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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "94583e30-e2ad-49ca-bb1c-283f61969ce5",
+   "metadata": {},
+   "source": [
+    "# **Deep Learning Image Classification with CNN**\n",
+    "\n",
+    "**Objective**\n",
+    "\n",
+    "The objective of this code is to classify images from the CIFAR-10 dataset using a pre-trained DenseNet121 model. The code includes steps for loading the dataset, preprocessing the data, augmenting the training data, defining the model architecture, training the model, evaluating its performance, and visualizing the results.\n",
+    "\n",
+    "**About the Data**\n",
+    "\n",
+    "The dataset for this task is the CIFAR-10 dataset, which consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. \n",
+    "\n",
+    "The ten classes are: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck. \n",
+    "\n",
+    "Each image is 32x32 pixels in size with 3 color channels, RGB.\n",
+    "\n",
+    "The dataset has been split in the following manner: 50k images for training, 10k images for testing\n",
+    "\n",
+    "Reference: https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cd157004-b419-4f27-b79c-32b9ee6dd0d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Imports the necessary libraries\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tensorflow.keras.applications import DenseNet121\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.datasets import cifar10\n",
+    "from tensorflow.keras.utils import to_categorical\n",
+    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d54aa8cb-3d94-4031-a446-e5eee6c3ff37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loads the CIFAR-10 dataset\n",
+    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
+    "\n",
+    "# Data Preprocessing\n",
+    "X_train = X_train / 255.0  # Normalizes pixel values to [0, 1] - this helps the model improve its ability to learn efficiently\n",
+    "X_test = X_test / 255.0\n",
+    "y_train_cat = to_categorical(y_train, 10)  # One-hot encoding for labels - each label corresponds to a vector with 1 at the index of the class and 0 elsewhere.\n",
+    "y_test_cat = to_categorical(y_test, 10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4e41902d-384c-49b7-b6e2-7c4bf7ac5a4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 25 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualization: Random Samples from Training Set\n",
+    "# This helps confirm the dataset's integrity and diversity\n",
+    "labels = ['Airplane', 'Automobile', 'Bird', 'Cat', 'Deer', \n",
+    "          'Dog', 'Frog', 'Horse', 'Ship', 'Truck']\n",
+    "\n",
+    "fig, axes = plt.subplots(5, 5, figsize=(10, 10))\n",
+    "axes = axes.ravel()\n",
+    "\n",
+    "for i in range(25):  # Displays 25 random images\n",
+    "    idx = np.random.randint(0, len(X_train))\n",
+    "    axes[i].imshow(X_train[idx])\n",
+    "    axes[i].set_title(labels[y_train[idx][0]], fontsize=8)\n",
+    "    axes[i].axis('off')\n",
+    "\n",
+    "plt.subplots_adjust(hspace=0.5)\n",
+    "plt.show()\n",
+    "\n",
+    "# Visualization: Class Distribution in Training and Testing Sets\n",
+    "# Helps to confirm that the dataset is balanced across the 10 classes\n",
+    "train_counts = np.unique(y_train, return_counts=True)[1]\n",
+    "test_counts = np.unique(y_test, return_counts=True)[1]\n",
+    "\n",
+    "plt.figure(figsize=(12, 5))\n",
+    "\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.bar(labels, train_counts, color='skyblue')\n",
+    "plt.title('Class Distribution in Training Set')\n",
+    "plt.ylabel('Number of Samples')\n",
+    "plt.xlabel('Class')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.bar(labels, test_counts, color='lightgreen')\n",
+    "plt.title('Class Distribution in Testing Set')\n",
+    "plt.ylabel('Number of Samples')\n",
+    "plt.xlabel('Class')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "48ddfa5d-b95b-41b4-b2b3-b014ba49e2a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/lib/python3.12/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[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m288s\u001b[0m 172ms/step - accuracy: 0.4486 - loss: 1.6219 - val_accuracy: 0.4642 - val_loss: 1.6542\n",
+      "Epoch 2/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m372s\u001b[0m 238ms/step - accuracy: 0.5762 - loss: 1.2448 - val_accuracy: 0.5240 - val_loss: 6.6582\n",
+      "Epoch 3/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m336s\u001b[0m 215ms/step - accuracy: 0.6349 - loss: 1.0760 - val_accuracy: 0.5031 - val_loss: 2.1518\n",
+      "Epoch 4/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m340s\u001b[0m 218ms/step - accuracy: 0.6817 - loss: 0.9320 - val_accuracy: 0.6785 - val_loss: 3.1231\n",
+      "Epoch 5/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m331s\u001b[0m 212ms/step - accuracy: 0.7334 - loss: 0.7748 - val_accuracy: 0.7669 - val_loss: 0.6808\n",
+      "Epoch 6/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m329s\u001b[0m 211ms/step - accuracy: 0.7554 - loss: 0.7353 - val_accuracy: 0.7100 - val_loss: 1.3602\n",
+      "Epoch 7/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m327s\u001b[0m 209ms/step - accuracy: 0.7017 - loss: 0.9077 - val_accuracy: 0.6668 - val_loss: 1.4545\n",
+      "Epoch 8/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m313s\u001b[0m 200ms/step - accuracy: 0.7174 - loss: 0.8468 - val_accuracy: 0.6995 - val_loss: 0.9865\n",
+      "Epoch 9/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m333s\u001b[0m 213ms/step - accuracy: 0.7187 - loss: 0.8267 - val_accuracy: 0.7559 - val_loss: 1.3768\n",
+      "Epoch 10/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m354s\u001b[0m 226ms/step - accuracy: 0.7742 - loss: 0.6522 - val_accuracy: 0.7368 - val_loss: 7.8946\n",
+      "Epoch 11/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m344s\u001b[0m 220ms/step - accuracy: 0.7880 - loss: 0.6287 - val_accuracy: 0.5612 - val_loss: 3.5514\n",
+      "Epoch 12/20\n",
+      "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m530s\u001b[0m 339ms/step - accuracy: 0.7711 - loss: 0.6693 - val_accuracy: 0.3777 - val_loss: 3.1805\n",
+      "Epoch 13/20\n",
+      "\u001b[1m 856/1563\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m2:38\u001b[0m 224ms/step - accuracy: 0.7089 - loss: 0.8771"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 23\u001b[0m\n\u001b[1;32m     16\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(\n\u001b[1;32m     17\u001b[0m     loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcategorical_crossentropy\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     18\u001b[0m     optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     19\u001b[0m     metrics\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m     20\u001b[0m )\n\u001b[1;32m     22\u001b[0m \u001b[38;5;66;03m# Train the Model\u001b[39;00m\n\u001b[0;32m---> 23\u001b[0m history \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mfit(\n\u001b[1;32m     24\u001b[0m     datagen\u001b[38;5;241m.\u001b[39mflow(X_train, y_train_cat, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m32\u001b[39m),\n\u001b[1;32m     25\u001b[0m     validation_data\u001b[38;5;241m=\u001b[39m(X_test, y_test_cat),\n\u001b[1;32m     26\u001b[0m     epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m\n\u001b[1;32m     27\u001b[0m )\n\u001b[1;32m     29\u001b[0m \u001b[38;5;66;03m# Save the Model\u001b[39;00m\n\u001b[1;32m     30\u001b[0m model\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdensenet_cifar10.keras\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/keras/src/backend/tensorflow/trainer.py:371\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator:\n\u001b[1;32m    370\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m--> 371\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_function(iterator)\n\u001b[1;32m    372\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n\u001b[1;32m    373\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/keras/src/backend/tensorflow/trainer.py:219\u001b[0m, in \u001b[0;36mTensorFlowTrainer._make_function.<locals>.function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfunction\u001b[39m(iterator):\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[1;32m    217\u001b[0m         iterator, (tf\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mIterator, tf\u001b[38;5;241m.\u001b[39mdistribute\u001b[38;5;241m.\u001b[39mDistributedIterator)\n\u001b[1;32m    218\u001b[0m     ):\n\u001b[0;32m--> 219\u001b[0m         opt_outputs \u001b[38;5;241m=\u001b[39m multi_step_on_iterator(iterator)\n\u001b[1;32m    220\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m opt_outputs\u001b[38;5;241m.\u001b[39mhas_value():\n\u001b[1;32m    221\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[1;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[0;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m tracing_compilation\u001b[38;5;241m.\u001b[39mcall_function(\n\u001b[1;32m    879\u001b[0m     args, kwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_config\n\u001b[1;32m    880\u001b[0m )\n\u001b[1;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[1;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m function\u001b[38;5;241m.\u001b[39m_call_flat(  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m    140\u001b[0m     flat_inputs, captured_inputs\u001b[38;5;241m=\u001b[39mfunction\u001b[38;5;241m.\u001b[39mcaptured_inputs\n\u001b[1;32m    141\u001b[0m )\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_inference_function\u001b[38;5;241m.\u001b[39mcall_preflattened(args)\n\u001b[1;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1324\u001b[0m     args,\n\u001b[1;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1326\u001b[0m     executing_eagerly)\n\u001b[1;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcall_flat(\u001b[38;5;241m*\u001b[39margs)\n\u001b[1;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mcall_function(\n\u001b[1;32m    252\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname,\n\u001b[1;32m    253\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;32m    254\u001b[0m         \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mflat_outputs),\n\u001b[1;32m    255\u001b[0m     )\n\u001b[1;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[1;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[1;32m    261\u001b[0m     )\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/eager/context.py:1552\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1550\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m   1551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1552\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute(\n\u001b[1;32m   1553\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1554\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m   1555\u001b[0m       inputs\u001b[38;5;241m=\u001b[39mtensor_inputs,\n\u001b[1;32m   1556\u001b[0m       attrs\u001b[38;5;241m=\u001b[39mattrs,\n\u001b[1;32m   1557\u001b[0m       ctx\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1558\u001b[0m   )\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1560\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1561\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1562\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1566\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m   1567\u001b[0m   )\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[1;32m     54\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "# Data Augmentation\n",
+    "# Uses ImageDataGenerator to improve the model's generalization.\n",
+    "# This includes horizontal flipping and width/height shifts.\n",
+    "# These transformations simulate real-world variations in the dataset.\n",
+    "datagen = ImageDataGenerator(\n",
+    "    width_shift_range=0.1,\n",
+    "    height_shift_range=0.1,\n",
+    "    horizontal_flip=True\n",
+    ")\n",
+    "datagen.fit(X_train)\n",
+    "\n",
+    "# Define DenseNet121 Model\n",
+    "# DenseNet121 serves as the base model.\n",
+    "# For CIFAR-10, the default classification layer has been excluded and a pooling layer has been added.\n",
+    "# The global average pooling layer helps to reduce the feature maps to a single vector.\n",
+    "base_model = DenseNet121(include_top=False, weights='imagenet', input_shape=(32, 32, 3), pooling='avg')\n",
+    "model = Sequential([\n",
+    "    base_model,\n",
+    "    Dense(10, activation='softmax')  # The output layer has 10 neurons, one per class, and has a softmax activation to output probabilities.\n",
+    "])\n",
+    "\n",
+    "# Model Training\n",
+    "# The model uses categorical cross-entropy, an adam optimizer (has an adaptive learning rate) and the accuracy metric to track the % of correct predictions.\n",
+    "model.compile(\n",
+    "    loss='categorical_crossentropy',\n",
+    "    optimizer='adam',\n",
+    "    metrics=['accuracy']\n",
+    ")\n",
+    "\n",
+    "# Model is trained for 20 epochs, with data augmentation applied during the training.\n",
+    "history = model.fit(\n",
+    "    datagen.flow(X_train, y_train_cat, batch_size=32),\n",
+    "    validation_data=(X_test, y_test_cat),\n",
+    "    epochs=20\n",
+    ")\n",
+    "\n",
+    "# Saves the Model\n",
+    "# The trained model has been saved in the keras format (versus h5) to ensure compatibility with future versions of TensorFlow.\n",
+    "model.save('densenet_cifar10.keras')  # Save in the recommended .keras format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8694ef6c-4369-43a0-a690-f1a4e5dd4312",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 30ms/step - accuracy: 0.7862 - loss: 0.6283\n",
+      "Test Accuracy: 0.7888\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluates the model looking at its test accuracy, generating a classification report, and a confusion matrix.\n",
+    "# Accuracy - % of correct predictions\n",
+    "evaluation = model.evaluate(X_test, y_test_cat)\n",
+    "print(f\"Test Accuracy: {evaluation[1]:.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "95278e37-7532-4444-a43e-ee9b1a943800",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 33ms/step\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "    Airplane       0.80      0.82      0.81      1000\n",
+      "  Automobile       0.85      0.91      0.88      1000\n",
+      "        Bird       0.84      0.65      0.73      1000\n",
+      "         Cat       0.71      0.52      0.60      1000\n",
+      "        Deer       0.85      0.69      0.76      1000\n",
+      "         Dog       0.66      0.82      0.73      1000\n",
+      "        Frog       0.77      0.91      0.83      1000\n",
+      "       Horse       0.81      0.85      0.83      1000\n",
+      "        Ship       0.88      0.83      0.85      1000\n",
+      "       Truck       0.75      0.90      0.82      1000\n",
+      "\n",
+      "    accuracy                           0.79     10000\n",
+      "   macro avg       0.79      0.79      0.79     10000\n",
+      "weighted avg       0.79      0.79      0.79     10000\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x1000 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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4zDG9eB2KZ19vOjevyujeTdDX1eLJy7doqKspJJ6vufnwFb77LlGqmFWe7zsrLo5F2Ld8oHRaRSV/q/TP+IwkLe3jU5j8ngfSauAKWtQrV6hjcnawZPtiL+m0inLGF25CYjL3n7xmaNeGuBazIjImgalL99Fj7DqOKvDH0NdiAli57Qy/7zzPovGdcLQ1Z6nvSToNW8WFbePR1S44z3KUKsTNYf/ZJGjDhg0MGjSIdevW4e/vj52dHQDq6upYWlp+cb20tDSUlJRQVv4xs9rsqOhShKNXn3Hyn5cABIRE07qWM+WKZ9SyRMcn02ryPpl1xqw9z9lFHbEx0+N1aAwAqw/eAqB6aRu5x7jD20tmesnETpRqOoG7jwKoWq6YdL66uirmJnn3UNb61UpRv1qpLy5v06iCzPTMIb+w9eAVHj4NpGZFZwXF5Er9aq5fXD579RHqV3NlyqAW0nlFrU0VEsvXxMYn0XeSL94TOrJww4lvr5AHVFWUsTAtOA/1/TxR9t50CgcbU6qXL/aFNRSvIMSkoqKc5edcX1dLJhEBmDG0NZ59FvHmbQTWFkZ5HpNEImH9rj8Z9GsDaS3x4gmdKddiIvtP3aBLi+oKi0nIuf/kN31cXBy7du2if//+eHp64uPjI112/vx5lJSUiIyMBMDHxwdDQ0MOHz6Mq6srGhoavHr1im7dutGyZUumTZuGubk5+vr69O3bl+Tk5C/ud8uWLVSoUAE9PT0sLS3p1KkTISEhmfZ95swZKlSogLa2NtWqVePx48cy2zl06BAeHh5oamri6OjItGnTSE1NldvxufowkFpl7XCyMgSgdFFTqpS04tT7pCgr+trqpKdLiIqVT3NXTsXEJgJgqK8tM//yzae4Nh1P1XYzGD5nO6HhMfkRXpaSU1Lx3X8ZfV0tShW3zpcY0tPTOXX5AU525rQdspKSTcbTqMdCjl64m+exjJ63iwbVS1G7kkue7/tLngeE4tp0Au4tptBzwkZevnmX3yFJJaek8sex63RuXiVvniSeDfkV04vX7/BoOZmq7abjNcWXV4Fffp9i4hJQUlJCX1crX2LyDwojJDyaWhU/nuca6qpUcS/GP/dfKjSm7/ahOSy3rx/Qf7ImaOfOnTg7O+Ps7EyXLl0YNGgQkyZN+uKHNj4+njlz5rBu3TpMTEwwNzcH4MyZM2hqanLu3DlevnxJ9+7dMTU1ZdasWVluJzk5mRkzZuDs7ExISAjDhg2jW7duHD16VKbchAkTWLhwIWZmZvTr148ePXpw6dIlAE6cOEGXLl1YunQpNWrU4NmzZ/Tp0weAKVOmfPFvTkpKIinpY4ISHR39xbLeu/9BX1uDa6u6kpaejoqyMjM3X2bPn4+zLK+hpsKUrj+x+8IjYhK+nAQqikQiYfLSfVQu60hJp4/NKHWrutK8bjlsLI3wDwxj7u9HaT1oOac2jsyXpp4PTly8T5+JG4lPTMHCVJ/dywZgYqibL7GERsQSF5/E0k2nGde3GZMH/MzZq350G7uefSsGUr188TyJY8/JG9x5HMAZn1F5sr/s8Chtz8qp/6OYnTkh4dEs3HCCxj0XcXnHBIwNdfI7PI6cv0tUbAIdPavkdyhS+RFTOVd7vCd0xtHWjHcRMSzxPUnL/ks4u2ksRgay71NiUgpzVh+mZf3y6OkortnpazGFhmX8EDM1lq1BMzXS401w5r5VBYJoDvtvWb9+PV26dAGgcePGxMbGcubMGerXr59l+ZSUFFauXEnZsmVl5qurq7Nhwwa0tbUpVaoU06dPZ9SoUcyYMSPL5rIePXpI/+3o6MjSpUupVKkSsbGx6Op+/BKcNWsWtWrVAmDs2LE0a9aMxMRENDU1mTVrFmPHjqVr167S7cyYMYPRo0d/NQmaM2cO06ZNy9bxaVWjBO1qu9B7wTEe+Yfh5mjG7F61CAqPZcdZP5myqirKrB/dFGVlJUauOpet7cvbuAV/4Pc0kINrhsjMb1m/vPTfJZ2scC9ph8cvUzl9+SHNapf9fDN55ieP4pzbPJbwyFg2H7hMr/EbOLFhJGbGiukT9DWS9Iy+HI1rutGvYx0A3ErYcP3uC3z3XcqTJOj12wjGL9rDnqVeaGrkX3L6uQafNGu6YkVFNwc8fpnG9iN/M6Bz3XyMLMOWg1eoX9WVImYFp/9ifsRUt4psU69HqaJU7zCTP45do0+HOtL5KalpDJjqS3q6hNkj2uZbTOVLFQXg85RAIpH8sInCf9mPWX/1FY8fP+batWt06NABAFVVVdq3b8+GDRu+uI66ujplypTJNL9s2bJoa39sfqlatSqxsbEEBARkuZ1bt27RokUL7O3t0dPTo3bt2gD4+/vLlPt0X0WKZNwt8KHZ7MaNG0yfPh1dXV3pq3fv3gQFBREfH//Fv2HcuHFERUVJX1+KEWB69xp4777O3r+e8PBVGDvPPWLlgVsMa1tRppyqijIbxzTF3kKfXybtzZdaoHELd3Pi4n32rBiElfnX2/ctTA2wsTTmeUDIV8spmo6WBo62ZlRwc2DJxM6oqKiw9eCVfInF2FAHVRVlShSV7QdXoqgFr4Mj8iSGO37+hIbHUKfrfMyqDsGs6hAu3XzK2p0XMKs6hLS09DyJ41t0tDQoWcyK5wGh+R0KAUHhXLj+mP+1qJrfoUgVlJi0tTRwcSzCi9cf36eU1DT6TfbBPyic7Yv7K7QW6FsxmZlk/Nj5vGk+LDI2X34IZYtoDvvvWL9+PampqVhbf+yDIZFIUFNTIyIi64u+lpZWjtq3syobFxdHw4YNadiwIVu2bMHMzAx/f38aNWqUqR+RmtrHX8MftpWeni79/7Rp02jVqlWmfWhqfvmDraGhgYaGxheXf0pLQ5V0iey89HQJyp/8XR8SICcrQ5qP30NETGK2ti0vEomE8Qt3c/TCXfatHIS9lck31wmPiiMwJAILk4LzyzmDhKQU+fXpygl1NVXKudrxzP+tzPxnAaHYFsmb2+NrVnTm4vZxMvMGTd9K8aIWDP61PioqBePimZScwpOXb6nq7pTfobD10FXMjPRoWP3LnfDzWkGJKSk5lX9fvaVSGUfgYwL08nUou5YMzNREltcx2RUxwdxYnz+vP6Z0iYybRpJTUrl6+ynj+zXP89iyRUlJDrfI/5i1XP+pJCg1NZVNmzaxcOFCGjZsKLOsdevWbN26ldKlS2d7e3fu3CEhIQEtrYwOdlevXkVXVxcbm8x3Qz169Ih3797x22+/YWtrC8A///yT47+hfPnyPH78mGLFFHfnxfHrLxjeriKvQ6Px8w+njKMZXi3LsfXUQyBjHCHfsc0o62ROh+kHUFFWwtwwo0YsIjaRlNSMhM3cUBtzIx0crTKSjlL2JsQkpPA6NJrIXHagHrvgD/aevIHv3F7oamsSEpbRx0lPRxMtTXXi4pOYv+4YzeqUxcJUn4CgcGavOoSxgU6mMXLkKTY+SeYX6KvAMO49eY2RvjZGBjos3niCxjXcsDA1IDwqjg17/iIwJFKhtxN/HpP/JzHZWBozoHM9ek/0oap7Map7FOfsVT9OXLzP/hWDFBbTp/R0NHF1kr0lXltLHSMDnUzz89KkJftoXKM0NhZGhEbEsnDDCWLiEunYrHK+xQQZP4S2Hb5Kh2aVUFXwkA/ZlZ8xzVhxgPrVSmFtYcS7iBiWbjpFbFwibZtUIjU1jb6TNnLvyWt85/YmLT1deq0w1NdGXU0xX3Ffi0lJSYme7WqyfMspHGzNcLAxY9nmU2hpqNOygYdC4sk1ZaWMV2638QP6TyVBhw8fJiIigp49e2YaB6hNmzasX7+exYsXZ3t7ycnJ9OzZk4kTJ/Lq1SumTJnCwIEDs+wPZGdnh7q6OsuWLaNfv37cv3+fGTNm5PhvmDx5Mp6entja2tK2bVuUlZW5e/cu9+7dY+bMmTneXlbGrDnH+M7VWNC/LqYG2gSHx+Jz/B7zdvwNgJWpHk2rZPwa/mtZF5l1Pcft5tL91wB0b1KGsZ0+dpA8Ojdj4DQv75NsP/MwVzH67L0IwC8DlsnMXzKxMx2aVUZZWQm/54HsOn6N6JgELEz1qV6+OGtndkdXgVXht/38aem1VDo9yTtjKIEOzSqxYEwH/n31lh1HrxEeGYeRgTblStpzaM1QXBwVN0jaHT9/Wn5ynCYtyYipfdNKLJ/chWa1yzJ/TDuW+J5m/OI9ONmZs3FOD6oUgBqP/BQYEknviT6ERcZhaqSLR+minFw/PM9qyL7k/LXHvA6OoHPzgtMUlp8xBYVEMnDaJsKj4jA21KV8KXsOrh6GjaUxAUFhnLx4H4CG3efLrLdr6QCqlVNMn7evxQTg1akeiUkpTFi4m6jYeNxL2rN1Uf+COUZQIackkUgk3y72Y2jevDnp6ekcOXIk07KbN2/i4eHBwoULGTFihHTEaB8fH4YOHSq9Zf6Dbt26ERkZSdmyZVmxYgVJSUl06NCB5cuXS5udPh8xevv27YwfP56goCDKly/PuHHj+Pnnn7l16xbu7u5ZjlZ9+/ZtypUrx4sXLyhatCiQcYfY9OnTuXXrFmpqari4uNCrVy969+6d7WMRHR2NgYEBGvXnoKRacD54b/cNzu8QMlEpgL9gCuLHsiAeJ+HHFZ+Ult8hFGgx0dE4WJsQFRWFvr5ixrKSfk/UmJjr7wlJaiJJf81UaLyK8J9KguTpQxK0f//+/A7lu4gkKPsK4pd7QfxYFsTjJPy4RBL0dXmaBNWcJJ8k6M8ZP1wSVDB6JAqCIAiCIOSx/1SfIEEQBEEQckg8QFX43KeP2hAEQRCE/6xCPGL0j5m6CYIgCIIg5JKoCRIEQRCEwkw0hwmCIAiCUCiJ5jBBEARBEITCRdQECYIgCEJhJprDBEEQBEEolApxc5hIggRBEAShUJNDTdAP2rvmx4xaEARBEAQhl0RN0H/cqx39C9RzXIwrDcrvEDKJuL48v0PIwo9ZtSwUzOe+KRXApgodzYL39RMVn5LfIUilpufheSSawwRBEARBKJSUlOTQMfrHTIJEc5ggCIIgCIWSqAkSBEEQhMKsEN8i/2NGLQiCIAiCfHzoE5TbVzalpqYyceJEHBwc0NLSwtHRkenTp5Oeni4tI5FImDp1KlZWVmhpaVG7dm0ePHggs52kpCQGDRqEqakpOjo6/Pzzz7x+/TpHf7pIggRBEARByDNz585l9erVLF++HD8/P+bNm8f8+fNZtmyZtMy8efNYtGgRy5cv5/r161haWtKgQQNiYmKkZYYOHcq+ffvYsWMHFy9eJDY2Fk9PT9LS0rIdi2gOEwRBEITCLI+bw65cuUKLFi1o1qwZAEWLFmX79u38888/QEYtkLe3NxMmTKBVq1YA+Pr6YmFhwbZt2+jbty9RUVGsX7+ezZs3U79+fQC2bNmCra0tp0+fplGjRtmKRdQECYIgCEJhJsfmsOjoaJlXUlJSpt399NNPnDlzhidPngBw584dLl68SNOmTQF48eIFwcHBNGzYULqOhoYGtWrV4vLlywDcuHGDlJQUmTJWVlaULl1aWiY7RE2QIAiCIAhyYWtrKzM9ZcoUpk6dKjNvzJgxREVF4eLigoqKCmlpacyaNYuOHTsCEBwcDICFhYXMehYWFrx69UpaRl1dHSMjo0xlPqyfHSIJEgRBEITCTI7NYQEBATID9GpoaGQqunPnTrZs2cK2bdsoVaoUt2/fZujQoVhZWdG1a9ePm/yss7VEIvnmwJ/ZKfMpkQQJgiAIQmEmxxGj9fX1v/mUglGjRjF27Fg6dOgAgJubG69evWLOnDl07doVS0tLIKO2p0iRItL1QkJCpLVDlpaWJCcnExERIVMbFBISQrVq1bIdtugTJAiCIAiFmJKSklxe2RUfH4+ysmz6oaKiIr1F3sHBAUtLS06dOiVdnpyczIULF6QJjoeHB2pqajJlgoKCuH//fo6SIFETJAiCIAhCnmnevDmzZs3Czs6OUqVKcevWLRYtWkSPHj2AjKRs6NChzJ49m+LFi1O8eHFmz56NtrY2nTp1AsDAwICePXsyYsQITExMMDY2ZuTIkbi5uUnvFssOkQQJgiAIQiGW05qcL2wk20WXLVvGpEmT8PLyIiQkBCsrK/r27cvkyZOlZUaPHk1CQgJeXl5ERERQuXJlTp48iZ6enrTM4sWLUVVVpV27diQkJFCvXj18fHxQUVHJftiSgvjI4x/Y1KlT2b9/P7dv3/5imW7duhEZGcn+/fsBqF27Nu7u7nh7e8stjujoaAwMDAh+F/ndT5EPDIlk2vIDnL78kMSkFJzszFk6sRPuJe2+O66vPUVeV1uD8f088axdFlMjXe49ec3Yhbu59dBfWmZM76Z0/aU6hnpa3HjwilHzdvLo+cc7AcxN9Jg++BdqV3ZBV1uDp69CWLTxBAfP3v7ifnPzFPlFG09w+Nwd/n31Fk0NNSqVcWTqwBYUL2rx7ZUV5NLNpyzbfJo7j/wJfhfNlvm9aVa7bL7FU1BjWr/7Lzbs+YuAoHAAXBwtGdWzCQ2ql8rVdnNzSS3bYoo0nk/1bFOD+aPbffd2c/sFt+6PP1m25Qxv30Xh4liE2cNbU61csVxt83sp6n2DnD1FPjg0kt/WHOb8334kJqXgYGvGvNEdcHPOuDsqNDyG39Yc4q/rj4mOTaBSWSemDWmFg41ZtrYfEx1NcVtToqKivvsa/i0fvie0WqxASU0rV9uSpCSQcGCAQuNVhALRJ+jy5cuoqKjQuHHjHK87depU3N3d5R+UAi1ZsgQfH5/8DuOrIqPjadJ7MaqqKuxa0p8rOycwY8gvGOjl7oPyNUsmdqJ2ZRf6TfGlesfZnL36iP0rBlHEzACAIb/Wx6tTHUbP30W9bvMJCYtm7/JB6Gp/vPtg9bSuFLM3p9PwNVTvOJtD526zYXYP3ErYKCTmyzef0qttTU5uGMne5QNJTUuj1aDlxCVkHhsjr8QnJFG6hDXzRn3/l6a8FcSYrMwNmTKwBWd9R3HWdxQ1KpSg88i1+D0LyreYzviMxO/oLOlr7/IBALSoVy7fYtp78gbjF+1hRPdGXNgylqruTrQbspKA4MzJWl4oCO9bVEw8rQcuRVVFBZ95fTjlO5aJXi3Q1824PkokEvpMWE9AYBi/z+rJkXUjsbYwosvwVcTn47VByKxAJEEbNmxg0KBBXLx4EX9//2+v8IMzMDDA0NAwv8P4qiWbTmFtbsiKyV3wKFUUOysTalVyzvavmJzS1FDj5zruTF26n8u3nvHi9Tvm/n6UV4Fh9GhdA4B+HetIa178ngXRf+pmtDXVaNOognQ7Fd0c+H3nBW4+fMWrN2Es3HCCqJgEyrrYfmnXubJ72QA6Na9CSaciuJWwYcXkLrwOjuC2X4BC9pcdDaqXYmL/5jSv655vMXyuIMbUpKYbDauXopi9BcXsLZjk9TM62hr8c/9FvsVkaqSHham+9HXi4gMcbEypXj5/al0AVm47S5cWVfm1ZTWcHSyZM6IN1hZGbNj9V77EUxDet1XbzmBlZsiCcR1xL2mPbRFjqnuUwN7aFIAXr0O59fAVM4e3oWxJO5zszJk5rA1xCUkcPHMrz+LMrrzuGF2Q5HsSFBcXx65du+jfvz+enp4yNSQ+Pj6ZkoX9+/dLD7aPjw/Tpk3jzp070jfhw/r+/v60aNECXV1d9PX1adeuHW/fvpVu50MN0oYNG7Czs0NXV5f+/fuTlpbGvHnzsLS0xNzcnFmzZsns/1vb/WDNmjXY2tqira1N27ZtiYyMlC7r1q0bLVu2/OIxSU5OZvTo0VhbW6Ojo0PlypU5f/58to6nvBz76z7uJe3oNnY9JRqNo1aXufjuv6Sw/amqKKOqqkJismx1dEJiClXcnbC3NsHS1ICzVx9JlyWnpHLp5lMqlXGUzrt65xm/NPDAUF8bJSUlWjXwQF1dlYs3/lVY7J+Kjk0EwEhfO0/2J8hHWlo6e07+Q3xCMhXdHPI7HCDj/P7j2HU6N6+Sb18wySmp3H4UQN3KJWXm16lckmt38y9Z/CC/3rfTlx7g5mKL12QfPFpMomnPBWw/dEW6PDk5FQANdTXpPBUVZdRUVbh+73mexZldhTkJyveO0Tt37sTZ2RlnZ2e6dOnCoEGDmDRpUrYOaPv27bl//z7Hjx/n9OnTQEYti0QioWXLlujo6HDhwgVSU1Px8vKiffv2MsnEs2fPOHbsGMePH+fZs2e0adOGFy9eUKJECS5cuMDly5fp0aMH9erVo0qVKtne7tOnT9m1axeHDh0iOjqanj17MmDAALZu3ZqtY9K9e3devnzJjh07sLKyYt++fTRu3Jh79+5RvHjxLNdJSkqSGZ48Ojo6W/v6kldv3rFx70W8OtVhePeG3HzwinEL96ChpkqHZpVzte2sxMYnce3uc0b1bMKTF28JCY+mTaMKVChtz7OAUCxMMtqYQ8NjZNYLCY/B1tJYOt1z3AbWz+nBizPzSElNIyExmf+N+p2Xb97JPebPSSQSJizeQxV3J1yLWSl8f0LuPXj6hkY9FpKYnIqOlgab5/fGxbHIt1fMA0fO3yUqNoGOnlXyLYawyFjS0tIxM9aTmW9mokdIWO6uMbmR3++bf1AYWw5cplfb2nh1qc+dR/5MXboPdTVVWjeuiJO9BdaWRsxbe5jZI9uhpanOul3nCQ2PydfjJmSW70nQ+vXr6dKlCwCNGzcmNjaWM2fOZOsWNy0tLXR1dVFVVZUOrgRw6tQp7t69y4sXL6RDeG/evJlSpUpx/fp1KlasCEB6ejobNmxAT08PV1dX6tSpw+PHjzl69CjKyso4Ozszd+5czp8/T5UqVTh9+nS2tpuYmIivry82Nhn9UJYtW0azZs1YuHChTJxZefbsGdu3b+f169dYWWV8kY4cOZLjx4+zceNGZs+eneV6c+bMYdq0ad88ZtmVni7BvaQdk7x+BqCMsy2PngezYc9FhSRBAH0nb2L55M74HZtFamoadx4HsPvEP5Rx/tiU9XmnUyUlkPBx3oT+zTHU06aF11LCI+NoWqsMPr/1oGlvbx4+C1RI3B+MmreLB08DOfb7MIXuR5Cf4vYW/Ll1HFEx8Rw8exuvqZs5vGZIgUiEthy8Qv2qrtI+cfnp89+kOR2VV97y+32TpEtwc7ZldJ+MB4CWLmHDvy+C2XLgEq0bV0RNVYXV07szet4OynpOQEVFmeoeJaj9WY1aQZHXd4cVJPmaBD1+/Jhr166xd+/ejGBUVWnfvj0bNmzI0X3+n/Pz88PW1lbmGSaurq4YGhri5+cnTVaKFi0qc7udhYUFKioqMoM4WVhYEBISkqPt2tnZSRMggKpVq5Kens7jx4+/mQTdvHkTiURCiRIlZOYnJSVhYmLyxfXGjRvH8OHDpdPR0dGZnuGSExam+jg7yMZaoqgFh87d/u5tfsvLN+/w7LsEbU119HQ0eRsWzfrZ3fEPDOPt+19P5ib60n8DmBnpERqWUTtU1NqUPu1rUbX9TOkdY/f/fUPVck70aluT4b/tUFjso+fv4tif9zi6dijWFkbfXkEoENTVVHG0zejnVs7VnlsP/Vm94zze4zvma1wBQeFcuP6YTXN75WscJoa6qKgoExImWwP7Ljw2U+1QXsrv983cRD/THaBO9hYc+/OudNrN2ZZj60cRHZtASmoaJoa6tOi3WOZHXUEhkqB8sn79elJTU7G2tpbOk0gkqKmpERERgbKycqZf/ikp376F8Uu/Uj6fr6amJrNcSUkpy3kfRrHM7nY/92FZdk6y9PR0VFRUuHHjRqaxDnR1db+4noaGRpbPaPlelcs48vSVbF+np/4h2HzS9KQo8YnJxCcmY6CnRb0qJZmy7ACv3oQR/C6KOpVduPfkNQBqqipUL1+MqcsOAKCtqQ5k1GJ9Ki1NgpKyYj6gEomE0fP/4Mj5OxxaPUTaMVL4MUkkEml/jvy09dBVzIz0aCiH275zQ11NFXcXW879/QjPOh+HNDh/7RFNarrlY2Sy8vp98yjtwHP/EJl5L16HZPkD6MMdYy9eh3LvcQAjejbJkxhzROn9K7fb+AHlWxKUmprKpk2bWLhwIQ0bNpRZ1rp1a7Zu3YqTkxMxMTHExcWho6MDkGn8HXV1ddLS0mTmubq64u/vT0BAgLQ25OHDh0RFRVGy5PdXR2Z3u/7+/gQGBkqbs65cuYKysnKm2p2slCtXjrS0NEJCQqhRo8Z3x5pb/TvVoXHPRSzaeIKW9ctz88ErNu2/zOLxHRS2z7pVSqKkBP++CsHRxozpQ1ry76sQth7M6HC4evs5hndvyLOAEJ4HhDK8WyPiE1PYfeIfAJ68DOaZfwiLx3Vk0pJ9hEfF0ax2GepUdqbDsNUKiXnk3F3sPvEP2xb0QVdbk7fvMmqp9HU10XqflOW12PgkXgSESqdfBYZx7/FrDA20ZfpPFfaYpq84SP1qrthYGBETn8jekze4ePNfdi/1ypd4PkhPT2fb4at0aFYJVdXsD/qmKF6d6tJvyibKudpR0c0B332XeB0cTvfW+XN9KgjvW8+2tWg9YAkrNp+iWR137vj5s/3QVeaM/DgExJFztzE21MXawpBHz4OYtmwfDX9yo2ZFlzyLU/i2fEuCDh8+TEREBD179sTAQLbNu02bNqxfv54zZ86gra3N+PHjGTRoENeuXcs0vk7RokV58eIFt2/fxsbGBj09PerXr0+ZMmXo3Lkz3t7e0g7MtWrVokKFCnyv7G5XU1OTrl27smDBAqKjoxk8eDDt2rX7ZlMYQIkSJejcuTO//vorCxcupFy5crx7946zZ8/i5uZG06ZNvzv+nCjvas/meb2ZvvIg89cfx87KhFnDW9G2cUWF7VNfV5PJA37GytyQiOh4Dp29zcyVh0hNy6iJW7LpNJoa6iwY0x5DPW1uPHhJ60HLiY3P6BCempZOu6GrmDKwBdsX9UVHW4MXAaF4Td3MqcsPFRLzhj0Ztwl79lsiM3/F5C50ap4/HVpv+72ieb+l0ukJizOamzs2q8zKqf8TMb0XGh5DvymbePsuGn1dTUoVs2b3Ui/q5HO/jfPXHvM6OILOzavmaxwftGroQXhUHPPWHePtu2hKOhVhp7cXdkXyJ3ktCO9b2ZJ2rJnZg3lrj7Bk00lsLY2ZPLAlLRt4SMuEhEUzc8UB3kXEYG6iT6tGFRj0a8OvbDX/FObmsHwbMbp58+akp6dz5MiRTMtu3ryJh4cHN27cwN/fn1GjRvH69Wvq16/Pzz//TJ8+faTNZElJSXTu3JkzZ84QGRnJxo0b6datG/7+/gwaNIgzZ86grKxM48aNWbZsmfQJtFmN7Pz5SM6QeTTn7G63b9++zJw5k/DwcJo2bcq6deukT7r91ojRKSkpzJw5k02bNvHmzRtMTEyoWrUq06ZNw80te1XQ8hgxWhG+NmJ0fsnNiNGC8LmCOAj/j3r7cl7LyYjRipaXI0YbtFuLklruhvWQpMQTtavPDzditHhsxn+USIKyTyRBgjwVxEuqSIKyRyRB3+9HTYLy/RZ5QRAEQRDyjxLyGOzwx0y0RRIkCIIgCIVYYe4TlO+PzRAEQRAEQcgPoiZIEARBEAozMU6QIAiCIAiFkhyawySiOUwQBEEQBOHHIWqCBEEQBKEQk0fH6B91GAaRBAmCIAhCISaSIEEQBEEQCqdC3DFa9AkSBEEQBKFQEjVBgiAIglCIieYw4T8rJiEV1FLzOwypgvicrvKTT+Z3CJlcnlQvv0PIpAA+EovUtPT8DiGThJSCF5Oxjlp+h5CJcgH80tRSV8nvEKRS8jCWwpwEieYwQRAEQRAKJVETJAiCIAiFWGGuCRJJkCAIgiAUYoU5CRLNYYIgCIIgFEqiJkgQBEEQCrNCPE6QSIIEQRAEoRATzWGCIAiCIAiFjKgJEgRBEIRCrDDXBIkkSBAEQRAKMZEECYIgCIJQOBXijtGiT5AgCIIgCIWSqAkSBEEQhEJMNIcJgiAIglAoiSRIULiXL1/i4ODArVu3cHd3z9G6tWvXxt3dHW9vb4XEBhAcGslvaw5z/m8/EpNScLA1Y97oDrg52wIwYs429hy/LrOOu6s9+1cNVVhMn1u08QSHz93h31dv0dRQo1IZR6YObEHxohYK26eZngZDGhanWnFTNFRV8A+LY/r+B/gFxQAw9ZdS/FzOWmadewGRdP39GgD6Wqr0q1OMKsVMsNDXJDI+mfOPQlh15hmxSam5js9370V8910kICgcAGeHIgzr0Yh6VV0BWLDuGPtP3yQwJBJ1NRXKONsytm8zypcqmut9Z9eyTaeYs+YwvdrWYvrQVqSkpjF37RHOXnnIq8Aw9HU0qVHRmfH9mmNpZqCwOIJDI5mz+jDn3p/jjrZmzBvTgTLvz3GJRMLijSfYdugKUTEJlHO1Y8aw1jg7FFFYTLHxiSzZeJzTF+8TFhmDazFrxg9oSRkXOwCc643Icr1RfTzp1b6OQmK6fOspK7ac4c7jAN6+i8Z3bi+a1iojXT5w+hZ2Hr0ms45HKXuOr886VkWIiUtkzpojHLlwh3cRsbiVsGH28NaUd7XPk/0v8T3J0Qt3pdeiim4OTPL6mWL2H69Fg2dkPk7lS9lzbF3eHSfh20QSJCfdunXD19dXOm1sbEzFihWZN28eZcqUwdbWlqCgIExNTfMxyqxFxcTTeuBSqroXx2deH0wM9fAPfIe+rpZMuVqVXJg/tqN0Wl1NJU/jvHzzKb3a1qScqz2paWnMXHWIVoOWc3XXRHS0NOS+Pz1NVTb2qsQ/L8IZtPkm4XHJ2BprE5Mom7xc+vcdU/fdl06npKVL/22mp4mZngbeJ57wPCSWIoZajG9eEjM9TUbvvJPrGIuYGzKhf3OK2pgBsOvoNbqPWccpn1E4OxbB0c6M2SPaYG9lQmJSCmt3nqfD0FVc3jUJUyPdXO//W277vWLLwcu4FrOSzktITObe4wCGdmuEazEromISmLJkL93G/M7xDSMVEkdkTDytBiylarnibJrXBxMjPV59do6v2naWdbvOs3BcJxxtzVi66RSdh6/m/NZx6GprKiSuiQt38e+LYOaN64i5iQEHT9+g++g1HF0/GgszAy7+MUWm/J/XHjFhwS4a1SjzhS3mXnxCMqWKW9PRswrdx63PskzdKiVZOqmzdFpdNW+vBUNnb8PvWRCrpv6KpakBfxy/TquBy7m8YwJW5oYK3/+VW0/p3roG7iXtSEtLZ/bqw7QfupI/t42XuRbVrVKSJRM/Hie1PD5O2aWEHGqCftCe0SIJkqPGjRuzceNGAIKDg5k4cSKenp74+/ujoqKCpaXlF9eVSCSkpaWhqpr3b8mqbWewMjNkwbiPCY5tEeNM5dTVVTE30c/L0GTsXjZAZnrF5C4UbziO234BVC9fTO7761bDgbfRiUzd/0A6LygyMVO55NR0wmKTs9zGs5BYRn2S7LyOSGDFmafMbO2GirISaemSXMXY8KfSMtPj+nmyad8lbjx4ibNjEVo1rCCzfOrgX9h26Cp+z95Qo4Jzrvb9LXHxSQyctpn5YzqwxPekdL6+rhY7l8i+lzOHt6Zpr0W8Dg7HxjLzuZdbq7aeoYi5IQu/cI5LJBLW/3GBgf9rQJP3tR6LxnfCo+Uk9p+6SZcW1eQeU2JSCif/vMfKGd2pWMYJgEFdG3H60n22HbrMsB5NMDOW/byduXSfyu5O2FqZyD2eD+pXc6V+NdevltFQV8Uin64FCYnJHDp3hy3zelOtXMbnfkzvphy9cJeNey8yoZ+nwmPY4e0lM71kYidKNZ3A3UcBVC338VqU39fM7CrMzWHi7jA50tDQwNLSEktLS9zd3RkzZgwBAQGEhoby8uVLlJSUuH37NgDnz59HSUmJEydOUKFCBTQ0NPjrr7+Ii4vj119/RVdXlyJFirBw4UKFx3360gPcXGzxmuyDR4tJNO25gO2HrmQqd/X2UzxaTKJO59mMnbeTdxExCo/ta6JjMxISI31thWy/lrMZD99EM7ddGU6Prs22/lX4xcM6U7kKRY04Pbo2+wZXZ+LPrhjpqH91u7oaqsQlpeY6AfpcWlo6+0/dJD4xCY/SDpmWJ6eksuXAZfR1tXAtlvnvkLfxC/+gXlVXalb8drIVHZuIkpISBnqKeS9PXXpAGWdb+k32odzPk2jScwHbPjnH/YPCCA2PkYlVQ12VymWLceP+C4XElJqWRlp6Ohrqsj98NNXVuJnFPt+Fx3Dhbz/aNKmskHhy4tLNp5RsMp7KbWcwbPZ2QsPz7lqQmpZOWlo6GhpqMvM1NdT4+86zPIvjUzHvr0WGn12LLt98imvT8VRtN4Phc/L2OAnZI2qCFCQ2NpatW7dSrFgxTExMiIuLy7Lc6NGjWbBgAY6OjhgaGjJq1CjOnTvHvn37sLS0ZPz48dy4ceOb/YiSkpJISkqSTkdHR2c7Vv+gMLYcuEyvtrXx6lKfO4/8mbp0H+pqqrRuXBGA2pVL0qx2WawtjAkICmPhhmN0GraSQ2tHZLqI5wWJRMKExXuo4u4k09QiT9ZGWrSpaMPWK6/Y8OcLStsYMKqpC8mp6Ry5EwTA5X/fcfrBW4IiE7E20qJ/XSfWdKtA59VXSEnLnOQYaKnRu7Yje/55Lbc4/Z4F4tlnMUnJqehoabBhTk+cHT7WOp66dJ9+k31JSEzBwkSfnd79MTFUbFPY/tM3uffkNUez0f8hMSmF2asO8UuD8ujpKKbZKeDDOd6uNgO71Oe2nz9TlmSc420aVyQ0LOPLydRYT2Y9U2Nd3gRHKCQmXW1Nyrnas3LLaRztLDA10uPw2VvceeSPvXXmZvN9J6+jo61BwxpuCoknu+pVdeXneuWwtTTCPzCMOWuP0mrgck77jERDXe3bG8glPR1NKro5sHDDcUoUtcTcWI89J29w48ErHG3NFL7/z0kkEiYv3Uflso6UdPp4Lapb1ZXmdcth8/44zf39KK0HLefUxrw5TjlSiMcJEkmQHB0+fBhd3Ywvl7i4OIoUKcLhw4dRVv5yhdv06dNp0KABkJE4rV+/nk2bNknn+fr6YmNj8819z5kzh2nTpn1X3JJ0CW7Otozu0wyA0iVs+PdFMFsOXJImQc3rlpOWd3YsQhkXW6q3m8G5qw9pXFNx/RO+ZNS8XTx4Gsix34cpbB/KSko8DIxm+emnADwOjsHRXIe2lWylSdDJ+2+l5Z+FxPLwTRRHhtekRgkzzvqFyGxPR0OFpV3K8Tw0jrXn5PeL1cnOnNO+o4mKSeDI+TsMnrmVvSsGSxOh6uWLc9p3NOGRcWw9eJk+k3w4+vvwTF/48vLmbQSTvfewfbEXmhpfv9inpKbRf4ov6RIJc0a2U0g8AOnpEso42zLmk3P8ycuMc7zN+3McMl/HJRLFVvPPG9eJ8fN3UrP9dFSUlXEtbo1n3XI8/PdNprJ7jl+jeb3y+f4F+kuD8tJ/l3SyomxJO8q3nMqpSw/xrFM2T2JYNfV/DJ65jdKeE1FRUaaMsw2tG3lw95H8flxk17gFf+D3NJCDa4bIzG9ZX/Y4uZe0w+OXqZy+/JBmtfPmOGWXaA4T5KJOnTrcvn2b27dv8/fff9OwYUOaNGnCq1evvrhOhQof+2w8e/aM5ORkqlatKp1nbGyMs/O3mxPGjRtHVFSU9BUQEJDtuM1N9DPdYeVkb0FgSORX1jHA2sKIF69Ds70feRk9fxfH/rzHoVWDsbYwUth+3sUm8Tw0Vmbei9A4LA2+XFvxLjaZoKgEbE1kq8W11VVY/j8P4pPTGLH9NqlybApTV1PFwcYM95J2TOjfnFLFrFm368LHfWtp4GBjhkfpoiwa3wlVFWW2Hb4qt/1/7u7jAN5FxNK45wJsaw7DtuYwrtx6yvrdf2Jbcxhp7zuOp6Sm0XfSRgKCwtjh7aWwWiDI+hwvbm/Bm7eRAJiZZCSEnzdXhEXEKrQDuZ2VKVsWD+DW4dmc3zGJ3SuHkpqWhs1nffL+ufucFwGhtG1aRWGxfC9LUwNsLI15HhDy7cJy4mBjxqHVQ/A/v4C7B6dzeuMoUlPTsLeSf3+yrxm3cDcnLt5nz4pBWJl//VpkkQ/HSfg2URMkRzo6OhQr9rFTnIeHBwYGBvz+++/06tXri+t8IJF8/xejhoYGGhrfd4eUR2kHnvvLfjBfvA75aoIRERVHYGgk5sZ51+lPIpEwev4fHDl/h0Orh2TZZCBPt/0jKWqqIzPP3kQny87RHxhoqWGhr8m7mI9NkzoaKqz41YPk1HSGbbtFcmr6F9eXB4lEQnLKl2+/l0ggOTn3t+d/SQ2PEpzdPEZm3rBZ2yhmb8GALvVQUVGWJkAvAkLZvWwQxgY6X9iafFRwc+DZZ18+zwNCsHl/jtsVMcHMWI+//nlM6RIZNa/JKan8fecpY/s2V2hskJGoamtpEBUTz8XrjxnVR7Zz7+5jf1OqhA0uTopp+s2N8Kg4AkMisDBV3PAGX6KjpYGOlgaR0fGcvfqIqQNb5Ml+JRIJ4xfu5uiFu+xbOQj7bHRUlx4nk7w/Tt9SmGuCRBKkQEpKSigrK5OQkJCt8sWKFUNNTY2rV69iZ5cxTkhERARPnjyhVq1aCouzZ9tatB6whBWbT9Gsjjt3/PzZfuiqtHkiLj4Jb5/jNK5ZFnMTfV4HhzP/9yMYG+jQqGbe9U8YOXcXu0/8w7YFfdDV1uTtu4x+T/q6mmhpfr0z8vfYevkVG3tXokdNB07dD6aUtQGtKtgw82DG3WJa6ir0rePE2YdvCY1JwspQi4H1ixMZn8K5901h2uoqrPzVA001FSbuvoeOhio673PViLhkclshNHv1IepWccXawpDY+CT2n7rJ5VtP2baoH/EJSXj7nqTRT26Ym+gTER2H796LBIVG0ryue+52/BW6Opq4OMp+WWtraWCkr4OLoxWpqWn0nrCBe09es2leH9LS0wkJy3gvDfW1UVeT/2WpV9ta/OK1hOWbT+FZx53bfv5sO3SV396f40pKSvRsW4sVW07jYGOGg40Zy7ecRlNDnZafNP/I21/XHyGRgIOtGf5v3jFv7WEcbM1p1biStExsXCLH/7zLmH6KT8YAYuOTZGp4/QPDuPfkNUb62hjq6zB/3TE865TFwkSfgKBwZq0+hLGBDs1q5V2z+NmrfkgkEorZm/M84B1Tl+2nmL05nZrnTU3Z2AV/sPfkDXzn9kJXW1N6/urpZFyL4uKTmL/uGM3qlMXCNOM4zV6VcZya5uFxyi4lpYxXbrfxIxJJkBwlJSURHBwMZCQvy5cvJzY2lubNs3fx0tXVpWfPnowaNQoTExMsLCyYMGHCV/sUyUPZknasmdmDeWuPsGTTSWwtjZk8sCUtG3gAoKKixKPnQew98Q/RsQmYm+hTpVwxlk/9VWHjp2Rlw56/APDst0Rm/orJXRRy8XsYGM3I7bcZ2KA4vWs5EhiZwIJjjzh2N+M9Tk+XUNxCF8+yVuhpqvIuNonrL8IZu+sO8clpAJS00sfN1hCAg8NqyGy/2aI/v1qrlB3vwmMYNH0LIWFR6Olo4VrMim2L+lGrkguJSSk8fRXCH0c3EB4Vi5GBDu4uduxfORhnR8UNAPgtQaGRnLyYMa5Sg27zZJbtXjaQauWLy32fZUvasXZWD+auOcIS34xzfMqglvzS0ENapn+nuiQmpTBh0W6iYxNwL2nP1oX9FHqOx8QlsmjdUYLfRWKop03DGmUY1qOJzHgyR87dQiKR4Fmn3Fe2JD93/PxpOWCZdHrSkn0AtG9aifmj2/HwWSC7jl0jKiYBC1N9qpcvzu8zu6OrwObMz0XHJjBj5SECQyIx0tfGs05ZJvZvnmfj8PjsvQjAL58cJ4AlEzvToVlllJWV8HseyK7j14j+5DitzePjlF0ZSVBua4LkFEweU5Lkpg1GkPp8sEQ9PT1cXFwYM2YMrVu3zjRi9Pnz56lTpw4REREYGhpK14uNjaV///7s3bsXPT09RowYwZEjR3I8YnR0dDQGBgb8G/AOPf2CM06FgXYBuysCKD/55LcL5bHLk+rldwiZFMQrRWqaYpsWv0dCSsGLyVin4H3ulAvgt6Y8++rlVnR0NLYWRkRFRaGvoGv4h+8Jx0G7UdbIXZN0elIcz5e1UWi8iiCSoP8okQRln0iCsqcgXilEEpQ9IgnKnkKbBA3ejUouk6C0pDieL/3xkiDRHCYIgiAIhVhh7hgtbpEXBEEQBKFQEjVBgiAIglCIibvDBEEQBEEolJSVlVBWzl0WI8nl+vlFNIcJgiAIglAoiZogQRAEQSjERHOYIAiCIAiFkrg7TBAEQRAEoZARNUGCIAiCUIiJ5jBBEARBEAqlwtwcJpIgQRAEQSjERBIk/Gdpa6igo5E3T1bOjoL4qLqrk+vndwiZ9Nh2K79DyGRdR/f8DiGTJ8Gx+R1CJk7muXsGkyKkphW8z52aasH70kwvQM8OK0ix/JeJJEgQBEEQCjHRJ0gQBEEQhEJJCTk0h/FjZkHiFnlBEARBEAolURMkCIIgCIWYaA4TBEEQBKFQKsx3h4nmMEEQBEEQCiVREyQIgiAIhZhoDhMEQRAEoVASzWGCIAiCIAiFjKgJEgRBEIRCrDA3h4maIEEQBEEoxD40h+X2lRNv3ryhS5cumJiYoK2tjbu7Ozdu3JAul0gkTJ06FSsrK7S0tKhduzYPHjyQ2UZSUhKDBg3C1NQUHR0dfv75Z16/fp2jOEQSJAiCIAiFmdLH2qDvfeVkwOiIiAiqV6+Ompoax44d4+HDhyxcuBBDQ0NpmXnz5rFo0SKWL1/O9evXsbS0pEGDBsTExEjLDB06lH379rFjxw4uXrxIbGwsnp6epKWlZTsW0RwmCIIgCEKemTt3Lra2tmzcuFE6r2jRotJ/SyQSvL29mTBhAq1atQLA19cXCwsLtm3bRt++fYmKimL9+vVs3ryZ+vUzHoK9ZcsWbG1tOX36NI0aNcpWLCIJEvD2PcmR83f599VbtDTUqOjmwOQBP1PM3kJa5vC5O/juv8TdRwGER8VxdtNo3ErY5GmcZVtMISAoPNP8nm1qMH90O4Xvf4nvSY5eyDhOmu+P0yQv2eM0eMYWdh69JrNe+VL2HFs3Qi4xtCpbhFZlrWTmRSakMPCPuwBUsDOkbglTHIx10NNUZfyhh/hHJMiUN9BUpaOHDaWt9NFUVSY4OokD94K47h8plxh9917Ed99F6Xvl7FCEYT0aUa+qKympacxdc4QzVx7yKjAMfV1NalRwZkL/5liaGchl/wDb9l3g4jU/At6EoqGuhmsJW3p3aYitldnHOHed5fzle4SGRaGqqkJxRyt6dKhPyeK2mbYnkUgYP2cz12//y7SRHaleyTXXMf7UfgZv3kZkmt+lZXVmDG0tM2/8wl1sP3SVSQNa0KNtrVzv+0t892Xx3nXPeO8AilQfkuV6k7x+xqtzPYXFdfnWU5ZvOcOdR/68fRfNpnm9aFqrrHT53N+Psu/UDQLfRqKmpkJZF1sm9GuOR+miConnW8cpNDyamSsPceHaI6JiE6ji7sSsYa1xtDVXSDy5Jc+7w6Kjo2Xma2hooKGhITPv4MGDNGrUiLZt23LhwgWsra3x8vKid+/eALx48YLg4GAaNmwos51atWpx+fJl+vbty40bN0hJSZEpY2VlRenSpbl8+bJIggqK4OBgZs2axZEjR3jz5g3m5ua4u7szdOhQ6tX79kXDx8eHoUOHEhkZqbAYL996So/WNSjnakdqWjqzVx+m7ZCVXNw+Hh2tjJM3PjGJymUc+LmuO8Pn7FBYLF9zxmckaWkS6bTf80BaDVxBi3rl8mT/V249pXvrGriXtCPt/XFqP3Qlf277eJwA6lYpyZKJnaXTaqoqco0jICKB3049kU6nfzwkaKgq8yQkjmsvI+hVrWiW6/f7yQFtdRUWnX1KTFIq1RyMGVTTkUlH/XgVnpDlOjlRxNyQCf2bU9QmI+HYdfQa3ces45TPKIqYG3LvSQDDujfCtZgVUTEJTF6yl65jfufEhpG53vcHdx++pEWjSjg7WZOWls6GHacZM9OX9YsGo6WpDoCNlQkDe3hSxMKI5OQU9hy5wpiZvmxaNgxDfR2Z7e05ckXuHT8PrBlGelq6dPrxi2D+N3I1zT75cgc4+dc9bj/0x8JUX74BZKGImSET+n3y3h27Rvex6zi1cRTOjkW4c3CGTPmzVx8yfM4OmtUum9Xm5CY+IYnSxa3p5FmZbmPXZ1ruZGfO3JFtsbc2JTEphVXbz9Fm8Aqu75mMqZGe3OP52nEq4WBJ97HrUVVVwWduL3S1NVmz8zzthqzkz63j0NbS+MbW8548O0bb2sr+iJgyZQpTp06Vmff8+XNWrVrF8OHDGT9+PNeuXWPw4MFoaGjw66+/EhwcDICFhYXMehYWFrx69QrI+G5VV1fHyMgoU5kP62eHSIIU6OXLl1SvXh1DQ0PmzZtHmTJlSElJ4cSJEwwYMIBHjx7ld4gA7PL2kpleOrETJZtM4M6jAKqVKwZAuyaVAPAPDMvz+D74/GLmvekUDjamVC9fLE/2v+Oz47RkYidKNZ3A3UcBVC33MQZ1dVXMTRT3hZUukRCVmJrlskvPM36Zmuqof3H94mY6bPzbn+dh8QAcuBdMY1cLihpryyUJavhTaZnpcf082bTvEjcevKSTY1V2Lhkgs3zWsNY06bWI18Hh2Fga53r/AL9N6CozPcqrFW16/ca/zwMp41oUgHo/yX5x9/u1McfO3uD5q2DKuzlJ5z97GcSeI5dYMacf7frMk0t8ACaGujLTq7adwd7KhMruH/cdHBrJlCV78Z3flx5jf5fbvr8k03vX9+N75+xYJNN5ffyv+1QvXwx7a1OFxlW/WinqVyv1xeVtGlWQmZ455Be2HrzCw6eB1KzoLPd4vnacVFVVuPHgJec3j8XZsQgAv41oi5vnBPaduknnn6vKPZ6CJCAgAH39j+fJ57VAAOnp6VSoUIHZs2cDUK5cOR48eMCqVav49ddfpeU+r52SSCTfrLHKTplPiY7RCuTl5YWSkhLXrl2jTZs2lChRglKlSjF8+HCuXr0KwKJFi3Bzc0NHRwdbW1u8vLyIjY0F4Pz583Tv3p2oqChpdeXnGbUiRMcmAmCkr63wfX2v5JRU/jh2nc7Nq+TbIF0x74+T4WfH6fLNp7g2HU/VdjMYPmc7oeExWa3+3Sz0NFjWxo1Fv5RmQA0HzHS/nPBk5UlILFWKGqGjroISUKWoEWrKSvgFyzdOgLS0dPafukl8YhIepR2yLBMdl4iSkhIGeoo73+LiM94rPV2tLJenpKZy5PQ/6Ghr4mRvKZ2fmJTMrCV/MLCHJ8aG8q9R+CA5JZX9p27Stmll6fmcnp7O8Nnb6NOhDiUcLL+xBflLS0tn/+kvv3eh4dGcufyAjp5V8jy2r0lOScV3/2X0dbUoVdxa4fv7/Dglp2T8QNFQV5OWUVFRRk1NlWt3nys8nu8hz7vD9PX1ZV5ZJUFFihTB1VW2SblkyZL4+/sDYGmZcb5/XqMTEhIirR2ytLQkOTmZiIiIL5bJDlETpCDh4eEcP36cWbNmoaOjk2n5h17wysrKLF26lKJFi/LixQu8vLwYPXo0K1eupFq1anh7ezN58mQeP34MgK6ubqZtQcatgklJSdLpz9tls0sikTB5yT4ql3WkpJPVt1fIJ0fO3yUqNiHfLsASiYTJSzMfp7pVXWletxw2lkb4B4Yx9/ejtB60nFMbR8pcFL/X09A41lx6SVB0IgZaarR0K8KUJi6MPfiA2KTs3RGx7M/nDKrpyJoO7qSmS0hOTcf7/DNCYpNzHd8Hfs8C8eyzmKTkVHS0NNgwpyfOWXyRJyalMGvVIX5pUB49HU257f9TEomE1b7HKO1ij4Od7MXx6o3HzPTeRVJyCsaGusyd2BWDT5rCVvkeo5SzHdUrllRIbB+cvHif6NgE2jSuKJ23evtZVFSU6da6hkL3/Tm/Z4F49v3kvZud9Xu369h1dLU1Zfrm5KcTF+/TZ+JG4hNTsDDVZ/eyAZlq2+TpS8cpJTUNG0tjZq85xLxR7dHWUmfNjnOEhEXzNuz7rsuKltfjBFWvXl36nfbBkydPsLe3B8DBwQFLS0tOnTpFuXIZ3R2Sk5O5cOECc+fOBcDDwwM1NTVOnTpFu3YZfUKDgoK4f/8+8+Zlv8ZWJEEK8vTpUyQSCS4uLl8tN3ToUOm/HRwcmDFjBv3792flypWoq6tjYGCAkpKSNDP+kjlz5jBt2rRcxz1mwR88fBrI4bVZd4AsKLYcvEL9qq4UkWNn2pwYt+AP/J4GcnCN7HFqWb+89N8lnaxwL2mHxy9TOX35oVz6TdwN/HgRfR2ZyNPQOBb+UpoajiYc8wvJ1jbalrNGW12VOSefEJOUioetIYNqOTLj+GNeRybmOkbI6KNx2nc0UTEJHDl/h8Ezt7J3xWCZL9OU1DT6TfYlPV3Cb6MU17F92frDPPd/i/f0XpmWlS3lwJr5XkRFx3P0zD/MXLyTZbP7YmSgy+V//Lh9/zmr53llsVX52nX0b2pVdsHCNON8vvc4gI27/+Lw78PzvKbTyc6c0z6fvHeztrJ3+eBMidD2w1dp1dADTY3cJ/fy8JNHcc5tHkt4ZCybD1ym1/gNnNgwEjNjxdTgfe04rZvVgxFztlOyyThUVJSpUaEEdasoNpH+kQwbNoxq1aoxe/Zs2rVrx7Vr11i7di1r164FMmqmhg4dyuzZsylevDjFixdn9uzZaGtr06lTJwAMDAzo2bMnI0aMwMTEBGNjY0aOHImbm5v0brHsEEmQgkgkGb1Vv3UBO3fuHLNnz+bhw4dER0eTmppKYmIicXFxWdYgfcm4ceMYPny4dDo6OjpTB7VvGbtgNyf+us/B1UOwMjf69gr5JCAonAvXH7NpbuYvtbwwbuFuTly8z/5V3z5OFqYG2Fga8zwgewlKTiWlphMQkYCFfvZqUcx11WnoYs6YAw94E5WR8PhHJOBsoUsDZ3M2/u0vl7jU1VRxeN9p1L2kHXf8/Fm36wLzx7QHMhKgPhM3EhAUxh/LBiqsFmjZhsNcufGIRdN6YWaSOWHW0lTH2tIEa0sTXEvY0nXwYo6dvUGnX2px+/4LAt9G0KLbbJl1pi3cQemS9iya2lMuMb4ODufSjSesmt5dOu/63eeERcZSvd3Hjshp6enMWnWQDbv/5OLOSXLZd1YyvXeP/Fn3xwXmj24vLXP19jOe+YewZno3hcWRUzpaGjjamuFoa0YFNwcqtp7O1oNXGNqt4bdX/g5fO05lXWw57Tua6NgEklPSMDXSpWnvRZR1ydk1Oa/k9bPDKlasyL59+xg3bhzTp0/HwcEBb29vOnf+eEPJ6NGjSUhIwMvLi4iICCpXrszJkyfR0/uY1C5evBhVVVXatWtHQkIC9erVw8fHBxWV7N+MIpIgBSlevDhKSkr4+fnRsmXLLMu8evWKpk2b0q9fP2bMmIGxsTEXL16kZ8+epKSk5Gh/Wd2GmF0SiYSxC3dz9MJd9q8YhL2VyXdtJ69sPXQVMyM9Glb/ckdJRZBIJIx/f5z2rczecQqPiiMwJAKLLL6A5UFVWQlrA00eh8Rmq7y6akY3QMln89MlEoUOey+RSKR9JT4kQC8CQtm9fBDGBtlP9nOyv+UbjnDx2kMWTu1JkWwm9RIJpKRkNCt2aFmDJnU9ZJb3Hrmc/l2bUKXC12t4c2L3sWuYGOrK1BT80rAC1T1KyJTrOnoNvzSoQJv3NynkFYlEQnKybEf87YevUsbZNk/63Hw/CUkpWd9AoJC9ZXGc9N/3QXseEMKdR/6M7tU0z+LJifx4gKqnpyeenp5f3d7UqVO/2g9WU1OTZcuWsWzZshzt+1MiCVIQY2NjGjVqxIoVKxg8eHCmWp3IyEj++ecfUlNTWbhwIcrKGV9Ou3btkimnrq6eo9Evv8eY+X+w5+QNNs3rha6OprTdWl9HU3o7cURUHK/fRhD8LgqAp68yajbMTfSxUOCdUJ9LT09n2+GrdGhWCVU533r+LWMX/MHekzfwfX/ba8j746T3/jjFxScxf90xmtUpi4WpPgFB4cxedQhjAx2a1iojlxg6elhz63UUYXHJ6Guq0sKtCFpqKvz1LOOuPR11FUx01DHSzmiiKGKQUcMSlZBCVGIqQVGJBEcn0qOKHdv+eU1sUioedoaULqLPwrNP5RLj7NWHqFvFFWsLQ2Ljk9h/6iaXbz1l26J+pKam0Xv8Bu49ec2m+X1IT0+XHkdDfW3U1eRzSVq6/jBnL95l+uhOaGupEx6Z0elbR1sTDXU1EhKT2bb3AlUruGBipEd0TDwHT14jNDyaWlUzkmtjQ70sO0ObmxpkO6n6lvT0dP44fp3WjSrKnM9GBjoYfZYcqqqoYGash5Od4saayfTenX7/3i3sJy0TE5fIoXO3mTKwhcLi+FxsfBIvXodKp18FhnHvyWuM9LUxMtBh8cYTNK7hhoWpAeFRcWzY8xeBIZEKGz7jW8fp0NlbmBjqYm1hhN/zICZ576VxDTdqV5Zf8izIh0iCFOhD5+ZKlSoxffp0ypQpQ2pqKqdOnWLVqlVs376d1NRUli1bRvPmzbl06RKrV6+W2UbRokWJjY3lzJkzlC1bFm1tbbS15XsXzca9FwFo6SWbTS+d2JmOnpWBjFthB8/cKl3WZ5IPAKN6NmZ077z7dXP+2mNeB0fQuXne32bq8/44/TJA9jgtmdiZDs0qo6yshN/zQHYdv0Z0TAIWpvpUL1+ctTO7oyun5h5jbXUG1HBAT0OV6KRUnobGMeXYI8LiMjo1l7c1pG/1otLyg2o6ArD3TiB77wSRJoH5Z57Svrw1I+oWQ0NVmbcxSay59JI7b+TTafNdeAyDpm8hJCwKPR0tXItZsW1RP2pVciEgKIwTF+8DUL+rbOfFPcsHUq18cbnEcOhkxoCVI6ZukJk/yusXGtUuj4qyEgGBoZxceIvomHj09bQp4WTN4mk9KWqb/TtLcuvijX8JfBtB26Z5W7vzJe8iYhg047P3bmHGe/fB/tM3kUgk/NLA4ytbkq/bfv609FoqnZ7kvQ+ADs0qsWBMB/599ZYdR68RHhmHkYE25Urac2jNUFze36Iub986Tm/Dopm6bD+h4TGYm+jTtnFFhnXP3uB9+aEwP0BVSfKh84qgEEFBQcyaNYvDhw8TFBSEmZkZHh4eDBs2jNq1a7N48WLmz59PZGQkNWvWpHPnzvz6669ERERI7yDr378/f/zxB2FhYVkOPJWV6OhoDAwMeBMSITNmQ35TUS54n5SUtIL3Eeix7VZ+h5DJuo7u+R1CJg9eF7y7bZzM5d+8l1uaanlba5odaqoFb4SWlNT0bxfKI9HR0dgXMSYqKkph1/AP3xPV55xEVTN3521qYhyXxjVUaLyKIJKg/yiRBGWfSIKyRyRB2SOSoOwRSdDX5WUS9NNv8kmCLo798ZKggncWCoIgCIIg5AHRJ0gQBEEQCrH8uDusoBBJkCAIgiAUYkrIoWO0XCLJe6I5TBAEQRCEQknUBAmCIAhCIaaspIRyLquCcrt+fhFJkCAIgiAUYoV5nCDRHCYIgiAIQqEkaoIEQRAEoRATd4cJgiAIglAoKStlvHK7jR+RaA4TBEEQBKFQEjVBgiAIglCYKcmhOesHrQkSSZCQpwpiu7FEUnCeF/TBug7u+R1CJu7jjuV3CJn8M7NxfoeQScE7wyE2KTW/Q8jESFU9v0PIRFWl4Lx7eRlLYb47TCRBgiAIglCIKb3/L7fb+BGJPkGCIAiCIBRKoiZIEARBEAqxwnx3mEiCBEEQBKEQK8zjBInmMEEQBEEQCqVs1QQtXbo02xscPHjwdwcjCIIgCELeEneHfcPixYuztTElJSWRBAmCIAjCD0Q8Rf4bXrx4oeg4BEEQBEEQ8tR39wlKTk7m8ePHpKYWvEG4BEEQBEHIng/NYbl9/YhynATFx8fTs2dPtLW1KVWqFP7+/kBGX6DffvtN7gEKgiAIgqA4H+4Oy+3rR5TjJGjcuHHcuXOH8+fPo6mpKZ1fv359du7cKdfgBEEQBEEQFCXH4wTt37+fnTt3UqVKFZnMz9XVlWfPnsk1OEEQBEEQFEvcHZYDoaGhmJubZ5ofFxf3w1aHCYIgCEJhJe4Oy4GKFSty5MgRBg0aBHwcJfL333+natWq8o1OyDOXbz1lxZYz3HkcwNt30fjO7UXTWmWky0PCopm+4iDnrz0iOiaBKuWcmDO8DU52mRNiRVm/+y827PmLgKBwAFwcLRnVswkNqpfKk/0v3XSKI+fv8NQ/BE11NSq6OTDRqznF7C2kZULDo5mx8hAXPhwndydmDW+No61ijpPvvov47rsoPSbODkUY1r0R9aq6Sss8eRnMrJWHuHL7KenpEpwdLFkzoxs2lsZyi8NcX5ORzVyo6WKOhpoKL0NjmbjrLg/eRAGgra7CiGYlqVfKAkMddd6Ex7P54kt2XHkl3cam/lWp5GQis90jt94wYuutXMe3aMMxFm88ITPPzFiPmwdmZCo7dv5Oth68wpRBLenVrnau9/0lC78Q0633MR29cIetBy5z98lrIqLiOLFhJKWK2ygsng9i4xPx3nCcUxfvExYZg2sxayYObEkZFzsAJBIJy3xPsvPIVaJi4ilb0p6pg1tR3MFSYTFdvvWU5VvOcOeRP2/fRbNpXi+a1iorXT7396PsO3WDwLeRqKmpUNbFlgn9muNRuqhC4vH2PcmR83f599VbtDQyrgWTB/wscy2QSCTMX3eMTQcuExWTQHlXe+aOaouLYxGFxJQbSu9fud3GjyjHSdCcOXNo3LgxDx8+JDU1lSVLlvDgwQOuXLnChQsXFBFjgdOtWzd8fX0BUFVVxdjYmDJlytCxY0e6deuGsvKPNxB3fEIypYpb09GzCt3HrZdZJpFI6DpmHaqqKmye1xs9HU1WbT9Hm8EruLh9PDpaGnkSo5W5IVMGtsDRxhSA7Uf+pvPItVzYMpaSToq/sFy59ZTurWvgXtKOtLR05qw5TPuhq/hz2zh0tDSQSCR0G7MeNVUVfH7rhZ6OJmt2nKft4JXSMvJWxMyQCf2aU9TGDIBdx67Rfew6Tm0chbNjEV6+fkfL/kvo6FmFkb2aoK+jyb+v3qKpoSa3GPS11Ng+sBp/Pwuj97prhMcmYWuiTXRiirTM2J9LUbmYCaO33+ZNeDzVS5gxuVVpQqITOfvgrbTcrquvWHriiXQ6MSVNbnGWcLBk+2Iv6bRKFp/T43/e5dbDV1iYGshtv1/j/JWY4hOSqeDmQLM67oyel3f9LScs2MWTF8HMH9cRC1MDDpy6QddRazi2YTSWZgas3XGODbsvMHd0BxxszVi55TTdRq/hhO8YdLU1v72D7xCfkETp4tZ08qxMt7HrMy13sjNn7si22FubkpiUIr0+Xd8zGVMjPbnHc/nWU3q0rkE5VztS09KZvfowbYeslLkeLtt8mlXbz7FsUhec7MxYtPEkbQav4OrOiejqKOY4CTmX42/ratWqcenSJeLj43FycuLkyZNYWFhw5coVPDw8FBFjgdS4cWOCgoJ4+fIlx44do06dOgwZMgRPT0+FDhuQkpLy7ULfoX41V8b388SzTtlMy54HhPLP/ZfMH92Ocq72FLO3YN6odsTFJ7H35A2FxJOVJjXdaFi9FMXsLShmb8Ekr5/R0dbgn/t5M47V9sX96dCsMi6ORShV3BrvCZ158zaCu48CgIzjdOPBS34b1VZ6nH4b2Zb4hCT2n7qpkJga/lSaetVK4WRnjpOdOeP6eqKjpcGNBy8B+G3tYepWdWXSgBa4lbDB3tqU+tVKyfWLoVcdJ4IiExi/8w73AiJ5E5HA1adhBITFS8u4FzVk/z+vufYsjDcRCez625/HQdGUtpFNNhKS03gXkyR9xSbK77OkqqKMuYm+9GVipCuzPCg0kknee1g6+X+oqebNDxmVr8TUpnFFhnVvTI0KJfIkFoDEpBRO/HmP0X09qVTWCXtrUwZ3a4SNpTHbDl5GIpHgu+dP+neuT6OaZSjhUIS5YzqSkJjMoTO5r7H7kvrVSr2/PrlnubxNowrUquRCUWtTXByLMHPIL8TEJfLwaaBC4tnl7UVHz4xrQeni1iyd2InXwRHceX8tkEgkrNl5gWHdGuJZpywlnaxYPrkzCYkp7MnDa2Z2ibvDcsjNzQ1fX1/u37/Pw4cP2bJlC25ubvKOrUDT0NDA0tISa2trypcvz/jx4zlw4ADHjh3Dx8cHgKioKPr06YO5uTn6+vrUrVuXO3fuyGzn0KFDeHh4oKmpiaOjI9OmTZNJopSUlFi9ejUtWrRAR0eHmTNn5uWfCUBSckY8GuofKw5VVJRRU1Pl7zvP8zwegLS0dPac/If4hGQqujnkSwwxcQkAGOprA5CcknGcNNU/1rJIj9NdxR+ntLR09p++SXxiEh6lHUhPT+f05Yc42prTYdgqSjebQNPeizj251257rduKQvuv47C+3/luTS1AXuH1aBtZTuZMjdfRFC3lAXm+hm/gCs7mVDUVJeLj0NlyjUvb82VaQ05NLIWoz1LoqOhIrc4X7x+h0fLyVRrNx2vKb68CnwnXZaens7QmVvp17Euzg5511zxIaaqWcSUH1LT0khLT5f5rANoaqhx4/4LAoLCCQ2P4adPEjMNdVUqlXXi1vvEO78lp6Tiu/8y+rpalCpunSf7jI5NBMDo/bXgVWAYIWHR1K7sIi2joa5GtXJOXLtX8AYf/vAU+dy+fkTf9RT5tLQ09u3bh5+fH0pKSpQsWZIWLVqgqlq4H0pft25dypYty969e+nZsyfNmjXD2NiYo0ePYmBgwJo1a6hXrx5PnjzB2NiYEydO0KVLF5YuXUqNGjV49uwZffr0AWDKlCnS7U6ZMoU5c+awePFiVFSy/lJISkoiKSlJOh0dHS23v6t4UQtsLY2ZueoQC8d0QFtLnVXbzxESFs3bMPntJzsePH1Dox4LSUxORUdLg83ze+dLG7tEImHK0v1ULutISScrAIrZW2Bjacys1YeYP7o92lrqrHl/nELeKe44+T0LxLPvYpLeH5MNs3vi7GBJSFg0cQlJLN9ymjG9mzKxf3PO/e1Hz/Eb2L1sINXKFZPL/m2NtelY1R6fP5+z5sxTytgZMqFlKZJT0zhw4w0As/bfZ0bbMvw5uT4paelIJBIm7rrLzZcR0u0cuvmG1+HxvItJorilHsObuuBspU/PtX/nOsZyrvZ4T+iMg60Z7yJiWOp7kl/6L+HMprEYGeiwcusZVFSU6dGmZq73ldOYHN/HtMT3JC37L+Hs+5jyg662JuVc7Vmx+TROdhaYGulx+Owt7vj5U9TalHfhGefx5zWJpkZ6vHkbnh8hS524eJ8+EzcSn5iChak+u5cNwMRQ99sr5pJEImHykn0y14KQ99dFc2N9mbJmxvoEBOfvcRJk5ThruX//Pi1atCA4OBhnZ2cAnjx5gpmZGQcPHix0NUKfc3Fx4e7du5w7d4579+4REhKChkZGG/GCBQvYv38/u3fvpk+fPsyaNYuxY8fStWtXABwdHZkxYwajR4+WSYI6depEjx49vrrfOXPmMG3aNIX8TWqqKmz8rQdDZm2neMOxqKgoU7NiCZnOt3mluL0Ff24dR1RMPAfP3sZr6mYOrxmS54nQuIW7efg0kIOrh0jnqamqsH52D4bP2Y5L43EZx6lCCepWLanQWJzszDntM5qomASOnL/D4Flb2bt8MAa6WgA0rlGavh3qAFC6hA3/3HvJ5v2X5JYEKSkp8eB1JIuPPQbALzCaYhZ6dKxaVJoE/e8nB8raGdF/wzXeRCRQ0dGEKa3cCI1J4sq/GbUff/ztL93mv8ExvAqNY8+wGrha6/PwTe6SyDpVZM9Vj1JF+anDTP44do0q5YqxYfefHF0/Mk+r9OtmEVP19zH1ef9+5Yf54zoxbv5Ofmo3HRVlZUoVt6Z5vXI8+PeNtMznx0kikeR7c8hPHsU5t3ks4ZGxbD5wmV7jN3Biw0jMjOXfJ+hTYxb8wcOngRxeOyTzws8OScZxUmg430UezVn5/f5/rxwnQb169aJUqVL8888/GBkZARAREUG3bt3o06cPV65ckXuQP5IPF4MbN24QGxuLiYns3S4JCQnS8ZRu3LjB9evXmTVrlnR5WloaiYmJxMfHo62dUbVaoUKFb+533LhxDB8+XDodHR2Nra2tPP4kAMq62HF+8xiiYxNITknF1EiPRj0WUrak/PaRHepqqjjaZnQCLudqz62H/qzecR7v8R3zLIbxi3Zz8uJ99q0cjJW5ocyysi62nPEd/f44pWFqpEuTXoso66K446SuporD+47R7iXtuPPIn3V/XGDWsNaoqihTvKjsXTvFi1pwTY7Nc6ExiTx9Gysz71lILA3LZCSmGqrKDG3iwiDff7jgFwLAk6AYXKz06VHLUZoEfe7BmyiSU9OxN9XJdRL0OW0tDVwci/DidSjKykq8i4ilSpuPPyLS0tKZseIA6/+4wJU/pnxlS4qJKT/ZW5uyzXsA8QlJxMYnYW6iz5Dpm7CxNMb0fc1GaHg05iYfaznCImMV0gE5J3S0NHC0NcPR1owKbg5UbD2drQevMLRbQ4Xtc+yC3Zz46z4HVw/BytxIOv/DsQkJi8byk0727yJiMPusdqig+EFzmFzLcRJ0584dmQQIwMjIiFmzZlGxYkW5Bvcj8vPzw8Ehoz9GkSJFOH/+fKYyhoaGQEY/hGnTptGqVatMZT4djVtH59tV4xoaGtIaJ0XSf1+78Mw/hNuP/Bnbt6nC9/k1EomE5OS8eX6dRCJh/KI9HLtwl70rBmJvZfLFsh+O0/OAEO488mdM77w7Th+OibqaKu4l7XjmHyKz/FlACDaWRl9YO+duvYjAwUz2HC1qpkNgREbHaFUVZdRVlUmXSGTKpKdLvjq2SHFLPdRVlQmNSfpime+VlJzKv6/eUqmMI60bVeSnCs4yy7uMWE3rRhVo17SS3PednZgKAm0tDbS1NIiKieev648Z3dcT2yLGmBnrcenGE+nt+skpqVy784xRfTzzOeLPSUhKUcy1QSKRMHbhbo5euMv+FYMyXQvsrUwwN9HnwrXHlHHO+AGUnJLK5VvPmDzgZ4XEJHyfHCdBzs7OvH37llKlZMdmCQkJoVgx+VSv/6jOnj3LvXv3GDZsGDY2NgQHB6OqqkrRokWzLF++fHkeP35cII5bbHySzC9Q/8Aw7j15jZG+NjaWxhw4cwtTQ12sLY3wexbIhEV7aVKzDHUqK7ap51PTVxykfjVXbCyMiIlPZO/JG1y8+S+7l3p9e2U5GLvgD/aduonP3F7oamtK2/31dDXR0lAH4ODZW5gY6mJjYYTfsyAmeu+lSU03mQ6S8jR79SHqVnHF2sKQ2Pgk9p++yeVbT9m2sB8A/TvVpd9kX6q4O1G9fHHOXfXj1KUH7Fk2UG4x+Pz1nO0Dq9O3bjGO3QmkjJ0h7arYMfmPewDEJaVy7VkYozxLkpSSzpuIeCo5mtCigg2/HXwIgK2JNs3LW/OnXwgRcck4WegxpnlJHryO4uaL3PehmLHiAPWrlcLawoiwiBiWbjpFbFwibZpUwshAJ1MfHDVVZcyM9XCys/jCFnPv05jefRJT2yYZiVdEdByBbyMIft+f7EMya2asL1MLI29/XX+ERAIOtma8evOOuWsO42BrTuvGlVBSUqJr65qs3nqGotZmFLUxZdXWM2hpqtO8XjmFxfT59enVJ9cnIwMdFm88QeMabliYGhAeFceGPX8RGBJJCwXFNGb+H+w5eYNN83qhq6Mp7Rupr6OJlqY6SkpK9G1fC2/fU9LaKW/fU2hpqtG6YcG7i1o0h33Dp51sZ8+ezeDBg5k6dSpVqlQB4OrVq0yfPp25c+cqJsoCKCkpieDgYNLS0nj79i3Hjx9nzpw5eHp68uuvv6KsrEzVqlVp2bIlc+fOxdnZmcDAQI4ePUrLli2pUKECkydPxtPTE1tbW9q2bYuysjJ3797l3r17eX4X2B0/f1oOWCadnrRkHwDtm1Zi+eQuvH0XzeQl+wgNj8HCVJ92TSoxokejPI0xNDyGflM28fZdNPq6mpQqZs3upV55loj57rsEQKtPjhOA94ROdGhWGYCQd9FMXbqf0PAYzE30adekIsO6K+44vYuIYdCMLYSERaGno4VrMSu2LexHrUoZSVfTWmWZO6odyzafYtLivTjZmbNuVg8ql3WSWwz3A6IY5PMPw5u64NWgOK/D45lz4CGHb33sQzJ8y02GN3VhfqdyGGirERiRgPexR9LBElNS06lazJRff3JAW0OFoMhELviFsOLkE9IlX9pz9gWFRDJw2iYiouIwNtSlfCl7DqweJtcBI783pvBPYjr4SUynLt5n+Jzt0vJeUzcBMKx7I0b0aKKwuGLiElnw+1GC30ViqKdNoxplGN6zCWqqGTdl9OlQh6SkFKYu2UNUTAJlS9qxcV4fhY0RBHDbz5+WXkul05O8M65PHZpVYsGYDvz76i07jl4jPDIOIwNtypW059CaoQrrK7hx70UAWnrJXguWTuxMR8+Ma8Gg/9UnMSmF0fP/IComnvKl7PljiVeBHCNIHnd3/ah3hylJJJJvXmKUlZVlsrwPq3yY9+l0Wpr8BjcrqD4fLNHIyIiyZcvSqVMnunbtKh0sMSYmhgkTJrBnzx5CQ0OxtLSkZs2azJkzR9pf58SJE0yfPp1bt26hpqaGi4sLvXr1onfv3kDGMd23bx8tW7bMUYzR0dEYGBjwJiQCff2C0watqlLwBpJMkuOAfPLy7U9l3nMffyy/Q8jkn5mN8zuETArid0FCATzHjXTU8zuETLLxdZhnoqOjsTY3IioqSmHX8A/fEx3XXUJdO3d30iXHx7K9V3WFxqsI2aoJOnfunKLj+KH4+PhIxwL6Gj09PZYuXcrSpUu/WKZRo0Y0avTlmoKC9KEUBEEQ/ntEc9g31KpVS9FxCIIgCIKQD8Szw75DfHw8/v7+JCcny8wvU6bMF9YQBEEQBKGgEU+Rz4HQ0FC6d+/OsWNZ9w8oDH2CBEEQBEH48eW4l+rQoUOJiIjg6tWraGlpcfz4cXx9fSlevDgHDx5URIyCIAiCICiIkpJ8Xj+iHNcEnT17lgMHDlCxYkWUlZWxt7enQYMG6OvrM2fOHJo1a6aIOAVBEARBUIDC3DE6xzVBcXFxmJubA2BsbExoaMYAVm5ubty8eVO+0QmCIAiCIChIjpMgZ2dnHj/OeFCiu7s7a9as4c2bN6xevZoiRfL+ad6CIAiCIHw/0RyWA0OHDiUoKAiAKVOm0KhRI7Zu3Yq6unq2xs4RBEEQBKHgEHeH5UDnzp2l/y5XrhwvX77k0aNH2NnZYWpqKtfgBEEQBEEQFOW7xwn6QFtbm/Lly8sjFkEQBEEQ8pg8mrN+0Iqg7CVBw4cPz/YGFy1a9N3BCIIgCIKQtwrz3WHZSoJu3bqVrY39qAfhv0xFWQmVH/XxvnmkILZlJ6QWvEFH789tmt8hZGL2y/L8DiGTkL0D8zuETFRUCt45XhCvS6lp4lmNhY14gKogCIIgFGLKfMet4lls40eU6z5BgiAIgiD8uERzmCAIgiAIhZKSEuS2dfIHzYF+2BosQRAEQRCEXBE1QYIgCIJQiCnLoSaoAPZzzxaRBAmCIAhCIVaY+wR9V3PY5s2bqV69OlZWVrx69QoAb29vDhw4INfgBEEQBEEQFCXHSdCqVasYPnw4TZs2JTIykrS0jPFMDA0N8fb2lnd8giAIgiAo0IfmsNy+fkQ5ToKWLVvG77//zoQJE1BRUZHOr1ChAvfu3ZNrcIIgCIIgKFZhfop8jpOgFy9eUK5cuUzzNTQ0iIuLk0tQgiAIgiAIipbjJMjBwYHbt29nmn/s2DFcXV3lEZMgCIIgCHlEWUlJLq8fUY7vDhs1ahQDBgwgMTERiUTCtWvX2L59O3PmzGHdunWKiFEQBEEQBAURj83Ige7du5Oamsro0aOJj4+nU6dOWFtbs2TJEjp06KCIGAVBEARBEOTuu8YJ6t27N7179+bdu3ekp6djbm4u77iEfFa2xRQCgsIzze/ZpgbzR7fLh4hg/e6/2LDnL2lcLo6WjOrZhAbVS+XJ/pf4nuTIhbv8++otWhpqVHBzYLLXzxSztwAgJTWNOWsOc+byQ14FhqGnq0nNCs5M8voZSzMDhcUVHBrJnNWHOf+3H4lJKTjamjFvTAfcnG0BOHbhLtsOXubek9dERMVxdP1IShW3Vlg8AJdvPWXFljPceRzA23fR+M7tRdNaZaTLzaoMznK9KQNbMLBLvVzvX0VZibEdK9O2tjPmhjq8jYhj2xk/Fuy6huT9g8I9qzrRrVFp3IuZY6KvRY0h27j/4p10G7bmetxd1z3L7Xebe5QDl57mKsZvnU8A89YdZf+pmwSGRKKmpkIZZ1vG9/PEo1TRXO37S3z3XsR330XpZ8zZoQjDejSiXtWMrg4L1h1j/+mMeNTfxzO2bzPKKyieL7l08ynLNp/mziN/gt9Fs2V+b5rVLptn+//W+Q3w5EUw01cc5PKtp6RLJLg4WLJuVndsLI3zLM7skkfH5h+0NSx3gyWamprKK44fXrdu3fD19QVAVVUVY2NjypQpQ8eOHenWrRvKyj9WZeEZn5GkpUmk037PA2k1cAUt6mXuFJ9XrMwNmTKwBY42Gefd9iN/03nkWi5sGUtJpyIK3//lW0/p0boG7iXtSE1LZ87qw7QbupK/to1HR0uDhMRk7j5+zfDujShV3JrImHgmee/lf6PXcmrjKIXEFBUTT+sBS6larji+8/pgYqTHq8B36OtqScskJCZRwc2BpnXcGTtvp0Li+Fx8QjKlilvT0bMK3cetz7T8/pGZMtNnrjxk6KzteNaRzxfZ0NYedG/ihpf3Kfz8wyhXzILlg+sTHZ/EmkN3ANDRUONvvyAOXHrK0kGZE68372Jx/lW2ib9ro9IMblWe0zde5TrGb51PAE625swZ0RZ7axMSk1JYs+Mc7Yas5O8/JmFqpJfrGD5XxNyQCf2bU9TGDIBdR6/Rfcw6TvmMwtmxCI52Zswe0QZ7q4x41u48T4ehq7i8axKmRrpyj+dL4hOSKF3Cms7Nq/DrmLzvhvGt8/vF61A8+3rTuXlVRvdugr6uFk9evkVDXS3PY80OZXLfp0eZHzMLynES5ODg8NWRIZ8/f56rgH5kjRs3ZuPGjaSlpfH27VuOHz/OkCFD2L17NwcPHkRV9ccZoPvzC6z3plM42JhSvXyxfIoImtR0k5me5PUzG/Zc5J/7L/IkCdrp7SUzvWRiJ1ybTuDuowCqliuGvq4Wu5cOkCkze3gbGvVcyOvgcIX8Aly19QxFzA1ZMK6jdJ5tEdn9tGpUESDLmj1FqV/NlfrVvnyjhIWJvsz08T/v8ZNHcYpay+eHVUWXIhz9+zkn/3kJQEBIDK1rlqBcsY+1LDvPPwIyanyykp4uISQyXmaeZ1Un9l38l7jElFzH+K3zCaB1owoyZaYP+YWth67y8GkgNSs65zqGzzX8qbTM9Lh+nmzad4kbD17i7FiEVg1l45k6+Be2HbqK37M31Kgg/3i+pEH1UnlWA5yVb53fs1cfoX41V6YMaiGdJ69zWxFETVAODB06VGY6JSWFW7ducfz4cUaNUsyv3R+FhoYGlpaWAFhbW1O+fHmqVKlCvXr18PHxoVevXvj7+zNo0CDOnDmDsrIyjRs3ZtmyZVhYfLw4z5w5k6VLl5KQkED79u0xNTXl+PHjWd6VlxeSU1L549h1vDrVKTBDo6elpbP/zE3iE5Kp6OaQLzFExyYCYKiv/dUySkpKGOhpfbFMbpy69IBalZzpP9mHv28/w8LMgF9bVqdj86oK2Z8ihIRFc+rSA5ZP7iK3bV59GEj3xm44WRnyLDCS0kVNqeJqxfh1f373Nss6mVHG0YxRq8/JLc5Pfet8Sk5JZdP+y+jraim8ORMyPmOHzt4mPjEJj9KZP2PJKalsOZARj2sxxcfzo0hPT+fU5QcM6lKPtkNWcv/Ja+yKmDCka4NMTWZC/stxEjRkyJAs569YsYJ//vkn1wH919StW5eyZcuyd+9eevbsScuWLdHR0eHChQukpqbi5eVF+/btOX/+PABbt25l1qxZrFy5kurVq7Njxw4WLlyIg8PXv+iTkpJISkqSTkdHR8vtbzhy/i5RsQl09Kwit21+rwdP39Cox0ISk1PR0dJg8/zeuDgqvhbocxKJhClL91G5rCMlnayyLJOYlMLMVQdp1dADPR3FJEEBQWFsOXCZXu1qM6BLfe74+TNlyT7U1VRp3biiQvYpbzuPXkNXR1OufTq899xAX0eDayv/R1p6OirKyszccoU9fz757m3+r0EpHvmHc+1RsNzi/OBr59PJi/fpM9mHhMQULEz0+WOJFyaGimt68nsWiGefxSS9/4xtmNMTZwdL6fJTl+7Tb7KvNJ6d3v0VGs+PJjQilrj4JJZuOs24vs2YPOBnzl71o9vY9exbMZDq5Yvnd4iZFOYHqMqto0qTJk3Ys2ePvDb3n+Li4sLLly85ffo0d+/eZdu2bXh4eFC5cmU2b97MhQsXuH79OpAxInfPnj3p3r07JUqUYPLkybi5uX1jDzBnzhwMDAykL1tbW7nFv+XgFepXdaWIAjv3Zldxewv+3DqOUxtG0KP1T3hN3cyj50F5HsfYBX/w8Gkga6Z3zXJ5SmoafSb7kJ4uYd6otgqLIz1dQqniNozu04zSJWzo3KIaHZtXYfOBSwrbp7xtO3yV1g0roKkhv/4SrWoUp10tZ3ovPE7tYTvw8j7FwJbl6FDX5bu2p6muQpuazmw5/UBuMX7qa+dTdY/inPUdw5G1Q6lbpSS9J24kNDxGIXEAONmZc9p3NIfXDuPXX6ozeOZWHr/4mPhVL1+c076jObRmKHWquNBnkg/vFBjPj0aSntGXsnFNN/p1rINbCRuG/NqAhtVL4buvYH4ulZRyP1ZQbhoJ5syZg5KSkkxLk0QiYerUqVhZWaGlpUXt2rV58ED285eUlMSgQYMwNTVFR0eHn3/+mdevX+do33JLgnbv3o2xccHr9V4QSCQSlJSU8PPzw9bWViZBcXV1xdDQED8/PwAeP35MpUqVZNb/fDor48aNIyoqSvoKCAiQS+wBQeFcuP6Y/7UoGM0r6mqqONqaUc7VnikDW1C6uDWrd5zP0xjGLdzNiYv32btiEFbmRpmWp6Sm0WvCRvwDw/hj6QCF1QIBmJvoU7yohcy8YvYWBL6NVNg+5enK7Wc8fRVCFzmfX9O7/YT3nhvs/etfHr4KY+f5R6w8eJthbSp8e+UstKhWHC0NVXacfSTXOOHb55OOlgaOtmZUKO2A94ROqKiosO3QFbnH8YG6mioONma4l7RjQv/mlCpmzbpdF6TLtbU0cLAxw6N0URaN74SqijLbDl9VWDw/GmNDHVRVlClR1FJmfomiFrwOjsinqAqu69evs3btWsqUkW0qnDdvHosWLWL58uVcv34dS0tLGjRoQEzMx4R76NCh7Nu3jx07dnDx4kViY2Px9PSUPtM0O3LcHFauXDmZfiESiYTg4GBCQ0NZuXJlTjdXKPj5+eHg4CBNhj73+fzPy0gkks9XyURDQwMNDY3cB/uZrYeuYmakR8N87IT4NRKJhOTk1Dzb17iFuzl64S77Vw7C3sokU5kPCdCL16HsXT4QYwMdhcbk4ebA84AQmXkvAkKwtsj8ZVoQbT14hbIutpSWcx8XLQ1V0j/73KSnS777DpguDVw5du0FYdEJ8ggPyN759KX1klLy5pz/sL/kr+xPIiHPPoM/AnU1Vcq52vHM/63M/GcBoZluWigo8qtjdGxsLJ07d+b3339n5syPd4xKJBK8vb2ZMGECrVq1AsDX1xcLCwu2bdtG3759iYqKYv369WzevJn69esDsGXLFmxtbTl9+jSNGjXKVgw5ToJatmwpM62srIyZmRm1a9fGxeX7qpr/y86ePcu9e/cYNmwYNjY2+Pv7ExAQIK0NevjwIVFRUZQsWRIAZ2dnrl27xv/+9z/pNvKrr1V6ejrbDl+lQ7NKqKqqfHsFBZu+4iD1q7liY2FETHwie0/e4OLNf9m91OvbK8vBmAV/sPfkDTbN7YWOtiZvwzL6XenraKKlqU5qaho9x6/n7uPXbFnQl7R0ibSMkb426mryvzuwV9tatPJawvLNp/Cs485tP3+2HbrKnJEfx3KKjI7jzdtI3r6LAuC5f0bSZGash/lnd2nJS2x8Ei9eh0qn/QPDuPfkNUb62tK75GLiEjh09jbTBreU+/6PX3/B8LYVeR0ag59/GGUczfBqUY6tnzRnGepqYGOmRxHjjES1uHVG4hgSES9zV5hDEQOqlbKm3fSDco3xW+dTXEIS3j4naVSjNBYmBkREx7Fxz18EhUbyc13FDFUxe/Uh6lZxxdrCkNj4JPafusnlW0/Ztqgf8QlJePuepNFPbpib6BMRHYfv3osEhUbSvK67QuL5ktj4JF4EfDy/XgWGce/xawwNtLHNg3F4vnV+D+hcj94TfajqXiyjOfOqHycu3mf/ikEKj+17yLNP0Of9Ub/2A33AgAE0a9aM+vXryyRBL168IDg4mIYNG8psp1atWly+fJm+ffty48YNUlJSZMpYWVlRunRpLl++rJgkKDU1laJFi9KoUSPpXVDCR0lJSQQHB8vcIj9nzhw8PT359ddfUVZWpkyZMnTu3Blvb29px+hatWpRoUJGNf2gQYPo3bs3FSpUoFq1auzcuZO7d+/i6OiY53/P+WuPeR0cQecCcqdRaHgM/aZs4u27aPR1NSlVzJrdS72oU7lknuzfZ+9FAFoOWCYzf+nEznRoVpnA0EiO/3UfgLq/zpUps2/FIIV0iCxb0o61s3owd80RlvqexMbSmCmDWvJLQw9pmVOXHjByznbp9MBpmwAY2q0Rw3o0lntMAHf8/GWO06Ql+wBo37SS9C6wfaduIpFIaPVJrPIyZu0FxneuwoJ+tTE10CY4PA6f4/eYt/OatEyTSo6sHNpAOr1hdBMAftv+N3O3/y2d36W+K0FhsZy9lfuxgT71rfNJRVmZf1+9ZefRa4RHxWJkoEO5knYcXDVEYTcDvAuPYdD0LYSERaGno4VrMSu2LepHrUouJCal8PRVCH8c3SCNx93Fjv0rB+Ocxzcn3PZ7RfN+S6XTExbvBaBjs8qsnPq/L60mN986v5vVLsv8Me1Y4nua8Yv34GRnzsY5Paji7qTw2PLb5/1Rp0yZwtSpUzOV27FjBzdv3pT2h/1UcHBGH7RP75r+MP3q1StpGXV1dYyMjDKV+bB+dihJstPW8gltbW38/Pywt7fPyWr/eZ8PlmhkZETZsmXp1KkTXbt2lQ6WmJ1b5GfMmMHSpUtJTEykXbt26Orqcu3aNa5cyX4/gOjoaAwMDAh+F4m+vmJ+7X+PgnKL/adSUtPzO4RMElKy36adV7TV87828HNmvyzP7xAyCdk7ML9DyCQtZ5f5PKGpVvDOp9S0gnMtiI6OxtrciKioKIVdwz98T0w6cAtNndwNvpkYF8OMFuUICAiQiTermqCAgAAqVKjAyZMnKVs2467Q2rVr4+7ujre3N5cvX6Z69eoEBgZSpMjHBLt3794EBARw/Phxtm3bRvfu3WXuigZo0KABTk5OrF69Oltx57h+vnLlyty6dUskQZ/x8fHBx8fnm+Xs7Ow4cODAV8tMmjSJSZMmSacbNGhAsWL5N0ihIAiC8N8lz+YwfX39byZtN27cICQkBA+Pj7XAaWlp/PnnnyxfvpzHjx8DGbU9nyZBISEh0goDS0tLkpOTiYiIkKkNCgkJoVq1atmOO8dJkJeXFyNGjOD169d4eHigoyPb8fPzHt5CzsTHx7N69WoaNWqEiooK27dv5/Tp05w6dSq/QxMEQRCEXKtXrx737t2Tmde9e3dcXFwYM2YMjo6OWFpacurUKcqVy+j/lpyczIULF5g7N6OrgYeHB2pqapw6dYp27TL6QAYFBXH//n3mzZuX7ViynQT16NEDb29v2rdvD8DgwR8fgKikpCS9wyknt6YJmSkpKXH06FFmzpxJUlISzs7O7NmzR9r7XRAEQRDkKa8HS9TT06N0adlHtOjo6GBiYiKdP3ToUGbPnk3x4sUpXrw4s2fPRltbm06dOgFgYGBAz549GTFiBCYmJhgbGzNy5Ejc3Nxy9H2Z7STI19eX3377jRcvXmR740LOaWlpcfr06fwOQxAEQSgklJSUct1fU979PUePHk1CQgJeXl5ERERQuXJlTp48iZ7ex75LixcvRlVVlXbt2pGQkCB9RJWKSvb7m2U7CfrQf1r0BRIEQRCE/46C8NiMD4+O+kBJSYmpU6dmeWfZB5qamixbtoxly5Z9scy35GjE6IJ4Z48gCIIgCML3yFHH6BIlSnwzEQoPD89VQIIgCIIg5J38GjG6IMhREjRt2jQMDPL/IZqCIAiCIMjHh4eg5nYbP6IcJUEdOnTA3NxcUbEIgiAIgiDkmWwnQaI/kCAIgiD89xSEjtH5Jcd3hwmCIAiC8B8ihz5B/NeToPT0gvNMFUEQBEEQhNzK8WMzBCE3CmKNonIBrMfVKoAPlyyITeKh+wrew0rNqgz+dqE8Fn7t+8dRUZSCeC1QKUDXgryMRRkllHNZlZPb9fOLSIIEQRAEoRArzLfI52iwREEQBEEQhP8KURMkCIIgCIWYuDtMEARBEIRCSQyWKAiCIAhCoST6BAmCIAiCIBQyoiZIEARBEAoxZeTQHCZukRcEQRAE4UcjmsMEQRAEQRAKGVETJAiCIAiFmDK5rxH5UWtURBIkCIIgCIWYkpJSrh+LUxAfq5MdP2ryJgiCIAiCkCuiJkgQBEEQCjGl96/cbuNHJJIg4ZsW+5xkxspD9O1QmznDW+d3OED+xXT51lOWbznDnUf+vH0XzaZ5vWhaq6x0+eFzt/Hdd4k7jwIIj4rj3OYxuJWwUVg8S3xPcuTCXf599RYtDTUquDkw2etnitlbZFl+xG872HzgMjOG/ELfDnUUFtfXjlNKahqzVx/m9OUHvHoThp6uJrUqOjNpQAuKmBkoJB5v35McOf/xOFV0c2DyANnjJJFImL/uGJsOXCYqJoHyrvbMHdUWF8cicolBV1uD8f088az9//buOq6q+4/j+OuCdKOEgSA2JnZtil2bTmcvmLHZ7dTZzpizsHWbgT0T42fnjDkMxMIOUERUGpU8vz+Yd15BxQH3Xsfnucd9zPs9557z5sK993u/dSqQz86Si9fvM2LmJgKuBKv3Gd6jOV9/VhtbKzPOXr7HsJ9/5+rtMI3jVC1XhNG9WlK5rBvJySlcvP6AdgMW8iIhKVtyvq5Cq3GEPIxIV97t84+Y/n37HDnnu4SGRzFh/jYOnLzCi4QkihZ2ZO7ozlQsXVgnefQ1U2bl5hWjpTssi7y9vdX9qa/ebt68qeto2eLclXv4bj1BmWIFdB1FTZeZnj1PoGzxgkwb2u4N2xOpVt6dMX0+1UqekwE36dr2I3b/OpgNc/qQkpxK+4ELiX+ekG7fXUcvcO7KPZzz5UxF41Vve56ev0jkwrUQhnRtysGV3+P7U3duBT/mi6FLcizPy+dpz2+D2Ti3D8kpqbQboPk8zVt1gEXrDvPTkHbsWzYEx7zWfN5/AXHxL7Ilw5zRnalXvRQ9x/lSu9MUDp26it+CfuqK34CvGtK7sxffT99AA+/phD+NYcv8fliam6iPUbVcETbN7c3hv67S0Hs69b+ezq8bjpKaqmRLxowcXDGUoF2T1bct8/sA0KqBZ46d822iYp7RrMds8uQxZMOcXvz5+yh+HPAZNlZmOsmjr5lE5khLUDZo2rQpy5cv1yhzcHDQuJ+YmIixsbE2Y2VZ3LMEvhvji8+oTsxctlfXcQDdZ2pYqwwNa5V54/b2zasBEBz6VCt5fvfprXF/zujOeDQfxYWrIdT0LKYufxgexciZG/ndpzddhuRcZeOltz1P1pZmbJ7XV6Ns6tDPafzNDO6HRVDI2T7b82x47XmaO7ozpZuNIvBqCLU8i6EoCkt+P8og78a09EprsZo/tgsezUezed9Zvv6sdpbOb2pixKdeFeky9BdOBtwCYNqvu2hRrzxd237E5MU76dnJi1nL97LzcCAAvcav4vreKXzepAortp4AYPKgNiz5/Qg+vvvVx74d8jhL2d4ln52Vxn2flfspUigftSsVe8Mjctaclfsp6GjLgrFfqMsKF8irkywv6WOm9/VhtuNknbQEZQMTExOcnZ01bg0aNKBv374MHjyYfPny0ahRIwCOHj1KtWrVMDExIX/+/IwYMYLk5GT1sWJjY+nSpQsWFhbkz5+f2bNnU69ePQYOHKj1n+v7nzfQqHYZ6lUrpfVzv4k+ZtInMXFprRa21ubqstTUVPpMXEWfLg2yrWsnu8XGPUelUmFjqZ1vzi+fJ7u/n6d7oU8JfxpDver//F2ZGBtRy7Mo/hfvZPl8eQwNyJPHkBeJml1Wz18kUaNiUVwL5sU5nw2HTl1Vb0tMSubEuZtUK+8OQD47S6qWK8LjiDj2Lh3MtT1T2LlkADUquGc5X2YlJiWzcfdpunxSQ2ezgXYfu0TF0oXxHrGUEk1GUveLafj6ndBJFn3O9D5eLpaY1duHSCpBOcjX15c8efJw4sQJlixZwoMHD2jevDlVq1YlMDCQRYsWsXTpUiZNmqR+zODBgzlx4gTbt29n//79HDt2jHPnzr3zXAkJCcTExGjcsmLzvrMEXgthrJa6dTJDHzPpE0VRGDd3K9UruFO66D9dhfNWHcDQ0IAe7evqMN2bvUhIYuKC7bRtUhkrLVSCFEVh7BzN5yn8adrrxdHeWmNfB3tr9basiHuWgP+F2wzr1gznfDYYGKho36wqVcq64pTPGqe8aed9HBGr8bjwiFgc/97mVjAfACN6NMfX7ySf919I4NUQ/Bb2w91Fs+U5p/zvyAWi457TqWUNrZwvI/cePGH5luMULezAprm9+aZNbUbO3Mz6//0lmf6ljIZ0/Jvbh0i6w7LBzp07sbS0VN9v1qwZAMWKFePnn39Wl48aNQoXFxfmz5+PSqWiVKlShIaGMnz4cMaOHUt8fDy+vr6sXbuWBg0aALB8+XIKFHj32JepU6cyYcKEbPl57j+K5IdZm9k8tzemJkbZcsys0sdM+mbEjI1cuRnKjiUD1GWBV4P5ZcNRDq74Xi/fpJKSU+gxejmpisL0YdoZZDv87+dp5y8D0m987SlSFCXbvuF+N3Yl88d2IWj3ZJKTUwi8FsKmvWcoX9JF43wacVSgkFZmYJAWZMXW46zdcQqAi9fvU7dqSb74tCYTF2zPnqBvsXr7nzSs6ZFjA9gzIzVVoWLpwozpnfZlqHxJF67eDmPZ5uN0bFFdMon3IpWgbODl5cWiRYvU9y0sLOjUqRNVqlTR2C8oKIiaNWtqfBjVrl2buLg47t+/T2RkJElJSVSrVk293cbGhpIlS74zw8iRIxk8eLD6fkxMDC4uLm95xJsFBgXzOCIWr6+nq8tSUlI5GXCL3zb+Qdjx2RgaarcRUR8z6ZORMzex9/glti0aQAFHO3X5qfO3eBIZh+dn49RlKSmpjJvnxy+/H+Xs1vE6SJsmKTmFbj8sIzj0KVsX9tdKK9CIGZvYe+wS2xdrPk8vW1vCn8ZoDBx/EhmLw2utQ//W3QdPaPndHMxNjbGyMOXR0xiWTvmG4NCnPHrZEpXXWv1vAAc7Kx4/TWsdCnuSVn7tjuZssWt3wyjkbEdOC3kYwdHT11g5rXuOn+ttnPJZU7KIs0ZZCTcndhw+r5tA6Gem9yErRosssbCwoFix9IMELSwsNO6nfatUpSuDtObIV/+d0T5vY2JigomJyTv3y4yPq5bk+LqRGmX9Jq6huJsT/b9qqJPKhj5m0geKojBy5iZ2Hb2A38J+uL42GLNds2p8XFWzEt1h4CLaNatKJx1+Q31ZAbod8hi/hf2wt7F494OyQFEURrx8nhakf55cC+TFMa81R/2vqVtmEpOSORlwK9u7X5+9SOTZi0RsrMxoUKM04+Zt496Dp4Q9icareikuXr8PgFEeQ2pXKsb4eduAtMH2oeFRFHN11DhescKOHDh5JVszZmTNjlM42FnRuPabJwZoQ/Xy7ty890ij7GZweI4MqM8sfcz0PnLzitFSCdIiDw8PNm/erFEZOnnyJFZWVhQsWBBbW1uMjIzw9/dXt+LExMRw48YN6tbV3ngOKwtTPIpqdsGZmxljZ2ORrjy3ZYp7lsCd+//MxrkX+pSL1+9jZ21OIWd7IqPjuf8okrDH0QDqN0bHvP+M+8hOw2dsZMu+s6yc1h0Lc1N1K4K1hSlmpsbY21ikq2AY5THE0d7qjWsJZYe3PU/O+Wz4ZsRSLlwLYe3M70hJVdS57azNMTbK/rel4dM3snnfWVb+3B1Li/TPk0ql4rsOdfHx3Y+7iwPuLg74+O7HzNSIto0rZ0uG+jVKo1LBjXvhuBdyYOKA1ty4F86a7X8CsHjdYQZ/05hbIeHcDnnMYO8mPHuRxKa9Z9THmLf6ACO/bcGl6w+4eP0+nVpWp7irE18PX5otGd8kNTWVtTtP0bFFNfLkMczRc71Lr85eNO02i1nL99K6YSXOXb7HSr+TzP6ho2QS700qQVrUu3dvfHx86NevH3379uXatWuMGzeOwYMHY2BggJWVFV9//TXDhg3D3t4eR0dHxo0bh4GBwQdby/6vOR8UTOvec9X3x/hsBaBji2rMH/sle45dpN+Pa9Tbe4xeAcCw7s0Y3qN5tudZseU4AK37zNMonzu6i07HIrztefq+e3P2HLsIQL0vp2k8zm9hf+pULp7teZa/fJ56p3+eOrVMe576fdmQFwlJfD99I9Gxz6hUxpWNc3pjaWGaLRmsLU0Z2+dTCjjaEhnzjB2HzjNp4Q6SU1IBmLPyAKYmxswY3gFbK3POXr5L237ziXv2z1pGi9cdwdTYiCmD22Jrbc7lGw9o03c+dx88yZaMb3LE/xr3wyLp8knNHD1PZlTycGXVzz2YuHA705fuoXCBvEwe3IZ2TatKpn8pN68YrVIy09ci3sjb25uoqCj8/Pw0yuvVq0fFihXx8fHRKD969CjDhg0jMDAQe3t7vv76ayZNmkSePGn10djYWHr27Imfnx/W1tZ8//33rF+/nvr16zN16tRM54qJicHGxoawJ1FYW2d/C8R/SQ6uM/ev5eTid//Wy4G5+kQf374cavTXdYR0IvznvXsnoVdiYmJwzmdLdHR0jr2Hv/yc8D1+DXNLq3c/4C2excXydZ2SOZo3J0hLUBatWLEiw/IjR45kWF63bl38/f3feDwrKyvWrPmnJSE+Pp4JEybw7bffZiWmEEIIIV4jlSA9ExAQwNWrV6lWrRrR0dFMnDgRgFatWuk4mRBCiP8imR0m9MqMGTO4du0axsbGVK5cmWPHjpEvXz5dxxJCCPEfJLPDhN7w9PTk7Nmzuo4hhBBC/OdJJUgIIYTIxXLz7DCpBAkhhBC5WHZcAPUD7Q2TSpAQQgiRmxmgwiCLbTlZfbyufKgDuoUQQgghskRagoQQQohcTLrDhBBCCJErqf7+L6vH+BBJd5gQQgghciVpCRJCCCFyMekOE/9ZqYp+XSBUD6/BqZf06Fem15JS9O+ZevqX/l2s1L7RJF1HSCfywBhdR0gnKTlV1xHUkrX4t63Khtlh0h0mhBBCCPEBkZYgIYQQIheT7jAhhBBC5Eq5uRIk3WFCCCGEyJWkJUgIIYTIxXLzOkFSCRJCCCFyMQNV1mfufqgzf6USJIQQQuRiubklSMYECSGEECJXkpYgIYQQIhfLzbPDpBIkhBBC5GIqst6d9YHWgaQ7TAghhBC5k7QECSGEELmYzA4TQgghRK6Um2eHSSVIAHAy4CbzVx8k8Gowj57EsPLn7jSvWyHDfQdPXc9KvxNMGtiGnp28tJbxp1928fNvuzXKHO2tuLpnitYyvOt5mvbrLrbuP0vooyiMjAypUMqFUT0/oXJZtxzJM8d3H7uOXuDGvUeYmhhRtVwRxvT+lGKuTup9+v+4mt93+Ws8rlIZV3b/NiRHMoH+/z3NW7mfqUt20r1dXSYObAPAriOBrNp2kgvXQoiMjmff8mGULVFIK3kAkpNTmPbbbjbtOU14RCxOea3p1KI6Q7o2wcAgZ0YuGBqoGPF1XdrVL4ujvSWPIuJYuzeQGWuOofx9EfMFwz6lcxPN393poPs07rdcfX/2wObUrVQE57xWxD9PxP/Kfcb/epAbIU+zPfPSTcdYtvkYIQ8jACjl7sywbs1oVLtMtp/rTeb47uN/f7/uzEyMqFKuCGNfe929ashP61m17SQ/DviM7zpq7z1TvJtUgt7A29ubqKgo/Pz8NMqPHDmCl5cXkZGR2Nra6iRbTnj2PIGyxQvSuWV1vEcsfeN+u44Gcu7yXZwdbLSY7h+l3POzdX5f9X1DQ+1++3jX81S0sCPThrbDtWA+XiQksWjdYT7vv4DTm8eSz84q2/P8GXCTb9p+RMXShUlJSWXK4p10GLiQP9b+gIWZiXq/+jVKM2d0F/V9ozyG2Z7lVfr893Q+6B6rt5/Eo1gBjfJnLxKpWq4ILb0qMmzaeq3leWnOqgOs2HKcBWO/oJR7fs4HBdN30hqsLc34rmO9HDnnwI61+aZlZXr/vI2gu4/xLFGA+cM+ISY+gSVb/6k4H/C/SZ/p29X3E5NTNI5z/sZDNh68REh4NHZWZoz4qi5bpnWhwhfzSE1VsjVzAUdbxvVthXuhfACs+99fdBn6C0dXj6B00fzZeq43ORlwk65/v+6SU1KZungn7Qcu5NhrrzuAXUcvcO7KPZzz6eY9MzNkdpjQmsTERIyNjXUdI52GtcrQsNbbv0k9DI9i+PRNbJzbm06DF2spmaY8hgY45bPWybnh3c/T502qaNyfNOAz1mz/kys3Q/m4aslsz7Pep7fG/TmjO1Om+SguXA2hpmcxdbmxcR4c82rvedPXv6f4Zwn0nbCK6cM7Msd3n8a2z5tWBSDkYfa3XmTGmYt3aPZxORrXKQtA4QJ52bzvLAFBwTl2zqoeBdl18hr7/roJQMijaNrWL4NnCc3KREJSCuGR8W88ju//AtT/DnkUzeTlhzn+63cUdrLl7sPIbM3c7ONyGvfH9P6UZZuPc+bSHa1Vgn7P4HXnkcHr7mF4FCNnbuR3n950GbJEK9n+DRVZn931gdaBZHZYVm3evJkyZcpgYmKCm5sbM2fO1Nju5ubGpEmT8Pb2xsbGhh49epCYmEjfvn3Jnz8/pqamuLm5MXXqVPVjoqOj+fbbb3F0dMTa2pr69esTGBio7R9NQ2pqKr3Gr6TvFw0o5a6dN5qM3A55jEfzUVRsNY5uo5Zz98ETnWV5l8SkZHz9TmJtaUaZ4gW1cs7YuBcA2Fqba5SfPHcTj+Y/ULP9jwyeuo7HEbFayfMmuvp7+mHmRhrU9MiRCmlWVa/gzh9nrnMzOByAS9fv81fgbRrV8sixc566FEJdzyIULWgPQFl3J2qUdWG//02N/epUcOX6xsGcXtEbn8EtyGdrntHhADA3NaJz0wrcfRjJg8fROZYdICUllc37zvDseVornq7EZPC6S01Npc/EVfTpotv3TPF20hKUBWfPnqV9+/aMHz+eDh06cPLkSXr37k3evHnx9vZW7zd9+nTGjBnD6NGjAZg7dy7bt29nw4YNFC5cmJCQEEJCQgBQFIUWLVpgb2/Prl27sLGxYcmSJTRo0IDr169jb2+fYZaEhAQSEhLU92NiYrL1Z5278gB5DA35tkPdbD3u+6hc1pWF47+kWGFHwiNimLlsL027zeLk+lHY21roLNfr9h6/xLejl/PsRRJO+azZNK8PeW0tc/y8iqIwdu5Wqldwp3TRf7p66tf04JP6nhRytiM49CnTft1F237z2b98KCbGRjmeKyO6+HvyO3COi9fvsysHx0JlxYCvGhET94Ia7SdhaKAiJVVhVM+WtH2tdTE7+aw/ibWFKf7Le5OSmoqhgQGTlh9m8+HL6n0OnL7Jtj+uEPIoGldnW37wrsf26V9Sr/dvJCb90y3W7dPKjO/REEszY67de8Jn368hKTk1R3JfvvmAJl1n8iIxGQszE1ZN76GzioaiKIzL4HU3b9UBDA0N6NFed++ZmWWACoMs9mcZfKBtQVIJeoudO3diaan54ZWS8s+LftasWTRo0IAxY8YAUKJECa5cucL06dM1KkH169dn6NCh6vvBwcEUL16cOnXqoFKpcHV1VW87fPgwFy9eJDw8HBOTtL7lGTNm4Ofnx6ZNm/j2228zzDp16lQmTJiQ5Z85I+eDgvnl9yMcXDkclQ47fhu90r3iQQGqlitC5c8msO5/f9GnS32d5XpdncrFObxqBBFRcazadpLuPyxj77KhONhn/5igV42csZGgm6FsXzJAo7x1w0rqf5cuWoCKpQtT+bPxHDh5hRb1Mh6snJN08ff04FEkY302s252b0xNdFPxe5et+8+xcc9pfpn4NaXc83Px+n1Gzd6Ms4MNnVpUz5FztqlXhvYNytJjylau3ntMuaJOTOndmIdPYlm//0JariNX1PsH3X1MwPWHXFjTn8bVi7Pz+FX1to0HL3H47B2c7S3p264my8e0pemA5SQkpaQ7b1YVd3XijzUjiY59xvZD5+k9fhU7lwzQSUVoxIyNXLkZyo5XXneBV4P5ZcNRDq74XqfvmZmVm7vDpBL0Fl5eXixatEij7K+//uKLL74AICgoiFatWmlsr127Nj4+PqSkpGBomDb4tEoVzW9y3t7eNGrUiJIlS9K0aVNatmxJ48aNgbTWpbi4OPLmzavxmOfPn3Pr1q03Zh05ciSDBw9W34+JicHFxeU9f+KMnTp/i8eRcVRsNVZdlpKSyti5W1ny+xEC/HKm8vUuFmYmlC5WgNshj3Vy/jexMDPB3cUBdxcHqpQrQtW2E1mz/U8GejfOsXOOnLmJvccv4bdoAAUc7d66r1M+Gwo523M7JDzH8ryNLv6eLlwL4UlkHE27zdA456nzt1i+5Rh3D8/E0FC3owPGzfNjwFeNaNO4MgAexQoQEhaBj+++HKsETfy2AT7rT7LlSFrLz5U74RRysmFQp9rqStDrHkXEEfIoSt2F9lJMfAIx8QncfhDB6aD73Nk6jJZ1Smm0KmUXY6M8uLs4AODp4UrAlWAWrz+Czw+dsv1cb/PydbfttdfdqfO3eBIZh+dn49RlKSmpjJvnxy+/H+Xs1vFazflOubgWJJWgt7CwsKBYsWIaZffv31f/W1GUdLV8RUk/E8LCQrOrplKlSty5c4fdu3dz4MAB2rdvT8OGDdm0aROpqankz5+fI0eOpDvO22ajmZiYqFuOslv75tWoW01zDEW7AQtp36wqnVrWyJFzZkZCYhLX7z6iZsWiOsuQOQoJSck5c2RF4YeZm9h19AJbF/bDtUDedz4mIjqe0PBInPLqZraKLv6ePqpcgkOrhmuUDZq8lmKuTvT5ooHOK0AAz18kYvDainOGBgYo2Ty76lVmpkakvvaelZqqpMvxKjtrMwo62hAWEffWY6tUKoyNcnYW4kuKopCYmDOvsTedb+Tfrzu/DF537ZpVSzfurMPARbRrVjXHKrTi35FKUBZ4eHhw/PhxjbKTJ09SokQJdSvQm1hbW9OhQwc6dOjA559/TtOmTYmIiKBSpUqEhYWRJ08e3NzccjC9prhnCdy5/0+Lyr3Qp1y8fh87a3MKOdtjb6NZkTPKY4ijvTXF37AuRk4YM2crTT8qSyEnOx5HxjFz2V5i419o9U3lbc+TnY0Fs5fvpelH5XDKZ0NEdDzLNh8jNDyKVg08cyTPiBkb2bLvLL7TumNpbkr407SxYFYWppiZGhP/LIHpv+2mhVcFnPJZE/IwgimLdmBvY0HzuuVzJBPo39+TpYUppdw1p8Sbm5lgZ22hLo+MiedBWCSPnqQN5r319wBlx7zWWplZ1+Sjssxavo9CTnaUcs/Phev3WbTuMJ0/ybkvGnv+vMHgznW4Hx5N0N3HlC/mTO+21VmzJ20ihoWpEcO/qsuOY0GERcRR2NmWsV29eBr9jP/93RXmmt+WNvXKcOjMLZ5GPyN/XisGdKzNi8SkdAOss8PEBdtpWMuDQk52xD57wZZ9Zzl+7gab5vZ+94OzyfC/X3crp3XHwtyUR3+/7qz/ft3Z21i84W/c6o1rCemSLJYo/pUhQ4ZQtWpVfvzxRzp06MCff/7J/PnzWbhw4VsfN3v2bPLnz0/FihUxMDBg48aNODs7Y2trS8OGDalZsyatW7dm2rRplCxZktDQUHbt2kXr1q3Tda1ll/NBwbTuPVd9f4zPVgA6tqjG/LFf5sg531doeBQ9Rq/gaVQ8+ewsqVzWjX1LB+OSP+PB4jnhbc/TjOEduXHvEet3+RMRFY+djTmepV3ZsWRgjo1VWLElrRL+WZ95GuVzRnehY4vqGBioCLodyoY9/sTEPscpnzW1KxXnl0nfYGlhmiOZ4MP4e3rdvmOXGDRlrfp+r3G+AAzu2pSh3Zrl+Pl/GtKOqUv+x7DpG3gSGYdzPhu+/qw2w7o1zbFzDp+/hx+86zGjfzPy2VoQ9jSWFf87x8+r/gAgJVXBw92Rjo3KY2NpyqOIWI6dv0fXSVuIe54IQEJiMjXLutCzTTVsLc14HBnHyYvBNOm/gidRz7I98+OIWHqOW8mjJzFYW5pSplhBNs3tjVf10tl+rjd5+bpr/drrbu7fr7sPTjasE/SB1oFQKRn134hML5a4efNmxo4dy40bN8ifPz/9+vXTGATt5ubGwIEDGThwoLrs119/ZeHChdy4cQNDQ0OqVq3K9OnT8fRMay2IjY1l1KhRbN68mcePH+Ps7MzHH3/M1KlTMz3OJyYmBhsbG0IfR2Ftrbt1dV6nj9eXycHehn8tRQ9DGerhLy8xh2YfZYVJHt13rb0ub+NJuo6QTuSBMbqOkE5OzWb7N2JiYijkZEd0dHSOvYe//Jw4eD4YS6usnSMuNoYGFQvnaN6cIJWg/yipBGWeHtY3pBKUSVIJyhypBGVObq0EHcqmSlD9D7ASpH+vViGEEEJojyqbbpk0depUqlatipWVFY6OjrRu3Zpr165p7KMoCuPHj6dAgQKYmZlRr149Ll/WnGmYkJBAv379yJcvHxYWFnz66acak5cyQypBQgghhNCao0eP0qdPH06dOsX+/ftJTk6mcePGxMf/c2mWn3/+mVmzZjF//nxOnz6Ns7MzjRo1Ijb2n9XuBw4cyNatW1m/fj3Hjx8nLi6Oli1baqzn9y4yMFoIIYTIxbQ9O2zPnj0a95cvX46joyNnz57l448/RlEUfHx8GDVqFG3atAHA19cXJycn1q5dy3fffUd0dDRLly5l1apVNGzYEIDVq1fj4uLCgQMHaNKkSaaySEuQEEIIkYu9vIp8Vm+QNs7o1durl3N6k+jotGUpXl4W6s6dO4SFhakXEYa0tfDq1q3LyZMngbSFhZOSkjT2KVCgAGXLllXvkxlSCRJCCCFysewcEuTi4oKNjY369urFwTOiKAqDBw+mTp06lC1bFoCwsDAAnJw011RycnJSbwsLC8PY2Bg7O7s37pMZ0h0mhBBCiGwREhKiMTvsXVcy6Nu3LxcuXEi38DCQ4RUZ3nUttszs8yppCRJCCCFys2xsCrK2tta4va0S1K9fP7Zv387hw4cpVKiQutzZ2RkgXYtOeHi4unXI2dmZxMREIiMj37hPZkglSAghhMjFVNn0X2YpikLfvn3ZsmULhw4dokiRIhrbixQpgrOzM/v371eXJSYmcvToUWrVqgVA5cqVMTIy0tjn4cOHXLp0Sb1PZkh3mBBCCCG0pk+fPqxdu5Zt27ZhZWWlbvGxsbHBzMwMlUrFwIEDmTJlCsWLF6d48eJMmTIFc3NzOnfurN63W7duDBkyhLx582Jvb8/QoUMpV66cerZYZkglSAghhMjFXp3dlZVjZNaiRYsAqFevnkb58uXL8fb2BuD777/n+fPn9O7dm8jISKpXr86+ffuwsrJS7z979mzy5MlD+/btef78OQ0aNGDFihXvvIC5Rm65bMZ/k1w2I/P08AoVctmMTJLLZmSOXDYjc3LrZTOOX7qfLZfNqFO20Ad32QxpCfqPS0hK4UVS5lfPzHH699mOuUnmvzVoi0oPKxwGepjJzFj/fnf6SB8rHHbNZ+g6Qjr3tw7UdQS1pBT9qZD9l0klSAghhMjN3vPaX288xgdIKkFCCCFELqbty2boE/3rvBZCCCGE0AJpCRJCCCFyMW3PDtMnUgkSQgghcrFcPCRIKkFCCCFErpaLa0EyJkgIIYQQuZK0BAkhhBC5WG6eHSaVICGEECIXy80Do6U7TAghhBC5krQECSGEELlYLh4XLZUgIYQQIlfLxbUgqQSJdOav2s9PS/5Ht3YfM2FAGwAeR8QyZdF2/vC/RnTcc6pXKMqPg9ri7uKQIxlmLtvN7OV7Ncoc7K0I2Pajevv2gwGEhkdhnMeQciVd+L5HcyqVccuRPG9SodU4Qh5GpCvv9vlHTP++vVazvBQb/4KpS/7H/44G8iQyjnIlCjFlcFsqebjqJA/AiXM3mbfqAIFXgwl7EsPq6T1oUa+CzvLMWr6XnYcDuXHvEaYmRlQr7874vq0o7uaks0ygf8+TLjIZGqgY8UUt2tXzwNHOnEcR8aw9cJkZ6/9E+fsCzMO71KLNxyUp6GBNUlIK528+YtLKY5y9FqY+jnEeQ37sXpe2dUthamLEH+fvMXTBAUKfxmV75ozeMwvVGZjhvqN6f0qvzvWzPYP4d6QSpAUqlYqtW7fSunXrDLcfOXIELy8vIiMjsbW11Wq2150PCmbN9j8pXbSAukxRFLqN/A2jPIYs/ak7VhYm/LL+CJ0GLuTw6hGYm5nkSJaSRZxZN7u3+r6hwT9D2NxdHJk0qC2FC+TlRUISv/5+lC5DFnN83Wjy2lnmSJ6MHFwxlJQURX0/6HYobfouoFUDT61leN3AKWsJuvWQReO/wjmfDRv3nKZN3/mcXD+KAo62Osn07HkCZUsUpMsnNfhq+G86yfCqk+du0r3dx3h6uJKcksKkRTto028+pzaMxiKH/p4zQ9+eJ9B+poHtqvFNswr0nrWHoHtP8CzuzPxBTYl5lsCSbecAuPUggu8XHeRuWDRmxnno9VlltkxqR6Vuv/E05jkAU7/zokn1onSbtpOImBdM6lGP9ePbUG/AKlJTlbdFeC8ZvWcCnNs2UeP+4VNBDP1pPc3rls+2c2eX3Dw7TAZGZ4Pw8HC+++47ChcujImJCc7OzjRp0oQ///wzU4+vVasWDx8+xMbGJoeTvl38swT6TVjFz993wMbKTF1+J+Qx5y7fY8qQdlQsXZiihZ2YMqQd8c8T8DtwLsfyGBoa4JjXWn17tXLzWaPKfFSlJK4F8lGySH7G9WtNbPwLgm6F5liejOSzs8Ipn7X6tvf4ZYoUykftSsW0muOl5y8S2XE4kPF9W1HLsxjuLg4M79Ec1wJ5Wb7luE4yATSqXYbRvT7hk/oVdZbhVZvm9aHzJzUoXTQ/5UoUYsHYL7gfFsn5oBCd5tK35wm0n6lq6QLsOnWLfadvExIew/YT1zkccBfP4v+00m06cpWj54O5FxbN1eCnjP7lCNYWJpQpktYybW1uzBeNyzHmtyMcPR/MxdvhfDf9f3i45aNexexrEX3Teyag8d7lmNeafccvUqtSMVwL5su282eXl7PDsnr7EEklKBu0bduWwMBAfH19uX79Otu3b6devXpERKTvJsmIsbExzs7OqHT8VzRq1iYa1PLgo6olNcoTkpIBMDExUpcZGhpgbJSH0xdu51ieO/efULn1WGq2n0jvcb7cC32S4X6JScms2X4Sa0tTPIoVyHAfbUhMSmbj7tN0+aSGzn6XySmppKSkavyuAExNjPgr8JZOMn0IYuJeAGBnba7jJOLU5QfUrViYogXtAChbxIEaHgXZf/pOhvsb5THg62bliY57waU7jwGoUNwJYyNDDp27q94vLCKeoHtPqFY6+94j3vSe+brHEbEcPHmFji1qZNu5RfaQSlAWRUVFcfz4caZNm4aXlxeurq5Uq1aNkSNH0qJFC/V+T5484bPPPsPc3JzixYuzfft29bYjR46gUqmIiooCYMWKFdja2uLn50eJEiUwNTWlUaNGhITk3LfUbQfOcfH6fUZ81zLdtmKuThRytuOnxTuJinlGYlIy81cdIPxpDOFPY3Ikj6eHKz6jurB6Zk9+/r4D4RExtO41h8joePU+B05cpkTj7ynaYBi/bjjK2lm9sbfVXlfY6/535ALRcc/p1FJ3b3RWFqZULVeEmcv28PBxNCkpqWzYfZqzl+8R9iRnflcfOkVRGDV7MzUqFtVpJVqk8dnoz+ajV/Ff0pXw7YM4Ou8rFm87y+ajVzX2a1LNnZDN/QnzG0Sv1pX5bNQmIv7uCnOysyAhKZnouASNx4RHPcPJziJbcr7tPfN1G3f7Y2FuSjM97AqDf8ZFZ/X2IZJKUBZZWlpiaWmJn58fCQkJb9xvwoQJtG/fngsXLtC8eXO6dOny1paiZ8+eMXnyZHx9fTlx4gQxMTF07NjxjfsnJCQQExOjccus0EeRjJuzhXljvsD0tRYEAKM8hvwyqSu3Q8Ip2/wHijf8nj8DbuJVozQGBjnzJ1S/hgct6lWgdNECfFSlJCt//hZIezN5qValYuxdNgy/RQOoV70Uvcat4ElkbI7kyYzV2/+kYU0P8jvotltz0fgvURQo23I0+T8axC8bjtC2SWWNMVXiH8N+3sDlm6H8Nslb11EE0ObjkrT3Kk2Pn3dSr/8qes/aTd82VenYoIzGfscCQ/i470qaDFnLwbN3WT7yE/LZvL0lT6UChayPB3rXe+brfv/fX3zWuHKm9tWJXFwLkoHRWZQnTx5WrFhBjx49WLx4MZUqVaJu3bp07NiR8uX/qfV7e3vTqVMnAKZMmcK8efPw9/enadOmGR43KSmJ+fPnU716dQB8fX0pXbo0/v7+VKtWLd3+U6dOZcKECf/qZ7hwLYQnkXE06z5TXZaSkspfgbdZseU4tw/NoHwpF/at+J6YuOckJaWQ186Slj1mUaFU4X91zvdlbmZCKff83Ln/WKOsSCEHihRyoHIZN+p0msT6nafo+2UjrWR6VcjDCI6evsbKad21fu7XFSnkwI7FA4h/nkBs/Auc89nQbdQyXAvY6zqa3vl++gZ2/3GRXb8MpKCTna7jCGBit7r4bPRnyx/XALhy9wmFHK0Z1L4a6w9eVu/3LCGJOw+juPMwijPXHnLm12582aQsszf48ygyHhOjPNhYmmi0BjnYmON/JevjBjPznmlomPal46/AW9wKDmfRhK+zfN6cIgOjRZa0bduW0NBQtm/fTpMmTThy5AiVKlVixYoV6n1erRBZWFhgZWVFeHj4G4+ZJ08eqlSpor5fqlQpbG1tCQoKynD/kSNHEh0drb69T9dZnSolOLByOHuXD1PfKpRy4bPGldm7fJj6xQxgbWlGXjtLboc85sK1EBp/VDbT58mKhMRkbtx7hGNe6zfuoyj/jF/StjU7TuFgZ0Xj2mXevbOWWJiZ4JzPhqiYZxw6dZVmH+tnU7wuKIrCsJ83sPNwINsX9dfLwaq5lZmJUbrZW6mpqRgYvP1DVqUCY6O07/WBNx6RmJSCl6eberuTnQWlXfPhH5T1StD7vGeu33mK8iVd8CheMMvnFdlPWoKyyctxO40aNWLs2LF0796dcePG4e3tDYCRkWYzqEqlIjU19a3HzGhw7ZsG3JqYmGBi8u+m9lqam1LKPb9GmZmpMXbW5urynYfOY29rQUEnO67efsi4OVto8lE56lYr9a/O+S4/LthGw1plKOhkx5PIWOau3E9c/AvaNavGs+cJzF25n0Z1yuKU15rI6Hh8t54g7HEULb0q5kiet0lNTWXtzlN0bFGNPHkMtX7+1x06FYSiKBRzdeR2yBPGz/OjmKsjnT/R3ViluGcJ3An5pxXvXuhTLl67j62NOS7O2m+hGjptA5v2nmHtjG+xNDfl0d/jpawtTTEzNdZ6npf07XnSRaY9f91icMca3H8cS9C9J5Qv6kjvz6qwZt8lAMxNjBjSsTq7T93iUWQ8dlamdGtZkQL5rNh2LK31KOZZIqv3XWRS97pExDwnMvYFP3avy5W7Tzhy/l6WM2bmPRPS1uzaeTiQsX1bZfmcOSo7Znd9mA1BUgnKKR4eHvj5+f3rxycnJ3PmzBl119e1a9eIioqiVKmcqXS8y6On0UyY78eTiFgc81rzedOqDPBunGPnexgeRd8JK4mIjsfe1pJKZVzZvngQhZzteZGQxM3gcDaOXk5kdBx21hZUKF2YzfP7U7JI/ncfPJsd8b/G/bBIunxSU+vnzkhM3HN+XLiD0PAo7KzNaelVgdG9PsFIhxW080H3+KTnXPX9UbO3ANCpRXUWjv9S63mWbT4GQMueczTKF4z9QqeVRX17nnSRafjig/zwZR1m9GlIPhszwiLiWbE7kJ/Xpi05kpKaSvFC9nQcVYa8NmZExLwg4HoYzYet52rwU/VxfvjlMMkpqSwf+Qmmxnn4IzCYTrO2ZusaQe+y7cA5FEWhVcNKWjvnv5GLF4xGpSiK9v4i/oOePn1Ku3bt6Nq1K+XLl8fKyoozZ87Qr18/WrRowdKlSzNcLNHW1hYfHx+8vb3TLZa4YsUKvv32Wzw9PZk7dy5GRkb07dsXRVEyvfZQTEwMNjY23Al9ipX1m7uQtE4P/9rMTXTfevM6fXxVvqs7Qoj3Ydd8hq4jpHN/60BdR1CLjYmhSIG8REdHY51D7+EvPycCboZhZZW1c8TGxuBZzDlH8+YEaQnKIktLS6pXr87s2bO5desWSUlJuLi40KNHD3744Yd/fVxzc3OGDx9O586duX//PnXq1GHZsmXZmFwIIYQgVzcFSSUoi0xMTJg6dSpTp0594z4ZNba9XBMIoF69ehnu06ZNG9q0aZMtOYUQQoiMyOwwIYQQQohcRlqChBBCiFwsO679JdcOE9nG29tbo7tMCCGEyCm5eMFoqQQJIYQQIneS7jAhhBAiN5PZYUIIIYTIjXLz7DCpBAkhhBC5mIpsGBidLUm0T8YECSGEECJXkpYgIYQQIhfLxUOCpBIkhBBC5GayTpAQQgghRC4jLUH/ccZ5DDDJoz913RdJqbqOkI5KL7/C6OFl5EWmZHQdQJHek51DdB0hnXw1B+o6gpqSkqDFs+XeDjGpBAkhhBC5mHSHCSGEEELkMtISJIQQQuRiubczTCpBQgghRK4m3WFCCCGEELmMtAQJIYQQuZhcO0wIIYQQuVMuHhQklSAhhBAiF8vFdSAZEySEEEKI3ElagoQQQohcLDfPDpNKkBBCCJGL5eaB0dIdJoQQQohcSVqCBAAnA26yYPVBAq+F8OhJDL7TutO8bnn1doca/TN83Li+rej7RYNszzNr2W5mL9+rUeZgb8W5bT+q79+4G8aUxTv46/wtUlMVShRxZtFEbwo62WV7njfmXL6XnYcDuXHvEaYmRlQr7874vq0o7uaktQwZCQ2PYsL8bRw4eYUXCUkULezI3NGdqVi6sM4y/bbxD+atPsijJ9GUcs/PlMFtqeVZTGd59DXTS7NX7OPHhTv4rmM9pg5uq+s4gO4ynQy4yfzVBwm8GsyjJzGs/Lk7zetWUG+f9usutu4/S+ijKIyMDKlQyoVRPT+hclm3LJ/b0NCAEd2b0q5pFRztrXj0NIa1//NnxrJ96ovlOthbMb7PJ3hVL4WNlRknA24xfOZmboc8Vh/H0d6Kif1bUa9aSSzNTbh5L5xZvvvZfigwyxmzLBePjJZKkJ5SqVRs3bqV1q1ba+V8z54nUqZ4QTq1rME3I5em237pf5M07h/88woDJ6+jpVeFdPtmlxJFnFk3u7f6vqHBPw2Xdx88oU2fuXRsUYMhXZthZWnKzbuPMDHW7p/0yXM36d7uYzw9XElOSWHSoh206TefUxtGY2FmotUsL0XFPKNZj9nUqVycDXN64WBnxZ37T7CxMtNJHoAt+87yw6zNzBjegeoV3Fmx5TjtByzkzw2jcXG2l0yvOXflHr5bT1CmWAGd5niVLjM9e55A2eIF6dyyOt4j0r8/FS3syLSh7XAtmI8XCUksWneYz/sv4PTmseSzs8rSuQd+2YBv2tSm98Q1BN0Ow7O0C/NHdyYm7gVLfj8KwOqfu5GcnEKXYb8RG/+CPp3r4TevNzU6TuXZi0QAFo//EmtLUzoP/ZWnUfF83qQyyyZ54+U9g4vXH2QpY1bl4jqQdIe9i0qleuvN29tb1xGzRcNaHvzQs+UbKzVOea01bnv+uEidysVxK5gvxzLlMTTAMa+1+pbXzlK97edf/kf9Gh6M6v0pZUsUwrVAPhrUKpPlN7z3tWleHzp/UoPSRfNTrkQhFoz9gvthkZwPCtFqjlfNWbmfgo62LBj7BZXLuFG4QF7qVitJkUIOOsu0cO0hvmhVk69a16JkEWemDvmcgk52LNt0TDK9Ju5ZAt+N8cVnVCdsrc11muUlXWdqWKvM3+9PFTPc/nmTKtStVgq3gvko5Z6fSQM+Izb+BVduhmb53FXLFWHXH5fYd+IKIQ8j2H4okMP+1/As7QJAURcHqpUrwpBpGwkICuZmcDhDft6IhbkJbRtXeuU4bvy68Q/OXQnmXuhTZi7fR3TccyqUdMlyRvHvSSXoHR4+fKi++fj4YG1trVE2Z84cjf2TkpJ0lFR7wp/GsP/EZbp8UiNHz3Pn/hMqtx5LrfYT6T3Ol3uhTwBITU3l0J9XKOLiQJfBi6j4yWg++XYWe/64kKN5MiMm7gUAdjr88Np97BIVSxfGe8RSSjQZSd0vpuHrd0JneRKTkjl/NYT61UtrlHtVL43/hTuS6TXf/7yBRrXLUK9aKZ3meJU+ZnqTxKRkfP1OYm1pRpniBbN8vFOBt6lbpThFXdK+RJQtXoAaFdzZf/IKgLr1+UXiP+/9qakKiUnJ1KjgrnGczxpWwtbaHJVKRZtGnhgb5eH4uRtZzphVL2eHZfX2IZJK0Ds4OzurbzY2NqhUKvX9Fy9eYGtry4YNG6hXrx6mpqasXr2a8ePHU7FiRY3j+Pj44ObmplG2bNkyypQpg4mJCfnz56dv375vzDFx4kScnJw4f/589v+Q7+n3Xf5YWpjSol7OdYV5erjiM6oLq2f2ZNr3HXgcEcNnveYQGR3Pk8g44p8nsHDNQepVL82aWT1p+nF5vh29nD8DbuZYpndRFIVRszdTo2JRPHTYjXHvwROWbzlO0cIObJrbm2/a1GbkzM2s/99fOsnzNCqOlJRUHOw1W+kc8loR/jRGMr1i876zBF4LYWyfT3WW4XX6mCkje49fwrXeEAp+NJjF6w+zaV4f8tpavvuB7+Cz8gCb95/Df8MPhJ+YxdGVw1i8/gib950D4PrdRwSHPmVs70+wsTLDKI8hA79qiHM+G5zyWauP023UCgwNDbizfyqPjs9k9ogOfDl8KXcfPM1yxqxTZfm/D7VDTMYEZYPhw4czc+ZMli9fjomJCb/88ss7H7No0SIGDx7MTz/9RLNmzYiOjubEifTf1hVFYeDAgfj5+XH8+HGKFy+e4fESEhJISEhQ34+Jybk38rU7T9G2cRVMTYxy7BxeNTw07lcu40adjpPYuNufTxumNTE3rlOWHh3qAVCmeCHOXLrD6m0nqKmjga3Dft7A5Zuh7P51kE7O/1JqqkLF0oUZ0zvtQ6t8SReu3g5j2ebjdGxRXWe5Xv+mqCgKKh1/fdSnTPcfRfLDrM1snts7R19b70MfM71JncrFObxqBBFRcazadpLuPyxj77Kh6Sq676tNI0/aN61Cj7EruXo7jHIlCjJlUBsePo5m/a7TJKek8tXIZcwb1Ym7B34iOTmFI6evq1uKXhrVswW2Vma06rOAiOg4mn9cnhVTvGn+3Vyu3HqYpYzi35NKUDYYOHAgbdq0ea/HTJo0iSFDhjBgwAB1WdWqVTX2SU5O5quvvuLMmTOcOHGCQoUKvfF4U6dOZcKECe8X/F/48/wtbt4L59dJ3+T4uV5lbmZCKff83Ln/GHsbC/IYGlDczVljn+KuTpzWUVfG99M3sPuPi+z6ZaBWZ6dlxCmfNSWLaD43Jdyc2HH4vE7y5LW1xNDQgPCnsRrlTyLisvwB9V/KFBgUzOOIWLy+nq4uS0lJ5WTALX7b+Adhx2djaKjdxnt9zPQmFmYmuLs44O7iQJVyRajadiJrtv/JQO/GWTruxH6t8Fl5gC37AwC4cushhZztGfR1I9bvOg1A4NX7fPzldKwtTDEyMuRpVDz7lw7i/NW0sYFuBfPybfuPqdlxKlfvhAFw6UYoNSu60/3zjxg8bUOWMmaVLJYosqRKlSrvtX94eDihoaE0aPD2qeWDBg3CxMSEU6dOkS/f2wcgjxw5ksGDB6vvx8TE4OKS/QPu1mz/kwqlXCibDX3t7yMhMZkb9x5Rrbw7xkZ5qFC6MLeDwzX2uR3ymILO2q2AKIrC99M38r8jgexYPADXHBwonlnVy7tz894jjbKbweEU0tGMJ2OjPFQs5cLhv65qDLw/4n+VZh+Xk0x/+7hqSY6vG6lR1m/iGoq7OdH/q4Y6qWzoY6bMU0hISs7yUcxMjUlNVTTKUlNTMTBI/6kfE582JtDdxQHP0oWZ8ssuAMxNjdMep2geJyU1FVUGxxHaI5WgbGBhYaFx38DAQL1+xEuvDpg2M8vcVOVGjRqxbt069u7dS5cuXd66r4mJCSYm/35KdtyzBO7c/2dNi+DQp1y8fh87a3P1h2ds/HN2HDrPhP6t//V5MuvHBdtoWKsMBZ3seBoZy9yV+4mLf8HnzaoB8F2n+vQZ50v1CkWpWakYR/+6yoGTl9kw983jqnLC0Gkb2LT3DGtnfIuluSmPnqR1Q1pbmmL29xuftvXq7EXTbrOYtXwvrRtW4tzle6z0O8nsHzrqJA9A78716TluJZ4ehalargi+W09wPyyCb9p+JJn+ZmVhikdRzbFk5mbG2NlYpCvPbZlef3+698r7k52NBbOX76XpR+VwymdDRHQ8yzYfIzQ8ilYNPLN87j3HLjH4m8bcfxRJ0O0wypcoRO9OXqzZcUq9T6v6FXkSFcf9sEg8iuXnp0Ft+N8fFzn81zUgbdzQrZDHzB7RnjFztxERHU+LuuXxqlaSjkN+zXLGrJKWIJGtHBwcCAsL0xhf8OqAZisrK9zc3Dh48CBeXl5vPM6nn37KJ598QufOnTE0NKRjx5z7EAsMCqZ1n3nq+2PmbAWgQ/NqzB/7BQBb959DURTaNK6cYzleehgeRd8JK4mMjsfe1pJKZVzZtniQukLW7OPyTBnajgWrDzB2zhaKFnZgyY/fUK28+zuOnL2WbU6bTt2yp+YswQVjv6BzDs+ee5NKHq6s+rkHExduZ/rSPRQukJfJg9vQrmnVdz84h7RpXJmI6Hh+/m03j57EULpofn736U3h/Lpbj0cfM4mMnQ8KpnXvuer7Y3zS3p86tqjGjOEduXHvEet3+RMRFY+djTmepV3ZsWQgpdzzZ/ncw2du5ofvmjNjWDvy2VkS9iSGFVtP8PPSfxZzdcpnzeSBrXGwt+LRkxjW7z7N9Fe2J6ek0n7QEsb1+YR1M7/FwsyYO/ef0HvimnRjh4R2qZTXmyzEG61YsYKBAwcSFRUFwN27dylSpAgBAQEas8GCgoIoU6YMU6dO5fPPP2fPnj2MGTMGa2tr7t69C4Cvry89e/Zk2rRpNGvWjNjYWE6cOEG/fv0AzcUSN23axJdffsmqVav4/PPPM5U1JiYGGxsbHoRHYm1t/e4HaMmLpFRdR0jH0lT/vgvo48tS14OYPxT6+LvTR6l6+DTlqzlQ1xHUlJQEEgKXEB0dnWPv4S8/J4LDsv45ERMTQ2FnuxzNmxP0uUP3g1W6dGkWLlzIggULqFChAv7+/gwdOlRjn6+//hofHx8WLlxImTJlaNmyJTduZLxexOeff46vry9ffvklW7Zs0caPIIQQIpfIzesESUvQf5S0BGWetARljrQEZY4+/u70kbQEvZ02W4JCHmVPS5CL04fXEqR/7/5CCCGE0JrcfO0wqQQJIYQQuVkurgXJmCAhhBBC5ErSEiSEEELkYv9c/ytrx/gQSSVICCGEyMVy82KJ0h0mhBBCiFxJWoKEEEKIXCwXj4uWliAhhBAiV1Nl0+09LVy4kCJFimBqakrlypU5duxYln+U9yWVICGEECIXU2XTf+/j999/Z+DAgYwaNYqAgAA++ugjmjVrRnBwcA79lBmTSpAQQgghtGrWrFl069aN7t27U7p0aXx8fHBxcWHRokVazSFjgv6jXi7dHxsbo+MkmhL08LIZqYn69zLQx0svyGUzMkcff3f6SB8vm6GkJOg6gpqSkpj2fy38PcXGxmR5dtfLz5qYGM3PHBMTE0xMTDTKEhMTOXv2LCNGjNAob9y4MSdPnsxakPekf+/+IlvExsYCUKqoq46TCCGE+LdiY2OxsbHJkWMbGxvj7OxM8SIu2XI8S0tLXFw0jzVu3DjGjx+vUfbkyRNSUlJwcnLSKHdyciIsLCxbsmSWVIL+owoUKEBISAhWVlZZ+gYfExODi4sLISEhenNRPMmUOZIpcyRT5kimzMmuTIqiEBsbS4ECBbIxnSZTU1Pu3LlDYmJithxPUZR0nzevtwK96vV9M3p8TpNK0H+UgYEBhQoVyrbjWVtb682bzEuSKXMkU+ZIpsyRTJmTHZlyqgXoVaamppiamub4eV6VL18+DA0N07X6hIeHp2sdymkyMFoIIYQQWmNsbEzlypXZv3+/Rvn+/fupVauWVrNIS5AQQgghtGrw4MF8+eWXVKlShZo1a/LLL78QHBxMz549tZpDKkHirUxMTBg3btxb+3W1TTJljmTKHMmUOZIpc/Qxkz7q0KEDT58+ZeLEiTx8+JCyZcuya9cuXF21O5lHpch8TiGEEELkQjImSAghhBC5klSChBBCCJErSSVICCGEELmSVIKEEEIIkStJJUgIIYQQuZJUgsQbvXjxQtcRhBBCr124cOGN2/z8/LQXRPwrUgkSGlJTU/nxxx8pWLAglpaW3L59G4AxY8awdOlSneW6desWo0ePplOnToSHhwOwZ88eLl++rLNM+mjixIk8e/YsXfnz58+ZOHGi1vMkJyczYcIEQkJCtH5u8d925swZVq1axerVqzlz5ozOcjRp0kT9PvmqzZs306VLFx0kEu9FEeIVEyZMUNzd3ZXVq1crZmZmyq1btxRFUZTff/9dqVGjhk4yHTlyRDEzM1MaNmyoGBsbqzNNmzZNadu2rU4y6SsDAwPl0aNH6cqfPHmiGBgY6CCRolhYWCh37tzRyblF1kVGRiq//vqrMmLECOXp06eKoijK2bNnlfv37+skT0hIiFKnTh1FpVIpdnZ2ip2dnaJSqZTatWsrwcHBWs8zYcIExc3NTQkNDVWXrV+/XjE3N1c2bNig9Tzi/ciK0ULDypUr+eWXX2jQoIHG8uXly5fn6tWrOsk0YsQIJk2axODBg7GyslKXe3l5MWfOHK1mGTx4cKb3nTVrVg4myZjyhqswBwYGYm9vr/U8AA0bNuTIkSN4e3vr5PwZ8fT0zPB5UqlUmJqaUqxYMby9vfHy8tJapjf9bb2aqVWrVlr9PV64cIGGDRtiY2PD3bt36dGjB/b29mzdupV79+6xcuVKrWV5qWvXriQlJREUFETJkiUBuHbtGl27dqVbt27s27dPq3nGjh3L06dPadiwIceOHWPPnj10796dVatW0bZtW61mEe9PKkFCw4MHDyhWrFi68tTUVJKSknSQCC5evMjatWvTlTs4OPD06VOtZgkICNC4f/bsWVJSUtRvxtevX8fQ0JDKlStrNZednR0qlQqVSkWJEiU0PuBTUlKIi4vT+jV5XmrWrBkjR47k0qVLVK5cGQsLC43tn376qdYzNW3alEWLFlGuXDmqVauGoiicOXOGCxcu4O3tzZUrV2jYsCFbtmyhVatWWskUEBDAuXPn1H9PiqJw48YNDA0NKVWqFAsXLmTIkCEcP34cDw8PrWQaPHgw3t7e/PzzzxpfQJo1a0bnzp21kuF1x44d4+TJk+rXHEDJkiWZN28etWvX1kmmOXPm8OWXX1KjRg0ePHjAunXrtPZ3I7JGKkFCQ5kyZTh27Fi667ds3LgRT09PnWSytbXl4cOHFClSRKM8ICCAggULajXL4cOH1f+eNWsWVlZW+Pr6YmdnB0BkZCTffPMNH330kVZz+fj4oCgKXbt2ZcKECdjY2Ki3GRsb4+bmRs2aNbWa6aVevXoBGbeMqVQqUlJStB2JJ0+eMGTIEMaMGaNRPmnSJO7du8e+ffsYN24cP/74o9Y+zF628ixfvhxra2sAYmJi6NatG3Xq1KFHjx507tyZQYMGsXfvXq1kOn36NEuWLElXXrBgQcLCwrSS4XWFCxfO8AtZcnKy1t4Ptm/fnq6sdevWHD16lE6dOqFSqdT76KKSL96DbnvjhL7Zvn27YmNjo/z000+Kubm5Mn36dKV79+6KsbGxsm/fPp1kGjZsmFKnTh3l4cOHipWVlXLjxg3l+PHjiru7uzJ+/HidZFIURSlQoIBy6dKldOUXL15U8ufPr4NEaeOnEhMTdXLuD4m1tbVy48aNdOU3btxQrK2tFUVRlKCgIMXS0lJrmQoUKKBcvnw5XfmlS5eUAgUKKIqSNhYnb968Wsvk6OionDt3TlEURbG0tFSPx9u7d69SqFAhreV4lZ+fn1KtWjXl9OnTSmpqqqIoinL69GmlRo0aytatW7WSQaVSZeqmq3F4IvNkdpjQ8Mknn/D777+za9cuVCoVY8eOJSgoiB07dtCoUSOdZJo8eTKFCxemYMGCxMXF4eHhwccff0ytWrUYPXq0TjJB2rf0R48epSsPDw8nNjZWB4mgbt26GBkZAWkzwmJiYjRuuqYvyy6Ymppy8uTJdOUnT57E1NQUSOsC1uaVwKOjo9UzH1/1+PFj9e/O1taWxMRErWVq1aoVEydOVLe8qFQqgoODGTFihM7Gu3h7e3P+/HmqV6+OqakpJiYmVK9enXPnztG1a1fs7e3Vt5ySmpqaqZsuWjnF+5HuMJFOkyZNaNKkia5jqBkZGbFmzRomTpxIQEAAqampeHp6Urx4cZ3m+uyzz/jmm2+YOXMmNWrUAODUqVMMGzaMNm3a6CTTs2fP+P7779mwYUOG46V08aackpLClClTWLx4MY8ePeL69eu4u7szZswY3Nzc6Natm9Yz9evXj549e3L27FmqVq2KSqXC39+f3377jR9++AGAvXv3arULuFWrVnTt2pWZM2dqZBo6dCitW7cGwN/fnxIlSmgt04wZM2jevDmOjo48f/6cunXrEhYWRs2aNZk8ebLWcrzKx8dHJ+cV/1G6booS+ikhIUEJCQlR7t27p3ET/4iPj1d69eqlmJiYKAYGBoqBgYFibGys9OrVS4mLi9NJpt69eyulS5dWNm7cqJiZmSnLli1TfvzxR6VQoULK6tWrdZJJH5ddUBRFWb16tVKjRg31NOsaNWooa9asUW9/9uyZ8vz5c63liY2NVXc9v/r31KNHD/XfU0BAgBIQEKC1TC8dPHhQmT59ujJt2jRl//79Wj+/PuvXr58yZ86cdOXz5s1TBgwYoP1A4r2oFEVRdF0RE/rjxo0bdO3aNV1XgfL31GtttSTo+1T0lJQUjh8/Trly5TAxMeHWrVsoikKxYsXSzX7SpsKFC7Ny5Urq1auHtbU1586do1ixYqxatYp169axa9curWcqVqwYS5YsoUGDBlhZWREYGIi7uztXr16lZs2aREZGaj2TPouLi+P27dsoikLRokWxtLTUdSQNUVFR2NraavWcMTExGoPF3+blftpSsGBBtm/fnm5G6Llz5/j000+5f/++VvOI9yPdYUKDt7c3efLkYefOneTPnz/DtVS04fWp6G+iq3yGhoY0adKEoKAgihQpQvny5XWS43URERHqWXTW1tZEREQAUKdOHfUsLW3Tx2UXXjp79ixBQUGoVCo8PDx0NgPyVZaWltjb26NSqXReAZo2bRpubm506NABgPbt27N582acnZ3ZtWsXFSpU0EoOOzs7Hj58iKOjI7a2thm+7rX9Re2lp0+faszGfMna2ponT55oNYt4f1IJEhrOnz/P2bNnKVWqlE5zvDoVXV+VK1eO27dvp5u6r0vu7u7cvXsXV1dXPDw82LBhA9WqVWPHjh1a//b+kj4uuxAeHk7Hjh05cuQItra2KIpCdHQ0Xl5erF+/HgcHB61nSk1NZdKkScycOZO4uDgArKysGDJkCKNGjcLAQPvzWJYsWcLq1asB2L9/P/v372f37t1s2LCBYcOGaW1hwkOHDqkHOuvbe0OxYsXYs2cPffv21SjfvXs37u7uOkolMk2HXXFCD1WpUkU5duyYrmN8EPbu3atUrFhR2bFjhxIaGqpER0dr3HRh1qxZ6vEJhw4dUszMzBRjY2NFpVIpPj4+Osmkj8sutG/fXqlcubJy5coVddnly5eVKlWqKB07dtRJphEjRigODg7KwoULlcDAQOX8+fPKggULFAcHB+WHH37QSSZTU1P1pSj69++vfPvtt4qiKMq1a9cUW1tbnWTSN0uXLlXMzMyUsWPHKkeOHFGOHDmijBkzRjE3N1d++eUXXccT7yBjgoSGQ4cOMXr0aKZMmUK5cuXU061f0lZ/e5s2bVixYgXW1tbvnGm1ZcsWrWR63avfzF9tnld01CyfkeDgYM6cOUOxYsV02mW3d+9epkyZwtmzZ0lNTaVSpUqMHTuWxo0b6ySPjY0NBw4coGrVqhrl/v7+NG7cmKioKK1nKlCgAIsXL063uN62bdvo3bs3Dx480EmmTZs2UatWLUqWLMmkSZNo164d165do2rVqjpbdiEqKgp/f3/Cw8NJTU3V2PbVV19pPc+iRYuYPHkyoaGhALi5uTF+/HidZBHvR7rDhIaGDRsC0KBBA41ybX+w29jYqCsWGfW36wN9apY/dOgQffv25dSpUxoV1cKFC2NjY0OtWrVYvHix1leyfknfll1ITU1NV8GHtOUYXv9Q1ZaIiIgMu6FLlSqlHtulbW3atKFz584UL16cp0+f0qxZMyCt2zyjcV7asGPHDrp06UJ8fDxWVlYaX0BUKpVOKh69evWiV69ePH78GDMzM52P5RKZJy1BQsPRo0ffur1u3bpaSiLex6effoqXlxeDBg3KcPvcuXM5fPgwW7du1XKyNFFRUWzatInbt28zdOhQ7O3tOXfuHE5OTlq/9AmkrckTFRXFunXrKFCgAJA2gLtLly7Y2dnp5HmqXr061atXZ+7cuRrl/fr14/Tp05w6dUrrmZKSkpg7dy7BwcF4e3urx3D5+PhgaWlJ9+7dtZ6pRIkSNG/enClTpmBubq7184v/FqkEiQ9GeHg4165dU18k1NHRUesZLly4QNmyZTEwMODChQtv3Veb3U+urq7s2bOH0qVLZ7j96tWrNG7cmODgYK1leun1K5Ffu3ZNvViirq5EHhISQqtWrbh06RIuLi7qlZDLlSvHtm3bKFSokNYzHT16lBYtWlC4cGFq1qyJSqXi5MmThISEsGvXLq234iUlJfHtt98yZswYvRrga2FhwcWLF/UmU5EiRd46S/X27dtaTCPel1SCRIaePXtGcHBwuiX6dTGuJCYmhj59+rB+/Xp1d5yhoSEdOnRgwYIFWu0uMzAwICwsDEdHRwwMDFCpVGT0EtL2mCBTU1MuXbr0xi6KmzdvUq5cOZ4/f661TC81bNiQSpUqqa9E/nKdoJMnT9K5c2fu3r2r9UwvHThwgKCgIBRFwcPDQ90drCuhoaEsWLCAq1evqjP17t1b3Vqlbba2tpw7d05vKhyQ1kXXsWNH2rdvr+soQNoV5F+VlJREQEAAe/bsYdiwYYwYMUJHyURmyJggoeHx48d888037N69O8Ptuhjs2717d86fP8/OnTs1viEPGDCAHj16sGHDBq1luXPnjnr69J07d7R23ncpWLAgFy9efGMl6MKFC+TPn1/LqdLo25XIU1NTWbFiBVu2bOHu3buoVCqKFCminiqvi7WnkpKSaNy4MUuWLNHZ5Sgy8tlnn+Hn5/dei5fmhFev2t6iRQuGDRvGlStXMpy8oe2rtg8YMCDD8gULFnDmzBmtZhHvT1qChIYuXbpw9+5dfHx88PLyYuvWrTx69Ei9fkmLFi20nsnCwoK9e/dSp04djfJjx47RtGlT4uPjtZ4J0hZJy5s3L5DWvfLrr7/y/PlzPv30U613XfTr148jR45w+vRp9QVAX3r+/DnVqlXDy8sr3XgTbXBycmLPnj14enpqtATt27ePbt26ERISorUsiqLwySefqBf6K1WqFIqiEBQUxMWLF/n000/x8/PTWp5XOTg4cPLkSZ1fE+9VkydPZsaMGTRo0IDKlSunWw29f//+WsmR2TWS9GVWJqR1g1WsWFEvLlws3kLbc/KFfnN2dlb++usvRVEUxcrKSrl27ZqiKIqybds2pXbt2jrJ5OLioly4cCFdeWBgoFKwYEGt57lw4YLi6uqqGBgYKCVLllQCAgIUJycnxdLSUrG2tlYMDQ2VrVu3ajVTWFiYUqBAAcXFxUWZNm2a4ufnp2zbtk356aefFBcXF6VAgQJKWFiYVjO91KNHD6V169ZKYmKiYmlpqdy+fVu5d++e4unpqfVrKy1btkyxsrJSDh06lG7bwYMHFSsrK8XX11ermV4aPHiwMnz4cJ2c+03c3NzeeCtSpIiu4+m1adOmKa6urrqOId5BKkFCg5WVlXLnzh1FURTF1dVVOX78uKIoinL79m3FzMxMJ5mWLFmiNGzYUAkNDVWXPXz4UGncuLGyePFiredp2rSp0rJlS+XYsWPKd999pxQsWFD55ptvlJSUFCUlJUXp3bu3Ur16da3nunv3rtKsWTPFwMBAUalUikqlUgwMDJRmzZqpf6e6EB0drdSuXVuxtbVVDA0NFRcXF8XIyEj5+OOPtX6h2UaNGilTp0594/bJkycrjRs31mKif/Tt21extrZWKlWqpHz77bfKoEGDNG653alTp5Rdu3ZplPn6+ipubm6Kg4OD0qNHD+XFixdaz1WxYkXF09NTfatYsaLi7OysGBoaKkuWLNF6HvF+pDtMaKhatSqTJk2iSZMmtG7dGmtra6ZOncrcuXPZtGkTt27d0koOT09PjbEZN27cICEhgcKFCwNpiwCamJhQvHhxzp07p5VML+XLl49Dhw5Rvnx54uLisLa2xt/fnypVqgBpM7Fq1KihkwX3ACIjI7l58yaKolC8eHHs7Ox0kuN1hw8f1lgsUReDkJ2dndmzZw8VK1bMcHtAQADNmjXT6lil27dv4+bmlm5trlepVCoOHTqktUwZeflRoavr9TVt2hQvLy+GDx8OwMWLF6lUqRLe3t6ULl2a6dOn89133zF+/Hit5powYYLGfQMDAxwcHKhXr57OLz8k3k0GRgsNAwcO5OHDhwCMGzeOJk2asGbNGoyNjVmxYoXWcrRu3Vpr53pfERERODs7A2kXu7SwsFBf1wjSLvYYGxurq3jY2dmlWwlZV940CNnZ2Vkng5AjIiJwcnJ643YnJyetX9W+ePHiPHz4UL34ZocOHZg7d+5bc2rTypUrmT59Ojdu3ADS1ukZNmwYX375pVZzBAYGMmnSJPX99evXU716dX799VcAXFxcGDdunFYrQcnJybi5udGkSRP1e4L4sEglSGjo0qWL+t+enp7cvXuXq1evUrhwYfLly6e1HOPGjdPauf6N1z+8dfXtWJ8pisKnn36qHoRcrlw59SBkb29vtmzZovVByCkpKeTJ8+a3PUNDQ5KTk7WYiHRLLOzevVtng/1fN2vWLMaMGUPfvn2pXbs2iqJw4sQJevbsyZMnT964OGdOiIyM1KgYHj16lKZNm6rvV61aVauD7AHy5MlDr169CAoK0up5RfaRSpB4K3NzcypVqqTrGACcPXuWoKAgVCoVHh4eOrsCOYC3tzcmJiYAvHjxgp49e6pnziQkJOgslz5ZsWIFf/zxBwcPHsTLy0tj26FDh2jdujUrV67U6mUOFEXR+N29Th9+d/o0QmHevHksWrRI43fUqlUrypQpw/jx47VaCXJycuLOnTu4uLiQmJjIuXPnNLqiYmNjM7wUSk6rXr06AQEBuLq6av3cIuukEiTeaw2QWbNm5WCSjIWHh9OxY0eOHDmiXsslOjoaLy8v1q9fr163R1u+/vprjftffPFFun3kwomwbt06fvjhh3QVIID69eszYsQI1qxZo9Xn6vXfXUa0/btTqVR627L48OFDatWqla68Vq1a6m5zbWnatCkjRoxg2rRp+Pn5YW5urrEUxYULFyhatKhWMwH07t2bIUOGcP/+/QyXEdDlhYvFu8nAaJHhh1RGdDU4s0OHDty6dYtVq1apLwtx5coVvv76a4oVK8a6deu0nkm8mz4OQtZHBgYGNGvWTN06tWPHDurXr5/uw3TLli1az1a2bFk6d+7MDz/8oFE+adIkfv/9dy5evKi1LI8fP6ZNmzacOHECS0tLfH19+eyzz9TbGzRoQI0aNbS22GTXrl3x8fHB1tY23baXK8nr07pFImNSCRJ6z8bGhgMHDqQb7Ovv70/jxo11NgtLvJ2xsTH37t1740rVoaGhFClSRC+6oHTpm2++ydR+y5cvz+Ek6W3evJkOHTrQsGFDateujUql4vjx4xw8eJANGzZoVEK0JTo6GktLSwwNDTXKIyIisLS0xNjYWCs5DA0Nefjw4TsvRSPdZPpNusPEG4WEhKBSqXRyMclXpaamZtjXb2RkRGpqqg4SiczQx0HI+kgXlZvMatu2LX/99RezZ8/Gz89PfT0zf39/nY3Je9O1Al+doakNL9sPpJLzYZOWIKEhOTmZCRMmMHfuXOLi4oC0aeD9+vVj3LhxOhl42KpVK6Kioli3bp36QpIPHjygS5cu2NnZsXXrVq1nEu/2ejfP6xISEtizZ490F+ihzF7qwdraOoeT6C8DAwMePXqk9TGJIntJJUho6NmzJ1u3bmXixInUrFkTgD///JPx48fTqlUrFi9erPVMISEhtGrVikuXLuHi4oJKpSI4OJhy5cqxbds2nbdUiYzpczePeDsDA4NMDc7OzRVYAwMDbGxs3vk8RUREaCmR+DekEiQ02NjYsH79epo1a6ZRvnv3bjp27Eh0dLSOksH+/fu5evWqukleFysOC5EbHD16VP1vRVFo3rw5v/32GwULFtTYr27dutqOpjcMDAzw8fF5Y/fcS5mZkSh0RypBQoOTkxNHjhxRz8J6KSgoiI8//pjHjx/rKJkQQlesrKwIDAzE3d1d11H0hoGBAWFhYTg6Ouo6isgCGRgtNPTp04cff/yR5cuXq8dyJCQkMHnyZPr27auzXP7+/hw5coTw8PB0g6F1sXaRECJ305e1nETWSCVIaAgICODgwYMUKlSIChUqAGnX7ElMTKRBgwa0adNGva+21i2ZMmUKo0ePpmTJkjg5OWm8+cgbkRBCF6QT5b9BKkFCg62tLW3bttUoc3Fx0VGaNHPmzGHZsmV4e3vrNIcQuZl84dAky3P8N8iYIKGmKArBwcE4ODhgbm6u6zhq+fPn548//qB48eK6jiJErvBqiy/o1yrWQmQnA10HEPpDURSKFy/OgwcPdB1Fw6BBg1iwYIGuYwiRa9jY2GjcvvjiCwoUKJCuXIgPnbQECQ1lypRh6dKl1KhRQ9dR1FJTU2nRogXXr1/Hw8Mj3YKN8m1UCCHEvyEtQULDzz//zLBhw7h06ZKuo6j169ePw4cPU6JECfLmzSvfRoUQQmQLaQkSGuzs7Hj27BnJyckYGxtjZmamsV0Xq59aWVmxfv16WrRoofVzCyGE+O+S2WFCg4+Pj64jpGNvb0/RokV1HUMIIcR/jLQECb23fPly9uzZw/Lly/Vq1poQQogPm1SCBDExMeqrQb/r6tG6uGq0p6cnt27dQlEU3Nzc0g2MPnfunNYzCSGE+PBJd5jAzs6Ohw8f4ujoiK2tbYaLoimKgkql0slVo1u3bq31cwohhPjvk0qQ4NChQ9jb2wNw+PDhN+4XEBCgrUgaxo0bp5PzCiGE+G+T7jDxVtHR0axZs4bffvuNwMBAnbQEvXT27FmCgoJQqVR4eHjg6empsyxCCCE+fNISJDJ06NAhli1bxpYtW3B1daVt27YsXbpUJ1nCw8Pp2LEjR44cwdbWFkVRiI6OxsvLi/Xr1+Pg4KCTXEIIIT5ssliiULt//z6TJk3C3d2dTp06YWdnR1JSEps3b2bSpEk6a3np168fMTExXL58mYiICCIjI7l06RIxMTH0799fJ5mEEEJ8+KQ7TADQvHlzjh8/TsuWLenSpQtNmzbF0NAQIyMjAgMD8fDw0Fk2GxsbDhw4QNWqVTXK/f39ady4MVFRUboJJoQQ4oMm3WECgH379tG/f3969eqld1drT01NTTctHsDIyIjU1FQdJBJCCPFfIN1hAoBjx44RGxtLlSpVqF69OvPnz+fx48e6jgVA/fr1GTBgAKGhoeqyBw8eMGjQIBo0aKDDZEIIIT5k0h0mNDx79oz169ezbNky/P39SUlJYdasWXTt2hUrKyudZAoJCaFVq1ZcunQJFxcXVCoVwcHBlCtXjm3btlGoUCGd5BJCCPFhk0qQeKNr166xdOlSVq1aRVRUFI0aNWL79u06y7N//36uXr2Koih4eHjQsGFDnWURQgjx4ZNKkHinlJQUduzYwbJly3RSCVq5ciUdOnTAxMREozwxMZH169fz1VdfaT2TEEKID59UgoTeMzQ0VF/W41VPnz7F0dFRpws4CiGE+HDJwGih915et+x19+/fx8bGRgeJhBBC/BfIFHmhtzw9PVGpVKhUKho0aECePP/8uaakpHDnzh2aNm2qw4RCCCE+ZFIJEnrr5dXjz58/T5MmTbC0tFRvMzY2xs3NjbZt2+oonRBCiA+djAkSes/X15cOHTpgamqq6yhCCCH+Q6QSJIQQQohcSbrDhN4zMDDIcGD0SzI7TAghxL8hlSCh97Zs2aJRCUpKSiIgIABfX18mTJigw2RCCCE+ZNIdJj5Ya9eu5ffff2fbtm26jiKEEOIDJJUg8cG6desW5cuXJz4+XtdRhBBCfIBksUTxQXr+/Dnz5s2Ti6cKIYT412RMkNB7dnZ2GmOCFEUhNjYWMzMz1qxZo8NkQgghPmRSCRJ6z8fHR+O+gYEBDg4OVK9enXv37ukmlBBCiA+ejAkSH5zo6GjWrFnD0qVLOX/+vEyRF0II8a/ImCDxwTh06BBffPEF+fPnZ968eTRr1owzZ87oOpYQQogPlHSHCb12//59VqxYwbJly4iPj6d9+/YkJSWxefNmPDw8dB1PCCHEB0xagoTeat68OR4eHly5coV58+YRGhrKvHnzdB1LCCHEf4S0BAm9tW/fPvr370+vXr0oXry4ruMIIYT4j5GWIKG3jh07RmxsLFWqVKF69erMnz+fx48f6zqWEEKI/wiZHSb03rNnz1i/fj3Lli3D39+flJQUZs2aRdeuXbGystJ1PCGEEB8oqQSJD8q1a9dYunQpq1atIioqikaNGrF9+3ZdxxJCCPEBkkqQ+CClpKSwY8cOli1bJpUgIYQQ/4pUgoQQQgiRK8nAaCGEEELkSlIJEkIIIUSuJJUgIYQQQuRKUgkSQgghRK4klSAhRI4ZP348FStWVN/39vamdevWWs9x9+5dVCoV58+ff+M+bm5u+Pj4ZPqYK1aswNbWNsvZVCoVfn5+WT6OEOL9SSVIiFzG29sblUqFSqXCyMgId3d3hg4dSnx8fI6fe86cOaxYsSJT+2am4iKEEFkh1w4TIhdq2rQpy5cvJykpiWPHjtG9e3fi4+NZtGhRun2TkpIwMjLKlvPa2Nhky3GEECI7SEuQELmQiYkJzs7OuLi40LlzZ7p06aLuknnZhbVs2TLc3d0xMTFBURSio6P59ttvcXR0xNramvr16xMYGKhx3J9++gknJyesrKzo1q0bL1680Nj+endYamoq06ZNo1ixYpiYmFC4cGEmT54MQJEiRQDw9PREpVJRr1499eOWL19O6dKlMTU1pVSpUixcuFDjPP7+/nh6emJqakqVKlUICAh47+do1qxZlCtXDgsLC1xcXOjduzdxcXHp9vPz86NEiRKYmprSqFEjQkJCNLbv2LGDypUrY2pqiru7OxMmTCA5Ofm98wghsp9UgoQQmJmZkZSUpL5/8+ZNNmzYwObNm9XdUS1atCAsLIxdu3Zx9uxZKlWqRIMGDYiIiABgw4YNjBs3jsmTJ3PmzBny58+frnLyupEjRzJt2jTGjBnDlStXWLt2LU5OTkBaRQbgwIEDPHz4kC1btgDw66+/MmrUKCZPnkxQUBBTpkxhzJgx+Pr6AhAfH0/Lli0pWbIkZ8+eZfz48QwdOvS9nxMDAwPmzp3LpUuX8PX15dChQ3z//fca+zx79ozJkyfj6+vLiRMniImJoWPHjurte/fu5YsvvqB///5cuXKFJUuWsGLFCnVFTwihY4oQIlf5+uuvlVatWqnv//XXX0revHmV9u3bK4qiKOPGjVOMjIyU8PBw9T4HDx5UrK2tlRcvXmgcq2jRosqSJUsURVGUmjVrKj179tTYXr16daVChQoZnjsmJkYxMTFRfv311wxz3rlzRwGUgIAAjXIXFxdl7dq1GmU//vijUrNmTUVRFGXJkiWKvb29Eh8fr96+aNGiDI/1KldXV2X27Nlv3L5hwwYlb9686vvLly9XAOXUqVPqsqCgIAVQ/vrrL0VRFOWjjz5SpkyZonGcVatWKfnz51ffB5StW7e+8bxCiJwjY4KEyIV27tyJpaUlycnJJCUl0apVK+bNm6fe7urqioODg/r+2bNniYuLI2/evBrHef78Obdu3QIgKCiInj17amyvWbMmhw8fzjBDUFAQCQkJNGjQINO5Hz9+TEhICN26daNHjx7q8uTkZPV4o6CgICpUqIC5ublGjvd1+PBhpkyZwpUrV4iJiSE5OZkXL14QHx+PhYUFAHny5KFKlSrqx5QqVQpbW1uCgoKoVq0aZ8+e5fTp0xotPykpKbx48YJnz55pZBRCaJ9UgoTIhby8vFi0aBFGRkYUKFAg3cDnlx/yL6WmppI/f36OHDmS7lj/dpq4mZnZez8mNTUVSOsSq169usY2Q0NDAJRsuBzivXv3aN68OT179uTHH3/E3t6e48eP061bN41uQ0ib4v66l2WpqalMmDCBNm3apNvH1NQ0yzmFEFkjlSAhciELCwuKFSuW6f0rVapEWFgYefLkwc3NLcN9SpcuzalTp/jqq6/UZadOnXrjMYsXL46ZmRkHDx6ke/fu6bYbGxsDaS0nLzk5OVGwYEFu375Nly5dMjyuh4cHq1at4vnz5+qK1ttyZOTMmTMkJyczc+ZMDAzShk5u2LAh3X7JycmcOXOGatWqAXDt2jWioqIoVaoUkPa8Xbt27b2eayGE9kglSAjxTg0bNqRmzZq0bt2aadOmUbJkSUJDQ9m1axetW7emSpUqDBgwgK+//poqVapQp04d1qxZw+XLl3F3d8/wmKampgwfPpzvv/8eY2NjateuzePHj7l8+TLdunXD0dERMzMz9uzZQ6FChTA1NcXGxobx48fTv39/rK2tadasGQkJCZw5c4bIyEgGDx5M586dGTVqFN26dWP06NHcvXuXGTNmvNfPW7RoUZKTk5k3bx6ffPIJJ06cYPHixen2MzIyol+/fsydOxcjIyP69u1LjRo11JWisWPH0rJlS1xcXGjXrh0GBgZcuHCBixcvMmnSpPf/RQghspXMDhNCvJNKpWLXrl18/PHHdO3alRIlStCxY0fu3r2rns3VoUMHxo4dy/Dhw6lcuTL37t2jV69ebz3umDFjGDJkCGPHjqV06dJ06NCB8PBwIG28zdy5c1myZAkFChSgVatWAHTv3p3ffvuNFStWUK5cOerWrcuKFSvUU+otLS3ZsWMHV65cwdPTk1GjRjFt2rT3+nkrVqzIrFmzmDZtGmXLlmXNmjVMnTo13X7m5uYMHz6czp07U7NmTczMzFi/fr16e5MmTdi5cyf79++natWq1KhRg1mzZuHq6vpeeYQQOUOlZEcHuhBCCCHEB0ZagoQQQgiRK0klSAghhBC5klSChBBCCJErSSVICCGEELmSVIKEEEIIkStJJUgIIYQQuZJUgoQQQgiRK0klSAghhBC5klSChBBCCJErSSVICCGEELmSVIKEEEIIkSv9H+NuRrHIvcVnAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Predictions and Classification Report\n",
+    "# Classification report share precision, recall and F1-score for each class\n",
+    "y_pred = np.argmax(model.predict(X_test), axis=1)\n",
+    "y_true = y_test.flatten()\n",
+    "\n",
+    "print(\"\\nClassification Report:\")\n",
+    "print(classification_report(y_true, y_pred, target_names=labels))\n",
+    "\n",
+    "# Confusion Matrix\n",
+    "# Visualizes the model's performance across classes\n",
+    "conf_matrix = confusion_matrix(y_true, y_pred)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels=labels)\n",
+    "\n",
+    "plt.figure(figsize=(10, 10))\n",
+    "disp.plot(cmap=plt.cm.Blues, xticks_rotation='vertical')\n",
+    "plt.title(\"Confusion Matrix\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Visualization: Predictions\n",
+    "# Correct predictions are marked in blue, incorrect ones in red.\n",
+    "num_rows, num_cols = 4, 5\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "for i in range(num_rows * num_cols):\n",
+    "    plt.subplot(num_rows, num_cols, i + 1)\n",
+    "    plt.imshow(X_test[i])\n",
+    "    true_label = labels[y_true[i]]\n",
+    "    pred_label = labels[y_pred[i]]\n",
+    "    color = 'blue' if true_label == pred_label else 'red'\n",
+    "    plt.title(f\"T: {true_label}\\nP: {pred_label}\", color=color, fontsize=10)\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7fa52dd-4c31-4136-9b66-c0bad9a013ec",
+   "metadata": {},
+   "source": [
+    "**Possible Improvements to the Model**\n",
+    "\n",
+    "There are several possibilities that could be explored for further enhancement of the model shared here.\n",
+    "\n",
+    "- The ROC-AUC metric could be applied, to help in understanding how well the model distinguishes the classes (beyond precision and recall).\n",
+    "\n",
+    "- Fine-tuning further DenseNet121: selectively unfreesing the top layers of the pre-trained model and fine-tune them on CIFAR-10. This should allow the model to adapt its high level features to the dataset at the same time it leverages pre-trained lower level features.\n",
+    "\n",
+    "- Introduce more diversity within the augmentations (for example, roation, zoom or cutout)\\\n",
+    "\n",
+    "- Applying label smoothing to soften the one-hot encoded labels may help prevent overfitting by reducing the confidence of the level's predictions\n",
+    "\n",
+    "- Dense layers or convolutional filters (L2 regularization)\n",
+    "\n",
+    "- Upscaling the resolution of CIFAR-10 images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "3e3b0dad-6cbd-4432-9864-148144383daf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/lib/python3.12/site-packages/keras/src/saving/saving_lib.py:757: UserWarning: Skipping variable loading for optimizer 'rmsprop', because it has 366 variables whereas the saved optimizer has 730 variables. \n",
+      "  saveable.load_own_variables(weights_store.get(inner_path))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True Label: Deer\n",
+      "Predicted Label: Deer\n",
+      "Confidence: 0.99\n"
+     ]
+    }
+   ],
+   "source": [
+    "# **Additional Codes Developed**\n",
+    "\n",
+    "# Tests on one image\n",
+    "from tensorflow.keras.datasets import cifar10\n",
+    "from tensorflow.keras.applications.densenet import preprocess_input\n",
+    "from tensorflow.keras.models import load_model\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Load CIFAR-10 dataset (for testing purposes)\n",
+    "(_, _), (X_test, y_test) = cifar10.load_data()\n",
+    "\n",
+    "# Loads the trained model\n",
+    "model = load_model('/Users/sylviaperez-montero/Desktop/project-1-deep-learning-image-classification-with-cnn-main/cnn_20_epochs.keras')\n",
+    "\n",
+    "# Defines CIFAR-10 class labels\n",
+    "labels = ['Airplane', 'Automobile', 'Bird', 'Cat', 'Deer', \n",
+    "          'Dog', 'Frog', 'Horse', 'Ship', 'Truck']\n",
+    "\n",
+    "# Selects a single image from the test set\n",
+    "image_index = 100  # Change index to test different images\n",
+    "image = X_test[image_index]\n",
+    "true_label = y_test[image_index][0]\n",
+    "\n",
+    "# Preprocesses the image\n",
+    "preprocessed_image = image / 255.0  # Normalize pixel values to [0, 1]\n",
+    "preprocessed_image = np.expand_dims(preprocessed_image, axis=0)  # Add batch dimension\n",
+    "\n",
+    "# Makes a prediction\n",
+    "predictions = model.predict(preprocessed_image)\n",
+    "predicted_class = np.argmax(predictions, axis=1)[0]\n",
+    "confidence = np.max(predictions)\n",
+    "\n",
+    "# Displays the image and results\n",
+    "plt.imshow(image)\n",
+    "plt.axis('off')\n",
+    "plt.title(f\"True: {labels[true_label]}\\nPredicted: {labels[predicted_class]} ({confidence:.2f})\")\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"True Label: {labels[true_label]}\")\n",
+    "print(f\"Predicted Label: {labels[predicted_class]}\")\n",
+    "print(f\"Confidence: {confidence:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "501f70cb-4055-4e85-8dd8-0f076c62bf53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly selects 10 images, as required by the project.\n",
+    "\n",
+    "# Imports the necessary libraries\n",
+    "import os\n",
+    "import numpy as np\n",
+    "from tensorflow.keras.datasets import cifar10\n",
+    "from PIL import Image\n",
+    "\n",
+    "# Loads CIFAR-10 dataset\n",
+    "(X_train, y_train), (_, _) = cifar10.load_data()\n",
+    "\n",
+    "# Creates a folder named 'images' in the current directory\n",
+    "output_dir = \"images\"\n",
+    "os.makedirs(output_dir, exist_ok=True)\n",
+    "\n",
+    "# Selects 10 random indices\n",
+    "random_indices = np.random.choice(len(X_train), 10, replace=False)\n",
+    "\n",
+    "# Saves the selected images\n",
+    "for i, idx in enumerate(random_indices):\n",
+    "    # Get the image\n",
+    "    img = X_train[idx]\n",
+    "    \n",
+    "    # Converts the image array to a PIL image\n",
+    "    pil_img = Image.fromarray(img)\n",
+    "    \n",
+    "    # Saves the image with a sequential filename (e.g., image_1.png, image_2.png, etc.)\n",
+    "    filename = os.path.join(output_dir, f\"image_{i+1}.png\")\n",
+    "    pil_img.save(filename)\n",
+    "\n",
+    "    print(f\"Saved: {filename}\")\n",
+    "\n",
+    "print(f\"All 10 images saved in the '{output_dir}' directory.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}