LV6ddnm+dPBbZliz2l/bcopNWtYf+r1c0wR/YNRGk+FPLQhkPkM6DrkMfGAzGvbwzXi2vJdQzyOjGzWJvMMUHXefY++Y55XF4Ea0q3jUYIru8c37MVT5n36NU8zv0enQ8/PH0d99WjoSiKoiiKoijKfKIPGoqiKIqiKIqiNB190FAURVEURVEUpemclEdj+YqzJDa1ljmvtZ7Po1a4e9EK4/Ntraghc8K03jZpMS0ni+839O6mxqyF1hbOTuyEcs1FXbXnka6Q9H01Wui5XjN1aRXWsdEK04sWLoZyLoea6V7yUwTYQMQmP8HoKK7ZPzKCfoIINc2ihcuwjijTFhGRI6OYC8EZI/H4dO5EtVqVRx5+1tzIKSQcns7R6O7FAyhOZKE8fGCf8XmbskQ4T8LQ+VqsecbXOYdDRMQnzWiZvDXpNHobuA51I7eFsmQCRyzWq1bDPmuTlpc1qNy3gtYhj8dx7LJHg3M0WDvLutkgdWfIxoN79eWtUH7oP37Z+HetZup/54KellTDV7PhtFXwmk16Y3up6eHxEqj5txPYrvFO9La0LFoA5ep69GyU8maeQ6WEa807Y+gniEex39bI9xPuQb+NOJT9kcRzLSISI79cdzSN9TyC14ewhRPUwgWYccFRICJmnwqvWANlO04ewCTuoz3UCuVX9wwZ+7DoONb04byZz056w5wAn8pcYIvXyAwyvAuz5DaIiFizeDJMbTn5J/gafUIy7dnedPxMJqPOAe+fVUXPFZ2lSsHb47Y6cY168D5/HyeINeNfc98HXc8Tf6oOfh3nO84ccy3zPGVa8B4uEqH7Lw/n9QhlcVSL7Ncx68jXs4AgFyjx9dDwvtApqAf5C+k9tj2Lf8Y7/rnzbPP1UBr9nLEO3AfZQ8WmXA3X5QMxjyMaw20a3uAZlwJXPRqKoiiKoiiKoswn+qChKIqiKIqiKErTOSHp1NGfV8szln716GexMi0LWyyZy4sWaDmu2aRTpSJuw6afehzHlE+USDZQruDP/pUqSadc+mmOXq9WWdoSsLwtaZ1q9NNcpVKhMraVZ+P7T0Q6VauypAvryU+QvM+glfX42Fk65TjVGe+tTm3nJH8+/j04uo+Zy8vaFvcVlJCUAvpfnaRTrsfHh/2xRD8blst4HvnzIqZ0in9CLdKymIZ0iqRSoRDKP8KhIJnB8YUDLJ3ySMbE570YsHQsy1YK1N7cD0zpFEswgipKS01T+8+USx3991z0v5n7mbmksSFRoz5p0+siIl6VlvAN4bkJ0VxRpX5c5TahPikiUqNteLQMc8jCaZ/nK+FtknSqytIEEeFf+rkONV4KmqQsVdpn0DdgPLZqNKfZVAleIrISoeuJa17+LDqOMvXzytTYqEydlznvfzOW8fYN+QXLogI3FLjdY+/4+NKpE1EPza4YOv7ytuaSugFz4Kz1ODnpVPAW/jDpVMCivCfwrmNLp6qlSQn2XF6DYfl1WqLV42tAgK6O5TazlXm5d95H0LEbf5utTPA+Zt1+wN9mOydmHfn1gHpxW3Db0e0w78NY9TfgRpOXrDX2MaN89N8n0v8s/wTetX//flm8ePFsb1PehOzbt08WLVo0+xv/ALT/KcdiLvqfiPZBJRjtf8p8o9dgZT45kf53Qg8anufJ4OCgpNNpM8xEeVPi+77kcjnp6+szzVdNRvufwsxl/xPRPqgg2v+U+Uavwcp8cjL974QeNBRFURRFURRFUU4GNYMriqIoiqIoitJ09EFDURRFURRFUZSmow8aiqIoiqIoiqI0nTf9g8bb3/52uemmm475en9/v3zjG9846e3ecccdcu655/7e9VIURZlrZpsPFeX3Yc+ePWJZlmzZsuUP3ta1114rV1111R+8HUU5lTSzz7/eedM/aMzGk08+KR//+MfnuxqKMiv6cKsoymuRxYsXy9DQkKxZs2a+q6K8ydEvU+YefdCYha6uLkkkEsd8nUO7FEVRlGCqHNynvClwHEd6enokFArOCPZ93wgvVZT5QPti89EHDZlMZ77++uulpaVFOjs75fbbb2+kHbJ0yrIs+Yd/+Ae58sorJZlMyl/91V+JiMhXvvIVWbBggaTTabnuuuukXDaTqRVlNjzPk7/5m7+RFStWSDQalSVLljT62K233iqrVq2SRCIhy5cvl9tvv73xoHvvvffKnXfeKc8995xYliWWZcm99947j0eivNYpFAqyadMmSaVS0tvbK1/72tfg9UqlIp/97Gdl4cKFkkwm5YILLpCHHnoI3vPoo4/KJZdcIvF4XBYvXiw33HCDFAqFxuv9/f3y5S9/WTZt2iSZTEZ/HX4D8/Of/1wuvvhiaW1tlY6ODnnPe94jO3fuFBFTRvLQQw+JZVnys5/9TNavXy/RaFQeffTRxq+y/+N//A9ZvHixJBIJ+dCHPiTj4+O/135n7vtf//Vf5R3veIckEgk555xz5PHHH4ftzNaXldc/1157rTz88MPyzW9+E66TQX0xSKJ30003ydvf/vZG+XjXa8Z1XfnoRz8qZ5xxhgwMDJzCo3ztoQ8aIvJP//RPEgqF5He/+51885vflL/927+Ve+6555jvv+OOO+T973+/vPDCC/LRj35UfvCDH8gdd9whf/3Xfy1PPfWU9Pb2yt///d/P4REobxQ+//nPy1e+8hW5/fbbZevWrfLd735XFixYICIi6XRa7r33Xtm6dat885vflLvvvlu+/vWvi4jI1VdfLTfffLOcddZZMjQ0JENDQ3L11VfP56Eor3E+97nPycMPPyw/+clP5IEHHpCHHnpInnnmmcbr119/vTz++ONy//33y/PPPy8f/OAH5fLLL5dXX31VRER27twpl19+uXzgAx+Q559/Xr7//e/Lo48+Ktdffz3s56tf/aqcc8458uyzz8rtt98+p8eozB2FQkE+85nPyFNPPSUPPvig2LYt73//+8XzvGN+5i//8i/lK1/5imzbtk3Wrl0rIiI7duyQH/zgB/Lv//7v8vOf/1yeffZZ+dSnPvUH7/e2226Tz372s7JlyxZZtWqVfPjDH258c32ifVl5ffPNb35TNmzYIH/2Z3/WuE4eTTwP6ouzcbzr9UwqlYp88IMflC1btsgjjzwiS5Ysaepxvebx3+Rs3LjRX716te95XuNvt956q7969Wrf931/6dKl/te//vXGayLi33TTTbCNDRs2+J/61KfgbxdccIF/zjnnnLJ6K288JiYm/Gg06t99990n9P677rrLX79+faP8pS99SfucckLkcjk/Eon4P/jBDxp/Gx0d9ePxuH/jjTf6e/fu9R3H8Q8cOACfu/TSS/3Pf/7zvu/7/nXXXed//OMfh9cfeeQR37Ztv1Qq+b4/OX9eddVVp/holNciIyMjvoj4L7zwgr97925fRPxnn33W933f//Wvf+2LiP/jH/8YPvOlL33JdxzH379/f+NvP/vZz3zbtv2hoSHf933/Ix/5iP++973vhPbr+35j3/fcc0/jPS+99JIvIv62bdt83z+xvqy8Mdi4caN/4403NsrH6otB/ezGG2/0N27c6Pv+7Nfro/3ukUce8S+99FL/4osv9rPZbDMP5XWD/qIhIhdeeKFYltUob9iwQV599VVxXTfw/eeddx6Ut23bJhdccAH8bcOGDc2vqPKGZtu2bVKpVOTSSy8NfP373/++XHTRRdLT0yOpVEq+8IUvvOl+glWaw86dO6VarcK81d7eLqeffrqIiLzwwgviuq6sWrVKUqlU47+HH364IUt57rnn5N5774XXL7vsMvE8T3bv3t3YLs+XyhuTV199VT784Q/L8uXLJZPJSH9/v4jIceeooL6xZMkSWbhwYaO8YcMG8TxPXnnllT9ovzO/pe7t7RURkeHhYRE58b6svHE52Xlqtuv1UT784Q9LoVCQBx54QFpaWv6QKr5uCXZmKcclmUzOdxWUNyDxePyYrz3++ONyzTXXyJ133imXXXaZtLS0yP3332/o6hWlGeTzeXEcR55++mlxHAdeS6VSjfd84hOfkBtuuMH4/ExpgM6Xbw7e+973ytKlS+Xuu++Wvr4+8TxP1qxZc9wFAJrRN050v+FwuPHvo18sHpVXnWhfVt64cF+0bbvh1T3KzMV/jne9nskVV1wh9913nzz++OPyzne+8w+v6OsQfdAQkc2bN0P5iSeekJUrVxoX2GOxevVq2bx5s2zatAm2oSgnw8qVKyUej8uDDz4oH/vYx+C1xx57TJYuXSq33XZb42979+6F90QikWP+CqcoMznttNMkHA7L5s2bGzdSY2Njsn37dtm4caOsW7dOXNeV4eFhueSSSwK38Za3vEW2bt0qK1asmMuqK69BRkdH5ZVXXpG777670V8effTR32tbAwMDMjg4KH19fSIyeS21bbvxa9up2K/25TcPJ3qd7OrqkhdffBH+tmXLlsYD6/Gu1zP55Cc/KWvWrJErr7xSfvrTn8rGjRv/sAN4HaIPGjI5sX3mM5+RT3ziE/LMM8/It771rZP6pvjGG2+Ua6+9Vs477zy56KKL5Dvf+Y689NJLsnz58lNYa+WNRiwWk1tvvVVuueUWiUQictFFF8nIyIi89NJLsnLlShkYGJD7779fzj//fPnpT38qP/rRj+Dz/f39snv3btmyZYssWrRI0um0RKPReToa5bVMKpWS6667Tj73uc9JR0eHdHd3y2233Sa2PammXbVqlVxzzTWyadMm+drXvibr1q2TkZERefDBB2Xt2rXy7ne/W2699Va58MIL5frrr5ePfexjkkwmZevWrfLLX/5S/u7v/m6ej1CZS9ra2qSjo0P+8R//UXp7e2VgYED+8i//8vfaViwWk4985CPy1a9+VSYmJuSGG26QD33oQ9LT03PK9qt9+c1Df3+/bN68Wfbs2SOpVOqYixW8853vlLvuukv++Z//WTZs2CD33XefvPjii7Ju3ToROf71+rrrroNt/cVf/IW4rivvec975Gc/+5lcfPHFp/w4X0uoR0NENm3aJKVSSd761rfKn//5n8uNN954UsswXn311XL77bfLLbfcIuvXr5e9e/fKJz/5yVNYY+WNyu233y4333yzfPGLX5TVq1fL1VdfLcPDw3LllVfKpz/9abn++uvl3HPPlccee8xYwecDH/iAXH755fKOd7xDurq65Hvf+948HYXyeuCuu+6SSy65RN773vfKu971Lrn44otl/fr1jde//e1vy6ZNm+Tmm2+W008/Xa666ip58sknG7+ArF27Vh5++GHZvn27XHLJJbJu3Tr54he/2PgmWnnzYNu23H///fL000/LmjVr5NOf/rTcddddv9e2VqxYIX/6p38qV1xxhfzxH/+xrF279pirODZrv9qX3zx89rOfFcdx5Mwzz5Surq5jeoguu+yyxn3d+eefL7lcDlQrIse+Xgdx0003yZ133ilXXHGFPPbYY00/rtcyls8iNEVRFEVRlDnmjjvukB//+MeNvA1FUV7/6C8aiqIoiqIoiqI0HX3QUBRFURRFURSl6ah0SlEURVEURVGUpqO/aCiKoiiKoiiK0nT0QUNRFEVRFEVRlKajDxqKoiiKoiiKojQdfdBQFEVRFEVRFKXp6IOGoiiKoiiKoihNJ3Qib/I8TwYHByWdTotlWae6TsrrAN/3JZfLSV9fn9j2qX1e1f6nMHPZ/0S0DyqI9j9lvtFrsDKfnEz/O6EHjcHBQVm8eHFTKqe8sdi3b58sWrTolO5D+59yLOai/4loH1SC0f6nzDd6DVbmkxPpfyf0oJFOp0VEpLUt03iabWtLw3ssG59ywyHzCadQKEG5XK5DuVKuQjkei0LZDlN1Lc/Yh+fh30IhPkSbSljvWq1Mda5BuV439+n6+DeLD91z8f01jC45/7yzoPzBD/0fxj5+/fATUK7UsN6lagHKvS103LYDxa72jLGPvfsPQ/mJ3+2E8ujh8ca/J+NX/EbfOJUc3cc3brtW4rGIiIi4Ph7/RL4IZT/gWxfPxXYvFfFcuy6ea4vOa09XF5RDAU/x+QLWgztDOpmEcjQex8/n8lhn6s+d3VgHEZFCEc99tYbH4UTDUHZd7I98FAmqk4hIrVqBcr1K+6BxlM1moezR/JCIJwL2geM/Re/x3em2KFWq8pmv/tOc9D+R6T543fWflkh0cl4q5ifgPakMjqlaLWdsZ+TwIJQtD4+5UsJyuYR91nLwXEpACtLac9ZD+dUd23Gb5XEoc7t7FK0UiUbw/TV8v4jZT8XD821R/xAaO6UyXhtq1L9ERMIRvB5wBFQ8hv125PAhKIdCWEfHuDbMfuzt7b0iIlKv1eTX//9fzHn/u/euv5ZEPCYiIqkM7jtOdbEcc35yaD6KUH9y6TQODg1DuVzDa3Y602Lsw6dmLc4yP/E35PU67sOzsBxPmfNTio6d57w0jU2PjjvT3QvlRCJl7MOrYj3C9vHvJaww7qPk4ufrrjl4x8dx/v/+//tDKD/0H79u/Nut12X744/M6TX4PdefK+Ho5L1Eor0N3rN/zxCUB7eNGds5/ewVUPZtbOdDO0ag3N6F14Dr/s+PQnnPyweMfTzyzG+hHKXryNvedj6Un9r+ayiPZ3GOrhTwvG75xV5jn/USnttEO+6zWMMxUK/iQFu0Bu8LOpeY42pw5yiUownsf8kM7vPwEexLoSi+f8GimLGP3hZ8mNy3Da9hqeT0Z+o1V37zoy0n1P9O6EHj6ERgWZbYUzcMDk1i/KDBr4uI8fMKl3nCme39fO0KwvxJ5/iTw2x1CvrZkC+ixluMbeDLoRA+BMTjeEEVEQnTQ5bHD0gevs7vt+hBI0oTcdBnZmsL3/fn5GfUo/uIxyLHfNCo0gXwRB40fLrhpuuA8aBxdN9HCXrQcOu8EXwPPzzHqOzSTRzfwCUC+obn4T557J38g4a5jyq9qU7Hzg8aZbpB4wcNbksRkRCdMm4rPl8iwePxVHB0P5FoVKJTDxr1Gtbv6N8bn7HNG/JwBM+F5WGf9OpY5i82bLpBCopbjcbwAhIO8/nnbeA++GY7RJ/3ZfYveE72QSNUp/nNMw+MvzTiB40QzV+Og3OeQx3MoXlXRMTibYZ4XqXzN8f9LxGPNb4ISCbwxiKRxPKJPWjgOOQHDd6HTQ+A/LqI+aDBT8MherA96QeNhPmgkaRj5zkvlcIbOX7Q4AeVZNK8efLo2MN0TZ3tQSN0Ag8aLo0bHstBD8dzeQ0OR53Gg0YkRscX4fFm9r9wlMawTdugMRyOYJn7eCxu3izzHMtl7j98HEeP7yhuje8RA+4BZ7l35ftj436Z2orbUkTEpvc4YfoMlWd9f2T28xMKYz1Ckd+v/53Qg0bjzSGn0YC1Ot/c0E2oeS0yfoFo78DBb1lYdut4YzFBvy7EwubJ8Dz8W4wGKneAeBw7WWumB8rZLD4VVgK+aQtxR6abqHAY61Ckb9KrNN88v3W3sY9wFCe+uouTdUsS65Cv4D5qFt4Eje7BbzVFRNwKnsO+hX1Qzmann25935daNegknzpcty6uO3X+LOy6FTregDlcYlE8D/xQUK9gm6ZTOKnV6QIZDbjIpugXC37QaG2hbyp4kNINW5x+XYhEzYcAnz7j0S9o/EDUQt/uWXQjUC7SrzJi3tRx24VCNAao3jWf6hRwwfTr/CBB3+Yf499zyZ4922fcbGK7HxjENomFzVrG6fmq7uF8Egrj+W0N4zd+xRx+8z+WNb81rNCve70LcE577oUBKIdpHq3W8DxUqjRf1fDXLRGRSgX/FnbwOCIheqDmX6Ppwuv59MAuIiWa/316kGht7YBydy8e99gEfvvpW+aDK08cdgjLsczkHOJUzWvPXBCJRCU69cuORXOLT08JlmPW0XGO/2USX9f5RtemX5UOHzli7GNoBL8RLtJ8wl908EMqvy42noNE2px30y04py0kmc8C+sUi09EJ5XoIB2alhH1eRCQepi9PuN4e/2KG7W9RW0cDzs+hgweh/OKW57GeM1Qfxpdac0A80iuRqYe4kUEc8xM5qk/A3WXBw1+B/Rye2/YOHLOdrXgd6e7Ae5LKUnOOTTyD5+n5F7dA+by3ng3lyDhek7c8+CiUl7xtAZR7z2419pkbxnm4IjhPrzwD+2My1Y51TmGf711I9xEi0tWLbbFnL87jy8/EbcZ249jdv38/lNPdpqrF9bDft3TQPdPM65UfMH8eA111SlEURVEURVGUpqMPGoqiKIqiKIqiNB190FAURVEURVEUpemclEejVnXFmtLWuqwHNQzPpkieVwUplVBzm0hgdVjz+P73Xwnl6//v/8vYB+vo2bjokAkskUSdmiOsD6XVtMKmiZpXfjEMmmS4++VDv4Hyd374Syi/vMfUXZcrqHOt5HEVgz2v7IAym0rZKNTZY65elCf9d3s7av5mHvukR8P0q5xKPM9vmERZ214nfX85YGWcWoVWSuLlwUi/zCsrCUmDowF9oUQrV7FvtlJGXathNiWvQ6mI58QLMD+FyB/FXrU6tYVjLNyAdcgFaH+N8U164wLpsD2aH8JkMDfMw2Jqs6vU/vHI9DiyA4yuc8GOnc83TMYJMiLyufG5L4hIJont0NneCuW6S/4uQR13nbTjFVqlRkRk9wvb8A+0T9fHtnOpnV0yrfrCJlZTm2ucD+ovDpk82Sdqk569XjY18rxYRaIFV73p7EItdTKFbfnEZtQos0lURAwfYSZBJuEpA3DVPnF9cjOp193GXOeXcC6p0nkJWhAjwis3sunexter5PfyqO+M51BzLyIyOIirD3F/4kUTarQKFXs6qrTinTHBiUicTMLDw7hCj03HdcYaMpRH2G8RYPKLkC+VzODc3HlqmxrdGCRi5spWu7bjdXz4ALalP8M/FbQ4xqmmWo6K70+Om9EDuKqbE6EV2haiZ0pEJN2yBModnW/B11N4P9aRxGN0qP/t2ktznYgsIB/Hkk70o+7ZtgvKPZl+3EcZ67DjKTzOM95iLuUaSmGfPXwQy51Lsc+fdhZuI0nzZ7lq3gOmupZD2eXFSGgOjdAqe7XDZJzPmbf/Tjf20eVruqG8d2Daf2WZVr1jor9oKIqiKIqiKIrSdPRBQ1EURVEURVGUpqMPGoqiKIqiKIqiNJ2T82i4bkOrzaE6HAgXDpvPMEZ6s/Da/Kgh433ksqhX373HXMM7SmvCe3X8DMvPSzVMPi2VMAkxX6AcjbKpux4exm0cGMK1sBPxViifcRZp7ahSHB4kIhKPU8ZIGrcplAzOuRI2+REqdfM4OBV97x7M8yjP0ARzrsJc4LpuQ8dfoCwSDh3jDBYRM304Q22aTqFmlgP7PPYQVEyRYjzCPiTKMyGPRtkjX0wb6s5L1P+ckNnuDiVkWWQMCZH+s07nOR7DdcRZQz35GewvUdLjFiZw3ITI98GhlEH+HvaBOOQDmenhYA/IXJFMRhvr4zsOnosk+VCOapkB6pf8nqhNGScUZOdSMGUiYC3+SB7P7/YBTLKNdVGCOc0FFml7ObQpaOiz5YbzGUolnPu5P4Q4FMsL8IGwBpkCp8aymCrse9iv6xUKRwzKROKgLUqqdHOTbevV5tafdpRSsSLWlE7dcqgO1D5ugJ8rSkGamQzOeWF63aXrysFDeK0bOoTXOhGResC1ZSZl8t9wmb1aderz7A8VEalRajfnXb2ybTuUffI+ZTrQsxjkQ+KwvAWU08JJ7TwmOBG9XjazrF58HnMzckeyVIfpevP1aC6wpN4I3+zuwvG0by+ex9NWof9CRGT1yvVQXtR9HpTZ99KZxDZPZ/D6ODaK1x0RkSLdZ2648G1Q3vL0FihH+nE+XL7iNCi/vPMVKPd2m/7W09+KnovND22Fsh3Fcz9CPqZXB7NQTreZfdxxyN/SugrKL/5uM5T3HcD74yjdJ+THzHGabKNrFlmZMi2tjX/Xyiee46K/aCiKoiiKoiiK0nT0QUNRFEVRFEVRlKajDxqKoiiKoiiKojQdfdBQFEVRFEVRFKXpnJQZPJWKN0x+NQ5E89HMV6uZRiWPXYRk/syTiXFBXy+Uf/Jv/xvK//qv/2bsIxZDM4tj4SFyDg8Hu9UraHDh0DDPCzC6UVBPugXNRcuWLYPy8pVL8QNk6jpyEE2NIiLhBBpNOdSIm7tawwMtV8nUXjEdnfkCmfIo4G5m282DF1wq1YrYU0GQbBjkModAiYgUC2hsDFP4TyaJxkg2RYfoPOfGzbCq7m40mHMA3wSFOFXIUN5BZnA2/HEg5dS7oMTZaRxaWSpiO3CIZVDbhQzTMQd0miFaM+FARV7oQcQMEuT5YWbIX1Dg31xQr4o0PLaUOhemgDjPMxcLsMO44EU8hH3Or1N/oXE+kUfzYyag3TkMj4MEqwUKVxSsZ4LGAZvFawHnzvNosQlacIDN4S59xxVNUfhhwRxb8RSau2Nkvq/Q3J3OtEI5FMbzU63iQgwiIuEQbrNthvlRRKRnQd/Uvk4iraqJVCt1CVmT5yOSwDazaD6LRMxFHaIxbIMkLTxSpuvdqzt3QnnXblxYoBgQrCgez8U4Z1VoDuOFJowQU5qfghaCqHNAKB07G4RHjGss7jPouLI5NG9zCGUyjePGpoEYtbBOL2150djH889ugbJbofnend7nvJjBw4fEmjruqmCbZtrxvicaxvEqItLXfgaU21r5vgaPlwMmvRD23xQviiMiAzsOQNkmg3kqg/Uq1vE4epehyX/cxvDHpWvRlC0i0n0mhf1G6BpawXrnaA2FGo2Z3z6AixeIiHQsxm1e9p8xoLT2BM5n44ezUF60rBPKI7vxPkBEZPnpOKcMjaJpPRmdvj/h4Nfjob9oKIqiKIqiKIrSdPRBQ1EURVEURVGUpqMPGoqiKIqiKIqiNJ2T8mhUypWGFjueRG1drcqaeVPE73qooXVs1F7aFAzVksK0kBzts1gICHWy8G+JJOlYLdRydsc6oByNYpPEYlh2HLPJLAuPY/deFOCxb6RAXoHiBGo/IyFzHzUKbmtJohdg4FUKLyQdddVDLXedtZ8i0hrHerb3oOZ0x97psCbfF5lrmXK5VGmkIPkUQMga8Lpr6sg5kC8VxzIbT+qkV47GsS8VAwK/stkslCvUzkdGUe+5aPFiKLM+mUPr/ABvQpj7JMn2WVPPQZnsXQkKY2SvST3IqzSDKumyqz6djwCPj00aetZuzzysekCg21xQnCiJPWWCaWtHzWsmiZrZQhHPtYiIZeEc1xZBjXyljO02SiGTNs2rndyHRWSCwvEs6rfsb0lRaJtFJh+f/HdOQKBjqYLnw4kcX2efaUOd9LLl/VAe2IGBV5OfaYVyexeOnT279kHZpzmgrQXbulgy58BUIgHleh3f4/vu1P/nxyMUjcUkGp+8LrKXJprE6yWH74mIUPczQv32DmAbbt2G52FsHD1CHpsexWx3thGxB4zDVRNJPAfJGF7rQjEzCJP7bFsnXtcTtA2bJskazdOOZX4Hy1r/KnlNihTOWnVo3NA8/eILLxj7GBocxD/QXDwzKNKbjzkwKSJT00nUx/ZIp/qgvPyMtxofz7QshDJfV1KJKpWxL+Qr9P4O9DSKiNQcnDN3vPwqlCPk7SqTf6dWwv65YCHOM7mC6e1yt+M22h305R6m+4KxYfSgtZA3s7sH+6+IGQz94//5CyhP7MF7xIwxl5GX0zPvMw8ewGtWnv178fbGv/3K8X2ZM9FfNBRFURRFURRFaTr6oKEoiqIoiqIoStPRBw1FURRFURRFUZrOSXk0yuVqw6PhWagd9Ek7bGRmiBja8Tqtz37Gaahr+8//5wehnCJNancPaqJFRGo11M+9un0AyitXLIfyi8+/ghsgn0iINK2Wb+oi22j96O/e9wMoDw4dhvIvH/g5lEcOofYumUJtnYipJ77wwnVQTq9FTZ9HbTt4GMvLl5oawNUruqBsW6i1/ebd014Sz/elUkFd4qnGFUvcqU7k0rrpddIKJxKoyRURiTioWa6V8VyWDT0iajk9EjhXfc6WEBncsQfKMepP3V3Y7ot6sQ9X6Lyx9tgNyGYQGosR8jrUangeI7QWOfsGomHzuBzOtKCyQwOlxN4Cylmw7IDvOKjeFV5f356eruoBHrC54H2XXyjRKe17gvwRcWqDcgU1yyIi+w+hjnbPPlzP36FxHifdd6ZO5zrA7JIl7Xg4ih6NEvm9QnQZ2LBmJZSTUTxXtYAMnprHGQ7kNSKPjiWUdxTCOnYvN9eqt2n8tndg+5/VeTqUYxHc5vLW9VAuVbAdRERicfQ5WHTRaolPziul8vzkaNjRuNjRyTra1P866XpoS0BWTRjPUzaPbfDyK6hnz02gJyNEWTd+QI6LT55FfotDXgchy088hucgEsM3pCn7Q0QkHMZ9RiJYTlC5p6sbyu2LsL9Fk+b1o+LS3EzjfSyHXrcYeU1qJfIObKN7j4D32DQ3uzPa0rPm3qORCvdKZKqtHQfngTx5F+IJ8zyFwtgmHs8DETz3IfIZTOTR2xAjX5KISC6HfdalObJ6BMdFRfD9i09bBOUlqzD3zEub9z0hBz0WxRLWq1BG71Mog9soTmDbLV1m+pCGR3Hslij3LNKC892ildh3wg6Oo/we06M2sOcQlM84A8dF/7Lpc1opHd+nORP9RUNRFEVRFEVRlKajDxqKoiiKoiiKojQdfdBQFEVRFEVRFKXpnJRHY9JkMSkS9F0UXrrG+v6mjtfUG5LPg9b5/d53fwzlTBd6CDa87QJjHwcPoub55z/5GZTf//53Q/l/fe+HUOZ8EBGscyUgO2H1SvSWcA5Bawt6ON5z5RVYx//4LZQXdLQLs2P7DijbpJVdvgg/kxvPQvmsVagXZ1+JiFnvukttMfP8BXlwTjHlmtvIz+BsiFQK9aAhMde5r5VJm0mZBS5p2w0bAXkIqrwovYiM51Fr2dqDWuCuDvRoVMjLwNkcHo2jOudRiEilSt6SBGq3HRuHuUfjLEQ+EEtM7W+liPvgHAufxnKF8gdiNrZd2DHX+I8Y6/5jvbwZvgyPDV9zhFcuiTeVB5SntfcTtKZ7zDLHyJFh9GvlSpQtRHlEYdI9j5RQU18uomZZRMTJ0Fgg/1IohG2Xz+E+9u7CHKCqj1rgoPyYCPWhGGnmfZpHOzp7cB+VLJSzWfStiYjYLrbNWztaoVxPYa7Jq5QJEaHjGBmm7CERcanfOg7Wu6tjch+cjzNXRGJxicSndOthygMgL1YqZo4RzunZv38IyoODWGa/hEPnOShHg/MmOLfFrBVlWtSPn1/U1Wt6MzkXI0Uei852nHcL5FNqp+tp7yLMexARKdM8W2ftP9WbM0oGh7A/Dh+gzAwREc5ncY7tiQnKOzrVJMJ1iUYm67Afh5MM7cW57awzTS9DrgP7aHs3eUspg6VQxHGWJ89QvUKVEJGIjeMiFMJyqhPLg8W9UPajvVCOhnHM7CQfg4iIkF8lO4L3oXZ4jOqAc/TuPfj+/hWmv6WlC/0qRbomeza2XcnFsRqO4PxZC8gaW9HTD+UlSzDvaE9pW+Pf1fKJe4T0Fw1FURRFURRFUZqOPmgoiqIoiqIoitJ09EFDURRFURRFUZSmc1IeDdu2GjkaLullWYcZhGWTpovEmjUXq1MjDeLQ9l1Q3rMHtXUiIqOHUXu5uAv16iFaWzyeRi1njNYmj4RYe27q0kKkLa9UeB1x3IZDOQZ10n6Wi6iZFhHxqX3LtI77hIfaxVwe9ZH5PGoZ6655HPU6rw1PaznD+Zh7fWipVBZvqt42aY3jtP79yGHUPIqIVGndZ7+GbRpL4HnhLIgcrXHuh1E7LGKuK5/OoBemUMC11kkCLtUq9k+PNLrGGvRijsUi9R/2r7A3JUK5GeWy2f9cD9vOJgOLx94Ri3N1sK2jUfZCmTh0jusz9POBOT1zwCNbdjQyQzpaUb+6PoZzSSTAo5Evo6fCp1yMhIPHHG7Ffn3Iw3FdD5jCk9RHXDo3DmmOq5RXso08GqE0ZbkE5DPUyLMQi6GeOBKlfAqyoXEuy+GCmVMRJQ18rkLjM4J1GLewz5UOoiY+MAuDTlmB5k13ai36aoBPZS5wwmEJhc019kVM31q1GpB3UsM2Gh0dhTJ7OGYbp0HXfYf6cI36V53qyfMX1+G05Zh99dYNFxr7HBtDDXydM3homwP790M5MYJ9Y8lS1OlPbhTrbVNniZF/JV/BOoyQbn9iwvRX+eR5MdoX9jH3c2A17DSyxiIZvNdKt+AYLtA5EREJRbA/ZVJYHh3DvlChNq+zR23MzMKJ0vhIt+FctHQNntvRZ9A7s+8V7AudGcySGNtrjv0jE+htKpTQr+LWsJ4JB+fxQgXnooPj5hy7+HT0DeVz+Jn9L2Nm3MgAvj4ew332LW819hFN4PnYvhu9cl58ev6oVWa/5z+K/qKhKIqiKIqiKErT0QcNRVEURVEURVGajj5oKIqiKIqiKIrSdE4uR8OyJv+T2ddwDofMTbsufoa9C+kO0pJPoJ49RGsmt2dwDWYRkfYu1Pi1LFkK5Uw36vNWrVkD5Z3PPgPlZBq1xROksRcR6WxdCeW8jdq4Lc+il+SBXzwM5UQKtd0TJXP9aZe0m6NHUP/YuwD9BbUIPkOSvFl819QZOnQ+2OfgzvjMfKzhHYnGJBKZ1F/WaM34sbEslLNHxo3PJ0g3nkiix4LzBXhNb580sY4TsE496d87u3Bt/wSfF/LFREhfWvVIF+wE6bMtKvG5Zz8VfprPZShkfv9QKPMa8bjNag2Po1rFsRoJYb09z/QIlUr4t3gcx0UoNO09CLmmV2Uu6Opol/DUOY7FuP+Qvt0xNaztLdgH0xkct1HqU2zzSLmosc/lzHHMQ7PuskcDz2+EdPgZymFZ0NcKZc8z167nvIlUCr1JiTj6WewIzqsujYP2ML4uIhKiPiZ1ynIizXsqisfpx7GtnYB+3p7GelYoeycenTznler89L9yqdTIjAgluP7YHy3LnJ84c6dcJu8eaeJ5buDzzF6tyc9gv69UArwwM2AfSCKBY6S9DfNperrNHI2ubswreurpp6BcGsfjtMiXFqHblbBtjt1I/Phzc5lymNgPc/gw6vaLxaDrPOUT0dxtzcijORFfbLPZtf+QhKOTjbXzKfQlLFmEY2dkFO+lRES80lugvOM/nodyoQXzdXp78FwXsnhdDwf4lUJ0rQnjZUQmDmO7O+M4VxXH0bc0fgT9r7Vxs93Hx/DcZinHLH8I7xtbMjhv978d72VzR8z7zOwQeufaenFctC/E+2ef5zcfPW0LVplZHfU67ndkBx5XzJ++NtSrmqOhKIqiKIqiKMo8og8aiqIoiqIoiqI0HX3QUBRFURRFURSl6eiDhqIoiqIoiqIoTeekzOAhJ9QwmLFpzHPRIOMFhFVxiE4yhaavt214K5Q/eOU7oFwqoKHLsczqp5JoIkx3ovm7hQzk1/znLJQL40doH2Q2CjC/JWJoZvu///xTUO7twX3u3b0Dym3daIDyA57/JrJodOzsQuNVIoHH7ZNhs7sL6xCNmWFzlrAZF7cZi0wH2XieLyLHN/k1G1dscRttg22Uo4UDoiEzaCoWIfMumSUjETQI1siIF6VAtopnmi3HxtGkP0qhRZHOVijnxvG8xpJoxK2RCZsN7SIicTJP1usUVFZiozZuM0vGs2TK7Bs1+kzVWF0A28IINEuQyTnAqDozkE9EpJDHkKNicbpPlyvzE5iWjIUaZnCLFgdwqf5uwIIJMTJ7Z2gOHD2C5juf2jXMCY++OQbZUMqBaNz2IVqggM/dkWHso5n2VmOfySQaC90qbvNIHo2IhSIaLs22CwiCo7IVwX3GF+A+OzNYz0IN58R83gz7yiTxfIRaKEBxKgyxTAGFc4Xnecc0AfN5NltMpEQG5GEKkeM24b4SiaChPsiMy6FzZTKD82fY/M2G8xIFkA4fOmTss7sPr/P9FPJnk9vbout4Wyv2pXDYbLu6sWgGBWPSwgHclmz+DlpQhWdFDtaduYDOfJjBpVQW35s87nCU+lsI65P3MBRRRGTfKy9BedfT+J7VV1yJm3RwLioXMFyvUjLH8GJaAGjnod1QLlEI59gw3vMdOpLF1x99BMoc9Csisv/IASjz7W89h23VdiYZt8t4v1IawTqJiBTzWK/dT6A5vO8MXHhm5SXYDoeHcKy/8jhea0REanSPHevEPt63+Izp91bqIoL3ssdCf9FQFEVRFEVRFKXp6IOGoiiKoiiKoihNRx80FEVRFEVRFEVpOifl0UgkI42AHt9DTVmNgn9q9QD9YJj0yRSGd/oq1FWuPRvDXVjzXKfQNhER4TAyCgl0XdSLdmQoHCiNOnyfNJJOyNRu5ooYIhOLY9u0tZDuvoK6Qr+UhXImY+rwk4vb8TM+HrtLHpkwBcflKGgwlsCQGhGR0RHUGToUfGPP1JebEvtTzvj4hESmtLaxMLZxNIJlK6D/8Znz/OMH2bHvxaL2KBdNfTzrkQ8dHobygg5sd0MDTYF8IQp0K3FomYiEyEti+iWwrxgejnKVymYgW5hCteJxCvuxsd7JBNbTIU10UIiXZeEZKpCmuTTDo1FhD8gcsWrpQolGJ89JyMb6tpA/zKugtlxE5Ixli6GcakGvlfgLsUxi34cffQHKwyMYJiUiIg6FPLKvjPqcR5r6KgWNhas4l4Q8cw6s5fEzZdoGjwsO3OT+UHPNuT3sYD2qr+yB8tIqhnstOr0PN0BeACvAY5WM4PlgP0GqdfLcs+9projH4415yaPzWiMPJI9rEdOTcWA/auT5PLCPgF8P8mjYNC54juNtBgXXzWR4GOfQHTtMXbgTpeBLmrvjGbwGt7S14gboWlALyKOtUPtWqY+WyLfD/gqPfEdBYYemd41CKWds058Hj4bt1MSZCiJdsfo0eK2Wx74VC5mBcEMH0HuwN4vnNrQd/RSLll4A5WSqC8pWQGZcNIT9yfNwHnbpOu5EyZspOKcWJ7DOfshsd8uj6zaNC8/GOg08h/6I+G70wdXEvHa0dGEfDyWxvw3vx3q278F72dwhvO+cGDb3USWvXF8fXo/+5J0XNf5dKlTkp9/E8Oljob9oKIqiKIqiKIrSdPRBQ1EURVEURVGUpqMPGoqiKIqiKIqiNJ2T8miI64pM6SsdGz/qR1jPb2psnTBqzKol1IL//IH/gHKYcguiFmrjklFTH8rLzBt6Uap3lfTtDq1zHyWtXTjAo1Grodbt05/+Myin6DhaWlEHnEqjfjQRNzMgnn1+K5Tvvvc7UPYF11TuWdAN5SLp7odHUBspIhIlzajroibVOsa/54pipSL1qYyALK11vXwRrhnd0UHadxEZH8fPOCHyDVSxjSzKaYlw5wrIikm3oC7VsnAfYRonrKvOT6BWM0Ea/lrVHFf5POo9w+RfGRvLQvnQQTz35RJrNU0NaiKFfbSjC7WyoQiPRWyrCumXeVyKmOvpz/RkiOC69fX5WENeRPYNDkpk6lg9yhJa3LsIyima70REXn6F1oG3BqDsh3E+CpGv7dAE5rJYttkHeX3/eCxO72CPBq3VT/kglSrq8g8fwQwMETNfwaXzY+yDzr9tY53qAf4C/lZsgsbCK7sHoVy2cBvVehbKI8PmOvLt6TYoR8jrlpzS/hs5MXOEbTuNsVMjD4DvYwsFeRgPH8ZjzuVQj85ZHFxmf0VQFoRDXhqHzjXnZHDeBO8zm81Cee/uPeY+yaOxYAlqy33KSEr65JWj3sU5YSIiVfJ7luneoVyntiPvE7dDsL8F6xGiC2115j4C2v5Uk4gkJDzlk2xJo98ssQRzHGoj5vy364XHocyJTdUJ7J8HxrDN2jrPhXKqxRzD+RpeR3oph2XsCHlJIvj+VauXQXm8jHPuQfI5iYh4o3ju63TerDiOidIEzk0VytloWWzemrtFPN+dvZT5k8GdjlOGV89K7PML1/B1QaSzHb3AF56G2Xb1+nRb1t0T96npLxqKoiiKoiiKojQdfdBQFEVRFEVRFKXp6IOGoiiKoiiKoihN56Q8Gq0tUXGcyWcT1k8n4qilayWtq4hI3UNdq1tBHeSuV9CH8NLOs6D8ny7bCOW2pJk34ZO+2CLNYz6PeRLlEmrlQiSKZA19sYB6UhGRCmncF/Xj+u29C3qgXKf1ty1BfV6lHJQxgOV0Co+9jzTz9Squ25xMUB5DCdtBRBoZFUcpV1BnHZqhvfUC/AmnGs+tizt1OuOk57cdPM+cqyEiEo9R9gutrx0J0TrV1B68Djq3l4hIdzd6Y3jt9ew4ejDKdK6P5HEt7DD5SkquqV3nTAn2aFRIyz4+jrkvhw/jPltbWwP2gfUez2L/scgXEEuh+jZC4yqZNNdYZz8LS5Bn6qbdAA31XGDFXLEik8dSy+McWHGxHI+YPpKqTzkFIeqnYdyGRzpui3JWwlFzHCZpXoyQV4TbtVrhPoX1Zs18Lm/OgYZHgzMEvONnCMTjpBcO8OBMTGRpGzj+CmXsYx15rFPnQiwXHXP85gs4NqIhmhO9yXFQ8+an//n+9PnjrIcY+14CvFblPF4XOBeD85g470To+hroMyBvn2dTX6C5mv07pQqOgewEnpNa1ZwDOd8qRnN9azvej3guBzBQXgVnE4nZVnxN9qvkQ6J5t5XmvGQCvZsiImPU33gczLx3CIiBOeUMDxQlFJ5s6+jpWXjNFvTxbd++y/h8he633nvpe6Gc89GTuGvry1BevqIfyi0ZLIuI9KxF70h6yRoo73kMvcCFg1l8/wI8B7UJyuEYMv2FoQjdf7TguHCLeB59GrxeHcv5QdMDFuqlrA4a3skF2Of7zsT+1tGBY8ApmcfRv6AfynEHt/HI9kcb/66WT3wO1F80FEVRFEVRFEVpOvqgoSiKoiiKoihK09EHDUVRFEVRFEVRms5JeTQS6UjDo9HaSisg03ruLW1mjkExj2t2HxlD7aUvqN/LHsb1/vu6cJu8xvnURgCH1mcPkyxtB+nTJ0qoYS2SpjBi5AWIVKsoluugcoXW166Tti4Wwm2WA9Zor9bxbzHKEPF82kcd9aRR0qx2tJvnp1RALWKF9Ln1Gfpdz5t7j0YsEWmcc5uekUfHcG3/toTpAYiQntijdaAtylCJJ1FDWyZtsOOaGuiWNO53/x7MTRgdQ69DmLTqPI6EfE2eoacX8UgL7PP3B9ThUuRvGQvTWvq+Kf6NWdh2Ph17NodjOVFn3xH2LUvMcbR4Eer0w+QtyOWm265en58cg0wiKtGpsZdwcTLJhHFOtC3zexyL5qwweVfilKFTpXZ2aYH2VBLXRhcx9ensyagUsd+XaM7zfc7AOH5ZxNTqsweD8xZm83BwDoeISJHWww/RZO6Tb2xoELexctmZUO5dYnr8SnnsV5yrdPQCwrkwc4UvlvhTfoJwDPsbxTxImbKTREQmyCPGHowQeQQ486JKc2A+Z/p14uR9SZD/xqN9coZKG3nE2BthB1x76uQZnRjB60F5Efom65zrQ2M16BvYOF2ni0UcN0LXbZt8HnHyAPL1SMT0lDpkBAlZ0+fDY5PIHNDSnpBwdPL89i7B6+N45RCUR8b3G5/vo+yNLvLOODXc5u4BzKyo9C3AciTALxHFbbh17H/pAvbPZRHM4MpH6RyswM8XJkwP7SCd63ASz3W5jH0lnMLXKw72nYBLsORzOJ8NvYg5GUlq277lmGvSkUAfbyJnXjtGd+G9wEj6RSgfnHG/XK+YPqZjob9oKIqiKIqiKIrSdPRBQ1EURVEURVGUpqMPGoqiKIqiKIqiNJ2T8miUXU+OKuImSMvKGtsjOVND3dbaAeX169dCee3ZmJvR24cas3wWNYBhXntdRCyS9pLEVEpl0tkL6n5LE6jtfPDXj0D5kc3bjH1296Nu8K//8kYoh0jnyzpMnzSqkZiZAZFK4RrVdVqrvFBg7T8eeI00hJGI2XY1F/W2lSrqXmdKQudBHirRWKSRXTGeRS1h1EbN4xitvS4iYvmoDW7vQj9FoYjHbxnr7GPfCYoSsWWWHBbKXGnPoJ7UojXmWW8fZI1xyYPBuQh1+hBnjHRSBkupZGpQS6T3NvTFtA/WVfM2hwYPGvvo7MDxzmvjz8xOsULz49FYlOyUWHSyr5VsPKZYmPxgATkrpy9FrXg8ieO6vR37w449g1AeDGG7BeUYsP+BPRgFysHgPsoCYd4e552ImB4M1vpzTkGdclAmJmj+CjgupkaaeIv8TQdpffz9AzhnnL7YzHrKhPF8sE/o6FGUbXOMzAW+7zfaulrBuYSzjUIBc7RL49TwxtC5Zk8G+xL4dRGRMmWqVCknwy5ixbpp/ll31tlQLlD/3bff1P6XaV7lPs25O9z/OGuGvSkiItEozps56rN12medvCg+tXWMPDYiZr25HjPHRZBX6lTTs2y5RGKTbVWZwPO4cwfmZkQDOmBHG57rFStXQnnbHmyzShF9uvkJ9CWEHHMuqtFcVKH+kiNfrlfAa1thF16HIivPh/K6i/E+VkTEcx6F8sEdQ1jPFN8XYL0zGbwf61uB85CIyMCz2N8mdmKfr1NWx5Ht2JaHS+gXXbgSj1NEZNuBASgPDWB729b0OHFrJ97/9BcNRVEURVEURVGajj5oKIqiKIqiKIrSdPRBQ1EURVEURVGUpnNSHo3sUL6xPvpZZ6LW7py1i6B8aBC9DiIiJUFN2P/vy1+GcoJkuc+++BKUv/w3/wvKh7OmDr9S5WwEfJa69NJLoPyR//QeKJ+/AY/r/Leij2Tn9XcY+7R5/XzS9pMFQ1zOAPCxjlHSuIqI7B04AOViGfV3cdL4sR+BtbSloqmvi9Ia/hlBD4PlTOsO58OjYTu22FOaVbYquLT2fzY/IYxFvpZInNbLJk9Aucw+A/asmDpe1uFapJueyGG9FnS34/tL2Hd80sMn02Y+COe05DifhraRbmmFclsU+28uZ2qgCxOo64+RXpl9RnWqUzjCuS/GLmR0DLWz6TTmHERnaJr5mOaKbTuPNPwpPI4X9uG5TKfMMbZvAH1mYqNvIEw+j2y+RK/T2vX5gDmQPDqFAp67eg23yTkbItivWQseIF8X1yU9us/jgHxpNILZb8Een6B6sEnKom1WK7jN321Bf93ggVZjHyEa0+kMaqUjUzkaVfa1zBGVSkWcKf9dKIHXiWQCx6QdEPXB44bblLM3+DywtyHoQlCnoVmqY0VSMbxWtWYw06mDyknK/TkyhrpxEZEcZXRls1msk5Hrg8fp2Mfv8yIiJfKBMJwdw34LLrPvUiTYGzITa0Z7z8c1eGDwgISmcjTKlF1yZA+2cWvI9DK0tmN2Q80n/xj5XopHMEcjdwS3GYmYflaXPD35XTtwGwWcM3OH0U9RG9wL5WrpDCgn159n7PMtq/He9je7/w3KBQ+PY8n5rbgBGlaje7A/i4jkDuBx2XT7PvAyXj8P7aW2pPyPlRf0GvsoC+7j4I4slM8/5/TpKluuiOB96bHQXzQURVEURVEURWk6+qChKIqiKIqiKErT0QcNRVEURVEURVGajj5oKIqiKIqiKIrSdE7KDP72t69oBKaduaobXiuX0ejUceZi4/OdfRjEUyqgqWtgHE2LTz79LJR/8eCDUD5yxDScWx46pGwyV7W0orFsw1uxTulRNO0s7UPzUSplGrUTaTS6OmQGT2ewrapVNNxEwvj+/YMY0iUi8ssHMDjw0MhhKJ+1AOsVCeMzZJ2Mq/kc1kFEpO5jW7kuh8xMG+S8IDfvKaZerTeejNlIVyLTbCnATBo2jNlouOLgKNflIDwOUzLbIBFDs25HB4aC5cazuA9y4tohPI8cPpQIm0OWAyBrbMwlA2i1huZMj8y/VoDLsEThjVzuorZzKHCtUCpAORIQtFQnA6ZxDmfss1KdHzNuseJKzZuse9ihMDcb5xYr4Hucau3454ozIoslCl06jHPeeB7bVUSEfNhGkJjNAaJ0unnhCH7dMt3j4tNYcWb5DssXfL9H2+SA0cnPIA4tQMB79MgAzHOES4siiIi0JPGcumQArtcnX3cpjG2usCyrMT5jtHiFzSdezHHshGhRBhpzbFhmg3OEFirhgL/Jz+B+E0kcFz2dGHDLC2hspUVg2Fxerplzu01m7gKNi5HDeF1PdrRCOZ1Gk3LQYgR5CrpkZrsmstG7paXFeA+HAnJA4sy5OWAYnnIK+SEJVSePo3QY26jVwuNpTZjHt2LZKii3ZNBEfWAA7/k8mgc8Wljg8EHTjOzRdb1AZvDiAQyu83jBgzIFFw/i5902XPRDRGTxmedCef15eB5feurfoZz0cP4Yr6BxOz8R0McjFObbSvdrBewQ4zm8RvME+urjtDCJiFhRbO+F/a1Q3rBxTePflXJNfvNTM8A6CP1FQ1EURVEURVGUpqMPGoqiKIqiKIqiNB190FAURVEURVEUpemclEcjEg81PBp7B7PwGgfgRKJmkEqoBXVnu3e9AuXtu1ELF+KAtRBVt27uwyMhWpg+09HWCuUXXtoM5QXtqBlc1vdOKHf34OsiIv/lP30Iyt/5X/8C5X+JPgDlZAZ1/JwNtHMPBsaIiGx/CbVwvQvw2LPj2LZVCptrbUVPQyxphgXVSWfNYTjhyHRbzodHI+RbEvanNKqkNXZDFFBYIH2iiCRTHL6FQXV0+BIiH0E8zm1mtkGYdNPs0SiXKaCIQgDZTxGN4TmocWCWBPT5KOqox8ZRs+qT7jrBWtqgIKgQ+zxwbPr0GdbPc70dzzyOSBzPR5IC+7LZaU9XLUAbPhe0pqKNwD6Lpk+b6hRx8HhERFoyeEw+BcQ5ZIhopbni8Ch6NEoUADlZETz/odDxv08ywvRmC9sL8mj4LpXJg0GTHHVB8H+JmJ4/ETP0MUT1sl3yO1E96+z78Mw+tHwR6q+TKTyHR09PuRqQhjcH2JbV8EA45HPhviPkkxIRSaQy9BZ8D3sT2K/F7w/qC2HykS3q6YNyMo6BfYeH0T/BbRtN4PvtAH9XPIzzJAf3DpHvMdGGc14kzGPGvDVif8r4OAa/cZhhnI4zk8G2Z19b0D44JHDmOAoKFTzV5Icq4kz5PztDdC9k41jpXmgG9qUpcDZB4Y3xOB5/awffK+Ex5wPCGw/RfeQE3WdmyjhnRikjsaMHz4tH53H3/p3GPoXud0M0B1s+HsfeF7ZDuUQhqoURc35pXYLXjkIE7x3Kw9j/wlUK0a1i29UDfGZ+FcfzYvJTLeqfbptS8cTnQP1FQ1EURVEURVGUpqMPGoqiKIqiKIqiNB190FAURVEURVEUpemclEcjf2RcwuFJQVtrivT7gnpXu2RqN8fHcW3rlWedB+WlK9ZAOTtO+kTSh/7qf2OuhohIpYL1YL3d//HOy6C8+gzM+0iSTrxImrW1azF3Q0RkQRdqEf/jF7+C8sFRPI4EaTXbOlEvGoqY2u5KEfWgYcH3VMtYtskrkKf1uN26eX5iMdQAsg1j5vr5QWvpn2rCMzwa5TLqAyMUQJBIm2t4J2Okw6V1zaukT3bp9WgU2zhorXXW6UbJL5HJoEa1RN6mOun82aPhBXg0OCsmRDrfupHVgG0Vi+FxxUiTKiLS2oF9/AhpYyukq/ap83BOQhCFAs4PqTT3Ry/w33PJyv4+iU3pcTlzgGXd4ZB5rtasXgjlZAL7g0WZJnYEz4VF/Xzk8G+NfYQT6CVinX2phHNBvYr19KgPeux9sMy2D9E+LNLRn7YEdfrVGvb7F3fsh/KSbtMLVyzR+PSx3ksX9kJ5994hKOcp+6VYxXlZRKSrG68XCxegx+poeEGxbI79uaBWq0ltKkPGjeB54FyXmhvwPSKbY4yXKeeF+nhHJ56X01euNLZRJ9/ZBHkZxkaPQLlC+4iTp8ghb06QhYznwNwE6vDL5HvM0DW7lTJV0inTw8hzGOdqRMnTyNke7PtoacH7ABHTw1AsYh8tV6bbdh5iNKSvbUnDq2n7eDz5Cl1nkuZ1JM3zHc2Z3QvRI7W/jGebc1v4Gi5i+nOKNF051N+Wnb4Myu/+zx+A8pEs9t/vfPffjH0e2vkilGPt6G2Id+A1NtyGno1Snvq4ZXpPWrqwPZeegXPVxATO65VxHIfjB7A8ut30WEQi2N5nrlmOb5iZrXYSNkn9RUNRFEVRFEVRlKajDxqKoiiKoiiKojQdfdBQFEVRFEVRFKXpnJRHo6MjKdEpfV46RmuYh7F88BDqxUREfvrA76D869++CmXHQY2jS2LMkF+j100dpZPCD43lUDf4//w/90A5Ecd9OjY1iU165ZCpjFzd3w/lvsWo6y15qIULk1cgSmtJ1+qm+C1E6zRX8qjdHM/SGv5JaktaQ75WNfXjpTKuZx6P8drk08cxHzkajjf5n4gIn4YqrQnN3gYREbeOYs3hcdRBdpAPwbKwzer0+WLR7OOdregNqZBudWICMy3sEG6Dj4PXYo8HrCFfoHqwT8Qi3bVLa5FXa7Rufdz0CJUpJyFJWmLOWmBtdzKBfotSxcw5yRdQ89xn90C5PmNc8LmYK1KRhMSnxqJFEmSyT4hbNz0A0TBqc2Mu9lPfQa3uK7uHobxrP5bZ7yVi6tXzeewf7PPpIY9YNIRzaJXWX49FzLHV1Y5z8ZIleO6W0hz5g3//BZT7SZv9X95/hbGPn//ycSjvOIC6+7XnroIyezBye/ZBeTyHniAREcN25eNJDk/p7sO2mVExFzi2L449Ofn5pFfndfE9z5wr+NyFQhF6B7YZezYyaTzPK1auMPZxZPAglA/sw3avUX6JRf01QXMe5x/VKqa2PEZZG7lRPLdHBtGv0zt0CMqL+xZBORIKyCBJJo5b5nm2Tn5Rj+4DEokAL1wb3jsMHsTx7sw4X1ZADsypJhOLNTwah7N4v9CxGP1n3Yv6jc9zThQ7TWIJnHuiEewbnNtSq+A1Q8T0ueQyOL/ZcTy3+/djf/35D38M5cPk9ylms8Y+y2nyzg0/B+W+ldgfF5yB14FEC7bd2D5zjj18EPvPivOXQvnIGGbFuFXK4KriOHrpV3j+RERa/VYoxxK4jVdfnG6rSkDW0bHQXzQURVEURVEURWk6+qChKIqiKIqiKErT0QcNRVEURVEURVGazkl5NKo1T2RqDfWQg1o6j/SIB4ZNffLz2w5D2bZ34xt4LXbyS/DS+QuXoKZeRKQtgzq0F15EHa8Two2cfTZqTMNhrEMmjVq6I8OosRcROXAAdZTxFGov159/FpT37clCecfWbVD2bXNt6Aj5OnpS9J4o1jObQz2fQ7kaboDHItGCOukIr18emtbnWZ4vrOc91fi+J/5UJ7B5FXHSq1Zq5jr3Fj1XF8uoae6Noua2Wsbji1G+SSJq6ignJnDN7USKzgt5NMzzgHU8HMtCuSVm+icKE+h3qLnYx2tVbJtciXTYUTzP0Zg5LYQr5CtycB+JOL7u1rH/1dg34pur4be34nhuo3JuYlqPG3JOXB/aTFLxlCSmzoFl4zHYNrU7+71EJMIeDcpmGc6iHviRzaj1rVF/sQJW08/lMafgLFonPhXFeq1/K85P7S04DgrkcwtF8BhEzIyabBY1yT/5xSNYR9LZf+hP/wTK/UtwHXoRkaXk+3h59w4oR2hR/jNOwzXgdw+ghrlWM/ugTzkmyRheT+pTfibbMufouSDsWBKZ8tDw9dCt4R88z/ThJch/1ZIhP2ER55JwmPsw9rdKgNeqSH+LkT8rRtd53kc7ZVpwLpDnmv4sn7YZT2AfrU7gHDi4E+8L+nowg4V9IiIiFs3lbW14vSxSPk0pn4WyTcEDLa2mv6qjC7MR/Jd3Yh2cGVlW8/A9cWdrXyMbKlbFMV4WvOYeHBowPr/j2eehbJNng6LSpJRHH4Hr4jVhPDtq7MOjsVmv4XnJkVemUsLzsuc/MJuonsHz7KdajX0maVyk29AnEu0mL0kE3394AO8h21qxH4iIhFrw3mHrFrxvzLTg/ciys3EOffHBPfj+toDrPNlOn3sG79FXrpu+llTdE/fp6i8aiqIoiqIoiqI0HX3QUBRFURRFURSl6eiDhqIoiqIoiqIoTeekPBo11xdrSh/p+aiDi5PWuK/T1Dh2xXF3EyXSWrKmlHT3vo+vZ0Ko2RURSYdQpxaukR69jvXe9tTL+Dp5GcK0jvOCTtRyioiIg1q1cdLM738R1xEfOoBeFaH1z8NJMx8kEkN96NZXUa9XKOEz4+rlrVAuFrAd3LqpryuSv4CzCiql6XryetZzQSqTlNiUbyQ7Tl4ZemQuVU3tsOOg1tehtdJrtC69Q9rhOOmbbVPiLeM51NgnU6jV7F2I67VvIc1qPI7nuVxCPWm9aB5XhPr8+ATmgwwdRJ1re18nlH3yW3hVUwPtOFgvHu/cFk4U2zqXR41qhMW4IhKnPJmZngwREWdGdoE9T1+ReJYn3pRPzffooGn+sgO8VuzrqNTRa1KpoEi2UsQx+Y6N74Dy8y9i/xERGdiP2/zApfiZMAV+8NL2Xo09GfgGzzLHfimH9f7VfzwK5cEhXKv+mivfDeXlLd1QdnOmB6e3Hf0ELVHsL1YN65mg8R4J8eXOzCEIU05NtY6683JpcvyVyuY4nAscxxZnqo4W+RIcyqPwPXMcR8nv0EPehLEszh31Oh6nR8aQA4P7jX1MHME+GyKvX5S8bS0tmHPA2UHsv2hvR828iEiJfGcx8rJxHsgEZSNseXYLlL2A61vfYpy701Rvtkuxf8q2+XyZk1g/5c3s2nMAyvshkyTgAnSKaU+2SWwqoyo/in6J0f14X7PP2mp8Pk3emR1H0IOSpliXWmEXlHe9SnlhxpgWqZL/y3dxPhsfxbkoTT5cj/K0Ykm8hidbTW9wqhXnrxDakmT/NrrPHMpimebYQ7vNjK6l5+B+84ewD59xNnrSUpQnUipiuwy/bM5h/gT22dXvXw3lAwem82iCstiOhf6ioSiKoiiKoihK09EHDUVRFEVRFEVRmo4+aCiKoiiKoiiK0nT0QUNRFEVRFEVRlKZzUmZwx7LEmTJmxSigi/L7ZHm/aZj5L3+KJtQXX8GwlWdeQNN0IYdm0Br5A1/c8oqxD4sMUhySEwlhvWsuGmQsC41rmUVoOG8JMAL96McPQnl8Ais6dhiNauEQGtVqPhoOfcs08XmCfxs5gm3T047bXLkcjWr1KhqDimU8bhERz+PAJzQGbZkRuOh5voxljU2cUlLJpMSmTMbkUxeLzJmVAKNSgQzxra1oLmWzns2ZgLQ4gcuJWSJih/DZvUAhTukMnoc2Mrju2z8E5RZ6f8II0BJpzWDwU5uP9RzOYt9gI6QnHKZnfv9QLKD5PhpBw2ZXN46LQhGPO0wGUDbWi5hm/GKZgwinX6+7ppF3Ljh0aERiU8fCCyLEaU5kE6KIiO2jUTZKiwWUKCSSgzZffBVD6iaKZmgm94d6jdp1HIO2HA/nPO7nEmD+Zko1PFfDhzE0MBnBfst5o9lRvBbYTsCliUzBbRSK1ZqmBUhoIZCeBPXZTjMUK0qDvljGtpKpc+7589P/yuWy2FPtwGZwNlmXAwzrPMcvXrwYyocPY3DYwUMYcshG25ERXGhCRKRUwP2yAT1CRu0am3VpQY3uBRg8FgqTY1hEhoex3hHaJ5vBeeGJIyNoZN4/gPciIobXWxbTYg8JMhUnk1jmkNJckRaFEZHubjQVr1mzBj8zY+523bqgVfzU88rQ0xKeGsvbd+2B1xLdeC1L9Zmhmw6FZO4ZwPmsrxfvERf3YxuO7MXzcugAzqci5oIFLvV5i+6lxrI4V3GGrk1zQNDInxjH+atr6elQ7lyMwdCHc09BecEybCsrIJRyYAv2l3IJ58jBXbRA0Djeh1oU6Nm/Ds+XiEg8gWOz2oOLQ1jF6X1aFTWDK4qiKIqiKIoyj+iDhqIoiqIoiqIoTUcfNBRFURRFURRFaTon6dGQhkejShp/l/SKdkDgTVsad7ekD3WSE/V+KB86cAjKA7tQkZiMmIE1NRf1xj7p7usU+ueQFpj1yYcP4D4Pk4ZeRGTpaahzdUmLWaPQJ5LWBgRmmSrAco58HhbW+8wzUG9cJ519nXTWdtQMTBPaL4dXzQwxCgo0OtXk8nmpVSfrzaFNoQJ6AvIBwXbsOYnHUdNcr2OfjpDfolrF8+iwMUlM3X4uj7rJTLoVyt096AEaHUPNaXYc9cqhFtTfi4hk2vBv3X3YFzILUIv5yp49UE6RttgP0IfmC6jVLlWxvWsueoI4DDGRwBDKZJISjcQM9orTe4oz/Av+3GdViYhIW7pN4kc15sZcgv3FDfCRsEY+RlpyIc9GPIKa2SeNgEdzHJ9/1hlQTkewHUuk1eXw1XDYDFudSbVuanNjCRxLvV2oOR6keTTOoW3kRXICwg4nKqg57mpHX1A7bSNO8+qlF10I5dNOw4ArEZE0aZTDFCx5tFqR0vwE9tm23fAbcP/ivhUE+zo6O7EN+5ctg3J2HPXrhQLq1WM0h4qI2BQc6Prse8FrWSKB/bO9tRXKE+TVDBpXNTJw8ntc6rMOzTVCdexsN72YbhX38cq2bVDu6MZ5t4P8dzbND9Go6VOLRrHevb0YqNjZOe1hqLNpdQ7YemCHOOHJ40j04DzRuQyvZXXLvL10c3gNfeGpF6BsnXEulFcsWwrlRQuxPwpdk0VEslm6FyBPmk3+qgh5YqMpPK4IzW37dr5o7HMim8VqUejt+e8/H8oJ2Yv7bMVxGY+aoc0jr+I+ktR/omGsZ6YDX1+2Gv0/iaQ5z7evwjl0aB96T5zc9Ge88uzzzVH0Fw1FURRFURRFUZqOPmgoiqIoiqIoitJ09EFDURRFURRFUZSmc1IejWqtLkdXkw6FSO8eRV1mPUDHW63ic00oSmtdh1A7F4/h+9/3nlVQbs+YGrNf/3Y/lAcPoV7P5SwE9/g6R9ZQH9UnziRMfod6iXIxSC/q0/OdQ/rQSNjUoDqCx3H6OaghXdqPOn3XJb04yen8AD2vEyK/Cq1HDdrbE1hbv9n4ntfQIVtkbOE1y2sctCEiBdInu7R+e72GHo1oGnWS9Tr2laB9UIyLlMrYF9IZ0mKSD6GlrRXK7NfhcyQiEo1h/6vUKLujFdumowu1xIUCaqDTAdrNhZQns28/ejbGxlA7a9tYz3Qvaj87u8wMA8c5/hwyMyfA54aeI9rb2yURP9o+5GmizItq1ZxbeEx55JdIpVBX+47z1uM+KA9g1xD62EREuklfnophHwvRNizSUnPmAK9DHw+YO9jP9UfnrYPyHsoHSMZxbHG+kWWb57dQQm1/PIaa5BT5BVoo16SnG30jdoAPxPDs0cRZmxqPlcr85Gg4jt0YJ/E4jlM+bzabAUWkTuPGo3E60wMwuQ/sj6x/D7rOWxa2a5z6Xyt5F9rasMx+i2oV5+VSycyOGR9Hbxv7VYxMC5prapQPMkA+NhGRvkWLoOzSNfCZJzEboYP8Lz096LeoBnQhzj5JJXGcLF8+7SuqVivyqLmJU0q6LSOhyGTbLVrZB6+Vs9im2aw5NxWzeO62P7cLyl0x3ObK3pVQbm3D9giz10ZEdr2yB8qdHdinY3R9GxnBbI6VZ+N9Zu/ihVD+l382j6uQRR/I4C700hUOrsY6rUHvyf4Dr0K5njTvMwsT2EeLo9hXYnQ/HWnF89F/LvmOAn5mKNexT1eLeP+ScqbvM0MBHtVjob9oKIqiKIqiKIrSdPRBQ1EURVEURVGUpqMPGoqiKIqiKIqiNJ2T82i4buPRJOHQGtA2az9NDb9P686HSOLleajNbG0j3b2Hz0W/+d1BYx9Dw6g3r9V5rXHcqU/b5FrnSlinftJZiog4EdLG0tLOZF+RVbRW8YIO1AzGk+b62skEad5Juzl8CPMWovT+kIP6vZAdtAbyLM+dM7W31omvodwsnFBIQlMehezYGLyWasEch1CAl8HmTBXKzbBJcxiJ4DYc6h31gLyJBHkuCqT9HRkdwX2E8VyHwrhPlzTViZS5vnaxgrrpchXHgF9DHWuNdNUFygRIp82MiyXLUJ+cK2B/Gxo6DOXWFtTFFkl7zPOFiIhPmvLxHO5jZgZAzTW14XOBL774Mnne2ePDOSvhcEDGAHmLQjROHcptOHsNantZ533P//svxj5a05RJQZkmcfIhVCpY7wp5lThLIhQyddHcp/p7UWt92iLMGipxzg3Nwzbni4jIHsowipI+u7UVs3XK1K/rlMPhueY1yvPwODiP4ei8Ys3Td3TlcsXoQ0eJUjYJ51kEwZ5FzrTooZyfiYks1cfME3HCeO4SNNbL1L+GDuF1PE9ZHRblHzlWkLeGsjuOcd6O0k6+kJ5ePM5w1By7+RxmIqUCMo1msm3rViiXaZwtXYZeABGRtjbcb7GEx9Hf3z+9vbLpVTnVVAtlcae8tnu2DsBrqTTOTX6Aj3NiFNuwTt3n8Ah6/eJRyuqg60q9njX2wT6jRBLbtKMLX3fDWKflZ+JcVfexP3b0mX3j8D68jhepD+96+iUon7NoLZQHXnoSyotWmte3xUvxfvjxF7JQLpTRO9KzFu+JvAjnJZkewso+/Nv4ML6eaJ/ehlc/cZ+u/qKhKIqiKIqiKErT0QcNRVEURVEURVGajj5oKIqiKIqiKIrSdE7KoyG2M/mfiAjpqT0qs9ZcxPQy+EXUGLq0pvy+A1koP/M71IEnk6ZWOJNg/TFuk9catshr4JMe1PNJ119CPbyIiFtFTfTEBB7XW87BdZjPPQe1xKy5zeVNfV6Z8hhq5C/gXIHDw6gRDIXw89Go2XZ2CPcbiaBe15+R9+HPfYyG2LbdWCu+RFklMdKIc3uJiPicYUBrrbMWvUa5GRFat98K8CFFU9gXfFqnfudOXDe8tRVfj9G69eNlXB8+KEejxpr6KL4nm8c+Wyxxtgy1S8Dy2CyL7u5FnevB4VEo10kjffgIam+DYjB6+1DXXyL9d2WGByIoo2IuSGfikkxM6obL1Afr1Tq9N8BPU6S29zmnAY/LIQ+PY2MfdD1Tr55Ik3bcoX5K0wvr28N0bljv7gcNfpr/OaOCtxE2xhJ+/uXduLa9iMjuAcxuWbUUdfVchxrtU8J0nI45ljhfyKZ8kKP5PSfifzgVWL4n1lSfcWnO88kb4QTkhETI++JSRgXnofQswDYePYwes2IJrzMi0vAwTb8HPWOlMo6BXA5f57GdoTk1GjavXfyZKB0nZ4zwXJ8hv0VbB2UOiEid/Cz5Cs5PfH3guJmxIziXL15mXufjdKxi4z7batO6+1LJ9DGdas5ccJ5Epu4dcjH00GW6MRtp+0vbjc+H0zjm/AzOoeMVvE7EyC84fgD7Slt7q7GPIyN4LTo4iPeN3V3oJSmS16WSxb7y2BPPYZ1987y1LUAviTuIc+SBnZiTsXjXMij3L8OMn9o41llEZMFyfE/HauzD+QHcZ0sM6/TKZpw/2fcrIjL8Cp4PK4bvOWxPt1X9JLKE9BcNRVEURVEURVGajj5oKIqiKIqiKIrSdPRBQ1EURVEURVGUpnNSHo16pSrWlCbY9lE7zLEKjhXgAXDwby0JfM5J21koL+lADeIl554B5e4Os/qcbVAuowY1QRp4Xgu/TrrXGGnU8kVT97r/IGr8FrXieserV6F2MZYgbSXptNvCqK0TESmVUKvI/oCIT3khJJ+rkf/FdU2RvBNBjen4BK2vP6MtPW/uTRrxWLzhsanW8ACr5FNwPVPDXyU9ciyOx5tI4nkr0FrYLE3nnA0RkRR5KFrSuM0UaU5rdew7CeobtTKW6wHHNVHGtkjHKX+G8j9Yn+zUsM8H+UCMHAz6jqK1pRXKlQrus1DA46xVzQycNiMLBcdaYUYmTlCGyVzgeX4jf6FQwDZxLGy3ag01zCIi4SSu2V4skJfBx20cIH3xU8+/gu8Xsz8k49hn8kXUNVdK7MUi3T6Jy+vkGwnyJ1jkMyuSR4ozBJIp7KN1mocH9uMa/SIiLSmcuzPUX8byeJz5Io7fJI3vYgHfLyJSpXo71AePav9LVTM/Yi6wPV/so3OvcZ6wDb0AIxT7cRIxPPe+i+e6mzIJ3FWnQ3nv3r3GPiYq6EWou+QnJBOYR2PZsXF+ilKWQiggg4c9YRHyYkbpum+FsL9yfo0TMLe3tKJvI0M5EeEjOLfbFl5fIrQPi81QIhKJU54Mefwqxenj8uYhS+jWa26U9FSWUz2M540zo4bPNuf4PaM4rl98FX0cPvWFNI35lnbMPxkapqAHEXnlZcysOGMJ3jdGEjjGW/vQn2PL8c9BrWD2jZBgPWMR7BtVmi+2b94G5Qv/E+YlPf7oL419DGzHsbbiHPQ0vjSEHowdlDPn0f3PKyM4TkVEPB/7aLWK56Nn4XTbuDX1aCiKoiiKoiiKMo/og4aiKIqiKIqiKE1HHzQURVEURVEURWk6+qChKIqiKIqiKErTOSkzuG05Yk8ld/kuml2sEJbrZMIWESmQ0SdMhubzzsVAEpvD9Az/cUDqF70nESeDHJmY7RAaBB0KtyK/l7S2ms9mS5e04h88NgKhwc4ic5HrYR3DsYCgpTDul4+jUsfPJDPYNtUaB9qZJlION6xW8DPWjAAoixt6DrAsS6ypUK4wBUL6Rn1Mo1I8QSFhZGCtkbmJwxqPjKORN5OmcCURieTQgBpPoJGxtwcDsI5kx6BcKWCYVYrqbB6nSJX+dJjqUOBwQwrycWlg+bbZxycoVMul/pShkLihHIYmWWSU7u3CsS4ikoqhoa5CBn9vhuHT4zC2OSKXyzeC0ioVXiwB22giYA5Md2Ofmqig+S4UwvKOITRPbtu7E8rtSWwzERGrSiFYEzSP0vurZCrltmWTca1mmlDZZMxBhGzWLVNIFhuCF3Sg6VNEpLsTg0453PLIGI4lpk5mVSdgPQEOmuVMTm9qQQK/Pj/9z/O8RtCoR+fBt3BOLwb0vyQtTuFbeBwxCp3jeZavuJ5vNuLgCM4f4xNoOq3Qgis2BS26xvUT+7PnmPMTh0h6fLPAC3lQuGGC5ulwxAzDc6gtErSgQYgWF2lLoHncJhN7rMUcuxwKaHF5RhiiJXO/IMbPf/LvEotOHueCdhyPXQu6qGzO8f2nXQjlS8/6IyhX68e+5xAR6Uth+PGLL2wx9hE6C+fhNavQDJ5qwTl2Ze8iKI+MUthxHMdV11JchEJEJNqGn6k4tDjNIewbhw9gIOmRnf1Q7u1bYexjwkczeIqM8nxPuPMpNMpbNHpLFXNBi3AKj3ViFMdef/d0W9VPIjNXf9FQFEVRFEVRFKXp6IOGoiiKoiiKoihNRx80FEVRFEVRFEVpOifl0ajWRY7K1lmhapGfwgmZzzCscTzq9zhKOMRaYdYgogbNcczq26S7LxdRC1wivWeU9J4hCitjbXmxihp6ERGbtP22g1q4aBS1dPEo1Zv2YQdo5IV8HaUyBQ1SWJVFZ4i9JuEQagZFzHr7PntkrBn/Nqt4qskXClKf0u2nKaxrPI/+CQ5eFBGJkgfAov7HOnI+xBy1seeZGlmXQnFa6C2hKIc4UYCkQ3p40lBzmNrkH8m/Q9rd2bIVOcgsny8Y76lWcBy1pbH9/Ti2peOgLjsUwuPs6kI97+RGyCtC5yM2o+14nM8Vbt0Xtz5ZT3eW+Wmc9K0iIuNjOI7D5L0qVLJQbrdQO37JWRiYFo+a47hE569GQWEeBzjSnMcBfewxYz+GiBn6Z5HuPkxjjee4Ygn1wu0Z9PyIiEQCjnUmFZrbuY/wcVkBwZQ0BYpH80hlahvsz5kranVXalP+EA6dM4LGHNNn4LK3kjyJHObp0hiMx7E/dnWa49ila0+IrtNHjmShXKpjW/qCbV6lsFAvbJ439mhwvUvsCaJ5NEZ9K2SbfdyhPh2h/uNZOMeFyQvA87ATDgi+NHxpWG97RsCrXZ/70Mhnnt/WCB6MRbC/pcjfkyE/kIhIWyuO60QCQw5T5PWzYiko2zF8fx+9LiKy8uK3QTlN+2xpxWtXkvw5h3IYtLpgMb6/wmnIIrJvGL10jz/8HJQHfLweHjmCfrLBV/ZjnXvwOEVEygXc786tI1B2Wql/xrD/1erYAVsiAcHQwzhHxj0KNY5Oj5u6feIeIf1FQ1EURVEURVGUpqMPGoqiKIqiKIqiNB190FAURVEURVEUpemclEdjy/MHGmtBd3ZhhkA6gXrQIJsBL8Lt0lrkvGayTzkGHmnjgrwMvCa3T8LIiQn0WLCWmLfphEhHyQurS4CXhLXmpIkOkbaWtZ5B+vMy6aQrVdxnNIJ6vDotcsza3ErVXAS5VqP1yylHY3R0WrvImti5YOYa8oUC6tBn0xJPgufaCR1fV846bN4mezpERKrUzqNj6B2JJnEbvs/r1ON5jGJR7ABdr0v+KJt8Rzb5PDyqI2uo6wEZAVHWMFPeg/jYVxZ04xrq0QhlZJQD1vC28Xy0tWGWgp2f1idXQiexiHcTKRSKjfHuk86b5w5uVxERoT4Wj1CmDq21Hw6hPrizrZX2aWYJzZYhUOfcjDoeB3tPWLfPfUFEpFQiLTmNNR47FRfPX2sXrskf5L/jw2LtvpGTQV4Sm+bIYkAfrJCPw8itmTrHpZNZRL6J+LbdyLlhu9ZEHq9t4ag5B9qGBxGPr1Y7/nGFKX8iGjH7QiaFmnj2KFJslJSLB6HsUtvyPJwK8O+w52JiYuK4r5eNvoP79KKmx69MPiKHfEl18saVqS35NiEU8D2vTx6NegGvH7Xx6WyEWkD/PdXYTkjsqbFZozbNlvC6MV40c20OHMJ8JZ5D4+TZqPvHv2bH2O8qIqkk9skOyuTpXYD5Jr1drVBu68Wsjrf3XAzlas08b+NL8H7kkmXvhPLIBPo79+4fhPLOvbvx9UOvGvtwXPSjVD3c58Jz8J68MIr9b2wvjqO6a147otT+dFsve3Ydavyb7yOOh/6ioSiKoiiKoihK09EHDUVRFEVRFEVRms4JSaeOymRmRpzzz+sstzgx6RRugxVDpnSKpQrmTzezSae43iyXMVQHFq93GCCd4t+wDVnR8X9isukn1wA1hNRnkTfM9jpLp8ylOc33cKT9TLnU0X/PhYTq6D5myr1C1DeqJMVw/aCl1+hnWPrp0JBOkbysViXJScA+Qg5+xuijJF1gyRfv063hPu2AffKSt4bEhOrtUNu59H1DtWbKBnyP2oakS/wZLlsWllmRKCISDuE+QiSFmdk2R/89VxK+o/spzZBx8M/+3H9KQUugWsfvczwn1niZZkPq+YdLpywaF7U692FePtmEj5WlU3xBYOkUSwLtINmZIZ3CfXIdWDpl0fmqBpwflq0eSzpVnvrsnPe/GXIZX2h5bjoxtcApkOTKFkmGZlkmlq8bpQD5TpnOS6WO47hKS4BzH+cy77MWMD/xXMyjokoypjKd+yJJ/yRoeX6fJKU0idVoZFRKdBx8KQiQ4LB0qsbL889o76PtPJfX4JlzA0casAzP4vYS816H51CHzpMhnaJD5TqIiITDx5+HeSntArVxmGTZvsXXR/O85Yv4mUIRZYzcv1i6x2OiHtDH+drg0QB3SU7v0ussdeJb28kP0XvoFHozDv3o9k6k/1n+Cbxr//79snjx4lk3prz52LdvnyxatOiU7kP7n3Is5qL/iWgfVILR/qfMN3oNVuaTE+l/J/Sg4XmeDA4OSjqdNr6BU96c+L4vuVxO+vr6ggMGm4j2P4WZy/4non1QQbT/KfONXoOV+eRk+t8JPWgoiqIoiqIoiqKcDGoGVxRFURRFURSl6eiDhqIoiqIoiqIoTUcfNBRFURRFURRFaTr6oKEoiqIoiqIoStPRB41TxJ49e8SyLNmyZct8V0V5HfH2t79dbrrppvmuhvIGRPuWoigKMtu82N/fL9/4xjdOert33HGHnHvuub93vd5IvOkeNPRiqyiKoihvLPTGTjkVPPnkk/Lxj398vqvxuuZN96AxG77vS53TeBXlDQInkCrK74v2JUVR3uh0dXVJIpE45us1SjNXTN5UDxrXXnutPPzww/LNb35TLMsSy7Lk3nvvFcuy5Gc/+5msX79eotGoPProo3LttdfKVVddBZ+/6aab5O1vf3uj7Hme/M3f/I2sWLFCotGoLFmyRP7qr/4qcN+u68pHP/pROeOMM2RgYOAUHqXyeqFQKMimTZsklUpJb2+vfO1rX4PXK5WKfPazn5WFCxdKMpmUCy64QB566CF4z6OPPiqXXHKJxONxWbx4sdxwww1SKBQar/f398uXv/xl2bRpk2QyGf1m5k2O53lyyy23SHt7u/T09Mgdd9zReG1gYEDe9773SSqVkkwmIx/60Ifk0KFDjdePfmN8zz33yLJlyyQWi4mIyL/8y7/I2WefLfF4XDo6OuRd73oX9MF77rlHVq9eLbFYTM444wz5+7//+zk7XuX1xfGuqbfeequsWrVKEomELF++XG6//fbGTd69994rd955pzz33HNwbVeUE6Fer8v1118vLS0t0tnZKbfffrscjZhj6ZRlWfIP//APcuWVV0oymWz0z6985SuyYMECSafTct1110m5XJ6PQ3lt4r+JyGaz/oYNG/w/+7M/84eGhvyhoSH/V7/6lS8i/tq1a/0HHnjA37Fjhz86Oup/5CMf8d/3vvfB52+88UZ/48aNjfItt9zit7W1+ffee6+/Y8cO/5FHHvHvvvtu3/d9f/fu3b6I+M8++6xfLpf997///f66dev84eHhOTxi5bXMJz/5SX/JkiX+r371K//555/33/Oe9/jpdNq/8cYbfd/3/Y997GP+2972Nv83v/mNv2PHDv+uu+7yo9Gov337dt/3fX/Hjh1+Mpn0v/71r/vbt2/3f/vb3/rr1q3zr7322sY+li5d6mcyGf+rX/2qv2PHDn/Hjh3zcajKa4CNGzf6mUzGv+OOO/zt27f7//RP/+RbluU/8MADvuu6/rnnnutffPHF/lNPPeU/8cQT/vr162G++9KXvuQnk0n/8ssv95955hn/ueee8wcHB/1QKOT/7d/+rb97927/+eef9//7f//vfi6X833f9++77z6/t7fX/+EPf+jv2rXL/+EPf+i3t7f799577zy1gvJa5njX1C9/+cv+b3/7W3/37t3+v/3bv/kLFizw/9t/+2++7/t+sVj0b775Zv+ss85qXNuLxeJ8HoryOmHjxo1+KpXyb7zxRv/ll1/277vvPj+RSPj/+I//6Pv+5DX061//euP9IuJ3d3f7//N//k9/586d/t69e/3vf//7fjQa9e+55x7/5Zdf9m+77TY/nU7755xzzvwc1GuMN9WDhu9PdqqjN3K+7/u//vWvfRHxf/zjH8P7ZnvQmJiY8KPRaGMSZI4+aDzyyCP+pZde6l988cV+Nptt5qEor2NyuZwfiUT8H/zgB42/jY6O+vF43L/xxhv9vXv3+o7j+AcOHIDPXXrppf7nP/953/d9/7rrrvM//vGPw+uPPPKIb9u2XyqVfN+fnCSvuuqqU3w0yuuBjRs3+hdffDH87fzzz/dvvfVW/4EHHvAdx/EHBgYar7300ku+iPi/+93vfN+ffNAIh8PwZcnTTz/ti4i/Z8+ewH2edtpp/ne/+13425e//GV/w4YNzTos5Q3CbNdU5q677vLXr1/fKH/pS1/SGzvlpNm4caO/evVq3/O8xt9uvfVWf/Xq1b7vBz9o3HTTTbCNDRs2+J/61KfgbxdccIH2xyneVNKp43Heeeed1Pu3bdsmlUpFLr300uO+78Mf/rAUCgV54IEHpKWl5Q+povIGYufOnVKtVuWCCy5o/K29vV1OP/10ERF54YUXxHVdWbVqlaRSqcZ/Dz/8sOzcuVNERJ577jm599574fXLLrtMPM+T3bt3N7Z7sn1beeOydu1aKPf29srw8LBs27ZNFi9eLIsXL268duaZZ0pra6ts27at8belS5dKV1dXo3zOOefIpZdeKmeffbZ88IMflLvvvlvGxsZEZFIauHPnTrnuuuugj/7X//pfG31YUY4y2zX1+9//vlx00UXS09MjqVRKvvCFL6gMWWkKF154oViW1Shv2LBBXn31VXFdN/D9fE3dtm0bXMuPbkOZJDTfFXitkEwmoWzbdkOjd5SZpp94PH5C273iiivkvvvuk8cff1ze+c53/uEVVd4U5PN5cRxHnn76aXEcB15LpVKN93ziE5+QG264wfj8kiVLGv/mvq28eQmHw1C2LEs8zzvhz3NfchxHfvnLX8pjjz0mDzzwgHzrW9+S2267TTZv3twwUN59993GRZj7tKIc75r6+OOPyzXXXCN33nmnXHbZZdLS0iL333+/4WtTlLlAr6knx5vuF41IJHLMp9SZdHV1ydDQEPxtZibGypUrJR6Py4MPPnjc7Xzyk5+Ur3zlK3LllVfKww8//HvVWXnjcdppp0k4HJbNmzc3/jY2Nibbt28XEZF169aJ67oyPDwsK1asgP96enpEROQtb3mLbN261Xh9xYoVEolE5uW4lNcnq1evln379sm+ffsaf9u6datks1k588wzj/tZy7LkoosukjvvvFOeffZZiUQi8qMf/UgWLFggfX19smvXLqN/Llu27FQfkvI643jX1Mcee0yWLl0qt912m5x33nmycuVK2bt3L7znRK/tisLMvA6LiDzxxBOycuXKE/5CZPXq1YHbUCZ50/2i0d/fL5s3b5Y9e/ZIKpU65rd573znO+Wuu+6Sf/7nf5YNGzbIfffdJy+++KKsW7dORERisZjceuutcsstt0gkEpGLLrpIRkZG5KWXXpLrrrsOtvUXf/EX4rquvOc975Gf/exncvHFF5/y41Re26RSKbnuuuvkc5/7nHR0dEh3d7fcdtttYtuTz/6rVq2Sa665RjZt2iRf+9rXZN26dTIyMiIPPvigrF27Vt797nfLrbfeKhdeeKFcf/318rGPfUySyaRs3bpVfvnLX8rf/d3fzfMRKq8n3vWud8nZZ58t11xzjXzjG9+Qer0un/rUp2Tjxo3Hld5t3rxZHnzwQfnjP/5j6e7uls2bN8vIyIisXr1aRETuvPNOueGGG6SlpUUuv/xyqVQq8tRTT8nY2Jh85jOfmavDU14HHO+aunLlShkYGJD7779fzj//fPnpT38qP/rRj+Dz/f39snv3btmyZYssWrRI0um0RKPReToa5fXEwMCAfOYzn5FPfOIT8swzz8i3vvWtk/q17MYbb5Rrr71WzjvvPLnooovkO9/5jrz00kuyfPnyU1jr1w9vul80PvvZz4rjOHLmmWdKV1fXMTWel112mdx+++1yyy23yPnnny+5XE42bdoE77n99tvl5ptvli9+8YuyevVqufrqq2V4eDhwezfddJPceeedcsUVV8hjjz3W9ONSXn/cddddcskll8h73/teede73iUXX3yxrF+/vvH6t7/9bdm0aZPcfPPNcvrpp8tVV10lTz75ZEMWtXbtWnn44Ydl+/btcskll8i6devki1/8ovT19c3XISmvUyzLkp/85CfS1tYmf/RHfyTvete7ZPny5fL973//uJ/LZDLym9/8Rq644gpZtWqVfOELX5Cvfe1r8id/8iciIvKxj31M7rnnHvn2t78tZ599tmzcuFHuvfde/UVDCeRY19Qrr7xSPv3pT8v1118v5557rjz22GNy++23w2c/8IEPyOWXXy7veMc7pKurS773ve/N01Eorzc2bdokpVJJ3vrWt8qf//mfy4033nhSS8FfffXVjfvF9evXy969e+WTn/zkKazx6wvLZyOCoiiKoiiKoijKH8ib7hcNRVEURVEURVFOPfqgoSiKoiiKoihK09EHDUVRFEVRFEVRmo4+aCiKoiiKoiiK0nT0QUNRFEVRFEVRlKajDxqKoiiKoiiKojQdfdBQFEVRFEVRFKXp6IOGoiiKoiiKoihNRx80FEVRFEVRFEVpOvqgoSiKoiiKoihK09EHDUVRFEVRFEVRms7/B3X8dIxDWWloAAAAAElFTkSuQmCC\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 3) Провели предобработку данных: привели обучающие и тестовые данные к формату, пригодному для обучения сверточной нейронной сети. Входные данные принимают значения от 0 до 1, метки цифр закодированы по принципу «one-hot encoding». Вывели размерности предобработанных обучающих и тестовых массивов данных." + ], + "metadata": { + "id": "d3TPr2w1KQTK" + } + }, + { + "cell_type": "code", + "source": [ + "# Зададим параметры данных и модели\n", + "num_classes = 10\n", + "input_shape = (32, 32, 3)\n", + "\n", + "# Приведение входных данных к диапазону [0, 1]\n", + "X_train = X_train / 255\n", + "X_test = X_test / 255\n", + "\n", + "print('Shape of transformed X train:', X_train.shape)\n", + "print('Shape of transformed X test:', X_test.shape)\n", + "\n", + "# переведем метки в one-hot\n", + "y_train = keras.utils.to_categorical(y_train, num_classes)\n", + "y_test = keras.utils.to_categorical(y_test, num_classes)\n", + "print('Shape of transformed y train:', y_train.shape)\n", + "print('Shape of transformed y test:', y_test.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iFDpxEauLZ8j", + "outputId": "40a5b5cc-d00e-4a72-96be-173384ae67b9" + }, + "execution_count": 30, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Shape of transformed X train: (50000, 32, 32, 3)\n", + "Shape of transformed X test: (10000, 32, 32, 3)\n", + "Shape of transformed y train: (50000, 10)\n", + "Shape of transformed y test: (10000, 10)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 4) Реализовали модель сверточной нейронной сети и обучили ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывели информацию об архитектуре нейронной сети." + ], + "metadata": { + "id": "ydNITXptLeGT" + } + }, + { + "cell_type": "code", + "source": [ + "# создаем модель\n", + "model = Sequential()\n", + "model.add(layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\", input_shape=input_shape))\n", + "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"))\n", + "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(layers.Conv2D(128, kernel_size=(3, 3), activation=\"relu\"))\n", + "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(layers.Flatten())\n", + "model.add(layers.Dense(128, activation='relu'))\n", + "model.add(layers.Dropout(0.5))\n", + "model.add(layers.Dense(num_classes, activation=\"softmax\"))\n", + "model.summary()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 510 + }, + "id": "YhAD5CllLlv7", + "outputId": "8b981b58-11a5-4140-8ffa-71b1c215f4d9" + }, + "execution_count": 31, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: 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" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1mModel: \"sequential_1\"\u001b[0m\n" + ], + "text/html": [ + "
Model: \"sequential_1\"\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "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_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\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;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\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;34m128\u001b[0m) │ \u001b[38;5;34m65,664\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_1 (\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_2 (\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" + ], + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ conv2d_2 (Conv2D)               │ (None, 30, 30, 32)     │           896 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_2 (MaxPooling2D)  │ (None, 15, 15, 32)     │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_3 (Conv2D)               │ (None, 13, 13, 64)     │        18,496 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_3 (MaxPooling2D)  │ (None, 6, 6, 64)       │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ conv2d_4 (Conv2D)               │ (None, 4, 4, 128)      │        73,856 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ max_pooling2d_4 (MaxPooling2D)  │ (None, 2, 2, 128)      │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ flatten_1 (Flatten)             │ (None, 512)            │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (Dense)                 │ (None, 128)            │        65,664 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dropout_1 (Dropout)             │ (None, 128)            │             0 │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (Dense)                 │ (None, 10)             │         1,290 │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m160,202\u001b[0m (625.79 KB)\n" + ], + "text/html": [ + "
 Total params: 160,202 (625.79 KB)\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m160,202\u001b[0m (625.79 KB)\n" + ], + "text/html": [ + "
 Trainable params: 160,202 (625.79 KB)\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ], + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+              "
\n" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# компилируем и обучаем модель\n", + "batch_size = 64\n", + "epochs = 50\n", + "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n", + "model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3otvqMjjOdq5", + "outputId": "8051fa3f-3332-4a92-ae75-11c9985bc1d3" + }, + "execution_count": 32, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 10ms/step - accuracy: 0.2664 - loss: 1.9466 - val_accuracy: 0.4806 - val_loss: 1.4130\n", + "Epoch 2/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.5057 - loss: 1.3726 - val_accuracy: 0.5646 - val_loss: 1.2276\n", + "Epoch 3/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.5814 - loss: 1.1935 - val_accuracy: 0.5916 - val_loss: 1.1488\n", + "Epoch 4/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.6156 - loss: 1.0997 - val_accuracy: 0.6424 - val_loss: 0.9974\n", + "Epoch 5/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.6488 - loss: 1.0081 - val_accuracy: 0.6694 - val_loss: 0.9562\n", + "Epoch 6/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.6746 - loss: 0.9450 - val_accuracy: 0.5854 - val_loss: 1.2591\n", + "Epoch 7/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.6922 - loss: 0.8931 - val_accuracy: 0.6830 - val_loss: 0.8941\n", + "Epoch 8/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.7087 - loss: 0.8355 - val_accuracy: 0.6966 - val_loss: 0.8782\n", + "Epoch 9/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7240 - loss: 0.8012 - val_accuracy: 0.6982 - val_loss: 0.8639\n", + "Epoch 10/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7408 - loss: 0.7496 - val_accuracy: 0.7090 - val_loss: 0.8516\n", + "Epoch 11/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7512 - loss: 0.7111 - val_accuracy: 0.7030 - val_loss: 0.8536\n", + "Epoch 12/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7594 - loss: 0.6925 - val_accuracy: 0.7074 - val_loss: 0.8410\n", + "Epoch 13/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.7756 - loss: 0.6547 - val_accuracy: 0.7056 - val_loss: 0.8658\n", + "Epoch 14/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7751 - loss: 0.6324 - val_accuracy: 0.7150 - val_loss: 0.8463\n", + "Epoch 15/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7858 - loss: 0.6145 - val_accuracy: 0.7090 - val_loss: 0.8894\n", + "Epoch 16/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - accuracy: 0.7950 - loss: 0.5918 - val_accuracy: 0.7182 - val_loss: 0.8696\n", + "Epoch 17/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.7974 - loss: 0.5649 - val_accuracy: 0.7014 - val_loss: 0.9135\n", + "Epoch 18/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8055 - loss: 0.5557 - val_accuracy: 0.7252 - val_loss: 0.8748\n", + "Epoch 19/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8142 - loss: 0.5281 - val_accuracy: 0.7068 - val_loss: 0.9660\n", + "Epoch 20/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8159 - loss: 0.5160 - val_accuracy: 0.7296 - val_loss: 0.9005\n", + "Epoch 21/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.8256 - loss: 0.4960 - val_accuracy: 0.7178 - val_loss: 0.9040\n", + "Epoch 22/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8328 - loss: 0.4789 - val_accuracy: 0.7272 - val_loss: 0.9039\n", + "Epoch 23/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8370 - loss: 0.4589 - val_accuracy: 0.7228 - val_loss: 0.9271\n", + "Epoch 24/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8402 - loss: 0.4509 - val_accuracy: 0.7172 - val_loss: 0.9669\n", + "Epoch 25/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - accuracy: 0.8411 - loss: 0.4476 - val_accuracy: 0.7210 - val_loss: 0.9331\n", + "Epoch 26/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8509 - loss: 0.4210 - val_accuracy: 0.7186 - val_loss: 0.9691\n", + "Epoch 27/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8477 - loss: 0.4171 - val_accuracy: 0.7214 - val_loss: 1.0069\n", + "Epoch 28/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8545 - loss: 0.4089 - val_accuracy: 0.7204 - val_loss: 1.0157\n", + "Epoch 29/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8588 - loss: 0.3994 - val_accuracy: 0.7152 - val_loss: 1.0545\n", + "Epoch 30/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - accuracy: 0.8592 - loss: 0.3959 - val_accuracy: 0.7118 - val_loss: 1.1099\n", + "Epoch 31/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8582 - loss: 0.3911 - val_accuracy: 0.7106 - val_loss: 1.1526\n", + "Epoch 32/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8668 - loss: 0.3691 - val_accuracy: 0.7220 - val_loss: 1.0838\n", + "Epoch 33/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8735 - loss: 0.3511 - val_accuracy: 0.7046 - val_loss: 1.1383\n", + "Epoch 34/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - accuracy: 0.8707 - loss: 0.3548 - val_accuracy: 0.7258 - val_loss: 1.1460\n", + "Epoch 35/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8704 - loss: 0.3593 - val_accuracy: 0.7208 - val_loss: 1.1223\n", + "Epoch 36/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8772 - loss: 0.3411 - val_accuracy: 0.7264 - val_loss: 1.1060\n", + "Epoch 37/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8840 - loss: 0.3180 - val_accuracy: 0.7236 - val_loss: 1.1325\n", + "Epoch 38/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 6ms/step - accuracy: 0.8772 - loss: 0.3432 - val_accuracy: 0.7246 - val_loss: 1.1593\n", + "Epoch 39/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8844 - loss: 0.3239 - val_accuracy: 0.7244 - val_loss: 1.1873\n", + "Epoch 40/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8886 - loss: 0.3056 - val_accuracy: 0.7154 - val_loss: 1.2173\n", + "Epoch 41/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8945 - loss: 0.2932 - val_accuracy: 0.7124 - val_loss: 1.2767\n", + "Epoch 42/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.8900 - loss: 0.3043 - val_accuracy: 0.7230 - val_loss: 1.2550\n", + "Epoch 43/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8941 - loss: 0.2936 - val_accuracy: 0.7208 - val_loss: 1.2914\n", + "Epoch 44/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8964 - loss: 0.2842 - val_accuracy: 0.7248 - val_loss: 1.2318\n", + "Epoch 45/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8933 - loss: 0.2893 - val_accuracy: 0.7212 - val_loss: 1.3048\n", + "Epoch 46/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - accuracy: 0.8979 - loss: 0.2843 - val_accuracy: 0.7208 - val_loss: 1.3156\n", + "Epoch 47/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9005 - loss: 0.2698 - val_accuracy: 0.7052 - val_loss: 1.3691\n", + "Epoch 48/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9024 - loss: 0.2705 - val_accuracy: 0.7152 - val_loss: 1.3893\n", + "Epoch 49/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8964 - loss: 0.2817 - val_accuracy: 0.7234 - val_loss: 1.3403\n", + "Epoch 50/50\n", + "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - accuracy: 0.8998 - loss: 0.2719 - val_accuracy: 0.7224 - val_loss: 1.2929\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 32 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 5) Оценили качество обучения на тестовых данных. Вывели значение функции ошибки и значение метрики качества классификации на тестовых данных." + ], + "metadata": { + "id": "Vv1kUHWTLl9B" + } + }, + { + "cell_type": "code", + "source": [ + "# Оценка качества работы модели на тестовых данных\n", + "scores = model.evaluate(X_test, y_test)\n", + "print('Loss on test data:', scores[0])\n", + "print('Accuracy on test data:', scores[1])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SaDxydiyLmRX", + "outputId": "f0044f7a-a270-4b1d-8fbb-d3bbcc767ecb" + }, + "execution_count": 33, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.7087 - loss: 1.3107\n", + "Loss on test data: 1.2974315881729126\n", + "Accuracy on test data: 0.7123000025749207\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 6) Подали на вход обученной модели два тестовых изображения. Вывели изображения, истинные метки и результаты распознавания." + ], + "metadata": { + "id": "OdgEiyUGLmhP" + } + }, + { + "cell_type": "code", + "source": [ + "# вывод двух тестовых изображений и результатов распознавания\n", + "\n", + "for n in [3,16]:\n", + " result = model.predict(X_test[n:n+1])\n", + " print('NN output:', result)\n", + "\n", + " plt.imshow(X_test[n].reshape(32,32,3), cmap=plt.get_cmap('gray'))\n", + " plt.show()\n", + " print('Real mark: ', np.argmax(y_test[n]))\n", + " print('NN answer: ', np.argmax(result))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "t3yGj1MlLm9H", + "outputId": "e656e509-1bd8-422a-90c2-ee93acc248b7" + }, + "execution_count": 35, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n", + "NN output: [[9.9996436e-01 1.7504866e-14 1.4552541e-10 2.9330904e-14 7.7180937e-13\n", + " 5.0618490e-18 9.6551863e-18 4.9504489e-15 3.5634086e-05 8.8595966e-09]]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Real mark: 0\n", + "NN answer: 0\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n", + "NN output: [[7.6167588e-03 9.8444516e-06 2.7863038e-01 2.1874362e-01 4.0668417e-03\n", + " 2.5500089e-01 3.5849182e-05 1.9295409e-01 3.3263098e-02 9.6785687e-03]]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Real mark: 7\n", + "NN answer: 2\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 7) Вывели отчет о качестве классификации тестовой выборки и матрицу ошибок для тестовой выборки." + ], + "metadata": { + "id": "3h6VGDRrLnNC" + } + }, + { + "cell_type": "code", + "source": [ + "# истинные метки классов\n", + "true_labels = np.argmax(y_test, axis=1)\n", + "# предсказанные метки классов\n", + "predicted_labels = np.argmax(model.predict(X_test), axis=1)\n", + "\n", + "# отчет о качестве классификации\n", + "print(classification_report(true_labels, predicted_labels, target_names=class_names))\n", + "# вычисление матрицы ошибок\n", + "conf_matrix = confusion_matrix(true_labels, predicted_labels)\n", + "# отрисовка матрицы ошибок в виде \"тепловой карты\"\n", + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=class_names)\n", + "disp.plot(ax=ax, xticks_rotation=45) # поворот подписей по X и приятная палитра\n", + "plt.tight_layout() # чтобы всё влезло\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 889 + }, + "id": "od56oyyzM0nw", + "outputId": "83256ffa-fa04-4348-e2ec-96d8fd183d46" + }, + "execution_count": 36, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n", + " precision recall f1-score support\n", + "\n", + " airplane 0.69 0.76 0.72 986\n", + " automobile 0.85 0.80 0.82 971\n", + " bird 0.66 0.60 0.63 1043\n", + " cat 0.54 0.55 0.55 1037\n", + " deer 0.66 0.67 0.67 969\n", + " dog 0.63 0.64 0.63 979\n", + " frog 0.78 0.78 0.78 1025\n", + " horse 0.74 0.73 0.74 948\n", + " ship 0.79 0.82 0.80 1003\n", + " truck 0.80 0.78 0.79 1039\n", + "\n", + " accuracy 0.71 10000\n", + " macro avg 0.71 0.71 0.71 10000\n", + "weighted avg 0.71 0.71 0.71 10000\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "По результатам классификации датасета CIFAR-10 сверточной моделью делаем вывод, что она удовлетворительно справилась с задачей. Полученные метрики оценки качества имеют показатели в районе 0.71." + ], + "metadata": { + "id": "RF4xK1cxamBc" + } + } + ] +} \ No newline at end of file diff --git a/labworks/LW3/report.md b/labworks/LW3/report.md new file mode 100644 index 0000000..13377bd --- /dev/null +++ b/labworks/LW3/report.md @@ -0,0 +1,530 @@ +# Отчёт по лабораторной работе №3 + +**Качисов Азамат, Немыкин Никита — А-0ё-22** + +--- +## Задание 1 + +### 1) В среде Google Colab создали новый блокнот (notebook). Импортировали необходимые для работы библиотеки и модули. + +```python +# импорт модулей +import os +os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab3') + +from tensorflow import keras +from tensorflow.keras import layers +from tensorflow.keras.models import Sequential +import matplotlib.pyplot as plt +import numpy as np +from sklearn.metrics import classification_report, confusion_matrix +from sklearn.metrics import ConfusionMatrixDisplay +``` + +### 2) Загрузили набор данных MNIST, содержащий размеченные изображения рукописных цифр. + +```python +# загрузка датасета +from keras.datasets import mnist +(X_train, y_train), (X_test, y_test) = mnist.load_data() +``` + +### 3) Разбили набор данных на обучающие и тестовые данные в соотношении 60 000:10 000 элементов. Параметр random_state выбрали равным (4k – 1)=23, где k=6 –номер бригады. Вывели размерности полученных обучающих и тестовых массивов данных. + +```python +# создание своего разбиения датасета +from sklearn.model_selection import train_test_split + +# объединяем в один набор +X = np.concatenate((X_train, X_test)) +y = np.concatenate((y_train, y_test)) + +# разбиваем по вариантам +X_train, X_test, y_train, y_test = train_test_split(X, y, + test_size = 10000, + train_size = 60000, + random_state = 23) +# вывод размерностей +print('Shape of X train:', X_train.shape) +print('Shape of y train:', y_train.shape) +print('Shape of X test:', X_test.shape) +print('Shape of y test:', y_test.shape) +``` +``` +Shape of X train: (60000, 28, 28) +Shape of y train: (60000,) +Shape of X test: (10000, 28, 28) +Shape of y test: (10000,) +``` + +### 4) Провели предобработку данных: привели обучающие и тестовые данные к формату, пригодному для обучения сверточной нейронной сети. Входные данные принимают значения от 0 до 1, метки цифр закодированы по принципу «one-hot encoding». Вывели размерности предобработанных обучающих и тестовых массивов данных. + +```python +# Зададим параметры данных и модели +num_classes = 10 +input_shape = (28, 28, 1) + +# Приведение входных данных к диапазону [0, 1] +X_train = X_train / 255 +X_test = X_test / 255 + +# Расширяем размерность входных данных, чтобы каждое изображение имело +# размерность (высота, ширина, количество каналов) + +X_train = np.expand_dims(X_train, -1) +X_test = np.expand_dims(X_test, -1) +print('Shape of transformed X train:', X_train.shape) +print('Shape of transformed X test:', X_test.shape) + +# переведем метки в one-hot +y_train = keras.utils.to_categorical(y_train, num_classes) +y_test = keras.utils.to_categorical(y_test, num_classes) +print('Shape of transformed y train:', y_train.shape) +print('Shape of transformed y test:', y_test.shape) +``` +``` +Shape of transformed X train: (60000, 28, 28, 1) +Shape of transformed X test: (10000, 28, 28, 1) +Shape of transformed y train: (60000, 10) +Shape of transformed y test: (10000, 10) +``` + +### 5) Реализовали модель сверточной нейронной сети и обучили ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывели информацию об архитектуре нейронной сети. + +```python +# создаем модель +model = Sequential() +model.add(layers.Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=input_shape)) +model.add(layers.MaxPooling2D(pool_size=(2, 2))) +model.add(layers.Conv2D(64, kernel_size=(3, 3), activation="relu")) +model.add(layers.MaxPooling2D(pool_size=(2, 2))) +model.add(layers.Dropout(0.5)) +model.add(layers.Flatten()) +model.add(layers.Dense(num_classes, activation="softmax")) + +model.summary() +``` +**Model: "sequential"** +| Layer (type) | Output Shape | Param # | +|--------------------------------|---------------------|--------:| +| conv2d (Conv2D) | (None, 26, 26, 32) | 320 | +| max_pooling2d (MaxPooling2D) | (None, 13, 13, 32) | 0 | +| conv2d_1 (Conv2D) | (None, 11, 11, 64) | 18,496 | +| max_pooling2d_1 (MaxPooling2D) | (None, 5, 5, 64) | 0 | +| dropout (Dropout) | (None, 5, 5, 64) | 0 | +| flatten (Flatten) | (None, 1600) | 0 | +| dense (Dense) | (None, 10) | 16,010 | +**Total params:** 34,826 (136.04 KB) +**Trainable params:** 34,826 (136.04 KB) +**Non-trainable params:** 0 (0.00 B) + +```python +# компилируем и обучаем модель +batch_size = 512 +epochs = 15 +model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) +model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1) +``` + +### 6) Оценили качество обучения на тестовых данных. Вывели значение функции ошибки и значение метрики качества классификации на тестовых данных. + +```python +# Оценка качества работы модели на тестовых данных +scores = model.evaluate(X_test, y_test) +print('Loss on test data:', scores[0]) +print('Accuracy on test data:', scores[1]) +``` +``` +313/313 ━━━━━━━━━━━━━━━━━━━━ 3s 9ms/step - accuracy: 0.9891 - loss: 0.0327 +Loss on test data: 0.03766785189509392 +Accuracy on test data: 0.9879000186920166 +``` + +### 7) Подали на вход обученной модели два тестовых изображения. Вывели изображения, истинные метки и результаты распознавания. + +```python +# вывод двух тестовых изображений и результатов распознавания + +for n in [11,43]: + result = model.predict(X_test[n:n+1]) + print('NN output:', result) + + plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray')) + plt.show() + print('Real mark: ', np.argmax(y_test[n])) + print('NN answer: ', np.argmax(result)) +``` +![picture](images/1.png) +``` +Real mark: 2 +NN answer: 2 +``` +![picture](images/2.png) +``` +Real mark: 9 +NN answer: 9 +``` + +### 8) Вывели отчет о качестве классификации тестовой выборки и матрицу ошибок для тестовой выборки. + +```python +# истинные метки классов +true_labels = np.argmax(y_test, axis=1) +# предсказанные метки классов +predicted_labels = np.argmax(model.predict(X_test), axis=1) + +# отчет о качестве классификации +print(classification_report(true_labels, predicted_labels)) +# вычисление матрицы ошибок +conf_matrix = confusion_matrix(true_labels, predicted_labels) +# отрисовка матрицы ошибок в виде "тепловой карты" +display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix) +display.plot() +plt.show() +``` +``` +313/313 ━━━━━━━━━━━━━━━━━━━━ 2s 5ms/step + precision recall f1-score support + + 0 1.00 0.99 0.99 997 + 1 1.00 0.99 1.00 1164 + 2 0.99 0.98 0.98 1030 + 3 1.00 0.98 0.99 1031 + 4 0.99 0.99 0.99 967 + 5 0.97 0.99 0.98 860 + 6 0.99 0.99 0.99 977 + 7 0.99 0.99 0.99 1072 + 8 0.98 0.98 0.98 939 + 9 0.99 0.98 0.99 963 + + accuracy 0.99 10000 + macro avg 0.99 0.99 0.99 10000 +weighted avg 0.99 0.99 0.99 10000 +``` +![picture](images/3.png) + +### 9) Загрузили, предобработали и подали на вход обученной нейронной сети собственное изображение, созданное при выполнении лабораторной работы №1. Вывели изображение и результат распознавания. + +```python +# загрузка собственного изображения +from PIL import Image + +for name_image in ['190 (2).png', '690 (1).png']: + file_data = Image.open(name_image) + file_data = file_data.convert('L') # перевод в градации серого + test_img = np.array(file_data) + + # вывод собственного изображения + plt.imshow(test_img, cmap=plt.get_cmap('gray')) + plt.show() + + # предобработка + test_img = test_img / 255 + test_img = np.reshape(test_img, (1,28,28,1)) + + # распознавание + result = model.predict(test_img) + print('I think it\'s', np.argmax(result)) +``` +![picture](images/190 (2).png) +``` +I think it's 1 +``` +![picture](images/690 (1).png) +``` +I think it's 6 +``` + +### 10) Загрузили с диска модель, сохраненную при выполнении лабораторной работы №1. Вывели информацию об архитектуре модели. Повторили для этой модели п. 6. + +```python +model_lr1 = keras.models.load_model("model_1h100_2h50.keras") + +model_lr1.summary() +``` +**Model: "sequential_10"** +| Layer (type) | Output Shape | Param # | +|------------------|-------------:|--------:| +| dense_1 (Dense) | (None, 100) | 78,500 | +| dense_2 (Dense) | (None, 10) | 1,010 | + Total params: 79,512 (310.60 KB) + Trainable params: 79,510 (310.59 KB) + Non-trainable params: 0 (0.00 B) + Optimizer params: 2 (12.00 B) + + +```python +# развернем каждое изображение 28*28 в вектор 784 +X_train, X_test, y_train, y_test = train_test_split(X, y, + test_size = 10000, + train_size = 60000, + random_state = 23) +num_pixels = X_train.shape[1] * X_train.shape[2] +X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255 +X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255 +print('Shape of transformed X train:', X_train.shape) +print('Shape of transformed X train:', X_test.shape) + +# переведем метки в one-hot +y_train = keras.utils.to_categorical(y_train, num_classes) +y_test = keras.utils.to_categorical(y_test, num_classes) +print('Shape of transformed y train:', y_train.shape) +print('Shape of transformed y test:', y_test.shape) +``` +``` +Shape of transformed X train: (60000, 784) +Shape of transformed X train: (10000, 784) +Shape of transformed y train: (60000, 10) +Shape of transformed y test: (10000, 10) +``` + +```python +# Оценка качества работы модели на тестовых данных +scores = model_lr1.evaluate(X_test, y_test) +print('Loss on test data:', scores[0]) +print('Accuracy on test data:', scores[1]) +``` +``` +313/313 ━━━━━━━━━━━━━━━━━━━━ 2s 3ms/step - accuracy: 0.9490 - loss: 0.1739 +Loss on test data: 0.18475718796253204 +Accuracy on test data: 0.9458000063896179 +``` + +### 11) Сравнили обученную модель сверточной сети и наилучшую модель полносвязной сети из лабораторной работы №1 по следующим показателям: +### - количество настраиваемых параметров в сети +### - количество эпох обучения +### - качество классификации тестовой выборки. +### Сделали выводы по результатам применения сверточной нейронной сети для распознавания изображений. + +Таблица1: + +| Модель | Количество настраиваемых параметров | Количество эпох обучения | Качество классификации тестовой выборки | +|----------|-------------------------------------|---------------------------|-----------------------------------------| +| Сверточная | 34 826 | 15 | accuracy:0.987 ; loss:0.037 | +| Полносвязная | 79512 | 50 | accuracy:0.946 ; loss:0.185 | + + +##### По результатам применения сверточной НС, а также по результатам таблицы 1 делаем выводы, что сверточная НС намного лучше справляется с задачами распознования изображений, чем полносвязная - имеет меньше настраиваемых параметров, быстрее обучается, имеет лучшие показатели качества. + +## Задание 2 + +### В новом блокноте выполнили п. 2–8 задания 1, изменив набор данных MNIST на CIFAR-10, содержащий размеченные цветные изображения объектов, разделенные на 10 классов. +### При этом: +### - в п. 3 разбиение данных на обучающие и тестовые произвели в соотношении 50 000:10 000 +### - после разбиения данных (между п. 3 и 4) вывели 25 изображений из обучающей выборки с подписями классов +### - в п. 7 одно из тестовых изображений должно распознаваться корректно, а другое – ошибочно. + +### 1) Загрузили набор данных CIFAR-10, содержащий цветные изображения размеченные на 10 классов: самолет, автомобиль, птица, кошка, олень, собака, лягушка, лошадь, корабль, грузовик. + +```python +# загрузка датасета +from keras.datasets import cifar10 + +(X_train, y_train), (X_test, y_test) = cifar10.load_data() +``` + +### 2) Разбили набор данных на обучающие и тестовые данные в соотношении 50 000:10 000 элементов. Параметр random_state выбрали равным (4k – 1)=23, где k=6 –номер бригады. Вывели размерности полученных обучающих и тестовых массивов данных. + +```python +# создание своего разбиения датасета + +# объединяем в один набор +X = np.concatenate((X_train, X_test)) +y = np.concatenate((y_train, y_test)) + +# разбиваем по вариантам +X_train, X_test, y_train, y_test = train_test_split(X, y, + test_size = 10000, + train_size = 50000, + random_state = 23) +# вывод размерностей +print('Shape of X train:', X_train.shape) +print('Shape of y train:', y_train.shape) +print('Shape of X test:', X_test.shape) +print('Shape of y test:', y_test.shape) +``` +``` +Shape of X train: (50000, 32, 32, 3) +Shape of y train: (50000, 1) +Shape of X test: (10000, 32, 32, 3) +Shape of y test: (10000, 1) +``` + +### Вывели 25 изображений из обучающей выборки с подписью классов. + +```python +class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', + 'dog', 'frog', 'horse', 'ship', 'truck'] + +plt.figure(figsize=(10,10)) +for i in range(25): + plt.subplot(5,5,i+1) + plt.xticks([]) + plt.yticks([]) + plt.grid(False) + plt.imshow(X_train[i]) + plt.xlabel(class_names[y_train[i][0]]) +plt.show() +``` +![picture](images/4.png) + +### 3) Провели предобработку данных: привели обучающие и тестовые данные к формату, пригодному для обучения сверточной нейронной сети. Входные данные принимают значения от 0 до 1, метки цифр закодированы по принципу «one-hot encoding». Вывели размерности предобработанных обучающих и тестовых массивов данных. + +```python +# Зададим параметры данных и модели +num_classes = 10 +input_shape = (32, 32, 3) + +# Приведение входных данных к диапазону [0, 1] +X_train = X_train / 255 +X_test = X_test / 255 + +print('Shape of transformed X train:', X_train.shape) +print('Shape of transformed X test:', X_test.shape) + +# переведем метки в one-hot +y_train = keras.utils.to_categorical(y_train, num_classes) +y_test = keras.utils.to_categorical(y_test, num_classes) +print('Shape of transformed y train:', y_train.shape) +print('Shape of transformed y test:', y_test.shape) +``` +``` +Shape of transformed X train: (50000, 32, 32, 3) +Shape of transformed X test: (10000, 32, 32, 3) +Shape of transformed y train: (50000, 10) +Shape of transformed y test: (10000, 10) +``` + +### 4) Реализовали модель сверточной нейронной сети и обучили ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывели информацию об архитектуре нейронной сети. + +```python +# создаем модель +model = Sequential() +model.add(layers.Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=input_shape)) +model.add(layers.MaxPooling2D(pool_size=(2, 2))) +model.add(layers.Conv2D(64, kernel_size=(3, 3), activation="relu")) +model.add(layers.MaxPooling2D(pool_size=(2, 2))) +model.add(layers.Conv2D(128, kernel_size=(3, 3), activation="relu")) +model.add(layers.MaxPooling2D(pool_size=(2, 2))) +model.add(layers.Flatten()) +model.add(layers.Dense(128, activation='relu')) +model.add(layers.Dropout(0.5)) +model.add(layers.Dense(num_classes, activation="softmax")) +model.summary() +``` +Model: "sequential_1" +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ +┃ Layer (type) ┃ Output Shape ┃ Param # ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ +│ conv2d_2 (Conv2D) │ (None, 30, 30, 32) │ 896 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ max_pooling2d_2 (MaxPooling2D) │ (None, 15, 15, 32) │ 0 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ conv2d_3 (Conv2D) │ (None, 13, 13, 64) │ 18,496 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ max_pooling2d_3 (MaxPooling2D) │ (None, 6, 6, 64) │ 0 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ conv2d_4 (Conv2D) │ (None, 4, 4, 128) │ 73,856 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ max_pooling2d_4 (MaxPooling2D) │ (None, 2, 2, 128) │ 0 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ flatten_1 (Flatten) │ (None, 512) │ 0 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ dense_1 (Dense) │ (None, 128) │ 65,664 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ dropout_1 (Dropout) │ (None, 128) │ 0 │ +├─────────────────────────────────┼────────────────────────┼───────────────┤ +│ dense_2 (Dense) │ (None, 10) │ 1,290 │ +└─────────────────────────────────┴────────────────────────┴───────────────┘ + Total params: 160,202 (625.79 KB) + Trainable params: 160,202 (625.79 KB) + Non-trainable params: 0 (0.00 B) + +```python +# компилируем и обучаем модель +batch_size = 64 +epochs = 50 +model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) +model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1) +``` + +### 5) Оценили качество обучения на тестовых данных. Вывели значение функции ошибки и значение метрики качества классификации на тестовых данных. + +```python +# Оценка качества работы модели на тестовых данных +scores = model.evaluate(X_test, y_test) +print('Loss on test data:', scores[0]) +print('Accuracy on test data:', scores[1]) +``` +``` +313/313 ━━━━━━━━━━━━━━━━━━━━ 2s 4ms/step - accuracy: 0.7087 - loss: 1.3107 +Loss on test data: 1.2974315881729126 +Accuracy on test data: 0.7123000025749207 +``` + +### 6) Подали на вход обученной модели два тестовых изображения. Вывели изображения, истинные метки и результаты распознавания. + +```python +# вывод двух тестовых изображений и результатов распознавания + +for n in [3,16]: + result = model.predict(X_test[n:n+1]) + print('NN output:', result) + + plt.imshow(X_test[n].reshape(32,32,3), cmap=plt.get_cmap('gray')) + plt.show() + print('Real mark: ', np.argmax(y_test[n])) + print('NN answer: ', np.argmax(result)) +``` +![picture](images/5.png) +``` +Real mark: 0 +NN answer: 0 +``` +![picture](images/6.png) +``` +Real mark: 7 +NN answer: 2 +``` + +### 7) Вывели отчет о качестве классификации тестовой выборки и матрицу ошибок для тестовой выборки. + +```python +# истинные метки классов +true_labels = np.argmax(y_test, axis=1) +# предсказанные метки классов +predicted_labels = np.argmax(model.predict(X_test), axis=1) + +# отчет о качестве классификации +print(classification_report(true_labels, predicted_labels, target_names=class_names)) +# вычисление матрицы ошибок +conf_matrix = confusion_matrix(true_labels, predicted_labels) +# отрисовка матрицы ошибок в виде "тепловой карты" +fig, ax = plt.subplots(figsize=(6, 6)) +disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=class_names) +disp.plot(ax=ax, xticks_rotation=45) # поворот подписей по X и приятная палитра +plt.tight_layout() # чтобы всё влезло +plt.show() +``` +``` +313/313 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step + precision recall f1-score support + + airplane 0.69 0.76 0.72 986 + automobile 0.85 0.80 0.82 971 + bird 0.66 0.60 0.63 1043 + cat 0.54 0.55 0.55 1037 + deer 0.66 0.67 0.67 969 + dog 0.63 0.64 0.63 979 + frog 0.78 0.78 0.78 1025 + horse 0.74 0.73 0.74 948 + ship 0.79 0.82 0.80 1003 + truck 0.80 0.78 0.79 1039 + + accuracy 0.71 10000 + macro avg 0.71 0.71 0.71 10000 +weighted avg 0.71 0.71 0.71 10000 +``` +![picture](images/7.png) + +### По результатам классификации датасета CIFAR-10 сверточной моделью делаем вывод, что она удовлетворительно справилась с задачей. Полученные метрики оценки качества имеют показатели в районе 0.71. \ No newline at end of file