Terrain_V5 Optimizer SGD with Momentum.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install necessary packages and import libraries\n",
    "# The first few lines ensure that TensorFlow, Scipy, and Matplotlib are installed.\n",
    "# pip install tensorflow\n",
    "# pip install scipy\n",
    "# pip install matplotlib\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline plotting with Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %matplotlib inline\n",
    "# this  command ensures that plots are displayed directly in the notebook after being generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building a Convolutional Neural Network (CNN) model for terrain classification\n",
    "# Importing layers from Keras for building the model architecture\n",
    "from keras.preprocessing import image\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.models import load_model\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.callbacks import LearningRateScheduler, ReduceLROnPlateau\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the CNN model\n",
    "The following code defines a CNN model using TensorFlow's Keras API. The model is designed for terrain classification with a series of convolutional layers, pooling layers, and fully connected layers.\n",
    "\n",
    "* **Convolutional Layers:** These layers apply filters to the input image, helping to detect various features (like edges, textures, etc.).\n",
    "* **Max Pooling:** This operation reduces the spatial dimensions (width and height) of the output from the convolutional layers, which helps in reducing computational complexity and controlling overfitting.\n",
    "* **Batch Normalization:** This technique normalizes the outputs of a previous activation layer, which helps stabilize learning and can accelerate training.\n",
    "* **Dropout Layers:** These layers randomly set a fraction of the input units to zero during training to help prevent overfitting.\n",
    "* **Softmax Layer:** This is used in the output layer for multi-class classification problems, providing probabilities for each class.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the CNN model architecture\n",
    "model = tf.keras.Sequential([\n",
    "\n",
    "    # Input layer\n",
    "    tf.keras.layers.Input(shape=(64, 64, 3)),\n",
    "    \n",
    "    # First convolutional layer\n",
    "    tf.keras.layers.Conv2D(32, kernel_size=3, activation='relu'),\n",
    "    tf.keras.layers.MaxPool2D(pool_size=2, strides=2),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "    \n",
    "    # Second convolutional layer\n",
    "    tf.keras.layers.Conv2D(64, kernel_size=3, activation='relu'),\n",
    "    tf.keras.layers.MaxPool2D(pool_size=2, strides=2),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "\n",
    "    # Third convolutional layer\n",
    "    tf.keras.layers.Conv2D(128, kernel_size=3, activation='relu'),\n",
    "    tf.keras.layers.MaxPool2D(pool_size=2, strides=2),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "    \n",
    "    # Fully connected classifier\n",
    "    tf.keras.layers.Flatten(),\n",
    "\n",
    "    tf.keras.layers.Dense(1024, activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "\n",
    "    tf.keras.layers.Dense(1024, activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "\n",
    "    tf.keras.layers.Dense(256, activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "\n",
    "    tf.keras.layers.Dense(5, activation='softmax')\n",
    "])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compiling the CNN model\n",
    "The model is compiled using the Adam optimizer, with categorical crossentropy as the loss function, and accuracy as the evaluation metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "# Categorical crossentropy is used since the labels are one-hot encoded.\n",
    "# Adam optimizer is used for its adaptive learning rate.\n",
    "\n",
    "model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.01, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Augmentation and Preprocessing\n",
    "This section applies data augmentation techniques using the `ImageDataGenerator` from Keras to generate more training samples and preprocess the images. These operations include rescaling, rotating, shifting, and flipping.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data augmentation and preprocessing for training and validation datasets\n",
    "# The ImageDataGenerator applies various transformations to augment the training data\n",
    "# Data augmentation for training dataset\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    rotation_range=30,\n",
    "    brightness_range=[0.8, 1.2],\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2\n",
    ")\n",
    "\n",
    "# Validation dataset (only rescaling)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading and Preparing the Data\n",
    "In this section, the training and validation data are loaded from the respective directories, and the images are prepared for the model using the augmentation techniques defined earlier.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 10000 images belonging to 5 classes.\n",
      "Found 500 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "# Assuming you've created 'train_subset' and 'test_subset' directories\n",
    "training_set = train_datagen.flow_from_directory(\n",
    "    r\"D:\\Jupyter Notebook\\Contribution\\Terrain-Recognition\\Data Main\\Training Data\",\n",
    "    target_size=(64, 64),\n",
    "    batch_size=64,\n",
    "    class_mode='categorical'\n",
    ")\n",
    "\n",
    "test_set = test_datagen.flow_from_directory(\n",
    "    r\"D:\\Jupyter Notebook\\Contribution\\Terrain-Recognition\\Data Main\\Testing Data\",\n",
    "    target_size=(64, 64),\n",
    "    batch_size=64,\n",
    "    class_mode='categorical'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define learning rate scheduler\n",
    "def scheduler(epoch, lr):\n",
    "    if epoch < 10:\n",
    "        return 1e-2\n",
    "    else:\n",
    "        return 1e-3\n",
    "\n",
    "lr_schedule = LearningRateScheduler(scheduler)\n",
    "lr_reduction = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_1                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_2                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4608</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                │       <span style=\"color: #00af00; text-decoration-color: #00af00\">4,719,616</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_3                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,049,600</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_4                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)                │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">262,400</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_5                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                 │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)                   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,285</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
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       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\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;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m896\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m128\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\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;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_1                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m256\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │          \u001b[38;5;34m73,856\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ max_pooling2d_2 (\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;34m128\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_2                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │             \u001b[38;5;34m512\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4608\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;34m1024\u001b[0m)                │       \u001b[38;5;34m4,719,616\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_3                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                │           \u001b[38;5;34m4,096\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                │       \u001b[38;5;34m1,049,600\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_4                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)                │           \u001b[38;5;34m4,096\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │         \u001b[38;5;34m262,400\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_5                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)                 │           \u001b[38;5;34m1,024\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)                   │           \u001b[38;5;34m1,285\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,136,261</span> (23.41 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m6,136,261\u001b[0m (23.41 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,131,205</span> (23.39 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m6,131,205\u001b[0m (23.39 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">5,056</span> (19.75 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m5,056\u001b[0m (19.75 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the CNN Model\n",
    "The model is trained using the training and validation data. The training process is monitored, and a learning rate scheduler is used to adjust the learning rate dynamically during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m157/256\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m49s\u001b[0m 500ms/step - accuracy: 0.5229 - loss: 1.3377"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DEEPANSHU\\anaconda3\\Lib\\contextlib.py:155: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
      "  self.gen.throw(typ, value, traceback)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m91s\u001b[0m 324ms/step - accuracy: 0.5454 - loss: 1.2619 - val_accuracy: 0.6116 - val_loss: 1.0092 - learning_rate: 0.0100\n",
      "Epoch 2/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 384ms/step - accuracy: 0.6499 - loss: 0.9221 - val_accuracy: 0.5385 - val_loss: 1.0488 - learning_rate: 0.0100\n",
      "Epoch 3/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 386ms/step - accuracy: 0.6694 - loss: 0.8682 - val_accuracy: 0.6964 - val_loss: 0.9067 - learning_rate: 0.0100\n",
      "Epoch 4/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 383ms/step - accuracy: 0.6761 - loss: 0.8556 - val_accuracy: 0.5769 - val_loss: 0.8990 - learning_rate: 0.0100\n",
      "Epoch 5/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 395ms/step - accuracy: 0.6960 - loss: 0.8154 - val_accuracy: 0.4799 - val_loss: 1.3479 - learning_rate: 0.0100\n",
      "Epoch 6/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 381ms/step - accuracy: 0.6957 - loss: 0.8010 - val_accuracy: 0.5577 - val_loss: 1.2049 - learning_rate: 0.0100\n",
      "Epoch 7/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m104s\u001b[0m 389ms/step - accuracy: 0.6986 - loss: 0.7900 - val_accuracy: 0.6942 - val_loss: 0.8291 - learning_rate: 0.0100\n",
      "Epoch 8/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 385ms/step - accuracy: 0.7016 - loss: 0.7891 - val_accuracy: 0.6346 - val_loss: 0.9920 - learning_rate: 0.0100\n",
      "Epoch 9/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 394ms/step - accuracy: 0.7109 - loss: 0.7606 - val_accuracy: 0.7121 - val_loss: 0.6805 - learning_rate: 0.0100\n",
      "Epoch 10/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 387ms/step - accuracy: 0.7222 - loss: 0.7268 - val_accuracy: 0.7115 - val_loss: 0.8132 - learning_rate: 0.0100\n",
      "Epoch 11/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 396ms/step - accuracy: 0.7127 - loss: 0.7529 - val_accuracy: 0.7969 - val_loss: 0.5509 - learning_rate: 0.0010\n",
      "Epoch 12/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 385ms/step - accuracy: 0.7466 - loss: 0.6743 - val_accuracy: 0.8462 - val_loss: 0.4400 - learning_rate: 0.0010\n",
      "Epoch 13/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 392ms/step - accuracy: 0.7516 - loss: 0.6700 - val_accuracy: 0.8080 - val_loss: 0.5094 - learning_rate: 0.0010\n",
      "Epoch 14/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 384ms/step - accuracy: 0.7515 - loss: 0.6670 - val_accuracy: 0.8077 - val_loss: 0.4937 - learning_rate: 0.0010\n",
      "Epoch 15/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 393ms/step - accuracy: 0.7529 - loss: 0.6555 - val_accuracy: 0.8237 - val_loss: 0.4842 - learning_rate: 0.0010\n",
      "Epoch 16/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 381ms/step - accuracy: 0.7646 - loss: 0.6375 - val_accuracy: 0.7308 - val_loss: 0.6430 - learning_rate: 0.0010\n",
      "Epoch 17/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m105s\u001b[0m 394ms/step - accuracy: 0.7601 - loss: 0.6366 - val_accuracy: 0.8348 - val_loss: 0.4645 - learning_rate: 0.0010\n",
      "Epoch 18/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 375ms/step - accuracy: 0.7570 - loss: 0.6484 - val_accuracy: 0.7885 - val_loss: 0.6484 - learning_rate: 0.0010\n",
      "Epoch 19/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 302ms/step - accuracy: 0.7581 - loss: 0.6354 - val_accuracy: 0.8080 - val_loss: 0.4724 - learning_rate: 0.0010\n",
      "Epoch 20/20\n",
      "\u001b[1m256/256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 295ms/step - accuracy: 0.7629 - loss: 0.6385 - val_accuracy: 0.8077 - val_loss: 0.5854 - learning_rate: 0.0010\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "modelZ = model.fit(\n",
    "    training_set,\n",
    "    steps_per_epoch=256,\n",
    "    epochs=20,\n",
    "    validation_data=test_set,\n",
    "    validation_steps=7,\n",
    "    callbacks=[lr_schedule, lr_reduction]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Training and Validation Accuracy\n",
    "This section plots the training and validation accuracy and loss over time to visualize how the model performs during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Training Accuracy: 0.7583\n",
      "Final Validation Accuracy: 0.8077\n"
     ]
    }
   ],
   "source": [
    "# Print the final training and validation accuracy\n",
    "train_accuracy = modelZ.history['accuracy'][-1]\n",
    "val_accuracy = modelZ.history['val_accuracy'][-1]\n",
    "\n",
    "print(f\"Final Training Accuracy: {train_accuracy:.4f}\")\n",
    "print(f\"Final Validation Accuracy: {val_accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QoFqUKfb29rRr1461a9fmuCun1+v56aefqFGjBvXq1cu1nYeHB2PHjuXxxx/n3LlzeWYqrVevHm+//TYBAQH8888/xaqnh4cHNjY2nDx5Msf6P/74o8j77NevHwALFy68b7mC3rEs6rksaa6urgwZMoQff/yRDRs2EBERkaPrN0DPnj0JDg7O9e/0448/otFo6N69e777N7Rw3/tvs379+lxly/u5FEIIU8jMzOSHH37A39+fnTt35nq8+uqrhIeHG4cp9evXj507d+Ya9nK3fv36cf78+fsO18nv77VhxouCMATahhZcg0WLFuV4bWtrS9euXfn1118fGMDa2Njw7LPP8sMPPzB37lyaN29Ox44dC1yn+6lZsybnz5/PEbzdvHmTAwcOFGl/9erVw9/fnyVLluQZrBsUprdUz549SUxMzDF8C9RrsOF9c3j66acJCwtj48aN/PTTTwwdOjTHPNE9evQA4Keffsqx3dGjRzlz5sx9612Y33UV4VwK05Pu38IsduzYYcxofbf+/fszc+ZMHnroIbp3785rr72GtbU1CxYs4L///mPlypXGC2i7du0YOHAgTZs2xdXVlTNnzrB8+XI6dOiAnZ0dJ0+e5MUXX2T48OHUrVsXa2trduzYwcmTJ5k2bVqx6q/RaBg1ahRLlizB39+fZs2aceTIEX7++eci77Nz586MHj2ajz76iMjISAYOHIhOp+Pff//Fzs7OmG0yICCAX375hVWrVlG7dm1sbGwICAjIc58FPZemcvz4cZydnXOtb9SoUY4ue+PGjWPVqlW8+OKL1KhRI9eYuylTpvDjjz8yYMAAPvjgA/z8/Pjrr79YsGABzz///H0D2P79++Pm5sb48eP54IMPsLS0ZNmyZVy7di1X2bJ8LoUQorRs2rSJsLAwZs2aled0T02aNOHrr79m8eLFDBw40Jg5u0uXLrz55psEBAQQGxvL5s2bmTp1Kg0aNOCVV15h1apVPPzww0ybNo22bdty584ddu/ezcCBA+nevTuenp706tWLmTNn4urqip+fH9u3b2ft2rUFrnuDBg3w9/dn2rRpKIqCm5sb69evZ9u2bbnKzp07l06dOtGuXTumTZtGnTp1iIyM5M8//2TRokU5hoVNmjSJ2bNnc/z4cb7//vsinde8jB49mkWLFjFq1CieeeYZbt68yezZs3N1ay+Mb775hkGDBtG+fXumTJmCr68voaGhbNmyhRUrVgAYr21ffvklTz31FFZWVtSvXz/PoXBjxozhm2++4amnnuLKlSsEBASwb98+PvnkE/r372/ycfKhoaF5ThNWtWpV/P39ja979+5NjRo1mDRpEhEREcYZQwzq16/Ps88+y1dffYVWq6Vfv35cuXKFd955Bx8fn1wzrtytML/ryvK5FGZkthRpolIyZEbO72HIpLh3716lR48eir29vWJra6u0b99eWb9+fY59TZs2TWndurXi6uqq6HQ6pXbt2sqUKVOUmJgYRVEUJTIyUhk7dqzSoEEDxd7eXnFwcFCaNm2qfPHFFzkyaebFkEE6Ojo63zJxcXHKhAkTFA8PD8Xe3l4ZNGiQcuXKlXyzUd+7r7yyUWdmZipffPGF0qRJE8Xa2lpxdnZWOnTokOOzX7lyRendu7fi6OioAMbslfllrizIucwvK3t+mVnzO1/5Pe7N/pqZman4+PjcN9v51atXlSeeeEJxd3dXrKyslPr16ytz5sxRMjMzc5S793wripodNTAwULG3t1eqV6+uvPfee8r333+f63yXxXMphBClbciQIYq1tfV9s2I/9thjiqWlpRIREaEoiqJcu3ZNGTdunOLp6alYWVkp3t7eyogRI5TIyEjjNrdv31ZefvllxdfXV7GyslKqVaumDBgwQDl79qyxTHh4uDJs2DDFzc1NcXZ2VkaNGqUcO3Ysz+zf9vb2edYtODhYeeihhxRHR0fF1dVVGT58uBIaGprn9SE4OFgZPny44u7urlhbWyu+vr7K2LFjlZSUlFz77datm+Lm5qYkJycX5DQarx1z5sy5b7kffvhBadiwoWJjY6M0atRIWbVqVb7ZqPPaV16f6+DBg0q/fv0UZ2dnRafTKf7+/sqUKVNylJk+fbri7e2taLXaHNejvDKS37x5U5k4caLi5eWlWFpaKn5+fsr06dNznSdAeeGFF3LV0c/PT3nqqafuex4elP37ySefzLXNm2++qQCKj49Prt8DiqL+vpg1a5ZSr149xcrKSqlSpYoyatQo5dq1aznK3Xu+FaXgv+sUpeydS2F+GkUp4sR5QgghhBBCVEBRUVH4+fnx0ksvMXv2bHNXRwhRxkn3byGEEEIIIVATfl2+fJk5c+ag1Wp5+eWXzV0lIUQ5IInKhBBCCCGEAL7//nu6devG6dOnWbFiBdWrVzd3lYQQ5YB0/xZCCCGEEEIIIYpIWqqFEEIIIYQQQogikqBaCCGEEEIIIYQoonKRqEyv1xMWFoajo6PMBSuEEKJMUBSFhIQEvL290WrlHnVxybVeCCFEWVPQa325CKrDwsLw8fExdzWEEEKIXK5du0aNGjXMXY1yT671QgghyqoHXevLRVDt6OgIqB/GycnJzLURQgghID4+Hh8fH+M1ShSPXOuFEEKUNQW91peLoNrQDczJyUkutEIIIcoU6apsGnKtF0IIUVY96Fovg8CEEEIIIYQQQogikqBaCCGEEEIIIYQoIgmqhRBCCCGEEEKIIioXY6qFEKKi0ev1pKWlmbsa4j6srKywsLAwdzVKxIIFC5gzZw7h4eE0btyYefPm0blz53zLr1ixgtmzZ3PhwgWcnZ3p27cvn332Ge7u7gAsW7aMp59+Otd2d+7cwcbGxqR1z8zMJD093aT7FBVLRf7uCiHKJgmqhRCilKWlpRESEoJerzd3VcQDuLi44OnpWaGSka1atYpXXnmFBQsW0LFjRxYtWkS/fv0IDg7G19c3V/l9+/YxZswYvvjiCwYNGsSNGzeYOHEiEyZMYN26dcZyTk5OnDt3Lse2pgyoFUUhIiKC2NhYk+1TVFwV8bsrhCi7JKgWQohSpCgK4eHhWFhY4OPjg1Yro3DKIkVRSE5OJioqCgAvLy8z18h05s6dy/jx45kwYQIA8+bNY8uWLSxcuJCZM2fmKn/o0CFq1qzJ5MmTAahVqxbPPfccs2fPzlFOo9Hg6elZYvU2BNTVqlXDzs5OgiWRp4r83RVClF0SVAshRCnKyMggOTkZb29v7OzszF0dcR+2trYAREVFUa1atQrRnTQtLY3jx48zbdq0HOt79+7NgQMH8twmMDCQt956i40bN9KvXz+ioqL47bffGDBgQI5yiYmJ+Pn5kZmZSfPmzfnwww9p0aJFvnVJTU0lNTXV+Do+Pj7fspmZmcaA2tDlXIj8VMTvrhCibJMmEiGEKEWZmZkAWFtbm7kmoiAMNz4qyhjemJgYMjMz8fDwyLHew8ODiIiIPLcJDAxkxYoVjBw5Emtrazw9PXFxceGrr74ylmnQoAHLli3jzz//ZOXKldjY2NCxY0cuXLiQb11mzpyJs7Oz8eHj45NvWcP5lxtRoqAq2ndXCFG2SVAtCi/sX4g8be5aCFGuSdfV8qGi/jvd+7kURcn3swYHBzN58mTeffddjh8/zubNmwkJCWHixInGMu3bt2fUqFE0a9aMzp07s3r1aurVq5cj8L7X9OnTiYuLMz6uXbtW6HoLkR/5v2ImF/+GmIvmroUQpU66f4vCuRMLS/uDosBLx8G5urlrJIQQooCqVKmChYVFrlbpqKioXK3XBjNnzqRjx468/vrrADRt2hR7e3s6d+7MRx99lOeYVa1WS5s2be7bUq3T6dDpdMX4NEKIMiX4D1g9Bmxd4dld4FrT3DUSotRIS7UonKhgSE+GjDuwe5a5ayOEEKIQrK2tadWqFdu2bcuxftu2bQQGBua5TXJycq6EeoYxqoqi5LmNoigEBQVJkighKovUBNj0f+ryndvwy5OQlmTeOglRiiSoFoUTdSZ7+d+fpIuPEEKUM1OnTuX7779nyZIlnDlzhilTphAaGmrszj19+nTGjBljLD9o0CDWrl3LwoULuXz5Mvv372fy5Mm0bdsWb29vAGbMmMGWLVu4fPkyQUFBjB8/nqCgoBxdxIUQFdjOTyAhHFz8wL4qRP4Hf05WezYKUQlIUC0KJ/ps1oIGlEzY+bFZqyOEKB1jx45lyJAh5q6GMIGRI0cyb948PvjgA5o3b86ePXvYuHEjfn5+AISHhxMaGmosP3bsWObOncvXX39NkyZNGD58OPXr12ft2rXGMrGxsTz77LM0bNiQ3r17c+PGDfbs2UPbtm1L/fOVVQcOHMDCwoK+ffuauypCmFb4CTj8rbo8cC6M+BG0lvDfb3Dwa/PWTYhSolHy67tVhsTHx+Ps7ExcXBxOTk7mrk7ltmwgXNkL7V+AQwsABZ7bA17NzF0zIcqFlJQUQkJCqFWrFjY2NuauToGNHTuW2NhYfv/9d3NXpVTd799Lrk2mdb/zWV6/N3ebMGECDg4OfP/99wQHB+Pr62uWeqSnp2NlZWWWY5emivB/plzQZ8L3vSDsH2j8CAxfqq4/8j/Y+BpotDBqLfh3N289hSiigl7rpaVaFI6hpTrgUQgYpi5v/9B89RGinFMUheS0DLM8THVPdffu3bRt2xadToeXlxfTpk0jIyPD+P5vv/1GQEAAtra2uLu706tXL5KS1LF2u3btom3bttjb2+Pi4kLHjh25evWqSeolKjZzfXeK8r1JSkpi9erVPP/88wwcOJBly5bleP/PP/+kdevW2NjYUKVKFR555BHje6mpqbzxxhv4+Pig0+moW7cuixcvBmDZsmW4uLjk2Nfvv/+eI/P1+++/T/PmzVmyZAm1a9dGp9OhKAqbN2+mU6dOuLi44O7uzsCBA7l06VKOfV2/fp3HHnsMNzc37O3tad26NYcPH+bKlStotVqOHTuWo/xXX32Fn5+fyf62iHLg+FI1oNY5QZ9Pste3mQDNnwRFD7+Ng9vyd11UbJL9WxRc0k1IilaXq9SH7m/C6XVwcRtcPQB+eSe5EULk7056Jo3e3WKWYwd/0Ac76+JdBm7cuEH//v0ZO3YsP/74I2fPnuWZZ57BxsaG999/n/DwcB5//HFmz57N0KFDSUhIYO/evSiKQkZGBkOGDOGZZ55h5cqVpKWlceTIEZkKRxSIub47RfnerFq1ivr161O/fn1GjRrFSy+9xDvvvINGo+Gvv/7ikUce4a233mL58uWkpaXx119/GbcdM2YMBw8eZP78+TRr1oyQkBBiYmIKdfyLFy+yevVq1qxZY0wyl5SUxNSpUwkICCApKYl3332XoUOHEhQUhFarJTExka5du1K9enX+/PNPPD09+eeff9Dr9dSsWZNevXqxdOlSWrdubTzO0qVLGTt2rHyHK4uESPj7A3W5xzvgdFdiQo0GBsxVE9yG/QurnoRxW8Fa5poXFZME1aLgorOSlLn4gs5BfbQcA8eWwN8zYNxm9Y+oEKLSWLBgAT4+Pnz99ddoNBoaNGhAWFgY//d//8e7775LeHg4GRkZPPLII8YxuwEBAQDcunWLuLg4Bg4ciL+/PwANGzY022cRoqQsXryYUaNGAdC3b18SExPZvn07vXr14uOPP+axxx5jxowZxvLNmqlDqs6fP8/q1avZtm0bvXr1AqB27dqFPn5aWhrLly+natWqxnWPPvporjpWq1aN4OBgmjRpws8//0x0dDRHjx7Fzc0NgDp16hjLT5gwgYkTJzJ37lx0Oh0nTpwgKCgox1h7UcFteRNS48CrObQZn/t9KxsY+RMs6goRp2D9y/DId/JbsTTpM2HtM2BlC4O+Aq10Ui4pElSLgjNk/q5614/eLm9A0Eq4dggubIV6fcxTNyHKKVsrC4I/MM/3xtbKotj7OHPmDB06dMjRMtWxY0cSExO5fv06zZo1o2fPngQEBNCnTx969+7NsGHDcHV1xc3NjbFjx9KnTx8eeughevXqxYgRI2QaJlEg5vruFPZ7c+7cOY4cOWIMNi0tLRk5ciRLliyhV69eBAUF8cwzz+S5bVBQEBYWFnTt2rVYdfbz88sRUANcunSJd955h0OHDhETE4NerwcgNDSUJk2aEBQURIsWLYwB9b2GDBnCiy++yLp163jsscdYsmQJ3bt3p2bNmsWqqygnLu1QE5FptDBoHmjz+V4414ARP8APg+HUavBuAR0mlWpVK7VrR+C/NeqyZzNo96x561OBye0KUXCG8dTVGmSvc/LK/oJu/xCyLspCiILRaDTYWVua5WGKLpqKouTaj2E8pUajwcLCgm3btrFp0yYaNWrEV199Rf369QkJCQHU7qIHDx4kMDCQVatWUa9ePQ4dOlTseomKz1zfncJ+bxYvXkxGRgbVq1fH0tISS0tLFi5cyNq1a7l9+za2trb5bnu/9wC0Wm2u8cvp6em5ytnb2+daN2jQIG7evMn//vc/Dh8+zOHDhwG1Vbsgx7a2tmb06NEsXbqUtLQ0fv75Z8aNG3ffbUQFkZ4Cf72qLrd9Vg2U76dmp+zx1lvfhpA9JVs/ke3CXUNk/n4fbl8xV00qPAmqRcFFZQXVVe/pntnxFdA5Q+QpOC3dvoSoTBo1asSBAwdy/LA/cOAAjo6OVK9eHVCDn44dOzJjxgz+/fdfrK2tWbdunbF8ixYtmD59OgcOHDB2OxWiIsjIyODHH3/k888/JygoyPg4ceIEfn5+rFixgqZNm7J9+/Y8tw8ICECv17N79+48369atSoJCQnGxH+gtm4/yM2bNzlz5gxvv/02PXv2pGHDhty+fTtHmaZNmxIUFMStW7fy3c+ECRP4+++/WbBgAenp6TkSrIkKbN8XcOsyOHhC97cKtk2756DZ4+p0rL+OhdjQB24iTOD8VvXZxhnSk2Tu8BIkQbUoOMOY6mr3BNV2btDxJXV5x0eQmfsuuRCi/IuLi8sRGAQFBfHss89y7do1XnrpJc6ePcsff/zBe++9x9SpU9FqtRw+fJhPPvmEY8eOERoaytq1a4mOjqZhw4aEhIQwffp0Dh48yNWrV9m6dSvnz5+XcdWiwtiwYQO3b99m/PjxNGnSJMdj2LBhLF68mPfee4+VK1fy3nvvcebMGU6dOsXs2bMBqFmzJk899RTjxo3j999/JyQkhF27drF69WoA2rVrh52dHW+++SYXL17k559/zpVZPC+urq64u7vz3XffcfHiRXbs2MHUqVNzlHn88cfx9PRkyJAh7N+/n8uXL7NmzRoOHjxoLNOwYUPat2/P//3f//H4448/sHVbVAAxF2HfXHW536dgU8DpBDUaGPiFOgVr8k1YNQrS75RcPQXEXoOo09nTmlnaQshu+OdHc9esQpKgWhRMYrT6RxANVKmX+/12z4N9VbgdAv8uL/XqCSFK3q5du2jRokWOx3vvvcfGjRs5cuQIzZo1Y+LEiYwfP563334bACcnJ/bs2UP//v2pV68eb7/9Np9//jn9+vXDzs6Os2fP8uijj1KvXj2effZZXnzxRZ577jkzf1IhTGPx4sX06tULZ2fnXO89+uijBAUF4eTkxK+//sqff/5J8+bN6dGjh7ErNsDChQsZNmwYkyZNokGDBjzzzDPGlmk3Nzd++uknNm7cSEBAACtXruT9999/YL20Wi2//PILx48fp0mTJkyZMoU5c+bkKGNtbc3WrVupVq0a/fv3JyAggE8//dSYPdxg/PjxpKWlSdfvykBR4K+pkJkGdXpBoyGF297KVk1cZucO4Sdg/SvSalqSLmS1UtdoAzVaQw/1uszWtyHuhvnqVUFplHIwmWBBJ90WJShkD/wwCFxrwctBeZc5vAg2vQGOXjD5X/WPpxAih5SUFEJCQqhVqxY2Njbmro54gPv9e8m1ybTudz7le1N2ffzxx/zyyy+cOnXK3FXJQf7PlICTv8LaCWBpA5MOglvhM9ED6m/KH4eoXcH7zoL2E01aTZHl55FwfrM63VmX19RM4It7w41jULc3PLFaMrEXQEGv9dJSLQrGMJ763q7fd2s1Fpx9ISEcjnxXKtUSQgghROlLTEzk6NGjfPXVV0yePNnc1REl7c5t2DJdXe7yWtEDaoBaXaD3R+ryljfhyr7i189c4m7AiV/KXlf29BS4nJWLwTAzj9YCHv4GLKzVVuyTq81XvwpIgmpRMIbx1FUb5F/GUgfds/7g7vsCUuJKvl5CCCGEKHUvvvginTp1omvXrtL1uzLY/iEkRatDAANNcBOl/fMQMEJtrV79lDr+tzxJioEtb8H8FrDuOfX8lCVX9kHGHXD0Bo8m2eurNYCu/6cub3oDEiLNU78KSIJqUTAFaakGaDpSDbzv3IYDX5V8vYQQQghR6pYtW0ZqaiqrVq3KNc5aVDDXj8GxJerygLlqI0pxaTQw6EvwDIDkmPKTuCwlHnZ+Al82g4NfQ2aquv702rI1raxhKq26D+Xu4t3xZfBsCimxsPG1Uq9aRSVBtXgwRSlYSzWoXUsMiRAOLoDEqJKtmxBCCCGEKBmZGbDhFUBRp8Sq1dl0+7a2g5ErwNYNwoNgw9Sym7gs/Q7snw9fNoXdsyAtUQ1MH1sJOid16OP1o+aupUpR4HxWUG3o+n03Cyu1G7jWEs78Cad/L9XqVVQSVIsHS4xSW5412rwzf9+rwUCo3kqdD2/v5yVfPyGEEEIIYXpHvoOIU2DjAg+VQBdnVz8YvlT9jXniZzjyP9Mfozgy09VW+vktYNs76u9h97ow/Ad4djc06A/1+qplg/8wb10NYs5D7FV17HStrnmX8WoKnbKm0dv4GiTdLL36VVASVIsHM7RSu9YCqwJk0NRooOe76vKxJRAbWnJ1E0IIIYQQphd3A3Z+rC4/NAMcqpbMcWp3g4c+UJe3TIcr+0vmOIWh16vZzr9uAxumqC3Rzj5qC++kQ9B4CGizwqhGD6vPZ/4sGy3thlbqmp1A55B/uS6vQdWG6lj5zdNKp24VmATV4sEKOp76brW7qXfHMtNg16clUi0hhBBCCFFCNk9Tuzn7tIMWY0r2WB1ehCbDQJ8Bvz5lvnmUFQXOboRvO6nTh90OAfuq0G82vHQcWowCC8uc29TpCVb2EHcNwv4xT73vZpifum4eXb/vZqlTbxJotHBqNZzbVPJ1q8AkqBYPVtDx1Pfq+Z76fGJldmAuhBBCCCHKtvNb1JZXjQUM/CK7VbakaDQw+CvwCFBbTleNUqeFKk0he2DxQ/DL4xB1GnTO6hzPk4Og3XP5J2izss0eu2zuLuApcRB6UF2u1/vB5Wu0Um9ogNoifye2xKpW0UlQLR6sKC3VoH5RGwwERQ87PzJ9vYQQQgghhGmlJWdnhe7wAng0Lp3jWtvBYz+Brava4vvXq6XTnfr6cfjxYfhhkJpszNIWOk2Bl4PULtL360Jt0Giw+hz8h3m7gF/aqbb2u9ct+Fzi3d8EN3+1i/vWt0u2fhWYBNXi/gqT+TsvPd5Ru5WcWQ83jpu2bkKIcqNmzZrMmzfP3NUQotyR744odXvmqPlwnH2gWymPtXWtCcOWqL8dg36Co9+X3LGizsAvT8L3PeDyLtBaQZtn1GC61/tg51bwfdV5SA3Gb1+BiJMlU9+CMHb9LkArtYGVrdoNHA38uxwu7SiRqlV0ElSL+0uIULuSaCygSt3Cb1+tATR9TF3e/oFp6yaEqJAkiBBCCDOJOgMH5qvL/WaDtX3p18G/hxrUgjqu+9JOtVtyaoLaip6eomblLuq80LevwLqJsKADnN2gBvDNnoCXjsGAz8DRs/D71DlA3V7qsrm6gOv12UF1Qbp+382vA7R9Vl3+82X1XJemlHg1s3pKPKQl5fw3LgvJ3wrA8sFFRKUWFaw+u9XOfyzJg3SbBqd+Ve8CXt4NtfNJ7y+EEEKICiMzMxONRoO2pMfjCtNQFHWuaH0G1B+gThdlLoGTIexfOL0Olg+5f1mNBWgt7nrWqo8c6yzUceEaC4i7Dvp0dduGg6D722ojUHE1GqL2zDz9e1ZPTU3x91kY4f+q49GtHcE3sPDb93wXzm9Seyn8PUO9wVDS4m7Axtfh3F/3L6fRFv7fuU5P6D+n5D9DFvkrJ+4v2jCeuhh/bFz9oPXT6vL2GeXmjpMQpUJR1Luy5ngU8Lu4aNEiqlevjv6eVoHBgwfz1FNPcenSJR5++GE8PDxwcHCgTZs2/P333yVxtgBYuHAh/v7+WFtbU79+fZYvX57j/ffffx9fX190Oh3e3t5MnjzZ+N6CBQuoW7cuNjY2eHh4MGzYsBKrpyhh5vruFOIaVtrfnblz5xIQEIC9vT0+Pj5MmjSJxMTEHGX2799P165dsbOzw9XVlT59+nD79m0A9Ho9s2bNok6dOuh0Onx9ffn4Y3VKpV27dqHRaIiNjTXuKygoCI1Gw5UrVwBYtmwZLi4ubNiwgUaNGqHT6bh69SpHjx7loYceokqVKjg7O9O1a1f++SdnluTY2FieffZZPDw8sLGxoUmTJmzYsIGkpCScnJz47bffcpRfv3499vb2JCSUcotaRRa0AkIPgJUd9Jtl3rpoNGqX5NrdH1xWyVRnm8m4o2YrT42HlFhIvglJUepY4fjrarB4O0QNqGt3h2d2wMifTBNQg9rl2kIHty5lN0qVpvNZrdT+3cDSuvDb6xzUZHEAR/9XslOb6fVwdDF80+7BATWo+Zn06ZCRAulJ2f/Gd26pNxISIyD+BsSFqj0Rbl1Se9uWImmpFvcXZRhPXcgkZffq8jr8+5M6rvrsX9BwYPHrJkRFkJ4Mn3ib59hvhhWoa9/w4cOZPHkyO3fupGfPngDcvn2bLVu2sH79ehITE+nfvz8fffQRNjY2/PDDDwwaNIhz587h6+tr0iqvW7eOl19+mXnz5tGrVy82bNjA008/TY0aNejevTu//fYbX3zxBb/88guNGzcmIiKCEydOAHDs2DEmT57M8uXLCQwM5NatW+zdu9ek9ROlyFzfnQJ+b6D0vztarZb58+dTs2ZNQkJCmDRpEm+88QYLFiwA1CC4Z8+ejBs3jvnz52NpacnOnTvJzMwEYPr06fzvf//jiy++oFOnToSHh3P2bOFm70hOTmbmzJl8//33uLu7U61aNUJCQnjqqaeYP1/tVvz555/Tv39/Lly4gKOjI3q9nn79+pGQkMBPP/2Ev78/wcHBWFhYYG9vz2OPPcbSpUtz3AQzvHZ0dCz0eRJ5SLoJW99Rl7tNBxcf89YH1O/ZmN9Bn6k+lHuf9blfK5lZXYbz2iZrvY2L6QLpu9k4qa2j5zZC8J+ll+DNoKBTad1P7W7Q8in45wf480WYuF9NIGdKMRfgz8nqDRyA6q1h8HyoUi//fzPDa+O/+b3/9nn8n7AtxJh4E5CgWtyfKVqqARyqQfvnYe/nsONDqN9P7aYhhCjz3Nzc6Nu3Lz///LMxMPj1119xc3OjZ8+eWFhY0KxZM2P5jz76iHXr1vHnn3/y4osvmrQun332GWPHjmXSpEkATJ06lUOHDvHZZ5/RvXt3QkND8fT0pFevXlhZWeHr60vbtm0BCA0Nxd7enoEDB+Lo6Iifnx8tWrQwaf3KiwULFjBnzhzCw8Np3Lgx8+bNo3PnzvmWX7FiBbNnz+bChQs4OzvTt29fPvvsM9zd3Y1l1qxZwzvvvMOlS5fw9/fn448/ZujQoaXxccqs0v7uvPLKK8blWrVq8eGHH/L8888bg+rZs2fTunVr42uAxo3VH/4JCQl8+eWXfP311zz11FMA+Pv706lTp0LVIT09nQULFuT4XD169MhRZtGiRbi6urJ7924GDhzI33//zZEjRzhz5gz16tUDoHbt7MzFEyZMIDAwkLCwMLy9vYmJiWHDhg1s27atUHUT9/H3u2qrX7XG6u+1skRrUX5+MzZ6OCuo/gO6Ty+94yZGZc+RXZgkZXnp/SFc2Aa3LsPOj6HPx8WvH6hjpPd/CbtnQ2aqOrd3z3eh7TPZ/74WVqY5lhlIUC3ypygQfU5dLm5LNajjY44uVgP1k6uh+ePF36cQ5Z2VndryZa5jF9CTTz7Js88+y4IFC9DpdKxYsYLHHnsMCwsLkpKSmDFjBhs2bCAsLIyMjAzu3LlDaGioyat85swZnn322RzrOnbsyJdffgmoLYPz5s2jdu3a9O3bl/79+zNo0CAsLS156KGH8PPzM77Xt29fhg4dip2die/Cl3GrVq3ilVdeYcGCBXTs2JFFixbRr18/goOD82wd3bdvH2PGjOGLL75g0KBB3Lhxg4kTJzJhwgTWrVsHwMGDBxk5ciQffvghQ4cOZd26dYwYMYJ9+/bRrl27kvkg5vruFOJ7A6X73dm5cyeffPIJwcHBxMfHk5GRQUpKCklJSdjb2xMUFMTw4cPz3PbMmTOkpqYag/+isra2pmnTpjnWRUVF8e6777Jjxw4iIyPJzMwkOTnZ+DmDgoKoUaOGMaC+V9u2bWncuDE//vgj06ZNY/ny5fj6+tKlS5di1VVkuXpA7U0I6pzU5TiwMbt6fdUs4tFn1N/QVeuXznEvZN1g8moOjh7F25eNMwyaBz+PgEML1LHiPm2Kt88b/8CfL0Hkf+rrOr3U/2supu3NZk4yplrkL/6GOmZBawnudYq/P1sX6PSKurzrE8hIK/4+hSjvNBq1i5s5HoVIojJo0CD0ej1//fUX165dY+/evYwaNQqA119/nTVr1vDxxx+zd+9egoKCCAgIIC2tZL7jmnvqrSiKcZ2Pjw/nzp3jm2++wdbWlkmTJtGlSxfS09NxdHTkn3/+YeXKlXh5efHuu+/SrFmzHGNEK4O5c+cyfvx4JkyYQMOGDZk3bx4+Pj4sXLgwz/KHDh2iZs2aTJ48mVq1atGpUyeee+45jh07Ziwzb948HnroIaZPn06DBg2YPn06PXv2vG8W99TUVOLj43M8CsVc351CJh8qre/O1atX6d+/P02aNGHNmjUcP36cb775BlBbjwFsbW3z3f5+7wHGZGPKXWPKDfu9dz/3fkfHjh3L8ePHmTdvHgcOHCAoKAh3d3fj53zQsUFtrV66dCmgdv1++umncx1HFEFGmpqcDNRuv74ldBOssrB1UbtQg9oFvLRc2KI+F7eV2qBeH3X2HkUPf7wAGalF209aEmx5C77vqQbUtm4w9Dt48rcKFVCDBNXifqKyun67+Rct4UFe2j4HDp5qsojjy0yzTyFEibO1teWRRx5hxYoVrFy5knr16tGqVSsA9u7dy9ixYxk6dCgBAQF4enoaExeZWsOGDdm3b1+OdQcOHKBhw+zeNLa2tgwePJj58+eza9cuDh48yKlTpwCwtLSkV69ezJ49m5MnT3LlyhV27Kg8c3KmpaVx/PhxevfO+cOrd+/eHDhwIM9tAgMDuX79Ohs3bkRRFCIjI/ntt98YMGCAsczBgwdz7bNPnz757hNg5syZODs7Gx8+PmVgDGcJKK3vzrFjx8jIyODzzz+nffv21KtXj7CwnC35TZs2Zfv27XluX7duXWxtbfN9v2rVqgCEh4cb1wUFBRWobnv37mXy5Mn079+fxo0bo9PpiImJyVGv69evc/78+Xz3MWrUKEJDQ5k/fz6nT582dlEXxXToG7VV1a5K9jRWongaPaw+l9bUWpnp6rRjoAbDptJ3JthXg5hzapftwrq0U5227ODXanAeMBxePArNRpZ+ZvRSIN2/Rf6is5KUmTKZg7UddH0d/noV9syBFk+aZw5EIUShPfnkkwwaNIjTp08bW9oA6tSpw9q1axk0aBAajYZ33nknV7bjwrpx40auH+y+vr68/vrrjBgxgpYtW9KzZ0/Wr1/P2rVrjRmTly1bRmZmJu3atcPOzo7ly5dja2uLn58fGzZs4PLly3Tp0gVXV1c2btyIXq+nfv1S6p5XBsTExJCZmYmHR87ugR4eHkRE5J0pNTAwkBUrVjBy5EhSUlLIyMhg8ODBfPXVV8YyERERhdonqEmxpk6danwdHx9fYQPr0vju+Pv7k5GRwVdffcWgQYPYv38/3377bY4y06dPJyAggEmTJjFx4kSsra3ZuXMnw4cPp0qVKvzf//0fb7zxBtbW1nTs2JHo6GhOnz7N+PHjqVOnDj4+Prz//vt89NFHXLhwgc8//7xAdatTpw7Lly+ndevWxMfH8/rrr+done7atStdunTh0UcfZe7cudSpU4ezZ8+i0Wjo27cvAK6urjzyyCO8/vrr9O7dmxo1ahTpPIm7xIbCrqws330+BrvSTexUYTUYAOtfhshTcPMSuPuX7PFCD6o9S+2qgHdL0+3Xzg0GfA6rR8O+L6DRYPBq9uDtkm+pSe+CsoYUONWAgXNNG/CXQdJSLfJnaKk2xXjqu7UYA6411WkODuXd3VAIUfb06NEDNzc3zp07xxNPPGFc/8UXX+Dq6kpgYCCDBg2iT58+tGxZvAv7Z599RosWLXI8/vzzT4YMGcKXX37JnDlzaNy4MYsWLWLp0qV069YNABcXF/73v//RsWNHY6vc+vXrcXd3x8XFhbVr19KjRw8aNmzIt99+y8qVK42JmiqT+3Whv1dwcDCTJ0/m3Xff5fjx42zevJmQkBAmTpxY5H0C6HQ6nJyccjwqqtL47jRv3py5c+cya9YsmjRpwooVK5g5c2aOMvXq1WPr1q2cOHGCtm3b0qFDB/744w8sLdU2lnfeeYdXX32Vd999l4YNGzJy5EiioqIAsLKyYuXKlZw9e5ZmzZoxa9YsPvroowLVbcmSJdy+fZsWLVowevRoJk+eTLVq1XKUWbNmDW3atOHxxx+nUaNGvPHGG8as5Abjx48nLS2NcePGFekciXscXqROQ+XXCZqONHdtKg47N6iVNd7/TCl0AT9v6Pr9kDpHsyk1GqyOqVYy4fcX1Fbx/CiKOq/4N+2yAmoNtHkGXjhU4QNqAI2ilP1Jg+Pj43F2diYuLq5CX3TLnP/1UKfAGr4MGps4g+vJ1bD2GdA5w8tBcndUVBopKSmEhIRQq1YtbGxszF0d8QD3+/cqj9emtLQ07Ozs+PXXX3Nk5n755ZcJCgpi9+7dubYZPXo0KSkp/Prrr8Z1+/bto3PnzoSFheHl5YWvry9TpkxhypQpxjJffPEF8+bN4+rVqwWq2/3Op3xvBKhZ6F9++WXCwsKwtr7/sDT5P/MA+kyY20id3/exldCgv7lrVLEcWwobXgHvFvDsrpI91tdt1S7aw5ZCk0dMv//EKDVQvnMLur+t9ji9V3wY/PVa9pzTVeqpc177tjd9fUpZQa/10lIt8mbqzN/3ajJMnbYhNU5Nry+EEKLEWVtb06pVq1xTEW3bto3AwMA8t0lOTjYmqTKwsFCnPzHcl+/QoUOufW7dujXffQpRGMnJyZw+fZqZM2fy3HPPPTCgFgUQskcNqG1d1UzMwrQaDASNFsL+hdsFu7FYJLevqAG1xgL8ezyweJE4VIN+WWOqd8+CqDPZ7+n16g2Eb9qpAbXWErq8ARP3VYiAujAkqBZ5i7sGaYnqtAAlMRZEq4We76jLhxdBQv7j7oQQFceKFStwcHDI81EZu2Gbw9SpU/n+++9ZsmQJZ86cYcqUKYSGhhq7c0+fPp0xY8YYyw8aNIi1a9eycOFCLl++zP79+5k8eTJt27bF29sbUFu6t27dyqxZszh79iyzZs3i77//zjFvsiieyvzdmT17Ns2bN8fDw4Pp00tx7t+K7FRWz5PGQ02XjFZkc6gKfh3V5ZLsAn5+q/rs20HNPF5SAoZBvX6gT4ffJ0FmBsRchB8GqS3yqfFQvRU8twd6vAWWupKrSxlV6ERle/bsYc6cORw/fpzw8HDWrVvHkCFD8i1vuBAHBQWRmppK48aNef/99+nTp+L3rS/XDOOp3euU3HyF9fpCjbZw/YiatGxAwRKeCCHKr8GDB+c7b7GVlcyNWhpGjhzJzZs3+eCDDwgPD6dJkyZs3LgRPz8/QM3ufPc8yWPHjiUhIYGvv/6aV199FRcXF3r06MGsWbOMZQIDA/nll194++23eeedd/D392fVqlUlN0d1JVSZvzvvv/8+77//vrmrUXGk38me7ilghHnrUpE1ehiu7FWzgAe+VDLHuHDXeOqSpNGoyca+OQBh/8DKx9TeDpmpYGUHPd6Bds+B1qJk61GGFTqoTkpKolmzZjz99NM8+uijDyy/Z88eHnroIT755BNcXFxYunQpgwYN4vDhw7Ro0aJIlRaloCQyf99Lo4Fe78GyAer0Wp2mgnP1kjueEMLsHB0dcXR0NHc1Kr1JkyYxadKkPN9btmxZrnUvvfQSL710/x+Fw4YNY9iwYaaonsiDfHeEyZzbBGkJ4OwLPnLjq8Q0GAgbX4frRyHuOjibOGN9WhKE7FWXSyMRmJO3miX+zxfhYtZwH/8eMHAeuPqV/PHLuEIH1f369aNfv34FLj9v3rwcrz/55BP++OMP1q9fn29QnZqaSmpq9iTj8fHxha2mKK6Syvx9r5qd1PT/Yf/AlX3q3HVCVALlIEekQP6dyhr59xAFJf9X7sPQ9bvpcNNnixbZnLzUccWhB+HMemj/vGn3b2gpdvaFqiXYCHa3FqMgZDdcPaC2Tjd7rELOOV0Upf5N0uv1JCQk4OaWf7bnmTNn4uzsbHxU1Hkry7TSaKk28GmrPocHlfyxhDAzQ4KntLQ0M9dEFERycjJQ8bvXlnWG82/49xDiQeS7m4/kW3Ahq5VRun6XvEYPq8/BJTCu+kLWeOp6vUsvsNVo4NHvYWowNH9cAuq7FLqlurg+//xzkpKSGDEi/y/y9OnTmTp1qvF1fHy8BNalSa8v2czf9/Jqrj6HBZX8sYQwM0tLS+zs7IiOjsbKyipXVmVRNiiKQnJyMlFRUbi4uBhvhgjzsLCwwMXFxThnsp2d3X3nwBaVl3x3H+D0OjXZlGdA6TScVHYNB8HmaWprdUIEOHqaZr+Kkp2krK7kqSoLSjWoXrlyJe+//z5//PEH1apVy7ecTqdDp6t8WePKjLhQSE8GC2twq13yx/Nurj6Hn1DnTazESQ5ExafRaPDy8iIkJKTA8/cK83FxccHT00Q/gkSxGP4dDIG1EPcj3918GLp+Syt16XCuAdVbw41jahfwts+YZr9RwRB/HSxtoVZn0+xTFEupBdWrVq1i/Pjx/Prrr/TqJfPhlWnGzN91waIU/otUqadmDkxPgpsXoWr9kj+mEGZkbW1N3bp1pQt4GWdlZSWtXGWI4YZUtWrVSE9PN3d1RBkm3918xIaqLaZo1CmSROlo9LAaVAf/Ybqg+nxW1u9aXcDK1jT7FMVSKkH1ypUrGTduHCtXrmTAgAGlcUhRHKU5nhrUlmnPpnDtkNoFXIJqUQlotVpsbGzMXQ0hyh0LCwsJmIQoCkMrda3OaiZnUToaDYZt78DV/ZAYrc5hXVyG8dQlPZWWKLBCD+ZLTEwkKCiIoKAgAEJCQggKCjLOaTl9+nTGjBljLL9y5UrGjBnD559/Tvv27YmIiCAiIoK4uDjTfAJheqWV+ftu3lmZ4MP+Lb1jCiGEEEJUBooCJ1ery9L1u3S51lTzByl6OLuh+PtLvgXXDqvLpTGVliiQQgfVx44do0WLFsbpsKZOnUqLFi149913AQgPDzcG2ACLFi0iIyODF154AS8vL+Pj5ZdfNtFHECZX2i3VcNe46qDSO6YQQgghRGUQcQqiz4KFTm05FaXLkAX8jAmygF/aoQboVRuCi2/x9ydMotDdv7t163bfuf+WLVuW4/WuXbsKewhhTno9RJ9Xl0uzpdqQAVySlQkhhBBCmNaprFbq+n3Bxtm8damMGj0M22fA5d1qS7Nd/lMLP9DdU2mJMkPmchE5xV6BjDvqnUy3WqV33Cp1wcpezToec6H0jiuEEEIIUZHpM+HUGnVZun6bh7s/eASAkgnnNhZ9P/rM7HnGZSqtMkWCapGTYTx1lXql21qstQCvpuqydAEXQgghhDCNK/sgIUxtoZbEVuZj6HYf/EfR93HjONy5pf5b+rQzTb2ESUhQLXIyx3hqA0MXcElWJoQQQghhGoau342HgqXOvHWpzAzjqi/thDuxRduHYSot/56lM+2tKDAJqkVOxszfZgiqjRnAg0r/2EIIIYQQFU16CgRnJceSrt/mVbW++vtanw7nNxdtHxeyguq6Mp66rJGgWuRkbKkuxSRlBoYM4BEn1TEjQgghhBCi6M5vhtR4cKoBvh3MXRthaK0uShfw+DA1izsa6cZfBklQLbLpM7OThJmjpdq9Dlg7ZCUrO1/6xxdCCCGEqEhO/ao+BwwDrfzsNztDUH1xO6QmFG5bQ9bv6q3Avopp6yWKrdJ9u5JSMzh8+aa5q1E23b4CGSlgaaNOVF/atBbgmZWsTLqACyGEEEIU3Z3b2YFY05HmrYtQVWukNiJlpmaPjy4oQ9bvepL1uyyqVEF1WOwdWn20jdFLjpCQkm7u6pQ9UVldv0s78/fdDF3AJVmZEEIIIUTRBf8BmWng0QQ8Gpm7NgJAoylaF/CMVDXBGch46jKqUgXVXs42VHexJS1Dz7bgSHNXp+wx53hqA0OyMplWSwghhBCi6E5mZf0OGG7eeoicGmZNrXVhG6QlFWybq/shPQkcPMGrWcnVTRRZpcrFrtFoGNjUmy+3X2DDyXAeaVnD3FUqW8yZ+dvAMK1WxCnIzJDpAoQQQgghCiv2mhqIoVHHU4uyw6sZuPhB7FU1sG48BICo+BR2n4/m0OVbZOj12OsscdBZYmdtQa+rv9IEuOrekYtno7CzVt+z11lgr7PEXmeJnZUFWq3GrB+trMjUK6Rn6rGxKr2et5UuYhnUzIsvt19g74Vo4pLTcbazMneVyo7orKDanC3VhmRlaYlqsjLpriSEEEIIUTj//aY+1+wEztKIVKYYuoAfmE/MkdX8L7Q+u89FczYi/8RlD1tvBy18csGHLeeO5VvOzjoryLbODrbtrS1wd9BRw9WWGq52Wc+2eDrZYGlRNjotp2XoSUrNICktg6TUzKxnw0N9nZiaQXJqJolZ65PTspeT0jKz1qnlUtL1DG7mzfzHW5TaZ6h0QXWdao408HTkbEQCW4IjGNHax9xVKhsyM7IzbpuzpVqrVe/gXd2vdgGXoFoIIYQQonBOGrJ+S9fvsuRKTBJ7LkQTGlKPtwHbK3+z7NxwUrFGo4Gm1Z3pXLcqLnZWahCZloku/jK1zkaSgSVpvl1pmmGdHWxmBaJ6Rd1/clomyWmZRBegLhZaDV7ONvcE26YLutMy9NxMSiUmIY3oxBSiE1KJSUwjOiGV6MRU9XXWckJKRpGPk5/kNNPv834qXVANMCDAi7MRCWw4GS5BtcHtEDWZhZWd2iXFnLyaq0F12L/Q/Anz1kUIIYQQojyJ+A+iToOFdXZSLGEWSakZHLx0k93no9lzIZqrN5Oz3nHlaZ071TU3+b+6N3BvNZTOdaviZm+deycHd8JZsKzVkaVP9cj1tqIopKTrja27ife24qZmEJ2QyvXbd7IeydyIvUN6pmJcB7dy7fd+QbeDzpKbSVkBckIqMYk5n6MTU4lNLnxSaJ2lNquF3QJ7a8scre13d4fPtU5niYOhK7xxu9JNulwpg+qBzbz5fNt59l+M4VZSWt7/gSubHJm/zdwVxJCsTKbVEkKIErFgwQLmzJlDeHg4jRs3Zt68eXTu3DnPsmPHjuWHH37Itb5Ro0acPn0agGXLlvH000/nKnPnzh1sbGxMW3khxP2dykpQVrc32LqYtSpliaIo6BX1uaS6PSuKwpnwBDWIPh/Nsau3SM9UjO9bWWho7edGl3pVsbk5FE59zzjXk9D8xfx3eiFr6q18ptLSaDTYWltga21BFQddgeqp1ytEJaRy/XZyjkDbEGTfuH2HtEz9fYPugrDUaqjioKOKozVVHXRUcdBR1VF9GJarOOio4mCNvc4SqzLSHb0oKmVQXauKPY29nTgdFs/m/yJ4op2vuatkfmVhPLWBYVotSVYmhBAmt2rVKl555RUWLFhAx44dWbRoEf369SM4OBhf39zXwy+//JJPP/3U+DojI4NmzZoxfHjObqVOTk6cO3cuxzoJqIUoZXo9nMoaT11O56ZOy9BzPjKBk9fjOHUjlsj4VDL0Cpl6PRmZipqE6p7X6jo9mZlKVln1OSNTn+O1gbWlNjvR1wNaRA3L+bWIZuoVDlyKYc/5GPZciCY6ITXH5/F1s6Nrvap0rVeV9v7uOOiyfteGjoBT38O5TeqUWZZ5BMSpCXBlv7pc13TzU2u1GjydbfB0tqF1zdzv6/UK0Yl3B913ciwnpmbcFRRbq4GyQ85guaqDDmdbq0qTPK3SRisDm3pzOiyeDSfDJKiG7JZqc46nNnDzB2tHSEuAmHPg0djcNRJCiApj7ty5jB8/ngkTJgAwb948tmzZwsKFC5k5c2au8s7Ozjg7Oxtf//7779y+fTtXy7RGo8HT07NkKy+EuL+r+yH+Buicy8V8xhmZei5GJ6oB9PU4Tt6I40x4PGkZ+hI9blqGnlsZadwq4IxWhWFrZUGgvztdsgLpmlXs8y5Yow04ekFCOFzelXdL9OVdoE8Ht9pQpY7pK5sPrVaDh5MNHk42tDLzqNDyohIH1V7M2nyWQ5dvEp2QSlXHgnWXqLDKUku1MVnZPrULuATVQghhEmlpaRw/fpxp06blWN+7d28OHDhQoH0sXryYXr164eeX85dWYmIifn5+ZGZm0rx5cz788ENatMg/82pqaiqpqdktOvHx8YX4JEKIPBm6fjcaDFZlq6eIXq9wOSaJUzdijUH06bB47qRn5irrbGtF0xrOBFR3xs/dDkutFksLDRZajbqs1WBhoVGfDevueW2h1WBlkfO1pVaDAjmyRycbxyJnGrNH350ILK8s04YEYSnpavDfwNPR2BrdqqYrOssCjOfVaqHhIDjyHQT/kXdQfT6r67cJW6lFyai0QbWPmx3Najhz4nocm/8LZ3SHmuaukvlkpkPMBXW5LLRUg9oF/Oo+NVlZiyfNXRshhKgQYmJiyMzMxMPDI8d6Dw8PIiIiHrh9eHg4mzZt4ueff86xvkGDBixbtoyAgADi4+P58ssv6dixIydOnKBu3bp57mvmzJnMmDGj6B9GCJFTRqoanAE0HWHWqiiKQuitZE5ej+PkdTWIPh0WT2Jq7ozMDjpLmlR3omkNFwKqO9O0hjO+bnZoNCXXbdhU+ZQM3cuLPB9yo4fVoPrsX+rvcYu7pvpVFHUea4C6DxW/sqJEVdqgGtQu4Ceux7H+ZCUPqm9dVruWWNmDcxnJhm5IVhYeZNZqCCFERXTvj1VFUQr0A3bZsmW4uLgwZMiQHOvbt29P+/btja87duxIy5Yt+eqrr5g/f36e+5o+fTpTp041vo6Pj8fHp4xcg4Qojy5shZQ4cKoOfp1K/fCpGZl8vzeEg5ducvJ6LPF5TJNkY6WlibczATWcs1qiXahdxb7cjru1tNBSkEbpfPl2APuqkBQNIXugTs/s98JPQGKE+vu8Zun/e4rCqdRB9YCmXny88QxHr9wiMj4FD6ey1U2m1BjHU9c3f+ZvA6/m6rMkKxNCCJOpUqUKFhYWuVqlo6KicrVe30tRFJYsWcLo0aOxtr5/K49Wq6VNmzZcuHAh3zI6nQ6drpIPvRLClE6uUp+bPFrqv+fCYu/w/E/HOXE9zrjO2kJLQ28nmlbPDqLrVHUosczb5ZLWQu0CfmyJ2svg7qDa0Epdu1veScxEmVKpIxVvF1ta+bly/Opt/joZzrhOtcxdJfMoS+OpDdxqZycriz4Lnk3MXSMhhCj3rK2tadWqFdu2bWPo0KHG9du2bePhh+8/n+3u3bu5ePEi48ePf+BxFEUhKCiIgICAYtdZCFEAd2Kzx9+WctfvAxdjeGnlv9xMSsPFzoopverRys+Veh6OWFtKAP1AjR5Wg+qzG2DA3OyGJONUWmU/4ZyASv8/fWBTLwD+OhVu5pqYUVnK/G2g1WZPrSVdwIUQwmSmTp3K999/z5IlSzhz5gxTpkwhNDSUiRMnAmq37DFjxuTabvHixbRr144mTXLf5JwxYwZbtmzh8uXLBAUFMX78eIKCgoz7FEKUsDN/QmYaVGsEHqXTEKEoCot2X2LU4sPcTEqjsbcT61/sxFOBNWlS3VkC6oLy6wS2bpB8U83eDpAUA9ePqcvlIIu7kKCa/gFeaDRw/OptwmLvmLs65lEWW6pBzQAOagZwIYQQJjFy5EjmzZvHBx98QPPmzdmzZw8bN240ZvMODw8nNDQ0xzZxcXGsWbMm31bq2NhYnn32WRo2bEjv3r25ceMGe/bsoW3btiX+eYQQwMmsrN8Bw6EEE3wZJKZm8MLP/zBz01n0CgxrVYM1zwfi42ZX4seucCwsocEAddmQaO7i34ACngHg5G22qomCq9TdvwE8nGxoU9ONIyG3+OtkOM90qW3uKpWujDS4eVFdLkst1ZCdrCzsX/PWQwghKphJkyYxadKkPN9btmxZrnXOzs4kJyfnu78vvviCL774wlTVE0IURtwNuLJPXQ4YVuKHuxiVyMSfjnMxKhErCw3vDWrMk+18SzRbd4XXaAj8uxzOrIf+c2QqrXKo0rdUAwzK6gK+4WSYmWtiBrcugT5DHb/sXMPctcnJEFRH/qdOMyCEEEIIIXL67zdAAd9AcPEt0UNt/i+cId/s52JUIp5ONqx6rgOj2vtJQF1ctbqAjTMkRak3SC5tV9dL1+9yQ4JqoG8TL7QaOHE9jtCb+d+Jr5Duzvxd1v4gutYCnRNkpGR3URdCCCGEENlO/qo+l2CCsoxMPZ9uOsvEn/4hMTWDdrXcWP9SJ1r6upbYMSsVS2uon9UFfNu76tRotm5Qo7V56yUKTIJqoKqjjg7+7kAlTFhmHE9dxrp+g5qsTMZVCyGEEELkLTIYIk+B1krNIl0Cbiam8tTSI3y7+xIAEzrVYsWEdlR1lGmeTMrw72dI0FunlzrlligXJKjOMiBATQJQ6bqAG1uqy1iSMgPJAC6EEEIIkbdTWQnK6vYGOzeT7/7EtVgGfbWP/RdvYmdtwVePt+DtgY1krumS4N9dHY5pUE/GU5cn8o3I0reJJxZaDafD4gmJSTJ3dUqPIaguiy3VAF7N1WdJViaEEEIIkU2vh1O/qctNh5t8978cCWX4twcJi0uhVhV7fn+hI4OaSSbqEmOpg/p91WWNFvx7mLc+olAkqM7iZm9NxzpVANhwopK0Vmekwq3L6nKZbanOSlYWIcnKhBBCCCGMrh2CuGtq/pl6fU2225T0TKatOcm0tadIy9TzUCMP/nixI/U8HB+8sSiepo+pz7W7lUjPA1FyJKi+y0BjFvBKMq465gIomeof47I6B55bbdA5Q2Zqdqu6EEIIIURld3KV+txwMFjZmmSXN2LvMGLRQX45eg2NBl7vU59Fo1rhZGNlkv2LB6jbC57eDI/8z9w1EYUkQfVd+jTyxMpCw7nIBC5EJhRso4xUSIgs2YqVFGOSsoZlL/O3gUYD3lnJymRctRBCCCEEZKTB6d/VZRN1/d5/MYZBX+3j5PU4XOys+OHptrzQvQ5abRn9jVhR+XUA+yrmroUoJAmq7+JsZ0XnulWBArRWZ6bDsSXwZTOY2xAu7SyFGpqYMUlZGR1PbWAcVx1kzloIIYQQQpQNF7dBSiw4eELNzsXalaIofLv7EqMXH+ZWUhpNqjux/sVOdKlX1TR1FaISkKD6HtldwMNQFCV3Ab1enQ/w6zawYQokhKtdqLe+o75XntzdUl2WGTKAS7IyIYQQQojsrt8Bw4o17VJiagaTVvzDp5vOoldgeKsa/DYxEB83OxNVVIjKwdLcFShrHmrkgbWllkvRSZyNSKChl5P6hqLA+c2w/UOIOq2us68KgZNhzxx1jsDTa9U/buVFeWmpNiQrizyt9hCwkHE9QgghhKikUuLg3GZ1uemIAm2SnqknMj6FiLgUwuJSCI+9Q3hcCrvPRxMSk4SVhYb3Bzfmiba+aMrqkEAhyjAJqu/haGNFt3pV2RocyYaTYWpQHbIXtn8A14+ohXTO0HEytJsIOgc1idaOj2DHh2qyCEtr836IgkhPgdsh6nJZb6l2rQU2zupFJOoMeDU1d42EEEIIIczjzHr1t2eV+uDZlIxMPVEJqYTHqYFyeGwKYXF3cgTQ0Ymp5NUBE8DTyYaFo1rSwte1dD+HEBWIBNV5GNjMm63BkVz8dy9K1HQ0l3aob1jaQvuJauv03Wnu2z0Ph7+D21fg3x+hzQSz1LtQYs6DogcbF3DwMHdt7k+jUcdVh+xWu4BLUC2EEEKICi4jU8+t5DRuJmY9klKJTkilx5Gl1AZW3GnHVzN3EJWQgj6fgPluVhYaPJ1t8HKyxcvFBi9nW6q72jIgwAs3+3LQICREGSZBdR56VbnNIt08+qQcgUuA1hJajYUur4OjZ+4NdA7Q9Q3Y+Brsng3NHgdr+9KuduGUh8zfd/NurgbV4UHAU2aujBBCCCFE4ej1CvEp6cQkpnEzMZVbSWnEJKnLhqD57vduJ6fn2ocHtxinOw4aWHirJRFKCgCWWg0eTjZ4ZwXLXs42eDnb4Olsa1znbm8tmbyFKCESVN8tNhR2fYrdiZX00ejRKxpOV+1LwBMzwa3W/bdt+RQc+Apir8Lhb6Hzq6VT56IqL+OpDYwZwCVZmRBCCCHKtisxSfx2/DonrsfmCJQzCtKkfBetBtzsrXGzt8bdXsfwtL1oYxQinZvz9kN91aDZ2YYqDjoJmIUwIwmqARKjYM9n6hRZevWuYIR3L0aH9OZOUl32utbkgX+mLK2hx9uw9hnY/yW0Hge2ZXhsSnnJ/G1wd7KyjLTyMW5dCCGEEJVGcloGm05FsOrYNY6E3Mq3nKONJVUcdLjbW+PuYI27g44qhsDZQYe7g7XxfRc7ayzuDpa/fR0Aj06j6dvEq6Q/khCigCp3UH0nFg7Mh0MLIT1ZXVerK/R8D+dqzbnx0TaSb98h6FpswZI3NBmmBtSR/8G+efDQjJKsffGUt5Zq15rq+O+UWIg+A17NzFwhIYQQQlR2iqIQdC2W1ceus/5EGImpGYDawtzPX8ew6rdxdbDFyc4GZwc7HO1ssLayVmcy0Vqq02FpDcuWYGGZvay1VHdkEHUWIk6q6xsNNdMnFkLkpXIG1WnJahft/fPUjNIA1VtBz3ehdjcAbIFeDT3480QYf50ML1hQrdWq+/h5hLr/ds+Bk3dJfYqiS0tWk6pB+Wmp1mjUQNqYrEyCaiGEEEKYR0xiKr//e4PVx65xPjLRuN7XzY4RrWvwaEtvvFb0gMNni3kkTXaATVbX8ToPgb17MfcrhDAlbWE32LNnD4MGDcLb2xuNRsPvv//+wG12795Nq1atsLGxoXbt2nz77bdFqWvxZaTBkf/B/OawfYYaUFdtCCNXwITtxoDaYEBTtVvNX6fC0Rd0DEzd3uDbATJS1KRlZVHMeUABWzd1ru3ywtAFPCzIrNUQQgghROWTkalnx9lIJi4/TvtPtvPRX2c4H5mIzlLL0BbVWflMe3a91o0Xe9TF69ZRdaidhQ6q1AO32uDiC47eYF9N/Q2mcwIrO7CwhnwHGirq0MSMO+pvS4A240vrIwshCqjQLdVJSUk0a9aMp59+mkcfffSB5UNCQujfvz/PPPMMP/30E/v372fSpElUrVq1QNubVEI4bJ6u/nFy8YPub0LAcLXrTR661quKo86S8LgU/gm9TeuabnmWy0GjgZ7vwdK+8M+PEPgSuPub+IMUU3nL/G3g3Vx9lmRlQgghhCglV2KSWH3sGmv+uU5kfKpxfbMazoxo48OgZt442Vjl3Oj4D+pzy9Ew4POCHUivB32G+jtVnwGZGVmvDesy1dll8pqJRghhVoUOqvv160e/fv0KXP7bb7/F19eXefPmAdCwYUOOHTvGZ599VvpBtasfdHkN7NzVbN0PSHZlY2XBQ408WPvvDTacDC9YUA3g1wHq9oELW2DHRzB8qQkqb0LlbTy1gaGlOipYkpUJIYQQosTkl3TM1c6KoS1qMKJNDRp4OuW9cdJNOLtBXW45puAH1WpBaw3I7xshypsSH1N98OBBevfunWNdnz59WLx4Menp6VhZWeXaJjU1ldTU7DuB8fHxpqtQt2mFKj6wmRdr/73BX6fCeWdgo5wZGO+n57twYSucXgsdX85uZS0LylvmbwMXv+xkZVHBZeucCiGEEKJcy046do31J8JzJB3rUq8qI1r70LNhNXSWefdwNDr5C2SmqdOBSg4YISqFQo+pLqyIiAg8PDxyrPPw8CAjI4OYmJg8t5k5cybOzs7Gh4+PT0lXM1+d6lTFycaS6IRUjl7Jf3qEXDybqF3LAbZ/UDKVK6ry2lKt0UgXcCGEMIEFCxZQq1YtbGxsaNWqFXv37s237NixY9FoNLkejRs3zlFuzZo1NGrUCJ1OR6NGjVi3bl1JfwwhiiQ9U09ITBI7z0axZF8I7/7xH6MXH6bjpzsYuuAAK49cIzE1A183O17rXY/903qw7Om29A/wenBArSjq8D+AVk+V/IcRQpQJpZL9W3PPuF1FUfJcbzB9+nSmTp1qfB0fH2+2wNraUkufxp78evw6G06G0b52IbItdn9Tbam+tB1C9kKtziVX0YJKS4LYq+pyeWupBrUL+OVdEB5UeseMOAWrx0DHV+QCKYQo91atWsUrr7zCggUL6NixI4sWLaJfv34EBwfj6+ubq/yXX37Jp59+anydkZFBs2bNGD58uHHdwYMHGTlyJB9++CFDhw5l3bp1jBgxgn379tGuXbtS+VxC3C1Tr3Dj9h1CbiZxJSaJkJgkrmQtX7t9h8x8EtDqLLX0D/BiRGsf2tVyQ1vQHooG146oPQKt7NSpVoUQlUKJB9Wenp5ERETkWBcVFYWlpSXu7nkHqDqdDp1OV9JVK7CBzbz59fh1Np2K4P1BjbG0KGADv1staPU0HP2fmm18/DbzJwaLPqc+21UB+yrmrUtReDVXn0szA/j2D+DWZTjynQTVQohyb+7cuYwfP54JEyYAMG/ePLZs2cLChQuZOXNmrvKGXmMGv//+O7dv3+bpp582rps3bx4PPfQQ06dPB9Sb47t372bevHmsXLmyhD+RqKz0eoXw+JTsoDnrOeRmEtduJZOemf/MLTZWWmq626uPKvbUqmJHTXd7Gnk74Xhv0rHC+CcrQVnjR8AmnzHXQogKp8SD6g4dOrB+/foc67Zu3Urr1q3zHE9dFgX6u+NqZ8XNpDQOXb5Fp7qFCEa7vA5BK+D6UTi3ERoMKLmKFkR5HU9tYEhWFnkaMlLBsoRvvkSeVsfGG5aTb4FdARPWCSFEGZOWlsbx48eZNi1nfpHevXtz4MCBAu1j8eLF9OrVCz8/P+O6gwcPMmXKlBzl+vTpY0xSmpcSzZ8iyq2U9ExiElO5mZjGzaRUYhLT1OXEVG4lpRGTpC4b3r9f4GxtqcXPzS4raDYE0HbUqmKPh6NN4VuhH1j5ODidNeyhMAnKhBDlXqGD6sTERC5evGh8HRISQlBQEG5ubvj6+jJ9+nRu3LjBjz+q40kmTpzI119/zdSpU3nmmWc4ePAgixcvLld3rq0stPRt4sXKI6FsOBlWuKDa0QPaT4K9n6ktnvX65juFV6kor+OpDVx8wdYV7tzOSlbWomSPt3/+XS8UuHYY6hc8+70QQpQlMTExZGZm5pnr5N5eZXkJDw9n06ZN/PzzzznW55c/5X77nDlzJjNmzChE7UV5pSgKkfGpXIpOJCbRECjnDJxvZQXLSWmZhdq3pVaDr7sdtbJanGtWsc9atsPL2bbgCWZN4dRvkJ6s/sbyaVt6xxVCmF2hg+pjx47RvXt342vD2OennnqKZcuWER4eTmhoqPH9WrVqsXHjRqZMmcI333yDt7c38+fPL/3ptIppYFM1qN58OoIPhzTBqqBdwEGdq/ro92or8clV0PyJkqvogxhbqstpUK3RqF3AL+9Uk5WVZFAdew3++01drt4KbhyHq/slqBZClHt55TrJL8/J3ZYtW4aLiwtDhgwp9j7LUv6UUpeaCL+OhboPQbvnzF0bk0pKzeBcZALnIhI4Gx7P2YgEzkYkEHcnvcD7sLbUUsXeGncHHe4O1rjZW1PFQYf7Xeuq2Otwc7DGw1FX8GF5Jc2QoKzlGPMP9xNClKpCB9XdunUzJhrLy7Jly3Kt69q1K//8809hD1WmtKvlRhUHa2IS09h/MYZu9asVfGNbF+g8Fba9Czs/gSaPlny35fxEZQXVVctp92/ISla2s+THVR9aAPoMqNUFWoyGtc/A1YJ1jxRCiLKoSpUqWFhY5Jnr5N6W5nspisKSJUsYPXo01tY559HNL3/K/fZZ1vKnlKoLW+DiNjWpVZsJ5u3BVkSZeoUrN5M4G57AuYh4zkSogXToreQ8y1toNfi52+HpZKMGxvbWVHFQg2Q3w7K9GjA76CwffJNHUeDidrBuWTaGZYWfUJOoWlhD08fMXRshRCkrlezfFYGlhZZ+TbxYfugqG06GFy6oBmj7LBxaCHHX4NgSaP98yVT0flITIS6rF0F5HVMN2dNqlWQG8ORbcDwr2UjHl7O7y4cFqedR51ByxxZCiBJibW1Nq1at2LZtG0OHDjWu37ZtGw8//PB9t929ezcXL15k/Pjxud7r0KED27ZtyzGueuvWrQQGBpqu8hVJ+An1OTVOXa7e0rz1eYDohFS15TnC0PIcz4XIRFIz9HmWr+aoo76nIw29nKjv4UgDL0f8qzpgY2XCmwfnN8PKx6BGm7KRCNbwm6HhILAvxEwxQogKQYLqQhjYVA2qt5yO4OOhTR48V+HdrGyh2zRY/zLsmQMtRoHOseQqmxdD5m/7amXjrm5RGTKARwaXXLKyo4shPQk8AsC/p3qxdvGF2FC4fgT8e5j+mEIIUQqmTp3K6NGjad26NR06dOC7774jNDSUiRMnAuTKjWKwePFi2rVrR5MmTXLt8+WXX6ZLly7MmjWLhx9+mD/++IO///6bffv2lcpnKnfCT2YvX9lb5oLqqPgU1vxzg30XozkXkUBMYlqe5WytLKjn6UiDrMC5vqcjDTydcLO3zrO8SV3epT5fPwrnt0D9viV/zPykJcOpX9VlSVAmRKUkQXUhtKnpRjVHHVEJqew9H0OvRvfvKpdL81Fw4Cu4eREOfqMG2aUpOitJWXkdT23g4gu2bnDnlpqR29Q/RtLvwOFv1eWOL2ff/fbrqAbVVw9IUC2EKLdGjhzJzZs3+eCDDwgPD6dJkyZs3LjRmM373twoAHFxcaxZs4Yvv/wyz30GBgbyyy+/8Pbbb/POO+/g7+/PqlWrZI7qvCgKRNwVVIfsVa81ZpaWoWfH2UhWH7vO7vPROeZx1migpru9sdW5QVbw7OtmZ/oM2gV17XD28s6PoV4f87VWB/8OqfHgWhNqdjFPHYQQZiVBdSFotRr6B3ix7MAV/joVXvig2sISerytJic58LU6jqo054o2Zv4ux12/Qb1oejeHSzvUZGWmDqqDVkByDDj7QuPs7pH4BcKJlTKuWghR7k2aNIlJkybl+V5euVGcnZ1JTs57rKzBsGHDGDZsmCmqV7HFh0HyzezXoQchMx0szDPN6NmIeFYfvc7vQTe4lZTdIt3Kz5Uhzb1pWsOFuh4O2FmXoZ+M6Xcg4pS6bKFTb1KcWQ+NBpunPoYEZS1Gg7aMJE0TQpSqMvQXsnwY1EwNqrcFR5KSnln48UENH1a7L4cHwd650PeTkqhm3sp75u+7ebdQg2pTj6vWZ6q9CQACX1RvhBj4dVSfrx+D9BSwsjHtsYUQQlR8hvHUVRtCYqTa6yosCHzalFoV4pLT+fPEDVYfu86pG3HG9VUddTzasgbDWtWgTrUynDsk7F81kaiDpzqcbu9nsGsmNBhY+kFt9Dn1xojGApo/WbrHFkKUGRJUF1ILH1e8nW0Ii0th17lo+jbxLNwOtFro9R4sHwpH/6cmLHMppSlEKkLmbwPDuGpTZwAP/gNuX1G7l7cYlfM9t9rg4KH+CAr7R225FkIIIQrD0PXbuzmkJaotrCG7SzyoztQrHLgUw+pj19lyOoK0rCRjVhYaejbwYESbGnSpW7XsTE91P9eOqM8+bdQb4Ef+B1HBELxOnWGlNBlaqev1ASev0j22EKLMKAd/OcsWrVbDgKbqH80NJ8OKtpPa3aFmZ8hMg12fmrB295ESD/HX1eUK0VLdXH2OClZbjU1BUWB/1njBts+CtX3O9zWa7ED66n7THFMIIUTlYkhS5tUse/ztlb0ldrjQm8nM3XqOzrN2MHrxEdafCCMtQ08DT0feGdiIQ9N78u3oVvRo4FE+AmpQk5MB1GgLtq7Q4QX19a5P1R5npSUjVR0WBtDyqdI7rhCizCknfz3LloFNvQHYfiaK5LSMwu9Ao4Fe76vLJ37ObkEuSYbM3w6e6gWovHP2ATt3tftX1GnT7DNkt9qd3NJWDarzYugCLuOqhRBCFIWh+7dnU6jVWV0OPawGaCZyJy2Ttf9c57HvDtJlzk7m77hIWFwKTjaWjG7vx/oXO7Hp5c6M71QLd4dyNle4otzVUt1WfW7/PNi4QMz57CzcpeHsX+r4eEdvqNOr9I4rhChzJKgugqY1nPFxs+VOeiY7z0YXbSc1WqtjfxQ97PjQtBXMS0XJ/G2g0Zi+C/i+eepzy9H5zzFpaKkOPQyZRbihIoQQovJKvpXda8wzAKo2APuqkHEHbhwv1q4VReGf0NtMX3uSNh//zdTVJzh0+RYaDXSuW4X5j7fgyFu9+HBIEwJqOKMx97zORRV7FZKiQGuV/TvAxgk6TlaXd31aetdnY4KyJ3PmYBFCVDoSVBeBRqNhQIDaWl3kLuAAPd4BjRbOblCTX5WkijSe2sC7hfoc9m/x9xV+Ai7vVBONdHgx/3JVG6p3w9OTIOJE8Y8rhBCi8jC0UrvWUgNBjUYdDgbq1FpF2WXcHRbsukivubt5ZMEBVh65RmJqBr5udrz6UD32/V8Plo9vx+Bm3oVPrloWXcvq+u3VNGfC0LbPgV0VuB2S3SW7JN2+ov5uQKNm/RZCVGoSVBfRwKxx1TvORpGYWsQ7otUaQLMn1OW/31e7NJWUitZSDdnjqk2RAdwwlrrJI+Dql385rfaucdXSBVwIIUQhGIJqr2bZ6wxdwEP2FHg3d9Iy+f3fG4xefJjAT3cwe/M5LkUnYWOl5ZGW1Vn5THt2vdaNl3rWpbqLrQk/QBlwPavrd422OdfrHKDTK+ry7tmQkUaJ+vcn9bl2t/v/bhBCVArSV6WIGns7UauKPSExSWw/E8nDzasXbUfdpsGp1WqSkks7oE5P01bUoCK2VBu6fUWdKd4UV7evwOl16nLg5AeX9wuEcxvVoDrwpaIdUwghROVjyPzt1TR7nSFZ2fUj6vzLVnkHwYqicCTkFmv+uc7GUxE5bui3renGo62q0z/AC0cb88x3XWruzvx9r9bj1Wkx40Ih6CdoPa5k6pCZkR1Ut5IEZUIIaakuMo1GY2ytXn8ivOg7cvGBNs+oy9s/AL3eBLW7x51YSMjqpl61vun3by7ONdSuXvoMiCxGsrIDX6tj2/175vyhk5+7W6pL4t9LCCFExWTI/O15V0u1uz84eqkzghgCxrtcu5XMvL/P03XOLkZ+d4jVx66TmJqBj5stL/esy57Xu7N6YgdGtvGt+AF1WjJE/qcu39tSDWBtB52mqst7PjPd7CD3urgNEsLVhKn1+5fMMYQQ5YoE1cVgmFprz/loYpOL0c2o81SwdlS7MZ/5wzSVu5sh87ejN9i6mH7/5qLR3NUFvIjjqpNisu82d3y5YNt4NgMre0iJze5WL4QQQtxPaiLcvKgu330D9+5x1VlTayWmZrD66DVGLDpI59k7mff3BUJvJeOgs2RE6xqserY9u1/rzpSH6uHrblfKH8SMwv5Vb6Q7eqk31vPSaiw4VYf4G/DPDyVTD0OCsmaPg2U5y54uhCgRElQXQ30PR+pWcyAtU8+gr/ex90IRM4HbV4HArORY2z+EzHTTVRIq5nhqA2MG8CIG1Ue+U7OuereAWl0Kto2FJfi2U5dlXLUQQoiCiPwPUNSA0KFazveyxlXHndnBK7/8S+uPtvHGmpMcCcnO3j1vZHOOvtWL2cOa0a62O1ptOc3eXRzG8dRt1JsRebGygc6vqst7P1dbt00pPhzOb1GXZW5qIUQWCaqLQaPRMGtYU7ydbbh26w6jFx9h6uogbicVodW6wwtqN6JblyBohWkrWhHHUxsYM4AXIRN3WpIaVIPaSl2Y6UWMXcD3F/64QgghKh9j1++cw4wuRiXybaja6moXFcSWoMukpOvxr2rPG33rc2Camr17SIvq2FpXgOzdxWHI/O2TR9fvu7UYDc6+kBgJx5aYtg5BK0DJBN8OULWeafcthCi3JKguppa+rmyd2pWxgTXRaGDtPzfoNXc3fwTdQClMNm+dI3R5XV3e9aka8JlKRW6pNnT/jgpWE7wUxj/L4c5tdWqThoMLt61fR/X56oGSzdouhBCiYrgr83dahp6fD4cy5Jv99Jq7m08P3eG6UgUrTSb/1+g26yYF8vfUrkzqVgcv5wqWvbuoFCW7pdqn3f3LWlpD16zfVPu+ULvem4Jen931W1qphRB3kezfJuCgs+T9wY0Z3NybaWtOcj4ykZd/CWLdvzf4aEgTargWcLxT63Fw8BuIuwafVAf7quDoqXYVy+/ZvgpoH3DnuiK3VDtlnaekaDVZWY3WBdsuMx0Ofq0uB7704HN4L++WYKFT74LfuqwmmhFCCCHyE6EG1ScyfHll3h5CYtSb5xZaDd3qVQWlM1xdx1iva+Dras6alk23Q9RrvYV1zinJ8tPscdg7V93uyHdq/priCtkNsVdB5wyNHi7+/oQQFYYE1SbU0teVDS91ZtHuS3y14yK7zkXT+4s9vNa7Pk8F1sTiQeOfLHXQdyaseUYd55sUpT4MU3DkRWMBDh7g5HVXsH1X4G3tCIkRatmKlPnbQKNRx1Vf3KaOqy5oUH16nXrzwr4qNH+i8Me1slGPdXW/+pCgWgghRH4y0lCizqIBXtiZyXUliSoO1jzXxZ8hLapT1VEHQX3g6jpjsjJxD0PXb69mBUsOZmGlTlu67jk4MB/aTAAbp+LVwdBK3XS4mmlcCCGySFBtYtaWWl7qWZd+AV5MX3uSo1du88GGYP44EcasRwNo4PmAP+gNB8GbYZB8U52uISEi/+ekKHVcT0JY9pRZ+XGqUfyLSVnl3TwrqA4qWHlFgf1fqsvtnst3TtAH8gvMCqoPQMsxRduHEEKICu1WUhor/1jPC/p0YhV7orTVmNipNi909885BVZWsjLCgiAlvuJes4vKmKTsAeOp7xYwXJ1a6+YFOPwtdH2j6MdPuglnN6jLcs0XQtxDguoSUqeaA6ue7cDKo6F8uvEsJ67FMnD+Pp7v5s8L3etgY3Wf7sZaLThUVR/3mzc5M0MNrPMNvrOW79yGgGGm/5BlhSFZWXhQwcpf3K5mYbV2UO9cF5UkKxNCCJGPtAw9Px68wpfbL9Av/SBYQYRdPf6e0C3vabCca6g5Pm6HQOhBqNen9Ctdlhnm8PZpU/BttBZqa/Wa8XDga2j7DNgWsWv9yV/UucS9mhes+7kQolKRoLoEabUanmznR88GHrzzx39sC47kqx0X+etUODOHBtCutnvxDmBhCU7e6uN+9JmFHzNcnhim1Yo6oyYre1DL8/556nOrsUW/uIJ6t1xjAbGhEHsNXHyKvi8hhBAVgqIo7Dgbxcd/neFy1rjpTs5hkAoNmneE+80rXauLGlSH7JGg+m5pSWreFChcSzVA40fUqbWigtW8NT3eLvzxFQWOZ8153UoSlAkhcpPs36XA09mG70a3YuGTLanqqONydBIjvzvEm+tOEZ9i4jmp81KRA2pQbyrYV1O7wkf8d/+y14+r49W0ltD++eIdV+eQnX089GDx9iWEEKLcOx+ZwJglRxj/wzEux6jjpj99JIBBVWPUAoabwPmp1UV9DtlTovUsd278o17jnaqDc/XCbavVQrfp6vKhhZB8q/DHv3YYYs6BlR00qcA9/4QQRSZBdSnRaDT0C/Di76ldebyt2qL58+FQen2+m83/RZi5duWcRpMd3D6oC7ihlTpghNrVrrikC7gQQlR6t5LSeOf3/+j35V72XojB2kLLc11rs/O1bjzWujqayFNqwfsN6QKo2Ul9jjhVtOCvojKOpy5E1++7NRgIngGQlpidU6UwDAnKGj8iY92FEHmSoLqUOdtaMfORpqx8pj21qtgTlZDKxJ+OM3H5cSLjU8xdvfLLcPc/7N/8y9y8BGfWq8sdJ5vmuHfPVy2EEKJSSc/Us2RfCN3m7GT5oatk6hX6NPZg29QuTO/XUE1EdvMSpCerrZzude6/Q0dPqFIPUOS6cjdD5m+fQnb9NtBqoftb6vKR7yAxquDbpsTBf2vVZUlQJoTIhwTVZtLB351NL3fmhe7+WGo1bD4dQa+5u/n5cCh6vWLu6pU/hmRl98sAfmA+oEC9vlDNRHN2+7YHNBBzHhKjTbNPIYQQZZo6bjqSPvP28MGGYOJTMmjo5cTPz7Rj0ejW+LnbZxc2TIvp0bhgw7FqZmUBl6m1VIpStMzf96rXF6q3Um9wFKa1+tRv6jSnVRsUPagXQlR4ElSbkY2VBa/3acD6lzrRrIYzCSkZvLnuFI/97xAbToYRk5hq7iqWH4bu39FnIS059/sJkRC0Ul3u+LLpjmvrqv5QAgiVVgUhRPmwYMECatWqhY2NDa1atWLv3vsHcKmpqbz11lv4+fmh0+nw9/dnyZIlxveXLVuGRqPJ9UhJqXg9sC5EJvDU0qOMW3aMy9FJuNtbM/ORADa81IlA/yq5NzAMSypoxmjD1FohElQDcOuyOs2ohfWDu8/fj0YD3d9Ul49+D/HhBdvun6wEZS3HqPsQQog8SPbvMqChlxNrJ3Vk2YErfLblHEdCbnEkRB1LVd/DkQ7+7gT6u9OuljvOdlYP2Fsl5eilJitLilKny7r3bvLhbyEzVb3L7dvBtMf2C1SPefUANHrYtPsWQggTW7VqFa+88goLFiygY8eOLFq0iH79+hEcHIyvr2+e24wYMYLIyEgWL15MnTp1iIqKIiMjI0cZJycnzp07l2OdjY1NiX2O0nY7KY15f5/np8OhZOoVrCw0jOtYixd61MHJ5j7X5vCslmrPAgaEhpbqqNOQFAP2eQTqlcn1rK7fXs3BUle8ffn3BJ92auKxfXOh/5z7lw8LgvATakDf9LHiHVsIUaFJUF1GWGg1jO9Ui96NPFh24Ar7L8ZwNiKBc5HqY9mBK2g00MTbmUB/d9r7u9O2phv2OvknBLKSlbWAC1vUi+DdQXVqAhxdrC53fNn0d5r9AtUxWpKsTAhRDsydO5fx48czYcIEAObNm8eWLVtYuHAhM2fOzFV+8+bN7N69m8uXL+Pm5gZAzZo1c5XTaDR4enqWaN3N5eT1WEYvPkLcHXXGjt6NPHizf0NqVrG//4aKkt39u6CtrPZVoFpjNai+shcaDy1GzSsA4/zUJuh6rdGoY6t/HAzHl6m/Ce6XtNSQoKzhILAv5jSoQogKTSKyMsbHzY53BjYC4GZiKodDbnHgUgwHLt3kcnQSp27EcepGHIv2XMZSq6GZjwuB/u50qO1OSz9XbKwq+PRZ9+PdPCuovidZ2fFlkBoH7nWhfn/TH9c3KwN4xH9wJxZsXUx/DCGEMIG0tDSOHz/OtGnTcqzv3bs3Bw7kPYTlzz//pHXr1syePZvly5djb2/P4MGD+fDDD7G1tTWWS0xMxM/Pj8zMTJo3b86HH35IixYt8q1LamoqqanZw5zi4+OL+elKzpwt54i7k059D0feG9SIwDoFbD2OuwZ3bqvTOFZrVPAD1uqsBtUhElQXO/P3vWp3VXsDXNkLez6DQfPyLpeWBKd+VZclQZkQ4gEkqC7D3B109A/won+AFwCR8SkcvHTTGGRfv32H41dvc/zqbb7acRFrSy2tfF3VINvfnWY+LlhZVKJh84ZkZXdPq5WRBgcXqMsdJ6sZQE3N0UPN6HrzotqlrF4f0x9DCCFMICYmhszMTDw8PHKs9/DwICIi7+kdL1++zL59+7CxsWHdunXExMQwadIkbt26ZRxX3aBBA5YtW0ZAQADx8fF8+eWXdOzYkRMnTlC3bt089ztz5kxmzJhh2g9YAi5GJbD3QgxaDXz/VGt83OwKvrGh63fVhoXrulyzszpsqbInK0tNhMjT6rIpk4R1fxOW9oN/l0OnV8C1Zu4ywX9Aarz6Xs0upju2EKJCkqC6HPFwsmFIi+oMaVEdgGu3knME2VEJqRy8fJODl2/CNrCztqBNTTcC/d3pH+BVuB8C5ZFhWi1DsjJrO/Uuc0IYOHhC05Eld2y/QDWovrpfgmohRJmnuWcYjKIoudYZ6PV6NBoNK1aswNnZGVC7kA8bNoxvvvkGW1tb2rdvT/v27Y3bdOzYkZYtW/LVV18xf/78PPc7ffp0pk6danwdHx+Pj49PcT+ayS3dfwWAXg09Cn8dLWzXb4OaHTHOLJEQoU61VRmF/QOKHpxqgJO36fbrFwi1u8PlnbB7Dgz5JneZ41kJylqMLpkb8kKICkWC6nLMx80OHzc7RrTxQVEULsckceDSTQ5eiuHQ5VvcSkpj9/lodp+PZtbms/Rr4sWzXWrTzMfF3FUvGU5e4OABiZEQcUrtKnYg68dc++eLn+Dkfvw6qmOvZF5RIUQZVqVKFSwsLHK1SkdFReVqvTbw8vKievXqxoAaoGHDhiiKwvXr1/NsidZqtbRp04YLFy7kWxedTodOV4J/l00gNjmNNf9cB+DpjrUKv4PwE+pzQTN/G9i6qoF4+Am4sg8ChhX+2BXBtcPqs4+Jun7frcfbalB9YiV0ngru/tnvRZ+Da4dAYwHNnzT9sYUQFY7ceqsgNBoN/lUdGN3ejwVPtuLYW73Y9HJn3hnYiI513NEr8NepcB7+Zj8jFh1k+5nIijkf9t1dwC9sUVutdU7Q+umSPa5f1rjqsH/VcVhCCFEGWVtb06pVK7Zt25Zj/bZt2wgMDMxzm44dOxIWFkZiYqJx3fnz59FqtdSokXeSJ0VRCAoKwsvLy3SVN4Nfjl4jJV1PA09H2td2K/wOCpv5+26GLOAhuwu/bUVxLSvzd3Hmp85PjdZQtw8ombB7Vs73DAnK6vVRb9gLIcQDSFBdQWm1Ghp6OTG+Uy1WTGjP5lc680jL6lhqNRwJucX4H47Re94eVh0NJTUj09zVNR1DF/CwINj/pbrcehzYOOe3hWm4+IKzD+gzsqf/EEKIMmjq1Kl8//33LFmyhDNnzjBlyhRCQ0OZOHEioHbLHjMmOzHTE088gbu7O08//TTBwcHs2bOH119/nXHjxhkTlc2YMYMtW7Zw+fJlgoKCGD9+PEFBQcZ9lkcZmXp+PHAFgHEda+XbPT5fSTHq8CM04Nmk8BWolTWOt7LOV60o2ddTU46nvpth3uqTq9XWaYCMVAj6WV1u+VTJHFcIUeFIUF1JNPB0Yu6I5uz9v+4816U2jjpLLkYl8n9rTtFp1k6+2XmRuOR0c1ez+Awt1Wc3QOhBdW7J9s+XzrENrdXSBVwIUYaNHDmSefPm8cEHH9C8eXP27NnDxo0b8fPzAyA8PJzQ0FBjeQcHB7Zt20ZsbCytW7fmySefZNCgQTnGSsfGxvLss8/SsGFDevfuzY0bN9izZw9t25ZQMFQKtgZHEhaXgpu9NYObF2E8r6Hrt7s/6BwLv71vB7X78e0QiLte+O3Lu5uX4M4tsNAVraW/ILybQ4OBgAK7PlXXnf1LPa6jN9TpVTLHFUJUODKmupLxcrZlev+GvNijDr8cucaS/SGEx6UwZ8s5vtl5kRGtfRjfqVb5TWrm3Vx9Ts2amqXZY6WX4MUvEE6ukqBaCFHmTZo0iUmTJuX53rJly3Kta9CgQa4u43f74osv+OKLL0xVvTJh6f4QAJ5s51u06SoNQXVRA0IbJ/WaduO42lrd/PGi7ae8Mkyl5d0cLK1L7jjdpqs34k+vhS6vwT+GBGVPgoX8TBZCFIy0VFdSjjZWPNOlNnve6M4XI5vR0MuJ5LRMlh24Qtc5O3nx5384dT3O3NUsPEdPNdM3ABoInFx6x/brqD5fP6p2HxNCCFEu/XcjjqNXbmOp1TCqvV/RdlLUzN93M4yrroxTa13LCqpLquu3gWeT7LnA178Cl3cBGjXrtxBCFJAE1ZWclYWWoS1qsHFyJ5aPb0vnulXQK7DhZDiDvt7H498dYufZKBSlHCU1M3QBbzAAquQ9P2qJcK8D9lUhI0VNWCaEEKJcWpLVSj2gqRceTjZF24khSVlhM3/fzTiueo86xrgyuV6CScru1W06oMluHa/dDVyLeDNFCFEpSVAtADV7eOe6VVk+vh0bJ3dmaAs1qdnByzd5etlR+szbw6/HrpWPpGadpqhjpHp/WLrH1WjuGle9v3SPLYQQwiSiE1LZcCIcKOI0WgAp8XDrkrrsWYyg2rc9aK0g7hrcvlL0/ZQ3qQkQFawul3RLNUDV+hAwPPt1K0lQJoQoHAmqRS6NvJ34YmRz9rzRnWc618JBZ8n5yERe/+0knWftZOGuS2U7qZlvO3hsBbjVLv1jG7qAy7hqIYQol1Ycvkpapp4Wvi4093Ep2k4i/1OfnaqDvXvRK2NtD9VbqcuVqQv4jeOg6MHZt/TyonSbBpa24FQD6vcvnWMKISoMCapFvrxdbHlrQCMOTO/B9H4N8HDSEZWQyqzNZ2n98TaeWnKEnw+HEp0g44eNDC3VoYchM8O8dRFCCFEoqRmZ/HToKlCMVmowTddvg1qG+aorUVBtmJ/ap03pHdPdHyYdhGe2g6Wu9I4rhKgQJKgWD+RkY8VzXf3Z+0YPPh+uJjVLz1TYfT6aN9edou0nfzNs4QG+33uZa7eSzV1d86rWSJ0TOy0BIk+VzjH1evj5Mfi2M6Qllc4xhRCiAtpwIpyYxDQ8nWzo16QYLaTFzfx9t7uTlVWWcdWGsc2lMZ76bm61Sq9lXAhRoRQpqF6wYAG1atXCxsaGVq1asXfv/e+erlixgmbNmmFnZ4eXlxdPP/00N2/eLFKFhflYW2p5tFUNNr3cme2vduWNvvVpVsMZRYFjV2/z0V9n6Dx7J/2+3Mu8v89zNiK+fCU4MwWthTq3KJReF/CTv8D5TWqm2Us7S+eYQghRwSiKwtIDaoKy0R38sLIoRruDKTJ/G/i0BQtrSAhX526u6BQlO0lZabZUCyFEMRT6irFq1SpeeeUV3nrrLf799186d+5Mv379CA0NzbP8vn37GDNmDOPHj+f06dP8+uuvHD16lAkTJhS78sJ8/Ks6MKlbHf54sRMHp/dgxuDGBPq7Y6HVcCY8nnl/X6DvvL10+2wXn2w8w/Grt9DrK0mAbUxWVgpBdWoi/D0j+/WFrSV/TCGEqICOXb3Nfzfi0Vlqebytb9F3lJ4C0WfVZVO0VFvZgk87dfnKnuLvr6y7eRHu3AZLG/AIMHdthBCiQAodVM+dO5fx48czYcIEGjZsyLx58/Dx8WHhwoV5lj906BA1a9Zk8uTJ1KpVi06dOvHcc89x7NixYldelA1ezrY8FViTn59pz7G3ejFnWFN6NfRAZ6nl6s1kvttzmUcXHqT9zO28te4Ue85Hk5ahN3e1S87dycr0Jfw5938JiRFqchWAC9sqT/dAIYQwoaVZ02gNbVEdN3vrou8oKhj0GWDrBs41TFM5QxfwkEoQVBvmp/ZuAZbF+HcQQohSVKigOi0tjePHj9O7d+8c63v37s2BA3m3ygUGBnL9+nU2btyIoihERkby22+/MWDAgHyPk5qaSnx8fI6HKB9c7a0Z3tqH759qzT/vPMSCJ1vycHNvHHWWRCWksuJwKGOWHKHVR9uYsiqIzf+Fk5xWwRJ6eTUDKzu4cwtizpXccWKvwYH56vLg+WpgnRCWPQ2JEEKIArkRe4ctpyMBGNuxZvF2dnfXb42mePsyMCQru7Kv4t84NY6nlq7fQojyw7IwhWNiYsjMzMTDwyPHeg8PDyIiIvLcJjAwkBUrVjBy5EhSUlLIyMhg8ODBfPXVV/keZ+bMmcyYMSPf90X5YK+zpH+AF/0DvEjL0HPgUgxbTkeyLTiSmMRU1v17g3X/3sDGSktzHxdsrCyw1Gqx1GqwsNBgpdVgodViZaHBQqvBUqvB0kKb9ay+Z1i2vKesrZUFDb2cqOfhiIXWRD9qCsrCSh0Dd3mXOl91tYYlc5ztMyAjRW0ZDxgOp36DC1vULuAejUvmmEIIUQH9ePAKmXqFQH93Gng6FW9nhszfpuj6bVC9lXrjNCla7VpeUteVssCY+buUk5QJIUQxFCqoNtDcc+dVUZRc6wyCg4OZPHky7777Ln369CE8PJzXX3+diRMnsnjx4jy3mT59OlOnTjW+jo+Px8fHpyhVFWWEtaWWbvWr0a1+NT4a0oR/Q2+z+b8ItgRHcO3WHQ5dvlUix7WztiCgujPNfV1oXsOF5r4ueDnblsixcvDrmBVUH4A2JZA/4NpROPUroIE+n6itIXUfygqqt0GnKaY/phBCVEDJaRn8cuQaUMxptAwMmb9NMZ2WgaUOfNup15WQvRU3qE6Jy+5tVdqZv4UQohgKFVRXqVIFCwuLXK3SUVFRuVqvDWbOnEnHjh15/fXXAWjatCn29vZ07tyZjz76CC8vr1zb6HQ6dDqZI7CistBqaF3TjdY13XhrQEPOhCdwPjKB9Ew9mXqFDL1CRqZefdYrZOqVvN/LNLyvNy5nZC3H3UnnvxtxJKVlcjjkFodDsoN2DycdzbIC7OY+LjSt4YKDrkj3l/J3d7IyRTFdF0BQ97d5mrrc/Enwbq4u131IfQ49pP4wsXE23TGFEKKCWvfvDeLupOPrZkePBtWKtzN9JkSeVpdNGVSDOq768i41WVm7Z02777LixnFAARdfcMz7d6UQQpRFhYokrK2tadWqFdu2bWPo0KHG9du2bePhhx/Oc5vk5GQsLXMexsLCAqDyTbckctFoNDTydqKRdzG72+UhU69wKTqRoNBYgq7HEhQay7nIBCLjU9kaHMnW4MisOkDdag4093GhmY8aaNf3cMSyONOpVG+VPQXK7RBwq22iT4XazfvGMbCyh57vZK93rQlV6kHMeXVqrcZDTHdMIYSogBRFYen+KwA8FViz+MOFYi5Axh3177Obf/EreLdaXYEP1XHVej1oi3GNKqsMXb+llVoIUc4Uunlu6tSpjB49mtatW9OhQwe+++47QkNDmThxIqB23b5x4wY//vgjAIMGDeKZZ55h4cKFxu7fr7zyCm3btsXb29u0n0aIu1hoNdTzcKSehyMj2qjDB5LTMjgdFq8G2tfUx43YO5yPTOR8ZCKrj10HwMZKq3Yb93GhuY8rzXycqe5im+8wh1ysbNXAOvSg2lptqqA6LRn+fk9d7jwFHD1zvl+3txpUX9gmQbUQQjzA3gsxXIxKxN7aguGtTZCp25CkzDPA9EGvd3OwdlCnm4r8zzRzYJc1hiRlMp5aCFHOFDqoHjlyJDdv3uSDDz4gPDycJk2asHHjRvz8/AAIDw/PMWf12LFjSUhI4Ouvv+bVV1/FxcWFHj16MGvWLNN9CiEKyM7akjY13WhT0824LiohhRPX4jiRFWSfuBZLQmoGR6/c5uiV24A6zUoDT0ceaVmdIc2rU83J5sEH8wvMDqpbjDLNBzj4NcTfAGcf6PBi7vfr9FLLXNxm+m7nQghRwRim0Rre2gcnG6vi79A4nroEAl4LK/DtoP59v7K34gXVej1cN7RUS+ZvIUT5olHKQR/s+Ph4nJ2diYuLw8nJ9N2EhbibXq9wOSbJGGAHXYvlTHg8GXr1q6LVQJd6VXmkZQ16N/LAxsoi7x1d+BtWPKp2y375RPErFh8GX7WC9GR4dDEEDMtdJiMVZtWC9CR4bo/px/QJIYzk2mRapX0+L0cn0uPz3Wg0sOPVbtSqYl/8nS4bqAa8g7+GlqOLv7977f8Str0L9frBE7+Yfv/mFH0OvmmrZjmffk29iSCEEGZW0GuTibMzCVH+abUa6lRzoE41B4a1UrsDxiWns+FUGGuOX+ef0Fh2nYtm17loHG0sGdjUi0da1qC1n2vO7uE+bUGjhdtXIO4GOFcvXsW2f6gG1DXaQpNH8y5jqYPa3eDcX2oXcAmqhRCVjaLAtcNwbCn0+xRsXfMs9sOBKwD0qF/NNAG1otw1R3UJ/e2tmTVf9dUDalI0bT43dcuja1ldv71bSEAthCh3KmCWCyFMz9nOiifb+bF2Ukd2vtaNl3rUobqLLQkpGaw8co3h3x6k65xdzPv7PNduJasb2Thlz1MaerB4FbjxD5z4WV3u++n9u3XX7aU+X9hWvGMKIUR5tWEqnPwFgn7O8+34lHR+O67m0DDJNFoAsVfVmRe0VlC1gWn2eS+vZqBzhtS47K7mFYVxPLV0/RZClD8SVAtRSLWq2PNq7/rsfaM7K59pz7BWNbC3tiD0VjLz/r5A59k7GfHtQVYdDSWtRnt1o6v7i35ARYHN09XlpiOhRqv7l6+TNbXW9SOQXDLzfwshRJml0UDbCery0e/Vsbr3WH30GklpmdTzcKBjHXfTHDc8q5W6WkOwtDbNPu+ltYCaHdXlkD0lcwxzkczfQohyTIJqIYpIq9XQwd+dz4Y34+jbvfhiZDM61amCRgNHrtzi/9ac4pVDapfCpAt7yNQXMX1B8O9w7ZA6zqznew8u7+ID1RqBoof/b+++46Oq0sePf2YmmQmplJBGCaElhE7oGCxoKKJiAyyAdVdRV2BdhWX3K7q/XdRVZHWFXZViW2RXhGUFgShdihAC0gk1lIQQWhqZlLm/P04mhfRkWibP+/XK697M3HIul+TmmXPO85zcULdzCiFEQ9Z9LJj84crJcr8HCy0an20/DcATgyNqXtWhOvYe+m1lHQJ+eot9z+NIudfh0hG1Lpm/hRANkATVQtiAt9GD+3u35stnBrBt+h28OiKSDi192J7fCQCf68cZ+Zdvmb36MEdTM2t+4PxclZQGYMjLNZ+X3VGGgAshGjGTL/R8RK3vWlDmrR8PX+TslRs09fbk/t71zHVRWnHmbzsH1RHWedXboTDfvudylHO7AU0l9vQNcnZrhBCi1iSoFsLGQgOaMPm2jvww7VYWvzCSi15qvl5Ezn7+ufkkw+duZvSHW3g//hhrDqSSfDkHS2W92DvmwbVk8AuDIb+peSM6xallUnyFQx+FEMLt9XtaLY99D9fOFr+86KfTAIzv15YmRhsm+rIO/w6xc6mroK7QpLmq8nAh0b7ncpRzMvRbCNGwSVAthJ3odDp6tmlKcLc7AJgRfYW7ooPx0Os4cD6Dv/2YxHNfJjD0rxvo8cY6Hpy/jZnL9/PljjMknLlK9uXzsOU9dbA7Z4GxFtlp2w4Eox/kpEPKXptfmxCiYZs3bx4RERF4eXkRExPDli1VDyU2m83MnDmT8PBwTCYTHTp0YOHChWW2WbZsGdHR0ZhMJqKjo1m+fLk9L6F6LSMhYqiaCpOwCIDDKRlsP3kZg17HxEHhtjtX5kXISgV0ENzVdsetiF7vfvOqrZm/Zei3EKKBkqBaCHsLHwxAu+y9fDKxLz/PvJM/jenGg31a0zXMH6NBT5a5gIQzV/lqZzJ/WHGAB+dvY+X7L0BeFqdMkcxN61l9r3ZpBk/ocJtalyHgQohSli5dypQpU5g5cyaJiYnExsYycuRIkpOTK91n7Nix/PjjjyxYsICjR4+yZMkSoqJKMlxv376dcePGMWHCBPbt28eECRMYO3YsO3fudMQlVa5fUcKyhM+gwMyin04BMKJbCGFNm9juPNb51IGd1NBze2s3VC3dYV61xVI0/BtoLZm/hRANk07TtDpmT3KcmhbdFsIlZVyAOV1UzerXToNXQJm38wstnErP5nBKBodTMjmckkHhhX18nv879DqNB8yz2KN1Lt7ex2ggKtSfqBA/uoT60yXUj8gQf3xNN5Wd3/M5rHxJ/ZHyzA8OuFAhGpeG+mwaMGAAffr0Yf78+cWvdenShTFjxjB79uxy269Zs4bx48dz8uRJmjdvXuExx40bR0ZGBt9//33xayNGjKBZs2YsWbKkwn3MZjNms7n4+4yMDNq0aWPbf8/CApjbDTJTyLx7PjH/bUZegYVlzw8iJrzia6mTze/C+j9Bt4fgoQXVb19faUdg3gDw8ILpyeBhsv857cV6LZ7eMP0sGDyq30cIIRykps96+c0lhL35h0GzCLh6Sg1x63RXmbc9DXo6B/vROdiP+3qhSmh99iac1rgUfjejOt5HREomR1IzSLqYRXZeIQlnrpJw5mqZ4wT5mfDz8sDXyxM/kwetPYJ4C9DO7Wb+6p/x9A3E18tDbWNSSz8vT3xNHvh6eeBr9ECvt1EWXCGES8rLyyMhIYHp06eXeT0uLo5t27ZVuM/KlSvp27cv77zzDl988QU+Pj7ce++9/OlPf6JJE9Xbu337dqZOnVpmv+HDhzN37txK2zJ79mzeeOON+l1QdQweEPMkbPwLWVvmk1cwnR6tA+jTtpltz1Oc+dvO86mtWkaCTxBkp6leXutw8IbobNFohrA+ElALIRos+e0lhCOED1FB9ZmfygXV5RxZpYb0GUy0vP8tnmnatvitinq1j6RmcDHDTFqm+iptojGcaP0Zjm5dzn8tt1TbTF9TScAdEuDFK3GR9GzTtC5XLIRwQenp6RQWFhIcHFzm9eDgYFJTUyvc5+TJk2zduhUvLy+WL19Oeno6kydP5sqVK8XzqlNTU2t1TIAZM2Ywbdq04u+tPdU2FzMJbfM7hGb8QrTuNE8Ouc92ZbSsHJX520qng3a3wMFv1fOiIQfV56zzqWXotxCi4ZKgWghHCB8Me7+EMxX3BBUrMMO6P6j1wS9CqYAaKujVLnIlO48L126QmVtAlrmALHM+WbkF3Dh0B5xdxFPBx9EFjyXLXEBmbkGp7QrIzM0nv1DNArG+lpoBSWlZbD9xmVeGR/Kr2PbSiy2EG7k5qNQ0rdJA02KxoNPp+OqrrwgIUNNX5syZw0MPPcRHH31U3Ftdm2MCmEwmTCYHDFv2C+FC6J20Or+GZ73Wc3f35217/NzrcPW0Wrd35u/SImJVUH1qC9w2vfrtXdVZyfwthGj4JKgWwhGKkpVxfg/k5YDRu+Ltfv5Y9Wj7BsMtUyvepgLNfYw09zGWfyNsLCxaRM/c3cwd2wP05cvHaJqGucCiAupca9Cdzxc7zvD9gVTe+v4Im49dYs7YXoQEeNW4TUII1xMYGIjBYCjXg5yWllaup9kqNDSUVq1aFQfUoOZga5rGuXPn6NSpEyEhIbU6pqN9lHU7f2ENo3Vb8czPAI+mtjt46n61DGgD3jacp10da7Kycz9D/g3wtGHiNUe5cRXSj6p1SVImhGjAJPu3EI7QrJ2qNW3Jh/O7K94mOx02vaPWh/0fmPzqf97W/cEUADeuqIC+AjqdDi9PA4G+JtoF+tC9dQCDOwYy77E+vPVAd5p4Gth24jIj/7aZdQcrH8ophHB9RqORmJgY4uPLVgWIj49n8ODBFe4zZMgQLly4QFZWVvFrx44dQ6/X07p1awAGDRpU7pjr1q2r9JiOlJh8lX9dbM0xrTWellzYV3HitDqz1qd21NBvqxYdwC8UCvNKSlI1NOcS1LJZBPi2dG5bhBCiHiSoFsIRdLqS3urKhoBv+AuYM9TwwZ6P2ua8Bg/ocLtaP1670lo6nY7x/dvyv5duoWuYP1dz8vnVFwn8ccUBcvMLbdM+IYTDTZs2jU8//ZSFCxdy+PBhpk6dSnJyMs899xyg5jpPnDixePtHH32UFi1a8OSTT3Lo0CE2b97M7373O5566qniod8vv/wy69at4+233+bIkSO8/fbb/PDDD0yZMsUZl1jGop9OAzp+CR2rXtj1qUoIaSvW+dSOHPoN6rkSUdRb3VDrVZ+T+tRCCPcgQbUQjlIcVP9U/r2LhyBhkVofMRv0NvzR7BSnlknr6rR7xyBfvp08mGdjIwD4YscZ7vlwK4dTMmzVQiGEA40bN465c+fy5ptv0qtXLzZv3szq1asJDw8HICUlpUzNal9fX+Lj47l27Rp9+/blscce45577uGDDz4o3mbw4MF8/fXXLFq0iB49erB48WKWLl3KgAEDHH59paVez2X1/hQAooc/A0Y/uHwcTm604UkcnPm7tHaxatlQ61Vbe9hl6LcQooGTOdVCOEp4UXbWs7ugIA88iuZAaxqs/T1oFuhyj8roaksd71TLC4mQlQa+QbU+hMnDwMy7o4nt1JJp/95HUloW9330E78fGcWkwe1sn0lXCGFXkydPZvLkyRW+t3jx4nKvRUVFlRvefbOHHnqIhx56yBbNs5kvdpymwKLRP6I50RGtoOd42PWJ6q22juKpj/wbcKloTrCjh3+DSlYGcD4BzFlg8nV8G+rKYlHtBumpFkI0eNJTLYSjtIwE7xZQcANS9pa8nrQOTm4AgxHuetP25/ULLvlj7/iP9TrU0M4tWTMlljuigsgrsDDrf4d4+rPdXM4yV7+zEEI4UG5+If/aqXrcnxrSTr3Y72m1PLoarp+r/0kuHgKtELwD1fxmR2vWDgLagqUAzu5w/Pnr49IRNeXJ0weCujq7NUIIUS8SVAvhKDodtB2k1q1DwAvzYe1MtT7weWje3j7nrucQ8NICfU0smNSXWfdEY/TQs/5IGiP+toXNxy7V+9hCCGEr/917nqs5+bRq2oS7okPUi0Fd1JBpzQIJi+t/klRrfeoe6ne8M1h7q081sCHg1vnUrfqo/B9CCNGASVAthCNZh4Bbk5XtWgCXk1QvR+wr9juvNag+sR4KC+p9OJ1OxxNDIvjvC0PoFOTLpUwzExf+zJ9XHSKvwFLv4wshRH1omlaUoAwmDQ7HoC8V8Fp7qxM+U1Nx6sNZmb9LsyYra2jzqovrU8t8aiFEwydBtRCOZE1WlrxDldDaOFt9f8cfwMvffudtFQNNmkHutcpLetVBl1B//vfSLUwYqBIcfbLlFA/M/4kTl7Kq2VMIIexn+8nLHEnNpImngXF925Z9M2o0+IZAdhocXlm/Ezkr83dp1mRlFxIh97rz2lFbkvlbCOFGJKgWwpFCuqvss+YM+M8TKsgN6gp9Jla3Z/3oDdBhmFq3wRDw0rw8DfxpTDc+mdiXZt6eHDifwegPtrJ0VzKaLcvWCCFEDVl7qR+MaUWAt2fZNw2eEPOEWt+1oO4nKSyAtENq3Zk91QGt1NQhzQJntjuvHbWRcwXSj6l16akWQrgBCaqFcCS9AdoOVOvWoXoj/qJet7fiedW1q1ddU3dFB7NmylCGdGzBjfxCXlu2nxf+tYfrOfl2OZ8QQlRE0zQ6BvkS0MSTJwZHVLxRzCTQGSB5G1w8WLcTpR+Dglz1QWmzSs7jKA2ttJY163fz9uAT6Ny2CCGEDUhQLYSjWYeAA3QeCe1vc8x5Ow4DdKqmakaKXU4R7O/FF08NYPrIKDz0OlbvT2Xk3zbz86krdjmfEELcTKfT8dqIKH6eOYyOQZWUmPIPgy6j1fquT+t2ouKh391B7+Q/p6zzqk9tdm47aqq4PrUM/RZCuAcJqoVwNGsdar0HxP0/x53XJ1BlWQU4/oPdTqPX63ju1g4se34w7Vp4c+F6LuM/3s57646Scv0GFosMCRdC2J/Jo5oRQP2eUct9S+s2FznVmqTMifOpraw91an7IasBVGI4u1Mt28jQbyGEe5AaBkI4Wut+Kphu1g4COzr23B3vUsPujsdDnwl2PVXPNk1Z9ZtYZq08yH8SzvHh+uN8uP44Xp56wpv70C7Qm3YtfGgX6EN4C28iAn0I9vNCr3dSWRohROPSLhYCIyH9qAqsB/yqdvtbM387M0mZlV+wmtedsg+OrbH77/d6sRSWDP9uM8C5bRFCCBuRoFoIR9PpYPBLzjl3pzjY9Bac2KBqZBs8q9+nHnxMHvz14Z4M7dySv/2YxKn0bHLzLRy9mMnRi5nltrcG3NYgO7xFSfAd4i8BtxDChnQ61Vv9/e/UEPD+z9a81rTFUqqn2olJykqLGq2C6iOrXDuoTjsMeVlg9IWgaGe3RgghbEKCaiEak7DeqiZ2Trqa09ZuiENOe0/PMO7pGUZ+oYXzV29w6nI2Z9KzOX05h9OXszmdns3ZqzeqDLhNHnrCW5Tt3W7iaaDAolFQqFFosZBfqFFo0Ypes6ilxVJqG438QkvRsmgfi0ZhodrO5Gmgpa+Jln4mWvqaCPQz0tLXi0A/Iy18TBg9ZMaMEG6l53j4YZbqrT69pWRucnWunVZVHAwmaBlpzxbWXNRo2PBnOLEezFlgqmQ+ubNZS2m16uOYJJ1CCOEAElQL0Zjo9Sph2S9LVWktBwXVVp4GPe0CVVDMTX+HWgNua5BtDbjPXM7h7JUczAUWjl3M4thF59XAbubtSWBR0G1dll4P9DXS0s9ECx8TBulVF8L1eflDz3GweyH8/EnNg2rr0O+gLnYf8VNjQV1UFvKrp+DEjxB9n7NbVLGzu9RSkpQJIdyIBNVCNDad4oqC6ni46w1nt6ZYdQH3hWs3OJWuguxT6dkkX8khv9CCp0GPQa/D06DDoNfjqddh0OvwMOjx0OvwMOiKlup7ta3ax0Nf9r0b+YVcyjRzKdNMepaZS1lm0jPzSM8yU2DRuJqTz9WcfJLSqg7s9Tpo7mMk2N+L9i19aR/oQ4cgtWzf0gdvo/zqFcJl9HtGBdVHVkHGBZUZvDquNvQb1ND1qLth+9/VtbhqUG3tqW4jQbUQwn3IX3ZCNDYd7gCdHtIOwvXzENDK2S2qlqdBT3gLNcfaGSwWjWs38lWgbQ24M1XQXRKE53Ep08yVbDMWDdKz8kjPyuPghYxyxwsL8CoOsjsE+dKhpS/tW6p547qazukUQthGcFdoO1jVrE5YDLf/vvp9rOW0XCHzd2lRo1VQfWyNQ/Jm1FrOFbh8XK23lszfQgj3IUG1EI2Nd3No1Vf1FhyPh5gnnN0il6fX62juY6S5j5HOwX5Vblto0biSrQLsC9ducDI9ixNp2Wp5KZsr2XlcuJ7Lheu5bElKL7Ovj9FAREsfFWQH+tIhyIf2gSrg9vJ0vbmHFotGZm4BV3PyuJKTx9XsPLyNHrQL9JZM7qJh6f9MSVA99HfVB6PFmb9dqKcaVO+vT0vIvgSnt0KH253dorLOFQ39btFRPYuEEMJNSFAtRGPUKU4F1UkSVNuaQa8rnmsdHeYPBJd5/2p2XnGgfaJUwJ18OYfsvEIOnM/gwPmyvds6HYQFNCEi0IeAJp54Gw34mDzKLo0eeJvU9z7Gkvd8jAa8TR54exqqDHI1TSPTXMDV7Dw1zD07jyvZeSpgLlpezc4vDp6v5qjtCovqjntQwFuen3JAa8EjBQ+XLZ0W6KMSzLXwISLQhyA/kwTcwrVE3QM+QZB1EY58B13vr3zbzFTITlMjfoK7Oq6NNaE3QORI2PO5GgLuakH12aKh3zKfWgjhZiSoFqIx6nQXbPh/cHIjFOSBh9HZLWo0mvkYifFpTkx42V6a/EILyVdyOJGWxcn0bE6kZXHikurdvn4jn/Rr1+mcsY01lh4UUrde6yaeBnxMBryLgu4mRgM55sLiQLmgKECuLR+jgQdNCTyUvxmA1ZbBHM1vVWXptHYtVAb3doE+RBQN7Y8I9CHY31TnIfAFhRayzYVk5RWQbS4gy1xAVm7Jera5gOy8Qm6PDCr6wEOIIh5G9QHj5nfg50+rDqqtQ78DO4PR2yHNq5Wo0SVB9ai/1rxMmCMUz6eWod9CCPciQbUQjVFID9Urk50Gyduh/a3OblGj52nQ06Glml9dmqap4eSW/75Iy6R/s7f9c2xr+yw55kKy8wpKlnmFZJuLlqVezzYXYI2Vb+QXciO/EMirtB1NPA009zHSzMeTZt5qyHszb2PRuifNfIw09zaqpY+Rpt6emDwMsOhDOKOOsXrQIc4OeqxM6TSVZK6kdNqR1EyOpFYecLdr4UN4oDfNvY1kF11btrmATGtwbC4gy1xYar0Ac4GlRv/WzbyNElSL8mKegC3vwZmtqpZyUJeKtyse+u1i86mtIm5VNaAzL8CFPdAqxtktUiyFcH6PWpeeaiGEm5GgWojGSK9XvdV7v1KltSSodlk6nY4WBRfhxLcA9Er9hl6Pvgkephrtr2ka5gJLmYA721xITlEg7mP0oJmPZ3HwXKe522lHVCBSxPDLUtrd+TrtAoMqzOR+rnTptFLl085VE3DXlNGgx6doKLyvyUMNgzd54GfywMdkoF2gC/YuCucLaAVRo+Dw/2DXp3D3exVvl2pNUuZi86mtPL2g451waIXqrXaVoDrtEORlgdGv8g8shBCigZKgWojGquOdKqg+/gMM/7OzWyOqsu1DsBSo9Zx0OLhC1datAZ1Oh5enAS9PAy3s1b7dC9UyajRcPQ0XD0DiVzD4xXKbehr0RASqod6VBtzp2cVBd0ZuQUmAbPQoDpR9vazrJfPIrQG00UNvrysV7q7fMyqo3vc13DkLTBUkJnTVzN+lRY0uCaqH/Z+zW6NY51O36qPmfgshhBuRoFqIxqrD7aAzwKUjcC0ZmrZ1dotERbIuqfmRAB2GwYkf4eePaxxU211eNuxbotb7PgUZ52HlS6qNA5+v1R/PZQJuIZwh4lZo0QkuJ6nAuv+zZd+/cVX9vgQI6e749tVUp7tA76F+v6cfh8COzm6RGhUF0Hagc9shhBB2IB/nC9FYNWkGbQao9aR457ZFVG7HPCjIVWXQ7v8HGIxwfjecT3B2y5T934A5A5pFQPvbofvD6v/WtTNwbK2zWydE7eh0qrcaYNcC0G5K3pe6Xy2bhqv/566qSVOIGKrWj3zn1KYAkHGhJKju9pBz2yKEEHYgQbUQjVmnO9VSgmrXlHtdze0EiJ0GvkElWYl//sR57bLSNNi9QK33fUrN1fdsAn0mqdd2/sN5bRNVmjdvHhEREXh5eRETE8OWLVsq3Xbjxo3odLpyX0eOHCneZvHixRVuk5ub64jLsa1ej4CnN1w6DGd+KvteQxj6bRV1t1oeWeXcdoCaaqRZoO0gaNnZ2a0RQgibk6BaiMasU5xantoE+Q3wj193t+tT1Qvcsgt0Hqle6/9rtTywDLLTndc2UJl8U/aBwQS9Hy95vd8zqobvqU0qiZlwKUuXLmXKlCnMnDmTxMREYmNjGTlyJMnJyVXud/ToUVJSUoq/OnXqVOZ9f3//Mu+npKTg5eVlz0uxD68A6DFWrVs/1LIqzvztoknKSoscpZbndqna2s5iscCeL9R6n4nOa4cQQtiRBNVCNGbB3cAvFPJzIHmbs1sjSsvLge3z1PotU1UvMEDrGAjrA4V5sOcz57UPSnqpu94P3qXqbjdtU9JL9vM/Hd8uUaU5c+bw9NNP88wzz9ClSxfmzp1LmzZtmD9/fpX7BQUFERISUvxlMJSdL6/T6cq8HxISYs/LsC/rEPDD/ysbkKYWBdUNoafaP0xNG0GDo6ud147Tm9V0EJM/RI9xXjuEEMKOJKgWojHT6VQWcJAh4K4m8UuV6btpW+j2YNn3+v9KLXcthMICx7cNIOeK6i0H6Pd0+fcHPKeW+75WyZ2ES8jLyyMhIYG4uLgyr8fFxbFtW9UfrPXu3ZvQ0FCGDRvGhg0byr2flZVFeHg4rVu3ZvTo0SQmJlZ5PLPZTEZGRpkvlxHSHdoMVFn3E4o+vMrLgfRjat1Vy2ndzBWGgFsTLXZ/GIxSzk4I4Z7qFFTXZi4WqAfnzJkzCQ8Px2Qy0aFDBxYuXFinBgshbMw6BNyaREY4X0Ee/PQ3tT5kChhuKtTQ9X7wbgEZ5+DY9w5vHqAyfhfkQnB3aN2v/PvhQ9RIiPwcVV5LuIT09HQKCwsJDg4u83pwcDCpqRUPEQ4NDeXjjz9m2bJlfPvtt0RGRjJs2DA2b95cvE1UVBSLFy9m5cqVLFmyBC8vL4YMGUJSUlKlbZk9ezYBAQHFX23atLHNRdqKNfN3wiIozIeLB9W8YJ8g8GsgvfBRo9Xy5CbIdcKHFjlXVG8/yNBvIYRbq3VQXZe5WGPHjuXHH39kwYIFHD16lCVLlhAVFVWvhgshbKT9bar0yuXjcOWks1sjAPb/RwXMvsHQ67Hy73t6lSQD+/ljx7YNihKUFX0w2u8pNeLhZjpdSY/6zx+DpdBx7RPV0t10zzRNK/eaVWRkJM8++yx9+vRh0KBBzJs3j7vvvpt33323eJuBAwfy+OOP07NnT2JjY/n3v/9N586d+fDDDyttw4wZM7h+/Xrx19mzZ21zcbbS5R7waQmZKWr4dGoDSlJm1bKzKhFmyYfjThiNtO9rNVUlpAeE9XL8+YUQwkFqHVTXdi7WmjVr2LRpE6tXr+bOO++kXbt29O/fn8GDB1d6DpceEiaEu/HyVxlZAZJ+cG5bhAo+t76v1ge9oALoivR9qigZ2GZIO+y49oFKQHb5OBj91JDOykh5LZcTGBiIwWAo1yudlpZWrve6KgMHDqyyF1qv19OvX78qtzGZTPj7+5f5cikeplIfXn1SkqSsoQz9tupS1Ft92MGltTStZOi39FILIdxcrYLquszFWrlyJX379uWdd96hVatWdO7cmVdeeYUbN25Ueh6XHxImhLvpdJdaOqMnQ5R15Du4nKQyEPd9qvLtyiQDc3B5rV1FCcp6jAWTX+XbGb2lvJaLMRqNxMTEEB9f9mc9Pj6+yg+7b5aYmEhoaGil72uaxt69e6vcpkGIeUJ9eHV6Cxxbo14LaUA91VAyBDwpHgrMjjvvud2qLJmHV9UfvgkhhBuoVVBdl7lYJ0+eZOvWrRw4cIDly5czd+5cvvnmG1544YVKz+PyQ8KEcDcdi4LqU5shv/IPvISdaRpseU+t9/911QErlAyv3ve1qmntCBkpJUmPKkpQdjMpr+Vypk2bxqeffsrChQs5fPgwU6dOJTk5meeeU8nlZsyYwcSJJT2Lc+fOZcWKFSQlJXHw4EFmzJjBsmXLePHFF4u3eeONN1i7di0nT55k7969PP300+zdu7f4mA1W0zYl5eyyLqplQxr+DapagG8I5GXCqapz4NiUtTpB9Bho0tRx5xVCCCeoU6Ky2szFslgs6HQ6vvrqK/r378+oUaOYM2cOixcvrrS32uWHhAnhboK6gH9rlXjq9FZnt6bxOrFe1X329C7Jnl2VdrHQMgrys2HvEvu3D9RwTq1QZUYO7lr99q5UXqsgD67Jh7Tjxo1j7ty5vPnmm/Tq1YvNmzezevVqwsPDAUhJSSmTJyUvL49XXnmFHj16EBsby9atW1m1ahUPPPBA8TbXrl3jV7/6FV26dCEuLo7z58+zefNm+vfv7/Drs7n+z5SsmwKgWYTz2lIXen2pLOD/c8w5zZlw4Fu1HjPJMecUQggnqlVQXZe5WKGhobRq1YqAgIDi17p06YKmaZw7d64OTRZC2JxOVzIEXEprOc+WOWoZ8wT4tKh+e52uJEPxrk/AYrFb0wBVvithsVqvSS+1lauU19ryHswbqNrRyE2ePJnTp09jNptJSEhg6NChxe8tXryYjRs3Fn//6quvcvz4cW7cuMGVK1fYsmULo0aNKnO8999/nzNnzmA2m0lLS2Pt2rUMGjTIUZdjXxG3QfMOaj2ke8WJ+VxdcVC92v6/J0AF1PnZ0KJjSc4OIYRwY7UKqusyF2vIkCFcuHCBrKys4teOHTuGXq+ndevWdWiyEMIuioPqtWoYsnCs5J1wZivoPWHQi9Vvb9VjPJj8VeKwk+VrB9vUsTWQeUGV84q+r+b7uUJ5rZRfYMu7kJcFBk/ntEE0THo9xE5T69bfkw1Nu1j1eyI7Dc7vtv/5Sicoa4gfQgghRC3Vevh3bediPfroo7Ro0YInn3ySQ4cOsXnzZn73u9/x1FNP0aRJE9tdiRCifiJuVQHd1dNw+YSzW9P4bC3qpe71CAS0qvl+Jl/o9ahat3d5rd1FCcp6P64yI9eUs8trFeTBiufBUgBd7oWuD1S/jxCl9X4cph6EwS85uyV142GETkVJZo/YOQv4xYMqcNd7QM9H7HsuIYRwEbUOqms7F8vX15f4+HiuXbtG3759eeyxx7jnnnv44IMPbHcVQoj6M/lCuyFqXbKAO1bqftULrNPDkCm1379f0RDwY2vhyimbNq3YlZNqzjc6iHmy9vs7s7zWlnfh4gFo0hzufk96zkTdBLQGvcHZrai70qW17DkaydpLHTkSfIPsdx4hhHAhdUpUVpu5WABRUVHEx8eTk5PD2bNnee+996SXWghXZM0CnrTOue1obKx1qaPHQIsOtd8/sCN0GAZoJb3JtrZ7kVp2HAbN65CoyVnltVL2lWRUv/td+SNfNF4d7wSDEa6cgEtH7XOO/NySnAV9JEGZEKLxqFNQLYRwU9bhgae3Ql62c9vSWFw+AQeXq3XrvM26sA6v3vMF5OXUv12l5edC4pdqvW8tEpTdrN/Tji2vVZAHKybLsG8hQJXoa3+bWrfXEPAj30HuNfBvBR3usM85hBDCBUlQLYQoEdgJmraFwjzH1jNtzH76G2gW9YFGSPe6H6fTXdA0XP1Be+AbmzUPgEP/hRtXVNm1zsPrfpymbR1bXss67Nu7Bdw9R4Z9CxFVNATcXkG1tTZ178cb9lB5IYSoJQmqhRAldLqS3moZAm5/GRdgX1F96djf1u9YegP0K6qn+/PHtp0zaR1SHvNE/f9QdlR5rdLDvke9C74t7XcuIRqKyJGADi4kwnUblzW9chJObVbH7/24bY8thBAuToJqIURZ1qD6eLyU1rK37R+pUQFtB0PbgfU/Xu/HwaOJSnyWvKP+xwNIPQBnd6pMvn0mVr99dRxRXqv0sO/o+6CbDPsWAlA5BdoMUOtHv7ftsa1TRDrcoUalCCFEIyJBtRCirHaxYDDBtWT7JbMRkHOlJPlXfXuprbybQ4+H1bqtymtZe6mjRoNfcP2P54jyWpv/WjLse9R7tj++EA2ZdQqGLYeAFxaUfEhmiw/fhBCigZGgWghRltEb2t+q1tf/SXqr7WXnPyE/G0J6qIzatmItr3V4JWSk1O9Y5kz45d9Fx61HgrKb2bO81oW9MuxbiKpYg+rTW203BeN4PGSlqg+yIkfZ5phCCNGASFAthChv2P+B3lP1ZPyy1NmtcT/mzJKyUrHTbJtAK7QHtB2khj4nLK7fsX5ZCnlZENhZjWCwldLltWyZsKwgD/77AmiFMuxbiMq06ABB0ep3xDEb5c5IKEpQ1vMR8DDa5phCCNGASFAthCgvpDvc9ppaX/0qXD/v3Pa4m92LVJbuFh1VqSdb61/UW52wSAWadaFpsGuhWu/7lO0zZ1vLa53caLvyWjLsW4iaseUQ8IwLkFQ04kSGfgshGikJqoUQFRsyFcL6gPk6rHxRhoHbSn6uSlAGcMtU+5SdiboHfEMg66IaBl4XZ3dC2kGV+KznI7ZtH9i+vFbpYd93vyfDvoWoirW01vEfIP9G/Y6191+qLGCbgdAysv5tE0KIBkiCaiFExQwecP8/wcMLTqxXvZ6i/vb9S8099G8F3cfa5xweRuj7pFr/+ZO6HWNXUYKy7g9Ck6Y2aVY5tiqvZc32bR323fV+27RPCHcV2lPVnc/PUaNF6spigcQv1HrMJJs0TQghGiIJqoUQlWvZWc2vBlj7B7hyyrntaegKC2DrXLU++Df2nXsY86SaF392h6rZXBvZ6XBohVrva8MEZTcLHwJBXetfXmvzO6pXXYZ9C1EzOp1thoCf3gJXT4PJX32gJYQQjZQE1UKIqg14HsJvUZmqV0xWPROibg4uVxmvvVvYf+6hX3DJH7m1La+V+KWqnx3WG1r1sX3brHQ6GPBrtV7X8loX9sKWOWpdhn0LUXPWoPro93Uvbbfnc7Xs/hAYfWzTLiGEaIAkqBZCVE2vhzEfgacPJG+DnfOd3aKGyWKBrUXB38DnVQZse7PWg97/jaqLXRMWS8lQf3v2UlvVp7xWmWHfY2TYtxC1ET4EvJpCzmVI3lH7/XOulORskARlQohGToJqIUT1mrWD4X9W6z+8AZeOOrU5DVLSWkg7BEa/klrS9tamv6qDXZBbMu+xOifWq+GcXgHQ7UG7Ng+oX3mt4mHfgaqXWghRcwYPiByp1o+sqv3+v/xbjWgJ6Q6hvWzaNCGEaGgkqBZC1EzME9BhGBSaYflzan6wqBlNg83vqvV+T9sv8dfNdLqS3updn9ZsiOfuogRlPR91TG861K281oXEssO+fQLt1jwh3FbpedW1qfCgabCnqDZ1n0m2L7knhBANjATVQoia0engvr+rHswLe+Cn953XlgIz/PQB7PwnnN2lylS5stNb4PxulUl90AuOPXf3h4qGVydD0rqqt712Fo6tUet9n7J/26xqW16rwHzTsO8x9mydEO6rwzBVNu/aGbh4sOb7nU9QI288vNTvGCGEaOQkqBZC1Jx/GIz8q1rf+Dak/OL4Npgz4auHIf6P8P2rsOBOmN0K/jkU/jdFJc65eLDuiXfswdqj2nsC+AY59tyeTUrmO+6sJmDd85mqN9suVmV+d6TalNfa/Ff1B70M+xaifoze0OEOtV6bLODWXuro+9SHdkII0chJUC2EqJ0eYyFqNFjyYcXzqtfQUbIvw2f3wqlNKnFapzjwaQmWAlU2KmERrHwJ5g+G2a1h4UhYO1Ml6rpyqnbDG23lfAKc3AA6Awx+yfHnh6KEYzrVjkvHKt6mML8kk28/ByQou1lNy2vJsG8hbKu2pbXMmbB/mVrvI7WphRACJKgWQtSWTgej56qyUBcPwKa3HXPe6+dg0Qg19LxJc3jif/DYf+CVJJiyHx7+DIa8rHpZjb4qOEveBtv/Dsuehg96wTvt4YsHYP2fVRmZzIv2b7c1AOwxFpqF2/98FWkWXpKQaNenFW9z5DvIugi+wepDE0erSXmt0sO+u94vw77rYd68eURERODl5UVMTAxbtmypdNuNGzei0+nKfR05Unb++7Jly4iOjsZkMhEdHc3y5cvtfRnCFjqPUDkNUvfD1TPVb39wuSqx2LwDhA+2f/uEEKIBkKBaCFF7vi1VYA2w9X04t9u+57t0DBYMh/Rj4N8KnloLrWLUezqdmpPbdQzc9SY88R1MT4bJO2HMfJVpO6wPGIxw4wqc+FFljV4yHt7rDHO6wtLHVfB7bJ3KbJ6XY5t2px0p6f0ZMsU2x6yr/kUZx/f+S/U03WxXUYKyPhPB4Om4dpVWXXmtTe+UDPse9a7j2+cmli5dypQpU5g5cyaJiYnExsYycuRIkpOTq9zv6NGjpKSkFH916tSp+L3t27czbtw4JkyYwL59+5gwYQJjx45l586d9r4cUV8+LdRIEahZFnDriJY+EyVBmRBCFNFpmjPGQ9ZORkYGAQEBXL9+HX9/f2c3RwhhtexZ2P9vaNERfr3FPtmizyfAlw+pgDiwM0xYDgGta3+cArPqWT+/Rw0hPp9QVBqskl+BPi1VsF7mK7zoq42aq1yd5c/BviWq53d8FUOaHcFigY/6w+UkFZD2L1XW69Ix+Kif6q2asr9u/762Ev9/8NPfoP1tMPG/Ja+f3wOf3ql6qR/+zCV6qRvqs2nAgAH06dOH+fNLas536dKFMWPGMHv27HLbb9y4kdtvv52rV6/StGnTCo85btw4MjIy+P7774tfGzFiBM2aNWPJkiU1aldD/fd0Czvmw5rpEH4LPFlFYH3xEMwfBHoPmHoI/IId10YhhHCCmj6bPBzYJiGEuxn1jspsffk4rP8TjCj/B3m9nNwIXz8GeVmqt/mxb1SvSl14mFTvtrWHG1SPbco+FWCf36Ou4+oZyMuE7Evq63xCxcfzCaok6G6rgu6sNFXHFSB2Wt3abEt6vQqkv38Vfv4E+j1T0su0e6Fadh7h3IAaVLu2fVhSXisoSoZ921BeXh4JCQlMnz69zOtxcXFs27atyn179+5Nbm4u0dHR/OEPf+D2228vfm/79u1MnTq1zPbDhw9n7ty5lR7PbDZjNpfkZMjIyKjFlQibihylgurkbSp3RWW/Z6317juPkIBaCCFKkaBaCFF3TZrBvR/CVw/BjnnqD7OIWNsc++AK+PZZKMxTvZbjvgSTn22ObWXyg3a3qC8rTYPca6oEVUVfxUF3mvo6X8nQd09vFQS2v61sIO9MPR+BH9+E9KMq2Vv729RQ933/Uu/3dUKCsptZy2sd/p8qrzX6fTXs+9JhGfZtA+np6RQWFhIcXDYgCg4OJjU1tcJ9QkND+fjjj4mJicFsNvPFF18wbNgwNm7cyNChQwFITU2t1TEBZs+ezRtvvFHPKxI20SwcQnpA6i+qrF7vx8pvk5+rRt6AJCgTQoibSFAthKifTnepP7D2fKZ6Eydvq3/wu3sRfDcV0FTJlgc+UT3NjqDTqQ8LmjSD0J7l369p0J2fA+hg6KuOaXdNePmrwHrXJ6q3uv1tcGAZ5F6HZu1KSus424DnVFC972vocq+atw8weo5k+7YR3U1zYTVNK/eaVWRkJJGRkcXfDxo0iLNnz/Luu+8WB9W1PSbAjBkzmDatZBRHRkYGbdq0qdV1CBuKGq2C6iPfVRxUH/lOlbvzbwUdhzm+fUII4cIkqBZC1N/wP6tyTdeSVQmrez+o23E0DbbOUb2pADFPqrJJeoPt2lpftQm6DUYI6uLwJlap/7MqqD66WrVxd1GCspgn1RBxV2Atr5V2EP41tmjY9wPqAxZRL4GBgRgMhnI9yGlpaeV6mqsycOBAvvzyy+LvQ0JCan1Mk8mEyeSgD8tE9aLuho1/gRPrIS8bjD5l37cmKOv9uGv9ThZCCBfgIn9BCSEaNJMf3DdPre/5DJLia38Mi0UF5NaAOvYVNfS3of3xZg26Q3u6XkAN0DISIm4FzQL/m6KSthmM6g9lV1G6vFZhngz7tiGj0UhMTAzx8WV/RuPj4xk8uOblkRITEwkNDS3+ftCgQeWOuW7dulodUzhZcFeVF6IgVwXWpV05paaMoINeFfRiCyFEIydBtRDCNiJiYcDzav2/L0LOlZrvW5gP/50MOz5S3w//Cwz7o5RrsZf+v1LLEz+qZfQY1xtW3f1hVQsdioZ91zFBnShn2rRpfPrppyxcuJDDhw8zdepUkpOTee655wA1LHvixInF28+dO5cVK1aQlJTEwYMHmTFjBsuWLePFF18s3ubll19m3bp1vP322xw5coS3336bH374gSlTpjj68kRd6XTQ5R61fvi7su8lFo1K6HC7mn8thBCiDBn+LYSwnWH/B8fjVRbt71+DBz+pfp/8G/CfJ+HY96AzwH0fQa9H7N/WxixyJAS0getn1ff9XCBB2c2M3jDpO8i8AB3vdHZr3Mq4ceO4fPkyb775JikpKXTr1o3Vq1cTHq6CpZSUlDI1q/Py8njllVc4f/48TZo0oWvXrqxatYpRo0YVbzN48GC+/vpr/vCHP/DHP/6RDh06sHTpUgYMGODw6xP1EHU3bP+7SlZWmK9q1hcWwN6ikoB9Jla9vxBCNFJSp1oIYVtnd8HCODW8eOznVc+Dzb0O/xqvyrh4eMHDi1XAJ+xv6/vwwyw1d/n5n2RUQB3Is8m25N/TBVgK4d3OkJMOE1dC+1vh6BpYMk6NHJl22HFJI4UQwgXU9Nkkw7+FELbVph8MmaLWv5sKWZcq3i7zIiy6WwXUpgCYsFwCakcaOBnufAMeWiABtRBC0RtKfg8fWaWW1gRlPR+RgFoIISohQbUQwvZum656QHMuw3dTVEbs0q6cgoXD4eJ+8AmCJ1dBuCQ0cigPE9wyxTWTqQkhnCdqtFoeWQUZKWooOEDvCc5rkxBCuDgJqoUQtudhgvv/AXoPVdv0l3+XvJd6QAXUV0+pTLNPr4WQ7s5rqxBCiBLtbwVPH8g4B9+/qkratRkAQVHObpkQQrgsCaqFEPYR2gNuna7WV/8Orp+H5B2weBRkXVQ92U+vg+btndtOIYQQJTybQMdhav3wSrXsM8l57RFCiAZAgmohhP3cMhXC+oD5OiwZD5+PUcnJ2gxUQ779QpzdQiGEEDezltYCMPpB1zFOa4oQQjQEElQLIezH4KGGgRtMkPoLFNyATnEqKVmTZs5unRBCiIp0uktN3wHo/hAYfZzbHiGEcHESVAsh7KtlJIz4i6pB3fNRGP8vVYNYCCGEa2rSDLo9CJ7e0P9Xzm6NEEK4PA9nN0AI0Qj0ewZ6jAeTr7NbIoQQoibGzIfR70svtRBC1ID0VAshHEMCaiGEaDj0BgmohRCihiSoFkIIIYQQQggh6kiCaiGEEEIIIYQQoo4kqBZCCCGEEEIIIepIgmohhBBCCCGEEKKO6hRUz5s3j4iICLy8vIiJiWHLli012u+nn37Cw8ODXr161eW0QgghhBBCCCGES6l1UL106VKmTJnCzJkzSUxMJDY2lpEjR5KcnFzlftevX2fixIkMGzaszo0VQgghhBBCCCFcSa2D6jlz5vD000/zzDPP0KVLF+bOnUubNm2YP39+lfv9+te/5tFHH2XQoEF1bqwQQgghhBBCCOFKahVU5+XlkZCQQFxcXJnX4+Li2LZtW6X7LVq0iBMnTvD666/X6Dxms5mMjIwyX0IIIYQQQgghhKvxqM3G6enpFBYWEhwcXOb14OBgUlNTK9wnKSmJ6dOns2XLFjw8ana62bNn88Ybb5R7XYJrIYQQrsL6TNI0zcktcQ/Wf0d51gshhHAVNX3W1yqottLpdGW+1zSt3GsAhYWFPProo7zxxht07ty5xsefMWMG06ZNK/7+/PnzREdH06ZNm7o0VwghhLCbzMxMAgICnN2MBi8zMxNAnvVCCCFcTnXP+loF1YGBgRgMhnK90mlpaeV6r60n3717N4mJibz44osAWCwWNE3Dw8ODdevWcccdd5Tbz2QyYTKZir/39fXl7Nmz+Pn5VRi810ZGRgZt2rTh7Nmz+Pv71+tYzuYu1+Iu1wHucy3uch3gPtfiLtcB7nMtmqaRmZlJWFiYs5viFsLCwuRZXwF3uRZ3uQ6Qa3FF7nId4D7X4i7XUdNnfa2CaqPRSExMDPHx8dx///3Fr8fHx3PfffeV297f35/9+/eXeW3evHmsX7+eb775hoiIiBqdV6/X07p169o0tVr+/v4N+gaX5i7X4i7XAe5zLe5yHeA+1+Iu1wHucS3SQ2078qyvmrtci7tcB8i1uCJ3uQ5wn2txh+uoybO+1sO/p02bxoQJE+jbty+DBg3i448/Jjk5meeeew5QQ7fPnz/P559/jl6vp1u3bmX2DwoKwsvLq9zrQgghhBBCCCFEQ1ProHrcuHFcvnyZN998k5SUFLp168bq1asJDw8HICUlpdqa1UIIIYQQQgghhDuoU6KyyZMnM3ny5ArfW7x4cZX7zpo1i1mzZtXltDZhMpl4/fXXy8zZbqjc5Vrc5TrAfa7FXa4D3Oda3OU6wL2uRbgmd/o/5i7X4i7XAXItrshdrgPc51rc5TpqSqdJLRAhhBBCCCGEEKJO9M5ugBBCCCGEEEII0VBJUC2EEEIIIYQQQtSRBNVCCCGEEEIIIUQdSVAthBBCCCGEEELUkQTVQgghhBBCCCFEHbllUD1v3jwiIiLw8vIiJiaGLVu2VLn9pk2biImJwcvLi/bt2/OPf/zDQS2t3OzZs+nXrx9+fn4EBQUxZswYjh49WuU+GzduRKfTlfs6cuSIg1pd3qxZs8q1JyQkpMp9XPF+ALRr167Cf98XXnihwu1d5X5s3ryZe+65h7CwMHQ6HStWrCjzvqZpzJo1i7CwMJo0acJtt93GwYMHqz3usmXLiI6OxmQyER0dzfLly+10BSWqupb8/Hxee+01unfvjo+PD2FhYUycOJELFy5UeczFixdXeJ9yc3Oddi0ATzzxRLk2DRw4sNrjOvq+VHcdFf3b6nQ6/vrXv1Z6TGfdE9GwyLNenvX20FCf9eA+z3t51suzviFyu6B66dKlTJkyhZkzZ5KYmEhsbCwjR44kOTm5wu1PnTrFqFGjiI2NJTExkd///vf85je/YdmyZQ5ueVmbNm3ihRdeYMeOHcTHx1NQUEBcXBzZ2dnV7nv06FFSUlKKvzp16uSAFleua9euZdqzf//+Srd11fsBsGvXrjLXER8fD8DDDz9c5X7Ovh/Z2dn07NmTv//97xW+/8477zBnzhz+/ve/s2vXLkJCQrjrrrvIzMys9Jjbt29n3LhxTJgwgX379jFhwgTGjh3Lzp077XUZQNXXkpOTw549e/jjH//Inj17+Pbbbzl27Bj33ntvtcf19/cvc49SUlLw8vKyxyUUq+6+AIwYMaJMm1avXl3lMZ1xX6q7jpv/XRcuXIhOp+PBBx+s8rjOuCei4ZBnvfOfLTeTZ73z74e7PO/lWS/P+gZJczP9+/fXnnvuuTKvRUVFadOnT69w+1dffVWLiooq89qvf/1rbeDAgXZrY12kpaVpgLZp06ZKt9mwYYMGaFevXnVcw6rx+uuvaz179qzx9g3lfmiapr388stahw4dNIvFUuH7rng/AG358uXF31ssFi0kJER76623il/Lzc3VAgICtH/84x+VHmfs2LHaiBEjyrw2fPhwbfz48TZvc2VuvpaK/PzzzxqgnTlzptJtFi1apAUEBNi2cbVU0bVMmjRJu++++2p1HGffl5rck/vuu0+74447qtzGFe6JcG3yrHetZ4s8613rfmia+zzv5VlfXkO4J43xWe9WPdV5eXkkJCQQFxdX5vW4uDi2bdtW4T7bt28vt/3w4cPZvXs3+fn5dmtrbV2/fh2A5s2bV7tt7969CQ0NZdiwYWzYsMHeTatWUlISYWFhREREMH78eE6ePFnptg3lfuTl5fHll1/y1FNPodPpqtzW1e5HaadOnSI1NbXMv7nJZOLWW2+t9GcGKr9PVe3jDNevX0en09G0adMqt8vKyiI8PJzWrVszevRoEhMTHdPAamzcuJGgoCA6d+7Ms88+S1paWpXbu/p9uXjxIqtWreLpp5+udltXvSfC+eRZr7jas0We9a51P27mzs97eda71j1prM96twqq09PTKSwsJDg4uMzrwcHBpKamVrhPampqhdsXFBSQnp5ut7bWhqZpTJs2jVtuuYVu3bpVul1oaCgff/wxy5Yt49tvvyUyMpJhw4axefNmB7a2rAEDBvD555+zdu1aPvnkE1JTUxk8eDCXL1+ucPuGcD8AVqxYwbVr13jiiScq3cYV78fNrD8XtfmZse5X230cLTc3l+nTp/Poo4/i7+9f6XZRUVEsXryYlStXsmTJEry8vBgyZAhJSUkObG15I0eO5KuvvmL9+vW899577Nq1izvuuAOz2VzpPq5+Xz777DP8/Px44IEHqtzOVe+JcA3yrHe9Z4s8613rflTEXZ/38qxXXOmeNNZnvYezG2APN3+aqGlalZ8wVrR9Ra87y4svvsgvv/zC1q1bq9wuMjKSyMjI4u8HDRrE2bNneffddxk6dKi9m1mhkSNHFq93796dQYMG0aFDBz777DOmTZtW4T6ufj8AFixYwMiRIwkLC6t0G1e8H5Wp7c9MXfdxlPz8fMaPH4/FYmHevHlVbjtw4MAySUGGDBlCnz59+PDDD/nggw/s3dRKjRs3rni9W7du9O3bl/DwcFatWlXlg8qV78vChQt57LHHqp0v5ar3RLgWedYrrvBskWe9a92PqrjT816e9SVc5Z5A433Wu1VPdWBgIAaDodwnNWlpaeU+0bEKCQmpcHsPDw9atGhht7bW1EsvvcTKlSvZsGEDrVu3rvX+AwcOdKlPfHx8fOjevXulbXL1+wFw5swZfvjhB5555pla7+tq98OanbU2PzPW/Wq7j6Pk5+czduxYTp06RXx8fJWfXFdEr9fTr18/l7pPoHpDwsPDq2yXK9+XLVu2cPTo0Tr93LjqPRHOIc/68lzt2SLPete6H+B+z3t51rvePYHG/ax3q6DaaDQSExNTnKnRKj4+nsGDB1e4z6BBg8ptv27dOvr27Yunp6fd2lodTdN48cUX+fbbb1m/fj0RERF1Ok5iYiKhoaE2bl3dmc1mDh8+XGmbXPV+lLZo0SKCgoK4++67a72vq92PiIgIQkJCyvyb5+XlsWnTpkp/ZqDy+1TVPo5gfcgmJSXxww8/1OmPM03T2Lt3r0vdJ4DLly9z9uzZKtvlqvcFVI9PTEwMPXv2rPW+rnpPhHPIs748V3u2yLPete4HuNfzXp71rndPrBr1s96xedHs7+uvv9Y8PT21BQsWaIcOHdKmTJmi+fj4aKdPn9Y0TdOmT5+uTZgwoXj7kydPat7e3trUqVO1Q4cOaQsWLNA8PT21b775xlmXoGmapj3//PNaQECAtnHjRi0lJaX4Kycnp3ibm6/l/fff15YvX64dO3ZMO3DggDZ9+nQN0JYtW+aMS9A0TdN++9vfahs3btROnjyp7dixQxs9erTm5+fX4O6HVWFhoda2bVvttddeK/eeq96PzMxMLTExUUtMTNQAbc6cOVpiYmJxlsy33npLCwgI0L799ltt//792iOPPKKFhoZqGRkZxceYMGFCmay6P/30k2YwGLS33npLO3z4sPbWW29pHh4e2o4dO5x2Lfn5+dq9996rtW7dWtu7d2+Znxuz2VzptcyaNUtbs2aNduLECS0xMVF78sknNQ8PD23nzp1Ou5bMzEztt7/9rbZt2zbt1KlT2oYNG7RBgwZprVq1crn7Ut3/L03TtOvXr2ve3t7a/PnzKzyGq9wT0XDIs975z5bS5FnvGvfDXZ738qyXZ31D5HZBtaZp2kcffaSFh4drRqNR69OnT5nSFJMmTdJuvfXWMttv3LhR6927t2Y0GrV27dpV+p/BkYAKvxYtWlS8zc3X8vbbb2sdOnTQvLy8tGbNmmm33HKLtmrVKsc3vpRx48ZpoaGhmqenpxYWFqY98MAD2sGDB4vfbyj3w2rt2rUaoB09erTce656P6zlPm7+mjRpkqZpqszG66+/roWEhGgmk0kbOnSotn///jLHuPXWW4u3t/rPf/6jRUZGap6enlpUVJRD/oCo6lpOnTpV6c/Nhg0bKr2WKVOmaG3bttWMRqPWsmVLLS4uTtu2bZtTryUnJ0eLi4vTWrZsqXl6empt27bVJk2apCUnJ5c5hivcl+r+f2mapv3zn//UmjRpol27dq3CY7jKPRENizzr5VlvLw3xWa9p7vO8l2e9POsbIp2mFWWGEEIIIYQQQgghRK241ZxqIYQQQgghhBDCkSSoFkIIIYQQQggh6kiCaiGEEEIIIYQQoo4kqBZCCCGEEEIIIepIgmohhBBCCCGEEKKOJKgWQgghhBBCCCHqSIJqIYQQQgghhBCijiSoFkIIIYQQQggh6kiCaiGEEEIIIYQQoo4kqBZCCCGEEEIIIepIgmohhBBCCCGEEKKO/j+4r5eQz81gNAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x1600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training and validation accuracy over epochs\n",
    "plt.figure(figsize=(12, 16))\n",
    "\n",
    "plt.subplot(4, 2, 1)\n",
    "plt.plot(modelZ.history['loss'], label='Loss')\n",
    "plt.plot(modelZ.history['val_loss'], label='val_Loss')\n",
    "plt.title('Loss Function Evolution')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(4, 2, 2)\n",
    "plt.plot(modelZ.history['accuracy'], label='Accuracy')\n",
    "plt.plot(modelZ.history['val_accuracy'], label='val_accuracy')\n",
    "plt.title('Accuracy Function Evolution')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the Trained Model\n",
    "After training, the model is saved to a file for future use.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save('modelZ.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from tensorflow.keras.models import load_model\n",
    "\n",
    "# # Load the model\n",
    "# model = load_model('saved_model/my_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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