main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNNs on CIFAR-10\n",
    "\n",
    "Comparing different CNNs performance on the CIFAR-10 Dataset\n",
    "- Just CNNs\n",
    "- Include MaxPool\n",
    "- Include Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "from tensorflow.keras.layers import Conv2D, Dense, Flatten, MaxPooling2D, Dropout\n",
    "from tensorflow.keras.models import Sequential\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run on GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-28 17:30:21.672404: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-10-28 17:30:21.691629: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-10-28 17:30:21.691723: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.config.list_physical_devices('GPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)\n"
     ]
    }
   ],
   "source": [
    "#@Load CIFAR-10 Dataset\n",
    "cifar10 = tf.keras.datasets.cifar10\n",
    "\n",
    "# Distribute it to train and test set\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reduce pixel values\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "\n",
    "# flatten the label values\n",
    "y_train, y_test = y_train.flatten(), y_test.flatten()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_valid, x_test, y_valid, y_test = train_test_split(x_test, y_test, train_size=0.5, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#@One hot encoding\n",
    "labels = np.array([0, 1 ,2, 3, 4, 5, 6, 7, 8, 9]).reshape(-1, 1)\n",
    "\n",
    "# Design one hot encoder\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "\n",
    "# Fit the encoder on the labels\n",
    "encoder.fit(labels)\n",
    "\n",
    "# Function to one-hot encode data\n",
    "def one_hot_encode(data):\n",
    "    return encoder.transform(np.array(data).reshape(-1, 1))\n",
    "\n",
    "\n",
    "# Function to decode one-hot encoded data\n",
    "def decode(encoded_data):\n",
    "    return encoder.inverse_transform(encoded_data).flatten().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = one_hot_encode(y_train)\n",
    "y_valid = one_hot_encode(y_valid)\n",
    "y_test = one_hot_encode(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Phase 1:\n",
    "\n",
    "VGG Style Conv layers followed by FCNN Layers and classification. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 30, 30, 32)        896       \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 28, 28, 32)        9248      \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 26, 26, 64)        18496     \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 24, 24, 64)        36928     \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 22, 22, 128)       73856     \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 20, 20, 128)       147584    \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 51200)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 100)               5120100   \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1010      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5408118 (20.63 MB)\n",
      "Trainable params: 5408118 (20.63 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-28 17:30:36.710784: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-10-28 17:30:36.710890: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-10-28 17:30:36.710928: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-10-28 17:30:36.980564: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-10-28 17:30:36.980651: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-10-28 17:30:36.980693: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:894] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-10-28 17:30:36.980732: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 18248 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4090, pci bus id: 0000:01:00.0, compute capability: 8.9\n"
     ]
    }
   ],
   "source": [
    "#@ Define model\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)))\n",
    "model.add(Conv2D(32, (3,3), activation='relu'))\n",
    "model.add(Conv2D(64, (3,3), activation='relu'))\n",
    "model.add(Conv2D(64, (3,3), activation='relu'))\n",
    "model.add(Conv2D(128, (3,3), activation='relu'))\n",
    "model.add(Conv2D(128, (3,3), activation='relu'))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(100, activation='relu'))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-28 17:30:42.488929: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8902\n",
      "2023-10-28 17:30:42.895932: W tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:231] Falling back to the CUDA driver for PTX compilation; ptxas does not support CC 8.9\n",
      "2023-10-28 17:30:42.895948: W tensorflow/compiler/xla/stream_executor/gpu/asm_compiler.cc:234] Used ptxas at ptxas\n",
      "2023-10-28 17:30:42.895984: W tensorflow/compiler/xla/stream_executor/gpu/redzone_allocator.cc:322] UNIMPLEMENTED: ptxas ptxas too old. Falling back to the driver to compile.\n",
      "Relying on driver to perform ptx compilation. \n",
      "Modify $PATH to customize ptxas location.\n",
      "This message will be only logged once.\n",
      "2023-10-28 17:30:43.295921: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\n",
      "2023-10-28 17:30:43.306405: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f4ea8021f00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2023-10-28 17:30:43.306416: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 4090, Compute Capability 8.9\n",
      "2023-10-28 17:30:43.309191: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2023-10-28 17:30:43.335532: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1563/1563 [==============================] - 13s 7ms/step - loss: 1.5817 - accuracy: 0.4206 - val_loss: 1.3060 - val_accuracy: 0.5310\n",
      "Epoch 2/15\n",
      "1563/1563 [==============================] - 12s 8ms/step - loss: 1.1627 - accuracy: 0.5853 - val_loss: 1.0379 - val_accuracy: 0.6440\n",
      "Epoch 3/15\n",
      "1563/1563 [==============================] - 12s 7ms/step - loss: 0.9573 - accuracy: 0.6603 - val_loss: 0.9788 - val_accuracy: 0.6514\n",
      "Epoch 4/15\n",
      "1563/1563 [==============================] - 12s 8ms/step - loss: 0.7942 - accuracy: 0.7220 - val_loss: 0.9260 - val_accuracy: 0.6790\n",
      "Epoch 5/15\n",
      "1563/1563 [==============================] - 12s 8ms/step - loss: 0.6307 - accuracy: 0.7772 - val_loss: 1.0450 - val_accuracy: 0.6648\n",
      "Epoch 6/15\n",
      "1563/1563 [==============================] - 12s 8ms/step - loss: 0.4594 - accuracy: 0.8371 - val_loss: 1.2774 - val_accuracy: 0.6608\n",
      "Epoch 7/15\n",
      "1563/1563 [==============================] - 12s 8ms/step - loss: 0.3173 - accuracy: 0.8892 - val_loss: 1.3787 - val_accuracy: 0.6466\n",
      "Epoch 8/15\n",
      "1563/1563 [==============================] - 11s 7ms/step - loss: 0.2256 - accuracy: 0.9211 - val_loss: 1.6246 - val_accuracy: 0.6458\n",
      "Epoch 9/15\n",
      "1563/1563 [==============================] - 12s 8ms/step - loss: 0.1728 - accuracy: 0.9404 - val_loss: 1.8164 - val_accuracy: 0.6530\n",
      "Epoch 10/15\n",
      "1563/1563 [==============================] - 12s 7ms/step - loss: 0.1380 - accuracy: 0.9541 - val_loss: 2.1730 - val_accuracy: 0.6392\n",
      "Epoch 11/15\n",
      "1563/1563 [==============================] - 12s 8ms/step - loss: 0.1203 - accuracy: 0.9609 - val_loss: 2.4525 - val_accuracy: 0.6404\n",
      "Epoch 12/15\n",
      "1563/1563 [==============================] - 12s 8ms/step - loss: 0.1108 - accuracy: 0.9638 - val_loss: 2.3864 - val_accuracy: 0.6350\n",
      "Epoch 13/15\n",
      "1563/1563 [==============================] - 11s 7ms/step - loss: 0.1044 - accuracy: 0.9658 - val_loss: 2.2729 - val_accuracy: 0.6400\n",
      "Epoch 14/15\n",
      "1563/1563 [==============================] - 10s 6ms/step - loss: 0.0959 - accuracy: 0.9688 - val_loss: 2.7196 - val_accuracy: 0.6344\n",
      "Epoch 15/15\n",
      "1563/1563 [==============================] - 8s 5ms/step - loss: 0.0879 - accuracy: 0.9718 - val_loss: 2.6623 - val_accuracy: 0.6434\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "model_history = model.fit(x_train, y_train, validation_data=(x_valid, y_valid), epochs=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting loss vs epoch curve of model\n",
    "loss = model_history.history['loss']\n",
    "val_loss = model_history.history['val_loss']\n",
    "epochs = range(1, len(loss)+1)\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, label='train loss')\n",
    "plt.plot(epochs, val_loss, label='val loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 - 0s - loss: 2.6374 - accuracy: 0.6378 - 357ms/epoch - 2ms/step\n",
      "\n",
      "Test accuracy: 0.6377999782562256\n"
     ]
    }
   ],
   "source": [
    "# Find and print model accuracy against test set\n",
    "test_loss, test_acc = model.evaluate(x_test,  y_test, verbose=2)\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Predictions over test set\n",
    "predictions = model.predict(x_test)\n",
    "\n",
    "y_pred_classes = decode(predictions)\n",
    "y_true_classes = decode(y_test)\n",
    "\n",
    "cm = confusion_matrix(y_true_classes, y_pred_classes)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Phase 2:\n",
    "\n",
    "VGG Style Conv layers and Max Pooling layers followed by FCNN classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_6 (Conv2D)           (None, 30, 30, 32)        896       \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 28, 28, 32)        9248      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 14, 14, 32)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " conv2d_8 (Conv2D)           (None, 12, 12, 64)        18496     \n",
      "                                                                 \n",
      " conv2d_9 (Conv2D)           (None, 10, 10, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 5, 5, 64)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_10 (Conv2D)          (None, 3, 3, 128)         73856     \n",
      "                                                                 \n",
      " conv2d_11 (Conv2D)          (None, 1, 1, 128)         147584    \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 100)               12900     \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 10)                1010      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 300918 (1.15 MB)\n",
      "Trainable params: 300918 (1.15 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#@ Define model\n",
    "model_maxpool = Sequential()\n",
    "model_maxpool.add(Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)))\n",
    "model_maxpool.add(Conv2D(32, (3,3), activation='relu'))\n",
    "model_maxpool.add(MaxPooling2D(2,2))\n",
    "model_maxpool.add(Conv2D(64, (3,3), activation='relu'))\n",
    "model_maxpool.add(Conv2D(64, (3,3), activation='relu'))\n",
    "model_maxpool.add(MaxPooling2D(2,2))\n",
    "model_maxpool.add(Conv2D(128, (3,3), activation='relu'))\n",
    "model_maxpool.add(Conv2D(128, (3,3), activation='relu'))\n",
    "model_maxpool.add(Flatten())\n",
    "model_maxpool.add(Dense(100, activation='relu'))\n",
    "model_maxpool.add(Dense(10, activation='softmax'))\n",
    "\n",
    "model_maxpool.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "1563/1563 [==============================] - 10s 6ms/step - loss: 1.5921 - accuracy: 0.4125 - val_loss: 1.2399 - val_accuracy: 0.5696\n",
      "Epoch 2/15\n",
      "1563/1563 [==============================] - 9s 6ms/step - loss: 1.1093 - accuracy: 0.6025 - val_loss: 1.0472 - val_accuracy: 0.6296\n",
      "Epoch 3/15\n",
      "1563/1563 [==============================] - 10s 6ms/step - loss: 0.9279 - accuracy: 0.6730 - val_loss: 0.9426 - val_accuracy: 0.6684\n",
      "Epoch 4/15\n",
      "1563/1563 [==============================] - 9s 6ms/step - loss: 0.8075 - accuracy: 0.7158 - val_loss: 0.8574 - val_accuracy: 0.7042\n",
      "Epoch 5/15\n",
      "1563/1563 [==============================] - 9s 6ms/step - loss: 0.7121 - accuracy: 0.7507 - val_loss: 0.8358 - val_accuracy: 0.7106\n",
      "Epoch 6/15\n",
      "1563/1563 [==============================] - 10s 6ms/step - loss: 0.6408 - accuracy: 0.7745 - val_loss: 0.8190 - val_accuracy: 0.7226\n",
      "Epoch 7/15\n",
      "1563/1563 [==============================] - 9s 6ms/step - loss: 0.5740 - accuracy: 0.7999 - val_loss: 0.8270 - val_accuracy: 0.7314\n",
      "Epoch 8/15\n",
      "1563/1563 [==============================] - 10s 6ms/step - loss: 0.5156 - accuracy: 0.8177 - val_loss: 0.7968 - val_accuracy: 0.7370\n",
      "Epoch 9/15\n",
      "1563/1563 [==============================] - 10s 6ms/step - loss: 0.4638 - accuracy: 0.8369 - val_loss: 0.8736 - val_accuracy: 0.7330\n",
      "Epoch 10/15\n",
      "1563/1563 [==============================] - 10s 7ms/step - loss: 0.4189 - accuracy: 0.8512 - val_loss: 0.9014 - val_accuracy: 0.7308\n",
      "Epoch 11/15\n",
      "1563/1563 [==============================] - 9s 6ms/step - loss: 0.3730 - accuracy: 0.8675 - val_loss: 0.9473 - val_accuracy: 0.7324\n",
      "Epoch 12/15\n",
      "1563/1563 [==============================] - 10s 6ms/step - loss: 0.3362 - accuracy: 0.8792 - val_loss: 0.9380 - val_accuracy: 0.7332\n",
      "Epoch 13/15\n",
      "1563/1563 [==============================] - 10s 6ms/step - loss: 0.3060 - accuracy: 0.8928 - val_loss: 1.0160 - val_accuracy: 0.7382\n",
      "Epoch 14/15\n",
      "1563/1563 [==============================] - 9s 6ms/step - loss: 0.2767 - accuracy: 0.9015 - val_loss: 1.0655 - val_accuracy: 0.7290\n",
      "Epoch 15/15\n",
      "1563/1563 [==============================] - 9s 6ms/step - loss: 0.2599 - accuracy: 0.9075 - val_loss: 1.0650 - val_accuracy: 0.7286\n"
     ]
    }
   ],
   "source": [
    "model_maxpool.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "model_history = model_maxpool.fit(x_train, y_train, validation_data=(x_valid, y_valid), epochs=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting loss vs epoch curve of model\n",
    "loss = model_history.history['loss']\n",
    "val_loss = model_history.history['val_loss']\n",
    "epochs = range(1, len(loss)+1)\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, label='train loss')\n",
    "plt.plot(epochs, val_loss, label='val loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 - 0s - loss: 1.0690 - accuracy: 0.7242 - 263ms/epoch - 2ms/step\n",
      "\n",
      "Test accuracy: 0.7242000102996826\n"
     ]
    }
   ],
   "source": [
    "# Find and print model accuracy against test set\n",
    "test_loss, test_acc = model_maxpool.evaluate(x_test,  y_test, verbose=2)\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Predictions over test set\n",
    "predictions = model_maxpool.predict(x_test)\n",
    "\n",
    "y_pred_classes = decode(predictions)\n",
    "y_true_classes = decode(y_test)\n",
    "\n",
    "cm = confusion_matrix(y_true_classes, y_pred_classes)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Phase 3:\n",
    "\n",
    "VGG Style Conv, MaxPool and Dropout layers followed by FCNN classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_12 (Conv2D)          (None, 30, 30, 32)        896       \n",
      "                                                                 \n",
      " conv2d_13 (Conv2D)          (None, 28, 28, 32)        9248      \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPoolin  (None, 14, 14, 32)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 14, 14, 32)        0         \n",
      "                                                                 \n",
      " conv2d_14 (Conv2D)          (None, 12, 12, 64)        18496     \n",
      "                                                                 \n",
      " conv2d_15 (Conv2D)          (None, 10, 10, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPoolin  (None, 5, 5, 64)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 5, 5, 64)          0         \n",
      "                                                                 \n",
      " conv2d_16 (Conv2D)          (None, 3, 3, 128)         73856     \n",
      "                                                                 \n",
      " conv2d_17 (Conv2D)          (None, 1, 1, 128)         147584    \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 1, 1, 128)         0         \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 100)               12900     \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 100)               0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 100)               10100     \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 100)               0         \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 10)                1010      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 311018 (1.19 MB)\n",
      "Trainable params: 311018 (1.19 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#@ Define model\n",
    "model_f = Sequential()\n",
    "model_f.add(Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)))\n",
    "model_f.add(Conv2D(32, (3,3), activation='relu'))\n",
    "model_f.add(MaxPooling2D(2,2))\n",
    "model_f.add(Dropout(0.1))\n",
    "model_f.add(Conv2D(64, (3,3), activation='relu'))\n",
    "model_f.add(Conv2D(64, (3,3), activation='relu'))\n",
    "model_f.add(MaxPooling2D(2,2))\n",
    "model_f.add(Dropout(0.1))\n",
    "model_f.add(Conv2D(128, (3,3), activation='relu'))\n",
    "model_f.add(Conv2D(128, (3,3), activation='relu'))\n",
    "model_f.add(Dropout(0.1))\n",
    "model_f.add(Flatten())\n",
    "model_f.add(Dense(100, activation='relu'))\n",
    "model_f.add(Dropout(0.1))\n",
    "model_f.add(Dense(100, activation='relu'))\n",
    "model_f.add(Dropout(0.2))\n",
    "model_f.add(Dense(10, activation='softmax'))\n",
    "\n",
    "model_f.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-28 17:49:49.641337: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:961] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape insequential_2/dropout/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\n",
      "2023-10-28 17:49:49.777362: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\n",
      "2023-10-28 17:49:49.777379: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\n",
      "2023-10-28 17:49:49.777842: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\n",
      "2023-10-28 17:49:49.778202: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\n",
      "2023-10-28 17:49:49.778359: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\n",
      "2023-10-28 17:49:49.778701: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\n",
      "2023-10-28 17:49:49.778755: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\n",
      "2023-10-28 17:49:49.778910: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\n",
      "2023-10-28 17:49:49.779393: W tensorflow/compiler/mlir/tools/kernel_gen/transforms/gpu_kernel_to_blob_pass.cc:191] Failed to compile generated PTX with ptxas. Falling back to compilation by driver.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1563/1563 [==============================] - 12s 7ms/step - loss: 1.7035 - accuracy: 0.3602 - val_loss: 1.3197 - val_accuracy: 0.5084\n",
      "Epoch 2/15\n",
      "1563/1563 [==============================] - 10s 7ms/step - loss: 1.2626 - accuracy: 0.5498 - val_loss: 1.0946 - val_accuracy: 0.6072\n",
      "Epoch 3/15\n",
      "1563/1563 [==============================] - 11s 7ms/step - loss: 1.0762 - accuracy: 0.6236 - val_loss: 0.9747 - val_accuracy: 0.6644\n",
      "Epoch 4/15\n",
      "1563/1563 [==============================] - 12s 8ms/step - loss: 0.9468 - accuracy: 0.6720 - val_loss: 0.8965 - val_accuracy: 0.6996\n",
      "Epoch 5/15\n",
      "1563/1563 [==============================] - 12s 7ms/step - loss: 0.8659 - accuracy: 0.7024 - val_loss: 0.8522 - val_accuracy: 0.7096\n",
      "Epoch 6/15\n",
      "1563/1563 [==============================] - 9s 6ms/step - loss: 0.8024 - accuracy: 0.7240 - val_loss: 0.8606 - val_accuracy: 0.7106\n",
      "Epoch 7/15\n",
      "1563/1563 [==============================] - 6s 4ms/step - loss: 0.7566 - accuracy: 0.7406 - val_loss: 0.8064 - val_accuracy: 0.7292\n",
      "Epoch 8/15\n",
      "1563/1563 [==============================] - 10s 7ms/step - loss: 0.7185 - accuracy: 0.7544 - val_loss: 0.7653 - val_accuracy: 0.7440\n",
      "Epoch 9/15\n",
      "1563/1563 [==============================] - 10s 7ms/step - loss: 0.6801 - accuracy: 0.7669 - val_loss: 0.7571 - val_accuracy: 0.7530\n",
      "Epoch 10/15\n",
      "1563/1563 [==============================] - 11s 7ms/step - loss: 0.6497 - accuracy: 0.7792 - val_loss: 0.7524 - val_accuracy: 0.7568\n",
      "Epoch 11/15\n",
      "1563/1563 [==============================] - 11s 7ms/step - loss: 0.6132 - accuracy: 0.7906 - val_loss: 0.8339 - val_accuracy: 0.7404\n",
      "Epoch 12/15\n",
      "1563/1563 [==============================] - 9s 6ms/step - loss: 0.6067 - accuracy: 0.7941 - val_loss: 0.7676 - val_accuracy: 0.7550\n",
      "Epoch 13/15\n",
      "1563/1563 [==============================] - 10s 6ms/step - loss: 0.5762 - accuracy: 0.8053 - val_loss: 0.7717 - val_accuracy: 0.7514\n",
      "Epoch 14/15\n",
      "1563/1563 [==============================] - 7s 4ms/step - loss: 0.5582 - accuracy: 0.8094 - val_loss: 0.7541 - val_accuracy: 0.7606\n",
      "Epoch 15/15\n",
      "1563/1563 [==============================] - 9s 6ms/step - loss: 0.5362 - accuracy: 0.8183 - val_loss: 0.7782 - val_accuracy: 0.7504\n"
     ]
    }
   ],
   "source": [
    "model_f.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "model_history = model_f.fit(x_train, y_train, validation_data=(x_valid, y_valid), epochs=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting loss vs epoch curve of model\n",
    "loss = model_history.history['loss']\n",
    "val_loss = model_history.history['val_loss']\n",
    "epochs = range(1, len(loss)+1)\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, label='train loss')\n",
    "plt.plot(epochs, val_loss, label='val loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 - 0s - loss: 0.7944 - accuracy: 0.7506 - 304ms/epoch - 2ms/step\n",
      "\n",
      "Test accuracy: 0.7505999803543091\n"
     ]
    }
   ],
   "source": [
    "# Find and print model accuracy against test set\n",
    "test_loss, test_acc = model_f.evaluate(x_test,  y_test, verbose=2)\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 602us/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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v0WgYOHAgKpWKv/76i88++4wtW7awcOHCGscgkr0gCIIgmEhhYSFDhw7l008/xd3dvXx6Xl4eGzduZPXq1fTs2ZN27dqxefNm/vrrL44dOwbAvn37uHz5Ml988QVt2rShf//+LFu2jHXr1qFS1WwsCZHsBUEQhDrhZpt9bV6gf0jPv1//Ho31vyZNmsTAgQPp3bu3wfTTp09TVlZmMD08PJz69euXj/p69OhRWrZsafDcmb59+5Kfn8+lS5dq9L+LDnqCIAhCnaDRSdHo7r6Me3M8+/8OwLZo0SIWL15cYfnt27dz5swZTp48WWFeamoqdnZ2uLm5GUz/96ivqamplY4Ke3NeTYhkLwiCIAg1kJCQYPC43MoGaEtISOCVV15h//79KBR3HqrYHEQ1viAIglAnaJGgRVqLl74a38XFxeBVWbI/ffo06enpPPDAA9jY2GBjY8Phw4d57733sLGxwdfXF5VKRW5ursF6/x711c/Pr9JRYW/OqwmR7AVBEIQ6wVht9tXRq1cvLly4wNmzZ8tf7du3Z+jQoeV/29ra8uuvv5avExkZSXx8PBEREQBERERw4cIF0tPTy5fZv38/Li4uNGvWrEb/+31RjT+qdRdsJLaWDqPcNzGHLR1CBYMDO1o6BOFuSazvQR46axzrU1dm6QgqsLYR5gCknh6WDsGAVKuEZEtHYXzOzs60aNHCYJqjoyOenp7l08eMGcO0adPw8PDAxcWFKVOmEBERQceO+vN1nz59aNasGcOHD2fVqlWkpqYyf/58Jk2aVGltwp3cF8leEARBEKpS+w56xr3Ifffdd5FKpQwePBilUknfvn358MMPy+fLZDL27NnDhAkTiIiIwNHRkZEjR7J06dIa70ske0EQBKFO0LfZ12IgnFo+LvfQoUMG7xUKBevWrWPdunW3XSc4OJiffvqpVvsF0WYvCIIgCPc9UbIXBEEQ6gRtLZ9vr8UK+6pUk0j2giAIQp1gbW325iSSvSAIglAn3Lxf/u7Xv3eTvWizFwRBEIT7nCjZC4IgCHWCRidBc5fD1N5c/14lkr0gCIJQJ2hq2UFPI6rxBUEQBEGwVqJkLwiCINQJWp0UbS1642tFb3xBEARBsG51uRq/zid7e0cNI6Yl0qlvDm6eZcRccuSjpfWJOu9k8n3v/CCArW/UZ+CYFEYviaMgR8ZX7wRx7ogrmUlyXDzLeKhvNs/OSMTRRVO+3vk/XNj+VhBxVx1QOGjo/n+ZPD8rHpkJP80WHQoZMjGDRi2L8fRTs3h0A47udTXdDu/RmAAeeyGT/5uQjoe3muuX7flwfj0izzpYJJZHR2QycHgmvkEqAOKiFGx9149TB12qWNN0hk1LZvi0VINpCdFyxnZvbqGI4JnJaXTun0tQmBJVqZTLpxzYuCKAxBjzjUPe4sF8/m9cCmEtivD0LWPp+EYc3a8ftEZmo2Xk9ETad8/FP0hJUYGMv/90ZfOqILLT7UwW04DBcQwYnICvfzEAcded+XJjGKf/8gZg8pyLtHkoEw8vJaUlMq6cd2fz+01IjDP9+VOomTqf7F99I5YGjUt4a1ooWWl29BqUycr/RTKuT0uy0kz3I4o+68j+rT4EN701KlZOmh3ZabaMWBBHUKMSMpLkfDw7hOw0O2Z8cg2AG5cdWD4inMFTkpiyJprsVDs+nhOKVgsjF8SbLF6Fg5brlxT88qUHizbdMNl+asIaY+r2eA7jFiXz/uxArp5x4MkXM1i+7TpjujQhL8v8IzNmpNiyaWUASbFyJBIdjwzJYfGmWCb1bUxclL3Z47npxlUFs59rVP5eo7ZsL+dWHQv54TMvos46ILOBF2ansGJbDC92D0dZIjNLDAoHLdevOLDva28WfHTNYJ7cXkvD5kV8+X49rl9xwNlVzfiFcSz6NIpXnmhxmy3WXma6gi0fNCY5wREk0HtgEgvePs3LwzoTf92Z6KsuHNwbQEaqAmeXMoaOi2bZBycZ80R3tFrr67mupXY96rXGC8XsLNpB78iRIzz22GMEBAQgkUjYvXu3WfdvJ9fycL9sNr4RxMUTLqTEKfhibSDJcXIeHZZe9QbuUkmRlDVTwnhp1XWcXG+V2OuHlzDz02s8+Egufg2UtOycz/OzEjh1wB2NWr/Mn997Ety0mKenJuEfoqR5RAHD58Wxd4sfJYWm+zhPHXThs1X+/GUFJeebrDGmp8ZlsnebB/u+8iD+moL3ZgWiLJHQ97lsi8RzfL8rJ39zITlWTtJ1BVve9Ke0SEr4A8UWiecmjUZCToZt+Ss/x7LljnnDGrJ/hydxUfZcv2zPO6/WxzewjEatSswWw6nDbny+Ooi/9lUcgra4wIZ5I5ry+0+eJMXac/WsM+sXN6BxyyK8A5Qmi+nE776c+suH5ARHkuMd+Xx9Y0qLbQhvkQvA3l31ufS3B+kpDsREuvL5+kb4+JXi42/Z79ft3HyoTm1e9yqLRl5UVETr1q3vOOKPKclsdMhsQKU0vNJTlUpp3r7AZPvdMC+Edr1yad0lv8pli/NlODhpyqvoy1QS7OSG15d2Ci0qpZQYMzQ9CLdnY6ulUatizvzuXD5Np5Pw9+/ONGtn+ZOfVKqj2+M5yB20XDntaNFY6oUo2XbqAlv+vMis92PxDlBZNJ7/utlsVpBrnlL93XBw1qDVQlG+eWKUSnV0fSQZhb2aKxfcKsyXK9Q88lgSqUn2ZKZZrtZIqJxFL6f79+9P//79Lbb/kiIZl0878fyUZOKj7cnNtKX741mEP1BISpxp2ur++M6T6xccefPHC1Uum59tw9drA+k99FYtQ5tuefy4wZ/fd3vS6bEsctNt+XpNIAA56eavJhZucfHQX5TlZhj+rHIybQgKM13pqyoNwktY8/017ORaSoqkLB0bQvw187VF/9fVvx15e2owidflePioGTY1hXd2RjG+V1NKiiyfXCUSHS8tSeLiCUfiIq0zadnaaRk9M57DP3hSXGja03hwwwLe2XQUOzstJSUyXp/xAAmxty5oB/5fHKOmRGLvoCHhhiPzJj2IWm2dJeDaPxvfOv+v6rin2uyVSiVK5a2TZn5+1SXjqrw1LZSpq2LZdvwsGjVEX3Lk8A+ehLUoqnrlGspMtmPTomAWbruKneLOvTqLC2SsGBFOUKMSnpmWWD69Tbc8hs+P45M5Ibz3Shi2dlr+75Ukrhx3QXrvfg8FE0qMkTOxTxMcnDV0GZjLa2vimDG4kcUS/qmDt5pdYq/A1b8d+N+xi3R9LIdftntZJKZ/m7wikeAmJUx/slHVC1uAzEbL3A+uIZHABwsamHx/SXGOTBnaGUcnNZ17pTJt8Xlmje9QnvAP/hzA38e9cPdSMnhYLHNWnuW1sR0pU1n+wu2/LD2evSXdU8l+5cqVLFmyxKjbTIlXMPPZpsjtNTg6acjOsGPO+9GkxsuNuh+AmPOO5GXaMaN/y/JpWo2Ey8ed+XmLH9uvH0cmg5JCKa8PC0fhpGHmhkhsbA0vDB4fl8pjL6aSk2aLo6uajEQ5W9+oj2/9UqPHLFRffrYMjRrcvNUG09291ORkWO6npi6TknxD/32OvuBAkzbFDBqbwXuzgiwW078V5duQeF1BQAPL1X7cNOn1RDr0zmf6U2Fkppiug+7dktlomft+ND71VMweGm7yUj2AWi0lJVHf7BN91ZXGzfJ44tk4Plip7xhYXGRLcZEtyQmORF5w46vfDtCpexqH9wWYPLaaqssl+3sq8jlz5pCXl1f+SkhIMNq2lSUysjPscHJR065rHkcPuBtt2ze1ejiPdw+c451fzpe/GrYupMuTmbzzy3lkMn2JfunzTbGx1TFnc+RtawAkEvDwK0Nur+P33V54BSgJaWn82gih+tRlUq6dd6Dtw7f6e0gkOto8XMjl05a59a4yEqm+GthaKBw0BDRQkm3RZigdk15PpFO/PGY+HUZagvEv9mvrZqIPaFDK3OHhFORa5nhJJLrbf38kOrjTfMFi7qmSvVwuRy437o+wXddcABKv2xPQoJSxcxJIiFGw72vjVyfaO2mpH27Yu1dhr8XZXU398JJ/En04yhIpr7wXRXGBjOICfVWYi2cZsn9qxXav96dt91wkUjj+swe7Pwxg2vpr5fNNQeGgISDkVicqvyAVoc1LKMiVkZFkmRKQNca08xMvXluTQNQ5ByL/1t96p3DQsm97xR7W5jBqdjInD7qQkWSLvZOWHoNyaBVRyLznG1okHoAX5ydy7IAr6Yl2ePqWMXx6ChqNhEO7jX+BXV2TVyTSY1AOi0eHUlIoxd27DICiAhmqUvOUiRQOGgKCb9XO+QYpCW1aREGeDdnptsxbd42w5sUsGtsYqVSHu5f+u1+QZ4O6zDQxjpwUyam/vMlIVWDvoKF7v2RatstmwZQH8atXTJdHUvj7mBd5OXZ4+ZYyZOR1VKUyTv7pbZJ4aqv2D9W5p8rHBu6pZG8KDs4aRs1IxMtPRWGeDX/sdWfL24FoLNDB5PoFR679rW8Hm/RwW4N564/+jU+Qvprz74NufPt+PdRKKcHNipi1MYoHeuaaNLbGrUt469uY8vcvLUkGYN9X7rwztb5J930vxXT4e3dcPTWMmJGKu7ea65fsmTc0hNxMy5TC3LzUzFgbh4ePmuICGbFXFMx7vqHBHQPm5uVfxpwPbuDsriYv24ZLJ5x49fEm5GVbrmT/2MgsAN7+Ntpg+ttTg9i/w9MsMTRqWcSqL6+Uvx8/X//cjP3fePHF2kAiHskF4MOfLhqsN/O5plw4bpqHJLm5q5i++DweXqUUFdpyI9qZBVMe5OwJLzy8SmneJocnnr2Bk0sZudlyLv7tzmtjO5KXY301IwBanQRtbe6zv4dHvZPodJZ72G9hYSHR0fofV9u2bVm9ejU9evTAw8OD+vWrPlnn5+fj6upKD/nT2Eispyf6NzGHLR1CBYMDO1o6BOFuSazwBCOxwhKOzvqqjqVGrok0BqmnZWqZbketVXIg+WPy8vJwcTHNRcvNXLHqZBfsne6+jFtSqGbmg7+bNFZTsWjJ/tSpU/To0aP8/bRp0wAYOXIkW7ZssVBUgiAIwv1IW8tq/Hv5oToWTfbdu3fHghULgiAIQh1S+1Hv7t1kf+9GLgiCIAhCtdT5DnqCIAhC3aBBgqYWD8apzbqWJpK9IAiCUCeIanxBEARBEO5bomQvCIIg1AkaalcVr6l6Easlkr0gCIJQJ9TlanyR7AVBEIQ6QQyEIwiCIAjCfUske0EQBKFO0P0znv3dvnQ1bO9fv349rVq1wsXFBRcXFyIiIvj555/L53fv3h2JRGLweumllwy2ER8fz8CBA3FwcMDHx4cZM2agVqv/u6sqiWp8QRAEoU4wdzV+YGAgb7zxBo0aNUKn0/HZZ5/xxBNP8Pfff9O8eXMAXnzxRZYuXVq+joPDreGwNRoNAwcOxM/Pj7/++ouUlBRGjBiBra0tK1asqFEsItkLgiAIggk89thjBu+XL1/O+vXrOXbsWHmyd3BwwM/Pr9L19+3bx+XLlzlw4AC+vr60adOGZcuWMWvWLBYvXoydXfWH8r4vkr20nh9SmfWMLjU4KMLSIVQQ+2UrS4dQQaMZmZYOoSIbmaUjqEBXUlr1QmYmqcFJxlx0xcWWDqECbX6hpUOoQJ2cYukQDKh1ZWbbl7GGuM3PzzeYLpfLkVcxwqFGo+Hrr7+mqKiIiIhbOWLr1q188cUX+Pn58dhjj7FgwYLy0v3Ro0dp2bIlvr6+5cv37duXCRMmcOnSJdq2bVthP7dzXyR7QRAEQaiKppaj3t1cNygoyGD6okWLWLx4caXrXLhwgYiICEpLS3FycmLXrl00a9YMgOeff57g4GACAgI4f/48s2bNIjIykp07dwKQmppqkOiB8vepqak1il0ke0EQBEGogYSEBIPx7O9Uqm/SpAlnz54lLy+Pb775hpEjR3L48GGaNWvGuHHjypdr2bIl/v7+9OrVi5iYGBo2bGjUmEVvfEEQBKFOuFmNX5sXUN67/ubrTsnezs6OsLAw2rVrx8qVK2ndujVr166tdNkOHToAEB0dDYCfnx9paWkGy9x8f7t2/tsRyV4QBEGoE7RIa/2qdQxaLUqlstJ5Z8+eBcDf3x+AiIgILly4QHp6evky+/fvx8XFpbwpoLpENb4gCIIgmMCcOXPo378/9evXp6CggG3btnHo0CF++eUXYmJi2LZtGwMGDMDT05Pz588zdepUunbtSqtW+g7Vffr0oVmzZgwfPpxVq1aRmprK/PnzmTRpUpUdAv9LJHtBEAShTtDoJGhq0Ru/puump6czYsQIUlJScHV1pVWrVvzyyy888sgjJCQkcODAAdasWUNRURFBQUEMHjyY+fPnl68vk8nYs2cPEyZMICIiAkdHR0aOHGlwX351iWQvCIIg1AnGuvWuujZu3HjbeUFBQRw+fLjKbQQHB/PTTz/VaL+VEcleEARBqBN0tRz1TicGwhEEQRAEwVqJkr0gCIJQJ2iQoKnhYDb/Xf9eJZK9IAiCUCdodTVvd//v+vcqUY0vCIIgCPe5Ol2yHzI0ilHjL7P761A+ef/WQDHhzbMZ+eJlmjTNQauVcD3alfnTO6FSmX6QlGcmp9G5fy5BYUpUpVIun3Jg44oAEmMUJtmf6+40HE/mYZusRGcnpbSxAznP+VMWYLg/eVQR7l+lIo8pBimogu1JnROKzk5/vRg45TK2mYYDWmQ/60feE4bPdb5bAwbHMeCpeHz9SwCIi3Xiyw1hnD7qA8DK9cdo1S7bYJ2fdgax7o2WRtl/VYYMv8YLE66y+6sQPl3bAoB+T8TR7ZEkwprk4eCo5uk+/SgqtDVpHE+PjqVTz3QCGxShUkq5cs6NTWsbkRTnWL6Mu6eSMa9eo03HLBwc1STecOSrjSH8+atxPqv/GvBUHAOeisM34J/P7roTX25sxOmjPvj4F7N598FK11s55wH++M3fJDE9PSaOTr0zCAwpRlUq5co5Vza925CkG7eGF528MJK2HbPx8FZRWizj8jlXNr8bSmKs4x22bDxSqY5hU5Po+WQW7t5lZKXZceAbL7a95w8Wqk5+dEQmA4dn4hukAiAuSsHWd/04ddClijWtg7aWHfRqs66l1dlk3yg8h/6P3+B6tOGXNLx5Nsve+osdWxuzfk0rNBoJoWH5Zqu+adWxkB8+8yLqrAMyG3hhdgortsXwYvdwlCXGv9hQXCkiv48XylAHJFod7ttT8Ft5ncS3mqBT6PcnjyrC743r5D7hQ9YL9UAGdnGl/Lc2LGeIHwU9PcrfaxXG+2FkpinYsq4JyQmOINHRe2ASC94+zcvDHyb+ujMAe3cF8cUnjcvXKS01zw+zUdNc+j0Rx/Vrht8luVzDmePenDnuzQsTrpollhYP5LDnqyCiLrkgs9ExcnI0y9efYfxTnVCW6j/P6csu4uisZumrbcjPtaV7/1Rmv3meV4Z24Hqk8U/amekKtnwYrv/s0NF7YCIL3jrFy8O7kBjnxLD+vQyW7/dkAk8NjeHUUW+jx3JTi/a57Nlej6iLLshkOka+EsPyj88yflCH8t9Z9GVnDv3oS3qKHGdXNUMnxPL6x+cY3S8Crdb0yXbIhBQGDsvgnekhxEXZ06hVEdPeiqUoX8Z3W0xzYVaVjBRbNq0MIClWjkSi45EhOSzeFMukvo2Ji7K3SEw1oUWCthYXSrVZ19IsmuxXrlzJzp07uXr1Kvb29nTq1Ik333yTJk2amHS/Cns1Mxec4r1VbXh2RKTBvHGTL/D9t6F8vfVW0khKcDZpPP82b5jh4AfvvFqfHRcu0qhVCRePOxl9f2lzQg3eZ0yoT/D4S8hjSyhtqt+fx/+SyevnZVBK/2/JH/TJXeNmmpLriT8MT26fr2/CgKfiCW+RW57sS0tl5GSZd6hjhb2aGYvO8P4brXnmhWsG877boT+2LduabyjfhZMfMHi/elFztv92mEbN8rl4xh2Apq3zWLcinKhLrgBs3xDKoKHxNGqWb5JkX+Gz+yj8n88uh/hYZ3KyDb9LEd1S+eNXf0pLTHd6WjihtcH71fObsv3InzRqVsDF024A7P0moHx+ejJ8/kEoH357Ep+AUlITTZ/YmrUr5Nh+N078po8nLVFO98ezadKmELBMsj++39Xg/ZY3/Xl0eCbhDxTfE8m+LrNoncThw4eZNGkSx44dY//+/ZSVldGnTx+KiopMut+JU89x4qgfZ0/7GEx3dVMS3jyH3Bw5b394hK27f+bN936nWcssk8ZzJ44uGgAKcs0zzrq0WL8/jZN+f9K8MhTRxWhdbPBfeI364y/htyQa+dWK43S7fp9O/RcvEjA7Etcf0kFjmuoQqVRH10eSUdhruHLBrXx6j37JbNu3n3VfHmHkxKvI5RqT7P/fJky/wMm/fDh7ynSl0NpwdFIDUJB36yLsyjlXuvZJw8mlDIlER9e+qdjJNZw/5XG7zRiNwWd30b3C/LDwPBo2yWff90GVrG06t45T5RcYcnsNjwxKISVRQWaqeS4oL592ok2nfOqFlAIQ0rSY5u0LOHnIzSz7r4pUqqPb4znIHbRcOW2epo3auvkEvdq87lUWLdnv3bvX4P2WLVvw8fHh9OnTdO3a1ST77NozkbDGebwyrluFeX4B+ouMoaOusvHDFsREu9KrbwIr3/2TCS/0JDnR+CXrO5FIdLy0JImLJxyJizTDVbNWh+fnSZQ2caAsSL8/23R925zbt2lkDw1AFazA6fcc/JdfJ3FVE9T++hNffj9vVCH2aBxlKP5p35fllpE9vJ7RwgtumM87G49iZ6elpETG6zMfICFWX6o//EsA6an2ZGXICQkrYNTkSAKDi1g+q53R9v9fXXvr2+NfHdPFZPuoDYlEx/jXIrn0txtxMbe+uytntmL2mxfYcfgQ6jIJylIZy6a1ISXB4Q5bq53ghvm8s+GvW5/drHbln92/9XksnvhYJ65cMP2Fx00SiY7xs6K5dMaVuGjD3/jAZ5IYPS0GewcNCbEOzHuxDWq1ecpIOz70x8FJw6e/XUCrkSCV6fjsrXoc3O1plv3fToPwEtZ8fw07uZaSIilLx4YQf800fYqMTbTZW4m8vDwAPDwq/6ErlUqD0YLy8/NrtH0vn2LGv3yBedM6UVZJZzupVF8S/fn7EPb/HAzA9WtutGmXQZ8BcWz5pHmN9ldbk1ckEtykhOlPNjLL/jw3J2GbUErK4rBbE/8pnBf08qSwu/5zyQ5xwP5iIc6Hssl5Tt+BKn/grZJtWbA9OhsJXhsTyX7WH2yN8wNJinNiyrCHcXRS07lnCtMWnWfWSx1IiHVm7+765cvFxbiQnSVn5Ycn8KtXRGqS8UsdXj4ljHv1IvNfiaj0u2QNJs65SnBYIa+NetBg+vBJMTg5lzFn/APk59oR0T2dOavOM3N0e25Em6bJKinOiSnDu9z67BaeY9aEjgYJ306uoVvfZLZvMs/3/aaJ86IIDivitZFtK8w7+KMvfx91x8NbxVMj45nzzkVeG/6AWT7zro9m03NQFm++HEpclD0NmxUzflG8vqPet14m3//tJMbImdinCQ7OGroMzOW1NXHMGNzonkn4dZXVJHutVsurr75K586dadGiRaXLrFy5kiVLltz1Pho1zsXdQ8n7Gw6VT5PZ6GjROovHnozlxWG9AYi/YXjCS4hzxtu35K73ezcmvZ5Ih975TH8qjMwUO5Pvz3NzIg5n8klZ1BCN5639adz0XxFVPcMfsqqeHJss1W23pwxzRKIB2wxVpe37d0OtlpKSqE/c0VddadwsjyeeucEHlfS4j7zoBkBAULFJkn1YeC7uHire23ykfJrMRkeLNlk8NvgGg7oPNEsnrtuZMOsqD3XJYOaYB8lKv3X8/QKLefzZBF4aHEH8dX0pNjbKmeYP5PLoMwl8sLxmw2ZWV4XPrmluhc+uc88U5AoNv/5kvNqgqkyYG8VD3bKY+UJbstIqfk+LC20oLrQhOd6Bq+dc2PHn73Tqlcnhn03fZj52bgI71vtz+Ad9Sf5GpAM+gSqemZhi0WSvLpOSfENfoxd9wYEmbYoZNDaD92aZt+nlbmip5bPxRQe92ps0aRIXL17kjz/+uO0yc+bMYdq0aeXv8/PzCQqq/hfs7GlvJozsaTBt6uwzJMY78fW2xqQmO5CZoSCwfoHBMvUCCzl13FwdYnRMej2JTv3ymDEkjLQEE7cP6nR4bknC4WQeKQvCUPsY7k/tbYfa3QbblFKD6bYpSkra3L4zl11cCToJaFxM9xWTSMHWTlvpvNDG+lqf7EzTHL9zp7yZOMywKejVeWdJjHPimy/CLJjodUyYFUlEz3Rmv9iOtGTD5h+FQt+PQfef7hRajQSJGUOWSMHW1vCz6/NYAsd/9yU/1xxt4jomzL1GRM8MZo9uS1pSNZrJJPrX7b5zxia311b4Hmk1IJFa15Nd7vQ7tDa6WvbG14lkXzuTJ09mz549HDlyhMDAwNsuJ5fLazyG77+VlNgSF2vYW7y0VEZ+vh1xsfrE9e32MIaNusr1aFeuR7vSu188gcEFLF/40F3vtyYmr0ikx6AcFo8OpaRQiru3/t71ogIZKhPcSua5KQnHv3JInx6Czl6KLFe/P62DTH8PvURC3qM+uH+TiirYHlWwPU5HsrFNVpI+VV+tL48qQh5dTGlzJ7QKKfJrxXj+L5nCh93ROhnnKzZy4lVOHfUhI1WBvYOa7n2TaflAFgtefhC/ekV075vMqb98yM+zJSSsgBenXuHCGQ9uRJvm/t+SYhvirhtuu7TEhvw8u/Lp7h6luHsq8Q/U9wVp0DCfkmIb0lPtKSwwTW3NxDlX6d4/laVTW1NSZIO7p77Zq6jQBpVSRsINR5Li7Zky/wobVjcmP8+WiB7ptO2YxeJX2pgkppETr3LqL28y0uwNP7tXbv2m/AOLaNE2m8VTH7zDloxn4rwoug9IZ+krLSgpklU4Tn6BJXTtm86Zox7kZdvi5atkyJg4VEopJ383T5v58QNuPDs5mYxkO301fvNinhybxr4dlivVj5qdzMmDLmQk2WLvpKXHoBxaRRQy7/mGVa9sBcw96p01sWiy1+l0TJkyhV27dnHo0CFCQkIsGQ4A330dhp2dlnFTLuLsrOJ6jCvzpnUmNdk8vU0fG6nv+f/2t9EG09+eGsT+HcY/ybgc0O/Pf1mMwfSMl4Io7KZP5vkDvJGUafH8PBlpkQZVfQWpc0NR++ovvHS2EhyP5uL2bSqSMh1qHzvy+nuRN9B4PdTdPFRMX3QODy8lRYU23Ih2ZsHLD3L2hDdePiW0eSiLJ567gUKhISNNwZ8H/di+ybInoP5PxjF0TFT5+1Xr/wLg3dfbcOAn01R5Pvp0on5fG04bTF+9sDkHfghAo5ayaEpbRr0czaK1Z7F3UJOc4MDqhc059Ydp7ihwc1dW/OxeeYizJ27t75HHEshMV3DmuHnuanj02WQAVm0+azB99fxwDnznj0oppXm7XJ4YnoCTi5rcLDsunnZj+vB25GWbvlkN4MNFwYyYnsSkZXG4eekfqvPzNm+2rg2oemUTcfNSM2NtHB4+aooLZMReUTDv+Yac+d18tycLd0ei0/23Qs98Jk6cyLZt2/juu+8M7q13dXXF3r7qarX8/HxcXV3pFfoyNjLz3l99J5qYG5YOoYLYba2qXsjMGs0w3/3n1WZjfZ3tdCWlVS9kZhI78yS8mtAVF1s6hAq0+RVvUbU0nbqs6oXMSK0r45BuN3l5ebi4mKYm7maueHL/KGwd7/67W1akYtcjm00aq6lYtGS/fv16ALp3724wffPmzbzwwgvmD0gQBEG4b4lqfAuxYKWCIAiCINQZVtFBTxAEQRBMTTwbXxAEQRDuc3W5Gv/effafIAiCIAjVIkr2giAIQp1Ql0v2ItkLgiAIdUJdTvaiGl8QBEEQ7nOiZC8IgiDUCXW5ZC+SvSAIglAn6Kjd7XP38pNhRLIXBEEQ6oS6XLIXbfaCIAiCcJ8TJXtBEAShTqjLJfv7I9nn5oPU+kbhsiZhkxMsHUIFsR9bbqjO22nwUoqlQ6hAV8/H0iFUICkosXQIFUiqMVKm2VnhqHcSG1tLh2BAogPMNBBfXU72ohpfEARBEO5z90fJXhAEQRCqIEr2giAIgnCf0+kktX7VxPr162nVqhUuLi64uLgQERHBzz//XD6/tLSUSZMm4enpiZOTE4MHDyYtLc1gG/Hx8QwcOBAHBwd8fHyYMWMGarW6xv+7SPaCIAiCYAKBgYG88cYbnD59mlOnTtGzZ0+eeOIJLl26BMDUqVP54Ycf+Prrrzl8+DDJyck89dRT5etrNBoGDhyISqXir7/+4rPPPmPLli0sXLiwxrGIanxBEAShTjDWePb5+fkG0+VyOXK5vMLyjz32mMH75cuXs379eo4dO0ZgYCAbN25k27Zt9OzZE4DNmzfTtGlTjh07RseOHdm3bx+XL1/mwIED+Pr60qZNG5YtW8asWbNYvHgxdnbV75guSvaCIAhCnXCzzb42L4CgoCBcXV3LXytXrqxy3xqNhu3bt1NUVERERASnT5+mrKyM3r17ly8THh5O/fr1OXr0KABHjx6lZcuW+Pr6li/Tt29f8vPzy2sHqkuU7AVBEAShBhISEnBxcSl/X1mp/qYLFy4QERFBaWkpTk5O7Nq1i2bNmnH27Fns7Oxwc3MzWN7X15fU1FQAUlNTDRL9zfk359WESPaCIAhCnXA3nez+uz5Q3uGuOpo0acLZs2fJy8vjm2++YeTIkRw+fPiuY7hbItkLgiAIdYIlbr2zs7MjLCwMgHbt2nHy5EnWrl3LM888g0qlIjc316B0n5aWhp+fHwB+fn6cOHHCYHs3e+vfXKa6RJu9IAiCUCeY+9a7ymi1WpRKJe3atcPW1pZff/21fF5kZCTx8fFEREQAEBERwYULF0hPTy9fZv/+/bi4uNCsWbMa7VeU7AVBEATBBObMmUP//v2pX78+BQUFbNu2jUOHDvHLL7/g6urKmDFjmDZtGh4eHri4uDBlyhQiIiLo2LEjAH369KFZs2YMHz6cVatWkZqayvz585k0adId+wlURiR7QRAEoU7Q1bIav6Yl+/T0dEaMGEFKSgqurq60atWKX375hUceeQSAd999F6lUyuDBg1EqlfTt25cPP/ywfH2ZTMaePXuYMGECERERODo6MnLkSJYuXVrj2OtUsn96TBydemcQGFKMqlTKlXOubHq3IUk3HMqXmbwwkrYds/HwVlFaLOPyOVc2vxtKYqyjWWJ8dEQmA4dn4hukAiAuSsHWd/04dbB6nUGMoTrH6RYdS9efp/3D2Sx7pQVHf/Ou9f6dd2bgcDwfmyQlOjsJyiYO5A3zQ11PfyUrS1cRMDGq0nUzpwVR0snVYJq0QI3v9GhsstUkftYUnaOs1jECPD3mBp16/XOclFKunHVl05qGJN249V15Y+MZWj2Ya7DeTzsC+OD1cKPEUBlPz2JGjz5H+/YpyOUakpOdePfdDly75gFAp04JDBwYTVhYDi4uKiZN6sv16+4mi+ffhgyL4oWXrrB7RyifvtcSgMkzztKmfQYeXqWUFttw5aIHm9c3IzHe2WRxDBgcx4Cn4vH11w/oExfrxJcbwjh9VD/o0Mr1x2jVLttgnZ92BrHujZYmi+m/pFIdw6Ym0fPJLNy9y8hKs+PAN15se88fanGveG189se58nPTv/3wuQ/rFgRbIKKa0QE6Xe3Wr4mNGzfecb5CoWDdunWsW7futssEBwfz008/1XDPFdWpZN+ifS57ttcj6qILMpmOka/EsPzjs4wf1AFliT4BRF925tCPvqSnyHF2VTN0Qiyvf3yO0f0i0GpN/wPLSLFl08oAkmLlSCQ6HhmSw+JNsUzq25i4KPOM6lWd43TToOGJtfrxVEZ+uYiCfh6owuyRaHS4bkvDe9kNUtc0QqeQovG0JenTJgbrOB3Iwfm7TErbOlXYnvuHSZQFK7DJNu4IZPrjFEjUJWf9cXr5Oss/Osv4JzsaHKefvwngi3Uh5e9LS41zsVEZJycV77xzgHPnfFmwoBt5eXLq1SugsPDWSGcKhZpLl7w5cqQ+r7560mSx/Fej8Bz6PR7H9WjDC9foSDcO7gskI80BZxcVQ0dHsuzdo4wZ8ojJfnOZaQq2rGtCcoIjSHT0HpjEgrdP8/Lwh4m/rr/I2LsriC8+aVy+Tmmpebs4DZmQwsBhGbwzPYS4KHsatSpi2luxFOXL+G6Lb9UbMIGXH2+G9F9f3waNi1m5LYrffzTPxaJw9yya7NevX8/69eu5ceMGAM2bN2fhwoX079/fJPtbOKG1wfvV85uy/cifNGpWwMXTbgDs/ebWsKvpyfD5B6F8+O1JfAJKSU00fbI9vt+wVLrlTX8eHZ5J+APFZkv21TlOAKFNCnhqZAKvPNOOrYf+Mtr+M+c3MHifPSmQemOuYne9BGUzR5BJ0LobDtNpfzyf4k4u6OwNE6njL1lIizTkD/HB/m/jJvuFE9oYvF+9oCnbD/9Bo2b5XDx96+SnLJWSk1Wz9rW7NWTIFTIyHHj33Q7l09LSDC+AfvtNf+Hh42O+4VcV9mpmLDrN+6ta88xIw1qZvd83KP87PdWBzz8NZ91nh/DxKyY12TQ1aif+MEyWn69vwoCn4glvkVue7EtLZWb73CrTrF0hx/a7ceI3NwDSEuV0fzybJm0KAcsk+7xsw9/d0xPySL4h5/wx09XCGJMWCRIjPEHvXmTR3vhVPTfY1Byd9IMJFORVfs0jt9fwyKAUUhIVZKaa/0cvlero9ngOcgctV06bpxmhMpUdJ7lCw8w3L/Ph8kYmPyFKizUAaJ0qLxHbxpRgd6OUop4eBtNtEkpx/TqD7CmBZqn1vHWcDE+IPQak8eXh3/lw53FeeDkGuUJjshg6dkzi2jUP5s79ky+/3MUHH+ylX78Yk+2vuiZMO8/Jv3w5e8rnjsvJFWoeGRBParIDmenmubiVSnV0fSQZhb2GKxfcyqf36JfMtn37WfflEUZOvIpcbrrPrTKXTzvRplM+9UJKAQhpWkzz9gWcPOR25xXNxMZWS88ns/hlhxeWalaoKWvojW8pFi3Z3+m5wc2bN6+wvFKpRKlUlr//7/OJa0Ii0TF+VjSXzrgSF21Y8hn4TBKjp8Vg76AhIdaBeS+2Qa0233VRg/AS1nx/DTu5lpIiKUvHhhB/TWG2/f/b7Y7TizOjuXLWlWMHa99Gf0daHW6bU1GGO1BWv/Jj4PRbDmWBclTh/+pTUKbFc00iuSP80HjbYZNWsZ3RmCQSHeNnXqtwnA795Et6ioLsDDkNGhUyemoM9RoUs3yaadp+/fwKGTgwmp07m/DVV81o3DiLl146g1ot5cCBkKo3YAJdeyUS1jiXV1/sdttlBj4Zy6gJl/S/uTgn5r3ayeS/ueCG+byz8Sh2dlpKSmS8PvMBEmL1JdTDvwSQnmpPVoackLACRk2OJDC4iOWz2pk0pn/b8aE/Dk4aPv3tAlqNBKlMx2dv1ePgbk+zxXAnEX1ycXJRs/9rL0uHIlSD1bTZazQavv766/LnBldm5cqVLFmyxCj7mzgviuCwIl4b2bbCvIM/+vL3UXc8vFU8NTKeOe9c5LXhD1CmMl1b678lxsiZ2KcJDs4augzM5bU1ccwY3MgiCb+y49SheyatH8phypD2Jt+/+4YUbBNKSX89tNL5EqUWh99zyf8/wxKj29Y01PXkFHd1M3mM8K/j9MIDBtP3fluv/O8b15zIybRj5Yaz+AUWk5pYWYfH2pFI4No1dz77TN8UExPjTnBwHgMGRFsk2Xv5lDDulYvMnxpxx9/PwX2B/H3SG3fPUgY/F8OcZSd5bUIXk/7mkuKcmDLsYRyd1HTumcK0ReeZ9VIHEmKd2bu7fvlycTEuZGfJWfnhCfzqFZGaZJ5atq6PZtNzUBZvvhxKXJQ9DZsVM35RvL6j3reWT7D9nsng5CFXstOrPxiLpWl1EiR1dDx7iyf72z03uDJz5sxh2rRp5e/z8/MJCgqq8T4nzI3ioW5ZzHyhLVlpFRNocaENxYU2JMc7cPWcCzv+/J1OvTI5/LN52snUZVKSb+irxqMvONCkTTGDxmbw3qya/6+1cbvj1PqhHPyDSvj6rz8Mlp+7+iKXzrgxe3TFC6i74bYhGcXpfNKXhqLxtK10GftjeUhUOoq6uRlMl18swja+lMCn8wym1xt1hfzB3uQ/Y7zPcsKcSB7qmsnMUQ9U+n36t6sX9H0yAuqXmCTZZ2criI837PeRkOBC586JRt9XdYQ1ycXdQ8l7G289HlRmo6NF6yweeyqWQT0fQ6uVUFxkS3GRLcmJTkRe8uCrn3+iU9cUDh8INFlsarWUlER94o6+6krjZnk88cwNPqikx33kRTcAAoKKzZbsx85NYMd6fw7/oC/J34h0wCdQxTMTUyye7H3qKWnzcD7LxodZNI6a0ulq2RvfyJ2Rzcniyf52zw2uLOHfbhjB6tMxYe41InpmMHt0W9KSqtEmKNG/bO20tdhv7Uik5t7/nY/T1xvr88tOf4Np63ed5NNVjTh+2AhVjDodbhtTsD+RT8aSEDS+ty85OP6aQ0l7Z7Suhl/lzNeCkKpu/TLtokvw+DCJ9GWhqP2MVRLRMWFOlP44jXmgWt+nhk0KAMjOME0/h8uXvQgMNGzeqlevgPR0419YVMe5U15MHN7DYNqrc/8mMc6Jb7Y2qry3vUSn/83Zmvc3d6ffWWhj/THNzjRf3x25vbbC8dFqQCK1fMbpMySTvCzb8s6DgvWzeLK/3XODP/74Y6Pva+K8KLoPSGfpKy0oKZLh7qlv/y8qtEGllOEXWELXvumcOepBXrYtXr5KhoyJQ6WUcvJ387STjZqdzMmDLmQk2WLvpKXHoBxaRRQy7/mGZtk/VH2ccrLklXbKy0iVV+8CqgruG1Jw+D2XzFnBaBVSpDllAOgcZOjkt9pxbVKUyK8Ukzm34v29Gj85/+5OJc3Xd54rC5Qb7T77ifOi6N4/jaWvtLzN96mYHgPSOPm7J/l5toQ0LmTcjGtcOOXGjWsVbxE0ht27m/DOOwd45plLHDlSnyZNsujfP4b33nuwfBknJyU+PsV4eurvMQ8M1F+A5OQoyMkxbqe4khJb4mINa2VKS2Xk59sRF+uCX0ARXXom8fdJH/Jy7fDyLmXIsGv639xR09WkjZx4lVNHfchIVWDvoKZ732RaPpDFgpcfxK9eEd37JnPqLx/95xZWwItTr3DhjAc3os33vIvjB9x4dnIyGcl2+mr85sU8OTaNfTssW6rX3xKcyf5vPNFq7q1qbWMNhHMvsniy/6+bzw02hUefTQZg1eazBtNXzw/nwHf+qJRSmrfL5YnhCTi5qMnNsuPiaTemD29HXrZ52qXcvNTMWBuHh4+a4gIZsVcUzHu+IWd+N9+tLVUdJ1Nz+kX/MBOfRbEG07Mm1aO4x61b2hx/y0HjaUtpa9Mkzqo8+kwSAKs2/20wffX8phz43h91mZQ2HXN4YlgCCnstGaly/jzgw5efNDBZTFFRnixb9jAvvHCe55+/RGqqEx9//AAHD97aZ8eOSUyffmtwjTlz9LdNfvFFc7ZuNd9DYwD9b651Fk88fR0nZxW52XIunvPitZe6kJdrulK0m4eK6YvO4eGlpKjQhhvRzix4+UHOnvDGy6eENg9l8cRzN1AoNGSkKfjzoB/bN5nvghvgw0XBjJiexKRlcbh56R+q8/M2b7auDah6ZRNq+3A+voEq9u0wcedcE6jLyV6i01muFaKy5wa/+eabBo8TvJP8/HxcXV3p5fECNlLr6SSiyc6xdAgVyDys76EXNz627EmrMg1eSrF0CBXo6t35djVLkBaUWDqEilRllo6gAk1ahqVDsHpqXRkHy74mLy+v2sPG1tTNXNFk22xkDnd/EakpVhL5/BsmjdVULFqyr+q5wYIgCIIg1J5Fk31Vzw0WBEEQBGMRvfEFQRAE4T6nT/a1abM3YjBmZtHH5QqCIAiCYHqiZC8IgiDUCXW5N75I9oIgCEKdoKPmY9L/d/17lajGFwRBEIT7nCjZC4IgCHWCqMYXBEEQhPtdHa7HF8leEARBqBtqWbLnHi7ZizZ7QRAEQbjPiZK9IAiCUCeIJ+gJgiAIwn1OdNC7x+nKytBJ7t0PwSykxhnD3ZhCXsm2dAgVuH6ntXQIFWR3j7F0CBXonBwtHUJFAdY3OqAuKdnSIVg9nc76Riu8H90XyV4QBEEQqqST1K6TnSjZC4IgCIJ1q8tt9qI3viAIgiDc50TJXhAEQagbxEN1BEEQBOH+JnrjV+H777+v9gYff/zxuw5GEARBEATjq1ayHzRoULU2JpFI0Gg0tYlHEARBEEznHq6Kr41qddDTarXVeolELwiCIFirm9X4tXnVxMqVK3nwwQdxdnbGx8eHQYMGERkZabBM9+7dkUgkBq+XXnrJYJn4+HgGDhyIg4MDPj4+zJgxA7VaXaNYatVmX1paikKhqM0mBEEQBME8zNxB7/Dhw0yaNIkHH3wQtVrN3Llz6dOnD5cvX8bR8daDqV588UWWLl1a/t7BwaH8b41Gw8CBA/Hz8+Ovv/4iJSWFESNGYGtry4oVK6odS42TvUajYcWKFXz00UekpaURFRVFaGgoCxYsoEGDBowZM6ammxQEQRCEe0Z+fr7Be7lcjlwur7Dc3r17Dd5v2bIFHx8fTp8+TdeuXcunOzg44OfnV+m+9u3bx+XLlzlw4AC+vr60adOGZcuWMWvWLBYvXoydnV21Yq7xffbLly9ny5YtrFq1ymAnLVq0YMOGDTXdnCAIgiCYicQILwgKCsLV1bX8tXLlymrtPS8vDwAPDw+D6Vu3bsXLy4sWLVowZ84ciouLy+cdPXqUli1b4uvrWz6tb9++5Ofnc+nSpWr/5zUu2X/++ed88skn9OrVy6BdoXXr1ly9erWmmxMEQRAE8zBSNX5CQgIuLi7lkysr1f+XVqvl1VdfpXPnzrRo0aJ8+vPPP09wcDABAQGcP3+eWbNmERkZyc6dOwFITU01SPRA+fvU1NRqh17jZJ+UlERYWFil/0hZmXUPaNCifR7/NyaRsBZFePqoWDqxKUd/9Syf/3PkH5Wut2FVA77dGGiWGB8dkcnA4Zn4BqkAiItSsPVdP04ddKliTeN5enQsnXplEBhShEop5cpZNzatCSMpznDwk/BWuYycEkOTlnloNRKuRzozf0JbVErjD7ozYHAcAwYn4Ouvv+KNu+7MlxvDOP2XNwCT51ykzUOZeHgpKS2RceW8O5vfb0JinJNR9l+6S0npLiXaFH0nVFmIDPtR9thF2KLN11KyoRTViTK0aVqk7hLsuthh/6I9UqdbHXqyOudU2K7TEkfkvatXDXc3pFIdw6Ym0fPJLNy9y8hKs+PAN15se8+fm6UUU3t6bBydHskkMKQYVamUK2dd2LS6IUk39O2STq5lDJt0gwc6ZePtryQvx5ajv3rxv/dDKC40zaNAhg6/yNARlw2mJcQ7M35MfwDeePsgrVpnGMz/aU8oH6xtb5J4KvPM5DQ6988lKEyJqlTK5VMObFwRQGKM5fpJWWNMluDi4mKQ7Ktj0qRJXLx4kT/+MMwz48aNK/+7ZcuW+Pv706tXL2JiYmjYsKFR4oW7SPbNmjXj999/Jzg42GD6N998Q9u2bY0WmCkoHDRcj3Ri37e+LFhXsRbi+c4PGbxv3zWHV5df489fvMwVIhkptmxaGUBSrByJRMcjQ3JYvCmWSX0bExdlb5YYWrTPZc9XgURdckEm0zFySjTLP/qb8U9FoCzRJ/LwVrks+/BvdmwKYf0bTdCoJYQ2KUSrNU0CyUxXsOWDxiQnOIIEeg9MYsHbp3l5WGfirzsTfdWFg3sDyEhV4OxSxtBx0Sz74CRjnuhulJik3hIcXrJHFiQFHSh/VlEwuxDXzfofvDZTi+Nke2QNZGjTtBS+VYw2U4vzcsOLDce5Dth1tC1/L3EybcIdMiGFgcMyeGd6CHFR9jRqVcS0t2Ipypfx3RbfqjdgBC0ezGXPlwFEXXBBZqNj5CvXWf7pOcY//hDKEhme3ko8fZRseLsh8TGO+AaUMnlhFJ4+SlZMbVH1Du7SjVgX5s3qVv5eozFs1fz5x1C++Kx5+ftSpXmfQdaqYyE/fOZF1FkHZDbwwuwUVmyL4cXu4eW/Q3OzxphqxEJP0Js8eTJ79uzhyJEjBAbeueDYoUMHAKKjo2nYsCF+fn6cOHHCYJm0tDSA27bzV6bG396FCxcycuRIkpKS0Gq17Ny5k8jISD7//HP27NlT082Ve+ONN5gzZw6vvPIKa9asuevt3MmpIx6cOuJx2/k5mYYlrI69sjh/3JXURPNdtR7f72rwfsub/jw6PJPwB4rNluwXTjS8aFu9sDnbDx2hUdN8Lp5xB2DcjCi+/7I+X29qUL7cf0v+xnTid8PE9Pn6xgwYHE94i1zirzuzd1f98nnpKfD5+kas+/JPfPyLSU2qfVx2Dxt+NxzG21O6S4n6khrFY3KcV9xK6rJAGQ7j7ClcWoROrUNicyuhS50lSD3NNyRFs3aFHNvvxonf3ABIS5TT/fFsmrQpBMyT7BeOb23wfvW8cLb/8ReNmhVw8bQbcdFOLH/1VlJPTbDns7UhzHjzClKZFq3GNMdLo5WSk3P735RSKbvjfFObN8ywVPfOq/XZceEijVqVcPG4cWqs7oeYasTMo97pdDqmTJnCrl27OHToECEhIVWuc/bsWQD8/f0BiIiIYPny5aSnp+Pjox/Gef/+/bi4uNCsWbNqx1LjX9ETTzzBDz/8wIEDB3B0dGThwoVcuXKFH374gUceeaSmmwPg5MmTfPzxx7Rq1equ1jcFN08VD3XL4ZdvzHNCrIxUqqPb4znIHbRcOW258cMdnfT3cxbk60ukrh4qwlvlk5tty9ufnWTrb0d4c+MpmrXNNUs8UqmOro8ko7BXc+WCW4X5coWaRx5LIjXJnsw045+sdRodygMqdKU6bFpUfr2sK9QhcZQYJHqAwneKyR6QS+7YfEr3KNGZeBity6edaNMpn3ohpQCENC2mefsCTh5yM+l+78TR+Z/vU97tyxqOzmqKC21MlugB6gUU8L/t37Px8x+ZMfsY3t5FBvN79Izny2928+Ene3lh9Hnk8prd12xsji76JqSCXOspQVtjTNZk0qRJfPHFF2zbtg1nZ2dSU1NJTU2lpKQEgJiYGJYtW8bp06e5ceMG33//PSNGjKBr167l+bBPnz40a9aM4cOHc+7cOX755Rfmz5/PpEmTqtVX4Ka7qpfq0qUL+/fvv5tVKygsLGTo0KF8+umnvP7663dcVqlUolQqy9//9/YHY+r9ZDolRTL+3Ge+KvybGoSXsOb7a9jJtZQUSVk6NoT4a5ZpE5NIdIyfGcWlv12Ji9ZfufvV039Rh74Uy8bVjYiJdKLXoyms/OQ0EwZHkBzvcKdN3rXghgW8s+kodnZaSkpkvD7jARJincvnD/y/OEZNicTeQUPCDUfmTXoQtdp4yUIdoyFvfD6oQGIvwXmFEzYhFU9y2lwtJVtKUTxu+EO0H6vAtp0tEgWUnVBT9E4xuhId9kNM99nu+NAfBycNn/52Aa1GglSm47O36nFwt2fVK5uARKJj/KxoLp1xKf8+/ZeLm4rnXorj56/9TRZH5FVPVr/9EIkJznh4lvL8sEu89e5BJrzYl5ISWw79Vp/0dEeyMxU0CM1j9Njz1AsqYPmSziaL6U4kEh0vLUni4glH4iItV9vwb9YYU1XMPcTt+vXrAf2Dc/5t8+bNvPDCC9jZ2XHgwAHWrFlDUVERQUFBDB48mPnz55cvK5PJ2LNnDxMmTCAiIgJHR0dGjhxpcF9+ddx1I9SpU6e4cuUKoG/Hb9eu3V1tZ9KkSQwcOJDevXtXmexXrlzJkiVL7mo/NdVncBoHf/CmTGX+UYATY+RM7NMEB2cNXQbm8tqaOGYMbmSRhD9x7lWCGxby2gu3OiZJpfpv/M/f1GP/dwEAXL/qQpsOOfQZlMyW9yp24DSGpDhHpgztjKOTms69Upm2+DyzxncoT/gHfw7g7+NeuHspGTwsljkrz/La2I6UqYxT6pDVl+K2xQVdoQ7lwTIKlxfh8oGzQcLXFukomFGILESK/RjDz8th1K0Tok1jG3QlOkq3lZo02Xd9NJueg7J48+VQ4qLsadismPGL4vUd9b41/4XsxPnXCG5UxGvDK+/fY++oZsn6C8THOLL1wwYmi+PUyVsXEjdiIfKKB1u2/kiXbgns2xvK3p9uVVffuOFGTraClW8dxs+/kNQU81dXT16RSHCTEqY/2cjs+74da4ypSmZus6+q5i4oKIjDhw9XuZ3g4GB++umnmu38P2qc7BMTE3nuuef4888/cXNzAyA3N5dOnTqxffv2Kjsf/Nv27ds5c+YMJ0+erNbyc+bMYdq0aeXv8/PzCQoKqlH81dG8XR5BoSWsfLWJ0bddHeoyKck39KXC6AsONGlTzKCxGbw3y/j/651MmHOVh7pmMnN0e7LSbyWk7Ex9bPHXDZsWEmId8fYrNVk8arWUlET9PqOvutK4WR5PPBvHByv17b3FRbYUF9mSnOBI5AU3vvrtAJ26p3F4X4BR9i+xlSAL1Cd2m3AbNFfVlH5ditNMfUy6Ih0F0wqQOOhL/f+twv8vm+Y2lGwpRafSIbEzTUe9sXMT2LHen8M/6EvyNyId8AlU8czEFLMn+wnzonioWxYzR7YhK63iBY69g5plH5+nuEjGspebozFirUxViorsSEp0IiCgsNL5V6/qj19APfMn+0mvJ9Khdz7TnwojM8V0d27UhDXGJNxZjX9NY8eOpaysjCtXrpCdnU12djZXrlxBq9UyduzYam8nISGBV155ha1bt1b7kbtyubz8loe7ufWhuvr+XxpRF52IjbSODicSKdjaac24Rx0T5lwlomcGc15sR1qSYRVdWpKCzHQ5gQ2KDabXCy4iPcV8tQ8Sie72x0WigzvNNwKdFtDfIYm2SEf+1AKwleD8phMSedXJW31NjcRZYrJEDyC311a4G0GrAYnUnKOB6JgwL4qIXpnMGd26wvcJ9CX61z89j7pMwtLJLY1WG1NdCkUZ/v5FZGdX/v1t2DAXgOwsc9au6Zj0eiKd+uUx8+kw0hKq3z5rOtYYUw3c7KBXm9c9qsYl+8OHD/PXX3/RpMmtUm+TJk14//336dKlS7W3c/r0adLT03nggQfKp2k0Go4cOcIHH3yAUqlEJjPuD17hoCGgfkn5e9/AUkLDCynIsyHjnyTl4KimS79MPn2z6l6TpjBqdjInD7qQkWSLvZOWHoNyaBVRyLznjXe/ZVUmzo2ke/9Ulr7ampIiGe6e+n4SRYU2/9xDL+HbLcEMmxDD9Ugnrkc60/vxFAIbFLN8unFK0f81clIkp/7yJiNVgb2Dhu79kmnZLpsFUx7Er14xXR5J4e9jXuTl2OHlW8qQkddRlco4+ae3UfZftL4EuwgbpL5SdMWg3KdC/bca+9VO+qr7VwvQKcF5oQO6Ih26In0ylbhJkMgkqP5Qoc3Wd+iT2EHZSTUln5di/5xpk8fxA248OzmZjGQ7fTV+82KeHJvGvh3mK9VPXHCN7gPSWDqlJSXFMty9/vk+Fei/T/aOapZ/eg65Qstbs1vg4KTG4Z9OoXnZdia5nXPMuLMcPxZAepojnp4lDBtxCa1WwqGD9fHzL6RHzzhOnvAnP19OSGgu4146y4Xz3tyIdTN6LLczeUUiPQblsHh0KCWFUty99c8xKSqQoSo1f/OitcZUE/+UAWq1/r2qxsk+KCio0ofnaDQaAgKqf6Lv1asXFy5cMJg2atQowsPDmTVrltETPUCjFgWs+t/F8vfj58YCsH+nD6vnNAag28BMkMChPcZJEjXl5qVmxto4PHzUFBfIiL2iYN7zDTnzu3PVKxvJo88kArBq02mD6asXNOPA9/rP+Lut9bGTaxk3Iwpn1zKuRzoz76UHSE00Tec8N3cV0xefx8OrlKJCW25EO7NgyoOcPeGFh1cpzdvk8MSzN3ByKSM3W87Fv915bWxH8nKMU/LQ5WopXFaMNkuLxFGCTZgM59VO2D1kS9mZMtSX9b2Sc58x7DTq9o0LMn8Z2Ego3VmK5j19bYisngzHKQ7IHzdtFeiHi4IZMT2JScvicPPSP1Tn523ebF1rmouyyjz6bDIAqz47azB99bwmHNjtT1izAsJbFwCwae9xg2VeeKQD6cnG7/zl5VXCrLnHcHFWkZcn59JFL6a+3Iv8PAV2dsW0eSCdJ566hkKhJiPDgT9/D+TLbdW/zckYHhuZBcDb30YbTH97ahD7d1img6U1xlQjFrrP3hpIdDW89+e7775jxYoVrFu3jvbt9Z22Tp06xZQpU5g1axaDBg2662C6d+9OmzZtqn2ffX5+Pq6urvR0HoqNxHrajbSFlbf7WZLMy/ydsaoisbOteiEzc91RUvVCZpbdvajqhcxM6mS5W0FvK8DH0hFUoLkcZekQrJ5aV8Yh3W7y8vJM1jR7M1cErVmK1P7ua9O0JaUkvLrQpLGaSrVK9u7u7kgkt6rSioqK6NChAzY2+tXVajU2NjaMHj26VsleEARBEEzGzA/VsSbVSvameqLdfx06dMgs+xEEQRDqoDpcjV+tZD9y5EhTxyEIgiAIgonUamSH0tJSVCqVwbR7rR1DEARBqCPqcMm+xvdKFBUVMXnyZHx8fHB0dMTd3d3gJQiCIAhWSWeE1z2qxsl+5syZ/Pbbb6xfvx65XM6GDRtYsmQJAQEBfP7556aIURAEQRCEWqhxNf4PP/zA559/Tvfu3Rk1ahRdunQhLCyM4OBgtm7dytChQ00RpyAIgiDUTh3ujV/jkn12djahoaGAvn0+OzsbgIcffpgjR44YNzpBEARBMJKbT9CrzeteVeNkHxoaSmys/slz4eHh7NixA9CX+G8OjCMIgiAIgvWocbIfNWoU586dA2D27NmsW7cOhULB1KlTmTFjhtEDFARBEASjqMMd9GrcZj916tTyv3v37s3Vq1c5ffo0YWFhtGrVyqjBCYIgCIJQe7W6zx4gODiY4OBgY8QiCIIgCCYjoZaj3hktEvOrVrJ/7733qr3Bl19++a6DEQRBEATB+KqV7N99991qbUwikVgk2etKStFJNGbf723VbCBBs9CVllo6hAq0gdY3SllOvwJLh1BB0B/WNzpgYk9V1QuZmVWWuqzwXIDU+MOH147UfG3hdfjWu2ol+5u97wVBEAThniUelysIgiAIwv2q1h30BEEQBOGeUIdL9iLZC4IgCHVCbZ+CV6eeoCcIgiAIwr1FlOwFQRCEuqEOV+PfVcn+999/Z9iwYURERJCUlATA//73P/744w+jBicIgiAIRlOHH5db42T/7bff0rdvX+zt7fn7779RKpUA5OXlsWLFCqMHKAiCIAhC7dQ42b/++ut89NFHfPrpp9ja3nrYR+fOnTlz5oxRgxMEQRAEY6nLQ9zWuM0+MjKSrl27Vpju6upKbm6uMWISBEEQBOOrw0/Qq3HJ3s/Pj+jo6ArT//jjD0JDQ40SlCAIgiAYnWizr74XX3yRV155hePHjyORSEhOTmbr1q289tprTJgwwRQxCoIgCIJQCzWuxp89ezZarZZevXpRXFxM165dkcvlvPbaa0yZMsUUMZqUp6+KMXOSaN8jD7m9luQbcla/1oBr5x0tFlOLDoUMmZhBo5bFePqpWTy6AUf3uppv/+3z+L8xiYS1KMLTR8XSiU05+qunwTJBocWMnnGDlg/mIZPpiI9x4PUp4WSkKEwWl6dnMWNGnaV9u2Tkcg3JKU6sfrcj16I9Kyw7ZdIJBg6I5qNPHmD3d+Emiaeq4/RzZOV3p2xY1YBvNwbWev8F35ZRuLMMdbK+uGEbKsV1jC32nfQ/6+yVSkpPatBk6pDYg7ylDLfJdtg20F/ja/J0ZC1UUhatRZOnQ+Yuwb6rDLcJdkidjFdd2eLBPP5vbDJhzQvx9C1j6YQmHD1w6zhNe/MajzyVYbDOqSNuLBjTzGgx/NfQ4RcZOuKywbSEeGfGj+kPwBtvH6RVa8OYftoTygdr25sspv+y9HmgOp6elMqYOcns2uDNR4uDLB1Olcz9UJ2VK1eyc+dOrl69ir29PZ06deLNN9+kSZMm5cuUlpYyffp0tm/fjlKppG/fvnz44Yf4+vqWLxMfH8+ECRM4ePAgTk5OjBw5kpUrV2JjU/0UXuNkL5FImDdvHjNmzCA6OprCwkKaNWuGk5NTTTdlcU6ualbvjOTcUWfmj2hEXrYN9RooKcyz7OMHFA5arl9S8MuXHizadMMC+9dwPdKJfd/6smDd1Qrz/YNKeHvbeX751pcv3qtPcaGM+o2KUSlN94wmJycVq9/az7nzvsxf1J28PAX1AgooLLSrsGyniATCwzPJzLQ3WTxQ9XF6vvNDBu/bd83h1eXX+PMXL6PsX+YjwW2iHTZB+uNe9KOajBlK/P4nxS5Uil24FId+Ntj4StDm68jbUEb6y6UE7LJHIpMgkYB9VxmuL9kic5OgTtSR/ZaS7HwlXsuMd9GmsNdy/aoj+77xYcGHkZUuc/KwG+/ODit/X6Yy/fO+bsS6MG9Wt/L3Go3hPn/+MZQvPmte/r5Uad7zgqXPA1Vp3LqIgUMzuX7ZtL8zozLzffaHDx9m0qRJPPjgg6jVaubOnUufPn24fPkyjo76AuXUqVP58ccf+frrr3F1dWXy5Mk89dRT/PnnnwBoNBoGDhyIn58ff/31FykpKYwYMQJbW9sa3QF3199eOzs7mjWr3ZX34sWLWbJkicG0Jk2acPVqxROnKQyZkEpGih2rX2tQPi0tQW6Wfd/JqYMunDroYrn9H/Hg1BGP284fOTWOk0fc2fRWSPm0lATT/uCH/N9lMjIcWL2mY/m0tLSKF5iensVMeOkU8xf0YOniwyaNqarjlJNpeCHSsVcW54+7kpponETq0MXw5+s2wY7CnWWoLmqwC5Xi9OS/hsYNANfxElKHlaBO0WEbKEHqIsF58K1lbPzBebAt+V+UGSW+m04dcefUEfc7LlOmklY4Xqam0UrJybn991aplN1xvqlZ+jxwJwoHDbPev8GamfV57pVUS4djdvn5+Qbv5XI5cnnF3LF3716D91u2bMHHx4fTp0/TtWtX8vLy2LhxI9u2baNnz54AbN68maZNm3Ls2DE6duzIvn37uHz5MgcOHMDX15c2bdqwbNkyZs2axeLFi7Gzq97vpsbJvkePHkgkt6/i++2332q0vebNm3PgwIFbAdWgWqK2Oj6Sx+nDLsxbH0PLDoVkptqy53/e7P3S22wx3GskEh0Pds/hmw31eH3DRRo2KyI1Uc6Oj4MqVPUbU8cOiZw+48+8Ob/TskU6mVkO7PmxEXt/uVUalEh0zJh+lG++bUpcvJvJYrkbbp4qHuqWwzuzG5lk+zqNjuJfNWhLQN6i4njl2hIdRXvKkAVIsPGt/PerztBSfEiN/AHzP0W7VYc8vjx2gsI8G84dc+Wzd+tTkGtb9Yq1UC+ggP9t/x6VSsbVy55s2diSjIxbzXc9esbTo1ccOdkKThwL4MutzVCauXRvrSYvT+DEr678/YfLvZXsa3v73D/rBgUZNlksWrSIxYsXV7l6Xl4eAB4e+kLC6dOnKSsro3fv3uXLhIeHU79+fY4ePUrHjh05evQoLVu2NKjW79u3LxMmTODSpUu0bdu2WqHX+Jvbpk0bg/dlZWWcPXuWixcvMnLkyJpuDhsbG/z8/Kq1rFKpLH+ID1S8uqop/yAljw7LYOcGX7Z/4E/j1kVMWJKAukzKgW9Ml7juZW6eZTg4anj6xUQ+WxPMprcb0K5LDvM/uMLsES25cNI0bYr+foU8OuAaO3eFs/2r5jRunM2E8adRq6Uc+FV/F8jT/3cZjUbCd983qWJr5tf7yXRKimT8uc84Vfg3qaK1pI0tQacCiT14vynHNvRWsi74pozcD1ToSsAmWILP+woktobJPnN+KSVHNOiUYN9Fhudc89ZunT7izp+/eJKWKMe/fikvTI9n2YYrTHu6JVqtaW51irzqyeq3HyIxwRkPz1KeH3aJt949yIQX+1JSYsuh3+qTnu5IdqaCBqF5jB57nnpBBSxf0tkk8dxLuj2eTVjLYqYMNE1fGJMyUjV+QkICLi63al0qK9X/l1ar5dVXX6Vz5860aNECgNTUVOzs7HBzczNY1tfXl9TU1PJl/p3ob86/Oa+6apzs33333UqnL168mMLCwppujmvXrhEQEIBCoSAiIoKVK1dSv379SpdduXJlhWr/2pBI4dp5B7asqgdAzCUHGjQpYeDQDJHsb0Mi1X/bj/7qye7P9Mft+lUnmj1QwIBnU0yW7CUSuBbtwZbP2wAQc92DBsG5DOx/jQO/hhIWls0TT0Qy+eV+gPXdC9tncBoHf/A2elu0bbAEv//ZoyvUUfybhqylSnzXS8sTvmM/GxQPydBk6SjYWkbmXCV+nyqQyG8dI/epdriOhbJ4LXkfqshZq8JjpvkS/uEfb10A3YhyJDbSkc2/naFVhzzOHnUzyT5PnfS/tc9YiLziwZatP9KlWwL79oay96eGt+bfcCMnW8HKtw7j519Iasq91z/JWLz9VUxYksic58MoM2EfHWvn4uJikOyrY9KkSVy8eNFij5U32qc1bNgwNm3aVKN1OnTowJYtW9i7dy/r168nNjaWLl26UFBQUOnyc+bMIS8vr/yVkJBQq5iz022Jv2bYfhp/zR7veqpabfd+lp9ji7pMQnyMYVtmQow93gHK26xVe9k5CuLjDS8k4hNc8fYuBqBF83TcXEv535bv+PH7L/nx+y/x9S3ixTF/89mm70wWV3U0b5dHUGgJe7/2rXrhGpLYSrANkmLXVIbbJDtsG0kp+OpWm7vUSYJtfSmKtjK8VspRx2kpPqQx2IbMU4ptAykOXW1wny2n8Fs1mkyt0WOtrtQEBXnZNvgHl5ptn0VFdiQlOhEQUHmB5epV/cV/QL2aF2juJ2GtinH3VrPu56v8dOMMP904Q+uIQp4YncFPN84glVr5jegWus9+8uTJ7Nmzh4MHDxIYeOtOHD8/P1QqVYUH0qWlpZXXePv5+ZGWllZh/s151WW0BqijR4+iUNSs41H//v3L/27VqhUdOnQgODiYHTt2MGbMmArL364TxN26fMqRwIaGCapeaCnpiebtKHQvUZdJibrgRGBIicH0eg1KSE8y3W13ly97E1jPsNmmXr180v9pY/31txD+Pmv4xV++9CC/Hgxh/37LPuyp7/+lEXXRidhIM5QItaC7Xf+6f05WurI7nLH+maWz4PWul58SZzc12enm+x0qFGX4+xfxW3bl3+GGDXMByM4y3Xf8XnD2D2fG9WpqMG36O3EkxCjY8aGvyZpdjMXct97pdDqmTJnCrl27OHToECEhIQbz27Vrh62tLb/++iuDBw8G9E+pjY+PJyIiAoCIiAiWL19Oeno6Pj4+AOzfvx8XF5cadZKvcbJ/6qmnKvwzKSkpnDp1igULFtR0cwbc3Nxo3LhxpU/oM4VdG3xZvesqz0xK4cged5q0KWbA85msnV15M4K5KBw0BITcOtv6BakIbV5CQa6MjCTTnwAVDhoC6t9K5r6BpYSGF1KQZ0NGioJvN9Zj9ruRXDzpyrnjrrTvkkOHHtnMGtHSZDHt2h3O6rf38czTlzjye32aNM5iQL9o1r6vv72toEBOQYHhhaBGIyUnR0Fikml6NFd1nAAcHNV06ZfJp2+G3G4zdy13nQpFJ5n+1rpiKP5FjfKMFpe1dqiTtBTtV2PfQYbUXYImXUf+52VI5JTfh1/ypxpNtg67ZjKk9lB2XUvO+yrkraTYBBivilbhoCHgX6V030AloU2LKMi1oSDPhqFTEvjzF0+yM2wJqF/K6JlxJMcpOPOHm9Fi+K8x485y/FgA6WmOeHqWMGzEJbRaCYcO1sfPv5AePeM4ecKf/Hw5IaG5jHvpLBfOe3Mj1nQx/ZelzwOVKSmSERdpWKtXWiKlIKfidEFfdb9t2za+++47nJ2dy9vYXV1dsbe3x9XVlTFjxjBt2jQ8PDxwcXFhypQpRERE0LGj/s6jPn360KxZM4YPH86qVatITU1l/vz5TJo0qUaF3xone1dXw6pUqVRKkyZNWLp0KX369Knp5gwUFhYSExPD8OHDa7Wd6oo678jScQ0ZNSuJoa+kkJog56MlgRzcbdn2+satS3jr25jy9y8tSQZg31fuvDPV9BcijVoUsOp/F8vfj58bC8D+nT6sntOYvw548cFiNU+PS+Sl+ddJjLXn9Zebcum06R74EXXNk6Wvd2XUC2cZ+twFUtOc+OiTdhw8ZPwkWl1VHSeAbgMzQQKH9hj/Dg9Njo6sJUo0mTqkTmAbJsV7rQL7DjLUGVqUZ7UUbC9DWwAyDwnytlJ8N9gj89CXviRyCYXflVG2RgVl+vv2HXrY4DLCuL3gG7UoZNXWS+Xvx8+7AcD+nd58sDCUkCbF9H4yHUdnDdnpdpz5w43P1wSZ9F57L68SZs09houzirw8OZcuejH15V7k5ymwsyumzQPpPPHUNRQKNRkZDvz5eyBfbjPdQ34qY+nzgFB769evB6B79+4G0zdv3swLL7wA6PvBSaVSBg8ebPBQnZtkMhl79uxhwoQJRERE4OjoyMiRI1m6dGmNYpHodLpqV0xoNBr+/PNPWrZsibv7ne+brY7XXnuNxx57jODgYJKTk1m0aBFnz57l8uXLeHtXfXLMz8/H1dWVHjaDsZGY9jadmtCp1ZYOoQKps7OlQ6hAF2Z9JyxJdLylQ6gg8IDl2s9vJ7GnpSOoSNKg9k8lNDbNpcofImRR0oq3ZlqSWlfGIe1O8vLyatzprbpu5oqGc1Ygq2Fz879pSkuJWTnXpLGaSo1K9jKZjD59+nDlyhWjJPvExESee+45srKy8Pb25uGHH+bYsWPVSvSCIAiCUBPmbrO3JjWuxm/RogXXr1+v0NHgbmzfvr3W2xAEQRAE4c5q3Cj2+uuv89prr7Fnzx5SUlLIz883eAmCIAiC1aqDw9tCDUr2S5cuZfr06QwYMACAxx9/3OCxuTqdDolEgkajud0mBEEQBMFyzDwQjjWpdrJfsmQJL730EgcPHjRlPIIgCIIgGFm1k/3NTvvdunWrYklBEARBsD6ig1413Wm0O0EQBEGwaqIav3oaN25cZcLPzs6uVUCCIAiCIBhXjZL9kiVLKjxBTxAEQRDuBaIav5qeffbZ8gfxC4IgCMI9pQ5X41f7PnvRXi8IgiAI96Ya98YXBEEQhHtSHS7ZVzvZa7XWNxiHIAiCIFSXaLO/10mk+pe1sMImD12p0tIhVCC9kWzpECrQWuFxSuhqfd+nvbHHLR1CBf1COlg6hIqsbIQ5AHRWVnAzZzx1uGRvRRlSEARBEARTuD9K9oIgCIJQlTpcshfJXhAEQagT6nKbvajGFwRBEIT7nCjZC4IgCHWDqMYXBEEQhPubqMYXBEEQBOG+JUr2giAIQt0gqvEFQRAE4T5Xh5O9qMYXBEEQhPucKNkLgiAIdYLkn1dt1r9XiWQvCIIg1A11uBq/Tif7z/44h2+QqsL0Hz73Yd2CYAtEBM9MTqNz/1yCwpSoSqVcPuXAxhUBJMYoLBIPWMdxenpsHJ0eySQwpBhVqZQrZ13YtLohSTccypfpNySZ7gPSCGtWiIOThiEdO1NUYGuW+ACkUh3DpibR88ks3L3LyEqz48A3Xmx7zx9LlgnsHTWMmJZIp745uHmWEXPJkY+W1ifqvJPJ9/3V+z5sWhnAoLEZTFiaBMDamYH8/bszWWm22Dtoadq+iDHzkqnf6NYgRH//7sRnq/y5cVWBwkFL7yHZjJqdgsyEZyxLHqfqeHpSKmPmJLNrgzcfLQ6ySAyPjshk4PDM8vNBXJSCre/6ceqgi0Xiqam6fOtdnU72Lz/ezGBQqgaNi1m5LYrff3S3WEytOhbyw2deRJ11QGYDL8xOYcW2GF7sHo6yxDIjaFnDcWrxYC57vgwg6oILMhsdI1+5zvJPzzH+8YfKj4tcoeH0nx6c/tODUVNjzRbbTUMmpDBwWAbvTA8hLsqeRq2KmPZWLEX5Mr7b4mv2eG569Y1YGjQu4a1poWSl2dFrUCYr/xfJuD4tyUqzM9l+I8/a8+MXnoQ0KzGY3qhVCT2fysG7XhkFOTK+eMePuc815LPjl5HJIOaSggXDQ3n25TRmvBdHVqot780KQquRMG6R6UZKtNRxqo7GrYsYODST65ftLRpHRootm1YGkBQrRyLR8ciQHBZvimVS38bERVk2NuHOLN5BLykpiWHDhuHp6Ym9vT0tW7bk1KlTZtl3XrYtORm3Xg/1yiP5hpzzx5zNsv/KzBvWkP07PImLsuf6ZXveebU+voFlNGpVUvXKJmINx2nh+NYc2O1PfIwjsZFOrJ4Xjk+AkkbNCsqX+e5/QXy9IZir5yxTymjWrpBj+9048ZsbaYly/vjJgzO/u9KkTaFF4gGwk2t5uF82G98I4uIJF1LiFHyxNpDkODmPDks32X5LiqS8OTmYV99KwNlVYzBvwLAsWnYswi9IRaNWJYyclUJGsh1pCfqEevh7d0KaljJsWhr1QlS0iihi7PxkfvjMi+JC05yyLHWcqkPhoGHW+zdYM7M+BXmWHTL3+H5XTv7mQnKsnKTrCra86U9pkZTwB4otGle16YzwukdZNNnn5OTQuXNnbG1t+fnnn7l8+TLvvPMO7u7mL1nb2Grp+WQWv+zwwpq6YTi66E+UBbnWMS62tRwnR2c1AAV51lM5dfm0E2065VMvpBSAkKbFNG9fwMlDbhaLSWajQ2YDKqXhZ6UqldK8fcFt1qq9D+YG8lCvfB7oeucLndJiKfu+8sCvvhLvgDIAylQSbOWGY5zbKbSoSqVcO+9Q2WZqzVLHqTomL0/gxK+u/P2HdVWVS6U6uj2eg9xBy5XTjpYOp/rqYKIHC1fjv/nmmwQFBbF58+byaSEhIbddXqlUolTeatfLz883WiwRfXJxclGz/2svo22ztiQSHS8tSeLiCUfiIq2jiswajpNEomP8rGgunXEhLto62lMBdnzoj4OThk9/u4BWI0Eq0/HZW/U4uNvTYjGVFMm4fNqJ56ckEx9tT26mLd0fzyL8gUJS4kzTD+TQbjeiL9jz/k9Rt13mhy2ebHg9gNJiGYENS1m5PQZbO/3ZtH23AnZ/6s3BXW50fTyXnHRbtr7rB0B2mmlOWZY4TtXR7fFswloWM2VguMVi+K8G4SWs+f4adnItJUVSlo4NIf6a5Y6RUD0WLdl///33tG/fniFDhuDj40Pbtm359NNPb7v8ypUrcXV1LX8FBRmvk0q/ZzI4eciV7HTLts392+QViQQ3KWHlRMt0FqyMNRynifOvEdyoiDdea2axGCrT9dFseg7K4s2XQ5k8sBnvTAth8LhUeg/OtGhcb00LBQlsO36WHyJP8sQLaRz+wROttup1ayo9yZb1C+sx64M47BS3Lwr1fCqHD/dF8vbOawSGKlk+vgGqUn2pul33AsYuSOa92UE82qA1ox8O56Ge+gt7iQnPWOY8TtXh7a9iwpJE3pzSgDKlxVtcyyXGyJnYpwkvP9qYPZ978dqaOOo3KrV0WNVys4NebV41ceTIER577DECAgKQSCTs3r3bYP4LL7yARCIxePXr189gmezsbIYOHYqLiwtubm6MGTOGwsKaNw1atGR//fp11q9fz7Rp05g7dy4nT57k5Zdfxs7OjpEjR1ZYfs6cOUybNq38fX5+vlESvk89JW0ezmfZ+LBab8tYJr2eSIfe+Ux/KozMFOu4ALGG4zRhXhQPdcti5sg2ZKVZV2li7NwEdqz35/AP+pL8jUgHfAJVPDMxhQPfWq4mJCVewcxnmyK31+DopCE7w44570eTGi83+r6izzuQm2nLpL5NyqdpNRIuHHPk+81e7LlxDpkMHF20OLqoqBeqIvyBGwxu2oI/f3alx5O5AAwen8FT4zLITrPByVVDWqIdm1YG4B+svM2ea8+cx6k6wloV4+6tZt3PV8unyWygZYdCHn8hg0dD26LVmr8pTV0mJfmG/phEX3CgSZtiBo3N4L1ZlrlDoEbMfOtdUVERrVu3ZvTo0Tz11FOVLtOvXz+D2m253PD7NnToUFJSUti/fz9lZWWMGjWKcePGsW3bthrFYtFkr9Vqad++PStWrACgbdu2XLx4kY8++qjSZC+XyyscCGPoMySTvCxbTvzmZvRt15yOSa8n0alfHjOGhJGWYJkTTWUse5x0TJh3jYhemcx+oQ1pSdbRrPFvcntthZOvVgMSqXU09ilLZChLZDi5qGnXNY+Nbxj/5NymSwEf/3bVYNo7U+sTFFbK05PSkVXS9USnA3QSylSGpVeJBDz99H0zDu5yxztARVhL03dUNcdxqo6zfzgzrldTg2nT34kjIUbBjg99LZLoKyORgq2dhao/LOS/Tci3y039+/enf//+d9yWXC7Hz8+v0nlXrlxh7969nDx5kvbt2wPw/vvvM2DAAN5++20CAgKqHbNFk72/vz/NmhlWxTZt2pRvv/3WbDHobx/JZP83nmg1lv/xTF6RSI9BOSweHUpJoRR3b32npaICGapSy1XlWfo4TVxwje4D0lg6pSUlxTLcvfQlvKICG1RKfQZx91Li7qUioL4+ITRoVERJsYz0FAWFeaa/3/74ATeenZxMRrIdcVH2NGxezJNj09i3w7L9QNp1zQUg8bo9AQ1KGTsngYQYBftM0O/CwUlLg3DDKl2FgxZndw0NwktJibPj8PdutOtWgKuHmowUW3Z84IudvZaHet06gX79oTftexQgkcKfP7myY50P8z6Kq/RiwVjMeZyqo6RIVqGvTmmJlIKcitPNZdTsZE4edCEjyRZ7Jy09BuXQKqKQec83tEg8NWWs++z/W6O8aNEiFi9efFfbPHToED4+Pri7u9OzZ09ef/11PD31tYNHjx7Fzc2tPNED9O7dG6lUyvHjx3nyyServR+LJvvOnTsTGRlpMC0qKorgYPO1Ubd9OB/fQBX7dnibbZ938tjILADe/jbaYPrbU4PYv8NyHb0sfZwefVZ/f/Wqz84aTF89rwkHdvsDMODpZIZOiiuf99b/zlZYxpQ+XBTMiOlJTFoWh5uX/qE6P2/zZuva6l99m4KDs4ZRMxLx8lNRmGfDH3vd2fJ2IBq1+S8e7eRaLh53Yten3hTmyXDzUtOyYyHvfncNNy91+XInD7rw5Xt+lKkkhDYrYfHmWB7sadpe8dZ0nKyVm5eaGWvj8PBRU1wgI/aKgnnPN+TM75a7XblGjFSNn5CQgIvLrbsj7rbGuV+/fjz11FOEhIQQExPD3Llz6d+/P0ePHkUmk5GamoqPj4/BOjY2Nnh4eJCamlqjfUl0Op3F6hhPnjxJp06dWLJkCU8//TQnTpzgxRdf5JNPPmHo0KFVrp+fn4+rqys9bIdgIzHfk9KqolOXWTqECiQ21nN8bpI6Wd/tOtrCIkuHUJHU8jVO/7U39rilQ6igX0gHS4dQga5MXfVC5qazrip3ta6MQ7rd5OXlGSRQY7qZK1qOWYHM7u77+mhUpVzYOPeuYpVIJOzatYtBgwbddpnr16/TsGFDDhw4QK9evVixYgWfffZZhUKxj48PS5YsYcKECdXev0UvWR988EF27drFl19+SYsWLVi2bBlr1qypVqIXBEEQhJowd2/8mgoNDcXLy4voaH3Nrp+fH+nphg91UqvVZGdn37ad/3Ys/kSSRx99lEcffdTSYQiCIAj3OysfCCcxMZGsrCz8/fXNjhEREeTm5nL69GnatWsHwG+//YZWq6VDh5rVZFk82QuCIAiCWZg52RcWFpaX0gFiY2M5e/YsHh4eeHh4sGTJEgYPHoyfnx8xMTHMnDmTsLAw+vbtC+g7rPfr148XX3yRjz76iLKyMiZPnsyzzz5bo574YAXPxhcEQRCE+9GpU6do27Ytbdu2BWDatGm0bduWhQsXIpPJOH/+PI8//jiNGzdmzJgxtGvXjt9//92gw9/WrVsJDw+nV69eDBgwgIcffphPPvmkxrGIkr0gCIJQJ5h7iNvu3btzpz7wv/zyS5Xb8PDwqPEDdCojkr0gCIJQN1h5m70piWp8QRAEQbjPiZK9IAiCUCdIdDoktXi0TG3WtTSR7AVBEIS6QVTjC4IgCIJwvxIle0EQBKFOMHdvfGsikr0gCIJQN4hqfEEQBEEQ7leiZG8K1thj08pGugLQlZRYOoQKpC5Olg6hAp3K+kZR7BvYztIhVPBt/GFLh1DB4MCOlg6hIon1jaJoLqIaXxAEQRDud3W4Gl8ke0EQBKFOqMsle9FmLwiCIAj3OVGyFwRBEOoGUY0vCIIgCPe/e7kqvjZENb4gCIIg3OdEyV4QBEGoG3S62t0abY23VVeTSPaCIAhCnSB64wuCIAiCcN8SJXtBEAShbhC98QVBEATh/ibR6l+1Wf9eJarxBUEQBOE+V6dL9p/9cQ7fIFWF6T987sO6BcEWiEivRYdChkzMoFHLYjz91Cwe3YCje10tFg+Ap6+KMXOSaN8jD7m9luQbcla/1oBr5x3Nsv8WD+bzf+NSCGtRhKdvGUvHN+Lofg8AZDZaRk5PpH33XPyDlBQVyPj7T1c2rwoiO93OZDE9PSaOTr0zCAwpRlUq5co5Vza925CkGw6VLK1j6frztH84m2WvtODob94mialF+zz+b0yi/jj5qFg6sSlHf/Usn/9z5B+VrrdhVQO+3Rhokpiq8vSkVMbMSWbXBm8+Whxk8v3t/CCArW/UZ+CYFEYviQPgo1khnP/DlZxUOxSOGpq0L2DY3HgCw0rL18tIsuOTOSFc/MsFhaOW7v+XwbA58chMdBa1xvPAoyMyGTg8s/y8GRelYOu7fpw66GLRuKpNVOPXTS8/3gyp7Nb7Bo2LWbktit9/dLdcUIDCQcv1Swp++dKDRZtuWDQWACdXNat3RnLuqDPzRzQiL9uGeg2UFOaZ7+ujcNBy/YoD+772ZsFH1wzmye21NGxexJfv1+P6FQecXdWMXxjHok+jeOWJFiaLqUX7XPZsr0fURRdkMh0jX4lh+cdnGT+oA8oSmcGyg4YnmuWuHYWDhuuRTuz71pcF665WmP9854cM3rfvmsOry6/x5y9epg+uEo1bFzFwaCbXL9ubZX/RZx3Zv9WH4KZFBtNDWxbR5clMvOupKMyV8dXqQJY935QPj/6NTAYaDawYEY6bj4oV310iJ82O919tiI2tjqGzE0wSq7WdBwAyUmzZtDKApFg5EomOR4bksHhTLJP6NiYuyjyfYW3U5d74Fk32DRo0IC4ursL0iRMnsm7dOpPvPy/b1uD90xPySL4h5/wxZ5Pv+05OHXSxqivlIRNSyUixY/VrDcqnpSXIzRrDqcNunDrsVum84gIb5o1oajBt/eIGrN19Ce8AJRnJpol14YTWBu9Xz2/K9iN/0qhZARdP34o1tEkBT41M4JVn2rH10F8mieWmU0c8OHXE47bzczINazo69sri/HFXUhMVJo2rMgoHDbPev8GamfV57pVUk++vpEjKmilhvLTqOt+uNazF6DMsvfxvnyB4bkYi0/u0IiNBjl8DJecOu5F4zZ5F26/g5l1GSPNinp2RwBcr6vP0tERs7YyfBaztPABwfL9hzcKWN/15dHgm4Q8U3xPJvi7fZ2/RNvuTJ0+SkpJS/tq/fz8AQ4YMMXssNrZaej6ZxS87vIC6O95zZTo+kkfUeQfmrY9h+5lzfPDTZfo9l2HpsO7IwVmDVgtF+bKqFzYSRyc1AAX/qvGQKzTMfPMyHy5vRE6WeS+QquLmqeKhbjn88o2vRfY/eXkCJ3515e8/zJPQNswLoV2vXFp3yb/jcqXFUg7u8ManfimeAfrq6sjTTtQPL8bNu6x8uTbd8igusCHhXkhyJiCV6uj2eA5yBy1XTpunOU+4exYt2Xt7G7ZbvvHGGzRs2JBu3bpVurxSqUSpVJa/z8+/84+2JiL65OLkomb/15apzrRm/kFKHh2Wwc4Nvmz/wJ/GrYuYsCQBdZmUA994Vr0BM7O10zJ6ZjyHf/CkuNA8X3GJRMf4WdFcOuNKXLRT+fQXZ0Zz5awrxw6apo2+Nno/mU5JkYw/95n/O9/t8WzCWhYzZWC4Wfb3x3eeXL/gyJs/XrjtMns/8+V/y+tTWiwjoGEJi7ZdKS+x52bY4vqvRA+UJ/7cdDug2GSxW5sG4SWs+f4adnItJUVSlo4NIf6a+WuG7kZdrsa3mt74KpWKL774gtGjRyORVF6yXrlyJa6uruWvoCDjdebp90wGJw+5mrRD171KIoXoiw5sWVWPmEsO/LzNm71fejFwqPWV7mU2WuZ+cA2JBD5Y0MBs+504L4rgsCLemNmsfFqH7pm0fiiHj98MM1scNdFncBoHf/CmTGXe04C3v4oJSxJ5c0oDypSm33dmsh2bFgXzyvvR2Cluf7bu8mQmb+29wNJvLhEQWsI7ExqhKhW1fP+VGCNnYp8mvPxoY/Z87sVra+Ko36i06hWtgc4Ir3uU1XTQ2717N7m5ubzwwgu3XWbOnDlMmzat/H1+fr5REr5PPSVtHs5n2XjrPClbWna6bYUr9/hr9nTun2uZgG5DZqNl7vvR+NRTMXtouNlK9RPmRvFQtyxmvtCWrLRbx6n1Qzn4B5Xw9V+GPeDnrr7IpTNuzB7d1izxVaZ5uzyCQktY+WoTs+87rFUx7t5q1v18qwOhzAZadijk8RcyeDS0LVqt8ZJszHlH8jLtmNG/Zfk0rUbC5ePO/LzFj+3XjyOTgaOLBkcXDQGhpTR+oJCRzdtzfK8HXQZl4eZdRvRZJ4Pt5mbo+/y4+VS8o+d+pi6TknxD3yQVfcGBJm2KGTQ2g/dmmf5OCuHuWU2y37hxI/379ycgIOC2y8jlcuRy47d79hmSSV6WLSd+czP6tu8Hl085EthQaTCtXmgp6YnWUwtyM9EHNChl9tCmFOTaVr1SremYMPcaET0zmD26LWlJhm23X2+szy87/Q2mrd91kk9XNeL4Ycs2f/T9vzSiLjoRG+lU9cJGdvYPZ8b1MuxQOf2dOBJiFOz40NeoiR6g1cN5vHvgnMG0D6Y3pF7DEp6cmIyssm4d//Tjulnr0aRdITvfr0depg2uXvq+GeeOuOLgrCaoUYlR473XSKT6prN7QV2uxreKZB8XF8eBAwfYuXOn2fetv30kk/3feKLVWEeVncJBQ0DIrdKCX5CK0OYlFOTKyEgyf4LdtcGX1buu8sykFI7scadJm2IGPJ/J2tn1zRaDwkFDQPCtqkLfICWhTYsoyLMhO92WeeuuEda8mEVjGyOV6nD30h+/gjwb1GWmqSqeOC+K7gPSWfpKC0qKZLh76i+IigptUCll5GTJK+2Ul5Eqr3BhYCwKBw0B9W8lH9/AUkLDCynIsyEjRV/r4OCopku/TD59M8QkMVSlpEhGXKTh/19aIqUgp+J0Y7B30lI/3DAhK+y1OLurqR9eQmqcnL9+8KR11zxcPMvISrFj17oA7BRa2vXMAaB1t1wCG5Ww9pUwRsyLJyfdli/fCqLfyDRs5abJANZ2HgAYNTuZkwddyEiyxd5JS49BObSKKGTe8w0tEk+N1eHe+FaR7Ddv3oyPjw8DBw40+77bPpyPb6CKfTuspwNV49YlvPVtTPn7l5YkA7DvK3femWq+BHtT1HlHlo5ryKhZSQx9JYXUBDkfLQnk4G7zlU4btSxi1ZdXyt+Pnx8PwP5vvPhibSARj+QC8OFPFw3Wm/lcUy4cN01v70ef1X8uqzafNZi+en44B77zr2QN02vUooBV/7t1DMbPjQVg/04fVs9pDEC3gZkggUN7rOc7b0l2ci2XjzuzZ4MfRXk2uHqV0axDASu+u1ReipfJYM5nV/lkTghzHm+OwkFL9yEZPPuaae6xB+s7DwC4eamZsTYODx81xQUyYq8omPd8Q878btnblYWqSXQ6y16qaLVaQkJCeO6553jjjTdqtG5+fj6urq70sB2CjcQc1bbVoyuzvjY8iY1VXNcZsMqYHCt7+p1l6VRlVS9kZtoi6+t9/m38n5YOoYLBgR0tHUJFt+kAbSlqXRmHdLvJy8vDxcU0F+Y3c0VE/6XY2N79nQPqslKO/ryw2rEeOXKEt956i9OnT5OSksKuXbsYNGhQ+XydTseiRYv49NNPyc3NpXPnzqxfv55GjRqVL5Odnc2UKVP44YcfkEqlDB48mLVr1+LkVLMmOIv3xj9w4ADx8fGMHj3a0qEIgiAI9zMz98YvKiqidevWt31I3KpVq3jvvff46KOPOH78OI6OjvTt25fS0ltNlkOHDuXSpUvs37+fPXv2cOTIEcaNG1ezQLCCavw+ffpg4coFQRAEQTC6/v37079//0rn6XQ61qxZw/z583niiScA+Pzzz/H19WX37t08++yzXLlyhb1793Ly5Enat28PwPvvv8+AAQN4++2379ih/b8sXrIXBEEQBHO42Ru/Ni/QNwv8+/Xvh71VV2xsLKmpqfTu3bt8mqurKx06dODo0aMAHD16FDc3t/JED9C7d2+kUinHjx+v0f5EshcEQRDqBq2u9i8gKCjI4AFvK1eurHEoqan68SB8fQ0fV+3r61s+LzU1FR8fH4P5NjY2eHh4lC9TXRavxhcEQRAEszDSELcJCQkGHfRM8fwXYxMle0EQBEGoARcXF4PX3SR7Pz8/ANLS0gymp6Wllc/z8/MjPT3dYL5arSY7O7t8meoSyV4QBEGoEyTUss3eiLGEhITg5+fHr7/+Wj4tPz+f48ePExERAUBERAS5ubmcPn26fJnffvsNrVZLhw4darQ/UY0vCIIg1A1mfoJeYWEh0dHR5e9jY2M5e/YsHh4e1K9fn1dffZXXX3+dRo0aERISwoIFCwgICCi/F79p06b069ePF198kY8++oiysjImT57Ms88+W6Oe+CCSvSAIgiCYxKlTp+jRo0f5+5sDuY0cOZItW7Ywc+ZMioqKGDduHLm5uTz88MPs3bsXheLWg3+2bt3K5MmT6dWrV/lDdd57770axyKSvSAIglAnmHsgnO7du9/xOTISiYSlS5eydOnS2y7j4eHBtm3barbjSohkLwiCINQNRuqNfy8SHfQEQRAE4T4nSvaCIAhCnSDR6ZDUooNebda1tPsi2csC/ZBJreehBrrMbEuHUIGmoMDSIVQkk1k6ggq02TmWDkG4S4Prd7Z0CBUsuX7C0iFUsKSZdR0nqU4CpVUvZxTaf161Wf8eJarxBUEQBOE+d1+U7AVBEAShKqIaXxAEQRDud3W4N75I9oIgCELdYOYn6FkT0WYvCIIgCPc5UbIXBEEQ6gRzP0HPmohkLwiCINQNohpfEARBEIT7lSjZC4IgCHWCRKt/1Wb9e5VI9oIgCELdIKrxBUEQBEG4X4mSvSAIglA3iIfq1E1DhkXxwktX2L0jlE/fawnA5BlnadM+Aw+v0v9v787jYzr7/4+/JpPMkl0iIiEJSUiCUMtdd3RR5a66Fa22VNM2im6itZSS209RN0G/bS1t0Y3Wbb2rtLSlqbsNilrjRjUkIouEBNn3zJzfH25hJIpmOSP5PB+P+WPOOTPnPZmT+Zxzruuci5IiW04cc2P5knakpTjVSYYO3XJ5fGQage0LcG9WxqzIEPZsb1o532Bv4vnXkwjrfREn1wrOp+n5ZmULvlvnVSd5qjN0zHnu6ZeDT2ApZSU2/HbAnk/neJOWaKi3DNUxOph4bkIaPfpm4+peTuJxB5a+5cvJ/zqqkueR5y7Q/9kLePqUAZB80sCq95pz4CdnVfKAdX531pjpekMizzEyKp2Nn3iwdIZPna9v5xJPfny7JX8dfp5+b6YBUF6qYdvslhzb4oapTEPAfXk88lYKjh4Vla/7bqYPKQcdyDxpxCOghFe+PVGruTr8JY8nXswgsEMh7p7lvPVSG/bEuAGgtTUT8Xoa3R7IwcunlMJ8LYd/cWH5fB8uZepqNUdtacy3y220p/HbBGfz8MBkTidY/hAnxLvy3pzOvBzem2mvh6HRwKz39mBjUzdfssFoIul3Bz58K6Da+S9MOU3Xe7N5+40gXurflU1ftOCVaQl073WxTvJUp+NfC9j8eVPGDWhD1LAAtHYwZ3UieqOp3jJUZ9zcJLrcm8fbE/x5+eFQDu10JnplPO6eZarkycqw47Nob8b0C+LVv7flyC9OzPgsCb+2xarkAev87qwx07Xadiqkf/gFTv9mrJf1nT1iz4E1HngGF1lM3zrLh5PbXRny/mmeX3OS/Ew71o6u+jvR+YmLdOhfN6M1GuzNnD5hz4fTW1WZpzeaCWhfyJrFLRgzoAP/fKUNLf2Lmf7xyTrJImpG1WJvMpmYNm0arVu3xmg0EhAQwKxZs1DqeO/JYKxg0vSDLJ7fiYJ8O4t5W79pxfEjTck8Z0/iSVe++DiYZp7FNGtedIN3q5kDO934YmEr9vzYtNr5IXflsX2TJ0f3uZJ51sDW9V6cjnckqGP9DVk79ZkAYta7k3zSyOnfjLwzzhfPluW06aheEdPpzdz78CU+nevDsX3OZCQb+NfClqQn63nkmUxVMv0a48L+/ziTnqTn7GkDK+Z5UVJoQ3CXutl2boU1fnfWmOkKg72JyYvPsOANX/Jz634I5tJCGzaMb83AOckYXa7u7JTk2XD43+70nZqKf498vEOLeHT+GVIPOpJ62KFyub9PT6X7c1k08Smtk3wHYl354l0fdv/gVmVeUb4tU58LYed37pxNMvJ7nBNLZrSibWghHt51k6fGrnTQq8njDqVqsZ83bx5Llizh/fff58SJE8ybN4/58+ezePHiOl3vKxP+y/7dnsQdaPaHy+kNFfzt7ymcS7fnQmb97OVf70ScM90fvIh7s1JAoWP3HFq0KubQL01UyQPg4Hz5Ryk/R73x6LW2ClpbKCvVWEwvK7Ghfbf62xG6ERsbhZ4Ds9Hbmzlx0OHmL6gn1vDdXc+aMo2Zncq+7S4c3lU/TS/fTvelTa9cAu613GbTjzlgKrfB/5rpHgGluHiXknrIeran69k7mTCboTBP/e+yWgpXx7T/M487t9ar22a/e/duBg0aRP/+/QFo1aoVa9asYd++fdUuX1paSmnp1T3GvLy8217n/b3TCGybw7gXet5wmf6PJfH8K8cx2ptITXZk6rgeVFSos1+0ZFYAr806xcod+6go16AosHBaG44dcFElj0aj8PLMsxzb50ByvDo7QADFhVp+O+jI06+mk5JgJOeCHQ8MvEhwlwIyktVr+20VXMyCb06h05spLrThrVGtSTllHW3R1vLdXcuaMvUceInA0CJe7R9cL+s7urkJGcfsefHrqu3sBVm2aHVmjM6WTRuOTSsoyLKrsrw1sNOZGfFGCrGb3SkqsM7uYNJmr5IePXqwfft2Tp683MZz5MgRdu3aRb9+/apdPjo6GhcXl8qHj8/tdZxp2qyYF8ce4+23ulJeduM9z59+aMlrIx7gjch7SE91JGrWfux06rQnDnw2neBO+cx4pR2vPd6Zj+f5M/rNRO4Kq5s2upsZMycNv6Biokf7qbL+a709wR80sPrXODbH72fQ8PPEbnbHrOKNL9IS9Yx+KIjXHmnLli+aMnFBMr5tStQLdA1r+u6usJZMHl5lvDIzjXmvtqK8tO5/FnPT7fj+LR8efy8JO/2dW0Cu0Nqa+cf7p9Bo4P1prdSOI6qh6u7XlClTyMvLIzg4GK1Wi8lkYvbs2YSHh1e7fFRUFBMmTKh8npeXd1sFPzAohyZupSz6NLZymtZWoUOniwwYnMSjDw7AbNZQVGhHUaEd6WmOxB93Y93339Hj/gxif2z55z/sn6DTm4gYd4Z/vtqO/bGX28zOnHQgILiAwSPOErenfk/lR/4zje598nh9cCAXMtTvbZuRYuCNp0LQG004OJq4lKUjanEC51L0qmWqKLch/czl9ScctSforiIeHZXFosl136P7j1jbdwfWlSmwYxFNPCr44PvfK6dpbSG0ewEDh2fxiH9nzGbNH7zD7Uk/Zk/hRTuWDQypnGY2aUje58i+lc14dsUpTGU2FOdpLY7uCy7Y4uhRXms5aoPW1sw/FifQrEUZU8KDrfaoHvjfpXc1ualOrSWpd6p+K+vXr2fVqlWsXr2a9u3bExcXx7hx4/D29iYiIqLK8nq9Hr3+z/+QHznQlNHP9rKYNu4fh0lLduTLVW2q/2fWKKABO7v6P1zU2irY6RSU61ZtMmvq7OqA6ilE/vMsPR7OZdKTgZxPVa+YVqe0WEtpsRZH5wq63p/Lp3PVLazX0thcPr2pHmv87qwvU9wuJ17sHWIx7fV3kklNNLD+Q89aLfQA/j3yGf39cYtpm95oRdOAEu596RwuXmVo7cwk/eJEu345AFw4rSc3XY9Pl8JazVITVwq9d6sSpoSHkJ9jnU0MlRrxHfRULfaTJk1iypQpPPXUUwCEhoaSnJxMdHR0tcW+poqL7UhOstwYS0q05OXpSE5yprl3Ifc9eJbD+5uRm6OjqUcJTz5zirJSG/bv8az1PHC596+379VeyJ4tS/EPLiA/15asDAP/3efCiElJlJbakHnWQOjdufQelMnHc1vXSZ7qjJmTRq9Hs5kxwp/iAhua/O/IojBfS1mJei1BXe/PASDttBHvViWMikolNdHAD/+u/sqGuvb8lHT2/+RM1lk7jI5mej2aTcewAqY+Xf1llfXBGr87a8xUXKit0megpNiG/Oyq02uD3tGMZ5Bl847O3oy9a0Xl9M5PXmTr7JYYXSvQO5r5bqYPPl0K8Ol8tdhfPKOnrMiGgiw7yktsyPjf5YIegSXY6mpemAz2Jrz9rub09CnFP6SQ/FxbLmXaMfWDUwS2L2L6qLbY2Cg0aXr5stf8XFsqyhvtld1WSdViX1RUhI2N5Qah1Woxq9ToWlZqQ/tOFxk05DSOTmXkXNJz7EhTJr58H7k5dXP00aZDPvO+OFr5/MWo0wDEbGzGe1FBzJsQzPAJZ5j0djxOLhVkpuv5YoEf362tv5vqDIi4fE3//21IsJj+f+N9iFnvXm85rmfvZOL5SWk0bV5GQa4tu7Y2YcX/tcSkUmdK16YVTFqYjFuzCorytSSdMDD16QAO7aybGzLdCmv87qwxkzV6eFoqGpuWrBsdQEWZhsD78ug/K8VimW+i/Djz69Xta+kj7QAYt+MoTVrW/H4TbUILmb/magfCl/7f5fXHfNmUfy1sSdjfcgD48LtjFq97Y1gIR39V72ZSN2QGanKS5g4eCEej1PVF7X9g+PDh/Pjjjyxbtoz27dtz+PBhXnzxRUaMGMG8efNu+vq8vDxcXFzo0/pVbG3UPxV4hXLhktoRqjDlq3852vU0OutoO76WUqbODXlELdBY35HkzITqryxS08x296gdwUKFUsZ/StaTm5uLs3Pd7CBcqRW9O7yBrfbP14oKUynbj82v06x1RdUj+8WLFzNt2jRGjx5NZmYm3t7evPTSS7z55ptqxhJCCCEaFFWLvZOTEwsWLGDBggVqxhBCCNEYSAc9IYQQooFrxMXe+hq5hBBCCFGrpNgLIYRoHOp5IJwZM2ag0WgsHsHBV2/HXFJSQmRkJO7u7jg6OvL4449z/vz52v7UgBR7IYQQjUVNBsG58rhN7du3JyMjo/Kxa9euynnjx49n8+bN/Pvf/yY2Npb09HQGDx5cgw94Y9JmL4QQolFQYyAcW1tbmjdvXmV6bm4un376KatXr+bBBx8EYPny5YSEhLB3717++te//umc1ZEjeyGEEOI25OXlWTyuHY31eqdOncLb2xt/f3/Cw8NJSbl8Y6KDBw9SXl5Onz59KpcNDg7G19eXPXv21HpmKfZCCCEah1pqs/fx8bEYgTU6Orra1XXv3p0VK1awdetWlixZQlJSEvfddx/5+fmcO3cOnU6Hq6urxWs8PT05d+5crX90OY0vhBCicTArlwc3q8nrgdTUVIs76N1ogLZrh2vv2LEj3bt3x8/Pj/Xr12M01v6YC39EjuyFEEKI2+Ds7GzxuNXRWF1dXWnbti0JCQk0b96csrIycnJyLJY5f/58tW38NSXFXgghRONQz5feXa+goIDExES8vLzo2rUrdnZ2bN++vXJ+fHw8KSkphIWF1fSTViGn8YUQQjQSNS3Yt/faiRMnMmDAAPz8/EhPT2f69OlotVqGDRuGi4sLI0eOZMKECbi5ueHs7Myrr75KWFhYrffEhwZS7M2ZFzBrrGgEtTv4lor1SSmvUDtCVfLd3RpNTcYJrRsaO+v7OZsR0kPtCFV8mfiz2hEs5OWb8Qm++XJ3orS0NIYNG8bFixfx8PDg3nvvZe/evXh4eADw3nvvYWNjw+OPP05paSl9+/blww8/rJMs1vffIYQQQtSFer43/tq1a/9wvsFg4IMPPuCDDz7485lukRR7IYQQjYNZ4XZPxVd9/Z1JOugJIYQQDZwc2QshhGgcFPPlR01ef4eSYi+EEKJxaMTj2UuxF0II0ThIm70QQgghGio5shdCCNE4yGl8IYQQooFTqGGxr7Uk9U5O4wshhBANnBzZCyGEaBzkNL4QQgjRwJnNQA2ulTfLdfZ3hA5/yeWJUekEti/A3bOct14JYs+P7pXzJ8w7xd8GZ1m85sAOV6aNbFeHmfJ44oVrMr0cxJ4f3Srnh7+WSs/+F/DwKqO8XEPCMUc+f9eH+CNOdZbpeo88d4H+z17A06cMgOSTBla915wDPznXW4abGRJ5jpFR6Wz8xIOlM3xUzTJg+AWeeCUTN48KTv9m5MP/14L4OHvV8nToXsCTo7NoE1qEe/MKZoxoxZ6tLqrlsdbtyehg4rkJafTom42rezmJxx1Y+pYvJ//r2CgzffW+N6vm+tJ/ZAYjZiYDsHRya/67y4XsczoMDiaCuuXzzD9SaBlYUvm6rLM6PopqzbHdzhgczDzwRBbPRKWgbVTVxvo0qj+/wWjm9O8O/PBlM6Z9GF/tMvtjXXlvSmDl8/Kyuu3WYDCaOH3Cnh/+7cG0JSerzD+bZODDma05l2pAZzDz2PMZzF5xgpG9O5N7ya5Os12RlWHHZ9HenE3So9Eo/O3JbGZ8lkRk37YknzTWS4Y/0rZTIf3DL3D6N/Wz9ByYzYvT01k8pSW/H7LnsReymL36NCPvCyL3Yv18X9cz2Js5fdzAtjVuTP/sjCoZrmWt29O4uUm0alvM2xP8uXheR+9HLxC9Mp4XHwrl4nl1RtVUK1NCnAMxq5rhF1JoMd0/tJD7HruAR4syCnK0rHu3JbOeDuHDPYfRasFkgjnPBeParIw5Xx8n+7yOxeMCsLVTCJ+SWmd5b1kjPo2vage9/Px8xo0bh5+fH0ajkR49erB///46W9+BHU344j1fdse433CZ8jIbsi/oKh8FeXW7P3SzTD9v9iButyvnUg2knLLn4zl+ODiZaB1UVKe5rvVrjAv7/+NMepKes6cNrJjnRUmhDcFd6i/DjRjsTUxefIYFb/iSn6tVOw6DX7zA1tVu/LDOjZRTBhZNbklpsYa+wy6plunAT858Pt+L3SoezV/LGrcnnd7MvQ9f4tO5Phzb50xGsoF/LWxJerKeR57JbFSZigttWPBqIC/PP42ji8li3kPPZNL+r/k08ynFP7SIYZPSuJCuJytVD8CRWFfSThkZuyiR1u2L6PJgDk9NSmXr556Ul1nBsMhXin1NHncoVYv9qFGjiImJYeXKlRw9epSHHnqIPn36cPbsWdUydeyey5q9+/h42yHGzEzEybVctSzXs7Uz029oJgV5Wk7/rs5pYRsbhZ4Ds9Hbmzlx0EGVDNcaMzuVfdtdOLxL/SYFWzszbToWcWjn1SYWRdFweKcT7bqqv2Nkjaxle9LaKmhtoazUsiCVldjQvlt+o8r0ydTWdO2dQ6f78v5wuZIiG35a70Ez3xLcvS83ycQfdMQ3uAhXj6u/m3f1zKUo35ZUKzgL2Jipdhq/uLiYDRs28PXXX3P//fcDMGPGDDZv3sySJUv45z//WeU1paWllJaWVj7Py/vjjfF2HdzRhF+2uXM+TY+XbwnDX09h1icnmDAkFLNZvb3Su3tlM2XBSfRGM5cy7Zga0Y687Po9JdwquJgF35xCpzdTXGjDW6Nak3LKUK8Zrtdz4CUCQ4t4tX+wqjmucHYzobWFnCzLf6vsC7b4BJbe4FWNk7VtT8WFWn476MjTr6aTkmAk54IdDwy8SHCXAjKS1cmlRqZdX7tz+qgD8749esNltn7uycrZvpQUafEOKGb66hPY6S4f8eZk2eHiYXmAdKXw52TqAJV3euV2ufWvoqICk8mEwWC50RqNRnbt2lXta6Kjo3Fxcal8+PjUbkes2G+b8ut/3Dhz0oE9P7oz/cUQgjoV0LF7bq2u53Yd2etM5MCOvD6kAwd3uhK16CQubvV7xiEtUc/oh4J47ZG2bPmiKRMXJOPbpuTmL6wjHl5lvDIzjXmvtqK8VG4Xcaextu0J4O0J/qCB1b/GsTl+P4OGnyd2s7uqHbDrM9OFdB2fTfdj7OIEdIYbF7X7HrvA21uP8taXx/H2L+adV9pQVmIFp+hvgaKYa/y4U6l2ZO/k5ERYWBizZs0iJCQET09P1qxZw549ewgMDKz2NVFRUUyYMKHyeV5eXq0X/GudSzWQe8kWL78S4vbU2WpuqrRYS0aykYxk+D3OiU9+PEzfIZmsX9qi3jJUlNuQfuZyu1zCUXuC7iri0VFZLJqsTs/3wI5FNPGo4IPvf6+cprWF0O4FDByexSP+nev9bEzeJS2mCnD1qLCY3qRpBdlZjaov7E1Z2/YEkJFi4I2nQtAbTTg4mriUpSNqcQLnUvSNIlPifx3IvaBjUr/Qymlmk4bffnXi+xXNWXv6V7RacHA24eBswtu/hLZdCoho341ft7px36MXcfUoJyHO8kqBnKzLZyFdm5XVeubbpig1Ozq/g9vsVf0FWrlyJSNGjKBFixZotVq6dOnCsGHDOHjwYLXL6/V69Pr6+8dr2rwUJ9cKLmWq0xP3RmxsFOx06u5hamxQNUPcLide7B1iMe31d5JJTTSw/kNPVZpdKsptOPVfezrfm195aZtGo3DXvQV8s+LGnUKF+tvTtUqLtZQWa3F0rqDr/bl8OlfdSznrK1PHe3N578cjFtPefz2AFgHFPDY6HW11/V//12ftylVLQV0L+GpxC3Iv2OLS9PJO75EdLtg7VeDTprjWM4tbp2qxDwgIIDY2lsLCQvLy8vDy8mLo0KH4+/vXyfoM9ia8/a6eKvRsWYp/SCH5Obbk59oS/moqv2xz51KWHd6+JYx4I5n0ZAOHdrnWSZ5qM/mUVGbKy7HlqdFn+XV7Ey5l6nBuUs6AZ87h7lnGzu/rr3g8PyWd/T85k3XWDqOjmV6PZtMxrICpTwfUW4brFRdqSY637PBTUmxDfnbV6fXpq4+aMnFBKieP2BN/+PKldwZ7Mz+sdbv5i+uIwd6Ed+urR1XNfcrwb19Mfo6WrLP1vyNrjdsTQNf7cwBIO23Eu1UJo6JSSU008MO/mzaKTEZHM77BlgXZYDTj1KQC3+BiziXr2b3ZnU735+LsXs7FDB0bP/BGZzDT9cFsADr1zKFlm2IWjg3kuakpZGfaseZtHx6OOI+d3gqOipUattnLkX3NODg44ODgQHZ2Ntu2bWP+/Pl1sp42HQqYv+p45fOXpp4BIOYrD95/05/WQUX0eSwTBycTlzJ1HNrlyhcLfOr0Wvs2oQXMX/XbNZku37wiZoMHi6f54+NfTJ/HMnFxqyAv25aTRx2Z9FQHUk7VX29816YVTFqYjFuzCorytSSdMDD16QCLXufisthvmuDibuK5Sedo4lHB6eNGpoa3JueCOtfYA7TtVMzbGxIrn788Mx2AH9Y14Z3xvvWex1q3J3snE89PSqNp8zIKcm3ZtbUJK/6vJaYK9fqEWFMmnd7Mb786seWT5hTm2uLStJx23fOZ8/XxyqN4rRaiPv+dj6JaEzWwPQZ7Mw88mcVTE63gGnu4fAc8TQ3OIN3BbfYaRVFvV2Xbtm0oikJQUBAJCQlMmjQJg8HAzp07sbO7+Y9jXl4eLi4uPOgwDFuNFZ1qt8K9P3OxFZ5C01hhxzqz6ebLCNBYX4csjc6KfgOs2JeJsWpHsJCXb8YnOJ3c3FycnevmEtortaK3U3iNakWFUsb2/FV1mrWuqHpkn5ubS1RUFGlpabi5ufH4448ze/bsWyr0QgghxG2R0/jqGDJkCEOGDFEzghBCiEZCMZtRanAa/06+9M4Kz6MKIYQQojZZRQc9IYQQos7JaXwhhBCigTMroGmcxV5O4wshhBANnBzZCyGEaBwUBajJdfZ37pG9FHshhBCNgmJWUGpwGl/F29LUmBR7IYQQjYNipmZH9nLpnRBCCCGq8cEHH9CqVSsMBgPdu3dn37599Z5Bir0QQohGQTErNX7crnXr1jFhwgSmT5/OoUOH6NSpE3379iUzM7MOPuGNSbEXQgjROCjmmj9u07vvvssLL7zA888/T7t27Vi6dCn29vZ89tlndfABb+yObrO/0lmiQilXOcl1rLATh9na/kaAVe5rKjIQzq2xwoFwFOvLZI3y8q2r3Tm/4HKe+uj8VkF5je6pU8Hl39G8vDyL6Xq9Hr1eX2X5srIyDh48SFRUVOU0Gxsb+vTpw549e/58kD/hji72+fn5AOwo+lLlJOJPsb59InGrrPG7K1U7wJ3BJ1jtBNXLz8/HxcWlTt5bp9PRvHlzdp37rsbv5ejoiI+Pj8W06dOnM2PGjCrLXrhwAZPJhKenp8V0T09Pfv/99xpnuR13dLH39vYmNTUVJycnNDUccjMvLw8fHx9SU1OtZuhCyXRrrC2TteUByXSrJNOtqc1MiqKQn5+Pt7d3LaWrymAwkJSURFlZWY3fS1GUKvWmuqN6a3NHF3sbGxtatmxZq+/p7OxsNf9QV0imW2NtmawtD0imWyWZbk1tZaqrI/prGQwGDAZDna/nWk2bNkWr1XL+/HmL6efPn6d58+b1msUKG02FEEKIO59Op6Nr165s3769cprZbGb79u2EhYXVa5Y7+sheCCGEsGYTJkwgIiKCbt26cffdd7NgwQIKCwt5/vnn6zWHFPv/0ev1TJ8+3araXiTTrbG2TNaWByTTrZJMt8YaM1mroUOHkpWVxZtvvsm5c+e466672Lp1a5VOe3VNo9zJN/sVQgghxE1Jm70QQgjRwEmxF0IIIRo4KfZCCCFEAyfFXgghhGjgpNhjHcMPXmvHjh0MGDAAb29vNBoNmzZtUjVPdHQ0f/nLX3BycqJZs2Y8+uijxMfHq5ppyZIldOzYsfKmHmFhYXz//feqZrre3Llz0Wg0jBs3TrUMM2bMQKPRWDyCg9W/X+rZs2d55plncHd3x2g0EhoayoEDB1TL06pVqyp/J41GQ2RkpGqZTCYT06ZNo3Xr1hiNRgICApg1a1a93EP+j+Tn5zNu3Dj8/PwwGo306NGD/fv3q5pJ3FyjL/bWMvzgtQoLC+nUqRMffPCBahmuFRsbS2RkJHv37iUmJoby8nIeeughCgsLVcvUsmVL5s6dy8GDBzlw4AAPPvgggwYN4vjx46plutb+/ftZtmwZHTt2VDsK7du3JyMjo/Kxa9cuVfNkZ2dzzz33YGdnx/fff89vv/3GO++8Q5MmTVTLtH//fou/UUxMDABPPvmkapnmzZvHkiVLeP/99zlx4gTz5s1j/vz5LF68WLVMAKNGjSImJoaVK1dy9OhRHnroIfr06cPZs2dVzSVuQmnk7r77biUyMrLyuclkUry9vZXo6GgVU10FKBs3blQ7hoXMzEwFUGJjY9WOYqFJkybKJ598onYMJT8/X2nTpo0SExOj9OzZUxk7dqxqWaZPn6506tRJtfVXZ/Lkycq9996rdow/NHbsWCUgIEAxm82qZejfv78yYsQIi2mDBw9WwsPDVUqkKEVFRYpWq1W2bNliMb1Lly7K1KlTVUolbkWjPrK/Mvxgnz59KqepNfzgnSQ3NxcANzc3lZNcZjKZWLt2LYWFhfV+C8rqREZG0r9/f4vtSk2nTp3C29sbf39/wsPDSUlJUTXPN998Q7du3XjyySdp1qwZnTt35uOPP1Y107XKysr417/+xYgRI2o8wFZN9OjRg+3bt3Py5EkAjhw5wq5du+jXr59qmSoqKjCZTFXuMW80GlU/YyT+WKO+g541DT94pzCbzYwbN4577rmHDh06qJrl6NGjhIWFUVJSgqOjIxs3bqRdu3aqZlq7di2HDh2ymjbM7t27s2LFCoKCgsjIyGDmzJncd999HDt2DCcnJ1UynT59miVLljBhwgT+8Y9/sH//fl577TV0Oh0RERGqZLrWpk2byMnJYfjw4armmDJlCnl5eQQHB6PVajGZTMyePZvw8HDVMjk5OREWFsasWbMICQnB09OTNWvWsGfPHgIDA1XLJW6uURd7cfsiIyM5duyYVezFBwUFERcXR25uLl9++SURERHExsaqVvBTU1MZO3YsMTEx9T661o1cexTYsWNHunfvjp+fH+vXr2fkyJGqZDKbzXTr1o05c+YA0LlzZ44dO8bSpUutoth/+umn9OvXr06HXL0V69evZ9WqVaxevZr27dsTFxfHuHHj8Pb2VvXvtHLlSkaMGEGLFi3QarV06dKFYcOGcfDgQdUyiZtr1MXemoYfvBOMGTOGLVu2sGPHjlofWvjP0Ol0lUcTXbt2Zf/+/SxcuJBly5apkufgwYNkZmbSpUuXymkmk4kdO3bw/vvvU1pailarVSXbFa6urrRt25aEhATVMnh5eVXZIQsJCWHDhg0qJboqOTmZH3/8ka+++krtKEyaNIkpU6bw1FNPARAaGkpycjLR0dGqFvuAgABiY2MpLCwkLy8PLy8vhg4dir+/v2qZxM016jZ7axp+0JopisKYMWPYuHEj//nPf2jdurXakaplNpspLS1Vbf29e/fm6NGjxMXFVT66detGeHg4cXFxqhd6gIKCAhITE/Hy8lItwz333FPl0s2TJ0/i5+enUqKrli9fTrNmzejfv7/aUSgqKsLGxvInWqvVYjabVUpkycHBAS8vL7Kzs9m2bRuDBg1SO5L4A436yB6sZ/jBaxUUFFgceSUlJREXF4ebmxu+vr71nicyMpLVq1fz9ddf4+TkxLlz5wBwcXHBaDTWex6AqKgo+vXrh6+vL/n5+axevZqff/6Zbdu2qZIHLrdnXt+PwcHBAXd3d9X6N0ycOJEBAwbg5+dHeno606dPR6vVMmzYMFXyAIwfP54ePXowZ84chgwZwr59+/joo4/46KOPVMsEl3cWly9fTkREBLa26v80DhgwgNmzZ+Pr60v79u05fPgw7777LiNGjFA117Zt21AUhaCgIBISEpg0aRLBwcGq/maKW6D25QDWYPHixYqvr6+i0+mUu+++W9m7d6+qeX766ScFqPKIiIhQJU91WQBl+fLlquRRFEUZMWKE4ufnp+h0OsXDw0Pp3bu38sMPP6iW50bUvvRu6NChipeXl6LT6ZQWLVooQ4cOVRISElTLc8XmzZuVDh06KHq9XgkODlY++ugjtSMp27ZtUwAlPj5e7SiKoihKXl6eMnbsWMXX11cxGAyKv7+/MnXqVKW0tFTVXOvWrVP8/f0VnU6nNG/eXImMjFRycnJUzSRuToa4FUIIIRq4Rt1mL4QQQjQGUuyFEEKIBk6KvRBCCNHASbEXQgghGjgp9kIIIUQDJ8VeCCGEaOCk2AshhBANnBR7IYQQooGTYi9EDQ0fPpxHH3208vkDDzzAuHHj6j3Hzz//jEajIScn54bLaDQaNm3adMvvOWPGDO66664a5Tpz5gwajYa4uLgavY8Q4s+TYi8apOHDh6PRaNBoNJWj47311ltUVFTU+bq/+uorZs2adUvL3kqBFkKImlJ/tAch6sjDDz/M8uXLKS0t5bvvviMyMhI7OzuioqKqLFtWVoZOp6uV9bq5udXK+wghRG2RI3vRYOn1epo3b46fnx+vvPIKffr04ZtvvgGunnqfPXs23t7eBAUFAZCamsqQIUNwdXXFzc2NQYMGcebMmcr3NJlMTJgwAVdXV9zd3XnjjTe4fniJ60/jl5aWMnnyZHx8fNDr9QQGBvLpp59y5swZevXqBUCTJk3QaDQMHz4cuDwCW3R0NK1bt8ZoNNKpUye+/PJLi/V89913tG3bFqPRSK9evSxy3qrJkyfTtm1b7O3t8ff3Z9q0aZSXl1dZbtmyZfj4+GBvb8+QIUPIzc21mP/JJ58QEhKCwWAgODiYDz/88LazCCHqjhR70WgYjUbKysoqn2/fvp34+HhiYmLYsmUL5eXl9O3bFycnJ3bu3Mkvv/yCo6MjDz/8cOXr3nnnHVasWMFnn33Grl27uHTpEhs3bvzD9T733HOsWbOGRYsWceLECZYtW4ajoyM+Pj5s2LABgPj4eDIyMli4cCEA0dHRfPHFFyxdupTjx48zfvx4nnnmGWJjY4HLOyWDBw9mwIABxMXFMWrUKKZMmXLbfxMnJydWrFjBb7/9xsKFC/n444957733LJZJSEhg/fr1bN68ma1bt3L48GFGjx5dOX/VqlW8+eabzJ49mxMnTjBnzhymTZvG559/ftt5hBB1ROVR94SoExEREcqgQYMURVEUs9msxMTEKHq9Xpk4cWLlfE9PT4vhQleuXKkEBQUpZrO5clppaaliNBqVbdu2KYqiKF5eXsr8+fMr55eXlystW7asXJeiWA5rGx8frwBKTExMtTmvDGecnZ1dOa2kpESxt7dXdu/ebbHsyJEjlWHDhimKoihRUVFKu3btLOZPnjy5yntdD1A2btx4w/lvv/220rVr18rn06dPV7RarZKWllY57fvvv1dsbGyUjIwMRVEUJSAgQFm9erXF+8yaNUsJCwtTFEVRkpKSFEA5fPjwDdcrhKhb0mYvGqwtW7bg6OhIeXk5ZrOZp59+mhkzZlTODw0NtWinP3LkCAkJCTg5OVm8T0lJCYmJieTm5pKRkUH37t0r59na2tKtW7cqp/KviIuLQ6vV0rNnz1vOnZCQQFFREX/7298sppeVldG5c2cATpw4YZEDICws7JbXccW6detYtGgRiYmJFBQUUFFRgbOzs8Uyvr6+tGjRwmI9ZrOZ+Ph4nJycSExMZOTIkbzwwguVy1RUVODi4nLbeYQQdUOKvWiwevXqxZIlS9DpdHh7e2Nra7m5Ozg4WDwvKCiga9eurFq1qsp7eXh4/KkMRqPxtl9TUFAAwLfffmtRZOFyP4TasmfPHsLDw5k5cyZ9+/bFxcWFtWvX8s4779x21o8//rjKzodWq621rEKImpFiLxosBwcHAgMDb3n5Ll26sG7dOpo1a1bl6PYKLy8vfv31V+6//37g8hHswYMH6dKlS7XLh4aGYjabiY2NpU+fPlXmXzmzYDKZKqe1a9cOvV5PSkrKDc8IhISEVHY2vGLv3r03/5DX2L17N35+fkydOrVyWnJycpXlUlJSSE9Px9vbu3I9NjY2BAUF4enpibe3N6dPnyY8PPy21i+EqD/SQU+I/wkPD6dp06YMGjSInTt3kpSUxM8//8xrr71GWloaAGPHjmXu3Lls2rSJ33//ndGjR//hNfKtWrUiIiKCESNGsGnTpsr3XL9+PQB+fn5oNBq2bNlCVlYWBQUFODk5MXHiRMaPH8/nn39OYmIihw4dYvHixZWd3l5++WVOnTrFpEmTiI+PZ/Xq1axYseK2Pm+bNm1ISUlh7dq1JCYmsmjRomo7GxoMBiIiIjhy5Ag7d+7ktddeY8iQITRv3hyAmTNnEh0dzaJFizh58iRHjx5l+fLlvPvuu7eVRwhRd6TYC/E/9vb27NixA19fXwYPHkxISAgjR46kpKSk8kj/9ddf59lnnyUiIoKwsDCcnJx47LHH/vB9lyxZwhNPPMHo0aMJDg7mhRdeoLCwEIAWLVowc+ZMpkyZgqenJ2PGjAFg1qxZTJs2jejoaEJCQnj44Yf59ttvad26NXC5HX3Dhg1s2rSJTp06sXTpUubMmXNbn3fgwIGMHz+eMWPGcNddd7F7926mTZtWZbnAwEAGDx7M3//+dx566CE6duxocWndqFGj+OSTT1i+fDmhoaH07NmTFStWVGYVQqhPo9yoZ5EQQgghGgQ5shdCCCEaOCn2QgghRAMnxV4IIYRo4KTYCyGEEA2cFHshhBCigZNiL4QQQjRwUuyFEEKIBk6KvRBCCNHASbEXQgghGjgp9kIIIUQDJ8VeCCGEaOD+PyiJ9gmwNmy2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Predictions over test set\n",
    "predictions = model_f.predict(x_test)\n",
    "\n",
    "y_pred_classes = decode(predictions)\n",
    "y_true_classes = decode(y_test)\n",
    "\n",
    "cm = confusion_matrix(y_true_classes, y_pred_classes)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "adverbs",
   "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}