11. Convolutional Neural Networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Starting with a Simple Example: Digits Data from sklearn\n",
    "Let's start our adventure into convolutional networks with a simple example of the digits dataset. This was not always such a simple example, but modern day computing power and open source tools has made it a significantly more tractable problem. \n",
    "\n",
    "Note that we will not be using the `tf.data` format for these example since they are sufficiently small enought to work with. \n",
    "\n",
    "Moreover, we will employ the sequential API in keras, which assumes that the output from the previous operation becomes the input to the next operation. Thus, we can build feedforward networks by simply adding operations in a list. Specifically, we will use the `model.add( )` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow version: 2.16.2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1437, 8, 8, 1)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "\n",
    "from sklearn import datasets, metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "print('Tensorflow version:',tf.__version__)\n",
    "\n",
    "tf.random.set_seed(2)\n",
    "np.random.seed(0) # using this to help make results more reproducible\n",
    "tf.config.list_physical_devices('GPU')\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "\n",
    "# NORMALIZATION, SO IMPORTANT, Like we showed in Neural Network Lab\n",
    "X = digits.data.astype(np.float32)/16.0 - 0.5\n",
    "y = digits.target.astype(np.int32)\n",
    "\n",
    "X = X.reshape((X.shape[0],8,8,1)) # reshape as images\n",
    "\n",
    "# Split it into train / test subsets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Split X_train again to create validation data\n",
    "#X_train, X_val, y_train, y_val = train_test_split(X_train,y_train,test_size=0.2)\n",
    "\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.config.list_physical_devices('GPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1020x690 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What do these images look like?\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# a helper plotting function\n",
    "def plot_gallery(images, titles, h, w, n_row=3, n_col=6):\n",
    "    \"\"\"Helper function to plot a gallery of portraits\"\"\"\n",
    "    plt.figure(figsize=(1.7 * n_col, 2.3 * n_row))\n",
    "    plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)\n",
    "    for i in range(n_row * n_col):\n",
    "        plt.subplot(n_row, n_col, i + 1)\n",
    "        plt.imshow(images[i].squeeze(), cmap=plt.cm.gray)\n",
    "        plt.title(titles[i], size=12)\n",
    "        plt.xticks(())\n",
    "        plt.yticks(())\n",
    "\n",
    "plot_gallery(X_train, y_train, 8, 8) # defaults to showing a 3 by 6 subset of the images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "____\n",
    "# A very Simple ConvNet Versus a Raw Pixel Input MLP\n",
    "Wouldn't it be great if we did not need to specify the correct filters (like in DAISY)? What if we could just let the weights of the convolution be found through neural network training methods. Of course, we can! Let's do one example with a simple CNN architecture and compare it to the performance of a pixel wise MLP.\n",
    "\n",
    "In general, the flattened images placed through a MLP can be quite accurate (as we have seen in the past). Even so, using convolitional filters and pooling should provide us with some better resilience to small perturbations in the images. \n",
    "\n",
    "**BONUS**: Let's start using the sequential API, rather than the funcitonal API. Also, we can use numpy matrices instead of the tf.data format for smaller examples. The format will mean that the models are not quite as fast for loading data, but since everythign fits in memory its not a huge concern. \n",
    "\n",
    "Note that, since we are not using tf.data, we need to normalize the input data directly, rather than as part of th model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.5.0'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Reshape\n",
    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "keras.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1437, 8, 8, 1)\n",
      "(360, 8, 8, 1)\n"
     ]
    }
   ],
   "source": [
    "NUM_CLASSES = 10\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eclarson/miniconda3/envs/mlenv3_11/lib/python3.11/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
      "2024-11-18 08:05:36.651586: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M2\n",
      "2024-11-18 08:05:36.651653: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 8.00 GB\n",
      "2024-11-18 08:05:36.651793: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 2.67 GB\n",
      "2024-11-18 08:05:36.652054: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2024-11-18 08:05:36.652500: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n",
      "2024-11-18 08:05:37.467072: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 23.1 s, sys: 14.5 s, total: 37.6 s\n",
      "Wall time: 30.3 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x3045f4690>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# CREATE A BASELINE MLP WITH THREE LAYERS\n",
    "\n",
    "# since this is not binary, we should go ahead and one-hot encode the inputs\n",
    "y_train_ohe = keras.utils.to_categorical(y_train, NUM_CLASSES)\n",
    "y_test_ohe = keras.utils.to_categorical(y_test, NUM_CLASSES)\n",
    "\n",
    "# make a 3 layer keras MLP\n",
    "mlp = Sequential()\n",
    "mlp.add( Flatten() ) # make images flat for the MLP input\n",
    "mlp.add( Dense(input_dim=1, units=30, \n",
    "               activation='relu') )\n",
    "mlp.add( Dense(units=15, activation='relu') )\n",
    "mlp.add( Dense(NUM_CLASSES) )\n",
    "mlp.add( Activation('softmax') )\n",
    "\n",
    "mlp.compile(loss='mean_squared_error',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "mlp.fit(X_train, y_train_ohe, \n",
    "        batch_size=32, epochs=150, \n",
    "        shuffle=True, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eclarson/miniconda3/envs/mlenv3_11/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)       │            <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,570</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)       │            \u001b[38;5;34m80\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m16\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_1 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m16\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m2,570\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_2 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,650</span> (10.35 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,650\u001b[0m (10.35 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,650</span> (10.35 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,650\u001b[0m (10.35 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "You must install pydot (`pip install pydot`) for `plot_model` to work.\n",
      "CPU times: user 45.2 ms, sys: 22.7 ms, total: 67.8 ms\n",
      "Wall time: 343 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# CREATE A CNN WITH ONLY ONE CONVOLUTIONAL LAYER AND ONE OUTPUT LAYER\n",
    "\n",
    "# make a CNN with conv layer and max pooling\n",
    "cnn = Sequential() \n",
    "cnn.add( Conv2D(filters=16, kernel_size= (2, 2), padding='same', \n",
    "                input_shape=(8,8,1),\n",
    "               ) )\n",
    "\n",
    "cnn.add( MaxPooling2D(pool_size=(2, 2)) )\n",
    "cnn.add( Activation('relu') )\n",
    "# add one layer on flattened output\n",
    "cnn.add( Flatten() )\n",
    "cnn.add( Dense(NUM_CLASSES) )\n",
    "cnn.add( Activation('softmax') )\n",
    "\n",
    "print(cnn.summary())\n",
    "\n",
    "plot_model(\n",
    "    cnn, to_file='model.png', show_shapes=True, show_layer_names=True,\n",
    "    rankdir='LR', expand_nested=False, dpi=96\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1437, 8, 8, 1)\n",
      "CPU times: user 21.1 s, sys: 14.3 s, total: 35.4 s\n",
      "Wall time: 27 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x306396f90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "# the shape of input (batch x h x w x channels)\n",
    "print(X_train.shape)\n",
    "\n",
    "# Let's train the model \n",
    "cnn.compile(loss='mean_squared_error',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "cnn.fit(X_train, y_train_ohe, \n",
    "        batch_size=32, epochs=150, \n",
    "        shuffle=True, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics as mt\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "def compare_mlp_cnn(cnn, mlp, X_test, y_test, labels='auto'):\n",
    "    plt.figure(figsize=(15,5))\n",
    "    if cnn is not None:\n",
    "        yhat_cnn = np.argmax(cnn.predict(X_test), axis=1)\n",
    "        acc_cnn = mt.accuracy_score(y_test,yhat_cnn)\n",
    "        plt.subplot(1,2,1)\n",
    "        cm = mt.confusion_matrix(y_test,yhat_cnn)\n",
    "        cm = cm/np.sum(cm,axis=1)[:,np.newaxis]\n",
    "        sns.heatmap(cm, annot=True, fmt='.2f',xticklabels=labels,yticklabels=labels)\n",
    "        plt.title(f'CNN: {acc_cnn:.4f}')\n",
    "    \n",
    "    if mlp is not None:\n",
    "        yhat_mlp = np.argmax(mlp.predict(X_test), axis=1)\n",
    "        acc_mlp = mt.accuracy_score(y_test,yhat_mlp)\n",
    "        plt.subplot(1,2,2)\n",
    "        cm = mt.confusion_matrix(y_test,yhat_mlp)\n",
    "        cm = cm/np.sum(cm,axis=1)[:,np.newaxis]\n",
    "        sns.heatmap(cm,annot=True, fmt='.2f',xticklabels=labels,yticklabels=labels)\n",
    "        plt.title(f'MLP: {acc_mlp:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_mlp_cnn(cnn,mlp,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eclarson/miniconda3/envs/mlenv3_11/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"3by3_kernel\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"3by3_kernel\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)       │           <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,570</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)       │           \u001b[38;5;34m160\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_3 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m16\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m2,570\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,730</span> (10.66 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,730\u001b[0m (10.66 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,730</span> (10.66 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,730\u001b[0m (10.66 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n",
      "CPU times: user 21.8 s, sys: 14.5 s, total: 36.3 s\n",
      "Wall time: 27.1 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# changes: \n",
    "#    1. increased kernel size\n",
    "cnn2 = Sequential(name='3by3_kernel')\n",
    "cnn2.add( Conv2D(filters=16, kernel_size= (3, 3), \n",
    "                padding='same', input_shape=(8,8,1),\n",
    "                ) )\n",
    "cnn2.add( Activation('relu') )\n",
    "cnn2.add( MaxPooling2D(pool_size=(2, 2)) )\n",
    "# add one layer on flattened output\n",
    "cnn2.add( Flatten() )\n",
    "cnn2.add( Dense(NUM_CLASSES, activation='softmax') )\n",
    "\n",
    "# Let's train the model \n",
    "cnn2.compile(loss='mean_squared_error',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "input_shape = [None, 8,8,1]\n",
    "cnn2.build(input_shape)\n",
    "print(cnn2.summary())\n",
    "\n",
    "cnn2.fit(X_train, y_train_ohe, \n",
    "        batch_size=32, epochs=150, \n",
    "        shuffle=True, verbose=0)\n",
    "\n",
    "compare_mlp_cnn(cnn2,mlp,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"two_layer_cnn\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"two_layer_cnn\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │           \u001b[38;5;34m320\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_4 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │         \u001b[38;5;34m9,248\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_5 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_3 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,290\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_6 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">10,858</span> (42.41 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m10,858\u001b[0m (42.41 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">10,858</span> (42.41 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m10,858\u001b[0m (42.41 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 911us/step\n",
      "CPU times: user 25.1 s, sys: 15.5 s, total: 40.5 s\n",
      "Wall time: 32.3 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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YofXxrbqu++LFC9y+fTtdN3EEgNy5c+OHH37AmzdvMHnyZI1l789WNWvWLMUjax88eIDbt29DqVRqtMfGxqZ4j8jISHTv3h0qlQqzZs1KNcv7GzW+P3uYmtQ+h40bN2Lbtm0wNzdP8fSXn376CcC7y/nevn2rbvfz88O2bdvg6OiI9u3bp/qeREREJC/Tp0/Hrl27cOTIEeTJk8egWZRKJW7fvo0HDx6ke50ff/wRFhYW6nruvejoaPz+++8AgIEDB2qs8/LlS9y+fVv98Jv3VCqV1tpzw4YNWL9+PerUqYN27dppLBs6dCiAd/Xrx4NGMTExmDZtGgBoDN5ZWlqib9++ePv2LSZOnAhJktTLrl+/jrVr18LMzAwdOnTQ+vumt44kIjGkfCY4UTYxfvx4JCUlYcqUKahRowbq1KkDV1dX5M6dG6GhoThz5gzu3bsHV1fXdG3Pzs4O48ePx6+//prhLOlZd9y4cVi3bh08PT3Ru3fvdG33999/x6lTpzBv3jxcu3YNNWvWxK1bt7Bnzx44OTlhyZIlKdZp3Lgxnjx5gkePHqFo0aLq9qlTp+Lw4cOoXbs2nJyc8Pz5c+zduxcxMTH4888/Ux2M+/hGjZ+bkt6hQweYmZnB1dUVBQsWxNu3b+Hn54dLly7BzMwMK1as0MgEAF26dME///wDLy8vVK1aFa1bt0ZkZCS2bduG5ORk9aOJiYiIiACgcOHCKFy4cIbXW7VqFU6dOqV1Wbdu3dC0adMMb/P58+coV64cihQpojFglJZixYphzpw5GDZsGFxdXdG5c2eYm5vDy8sLz549wy+//JLi3ke7du3Cjz/+iF69emHt2rXq9ri4ODg7O+Pbb79FiRIlYGpqivPnz+PixYsoV64cduzYkeLBLu7u7vjxxx/h6emJihUrokWLFkhOTsb+/fvx/PlztG/fHj169NBYZ9q0aThz5gwWLFiAixcvom7duggNDcU///yjvlG4tpufZ6SOJCJBSETZ3M2bN6UhQ4ZI5cuXl/LkySMpFArJxcVFat68ubRq1Srp7du3Gv0BSAUKFNC6rbdv30pFixaVAEgAJKVSqbd1e/XqJQGQPD09M/T7RUZGSsOGDZMKFy6s/t1+/PFH6enTp1r7FylSRAIgPXr0SKN9//79UsOGDSVHR0dJoVBIzs7OUocOHaSLFy+m+f4HDx6UAEhubm6fzTpz5kypSZMmUsGCBaWcOXNKFhYWUvHixaXevXtL165dS3U9pVIpzZs3T6pQoYKUM2dOycbGRmrRooV0/vz5z74nERERZV9p1V6fmjBhgtZa630NltZr/vz5kiRJkoeHhwRA8vDwSNd7Pnr0SAIgFSlSJP2/1H/27t0rff3111Lu3LklS0tLydXVVVq7dq3Wvp6enhIAqVevXhrtiYmJUp8+faTSpUtLlpaWkqWlpVS5cmVpxowZ0ps3b1J9b5VKJf3999+Sq6urZGlpKeXKlUuqXr26tHjxYik5OVnrOq9evZLGjx8vlSpVSjI3N5fy5s0rffvtt9KRI0dSfZ+M1JFEJAYTSfpoPiMREREREREREVEm8J5MRERERERERESkMw4yERERERERERGRzjjIREREREREREREOuPT5YiIiGTq5s2b2Lt3Lx49eoTo6GiMGjUKNWvWTHOdGzduYP369Xj69Cns7e3Rvn17NGjQIGsCExEREVG6GKrO40wmIiIimUpISEDRokXRt2/fdPUPCwvDzJkzUb58ecyePRstW7bE8uXLce3atS8blIiIiIgyxFB1HmcyERERyVTVqlVRtWrVdPc/evQonJyc0LNnTwBAwYIFcfv2bRw4cABVqlT5QimJiIiIKKMMVedxJhMREVE2olQqERcXp/FSKpV62fa9e/dQsWJFjbbKlSvj7t27etk+EREREaVOhDrPaGYyKSMeGjpCuuTKX9/QEYiIKAskJT7PsvfS57+Bu05chpeXl0Zbhw4d0KlTJ523HRMTg7x582q05c2bF/Hx8UhMTIS5ubnO70HZE+s8IiIyNllV68mtzjOaQSYiIiLSnbu7O1q1aqXRplAoDJSGiIiIiPRFhDqPg0xERESGpkrW26YUCsUXKzZsbGzw8uVLjbaXL18iV65cnMVEREREpI3M6jwOMhERERmapDJ0gnQpVaoUrl69qtEWGBiI0qVLGygRERERkZGTWZ3HG38TERHJ1Nu3b/H48WM8fvwYwLtH1z5+/BgREREAgM2bN2Px4sXq/k2bNkVYWBg2btyI58+f48iRI7h48SJatmxpiPhERERElApD1XmcyURERGRoKsOc4Xrw4AGmTJmi/nn9+vUAgG+++QaDBw9GdHS0uhABACcnJ4wdOxbr1q3DwYMHYW9vj4EDB2bosbZEREREsiKzOs9EkiRJL7+BjvjUESIiMiZZ+XS5xOAbetuWef7yetsWkb6wziMiImOTVbWe3Oo8Xi5HREREREREREQ64+VyREREhmagadRERERE9IXJrM7jIBMREZGhCfLUESIiIiLKIJnVebxcjoiIiIiIiIiIdMaZTERERIamSjZ0AiIiIiL6EmRW53GQiYiIyNBkNo2aiIiISDZkVufxcjkiIiIiIiIiItKZEINMl69dx+BfPdCwTXdUqNsC3mcufHadS/6B6PjjEFRt0BotOvXB7gPHUvTZsnMfmrbvhWoN26Br/xG4fvOOXvL+NLAX7t/1wevYB7hwbh9quFZJs3/79q0QdP00Xsc+wFX/42jRvFGKPpM9RuHpE3+8enkfRw5tRcmSxZjTyHKKlJU55ZlTpKyi5NQblUp/LyLBsM6T93FUlJwiZWVOeeYUKasoOfVGZnWeEINM8fFvUaZkcUz4ZVC6+j8LDsHg0ZNQs1pleK1dgh86tYPHrAU473tF3efQ8dOYvWglfurTHTvWLEKZksUw4OeJiIyO0Slrx45tMHeOB6ZNn4catZojIPAmDh7YBEdHe639a7u5YtOGJfD03ALXms2wd+8R7PRajfLly6j7jB41CEMG98GgIWNRp15rvImLw8H9m2BhYcGcRpJTpKzMKc+cImUVJac+SZJKby8i0bDOk+9xVJScImVlTnnmFCmrKDn1SXZ1nmQkEsMfpOtVunRp6dDODWn2mTllvPRds2812ob91E/68Yeu6p/bt2steYz9Rf3z29B7Ur06taWl8/5Ic9s5FPnTfPn6XpEWL1mj/tnMvID07FmwNG78DK39t23fI+3ff0yjzcfnirR8xXr1z8HBIdLoX6eof7a1LyPFx8dLXbsP/Gwe5syanCJlZU555hQpq7HkzEpv71/U24vIGLHOk+dxNLvkFCkrc8ozp0hZjSlnVpFbnZfhmUyxsbHYs2cP5syZgwkTJmDChAmYM2cO9u7di9jY2C8xDpZhAUG34fbJlLu6taojIOgWAECpVOLmnXtwq/Ghj6mpKdxcq6j7ZIZCoUC1apXgfeKsuk2SJHifOAc3t+pa13GrVV2jPwAcPXZK3b9YscLIl88Z3ifOqZfHxr7CpUtX4VZL+zaZM2tzipSVOeWZU6SsouTUO5lNoybjxTovdaIcn5iT/4Yyp7xyipRVlJx6J7M6L0ODTPfv38fw4cNx6NAhWFpaoly5cihXrhwsLS1x6NAhjBgxAg8ePPhSWdMtIioa9na2Gm32tjZ4/SYObxMSEB0Ti+RkVco+draIiIrO9Ps6ONjBzMwMYaERGu1hYeFwcXbUuo6LiyNCw8I12kJDI9T9XZyd/mv7pE9YBFxcnJjTCHKKlJU55ZlTpKyi5NQ7SaW/F1Emsc5LmyjHJ+bkv6HMKa+cImUVJafeyazOM8tIZ09PT9SuXRv9+/eHiYmJxjJJkvD3339jzZo1mDFjRprbUSqVUCqVGm2KjAQhIiIiIr1inUdERES6ytBMpsePH6Nly5YpCg8AMDExQcuWLfH48ePPbmfXrl3o3bu3xkufHOxsEfnJmarI6BjktrJETgsL2NpYI0cO05R9oqLh8MlZr4yIiIhCUlISnJwdNNqdnBwR8snI6nshIeFwdtIctXV2dlD3DwkN+6/tkz5ODggJCWNOI8gpUlbmlGdOkbKKklPvVMn6exFlEuu8tIlyfGJO/hvKnPLKKVJWUXLqnczqvAwNMtnY2OD+/fupLr9//z5sbGw+ux13d3esXbtW46VPlSuUhe+VAI22i35XUblCOQDvrgX9qkwp+F6+pl6uUqnge+Wauk9mKJVK+PsHolHDeuo2ExMTNGpYDz4+V7Su4+N7BY0a1dNoa9L4a3X/R4/+xYsXoRrbzJMnN2rWrAofX+3bZM6szSlSVuaUZ06RsoqSU+9kNo2ajBPrvLSJcnxiTv4bypzyyilSVlFy6p3c6ryM3CX80KFDUrdu3aQ1a9ZIfn5+0t27d6W7d+9Kfn5+0po1a6Tu3btLhw8fztQdyNN60kfMkyAp8PxRKfD8Ual06dLSqkVzpcDzR6Un1y9KieEPpFlTJ0q/DPtJ3f9hwHmpcqWK0h+Tx0u3L5+S1q9YKJUrV1Y6uX+7us+eLWukChXKSzvWLZdu+Z2UJoweLrlWryYF37ms01NHunQbKMXHx0u9+wyXylf8WlqxcoMUFRUt5StQScqhyC+t37BDmjnrL3X/evXbSImJidKo0VOkryrUl6ZMnSslJCRIlao0VPcZO266FBUVLbV17yVVrtpI2r3nkPTgwWPJMnexTN/Vnzn1m1OkrMwpz5wiZTWWnFnp7c0TensRZRbrPNZ5zCl+VuaUZ06RshpTzqwitzovQ/dkat68OaytrXHgwAEcPXoUqv/ubm5qaorixYtj0KBBqFOnjt4HwoJu30OfoWPUP89etBIA0LZFE8yY+AsiIqPwIvTDVLiC+V2wZM5UzP5rBTbu2A1nRwdMGTMCdT+6u3yLJt8gOuYlFq/aiIioKJQtVQLL/5ym0zRqANixYy8cHewwedIouLg4IiDgBlq26oGwsHc3NytcKL96vwHARZ/L6NFzCKZO+RXTp43BvfuP0L5DX9y4cUfdZ87cpbCyssTypbNhY2ON8+f90LJ1DyQkJDCnkeQUKStzyjOnSFlFyalXgjwthLI31nmfJ8rxiTn5byhzyiunSFlFyalXMqvzTCRJkjKzYlJSEl69egUAyJMnD8zMMjRelYIy4qFO62eVXPnrGzoCERFlgaTE51n2XglBx/S2LYsK3+ptWyRfrPOIiCi7y6paT251XqYrBjMzM9ja6nY2iIiIiIiMD+s8IiIiygzdTksRERGR7mQ2jZqIiIhINmRW53GQiYiIyMAkSYxH0hIRERFRxsitzjM1dAAiIiIiIiIiIhIfZzIREREZmiSvadREREREsiGzOo+DTERERIYms2v1iYiIiGRDZnUeL5cjIiIiIiIiIiKdcSYTERGRoclsGjURERGRbMiszuMgExERkaGp5PXUESIiIiLZkFmdx8vliIiIiIiIiIhIZ5zJREREZGgym0ZNREREJBsyq/M4yERERGRoMnvqCBEREZFsyKzO4+VyRERERERERESkM85kIiIiMjSZTaMmIiIikg2Z1XlGM8iUK399Q0dIlzcBGw0dIV2sKvcwdASibCOnmbmhI2Qrb5MSDR3B+MhsGjXJj23hxoaOkC6vLy4xdIR0yV17sKEjEKVJpNqJdYl+ifTZZxmZ1Xm8XI6IiIiIiIiIiHRmNDOZiIiIZEtmZ7iIiIiIZENmdR4HmYiIiAxMkpINHYGIiIiIvgC51Xm8XI6IiIiIiIiIiHTGmUxERESGJrNp1ERERESyIbM6j4NMREREhiazR9sSERERyYbM6jxeLkdERERERERERDrjTCYiIiJDk9k0aiIiIiLZkFmdx0EmIiIiQ5PZNGoiIiIi2ZBZncfL5YiIiIiIiIiISGecyURERGRoMptGTURERCQbMqvzOMhERERkaDKbRk1EREQkGzKr84S5XO6ngb1w/64PXsc+wIVz+1DDtUqa/du3b4Wg66fxOvYBrvofR4vmjVL0mewxCk+f+OPVy/s4cmgrSpYsppesWw+eQvP+4+HacQi6jZ6J63cfpdpXmZSM5dsO4LsBE+HacQg6jJiGc/43Uu2/eudhVGo3ELNWbdc5pyj7VJScImVlTv3m/N+AH3Dj1llERN3GydO7UN21cpr93d2/g//V44iIug3fS4fQtFkDjeXjJwyH/9XjCA2/gafPr2Hf/g1wrVFF55wiZRXlsyci/RHl+LT16AW0GDYTNXpNQPffFuP6/aep9lUmJWP5P8fRcsQs1Og1AR3HLsD5gDsafa7ceoihc9aiyaDpqNxtDE74pV4HZoQox1FRcoqUVZSconznRdmfImUV5bOnzBFikKljxzaYO8cD06bPQ41azREQeBMHD2yCo6O91v613VyxacMSeHpugWvNZti79wh2eq1G+fJl1H1GjxqEIYP7YNCQsahTrzXexMXh4P5NsLCw0Cnr4XOXMWeNFwZ2aYVt88ajTNGCGDhlESJjYrX2X7xpD7yOnMG4/p2xe5EHOjb7GiNnLseth/+m6Bt07zF2HDmL0kUL6JQREGefipJTpKzMqd+c7du3xB8zJ+CP3xeiXp1WCLp+C7v3rEs1Z61a1eC5biHWrduOurVbYv/+Y9i6bQW++qq0us+9e4/w888eqFWjOZo26Ygn/z7Hnr3r4OBgl+mcImUV5bPXK5VKfy8iAYlyfDp8MQBzN+7HgO8bY+uMYShTOB9+mrkakS9fa+2/eMcReHn7Ymyvttg1+2d0bFwLI+etx63Hz9V94hMSUaZIPoz7sV2mc31KlOOoKDlFyipKTlG+86LsT5GyivLZ65XM6jwTSZIkQ4cAADPz1AdOLpzbB7/LARg+YiIAwMTEBI8f+mHJUk/MnrMkRf/Nm5bBytISbd17qdvOn92HawE3MHjIWADA0yf+mL9gBebNXwEAsLbOg+Bn19Cn30hs37431SxvAjam+Xt0Gz0TFUoVwfj/dQUAqFQqNO03Dl1bNkTf9s1T9G/84xj079gCXb5roG4bOXMFcloo8MfIPuq2uPi36PzL75gwoCtWbj+IMsUKYUy/TqnmsKrcI82cxrRPs0NOkbIyZ8Zz5jQzT3XZydO74H8lEL/87KHOeefeBSxftg7z/lyeov+69YtgaZULHdv3U7edOPUPrgfexPBhE7W+R548ufEi9Dpafdcdp05dSDXL5xhL1rdJiWnmNJbPPinxudb2LyH+wAK9bStXyxF62xaRvuS2TPvMt7EcnyLOzk8zZ/ffFqN88YIY/9+AkEqlQtOhf6Brszro26Zhiv5NBk1Hv3aN0KVpHXXbz/M3wMJcgT8Gd0nRv3K3MZg/sica1SifZo7ctQenudxYjqOfI0pOkbIaS860aifAeL7zQNp1ibHsz/Qwlqwiffav41K/4kif5FbnGf1MJoVCgWrVKsH7xFl1myRJ8D5xDm5u1bWu41arukZ/ADh67JS6f7FihZEvnzO8T5xTL4+NfYVLl67CrZb2baaHUpmEWw/+hVulcuo2U1NT1KpcDgF3HmpdJzEpCeYKhUZbTgsFrt68r9E2Y+VW1K9eAW6Vy0FXouxTUXKKlJU59Z+zatUKOHnywzYlScLJE+dRs1Y1revUrFUVJ0+c12jzPn4GNWtq769QKPBjn66IiYnF9eu3MpVTpKyifPZEpD+iHJ+USUm49eg53CqUUreZmprCrUJJBN5LOQMdABKTkmGu0LwFqoW5AtfuPM5UhvQQ5TgqSk6RsoqUU4TvvCj7U6Ssonz2pBujH2RycLCDmZkZwkIjNNrDwsLh4uyodR0XF0eEhoVrtIWGRqj7uzg7/df2SZ+wCLi4OGU6a/Sr10hWqWBvY63Rbp83DyKitV8uV6fKV9iw9zieBIdCpVLh4rWb8L54FeEf9T901g+3HvyL4T+4Zzrbx0TZp6LkFCkrc+o3p72DbSo5I+CcSk5nZ0eEh32+f/MWjRASFoTI6NsYMrQP2rT+AZGR0ZnKKVJWUT57vZNU+nsRCUaU41P0q7h3dV7e3Jr58+ZBRMwrrevUqVQaGw6exZMXEe/qvOt3ccIvCOGp3EZBH0Q5joqSU6SsouQU5Tsvyv4UKason73eyazO0/sgU0REBJYuXZpmH6VSibi4OI2XXI3p1wmF8zmh7ZDJqN5hCH5fuQ1tG9eBqakJACAkPAqzVm3HzJ/7wMJc8ZmtEVF2ceb0RdRxa4nGDdvj2LHTWL9hcarXqhuaSFmNlsyu1SdxiVbnGfr49GvP1iji4oB2o+bCtecE/LF2D9p+4wpTE5Msy0AkJ4b+zpPhGPVnL7M6T++DTK9fv8bp06fT7LNr1y707t1b45WaiIgoJCUlwcnZQaPdyckRIZ+Mqr4XEhIOZyfNkU1nZwd1/5DQsP/aPunj5ICQkLA0s6fFNk9u5DA1TXGT78iXr+Bga611Hbu8ebBw/E/w3foXDv89A3uXTIZlTgsU/O/3vfngX0S9fIXOP/+Oqt8PQtXvB+HyjXvYfOAkqn4/CMnJGf9DE2WfipJTpKzMqd+ckRHRqeR0SHHW573Q0HA4On2+f1xcPB4+fAI/v2sY/NNYJCUloWev1O/Dll2yivLZE8mVvus8QJzjk20ey3d13ic3+Y58+QoONnm0rmNnnRsLfukFH89pOPTXWOyZOwq5cpqjgNOXuxmtKMdRUXKKlFWUnKJ850XZnyJlFeWzJ91keJDp8uXLab5u3Pj8Y1fd3d2xdu1ajVdqlEol/P0D0ahhPXWbiYkJGjWsBx+fK1rX8fG9gkaN6mm0NWn8tbr/o0f/4sWLUI1t5smTGzVrVoWPr/ZtpodCYYZyJQrDN/C2uk2lUsE38DYqlyme5roW5go429siKVmF4xevokHNd49xrFW5LHYu/A3b509Qv8qXLIKWX9fE9vkTkCNHxscJRdmnouQUKStz6j/n1atBaNCgrkbOBg3r4JKvv9Z1LvleRYOGdTXaGjaqh0uXtPd/z9TUFBYWad9IMTtkFeWz1zuZTaMm45XVdR4gzvFJYWaGcsUKwPfGh/tmqlQq+N64j0qlCqe5roW5As52eZGUrIL3pSA0rJ72jb11IcpxVJScImUVKacI33lR9qdIWUX57PVOZnWe2ee7aJozZ47Ob6pQKKBQpP/Sr/kL/4bn6vm44h8IP7+rGDa0P6yscmHtum0AAM81CxEc/AITJs4EACxatBonvL0wcsQAHDx0HJ07tUX16pUwcNCv6m3+tWgVxo8bhnv3H+Lx46eYMnk0goNDsWfPEZ1+t55tm2DiwrX4qmQRVCxVFBv3nUD820S0a/zuqSLjF3jC2d5GfX+lwLuPEBYZg7LFCiI0MgbLtu6HSpLwo3tTAIBVrpwoVUTzyXu5LMyRN49VivaMEGWfipJTpKzMqd+ci/9ahRV//wl//0BcuRyAwUP6wNLSEhs3eAEAVv79J4KDQzDZ492xc+kSTxw+uhVDh/XDkcMn0KFja1SrVhHDhowHAFha5sLoMYNxcP9xhISEw97BFv8b8APy53fBrn8OZjqnSFlF+ez1SpDpz5T9GaLOA8Q5Pv3wXX38tnw7yhcviAolCmLjoXOIf6tEu29cAQATlm6Dk501hndpAQAIvP8vwqJiUbZIPoRFx2LZzmNQqST0bv2NeptxbxPwb0ik+ufn4VG4/TgYeXPnQj4H20zlFOU4KkpOkbKKklOU77wo+1OkrKJ89nolszovw4NMNjY26NevH2rUqKF1+ePHjzFmzBidg31sx469cHSww+RJo+Di4oiAgBto2aoHwv67AVjhQvmh+uiDu+hzGT16DsHUKb9i+rQxuHf/Edp36IsbN+6o+8yZuxRWVpZYvnQ2bGyscf68H1q27oGEhASdsjav54rol6+wdMs+RETHokyxgljmMVR9M/CQ8CiN6/ATE5VYvGkPnoVGwDKnBepVr4DfR/4I69yWOuX4HFH2qSg5RcrKnPrNuXPnATg42mPibz/D2dkBgYG34N6utzpnoU9y+vr6o0/vEfjN4xdMnjIKD+4/RpfOA3Dz5l0AQHJyMsqULoHuW9rD3t4WUVExuHIlEE2/7YRbt+5lOqdIWUX57ImyI0PUeYA4x6fmtSsjOvYNlnodRUTMK5Qpkh9Lx/aBfd53l8uFRMao76sJAInKJCzZcQTPwqJgaWGOelXKYsagLrC2yqXuc+PhM/SbvlL989yN+wEAbb6ujmkDM3ephyjHUVFyipRVlJyifOdF2Z8iZRXls6fMM5EkScrICrNmzULRokXRuXNnrcvfFx/btm3LUBAz88zPyslKbwI2GjpCulhV7mHoCETZRk4zI5lqm028TUo0dIR0SUp8nmXvFf/P73rbVq7vx+ttWyQ/X6rOy21ZTB/xvriIs/MNHSFdctcebOgIRGkSqXYSpS4RhUif/eu4R1nyPnKr8zI8k6lNmzZpjly6uLjAw8NDp1BERESyIrNp1GS8WOcRERHpmczqvAwPMpUrVy7N5Tlz5sRXX32V6UBEREREZBis84iIiEgXGR5kIiIiIj2T2RkuIiIiItmQWZ3HQSYiIiJDy9jtEYmIiIhIFDKr80wNHYCIiIiIiIiIiMTHmUxERESGJrNp1ERERESyIbM6j4NMREREhiaz4oOIiIhINmRW5/FyOSIiIiIiIiIi0hlnMhERERmaJK8zXERERESyIbM6j4NMREREhiazadREREREsiGzOo+DTERERDJ3+PBh7Nu3DzExMShSpAj69OmDkiVLptr/wIEDOHr0KCIiImBtbY1atWqhW7duMDc3z8LURERERPQ5WV3n8Z5MREREhiZJ+ntl0IULF7B+/Xp06NABs2bNQpEiRTBjxgy8fPlSa/9z585h8+bN6NixI+bPn4+BAwfi4sWL2LJli657gYiIiCj7kVmdx0EmIiIiQ1Op9PfKoP3796Nx48Zo2LAhChYsiP79+8Pc3BwnT57U2v/OnTsoU6YM6tWrBycnJ1SuXBl169bF/fv3dd0LRERERNmPzOo8DjIRERFlI0qlEnFxcRovpVKptW9SUhIePnyIihUrqttMTU1RsWJF3L17V+s6ZcqUwcOHD9XFRmhoKK5evYqqVavq/5chIiIiIjUR6jzekymDrCr3MHSEdIkPPmvoCOmWK399Q0cgStPbpERDR6DsTo83hNy1axe8vLw02jp06IBOnTql6BsbGwuVSgUbGxuNdhsbGwQHB2vdfr169RAbG4vffvsNAJCcnIxvv/0W33//vX5+AcqWRDmO5q492NAR0oV1Hhk7Ub7zpH/87LWQWZ3HQSYiIiJD0+Ojbd3d3dGqVSuNNoVCobft37hxA7t27UK/fv1QqlQphISEwNPTE15eXujQoYPe3oeIiIgoW5BZncdBJiIiomxEoVCku9iwtraGqakpYmJiNNpjYmJSnPV6b9u2bfj666/RuHFjAEDhwoXx9u1brFy5Et9//z1MTXklPhEREdGXIEKdx0qQiIjIwCSVpLdXRpiZmaF48eIICgpSt6lUKgQFBaF06dJa10lISICJiYlGGweWiIiIiLSTW53HmUxERESGpsdr9TOqVatWWLJkCYoXL46SJUvi4MGDSEhIQIMGDQAAixcvhp2dHbp16wYAqF69Og4cOIBixYqpp1Fv27YN1atX52ATERER0adkVudxkImIiEjG6tSpg9jYWGzfvh0xMTEoWrQoxo8fr55GHRERoXFGq3379jAxMcHWrVsRFRUFa2trVK9eHV27djXQb0BERERE2hiizjORJCljc66+EDPzAoaOkK3wqSNERLpJSnyeZe8Vt2yo3rZl+dMivW2LSF9Y5+kX6zwiIt1lVa0ntzqPM5mIiIgMLYPX2BMRERGRIGRW5/HmCUREREREREREpDPOZCIiIjI0A94QkoiIiIi+IJnVeZzJREREREREREREOuNMJiIiIkOT2RkuIiIiItmQWZ3HQSYiIiJDM44HvRIRERGRvsmszuPlckREREREREREpDNhBpl+GtgL9+/64HXsA1w4tw81XKuk2b99+1YIun4ar2Mf4Kr/cbRo3ihFn8keo/D0iT9evbyPI4e2omTJYrLJevnadQz+1QMN23RHhbot4H3mwmfXueQfiI4/DkHVBq3RolMf7D5wLEWfLTv3oWn7XqjWsA269h+B6zfv6JQTEGN/ipaVOeWZU6SsouTUG5VKfy8iQYnyvRchJ+s8/hvKnPLLKVJWUXLqjczqPCEGmTp2bIO5czwwbfo81KjVHAGBN3HwwCY4Otpr7V/bzRWbNiyBp+cWuNZshr17j2Cn12qUL19G3Wf0qEEYMrgPBg0Zizr1WuNNXBwO7t8ECwsLWWSNj3+LMiWLY8Ivg9LV/1lwCAaPnoSa1SrDa+0S/NCpHTxmLcB53yvqPoeOn8bsRSvxU5/u2LFmEcqULIYBP09EZHRMpnOKsj9Fysqc8swpUlZRcuqVStLfi0hAonzvRcnJOo//hjKnvHKKlFWUnHolszrPRJKM4wJBM/MCqS67cG4f/C4HYPiIiQAAExMTPH7ohyVLPTF7zpIU/TdvWgYrS0u0de+lbjt/dh+uBdzA4CFjAQBPn/hj/oIVmDd/BQDA2joPgp9dQ59+I7F9+95M/x7GkjU++Gy6M1eo2wIL//gNjb+uk2qfeUtX48wFP+zeuFzdNmrSH3j1+g1WzJsOAOjafwQqlC2tLmhUKhWauPdEtw5t0O+HTqluO1f++qkuM5b9mR6iZGVOeeYUKaux5ExKfJ6p/JkRN7ef3rZlOWqV3rZFpC9p1XmA8XzvP8dYcrLO47+hzMmcomY1ppxZVevJrc4z+plMCoUC1apVgveJD/+YSpIE7xPn4OZWXes6brWqa/QHgKPHTqn7FytWGPnyOcP7xDn18tjYV7h06SrcamnfZnbLmlEBQbfh9sk0xrq1qiMg6BYAQKlU4uade3Cr8aGPqakp3FyrqPtklEj7U5SszCnPnCJlFSWn3kkq/b2IBCPK916UnJnBOi97ZGVOeeYUKasoOfVOZnVehgeZEhMTcfv2bTx79kzrstOnT+sl2HsODnYwMzNDWGiERntYWDhcnB21ruPi4ojQsHCNttDQCHV/F2en/9o+6RMWARcXJ1lkzaiIqGjY29lqtNnb2uD1mzi8TUhAdEwskpNVKfvY2SIiKjpT7ynS/hQlK3PKM6dIWUXJqXcym0ZNxiur6zxAnO+9KDkzg3Ve9sjKnPLMKVJWUXLqnczqPLOMdA4ODsaMGTMQEfHuj6Js2bIYMWIEbG3f/YMTFxeHpUuX4ptvvklzO0qlEkqlMpORiYiIiEjfWOcRERGRrjI0k2nTpk0oVKgQ/v77byxcuBC5cuXCb7/9pi5G0mvXrl3o3bu3xis1ERFRSEpKgpOzg0a7k5MjQj4ZrXwvJCQczk6aI6HOzg7q/iGhYf+1fdLHyQEhIWEZ+l1EzZpRDna2iPzkTFVkdAxyW1kip4UFbG2skSOHaco+UdFw+OSsV3qJtD9Fycqc8swpUlZRcuqbpFLp7UWUWYao8wBxvvei5MwM1nnZIytzyjOnSFlFyalvcqvzMjTIdPfuXXTr1g3W1tZwcXHBmDFjULlyZUyaNAmhoaHp3o67uzvWrl2r8UqNUqmEv38gGjWsp24zMTFBo4b14ONzRes6Pr5X0KhRPY22Jo2/Vvd/9OhfvHgRqrHNPHlyo2bNqvDx1b7N9BApa0ZVrlAWvlcCNNou+l1F5QrlALy7vvarMqXge/maerlKpYLvlWvqPhkl0v4UJStzyjOnSFlFyal3MptGTcbJEHUeIM73XpScmcE6L3tkZU555hQpqyg59U5mdV6GLpdLTEyEqemHcSkTExP0798fq1evxuTJkzFs2LB0bUehUEChUKT7fecv/Bueq+fjin8g/PyuYtjQ/rCyyoW167YBADzXLERw8AtMmDgTALBo0Wqc8PbCyBEDcPDQcXTu1BbVq1fCwEG/qrf516JVGD9uGO7df4jHj59iyuTRCA4OxZ49R9KdS+SscXHx+PdZsPrn58GhuH33AfJa50E+FyfMX+aJsIhI/PHbKABAp3YtsWXnPvy5ZDXcWzXFpSsBOHLiDJbOmareRs/O7pgw40+UL1sKFb4qg43bdyP+bQLatfw20zlF2Z8iZWVOeeYUKasoOYmyG0PVeYA433tRcrLO47+hzCmvnCJlFSUnZV6GBpny58+Phw8fomDBghrtffv2BQDMnj1bf8k+smPHXjg62GHypFFwcXFEQMANtGzVA2Fh76ZvFy6UH6qPpo5d9LmMHj2HYOqUXzF92hjcu/8I7Tv0xY0bd9R95sxdCisrSyxfOhs2NtY4f94PLVv3QEJCgiyyBt2+hz5Dx6h/nr1oJQCgbYsmmDHxF0RERuFF6IfphQXzu2DJnKmY/dcKbNyxG86ODpgyZgTqfnTH/hZNvkF0zEssXrUREVFRKFuqBJb/OS3T06gBcfanSFmZU545RcoqSk69EuRpIZS9GarOA8T53ouSk3Ue/w1lTnnlFCmrKDn1SmZ1nokkSemec7Vr1y7cvn0b48aN07p81apVOHbsGLZt25bhIGbmBTK8DqUuPvjs5zsZiVz56xs6AhFRCkmJz7Psvd5M7a63bVlN2qS3bZG8sM4TB+s8IiLdZVWtJ7c6L0ODTF8Siw/9YvFBRKQbDjIR6Q/rPP1inUdEpDsOMn0ZGbpcjoiIiL4AQZ4WQkREREQZJLM6j4NMREREhibI00KIiIiIKINkVueZfr4LERERERERERFR2jiTiYiIyNBk9tQRIiIiItmQWZ3HQSYiIiJDk9k0aiIiIiLZkFmdx8vliIiIiIiIiIhIZ5zJREREZGCSzJ46QkRERCQXcqvzOMhERERkaDKbRk1EREQkGzKr83i5HBERERERERER6YwzmYiIiAxNZme4iIiIiGRDZnUeB5mIiIgMTWaPtiUiIiKSDZnVebxcjoiIiIiIiIiIdMaZTERERIYms2nURERERLIhszqPg0zZVK789Q0dId3i7u4xdIR0sSzd1tAR0sVSYWHoCOkSp0wwdAQioyHJrPggIt2IVOfFB581dIR0EWmfEpFY5Fbn8XI5IiIiIiIiIiLSGWcyERERGZrMznARERERyYbM6jwOMhERERmaSl5PHSEiIiKSDZnVebxcjoiIiIiIiIiIdMaZTERERIYms2nURERERLIhszqPg0xERESGJrPig4iIiEg2ZFbn8XI5IiIiIiIiIiLSGWcyERERGZgkyesMFxEREZFcyK3O4yATERGRoclsGjURERGRbMiszuPlckREREREREREpDPOZCIiIjI0mZ3hIiIiIpINmdV5HGQiIiIyMElmxQcRERGRXMitzhPmcrmfBvbC/bs+eB37ABfO7UMN1ypp9m/fvhWCrp/G69gHuOp/HC2aN0rRZ7LHKDx94o9XL+/jyKGtKFmymKyyipJzy96jaNZzOKq36o1uwybh+u0HqfZVJiVh2cZ/0KL3SFRv1RvtB47DOb8AjT7Neg5HxWbdU7ymL/bUOaso+7T//37A9ZtnEBZ5CydO/YPq1Sul2b+dewtc9j+GsMhbuHjpEJo2a6CxfNz44bjsfwwvwoLw5NlV7Nm/Aa6ulXXOKcr+FCWnSFlFyUlE+iPK95459Zfz8rXrGPyrBxq26Y4KdVvA+8yFz65zyT8QHX8cgqoNWqNFpz7YfeBYij5bdu5D0/a9UK1hG3TtPwLXb97RKed7IuxT5pRvTpGyipKTMkeIQaaOHdtg7hwPTJs+DzVqNUdA4E0cPLAJjo72WvvXdnPFpg1L4Om5Ba41m2Hv3iPY6bUa5cuXUfcZPWoQhgzug0FDxqJOvdZ4ExeHg/s3wcLCQhZZRcl5+NRFzFm5CQO7f4/tS6ajdPHCGDBhJiJjXmrtv2jtDngdPIFxg3ph99+z0allY4yYOh+37j9W99ny1zSc3LJE/Vr5xzgAQLP6tTKdExBnn37fviV+nzkeM//4C/Xrtsb167fwz551cEglZ81a1bBm7UKsX78d9eq0woF9R7F563KU+6q0us/9+48w6pfJqF2zBZp92wn/PnmGXXvXw97BLtM5RdmfouQUKasoOfVKJenvRSQgUb73zKnfnPHxb1GmZHFM+GVQuvo/Cw7B4NGTULNaZXitXYIfOrWDx6wFOO97Rd3n0PHTmL1oJX7q0x071ixCmZLFMODniYiMjsl0TkCcfcqc8swpUlZRcuqVzOo8E8lInqdnZl4g1WUXzu2D3+UADB8xEQBgYmKCxw/9sGSpJ2bPWZKi/+ZNy2BlaYm27r3UbefP7sO1gBsYPGQsAODpE3/MX7AC8+avAABYW+dB8LNr6NNvJLZv35vp30OUrMaUM+7unlSXdRs2CeVLF8eEIb0BACqVCt/2GIaubZuiX+c2Kfo36joY/bu2Rdc2TdVtI6cugIWFOWaO0V7AzFq2Aad9r+KA558wMTFJNYtl6bapLgOMZ59aKtI+mJ449Q/8rwRi1C+T1Tlv3T2PFcvXY/6fy1P091z3F6ysLNGpQz91m/fJnQgMvIWRwydqfY88eXLjeUggWrfsgdOntJ+VjFMmpJnTWPbn54iSU6SsxpIzKfF5pvJnxssfGuttW3k3eOttW0T6kladBxjP9/5zmDPjOeODz6Yrc4W6LbDwj9/Q+Os6qfaZt3Q1zlzww+6NH+qVUZP+wKvXb7Bi3nQAQNf+I1ChbGn1wJVKpUIT957o1qEN+v3QKdVt58pfP818xrRPmZM5Rc5qTDmzqtaTW51n9DOZFAoFqlWrBO8TH/6BkiQJ3ifOwc2tutZ13GpV1+gPAEePnVL3L1asMPLlc4b3iXPq5bGxr3Dp0lW41dK+zeyUVZScSmUSbt57BLdqFdRtpqamcKtaAQE372ldJ1GZBAtzc402CwtzXL2hfZq0UpmE/SfOwb3ZN2kOMH2OKPtUoVCgStUKOHnyvEbOUyfPo2bNqlrXqVmrGk591B8AvI+fRc1a2vsrFAr07tMFMTGxuH79VqZzirI/RcgpUlZRchKR/ojyvWdOwx9HA4Juw+2Ty2rq1qqOgKB39YZSqcTNO/fgVuNDH1NTU7i5VlH3yQxR9ilzyjOnSFlFyUm6yfAg07Nnz3Dy5Ek8f/5u1O/58+f4+++/sXTpUgQFBek9oIODHczMzBAWGqHRHhYWDhdnR63ruLg4IjQsXKMtNDRC3d/F2em/tk/6hEXAxcUp22cVJWd07Cskq1Swt8mr0W5va43IaO2Xy9WpXhHrdx7Ek+chUKlUuHDlOrzP+yE8KkZrf+8Ll/HqdRzaNv06UxnfE2Wf2tvbwszMDOFhn+aMgHMqOZ2dHRCWjv7NmzdCcOh1hEfdwuAhfdCudU9ERUZnKqco+1OUnCJlFSWnvkkqSW8vIl1kdZ0HiPO9Z07DH0cjoqJhb2er0WZva4PXb+LwNiEB0TGxSE5WpexjZ4uIqMzVJIA4+5Q55ZlTpKyi5NQ3udV5GXq63LVr1zB79mzkzJkTCQkJGD16NBYvXowiRYpAkiRMnz4dEydORIUKFdLcjlKphFKp1Ck4kTZjf+qJyQtWoU2/UTCBCQrld0bbpl9j95HTWvvvOnIK9WpUhpO9rdbllH5nzlxEvdqtYG9vi14/dsHaDYvQqMH3iAiPNHQ0IuMnSNFA2RvrPCIioi9AZnVehgaZvLy80KZNG3Tp0gXnz5/HwoUL0bRpU3Tt2hUAsHnzZuzevfuzxceuXbvg5eWVrveMiIhCUlISnJwdNNqdnBwR8slo5XshIeFwdtIcCXV2dlD3DwkN+6/NESEhYR/6ODngWsCNdOUSOasoOW2t8yCHqWmKm3xHRsfC3jav1nXsbKzx1+SfkZCYiJjY13Cyt8X81VtRUMsodnBoOHyuBmH+byMyle9jouzTyMhoJCUlwdHp05wOKUb/3wsNjYBTOvrHxcXj4cMnePjwCfz8ruFqwAn07NUJ8+Yuy3BOUfanKDlFyipKTqLsyBB1HiDO9545DX8cdbCzReQnM5Iio2OQ28oSOS0skMPGFDlymKbsExUNB7vMn1AUZZ8ypzxzipRVlJykmwxdLvf06VM0aNAAAFC7dm28ffsWbm5u6uX16tXDkydPPrsdd3d3rF27VuOVGqVSCX//QDRqWE/dZmJigkYN68HH54rWdXx8r6BRo3oabU0af63u/+jRv3jxIlRjm3ny5EbNmlXh46t9m+khSlZRcioUZviqVDH4Xv1wcFCpVPC5FoTKX5VKc10Lc3M4O9ghKTkZx8/5oWHtlNfj7j56BnY2efF1KvcWyghR9qlSqcS1q0Fo0ODDTTVNTEzwTYM6uHTpqtZ1Lvn645uP+gNAw0Z1cclXe//3TE1NUtwfKyM5RdmfIuQUKasoOfVOpccXUSYZos4DxPneM6fhj6OVK5SF75UAjbaLfldRuUI5AO/u9/JVmVLwvXxNvVylUsH3yjV1n8wQZZ8ypzxzipRVlJx6J7M6L0MzmT5mamoKhUIBS0tLdVuuXLkQFxf32XUVCgUUCkW632v+wr/huXo+rvgHws/vKoYN7Q8rq1xYu24bAMBzzUIEB7/AhIkzAQCLFq3GCW8vjBwxAAcPHUfnTm1RvXolDBz0q3qbfy1ahfHjhuHe/Yd4/PgppkwejeDgUOzZcyTduUTOKkrOnt+3wIS5K1C+dDFULFMCG3YdRvzbBLRr+g0AYPzsZXBysMWIPl0AAIG37yMsIhplShRBWEQUlm38BypJhR87tdLYrkqlwu6jp9GmSX2Y5ciR6XwfE2WfLl60GstXzsXVq9dx+XIABg3+EZaWlti44d1Z5xV/z0VwcCimeMwBACxbuhaHjmzBkGF9ceTwSXTo0BpVq1XEsKETAACWlrkw6tfBOHTgOEJCwmBvb4f+A35Avvwu2LXrYKZzirI/RckpUlZRcuqTKNfYk3xkZZ0HiPO9Z0795oyLi8e/z4LVPz8PDsXtuw+Q1zoP8rk4Yf4yT4RFROKP30YBADq1a4ktO/fhzyWr4d6qKS5dCcCRE2ewdM5U9TZ6dnbHhBl/onzZUqjwVRls3L77Xe3Y8ttM5wTE2afMKc+cImUVJac+ya3Oy9Agk5OTE0JCQuDi4gIAmD59OhwcPkx1i4iIgK2t/u9ts2PHXjg62GHypFFwcXFEQMANtGzVQ30z4sKF8kOl+jCsd9HnMnr0HIKpU37F9GljcO/+I7Tv0Bc3PnrC2Jy5S2FlZYnlS2fDxsYa58/7oWXrHkhISPux6tklqyg5mzeojaiXr7BkvRciol+ibPEiWD5jDBz+u1zuRXgkTEw/PBUuIVGJReu249mLcFjmskD9GlXw+68/wTq3lcZ2fa4G4UVYJNybfZPpbJ8SZZ/+s/MAHBzsMH7iSDg7O+B64C20b9dbfTPwggU1c17y9UffH0fgt0m/wGPyKDx48BjdugzErZt3AQDJyckoXboEunX/Hvb2toiKioH/lUA0/7Yzbt/S/hTA9BBlf4qSU6SsouQkym4MVecB4nzvmVO/OYNu30OfoWPUP89etBIA0LZFE8yY+AsiIqPwIvTD5S8F87tgyZypmP3XCmzcsRvOjg6YMmYE6n70BKkWTb5BdMxLLF61ERFRUShbqgSW/zlNp8vlAHH2KXPKM6dIWUXJSZlnIklSuofVjh49CgcHB1SrVk3r8s2bNyM2NhYDBw7McBAz8wIZXoeyh7i7ewwdIV0sS7c1dIR0sVRYGDpCusQpedAn45aU+DzL3iu6fQO9bct25ym9bYvkhXUefQnxwWc/38kI5Mpf39ARiCiLZVWtJ7c6L0MzmZo2bZrm8m7duukUhoiISI7kNo2ajBPrPCIiIv2TW52XoRt/ExERERERERERaZPpG38TERGRngjytBAiIiIiyiCZ1XkcZCIiIjIwSWbFBxEREZFcyK3O4+VyRERERERERESkM85kIiIiMjSZneEiIiIikg2Z1XkcZCIiIjIwuU2jJiIiIpILudV5vFyOiIiIiIiIiIh0xplMREREhiazM1xEREREsiGzOo+DTERERAYmt2nURERERHIhtzqPg0xEREQyd/jwYezbtw8xMTEoUqQI+vTpg5IlS6ba/82bN9iyZQsuXbqE169fw9HREb169UK1atWyMDURERERfU5W13kcZCIiIjIwQ57hunDhAtavX4/+/fujVKlSOHDgAGbMmIEFCxYgb968KfonJSVh+vTpsLa2xs8//ww7OztERETA0tLSAOmJiIiIjJvc6jwOMhERERmYIYuP/fv3o3HjxmjYsCEAoH///vD398fJkyfRrl27FP1PnDiB169fY9q0aTAze1dGODk5ZWVkIiIiImHIrc7jIBMREVE2olQqoVQqNdoUCgUUCkWKvklJSXj48KFGkWFqaoqKFSvi7t27Wrd/5coVlCpVCqtXr8bly5dhbW2NunXrol27djA15UNriYiIiL4UEeo8DjKRwVmWbmvoCOkSH3zW0BHSJVf++oaOQEQZJZnobVO7du2Cl5eXRluHDh3QqVOnFH1jY2OhUqlgY2Oj0W5jY4Pg4GCt2w8NDUV4eDjq1auHcePGISQkBKtWrUJycjI6duyot9+DiLIHUeoS1nlE9MXIrM7jIBMREZGB6XMatbu7O1q1aqXRpu3sVmZJkgRra2sMGDAApqamKF68OKKiorB3714OMhERERF9Qm51HgeZiIiIspHUpkxrY21tDVNTU8TExGi0x8TEpDjr9Z6NjQ3MzMw0pkwXKFAAMTExSEpKUl+/T0RERET6JUKdx5snEBERGZikMtHbKyPMzMxQvHhxBAUFqdtUKhWCgoJQunRpreuUKVMGISEhUKk+nJZ78eIFbG1tOcBERERE9Am51XkcZCIiIjIwSaW/V0a1atUK3t7eOHXqFJ49e4ZVq1YhISEBDRo0AAAsXrwYmzdvVvdv2rQpXr9+jbVr1yI4OBj+/v7YtWsXmjVrpqe9QURERJR9yK3O4ylHIiIiGatTpw5iY2Oxfft2xMTEoGjRohg/frx6GnVERARMTD6cOXNwcMCECROwbt06jB49GnZ2dmjRooXWx+ASERERkeEYos4zkSRJ0vPvkSlm5gUMHYEoTXzqCJG8JCU+z7L3el67kd62VeDiCb1ti0hfWOeRsWOdRyQ/WVXrya3O40wmIiIiA9PnU0eIiIiIyHjIrc7jPZmIiIiIiIiIiEhnnMlERERkYBl9WggRERERiUFudR4HmYiIiAzMOO6OSERERET6Jrc6j5fLERERERERERGRzjiTiYiIyMDkNo2aiIiISC7kVudxkImIiMjA5FZ8EBEREcmF3Oo8YS6X+2lgL9y/64PXsQ9w4dw+1HCtkmb/9u1bIej6abyOfYCr/sfRonmjFH0me4zC0yf+ePXyPo4c2oqSJYvJKitz6i/n5WvXMfhXDzRs0x0V6raA95kLn13nkn8gOv44BFUbtEaLTn2w+8CxFH227NyHpu17oVrDNujafwSu37yjU873RNinzMnjkwg5iUh/RPneM6f8crLOk+9nL1JOkbKKkpMyR4hBpo4d22DuHA9Mmz4PNWo1R0DgTRw8sAmOjvZa+9d2c8WmDUvg6bkFrjWbYe/eI9jptRrly5dR9xk9ahCGDO6DQUPGok691ngTF4eD+zfBwsJCFlmZU7854+PfokzJ4pjwy6B09X8WHILBoyehZrXK8Fq7BD90agePWQtw3veKus+h46cxe9FK/NSnO3asWYQyJYthwM8TERkdk+mcgDj7lDl5fDL2nPokSfp7EYlIlO89c8ozJ+s8+X72ouQUKasoOfVJdnWepAcqlUrnbeRQ5E/15et7RVq8ZI36ZzPzAtKzZ8HSuPEztPbftn2PtH//MY02H58r0vIV69U/BweHSKN/naL+2da+jBQfHy917T4wzSyfe4mSlTkznjMx/EG6XqVLl5YO7dyQZp+ZU8ZL3zX7VqNt2E/9pB9/6Kr+uX271pLH2F/UP78NvSfVq1NbWjrvjzS3LdI+Zc6syylSVmPJmZUeVPhWby8iffvSdZ4xfe+ZU745WefJ97PPDjlFympMObOK3Oo8vcxk6tatG549e6aPTaWgUChQrVoleJ84q26TJAneJ87Bza261nXcalXX6A8AR4+dUvcvVqww8uVzhveJc+rlsbGvcOnSVbjV0r7N7JSVOfX/2WdUQNBtuH0yLbRureoICLoFAFAqlbh55x7canzoY2pqCjfXKuo+mSHKPmVOHp+MPSeRnHzJOg8Q53vPnPLMmRms85gzK3OKlFWUnKSbDN34e926dVrbVSoVdu/ejTx58gAAevXqpXuy/zg42MHMzAxhoREa7WFh4ShbpoTWdVxcHBEaFq7RFhoaARdnx3fLnZ3+a/ukT1gEXFycsn1W5tT/Z59REVHRsLez1Wizt7XB6zdxeJuQgNjY10hOVqXsY2eLR/9mvtAXZZ8yJ49Pxp5T3yRJXjeEJONkiDoPEOd7z5zyzJkZrPOYMytzipRVlJz6Jrc6L0ODTAcPHkSRIkVgZWWVYtnz58+RM2fOdG1HqVRCqVRm5K2JiIiyLUll6ARErPOIiIi+BLnVeRkaZOratSuOHz+Onj17okKFChrtgwcPRsGCBdO1nV27dsHLyytdfSMiopCUlAQnZweNdicnR4R8Mlr5XkhIOJydHDXanJ0d1P1DQsP+a3NESEjYhz5ODrgWcCNduUTOypz6/+wzysHOFpFR0RptkdExyG1liZwWFshhY4ocOUxT9omKhsMnZ70yQpR9ypw8Phl7TqLsyBB1HiDO95455ZkzM1jnMWdW5hQpqyg5STcZuidTu3btMGLECPz9999Yv349kpKSMvWm7u7uWLt2rcYrNUqlEv7+gWjUsJ66zcTEBI0a1oOPzxWt6/j4XkGjRvU02po0/lrd/9Gjf/HiRajGNvPkyY2aNavCx1f7NtNDlKzMqf/PPqMqVygL3ysBGm0X/a6icoVyAN5dr/xVmVLwvXxNvVylUsH3yjV1n8wQZZ8yJ49Pxp5T31SSid5eRJlliDoPEOd7z5zyzJkZrPOYMytzipRVlJz6Jrs6LzN3C4+Pj5cWLVokjRo1Snry5InUpUsX6enTpzrdgTytu7536TZQio+Pl3r3GS6Vr/i1tGLlBikqKlrKV6CSlEORX1q/YYc0c9Zf6v716reREhMTpVGjp0hfVagvTZk6V0pISJAqVWmo7jN23HQpKipaauveS6pctZG0e88h6cGDx5Jl7mI63dVflKzMmfGcaT3pI+ZJkBR4/qgUeP6oVLp0aWnVorlS4Pmj0pPrF6XE8AfSrKkTpV+G/aTu/zDgvFS5UkXpj8njpduXT0nrVyyUypUrK53cv13dZ8+WNVKFCuWlHeuWS7f8TkoTRg+XXKtXk4LvXNbpqSPGtE+Zk8cnY86ZlW6Xaa63F5GusrrOM6bvPXPKNyfrPPl+9tkhp0hZjSlnVpFbnZehy+Xey5kzJ4YMGYLz589j2rRpUKm+7EWGO3bshaODHSZPGgUXF0cEBNxAy1Y9EBb27oZhhQvl18hw0ecyevQcgqlTfsX0aWNw7/4jtO/QFzdu3FH3mTN3KaysLLF86WzY2Fjj/Hk/tGzdAwkJCbLIypz6zRl0+x76DB2j/nn2opUAgLYtmmDGxF8QERmFF6Efpm8WzO+CJXOmYvZfK7Bxx244OzpgypgRqPvRExBaNPkG0TEvsXjVRkRERaFsqRJY/uc0naZRA+LsU+bk8cnYcxJlV1ld5wHifO+ZU545WefJ97MXJadIWUXJSZlnIkmSpMsGIiMj8fDhQ1SsWDHdN4TUxsy8gC4xiL64+OCzn+9kBHLlr2/oCETZQlLi8yx7r9ulv9PbtsrePai3bRGxziO5YJ1HJD9ZVevJrc7L1Eymj9nb28Pe3l4fWYiIiGRJt9M9RF8O6zwiIiLdyK3Oy9CNv4mIiIiIiIiIiLTReSYTERER6UZSCfK0ECIiIiLKELnVeRxkIiIiMjBhHklLRERERBkitzqPl8sREREREREREZHOOJOJiIjIwCSZneEiIiIikgu51XkcZCIiIjIwuT11hIiIiEgu5Fbn8XI5IiIiIiIiIiLSGWcyERERGZjcbghJREREJBdyq/M4yERERGRgcrtWn4iIiEgu5Fbn8XI5IiIiIiIiIiLSGWcyERERGZjcbghJREREJBdyq/M4yERERGRgcrtWn4iIiEgu5Fbn8XI5IiIiIiIiIiLSGWcyEaVTrvz1DR0hXd4EbTN0hHSxqtDZ0BHIQGxyWhk6gtGR2w0hiYiMDes8/WKdJ1+s81KSW53HQSYiIiIDk9s0aiIiIiK5kFudx8vliIiIiIiIiIhIZ5zJREREZGAye+gIERERkWzIrc7jIBMREZGByW0aNREREZFcyK3O4+VyRERERERERESkM85kIiIiMjC5PXWEiIiISC7kVudxkImIiMjAVIYOQERERERfhNzqPF4uR0REREREREREOuNMJiIiIgOTIK9p1ERERERyIbc6j4NMREREBqaS27NtiYiIiGRCbnUeL5cjIiIiIiIiIiKdcSYTERGRgalkNo2aiIiISC7kVucJM5Ppp4G9cP+uD17HPsCFc/tQw7VKmv3bt2+FoOun8Tr2Aa76H0eL5o1S9JnsMQpPn/jj1cv7OHJoK0qWLCarrMwp389+64ETaN73V7h+PwDdfpmO63cfptpXmZSE5Vv24rv+Y+H6/QB0GOqBc1eua/RZunkPKrXuq/FqM3CCzjlF2Z+i5BQpa5/+3eF//QSehV3HkRM7ULV6pTT7t2nXHBcvH8azsOs4c3EfmjT9RmP5omUzERF7V+O17Z9VOufUFwkmensRiUqU4xNzyjOnSFlZ5/Fv1Nizss7L3nWeEINMHTu2wdw5Hpg2fR5q1GqOgMCbOHhgExwd7bX2r+3mik0blsDTcwtcazbD3r1HsNNrNcqXL6PuM3rUIAwZ3AeDhoxFnXqt8SYuDgf3b4KFhYUssjKnfD/7w2cvYc6qbRjYtQ22LfBAmWKFMHDSfETGxGrtv3jjLngdPo1xA7ph99Lp6NiiAUb+vgS3HjzR6FeicH6cWD9P/Vo3a2ymMwLi7E9RcoqUtd3332Ha7+MwZ+ZiNKrfDjeu38aOf1bDwcFOa/8aNati5Zp52LR+BxrWa4eDB45j/eYlKFuulEa/48fO4KuSddSv//X5OdMZiUi/RDk+Mac8c4qUlXUe/0aNPSvrvOzPRJIko7gNlZl5gVSXXTi3D36XAzB8xEQAgImJCR4/9MOSpZ6YPWdJiv6bNy2DlaUl2rr3UredP7sP1wJuYPCQdwfEp0/8MX/BCsybvwIAYG2dB8HPrqFPv5HYvn1vpn8PUbIyZ/b97N8EbUszZ7dfpqNCqWIYP7A7AEClUqHpj6PRtVVj9O34XYr+jXv9jP6dWqFLyw9nNkb+vgQ5Lczxxy/9Abw7w3XS5yp2/DU5zff+mFWFzmkuN5b9+Tmi5DSmrDY5rdLMeeTEDlz1v46xo6aqcwbeOoO/V2zAX/NXpui/ynMBLK1yoVunAeq2w97bERR4C6NGegB4d4Yrb15r9Ow2KM33/lhE7N1099XVMee0vw8Z8W1o2scAIkNIq84DjOf49DnMKc+cxpSVdR7/Ro09qyh1HpB1tZ7c6jyjn8mkUChQrVoleJ84q26TJAneJ87Bza261nXcalXX6A8AR4+dUvcvVqww8uVzhveJc+rlsbGvcOnSVbjV0r7N7JSVOeX72SuVSbh1/wncKpdTt5mamqJWla8QcOeB1nUSlUkwVyg02nJamOPqzXsabU+CQ9G4189o0W8Mxs5diRdhkZnKCIizP0XJKVJWhUKBylXK4/TJCxo5T5+6gBo1q2hdx7VmFZw+dUGj7aT3ObjWrKrRVrdeTdx6cBE+Vw5jzrzJsLWzyVTGL0Fu06iJPibS8Yk55ZdTpKys8/g3auxZWefJo87TaZDp7du3OHnyJLZs2YLDhw/j1atX+sql5uBgBzMzM4SFRmi0h4WFw8XZUes6Li6OCA0L12gLDY1Q93dxdvqv7ZM+YRFwcXHK9lmZU76ffXTsKySrVLC3tdZot7exRkT0S63r1KlaARt2H8WT4FCoVCpcvHoD3hf8ER71oX/F0sUxfUQfLJs8EhMH/YDnoRHoPXYm3sTFZyqnKPtTlJwiZbW3t4WZmRnCwzVzhodFwCmVnE7ODggP+/T3ioCTs4P65xPHz2LwgF/xfetemDJpLurUq4ltO1fB1NToz7UQGUxW1HmAOMcn5pRnTpGyss7j36ixZ2WdJw8ZerrcyJEjMW3aNOTOnRsRERHw8PDAmzdvkC9fPoSGhmLnzp2YMWMGnJzS/qNTKpVQKpU6BSeirDHmf10xZdFatP1pAkxggoL5HNG2SV3sPv7hrEZ914rq/y5drBAqli6O5n1/xZFzl/F90/qGiE2kYdfOA+r/vnXzLm7euIMrgd6oW78Wzp6+aMBk76gMHYAIrPOI5Ih1HmUHrPOMS4YGmYKDg5GcnAwA2Lx5M+zs7DBnzhxYWlri7du3mDNnDrZs2YLhw4enuZ1du3bBy8srXe8ZERGFpKQkjZFKAHByckTIJ6Oq74WEhMPZSXMk1NnZQd0/JDTsvzZHhISEfejj5IBrATfSlUvkrMwp38/e1joPcpiaIjJa8+aPkTGxcLDNq3Udu7x5sHDiUCQkKhHz6jWc7GywYJ0XCqZytgEArHNbokh+Zzx9EZZqn7SIsj9FySlS1sjIaCQlJcHRUTOno5MDwlLJGRYaAUenT38vhxRn8z725PFTREREoXjxwiw+iP5jiDoPEOf4xJzyzClSVtZ5/Bs19qys8+Qh0/PH7t27h44dO8LS0hIAkDNnTnTq1Am3b9/+7Lru7u5Yu3atxis1SqUS/v6BaNSwnrrNxMQEjRrWg4/PFa3r+PheQaNG9TTamjT+Wt3/0aN/8eJFqMY28+TJjZo1q8LHV/s200OUrMwp389eoTBDuZJF4Bt4S92mUqngG3ALlcuUSHNdC3MFnO1tkZScjOMX/NHArUqqfePi3+JpSFiqBc3niLI/RckpUlalUomAazfwdYPaGjm//qY2/C5d07rO5UvX8PU3tTXavmlYB5cvXU31ffLld4adnQ1CQ7QXNERyl1V1HiDW8Yk55ZdTpKys8/g3auxZWefJQ4ZmMgHv/ggAIDExETY2NhrL7OzsEBur/fGYH1MoFFB8coO5tMxf+Dc8V8/HFf9A+PldxbCh/WFllQtr1727s7rnmoUIDn6BCRNnAgAWLVqNE95eGDliAA4eOo7OndqievVKGDjoV/U2/1q0CuPHDcO9+w/x+PFTTJk8GsHBodiz50i6c4mclTnl+9n3bNcUE+evxlcli6Ji6WLYuOc44t8moF2TugCA8fNWwdneFsN7tQcABN55iLDIaJQtXhihkdFYtnkPVCoVfvy+hXqbc1dvQ4OaVZDPyR7hUTFYunkPcpiaosU3tTKdU5T9KUpOkbIuW+yJxctn4drVIPhfDsTAQb1gaZkLWzbuBAAsWTEbL4JDMX3KnwCAFcvWYe+hjRg0pA+OHjmF7zu0RJWqFfDzsN8AAFZWlhg9dgj27T2CsNAIFC1WGJOnjsajh09wwvtsqjmykig3cqTszxB1HiDO8Yk55ZlTpKys8/g3auxZWedlfxkeZJo6dSpy5MiB+Ph4BAcHo3Dhwupl4eHhyJMnj14DAsCOHXvh6GCHyZNGwcXFEQEBN9CyVQ+E/XcDsMKF8kOl+jAJ7aLPZfToOQRTp/yK6dPG4N79R2jfoS9u3Lij7jNn7lJYWVli+dLZsLGxxvnzfmjZugcSEhJkkZU55fvZN69fE9EvX2Hppt2IiI5FmeKFsGzKSNj/dzYqJDwKpiYfDoSJiUos3rgLz0LCYZkzJ+q5VsTvP/eDdW5LdZ+wyGiMmbsCMbFvYJs3D6p9VRIb506AXd7MHw9E2Z+i5BQp6+5/DsLewQ5jxw+Dk7Mjgq7fQqf2fREe/u5JNgUL5tPI6XfpKgb0/QXjfxuBCR4/4+GDx+jZbTBu33r3ZJzk5GR8VaEMOndzR968eRDyIgynTpzHH9MXIDHROO4bo5JX7UFGzBB1HiDO8Yk55ZlTpKys8/g3auxZWedlfyaSJEnp7bxjxw6Nn0uVKoUqVaqof96wYQMiIyMxYsSIDAcxMy+Q4XWIKKU3QdsMHSFdrCp0NnQEMhCbnFaGjpAuEbF3s+y99rl01du2Wods0du2SF5Y5xEZP9Z5ZOxEqfOArKv15FbnZWgmU8eOHdNc/sMPP+gUhoiISI5UMptGTcaJdR4REZH+ya3Oy/DlckRERKRf6Z5STERERERCkVudl+mnyxEREREREREREb3HmUxEREQGpvp8FyIiIiISkNzqPA4yERERGZjKRF7X6hMRERHJhdzqPF4uR0REREREREREOuNMJiIiIgOT2w0hiYiIiORCbnUeB5mIiIgMTG7X6hMRERHJhdzqPF4uR0REREREREREOuNMJiIiIgNTyet+kERERESyIbc6j4NMREREBqaCYauPw4cPY9++fYiJiUGRIkXQp08flCxZ8rPrnT9/HgsXLoSrqyt+/fXXLEhKREREJBa51Xm8XI6IiEjGLly4gPXr16NDhw6YNWsWihQpghkzZuDly5dprhcWFoYNGzagXLlyWZSUiIiIiDLCEHUeB5mIiIgMTNLjK6P279+Pxo0bo2HDhihYsCD69+8Pc3NznDx5MtV1VCoVFi1ahE6dOsHJySkT70pEREQkD3Kr8zjIREREZGAqE/29lEol4uLiNF5KpVLr+yYlJeHhw4eoWLGius3U1BQVK1bE3bt3U83r5eUFa2trNGrUSO/7goiIiCg7kVudx3syZZAihxi7TJmcZOgIZCBWFTobOkK6xD08bOgI6WZZvLmhI2QrMW/fGDpCtrZr1y54eXlptHXo0AGdOnVK0Tc2NhYqlQo2NjYa7TY2NggODta6/du3b+PEiROYPXu23jITGQvWeWTsWOfpH+s8/WKd92WJUOeJ8S8pERFRNqbS47bc3d3RqlUrjTaFQqGXbcfHx2PRokUYMGAArK2t9bJNIiIiouxMbnUeB5mIiIgMLDPX2KdGoVCku9iwtraGqakpYmJiNNpjYmJSnPUCgNDQUISHh2PWrFnqNkl6l75Lly5YsGABXFxcMp2diIiIKLuRW53HQSYiIiKZMjMzQ/HixREUFISaNWsCeHezx6CgIDRvnvLygfz582Pu3LkabVu3bsXbt2/Ru3dvODg4ZEluIiIiIkqboeo8DjIREREZmMrEcO/dqlUrLFmyBMWLF0fJkiVx8OBBJCQkoEGDBgCAxYsXw87ODt26dYO5uTkKFy6ssb6VlRUApGgnIiIiIvnVeRxkIiIiMjB9XqufUXXq1EFsbCy2b9+OmJgYFC1aFOPHj1dPo46IiICJiQGrIyIiIiKBya3OM5HeX2RnYGbmBQwdIV341BEi/eBTR8jYJSU+z7L3+rtgD71tq/+zjXrbFpG+sM7TL9Z5ZOxY55EIsqrWk1udJ8a/pERERNmYIc9wEREREdGXI7c6j4NMREREBibxajQiIiKibEludZ6poQMQEREREREREZH4OJOJiIjIwOQ2jZqIiIhILuRW53GQiYiIyMDkVnwQERERyYXc6jxeLkdERERERERERDrjTCYiIiIDkwwdgIiIiIi+CLnVecLMZPppYC/cv+uD17EPcOHcPtRwrZJm//btWyHo+mm8jn2Aq/7H0aJ5oxR9JnuMwtMn/nj18j6OHNqKkiWL6SXrgAE9cfv2OURH38GZM7vh6lo5zf7ff/8drl3zRnT0Hfj5HUGzZg3Vy8zMzDB9+lj4+R1BRMQtPHx4CatWzUO+fE465xRln4qSU6SsouTcsvsQmnX7CdWbd0W3wWNx/fa9VPsqk5KwbP0OtOgxGNWbd0X7/r/g3KWrGn2Sk5OxyHMLmncfBNcW3dCix2As37ADkqTboV+U/SlSVlFy6ovKRH8vIlGJ8r1nnSff470oWUXJyTqPf6PGnlNf5FbnCTHI1LFjG8yd44Fp0+ehRq3mCAi8iYMHNsHR0V5r/9purti0YQk8PbfAtWYz7N17BDu9VqN8+TLqPqNHDcKQwX0waMhY1KnXGm/i4nBw/yZYWFjolLVDh1aYNWsiZsxYiNq1WyEw8Bb27t2QalY3t+pYt24R1q3bDje3lti37yi2b1+Jr74qDQCwtMyFKlUqYObMv1C7dkt06TIApUsXx44dq3XKKco+FSWnSFlFyXn45HnMWb4OA3t2xPbls1G6RFEMGDMdkdEvtfZftGYLvPYfw7ihfbF7zQJ0at0UIzzm4Na9h+o+a7buxva9RzF+aF/s8VyAkf17wHPbHmzedTDTOUXZnyJlFSUnEemPKN971nnyPd6LklWUnKzz+Ddq7Dkp80wkXYd29cTMvECqyy6c2we/ywEYPmIiAMDExASPH/phyVJPzJ6zJEX/zZuWwcrSEm3de6nbzp/dh2sBNzB4yFgAwNMn/pi/YAXmzV8BALC2zoPgZ9fQp99IbN++N9UsihxpX2F45sxuXLkSiJEjJ6mz3r/vg2XL1mLu3GUp+m/YsBiWlpZo376Puu306V0ICLiJYcMmaH2P6tUr4dy5fShdujaePg3W2keZnJRmTmPap9khp0hZjSVn3MPDaebsNngsypcpiQnD+gEAVCoVvu0yEF3dW6BfV/cU/Rt16o/+3b5H13Yt1G0jJ8+Bhbk5Zo4fDgAYPP532NvaYOroQan20cayePNUlxnL/kwPUbIaS86kxOeZyp8Z8wv30Nu2Rv67UW/bItKXtOo8wHi+96zz5Hm8z05ZjSUn6zz+jYqQM6tqPbnVeUY/k0mhUKBatUrwPnFW3SZJErxPnIObW3Wt67jVqq7RHwCOHjul7l+sWGHky+cM7xPn1MtjY1/h0qWrcKulfZvpzVq1akWc+Gi7kiThxIlzqFmzmtZ1atWqhpMnz2m0HTt2BrVqae8PvPvSqFQqxMTEZjqnCPtUlJwiZRUlp1KpxM27D+FWrZK6zdTUFG7VKiLg5h2t6yQmKmFhbq7RZmFujqtBt9U/VylfBr5Xr+Pxf0X7nQeP4X/9NurVrJqpnKLsT5GyipJT31R6fBGJRpTvPes8+R7vRckqSk7WefwbNfac+ia3Oi9DN/5++PAhcufODSend9eJnzlzBseOHUNERAQcHBzQvHlz1K1bV68BHRzsYGZmhrDQCI32sLBwlC1TQus6Li6OCA0L12gLDY2Ai7Pju+XOTv+1fdInLAIuLpm/Bt7BwfZd1rBPs0agTCpZnZ0dtfZ3/i/rpywsLDB9+jhs374Xr169zmROMfapKDlFyipKzuiXr5CsUsHeNq9Gu72tDR491X7GoU6NKljvtQ/VK32FQvmd4eN/Hd7nfJGs+nA47tvVHa/j4tHmx+HIYWqKZJUKw/p0RasmX2cqpyj7U6SsouQkyo4MUecB4nzvWefJ93gvSlZRcrLO49+oseck3WRokGnZsmX44Ycf4OTkBG9vb3h6eqJx48aoX78+goODsWLFCiQkJKBRo5Q34vqYUqmEUqnUKbgcmZmZYePGJTAxMUl1ijWRXI0d/CMm/7kcbX4cDhMAhfK7oG2zhth9+KS6z5FTF3DA+yxmjR+OEkUL4c6Dx5i1xBOO9nZo26yBwbITGcV16yR7rPMMi3UeUepY55HI5FbnZWiQ6cWLF8iXLx8A4OjRo+jduzeaNGmiXl6yZEn8888/ny0+du3aBS8vr3S9Z0REFJKSkuDk7KDR7uTkiJBPRivfCwkJh7OT5hkiZ2cHdf+Q0LD/2hwREhL2oY+TA64F3EhXLu1Zo99ldfo0qwNCQrRnDQ0N19r/05FYMzMzbNq0BIULF0CLFl0zfXbrXU4x9qkoOUXKKkpO27x5kMPUNMXNHyOjY2BvZ6N1HTubvPhr2hgkJCYi5uUrODnYYf7fG1Hwoyf0/LlyA/p2aYcWjeoBAEoXL4Lg0HCs2vJPpooPUfanSFlFyalvojwthLI3Q9R5gDjfe9Z58j3ei5JVlJys8/g3auw59U1udV6G7slkYWGBV69eAQCioqJQsmRJjeUlS5ZEWFiYtlU1uLu7Y+3atRqv1CiVSvj7B6JRw3rqNhMTEzRqWA8+Ple0ruPjewWNGtXTaGvS+Gt1/0eP/sWLF6Ea28yTJzdq1qwKH1/t20wPpVKJq1evo2HDD1PJTUxM0LBhXVy65K91HV9ffzRooDn1vHHj+vD1/dD/feFRokQxtGzZHVFRMZnO+D6nCPtUlJwiZRUlp0KhwFeli8P36nV1m0qlgs/V66j8VZk01nx3fb6zoz2SkpNx/KwvGtapoV729m0CTE01D3s5TE0hqTJ3fkGU/SlSVlFyEmVHhqjzAHG+96zz5Hu8FyWrKDlZ5/Fv1Nhzkm4yNJOpSpUqOHr0KAYOHIhy5crBx8cHRYsWVS+/ePEiXFxcPrsdhUIBhUKR7vedv/BveK6ejyv+gfDzu4phQ/vDyioX1q7bBgDwXLMQwcEvMGHiTADAokWrccLbCyNHDMDBQ8fRuVNbVK9eCQMH/are5l+LVmH8uGG4d/8hHj9+iimTRyM4OBR79hxJdy5t/vprFf7++09cuRKIy5cDMGRIH1haWmL9+h0AgFWr5iE4OASTJs0GACxZ4omjR7dh+PD+OHToBDp2bI1q1Spi8OB3d8o3MzPD5s3LULVqBXz/fR/kyJFDfR1/VFRMpqeji7JPRckpUlZRcvbs0BoTZi1G+dIlULFsSWzYeQDxbxPQrllDAMD4mX/BycEeI/p1BwAE3rqLsIgolClRDGERkVi2fjtUkgo/dmmn3uY3tV2xctNO5HNyQImihXD7/iOs99qPds0bZjqnKPtTpKyi5NQnUW7kSNmboeo8QJzvPes8+R7vRckqSk7WefwbNfac+iS3Oi9Dg0zdu3fHb7/9Bg8PD5QoUQL79+/HzZs3UaBAAQQHB+PevXsYNWqU3kPu2LEXjg52mDxpFFxcHBEQcAMtW/VQ30ixcKH8UH1007eLPpfRo+cQTJ3yK6ZPG4N79x+hfYe+uHHjw9MK5sxdCisrSyxfOhs2NtY4f94PLVv3QEJCgk5Zvbz2w8HBHpMm/QxnZ0cEBt5E27Y91VkLfZLVx+cKevceBg+PUZgyZTTu33+MTp3+h5s37wIA8ud3QevWTQEAly5pPgq0adPOOHvWJ1M5RdmnouQUKasoOZs3rIuol7FYsnYrIqJjULZEUSyfOQEO/02jfhEWAROTD2erEhKVWLRmK569CIVlrpyoX6sqfh87DNa5rdR9xg/ti8WeWzF94d+IiomFo70tOrT6Fj/90CHTOUXZnyJlFSWnPsntWn0yToaq8wBxvves8+R7vBclqyg5Wefxb9TYc+qT3Oo8E0mSMvQ7v3nzBrt378aVK1cQGhoKSZJga2uLMmXKoGXLlihRQvtd4T/HzLxAptbLaoocGRqXMxhlcpKhIxClKe7h4c93MhKWxZsbOgIZQFKi9ifcfAl/FOmht22Ne7JRb9si+WGdxzqPSB9Y55EIsqrWk1udl+F/Sa2srNC9e3d07979S+QhIiKSHZXsznGRsWKdR0REpF9yq/PEOF1DRESUjcntWn0iIiIiuZBbnZehp8sRERERERERERFpw5lMREREBiavSdRERERE8iG3Oo+DTERERAYmt2nURERERHIhtzqPl8sREREREREREZHOOJOJiIjIwFQmhk5ARERERF+C3Oo8DjIREREZmNwebUtEREQkF3Kr83i5HBERERERERER6YwzmYiIiAxMXue3iIiIiORDbnUeB5mIiIgMTG5PHSEiIiKSC7nVebxcjoiIiIiIiIiIdMaZTERERAYmtxtCEhEREcmF3Oo8DjIREREZmLxKDyIiIiL5kFudx0GmDFImJxk6AlG2YFm8uaEjpFt88FlDR0iXXPnrGzoCEZHQWOfJl22u3IaOkC7R8a8NHSFdRKrz3gRuNnSEdLGq1M3QEYjShYNMREREBia3G0ISERERyYXc6jwOMhERERmY3K7VJyIiIpILudV5fLocERERERERERHpjDOZiIiIDExe57eIiIiI5ENudR4HmYiIiAxMbtfqExEREcmF3Oo8Xi5HREREREREREQ640wmIiIiA5NkN5GaiIiISB7kVudxJhMREREREREREemMM5mIiIgMTG7X6hMRERHJhdzqPA4yERERGZhKZtOoiYiIiORCbnUeL5cjIiIiIiIiIiKdcSYTERGRgcnr/BYRERGRfMitzhNmJtNPA3vh/l0fvI59gAvn9qGGa5U0+7dv3wpB10/jdewDXPU/jhbNG6XoM9ljFJ4+8cerl/dx5NBWlCxZTFZZmZOfPXPqnvPytesY/KsHGrbpjgp1W8D7zIXPrnPJPxAdfxyCqg1ao0WnPth94FiKPlt27kPT9r1QrWEbdO0/Atdv3tEp53si7FORcuqLCpLeXkSiEuV7z5zyzAkAffp1w5VAbzwNDcRh7+2oWq1imv3btGuOC36H8DQ0EKcv7EWTb7/WWL5o6R8If3lH47Vt5yqdc4qyT0XJufXASTTvPw6uHQah26jfcf3uo1T7KpOSsHzrfnw3YDxcOwxCh+FTcc4/SKPP0i17Uant/zRebQb9pnNOQJx9KkpOfZFbnSfEIFPHjm0wd44Hpk2fhxq1miMg8CYOHtgER0d7rf1ru7li04Yl8PTcAteazbB37xHs9FqN8uXLqPuMHjUIQwb3waAhY1GnXmu8iYvDwf2bYGFhIYuszMnPnjn1kzM+/i3KlCyOCb8MSlf/Z8EhGDx6EmpWqwyvtUvwQ6d28Ji1AOd9r6j7HDp+GrMXrcRPfbpjx5pFKFOyGAb8PBGR0TGZzgmIs09FyUlE+iPK95455ZkTANp93wJTfx+HubOWoPHX7rgRdBvbd62Gg4Od1v41albFitV/YtMGLzSq3w6HDnhj3eYlKFuulEY/72NnUL5UXfXrf31/1imnKPtUlJyHz/phzpodGNi5FbbNm4gyxQph4OSFiIyJ1dp/8aY98DpyBuP6d8XuxVPQsfnXGPnHMtx6+K9GvxKF8+PE2jnq17qZv2Y643ui7FNRclLmmUiSZBTDYWbmBVJdduHcPvhdDsDwERMBACYmJnj80A9Llnpi9pwlKfpv3rQMVpaWaOveS912/uw+XAu4gcFDxgIAnj7xx/wFKzBv/goAgLV1HgQ/u4Y+/UZi+/a9mf49RMnKnPzsmTP9OeODz6Yrc4W6LbDwj9/Q+Os6qfaZt3Q1zlzww+6Ny9Vtoyb9gVev32DFvOkAgK79R6BC2dLqgSuVSoUm7j3RrUMb9PuhU6rbzpW/fpr5jGmfipAzKfF5pvJnRv+iHfW2rb8f79Dbtoj0Ja06DzCe7/3nMGf2zWmbK3eaWQ97b8c1/+sYO3qaOmvAzdNYtXID/pr/d4r+f3vOh6VlLnTvPFDdduj4NgRdv43RIz0AvJvJZJ3XGr26D07zvT8WHf86zeXGtE9FyfkmcHOqy7qN+h0VShXF+AHdALyryZr2HYuuLRuib4cWKfo37j0a/Tt+hy4tG6rbRs5chpzm5vjj574A3s1kOul7DTsWTEr1fbWxqtQtzeXGtE9FyZlVtZ7c6jyjn8mkUChQrVoleJ/48H/yJEmC94lzcHOrrnUdt1rVNfoDwNFjp9T9ixUrjHz5nOF94px6eWzsK1y6dBVutbRvMztlZU5+9sypn5yZERB0G26fTAmuW6s6AoJuAQCUSiVu3rkHtxof+piamsLNtYq6T2aIsk9Fyalvkh7/RyQaUb73zCnPnO+zVq5SHqdPfbgkXpIknDl1Aa41qmpdx7VGFZw5dVGj7aT3Obh+9O87ANStVxM371/AxcuHMXveZNja2uiUU4R9KkpOpTIJtx78C7fK5dRtpqamqFW5HALuPNS6TmJSEszNFRptOc3NcfXWfY22J8FhaNx7NFr8bzzG/rkKL8IjM5XxPVH2qSg59U1udV6GBpnWrFmDW7cy/39yMsPBwQ5mZmYIC43QaA8LC4eLs6PWdVxcHBEaFq7RFhoaoe7v4uz0X9snfcIi4OLilO2zMic/e+bUT87MiIiKhr2drUabva0NXr+Jw9uEBETHxCI5WZWyj50tIqKiM/2+ouxTUXISZUeGqPMAcb73zCnPnABgZ28LMzMzhIdpDgSEhUfCydlB6zpOzg4IC9P83cI/6e/tfRaDB45B+za9MdVjDurUrYGtO/+GqWnm5gGIsk9FyRkd+xrJKhXsbaw12u1t8iAi+qXWdepULY8Ne47hSXAoVCoVLl67Ce+L/giP+tC/YulimD68N5ZNHo6JA7vjeWgEeo+bgzdxbzOVExBnn4qSk3SToafLHTlyBEeOHIGLiwsaNmyIBg0awMbGJsNvqlQqoVQqM7weERFRdqQydAAisM4jymq7dx5U//etm3dx88YdXA7wRt36NXH2tI8Bk1FmjenXGVOWrEfbwZNgAhMUdHFE28Z1sdv7vLpP/eofbhhfumhBVCxdDM37j8WR85fx/bf1DBGbvjC51XkZHiafOHEiqlatin379uGnn37C7NmzceXKFahU6d91u3btQu/evTVeqYmIiEJSUlKKswROTo4I+WS08r2QkHA4O2mOhDo7O6j7h4SG/df2SR8nB4SEhKX79xA1K3Pys2dO/eTMDAc7W0R+MiMpMjoGua0skdPCArY21siRwzRln6hoOHwyuykjRNmnouTUN7lNoybjldV1HiDO95455ZkTAKIio5GUlARHJ80bEzs52qeYkfFeWGgEnJw0fzfHNPoDwJPHzxAREYVixYtkKqco+1SUnLbWuZHD1DTFTb4jY17BwTav1nXs8ubBwvGD4bttMQ6v+gN7l06FZS4LFExlxhsAWOe2RJH8znj6gv9fxFhy6pvc6rwMDzIVLlwYvXv3xooVKzB06FAolUrMmTMHP/30E7Zs2YKQkJDPbsPd3R1r167VeKVGqVTC3z8QjRp+GNU1MTFBo4b14ONzRes6Pr5X0KiR5ihwk8Zfq/s/evQvXrwI1dhmnjy5UbNmVfj4at9meoiSlTn52TOnfnJmRuUKZeF7JUCj7aLfVVSu8O56f4VCga/KlILv5Wvq5SqVCr5Xrqn7ZIYo+1SUnETZVVbXeYA433vmlGfO91kDrt3A19/U1sha/5vauOx3Ves6l/2uof43bhpt3zSsg8t+11J9n3z5nWFnZ4PQEO3/Zzs9OUXYp6LkVCjMUK5EYfgG3la3qVQq+AbeQuUyxdNc18JcAWd7WyQlJ+P4BX80qFUl1b5x8W/xNCQ81YGr9BBln4qSk3STocvlNFY0M0OdOnVQp04dRERE4MSJEzh9+jR2796Nbdu2pbmuQqGAQqFIs8/H5i/8G56r5+OKfyD8/K5i2ND+sLLKhbXr3r2P55qFCA5+gQkTZwIAFi1ajRPeXhg5YgAOHjqOzp3aonr1Shg46MOjIf9atArjxw3DvfsP8fjxU0yZPBrBwaHYs+dIJvaGeFmZk589c+onZ1xcPP59Fqz++XlwKG7ffYC81nmQz8UJ85d5IiwiEn/8NgoA0KldS2zZuQ9/LlkN91ZNcelKAI6cOIOlc6aqt9GzszsmzPgT5cuWQoWvymDj9t2If5uAdi2/zXROQJx9KkpOfZLbNGoyfllZ5wHifO+ZU545AWD5Ek8sWjYL164Gwf9KIAYM6gVLq1zYsvEfAMDi5bMQ8iIU06fMAwCsXLYeew5uwE9DfsSxI6fh3v47VKlaAb8Mf/dEMSsrS4waOwT79xxBWFgEihYrBI+po/Ho4ROc9E7fU221EWWfipKzZ9tvMXGhJ74qWQQVSxXDxn3HEf82Ee2a1AUAjJ+/Bs72Nhje83sAQOCdhwiLikHZYoUQGhmDZVv3QSVJ+NG9mXqbcz13oEGNSsjnaI/wqJdYumUvcpiaosXXNTOdExBnn4qSU5/kVudlepDpYw4ODujUqRM6duyI69ev62OTGnbs2AtHBztMnjQKLi6OCAi4gZateqhvple4UH6NadwXfS6jR88hmDrlV0yfNgb37j9C+w59cePGHXWfOXOXwsrKEsuXzoaNjTXOn/dDy9Y9kJCQIIuszMnPnjn1kzPo9j30GTpG/fPsRSsBAG1bNMGMib8gIjIKL0I/TNUtmN8FS+ZMxey/VmDjjt1wdnTAlDEjUPejp1+0aPINomNeYvGqjYiIikLZUiWw/M9pOl0uB4izT0XJqU8qSYzpzyRPX7rOA8T53jOnPHMCwO5/DsHe3g5jxg+Dk7Mjgq7fQufv+yH8v6eCFSyYD9JHWf0uXcXAfqMwbuIITJj0Mx4+eIxe3Qbj9q17AIDk5GSUL18anbu2Q968eRDyIgynTp7HzOkLkZiY+XuaibJPRcnZvH4NRMe+wtLNexERHYsyxQpimccw9c3AQyKiYGpqou6fqFRi8cY9eBYaDsucFqhXvSJ+H9EH1rkt1X3CIqIxZu4qxLx6A9u8uVGtXElsnD0WdnnzZDonIM4+FSWnPsmtzjORpPT/xoMHD8bMmTORJ49uXwBtzMwL6H2bRET6EB+c+TOKWSlX/vqGjpCtJCU+z7L3+qHI93rb1oYn/+htWyQvrPNIzmxz5TZ0hHSJjn9t6AjZzpvAzYaOkC5WlboZOkK2k1W1ntzqvAzNZFqyZMmXykFERCRb8jq/RcaKdR4REZH+ya3O08vlckRERJR5KtmVH0RERETyILc6L8NPlyMiIiIiIiIiIvoUZzIREREZmCSzM1xEREREciG3Oo+DTERERAYmt0fbEhEREcmF3Oo8Xi5HREREREREREQ640wmIiIiA5PbDSGJiIiI5EJudR4HmYiIiAxMbtfqExEREcmF3Oo8Xi5HREREREREREQ640wmIiIiA5PbDSGJiIiI5EJudR4HmYiIiAxMkgw7jfrw4cPYt28fYmJiUKRIEfTp0wclS5bU2vf48eM4c+YMnj59CgAoXrw4unbtmmp/IiIiIjmTW53Hy+WIiIhk7MKFC1i/fj06dOiAWbNmoUiRIpgxYwZevnyptf/NmzdRt25deHh4YPr06bC3t8f06dMRFRWVxcmJiIiIKC2GqPM4yERERGRgKkh6e2XU/v370bhxYzRs2BAFCxZE//79YW5ujpMnT2rtP2zYMDRr1gxFixZFgQIFMHDgQEiShOvXr+u6G4iIiIiyHbnVebxcjoiIyMD0ea2+UqmEUqnUaFMoFFAoFCn6JiUl4eHDh2jXrp26zdTUFBUrVsTdu3fT9X4JCQlISkpC7ty5dcpNRERElB3Jrc7jIFMGWZil/PCMUUKS8vOdiChdbAo3MnSEdHkTuNnQEdLFqlI3Q0fI1nbt2gUvLy+Ntg4dOqBTp04p+sbGxkKlUsHGxkaj3cbGBsHBwel6v02bNsHOzg4VK1bMdGbK/kSpn0TBOk//ouNfGzoCGYgodQnrPALEqPM4yERERGRgUiamP6fG3d0drVq10mjTdnZLH3bv3o3z589j8uTJMDc3/yLvQURERCQyudV5HGQiIiIysMxcY5+a1KZMa2NtbQ1TU1PExMRotMfExKQ46/WpvXv3Yvfu3fjtt99QpEiRTKYlIiIiyt7kVufxxt9EREQyZWZmhuLFiyMoKEjdplKpEBQUhNKlS6e63p49e7Bz506MHz8eJUqUyIqoRERERJQBhqrzOJOJiIjIwCRJf2e4MqpVq1ZYsmQJihcvjpIlS+LgwYNISEhAgwYNAACLFy+GnZ0dunV7d4+F3bt3Y/v27Rg2bBicnJzUZ8dy5syJnDlzGui3ICIiIjJOcqvzOMhERERkYPp86khG1alTB7Gxsdi+fTtiYmJQtGhRjB8/Xj2NOiIiAiYmJur+x44dQ1JSEubNm6exndRuOklEREQkZ3Kr80wkQw6rfcTMvIChI6SLKE9H4VNHiPRHlO99lP86Q0dIF1GeOpKU+DzL3qtZoRZ629aRp4f0ti0ifbGyLGroCNkK6zwi+eHT5fQvq2o9udV5nMlERERkYPp86ggRERERGQ+51XkcZCIiIjIwfT51hIiIiIiMh9zqPD5djoiIiIiIiIiIdMaZTERERAZmJLdHJCIiIiI9k1udx0EmIiIiA5PbNGoiIiIiuZBbncfL5YiIiIiIiIiISGecyURERGRgcnvqCBEREZFcyK3OE2Ym008De+H+XR+8jn2AC+f2oYZrlTT7t2/fCkHXT+N17ANc9T+OFs0bpegz2WMUnj7xx6uX93Hk0FaULFlML1n/N+AH3Lx1DpFRd3Dq9G5Ud62cZn939+/gf9UbkVF3cOnSYTRr1kBj+fgJI+B/1Rth4Tfx7HkA9u/fCNcaVXTOKco+FSWnSFmZU785RfnOA8DWAyfRvP84uHYYhG6jfsf1u49S7atMSsLyrfvx3YDxcO0wCB2GT8U5/yCNPku37EWltv/TeLUZ9JvOOUX57PVFJUl6exGJSpRjqSg5RTmOipJTpKzMKc+cAOs81nnGQYhBpo4d22DuHA9Mmz4PNWo1R0DgTRw8sAmOjvZa+9d2c8WmDUvg6bkFrjWbYe/eI9jptRrly5dR9xk9ahCGDO6DQUPGok691ngTF4eD+zfBwsJCp6zt27fCzJkT8cfvC1G3Tktcv34Te/asTzVrrVrVsHbdX1i/bhvq1P4O+/YfxdZtK/HVV6XVfe7fe4hffp6EmjWa4dsmHfDk32fYu3c9HBzsMp1TlH0qSk6RsjKnfnOK8p0HgMNn/TBnzQ4M7NwK2+ZNRJlihTBw8kJExsRq7b940x54HTmDcf27YvfiKejY/GuM/GMZbj38V6NficL5cWLtHPVr3cxfdcopymdPRPojyrFUlJyiHEdFySlSVuaUZ06AdR7rPONhIhnJrc7NzAukuuzCuX3wuxyA4SMmAgBMTEzw+KEfliz1xOw5S1L037xpGawsLdHWvZe67fzZfbgWcAODh4wFADx94o/5C1Zg3vwVAABr6zwIfnYNffqNxPbte1PNYmGmSPP3OHV6N65cCcAvP3uos969dxHLl63Dn38uS9F/3frFsLLKhQ7t+6rbTp7ahcDAmxg+bILW98iTJzdCQoPQ8rtuOHXqgtY+CUnKNHMa0z7NDjlFysqc+v3eG8t3HgCi/NelugwAuo36HRVKFcX4Ad0AACqVCk37jkXXlg3Rt0OLFP0b9x6N/h2/Q5eWDdVtI2cuQ05zc/zx87v8S7fsxUnfa9ixYFKa7/0xq0rd0lxuLJ99UuLzdP9OuqpfoLHetnX2ubfetkWkL1aWRdNcbkzHUhFyss5jncec8sv5JnBzmllZ52V8n2ZVrSe3Os/oZzIpFApUq1YJ3ifOqtskSYL3iXNwc6uudR23WtU1+gPA0WOn1P2LFSuMfPmc4X3inHp5bOwrXLp0FW61tG8zvVmrVq2AkyfPa2Q9eeI8ataqpnWdWrWq4uSJ8xptx4+fQa2a2vsrFAr06dMVMTGxuH79VqZzirBPRckpUlbm1H9OEb7zAKBUJuHWg3/hVrmcus3U1BS1KpdDwJ2HWtdJTEqCubnmAFtOc3NcvXVfo+1JcBga9x6NFv8bj7F/rsKL8MhM5xTls9c3FSS9vYhEI8qxVKScIhxHRckpUlbmlGdOgHUe6zzjkuEbfx8+fBj3799H1apVUbduXZw5cwa7du2CJEmoWbMmOnfujBw5cugtoIODHczMzBAWGqHRHhYWjrJlSmhdx8XFEaFh4RptoaERcHF2fLfc2em/tk/6hEXAxcUp01ntHWxTzVo6lazOzo4IC0vZ39nZQaOteYtGWLduESwtcyEkJAytW/dAZGR0pnKKsk9FySlSVubUb05RvvMAEB37GskqFextrDV/B5s8ePTshdZ16lQtjw17jqF6+VIo5OII38Db8L7oj2TVh3/gKpYuhunDe6NoAReER73E8q370HvcHPzz12RYWebMcE5RPnui7Cqr6zxAnGOpKDlFOY6KklOkrMwpz5wA67x3bazzjEWGBpl27tyJvXv3olKlSli3bh3Cw8Oxb98+tGzZEiYmJjhw4ADMzMzQqVOnNLejVCqhVKY9zZc0nTl9EbXdvoO9vR1+7NMFGzYsQYNv2iFch5FkIjJexvCdH9OvM6YsWY+2gyfBBCYo6OKIto3rYrf3h7Py9atXVP936aIFUbF0MTTvPxZHzl/G99/Wy7KsohPlzBRlb9mxzjOGY2l6iJKTiLIP1nlZR251XoYGmU6dOoVBgwahVq1aePz4McaOHYvBgwejfv36AIACBQpg48aNny0+du3aBS8vr3S9Z0REFJKSkuD0yRkfJydHhHwyWvleSEg4nJ0cNdqcnR3U/UNCw/5rc0RISNiHPk4OuBZwI125tImMiE4166cjq++FhobDyUlbf83R3bi4eDx8+AQPHz6Bn99VBASeRK9enTF37tIM5xRln4qSU6SszKnfnKJ85wHA1jo3cpiaprj5Y2TMKzjY5tW6jl3ePFg4fjASEpWIefUaTnY2WLD+HxT85Pf9mHVuSxTJ74ynL8JS7ZMWUT57fTOS2yOSzBmizgPEOZaKklOU46goOUXKypzyzAmwznvXxjrPWGTonkzR0dEoUeLdNLaiRYvCxMQERYsWVS8vVqwYoqM/P7XX3d0da9eu1XilRqlUwt8/EI0afhgpNTExQaOG9eDjc0XrOj6+V9CokebIapPGX6v7P3r0L168CNXYZp48uVGzZlX4+GrfZnoolUpcvRqEBg3qaGRt0LAOLvn6a13H1/cqGjSso9HWqFE9+F7S3v89U1NTmFuYZzqnCPtUlJwiZWVO/ecU4TsPAAqFGcqVKAzfwNvqNpVKBd/AW6hcpnia61qYK+Bsb4uk5GQcv+CPBrWqpNo3Lv4tnoaEp1rQfI4onz1RdmSIOg8Q51gqUk4RjqOi5BQpK3PKMyfAOo91nnHJ0EwmGxsbPHv2DA4ODnjx4gVUKhWePXuGQoUKAQCePn0Ka2vrz2zl3Q2/FIq0n9L2sfkL/4bn6vm44h8IP7+rGDa0P6yscmHtum0AAM81CxEc/AITJs4EACxatBonvL0wcsT/27vzuCjqxw3gDyuXqNyXCKKo0Fe/mAceqSVqh1ehSeZVeaYpWn4zLdS8K2/zwl9qkhoKouKR4RWWeECKgqiJgKSFCAgLnrA4/P5Q1zYWFBicHeZ59+IPxs8uD7Pu+vSZz8yMxr6fD+Hd/n5o3bo5xox9crvF5SvWIfCLCbicnIq0tGuYNfMzpKffwK5d+8uzS0pYsXwdvlu7GGfizuHUqbMYFzACFhYW2LRpGwBg7drFSE+/gRkzFgAAVq/6HvsPhGLChJGIjIyC/ztvolUrb4wP+AIAYGFRE5OnBOCnvYeQkZEJO3sbjB79PlxcnLFzx08VzimXfSqXnHLKypzi5pTLex4A3vd7DdO+3YCmjd3h3aQhNu85hHv3C9Hn1Y4AgMCl38PJzhofv/82ACDhUioyc9R4oaEbbtxUI2jrHgjFxRjW9w3tcy7asA2+bZqjroMdsnLysHrLbtRQqdDjlbYVzimX115MSltGTYZJqp4HyOezVC455fI5KpeccsrKnMrMCbDnsecZjnJNMnXq1AkrV66Ej48PEhMT4efnh02bNuHWrVswMjLCjh070L59e9FDbtu2Gw72tpj55SQ4OzsgPv48evUeor2QYn03FwiCoB1/4uQpDHk/ALNnTcbcOVNwOfkK+vmPwPnzl7RjFi5ajVq1LLBm9QJYW1vi2LHf0evNISgoKKhU1u3b98LewRbTpk+Ek5MDEhIuok+fD7RZXd3qQfjHxdRiYuIwbOjH+HLGp5g56zOkJKdhwLsf4sKFJADAgwcCPD0bYfCWfrCzs0FOjhqnTyfgtdfewcWLlyucUy77VC455ZSVOcXNKZf3PAB0f7kNcvNvYXXIbmTn5sOroSuCZkzQXiQyIzsHKpWRdnyhRoOVm3fhrxtZsDA3Q6fW3vjqk+GwrG2hHZOZnYspi9ZBfesObKxqo9V/GmPzgs9ha1Wnwjnl8tqLqVhh5YMMk1Q9D5DPZ6lccsrlc1QuOeWUlTmVmRNgz2PPMxxGxeU4QVAQBERERCApKQleXl7o06cPjh8/js2bN6OwsBCtW7fG8OHDYW5e/ivNG5vWK/djpGBmXL4jc1IpKDKMC24SVQdyed/nxP0gdYRnUqv5IKkjPJOiwr+f289q4/KKaM/1e/pvoj0XKUtV9rxaFg3ED6xg7HlEynMnIUTqCM9ELj0PeH5dT2k9r1wrmVQqFd5++22dbR07dkTHjh1FDUVERKQkSrsgJBkm9jwiIiLxKa3nlWuSiYiIiMSntHP1iYiIiJRCaT2vXHeXIyIiIiIiIiIi0ocrmYiIiCSmtGXUREREREqhtJ7HSSYiIiKJKW0ZNREREZFSKK3n8XQ5IiIiIiIiIiKqNK5kIiIiklixwo5wERERESmF0noeJ5mIiIgkJijsXH0iIiIipVBaz+PpckREREREREREVGlcyURERCQxpS2jJiIiIlIKpfU8TjIRERFJTGnLqImIiIiUQmk9j6fLERERERERERFRpXElExERkcSUtoyaiIiISCmU1vM4yURERCQxpS2jJiIiIlIKpfU8TjKVU0GRRuoIRGUyMzaROsIzkdN7SS5ZazUfJHWEZ3I3ZZ/UEYjoOZPL5yiJj71EXNyfyiWXnncnMVTqCCQxTjIRERFJTGnLqImIiIiUQmk9j5NMREREElPaMmoiIiIipVBaz+Pd5YiIiIiIiIiIqNK4komIiEhiSltGTURERKQUSut5nGQiIiKSWHGxIHUEIiIiIqoCSut5PF2OiIiIiIiIiIgqjSuZiIiIJCYobBk1ERERkVIoredxkomIiEhixQq76wgRERGRUiit5/F0OSIiIiIiIiIiqjSuZCIiIpKY0pZRExERESmF0noeJ5mIiIgkprRl1ERERERKobSex9PliIiIiIiIiIio0mQzyfTRmA+QnHQSt/NTcDx6D9r4tChzfL9+vZF47lfczk/BmbhD6NG9a4kxM2dMwrU/43ArLxn7f96Kxo0bKiorcyr3tf9w9Hu4cDEaN3Mu4civEWjt82KZ4/v27Ym4M4dxM+cSYmMj8cYbvto/MzY2xpw5nyM2NhKZWReQnBKDtWsXw7muY6VzymV/yiWnnLJu2RWJNwaPQ+segzEoIBDn/kgudaymqAhBm8LR473xaN1jMPp9+BmiY8/qjHnwQMCKDVvRfcg4+PQcjB7vjceazeEGc2RJKC4W7YtIruTy+cScyuwkAPcpe55h55RT1q0//YLuIybD5+3RGPTpXJxLSi11rKaoCGu27EbPUZ/D5+3R8B8/A9Gnz+mMWR2yC83fHKHz9daYqZXOKRal9TxZTDK9885bWLRwBubMXYI27bojPuEC9v30Ixwc7PSOf6m9D37ctAobNmyBT9s3sHv3fmwPX49mzby0Yz6bNBYB44ZjbMDn6NDpTdy5exf79v4IMzMzRWRlTuW+9v369cY330zD1199i44deuHcuQvYtWtjqTnbtWuF4B+WY+MPoejwUk/s2XsAW0O/Q9OmngAAC4uaaNGiGb75ZgU6duiNgQPGoIlnI2zbtq7CGQH57E+55JRT1sio41i4ZiPGvOePsDXz4enhjtGfz8PN3Dy941ds2IrwvQfxRcAwRKxfgv69X8MnMxfi4uUr2jHfh0YgbM9BBAaMwK7vl2LiqMHYELobIRE/VzinmIpF/I9IjuTy+cScyuwkAPcpe55h55RT1sijsVi4LhRjBr6F0GUz4NXQDWO+XIqb6ny941du3onwyF/xxehBiFg9F+/08MXEr1bhYsqfOuMa1XfBLxuXaL9+mP95hTOKTWk9z6jYQA7jGpvWK/XPjkfvwe+n4vHxJ9MAAEZGRkhL/R2rVm/AgoWrSowP+TEItSws4Nf3A+22Y0f34Gz8eYwLePiX7dqfcVi67P+wZOn/AQAsLesg/a+zGD5yIsLCdlf495BLVuasvq+9mbFJmTmP/BqB06fj8en/ZmhzJl0+gTVBP2Dx4qAS43/YuBK1atWEf78R2m1RR3YiIeECPp6g/whBq9bNcfTobnh5dsBff6XrHVNQpCkzp6Hsz6eRS05Dyno3ZV+ZOQcFBKKZVyNMHf/w75wgCHht4EcY2KcHRg7sU2J813dHY9Sgvhjo1127beLMRTAzM8U3X0wAAIyb+g3sbKwwe9JHpY75N1O3so/8isnZ+j+iPVeG+qJoz0UklrJ6HmA4n09Pw5zlz1lWLzGUTgLIp5ew5yn3vSSXrHcSQ8vMOejTufhvk4YIHDMYwMOe9/qwzzCwdzeMeKdnifHdPvgfRvXvjQG9nqyymvjVKpibmeLrT0cBeLiSKerkGWxbPrPMn/1vZp6dyjW+opTW8wx+JZOJiQlatWqOw78c1W4rLi7G4V+i0b59a72Pad+utc54ADhw8Ih2fMOG9VG3rhMO/xKt/fP8/FuIjT2D9u30P2d1ysqcyn7tW7b8L6KijunkjPrlGNq2a6X3Me3atUTUL8d0th069BvatdU/HgCsLOtAEATk5ek/IvEsOeWyP+WQU05ZNZoiXEhKRftW3tptKpUK7Vt5I/5Ckt7HFBZqYGZqqrPNzMwUZxIvab9v0cwTMWcSkfaoDF9KSUNc4iV0atuyQjnFVlxcLNoXkdzI5fOJOZXZSR5n5T59gj3PsHLKKatGU4SLyX+i/YtPJl1UKhXatWiK+Espeh9TqCmCqYnu5Kq5mSnOXLiss+3P9Bvo9sH/0GPkFHy+6Dtcz7xZoYxVQWk9r9x3l8vNzcWBAwfwxx9/QK1Ww8jICI6Ojmjbti18fX2hUok7b2VvbwtjY2Nk3sjW2Z6ZmYUXvBrpfYyzswNuZGbpbLtxIxvOTg4P/9zJ8dG2f43JzIazc8XPL5ZLVuZU7mtvZ29Tak7PUnI6OTkgM7PkeCcne73jzczMMGfu59gWthu3bt2uUE657E+55JRT1ty8fDwQBNjZWOtst7OxxpVr+o+WdvB5ERvD96K193/g5uKEk2cScTg6Fg8EQTtmxIA+uH3nHt4aNhE1VCo8EARMGDYAvbu9XKGcYlParW3JcD3vngfI5/OJOZXZSQDu039izzO8nHLKmpt/61HPs9TZbmdtiSt/Xdf7mA4t/4tNEQfQ+r+ecHN2QEz8RRw+HqfT87w9PTD3k+FoUM8ZWbl5WLNlN4Z+/g12rJyNWhY1K5RVTErreeWaZEpJScGcOXPg7OwMU1NTXL9+HZ06dUJRURE2bdqEqKgoBAYGombNsl9IjUYDjabsJZREJD/GxsbYtHkljIyM8PHH06SOQwrx+bhhmLlkDd4a/gmMYAQ3Fyf4veGLiMgo7Zj9v57AT79EY37gBDRyd8OllDTMXx0MB3sb+L3uK114IgPCnkfVCTuJ+LhPSQpTPhyIWSuC4ffRVBjBCK51HeD3akdEHHqywuplnycr4D0busHb0wPdR0zG/uhTePt1wzigqCTlmmQKDg5Gr1698M477wAAfvvtN+zfvx/z5s3D7du3MXv2bGzduhXDhg0r83l27tyJ8PDwZ/qZ2dk5KCoqguO/ZtMdHR2Q8a9Z1ccyMrLg5Oigs83JyV47PuNG5qNtDsjIyHwyxtEeZ+PPP1MuOWdlTuW+9jezc0vN+e+jFI/duJEFR0d943WPlDwsHqtQ380VPXsOrNQRQ7nsT7nklFNWGytL1FCpcDNXrbP9Zq66xOqmx2ytLbF89mQUFBZCnX8bjnY2WLruR7jWddKOWfzdZowY4IceXToCADw96iP9RhbWbYkwiEkmuSx/pupNip4HyOfziTmV2UkA7lOAPc+Qc8opq41lnUc9T/dUy5vqfNjbWOl9jK1VHXw7bTwKCjVQ37oNR1trLPshHK5ODnrHA4BlbQu4uzjh2vXMUsc8T0rreeVa83zlyhW88sor2u87deqE1NRUqNVq1K5dG0OGDMHJkyef+jx9+/ZFcHCwzldpNBoN4uIS0LXLk4tyGRkZoWuXTjh58rTex5yMOY2uXXUv4vVqt1e0469cuYrr12/oPGedOrXRtm1LnIzR/5zPQi5ZmVPZr/2ZM4nw9e2gk9O3SwfExsTpfUxMzBn4dumgs61r106IiX0y/nHxaNyoAXr3HoycHHWF8v0zp1z2pxxyyimriYkxmnp6ICYuUbtNEAScPJOIFx/d6aY0ZqamcLK3RdGDBzh0NAZdOvho/+z+/QKojHT/yauhUqFYMIx/9JV2a1syTFL0PEA+n0/MqcxO8jgr9yl7nqHmlFNWExNj/KexO2ISnly8WhAExMRfxIulnNb3mJmpCZzsbB72vONx8G3fotSxd+/dx7WMzFInrp43pfW8cq1ksrKyQm5uLpycHh4dzsvLgyAIsLCwAAA4Ozvj9u2nz2qbmJjA5F8X7yrL0m/XYsP6pTgdl4Dffz+DCeNHoVatmgj+4eGV6zd8/y3S069j6rRvAAArVqzHL4fDMfGT0dj38yG8298PrVs3x5ixk7XPuXzFOgR+MQGXk1ORlnYNs2Z+hvT0G9i1a/8z55JzVuZU7mu/Yvk6fLd2Mc7EncOpU2cxLmAELCwssGnTNgDA2rWLkZ5+AzNmLAAArF71PfYfCMWECSMRGRkF/3feRKtW3hgf8AWAh8Xjx5AgtGjRDP79RqBGjRpwenRkISdHXeFTJuSyP+WSU05Z3+/XG1MXrEIzLw94ezXGph37cO9+Afp09wUABH6zEo72tvhk5CAAQMLFy8jMzoFXowbIvJmDoI3bIAjFGPaun/Y5O7/UGt+F7EBdR3s0auCKP5LTsHH7XvTp3qXCOYmqG6l6HiCfzyfmVGYnAbhP2fMMO6ecsr7f53VMW7oeTRs3gLdnQ2zedehhz3v14WrzwCXr4GRng48/6AcASLiUisybuXjBoz5u3MxFUMguCIKAYW/30D7novWh8G3bAnUd7ZCVo8bqkF2ooVKhR+d2Fc5JFVeuSaY2bdpg7dq1eO+992BsbIzt27ejadOmMH10V5/09HTY2tqKHnLbtt1wsLfFzC8nwdnZAfHx59Gr9xDtRerqu7lA+MeFv06cPIUh7wdg9qzJmDtnCi4nX0E//xE4f/7JnYYWLlqNWrUssGb1AlhbW+LYsd/R680hKCgoUERW5lTua799+17YO9hi2vSJcHJyQELCRfTp84E2p6tbPQj/WN0RExOHYUM/xpczPsXMWZ8hJTkNA979EBce3enLxcUZvXu/BgA4GfOzzs/q/sYAHD369KPe+shlf8olp5yydu/SATl5+VgVHIbsXDVeaNQAa74OhP2j0+WuZ2bDSGWkHV9QqMGKDVvx1/VMWNQ0x8ttW+KrKQGwrF1LOyYwYDhWBodi7vJ1yFHnwcHOFv69XsNH7/lXOKeYlLaMmgyTVD0PkM/nE3Mqs5MA3KcAe54h55RT1u4vt0Vu3i2s/jEC2bn58PJwQ9CsibB7tOooIysHKqMnPa+wUIOVm3fir4wsWJibo5OPN77630hY1rbQjsm8mYspi/4P6vw7sLGqg1ZNG2PzoqmwtapT4ZxiUlrPMyoux298//59BAUFITY2FoIgwNPTE+PHj4ej48Ory8fHx+Pu3bt46aWXyh3E2LReuR9DRCWZGZfv6LFUCop4UVilupuyT+oIz8TU7cXn9rOsape9RLw88m7rvwUw0dOw51FVYC8RF/cnGbo7iaFSR3hmZp6dnj5IBErreeWaZHqssLAQgiDA3NxctCAsH0TiYPkgQ8dJppKUVj7IsLHnkZjYS8TF/UmGjpNMJSmt55XrdLnHHi+bJiIiospT2jJqMmzseUREROJRWs+r0CQTERERiUcudwshIiIiovJRWs9TPX0IERERERERERFR2biSiYiISGLFUNYRLiIiIiKlUFrP4yQTERGRxJS2jJqIiIhIKZTW83i6HBERERERERERVRpXMhEREUlMaXcdISIiIlIKpfU8TjIRERFJTGnn6hMREREphdJ6Hk+XIyIiIiIiIiKiSuNKJiIiIolJvYw6MjISe/bsgVqthru7O4YPH47GjRuXOv7EiRMIDQ1FVlYWnJ2dMXjwYLRq1eo5JiYiIiKSB6X1PK5kIiIiklhxcbFoX+V1/PhxbNy4Ef7+/pg/fz7c3d0xb9485OXl6R1/6dIlfPvtt+jatSvmz5+PNm3aYOHChbh69WpldwMRERFRtaO0nsdJJiIiIgXbu3cvunXrhi5dusDV1RWjRo2CqakpoqKi9I7ft28fWrRogbfeeguurq4YMGAAPDw8EBkZ+ZyTExEREVFZpOh5nGQiIiKSWLGIXxqNBnfv3tX50mg0en9uUVERUlNT4e3trd2mUqng7e2NpKQkvY9JSkrSGQ8AL774Ii5fvlzB356IiIio+lJazzOYazIVFf4t6vNpNBrs3LkTffv2hYmJiajPLSbmFJ9csjKnuOSSE5BPVuZ8fsT8NzAsLAzh4eE62/z9/dG/f/8SY/Pz8yEIAqytrXW2W1tbIz09Xe/zq9VqWFlZ6WyzsrKCWq2uVG6q3tjzmFMscsnKnOKTS1bmFJdccpZFaT2v2q5k0mg0CA8PL3VWz1Awp/jkkpU5xSWXnIB8sjKnPPXt2xfBwcE6X3379pU6FpGo5PK+Z07xySUrc4pPLlmZU1xyyfm8yKHnGcxKJiIiIqo8ExOTZz7SZ2lpCZVKVeLolFqtLnHU6zFra+sSF4vMy8srdTwRERERiUMOPa/armQiIiKishkbG8PDwwOJiYnabYIgIDExEZ6ennof4+npiXPnzulsS0hIQJMmTao0KxERERE9O6l6HieZiIiIFKx37944fPgwjhw5gr/++gvr1q1DQUEBfH19AQArV65ESEiIdnzPnj0RHx+PPXv24O+//0ZYWBhSUlLQvXt3iX4DIiIiItJHip5XbU+XMzExgb+/v8FfHIw5xSeXrMwpLrnkBOSTlTmVoUOHDsjPz0dYWBjUajUaNGiAwMBA7bLo7OxsGBkZacd7eXlhwoQJ2Lp1K7Zs2YK6devis88+Q/369SX6DUiJ5PK+Z07xySUrc4pPLlmZU1xyyWmopOh5RsXFxcVi/yJERERERERERKQsPF2OiIiIiIiIiIgqjZNMRERERERERERUaZxkIiIiIiIiIiKiSuMkExERERERERERVVq1vLtcZGQk9uzZA7VaDXd3dwwfPhyNGzeWOpaOCxcuYPfu3bhy5Qpyc3MxadIktG3bVupYJezcuROxsbH4+++/YWpqCk9PTwwZMgQuLi5SR9Nx4MABHDhwAFlZWQAAV1dX+Pv7o2XLlhInK1tERARCQkLQs2dPDB06VOo4OsLCwhAeHq6zzcXFBcuWLZMmUBlycnKwefNmnD17FgUFBXB2dsbYsWPRqFEjqaPpGDdunPbv6D+9/vrrGDlypASJ9BMEAWFhYTh69CjUajVsbW3RuXNn9OvXT+fuE4bg3r17CA0NRWxsLPLy8tCwYUMMHTrU4D7ziUg87HniYc+rWux54mDPExd7HlW1ajfJdPz4cWzcuBGjRo1CkyZN8NNPP2HevHlYtmwZrKyspI6nVVBQgAYNGqBr165YtGiR1HFKdeHCBbzxxhto1KgRHjx4gC1btmDu3LlYsmQJzM3NpY6nZWtri0GDBqFu3booLi7Gr7/+igULFmDBggVwc3OTOp5eycnJOHjwINzd3aWOUio3NzdMnz5d+71KZXiLH2/fvo3p06ejWbNmCAwMhKWlJa5fv45atWpJHa2Er7/+GoIgaL+/evUq5s6di5deeknCVCVFRETg4MGDGDduHFxdXZGamorVq1fDwsICPXv2lDqejjVr1uDatWsICAiAra0tfvvtN8yZMwdLly6Fra2t1PGISGTseeJiz6s67HniYM8TH3seVbVqN8m0d+9edOvWDV26dAEAjBo1CnFxcYiKikKfPn2kDfcPLVu2NPijLwAwdepUne/HjRuHkSNHIjU1FU2bNpUoVUk+Pj463w8cOBAHDhzA5cuXDbJ83L9/HytWrMDo0aOxY8cOqeOUSqVSwdraWuoYZdq1axfs7OwwduxY7TZHR0cJE5XO0tJS5/uIiAg4OTkZ1HsJAJKSkuDj44NWrVoBeLg/o6OjkZycLHEyXYWFhYiJicHkyZO1+7B///44ffo0Dhw4gAEDBkickIjExp4nLva8qsGeJx72PPGx51FVM7zp6kooKipCamoqvL29tdtUKhW8vb2RlJQkYbLq4+7duwCA2rVrS5ykdIIg4NixYygoKICnp6fUcfRat24dWrZsiebNm0sdpUwZGRkYPXo0AgICsHz5cmRnZ0sdqYRTp07Bw8MDS5YswciRIzF58mQcOnRI6lhPVVRUhKNHj6JLly4GtzTZ09MTiYmJSE9PBwCkpaXh0qVLBvc/TA8ePIAgCDAxMdHZbmpqij/++EOiVERUVdjzqh57njjY88TDnic+9jyqatVqJVN+fj4EQSgxI29tba19E1HFCYKA4OBgeHl5oX79+lLHKeHq1auYOnUqNBoNzM3NMWnSJLi6ukodq4Rjx47hypUr+Prrr6WOUqYmTZpg7NixcHFxQW5uLsLDw/Hll19i8eLFqFmzptTxtDIzM3Hw4EH06tULffv2RUpKCjZs2ABjY2P4+vpKHa9UsbGxuHPnjkFm7NOnD+7du4eJEydCpVJBEAQMGDAAL7/8stTRdNSsWROenp7Yvn076tWrB2tra0RHRyMpKQnOzs5SxyMikbHnVS32PHGw54mLPU987HlU1arVJBNVrfXr1+PatWuYPXu21FH0cnFxwcKFC3H37l2cPHkSq1atwqxZswyqgGRnZyM4OBjTpk2Dqamp1HHK9M+jGe7u7toycuLECXTt2lXCZLoEQUCjRo0waNAgAEDDhg1x9epVHDx40CD/YX8sKioKLVq0MMjzyU+cOIHo6GhMmDABbm5uSEtLQ3BwMGxsbAxunwYEBCAoKAhjxoyBSqVCw4YN0bFjR1y5ckXqaEREssKeV3nseeJjzxMfex5VtWo1yWRpaQmVSgW1Wq2zXa1WG/z5xoZu/fr1iIuLw6xZs2BnZyd1HL2MjY21s9oeHh5ISUnBvn378OGHH0qc7InU1FTk5eVhypQp2m2CIODixYuIjIxESEiIQV50EQBq1aoFFxcXZGRkSB1Fh42NTYmC6erqipiYGIkSPV1WVhYSEhIwadIkqaPotXnzZvj5+aFjx44AgPr16yMrKwsREREGVz6cnZ0xa9Ys3L9/H/fu3YONjQ2WLl1qsNdrIKKKY8+rOux54mDPEx97nvjY86iqVatJJmNjY3h4eCAxMVF7m1hBEJCYmIju3btLnE6eiouL8f333yM2NhYzZ86U1RtaEARoNBqpY+jw9vYucZeZoKAguLi4wM/Pz2CLB/DwIpYZGRkGt5TWy8urxGkS6enpcHBwkCjR00VFRcHKykp7wUVDU1BQUOLvokqlQnFxsUSJns7c3Bzm5ua4ffs24uPjMWTIEKkjEZHI2PPEx54nLvY88bHniY89j6patZpkAoDevXtj1apV8PDwQOPGjbFv3z4UFBQY3Kzs4w/yxzIzM5GWlobatWvD3t5ewmS61q9fj+joaEyePBk1a9bUHj20sLAwqGXAISEhaNGiBezt7XH//n1ER0fjwoULJe6aIrWaNWuWuM6BmZkZ6tSpY3DXP9i4cSN8fHxgb2+P3NxchIWFQaVSoVOnTlJH09GrVy9Mnz4dO3bsQIcOHZCcnIzDhw8b1JHNfxIEAUeOHEHnzp1Ro0YNqePo1bp1a+zYsQP29vZwdXVFWloa9u7dq72bkyE5e/YsAGiPvm7atAn16tUzuM98IhIHe5642PPExZ4nPvY88bHnUVUzKjbkKcsKioyMxO7du6FWq9GgQQMMGzYMTZo0kTqWjvPnz2PWrFkltnfu3Bnjxo2TIJF+/fv317t97NixBvXmDgoKQmJiInJzc2FhYQF3d3f4+fkZ/F09AGDmzJlo0KABhg4dKnUUHcuWLcPFixdx69YtWFpa4oUXXsCAAQMM8kJ7p0+fRkhICDIyMuDo6IhevXrh1VdflTqWXvHx8Zg3bx6WLVsGFxcXqePode/ePYSGhiI2NhZ5eXmwtbVFx44d4e/vD2Njwzo2cfz4cWzZsgU3b95E7dq10a5dOwwcOBAWFhZSRyOiKsKeJx72vKrHnld57HniYs+jqlYtJ5mIiIiIiIiIiOj5MtwTg4mIiIiIiIiISDY4yURERERERERERJXGSSYiIiIiIiIiIqo0TjIREREREREREVGlcZKJiIiIiIiIiIgqjZNMRERERERERERUaZxkIiIiIiIiIiKiSuMkExERERERERERVRonmYiIiIiIiIiIqNI4yURERERERERERJXGSSYiIiIiIiIiIqo0TjIREREREREREVGl/T+J1+8nN+OZxAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# changes: \n",
    "#    1. increased kernel size\n",
    "#    2. add another conv/pool layer \n",
    "cnn3 = Sequential(name='two_layer_cnn')\n",
    "\n",
    "num_filt_layers = [32, 32]\n",
    "for num_filters in num_filt_layers:\n",
    "    cnn3.add( Conv2D(filters=num_filters, \n",
    "                    kernel_size=(3,3), \n",
    "                    padding='same',\n",
    "                     ) )\n",
    "    cnn3.add( Activation('relu'))\n",
    "    cnn3.add( MaxPooling2D(pool_size=(2, 2)) )\n",
    "    \n",
    "\n",
    "# add one layer on flattened output\n",
    "cnn3.add( Flatten() )\n",
    "cnn3.add( Dense(NUM_CLASSES) )\n",
    "cnn3.add( Activation('softmax') )\n",
    "\n",
    "# Let's train the model \n",
    "cnn3.compile(loss='mean_squared_error',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "input_shape = [None, 8,8,1]\n",
    "cnn3.build(input_shape)\n",
    "print(cnn3.summary())\n",
    "\n",
    "cnn3.fit(X_train, y_train_ohe, \n",
    "        batch_size=32, epochs=150, \n",
    "        shuffle=True, verbose=0)\n",
    "\n",
    "compare_mlp_cnn(cnn3,mlp,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"two_layer_few_filters\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"two_layer_few_filters\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)       │           <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">12,900</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,010</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)       │           \u001b[38;5;34m160\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_7 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m16\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │         \u001b[38;5;34m4,640\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_8 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_5 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_4 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m12,900\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_9 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,010\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_10 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">18,710</span> (73.09 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m18,710\u001b[0m (73.09 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">18,710</span> (73.09 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m18,710\u001b[0m (73.09 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n",
      "CPU times: user 28.2 s, sys: 15.7 s, total: 43.9 s\n",
      "Wall time: 33.8 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# changes: \n",
    "#    1. increased kernel size\n",
    "#    2. add another conv/pool layer with increasing num filters\n",
    "#    3. add more layers once flattened\n",
    "cnn4 = Sequential(name='two_layer_few_filters')\n",
    "\n",
    "num_filt_layers = [16, 32]\n",
    "for num_filters in num_filt_layers:\n",
    "    cnn4.add( Conv2D(filters=num_filters, \n",
    "                    kernel_size=(3,3), \n",
    "                    padding='same'))\n",
    "    cnn4.add( Activation('relu'))\n",
    "    cnn4.add( MaxPooling2D(pool_size=(2, 2)))\n",
    "    \n",
    "\n",
    "# add one layer on flattened output\n",
    "cnn4.add( Flatten() )\n",
    "cnn4.add( Dense(100) )\n",
    "cnn4.add( Activation('relu') )\n",
    "cnn4.add( Dense(NUM_CLASSES) )\n",
    "cnn4.add( Activation('softmax') )\n",
    "\n",
    "# Let's train the model \n",
    "cnn4.compile(loss='mean_squared_error',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "input_shape = [None, 8,8,1]\n",
    "cnn4.build(input_shape)\n",
    "print(cnn4.summary())\n",
    "\n",
    "cnn4.fit(X_train, y_train_ohe, \n",
    "        batch_size=32, epochs=150, \n",
    "        shuffle=True, verbose=0)\n",
    "\n",
    "compare_mlp_cnn(cnn4,mlp,X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fashion MNIST\n",
    "Okay, so we are honing in on the best performance for the digits data. But, we really need something a bit bigger to work on. Enter: Fashion MNIST. We have already used this dataset in the past, where we got about 84% accuracy with our custom MLP. Lets see how well Keras and tensorflow can perform!\n",
    "\n",
    "Now let's load in a more diverse, harder to classify dataset: Fashion MNIST\n",
    "https://www.kaggle.com/zalando-research/fashionmnist\n",
    "\n",
    "**Labels**\n",
    "\n",
    "Each training and test example is assigned to one of the following labels:\n",
    "\n",
    "- 0 T-shirt/top\n",
    "- 1 Trouser\n",
    "- 2 Pullover\n",
    "- 3 Dress\n",
    "- 4 Coat\n",
    "- 5 Sandal\n",
    "- 6 Shirt\n",
    "- 7 Sneaker\n",
    "- 8 Bag\n",
    "- 9 Ankle boot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (60000, 28, 28, 1)\n",
      "X_test shape: (10000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "# more data for handwriting recognition?\n",
    "# Let's use Raschka's implementation for using the mnist dataset:\n",
    "# https://github.com/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb\n",
    "import os\n",
    "import struct\n",
    "import numpy as np\n",
    "# from sklearn.preprocessing import RobustScaler\n",
    " \n",
    "def load_mnist(path, kind='fashion_train'):\n",
    "    \"\"\"Load MNIST data from `path`\"\"\"\n",
    "    labels_path = os.path.join(path, '%s-labels-idx1-ubyte' % kind)\n",
    "    images_path = os.path.join(path, '%s-images-idx3-ubyte' % kind)\n",
    "        \n",
    "    with open(labels_path, 'rb') as lbpath:\n",
    "        magic, n = struct.unpack('>II', lbpath.read(8))\n",
    "        labels = np.fromfile(lbpath, dtype=np.uint8)\n",
    "\n",
    "    with open(images_path, 'rb') as imgpath:\n",
    "        magic, num, rows, cols = struct.unpack(\">IIII\", imgpath.read(16))\n",
    "        images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(labels), 784)\n",
    " \n",
    "    return images, labels\n",
    "\n",
    "X_train, y_train = load_mnist('data/', kind='fashion_train')\n",
    "X_test, y_test = load_mnist('data/', kind='fashion_t10k')\n",
    "\n",
    "X_train = X_train.reshape(X_train.shape[0],28,28,1)/255.0 - 0.5\n",
    "X_test = X_test.reshape(X_test.shape[0],28,28,1)/255.0 - 0.5\n",
    "\n",
    "print('X_train shape:', X_train.shape) \n",
    "print('X_test shape:', X_test.shape)\n",
    "\n",
    "NUM_CLASSES = 10\n",
    "\n",
    "\n",
    "# make one- hot encoded versions of the data\n",
    "y_train_ohe = keras.utils.to_categorical(y_train, NUM_CLASSES)\n",
    "y_test_ohe = keras.utils.to_categorical(y_test, NUM_CLASSES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eclarson/miniconda3/envs/mlenv3_11/lib/python3.11/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ flatten_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">784</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">78,500</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">5,050</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,550</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">510</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ flatten_5 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m78,500\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │         \u001b[38;5;34m5,050\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │         \u001b[38;5;34m2,550\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m510\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_11 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">86,610</span> (338.32 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m86,610\u001b[0m (338.32 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">86,610</span> (338.32 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m86,610\u001b[0m (338.32 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "Epoch 1/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 7ms/step - accuracy: 0.7613 - loss: 0.6601\n",
      "Epoch 2/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 7ms/step - accuracy: 0.8531 - loss: 0.4027\n",
      "Epoch 3/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 7ms/step - accuracy: 0.8664 - loss: 0.3645\n",
      "Epoch 4/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 7ms/step - accuracy: 0.8768 - loss: 0.3408\n",
      "Epoch 5/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 7ms/step - accuracy: 0.8811 - loss: 0.3254\n",
      "Epoch 6/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 7ms/step - accuracy: 0.8855 - loss: 0.3155\n",
      "Epoch 7/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 7ms/step - accuracy: 0.8863 - loss: 0.3113\n",
      "Epoch 8/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 7ms/step - accuracy: 0.8898 - loss: 0.3063\n",
      "Epoch 9/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 7ms/step - accuracy: 0.8903 - loss: 0.3083\n",
      "Epoch 10/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 7ms/step - accuracy: 0.8911 - loss: 0.3043\n",
      "Epoch 11/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 7ms/step - accuracy: 0.8930 - loss: 0.3060\n",
      "Epoch 12/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 7ms/step - accuracy: 0.8949 - loss: 0.2999\n",
      "Epoch 13/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 7ms/step - accuracy: 0.8942 - loss: 0.3046\n",
      "Epoch 14/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 7ms/step - accuracy: 0.8969 - loss: 0.2971\n",
      "Epoch 15/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 7ms/step - accuracy: 0.8950 - loss: 0.2958\n",
      "CPU times: user 2min 42s, sys: 1min 5s, total: 3min 48s\n",
      "Wall time: 3min 23s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x31a017e90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# make a keras MLP\n",
    "mlp = Sequential()\n",
    "mlp.add( Flatten() )\n",
    "mlp.add( Dense(input_dim=1, units=100, activation='relu') )\n",
    "mlp.add( Dense(units=50, activation='relu') )\n",
    "mlp.add( Dense(units=50, activation='relu') )\n",
    "mlp.add( Dense(NUM_CLASSES) )\n",
    "mlp.add( Activation('softmax') )\n",
    "\n",
    "mlp.compile(loss='categorical_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "input_shape = [None, 28,28,1]\n",
    "mlp.build(input_shape)\n",
    "print(mlp.summary())\n",
    "\n",
    "mlp.fit(X_train, y_train_ohe, \n",
    "        batch_size=32, epochs=15, \n",
    "        shuffle=True, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [\n",
    "    \"T-shirt/top\",\n",
    "    \"Trouser\",\n",
    "    \"Pullover\",\n",
    "    \"Dress\",\n",
    "    \"Coat\",\n",
    "    \"Sandal\",\n",
    "    \"Shirt\",\n",
    "    \"Sneaker\",\n",
    "    \"Bag\",\n",
    "    \"Ankle boot\",\n",
    "]\n",
    "compare_mlp_cnn(None,mlp,X_test,y_test,labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">5,208</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1176</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">117,700</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,100</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,010</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m24\u001b[0m)     │           \u001b[38;5;34m240\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_12 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m24\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_6 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m24\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m24\u001b[0m)     │         \u001b[38;5;34m5,208\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_13 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m24\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_7 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m24\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_6 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1176\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_12 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │       \u001b[38;5;34m117,700\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_13 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m10,100\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_14 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,010\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">134,258</span> (524.45 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m134,258\u001b[0m (524.45 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">134,258</span> (524.45 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m134,258\u001b[0m (524.45 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "Epoch 1/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 6ms/step - accuracy: 0.7146 - loss: 0.0376 - val_accuracy: 0.8635 - val_loss: 0.0196\n",
      "Epoch 2/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.8772 - loss: 0.0176 - val_accuracy: 0.8848 - val_loss: 0.0167\n",
      "Epoch 3/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.8976 - loss: 0.0150 - val_accuracy: 0.8919 - val_loss: 0.0157\n",
      "Epoch 4/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.9093 - loss: 0.0134 - val_accuracy: 0.8972 - val_loss: 0.0151\n",
      "Epoch 5/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.9166 - loss: 0.0122 - val_accuracy: 0.8975 - val_loss: 0.0148\n",
      "Epoch 6/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 6ms/step - accuracy: 0.9249 - loss: 0.0113 - val_accuracy: 0.9050 - val_loss: 0.0140\n",
      "Epoch 7/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 6ms/step - accuracy: 0.9311 - loss: 0.0105 - val_accuracy: 0.9026 - val_loss: 0.0143\n",
      "Epoch 8/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.9365 - loss: 0.0099 - val_accuracy: 0.9043 - val_loss: 0.0145\n",
      "Epoch 9/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.9414 - loss: 0.0092 - val_accuracy: 0.9038 - val_loss: 0.0146\n",
      "Epoch 10/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.9456 - loss: 0.0087 - val_accuracy: 0.9034 - val_loss: 0.0148\n",
      "Epoch 11/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.9496 - loss: 0.0081 - val_accuracy: 0.9052 - val_loss: 0.0146\n",
      "Epoch 12/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.9541 - loss: 0.0076 - val_accuracy: 0.9046 - val_loss: 0.0150\n",
      "Epoch 13/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.9550 - loss: 0.0073 - val_accuracy: 0.9064 - val_loss: 0.0149\n",
      "Epoch 14/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 6ms/step - accuracy: 0.9582 - loss: 0.0069 - val_accuracy: 0.9064 - val_loss: 0.0150\n",
      "Epoch 15/15\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 7ms/step - accuracy: 0.9590 - loss: 0.0068 - val_accuracy: 0.9084 - val_loss: 0.0151\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step\n",
      "CPU times: user 2min 12s, sys: 1min 12s, total: 3min 25s\n",
      "Wall time: 2min 53s\n"
     ]
    },
    {
     "data": {
      "image/png": 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KZGRkhj02KChIuXz5shIREZHi1zhgwAAFUH766Se15fPnz1cApWXLlgkeExgYqFy+fFl59+6d2vKnT58maBseHq7Y2dkpgLJ79261+6KiopSKFSsqgLJ9+3bV8vfv3ytdunRRAGX69Olqj/nxxx8VQGnWrJny5s0btft+//13BVDq1q2boteeWF8LIYQQQndoqgfu3LmjbN26VTl58qTa8rFjxyqA4urqqra8V69eGpdrMnHiRAVQJk6cmKJ87969Uy5fvqwEBgamqL2iKMrNmzeVXLlyKWZmZsqtW7dUyx8/fqyUKVNGAZTTp0+rPSYiIkK5fPmyEhQUpLY8LbWXl5eXoq+vr5QtW1YJDQ1VLX/9+rXSrFkzBVAmT56sWv7gwQPFwMBA0dfXV/bt26e2rrt37yoWFhYKoOzatSvR13zkyBEFUL799tukO0cIke3JVUGF+IzEXh588uTJ1K1bl/r161OnTh3y5MlDSEgIx48f5/r162pXwEyKmZkZY8aM4ddff011lpQ8dvTo0axatQpXV1d69+6dovX++eefHD16lDlz5nDhwgVsbW25fPky27dvx9LSEmdn5wSPadasGXfu3OHWrVuULFlStfyPP/5g79691KtXD0tLSx48eMCOHTuIiIhg9uzZtG7dWm09OXLkwNXVlaZNm9KlSxe6dOlC8eLFOXToEF5eXjRo0ICRI0eqPWbs2LG4u7tz6NAhKlSoQKtWrVRH3Xl4eGBkZMT8+fNT9NqFEEIIITQpXrw4xYsXT/Xjli9fztGjRzXe980339CiRYtUr/PBgwdUrFiREiVKcPv27RQ9plSpUsycOZNhw4ZRp04dvv76a3LmzImbmxv379/n559/pl69emqP2bp1K3369KFXr16sXLlStTwttVft2rWZOHEiEydOpEqVKnz11Vfkzp1bdeXT+vXrq9W0hQsXZvz48UycOJHWrVvj6OhIhQoVCA4O5r///uPFixd07NiRNm3aqD3Ptm3b2LZtGwDBwcEAnDlzRlUHm5ubM2vWrBT1mRAiG8nqkT0hRMYLCAhQhgwZolSuXFnJmzevYmhoqFhbWyutWrVSli9fnuDXO5I4EurNmzdKyZIlFSBVR6yl5LGp+bU0rkePHinDhg1Tihcvrnptffr0Ue7du6exfYkSJRRA7RdQRVGUnTt3Kg4ODoqFhYViaGioWFlZKV26dFHOnDmT5PNfunRJ6dKli1KwYEElZ86cSrly5ZQJEyYor1690tg+NDRU+fnnn5UKFSoouXLlUgwNDZXixYsrvXv3Vi5fvpzi151UXwshhBBCN6SmHkjuiLWkbnPnzlUUJfVHrN26dUsBlBIlSqT8RX2wY8cOpXHjxkqePHkUY2NjpU6dOsrKlSs1tnV1dVUApVevXgnuS2vt9e+//yqNGjVS8ubNq+TKlUupVKmSMnXq1AS1c6xt27YprVq1UszNzZUcOXIoefPmVerVq6csXrxYiYqKStA+ti8Tu6Wlz4QQWU9PURQlU0bwhBBCCCGEEEIIIYT4jMjFC4QQQgghhBBCCCGESAMZWBNCCCGEEEIIIYQQIg1kYE0IIYQQQgghhBBCiDSQgTUhhBBCCCGEEEIIIdJABtaEEEIIIYQQQgghhEgDGVgTQgghhBBCCCGEECINZGBNCCGEEEIIIYQQQog0MMjqALrq9fa/szpCiuTtOj+rI6SYYQ7ZnTNS1PuorI6QYkpWB0ihsgUKZ3WEFAmMCMrqCCmSQ197fht6Hx2d1RFSJOrdg0x5nsjwmxm6PkPz0hm6PiHS6/WueVkdIcXydpyZ1RFSROq8jBWpRXWeXlYHSCEb06JZHSFFrj65n9URUkRfT1u2PEQr2vHXiNR5n458QwkhhBAic0W/z+oEQgghhBDiU9DBOk97fu4XQgghhBBCCCGEECIbkSPWhBBCCJG5FO04NVYIIYQQQqSSDtZ5MrAmhBBCiMylJXPOCSGEEEKIVNLBOk9OBRVCCCGEEEIIIYQQIg3kiDUhhBBCZCpFB08REEIIIYTQBbpY58nAmhBCCCEylw6eIiCEEEIIoRN0sM6TU0GFEEIIIYQQQgghhEgDOWJNCCGEEJlLB08REEIIIYTQCTpY58nAmhBCCCEyV/T7rE4ghBBCCCE+BR2s8+RUUCGEEEIIIYQQQggh0kCOWBNCCCFE5tLBUwSEEEIIIXSCDtZ52eaItc2bN/PLL78k2WbSpEmsXLkycwIJIYQQ4tOIjs7Ym8j2pM4TQgghdIQO1nlpHlhzcnJK8rZ58+aMzAnAqFGj+Prrr5NsExoaipOTE7dv39Z4/5YtW1iwYAEQ8xo8PDwyOmaG2Xg6gNbTN2E7ZiU9Fu7A/25Yku3XnrhI+7/dsBuzkpbTNjJzx1neRkap7t985jJd5/xHg/GraTB+NT0X7eDklXvpzvnjD70IvHaWF89ucPqkO3Xr1EiyfefOjlz0P8aLZzc473OQ1q2aJmgzaeIo7t3x4fnTQPbt2UjZsqXSnXPgwJ5cuXKSJ0+ucvz4NurUqZ5k+06d2nDhwiGePLmKp+c+WrZ0UN1nYGDA1Km/4+m5j/Dwy9y86cHy5XMoVMgy3Tm1KeuPP/Ti+rWzPH92g1Mp3Pb+/sd4/mHbt4q37Tt0aM3uXesJfniRyHcPqF69crozxubUhn30m75dOeS1Hd+7J9m0x5WqNSsl2rZs+dIsWDGDQ17buRLqSc8B3dO9ztTQlj79YWAvrl49zdOI65w4voM6yeTs1Kktfr5HeBpxHW+vA7SK814CaN++Fbt2riPogR9v39yjWjXd6k+hO6TO+/Q2nrxI6ylrsf11KT3m/Yv/nZAk26895kv76eux+3UpLf9Yzcxtp9TrvFMX6TpzEw1GL6fB6OX0nP8fJy/fSXdObfp80pb6SVtyasu215Z6FKB7ny7s99yKz53jbNjjkmRdVqZ8Kea5/MV+z61cCjnHdwO6JWhT274GzmtmccR3J5dCztG0deMMyakt2/6HH3px7eoZnj0N5OQJ92TrvM6d2uLvd5RnTwPx8daw7du3ZteudTwM8ufd2/tUlzpPpFCaB9aWLl2quvXu3RsjIyO1ZV999VVG5gQgT548GBkZJXp/VFRUovfF8vLyok6dOhkZ65PYd+Ems93PMbB5TTYMb49NITMGuezl8YvXGtvvPn+DBXu8GPhlTf4b1ZmJXRuy3/cWC/d6qdpY5TdhWOu6rB/WnvXD2lO3bGFGrDpIYPCTNOfs2vUrZs2cyJSpc6hr1wpfvwB271qHhUVBje3r2ddh3RpnXF03UMe2JTt27ONfNxcqVy6vavPLqEEMGdyXQUN+p37Ddrx89YrdO9eRK1euNOfs0sWRGTPGMW3afOrVc8TP7zI7dqxJNKe9fW1WrVrIqlWbsbdvi7v7fjZvXkqlSjYAGBsbUaNGFf76awH16rWlW7eB2NiUZssWlzRn1LasXbt+xcyZE5k6dQ62dq3w8wtgVzLbfu2HbV/XtiXbNWx7ExNjTp32YMyYaenKFj+nNuyjrdt/ye+TR+A8azmdmn/H1UvXWb5pIWbmphrb5zbKzb07D5g9dRGhIeEZss6U0pY+7dKlHX//PZ5p0+ZhZ98Gf/8Adron/V5as3oRK1duxM6uNTvc97Fly3IqVUq4j44d92eac8WnLf2ZkRQlOkNvIuNJnfdp7TsfyOztpxjYsg4bfuqCTeGCDFq6k8fPX2lsv9v7Ggt2nWNgizr893s3Jn7twP4LgSzcfU7VxqpAHoa1tWf9T11YP7ILdcsVYcSKvQQGP05zTm36fNKW+klbcmrLtteWehSgVfvm/Dp5OItnu9D1y15cvRTI/zbOT7QuM/pQ682dtpiwRGo9I2Mjrl66ztTfZ2ZYTq3Z9l3aMfPvCUydNhc7u9b4+Qewa+fapOu8Nc64rtyIrV0rduzYi9uW5VSOV+edPuXJmLFS56WHLtZ5eoqiKOldydGjR1m5cmWyh+9funSJtWvXcv/+fXLkyEGxYsUYNmwYFhYWbN68GU9PT9q1a8emTZt48eIFNWvWZODAgaoia9KkSZQsWZLevXsDMHjwYBwcHAgODsbT0xNbW1uOHTum9pyVKlVi0qRJAISHhzNs2DCWL1/OL7/8QljYxyPALCwscHZ2BmD//v24u7sTHh6OpaUlnTt3pnHjj6P/Tk5O9OvXDy8vLy5duoSpqSk9evTA3t4+xX32evvfSd7fY+EOKhczZ3SH+gBERyu0/HMj3RtUoq9Dwl+1pm87za3QCJYOaKNaNtv9HP73wlg5yDHR52k8cQ0j29rS0ba8xvvzdp2fZM7TJ93x9PJl+IhxAOjp6XH7pifOi135e6Zzgvbr1y3BxNiY9h17qZadOuHOBd9LDB7yOwD37vgwd97/mDP3fwDky5eXoPsX6NtvJJs370g0i2GOxKcMPH58G97efowcOUGVMzDwLEuWrGTWrCUJ2q9ZswhjY2M6d+6rWnbs2FZ8fQMYNmysxueoXbsaJ0+6Y2NTj3v3ghLNkpzskjXqfdJ/wJw66Y5XvG1/68O2n6lh26/7sO07xNn2J0+44xtn28cqUaIogdfPUaduC3x9LyWZAyCpD7HstI+WLVA40fs27XHl4oUApoyeqcp59MJO1i7fzLKFq5J4hXDIazurlm5k9dINGbLOwIik99/s0qc59JP+bejE8R14e/syYuR4Vc4bgR4sXuLKrFmLE7Rfu2YxJiZGdOzUR7Xs+LHt+PldYsjQMWptS5QoyrWrZ6hr2xI/v4AkcwC8T+Iw9uzSnwBR7x4k+1oywtvrpzN0fbnK1c/Q9Ql1Uueloc7bNS/J+3vM+5fKxSwZ3bkR8KHO+2MN3RtVoW+zWgnaT//3BLdCn7D0x48DmrO3n8b/bggrh3ZM9Hkaj13ByHb16GhfMdE2eTsm/gd5dvp8SqrOg+xTPyUnu+SMTKbOy07bXi+JnNmpHrUxLZrk/Rv2uHDx/GWmjZmlynro/A7Wu2xh+cLVST52v+dW1izbxJqlGxNtcynkHEN7/8LhPceTXNfVJ/eTvD+7bHt9vaS2fMx28/L2ZUScnDdveLJ4sSszZ2nY9msXY2xiTMeOvVXLThzfga/fJYYMGa3WtkSJoly/dpa6dVvgm4I6LzqJIZXs0p8gdd6nlGlzrL1//56ZM2dSqVIlZs6cydSpU2nWrBl6cd4wISEheHh48Ntvv/H7778TEBDAtm3bklyvu7s7JUqUYMaMGXTu3Jk//4wZXR4/fjxLly5l1KhRqrZeXl5UrlwZY2Njpk+fDsCgQYNYunSp6v8eHh64urri6OjI7Nmz+fLLL1m8eDEXL15Ue95NmzZhZ2fHzJkzadiwIfPmzeP+/aQ/pFIqMuo9lx+EY1f24x/h+vp62JUrjN+dUI2PqV7CioD7j1Sni95/9IyTV+/RsILmD/j30dHsvXCD1++iqFYibYeKGxoaUqtWNQ4dPqFapigKhw6fxN6+tsbH2NvVVmsPsP/AUVX7UqWKU6iQFYcOn1Td/+zZczw8zmNvp3mdKclZs2ZVDsdZp6IoHD58ElvbhMUrgJ1dLY4cOam27MCB49jZaW4PMR9o0dHRREQ8S1NObcqa2LY/nMy2P5zEtv8UtGcfNaBy9QqcPv7xlCVFUThz3IMadapmm3XGrFdb+tSQWrU0vJeOnEh0nXb2tdTaAxw4eAy7NGZIec7s359CJEfqvJSLjHrP5fth2Nl8rNH09fWwsymC323Np4NWL2lFwL0w1emi9x894+TlOzSsWFxj+/fR0ew9f53X7yKpVtIqTTm16fNJm+onbcmpDdteW+rRmKwGVKpWgTMn1Ouys8c9qZ6Ouiyjade2r6q2LWO2/Qns7RN7LyXc9gcOHPuktZG29KdIv0y7Kujr16959eoVtWvXxtraGoCiRdUHfRRFYfDgwapfLhs3bpyg0ImvSpUqtGvXTvX/0NCYgae8efNSoEABtbZxTw/Ily8fAMbGxmrt3N3dadKkCS1btgSgcOHCXLt2DXd3d6pUqaJqZ29vT7NmzQDo1q0b/v7+7N27l379+iXIGBkZSWRkpNqypMbfn7x8w/tohYJ51U+HKJjHiNuhTzU+pk3NMkS8fEOfJTtBUYiKVuhqX4F+TWuotbv+8DE9nd15F/Ueo5yGzOnZnDJWaTstzNzcDAMDgwSnoYWGhlGhfBmNj7G2tiAkVH2uuJCQcKytLGLut7L8sCxem9BwrK3TNgBobm4akzM0fs5wyieS08rKQmN7qw8548uVKxdTp45m8+YdPH/+Ik05tSlrYts+JDQs0Zyatn1oSOI5M4K27KOmZgUwMDDgUZj66TrhYY8pVbZktlknaE+fxubUtM+VtymrOaeVBSHx30uyj34aWnJYv0g5qfMyoM7La8zt0AiNj2lT2yamzlu0DRSIio6ma/1K9Guu/kfU9aBH9Fzw38c6r08rylibJZEmcdr0+aQ99ZO25NSOba8t9ShAgUTqskdhjylVrsQnfe7U0LZtH3+dMe+lROo8awuN+4rUeZ+ADtZ5n2RgLTw8nJEjR6r+37FjRzp16kSTJk2YNm0aVatWpVq1atSrVw9T04+DOhYWFmpzaxQoUICnTzUPJMUqU0bzDhnfq1evCAgI4Icffkiy3f3791WFVKwKFSqwe/dutWU2NjZq/y9Xrhx37mieIHbr1q24ubmpLVv1bcbO/+F54yEuh30Z06E+VYtbcO/RM/7ecZalB88zoHlNVbuSFvnZNKIjL96846D/LSZsPs7yH9qkeXBNxEwau3atM3p6eokekp9daFNWIcRnLPp9VicQ6SB1njqNdV6vjD1txTPwAS6HfBjTuRFVi1txL/wpf287xdL9Xgxo8bGmLGlZgE0/O8XUeb43mLDhMMsHt0/z4JqIoS31k7bkFEJ85nSwzvskA2umpqbMnPlxvoY8efIAMYfjt27dmgsXLnD69Gk2btzIuHHjVMVLjhw51Najp6dHclPApXSCvgsXLlC0aFHMzc1T81IyRMeOHXF0jDfP2YFFibY3NclNDn09Hj1Xv1DBoxevMc+reVLfxfu8aVurLJ3sYuZKK1fIjNfvopjy70n6Na2Bvn7Mb6eGBjkobh7zK26louZcuhfO+pOXGN+5YapfV3j4Y6KiorC0Uu9TS0sLguONoMcKDg7DylL9VwErK3NV++CQ0A/LLAgO/njaq5WlORdSMLeB5pxPYnJaxs9pTnCw5pwhIWEa28f/ZcDAwIB165wpXrwIrVt3T9fRatqUNbFtb5XKbW9plTBnRtKWffTJ4wiioqIoaKH+h4+5hRnhoY+yzTpBe/o0Nmdq9rngkDCs4r+XZB8VIgGp89RprPOOLE20faJ13vNXmOc11viYxXs8aFvbhk72MVeoK1e4YEydt+UY/ZrXVq/zLPIDUKmYBZfuhbL+uD/jnb5I9evSps8n7amftCWndmx7balHASISqcsKWpgRHpr2C4xkNG3b9vGPNot5b2ieOik4OEzjviJ1nsgIn2SOtRw5cmBtba26xRZcAKVKlaJjx45MnTqVYsWKcfLkySTWlHoGBjFjhdHxJor29PRMcJWoHDlyJGhXtGhRrl69qrbsypUrCU5nuH79eoL/FylSRGMmQ0NDjI2N1W5JMTTIQcUi5ngEPlQti45W8AgMSnQ+tDeRUcSfxzt2wkcliandoxWFd1FpO1QzMjISHx8/mjp8HJTT09OjqUNDzp711viYs+e8adpUfRCvebPGqva3bt3l4cMQtXXmzZsHW9uanD2neZ0pyXn+vD8ODg3Ucjo4NMDDw0fjY86d86FJkwZqy5o1a8S5cx/bxxYwZcqUom3bb3n8OCJN+bQxa2Lb3iGZbe+QxLb/FLRnH43iku8V6jWqq5bTvlFdLnj5Z5t1xqxXW/o0Eh8fDe+lJg0TXee5sz5q7QGaNW3EuTRmSHnO7N+fGU6JztibyFRS56lLU51X1AKP6x/nbIuOVvC4/iDR+dDeREYlmMg7djAt2TrvfdqOHNCmzydtqp+0Jac2bHttqUdjskYR4HcF+3h1mV2juvimoy7LaNq17f1x0LjtE3sveatlgJj30qesjbSlPzOcDtZ5mTbHWmhoKAcPHqROnTqYmpoSFBREcHAwX3yR+l/QkpI/f35y5szJhQsXMDMzI2fOnOTKlYsLFy6ozdEBYGlpycWLF6lQoQIGBgbkyZOHdu3aMXfuXEqVKkXVqlXx9vbGw8OD8ePHqz32zJkzlC5dmgoVKnDy5EkCAwP58ccfM+x1fNeoCuM3H6dSUXOqFLNg3cmLvH4XRfs6Mb/6jtt4DMv8xgxrHfPh3LhicdaeuEiFwgWpWtySu+HPWLzfm8YVi6uunLdgjycNyhfFukAeXr2NZM+FG3jdfMji71ulOefc+ctwdZmLt48fnp7nGTa0PyYmRqxctQkA1xXzCQp6yNhxfwGwcKELhw+5MXLEQHbvOcjXTu2pXbsaPwz6VbXOBQuXM2b0MK4H3uT27XtMnvQLQUEhbN++L805FyxYzrJls/H29sPLy5chQ/pibGzM6tVbAFi+fA5BQcFMmBBztVZnZ1f279/E8OH92bPnMF27tqNWraoMHhxzJRYDAwPWr19CzZpV6NSpLzly5FD9YvL4cUSCuVY+x6zz5i9jhYZtvyrOtn8Q9JBxH7b9ooUuHDrkxogRA9mz5yBOH7b9j3G2valpAYoXL0KhQjF/WNjYxJwCFBwcmuZfk7RlH135z3r+WjiRi76X8fO5RK+B3TEyNuK/je4A/LVoEqEPw5gzLebqQYaGBpQpXzrm3zkNsSpkQYUqNrx6+Yq7t+6naJ1ppS19On/BMlyWz8Hbxw8vzwsMHfo9JiZGrF69GQAXl7kEBQUzfvwMABY5u3DwwBZGDB/Anj2H6Or0FbVrV2PQ4I9XCTM1LUCxYoUpHG8fDQkJ++z30QyVxFVShXaSOi91vvuiOuM3HKZSMQuqFLdi3TE/Xr+LpL1tBQDGrT+EZT4ThjnGXIm0caWSrD3mS4Wi5lQtbsXd8Kcs3uNB48olPtZ5O8/SoGJxrE3z8OpNJHt8ruN1I4jFAxK/OnxytOnzSVvqJ23JqS3bXlvqUYBV/2zgzwUTuHThMv7nA/huQDeMjHOzdeNOAP5cOJHQ4DDmTYu5crmhoQFlbErF/DunIZbWFlSoXI5XL19z93ZMrWdsbETxUh9/GChavDAVKpfjacQzHj7QfDGU5GjLtp8/fykuLnPx8fbF0+sCQ4f2i9n2q2NyrnCZR1BQMOPGf8i5yIVDB90YMSKmznPqGpNz0KDfVOs0NS1A8WKFKVQ4Zq5Q1baXOi91dLDOy7SBtZw5c/LgwQOOHTvG8+fPMTU1pWXLljRv3jxDnydHjhz06dMHNzc3Nm3aRMWKFencuTO5c+emdOnSam2/++47Vq9ezaFDhzAzM8PZ2RlbW1v69OmDu7s7rq6uWFpaMmjQICpXrqz2WCcnJ06fPo2LiwsFChRg+PDhCX7tTI+WNUrz5OUbluz3Jvz5a8oXLsji71uqJrp9GPFC7Upb/ZvVQE8PnPd5E/r0FaZ5ctO4YnGGtPo4qe3jF28Yt+k44c9ekSd3TmwKmbH4+1bUs9H8C2xKbNmyAwtzMyZNGIW1tQW+vpdo69hDNclq8WKF1X4tPnPWix49h/DH5F+ZOuU3rgfeonOX77l06eOvxzNnLcbExJh/Fv9NgQL5OHXKk7btevD27ds053Rz24m5eUEmTPgJKysL/PwCaN++pypnsXg5z571pnfvYUycOIrJk38hMPA2Tk4DCAi4BkDhwta0a9cCAA+PvWrP1aLF15w4cfazzxq77SfG2faOcbZ9/JxnznrxXc8hTE5i27dzbIGLy1zV/9evi7ns/B9TZjNlypx05czu++ie7QcwK1iAob8OxMKyIJcvXqN/t2GqSW4LF7FGif54VIKltQXbDq9T/f/7wd/x/eDv8DjlTc+OP6RonWmlLX3q5uaOhbkZEyb8jLWVBb6+AbT76rs4+2gRouP06dmz3vTsNZTJk37hjz9+JTDwNl279iMg4GNOR8cvWb7s4764bm1M8Ttl6hymTv2476aGtvSnEEmROi91WtYsy5MXr1my15PwZ68oX8ScxQMcKfjhVNCHT+LVeV/WjqnzdnsQ+vQlpnmMaFy5BEPa2KnaPH7xmnHrDxP+7CV5jHJiU6ggiwc4Uq98sTTn1KbPJ22pn7Qlp7Zse22pRwH2bj+IWcECDPl1AOaWBbly6RoDu49Q1WWFilihxMlqYW3Bv4fXqv7fd3AP+g7ugccpb/p0GgRA5RoVWbl1iarNb3/EzH+5beNOxg6fkqacWrPt3dwxtyjIBFXOABzbxa/z1N9LPT9s+yl//EZg4C26dO3HpXh1nsvyj9t+3YdtP2XKHKZM/bz/FhHpo6ckN7nFZ2DFihVER0drvJJTWjg5OTFq1ChsbW3TvI7X2//OkCyfWt6u87M6QooZ5si0cWKdEPU+KqsjpJi2fIiVLVA4qyOkSGBEUFZHSJEc8c9/z8bea8kvd1HvHmTK87y9eCBD15erypcZuj6hXbJlnbdrXoZkyQx5O85MvlE2IHVexorUojovqavsZic2phk3+P4pXX1yP/lG2UD809+zs2gtGVKROu/T0YlvqOLFiye4upMQQgghsoiWDDQK7SB1nhBCCJGN6GCdpxMDaxl9GoIQQgghhMgepM4TQgghRFbSiYG1jLZ58+asjiCEEEJoLUVJ21UKhcgMUucJIYQQaaeLdZ4MrAkhhBAic2nJpdOFEEIIIUQq6WCdpz0zPwshhBBCCCGEEEIIkY3IEWtCCCGEyFw6OKmtEEIIIYRO0ME6TwbWhBBCCJG5dPAUASGEEEIInaCDdZ6cCiqEEEIIIYQQQgghRBrIEWtCCCGEyFzRune1KCGEEEIInaCDdZ4MrAkhhBAic+ngKQJCCCGEEDpBB+s8ORVUCCGEEEIIIYQQQog0kCPWhBBCCJG5dPBqUUIIIYQQOkEH6zwZWBNCCCFE5tLBUwSEEEIIIXSCDtZ5MrCWRfJ2nZ/VEVLkddCJrI6QYkaFG2V1hBTRy+oAKaRkdYDPUGBEUFZH+Ky818Ffw4QQ2iFfx5lZHSHFXt85mNURUsSoRPOsjpAiuQ1yZnWEFInM6gCpoC016fWIB1kd4bMSrWjLlhdCBtaEEEIIkdlkUFQIIYQQ4vOkg3WeDKwJIYQQInPpYMElhBBCCKETdLDOk6uCCiGEEEIIIYQQQgiRBnLEmhBCCCEylaK8z+oIQgghhBDiE9DFOk8G1oQQQgiRuXTwFAEhhBBCCJ2gg3WenAoqhBBCCCGEEEIIIUQayBFrQgghhMhciu79kimEEEIIoRN0sM6TgTUhhBBCZC4dPEVACCGEEEIn6GCdJ6eCCiGEEEIIIYQQQgiRBnLEmhBCCCEylw6eIiCEEEIIoRN0sM6TgTUhhBBCZC4dPEVACCGEEEIn6GCdJ6eCCiGEEEIIIYQQQgiRBnLEmhBCCCEylw6eIiCEEEIIoRN0sM7LNkesOTk5JXnbvHlzVkfMdD/+0IvAa2d58ewGp0+6U7dOjSTbd+7syEX/Y7x4doPzPgdp3appgjaTJo7i3h0fnj8NZN+ejZQtWyrdOTf8606Lzr2o5fAV3fuPwD/gaqJtI6OiWLJiHa269qGWw1d06jWIk2e91Nq8fPmKv+b9w5edelHboT3fDvwJ/8uJrzOltKU/Y7Nev3aW589ucCqFWf39j/H8Q9ZW8bJ26NCa3bvWE/zwIpHvHlC9euUMy6kNfaotObUpq+TUzZwZJjo6Y28i25M6L6GM/q4HmDhxFHfv+PDsaSB7M6rO27qbFl/3p9aXXen+4y/4X76WaNvIqCiWrNpEq28GUuvLrnT6fgQnz/motXF23UCVJh3Ubu2+G5zunKA9n6UDBn7HpcsnCH98hSPHtlK7TvUk23fs2Aaf8wcJf3yFcx57aNGyidr9Y8YOx+f8QULCLnHvwQXcd66hTt0a6c6pLf2pLTkBfvihF9eunuHZ00BOnnCnTnJZO7XF3+8oz54G4uOtocZv35pdu9bxMMifd2/vU71apQzJqS19Kjmlzssuss3A2tKlS1W33r17Y2RkpLbsq6++UrVVFIX3799nYdrERUVFZch6unb9ilkzJzJl6hzq2rXC1y+A3bvWYWFRUGP7evZ1WLfGGVfXDdSxbcmOHfv4182FypXLq9r8MmoQQwb3ZdCQ36nfsB0vX71i98515MqVK8059xw8xt8Ll/Jj32/ZsmIh5cuWYuBP43j0JEJj+4VLV7Fl+x7GjPyR7Wv/h1OHNgwfPYXL1wJVbSb8NZ8znueZPmEUW9csob5tLfoPH0NIWHiac2pLf8ZmnTlzIlOnzsHWrhV+fgHsSibr2g9Z69q2ZLuGrCYmxpw67cGYMdPSlS1+Tm3oU23JqU1ZJadu5hQiPaTOU/cpvutHfXjfDx7yOw0+vO93pbfOO3ySvxev4Mfe3diybA7ly5Rk4C+TE6/zXNaxxX0fY4b1Z/uqhTh91ZLh4//i8vWbau3KlizO0X9dVbfVC6enOWMsbfks7dy5LdP/Gsv0P+fTsL4jF/0vs237qkRz2tnVwnXVfFat2kyDem3ZufMAGzf9j0qVbFRtrl+/xU8/TcSubitaNO/KnbsP2L5jFebmZmnOqS39qS05Abp2acfMvycwddpc7Oxa4+cfwK6daxPNam9fmzVrnHFduRFbu1bs2LEXty3LqVxJvcY/fcqTMWP/TFc2tZxa0qeSU+q87ERPURQlq0PEd/ToUVauXMnKlSsBuHTpEpMnT2b06NFs3LiRu3fvMm7cOGxsbFizZg2nT5/m9evXlC5dml69elG2bFmN6wHw8PBg1qxZql9Gb9++zapVq7hx4wZ6enpYW1szYMAAypQpA8CVK1dYv349N27cIF++fNStW5dvvvmG3LlzAzB48GAcHBwIDg7G09MTW1tbBg9O/lc3g5xFkrz/9El3PL18GT5iHAB6enrcvumJ82JX/p7pnKD9+nVLMDE2pn3HXqplp064c8H3EoOH/A7AvTs+zJ33P+bM/R8A+fLlJej+Bfr2G8nmzTs05ngddCLJnN37j6BKBRvG/jwIgOjoaJp37Mk3Xb6i33dOCdo7fPUtA3p1o3vndqplI8ZMJVeunMyY+Ctv3r7F7stOLPhrIl/Ut1W1ceo7lIb2dRg2oFeCdcYyKtwo0fuyS38C6CV6z4fnOemOV7ystz5knakh67oPWTvEyXryhDu+cbLGKlGiKIHXz1Gnbgt8fS8lmSO5D4bs1KefQ05tyio5P9+cUe8epOk1pNbrXfMydH1GbUdk6PrEp6ULdZ5hMnXep/iuv/vhfT83zvv+wf0LfJ/M+/7VnYOJ3tf9x1+oUr4cY0cMAD7UeU79+KZjW/p92zlBe4fOfRjQoyvdO7ZRLRsx4S9y5czFjHEjgZgj1g6fPMe/LvMSfV5NjEo0T/L+7PJZmtsgZ5I5jxzbio+3Hz//NFGV8+r10/yzZBVzZv+ToP2q1QsxNjGia+d+qmWHj/6Hv18Aw4eN0/gcefPm4WGIP45tvuXo0dMa27yJepdkzuzSn8nJTjn19ZKu8k+ecMfL25cRcbLevOHJ4sWuzJyl4X2/djHGJsZ07NhbtezE8R34+l1iyJDRam1LlCjK9WtnqVu3Bb5+AUnmiE7mz//s1KeSM2NzSp336WSbI9ZSYv369Xz77bfMnTuXEiVKsHbtWs6dO8fgwYOZMWMG1tbWTJs2jRcvXqR4nQsXLsTMzIzp06fz119/0aFDB3LkyAFAcHAw06ZNw87OjlmzZjFixAiuXr3KihUr1Nbh7u5OiRIlmDFjBp07JywyUsvQ0JBatapx6PDHQS1FUTh0+CT29rU1PsberrZae4D9B46q2pcqVZxChaw4dPik6v5nz57j4XEeezvN60xOZGQkAVevYx/nUHN9fX3s69TA9+JljY95FxlJzpzqBUeuXDk57xczyPM+6j3v30eTK6dhgjY+fkkPBCVGW/ozqayHk8l6OImsn4K29Km25NSmrJJTN3NmOCU6Y2/is6DrdV56vutj3/eHM7zOu4F97WqqZfr6+tjXro5vItN+vIuMImf8Gi5nLs77q/+hf/fBQxw696FV94H8NnUOD0PC0pQxlrZ8lhoaGlKzZhWOHPm4TkVROHL4FLZ2tTQ+xtauJkcOn1JbdujgcWxtNbc3NDSkT9/uREQ8w99fcz2ekpza0p/akPNj1qpq7+OY9/0J7O01b0s7De/7AweOfdLvcm3pU8kpdV52o1UDa05OTlSrVg1ra2sMDAzYv38/3333HTVr1qRo0aIMHDiQnDlzcvjw4RSvMzw8nKpVq1KkSBEKFSpEvXr1KFmyJADbtm2jUaNGtG3blkKFClG+fHn69OnDsWPHePfu4688VapUoV27dlhbW2NtbZ3u12luboaBgQGhIeqnPoaGhmFtZaHxMdbWFoSEqhclISHhqvbWVpYflsVrExqOtbVlmnI+iXjG+/fRFDQzVVte0MyU8MdPND6mgV1tVm/8jzv3HhAdHc1pDx8OHTtN2KPHQMzhzNWrVOSflRsIDXvE+/fvcd93GN+LVwgPf5ymnNrSn0llDUll1tCQcKwSaZ8RtKVPtSWnNmWVnLqZU4jMoOt1Xnq+65N631ultc57+pz30dEUNCugtrygaf7E67y6NVi9ZQd37gfF1HleFzh04gxhcdpXq2TD1N+H8c/fExk/8gfuPwyh57AxvHz1Ok05QXs+SwuamyaSM/G6zcrKgrDQ5Nu3at2U4NCLPHpyhSFD+/JVu+949EjzdkqOtvSntuSMmzX+emO2peb1WltbaPyckBpfcmZ0TpF+WnVV0NjD9gFCQkJ4//495ct/PNfYwMCAsmXLcv/+/RSvs23btvzvf//jxIkTVK1aFXt7e1XRdOfOHe7cucOJE+ojxoqiEBoaStGiRRPk0iQyMpLIyMgUZ/qc/T58IJNmLKDdNwPQ04NihQvRoe2XbN25X9Vm+vhRTJg+l6YdepAjhz4VbcrSuvkXBFwNTGLNQgghtIaWTEQrMpfUedrv96H9mDTTmXY9h6AHFCtiTYfWzdi6+5CqTaM4R1SUL1OSqhXL0aLbAPYeOUnntl9mQerPw/FjZ6hv35aCBU3p3bcbq9cswuGLjoSFPcrqaEIIXaODdZ5WDayldjI+PT094k8hF38yXCcnJxo2bIiPjw8XLlxg8+bNjBgxAltbW968eUPz5s1p06YN8Zmbm6c419atW3Fzc0tx7vDwx0RFRWFpZa623NLSguBEDpUPDg7DylJ91NvKylzVPjgk9MMyC4KDQz+2sTTnQjJzbSXGtEA+cuTQ51G8Xy0fPX6Cebyj2GKZmRZgwV8TePv2HRHPnmFpXpC5S1ZQtPDHX4CLFy3MSueZvHr9hpcvX2FhbsbP46ertUkNbenPpLJapTKrpZV5gl8xMpK29Km25NSmrJJTN3NmOC05rF9kLl2v89LzXZ/U+z65OVUTY5o/Lzn09Xn0OEJt+aMnTxOv8wrkZ8G0MR/qvOdYmpsxd+lqiha2SvR58uXNQ4mihbn7IDhNOUF7PksfhT9JJGfidVtISBgWlsm3f/XqNTdv3uHmzTt4el7ggt9hevZyYvasJanOqS39qS0542aNf7RZzLYM1fiY4OAwjZ8TUuNLzozOmeF0sM7TqlNB47KyssLAwICrVz/O8RAVFcWNGzdUvzDmy5ePN2/e8ObNG1Wb27dvJ1hX4cKFcXR0ZNy4cdja2nLkyBEASpUqxYMHD1SH/se9GRikfEyyY8eOqsl140+yq0lkZCQ+Pn40dWioWqanp0dTh4acPeut8TFnz3nTtGlDtWXNmzVWtb916y4PH4aorTNv3jzY2tbk7DnN60yOoaEhlcqX45zXBdWy6OhoznlfoHqVikk+NleunFhZmBP1/j0Hjp7CoVG9BG2MjXJjYW7G02fPOe3hTdNG9mnKqS39mVRWh2SyOiSR9VPQlj7VlpzalFVy6mZOITKbLtZ56fmuj33fO2R4nVeGcz5+qmUxdZ4f1eNclVCTmDqvYEydd+wMDg1sE2376tVr7gUFY1FQ82BdSmjLZ2lkZCTnz1+kSZMGajmbONTH45yPxsd4nDtPE4cGasscmjbEw0Nz+1j6+vrkypX0hRSSyqkt/akNOT9m9Vd7j35832velufOeavlAGjWrNEn/S7Xlj6VnFLnZTdadcRaXLlz56ZFixasWbOGPHnyYG5uzvbt23n79i1NmzYFoFy5cuTMmZMNGzbQunVrAgMDOXr0qGod7969Y82aNdjb22NpacmjR4+4ceMGdnZ2ALRv356xY8fi4uJCs2bNyJUrF/fv38fPz4/vv/8+xVkNDQ0xNDRMvmEcc+cvw9VlLt4+fnh6nmfY0P6YmBixctUmAFxXzCco6CFjx/0FwMKFLhw+5MbIEQPZvecgXzu1p3btavww6FfVOhcsXM6Y0cO4HniT27fvMXnSLwQFhbB9+75UZYur59cdGTttNpUrlKNKpfKs3byN12/e0uHDofyjp8zC0rwgI3/sA4DfpSuEhD2iQrnShIY9YvGKtSiKQt9vu6jWeeqcN4qiULJ4Ue7eD2K2swulihelQ9sWac6pLf0JMG/+MlZoyLoqTtYHQQ8Z9yHrooUuHDrkxogRA9mz5yBOH7L+GCerqWkBihcvQqFCMb8Y29jEnNYSHBya5l+9tKVPtSWnNmWVnLqZM0Pp4CkCInU+9zrvU3zXx77vAz+87ydlRJ3XtT1jp8+ncvmyVKlYjrVu7rx+84YOrZsBMPrPeTF13oDvAPALuEZI+CMqlC1FaPgjFq/cGFPndeuoWufMxa40qV+XwlYWhD56grPrBnLo69OmWeJXd08JbfksXbRgOf9bNhsfHz+8vXwZPKQvxsbGrF0Tc9Tj0mWzCQoKZtLEmQAsdnZl7/6NDB3Wj317D9Olaztq1arKsCFjADA2NuKX3waze+dBgoPDKGhuyoCB31G4sDVb/9ud5pza0p/akhNg/vyluLjMxcfbF0+vCwwd2i/mfb86JusKl3kEBQUzbvyHrItcOHTQjREjBrBnzyGcusZkHTToN9U6TU0LULxYYQp9OLNHVeOHhEmNLzmzjg7WeVo7sAbwzTffEB0dzcKFC3nz5g2lS5dm7Nix5MmTB4A8efIwdOhQ1q5dy6FDh6hSpQpdu3Zl6dKlQMwvOc+fP2fRokU8ffqUvHnzYmdnh5OTEwAlSpRg0qRJbNy4kQkTJqAoCtbW1tSrl/Doqoy2ZcsOLMzNmDRhFNbWFvj6XqKtYw9CP0xeWrxYYaLj7LBnznrRo+cQ/pj8K1On/Mb1wFt07vI9ly59/KV35qzFmJgY88/ivylQIB+nTnnStl0P3r59m+acrZt/wZOIpyxavpbwx4+pUK4M/8yeojpF4GFIqNqlp9++e8fCZau4HxSMsZERjerVZfr4X8iXN4+qzfMXL5n3jyshYeHkz5eXL79oyLCBvTBMxa/H8WlLf8bNOjFOVsc4WYtpyPpdzyFMTiJrO8cWuLjMVf1//bqY0wL+mDKbKVPmpCtndu9TbcmpTVklp27mzFA6eIqASD1dqPMy8rt+1of3/ZI473vH9NZ5TRvG1HmuGwh//IQKZUvxz98TMf9wQYOHIWEJ6zyXddwPCsHYKDeN7GszfcxItTovJOwRv06ZTcSz55jlz0/NqhVZt3gGZgXypzknaM9n6b//7sLcoiDjxv+ElZU5fn6X6dihd6Lb/tw5H/r2HsH4iT8zafIobgTeptvXAwkIuAbEnP5c3qYM327oTMGCpjx+HIG3tx8tvnTi8uXrac6pLf2pLTkBtri5Y25RkAmqrAE4tvsuzrYvopb17Flven5430/54zcCA2/RpWs/LsW5Kq+j45e4LP9Y46/7UONPmTKHKVOlxpecWUQH6zw9Jf7kFCJTGOQsktURUuR10InkG2UTRoXT90tnZtFLvkm2IB8MQuieqHcPMuV5Xv/3Z4auz6jTmAxdnxDpZagldR7AqzsHszpCihiVaJ7VEVIkt0HaTr/MbG+i3iXfSKRK3EHm7Cxa/vzXWVLnfTpafcSaEEIIIbSQDp4iIIQQQgihE7K4ztu7dy/u7u5ERERQokQJ+vbtS9myZRNtv2vXLvbv3094eDj58uXDzs6Ob775hpw5U/5DiQysCSGEECJzycCaEEIIIcTnKQvrvNOnT7N69Wr69+9PuXLl2LVrF9OmTWPevHnkz59wyoGTJ0+yfv16fvzxR2xsbHj48CGLFy9GT0+PXr16pfh5tfaqoEIIIYQQQgghhBBCAOzcuZNmzZrh4OBA0aJF6d+/Pzlz5lRdETy+q1evUr58eRo2bIilpSXVq1enQYMGBAYGpup5ZWBNCCGEEJlLUTL2JoQQQgghsocMrvMiIyN59eqV2i0yMjLB00ZFRXHz5k2qVq2qWqavr0/VqlW5du2axqjly5fn5s2bqoG0kJAQzp8/T82aNVP1kuVUUCGEEEJkLjkVVAghhBDi85TBdd7WrVtxc3NTW9alSxfVVb5jPXv2jOjoaAoUKKC2vECBAgQFBWlcd8OGDXn27Bnjx48HYq60/OWXX9KpU6dUZZSBNSGEEELolKyY1FYIIYQQQqRex44dcXR0VFtmaGiYIeu+dOkSW7dupV+/fpQrV47g4GBcXV1xc3OjS5cuKV6PDKwJIYQQInPp4KS2QgghhBA6IYPrPENDwxQNpOXLlw99fX0iIiLUlkdERCQ4ii3Wpk2baNy4Mc2aNQOgePHivHnzhqVLl9KpUyf09VM2e5rMsSaEEEKIzKVEZ+wtFbJqUlshhBBCCJ2QRXWegYEBpUuX5uLFi6pl0dHRXLx4ERsbG42Pefv2LXp6emrLUjqYpvaYVD9CCCGEECIb0YZJbYUQQgghxKfl6OjIoUOHOHr0KPfv32f58uW8ffuWJk2aALBo0SLWr1+val+7dm0OHDjAqVOnCA0Nxc/Pj02bNlG7du1UDbDJqaBCCCGEyFw6OKmtEEIIIYROyMIpP+rXr8+zZ8/YvHkzERERlCxZkjFjxqhqv/DwcLUj1Dp37oyenh4bN27k8ePH5MuXj9q1a9O9e/dUPa8MrAkhhBAicylKhq5OGya1FUIIIYTQCRlc56VWq1ataNWqlcb7Jk2apPb/HDly0LVrV7p27Zqu55SBNSGEEEJoNW2Y1FYIIYQQQnyepBoUQgghROaKjs7YWwpl5aS2QgghhBA6IYvqvKwkR6wJIYQQInNlYZHk6OiIs7MzpUuXpmzZsuzevTvBpLZmZmZ88803QMyktrt27aJUqVKqU0HTMqmtEEIIIYRO0JLBsIwkA2tZJIeWFONGhRtldYQUe75/SlZHSJF8LcZndYQUsTIpkNURUizkZURWR0gRY8NcWR0hRV5Fvs3qCEJ8Mlk1qa3QLTn0c2R1hBQzKtE8qyOkyPPlPbM6Qork778mqyOkSHnTolkdIcWuPrmf1RFSxNrENKsjpEjQi8dZHSFF9JJvkm1k7YxiIjuQgTUhhBBCZC4la3/JzIpJbYUQQgghdEIW13lZQQbWhBBCCJGplGj5bVcIIYQQ4nOki3WedpyPKIQQQgghhBBCCCFENiNHrAkhhBAic+ngpLZCCCGEEDpBB+s8GVgTQgghRObSwbk3hBBCCCF0gg7WeXIqqBBCCCGEEEIIIYQQaSBHrAkhhBAic+ngpLZCCCGEEDpBB+s8OWJNCCGEEEIIIYQQQog0kCPWhBBCCJG5dHBSWyGEEEIInaCDdZ4MrAkhhBAic+lgwSWEEEIIoRN0sM6TU0GFEEIIIYQQQgghhEgDOWJNCCGEEJlL0b1JbYUQQgghdIIO1nlae8Ta4MGD2bVrl+r/Tk5OeHh4ZGEiIYQQQqRIdHTG3sRnR+o8IYQQQkvpYJ2XZQNrzs7OODk54eTkRPfu3Rk6dChubm68f/8+qyJlOz8M7MXVq6d5GnGdE8d3UKdOjSTbd+rUFj/fIzyNuI631wFatXRQu799+1bs2rmOoAd+vH1zj2rVKmVIzh9/6EXgtbO8eHaD0yfdqZtMzs6dHbnof4wXz25w3ucgrVs1TdBm0sRR3Lvjw/Ongezbs5GyZUulO+fGIz60HrME28Gz6DF9Nf63gpJsv/agJ+0nLMNuyGxa/r6YmZsP8TYySnV/6zFLqDFwRoLbn+v3pzvrjz/04vq1szx/doNTKexTf/9jPP/Qp63i9WmHDq3ZvWs9wQ8vEvnuAdWrV053RoBe/bpz1nc/Nx764H5gAzVqVU2yvWP7Fhw7586Nhz4cPLWVpl82Urv/wZNLGm8/DO2Trpzaso8C9B/wHf4Bxwl9dJnDR/+jdu1qSbbv0LE1Xj4HCH10mTMee2jRsona/aPHDMfL5wAPQy9y5/55tu9cQ5061dOdU1v6VHJm/D4qREpInZe8gQN7cvXqKSIirnH8+PZkP5s7dWqLr+9hIiKu4eW1n5Zx6jwDAwOmTh2Nl9d+Hj26ws2bnri4zKVQIat059SWz6eNXjdovWgvtn9to4frEfwfPE607fdrjlNj2n8JbkM2nlK1efTiDePdvfhy/m7sZ2xn0IaT3Hn8It05AX74oRfXrp7h2dNATp5wT7bG79ypLf5+R3n2NBAfbw11XvvW7Nq1jodB/rx7e5/qGVTjd+/Thf2eW/G5c5wNe1yoWjPx9ZYpX4p5Ln+x33Mrl0LO8d2Abgna1LavgfOaWRzx3cmlkHM0bd04Q3Jqyz4K0PP7bpy6sJdrQV5sP7CO6rWqJNm+bfsWHD67g2tBXuw/+R8OzdVr57uP/TXeBg7tna6c2tKnGf03E8DEiaO4e8eHZ08D2St1nkihLD1irUaNGixdupQFCxbQrl07tmzZwo4dO7IyUrpERUUl3yiFunRpx99/j2fatHnY2bfB3z+Ane5rsLAoqLG9vX1t1qxexMqVG7Gza80O931s2bKcSpXKq9qYmBhz6rQHY8f9mWE5u3b9ilkzJzJl6hzq2rXC1y+A3bvWJZqznn0d1q1xxtV1A3VsW7Jjxz7+dXOhcuWPOX8ZNYghg/syaMjv1G/YjpevXrF75zpy5cqV5pz7PC8z2+0wA9s2YMPY3tgUtWTQgs08fvZSY/vdHgEs2HqMgY4N+G9SPyb2bM1+ryss3HpM1Wbd6F4c/Huw6vbPiK8B+LJ2hTTnhJg+nTlzIlOnzsHWrhV+fgHsSqZP137o07q2LdmuoU9jt/2YMdPSlS2urzq2YuLUX5kzYzGtmnQl4OJV1v37Pwqam2lsX8e2Bs7LZ7Jh7X+0/KIL+3YdxmXtQspXLKtqU6P8F2q3kYPHEh0dze4dB9KcU1v2UYBOndvy519j+Gv6Aho1aIe//2X+274K80Sy2trVYsXK+axevZmG9R3Z5b6f9Rv/oWIlG1WbwMBbjPp5EvVsW9PySyfu3rnP1h2rE91OKaEtfSo5M34fzTDRSsbeRLYkdV7i4tZ59vZt8fe/jLv72iTrvNWrF7Jy5Sbs7Nrg7r6PLVuWUenD572xsRE1a1Zh+vQF2Nu3oVu3AZQrVxo3N5d05dSWz6d9AfeZfdCfgY0qsOH7pthY5mfQxlM8fvlGY/s5Xew5OLyN6uY2oDk59PT4smJRABRFYaTbWR48ecncrvZs7NeUQvmN+WHdCV6/S99+0LVLO2b+PYGp0+ZiZ9caP/8Adu1MetuvWeOM68qN2Nq1YseOvbhtWU7leDX+6VOejBmbcTV+q/bN+XXycBbPdqHrl724eimQ/22cj5m5qcb2Rka5uXfnAXOnLSYsJFxzG2Mjrl66ztTfZ2ZYTm3ZRwHadWzJ+Km/MO/vf2jr4MTli9dY65Z47VzbtjoLl81g07r/aNOkK/t2H2bZ2vnYxKmda1doonb7ech4oqOj2bPjYJpzakuffoq/mUZ9yDl4yO80+JBzl9R5qaeDdZ6eomTNCbDOzs68fPmSX3/9VbVs6tSpvH79GkNDQ0qWLEnv3r1V9/3999+YmJgwePBgIOYUgTZt2tC2bVsg5hSBUaNGYWtrC8Ddu3dxdXXl2rVr5MqVCzs7O3r16kXu3Lnx9fXl77//ZunSpZiYmKiew9XVlbt37zJx4kQArly5wvr167lx4wb58uWjbt26fPPNN+TOnVuVwcHBgeDgYDw9PbG1tVXlS06u3MWSvP/E8R14e/syYuR4APT09LgR6MHiJa7MmrU4Qfu1axZjYmJEx04fj+w5fmw7fn6XGDJ0jFrbEiWKcu3qGeratsTPLyDJHO+TOfTy9El3PL18GT5inCrn7ZueOC925e+Zzgnar1+3BBNjY9p37KVaduqEOxd8LzF4yO8A3Lvjw9x5/2PO3P8BkC9fXoLuX6Bvv5Fs3px4Qf58/5RE7+sxfTWVSxZidPcvAYiOVmg5ejHdHWrTt5V9gvbTNxzg1sNHLP3p469ts7ccxv9WECt/7aHxOf7edJAT/jfYMWUAenp6iWbJ12J8ovcBnDrpjle8Pr31oU9naujTdR/6tEOcPj15wh3fOH0aq0SJogReP0edui3w9b2UZA5LkwJJ3u9+YAO+5y8y7tdpqpyeFw/humw9zvOWJ2i/xGUWxiZG9Or28T3ivn89ly5e4fef/tD4HC5rF5Anjwlfd/g+ySwhLyMSvS877aPGhkl/2R0++h8+3n6M+nmSKuvla6f43z+rmTv7nwTtXVctwMTEGKcu/VTLDh35Fz+/y4wcPk7jc+TNm4cHwX60a9uDY0dPa2zzKvJtkjmzU59KzozNGfXuQZpeQ2q9mtk3Q9dn/MuKDF2fSD9dr/Ny5y6e5P3Hj2/H29uXkSMnADHv+8DAcyxZslJjnbdmjTMmJsZ0ilPnHTu2DT+/AIbGq/Ni1a5djVOndlKunD337iV+lH5UdOJHEWanz6fny3smel8P1yNULmTK6FY1AIhWFFou2EP3umXoW798oo+LtdYjkCXHAjg4vA1GOQ248+g57f85gNuA5pS1yKdaZ7N5uxnapBKdaiZ+ZEj+/muSfK6TJ9zx8vZlRJw+vXnDk8WLXZk5S0Odt3YxxibGdOzYW7XsxPEd+PpdYsiQ0WptS5QoyvVrZ6lbtwW+ydT45QoUSfL+DXtcuHj+MtPGzFLlPHR+B+tdtrB84eokH7vfcytrlm1izdKNiba5FHKOob1/4fCe40muC+Dqk/uJ3ped9tHCeZL+0XL7gXX4+lxiwm9/qrKe8z/AymUbWDw/4SC4s8tMjI2N6NN9iGrZtv1rCfC/ypifNf/ds2zNfPLkMaZ7x/6J5gh6kfjRnJB9+jTxv6Y+PMcn+Jvp7oecc+PkfHD/At8ns+2TGlDJLv0JUud9StlqjrWcOXNmyK+Bb968Ydq0aZiYmDB9+nR++ukn/P39cXGJ+cCqWrUqxsbGnDt3TvWY6OhoTp8+TaNGMYfXBgcHM23aNOzs7Jg1axYjRozg6tWrrFihvlHd3d0pUaIEM2bMoHPnzunODmBoaEitWlU5fPikapmiKBw+cgJ7u9oaH2NnX0utPcCBg8ewS6R9xuWsxqHDJ9RyHjp8Ent7zc9rb1dbrT3A/gNHVe1LlSpOoUJWHIrzWp49e46Hx/lEX3tyIqPec/luMHYVS6iW6evrYVehJH43NX+4VC9ThIC7warTRe+HRXDy4g0aVimT6HPsPhdA+/rVkhxUS05ifXo4mT49nESffgqGhoZUq1GJE0fPqOU8eewstetqPpWltm0NThw9q7bs6OFT1K5bQ2N7c4uCNGvRmA1r/0tXTm3YR2Oz1qhZhSNHPp6GoigKR4+cwta2psbH2NrV4mic9gCHDp7A1k5ze0NDQ3r37UZExDP8/S+nOac29KnkzPh9VIj0kjovRmJ13pEjJ7Gzq6XxMfYa6ryDB48n2h4gf/58REdHExHxLB05s//nU+T7aC4/jMCulKVqmb6eHnalLPG7n/QAQqxtF27TslJRjHLGXNft3fuYH5ZzGXz8U0lfT4+cOfQ5f/9RmnJC3G0fv847gb295m1pp6HOO3Dg2Cf9PDc0NKBStQqcOfFxXkNFUTh73JPqdZKe9iMzacs+GpPVgKrVK3Hy2MdaOLZ2rpVI7VyrbnW19gDHD59OtL25RUGatmjExrVb05FTO/r0U/zNFJvzsNR5Ig2yxcCaoij4+fnh6+tLlSpJn2eeEidPnuTdu3cMGTKE4sWLU6VKFfr27cvx48eJiIhAX1+fBg0acPLkx53R39+fV69eYWdnB8C2bdto1KgRbdu2pVChQpQvX54+ffpw7Ngx3r17p3pclSpVaNeuHdbW1lhbW2vMExkZyatXr9RuSTE3N8PAwICQ0DC15aEh4VhZWWh8jLWVBSGh4SlunxFic4bGO9w7NDQM68RyWlskeF0hIeGq9tZWlh+WxWsTGo61tSVp8eTFK95HKxTMa6K2vGA+Y8Kfaj4VtI1tJQZ91ZA+M9dR58eZOI77H3VsitOvTT2N7Q9fuMbz12/4qn769t/E+jQklX36qbe9WcECGBgYEB6mXlyGhT3CwtJc42MsLM0Ji9c+POwRFpaaD4Pu2r09L168Yo972k8D1ZZ9FKBgQVMMDAwIi/8+Dk18W1pZmROagvatWjUlKMSfsMeXGTykLx3a9eTxoydpyqktfSo5M34fzVA6eIqALpM6T53qfR/v8zskie9uKysLQhO878MSbZ8rVy6mTh3N5s3bef48bfOCacvn05NXb3mvKBQ0UT8qvKBJLsITORU0Lv8HjwkMe0bHGiVVy0oWzEuhfEYsOHKJZ6/fEfk+GtfTVwl5/prwF8mvMzGqGj/e64/57tb8+q2tLTTWhZ+yzitgFlPnPQpTH5h8FPYYc8u0TyWR0bRlHwUw+1Dnxa+dw8MeYWGluRa2sDQnLDRerR2aeK3dpdtXvHzxir07034aqLb06af4mympnFZS56WODtZ5Bln55D4+Pnz33Xe8f/8eRVFo0KABXbt25a+//krXeh88eEDJkiVVh/IDVKhQAUVRCAoKokCBAjRs2JCxY8fy+PFjzMzMOHHiBDVr1lSdMnDnzh3u3LnDiRPqo8WKohAaGkrRojFzMJQpo/kIpri2bt2Km5tbul6TyFyeV+/isucsY75pQdVShbkX+oS/Nx1k6a5TDGjbIEH7baf8aFC5NJYF8mZB2s9Tt287snXLTt6+fZd8Y5Gk48fP0LCeIwULmtKrTzdWrllI0yadEhR3QmQWRUuu8CTSR+q8rGFgYMC6dYvR04OhQ8dmdZxsb5vvbcpZ5qNqkY8DRoY59JndxZ5JO31oPGcnOfT0sCtlQYMyVkmf8yVEFnL6tiNbt+yS2llkOV2s87J0YK1y5cr0798fAwMDTE1NyZEjBxBz3nH8qd8y+ipSZcuWxdramtOnT9OiRQs8PT0ZNGiQ6v43b97QvHlz2rRpk+Cx5uYffyVIyQSBHTt2xNHRUW2ZqVni8z2Ehz8mKioKK0v1UWxLK/MEI9OxgkPCsIr360VS7TNCbE5Lq3jPa2lBcGI5g8MSvC4rK3NV++CQ0A/LLAgODv3YxtKcC8nMCZYY0zzG5NDX49Fz9aPTHj17hXl+E42PWbzjBG3tKtOpYcyh1uWKWPD6bSRT1u6lX+v66Ot/PN0z6NFTzl2+w+wfOqYpX1yJ9alVKvv0U2/7x48iiIqKSjCpvoVFwQRHXMUKCw1PMEmnuUXBBL/EAdjWq0VZm9L8+P2odOXUln0U4NGjJ0RFRSX4FdLSMvFtGRISjmUK2r969ZqbN+9w8+YdPD0vcN73MD17OTFn1pJU59SWPpWcGb+PCpFaulznmZklfiEj1fs+3ue3VRLf3SEhYVgmeN9bJGgfO6hWvHgRWrXqluaj1dRyZvPPJ1PjXOTQ0+PRS/X5QR+9fIu5Se5EHhXj9bso9gXc58fGCa92WamQKZv7N+P5m0gi30djZpKLHq5HqFRI8+T9KaGq8eMdqRLz3R2q8THBwWEa68JPWedFPI6p8wpaqB+dVtDCjPDQlJ1emxm0ZR8FePyhzotfO5tbFCQsRPMPnWGh4QnO7LCw1Fxr29rXoqxNKQbrSO38Kf5mSipncnNTpzZndutPkX5Zeiporly5sLa2xtzcXFVsAeTLl48nTz6ephQdHc29e/dSvN4iRYpw+/Zt3rz5eKj2lStX0NPTo3DhwqplDRs25MSJE3h5eaGnp0etWh/nNihVqhQPHjxQHfof92ZgkLrxSENDQ4yNjdVuSYmMjMTHxx8Hh49HRunp6eHQpCFnz3lrfMy5sz5q7QGaNW3EuUTaZ4SYnH40dWiolrOpQ0POntX8vGfPedO0aUO1Zc2bNVa1v3XrLg8fhqitM2/ePNja1kz0tSfH0CAHFYtb43H5jmpZdLSCx5XbVCuteeLWN+8i0Y83V1rsYJoS76fK7af9MctrTKOqyf+qnZzE+tQhmT51SKJPP4XIyEj8LgTQ8IuPF37Q09OjYWM7vD19NT7G2+OCWnuAxg718Pa8kKBt9x6d8T1/kYCLV9OdUxv20disF85fpEmT+mpZv2hSHw+P8xof43HOhy/itAdwaNoAj3Oa28fS19cjV86cac6pDX0qOTN+H81QOniKgC6SOk+zxOq8Jk0acO6cj8bHnNVQ5zVt2lCtfeygWtmypWjT5hseP45I1evQnDP7fz4Z5tCnYqECeNz++AdmtKLgcTuUakWTPm1x/+UHvIuKpm2VxC8qlje3IWYmubjz+AUBD5/QxKZQmnJC3G2vqc7TvO3PnfNW6y+AZs0afdLP88jIKAL8rmDfqK5aTrtGdfH18v9kz5ta2rKPxmSNwt83gAaN7dSyNvjCHp9EamcfT1+19gANm9TT2P7rHp3wO3+Jy5eupTljTE7t6NNP8TdTbE4HqfPSTwfrvGwxx1p8VapU4fz58/j4+PDgwQOWLVvGy5ea58LSpFGjRuTMmRNnZ2fu3r3LxYsXcXV1pXHjxhQoUECt3a1bt9i6dSv29vYYGhqq7mvfvj1Xr17FxcWF27dv8/DhQzw9PVUT435q8xcso2/f7vTo0YUK5cuyaOGfmJgYsXr1ZgBcXOYyZcpvqvaLnF1o0aIJI4YPoLxNGcaNG0nt2tVYvGSVqo2paQGqVatExQrlALCxKUO1apXSNUfD3PnL6Pf9N3z3XVcqVCiL86K/MDExYuWqTQC4rpjPtKkfr0y5cKELLVs0YeSIgZQvX4YJ43/6kNNV1WbBwuWMGT0MR8cvqVKlAitd5xMUFML27fvSnPO75nX576QvO874c/NhONPW7+P1u0ja14+ZgHWc604WbD2mat+4Wlm2HD/PXs8AHoRHcCbgFot3nKBxtbLk0P/4tomOVthx2p929apgkCNj3k7z5i/jew19uipOn06N06eLPvTpiA99Ol5Dn5qaFqB69cpUrGgDxGz76tUrp2vbL1u8im96dqFrt/aUtSnNX3MmYGRixKZ1MROmzl/yJ79PGKFq7/K/tTRp1oCBg3tRplwpfvptENVqVMF12Xq19ebJa4Jj+xZsWPNvmrPFpS37KMRsy159uvHNt52wKV+GufOnYGxszNo1MacY/W/ZLCZO/kXVfsnilTT/sjFDhn1POZvSjB4znJq1qrL0fzFX6zI2NmLCpFHUrVuDYsUKU6NGFZyXzKBQYWu2bt2d5pza0qeSM+P30QyjRGfsTWgVqfNgwYLlqjqvfPmyLFz4JyYmxonWec7OK2jR4guGD++PTZw6b8mSlUDMoNqGDf9Qu3Y1evceRo4cObCyssDKykLtdaeWtnw+fWdXjv/O32aH3x1uhj9j2p7zvI58T/tqMReuGrfDiwVHLiZ43Dbf2ziUL0wB44RHJu6/fB/PO2Hcf/KSI1eD+GH9SRxsClO/tFWacwLMn7+U7/t257seXahQoSyLFk2PqfNWx/TpCpd5TJ0Sp08XfajxRwyIqfPGxfTpksUrVW1MTQtQvVol9TovnTX+qn820OXb9rR3akPpciWZ8PdvGBnnZuvGnQD8uXAiI8Z+PArU0NCACpXLUaFyOQxzGmJpbUGFyuUoXrKoqo2xsZGqDUDR4oWpULkchYqkvU+1ZR8FWL54Nd17dqZLt68oa1OKP2ePx9jYiM3rt8W8lsXT+G38cFX7Ff9byxfNGtB/cE/KlCvFyN9+pFqNyqxcvkFtvXnymtC2/Zds1LHa+VP8zRQ/p6vUeWmjg3Velp4KmhgHBwfu3LnDokWLyJEjB23btqVy5copfnyuXLkYO3Ysrq6ujB49Wu0y7HFZW1tTtmxZAgMDE9xXokQJJk2axMaNG5kwYQKKomBtbU29eponr89obm7uWJibMWHCz1hbWeDrG0C7r75TTXRbrFgRouOM3p49603PXkOZPOkX/vjjVwIDb9O1az8CAj4e8ePo+CXLl81R/X/d2pjLuU+ZOoepU+emKeeWLTuwMDdj0oRRWFtb4Ot7ibaOPVQ5ixcrTHScc6zPnPWiR88h/DH5V6ZO+Y3rgbfo3OV7Ll36mHPmrMWYmBjzz+K/KVAgH6dOedK2XQ/evn2b4PlTqmXdijx58YolO04S/uwl5YtasniYEwXzxZwK+vDxM7WrefZvUx89wHn7CUIjXmCax4jG1coypENjtfWevXKbh4+f0aFBtTRniy+2TyfG6VPHOH1aTEOfftdzCJOT6NN2ji1wcfm4jdevizkF8I8ps5ky5eM+kRo7tu7FzNyMUWOGYGFpziX/K/ToMlA1b1fhooXU9lEvjwsM6f8rv44dxm/jR3Dr5h2+7zGUq5cD1dbbvlMb9PT02PZv2gd+4tKWfRTgv393YW5uxphxI7GyMsff7zKdO/RWHfJftKh6Vo9zPnzfZwTjJ/zMxEmjuHHjNt90+4HLATG/Vr5//x4bmzJ8820nChY05fHjCHy8/Wj15ddcuXw9zTm1pU8lZ8bvo0JkBKnzYuo8c3MzJkz4CasPdd5XanWe+vv+7FlvevUaxqRJo+LUef0J+PB5X6SINe3atQDA01P9D6sWLZw4flz9yoIppS2fTy0rFeXJy7csORZA+Mu3lLfKz+JuDSiYJ+ZU0IdPXxH/ou23Hz3n/L1HLOmecO5cgPAXb5h9wJ9HL99gkSc3jlWLM6BRxTRnjLXFzR1zi4JMUPVpAI7t4tf46tu+54c6b8ofvxEYeIsuXftxKV6N77L8Y5237kOdN2XKHKZMTVudt3f7QcwKFmDIrwMwtyzIlUvXGNh9hOqCBoWKWKnNo2RhbcG/h9eq/t93cA/6Du6Bxylv+nSKGYCrXKMiK7d+nIbitz9GArBt407GDp+Sppzaso8CuG/dh1lBM34aPRgLS3MCLl7hu64/JFo7e3v4MmzA74waM4Rfxw3n9s079O8xnGvxauevOrVGT0+P7f/uSVe+WNrSp5/ib6ZZH3IuiZPTUeo8kQJ6SvxJLkSmyJU78UPOs5P3WjTx4PP9aftCzmz5WozP6ggpYmlSIKsjpFjIy4isjpAixobJz9WTHbyKlC9lXRX17kGmPM/LP77N0PWZTFiXoesTIr1y5y6e1RFSLCo6Y+e3+1SeL++Z1RFSJH//NVkdIUXKFdA8HUp2dPXJ/ayOkCKF82SfK6YmJehF9pknLyl6yTfJNrRlQEXqvE8nWx6xJoQQQojPmBb9aCOEEEIIIVJBB+u8bDnHmhBCCCGEEEIIIYQQ2Z0csSaEEEKIzKUlV3gSQgghhBCppIN1ngysCSGEECJzackVnoQQQgghRCrpYJ0np4IKIYQQQgghhBBCCJEGcsSaEEIIITKXDp4iIIQQQgihE3SwzpOBNSGEEEJkKkUHrxYlhBBCCKELdLHOk1NBhRBCCCGEEEIIIYRIAzliTQghhBCZSwdPERBCCCGE0Ak6WOfJwJoQQgghMpcOFlxCCCGEEDpBB+s8ORVUCCGEEEIIIYQQQog0kCPWhBBCCJG5FN2b1FYIIYQQQifoYJ0nA2tCCCGEyFw6eIqAEEIIIYRO0ME6TwbWRJL09fSyOkKKmbaalNURUuT5GeesjpAiVl/8nNURPjvv3kdldYTPivZ8OoHulRdC6LYc+toz20pU9PusjpAiZgPXZ3WEFHn636isjpAipb9dmtURPjuv37/L6ghCiCwiA2tCCCGEyFSKDv6SKYQQQgihC3SxzpOBNSGEEEJkLh0suIQQQgghdIIO1nnac5y6EEIIIYQQQgghhBDZiByxJoQQQojMFa17V4sSQgghhNAJOljnycCaEEIIITKXDp4iIIQQQgihE3SwzpNTQYUQQgghhBBCCCGESAM5Yk0IIYQQmUsHf8kUQgghhNAJOljnycCaEEIIITKVouhewSWEEEIIoQt0sc6TU0GFEEIIIYQQQgghhEgDOWJNCCGEEJlLB08REEIIIYTQCTpY58nAmhBCCCEylw4WXEIIIYQQOkEH6zw5FVQIIYQQQgghhBBCiDSQI9aEEEIIkakUHfwlUwghhBBCF+hinafVR6w5Ozvj5OSEk5MT3bt3p3///kyZMoXDhw8THR2d1fHS7YeBvbh69TRPI65z4vgO6tSpkWT7Tp3a4ud7hKcR1/H2OkCrlg5q97dv34pdO9cR9MCPt2/uUa1apYzJ+UMvrl09w7OngZw84Z5szs6d2uLvd5RnTwPx8T5Iq1ZN1e7v0L41u3at42GQP+/e3qd6BuUcOLAnV6+eIiLiGsePb6dOnepJtu/UqS2+voeJiLiGl9d+WsbpTwMDA6ZOHY2X134ePbrCzZueuLjMpVAhqwzJunH/aVoP+4u6vcby7fhF+AfeS7RtZNR7/vnvIG1HzKBur7F0/X0ep3yvpmudKdVvQA/8Lh0jJDyAQ0f+pVbtakm279CxNZ4++wkJD+D0ud182aJJom3nzp/C0xc3+HFQ73Tn/PGHXgReO8uLZzc4fdKdusnto50dueh/jBfPbnDe5yCt4+2jAJMmjuLeHR+ePw1k356NlC1bKt05QXv2U23p0x9/6MX1a2d5/uwGp1KY09//GM8/5Iz/+QQwceIo7t7x4dnTQPZmYE5t6M8ME61k7E18lj73Om/AwO8IuHySR4+vcvTYNmon83nfsWMbfM4f4tHjq3h47KVlyyaq+wwMDJgy5Xc8PPYSGhZA4I1zLFs2G+tClunOqU2fTwMH9uTKlZM8eXKV48e3peA7tA0XLhziyZOreHru0/Ad+juenvsID7/MzZseLF8+h0IZ0KcbT16k9ZS12P66lB7z/sX/TkiS7dce86X99PXY/bqUln+sZua2U7yNjFLdv/nURbrO3ESD0ctpMHo5Pef/x8nLd9Kds3e/7nj4HeBW8Hl2HdxIjVpVk2zv2L4lJzx2civ4PIdPbaPpl40TtClnU5qVGxZx9c45bjzwYs/hTRQpWihdObVpH+3b7xu8/Q5xL8SPvYc2UzOZPv2qQytOe+7hXogfx07voHm8Pl24eDphT6+q3Tb9uzzdObWlTzO6zuvQoTW7d60n+OFFIt89oHr1yunOGJtTG/ozw+hgnafVA2sANWrUYOnSpTg7OzN69GgqV67MypUrmTFjBu/fv9f4mKioKI3Ls5MuXdrx99/jmTZtHnb2bfD3D2Cn+xosLApqbG9vX5s1qxexcuVG7Oxas8N9H1u2LKdSpfKqNiYmxpw67cHYcX9mWM6uXdox8+8JTJ02Fzu71vj5B7Br59qkc65xxnXlRmztWrFjx17ctiyncrycp095MmZsxuWM25/29m3x97+Mu3vSOVevXsjKlZuws2uDu/s+tmxZRqVKNgAYGxtRs2YVpk9fgL19G7p1G0C5cqVxc3NJd9a9Z3yZtXYnAzs1Y+O0YZQvXogf/3Lh0dMXGtsv2rIPt0Pn+L1Xe7b+/RNdm9kxcs5qLt9+kOZ1pkSnzm35c/oYZkxfQOOGX3Hx4hW2bluJeSJ9amtXCxfXeaxZtYVGDdqxa+cB1m9cQsUPfRqXY7sW1Klbg6Cg4DTni9W161fMmjmRKVPnUNeuFb5+AezetS7RbV/Pvg7r1jjj6rqBOrYt2bFjH/+6uVC58sd99JdRgxgyuC+DhvxO/YbtePnqFbt3riNXrlzpyqot+6m29GnXrl8xc+ZEpk6dg61dK/z8AtiVTM61H3LWtW3Jdg05R33IOXjI7zT4kHNXBuTUhv4UIit8rnVe586O/PXXOKb/OZ8G9dvi7x/A9u2rE33f29nVYuWqBaxetYn69drgvnM/GzctVfu8r1GjMn/9tZAG9R3p3u0HytmUYcuW9P2BrU2fT126ODJjxjimTZtPvXqO+PldZseOpGvnVasWsmrVZuzt2+Luvp/Nm+P3aRX++msB9eq1pVu3gdjYlGbLlvR9h+47H8js7acY2LIOG37qgk3hggxaupPHz19pbL/b+xoLdp1jYIs6/Pd7NyZ+7cD+C4Es3H1O1caqQB6GtbVn/U9dWD+yC3XLFWHEir0EBj9Oc86vOrZi0rTfmD1jMS2/6ELAxSts+G8pBc3NNLavY1uDJS4zWb/mP1o07sze3YdwXbeQ8hXLqtqUKFmMbXvXEnjtFp3b9aZpg47MnfkPb968TXNObdpHO3RqzR9/jmbWDGeaNe7IpYtX2LzVBfNE+rSubU3+5zKbdWvcaNqoA3t2HWLVemcqVCyn1u7QgeNULtdAdRvw/U/pyqktffop6rzYv5fHjJmW5lyacmpDf4r00VMURTuGADVwdnbm5cuX/Prrr2rLL168yB9//MHAgQNp1qwZTk5O9OvXj/Pnz3Px4kXatWuHk5MTnp6euLm5cf/+fUxNTfniiy/o1KkTOXLkQFEUtmzZwpEjR3j69Cl58+bFzs6Ovn37ArBv3z527drFo0ePMDY2pkKFCvz8888pzp4rd7Ek7z9xfAfe3r6MGDkeAD09PW4EerB4iSuzZi1O0H7tmsWYmBjRsVMf1bLjx7bj53eJIUPHqLUtUaIo166eoa5tS/z8ApLMkdzucfKEO17evowYMU6V8+YNTxYvdmXmLOcE7detXYyxiTEdO/ZWe62+fpcYMmR0gpzXr52lbt0W+CaTE0BfL/Fx4uPHt+Pt7cvIkRNUOQMDz7FkyUqN/blmjTMmJsZ0itOfx45tw88vgKHx+jNW7drVOHVqJ+XK2XPvXlCiWSJOLUjydXw7fhGVSxdlTJ8OAERHR9Ni6HS6t6zP9185JGjffNBU+nVoSrcW9VXLfpq7hlw5DZk+uFua1glg9UXS+/OhI//i4+PHLz9PBmL6NODqSZb+s5q5c/6XoL3rqgUYGxvxddf+qmUHD7vh73+ZkcPHq5YVKmTFoaP/0qlDHza7LWeJsytLFq9MMsvLd28Sve/0SXc8vXwZHmcfvX3TE+fFrvw9M+E+un7dEkyMjWnfsZdq2akT7lzwvcTgIb8DcO+OD3Pn/Y85c2NeZ758eQm6f4G+/UayefOORLMY6OdI8nVkl/00KlrzH6uxskuf6iWZEk6ddMcrXs5bH3LO1JBz3YecHeLkPHnCHd84Oe9+yDk3Ts4H9y/wfTLbPqlP0uzSnwBR7x4kel9GevpdswxdX/41hzJ0fSJ70OY6z8S4ZJL3Hz22DW9vX37+aSIQ876/dv0M/yxZxezZSxK0X7V6ESYmRnTp/L1q2ZGjW/HzC2D4sLEan6NW7WqcOLGD8jb1uX8/8brkbVRkovdlp88nwxxJz2Bz/Pg2vL394n2Hnv3wHZqwT9esWYSxsTGdO/dVLTt2bCu+vgEMS6RPa9euxsmT7tjY1Ev0O/Sx28gkc/aY9y+Vi1kyunMjAKKjFVr+sYbujarQt1mtBO2n/3uCW6FPWPrjV6pls7efxv9uCCuHdkz0eRqPXcHIdvXoaF9R4/2lv12aZM5dBzdywcefsb/GDDDo6enhfekwK5auY9G8hAO2/6yYjbGxET27DVIt23lgA5f8r/DbTzG14hKXWURFRTF04O9JPnd8Ya+eJnpfdtpHTY3yJPk69h7azAUff37/ZYoqq2/AMZYvXcOCucsStF/mOhdjYyO+/foH1bI9Bzdx0f8Kv4yM+exYuHg6+fLno9e3g5N87rievE76h/Xs0qdZUefFKlGiKIHXz1Gnbgt8fS8lk0TqvPh0sc7T+iPWNKlSpQolSpTAw8NDtWzLli3Y2toya9YsmjZtyuXLl1m0aBGtW7dmzpw5DBgwgKNHj/Lff/8BcO7cOXbt2sWAAQNYsGABv/zyC8WLFwfgxo0buLq64uTkxLx58xgzZgwVK2r+0koLQ0NDatWqyuHDJ1XLFEXh8JET2NvV1vgYO/taau0BDhw8hl0i7TM25wn1nIdPYG+fsDAAsLOrrdYe4MCBY4m+rozNqd6fR46cxM5Oc057Df158ODxRNsD5M+fj+joaCIinqU5a2RUFJdvPcC+ysdfovT19bGvUha/63c1PuZd1HtyGqoXm7lyGnLh6u00rzM5hoaG1KhZhaNHTquWKYrC0SOnqWtbU+Nj6trW5OiRU2rLDh06odZeT0+Ppctns2D+cq5cvp6mbPFz1qpVjUPx9tFDh09ib695n7O3q63WHmD/gaOq9qVKFY8Z/Iuzfzx79hwPj/Pp2o+1ZT/Vlj5NLOfhZHLG/3zSlPNwJuTMbv2Z0ZRoJUNvQrd8DnVezZpVOBLnO1FRFI4cPoVtIp/fdnY1OXJY/Tv04MHj2Nkm8XmfLy/R0dE8ffp5f97HZq1ZU0PtfPgkton0kZ1dLY4ciVc7H0j6OzTfhz5N63doZNR7Lt8Pw86mqGqZvr4edjZF8Lut+XTQ6iWtCLgXpjpd9P6jZ5y8fIeGFYtrbP8+Opq956/z+l0k1UqmbeoHQ0NDqtWoxIljZ1XLFEXhxLEz1LatofExderW4MSxM2rLjh4+RW3bmNNx9fT0aN7iC24G3mbDv0vxv36CXQc30qpt2v8A17Z9tHqNyhw7ql47Hz96mjp1NdfOderW4PhR9T49cugkderWUFvWoKEtAYGnOeO1l7/nTMLUtEC6cmpDn36KOu9T0Jb+zGi6WOd9thcvKFKkCHfufJxboEGDBjg4fDwyZ8mSJXTo0IEmTZoAYGVlxddff826devo2rUr4eHhFChQgKpVq2JgYIC5uTlly8YcyhweHk6uXLmoXbs2RkZGWFhYUKpUxp3TbG5uhoGBASGhYWrLQ0PCKW9TVuNjrK0sCAkNT9Deysoiw3IlmjMkXs7QcMqXTySntQWhIeo5Q0LDMiVnaLz+CQkJx8amjMbHWFlZEBqv/0NCEs+ZK1cupk4dzebN23n+PO2nVz55/or30dEUzK/+i1fB/Hm5FRSm8TH1q9mwZvcJalcoTTErM85dCuSw50Xef5h/Ji3rTE7BgqYa+zQsNBwbm9IaH2NlZU5o2KME7eP26cifBhIVFcU/yRyhllKqbR9vnwsNDaNCec3b3traIsF7LyQkHOsPOa2tLD8si9cmNBxr67TPu6It+6m29GliOUNCwyifipxxP0eTymmVwTmzW38Kkd1oc51X0Nw00fe9TSLv+5jP+4TtrazMNbbPlSsXU6b+zpbNOz77z/uYrJrrkpiaNDV9mnjt/PE7NO19+uTlG95HKxTMa6S2vGBeY26HRmh8TJvaNkS8fEOfRdtAgajoaLrWr0S/5up/QF8PekTPBf/xLuo9RjkNmdOnFWWsNZ9imByzggUwMDAgLEGd94iy5TTXeRZW5oSFJqzzLC1j9lFzi4LkyWvCkBH9mDFtAVMnzcGhWUNc1synS7venDnlleqc2rSPmn2oneP3UWjYI8omUjtbWpknrLXDHmEZ531/6NAJdrof4O6d+5QsVYyxE35i47/LaN386zTNRaktffop6rxPQVv6U6TfZzuwpigKenofDyAtU0Z9x719+zZXrlxR/XIJMafHRUZG8vbtW+zt7dm1axdDhw6levXq1KpVi9q1a5MjRw6qVauGhYUFQ4YMoUaNGtSoUQNbW9tEz2mOjIwkMjLxw+yFdjMwMGDdusXo6cHQoZpPHfiUfu3Zjj+W/UuHUbPQ09OjqJUZ7b+ow7ajnpmeJT1q1KjCD4N607jBV8k3FqmW1fupEGq05NdHkX1JnZc4AwMD1qxdhJ6eHsOHj8vqOJ8FAwMD1q51Rk9PL9HTRD8Vz8AHuBzyYUznRlQtbsW98Kf8ve0US/d7MaBFHVW7kpYF2PSzEy/evOOg7w0mbDjM8sHt0zy4ltH09WPer3t3H2bp4tUAXPK/Qh27GnzX5+s0DawJ2PbvbtW/LwdcI+DSVbx8D9Ggka3aEYdCZCodrPM+24G1Bw8eYGn5ccQ2fjH05s0bnJycsLOzS/BYQ0NDzM3NmT9/Pn5+fvj5+bF8+XJ27NjBpEmTMDIyYsaMGVy6dAk/Pz82b97Mli1bmD59OiYmJgnWt3XrVtzc3FKcPTz8MVFRUVhZqo+eW1qZJxiZjhUcEoaVpXmK22cEVc54o/yWluaEhIRqzhkcpvYrC4CVpUWm5LSM1z9WSfRPSEgYlvH638oqYc7YwYrixYvQqlW3dB2tBmCa15gc+voJLirw6OlzzAvk1fgYs3x5mPdzL96+iyTixSssTfMxb+MeiliapXmdyXn06InGPrWwTKpPw7GMN0ln3Pb16tfBwqIgl658PPTZwMCAadPH8OPgPlSr/EWqc6q2fbx9ztLSguDE3kvBYQnee1ZW5qr2wR/2bSsrC4KDP+7nVpbmXEjBHAzJZs3m+6m29GliOa1SmTPu52hSOVMy/0Zqcma3/sxw2n9BR5HFtLnOexT+JNH3fdKf95raqx8FETOo5kzxYkVp06a7Tnzex2TVXJdYWpoTHJyaPk34nRvzHepM8eJFaN06fX1qapKbHPp6PHr+Wm35o+evMM9rrPExi/d40La2DZ3sKwFQrnBBXr+LYsqWY/RrXls1YGVokIPiFvkBqFTMgkv3Qll/3J/xTqmvnx4/iiAqKgqLBHVewQRHUMUKCwnHwjJhnRfb/vGjCCIjI7l+9YZam+tXb2KbyBQyydGmffTxh9o5fh9ZWhRMcDRTrNCQ8IS1dhLtAe7cvk94+GNKlS6RpoE1benTT1HnfQra0p8ZTgfrvM9yjrWLFy9y9+5djcVUrNKlSxMUFIS1tXWCm75+TLfkzJmTOnXq0LdvXyZNmsS1a9e4ezdmTqrYXzR79OjBzJkzCQsL4+LFixqfq2PHjqxcuVLtlpTIyEh8fPxxcGigWqanp4dDk4acPeet8THnzvqotQdo1rQR5xJpnxE+5myontOhIWfP+mjOec6bpnHaAzRr1ijR15WxOdX7s0mTBpw7pznnWQ392bRpQ7X2sYMVZcuWok2bb3j8OCLdWQ0NDKhYqgjnLgWqlkVHR3PuUiDVymmeSyNWrpyGWJnlJ+p9NIc8LuJQu3K615mYyMhILpy/yBdNPl4wQU9Pjy+a1MPT47zGx3h6nFdrD+Dg0FDVfuPGbdS3b0vD+u1Ut6CgYBbMW0anDr3TnNPHx09tn9PT06OpQ0POntW8z509503Tpur7aPNmjVXtb926y8OHIWrrzJs3D7a2NdO1H2vLfqotfZpYTodkcjqkIKdDJuTMbv0pRHbyOdR5589fpEm879AmDvXxSOTz/ty58zRxUP8Obdq0Iec81D/v16x1pmyZkjg6fqszn/exWc+f11A7OzTAwyOxPvWhSZN4tXOzRhq+Q50pU6YUbdumv08NDXJQsagFHtfvq5ZFRyt4XH+Q6HxobyKj0NdTn8Y9djBNSWLK9GhF4V0iV85NTmRkJH4XAmj4hb1qmZ6eHg0b2+PtcUHjY7w8L6i1B2jcpB7eHr6qdV7wuUiZcuqnVZcpW5L7SVz0K7mc2rSP+l64ROMv6qllbfRFPbw8NdfOXp4XaBSvT79wqI+X54VEn6dQYSvMzAoQksiAckpyakOffoo671PQlv4U6af1R6xFRUURERHxYSLRCC5cuMC2bduoVasWX3yR+C80nTt3ZsaMGZibm2Nvb4+enh537tzh3r17dOvWjaNHjxIdHU3ZsmXJlSsXx48fJ2fOnFhYWODt7U1ISAiVKlXCxMSE8+fPEx0dTeHChTU+l6GhIYaGhql6XfMXLMNl+Ry8ffzw8rzA0KHfY2JixOrVmwFwcZlLUFAw48fPAGCRswsHD2xhxPAB7NlziK5OX1G7djUGDf54hRNT0wIUK1aYwoVivrhj520KCQlL80j9/PlLcXGZi4+3L55eFxg6tB8mJkasWr0JgBUu8wgKCmbc+L8AWLjIhUMH3RgxIianU9f2MTkH/aaWs3ixwhQqbK2WMzgdORcsWM7y5bPx8fHHU9Wfxon2p7PzCg4c2Mzw4f3Zs+cwTh/6c/CH/jQwMGDDhn+oWbMKHTv2IUeOHKoj9x4/jkjXKSHftWnE+H82U7l0UaqUKcraPSd5/SaSDl/EHO4/dvEmLM3yMbxbawD8Au8S+vgZFUoUIvTJM5b8e4DoaIXe7b5I8TrTwnnRCpb8bybnffzx9vZl0OA+mBgbs3ZtzK/2/yydxcOgYCZPmgXAksUr2b13PUOGfs++fUfo3MWRmrWqqK5m9uRxBE/iFayRkVGEhIQReP1WmnPOnb8MV5e5ePv44el5nmFD+2NiYsTKVTH7qOuK+QQFPWTsuA/76EIXDh9yY+SIgezec5CvnWL20R8Gfbwq3YKFyxkzehjXA29y+/Y9Jk/6haCgELZv35fmnKA9+6m29Om8+ctYoSHnqjg5HwQ9ZNyHnIsWunDokBsjRgxkz56DOH3I+aOGnIEfck7KgJza0p8ZSVsmohVZ73Ot8xYuWM7SZbM57+OPl9cFBg/5HmNjY9as2QLAsmWzCQoKYeLEvwFY7LyCffs3MWxYP/buPUKXru2oVasqQz9cUd3AwIB165dQo0ZlunT+Xuc+7yHmO3TZstl4e/vh5eXLkCF9MTY2ZvXqmD5dvnwOQUHBTJgQ06fOzq7s379J9R3a9UOfxv0OXb9+CTVrVqFTp74Z1qfffVGd8RsOU6mYBVWKW7HumB+v30XS3rYCAOPWH8IynwnDHGMGVBpXKsnaY75UKGpO1eJW3A1/yuI9HjSuXIIcHwaJF+w8S4OKxbE2zcOrN5Hs8bmO140gFg9wTHN//s95JfOXTMf3/EUuePvT/8eeGJsYsXHd1pjn/Gc6wUGh/PnHXACW/7OG/3atYuCQ3hzad4z2ndtQvWYVfhkxUbXOJQtX8M+KOZw95cWpEx44NG/Il62a0Nmxd5pzatM++o+zKwuXzODC+Yv4ePsxcFAvjE2M2LA25pT1Rf/MIPhhCFMnzwFg6ZLVbN+9hh+H9OHAvmN07NyGGjWr8PPwmCvfmpgYM+r3Iezcvo/Q0HBKlirGxD9+4dbNOxw5dCLRHMnRlj79FHWeqWkBihcvQqF4fy8HB4em+e9QbenPjKSLdZ7WD6xduHCBAQMGkCNHDkxMTChRogR9+vThiy++UP0iqUmNGjX47bff+Pfff9m+fTs5cuSgSJEiNG3aFABjY2O2b9/OqlWriI6Opnjx4vz222/kzZsXExMTPDw82LJlC5GRkRQqVIjhw4dTrFixDHtdbm7uWJibMWHCz1hbWeDrG0C7r75THU5drFgRouPssGfPetOz11AmT/qFP/74lcDA23Tt2o+AgKuqNo6OX7J82RzV/9etXQzAlKlzmDp1bppybnFzx9yiIBMmjMLaOianY7v4OT8eC3r2rDc9ew5h8uRfmfLHbwQG3qJL135cipfTZfnHPOvWxVwifcqUOUyZ+jF/ari5uWNubsaECT9h9aE/v1Lrz8IJcvbqNYxJk0bF6c/+BARcA6BIEWvatWsBgKen+gdYixZOHD+e9jkNWtWrzpNnL1nstp/wiOeUL1GYxb/3pWD+mNM2gx9FqH6pBHgXGYXzln3cD32Mca6cNKxRgWmDupHPxCjF60yL//7dRUFzM8aMG4GVlTn+fpfp1LGPalLWosUKqfWpxzkf+vUdybjxPzFh0s/cuHGHb7r9yOUPffqpbNmyAwtzMyap9tFLtHXsodr2xeNt+zNnvejRcwh/TP6VqVN+43rgLTp3+Z5Llz7uozNnLcbExJh/Fv9NgQL5OHXKk7btevD27dt0ZdWW/VRb+jQ258Q4OR3j5Izfn2fOevHdh8+nxHLO+pBzSZycjhmUM7v3Z4bSwVMERNp8rnXev//uxNzCjHHjR2JlZYGf32U6dOilet8XjVfnnTvnQ5/ew5kw8WcmTf6FG4G36fb1ANXnfeHC1jg6fgnA2XN71J6rVctunDjxeX/eA7i57cTcvKDqO9TPL4D27Xsm+R3au/cwJk4cxeTJvxAYeBsnJ/U+jf0O9fDYq/ZcLVp8neY+bVmzLE9evGbJXk/Cn72ifBFzFg9wpOCHU0EfPnmhNn9g/y9ro6cHzrs9CH36EtM8RjSuXIIhbT4esfn4xWvGrT9M+LOX5DHKiU2hgiwe4Ei98mnfZ3ds3UtBczN+HTMUC0tzLvlf4ZvOAwn/cCGqIkXV6zwvjwsM6vcrv40bxujxI7h14w59vh3K1csfz5jYs/MQv/00maEj+zNlxhhuBN6mX88ReCRypktKaNM+uu2/PRQsaMZvY4ZhaWXBRf/LfN2pH2Ef+rRo0UIocbJ6epznh36jGD1uBGMn/MTNG7fp9c1grly+DsD79++pXNmGr7t3IH/+vAQ/DOXokVP8NXU+796l/Ud+benTT1HntXNsgYvLx79D13/4O/SPKbOZMiVtf4dqS39mKB2s8/QURdG94cRsIFfujCvOPiVt2j309bTjzOaIUwuyOkKKWH3xc1ZHSLGX795kdYQUMdDPkdURUiQqOm2njmQ2veSbZBva8kka9e5BpjzPk85NMnR9pv8ezdD1CZFeJsYlszpCir2Nyj4XXkiKYQ7tOB7gsdvIrI6QIqW/XZrVEVIs7NXTrI6QIqZGebI6Qoo8eZ2+OaEzi9R5GU/qvE9HO76hhBBCCPHZ0MVTBIQQQgghdIEu1nkysCaEEEKIzKWDpwgIIYQQQugEHazzZGBNCCGEEEIIIYQQQmi9vXv34u7uTkREBCVKlKBv376ULVs20fYvX75kw4YNeHh48OLFCywsLOjVqxe1atVK8XPKwJoQQgghMpWig79kCiGEEELogqys806fPs3q1avp378/5cqVY9euXUybNo158+aRP3/+BO2joqKYOnUq+fLl46effsLMzIzw8HCMjY1T9bwysCaEEEKIzCUDa0IIIYQQn6csrPN27txJs2bNcHBwAKB///74+Phw5MgROnTokKD94cOHefHiBVOmTMHAIGZ4zNLSMtXPKwNrQgghhBBCCCGEECLbiYyMJDJS/QrWhoaGGBoaqi2Liori5s2bagNo+vr6VK1alWvXrmlct7e3N+XKlcPFxQUvLy/y5ctHgwYN6NChA/r6+inOKANrQgghhMhUciqoEEIIIcTnKaPrvK1bt+Lm5qa2rEuXLjg5Oakte/bsGdHR0RQoUEBteYECBQgKCtK47pCQEMLCwmjYsCGjR48mODiY5cuX8/79e7p27ZrijDKwJoQQQojMlcUDa1kxqa0QQgghhE7I4DqvY8eOODo6qi2Lf7RaWimKQr58+Rg4cCD6+vqULl2ax48fs2PHDhlYE0IIIYTQJKsmtRVCCCGEEKmn6bRPTfLly4e+vj4RERFqyyMiIhIcxRarQIECGBgYqJ32WaRIESIiIoiKilLNu5aclJ80KoQQQgiRAZTojL1FRkby6tUrtVv8uThixZ3UtmjRovTv35+cOXNy5MgRje1jJ7X95ZdfqFChApaWllSqVImSJUt+wh4SQgghhNBOGV3npZSBgQGlS5fm4sWLqmXR0dFcvHgRGxsbjY8pX748wcHBREd/fKKHDx9iamqa4kE1kCPWhBBCCJHJsmrujayc1FYIIYQQQhdk5Vy6jo6OODs7U7p0acqWLcvu3bt5+/YtTZo0AWDRokWYmZnxzTffANCiRQv27dvHypUradWqFcHBwWzdupXWrVun6nllYE0IIYQQWi2lc29k5aS2QgghhBDi06pfvz7Pnj1j8+bNREREULJkScaMGaOq/cLDw9HT01O1Nzc3Z+zYsaxatYpffvkFMzMzWrdurfYjbErIwJoQQgghMlVG/5KZ0rk30iKjJrUVQgghhNAFWX3191atWtGqVSuN902aNCnBMhsbG6ZNm5au55SBNSGEEEJkLkUv+TafQFZOaiuEEEIIoROyqM7LSlINZpH30Vk8jPsZypcrd1ZHSBHThsOzOkKKPDk+J6sjpFieBsOyOkKKGOjnyOoIKRIV/T6rI6SIktUBhNaJO6mtra0t8HFS28R+2SxfvjynTp0iOjpaNbiWlklthW55G6X54hki7UrktczqCCmSv9OsrI6QIk83a0ftBJC36/ysjpAiuXJ8miOndZXUeUKbyKy7QgghhMhUWXW1KIiZ1PbQoUMcPXqU+/fvs3z58gST2q5fv17VvkWLFrx48YKVK1cSFBSEj48PW7dupWXLlhnYI0IIIYQQn4esrPOyivzUKoQQQohMpURn3SkCWTWprRBCCCGELsjKOi+ryMCaEEIIIXRKVkxqK4QQQgghPk8ysCaEEEKITKUth/ULIYQQQojU0cU6TwbWhBBCCJGpFB28WpQQQgghhC7QxTpPLl4ghBBCCCGEEEIIIUQayBFrQgghhMhUuniKgBBCCCGELtDFOk8G1oQQQgiRqXTxalFCCCGEELpAF+s8ORVUCCGEEEIIIYQQQog0kCPWhBBCCJGpFCWrEwghhBBCiE9BF+s8GVgTQgghRKbSxVMEhBBCCCF0gS7WeXIqqBBCCCGEEEIIIYQQaSBHrAkhhBAiU+niL5lCCCGEELpAF+u8z/qItYiICFasWMGQIUP45ptv+PHHH/nrr7/w9/fPsOcYPHgwu3btyrD1xfXjD70IvHaWF89ucPqkO3Xr1EiyfefOjlz0P8aLZzc47/N/9u47LIprjeP4d5EioAakWmIvib2DUa+iiSVq7ERNrLF3TTGxGzX2LmrsDWPB3ht2o6hIExsaKwKiInaB5f6Brq7sIq4rMO77uc8+z83smdkfZ2d3X8/MmdlLvbo1k7UZMfwXblzz5+GDMHbtWEWhQvlNJmfHzj/gH+zLzahgdvmupWz5Uim2/65xXf49tZObUcEc+ncLX9eurvX8zDnjiI69qPVYvX7BB+cE6Nq1LefPH+H+/QscOrSRChVKp9i+adNvCQjYx/37Fzh5chd16nhonjM3N2f06N85eXIX0dHnuHLFjwULppAjh/MH51y151/q9ZtAxQ7D+GH4bIIv39DbNi4+gbkb9lF/wCQqdhhGi0EzOBp48YO2mVpK2UcBunRtw9lzh4m+d579BzdQ/h3vfZMm3+J/Zi/R985zwm8HtevU0Hp+0OC++J/ZS+Sds9y4FcCWrcupULHMB+dUSp9KTuPvo8aQmGjchzBNUufJ99ObWndswb5Tmwi8foTVOxZTsmwxvW0LFS3AjEXj2XdqE+ejTtK2S6sP3ub76NatHRcv/EvsgzCOHN5ChXf1adP6BAcdIPZBGP6n91L3rT5t3Kge27Z5czs8mBfPb1K6lHFyrjoWSr2xq6k0aAk/ztxM8PU7KbZfcTiERhN8cBu0hDpjVjFx83Gex8Vrnl/oG0jrGZv4asgyPEZ602/pHq5GxXxwTqXsowDtfmrJvwG7CAs/zZY9KylTrkSK7es3qs2B45sJCz/N3iPrqfl1Na3nb94L0fno1rvDB+VUSp9KTqnzMopPdmAtKiqKgQMHEhISwo8//sikSZMYNGgQJUqUYOHChekd751atPiOSROHM2r0FCq61SUwKJTt27xxcnLQ2b6yewW8l3uxePE/VKhUh82bd7HOZyHFixfVtPn1lx706tmRHr1+56uqDXn85Anbt3pjZWX1yeds3PRbRv31BxPHzaJmtcacDT7P2vULcXTMrrN9xUplmbdoCt7L1uJRtTHbt+1l2UovvviysFa7vXsOUazQV5pHl44DDM74SvPmDRg/fghjxkyncuUGBAWdY/Pm5Xr71N29PEuXzmTp0jW4u9dny5bdrFkzj2LFigBgY2NNmTIlGDduBpUr16dly64UKVKAtWs/7HOw83gQk7y307VJLVaN7knRPDnoPn4xdx880tl+ls8efHxP8nvbhmwY348WNSvRf9oKzl0NN3ibqaGUfRSgWbP6jB03mLF/TafqVw0ICT7Hxk1L9WZ1cyvH4qXTWbp0DVUq12fr1j2sWv235r0HuHTpPwYMGI5bxbrU/roF167fYtPmpXr3/dRQSp9KTuPvo0JkFFLnyffTm+o1+obfR/bDa9ICmn7dhgtnL7Fg9UyyO9rrbJ/ZOjM3rt1i8uhZREVGG2WbqdWieUMmThjG6DFTcXOrR1BwKNu2rkixzlu+3IvFS1ZRya0umzfvxGftAooXe92ntrY2HDt6kkGD//qgbG/aFXCFyVtO0PXrsvzTtxFFcmSnx8Kd3Hv0VGf77WcuM2PHKbp+U5b1vzRjeIuq7A78j5k7T2nanL5ym++/+pJlvRoyt3Nd4hPUdF+wk6cv4gzOqZR9FKBhk7oMG/0bUyfMoZ5HC0JDLrDC528c9NRk5SuVwWv+BFZ5b6BujRbs3O7LghUzKPplIU2bsl9U13oM6DUEtVrN9s17DM6plD6VnFLnZSSqxESljAG+n7Fjx3Lt2jWmTZtG5syZtZ57/Pgxtra2REdHs2jRIoKDgzEzM6N06dJ07NgROzs7ACIiIli2bBmXLl3i2bNn5M6dm1atWlGqVNKZTiNGjCA0NFRr22vWrElVPnPLXCk+f+zIFk6eCqRvvyEAqFQqrl45idfsxUyY6JWs/UrvOdja2NCoSTvNsqOHtxAQeJaevX4H4MY1f6ZO+5spU/8GIFu2rITfDKBjp/6sWbM5Vbkzck67zLZ6n9vlu5Yz/sH8/sufmpxB5w4x/+/lzJg6L1n7BYunYWNrTWvPrpplO/etISToHL/0Hw4knbH22WfZaNu6h97X1eVx3PMUnz90aCOnTwfRv/8wTdawsOPMmbOESZPmJGu/fPksbGxsaNaso2bZwYMbCAwMpU+fwTpfo3z5Uhw5soUiRSpz40a4zjb3D01JMecPw2dTvEBuBrX7DgC1Wk3tvhNo9U1lfvquerL2X/caS6dGNWj5TWXNsgHTvbGysGBsD0+DtvlKlip99D6XkfbRzOaWep8D2H9wA/6ng/h5wHBN1guXjjF3zlKmTJ6brP3SZTOxsbWmRbNOmmW+B9YTHBRK3z5DdL5G1qxZuB0ZTINvf+DAgWM62zyLf5FizozUp5LTuDnjX9wy6G94X1dK1jbq9goE7zbq9kTGJ3We6X0/FbLLqfe51TsWExIQyqg/JmpyHgjYyooFa5g/c2mKf+O+U5tYOm8Vy+b9Y5RtXnlwO8XXO3J4C6dOB9LvjT69cvkks2cvZuKk5H3qvWI2NrY2NGnSXrPs8KHNBAadpVevP7Ta5s2bm0sXj1OxYm0Cg0JJyYM1+msngB9nbqb454780fgrANTqROr8tYpWVYrR0SP52fRjNx7jv6gY5nX5VrNs8pYTBN+4w5IeDXS+xr1HT6n550oWdvuW8gVy6M2StcV0vc9lpH3UNUvKg65b9qwk0D+EIQP/0mQ9GbyXxfNX4jU9+QGB2QsnYWNjTftWPTXLNu/25mzwBf74+U+dr7Fg+XSyZLGlZZNOOp8HiHh0P8WcGalPJadxc0qd9/F8kmesPXr0iICAAOrUqZOs2AKwtbVFrVYzYcIEHj16xMiRIxkyZAhRUVFMmzZN0+7Zs2eULVuWoUOHMmHCBEqXLs348eOJjk46svXLL7/g4OCAp6cn8+bNY9685AM0hrCwsKBcuVLs8z2sWZaYmMg+3yO4u5fXuY67W3mt9gC79xzQtM+fPw85criwz/eI5vnY2If4+Z3B3U33Nj+lnKXLFOfg/teDCImJiRw8cIyKlcroXKdCpTIcfGvQYf++I1SoVFZrWZWqlTh3+V+On97JxCkjsM9uZ1DGN7OWLVsS3zf+/sTERHx9j1CpUjmd67i5lWP//iNay/bsOYSbm+72kPTlq1ariYmJNShnXHw85/4Lx7346yNmZmZmuBcvSFDYdZ3rvIiPx9LCQmuZlYUFARevGrzNd1HKPvoqa9myJbTey8TERPb7HqWSnveykltZ9vse1Vq2b+8hvfuKhYUFHTq2IiYmluDgcwbnVEKfSk7j76PGlJioMupDmBap85LI99OrnOYUL/0Fxw75aeX895AfZSqUzDDbTNquBeXKlcT3rT719T2Mu7u+Oq+8VnuAPXsOftTv87j4BM7disat0OvBTDMzFW6FcxJ0LUrnOqXzuhB6865muujNu7EcuXCDql/k1vs6j54lnan2mY1hZ9koZR9NympOydLFOHzwuFbWwwePU66i7st+lK9YmsMH/9VadtD3GOX1tHd0cqBW7f+xasX6D8ipjD6VnFLnZTSf5MBaREQEiYmJ5Mql/2hhSEgI169fp0+fPhQoUIDChQvTq1cvQkNDCQsLAyBfvnx888035MmThxw5ctCyZUtcXV05dSrplOYsWbJgZmaGtbU1dnZ2miOgb4uLi+PJkydaj5Q4OmbH3Nw82anpUVF3cHVx0rmOq6sTkVHa1z2IjIzWtHd1cX657K02UdG4uhp2rS2l5HRwsMfc3Jw7d7Rz3omKxllPTmcXR+5Evf13RePs4qj5b9+9h+nZ9TeaNmzHyGGT+KpqJVavW4CZmeEfK0fHpKxROl7b1VV3VhcXJ53tXfT8bVZWVowe/Qdr1mzm4UPDpljef/iEBLUah8+yaC13+CwL0Q8e6lznq5KFWb7jCNciolGr1fwbfAnfU2e5E/PQ4G2+i1L2UQCHV+99sqz630sXFyed++nb7evWq0lEVAh375+nV++OfNewDXfvpny0Uh+l9KnkNP4+KkRGIXVeEvl+SmKf3Q5zc3Pu3rmntTz6zj0cnXVPtUqPbcLrPn3770/67db997u6OiV7DyKj7uitDYzh/uNnJKgTcchqrbXcIYs10Q91TwX9tmxBetQuR4c5W6nw+yIajF9LhQI56FSzjM72anUiEzcfp0w+Fwq5GnZ5CqXsowDZNf8euau1PPrOXa1/X7zJydmR6Cjt9neionFy1t2+RcvvePzoCTu27jU4p1L6VHJKnZfRfJJ3BU3N7NabN2/i4OCAo+PrL6bcuXNja2vLrVu3KFSoEM+ePWPNmjWcOXOG+/fvk5CQwIsXLzRHMlNrw4YN+Pj4vPffITK2DeteX8z4XOhFQs9e4HTQPqpUc0t2dCmjMDc3Z8UKL1Qqld5poh/Lb20a8OfCDTT+dSoqlYrcztlp9L9ybDx4Ok1zmKJDB//lK/f6ODjY075jS5Ytn4VH9SbJijsh0kqiOr0TCCWTOk+IjOfk5dss9A1kUOOvKJnHiRt3Y5mw+Tjz9p6hy9dlk7Ufu/EYYZH3WdJd9zRR8f6+/6EJG9Zu5fnzlC/pIcTHZop13ic5sJYjRw5UKhW3bn3YHOJly5YRHBxMmzZtcHV1xdLSksmTJxMfH//uld/QpEkTGjTQ/tFYb1dYT2uIjr5HfHx8sqMXzs5ORLw1Mv1KRMQdXJy1R71dXBw17SMio14ucyIi4vUp3C7OjgQEnk39H6PAnHfv3ic+Ph4nJ+2cTs6OROnJGRWZ/GiQs7Oj3gvcAly7eoPo6HsUKJDH4IG16OikrM46XjsiQnfWyMg7Otu/fRTD3Nwcb28v8uTJRb16rQw+Ww3APqsNmczMkt1U4O6DRzh+llXnOtmzZWFa/zY8fxFHzKMnONtnY9rqXeRyzm7wNt9FKfsowN1X732yrMnfy1ciI+/o3E/fbv/kyVOuXLnGlSvXOHkygIAgX9q282Syjmv2vYtS+lRyGn8fNSa1Qk7rFxmT1HlJ5Pspyf17McTHx+PgpH3Wk6NT9mRn+6TnNuF1n759tlnSb7fuKZYREXeSvQcuzk56awNjsLfNTCYzFXffOjvt7qOnOL51Ftsrs3edpn65QjR1S7rAeuEc2Xn6Ip5R647QqWYZzMxef++P3XiMQ+dusKh7fVzs9F8j+V2Uso8C3NP8e0T7jEdHJwe9/764ExWd7AxJJ+fks2oAKrmXo1CRAnT/6VeDM4Jy+lRySp2X0XySU0GzZMlC6dKl2bVrF8+ePUv2/OPHj8mdOzd3797VOip58+ZNzXMAFy5coHr16lSqVIk8efJgZ2fHnTvJByvU6pSHZC0sLLCxsdF6pCQuLg5//yBqelTVLFOpVNT0qMrx47rP7jl+4jQ1a1bVWvZ1rf9p2v/333Vu347U2mbWrFmoVKksx08YdsaQknIGBpzlfzVeXzRfpVLxv+qVOekXoHOdU34B/K96Za1l1T2+4pTfGb2vkyOnC9mz2xGpZwAstVnPnAnGw6OKVlYPjyr4+fnrXOfECX9q1KiitaxWrWqcOPG6/atBtYIF81O//g/cuxdjcEYAC3NzvsyfkxNnwzTL1Go1J85eplShPCmua2VpgUv2z4hPULPPLwSPcl9+8Db1Uco++irrmTMhWu+lSqWihsdX+J3Q/d77nThDDQ/t996jZlW9+8orZmZmWFmlfCOFlHIqoU8lp/H3USEyCqnzksj306uc8ZwNPE/lahW1crpXq0jAqeAMs82k7cbh7x+Mx1t96uFRlePH9dV5p7X6C5LqvI/5fW5hnokvczniF/b6RgxqdSJ+YeGUyqt7qtmzuHjevhqKmSrpH9eJJJ1lmpiYyNiNx/ANuca8LvXIld2wA6evKGUfTcoaT3BgKFX/56aVtWp1N/xPBupc5/TJQKr+z11rWbUalTmto33LH5sSeOYs585eMDhjUk5l9KnklDovo/kkz1gD+Omnnxg6dCiDBg3C09OTvHnzkpCQQFBQEHv27GHKlCnkyZOHmTNn0q5dO9RqNQsWLKBYsWIULFgQSDoi6ufnR4UKFQBYvXp1sukHTk5OnDt3jipVqmBubk62bNmMkn/q9PksXjiV0/5BnDx5hj69O2Nra82SpasBWLxoOuHhtxk8ZBwAM2cuxHefD/37dWX7jr1879mI8uVL0a3Hb5ptzpi5gEF/9OFS2BWuXr3ByBG/Eh4eyaZNuz75nHNmLWbW3PEEnAnB/1QQ3Xq0w8bGmn9WrAPA6+8J3A6PZPTIyQD8PWcpm3esoEevjuzedYCmzetTpmwJBvQZCiTd1vzX33uxZfMuoiKjyZc/DyP+/JX/rlzDd99hvTlSY8aMBcyfP5nTp4M4dSqQXr06YmNjw7JlawFYsGAK4eERDBs2ISm712J2715N376d2bHDlxYtGlKuXEl69ky6a4y5uTkrV86hbNkSNG3akUyZMmmOlN67F0NcnGG3OG9TrypD//aheP7clCiYmxU7j/L0+QsaV0+6+O7guWtxts9G3+/rABAUdoOo+w/4Im9Oou49YM76fagTE2nf4H+p3qYhlLKPAsyasYC/50/G3z+I06cC6fnyvV+xPGmK0bz5kwkPj2DE8KQ7lM32WszO3avo3acTu3b60vzle9+n1yAAbGys+XVgT7Zv3UtExB0cHO3p0rUNOXO6smH9doNzKqVPJafx91FjUcqFaEXGJXWefD+9acnclYybOZyQwHME+Z+lXddWWNtYs37VFgDGzRpB1O07TBmTdAc+CwtzChYtkPT/LS1wyeHEFyWK8OTxE67/dzNV2zTU9OnzWLhwKv6nAzl5KoDevTtha2vN0mVJfbpo4TTCwyMYMvRln85ayL69PvTr14UdO/bh2SKpT3v0GKjZpr29HXk+z0mOnK4AFCmStI9HRN4x+My2NtVKMHTNIYrldqTE5054Hwnh6Yt4GlUoAsCQVQdx/syGPvWSBh//92UeVhwO4YucDpTM48z16Fhm7z7N/77MQ6aXI25/bTzGjjNXmNbua2wzWxD9MOl6hFkyW5LZwrB/liplHwWYN3sZU73GEBhwlgD/EDp1+xFrG2tWr9wIwLTZfxFxO4pxo6YBsPDvFfhsWUyXnu3Yt/sQjZrWo1SZ4gzsP0Jru1my2tKgUW3+HDrpg/K9opQ+lZxS52Ukn+zAmouLC+PHj2f9+vUsX76c+/fvky1bNgoUKECnTp1QqVT89ttvLFq0iOHDh2vdhv2Vtm3bMmfOHIYMGULWrFlp1KgRT59qnxLt6enJ/Pnz6d27N3Fxcam+Dfu7rF27GSfH7IwY9guurk4EBp6lfoMfNRepz/N5Tq0jqP8eP8WPbXvx58jfGD1qIJfC/qNZ8584+8ZRi4mTZmNra8Pc2ROws8vG0aMnqd/wR54/f/7J59y4fjsOjtn5fVAfnF2cCAk+h2eznzTXmMqdO4dWzpN+Z+j6088MGtqPwcMHcOXyVdq27sn5c5cASEhIoFiJonzfugmffZaViNtRHPA9ytjR03jxwrCBqld8fLbi6OjAsGEDcHFxIigolEaN2mr69PO3+vT48dO0b9+H4cN/YeTIXwkLu4qnZxdCQy8CkDOnKw0bJt3y2M9vp9Zr1a79PYcPH8cQdd1LcT/2MbPX7SX6wUOK5s3B7N864PBy2mZEdIzmSCXAi7g4vNbu4ead+9hYWVK1TFHGdPckm611qrdpCKXsowDr1m3D0cmBIUMH4OLiSFDQOZo0bq/3vT9xwp+O7fsxdPjPjBj5C5fDrtLy+66a9z4hIYGiRQrywz/NcHCw5969GE6fDqL2N56ce7kvG0IpfSo5jb+PGkui2vQKLmFcUufJ99ObdmzaQ3YHO3r/1hUnZwfOhVykc8s+mpsP5MzlSqL69aCps6sTG329Nf/9U882/NSzDX5HT9O2SbdUbdNQa3224OjkwDBNn4bSoGGbN37rcyWr89q27cXIkb8x6s+BhIX9R/MWnTgb+rpPGzT4hoULpmr+29s76VIPo0ZNYdToKQblrFOmAPcfP2PO7tNEP3xK0ZwOzP6pjuaGBrdjHqF6o87rXKsMKhV47TpN1IMn2GfJzP++zEOvuq/vUrj23/MAdPpb++DeSM9qmgG796WUfRRgy4adODjY88sfvXBydiQ05DxtWnQj+uW/R3K99e+R034B9OoykN8G9WbgkL78d+UanX7sw4VzYVrbbdS0HiqVik3rDD9o+ial9KnklDovI1ElpuYKsMLozC3138lKGMYus+HXaEhLj+Myxhfeu9w/ZFghlh6yVOmT3hFSJbO5YdMv09qzeLnoramKf/Fh16xKrfNFvjXq9r64aJx/TAhhLFLnGV8hu5zpHSFVrjy4/e5GGcCDNcqonQCytpie3hFSxTWLfXpHSJWIR4bdGV4on9R5H88ne8aaEEIIITImOaQnhBBCCPFpMsU6TwbWhBBCCJGmTHGKgBBCCCGEKTDFOu+TvCuoEEIIIYQQQgghhBAfm5yxJoQQQog0pTbBu0UJIYQQQpgCU6zzZGBNCCGEEGnKFG/DLoQQQghhCkyxzpOpoEIIIYQQQgghhBBCGEDOWBNCCCFEmjLFu0UJIYQQQpgCU6zzZGBNCCGEEGnKFK+9IYQQQghhCkyxzpOpoEIIIYQQQgghhBBCGEDOWBNCCCFEmjLFi9oKIYQQQpgCU6zzZGBNCCGEEGnKFK+9IYQQQghhCkyxzpOpoEIIIYQQQgghhBBCGEDOWBNCCCFEmjLFi9oKIYQQQpgCU6zzZGBNfDJinj1O7wiflCxV+qR3hFR7Gn44vSOkinXOaukdIVUymSnjZOYEtTq9I6Sa6ZUXKTPFa28IIT5MWEx4ekf4pGRtMT29I6Ta45DV6R0hVWxLfJ/eEVLF0SZbekdIlegnsekdIdUsMsmwyptMsc5Txr+ehBBCCCGEEEIIIYTIYGRoVQghhBBpyhSnCAghhBBCmAJTrPNkYE0IIYQQacoEbxYlhBBCCGESTLHOk6mgQgghhBBCCCGEEEIYQM5YE0IIIUSaMsUpAkIIIYQQpsAU6zwZWBNCCCFEmjLFu0UJIYQQQpgCU6zzZCqoEEIIIYQQQgghhBAGkDPWhBBCCJGm1OkdQAghhBBCfBSmWOfJwJoQQggh0lQipjdFQAghhBDCFJhinSdTQYUQQgghhBBCCCGEMICcsSaEEEKINKVOTO8EQgghhBDiYzDFOk8G1oQQQgiRptQmOEVACCGEEMIUmGKdJ1NBdfDy8mLChAmpbh8VFYWnpydXr179eKGEEEIIIcQHkzpPCCGEEMaUoQfWYmNjmT9/Pt27d6d169Z07tyZMWPGcP78+fSOlia6d2tH2MXjPIq9zLEjW6hYoUyK7Zs1a0BI8EEexV7mjP9e6tWtmazNiOG/cOOaPw8fhLFrxyoKFcovOTNYTiVlVUrOf9ZtoXazdpTz+I5WnfsRHHpBb9u4+HjmLPKmbosOlPP4jqbtenDk+CmtNgkJCcyct4w6zdtT3qMRdVt0YO7ilSQmfvh5z0rp025d23HhwjEexFzi8KHNVHhHzqZN6xMUuJ8HMZc4fWoPdet4aD3fqFFdtm31JvxWEM+f3aBUqWIfnBGU05/du7Xj0sXjPIy9zNFU5gwOPsjDlznrvpWzceN6bN+2kojbIcS9uEXp0sU/OKMxJaIy6kMok9R5yvl+UkJOJWWVnMbNuWqbL3V/+o0KTbvS+ufRBF+8ordtXHw8c//ZzLedf6dC06407z2cI6eDtdosWLuNVv1H4e7Zg+o/9qPv6Jn8dzPig3OCcvq0Q6fWnAzay9WIALbvXUXZciVTbN+wUR0O+23jakQA+49uotY3/0vWpnCRAiz9x4uL1/y4cus0O33XkCt3jg/KqZT+7Nq1LefPH+H+/QscOrSRChVKp9i+adNvCQjYx/37Fzh5chd13qibzc3NGT36d06e3EV09DmuXPFjwYIp5Mjh/ME5jcUU67wMPbA2efJkrl69Ss+ePZk+fToDBw6kWLFiPHr0KL2jfXQtWnzHpInDGTV6ChXd6hIYFMr2bd44OTnobF/ZvQLey71YvPgfKlSqw+bNu1jns5DixYtq2vz6Sw969exIj16/81XVhjx+8oTtW72xsrKSnBkkp5KyKiXnjr0HmTBzHt07/sDaRTMpWig/XQcM4e79GJ3tZ85bytpNOxjUvzubVvyNZ+Nv6fvHKM5dDNO0WbhiLas3bmPQgB5sXjmPAT06ssjbB2+fzQbnBOX0afPmDZkwYShjxkzDzf1bgoND2bplud6c7u7lWb5sFkuWrMLNrR6bt+xi7doFFCv2OqetrQ1Hj/kxeMhfBud6m1L6s0WL75g4cTijR0+hkltdgoJC2faOnCte5qxYqQ6bdOR81Z+DBo0xONfHpDbyQyiT1HnK+H5SQk4lZZWcxs2587AfExesplur71g9bThF839Ot2FTuRsTq7P9rBUb8Nl5kD+6tmbj7NG0qFeD/n95ce7yNU2bUyEXaVnfgxUTBzNv1M/EJyTQbdhknjx7bnBOUE6fNmpSjxFjBjJ5vBe1qzfjbMgF/lk/H0fH7DrbV6hUhjkLJ/HP8nV887+m7Ni+j8XeM/niy8KaNnnzfc6mnd6EXfyPpg3b4VGlMVMmzuH5B/SpUvqzefMGjB8/hDFjplO5cgOCgs6xeXPKdfPSpTNZunQN7u712bJlN2vWzKNYsSIA2NhYU6ZMCcaNm0HlyvVp2bIrRYoUYO3ahQZnNDZTrPNUicY4xeIjePz4MR06dGDEiBEUK6b7zIWtW7eyf/9+oqKiyJIlC+XLl+fHH38kc+bMABw4cIAlS5bQr18/li5dSnR0NF988QU9evTA3t4eALVazfLly9m/fz9mZmbUrFmTmJgYnjx5wm+//QZAQEAA69at48aNG5iZmVGkSBHat2+Pq6srkDRFoFevXkyYMIF8+fKl6u8zt8yV4vPHjmzh5KlA+vYbAoBKpeLqlZN4zV7MhIleydqv9J6DrY0NjZq00yw7engLAYFn6dnrdwBuXPNn6rS/mTL1bwCyZctK+M0AOnbqz5o1hg0ISE7j5lRS1oyU82n4Yb3PtercjxJfFGHwzz2ApM/8103a0rr5d3Rq45msvcd3P9ClXUtaNWuoWdZv0GisrCwZPzzpO6HHr8NxyG7HqD/6622ji3XOanqfg4zTp5nMUj7mcvjQZk6fDqRf/6GanJfD/Jg9ZzGTJs1O1n7F8tnY2lrTpGkHzbJDBzcRFHSWXr0HabXNmzc3Fy/8S8VKdQgKCk0xR4I65Z/ajNKfQIrH2o4e2cKpt3L+9zLnRB05vV/mbPxGziOHtxD4Rs5X8ubNTdilE1SoWJvAwLMppEgS9+LWO9sYwx6X7426vW8iVxt1e+Ljkzov43w/fQo5lZRVcr5/zsch+r/jW/88mhKF8zOo2w9A0me+dodfadWgFj+1+DZZ+1rtBtDZswEt678+U6n/X15ktrJk7M+ddb7GvQcPqfFjPxaN/Y0KJYrqbANgWyLl37aM0qeONtlSzLl97yoC/EMY9NtoTU7/s/tZOG8Fs6YtSNb+70VTsLGxpk3L7ppl2/asIiT4HAMHjARg7sLJxMXH07vrwBRf+03RT3QPjr6SUfoTwCKT/kvXHzq0kdOng+jff5gmZ1jYcebMWcKkSXOStV++fBY2NjY0a9ZRs+zgwQ0EBobSp89gna9RvnwpjhzZQpEilblxI1xvlqdPr+l9zphMsc7LsGesZc6cmcyZM+Pn50dcXJzONiqVig4dOjB58mR69uxJSEgIK1as0Grz/PlztmzZQq9evRg5ciTR0dEsX75c8/yWLVs4cOAA3bt3588//+TRo0ecPHlSaxvPnj2jQYMGjBs3jmHDhqFSqZg0aRLqd/yjzlAWFhaUK1eKfb6vBwsSExPZ53sEd/fyOtdxdyuv1R5g954Dmvb58+chRw4X9vke0TwfG/sQP78zuLvp3qbkTNucSsqqlJxxcXGEXriEe8UymmVmZma4VyhDYMg5neu8iIvD0tJSa5mVlSVngl4PSpQp8SUnTgVw9fpNAM5fuoJ/0FmquVcwKCcop0+TcpbE941tJiYm4rv/sN5turmX02oPsGfvQdw+4LOSupxK6c/kOX3fkdM3hZxKYIpTBIQ2qfOU+/2U0XIqKavkNHadF8+5sGu4l/5Ss8zMzAy3MsUIvHBZ5zov4uKxtLDQWpbZypIzoZf0vs6jx08A+CyrrUE5QTl9amFhQakyxTl08F+tnIcP/kuFSmV0rlO+Ymmt9gAHfI9o2qtUKr6uXZ0rYVf5Z918Qi4dYfveVdStX8ugjK9yKqU/y5bVUTf7HqFSpXI613FzK8f+/W/VzXsO4eamuz0kDQCq1Wpi9JypmdZMsc7LsANrmTJlokePHhw8eJD27dszdOhQVq5cybVrr0dZ69evT4kSJXB2dqZEiRK0bNmSf//V/lAnJCTQuXNnChYsSIECBahbty7Bwa/n0W/fvp0mTZrg5uZG7ty56dy5MzY2NlrbcHd3x83NDVdXV/Lly0f37t25fv06N2/e/Ch/u6NjdszNzYmKjNZaHhV1B1cXJ53ruLo6ERl1R2tZZGS0pr2ri/PLZW+1iYrG1dWw+diS07g5lZRVKTnvx8SSkKDGIbu91nKH7PZE37uvc50qbuVZtmo9127cQq1Wc8zPn30Hj3Hn7j1Nm05tPKn3dXUatu5Cmf81oEWHXrTxbEyDOsmv05BaSunTVznfft2oyGhc9OV0cSIyKjrV7Y1Baf35ds7I98z5sfvT2ExxioDQJnWecr+fMlpOJWWVnEau82IfkqBW42CvfQaWg102ou8/0LnOV2VLsHzjbq6FR6JWq/n3zFn2HfPnzj3d7dVqNRPmr6Lsl4UonDe3QTlBOX2a3cEOc3Nz7kTd1Vp+J+ouzs6OOtdxdnHkzlt13pvtHZ0cyJLVlt79OrF/3xG+b9qJ7Vv3smj5DCpXqWhQTqX0p6OjfVLOt+vgqGhcXXXndHFx0tleX51nZWXF6NF/sGbNZh4+zBiXUjDFOk//OYsZgLu7O+XKleP8+fNcvHiRgIAANm/eTLdu3ahRowZBQUFs3LiRW7du8fTpUxISEoiLi+P58+eaedBWVlaaU/kB7O3tiY1NGsl98uQJ9+/fp1ChQprnM2XKRIECBbQuQn779m1Wr15NWFgYDx8+1BzBjI6OJk+ePO/8O+Li4vQejRVCZCy/9+3KiPEzaNi6CyoVfJ4zB43rf8OGrbs1bXb6HmLr7v2MH/EbhfLn5fylK4yf/jfOjtlp9O036ZheCCGUQ+o8IURaG9ilFSNnLqFR98GoUJE7hxONvq7Cxr1HdLYfM9ebsOu3WDL+d53Pi3czM0s642jndl/mzV4KwNng81R0K0vbDt/z79GTKa0uUmBubs6KFV6oVCq900RF2sjQA2sAlpaWlCpVilKlStG8eXPmzp3LmjVrKFasGOPHj+ebb76hZcuWZMmShfPnzzN37lzi4+M1BVemTJmSbfN9Lys3fvx4nJyc6Nq1K/b29iQmJvLzzz8THx+fqvU3bNiAj49Pql8vOvoe8fHxOLtoHxVwdnYi4q0R9FciIu7g4qw9iu3i4qhpHxEZ9XKZExERUa/bODsSkIrr7kjOj59TSVmVktPeLhuZMplx962z0+7eu4/jW2exvZLd3o4Z44bx/PkLYmJjcXZ0YOqcReTO+fofbpO9FtLpR0++/boGAEUK5ud2RBQLlq8xeGBNKX36Kufbr+vs4pjsCJ8mZ+QdXN46yplSe2NQWn++ndPlPXN+7P40NqUcfRQfn9R5rynl+ymj5VRSVslp5DovW1YymZlx97729Le7MbE42n+mc53sn2Vl+pDePH8RR8zDRzhnt2PaUh9y6zgb6K+53hw6GcjisQNx1XPh/tRSSp/euxtDfHw8Ts7aF9Z3cnZIdhbVK1GR0Ti9Vee92f7e3Rji4uK4+Nb03EsXrlDJXf/0xpQopT+jo+8n5Xy7DnZ2JCJCd87IyDs6279d55mbm+Pt7UWePLmoV69VhjlbDUyzzsuwU0H1yZ07N8+fP+fKlSuo1Wratm1LkSJFyJkzJ/fv657apY+NjQ329vaEhb2+219CQgJXrry+RfPDhw8JDw+nadOmlCxZkty5c/P48eP3ep0mTZqwZMkSrUdK4uLi8PcPoqZHVc0ylUpFTY+qHD9+Wuc6x0+cpmbNqlrLvq71P037//67zu3bkVrbzJo1C5UqleX4Cd3bfBfJadycSsqqlJwWFhYUK1qYE6cCNMvUajUnTgdQusSX+lck6bpqLk6OxCcksOfAUTyqVdY89+zZc1Rm2vP9zczMUH/AvWCU0qdJOYPx8KiildOjRlW92zxx3F+rPUCtmtU48QGfldTlVEp/Js/p8Y6cHinkVAJTvPaGSB2p8zL+91NGy6mkrJLT2HWeOV8WysuJoNfXzVWr1ZwIPEfpogVTXNfK0gIXB3viExLYe8yfGu5lNM8lJiby11xvfP/1Z8GYX8mtZ8re+1BKn8bFxREUcJZq1d21clb9nzun/AJ0rnP6ZKBWe4D/1fhK0z4uLo4A/xAKFs6v1aZAoXzcTOFC++/KqZT+PHNGR93sUQU/P3+d65w44U+NGm/VzbWqceLE6/avBtUKFsxP/fo/cO9ejEH5PhZTrPMy7BlrDx8+ZMqUKXh4eJA3b16sra25fPkymzZtokKFCri6upKQkMDOnTspX748Fy5cYM+ePe/9OvXq1WPjxo24urqSK1cutm7dypMnTzTP29rakjVrVvbu3Yu9vT3R0dF4e3u/12tYWFhg8dZFMt9l6vT5LF44ldP+QZw8eYY+vTtja2vNkqVJd8RYvGg64eG3GTxkHAAzZy7Ed58P/ft1ZfuOvXzv2Yjy5UvRrcfrOxTOmLmAQX/04VLYFa5evcHIEb8SHh7Jpk273iub5Px4OZWUVSk5237fhMFjJlP8i8KUKFaUFWs28vTZcxrXTzqz7I9Rk3B2dKB/96Q7VgadPU/knbt8UbgAUXfuMnvRChITE+n4Q3PNNmtUcWP+0lXkcHGmUP68nLsYxrLV62lSv7bBOUE5fTp9xnwWLpjCaf8gTp0MoHfvn7C1tWbZsjUALFw4lfDwCIYOHQ/ALK+F7N2zln59u7Bjxz5aeH5H+fKl6NHz9bQKe3s7Pv88JzlzuABQpEhSQRwZecfgM7GU0p/Tps9nkY6cS9/IeSv8NkNe5pw1cyH79vnQr19XduzYi+fLnN3fyGlvb0eePLnI8VZ/RkREKerMNvHpkjpPGd9PSsmppKyS08h1XuPaDJm6kGKF8lGySH5WbNqbVOd9nTQwMWjKAlwc7OnbrhkAQReuEHX3Pl8UyEPk3fvMWbkJtVpNh6b1NNscM2cFOw6dYPrg3thaZ9Zcry2LjTWZrSyTh0glpfTp315LmT5nLIFnQjhzOpjO3dtiY2vNKu8NSbnmjuN2eCR//TkVgPlzl7Fh2zK69WrP3l0HadzsW0qXLc6v/YZrtjl75iL+XjSZ40dPcfTwCWp+XZXadWvQtEE7nRlSQyn9OWPGAubPn8zp00GcOhVIr14dsbGxYdmytQAsWDCF8PAIhg2bAICX12J2715N376d2bHDlxYtGlKuXEl6vqybzc3NWblyDmXLlqBp045kypRJc/21e/di5NIE6STDDqxlzpyZwoULs23bNiIjI0lISMDBwYFatWrRtGlTLC0tadu2LZs2bWLlypV8+eWXtG7dmlmzZr3X6zRs2JCYmBi8vLwwMzPDw8ODihUraoouMzMz+vbty+LFi/n555/JmTOn5vbwH9PatZtxcszOiGG/4OrqRGDgWeo3+FFzSm2ez3Nq3a3q3+On+LFtL/4c+RujRw3kUth/NGv+E2fPXtC0mThpNra2NsydPQE7u2wcPXqS+g1/5Pnz55Izg+RUUlal5Kz3dXXuxzxg1oIVRN+7xxeFCzJ38ijNVNDbkVGYqV4fCXn+4gUz5y/lZngENtbWVKtckbFDfyVb1iyaNoP6d2fm/GWMnuTFvfsxODlmp0Wjb+neobXBOUE5ferjswUnx+wMG/Yzri5OBAaG0vC7Npqcn3+eC7X69dl7x4+fpm273owc8St//vkbYWFXadGiE6Ghr3M2aPANC+ZP0fy394rZAIwaPYXRo6calFMp/fkq5/A3cjZ4I+fnOnK2aduLkSnkbNigNgsXvu63ld5Jt3P/c9RkRo163c/pRa2Mg4/iI5I6T1nfTxk9p5KySk7j5qxbrRL3HzxktvdGou/HUrTA58wZ2R+Hl1NBI+7c06rzXryIY9aKDdyMuINN5sxUrVCSvwZ0IluW1zc1WbPjAAAdB03Qeq1RfTvQ6GvtM57eh1L6dNOGHTg42vPboD44OTtyNvgcrZp1IfpO0g0NcuXOoZXzlF8APTr9ysAhffljaH/+u3yNDj/05vy513da3bF1LwMHjKR3/y6MHj+Iy2H/8VPbvvgd133WVmoopT99fLbi6OjAsGEDcHFxIigolEaN2uqt844fP0379n0YPvwXRo78lbCwq3h6diE09CIAOXO60rBh0sF8P7+dWq9Vu/b3HD583OCsxmKKdZ4q8X0vRCGMwtwyV3pHEOKT8TT88LsbZQDWOauld4RUyWSmjKsEJKiVcwUHpdQXcS9upcnrbHL9sEHotzWKWGnU7QnxoaTOE8J4HoesTu8IqWJb4vv0jpAqjjbZ3t0oA4h+EvvuRhmERaYMe76SlqdPr727kRGYYp2njH89CSGEEEIIIYQQQgiRwShjaFUIIYQQnww5VV4IIYQQ4tNkinWeDKwJIYQQIk0pZxKvEEIIIYR4H6ZY58lUUCGEEEIIIYQQQgghDCBnrAkhhBAiTalVSrmdgxBCCCGEeB+mWOfJwJoQQggh0pQpXntDCCGEEMIUpHedt3PnTrZs2UJMTAx58+alY8eOFCpU6J3rHT16lOnTp1OhQgV+++2393pNmQoqhBBCCCGEEEIIIRTt2LFjLFu2jObNmzN+/Hjy5s3LmDFjePDgQYrrRUVFsXz5cr788kuDXlcG1oQQQgiRptRGfgghhBBCiIwhPeu8rVu3UqtWLTw8PMidOzedO3fG0tKS/fv368+rVjNz5kw8PT1xdnZ+z1dMIlNBhRBCCJGm1Ol86Y30mCIghBBCCGEKjF3nxcXFERcXp7XMwsICCwsLrWXx8fFcuXKFxo0ba5aZmZlRsmRJLl68qHf7Pj4+ZMuWjZo1a3Lu3DmDMsrAmhBCCCFMxqspAp07d6Zw4cJs27aNMWPGMG3aND777DO9633oFAEhhBBCCPH+NmzYgI+Pj9ay5s2b4+npqbUsNjYWtVqNnZ2d1nI7OzvCw8N1bvv8+fP4+voyYcKED8ooA2tCCCGESFNq0u+UtTenCAB07twZf39/9u/fr3WE801vThE4d+4cjx8/TsPEQgghhBDKYew6r3mTJjRo0EBr2dtnqxni6dOnzJw5k65du5ItW7YP2pYMrAkhhBAiTRn7blFKmCIghBBCCGEKjF3n6arpdMmWLRtmZmbExMRoLY+JiUl2FhtAZGQkd+7cYfz48ZpliYlJ6Vu2bMm0adNwdXVNVUYZWBNCCCGEoilhioAQQgghhPh4zM3NKVCgACEhIVSqVAlImnUQEhJC3bp1k7XPmTMnkyZN0lq2atUqnj17Rvv27XF0dEz9a39YdCGEEEKI92Psi9o2UcAUASGEEEIIU5CeN6lq0KABXl5eFChQgEKFCrF9+3aeP39OjRo1AJg1axbZs2endevWWFpakidPHq31bW1tAZItfxcZWEsnebO5pHeEVLkeG5neEVItd9bUjyinpztPY9M7Qqo429ild4RUs85ZLb0jpMr9HuXSO0Kq2M/2T+8IqWJulim9I6RavDohvSNkKO976/R3UcIUAWFaSjnkT+8IqRZ097/0jpAqhexypneEVLmqkNo5h619ekdINdsS36d3hFS537FkekdIFftFwekdIVWUVOfFJcSnd4QMxdh13vv46quviI2NZc2aNcTExJAvXz4GDRqkqfOio6NRqYw/8icDa0IIIYQwCek5RUAIIYQQQnx8devW1VnXAYwYMSLFdXv27GnQa8rAmhBCCCHSlLEvavs+0muKgBBCCCGEKUjPOi+9yMCaEEIIIdJUel57I72mCAghhBBCmIL0rPPSiwysCSGEEMKkpMcUASGEEEII8WmSgTUhhBBCpKn0vKitEEIIIYT4eEyxzpOBNSGEEEKkKVMsuIQQQgghTIEp1nlm6R1ACCGEEEIIIYQQQgglkjPWhBBCCJGmEk3worZCCCGEEKbAFOs8GVgTQgghRJoyxSkCQgghhBCmwBTrPJkKKoQQQgghhBBCCCGEAeSMNSGEEEKkKVM8kimEEEIIYQpMsc6TgTUhhBBCpKnE9A4ghBBCCCE+ClOs80x2Kqinpyd+fn56nz979iyenp48fvw4DVMJIYQQQogPJXWeEEIIIdLKJzuwFhsby/z58+nevTutW7emc+fOjBkzhvPnz6dq/aJFizJv3jxsbGxSbOfl5cWECROMETmZHzt6ctB/K6E3/2XdrqWUKls8xfb1vvua3f+uI/Tmv2w/tJoaX1fRet7BKTsTZo7gWMguQq4fZfHqWeQr8PkH5+zerR2XLh7nYexljh7ZQsUKZVJs36xZA4KDD/Iw9jJn/PdSt25NrecbN67H9m0ribgdQtyLW5QunfLfnVptfvqew2e2c/6WHxt2r6B0uRIptv/2u2/Ye3wj52/5seOwDzW+rqr1vI2tNSPH/8Gx4N2cu3mC3cfW07p9C6Nk7dK1DWfPHSb63nn2H9xA+QqlU2zfpMm3+J/ZS/S985zw20HtOjW0nh80uC/+Z/YSeecsN24FsGXrcipULPPBOdt09OSQ/zbO3TzO+l3LUrWP7vl3PeduHmfHoTXJ+tTRKTsTZo7k35DdnL1+7OU+mueDc3bv1o6wi8d5FHuZY6ncR0OCD/Lo5T5a7619FGDE8F+4cc2fhw/C2LVjFYUK5f/gnBZVv8V22AKyTFyHTf9JmOUpnPIK1rZYNeuG7cilZJm0HttBc8n0ZfnXz1tZY9WkE7bDFpJlgg82fSdg9vk7tplKSunTrl3bcuHCUWJiLnLo0CYqvOOz1LRpfQIDfYmJucipU7upU8dD85y5uTmjR//BqVO7uXv3PFeunGThwqnkyOHywTmV0p/GolYZ9yFMz6dQ53m2b8pWv7X8+98+lm6bR/EyX+ptW6BIfiYuGM1Wv7X43z5C687J643mbRuzet8SDl3cxaGLu1iyZS5f1XT/4JxK+X5q3bEF+05tIvD6EVbvWEzJssX0ti1UtAAzFo1n36lNnI86SdsurT54m+9DKb9NSqmdlbKPAlhUb4jtmKVkmbkZm4HTMMtXJOUVrG2xatkT2/EryTJzM7YjF5CpREWtJio7BzJ3+I0sk9aQZcYmbIbOeXcN+Q5K6VOlfJaU0p/GYop13ic7sDZ58mSuXr1Kz549mT59OgMHDqRYsWI8evQoVeubm5tjZ2eHSqX7nVSr1ajVH2/2cP3GtRk0agAzJs7ju5qtOX/2EkvWeuHgaK+zfbmKpZg27y/Wem+ioUdr9mw/wJxlUyjyRUFNm7nLpvB5vtx0bdOfhjVbc+vmbZatm4u1TWaDc7Zo8R0TJw5n9OgpVHKrS1BQKNu2eePk5KCzfWX3CqxY7sXixf9QsVIdNm3exTqfhRQvXlTTxtbWhqPH/Bg0aIzBud5Wv3EdBo/6hekT/6ZBzZacC7nA0rVzcHDMrrN9uYqlmT5/HGtWbKC+x/fs2b6fv5dPo8gXhTRthoz6hf/V/Ir+3QbxdeUmLJ7rzcjxv/N13eoflLVZs/qMHTeYsX9Np+pXDQgJPsfGTUv19qmbWzkWL53O0qVrqFK5Plu37mHV6r8pVuz1D/WlS/8xYMBw3CrWpfbXLbh2/RabNi/FUc/fnxpJ++jPzJj4Nw1rtubc2YssXTs7hX20NNPnjWWN90YaeLRi9/YDzE22j04lT77cdG3TjwY1W3Hr5m2WG2EfnTRxOKNGT6GiW10Cg0LZ/o591PvlPlqhUh0269hHf/2lB716dqRHr9/5qmpDHj95wvat3lhZWRmc07xsVawad+L5zn94MqkfCbf+w6bbn6iyfKZ7hUzm2HQfhVl2Z54tGcfjv7rxbPUsEh/c1TTJ3LI3mYqU5dmKKTye0Jv4C2ew6TEK1WeGv++gnD5t3rwhEyYMZcyYabi71yc4+BxbtqzQm9PdvTzLls1kyZLVuLl9y5Ytu1i7dr7ms2RjY03ZsiUYO3YG7u7f0rJlFwoXLoCPz0KDM4Jy+tOY1EZ+CNOj9Dqv9nc1GTCiF/MmL6Z1nZ+4FBqG1z9TsHew09k+s7UVt66FM2PMXO5ERutsE3X7DjPGzOWHOj/xY91OnDzqz9TFYylQxPB/bCnl+6leo2/4fWQ/vCYtoOnXbbhw9hILVs8ku56aJLN1Zm5cu8Xk0bOI0tOf77vN1FLKb5NSamel7KMA5uX/h1XzzjzfuoInf/Ui4eYVbHqPQZU1hVqv71jMHFx4Nm80j0d05tmK6STef2OftcmCza9TICGeJ7OG8HhkF577zCfxSeq+C3VRSp8q5bOklP40JlOs81SJiYmf3BTYx48f06FDB0aMGEGxYrqPLHl6etK1a1f8/f0JDAwke/bstG3blgoVKgBJUwRGjhzJ4sWLsbW15cCBAyxZsoRevXrh7e3N7du3qVatGgcPHtTa7vDhwyle/N1nWBV0LJfi8+t2LSXoTCgjfx8PgEql4kjQDpbNX8XfM5Ykaz9jwTisbazp3LqvZpnPzqWcC7nA0F/+Il/BPOw7sZG6VZpz6cIVzTZPhO5h0phZrFmxUWeO67GRKeY8emQLp04F0rffEM02/7tyEq/Zi5k40StZe2/vOdja2NC4STvNsiOHtxAYeJaevX7Xaps3b27CLp2gQsXaBAaeTTEHQO6sjnqf27B7BUFnzjJ84FhNzmPBu1k6/x/mTl+UrP3MBROwtrGmU+vemmXrdy0nNPgCQ34ZDcDOI+vYtmEXMyfP07TZvO8fDu47wuS/kv/tr9x5Gpvi37H/4Ab8Twfx84DhmqwXLh1j7pylTJk8N1n7pctmYmNrTYtmnTTLfA+sJzgolL59huh8jaxZs3A7MpgG3/7AgQPHdLZxtrFLMef6XcsIOnOWEW/so0eDdrJs/irmzlicrP2MBeOwsbGm0xv76LqdSzkXcpEhv4whf8E87DuxiTpVmr21j+59uY9u0JvlemyU3ueOHdnCybf20asv99EJOvbRlS/30UZv7KNHD28h4I199MY1f6ZO+5spU/8GIFu2rITfDKBjp/6sWbNZb5b7PfR/7m36TyLh+iWer0vaJioVtsMXE3d4Ky/2+SRrb/FVXSxrNuXxX91BnZB8gxaWZBm3hqcLR5MQeur16/w8lfhzp3mxfYXeLPaz/fU+BxmnT83NMqWY89ChTZw+HUj//sM0OcPCTjBnzhImTZqdrP3y5V7Y2trQtGkHzbKDBzcSFBRK796DdL5G+fKlOHp0K4ULu3PjRrjeLPG63qOXMkp/AsS/uKX3OWOamudHo26v/3X9+7P49CihziuXo2qKzy/dNo/QgHOMHzwVSPrc7zi9nlWL1rFkVsr781a/taycv4aV89e+M8f+0O1MG+XFpn+26W0TdPc/vc9lpO+nQnY59T63esdiQgJCGfXHRE3OAwFbWbFgDfNnLtW7HsC+U5tYOm8Vy+b9Y5RtXn1H7ZxRfpty2KY8QJiRaucbD3UPfkLG2kfvdyyp9zkAm4HTSLh2keerXr7PKhW2Y5cTt38zL3atSdbeotq3WNZuzuPhnXXXeoBl4w5kKlicp5N/SfG132S/KDjF5zNKn0qdJ3XeK0qo8z7JM9YyZ85M5syZ8fPzIy4uTm87Hx8fKleuzKRJkyhbtiwzZsxI8Ujn8+fP2bRpE926dWPKlCl06NCBypUrU6ZMGebNm8e8efMoWrSo3vVTy8LCnBKlv+TYwROaZYmJiRw7eIKyFUvpXKdshZIcfaM9wOH9/1K2QlJ7S0vLl3/DC61tvnjxggpuZQzMaUG5cqXY53tYa5u+vkdwdy+vcx13t/L4vtEeYPeeA3rbG8Or/jxy8LhWzqMHj1NOX39WLMXRN9oDHPI9ptXe3y+AWvWq45LDGQD3qhXJXygvh/f/+wFZLShbtgT79x/Ryrrf9yiV3HQPylRyK8t+36Nay/btPUSlSrrbW1hY0KFjK2JiYgkOPmdgzqQ+PfrWPno0hX20XIVSGWYf3feOfXRfCvto/vx5yJHDhX2+r9+j2NiH+Pmdwd3NwP04kzlmuQuRcDHw9bLERBIuBmCWT/d3inkJNxKunseqeTdsRy3DZuAsLL9uAaqXX+tmmVBlygRxL7TWS4x7QaYChk9lUUqfJuUsia/vW5+l/Udw0/NZcncvp9UeYO/eQ3rbA3z2WTbUajUxMSkPmKecM+P3p7GZ4pFMYTxKr/PMLcz5slQRThx+fdAjMTGRE4dPUaq8cS5/YWZmRu1GtbC2yUzQ6XcfnNRFKd9PFhbmFC/9BccOvb6mXmJiIv8e8qNMhZQHOtJym0nbVcpvkzJqZ6Xso0BSrZenMAnnzrxelphIwrkzmBXQPQ3cvLQ7CVfOY9WqJ7YT/sFm6Fws637/utZ72UZ9/SKZOw/GdsIqbAbNwqJqXYNjKqVPlfNZUkZ/Gpsp1nmf5MBapkyZ6NGjBwcPHqR9+/YMHTqUlStXcu3aNa121atXp2rVqri6utKqVSuePXtGWFiY3u0mJCTw008/UbRoUXLmzImNjQ2Wlpaa6QR2dnaYmye/0WpcXBxPnjzReqTE3iFpO9F37mktj75zDydn3aeMOjo7cvfOXe32UXc17a9cusqtG7f5ZUgvsn2WFQsLc7r0bkeOXK44uTilmEcfR8fsmJubJzuFPjLqDq56tunq6kRk1B2tZVGR0bgYmCE17B3sk/ozSlf/6D7LzcnZkei3+/OOdvsRv48j7MIVjofs4WLEKZasmc3w3/7C79+Uz/ZJiYOjvc4+jYrS30cuLk7ciXp3+7r1ahIRFcLd++fp1bsj3zVsw9279w3KqenTZPvo3RT30WTt39hHL7/cR38d0luzj3bt3Z6cuVxxdtF/NmJK9O2jUe+5j0ZGRmvau7o4v1z2VpuoaFxdnQ3KqbLNhipTJtQPtd+PxIcxmGXTfURZ5eCKeekqYGbG079H8mL3Kiw9GmNZ2zOpwfOnJPx3Dss6LVFlyw4qM8zL1yBTvqJ6t5kaSulTTc63PhuRKXzfuLg4EZUs5x297a2srBg9+g/WrNnEw4eGTblQSn8aW6KRH8K0KL3Os8v+Gebm5tx76zfx3p17OOj5DU2tQl8U4EjYbo5f82Xw+F/4ueMg/rt41aBtKeX7yT570vtyV0fd7Ghgf36MbYJyfpuUUjsrZR8FUGV5WevFxmgtT7HWc8yBebmqYJaJp7OG8mL7Siy/boblt6+vCWjmmAOL/zVAHXWLpzMHE3doG1ae3TF3/9qgnErpU6V8lpTSn8ZminVe8urgE+Hu7k65cuU4f/48Fy9eJCAggM2bN9OtWzdq1KgBQN68eTXtM2fOjLW1NQ8ePNC7TXNzc611UmvDhg34+CSfypWW4uPj6dH+F8ZOG8aZyweJj4/n2EE/Duw5ovf6IiJl7Tq3omyFUnRq3YdbN8Kp9FV5Rk4YRGTEnWRnZmUEhw7+y1fu9XFwsKd9x5YsWz4Lj+pNuPNWEZRe4uPj6d7+Z8ZNG07A5UPEx8dz9OAJDuw5ArKPJqNSqUh89IDnq70gUY365mWef+aApUdTXuxaBcDTFVPI3KovWf5cSmJCAuqbl4n3P4TZ54XesXXxLubm5nh7z0algt69B6d3HCFMjtR5ul29fJ1WX3cgS7Ys1GpQgz9nDKZT094GD64JZcnov01Kq53Tm0qlIvFhDM9XTE+q9a6H8dzOEcvazXmxzftVI9TXLvFi0xIA1DcuY5YzH5b/q0/88b3pF17hMvpnSWQ8n+zAGiRNLStVqhSlSpWiefPmzJ07lzVr1mgKrkyZtOdtq1QqUrrknKWlpUGDUE2aNKFBgwZay0rm0X/tjft3Y4iPj8fRSfvioI5O2bkTpXsQJDoqGoe3LoDo6Oyg1T4k8BwNPVqRJWsWLC3NuXc3hnW7lhIcYNh0wOjoe8THxyc7m8jF2YmIt0bQX4mIuIOLs/bovLOLY7IRd2O6f/d+Un866+of3ddsuBMVjePb/en0ur1VZit+GdKHbm37s39P0qm650MvUaxEUTr3bGdwcXA3+r7OPnV21t9HkZF3kh091NX+yZOnXLlyjStXrnHyZAABQb60befJ5Elz3junpk+T7aMOKe6jydrr2EcbeLQka9YsWFhacO/ufdbvWkZwQOh7ZwT9+6jze+6jLi6OmvYRkVEvlzkREfH62m4uzo4EpOJagLokPo4lMSEBs6z2Wqc7q7LaoY7VfVahOvY+JMRD4us11JE3MfssO2Qyh4R4Eu9G8HTWH2BphSqzDYmx98nc7jcSoyMMygnK6VNNzrc+Gy4pfN9ERt7BOVlOp2TtXxVbefLkom7dlgYfxdTKmcH709iUcocnkbFl5DqvasHaetvH3HtAfHw82d/6TczulJ27en5DUys+Lp4bV5OuoXMu6ALFS39J604tGPPbxPfellK+n+7fS6qbHXTUzW+fcZWe2wTl/DYppXZWyj4KkPjoZa2XzS71td6De5CQoF3rRVzXrvUe3CPh9nXt9SKuY16uikE5ldKnSvksKaU/jc0U67xPciqoPrlz5+b58+dG3aa5ufk77xplYWGBjY2N1iMlcXHxhASe46v/VdIsU6lUVP5fJc6cDNK5zplTwVrtAapWd+PMqeTtHz18xL27MeQr8DklyxRj744DKebRnzMOf/8ganq8HiRUqVR4eFTl+PHTOtc5fuI0HjW1BxW/rvU/ve2N4VV/Vvmfm1bOr/7nhr++/jwZpNUeoGoNd017CwtzLC0tkr33CQlqzMwM/1jFxcVx5kwINWq8/jFUqVTU8PgKvxO6T5P3O3GGGh7aP54eNavi55fyafVmZmZYWVkamPPVPvp2n+rfR/1PBSXbR6tUd9e5jz58+Ih7d++Tr0AeSpYpxh4j76M137GP1kxhH/3vv+vcvh2ptc2sWbNQqVJZjp8wcD9OiEd9M4xMhd+4bolKRaYipVFfvaB7lf9CMXPKoXU2n5lTTtQP7iYNuL3pxXMSY++DtS3mX5QlPsTwo8JK6dOknMF4eLz1WapRhRN6PkvHj/trtQeoWbOqVvtXxVahQvn59tvW3LsXY1A+7ZwZvz+NzRSvvSE+PqXUefFx8ZwLukilqq+vhaNSqahUtbzB10PTx8xMhYWlhUHrKuX7KS4unrOB56lcraJWTvdqFQk4lfJF2tNym0nbVcpvkzJqZ6Xso0BSrXf9Epm+KPN6mUpFpi/KoL6i+ySHhMuhmDnn1K71XHKhjnld6yVcDsXMJbfWemYuuUi8q//GXilRSp8q57OkjP40NlOs8z7JM9YePnzIlClT8PDwIG/evFhbW3P58mU2bdqkuRuUsTg5OREYGEh4eDhZsmTBxsZG5/U33teiOd5MnDWS4IBQAv3P0qFba2xsrPH5J+kuH5O8/iTidhSTRs8CYMnfK1m5eT4/9fiR/buP0KBpHUqUKcbgAaM126z33dfcu3uf8JsRFC1WiKFjfmXP9gMcOXBcZ4bUmDZ9PosWTuW0fxAnT56hT+/O2Npas3TpagAWL5rOrfDbDBkyDoBZMxeyb58P/fp1ZceOvXh6NqJ8+VJ07/GbZpv29nbkyZOLHDlcAChSpCAAERFRBp/ZtmD2ciZ7jSIo4CyB/iF07PpjUn+u3AjA5NmjibgdxcRRM5Jy/+3Nqi0L6dSjLb57DtGwSV1KlinOoP6jAHj08DHHj5zkj5EDePbsObdu3MatSnmaft+A0UMnGZTxlVkzFvD3/Mn4+wdx+lQgPXt1xMbGhhXLk6aZzJs/mfDwCEYMTzr6PNtrMTt3r6J3n07s2ulL8xYNKVeuJH16Jd3dxsbGml8H9mT71r1ERNzBwdGeLl3bkDOnKxvWbzc458I5K5g068+X+2jIG/voJgAmeY0i8nYUE0fPBGDJ3//wz+b5/NSjDft3H6Zh0zqULFOMwQNGabapvY8WZpgR9tGp0+ezWMc+uuSNfTQ8/DaDX+6jM2cuxHefD/37dWX7jr18/3If7fbGPjpj5gIG/dGHS2FXuHr1BiNH/Ep4eCSbNu0yOOeLAxvJ3Lo/CTfCUF+/iEX1RqgsMxN3Iuk0/sw/9Ef94C4vti4DIO7oDiyrNcCqSWdeHN6KmVNOLL9pQdyhrZptZvqiLKBCHXULM8ccWDXqgDrypmabhlJKn86YsYAFCybj7x/MyZMB9O79E7a2NixblnTnrYULpxIeHsHQoUl3tvXyWsSePWvo27czO3b44un5HeXLl6Jnz6Q7MJmbm/PPP3MpW7YETZp0IFOmTJrrcty7F5PiRdRTopT+FCKj+BTqPO+/VzFy+mBCA89zNuAcrTt7Ym1jzeZVSXfv/HPGEKIi7jDrr6Q7vplbmFOgSD4gaSDP2dWJIsUL8fTxU80Zar0GdeWY73Fu34zENosNdZt+Q/mvytKz1QCDcyrl+2nJ3JWMmzmckMBzBPmfpV3XVljbWLN+1RYAxs0aQdTtO0wZk3QHPgsLcwoWLZD0/y0tcMnhxBclivDk8ROu/3czVds0lFJ+m5RSOytlHwV4sXc9mdv/QsK1S6ivXsCiZpOkWu/YbgAyt/8FdcxdXmxcDEDcoa1Y1miIlWc3XuzfjJlzLizrtiRu/6bX29y3AZvfpmBZ93viTh8iU76iWFT9lmfe0w3OqZQ+VcpnSSn9KT7MJzmwljlzZgoXLsy2bduIjIwkISEBBwcHatWqRdOmTY36Wl9//TWhoaH8/vvvPHv2LNW3YX+XbRt3k93Bnn6/d8fR2YFzIRfo4NlLcxHVHLldtY74+J8Mon/XwQwY1IOfB/fi2pXrdG87gIvnL2vaOLs4MnjUABycHLgTGc2G1VuZNXn+B+Vcu3YzTo7ZGT7sF1xdnQgMPEuDBj9qLiT5+ec5tXL+e/wUbdr2YuTI3xg9aiCXwv6jWfOfOHv29Vk5DRvUZuHCqZr/XumdNFXxz1GTGTVqikE5t23chYOjPQN+74GjsyPnQi7Q3rOH5mL6OXO93Z+B9OvyBz8P7sUvQ3pz9cp1urbpx8Xzry963LvzQH4b2pdpf4/Fzi4bt27eZtKYWXgvXmtQxlfWrduGo5MDQ4YOwMXFkaCgczRp3F5vn5444U/H9v0YOvxnRoz8hcthV2n5fVdCQy8CSRdjLlqkID/80wwHB3vu3Yvh9Okgan/jyblzlwzO+Wof7f/GPtres+frPk22jwbSr+sgfh7Uk18G9+Lqlet0S7aPOjF41M9JUwcio1m/eiuz3rgluyFe7aMj3thH67+xj+bRsY/+2LYXf6awj06cNBtbWxvmzp6AnV02jh49Sf2GP37QmRLxZ47w3PYzrOr9gCqbPepbV3jy93ASH8UAoLJ3wuyNKUyJMdE8mTuMzI07YfvbTBIf3CXu4BZe7FunaaPKbItVg7ao7BxJfPyQ+KBjPN+2XO8t21NLKX3q47MFR8fsDBs2ABcXJwIDQ/nuuzZ6P0vHj5+mXbs+jBjxC3/++RthYVdp0aKz5rOUK5crDRsmTe86eVK7cKld25NDhwwbAFZKfxqTUi5EKzKmT6HO273ZF3sHO7r/1gkHp+xcOBtGr9Y/cy86aUqYay4Xrc+9k4sjq/Yu0fx32x6tadujNaeOnaFLs94AZHew588ZQ3B0duDRw8dcCr1Mz1YDOHHoFIZSyvfTjk17yO5gR+/fuuLk7MC5kIt0btlHUzfnzOVKovr1N4+zqxMbfb01//1Tzzb81LMNfkdP07ZJt1Rt01BK+W1SSu2slH0UIP70IZ5n/Qyrhm2Sar2bV3gycwiJD2MAUGV31q717kfzZMYQMrfogu3QOSTGRBPnu5EXu173l/raRZ7O/ROrxh2wrP8D6ugInq+dS7zffoNzKqVPlfJZUkp/GpMp1nmqxJQuNiE+moKO+m/rm5Fcj41M7wipljurYXeOTGt3nhp2u+a05mxjl94RUu16rGGnu6e1+z2U8bm3n234nW3TkrlZpnc3yiDiP3AwM63Ev7iVJq8zJu8PRt3e4Gve724kRBoql0P/tXQzmqC7/6V3hFQpZJczvSOkylWF1M45bA2/Q3hau/FQ93XdMpr7HUumd4RUsV9k+HTmtCR1nvFJnffxmNQ11oQQQgghhBBCCCGEMJZPciqoEEIIITIupVyIVgghhBBCvB9TrPNkYE0IIYQQaUquQSGEEEII8WkyxTpPpoIKIYQQQgghhBBCCGEAOWNNCCGEEGnKFKcICCGEEEKYAlOs82RgTQghhBBpSq1K7wRCCCGEEOJjMMU6T6aCCiGEEEIIIYQQQghhADljTQghhBBpSm2Sl7UVQgghhPj0mWKdJwNrQgghhEhTplduCSGEEEKYBlOs82QqqBBCCCGEEEIIIYQQBpAz1oQQQgiRpkzxblFCCCGEEKbAFOs8GVgTQgghRJoyxWtvCCGEEEKYAlOs82QqqBBCCCGEEEIIIYQQBpAz1tLJtdjI9I7wybnxMDq9I3xSrsdGpXeEVDNTqdI7QqrYz/ZP7wip8uTq7vSOkCo2+Wqnd4RUs8gkP7dvMr3jmMLUBN39L70jfHLCYsLTO8InRUl1szKqPLBfFJzeEVLlafjh9I6QKtY5q6V3hFSzMrdI7wgZiinWeVLpCyGEECJNmeK1N4QQQgghTIEp1nkyFVQIIYQQQgghhBBCCAPIGWtCCCGESFOmeFFbIYQQQghTYIp1ngysCSGEECJNmV65JYQQQghhGkyxzpOpoEIIIYQQQgghhBBCGEDOWBNCCCFEmjLFi9oKIYQQQpgCU6zzZGBNCCGEEGkq0SQnCQghhBBCfPpMsc6TqaBCCCGEEEIIIYQQQhhAzlgTQgghRJoyxSkCQgghhBCmwBTrPDljTQghhBBCCCGEEEIIA8gZa0IIIYRIU2oTvPaGEEIIIYQpMMU6TwbWhBBCCJGmTK/cEkIIIYQwDaZY55n0VNCoqCg8PT25evVqekcRQgghhBBGJHWeEEIIIdJCug2sxcbGMn/+fLp3707r1q3p3LkzY8aM4fz58+kVKcPp3q0dYReP8yj2MseObKFihTIptm/WrAEhwQd5FHuZM/57qVe3ZrI2I4b/wo1r/jx8EMauHasoVCi/5MxgOZWUVSk5u3Vrx8UL/xL7IIwjh7dQ4V05m9YnOOgAsQ/C8D+9l7pv5WzcqB7btnlzOzyYF89vUrpUsQ/O+IpS+vSfDTuo07Ir5Wt/T+vuAwk+d0lv27j4eOYsXUO9H7pTvvb3NPupP0f8/LXa1GnZlZIeTZM9Rk+b90E5ldKfXbu25fz5I9y/f4FDhzZSoULpFNs3bfotAQH7uH//AidP7qJOHQ/Nc+bm5owe/TsnT+4iOvocV674sWDBFHLkcP7gnMaiJtGoD5HxSJ33bkr5flJKTiVllZzGz3np4nEexl7maCpzBgcf5OHLnMnqvMb12L5tJRG3Q4h7cYvSpYt/cMY3syqhT/9Zt4XazdpRzuM7WnXuR3DoBb1t4+LjmbPIm7otOlDO4zuatuvBkeOntNrUbtaOElXqJXuMnuz1QTmV0p9durYh9NwR7t67wIGDGyn/jjqvSZNv8T+zj7v3LuDnt5M6dWponjM3N2fUqN/x89tJ1J1Qwi6fYP78ybhKnZeu0m1gbfLkyVy9epWePXsyffp0Bg4cSLFixXj06FF6RTKK+Ph4o2ynRYvvmDRxOKNGT6GiW10Cg0LZvs0bJycHne0ru1fAe7kXixf/Q4VKddi8eRfrfBZSvHhRTZtff+lBr54d6dHrd76q2pDHT56wfas3VlZWkjOD5FRSVsXkbN6QiROGMXrMVNzc6hEUHMq2rSv05nR3L8/y5V4sXrKKSm512bx5Jz5rF1C82OuctrY2HDt6kkGD/zI4l86sCunTnb5HmDhnMd3aebJm3iSKFMxH19/+5O79GJ3tZy5cic/W3fzRuxMbl0zH87s69Bs6gXOXrmja/DN3AvvXLdQ85k0aDkCdGl8ZnFMp/dm8eQPGjx/CmDHTqVy5AUFB59i8eXmK++jSpTNZunQN7u712bJlN2vWzKNYsSIA2NhYU6ZMCcaNm0HlyvVp2bIrRYoUYO3ahQZnNDa1kR8i45E6L2VK+X5SSk4lZZWcxs85ceJwRo+eQiW3ugQFhbLtHTlXvMxZsVIdNunIaWtrw9FjfgwaNMbgXPqyKqFPd+w9yISZ8+je8QfWLppJ0UL56TpgiP46b95S1m7awaD+3dm04m88G39L3z9Gce5imKbNqgXTObDZW/OYPy2phq7tUc3gnErpz2bNGjBu3BDG/jWdKl/VJzg4lE2blunN6eZWjiVLZ7Bs6Wq+qvwtW7buZtXqt+u84owbN5MqXzWgVctuFC5SkLVrFxic0dhMsc5TJSYmpvkQ4OPHj+nQoQMjRoygWDHdZ3p4enrStWtX/P39CQwMJHv27LRt25YKFSpo2ly/fp0VK1Zw7tw5MmfOTKlSpWjXrh3ZsmUDICAggHXr1nHjxg3MzMwoUqQI7du3x9XVFUiaItCrVy8mTJhAvnz5UKvVzJ07l4sXLzJkyBAcHR05efIkPj4+3Lx5E3t7e6pXr07Tpk3JlCmTJmenTp04c+YMISEhNGzYEE9Pz3f2gbllrhSfP3ZkCydPBdK33xAAVCoVV6+cxGv2YiZMTD6yv9J7DrY2NjRq0k6z7OjhLQQEnqVnr98BuHHNn6nT/mbK1L8ByJYtK+E3A+jYqT9r1mx+Z2bJ+fFzKilrRsppplLpfe7I4S2cOh1IvzdyXrl8ktmzFzNxUvKc3itmY2NrQ5Mm7TXLDh/aTGDQWXr1+kOrbd68ubl08TgVK9YmMChUb4ZX1O/4us0offrk6u4Uc7buPpDiXxRicN/OSX+XWs0333ehVZNv6dS6abL2NZv/ROcfmtOqST3Nsv7DJmBlZcm4wf10vsb4WQs5+O9ptq3wQqXn/bXJVzvFnBmlPwEsMum/pOmhQxs5fTqI/v2HaXKGhR1nzpwlTJo0J1n75ctnYWNjQ7NmHTXLDh7cQGBgKH36DNb5GuXLl+LIkS0UKVKZGzfC9WZ5+vSa3ueMqXO+Fkbd3vyra426PfFhpM6TOk/qJ8lpzJz6qzw4emQLp97K+d/LnBN15PR+mbPxGzmPHN5C4Bs5X8mbNzdhl05QoWJtAgPPppAiybv+UZ1R+vRp+OEUc7bq3I8SXxRh8M89gKQ67+smbWnd/Ds6tUn+/efx3Q90adeSVs0aapb1GzQaKytLxg//TedrjJs2l4PH/Ni+eqHeOs86Z8qDbhmlPwGszC30Pnfg4EZOnw7k5wHDNTkvXvqXuXOWMnly8jpv6bJZ2Npa07zZT5pl+w9sICgolL566rxy5Utx+PBmihb5ips39dd5j59c1fucMZlinZcuZ6xlzpyZzJkz4+fnR1xcnN52Pj4+VK5cmUmTJlG2bFlmzJihOdL5+PFj/vzzT/Lly8e4ceMYNGgQDx48YOrUqZr1nz17RoMGDRg3bhzDhg1DpVIxadIk1Ork455xcXFMmTKFa9eu8eeff+Lo6Mi5c+eYNWsW9erVY8qUKXTp0oUDBw6wfv16rXXXrl1LpUqVmDRpEjVrJj+d9H1ZWFhQrlwp9vm+/tJLTExkn+8R3N3L61zH3a28VnuA3XsOaNrnz5+HHDlc2Od7RPN8bOxD/PzO4O6me5uSM21zKimrsnKWxPetnL6+h3F3L6dzHTe38lrtAfbsOfhB72vqs2b8Po2LiyP04mXcy5fSLDMzM8O9XCkCz+qeJvAiLg4rS+2Cw8rKkjPB5/S+xtY9h2hSr6beYutdlNKfFhYWlC1bEt83tpm0jx6hUiV9+2g59u8/orVsz55DuLnpbg9JhaFarSYmJtagnMaWaOT/iYxF6ryUKen7SQk5lZRVcqZNTt935Hy7znsz58eilD6Ni4sj9MIl3CuW0SwzMzPDvUIZAkN0120v4uKwtLTUWmZlZcmZIN2DkXFxcWzdvZ8m9WubSJ1Xgv37j2rl3O97lEp66jY3t7Ls9z2qtWzv3kO46akLAT57Wec9eCB1XnpJl4G1TJky0aNHDw4ePEj79u0ZOnQoK1eu5No17SPl1atXp2rVqri6utKqVSuePXtGWFjSKaU7d+4kf/78tG7dmly5cpE/f366d+/O2bNnCQ9PGqV1d3fHzc0NV1dX8uXLR/fu3bl+/To3b97Uep1nz54xbtw4YmNjGT58uOZIqI+PD40bN6ZGjRq4uLhQqlQpvv/+e/bu3au1fpUqVfDw8MDFxQVHR8cP7h9Hx+yYm5sTFRmttTwq6g6uLk4613F1dSIy6o7WssjIaE17Vxfnl8veahMVjaurYfOxJadxcyopq9Jyvr3NqKhoXFx0b9PV1SnZ3xUZdQcXPX+XsSilT+8/eEiCWo2DvZ3Wcgd7O+7ei9G5zlcVyrJs7Rau3QxHrVZz7FQA+w4f5869+zrb7zvix8NHj2mk47oXqaWU/nR0tE/KGfV2zmhcXXXndHFx0tle3z5qZWXF6NF/sGbNZh4+zBjT8ExxioApkTovZcr5flJGTiVllZxpkzPyPXNGRer/DTUWpfTp/ZhYEhLUOGS311rukN2eaD11WxW38ixbtZ5rN24l1Xl+/uw7eIw7d+/pbL/v0L88fPSIxt9+Y1BGUE5/Oryq83Tk1LfP6a7z7uDiovv3x8rKilGjf2et1HnpSv/clI/M3d2dcuXKcf78eS5evEhAQACbN2+mW7du1KhRA4C8efNq2mfOnBlra2sePHgAwLVr1wgJCaFNmzbJth0ZGUnOnDm5ffs2q1evJiwsjIcPH2qOYEZHR5MnTx5N++nTp5M9e3aGDx+uNdp+9epVzp8/r3XkUq1WExcXx/PnzzVzrQsWLJji3xoXF5fiEVshhPiU/d67IyMmzeG7dn1QAZ/ncqVR3Zps3OGrs/2G7fuo6lYOZ8fsaRv0E2Rubs6Kl9Np9U0TFeJjkDpPCCFMw+99uzJi/Awatu6CSgWf58xB4/rfsGGr7kuLrN+6i6ruFXDWc40xkXrm5uYsXzELlUpF375D0juOSUu3gTUAS0tLSpUqRalSpWjevDlz585lzZo1moLr1fUtXlGpVLy6JNyzZ88oX748P/74Y7Lt2tnZATB+/HicnJzo2rUr9vb2JCYm8vPPPye78GzZsmU5fPgwFy9epESJEprlz549w9PTEzc3t2SvYWHxelrTuy5muGHDBnx8fFJs86bo6HvEx8fj/NaotLOzExFvjaC/EhFxBxdn7VFvFxdHTfuIyKiXy5yIiIh63cbZkYBUXDNAcn78nErKqrScbx8RcnZ2JDIySuc6ERF3kv1dLs5OyY5eGZtS+tT+s6xkMjNLdgHbu/djcMhup3Od7HafMWP07zx/8YKYBw9xdszO1HnLyZ3DJVnb8IgojvsHMXWk7mtypJZS+jM6+n5STue3czoSEaE7Z2TkHZ3t395Hzc3N8fb2Ik+eXNSr1yrDHMUEFHNav/gwUufpppzvJ2XkVFJWyZk2OV3eM6ezS/LfUGNTSp/a22UjUyYz7r51dtrde/dxfOsstley29sxY9wwnj9/QUxsLM6ODkyds4jcOV2TtQ2PiOT4qQCm/fVhg0BK6c+7r+o8HTn17XO66zwnIt866y1pUM2LPJ/n5ttvpc5Lb+l2V1BdcufOzfPnz1PVNn/+/Ny8eRMnJydcXV21HpkzZ+bhw4eEh4fTtGlTSpYsSe7cuXn8+LHObdWuXZvWrVszfvx4QkNfX4C8QIEChIeHJ9u+q6srZmap77omTZqwZMkSrUdK4uLi8PcPoqZHVc0ylUpFTY+qHD9+Wuc6x0+cpmbNqlrLvq71P037//67zu3bkVrbzJo1C5UqleX4Cd3bfBfJadycSsqqrJzBeLyV08OjKseP++tc58SJ01oZAGrVqvZB72vqs2b8PrWwsKBYkYKc8A/SLFOr1Rz3D6L0G3dV0sXK0hIXJwfiExLYe+g4HlUqJmuzcacv2e2y8b/KH3atE6X0Z1xcHGfOBOPhUUUrp4dHFfz89O2j/tSoUUVrWa1a1Thx4nX7V4NqBQvmp379H7inZ5puejHFKQJC6rxXlPT9pIScSsoqOdMmp8c7cnqkkPNjUUqfWlhYUKxoYU6cCtAsU6vVnDgdQOkSX6a4rpWVJS5OjsQnJLDnwFE8qlVO1mbDtj1kt/+M/1WuZFC+V5TSn0l1Xgg13rjLvUqloobHV/id0FfnnaGGx1day2rWrMoJP+06b/kKLwoVzEeDBlLnZQTpcsbaw4cPmTJlCh4eHuTNmxdra2suX77Mpk2btO4GlZI6deqwb98+pk+fznfffUeWLFmIiIjg2LFjdOvWDVtbW7JmzcrevXuxt7cnOjoab29vvdurV68earVac4HcL774gmbNmjF+/HgcHR1xd3dHpVJx7do1bty4QcuWLVP991pYWGgd+UyNqdPns3jhVE77B3Hy5Bn69O6Mra01S5auBmDxoumEh99m8JBxAMycuRDffT7079eV7Tv28r1nI8qXL0W3Hq/P+pgxcwGD/ujDpbArXL16g5EjfiU8PJJNm3a9VzbJ+fFyKimrUnJOnz6PhQun4n86kJOnAujduxO2ttYsXZaUc9HCaYSHRzBk6Mucsxayb68P/fp1YceOfXi2SMrZo8dAzTbt7e3I83lOcrw8ElekSNI0oYjIOx90xFMpfdq2RUMGj5tJ8SKFKPllYZb7bOHps+c0fnlNtEF/TcfZyYF+nZPONAkKvUhU9D2KFspHVPQ95ixZjToxkQ6tmmhtV61Ws3GnL9/V8cD8rTNZDKGU/pwxYwHz50/m9OkgTp0KpFevjtjY2LBsWdIdkBYsmEJ4eATDhk0AwMtrMbt3r6Zv387s2OFLixYNKVeuJD17Jt3RytzcnJUr51C2bAmaNu1IpkyZNGdt3rsXI1PWxEcndd67KeX7SSk5lZRVcho357Tp81mkI+fSN3LeCr/NkJc5Z81cyL59PvTr15UdO/bi+TJn9zdy2tvbkSdPLnK8PLNeU+dFRJlGnfd9EwaPmUzxLwpTolhRVqzZmFTn1U+6Jtofoybh7OhA/+4dAAg6e57IO3f5onABou7cZfaiFSQmJtLxh+Za21Wr1WzctodG9b7G3Nx06ryZMxYwb/5kzvgHc+pUAD17/YSNjQ3LlyfVefPnTyY8PJLhw5PqvNlei9i1ezV9+nRi5879NH9Z5/Xu9Qfw8uDpyjmUKVOc5s1+kjovg0iXgbXMmTNTuHBhtm3bRmRkJAkJCTg4OFCrVi2aNm2aqm1kz56dUaNG4e3tzZgxY4iLi8PJyYnSpUujUqlezjPuy+LFi/n555/JmTOn5tbv+tSvX5/ExETGjh3LoEGDKFOmDAMHDmTdunVs2rSJTJkykStXLqPcEepd1q7djJNjdkYM+wVXVycCA89Sv8GPmgsZ5vk8p9Zdr/49foof2/biz5G/MXrUQC6F/Uez5j9x9o279E2cNBtbWxvmzp6AnV02jh49Sf2GP6b66LHk/Pg5lZRVMTl9tuDo5MAwTc5QGjRso8n5+ee5tHIeP36atm17MXLkb4z6cyBhYf/RvEUnzoa+ztmgwTcsXPD6znTe3km3yh41agqjRk8xPKtC+rRuzarcexCL15J/iL4XwxcF8zN3/FAcX04FvR0VjeqNsz2ev4hj5qKV3AyPxMY6M9XcyvHXoL5ky2Krtd3jp4O4HRlNk3q1DM72JqX0p4/PVhwdHRg2bAAuLk4EBYXSqFHbN/bRnMn20fbt+zB8+C+MHPkrYWFX8fTsQmjoRQBy5nSlYcPaAPj57dR6rdq1v+fw4eMGZzUWdaLpTREwJVLnvZtSvp+UklNJWSXnx8k5/I2cDd7I+fZv6L/HT9HmZZ2nL2fDBrVZuPB1nbfyZZ3356jJjBr16dd59b6uzv2YB8xasILoe/f4onBB5k4epZkKejsyCrM37ub5/MULZs5fys3wCGysralWuSJjh/5KtqxZtLb778kz3I6Mokn92gZne5NS+nPduq04OmVnyND+L+u8czRu3E6TM/fnuVCrX9dFJ07406F9X4YN/5kRI3/lcthVWn6vXec1aJA0yHn8xA6t16pbp6XUeelElZhogn91BmBumSu9IwjxyXjzxz0jU8qPzJOrui82m9HY5DNOYZYWLDKl6yVNU+3p02vvbmQEP+ZN3eBKaq24tv7djYRIQ1LnCWE8yqjyUMxVpZ6GH07vCKlinbNaekdINSvz9ztrOb08fnI1TV7HFOu8DHWNNSGEEEIIIYQQQgghlEIZh9CFEEII8clQK+a4vhBCCCGEeB+mWOfJwJoQQggh0pQp3oZdCCGEEMIUmGKdJ1NBhRBCCCGEEEIIIYQwgJyxJoQQQog0pX53EyGEEEIIoUCmWOfJwJoQQggh0pQpXntDCCGEEMIUmGKdJ1NBhRBCCCGEEEIIIYQwgJyxJoQQQog0ZYoXtRVCCCGEMAXpXeft3LmTLVu2EBMTQ968eenYsSOFChXS2Xbv3r0cOnSIGzduAFCgQAFatWqlt70+csaaEEIIIdKU2sgPIYQQQgiRMaRnnXfs2DGWLVtG8+bNGT9+PHnz5mXMmDE8ePBAZ/vQ0FCqVKnC8OHDGT16NA4ODowePZp79+691+vKGWtCCCGEMCnpcSRTCCGEEEJ8XFu3bqVWrVp4eHgA0LlzZ/z9/dm/fz+NGzdO1r5Pnz5a/92tWzdOnDhBcHAw1atXT/XryhlrQgghhEhTiYmJRn28j/Q6kimEEEIIYQqMXefFxcXx5MkTrUdcXFyy142Pj+fKlSuULFlSs8zMzIySJUty8eLFVGV//vw58fHxZMmS5b3+ZjljTQghhBBpKj3vFpVeRzKFEEIIIUyBseu8DRs24OPjo7WsefPmeHp6ai2LjY1FrVZjZ2entdzOzo7w8PBUvZa3tzfZs2fXGpxLDRlYE0IIIYSixcXFJTtyaWFhgYWFhdayV0cy3xxAS6sjmUIIIYQQ4v01adKEBg0aaC17u8Yzho0bN3L06FFGjBiBpaXle60rA2tCCCGESFPGvuGAEo5kCiGEEEKYAmPXeboOluqSLVs2zMzMiImJ0VoeExOTrPZ72+bNm9m4cSNDhw4lb968751RBtaEEIr3vtdYEimzyVc7vSOkytPww+kdIdWsc1ZL7wgZirFvw66EI5nCtKjSO8B7kF9Q4zJTKePdVyuodlJKUqW89zYKqUme3jyQ3hFSzTp3jfSOkKEYu85LLXNzcwoUKEBISAiVKlUCQK1WExISQt26dfWut2nTJtavX8/gwYMpWLCgYa9t0FpCCCGEEBmEEo5kCiGEEEKIj6tBgwZ4eXlRoEABChUqxPbt23n+/Dk1atQAYNasWWTPnp3WrVsDSQdN16xZQ58+fXB2dtbUiJkzZyZz5sypfl0ZWBNCCCFEmkqvmxek55FMIYQQQghTkJ43qfrqq6+IjY1lzZo1xMTEkC9fPgYNGqQ5gBodHY3qjbNL9+zZQ3x8PFOmTNHajq5LiqREBtaEEEIIkabSc/p2eh3JFEIIIYQwBel9mZ66devqPWA6YsQIrf/28vIyymvKwJoQQgghTEZ6HckUQgghhBCfJhlYE0IIIUSaMvbdot5XehzJFEIIIYQwBeld56UHGVgTQgghRJpKr7tFCSGEEEKIj8sU6zyz9A4ghBBCCCGEEEIIIYQSyRlrQgghhEhT6Xm3KCGEEEII8fGYYp0nA2tCCCGESFPpfbcoIYQQQgjxcZhinSdTQYUQQgghhBBCCCGEMICcsSaEEEKINGWKUwSEEEIIIUyBKdZ5MrAmhBBCiDRlineLEkIIIYQwBaZY58lUUCGEEEIIIYQQQgghDGBSA2teXl54enpqHh07dmTMmDFcu3YtvaPp1L1bO8IuHudR7GWOHdlCxQplUmzfrFkDQoIP8ij2Mmf891Kvbs1kbUYM/4Ub1/x5+CCMXTtWUahQfsmZwXIqKauScl66eJyHsZc5msqcwcEHefgyZ923cjZuXI/t21YScTuEuBe3KF26+AdnfDOrUvpUCTn/WbeF2s3aUc7jO1p17kdw6AW9bePi45mzyJu6LTpQzuM7mrbrwZHjp7TaJCQkMHPeMuo0b095j0bUbdGBuYtXfvBFWpXSn8aiTkw06kMIUGadZ8zfJoDhw3/h+jV/Yh+EsdPEvu+VlLVbt3ZcvPAvsQ/COHJ4CxXelbNpfYKDDhD7IAz/0zrqkkb12LbNm9vhwbx4fpPSpYp9cEZQTn8qJSco671XwvfTP+u3UrtFR8rVakKrLgPeXect/oe633eiXK0mNG3fiyMnTidrF3knmoF/TqJK/VaUr9WUJu16EnL+0gflVNI+agymWOeZ1MAaQJkyZZg3bx7z5s1j2LBhZMqUiXHjxqV3rGRatPiOSROHM2r0FCq61SUwKJTt27xxcnLQ2b6yewW8l3uxePE/VKhUh82bd7HOZyHFixfVtPn1lx706tmRHr1+56uqDXn85Anbt3pjZWUlOTNITiVlVVLOiROHM3r0FCq51SUoKJRt78i54mXOipXqsElHTltbG44e82PQoDEG59KXVSl9qoScO/YeZMLMeXTv+ANrF82kaKH8dB0whLv3Y3S2nzlvKWs37WBQ/+5sWvE3no2/pe8fozh3MUzTZuGKtazeuI1BA3qweeU8BvToyCJvH7x9NhucUyn9aUyJRn4I8YqS6jxj/zb98vJz37PX71R5+bnfZiLf90rK2qJ5QyZOGMboMVNxc6tHUHAo27au0JvT3b08y5d7sXjJKiq51WXz5p34rF1A8WLadcmxoycZNPgvg3Mly6mU/lRITlDWe6+E76cd+w4xYdYCurdvxdoF05PqvJ+H6a/z5i9n7eYdDOrXlU3L5+DZ6Fv6DhrDuYuXNW0ePHxEmx6/YWFuztyJI9i0fDa/9PyJbFmzGJxTSfuosZhinadKNKF7oXp5efH48WN+++03zbLz588zbNgwFixYQLZs2VixYgUnT57k7t272NnZUbVqVZo3b465+evL0a1bt44dO3bw4sULvvrqK7JmzUpAQAATJ05MdRZzy1wpPn/syBZOngqkb78hAKhUKq5eOYnX7MVMmOiVrP1K7znY2tjQqEk7zbKjh7cQEHiWnr1+B+DGNX+mTvubKVP/BiBbtqyE3wygY6f+rFlj2D8KJadxcyopa0bKqUoh59EjWzj1Vs7/XuacqCOn98ucjd/IeeTwFgLfyPlK3ry5Cbt0ggoVaxMYeDaFFEne9WWbkfpUCTmfhh9OMWerzv0o8UURBv/cAwC1Ws3XTdrSuvl3dGrjmay9x3c/0KVdS1o1a6hZ1m/QaKysLBk/POl3o8evw3HIbseoP/rrbaOLdc5qep/LKP0JEP/ilt7njKlarlpG3d7hW/uMuj2hTBmpzrN4R533MX6brr/83E9943N/62YAP73j4xSrPAAAU+hJREFUc5/Sb1NG+n56l4yS1UyVUlWS9L6dOh1IvzdyXrl8ktmzFzNxko73fsVsbGxtaNKkvWbZ4UObCQw6S69ef2i1zZs3N5cuHqdixdoEBoWmmONdZ4FklP58l4yUUynv/bv++Z9Rvp+e3DyQYs5WXQZQ4svCDO7fHXhZ5zVrT+tmDen0Y4tk7T0at6VLW09aNW2gWdZvyF9YWVoyftgvAEydu4QzwaEs85qQ4mu/zTp3Db3PZaR9VOq8j8fkzlh707Nnzzh06BCurq5kyZI0Cm1tbU2PHj2YMmUK7du3Z9++fWzbtk2zzuHDh1m/fj0//PAD48aNw9HRkd27dxs1l4WFBeXKlWKf7+t/NCYmJrLP9wju7uV1ruPuVl6rPcDuPQc07fPnz0OOHC7s8z2ieT429iF+fmdwd9O9TcmZtjmVlFXpOX3fkdM3hZwfi9L7NKPljIuLI/TCJdwrltEsMzMzw71CGQJDzulc50VcHJaWllrLrKwsORP0etC0TIkvOXEqgKvXbwJw/tIV/IPOUs29gkE5ldKfxqYm0agPIXRRWp33Ib9Nrz73vib4fa+krEk5S2q9l0nv/WHc3cvpXMdNx3u/Z8/Bj/p9rqz+zPg5X2dV7nuf0b6f4uLiCL0Yhnv5Mpplmjrv7Hmd6+is8ywtORP8eiBy/5ETFC9amAFDx/K/hj/QvGMffDbvNCgjKGsfNSZTrPNM7q6g/v7+tGnTBoDnz59jb2/PwIEDMTNLGmNs1qyZpq2zszPh4eEcO3aMRo0aAbBz505q1qyJh4cHAM2bNycwMJBnz57pfc24uDji4uJSndHRMTvm5uZERUZrLY+KusMXRQvqXMfV1YnIqDtayyIjo3F1cUp63sX55bK32kRF4+rqnOpskvPj5VRSVqXnjIy6Q9H3yBkVGY3Ly5wfi9L7NKPlvB8TS0KCGofs9lrLHbLb89/LQbG3VXErz7JV66lQpgSf58rB8VMB7Dt4jAR1gqZNpzaePH7yhIatu5DJzIwEtZo+XdrRoE7ya1+khlL609iUUiQJ5VFynfchv00pfe5dPvHveyVlfZXz7W1GRUVTtGghvTl17Ssfsy5RWn9m9JxvZlXqe5/Rvp/uP3hV59lpLXewt+O/a3rqvErlWLZ6IxVKF0+q804Hsu/Qv1p13s3bEazetJ22no3p3MaTkPOXGDt9HhYWFjSq9/5nYSlpHzUmU6zzTG5grXjx4nTu3BmAR48esXv3bsaOHctff/2Fk5MTx44dY8eOHURERPDs2TPUajXW1taa9cPDw6ldu7bWNgsVKkRISIje19ywYQM+Pj4f5w8SQghhFL/37cqI8TNo2LoLKhV8njMHjet/w4atr89W2el7iK279zN+xG8Uyp+X85euMH763zg7ZqfRt9+kY3ohBEidJ4QQQrff+3RhxISZNPyx++s679uv2bBtj6aNWp1I8S8K0a9r0jTML4sU5NKVa6zZtN2ggTVhOkxuYM3KygpXV1fNfxcoUIB27dqxb98+ypUrx4wZM/D09KR06dLY2Nhw9OhRtm7d+kGv2aRJExo0aKC1bL1dYb3to6PvER8fj7OLo9ZyZ2cnIt4amX4lIuIOLs7aRy9cXBw17SMio14ucyIiIup1G2dHAlJxbSjJ+fFzKimr0nO6vGdOZxfHZEeFjE3pfZrRctrbZSNTJjPu3ruvtfzuvfs4vnUW2yvZ7e2YMW4Yz5+/ICY2FmdHB6bOWUTunK9/MyZ7LaTTj558+3UNAIoUzM/tiCgWLF9j0MCaUvrT2Ezo8q4ijWWUOm+DAXXeh/w2pfS5T801QN8nZ0b8flJK1lc53z7jyNnZkcjIKJ3rRETc0bmvfMy6RGn9mdFzvplVqe99Rvt+sv/sVZ0Xo7X87v0YHB301XmfMWPsEO06b+4SrTrPycGegnnzaK1XIO/n7D141KCcStpHjckU6zyTvsbaK2ZmZrx48YILFy7g5ORE06ZNKViwIDly5CA6Wvu0zZw5c3L58mWtZW//99ssLCywsbHReqQkLi4Of/8ganpU1SxTqVTU9KjK8ePJbwkMcPzEaWrWrKq17Ota/9O0/++/69y+Ham1zaxZs1CpUlmO67jNcGpITuPmVFJWpef0eEdOjxRyfixK79OMltPCwoJiRQtz4lSAZplarebE6QBKl/gyxXWtrCxxcXIkPiGBPQeO4lGtsua5Z8+eozLTvjixmZmZwbcCV0p/GpspXntDpB+l1Hkf8tv06nPvYYLf90rKmpQzWOt9ev3e++tc58SJ01oZAGrVqvZRv8+V1Z8ZP+frrMp97zPa95OFhQXFihTixOlAzbKkOi+Q0sW/SHFdrTrv4DE8qrppnitbshhXb2hPJb124xY5DJxiqaR91JhMsc4zuTPW4uPjiYmJAZKmCOzcuZNnz55Rvnx5nj59SnR0NEePHqVgwYL4+/vj5+entX7dunX5+++/KVCgAEWLFuXYsWNcu3YNFxcXo+acOn0+ixdO5bR/ECdPnqFP787Y2lqzZOlqABYvmk54+G0GD0m6hfzMmQvx3edD/35d2b5jL997NqJ8+VJ06/H6zlgzZi5g0B99uBR2hatXbzByxK+Eh0eyadMuyZlBciopq1JyTps+n0U6ci59I+et8NsMeZlz1syF7NvnQ79+XdmxYy+eL3N2fyOnvb0defLkIkeOpM99kSJJ10iIiIj6oKOISulTpeRs+30TBo+ZTPEvClOiWFFWrNnI02fPaVw/6cyyP0ZNwtnRgf7dOwAQdPY8kXfu8kXhAkTducvsRStITEyk4w/NNdusUcWN+UtXkcPFmUL583LuYhjLVq+nSf3aOjOkhlL6UwglUEqd9zF+m1597sNefu5HmND3vZKyTp8+j4ULp+J/OpCTpwLo3btT0nu/LCnnooXTCA+PYMjQlzlnLWTfXh/69evCjh378GyRlLNHj4Gabdrb25Hn85zkeHnmjaYuibxjcF2ilP5USk5QznuvlO+ntt83ZvBfU5PqvC+LsGLtJp4+fUbjb78G4I/Rk5PqvG7tAQg6e4HI6Fd1XjSzF60kUa2mY+vX195s49mINt1/Zd6yNdStWZXgcxfx2bKT4b/2MjinkvZRYTiTG1gLCAigS5cuQNKdoXLmzEn//v0pXrw4APXr12fRokXExcVRrlw5mjVrxtq1azXrV6tWjcjISJYvX05cXByVK1emRo0ahIWFGTXn2rWbcXLMzohhv+Dq6kRg4FnqN/iRqKikI6t5Ps+JWq3WtP/3+Cl+bNuLP0f+xuhRA7kU9h/Nmv/E2bMXNG0mTpqNra0Nc2dPwM4uG0ePnqR+wx95/vy55MwgOZWUVWk5h7+Rs8EbOT/XkbNN216MTCFnwwa1Wbhwqua/V3rPAeDPUZMZNWrKB2dVSp9m9Jz1vq7O/ZgHzFqwguh79/iicEHmTh6lmQp6OzIKM9Xrs8+ev3jBzPlLuRkegY21NdUqV2Ts0F/JljWLps2g/t2ZOX8Zoyd5ce9+DE6O2WnR6Fu6d2htcE6l9KcxJSrk6KNQHqXVecb8bZr08nM/543PfQMT+b5XUta1PltwdHJgmCZnKA0atnnjvc+llfP48dO0ffnej/pzIGFh/9G8RSfOhr7O2aDBNyxc8Lou8X5Zl4waNYVRow2rSxTTnwrJCcp77zP691O9Wv9LqvMWriD63n2+KFSAuZP+fKPOu4OZ6vUEvaQ6bzk3b7+s89zLM3boz1p1XskvizBtzGCmz1vK3KX/kCuHCwN7d6ZBbQ+DcyppHzUWU6zzVImmOAHWyEaNGoWdnR29e/dO9Trmlrk+YiIhTIvq3U0yBPmyNa6n4Yff3SiDsM5ZLb0jpEr8i1tp8joVchi3P07dVs6+IJTHkDrPQkF1nvw2GdebB2wyMkMvYSD0U8p7r5R//j+5eSC9I6Sade4a6R0hVaTO+3jkGmvv6fnz52zdupUbN25w69Yt1qxZQ3BwMNWrV0/vaEIIIYQQ4gNInSeEEEKI92VyU0E/lEql4syZM6xfv564uDhy5szJzz//TKlSpdI7mhBCCKEISrkQrTA9UucJIYQQH8YU6zwZWHtPlpaWDB06NL1jCCGEEIqllGkowvRInSeEEEJ8GFOs82QqqBBCCCGEEEIIIYQQBpAz1oQQQgiRpkxxioAQQgghhCkwxTpPBtaEEEIIkaZM8TbsQgghhBCmwBTrPJkKKoQQQgghhBBCCCGEAeSMNSGEEEKkKbUJXtRWCCGEEMIUmGKdJwNrQgghhEhTpjhFQAghhBDCFJhinSdTQYUQQgghhBBCCCGEMICcsSaEEEKINGWKUwSEEEIIIUyBKdZ5MrAmhBBCiDRlilMEhBBCCCFMgSnWeTIVVAghhBBCCCGEEEIIA8gZa0IIIbRkNrdM7wipYp2zWnpHSLUnV3amd4QMxRSnCAjTInu46ZLvN+MzU6nSO0KqKOW9z2ppnd4RUsU6d430jpBqUudpU8pnwZhkYE0IIYQQacoUpwgIIYQQQpgCU6zzZCqoEEIIIYQQQgghhBAGkDPWhBBCCJGmTHGKgBBCCCGEKTDFOk8G1oQQQgiRpkxxioAQQgghhCkwxTpPpoIKIYQQQgghhBBCCGEAOWNNCCGEEGkqMVGd3hGEEEIIIcRHYIp1ngysCSGEECJNqU1wioAQQgghhCkwxTpPpoIKIYQQQgghhBBCCGEAOWNNCCGEEGkq0QTvFiWEEEIIYQpMsc6TgTUhhBBCpClTnCIghBBCCGEKTLHOk6mgQgghhBBCCCGEEEIYQM5YE0IIIUSaMsUpAkIIIYQQpsAU67x0OWNtzZo1/Prrr3qfP3DgAO3bt/+g1/Dy8mLChAkftA0hhBBCGJ86MdGoD5GxSJ0nhBBCmC5TrPMMGli7ePEi33//PWPHjjV2HsUZMWIES5Ys+Sjb7t6tHWEXj/Mo9jLHjmyhYoUyKbZv1qwBIcEHeRR7mTP+e6lXt2byvMN/4cY1fx4+CGPXjlUUKpRfcmawnErKqqScly4e52HsZY6mMmdw8EEevsxZ962cjRvXY/u2lUTcDiHuxS1Kly7+wRnfzKqEPu3StQ1nzx0m+t559h/cQPkKpVNs36TJt/if2Uv0vfOc8NtB7To1tJ4fNLgv/mf2EnnnLDduBbBl63IqVCzzwTmV0p//bNxBndbdKV+3Fa17/k7w+Ut628bFxzNn2Vrq/diT8nVb0azzzxzxO5OsXeSdu/z+13SqNm5PhXqtadJpAGcvhH1wVvHpkzrvNanzlJNTSVklp3FzduvWjosX/iX2QRhHDm+hwrtyNq1PcNABYh+E4X9aR53XqB7btnlzOzyYF89vUrpUsQ/O+IpS+rRTlx8JPHuA29Fn2bPfh3LlS6XYvlGTepzw38Xt6LMcPbGNb2pX19t2yvQ/uf8ojG492n9wTqX0p7HrvNlLV1OyVnOtR8P2fT44pzCcQQNrvr6+1KtXj3PnznHv3j1jZxJAixbfMWnicEaNnkJFt7oEBoWyfZs3Tk4OOttXdq+A93IvFi/+hwqV6rB58y7W+SykePGimja//tKDXj070qPX73xVtSGPnzxh+1ZvrKysJGcGyamkrErKOXHicEaPnkIlt7oEBYWy7R05V7zMWbFSHTbpyGlra8PRY34MGjTG4Fz6siqhT5s1q8/YcYMZ+9d0qn7VgJDgc2zctFRvTje3cixeOp2lS9dQpXJ9tm7dw6rVf1OsWBFNm0uX/mPAgOG4VaxL7a9bcO36LTZtXoqjY3aDcyqlP3fuP8rEuUvp1rYFa+ZOoEjBfHQdOJq79x/obD9z0T/4bN3DH71/YuOiaXg2rE2/4RM5d+mKps2Dh49o23cI5ubmzBk3mI2LpvJrt7Zky5rF4JzGlGjk/wnjkjrv41PK95NSciopq+Q0cs7mDZk4YRijx0zFza0eQcGhbNu6Qm9Od/fyLF/uxeIlq6jkVpfNm3fis3YBxYtp13nHjp5k0OC/DM6lM6tC+rRJs28ZPXYQ48fOpEbVRoSEnGfdxsU4OumuySq5lWXB4qmsWLqW6lW+Y9vWPaxYNYcvixVO1rZ+w2+oULEM4eERBud7RSn9+THqPIBC+T5n/9r5msey6aMNzmhspljnqRLfcwLss2fP6NKlC+PGjWPNmjXkyZOHpk2bap4/e/YsI0eOZOjQoXh7e3Pz5k3y5ctHjx49yJkzJ5A0ReDkyZNMnDgRgIiICEaPHk3ZsmXp2LEjBw8eZMmSJVpHCE+ePImPjw83b97E3t6e6tWr07RpUzJlyqQzp5eXF48fPyZ//vzs3LmT+Ph4qlSpQseOHTE3T7q0XFxcHMuXL+fYsWM8ffqUAgUK0K5dOwoVKqTZTmhoKMuXL+fatWtkyZKF6tWr07JlSzJlyoSXlxcHDx7Uet1Zs2bh7Oz8zn40t8yV4vPHjmzh5KlA+vYbAoBKpeLqlZN4zV7MhIleydqv9J6DrY0NjZq00yw7engLAYFn6dnrdwBuXPNn6rS/mTL1bwCyZctK+M0AOnbqz5o1m9+ZWXJ+/JxKypqRcqpSyHn0yBZOvZXzv5c5J+rI6f0yZ+M3ch45vIXAN3K+kjdvbsIunaBCxdoEBp5NIUWSd33ZZpQ+zWxumWLO/Qc34H86iJ8HDNfkvHDpGHPnLGXK5LnJ2i9dNhMbW2taNOukWeZ7YD3BQaH07TNE52tkzZqF25HBNPj2Bw4cOKazzbP4FynmzCj9CfDkyk69z7Xu+TvFixZicJ+k/lGr1XzTshutmtSjU6smydrX9OxM59ZNadW4nmZZ/xETsbK0ZNygvgBMnb+CgJDzLH3PIssyd8n3am8ol8++MOr2Ih+cN+r2TJnUeVLnKTGnkrJKzvfPaabSX+kdObyFU6cD6fdGziuXTzJ79mImTtJR562YjY2tDU2atNcsO3xoM4FBZ+nV6w+ttnnz5ubSxeNUrFibwKBQvRleedeUtYzSp1ktrVPMuWe/D2f8g/nt55GanCEXDjN/7nKmTfk7WfuFS6dja2NNyxZdNMt2+/oQEhzKgL7DNMty5HBhz4F1NG/cgdU+85njtYS5s5fozfHwxdMUc2aU/oS0r/NmL12N79GT+MybpPd1dZE67+N57zPWjh07Rq5cuciZMyfVqlVj//79Oi9Ot2rVKtq2bcu4cePIlCkTc+bM0bm9a9euMWzYMKpWrcpPP/2ESscX57lz55g1axb16tVjypQpdOnShQMHDrB+/foUs4aEhHDr1i1GjBhB37598fPzY+3atZrnV6xYwYkTJ+jZsyfjx4/H1dWVMWPG8OjRIwDu3bvH2LFjKViwIBMnTqRTp074+vqybt06ADp06ECRIkWoVasW8+bNY968eTg6Oqa6L/WxsLCgXLlS7PM9rFmWmJjIPt8juLuX17mOu1t5rfYAu/cc0LTPnz8POXK4sM/3iOb52NiH+Pmdwd1N9zYlZ9rmVFJWpef0fUdO3xRyfixK6tOyZUuwf//rbSYmJrLf9yiV3MrpXKeSW1n2+x7VWrZv7yEqVdLd3sLCgg4dWxETE0tw8DmDcyqhP+Pi4gi9eAX3cq+nWJiZmeFeriSBoRd0rvPiRRxWltqDn1aWlpwJeV10HDh2imJFCzJg5CSqN+tIi66/4LNtj0EZhWmROk/qPKXlVFJWyfkxcpbUqtuS6rzDuLvrrjHcdNR5e/Yc/KD9L/VZldGnZcqW4MD+13VbYmIiB/cfo2KlsjrXqVSpLAf2ax8E9d13WKu9SqVi7oJJzJw+n/Pn9E+DfJ+cSujPj1XnAVy/dZuanp2p+2MPBv41jduRdwzKKIzjvQfW9u/fT7Vq1QAoU6YMT548ITQ0+Qh+y5YtKVasGLlz56ZRo0ZcuHCBFy+0zy64cOECI0aMoGHDhrRs2VLva/r4+NC4cWNq1KiBi4sLpUqV4vvvv2fv3r0pZjU3N6d79+58/vnnlCtXDk9PT3bs2IFarebZs2fs3r2bNm3aULZsWXLnzk3Xrl2xtLTE19cXgF27duHg4MBPP/1Erly5qFSpEp6enmzduhW1Wo2NjQ3m5v9v787DqigbNoDf7Igk+6IgJKK577hGKr25FOaGYuKKuXwu6Wsqmpnivr5milmmuGWGoGVlVoph7mAuGCoKAiHJKsoi6+H7gxw9nqOizmFm4P5dF9fFmTPg7VHg5nlmnscQJiYmsLS0hKWlJfT1X34/CFtbaxgaGiItNUPteFpaOhwd7LR+jKOjHVLT1L+YUlMzhPMdHez/PfbYOWkZcHR89swrc+o+p5KyKj1n6nPmTEvNgMMTzheLUl5TG1urJ+R88mvk4GCH9LRnn9+rtxdup11G5p2rmDzFH+/2GY7MzDsvlFMpr+eduzkoValgY2WhdtzGyhKZWdlaP6azRyvsCP0Bicn/QKVS4WTURRw5fgbpWQ9fq+R/UhFy4Fe4OtXGpuUfY3Cfnli+IRjf//L7C+UUmwplor6ReNjz2POUllNJWZlTNzkf/5zlHUP753R0tNPaC9nzytnYlPe89LRMtePpaRmwd9A+sWDvYIv09Awt5z/8e02bPh4lJaX4YuP2F8r1OKW8nrrqec0bNcCiWZPw+bK5mDd1HG79k4aR0+YhL//pV/lVlurY8wyf5+SUlBTcuHEDM2bMAAAYGBigc+fOCA8PR9Om6ot3u7q6Cu9bWVkBAO7duyfM9GVkZGDx4sUYMmQI3nnnnaf+uQkJCbh69arazKVKpUJxcTEKCwufeM+zq6ur2nMNGzZEQUEBMjMzkZ+fj9LSUrz22sN7qg0NDeHu7o7k5GQAwK1bt9CwYUO12dXXXnsNBQUFyMrKqvCsZXFxMYqLiyt0LhFRdXUs4hQ6d3wHNjZWGOU/BDt2bkD3rv2Rnp757A+uRmZPGo0Fazbh3dFToQegbh1H9O3ZHd8dOiqcoyorQ9OGbpj6vh8AoHEDN9xISELID7+i72MbR0ihOm7DrgTseex5RERia9mqKcZPHIluXfpKHUURKtLzPB+5S+S1+kDzxg3Qc+j/4ZffT2LA229KkFpddex5zzWwFh4ejtLSUowfP144VlZWBiMjI4wZMwZmZmbC8UfXxHhQWFQqlXCsVq1asLa2xokTJ9C9e3e1j31cQUEBBg8ejA4dOmg8Z2Rk9Dx/BUns378foaGhFT4/IyMLJSUlGrMC9vZ2uP2ESzxv306Hg7366LyDg61w/u3UtH+P2eH27bSH59jb4kIF1oZiTt3nVFJWped0eM6c9g62GrNXYlPKa5qZcecJOZ/8GqWmpsPO/tnn5+ffR3x8IuLjExEZeQEXLoVjxMjBWLNa+y1mT6OU19PK4hUY6OtrLGCbeScbNtaWWj/G2tICny0KQGFREbLv5sDe1hprN++Cc+2Hs6l21pao71pX7ePcXJxx+NiZF8pJ1QN73othz5M2p5KyMqducj5+tVl5x0jT+jG3b6dr7YXseeUyM8t7np29+gYAdva2GleHPZCWmgE7O1st55fn7NTZA3Z2Noi+ekx43tDQEIuXzcH/TRqFlk27PXdOpbyeuup5j6tlXhOuzrWRJMKmEPRiKnw9e2lpKSIiIjBixAisXLlSeFu1ahWsrKxw/PjxZ3+SRxgbGyMgIABGRkZYsmQJ7t9/8mWLbm5uSElJgaOjo8bb0y7JT0xMVLst4fr16zA1NYWNjQ0cHBxgaGiIa9ce3ttcUlKCuLg4ODs7AwCcnJwQGxurNuJ67do11KhRA9bW5buiGBoaqhVJbfr37y8s0vv4Yr3aFBcX488/L8Gr++vCMT09PXh1fx2nT5/T+jGnz5yDl9frasf+8+Ybwvk3bybhn39S1T7nK6+Yo3371jh9RvvnfBbmFDenkrIqPWf3Z+Ts/pScuqKk1/T8+cvo1q2LWs5u3Tvj7Jk/tX7M2TPn0a17F7Vj3b1ex9mz2s9/QF9fHyYmT99I4Wk5lfB6GhkZoUlDN5w5Hy0cU6lUOH0+Gi0f2aFMGxNjYzjY2aCktBSH/ziD7p09hOdaNWuEhL9vqZ2fkJyC2k+4jaOyqcrKRH2jl8eeV449T3k5lZSVOXWRMxrdtfY87R3jzJlzahkA4M03PV/q/1/FsyrjNb1w/jK6duuslvONbp0Refa81o85e/a82vkA0L17F+H8b/d8h9c7voM3OvcR3lJSbmP9p19hYL/RL5xTCa+nrnre4/Lv38ffKamwe8JgXWWrjj2vwlesnTt3Dnl5efDy8tKYdezQoQOOHj2KHj16PNcfbmpqijlz5mDp0qVYunQp5s6dC1NTU43zBg4ciBUrVsDW1hYdO3aEnp4eEhMT8ffffz91zY6SkhJ8/vnnGDhwINLS0hASEoJevXpBX18fpqam6NGjB3bu3Alzc3PY2tri+++/R2FhIby8vAAAPXv2xMGDB7F161b06tULKSkpCAkJwTvvvCMUPTs7O1y/fh1paWkwNTWFubm5Rgk0MjJ67hnXtes2I3jLWpz78xIiI8/jgyljUbNmDWzb/i0AIHjrOqSk/IO5Hy8HAKxfvwXhR0Lx32njcfDnw/Ad3Bdt27bAhImzhM/52fqv8NGcD3D9RjwSEv5G4IKZSElJxfff//Jc2ZhTdzmVlFUpOT9dtxlbteTc/kjOWyn/4ON/c25YvwVHjoRi2rTx+Pnnwxj8b87/eySnlZUlXFycULu2AwCgYcP6AIDbt9NeasZTKa/phs++wheb1+DPPy/hXNRFTJrsDzMzM+zaWX7Fxpeb1yAl5TYWzC/fEXBjUDAO/boHUz54H78cCofPoD5o06Y5Ppj8EQDAzKwGZgZMwsEfD+P27XTY2Fph3PjhqFPHEfv3HXzhnEp5PUf49MHcFRvQtGF9NG/kjp1hP+F+QSH69ewOAPho+Wewt7XBtH9v67x0JRZpGVl4rX49pGVk4vMdIVCVqTB6SL+Hn3OgN4Z/MBebvw5Dz26dEX31BsJ+OoxP/jteW4RKVx1vEZA79jz2PCXnVFJW5hQ357p1X2LLlrX489xFREZdwJQp75f3vB3lObdu+RQpKbfx8bx/c27YgiOHQzFt2jj8/PMRDB5UnnPixADhc1pZWcKlbh3UruMI4JGel5peLXrexg1bsfGLVTj/ZzT+PHcJ/zdpFGqa1cDXu8p73udfrsI/KalYuKB8R8ovNm7Dj4d2Y9KUMfj1l6MY4OONVm2aYdoHcwEAd7Kyceex9cRKikuQmpqOG9dvvnBOpbyeuuh5qzdtR9dO7VDHwQ7pmVkI2hYCA3199H5s4FAq1bHnVXhgLTw8HM2bN9d6KX/Hjh1x4MABJCYmPncAU1NTfPTRR1iyZAmWLVuGOXPmaJzTqlUrBAQEICwsDN9//z0MDAzg5OQkFKMnadasGWrXro358+ejuLgYXbp0waBBg4Tnhw4dCpVKhfXr16OgoABubm6YO3cuzM3NAQDW1taYM2cOdu7ciZkzZ8Lc3BxeXl4YOHCg8Dn69OmDoKAgTJ8+HUVFRRXehv1Z9u49ADtbayz4ZAYcHe1w8eJfeMd7GNL+XQDcpW4dtRnUU6ejMGzEZCwMnIXFiwJw/cZNDPQZg7/+ejhTu2r1RtSsaYZNG1fC0rIWTpyIxDt9hqGwsJA5ZZJTSVmVlnP+Izm9H8lZV0vO4SMmI/ApOft498CWLWuFx7u/Lr9VceGiNVi06H8vnVXur2lY2E+wtbPBx/Omw8HBFpcuXUH/fqOe+JqeOfMn/EdNw7z5H2JB4AzE3UjAEN/xiImJBVB+pcxrDevD75uBsLGxQlZWNs6du4Qebw3GlZfYOUopr2ev7l2QdfcegrbtQcadbDSq/yo2LZ8L239nHf9Jy4Ce3sNf5AuLirF+6x4k/5MKsxqm8OzQGktnf4Ba5jWFc5o1csengTPx6Zbd2LQzFE617TFr4ih4/+eNF85JVRt7HnueknMqKStzipwz9AfY2tngEyFnDLz7DH+kkzip5Tx9+hxG/NvzFi0MwI0bN+Ez6H389cgOjd7eb2HLVw973tf/9rxFi/6HRYurfs/bH3YQtrY2+OjjabB3sEP0pRj49PcXNjRwfizn2TPnMdZ/OubO+y/mLfgQ8XEJGDbk/3Al5uV3/3wapbyeuuh5qemZCFjyKbLv5cDKohbaNGuErzcshbWlxeN/PFUSvbLqOJwoA4bGTlJHIKoy9J59iiwo5ZutqeGL3X5Z2QpKip59kkzkxx+SOkKFGDs3r5Q/x8K8vqif725unKifj+hlsecRiUdfTxlNTym3rL1iXEPqCBWSUySPHS4rgj1PXXXsec+1eQERERHRy+KcHhEREVHVVB17XoU3LyAiIiIiIiIiIqKHeMUaERERVSql3C5DRERERM+nOvY8DqwRERFRpSpTzIqDRERERPQ8qmPP462gREREREREREREL4BXrBEREVGlqo63CBARERFVB9Wx53FgjYiIiCpVddwtioiIiKg6qI49j7eCEhERERERERERvQBesUZERESVqjouaktERERUHVTHnseBNSIiIqpU1fEWASIiIqLqoDr2PA6sERERUbVy6NAh/PDDD8jOzoarqyv8/f3h7u7+xPNPnTqFb7/9Funp6XB0dISfnx/atGlTiYmJiIiIqCKk6HlcY42IiIgqVVlZmahvz+PkyZPYsWMHfHx8sGLFCri6umLJkiW4e/eu1vOvXbuGdevWwcvLCytWrICHhwdWrVqFpKQkMV4KIiIioiqlOvY8DqwRERFRpSoT+e15/Pjjj3jzzTfRvXt3ODs7Y+zYsTA2NsbRo0e1nn/w4EG0atUK7777LpydnTFkyBC4ubnh0KFDz/knExEREVV91bHncWCNiIiIFK24uBj5+flqb8XFxRrnlZSUID4+Hs2bNxeO6evro3nz5oiNjdX6uWNjY9XOB4CWLVvi+vXr4v4liIiIiEiDEnoe11iTSEnRLVE/X3FxMfbv34/+/fvDyMhI1M8tNqVkZU5xKSUnoJyszCk+pWRVSs4nEftnYEhICEJDQ9WO+fj4YPDgwWrH7t27B5VKBUtLS7XjlpaWSElJ0fq5s7OzYWFhoXbMwsIC2dnZL52bqi72PPlnZU7xKSUrc4pLKTkB5WRVSs4nqY49j1esVRHFxcUIDQ3VOnIrN0rJypziUkpOQDlZmVN8SsmqlJyVpX///ti2bZvaW//+/aWORSQaJX3NKyUrc4pPKVmZU1xKyQkoJ6tSclYWJfQ8XrFGREREimZkZFShGd1atWpBX19fYxYyOztbY3bzAUtLS40Fb+/evfvE84mIiIhIPEroebxijYiIiKoFQ0NDuLm54fLly8IxlUqFy5cvo2HDhlo/pmHDhoiOjlY7dunSJTRo0ECnWYmIiIio4qTseRxYIyIiomrD29sbR44cwe+//47k5GR89dVXKCwsRLdu3QAAGzZswO7du4Xz3377bVy8eBE//PADbt26hZCQEMTFxaFXr14S/Q2IiIiISBupeh5vBa0ijIyM4OPjo4jFDZWSlTnFpZScgHKyMqf4lJJVKTnlqHPnzrh37x5CQkKQnZ2NV199FR999JFwyX9GRgb09PSE81977TV88MEH2LNnD7755hvUrl0bM2fOhIuLi0R/A6qOlPQ1r5SszCk+pWRlTnEpJSegnKxKySlHUvU8vbKysjIx/yJERERERERERETVAW8FJSIiIiIiIiIiegEcWCMiIiIiIiIiInoBHFgjIiIiIiIiIiJ6ARxYIyIiIiIiIiIiegEcWCOdKikpwcaNG5GWliZ1FCIixcrIyIC2vYbKysqQkZEhQSIiIvY8IiIxsOcpHwfWFCwlJQVbtmzBwoULsXDhQmzZsgUpKSlSx1JjaGiIM2fOSB2DJOLr64u7d+9qHM/JyYGvr68EiZQvJiYGpaWlGsdLS0sRExMjQSJNpaWlCA0NRWZmptRRqoxJkybh3r17Gsdzc3MxadIkCRIRka6x55HcseeJjz2vemLPUz4OrCnU6dOn8eGHHyI+Ph6urq5wdXXFzZs38eGHH+L06dNSx1Pj4eGBs2fPSh3jmZQ+65qfn4+zZ88iOTlZ6ijPVFxcDENDQ6ljIDExscJvchEYGIjc3FyN4/n5+QgMDJQgkSYDAwMcOHBAazGkF6enp6dxrKCgAMbGxhKkISJdYs8TH3te5WHPe3HsedUXe56ySf8dj17I119/jX79+mnMBoWEhODrr79Gx44dJUqmqXbt2ggLC8O1a9fg5uYGExMTtefffvttiZKpezDr6uPjI3WUCvnf//6HJk2aoFevXigqKsKcOXOEsjh16lRJ/w8cPHhQeP/IkSMwNTUVHqtUKly5cgVOTk5SRFMza9asCp/77bff6jDJ89H2gzcnJ0ftdZZas2bNEBMTA3t7e6mjPNOsWbO0vqYAYGxsDAcHB3Tr1g3NmjWr5GTA9u3bhff37Nmj9v1TpVLhxo0bePXVVys9FxHpFnue+NjzxMOep1vseeJiz6PKwIE1hbpz5w66du2qcdzT0xMHDhyQINGThYeHw8zMDPHx8YiPj1d7Tk9PTzaFC3g46+rt7S11lGe6cuUKBgwYAAA4e/YsysrKsG3bNkRERGDfvn2SFq6ffvpJeP+3336Dvv7Di2MNDQ1hb2+PsWPHShFNzYYNG6SOUGGrV68W3g8KCoKRkZHwWKVSITExEQ0bNpQimlatWrXC7t27kZSUBDc3N40y2K5dO4mSaWrVqhV+/fVXuLi4wN3dHQAQFxeHxMREdOvWDcnJyVi0aBFmzJgBDw+PSs2WkJAgvP/333+rXQFgaGgIV1dX9OnTp1IzEZHusefpBnueONjzxMeepzvseVQZOLCmUE2bNsWVK1fg6Oiodvzq1ato3LixRKm0CwoKkjpChSll1hUovyTc3NwcAHDhwgV06NABJiYmaNOmDXbu3Clptgf/5oGBgfjwww+FnHJjZ2cndYQKMzMzE96vUaOG2mXhhoaGaNCgAd58800pomm1ZcsWAOrl+1Fymhm+d+8evL29Na5iCAsLQ3p6Oj7++GOEhIQgLCys0gvX/PnzAQAbN27EqFGj1P4fEFHVxZ6nG+x54mDPEx97nu6w51Fl4MCaQrVt2xZff/014uPj0aBBAwDA9evXcfr0aQwaNAhRUVHCuXKZMSgpKUFaWhocHBxgYGAgdRytlDTramtri9jYWJibm+PChQuYNm0agPJFLuVyL/6DHxZKkpycjIyMDJSUlKgdl/rraOLEicJuQf7+/rK6HUAbORWqZzl16hRWrFihcbxLly4ICAjAhAkT0KVLF/z4448SpCs3ceJE4f0HiwXb2NhIFYeIdIw9TzfY88TFnice9jzdYc+jysCBNYV6MEvw66+/4tdff9X63ANSf+MrLCzE1q1bERERAQBYt24dHBwcsHXrVlhbW6Nfv36S5nuUkmZd3377baxfvx6mpqawtbVFkyZNAJTfOuDi4iJxuocyMzMRFRWltcSMHDlSolSaUlNTsXr1aiQlJWl9XuqvI6B8y+0//vgD/fv3R+3ataWOU2FFRUWy+SVAG2NjY1y7dk3jypBr164JucvKytRuy6hsKpUK+/btww8//ICCggIA5TPa3t7eGDBggNptOESkfOx5usGeJz72PPGw5+kGex5VBg6sKZQcvvlX1O7du5GYmIgFCxZgyZIlwvHmzZtj7969sipcDyhh1rVnz55wd3dHZmYmWrRoIXzDdXBwkM0W59HR0Vi5ciXs7e2RkpKCunXrIj09HWVlZahXr57U8dQEBwfDzs4O8+bNw+TJk7F06VLk5uZix44dGD58uNTxAAD6+vqoXbs2cnJyZF+4HhSE3377DXfv3hV+0dqzZw/s7e3h5eUldURBr169sHnzZsTHx6N+/foAytfeCA8PR//+/QGU34Yj5eKxe/bsQXh4OPz8/PDaa68BKL8lbO/evSguLsZ7770nWTYiEh97nm6x54mDPU9c7Hm6wZ5HlYFDn6RzkZGR8Pf3R6NGjdR2ZKlbty5SU1MlTKapsLAQn3/+OYYNG4bp06cjIyMDALB161Z899130obTon79+mjfvj1MTU2hUqmQkJCAhg0bolGjRlJHA1Betvv06YM1a9bAyMgIH374IT7//HM0adIEnTp1kjqemuvXr8PX1xe1atWCnp4e9PX10ahRIwwdOhTBwcFSxxMMHToUu3bteuKMq1zs27cPERERGDZsmNpCrC4uLjhy5IiEyTQNHDgQ48ePx40bNxAcHIzg4GDcuHED48ePFxaO7tGjBwICAiTLGBERgQkTJqBHjx5wdXWFq6srevbsifHjx+P333+XLBcREXue7rDniYc9T1zseeJiz1M+XrGmYDExMThw4ABu3boFAHB2dsa7774ru0Vt7927BwsLC43jDy5zlRMlzbpu27YNLi4u8PLygkqlwvz58xEbGwtjY2PMnj0bTZs2lToibt26halTpwIADAwMUFRUBFNTUwwePBirVq1Cjx49JE74kEqlQo0aNQAAtWrVQlZWFurUqQNbW1ukpKRInO6hoKAgFBYWYubMmTA0NNS49F4u5TAiIgLjxo1D8+bNsXnzZuG4q6urrF7PBzw9PeHp6fnE56W+xSE3Nxd16tTROO7k5ITc3FwJEhGRrrHniY89T1zseeJjz9MN9jzSNQ6sKdSxY8fw+eefo3379ujduzeA8vvEFy5ciEmTJuH111+XOOFD9evXx59//inkfDCbGR4eLqtto4HyWddp06ahYcOGsp91PX36tPADIioqCmlpaVi7di2OHTuGPXv2YNGiRRInBExMTIT1NiwtLZGamoq6desCKC/iclK3bl0kJCTA3t4e7u7uOHDgAAwNDXH48GE4ODhIHU8gp/VKniYrK0tjLQugfA2Lx9dgoWdzdXXFoUOH4O/vr3b80KFDkt66QES6wZ6nG+x54mLPEx97XvXEnqd8HFhTqP3798PPzw/e3t7Csbfffhs//vgjwsLCZFW43nvvPSxduhTJyckoLS3FwYMHkZycjGvXriEwMFDqeGqUNOuak5MDS0tLAMD58+fRqVMn1KlTB15eXvj555+lDfevBg0a4OrVq3B2dkbr1q2xY8cOJCUl4cyZM7Ir2wMGDEBhYSEAwNfXF8uXL8f8+fNhbm6O//73vxKne6hbt25SR6gQZ2dnXLlyRWOr+9OnT8tu3RWVSoUff/wRp06d0rr4shxmh4cNG4Zly5YhOjpa+NqJjY1FZmYm5syZI3E6IhIbe55usOeJiz1PfOx54mPPo8rAgTWFSk1N1botdLt27fDNN99IkOjJGjVqhJUrV+K7776Di4sLLl68iHr16mHJkiWy2tUIUNasq4WFBZKTk2FlZYULFy5g7NixAMrXD5HLzjEjR44UyurgwYNRUFCAkydPwtHRUXYzcq1atRLed3R0xKefforc3FzUrFlTbVZbCvn5+TAzMxPef5oH50nNx8cHQUFByMrKQllZGc6cOYOUlBQcO3YMs2fPljqemr179yI8PBze3t7Ys2cPBgwYgPT0dERGRmLgwIFSxwMANGnSBOvWrcMvv/wi3BbWoUMH9OjRA9bW1hKnIyKxsefpBnueuNjzxMGep1vseVQZOLCmUDY2NoiOjta4BPfSpUuwsbGRKNWTOTo6YsKECVLHeCYlzbp269YNa9euhZWVFfT09NC8eXMA5YuzartHXwqPXlpvamqKcePGSZjm+Zmbm0sdAQAwevRofPnll7CwsMDo0aOfeq5cdpLz8PBAQEAAQkNDYWJigpCQENSrVw8BAQFo0aKF1PHUHD9+HOPHj0ebNm2wd+9edOnSBY6OjnBxccH169eljiewtrbmrlBE1QR7nm6w54mLPU8c7Hm6xZ5HlYEDawrVp08fBAcHIyEhQW1L3oiICIwaNUracI+Jj4+HoaGhMGsZGRmJo0ePwtnZGYMHD1bbSUZqSpp1HTx4MFxcXJCRkYFOnTrByMgIQPlW3XJafBco/z+QnJwMoHyNC7lcIr569eoKnztjxgwdJnm6B7cqPHhfCTIzM9G4cWPMmzdP47nY2FhZXRmQnZ0tfH2bmpoKs8Vt27aVTYEFgLy8PISHh6stZO7l5SWb2WsiEg97nm6w5+kGe97LYc/TLfY8qgzy+UlHz6VHjx6wtLTEDz/8gFOnTgEo3zVk2rRp8PDwkDidus2bN6Nv375wcXFBamoqPv30U7Rv3x6nT59GUVGR7AqiUmZdAaBjx44AgKKiIuGYnNZmuHv3Lj799FPExMSoXeLetGlTTJs2DbVq1ZI036M/qMrKynD27FmYmZmhfv36AMqLYl5eHjp06CBVRADll4dre1/OFi9ejEWLFmnMBl+9ehXLly/Htm3bpAmmhbW1Ne7cuQNbW1s4ODjg0qVLcHNzQ1xcnPCLjNTi4uKwZMkSGBsbw93dHQDw008/Yf/+/Zg7dy7c3NwkTkhEYmLP0x32PPGw54mDPU+32POoMnBgTcHat2+P9u3bSx3jmVJSUoTdTE6dOoUmTZpg6tSpuHr1KtatWyerwrVo0SJ4enqiffv2sp8dUKlU2LdvH3777TfcvXsX69atg4ODA/bs2QN7e3t4eXlJHRFbt25FQUEB1qxZA2dnZwBAcnIygoKCsHXrVkybNk3SfBMnThTe37VrFzp16oRx48YJa5eoVCp89dVXwvbscpGXl4cbN27g7t27KCsrU3uua9euEqVS16BBAyxevBjz588XXr+YmBisWLECgwYNkjiduvbt2yM6OhoNGjRA7969sX79eoSHhyMjIwPvvPOO1PEAANu3b0e7du0wfvx4GBgYAABKS0uxadMmbN++XXa3MBHRy2PPEx97nrjY83SDPU9c7HlUGeSx8iU9t8mTJyMnJ0fjeF5eHiZPnixBoqd78EMhOjoarVu3BgDY2trKbituZ2dn7N69G+PGjcP//vc/REZGynbL6H379iEiIgLDhg1Tu83CxcUFR44ckTDZQxcuXMCYMWOEsgWUv8ZjxozBhQsXpAumxdGjR/Huu++qLQisr68Pb29vHD16VMJk6qKiojBx4kQsXboUW7duxbZt29Te5GLChAmwtbXFihUrUFxcjMuXL2P58uXw9fVV2+VODvz8/DBgwAAAQOfOnREYGIgePXpg+vTp8PPzkzhdubi4OPTt21coWwBgYGCAvn37Ii4uTsJkRKQL7Hm6wZ4nLvY88bHniY89jyoDr1hTqPT0dKhUKo3jxcXFyMrKkiDRk7m5uSEsLAwtWrRATEyMsKtRWlqasI24XIwePRojR45EdHQ0jh8/jg0bNkBfXx8dO3aEp6enrC7PjoiIwLhx49C8eXNs3rxZOO7q6oqUlBQJkz1UVlamdW0VAwMDjRk4qalUKty6dUtjQeBbt27JKuvOnTvRvXt3vPfeezAxMZE6zhPp6+tj2rRpWLZsGRYuXIjExEQMHToUvXr1kjqahpycHLzyyisAgIyMDJw/fx5FRUWyuuzezMwMGRkZcHJyUjuekZEhu5l2Inp57Hm6wZ4nLvY88bHniY89jyoDB9YUJioqSnj/4sWLapexq1QqREdHw87OTopoTzRq1Ch89tlniIyMxIABA4Qdrk6fPi2rhS0f0NfXR8uWLdGyZUuMHTsW586dw759+xAeHi6rBS6zsrI0dgsDykuOXGZfmzVrhuDgYEydOlXYKjorKwvbt29Hs2bNJE6nrlu3bti0aRNSU1OFtQ2uX7+O7777TlbrmWRlZaF3796yLFuJiYkaxwYNGoR169bB09MTjRs3Fs5xdXWt7HgakpKSsGLFCmRkZKB27dqYNm0alixZgsLCQujp6eGnn37C9OnTZXErVqdOnbBp0yYMHz5c+L557do17Nq1C126dJE4HRGJhT1P99jzxMOeJz72PPGw51Fl4sCawqxatUp4PygoSO05AwMD2NnZYcSIEZUd66lcXV2xZs0ajePDhg1TuxxbbrKzs3HixAn88ccfSEpKEn4Iy4WzszOuXLmiUbBPnz4tm92Y/P39sXLlSkyaNAm2trYAymdeXFxcMGXKFInTqRs+fLiwUHR2djYAwNLSEu+++y769OkjbbhHtGzZEnFxcWpb3MvFrFmznvjc4cOHcfjwYeGxHH552bVrF+rWrYspU6bg2LFjWL58Odq0aYPx48cDKF875vvvv5dF4RoxYgT09PSwYcMGlJaWAgAMDQ3x1ltvyeY2BiJ6eex5lYc97+Wx54mPPU887HlUmfTK5HTtK1XYpEmTsGzZMsl326lq8vPzcebMGRw/fhwxMTGwt7eHp6cnXn/9da2zhlKKjIxEUFAQ+vXrh7CwMAwaNAgpKSk4duwYZs+ejRYtWkgdEUD5zGp0dLSwdbSTk5Nssj3Jg2245bKw8aNXMNy7dw+hoaHo1q0bXFxcNG7BaNeuXWXHE6Snp1f4XDlccTFmzBh88skncHV1RUFBAUaOHIlly5YJtwbcunULc+fOldWaJoWFhUhNTQUAODg4yHJGm4heHnuebrDniY897+Wx5+kGex5VJl6xpjAbNmxAu3btsGbNGpiamkodp0J8fX2f+rwcZjQeGDt2LMzNzdGpUycMHTpU2I5bjjw8PBAQEIDQ0FCYmJggJCQE9erVQ0BAgOSF5vLly9iyZQuWLFkCMzMztGjRQsiUn5+P6dOnY+zYsWjcuLGkOZ9ELkXrgUevYHggLCxM67lSfj3JoUQ9j9zcXGH9H1NTU5iYmKBmzZrC8zVr1sT9+/clSqediYmJ8P+TZYuo6mHP0y32PHGw54mLPU832POoMnFgTWEcHR2xf/9+rF+/Hk2bNkW7du3Qrl07YV0DOZoxY4ba49LSUty8eRMREREYPHiwRKm0CwgIQLNmzWR96wJQ/hru378f3bt3x7x586SOo+Gnn37Cm2++qbW4mJmZ4T//+Q9+/PFHWRWu7Oxs7Ny5E5cvX9a6vbmURUZOv5Q8r+TkZGRkZGisByPljOuj9PT0nvpYLkpLS7F37178/PPPKCgoAFBeEnv37g0fHx+ti0cTkfKw5+kWe5442PPExZ6nO+x5VFn4L6QwPj4+8PHxQUZGBs6dO4fIyEhs374dzs7O8PDwQLt27fDqq69KHVONh4eHxrGOHTuibt26OHnyJLy8vCRIpd2D2bZ79+4JOy7VqVNHdrdiGBgY4Pvvv8cbb7whdRStEhMTn7oeQMuWLfHDDz9UYqJn27hxIzIyMjBw4EBYWlrK7gdvbGwscnJy0LZtW+FYREQEQkJCUFhYCA8PD/j7+8PIyEjClA+lpqZi9erVSEpK0vq8XEpkUFCQ8JoVFxdj8+bNwgxhcXGxlNHUbN26FWfPnsWwYcOERW1jY2Oxd+9e5OTkCLvwEZGysefpFnueONjzxMeepxvseVRZOLCmULa2tujZsyd69uyJ+/fv4/z584iKikJgYCBq1KiBtm3bokePHqhbt67UUZ+oQYMG+OKLL6SOoaawsBBbtmzBsWPHhJksfX19vPHGG/D395fVJbnNmzcX1geRm7t37z51ZsXAwAD37t2rxETPdvXqVSxcuFB2v7A8sHfvXjRt2lQoXElJSdi0aRO6du0KZ2dnHDhwAFZWVrK5OiA4OBh2dnaYN28eJk+ejKVLlyI3Nxc7duzA8OHDpY4HAOjatavaY09Pz2eeI5Xjx49j2rRpaN26tXDM1dUVNjY2WLduHQsXURXDnqcb7HniYM8TH3ue+NjzqDJxYK0KqFGjBjp37ozOnTtDpVIhJiYGkZGRiI2NlW3hKioqws8//yy7Wxu2b9+OK1euICAgAK+99hqA8h/EwcHB2LFjh6y+qbVq1Qq7d+9GUlIS3NzcNNZikfISbGtrayQlJT1xIeDExERYWVlVcqqns7Gx0bgtQE4SExMxZMgQ4fGJEyfg7u6OCRMmACjPHxISIpvCdf36dXzyySeoVasW9PT0oK+vj0aNGmHo0KEIDg7GypUrpY6IiRMnSh2hwoyMjLSubWJvb8/bA4iqOPY88bDniYM9T3zseeJjz6PKxH8lhQoMDMSMGTPUFmAEgIKCAoSFhWH+/PkSJdM0evRojWP379+HiYmJ7LbiPnPmDKZPn46mTZsKx9q0aQNjY2OsXbtWVoVry5YtAMrXudBGykuwW7dujW+//RatWrWCsbGx2nNFRUUICQlBmzZtJEqn3ahRo7B7926MHTtWlrPDeXl5sLCwEB7HxMSozWrVr18fmZmZUkTTSqVSoUaNGgCAWrVqISsrC3Xq1IGtra1w+w1VXK9evRAWFoaJEyeq3dKwb98+9OrVS+J0RCQ29jzdYM8TB3ue+Njzqjf2POXjwJpCxcTEaCwSCZR/AV69elWCRE82cuRItcf6+vqoVasW3N3dYW5uLlEq7QoLC9V+qD1gYWGBoqIiCRI9mVzWLtBmwIABOHPmDKZOnYpevXqhTp06AMq3tf7ll1+gUqkwYMAAiVOq+/TTT1FYWIgpU6bAxMQEBgYGas8HBwdLlKychYUF0tLSYGtri5KSEty8eVNt1rKgoEAjs5Tq1q2LhIQE2Nvbw93dHQcOHIChoSEOHz4MBwcHqeMpwurVq9UeR0dHY8KECcJtLAkJCSgpKUHz5s0lSEdEusSepxvseeJgzxMfe171w55XtXBgTWESExOF95OTk5GdnS08VqlUuHDhguwuu+/WrZvUESqsYcOGCAkJweTJk4UZuKKiIuzdu1dYSFIOVCoVfv/9d5w9exbp6ekAAAcHB3To0AFvvPGG5AuyWlpaYvHixfjqq6+we/dutedatWqFMWPGCNtfy8XjvxjITevWrbF79274+fkhMjISJiYmarttJSYmPvGWDCkMGDAAhYWFAABfX18sX74c8+fPh7m5Of773/9KnE4ZHt9trUOHDmqPbWxsKjMOEVUC9jzdYs8TB3ue+Njzqh/2vKpFr0zON5uTBl9f36c+b2xsjNGjR8tqByag/PLm8PBw3Lp1CwDg7OwMLy8vrdt0SykpKQlLlixBSUkJXF1dAZT/IDMyMsLcuXNlsZZJWVkZVqxYgfPnz8PV1VVtljApKQlt27bFrFmzJE75UG5uLm7fvg0AcHR0lN3stVLcu3cPa9aswdWrV2FqaopJkyahffv2wvMLFy5EgwYN8N5770mY8ulyc3NRs2ZNyX8hICKSK/Y83WLPEx97njjY84iUjQNrCpOeno6ysjJMmTIFS5cuVdse3NDQEBYWFtDX15cwoaa4uDgsWbIExsbGcHd3F44VFRVh7ty5cHNzkzihusLCQvzxxx/C+gBOTk7w9PTUWENCKkePHsW2bdswc+ZMNGvWTO25y5cvY9WqVfD395fNLjdKVFRUpHELjlx+OcjPz4epqanG13lubi5MTU1lt8Dp7du3cfv2bTRp0gTGxsYoKytj4SIiegL2PN1jzyP2PPGw5xGV48CaApWUlODLL7+Ej4+PLBfffNwnn3wCR0dHjB8/XlgboLS0FJs2bUJaWhoCAwMlTqgsixcvRrNmzdCvXz+tz+/btw9XrlzB3LlzKzeYwhUUFODrr7/GqVOnkJOTo/G8nNc6kaOcnBysXbsWf/31FwDgs88+g4ODAzZu3Ahzc3OMGDFC4oRERPLEnle9sefpBnueuNjziNTJa8qLKsTQ0BBnz56VOkaFxcXFoW/fvmoLbhoYGKBv376Ii4uTMFm5qKioCr/JQWJiIlq1avXE51u3bo2EhIRKy1NV7Nq1C5cvX8b7778PIyMjTJgwAYMHD4a1tTUmT54sdTzF2b59OwwMDLBx40aYmJgIxzt37owLFy5IF4yISObY88TFnkcAe57Y2POI1MnrWlKqsHbt2uHs2bPw9vaWOsozmZmZISMjA05OTmrHMzIyhG2apbRq1aoKnyuH2azc3FytO1o9YGFhgby8vEpMVDWcO3cOkydPRtOmTfH555+jcePGcHR0hJ2dHY4fPw5PT0+pIyrKxYsXMXfuXI2FV2vXri0sxExERNqx54mHPY8A9jyxsecRqePAmkLVrl0bYWFhuHbtGtzc3NRmCgDg7bffliiZpk6dOmHTpk0YPny4sOPStWvXsGvXLnTp0kXidPIoUc9DpVI9dbttfX19lJaWVmKiqiE3N1fYHrxGjRrIzc0FADRq1AibN2+WMpoiFRYWanxfAspfZyMjIwkSVR1FRUWyWQuIiHSDPU887HkEsOeJjT1Pd9jzlIkDawoVHh4OMzMzxMfHIz4+Xu05PT09WRWuESNGQE9PDxs2bBCKgKGhId566y34+flJnE6ZgoKCnvhDq7i4uJLTVA0ODg5IS0uDra0tnJyccPLkSbi7uyMqKgo1a9aUOp7iNG7cGBERERgyZAiA8u9LKpUK33//PZo2bSpxOuVRqVTYt28ffvvtN9y9exfr1q2Dg4MD9uzZA3t7e9ntEEhEL4c9r3pjzxMfe5642PPExZ6nfBxYU6igoCCpI1SISqVCbGwsBg0ahKFDhyI1NRVA+Q83bbMcUjh48GCFz5VDka3ILlDcKer5devWDQkJCWjSpAn69u2LFStW4JdffkFJSQlGjhwpdTzF8fPzw6JFixAfH4+SkhLs2rULf//9N3Jzc7Fo0SKp4ynOvn37EBERgWHDhuGLL74Qjru4uOCnn35i4SKqYtjzxMOeRwB7ntjY88TFnqd83BWUdM7Pzw9r166V7c5WkyZNqtB5D2ZjqXpIT09HfHw8HB0d4erqKnUcRcrPz8ehQ4eQkJCAwsJC1KtXDz179oSVlZXU0RRnypQpGDduHJo3b44RI0Zg1apVcHBwwK1bt/Dxxx8jODhY6ohEVE2x55ESsee9PPY88bDnKR+vWFOQ7du3w9fXF6ampti+fftTz5XTzEvdunWRmpoq28KllFlh0o3Y2Fjk5OSgbdu2wrGIiAiEhISgsLAQHh4e8Pf353oRL8DMzAwDBgyQOkaVkJWVBUdHR43jZWVlKCkpkSAREYmNPU832POqN/Y83WHPEw97nvJxYE1BEhIShLUrlLTN9pAhQ7Bz5074+vpqXYDXzMxMomREQGhoKJo0aSIUrqSkJGzatAldu3aFs7MzDhw4ACsrKwwePFjipMqTl5eHGzdu4O7du3j84mjexvJ8nJ2dceXKFdjZ2akdP336NOrVqydRKiISE3sekfjY83SHPU887HnKx4E1BZk/f77W9+UqNDQU3t7eWLZsGQBg5cqVWs+T025NGzdufOrzEydOrKQkVFkSEhLg6+srPD5x4gTc3d0xYcIEAICNjQ1CQkJYuJ5TVFQU1q9fj4KCAtSoUQN6enpqz7NwPR8fHx8EBQUhKysLZWVlOHPmDFJSUnDs2DHMnj1b6nhEJAL2PN1jz6t+2PN0gz1PXOx5yseBNdKZvXv34q233lJEOXwgLy9P7XFpaSn+/vtv5OXloVmzZhKlIl3Ky8uDhYWF8DgmJgatW7cWHtevXx+ZmZlSRFO0nTt3onv37njvvfdks4C1knl4eCAgIAChoaEwMTFBSEgI6tWrh4CAALRo0ULqeERUDbHnkRKw5+kGe5642POUjwNrClVQUIDvvvsOly9f1nr5rZwWX23SpInUESps5syZGsdUKhW++uorODg4SJCIdM3CwkLYfr2kpAQ3b95Um7UsKCiAgYGBhAmVKSsrC71792bZElHjxo0xb948qWMQUSVgz9MN9rzqhz1PN9jzxMeep2wcWFOoTZs24cqVK/D09ISVlZXG5bdyIddcz0NfXx/e3t5YsGAB+vbtK3UcElnr1q2xe/du+Pn5ITIyEiYmJmjcuLHwfGJiotbFROnpWrZsibi4OP6iQkT0AtjzKg97XtXGnqcb7HlE6jiwplAXLlzA7Nmz0ahRI6mjPNXUqVOfeY4Stg++ffu2sKAwVS2+vr5Ys2YNFixYAFNTU0yaNAmGhg+/NR49epSXYL+ANm3aYNeuXUhOToaLi4vaawoA7dq1kyiZcowePbrC5yrh+ygRVRx7XuViz6u62PN0gz3v5bHnVS0cWFOomjVrwtzcXOoYzzRo0CBF7Qb1+Pb2ZWVlyM7Oxp9//slFOKuoWrVqITAwEPn5+TA1NYW+vr7a89OnT4epqalE6ZTriy++AACEhYVpfV5Oi1nL1ciRI6WOQEQSYc/TDfa86oc9TzfY814ee17Volf2+KINpAjHjh1DVFQUJk2aJNt72319ffHll1+qLRgqd4GBgWqP9fT0UKtWLTRr1gzdu3fnGgxEJDtFRUUwNjaWOgYRiYg9TzfY84hIadjzlIFXrCnIrFmz1NayuH37NsaOHQs7OzuNy29XrFhR2fEUTaVS4cCBAyguLkZpaSmaNm2KwYMH85sY0XOKjY1FTk4O2rZtKxyLiIhASEgICgsL4eHhAX9/fxgZGUmYUnm2bt0Kf39/jeMFBQVYsWKFonblIyLt2PN0hz2PSBzsebrBnqd8HFhTEA8PD6kjVFn79u3D3r170aJFCxgZGeHnn3/GvXv3MHHiRKmjESlKaGgomjRpIhSupKQkbNq0CV27doWzszMOHDgAKysrtR256NnOnz+PkJAQjZ3Mli5dKmEqIhITe57usOcRiYM9TzfY85SPA2sKMmjQIKkjPBcl3Vt/7NgxvP/++3jrrbcAAJcuXcLy5csxYcIEjbUYiOjJEhIS4OvrKzw+ceIE3N3dMWHCBACAjY2NRnGgZ5s7dy7mz5+PmjVr4p133sH9+/exZMkSGBgYYM6cOVLHIyIRsOfpDnsekTjY83SDPU/5OLCmUBkZGdDT04ONjQ0A4MaNGzh+/DicnZ3xn//8R+J0ypORkYHWrVsLj1u0aAE9PT3cuXNHeI2J6Nny8vLU1tuJiYlR+9qqX78+MjMzpYimaI6Ojvjoo48QGBgIPT09nDhxAkZGRpg9ezYXXSaqgtjzxMWeRyQO9jzdYM9TPk7RKNRnn32Gv/76CwCQnZ2NRYsW4caNG/jmm28QGhoqcTrlKS0t1Vhnw8DAgFuvEz0nCwsLpKWlAQBKSkpw8+ZNNGjQQHi+oKCAi0O/IFdXV8yePRvffPMNTExM8NFHH7FsEVVR7HniYs8jEgd7nu6w5ykbr1hTqL///hvu7u4AgJMnT8LFxQWLFi3CxYsXsXnzZvj4+EicUHmCgoLUFtosLi7G5s2b1XbjmjFjhhTRiBSjdevW2L17N/z8/BAZGQkTExM0btxYeD4xMRGOjo4SJlSOxxcyf8DIyAh37tzBvHnzhGNcyJyoamHPEx97HtHLY88TD3te1cKBNYUqKSkRdoiKjo4WFpB0cnLCnTt3pIymSF27dtU45unpKUESImXz9fXFmjVrsGDBApiammLSpElqu9kdPXoULVq0kDChcnAhc6Lqiz1PXOx5ROJgzxMPe17VwoE1hapbty5+++03tGnTBpcuXRIWkczKysIrr7wicTrl4a5QROKoVasWAgMDkZ+fD1NTU41FoadPn87L2itIaQuZE5F42PPExZ5HJA72PPGw51UtHFhTKD8/P6xevRoHDhxA165d8eqrrwIAoqKihFsHiIikYmZmpvW4ubl5JSepWkpKSnD37l2UlZWpHbe1tZUoERHpAnseEckZe55usOcpl17Z4/9qpBgqlQr5+flq38DS0tJgYmKitlsLEREpW0pKCjZt2oRr165pff7bb7+t5EREpGvseURE1QN7nvJxYK0K+O677/DWW2+hZs2aUkchIiIdmDdvHvT19dGvXz9YWVlpPP/gahYiqnrY84iIqjb2POXjraBVwP79+9GpUycWLiKiKiohIQHLly+Hk5OT1FGIqJKx5xERVW3secqn/+xTSO540SERUdXm7OyMnJwcqWMQkQTY84iIqjb2POXjwJrChIaGorCwUOoYRERUifz8/LBr1y789ddfyMnJQX5+vtobEVUN7HlERNUPe57ycY01hfH19cWXX36ptmhtRkYGrK2tNbY7JiKiqsHX1/epz3NRW6KqgT2PiKj6Yc9TPq6xVgVw+10ioqpt/vz5UkcgIomw5xERVW3secrHgTUF0tPTkzoCERFVoiZNmjzxuaSkpEpMQkS6xp5HRFS9sOcpH28FVRhfX1+YmZk987zg4OBKSENERFK4f/8+Tpw4gSNHjiA+Pp63CBBVEex5RETEnqc8vGJNgQYNGlSh0kVERFVLTEwMwsPDcebMGVhbW6N9+/YYM2aM1LGISETseURE1RN7nnJxYE2BunTporaoLRERVV3Z2dn4/fffER4ejvv376NTp04oKSnBzJkz4ezsLHU8IhIZex4RUfXBnlc1cGCNiIhIppYvX44rV66gTZs2GDVqFFq1agV9fX389ttvUkcjIiIiopfAnld1cGCNiIhIpi5cuIDevXujR48eqF27ttRxiIiIiEgk7HlVBwfWFIYLFxIRVR8LFy5EeHg4Zs+eDScnJ7zxxhvo3Lmz1LGISEfY84iIqg/2vKqDu4ISERHJXEFBAU6ePImjR4/ixo0bUKlUGDlyJLp3744aNWpIHY+IiIiIXhB7nvJxYI2IiEhBUlJSEB4ejmPHjiEvLw8tWrRAQECA1LGIiIiI6CWx5ykTB9aIiIgUSKVSISoqCkePHmXhIiIiIqpC2POUhQNrREREREREREREL0Bf6gBERERERERERERKxIE1IiIiIiIiIiKiF8CBNSIiIiIiIiIiohfAgTUiIiIiIiIiIqIXwIE1IiIiIiIiIiKiF8CBNSIiIiIiIiIiohfAgTUiIiIiIiIiIqIX8P/x1HGVx9bdBgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# changes: \n",
    "#    1. Baseline: 2 conv layers and two output layers\n",
    "cnn1 = Sequential()\n",
    "\n",
    "num_filt_layers = [24, 24]\n",
    "for num_filters in num_filt_layers:\n",
    "    cnn1.add( Conv2D(filters=num_filters, \n",
    "                    kernel_size=(3,3), \n",
    "                    padding='same') )\n",
    "    cnn1.add( Activation('relu'))\n",
    "    cnn1.add( MaxPooling2D(pool_size=(2, 2), \n",
    "                           ) )\n",
    "    \n",
    "\n",
    "# add one layer on flattened output\n",
    "cnn1.add( Flatten() )\n",
    "cnn1.add( Dense(100, activation='relu') )\n",
    "cnn1.add( Dense(100, activation='relu') )\n",
    "cnn1.add( Dense(NUM_CLASSES, activation='softmax') )\n",
    "\n",
    "# Let's train the model \n",
    "cnn1.compile(loss='mean_squared_error',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "input_shape = [None, 28,28,1]\n",
    "cnn1.build(input_shape)\n",
    "print(cnn1.summary())\n",
    "\n",
    "# we need to exapnd the dimensions here to give the \n",
    "#   \"channels\" dimension expected by Keras\n",
    "cnn1.fit(X_train, y_train_ohe, \n",
    "        batch_size=32, epochs=15, \n",
    "        shuffle=True, verbose=1,\n",
    "        validation_data=(X_test,y_test_ohe))\n",
    "\n",
    "compare_mlp_cnn(cnn1,mlp,X_test,y_test,labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3136</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">803,072</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,570</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m320\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_8 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_9 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_7 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3136\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_15 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │       \u001b[38;5;34m803,072\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_16 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m2,570\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">824,458</span> (3.15 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m824,458\u001b[0m (3.15 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">824,458</span> (3.15 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m824,458\u001b[0m (3.15 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "Epoch 1/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 28ms/step - accuracy: 0.7215 - loss: 0.7734 - val_accuracy: 0.8721 - val_loss: 0.3541\n",
      "Epoch 2/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 24ms/step - accuracy: 0.8679 - loss: 0.3653 - val_accuracy: 0.8905 - val_loss: 0.3050\n",
      "Epoch 3/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 23ms/step - accuracy: 0.8905 - loss: 0.3061 - val_accuracy: 0.9006 - val_loss: 0.2714\n",
      "Epoch 4/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 24ms/step - accuracy: 0.9033 - loss: 0.2735 - val_accuracy: 0.9089 - val_loss: 0.2550\n",
      "Epoch 5/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 23ms/step - accuracy: 0.9100 - loss: 0.2512 - val_accuracy: 0.9095 - val_loss: 0.2465\n",
      "Epoch 6/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 21ms/step - accuracy: 0.9165 - loss: 0.2319 - val_accuracy: 0.9135 - val_loss: 0.2363\n",
      "Epoch 7/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 23ms/step - accuracy: 0.9195 - loss: 0.2211 - val_accuracy: 0.9153 - val_loss: 0.2357\n",
      "Epoch 8/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 23ms/step - accuracy: 0.9231 - loss: 0.2086 - val_accuracy: 0.9183 - val_loss: 0.2329\n",
      "Epoch 9/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 23ms/step - accuracy: 0.9269 - loss: 0.2017 - val_accuracy: 0.9187 - val_loss: 0.2286\n",
      "Epoch 10/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 22ms/step - accuracy: 0.9312 - loss: 0.1928 - val_accuracy: 0.9205 - val_loss: 0.2266\n",
      "Epoch 11/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 24ms/step - accuracy: 0.9325 - loss: 0.1854 - val_accuracy: 0.9212 - val_loss: 0.2354\n",
      "Epoch 12/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 22ms/step - accuracy: 0.9349 - loss: 0.1798 - val_accuracy: 0.9222 - val_loss: 0.2330\n",
      "Epoch 13/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 22ms/step - accuracy: 0.9378 - loss: 0.1711 - val_accuracy: 0.9229 - val_loss: 0.2261\n",
      "Epoch 14/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 27ms/step - accuracy: 0.9383 - loss: 0.1693 - val_accuracy: 0.9190 - val_loss: 0.2355\n",
      "Epoch 15/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 24ms/step - accuracy: 0.9414 - loss: 0.1633 - val_accuracy: 0.9205 - val_loss: 0.2325\n",
      "Epoch 16/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 24ms/step - accuracy: 0.9436 - loss: 0.1587 - val_accuracy: 0.9235 - val_loss: 0.2302\n",
      "Epoch 17/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 25ms/step - accuracy: 0.9443 - loss: 0.1562 - val_accuracy: 0.9215 - val_loss: 0.2357\n",
      "Epoch 18/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 22ms/step - accuracy: 0.9448 - loss: 0.1524 - val_accuracy: 0.9199 - val_loss: 0.2333\n",
      "Epoch 19/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 27ms/step - accuracy: 0.9463 - loss: 0.1497 - val_accuracy: 0.9256 - val_loss: 0.2327\n",
      "Epoch 20/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 27ms/step - accuracy: 0.9473 - loss: 0.1484 - val_accuracy: 0.9249 - val_loss: 0.2410\n",
      "Epoch 21/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 24ms/step - accuracy: 0.9483 - loss: 0.1436 - val_accuracy: 0.9238 - val_loss: 0.2458\n",
      "Epoch 22/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 23ms/step - accuracy: 0.9482 - loss: 0.1427 - val_accuracy: 0.9251 - val_loss: 0.2437\n",
      "Epoch 23/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 23ms/step - accuracy: 0.9515 - loss: 0.1361 - val_accuracy: 0.9273 - val_loss: 0.2459\n",
      "Epoch 24/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 23ms/step - accuracy: 0.9524 - loss: 0.1347 - val_accuracy: 0.9227 - val_loss: 0.2503\n",
      "Epoch 25/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 22ms/step - accuracy: 0.9535 - loss: 0.1333 - val_accuracy: 0.9214 - val_loss: 0.2638\n",
      "Epoch 26/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 24ms/step - accuracy: 0.9541 - loss: 0.1304 - val_accuracy: 0.9260 - val_loss: 0.2451\n",
      "Epoch 27/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 24ms/step - accuracy: 0.9540 - loss: 0.1269 - val_accuracy: 0.9238 - val_loss: 0.2608\n",
      "Epoch 28/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 23ms/step - accuracy: 0.9566 - loss: 0.1259 - val_accuracy: 0.9260 - val_loss: 0.2490\n",
      "Epoch 29/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 22ms/step - accuracy: 0.9564 - loss: 0.1250 - val_accuracy: 0.9232 - val_loss: 0.2724\n",
      "Epoch 30/30\n",
      "\u001b[1m469/469\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 23ms/step - accuracy: 0.9559 - loss: 0.1249 - val_accuracy: 0.9264 - val_loss: 0.2354\n",
      "CPU times: user 2min 34s, sys: 1min 30s, total: 4min 4s\n",
      "Wall time: 5min 35s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Copy TensorFlow Architecture from \n",
    "#   Deep MNIST for experts\n",
    "#   https://www.tensorflow.org/versions/r0.11/tutorials/mnist/pros/index.html\n",
    "\n",
    "# Manipulated to mirror parts of this network:\n",
    "#   http://ankivil.com/mnist-database-and-simple-classification-networks/\n",
    "\n",
    "cnn2 = Sequential()\n",
    "\n",
    "num_filt_layers = [32, 64]\n",
    "for num_filters in num_filt_layers:\n",
    "    cnn2.add( Conv2D(filters=num_filters, \n",
    "                    kernel_size=(3,3), \n",
    "                    padding='same', \n",
    "                    activation='relu',\n",
    "                    ) ) # more compact syntax\n",
    "\n",
    "    # max pooling\n",
    "    cnn2.add( MaxPooling2D(pool_size=(2, 2) ) )\n",
    "    \n",
    "\n",
    "# add one layer on flattened output\n",
    "cnn2.add( Dropout(0.25) ) # add some dropout for regularization after conv layers\n",
    "cnn2.add( Flatten() )\n",
    "cnn2.add( Dense(256, activation='relu') )\n",
    "cnn2.add( Dropout(0.5) ) # add some dropout for regularization, again!\n",
    "cnn2.add( Dense(NUM_CLASSES, activation='softmax') )\n",
    "\n",
    "# Let's train the model \n",
    "cnn2.compile(loss='categorical_crossentropy', # 'categorical_crossentropy' 'mean_squared_error'\n",
    "              optimizer='rmsprop', # 'adadelta' 'rmsprop'\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "input_shape = [None, 28,28,1]\n",
    "cnn2.build(input_shape)\n",
    "print(cnn2.summary())\n",
    "\n",
    "history = cnn2.fit(X_train, y_train_ohe, \n",
    "            batch_size=128, epochs=30, \n",
    "            shuffle=True, verbose=1,\n",
    "            validation_data=(X_test,y_test_ohe))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(history.history['accuracy'], label='training')\n",
    "\n",
    "plt.ylabel('Accuracy %')\n",
    "plt.title('Training')\n",
    "plt.plot(history.history['val_accuracy'], label='validation')\n",
    "plt.title('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(history.history['loss'], label='training')\n",
    "plt.ylabel('Training Loss')\n",
    "plt.xlabel('epochs')\n",
    "\n",
    "plt.plot(history.history['val_loss'], label='validation')\n",
    "plt.xlabel('epochs')\n",
    "plt.title('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_mlp_cnn(cnn2,mlp,X_test,y_test,labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.23536495864391327, 0.9264000058174133]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn2.evaluate(X_test,y_test_ohe,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "# want to save this model for later?\n",
    "cnn2.save('large_data/mnist_cnn.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "cnn_mnist = load_model('large_data/mnist_cnn.h5')\n",
    "compare_mlp_cnn(cnn_mnist,None,X_test,y_test,labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}