Noise_Classification_Generator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !unzip './content/Phase2.zip' './content/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import cv2\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGE_HEIGHT = 224\n",
    "IMAGE_WIDTH = 224\n",
    "BATCH_SIZE = 32\n",
    "EPOCHS = 20\n",
    "LEARNING_RATE = 1e-2\n",
    "NUM_CLASSES = 3\n",
    "CLASSES = ['Gaussian', 'Periodic', 'Salt']\n",
    "N_CHANNELS = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = './content/DataSet2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plt_figure(images: list, titles: list, cmap='gray', rows=1, columns=1, w=5, h=5):\n",
    "    fig = plt.figure(figsize=(w, h))\n",
    "    count = 1\n",
    "    for image, title in zip(images, titles):\n",
    "        fig.add_subplot(rows, columns, count)\n",
    "        count += 1\n",
    "        plt.imshow(image, cmap=cmap)\n",
    "        plt.axis('off')\n",
    "        plt.title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def convert_to_ms(image):\n",
    "#     image = np.fft.fftshift(np.fft.fft2(image))\n",
    "#     image = 20*np.log(np.abs(image))\n",
    "#     return image\n",
    "\n",
    "def convert_to_ms(image):\n",
    "    if(N_CHANNELS==3):\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "    image = np.fft.fftshift(np.fft.fft2(image))\n",
    "    image = 20*(np.log(np.abs(image)))\n",
    "    image = (image / np.max(image))*255\n",
    "    image = np.uint8(np.round(image))\n",
    "    if(N_CHANNELS==3):\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)\n",
    "    image = image/255\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras.utils as image\n",
    "\n",
    "def load_test_image(path):\n",
    "    img = image.load_img(path, target_size=(224,224))\n",
    "    x = image.img_to_array(img)\n",
    "    x = x/255\n",
    "    return np.expand_dims(x, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Denoise Image</th>\n",
       "      <th>Noise Type</th>\n",
       "      <th>Noisy Image</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Without-Noise/cast_def_0_0.jpeg</td>\n",
       "      <td>Gaussian</td>\n",
       "      <td>With-Noise/cast_def_0_0.jpeg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Without-Noise/cast_def_0_2.jpeg</td>\n",
       "      <td>Gaussian</td>\n",
       "      <td>With-Noise/cast_def_0_2.jpeg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Without-Noise/cast_def_0_22.jpeg</td>\n",
       "      <td>Gaussian</td>\n",
       "      <td>With-Noise/cast_def_0_22.jpeg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Without-Noise/cast_def_0_25.jpeg</td>\n",
       "      <td>Gaussian</td>\n",
       "      <td>With-Noise/cast_def_0_25.jpeg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Without-Noise/cast_def_0_33.jpeg</td>\n",
       "      <td>Gaussian</td>\n",
       "      <td>With-Noise/cast_def_0_33.jpeg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Denoise Image Noise Type                    Noisy Image\n",
       "0   Without-Noise/cast_def_0_0.jpeg   Gaussian   With-Noise/cast_def_0_0.jpeg\n",
       "1   Without-Noise/cast_def_0_2.jpeg   Gaussian   With-Noise/cast_def_0_2.jpeg\n",
       "2  Without-Noise/cast_def_0_22.jpeg   Gaussian  With-Noise/cast_def_0_22.jpeg\n",
       "3  Without-Noise/cast_def_0_25.jpeg   Gaussian  With-Noise/cast_def_0_25.jpeg\n",
       "4  Without-Noise/cast_def_0_33.jpeg   Gaussian  With-Noise/cast_def_0_33.jpeg"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel('./content/Labels.xlsx')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Gaussian\n",
       "1       Gaussian\n",
       "2       Gaussian\n",
       "3       Gaussian\n",
       "4       Gaussian\n",
       "          ...   \n",
       "1245        Salt\n",
       "1246        Salt\n",
       "1247        Salt\n",
       "1248        Salt\n",
       "1249        Salt\n",
       "Name: Noise Type, Length: 1250, dtype: object"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Noise Type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>images</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./content/DataSet2\\With-Noise/cast_def_0_0.jpeg</td>\n",
       "      <td>Gaussian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./content/DataSet2\\With-Noise/cast_def_0_2.jpeg</td>\n",
       "      <td>Gaussian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./content/DataSet2\\With-Noise/cast_def_0_22.jpeg</td>\n",
       "      <td>Gaussian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./content/DataSet2\\With-Noise/cast_def_0_25.jpeg</td>\n",
       "      <td>Gaussian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./content/DataSet2\\With-Noise/cast_def_0_33.jpeg</td>\n",
       "      <td>Gaussian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>./content/DataSet2\\With-Noise/cast_ok_0_9397.jpeg</td>\n",
       "      <td>Salt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>./content/DataSet2\\With-Noise/cast_ok_0_9411.jpeg</td>\n",
       "      <td>Salt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>./content/DataSet2\\With-Noise/cast_ok_0_9464.jpeg</td>\n",
       "      <td>Salt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>./content/DataSet2\\With-Noise/cast_ok_0_9494.jpeg</td>\n",
       "      <td>Salt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>./content/DataSet2\\With-Noise/cast_ok_0_9516.jpeg</td>\n",
       "      <td>Salt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1250 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 images    labels\n",
       "0       ./content/DataSet2\\With-Noise/cast_def_0_0.jpeg  Gaussian\n",
       "1       ./content/DataSet2\\With-Noise/cast_def_0_2.jpeg  Gaussian\n",
       "2      ./content/DataSet2\\With-Noise/cast_def_0_22.jpeg  Gaussian\n",
       "3      ./content/DataSet2\\With-Noise/cast_def_0_25.jpeg  Gaussian\n",
       "4      ./content/DataSet2\\With-Noise/cast_def_0_33.jpeg  Gaussian\n",
       "...                                                 ...       ...\n",
       "1245  ./content/DataSet2\\With-Noise/cast_ok_0_9397.jpeg      Salt\n",
       "1246  ./content/DataSet2\\With-Noise/cast_ok_0_9411.jpeg      Salt\n",
       "1247  ./content/DataSet2\\With-Noise/cast_ok_0_9464.jpeg      Salt\n",
       "1248  ./content/DataSet2\\With-Noise/cast_ok_0_9494.jpeg      Salt\n",
       "1249  ./content/DataSet2\\With-Noise/cast_ok_0_9516.jpeg      Salt\n",
       "\n",
       "[1250 rows x 2 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({\n",
    "    \"images\" : [os.path.join(base_dir, dir) for dir in data['Noisy Image']],\n",
    "    \"labels\" : data['Noise Type']\n",
    "    })\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle \n",
    "data = shuffle(data, random_state=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen=ImageDataGenerator(\n",
    "    # preprocessing_function=convert_to_ms,\n",
    "    rescale=1./255,\n",
    "    horizontal_flip=True,\n",
    "    vertical_flip=True,\n",
    "    validation_split=0.2,\n",
    "    # shear_range=0.2,\n",
    "    # zoom_range=0.2,\n",
    "    # width_shift_range=0.2,\n",
    "    # height_shift_range=0.2,\n",
    "    # rotation_range=5,\n",
    "    # fill_mode='nearest'\n",
    "    )\n",
    "test_datagen=ImageDataGenerator(\n",
    "    # preprocessing_function=convert_to_ms,\n",
    "    rescale=1./255\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1000 validated image filenames belonging to 3 classes.\n",
      "Found 250 validated image filenames belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "train_gen = train_datagen.flow_from_dataframe(\n",
    "    data,\n",
    "    x_col='images',\n",
    "    y_col='labels',\n",
    "    classes=CLASSES,\n",
    "    class_mode='categorical',\n",
    "    target_size=(IMAGE_HEIGHT,IMAGE_WIDTH),\n",
    "    shuffle=False,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    subset='training',\n",
    "    seed=0\n",
    ")\n",
    "\n",
    "val_gen = train_datagen.flow_from_dataframe(\n",
    "    data,\n",
    "    x_col='images',\n",
    "    y_col='labels',\n",
    "    classes=CLASSES,\n",
    "    class_mode='categorical',\n",
    "    target_size=(IMAGE_HEIGHT,IMAGE_WIDTH),\n",
    "    shuffle=False,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    subset='validation',\n",
    "    seed=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_figure([train_gen[10][0][29]],[f'{train_gen[10][1][29]}'],rows=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
    "\n",
    "# Define callbacks for early stopping, model checkpointing, and learning rate reduction\n",
    "my_callbacks = [\n",
    "    EarlyStopping(monitor='val_loss', patience=5),\n",
    "    ModelCheckpoint('best_model.h5', save_best_only=True, monitor='val_accuracy', mode='max'),\n",
    "    ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3)\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VGG16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 25088)             0         \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 25088)             0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 512)               12845568  \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 512)               0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 3)                 1539      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 27,561,795\n",
      "Trainable params: 12,847,107\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.optimizers import Adam\n",
    "from keras.layers import Flatten, Dense, Dropout\n",
    "from keras.models import Model\n",
    "\n",
    "\n",
    "\n",
    "# Load pre-trained VGG16 model without the top layers\n",
    "pretrained_model = VGG16(weights='imagenet', include_top=False, input_shape=(IMAGE_WIDTH, IMAGE_HEIGHT, 3))\n",
    "\n",
    "# Freeze pre-trained layers for fine-tuning later\n",
    "for layer in pretrained_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "x = pretrained_model.output\n",
    "x = Flatten()(x)\n",
    "x = Dropout(0.25)(x)\n",
    "x = Dense(512, activation='relu')(x)\n",
    "x = Dropout(0.5)(x)\n",
    "predictions = Dense(NUM_CLASSES, activation='softmax')(x)\n",
    "\n",
    "# Create full model\n",
    "model = Model(inputs=pretrained_model.input, outputs=predictions)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss='categorical_crossentropy',\n",
    "    optimizer=Adam(learning_rate=LEARNING_RATE),\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      " 9/32 [=======>......................] - ETA: 4s - loss: 45.0279 - accuracy: 0.5682"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32md:\\Uni\\CV\\Project\\Image_Denoising_Classification\\Noise_Classification_Generator.ipynb Cell 21\u001b[0m line \u001b[0;36m3\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/Uni/CV/Project/Image_Denoising_Classification/Noise_Classification_Generator.ipynb#X25sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m history \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/Uni/CV/Project/Image_Denoising_Classification/Noise_Classification_Generator.ipynb#X25sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mwith\u001b[39;00m tf\u001b[39m.\u001b[39mdevice(\u001b[39m'\u001b[39m\u001b[39m/GPU:0\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/Uni/CV/Project/Image_Denoising_Classification/Noise_Classification_Generator.ipynb#X25sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m     history \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mfit(\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/Uni/CV/Project/Image_Denoising_Classification/Noise_Classification_Generator.ipynb#X25sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m         train_gen,\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/Uni/CV/Project/Image_Denoising_Classification/Noise_Classification_Generator.ipynb#X25sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m         epochs\u001b[39m=\u001b[39;49mEPOCHS,\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/Uni/CV/Project/Image_Denoising_Classification/Noise_Classification_Generator.ipynb#X25sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m         batch_size\u001b[39m=\u001b[39;49mBATCH_SIZE,\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/Uni/CV/Project/Image_Denoising_Classification/Noise_Classification_Generator.ipynb#X25sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m         validation_data\u001b[39m=\u001b[39;49mval_gen,\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/Uni/CV/Project/Image_Denoising_Classification/Noise_Classification_Generator.ipynb#X25sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m         callbacks\u001b[39m=\u001b[39;49mmy_callbacks,\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/Uni/CV/Project/Image_Denoising_Classification/Noise_Classification_Generator.ipynb#X25sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m         workers\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m,\n\u001b[0;32m     <a href='vscode-notebook-cell:/d%3A/Uni/CV/Project/Image_Denoising_Classification/Noise_Classification_Generator.ipynb#X25sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m     )\n",
      "File \u001b[1;32md:\\Programming\\Anaconda3\\envs\\tf\\lib\\site-packages\\keras\\utils\\traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m---> 65\u001b[0m     \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m     66\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32md:\\Programming\\Anaconda3\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py:1564\u001b[0m, in \u001b[0;36mModel.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1556\u001b[0m \u001b[39mwith\u001b[39;00m tf\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mexperimental\u001b[39m.\u001b[39mTrace(\n\u001b[0;32m   1557\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m   1558\u001b[0m     epoch_num\u001b[39m=\u001b[39mepoch,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1561\u001b[0m     _r\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,\n\u001b[0;32m   1562\u001b[0m ):\n\u001b[0;32m   1563\u001b[0m     callbacks\u001b[39m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m-> 1564\u001b[0m     tmp_logs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrain_function(iterator)\n\u001b[0;32m   1565\u001b[0m     \u001b[39mif\u001b[39;00m data_handler\u001b[39m.\u001b[39mshould_sync:\n\u001b[0;32m   1566\u001b[0m         context\u001b[39m.\u001b[39masync_wait()\n",
      "File \u001b[1;32md:\\Programming\\Anaconda3\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    151\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32md:\\Programming\\Anaconda3\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    912\u001b[0m compiler \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mxla\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mnonXla\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    914\u001b[0m \u001b[39mwith\u001b[39;00m OptionalXlaContext(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 915\u001b[0m   result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_call(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n\u001b[0;32m    917\u001b[0m new_tracing_count \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    918\u001b[0m without_tracing \u001b[39m=\u001b[39m (tracing_count \u001b[39m==\u001b[39m new_tracing_count)\n",
      "File \u001b[1;32md:\\Programming\\Anaconda3\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:947\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    944\u001b[0m   \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n\u001b[0;32m    945\u001b[0m   \u001b[39m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[0;32m    946\u001b[0m   \u001b[39m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[1;32m--> 947\u001b[0m   \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_stateless_fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)  \u001b[39m# pylint: disable=not-callable\u001b[39;00m\n\u001b[0;32m    948\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_stateful_fn \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    949\u001b[0m   \u001b[39m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[0;32m    950\u001b[0m   \u001b[39m# in parallel.\u001b[39;00m\n\u001b[0;32m    951\u001b[0m   \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n",
      "File \u001b[1;32md:\\Programming\\Anaconda3\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:2496\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2493\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n\u001b[0;32m   2494\u001b[0m   (graph_function,\n\u001b[0;32m   2495\u001b[0m    filtered_flat_args) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[1;32m-> 2496\u001b[0m \u001b[39mreturn\u001b[39;00m graph_function\u001b[39m.\u001b[39;49m_call_flat(\n\u001b[0;32m   2497\u001b[0m     filtered_flat_args, captured_inputs\u001b[39m=\u001b[39;49mgraph_function\u001b[39m.\u001b[39;49mcaptured_inputs)\n",
      "File \u001b[1;32md:\\Programming\\Anaconda3\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:1862\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1858\u001b[0m possible_gradient_type \u001b[39m=\u001b[39m gradients_util\u001b[39m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1859\u001b[0m \u001b[39mif\u001b[39;00m (possible_gradient_type \u001b[39m==\u001b[39m gradients_util\u001b[39m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1860\u001b[0m     \u001b[39mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1861\u001b[0m   \u001b[39m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1862\u001b[0m   \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_build_call_outputs(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_inference_function\u001b[39m.\u001b[39;49mcall(\n\u001b[0;32m   1863\u001b[0m       ctx, args, cancellation_manager\u001b[39m=\u001b[39;49mcancellation_manager))\n\u001b[0;32m   1864\u001b[0m forward_backward \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1865\u001b[0m     args,\n\u001b[0;32m   1866\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1867\u001b[0m     executing_eagerly)\n\u001b[0;32m   1868\u001b[0m forward_function, args_with_tangents \u001b[39m=\u001b[39m forward_backward\u001b[39m.\u001b[39mforward()\n",
      "File \u001b[1;32md:\\Programming\\Anaconda3\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:499\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    497\u001b[0m \u001b[39mwith\u001b[39;00m _InterpolateFunctionError(\u001b[39mself\u001b[39m):\n\u001b[0;32m    498\u001b[0m   \u001b[39mif\u001b[39;00m cancellation_manager \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 499\u001b[0m     outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39;49mexecute(\n\u001b[0;32m    500\u001b[0m         \u001b[39mstr\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msignature\u001b[39m.\u001b[39;49mname),\n\u001b[0;32m    501\u001b[0m         num_outputs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_num_outputs,\n\u001b[0;32m    502\u001b[0m         inputs\u001b[39m=\u001b[39;49margs,\n\u001b[0;32m    503\u001b[0m         attrs\u001b[39m=\u001b[39;49mattrs,\n\u001b[0;32m    504\u001b[0m         ctx\u001b[39m=\u001b[39;49mctx)\n\u001b[0;32m    505\u001b[0m   \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    506\u001b[0m     outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m    507\u001b[0m         \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msignature\u001b[39m.\u001b[39mname),\n\u001b[0;32m    508\u001b[0m         num_outputs\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    511\u001b[0m         ctx\u001b[39m=\u001b[39mctx,\n\u001b[0;32m    512\u001b[0m         cancellation_manager\u001b[39m=\u001b[39mcancellation_manager)\n",
      "File \u001b[1;32md:\\Programming\\Anaconda3\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     52\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m   ctx\u001b[39m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 54\u001b[0m   tensors \u001b[39m=\u001b[39m pywrap_tfe\u001b[39m.\u001b[39;49mTFE_Py_Execute(ctx\u001b[39m.\u001b[39;49m_handle, device_name, op_name,\n\u001b[0;32m     55\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[0;32m     56\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m     57\u001b[0m   \u001b[39mif\u001b[39;00m name \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "history = 0\n",
    "with tf.device('/GPU:0'):\n",
    "    history = model.fit(\n",
    "        train_gen,\n",
    "        epochs=EPOCHS,\n",
    "        batch_size=BATCH_SIZE,\n",
    "        validation_data=val_gen,\n",
    "        callbacks=my_callbacks,\n",
    "        workers=1,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/8 [==============================] - 2s 279ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99        78\n",
      "           1       0.99      1.00      1.00       102\n",
      "           2       1.00      1.00      1.00        70\n",
      "\n",
      "    accuracy                           1.00       250\n",
      "   macro avg       1.00      1.00      1.00       250\n",
      "weighted avg       1.00      1.00      1.00       250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "predict = model.predict(val_gen)\n",
    "print(classification_report(val_gen.labels, np.argmax(predict, axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Calculating model accuracy\n",
      "8/8 [==============================] - 2s 227ms/step - loss: 0.1412 - accuracy: 0.9960\n",
      "Test Accuracy: 99.59999918937683\n"
     ]
    }
   ],
   "source": [
    "print(\"[INFO] Calculating model accuracy\")\n",
    "scores = model.evaluate(val_gen)\n",
    "print(f\"Test Accuracy: {scores[1]*100}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAGzCAYAAAAMr0ziAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABDyklEQVR4nO3deXxU9b3/8feZyUYgCxDIIlFkCwHDIkIuuIEgAQIF60WlVFCLtl7wSlGr3PtTUdub2t5aewtFbS9gq7i2oBcQQRSQTdkiYZHNkISSBRAyJEC2Ob8/khkSSAJDZjJLXs/H4zzInPM9Z75fj2HezOd8zzFM0zQFAADgwyze7gAAAMDlEFgAAIDPI7AAAACfR2ABAAA+j8ACAAB8HoEFAAD4PAILAADweQQWAADg8wgsAADA5xFYAACAzyOwAPC4RYsWyTAMbdu2zdtdAeCnCCwAAMDnEVgAAIDPI7AA8Ak7d+7U6NGjFRkZqTZt2mj48OHasmVLnTYVFRV64YUX1L17d4WFhal9+/a65ZZbtHr1amebgoICPfjgg+rUqZNCQ0MVHx+v8ePH68iRI808IgDuFOTtDgDAnj17dOuttyoyMlK/+MUvFBwcrNdff11Dhw7VunXrlJqaKkmaM2eOMjIyNG3aNA0aNEg2m03btm3Tjh07dOedd0qS7r77bu3Zs0ePPfaYOnfurKKiIq1evVq5ubnq3LmzF0cJoCkM0zRNb3cCQGBbtGiRHnzwQW3dulU33XTTJdvvuusurVixQvv27VOXLl0kSfn5+UpKSlL//v21bt06SVK/fv3UqVMnLVu2rN73OX36tNq2bavf/va3evLJJz03IADNjpIQAK+qqqrSqlWrNGHCBGdYkaT4+Hj96Ec/0oYNG2Sz2SRJ0dHR2rNnjw4ePFjvsVq1aqWQkBCtXbtWp06dapb+A2geBBYAXnX8+HGdPXtWSUlJl2xLTk6W3W5XXl6eJOnFF1/U6dOn1aNHD6WkpOipp57Srl27nO1DQ0P18ssv65NPPlFsbKxuu+02/eY3v1FBQUGzjQeAZxBYAPiN2267TYcPH9aCBQt0ww036C9/+YtuvPFG/eUvf3G2mTlzpg4cOKCMjAyFhYXp2WefVXJysnbu3OnFngNoKgILAK/q0KGDwsPDtX///ku2ffvtt7JYLEpMTHSua9eunR588EG98847ysvLU58+fTRnzpw6+3Xt2lVPPPGEVq1apd27d6u8vFy/+93vPD0UAB5EYAHgVVarVSNHjtRHH31UZ+pxYWGhFi9erFtuuUWRkZGSpJMnT9bZt02bNurWrZvKysokSWfPntX58+frtOnatasiIiKcbQD4J6Y1A2g2CxYs0MqVKy9ZP2fOHK1evVq33HKL/u3f/k1BQUF6/fXXVVZWpt/85jfOdr169dLQoUM1YMAAtWvXTtu2bdOHH36oGTNmSJIOHDig4cOH65577lGvXr0UFBSkJUuWqLCwUPfdd1+zjROA+zGtGYDHOaY1NyQvL0/Hjx/X7NmztXHjRtntdqWmpupXv/qVBg8e7Gz3q1/9Sh9//LEOHDigsrIyXXfddbr//vv11FNPKTg4WCdPntTzzz+vNWvWKC8vT0FBQerZs6eeeOIJTZw4sTmGCsBDCCwAAMDncQ0LAADweQQWAADg8wgsAADA5xFYAACAzyOwAAAAn0dgAQAAPi8gbhxnt9t17NgxRUREyDAMb3cHAABcAdM0debMGSUkJMhiafw7lIAILMeOHavzrBEAAOA/8vLy1KlTp0bbBERgiYiIkFQ9YMczRwAAgG+z2WxKTEx0fo43JiACi6MMFBkZSWABAMDPXMnlHFx0CwAAfB6BBQAA+DwCCwAA8HkBcQ0LAACmaaqyslJVVVXe7gpqsVqtCgoKavJtRwgsAAC/V15ervz8fJ09e9bbXUE9wsPDFR8fr5CQkKs+BoEFAODX7Ha7srOzZbValZCQoJCQEG4i6iNM01R5ebmOHz+u7Oxsde/e/bI3iGsIgQUA4NfKy8tlt9uVmJio8PBwb3cHF2nVqpWCg4OVk5Oj8vJyhYWFXdVxuOgWABAQrvZf7vA8d5wbzi4AAPB5BBYAAODzCCwAAHjJ0KFDNXPmTG93wy8QWAAAgM8jsDSi+GyF/rz+Oz394S5vdwUAgBaNwNKI8iq7Mj7Zp/e25Snve25GBAD+wjRNnS2vbPbFNM2r7vOpU6c0ZcoUtW3bVuHh4Ro9erQOHjzo3J6Tk6Nx48apbdu2at26tXr37q0VK1Y49508ebI6dOigVq1aqXv37lq4cGGT/zv6Eu7D0ogOEaH6ly7ttenwSa3IytdPb+/q7S4BAK7AuYoq9Xru02Z/370vpik85Oo+Wh944AEdPHhQH3/8sSIjI/X0009rzJgx2rt3r4KDgzV9+nSVl5dr/fr1at26tfbu3as2bdpIkp599lnt3btXn3zyiWJiYnTo0CGdO3fOnUPzOgLLZaT3idemwye1nMACAPAQR1DZuHGjhgwZIkl6++23lZiYqKVLl2rixInKzc3V3XffrZSUFElSly5dnPvn5uaqf//+uummmyRJnTt3bvYxeBqB5TJG9Y7Ts0t3a9fRYuWePKtr23MXRQDwda2Crdr7YppX3vdq7Nu3T0FBQUpNTXWua9++vZKSkrRv3z5J0r//+7/r0Ucf1apVqzRixAjdfffd6tOnjyTp0Ucf1d13360dO3Zo5MiRmjBhgjP4BAquYbmM9m1CNaRrjCRpxe58L/cGAHAlDMNQeEhQsy+efIbRtGnT9N133+n+++9XVlaWbrrpJv3xj3+UJI0ePVo5OTn6+c9/rmPHjmn48OF68sknPdYXbyCwXIH0PvGSpOW7CCwAAPdLTk5WZWWlvvrqK+e6kydPav/+/erVq5dzXWJion72s5/pH//4h5544gn9+c9/dm7r0KGDpk6dqrfeekuvvvqq3njjjWYdg6cRWK5AWu84WS2Gsv5ZrJyTpd7uDgAgwHTv3l3jx4/Xww8/rA0bNuibb77Rj3/8Y11zzTUaP368JGnmzJn69NNPlZ2drR07duiLL75QcnKyJOm5557TRx99pEOHDmnPnj1atmyZc1ugILBcgXatQzSka3tJ0vIsvmUBALjfwoULNWDAAI0dO1aDBw+WaZpasWKFgoODJUlVVVWaPn26kpOTNWrUKPXo0UN/+tOfJEkhISGaPXu2+vTpo9tuu01Wq1XvvvuuN4fjdobZlEnjPsJmsykqKkrFxcWKjIz0yHu8+3WunvlHlnonRGr5v9/qkfcAALju/Pnzys7O1vXXX6+wsDBvdwf1aOgcufL5zTcsV8hRFtpzzKbsE5SFAABoTgSWK9S2dYhu7lYzW4iyEAAAzYrA4oKxKdWzhZYxWwgAgGblcmBZv369xo0bp4SEBBmGoaVLl9bZbhhGvctvf/vbBo85Z86cS9r37NnT5cF42sjesQqyGNqXb9Ph4yXe7g4AAC2Gy4GltLRUffv21bx58+rdnp+fX2dZsGCBDMPQ3Xff3ehxe/fuXWe/DRs2uNo1j4sOD9Et3WvKQnzLAgBAs3H51vyjR4/W6NGjG9weFxdX5/VHH32kYcOG1XnmQb0dCQq6ZN+GlJWVqayszPnaZrNd0X7ukJ4Sr7X7j2t5Vr4eG9692d4XAICWzKPXsBQWFmr58uX6yU9+ctm2Bw8eVEJCgrp06aLJkycrNze3wbYZGRmKiopyLomJie7sdqNG9opTsNXQtwVndKjoTLO9LwAALZlHA8ubb76piIgI/fCHP2y0XWpqqhYtWqSVK1dq/vz5ys7O1q233qozZ+oPBLNnz1ZxcbFzycvL80T36xUVHqxbu3eQJC3fVdBs7wsAQEvm0cCyYMECTZ48+bI38hk9erQmTpyoPn36KC0tTStWrNDp06f1/vvv19s+NDRUkZGRdZbmlF4zW2h51rFmfV8AAFoqjwWWL7/8Uvv379e0adNc3jc6Olo9evTQoUOHPNCzphvRK1YhVosOFJboQCFlIQCAd3Tu3FmvvvrqFbWtb2avP/FYYPnf//1fDRgwQH379nV535KSEh0+fFjx8fEe6FnTRbUK1m09qmcL8QRnAAA8z+XAUlJSoszMTGVmZkqSsrOzlZmZWeciWZvNpg8++KDBb1eGDx+uuXPnOl8/+eSTWrdunY4cOaJNmzbprrvuktVq1aRJk1ztXrNJ7+MoC+UrAB7HBACAT3M5sGzbtk39+/dX//79JUmzZs1S//799dxzzznbvPvuuzJNs8HAcfjwYZ04ccL5+ujRo5o0aZKSkpJ0zz33qH379tqyZYs6dOjgaveazYjkWIUEWXSoqEQHCrmJHAD4FNOUykubf3HhH7BvvPGGEhISZLfb66wfP368HnroIR0+fFjjx49XbGys2rRpo4EDB+qzzz5z23+irKws3XHHHWrVqpXat2+vRx55RCUlFz7P1q5dq0GDBql169aKjo7WzTffrJycHEnSN998o2HDhikiIkKRkZEaMGCAtm3b5ra+1cfl+7AMHTr0st8oPPLII3rkkUca3H7kyJE6r/3xEdgRYcG6vUcHrd5bqOVZ+UqKi/B2lwAADhVnpf9KaP73/Y9jUkjrK2o6ceJEPfbYY/riiy80fPhwSdL333+vlStXasWKFSopKdGYMWP0q1/9SqGhofrrX/+qcePGaf/+/br22mub1M3S0lKlpaVp8ODB2rp1q4qKijRt2jTNmDFDixYtUmVlpSZMmKCHH35Y77zzjsrLy/X111/LMAxJ0uTJk9W/f3/Nnz9fVqtVmZmZCg4OblKfLsflwIILxvaJrw4su47p5yO6O08kAACX07ZtW40ePVqLFy92BpYPP/xQMTExGjZsmCwWS53rQF966SUtWbJEH3/8sWbMmNGk9168eLHOnz+vv/71r2rdujpgzZ07V+PGjdPLL7+s4OBgFRcXa+zYserataskKTk52bl/bm6unnrqKedjdLp39/yNVAksTTC8pix0+Hip9heeUc+45p1eDQBoQHB49bcd3nhfF0yePFkPP/yw/vSnPyk0NFRvv/227rvvPlksFpWUlGjOnDlavny58vPzVVlZqXPnzjV6Y9UrtW/fPvXt29cZViTp5ptvlt1u1/79+3XbbbfpgQceUFpamu68806NGDFC99xzj3MyzKxZszRt2jT97W9/04gRIzRx4kRnsPEUntbcBG1CgzS0h+MmcswWAgCfYRjVpZnmXlz8pn3cuHEyTVPLly9XXl6evvzyS02ePFlS9YSUJUuW6L/+67/05ZdfKjMzUykpKSovL/fEf7FLLFy4UJs3b9aQIUP03nvvqUePHtqyZYuk6ocW79mzR+np6fr888/Vq1cvLVmyxKP9IbA0kXO20C5mCwEAXBMWFqYf/vCHevvtt/XOO+8oKSlJN954oyRp48aNeuCBB3TXXXcpJSVFcXFxl1wDerWSk5P1zTffqLS01Llu48aNslgsSkpKcq7r37+/Zs+erU2bNumGG27Q4sWLndt69Oihn//851q1apV++MMfauHChW7pW0MILE00PDlWoUEWfXeiVPvyuYkcAMA1kydP1vLly513h3fo3r27/vGPfygzM1PffPONfvSjH10yo6gp7xkWFqapU6dq9+7d+uKLL/TYY4/p/vvvV2xsrLKzszV79mxt3rxZOTk5WrVqlQ4ePKjk5GSdO3dOM2bM0Nq1a5WTk6ONGzdq69atda5x8QSuYWmiNqFBGpbUUSv3FGh51jH1SuA6FgDAlbvjjjvUrl077d+/Xz/60Y+c61955RU99NBDGjJkiGJiYvT000/LZrO55T3Dw8P16aef6vHHH9fAgQMVHh6uu+++W6+88opz+7fffqs333xTJ0+eVHx8vKZPn66f/vSnqqys1MmTJzVlyhQVFhYqJiZGP/zhD/XCCy+4pW8NMcwAqGPYbDZFRUWpuLi42Z8rJEn/980xPfbOTnVuH64vnhzKbCEAaEbnz59Xdna2rr/++ss+uw7e0dA5cuXzm5KQG9zRs6PCgi06cvKs9hxzT/oFAAAXEFjcoHVokO7o2VFS9a36AQBoTm+//bbatGlT79K7d29vd88tuIbFTdJTErQiq0DLd+XrF2lJlIUAAM3mBz/4gVJTU+vd5uk70DYXAoubDOvZQa2Crcr9/qx2/9OmlE5R3u4SAKCFiIiIUEREYD8ihpKQm4SHBOmO5Oqy0LIsL9xdEQBauACYQxKw3HFuCCxuNDaFm8gBQHNzlDzOnj3r5Z6gIY5z05TyFCUhNxqa1FHhIVYdPXVOu44Wq29itLe7BAABz2q1Kjo6WkVFRZKq7yHCdYS+wTRNnT17VkVFRYqOjpbVar3qYxFY3KhViFXDk2P1f98c0/KsfAILADSTuLg4SXKGFviW6Oho5zm6WgQWN0tPia8OLLvyNXt0T1I+ADQDwzAUHx+vjh07qqKiwtvdQS3BwcFN+mbFgcDiZkOTOqh1iFX/PH1OmXmn1f/att7uEgC0GFar1S0fjvA9XHTrZmHBVo3oFStJWsFN5AAAcAsCiwekM1sIAAC3IrB4wG09OqhNaJCOFZ/XzrzT3u4OAAB+j8DiAWHBVo2ouYnc8l2UhQAAaCoCi4ek90mQVH0di91OWQgAgKYgsHjIrd1jFBEapPzi89qZd8rb3QEAwK8RWDwkLNiqO2tmCy2jLAQAQJMQWDwovU/1bCHKQgAANA2BxYNu6R6jiLAgFdrKtD2XshAAAFeLwOJBoUFWjexV/ewEZgsBAHD1CCweNrZWWaiKshAAAFeFwOJhN3eLUWRYkIrOlGnbke+93R0AAPwSgcXDQoIsSutdUxbi2UIAAFwVAkszuDBbqICyEAAAV4HA0gxu7hajqFbBOlFSpq+zKQsBAOAqAkszCLZaNMpZFjrm5d4AAOB/CCzNxFEWWrm7QJVVdi/3BgAA/+JyYFm/fr3GjRunhIQEGYahpUuX1tn+wAMPyDCMOsuoUaMue9x58+apc+fOCgsLU2pqqr7++mtXu+bTBndtr7bhwTpRUk5ZCAAAF7kcWEpLS9W3b1/NmzevwTajRo1Sfn6+c3nnnXcaPeZ7772nWbNm6fnnn9eOHTvUt29fpaWlqaioyNXu+axgq0WjbmC2EAAAV8PlwDJ69Gj98pe/1F133dVgm9DQUMXFxTmXtm3bNnrMV155RQ8//LAefPBB9erVS6+99prCw8O1YMECV7vn08akUBYCAOBqeOQalrVr16pjx45KSkrSo48+qpMnTzbYtry8XNu3b9eIESMudMpi0YgRI7R58+Z69ykrK5PNZquz+IPBXarLQidLy/UVZSEAAK6Y2wPLqFGj9Ne//lVr1qzRyy+/rHXr1mn06NGqqqqqt/2JEydUVVWl2NjYOutjY2NVUFBQ7z4ZGRmKiopyLomJie4ehkcEWS0adUP1tyzLeLYQAABXzO2B5b777tMPfvADpaSkaMKECVq2bJm2bt2qtWvXuu09Zs+ereLiYueSl5fntmN72ljnbKF8ykIAAFwhj09r7tKli2JiYnTo0KF6t8fExMhqtaqwsLDO+sLCQsXFxdW7T2hoqCIjI+ss/iL1+nZq3zpEp85WaPN3DZfKAADABR4PLEePHtXJkycVHx9f7/aQkBANGDBAa9asca6z2+1as2aNBg8e7OnuNbug2rOFKAsBAHBFXA4sJSUlyszMVGZmpiQpOztbmZmZys3NVUlJiZ566ilt2bJFR44c0Zo1azR+/Hh169ZNaWlpzmMMHz5cc+fOdb6eNWuW/vznP+vNN9/Uvn379Oijj6q0tFQPPvhg00fog5w3kdtToArKQgAAXFaQqzts27ZNw4YNc76eNWuWJGnq1KmaP3++du3apTfffFOnT59WQkKCRo4cqZdeekmhoaHOfQ4fPqwTJ044X9977706fvy4nnvuORUUFKhfv35auXLlJRfiBorU69srpk2ITpSUa9Phk7q9RwdvdwkAAJ9mmKbp948PttlsioqKUnFxsd9cz/Ls0t3625Yc3XNTJ/3mX/t6uzsAADQ7Vz6/eZaQlzjKQp/uKVR5JWUhAAAaQ2DxkoGd26lDRKiKz1Vo4+ETl98BAIAWjMDiJVaLoTHMFgIA4IoQWLwovU+CJOnTPQWUhQAAaASBxYtuuq6tOkaE6sz5Sm04dNzb3QEAwGcRWLzIYjGcT3Dm2UIAADSMwOJljmcLrd5TqLLK+h8QCQBAS0dg8bIbr22ruMgwnSmr1JcHmC0EAEB9CCxeZrEYGp1SPVtoRRZlIQAA6kNg8QHOstDeQp2voCwEAMDFCCw+oH9iW8VH1ZSFDlIWAgDgYgQWH1B7ttDyXce83BsAAHwPgcVHpFMWAgCgQQQWH9E/MVrXRLdSaXmV1h3gJnIAANRGYPERhmFoTArPFgIAoD4EFh/ieLbQZ/soCwEAUBuBxYf07RSla6Jb6Wx5ldbuL/J2dwAA8BkEFh9iGIbzniw8WwgAgAsILD7GMVtozb4inSunLAQAgERg8Tkp10QpsV0rnauo0heUhQAAkERg8TmGYSg9pfriW2YLAQBQjcDigxzXsaz5tlBnyyu93BsAALyPwOKDeidE6rr24TpfYdfn31IWAgCAwOKDqstCjmcLURYCAIDA4qMcs4U+/7ZIpWWUhQAALRuBxUf1io9U5/bhKqu0aw1lIQBAC0dg8VGGYTi/ZVlBWQgA0MIRWHyYY3rzF/uLVEJZCADQghFYfFhyfIS6xLSuLgvtK/R2dwAA8BoCiw+rXRZithAAoCUjsPg4R2BZe+C4zpyv8HJvAADwDgKLj0uKjVDXDq1VXmnXmn3MFgIAtEwEFh9XXRaqvvh2GWUhAEALRWDxA45nC60/cFw2ykIAgBaIwOIHesRGqHvHNiqvsuuzvcwWAgC0PC4HlvXr12vcuHFKSEiQYRhaunSpc1tFRYWefvpppaSkqHXr1kpISNCUKVN07NixRo85Z84cGYZRZ+nZs6fLgwlkzBYCALRkLgeW0tJS9e3bV/Pmzbtk29mzZ7Vjxw49++yz2rFjh/7xj39o//79+sEPfnDZ4/bu3Vv5+fnOZcOGDa52LaA5Hoa4/uBxFZ+jLAQAaFmCXN1h9OjRGj16dL3boqKitHr16jrr5s6dq0GDBik3N1fXXnttwx0JClJcXJyr3WkxusdGKCk2QvsLz2j13kL964BO3u4SAADNxuPXsBQXF8swDEVHRzfa7uDBg0pISFCXLl00efJk5ebmNti2rKxMNputztISXCgLNV5iAwAg0Hg0sJw/f15PP/20Jk2apMjIyAbbpaamatGiRVq5cqXmz5+v7Oxs3XrrrTpz5ky97TMyMhQVFeVcEhMTPTUEnzKmpiz05cETKj5LWQgA0HJ4LLBUVFTonnvukWmamj9/fqNtR48erYkTJ6pPnz5KS0vTihUrdPr0ab3//vv1tp89e7aKi4udS15enieG4HO6dWyjnnERqrSb+nRvgbe7AwBAs/FIYHGElZycHK1evbrRb1fqEx0drR49eujQoUP1bg8NDVVkZGSdpaUYy2whAEAL5PbA4ggrBw8e1Geffab27du7fIySkhIdPnxY8fHx7u6e33OUhTYeOqFTpeVe7g0AAM3D5cBSUlKizMxMZWZmSpKys7OVmZmp3NxcVVRU6F//9V+1bds2vf3226qqqlJBQYEKCgpUXn7hw3X48OGaO3eu8/WTTz6pdevW6ciRI9q0aZPuuusuWa1WTZo0qekjDDBdOrRRcnykKu2mVlEWAgC0EC5Pa962bZuGDRvmfD1r1ixJ0tSpUzVnzhx9/PHHkqR+/frV2e+LL77Q0KFDJUmHDx/WiRMnnNuOHj2qSZMm6eTJk+rQoYNuueUWbdmyRR06dHC1ey3C2D7x2pdv0/KsAt07sOGp4gAABArDNE3T251oKpvNpqioKBUXF7eI61myT5Rq2H+vldViaNt/jlDb1iHe7hIAAC5z5fObZwn5oetjWqt3QqSq7KY+3UNZCAAQ+Agsfsp5E7ksZgsBAAIfgcVPOZ4ttOnwSZ0sKfNybwAA8CwCi5+6rn1rpVwTVVMWKvR2dwAA8CgCix+7UBbi2UIAgMBGYPFjjrLQ5sMndYKyEAAggBFY/Fhiu3D17RQluymt3M1sIQBA4CKw+Ll0ni0EAGgBCCx+zvFsoa+yT6rozHkv9wYAAM8gsPi5Tm3D1S8xWnZT+pSyEAAgQBFYAsDYmrLQMspCAIAARWAJAKNrykJfH/leRTbKQgCAwENgCQDXRLfSjddGyzSlTygLAQACEIElQKT3SZDEbCEAQGAisASIMSlxkqStOd+roJiyEAAgsBBYAkR8VCsNuK5tTVmIb1kAAIGFwBJAHLfqX5FFYAEABBYCSwBx3ERu65FTlIUAAAGFwBJA4qLCNLBzW0l8ywIACCwElgDjKAstJ7AAAAIIgSXAjE6Jl2FI23NO6djpc97uDgAAbkFgCTCxkWEa2LmdJMpCAIDAQWAJQI5nC1EWAgAECgJLABp1Q5wMQ9qZe1pHT531dncAAGgyAksA6hgRptTrq8tCn2TxbCEAgP8jsAQox7OFllEWAgAEAAJLgBrVO04WQ/om77TyvqcsBADwbwSWANUhIlT/0qW9JGYLAQD8H4ElgKUzWwgAECAILAHMURbadbRYuScpCwEA/BeBJYC1bxOqIV1jJPEtCwDAvxFYAtwY57OFjnm5JwAAXD0CS4BL6x0rq8XQ7n/alHOy1NvdAQDgqhBYAlx1Wah6thBlIQCAvyKwtADpjrLQLgILAMA/uRxY1q9fr3HjxikhIUGGYWjp0qV1tpumqeeee07x8fFq1aqVRowYoYMHD172uPPmzVPnzp0VFham1NRUff311652DQ1I6x0nq8XQnmM2ZZ+gLAQA8D8uB5bS0lL17dtX8+bNq3f7b37zG/3P//yPXnvtNX311Vdq3bq10tLSdP78+QaP+d5772nWrFl6/vnntWPHDvXt21dpaWkqKipytXuoR9vWIbq5W/VsIW4iBwDwR4ZpmuZV72wYWrJkiSZMmCCp+tuVhIQEPfHEE3ryySclScXFxYqNjdWiRYt033331Xuc1NRUDRw4UHPnzpUk2e12JSYm6rHHHtMzzzxz2X7YbDZFRUWpuLhYkZGRVzucgPb+1jz94u+7lBwfqU8ev9Xb3QEAwKXPb7dew5Kdna2CggKNGDHCuS4qKkqpqanavHlzvfuUl5dr+/btdfaxWCwaMWJEg/uUlZXJZrPVWdC4kb1jFWQxtC/fpsPHS7zdHQAAXOLWwFJQUCBJio2NrbM+NjbWue1iJ06cUFVVlUv7ZGRkKCoqyrkkJia6ofeBLTo8RLd0rykLcfEtAMDP+OUsodmzZ6u4uNi55OXlebtLfsE5W4jrWAAAfsatgSUuLk6SVFhYWGd9YWGhc9vFYmJiZLVaXdonNDRUkZGRdRZc3shecQq2Gvq24IwOFZ3xdncAALhibg0s119/veLi4rRmzRrnOpvNpq+++kqDBw+ud5+QkBANGDCgzj52u11r1qxpcB9cnajwYN3avYMkafmu+sttAAD4IpcDS0lJiTIzM5WZmSmp+kLbzMxM5ebmyjAMzZw5U7/85S/18ccfKysrS1OmTFFCQoJzJpEkDR8+3DkjSJJmzZqlP//5z3rzzTe1b98+PfrooyotLdWDDz7Y5AGirnSeLQQA8ENBru6wbds2DRs2zPl61qxZkqSpU6dq0aJF+sUvfqHS0lI98sgjOn36tG655RatXLlSYWFhzn0OHz6sEydOOF/fe++9On78uJ577jkVFBSoX79+Wrly5SUX4qLpRvSKVYjVogOFJTpQeEY9YiO83SUAAC6rSfdh8RXch8U1097cqs/2Fenx4d318zt7eLs7AIAWymv3YYF/SO9zYbZQAORVAEALQGBpgUYkxyokyKJDRSU6UMhN5AAAvo/A0gJFhAXrNudsIS6+BQD4PgJLCzW2piy0jLIQAMAPEFhaqOHJHRUSZNF3x0v1bQE3kQMA+DYCSwsVERasoT2qy0IruFU/AMDHEVhaMOdsoV2UhQAAvo3A0oINT45VaJBF350o1b58ykIAAN9FYGnB2oQGaVhSR0ncqh8A4NsILC0cZSEAgD8gsLRwd/TsqLBgi46cPKs9x2ze7g4AAPUisLRwrUODdEdPR1mI2UIAAN9EYIHSUxIkURYCAPguAgs0rGcHtQq2Kvf7s9r9T8pCAADfQ2CBwkOCdEdydVloGbOFAAA+iMACSdLYFGYLAQB8F4EFkqShSR0VHmLV0VPntOtosbe7AwBAHQQWSJJahVg1PDlWErOFAAC+h8ACp3TKQgAAH0VggdPQpA5qHWLVP0+fU2beaW93BwAAJwILnMKCa5WFdlEWAgD4DgIL6nA8W2hFVr7sdspCAADfQGBBHbf3qC4LHSs+r52UhQAAPoLAgjrCgq26s1d1WWgFs4UAAD6CwIJLpPepfrYQZSEAgK8gsOASt3aPUURokPKLz2tn3ilvdwcAAAILLlW7LLSM2UIAAB9AYEG9mC0EAPAlBBbU65buMYoIC1KhrUzbcykLAQC8i8CCeoUGWTWyV5wkbiIHAPA+AgsaNLZWWaiKshAAwIsILGjQzd1iFBkWpKIzZdp25HtvdwcA0IIRWNCgkCCL0nrXlIW4iRwAwIsILGjUhdlCBZSFAABeQ2BBo27uFqOoVsE6UVKmr7MpCwEAvMPtgaVz584yDOOSZfr06fW2X7Ro0SVtw8LC3N0tXKVgq0WjnGWhY17uDQCgpXJ7YNm6davy8/Ody+rVqyVJEydObHCfyMjIOvvk5OS4u1toAkdZaOXuAlVW2b3cGwBASxTk7gN26NChzutf//rX6tq1q26//fYG9zEMQ3Fxce7uCtxkcNf2ig4P1omScn2d/b2GdIvxdpcAAC2MR69hKS8v11tvvaWHHnpIhmE02K6kpETXXXedEhMTNX78eO3Zs6fR45aVlclms9VZ4Dm1y0LLmC0EAPACjwaWpUuX6vTp03rggQcabJOUlKQFCxboo48+0ltvvSW73a4hQ4bo6NGjDe6TkZGhqKgo55KYmOiB3qM2ykIAAG8yTNP02FzVtLQ0hYSE6P/+7/+ueJ+KigolJydr0qRJeumll+ptU1ZWprKyMudrm82mxMREFRcXKzIyssn9xqUqq+wa+KvPdOpshd76Sapu6U5ZCADQNDabTVFRUVf0+e2xb1hycnL02Wefadq0aS7tFxwcrP79++vQoUMNtgkNDVVkZGSdBZ4VZLVo1A3V37JwEzkAQHPzWGBZuHChOnbsqPT0dJf2q6qqUlZWluLj4z3UM1ytsc6yUD5lIQBAs/JIYLHb7Vq4cKGmTp2qoKC6E5GmTJmi2bNnO1+/+OKLWrVqlb777jvt2LFDP/7xj5WTk+PyNzPwvNTr26l96xCdOluhzd+d9HZ3AAAtiEcCy2effabc3Fw99NBDl2zLzc1Vfv6FksKpU6f08MMPKzk5WWPGjJHNZtOmTZvUq1cvT3QNTVBdFqq5idwuykIAgObj0Ytum4srF+2gaTYdPqEf/fkrRYcHa+t/jlCwlac7AACujk9cdIvAlHp9e8W0CdHpsxXadJiyEACgeRBY4BKrxdBox2yhXTxbCADQPAgscJnjJnKf7ilUeSWzhQAAnkdggcsGdm6nDhGhKj5XoY2HT3i7OwCAFoDAApdZLYbGMFsIANCMCCy4Kul9EiRJn+4poCwEAPA4Aguuyk3XtVXHiFCdOV+pDYeOe7s7AIAAR2DBVbFYDI1Jqb74dhllIQCAhxFYcNUcs4VW7ylUWWWVl3sDAAhkBBZctQHXtlVsZKjOlFXqywPMFgIAeA6BBVetdlloeRZlIQCA5xBY0CRjHWWhvYU6X0FZCADgGQQWNEn/xLaKjwpTSVml1h9gthAAwDMILGiS2mWhFZSFAAAeQmBBk6VTFgIAeBiBBU3WPzFa10S3Uml5ldZRFgIAeACBBU1mGIbGpPBsIQCA5xBY4BaOZwt9to+yEADA/QgscIu+naJ0TXQrnS2v0tr9Rd7uDgAgwBBY4BaGYTjvycKzhQAA7kZggds4Zgut2Vekc+WUhQAA7kNggdukXBOlxHatdK6iSl9QFgIAuBGBBW5jGIbSU6ovvmW2EADAnQgscCvHdSxrvi3U2fJKL/cGABAoCCxwq94JkbqufbjOV9j1+beUhQAA7kFggVtV30Su+lsWykIAAHchsMDt0msCy+ffFqm0jLIQAKDpCCxwu94JkercPlxllXatoSwEAHADAgvczjAM5z1Zlu865uXeAAACAYEFHuGY3vzF/uMqoSwEAGgiAgs8Ijk+Ql1iWqu80q41+wq93R0AgJ8jsMAj6paFmC0EAGgaAgs8xhFY1h44rjPnK7zcGwCAPyOwwGOSYiPUtYOjLMRsIQDA1SOwwGOqy0LVF98uoywEAGgCtweWOXPmyDCMOkvPnj0b3eeDDz5Qz549FRYWppSUFK1YscLd3YKXOJ4ttP7AcdkoCwEArpJHvmHp3bu38vPzncuGDRsabLtp0yZNmjRJP/nJT7Rz505NmDBBEyZM0O7duz3RNTSzHrER6t6xjcqr7PpsL7OFAABXxyOBJSgoSHFxcc4lJiamwbZ/+MMfNGrUKD311FNKTk7WSy+9pBtvvFFz5871RNfgBcwWAgA0lUcCy8GDB5WQkKAuXbpo8uTJys3NbbDt5s2bNWLEiDrr0tLStHnz5gb3KSsrk81mq7PAdzmeLbT+4HEVn6MsBABwndsDS2pqqhYtWqSVK1dq/vz5ys7O1q233qozZ87U276goECxsbF11sXGxqqgoKDB98jIyFBUVJRzSUxMdOsY4F7dYyOUFBuhiipTqykLAQCugtsDy+jRozVx4kT16dNHaWlpWrFihU6fPq3333/fbe8xe/ZsFRcXO5e8vDy3HRuewbOFAABN4fFpzdHR0erRo4cOHTpU7/a4uDgVFtb9V3dhYaHi4uIaPGZoaKgiIyPrLPBtY2rKQl8ePKHis5SFAACu8XhgKSkp0eHDhxUfH1/v9sGDB2vNmjV11q1evVqDBw/2dNfQjLp1bKOecRGqtJv6dG/D5T4AAOrj9sDy5JNPat26dTpy5Ig2bdqku+66S1arVZMmTZIkTZkyRbNnz3a2f/zxx7Vy5Ur97ne/07fffqs5c+Zo27ZtmjFjhru7Bi9zXHzLbCEAgKvcHliOHj2qSZMmKSkpSffcc4/at2+vLVu2qEOHDpKk3Nxc5edf+MAaMmSIFi9erDfeeEN9+/bVhx9+qKVLl+qGG25wd9fgZWNqrmPZeOiETpWWe7k3AAB/YpimaXq7E01ls9kUFRWl4uJirmfxcaP/8KX25dv08t0punfgtd7uDgDAi1z5/OZZQmhWjlv182whAIArCCxoVo7ZQpsOn9T3lIUAAFeIwIJmdX1Ma/VOiFSV3dSne5gtBAC4MgQWNDvHTeRWZFEWAgBcGQILml16rbLQyZIyL/cGAOAPCCxodte1b62Ua6JqykI8WwgAcHkEFniF89lCWTxbCABweQQWeIWjLLT58EmdoCwEALgMAgu8IrFduPp2ipLdlFbuZrYQAKBxBBZ4jbMsxE3kAACXQWCB1zhuIvdV9kkVnTnv5d4AAHwZgQVe06ltuPolRstuSp9SFgIANILAAq/i2UIAgCtBYIFXja4pC3195HsV2SgLAQDqR2CBV10T3Ur9r42WaUqfUBYCADSAwAKvc9yThdlCAICGEFjgdY7ZQltzvldBMWUhAMClCCzwuoToVhpwXduashDfsgAALkVggU+gLAQAaAyBBT7BURbalnNK+cXnvNwbAICvIbDAJ8RFhWlg57aSpBVZzBYCANRFYIHPcJSFVmRRFgIA1EVggc8YnRIvw5C255zSsdOUhQAAFxBY4DNiI8M0sHM7SXzLAgCoi8ACn+J4ttByAgsAoBYCC3zKqBviZBjSztzTOnrqrLe7AwDwEQQW+JSOEWFKvb66LPQJs4UAADUILPA56X0SJEnLKAsBAGoQWOBzRvWOk8WQvsk7rbzvKQsBAAgs8EEdIkL1L13aS2K2EACgGoEFPimd2UIAgFoILPBJaTVloV1Hi5V7krIQALR0BBb4pJg2oRrctbosxLcsAAACC3xWekr1bKHlWce83BMAgLcRWOCz0nrHymoxtPufNh05Uert7gAAvMjtgSUjI0MDBw5URESEOnbsqAkTJmj//v2N7rNo0SIZhlFnCQsLc3fX4GfatwnVEMpCAAB5ILCsW7dO06dP15YtW7R69WpVVFRo5MiRKi1t/F/IkZGRys/Pdy45OTnu7hr8UHpKzWyhXQQWAGjJgtx9wJUrV9Z5vWjRInXs2FHbt2/Xbbfd1uB+hmEoLi7O3d2Bn0vrHaf/XLpbe/Nt+u54ibp0aOPtLgEAvMDj17AUFxdLktq1a9dou5KSEl133XVKTEzU+PHjtWfPngbblpWVyWaz1VkQmNq2DtHN3WIkcRM5AGjJPBpY7Ha7Zs6cqZtvvlk33HBDg+2SkpK0YMECffTRR3rrrbdkt9s1ZMgQHT16tN72GRkZioqKci6JiYmeGgJ8wFhHWYiHIQJAi2WYpml66uCPPvqoPvnkE23YsEGdOnW64v0qKiqUnJysSZMm6aWXXrpke1lZmcrKypyvbTabEhMTVVxcrMjISLf0Hb7j9Nly3fTLz1RpN7XmidvVlbIQAAQEm82mqKioK/r89tg3LDNmzNCyZcv0xRdfuBRWJCk4OFj9+/fXoUOH6t0eGhqqyMjIOgsCV3R4iG7pXlMW4uJbAGiR3B5YTNPUjBkztGTJEn3++ee6/vrrXT5GVVWVsrKyFB8f7+7uwU85ZwtxHQsAtEhuDyzTp0/XW2+9pcWLFysiIkIFBQUqKCjQuXPnnG2mTJmi2bNnO1+/+OKLWrVqlb777jvt2LFDP/7xj5WTk6Np06a5u3vwUyN7xSnYaujbgjM6VHTG290BADQztweW+fPnq7i4WEOHDlV8fLxzee+995xtcnNzlZ9/4V/Kp06d0sMPP6zk5GSNGTNGNptNmzZtUq9evdzdPfipqPBg3dq9gyRp+S4uvgWAlsajF902F1cu2oH/+vv2o3rig2/UI7aNVv38dm93BwDQRD5x0S3gbiN6xSrEatGBwhIdKKQsBAAtCYEFfiOqVbBu61E9W4hb9QNAy0JggV9J73NhtlAAVDMBAFeIwAK/Mjy5uix0qKhEBwpLvN0dAEAzIbDAr0SGBeu2Ho7ZQse83BsAQHMhsMDvjK0pCy2jLAQALQaBBX5neHJHhQRZ9N3xUn1bwGwhAGgJCCzwOxFhwRrqLAsxWwgAWgICC/wSs4UAoGUhsMAvDU+OVWiQRdknSrU33+bt7gAAPIzAAr/UJjRIw5I6SqIsBAAtAYEFfstRFlpBWQgAAh6BBX7rjp4dFRZs0ZGTZ7XnGGUhAAhkBBb4rdahQbqjZ01ZKIuyEAAEMgIL/Fp6SoKk6utYKAsBQOAisMCvDevZQa2Crcr9/qx2/5OyEAAEKgIL/Fp4SJDuSK4uCy3L4tlCABCoCCzwe2NTam4iR1kIAAIWgQV+b2hSR4WHWHX01DntOlrs7e4AADyAwAK/1yrEquHJsZKYLQQAgYrAgoCQnhInibIQAAQqAgsCgqMs9M/T55SZd9rb3QEAuBmBBQEhLNiqEY6yEM8WAoCAQ2BBwKj9bCG7nbIQAAQSAgsCxu09Oqh1iFXHis9rJ2UhAAgoBBYEjLBgq+7sRVkIAAIRgQUBJb1P9bOFKAsBQGAhsCCg3No9RhGhQSqwndeO3FPe7g4AwE0ILAgotctCyygLAUDAILAg4DhmC32ym7IQAAQKAgsCzi3dYxQRFqRCW5m2UxYCgIBAYEHACQ2yamSvC7fqBwD4PwILAtLYWjeRq6IsBAB+j8CCgHRztxhFhgWp6EyZth353tvdAQA0UZC3OwB4QkiQRWm94/TB9qNanpWv1C7tvd0lwLeZpmTaJXulZK+q+bOy1rra66sks9bPjvVmrf3stfYzq6qPY1gkGdV/Ohej7p+XbK/dpubnBo9hqXUM4zLba2+r5z3rfQ/Dm2eoxfNYYJk3b55++9vfqqCgQH379tUf//hHDRo0qMH2H3zwgZ599lkdOXJE3bt318svv6wxY8Z4qntoAdL7xOuD7Ue1IqtAz4/rLauFv2yg6g/m2h++ZlXdD+LGPqgv++Fd6wO8vnV1PtSrLnq/qkbWNRYW6uvDRWGh3nUXHcOs8vaZ8Q+NBprGgpeLocutweziPxsagy56fVEba5A08pde+0/vkcDy3nvvadasWXrttdeUmpqqV199VWlpadq/f786dux4SftNmzZp0qRJysjI0NixY7V48WJNmDBBO3bs0A033OCJLqIFuLlbjKJaBetESZm+zv5eg7v6+LcspinTtMusqpJpVsm0Vy92e5VMu12q+Vn2qup2Na+NqurX9poPHsc2RzvZ7TJr1qvmmLLbne8hu10yq5zbqz/8ah1DjvX2mg+66n3laO/8+cJ2mVUyao5pmFWS3axpV93WcLZztLVfWO/42V4lyS6j1nqLWSnDXlWzrlJGzftYzKqa9Q0vFntl9X6ye/tM+x27YZUMi+xGkPNP02KVaVxYZFhlt1hl1mojwyK7Jah6uwxJZvW5lllzvk1Jdsm8sP7i7YZq/j+RWd3ecQ5NObdV76OaPx3HNWvOt2OfC386118N09GflqfCCFGwFwOLYZqm269ITE1N1cCBAzV37lxJkt1uV2Jioh577DE988wzl7S/9957VVpaqmXLljnX/cu//Iv69eun11577ZL2ZWVlKisrc7622WxKTExUcXGxIiMj3TeQqkpp1X+673hodhsOHdfBwhK1CbEoPMQii2mXoSoZpimL7NUfdKr+2aj1s7XmL77qNtV/QVpMu6yq+blmcRyn7nrTud1i2mttN2W9qI1jP6vsshpcHOxtFaZVVbLULFZV1vq5ShZVmRZV1pzFSl3a1i6LKmsd40LbWseQVZWm5ZJj1GlrWi897sVtzYuPW0/fzIb6ULvN5fpb86/sgGQ6fxcNqebPuusM5++r6dxmXLyPUXe74ziO7Y79a7+fo/2FfUwZxoVj135Po7F9nH9e2G4xLu5r7T7V3td+SR/rtDHq7iOLVTNeXOjWM2Cz2RQVFXVFn99u/4alvLxc27dv1+zZs53rLBaLRowYoc2bN9e7z+bNmzVr1qw669LS0rR06dJ622dkZOiFF15wW58bZNqlry4NTPAft0i6JUiSXdJ5L3TAzX/P283qv2KqLkSmBn6uXqpq/gqq3cZ+URvTuLDdrL3NqNVGhvO1WWubKYtMozp6OdsYVudfn3Ico+Zf2PaatjIM2Q1rTRtDZs3PpnHhmNU/W50/22VVleGIfHV/ths1H7CGY9xW5/Gr29X+2fFhXHe7adQ/B+FK/0lnXuG/2K/8eC5opLHjL/kQebePpmnKMAznr4RhSIYM5++IoQuXiBgy6lwy4ngt575GnX2MWutrH9+xxdGmvuM5+1HT3Kjn+Bd+vnB81dqvbh8uPf7F/ai3Xa33vbhfl+xXq3+X60ed9fWO6+I+XNoPR7sgL5fV3R5YTpw4oaqqKsXGxtZZHxsbq2+//bbefQoKCuptX1BQUG/72bNn1wk4jm9Y3M6wSLc+4f7jolkV2s6rtNyULBYZhkWmpfrraxkWyWKRjKDq38o666t/NgxDsgRVt5NFhsVas626jeH42bDKsNRsr/nZcQxLzc+GESRZjJpjBFX/5WcJkmGxyjAsktXq/Ll6vUWGYZWs1ccwavaxGIbzL6Bgw1BIrb9kLRf9ZQMAgcIvZwmFhoYqNDTU829kDZKGP+f594FHxV6+CQDAx7n9PiwxMTGyWq0qLCyss76wsFBxcXH17hMXF+dSewAA0LK4PbCEhIRowIABWrNmjXOd3W7XmjVrNHjw4Hr3GTx4cJ32krR69eoG2wMAgJbFIyWhWbNmaerUqbrppps0aNAgvfrqqyotLdWDDz4oSZoyZYquueYaZWRkSJIef/xx3X777frd736n9PR0vfvuu9q2bZveeOMNT3QPAAD4GY8ElnvvvVfHjx/Xc889p4KCAvXr108rV650Xlibm5tbfSFijSFDhmjx4sX6f//v/+k//uM/1L17dy1dupR7sAAAAEkeug9Lc3NlHjcAAPANrnx+8/BDAADg8wgsAADA5xFYAACAzyOwAAAAn0dgAQAAPo/AAgAAfB6BBQAA+DwCCwAA8Hl++bTmiznufWez2bzcEwAAcKUcn9tXcg/bgAgsZ86ckSQlJiZ6uScAAMBVZ86cUVRUVKNtAuLW/Ha7XceOHVNERIQMw3DrsW02mxITE5WXlxeQt/0P9PFJgT9Gxuf/An2MgT4+KfDH6KnxmaapM2fOKCEhoc4zBusTEN+wWCwWderUyaPvERkZGZD/EzoE+vikwB8j4/N/gT7GQB+fFPhj9MT4LvfNigMX3QIAAJ9HYAEAAD6PwHIZoaGhev755xUaGurtrnhEoI9PCvwxMj7/F+hjDPTxSYE/Rl8YX0BcdAsAAAIb37AAAACfR2ABAAA+j8ACAAB8HoEFAAD4PAILAADweQQWSfPmzVPnzp0VFham1NRUff311422/+CDD9SzZ0+FhYUpJSVFK1asaKaeXh1Xxrdo0SIZhlFnCQsLa8beumb9+vUaN26cEhISZBiGli5detl91q5dqxtvvFGhoaHq1q2bFi1a5PF+NoWrY1y7du0l59AwDBUUFDRPh12UkZGhgQMHKiIiQh07dtSECRO0f//+y+7nL7+HVzM+f/o9nD9/vvr06eO8A+rgwYP1ySefNLqPv5w7B1fH6E/nrz6//vWvZRiGZs6c2Wi75j6PLT6wvPfee5o1a5aef/557dixQ3379lVaWpqKiorqbb9p0yZNmjRJP/nJT7Rz505NmDBBEyZM0O7du5u551fG1fFJ1bdezs/Pdy45OTnN2GPXlJaWqm/fvpo3b94Vtc/OzlZ6erqGDRumzMxMzZw5U9OmTdOnn37q4Z5ePVfH6LB///4657Fjx44e6mHTrFu3TtOnT9eWLVu0evVqVVRUaOTIkSotLW1wH3/6Pbya8Un+83vYqVMn/frXv9b27du1bds23XHHHRo/frz27NlTb3t/OncOro5R8p/zd7GtW7fq9ddfV58+fRpt55XzaLZwgwYNMqdPn+58XVVVZSYkJJgZGRn1tr/nnnvM9PT0OutSU1PNn/70px7t59VydXwLFy40o6Kimql37iXJXLJkSaNtfvGLX5i9e/eus+7ee+8109LSPNgz97mSMX7xxRemJPPUqVPN0id3KyoqMiWZ69ata7CNv/0e1nYl4/Pn30PTNM22bduaf/nLX+rd5s/nrrbGxuiv5+/MmTNm9+7dzdWrV5u33367+fjjjzfY1hvnsUV/w1JeXq7t27drxIgRznUWi0UjRozQ5s2b691n8+bNddpLUlpaWoPtvelqxidJJSUluu6665SYmHjZf0X4G386f03Vr18/xcfH684779TGjRu93Z0rVlxcLElq165dg238+Txeyfgk//w9rKqq0rvvvqvS0lINHjy43jb+fO6kKxuj5J/nb/r06UpPT7/k/NTHG+exRQeWEydOqKqqSrGxsXXWx8bGNljvLygocKm9N13N+JKSkrRgwQJ99NFHeuutt2S32zVkyBAdPXq0ObrscQ2dP5vNpnPnznmpV+4VHx+v1157TX//+9/197//XYmJiRo6dKh27Njh7a5dlt1u18yZM3XzzTfrhhtuaLCdP/0e1nal4/O338OsrCy1adNGoaGh+tnPfqYlS5aoV69e9bb113Pnyhj97fxJ0rvvvqsdO3YoIyPjitp74zwGeezI8EuDBw+u86+GIUOGKDk5Wa+//rpeeuklL/YMVyopKUlJSUnO10OGDNHhw4f1+9//Xn/729+82LPLmz59unbv3q0NGzZ4uysecaXj87ffw6SkJGVmZqq4uFgffvihpk6dqnXr1jX4ge6PXBmjv52/vLw8Pf7441q9erVPXxzcogNLTEyMrFarCgsL66wvLCxUXFxcvfvExcW51N6brmZ8FwsODlb//v116NAhT3Sx2TV0/iIjI9WqVSsv9crzBg0a5PMhYMaMGVq2bJnWr1+vTp06NdrWn34PHVwZ38V8/fcwJCRE3bp1kyQNGDBAW7du1R/+8Ae9/vrrl7T1x3MnuTbGi/n6+du+fbuKiop04403OtdVVVVp/fr1mjt3rsrKymS1Wuvs443z2KJLQiEhIRowYIDWrFnjXGe327VmzZoGa5ODBw+u016SVq9e3Wgt01uuZnwXq6qqUlZWluLj4z3VzWblT+fPnTIzM332HJqmqRkzZmjJkiX6/PPPdf311192H386j1czvov52++h3W5XWVlZvdv86dw1prExXszXz9/w4cOVlZWlzMxM53LTTTdp8uTJyszMvCSsSF46jx67nNdPvPvuu2ZoaKi5aNEic+/eveYjjzxiRkdHmwUFBaZpmub9999vPvPMM872GzduNIOCgsz//u//Nvft22c+//zzZnBwsJmVleWtITTK1fG98MIL5qeffmoePnzY3L59u3nfffeZYWFh5p49e7w1hEadOXPG3Llzp7lz505TkvnKK6+YO3fuNHNyckzTNM1nnnnGvP/++53tv/vuOzM8PNx86qmnzH379pnz5s0zrVaruXLlSm8N4bJcHePvf/97c+nSpebBgwfNrKws8/HHHzctFov52WefeWsIjXr00UfNqKgoc+3atWZ+fr5zOXv2rLONP/8eXs34/On38JlnnjHXrVtnZmdnm7t27TKfeeYZ0zAMc9WqVaZp+ve5c3B1jP50/hpy8SwhXziPLT6wmKZp/vGPfzSvvfZaMyQkxBw0aJC5ZcsW57bbb7/dnDp1ap3277//vtmjRw8zJCTE7N27t7l8+fJm7rFrXBnfzJkznW1jY2PNMWPGmDt27PBCr6+MYwrvxYtjTFOnTjVvv/32S/bp16+fGRISYnbp0sVcuHBhs/fbFa6O8eWXXza7du1qhoWFme3atTOHDh1qfv75597p/BWob2yS6pwXf/49vJrx+dPv4UMPPWRed911ZkhIiNmhQwdz+PDhzg9y0/Tvc+fg6hj96fw15OLA4gvn0TBN0/Tc9zcAAABN16KvYQEAAP6BwAIAAHwegQUAAPg8AgsAAPB5BBYAAODzCCwAAMDnEVgAAIDPI7AAAACfR2ABAAA+j8ACAAB8HoEFAAD4vP8P/U+EHiNnFa4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(history.history)[['accuracy','val_accuracy']].plot()\n",
    "plt.title(\"Accuracy\")\n",
    "plt.show()\n",
    "\n",
    "pd.DataFrame(history.history)[['loss','val_loss']].plot()\n",
    "plt.title(\"Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 15ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxoAAAEOCAYAAAAOmGH2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9ebBnaV3f/3rOfs53vXt33963me5ZEWZYZwZZREVEEAETLcBYLkGxKhUlxgoKhqQkVWoVETWmhERKY1QQNWpARVEWHWBg9p7pvXv6dt/9u5z9PM/z++PmOXPunQGGZBDG3/dddeve+/2e5dnOc96fXWitNRNMMMEEE0wwwQQTTDDBBE8jrK93AyaYYIIJJphgggkmmGCCf3qYCBoTTDDBBBNMMMEEE0wwwdOOiaAxwQQTTDDBBBNMMMEEEzztmAgaE0wwwQQTTDDBBBNMMMHTjomgMcEEE0wwwQQTTDDBBBM87ZgIGhNMMMEEE0wwwQQTTDDB046JoDHBBBNMMMEEE0wwwQQTPO2YCBoTTDDBBBNMMMEEE0wwwdOOiaAxwQQTTDDBBBNMMMEEEzztmAgaEzwlfOADH0AIwfnz57/eTZlgggn+kfDiF7+YF7/4xU/b9c6fP48Qgg984AP1Zz/3cz+HEOJpu8cEE0zwzMTTvd9M8I2BiaDxj4Rz587xYz/2Yxw/fpwoioiiiJMnT/LWt76Ve++99+vdvAkmmOAZBCP4m58gCDh+/Dg/9mM/xrVr177ezZtgggn+f4L77ruP173udRw4cIAgCFhcXOTlL385733ve/+fr33lyhV+7ud+ji984Qv/7w2d4OsGobXWX+9G/FPHn/zJn/CGN7wBx3H45//8n3PLLbdgWRYPP/wwH/rQh7hw4QLnzp3jwIEDX++mfklIKSnLEt/3J9rHCSb4OuMDH/gAb3nLW3jXu97FoUOHyLKMv/u7v+O3fuu3OHDgAPfffz9RFP0/36coCgA8z/t/vhZsWTQOHTrE+9//ft785jcDUFUVVVURBMHTco8JJpjgHwef+tSn+OZv/mb279/Pm970Jnbt2sWlS5f4zGc+w5kzZzh9+vRXdT1jzfjrv/5rAD772c9y2223bdsvJnjmwfl6N+CfOs6cOcMb3/hGDhw4wF/+5V+ye/fubd//wi/8Au973/uwrG9s45Jt29i2/fVuxgQTTNDAt33bt/Gc5zwHgB/8wR9kZmaGX/zFX+QjH/kI3/u93/t/fd0kSYii6GkTML4cHMfBcSavogkmeKbh3e9+N71ej7vvvpt+v7/tu+Xl5a9Poyb4hsM3Nrv9J4D3vOc9xHHM+9///icIGbD1kn3b297Gvn37ALj33nt585vfzOHDhwmCgF27dvEDP/ADrK2tbTvvzW9+MwcPHnzC9Z7M3/ljH/sYL3rRi+j3+7Tbba677jr+7b/9t9uOee9738sNN9xAFEVMTU3xnOc8h9/+7d+uv3+yGI2PfOQjvPKVr2TPnj34vs+RI0f4+Z//eaSU26794he/mBtvvJEHH3yQb/7mbyaKIhYXF3nPe97zlMZwggkmeGp4yUteAmy5agJ88IMf5NnPfjZhGDI9Pc0b3/hGLl26tO0c83x+7nOf48477ySKonp/eDKf6eXlZf7Fv/gXLCwsEAQBt9xyC//tv/23J7Rlc3OTN7/5zfR6Pfr9Pm9605vY3Nx8wnFfKkbjgx/8ILfffnu9J91555189KMf/b8ZlgkmmOBrgDNnznDDDTc8QcgAmJ+fr/9+//vfz0te8hLm5+fxfZ+TJ0/yq7/6q1/22n/913/NbbfdBsBb3vKW2k20Gd81wTMDEzXS1xh/8id/wtGjR3nuc5/7lI7/2Mc+xtmzZ3nLW97Crl27eOCBB/gv/+W/8MADD/CZz3zmq3ZbeuCBB/iO7/gObr75Zt71rnfh+z6nT5/mk5/8ZH3Mb/zGb/C2t72N173udfzET/wEWZZx77338vd///f8s3/2z77ktT/wgQ/Qbrf5V//qX9Fut/mrv/or3vGOdzAcDvlP/+k/bTt2Y2ODb/3Wb+W1r30tr3/96/n93/993v72t3PTTTfxbd/2bV9VnyaYYIInx5kzZwCYmZnh3e9+N//u3/07Xv/61/ODP/iDrKys8N73vpc777yTe+65Zxs5WFtb49u+7dt44xvfyPd93/exsLDwpNdP05QXv/jFnD59mh/7sR/j0KFD/N7v/R5vfvOb2dzc5Cd+4icA0Frz6le/mr/7u7/jR37kRzhx4gQf/vCHedOb3vSU+vHOd76Tn/u5n+MFL3gB73rXu/A8j7//+7/nr/7qr/iWb/mW/7dBmmCCCZ4WHDhwgE9/+tPcf//93HjjjV/yuF/91V/lhhtu4Du/8ztxHIc//uM/5l/+y3+JUoq3vvWtT3rOiRMneNe73sU73vEOfuiHfog77rgDgBe84AVfk75M8DWEnuBrhsFgoAH9Xd/1XU/4bmNjQ6+srNQ/SZJorXX9u4nf+Z3f0YD+xCc+UX/2pje9SR84cOAJx/7sz/6sbk7rL/3SL2lAr6ysfMl2vvrVr9Y33HDDl+3L+9//fg3oc+fO1Z89WVt/+Id/WEdRpLMsqz+76667NKD/+3//7/VneZ7rXbt26e/+7u/+svedYIIJngjzPP7FX/yFXllZ0ZcuXdL/43/8Dz0zM6PDMNTnz5/Xtm3rd7/73dvOu++++7TjONs+N8/nr/3arz3hPnfddZe+66676v9/+Zd/WQP6gx/8YP1ZURT6+c9/vm6323o4HGqttf7DP/xDDej3vOc99XFVVek77rhDA/r9739//fnOPevRRx/VlmXp17zmNVpKua09SqmvbqAmmGCCrxk++tGPatu2tW3b+vnPf77+qZ/6Kf2///f/1kVRbDvuybjCK17xCn348OFtn+3cb+6+++4n7BcTPPMwcZ36GmI4HALQbref8N2LX/xi5ubm6p9f+ZVfASAMw/qYLMtYXV3lec97HgCf//znv+o2GK3lRz7yEZRSX/KYy5cvc/fdd39V1262dTQasbq6yh133EGSJDz88MPbjm2323zf931f/b/nedx+++2cPXv2q7rnBBNM8Dhe9rKXMTc3x759+3jjG99Iu93mwx/+MB/60IdQSvH617+e1dXV+mfXrl0cO3aMj3/849uu4/s+b3nLW77i/f70T/+UXbt2bYv/cF2Xt73tbYzHY/7mb/6mPs5xHH70R3+0Ps62bX78x3/8K97jD//wD1FK8Y53vOMJsWuTRBQTTPCNg5e//OV8+tOf5ju/8zv54he/yHve8x5e8YpXsLi4yB/90R/VxzW5wmAwYHV1lbvuuouzZ88yGAy+Hk2f4B8RE0Hja4hOpwPAeDx+wne//uu/zsc+9jE++MEPbvt8fX2dn/iJn2BhYYEwDJmbm+PQoUMA/1cP5Bve8AZe+MIX8oM/+IMsLCzwxje+kf/5P//nNqHj7W9/O+12m9tvv51jx47x1re+dZtr1ZfCAw88wGte8xp6vR7dbpe5ublamNjZ1r179z6BJExNTbGxsfFV92mCCSbYwq/8yq/wsY99jI9//OM8+OCDnD17lle84hU8+uijaK05duzYNoXG3NwcDz300BMCNRcXF59S4PeFCxc4duzYEwSAEydO1N+b37t3736CkuW66677ivc4c+YMlmVx8uTJr3jsBBNM8PXFbbfdxoc+9CE2Njb4h3/4B376p3+a0WjE6173Oh588EEAPvnJT/Kyl72MVqtFv99nbm6ujgObCBr/9DGJ0fgaotfrsXv3bu6///4nfGdiNnYWwHv961/Ppz71KX7yJ3+SW2+9lXa7jVKKb/3Wb90mHHwpzd7OQOwwDPnEJz7Bxz/+cf7X//pf/Pmf/zm/+7u/y0te8hI++tGPYts2J06c4NSpU/zJn/wJf/7nf84f/MEf8L73vY93vOMdvPOd73zS+2xubnLXXXfR7XZ517vexZEjRwiCgM9//vO8/e1vf4L15EtlrNKT7MoTTPB/jdtvv73OOtWEUgohBH/2Z3/2pM/eTgGgqXGcYIIJJvhq4Xket912G7fddhvHjx/nLW95C7/3e7/H933f9/HSl76U66+/nl/8xV9k3759eJ7Hn/7pn/JLv/RLX9LTYoJ/OpgIGl9jvPKVr+S//tf/yj/8wz9w++23f9ljNzY2+Mu//Eve+c538o53vKP+/NFHH33CsVNTU0+awcVoFJuwLIuXvvSlvPSlL+UXf/EX+Q//4T/wMz/zM3z84x/nZS97GQCtVos3vOENvOENb6AoCl772tfy7ne/m5/+6Z9+0vz2f/3Xf83a2hof+tCHuPPOO+vPTbabCSaY4OuHI0eOoLXm0KFDHD9+/Gm77oEDB7j33ntRSm2zahhXSVMLyKTzHo/H24SaU6dOPaW2K6V48MEHufXWW5+2tk8wwQT/ODDKj6WlJf74j/+YPM/5oz/6I/bv318fs9N988kwcZX8p4GJ69TXGD/1Uz9FFEX8wA/8wJNW7G1q9I3mcaeW/5d/+ZefcN6RI0cYDAbbqoovLS3x4Q9/eNtx6+vrTzjXvLzzPAd4Qupcz/M4efIkWmvKsnzSfj1ZW4ui4H3ve9+THj/BBBP84+G1r30ttm3zzne+8wn7idb6Cc/8U8W3f/u3c/XqVX73d3+3/qyqKt773vfSbre566676uOqqtqWwlJK+ZSqBX/Xd30XlmXxrne96wnazokFdIIJvnHw8Y9//EmfyT/90z8Ftlwln4wrDAYD3v/+93/F67daLYAnVapO8MzBxKLxNcaxY8f47d/+bb73e7+X6667rq4MrrXm3Llz/PZv/zaWZbF371663S533nkn73nPeyjLksXFRT760Y8+qZXgjW98I29/+9t5zWtew9ve9jaSJOFXf/VXOX78+Lag8Xe961184hOf4JWvfCUHDhxgeXmZ973vfezdu5cXvehFAHzLt3wLu3bt4oUvfCELCws89NBD/Of//J955StfWceZ7MQLXvACpqameNOb3sTb3vY2hBD81m/91oQITDDBNwCOHDnCv//3/56f/umf5vz583zXd30XnU6Hc+fO8eEPf5gf+qEf4l//63/9VV/3h37oh/j1X/913vzmN/O5z32OgwcP8vu///t88pOf5Jd/+Zfr/eJVr3oVL3zhC/k3/+bfcP78eU6ePMmHPvShp+SPffToUX7mZ36Gn//5n+eOO+7gta99Lb7vc/fdd7Nnzx7+43/8j191uyeYYIKnHz/+4z9OkiS85jWv4frrr6coCj71qU/xu7/7uxw8eJC3vOUtXLt2Dc/zeNWrXsUP//APMx6P+Y3f+A3m5+dZWlr6stc/cuQI/X6fX/u1X6PT6dBqtXjuc59bx61O8AzB1yPV1f8fcfr0af2jP/qj+ujRozoIAh2Gob7++uv1j/zIj+gvfOEL9XGXL1/Wr3nNa3S/39e9Xk9/z/d8j75y5YoG9M/+7M9uu+ZHP/pRfeONN2rP8/R1112nP/jBDz4hVeRf/uVf6le/+tV6z5492vM8vWfPHv293/u9+pFHHqmP+fVf/3V955136pmZGe37vj5y5Ij+yZ/8ST0YDOpjniy97Sc/+Un9vOc9T4dhqPfs2VOntgP0xz/+8fq4u+6660nT536pFL0TTDDBl4d5Hu++++4ve9wf/MEf6Be96EW61WrpVqulr7/+ev3Wt75Vnzp1qj7mSz2f5rtmukmttb527Zp+y1veomdnZ7Xnefqmm2560vSTa2tr+vu///t1t9vVvV5Pf//3f7++5557vmJ6W4Pf/M3f1M961rO07/t6ampK33XXXfpjH/vYl+3vBBNM8I+HP/uzP9M/8AM/oK+//nrdbre153n66NGj+sd//Mf1tWvX6uP+6I/+SN988806CAJ98OBB/Qu/8Av6N3/zN5/AKZ5sv/nIRz6iT548qR3HmaS6fYZCaD1RQU8wwQQTTDDBBBNMMMEETy8mMRoTTDDBBBNMMMEEE0wwwdOOiaAxwQQTTDDBBBNMMMEEEzztmAgaE0wwwQQTTDDBBBNMMMHTjomgMcEEE0wwwQQTTDDBBBM87ZgIGhNMMMEEE0wwwQQTTDDB046JoDHBBBNMMMEEE0wwwQQTPO2YCBoTTDDBBBNMMMEEE0wwwdOOp1wZ/Hf+5+8TTlVYuaRTzTBKU9Kug7ICelqjBIziDfrBDNZqztAe09vVQ7gh1bDC0mPi3KLQktILsEVMZFlo30f4IY4ewzjBHdtUbhc6FblSRJkg9hzcXOJVisS18DwFykImkplZl9WNAkJBEFR4KmQ82MBTHRKrQvku03lIWaQMHUVhKUIrxvZapMoiDDzaVKwVORKbaLPAsUuUJ8hlhRd4FIViYAk6gYOftqCskIwJIo/KcoidMaFqUxUWfmjjWgXjUUXf9lkuN8myil2pIGl3yDo2upSEuYMfgpp30Gs5hTWFTYoblKSxxstjfEtQpDmp71GJHMtymPFskrWE1uwMuSixnRK1KtlMbapQYVUlgZ6nokI5ksK3mHVcwtAlBMpKYNvTCF8SRxq/PYVVDWmLgMCP0KFPtygJ5gRlWuAUBZGeZZTFpGKTUkqyrOTa6hrxSoZjO+jxGCVdSl+yEXrYo5K+45NFimlPIIuEfFgROFOkjk2r3yW1RwxHQ3a7XVTaInUzHJ3ia4uhcAgcm7WNAZE/hUgrMt/CCUs6siAtQvpTPqNxgS3aFHoNR/toX+IFPskoo9P1cSqBm+UUPrjBFCO9ApZExi1CN2GUQYcp8AviLMWWLnNBSKwylBoThx5OFdCXFm5Lsbla4M+EZFIyHla0PIcsS5gJQzJLUso2ou0QDGNC4ZI5IDslXpmxvpYS+n2cIIC2QPk+o5UV2kWAqroU1pjC3qTnRURRyCYF016LaizZLCRhlhEFARtFitfp8O3f8s1fw23ha4fPfvaz+L6P53kEQYDneQghsCwLx3GwbRvbtgHQWmNZFmVZUhQFUkrSNKWqKhzHQSmFZVnYto1SCs/zsCwLKWV9fpZllGVJWZYkSYJSiqqqyLKMJEmwLAvLsvA8j1arRRiGdRuSJKGqKqSUDIdDiqKgKAriOKaqKgBc10UIQVmWOI6D7/sEQVD/DsMQrTVFUWBZFmma1v21bRvP8+r+CiG2fWauZ9s2WmvyPK/bUFXVtv5KKamqqr6G+cwcU5YlVVWhta6vbebAdV201mit63sC2+ZBa42Usr6/lBLXdQnDEM/zUEoB1P00x0spEUIghCDPc6qqqsfVHOd5Xt2uPM+xbXtbv3aOc1VVuK6L7/v1+ARBQFVVxHFcj1UTeZ7Xc18UBUIIHMfBsrZ0bZZl1d+XZUkcx6RpSlmW9RgIIQBQSuG6Lq7r0m636Xa79bibdppjh8MhSZKQJAllWdZrOoqi+jnwPA/f9wnDkE6ng1IKx3FwXbduv2mf1rpuJ1A/M5Zl1eOjta7XbVEU9fnNY01bd+/e/fQ82P+I+Iu/+It6voMgYDAYMBgMmJ+fx/d9hsMhsLV+i6LgwQcf5IYbbmAwGNDv9zl37hz79++n3++TZRntdpssy3j44Yfp9XoIIVhZWWFzc5Pbb7+dOI5ZXV1lcXGR8+fPs7CwQKvVwvd9BoMBURQxHo/ruYPHn5ndu3ezublZP/tKKcIwZDgcEoYh7Xab4XDIhQsXiKKI++67jzvvvJNer0ccxwBkWcbKygrtdhulFLt27SJNU1ZXV/E8j9nZWdI0Ze/evSwvL9frz7TN932mpqZIkoQ4jvF9v17XSql67Zq2CyEIw5Dl5WWKomB6ehohBGfOnOHQoUMopXjkkUe44YYbcByHoijq59dc44EHHuDkyZNcuHCBqakper0eKysrzMzM1Pe4ePEi+/fvZzQacc899/D85z+f8+fPc/jwYc6dO4cQggMHDrC2tka326XX63H+/Hnm5ua4cOEChw8frvcPIQRSSi5evMiePXsASNOUtbU19uzZQ7/f59q1a0xPT297zk0Zuf3797OyskKWZQRBwMbGBt1ul+FwyNLSEgcPHqyP7Xa7FEVR7/kXLlygqir27t3LhQsX2L9/f31clmU8+uijLC4u8thjj3H06FHm5uZYXl4mCAJWVlYAaLVaPPbYY8zOzuI4DkEQ1PO9ublJlmXb9pder8fm5mY9h5Zl8cADD/DCF76QtbU1Dh8+zNLSEisrK1y6dAkhBNdffz1xHPMXf/EXfPd3f3d9nmnzzMwMFy9eZPfu3QwGAwDa7Xb93lxZWSEMQ6IoIk1TgiBAa83rXve6r/jMPmVBo4OLk3WpKkGS5siWQFoxbaUohpLAt2nZLapMolsFtleSyph0UOJ2Q2zp4qic0BIsJxnR9BRlvIkvbawkxm75VLQZ2Wu47QSRZ4iwR+xobK3wnQTth7jtkHFRYJc2rpcz0CWZUzBXhQyGJWMHur7Aw6dIPapMEMsKl4Ru2GVUWminwMkLLEISVyCEwteaSkusqQjHDajyDFFW5MkmQWuemUohgFwXlEohog7SsbCthL4XUK4nZLKFn2kqYdH2IuKuQG+EuFXJOGwhPPAzReLnFJbFWLjY6yXzrofYLJC2j4eNnQ4R2kM5YPldrHRrQbuOi11p7JYmHSyTlxZuECCiFm7kojcG+D0HJ4/RQuIXFlE0C0mFV7iMK0k0FeLpklSkzLg2kOBIG6twkUIRigzwyRLwBbhehFQlsWXh0yFfT3FjRatskXiaMrTJKgutSjotH53EiCjA8QURHp4qSQOBFi5ltYmwIpQVIRJFVEaMxprQzwiqNVLPId/ceqHaXouuCOhNh2wOVnGrHEt45JFHZVckpYWuBJmX0eq3SbMCz4PKE9huSFmB0hC7AVkiicqKTruPKBKGpYdrBczqBB0XOLbE8hXKVWSqIFcFftDGSQtsWzAcV/iZwOu42JaDUgIRpli2QJUlietj41IVFVaRkFUJ/tQMmbbwUo3tWLjtWRzPx6eiKjRuAZ7VRVmSylEE2FhOH6tU6LQi6HaQQlHKMZ5yccKQ1HIRShKW3lN9bL/hYAiUbdsIIWjWCzUkypAmoCbQTdJqzjXfGzJqhAZDyAypbL5Azf2EEHU7mhu4lHLbMY7j1C9uI5CYF4wh9qYdzWNNfwwhNP+bH/OyU0rV/bEsC9d16+uY6xtBYGc/Tdsdx6nvb75XSiGl3EZCDREwbWxe03xmxtOMdbPtVVXVwo6Usr6WUqoe87Ist82t+dx8Z4i24zh1ex3HqYmK+R+or9s8xlzP3MN8X1VVvU6a9zbHZ1lWj4kZF9MuM3/meDMXRghprsnmHBnibtZJs01GMLEsq34hNwWw5ppsjq8RDJpj31xT5rdpv5k/00bTH9P/nWvPtLU5/s9EjMdjpJScOnWKEydO8Nhjj9HpdJBSkiQJRVGwb98+yrJkbm6O8XjMzMwMVVVx0003kSQJ4/EYy7LY2NggjmOOHDmCUoq1tTW01jz72c8GoNPpANDr9UiShKtXr3Lo0CHKsuQf/uEfeNaznkWaprTb7VqIHI1GXLp0qV43SikuX76MZVnMzc0xGAyYmZmpCefJkyf5/Oc/z4te9CI6nU5NLjudDisrKxw4cADP88iyrN4PjMB75syZWvCBrbW8vr7O+fPneeELX4iUkvF4TJqmALWyZDAYsHv3bi5evIjneRw5cqTe/65evcrMzAxpmpLnOb7vMzs7y6c+9Sle9KIXMT09TZ7ntcCf5zkPPvggi4uLzMzMMDMzQ6vVwnXdeg+cmpqqn83l5WVarRaO45AkCa985StJ05QjR46gtWZqaqpW0szNzRGGIQC7d+9GKcWJEyeQUmJZFkmS1MoGx3GI45goiuh0OkxNTXH+/Hk2NjY4c+YMx44dI4qiegwNzp07R5IktFotsiyr2zUYDNjY2ODQoUN0Oh2GwyFSSh566CEOHTpEkiREUVSvp3379rGxscHc3BxxHON5HidOnCCOYw4fPkyWZVy7do0zZ85w+PBhwjCs95ldu3bVe7fruhRFwenTp5mdnWVpaYldu3bV74YrV67gOA4zMzPcc8893HDDDfi+z+bmJhcvXmQ8HuP7PkVR8LznPa8WHHq9Ht/zPd9TC+EA+/btQwjB+vo67XabNE0Jw5DxeMz999/PoUOH6nUVRREAo9GIIAiYmpp6Ss/rU3adCiKHuBpSSkkr6qJLiT8AO8/wA4dCemRFQRXKLQKKQ55Bu+1sCQpak7slG5XGmw6QYgPLGSJCAW4H22rjygzbcxgPSiwdYuWC0PURlUbZIWvDEmeY0s5jbEZUnRJlhwShSyYLcjtkmElWK59RliMqSdeyGaQjck/g2hKHmFavQ+ZBGICbD/FVRkSA71hoH6zCRm0WpJmDa88RWIogV1iFQPsO4ZRDpWL8AJLBOm5i4QgbX+bIKkE7kqEcMh7leJlNp+PghBZhKMlcGzvxCDW0VY6db42V42cIsQappCwFmQupVeLg4uY2UeyisgocC1mBjHo4nS4akKWmKAZYgULgowObwBd0HYdqXKEthSAj8C20nZPqTRwlEVmBW0qKakzbT1F6TKfTwulYyFyg7QDlRoxLG6RgbZQysjzWbMidDD8SuKrE0g6+16HQPl5L4U8JqqzELnyKLKJMBNiCRAosGeI4Gu0JWg4EUw7CKyjReJWDPVURHuzhBAG5zClHMbZn47UsbMvFwqLjgXBDgm6IG45QyRBvTeLkFqoY0SozOgWkcU6r5eM6mnbfo0ollS3x+yOSbslqkVAGFQUa6Su0n0M1xLcdXDxa7R4qgGDWx4pskKCHLoXwabkRlrRw3Ta25SKlRtoZ3Z7eWvNZgTNO0akkTV20dAktF7eQFFmJKkYoLYkdSRUKBnqEyiV5nmGRE5QZ5UBi2S2UJ1AB0Krw2wKhrj3Vx/YbDq7r1oSoqSk3P00y2CSPZVluI9pNomUIWlEU5Hle/xhCvJM4ArXlwPO8WuBQSlEURf1yNYJAUysPjxM/Q/RNewyBNkJL80Vmzmv2uSxL8jwny7JaQ7ZT0AG2kVLz0ySLxjphNONBEBBFEWEY1tYV873neTXBbI5xk9TvtEY0hZomyW5aoMzYmvOaAtXOuTTCleu6dZubFqAoirZZhcwcmeObAt1OgbVJ3M0YF0VBlmVkWVa3oUm8m3NrrmH61/zeCHXNddSco6bFxMypsRI1LTDmGTD3Mf8322vWg/lpttuc2+zjzmeoOZfN/81nO5+HZxLMOHQ6nVr7vrCwUH9utPH33HMPWmsWFhbqvnY6nVowbz5XxhqitebKlSs8+9nPri1KxlI2GAy4+eab6Xa7dVsOHTpUW/fm5+dZXl7m85//PFVVMTc3B1CvzbIsuXr1KlmW0e12GY1GwJbW+MyZMzXRN+eYtbW4uFj38/Tp0/V1pqamsG2bI0eOsLCwsG0N+77P0aNHuXjxIlprHn74YZRSjEYjhsMh7XabxcVFwjDk2c9+NsePH2dpaYnl5WVc1+Uzn/kMSZLUYyKEII5jXvCCFzAcDutxvPfee9nY2MCyLG644YbacuP7PqPRiN27d9f7/d13313vKXNzc0xPT1OWJbt37yaOY1ZWVrhy5QqdTod2u11bJQeDAcPhEMuyaivSqVOnCMOQVqvF2bNnAYjjuLYuKqW4dOlSTbiXlpa47bbbmJqaYjweA7C2tsb6+jqWZW2zUhtFhuu6HDx4ENu26fV62xQOYRjW49Pv99m9e3f9zlhYWGBzc5OyLOs9F6jHXynFwYMHOXXqFEVREAQBQRDU748gCMiyjDRNOXDgAFEUYVlW3b4oihBC1JaF6elp1tfXmZmZYTQabVufRsg261wIUQuG5lkxe2xVVZw7d46iKGrh7fDhw/Vc+L6PEILZ2VmOHTvG4uJibVX5SnjKKg1RSjwPrLLFcFyAZ+FFLq2oxWiYga3wp1oUeYHjRRRFG6esyGSGxsW2LbQ9g+NIiDRe5WHlHYQDepyQxyW26yClRdgN0Bp0laGzFHucQjcgatvYwsaSHlIIqtUUtxsgRUHRBq/Q+G5A4WZIXeB7ARt5TtQGnSskLnY7IF0aEE15WJXCsXyGaUwnTBG6JE9LHG2TCChlRdDzKLMcWhZVUuB4fVI5QmcWaqgI2jOsVTG+cHB9GzwXrQsc6dIpA4RfsKlKWn6FshWCAns9w1ELOHbK1LTLSpnT6Tu4G2N0mmFNTTEuhgT0SPIYr2NTFZrYBb+ocMixdUCpbSrtUIw12nFJ7RxLVkgtCIVDZmmEvQlOQKoVhVR0xxpZlITRPNJysEobz+vgdlt0pWYkM6Rv4VU9tBxRWhWhDtBSQ9AiHxUUboXyHRzPRg8KykIQuAV5VRG6CvmYxKog7+eUVkBRhch4jCPaOK5DHg/I7ZKyyIh0hJVvkRlluTihQ56nOLYmCHLKMka7AVr7WwJNJSltiR1aeLHG81okeUnQ7xN7FZ5lg7aoULhOQV6t4ngO43KdouNQlhI9ANutmO70KZMMtEOS9vClQpUJ0tPkMiGQPjpJqQIb4Qksx0MPFO6mhSs8XN8hljluIrFbFlp56I2MVm+KNBXglwxkTMeJcAoYJAWW0nhhREWKcMDRIUka0/FAJjnduWm0UzJIS/KkoOXZ2H6EXWkoJI7v4k51v+Lz+o0KQ1SbRBTY9tts6OYzQ1TN3wZNjXXTTcRYAswLrikgGLJoyIa5jiEDTc18072nSUqBbcTWaOINCW5aEXZaMZqChCGPxtXJkFij+Tf9Ni+g5tiYsWy6NxlibD5rEtqyLGttvSGnZox2kvOmMNAUspqfN/vTPKcpSDXH1VgCmtYMQ2JM3zzPq7WUpq9PZoFoCnA7hVPTLyPYGBJuhKmmBWon2W7OsVknxhrRtJQYIrLT6tF0bWpaMJpCqVknO10FzXXM2jNCi/ne3HunJa9pkWqu3ycTqJpzYYjUMxGu6/LYY49x7tw5jh07Rp7nXL58maqqOHToEJcuXeLw4cPb1ky32922rpaXl1lYWODSpUvMzMwwPT1NkiQA3HDDDWRZVlsT8zyv3Wnm5uZotVr1fLVardpFcWVlhdnZWXq9HsATBNLDhw+zurrK2toa9957L69+9asZj8c1Yb/11lsJggApZa3x39zcpN/v47oum5ubNWktioKZmRmEEKytrTE7O7tt7vv9Pg8++CDXXXcdAAsLC1y5coX9+/fX6yCOY9bX12s3ql6vx9TUVE2ENzY22Lt3L61WC9gi5rt27aotPI7jsLCwUO9RxtVHCEG73eb+++9n7969tWvTN33TN3Hu3DmOHj3KlStX6PV6tFotVlZWaLVazMzMsLCwQJIkhGFY72+9Xo+qqhiPx7iuy+zsLEEQkOc54/GYAwcOUJYlZ8+e5ejRo3Q6He69917OnTvHy1/+cnq9HtPT0zWBn5ubQynF0aNHWVpaot1uc/XqVWZnZxFCcO7cOfbt24fv+0gpOXHiRC2A7dq1C6B2OTR9X1lZodvt1utFSkm73WZ5eZkHHniAF7zgBfVzfPnyZebm5ti9e3ftNjc9Pc3s7GwtwMzOzvLFL36Ro0ePMhwOOXToUL3vBEFQu1pNTU3RarVqV8KzZ88yPT1dr4FWq1Wva7M+jFAXRRFRFNV7pW3bHDhwoN6fsyxDSslgMCAMw3q/Xl1dreftqeIpqzTyXGFrn0qN0U6FJTxSHVJaHm6rg2/bOHGGq3xEZeE5EbgRZccnbDloy6GMMzp2SivLqFbXcUtFluXga/pTFkJY2I5NN3RBWLhOhO5EVJGFyhVhIYlVwqYsqDKJ67aRRYEmQKUCN1Y4Xki7iPF0gQp83NaWaUoFJaqdI9MYyxKoOAFdUegSpwjQ0iJwwS2hlBn4DlORTVaOSANB7CtKrXAziZc5tGxBrFIs2cUPumC3yYqQrPBZHUMlbLJyzDDPULpC5xKZedipwA1tMidFS0WxMiZdG5GNFLbjU1klutRUsUs50OA7CC+mlCOoIK08tIoYxSFlApbl44kIr1CErkehO6hCUmU2uQpxpiIyz0a5IYHXI6s6tFtzFJ5FjI3bDXG8kCr1CTOblrJRmQ+yxFMWlhIUQYZjldgppNUYhMaVmqqwcN2AIBRk1ggnsMjGHlEUYPVCdGSRE9PuuHT8DsKGXHhYbhs/dxEqQjs2VS9E9VuUToYUHhEhuaVBtLAcD1HZuIWPIzNyKjx8GMfYKsOyXCwrwmt5UGlKpaiUxaDMcAOJmyscqRFDQaQi5sI99IM+nrJQYkQVZghP4Ns5ORWJblPlLSyt0GVK5GkGaUJmKcZlShBJfD+mqAqgou9qbMvBthw8K8QuXfJxTq4qrMinRRtPaYQcU6UJlu2DUjhyCicL8DKNZbtYePhth1IWUGq0SJBRRehpQhKCdoC0MvIiZrNMn/ID/o2GndrjJjEz/zeJq0GT0BkCuVPTv5PIN+9piJ3RkhtXg52uLjtJt7GSmPgG89O8nrEemI17p2tYk4Tu7HfTGmPIoemf0Ywb60xTS23GonndJkk2FgAj2JkYAGM1MH8bdwNDipsuXTuFODMGBma8m65UO4WPpruOGXvzYzT8TYtF07VuJ5l/snVkiLkZw+Y4NoUP83kTzflsEnIzfmZOzW9jjTDWr51zaMbNtM8IKwa2bdcuHcZqY65n5qDZr51za8bcCMHmXmat7RSCm9f7pwSzlqMo4uzZs8RxTFEUHD58GMuyuPXWW4Gt2IYLFy7Uz39VVWxsbPDwww9z8uTJ+vlKkqTWbJ87dw7Ysj7cfffdrK2tATA7O0u320VrTavVqjXDhw4dYjgc1hpio0k/d+4cvu9z7dq12mXJaPJvvPFGXv7yl9eacYA77rijFkSLoqDVatVWA6VUvQdMTU1RliWzs7N4nkcURTXBN/dYWVlBSsnNN99MmqakaVqvOSOsGIuQIe1GgbC5uUmSJOzfv5+1tTWKouDixYsopbjpppvqZ7ndbtexa5/+9Kd58MEHKYqC8XhcCwn9fr92x3Ech2vXrnH69Ol6j9jc3ERrTb/fx/d9zp07ty3mzrbtbcJ2mqZEUUSWZVy8eJFr165x6tQpRqMRWZaxf/9+2u02ZVly9OhRXvaylzEYDLh69SqPPPIIly9f5rHHHuPee+/lvvvuY2VlhdXVVb7whS+we/du+v0+QmzFMgBsbm7yuc99jiRJyPOcM2fO8MUvfrGOASrLkjNnzqCUYvfu3fV8OY5TCzBKKebm5rh69Spra2s1mTfCr5lnE7tn4teuXr1KmqYkSVLv2WbdmXVi7mfWnmVZHD9+nDiOa3co4z6WpimWZZFlGX/zN3+DbdtMT0+zvLxMVVXcd999aL0V23b+/Pna2mYEV+N+BpAkCefPn/+q9pWnbNFQkUZh0+rllCNFVrYYS42fQrURI+wCu6cphCRSEWmZInxJpAuyIqPVarErgCKtUANN2+qR+yV2VDCOLQLhoz1NUUl0UmFXFo5KGKc5URkgPZ9cj7YeEgJ8W5NXkqJQVOWYWd8j1yUDpWkH0+SDBJnmkFWEvsRrR6zFKR07YCgL+p5DOSoIpjyqPEfjk1YCYXu43TbWKMayCnztoLTLuMop3YLIiqniMX7Lx3cEclzgKklaFGgdgBzRDiqEtql8FytXtLSNFbh4gN3ycApB4QgC2lhKMj0e0/J9qjzDsWy0ipmPNGkFKnQpsgrHcwgjiaMtytii6wZkVYnSGeOypNvuUVQJrlsSZwV51ML1YnQu8asOnhMi7RzZ7hJHDmE1wlIZFDZ+u0MsM1wtCLOSlufhBB5KC5QqKfMYkVdsxEPGdk6WKUIUMhmR5RqvLdFEiGKGtiMphkMKx8eS4NqSNM1RtsCOHYp4k2oKvFIRegGgcGwH23FwrII0rxCWR1HkBL2IltNilMREboWKLAZjEFpR+hXrjk/bLrFFxTDfJHS6BK6NTEuSQpK1WgShRCYljhB4qmJwdR0RKFzPR7otfFcxzlK6YYAoMmhpKtnDpwVUSCnod/vosGI8GKOtAjyJQjCWKa3AReUFQ1FC9X82RtVCewJdbtAXPjngRA4tUVF4ikoIqmwMVUYrajHtuojCoRKSqkxx7JCesHHckiDqI2XOhhpi+4K2H5IX+Zd/WL+B0SROTe10kyQ1LQDNoOCdJLapzW3GOjyZK4x5gcHjQc5lWdZEdqerU7Nthsw2yV8zmLrpNmWut9Mtx7TN3Bu2CwpNomvu2RQsdrp/mReAaYvR3pp+NjXg5rymv7ixVph2NPvcFKqeTJu+0xJirtG0ZDSP22nVaFo0mlaG5lg156dJ6JvuQ02rSrMN5jrNsTL3N+PTdP0y5+68/05rgBkbE1huBJhmzFBzzRkYYcNoyE27DGkz7Wpat3auy52CbvM+TQvgzufJWA9N/5uCzE7B65mC9fV1AObn59nc3MS2bW677bbaD31jYwPHcRiPx+zduxfHcXj44Yc5fPgwo9GodjU5f/48SimyLKs1vfv3768J3+LiIkmS1BYo4wZj9p6lpSVmZma44YYb6jH2PI8bbriBRx55hKtXrzIajRiNRhw4cGBb0oXl5WX279+P67qcPn2akydPEkURZ86cqYV313VrtxcjbBiYBAy2bdNqter1IYRg7969tVWvLMua0Jq9an5+nqWlpTqe6vLly7RaLXq9Xu1utLCwwE033URVVRw+fJjBYECe56ysrHDjjTeS5zmnT59mfn6eb/mWb9kmcJu/+/0+ZVmyvr5eWwBe9apXYdt2HT+TJAntdhuAvXv3kqYpvV6Pq1evMj09vU1h4fs+nU6HqqpqQXFlZYV+v8/y8jK7du0iSZLadbXVaqG15ujRo6RpyoULF9BaMz8/TxAEtQXBxG89+uijdbC6ZVlMTU1xww03IITg6tWrXHfddWitSZIEIQRTU1PceuuteJ7HYDCgKArW19c5cOAA1113HX/7t3/L4uIix44dq4PmbdvmoYceQmtNmqbceOONdQICozwz+8uhQ4ewLIvV1VWmpqbq/fLq1atcuXKFNE257rrr6vmybbsO2DeuflmW1bE4Zs97yUtewrlz57jnnns4cOAAw+Gw3guWl5drN7H19XU6nQ7j8Zjjx49z7do1hBC1a+FwOKwFkq+Ep2zRUF2btCrYqDRFoHB0xmxL4jnrBH2JcB0cDX4+RCQJFKu0vAKVethuQGLZFFKA7SOCiBSNtFukaxpyl83EQqqMjhOgbI9cWVSuhUOCcH2E6yICD3ucI1aHqI1V/CpBpjEzfo8kc8jShEjmDIcZXhBCmuCGkGGRrY/RhYVlu0SdEBG20NMdVktF1e6RSouKHiJyGOcpsYyRyiWTGjwLd2wRui6qlRN2A6StqWROIgqwoB3ZdNyK0tbYbkTuulQ22N2QTDgkVUpRpJRphqo8ytJhM8kYpZqstMk2h2RDTVVKpMhJGOC1E+w8pyo12q6IsNFlgWhNQdglkaCsANES5NkYKwZrmOELhyiSuMLGW/dwUgvXtijLiqocEkQwF/n0vC59x8PTkp7j4bQjUgd8R6B0inAcyqJEEZBIHyksRGVTpTnjUiKlS4UgTjPKDRdpKzQpOoJut2LKKfFCF98PsXMb19YEIqMl2lhFG6uqQOfkVYpWOa3pLlE3IBMpFDmyTCnVgFKUFEFJUQla3RbKEQS6Qg7WEbnEsgVIUGVGebUiS9It0oOPVgGFE6BtlyKOEWKEVQq8xMGqPMpNcAsfSpe27FNITWoNiPMKUfjIVOPqFBFL2qLHKG6Txz52x0NO+QRdn0KA9Nt4oUs5ZVGQQFrQ9aexLQfSiiy2WJUV47JAS/CiAoGL7bpkxQj8gkKn2J7E0SWl9IhKH9eOGFoQtdo4wib0OrSDhaf62H5DwhDspgWiKVzsdMExGtzmj5Syjm8wvvfAE9xQDBEDtrlBmRdzk5g3NcnNthpyGcfxNu2RcZVqCjGGcDTJp/ltCK6JNTCa/GYg8U4NezO+oRkz0vTfb8avNDXf5tjmeBpy++ViG+BxP3gzFqadps3NtjdjPprzt1PoM2R+5/2b7dzpbtW0VJmxaPbpyYS1JhE3AmAzfqVpRWnGXBghY6e7XNPVyGS5MoTArIkm0dspkDXn3fS5+blZO02BsGkNMesvTdPaetR8DnZaAJuC1k7BszlmzXX+TEIQBADbBLQzZ87UQbZG4xyGIUIIiqKo3ZlmZmYIgoDPfvaz2LbN1NQUs7OzADzyyCOcP38e2MoYZNyRDExgsDnGWEQNaTexYVJK9u3bR5IkHDlyhBMnTtRWvE6nQ5ZlzMzM8KlPfYpOp8Px48dRSpGmKfPz83VsTxRFHD58mC9+8YsIIdi1axe7d+/etk6vXLnCgw8+SBRFSCmJ45jLly+TZRlArVXPsox+v8/MzAxXr15FSllnPTKWNtgKSL7xxhtr15gwDJFScu3aNVqtVi3cua7LsWPH2Lt3L51OB9/36Xa79Pt90jTlscceIwxDZmdnWV1d5fLly+zdu7ceJ4CzZ88yHA7rwH6z51y+fBmgDjYvioKHHnqIpaWlWli7cuUKq6urHDlyBMuy6PV6XLt2rX5WTEA/wLVr1/jkJz+J53l1FqskSbj33ntrQdK27VrwM9aeJEn45Cc/WVsszD7x8MMPs7GxUc+16f/6+nptJRNiK5Zhc3OTKIoYDoecPXuWy5cvMz8/z65duzhx4gQAjz32GHmeU5ZlHf8C1G5xCwsLnD17ts6gGIYhe/fu5bnPfS62bW9z4zJB8iYT23A4ZO/evbVroZSyjuuYnp6m2+2yvLyM53lsbGywuLhIq9WqhT8TM2PmLMsyjh07xmAw4OzZs/Vz9ZXwlC0aSWEzFbrECmI7xxcV4QgKp8LpVNiFj5QBrm1jVTbzU10GZIjAo9LAKCfzAjzdJhnEhKGLrSVVFJCUChH6KC0ZbWzS7vewcptMSVxsbO0ihoo8GDHWKX4wjWeFUFo4WiPtCqsHQkIkcyw7Qqc5SrbIcxenpZF42KWFTgqEk4ESuF6AS4BQDpVbYVseg6WzWFGPWW+KUivGSkMcI7yAnh8yTteQMqAThIwrhWgrLGeMiH1SneF5girbxA4jQh1RjDOUKrGzjFR4+IGiVD6RJUicAitWtFyBlhLLs3CwKR0XYfnkSApZoUULz1KUWUVhJ3iWw3qZkM/YyByE7aHsDDfUVFohWh5WWCCFA0LiKcjHG+AL2nN9XKeCysKzZxgqbyu7lJBUCizXo6xyVKGI/ZxWFHItLdEiQxeSOC8ZjxOsQhCgt0h/p4M9ksg4RoWCypFYlksZQyFLpqYCnNLBqSzGeUqRJtiei+PYlJ0QOx9QDl0kbbI8ReUZtg6xCkGuHIoyoxIVng4ohhv02hJPBOB3UZ6NEBpPeohAkiUS0Q3xPAuZaZKkxEIRhCFiOqA99Iljn0wMsVSF0wqohItQFcNNhRIRrY7GJkE6BXZkMy4LfNXbcnOyCpRdkmYV070eaZaTOiXZckXg2LgBoHLaQYBOJKtjheNYOKQoO8AXAcJVxBlUlsQVGVFkU2lwcQikh+tVKCsgy0vW1xNcK8BeH1BWPlcHOcJ5ZrtONf3uDZHcaVEwRAqoiZwhDUAtaDQtH0CtcTSa/maAnzne3N+8KJrZn8yxTcLdJMiG/O4Mam+mm93pm/1k5Bl4QmCxIcU7teM7CXWTUDYJo7mWcbdoxhU0M041Y0jMeYb8mn6a6zStF82+PZkVomkBaQp8OzX+zbE1bWz+Nu3cGWPRJNbNPj2ZNasZpG+sUSYBwE5LT9OaYfrdnNemi5Jt27Vm0Xxm+mQsEc0x3SlImbY1LTM740+aglQzLbFSqhaOnmydNsd2p3Cxcy012/pMhPFJN0THzM2RI0dI05RHHnmEqakpdu3aRVEU7N+/nziOuXr1Krt27eKFL3whg8EAKSWXLl3i5ptv5vjx46RpytLSUp0yNsuyei5Npp19+/Zx6dIljhw5ghCC0WhEu93Gdd16rWqt6++b1jgTi9Vut9m/fz+PPfZY7Z5nXGzyPOeWW26pMxn1+31Go1HtZqX1VhBvmqa4rksURTz88MO1f32n06nXttn/lFJ0Op3azcyswePHj/O3f/u3tZtTlmXs2rUL13XrlMyPPPJInXnIWEdMKtSlpaU6rmM0GnH+/HkOHTrEgQMHgC1B5+jRozX5hq29/fLly9x2222Mx2Ns2ybLMj7zmc/w3Oc+lziO2b17N1prNjY2aiEpyzLOnz9fp3o1AdCnTp0CtoLmNzY2anel8XjMYDCoXamMy90tt9xSx6VcunSJ++67j5tuuqnOaCXlVjazxcVF7rjjjnr+z58/z/z8PPv27ePy5cusr69z+PBhLl++jFKqjt0xe8nMzAxxHDMej7ly5QpKbaU2PnToEA899BAXLlzg+uuv5znPeQ4PPPBAndmr3W6jtabdbrOxscHly5fZv38/UkouX77Mnj17cF23VrJtbm4yGo3qlLcbGxv0+/06TsnM6alTpzhw4AALCwt1ZikjRM3MzNTvZBMkPz09Xa8/13VZWlpidXWVvXv34rouhw4desqZ656yoFFtJpTeVsrSsG9RTnkoKUlTRaB9klLi5OBaNi3hkYsWUtsUIqMgpW1F5KlEUlGkOa7K8HVIYDsIXSCyTVRU4rc9NDkySgmlRSTnkCIlFpuEfoBtK8o4I6naeI6FDErG1TpSKuSUQsclpQORb9MtbFTlUloZYculNXCRVkxpSUThE2hNIDSyl+IONZ49ojs9RZZCjk/qDHBdB1e10a4mKRKUFyAzSBkStnycWJGNQvIkR7cC7DhmVkSkwqayLZSjoMpxCLFCGxyH0WZMEAqIM3LZwvId/BiU75F6EpmVYIHbtfD9EtKAWGvyUuF5beJ4gNvSRNqjSAuE9qm0xsqGVK6PlUuEY2PbLrlTYImSVmuKji+wUrBKj7EjcH2FH2Roq0S4HSzXI5AVparIlCCwWriFRSArRlJiiZCWNSTLNdguWTmmSkp8xwGnhRVaVCrByitG4yHtcAo3y0hXFTIVaEvhRl3y0KXl5OhxgRwrNA6O7zMebGD5QEsSEJJUJdJxcLRNF4+VtKLdmiIRkjiusOwClQgcT+BXFYHVp2xnFK5GakE79yidITqoWBusE9rTeKXCccDFwtJtEuVR2oIxBWEnJMgdhF3h+g6jcYyLh1cI4mxMvxdQEGCXPmFpY2frxKlGVwpPVmingyUCvMpH5YLYynBCi77loHEYu7PIXKLzMZYO6AQlbpHj+jaFnZNbbaqsouP7FGOBRDAz0yHfzJG+h2dXTAUWRVo+1cf2Gw5Nd5CdxNkEDTZdZZqWBkOYdqaMbQoO5jxD4pVStTk/z/NtRM9okUw7mjUtmnEghqQ2Yy8MOdxpkWm6HJn2NQUCQxabVpAmaW3WYWhaGZqa/ebYGeHEjK0RsMwx5jpGQDOEbOf4PxlhbcZYmHOAbQKBITRmrMxnO4PYDak11zRE1xzXFKTMvDfjP5rCRnPujWBkgmRNHQ7TzmYbm4JFk9Sb6zQFpZ2WEjNPzSBqM49m3I2wZAQ00xfT753z2RTwmvMCPCGTVLON5lpNYbHZzmZ8i7F+NS1ezQDzZyJMYLDJhmNiAkzdk6mpqdpN7+rVq3S7XeI4xrIs9u/fzwMPPECn02FxcbHWEBsLwObmJrOzs3z605/mxIkT9ToYjUbs37+f06dPMzMzw/z8fH3vgwcPcvr0afbt28e5c+dqAWB9fZ3jx4/T6XQ4ffp0TZa73S6DwQCtNYcPH65rv4zHY7IsY3p6ms3Nzdo1aNeuXduyMFmWVVsujh07xvnz5zl+/DgzMzOsrq7WQlFVbdWsmZmZqdtz5coVtNYsLi4yGAzY3NysXb9MjJlSiiAIOHjwIFJKjh49Wu8To9EIrbfcfkxsSrvdrmMxgG175nA4ZGVlhfn5+Xq9Xrt2jbm5OVZWVhgOhywsLJDnObfddhu+73Pw4MHacmjbNqPRiKmpKdrtNisrK3Wsg8m0tW/fPpaXl2vXpyNHjlAUBZcuXWJxcXHbfmBZFp1Oh6WlpdqN7I477kApVQdv53lOt9vlwoULdXpaY7k9d+4cBw8eJAiCOsZkZWWF/fv3EwQBaZqysrLC3NwcQmwFxVdVxYEDB1heXmZpaYnp6Wk8z2P//v184QtfQGtduyv1er06q9jc3FydxMC2bcbjMSdPniTPc9bX1+s0vr1erxaChRB4nlevMdhKBV1VFcPhkGvXrtUpeh3HYd++fbRaLc6fP8/09DSWZdFqteoEAPv378eyLEajEXv27KljmTY3N+t0zk8FT1nQmLZdlLDB8lBxSiWH2KqLnQVUtkPH8XFCyKuCogInluA7uLYmKmyErZCxJvRTrJkEP7SJ05igN4WbudiOIAkkKqnIpYUf+MhNwUjYQI4dWajKwxURozLHd8ByClqugydbVPkYp+0xijWO1yIvwVUOle8yHI1R7ZzIjygyTUiHXLvEtkDpAjeV2B5kroVIXSyx5e4U4CEqiVDlVjpfS5OXGbQ8RkiCQjGV2yTCJvRLVKbwgg45LmOV0LIETiHRXh+NpLC30tAGfguncpGFQ1rkdLstROZRSkU6iHHbDqUlkOMSsAl0isokLVuQqK0XT3vs4DoaL5OkugLfppI+rgI/kdhlRJyM8LuAqNAtjS4LHMuhJW0StyJhTFFU+DgoETEtUrRvoStF1HIp84S1zYJUjSiTmI1NTVqmyFRRVusULoyGKYXvYGmJXUgqVxA6IYmXkSZrzNk9hOUwlDl+2yVTENlQ4aEDidIaz5ZoW1IITatqQ5pTFBm4LpQVtoK8jJhVOVUyhF4XHbWgzGi1PGIqVCwp1zPKPKUd9chkhlLgSdBTPSwiWtU0wXjMyC7B91GhRWhp8qFG2gG5HqICH8eBvMoJWy6V42Nrm5lOReUrnDQjCjpsbhbEykWi8YWk8lOqIEdIh8BzEFZBhEfuVORlia5AOyMsT2FJC9sTiExAYVHZLpZjEzoOurJRrsLpSsrcYyAKomlFNRpj2RLbaSH9/lN9bL/h0NSON2MDjL+w+b9JlJsEtymoNEmy8XFtEuZmkPLO6+3MSmRIWdPn3/wGalenJlFtXqsp0DQJ305i23SnMWgKFs1MUmZ8mgKGGZ+dLkmmbzvbbYhp01Vs5xg2iXSTgDfT7TYDoJvHmj4a4cAIUk2BsWn9MNdralp3CmVmPsxYmf7s1Pybc42bkda6Dipvau6Ne5K5rrF0mbY1tZCmL03Bpvmd+W3maafwstOK1LRG7XTpMzDnGCtOc4ybFqum9a8pnDTb1xRKzb2aguwzvX4GbLncPOtZz2JjY4OHHnqIb/qmb2I0GtXBzSZwWAjB/v37t7m4pGnKsWPHsCyrJuZTU1NcunSJ3bt3c/PNN7O+vs6LXvSibecbomZIt8kuZPadXq9Hv9+vhV6TmtSsO1M3o9fr1QHox48fr2MsjCXDEOrhcMgjjzzC8ePHawJfFAWj0Yi5uTmGwyH79u1jbW2N1dVVZmZmcByH5eVl1tbWmJ+fry0NRVHUz0cQBLVgY2pclGXJAw88wE033bQta5JSisFgUGd9Mql5+/0+g8GAffv2MT8/T5ZlRFFUByErpbh48SL9fp99+/bRbrdr96jmc2kSZwyHQy5dusTc3FytJPrQhz7Ec57znHrOzTMyGAxotVo1AZdSEoYhJ06c4Nq1a3WGsCiKau08bNWKMG6O99xzD2VZMh6PmZqaYnFxkUcffZSNjQ0WFhZwXZdPfOITPP/5z68LQGq9lQTA1PmwLKsuZDg3N4fWWzEkzYxk5ln0PI+FhQU2NjbYt28fWZbxyCOPcOLECZ73vOfVgk1Zlnz+859HCFFbp8wcSLlVC6XVatXWE9hy55ubm6v3K5N1bzQa1XtMq9Xik5/8JI7jsLi4uG2PMn3r9/v13JhMZ1prLl26VMcXRVHE6uoqcRzz4IMPctNNNz3lzFNPecdpRV1WixFVYDGqcuxS07NztOsRuD7SyhFeiFck2K7EdQWDMqHjBrjaJS5j3KAHIkW7HoXyoQtYCpUpdGXj2hGVUDgoLNfF6lloWdHXEWMhyFMHuwA39AlsgeUF5I7GVj55uQlJRGmXiDzGchRKeGjLwrMlnh9ilwmRcBCWIPUUudZ4SqJTRWZptG4xyjew7IogTSktiatcbDsldypGKRSuoCOgo21sxyapFHma4bV9KksRuRYpBXlR4AioYgtHV2zEy7R6M4S2TyYVQUfjRTblekEVXyMTDtoLCDwbkVvYOiK1Yyws9IYiDFzoWzi5RmhBPpDgOrhhgF8l5NIlsWC0uUzXmSeWl+l0PTx3mnIkuLZ0DTcMCDsWVmUh0wy7BVF/ClHZOHrEWIWIEgItKMeKQueofMhwNGRzeROpBUWRMqgysuEGlueSCkU+VAjpMi0syGBUxTh2m1bg4nsVDi4FJValCHWI6wT4dkUuJJ6SoDJKKfG8NsNBtuVP3REoHAJbw7hC6RFerwVum8JXZImm6wUUlSTUPtrJwaqI7C4ytpFlSqvTIa8c0k1Nu98nH40RrkL7AcptobIhge2xr9UiswqW45KW66BcjWNHpHKI71rYlSbLNTJTUIG0NrB6AYmsyKuYhVaLqOpQ9XuMlnOGOqYVbMWMuEisqMt47JKnObbUtFo+OtN4NrhTHnlpo8cl2kkpGaOKLvOuTyuyWB0NKNsO7W4HlWkKKyaR0VN9bL/hYIjRTp9845dsYDZDc7zjONtcYJopYA2Ja2qjja9uM2C6qX1p3ttooHcGFTeJubn2Ts24IaxNgriTAALbiKHRiDe1zDuLwDWFniaRbBLNppuVuYaxyjTJZXNMmgHWTTLcFDDMsTszNTWFqqabUVMIaZLs5hgYkt8Uipo/TcuAaXfTTas5z0qpuh9N7b0Rckwaxub8mOs24y2a1ZGb8SDNmBjz4n6y7FxNlzUzBk3LRdNVqhmH0RRQdt7TfNd0j2qurZ1ubM3rNS005tzmeDaLQT6Zi94zBTfeeCOj0QjbtmsXp927dzMej1leXqbdbnPp0qW6UrfJFnbu3Lk6CNuk/lxZWaGqqrqa9MrKyjZ3Nsuyas2tqXpv5tUELZvsP6bq9mc+8xluuummmtyb2AAzT6begW3bdSafKIro9/u1q4vRXpuid02BwrK26kn8zu/8Dq961at48YtfXAsPc3NzTE1N1fUNmj70juPUMSDNfaPf73Py5Ek2NzdZX19nYWGB1dVVbrjhhvrcpaUl9u/fT1VV3H///Zw8eRLP87jvvvvqmhnPetazKIqCRx99lKNHj/LZz36Wv/mbv+G5z31uTfQvXrxYF7Mz9SC01nXF7jRNabVa3HTTTdx3332cOHECIbYCu01xu6WlJRzHqTNwlWXJqVOnqKqtGhYXL17kwIEDCCHqeIfNzU0cx+HMmTO1BWxubo5Lly7VVoHdu3ezvLzM/Pw8L3nJS+q+m+fIkPLp6ena+mGyAq6vr3PLLbfUGn8pZe3O9OCDD9bXevjhhzl69CjPf/7z60rwe/bsqavGB0HAwsJCHexfliVf+MIXOHz4MAsLC4xGIwaDAXv27EFKycrKSu1id+TIkfo+p0+f5tChQ/X+evz48dqN8NKlS1y+fLku5GeC8Nvtdm2VMcX+TIasU6dOsXfvXo4cOcJ4POb5z38+Wut6fL8SnrKgkVqKVjRNvBkTWSFCa0Jf4Fg20gZsG5VkOH5InidoYeE7Lar/Q/ocbEDg+ZpKVziVIuxMk45SHEdgBy62HZBlCaEXkA5jlKWxlcVyWVFabcJWzqanCAsXkpjKthFIxuMCO2iBdHFthyIbYhcJ4yLFVzbTvT3kacHAKwgrKKRC+BaB10FLTeJWiBHIDDw3QFUjOu0e6AAtC6oqxnXaxG6F9gK0stBliV3lOF5Amkq0rsiLhFEQIQIfGwslc3SoyF2Pbq8LRUKORekFZCol8CV+pJASZGmh7ZQgFVR2hRIKXYFtg9e2kNLFFRaKjEQLnMiCMmE9kVhag0pAl3iJZqRiNt0MXaSM1lLc3hSg6QsXr8rIHIGsKmzPIxAanRWEToRKwem55PYQVEWetEgLn7GG3JeIoiCVMWMnI7XW8PIuwmkhNzOEgjWVYykXzw8ZiA1aKYjIpQwThAVOAb6SjIohKhCUClp+hM5b+Eox8HOqtruVOtbywIJ8FEPgozsRRRphZSMkgn5rGjfN8AqLpJLk2qUVBQzzkkpl9Jw+cWYjfIupKmU8WEMicKcsnApUmWC5Fo7UlMUYrwctayv1bB6PcWVCnoO0CmxLgdTYfY0d2fhSIUWBMwDhtSGzEJWFWK2IRA9tlcRyTAVEgY1VWXi2wnIrbGtLeI6cNn6nZJjH6NjCEQLR9gj8LsPU5+raBsF8i1BbTNGmzEckqYPTCdBq/FQf2284GBLddFEyGmQjbBgC5/v+E1ytdmq5zXfN4wwpNUTZfG5IcdMFp6nZN6SwaW1pxjbs1GgbItqEuW/T5Wun9tm4+TT99I0mtBkH0ewnPK6dbhZ5axLnpmuX6dvOv5sChblmU+hrBlwbV5Smhr1pXTDXbZJcEyOzk9DuFDyaxLv5f9PC0Ty/KXg153Tn2mqeY65l5tp13W1ChVl/5timJadZ7LE5Tmbsm7E/xv2r+WP61tRuNttoxs3004x5s7ZGM5bGCG/NOKGmsNKsC2Nikcy4NhMWNM95pgoazTV5/fXX13UCTEaiixcv0u1260DjhYUFer0e+/btIwzD+pm+cuVKXQfDuCO5rlvXWwDqYOGLFy9y/PhxxuNxHdwrhKjTue7atasO1j906BBKqbq2RqvVqtMjmzm++eabKYqiript1qbruly6dKlO+drtdllbW2N6erp2BTOWgBtvvLEmhWb93n///dx4443Mzs7WY2SssUmSoPVW1qRut1sH1Qux5c7kOA6HDx/m4sWLnDhxoh5XE5RuXKU6nU6dEtcEFO/bt68O3DbxJbfffjtFUdQFE5eWlpibm6PT6dRuXIuLi3W2qDAMa7ceEwdjBC3z7Lfbbebn5+t7GZfQY8eO1VXTr169yvnz5+n3+3VtE2Nx2LVrF1przpw5w+7duzl69ChKbcV0mOrb8HhRS9/3SdOUz33uc1x//fVEUcTGxgbnzp3j8OHDTE1N8YUvfIHFxUVGoxHXrl3jlltuYXl5uRZwr7/+esbjMd1ul5tvvhkhBBcuXKjjPbTWdeYzM5dpmnLx4kUWFxfpdrt1kHm3263XsGVZdX92Fmw8ePAgy8vL9Pt9PM+r0xvHcczs7CxxHHPhwgX27t1LVVUMBoO6jf1+n7W1NTqdDmEY4jhObeFYW1uri/tZlvX0B4PHVUZLFAihsJSN9Cs28oROGEBRojOBJSyk6yCVRTlKabdmSOUQN7Dx/QWSIqXIJF7LQtg+62sjosDBDz3iJEOva4KWi1VUqDzB7oRkGznuQgerGFGNS6qWzdqwYsoPEJkCq8DqbLmc5IOK9myHyoNQBFhhQYXLRhzTabdQVoR0NI4nWC8y/MqiHA7wu7NYSUXmSIKWg5P4jDYHUHrIQCK0TW/Ko1tVSBS+tikcSIcbTAezTPdDlCiRQYFbuIwySaUKylwjwhBhC+SgoO3YJDJHZYLECXCwsSpJWWYELYtKpGRrKSoIyAMPqxI4jmZjMETiw0qJ9DVZCnOuxWA0ZJhapLZLma/SdirCqoVqS2JdIsYKAg97lNDtapL1gmHHw8Ui9DrotYp8vWCh04M2FHGMrSQjVSECyebaBqU1RUqBzhzytOTKWoUqK9zcZz1exXELWo5H4AviUiE8F53lZGVGN4oYiYSx3vKPdVyNyof02i5u6REXIVU+wmkJirHAdjx818YJNJZwyJKSuWia1JVUTknoZ2QDietr8vUlZOVTzNrEgxzluLiixMtidCtEehJPDpCZRkQakWdEbp9KWOhSYcmYVhCR2RU5LtbIRVYagjFhbuF2PTxh42cuTlvjSpuqKPA9heX7rA0HWKGLV2qyQY7yukRK0/ELchwGI4E33UcKqApwhUWmKgotaVsWwlKkeYIcVzh+B63AyQPifIzb3Uo5XNiaqU6L9LExuQ2qZyO0pNNvPdXH9hsOO108DKEyxK5J6Jq+6eZFY4hjkzAa4mAIflmWdYYOQ8LMsU3zfZM4NzMqGU2SIZ1ATeSNVrMpEBlBwQgkRkNtztvpxtN0DWoSz6aL1E5LBrDNLUkIUWvmdmq1m6428MTq0E3rQ/Pv5vw0syg1BbmdJHWnZt+0tRmM3ST2Bk3hzbRhp4XCXNNYKJpCgBFYd7bFkPbmZ2Z+m1aqZqrcpsXDxERYllUHjjctNOZ4Y5Uy1zfzbdaaEfq0fjygu2nBMfPSHBdj2Wu6g5n1a8aiKSyaNu98ppptNm1sZkgz99opqD1TUJZlnRUojmNarVbtUuP7PnNzc2xsbOD7PvPz89v2h263y+XLl1leXubgwYO1W1EzK5gJzN27dy9KbcV2GZcgs0eYFLoLCwsotZXVyfd9HnroIaqqot1uc91112HbNnme89hjj9XuOrfffnstoJi4srW1NaSU9Ho9Op0OruvyyCOPcPjw4W1KFlPBHKDf73P27Fmuv/56Ll26xMmTJ+sA3Xa7XceBPProo+zdu7deD0EQ1IHNFy5c4Pjx4+zZs4dWq1W7Zm1sbNRr2LhSCbGV0nV6erom8EKI2gphArGbFtPdu3ezubnJwsICe/bsqfe/xcXF+hkyVb/jOK6zRdm2zdGjR/n85z/P7bffjhCizqxlgqt3795NWZasrKxQliWLi4sURcHc3Fy99x89epQkSSiKgjRNa4WWiRkxsQyu69axPUo9HjhvLA3G2mQ+MxY0KSUHDhyoSTnAuXPn6j5fvHiRdrtNmqa1i5XWWzU0Pve5z3HrrbfW42DqjRiBtN/vc/XqVY4cOVK/f/bs2cO5c+c4deoUhw8frq12Zl8Lw7C2li0sLPDHf/zHvOIVr2DXrl0MBgMeeeQR4jiu3eTW1tbqdxc8LlibNXLs2DE2NjbqZAe2bdcCdJZltaD3lfDU62gEmkrF+HMddKzwIg+3aoHKEHYOXoQqbYq8QlgB7V6FECWdsMsw2UTbJUIo4ioiKhQCl44fMRrFW+lFixzP1WzmCe0pH0SbcZ7QnwnJBoLI17huiByC51toz0NrBxuBpwviIkcJF1FuoKqSQgfYjktpg7R9wMIaZrhRh9HGZYpQIYRD4ISgLTJboawxLbuHCiMGUuJhU+BjIxmtbSDHAa1ZQWmlKCkonQ6rbknLsomTMcqy8S0fx9qqDG7hkEmJrhII2pSVIvBdnAySNCYrfXw0UWihUMhcIsOt9KhO4RCWLpWGVHpb46rWIPOxlcVVWaGcFFl6jNaHRNGYkawY64zyqqIdeUitUJUk27SxqwgtXLQl8MMWSTqkVdk4foeVPMXeGNIKfZIccg2kmnGckOclm8MN/MpitBHD5gAvsiiLLauDKiRpNYDAxbY0VhiRbCQ4gMpLcjQVIb6T4mrI7TbDdEQpXQphkwVj7LGFzqEtFJGUZMJDuBJHWcSZwNU+johxZIxLj5aWZH6JzHKs3MV1bXAUlSrxrC4BmqJMKEpJlwCtC3ozIVlSIu0WQRfyzZQkrigDgXIlKpMIbPyxhWf5JOOSbuAiZEWeFriWi6gy4rFABhbCnsYOMwQQhBbatinKHK/IsWwPy4PRcJPAbZG6Lp3Ixc96xEUMPuQiQ0lBL+pS+oJYS7LBKsryEKMxrh/hy4o4V2hLY0/btPUmlorYEPLLP6zfwDAvgCb5NoS7GROwU8tuNtImaTMkvknimsXZmpr4pnvTTmHAbO6GjJkUjEb4MS8C295KA2l8/JvXB7aRY9Ouqqq2WRoMyW0KBrDdstAkn4asNi0hxkXK+CE3SaVplyHSTTK/M17CtMWM8c5xMu0yaAoaTXepptBnXqTNgPxmO3YWpmvC9GVnyldDuM28mVSvRghsrqXmGtsZB9P8bdrZ7IPpa/N+zf41yfnOgPvmfJvsNs3sVE9m5djZ7+ba3xlY3hQ8DJqCxk7B3fxvhB7jUteMqWm6xT2TIISorQ9SbqV0XV5e5tChQ2RZts1PHqifhfF4zLVr1zh06FDtc/7QQw8xNzdHFEW0Wi3yPK/J6ZUrVzh+/Hjtgrm6ukoURXXBPBMLsLGxwYULFzh58iSLi4usr68zPz9fu9pYlsX8/Hxd0+Kxxx5DKVXXNoCt9fSZz3yGl770pVy7dg3f91lcXKzXl5RbGYfMvO7du5d2u80dd9zB8vIy09PTwONkdWNjg6tXr7Jnzx583ydJEvbt21e7EM3Pz7O+vk6r1aoL+vV6PVZWVmqN/4kTJ8iyjDiO8TyPtbW1WhjZvXs3Fy9e3JZut9Vqsbq6SqfTqV3C8jzH930eeOAB9u3bV7uaLSwssLy8zMzMTG3JNs/T2bNnayvMyZMnCYKA8XjMI488UqfrNeO7e/fu+j1wzz33kGUZ119/fe0aadoBj9ciKcuS6enpOp1sWZZ1+muzXzWVCrBlMfnc5z7HzTffzMGDB/niF79Yu2kaITPP8/rZMsqu2dnZOijbKMm03soa9uxnP7uO6zDvNfOeMMUZTQYqM8YXL15kdXWVgwcPblMUaa3reijG1U8Iwbd/+7dj21tB+XEcc+jQoW3WZcdx6mJ9x48fr8eg1Wpxyy23UFVVvV4ty+LkyZPEcVxbPYzQ+5XwlAWNMApxRy5JViH8Cl9uVePOKokbBeQiw3c7CJ3TsiVSxlQhZHmApW2qcUortKnaPi1XkRcJha3od9pgS0rLR1UZoZBUQ5BtgW27pNlWIbRCOejQAlHhexZKF3j2GC1dqthlpusztCoc18GP2shMMcgzQqeFUyVolZJVimo8xO35+HEFiYIwQOYD7MCl24lY2xjTd3xCy6OyHdquixxnaKHAt3E8h2RcEFQ+RZ4w8qHKXYJWmzwuqWxNhYdHhYg0kdaooU3ZFsiuBZuacVpgeSGqstkox0xPdUk3E2zpMPQ9Krtg3pGMNtdY3iyxrBb5Zgn5BmXsEgUWI6sEJ4WhpFjP8XeF5JVCqgqtfFydkzkKqSW+H5IUGYUssKVD1UnpCMXAd4mTTcISnFBwbXWAuysiT1Na3YhcVTh5gRtLNuQGRVnSLjWrg5jSE8jCQhUlfqRIixSLFnlcURYloe+gy4qNRDOMNulHimpdIjsWlqXQVQ4CsFwKbKp4iAN0fIFwfNLBBt3ODAgXVaRoJbeqZnsFI0A6Ar/jorIMLSJ85VIKxVgq/FFGaiV4Xg/PbVGWkjTRbMaaVqSJ9YBKukihscoK8pKOG2K3NeWmRWZD6QLjlJZlUdop0rdwvACnEuhSUQoXuwQ3cAncknRQQBmQCQtdZfjTEU7xfzSzriQdZ7jCou8WqEriCJeNgaI166KqTTwRUbktyoHCt0ChKJVFFAqYBmwbOWxB4aBaz0yCANRpDo3Q0CS4sJ1YA7WrTNNtpunbbjbMnaSx6cZitP7NTdncw/itNwvvNX2YYXshu2b8hHmhNN1imsTYnNO0ihhtuPnbtLWpnTfHN4UTIwQBdUXpnTUgDKlpWkyA2t2q2femgNIU6Mxv87dB0z2oGQdg7m3uY8araSlpuuvsdJ9qvvSablPweBayZt2NnePUDPZv/m/aY47daQVrCktNIawp4DSJhumvsbo0BQizLnZadppuUDuzcpk2mHkx9zPHNfvc/Nu0xYxXc82bNdMshGjc6prCjCFcxj3omYYwDInjuF4LJjZjZmYGz/PIsoyrV6/WWaNc12VlZQWlFLOzswyHwzrzT5ZltVtRswieWWtJktQuJ1pvFdozbipm/K5evcr+/fvrjEwmtaqpi6G1ZteuXVy4cIFTp04xMzPDXXfdxcbGBpubmzVZ279/PxsbG0xPT9exJ48++ijXXXcdo9GIBx98kO/4ju/YlmXNBGI3g7QBzp8/X7vl7Nq1i42NDe655x5OnDjBYDAgSZK68Jzx+zeZrqanp7npppsYDocsLS1x/fXX18/P9PR0nSXKWIDM2t3c3Kzn48KFC+zevZtjx47V2aDa7Xbt0laWJffeey9BENTxCqbKt7Fg2LZdV7Q21qmlpSUOHz5MmqbkeV4HPdu2zc0331wXPFxfX2dqagohtgLITVV382OsGNPT03UaX9MuEwNk1tnKygqLi4s8+9nPxrK2Mi7t3bu3Lvxo1uGlS5fqDGEmgL+pIDKWN2DbXJl4GiNg2LbN6uoq+/fvJ01TZmZm6poYQojaimZcmGx7K+j90KFDLC8vA1t739LSEr1er84kZur+mNTB5n166623MhwOmZqaoigKzp49y/79+2ur18GDB2vh0SRZMJnGdiqLvhSeukVjoyLXoBwfV9hUloXdKnFXJcQWrZ6LU5SM0QwHOYHbx1IhYVlSiCE2IYVyqdQ6leUhK0VVbpD7kGYCx62oMk0naBOXBcU4pmW3adsRsgvpaEiqKnw/RJQVOhcEfQs7Lxi5feJUg8gZ5wo7KVFlTuhsZVDwXEmykaA6EWXh4xAhkw16XZfclqiiwqpSlO3R9gIEPlpoqkISZhZKaAoBWCXJqMKWIVXu0fJyokJRtmwqPcbyS6gUfuXjScEoS7B9TeBEyNIisBJyaTF2YMrzcYZjqsxmI8+Rfo6TlBS5RyxytC9Ro4KqLEnWVgm8iFEFtsqxSqDVIi4LXF1BT1PpBKEV2vbJ7ByZVhQqR3g+LhKdpfT9kGpjhFi2aO2ZQ1Qb9AvBalaip2NC6SOWSkJPkyfrJFaHYGiRb6aUVoEqCjadklEqsLSFW1SUecnYU/R0hFIW1Shm2gnISBn6MQWaiBauauOEmmy8SavVJi9HVE6GvwEyEfSn5xkO1rALn7IUuGVFxpgkSFkIbJTjYvshVV7ihgopLSgdbMuj8kqEkhRpSsvqYimfsqNxApu1PCbSLkLkdGxJtXEV1wpxCont+zhOi0hlKAuGZYXfs8iTjG5/mrAaklUKvz2DsiosNHmq8XwXz3ZJdYovRyBthGXjdV1CJRkJxVinBGGH0TBlxrHo2D6DwZggmCVVGu1ntKddBkVOFLRp2x5jISG0wU2w+y5yQyDKMa69JURawkNFJVX1zM0a0yRlOwN1m1p5Q5x3BkU33XGa2TN2aneb/xtBwwgFO0myiY2Ax4WKpgtNMwjYaKQMOWxqTnf61RvNWPNlbNx/DDE0/dtZuM+geS3jPrAzcLypTTdCgLnXTpJvrmPGf+d5ZgyalqYmaTW/Tb+bMSRNUg/b3caawp8Zz52CUpOAP5nWv3mMIdvGlc20e6f1oSkQ7hzP5vg022/+N+ulGQfRnEdDSo1mvVk9vhnLYqwbxp2v2Q6zPs1z8GRuXzvbZ65r5q05x02Xr6YwZ2DuYYrLPROxMy7K8zwOHz5Mt9slTVOqqmJ9fZ2LFy8SBAGe59X1M5aWlti9e3ft5nP48GGA2s1oNBrVdQL27NnDaDTiwoUL7Nmzh8FgUNe8MPUfAPbs2VO7c4VhWBdzm5+fr+e3SeKDIGBpaalum3HHOnbsWO0SZtpv4heGwyGvfOUrOX36NAcOHCBJEnq9HufOncPzPHbv3l0/dyZOpGmxmpqaYmZmpn6OfN+vswkNh0NmZmbq9WLIe6/XY35+njNnzjA3N1cTfrMf7Nmzp95XTVtNNXCTanVjY6NO01qWJa1WiwsXLuD7Ps95znMYjUZ1muAoimpLbZqmtUAUBAGnT59m7969zM3Nce7cOfbu3cvDDz/MnXfeSZIktTXBxIwYQcHUi1hdXaXb7TI/P899991Xx4o093Pb3kouYObPxFiYtMSwZXkysSTGwtXv92m327V7nbGYmCxRZ86cqQs29nq92rJmLBTHjx8nSZK6mB/A7OxsbZUx8RrHjx+vY0PuvfdewjBk9+7dnD59GsuyWF5e5rrrrqtTPc/NzdVpgvM8r7NkNePLzNgtLy/XblE33XQTQB2jY/a2IAhqq1zTKvRU8JQZS2QJpKXwyowst7BnBIwybNel8CWFHeKUCVXp0+6FuEpiywx7oc1qrMlzRem5OOkUduluadc9EE5M227jC8mGdqiUg/ASvFLR8gTCgmyUo6SFFSfYnk8lJJ5nUxWaspI49gg/aFGNtqqIW0IjLQEtC0t42EnBdNRhVbYpRwWisOi3D6DZoC0ElQ/4Fk41RctxuDp6DDXlICyLYlAhY0FuR/itFiIvEBSotkPluqiRRJUKNczxuz7FQCGCAblW5CU4dIgrRd/SWBslRRnS9kOqZIW8KmmHs4wLhe0KSg/YTPHTjIGdUxQxhc4RqmQ4qpC2oqRDkSnieJOWv+Ua5YQOUJDLmHLkoVxJyValdtv1WY838VWIzh0KSxHolGAckcUbKC+AsY2tYxK5zmLZY+zaCBIUY1aTaqviuAPLRcYgyyBVtP2IyNFsehBrFytO6HZsWlM26WANr+VguRFFsYFrObieTzqu0MJnpHP83MfyQqxiTOVlxFLhhB6bJeh4zEwgyOMBSmpKt01egJMIXK9PKSWiHFImPu58iFsoxlWByB1oV6jCxUkVqqxwxhWVDUyB62kiz0EUHkQOJRKlKgaqQrlQqhwtHNoyoBgUVK7EbUM82kRJl667FZxvBwFZWoCr6HdaDNc3iVwbWVqsZQV+38fVknGV0p53yAuLMq7wZ0sGxQjthLjYBJGHhUWVZ4zjBB8Lq6/RRQurFPh9fyvzV6XQhYMUNlpldOUzM4gTHieMRshoar6b8ReGDMLjVo1mYHCTTDeJsjnfbIBG823cn8x9DIFsWkR2Xm8nGYbHA6Kb5K+pBTfaySZJN/cxx5g+7bTENOMbmi45Tc18k3DvJOPN9pk2GOHBfNZ01wJqN7FmkDU87j7UJLs722La3LSWNF2qmgHnQE2odlo5zHg258C0w7j8NAVHc76x8DSDQpuxMs01tLMPTTeynTEh5lxzn53HNwVIMz7N+W5qTs38m/uYY3fGVDSvv1M4a1osmi5bTUGqGfTdfNaMkN0kVM1+PRNh6kPMz8+zsrJSE72HHnqonoNbb70VrTWnT59menq6DnxdXFysrQBAHdi/tLTEzMwMJ0+eZGVlpU5Hawi2IeDT09O1JWL//v3Mz89z9913o7WuFQBTU1Pbqinbts2FCxfqauE33XRTHfxriqY98sgjdbB6kiQsLCzQ7/fZ2Njg2rVrTE1N1VbF8XjMaDTi0qVL7N27l2vXrrGxscGhQ4fqat62bfPpT3+ab/7mb2ZpaYk9e/bU2ZpgK1j43LlzTE1Nsba2RhRFdeE5Y3kwRLvX67G2toZSqo4hOXv2LNPT09i2zfLyMouLi7WV7Pz587UQMj09jdaapaUlTpw4wXg85pZbbqnjRpaXl7lw4QLtdpvZ2VmuXLnCuXPnaLfbhGFIGIZ1wUWtdZ3a1qQ1Nq49YRjSarXo9/u1MBnHcW2FMXtVkiQ8+9nPZmVlpd4rVlZWapezKIoYj8esr6/T6XRqq6p5Ps37ZWVlpc78ZCyRvu/XWv9mHSdTVdwoRsyedPnyZQ4cOFAH2JsYnvX1dQ4dOkSapgRBwLlz5zhw4ACnT5/m4MGD5HnOzMwMnU6H1dXV2h1KCFFn3NL68foeRkgzyQe01qyvr+M4Th1ns3v3bo4cOcKVK1dqq4XJKLW8vFwL00tLS+zbt4/hcIhSqhZGvhKesqBRSkERuthegp1EMEjQpUJHAbbjYK2lOG0fISyKxGMkc/rzIYNk5f9kIrKJnArd9vHGkkinVJaHLS00CjwHu28RJxt0HZescClsD7+o6EqPsltheR7pKAXLQjo9MpVgRRWurRHxJrGWaFxi20YWFsUwI3AqBglMhT1aSqN7NtVQ4pDgepoiyZGBg6cVji4YjDZxbZt8XOD7Ec5UB8vJ6DkKK4tRto+2c2gpNtZzXMfHp0L7AbKwwAUtPaSuEFKj0wIChw1d0eo6pCspblkStDVCVWyOLhMXFlSCYbZOWWRUpUV6NUVYktzJsbSFVQlwKjqejTVS5H7B2C0IlUJVgizwKQMLOU5xpEcuClQ6YsaKcEuJ76TYlGRxyFiVFMn5rQxdgUa44CUZIT6bq6tsFjmu7RKFXZSTI+N1Ks9nuKFwWxDYPrZOWC3WEG4btKDoOAxHCbbjEWMTrgp8NPizSMasrS4zFc5gCwmZJqkkVVKSyQIvcMlTReFu4khFccVjONuh3QlQUrKeVgREFHaBnW8SJDnudA8ZemzkGYH08CR0ex1yxhBYuLmg43TIbYWQMVmSUNoWTq+HXMupZLE1Tl5OaNuUaUErKlFOm6KEghhLuoyxAMVcFJEmKUibtQ1J2LGRsmJpReLKCM+3GJUpKrSwbJdsXDDVcpBFSpp1CaOIqtJIMaDUOdVQko1cEBqnExD2NVoq/HaXZGUIrkZGJVZW0HJ8YpEhYxs76KLt+Cm/lL/R0CSNTQJlNnPzXZNANd1b4PHAbUOamhrqZtpTc11D4Ix20XzXdG1qkuSdLj5NErkzC1RTk2xcogx5NJqjpn+9IfRNNF3BjObcHGf62AxWbY5d0/pixsyQ7ifThDfJ/5MR1p3a8SZx3dlfM0/NeWu6FpmXsvnc9KkZJ9C0TjVdiKqqqjPbGDeNZnCzsc6Y9JZmDJrWpqYFzHzfRHN+myTczLeJxdkZx2LObcZMmFicncJXM+DcaAybsSvNFLta622CkrF8+b6/7XpmHTVdzZrz2Wx/U2DcuV6eqTAa4jAMuf/++2tf8vn5eaIo2iY0Li4uYlkWz3nOc+og1vF4XBdyM4GvRgtuskoZ4mncp3bt2sWRI0c4c+YMR44cYWFhgSzL6qJwpkbH+fPnOXnyJIPBoF7Lp06dYv/+/UxPT9eF7ow7jZmPbrfL+vp67TZnBOzl5WUsyyLLsnotGAtGURS0220efvjhugr5zMwMWmviOK7Tne7du7fONOR5HpcvXyZJEubm5jh9+nSdOva6664jjmN6vR55ntcVwc14CSE4ffo0x44dq7Ns5XnO3r17yfO81uDPzc3V6XiNZh4gTVPSNCWOY+bn5+t6DbZtc+nSJfbs2cOpU6dYWFioA6dhK7h6bm6Ooii4fPkye/furYP8y7Jk165dRFFEt9vl0UcfRcqt7HdLS0scOnSojvnodDp1EUSltupThGHI9PQ0U1NTDIfDOiVtFEU8+OCDHD58uLagXL58meuuu64eO2PJNPNs9pwgCJBS8thjj9UC2Llz53je855X76ntdrtem2YvPHToUB1DZOKFhNiq5WLSAg8GA2ZnZ+tMWmaczDvFBLlrrdmzZw9ra2scPHgQ27b5/Oc/T7fb5dixY/i+X1sxTLrha9eucerUKZRSdaE+2Kq4rvVWmuaDBw9y+fJlFhcXOXPmTF0x/ivhqQsaWuHpAOHvohyOsIWP8F0cy0VXNrLtUFk2zrSDo3LcPMBOYgQOghzPjkh1jFttUNoOygqoXBtRgFOBKHJ6PiS2RoqctiVQVUYqHDo9qBjjlBHSCUEXRNgopcENKcmQoSRqhayNK/qhxIpTSG36C21WHEh1RjXOwA0IIw+qIZujDD900KlNkjsov4I0peNCK/BRlcegUrhuRZXGRFEH5STIEqqBQ2QFyHxMQoEb+iSbY6gUgefh2h5KK6xQkBdDgtk26WjLTWaYDdGpZjMpyItVsrENiU1SbJDkAleIrQB7DVUlaXkepQdVWbI6WEVLh7GysccOvufi2DlZmuJLwajKGecZM4GAliBTJTpzqESBzGOcVBK1LEoEusyxyiH028RrMdLtsZxrLEtjhQFqXCDsgkiDGhQEBYyHI+gHCM/FqjRZmuE4LvZY42obi3KreJ+0qCKHDE1AiOuFpIVAe4KizFGWRdjKKRKJX4LvBxTrKY7fw3ZyCmmxmbbRSmKPYrzQJqk0rsjQrYpi1MaxIHfB67hYq5LBKGZ6uqRMcjpBD6wWY7GB5xdoy0bkkioFpMIOQuLAw3ZdSArkOMfWNlJVWF2BKzzs2Meq5Fb/ggBLC7DaeOsFgZBI1yZwAsabkki4RF7AIEkpRYHtdMmVxlEOCk2iErqpS6c9RRUVFHlMS9lobTPYrJjWHfJiQJZvkmpFmHjYQ5s4dQhaOV5oY09bFOMRbTt4qo/tNxyaaWWb5BAer4ZsjjGaY7NxGo2h+c6g6RZkvmu6H+3Uku8MxG5qoc31mi+Rne5PT6aRbwbtNgl8UzDY+ftLXbNJek1/jNasSZzNC2pnbIURUEz7mpmqmtdvHtPUvMPjQkrTlas55sb60bQsGMLenIcm4TfE1/TN+MQ3CXnTGmM0gk0LVHPt7Izxabap+WMEKHOP5jg0rR/NMWq6iRnybyxlOy0BzViKZgzGTguCsTA0r9cUgpsCrrlu0xWvuWbN9Zr3asb2NMew6Vpm2tMUSJ9pMO5OYRhy++231xmQLMvii1/8IkeOHKHb7dYBviZbnCFXu3bt2raumj7yjz76aO0CMx6POXz4MLOzs3XKYLNmi6KoC62dOXOGPXv2bHNhMoUAlVJ1CltTx+Gmm27aJlR7nsf8/Dxra2s8+uijHDlypLYgaL0VpCuEqIunmbXebreRUvKc5zyH8+fPMxgMEGIrM9S1a9fqgoDG1ccI7Xv27KHf73Pfffexd+9efN+vEygY161z586xb98+YOtZP3XqFK1Wi8OHD9dF48Iw5NFHH0UpVbsbnTlzhjNnztTBx4cPH2Z+fp75+Xn+/M//HNd1WV1d5fjx47UFxlhaer0ed91117ZnbGpqCq01Z8+exfd9Tpw4Ubuhzc7O1uN7+vRpdu/ezaFDhxiNRtx///2cOHGitjI3szN5nlfHsMRxvM21MAzDuuL3vn378DyvTiG7d+9ekiSp9yhT/PFv//Zv6wxjZg3dcsstHDhwoLamHz9+vH7emwqlptKmKIo6BXDTYmxZFnv27EGIrXoi4/G4Ft7g8fdkt9vl/PnztaC1srLC3r17kVJy7tw5vumbvonLly8DW3vB7t278X2f1dVVhsNhXQ2+1/v/qPuvJ02O7Lwf/2Rm+de1N+MxBgNvuAuCWPIbokzIXCikkPRH6lIXCjEYvKBol9zlLrAAFjMYg/E93dPudeUrM38XrUzUNPdH4pKoiImZ6X7fMllZWec553meM+HZs2e0bcvq6ippmvq109HLwjD0z+EP2X4w0JCpJBAa2RhqNCJRBKGh1SVaZHSdOeOT1wphYxZlS6MrwmCEMR3dMMA0DaWwdCF0UpN3BWvRhC4OULGkfHVKECWYMgTTUTYapVLapiYQCUanzJuOjfURZtESVwLbtNSJoRaWVkEQT2jrOdGKRcWasjzBJAFR3EEt0GWHSFs0AsIB1mrKcskoGNHoDjvMqLscPasgitBZgGlawlRRBTkiiKi6AGUCjGgJVUdLRFVITJcSxBbaEqsMyWTAvChIdII4bbBlxaumYS5KukVDW9dIc9b0jbJmWQjaccxQGSIjMW0JKBZVgy47dBbTRQLblsTaUGvJiRkQli3lckHUSswoxQSCqrNUhSENIO00p7LD1IJM1TRtQ5AmHExz0iBirbWoIGImCgSKLh4gi5ZC50RZRBtKWJ6gCkmrA4xuaec5kc5RdkAtNckwposFXbEgDRNs3RCsBwRFTlW1SCnQUQdyAFHGOIF8WjBIUuImRUiJGSUsFgWJjCFQdG2FEBGNlTSLAjWQpAyRIqdtZwQyJIolXdcghy1KDWjCjqVpwAbU7QmEFlRGbZcEYYDONaWyrEoL7ZQwGJMXC6I0xY4MptWsdJpaBVRCkIiYsrPMT2tMa4jGc9qwZoEkiVJkOkDaJYv8mMTEjCPOLHS7ApkMaReW9bjDKI0ZJhBCczojGoyQZUilG+JE0dRLlgkEA4UwFl0LlJREQYawkq4rUUoidcqy+nFrNM5TWJzjhsvM9PsAOB2CCzr7wZILrN3C/7uy5/3P9YOy/gLvft6nc/WrH/1MN3yfzXbZ6fPZ4T416XyW3FVi3Lme16g4Pq0LHPsUGjh7qbjgyZ2zc1LpVzbc9fVBRN/9qn+O7tz69K7+OLnPnA+g+wGtu5Z+Rr0P6Prj16+GuDHvV2Hc59u29ePsAsV+kF5VFVVVvVZx6FOf3L4cMDsvPj9fDXLH749Vf15Za18b6/5194GKO5e+KUB/DvcBdb/i4c7HXYObE258+sDCgbb+989XLH7X/XPP2Pnq3I9pOw8sXSCdJAm/93u/R57n/hqDIGAwGNA0DbPZjE8//ZTj4+PXxsONpXNf+u1vf8tnn31GFEVMp1NGoxH37t3jwoUL3Lhxg2fPniGl9B2Z33zzTU+9grOmf45mJaVksVj4fggff/wxBwcH7O7uAvhKCpzx8geDwWvidMfZPzo6Ynd318+b880Doygiz3Pf0M/ReoIg8D0YLl++7IXSX331FXA2L46Ojtje3n6N1uMCzrIsuXjxoheLB0HAcrlkZ2eH5XLJZDLhf/2v/8WzZ8+4fv06Dx8+5Pj4mLfeeoujoyOapuHnP/85N2/e9I59n376Kd99950X2Tsr3ocPH/Luu+/6e+eoXaPRyDsw3blzh52dHS/edsDo0qVLDAYDnxhwHdin0ynD4dBTxNwaEgQBDx488ADACdnrumY4HPLw4cPXqFNJknjL5JcvX3pHMNcPY7lc+nfNO++8A8CLFy9YXV3l5z//OZ9++imPHj1iNBrx5MkTbt++TRRFXu/j1tQ0TbHW8uTJE/b29jy16saNG0h5ZgEMeCcuZ508Ho/pus7fRwfAqqpiNBoxHA5p25bd3V1Pj3Pzf3Nzk/X1dR49ekQcx7z//vtcunSJ09NTvwa7da2uaz+Ow+HQz91/bvvBEUuARtaaPMhpEoMRKZNmjUhAHFuWAmwVoeqGQpVESjJaW6WZh9jOwKIiyCQmDAmsItCKMm+ok4bSGGIUMhzSioDMpKjEsFzmrI6HHMuGuGnIdExKSVm1dGGC7WoCKahqwWA4QAYxVnSIOqRRY8yghKUh0xkhLdNuTmU0XR2RyiHEAowljsZYOSUbdugmQEcJNlbETUNkLEssNkwhmFIXFTIZEnUtgoJQRegWKtsiY0UUDDB1SzyAJixZtYauhfk0h6ylUgVybmnngo4lcWgRRhLGAXEHUoE2DXmjCXRHCCgZUQeKhVlCVyLriiAY0KWSzpSEVYhqM8Isw1iJNA2LZYWMBjR0LOwhtZREjWAhK4zWRKIljQcEImZ/f8ZoKOlUR9NYlCwZScNkrFgcFxw3grVBi1UhKg6hkSgkIKnsWRCzKFtMAVIpwiwgUpq0OCGWklx3BDqgPpwzGGaIVJMvjpE6JK8lreyIa01tS0asI3TANK9RumTZWLa2BmBahiqgkwH54swlqmoLdNuSJgGZaBCTmJqYwWrM4nSGFgHIgLAckZqYvNtnlERkQN0UTMYD2rwmFhGhDMhPp4goplMKqwsKY+kkTIYx7aLE2ISmXRBlCiVDlBa0ZYG0MXI0QJ3as+aCdc3aMKGkJCXENDVqskJTN3R1zmBlE2FDytNjwhFo2aHGI0bGoMWAgc5pS0hUzTDLyLVGiRG6kqhQoc3pP/O0/svd3Muxn4V3AZezpXXe8n3ufz8T7igtLqvtXrp9e8DfxW93WTv393m9QF+X4TKe/QqACy4djcm9sNw19Z2kzmfnz1dB3LW7gLbvdOO+27fylfJ7N6w+dUZr7e03+zQo+F5n4Y4D31cZ+hWB84HpeUDXD5L7IOV81cYBo34FqE9nckGA+64DF33alttPn37VNI33qHdZezdfmqZ5rUu8lNIHBw4o9MFXHwS5c3Lj4O5VGIakafoa5eh85cndCzc3+5Wr/jj19RN9MHveCMEd+3cJt93vHHXGVTP696F/7/vZ0v7cOg+M+hWsH9NWliXL5VnTUmfd2neC+vzzz/nkk098ENqnIJZl6TUabmyTJKEoCqQ869rtRONJkvimfy6Ad03TlDrr/XN0dOSbzrVty3Q6ZXNz0/ePcFQfZ+Pq6DpufszncwaDgee6z+dz36FZ67O+Gm3b+u+4eXd0dOQtc6WUbGxssL29zZ/8yZ/wr/7Vv+L4+Ji6rvnggw/QWrO/v0+app7uNZlMvH5nc3OTo6Mj7t2754HYzs6ONy8YjUYcHBwAZ3PSdaN++fIlf/VXf8V3333nbVi3trYYj8cMh0PfPTwIAi8811rzi1/8gmvXrnnB8ng85urVq8xmM99krqoqbt68+ZoWYDKZsLm56e1kX758+Rq1Ns9zHj58yOXLlxmNRr66U1WVF5U7Mfn6+jq7u7tkWeYbJFZVxenpKVtbW6ytrREEAXVd++TP4eEhly9f9oDSOT2599aFCxf8mhQEgdc4/OxnP2N9fd0Lqre3t3n8+DE3b970Qvj5fE5RFBwfH3Px4kVWVlY8he2Xv/wlg8GA3d1d39jQJR7CMPRd5589e+apVE3TMBqN+NWvfsV/+A//gfF4jJTSa5qcjftyufTXfv36dZbLJV3Xsbe35y17HWhxc2BtbY319XX29vbY2Nj4Qc/sDwYaiQyIakuXBthYoERKc2wZpAa9LBC6xqiYLsog6ggKwWzeYG1DtiKpTUhRLkizFWLTYZY1W+NVjpcV40wQRNmZ4FsJVFeidclaIOiaA6I0xQRr6GnJMLCgc7RqyGVMiGAtW8WKKeX0lCRbRSQdZdNSW0M2jBB0VGmAihSDSBDpGt3UkGcUNiILJWoYssgXyDIjiSKQHUWV05iGZH1InU8JJKRJQqcbTKPQpaAJO+I4xAiLjVt0fdY5sV621NEMvWhAh8ybAmMritmMJg+RRhAIKHJoFFRBRdl2hF0KbUDeSoZBiGxqNA0LWxFlAYsCQilZLju6RcsgHrBYlBBUlPUURUowaGlkRNZZ5t2SKE5ICkOpO7I4QUYx06NTwmDBcNSiBgYCgQhXoOlQZUkbwUhKAl0RxEPaFkgjpMyZVzVpOiJQmunxjDgb07WWiYZFCk1rMd2SsB5h45QwkoSRRIkUKM/AWKcYTCJOmgajFVZbknFGO28pq4Y4GdKWORfHIXVboZOUzkjiJCQuAipTEMcdSoQ02oCKCPOG2ERUdctwMqE6WTCIJXU9o1EBa1trVF1J0MWgEorSEIaCZBPKZU2UrKPFkoPpIVk6IgotXRdTLEsyJWhFiTQB1hgaXaDLAWFYYmqJiCQiVpR1yCDpzsazqClCTbAaIGhpuoZhJiibJRkJarJOTkMSxuQzUHVLGE3pZE2crtG2FfPuBEyK6mJCAzat6Iofpy0l8Fpg7BZll+V12Wn3M8dj7mebXSAI32d7gdeCiX7Gt5+57msm3Of6jlP9bLhz63DBXj8zfL7C0q+8uPN1QaADJH0ajhsHBzL64KMfPLuxcAFxv6rggJfjbp8HN/0mb/1M+nkRM3xPqenTqfpZcnf97hr6QW1/HPpVA7ffPu3HfbavBfldFK7+Pe+Dv34g3nfM6vdN6VdNHCDsV0IcWHT3tg+u3HX0KVvnaXb9eePOsf85d87uHNy961canC6oH/C78+2PUb/q0Aep/XnWN0/o3wc3HvC9TbC7J/2qzI9x67qOtbU1fv3rX3Pr1i2uX7/urTvruuYnP/kJi8WCsixZWVmh6846H08mE7qu4/j42Dsg9at0LgA7OTnxdKsbN25weHjohd1OoGyt5cWLF1y6dAkppQ+IHUhwNKaPP/6Yo6MjvvrqK27dugWcWZu6NW59fZ0kSXxjOFd1+M1vfuMDYVfFdM+0tZbbt2/z9OlTbt++zcHBAU3TMJ1Oeeedd2iahjAMvY7hm2++4YMPPuDw8JBXr15xcHDApUuXvAYKzvpd/N7v/Z4HW87dSGvtm/iFYeiriE+ePGGxWPgA2H3OWYX3xdFCnFmy3rx5k+fPn9M0DU+fPvXPkRDCaxmuX7/urXEdpct1Aj85OWF/f5/79++zubnJu+++y9HREcPh0He8vnDhAtZa7/K2vr7Oy5cvfYfro6MjH1w7m97JZOIb0kVRxKtXrxgOhwwGZ41x5/M5V65cAfBzxtGxnM1yXdfs7e3594WbMxcvXvQVaHesq1ev+muy1pIkCXmeM51O+fjjj70OyNnrXr16ld3dXRaLBaurq9y/f5/d3V3fUyQIAu8o9Zd/+Zf8wR/8AWtra3RdxwcffMByufSOZ67Kdnh46Kttw+HQV3J2d3d58uQJr1694s0332QwGPjn6uLFi9y7d4+nT5+SpinHx8c/2CL7BwMNMTBgJHEV0HSW8U7EviyoK0UySilNQUSBMimBjbGBIFaSMq9ohSIMBRkTUhPRFBVhmrJsSuxgQNsa2iYnGiiKvCZFYURDEgvaKKVZnhKl61SkqEphh5bOxETpiDGGZlZh1iANQ2zVYKKCuAvpcoONp2ipMJ2EqEF2EgwsO0EUNpDUtEULc0GSjIlFjKRimhcwjFAIqA1ZnGK6kLbpkKHGNi0RZx2jq9qe0V0aQxAGtFIQzg16Iag7yWw6pY472vmM5YkmDCWVPkVbg0rW0VSoRUDWShKRMi0LKmtIKgG2ozULrK4odIpoB4RBwkle0ZYFzdAQyAFYQziWlKcNIjQ0VU3cFgykpga6ShIHinZRo1KDigShBrUA3VYMs4SFalGipguWtFJSnzQQJmBaikYT65xMRXSVplE5aZayPhojhKKuoe40sRZErSEwKQtTMUgimlrT5g1JpoGCfCqQywBrW9q6Im4j6kyRLzsGjSWerNGWc4QISLXAWIHIDblVLLolAxUg65ZKxgjbITUUMmBVRFRzi41CqrZAGUNiLOHQoMWMOsgQlcUCdVkTp6touzyj00UJ2rbErYJwTNdBmmmUsVRBQJm3rA03UFXDIOqYzkuEgIABsbDMK0ETwDiVNLajLmtkU9MkHTbIkDZnvDLBTmtilSMUZFFEgCbqKvKiQQ4TYmEpqyHNeIiKQNoO29XkxZzVeI2uMkzCH29ncNdN1QXUfZcol3l2AaB7uTu/cBeU9bO0LoPtgk5He3EBpNtPn5Pez2gDr1nFuv31+fPud+6l7H7utr6guX9NfU5+/7MOLPWDW8Bnxx3g6AeR/SpI36a3b6naP8bvEt27Mfxd7kTuHrjrPZ9N79Od3NbPkLuXr9tfn1LWpwK5a3H77oPIPk2tXwlxJXs3Pi6A71cq3HX2z62f4e9rLvoAwVWi+kCv71jWrzD079V50NcPWB0QdufvvtsHGY6mEgSBDyLdGDuHtaqqPHjr097cvHHBan+u9+eYG6N+MODAqeuj8GPcXJXogw8+4PPPP2cymXhA8eWXX/LJJ5/4jLSbO+4ZdFatk8mEo6Mjr+NwNp+ODlKWJW+++SZaa08zcZlkx4G/cOGCb2YWhqHvnZDnOfv7+1hrfQfymzdvvkaj61chnd3p48eP+eSTTxBC8P777/tqhuu8fXBwwNOnT1lfX/dV3YODAw+6XLdtB5SiKPKC7v39fVZXV/n666/5wz/8Q9bW1njw4AFKKe8Q5dYA9zx99dVXrK+v+27TL1++5Ne//jVJkvDq1Sv/jL569Yqmadje3vZrT9/NDuDg4ICqqjzdajabebHyZDLh+fPnRFHkO7w3TcNyueTw8JCTkxMODw+5evUqaZqyv7/PixcvODg44N133/WWuvD9M/348WPW1tZ8xchpH9q2ZTgcsr+/z8HBAePxmMuXL/PVV1+htebSpUv++djY2PBViLt373Lr1i2UUpyenvrAf29vz4vlr169yosXL+i6zjtxra6uehBx+/bt19wTlVJ8/fXXXL9+nTzPuXDhAo8fP/bVsK7rWF1d5Y033uDk5IQnT57wxhtveNqau9bpdMpsNmN3d5c/+qM/8lX7hw8fEgQBBwcHXs8jhOCrr75ia2vLa2isPdNrlGXp6b0rKyt+P9aeNQPc2Njg448/5v79+4xGI95++23u3bv3g57ZH95H40jDJKESgtFC0T0vuDgYEYSaZasJxSbtsiSShqBVaGloyiURimBhUFqDrpFdSzYQBEFGkCtkDl0qMOGCaS5IKom1km6YsMxAdYYJFtsU2HCACCNaWtJBQqcDFtOacQzTRiHkCJsqyqVg2LTESEw8RLQaqQXhWGNzTSWSM75bVxCHIWQaawe0pkPrmi4vMEYgVhMiMSLuEtq6RJoCqKmqjk2ZsShrTDhEmwZsDLqlVUs6DGWXs6xhbg1tURLYmk53yAyqZkZoBCKSlIsZRCllkaODhnZeYkgJdUdloWs0sUyI4oDZSYsUJblSLKxllESkUUwlLKINaQ8XRDrCNgqrOw7yZ2ysDKkOQxIVYwKLjCKqxRwhFToI6EYD2tOasiloWoMdKtowZCwHUHa0TY1KMkIrCHRFnYTQRdhGM+sa0jAgySqsjLEqxYYdrW7QVYUIJDQSmgoRCZpUIDqJaVtE1lDXIQED6kiiEk0mA9JQIc0MsQKRlZRlQVtZZCCIg5ZSn1V0ajp03tF2mvE4Is4mHB7PIe4IZEZmRlgRYTMIggGpENRURIS0WqCiDm1zhBEsGkM0NHRxjbAxWiUoG9C2hiRqETLBrKScWkhVhZU1QkrCJKJtzJmWZ1TRljlhkBHaCbJZogZDwpSzsW4binmJxZLaFB1ISnlKu5yzurKCaSyGgE6uEDWak6ZgMIhRlWUpWtJVQRBqqpkgHmY/9LH9F7e55kgOGJxvsnaez+8CJbfgA/7F3g824fuAv191cItr/7N9wOAy2H16iysr97Pjv0vb4PbZp1a5P/2eGOepLOcz1i7o7gfRLnjvO031g2F3Hf3j9ysf/etz+4LvBffudy4gcEGtAwF9YNEHZMYY/yJ2QbyrOjhaTz8YdwGPs/F05+My+i5T3698uGClT+nqA6zzlSNXDejrFfrXcJ461W+k158/7nzdz5w3fx/onZ837p71qzN9oNcHUv2Khvu7L3Q/DyTcNbn/9ysj7vk5L55313werLnPOTD8Y27Y98Ybb/g59Pbbb/Pq1StvnfzJJ5/4qkO/V4hrWubEsU3TsLKyQhRFLJdLfvGLX/DZZ5+htSbLMpIk4cmTJ0wmE/881XXtO2O3beuF5a5p22g0Io5jyrLk9PSUnZ0db0+6tbWF1prNzU0Gg4HPugdBwNOnT1ldXUVrzbfffsve3h5//Md/zN7eHkqdNWCcz+ekacqNGzdYLBbe8tTNj+vXr3Pv3j0mk4kHB1EUcXJygrWW5XLJ0dERP/nJT2jblmfPnrG1tUUQnHURd8LhL774gt3dXZRSfPnll7z99tueStN/VldXV3ny5Imv/lp7Zj17enrq7XuPjo58ZcFVq934Sil5+fIlWp85biVJ4i1bnSbru+++88/Axx9/TFEUHB0d8d/+23/j8ePH3pHr1atXCCGYz+d+jty8edP3rHB0tclkwt7enq+IDQYDNjY2ODo68vfRGONF9G5/jsY0m814+vSpt3oNw5ArV674fhr7+/v84he/4J133uEnP/kJWmvm87lf8111x60LQRB421mnQ3Fuam59BXxjv8uXL/Ps2TO6rqMsSyaTCXDmCrVYLLwb1HK59GvlZDLx7548z3nx4gWfffYZRVH4tcEJ9B0FeTAYMBgMfLU8TVOuXr1KkiRMp1MODw8BPFj9IdsPV5UOBtTlkiQJEKEgtRKhlxwtl4RqSChb4jCkXBYE8QAhLINUEaUB+bJDpRnRaUtgO6ouxNQNQbxCmk/RwwzVSRZNiYgEnRFYUrr2TPgcVREGQSQ0eVtTS0tUVGSZJBgH6LwlySVCVRzXkkwY0ok9a1pnEzobMA6gPbYIVWPKEjHKzhrvvZyTrY9oqw45hLZsGQwSWgG6KqnqimXbYo1BtZZsFGPjgFNjacOAPFlgO4EpLIKWtlwwb3PMsqasFWUHNreUr0rsqkHLM8rVsqsJVERrOkzRoUKgtiyalm6SIpolTWSoKsnCCuopLA6PCRKDCGKKdoEgYToVmDCka3MyFdLMljRHBjmw1PWCfHlCGgwpZER+UoBuieKQWIWUnSAs5qwPE44qMGOFmnZEsSaeGKRKUEULaQOznDCKmVtLrCRKSxZpi1UGTkuCOMNWJVoYwlBSi4ZBq6jKAhueTTQzD87AlS0Zp5rUasoqIm8kWdAQSE2tVpGJRNuQpmtYHU0IlhpoKJYtgzQhtzWVtmS7CUEhafKCZtnRqiEyCFmVgnZ6ggiGHJ8s2Bqs0KgQEwyopSGIU0RXo/IGGxm0soi6JIksmA7dSQwNWTBCRBC0S7QOkaYjDAUneUWqJnS6pG0kXd2STAQ2USyPF6y0EjGwNIUmNBmVimg7Q2IUJBEEHSrW1HrJaHWbuhPIEGJzZvU7Dy3JQGLNEl3CcDQiDgNY5qymQyTzf/pZ/Re8LRaL1yg5fX//Pm0HXnemgte56P2MeJ+C0hf8nq8eOCtUt29nbZkkic94uiC2T6nqB3PwevbdncvvAkr94M/9cS+d89Ub91338/53+sFwvyrRp9r0g253nP4Y9QNTlyV1AUDfUSeOYyaTiaeHnKciucCnX9Xpn7fT2Lhgti+2di81BzxcB93hcPiPqkj9KkR/HN2ccGPu7pELSBzo6dOOXMDdp7ydpyH1KVVRFL123uerF30wdv783Jj1wcb5ylL/fvZNBfq9XvrgwIFLd45Sytdsd/sVpT7Y7l97//5UVUWe5z/aikZRFP6+uUqGG9eXL1/yzjvvvFYNyrKMO3fueGHzcrnknXfeoa5rnjx5wubmJpcvXybLMg8k3LoxHA45PT31zlPGGJIk8dQT1wjOZesPDw99Nno8HgN4as/z58958803WSwWXuPxl3/5l7z55ptYa724eXt7m4cPHxJFER999JEPNK0960dx+/ZtTk9P2d/f9xoNYww3btxgf3+fsix58eIF6+vr3lnJjcdoNGK5XLK1tcWzZ8/Y39+nrmu2t7f527/9W6Io4v/+3//rq25OBH737l2SJPG2r8PhkJWVFcIw5OXLl2xvb3N0dMT6+jonJye+CiPEmZPV+vq677LeNA0XLlzw7lbO5tbRzd555x1WV1dZWVnxPSnyPGdzc5MkSbz25ODgwCef6rr2lraHh4fcvHmT6XRKlmVcuHCBk5MT7xJ26dIl/uZv/sbPnV//+tceZMZxzGw2Q2vtQdrW1pZ/Xp1TVhAE3oHK3Yv33nuPf//v/72nkPXXM9e/QinF4eEhn3/+OX/0R3/ke6E8ffrUr5HL5dK7X6Vp6isqly9f5tq1a57m9/XXX/tu48+fP6csS1ZXV70RgdPFuCZ7y+WSGzdu+MrFYrHw6/p4PKYoCn7961/z7rvveiDsdDCu+WRd19y6dcuvt9euXftBz+wPBhpNWQIt7akhFRqRhchBBLYliBM6DPNasZaCGDYUtWEcxixKi56Drg3pKKKsSoI45mQ5x2pNFlcEYYQpLEoFBDKmKS0yqKmXmlG2ApGlXB5hxxY174gAFSTM8xypwjPhz+mUOq6JO0UoBNNSEViLKBVlXRJZS9UoTCSRxIQqZBDVBCsZeVcg2xGBEBC11AtL2eQMUsUgi9HWMJvPkBtDtNXoVlGWAVnYYGSAagOa+pRiuaC1BbkOicwAmy+gqxADQdeABbSuMGlCpw1aB1SmZtA1CN3RYXm5nJ/ZyrY5XVlSz5ckA4MJM0Ri6ZRFhpKgqrHCorsQGcWEsiWMQuxIY6KEYlGwuboOwhK1ISGScCXEsIKxIa0pSVHEZUfdLZjl0L04gUATx4rl9Ag5yAiNoSUhCcZ0hSUxDVGc0CQSo0q60qIYIE1Jk0lUAG3REYaayibIbkqQDGnrENVa6AxBGxCGkiZs6IQiiwPqvGW4qqgbQ1cYZK4JJjHzZkqWBBSlQo7XWMwKskxR65j8RDOkQRoJoUQGlrCVLA4aQjp00hEF2dnv0pZKl0TxiHleMgwCwsggOGtCGQ8zmrYkzhTVTJw5rHWwyOVZr5RGIq3C1B1ZGCFaidUt0ShAmJB8b06gDKLVdJsCRESpA0TRYLMGaWtCMaCpThGbE+p5wyhJ0a2gqCAtII4r2kawsr7Oq1mJjUKSTKI6S1UsSCOB7Cq0Cv/ph/Vf8OaCzz5g6Gd9+wFgP0BylI8+77yfYXaBgQvA+/0snNOTqzQYY3xQ7cCGAyN9K9g+h99liOD7Pg4u4OsHuP3g/HyACfgAvA8KHFWpn/HvB7aOJuGutV9V6Wfm+1oFBwr6AWofbDiKmBsDBwAcRadfdXLn467ZjZc7F9eN2V2L6wHgqgIusJ3NZj7b+/jxY18xcF72m5ubbG9vs76+7oWqrtJ0PtvfD/D7Y3A+09+fH/151qdi9e9THyj09SfuMw749XUo7vP9MelrNs7/7e69m4cOaPQpWO46HGA8XwE5D8D7uqX+ufX1Go4+WFXVa4LqH9vWdR2np2eGGI6b7u6lC6z7Gi2lFJcuXXptzjx79gyttRez3rx504/Xs2fPuHnzJvfv3/fZ9ZOTEy/A7ScnnEbA6UAuXbrEzs4OT58+9fcyDEPvlvTgwQPatuUnP/kJQRD4oPov//Iv+eyzzwiCwAd1SZL4KqGbuzs7O8xmM/b397ly5cprCQX3execOkEv4PtgvHr1iv39fe7du8eLFy84Ojryz5pbm9z6NZvNWC6XzOdzpJSMRiPfUX1nZ4fpdEqe57Rty/Xr1zk6OvIC+s3NTf/Mbm1tMZvN+OCDD3jy5AknJydsbm76pnfu3t28eZMoijg4OKDrOq+TEUIwnU4Zj8dcv36dpmm4d+8eN27c8M/iN998w+PHj+m6jjiOuXPnDuPxmL//+7/n8uXLTCYT/uzP/ozPPvuMg4MDrLWcnp4ShiEfffQRRVEQhiGz2YzZbOaF49988w1XrlzxTRBns5l/F7j7vLOz4+fgfD7HWsve3h7D4ZDj42MuX77M3t6edxgbDAZe/L61tUVRFOzu7vLq1Svf7+Q3v/kNFy5cYDKZ8PTpU78uuoZ+Dx8+ZGtri6476x+zWCx47733EEJ4qtnGxgaXLl3i2bNnWHvWBfz69escHBz4zun9RFWSJLz//vsEQcBisSCKIm7cuIExxveFiaKI4+Nj1tbWODk5YWdn5wc9sz9co9GVRFlMFK1h25yyK5EnlvXRgM42JCbB1BobjSimFeEoJA9bClLMisU2HUd1SJKmCFWwNhmzOG1ACkqpkaSkqmHZlGQr6+RFy1hIhDTM50sabdGFRQaSKBaEtqZsOoLhGjapUKsNQXX2MCRqznQRUJQhWgnEZoTRDaNWU2lFbQRBYQlkBmGDUYYutbTFlEZJOhESr67SGYOsNUooBmsbHOmGtQAUHSUFgU0xOqWUJZE6o9osTUixqDgpO6JA09FgWlh2JWY5ppu3RKspnSkRcsHiZEqhoV5ouqCmFEt0B7JKSMMxK1fWactjjEqo6hqkQTU1VsVYJJ2FzGiUVdimQiURupsTRyFi3iBkR6ugtoJ6JKjbkm6xZGsjJUqgA/KFRVmLGVpspImHExZlRVAvqSpNkxdkkSAJa4bdABtZKl2iFiPSJqQLoWkkJtAELSgV03QCFSpUHGHngkApTFyzzC0qHbDUHdWyRoqQNCpJA0EnFe0cEitIxyHTukTFUHctCgiKJQJDo1MSlTIYxtTHL1hPUtpOszQVMraUukGgUUWICIecVJJQGQY7q3R2QGhLAqtQtsIOhggzoQsLVAttJ1gbD6hIWeSnrE3WyGvBvGnZyiymBCtGdAaGg4wwUxw3lkEsUW0AMmNcZyxlSxgakBZ0QdxZ2lATYimnp4guROsAOxmQyRZla0gH2Fxj4prxiiZvLTIxiGKJsIqOAGkG1LL9oY/tv7jNBTfnA8E+NanfX8H97jwtpp9ldvvoU3xcxqUfBLqXlwvG0jT14MRVMPqUFxfsOT61Cxr6x3JgAHiNatOn+LitL3R35++284Gyy1C7QNhRxfpj0heHuutydo6DwcADK/cddx3u+vvUqz6dqmka34yrT4vqX7sL2lxFxAFCd07uHriAzIlGXaDusqyLxYKTkxMv9Lxz5w5ZlrGxseH99zc3Nz0IcmPYb47n5k4/EHf3qw/o3Fg5ioAbj74Auw/izs/B/nztg8d+lcwBtj4lylVh3Hi4e+HcafrVIXhd0+LO2YG9PjB3AWaftnVes3GeauXoRK6a9WPcXDO427dve4qHqzZ8/vnnvPvuu95IwnUzdpnXLMt8Rc1Zsbpg7cGDB1y/ft2vC1euXKHrOl+FTdPUZ+2dtub69evewWpra8vTTVxQLeWZq9PVq1dZX19nY2OD09NTX2Fwmf+madjf3/ciaK01e3t7HB4esra2xi9/+Usmk4nvsXHr1i0/f9x6kuc5KysrHB4eeorWyckJFy9e5PHjx5yenrK3t8fTp0+96N1dm9OxOFAfBIHXPrz77rscHh5y8eJF2rZlfX0dpZRv/Pbw4UNf2XHJBrcuWmvZ39/3WXbnAvbq1SuSJPFWv5cvX/aVC1dtreua2Wzmn9eqqrh//z7Xrl3zQna3j5/+9Ke8fPmS8XjMd999x2w248WLF75T99WrVzk8PPQZ+yiKePvtt72Qu+s60jTl3r17XLp0ic3NTb777jveeustT5NyZgIOAD569IjxeMzh4aEXlFtrvUOYEOI1ypF77ubzua8sPHz4kMFgwGQy8X06iqLgvffeA6CqKsIwJMsy31CybVuuXr2KEMJXUxygdPfHUdOOjo581/Su63j+/Lmfu0mS+MrFYrHAWstwOPQmCt999x1Xrlx5jc4VRZE3bllZWXltvfynth/esC9epW4KRqomDFN0rghixbK2CNVRL6boIMQGQ5LREBkb6CDTsFSSNqgYyJSoEXQyhNCSJi0qSqmNPtNdKMXK2gb5aUkqxiz1HKELxgNBaQeYRJGOJF1R0U4bomCItoaCmmQgyOSAvFmQ1wvCaJV4NaLqSkIkspG0SiMISWgICanqAtO1tCGYakEgJEIHRCNF1zQsqpZhHGLbhgBBPLPEmzGdbJgMBOVJxaKuyXWLyTVGNRRNSas74mSBUQmqEnB4pmXQFtoMDl48o5jPsGFNpyVECU0JNlLIVhJEC2QaM5MtstbYVqPVjLq2UHYM0gHGKpQIUaqgbWuqVqAjiypDpLTYyJIvA1QkkTbFNHMQikRXGBnSnbZoKUhXU6IkQtgpyUpC1UikNTRdS9eEZHFG1LZomVMvZshkzsnUECUrDJMBM7vAYgkbSRKFaGmwgaSWFaGQaDUmC2OWck4sAhIpKOZTqtEIScJwMKA4fEGcRag2Qg4a8rbDNim2M+i5pNCQjWJEVRNmEUVZEa8lVNMpQSqYYWk7qKUm7TqCgaCawspIYYIlRmVATGM0dnkMQ8VwEDKrG0SzJLIZcQWtHSMTgekW6EQghzHYljaoGCdjympOEmtMEtFJqLVAz3OkhigNaQuNDA26WxAoixEKEwpClaIigUhGqDwkCJeEsSSLQ/L5Ai06GA44qiuMjMlKSxqkpFSYukbEZxntKIipjEYU3T/3uP6L3YqieC0A7L+Q4PVu1v0AsU8nOq87cNv5bHc/2HLZGJeVHAwGr5WzXQbaBfyAp1o5XUM/AEySxAfr7hj9oNEdu19BcMEs8Frl5Dxv3+3LaTTctfUrPC6wdpnPKIqoqoqdnZ3XxOfuBeSurZ+xPF956Wsi+kDIAbUwDP2x+uPtdBt9qlJfN+OO4ypBQXDmc7+xseF/74BHnuccHR1xcHDA8+fPUUqxtrbGzs4Oly9fZmVlxdMl3Pg5ENCnv7lzcGPfvyfu3vYrP+6zbp9uXPp0Kvcza+1rFS53X35XdaNPo+rTqfpj7c7NjVlff9OvbvXBkJuvffDptr4+xf3OUQedCYMTP/8Yt/F4TJqm3kFqNpv54PzmzZusr6/z5MkT2rb1oA/OxtWtP84y1lGlHj9+zPr6Ot988w03b970gW0cx6ytrXnXnpWVFU5OTrzjz927d/n000/9mvT06VPfsds1O7t37x7b29teJ+K0EXVd+8z/f/yP/5HDw0OGw6HvV+Hcnqy1vPXWWz67v7q66ufEN99847POaZp6Z7miKHzC4H/+z//p+yFcuXKFb7/91rsv9dep8Xjs6WOPHj3i9u3bvqGb61odRRGrq6vMZjNOTk78+lhVlQfQq6urnJ6esrq6SlmWhGHo7XIBfvvb3/LJJ58QhiEXL14E4MmTJ75juHOAklLy5ptv8ujRI4qi4E//9E/5+OOPfbIgyzLiOCZNU6/TePbsGUVRUJYlSZLwzjvv+Hviek98/fXXvhLkdDWnp6eMx2PeeustAO+yNJ1OmU6nr53/N998w61bt3jrrbd49eoVN27c4MqVK0ynU+bzuX+2Hz16RBAEXL9+3dPrVldXUUqR5zkvX77kX/2rf8WdO3e8bmY+n7O+vu4rQV9//bVPYLj5PJvNuHPnDu+++67vUO/eI6urqz4Z8+abb/r1Pk1Tr5ex1vL48WOuX7/O6emp784OeFrt4eEhb7zxBnme+6q1u/6VlRUAP5b/43/8j3/2mf3BQGMQBRibEgQlbV2jgwSs4rRcEIQV49UNZkVBmDTUc0VoMoLQIOyc1cGYatpA3pEPFMpI4gpCNcFUhoFUdGmDbi2mrIgTKJczVGoRCuJAI9F0cgBVQ1dCUxuSLERFNVZIlrVioVsklihapTUKZEvdHaPEkCCNkdrQyhoxFeQBhMX/a4AVTWjbBiM0SkjyeoFSA4JohJYdrSwxpiUNQ6plQaNbbGtRcYSsTomKkhf5glp1xLohFC31tEStxDRlxWlZUC1mmHlBHXaUlSJLIxqjMGWEQEDU0CIYDwZEgaIxLVkk6RYWZEArQAULQjFGFwJtLEWoCYOOOEvoatCNJZaCuoPAdrTaQBZyujhmY20V09SENbAaUp80KG0oZw0mTZiMh+Tdkulco8eaaBRQzWu6MiRqJS0VJBGtCEmURi8tSpQU8ohpWbBmx2i9QjhKoJ6RTGIC0VGVOYFoWFUxpYSpbhgmE4wJCZsGuoI4XaPWAgrDJFXsBYbACNK6JJBjWqEwwYBZM2NMCzJhNj9kKAQBQ4oc5CSksjW6a4jaBhkkdPmcTnaoiSXvWvJXLZHWYAccNAVJmhG0GpNq6tSSVgGRkcxSiaLFiIBFGZKkAt0ZwnCICFqC1iAnKdSGojLIUCFUiBUd0sbMhGEzC9F5QTCZ0Bw1rMUJS3vWdFE2DWmYnFkiS4G2UNQBUSsJQtBdTd5o4skAU3fIRBENocxbumHHIPxxdvSF77nwfZDgAmxXXXBC7PMUIvf78xnfPuhwL6Hzmgn4Psvu+iS4xbXvDNUPnvvZmn7G2AWMLqv2u0S3fR2KK+/3AYnL7PdpMu77Lhh3QZJ7iZyvtLjA352/8953wYMTaTqQ5Y7rwE7/s+f3774H3+szHM2snz3Lsuy1bH//vrqA111HP7B296FfYRoMBqyurnLt2jWMOXPjcVnYb7/9ll//+tc+03z16lV2dnZ8TwM3Jxx9rK9P6Vcf3NxzwMhVI/rg6nx1zY2zCw77ou4+YHbj6DKw/QpQH/C683VVmf55un33wWd/HvZB2/nxdtfTfxYcwOjrGPp6jR/j5p6Pp0+fep3AtWvXePToEdvb27Rt6/tEwFnzOaUUv/jFL3yGtigKL9wejUaMx2OapuHatWtUVcWLFy+w1nptBuB7DBweHvqKysrKCg8fPuTRo0e8//77SCm5ffu2DypPT0/J89z3gXG9Pf7iL/6C//Sf/pPXzDx79ozDw0NOT0+Josj33jDmrIeBszwF/HfiOPZ9HJ4+fUqSJKytrfH06VNevHjBq1evyPPcO0Tlec4vf/lLbyWbpilXrlzxc+Tq1atetO0E8UdHRxhjePbsGYPBgMPDQ6w9E34HQcD9+/fZ2dlhfX3dN4hbLpfeGtaBhvF4zNOnT7ly5Qp/8Ad/AJzZxrr7NB6P0VpzcHDAbDbzGoLt7W2vG3M6DAcs3bgeHBygtfY0JKeNefTokdeJPHjwgK2tLd5++22yLKMsS46OjhiNRty/f9+vU656+/jxY089vXr1KqPRiJOTE4Ig4Pbt2xhzZmXsenL88pe/9AL0k5MTuq7zFrRxHPPixQtvsQtnz/GFCxe4d+8ey+WSzc1N7y5ljOH58+dsbm6yXC65deuWryi4CoyrUNy5c4fhcOgtnpfLpQeCDphKKfn22295++23/b3+6U9/ynfffUcYhr5RIpw1m7x37x7vv/++rxxevHiRZ8+ecevWLe/e5Sxvf/azn/2gZ/YHA41YKpZRTNMGGFPAQGC1ZnMc0BYBKjesdgFhWNPqlEAK0smARZOTaIORinJiQebEJKhaM5tq1FgyDhtmjcaGBl1bCM4y8rEMUEJz2mrSkSRa5pRGo1RInAV0TYExZz0+gtOYdqgQwZB5XaAsqHlNbId0rcUMG2zbIjuLlZAvGzJrEbHitK4YUEPTMR9nxETUy4pUCSIdEgVDymAOicSUhiYK6eZL6qxk0c2xZc5k0jCdaUQpafKOojQcT79lNm8pjCSVmjUZEsYpMR1GC1pikiHopkZZzXiywfF0n5VhSpIJqllBUbakg5QoGFBqhQ2bs2uwozOHJUAUAVppqlRQyppIV8RWYpWlPqkRYUchS9oOIguibRAJ0LbkIiCfz+kCRWAMwyxE6w79fIEwCWWwJNtIIUkwpsLUYEqNbedMS0MSDRi1kjYOEKZkvl8R2obnC81GGjAMBnR2zGKYUo8MgVglFSFKnnWXL7qOMBiQDSJyO6ecaVbGGWGzQI8Ny7JgECY0y+lZ0xERE6wNCRYaq0A0MEkU07YhU9BVAZGyaF3R2RRNhugCjG3RMsBMLHKYYhqBbiXz5hQVScbZhKKEvCro4gCpVogx1LKi0QLTCU71kkC1pEKiRASmYjSWLGcFncxgdQurOywtlakIg5D8wJKohGWdU5aK4WBA1dYUgSBTikRYRDakLSqE1cSdJh7W6GiFWpWMuiHFcUFHTpwMiYQiFT9O/3vAL34u+OzrGlxmrR/kun+7DJcLmPtBUp+/D6+DhT5I6VNY+mLrfkWlL7B1+4Lv6Sl9UOP23QcfLlvqskou+93Xn/SrG32OrDuOy4T3s+2OCub2nSSJ9+wHPEBx5+eqK31aWhAEZFnmHUfcePQ57lprL4Z25xRFEcPh0Acf/cqBs2F1WfZ+tapPSeuPWR9g9HUYbjzcvYiiiLW1NZ/NffbsGV9++SWff/45v/rVr9ja2uKdd97hnXfeYXt725+D8/HvV3764+zuYz/g7lc3+mCgr9fo35Pz4u9+lcjNpz5IOA9E3b1y3+3PO/d/t7mx61dc+tUwN+bnnbzc9fU7kbt56f7/Y9zatmV/f99rMba2tjwNaDKZvJbxbpqGb7/91jsoff7553z66ac+YHU9Etx65DK6Lqvcp/hlWUYYhly6dAk4axy4tbVF0zT89Kc/pWkabt265XUCeZ6jlGI0GtF1HX//93/Pz372M4bDIb//+7/vAYybs9euXWM4HKKU4vj42AfpX331le/8vL6+ztHRkQfmbk4tl0vu3LmDMYbxeMyvfvUrdnZ2uHPnDmtra1y8eJEnT54wGo1I05T33nvPP4Ou07czB9je3ubk5IRbt27x9OlTjo6OyPPcW78eHBz4Y9+8edNn6dM05cWLFwC+geFkMmE6nfLq1SvatuXo6Ihr1675a1hZWWE4HPLkyRN/D3Z2dnzjvC+++MJn893zMZ1Ovdj54OCAOI69jsY1JXzzzTdZW1ujLEtfgXKdw9944w2vSXnx4gVRFHnL2NFoxPPnz5lMJiRJ4pNWDsyMRiO/5p+ennLp0iXu3LnDysqKp866dcitp03TcPnyZU/Dc9W0PvWrT3udz+dsb28zn889Tcx16X758iWj0ci7oV25coXDw0PKsvTPxoMHD4iiyDtOhWHo7Wvd/B6NRp6+6cbs9PQUKSWffPIJy+WSZ8+ecfXqVfb29hgMBjx79ozLly8jhGA4HHJ4eOiTTv/c9sM1GvOWwcWAaZ4zXl1nOqsZoFELQZIOCeKQZtmgOkWYdDTNAXUzRHSSXAwIBwGWYyZNijSKMLGsbEhOiynzLkI1IU1bEU4GaN0wEANaXdI2mkZHRFoj8pwoGYERVKYjHAToqkLZITrWqMWcIBzTdS1CBsgGogFUCqpCUNeKDIhjGKaWNu9oZy12rDG6Qw1TRoMIsRSsR4p5t2BuLWbZQKigKdGhpalLyuNTjuY5EmiOjghqUNOAw5Mjjo9yiFJQksFAkTAiyBLak1PaWiIbSxLFxLqkCju6KmYQaqYHr0iCEUf7p6yujwilgbUYWRjy04q6CxnEFhF3RPEQnZ/S2A65Zhk0AZaWI61ZTUKK2iLHIMqAjTgB3bFIBF0aocqWSIIYjZBas940iGjIfNYhVIlIRyAShlGHNoZwqWnmLdE4pe1aWpGRrQ5pRcmyPnPksh2IriJNAurCkgQJB69mVGlIoqaEZkkiAkahQhuLKRQqluiwQUjDspxSCQ2RJF/UXNkcUHQaHUrauiKJFGUsyVVItJiT2JSmm9MFObrp2EzGmEFAJaFrJTaS1FhUE9CpIeX0mPVdgWk65HIK4YSZbcnCFaCjneUMgyF52TDIVmibHNmVxGnGaVkiZUwWxwzkEBU2Z5qlWGA7TRgp0kRyurSUYU0WS7pKIYwitgYRtiAEWaOp2iWT1VWWYU1ZSMZKY7oFadQihyFNWZNEEVp0iHnOrOyomg6bhmQNdNawlOKffFb/JW/9wAte75oN/KOg73wG171s3Euyvx+3334Hbbfvfm8NJ/zuB+f9oOu8INwd1wWJrjLSz1QLcWbz6LLQ/UpFHyj0wcB58NHXS1hr/YvLBbZ1XXtnoX5lx41TH1g4Zyj3p8/x73e97lO5zldm3P+jKCLLMs9d7593v+Li6EX9n7vP9zP6fRDUt/R1Ly03bv0O31EUcfPmTQ88nj9/zrNnz/i7v/s770V/48YNL050594fJ/enn+F397avz+hXF1zQ0K+Q9e9df045cb27Phf8O4pb/7xchadf8ehXHPrif3dsN/fOg5j+89QX77vv9ask7jjunH+M28rKis/Wv/32276i5voyuEZqWXZmAx4EAR9++CGrq6s+sPz88895//33vZjcdf92Ynk4GzeXEZ9Op/zmN79hPp/zySef+GDTORU5apLj48NZBcQ9w3fu3PFVl76ewzkuuXvistsHBwdeI+GE3E6Dsb297R2hHEf/+fPn/PrXvwbOdCgu+3758mW2t7eZzWa88847jEYjHzzO53MeP37MG2+84as7cBasnpyc8Dd/8zc+q7+7u8vdu3fRWvPv/t2/4+HDhz7oN+as8aDrHZKmKXfv3mV9fR1rLdPplIsXL/rPPX361M9vR9M5PT1lc3OTjz/+2Nv0umqDEIL19XXfL2k4HHpHKPc8W3vWsfyTTz5hb2+PyWTinasuXrzoKZRuTkRRxHg8ZnV1lRs3bvDLX/6SNE15+fIl8/ncW6qfnp56UNS2re9jkWUZ4/GYtm1ZWVnh+PjY01jd2vf8+XPW19cZj8e+j8n6+jqz2cyvI0mSeL2Fc836zW9+w6effsqTJ0/Y3t72cyyKIjY2Nnjy5MlrmsOLFy9ydHTkqxquT4ij0TlqlNNdKKW8Y9doNPLrm6sIvXjxgqtXr3Lt2jV/3m7OffHFF3z88cdsbGzw6tUrb8rwz20/GGgMV0dnSEgalFoShQFZCSZMmHUVQbxgfRN0mzK1HZ1KMXPLaByg2yWd1Kha0LWSsrOkUkFQEgyGdFFCOisYZDHzYkoUGNJondlBzcpQEQwi6rnE2hXCOieeCMQ4Ylm2SBHTFoZmoFksczZVRxhkaDJGkaGVIIkIbIFNwdaWto6hhjiNUG2FSRLCOEbmNcVJzkCssZwvSIIBOqixgaRpC+qoI28KmHeUuqLJK4RR5FYxfX7A7GSBFQrLGYWpVCGhjJCdZf78hNUooNQFdajougKCkC62bA0H5EvLKBXEZYStI0QlKJMITEISNWSBJY411oIeBZhmQTqGrhtQnzS0XYvZGbIyN4xNhwpjmtDQRrCsC9ZJWeQdWtTEQQS2I2hrRKNRgWE2nyOrjng9QcgGQk1e56wnq7RVDW1DFQUI06EimBUnjJRC6oYgGdJYw8w0RKUlDgLCvCNSE4wOmE8LFi9rVtYS6kHHeH2dOIqoZjkrWYARZ1bB8TCDriQbrzCvC4bRgLaasxIPmE4XmLUEI8B0HVG0IB5klNOKMFActRViHwIRkIQJp1FLJiyBaBF1zcpwBWsbsiiABOqqJkwjVKlIE4vBkuc5a5MBRTUn6DqEEjS6JRmHNEVFHA/ougraAkpDJTRqNEKKFj3PGZYVdiQxXXBmx6wMTWRpy5o4VUglEE2LKVqkiDD1HD2MaIqSJBEYE9ARQrBFl9cMTUg37IilYFF2BLojkZJW/3j7aDgHqH4G2AXSfSedfs8EF0i4wM5Rq1yw5wJvF5S5LJILMn8Xn/083cUFl/0A2gXrLqPmAkYHGuD1BnbuWO5F4wLvfqCntfZdYvvX1g+AHVhxFC9HT3LZ2vl8/hrVylV63Dk5uo871ziOvY7CgTSX2XYBbx+0OV6vG7P+2LmXl5TyNRG0G/e+W5QbRxeUuWO7zGXfacndc2OMDyicSBXwY5IkCRcvXmRnZ4e3336bPM85PDzk0aNH/Pa3v/WZzu3tbf+C7VPtzlc03Pn3nZ9cFtAY4+dAHwzA900cz993928HXvrj1Z8Lbm739+k2d47u/vV/1gcLffDjNrd/d8/6gLhPB+zPzx/bJoTwwlknprbW8vDhQ58tdtqGvohaKcWnn35KVVW88cYb3vbz7t27rK6uekH0lStXKIrCW8wCXLp0yR9LSsmjR4/48MMPvTg5CALm8zlff/21dylydCDnmOS6kbvM/sbGhndW6gerADdu3GA0GvHNN9/w7bffsra2htbaC44Br13427/9W37/93+f7777zve4cXoLV/m4cuUKp6enaK198F5VFcPh0LsnGWPY3t7m2bNnvrP4q1evqKqKpmm8a9J3333nAdTly5c5OjrixYsXvmGho1D1zThck7vT01Nms5m3ls3zHCkl29vbvtu0W3u++uorVldXOTw85MWLF5ycnLC7uwucgc2dnR329/e9He3GxobPsjtzAGvPeknkeQ7g+5+4atHR0RFvvvkmf/iHf+g1FoeHhyyXS/b29vx7yFHfvvzyS/76r/+aS5cusb29zQcffOArqI8fP2Zzc5OtrS2SJOHChQs8ffqUMAy5cOEC0+mUqqr+Ed3RrS+LxYKu6/j//r//jwcPHnDlypXXKpVaa2+N7Lb9/X3vOrW3t8fa2pq3AnYAGM7WCNcMUQjB8fExm5ub3hXL9beKosj/PAgCfv/3f5+2bTk8PPR2y2EY+qrfD01W/GCgschnmJWGqJHURck4GSK6hjg1BLohWgjaRchMlZBk2LbEhAFqnBJ3OfkrQZQNCdIAW+UUbUmgYjAx2gqaoCFdKCbB4Mz9Z16Rqow6hbZaEHdD4iChLkKa+IyLFq2v0HUV1oJtBNFaSCcMXVURmI5ZNSMKI6plRxwoslTSxor2tEMLOK4aJnFIEoaUbUUnZmwPxpgupxgGaN0SIAhETLmoKUVBUR4iCkFx2nJcnDIt53THLYVeIrsQEaRIvaTpaurAQnnWXXwYdLRGkAxbWms5sZagFawD8+kRdQJ0hig2ZOtD8rxFEBC0mtpaWtURAp3VZEHCfFZihGE0GLE0lrkIkPOGVdFQLTTDqKZgwWCyAm3FojEEg5iuOQNniYA6GtKhQWtMsGC4ESEXNeHA0iQpJLDUC6wKicMIg0QHisQKwkDS6QGlUUykJGglIzlCI6lThZq3qKDBCoFaCwlMRNVqTpcV85P7jK+tEgxWCAYRyjbEVUJQWKyyNEVOnBny6UsyGVAnm4wubrA3PSRUBUoIGg3l6ZwgXMPECiE0rSnZWB9QT3NE3RBkAYw0lDOWXUCcdNSmIyyHlMtjRskGg0TSlZDXNdlKRB1BrBR1B7UesRQlY6GptaEW8qxnS9AyjFYIKkHbKro0phECW58i2hFt05JIkEYR6BazNKTDVRZlQxgW1MIwX9aMRpcougoZacLA0JUZWSWpiiWt6MhtRpiHiA3JJAkI9fyMYtX+sHLlv8StT+3p01AAH3z17TjhdeqIe8m6LE+f498XcsP31J5+RrtPT+kHX33tgMuk97PJ/Wx3P+hzmhL3GRcgOjqEEN+7Pbnz7oup3bWWZelL2w7QjMdj35XY0bPcC9NpTPrdvN1LxI2vq6QA/wg4uHF1lRhXlXDj6qoffc2IG4O+hsb97Y7nzqtP13L31FWa3Li7sXbf7Y+fA0PueH2xtZsnk8nE00Ju3Ljhwcaf//mfMxwOuX37Nrdv32Z7e/s10NkHue64LmPYrwq5cXJz9jzlqk+n6u/fHcOdv/vbVdsclau///OVkz6Qdsd04KuvP3HX0jcTcGPbr/L1gfX5KuCPbXPC3g8//JAgCHx148aNG0gpfWb6u+++46OPPvJapTRNPf3QGMOdO3e4ceMGV69eBc4C5FevXnn6kauAvPXWWx6suwSB00s4gOKy2bdv3/Y2qa5vxGQy8aAjjmNWV1d5+fIli8WClZUVf99c1bCqKq9RcI5PKysrPHjwgNlsxt7eHk+ePPEB/8uXL3n+/LlvXri7u8v169c5Pj72iQmXzVdKcXJy4m19pZTcvXvXz73Hjx+zvb3N9evXfT+MpmlYLBYebLjraduWR48ecf36dR+0b2xs8OLFC09TmkwmFEXB5uam79/w4YcfYq3lm2++YTKZ+LG9desW29vbPH36lPl8ztbWlnd06mu9HG3p6tWrjMdj1tbWfDM818PCJYPceu0siJ22wfWe2NjY4NGjR6ysrHixvgMZg8GAy5cv83d/93deNL6ysuL7W/zFX/wFjx494r//9//OBx98wN7enrcWdpqQq1evotSZCYejvblqzNWrV73Yen9/3wvH3bg5wbbWmgcPHvDBBx/w9OlTLl686AXg165d893WHf00yzK+/fZb3nzzTcbjMWEYenOAo6Mjdnd32dra8nPeUfwcbe/u3btcvHjR06v29vY4ODjwndHLsiSKIvb29jzw++e2Hww0urRAlBFtFRPGFhN3mCihrSWBEiSRom04600QVqCh1B3HVcHESMI0oZnmyKgjixVFNcDoAY2OCHSAoCIwik63dMbSDTRBnNE1liTMaJMASUmkDGAITUv9KmclSwlSyf5CE6sVTGeQ1mB1TTQY0uoGMYAqsAgTUpgGPbCMogxZtTSyRr5qESImFglt1WAzxdwYwrImxrAUNUYJ1BJkHXL0co/vni2Y5UfIUUonalK1RpR0NLWmDSuKcEqSbiCEJtAhgcrobAe6IzIdaTxAFy1ZFrKsJe0pRMMh05kmHFUMVEVbp8xtgJIdkTzTYRjZUhQd1kiETJiVlmCpiYY1pomYJg06rBHtiFSt0ZVLVBag0wRKwVBniM5gZEY5L4mGKYFSSDlAyxibSorTGSuZwC470tWYBSMaKQmJsGFBEIS0teWwmZOKkMWyJhQBHRCEDbId0nU5sY1R1RLGASOZIWKDMQJZKZ7dOyWIC4aXhlzYWCeINFJqLIpOG6JGoUwE2RrzRcHICgYypmtz5HBIpxKoDbZuIapo0xahLfnpEYNwnSAXNGkIxtCGJaI7E7XLJEAAw1FGXDXUzYBQhthQEswaYjOhSGrC/9cBfagkyWlBFkxoiwV6rOm6hOXSMmojjOho4oQ6L4iTCZFKCaWhbWcEKdhcodKYzrbowDAaraLbkkHckckpjWnIZEAXWJROEFKRiBOGg5hFLZGDhEWzx1CMCYKUJtBo9cP7bP5L21wGxwXp8H0Q74BCP4B1f/oB6fl9nK9GuICqL6buA4Y+Lcll4OH76or7t/t/P6g8n23u06P6maogCLxG47wIul/ydz71y+USpRSbm5tcuHDBv7DOZ7bDMPRAwGWT3EvYvbBdYO+oVn0tggt0+/oA92IGfAbe3SuXHXe/749VP8h1jib9aooLus/T3tx4lGX5j7L0fX6zu4fuvvWDcHdN7rtpmnpXqpOTE77++mt+/vOf89VXX/Huu+/ywQcfeIpKH6z274Wr/vSrMW7c3L+d5sGNc7+C0wcn7jzd+PQrHH2wfV470gcBffF+n1bnwOXvEqH3Rd/umXL76o+fu/4f4+Ya6B0cHHiKzXA4ZDab8eWXX/Jv/s2/oWkab2PqePDu3tR1zVtvvcW9e/d48OABH3/8sR//xWLBcDj0otrRaOTpJW+//TYvXrzwzj5lWfLFF18wmUx4++23GY/HvsHdeDzm5cuXXmvgOpW75muO5hLHsQ8C8zxnOBz6e316esrp6SlJkvDLX/7S03i+++47RqMRe3t7XoCc5zmDwYD333/fZ7JdYsJpDLa2tjzocDSnnZ0dnj9/7gN0KaV3fRNC8Nlnn/FXf/VXxHHM5uYmQRDw/Plzjo6OvGbD8ffbtvXBu7NLffHihaeiueqF6+PiXLneffddvvnmGxaLhR/fPM8RQrCzs0MURT64bduWnZ0dD5ScqN8Zc7iEgKOeWWs5OjpiOp1y4cIFnK2vs+912gTAB+zD4ZDxeMxoNGI6nRJFES9evPB0IwcGL1y4wOnpKb/4xS+4efMmGxsbvtnezs7Oa2t3EAS+KrCyssJyufRjJKVkfX3dd38vioKVlRVWV1fJsszTxgB2d3e9c9zW1pYHPS7R5Gixo9HI78/9cVWIuq79dwFOTk5IkoQ8z9Fac/v2bb744ovX+hs5OhbAixcv+OSTT9je3vZdwv+57QdHLMlwg9OTDhElxEFEMDs9c0fKBpjlActGEA9XoS3IbEo0CGmrDmU4026EJTZMmRuNMSXR2oiibAnDgNh0VHVNOR4gixBtNDKztO0MZQR1nRKPI6pFQxIZ2lgRJBPMsiafdehanwWSMiXoDJWqqDEEuqVtS3QjiZKUri5REdjAUpiGgJioEazsrNNUM5o8xLaC9rRgYgMmJmXRFixNzTJveHl4yPHRU44f75F3CXEQYhdLBumIqm2xzRyZpYRBCAhULSlNhYw6aq0xWKLaIlWENIIoHDI7WRAIaIip24qt4RgZSawy1FZgmTIRiuWiJhwJEsWZWJmOtoY0EZhRxTAYUhnLrBoQCNBC0IUhUbKGrnI6WRI0DaqVkIyZVTliI6GxDaGCxNY0Rc0gjQkHoALN+nDMMqgw9QlxMqJqclQUoFtBZhWl7YjiBBlaqC3KCrI4pcsbwkSyrBrUREICBCnCzhDVKWJlzGrdgRK82n9C+2rKcGOF7QsXECpCUtAWZ/NselISSEk9WBJYSRaGlLnFJhZES7iWECUCo2taIVCyQYULCDumjUDVsBYL0jRmOisIC0NtJGqQUrQNcT1jfbROLGpMkHFYzFkLBtRHBiULoklLp1oaCbaBIDyjetWDhqBrqeWAthUIoSFsaIuKJMlo4gBhIjplUZmk7SBEEGlLq3KGQYhoIYhiyhoCbdH1CV3RwFpMLAPCKKSoX7E6yqAsyKuOMA2pxY+zoy+8Ltx2gXi/FPy7tBEuw98PjvtUqz5vvU/XcdSU/r76f/e/08/ungcZ7jvOHtR9r9893J2zyyT33X/6v3eBZVmWTKdTX/bf3d317ibuu30L1X4lwOkA+lWGvlDcHcOdq8vUu/N2gYyU0mfy3bk6sXc/OO6DqD7A64+TE3i7P/2M+nm6VT+j7ypX/UpBH3D076dzc3LgyVlr9gGU4yJ/9tlnPij7xS9+wbfffstHH33EjRs3fFDirsUdoz+33LW68++D3z4Nrv/dPtjogyr3xwlL+6DqfBXEWuv1Mue/f15ncf7fTuTt5kRf99MXgrv55YwEfmxbVVX8+Z//Of/1v/5XH+gEQcDe3h63bt0CzjK19+/f58qVKyyXSx49esTu7q7veVFVle8GDXi6kKON9Ln2gKcQuWDdjfsnn3wCnD03jtayu7tLVVW89957vHz5kqqqWF9fJ89zjo+PuXTpkr8Hjm7j7p+7Z/fv32d1dZX9/X2Ojo6o65rFYsEXX3yB1poXL16wvb0NwObmJm+99Rbj8dgH6Nvb2zx//pyu63yVoes61tbWuHXrFv/wD//A9vY2jx49IkkSv8ZOJhOapmEwGHD9+nX+7u/+zgOkK1euUJal165cvXrVJ0i2tra4f/8+WZb5pn6XL1/m4ODAOxutr6/z/vvve/Cxv79PmqZEUUSe5/753d/fxxjDYDDg448/5le/+hWvXr2i6zrvqORAn1vfHeh2gMtl7F1CYGtry1OPlsul1505KtPGxgZ3797FGOOrxu7zRVHwySef8A//8A8+QVIUhQdJQRBw584d3njjDa97cE3zHF3MJSTu3bvH5cuXfZ8jtwY2TcPq6ipff/21ryg9evSIsiy5f/8+cFY9c1Svhw8fMp1OffV3Op36pIQxhqtXr9K2rb+3v/3tb9nY2PDvjel0irWWzc1NT+Wdz+e+U3uWZWxubrJYLDz4do0PXZJuZWXltXfcP7X9YKAxa1uagSYSR2C2qI2haDvsooUgRg0iykZh7Yi8MlS2gXpJPBxT25C6aQnFmeNToxJsqYnKEmEkOtAoBUfNgiy3DEYJbRkxSEYU1SlJrBCLkrJaIBIFtUWGMUEcI8qUqCtpxQIZhjRdSBMYlIr+n+5hFyum5E1BINfQQtAsG1Yig0jOzud0MSdctJSmI8jGBEazGUbkJy2vns05YMq9g31evHhE2uYEgSBDIWWICCtkaNEmRwQBeamIB2NsVVCVBeFqgm477FKQJoZFZRhMYioNVV2xmsakw4Rs3hHWhixWnBpBYAaYLmASx1SnxzRVjkrGBDZAWkW1klBVNWnREYcwtQVNo0nSlDhIUeasWV5RlahoQNNIwq7CUmPsFJWMoVYI2WBsiKhBSIVVLbqcc1zMSeSAzioC2bAoSuxowkDXiCAk0wEqGVEJgZ53ZMKQrK+xLE6pRE1rDCJZxcYdumuYL09QdUU2TpE2QIYGG0XsjK9SL3JeHj7m9PiY7d0bTEZDmvUEoQ1N0RBlY8pZR2tqZBDQWcluFnC62EPEAwgmWCFRlUGPU6pGEkcJQaywdclEDimLGkWMWhjWVkIWbYENxqjEUAYFUaOZ6Rk6EZiiY7KyjugaTGBBJsgkolhWmDKkNsBayKyBAZJ02UEYk4Ypy7SgsJokW6U+bbHlgsIIgqElDASv2iVJlFAvNZEpafUZABkOMxaRpSMiHg6g1qi2BCup24Ag6Cgbw6qICfSPs9EW4CkxLoh2gW4/SOsL/FwQ2890u8C4X2Ho06zcYtoP7uEscHTBtwvS+8c83wSur8PoU1McBeI8uOkHou78+y9Bp5MoisJ3v71w4QLvvfceo9HIf89dv9vcGDjw1Acazo7SnWdf89B32nLX6fjE7txchcRVLNzffYtYR11yGgxHC+qPn9t3v+IC3zcTdIGVo5r1tTMuuHfn2h9z97cDGO7c3N8OADoQ2KeS7e7usru7y8nJCffu3eOv/uqv+Prrr/nwww+5fPmyP18XjDs+dB+gOtDgwIaba+4a3L3vV+j6YMVlNft/+pqJ/v/dmDig4OZa3zXKfa4PGvuWvv3KiKt4ue+6z7m53gf4P6bt0aNHXmNwcnLCaDTyz8fKygrffvstV65c4eLFi16g/dlnn3mxsxCCr7/+mo8++oj5fO758K9evfpHlDO3ZozHY09/EkJ4h54HDx6wWCz41//6X/vscn8NOj4+5tq1a/65DoLA03gc/UUI8ZorkgvsXLbc0YKqqvKNAHd2drhx44a3d3aiZCdiPq/dcgLm09NT/vf//t+srq7y6NEjLl26xGKx4MKFC8BZM0RHIXr8+LFfa09OTvizP/sz3njjDd8Y7+nTpwyHQx4/fuzHFmBra4s4jvn666+x1vrqw3w+930/nGPXW2+9RV3XrKyssLe391oTSxf8O3qRE5w7KpIDytPp1PfCcIGze65OT08JgoBbt25xeHjogZfrV/Ls2TPeeOMN/vqv/5ooivx+XWVpd3eXR48e8Sd/8ievJbZc1cK5krl7d+nSJU5PT/2a5SxpXRWlbzoyHA559uyZr2wNBgMuXLiAMYb9/X201h74jUYjZrMZXdext7fne0A5q97hcMhisUAIwf379/mjP/ojjo+P0frM1viNN96gqipfdXE2zZcvX/bgUUrJlStX/Ni4db6/pr98+dJr2Mqy/Ef6sv9/2w8GGqPVhKgoEUvo1DGMBXKZ0OUFUkhUVDCOIsoiIs5qhAYZD4hlSsSMwnaYPEIpQaRTmmPNYCwoogYdBsQ6ZkDGZLNG6pYIzcnRMclAYtDE0QqhCEhFh+0kHRJjK2y6II0CxuEEYUryuiXuQEaS6cAQioo0USghMbZDVpAklrqeUklQ8ToDKxGhJS4bRJDTlB1HNRyfNHz35Lcc1gUHhwcMpUU3hpqQYAJdvkCEIEVO0GoClTIODeWyIFaCdDzkGGiWJavJGD2sicOWrpwRqggjB1TU6EqBSAjDmmN7jJEjlsc5Mg6wpWI03KKJp0TKEitBWwqMNoTtlE6FiGxE0mqSJKKdz6FT6CSmWS4II0lX1URySDkXKBUSJi2LriA0IyaDhtZ2BDJERREyFoRzS2tL5HiCyjukNMQ6xM4M8VDT6jkzAsKwRbUhbZyA6mhtjQpCEiWo2hJdzjCmIR2MWItiqtwSqwhtFTIb0mhDUJYksUHpgCixnD5/gh6vksorGClQSUBtS0IE4SAllAIRQNMsGcarVHXLq+KQSTghDhOEFLxalGRyQmob0iChahSNCZFhhwwFxi6wuoNlQhWEtKIhbTuyeEBV5IhQMZczZBiia0UgBGLWYLuAehigrCTRAZ0sifSULFulCENq3WGlIBlFiOZMNC/rAbopsFVDOJxQ1zlNAN0gIlEJmekIiopmniPSIWpcY+oOUye0ujrrTB+UqLAmlAICQRj9eKlT/ey6C6SapvHBrgukXdbfBU99zcP5ikc/i+vAiAtY3UsHvg96+yAEXu+sfJ4T3zQNdV2/Vl3oOwK5ILuvgXDHdoDGBYuLxYKDgwPyPGdjY4O3336b3d1dkiShrmsf2LjFux90O3DjAhk3Pn0Kj7suF8A74b0LnvsZ8fOBb5+e5caxH6y6az6vI3Dn6yoZfeG1+0yfbtYPqPv6Gbf1dQku0+eqM+4c3fX1dRxuLvWP5YIsJ9B89uwZd+/e5U//9E+5fv06b7/9trdrdFUSrfU/omH0KUsO+LhAwo2Jeyn3u7n3aYD9yky/quHucX+OurnXBzd9oNH/fB9gna9guXN2GiH3XUcn+rECjZ/+9KdIKdnb2/P32ekq5vM5T58+pW1b3nrrrdfAMHxvJbu+vs69e/eIooiPP/6Yo6MjDwBcLwsHxhyA2dvb843cHjx4wLVr1/jggw+88DgIAk5PT3n27BnXrl1jNpuxurrK06dPuXHjBqurq2xsbPD111/7XgR37971z+poNALg888/5+uvv8YY4ztK37x504uY/8t/+S/e/tTd97t373Lz5k1GoxGvXr3i7t27nj7jvnt0dMTq6ip//Md/zG9/+1svok+SxFdpdnd32d/fZzQaveaMtba25vn6bsydFSvgbX9fvXrFq1evGAwGfPDBB9y9e5cwDLl8+TJPnjzh9PSU4+Nj4jhmPB7z7Nmz12h+rvnejRs3/LU72926rllbW/PJAzfHr169ytraGoeHhxweHhKGIU+ePGFjY4PNzU3u3bvHzs4OcRyzXC69e9Jbb73FrVu3EEKQZZkP2rvurAfG3t4eV69e5eLFi6ytrfHs2TMPXqSUvqv64eEhb775Jg8ePPDNMK9du8arV6+8pmFjY8PTvowxnJyc+ASQq4YNh0P29vbY3Nykqirf1fudd97h7t27nv7nqHxwVolzVFvXuX57e5uyLBHizMVsNpt5sb0TfXdd53uUOIqeA6w7Ozu+0rS2tsZvf/tbX00KgoD19XX+/M//nOvXr7/2zvinth8csRT7NWEQUlQDSrmAJmSiOpKBQscTrO6o5wUBglwXqAxMDsvFlEHWkJmQMkmgPutXoTJNSY2MBEEcwauWNFJgK+o6JxinjAYxUlmKLmBZ1gRhi8lrKmPOAEtkMYmEWrCczc5oWCrA1CWdNETaksZDOlpIIsquJSKgqQpG4ZAhCUUnmc4PWBlPSIVl3tSURcDBiwO+uPsty6OviQYbjFSGNC2dFMhEELQhtWywJiOvWqRqqY1BqIjWGIYbKXneYIuU9cEKSuZUTYzJIQ4MTVNgiDAmRNuGRIU09Zk7UalzokghYktX55weG4IVS6VrdBhgS0ncZqgmQschVWoIhAYpiQcTuqKl00vUKCZsBPKkI9yBetygZMKgWyXvSoRo0EXLvCvJ4hWSNqTSFXUQIVtLvujOAiVSTAjhMqdVIWrQUpQntGFC3RQIuaQIhigREAws9bTE6hg5kDQ0tIuC8SBmZZTRVhVBdpatGScpQmlGNqJlnWlVM1qL6MwJX96Zs31lg63VFdJgDK2hiQVZGKKrJZUICOMBop2zlY2Juoyqy0lyWE1C8mWJ0IaDpCWuNYMgxCQSu3rWwTsSCa1YIpuQiZjQWENXN4zXY/RJguoGCNVhEwhtDZUhGA2oMkW4aDHLijTu0G1BpwNqFRIHKXFtKE7mZxSnXGMCQbwxIDYWYWakNqUpJCoKoahQbYRGE49TlkXJcDOhqQs6FSJNSBxaWl0RJoJRGlBVZyYLP9bNBYvnA7F+gHg+U9wvMfc5/f3ANQjOmqn1NRfu+31aDHxf9ThfFXGBnxMt9jPEfb1IX8jbz8T3z8VlPuGMm/zkyRNPFfjggw+4desW4/GYoiiYz+d+TFwA7c67nwk15qyzscswuWC3T1fqAy4HNFwGynF7+8c5L8jv3ycHKtzYnadgubF2WfLzYmv4XhNz3vLVnWNfiO4+f74y4bJpbr643/fvhwsI+yCwL8AX4sxzfnt7mydPnvDb3/6Wp0+fcvv2bT766COfHe1XFM4H+OcBUZ+25sagT//qayBcFcHRx/rA1d3j/r7O06Uc8HDnBd9XfRyYcM9JX8fU35c77z5Q+zFu7vnLssw/G46PHkURb775pk9KuLHI8/y159o1inMiZjc/Z7MZu7u7HB4eMhgMuHPnDp999pmnOa6trVEUBbPZjNlsxqVLl2iaswaqZVnyxhtv+IZ1R0dHHmy4Z+Dk5ITNzU2klDx9+pS3336buq6ZTqfs7e2RJAnPnz+nLEvvQmSt5Y033vBB8c7Ojrecffz4MUIIPvzwQw+QXLZ7Npvx3nvv8erVK05OTgjD0DfcW19f92tJEJx1Ib9x4wbz+ZyrV6/67HnXdXz88cd89dVXvju6a5Do6D91XfPs2TNf+XP2wk6EHYYhw+HQZ+cXiwUAg8HAJxCcM9WHH37I2toaz58/5+LFi1638urVK0/1OTg48M1CXffzJ0+eeEONra0tXxU+PT3lwoULnga1WCxYW1vj5s2bRFFEURQsl0sAr62p65q7d+/y0UcfvQZYb9++zZMnT/j8888RQvDo0SOGwyHD4ZDj42Pfb2UymTCZTLh06RJRFJGmKf/wD//AcDhkZWWF3d1dJpOJnyN5nnPr1i3u3bvndS9ra2u899573Llzx68ta2trJElClmVIKSnL0s/Hsiw9EPnJT37C3t4ex8fHGGPY3d19bU3b2tpiNpthjGE+n3sTg2+//dZfv1tLnj17RhAEvheMu09vvPEGKysrvHz58gc9sz84YlkLJyyLBcZYsmyNoDOMTcnxImcYpCxnOdX8JePJBuUCMhOSqoSClsIYytwySHNCAclkRFsVaKOwQkFjELFEkhO2gkBZOixWdBjTooWmChOSUUNbdCytIgskUb1A6xhdNyRGEqaKaafprCGxMNEhzckpaiWg0xWGAYtWgwlZDUOEqQjiBKKAWdMxDEc0ec7Dx4/58st/oJSaLFkjrw3GFiSBoVANsdUUrYJQ0OYWHQSMBglNPSc0BiEjZocdRlqGWqJkjQ6hni8ZqhgRpdimJVEDRAh2lhPESxoTINqQQZJhVENVnDDcSICYTgY0i5LOdsiwg8ogRIKxAp1r6rQlQdICdaYRsSLI50ibEa5nlPUCqwHZ0GhJoiLaNKERESJvSWxHoGdENkEmLTqOkFphk5KuawjskHigkCamXUhEVhAIS2EgCQSRFkRGIdsYoyxBJ6BrKIEsHlBMNdlAUMqWzlZkqUUbS1slYEui4YSg0gglMIMNBiLg4O5T9MqUKzffRIcTrBZUoka1sNQ5KgwYdh1RMKGxNVkcMlsuCBJBthOhpx2xCEmCkDiVLLsG0QkCnWJESdcaRllErgyFUARVTVVJBlKRiI6gs5jIoAKwcYNNEkSroKuQOqFc1KTpgKoTGHsmvFuzA2KrqJYLomyItZJBIDDzgtYWGFNjTcpIhQhpyYYxtrXMREmcSETXQq1J0pZGB5BFhElAVWsoG4wC3f54NRrupQL8I1ErfJ+97+sozouAXaDRD8pcoOACRhcgu4DbBdSuy7Pbx/kKhgso+yDDBWUuWHBUr34vjz4wct/puo7nz59z//59Tk5OuHnzJh9//LHv9dCnhvVtSN21up+78+of2wGRvoalD6T6gurz4KBfregf2wWnfTDnqjplWb5WMXEVFZfRcta5jjLSBxb9fTog1q8euZ/175sbnz6tqV+FcdQTBzr6mfw+Nez8PsMw5Pr16+zs7PDtt9/y1Vdf8fTpUz777DPeffddL6w8X21x43W+ouE+2xfQ9+etA2DnM3/uPB1Q6AOU/jPQtzx2z4Gbi/1Khvt9/7np07zcufTvz4/V3nYymXhRM+ABNEAcx1y6dMn/7vHjx2xtbfHll196t6cwDPnpT3/q576znXWVVSklly5dQinFT37yE3/cDz/80GfZP/roI+7fv8/a2hpwptFwzkrj8ZiyLNne3mZ9fZ3vvvuOoigoioKdnR3vNHX9+nXPoT89PeXOnTvcu3ePtbU1LxDO85y33nqLt956y4uiHz165NcD9+w7AOUqE0mSMJlM/LPg7J6//PJLHxw7sDSbzUiShGfPnrG1tcWTJ08IgoCjoyM/Xk5D4Obz48eP/bG77vueN27NdOBZCOG7Zd+4cYPHjx/z8uVLsizzFCAhzsTLTig/Ho+J45ijoyMA1tfXuX//PtevX2dtbY2VlRV+/vOfe8AmhODJkyckSUIURb5y4oL5Psh3VN0HDx54Ufn29jZhGPLgwQPqumY8HvPGG2/4fTjNztbWlq8+LZdLHj58SFEUTKdTPvvsMw+GHLVrZ2eHu3fvkqYpn332mX824zj2mrGLFy/66owTfTudnKt8uzWwaRoePnzI+++/73t7uMrSV199xU9/+lPatvVz6PLly2RZxnK5xBjD06dPuXr1Kt98841PuFy/ft3rX9z1dV3HwcEBw+GQpmmYz+esrKywsbHB6ekpo9GIqqq4e/eu10T9c9sPBhrL6gRTl0QKEhMxazuMCoiDVSyKMMtQ6gKtVQTBnNYIRqJGUlLPNeMwobU1XRLAokN0Fq0CgrCmCzpEIGi7mlK3DLIRNoypg46kg9RYdCtBC6o0JREhclbTyhHBIGNpc1QX0ZmOMIahGKBpKYVCC5D5gmEWIYMQFSryw5paSVACNUkQdUa7LHh2+IK///UdZk9e0pGjwghTRbRSk60YbKUIGFHbJXGoieIEEQ2ZlhWLZklUFYRhQGRbRC1RA80iP0LHGUbHyDiCfIltB8hOIZOGbt7Q0qJXQ9oFtG1DULSoKEGFMYGS5MuKWVCSBQnRUhFoiQ4NkVLIRFLO5gQmJA4zOlOSDlIWiwVxGBGkKeWyYqASShkyLw2VaAkHligV1IsS1Uq6UCI1jIdrSI6ZmpZRFGNLiERC3XSYMEPIDm0tqDG6lKTCUGGIhxLRFXQnNSYKaU3AJJCEJqGzinQt4Om8IlKGSWTptEQrhRWCMklpUo1VMcgQkZfUVcfGzgXsvOa3f/trRjevsButUIcNSa0IhyPK6pQUSVEvMCZB1xEiTZEiQsuOcBjRHlVkQYioFQiLlIYklrQ6YBAouk7QTQuUSYjUAJFrgkTSNAVNaGhOBfHaCiIZIcoGlTRYKxBRiKlCptag1JjACAZDiSw1YW1ZG6fMpSFJhtSzKbWZQziEOiIQGdouyVYUHfpMNN8NiIuAaTkjyDICEyEKQW5atDDIEibROmXRoLKDH/rY/ovb+gEmfC/W6wek/YCpz7d1PvfuZecCcAcS3EvHvej6Wou+7Wpfx+D+dgFvn3PaF/y6AKSfye7TnPo6EqfF+Prrr3n06BHj8Zj//J//Mzdu3PAZeffiy7LstYy4CyDc51w1ok+/cdfcp+K4F/t5vYA7TxcAu8/36Wv9MXf/dtfSr4L0K1AusOkH+a6a4a6nP779v/vZdgdO+ufk5kk/k98/z77drduckLZf7enrTPoVDkfReOedd1hZWeGLL77gT//0T9nf3+cP//APvWNMP/vvxscBQ3ct7nNuvpyf631Knpsbbq5UVUVd175DrwPNbuuPbx8Iun33QUr/Z+fvZf++9Y/Rr9D8mLZvv/2Wra0tT1u5cOGCD2ZdQOvsSa9cuQLAz372M98LYX9/n6qqeP78uacEwfcdx2/cuOHH29nXumMB3nWoT5l0PSSqqvJiYmstv/nNb9ja2vKWsE7LtFwuKYrCU8D+5m/+htls5k0iRqORNy9wfHnnRKe19sJsIQTz+ZymabwA2NGD6rrm5OTEPxfT6ZTLly8jpeThw4d+TVtZWaEoCuq69ta6dV1z+/Zt8jz3naWdfauryPXX2LfeeovvvvvOr5FN0xBFEVtbW34sXa8Ot07cvHmTO3fu+K7nbo2rqorV1VXKsvTi/cuXL/sExcuXL1lZWfGdq6uq4smTJ+zu7jIajbDW+s7bjrrk3MmSJCGOY65cucLJyQnvvfcei8XCP8Oj0Qgppe9V5Hqn1HXtbYYB7zD25MkTiqLgq6++QinFH/zBH3B4eMiTJ098dcy53TVN4xsyrqysMJ/P0fqsN4rTeTi7Wadrs9b6AL/ft2JjY8NXmcuy5ObNm8znc18ldi5SrreLE64vFgtGoxH7+/u8/fbbPlnkqGeuV8pwOCTLMk8z1FpzfHzsO8bnee7nxw/ZfjgHQ0rMMCERlmaRkw0HLEVLGiTYrqaWJSQxooyItGSuW4owxtiaLANJQywiMBGjFcV82hHFA7CSheloQ4HSKenqkLoziNAim4CuA5kpTNdgG4EyYOqcIB2cURSaBitSrNKEXUvSKRamJlYllZYoEyOVocw0oi6xKiFZ3aBrG5q6QasTDBX37nzFs/t3mB5VEAVoLLbStCJEWs1Qj1k0Bmk1YZrShYYwzWCuWZOSWmiiyZh81pKLhrXNXeg6ZFDQNpa4azHWIuOITjd0VjCmY64ENS3dvGBMShNGWDqkmNIIMPOAWI0ZaINpBXndMslCmkWBigSiChEyhErQyRnWjCmPK6TShImkbReE1lJNDcF6yShWdDYkrGpsPSNOOyI7IRSGplMclBVSA0pTqZpKa9ayBK2nqE5TKoEykjScUJUVtqsYpBFdV9EtNXYkiVpBOg4xbUNjNYVcoAxk0YRo2YGsKGzNdrhCaTTQktYdVoYsqhlZpAhVQlGGJJsJ2ViDPmL/7ikXtnbY3F7lqKwZjmKaoiNuAmxgkFFJpC3lwjBuQ0IBehRSFDWiCgmzs+pSbhRNXRJEGSqGbBwju5pGhei8ZTqtSLdidGgYhBl122FLSCchVWjJVlrUokJKS5RE5EdHjEYTuqqmagNUoghKQxcb5GFORAKhoAkirIxRRlEWNfPWMEkixmmMXEogYc126K6ilho5TskWEggobUvdGYIo5qzpyo9zcy8U55Djgn8X1LqF1PHrXcDbDyr72dl+QAXfV0n6drBSSh8YOFpSX8twPkDsVzhcFt41musHnX1Hpr7+4fnz53zxxRcURcF7773HJ5984jN3Xdf5YMFVGNzWF6i7ANxdW1VVHnD0g/o+VctVbty++tQhd25Or+GC3b72wH3GvaD7uoc+AOsHsk6/4oBc/3d9sXdfwN8/J/fv/n12oNOdYz877/7tMqiAnz990Od4+13Xef1Lf3P339EY7t27x1dffcWLFy/4t//233L79u3XaHzuTx809IFxH9ic17XA96DD0eEcuHDBXV9w39fouLnq5rubQ+66+gGamy9uX/1Kkvt/H9T9WIGGowyl6VlCza0jbk7MZjOyLPN9dtxn2ralLEsf7LlGc1evXmUwGPDVV1/5rK61lpOTE0+Dcll7+J7q5iharm9GlmX+WVosFr4XxPr6uqczOSDy4MEDsizj4OCAv/3bv0VrzaVLl/w1vffee6Rp6p2yABaLBcfHx0ynU3Z2diiKgslkwnK5JM9zdnd3efXqFTs7OxweHnJ0dMSFCxdomoY0TTk8PKRtW7a3tz1IePXqFePxmGvXrrFYLHx1w9neunn7+PFjLzafz+fcuHGD/f1971j03XffeSC1urrq9VPHx8c+UHUOVVeuXGF/f99rF1ywvLKywtraGoPBgIODA9/0z83pCxcu8ODBA8bjMbu7u8Rx7CtbH330EcPh0AvH3bPt9CJ5nvsEU1VVvvLlLHfdWvXVV1/xe7/3ezx//hwhBD/72c/45ptvADyYdT1FPv30U27cuMGDBw948uQJy+WS//N//g+7u7u+G7gbT6WU7+g9GAw80HTUsN3dXU+NdVUEtz6WZembEA6HQ5Ik4f79+1y9etX3jHENJd2zPZvNfB+M9957z1e7lFKsra359booCh4/fszGxgZffPEFn376qe/N4sCdcy+7fPmyr4Q5PZID2P/c9oOBhjU1aEPTtCTJAF3m7K6PyKsZbSKws4TAhmhREuuGUAyQaYzoplALTD0gHQwhFCyOpzSRAKNpCoNJaxIdMogCZmV91lk7jwmtpRYVeh4Qth3aNKREJGnEYb5kOMxoa4FQYMqSvC1p1QrLtsQqC3GHVSmpiDgtBoTWEKiSopkjakkahpwupnz1y1/z4LePqLslNJLyNCceSVSioOoIw7NGd9rUjEYBRdeRTyHucnYHCW3eskQxa1qmBpJxRpWDTEborCAxLUrGDCNFXlriUGDqlqJWdFGEXuTEXUIdQh7UpFbQFhV2HJ3RnZTk/0fdn/3okWTnHfAvIvfMd62FRbK479PsfaanpZmRNFoNGZB9ZcDwP+cL3xnQhWBbgATbkrX0SNOzdE93k93cWdxqr3fNPSK+i1JEJ8vzWf0B38V0AgSLrPfNJSIy8zznPM9z4kmDDAIW0YBp2eLnS1SvxZgWJSVq5LHEw5vPGXg+7TjFLErafIH0+9Q0DBqfgIbdWiFUS5AIaDP8tEdVzhGjAN/khLlBmpRkKVA6QJcpvlKkmWYUBORVy9FyRpgl9Och+V5NnIwZZB5LkxOFBa2pKWVK3oInNB4hlVqiPB9ZQZT6CL+BcEnRHDGKV5nmEpoMT2skCiWXKBMxWlkhki1H+ZKXe/vMlgvOrF/CDyKCTOGZktB4FNMaU4I/ihH1ABVrZGkwTU52ao1ZVSFbj6aZI8IQT1c0i5Zlq9AyoJZL+tWcQZZQtxoKiapqmoFGBjF+McZPWzzjIeZLCBPaqmFlfJq2bVkJBPPS0IqAKlAErU8VS7wxRHND6ilyvyaoDJ7o42lJPV0Q1UOyTDBrKkI/JG4jluUeVV7R984yOSyII59aLNBEjKpvb2dwG7jZoLlLFbH/d5LWdJJv/+v2aYODsixdcAVfU5Asl7jrbtUFGd1A0mbxbcBng8ouFcn+rls5yPOcBw8e8OWXX9Lr9fiTP/kTLl++/FpQaoXG3WDfBnw2mLH9M2wgb8GJBU0WhHSbFdrz6OpHbEDUBQwnxdf2c5aX271mC2C6laOTQWpXlG097bsAzb7Eu1UgC2JOZvy7Oh0rzrbf62ZKLeiw499dE/ZYdv826O+Kqk9WBuI45q233uLs2bN88skn/Pmf/znf//73+f73v++6N9t11K3odKth3YDI9uTo6lW6a90KTrvA4qQA3J5bF+R0qVXdCpBdT91x6ILG7pzacbT32bdx29racrxzK1C1a8PeX4C7560d6erqKjs7O6yvr5OmKZcuXXL6iCAIeOedd3jw4MFrov7JZIJSyjn/WNpTlmXO+tRSsH7xi1/w/vvv89lnn3H+/HmXVbe2swD7+/tcvHiRXq/H//k//8c1xbPUmYsXL/LBBx/g+z6DwYDJZOKc4k6dOuX48ovFwoEn3/dddnp/f5+bN2+6xMZkMuHMmTPueWE7c589e5bt7W1H/bJN9iw1ZjKZ0Lats4tNksTdc0EQuM9HUcSrV6+4ceMGBwcHbn4sOLKVoPfff5+7d+8Cx31QlsulA0ue57GxseEqOhas7O/vO7vvOI75u7/7O1ZXV7l27Rq9Xo87d+4QBAEbGxtUVUWaplRVxXK5ZD6fu+qFvVdskFyWJWVZOgpQURRsbW1x+/ZtHjx4wPnz510fFAsIX7165Srjly9fZnt7m52dHZIk4d1333X3tR0DO7a7u7vMZjPOnj3rnpV7e3usra2R5zmbm5u0bcvTp0+5ffs2u7u7bG1t8fbbb7/2jsrz3DkT2iSK1VhcunTJ0VttI8MgCNzc2Y7qR0dHfPzxx9y6dYvhcMh8Pme5XPK9732Poih46623HEjr9/vs7e1x4cIFTp069Vq/IGOM6z3SdUf8f23f+EkjwgbTNuRRTO75lFoynZcsKkGx9Aj9BhEWROOaMNMMUkPoNRgvpA0CmkzQxmAiTUmF8BtUtSBIQ3wTQSAoZjWhn6GiHqVpMUGN5/VJwoB+L8QPh9RSoxKPNB2QL3IiPIZDgehp4sinamaIUNCEAxoVYrRg2UQ0TQmeRBQBiRzgp5L5wSs++buf8fjOM7SUNJ4gNz69jSEiDNivZ3g98PuKmTIEiUFqxapIiAz4OmC+aMi1YVmA73uMRzFhIxAaWlNQLRvqSQ1KMdcKr22gVrRtROEporBiGClkXVCrmkCAEQlRNETkEUqB8kqGowFhzyPutaQ9SbIhWFYzTOMRJjHtdI5YKNLaUC0U5XxG603xRAaFT38Y/wuah0xGVNS0UUuKIKgNQtb4TYlfVSzaGhDUpkE0C/y2QEqfvJXsNgZNSpC3mMkUY0qGgzG6blBG4UtJ7SvawCP0PfwwQJiMaiGh1ceaBBlQ6hYtfKSJCKOLlG2PIOwR9CKQcGqYcHoV1mJDFgX0R0POjDc4tbHBZDrn6c4RuvUJ8PBCDy0hyobIwYAwNJR+zdFkSa0UjYmZ7mn8RUKVZ/gmIzQgaNChRiJp85ZYS8ywz2IQoGRIW0uU8QlqHz+vaI6O0HtzyiMPvJREpCRRil7M8XQLSUQyEgStIEjGLFTIxC+ZGEMdjlB+clypi1uS3phCppjVHs1qj0khiIKAZTsnVyXCS1gZnsEELePzPeJRC4kAXeAF3+zm/k3cbCXDaiVOVgm6vHobGHcDLjgOILr8/G4/BVuitpzt7jHti9nSVU4G193eELZS0LV/tYFZN2i253dwcMCnn37KV199xZUrV/jTP/1Tx1+1mWsrbLZNlboAyQbbv473b8eqGxzawLd7br8uuD0JEro0ny7v/6QeoGkaZ8W7WCxc1nS5XLqfbVbOjvdyuXT/tt+3gK07Z/aFBV+7JnUD+K6GwOpITlYQbBXB0gvsXNk11e0i3K1C2LHqvjTtdY9GI773ve9x6tQpfvKTn/BXf/VXPHv2zK2l7px19Q62gtIde7tmu5oUq3exa7A7VidpgF03qa59r+3U3B03e/90dUXdOe7S5uwa6dLbvm3buXPniOP4NQqizWx3K28ADx48II5jJ8jWWvPo0SOWy6Wjhhhz3D372bNnxHHMP/zDP7jnwObmJovFwo07HK8Dy3v/6KOPXCb58uXL/OxnP3PULCmPm9/9+Z//OQ8fPnRVhKdPn/LZZ5+5Zp1BEHD9+nX+5E/+hJs3b9Lr9ZBScnBw4ETf+/v7Tsi8sbFBHMeMRiOqqnJiZqvrCMPQ0f9sleH58+cEQcDFixcpioLDw0O01qytrdHv950uwnaHttdpg3Upj00M3n77bTzPY3V1lSRJePnypeP0W4cqq5Oz93zTNHz66aeuGtK2Lf1+n/39fXcMIQTvv/8+TdPwk5/8hO3tbdq2ZbFYOL3C6uoqV65cIc9zHj16RJIkrK6uOm3NdDpluVw6IbYQx301bDXAUkqfPHniKiJCCLIsc7S5tbU1nj596ppAvnjxgtlsRpZlhGHI6uqqe/7ZyqHtlv5nf/ZnXL58mX6/T1EUr13D3/3d37G9vc3h4SG9Xo/Hjx+76pZSis3NTaSUbG5ucvHiRX7yk584SpOUkkePHjGfzx1wvnHjhnt2WYDWNA1vvvmmexZvb287K+gHDx7g+z6nT59+zTRkfX3ddba33cWts1Q3kWXvG3sP2Yrh06dPv9E9+40rGhEaL0spWw/RKAhbjCwZ6R7zskL0FR4ecR7SLEvisaA+EvTDHgtRQeChKNBNQBBGlMaQiYCFbEm0oBQBjVmSJQatG2RoiOOAeqGhbNF+jiclBBmGAJIatdQ0s0OM8oiyPqZfEqY1flUjhUG1EYVpELVhEGhk5qMOK6p8woujKb/6h4/ZevaYXEIdQhb6BGJJa2JmVYU3XiNvDepgQVOD31PMRcBctijfpwljVDXBrwS676MPFvQ9Q1VCG4MnDFkY48sUJQyi9dFoTCBp4pJSN3h5SOj3qLKcMIgI5i1qKFGNRrYNjTSEtce8yVG6QWYhlalopU/iryC9lOnhDC9I8EeGYhLgRyHSV0ReDy0UKm1o/Jhi1pCqAE1N1JN4RmOiFs0MrwLtaWhi0n6LrheEXoSREi+NmJQVofBQNLR1gCcygtijDVqacoaMYhZlQTr0qXRImnkU0zmxHpIvMqRcIuQMiQ+eTxpJirIkxhA2EEcJjSlpEGgvYFK2SM+Q9A09r8dwOEKFJUGSIuMVnr94yaOHj1i/uE4chkRJhGcafC+jNQ2imeCHPfzUJwkjqkWAPzA0dQtRRKA8pF8xWMnYP6wI4oy+Lzg6KNBRRNpr0Z7GRIJGBJRVw7lzA2grtDI0kaGdL/BNglSGZTGh8lJkbAi9luKgQHmKVCtMsWShDL4Eco1vPJpwQtyXtEazXC5Iewl+EjCY+VThkibtsWgWpEFGWeQUoiX2QzwPFurb6zplX7b2oW+z4TYYtZlbG2x1g/JuNtdmXuBrytFJAGCD65N9OuxWFMX/JcTuCpZtBqpLb7Ii3q6WYWdnh5/97Gc0TcOHH37IW2+95TJc8HXW/yRf34IdCxgsv9Zm0G2n7S7AAtw5dS1tu1uXq2//3eXqdx217H5t4NxtotjNhnerSyeFz/B6I0b72S51ys63DVjs1gU+3e93hcp2/Oxmz92CLXs+XRpeV6/TrWLY352kZNl5D8OQW7duuT4D/+2//Td+/OMf88Ybb7i1++sqb/Y6uuNmdUNdm1r7pzsP9ru/Ti9j56/7ve7vupSx7rWcpBTa+bVjdXL+vk2bnT8rsF5bW2NlZcU1mrOB/927d53N6L179/id3/kdR5c5Ojpywm1rDbpcLtnb2+P3f//3EeLY5UcpRb/fd30SbMXUBu+/8zu/w1dffcW1a9fY399nfX2d6XT6mnPcO++84xoHbm1t8eDBA9dleTQa8d3vfpdbt27R6/Vc0NcFs3DcrK2uaw4PDzk4OCDLMldVOXPmDE3TOHrQ4eEhT58+5dy5cy7otc+4yWTivvvkyRPu3bvnbHB/9KMfsbm5yYsXL+j1euzt7Tnq1927d9nZ2eG//tf/yng8dpWamzdvcvfuXYbDoavuCCEc3SdNU3dPCiFcwsXzPM6ePcvOzo6jmFnXpAsXLrhg+dKlS5w6dYrd3V2WyyU7Ozusrq6ytrbmhNrWItz22LDXaSlb9r6CY33NBx98wGKxcM95Cxjquub8+fNorfn000+5fv26azBodR9PnjxxQNDS0g4ODjh//jzz+Zy33nqL5XLpKFZHR0eMRiPeeOMNtre3WS6XPHnyhB/+8IcOJO3v77trPHfuHKdOncLzPJ4/f87Ozg4vX75kc3MTY4xrPvmDH/yAIAhYLBauqmUpc6urqxweHrpqltV+GGO4cOECVVWRZRlSSo6Ojpxwvd/v8/HHH1OWJbdv3+bWrVsOZN67d49Lly65e1AIwalTp17rn/L/2r65va3R6GnLoOdBH5ZVS9yEBLJktSeYtBLpZ5S5oI4TdNuQNzVaeHj9IaoyVN6MdrlkkMUIFYD2ETqnDTwgRoctfj6jHyXMlaCczViN19he+CyqBhEbQqnJ6wYlWrKNHmoyI88NJm5RXkk2ylBzqNQcLxigc8Nar0/rtzTTimqyx5df3uPOo0MmR7tkoxDPh3nVQAUy8mgrST+OaOoluhA0osUfZ/j9BKXmeK2hKWImywm+8Okpgb/MEXKAkg1JEjEt5hAF5G1JL1rB1BW9uEKHPpO8pJ/BoFU0oqEyMY0OiZcBqddyoEvMvCZUFemgz+xwzmitT05NKxSNFgTapzABWlVEBgIdohqFFIJQa0oj2FkqhiLCqIK2MAQE5JGmKAsGasByMSVY8TEETBY1JjT0RUBZzuh7PQIyTFBSypKhaWllC42PQpD0NNLAop2wvjlkuS0QacRsMcVPYnRTkuqMFkMb5ERSU2pJKBqOVI4nPZIzI/LDJZFqUaLBEw1hHGCWPmkSIv2GKBjhZ2NK1ZBkglZpNs6c4dRghaf7D3m694zNUyvkXp9U9BiZJQaftvZpPYFpQySCIJ2ickGWhASeT14HVE2InsLaYIVi3lC3M9KhYNFoPF0TNh5zqfGpadOQF/mEoUkYrq3QUCMGGlUpqlzTi32UrikXhrpRDNIx0vPI+in5ZJdIz6k9j+HaALNb4/slzaLB89dQKqP2aqbzI2LlM5s16KIgjFKiRGHqGik8giSk8grE2rfTlhK+rgbA14Ghpb/YwMgGZt3A0VYjuiDCBod2v93A0X6nS5PpZsxt5cPSkmzA16UGdSlJ3eN2M8zWKtX3ff7gD/6AK1euALgsY9dNyQYewGvX062inMy822xbXdfuuzbT381M/bpx6I53N1A9+f9dWpitMNl/Ww623exn7Xx1g2Z7TlLK18TgNmjqVk+6x+7S5E5WsbrUNHs8mwG1gK87V/a73WAcvq5qnawydKlrNmOtteb06dMMBgPu3LnDX/zFX7C3t8f3v/99B/zs9XS1Ml372m4Vzc59d57sz3YsLRizx+/S3LpzelKv1K3MdLUbtspjz68LSk+ClW/bZoHmcDhkdXWVr776ivPnzwPHDed2dnY4ffo0w+GQOI4ZDAZ897vfdfNsq16+75OmqesK7nkeb7zxBmmaOqE24BoC2l441pns6tWrPHr0yNFfsiwjjmM2NjZo22Nr+LW1Ndq25W//9m95+fIlr169cuM+Ho+5desWN27cQKnjpm7j8diB8v39fadVs/1zHj9+zObmpruWLMsIgoB/+qd/4u2333aORSsrK8561GbWrT5rOp2yublJFEVOlGw7ottM/MWLFwH47LPPWFtbw/M8rly5wv3796nr2gW1ZVkSxzF7e3uO92/vWwtoAOfIZWmfYRjy8ccfO7vWJElcVt6u3eFwyHQ6df0kzp49y+nTp9ne3na9J9I0dXSjy5cvO7rcrVu3ePHiBevr6+6ZUZYlR0dH/M3f/A3/6T/9J169euXuFZuht0B9Y2PDVWitjuPs2bMcHh6yvb1NmqauG7fWmtFo5HQwQgjeffdd7t27x4sXL3j48KHT/lhg8L//9/9GqWPnsbfeegs41oBYkwKljvv57O/vc/v2bUdLffnyJVeuXHEdzoui4OHDh9y8efO1+9uaIlj7YK21awwYhiGz2YwoipzNsdXQWN1P0zTOhMBqjiwTwTYPfPTo0Te+Z78x0AiriNb30TogIibRkAQCr6qphSKIM4pKM+xByYJJqUjSFOHVxA20VYUX1dTCx2gFqqJKY9JQYbyQZtoSaI9GefRCj7aqiPyA+aJAa/Bin7DW1Kog0JLAj9DaR4oYP/YQfk2TG2RR4gtJHkgi3yc1Piqv4YzH0XbB1qOn/OzTz1kuWxZ+QFJJwmrGSjjECwUHs5xeJgkaTVkoqhC0SakXSwLZp4kEjRZkAmKtmPk9aikY+g1Lv2Y6mRDEa+CD30rS3gAhfWJRIYWhqmqEL/Ckj4dHK3KqekGrMgrjY8KUoKioPA1SIxH4g5jD+ggjUuq5QVeQjjWVKWh0SZo1NKUHh0v6akAdKmRTkUmfVgQEfoooluS1glDSFCG5bwhlhteGNJUki4fodo5sClZ7IYVuaZXCT3wCqagbTRDHtFRIX1EuS2I/JMkyDnYa0kZiqoY4yMiVApWhVYgIWnSiaXSLNAmtgXCQkUUhVT5HFHPUsEfTRqippmkrRkmCTA2rp1ZJmxjPazCmoZcOqJcteFN0GLEWnaUXr7H18j7jUY3oQTJIENQIX5IAUZgyW04Zpi1VLhlGPvO6ptYa3/PwjU+eF8duaV6NbCSx5+OFhqj1aJsaL04oWsNyFtLvhSymM9q2QvQy8CEZpuiyoi01cuBjwoa21qS+YFEtyEYZ5vCA8fAMRa7QJiVsFA2CoKyJooC5aFCiIQ9ADkbIukIYD12USFng5yH7+YxeGpDPqn/1fv1N3bpBfFeE16X5dLO1XZ6qFTjbILZbhegGkDY4sy89y/G1D83u54HXGm51QYndj926NqNFUXDnzh0ePHjAqVOn+KM/+iPW19dfc2OBrwNtW72xOhJ7fBsA2DGxL9luJcBeS9d9qjteJ61T7TG71pddgGOBgz1e91jdMTkJzLr0tW6V6aTuo1sR6VLBuoG33V/XRcx+14JAGwxa8GnP/aTGoFvpstdkj2+73tp57FL3umNjj2sDjSAIGAwG3L59m3v37vEP//APLJdLPvzwQ+ekY8e0W/Xpan7s76zOxn7GzoPdhx0Dm13tjrOd4y6A6s6z/awFsV1BfResdZspnqzCfNs2Szu7ePEixhxbtx4eHrprtvSWM2fOIIR4Latu6WuLxYK1tTUePHjA2tqa4/LDcXB99uxZguC4+3Ecxzx+/NhpdLrZ74sXL9K2rQu8PvjgA86fP8/29rbL+N67d4+trS1HPbE9dN58803OnTvH1tYWZ8+edeJlqzV4++232dvbc839tD7uiWAtWLXWLqv91ltvuaz2r371Kycut70v6rrmzp07/N7v/R4vXrygaRouXrz4L3TqktFo5ITybduyu7vrOpG3bcurV69o25Y33niDxWLB9va2o4Ra69PpdOq4/1ZDcnBwwMWLF9na2mJ9fd2tT6tzkVI6DY19jhlz3C/DgjopJcvlknfeeccBK9t8z86ntbq1YvI8zxmNRvi+z/b2NsPhkF6vx6effsqHH37I2toa586dYzabuf4jk8nE0S5t1t92SrcND7MsY319naOjIyaTCfv7+y4w79qR37hxwwHhFy9euPmztCTbp+Tx48dcuHCBMAxdPxdb2bDPB5s4+8lPfsKbb77pWAC+f9w76o/+6I8oisI9Gz/66CO+853vAMcObR988IEDslYX8+zZMyeItxqgV69eueuz2qG1tTUODw9JkoSNjQ2Ojo6chbLVrnyT7Zs/abweauQh+8edruUyoEbR+DW+fwplImTcIHqGvidYD1JiWiIxxbQTZFQggxgdRPjSh1AiwoS6MNTlnMVijiladNnnKF+gjGBWBiwN9PoJA53gqZA4Sgl9gagneBg836dFEo4yhsMBAkk5bYkmHsFkgScbZF8wyxd8/NHP+PkvfkVZKby4IaoqAk+hQokOAnaXOWHSwwtLZn7FQbNkWs7Ry4qk9mlmPs0kpZoULM2CWRMj65bGzymUjwhS5ngY6dEmEUoXBApktQBhmLUhhYrx/WP6006TszwsiUpBX2gCr6HWDZgaL4A89qlEjjA1TR3hzxS9wCcNBE1ekCmBbyTR+oiCnDxoWUY57ViCNqhlhSxKlodHLJoCLWs0mjI0zPSCo+aAg6NdlgczvPmSTEiyRKBqj1CGENc0RU6z1LRSQByBUqRhRD8KKVHM5oqAEG8gSZuWZlkQEFDUHgwC5Kim9EtK5WGCFNOG6HyKnOf485RIbKABIyEPWhotKVRBLD1CGZOuZAxCg2oOmS9LqkbSqACqnH5PMRABq9kKxfKIfL6D0YJAS1rPY0ZLWdT4UlMHESpJmauKhZDISNDrZxg0BRqTGZCSvPVo8Kl8j/32gELMyDxFj5qRyDGiJAsPUdRESuKLANMoCmpUAOQlPeMTeccaFy0TQtWDdJ1aSUQLMhkwa0NWBufQOmPeKpLEI4syIt8nzCKiICEUDUuvwEv79AYBsZcjDfgT/a/drb+x28nsM3ydXe8GTt3gtVvh6GaT4XUh7sk/9lhWx9DNDtsXlBUGngzKutkhW363gGS5XPL555+ztbXFrVu3+LM/+zMX1HQdcOI4dkJKz/Pc8azjUNd1yP5tg10rarS6hq6LkM1GWyDx6wLGLpWpCwZsJaVL5bFBfFcH0aWhWV2JzdgmSUK/33ecbst1/3VBtA3KutUHCz5O6hrs8ezvumujO9ddob4N5K2osygKyrJ0Y6O1duPb1fF0g6pu4G2zq/a4w+HQ+d3/7Gc/42/+5m/Y3993YKhL+bLXfbIaZs/Tzr8FP13QbQOcLq2rq62woMYCoe5a7Y5hl/bXHdMu2O2Cm2/jtrGxwfr6Oo8ePWKxWPD8+XPOnz/P2toat2/fpt/vu0BwuVy6KpTlu1uhtBDHze/s2rXgdGNjg2fPnrkM7/r6Om+++SZZljEej1ldXXV9NmyGN0kSrly54hrSXb58mbqu+cUvfsFf/uVfOvpSr9djc3PTaSlsdcFSj3zfpyxL4Nhc4tSpU84YAXBuP0mSOFG1dQHqJlgsANvZ2cH3feI45nvf+x7j8dhlp1+9esXdu3edha8xxgX329vbrK6u8uWXX6K15vz58w542WpPr9dzn4Xj+97a51ptx9raGovFgpcvX/L8+XNnM9ut+Hmex/b2tttP1+DBgvQ8z2nblkePHrmGfEVREIYh169f5/Dw0DlO9Xo9rl27xsOHD13FxWp63n33XYQQrpfG1tYWcExHHY1GPHnyhPl8ThAE7O7uumeXbYw3GAwoy9I5eI1GI4wxjoZrnaGePn3KbDZjc3OT7373uyRJ4qrc1ubc8zwH8D766CO2trY4Ojpy+pLd3V3XKA/g3/7bf0uapq4JoH1uWB2JTfysrKzg+z4vX77k2rVr7OzsYIzh4sWLaK3JssxpQXzf5/Hjx+zt7ZGmKadPn3br3ILtMAw5d+4ceZ6ztbXFy5cvMeZY//NNxeDfuKIxywyN8BmFmranaOOWSFUkKqbxcnzV4vtL6lri1R75YonXG6JpibyYOGopGtBNgR8PkdpDlhoRZghRkawp6lbQGE2kA2gNrefRV4b8cEIjUhYIxoFPbBqmtaIShTJAqQABAABJREFUS8p6Tj/OEGVKPmuoloZ+4CPSkNrTLAo4evmMjz7/GQf3dqjMnEoZfKUxbY2KRygvp2VK1g+pm5JcKgo0UU9gwgHlYUUrE3Q0I9Q1o3EAUrH0Gsqjin4saNqaJuvTi9bwlwbSgLbKKfMamWmmjaaf+EjpE2pNPqsZxz4qhLKOqEyNlywJ8GhlQ0uMzhWVXkDkQdxDS4GvJc2iwRifMIkJfIM5rBEayjZBBoLyxS6Rn5KXhizNqWJNW1dEESip0H7McpkjPeibCKKQsihYqJpYGmpP4+WC0TBGKI8iL4lHMNuek/oei2aGH7ZoP0ZMW2pRQBZTJDERNU1xiIgSAi9FqYgoFfh4+CrHEw2jfo9KhSwrTb8WiFxypJcMhn1MqxikgvG4TxymRGGGpmC9f5m60LQITC6IkxjfT8hTxeogIZoN8ZcVe3eec/ati4TakBCjpwXeGMQ0IA4VQiiEX4GnUUEPoQWeyWiPajxfspL5LFSBmsZEvVOgp8ymz5HJGFOEtB6oADx5zPFPvIiqKYlGPUxVgtKwaCHM8bwUfVTCeo8q8lEzweqgx6E6As9ncjjD61WEgUGXEl3FRFmEnjf42qcWBiFq2r2CWEYMwjGFrmnT8pvetr+RWzd47FYebPBogyob/NnsM7wuwLaZ3q7A1/5fFEVO72BfevZFbAWKJ61wu8GGzSx3j6O1ZjKZ8Ktf/Yrd3V3effddPvjgg9f0F1EUOcqRzeR3g9yTItzuz0IIV8bugi/7MrZ9Nex42cD0122WLnOSQtW9VuC1gL2rfbEvmG7QejKg7WolLGCx12gD5W7FqavBsOdjaWDdOe0CSsDpM+y/LS/YzpGtenQtZu04d92dbDXFjrmlzlk6ge2kHobhaxqWXq/Hd77zHeI45s6dO1RVxR//8R+zubn5WhWkC6a6YNmuJysQtmNp10gX4HXPuVs1OflzV5tiv9cFEyeNDU7OcVd/823b8jx3QZEQgqtXr7rg+rPPPuP06dPcuHGD+XzO559/zne/+12MMWxvbzMYDBzP3fYi8DyPe/fuuc7h1tXIipzX1tZ49eqVe4Zsb29zdHTkKiEW+FiB+cHBAa9eveL+/fvOjlUpxb//9//erQObbBiPx4Rh6MTUa2trTmdiTRh6vZ6by/39faSUTKdT7ty5w49//GMGg4F7Fuzs7LhgU2vNBx98wNOnTxFCuK7jvV6PlZUVoijiwYMHzgLW9lhQSrG6usrW1pbrM3L//v3XAHiapmxubvLll1+6BorT6ZS///u/ZzAYOAoXHK/1M2fOOOvewWCAEMKNcZqm7v8Xi4Ubr7W1NQ4ODpx4+smTJzRN44DRhQsXmM1mHB4ecvPmTT777DOGwyE7OzuuimGMod/vu2qh1fP85Cc/YTgcAl/bSO/t7XHjxg3Onj3L8+fP2djYoN/vc/bsWT799FMODw9fSwL1+302NjbY3d11zmX2fAaDAZ7nOR3QzZs3KcuSS5cu8fz5c6dzsQL2wWBAlmXcv3+fd955ByGEq2bZpns2wWGr9C9fvmQ2m3Hjxg1Hd7OWwM+ePXN2yXatWSD06tUrB6bfeustZ0/8/vvv8+LFC1cFsvTDxWLhKHZnzpxxybs8zx1l8V/bvjHQSPIAGZY0dYsWIIQiHcaYykcqjVoaTJJhtCaqA0apJK8LfJMRCA2BJFcVSjfHlBl8on5CqRq8vCaOQopmSjYaoeoQrzD4qqXRsEAxiEGpljKviIMYESgCBUGWETQtWVGSKMEs9fCGhoUuMaWhmR3x5af/wKuHj2llSF4L9LxAJpom8slFTl+2KFraSlALQZRr4tZDJz3qmcGLNQQti0VF5nuMFgWTyBAOMlQ5I04zZOWzOy3ISEhTzUI06CBEU1NJQyT+JZNUtbSFQcmYqmloaiA1SNOCLsD30USUdYsXxjTax2sWSFHTeBF5keOLBFpJmc8RXkBVK7LMw8wW5AcajYC+R9D38ecLSqGp/QYlIrI4gu0FYaBptAetoREHNLGgDnwWc8UwUSCH7O0vWR2meLpl/1VDFqQs/AohFW0TkwqB1BVBlVE3MdKrkSqAqofxJLou8TwfrzVEAejcILIMU7dITzHMfFTaIrKUZNEw1B5hGJP4Y6LxOq0oaAz4XgpLxWA1Y2cyIWgUMg45XByiooielzDaGBHqlq3dBfefPeTS2lWElIgkp2kbdBlxerRB3i7wW41uAqpcUFMQJpI29JAkHC5y8DVZL0M0AkkIvsbUiloJjKpJy5B+ktCWCtUoQqXxg5ojP6AfRDS+pBUQCskgllQ6oJrVqDZnNm8QkWI0DFhIjdYV0hhiP6BtawotUalEKEE6GJA3NXVeUiDQxPQHK1BM/398Nf/mbDYAPAkKLGfeBvTwuguUDSa72eeu5gG+bprXrYzYQM7SHqx7TFcjYY9lz69bxYCvOeG2CV+e53z44Ye8/fbbjrttg/KuyB1wzkLAa8LgrjC9m2HuUpsscJFSOrDS7/f/r0D8121dipUdL8tBthWMLsix59ilaHXn5mTgbMfb7uOkpsBu3cpIt7rUBWNdmtDJ6oYN3Ltidhss27HsitRt4zEbqHeF3vZz3fOxFDRLQ7HXbr/fpY1dvnyZMAy5d+8ef/mXf8nv//7vO/rOyXG3195dS1Zo3wWJXcDQdYjqzk0XAHYpYt2KVbcS1K3qdSlSlobVpd19Gze7Fvv9vgvuLY++1+vx6tUr6rrmwoULvPnmm3ie55IKs9kMIQQ3b97k6OiI8XgMwObmJnEc8+DBA7TWrK6uum7fYRhy+fJlXr586VyZ1tbW+OKLLwiCwFnE2ufKy5cvXTVyMpmg9bG703g8ZjqdcuvWLXf+NtscRRHPnz/nypUrFEXhAK9t1mbXjBUr2z4OWZaxtbXFlStXmM1mpGnK2tqaO/5isUAI4ehmdn3duXOHGzduEIah69tgK6q2cmvMsRuX3ZcNWvM8dyJnCz4uX77McDh0Iu4rV67w8OFDNya2o7fl/r/xxhvs7e25DtRJkvDs2TPefvttB9g9z3MWshcuXODp06dcvXrVXcfOzg5BEDAcDomiiOvXr7tqoed5rtrqeR6PHj3i7NmzbmzX19dZX193lQYrHjfGMJ1O3fOwaRoePnzo3KGMObYcP3/+PNPplDzPmc/n3LhxgzzPKcvSNQC01LC6rtnd3eXGjRuuYtXr9Xj27Bmz2YwHDx5w7tw5J7o/OjrC8zwuXbrk1oe167XPqpcvX7ox1Fpz9uzZ1yy/syxz9KYvv/wSz/PY2tri0qVLLBYLrl+/zsWLF5nNZvT7fW7fvu3eLw8ePGBjY8MZJNiGgru7u66rvAXqXYH4/2v75hqNtqEXShbLBZ6foGNB3ZrjDt/5DC+M0CR4laEWEGSCoPIJZilazkB56LaPGc5pq4q0FlArQpXSeAW1MRh1nDHMwh7oFu0ltG1J6ifETYmazphEMW0cMgz7zKsa2SiUOLY3bc0S6fvMqxDI2H+5xS8/+YiDl0tMKWgyBTGYxKcKAnxKtK5pGgk+qHBJOhjAYU7tgxdKYmUQnkTrBeuyj1QpeT5HjMFvNCIN8ERIKxWJL5BNyWRZEQQZ0jNIZgxVTLv0WMwXQEvQ6xNgKEqFDMAzhiD0ifSIhVygdU5PREwOSooIIhEQVyWtaIhiWMoaQkFW13htyb6qiAkIRUgbCVS7xNNz8qlBSE0U+bSlJk0D8t2cJPVBeqg2Q5UaUS+pFOjC4MdQK0PRNGRRzP4sR5qSpo0QrcDEHl4eU4YZy6MjBlGFCDwWhxMyP6HWMUG6Sq0PWdY+UWNIjEEMYgJZY1oFfoAwx+5i6dCnVIokHKB8jzr1CGKBKBVnNtbJyxavlxEEmqWA8XgdsVzSSoGXRKylI6bTBY3pE0YLzp47S/X4gPsv73F28yYbaYRWBjJD1e4hWp9at2BCVOOhUg+FRrSaKJHIqE+pSspqgacVplEEaZ+mblmJWjwt0IVAe5rZfEl8yqf1oDENqs7xg4Sl9PCkAi1pmgJdSvxYYpoSL/LA+LTLgkCEVMREvo9X+RxWmpEfIfw5o2REvSzwjWSa+Eg5oK1q6smEIP72NuyzwZ/dujQaGwTZAK1bvbAvvm6AaAPebmbdViVsNaMb/NrPdAXngBPMAS6b3bW+tSLQx48f0zQNP/jBD7h165YL2DzPc+5YNqCxP9sgt3u+XYG5pUXYz3V5+N3seDczbekANjjuZra742yDE3ss+7cFLnYsu5Qm+zPgyvTdwFkp5TQmdtxt8GXBluUVd0FGN6i2+7Ogxs5hd2y6c26D6a4lrN2vBWzdqkU3YO8COwtEgyB4bR9dwX43MLfX1B3PS5cukaYpn3zyCf/9v/93fu/3fo8LFy64c+le38mqTnd+utTAX6fjOSnWtuvaAh977hYUdcetC/YswOpWMGzV5Nuq0bAVKCtuLcvSceNtIGcF09ZM4Ze//CXnzp1jd3eX3/7t30Zr7ZyculqjmzdvorXm2bNnVFXFW2+9Ra/X4+DggJWVlde6SN+8edN15bZz/+jRI46Ojnj8+LGrvF25coX333+f8XjMYDCgqiqnPbCC552dHW7evInnea4Zm+3zcP/+fd5++222trZc928r1B2NRpw7d86JkHd3d91aj6Lotc7W3YTH2toaz549I01Tzp07x7Nnz8jz/DUziLfeeosnT564jPva2hpxHLOzs0PTNDx//hzP85xw+ty5c8Dxc+Px48csFgvnZiTEcU+Ply9fcvbsWSfUtlSwKIpYX19nPB6zt7fntB6XLl1y97Ltym3X/Xw+5/z586RpytbWlqtcWPe82WzmqiRW7LyyssL29ja+7zu7brsWbBNBrTXr6+u8evXK6Rc2NzedvfRoNGJ/f9/Zfw+HQ4QQruO2tYm1DSIt/fLRo0fuGbm3t8etW7c4OjoiCAJ++tOfOqqpFZdbjcl8PnegxfZ9sfSo7vPPPoOGw6F7j7169cpVaGwi7Pz5867XyHA4fM1gwJocVFXFzs4OFy5cwJhjFy/bEdyY4/41nud9Y43GNwYa/WGAqgy9rE9dK8o5hKFP3oR4vgEj8CtN0HrklMhAQxOgAoPuJeSRJila6vb4YVkmBhMWkOekQUagaqZxj9TEZJ4g1zUiqKhGEn+RIJo50ShlnMR4bURZ5cRpQnlUE48Mi7IiIaPWFVUtaedTfvWzu2ztvcRoD+1F+IVPkbaotIFFja5DWlOTDUKqIicbZzRFSKVier5C6wYRepS5gDjBExol5hTSI6orvKaiZxLyoqHWMUWtGWQgAgOtoij2CYcxrSoRQYDCw6srRFGgW/UvlCSBiEJ0G+EZjdA9tCooTEgYRSjmeG1Aq6EuWowS1E1FHAoWniIKe2RaUFc1y1KSa40nJWq5pDUBVeZTNDmpTKiOppgowygoK4NQDYEnkamHT4uioWolVV0RZSlLoQhoCf0BUgjiUlCVS8RYoKuWURZS65xStUgTgAloiilSeIRZSKANcV8i05h8pknaPl4ETd2QhDFe2hzTu7wEz0vx4pz+IMLTDUYrFjuHJKmPJz2q2KfdrfBEi9KaiIBxnFAUExLpIyKf0EsptM+Fyz4vXmmeP3zKxo0LxGmNUj6+TsmFR+qVyF5CoeesBhn5DGQakyY+9aQhThOaoqDRBcaHqknoCcVKFLBT5mSjlJloCFdOQzknPhVTV4p1k6DyBj+qEFoCU4JAUs8aQiR+EhPogHLRIPoNwo+JtWSt8amrkCxR1LokwtAWFbPJgmQtYRB51GWL1A1y0NIW307/e/haZGw3K8K0QrluEAVfdwGHr+k+3SCtW8Gw+7CbfXBamk+XOmN/tp/rBqg2MLT9DiaTCffv30dKyR/+4R+6zJSlI9hMeJduZQFHkiQOyHT50za4tPSdbhBpX/Tdz9lKgD3Proj+5JjYcbP7sluXmmSBnf2cDXC7fS6sHsJ+145Jt3+B1trZWloQ2dV/dM/Tfr5bxei6jQHu2uz42ePa/Vrg9+v6Q3S1C3Zf3X13xfS2OmDH2GZRLZWq67Zl58ae29mzZ/E8j48//pj/8T/+B3/4h3/owIYN8Ow664LablXopObGjnl3rLpi/i4Y7N4LXepat7Jjx+FkZacLFr8pv/o3bfvFL37B5uYmFy5ccN24Pc/jk08+Ic9zLl++7ObZdk5+66232N/fJ45j1zzPBlPWrrQL9M+dO8f+/j5FUXD37l1u3bqF7/vOhSiKIqf5sN24j46OePnyJY8fP2a5XDIajTh//jw3b97kzJkzzj3q1atX7O7uusZqX331FWfPngXg/v37jtJzcHCAMYazZ88SRZFrMGibDRpjuH//PufOnXNC38Vi4TQMX3zxBWfPnnXN9g4ODpwrnhW8r62tOXHvdDp1mggLgIQ4tjG1lKXHjx8TRRFw7Hg1m82cC5cVkFsg9c4773B0dOT4/7a6Ydfz/v4+nucxGAw4PDxECMG9e/dc5+ozZ864hnn9fp/V1VWXVNne3ub69etorXn48KEDOQ8fPnTgY3Nzk08++cRVLnz/uIfGs2fPuHz5srvXLchI09RVvKww3IrlbXC9srLiHLa01q4DuF03WZY5imRZlty8eZPt7W22trZcdfPixYt4nufG3dLZnj9/zrVr15wG7eDgwFVLxuOxa6Joq2i267rneQ4sLJdLBzhttQdwY2Kf1Uod9/+o6/q1ypSlhXme5/qFWGdGuzYODw8ZDoc8evSIq1evfqN79hsDje2jihRFGgnyssEfZ4QiRlSS2vMxYoW8zfGbmrW1jGkxR2jQScFifkjgrZIOfKrtOWUY4Xma5rBBxT61LPGbBj8oSeqQxURRyBBtPFS9JGxb8lqT+iEy8ZjPjyCA+ayANMR4PtqHqtQEIRwsF3x57xe8XDxDccRRHpKJAa0qkBqCtibqD5gWHkNjCKsGFaUoDG2xIIp8ynyOzCWNCUlCn3rh0xoPkzSMRYyMNPVSUMcetV/TbyNEL8GUU/xMUBeamBEzY0hViJQ+o8yQ7wvqsKXWIGTNcCmZ5RIZGObVPnE2oF14zJo5g4GHnAj8sgavwmQVTRxTSs1ysSSNoKk0bRsTiJTYmxImfepWULQGL4tpZgopEhqvwTOSo/kcIfvUoqWtlmQrY4rZjDhISJIUXRva4NjSN2g8TGNoFyVKl8zFCrIf0C72aBeCIhyB8fBMS1tWzFMP00rEfEK8hDT1Wc5BDGNiMUTKECEaIpnQFi06DdFNRTs06LxkZTQkSXxE5dFGHrXWFE3LaOzT1iW+DFBBAhwQxjG6UuiixfdiVFFR9iR+NCLC5/JGQKxeMN15wdql87RSMTcGIUoqGVAfVAwSSVnOkXWAaCPKVkNZghJUGGTkUxUKehOQDYW/gp8oZs0SYZYEsqKpDf5c40URwk+JU4NIFVotkEYSRkP8XBF4Aq1jQjKCeIrRGtkaYukzUS2VNtRC4AtJJWC+v0AEHm2tocnxw4wyFHiyh/a+nZlI+LqPhqWlnBSv2qCpmwW2gaMVD9vAs5tVt4GCDbC6QKSbIe4G710qTteZyQKFqqqYz+c8fPgQz/P4wz/8Q/dgtVWMrmjcBnY2WLfX0KVSdUXDNutsaV0ne1/YqoWtBtjz6u7bVk1ssG3/v6trga/7VNhz6FKdLF3LBqpVVTnthB0vwHXttdk1K6TPsszt02a8bEBh/7YUsK4bVTcAtuNvx6dbATg5Xt157Tot2UDfBjtdWpxdZ90xOUmxs1WVPM8BHIi0mUM7vvbl/P777/Pzn/+cv/7rv+aP/uiPHNiwx+yu5ZP6I7uW7Xx0gUS30tOtbHR/1wXl3f3bfXdBnv1dVwfT1Sh927YrV65wcHDAs2fPGI/H/PKXv+TSpUv0ej3OnDkD4LpxHxwcsLa2hjGGhw8f8tu//duuSmeDMmOOha2//OUvuXHjBi9evODq1auMRiNXxXr8+LEDH3Z8bddwOLaztpqMw8ND0jTl5s2bvP322xhz3IPBrmNrHHHnzh2uXr3Ku+++y+PHj11nbnsfDAYDdnd3XcZ/bW3N3e+2O/pgMMCYY6ctKaXLdAtxrF15/vy5W1vj8dgBA+uI9fnnn/PDH/7QPQNWVlYcT79bHRDi2L0ryzJ6vR737t3j1KlTDAYDjo6OHK3n/Pnz3Lt3j8lkwtOnT7l48SJZlrln27lz59yzwoJEgBs3bjialKWrCSG4ceOGq0x4nudcrOI4Znd3l1OnTjknrsePH7u+H9Yd6datWyilKIrCaVOsRqSqKtdDZDKZcPv2bYIg4OHDh2xtbXH16lUn9rdd17tV6Rs3bji3q93dXdeDpJvMMOZYnJ5lGU+fPmVjY4OiKLh16xY7Ozu8evUKIYTT53z11VdcunSJt99+m0ePHnHp0iW2t7ddl3T7LN7Z2XHanjzPXYWrrmuuXLniDA66NFwrLu9SK589e8bZs2f54osvuHr1KmEYOuBxeHj4WsNVIYQ738FgwOXLl5198b+2feOIRQ4Feixpo4h+f0xYCqpWszQF1AFSG7IkRsc+c3LassY0Da06bqIWNJLpYooYRERRjO8lJFGK1AGmNrS1pJ1EHDTgjSKkL9HLhmEVERkoTUMTSY4mS6IoxeQNnggJGkkwr4l6hmYoKKuSrXuf88UXX7F/UNOUHpHSREbQjyL8RoI+tuLVkSb0FX7kkXgRZuERaqiVxMg+fT9j2F9BtjGe8VCqRreaOq4plEC0AT0EwoMymNKoV7TMKBY5daGpWkm/jZF5gyoUutYoUiZHmmlhkF5E2/pUS4mqDUZ4FIVGlQsi5TOrppRJi+8ZwszDCyAqDKMmJG0ifDGkbiU6bGm9KYce5PWUsqmoa0XQVMRxQ9IPkEGCEgFeLKiTBj9rMHHLvM4JIw+V1pSixvg1WlcYA02ryZsFcZrh64AGSdOEUPgEEnKvptKg6hLfFyizR7M8IPEayrphsZgzyQ9YLHMCDCZbIDPDMqnQg4AVlTL2U5RRrK5kBI0hF4JplVFriQo8KuVR5DlBaRgMQ4Y9SSR9pmXNMi8JwoRatMi+RoYlwzikbAOUF3H67CmKGB5u79DoAOqaoGhR8wa/DlnqjCKQiFMNJsmZt0dM5RQDpJHHIJRkTcCg7bOWbjLXhtwoliogP2oIo5gw9sgbKNsIihZVKsIoYEUOGIkRug6Rg5Q6k9BbUCQTqgCKQtEaBUJiak1vEBCGLUki6CcJg9UhYVixFA2N0IR9RTaAqlwQ599e6tTJrt0nQUA3W939A19TZWwQ3BUr24dhF3B0M702YPZ93zXH69JN6rp2HFub7Z3P5zx58oQgCPjxj3/MtWvXXMb6ZBM/GxzYB7s9ts2qWjejOI5dgGkD4y7lqFvdsb9L09TRjrrUmC4FyYIu+Fqo3QVv9jpt8NkVDXevyR6zm323QakFJdbdyQY9dszsOHedtLqgo1up6Y7RyUx+lxLUzcTbObbX2N2nFfzb+bVbV9PTzfpbk4AkSYiiyH3fjoG9li49oQs4bPfl733vexhj+Ju/+RuePXv2Gt2uW4WxYNaenz1WV7Nix8Ku6e64dOkwJwFQlyoFr4MTu7bsH7uGbCXt27h1u4ErddyLwD4TukGRHesXL1644M7Sxg4PDzk6OnLCZYBr166h9bHDkp0fO442u/7w4UMA5vM5bduytbVF0zTcvXuXhw8fOmH1rVu3uH79OlmWsVwu+fLLL1+rMD179owrV664dWWb1N29e9e50Y3HY9q2ZWNjg62tLdq25X/9r//laFxwDMKfPHni3JMA9vf3nTPQqVOnuH79Op7nMZ/PnbBcSslgMODChQt88cUX7O3tuYaBKysrTlh8MhnU6/XY3d113beDICDPc37/93/fPTeGwyGDwYAf/ehHbGxsuGd2GIa8fPnSPXOEEM5968WLF05roZRiPp87lzbbS8P2KrGOTleuXCFJEsIw5NWrV0gpmc/njuple3dYylPTNDx79sw9K9u2ZT6fu/n8i7/4C3Z2dhiNRly8eJHd3V2apmFvb4/lcsmFCxfwfd/15miahqdPnzrrV9tIcTweu34Zjx494vDwkMViwRtvvOGshJum4cyZM7z99tvOhSzPc1cBt26Djx8/Zn9/31WGbEUZjiteQgju379PFEVcuHDBVcgePHjAs2fPXFLE0grtevZ9n83NTW7cuEHbtq5CpvVxB/BuvxfrOGjMsZHKeDx29FnrkPavbd+4ohHLmiQzTPdK+m2GRpLHDXGvR1MWtE0Os4LYj9CLFiUFcRijZYTQJaZWkKYkTUNioFEeTQpBVVHkLXEdEUQZTaNZLBfEWcgoNKi6omo1cc+wu1jiZyF5VSG8iFZVGM/HRCm1LijmLS+fP+WLrz6nymuKpQadYlpBQYnARweGcJBCK9B1Qa0NtSqI/IAgKpnVC9q2x2A85uDlko0oJYg8dOij9ndojKTVIWtxwCKvyVWNaRRGVXi1QQUQ+xVFZdB+gqbBG3hMdmsaKalbiJSH3ygQLYemxsg58yZGewGmNXhAWBuQilIWTIVBmJBlLpCLBYNeBmGILho8FF4t8NIQWQtEXWIi6KcxUdnSVg1lUVMog0g1PX8VVI4vfEQBbeAjYoCWcjYhzXqEBsqqQXmarBfSFhM8YVBBTV5pwtCnl0lUuUQ0CToeYnxBKFuiWLA4KomGKzQyJw0jtCqZzffx0pBhpoi8AL9pWDTg+QGrYZ9lk9MfrtJPR8yLhKqcMJQRdVHRUhP215C1z3Q5RUhB5WniUONHDRADObL18TLBINSU4jjoX1kZs7N7xHRvj83TY4QfItuAqJ+Q4yNFS6xDGj+kbFr8lYwqb5GiQKgMKRviIOQorxBhg+8riFJK5bMoNa3UeIHBUyUNAWmU0LYVB/OK0aCPpzVBGh1nbiqBiQKCBnzhHzcxPNojSTKWSoMfUtcLItGjWU6JkhSRBKTBcQfxOJAEQUy5zL/pbfsbt3UDy5NZ/y7gsJ+14MH+2wZ/3QxuN2t7Erh0KwzdDHKXpmQDQ9u7wWbrd3Z2APjRj37EtWvXXgs2bRXD+qdbkbUNJrqBpQ327GYDaTgOBLtaEau/sIGhDXhssG5fkvb4J6lR9vrsPrt/W7BiAY0NYO1xbMXCVnNsZt/Siux4dqsgXR1KkiRuHAeDAb1ez523FWV2K04WNNiXdnc+ulQqCza7jfBsoNBtXmg/39U6dF22ujSIrs1rN8Nnnb3smFm7Sgu4LGC1OpRTp07x/vvv84tf/IJ//Md/JAgCTp8+7dZ2d711Qa49n+612nOwgWCX0mUBUBdE2XPp3jP2e13DgC6trlvV6oKTb9MmhODChQsO2NkgqGsbnec5/X7fmQNMp1Onb7CORnme8/LlS9bX19nZ2XHBezcgs+u6LEvOnTvHrVu3mM1mZFnGdDplMpk4x6XpdEqaprzxxhu8/fbb9Ho9tD4W6lrXICvUvnjxomu+BscVmIODA95//32UUs5ZyVKqTp06RVVVfPjhhwghHA0qCALW19dfA0vb29uvOZwNBgNnqXrq1Ck+++wzZ0m9ublJGIb89Kc/dYJf21+iaw1sqyvL5dL1JNrd3UVKyerqKj/96U8B3DjO53MmkwmvXr3i1KlTLhlgky62I7Z1goqiiPl8zrlz50jTlLIsefHiBVVVcfHiRc6ePcudO3c4c+aMS9p0u6jbhoy2eWpZlq5x4mw2c5UU+wzf29sjyzK2t7edA5StMtpKdleUP5vNXGPHFy9eoJRiOp2ysrLC3bt3nfC8S7PSWjtAai2B33zzTVeFWF9f5+OPP+YHP/gBn3/+OUVROP3FixcvODw8ZG1tjfPnzzsgZtfP9va20wW+++67rK6usru76+hQZ86coSxLPM/j8PCQ6XTKmTNnXHWq1+u5c7PPO/vu6vV6/OQnP+G73/0uvV7P6aAWiwWbm5v4vs9oNOLg4OD///a2cSBQy4ikUYhYUvqGxq+J64LAiyhK8L2YVraYQBNHMRjBvFjgSUUURMhaE/iGSV1CVSBMhA48EhEgUw+hGkJREPT6FFTI2KcqDaZtqeeKfnqOikO0mOGlPZLa0EpBRU2zaDnaPeD+4+fMZhWpqkkCQRvFqDpmWWlMKBDhAtUYgrwkCVoCIfGjhNLTlMYnydaYFjVeBv5Kj6P5FH8xw6yfOe7boWGRz1mqhFLAYl4Qm4owSCgDgZEwDGK8ckGVaZSqWcznqHiAqQy+1mg1Jx0lzHNBpQVS1AQ1+EFMmc/x1nzK+RK/9QiimCANaaeKQEoCGVGHChFohOwTmYLA+Ig4YjBsqZctqZeB8umfDslnR/T8Hkp51BTIxQKtQccZPiWh0QgvYJI3pEEI0icIYhTgeRqxVGilUGELaQ2NoG4XtE2ApzxmRUOQtbR5xZlMUhYVOjYsyyMyPyNvfBo9peyD34wwSpJGhqQn8Y1PEGkiWROaPn6QoY4UcZzj6wYZp6StxGjFYZXTFkuyKCBMJcJAEg/xdYkwkoAIoSSLSU3llxwd7VI0DWUFUZCQHxTsVJCdGTKOE0S+RLQl8SCkWPagqemHCYt5zmGtGA4lKp8zWFlHeiAbTVtB7Xvk+3tkocHodZTnEdYtWZAQ9mIMAXHr0/owo0UGhrKa04uGtCZFqIhgWcBKhK4b8BOW2ifSHmJRIpOIqqwRiSFAYaoIEaeougDtI1tJNFh+09v2N26zgbzW+jXtgv1dl24CX4MNmwk8SX2xD8duIH5Sw9FtemYDdPvHggJ7PJuZtyK9d999lytXrriMXvc8ujoLG/yebIbXpdDY4Lh7PPuzPZcoilwQYL3S7bXaY9qKgaUzdKk/lq9ur8+Odxfc2P1Zupbl5U4mE6bTKfP53F1Ht3pkz9eKIG3Wzo5vl2aVZRmDwcA5oFy4cIHxeOwqOhbkWJcYe/3WnckCMBswWWBj58degz1etxJT17XjFHeBapfOZaliFhDadXGygnPye/Yz3V4eq6urfO973+Of//mf+fu//3v+4A/+gNFo9Nrcdtek/f+u+1p3vXZ1Jl2aoP19l/rX3addd/bv7jq3x7QCVSG+Ngr4tm11XXNwcMBoNHJz3QWuXR3G0dERly5dwvd9Hj16xJUrV5zWZmdnhzRN8f3jJmp/8Rd/wY//xS72yy+/dN2xF4uFowy+ePECKSV37txxtq6vXr1yznNra2tcu3bNucNZwDudThmPx7x48YJz58450fja2hpPnz7lxo0bjo4JOFB9//59rl27xieffMKFCxdYWVlxyQhrfbu/v48QgsPDQ54/f87p06dZLpfs7OywurrKwcEBz58/J8syJ1z3fZ+iKPjZz37G5uams3ZtmsYF3xYgvHjxgvfee4/9/X22trZcQsI+13Z2dpBSMhqNuHv3LqPRiOl0SpIk9Ho9Hj165ITaQRDw9OlT12AxTVPW19ddczvba8P2ILJAaDabuYqCBTkrKyvufnn16hVra2vcunXLAU2rfbGJG/ucXi6XvHr1iu9973tOHG6rhFZ/YZMLth/S3bt3HX1tY2ODxWLh3M0ARyGyfSg++ugjNjc3WV9fR6njZnlKKSaTiaPHKaUYDocsl0veeustHj586Ob98ePHnDlzho2NDabTqUtWHR4eOgqYfYZHUeTcp9q25ebNm8DXGjXAib5932djY4PHjx9z+vRpl4BaLpf86le/4t133wXg+9//Pm3bur4/lkZ19epVhsOh03RY17Z/bfvGQEPlIWG/R1BXzMoST0QkNWS1oszAa0tUFRGvDljqEj1b4LNAKDBBjNAlSQueJwkwpKsRlR+SH1YEXgL4yJ4ilqvMqyV106AjiRAhQkgaA8O0JZgqwjRh1rZ4ocDkHnEsmR0sePzsKQ++fEqJpJem5HtTZCyYRyVNo0mSAUZ5DGPJYq5oW/D8kCbzUG2Dahp60YCojZk8zZG+j6clnpQIvaDqG2Ti4+UVyyaiwKMfAk1OJULyVhD7AYdtjkh8TKjoNQ2m1cxkS2kMqRfTLn0WixblKUSoaOcxZqGJRj6+H2AOG4TyMRnEaYipNEHisTKO8cyAo2aOl+eEoaQOEygqyllBGfvEgYdHQxWFFLKhlYbFfB+ZRQR1RNhCkaV4/R5BLyANDbWEauJD3aDLmMKrSX2fXGuqukEECZXxaZc1rR8wFh5+fpzN97MENZ1D4aO0wviStinxspScJaqV+FIgVUtb5ewrQ69vCKkYZJsERpBkY9oKEIZStGAMq2d6tKZisr8glj3CXktPhLTTBpUGmDDDtD5em2Bo6AUhpWpY1DkHu1NE3RCkIY2q8EJFupGxs7/NZpkwCwt6vsQEmly1DACZSZRXkumW3qhHrBSeV1M0DT6SQCt0BLqU9KRHErfgS5pFjfED0nGI1BqvXSD1EC/pkzcTBgZM6NMYn0ou8KuSJO0xiySjXggLRSUVYeahakGiI+ZejVYB+B669FnIglBFFC0YHRB6wf/7Zv2WbLba0A2q7IujmzW3wVg3sOxWJOzW5a936UldQWw3k9w9Xpfnvre3R9u2fPDBB1y4cMHpC2x2GXDBcJey1HV26VKfuudtXyTd4NFm861YNEkSRw+xW7dSY18oXZAFuKDcOo50KzB2HzbQsADBvkgsuFgsFo5+YOkOtlGfPZ88z18DcHaeZrOZA1I242+z+b1ej7W1NdbW1jhz5gxnz551tDJLZbJjY8+te972OBasdasDXUBg9Sw2mLLzb8fIBhs2UOqK2i3l7OQYWy7/SUqanWtLnXj//ff5+OOP+eijj/jxj39Mmqbu87aK0dWVdA0JutUXC2ZOZta7YNNeb5eiZcGb/b29hpO6DLt2v60VjdXVVXeP2PVsBcyHh4fM53PyPGdlZYXV1VUePXrEmTNnWF9fd3S+X/ziF1y7ds05MrVty+/8zu+85gr18uVLVlZWePbsGefPn3fZcVsh+Oijj5yY/OrVq67bts3QK6Ve6yZ979493nzzTcqyZGVlhdOnT2OM4Tvf+Y6z57Vr1nZqvnr1KsYYzp8/T57n3L17lx/84AcEQeBoW9aNqeuGdPr0aVcNFUK4xEXTNE5cvrOzw61bt9jd3WU0Gh07fv6LwNtW4KxT0/379zlz5owDFU+ePHEVWOvoZIFXnuc0TcNgMODMmTPcu3ePPM85ffq0c+0aj8dOAL6+vu6axz169IjZbObAxaNHj4iiiJs3bzKdHtu6X758mRs3brBcLhkOhxwcHDjR83Q6pdfrsbq6ymw2ew0UWWB+5swZVldX3b1hq1q+73P27NnXEl22Sm07bS8Wi9cspG2gXtc1t2/f5sGDB+R5ThRFztFqOBwyGo148eIFz58/d2voq6++YjweOzrkpUuXODw8fE2/ZityYRjyq1/9ivfee88J1rXWzvXKVj8mkwl7e3vcvXuX27dvO+H2J598gtaa999/371foyji1atXDrScPn2apmlYLpc0TcN3v/tdXr58yc7OjqtQ2+SPtfv95JNP+NM//dN/9Z79xkCjlh6ULR4SmdWYekEsJdKEhEcN6ysB89BQt5LESxEBlM0SPzBI6YOQSJMj24hMZVT5kmWt6XtjynyJjiVF6dNDIjzoDQ3NfoHRISpc4Ld9WlMg6pBGLVGDBGE0TVMzXfq83D/i0eNHxBqqJicPPVojGcgBpc4ZZD61KlG+JpQR+CmlbhGmR69Rxx3LW0UQtIyGIVQZtawptaSNUmTm4bUhRR2TVjWpV1HmilYqPA8IEhZHgh6GallgPDAqYnpUksQ+sVEgFQvlkXsavzLg1wilYGlIBiG1p2iVRmtF7HlESR8v6bM3fclqmFKpmmK5jSbADwLKpkKlPfBjVsYRQjSUucEPBEamDH2P+Yqg8Qta5eN7Gj0e0dYGr5hRtEtUnVBpQ1B79JMREoEXROggoucvmQQRiRZ4zQIRZahWYaKEaV0hG4+kB6qRLIuGSRHgR4IwSxHGwxOKwIR4GoqmOKaq1Smm8EmShnZ9ynA0IJYpcWjwY4FcQhRHMG8ATeoHaN0ilhpJQyB9dK6IewFhXaNahd+LKULNfNqyKHYxhMyKGoNHXfpQVKigYePMgL2d56zLK6RpRtRL8CJNPm2IZglhFNFKDeKIgyok9iWqqYmkgFAgsz5hrsgrQyIEzWJJFvfwJMxUjdcm9IKE3G+phCEbDcm0omxy4kVO4mfkUjOvZ7RlxvRQEyU+SSSQ7YK60TRGokxBFMaAJEg8AiFJpEYUOUpJ6H+zLMJv4tblvHdpH5byYAOprn7CBky2LG2DUwsUTgIMGzye5OZbIbX9bjdwt7+zzZc+/PBDrl275hrw2QC4mzm23/F9//9qVmezYV1alw0M7WftZoPqLMtc8GxfNN3Ao9s3wo5ll0LVDVS64MhWHoIgcG4pNktvQUQcx5Rl6V56lpJyciyFOLbUtI29LPfZztFyuWQ2mznQcnh4SNM0HB4eOnvLL7/80gEimzm9fv06o9HIcb5tsNKlu3V1N1VVuSDKjpWlfNmxsd/pVpNO2u526XndxosWCNo5s9dnaWwWQHUpcLYvwU9+8hN++tOf8lu/9VuvUea6a6/rOtW9H+xasp/vNgG0a9X+6QLUk1WRLrXQbnYsrZj/26rR6PV6HB4euvl98uQJFy9edBQfS1e0mfObN2/ys5/9jDRNHcXE6jAsoJ5Op4xGIxcEW7C8srLiaEzT6ZSyLFlfX+fOnTtsbm663jSnTp3i7NmzDnjYfdtALwgCbt26RdM09Ho9F5zae9U6XJ0/f566rnn+/LkDRl3d0JtvvsmTJ084ffo0vV6Ppml48eIFt27dcsHfYrFw9J4rV644rYbtPxGGoQuY9/b2XNdyu6ZtU7bRaMR8Pmc2m9E0DV999RV3797lvffew/M815E8iiIuX77Mzs4O586dwxjj7Fl3d3dZX18HYDKZcPr0aVZWVgiCwAXFcEzviqKI4XDI1atXnfDd6hKMMY5e9Pnnn/O9733P6assddV2/7YA3TbsC8OQJ0+euPG1DQ7t/1k7V7u27LxZDcbZs2ediH5lZYX9/X2MMWRZhud5rnv71tYWxhjn+NRtEmupUk3TOGtjK+4+OjpyFf4rV67w5ZdfurVi72vbJ8P3fR4+fMjGxgZRFPHee+9x7949V1EZDAbAMYXOWh5vb2/zwx/+0CWibCXo8ePHLtlie5mMRiOXIPn8889dxXB7e5sgCLh8+bKzbbaVwG+yfWOgUWqfUDQ0Xkzgn6Itc5ZBjowhJsA3Bi8MyMt90lDQy0Kq+YBIeQgaijynTQNMLUhbw3wBvhdiejVx0jDxNH4T03gLBkGPpjWYLIDCox9GqFlBXgu8nocyPYQStEbjBUv2prt8dOdX5IslRD6Z79M2NcEowQCN0nilRquKYJQwKRuWgU+kJWE1RSsPZICWIXvbc8bDETL0IfRo8ho/U9SzgjiMaeuWPhqjWgK/QeeCpL/B7lGFGUTkSBrjEScRrcpJhgFls8TzNVUBRi8ZeBVHRmC0II0DPGloPU3gK7LYRxUD/EQzK5fERrAqM7w8QI5DWk8wJCCIBEESUdUSIp/esIFSsRz30GGI3q8YeBFGNiRnM4LaR6mWOB2gtcQczpjLJbXRBE2FEQak5rDOGYiUvoFkZUySGPKDOTJcoxQC2gWmClAlSAqCymc6W1DrEEGAbA3FoaZVJX4U4gWaSFWIFQ+ERvoVOlGUaQ9RLxALg/8qZGO1h5omIHssihqlSqTvMVxf4fDwiH4YUOwfIUY+zQz6JqNREmVKxishuwcHTOYFTdtSlgrZD9D1FC+NmU8VFQZVB6xtnGO+KNjwBgQYZOtD4uHFijZXmFriiR6xkZioxjcemAaFh8krpFL0sgzTCIw0yKYkzNbQZY2IDJVUFN4cbRRtM2JmYqSaU1c5jRkRJD5F2ydBEsgeIRD6JctJReCtUqucJPbQZCh8GuYExqMqfYoA/L4g4OCb3ra/cVuXQmL/3c3MdyksXcrHye92gQjwWo+GLn2qy+23waOtQnR7KWh9bCdoGyG9/fbbpGkKfF1xsHQZS53pUpa61J+uyNrzjpvI2Re5reJ06Sv2Zd2ttNj9d8+3G1wCLmiw59il3NimWoAToZ/UJdhqxmKxcEDPHtdm3LsUHeuCZedESukqHfbc0zRlNBrheZ4bL2MML168cNdtM84WTNgS/MWLF7lw4QJnzpxxgYa1V7Rj210DXerdSTDRpcvZ79h10K0WdPUMdi11KVLAa3NrM+gWfNoxtev40qVLzOdzPv30U9I05e2333aft9nR7hq3x+y6ngGvVaS6VQ+7DiywsmChW83onru9TntMe5/Zn7+N29HR0Wu0PaUUDx48AHDmCePxmPF4TK/XA+C9996jrms++ugjoiji4sWLLptrdRRSSke5snM2m80AXMXNUlkAF6CPx2OyLOPOnTvOLcj2wbAi20ePHrleBV999RU3b950NCIr0rZUsKZpXEfo6XTqKFZHR0ecO3eOCxcuoLVmNpvheR7nzp1zeoVTp065dbC+vu66VNvma1prNjc3KcvSVX+ePXtGHMcMh0MHlp4/f869e/d4+fIlgKN4xXHMp59+ymAw4NKlS2RZ5nrbXLt2jY2NDWehenR0xHA4xPM8py3QWrvM+IULFxz4efTokUs02CZyFnxduXKF7e1tbt26xU9/+lOqquK//Jf/AsAPfvADpJRcv37d9a/I89z16rDaFXssOBZ+a62dEH0ymbjn6nQ6dX02tNbcunXL9dPoJoGEEA6AXb9+nf39fUdjTdOU/f191/ncOlfN53MODw9ZXV1lf3+fq1evviaqts5d3/nOd2iahoODA5Ik4ejoCGMMi8XCPdd93+fWrVv8/Oc/dwmWCxcu4Hkee3t7vPnmmzx69IjHjx/z/e9/n+3tbWd9bJ+Bm5ubPH782HVBz7LMvYufP3/O1atXHZhMkoTt7W1XObFd7X9dQuPXbd9co6Eqqqqh9FsyA2kwoDAG1Q9pIlD1gqJVjFpJVHlUVcPIBJh2AVUC8YhStWjZMDUTWIkIejHVfMqgSWnrijDIMX6BXpR4whBKD3RLM8lpVxJU3cNXDbXXokyNMSFlEPCrJ0/IDwo835CbJUMvpCpr6BmeFQvCgUS1OUb3KBclQRTgIwgjQ7IesyyXJKWC0KeJQ9rGxzQlsfYRno/yW7whqKImCGKKyqdNDPOlZjUbUFRLAk+Rth6eb2hEi5opfK1QtDRxSFNqglxgTANS4wcCoSTS1BBGJIlAipZQe5hRwHB1jbQu0RPNcLzC0XxGGEb40bGYug2hLKAXKVTUUpgKWTQk6ZBaGpZRS64DvHCIaZckngeNxjcw1w398RDahDAO0aqkxZAQsFb6IAV58ZJptaTcV4RBzMrYpwkS8p2CshKY5Li/iFgapPCItE8vlvimYSkkSeARpjUH9YTIG6MOJNlmRFM0CC2p8pIogLJYUJSGIyXwRpKVJIYA1GgF8oJmahjIHnVV4A/7CB0ShDULFuiwh2ojPOGRVxW0EPgDFvGSVEpakzKflYQiIBl4pHHA4nBBXSkORMHg1JgwWiNYFIgwYVnVJGmfMm6ophWj4Fi0lso+9VJhQvCClJAEFTTU7RxhfGZNjez5NElB4icERcVMecyXNXE/IJMpeWpIhiNEm5MmAs9rCFjQi2OWZUMQGEw4oa1B6B6hMJR5gewLdB0SxSGSlljmxM23M0AAHEe/K3y1WeWTwVc3S2uD7G6Guhvk2YDRctO7++5mgrtUkq6b1dHREQ8ePODSpUt88MEHjvfdrbic3NdJnrylednKSlf83OXH24C3G6DafXSv7yTlqauBsN/tVmna9rhBk3UCsXaUURQ5qokNMPI8dz7uXaqA3brUrq6IslsNsMFrt/JgtQ/W1clSgmxTK/tSXS6XPH78mAcPHrCzs8NkMuHFixf89Kc/5dSpU9y4cYNbt245Jxy7Puwa6drv2mPa+ThZdbJz1qU/Aa9ZvHbBU5eKdXKN2v3bRl7d/7fA44033mCxWPDLX/7SdSy242M/Y8ew6zLVBdQ2S9ulFXbvg+652DXWpWTZz9prsfQie9zuvr9tWxzHTuy6v7/vfn727Bn9fp+HDx/y/vvvEwQBaZq6+zJNU/7gD/6A5XLpKhhhGNI0DXme8+mnn/LOO+/w8uVLp72yzwSbdbYVEMC5B21sbLiGdlJKNjc33bp59uyZy3hbkCGl5O7duy77bytMtnfLZDJxzdpOnz7tulffvn3buSRFUcTGxgbz+dwJrcuydNf8+PFjwjB01q9xHFMUBa9eveLcuXPOOUkp5bqi/+pXv2J/f5/9/X0+++wzJ9S2wf9oNOK3fuu3ePnyJUopzpw5w3w+f02PVBQFcRw7oPfo0SNXvZBSOiG9fZ5Y8GXH8OOPP3Zj8eWXX3L69Gm++OIL+v0+Gxsb/NEf/RFPnjxxiYrDw0PW19f5q7/6K958803eeecd4jhmb2/PUYKWy6UDcPbes/Nr3yEW6D158oS9vT3W19fJ8xwhjm2Qx+OxAy/2epVSDrwMBgPn0hWGIdeuXXN9Sfb29tzz2gJBY4yrEty4ccM5hY1GI958803+6Z/+yTltWU2IXS9WY7G1teWMA+w12b4eDx8+dP03fN/nypUrKKX48ssvXQXP933ef/99tNa8evWKxWLhAPxgMKAoCrfWbWNHa/NrDVa65hT/r+2bdwaXATItCBTEIkNGirQfUE4N8xoCERF6Cl2ETBV4yQBVFdDzyNWcOIgYlBIPQd03aOlRqylGlSznNcmpdaJ2jpI9lkFLRMS0XCLqmkhCfaCQNIhI4Yc+WTJiMZ3y7ME9Ht67S6U0WRIzDnzKeUMS9jB1w5kopDItMvHAF1TKEIgW3dYEkSFXPqqVx52q6wp0TmF8TKuJWoFY5hi9QKZ92tyw5JCoJ6EUhIS0ccDkcIrQKV6s8GRNmviIaklRtgh/iFkUtJ4BFKb1aEKOxex+hFAKVWm8yGMwGOEtFEEgySIPVaYQtkhTk656NKmmrzXVsiYNY3JTUJqGsIxRxqDyACEaqkYR++GxuDTL0PMFUga0sWLZLkCkNAHQGqJW0jQ+JjLIIKZHCYGH6q3TM4K8ylF1SVnPKCYzmmmOSX2SwMOoBN+ERL5PoxeoYooQIcrEyMijnZcMgoSibFFNQbPjE/sSKQbsiyOWwxRqQ5sNKLef4hUj2mHJeH2DOK/AQBjEGLOLqj38OKOsDF7VkmQxtT5ivlwwexBRlg2tLkg8hcmPSIdr5F7DyihE5SVyBmLegJGcPneK/ZcvCJoWIQ0y62GkwF8tUWHBfFowGqTkZYU2hmUgkUGMCFuUlNSeJlKauVIcyIrMr/GpMDKlXM7xw5qejBmGMZWeEEnNqhez1DMkDb4uCVRKRcBS1xQzSex7CG1QWjMcCrxWE655zH1JqcF4E3oiIPKHlObbSXmAr5umdUFFlxbUDYxscNYNnLpORfb/usLE7r9twGkDTBsMdl2hbOB7//59giDgww8/dJkaG9x3qyEWrNjM8cnAu6sZ6AaFcJwBtSJqW7LuBpo22LTnV9e1e5BbgGSpTF3R9+7urrMyjKLIgQu7T1vqF0JQFIULOLsZ7a6mwY67pWzYa7OBaRcM2rHugjJLy7Jz0Q3gLfUtiiJ6vR6XL19md3eXR48e8fz5c6bTqcvmffbZZ4zHYy5fvszly5dddtKCl+582+pMtyrQ1WJ0KXT2jw0MusC3e30n/9jf2Tm1gsgu0LLA8/bt27x48YKPP/6YXq/nggSt9WuAtFv56p53lypm11rXgcuu425Vxv5ts7F2zXbHwFZmvmkm8jdxOzg4QErJZDJxtJwsy3jvvfd49uwZH374oav2WRHwgwcPuH79uqOICCGc81AUHbsCZlnGo0ePGAwGTvBqx7Df73Pv3j3ntjMYDFhdXWWxWDhAfZK6ppTi0qVLLhi1TejW19eJoojZbMbGxoa7D23GH752B5vP546GZRupRVHEYrF47f7qJi6UUnz44YdOoC6EYGtry4HeoiicEH44HPLgwQPu3bvnBOPj8ZjRaMS7777rXIj29/cZDAYopXjvvfdo29YJpH3fd9QiC/gs3z8MQ/r9PvD1M+zFixdOCyPEscXtZDLh4cOHvP322+75ce3aNb788kvOnz/Pp59+yqlTp5yO5OrVq074/MEHH7C/v89iseCv/uqvePLkCZcvX+bf/Jt/46hVvV7PWbYeHR1x+vRpp39YX19nsVhQliWDwYDvfve7zpkrSRIuXrzoHMaspXFVVYxGI7IsY3V1la+++uq1pJKdiyzLnAuXHYOzZ8+S5zkXLlxga2uLu3fvurkIw9Bpiqx7lm0SuLu763o32Qqc7WM0n89pmobpdEqWZQghWCwW/PSnP3WJj16vx+nTpxFCMJ1OuXDhgqtYwdfv18lkQpqmPHnyhDNnznB4eMj58+edUcFnn33Gu+++y/b2NpcvX/5G9+w3BhqNF5EQEgkJgUTXLf5CENYNQRgic48q8SjCGq82mKCl7gfIxZzQT+hFKYdFSWsUAxmgJg0iThGBoD3ToPUrZCKp8gQhB2hTkQxi5JFCaIOWkImaspaEfkslSu4/fMydT39KqBr8nsGIlnJ5gCkFQvoEJsZvDMqXSD+licCrMkrjI4McLWYki5T8oGUxKllWCuNHjKRPNE5Z5DmqDpClwg8bciXx05SyyRnqhIyGyeKIqAeLZYnvxTTFlCjwWBofk4IsNXIi6a+ACkOCVY/U95lyLHTvaYnJepgmR7U9ekOFV/nkkxm+iYliRTnP8aIYr5KYWhK0DWGbY0TNojIQSpqqRgx6yKakmhV4wzG1yPGXkqL8F65gDAES6Wv8ICfSY0yjGCQRM6GOKyL9iFKXxEISqR7J1TMcbW+TygRRLtFrCZP5HCkDYi/AS8EohV+CFhFLLyCJh7T1IVJ4iLpgkPWYNC0+Gk/7NO2S/rDH1ChSP+Fof8qUirNxwLT0CQ4DkjWfeJBSFUfUBrJBhJEaQ4PMApoyQxUFVa0QFNS+gmrJYl7giYwX+wW9UBIENXFwbEmMFxAJxWJnzqm18+zvPWIDSW90mqVeIPwINa+JpSAyMAh6zOZzCrEkjBpioWkIKCpNEAiCqIfyW7K4oR9GiDIlLxRhNkQ2S6Jcg2mokz4yHJD4gsOdnEQE6NSnlD5pGtNOD8j8FH8IYVOiSk2lWxazBjXySXoeHhKdh8xNy3xR/+s37G/oZrPdNhC1gVNXM2GD264YustL79JKbDavG4x1+fY2eO5WCmwG3AbOT58+pSgKfvSjHzmXkJMZ/S633gYS3eDC7r8rTrUAxV6bBRpt27og1QYWXfqR/Y7djw1y7bXYzx4eHvLVV1+xvb2NEMeiSqufsADq8PDQ0bQs+LDnacfJjqPnecRx7Gwa7TWf1Mic1IhYhyhLEQBcptgG4vZveJ0CFIYhp06dYn19nXfffZe9vT0ePHjAwcEBBwcHvHr1igcPHrC+vs7169e5efOms83tUsnsedgAvEuNssFmF0B0x6HbJd2eW5fKZ+fhZNXN8q1tgGFBhA0Gv/vd7/K3f/u3/NM//RN/+Id/6AK1Ll2qW42z6w6+Bht2bVngac/DgmALWE8Kxbs0vO6xunPXvb++TZvlxtustDUssAGsUopnz57RNM1r9rGWr390dOT6oGxvb7vg3Aavln5z9+5dvv/973P//n1nlTsej9nc3MT2dLD3jBVz2yz++fPnmU6nrmkgHCcanj175rLfdg1Jedz/4YsvvuD999931Yzd3V1H8en1eq9Rdl69ekXbts7O9tKlS45PH4YhRVFw7949lzQZDoesr68znU7Z2dnB8zzu3LkDwP/8n//TPXdtlcGeQ6/Xc8/q8+fP88tf/tIFz6dOnXJjnqYpBwcHvPHGG054vlgs3L1m1521ubZWtisrKzx+/Jg0TZ0w3VahjDFcuHDB9dL47d/+bSdof/78uXPl+5u/+Rtngftnf/ZnfPbZZ9y/f5///J//MxcuXKBpGv7dv/t33Lx5k62tLeq6Zmdnx1G07L1uK1v22bRcLllfX2d/fx/f95lMJu6ZZbVsV69eddaz1llKCMF8PnfVnv39fc6fP8/+/j4AL1++5NKlS5RlSb/fp65rzp07RxiG3L9/n83NTacHuX//vqsyWFpfnuecOnXKPb9+8pOfcP78ec6cOUPTNPT7fSdwf//99927zPd9PvvsM958800uXrzI4eEh77//vnNgK4qCKIo4c+YMe3t7zubYVoYAV0Gxz6eNjY1vdM9+Y6BRBYpQp+iFJjCgyoiyqPDTDNVWDIYJi3yJ9BviRNBGBt/T+KGHkX3a/FjkbbREFiGe9JgZRdxfZTE5QOuWopCYoqX0FiAqMtUjiH3KHIQf4GUJmW5QM4hkw/bWF0z3copWcUqGLFrBfB7j+wEyg0B7LCYFfhZSVzneMEZoQZLE1EYhmzVM5bGy3jJranQc4QUZIeaYaiVyWjMjSQW10hAaAhXTVpJGFniBoZpWtL5PZAJCA7IW5EcVdeATpQJkixi1BP1VEqOpa0k6zgijiCRMKHaXpIMUKmhp0Z5PsiaR9SrFco4pW+Qooli2ZJFE9BNqoC4XmLrB9wKqfEmcDKmNQbchq14PL4o4WLSgIQkivLZGyhgZaGQpwQjyWUHsJ9SmRMkG5SuUSjFlTjxYpW0Vbb1HnAm8qMdKuIooSk6NNnj6YoZkgmkLZNniyYhc+YhAodSSQEtUGdAagVY1Mouo25J2oYkiDzNfEBcBfthS5iXRSkp7uKA2ioNWMchOYYzHxniFqoIyn0ETEYYCEbUcFi+oF0sCE1F5JZ4CT7Ukcca8hqwHYd3ge6CVRBhNrpdkQYgyOQeLml5/yM7BPpE/oBIC30BPD1l6FaUQaNPQihYR+oQSPNHHLAu8UDMtG3w/ZpikLOeCcCRR0xlBEFPVMamXoJuWMInp9SKODvYRjaBoFcLPCP2AyE+YzRqiJGImC2gSODCMegIPjZISOa8ITUBjjkFiXTcM9TcrV/4mbjZD36W1WNciG9R1KUM2CLR8/y71yWobbKBrg7Vupt4CFKs/sBl9+9m9vT12dnZ47733uHz5snP6OElxsbQAexx7HjYI7GbwbfBgM1vd87FaBxsonXSXgteDTOs4Yj3eLY3hs88+47PPPnOAYDwes7Ky4noB2POzdLMuILJZdCm/7n1hhd27u7u8ePGCo6Mj54Wf5/n/pROwc2j/hGHIYDAgyzKyLOPUqVOcP3+eNE3duVtferufblVEKUUcx9y6dYs33niD6XTKJ598wmeffcZ0OmWxWLC9vc3W1hZvvvmma9JmLW8thaprIdwVY1owZakANriHr9267Nh3gYYFINZWt0utqqrK8ZetZ739XhAEDAYDvvOd7/DP//zP/PznP+cHP/jBa5qLbuXKrqNuNrwLOiwP+6QhgB3/Ltju7sfuywIxCx6/rUJw+Ho8yrLkyZMnnDt3zl2/tW3d2Nhwz46iKBiPx+45srOz4wIkS3Pq3tN2rt9++22qqnJuQGfPnuXcuXPAMWiYTqf89V//Nf/hP/wH1yjtd37nd7h48aIb4yRJOH36tAtSb9++DeAsopfLJbdu3UJKyZ/8yZ9w584dmqbh8ePHrK6ucv36dffcsrSbIAhcoGmtd201z9KEjDGOdhjHsaN5TadTPvvsMz7//HO2t7edYcUHH3zA6uoqly5dckYOvu/z9OlTqqri9OnT7O7uumfkw4cPX3N+s5t1pLPd0ReLBWtra2itnbNdmqasrKwQRRGffvopk8mE73znO64fibXB/ud//mf++I//mPl8Tr/fd8DNJmFss74vvvjCgcaiKHjzzTe5ffs2H330EZ9++qmjyv3e7/0e4/HYUYBsl+vV1VXX+8eOna1EWZqYBST2WREEAWtra3z66adsbm5SVZXrOG6fPePxmLW1NQ4ODhxNKkkSiqLgiy++cBW2Fy9ecO/ePc6dO+cAnp0rq3FZWVlxz0BLgbWauHfffddZF9v7ejwec+/ePc6cOeOos1tbW7z11lssl0tX3bCd7EejESsrKzx//txV6H7xi184Otk777zzmvh7Op1y7tw5JpPJN7pnvzHQaKcTwvEKhZS0UuAjqUuBTpb4g4iFKlkdREwbiAPJfL5EaoEOM2qRQxkTBhGqzdFS0kZLDD2m+3P6osW0KS0+Ji4I/JK2qcGHytR4BGjjM68UQVkwPrXOP/7qH9h68AKjeiSjlkWVQyhJpI/JfIgaqmVJ3tZEnqYqKlKhEbVPo8GoGqEyml7JoTgiEJAhUO2EuidZHijWe4JlWiH8AcpERGbJojgiIKauy2Mxukxo2wKhKnQ950gCfkjmCYI6QiYBctxgRMzYjxAl9NKA6dRjLnL6/ZCsXdIkNQMToIMQU9T42iPUkoVIkHrJsJ9Q+CVxJrl+/hbVssGfTij1An84wjMZi/yIQS/DI6ZWihvhdxByybypafICkQkKJei3HrmqKbxDyoMlNC1JP6aSEUetYpyuQKNpAoVXK0I/oDSCsJcQAYE2nD+7iiKknC2Z5IeosGIch9SVJNQt00rTNIZW+AiWzA9KomFGLAKU8dFly2iYUug5/fUxPS+mLGvq/YZFo2nSDBM0eCpCxCGhzggbydI7QB/5aDPHtBIhJXGpiVVKrhRH85pwkKLqBqECFosCjaYX+ggdcUhBMoig8ahFwjRveDw54vzZSxhRgDKstoq2F9Eu5wipEcZDGB/RBsSxT9XOaYUg8g1NAWWhCH2o25b1dUHk5ehG4KcJJoRZU1GlHrHfcHrQZ1Eu8fyQumnwtGFWGgZpTWpSqqRHlWi0kcjUpylbRBtCXFLqOYhjt7Fv62Y7n9oAqNuETWvtMsQ2eLe0FhsIWL5+t1P2Sach+Np61Aaa3SDNVj1sg6RLly7x5ptvuge8fZFVVeWoAV1b1f9vmpIuDacLnizdxu7Pgi0LpLpOXN0MtA2QLdCQUvLw4UN+/vOfs7W15Y5vvfstJcMGlk3TuJeGtdrtBteHh4fcuXOHhw8fsr297USSXd2JPQerbbBBvD3Xbp+OnZ0d932bHQ3DkOFwyObmphN69/t9JwitqsoBhK5r2Llz59jY2ODWrVt8/PHHPH78mKqqePToEdvb21y/fp333nuPlZUV17yrSyMC3PXb87VVri6dq1tFsvNmAaS9ZjseFozY3/u+/5r7lXVjsWtaSsmFCxc4ODjgiy++4NSpU9y6deu18SvL8v8CrXYNW8DQ1YF0QVAXaHcpiXYO7FhY0NE9jq2EfBu33d1d16fi5s2b7O7usr29ze3btynLki+//NK5QNnqz3A4dL0drAB5sVgwHo+duPvo6IgkSRxlpG1b1tbW+Md//EfeeOMNVlZWSNPUmTs8fPiQ//gf/6Oj9E0mE9e1+uzZs6yurroeCBsbGxhjuH//vssmb25uugDSVjVWV1d58OAB586dc45QL168YDKZ8Lu/+7tON2AD/C+++ILbt287upJdM7/61a/4/ve/jzHGNd/7xS9+wT/8wz84mpEVf1+7ds2BGNsvwTZ0s5Qbq8l4/vw5m5ubPH36lB/+8Ifcv3+f5XLp+PxPnjxxHH/rPnfmzBn+/u//nslkwve+9z3yPOfhw4ccHBw4dysLEsbjMVtbW/i+z9WrV91zbmVlhVevXjEcDtnf33euTvP5nGvXrpGmKffu3ePy5cvs7++7cxqPx+zu7vKzn/2Mhw8fcv36dX73d3+XtbU14jh2Nri2OeD58+fde6Tf7zu9h6V3DQYDtra2OHPmDC9fvmQ4HPL8+XOCIHDrA46rFlEU8ezZMzzP4/bt2+zs7LiElwVT1ikLvk4O2XfP6dOn3fk3TcPKyorr8dLr9Tg4OHDAuG1bxuMxaZoym82I45iLFy860GNF6L1ej62tLUcPtPbIjx8/duYA4/GYyWRCv98niiKn49BaOwG8tVA/PDz8RvfsN9doDE+jPIMfzshLRTyKkK1GmZZFLhmKPsIY4hVJO1Uk7ZgmUZSeISh3kJVEe5I4Cphrhd/PCNuIsJnT04Z22VD2DLXfYNoGGUZ4YkBQ5cyTAtE0hCqmt9bn2dF9PvnqPpWf4kUFyIogyyjKGd5oADVEZUirJOGpHq0AJf4/1P3XlyVHnt8JfszM9ZWhIzNSawUNFAqFUj3TbEE5fYY8nJ0Hkg/73+zLvu7yhbMPe0guq5vsZld3V3VVVwFVUIkEkEiB1DIiQ8fV17X5PgTN4JnsncbZM2e34efkiciIG/e6m5uZ/8RXFJSVj1YKiYNbZCg3I/Nz8rhCpBIVgig89MSh6eYMp2NEYwa3cKhyySTLSDyPIC5p5S6lEoSRZOzCOPlveuaOSxJntFHMdT1mV5bYLEakowTphvgyQeRTmq0uVerTFDmq0BSuJE80IisYC82MI/DSfana9sFF2k4Hx3M5MNeguXCYaSLYvncLKskonpInY/RkxE78DNFwEXmXYvCMGV8gmiWtoI3rNgjbIUtzM5CCkmcoByM67QZ78ZSHd57iFAm+I9HDbZxWg6bw8ByXliyJ+ymebCKaFUr1ibI2raBNtzPHYLjD2mAL2fAosooKHy/wEOmUQEAhPQLdQlNS+iWEATsSOi0f15PsDCc4XgenhEZRkG4/ZDx3GDUe0/R8Sh1QhILxSJBNexBUSKfNNM/xHEEepwReizJIGA738BsBU0/hCw/lCUajCUHgEkwrUqVxIkU8zZidadIbbJHNKLr+MpVWFG2XIdDuROSJJio9AjRNP2aYg+O3aOWaajplqiQzrkTGILsOk2qbhWKOfOqQNjJUDFXlQNCh8nOmyYSo2UENNbmMaUYOytP4QROBopI5+Aqdd8hGUwopmUyGtJoNWo67Pw8n314yuOd59uEfx/FzSUe92m2+1rkILwa6pjNhEoR6cFznbLzoz2Aq1KPRiAMHDvD6668zMzNjuyx1aFYdMlXnlsDzkqt1iI0JQv82CVvDezCB4IsdgnowaAJI13UZDAZcvnyZO3fuWChUs9nk8OHDHDx4kCiKbELh+76tthtSsem0bGxs8NVXX3H37l37cK1Xx+sSr3UfkDrEy4xxvZL5YjBsAn8hBM+ePePmzZtIuS/pePTo0efI3qZaWL9/BppgjP4ePHjA9evXrRvt/fv36ff7HD9+nHPnzlnyuRl/0y0z11fnzxhlFcBWMM3PzJwzyW896a1zIsy5mntrPtskl+b9Pc/jlVdeYTKZcO3aNUt6NWNTP18z317kitQ5SvXkoz736pCpF+cmfN09MX/7bTXrA9jY2GBubo5f/epXvPnmmywtLbG4uGihicvLy+zu7jIajfA8j7Nnz1rvAdj3YdBa0263uXv3Lv1+31aaTXJrIHznz5/n7NmzzMzMcO/ePet9YZKcsiz5+c9/zne/+13effdddnZ2WFpaYmtry64d0ynMsoyXX36Zmzdvcvz4cZuoGwWxstw3cDt69ChLS0s8ffqUfr/PmTNnnoOAlmXJ+vo6x48f5+DBg7b4YRKWXq/H66+/zmAwYGtri/F4zG9/+1vLUfA8jwsXLvDGG288Z2ZnoDwbGxt8+eWXNtBcWlri8ePHttu7sbHB0tISn376KU+fPmVlZYX79++ztraGEII0TZmbm2M0GqGU4oMPPrCB+dWrVxFC8ODBA7vODezSFDTeeOMNnj59amGxZ8+etX4R8/PzdLvd5zqA5hmyvLxsYU5VVXHo0CH7OgOX++KLL3j69Cn/9J/+UxqNBuPx2HZTDx06ZANvY/pnoGdm//zBD35Ap9Oh1+vZzrWRCD9w4ICVnu10OmitrXLU/fv37TUuLCyglKLX69k9USnFnTt3WFlZsePbaDTY3t6m1+uxtbXFyy+/zMLCAgsLC3bfqhsXGu6SeY4Y08m1tTUWFxfZ2tpidXXVQlVNhzkMQy5dusT6+jqnT58miiL7bDJdDHMvHWffwPHEiRO2e/ZNDlGZXe3vOD69chuSAi8sGMW7yMDDrTxygMojcnyCPGM7GVP6Lj4euA5VpAmLIenYQTd9dJXjx4rRNMcVAldP8X3QRcggTchFheOVlJmDjgtCz2HqpLiFg+cqopkWf/qXP+fGlzfIihThBUyZ4mYCp5qicxe3EKjQRcuCCYLhJKfrapqZx06S4oYNWkwpgpTYSSmnFUrPoCKJHiX0h5pGS5OplNGkIsw9VFki3ZxEaMpYI8c5zVabwd4OaQVDnVN5OS3VQEUeS1FEo90kUQo3itDpHtVI4ogCp6EY7GmyvEkrKujoijSvSNwcL/SYBiHzjQZzjS5OURB0A1b3xuyu7aCyAUmRMxxPKaZTVOQz2R0yrDJy9qFqnt8kqvYnyjQBpzNhMs2o+gGNRgGBx+L8IqHjs3hgljOnTjPX6pJMB2TeHCSarb1V1lefITOJDpsIv6AaTEBIXOEQtgpGkyFT1aQRRlSDHoN8P8MdpxPGqUCnJYGowC0YTlPmVUCS54SOw5ScMghptiEoA9J0StNrEwYttJ7SbLi0Z5aoSs1st0OjMYMTROxOU1xiJtOMUigqKfFVgZsU+EGE9nLc6T6sDdWkcFMK5aBFG3da0VAOE91HNR1cR1IqReQ3cCZDDiyeJojmmJJSOj5Q4OuKMi/wQ4WjNekY5EyDRuki1S64LQoCIkpmZrpM3RGhDNDxLGVYUVX7hoMzgaTsKpKipOFWZEOXkhK3oUh1ies10dslQRtcRxBXGplqRKxBakoFQdBEKknRG3LpB2/+f/mY/v/v8e///b+36hYm0TAPCqMMYx5+deUmwxEwG6zpAJhg3MgPmu2sTvqtmxCZh7rRkX/77bet83f9XIDnKr5G9x6e90MwXI96p8O8pq7EJMTXLt5GE99s5GYDh6+ViAALCTNdjO3tbZsAHDp0iOPHj9trNmPQaDSe40cMBgOePXvG7du3uXv3rq1YmjZ7PTEygZGpgpsHI3yd9JnrMxV1k8zUYUYmmTN/Y86lPi5BEFglmfPnz3PmzBlbVTVjaiqsBpucpimPHz+2SYY55ufnOXHiBIcPHwaw/Bhz/03wU+9a1Cv/JtAx12WCLsCOr5k79TExXZi6ZG2dM2KgEkEQsLm5yccff2wT2/p4m3lcn0dmDA20ysBsTMJq5k5dA79OSDdJVn2N1e+tSR7/1b/6V/8HrOr/3x6/+MUvcF2X69evc+HCBR4/fszi4iKbm5tWFOHChQscOXLkOfGDVqtlHZ2XlpZI05Rnz57Z6jbsd/q2trZoNpt2XA8cOIDWmqdPn9pOglKK3d1dSwo2+1S/37d/q5Ti7t27HDp0yPIkptMpYRja7orpUD169IizZ8/SarUs8duQfQ2xd21tjdnZWS5evIjWmjt37jAYDPjhD39Iv9/n6dOnnDlzhiiKLE/l/fff59q1a5bEfuLECS5dumR5XGEY8sUXX1gTv3a7zb179yw2fzqdopSyRny7u7s8fPjQdgtMJ+POnTt2nbuuy/nz57l69SqwX8CYm5vj1Vdf5caNGxb20+12abfb3L9/HyEEhw4d4tGjR8D+ftNqtayx38zMjC06GAibIZobNamNjQ1rONhut63K33A4tFDRv/zLv2Rvbw+lFMePH2dmZobz589b+GeSJMzMzLC+vm73uqIoLHm9vs/l+b75Yb/ff86bpC6GYQop29vbLC4u2gTFJPxRFHHt2jXOnj1r/YZWVlZYWVmh3++TZZmVnZ2ZmeHu3buMRiPiOKbVallZ3M3NTcv3WF5e5sCBA9y8eZOLFy9a6KnZ5438r4GEZVnGkydPLLn90aNHNJtN69g+GAy4f/8+Fy5csOdsTEzzPOdf/+t//Xeu2W/c0agAXeYUjsBpRgglyCcCd6aDm/Upi4JepvEcTaMJRS6RpUO6N6WoQkQgUY4gHidElUfTyVChYlwoHM8lzgrSqUZWLrFOcVULr60pJjEyF3izLRaaAVdvPOT+w226nkPRGjLNI5K8QcdNEblGNxSDJMMrC0QpqAaadkMh/Yq40LQCl8wFFYR4g4oQh6l2SEVJPimpKkHYrFC+h8o0HS8nmQxoeR79qkeZS4rKw+kq1osxOiupPJcKTagcZma7NLo+UeVSiYpSV6hBTDoJ6EY5Q6WRnkvQUeg0J6sGCL+Jli7tZosDJw4SF4rps032Bnd5uPaI7X5Jkmmk69BGUfkVvq/3lZ2ylCqb4kVNqrxA9zK8mQzhhRTTCWWW4IYOxThD5jAeJYRTnyfPbjL1BOUNwc/+4hd0FkOazTYvv/QKJ5ZPcv7oETrdNqtPtkiygiIpCVoOwitIJgXFpKLp+6QUlGVGoJvMzihaUYu98ZCHj3pUTInCijyJCcMGIlX4ToHUEzxVIRzFcK9BqTxaTolTZPhVn8oLid2IMpsS4RCXExgXNN1lhI7x8hytBNNqimq0kWmIbAkkOamG0veodInQEhl0yMYpEo0TuOggpSmhSnMKz8PzfKrCYZR7VIM+867Cz2KQDqXj4EUuw0SQVoLQV7gNiecpsr0c2ezgSgVZyqTMcAuXaVGQexmyGtHyBWQCx6lACvROjqgqyq5DLnM8UnQVEGQVVX8LVbUYD1KyICWK2gQpeDLHQZAgGFU5getRhurvXK/fhqOuqGOCyzo51lRV4GsFIQOFefEwf2M2PxNcmKDRVNiLoiCOYzY2NvjOd77DysrKc8RZUwE3wbppPdcJu/VOhAmkDXTFHHWsvTGRMl+11hZ+AVjjJiMXaALP4XDIe++9x/Xr120wvLi4yMWLF1lYWLDvYz73xS7MrVu3uHz5Mvfu3bPmTXWTu/q4mfE2iU69Il6/D+b7OtejnpDVO1OmEyCltA9p89kmadze3ub69escPHiQ8+fP23tixr/u3B1FEa+88gqnT5/m5s2b3Lt3jyRJWF9fZ319nbNnz3Lx4kULFzOJkUk8TNBWP1dzD+scoTp06kW4Un2evcg3qgsI1LteVVVx4MABTpw4weeff06r1eLkyZPP3bN6glEfa/PVnJuZJ+bnLzqbm3Orj725ljohvM6R+bYdJkFuNBrs7u6ysLDAaDQiiiK++93v2u7Q7du3aTabOI5jIU8GhjMej9ne3rbdj2azSRzHlothfDEMt8B0DR8+fGjdumdnZ3n06JG9B9vb22xvb1uTQDO+vV7PQk7MXDTwL1PRN8mfcd4GnjN9M07PxnDT8KFM9X00GnHkyBG773z66af89Kc/tZ0A3/d59dVXOX/+vE1m7t69y8mTJ6088Pr6OsvLy5b/MRgM2N7eZn19nbt379pOalEUHD161MKo7ty5Y/lSL0oom/W+ublpIW337t3j4MGDPHr0iCdPnvBHf/RH/OQnP+HOnTucP3+eqqqsEpPjOIxGI+snMjMzw29+8xsOHz7MuXPnUEpx7Ngxa5xo5oXv+2xtbTEzM2M7psalXcp9Jb4bN25Y6NLZs2ft2rt79y7T6ZRjx47Zqn9RFLRaLVzXZW1tzXbFnz17Rq/XY2lpyXInlFJ0Oh2yLGN3d9dK+gLWZdvsmaurq5w6dcqub9OZ39nZsXPbrN08z1lZWbHy31euXGEymXDr1i1arZblwpi9pN1u0+v1bGHqxo0bHDp0yCY+hs9n5IjH4zHdbpeXXnrJ+ruYYkoYhnZvN5LCpovyTY5vnGgoNSTzXbJKIHNFOippuIoqGTBxgTJABhVUJVWpCBqKtHLwlAQhiElwgChqkE5iQk+QxBVKNCl1heeDLAuU6+NHTeJySsWEoCsocp9W4dLvj7nx2fuUgwmjUlGVTSpRseSAHEOfFqKQKClxPUkxzvE7FSJRFHlIxQRfCtyyQAhN6lYQFyQJJDrFa4Z4ymEyTBkOSypV4ClBURXEpUtVdvdJ5G7FeGMIDRdcQXc+opU0mTvY5vDyItubQ3RVMUp3CRZmKYcOVQvytKJMFKoM6XRySpXRYZmVhQaNmYPsjNZ5unqXB/e26a3t4ldTCnK2fUWjExKiCZw2o8EIv5LgFuz0prhKMO5PkVqjGoLedILMUhqyQz6KUcUAKkGip8hSkGQZjgdOIRAZaCQbG2NcOWL11irKdThz5CgnX7nI/PIhurKNIzO2hluMR2MmaBKdI9OIaD5Ej4Yo14GoRPqCxahBa26GvWcbTHvPcFyPuEyIcWn5AYF0mQyHqCSn23FI1YSJ9EBMqSqJkzSgkGRFRavjMNUJeTMln0oc1SQLJGlZEiqHPJ7g6C5ZOkGEOSGKYZIiHIHnlZSJxPV9ZJXhSMkkyXACyYIbkRAgUkVeZLh+k83HW3S7Ps0oxJ1KSsenyhVNUaADidQlSnpM9sY4yqVVKZwiI59W0HZIvQm6bOG0ZmgkCndYgqzwmh7DcYqWJZ35BdRoQtUOGI0cVAwtOaT0PGJR0fQCkhL8cg7XyYmqEVmVkaoGWbKf6Hsy/qbL9u/d8bepTJlEwwR9dV6GCZxeDJJMAGUw6nXcff19syyz7XHzGZPJhKWlJU6cOGFb4iYBMZXfOgejTiiuV8KzLLMqQ/C1ChPwXGXfBKgmcTGBtrkG89Cpqzfdu3ePX/7yl9y/f99yNE6fPs3rr79uq6/wPK7XPIxu3LjBJ598wtOnTy0B08CiTBBeV7YyyYdJfOpQqfrv69yB+tiY86tX/uuvLYrCXpt5T3PtJvgbDod88cUXPHjwgFOnTvG9733PSjj6vm8f4KZz9dZbb7G0tMSVK1d49uwZRVFw9epVdnZ2+M53vsPc3Jy9RiN9a+aXCe7N7+uKUvWA3Nz3etJo7vOL860OlzK/q89t3/c5ceIE9+/f55NPPmFubo75+fnnXmekK1/kmph7UIcYmmSozsEw42t+b15jzqt+PfXu27ft+PnPf87v/M7vWIM3A/UIw5CtrS3a7TZra2tU1b5XgVEHMvyMwWDAYDDg6dOndi8yHayFhQU8z7NSolEUWbIvwLFjx57jwhw6dMh+/szMDKPRiOXlZYbDIWVZsre3x+LiIh9++KHtLB04cIDFxUXL8fnggw947bXX2NjYoNlsWrlSYxa4u7vLD3/4Q7a2tphOp3ZuLy8vA1j8/kcffcSZM2f46KOPuHz5sq1Yv/baawRBwAcffMC5c+coy5Ld3V1+/OMf8+WXX9Lv922HpdPp8OWXX3Lt2jUL21lcXLTdVAMl+uqrryiKgu3tbaSUHDp0yBLBZ2dniePY7huLi4uWeP3kyRPeeOMNbty4YdfNJ598YtfbnTt37Hqcm5vj6tWrlGXJpUuXuH79uvV/MPcwTVN+9KMfceTIEdI0tddguiau69q1H4Yh3/nOd5hOp9y6dYu1tTXiOOazzz5jY2ODw4cP8/3vf59Go0GaptajxECRjEqVKTgZ35JGo2EVqgwUykCNJpMJURRZ7oQxNKyqiuPHj1tuVxAElmNj9qfRaGRVr4y53h/8wR9w7NgxvvjiC9bW1jh+/LhVHjNeKqaAY0jfcRxb5b6lpSV2dnbodDqU5b4PyNramvVo2d7etpA1k8TOzMwQhqGFBG5ubjIajZBScvDgwW+0Zr95ouEnzAiH7f6+6k7bbyN0iU5KgkoRJjla5ZTSp9KayWiE9AoKxyVwClQRIHOo0ik680hVju+GiHFJrjJcV9GMfKZJTJLlxEVBVQl8HJTfwnFDvvr8JsNJDxVMCd2A1AmZTlKqrKJwFE0ayLJi6uZUegKlxnFcqgAY7iIbPtL1GexMCIVLMk3xtCKckyg3wBkostxFFROCrES0Pcgq3EZEJiThJCdNcnSqCTwXoUtwXMJmm5WlJcJI4VYdZoKSONmD1hx5rnCUQEiNLkJavkIzJXB8zh5YwCm6PFtfZe2rD5iWGwxHu/QnDcpAUyEQImJuJqLpZoRTl7FIyD3FJMmQY0lJSR5rIhkiuy5FkVBIB6ELBuPR/lhSkQeACgmzklKWFDpFIcmUJAg9tEhRrkNDRRRa8mwn5tkvrtNYfMq5Qwe58PIpjl08S6c7w2S9x/0bX7GbTdhLBzSFQEYKXMUk8el6Dq3mlJlwgc1Hks3eCGcyYSZQSD+gX43xug2IQVPRDdpUCKZS4IYeqpQEjsN4OKBfCcSoxCkzmm6FDzTbDr6TozMQeYHPiKKsqNqKcjyh6wSMtcIVLrnvUDkZxbBCFgmh71FlGYO4onTH+FRIT6GkZmVpga21DfSBZTrtJeKiwItLGt0WhDGTjeE+KTzQNLodxnlCpB1ai4pxlZGnkrAZEFNAoOlEHn4u0ZlGt9poSnSiiGWDeG+TbreFlBPirAJaUGSEoYMYuuQMyEuXNAPdiGhFLqEWTEuFE347SZzAc0FO3Y3aBEUmEISvvSlM4FaHWNV/Z5IR4LkkxiQO5sFVr6S/9NJLVgqzrjBkgsi6Uk8db1+HTAGWK1KvZJvXGJiL6XrUOzZ1XH490AW4fPkyP//5z9nZ2UEpRbvd5t13332uSmp4KaYiKoTg1q1bXLlyhY2NDRt8mgDVjFXdK8OMpVHsMX9TJ6WbroW5vhe7Oi8mY+bvzb02PzdJmflsk9TUA3zY70DdunWLx48fc+7cOS5dukS73bbnZMZMKWVVrS5fvszdu3ctd2N3d5d33nmHubk5m+jAPvTKdJIMJ6OeDJrrM5yLeqfLzFH4mv9gfEvqpHGt9XOqYoDlbvi+z0svvcRf/dVf8eWXX/LOO+/YsTXO02ZsDVemzv8xn1M/DwNhe3F91TsW9TVX72Z8WxONP/qjP6IsS7744guOHTtm4UkmYFtfX2c6nXLixAk7tq1Wi93dXVt1bjabvPPOO1bZp66A9+zZM44fP26hb47j0G63LTzFqDIZonNVVVy9epVTp05Z1SBz/0x1+Hd/93ftz4yzs6mmv/vuu3YffPbsmXXFNjKjJ0+e5P79+7TbbcuXMOaAAA8ePODll1+m0+nwH/7Df+Dx48eEYUgYhrz11lu20/Fv/s2/4dq1a5YobCrmg8GAN998k36/zx//8R9z7949er2eTcwHgwGnTp3i4cOH9Ho9Lly4wLVr16iqisXFRbTWbGxsoNS+S/lkMmFzc5NDhw6xvr5uuzKPHz9mZmaGzz//3ApT1OdjvZNz8eJFVldX7fr94osvbIHE7GGmCPOTn/zEEtsXFhY4e/as5ag8fvzYPiOazSbnz5+3hQ+TmK6vr/Pw4UMeP36M53kcOXLEuq8PBgOyLLMJhtaamZkZbt26ZUn45hiNRnavMvv79vY2b7zxBgCzs7MWvjYcDvnVr37FoUOH2NnZYXZ2ln6/z8bGBp1Oh+PHj/PGG2/gOA57e3tIKTl27Ji9nuXlZSaTiXVrN5LGBpa8uLhIp9OhKAqrHmj8QLa2tjh37hx7e3skScLCwgK7u7scPnyYL7/8Etd1OXXqlN2ve72elS82PkdmTTx8+PAbrdlvnGhkZUymC6RqUEaKNInxdE7ge0wmOaIZUOUaUe0Hbi4hQuTEpUapJp5TUhQxTqIppQtugNAFwvEQlSKdjnBchZdIJsmY1lyLQb9AD2BhFjYnz7h17yaO36ahJKpKcWJB7rikJJSug5jkyFxRZYJM5bTn25AmFKkkdSUtGeASsuPnSCrCsEE8camSAYQ+SQOmwylN1cJvCCa9AQ03IFUxhV8wmsR4fkiyNyQIHCZas3LoOEF3gSRP0HsDBqJHJVNUI0FpRbFbUaiUTuCTZRoROhw/cpTQC3iw/ojJ1hrT/i6re0NKocmGPkmS4TZDCCWOdGkkDm7sIsuCspgyHMU4StAIPBiXBBUot6DKJEp2cLOSPBuQZykqUAgp8R2FKl1y5eL6kjBUiKJCJCWOI3CDJkHLQ0UBXhYQqTYeAXGZERdDrlz+ku49j6VTR/jOiYsc+4M/5Gcff0C8tkYofJywIK8KPL9LQUYndFBqnvDIPPMHRtx+8BhnkOJ2G+hJhzzfJpYCJ3BJiPFURDYNKVoOSiX09tZxmnOMRhndUJJUFcEwx5spKHVBq9mhiiRlUaDznGqUomOJxiWrcrSKEFEbkcU4cUno+yRklDk4sskkynBaDp2JwzTPyAJQoWJSeDSTgpnZDJGkiNk2I0cjJ4JWOEOaaHIUg+kQ7QtcIvIkR1YlYauLTDNU6hB1S5JsyHik8MOQqHIIOwFZnOIKQV5ohnFGJ2zgqpy0qsjKivG0xBUeviNJhWbsgFsW+KkEJ6fKS769/YyvA3H4OhA1waupFNYDoP9P8I56i9g8OOqQIBNUGe8Gg9Mfj8csLCxw4MAByrK0lTcT4JnPNMpJ9c83QWmdg2CCzhcfgsBzAaCBTJhg33Q4zN+aB9PPfvYzPvjgA0vsnJub40c/+hGLi4sMh0ObVBkeiyFnfvjhhzx58sSOhSFy+75vdenhazUrkxwZmBbwnHTqiwmEOVczpi9ChOpqW/V7bTothttQT9rq5PM610PKfTffy5cvc+3aNU6fPs3FixeZmZmxY2igUFEUWS34K1euIMS+Edsvf/lLzpw5w/Hjx+1Ym8qmGeu6EaNJfkwwYeZNfc7WOxcGi16HktUTYgOrMu9lCPkLCwscP36cW7ducejQIY4ePfrc/TdYcTOHX5yT9ftSXx8vwvbMZ5pkt/76+jl/G484jsnznDNnzqCU4ty5c88Zol2/fp0333zTKrB1u13bOZtMJhw+fJjhcIjWmvF4/JynjbmvYRhataUbN25YVbd79+6xtbXF2bNn2dvbs0Ry4+odRZGFPG1sbLCxscGpU6dsAmv4ClVV2cp5HMdsb29z8OBBK5FqOl7dbtfOm0ajQRzHVpnPBMEA77//Pn/2Z3/G0aNHiaKI48ePc+bMGeufMZlM6PV6HD582O4TBw4cYGdnh3v37nH9+nWePXvG3bt3bdfmD//wD/nTP/1TXnrpJVugeeWVV7h27RpSSs6ePWuLGkZae29vj+FwaH1vTKEmiiK7BrTW1nPilVde4fr16/Zewf68v3//vjXLu3//vi1GvLh3aa2tUWOapvz2t7+1Ihezs7PMzMzYgHlzc5NGo8HGxgawLwpw4sQJ7t69y4MHD6wC2UcffWS7qAbuamSCNzc36XQ6NJtNrl27xuzsrC2OBUFgu5aGg2WM7pIksR1ksxe/+uqrDAYD6110/fp1zp49a5UJlVJkWcaJEyeso3scx6yurhJFEVprjh49yuPHjzly5Ah37tyxz4x+v28VvVqtFhcuXLBQW9/32dnZIYoiVldXWVpaQmvNkydPOHfuHMPh0K4Hsy+aDk2/3+fAgQOWu1Hvrv/vHd840XBGnf0gVSWUaHRaEVaSPmO8Vpu9vMAPPXSvpONUDCdDZt0mQTVElBMCNyKWDk7Qwqsk5bTA9wW0FG5WUtAgkzntWYXeLsh7Iw519lUBmsLlk7v32BsMoKhIXUgzH5FlCCdgmoyIUGjhoN0pnmzg5JLJtIenApxI4VYKypQky+n4BVI6eAJKR1OoECU8BqNtCCKyYorvS5oC4kkfWQhkkpHLirTXA+UThG3OXTxNIQXPtrYJipyJk1IpiSxKijzDqSQyELhFTKQcjp47iejMsfr4Dvdv3UbmJceOHGAnHSGmBWlWMq18hExpRwrHySljAX4Agcd0b4dKxkRJSqU8BsWISkpkQ1J6Aj2OwfEpKfGckKJMKaWL74Z4KqcqCzpdSKWiwkd6AV6V0PQiSu2T7GT4oUCUBYUzpIqm5HnGaJiTpgGbe3vce7TKveZNzr7+BksrB1jqLLBxfx3BGJkLpAoQOSg/QuEQSM2S16HLHM/W7zLNJtD2QSyg3R26oSJJBYPhDo3uHDodMxlr8sylDBNQFa4TIQgBj6Q/wc0DUlGRZ0Oc0qNsSYgKnKnEEwFJDsgYRh4NEVJVCVXl4BQTkDlO4TJNShajkFxm+JFPXqQkvSnNuQU2xiMaaUHTUSSTPsr1KR1QTgN3VlHmCbmIWXBD0v4UigDHbzHNJL4e4XoVxaSgqlq47Sa+IyjyCcUwJWh2GI1Sos4iukwJ8oKq0kTtJjkTXFeSZQVl7CO9FFWG+JUiT0aUUhK4ikGV/x2r9e/vYar2RprTBLIm0agHP3U8eh0yYgICkzyYDbH+Xi9CtMqytBtonfxtqv7mQWHO0Wz0dZUrE4DXz+PF8wRskFon89ZJwvXrNue9t7fHBx98wGeffUZV7auxHDlyhLfeegut91VTjB66+fvhcMgHH3zABx98wGg0QghhMcgm2TAPtjqUxlTM4WsYkzlnA9cx1XOTWBloCTzfqYD/nmtT53yYazZBvKkcm88xY2+SIoPzNoH3ZDKxCkDvvPMOJ06coKq+ljQ2rz1//jyO4/DZZ59Z6d7f/va39Pt9qw5UT4TM+xt38LoD+4teFy8qYpmA4cX5ZiqZLyafdb6RUopLly7x+PFjG6gYMzaTwL0IdTLjbzoeZrzra6V+D+rdpRfnZx0WV1dF+zYd9STQBE8mKJxMJpw5c8ZW1g1p26zhra0tyrJkMBgwOzvL0tKSTf7jOCZNU+tAbWAwVVUxnU5xXZdOp0O73UZrTaPRsPfDKBbVu7We53Hw4EE2NzeZmZlhZ2fHdheMBOtkMrEu5Lu7u5YwPD8/b7toWZbx0Ucf8U/+yT/hq6++snyRnZ0der0eDx8+5MqVK5bM/Oabb3Lu3Dl832dubo6bN2/S7Xafe9/FxUWm0yk/+9nPePr0KUVRWDEGE9j/xV/8BWVZWqncF/lx6+vrNvFfWlpibW3NQkLPnj3L8ePH+eyzz+xYLS4uMhqNOHHiBB988AFSSu7du4fnebb7YWCeRn3pyZMnGL6BSSZNYcqsk88++4w//MM/5P333ydNUx49emTX86lTp9jb2+PixYtkWWa9OGDfid3zPE6ePMnRo0f5/PPPuXr1Kq+99hp/9Vd/xauvvsrRo0dtB8p0uE3notvt2uS10Wigteb27dtUVWVNHcMwtBy1yWRCkiSWGG7mE+z7j5w+fdpC9g4ePPgczHV2dtbyTwwEcGVlxXJrbt++zdzcHA8ePLDmkp1Ox3ZSjXO7ETgQQlhzxJmZGYbDIXfv3uXixYs2CTaf9/jxY2Dfm+Pw4cOWcA9Y7snfdXzjREOiqTQ0/ZBpOoKGj84EgZRkcYYz1QQCqtBFpDECmKoMcslM6TEEXE9TOhmVivCkoJjmJGVKNBvA1GNUSTw3p7EUMR046Fgzd3CB+4+fsrHew3MclCxx04potsVkd5fQd+k6S4yHQxwnRzoN4jwjyxM8x6fAxQ19gr5AdhRlLgi1h3Qq0lzjeiCyjEkvp4uPrkq0X1FqiVaCwqtwuzP0d0GlJU0hcWabnLp0BpkUZLtTOnlOnI6oIpdMaWZKl/4oofIStIDjx1Y42DnAs16fG59cZ231CXo85eihI6zv7RBn4GUTcmK8sE2uFSLLkFqD66E8mKYJsefhFg0IIFeSWb/DOBlTOlOcpMTxQ/rJBKEEOvRplAKlHJQrcWVJ5lZQFWTjCoFP4eZUpUeaTMmYUGQebpox8iR5r0RGgoKEvd0dijSgpTRBs+LhTo97u/c5sLLCOy//gB///svcu/OIzfUp/SzBjVwYKaT0aIYuo3xEuxvihIfYHmwS91dpOXN0Dhyh19skqCSFaKBGCSoE5bl0WxGF2u+OpXJKS4a40kUJhwpNNh2jSpemHzGRPqlXkWgoKg8d5gRKoPOMoSpBZohC4WcVjnQRoSaSUO3pfd+XeUmlNUIp/EwzGg8YTwNUa5a0TGmIDuVgQDKnYVgRuC5hUxBmHikxzU7ERKfkYUojbgIuukhohg65AqELpFOhfY/hNMOtUqq4wGv5+x0lP6ToD5FlRVUFtFou/X6O367IhWI0GeJGAkRAOh3Qib7pqv37dxjoR51AXMeXw9cV2Tpe/UU8eh1mUk9eAOuZYb6WZWmhDkePHrWbez3BqSsHmaDNwGHqUCL4WoGq/sAzFSxzbXWOSB3CYpKeeuV6e3ub999/35JPPc/j6NGjvPrqqzbZqfMwhBA8evSIv/7rv+bWrVtMJhOLVTfXYUja9Q5FHfJkAm/znvUHt7lP9ftlTPHMuZvv6x2cOnY9CAILC6rD4UyQWIeQ1cnQ5r3r9zmOY5s8vPbaa7z66qsEQWChT5PJxFb3HMfho48+YmtrizRN+fDDDxkOh7z88sv22s2cqCcH9cSv3okyCZHpuphuVH3u1pM4A2kzQYZ5jRlHKSXdbpdLly7xySefcP/+fV566aXngn9zP8z7mrlskur6udQ7eGae1s/HJJv1czaJ77cVOmWOsix57733eOWVV1heXrZJ6ldffUWn06GqKhYWFuy9bjabvPTSS2xtbVky64MHD8iyjIWFBaIosiILhg9kOBFVtU9oXlhYsIReQ5o2ia/pmBnZaZPoGAhXr9ezXZQnT57w1ltvMRqNrIfNqVOnAKxbuZmL6+vrHD582BLITWA7nU5ptVrcunXLwvVeffVVut0uW1tbzM3N4fs+8/PzPH36lAsXLjAcDllcXORP/uRPrOeHqYo/e/aMIAisWZ/5WpYla2trlGXJ7du3UUrZ7sXKygp5nvPkyRPm5uZYW1vj1VdfZTwe2wRPCEGSJDx9+hQh9ontKysr1hH78ePHPHz48Lm913Am7ty5YyV2DQdjOBw+1+mTUtpA38zpW7dusbS0xC9/+UuOHDnC3bt3ef3119ne3ubu3bt873vfY3NzE8DyFU6ePGm7VKurq5YIfuDAAWZmZqw8+IEDByxxv9PpWHWsRqPBtWvXbEfMdF6NIIFJaA353qg3GVjpqVOn6PV6wP6zzCSiX375JW+//Taw/2wzBH1jfPjmm2/y4MEDVldX7R5nyOGmk1KWpYXZwtceQ59++imO41jvln6/T6/Xs4Ws6XTKnTt3OHr0KJcvX+b8+fNW/c44tH+T4xsnGqIJzahJHO9SxZpYVuSRQ0MV5IMp7iRD+yFhs4n25X5VfJoSRhHjEmIE5ThFNj3yVFMmJY7nUk4lebqvpFQNKyopmOQZoHBwCcMm9zc+Y9qbUMQuqZ/SbjsMi12isEK6GcMR5MJD+CXNyiEe9CiVS7Nq4WoXPdG4gUs/nhC6PmXgQqzJRylB2aByFbFwcGSGPy0IHHAcl5ErScoCv9+nSktUoHCbc1x66TzC9djaWycXCZUr8MIuXlqxm06p8pCGVKRlyaWjl2gudHj/019x69FNxsMJIi9ZXDyMbCp27+8xGU/IRYFIXSoksq0onBK0j5M7ZGVKEud4bUmRShIXms0mWS9BChdy0GiU4zDXDdAqR4gWjlAoWaEdTZmmFDIgrVKCsEk5LUjLhCrPyIoc2Qpp+5IqzsiqDBF45K6HE86gBhNKOUakFVPtUDgO+TBmvHOfjQfbrD46zRtv/Yg3z1/kweNbjPYmTHfGeDMFwlcgS2RT0nHbhFkT52HIs7UnBE3JzMJRJnsDumqKIwoGmUR7MWM5JMBHFopJnCE7HnJvQMON0CXkjsJrRrQiQZpO8CIP6SoQDhGSohDkDZdkMkSlBU6rgypKHKFwg4pIBxSJwyie0tEuUilkU6PTLVZm5xjuTImICBsBVZbiuwGTQuAWOZHw0P0EPRugG5A4A7zMI+s75DpFJgndlXlGeUbQEuSJQ6vdJS0qhJK4VYXrZyROxtQvUUlJpzXHMCkRfkmWVzRCSakllZcTBj7lVJEXY9woAvHt1cCvw3VehH+Y7kM9MK4H7eaBbroDdThO3ZnZBHUGGpPnOTs7O7ZyZiA65qFUJyvXg13gv+Mn1APmekBclqWtxpmEwzwETfBpzlkpZdvjvV6PX/3qV9y7dw/YVwB59dVXOXfunOVgGChGs9lECMHHH3/Me++9x+7u7nNO56YaboJj8zvzIDPjWYcG1VWjTHBTN1CsJ4B1SFW9K2Her97h+Nscyc0Y1QP6Ol+kDoerQ7bq53/58mXW1tb4/ve/b8nUplOitWZubo63336b999/n2fPnpFlGZ9++ilZlvHaa6/Z+VE/p3rHxRDbzVww8CPT6aib45kkpR6w13kw9Ye6md9mzI4ePcpXX33FtWvXWF5etgpE5trr0Ly6tPOLmPYXOxgmua3DrExSZ+auuV/15OPbdBgVqTiO+cEPfsDOzo4lgQsheP311zlw4ABXrlyh0WjYeWi8c+7fv88PfvADfvvb33Lq1ClLDO/3+7z22mscPHiQOI6J45hms2mTWkMsNnPNzB1DujYqPkYOttlsMh6PrbO1UsoGiIcPH2Ztbc1W782hlLKSpL7vk+c5Bw4cYDgcWola46p948YNfv3rX+P7Pq+99pr1aNjc3GR5eZk/+7M/45//839Ou91mYWHBSkOPx2Pu3LkDYD0eTp48ya1bt6iqigsXLrCxscHs7CxC7BO419bWnoM5NptNq4L3xRdf8Lu/+7tcvXqVxcVFdnd3WV1d5fbt2wC89NJLXLt2ze6fx44ds53ljz76iMlkYn1ozHw3ylwnT56k2+1y9epV2819/fXX+fzzz5/jif2n//Sf7Hp88803+fTTT3n06JFVH3v8+DGdTodOp8Pp06e5e/cuGxsbnD9/njRNbcJglMx2dnaQUvLpp59aOeq6nLcQwt63yWRCVVXcuXOHTqfD7Oys7aCZNRtFkYU/Sblvzvjxxx9z9uxZ3n33Xb766is732ZnZ+3c8n3fGu+Z/cnsB7du3eLcuXMkScLOzg7b29sWvmec2Q3srqr2/TIePHjA4uKifa5cuHCByWRizfyMXPBoNLLPhu9///s2GYd9M8KDBw+iteb06dPfbM1+08Wtxi2S0YjAcYiznEpWSKdAVxJBG2/BYyq22Z7epeGsIApBoyopRil+I8R3M6QuEVlFmZU4nqAQJU67IK0qGsKnQ4ByNJHyyTxBONNmvPOMvacP0GjmllpkuSJLR/ilItM5YenR6Aji/giR+hSeJAhDmmqGcVYQBpA2FGVP0XS6eK6k0JopOU4QQCXAz5lNKhKRkgsPxwtJ5IRJfxf2cqa5IAgEc7NzvPadtxkMM9bv38MTCW4nYpSVJPmYpszxpUuRa5YXZ1k4e5TVa/f4+S/+lK1Rj0KWKNkhWtIcWp6hSAomQu9LoypBHvg05nLcSUaeS9SCIhkO2ZuA57bQWUahc5wsIKlitDuh7S7iRR20TAllg0kucFVFkVXkIYzjBJXEKKEoqwDikkCWNFSbkVQMuwk+Yt+TQjVxgwxXDyh8h1xWVJMCN3WQHfBTF5RkXOQETkAaRYx1xoeffcLdRxv8D//jH/LdH36Hnc09Ht+/ziRJybTGQ4AKGTqaSFYcOHaRrKp4+OQhKwcbNA/Ps761ymCakEsFJeRSo1RGoCMWCMmSHKImuqjIdYUTBAzihOi/SSkHE4dSZ+Q6RVSaomzjdQvUsKAdtsg9j3E1ZibyeDaYEDpd3FjTiFySYkTHc1FZTO54KE+RJhMawRzznXlGkymTbEyRRBROheuXOLrBsMwIPRcnn1I2M9pli8iRpC1F5oBbhPvdDFcznExoRC5eUFFJiSMaoEsKtyJINHGp2SkLnCyjpTwasqCsNAEB8SQlr0pSN8ORBe3s2ylLCV9LddYhNUEQIMS++oYJ2syD3ARnZtN+EfphCMKmWlOHSpmAYDweMxwOefXVVy22tR5sGkIv8FzyUocHmXMyHQBTQTQdBwOXMPArgzk21wk8RwgPgoDhcMhvf/tbHjx4YGFPly5dsjAgA3EyTtpZlvFXf/VXfPzxx1YX3byX6Y6YoN0obZlg2QShZozrQagJjM3/6/h+c4/M9Zvqep1QXe8Mmaq6+Wqc1U3wXue4mI6ICb5N96cexL3Im8iyjIcPHzIcDnn77bc5c+aMfW+TRMzOzvLd736X999/n9XVVfI857PPPrOYcNd1GY/HFt9ukjQD3zLJ1oudM2MiWO9ymHEy72P8MEyia+az6SIYA8VGo8G5c+d4//33efToEefPn7fros4Bqnc1six7rttmOiZm/ps5Xxc/MMHCi+T9Ojzs23bs7u5aUzPXdZmbmwP215zpVj548IB33nmHe/fu2aDKBLBhGFrH6eFwaImyhgdhkmQjG2vG6/Hjx5w8edImjqZDYXwxDh48aMm3rusyMzNju22mC2FI5Y6zb4RmxCCklPT7fapqnyxusPqGsDwYDCz06MiRI7z//vtcv36d+fl5Tp8+zbFjxzh8+LCdF0mScPToUTY2NixP4YMPPiCOYy5fvszCwgJra2t2X/3qq6+e24/rXWGzzs+ePcv169cpy32PjjAM+fTTTzl79ixXr15lMpnw0ksvce/evedEIz7//HML22m32zx48MCakAohrGSv6diZRMBU9FdXV/E8zxYcvvrqK8v9MIUG09WYnZ3ll7/8pU2s62vpyZMnVkHJ930uXLhAWZYsLy9bIQkzn1ZWVrh37x4nTpzgiy++QCnFgQMHrEFiu922PDHD0Tly5AhbW1vAvnu9cao3ML2jR4/ieR6fffYZ3W6Xubk5FhcXSZLEFp5MN+TZs2e2Q2uKDM+ePbOJpimWPXjwgPn5ebsfG2+NEydO2L3p6tWrHDx40HatTDFldnYWKaWFUl2/fp3l5WWOHDliOYJ1eDLsPy8PHz5s4cVra2vfaM1+40RjUkypPIXQGTJQtKKAXCkSUZCXKYmjcYWLJzvIckpHRmTJmMKLcDT4CPb6Yxq+T+EUeKUmEgEjneJWLlUqiKnQicDXJU4kqbyMm9duotHIqCQpB+A5pKlirjtLNthkSkwiwfFBTMagFVW7RRErlJcy1EM8XFJctCgp84xQeCACROAS52MqqYnzGMdpMIlcKIHRmIbyif2KUg9pdlv8zg++z6Cfsb36jHgyJhUpjvAIMkFJhRP5VInkxLmjLM0G/PLLD7hz/yajcYKcCiIpUU2fOZ3T8QIeb+zSHw8IfEXohyAlaRHjhREUkp2dEX4pmO/MUMZjEDleS5CNXbLSQ8xKYp2QTDO67TZp6ZKPEtBT8kiDKveduPMAXwc45RgyQdJs4JQxvoK5PCQRmswD5U7xHYhHGjEucTOHqavRnqYsKtxqgvQ6dLwGiJxBoqhkSKIqtrIBf/5Xf8z69hr/y//8LznWcrjy5T328gGV0ohYQj9BtCp0PGGltUDjeIsHw8e0Rcbywgr31goaokD4Dr5QOJVkMooh6lAISVg5ZKWDChROlaKKgjgVyMwlK8ANQ8JKI3wHqhwmU+b9WUZxSq7HOJHP9jijMePhZVP8YAZ/3kNPBeWkJEfgznZJE5fIc1lfe4LQTaq8wpuR+ElO2PYppUAl431Fs9JB5x5lUeAqQVGENLoBhDmOBjFJEZXAqSKUK1FyTKEUk8IlFeAqByJFJl1mOyFJljIZl1RFiRdmjPKUSqXgRrScNmKSoNW3lw5eD2hfJLqaoN0kCHVd8fpRV98x7wU8F/SZYCDLMtbX158j5tUr+nWpUpN0mCDMwCGA51SATOBsAvYXz8t0LUyAnCSJDSYAy0X4+OOPWV1dtcHg+fPnrb+CgUI1Gg2rmPPTn/7U6tnXOQJmXE2yZcy+XuwU1VWHzPm+qNiVJImVX6wrHJkuRb2TY67TJAN1orT5HBNsmPcyY/i3cRjqRHVz/+sQMHM/TcX4N7/5DePxmJdfftlCSQxOvdvt8tZbb5GmqVWG+fzzz4miyOK1x+Pxc/fOdCvMedf9WEwCYc7JzDGTQJjOjPlq7r1JnkzCUZ/PRjHp2rVrLCwsMDc399wYm78x35s5ZMbKjLVJvs38r6uzmXM1413vfNST6G/bYarGUbSPI62qfWfsjY0NNjc3uXDhAmmacvDgQabTKaPRyBKiDxw4wHg8tvKkS0tLxHHM4uKiTXD7/b7tDNy6dYs333yTZrNpPXk8zyOKIj788EO++93v2n0kTVP29vYAbPBYVRXPnj3j0KFDlgtiXJ+zLOPw4cNIKe01PXz40BJwjSGjkSo9c+YMP//5z62HwokTJ+w4mGDx4MGDHDx4kDfeeIP79++Tpilra2tcuXKFoiisepHZX00SVodVGk7Fo0ePeOWVVxiPx1bS1vM8fv/3f5/Lly/TaDR48uQJKysr9Ho9Pv74Y4QQVslLKcV0OrVdDWMEaBI8x3E4cuQIg8HAQlx3d3cJgoCVlRVGo5ElZg+HQ2ZmZmwBY2NjwworRFFEkiQ8fPjwOf7S+fPnuXbtGhcvXuTx48fcuXPHQuROnTpFVe17QSilrAP7cDjk3Llzz8kkP3nyBNgnkBsy/fz8vOWNhGHIZDKxwfulS5cYjUaMx2Pm5+eBr3173n33XZ48eWK7G+PxmIMHD1qOURzHHD9+nMFgwGg0YnZ2lmfPnqG1ZmVlxQoOFEVBt9ul3+9bg8G9vT2WlpaYn5/nyZMn1rE8CALa7TYXL160ZpJCCHZ3d1laWmIwGLCwsIDv+5YrZOCGZj6bpEOIfZ+PM2fOMDMz843W6zdONOYWfPp7E4qxg2o5lLkkn/437DQ5SoLf6uCVKaWY7vttFPM4OkMVDu4oYK7rU4kc4fowTkFBlUkQBVlV0WnM0B/2UF5OEDbp7415vDUmnQhc6VNpD5WmhEFAUUpGqstUVwSOi9soEY0SmY4pgxLPE8SxT9SahapABQWiKmj4BxhMh3hMKbMxBIoid3CDEEdJ5kuXcVlA5KNHOY50WZxb4h//3j9iUpXcf/oh/VFGkDuUukEpK3JS9DTB6UYcPnaKwNH84i/f58HOU0bjBD/ykVriem1m59ssH5pnbbxLfzyhkVdoV5MUOZkuqXSG39IEUlEVDqLMSXsT4iqHsMDJBMV4SOI3cXo+bkej8wlbz2JKRyEdcFLQaYbOwXEkelIi5iRRJBju5aS6h8IlLxKWG7P4RYBI+xQ6Y6A0hS4IXUmSS7ozPjqvGOcVwgkp0xzR8MknfUJfE+eSKoe8nBIL+Js//Tmbj1f5P/+v/wvf/R9+n6u3rvDo7kPKXEPlMalyaDh0gg6FSjjsS+7evsrxlRYHVg4w7m0xHU5p5RE6yHG6bcZ+hSoUsspxGgonKWjnkkx4JNMczw2IBKh8iuM5xOWY3G9QaB8/rvCbCi9QqCxlZqbFVEGcTIn8FDHWxIliHDhEqiIrUsoqJXAlo0GM38pxw4B4OyakTV5CVSXEE0mncKgcSRZViEwT64oyiwm9CnJN0XJwmyW63yQLfRACjzYRFdvbU9KZiiiXyFxSVBWeO6WDIA0iknJKGShC4VNUEuUFyOGA1BHIyP+my/bv3fEiadUElyYoMsGbgdHUXadNYFSXRq3DpOqdChNM9Xo9dnd3eeONN2g2m88FveZ86hCVFxVNXoQEme9NpbteSa8HmfWEqv5zY3xkZGiV2jd3Onz4MOfPn7fdlSAIrJb5kydP+OlPf2ofcEZ6tR5omgDdVOnh6+C5DoMyY2cq+KYTUocsGAJ5PVkwCaCpqtcTHUOmVkpZ0nJRFERR9Bz/o04QN2Nu7qMx3IKvuSz15K8OqTMdmCRJuHLlCtPplO9+97sWnmKuZ3l5mR/+8If89re/ZXV1FcdxuHr1Kr7vc/jwYcu1MQGR6e6YazMVZnOOpiNlKrym6vtiomzw2fXqcH2MTRBkuho/+9nPuH37Nq+88spzycGL0DWTYNShUHW+jbkWk8yY39fnb/09v63QKVMFT9PUqkeZe7KwsMC1a9e4cOECd+/e5fDhwyilLDzn0KFDCCGsbKwZE9/3WVlZ4csvv6TdbhNFEadPnyaOY1599VXyPGc8HuO6LkePHrUBtLln3W7XQqiqquLQoUM8efLEJg/GSPLhw4dEUcSNGzdYWlri7Nmz9r4qte8k/tZbb7G7u0u32+Xzzz/njTfesJ4Ln376Ke+//z7vvPMOa2tr/PjHP7aFhziOOXnyJBsbGzbxabVa/OQnP+HJkye2y2mu49ChQ2xvbzMajbh06ZJ1975+/ToA586d4/Tp02RZxoMHD0jTlJWVFQ4fPsyDBw/QWrO5uYnWmnv37tm17bou7XbbKvoZmBXsz2kjpfvJJ59Yd/GVlRXKsuTx48c2kYvjmF6vh5T7fg0G0lSXQ79z5w5lWdJut+n3+8+tl7Is6XQ6vPTSSziOQ6/XI8syptOpLSIdOXKExcVFOydMEG9kZrvdLl9++aXtjAoh7Brf2Njg4cOHfO9737NBvuEGmSTqwoULlmzfbreBr4sNRsmqLEv6/b49336//5xc8rNnzzh58iT9ft/Cqwz/an5+nsFgYHkgZVla40khBGtra5w5c4YTJ07w5Zdf2sKZ2efMs8R1XWteaSDKJnmbTCZ2TxuPx8zMzNjks861/N87vvFOsz0coVVFMCepggLpaYIyJRAaVVWMxxWDsctkkKHjCq0DCHxKNyPTfSb9bXKVk2UxeZUxIEd6GYFTQhQxTTXJdAvhZOTaI2zPc397m3Fc0vZm6KomkSvxpUNQSogL2h60c02rlPtY+TJmnDlIEZEVJVURkwzHTKYl7lShKp+dfo+EEqky8DS+jEDOopVL6kBW9XDLlPE4pZpKZNjkx//4D2hFEXfv3KPMJUpq/BnwWhWVynCCJs2FBV565V2ywuNPfvlrbvUfU+YFkfLIJhXToqLoFjCTI3TC7tNNRuMRjnToFA6OnOKIknZ7lsRvMZxWlIVHUoXE0qPINXlSIXSJGyhk4BBMJGyWVFmDJFdkaUI+GFMiKMoK6TZIdUXQUUzGe/SnGY4fopRLUqaIyGerHLLeXydJUooipSwKZCGRXkDsF2yMJsSl2odBVfsVy53xLkMkic4pnBjhakQJcTkFMq5/9in/l//r/53Pr9zkR999l8WjR4i9Jk2l6Do+WSIY6gy34RG6XWaXjvJk9xHzyuXw8gm6ShGqikxlxNMJKpNUkxFST8n1DhUj9sqE7SLBcUqcokBlJbORJol3casAP6uIlGYoegzSmFE8ZlRKkliQ9lJ8TzGlh9Y5ri/wcCkK0GQgp8ROhj87z9rTXbI9TV64yJkOKnaRA43ThNQFoUK8zKMsJKgct1mwpfq4jRytJySFT9IISYoRYTUldCU6lHSXGjQdFxkIpKcIhKTSggkNyBXtqInvu+hyDE5FUiRkgSIuPUaT7O9cr39fj3qgY6rh5kELz/sAvFjZrgeCpvpcr9y/6EdhKolaa/ugrv++TvytE6XNw8S0rE1QaZKHeoJRTyTga9O+OqTIBNCmxX79+nUePHhAnud0Oh3Onz/PSy+9ZHkrppvRbrd58uQJ/+W//BervhIEgX0fA+Uxn2dw5QZHbs67nqiZKrm59hf5ECZJqN8H08ExgXN93E2iY5IWg19+MQmpG86ZhK7OvxgOh7aC1+v17PeGeGjG1kDbzDjnec7Nmzd57733SJLEGvqZrtTS0hLvvPMOBw8etG7jxqCrjr83fBYz78wY1a/FzLE6HM4EV2asjR9CHb9vujbm9eZ9kiRhZWWFubk5bt++ze7urn0f8151iJ85H7MO6vfU3DtDnDd/W09E6mpg9U7Wt+0wye76+rqF15hkwfM8/uAP/sBKjQohmJmZsRChx48fU1UVR48eJQgCBoMBS0tLRFFEr9fj9OnTJEli4YzPnj0Dvi5qlOW+ydmHH35It9u1JGDjXm0S4L29PYqisJ23jY0N7t69y+zsLEeOHLGCAJubmzx48IDd3V2OHDnCpUuXbHElCALOnTuHEIJ2u821a9e4cuUKs7Oz7OzscP78eatuZdZC3cPjypUr/Nt/+2+5e/euhYSZZNc4lRsfkHPnzj0HWRRC8NFHH3H+/HnG47EtcOzs7PD5559bdavFxUULAavvz0b5aWtrC6UUH330Ea7rMjs7y+XLl7l69ap1yK6qii+++IInT57YYkGj0eDEiRO2Y9PtdllaWrJ7bl0trygKnjx5wmQyea5b7jgOX375Jevr61y9etUSo+M4ZjQasbOzw4kTJxgMBszNzdnu2N7enoXEnTx50s6jo0ePWjNRpRQvvfQSr732mt1vDUTN7IMAg8EAwHZqjHGfIdub/Q/2RUE++OADimLfeR32n2FHjhyhLEtmZ2eZm5uzibbp7BrYnSnyGAhoWe6bHppu1NraGjMzM9bA1HSeFhcXGY/H9Pt9661hIHw7Ozusr6/bZ5Hpzqyvr9ux+ibHN040JlNBkmvyIockJZ8mCE9SlJogatMIFJ18TKVcpmOFSsYM++sUuUvhO/SifD9Yzl2k1oShIi0ykBon1TRUSFZktFswu9gm28vYfrCJyiqmeUZCTJFNKGJwqwAnrgiCRRzVQlISeBrfEwiRQ65xcVFuRXe+S6OrKKIJYZDR7UAoS9KypNQeJQqpK9pByEzVREpF0ZA0HYcg0Pyjf/RPOHz6FF+s3adfFGzvpSgajLKSYVXh+w18qTl38nW2t3p88MkvyEcJVSwocAj8ACFK/EZMq+Ez31ri6dM1shhKqcilYOKVOCogqkIkBe54il+WBIHE91xUOaUTVoSEJFXJ1HfQVUFaTpHCoSxifC/B0TkITekKUl2QJH2KKmOS5XgiQBcB0zQljafEOmU6Lch1SeFNEVTIHEohiYuA0aBAU6EcRZ6luK4iS0vKMsBvdolHJTqtIClxRwlBIhFTSZwq1GKHx5sb/G//j/8b//l/+3N+793f49VzJ6ncdN9c0HWgzHGkIFCKQ0vLzM8eZOPZLrOew9zSPKkf42UQSIGfCXJcitxBiCYUHkkCWTJB+h5RCJnIGE1TqsChjCTVdAwTSdTo0lYRTTxEEZD7kjCIEKVPJGcpVIAUArdMKMSETBT4XhvKklLutzIrpSjbPpuTbSaMaCifKA1xWgFxqBmqMart0PV9AgVNFPlUIHCRoxRP5Ryc6SAqQU9OGe4ljMcVrtvA8RSDskCETZzOAuFsgNfwqTyfcpDhFBKdeRTDKZmEwndBtr7psv17d9ThTvUHxouQqnqXwVTJDFTHcBLM/83fAs+972g0Yn19nSNHjjwnIWqqzyYwMcFwndxrFGzga+iRCY5NsF4PIuuBtzmXuo+B53kEQcCdO3e4fPkycRzj+z7Hjh2z0B/T8ZiZmaHT6fD48WP++I//2Fb56rCjuvqRMaLr9/s2sTBBUR0CVOdQmOswlXBjbvgivKk+vvUug0ksTEfEtP0NBMNgz+tdF3PPTEBTh/EY2JZJLqbTqSXSJknyHPzMBFUGIz2ZTLh//z5XrlxBa205LSYJO3DgAD/84Q+Zm5uzhmufffaZhabUK4HGUdgkFvXvTQD/IiG4npjVoUsvzmnD76hzLhzH4cyZM+zs7NiO1YuHud/mn0ncXrw39cStfn71roZJME1A+m085ubmSJLE6v+vrKzw7Nkz62QMWNUgA6eCfdKu8Y6YTCZ8+umntpKtlLIKOh988IGFl8zNzdmA7tixY7TbbauCZKrrxq36vffes8G71trCfoxxpOmsxHHMoUOHWFtbY3d3l1OnTtnzNEpHk8mEW7duEYYhc3Nz7Ozs2GrzsWPHbAfCcLWCIODYsWPMzMywvb3Nhx9+yM9+9jN2dnY4duyYlfYGuHDhAlJKG2TOzc3xZ3/2ZzaAfe2113j77bcJw5D/+B//I9vb23Q6Hc6ePYvjODQaDbrdLu+99x6vvvoqJ0+e5Pjx43S7XZrNJq+88oqFMJrg+8c//jFBEFijwfn5eZtsHz9+3MKjkiTh4MGDtjNjEsgbN27YLqtJ/M0+dPz4cet5UpcRN+O4u7tr9z/f958zXbx9+zaXL1+286fb7SKEsDLD6+vrnD592sIwi6Jgb2/P8hNM99cUw2ZnZ22Rw3SvjXjB3Nyche6dPn2ac+fOWZNV08F6++23bSe1KApLzp5MJuzs7PDJJ5/QaDSswEhZlrYrZFy8Xde1c9wkbcPh8DloqBn7qtpXU1tZWWF+fp6ZmRnLPzFJXafTsR2r7e1t63pvIKrf5PjGiUbkKNyqAUWE8ly80EHJFBWXlMOSwHfQJARK4PoemQDH6zAZC9ABTnOWJK72A+Ex5KOE0TBFlA2cMsNvaqpmQJVUhA3B+nAdL41pBBLVTlFtRaIgXKgQLYlYaJDkBZEbUDoSZ+jS0bPMN5uEAhzhUklJHucoHVIFLaZ6igg1QTMk8uYIVYQoKyI0rRySWJNMY4aDbUoX3vqdd3nzjZe48uEVHmzuMk4T2rPzeF5IRYRLhOf7nH7pLM+ePOBnf/Gf2X32CBFPyPKcwi8YTnK08ghUl+XmEuM4ZbeYkOkxrTKj6XqIVkBzaZZSVkzLEhU4+CKErKKaJlSiYJQkUJb4pUORlYRZglA5e9MeeZaRTHJUUeEIhzIdEmU5uoQyl8hcgY6phlPkGKTjIn0XPc1gUtKImkhXUZUKR7oot0T5EjfPiPeGlFITSggcSVFlJJMx7VaILBV+6eG4kokqEJGH4yjSWIGseLr5hP/w5/+en/7pv+eHr5/l7e+/ixO4ZFVBkZYU2Qhdxbgo5uYXmJmf5f6jB3jNA2g1g3QbeEGDtHTRMeipwMkcqnGO5xbMNyLEOGMQ9/HmApQfoCcuySRDBAVSZrjNkm5L0BItZn2fIB/gi5JKasppwHArJ0s0eSVxpSIbCkZxSFG1CERAFUjWN5/SkIKGL3CFA4FL7CmmaUFQaAIvxGlE6FKT5AI/FuhUQqGIWg26HQ85TfbvTRIjcHCKAl0OmYwyHD8gFWMm/QGTyS5alcTTDNdpIFRIUUzpNjy8UtBVAd63U/4eeJ6j8eJhWrgmiK4Tx41KTv3nL2LYTWBsXrOxsWGrgoasXa8S1zH3Jmg2D6h6EmSCsnoyUSeGm6MO2zH4fNM9CcOQvb09Pv30U+tOfeTIEdvWN4lPs9mk0+nw4MEDm2TU+Sj1ANPwSOpV9DqkqU76NsmOOed60G/Gw4yDGb96Z6XdbjMzM8PMzAxzc3NWwaXRaNgHX52/Yu6LefDD13AzM7b1yl8drmZea/Dw5gFf55iY6yyKgjiOmU6n7O3t8cUXX6C1pt1u02g0bIJ35MgR3nnnHaIosjjk69evW9iNua8m2aj7s7wYmJv7a35n5oa5J39b16z+D/aTUMMdmJubY3Z21hJg64l3HWJmxqienJujntTVOx11IQCTeNbn0rfxMME4wObmJtvb23iex/r6Ouvr67ZzYDofBkZ34MABHMdhMBhQVRWvvfaaNUvzPM9K2xq4izG6fPjwIQsLC0gpefLkCVLuOzM3Gg2L8RdCcOnSpef8aBYWFhiPxxw6dMh22oQQlpcxNzfHD37wAxzHYX5+3q4NKSWnTp2iLEt2d3fp9Xp88cUX1pn87bffpixLDhw4QLPZtF2T1dVV1tfXuXHjBp9++inb29u22g9w8uRJHGffP2hlZYXxeMzNmzdtAnXo0CFmZ2ftuj127BhFUXDo0CGWl5fZ2tqykJ/r169TVRVRFHHy5EmL1ddac+PGDZvAmar4e++9x/b2tpXPNdCkwWDAgwcPbIW+1+uxtrZmFZTq8rAG9gZw4sQJq6pk+AQGFnv69GkOHTpkoZvmMBDJixcvWv7M7OwsGxsbVircdKW03lewW15etr4jzWbTEtnzPLcJl3Eor3ekTefMjNnhw4ftHmr2l7IsWVxcJAxDyzPJ85y5uTk8zyNNUzY2Nrhy5YrlxB0/fpzxeMyNGzdYXV21yUKr1WI8HrO+vo7jOPT7fWC/CGXcz33f5969e9a80nie1JO2uiT3zs6OTT6Mh0ar1SLLMnZ3d22X8Jsc35ij4RY5QXOWcbaL0AUUFTotSCtNZ7ZDLBUibOFWGuU7FJMRJDF+wyETHl7lkjqCMkspdQ4BhHMtRF8TuIqJjGnrEFk6ICK219fxUnBFSqcdMJkIvFaDRJYk0zECSVhIRKCYli6TpEcDF5eAcjIB6eA1XIhjKlkxH3YYb4CrCpI8QfoBVaJxqgytNDvZCOG75H2IRMTJi8d45X98jb/+9UdsPt3GTRKcSQmOYio1ge8y47gcOn6M1c0BH1+9jARcPEZlRum7EBekmcbRGq/ToVSK7WdPSIc5smyiVEEQ+AidEccZyglxREI+LohTCaRIFYAjUUgKqUGCm2m06zIpBcIJ8JseIs3I4wylGgRlgXAK/EpQ5jFSVaS6QEgHnRd4QqF1BTIgSVP8RkaiS8oUnEGJFJrpSOG5EaF0cKVLRoymIo6HeKKL0JKCMW7LJ01dtAZRpniygIlDrEpcXDK9x3/4z/+WYjThn/+f/ld2+332vrxJVqZIoYg6LpNpgp8r/EbEZOjwbHWL7tw8g8keTpmjyAi8CpwCqfaoZIlb+GjpMxnlOEHBziAmVCmB06RyPQqVonRFkjtM8wJVxJTtCkdrcgSRyKi8XVpekyrV6Ewi84hWKEjlmDm/i66GNBc77GxsU46HOE6A43sUUpJOprRaTZI4R1Ue/e0RbR3S6gboiab0CoTOySuXSZnieYKmH6BKSVJqVFNBmuPICF1Akia0hEupFZUrcfsVlZBMREbgS+K0oBF1SdIhsvnthDzUj3qlF772DzCBfx1WZYLhuhSreQ8TVJkEwvzddDpla2vL4lzh6+q0qWDD15KjJpEx1fJ61awOjTIPIfO9Occ6/MaotZjrM8Zet2/ftm3zVqvFa6+9Zt2BTcW70WiwtrbGn/zJn7C6umoD2jpHwcAz6t0Zk6yYa6tXvs3v6gFqne9igl/z8DOQrHpAbzTszf2oP6DMA90E/Sap01rbxKWqKiv7ayp+ZkzNdZhAwHSU6mRl8x5GecxU5UzgPB6Pefz4MXt7e4xGI773ve9ZNSozL86dO8d4PObDDz/E8zxWV1eZm5vjwoULNpkz3YC65nwd3vfivKgnvvC1EpWBsZij3lGqd40Ml+XcuXNcvnzZejTUuSEmqTZJb73rV1WVDVxMkPsiJKrelTG/+7bCpgAr3WnM9Uxl1ZBo19bWrGyrMXgErGKRSSwNXt2ojHU6Hcu3eP311211vd7ZNK7gJsAz8+rzzz+3kp9bW1vs7u7SarVotVoMBgNarZadJ8Ph0Aara2trzM3N2aq4WVvdbpc0TTl06BD37t2zHgmvvPKKXY9zc3O2u3bx4kXG4zG/+MUvuHz5sk2q6mtxbW3NJh5aa2sc2W63OXLkiPVnWF1dRQjBqVOnaDQa3LhxA9/3OXToEJ7nWanwS5cucf36dRzHYWdnxxYHzLw36/z111/H931+85vfsLu7y9zcHPfv32c6nQJY48TJZMLa2prtzCq172zt+z4vvfSSVboqy699PerwNrOmvvrqK7u31AsSly5dYnV1lbt376L1vmjGf/2v/9XuW8PhkB/96Efs7e1x4cIFC8E05ne+79NoNFhfX+fgwYOMx2M8z+Mf/sN/yK9+9StOnjzJ6uoqCwsLvP322xa62ev1GI/HdLtd2/Hc3Nzk8OHDHDx4ECGEJZbPzc3ZrkRVVXYvML+fnZ21ECzDLTHr38ghmw7K1taWvQeTyYRDhw6Rpil37tzhlVdesRwMIQSrq6s0m02m06lN0pvNpu2IGWjg9vY229vbFvZn1tDfdXzjRKPsdJmkU7RXIAKHIsnwgzaqWRF7kskYXAoqlRH0PbxMQ6SoNCRxSupp/NCHVkixU1GOS5KqQOaaZ5OKZkuSNzRLix36ewmTfk57zkWVDfp5gVAZLRXikON7GaUr0E5Fnjl4KajGIZpOSm+a0VieJx7v0ZiP0MW++lWWTqGSaCkIOiHD3i6eUKjSIZQRpacZZBlN3wXZ5B//+H9ie32Nh88eMJhs48gKP2yipI/Ic6QjOXpihWE25JMPfkO/v4MQklJXSKVgMqHMJIE7Q9tJmWt2mKQJ4zRBSI9W1KVgjBYgE+jrDEcEeF4FacYozanCBqoocUVKKTVu1CEdDAlpk6QjgrLCcxRVnlNVkkTmyDIhTTS+6yIdhecpSiS6jKnKnCLw0GmCyARIaDQDsmJAVVb75nrTHBUJWt2KolDk5OR5SpFIUlXgd1sUOodkgmpA5WjEKCarwFFNsiInlwUqiiiHPfq6YDh2+H/+8Z+QuT6vv/19WtETpmWOTqakSUSZCfwqYVyWtBcWGT18SOV4LK8cYPfJI6YywxUuSQjSrygQuI4iIGaaClQzxPMCRAGZP8WtBO20Qy5TirhAOQFBpCg14HsUsqTMI5QHTplSKZ/RNKfTXEZle3hBQU6CyCVO7OLIWZ6Np8zPt6kocIscT0ikFlA4BFlCFCpoVIgKQtWgVAE5mo7nM+rFTD0H7QTIRODIjJyKqBlA4RPnUyLlEAQN0jJGZRrtKgj9/cQor1BjTT5OUJ2SUn97g4R61c4cdQKs+b/5agK3emALz8NJ6tVu87qNjQ1bZTSStvXgzeiRGyiLgf0Yedp69d18HjxP6DXJSV1tqM7PqEvU3r59m8FgQBAEtFot3nnnHebn558LZqMoYjqd8pOf/ISnT59ayJi5JnOudcKjOcz5AJZI3mg0LNnvRbiVecibMavfB9PZqFfw610oo3hi7odJYEzF1oxjvbJfv07jJltXFzMQCUPSNO8J2ISp3k2qE8rrsBTjwOv7Pm+88YZNImE/6HjjjTfY3t7mzp07OI7D06dPWV5eZnl52ULd6tyWOva8nujVpYpf7HTVuwf196zj3+sQBiklR48e5d69ezx9+vQ5fLbprtW7bPVkx3T7zHu/2PH42zhP5hr+tnn0bTiazSZra2scOXIEpZSV4jx48CCTyYTl5WVgX9Vra2vLGrJtbW1x6NAhK+dpginYTwRv3rzJkSNH7D2tuzib9W/IuvXiwJUrVzh+/DjD4dAGZsZzw3QyB4MBp0+fRkppuyMGd2+6W0a44uTJk4RhyIULF9jd3eXWrVu2Ur+8vGxdyo8ePWrJ8MPhkJ///OcURcGJEye4fv26NeIz89LsZ2Zuf/HFF7zyyit89tlndt0ah/Pt7W2OHDnCyZMn+dWvfkVVVayurvL2228TBAG7u7vcv3//v4NAmoIDYLklRo5YSsmDBw9YWVl5jvN0//7955J5wz87evQoT548sd0Hs0cYVTwzv839PHz4MNvb2zQaDVt46na7lhhtEqB/8A/+AX/6p39q4T9vvvmmheQaQraBtZp9GPYVp3q9HrOzs4zHY8IwtN2Gd955hziOOXXqlFUkM12JeoekKApr0mfmwLVr1/jd3/1d2u02Ukq7J5VladW5ZmdnWVtb4+WXX7Yck7/+67/m93//95lOp7z22ms8ffqU9fV1kiSx3QnTwTOiBMZrxswFo9bXaDQsn9F0fU1XzziHb29vU1UVr7zyCq1Wi88//5wzZ858ozX7jaFTYZThODlShkx3U9yyjUw12SCnLARB0yeTgkleMAglpfKpxiAL8FSJXwn8SY4eJ3hS0/Vm8HJBFDg4lMjSxfMjunNNKjEmXIay6VI6HqFTEaiSWOdMCweNj68EZTEhH4+p8oJGs8moqhArHcYh+N0OonSpsooiVmTCJ2k7aLdB2S/plB7tQBBUCqIm20lKL5lQhQ4//ge/R2O2werlL2kNY4LSYWF2hrDjE874RIHm6OFlduIJf/5ff8ZwZ0SVKCaTGESJ5wuUU6GqEuUUBEstaEnSzT5JL0XrHMQAxBDXmSKkJhCKNBsiegmFBj/y8YoUt0rIHIc8L2EyJVAlRTqFHJxAkKuSKs+ROQTCwytztFORKYe4LEjygrwEKpfQjwgcFxm4FI4kDCt0HJMkDkXDIwkdpNS4mUMcpyS6QDRdXKFpeppmJSkHU0Q8IAw9ZNmm6Eu0dNFhSVntd0+qsGQ82UNFLk7YgELS7w/5L7/8z3xw+VdcPH4MXykKNHoypiUgQ+GoWeb8JVbmTyEngmx7TLPTJow8SumC3oeNTUoYiwbBzDKuq3CAajiizAME+9C+iZbkQUXq90FN0KVEphEqjtGlS1wFZImP0vuk7MirGIcFuevgecG+AaIAUeR0mvvzyJNNPASqyAkChRAK5VRooSmrlFiUlG5ApjSiWeCqFuU0wtUuTtUgKwMmusTvBIRViqMkqUzJnRJnziHvQqJThrtjAj9HlQO0SMmdArUUkjYhjx2KbzkZ/MX/G4yxaT+bwPLFarGpBtWTirqhngni4jhmfX2dAwcOcPjwYauIBF8nMaYqWA8I6xCkuqpUPZgzPzNwnjr+3QTm9e6M8b8w5MvDhw/zyiuvcOLECdt1MElGWZb8+Z//OQ8fPnyO2GkewHUi/YsQL/NehncShqF90NRhMkmSMB6Pn+sYmTGuj7VJAupJilKKKIoIw9B+NTyIF++rOa969d8kX/VAxFyD+dz6A7KebJiEpl6pNN0Vc57mwT2dTvnyyy+5ceOGHT+TPPi+z3e+8x0WFhZsx+PBgwdIKa3ijEkq6h0L87kmWDd8CHP+9fM0c6g+X83fJkliX1dPtjudDkePHmVra4ter/dcUmmSKvM5BpZnCOB19as6vNAcJrAwwVc9Ifk2Ho8ePWJ+ft7yKJaXl20l28xHwCYfZs5lWcaNGzcAePjwIb1ez3o0tFotgiCwrshCCJ4+fYrv+zY4U0px8OBBYD+ZN1XeI0eO2G6dUW4yimam49hut3n27JmtINc7Spubm2xtbfH48WPL14njmOXlZTY2NqyhmvH5MPf2wYMHtrr/3nvv8cknn/Dhhx9y7do1tNYMBoPnEoCZmRnm5+ftXPn+97/P3t4e3/nOd/i93/s9Czc12P6vvvqK3/zmNxan/y/+xb/g7t279Pt9CzkzkLRWq0UURdZE0yS2Z86csQGsSZKn06lNvg8ePGgTuXqRZjKZcPPmTZIksTLQVfW1GIYpCHU6HXtPsixjfn7+OQNE0xk1PI84jnn//fft+j5z5gxXr161ydVvf/tb7ty5w2g0Ym9vj9nZWfI8Z29vzybz4/GYyWRCWZb84R/+If/sn/0zZmdnLSTWdV22t7dZXV1lcXHRksg3NzdtYmX2/cXFRX74wx/aYkKapszMzFAUBbdu3SJNU+t5opRic3PT+mR0Oh3L41hdXeXMmTN27RvfEs/zWFpasvy/ZrPJ0tISQgirwmVcxE+fPk0URezu7jIYDDh16hRLS0vcv3+fvb09Dhw4QLvdptvtWkPDb3p841e6WqM8hcphbn6JSZFQOSmUMXI0oqVjOk5FV4a0yJGiomwF9IsSwgaB4+Nmc+gyJE2nlE6KqzQq14QUtJshi402jtSUZcpst0uzMUt3dpGo2caLmjiBh6dymo0G0m0itEejE9DoaioS/LDDeHcdFWh2pxMKGUOZ4AUVUgNaMhontHwf6UuSwiUJpuzF2xTaQzghP/rO9/jRO2/zwUdXebSa0t8pSIXLsMjQYsJED5k/vIQTRvz6b35Dvz+kKic0I0GkA4RwScc5RSxw/RBfCZqtNnE6ZTub4rkOnaINlU9RNSkLF1cpXGKcSUpMuk8iDiJERyHDCjGFqNGBJMYpJBLwhUPhhqSOZFJUBEEbpE+hBChNWQ5Rbozvlyg/RTkJCYIkk6TTGNdXjKY5GRVQkaQpaaFRSjBOY9JCU5YTVJlRpRF5rPG1wlcNPFwyzT6pvCxJ8pxyMCVNCrIUqn4fN87QGTijHKVdJmXG1pMeP/3ZT7m5dpOTJ8+hSh9RxcR6j7yKEUwp9ZSwHSKkw96ox1x7nqYIiFRBN4e2bhMVAaQl/f6ETJeUoUBVGVK7lEVAPC3RsoGbtZGyTRR5qLIgcHJasw3aLeh0S9xWReaU9HVKFHrMq4JW00epDFHleK0WnuOzoCPKdETmlvhhk27UoHAUWT4liweAixN2CMYButAUTgKipKpiRlXFtGwSTyViZ0CkYFKUEAQkZUUWV4iyJHMnJJMpfqdF2WqS5Tk5mqLKabiKqgSn5eAEQ5Y6wTde4H8fj3r13wTpdVy6UTcyFfJ6oG+w53WCtgnEzM9Mle+ll16i1WrZile9Sm0ehHWYi3nQTafT5xKQOjegzh0wvzMJijlMVa7RaBBFEaPRiDiOabfbHD16lDfeeMN2GgAr7frzn//cPvTqiUNdkQu+dis3QbzhdszMzNjrNepQJvA1pOnRaGSxz6aqZcbYBOT1cTHXbqqM5msURQRBYMfNBMAvwnnMva13ewxJdzwe26DHfJ7BIhtTrbpBnnmImk7Wi4mo+Wo09z/++GMeP35MEAQ2sDDByJtvvsnMzAxRFFlTsDAMaTQaVtXLdB3qSV69M1AP7s3cNf9MImDGtJ5smOpyvdPiOA7Hjx9HqX336LoqV32O1zkg9WSmTvivK0qZOVNPUv+2ZOTbdJw/f55Tp07R6/W4ffs2H330EUIIjhw5Ysd0Op3azpa5zvn5ed59910cx2F5eZmVlRUuXbpkO1RmPty5c4e1tbXnugEmSfF9nyzL2NjYsLAeKSX37t1jdnaW+fl5er2ePQellD3XdrttCecAR44csQRiIyVr9p/hcMju7i55nluyu/HdMF0R13XZ29vj3r17XL58mcFgwHA4tPuSSSqUUhw+fNjyWUxl/9e//jVpmnL37l1+85vf4DgOL7/8MqdPn8Z1XUajkeWOlWXJ1atXeeeddwBYXl5mZ2eHhw8fcvfuXesxYqresD+vDQ+qPod3dnbsnrGwsMB0OuXMmTMWsmq6KyaJNtCvixcvPifiYQjgjx8/BniuIz0/P8/58+etieCZM2cIw9Byagw8aXd3F601169fZ3V1lb29PYbDITdv3rTdhMXFRaSU3L171yrXjcdj4jjmwIEDnDx5kpWVFcshMVC+Bw8eWFUmo2Q1GAzodrtUVcVwOKTX69lOijmePXtGVVWsrKzgOI7lxC0vL1u/nTRNeeutt6xBZbfb5dGjR9bh3IyB6ZLMzs6yublpr7/f77O9vU0cxzx69MgmrKZ4YiCJnucxHA5ptVp2Pe3u7tr9ykj2/l3HN4ZOTVMHVeY02j5VnuKHDdI8xWs30JOErFDoqqKSApFpQsdnkm8TLUZoRyEmgkEKrlsiAp/ptM9Mu0NcTkkaIZNxzKkD84zGOeNBRZ410TKm8HPS2KfbcJmViqQ3QQYeu8kEPxWEvqacVGSOoKoEK+05RJLTnG0ynE6RzQi/UpSpphMkjPKSCZrKcxCqhZQB7UlKW0q89hle+/4PefjkKx4/vs9UTfBChdsQeBmMBEQzPp25GX7xs/cYbG6C65DoHJcMX0mmWYbAQbR8RklOO1cUk5xR2afZcchLgZf5xHlCI3BIC0BW5IWkcH0qB1Qhyb0pRZrieBWBF5KOhlS+h5QuSZIR+A5JmlHkJRKXfjbEp0GepwinwsMhn2aUDigBRVUgVE5ZKVy/hc41BYJSQMd18EtNUkiElDQ7ESM9QRclSR7T8DyyTFHlMV4Q4ao2vWKK71a4soS0IPAdcr9kOspplBXa0STZiKTKqUoHr92mUoLdZzv88s/+gn/5P/9Lzrx0hLtP7lMUmm6rTW84IBEBaSrxBHhii2TQJ9ABeTVk5LnMeCGtwifXIICgEYEGx9fgpqhc0JIgWxPSyqFRuYgRiKpJWgniWKClpBs0KMcFeRLjhSFuw8F1S9IK3FThzy4jdEHCCFodhNvE1QVuJhi5Ci/VaF+h3CZ+JVBpjrPYJI4zSkdSTcaEgaLySqqqhY4HOK2UyA3IMxfVcCj6AxpuiFIRXllSqYppPKbhLzDNc0JfUPU0XqBxA5dhXDHIIibbgiOnvunK/ft31NVy6i1zEzzB11h304o3QduLleaiKCxh2Pzt5uYmQRBw6tQpi782f2eCa/Na8xA0AaU5zEOxDg0y72GqZHW+ggk4TNBoSNTm4a+15tChQzb5qcOclFJ89tlnfPbZZ88lGaYSa4IdE/zWuwN1fkY9kDT/f9HrwTyM68F6PZitQ2vqpPd6Z8IEtOYe1av2JoCuezkYyIN5oCr1td+GEMI+vF/87BddreuwMXONhtNhxtPMHWPI98UXX1i1KZMolmVpYRCPHj0iz/ddgQ8fPsz8/LxV8qlDp+r8B1Oxhq+7V+a8zbmZ8a5348y9ePH3ZVkShiHLy8vMzc2xvr7OsWPHnkt0DUzHcGfMHE3T1OLizRi92Kkw5/Zih6bOgfk2Hdvb29bB2XjjLCwscPv2bbTWzM/P2+B4bW2NjY0Ny9XQep/8v7u7a/0IiqKw8p4LCwscO3aMqqrodru4rku/38f3fRus1hPKmZkZtra2WFhYIE1Tzpw5Q6/Xo9Vq2Sr37u4uJ06csHuR+dvpdGpVrYqi4NGjR5w5c4Yoirh9+7ZdB5cuXeLVV19lOp3y7NkzgiCg2WwyPz/P9evX+fjjjxkOhxYyVFUVMzMz9Ho9zpw5w5MnT/A8j3a7zeLiIru7u4xGIxYWFtja2qIsS8uLu3//PkWx7xMxOztLv9+3+6WUkl//+tf4vs+DBw/s3BRCcO/ePWA/4NVaW76BgWr1+/3niktmjh47dozJZMKDBw8wpGZDCjf7zI0bN2zHwTiEZ1lGEATWwK/T6dhEyuxnGxsblGXJ3bt3mZmZYWlpiUePHlFV+8pdd+7cQUppJWCNAISBYPX7fZs8DIdDTp06RZ7nbGxscP78eQ4dOsTt27f5vd/7PXzf5+bNmxZ2WlUVr776quWdjMdjjh49agtSdV6gIW/PzMzYhNkkx6bDYKB8f/7nf85bb71l96UkSWi1WmxtbZGmKfPz8yRJwvr6OufOnWNzc5Nz587R7/etWEAcxywtLdki0dLSEtvb24RhiOd59Pt9iwww98icj3mmGf7PV1999Y3W7DcuaUzjlCDLSfsDNOBXkrLIcKocX7j4WY6fD9HxHlU5ovILiAJauYuzlZKPKkSUonWCKwQqCBmTkVcCR4DXbeG4DZ6tjdndjCEFKTPIE+a7DTzRwAGCzhwiauI5gqgVEjRCCJq0PJeOkzHT6hB5HVp+l0C1UGkEU41UCYQhbqNL0QzxnRaOG1JJhWh4qI7Lm2+/Smd+iTu3HuCVMQ2vQIYF3cBF6pK2jDgULfHpb37F3fv3KUNB5Rfk2mGUgnYDXOGg8gRXp0hdoFr7sr1OXJAORnhFQUVMKTS6VASBS+ak6ETjSR+ZBegyR2UpTiVJBg5yMCYQgkB4MEzJq5TMneA3Anw3wlOKUFfodAcvKHCbGqdRECi171adZohcoEoHyX7lPytKfClwsorSbVAgEaUijh0m0wpVhkSiQeQFaF2iKoEWmmmR0csmuJFHGTkIJ0I7zr4JY+oipEJXEq0dskow1RVaFEiVkOcDUBn3bz/mv/z5/wuHlG5rBq0FJDk6kTAW6KSk8lo41QzrOyPKrkcRNigLl3GVkqmCVkvjexJXRXhTnxyXpNymlCXarajKPiqQVFpRDkpcXRA1K0oyvEyihzGeL2k0ffwgIHJmCL0lZNlGKhdnPEAXE2IC8rLioGqT9gZoWVJWE6J2QKty6QgfWRSgAlRe4GgBKRA00Y2QON5FlDu4TUUpXZTrEU9jxpsDIiXJdMWkFEz7iumOglyh4yFOkTLam9JoR+jUYRL3CaIKr+Eh3W8nthq+rrCaFnPdF8HAfczP6sFunUdgAv968GweSqPRiK2tLY4ePWqDy7r6Enyt0GOCLRME1v0n/raqsalim+DfHPXrMK19A5kyD79ut8vJkyc5ePCgDaLNdfR6Pf7mb/6G6XRqYU4G1lOXdKyfj8EUR1FkYUzNZhPf9wnD0FbDjeyh6SDUg3nzsDfXbl5r4FXm33A4fE521lRc9/b26PV61sug/hrz2YY3UZcFrsN2zLnVycx16JqZK4YzUiff13kfJgEw88DMmc3NTS5fvmyTMSMp2+l0eOutt5idnbUB2q1bt3Bd16ppGYiYUa56Ue2pDqeqd+bMz4xefb1zYYJKEywZbX8DCzlz5gxJkrC1tfVcp64O46vDrsx9rHdc6lA3s7bq52wqwkYd7Nt2GD8F13XpdrtWcefo0aOMRqPn4GHGl6XO1XFdl7t377K3t/dcYHX48GF2d3efKyT0ej2m0yl37tyxsLbV1VV773u9Hl9++aXdH3q9nlVkMx3Go0ePPscTcF2XmZkZrly5Ys8BsJ/z7/7dv+Pzzz/H8zwOHTpkE0gDKzV8hcFgwF/+5V9aSI5ZG5cuXcLzPAvJEkJw69YthBAMBgNrvGdEHEz39fTp0/Z96oUfs0Zv3rzJqVOnEEKwsrLC0tKS3Z/Pnj1r96Pjx4/bjnSj0aDX6xFFEWfOnLEJi5nXv/zlL58b893dXWsCWOfKVFXFgwcPrOy1WQ+DwYB+v89gMLCcN3NNZo7keW6LHFJKDhw4wP37923Qb877O9/5DmfPnrUmoB9++CHb29tMJhNef/11Go0Gc3Nz/M7v/A4LCwvcuXOH48eP8/DhQ7Is4969e3YvNXBg4wZ//PhxpJRWRrfdbv933DbzDDTCAUtLS7iuy9OnT20x4p133mEwGCCl5KuvvmJvb8+qT4VhSJIklnPR7/d59OgR9+/fZ35+nhMnTlCWpVU2NMUl4DmhDikle3t7PHz4kGfPnnHt2jXKsqTX61mvHrM2/g93Bu9WPknZJ/MdimGFkxTMzzXIypJ+mRJMS4SSVI5EOy5DTxM4LQYbQxAuOkxxRUWoWmgtkaGkTDN0IBBFRQtNMezx6PEj+vEWYq9iJippCYdxChWSIssRHQedxETdDk5Z0cglQWPEkBhiiawgEz5KZ3RbkmQvJxVzKMbEUYEXCuQkww9DRCVRsaBqKmY787z1g9d5/+MrPOtt0hsP6LY67E2HjOMhXa/NwmKHp/fWuLO6CUFJmQnaTpsimpBmDpO4xNEVwlPoFAJXMbvcIe3HOELhBy6VKCkqgd90UTn0RlNU5DFliFQZjdJFFzmuVKS5AsdBeILxeIiocpQocSIJBZCmFDrGFRpZKaTjoCtFNSwpJeRKEBcZjYbE0w6TKseVJWUxRKCQlYcTKvJsii4kpdh3xK4oKXNF4ZR4uqCsNE5TURUejpSkpUteZoiigqQgCjyEcHAcmFQSNxDkEogrmlGHPJuQDFNCD0QWI5TP/QdP+JvffsLv/8E/YbA1orc+xPEDXCXQkyl5UdEMZ1B4DHbHhPNLEA0oy4w8gUHap+FKkjgl+H9z96c/eqTZeTf4u2OPePbcM8nkvhRZVaytq7rVXWpZrZFHrwzJkGED8w7swdiDMV5g/iB/MQz4kzHwAsOb1C3bktqSuqvV3VVFssjivmQmM5nbs8cecc+H1LkrSGlG9VE1ASTIJJ8llns51znXdR03BM/DqW3KLKbu+3iqwncKCnViUGBpzWRWkNoLtAOHIk5QbQ8vdVGVxlY5de7S9lxU4GDnE1xl4/tdrLKm1WrxfGeHaTug1fIorBhd+3TDPrquqCuLeQZ1qOhnLnpWUTs5oRXidVys3MYtYGZleC0XJ4bEydB5glP3yGwfX6V4do0uK7x+G+VlZGVCy3UIscnylCouCXTr607bv7VHMysohwRrTa550y3JsiyzIEpw9/rrjo+P0Vpz8eLFv5aeJRav8mcTMEgm/HU+vIAS+KuVjqamRM7B930GgwEvX75kNBpRliVra2umG7Vkw+Xz//iP/9iUtcVyVqoQsgFJgCnVDtkgOp2OyfJJUCDCdhE7StZRNhEBVlLNEXAg17qwsIDnebRaLdbX1805nTt3juXlZUajEc+fP2c0GpnvEqpKHMevgAQBBxKISQDQFEi/LpQWICHnLIGPgAl5Tq9rDiTokaBd7vP29rbJFjcF2BsbG7z33nt88sknhGHI8+fP2dnZMdaiTZveZsVHnLSaY0KutwkIm7Qz+b/Xx1WzKmRZFqdPn+b27duvUCzkpwkyX59Lcp5NXYd8n1Q7ZC40X/NNPKIo4tGjR4abX9c1z58/Z3FxkZWVlVeqaevr66yvr5ssuViq3rhxw/S7EF1QHMfGFvXnP/853W7XWJYuLCzQbreNvkjrk2agp0+f5saNG/i+D8DVq1f52c9+xsOHD7l27ZqpQIlV9OXLlwmCgOPjY65duwacVGiOj4/J85wf//jHphkfYNYo+b4oikzw/p/+039id3eXzc1NM46Ojo64desWb775pqEByb2QHh5S7Tk6OjJBuWT7pRKcpikPHz40c6Wua6OlqKqTTtznzp0zz+POnTuGMijzc3t724iQBcjLutmkucp8b7VatNttDg4OzPiV7uJaa5PkkDX26tWrHBwccHh4yOnTp401rfQT+dnPfoZt21y7do2HDx/S6XSo69pUr65cucL29rahiN28eZPV1VW+//3v88tf/hKAn/zkJ7Tbbf7pP/2npr+FWMdqrXn06JEZZ7/927/NZDJha2uLIAjI85xHjx4Zm+N+v28aRW5tbbG0tGTu08rKCtvb22bNLcuS+/fvc+PGDTY2NiiKwrhEiYbtzTffNIG/1idi/fPnzxuwIv0xxMHr/Pnz7O7uGnArVYmHDx8a6m1RFMYdS3qFdDod/vRP/5Tvfve7jEYjbt++zW/+5m+adfDrHF8baBS1ReZ2SGsIgxLLglnmEfoOnZ4iSwusoqbrWIzygqWgTTUak+ocK1ggr2OsQFMkiqLWVHZJMte0OxpaCguX8eGE0d5jCj3BtwJe7qU4dpfSn9Nd8CmLknQ0ZnVpgF3Y5KrGtVwmeYnv95jY+4QkBL4imQ+x6pJBa0BOi2lcYEWKyrKxHQfPyairHLuIcErFb9z4O9SzQ+599jmqLOk7Cyi3RbfboiwndIMOvt3mF/d+xPFwgrLBskIYV3hBwTzJsWuFXebUnovOK6Kwj1UVqFKT+g6BZ+PYbUZlhpXPyIoSy/bIhjmRBVVlUzoKlE9VZZSWQtk1SZbScmzKyCXXOXamceoCN9I4voPlOLj1SZA8K2rcAlRZESsb11NYdUaRKQKnJP5LgODXDto+yRY4niZNa7ArfFujHRcLF7dwsK0K7dhklUJrCCixygyrslDKxutF1NMMVZVUKiUKI2xcitJCWTlZEmN7HmiHMs1Alzi2TTJVfPLjz9g8fZ2zZy7ycOcBk7mFZxUEvQJrklHZLqFlUZQeoeti5Q7ZPEVZJaWOsJRHEOSUeYJddKjKCkIocEhimyioiWqLurfIrAav5xAomzYObq/FUZHj2B08T+H4miCyScqCUgdoxyUvFflohNZQtyFcaFN6NbZvMa4znDDAt3NKVdFB4Ycd0gIsyyHJZtjHGWHXZ5alLGBjqRxbWfgoqjDHDdu4yUn5tABynaOKhNoKIA6xywx/sEoyHeEVDnXuoewR2vpmUh7gKzG4bDgS6DeDTPlTNphmoC1Uh2Y223VdgiCgLE+aKYldo7h+NAXDonmQYF2yXPCqy4/8LgGj8PyBV5r8SUDb7IUhXXgfP37M1tYWnU6H1dVVut2uybJJoHr79m1u3rxJlmWvaEeaYEYC/aYjS1P01+T3yj2WjLrcqya9R+wcpeOv6Cm63S6XL1/mt37rt0xjLcs66Tobx7GhqTSdo0QQOxqNODo6Mjzn0WhkrlUcvgATkInGRa719ay/VHYEzAnAbAYpMi6aeh0ZT0JvkiD+l7/8JadPnzbVCzEfuHjxIl988YXJkt66dcu4/jQpYJJRbvLPZYwIyGtSl+SeS1WhGdTLNTTpE3JfV1dX2dzc5Pnz55w+ffqValkTJMhckDnSrHoJaBFQKVUcua9NYe438RDQu7+/j+d59Pt94+T06aefsrKyYubP+fPnSdOUH//4x3znO98xYFxoKl988YUReQ+HQx49emS48LKuFEXB/v4+8/mcwWBgKnaDwcA81yzL+A//4T/wgx/8gA8//NBoEXZ2drh48SJFUXD//n3efvtt4jg2iY4kSXj27JnR2Tx+/Jhr165x7do12u02WZaZRoRxHBve/u7uLjdv3mQ6nTKbzYiiiPX1dd5++22ePn1q3MtEL3D//n08z2N9fd30ahDaXV3Xr1COnj17ZuxsHz58aMbfcDg09tV37941bkTNhIgkXqIoMkJiy7I4d+6caUi5trZm1pTmWi5rNZzQdcQ+tVl9bVInP/30U1MtuH//vrGLrarKOFkB3Lp1i3PnzplGe8fHx7RaLcbjMb7vm/OXBnoHBwecPXuWBw8eGPFzkiScOnWKly9fMp1OmU6npgeG7/s8e/aMq1evmkahu7u7pjv9ysoKWZYxnU5ptVqkacrCwgJpmnJ4eGjoc+1229g22/aJQ16apobSJ6Cx3+8zHA45d+4cP//5z3n06BEffPAB77//Pnmes7Ozw/b2tnEgOzw8ZDgcmt4vYuMsfUg2NjbMGnZ0dMTa2hpLS0umc3qv1+Pw8NDste+88w5JkhAEAevr619rzn5toGG5Ngt2i7ieY1klpVtSq5J4XhN1wFtwKQ+KE3G31hTpjEoB1LhlSYkinyX4TolyNJHVxfZtdFZgz2Dxco87X35BPJ9RlQW5o0gz8FWOns+xlcILFplNM+J0ipMpur0ecVlh5S62LvGdDoETkTm79KyIwO6gaJMmKYNOj3iWENeaWZngWx52y0GncO3iFS5cvc5/+p//b/bGO/S9EGUXYGdoQOEz6HW4/eWXTGdHeErjxpB6FUNVY801VhiQZ3PIFSqvqPDRdsDh0QiUi5dX1K5PMU3BykiKGZHbhVoRElPWUNSK0guosglB7RKFLrNsju92qKoxtVPSWfZwywA/r8HzqOI5de1R4WHVGX5VoQJw6xa6Kmh1XYqjmlQpfDeiqKCOSrSuICuIggWm+UvCbgtdZOha4wQ2eZziWN5f9teo6TgtSl1T1hW+55DnBcrzoEhwCcnrBNwOtbJxvQivGFM7LqXy0arAskGXFlqDsjR5pcnjCX/+4z/i7/5vf5d+p0s8fYmOa2xlEYYBOQWObZPiMTzaYqU9wI5cMrumzCoKOyOrMrpuF9/2sKwZbn8ZK4ZOtAQJqGpGuAh+3UYXFY6bU9garxvSK3rksUuiYwIdQWIxTSqiVox2W9TtCEZzWo5P5tZYdok3tfF0jee10L7FcXaAimsct0UvHuO7PqobkRzVODhoYiytKUufXhhhuQEzC3RRUuY2BQ7z6ZTuio9qB2RVQDWeE/gzilARZyU9vwfOlFkWox2NW2Rfd9r+rTtkoxcgAbwS2DedhiToF0AiAVozO9vkxAql54MPPmAwGJhMunyOVB6aWeemyLYp6m1Sp5o6CAnumv0nmnQwed39+/dNAyjJtDY54pZlEccxf/Inf8J4PH5FLNykSjQ1LM37I9l2yXLLZ0t1odkoT669SUdripJ7vR4XL17kvffeM25YEtg2Rd2TyQTAiOUlcyadj4W69vLlS+7cucOjR4+YTqcGsEnmrWn72xSRNysdTdcpCWCawKRZxWpm75sC9mZl6vDwkNu3b/Prv/7r5vnCCef4rbfe4vj4GNu2zSZ96dIlU+Vp9miRxmKS6W3ef/l7s4LRBI3NgAwwlIkm1ct1XTY2Nnjw4AGTyYSVlZVXhPiv07fkOcmzFMAm903+TSo/IoiVsfVNPMS5yXFOuiNPp1MeP37MRx99xD/+x/+Yhw8fcnR0xMLCgukC/dFHH5mKmNB6xEL2zp07WJbFo0ePuH79OnV9UsGezWaGGjIajUxG+L333uPx48f0ej2zBsVxzMcff8zR0ZER5I7HY86dO8f29rbphxDHMUmScHx8zLlz57hz5w6rq6vGLOK73/0uq6urxHFsmvgJV35hYYEXL16wu7vLf/kv/8U0jBOK1N7engHy58+f58yZM3S7XX70ox+Z8bK8vGxe30xE5HnOwsICvu+b/gsvXrzAtm3TL6OqTpzbpKqyvr5u3P2CIOD69et4nscvf/lLXNc1epBOp2O0BK1Wi7o+aYr4/vvv8+DBA9I0pdfrcfv2bSOEFi1Fs4Focz2WivF4PObmzZs4jsOzZ884d+6c6Z8CmKaDt2/fxrIsvv/97/Po0SNc1yXLMlqtFvP5nE6nY8BQURTcuXPHADChR0llS2tt3LXyPOf4+JiyLLl58yZra2uGzio9e773ve8ZCpRUnwaDAbPZjIWFBebzualMyHjL85zRaMTa2hpvvfUW0+mU7e1tlpaW2NnZYWVlxThQffzxx2bNE6re2toau7u7jEYjXNc1oLy5Vsznc+CEitjUDpVlaSiDS0tLALz//vvEcWyoe7Ie7u/vf605+/VXGi8jsWs81yWNI3zm6NkIx2uB3SLLSwoLElXjhgqdQalCatcnbEVQJFhBmzKZox3I0oRCO9hpje1HxOWcw+EuukyZZC52CoOlNmUak8cVh0czFpYsfM9jOBwTOg7j/Yzc7uFbJS1qotJhliR4tYUbtKnsAD2psDsDnLrEtgu6gYsXeOhSUekZvRWLN9+9zvhoh+O9EWeiZap0zgiNowpUWDLw2iT5jDv37mADmfYpK41XOxR2QlUr0rxC2zaWr3GzAseqcciZ1xbarVj0FeMsx7Fd4nSIi43OS3KVEPRaFPMSjUNZFviODVnJZJZQORb4NW4YsbzYJ0otytyl3baZFBkt7YBj4Xd8HG8BgLEeoyyf/rSL7btUnZTZwZRaWYRBh3k2I7IcinRGUY7xXB9XeydgwC1JU4XKbSo7wI8i8npKmsyx7Bw7sChmmsAJKTJFWRfYgQ1eRenYQE2ZZKiqRlsutltjORZ1DqV2qFyN9jKKpMDVNk+ebfEn/+t/8vf+t9/j5WROPpnjBB5pkJPPS2xdEdmaItfovMS2PHRVoByNrhTa8ol9m9rO8G2PlqWgBW5vgOtl6EmJF4TYqqKoLbxwhTQ5ZhIrOl6Nbse4Ts08q8ksG4cIPY5JXIWuUjzH5iAb4wNLi8tMd2LKKMBXLvPREKejaS8v4LQjitmYOvSpi5zAhqrO8cOAMPJwCZnlGUWSg6/xLYs6m2K1ApzIpbIUKk0g0/TsCl1mzBwPN9DkkwmJUxD1Q8I4Is++mZlIwHQjfV3YLEFYs9LxOrVKDgm2hN4ilo+SMbty5cor/HTZrIRnL98HX4l7sywz59PMmjeD2KYDFHxV8RB+sed5VFXFkydP+OlPf2qEm0JFgq/0Cb7v88knn7C9vW2C4ybIEAF1E2hIEClBo2TK5V6kacp4PDa6CDknabQn9CL5vzAMOXfuHBcvXmRzc5OFhQWyLGM2m5kgWAJy4UbLNYpAX1xepDIk2eU33niD/f19tra2+OKLL9jb2zObmZy/VIOkciCaBXnGwCt2no7jmM7dTbrV69nO5vlJwO04Do8ePeKjjz5iMBgYAKqUMnoeCQI/+eQTzp49aypi8qzLsjQBuoCNJpVGfppNBYUHLhlsGXNNSqAI4uU9a2tr+L7PeDxmbW3NjOEmZUte26y6yWfL0aRUva5Jkvv0TTyaz2I8HgMYAXJd1zx58sQAM+ka/+TJE65du8ZwOGRlZYXnz58bBx/p6/DRRx8Zio247Dx79ozBYMB3vvMdvvzySxYXF5nP51y6dIn9/X3TT2BlZYWVlRXW19d5/vw5SZJw+fJl4xq1vb3NxYsXSdMU13VZXl42YEPWHxECt1otut0uvu9z7949I8bd3d3F8zyjCXn27BkbGxvG3EHGc1VVHBwcmGz4hQsXePHiBefPn+fmzZtmTet0OiY5ABjRMcDm5iaj0ch0g15dXSUMQ7rdLrdu3SIIAvb29gwoaLfbnDlzhl/84hdsbm6yvLxs6GnS3E4cu0S/lWUZ3/ve9+h2uziOw6/8yq/wJ3/yJ9y9e9c8z+l0imWddP9+8eKFoV4KfQgw41kpxaNHj16xEv7iiy/MfKzrmp/85CevVPRkXlRVxR/8wR8wGo3wPI833niDzz//nMlkwqeffopt26a/RJ7nxgpbqj/Hx8dsbm4aI5D5fE4YhvzGb/yGAZYXLlzgk08+4caNG2ZdkTWkrmtDSxW3MOmTIja1165dYzwem0qb1pqNjQ0D3oQGK2uD7DkyBwaDAe+//z4//OEP+eijj4y+7smTJ5TlSUND6VFyeHhIr9czjU211ozHY9MhXMTj4nD1Nx1fG2jUrktdztCzPlUZU3gat9uh9mvSrKJMCjyvJLZsVBXilxWuzsn9mria45cBuoY4q3Fdj9JRFNMU23Op2zVVUnO0e0BVtYg8hzKfkyUJ8zQmVD7x0EIVQ9pRiLLbpG7FSzfFHscMLBdb2RSOiyJgubvGLClJrQprqYXyfAIVohwLWxdYZUpe1wR2j4VBjwuXr/C//vgPKbVGY1M7A1x1TDZN6fTaLHgh//OPf8ZseEwVKSLPQ/ktsmqGnRf4XoCnKxI0tF0836XSNtp3afkORVqS5jlVWhO1HMLaIctTMrvGcyomsxSXECutsO0aXWk81ybwQtqrLfylCKUd2rpHzgRnSRN6HaLIopuC569BUIHKcNOcqrVIkvs4iQcOWH6F3lDMJmPiYM7xPCd9NiePXCKtid0Ty1wmNrlT4vk+qoRC15TThMKp8S0L37awsNGhwyRTOMqjtHO0qinKmn6vSzGtKcsZruURLIaMD6aouk1e5ChHoV1NrSs8rbGVRUbG4yeP2Rlusbi8wmH+knk+Qeuanh2RzxKChYCqcIirjMhroWeKRT/AszJSP4SgRbu2CS0PKwS79LAjcGyXaQQqcvGGx9C2mMYZjg12XhP5HiklSo0J2y6uZcFxhfJ8ImtGMdJ43S7+hodTWngu2H5O4c7w7Qin41JVmikF9tERS+0BJB6zokL5Pp5yoGsziwscPUNHHkmtqesS3w4IdIXWc6woIFAuKTmW61JXLo6ysWpNXuUELR8nrqnnNtU8oXC+uX00pFGcgAgJ2l6nUTWDSAmmZSMVl6ImMAmCgMlkYixkJaiTDK7w9oXjD7yiD5AAvBnAy2skaynn2vx3EU82s/T379/nwYMHhnK1tLRkqFECHobDIb/85S9N4y/4KliVErWAKKFOCQdavlsy/5LtF0G2VBxko5HKgQT5cNL0bG1tjXPnzrGxsWFsCqUSIPQO2YgFGIq+Q6oqkjGX5yfZc7HtXFpa4vz582xtbfHzn//clP8B87pmVl8qGjIOxEZXrlmu5/+b+5SMLwnw5d7W9Ul33L/4i7/gd37nd17J9ruuy8WLF9na2kKpk/4JotWQ+y3gpUlXkvHSDPYlgJEAQsBjsyImvzerG8050O/3Dd1CKWXE202Bd7NaIoeA46ZIXe6jgFe5L82x/E07ZrMZDx8+ZGlpyfDXFxcX8X2f+XzOmTNnDPCWBo6nT582lBHLsgyAk8y2rC/iVLS5ucmVK1fY29szdDuxjhZgEEURcJIRFpvWJ0+e8Oabb1KWJZ9++qnRZFy9ehXHcQyQlrVhMpkY7YRQaqSCVlUVH3zwgclMX7hwgZ/97Gc8evSIlZUV7t+/bygy0j8kz3MzPqVi8fjxY+CEQiRrmWVZXLt2jS+//JJr165x//59U21uukrJnBiNRqbpnlRl9/f3iaKIjz/+mJWVFU6fPs3CwoKpwImeQCpIArJEAyBgQp6TWMWePXvWOCXt7e3x7/7dv2Nzc5PhcGgC9Hv37nH9+nXTt0Sy9NIhfTabcerUKR49emT2j+Y6IRVJWRelSahoFXZ3dw31cDQasbGxYa7h4cOHnDp1is3NTUMV6/V6PH/+nAcPHnDt2jW63S6PHz/m3LlzvHjxwuh/3njjDR4/fmwoR3Ecc3x8bMBDr9czdDkxKXjy5AmLi4s8fPiQS5cumYaUs9nsFart4eEhq6urxsHsnXfewXVdQ2c9Ojri+fPnvPPOO9y7d89cVxAEHB4esre3x2g0ot/vc/r0aWPzu7q6SpZlnDlzhk6nw4sXL3j27Bmrq6vs7u5+rTn79YFGGRPOKuwasGuUpUmrjDTPcR2bKFBUlY3v2Lga5nlJoWMK7VI5YFmaeVlSLoa4hU3L9vGDityrUO2SeTwlsB1it8BWc9LZBF1rbBTjKsFbblOWGfPpiNgtCfAZDDqMJ7vMHKgti1i51PhYlo2ufIKgx6w8pOdllGWLVi+kGy3jxFOSas5auMQbl68yz0qej/eolIVf51i+S2X72AR0aPPk7n12nu5RlA62hpwpqojxXYfaalHGOY5V44UBpa/JUgV1TaZLfNultmKmusKOApLDY+y6xg8jkqJAFQ6eZZGUCbWqISgICOj2epy+/jYtz6EoM8ZxyqLrQmuRpJ7geyFhF/I6A1VQdmxyFdLDY3pwTNayCNsaz3ewK4eWtgmAeXuAF85YPLvGcGfI7PiIo8mIYhRj6YqjEpSXU0YWdQVu6lBbBaUPLaeDKjW6TLAdF78qieyaTCsK7VHZULoJ5IpWJ2Q0zrEcm7KaQmkThYpC51RZgaUtdHnizKXGCff+/BYf/sr/GddqUyf72DrDCZfIdE0yz7GApCxYHTi4+sTZKdca17bxLJelukOd58yqEtutcfQElUeoyiM7muPaLaxpTmWVdLw2QasmK+bMIodKh7SUR5bPsXwbV41Z7Ng4KxHjScZ85pJPa9Iwo85srGGG1StJbI+FbhvlgKUDqnxK24kIl0MOjw6xvA7lTKNiC+WF1IVNpy4prJpCaVzbpRX4OGiKOQQqIrMt0nlC17MZhDZJWpDrjND1mWUxtuPQ9fyvO23/1h2vNywTjiy8mhWW4K3pONUU3UrgLZuaCAUvXbpkvMObAZlsNFLybTogNelEci4SeMjfm1QjCZCbNC4J6vb39/nss88YjUbG4tayTiwewzAETgLln//857x48eIVqotQfQRgtNttQ9WRAFHOu1kFEn63ZPqbGesmIJH7LZvpjRs3OHv2rHH6AozzlFBS5L1NsbkE1ZLdk+BWgqO6ro0eRbjAFy9eZDAY8ODBAx4+fGjupdx7uX55v1Sa4jim0+kYYCDPvilmFp50sxrUzPg3QeOjR4948eKF4SXLMz9z5gz9fp+joyPiOObzzz/n0qVLhGH4SvWjSdcSwCPgrPlM5DybLlivAwv5DAEeAiaEd37v3r1Xrk/u1ev9OV4Xnzfnl1BvmmJ6OQ+pRn3TDtu2OXPmjKHyjcdjNjY22N3dNXSmyWTC2toadV0bV6GHDx+ysrJigL7QVx48eMD169eBk2d26tQp9vb2WF5eNoBPaC3SvX53d5f5fM6FCxdMBtuyLC5cuGDm9I0bNzg8PCSKoldoc3Vdm74KT5484cyZM7z55psG+Mo5VVXFdDo17kXPnj0zXbZFu3Tq1Cnm87mxj5XPF02HVEn29/cZDAbYts2NGzeMO19VVdy5c4cLFy6YoFxE2nAyjhYXF9nb20MpZTpJZ1nGr/7qr6KUMg5bZVnSbrdN5/QoilBKmV4Y+/v7vPPOOyZA7vV6DIdDjo+POX/+PEVRsLy8zOrqqnHNunTpEr/xG7/BT37yExYWFgy9sSxLbt26ZdZvGd937twx67ucs8ypqqo4d+4c9+7dM05gS0tLPHv2DNs+aao4nU7Z2dkxa5jY9N68edNo8qQSO51OzXgTvUlRFPzsZz/j29/+ttEJivVuHMeEYcjm5qapyEkzRkkMSd8NcQKT9e/hw4dGb1HXNaPRiGvXrpl17+XLlwacTKdTLl26xGg0Mq5jIhR/9913DWBqJvRWV1eNYcDa2poRyG9sbBiRuiTJxLpZ9revc3xtoNEuNamVUtYZhbLxsoIwcKgsm6Koqb2KKo6pqw5JPsYKAupME4UasNFBRZTmpMMhdmeDfJjBX4pgF9w209GQyagi7yjqrMC1a5xBgE4UurIoSoVdhKTYoEt0DuNnM7yBR8KMTDt4VNhWRRaX2KVDHjtEPZ9kPCVvhczdEbXj4HhdLnbXWFpbpLe2zOHuDrqwqQKXLJxj2QmUGa4TYAUlnz69y6QeYymXcjJjeWWVcRxTVRptaywPtCoJIpjGGoI+XlHgaUUyneL5IWWpqRKF5bpoXTBNUwK3ptIeylZELZckzvBbfS6fusqVzUsUdQk6Z1anrPktSq+mo2sGQZeZa2ElJXbLwQk8KBS2H1BXMe3OgFIXEEJNia49MjvFinqotCSIHJJKs96LSBdKBkcdJotzDrMD1L6isib4OkTlBcrJsHybUkExzSjyCtVS6CzDqSxs5VDYFoGlyZMYFdgUpU2iC2pb43U7xNMYP7RYGNjs7abkeY0dtlBJQVmUJEXF7S/vcurqdRYWuxxpHyv3qKYuQbRKkU/w2xXr3RXmZYTb6ZDNDnDbPSpssrTiuD4icHu49oCqmuPhoT2LRc8jsFyKeYVyPVoBOHTJshl236caz050OE5N1ymg46OLDlWuifOcyVHC0uo6c53jTHMcxyPTsNLqEemMwMpJ5zn9ICLXHomuqNIa22+ThRYLTpcg8FD2CeWrg01Z+kxUicYmnmpqrZnpMVEUoucWWWBTKIdWrcishHnlUe0n2FFNlYNKvpkBAmCy37IoS7CW5/krVBIJ3polbgmgmwJvCdAODw/J85wLFy4YobcEVbKYNkW0TWF0MyvcpJvASeZXzlU+TzZ0eV0zY/3ZZ5+xvb1tvrPpdAQnQdLe3h6fffaZsW2UjVHAkQjKhfLU1C4I7UrOVX5eB2ZNJyPJYAZBQLfb5dKlS1y7do1Op2MyqU3QIkFr035VbHpFFOh5nrF+7ff7DAaDVyhqTYtQqYK0Wi0+/PBDrl69yi9/+UsODg7MfZWsZl3XRgwp4EfORe6fVFbEnrNJhZPgRvQgTZG28KA/+eQT/sE/+AeGayzaBcmahmHI7u4uw+GQxcXFV7L/zSpa02SgWaEQcCNjSUCuAAKpjsn4kmffBEnr6+s8fvzYCLmbQAww4/t15yj5DqlmCS1PAJJcQxPAf9MOrTVRFHH69GnyPGdlZQWAwWDAdDo1vQcODw8NBQ4wzc1E1Cw8ejFCePnyJWtra3z22Wf83u/9HsPhENd1OTw85C/+4i/4+OOPybKMTqdjAnkJ5iXwl3Enc1hAxmQyIY5jFhYWSJKE3d1d1tbWzDyUNaXZCE4a9UVRRFmWfPbZZ8YKVqoUWmvef/99fvrTn5rgUdbXpaUl4xAnCY9Wq0VVVfz0pz81gO3Bgwe8fPmSbrdraF7wVXXz1q1bxt1ubW2NOI55//33+fa3v01d16ax4GQyMfSmJlgWXcnp06eNs1Wr1TI9bL71rW8Z29eqqoxzoDTk63a7fP/73zeAQNb6wWDA2tqaEdRL9Vjsh8+fP8+zZ8+MmH4+n3Pv3j2q6qvmdJJY+da3vmVMIvr9Pjs7OywvLxvq6a1bt0ywvbKyYoTlOzs7bG5usra2xtOnT41F7c7OjrG0jaKIv//3/76hte7t7XH+/Hkzfk6dOmWeuzR8PDo6otvt8uzZM06dOkUYhqYn0/r6urEUl8qY9Mj41V/9VRYWFrAsi3a7jeOc9IyR7uKnTp0y1Kz9/X3W19cN8JbKqQCo3d1dYx8tlDAZ161Wi+fPn/Pmm29+rTn7tYHGNFdkWhO5FbFnUaUlfqHRQNjJKFKb2vXRqsYvbLI6Jmgv48Y1iV9SWC5WaePYHlk+xem3QUe4cYFfW3x58ASiBM/R5GGbvt9nNj0ksQuisEu5lRG2PdLAIy9s6l4BlUWpa3Tp4LsLtF2bPJ9QooizFF9PaLsRtmVjFTMqXTFOpmwsO3S6EUuLK9g1vNh9SWlbVHNwnRaVk+H5fVRucTjJOJpMKaqc0LXJao9hMiF0bFLHoc41tdfCq1zSYUw7aKPTChefvMip7QycEFWdBNXKb5NlJZ7nYOkCKp8sH2MpWIp6vHnxQzY3Nwgcm+FwwlGcohwfP9A4nRZ26GAXFr1+wOz4CD/sUBU2HglV4ZJ7Be78mE7eo/Y6JHWJ17WZ1zmtqKKYxoRliIpKHF3gWV2CXoh/vg2zbVTwktlkB5VqKq2ZzFI6rk2d26SFjacKfA9mQJxluK7C0Ra60hSTDNux8aIuYRihVEyWjHHqk03y6EVON+jiVQnK9YiLCqUVtfaIk4JnTz7n+ub3yPct0jCgrDW6nBK6FqHXo31qjTv3XnDWH5AudLFClwyHMnLpnuBZyjLG1Q7agiwbUtYWyoHCLnF1gM5hVieEnZB8XtKij+1bRC0LPZmhSwvsNkWt6IYO2D6z2RGWW1GrHoHfZRIf/2Vneo2yoChqpsyxIpfCSphNUlzPxas93FShdImKU7wgYh545BXU3py4zLEshVY1rtJY5YS0DqhcRaxsLMujKkI6jo1yS2zbIQ8cVDH9utP2b93R1DVIRryZZZUMrwTXAj6kz4KAkyY4sCzLNHg6c+aMCQBfbwLXzPQ3g/dmFr1ZPWlmjZVSRl/SzK5Lxl849ZKtdxyH06dPG759s9HX559/bjJcTScgwPRsEFvNpmi9eW5NACYUAOGli5OMABDAbMrf//73efPNNzk6OjKOMU2gJNf18uVLnjx5wpMnTzg8POTw8NAEZU2NjG3btFotFhYW2NjY4PLly1y4cMEENnVdE8exOfc8z1ldXeXv/t2/y+eff86TJ08YDofmHJpVDqF5NAX8EjCLMFqAqzyXZhWqKaQVAJvnOU+ePOH4+NiIIOU733jjDZ4/f24E7Nvb20bcKZ/TpExJxUK0J3KeTVApAvU8z1+h8cl3CoiV5yo6ol6vRxiGxnWnSX+Sz5AmlXJvXxfLNyldTVoXfEVP/CYe3W7X2IfmeW5sWkUM++zZM5NNFlAyn88ZDoevBFyu69Ltdrl48aIJcNfW1rh69arRI21vbxuNwL1794xV7s2bN1leXjbj4PDwkIWFBQMKm0H9fD43AuEkSXjw4IFpCCdi4suXLxvKVV3XZmyK6Hhra8vMl2aV8uXLl8a9StaIN9980wTGZ86c4ebNmwbIPH36lKdPnxpXqKOjI1ZWVvjBD37Av/k3/8Y0F71+/TqPHj0ya2i73WZlZYVWq8Xv/u7vcuHCBdPZWux2Pc9jZWXFrBVPnz416/wHH3zAwcGBGe+SLT8+PubFixc4jsPx8TGz2YyLFy8abUKSJEb0XpYlDx484OOPP+aHP/whk8mEixcv8tZbb7G1tUVRFMbStaoqBoMBW1tbphO5rC9VVbGysmLuvW3b3Lt3z1TGpUr78OFDs65XVcWf/umf0m63abVarK6ucuvWrVcqBBcuXDAgybZto/uybdsIxqUaIOutUF3H4zG7u7u8+eabhGHI/fv3uXr1Kpubm8BJck2SPOLiJZS/7e1tHMfhrbfe4ubNm7z//vtmrZM99ezZs4ZGJdSs69ev8/LlS6NPFF3SdDo1IE/uo+gNZXyK4cFfp6H8646vDTSc0KawArTKGRCi7Q62mlJkGr9qUdYzlHIp3QDXA9trkZUFuiwIPJtqXlMXQOUyKSx8e07X1vgtHy9oMZuMycop6VRRehOKzKJthUS1RzYpIHKwQxuVDVFuQDwqKZUiGrTwkoK6yDiipKxzbKvE8UGnE3SmiS2NTUpaubTqgkwd07tyiY4XEc+m1J7CTVy6BJR+iyTOsKwC2vDZH/6YLI+xqCjrBN8uqauA0u6SZBOC0kNryKwcp6ioqpi65ZNnJZ5j4dhQZgV2lmNRUeUplu/h1BorTnGClElZs3r1Er/93q9ydnCWgzrmxdOnVNWcIHSoWh2UTqmSguO8otPqEh+NiRyLJMuIei7BxGFSz2n54Ad9gmKB3HaYplOiwiO2WqR1gu1pVOoRKgsdWPg4VJVFoXMuLC0z8EOORwOOnu+g3QzH8jlOjqiDEsoCx7ZwCwer1GR2jR9auKlN4FlM0oI88ammGdVCRaltHLpYLU2ZpdiuTZYVuC0bi5rK97DzmkJDkaY8uv+YZ1eus7a0ypPjl0BNEAYUZUaJy8HeS1YXWtRuQbflkiUuaZZQzibEiYO3YOHaFtr1SBMI8bFVDbamzHyKtMBywFIJUbtHnSqSGtIigRiY5LS1T9HTuB0PChuKksxyGbSXsKoaVZd07DazYQmhi4VF6FvMqFHKxok7BJaD43g4uCe6pDLDd0IoanzXIs9L1BiywCF0HIKiotY1sevidxxIEqrSZZQNUW0Hx3fQ1owocaj7IeXCNzMTCV9x5yXrDJgADb7qLdF00ZFFUwK6ZnAlAdhwOGRpacm4O8l7ZRGVILrJ729Stl4X0cr3yKYjnynaCfF3b1Y4nj59asrurusaGpfoQ1zXZTabcfPmTebzuXkdYChSkkFrtVqGMvXXUYukgR18RaeSeyeASWhl58+f5/Lly/zKr/wKZ86cMRuQZFwFKBVFwc7OjukD0NRnrKysvEKHkvskwX8QBLx8+ZK9vT1u3brFmTNnuHz5MufPn8fzPKMbkSB8MBjw3e9+l8FgwM2bNw34FMAgYE2qMc0gATD3XHqIyDOzLMvwwqVKJmNEMr1xHHP79m3TEAtOgvy1tTXOnj3Ls2fPSJKEp0+f8v777xsetlRHpMojQEOoTBLYNoN5odhJtvR1m9nXQa+MFd/3WV9fNxQWcViScdn8bvn/pji9yVtvUsfk3jVB5TftkAro48ePOXv2rKG/RFHEkydPjEBVa83u7i7nz58374vjmH6/b4St0tFbAtXRaGTG8vLyMgsLC8xmM54/f873v/99sxZdv36dhYUFQ8ESStX9+/dZXFw0QuHz589zcHDA4uIik8nEUPLeeOMNnj17ZuiJkplvapJarZb5t3v37rG/v88PfvADxuMxd+7cYTAY8OTJE5aXlzlz5gx7e3s8fPiQW7duGc2WzLuXL18yGAzo9/scHx+zuLjI9va2AT9//Md//AqN8YsvvuCtt97i7t27Zq356KOPePfdd43phCQCJFkk6+SNGzfodrvs7u6aSkccx+beiqPV8vIyvV7PrNPD4ZD19XV2dnZot9vMZjPu3bvHmTNnTJXHtm1evHhBt9slSRLu3r3Ll19+acD3jRs3uHXrlsnAV1VlKkKXLl0y3buFwgsn7m+bm5tMp1OjQZD1R+bP22+/za1btxiNRqYBYRRF7OzscO7cOaqqYjwem8qY7GGnTp3iT/7kT7h27ZoBWM25fOnSJUPtPX36tOmx4XkeT58+5fz587TbbS5fvkxd10bg3+v1DMAMgoCzZ88SRZEZx1LVF2pVmqZGbyT3Rqpxi4uLjMdjjo+P6fV6xm1vbW2Nfr+P67omSXJ8fGz+vru7y7vvvvu15uzXBhpaa/I4o7XgYeU5aJ8KCz+ymQ4rcDI6A58iLompsUtNoG2cTkKmK3RWEFk9SnJUO8SqNMwqoo5DNUmYjhNIBniWhavn1HbBzE4pa4citfBamrhIKCqLaDCgSkek2ZwsK7AIqQoX14MaRVV6YGV0PJe0KigS8EIHr2phVxnuahfltCksxdRNmOXHtDybvOsxmedE3Q7jyTHDvZccHj2jrEq8wKLKNMp3qUoLVcZ4rvOX2dYpVpISuT6JVji6wMZGKwWlRa5S7Mglcl3yPKbwQ8rCwXM86krx1tU3+J1/9I/ZXFhg8mKP44d7FEoTtFsE3T7jZIpXBZTaJ/J8PC/H1TaO9nHTAW7aptuDdrdAVRpXhTh2G2UPWS9bpDVoy2XuQznMGL2cU1Q1jh/gWYrAqykCj7p22Fjtcf38JZ4sLPD40T307phO1WGiY7ptjyzPyNKSrm+RWBaVdqirkhkeWeTjZhZurcnSjDSwcS0XVZXYuY1yawpydGXjBz2KaUwZ1PhuSVVoDkYxdz+7z/Lv/oByts2gOyDPS/KqJk9OMoD9NYvnz7a4eOoiZZmRkaGUjxX5BJ6Ncl0Kp6SsSyq3RZbM6VgWlpNTUdLutKjsiBSFpWPSuUVnqUNdprgLAbYTkFQlx/v7tN0W3c4mSezgeQ7zdIzn+Ki0oB1UzOop8biN0/EItIOVOThRTW0pCjSlH+FbLqF2qFywNOggJwDqVHGYF0RLHpXWlKrGyj2cUlPmmtyqqFohbd+BMidYGWDHCm1pZln6N87Xv62HAAAJjIS/KwuwBD+y+UjwLAF/k/YhgWVdnzRhkoVaglbpEyGZGKE0yNEMxCTIazpeyebVFA6KbkCCRQnm8zzn1q1bJ31p/pLHeuHCBbNh1XVNGIY8evTIdFht8oulGiE8YBGBj8djs2HIhi7BMXyl7WjqV5q6hNOnT3P58mU+/vhjzp49C/BKZUa+88WLF/yv//W/TBZTKhIS6EsQ2wxcmw5ITbF5VVU8ffqU3d1ddnZ2+Na3vkWr1TLBTzMj++677xqx6euUIwnQm7Q6Of88z42WJYqiV+h38n6xYhRA26wcPXr0iG9961sEQWCuQ7oa7+zsEIYhw+GQw8ND01OhKXZ/vfIigEE+S/QfTW2GgCG5tub4k+uU83Rdl6WlJQNImyLW18XdQpVqAnOZF0JHa94/yYrK933TDtEQiP2rUooLFy6QZRmj0YirV69ydHRkNANCy+v1erx48YJOp0O32zUZcFlbLl++jFKKyWRiOPsy1958801GoxGDwcBUtNrttsmKyzpx4cIFAy7b7TZPnz7l7NmzPHz4ENu2OXXqFH/2Z3/G5uYmH330ES9evOD69etYlmWaCYoBgATuT58+5fPPPydNU/7H//gfwMn4GQ6HOI5jBOUi0J3P56yvr9Pr9bhz5w5hGFKWJbPZzDQOTJKE3/u93+PevXs8f/6cTqdjrEplzMp7L1y4wD/5J/+EVquFUorZbGb0ZjLfRMfV6XT4/PPPUUpx//59hsMhoufq9Xo4jmOqRIAJmNfX11lcXOT58+dsbm6apMDy8rKho8m69e6777KwsMBPf/pT0+jw8uXL3L17l88++8zMhYODAwD+2T/7Z/zbf/tvTcNAz/MYDocmYXPt2jU++OADbt26xXQ6ZXNz0+hVpGJ779490wvl6tWrdLtdut0uOzs7pGnK7//+7/M7v/M7WJZlQOzx8THr6+vGnEAAlhgYCOAXGqgAIjgRid+4ccNQtHzfNw5c0qcJMFRVqXBeuXKF4XBIEAQsLy+bqsPh4aHpnfTGG2+wurrK559/bvZGcUGr65rFxUVevHhhLHRnsxlnz541ds+yx/V6Pba2tr7WnP369rZlRlAu4M5qsDMqCo7iY9pexMCzSdyISeHiZRUWNbmt0HZIVszRtkPpQsgchYWtCqgL0rRkaWWF0fiAWntUlkXuZ+ReTZBrytlfig67UCjAb6Mcn+loRlb4hK0OTp6gYvA7itKzIK6wbI1TB8zzCseDoBPgFpr5bEK42GFWlhRVSpmlPLj3kOx4Cun0pIX7LKfl1owmOS+f7jM/zqhtm7yyCfHJYwvHn6FaOfk8IC0VdqBwOgPi4fykyhJAXWYkFuSVh6dd6lmO7vj4OExGKZ7bwrV9Lp1f5Xf+/j/gyuYlHj57yuePHqKSktCKcbstaqegFZTYmaKVOlRoVhY7eFGPItMUU49K+3T6IVa7ZD4pIa6BGtdStDsezGNQA/xc0V5bh8GEF8cjsjrBcSvcvEDHEzynS9TrUtk2q4uncCvFM30Pd79ETx1GxRTfa5NXc2rlUVgl1TAhz0NSQPk1wUKB44cnDl55Qivy6bR7HNoxZZyjKg3KInccVjeXGefHKKtkejSkRjF+ucXh9i4LVp/cthhmGU4S0G93ccsJIRldx6HKIA0UlmphKYcqTckTjVdFuHjYviLseIySlCRVuC2PfhCS6ILcsdBHJxnpxaCHp3yGRYJyfYbzgt7Aw+n2qIoU18tI9mYc1T6hcrAGFlVQc7R/RHi+S1xWeElNmFfYKwVpVWLrAb2+T5HPiFrLRLZi6uXEdYUq5gTax/JrFisbpyjRhFiOi5WXUMf4PtRBjZW4MFfU3YJ5XaMSjWW7WN9M+3vg1YZ9EijJ70J/gK8ausn/SdAmG5QEwUop0z/j7bffNlQWyeY1+zI0QY6ci1Q1JCiToxlkAybIlMBQ/k8qJPP53HCbLctic3OTMAzN9cjrZQOTwFWoQZI1F+pUu902mzq8aoPYFCZLwPh6l2yAxcVFzpw5w9tvv23cSCRbJSCjrms++eQTfv7zn6OUMg5ZorGQoLYJ4OQcmm5bAtIk8Oh2u9i2zcuXL/nRj37E22+/zalTp0zgJ+LZIAi4fPkyT58+5fj42LhGSRa1SS9q9pGQrKCAMHkWTUodYGho8n1Cr5rP5xwfH3Pp0iUDdqvqxG1ocXGR2WyGZVkcHBwY+oqM16bIXIIyGRcyTmT8Nu+hjOcmOJRreN1tS7jQcq7SJVzGQhOQyfvlO2QMNKtETXMDoSRKsPJNOyRYlPvaFAMvLi6azs1yVFVlaH+9Xs84H9n2SR8AEcVLZUycn372s5/x3nvvkee54evLc7Ntmzt37tBut5nP5zx//pyFhQXOnDljKpOiF/ryyy9J0xTP81hdXeXatWtG5Hvp0iWeP3/OYDDAsiyOj49Nv4nJZEJVVTx+/NhQfK5evcpf/MVfmAZuWmueP39uKpIvX740QSecJGx+4zd+gx//+MfGcU8a8f37f//vDf1OBOaAme9vvPEGnU6Ht956i9lsxrlz50xCJ89z0/BuPp9zeHjIZ599ZjQlaZpy+vRpk1BaXl4mCAKjOxJ9VZIkhvL6ySefEAQBBwcHnDlzhoWFBVP5Pjo6YmNjg6tXrzKfz7l8+TKO43Dv3j0eP35semPASbU0DEPm8zmTyYR/8S/+hWnoKHqcNE2NPuP+/fuGJiUGHPBVIqXb7RKGIXfv3uXJkyc8f/6cXq/H5cuXTRf1v/f3/h4LCwvs7+8bu+TmXmRZljEYKIqCwWDA0dGRodJtbGwQRREPHjwwHdib2pKHDx9y/vx5414oDotxHLOxsWGuJQxDPv/8c77//e8bClSn03lFp/Hs2TPqujZNV+fzualwA0Z3J/vEo0ePyLKMU6dOASduVxsbG0wmk1cSd/+/jq/fGdwr0L5mNs2IbJvKt+k6LYq5TRkqStfBUw6RW5LXoG1NZSVo24G8wrUUcwVJURBYEXlRYVkOsVNwVMyoijlxUaO0hSbHKS1CZ8AknuN7mjKdUZUWvq9xqhPvdb8dEqqIWZUQlxPiGrTjEVgKVWe4yiOZpxRVQeS6tJYGJOqIXsvHrWpe7uzx5NE9RqOXFHGNDno4rkM9mdD3Mrae3sRRFr4ChYtdlwStFoXtU9s2yge/gDIJydsVhVfhAfOspNQuLhpHpbi6xvVccg2uExL5Fnk5Q/X7/KP/yz/lV77/MZ/cvsfte9vEMx9le6hqjj1PcawaVduUdpelDRfH9jnMJjhOwZLjUPopYWCByvBKn8p3mM5iag8sp8WRsrDtE7enVuARZ8csWi6XVtZJ3YqkLkl2R/R6Ab1em6SaEbc1OrE4s3gFrBN3pDx9jqVsSjKKyKNutenpEsoReVlhqTm15TCbp/iqpNA23aBNjUU5TvGiFEc7xLoisVOq5AA76dHSNpMqOcnYqprtozFbT/b5zo0bbB28wLYsglChVYXyQkbHKX6nh9tu07crXr4cQa2pixplgcor6r4ijFrExZh+xyWtQ3Rhk7kOoRcRtSoCdUyqaxI1J8wdWp0OynFwJxXzdIrnO9ROyKQq6Lc7lFWBVh5eoej0PMokwSldlvsdpsczOosttCqw3QBUF50kBHXJMD+i9kK6yiapc/woIi18LCunTjNUkVE7PsXcIuzAuHYIHZd2EFDaCnemmacOlufg+Q55pfD0NzNAAIwdbFODIcG4bHSyIUgloSn2bbr7yGIuLierq6vAVxlvCb7hKxH6eDw2ouVmZljOR+1SJ+oAAQAASURBVII4ySBLECnnKYG9VEAk8/jkyRMODg4MWDh//vxfEZbP53PDU5ZN0bZt4/ok2XU5t9evV6o+8iNAoSkSF5ckES1euHDB6FaSJDFldRHM/vCHP2Rra4tWq2UoTk3wB1/1gJBs2us6AHkOAuSamhHh+or4+/333zdgQ0ToWmveffddw+1u9r8QgCZZexGCS0DfBI69Xs9oaZpCaRGtC8iSQHVnZ4fr168bwCfZPKFP1fWJO5AE/nIeTepd83c5LxkfwCte/6IzkvspgQh8BRaE1taszkgwJsCmSQ1sVuFECyL3v6nPaLqayd9lbnwTD+lD8/777xvQa1kWnU7H3CuAp0+fsra2xq1bt4xtp1QYO52OyS7P53NarRaj0YjDw0PgpBp48+ZNVlZW8DzvFVAp/SJ6vR69Xo8333zT2OAK8Ol2u4zHY95880329/cNeJTvkx4cVVUZPc7m5iZpmjKZTIyN6pMnT7h48SKffvqpoeBJpeEXv/iFCeTH4zFJkpjqmVA9Dw8Pabfbht53//59QwGDkzG6tLTE/v4+lmVx/vx5ZrMZ7Xabb33rW1y+fNlY7DYTQLZts7u7y+///u8bWs0PfvADQ4W8e/cub7/9Ni9evDAN+GS+yviWeyomFfP5nC+//JK7d+9y7tw5cz+EningptPpMJvNuHLlCv/xP/5H9vb2TAVhd3fX2NHKd+7v77O4uMjBwQEvX74040jW4hs3bnDv3j1s2zbJDqmi2LZtqlgAP/nJT1hZWeHs2bNmPtq2zePHj81rhVp7eHiI1tr0BlldXaWua6NL8TyPpaUlHj9+zHQ65dSpU8Zw4Msvv2R9fR3Lsrhx4wZHR0fGper8+fM8fPiQ8XhMFEX0+30DTn/zN3/TjHexwnVd1/SZGo1GvPnmm6yurrK9vW1A8dHRkUnsPHr0yDihfec73zHATcTtL168YG9vzxgt/E3H1yZ7136bqa2pwwBnYFN74Do+TugS+0vo1MGpcyazEgcfldcUSYlrt0gyReE6ZK6HH3XAKvHCNpEfEZUW5XHJNEuw3JzIChioPtN5Rjyf4toW89zGirr0vJquVuR1QtAuKOs5Izdl7o8hsvGwaU+ABCpPQWihcpui8MiDBdLawbY96lHAoLPMs50tjp/Pmc5cStslLjPiZMJxPOfO7iOG8TF+CLqsyHVGGhak9TFZVqCHGi+BtuNBkVIfZrTciHpe4FcaS9tEtFnwHFStOFEnp2R1RqVjOisd/tH/8X/l1//Bb3E8Sbhz6xFO5tHthlju9KSBnnIJOn+Zje8sYuGRFxlV7tP21mgXPj3PplIVC6GPPS+opy6+a+M6LvEkI58qMt3Cd0ryJCboROj+Al6vhb/QYW11hQuXLhAttbDaim6/i6c6hJ0+QdtlY2WRc2+8TbR+CmcxRHk2vahFUCkmk5RMh5SOT7To0fEhLDyU8ggtn2ycMU9TEm2Rp5oChbfWI6rbLKoQTUbYcVlc7tFrn0ZVLbJCsfXsKdMyxbVd1jrruEt9DlsV4ygg9Nr4lst0OsYvEgI1YtCBSpVoP6JwO0ReRJ7kkGmyWhMGHm7LQ1UuTqE4PiyZTSICFdDyFZmfMi+nHJYlB2VOb3kFP3ew5jVqWtBRmiWnwLVyjsdj8qIi6ZfUuKhCY1s5eVpxPKyp84w21YnuxLJQlUPpe6SVpqygyAKSCsqkxC09PNWFKqayJqS1xrMWSUvNeJozt2sKL0PZFbXOwbaJdEmgv7klDalkNEWtEuxLoC9OSEJ5aFKCAJPZlsBJxH5BEJj3StDXLE/LIire7RJU53n+SgYaMEGfdNluir+lsZsAmizLuHPnjgn8RCzYdF5yHIetrS3TR0IAiVCmmtamkpGUAFMAj7h9tFotA0okqBGnF6kSnD9/nitXrvDOO++YyoiIssMw5ObNm/zrf/2vTddicYqSYBwwm3sTaEiWTwBAkz4lmXSllOlfINnRhYUFdnd3+fTTTw0IEfrHbDYjCAI+/vhjEyDIa+RHMthSuRCqk5ybPB+hnonuRN7bbrfp9/v0ej1z/w4ODgytqkllOn36tNGujMfjv1JVEsAgz0UAslDbmnoLAYFCP2v+f1PU3xSrSkVEmvQ1aVXNe90Uvkt1RH6a3yHPqemU1gRI37RD1oz33nsPONE3yZx+9uwZSp00o9Na88YbbxAEARsbGywtLZkx1KzwwFdg9JNPPmEymeC6LpPJxOgIxCJXmstlWWY+z3Vder0ee3t7Zm2ToFhA5/r6uqnyra+vm6rAv/t3/w7ABLQCAAQkPH36lM8++4w/+7M/4/j42IxlaZzXTLZsbm6asSF9bAaDAbdv30ZrzYULF6iqilOnTpkEyY0bN7h69SqXLl0yIP7o6MjYyi4uLhrxsYwl27Z5+vQph4eH/MEf/AGHh4dGC3J0dMTR0RGnT5+m1Wq94iYnlcF//s//uakaiFj+Rz/6EWma8vjxY3zf57d/+7fZ3t7mk08+YTQaGRF1mqZsbGwQxzHLy8ucPn2aX/u1X+P69etcvHjRBPLb29vs7OyYfUJ6XtR1zVtvvWWC59XVVdNvAzC2v0KTleTKdDrFdV3eeustc59GoxEvX76kKApDC+v3+5w6dQqlFOPxmO3tbd555x0+/PBDrl27RlmWxrpc5rrrupw/f548z815+L7Pt771Lfr9vtG5pGnKxYsXzTyWxo6PHz9mNBoxGo1Mdf3q1ausra2ZfXFxcdFYNAvlTpJLWmvDCtjb2+PMmTNcu3btFapyc72Q38+fP8/GxsbXmrNfG2gEqs3ACfAtB5XWeIWmUjXKypiXMyLLw9I1tWeRD2dEaYnKLZJ0Si9waBU5nSwh0CNCPUdlY5Trk2ExOZ5ThD5RtwXuGFRGrdqUVkFWj7AjUFVAnnjM5oqW26Xd7VHaFXU2wannhLpAZ3N0CJmn8GqLusrw+yFRp0LrmpKCsojpDDrkKmP7yTPsZE4xjpmNp9jjI+qZQ5bbvNya0PbOE0QrOEEL8pJiXhAUCjVLqYsM7Voc2TGFVWOXDlZq4Qc2Hg62rslVirY9bNuFECw7xMsVbuTza9/+Hv+Pv/u/Mz8Y8eP//t8ox2NqnVDNx3SrCrey0ZWNU/Zoa49s9JI0n5IVKStRi6jSJK5H0I2I2iHDcU5mWWgnw2uFBM4CkbJoKYfSjSlVTWjluFaJCi2OqJkql9SzUL2IjXOrLJ3eILYVUauLF/ikVorbdml1YPPyMv2FiHanzWw65ejokHbfo6pyLFUTTyvS2sHSOU6ZYOVTAjcjLGGSFNTKpe16REd9lqIBgZsT6IK6KlC2jeV6WH5Eqmv2jnZ4vL9L1Q2ZTytISsKywkrjE5DW7xIuhSivTb8zoOtadHoBdS8g92MCx8H3WrR6HYKow3wWU2VDQntGHab0eh5WL+DFaMRwNqXWCa0gpOdWaA3H+3Nsx6MTLlOmFsODlL2RZjrRJFVJrk8smMs0o8om+F2fUs2o7ATXtdHVlCqOCVTNoMqZjSZMqgDV6TEvU8IKIsDTinkwoe4WBI5DS9e42YwQmwhFVNhYtkuk2+jEIy8KhnlOFfS/7rT9W3e8Xj1odsWW7GszAGiCkmYgK4GkZVlMp9NXMscSdDUXx2b33ddBRDOoF+AgwEMW26b+QER5EnAeHR3x+PFjc/5SgpYAseli8nofA/mRKgt8RdN6HajIpiHX2qTrwFe9E5aXl7lw4QLvvfceVVUxGo1MpcC2bY6Ojvjv//2/k6YpnU7nle7cApbkmptUN/ldgKGc1+sVGHmuwgmWKla73WZnZ4c//dM/NYJqqQAkSUK/3+ejjz4ygsWmJaycA2BAiGR2hUs/mUwM5UnE9M37J/0o5HPn8zlbW1sm6MyyzNANmjqGFy9e/JXxKuclQKNZdWtqMWT8CpCU5ybvFeAk46A5BuTndRqhjOfXtUpS8WrSy17XkTQrUN/kioYE4lVVmcrY7u4uZ8+eZW9vj2fPnvHo0SNevnyJZVkGZE6n01foPUmS0G63CYKA2WzG9773PdM4bjKZGO58p9PhwYMHpmJx48YN4KtKlNCyJFAUHYnQ+WQcVFVFp9Ph4sWLfPbZZ/ydv/N3DJiJ49jYnNq2zfLyMk+fPjXahCtXrvCzn/2M0WjE3bt3TSd06Yvz6NEjAzplDZTEQZIkHB4eMp1OOT4+NrS5Tz/9lLqu+fzzz012/caNG3zve99jeXmZs2fPsr+/b6xzwzAkTVO2trb4r//1v+I4Djdu3DDr5ueff05ZlgYERVHE8fExw+GQ7e1tNjc3+Zf/8l+yv7/PjRs3WFlZMYH7rVu3zLq8s7PDlStXgBMQtr29bZoKHhwccHR0ZP691+vx1ltv8cYbb5is/OuV7yAIjMbh9u3bpn+JrOm/9mu/Rq/X49KlSwRBwNtvv22A39OnT5nNZpw5c4a7d+9yeHjI1tYWs9mM4XBodHftdttUubvdLpPJhO3tbba3t42bnGVZbG1tmQrXbDYzrlrLy8vYtm1oszs7O6avhgiwHz58aGzHwzBkNpsZICw2tMfHx0wmE7OGievV8vIyL1++ZDgc8sUXXxCGoem98fTpU5RSbG5usrOzw+HhIaPRiIWFBdNjY21tzdCsZP583T4aXxtoFEmOnyvyvGJWuJR1hZva+PTx3YJEDZklc8LQo7O8AG4LXUyhqqniEtvpYtlt5lNFoUNsrwW2R+ZrJq05bacNMxu/ADUvCD2bCk0YLqBcD1XMKfM5tZOTFEPmeUm3XKCbtOhYAV6uCWrQeUzHCXDyAKs4WZjn84Ism1BMJvjOAK/b5+BozLwak4YpytFoT5OWHmU8Jd0/Yr4/x6s8ugRUaYZl1dhewCTJsRwfHBulcqjBsn2s0GHiQeF5JLHGdhQFNXHl4rVDHEApqFCsrJzi//h//r9YWujxv/78D3lx8AK7SnCqkrDr0Ot08KKQwUab2rV4dnRMMFB0vYB+26JjZxTzCXbpUpQ2VVFAcCJwVpVHOs/J1Ry7VaC8lGWvi63aFG4Lz27j1hZrgwWWO22iMGCYzZgMc5QKOd1bhyplNj4gSYYcJ3MS16LVX+D06mXCsEOrdeKMM5vkhFaAKl2qClId47QiChxyz6Z0HUpV49YlwTSjzGaUdcG8THG6EYEbUVke2bzATWsG7S79XovMKdkf72PbOW4nw9UWxJrACii8ikxVFHFG7FbM6oKsBs9y6HkWXStgmsQMZ0fEwxnpeI7vQcsKmMcxxwdDJsdjXO+Y/pqD3w5Jp9XJa7OEMIQ4yZnMKtI0p1IF0YpLSITyPGIHugOXMLdQbkYRJFiqxOtbLK5EVKXHpIjJyxSlfXINHSuglcUETky3p1C6QrkWWd8is1vMExvlKSzlom0L32/jBT59KyNQGegKy1IEUQuiEPLZ196Q/7YdTdqJbLwSjDeBRrMkLTSjZkZYQIEEs9KRVgJX4dJLozOpbEgQLdn2ZqY+yzJDu5GgUUCCnPNfF5BL11UBJYPBwATa8/mcPM+ZTqdsbW0ZG8pm4AdfuWcJ/aVJTxIdx+uVC+CvOBY5jsPZs2c5ffq0aaKVJInRqRRFwX/+z/+Z0WiE53mmstOsFEhwK5lIoTBJgN6kBjWz5UqpVxoHyvVIAFWWpXFT+eSTT8z9kiClKAquXr3K+fPnTRDZDNibVCFxQmlWeeTZC+VOKg1Ndybhz8s1ShAhHOckSQwFRyyJhT71Onh4HVi8HtQ3qwlN/vvrQEMoFM1+HfJeGUvyexPwNClC8oyaz0pASDMrKT///3BIECZVI3HAuXjxoqGSiOPU5cuXefnyJe12m+985zsG8AsIv3v3LrZtMxgMjIhb5uBnn31mqFISuPf7faPhkXFsWZaZT9JYTSp/ImiWRmxiGDEcDvnJT37C1taWmcdimytGFq7r8uGHH3L//n3efvvtV6qrvu/z0Ucfsb29bQCruD5JQkRAijjgCTXn+vXrtNttUwXa2Njggw8+4Hd/93dZXFw0gPT8+fNmjB4fH/NHf/RH3Lx503SuFm2aaJwkWO/1ekwmEz7++GOjgdva2jL6lGfPnrG7u8u5c+dMckBA+e3bt412ajgc8uDBA46Pj43IWqhhshaura1x6tQpfuu3fsskepoVv52dHVO9fPvtt0nTlEePHhndwoMHD8y63u/3DdXrN3/zN021V7Q2WZbxxRdf8OTJE6IoMt8n35XnuXHjms/n5hmK0QBg3JzOnDljKtpS7ZJGnZIcsSyLx48f8/z5c2NMIdWaDz74gM3NTSMkL8uSxcVFTp8+TVEUPHjwgLquDbiQyog4Z92/f5+lpSVzj6qq4q233jKJr4ODAw4ODuj3+8DJWieVvbo+6aHydY6vDzS0y6w4WTADN8JSLlMS0mJKN4YgaNHxW1SJZlblVI5LK4ro9BVuN+DY8khqjRtEzGNNdVhSjzJUkuNMa/S8pHILCLu4nSVUUeKGLqmbYdU2VuDRHbRIs5ocH7dqMZ+nHFsFU2zKPMQPV9CuS9UuGEdzXuia6VGMVStCVWCnNpNncy4sLzDce0mWpczIqLKccKwZZBVha87BZMjL+RGZP2KPIbrnQ2STYkHg4UYuXuRCdRLYV1WMzuc4zoyiSokDl9L28Z02Kj+heFnKwdNtFoOA//13/gHnL77BzQdf8PjBNkXVI3c9yg7MHEVeutjtDnnqsfP8OYsLfezSIqUkqSLSzGEpWMTOIM1sAqeDzhQuLlaeELo108Mx00xjKxtd1WjPRzsdqDSRC1GoaOc2znFFt2OjdMXo5T7pPCPot1C4FHWEr7sMUpteqVlwLHq9kNWVNgE1lS6Zljl2z0FhoytNXJT4voPX8qmVSxlqdFiRuy557JJZBbXr4FhLoGyq4hhPpegiwXNSen2ItOL4+JA8nmOPhqTHE9qtgJYHOA5ZDbblsWCHLLk9ZqrEdiLKrCTLU1Sd0Q/alLlGZwnj/RcMD54T2CWrAxc7mPNidsQon1PYGsfN8AOLvBySxSNafR//dAu7nTCvDhlNhmg7Jupqeq5iODnAiiO8eYKnbGrPAq0J4opOXRHUHn5nAAt95q5H5btUAagKsiSnimzm2qKaxgRFTeR52EFArgusOiOfZsTTiv1JSapL5k5ObOVk5RALi9L+5lKnZGOQYA14JVPcpFLBV85DEmjK5tMMDGezmREaNykrwCvBnmSCmhqAJsVGAnGpJEiAJ6BBNv9mJrgsS9N7QSgLKysrJElCHMfGl3x7e5vhcMh8Pn8l+y/ZdKHdSCAoVC/ZhCR7L0Fr0yK1CZaWl5dZWlri+vXrhnsrgsAoivizP/szbt++bXjq8JXwWv6Uc5JgVgCYdASWAESuUUBCsyeFgDDh8MpnCFC4f/8+Ozs75toE4DiOw0cffWTEo81qltwzua6m+FsqAlqfiKeFetQER0LDa4KinZ0dRqORaYAlQnUJBKRiJqLqJhhsAiE55NnBVwYHYlfbrKbIz+u6m+Zz1VobUaYEQU2Q1/ycJmiRzxRHsaa+qEm5+qZSpwBDj9ne3jZVBgl8ZY5IdcPzPPPsDw8PzVjc2tpie3vbNFLb2toyAW6/3+db3/oWp0+f5sKFC2xubppO4UJ3BFheXjYUln6/b+639F+RgG04HBpzhNlsZnpiXLlyhTAMTZ8FqXqJpuiTTz7h8PCQP/qjPyLLMkO9FAvdCxcumL4v586dM7qECxcuoLU2HP35fP6KFXK73ebevXsEQcCHH35Ir9czFCXAVGhfvHhhEhHj8Zgf/ehHPH78mLquGQwGZo2TZnbD4ZD9/X2KojB6COmvIXQ2OKlS7O7uYlkWL1++5Ld/+7fp9XpGrxKGIffu3TNNFm3bNlUNmdej0Yg4jul2u6b5oe/7vPXWWwaIyHyV5+B5Hi9evODjjz+mqirTl+TevXtMJhOSJDGUI3EVa9I2ZQ4dHx8b+9zV1VUzr548ecL+/j67u7sm2XB4eMjKygpFURjLWKFjPX/+nP39faPpkM7cYRjy3/7bfzPidMuyuHr1Ku122yTP9vb2+PM//3P29vYYj8cEQWCMEKSad+7cOa5fv85gMGBpaYmrV68aC9/t7W1OnTrF/fv3sW2bhYUFAPb29ijLkvF4TBiGpgry8uVLlFLs7u7y5MkTZrPZ115DvnbEEjo1lueTTTOmaYydlyhlwWKISl3qaUFaOai2TzodYlsldVkTpQ7antOuKxzlU9Ql3aCF8jROWeNWPnPHpoxsaqUplUM6nmMFLqUF+TSlmIxZO7VOqYe0ihBPh0xGU2q7oBsoihhcx6H2NA4d1KzGosDzFM5SiJVMcDwXlwLL6zHodbnz4AmjoymdVsjcraksTVxU1BYkek5u53ihT2Qtoso5sZegkwqtajKdQBESeDZekRK0u5BUkMypHBfXdUBXOEmB53qkOcwri5VBi3NXTvOP//n/ndH4kE8++XPSosbVilTFRBU484QqCNC5z9OtPXq+jWP75EVF6Dl0oxbKKki9hHmWg9MhLyv8qsDPbOJ2QBJkkGbklSKdFwSOS2vg4IYlgRfgWiFuGxxL4+JyPCuI44qg1ozjEf60TSvwmeZz6nhOnpT0goh2Hyp9lu15wcapiBc7zyjbDgVj7ChA1x5YNWlV4xQO5aRE6wQr9NGeh6Nc5nlGz+2Sj0oqHdDpt/F7FeNxRZ5ktIOIsNelzHMms5z1/jrRdE6PmgyfjtWmrsB3A4rhPk6SEbg2ORl5MicIXQpK4jhjOVzCDwParRZFmrB7NMNJFUtvDAi1SzUsKOYVNW2czGatt0Bcu0zGQ8oqp19D4AyYxofMrZh+sYJd1JC1yG2XvOfhhC2SCmzbAddmnlTU2sG1IZtPsd2SqlURli51VRLaLWyrxGp1qScueXyE0/NwrTbTsgZylO9CMYGu/5c6J59CexCnhH4J6psrBpcgqpn9ha96WTT/vZmZluycCAkBYyMKsLm5aWgMkvkWUCOBX5OyBSebuWQtmxn6sizNd8nGb1kWs9nMUAgAozPY2dkxAbSUlaUaIJv7ixcvDD2rSY9pin6bQnRZwMXVQ+xtm5qCpouRbKT9ft84J0mALaX8g4MDfvKTn5gMnFQY5Psl49qkismf8JVLV5NO1aQ1CTCSDVkAZVPDINbAruvy5Zdfsrm5aWxKBURcvHiR58+f8+LFC3Nt8h3ypwClZrVKbDtF3yPPQjjIEsTI+BKRqAAeqfzYtk2/32d/fx8Rl4vbi4zLJq2sWUmRZy7gr0lnkqNpLyzPVwJXqcRJYCPBqoxpGePyvU2dh4yrZoVF/k3uuQDZ5ud8045ut8toNGJ1dZXTp0+bZ7ixscH29jbnzp3jwYMHZhx997vfZXt7m+XlZfN8RC8hCYC33nrLgEoZG1tbW1y4cIE0TY2eR+htzfUlTVPTI2dzc9NYxcrc8jyPzc1NY44gtL719XU8z2NxcdFYii4uLpoq6H/9r//VVG3kecrzW1tbMyDzz/7sz1haWmJzc9M4GW1vb5sqVpqmhnYEJ/N+a2uLqqrY2NhgNBpxdHTEr/3ar/EP/+E/xPd9Ll++zGg0MnNoPp/z4x//mOfPn5PnObPZzGgyRAeltTaUng8++ICtrS2yLOPg4MA0L9Ras7Gxwd7eHvv7+ybBMp1ODYg6ODjg+vXrHB4ecnBwYLRsslbeunWLjY0Nzp0794pVseM4DAYDfv3Xf53Hjx+bfhbymU3qlyQcRLwta19RFHz66afGEvjmzZumZwjAjRs3+OKLL7hy5Qp5nrO4uEiapvT7fYbDIaurq2itWVlZYX9/n5/+9Kc8fvyYt956i+PjY9rtNo8ePWI+n3P9+nXjTvjZZ58xGAyMrsW2bVMJKsvSmBwkSUKn02F1dZU7d+6wtLQEwGg0Ik1T2u02W1tb+L5Pv983WjmhYz169IhOp0OWZYYSurGxwXQ6NTQosS8WjZrW2ojaFxcXTWXp3r17vPPOO19rzn79zl/zOW4RQ5oTKoew0yeK2qTzKYWXk2QTsrLA0xl930G5mlqV1BMFmUvhgtKKpEiZVSNyu6Qig7LCmU8JrBl+6JDqEqUVvcCj4/q0rYggGnAwmTNJZsRZzTyJKfEp8CmOEqgdxvGE2WxIFk/Ixkf484oVFZDNC2odUtWKmVuj2gElJftb2ziVx2w0J8vAyi18JyTNLJ48f4qtS+LpFJVM8YuStvLwSwhSi25lY2Up00mC3cpIkillWmDXFi3XBp2i8hnUNUmcUTkedeDiLPX5h/+3f8QgCvjy88/ZfXmME7ZRdkFPuVTKpeVF5FbMs53bxKN9lloLeEWNXyvqqiT3C5JyTFWkRN0IVSR4ZAS+g9VuYTsKNc3oRRF24dELF2h7Hv58TpDUOJFL3Q6ZlS4Hk5xxPGUe1+RpRZkVzPMZw+MDVMsm6lj4gUPpRRR2hApCgqDH4qnL2LpFaC1QaOh0fALPI7QidKbJHUVRaqwIOu0IXaUoVVKoKZadk1djHD3H8wsqKnToU9pTlCqI0wrPsgkUTIZHzPWcoTNhr6g4nExIYptkGmCpktQtGCVzirwkG5c4OqLKfYJwiYX2gMPpAYejbbJSoe1FVlfPgVYMnz6nns3x/QBbOSjHI8tK4jqiHdj0rIAwLVHaxvdCBq0FQr9Hmado7dCPlrFaFtO8JlcWTgmtrEUxKagjH8cHpy5xk4zQC3HqFrgRfqdFNo7Jp5DlJXVkQWuJKm9R5CmENcpz0G6FUhn9vCIsArwh9J0Kt1a4yqfjfD1Lub+tR1OD0czkNvn98tPcLIWW07TwnE6nlGVJr9czgXVTlwG8Yl8qWfsm/UiCxiYdRs5TMvpNoa98lgQZIkb3fZ+FhQXa7bapjAhtSHjRTbAjAKdJl2rqQpogq6ltEc5+U6si5ewwDLl8+bKhFvX7fZOd/+M//mMAc91NgbJk58MwfEVwKoGubPadTofFxUWTlZPsrnCE2+32K++V59qsDAl/OI5jHj16RK/XY2FhgU6nY0S3H374If1+39CjJJCReybBggCjJrgRsa4Aw+ZzbdLeyvLE7leyjyLyF/vNTqdjQI7Ykzb5700NTrMaJ4G/ACD5aQJXAZxS9RFqilTW5EeuW8BBs7IjQUhT3yTX2vwMcSOTSs5fJ4b+Jh27u7s8fvyY7e1tjo6OODw8ZHFxkWfPnpkGe+fOncO2bb797W9zdHRkqoeSkEiShOPjY65evcqZM2fQ+qTpp2VZxur1wYMHjMdj9vf3TfZe6DaSfQfY3t7mhz/8oamYiGMZYMDy06dPTXO+qqr44Q9/yPb2Nkp91QPk2bNnbGxsGHrK6dOnAV5xpdvf3zeOTjs7O9y8edP0irlz544BRlLta3aTHgwGnDt3jna7DWC0Gbdv3+Y73/kOV65cMSB9NBoxnU5ZWVlhaWmJmzdvcufOHVMZfOuttzh37pz5/3a7zbvvvvtX1lWp4giAF2Bl27Z5H8B4PObtt982WXXpeC6VlF6vZ3oVDYdDQ00SGlqv1zPjXsCl/JvQSAHTbFBAiAjoZa8QobRovWazmXGmU0qZLuudTofPPvuMJEkIw9DsLb1ez/w+nU5Nj6KHDx+art/Xr1/n3XffNU0Hnz9/ztbWFg8fPjRruIBJcQxbXV1lYWEB3/eNxuPixYvEcUyr1WIwGBjwtLy8bOxz4cTu9/Lly5w+fZoPP/zQUHnv3r1r9tMPPviAMAy5cuUK4/HYiMVFrC/Xv729bXQ1H330kUl8/U3H165o+GFFXMyxex2quCZsBeiiYJBFqLrAW4ooChudK2apjx1oVLvEyxJqbVHUHrmVE/oBpV3j1QW1rlBWgVd7+I7P8CjBdnxUG4oqJY8z7CKkpMBycgZWyHFYUeiMbmuRl0eHZIFNBLQGPabjKb0wJCNAUTHNNE7XwUoqZsWcsrZZXLSZj+cUgGq1qOMZWZ1glzXTSmEHJxuXrjXxfIY/d4j6A3RZ0+rPKGNFmmQELRs7d0jzHMsqUTbYno2tHNKkpmUX1I5NVVsMuhG9wONXPnqT/9PHv04WjxjtPMfKNX6kUJ5DNU9YW99gODvm8NkxvDxiY7BGFVWEPYs0Vti2op7mBLlNy+mClRFEJZOkouNFHMwmlCi8ugOVxeKiR1KNoevj2j6VUmRpig59kpdj3HROOUvp+n3GXk0xGaLtAm0rXg5rvMUO06MSx9OUhWJeKZy+R8sKKCvwnBA9nnOkC5xa4ykIQpjULrYboUmZp5q6LMmVTWRndIKIWRWTOgGu1aVSLm4Koddibzwnz6Ys+qCqillrmdANyWyoijadQYmf5YxTTXxY4g5CvA2fVCuoFWUyI6ckqEocFdBe3SCwE7JkTIEm6i3Q99tUWrH/ImEhiqltH0IXlGZUTSjTArfuYlc5cZmhg5LArum2uhynNUk1p1XOqMkIdUhZJjiJQ64yvH6fqozxbIeyjFC6pBhntDoBKAewUNrGLlycMsOLHA6TjFy7LCmHgJTIU0yzhLJ2yMYple9Q2Qm+7lCXCVVeMFGKM1934v4tO5pBrgRmkgmXjJ0E09LVW6oYQnkSsaQADQmkmkGolKLFyUcpZXipIj4Uq9dmkNYUOb8u4mxSWoQKIwFqu902walw5IXvLwBlNBq94kAkm5Nk1JpZbwlO5b1aa5IkMRSQpn6jGVCeOnWKCxcuGA66ZPrv3bvH06dPTcMxuT7JEkpHcvnOpvZDqh1lWb6SvZRgu+lOIsGyACR5jVQLmtcXhiE7OzvEcWyCD6GPXL16la2tLXZ3d01ALM9IgILneaZ8r5Qy96bpIFYUhaEuSRAlwEnGmwglm+BOMs+S2ZRnJdcu5ynn1HQ3a2a8X3eVquvajBv5/qZ+Q4CDAE4Bd00Q3qQOylwSKpg8dxkfzWqQXN/rdLRv2hFFEefOnUPrE0vVs2fPmuyyuACtrKywvLzM/v7+Kz1vZrMZURRxcHBgeu2cPXvWNEjUWtNut8myjO9+97scHh6yvr5uAO+lS5cYjUZMJhOyLOPs2bO89dZbXL9+3TT6u3z5Ms+ePSMIApRS/MEf/AHf//736Xa7/OIXv+Ddd99lbW3NrHlZlrG4uMh3vvMdQwMbDofcunWLy5cvM5lMaLVafP7550br0e/3WV9f5/nz52auSY+O58+fc+rUKe7cuWPWW4ArV66wvb3NZDIxVbeVlRXW1tb49re/Tb/f5+joyDS/W11dJQgC7ty5w1/8xV+YLuVClWwG1dIdXahXjx49Yn193VQvxE66rmuePXtmdBXLy8usra2xs7Nj3JGale4PPvjAOFGJ0FuC3zfeeAOAhYUFtre3zVx/4403uHv3Lp1Oh8lkwtraGp9//rlZtwF+/vOfU1UVX375pTGXkHtV1zVnz56l2+3y8OFD7ty5g1In/VXEtfDnP/85URRx9+5drl+/bswpfvzjH3P16lXz++3bt1laWuLJkyesr68b++SyLBkMBsZl7/z586Y797179zh//ry5t9vb26Ynk+u6DAYD6rrm1q1bDAYDdnd3WV9f5/j4GMuymEwmlGVpQJusQ19++aX5zHPnzrG9vW0qJOPxmKIojAWuABDpeC/fLY5ZgNEjfp3jawONyUThuApdxKTaR8UzNODGJfZil1mVU6Q5dpmR4qOzig6a0AmInQynrpi7Lk4Ndm1jA24YcTQ55kilhAW0WhbJzCUrClQHgqjN8HCEG5Y4gU2pHRY8iyJdoMxL+p2AMnfIxwW6gCKogAIPh1lWUfkBfcehroZkac2CslltBxxN5yTThGqWg+PSjmraTousSNnfPaYgJU1K2u0Oc12gdILru2B5qHhMbPugoLZTXHywQvK+R2XllNMS13bRZUThuVShT0ZKHXS5evYSnSDiePslk6May16gPIqpixG+b5PGKePpIZPhHMdt4w6W0XZIneVg++QJBC3IE8XIrvHcAj9yqEvNhBFl4lJHDkUNbpLhLtRUNhS+RVkV9KMAjjP07Ag78BjPQE0z6nSPWVwQpzlR16Gc5FROhYosuh2LYpahSxsn83DrlKVui9HpAVu6ICy6qCqFPKWwK5ygh4oTsBLyWQE6w29FUFSUQUChK7ywg11XOElC7WtCLJa6q+QqZ393h1qXzDJo708p5hlVXOE5KaWVU8QlrVaATwurCBkzpKoynN4iUV8xzWfkkym1ndO1BrR6HZTWZHHKKJ7SiQICImoqlJriOTZlUaPymlZhYSmf0nVZbPmU85qyUqgwIM1LAmq06xM7NWibLE3pdbrYtkVFTVmnOIFDUda4Vk4QDRiSELkZvlcRFxAGXYoiISszjo4Pafd6+LZHPBux6EVk2ZS6tqHjkthTPKumDH2K2kYFAVYnR3mdrztt/9YdkmmTYEgqAyIWbAqKJYgVICAiuyzLCILAZGakSiHZNtFtNANnCUYlsGu6sjQ1ERJINvs3wFfuMk2RujSCA4zgbzAYGMqEXF+SJIzHYxPkCoWlSdmSoFgy33CyQUiQK5UUCaCbgaJt28ajX0Sfcr4ShN6+fdsIpmWzybLMUGqky7bQyWazmbkfEhBJcN+8L3JP5DnJvZP3iCBSGojJ/ZWmUHmec3R0xJUrV16x3/U8jzfffBPLsjg6Onrlmcl3Nw0E5FlL8N0EQhLky//LfZZxdnBwwIULF8z9knvmOA7dbtfoMzqdDv1+3wSZcv+bgm+x2pXgpamJkEBfgobXK2nNqpWcuwBj6ercFM5LFlbOoyn2lnOQ8dEE+N/0QzLO6+vraK159uwZ6+vrRthr27Zx1REg6jgOq6ur7O/vmznr+z6Li4uGQjabzcw8eP78ORsbG1y/fp39/X0ODw/pdDom0AdMEqHZH+XcuXPEccypU6d4+PAhV65coSxLhsMhi4uLfPzxx3zxxRdorVlYWGBxcdF0rL5w4YKhSkmy5fbt2ybgA7hw4QKHh4cm0y/j0bZtDg4O+M//+T+jtTa9MmQ81XXNT3/6UwaDAVEUmbHWbrexbdto3CzL4rPPPuPChQsmCfE//sf/MKLgjY0NXNc1DnL7+/ssLS1x+vRpA+Y++OADvvjiC3NfXdfl0qVLjMdjE6SmaUqv12N3d5d79+6ZdUeqeELl2dnZQamTLuNiRytV7AcPHnDp0iX29/dN13ZJ8uzt7Rmw8Pnnn1NVFdevX2dnZwfHcTh9+jR7e3tmXZIkgCQO+v0+n332GYBp8vjGG2/w/PlzQzES62upaiileO+990xCaTabsba2xu7uLn/4h3/I+++/z9raGouLi8RxzPPnz/E8j/39fdPjRdY+z/N49uwZ586do9VqcXx8/Aq9t65r3nnnHba3t7l8+TLj8Zi9vT0zriTBJNqc6XRKEATGNerg4IDFxUUDoL/44gs+/PBDXrx4weHhoXFB6/V6vPPOOzx69IiVlRVj2SxgVLqv/41z9utO7nChTTnV+H5KUCdYyiN3cuz+SR8HW/snjdO8hMXII8lm5DjslUBW0tUJmZMQ0KN2QyYqpxcouq0lnE5APJuRHqfoUhO1ApwkJS0z/KWQwLaIs4K6HZElU7yBdbIpOTWuXWEXIVWRUeiKaNAinSUQOERWQh1bjLISK/Bxoh4LvUWS2YwinzKP5+S2Q7vj/2VjwIDpvGAyrfF9i6RIqcuCSV1w7uIVDiY5eeSTzhLa2sO1fOajGb4bUtUFZVljV+D4Nrl28LHplAX+CNqLNmcvXaX2PO5vP2NUzQh8l7SOSMYxrRYcTQ/Z2tohLqcsdpYJVjyUqmECbgeU5ZLvvmB1sIDbixhVGaM0oVNAVdRUdgHaQ0UKS7WZHk1wtA1FQRA4ZFWGoy3mwHxnzrSaoRxNPcuo/YI8tmnlNqVWTEcFdpkzWB7wcrKPtjWlV6J8j1ka0+90mS1UbO9uY1sBfqUpnIBZoWiFi9TlMW5gkeOQUtLxXZJK4VgOdlEzjSGMcuq6wK+WcbHwlMOpwSJ2nBDnUyjmHO/vs7C8QZ7GDKcxvrJwxzWTFZ+qmkNSo2yfl/NdVgKfQdRHeQ55PmUcz6gcn9DvsNgvGKcJR0/HLJzaIFpow2hMniQEKiBwfPZHBZWaEHRTimwTL9J4pctkekQ79E863JcWdkdj2x5BXRKOcrrLLdK5pqoshkmBVxYs9x0Cx2bB64FOScoUnSlsFeMs2NQzcDp9vLKHqixiJ2RaQLTYgemUyvKI1CKWA1k+Q1kF7c4Ap5hRW99MbjV8xetvBleSCZbAt+mmI8GlBNxyyMYuDcocxzHi5NlsZhqfScAMmOBf6C5NbYQEmXVdm+qInIscTWqMUIZk4RUhqPjOy7kLr/b4+PiVCkfT2Uruw+sWqBIsN8XpTd2KVDOE+y2dhpt0Jc/zGA6H7O3t0e/3TTOqZk8F+Rx5nzjAyOua19Pk+gtQamYg/zodh1Da5PembkGCCdGLyLVIZlgEn4B51vL3JiWrCeCa2omqqkwAKWJxaVwoQdhkMjGgVqoxURQZUb8EEVLhiKLoFR1Gkyoi1yXXKPdTwFmzctYcf/Bq93eh1AkwEStTGVPyWX8dkGmCJfl+mTNNoPpNPeS57e7uMhgMOHPmjBHCLi0tGUMG4bYrpYwpgwiv5b7meW4yzGfOnGEymZieCU+fPuW9994zYzYMQ46OjhgMBqRpyuLiIkopPv/8c65du2bciWTOvPHGG3ieZ8axWOn2+312d3dZXV3lD//wD013enGOWlpa4sWLF2ZuCfWn1+vxi1/8gtFoxPnz5zk4ODDjqan1KcuSpaUlfvM3f5N/9a/+lRkX586dYzgcopTi2rVr3L59G8/zuHLlihm3o9GItbU1tNbcvHnT6CSkOzpg5oFt2/zWb/0WDx48MJUSpRR37twxCZZ79+7RarWM+5Jt25w5c4Z79+4Za1bHcVhcXGRnZ4eiKIyTk9A/AdbW1njy5AnPnj3DdV0ODg44Pj5mZWWFo6Mjzp8/b4CK7/usra1x5coVfv/3f9+M+bt375r9R+7DuXPnjJXu7u6uceG6efMm8BUwX1lZYWdnxzS4q6qKbrdrAJ/02gnDkF6vR5ZljEYjXNdlcXGR4XDIwcGBWXdWV1cZDAYmefTs2TOiKGIwGNDpdLh//z6nTp2iLEv29/cNPVjWFtd12d3dZXl5mcPDQ1zX5c033zRW77JHCJV3OBxyeHhoKn+i5ZNE0Le+9S22trZQSrG6umqSLFpr9vb2jCZIOtiLkF80In/T8bWBhpd5FEnMvHLwux4VFlmZUGoPXYEqx1jdDl7RJ5tpVNihjgsKXbFmLWOXMVHooosCsBmGLoWjmOxP0AczKGqqIsfuWeTaw/cGBMczUsthqAqWFjc4LicE7TZuXlIVHnZL06pr9p0c8jltP0clh4StDlnpEnkORVVSJS5e5YLuoFmk1nMqKyfsKDyrRZLHhKHNi8MJ0zJDZxW2Bt+CsoA6UMymKe2gR1JnRIFFPssI7TZOVKHqGr9yiLSNVjV25HGcaxw9h8LB7i6iK83q5gLj8QG3vrzJvDjC8XyySsFSwJAp8/KYpKpxeot0nC7h0KLslMR+DZWLLg6oI5dpDIHaowhy0rSmVfrgu7h1wqzSOJVPojKiTkRVhlhBhVWXDMcpCpfSn1PZUJKRTI5xtE9VKZSqSGc5Wvn0FjtM53ssOi69bsDxYQptn0TlDNodvLjAXfLobvQ52NvHjmzSuMDRmtSvUHaGH7ioaUVdlKRFiR+0cKkp7ArbKwk8n/msQquC3C051e1h9docHe6TTRT7dcEZpyAIFLXn4OY+TqlQrkc2q7BWurR6AS1s0nFJOc+YVRVha4Ww7WJpmMQlYeTTsgNy3yIOa/K8JhileO4i1WhGYU9xIo/Ogo/juViFwrNzSksRdkHpmjLqUtUKt5NipRVFGaP9CHulw6S0KKqaMGwRVTG15zDXFrP0gHawgMoUHm3ssGA43cPL+/ihhVUqZmmBck4C0LnrU09LZqpFNimxMxvVKXECHyfNqaoxRZJhOe2vO23/1h2SrZWAtCnWFnqIBJACFuQ1TQAiwVaapqbsLVUQCTDjODbZ9WZwLJ8l1RH5PgEsEuAJqJCAThxdpAeG67pMp1PgKypYt9s1r5dNSmhgEog2OfLNYLApdhZKhZyrBOySKZfARzbjTqfD0tKSoS40A0xxEZGAWe5Ps0oBmPst/FwJhCWolXN/vT8D8IqeQMCVPEs51yaFSIBOq9Uym/KpU6deAUjLy8tsbm4yHA6ZTCZmHDSfsQSd4nwj3yGHBO0C4qTKAF81fpQqmPTZEGG2UI+aond59vJM5HXN721mvJtVCwEP8tylqiGBbFOT1HSXgq/6ZjSvSaoW8nd55s0x1qT7NTUu8vNNPKqqMv0tBETK9UqjxbquTRfu6XRquORNFzChSglNZDqdsri4SKvV4sWLF7TbbY6Pj3Fdl36/z+3btzl37hwvXrwwldmiKDh16pTREIjOYjQasb6+bsZEUxcxGo2MhuTSpUscHByY19q2zXw+Zzab0e122dvbM+5EdX3SzfzatWvcu3eP9fV14IRKtrCwYPpotFothsMhz58/N/Q+OLEDFmcuoSMB/x/q/uxJjys770Z/e+ec71hzoTATIAmQTbK72d0Kqa3+WrL0HYfDjo7jc3Mi/Jf5yhcO+04epAjZDocntdSDKDabJMABxIxCoeZ6x5xz73NRWpsJ2J/NE+ELMSMQKBSq8s3MPeR61nqeZ3Hr1i1OT0+5cuUK0+nUrZUf//jH/LN/9s84Pj526zmOY3q9Hp999hmDwYA7d+64OSfBuoB7rbXTHBwcHHD58mXu3r3L22+//VJfC9HfCdVM+gqJi5WsH8BVh0W3IPTaw8NDer0eBwcHvP76627f/J3f+R0++OADnj9/zo0bN3jx4oV7ByVJwvb2Nvv7+wyHQ5RSPHr0yH1WmqZsbGy4KmKSJM4MQN4dDx48cP0wxCFOzB4uX77MvXv3nED/4cOH/PSnP+Xs7IyPP/7YdZXf2toiSRJ+85vfcHZ25vpVzGYzB5Q9z6MoCgCnnZC9UxJfQgeU6pGskzzPXXM9Ad1Srd7b22M0GvH06VNHK5T36rVr11gsFq6hap7nHBwccOvWLQcc0zT9Rmv2G9dRe/WcZXDE2uYAr4L5IickYuh7BIEl8Eu8tmJpa2zMedY/akiilkW1ZFb7TKYeufbJigntyZywNPhtSzKIiMIILxgQsQ5VQ+tbmijEoNhsFf7phD4G2/Yoi4SoUdhckVcar+cR9VLStT5+UaHLCrtsKeYtjakYaY8k9qltThp5mKIiQEOcMy0PqNsSvWhYswn2rESbBm0byqxB6xRV+MzODvCDlp6JMblBKYNp52jAWI/GaArlYyJDXpR4KoeoRiUtZ8tjBqsrbG9fZu/BY872ntPOFyzanHAtYJz46Lbixd4xFp/p6YzGr2lajWkbGk/TqgIV+UR9TakmzKoF9ZlGnSnmTct+UTIJSgIqmhbmJRxPcvJiRnMyZbL0mC8Sjtsl/rKlzJZEdctgkGC1jy4Umpo6aGhLQzudg1EcLg4IRgnTMEMZ0EufYmHwfc0wDdlcW8dXipKCoFeSpjWhUdDGzKc11vrneo0qhDrCVA260TRtwfJ4SjHTzBcFs8M9pvOMsvbRXsisnGKrAl00mHlJP0hIMawPEnQvpDeK2Or7REFME6XEQR8vWEGRU8Rn+LSMvBA/CjievCCvckZ+jygdsJjOse2cqG2JA0WuPaaZR6t6NN6YvNJMixmlb8iigFJFFM2SIGjwG59BC2thzChdo1GKwjOoqESnS3SrifyQ1bUxUT/C04phEmDbDFrDdrBGVEcUysf3G9JhTuhZrG2Im5bEg8044WIC4aCESKPaEK8OCTJL1FtDBd9ee1sJtCTgkcDxVTF0FxRIoC5Z5y5YkMyyBGfyUuoKZSXw7Yp3pbIh2oGuq0s3MBSqhAANEevKz0rFRICDWBHK7wMuoOz2SxAQ0M0sS1AoWoJuprsbaHZdhrq9L6TjtdyvuCU9ePDAZSzl5SZ2t116RVeTAV8Hyl2w1qXgyAuuK+iX73ezavLzg8GAlZUVxuOx61osIFGqGt1qlwQwXVtfASIyB2Tcux2IZe50gZt8bzgcsrq66gBlt0olYu8uKOmCX+F4i54lTVOXpe1WM+R5dClVXZpctxO8VJMEuHWBWFen1L3f/yfQIfchf14FQK/SEKXK9G08xMZzNpu9FJQJqLXWslgsnKuUUGSePHniqgXD4ZAsy9jb28OY854AEowLaBHK3mQy4caNG5yentLr9RydRoDqb3/7WyfQ3dzc5PXXX3dBoqxjoWAdHBw4Co/Yl47HY6ef+uCDDzg9PWV/f5/19XWqquLatWtOeyJNCBeLBVmWsbu76xoFSiWurmv++3//7y8lcaTiYK3l4sWLbp5dvHiRtm05PDx0lbOzszP+8i//EmOMqyhubm7y1ltv8dZbb7G+vu7mmVLnJhKSWf+DP/gDVldX2dzcJI5jLl68CJxTjba3t11zTrHgFspTmqauazmcJz6+//3vk6YpSZKwubnJeDzm6tWrrK+vM5lM+PWvf+0oX6LhmM/nrjfH/fv3ncj/+PiYN99802Xlt7a2+OUvf0mWZfzVX/0Vjx49eol6WVUVR0dHHB8fs7e3x6NHj9z+OBwOuXnzJsPhkKIonBV3mqbMZjP+8i//kq+++sqNhTGGR48eOWtuofnN53OOj4959OgRFy9edM9EKkbXrl0jSRIeP37s5uGdO3dYLpcvWbIfHBywurrKhQsXnFZN+rksl0vu3LnD5cuXHbh7/vw5H3zwgXsHAM7gYLFYuI7f0g9FTA2Efiu9Nr5pZfQbAw3TlKyHQ5ZFBX6Ebg3FomJaTmltTuCNMXVApD0CryG2Fj23xDogSCL6fUXSbyh1QBZ5eL6Hb1ssOTGgMAwJ8VRDOoxomjlxvyIaBuh+St3XqFrRzhrqfoaKDEHdEtctYaGwQUobjFGDMU2hz+1F84za1thQE0cWO/aIxj5B0sd6ivJsTj9UjAch0XiMCkOmy5xW+ZQqIkmH1GhMU1HPc+bzFjv2sRZ0tEIRGerQUCpNlCi8NKUtfTwCtPYJpwPKBfjRiGtb1xmhef7gEbNpjZ8M8VRIrwE1q5idGKYqoq5qkjiGRKFQ6DYG06MJFNVsRrMEWyjqpaZpNXVSYBIPVVUEOsIuWmw5o82OiKoljZ1TthnL6ghVzUmmFe2yxs4Mi/0lZ7mhCQaEytIrFGHjUYYNk/wM27TMjysiE9JLQkpb01qFmltUpfCqEL8K6QchgdZEgaYJwvN+ECqin/bBU/RCnyQO8HsVKA+7zAmjdap0RDgMMGqBDWom82MWZxMG6ZCNwRobUZ8sbyhNST6dYysPvSwJLKSJJTs+I987Yty2rA/X6G8M8KMx2dkSbT0iFIFX4Yc1RduSBBEX+320skxOKyazmqC/QtDrQ5zhqyk+faKVMTr1MVajvQh/mFCHNUVQo31NXhsCldLMZ2hviW3mxKkmaD2qbEE/KUEtsKZEoygXFSPfo1cXlE2NjXuENqSn+9jSP39maQgsqZscVde0VY5CUXlLPFtj+kOWOiKrK8xR8U2X7d+5o0u3kQBawEb3625WuEtN6XLZxSWo+7UAFdncu7z+bhM6+Tm5BqHUdAFK9xDerASOkl0X/UEXIElA2aXPdN2jurQZCaalHN7NQAvI6mbKJYPdrYTIuSRrKu5O0+mUk5MTxxkW5xoROncFkHINXVpXFyxJ4NylcEnlR6oEMq4CrLoUIvlauhivrq4yGo1cUHF0dOSsGGUslVIuoOsCna4AvqsZ6bqDydwRoCPzqkv/kq8lIJfKTvf6rbWu70JXIyM9KqRTu5xDxvnVqsX/DAB0n6XMefk9qVR1dR9dMCjnlXXRBX1dqpv83dWCfNsrGhcuXHDBojiqed657an0Q5FnORgMnAWq55331tje3ubhw4c0zbm152uvveaMHFZXVynL0rmnyf4htKsrV664zvFCk1pfX+fChQusrq7y4sULnjx5wldfffWSQ55UylZWVvB93zlNjcdj3nzzTacrWV9f56c//SlPnjxxdJmrV6/y4MEDrLU8fvzYjVsYho6XX5YlP/zhD7l27RqHh4esrq7y3e9+161Z+DqpIdRAqc5NJhPefvttrLXOano+n/PkyRMePnzI66+/7tZ4URQuIH/99deB8/l36dIlB9Z//vOfv0SBPDw8fMmO+ujoiOFwyEcffeRoRFeuXHG2uFEUOceozz77jKZpuHz5sqNaDodDB76vXbvGkydPKIqCK1eu8P7771PXNQ8ePCAMQ2eksLW1xdnZGR999BFFUbg5IU5cArZkrwZeAuSyv8ha/d73vsfx8TEff/yxA6l37txxzWPff/99bty4wdraGkdHR6yvr7O/v++6hQdBwHvvvce1a9ec697Vq1cpy/Klfhf3798nTVN+8pOfuGuVexLNWZZlTjsjALxtW0ajEVqfN6L93d/9XS5cuOC0L+IseOXKFVctkyTXwcGBA+Bte97J/sKFC+R5zubmppsH0j/kmxzfODW6SBOoFJ5RKN3S8w1+EJHEAVUeUwctnm+ol0tCPSBUfYwuMdOW3J53l7atIjEKLxiR25KiVmSzkiJT1FWEqixEBbVSBK0hDkJqP2BpQfkj4llODygLj7otaOqS3GqCpqWtK/LM0Gro64oo0TQKgrqhtA2zrCGNYpbLnN3FKU3TcmFzjVkeUuYeJspo9QmtylgahfVagjgjqCq8UNNYiANF2VrCJMZkOaqtCfubKG0IDZgqowg0VjV4gU/beIRRj2Yx43u3X6P0G7548QyfCJtZIi8mOynxdcPsLKdeVKQDQ5blBIxpg2PK0serRwS1TxKPaHTLaVExWg1p4harLWWzZFE2NHPotT6LxqKThryp8HyfpdI0i5zIH+BhUaog6VlywNYlRXzGvJ7jlRbKCD9VaJtQVBXQUkxPuNIbcJTN8LwF1qsJ4iFBFTAcLImDPtaHomloCkVl54z9AX5bkLUK7VuUzvHCHj4xSW2ZNIbagmrA66VMShgTMB772NQSRyGzRUGvOWba9NgarODNK6ZtSxok5IcZ8YUV0jBA2ZRoGDDy+pyaE1SlWEQJfT9hLelRFD51Caow1KZmvNmDRctgrY9vYKQ8ssqjURAPW1o/JDusuWB8vFbTBBqTW/ysRocplfKpmyWjXkKjPJowoQkTqrIlXR9QqQa1NKykG0CCN24wdo6OBngz8BqPNjaYylLnLcZC4IdUVmO8CKNbTBxgG5+ktWg/pVgu8b0Wq7/9gs4uzaPLL+9mobvZWfn3/6xLtwSkeZ67qkU3gJKXhZxfAkF5+UuQ+mojNDm69qnyuV2QMZlMHMiQIFeurUttkcyy/L7QoLrARfjVEqx3ufjwNeiSc3VFwOJiIqBrNpu5TOTh4aGrtkgwADhHJQmGulUEefbdF6/QxgSEvfpc5D66VQDJ0Kdp6gJzCfrkeYmnuzTGkyy+PL8u1atLOesKr+X5i5hXBMBBEDiRuQRkAhqFSy1j/qqGp1tZ6tL94GWqmMxnOY/MLxnbbkWrW62Rn5G/u+eT+5Nxl2vvzg2Zn93GfDJG3bHoVqhkrkhl5Nt4SH8H6WkjVQMR0/q+z+7uLsPh0FE81tfX2dnZYW9vD6XOm/v99re/5fr166ytrbmM9MHBAWma8uLFC3zf5ze/+Q3vvfcea2trlGXpALrMI+kj0bbnVstXr151WqwvvviCW7duub4fUv3a2trixYsXeJ7H2dmZAzI//vGPmc1mPHv2jLOzM6qq4quvvnI2stIjoSvw7u4lf/3Xf+2e0T/9p/+Uf/tv/61bx7IeL168SJ7n3Lt3z62xLMucA9/JyYmbt6J7kmrJzZs3SdOU733ve3z++efs7u66/gxS2ZVA9p133uHu3btOkCz3PhgMXH8GcfuSDuLi5iQJjhs3brgqwN7eHuvr64RhyKeffuoqQp9++imvvfaaq0799V//Nevr65RlyXA45OrVq2xubvKrX/3K7XHXrl3jiy++4JNPPnGA5tW1InqPa9euOW2F6Dp83+cv/uIv3DqVbvHvv/++ozcNh0MODg6w1rKysuKep9aanZ0dN75FUTiAcffuXYIg4OTkhJ2dHZdA+vLLL9nY2ODFixfcuHGDCxcuOHpTGIZcvXrV9QcRsf9HH33EH/3RH6H1uTPjgwcPWFlZIY5jHj58yNrammsKKV2/+/0+169f5+nTp+5dY4zh9PSU6XTKxYsXHUBZLpe0bUscx99ozX5joDFVijD2SXqGylq8IMSvPOpCoVoPG1S0bYUONbltSXzNsm4ZJX1YKmZliR1qkrZCWYv2PFToE6QJRQVtVROMFQaLqQ2KIaUN8AONryrms2P6UUBqDfMmx1gPL02wpqAqKoJ+ApmhIaT0WuqipfQSyrql1x9i2hbPBtAUqNOcuqxoYw8ValZGKct8gVUG1U7xPMCP8VRA1c4IqxoVhMxfHBB5CWEakpkFpg1Z5jmxB6EJ0aGiNhV4hoiEytYEgUcvTHjtO9epsbSlRzOvCLY0rbLkpoZ8znQxwWsnqMoQxT0meQPRHC/uU2eg5nP89YAyy/F6fZrIp65meMqjKTWjJMF6hkIZaDyqumE18lmelRzVlsQLqZY1vUTRNDVWF6S2xZ402F7FsG1Z1AmFp4iaOZ6fks/n6DikmVSsXN3htFri94YsdY6NDLqxBN4AXUW05RJUgRcY0kGIrUrOsgW94QplqdGhwprzxnbH0wXGD/B7QJHTTAN8vUoeNjzNTkkIoK9JIp8qKKltQG+0RtVfMqvOmNY5o3DI0B9SKkPTq1kNzsX5RTrA00t0H1qrCRNFS0xOQ5l6eFXMSh9KL2OceOQoKp3iz/rgxyS+ogoKqmiMKX1UUWFp6UcpBkPWLtG+orIecRCc61m0T7s8o6w1dhjjaZ9I98lLgx8uaFuLyUJqawgCRWorFjMovIZ23AOTEeuWBQm0JUNfk1UtOq2olhoblNh0jiXEqJAi/PaKwSVwk829q1eArwXXXW5793vdAFsy/pK1Ea6v/I7QquQ8snFKYNcNtOQl0P09adAlG75S6iX9hgjpXuW+G2Mc6BGRupSzJdiT88iL69Wu391n0/0jh3xeF3hJ8D6fzzk7O2M+n1MUBWdnZ/T7fffMuxUBuVbJ3Hb1C13aj4A1uV74Wvgt1YBuKT3LMsdVlqBZen1IEz35XWvPbUqF4iJuS6PRyD0n+NreVjL+cq3drL9cu1CguvQzGVv4GtjK9crYdM0I5CXa1T1IFtha6/Qbcp6uJqWr+ZHA6VUA0l0D8v8CEiSDLOfsVtm6oOjVQ87fPW8X5Mq8Ecetb+MhIP5VMbyARqmKCUXv7OyM09NTx5V/7bXXnLjaGEO/32c2m7lMdxiGzopWgjjJaB8eHpJlGZcuXXIaraOjI27cuEHbnruf7e7usra2xhtvvOFMGKRpn3TEPj4+diLww8NDNjc3mc/n/PKXv3R72u///u/zi1/8gmfPnvHOO++wsbHhLEmttezt7blgeGVlhXv37jnw9ad/+qccHx9z8eJFdnZ2+PWvfw2c9/yw1nL79m1WV1dZX193e8/BwYFzrHvy5IlzfErTlKIomM/nVFXl9FTHx8ecnp4yHo8pioI0TV0VYzabkSQJ7733nnNX6vf7HB0dkWWZoyZKn56yLF21R3QFZ2dnDAYD3njjDUajEf/hP/wHNjc3XTM8We+LxcIFwfP5nO3tbW7evMnz58+ZTCbs7u5yeHgInO+TRVHws5/9jLt37/LVV185AbXsbWtra1y6dInnz5/zD/7BP+DP//zPuX//vltbGxsbvPvuu6yurvKv//W/do0/t7a2AFx15cWLF05HJzq5+/fvc/v2bfcstra2ODk5YX19HaUUx8fHjrIrZgbdZod5nrO2tkZVVXz++eesrq5SVRWnp6duDz49PXW2wnBOp7t69aqbc5999hmrq6tYa5lMJq4D+pMnT9z3tre3nUap3+8744PRaMR0OgVwjo/f5PjmrlNBgF365G2O30/Q0RJT5lSJh68DvDqhaBuCFDylqYs5nm2omNLolvX1TeY6pykqbFuf92toU8IkIkzBtC155ZHEMXVlwA/JTY1f1xgNvdgj0zV5Y+j3ejRZxVlbEiceptFokxNHCm/pkQHtMELNclaTMcuqwXotpq6IEkOjF0RRQElA1XqE2QxKzXzu4YdDRnHJMtM0bUswSFlOjwnjhGZZ4sctUbpJkwyomONVObZJMKoFNGN/yDRfUNgFQWIYMKSYlTSVpZwtSDE0KwFLf0le+HiRD6oFr8YDVONT14YmafAWEckKEBRUOudFXRBan62+x+kko5cGLHNLaC26qajLGlCURUNdtkzTEqtytgZrLCpLolqqyYJWWcJRzEmdEwc9lgclpV1gexWVsQR+iFElXlCzrCuWNmVVexSUqKAhX0I4qfGXJb0VjzaGYl4Rj/vQtlTWowwM0eYKgR1gioY4vsBimmOSBhsHFPkCv9Cs6nNRbdy24GnqvKLRHkkYstibkvgrbL29SkjMWTYjDCJqG0DkE6gIPEVjcoKkR1gbvLpE24rNeIUwiamyDF9FhF5AEK+QJoqsOqb1DHUUESQ90tpStwbPg6aOKJaWEEXrK6JQk1hoCo0ZNNg6J/R8oriHxcNmBd5qTKNien5CSIIlovYN1i8J04gyL897quRzdOhTFBWNv0njKXTeEsY+LRlhv0fbGHKlScIe84NjdBiivQQbD6kyUI2HDb9ZFuHv4iFBXDebKxnabkAEL3PrJYsnQX434y+lXMkQdTn6EqhKsNelMnUDXREhdi11JZB8NXNojHEULOniKkGhdOLuAoMu9afr9tTN+AvQ6QaiIgDsCpjhZfGxAAe5rrquHX0kz/OXOsZKwC8BsNilSra8G5R2KT7yPGSMZAzk+rv32hWfyn3K/cRxzHg8drxiAQMyfpKxh6+tWoXTLJ2B5f671ZcuYJDPFGcc6UTeBbgyxl3rWxkD+V7bto5iJRWDV0GCUHa6IE2qY3I++X8Rj3bpXN0MKuB0GwJ6ZL3IHH2Vmib3LUCkS1PrZiTlXF3tzLe5oiHBYhiGrK+vOyAt3biF02+M4ec//zk//elPefDggevc/fTpU7a2tlyAKMBtZWXFPZPBYMCPfvQjtycURcHp6SmDwcAZQIhhgQiEnz17RhAEzrVHAjNxcJP9SxrQCVf+0qVLjEYjvvjiC0d9WiwWfP755/zkJz+h3+/zb/7Nv3FmB0LHgfO1IMClrmsGg4Fb/23b8uTJE548efLSPPM8j/v377O5uck//If/kOvXr1MUhePfi1BewNfKygpvvPEGP/zhD7l7965zzdJau2y953ku0+77vutv9Nlnn/H666/z3//7f+fmzZuOwiTrUoTWUinVWrvmi1tbW6RpyuPHj9nc3HRdsyVhAThbb9nT/+iP/ojPP/+c8XjMfD7n5OSEx48fu3tXSnH37l3XFbsLWGW8fvKTn/CLX/yCmzdv8s//+T/nxo0brjdHv9/n4OCAg4MDPvroI3dOqZDOZjNOTk5e6m0h607Ov1wu2draomnObY//5m/+hnfffZft7W2SJOGDDz5ge3vbVT2EzibCdOkH9ezZM7a2tpxeQ5IvMvbL5dK9G+7du8fBwYHT7t29e5cLFy6wtrbGbDZjbW2N1dXVl5JHQmvtJnI++OADqqrirbfeckm0b3J8Yw7GJJtTlQVrcYStSnQT4jWKYJpjcigri5qmRJOQ8KwkUQGh9jBeQxCnLOozgtBgNZQmJE7/dmKVJaFv8TWMgpggjtGrmmigiNqWtPRpqgQb+RAFRHFMaz1qHRDVMb1wQOCHxO0QRUTJGYnXkFYFG4EmoWbgafw6INaKIPMYDvvE6TamjqnLhsoqbN6jzXsUsxI1WeAtJ1iV09qCKAjxqhbfizgrIsqlj9+mhEWIyg1ez6eJLKiQpsqxpqHhb336m4KcmrNFxenxlP3ZKVVk8AYrBKsDhlFCm6Uo5VM3Icsqo1kUxI3FlCH5xKOsLG1jiKsW7cPcX9KGNflsRlC1zM2cA04pIkOlWvyhTzSwTG3GrPI4OMqomoxZcUZeFRR1zjKryT3Doq0xjaUODBUFYeuR5RnLrMTHJ8tqTlqL5/vgeYSlIop8+knCeKPPYFCzspoShJqyrKhsg1dWqKzGX0B9kpOkmmk5x4ZgbIWXapKepWxrlhZKfHL/jEZlBFnDYjJnVub0xglbcYRv5uBlqNAnSkeMhn2iNkA3NVoVbK70aY3FN5pR6pOQkIY9vNCjPxoQBz024oRUg/INej0kioZ46TqLhaGdzNG2YWoygkFJGrQkSUsw6tEEmjTy8HzDwI/oo0nGEIYNHjG9oE/ZtJSnFV7lERFR5RmVrWitpc0VsUpoGssgXIFmRJkkWJsTTOeMTE2ifapC00xnrCQxVeAxmWckvQTSmCAJaec+kU7RtiIy02+6bP/OHd1A6NUKxavBU1dD0c3cd4MkCZjlZSEbqwSV8LWtJ/A/CMLrunbBv2R4u/SYbnVFXopSPVkuly6Qkxd0FEXuZwVUSaZaqEvdoLh773IvIhbuUmzkd7p6lS7Fofu5onGQzK/oWAToSIDcfd7ysuyCmq74WsBHHMdOlyDX2aW4dYGTfM6rn9kN3uUZd7UP4jrTnRsCBmR8u+d5dU7Jc4OvrTi7nyX0NmlAJQF+V9Tdrc5I9eJVGl03SOkKu7u0ExmjLijpVoC6QKVLCZNzdO+r+/er86I7b7tzq1sFe7U6JmP2bTuUOm8U9uTJE05PT6nr+qW+GnLfk8mEq1ev8vOf/5zNzU12dnacLqIsS05OTlhbW3Mc+bZt+fTTT11FKQxDJpMJSZLw1VdfcfHiRUdz6vV6zm55Op269d/Vw4ilLpwLbQ8PD9nd3XXg8fj4mK2tLfb393nx4oWzYJa5nuc5f/EXf+FsV2V/koqgJAAkySLXLYB+e3ubS5cuueBRnLqkJ4S1lvl8zu7urqMbiuB4uVy66ojW5yLgx48fc+XKFfb29rDWusw2nK/PK1euMJ/PmUwmbG5uOmH6YrHgtdde4/DwkBcvXrh+SFK1DIKAP/7jP2ZjY8PpPuq65tKlSxwdHTEej2maxjliSZZ/Mpm45olFUTh9jdAn3377bXq9ngN6Mnd832c2m71kBALn+91yueRP/uRPeP78OV9++SXL5ZLf/OY3fPrppwwGA37wgx84PVAcxy74X1lZcRS88XjM7u6uu96bN2/Sti3Xr193FbTRaMTKygrT6fQld7GiKJyeQ9wEpSoitEgBZn//7/99tNZsbm66Xi4COIIgcPf34YcfcuPGDXq9Hg8ePHDrXubo22+/zXQ6ZblccnBwQBRFHB8fO8qpABbRn1y6dMlVzqV7/f/u+MYVjdgLiUNFfnaGHfYw5Tm9IGaIivtMlhmeAb/R5F7AcjrHjyJa5WFZoocezSLD15ClhrYOCOMehileXFPMLCktbVESNBVhYkhHIcWixTcBQQWh0ai2pKwqfJ1QexVZE6B8UMqei6TjhDAO8YoCL0yYmwI/bEh1iB/5HLcti5MluszQXsVoHDD0R+TNlCRraQJLFlpIYtqlIk7ANxGeDljqJTbWLNspcZJgCagzH3W2YGPnAtmhARXhtUtCP6DOPZZK48UBD4+es3HpElmU0C9C7KLFeIZaFYRYejVUccR0CUHgc3aWkyQpsT5vwKfHQ7SdUh7n+FVM3Wb0CVDpebfpamKZUxIFLe1xgN/mqJWYBYZmXpF4BtuAH1TkDXhli28tja3obabMljlRrRj0x5wWBcpYFlVDP/CZHRzivWYZhauclGf0VmvaUlFnAW07pKgWYFLaYo4fKsJ+TJLEZGcLkhb8JcQ2IxxEeF6Po6xhEAeopiaIFdbL8NIIY8GvQ2qTkxiYndZkG4pCx6RrF9kMS6oqo7YBzSAlSDSmWDLy+3i9hHre0hyGxIMW/JxIx1jfUqiKIMzoj3tU/ZhyFpJqH69YEqkS47cMvRi0JSoNtS6I/ZjK1pgCzlRF2LeY1kfHPfJFTrtcMlwbkakWs5gSJgPatCXrtaQK1LIhXdugnRak4z6m5+EZi1KarK1YGYQoVaGHLXlekzaaJvTI5gWBbQiDlhIo4hiPAJMXNKok1BqKb2eAADjLya5TjgRX3QqDvEi7wS583ZtCgkYBHa86R0mF4VU70a4OQvQd3YCvCwwkAJTSflcTEgTBS7QeCai7NK3udcv/SwAtwd6rAuRu5hG+BjvwdSDZDaa7gmsJNrpBqAS0XR5/t4ogJfouSJPAu6tVkRe0PMcuYJJn171GCbwFJArNTDjuci4JfARcdAFIFzC+SinrAsmurgS+plh1q1evNs/rgkOtv7YM7o5Nl/6UJAlJkjjnHvkcGbvuc5Ln+CrdrwuWJJvbHUM5r1SpugBF5nN3Dci6kXXUBabdSl13PsgcFGemb+MhNDIJ3OB83onOQta/dJUX3YRokoR6sr6+Tp7nrK6ukmUZX375JW+//bab/9Za1tbW8H2f3/md33EJjcFgwMnJCYDr2yFfr66u8uWXXzIajVhbW2MwGDhQs7Gx4TowN03D5uamCzgnkwmj0chRaDzPYzwe88UXXzhK1KVLlxyo2tnZ4fDwkCtXrjAej121ZTabuT0ujmPnViT/lsqBBI+Hh4e88cYbzkZWHKAODg4Iw5C6rnnzzTe5d+8eURSxsbGBUueN6X7xi1+QZZkL/j/55BNX5X3w4IFzl+r3+4RhyNbW1ksVHakuep73Un+MoijY3NzkwYMHKPW1/k6aoV6/fh2tz21xpcu5uIV1ky3dSmCSJNy4cYPPP//czZlLly6xu7vLxYsXuXTpkmuiFwQBP/zhD/nss88cRUv0ND//+c/dPrO3t8fm5iZZlnHv3j22tracu9O1a9d49OgRw+HQ6X2MMdy5c4cLFy5gjOG//bf/xrVr11BKuR5Hw+GQnZ0dB4SiKHLvnbIsGY1GfPTRR27/fPHihet3IXRIcWKT/iUi5u++D9M0dZUp0Xdcv37drSPP85zTWtOc9xr68Y9/zLNnzxy97enTpy+Bzf/V8Y2Bhg4TaJcYPyCuzzPrWRkTqJqwzLCmJFpNKaaWMCoY+iGlfy6cNXqKl/u0pUdLzWDLwqLFFgUxCq9JCdYimqIgai2mDllWGrViCccxdmoplj5RUBAkPkGeUlc1rbXoyYKkCdFeSdZrSfCJ8gW133BWNvgEpGkf5RnqScFKGtP2L/Di8RGhApUqpuUZNomxaYxpE+q2IlKaGEObNRQKPOufd+muGgJaEtMSJ4pobZ1ysaSaL9E9i9ckFIQEUY8mL2gDsKbm9HCCqVtWoj55PaefetDWNFVB3Uxo/ILaX5LMPDwVMFnMWO/1qWxDE7ToIiGOE6K1hvK4IF0dsFAtumrIzpaYyuLXijT8WxcULNk0wwQapXzMQqFNQ25qojiiOFuQeRErPR+ljtB5Bb2IxekpqlRY4xPokGx2ytu/e5t0Y4A93qNXepSnltgovMgjJ2BhFGXU0uuv0tRLiqIm9hNMo2iShkZpbNOjiSPyZY2qAmaqAWqisoAUcpuTMkIpTd/4qMKiTchkCS0BVvuU5CR+QuJZgtWWlThlYTWeGdLMFG1Qo5KA7fULoHxK/5jloiBRBf3egLJW5MdLNB4hAX7lM06hTVKqhc9qMqZeLglsSKN6FASM+z6mUrRlztyriAtDf3NAUS1QgQZfY2wMSUgdt+fWs0tLbMHWc/woZtlU1CYj6o8Jm4K+yWntEqUVTR1Tej5hkhDYBhUZmnKAaWeMB31sVWOWOY2BsK0JwpDGbn7jl/LftUOCXCmTyyF0iFcztRI0dqkuXQ681tr5ec/n85eCsi7F5FWdhvTREH2HUJeE3y9VD6GySOWjy/OH89K9iD+71qMS3Pq+z2AwcMJiKUlLdrwbKHb1E90sJeDu6dUMttyvZJgkiJFgU5x0pAlW9zxFUbwEhLr9Obr9I+Scr4I+GT/RM8i1S2ZNnlO3YiHib/ksqZBsbW05TYaMr9j0yve6lKNuIC/g6FUNRxeMSpAlc6Ab7ItdqVgDy7jJ+brB+6si664JgHx2VxD+KhVOnpnMXZlX8nMyd7qaEJkvXaqJgO3uGul+dlfbJCBMqloSkHcB0rftkAz3eDx28797TzJXptOpM0nwfZ+NjQ1HE5LAVFykfud3fuclZ7GjoyM2NjZcM73ZbMZrr73m9gpxpvryyy9fsky+efOmo+EJwH348KELZkWs7HnnLmyPHj3i0qVLrvdEN2AEXCfwg4MD1+k5SRLefvttLl68yOeff87p6Snz+dwF70Llk2TDxYsXefz4sVsD0+nU0cL+3b/7d/zsZz9jOp3y2WefsbKyQr/fd5TFZ8+eOe0GnCcRvvzyS6qq4tKlS+R57rqNLxYLV0EMw5Dbt2/z/PlzptMpu7u7vPfee4zHY/I8Z2dnh+l06oCG0NIE2AsN9tKlSzx9+tTtX7/97W9pmobj42NnDSxdrz/77DOUOnfBkrkhSa2qqnjvvff4zW9+g1LnhgBt27K7u+t0PbL2RqMRt27dcuL4t956i88++8zNP2stV65c4eTkhDfffJNLly65ClpZljx79ozBYOCqZ1VV8cUXXzAej/nyyy+Josi5nR0fH1OWpet6rpRiZ2eHTz75hDAMXbVtPB47C9+2bTk5OeHq1at8+eWXrl/Jf/kv/4Xvfe97DuCura2594Q818uXLwPnWibR1QRB4NzErl+/7vrDVFXlPv/u3bvAeV+md999F8/zvjH98htTp6KqIVhNCIYJWVtyZg1N31J4S/IgQ9UhhfaYxzOOtWXhN5RVRnu6ZGB9qqqlyWZ4bU52BPnMIw4H5+Lt0McWOXWpKLwENYgo0pqlsewdFGRlS9RrsElN3Rh8kxN4hgRNOvDRa4YyavAGCUk0IB6sQNAnCn10EKN7Gt/X9JMhVD4qtDRNBXHIslySxj1q07DIFoS9AM9T1E2NZw2BAj/WtEGNrZf4TUVTzah1S02PiIS+H9KWLT4plcnQlU+Joh1qCmux1pBPlqSDFUwDdmkxRYCtz521bLpB22rC0tBQkzczKgxzSqrSR1ce+jBHLSryGlTks5xOWSzOyO2UOKpZWU+IVcbsbMLpbMLpfEqlLVU1I1QlOtSU1qKDALs4JbCasVVUdc1kCab1mSuPhQlYeIraTFkWz5lXB0RJRD+FeNDiKdjsrXBha0gQWqJEE+uWSM0o8gm1TvDihLKpCXVEmSvOyhaUh581+LZiELTUywprIgwhiRpQNSGmMvihoW49ionBVgVR3bI6jKinE3zbMs+XVHWLPxqxbA1+q2jKGVYtmb/Yx08sOXPmdcHhTNEGfQbbF6jwyHOPbBZizhoSa8gaQ76EbFpiIs20yamVRxAkWKtIW6hnC3oqYDQakOiCcdrDxj5Nk2AnhjSL6bVj1CwmMT28eUEvC9AKgijA8zWqDqDU5IuapoFaxfhBxMDr4019kkDTBC2LWYuXWUJVM2ssJ0VLufTJGoVOUpTvExjwltk3XbZ/545XqTGvZuC7Dj7dEm/XLUdAhPxu14q2S1eCr3nw3SoJ4ITj3cyvVBfkOqqqYrFYsFwuXQDS7eUh19+9dtEZCA0iCAJnJysBzat9GLq9NSQA79Jvutl2yaJ3s+oSaIp9YtedS3zqu8BhsVgwn89ZLBYuW1jXtdNACCWqS38SioboKrrieXkO8v0ubUgE+3J+6aIrQYSM4YULFxxYE9esyWTCycnJS5WDLo1LAnO5ZgEWXcqSBDzydfeZC0AVa1MJ6iXoFx0O4Gx/BSjIfBIg1u31Ir/f1ebI73QrYHLN8rOv0tQEMAnw6YIcGfduNaM7d1+tML1aRes2Jvw2HovFgjt37jCbzdwc7ILs9fV1l4F/8eKF0y5NJhOiKGI8HjsLVplzf/3Xf+1+X0TBcqyurrKzs+PA8tHREVprnjx54ihWWZZxdnbm9EBid+p5Hrdu3aJpzpsCPnv2zM2nrmEE4ABDFEVcvnzZrcXvfOc7LtO/vr7OlStX+OKLL/j5z3/O/v4+ZVly+fJl11husVhw5coVPM/jzTffZH9/H98/75khvHxJTvzhH/4he3t7bG9vs7W1xa1bt1hZWXFOXWma8sYbb9Dr9Xj69Cnb29t4nsfOzg6np6dOo1KWpetTcfnyZUajkdOODIdDLly44Fy1pGeIVPmkyeDa2prrk/Haa6+R57lrmijaDBEyS5+Ts7Mzp4G6desWv/u7v/uSKYiAr88++8w9E8D1DJGM/Y0bN6iqitFoxJ07d5jP544utb+/7/ZlWadlWbKzs+OscUejEUdHR8RxzOXLl3nzzTfxfZ8333zTOZUJVU+qyUVRuAaPXbrm8fEx4/HYBfLyHlJKOZct6RWyvb1NFEUURcH777/PZDLh7OzMafRk7Uv1+tmzZzx8+JC6rrl27ZoT6vf7fTY2NpzZQNu2PHz4kMViwdOnT9nc3OTo6IjNzU3n9vV/XKMR64hqYbGVxSRjlmGCjyYKexDFmJWMsDplc2VIoEIaBSQe/iCgDA2hVrQDn8YsaY/mRGFEWyxQaoxOekRJStCL8PUqOumTpNDvlSSJoR8YbFvRYlHaMlElS6vxgpjC17RNBLqPXwdYFGUcEoQjVjcu0hv0yLOaLK+YFzUnkyV5AnqzpfVaVJPQTEvWejGreoDnW7wwxYQRfhRiLDStIdYe/SgiCDyaAIq2osgXjHSF1hkhhmjhU+egrCKpG2zToEyGsiXT+ROsnTOMfZJ+xCKGpD/CZ42EiDAIsHlEwBY6TLBk7J/tUvjH5OaMwitpqzOqacWiaFiaBXWlyDOfbDJltn8GVQHpGXVbsDQZJ0VFsWyoqpymylF1SbGomGnNdHZAaebUXsBkXoM3QOUBaENvNSEYxCztEhuFRCFoT3O6mBOsB3j9hiq21L5mdjIlajWrxPhlSWI1XmPpeR6DviaMfTSK0Pdoa0VVzjFRQex72AU0eUVba8LQI7c1S9UjWhlixppyAH6S0DQ+ND66bNA9Re9yn0GoaYuahWc5sQ2n80OUp8AbEJZ9mnpK0lMMvAjT9NBtQICiFzfEQcTUKJZNhq0r0hBsZMj9mLbQRMpgohLiin6q6UceplXUVcyssJSziv5QY4yH8hOULYlsSWgtA2sh9DCexvM9PEq0V+CPhqA1uvAxWUObe8xpmOsG3xhsZfBDsFSU6pRQaVCgIlBehPGhqUPy1qPtfTOnh7+Lh9hPSqD1Kme8Cyi6Yl34OqsvAEF6DQAvZX27v9/NZklZX3pTdDPWEnRJMCviaAkksyxjuVy6YKGqKgckJNhrmoYsy9yLSF6i3WBWGnRprR0dR/4tgKkrwpZzdDPq8izkZ+QaJTPW7b/heR6XL192Qa4ABuE2izuWAK8uuOvqQbpgTYLdV/UDXd2GfF/AWZZlHB0dcXBw4CovAhY977xDcrdCUpal6wMi1ygv4W4Q3R07qcZIlaQbZMt8ExAkgEjGI01TVx2TLubwtcORdMwV0PPqPJVrelVcKoChq8Xo0nu6wEOCGJlPkoEUipeARbn3Vy2E5fPl/K+C0levR8Se38ZjNBpx/fp17t27R9u2vHjxgrIsWV1ddW5j6+vrGHPeTVsqQELd+fjjj90+ZO15v42LFy/y5MkTPvzwQx4+fIhSir29Pde5vtt5fmdnx+kgbt++jbXnDfFkXEV4Lll+mfNSfamqyvUr6IL1xWJBXddsbGzw1VdfuTV79+5d6rp2wu9Hjx6hlOLtt992c0jG84033kApxeeff461lnv37rmANQgCvvOd72DtuWGCUHKkYnH9+nWnW5KAeWVlhdPTU54/f04YhpydndG2rXOJksanL168cPvIwcEBcF79OD09BeCHP/whP/7xj7l27ZprdLeysuK6cidJQlmWzpVquVxy8eJF1zNFhP4iGI/j2FV/pEIkgGNjY4PFYoHv+/z0pz9lZ2eHra0t/vRP//Sl/UP2rNu3b/PFF1+4PaWqKkeD+vTTT131Q/ZWSRhtb287wbesbdnbZYzl60uXLrmAXuhS8/mczz//3Gk99vb2+OCDD1xnb5l7Moel8vLgwQNn9CH7isyvixcvcuXKFZegEmOQ4XDo3iVSgcvz3PWIESAqXepFQ2SMYWNjg+PjY2dt+9prr/Ho0SPXnf5/d3xjoGGjAt/kqNwwaD3iRUGUW+IipskDmqbCT3u0dUqvjWh1j9yHNijJlhplLFHaEqcDxuN16iJH6ZK43xIuM9I2oBcO8VROG5fYcIwpIsLQwx/2yeoQZRO0H6HTAcMNRZ7klCjMLCcMfZQNMXlJMzOUpaFdLAjrktAC1mN9nFLXcxIfohMIrSKKehijMMuQHjH9MKatFF5l8Jqa1POItSaoK4JwgIpG1G2IbaEtFszbOcGgRzIM8Fct/bghiKA1FuNp4rRP0Ebs7R1RHJ7gtwFE0BQHVF5Fpmu8EPwgoGpbgrDA8yv8IIBJSVxadKApPcN8PkM3OUXeYqsYjUFX5w0UG1qWpWJRBwR1TVBDXFUkbYkhIKsqyhhqXZETUfY2OCsaZvvPCfqKuVkQTGu86YJq/4CjwymzWYkpKm7deB1fh8RNTMyQPNc0tWZT9emXHot5wazyQQWEtFR5Q7HIyKopJmgIhyHhag+GHiqNqYqGwGrSuCGwLcpUVLmlGY5YZkvMfIldWhrtUUQWygaTWAb9gJXVEXESkOgSz28JjAfEqJUe4ZUReqxow0OavqYN+2SVZXI0o6grGlsy9C/Q+j4BOalKiFKfdE2xnJ9CU6M3E0wU0CsD0sxibMtsllEYD6tTbNsQlRoTNrQXDMtwSRCEpP2EXhySjhTxuCSMElTmk2c1NvHQQYlHgzKaXurTLBosEVEUUJca7XkkPUtTabTpEUXpuSUzllA1xCZj4DXUKmepTr7psv07d3Qz+V2uvgRYcgj4gP+xN4H8rGQfu7QeqTx0BeTwtRuSZJEkUO5SY7q0lm6lQ4IACbS7mWahQHV1CwJ45DO6GWQJbOWQoFyyb90gUa5PAJP8f7dpWzezPpvNODg4eIluBjhetQRaxpx3+xULWsnAdgXCxnxt0ftqZQC+Bh9SBeiKuuWZSsAvzyfLMhaLhXtWXZvYtbU1N/4SVIhrjIxX9167TRLh6wBfnlE3kykBhVSMulUkAR2SHe1WIESPI1SKXq/nziMBYHeOvUp5elWjI0G+PI8uqOhSnmSuC8jsPlO5V9EpdOdCl0LVBVldepkAKamyfRsPuaeLFy+6TtnyPB8+fOjm9KtrJgxDLly4wHg8Zmdnh6Ojo5cyvlEU8frrr/P3/t7f48KFC0RRxM7OjksGGGN48eIFz54948MPP2S5XPLFF1844LGzs8NoNHKOTKurq25vOj4+Znd3FzjvA/LixQvW1tbwPI/19XXG47Hbd0TMLCC6W7l7//333fhqrcmyjNu3b+N5Hn/zN3/jxNeyFnd2dhxAunbtGg8fPuTDDz/kj//4j6nrmgsXLrhsuiRUvvjiC2ftLP0tpMIyHo8ZDocMBgOyLOPixYtubl6+fNlVjkTDYq11Oo39/X3n+OX7Ptvb25RlyYMHD9zes1wuGY/HvP76626dVlXlKo7iOuV55z05ZJ8RzY08t6qqePHiBYeHh3z00UdcvXr1pfUH53rBmzdvsra2xuPHjx1weP78Oaenp2xvb7O7u+u6ZMt+3N3bd3d3XcM8wPVukepFnufs7+87+p61ljt37tA0jVt/QjM9PT3l+vXrvPnmm2xsbBAEgatWLBYLt3dubm6ysbHhTAykoib7lThO5XnO8+fPnYXy7u6umzvz+Zzlcsnq6iqnp6c8fPiQe/fucXR05PYbqejJ+jg5OXG9Qe7cucPu7i7Pnz//Rmv2GwONPFsQ6ohmJaIM5wz6IVpDUxWkXssgDikrTVuW5GpJsyjxaoMaGCpPM8ka7LIi0gFhGhH3EsrG0C5rfPpUtUU1QDwiqPtU0wpVNeeB5PKYXq+l1wto6oaVxifMa3xtGKYxnldQ+4Y8XOIPEnzd4tsSqxX9IMFvI2xgaNolts7wjTp3MFIVkcnxRzGNWYKZM4qGeBqSsEcbe9TrCs8H8MgXC1RZ0E/G2KpBa49lrtG5BeVhKNG+pdAVSnsMNYQqRydw//kpDx4/J4n6NDam769AUaJM9be2wD0Wkc/TPKOsGrxZRaxSFosldnGGqkvaOKE2c6bVhNmyRbctytQUhaGoWyqjKPKIha2p54ZoFtJmAZOyxGbtOT1NV5iFJjYBoa/p6wDbBniNQnmWom2oygK/bWjzgPXtTS7dfI0Xz5/TX1mhyVPaumF+csTp0VMW1QnTfHmub4nPKWm9NMGGHrnfEoWWYRrQNEtUUDPqDyjzkmWRs8xKFg1ktkVVhnrSUGQ5oT0Prqu2JU02CKseoQkxbc3AKNS84WS3pqg1pVIkOiKsY+ykws5m5GFI08ZgasIwI01a+uPhOaCwz1iaKTYFnWh0MmJeQ6A9AnyMgdpXNH1DE/jUESyDEqNq+lqT6JSygmZaksSWHmCqiAJoNVQEnIY1Zq0miEIaFD5LvHmBryy9VNHkFp36WJtjw5ZlXdEsW2zQI/c8apPQVoaoBD1rULagCQwmjFDemLb59orBxZmpm3mWILZLN+oG/V16UhcQyL8l6OxWH7rN4ITuI1nNrii6SwOSzFBX9/GqQxPwkpBWxM3iYCSZNbkWEdfJz0iVQjJvXW5/9x67oEACpW4zP6FwdOlkWZY5K0upkEhPiiAIOD4+fknbIsEp4DL9cv6iKBy1qqsNkeBYXmhdzYhoKrricgkWPO+8j8bKyooDh8YY5vO5swjt0rcAnj9/zsnJieuP0A2uu3S6rv5DQJlUh7rAsEs/kkNoG+IIJueTuSOUDZmjXVAq4yJztAs+X9V1CAjp6kyAl66rq/+Rc8t9dul83S7iXYvn7v12NUJdmtarlLFv4/HVV1853cLrr79OXdfcuHGDfr/P5uamAwR5njuaT5Zl7O/vk+c5T548caDs17/+NZ9//rkTeUv17eTkxFUwsyzj+PjYUShHoxF/+Id/6HpASBft3d1dPvzwQyd6jqKItbU1Z7cbRRGrq6sAvP322w6klGXJYrEAcP00xHRA9iQBy7I/Cc1zuVw60bQAlIcPHwLnc2F3d5fJZIK11jWc297e5uOPP3YuQ+L8dHx8DJyDC+lILQ0Ox+MxGxsbDIdDpxOSz6jr2lVYzs7OnMZDMuYiwj8+Pnb/vnPnDqenp2xsbDj6mjGGJEk4PT3l7t27bi8VKpw0FsyyzCWNmqbhypUrzhHs2bNnrjeE2MMeHR3x2WefsVwuuXr1Km+88YYL7u/fv+/eF92+SFK5Xltbc80B33rrrZcSYPP5/CXL4+PjY4IgYDabMRqN+OUvf0mv1+P58+eOAqaU4tatW+zt7XH//n3u3LmD1toZDLRty507d7h3755LWkmlKssymqbh8PDQVayPj4+x9rzzuZzj9PTUzSfZp4W6JiBEfr5pGi5cuECv1+PSpUsOlMjvSqLLWsvOzg63b9/mtdde40c/+pH7vW9yfHONBh4UoPKaQPtgG5Sp8IKA08M5cZtS1hm2zeklMaa06ExjypaGBbP8jMmsIqelsBlerFCphzKQrvfweilN1dJOJ3inc/rLglB75LOSqIoIowHl1OC1AcYqllVKtYB6UhMlMX6pCdsSXS6AHGzBUvlkLZRWEfT75MqStyFDtUrc6xM0PqrRZKaiSVr0hibe9PFXY+ZBTa0a2gqyIsAUIcY0NMUc1c5oFdSmwqqaualJhgP8JKFG4ZsSrVps6RHhkzctZZXz5MUzBhcSwkCREuPVmiT1SVf6xL0VPB2gYw9jU/zNCyzDgGcHE0wdEkQKMOSJJRoYBrqmmk6Y50vKyuA3C/JiibY95nkFoYK6RIU90rZhUJeUp3OGVhNRUzcneKHGJillUWGqmqm3YKJqTictZ6clpvV5890fMlzdoqlqynpJnk0JVMRgdZ16w+esmuFrjUeNH8ZUytD6Da3VROEQP7aQthS0mBJYFARJSKUNeKCVRRcVQVFhF6ckETTtgtzPWNlIWdvos7o+wAs1s6Jgmc1ZTE9YHQ4J64ootlQmZ5nX4FkCHRC1ARQ1Yb4kImc1CWmrJWWu8WJDL4poqvNO7nl5hsoMKE0QB7S2xjQhpgY1SPG9AZ726BlDRU6TVvhJTOr3GSofyoIg8gniiLoqqZY1o6aHPs6p2oZ4xccUOdoY6rZmoXMY9jCeIh32wfMZ9T3SXosyBR4tgVXUJqPWCUZ7xJGPNQ2L+YK2aAnst1fEKQE44IJ+yS5LQC4vFwnKuz/TtVSVSkHXPalL6Xk1KJQXdJeK1A0MJRiTwFQ2+e55XuX+SykfzoPDk5MTFyQKJUmCPfkdCRqERtEFU10AICCgm2l/taJTFIWzs5UMa/eFLJoI8aaXl5AEp12hsOgMXg3mhT4kz1Ceb1enIX+6HGYJ2owxzmZSspFlWXJ6esrR0RHXr193Jfzlcsn+/j6np6d89dVXLBYLp9kQOlsXbHXHpCu67jqadatFUvGCr8X00rBquVy6eSmHVF56vd5LAFhAq8xHmUddMNMFJ91qUbf60qXDyf10xdxyzfA19UnE+gIculqLVylV3Sxul/72Kkj8Nh1XrlxxVrZFUXDt2jVXsRAam+d5rK6uOlcc3z/vRn3//n0uXbqE1pqLFy/yxhtvOCGv9BlI05TZbOaeYxRFbG5uMhgMOD09JU1TJpMJd+/e5Y033qAsS65evcpbb73l3JQEREsTQHG4evr0KZcvXyZNU0e52t/fd+O1WCycGFfGWGxi33zzTXZ3d/E8j6tXr6L1eR8L+WxjDLu7u1y9evWl56W1ZjgccnZ25ioM8/ncmWAodd6bQ3rcXLp0iddff5319XWapmF7e9vRaEajkaNRGWM4Ojri1q1bXLlyhdFoxP/1f/1fbGxsMB6PsdYyHA5dzwvpUyIN43Z2dpxoOo5j55glonZJfPT7fS5evOgcumR9iMvX9evXWV1dZTAYcHZ25gDh6empG8Pf//3fxxjDw4cPnU3vpUuXXBVFkiOi2xAq3O7uLtvb2/zsZz/j0aNHzlTi6tWrboz6/b7TxmxsbHDt2jU2Nja4ePGim1NyfhHNh2HodD6SjJKeGG+99RZRFGGt5cGDB5ycnLhnNhwOKcuSFy9e8NVXXzmHqaZp6Pf7rK6u0uv1iOOYwWDA1tYWi8XCzRtxCLt+/bqjQL3xxhuOItetji6XS7cXnp6eusr306dP+eCDDwD+z7tOeTX4aUikPQql8doaaDlrSpROmM09TBiQ5TVrKsGsaKqyxg8jRn7NOFzHlBF1URIFFl0v8byIvFkSrTQwq2nrll7kgy6xKqLCY7iVU80U2lT0tD7P3KclrTVsrg6o8yWq9aEyeMsYz9MQ+vTahiZS1IuKfpgyWxiC/pilLZlmGbWnKI0mI6fnxdRVTrIyIjo+JsVQNBm2KikXChOmZGHL0A9prEflN1hieraFKqNoFIcnITvDEXnoU1XnzQYbWlTeEgYBy9khH37yId/54duEA4+giQmjlKyZk6Y91scTNpM+/aigzFqq5Yw0rmhaxYtnC4YXDH07pQaSeJ3Q+tSVpvI1KvZpm5b56QlENdiSiW/wsRTThn6qaPyGoLacTOfEraK/uc6sWJJW58GK1x+gMtgIQ45YktmKaBzz45/+PrNlRluDbjySGGoN5bRhsbAc7WU0XkSYJNi2pqkzWmsJKouXBqgEVFWyEq1wuDdB9xPCpKWvhlRWQdGSlQ0qTlDKkJ8tOatKiFMGUUacVLRDj6aqUek6TdAwGkakg5DpLARt0EFFrFs8U0EGbVOT+CErTUJgDfN6Ruv30QpM09IYS+BHxH5A0dYEcUgQByyKCmsCBis+bVWhFdg6J+kZkjygzQ25rhiNh5Q15LOUsB9S5i2Jrwh659UzrwGlUrKqIOkpbC/laDoniVYYqppiWRGmEdUyxxpNP0woiyXGs6z3VlnkGVhD1ir81se0iiiMCFcLqiqnWlb/u+X6d/bo0oDg6+BJKCKSyZcAUYI08ajv8vKlIvJqsCuHBPMSbAlAedV9SCgqwEtVjW4mHL4O4uTnoyhygYUEbyIK7VJj2rZ1nyVVCBGByt/yYumCpC6P3xjzUkDZzehLcCU8aaFwTCYTd/3SKKqqKlcS7zoxSTY/y86NBiQA6dKh5Gt5tt1+FEIRkfNZax2/uGkaR7eQ8RIet9gsTiYTdx9KKR48eMDjx48dzU642N3Ph68NA7pfCzDrzq9uVUCCdxkPmVvyfyKOF765PKcu4OpWnroVKfnMV8Ej8D/Me6k6yVzvjrVogISK0v287v3JXJT52j23zBkBX13anVRtvo2HVAObpmF3d5eyLLlx44YL6tu2JYoilssld+7ccU3dLly4wIULF1xTO9mL0jRlf3+fjY0NJpOJG/OzszOGw6ETvHqexw9/+EMH+o0xbo2Nx2MGgwGXL192VY7/8l/+C9vb23zve99DKUWv1+PixYvs7u6yubnJeDx2GpJuJTQIAlZXV3n8+DHWWhdc37lzxwWrMq/v3bvHrVu3uHv3Lsacu0mJtWsXwMo9A7z55psuw7+7u+v2m7IsnXtTr9djdXWV2Wzm3L2Ecvf06VMWiwUnJydcuXLFubbVdc29e/fY3d3l0qVLrhpwenrK5cuXnQ7g4cOHDIdDTk9PybKMwWDAcDh0VrVS1RFgL9bDWmtHEdre3uajjz7ixz/+sXPs2t/f58qVKw4knJ2dOT2JPDM4rww8fvyYP/qjP3LvEXl+0v/j+fPnbs6UZcn9+/df0sKIyUC3Si36kidPnlDXNWdnZ66qI++fsizZ3993yR/RZsi+KzQ26RY+GAy4d++eo6D2ej329va4cuWK69JureXatWsEQcDp6akTc4vu5cqVK466JxogcTATALlYLDg+PnaVMvksqbgHQcDDhw+ZTqf8wR/8gQMy3erw/+r45n00GFFNcmzfp6ohGPaYqBm28Yg9TTAKqCctUb8HyqKVRxLUeLbFqITprMRLU7Rn/1b/YNGJh2r66GyOl1dY1bIkQbfnWfdY1RSUROMBdZljWoUfhDSRTwIcH50xHEZYW6H8EO0pdO1RktOqGr88o8VS1+DXLcvGw84b1PoaG+vbHDRHxCpgkNZMa49q0TIqYewPKNsldTAk7nlUbY7NW1Rb0rMVtoloVECjNQEBoW0ospozlVOXIUXVEDUNoyQgQ+FFENqW47MDTg+P2FpN2DuY4c8bkqBH2++zOtxiK3jBl8cN9bwhUD5e69GLemgTMH+6T389xUtSJsU539+vDXVhUcGMsG1R0SqVMqyv9JlPFgRpSjYrzwN4ajINxUBDrlDVnGq+oC01gd9jVmaoqKbMaipb0PMarm++xncu3eKzzz+mig2DoE+Rl+hsSZtDMFec7s9olaXNztC9HrnNIPDoeRFx3yNJR4SZobaGYBxQR7CYJDTLCZWtCFSACQJQDXm+YKBjam0IgpAoXsVjRIsi9DWrQUTdVszKGj1vyK2HZ1tUsSAJNXgRpW4I0yFxa5iVDfiadMVHNQarFX7Zw9qQoLTY2MerDRpFrhs24pSqCaizJfEAqiynbRVem5z3MgkCgj4sFseQpqihYmYq/CQkthGqLIlsShAllPo5+BE5GkxIGm1hwoiibIl6PuG8JhqEmDanMDVzW+O1PidZRlFnBCpgpEqCUUimDWUVEHoK2yyJ7LeT8gAvW3EKj13AQ5ea0ratC0q7gZMExRKsithSuqFKMNcFKPL7XVqOZHe6lYounUU0ErJZS+AoTZDkWoR2IIGblPVFH9AFORK8dgNf0ShEUeTO2z2fCLS7dCe5l67lrlAJZrMZn3zyCT/4wQ9cxUMy65cvX+aTTz5xQUOX398NhiWIkzGSoxuow9eibKl6yNgIQJEMc9M0DAYDRzXx/fOGWaenp7z99tvODWg+n9O2593Af/3rX/PixQu2t7fp9XpObCnVIMl2SvAgQki5DwFvcgjVowueJGiXgK/rM39ycuJ6HkgVpvv/3aqazFd5FvJHnmEXdHTncFebISCtO0dEeCpBsYCFV+/zVUqYaES6a6dLrXq1YvZtO/I8dyBUrKOVUg4gaK25efMme3t7bG1tMZ1O+eqrr7h16xabm5vOwrjf7ztQ+d5773H//n2XFVbqXEz99ttv8+LFC77//e+TJIlbY1JFFUqgUDRlTbZt61yHJMA05twGemVlxQWJIoTe2NhwuoZHjx65Ts2e5/H06VO3N12/fp2HDx/y6NEjfvKTnzCdTnn06BHGGEdL1Vpz5coVgiBwlqSPHz92upHf/OY3rpohBg37+/v86Ec/Is9zrl27RlVVvPbaa9y+fdvR1L744gt83+fy5cv84Ac/4KOPPnIOWmLzvVgsmM1m7O3tcePGDYCXEkSz2cxVoMTCW8wXJAHS3VtlvxFx+f7+PpcvX2Z/fx+ttdNxhWHo6EH37t0jSRKWyyXXrl3DGMO/+lf/6iWt1q1bt/jNb37DycmJq1qJuYCI1+fzOXEcc+vWLT788EO310qz1tXVVaIoYjAYuPVljOHSpUtOQ/T48WPatmV1dZX19XVXOZVKrgAGrTV7e3tEUcTh4SF/9Ed/5Bo5iuh+b2/PVWk++OAD3nnnHS5fvuwsgYuicBa83/nOd16qKnU1Hmma8uDBA1fZWy6XPH361IH0yWTC2toaa2trznHqu9/9Lnt7e1y6dIkvvvjCuYoJRet/d3xj6pQZt+i0zzKHxQLOZhVBm+BHAYQedTEnMIrlicWYiLatqHyfdunjZw1hC35YolRDnPj00xXqosR4Hv24h60L/CiioECFAYPNEQkeap5ishK/MrQULMopOl+iC0MvHFIVA6wZ0maWvtXowJIEmtBE9L0+Cav0kxGJp0mqmGGaUKuGXhrh+w1+GDHPWlpiglWFNxrQG41RpsZoj8W8oprk+FFClabkUR/ra7zQYiIPG0BtDJWuyRXgp6RmiKp8KmNJk4DlvEEXsHhxxt3P7hCHKb0wQWUtVT3DlHPCOGLn2hX6qqUtcxZtzaKGKjdYm0NjeDbNeT6bMj05RVU1NtGQFQTLBt+EhNYSRT6zvTn9uiI/WxJYSzGpUWWC549RjYK2IT+c0WIwA02jMtpiiZkXnC0WLDxLHVr+4f/n/80iy/niyy8w+BSnBhMY0p6iTTSHi1PKvMArI9Axk6wgxidpPKJeDPOa4rgCNaIkJIw1y+ND/Cih0QrtNfihBduidIqX9vCThBRDYBZEsU/aG6BNRFN5zIuGiR9gVobQrynnL4gaS6gC8GOKoibPA/L5jHlxhh8H+MMNskYTtBbmBl1H6LpEBQWT8gSTKHJbkkQxpV1A3eJXPfzCok0FfklsoGpmmLjCGEtuSgxTlDLEYcDKqsLz5sQBtGlDYZaEQYSpwLMxgd+nZyJWG4utM0w9oalr5mcl80kLKNIoJlUhYWBJBiGgqLyCOloQUDFODW3p4ZsRg6D8psv27+zRpQV1aSbyYpaNu5uxfdWtSrJ2wqfv9o/oVhK6+go55He75+pm7F8VdouIXagqEkx0rR2FQy/iZQl+JLAB3DXK70nA2s10d7n3XYFw10VInkVXUyAuXU+fPmUymbh7zbKMyWTCeDx2HYq7+hahB4n2pJvxFnAmFZkuvawrEJcXsLjYSFbt8uXL3L59+yVv/jAM2dvb48KFC6ytrTnKlFQ0nj9/7jzdhQ/erTLB11oEAYgSlAsAE6BorXUdbrvOZDLGkrkT4bvMmel0ynw+d4FoVwsi81LuvatDkWclVYkuRQp4ib71qrai+8y7Yy69E7quZN3x6NIB5f+7YFsArcytLtj/Nh5BELC+vs7ly5dZX1/nwoULHB0d0bYtZ2dnfPrppzx58sQF9hKAxnHMgwcPKIqCIAgcCFdK8ezZM0crEZ7/u+++y8nJCRcuXOCTTz5xGg8Bnzs7OwyHQ6f/EurS8fExm5ub3Lhxg4sXLzqNx71795xTnjTES5LEOcaJwFn6FiilXHVCxmo6nTpAenZ2Rq/XY2try+k/1tfXaduWJ0+e8PHHH7t5JPNE7LfFkOLo6IjPP/+cKIq4c+cO1loODg4oioK1tTW+//3vOwqpzB/RkKyurrq9V8TJ77zzDjs7O6ysrLC3t8fBwQG3bt2iKArm8znb29uuitC255aw+/v7TlsmrkxCRzo4OODs7Mzp5nZ2dvB93yVLZF8VQwlx0JJx3tzcZH9/3/XUEE3JRx99xO7urkuS5Hnu3K5u3rzpwP/FixddPwupGnie51yf3njjDVZXV101QCiW8nNCZVosFq5beb/f5/vf/z63b992eqD5fE6/30drzfe//30+/vhjN8+SJOHFixe8+eab3L59m7W1Nd57772XKLkCFt58800HgETn89vf/pbj42Mmkwnb29sO5B0dHTm9nFLnjRGDIGBnZ8dpTkTXcnh4yOHhoetYfnZ25t4j3+T4xkCjXEQ0TY0OFd5A0VJSsyDwWuIwxCQRySBi1DMUVQGxAey5A9HMEoUD/LIl9iMq33JaL8h0g+4bjG4JdUrceOiypaiOmcyOWLQxZWTwAg+dBYTEmDZExSE6yglVDdWcOpsQtIakH2KWBVopgtDDMwm28miaiiCyMGqZ+i3VvKWPJW00XtsSRz38WhOYEWl/BT1MaXopeVPRap8yTMiMpp4ZsqIgVz5t2RK1JbHOaaylmBbkkyMajtD1MXFQs1BwakF7iigeMjvL+ezDT5iendJLNF6SkIbrhI2HLiuG6z2217eIV/r0Y02NITeG0lPUQQ9TKOpCU+qA+emC2fMpbVmwqDOmtsX6NXHV0ouGtFEfAh9tSmzskduGrF1iJ0DjUZUe3lSj8ob5bEZ2mqNnJaqsqPKaN3/wU97/3u9w55cf4FtLvpjRVIqy0kzKlpla8vzZE+JyxqrOKOcFFJYkHUJ/xDS0FL2EIA0ggLY8DwTC/pC6OsGrPJRR1ArapqXKFvStwZs3lK2i9RuS/oDEjzF2SaYyTFUwblcY7SuipcIb9CH2iTxDYjWqNoxWLJiKKLToIeTekuq0RJctbb7EYDGqIKsbeuGQ0PfoBx40Bpt5UGvyGow3wDOKwMbk0yU2MORRTdmUYDz6dYRdauICikVLY1NqE9KmUKic6TICE9FXMdEiJ1Ity3mJ3zRolWODBpVq2jCgtRprFS05jarxW0sUeQRxjVpWFPOKszKnZEZrMhr97fW/7/ajkEBMMuOS+e3qNbrZWgksuwFWv993Gaau4Fay3vJ7rwKYVzn9smF2tR1xHDt3Hml41+/3XxJ3j8djxuOxuw8pS3cpUsKRls8CnCi3e71dgXE3iJZzC+ARMb1ci3RylYz5bDbjww8/dIGpZNHKsuT69esuUBDBpgSlXZDWBVwSHHfBjAQwr34tAbOMs5TYuyBld3cXay3vvPOOc7+SZ5NlGb/61a9c9u1VLUa3t0f3ecmz71oaL5dLNzfOzs5cQ0cJnCQLmqapy/RLReH58+csl0uWyyXT6dRlX7s6FXGwkucv8/hVoXYXvMk86zpmyZwUINl1MhOhcHfeSqDWpS28On/le1IVk+uXf3fH+Nt2SGAvAeLW1habm5usrKzw/PlzNjY2nB7h5OTEVRAk62+t5ec//7kLxLTWpGnKyckJjx49Iooirl69ShRFXLp0iY2NDV577TUAN/ckE97r9RgMBvR6PXZ3d/F9n62tLWcuIHvQeDx2Tc583+f58+f81V/9FQcHB8xmM7cfVFVFv9/n6tWr3LhxwwX4AjxkTUjXcKEx7ezscHBw4JrGSdNAWVdheN6ZO89zvvjiCxaLBbdv32Z7e5s4jl0X77quGY1GjgpVliU/+MEPmM/nzlRCsudt27K1tcXp6SmPHz+maRqXwd/a2nJVzqdPn7q94ZNPPiEIAjY3N7ly5QpbW1vcuHGDNE05ODhwSYWyLHn8+DHXrl3D9312dna4dOkSw+HQrR15NqL/ms1mbp+U8f+v//W/OjrRrVu3HBiQNQu4vVfAatedSUBNr9fj7t27rnIu+3YYhoxGI27evIm1ljzP3fMUsCNOVFLVns1mRFHEr3/9a5egEmpXEATs7u7yySefuP14f3+f7e1tBoMBv/3tb939SlJJaKpKnYvmr1+/7tbJfD7nwoULbG9vO7Alc6IsSwaDgatira+vu95F4iq2t7fnqiBdBzDR+XzTqug3BhreWkiQakzPoIKWXi9nMMwIA4UxDUnjE/WhIaNtCnrWEhQWLxjQeKCDCqMKTuZTSt2w9JaYoCFr5pT4RIMVDs+e0Fdzxv0eFREmbakWGUXTUI5qFsOKMLGwBF3HVCpG2xDPg2SkWNQlXrxBlkOjILchuu+hU0sTalTb0otrrM7wxyPK4QA79ml9TRz1iMqGXj8gSkOU0SSBRamGINa0dUmbRtRxgA5KgqglCHyKsqVtCqLQB+thS58yLFj4y3N6U2nw8ZnniiUeuydnfPHVFyiVEg1SbLXAbzVhf0S6ss7rt7/Pxa1LVD1NNNSMYk1WFTRei+eDtyzJFzlVXWB1TWsKyAvmWGq1xFYLFvWEuinIyxlLWpYKCqtZHJ2hmpK6asDWRGnA8fyYuS7wohCTK0KjeePiDv/ff/yPmUwPOFseE2vF0CtJ+hWeB/lSc7D/gv3TXdowhCBgtDFgEGqSxlDOGup8gacL0oGH5y9omOMrD1WXpKElCqCtE8oiQOmY1lQEqqa0E+LQI9UpK+NV1kYxqlwSWw8VDSjqDD8xeGWP9d4asY6odcLCGNABSewRJz42DJnPM/S0YJhDW4ZUreK0qtEqpNeP8CKDbmqMF5MVhpOZpaCm9iom8ylBOuZs1lKFfbQf47eWtUFCRQODVYhCTBHQ83sEJqAxirKqiCwMPEs/hDNVUtiGpjUk/YZgEKLpo+OYgiX+oEDR0oYGb6ixFZSFZVYW1E0PpQbQH9BaRVGUZLSE3vD/33fz35lDeO9dzn+XPtIVPsuLFXD/3616iPZAMpld4asEVF2ay6u9CLrZZPiaTiUUBAkiJdsvZfLuC7ff779U8ldKObtNCeQkcyrgpJtRlqz1/4zrKi/CbhDfpQ6JY5Yc8hme5/H8+XOXRZPqi/D+b9265XjCEswKXUvGQipI8udVvUYXlAiQE5qV/ExXcyEWoWdnZ0wmE37v937Pvewk8xyGIY8ePXIvT2mw16WOda/p/2kMu4L6roWvgA0BS+IKJNlt0ZlUVeUccrpVjq4+SMay2wumq+eR5wi4gLhL3ZPnJn/k+kWnIz8jYKmrJXrVlU0AUlfAL393q0IyHq/qXb5th1S3pN/D3t4eL168cDScIAi4f/8+ZVny3e9+l5WVFd555x1OT09d073333+fe/fu8ezZM/dchFZVlqULpI+Ojnj8+DFZlvHo0SM3Fq+//robY9H7vPbaa9y6dcs17vsP/+E/OKGuZOU//fRTwjDk5s2bvPfee65pmlhTyz4moFS6XkuAuL6+7ugwsleFYeisc/v9PgcHB9y9e9e5BcmYP3v2DPi6avrll1+yu7vLBx98QBiGnJ6eujUqnP3XXnuN9fV1PM/ju9/9rqM4SfZb6/OGcfv7+xwfHzva0aeffopSylV1ZG/d2NjAmHPTjCdPnrBYLPjiiy9c87u1tTWXXY+iiKOjI3zfdxXPyWTCs2fPePLkCRcvXmRra4ter+eaAFpr+fWvf83f/M3fOPG4AGzRRdy+ffslbZ+I3q9fv+6qSuImd+fOHQ4PD50WR5650Mpu37790jtK9hnp7SH0su56HI/HvHjxwnURF13Qs2fPWC6XzGYzXn/9dbTWrK+v0+/3iaKIx48fOzBizLn97OXLl196Z4o5iDHGUZHX1tZ4+vSpS8bVdc3v/d7vsbm56QTmDx48YDabOdB9+/ZtwjDk8PCQZ8+ecXZ25kT5Ut09Pj7+Pw80TqYLltF507yhGjLwNhj5OzQmR8UTVLNgPjtDGYUh4qyosQHYtsQfwlxVnM4z+kFAiCUOY8ZegKk8dBhS2Ap/cwU7OO9joTMI2pheeoU4WcULE+yyR9D28PUASouOa3QMvh7QeIbaWvJ6Qj/2zh2VbIOpatrSp7FD1LKlLZZk2ZKlNURJQGBbvNxQ+zU29ND9AeP0EhfWbmDL5hyUNJZItUR1SVB60BpoDZmpmYeaNo4J/BBrQ0xeobSP7fVRkaIXK0xoUGmNPzzvhfHXf/0bDqZH+GlKbUC1miBQ9EPF9s4lLu5cQwcJeQ1V3NJbC4l6EK/06AVDNvtjPBuRTwum85rMgjkzzPcLXpRzlrYkO2tQuWLgtyibs2hy/OEGZTNgWuYcVjMO8n1sWKM9H+P5lFajB33+X//4j/GM5csv7lCbJUs/pjFr9IIeYa4o8oInv71P2EAU9SmXAaqEyPcx1ERRzupY0dMaL/PPe5/YgGKyZIRPnBu8UINfoW1DokNsE3CU13i94BwIrI9Z3V4nTQKCyMMPIiKvxyAEj5ZnZ0+ZmhyfCFXVmGpGG2imLdi8Im4MSRzipy3NpmEetPjRgGHfI0hCirOWzPPJ8jltmzHQEf1xijeAwchgVcF8kREFEXUdgRdjc8t8Nmc87hNFlqA/Jeh7VFaxbAtoI+Ksh6f6lI3FK3PShWE2sVQ2omw9KlUT2IYozInsnBhNjEaRsqgCqtrihx6DcI1UrVEtoKwa+r5mrDz6OmGqv71icHi5cVi3gvA/+zd8LaLtajvkZwQ4nJ2duYBO+PiAAzVdbrsEzpLV7eo6umBHKiryQu2Cla4d7/b2tstgep7nnEuU+ro5l2TeJPMtgR/g3J5eFbR3aUDdawXcdUkA2nXJElrE3bt3advWlfPlOaytrfHaa6/x9OlTAC5evOhK9HJfXUtOEXv2ej0X4Mj4dJsOdhvfdTPvYlksvva///u/z8bGhrsnufbpdMonn3ziXFik4iPnk7nRzdgLoJPxE3G2aGmkoZUEb5LZl0BCggyh3llrXQUDzgN5oXx1wYTMYflMAb6vai5kbGVeSrWqq+WQMZbKk3jgSyf1PM9fAndy74CrHnWrKF0AIUFm91l119C38RiNRvT7fbIsI01TiqLgjTfecM5Eh4eH7Ozs8MknnzCdTl2DtTfffNMJ4Ou65ubNm66r8/HxMWdnZ+48AvauX7/OzZs3nXYDcAJf3/ddsNU0DXfv3sXacxvZ0WjE22+/7cZcQL44/Mznc+7du+eyxf1+3wmk4bzp3vvvv/+S+4/neTx58oQrV66wv7/Pu+++y/HxsXNpUurc7vTChQuOhtOtpEk1Rrpjy7NYWVnhl7/8Jfv7+/zLf/kvefLkibOGXVtb47PPPuPSpUv85//8n1+yy5beFePx2D1b0T6JtuzZs2fM53MH7KVPhmhbmqZhY2ODwWDA06dP2dvbI8sy+v0+nucxGo3Y2triyZMnHB8fu2ryG2+84e7zyZMnTg9S1zW7u7tsbW1RlqWjVgmoOD4+dh3bZQ3/6Ec/IgzDl+aKVLO6OrTvfOc7TgMznU5dxVBE9qPRyCWjLly44PaBzc1NV+34xS9+4bRpWZYxHo+5ePGiA3BnZ2ekacrFixdd9/rpdEqapvi+z+bmJlmWEccxe3t7PH/+3CU9bt686Zq2ynWnaepsenu9Hg8ePADgwYMHrvP7zs6OA5PD4RBrLXt7e2xubvLd737XUQ/X1tY4Pj7mF7/4BQ8ePHC9Tb7J8Y3F4EGgKcuGOs/QUUxZ1oR2iTU+kdEUbUGiV8hNAb5BBT0aU0MKBBV+OGajNyS0LflyAUqzUBUrgx7TrCDq9ehVM4rJBM8kRL0RZ5MZUT+iOm2JRh5xeJ41LvKaOImJKdFeSaUVrbWEcYwKa4IAykwRDUPsoibxYKlyUDGBH1BmLc2kIMVjUmRES4OJA0xTE1QeO5e3uffZfVAR/SAlKCxlVRKtlST1iEVlMJGi0Dk6SPDLFt+2+EGMbz3yqgZT0TaGSif4yqO2ObSaulG82J/y0d/8hj/8e3+IDkJsqrHakJURfmC4/fYtzto5j+/vMj2d0A8MTd2iAmibksWJIio1qu1B2FKbBrvYR3s5numdNy1shuh2STv3UFFI6S8pZ6d4fp9g2DAYwPJFycrmGrNZhY5rwsjwh3/4f/O9N2/z+d2vqHRDHVY0ekm9LPHCkDxb8OD+5yzPJihlmZUTTJgSK4NVLdr30a0lDUZEQUDsG06OMpqZJYwHlMrQlopikaM9hZ/6ZN4cqPF9TR2EqLZmvN5nuDqmbBXaJtBqdJDh64i21awMLKsrFzg+brC6pN9bxWssfpPjmYCg0uhBArWHNtBWNYOwBzqitAUmzClnZwR+TNtoMjPFhAodp/h+H68x1IUiamuCpqGeeOdjFYYkXp9QabK8JPBLEqNJLRirsFVD6fmUiYdvY7ylIog8qJdoL8bWCiJY6JrCWvy6plRgiUisT6ktjW2I6/p8/kce/TiFqiIcjGjxyKfLb7ps/84dQiORwKzLu4evvbvl57p0Kslkynm6InJx05DAUoJ5CQSlQtLNGHf1D93svGTTXxXwSsAGX2ektdYuqyY/L1n7Xq/nAkmhJDx79szxvLtUoOVy6aodkrWSIEU+T6oX3cBb7kOC4CRJXFl9Pp/z+PFj3n333Zde/sYYx1n/6quv8DyP69evuwBUBK0CNIRuIpQByZwLmBCAJUH8cDh0PysVifv373NycsIPf/hDxuOxqxTI+ebzOb/97W9ZLpdOqCsuNV2HqVf1D/J8JTiQ8r5cj2QZxVZY5sXq6qrL3gnIk79fvHjhxkd41l2jApmvXQ1Ldy4K9UPuDb6u8siclHuQsTXGuK6/ciRJwmQyYbFYOMpIV3jfpZbJny7Fpnutr2oyZF18G4+TkxN2d3e5cuUKWmvef/99Pv74Y8f5f/TokRuHbm8EcXPKsoz5fO6a2cn6EdqLtee2oleuXOHLL7901EgZg7quXTVF/si6k7W1WCyI45jj42OuXLnC+vq6C7KFXiUOR1I9kbVz7949PM/jN7/5DVEUcePGDZ4+fcrNmzf56quvmEwmeJ7n3IhEayCA/fHjx1y+fNlpe2SPef311zk7O3upiWYXlL/99ttcuXLFibOXyyVffvklV65cIU1TTk9PefLkiQPb0l9mb2+PNE25dOmSo4JJhVRc+YQeJHtdv99nPp+7jumDwYCVlRW3zrIso9frcefOHZd5FwD0ve99jyA4b2Y3Go3Y2Nhgb2+Pd999lz/7sz9jsVgwGo34i7/4C9bX1x2VbTQaOYOHfr/P9vY2k8nE9bIQ8LG3t+fW/fPnz93+8dlnn7mKp+d5brzfffdd5vM5v/rVr3j33XfRWvPkyROm0ym9Xo/NzU1msxl//Md/zIULF1zlR/YqSVLNZjMuXrzoqo0CrN555x2X7FpZWXFuUU3T8F//63/ln/yTfwLgdBNC87t37x5Xrlxx+7FY0Yr2ZXt7m6+++oqjoyMHXsQF8fT0lJWVFe7fv+8ctow573Py1ltvsbKywsbGBj//+c+/0Zr9xjtNWNX0wx7LQUBetvQDjQ5CZmXDwhpiFWGUxgtDwsDjzFf0lSUtS4rS4KmMMBiSeTmB5+OpkDMDvm5BaXrjHgezChvEKGJqDf3BEFVlMOhR+6cU8YDWWPJmwbLxWFceYaspfEsDtG2NVxusrjCFoTIJXqTI5qeoyKJMQl0nlHWOnR6zsnqBeHKKt1FhdEpxfEo/8Umo2V6NmR2nzM8ymqbCHwUsmpImnxMS0kuHmPYYakuMRVcFZRxQNSVt3WB8y6DXp557BP55cxhPaWIgWzbcf7LH5qUv2d5aJ2gs2vepVEsvTFHjlD/40Y/5G/sxvy5/jfJawshQZDNUP6KpIYgqotKnpUaHlqww9JRCT+cEekBmlsSJYhoY5vMpHpak1cwWJ3/bvyPApqvMpgavUQzHK/zu773LW+/+gE+f7FH6BX4YUS1igtBHDwvasmB+ss/+0xecNAbT1qyvxtSVpiWlzBdEA4gyRbQc078SkfZ8zrIWT+eUrTnP/DcKrQ1WafI2J0DjeYqqBKVTxnGPC5uXSHublFlDf+hjdAsRFLZgY3WTYlpSLWYQNgw8TV5bPDwaFaLGHroOqecZZWuxVtGLPU6LBU1e4Sea1Afjgxem5Avw/ZxxGDCrDfMiI8osUQSlLuiPAxZmAX5IeZoz2PbwemtE3pCwsESmpIn6KAVBAEUzQ0cRlUoJwpBBURAVc+rTijJOmMdLPALG4Wu0fkvWZvSpUcrQ9mCxWBKkLevDmKoxFMWMhJRlbvG8CZH+9jbs01o7ikq3giAZGPg6WyuHBP8CBiSo6lJ8ZOMVy1g5T5fC0tV6CEgQMZzoNCQ4lMy5cIaFDyyBhtCz2rZ1HG0JFrIs4+DggOvXr7uSuZTt5UUllplCy5IgXz5DKF/y+QJyunoFAUNyfxJoSCARhqET9EkmTAIM4dq+/vrrPH36lOl0yptvvsloNHJ8XwF4EqgI2JL/g5d7Tsj3xXo1CM6bVz169IgkSfjd3/1der2eo590BdSff/65cxLqUpO6PyNBtQTyEnALGOkGUAJS5Zrh6+qWCEvX1tZIkoTZbOboRXmeu2ZhURSxsrLiMpIyl6TaIfNEvpbPFkpYtzLVBQFyL+IuJeORZZkDKAIoRJjbrVzIOpIKheiA5DokIJAxk3F6VUD/bQUaQpExxnB6esp0OmV3d5eVlRV6vZ4b12fPnrlqXL/f5/PPPydJEhfQSvVH6JziPnZ0dMRrr71G27asr687gbFoiQ4PD7lx44YTDxdF4SoL0rRT6DwihJ7NZmxubvJXf/VX/PCHP+TLL790RgIy97Isc25q165dY2Vlhf/4H/+jq5Q8ffrUzRvpxSFUrO9///vcuXOHf/SP/hF/8id/wueff+72u52dHcbjMZ9//jlNc97gbnV11T2je/fukaYpd+/e5Q/+4A84ODhgOp06sB5F0UsaMzF6+NnPfsaf//mfOx1TkiTcunWLR48ecXx87FyUpCKYZZkDgt0GmXmec3Jy4irGaZpy/fp19vf3aduWDz/8kLfffpvj42On1ZD5++DBA1ZXVxmNRvz7f//v+df/+l+7BINUZYVyJIH6ysoK4/GYp0+fuspUv993wfVisSAMQx4+fPjSPts1VphMJo5qJ432vv/976OU4vDw0CWKnj59ysOHD7l+/brT1cznc65eveqqCKJHWVlZcVbLols5PDzEGONE43VdOzME0dk8ePCA7e1thsMhb731FgB37txxiar19XW01pyenjqgJxoXMQrZ2NggTVM+//xzVy28c+cO29vbHB4eopRiZWXFdXN/8uQJnudx7dq1b7Rmv/FO4/cjzLHBCyOSyBLqlnJS01sf0PglbROgKsgnJaWe4adreJHCtprY6+EZyHRFYVOGRtMWM8Yjha0gsi3ZQON7Cm/gY2cNkdXUNPSHHrVn8W1Ck+f0vIg6svTSFfyyoDY11kJkFJWpzsXD+CRJgdc2eKMhhdL0I2jOCjxfEdFDtT79kSKaeMyqHL9JGHoD/FTTDxTpSoq/sgIzaHRLrUCXPcKqoVUZ82lGqWvCaIBua8LQJytKjIIwDql0Q1YrWgp8A7HvURcthYUoDDh98pjftoaf/t+/T//iDrEf0StrYiCOIuiH/O5Pf0BvxfDrD35NYWcEaUqel2hTk4QedV0w6qdkTUHq9TBVgL8SU2YVIT4WhV1U9HWIbSNaY1jRFustqEoPv9ejpuViEvM7773PW999h7PnM868KYOhj98G+G1JvawpGo/ZfJ/fPLzHbH5EnHjMC0NWlmhjaYxFewl10zLwE6qioZgFNLWmNIai9imMJU6gthajNZoSP/SwtY/f90gbj9AzJEnIUG2yisKECjXw8LOWctKSxiGnRzlBYGmpUGnDdK4YREMoLNNA4fstSe2TtIYw7FFpRTjwwTvCszFndUY9MuhqTLmwJFFKsyzJEwOJQtHijRV5WRCPEhbZDO1ZbO2xMl5h0F8jaMbEZYEN8vOqiI4J+5p2viDB0FqYVjWrqyG6ytCeJV0b0cYeOYaeCQm0pqxyNJbKanQMxltCMaTIwTMNiefhRTHNosVLIcsVvfDbDTQkSJIXC3xdXejy9iUwk8ykZK4lsyvBpO/7zl9dXgbASxlc4KWgvGtL2q1qSNAmf3cBgVARJOCVCsR4PGZ9fd1tyBIUCJ9V+LqiCTg6OnLOVBIs9vt9dz2SVZLAXYCG/GmaxlUtunQiAQRyb0EQOLGf1pqNjQ3nriL0sjRNuXXrFk+fPuUXv/gFV69edZ738lKVzxSQCF9XlLoUIKkcwbnT1f3795nNZty8eZPLly8DOG53l8px9+5dPv74Y+I4dnOgS3OTcZP50KWUCVdf6FldapSAOgngus8JcB75ooMAOD4+doJi4chvbW25a+pWtCRw7/L0u45jXSpT91nJOhBA8T/TWAjgFDtLObpUv26VROa7AKlXdSXd9Seg+ttKnfr000955513nDA3z3MuX77sqlxXrlxhsVi4RMLTp0/Z2Njg9ddfd1ao8/mcK1euvCRoTdPU0e1+9atf8ZOf/ITxeOx46tLzZWNjg0ePHvHGG2848L5YLPjoo4+w9rzB3Gg0YjgcEkWRCwqlWre7u8v777/PgwcPuHz5MsPhkGfPnjmR8HA4xJhzwffa2hq/93u/x5/92Z9RliXvvfceH374Icvl0nXtttZy6dIl7t27x69+9StnGiHzZmVlhY8//tiNuYiztdauclcUBf/+3/971tbWnChYAuH9/X2eP3/OixcveP/99xmPx/yLf/EvnEPW+vo6jx49omkajo6OAFhbW2Nzc5OiKHjx4oXrSg64CsRoNGJzc9P9fpqmTi8znU5544033Nq5d+8e165d42c/+5l7/hLkX758mZOTEz744AO3FldXVzk5OXH7KuCqzVEU8eWXXwKwurrKD37wA/7Tf/pP3Lp1y5kvDAYDZrMZ8HU/pu56DoKA69evc/36dS5fvszu7q6rsgmoEvpjnucO3AjddGNjg7t377qK09bWllvbSZJQFAVffvklo9HICddHo5FLGMRxzOHhIZubmyh13ltFqLBra2uukvH06VOqquLx48fcuHGDBw8eYK0lTVNHoY3jmL/8y7/k4sWLrK6uuv4nss8IZVa6rd+/f9/R/7oV2P/V8Y2BxjxvWBtHBCbDazSeavE2VpjokkGSopYW1TaMBjFLrajMkrxMKPwQq0p8L8ComtbzyXVLGvbIl5Z+AGEEVWC5Evd5cXaC1x9QzxaoxlLXmtwPzjUajSbwU6KkwkYVRVMyDCJs36ed56QYkiInD1J0ElMvK2bLGq8NqY0mixqCfIYXaOIoQRc1/z/q/uPJkuTO70U/7h76yNSqVJboEt3VAujGDAQHmAF5RxC882ZBe1yRa/4/5I5GM245NmY0jJYXGo0GWlR1dVV1aZGZlTrzyNDub5Hj3lHF+970NXsLIMzSUh0Rx93D4ye+Ym1xlePPM3qxQnVDJtUEv4aF06fwd5+RPpsQlTUi+2cZVl8TEGACSaE0gTInLtK6RhmPvCiRvkAJTaVLhFKE3Rb1YAoaUpUiqAnCgIcPH9P9OOZrM99BJUsoHdDxY+JYMMEQeJqvf+09cgOf37vB3vaYqqrp+h7Sj2i1K/LREDHQ5LqF9hQH+2P6HQ+tUqQKSAPNQhSTTwX4BjlWoPtov8LIisunFvj629eQnQUOJ0cEdQypoqg0UaekCjTZpCAf1Nx5dJutzSfUVUFYCrwEykoQCg9hoCRH1jWHVUpfxcSZJquPmezt4IcdAgHVpDgxodMaKg2eh6dgOpkQEhIoaLckSxfOgagwSZdxUdEOJDIsqbMMry4wcz46ADOWSJ2gihxPF/REC50VZFWK1+1SHw8oPUmm23TCmCKd0JERWgUEWUGoArKWpCo8ijKmlYSoYswkK/C8CK+uqEsPRI0xKUZriqJEqQmiBYnqUmWS2dmauhpRxF1CXVLIhFadM32xja8N2g+pdUE19gmrGtMJSKt9TDlCxh0meUbXjynrGuXXJMqDElTlk+sCYTx0WhIEfVph8C9drr+2h8XqNonMtnoPX3QdbPXQJiVNeIiVZLWEN+vsbP/evLHY5zaTjmaQZt+jaV5mg2UbwNnH2/dsejTYauf8/Dybm5uu6ryzs+NudJaMPBqN6Pf7LkGxAXPTE8ImF01pXhuQNrka9rNaHokNou252oDYEuYtJnd+ft5hzC1uvN1uc/nyZc6dO8eDBw/42c9+5oIAK5XYhEjZSqQN+u3YWDz58fExg8GApaUlrl+/7uBYdjytQZ41+PrlL3/pEpXmPDS7UcBLgXFz/uznr6rKwY/s+NgOl+2iWPJnr9ej3+8zGAycKVlZljx9+tQ930I6oihy3Y5mIvpqp6TJu2liu23wZP/eJK43lcGaPBHbBdvb23OwLwuxsa9jx8AmFrbz0xRFaH41z+s3maNx5swZBoOBMyiz1+uNGzf4oz/6I0ajkesyNjlHtir793//9/zbf/tvXUfCVrIt18ESn23l1zrDWwUhuwc0k5nV1VXOnDkD4Kr+ZVny+PFjp0hk4YinT592Y2+7jpYg/dWvfpW7d+/S6/VIkoTl5WU+//xzoigCTnhXURSxvr7OzZs38TyPS5cu8fd///duvdqk1875w4cPuXr1Krdv33YB4uHh4UsdYdtV/bM/+zNnJnj69GmWlpacQt1bb73lnK1fe+0158nz7rvv0ul0GI1GDAaDl3hEYRhy/fp1l+ABzgywKAoHV1pZWXHX63g8pt1uu+C93W6zurrKd77zHYqi4N69ewBufp4/f84PfvADdnd3XRJzfHzsii1zc3Puul1ZWXnJ0G4wGPB3f/d3rK+vu+fUdc329jZra2scHx+7fWhxcdGpMK2trTEej11CZyFxu7u79Pt9ptMpd+/edYnDuXPnXHfIkrLzPHfrTkrpEs0mX6/X67lxsSTzra0trl+/7nhDds84ffo0VVU5Ppmd13a7jTGGGzdusLCwwN7eHhcvXuTUqVPs7u4CJ/cxmzgcHh7y1ltv8fTpUzzPc7LBv/jFL7h48SJf//rXHeRrf3//S12zXzrRmPFTtF+SjkqqQtEOAiZVdkK61hUUPpWSaFXQMgFtP+BorInDgkKVCBnQ9mKmtcH3c7wopko1VRvKYY0YKTAt8nyEN50iw4A6kGRlSoRPXldIXzDUOVJ6xOUEU0IZdvBGFYP9Kf2lLn6gmMqaXCtKpSjygpCTcxahpJaCHgn5MGRfFEgy5vw+lYoZZRMCIVhbSlBqnicP5jhsbVMUx3Slx1gUZJmHEJpO1CVMj6jTCX7cYTKt8A20jKDUBr82iFKTdyIOj47plj4tr08YCrK0ovIjTAy37zzAb7X5P/6P7xGGiqGZYCY+2o+IIkmeT3l75U2WhM8vzG02h8dUwxRTlQTtgDqWIAXyaEp3RRDpkLgS5McK7fuE0lBkKcqLKSuoYuiFXaLIZ/XqKqdnFtHlAr70EMURWViga4OoodwaEHZDjrycB1t3efpgAz02GNFHTGtiVVNzovU9F3cJakEV+pi6RMYpo+Exx9mUIIiYFkfoMEDJFpQFbSlJpU+VgtAeYTdABD6xSrhw9Qprp+eowoxysEtMH/KMmBA502Z4sMNcOE+e1kR1hBcnmHiCNynQYURdBsz4in1RUMcJejqhlimp71PmNX1ZMy001WyIrLtEU0PbE1SJJig1vtdibEpKPWSSRwg8EuFT1f8cyFYBKvTx+x6DwZSwM8ugnCD2UurYZ1rV1GVFtxND7lMMNISQ10NkmRD6EaJOCXRJXLUoJobSE9R1QT2WeAQI4eEFMM4MuYpoLUs8FVBNU6bjCXDqy166v1ZHkwhuq2pN/Hiz6mxvrhZ/bINa+AIT3/SoODo6Ynl52XVA7M3WBuj2BtMMzJpBsv2bDdZtJcvekO2Nq3kjtSRfq19vibxpmvLo0SOuXLniKp6Hh4fMz8+787UkcdvxyLKMJElcMKO1dsGxPYem6pM9hyaExo5xXZ8Y7+3v7/PBBx8Qx7HDeVsokIVjWenCJEn4yle+wmg0YnNzk2fPnrkgp9PpuC8bMFjYg02KLI9kbm6Os2fP0u12nV6+NTmzSdbBwQG3b9/m3r17rjthD5tA2Lm3N1I7L3au8jx3OHG7PqbTqXMYtnA3K2trK3RhGDqeiFWlsj8/ePDAQV2sm7kxJ14czSSiSc5vdsXsz/Z/9tybwgZ27dv/vzp39vmj0Yjt7W1WV1fd+NixehVGZxNm+z+buDW/2+DWXnd2jH/TjufPn3Pt2jU6nY6rUEdRxO///u+7SvD6+rozxzs8PGR2dtZdK3/8x3/sErdnz57x9OlTF1QvLy+7runOzo4rIERR5IJFrbXjW8GJt0UYhvR6Pba2tvja176G53mcO3eOXq/nEk07F3DCv7GStFJKer0e3/zmN9Fac/36dXeNr66uvhSA2kT10aNHbv94+vSpUzO6ePEih4eHrK2tOY6Gle2N45iNjQ3W19c5OjoCTro4Fy5c4PPPP2d1dZXPPvuMvb09FhcXCcOQ8XjsOA7WJ8cYw6VLl7h27Rp/+Zd/yQ9/+EOWl5dJ05RWq8XBwQFSSjqdjiNPnzt3joODA6ccde7cOcdFsVCdra0tt79+/vnnPHv2jCRJuHbtGvfu3XPdR8ujWV1dBeBHP/oR9+/fR2vN+vo6S0tLHBwckGUZGxsbzuF7a2uLBw8euCS73W6ztLTE1tYW9+/fdxw3wEHiqqri8uXLPHr0iOFw6Aotp06d4t/8m39DnufOg8XCand3d10StLKywsHBAevr6wRB4PZ420kbj8c8f/6c+fl5Dg8PHURN6xPRjjAMOTw8xJgTpbGLFy+668B2KixM0j43TVMODw+dZ4blpFkpZuvCPjMzQ6fT4Uc/+hEXLlzgjTfe4OHDh9y8eZONjQ0uXLjA5uamu8Zs92R3dxcpJTs7O66D9S8dXzrRyAaaOABZKdptgV/nBCLAz/r4k5zazwmjCp3GpNMaFU8JRIwYeyzORwyzMVpXeFOFNoL96gipoMxDWp5PkR+Qt8GfLmG8I0qpEMpHTEEGJaZMkVGMGWm8AlIBsVRkVUGryukv9BChIitqvMrHywSiNgQLMeLoEAqDF7YxYUSaw5QUMTB0ehlJJDkYDKiTGp0OycddelXE5bVLbG8dsVlNmBYTjBT4tcZ4EaPhEM+XGC3IiopCTwlDD+UX1CpABDEqV8hRRi+IMORUOqOsDZ5WTLMxrbZHWPrc+eA2y/1LfPtfvYPWmuF+zmwbpK8ZH2lIeqzOrvBvf2+FJ4f7PHp2g+HWAcOBwcguxXSM9CRB6eF5AbVXYYyAQuN7kiqGdisknAYEsWD2zByn2wvMrZymbnvkQ59Ex1ShYqAzRMegNNRRl4PRiI2Ne9y7cw9/WKMCj0lQAhEqh7o2tBPDhAwRJ1TjITL1mfopIqvxlCGUMXk9pjQFhe8RB4r0eIzyC3zpk2cGFfoEYcRMZ45Tp8/g1SWMDZ1cocIM6QVEWYsjIJpvESSSzh6kgynhKQ+jMra8lIV2Fw4MY6PRkaEQPiJpo/yKWJdU1JSRh280sfGp9YhISqQQkNcIP0eXBa2gpKw0qiXwyoI8q6iKiH4voOd7aDx0KYi7i3h1jREGPdtDqghVSrzCwxOKMFQECzkTPyMqWvhCUfgSIQuKusSPJabSJKaLyI+JtSAMJKXOMV4b/IJWFyI5pcprfF+i+r+Zjr7wBa692XWwh8X3266HrRQDDkJjEw2rP25lUYX4Qm/cBlHN4NxWk5odEsDBBppEc/jCEM4GJzZAsM8Lw9ARTcfjMUmSEEWR818Yj8fu5m3x3bajsbi4yObmJsPh0AWi1lXXyvVaHHkzuGhyTWzQDV8Er/Zn+zgLtbLwM6UUN2/eZHFxkeXlZUfIb/qS2DFRSrG8vOwCbFtd39/ff8l7w8KzbAJi5R/jOHZzk+c5cRw7aMvm5iY3b95kZ2fnpSDctuGbQXCzk2XHwCaE3W6XVqv1EmfDjqMdM9vlsUnVzMwM3W7XVVStyhOcmJc9e/bMVVWNMe5xTeO+5vppJnlN3o+dH/t3ezQ7DM1zbHIn7ON3d3cZj8cuSbKBTPNxtnLZnJPm+Nnza/7tVQjZb9ph1YDu37/vCLk2MG+1Wpw5c4bbt2/zla98xXmk2GvPjqPF60+nU95++20ODg6Iooi//du/dXAaYwyTyYRWq8Xq6qrrPsIJBNASl33f5/Tp02itHYZ9OBzyP/7H/+A//sf/yJkzZ9jf38cY466Dra0tVldXHdG/2+0yGo14/vy542DYfcP3fc6cOcODBw/c2rG8gE6nw9LSEp1Oh/v373N0dMSVK1cIw9D5+QC8/vrrnD17lv/1v/4Xp06d4tmzZ1RVxfe+9z3+9E//FGMMz58/d9KrWp/48Vy5coVOp+O4IXYc5+bm2N7e5g/+4A+4ffs2o9GItbU1BoOBIzzbbq81nLNFHasMZYNda2SolHIV9bquWVtb47d/+7fJ89zBjqwZYlMw49GjR1RVxXA4ZDqdOulY68Btv587d467d+/y7W9/m08//ZQzZ87w8ccf8+1vf9splNlr1wqLWM7Wf/pP/4k///M/Z35+nr29PXq9nitG/OhHP+LixYvMzc1x48YN/uRP/oSf/vSn7rGrq6ssLS05aW0rsjEzM+NUquyeb/cyY07Uy2xHYjKZ8OTJE4IgcDwSWzSxME+LBOj3+6ysrFDXtTNItPvD3t4eaZq6zq/tXt24ccN14JeXl5mbm+PJkydODc3CSR8+fMjly5dZWlpid3eXs2fPfqlr9ksnGlF7EaMqRHWErqAWgqwuyKoRSWgQXo0Ja9Iio+hKWomPVxsCnTDJUvzIIxhLchVRqRAzyfE9qHTIROd4SYcwLZFijPJqqsxACB4KT0fIsSDyC+pAoLOcugoxcQJqQKVqTJSggwCtc4bpmMWkj8lB5ilaKpR3Ehge5ANacUxmpigzSxAkCHKEPyFq+5h2RFoXxGnI0twipy7MsbP3iML30XVN6YFHRVCfcAQCv0OZ55hQQeBTFBAXCUUqSLwaEQm0yKlDKCqNUAYRlkQiIBvXmHyKTCR/9cP/RR7s8/ab79KL2gzrDFEYpuMReAWiFkR4nDuzwszpmMnjIU92xwzLEYdbR+zsjjkaFPjzU7zII+m0UZ4hSSQVFZ12j3ixz+pKi3kVoYoAU9UEJiePDaNcQQV+EqNVSW1qhvtDbj95yK1bH9PyJDqQVDLAa4MZpEgTURY5lTjxsEirip7xCSOfOo/xKDBhyF6WokyJmiqkCqh1Tqk1WSEIRY3UBtKAdhBx+vQZenNzFLpGmRBJRT3KIEmIugGB1sTePKbKyVo+YRUQCh9dBkRR54QcH0kORin+SBB0FpmmOW1PkeWGjqyZlEP6UQJZRRFIxu0CnRbUFbSjHpQFfh0xPhyjFhQmDMmkhizHX1hCBjVxGCKUptaGvJgQ+h4mUgwLmG2FZGVOWfr4Eko/gVpRTVIoT6B2edcjiCJqBMZPkJWHycALJYFX0Ir7TDIPvysp1ZRaJWT1FFlI/PjLuXH+Oh42GG0Ss5syoTbYV+pEd73J0wiC4KXgzHI2rOTh8fGx4zdYCJMNsGwF2QZncBLENom6tmps+QNNZR97Pq8Gr6PRyN0M7A3BytseHh7y4sULRxS3Ci82MLCSvPAF/MaaTlnFFqtmY7s5NnGw7w9fVKhtgtHEEgMONmDH8cmTJ2xsbHDp0iUndWnH31Zs7ePtjc2qu9g5sP/L89wlF/Z59rXs52gG1w8ePOCjjz56SbJTCOEShVc7XfbLzpEN8Gwno5lwjcdjjo+PHQTGYqltpd/3fae9byFtds6n0ym3b992FdMsy5xRo1XHsepFzaMJUWueq50X+1mayWAzcbLr8lUVKCFO/FhsZ62ZZFkYjh0/m0A0x61JVLfn03xPu55/E4+6rvnZz37G7//+77Ozs+OSZUue39/f5+2333ZKPnYOT5065RR7tNbMzMw4EzubvL799ttODMCSfkejkRNFuHXrliN4W1z9o0ePHIHcClNMJhP+w3/4D7RaLUestnPz/vvvuwRpPB6zs7PD7Owsu7u7rK+vOzK153mcPn2a8XjsCMv9fp/nz587EQnbqbl7967rGFona7sOAD7//HN2d3cd1t+qQlnC+p07dxxvwJKPrXeFVceCkyTP8zwGgwHtdptr166xtLTkeBQ2eJ2ZmeHo6MgRscfjMePxmEuXLjk1piiKGI/HTKdT15kej8csLCywurrq1J729/cZDAbuHJMkYW9vjx//+Mfcvn3b3TeKouCTTz7BcuRsl8ryYqSUXLhwgR//+McuSXv99df52c9+5hKZpaUlty9YAY1r167x3//7f3fJzfXr11lYWHBz8t577zlS+e/8zu+wsbHB4eEhly9fZmtri9/93d9ldnaWo6Mjbt26xbVr1xiPx3z/+99nbm6O9957j06n4wjhtottoU/D4ZDnz59z+fJlAO7evcvKygqdTgchhDNptEWr0Wjk9tN+v0+e5/zP//k/6ff7vP322/zDP/wDCwsLzM/P8+GHH7oOlpXiVko5p3bP85ifn2dhYYFut+sSOLtGl5eXv9Q1+6UTjXF6TCAikrhDalJkEDMn4MgcE7a7pMcaqpIwajEdp6R+TFzmjEcZSZSijWJSSkQi8amIRYlUMNUppcjoq4DlTo/cFJTDhMxMkHVNOwjJRhl+u6LSNeN2QBhKWplHPq1JhQZPovIRWRlhZISM+gxriL0pvm+QUR9hYHCQ0Q/mKMqKxBOYusJMIOr0qFZgfJhRZSVxqPBmIlrKsLzVYynusPF8jGlJRAEm0JBIdFFT1xMCE2LwT3w3jMEXJZqAymiMUUyqABnUmLqiVUvSBLQ2kEnCdozOPcrtnI/+8Z/ID1LeffdrJLNtynKM8SZIb4zxYybCI5CKVjpPMpMQtDLaYYu9tZSN3SF1cURgpoSdDsO9AcbTLPfmyEpBMq9ASjwifCWgVsRJjNQGgWFkhvSCkGqaI7yY3d09nm7eY+fFJkUiyKcVIlXEoSAepUhTM82H6CwApdBSYrIcEUi0VzCWgoSCulRIY6h1jcJD1xV0NH6tEJXCQ6KiNqNsSrwKl86s0Q8DqtqgTYk0UOPR1SVHVUbpCzwMpjyBhLWUh9YVvq4RVYs6hUgXdJMOk0lOPS1oBRW+1uS+ROERSgm+gdJD6BJtNNIL0WlJOs4xAqqixJ8N8MMYPygY5hPi1WVas4uEQYapx2h8hJ/j5QKJhxIloTGUukalNaNRSqk8yqAiDqDXjjnOK/K8PHGWDwKquqSlKvygYCI8PNE6cZLXgrKaMjYGgi66HtMymrT0OdBjLv0/vTv/mhw2SLZBUVMZp6nqYYM0+7ttO9vHW4lBG0T1+33u3bvH8fGxa63DFzCX5uvZQLkZiDVJurYL0qyE2+DMdlyaFWyrfT8zM+MgPTaBsS1o+3wrobm6usru7u5LXQdbNXx1vPr9Pt3uiUljE4ffrGY3ieH2dWygbdvnNhguioLRaMSNGzfY29vjzTffdHCtZpXcBudWeaVJqG4GzTbYtSRFC4+y46m1ZmNjg/fff9+ROO3c2zG2cLdmp8SOmcUzW05Jq9VyXB2b6FjY2ng8dmNokxhbeV5eXmZ5eZnFxcX/jSOTZRlPnz4FcMHZ2tqaS/5s56PZ2WpCn+zfbDLXdOR+Vd3JzkNzfTY7GXb9bW1tuTXQfEwzkbGf0SZ0zUSjeX00ux2v8jZ+0w4ppYPH2MMWDbIso91uu4qvDZSePXvmugVCCBYWFhx5eTwec+rUKdflee+99xzE5/Tp0wyHQ+c2fu7cOafWEwQB29vb7vq0+0ld166bKeWJ4/Ts7Kybn7Nnz7qg1pgTsu3MzAwAt2/fdiRzm7SePXvWGXCur69z//59giDgzTff5O///u95/fXXieOY5eVllwjt7+87mdk33njDSUd7nsfW1paTSbWkX1sE6PV6HBwcOHf0p0+f8oMf/ICLFy9y/fp1B8d8/fXXGQ6HfPjhhywuLjr39K997Wvs7e3x6aefEscxWmsePXrkYFzWWPB3fud3ePjwIYPBgLm5Ob7+9a8ThqHbP8fjMYuLiwwGA+q6ZnV11VX+33//fYqi4Gc/+xlKKWfWt7a29lKAXFUVu7u7zvU6CAJn0AgnHUOrYGXVsSwE05631ppPPvnEdcvKsnTvtby87Ioa9mtxcZG/+Iu/cMT0a9euORft58+fuz1VKcV3v/tdut0ux8fHbG5ucu3aNQfvWl9fd+pgx8fHLuFI05QLFy44EQwLJzs8PHT7xMbGhuuk7+/vu47UixcvXJLd7XbZ3t52Uuyzs7NOAc0qgp0+fZpf/vKXLuGyEsOj0YhLly45U8Uvc3zpRKMdl+jap9YKURpMJClHKf1OSD0ZERhN5PUZVz5RkaGKMUHgE0SA32VclvgdH50HVAJ82WU4OibpgS99qoOUuDdPf1Kxw4AJU7phj3GdMu2G5HVJW0V4hyV9r0J3c3I/IJjEJKFgUtcUkwlenJG0WqRphhfURF7CqAzwpUaIEBn7+IWhLg5P1KdEhEhzOv0Wo6yio3yU6pBHBlEmLJ+5QvvzTcLdMZEpqFs1E89DVzmRUmSVIBMlfuWhZUktIJeSShq0jMCkSM8D4+P7ITob4CWSUoGnNEL5lLFBqJKylNy48SFDMeDihauc6i9gCkPbC6kijdQl2ThHakm308aMQ6SnWZqNCSIPz5vBq3L6ssXT8BCjFDKqWW73MDUc11OCOCHbHyMqhdQKMZ0iZgNEFlLmGi0ShtMjfvXJ+zzfOaZOJSQtYIAsJXlakkUGGRQYKSl96Ho+ZZYhqxEqCKhlTBBL6nHNNM+pCoORAe2gRVFVyLGGicSTgkppVKzod3qcX1unN7OIL318kyGDCC0SYpHjhwnSa9MyQ4QHaVig8xLje2RVjowj1EQhI6h8jSg1WhhCXRMqyeg4pd2bZSQrWniUVYxfGqIyR6gQ/JAo8ZiOBhi/xg8jglZMMa3Q1GAEs70IRMoog0gF9HKBHAvGoxq5EiLynI7qEkqBmfVIizFRCQiNFDH7RynJXAutCjwhyUufSsWIqiA0Gr+VEHkdpDkxhPRnPHoR1Noj1G08FCICVbS/7GX7a3c0IUvNr1eJzhbjDl8EZrY7YaEkTRhNq9Wiqio2Nzedvv6r1fBmNboZbDWJ1vZ1m10QG3g3oVw2AbESrkoppz61s7Pj8NiHh4cMh0MH2wBcNbTf7zs4ThiGLjloVq9tYGhN9+zNzgbpdjxskNRsk9tztJ+l6SdiX/fw8JCPPvqIg4MDLl265G62zfGx420VdmxCCLjulIWE2JuUlJLJZMKzZ8+4d+8eDx8+dO66TUUtuybq+gt/CHvzaiYa9n2shK3ltDSlgG0Xoxmw2/fr9Xou4LB+CM1k8smTJ46YWpYl3W6XtbU1x92wVe9ml6UJ67Nk7ib5u9mBgi9Uz2ySahO7V19Pa83u7i4HBweu8m3VupqJYJOcbzt8lsPR5F/Yn23C85vO0bDQkb29PR49esTy8rIL5C3cyPrYHB8f43ke29vbzM7OcubMGTdP9vrd29tjfX2dMAzZ3Nwkz3POnj3rYC1JkrC/v8/u7i7z8/Okacru7i6nT5/m3LlzjEYj4Au+1NzcHH/wB3/ggvpmIaCplGRdsi1EJUkSLl68yIcffsjVq1eZTqeUZek4DYPBgF/96leOjPyP//iPlGXJ0dERw+HQCQfcuXPHKRVJKfnss89c0lmWJZ988okLPp8+fcqVK1fcHmx9XiaTCXfu3HFFA6UUjx8/Zm5ujrIs2dvb4/Tp0/R6Pfb39x1pem9vDyEE3/jGNxgOh8635Be/+AXdbpeNjQ3W1tZYWFjg7bffZnZ21iVE4/HY/d/CVS2X4uDggMXFRT755BN+/vOfc3x8DOBgrzs7O3Q6HQCWlpYQ4oQ7eunSJR49egScKEzVdc3169e5desWaZry8OFD14VaXV3l8ePHwMk1c+XKFW7fvu2Sw+XlZdeVWVxcdPN78eJFVldX+clPfuK8i6zwxWQyYWVlhY2NDcqy5MqVK66rcfr0aabTKZ1OByklz58/p91uv6TQZ2F2lvNy9uxZp4xm5+rSpUtuDdZ1zfnz55mbm3NGlu+//z7Xr193XS+bROzu7hJFEadOnWJ/f5+1tTXyPGdvb484jp1Eu+2MxHHMa6+9xu7urpNCf7XD+//t+NKJhpElIJGhxBchhYqo8gITlkgigkST1TlVmuLFEi8DWfiIbohKEtTRmGJa4mmfyfGYbt8nDlqYaUjUTakVVNmUUAZ0OwuMJlPyzJD0uhzvDgh8j4lfEwaGLA3JJwoRSFp9g86m+HXCYqdN7WVU6ZhO3EVnGdr4YBLMtCZEgaxR3oQsiNClR6hSyggC4bG8Msdg7zl4EcVwSJgVnF7qc/H1q+wfD9nZ2oawAFlCZajqEC+QFDrH5Dle5OMnEXkWgRlDXSNMReCf4BI9TjxE9GGF1zKgFdILCEmZ+JAVNaKo+fyTRzy88ZTXls9w6cI60dkLFElFWE0IjUdW12zXKV1PQFWRURHOeIwnOTPdLkeZR3t2Bp1XTL0RJFAeT/B8icwNRlXIJCGTEAYJgZQQTDg+PGJzc8hnjz6jPjxCGiiUoBqCF0kiMvwwIlU+WgkGOiVQEdNJSSf2UH5CLgSe5yPKQ0QU/LOylEBiwKswpcZkHpFXk9YFkQmIS8XC2TVOXzpLb3mGojC0pQEtwZN4QqH9CGUqvCBA5gJTaEToEYkeJqug8mkFhroYUBMgWwFhXaNURFanxP2YluehdMi4PqYtKghCMgS6EkhGCCSJ12OvOkR325SpROopVVqihE878PHKGjEKiX0fE2cIPISQjI7GdAOJF9XUmWF6lEILpqKG0KMqNUnggUlBV8g6hALijoS2oR8m5JlESIFRJYHuoqhPXOaBPAsxvsLTx8St/pe9bH/tDtsVaMqWvgoLsT+/+jzL27AVuCYcxRLirCmRPWzgZ2+Y9gbWrPY2q8S2Ut7Es9uKsX1s83VsJctW5lZWVrhz5467OVtIzrlz514KVH3f59SpUwyHQ1ett4GBfW9LtrbdBctLsGNk8fpNbolVKGma/zXVZexnb3YnbNV1f3+fJElcW92SxC1B0urJW06MHd8mydV2Ffb29tjd3XWkRxsw2DG2yYoNum312SYaNmlq8gmayYlVhrLwiiZMxCYe9nXgRPJxeXmZpaWll8QAbIBz69Yt9xoWE9/pdJhOp06atNl1sJ/dzqflYzRN+Ox52HVjuUJW5KAJbWt2zWzCnGUZc3NzjhfQTG5tEtUUJ2jyPppro5nQ2Gqq/f038fA8j+XlZVqtFu+88w7Pnz9HSumUwax0p63W37lzxwXtVhlpaWkJgG63y9WrV93YXLx40fFiLGbejmlTlnl+ft551kynU5cs+77P7u6uc3t+7bXX3P5z48YNzp075yCfFr7105/+lPF4zJtvvulgUlVVOUM568Hz9OlTzp49y9HREXfv3nXr1/KHtNbOALAsSxYWFojj2CVPFv5V17UTSDDGsLu7y8WLF53kq70+Z2ZmiOOY8XjMr371KxdoLiwsMBqN3LmNRiPCMGR1dZUwDHn48CG3b9/m4sWLxHHMxx9/7KSEl5eXuXr1quts2gq7hXRprWm325Rl6VSd7t69S6vV4m/+5m+4deuWg2laA0QpJfv7+y6pHwwGXLx4kU8//dQVWMqydG7et2/fdntZq9V6ybl8aWmJx48fI4Tg4sWLrpvy+PFj7t+/z2//9m87n42iKJzYh0287ty54/aDJEl499132d7eptVqOcd5W5h5/PgxrVaL27dvc/bsWXq9Hqurq3zwwQdOQerFixe8/vrrvHjxwrmBe57niPatVovJZMLCwgKDwcAJF1hhlCzLuHbtGv/0T//kElTbgep2u/zkJz9ha2uL5eVlXrx4werq6kscQDvXWZaxt7fnFMCsI/mX3UO+dKKRSw9tptSZD0pRpgZfhKigIKoSjC4oshFKthFeRd1rMz4u8Yop7X7NtC6pU0VblSgKJlVBnMQUuU/pC8ooohbg9SvkvqYbrzLKJ4xrzXyrh55WaEp01yevI1RL0GmFiGnF0JN0A0XZyqgygZd5hLWmkiEyTegkNYXIkAEIv8toNMaLfFptRTUpSFMNk5SZU7Ns7YR0pgW+BJVAuxXw5qVz7O5sUOQF4+EeYZWTKVBxgimgrQQ6yJiGOX4tkEajYo8sz/Br0LomUBJDiQw9BIo0K2jHEWma04/6TKp9Ck8TyBI9zBhWNfcmQwaDTfb3trn0xjW8JGFQpMgKaAdUEtREUvuKfJQSVoJxVRH4ECnNRNYkIqaeQujH1EVOwYiFVotCKA7zEcNSE+/UbG1t8mTvMaPn2+xPMzq9HtXwGM+PKLRhmudEoQAVEOmCLCuJKvCqKfhQe4Z0qjFGYJRGewGhCPFNhVE11Ck5UAufUPkIo1EqIWhFeEHA8vJp5tZP4VFhqMizkLEZcnqmRykUoSyIa8VUw1QbvFwSKRgWR/QjD13WFKMWXdFhIkvSKscXhml5iEg8fNmiKmoySnqdPomRDI5rgk6LqtDo2kOHHqXJUS1JkU/ws5BoNkHWU5JAIGWNbMWISYCSgkrVlBKCnqGuM4ooJhuNqOMINetR5ylCCaSXEAUeHI/IBzm+DhB+TbfXJelFFIHCaEkUhuRZTtgLEEYiak1ReoQip1QZnldQmZo8m7D0//j2/OtxNINdewN/lezbhDXBF/Ccpvyp/R1wEoJJkjAej1+Som12Kuzj7femVGtTQtZutM3XsBuqJSA2eRK2elhVFadPn2ZmZsYRva3e+cHBAfPz88AXXgjWPMoSjC3noGnA1iQiWwiT53mO5GqDVBvwNrkmzTFsEmatGpMN9m2CI6VkMBg4krZNOpaXl4mi6CVOhe1yWAL2cDjk4ODAcRlscmAJ+PZ8bNJjzAkB3nYW7Bg3idLNLleTI2OTGTsuNkm0CaIltrfbbadeZqt8zcDMYq6fP3/uuhnGnKi7vPbaa04xq9npsu/VvBk3uxTWC6TJ/7DjZX0fLETEftk1bZOAuq6dbKTtFNlOTpPL1Ey8mudnP8eryXqzQ2i/fhMPrU+khI+Pj2m323z00UcURcEbb7wBnHQ89vf3WVpact4ClsTc6/Wc4pINEO21ZCE7rVaL8Xj8EoxmcXGRFy9eEIahU/CxHbHDw0MnD2t5APY1LEl5fn6e1dVVptMpV69edfvF1tYWly5dctDQfr/PpUuXKMuS+/fvMz8/7+B3Vo7Unpetcn/zm9/k+9//PlJKFhcXefToEW+++aaTgbV7X1VVdDodF8TbqvyLFy8cN6wJizw6OnIqco8fP6YsS27cuIHWmm9961vs7e2xsrJCWZYsLi66NXtwcMD58+ddErG8vOyC1FarxdOnT8nznNdff52trS2eP39OGIauI2PfOwxDl9D86Ec/clX7N998k48++shd+5anMzc351T/Pv30U4w5UaZqOlx//vnnzt9oe3ubP/zDP+Sv/uqvUEpx+fJl/vIv/5KFhQV2d3f58Y9/zDe+8Q0ePXrkEry1tTWnktVqtdjc3HTGfXme88knn7CxsYHnebz22mvMzMxw4cIFfv7zn3Pp0iX6/T5vvPEGaZo6pcLXXnvNFdH29/c5deoUm5ubjqcynU5pt9sMBgOm0ylHR0e89tpraK2dUa3llZ0+fZrbt29z/vx5x7X4/ve/z97eHq+99hoPHjzg0qVLrtNujHFrV2vt+DO7u7suiWl2X+3eZWWDLdz0Xzq+9E7TqSImosALJHWlUUqgmVKPNUN/ivEg7vTIhhXj4zHdTg/P96hbgpGBMPIJvJgqqVClJEoCsjKFvqQwGVXiEwSK4mBMWaYorVAmpawERTGhHyWY2iMtJ2hdIEcho8Mx0ewsZgypn+KpGp2D50VUhaRMFcSaUk8Q8YSRkbTrgqTbQhWGdJyiVJuOLKgqw3TfsNhbRE8mFEKSqw5hCUuLi3zlzTfZOzxEVLsMUx+vLCHLqQsPGYJoxYTSUA898smAKPEJAIOHVJI0HxPEPkdFCRJ6bR9UhjEzlCLAjzqU3piq1vhKkSQxRVby7OCIncFN7m484fz6RVbX1licX6Qdttk/2qEtOvSiFpODlNIIDBW1qSmqEYYYX9fU0xyVzBAFUKgJR1MDwrDz4gU7+wek29vsjnbISvBGKUrU6MERZmTwVM5sP2I68qiVYRAUVFWOrgqUjKiUBpFTZSU1Ck+HFCKnmJRoz0PXNZk+ye6V7FGLCbWYoiuN3w0pi4xzC2d58/I6S94KqjDkVUaFwPcDjrwa6SWUoxJaAZ6aYIQm7C3SLQr8SDM1E6TUqH5CbUJ0PiEOWuhS48UelFOCuI2RBuX1UZUmNT6VN8X3KurKUEiNYoyKFJ6KyMsSmRiKIifyJN2gTSBCZCqhUzOtJJVIMCInlJpYRmTjNqXWSC2o/ZKg1yLIDGU+wo8S5EJAkhpMBZmRdHsBOSMiOoShIqs9kjigngKJjxcoJIo6NUQtSV5UFHlJOPmyV+2v39EM1pqJxauYcRsE2kDaVn9tYGU5A7ayb0nLNmjs9/sAjtTZfH6Tq9FU/LHVbPv+Nhiwj7MbrA14mzwNi/O31daNjY2XEqT79+9z+fJl19K352Lb8RaT3ewy2ERmOp26APPo6OjkWlLK6aXboLapvmQreTbBsHyBZsfg/44cbwN6m0BsbW2xt7fn4B/NhLCZNDSr+3bsmtX0JkTIzovt5NjztXNhu1D2+TbJUkoxGAwcD8NyMV6VHJZSOsUrpRRra2tOocpCDmywPhqNuHPnDkEQuGryqVOnWF5efknO1q4hq5xluwoWhmOThSiK/jcSug3+LXSj2blrKklZPomFgtgk6VXhBNvtaXY6mgl6M/loru1X5+PLwh5+3Q6Li7edgXfffZfxeAycfOanT5+6avfa2hrnz59/SXnuzTffZGdnx3GltNZ88MEHDnde17VLVD/44AOyLOO9997jtddeY3Nz0yWj9+/fd7Atezx48ABjDG+++aYjqFslNDuHeZ6ztLTkEpTj42P29/edg7P1trHGf4BL/G0F/saNGy7x/6u/+ivXMbt37x6dTodr1665Lu/Nmzedv8RwOHRE6PX1dWf6Zzu09r3W1tbY2NjgT/7kT/iv//W/vjT+Sil+/OMf86//9b/m/v37wIn5pZTSuW1bmGMQBLx48YJOp+OC2PF4zNbWFvPz80RR5HxGrCyxvZ63trb4+OOPXZfaeov83d/9HWEYcvXqVXq9nvMEsXLDdg+wY3Lr1i23Pv7zf/7P/Lf/9t8wxvDGG2/w93//91RV5YjReZ7z7rvv8nd/93f0ej329vaoqoqHDx/y9ttvY4xxalIvXrxwEKRut8uDBw9cEgUnvMGvfOUrzmjRBukWrmald6Mocvu8Vf6K45iHDx/i+76Dgq2srPDkyRPOnz/voIBWDOXevXvOtPKtt96i0+m4jvje3p7rvLXbbXZ3d/na177Gp59+ipTSdYVOnTrlODFXrlxxiYaVEd/Z2aHX6zk1sZ2dHZe0/0vHl4dO5ZrQxOhAE0ifaTlEdQOk6lIVJzewtNDkicKfXyUqS47TIYwq4iqmRQi6ZKzSE3WjIkRPDSavqT0FY0kVSKYmYGa5x1G+hVyK2RsPqL2ETHqExSGdUlAlHhU5QnUwlUDEHmkFwTAlDGeQUUk9yoh1i0IYxiKipSRhWVPlU0JZQh7izyxQm5o6BCYnCU8rVIxVQaAidBVTZQV1B1aXT/HVa6/zi+mU42eP8cOYsojwkpLKVIiqRz0d0fJz2r0E5ZWUmWBsBGkNmhA9Log8jyIIycc5QU9SB2P2pyNkpAlqaEdtJkc5pc7AVASx4nh0wKg45mAwIL57j9VzV5jFY/7ULGmvIM4gCUEIiVf5lOqkuyBryIsCowTjfI9JNWFv+4BqqjncOeS4HLC3O0VPD/BmK6q8ReklhKqErEKoEE9J4mGOF4Xsl8cIT6CFoE5DpBzT6Qbkk4qgBONrRrUgGWtiLRmlKXiglYcuE0xd4WlDpUuUkZhqynx7gXdeu8zq/Cyh9kiLjFBEeNqgEw+8krTMaCdzKC9E14JSQlZkmGhMLhSmihDCR+iUvAqog5B2O6Ecjkm1oksXM5VMO4oyG6CmUKGg7SPKDExNTIug1tS+Qe9rpJbImRC/HEEt6Z07jSGhNjGBJ5EyR0wNk0AgwwrkDJWSEEhKYWDSwi8FGMlEV/R8iQolVRbSnzH0wg669ggQmFJTCk0Q+UTao/IMGE050ngtkJ6HNBXpSOGLDtr/zZW3bVbPbaDVJLXaQKnZdWh2FZpkaPs3G+TZRMOSN213ohm4NvkBTez8q92Opl9FE37UxMBbaIytyI9GI3zfZ3V1lX6/z9HRkasi7+7u8uTJEwehsn+Pooh+v0+apo6o2vQPsTdLG5hasycLGbJV92Zlv1nptp/BfiY7ns0kwSpa2TkBXoLl2Ndvnnezsm9fx8KbbODb9MewwbYNvm0no5mY2KMZkDVlRK3fhSX9Nj+P/Qw2WE+ShCRJ6HQ6zM3NOeUuq29vCeUfffQR+/v7Dt6VJAlvv/023W7XrSv7GZo8oSbXqFnxs10nu+5s98vCBZt/a3YcbCcmDEOHue90Ou69LLQnSRLX+XiV2N2E3tk5sY+xCaJdSzZI/U087OdNkoQHDx6wu7tLHMe89957DqpX17UL1D/88EPeeecd7t+/zzvvvMNgMCCKIifzubu7y9tvv+2gNbazaJWDbLEiTVP3+ltbW26NNPez69evU9c1z58/59SpU/zgBz9wylbnz5/n/v37xHHsulvGnLjPa605OjpiZmbGeSpYvDyceHVsbW3x5ptv8vrrr3Pv3j2Ojo6c+ILdV2yF+caNG+zv7zsI0ObmJn/yJ3/Cn/3Zn+H7PisrK7z//vvAyR60trbm+C5nz57l7t27CCH40z/9U7797W/zwQcfkKYpc3NzaH2iqPcP//APVFVFu93mv/yX/+I6tKdPn+a1117j9u3bDu9fVScu91YG28Jc5+fnHfl8MBjw4Ycf8vTpU46Pj1lbW3Pw0gsXLnDv3j02NzddMn7nzp2XuG/dbpder+e6JlJKHj58SBRFnD17loODA/70T//U7eOPHj3CGOOSpOfPn+N5Hj/4wQ9ot9vs7++zvb3NdDrlW9/6luNiwIm7uVLK3WuUUty9e9fJq49GI959912UUjx79sy5nNuug4XE2gKYVSecm5vj888/580333RJQa/XY2dnxxlMZlnmRE8AB9PLssz5SNmOkOXMAG6ftDwPa05qHeytsMH29jYHBwcsLy8zHA6dStnZs2cdpM7ei5u+Hv+/ji+daBx1Q9TA0A9CJlWB8irqTGKoCQCET25yIr9FOqkwXs3cYguDx7g+QlcZtejgFYI61xSRJk01QRzjkYL0SHVB29OEIiLtzzLZHZHkbZI6ZDQcE/daaFWTVj5RItHjmmJk0L6PB8huxDAtUUKSqZpuF/yWYV7UGCOp8hDVW8DLJmByxtkQE4Qc7wX0+y3KyQAhKugKygr8oaYdBBxNcqKgy7WzF9ne2uLFcI9JAWI8wCs1XicilJJp0cKLPAI/YFJNUaEhFAbT8pCTiloLkAIvqzEqRE8kZZWBAfPPJnm5maJMhVARFObEhTuOULWhrlOOBiXjzz9BTw3eZxVxL2a+u8zs6hz1oCCsCrKkoirbmKJAZ0MyIRkc7SA9iRnXTHLI8wwfQ2UUSeITZTEjbVBxQSl8giAiHQ2R7YT9qQdM8T1DiEdZwsTLT5ShUoOUEaOqJACisEQaRaAiWkmOFAo/rSEIqUVFYSCQIYHRdBbXWFy8xPrlq/hhhzEVWVghOx3q8ZB+bwZRpLR8iZ9XiHrMeDwgWFxGH1dMRUylh3hzAXmZ0zV9JkWN54XU5hjjB0TKx4SCaCLwa8U0qZF1QVQJVLtHnoUoKmpdoKsQT2pkYqglFCXUfodYRmiviy98dDomMBJtfIQKmQ19fC9kkNegNG3PUBSaQAaMsyPC7ixzQYhUNWJiSNoJtdCMM40oDd2ewvN8pABtDEaUSFFTlhKdhGivJKuH6BpanS61KUjT8Ze9bH8tjyakx96kbfWvCVmygWPT0AxeVtCxwarlL9hAzeJg0zR1Qau9sRdF4WR2beJhX9cG3DZQe7WKbwNM+zxb4baKHfbGu7Ky4qAT9uujjz5ybsU2MI2iyJldDYdDB7mK49jBW2yAbQNrWzG0CVKT12IThSYZ3j7OJgE2wGzCp+znssmNhVc1Cds2iLfj0JzH5us3teDtY5qJj038mnA2WwVsJqJWicoG5kopx8uw59lMlF6VO46iyMFc4jh2iYvF2T9+/JiPP/7YVcftjfO1117737oCNjFo8jTsvNpg3sLLbEJrYTfNwL+5du3/7e92DC23oN/vu0SjCXGzY2GvETuOzSS92U1pwmGaCd9vakdjc3OT9fV1sizj3LlzzsclTVOm0ylPnz51xOA0TZmdneXw8JDd3V2n3HPmzBnu3buH53mu49DpdFyyL8SJi7gVMfjKV77C6uoqq6urHB8fu3G2/hvHx8euGt9UnXvw4AFf//rXHdQqiiIePnzI0tKSgy1dunTJQV5sd9aYE4nWq1evsrm5SVmWnD9/ntFoxOzsLH/4h3/In/3Zn7nug+WpWInY27dvu87KjRs3kFJy9+5drl69yq1bt3j48KFbW9YUEHCKWsfHxwRBwGg0cufVbrcZDodcvnyZ/f19tydbqOjjx4959OgRN2/e5Cc/+QnXr18nSRKGwyFf/epXmZub4969exRFwdHREffv32dtbY3hcMi1a9f45JNPXKfSwpPu3r3L9evX3d+llPze7/0ev/jFL6jrmq9//ev89V//NYCDHB0fH/PGG29gjOHTTz/lG9/4Bg8ePEDrEwU52+21xQhLxm/ywKxcuoVI9ft9wjDk4OCATqeD53k8efKEKIqcAMGzZ8/Y29sjDEMuXrzI+vo6w+HQSQJvb28zGAyct8XMzIwTLTh//rzrdF+9etWdZxiGbG1tOTO/paUldw+wYgK2C2dlZ22ysLS0xN27d50zuU365ufnXRFodnbWwQRtAt1ut13XO4oipJTOcPbUqVPcu3fPdQNtMeZfOr50oiELiZiOKKWHCkOCuM3xJCUJPIKowKgMPVZUR1OkKRCtHC8/wZ+LOGbcqlEHJVVWo7w5sukUvy3IdEGAh9AZhfFoRS3Cno/QOWIi8MoxZjQhihRFr0WZZ2jlU8mU+HRIdpTSJqQsa2qhkMoQRC0qvyKrpvi+hyxK6lwTxn2oNMLL0V6NqqbEoSHQbaaDmihVmLiiMAatRnRPSbKJj8wgkYZwpstX3/ttNquUB7c+IQwNaCiyKVXgIX3FpAKjIKpCTCSZ6SkkARWCMRJCTTouT3w4Ch9RCwKl8AKFVxmE8ZGxh6dqJpFAmRMeRyZqYhVQTTJaYUyuSgrho3dSzO4DtncfYYSiGuV0qwhTCw5FiRf5mKokaVfoUjI9riGMEYEh9n3KUpOWhjIIKLTA1AYVlVAXxHgEQYugKMgTiakjiuGUEgXtAFFL8ipAqwKhNEZESCSVrlHZFKMNA10TejEGqOucQLRRusCLJGvLp3nzK1eJz8zjmYq2CQlVhTIpoYypJykoSZ0JFDX7tSTuJhRFTjAXItKUVpgwGhlQAnQKRqBSSRhUDKYl3oyiNiFjrYmMJk5PgowqqEmHY0ycoHKNzgS5iQnqY/wEdBwRDKco1aK/ehqp2oRBzbgwDERE4lfE7Qpd5RzvDKDbIyBC6JIoLFESOjLAqBGIPh3ZIkgUGQIZtglMgZE1yvMRKiYrSmSkQdaIEnzhEcSCKhfUoofwQGSSUqfodPplL9tfu6MZnDWDLxsAWTiUrTDbwLkJ3WlWxl8lPPf7fedA2263nUJHEyr0Knm2eV7NILJZDbbvaW+uNoCzMJqmLK1tc29sbDAajZwM4OHhIffu3eP11193lVBLGlxeXnZdgOPjY/I8d5t804nbEkttdct2ZixEw8KjAPe9CZOxN6amE/WrWH/7uFfVi15NRl7tmjSr+DbRaHaO7NEk/jfPq3nY5zfn2kr1NmFa9jF2LpuBdqvVotvtuhtrk6h9dHTEL3/5S+dYXhQFnU6Hd955x5FRX+0mNeFfTa17G+g3eRt2fTc7Evb35nq1v1sCvzHGVRZbrdZLXQvbZbLwvea428c0E+cml6N5rVk8+W9qR2NhYcEZSfZ6PZRSvHjxgvn5eXZ3d5mZmXESxpubm8zPzzMajbh48aLzg3nx4gWnTp3i0aNHXLp0yY2jhQHu7u5ijGFubs7tQ9///ve5cuWK41wIIRyHYnt7+yW1pLm5OZRSfPvb33YV/aIoHNTqgw8+YGVlBcDxTWwn0AbZly5dIs9zdnd3mZ2ddTK5Fy5cYH19nY8//tjBxCwv6sKFCzx69Mitgdu3bzuVtcFgwPb2NnBi4GdViDY2NhxE6+HDh1y8eBEhBKdOnXIdIYBz584xNzfHzs6OkxAGePbs2UtFBQuT+uSTT7h+/To/+clP+MUvfuEEL5r7k1WE+vTTTwmCwK35Cxcu8Mknn/DOO+84+Gan0+H3fu/3+Ou//mu3V3/wwQd0Oh0mkwl3794FvuBPWclYy+GxBP/Dw0O3Pzx+/NjBoaxBnzVRtNy9d955x6nVWZ6GNXqcTqcopfj0009dgjkzM8Mf//Ef0+12uX379kueP7aYZoxxJq7GGJ48ecLZs2cpy/Kljpbv+4643e/32d3d5ejoiMXFRSaTiUt+rGiH7cq99tprPHnyxHlLWS8Sa7ZnoVfXrl3j5s2bnD59mq2tLaduKIRw6xag1Wpx8eJFbt++zbVr15wCof3/v3R86UQjHCriUDHQJaLyKI+PCXoJVTXFxJrsOKfth7R8gazBTCSlUBTFCF/GFMQECdTsEaoTXK4KImaikGx0BCpEj0sINNOjmrDuELUMw8EAP/QJMFBkKBOghE+aZqRiiql8VGDwAkmej+inLdLREUk7Jmr3MfmQalIh2z2yqmQmDjic1sx6ChXNoMuCqMho+z66EBAY1LRFGUSkQ4+6mlKWNWUNZekzP3uWN65cZPJik+OdAVKXFAVMp8dEcQsvNwgfDJrpsCRsxySeRyFjIuHh+ZCsgkkLykIhPZ+8HDAuKyLVQUw0cVeT1oa0kPgE+J5Ppx1QTFPCQCBMTVgJprog90p6UUI6PSYzLeqqJgoEhhCvzuiqiokpmWYVqBb1bJ90mDEfe9R1Tu2B58eEnRg1SikzhZd5lD4kvqHKU6Q0lIOaogqgMiBLYj+kMIbaeOhcItDQBS0lZZbiBRITtKgLg1QBuRmhqQhrj0obls8s8ub5dS6152FcYmRGXgt0pan8MZ2lU5S5wjMeQXmIUQVh0qHvtxlPp3jCUKYhuuURmRJZhOQTg9dKSboBR1lE2ZOUZU673aFuBVTZAJl0KcoMIQy+F5MXFZXJiWSHGhBRm6zKYVrRmVsg9hVJpGBaUgeCQJWkfkplavRUkihB1W2TKR9hKkJpKIKEg3TCfNjBrxSV0QSJh/BqYh1TRYagUhgRUteC0uRIJfAqjVY+qCmRlFRolDTEoqKoUuqJh1IaP/a/7GX7a3fYqpQNQm2QZI32bFBs/9cM1mzV11bx7cYOuKBvbm6Og4MDd3O2HQ8bADSruE3FIysz+yqmvVnBtu9hH2+JvXBSOW1Wr9vtNrOzs47AZzsEd+/eZXZ2ltnZWZdYpGlKq9VidnbWdStsZbUJPXqVa2HPpa5rV4W0MpP2pt8MNptJlK1ENTs0zeC4+fnsmNjxaAazryZqNomw524TAzuO9rmvVtUtn8SOre082HVhA3obVFlpR1uFtKRFW5mz47+ysuJIpfacpZR88MEHfPrppw7HDHDhwgXm5+edG7NdI5ZHYsfEQpfs0eQ82M/Q/Ox2LVmYi4UwWBUgY4wzYDs6OnLE5Sb3xK5926lpeqJkWfZSwmWTFjs/zS6IHd8ml+M37bBGb0+fPuXatWusrq4SxzF1feImHYah81exZGU7xuPx2JGhbXLQ5D7Z6+rcuXOuYn3q1Cnu3LnDO++846q7t2/fdmvl2rVrLlC1UrR2HZw6dYrt7W0WFhZeSk6/+c1vorV2RnatVotvfOMbDAYDgiBgYWGBvb09lFJcuHCB7e1tJ0lr1ZqSJMHzTkzdrAnlvXv3CIKAt956i9u3b7uCxc7ODo8ePXLryCoo3bp1ywWqnufx1ltvcePGDYQ4kQq2OH7b6dje3nacCPhCZMImWU1Y2JUrV1wSYpW5moGp3dObHemtrS23h+d5zs2bNx00aDQa8ed//uduz7CvZ9dxHMdONezw8JCzZ88yGAw4d+6c484kSeKC+KdPn7pr6vDwEGOMU+x69uwZZVly8eJF3nvvPZaWlhzn5ejoiP39fZ4/f85Xv/pVJxFuuV8Wqqm15ty5c65j3+v1mJ2d5eDggCzLWF9fd0WTVqvl9kTA7W9FUfD5558zGAx4/fXXHQnb3iMtvyNNU77yla+wtbXlij22yGXXnF3XQggn533jxg3yPOfu3bvOq2l3d9fNnfV6sffbmZkZdnd32d3d5fXXX3ed6H/p+NKJxkSlxAHM+xFF0Gc0maLHFUpUyLpAVIbSgKlPEoiSACnESXBZFrT8CBN4ZHmPPZOTJAFBWVGKClEZ2l7MTDhlmldMlSEMfcIyJpxZ4Ohgi7LU9EWCCHyKYoAhJS9KPLVIVuUIEWBESBT55NmEIFcoAb5UlJ6kTkv6WjIsB9Qtj1QqqiLDlxIV9RjIMVVc0C0F7YlkXCjKxEckBYnnY0ZT+u0WE5Hx1hvvMN2u+GTyEdOjLdqtAGpBXsHs6hJ5NuD4uEAKTT5N0ZEhB4wp8XNJFIf4nqTntzk8zuh0EuqoIss9tKmpTU2mPDwNMwYmpiIvKrSpEJ7HOD8iEAEYhacEw7Gh9jsEsaQKDKkuMXVOHkWUeYqSAWWpqcocFXq0Q03sKzCKfiKZDgaIw5qqLqnDAFNX+KaiLGpCElKvovZyfJGBrMl1QTX28aWPJMVfbFGqhHyQE4SafruDGVQEIgIzRespvhT4souRMLfU48K165y7eIVuK0FoQSoSVFQTVDVxawnpgdk/pr8wzzAQzPR6DCrDYDokkgFmqpjKgllSsqokrAOiTsIkzal7hoAE5UuKLKVMc9pqgkgE46ImjQJUOaFTa8KpRKiAMh8TeYJuK0bXA8bihIfU7syidYbWhulwSh1BUQqiCioKRkFI6Rt8UaGKEG1KaqXoz8wSiwjPxOiyZKIhDjqQZ6iqQNY+mSnxlU8gNEJUGC8k1AojFUWdk5WKQFVQp5jSZ5Jl+KQE0Zczyfl1PGxlr1nVbUJ/bIBqb6jNQN9Cd+wN2wZNFn4jpXSkX6uAYjfiJjTLvm6TyGx5CPZGbINcG2A3gzUbODcr1xaaZQNpz/NYXFxkZ2eHw8NDF+QVRcGNGzf42te+Rr/fd8F5WZZ0Oh263a4LhiyOt3mDbnZSLLzI4nNt0mXH18pv2sDTjrF9TzvuNmC1PAFbMbe/NztOze5E8/cmkdSOh/1ctvLedIRvVtrtDdUG8zb4byY6Njmy0Bcb8Nv5s4G7lJLZ2VkWFhZYWloiSRL3eLu+tra2+MlPfuISlqqqmJmZcQRIS9q0Y9NMlJrnbcfAjo8dW6s81axe2rVlx8KOi+2ExHFMGIY8ePDAkS+bSZmdwzAM3f/sOrY3++a52OACcIFc873t438TD61PDMds9f78+fNu35ifn+fGjRusrq46x2P4Ql54c3OTwWDA9evXWV5e5saNG3S7XUajkeN6nD59mslkwpkzZ9x1Y6vXxhiWlpbodrssLi5ydHSE1poLFy7Q6/XY2NigrmvHCdJaO+dvK+9s3cnX1tbcNfrkyRM2Nzf53d/9XW7cuMHy8jK7u7uOP2DhgHt7eywvLxMEAUtLSw6SdXh4yLNnz1yCaTsk1uuhCZ+z8//+++8TRRFXrlzhs88+Q0rJrVu3+IM/+AN+8YtfOM7J7du3HdzJFjIA3n33XXZ2dhwB2sI+7b7ys5/9jO9+97uMRiP29vZcN9Z6ljx+/NglJZa3cP36dW7evOn2b2O+8MixCWQTXmshXt/73vfceQgheP78OTs7O26Pb7fbzM/Ps7W15a7LdruNUopz5845R/GqqlhZWeHUqVO0Wi3m5+fp9XpOgKKua5aWlpibmyOOY7Is4y/+4i8YjUasr6/Tbrd59913nVrUxsYGFy9edMIBNul89uwZg8GAFy9eMBgMWFxcdHuN3Tutt9LVq1fd/NjPnyQJ0+mUnZ0dt0YtP8cWKD777DM3FxsbGywsLHDv3j3OnTvHeDx23R3b7d/e3ubMmTOsrKxQFIUzEGy32079zkLBrErWl3UGl//yQ06Odl9iqphpXXGcFnhRhO8LdATEitIbI/ryJJgsQsamRPc8wrCNLARplnI43iejxpMeMjOUtUHkkiKPKcc5U5WfQHgqQSwTQmbwYp+oXRFphTQx6TClVUfIKiD0OkRBTWYqxnmNzDvUgSSIJQSGTMBUCIoATCzJZ0LKMKDjd0kiHy/wSCtNpgr80COcagwB48SDwKPrSUSuKIYhnXaPQihoeZxaneGbv/Xb/O63vkF/bpF0WpCnisrzOcqOOK4LVOzheRFe0qHOcsKyJEl8POWTDo84GgwZZgNKk5OPR1TTDM8DGWqkiui2YhQepS8glHiqQgGkUxKvSxh5dIVCj3LKuqaqK4ysCH0BNVQSQiORlUR5kiqsqEMP0pLZSKGyEj3SUClaYZdxNqWuJbFvkF5KIDRS+0zzknxaIHMDlaLSAtXqoVWNrgwm8JiWKT4VJpX405jRoeagTBlneyi/IIgEypMgFd3ZDpfPneeNS2+js4jRBLQKEUEIpqISEmFaqEoy2+0yLQ8oPcXBQYVJKwyCslYUeUVnpk3stQirLmVZo8QYE1Wk4yliMEaOaxJPoCJDXWjKiSQYFPSGGaJUkCm0B8FMRDTXR6oho+kxqIj5/gwq9GnF84SeT60K2mGHWPvEVYxXt0naC1SeJCslqdYYURJ5ilB7xBJ84eNHINuKMJBEAvzARykPVSnacUgcKaSRKOljTMWonFARIWqPqPbJRiWm9pGZRCYeVUeyy5erIvw6Hq/Kmb4KS3m1Am5VdmwgZr9e9ViwN5QoipymuIU/NANiG/TZzoD9uVnht1Vim3zYgMEG97YSbSFNNsC0wb59DxuMWJ7FZDIhyzJevHjB/fv33RhYfkaapnQ6HYeRtePVNI1rejUALqC1Nyfb8rZBOHxB7LY3oCbsyB72nC3fwT7PJnaA+3yWJ2EVnOyNv1mBbxK0m6R6mxQ0kyEbSDcTH/v+Vqt9NBoxHA7dZ7dBuB07m6gkScKpU6dYWVmh1+u559mqf5qm/OhHP+Lw8JDpdOogAFevXnXBhQ3+bXLSTMDserMyw80k2I6nTZaaPJrm69kuVXMtWdnde/fuOfO2ZufPVpybXQ1LMLdBaJMLYtekTfya/I5mEv+beFjolzGGhYUFtz9sbGzwq1/9irW1NReUjUYjp9ZkJYLH4zErKyu0220uX77siMjnz5+n0+mwvb1Nmqbs7+87VR07jtZ/I45jnjx5QlEUHB4eEkURxhhOnz7Ns2fP3Bq2ycCHH37oVK+63a5TqhqPx84gUCnFRx995Dprv//7v++KCEmSOIjVgwcP8DzPGd/Nzc25dXTmzBneeecdJ20rpeTKlSvuem/Oudaas2fPcufOnZcKPj/96U+5fv06T5484caNG1y4cIEkSeh2uy9BJjc3N4ETqJJNQJqdSwt/KsuS1157jd/+7d/mu9/9Lp7n8eDBA+fGLqXk4OCAvb09bt686ZJ4+/3FixcAXL9+ne9973uuC267AFprfvCDH6CU4smTJxhj+Na3voXneayurvLuu+9y5coVt6crpVhYWODatWuOO9FUlHvx4gU3btxgMplw9epV2u02CwsLzj3bnmtVVXz++efkec7BwQEff/wxURTxW7/1W5w5c4anT59y8eJFtD5xCLcKeIPBgDAMHTTLer9YyV2bzGZZxszMDMYY1tfXXyqgPXv2jNFoxMLCgitMFEXhpMmfP3/OxsYGly9fZmFhAc/zePPNN3nrrbcAXMFhOp26rt/Zs2ddAqu15ubNmzx+/JitrS06nQ4HBwcOwmXVxP7/Dp0yvqTqG6Ya5HFOUBbowODh4UUd8hKYCITyiOOSaeghKkl6XBNoQ7wmmAw0oi6YCztkfk2qJyS5oddvUU5TaiXIvTFKJvitmEQPyDMPb+pTqiGyUkzMhKwMMTolnfiIVg+tM0Q2xI/6aN+jmEhqkRNGESYFVYTIfowODS3to7OcalphwoTubEg+TimmGoOiFil14NEOfcJaE4sEv2/I80MEbbKJJhCwMLvM21/9Kk9fbDKqRkyOU0xdwGGOjEOiMKQ8TPGMJKwjahVS1QVJ6HE0FghChmVNN4pod+fZ3TuGMkebkklek5Qag2ZfF3i+T6vSqMpDezF5LQmJGRQTpFIYJLKq6CqYlv8su1hMqCa7hK0uaQG+3yIiQciUqsrJp4bI75COMvwQRCJIRzneCIJAUOkCr2WYTgxlmlMIg2aKHwSYShOEEIUhgzylOjLUXg4iIs0qgtCgAqirE2hVPYYwjllcbHPp/Dnee+e3mFvscDQpqQJBUWXkeUHgBbRnOuhyQpYVyEohQ/CzDM+vKYsaoXxUoOh7HoOiRIQdTDakiAOOswLJCRQsidscyyGJ0Ii8xsj4xChPGEzlE3otVFWTRxWZ1kzHIyLlgwzoSUV6cMjcqdPkqkusapKwRmY1qobEGyGkT54a4laILDRlECKznLo3i1ABINFGonONHyiEgbyukKZCKolqSYySpNmU0AspMPhSYQJJmhfIogKmRL5HkZVkkwFRu8eoTvG832zVKXvYG4m9Odkg0wZC9vE2ILWBlg36bPXH/t2S52wnYXd31xm0NYPZJim8KRFqK+LNG2az82EDTVshbjo1w8sVYvs5l5eXHZTLVqGVUjx//pzl5WWWl5ddEG4xt5avMZ1OXcXPJjrN7oSVstVaOzjVq+fWNGizlcFXIT72vK2vRRNm06zIW1y8nTMLCbCwFXvTtIlaszLXJIU3+Rg2kG4mK3YubIXRJgP2XC1cygbtTfz1xYsXWV5edlCWPM8dn6UsS375y1/y6NEjx92pqopz585x9uxZZ4Jlg5HmeTXhfO1228Fwml2D5jq2CYZNOiz0y3ZebOBvx8AYw8bGhlPisclzs5rdlOy1Y2Wd2O28NRM1eBkSZ4PMZkfrN/F4+vQp58+fp9VqvbQGzp8/z7Nnz1xQfvnyZdI0dV2rOI4xxvD6668787vJZMKLFy+YnZ11im77+/uuYNHkHWxubrpxGw6HDkonpSRNU46OjphMJiwvL9PpdNjZ2XFJ8sWLFzk4OHBzZ/0QbDevrk8kZ2/fvs3bb7/t4HO2kDAYDBwZ+OjoyMGdzp07x6effsq3v/1tptMpP//5z9nY2HAVd9slsx0B3/cdzNAYw8HBgdtLjTGO4KuUYmVlhQcPHjh3bNsZeeONN7h586bb0xYWFpxnhl1TQgg+//xztxb39/fdtWKLB82OnTXEhC84X3Zfmk6nBEHA0dGRg5QuLy+zurrqnNKtW7u9jnZ3d/m93/s9Hj9+zCeffOLgoHbPWllZYX9/n7fffps8z3n27Jnby8Mw5NSpU5w7d44sy3j27Bnnzp1z/L9ut+uSpR/84Afs7e1R1yfGfr/7u7/rCOl2Dqyp4dbWFhcuXGBmZoZer8fW1hb9ft/5+5Rlyfr6OsfHx3S7Xc6dO0eapjx58oQ33niDzz77jLm5OZIkYXFxkc8//9zxPOAk8ZuZmWFvb49PP/30JQM/Y05U1N5++212dnY4Pj6m0+nQ6/Wcx1O73XZywNeuXWN2dpaNjQ3XgbOJ3YsXLxBCsLi4yMHBwZe6Zr90olEMh6TKQymfxBsDAUWuMMYjnRbUOqSMWxTZ4ATT7wm8PEP12icGbMKgooROaND/7CugtU8UQaEnTKmJPJ/IRAgMRZWCH9DVLQayRTkLTKYIX1N6FS2l0NMCr8wRyiNsRRgxpagjAq9FQI00JZNA0JYeeVERFwKdG1IZkmmDrzT1OIWpIFYRhadptbqM9YTKAx0L8p0hge8RzoRMJtCSfTxfo3XGyozHH33rGwhZ8tnNT6irnLosMdpDtWOicyHjvQlCJmgPSmWoh0fEvR4EAcUoo9WKKEWFEYoyLQliQ12WTCpNFdT45QmRmlJhgpg8mxIbEKGi8g1RodC6pAhSxpmkSAXaC1FejQgCSgwirzAYEpVBVaIRpIFHEU4ZD0ckMkBITTeUKK+DNoZMTKkmGbIWaKXxfQHtNsVYo8qcWmsKT2JCH+qSqjQoU5PpjGI8pRVHjIqCpG1QCcypLq+fuszFi+v0en0IDYH2yesJgUyQBgLfp5ICmUyRdQ/Ph6KqyZggMOi6xgtihAkpqwEBLfLDDNGJ8JSHkhVipPH7MdOqAJlAZPANVOMR06hNoBStyOB5BbVUaKOoy4QwLcn8gElVEsfQSnzCoEK3crLKQ9U+uh7QSWKmkSQ1Bd7BEC8P8BOfwIPak5S1gUoQzXrIYY4vImTkoaThuC7wcwhrj9RUBG1BraGiRhtJPqnwYo8o8Ch0TRh45GmN8EpUR1GZlES2iMxvdkfDQqZsMNyEntgugg3IbQW5mUw0eRw28LZVX4stnp+fZ2dnh9OnT7ukokl4tkFhlmUuEG/CC+zRlCD1PM+RaJuwn+b5NAm6FuKxvr7OaDR6qfKUZRlPnz6l1WrRarUAXMXVJhu29W+DTnvedV27AMoGwZaAbRM0W+FuBr7NLpE956Yqlg1qbOBmOxq2+mUdsm2yEccxURS5LkAzeWn6eTR9ImzCaJVdbBLTTKYAlwDaz2g7PHaere+FhVD1ej2uX7/O2tqam3ObfNlA/O7du/zqV796ybtjYWGBr33ta5w5c8YprdhE18K27JfW2nUzbOfKJp72vO34NrtHzSpws8tgx8SueRvQzc7OvvR5mwmtTbJtZ89W9221tsmTaSbYgFtDzfP9TTzOnDlDEASkacrGxoYjgV+8eJG5uTk2NjYcFOX4+Jher0e/33dQlV6vx/HxsTMqW1tbc5XiJEnce9iuiSXTWsNxO8tAAAEAAElEQVTNjY0N1024e/euS/rX19f56KOP+MY3vuGM4+I4ptvtOv5Br9dzqll2jv76r/+af/Wv/pXbC+7cucP6+joPHjxgbW2NPM/Z3t6m2+06Dpc1F5ybm+Ps2bPcvn2bJ0+eOPndhYUFNy5Xr151XAu7XgHXBQEcL21nZ4eVlRV++MMf8sYbb3DmzBkHObNJ7Keffurmoqoqrl69yieffMJoNOLUqVOusg4nSfR7772HMYabN29SFAXr6+scHh7y+eef0+l0OHXqFJ999hlJkhDHsZuLJmxQiBMe38bGhnvvIAh49913uXHjBkEQ8Oabb3L37l1X/X/48CG7u7vAy4IV9tr//PPPuXPnjoO62WD+9OnTXL58mX6/z4sXL3j33XfpdDqOzG87Ze+//z7Hx8fASSL63e9+lyRJePToEb1ej5mZGQD+9m//lu985zuu2FDXtfOrqKqKyWTi5LajKHLFsbIsnWpelmVcunQJwPFgzpw5w+3bt51TvXUT11qzsrLikjlLfF9fXyeKIvb39wmCwCm12X1/f3/fQTMPDg5ckWNtbY3t7W137xoOhywuLjpPjS9zfOlEIw47ZEJSl4paS4gilMwYTUtask1QTyizAUb46MKjKipMIjChohI+JhvSSQRFnTEcaqLaJ/bkCTG8yIn8iJyCJOiRTQsYC2Skafs+KplFeV0Ohy+ICgW9kFE6IQ4MeWHwFzpoEdIaTAhVSN2qqGtFQUkSauosp4y7VEVApKa0hcSLFpjmm6Ch312gyMZIEzE5lCSrC1TViOlE0wtblEWEnFaEsuKonjCuDF3lE6Qd1k8n/L/+/RKdVszNWx9Th1MODg2D3QkqrsmqDOX7SArIK3r9LmmewnSKX/sc1inC5PRaHoXu4aMoqikjQCh9YjbnKQoRkOshyJy6LtFpTKQ8IgmognGtKbSHiANCrUAJxmFNx1REdchxVZPVJSrzqRNJUZeoWuMJH2MU41FJV4SgSkZlDYVAhKBlQSACdF3DsKQVBEz0lFAqlFAEZU17JiIdQJVqlCfww4jAVyQ6ZKE/Q39xhnML57j+xmu0kw6+LxGlRlYB3aiN8aElEhZaIXt1TkdKfGpyHaIKCDtdSI9JZID2fOS0JOi2mByUZHWN9FsQgxQVSScmCEuMDFGVx1QUjOMpM1WLuNDEc/NkZowpKorC4AmJqFK8BUEiCpZkB/BoLyfImcUTz420QAiofBhTosc1s0kXHSpqJZmmkhBBXik87ZFEAR41XngSEBkpqaoJSRigc4Ooa4yUpIUmCiJykxFoSSEFmIzKaEqtMIcZJQVGaKqy5rg+opZtetmXRjz+2h3NqlWzst7E2jfN2prBsQ0+4Qt37WaQ3yRAW/y0VZexFSO7EcMXErC2gmeD0iaW2QbBFsdqb8624m4DyGbF3gaC9ua9srJCnudOt90GslaO8LXXXiOOY6fyYcyJtr7FV9v3sUH7q5wAwHkClGVJHMcuObEQpqaClA0YmjfeJlfDnmPT2M4Gs7bD0nydZlJg58SOdZPM3+yu2DlsVkDt+9pEotnZsolnM+G01eFWq8WVK1dYWVlhfX3dBYdW0tYYw9bWFh9++OFLfiDtdpu3336ba9eusbCwQBRFzpfEihY012cz6bKfz35vQvRsgtGEqjSlm5uPs3/b29vjyZMnzMzMEMexu1aa0KxmkvhqgtusjNu/28dZvoZd4/bzf1nYw6/bsb29zcrKCkKcKCMBtNttXrx4wePHjzl9+jSPHj1yylC2AzgYDLh69SrHx8cuWbFO2fbLGMP58+dd9ysMQxe028T79OnT7lqy8tR7e3vcvXuX73znO24fsfM4Ho/d9Sal5PTp025eqqria1/7GsPhkN3dXeq65oc//KHzQbBmd5ZrZN2oASeh3Gq1mE6nLCwsOJjg3bt3+ff//t/z8ccfs7GxgRCC4+NjVxCwxZinT586aM/Dhw8pisJJgt++fduJVthr00qqHh4eAid7wI9//GOiKOLf/bt/R1EU/M3f/A1wkgjMzs6yublJkiS89dZb7O3tsb297caormunvHTmzBlnVOn7PmfOnOHOnTtEUeR4CufOnePhw4cO3mZlYLXW/F//1//l9qOqqlyl3167VtJ4ZWWFn//857RaLS5fvux4G/fu3WNhYYEzZ844jpdNmgaDAWma8lu/9Vt89tln/PKXv3QdCivw8d5775GmKd/85jepqorZ2Vn29/f5+te/TlEUnDlzhuPjY8eH6PV6nDt3jhcvXrjr9vHjx6yuriKEcG7evV7PqYUppdw+b/fn1dVVl0x4nsetW7fY2NhAa02/32d+fp6joyPefPNNNjc3OTg4cF2t6XTqpJabAhHNvcf6nNR1zZMnT7h06RJxHHPz5k3OnDnzpa7ZLy9vS0k40WQ6ogwMytfoqmCu06HOxhBXRJXPeFKhPPCrmHwyQeghtUjw04q8nBK2E8IERHTSUchKgdQdjOehTUClK1Qi8SYGpRJMMkYNJJFIiOIZJmKAliCFz0QIkr6krlOMXyHbAcVQIOQJ7MDvKupCk1UeXpaRl4Zo3oMspcxz2m2fzBhEXOHXEj+sUR1BLXJi3WI42Ke/1MVPodSadiTAa5OOcqQfUa5G9KTgzfZ5OnmF9DxuffABUTBmIgx1blieW6E8PqKqChIZkqY+la5Igpo69cjrHOkLhsOU/kxEVYXUtU93JqQoJxD5eJWkKFMiv6CIJHmRoyqDqEIGuqLTC5CTAJPViKRGl5oAj3Y0hzIDalOc+JcoiahBFhOiqsArO5TSJ6hyfD8kUj55mZFUHnk2RVQFiR9SeYqiqpCeQIaGeCKpRYtsWuMZmB4V1LVGdiooNJXxMRIuvHaG9aULLCzMsXZxic7cHGIMk38mto91TTzy6S7GFNMD0npKJGcpM4X0oDQZRZLSUhXd7gz7+2PijkB6OZUJTkwOI0GWS+LiJPlp+S3KiaEsIA4M6TRFF6BbbdoLhnhaUOxl1K0+UheEpiKbluSxRgpF0PHRBPi0qY81c3FCHk5IOh5V2sYkPgLNeD9DtbqkVUWcCMKOh8wVfhTRDgOKesJYgUrH+CoiCCSBFojEpy4qyKZ4wicvSkwrYpyeeHmoMgPh4/kaWh5mkKJUlx3GEETMqIBAjb7sZftreTSVkJrwnWZX41V1n2bCAS93Gl7ldUh5InObJAmbm5ssLy+7boTlYtjg2gaCtp1vz68JMbLBpPXIsB0EG2hbwrntSFiCpz1Xi1dO05Tt7e2XnMUtdOPs2bPAF9AiS2y3kAV77ja4toG/DYZttdJ2E+zNyI5hM/Btqms1uxv2te3rNefE8gksVMfOiYWDwRfJwqvE9SanpQkvasK07Pk1E5MmcdzOeZNvUJYlvV6Ps2fPcvbsWafwYpMqW9Hb2tripz/9qVNgEUK4wOc73/kOZ86ccbKazaqjrXzaz9ZcmzYpa3JjmpwNezQTa9udaSa69st6FXQ6nf8tCbHPt+uxCeVrwrRsImT5KPY9mhym5rz+pkKnFhYWMMY4qJsd16IoXOD3+uuvU9cn/jaPHz9maWnJBVgW6/7WW29x//59pJS8ePGCKIpc8GahSVacwe4Pa2trwElSbYnjc3Nz5HnuPCGKomB2dvalsR+Pxw7SpZQiSRI3Hxb/Pjc3RxRFTCYTfvCDH/Duu++yt7fn1IPSNKXdbjuRiNXVVWcieOnSJZIkYX5+nu3tbfI85x//8R/dGrbXub02LLfE8zzHK7HXo4V02c9Z1yeeLqurqzx9+tTJbne7XeI4dkpHd+7cYWNjw6218+fPMxgMqKqKx48fuy6hle7tdrvs7e25btTBwQFpmvLWW29x7949NjY2XIB7+/ZtoihyBnLD4ZCjoyN3zZ0+fZosy3j33Xf54Q9/iNaaixcvOmWp6XTq1LT29vbcON69e5cwDF3n5p133mF1ddXJ+Npr0hjD4uIig8GAhw8f8umnn7K4uMizZ8+4cuUKS0tLLrE9PDx0ruK2aGSV76z8soWrHRwcuOKFUsrBfcfjMQsLC2xvb/OXf/mXfOUrX3HX7sbGhuPx7O3t4fs+h4eHrnhiuUrGGEcYf+eddxBCOAhUk19ovUAmk4krksFJolgUhZPn3d3d5b333iOKIn760586vtKXOb50ohEZgWwlSFFitM+krGjHLTwBU18hajC1JPSnJIFC+5Kh8hEiROgKo2pknHBQpfhhjJdPiVsek1QReZJKDQl6bYrU4NeGMILp8ZSwp2nNzzKZpLRyhee1yPMRg6wm8udJspos3aSc7ZMGEbqV0up0aR9JcmGY1oJwpqKdBrR6hmlZUwsPFZdEKsQzJxr4YRySj0t63Zjx8J+x4XGbtMzp9FvUZUQUK/y4R1xNCaWPDlMCFVFlBRdfO8v/e+V7hK2QX/z8F9R7e0wnmt2dfUJf43d89L5PGCpkfNIVMB54+FSepvQT8rHB79SorGQwOCJOPIwnOBjneFVBUAN+jM4LlJJ0FtoYT3N8lOH3FLLwCP0OGUOm4xIlM+IwQaopWVij6pS8Vhjt42OYpBOCJCQMEsppSullTHJJVdZEoQI88kqg0SgTU+YlxqsJVUSeVwRtnyIzoCW+0eisQtQV7V6HK29f5sqps8ytLBGFMZ2ZNeppjsgzRnXK3FIfX8V42tBOFIXfZuxJelGbejphmmkqA55qEQPZaIQMDGVV4ukAL60JK5+YgkIckxc+XgDC5Ci6RKqi9iXduI+KKibTQzIRU4xK8AM6UjAROSpIkArCKKFDmyJVeP0Ik7ToeCmFOmKKRNUeQvvovEIFHn4sSBkSR12SskKmJWEsSIsxRS5QkUQVIKqaIqoQSlBVY0ztY8IQVVUIkaNFSDoc43sleDXDYU4d5BhtKIzCCEWix3hUiHHOCE2RVKx86Uv81+uwlVe70TZ5EbY93qz4WsysrXIJIV4KUG0HwbrFwheJgsWx7u/vO6UW20loyo42g8kmxr1ZubaBZJOIbZ/TJLdbaIs13LOvGYYhly9ffkmZyAapzc3dQoMs1Gt2dpYkSZzjOeDGwgavNrmxBGILSWpWrJtkTxtsNKvdloxsH9PEUNvHWghO87nNZAx4KXmzSUEYhk5xxxLrLW+hyQGxsLFm16f5mk1FLwt7Wlxc5MKFCw761ORDlGXJwcEBP//5zzk6OnJjYgMLm2RYwzf7Xk3zQHt+NkhrdpPsZ7TPs+upCZuyh00aqqp6SebUEmkfPHhAr9dziZwNDG0CYfH1r5K67Tqwr2nXcpPP82pXxFa1v6zZ1q/bce7cOZ4/f05ZlhweHuL7vvPMsd2G58+f0+l0kPILJ+Nut+uM2fb399nf36ff7zsH9mbC2+v1nHv4/Pz8S0RywAXJgNtPrBfLrVu32Nvb48yZM2xsbLhkwwoQzM3NcfPmTbdnra+vs76+7uZpOBxijOEf/uEfeOutt/jhD3/o5v0rX/mK60Q0oSudTofV1VX29/c5PDzk6OiINE3pdrtu3dv1vLi46PZGpZTrMDQLFlVVsbCwwBtvvMH9+/dZWlpy8Mzr16/z/vvvM5lMHH/K7o2WQyHliUGgLcxYTkKz+/fgwQNarRaXLl1yECbLMbCyqtYTyXZTLEdJ6xPpWAtd2t7eJgxDbt68CZxAL3d3dx288u233+bWrVsIccLxunTpEs+ePSMMQ4bDIcfHx6ysrDA3N8fCwoLrQFhjw83NTeI45uOPP3bja31NvvWtb/HNb37T8e4mk4kToej3+0RR5DpnMzMzhGHouiUWijoajWi1Wpw9e9bxc2xBIEkSl6gmScLc3BxwwnuxMrjz8/MMh0MePHjAhx9+iDGGy5cv8/DhQ65fv8758+fZ3Nx0xSIr/76+vs7Ozo5bX1VVMR6POX/+vIMhKqUYj8cuqZ1MJrz11lvMzc0xGAy+1DX7pRONOk8YxzVFYEhESLR7iBSCoQnwZZsyG6A9Tej55IOaQkwQnkcceeBrjKgQBEQqQZAzMpojbZCJxKgcVZVEVUaFR11nFPhEHR8tfPI8peVVpJMcjy6FqYjDAIkkNSm9ZIaqnKFQY7wkYjo4Rqg2hpBMp3ilx2A6AQNRL8Hz2lTZhELU1OMKhUb0NEkYkU4zpPSo/BpBRVZXdP02vuhAlXG+2+W4EFRlwbCW6KhNlo1ot7tcXEr4P//oeyz1+vzgRz/mwd3nSArqCpIpCE9QcXjiBJ4JojmfPCuJghCfGuNL8mybyjcEU4NJfbK8QuiCIIzRQuBHM3heShKUnL9yho3tQ1aTRWo9QR+PmZlbQHUXCesTWNBEVwy2X6DrgmlagoyIlIFM0A4Nvh9SSR8dlRxnI4zR1EpgooQsL4laPqbUVLU5cTNXHsOjCWGiEDJDSh9Za/xAUuqE9lqbS5cusTI3R295gd5Cl7jyEdkEKX2q0CNQglE9ohcEBHHnRBGq8mhHHQo0ZV3SFRW6NkjtU1IxTcBUAXnt4VcVMlYoIxhmhqjVYjKtGFQDaj+iDmLCrKQqMkyvxfE2eIGgKI9pRTPUtWJSDRAyJ9cg8hLpG+qgIvA18zOzqMBHVopMTzBlSoBiWkmCuENZjZGVIvALiizDI0ICUSUxQUCeaoLMw+uA1px4kxQ5YeBRGEVxnKG9GGFSTJWSJBHpYIDszJCXmsLkRAo4NChZc6ynGBGiK03cDpH85vpoAC8F4Pam0wzoXq3G2oCsWZFvukDbhMBWmW3gPT8/z8bGBs+fP3cGWs0KcxMq1HwfG2RbJRKbBMEXHBN7c7XQI/var8KShBAOC7u4uIiU0hlqWTKrrUofHh46ZRNLPLfV9yRJ2NracgZM9u/GGIezNca85HBruxs2AG5CnmwVrVmBb3o82KDazoH92aotwf+9y7SFXdgkozl3Nii3Y25vkvYm1/SHsI+z3AMbONtgeXZ2lvn5ec6cOePkFm2SYYOvvb09/umf/slp+dsEd35+nu9+97ucP3/eySHb921yIez5vSp1a9das9Niz9km0PZ/NkGzCmeAS75s1+vOnTuUZcni4qKrHtqAzwYGcRyTJIlLQu3/LGytmSzac7IwMzt/dh3YNfCbytOwOP3pdMpwOHSB+/r6unNFjuOYdrvtrkOLNZ9Opy/NiSXse57HvXv3HCZ+ZWXFGX0eHBxweHjIysqKUwcaDAZORcoYw9OnT906XFlZYTQaUVWVk841xjiviYcPH9LpdBxp/MGDBw7yZzkNT58+5Z/+6Z/c5/rggw8w5oTIvry87BIdG7QuLy/zi1/8wu0Zdl+8cOECnnfimwFfdH8fPnzo1vDMzIwjqpdl6bo3e3t7rjvQ6XTcayh1op5klZeklE4lyfqGGGOYmZlx5GYhhCtkfPe733XyuXEcOz7D0dERp06d4tatWy91dZMk4fj4mGfPnvHWW29x69YtpJQ8e/YM+GKtX7lyhefPn7tuk/WCsNDEtbU1Wq2We97S0hL9fp+HDx/y9a9/3Rn/WfGIyWTC0tISq6urzizvhz/8If1+n8lk4sQnrly5wuPHj11ic+nSJQaDAU+ePGE6nXLlyhVngmghvPfv3+fs2bNkWcaTJ08cD+PUqVPuHvj48WPOnTvH66+/zuHhoZs722WyxY00Tbl69SovXrzg8PDQcVwePHiA7/s8ffqUS5cuOTjV2toa4/HYwa36/T7T6dTN8+zsrCv6aK0db8caHJ49e5Z+v8/x8bHruP5Lx5dONKqiIvenSONR1TleXOMLj0r76LEhyWqqOQ+RdKjyAj/XJMqjinymRQ5VgC5z8EICYlRsCMIAMy4pphVGKvIopZ37dKoueVhiWhV6apB+jO+lePNdzLhFXaWgFQVTklabuqjxzIm6UD7UeIGiTgyinOC1DMYoOvEcVSCIfXli/Od5VJXA+CFpmdKuIjLp0W0FVFlNoaYst3rovCJMa3Q1hn7EuB7RWooxlY/IQ0SqiXsJspaIqmRtaZE//O4f0QkW+GH8E54+uMXhKGVa13hhjg4rZv0QkWl0Nka12hRHFX5VIJMAkYVMjSbzciIjiP0I2WuR+CF1mhMuRvRrmI4Ue4/36c2FqKhNu5pBLBtWzp5FFzUyTUnimN1JRi9ocfPOHXw/pkhLJtMJSehhqjbTKVTxGC2mBJ6HDGqmFUzTDImmqE+kc3v9JUxdkZUpKlJkgUDlAlON0YRIr8vK6iwXr11lrhuztLBC0gHlRURJiMolw1JRqpp2HJGECYn2qXzDzuSIAEGW5fjtGD/QHKYGEZ5U40ql8MMYP9cgSlq6ZFT7tAIPIQICv4VoV0xlRemFlFWFSaCjO+RHI2QQ4KcevfYMWaoRSKqJxo89Ar9GRAm0Q8pM0JvvoYIYMahRvk8YQeJ75LVAeYo0HRFGPsaLqUnxWhHUHtW4ZhIqvNjgqRwtC5gaCgx1LShlTj0NKDxDFdZUVUpcexRpRm5KUlHhj4eooIMSNfn4GFVLxrlHIXLi0CNs90EYvGP9L16vv85HMwhrVmGbXgO2st/E9Ter5U051CamH74wkbP46kePHjEYDJzhFZxAAmxQ1gzE6rp2XAkbjBljXAvfGOMCxCaG3/6cpilBEDincFtBa7VajEYjlpeXkVKyubnJ3Nwca2trbG5u8stf/tJVG69cueLew9502u026+vrbGxssLW1RZqmDAYDlxTY4DSOY1qtloNH2ISiCblpJmxNPkBTEtfevO3ntN9tgByGIWmauiDNJh/j8filar0dX5sU2e5F06PEJm12vGxny45/k7MTRRGLi4ssLS1x/vx5l7w1X8/CpX70ox/x/PnzlyAjMzP/H+r+rMmOKz3vR39r5Zx7rnlAYSZAgk2yuyVK7JblDtmWHf84Dl345nwEfzH7xhFS2ApbbUshqVvqVrvVbBFsAMRIYiqgUIWa9pBzrrXORXktJiCfI16cCzEjGCSBqr1zWJn5Pu/7DBN+9KMfcePGDUf96FK6ujS8LgXL0vu6kzZbxNrJVJcq150g2fMLvHE+bQF09+5dkiRhMBg4+9ou5azX6znLY3te7SSvqxHqTuS6blOWC2/vO3ts3YnLt2mzYmwhBMvLy2h9ZtMax7ErYM+dO/cGFdPej1prR2taLBbO3ta6v9n7dzqdkmWZ0+30ej0GgwEnJye8fPmSlZUV58KTZRkrKytubSdJQlEUrrt/7tw5lFLOpcdqtp48ecKVK1e4cOGC0xLZ7IX5fM7777/Pn//5nztajpRnQZPb29ucO3eOS5cucevWLUc5Go/HnJyc8C//5b/kiy++cPva1b3ZQD4LrD3P48aNGzx9+tS5U9lngV1nNjTOrpevvvqKwWDAe++9x1/91V/9IzBun1nT6ZTf/OY3pGmKUooLFy7w5MkT/uEf/gHf97l27Rrr6+v8t//231hZWSFNUw4PD99Y01mWvWG3/fLlSzY3N90ko2kawjDk2rVrCCHc87VpGgeSrBbn4OCAV69eIaVkd3fXPReXlpaYTCYuHdvzzpLmNzc3Mcbw+vVr9vb2ePnyJbu7u25NfP/732d9fZ0wDNnd3XUF+IMHD7h9+zZ/9Ed/RFVV7O7u8vDhQzfBWl5eZjQaOVfBra0tBoMBd+/eZWtry533S5cuEQSB063kec5gMHDvjcPDQ9577z03lTo9PeUXv/iF02TYBtj3vvc9lpaWePbsmZui2OfC3t7eGxTOk5MTp/WZTqccHh6yvLzMlStXuHXrlgNrtoFm09j/qe0bA41mWBBVKYE/RImSstD4aUJdNaTSpxExQoQ0VY6/pPEqTV1p5gq8yqfSkkhIgnBCa44ZByFFDkiBZxoK6YEKUX5KHnoUnqEpamLRYpRG5BGrwz67sxl+EqLyljT08LRPrQW1mpH0Q3zdnOkQdEMkPISAOBI0okYELWoa43kw6AlaEVKREIdnQXlN01LENeEwoEcPjxotE/xWEsY14dIyspcwKxf0dUIzfYXRktO2QmvBqhfhBS3jpTE/+Bc/oL/V48d/rsk//YxW1VTK0LaKuW5gEFBJjSglqoBk2KNVNV4UEBzmBOM+Ay/FGKgDRRBolgdDjk4rtB8yHq/SX11hFhRsThKakwIR91GtZiWMeH065UhrysOKarogGUTMpxm1r85e+l7L7DijFeCJCiFq/CBCtym0BXFgkJVPVdZIKZgdH+LHMdILaCnxakXY+vgyob++yvntSyxvTOiPh6wsDVmeTFAiJ5QBVa1oKx8jFKInicKYvklJ+pJalZzvx0QyZL/M0HlIJRWtrwl1iGgNnmwITUCcQq08VNESGkHpCYys8coMrWuSoAeqwY8ETVNRlYoeKa91RRRqgjimxpBXBpNEmKBEeNCGPqnwGA9S4ihkoRYMvQCEwCekaSp0WBOmMNYSETTUlUbVDYk0VKVPz/ORoYdSFVpqcq0wMiKtJUJ5tEGEKguUKMkbqFVF68WEQlJNFcoEyMRDCQiqGiN8dODT+j6+TkhDn8arKVvJMB5909v2n932Nk3JFqpdzYQt4GyxZAtZ28F5m1suhHAFsy38LFVgPB4jpeThw4e89957AO7vuwW37YRbepV9cVpAY/fBThssFcb+uf0s2xXsulPZcb/97J2dHax/+5UrV9jZ2XFUCkuFeu+995wdpy2WAFZWVlBK8eLFC6xI2x6PnYLYF5UNlIKvnba6QMLuq/0OY4zrnncL6a64vJtHUBSFo07Ya9dNV+7SMew5sZazdrMAwjpy2X2zOSdWbxAEAcPhkI2NDVZXV91ov7se7HV8/fo1P//5z3n+/Lmb+NgO6w9+8AO++93vOhpGVxNi3ahsYdYFuF1wYYv27rm03wH8o/Vp16X9/S7gu3v3LtPp9B/ZMFt3rjRN3Zp7G+TYtd8V21swYacnXVBvC8XuxOnbuM3ncw4PDzHGsLKy8oYOa2dnxwXH2WLRPgeOjo6cBmIwGLC3t+cyEnzfZzKZ0DQN58+f58mTJ0ynU27evMn58+e5ePEid+/edTagURTx8uVLt3atecNwOOT09BRjDHfu3AFw97KlndhnRq/X49atW65wW1lZYTqdMh6PSZLE7d+nn37q7tc4jnn16hWLxcJ14Nu25ebNm2xtbVFVFVEUcfHiRYIg4PT01E0+yrLkzp07DIdDLly44OxSb9686TRx3fVy/fp1PM/j8ePH7p6w4YBbW1tMp1O39uz1sCDZAnQbVmcnBL1ez9GZPv300zeaMJYCa92y4Gsht5SSK1euUJYl29vbFEXBw4cPXfPh9u3bXL9+nb29PSdevnz5Mk+ePHFTG5tv0g29U0rxzjvvONqivQ8vXLjAYrEgCAIePnzo6JdFUfD555+743zvvfcoioJ33nnHNQHW1tb40Y9+5Cbt1ia5bVvnSGWPdXt7m5OTEzzvLDjQvguTJEFr7VLjrR0vwMuXLxkOh84x0YLBMAw5ODigbVvef/99bt26xerqqtvHIAiYzWZUVeXAtg2GtfeJtVA2xvDZZ5+5CZ61BDfG8OWXXwJfv4u+yfaNgYbXG4MQTGenpCsJOkwps5qUmGSoqUNQlceJqOlFhuUoYL44QZUVchAzDCVqplHCoE1Ea0J8KUEXYDQjf5nGaMo2J+zFCFIGEVTFa5SK8UqPvu+xsjkmn4Uc1vtI0+AnmgaN3yakcQrhCaIQ1LUBf4DwCzxPcZpnCC0I44R+PKBVC6q6Idc5QS+EpkekFZKIaprhJS1T47OcBBi/YTLeoRePKNsCP0uo8pJ0OOa0bohUQIihqloaGbCaGvoBfP/d7zEc9viztM+t27c52T8kUX2Kcg5RizQBWTknSX1msiTRGuWHqNBjKU5RLZQmQ84EptfjeDHDJ+b9f/lbJMkSr49nVMUhk6hHs+Lzav9MwF21BYenGccZ5IcLVH4MkSGSPiaAqq2RZY0XhxRVTc9LqBuPuqwJQkEYBIRC0QKh76Glj5YKv21oixrpK2IlSPDorW+ydXGL7aVV0lFKb2PEaHlC3AxoZ4KizM/AZBTSD1KkBC8OaduaLIyoZ9AsCuJhwEBOkLWhDVs8qfB8SS01oippyhIRJOg6wIsE1aJE9mN8z6MhQLWakT6zmI2CgJkPntb4bYsvPPAkVVmTt4YgjDFC01Mx86ygtz2kFxmWRglxFCGqBpRGUeM3AZPeWaCTF0uKxkDZUJsFfh1Qqxa/nzKVCtN69EIoRUFeSUzU0sxKwniZtoz/Dy2tZOyllH5A3QYIL0LVC3wRkGWCql0wkIZKlURJn1T8n/2vBbWsCeIU450AW9/01v1ntdkitivq7tKhLJ3HUkK6FKIuZcd2iy21x3bRuj9jKSrj8ZiXL1/y7NkzZznYLdDsi98WlvAmtaTLbe92+uDNLBD7ggBcBzEIAvI8f4Nj73keOzs7VFXF3//93zu6jdV/PH/+HK21C4uyha3Vi0wmE0f1At7IrLDn0u6zBRt2stGledli1h6jBRp2bG4LdwuUAFf02sLEpmDb6we80XHvJpbbn+mK6K2bl50YWJBnczuMMe5Fu7Gxwfnz59ne3nai0i5lLQxDnj9/zk9+8hP29vbeEGAPh0N+//d/n9/93d91Kc12X+wExYIam+rbBQbd7r8FgG/rdexxWaBh15jV7NjzY3U8e3t73Lx5010nC0atda0FeVbv051mdKd9gAOxVihs14Bdo11djQX3Xdrct2m7ePGio3p03c1ms5lrBuzu7vL+++87Goy9DlprXr586RoAf/3Xf00YhvzRH/2RA7rWBchqHq5du0ae55w7d46iKNxEcjgcMhqNaJrGiXLzPGc8HmOM4cMPP3S2unEcu8LcNgVWV1c5Ojpie3ubg4ODNyaQT5484eTkhOXlZacjMMawWCyci9Evf/lL577V7/fp9/usra1RFIUDG2VZcuPGDV68eMGdO3ec7suuxZcvXzprVTijEx0eHlLXNV988QVKKS5fvuyeB19++SVCCF6+fOmAnW16jEYjPvroI/7mb/7GNS+sLiCKInq9HpPJhKqqXObIcDgEzu6d2WzmbHTh64n36uoqeZ47rcjTp085ODhwoP/cuXPcv3+fR48eAXD58mWKouDo6Ij19XVmsxm3bt3iwoULPH36FCkl77//vps0nT9/nq2tLV6+fMnR0RG/9Vu/5ahZWp8lbluBvaV4/s7v/A7vvPMOUkpHjbU6sIcPHzIej7l79y7Ly8uuIC/LkrW1NZ4+fcp3vvMdR8GbzWakacrFixddPot9ploanF3bAJubm078/ezZM9bW1hBC8Kd/+qccHh4ihHBUq4sXLzIcDvn0009dWKTVLNksoCiK3PPHApjhcOhMDg4PD3nx4gXnzp1zmUnLy8sopRiNvlnT8xsDDaopJk0RbUVt+gg1QjVnjknzoqWlYQLge3g6xNMSL5zQEz6VPqUhIoxDyqaijaCJGtppRa0zktQjCDSiEPS8EFXUJIGgmGaQBLSyQvR7tKZhGBuKk5y1UZ95pfCEAVETpinZrEKVNcQepoR0oCjaBiM8ltMV1LREmoSy0VApPCkQpiAeSdRBjRQtZTUgVoI4GdCqBpShN4rw/Yg6y9GRxyAICSYRRVsT+VMwiqjxMSKkUYpZ1eD1EuRcsjLY4f/1//y/2d78Djf//q/48uET8D0CrWkrg24FrbdANYCJKOuGpXPLhH5EtchhXpAGEygVRVlz/aPzjFaX2DsqKI1gMhiymJ5wkhlmlSZfFMzyffKjKVIlNFV7JubWiqqpQBlS4VMXLco3RAYwOakf0no9jKcpKgGhR+S1tAayuqDvxegqp0WzPFpn2BuwurVFOAo4t7FOpMdcONejH4cIlikbn6g/ohIS5bVEUUB/eUTsh5TGQ+tTqkVJ4Ek0EcbrszidkSYKVdZI36c2htCLaYIahE+tI3w0phX0JwmtzOlh8PyUyg/OLJV7PY4XGV7l4fmCWrSEUlJ7McelJg0bJAq/l1CVBTJO0dUxg+VVtO9TqJowCjBNSaIkrS95dXKC0Qlpfhb4F/sBSSupwoogHFMYhTIVXiMotMSXPVJP0VQVURBTFTUUgiasoCc4WczOwh3TIUezmtB41NkCpSSj1QElHloMaLRAGE0TetR+RZSHNMeKMi6/8W37z22z9qG2ELYvJFuEdh2o4GvnIvvz3c0CEjv16Ap2LWARQjAajdzY3HaiLJCxBbelXnWBQ1dE2QUg3X3pUmZs0WsLZgswukJx+/9SSi5duoTWmufPn9O2rSu67Si8rmuuX7/O0tKSK0LsPlkqle/77O/vOwG0PZ+2C2gzH7paEPvftmiy3uldEbkt3rud9K52wH5WN+yre63sd1rg0A2Us903+3MW0NgJkf1uz/NYXl7m3Llz7t8rKyuu22evs70md+/e5fPPP+fo6MgBAWMMw+GQTz75hN/+7d9mMpm4KZcFXtbRy+pBuhx3ey662RtdvYoQwoEZuybtGrDgwK5P+z322D///HMODw9dsWjXpaVm2JwSS6Oz19SuT7tW3hb2d4Gw3XdbpNjvsNfs27jZ89Lv9wmCgMVi4RKbrRPPixcvuHr1Km3buoC/k5MTlFK89957DkhGUcTy8jK/+tWv+OSTT9jY2HDiaFvkWptUSxGyQWxN0zjNVJZl7O/vMxqNGA6HDjzbMDfrGmUpRd/73vc4PDx0YMNaz1ZVxd7ensvMuHz5MkopfvnLX7oO9t27d92k8vj4mA8//JDRaESapsxmMwdm7GTg9evXnJ6ecvXqVZ4/f86LFy8c0LBU0AsXLvDgwQMGgwFhGDpb7Q8++IB79+7xox/9iJ///OfuGRfHMcfHx66hY2lff/u3f+sK6/X1dR4+fOjWm03g3tjYIM9zfvu3f9sV6Pa6bm9vO3qY1cF0myWnp6fcuHHDpVXbAt6ufattsJPXpaUl93x59eqVC+uz74p/9a/+FfP5nN/85jeuuP7Lv/xLp5/5yU9+wu7urnuebW1tEUURf/AHf4AQZxbAR0dHbGxscO/ePX74wx9y9epVDg8PSZKE8XjsxNT2uWAnZuvr605gba+RtaptmsYF8CVJ4pyy9vf3GQ6HPH/+nJ2dHd555x3iOOaLL75w7l9wBpZv3LjBlStXmM/naK1dY6armTl//jwvXrzg5OTETQOn0ykvX77kxo0b7O3tYYzh1atXjk577do1bt++zblz59xz5Z/avvlEw/M4zQ2xPyJqfGgUtecxUy3RckDgCdpFRdp6SCWpfR/pGZSEPkPyqkb2E8TxnIHXp85PGaYptRiTy5KZKOhJDy0UpmwxJidd67EoFLHv41ESKx+ajGE/YtHUpDpiNtMkQULc1ugEmjJCxzFFk+EFCr8WeNKnwSMd9WirGj2fYVaGRP0RbZGjjuakfkqFRCaaoFVkbUk/kEziHkHgU7QFYQKQoIOQqSqRXo+0ahgFAa/bklk7YxQlVPX/sZnM5uTtFK0MF6++y/nBmNsX7/Fw9zfsP9lj0eT0pEYXGtUo5DDCb6E+kUQjAcbDWx0Q+Cl+lHBueJXtjTHzwyleVRA2DXXeMG1rinxGURc0i4rMaOppgGkXVLHC74WoGoh8mlZBXSAM+EYhpCBrWgJRk3pDqrYiTFNC2aKCGt14jESEUQoz6rE9jhktbbMymBD1YpbX1tje3iIwIaInaBqB12h8b0prBBoP2Wo2L64TE1O0oJSmFhpPCNAeReiRtEfIfkPlDWjnDbG/wIQa4wU0RUAa+5ieINKSIGvI8xY/lZRtjZ/NkP0xJgwRFRgVUwcVYevTpAIpYxaZT7RkOCkXBLWPWGSY1iMdDUnjiEG8SSAlrRS0coYfNTQtKGL8/gCpBGko0UZBLijagMbTqHpO3A8wdY0WAQvVUgUB/sKnqivk0CMc9VlkBX6QoivQXkkc+7Qmwg8EQSoJgx5B6GNCRRn0kLpCmIyo8GnrgFbVEAlofIr221kgAO4F3eXz2m5+N2vg/0aPsv+2RXXXprVbVL8tHrcd5el0yvHxMaurqwCuiLZWf3azxSfwRve4O/XoUq9s19/un/3zbq6BpVUEQUCv13MA5vLly2itefbsmbPWtJ3yo6Mjbt68yTvvvMP6+rorjO0++r7vBKqHh4eOsmFfKvYl0AUZtgC1Qj9btHU7911KVRcAdcPmbCHWnexY/3orTreWwPbvLVixLyzbubTfIcSZr7ud2tiibWtri9XVVUajkaMg2EIbziY6Dx484P79+w5cdKlzH3zwAd/73vdcMWa7xvY82qmL1QjZtQG4CUdXh9GlMHWnBfY8dx2puufVHr/v++zt7XHr1i033bBrxF4jCzC6xgBdINelGdp9tt/ZBYNdQNK9h95e89+mbTgccvPmTS5fvuyoKvY5Mp/PqeuaH/3oR1RV9YYzk5182G6wDT2zBZe9jjYt2VKzhsMhT548cWv2s88+YzweMx6PqeuaV69eOevTKIo4PT1lbW3NTSGs011VVaysrHDlyhWOjo4ccLUUxGfPnnHp0iXef/999vb2XAF+9epVjDH88pe/dBkhlkZYFIXj8HdFvb7vc3Jywnw+d8F7tqC0drhN07hQu+PjY95991329/ediN4Yw71797hy5YrTYlgQY8NE19fXuXDhAoeHh+4cdbOH0jTF930XKmgnvkmSOE2IBRF2XQdBwPb2Ntvb29y8eZMXL1446uPS0hK3b992mgqbi9G1502ShGvXrnHt2jX+5E/+xNHi4jhmeXmZ69evOzeuO3fuMJ/PXUc/SRJ+8IMf8PTpU/76r/+aly9fusmq3a8PP/zQJaxfvXrVPdOuX7/unpvWPKAoCkdds/fp3t4e58+fJ8sydnd3OX/+vBPhdxs0dp/DMGR1ddWFNR4cHPDhhx+yWCz4/PPP+e53v8uvf/1rdnZ2yLLMPacuXLhAmqb8/Oc/f6Ox132GfP7557Rt69a8bbKkaereobu7u3z/+9/n+PiYvb09fN/n+9//PovFwl3Pf2r7xkBjnhlaAUdBTlBUZzSVyKdf+5isQUcheZ0R9GI8r8c8X5BKDz3TeEmI8iGfZXihR8/XqFByND8i9daIidGRpg4FKBCyRYUBRjV4eUlo+jRpQBvG0CqWRhG9bMG+1pjUoOYNoYESRUUISUwUCXRbnbkBCQGDFuEleKeCNOih8Hh1fMw4HoOA3GtAJgjp0+qWpV4fKStIe7RVifChaiVCeuCXBKqlnJYM+kOMr0irY3p+jBYSRUhZ52Rtg9fm+ExJ/AHR8pDf2XmXG8fn+N9//wVfPv2K2eEe1aIlEjGmPBNAFvVrjB8R+BF+JUk8n7g/ptEtB1lGWUzxvQZdKOanM6aqRh3nmFJT5AtMrOjLEWVr8LSkmJfoViL1gEYdE6BoIh8T+rSNRtQeIlUUJqeRGpVPKTxBFEtioYmkxFsZs3lhh2Xhs7Q0YZj0SUdDelub9HoTjG4JBMxo6JUZ/b4grzzW4oR4CL5uqQ9fcuq3RNEQ8JifNIx7gkiACRTeoiLyVhFxg/Z86rploeaIRjIMA45e54hhjygZEDctngdFXRFGEOkASUNev0YtIsKBJkojorhHTcV4AXomEGVE6oMfxWhpaExDNFnCi0uqkwKdDGhSQRAkNGWFyBuMbxDDhqpUeH6IJ4bIKMeLDKrwCZSmaRUqFhjdIBawyCu8QUwbRLSNxvNqhCqR9RKDxGdRLJCVh/B7tL7CtIpEtmRtgSdCfJkTpCOafIGQhqg3Rns+6IL+4ttJeQDcg8nyZG0hZAvE7gPRFtNv8+G79JS3Ra1WxN0FKr7vs7S0xGKx4PT0lKWlpTcEst1uerdgtHoJCza6kwvr8NMVDNsXiaWudDvutuiPosjlW1h61YULF9wD3b5MulOCO3fusFgsuHjxouM4d4XPw+GQOI5d4rEtrLp6ACv8tgVoV9PRPWddIAG4Y7TgKo7jN8CD/bdSijzP30hTttcTcNqNsixdh9MW5Rbw2JCrra0tp08YDAZO3N61xLXHdnx8zGeffeZCzGz3WCnlgvy+//3vs7Gx4fbB+sVbzUie5+4Yu2vubZcpu1Yt6LT7YdeHBSrWIao75bDnw+7j//7f/5vZbMbq6qqbjHS/w66rLvjoFgrd/3/7H3tNukYF9r7qisi/rdSp09NTrl+/7tYxnJ1Ta1+7tbXF69evnXh7eXnZJTrba2qzDiwPfnl5GStwtQ5IFjC/fPmS5eVlXr16xerqquuIG2PY3993TkEWbFuhcVVVjhe/tLTkdEb2s1+/fs14PKZtW46OjphMJjx9+pSjoyPOnTvHaDRyQnPrgPfgwQO0PgvMOz09de4/58+fJ89zF7AHZ/fuZDJxTYfz589zfHyM1prd3V3XsIGzNbq3t+cKWnsvnD9/no2NDUePstoWC4xtOrsQgqtXrzKfz1ldXeXly5c8fPjQrcknT55w+fJlhBCOBmSLdftssufMus/Z83ft2jUePnzIxsYGd+/edb9nszVevXrF8vKyE5KHYcijR4+c49RwOHQ6mmvXrhGGIcfHx856dzgcOv3OyckJbdvy6aef8uTJEy5cuOAsbjc2Nnj33XfZ2triiy++cEGwvV6Pg4MDTk5OuHr1Kq9evXqDnmafr3Ecc3BwwPb2NnVdMxwOuXTpEhsbG+zt7VGWJc+fPwdgeXmZ+XzO1tYWcRy7KcSTJ0/48MMPEUKwtrbGZDLh0aNHPHjwwF3nDz74gNevX9Pr9fjiiy/c9bW5I9Zid3Nzk+vXr/O3f/u3XL16laZpmM/nCHFm4/zs2TN833f7a+8hS/na3d3lt37rt77RPfuNgYaehPSOFf7MJ4wCfBQqb5B+SNAaZvMasTwg9mPyWUkvMlRliTA+wpcEpY9ucrSUnMYNXtOynMYYD0QBZZNjvP6Ze1McY5TBeB6mH+NnNTUx8/wIYzyGvQFDb5myLmhkg45r6vYsdTxIY7TKaEvoJQNkpKl1SRj0yHNDMAyQbYOqM/p9A9NT6tAgdM1KmNBGIf3RhPFwgFIZTZHhKwPKIwBqb0YrBf0I0lVJ2dQEg4RlvUHWvOD18QGqkuzlNQhNP0ih1GgtUH0o8obJ8pjf/3/+gOu7H3Pv7h0On/2Gg1evWTQFyq+RRUN2VCLjkIAE1Rp0ekyVa7JZRKugzA9RjUeQDChnJa3nE0YVIvBpvIgyr8kCQ1NkxApCIWlChUl9VOtBXVPXBX40omlygkZTi5rhZAl5oqhqSFOftbUl4nDIaHWIDHuMZMywl7K8tkUykER+SL6oWBmE+GGILlvCVqCVj0pTvDTFT85seqs0Im9BtidEQUCSgAbMoiJRHr1+n9liShwlmCggqAW6nuIHEfNFgFmCNmvI/BzpSVQzZhCFZLok8hV7RUZv2COcSDxairYkKny0l9F4Q3Qj6MkecdsQ9SOyqMYf9gh9gao90nBIayCSCUpP6ckYMU4oVYGJElS9QEaSJtQIL8RrauJAUkQLtOxRz32iOKZFk6yGaAKKWcbID6laQ51IKA+QYUygBV4qaBpN0ZaESwVlI6iOBF5UEow0VTFlFAYsphW9JGZRtQRBAt/ibqTt/rxdeL292eLYdrttl8y+RG2hZ4sy2zG2W7e4svx9+5I/PDx07h5d4a0FExaA2N+1nc6uoLcrrIWvna7sRMEWdbbItNSn6XSKUspRFGzxfPHiRecC0g12s+frwYMHnJycuKRYa3Noj9Hatq6srLgiptult4JBu5/2uLq0H/vnNtPDnucunc3+0+3U22O2Fq5dYGgBlp1gWSoVfK1jmUwmXL58mYsXLzqqQRek2GLZXhcrxt/d3eWzzz5zXu62m2sLxu985ztcuXLFAcvuWquqivl8Tp7nboLUzb94m7pnvz8MQwfY7P5Y2l13umWLJXssFixorbl37x5ffPEFo9GI0Wj0xrmyE44uRaorqrdrtAti7UTK/owFuna92s2uR3sOvq1TDdv5tYDbAjWlFPfu3ePSpUukaUqWZU4vYTMb2rZ1Wi3rRCfEWZCZpYI8evSItbU1lz69srJCFEVcu3aN3d1dTk5OWFlZQWvNzs4OcLaWbUG2WCw4d+6cE3YPBgNWVla4ffu2ayC8fPmSfr/Pq1evXBfZFor2eNI0ZWNjw+UarK6uMhgM+PM//3MHmJaXl9nb2+Pp06esra25xsPm5ia7u7tsbm660LwoinjvvffcsR0eHro8HwtsbYq5BUUHBweORgU4epHVlMxmM7fem6ZxmQ9dSqOdNFnwZKcnx8fHb5hBrK+vM5/PKcuSu3fvuinCixcv3OdYiqz9tzFnrlD/5t/8G/74j/8Y3/e5evUqJycnzGYzrl+/zjvvvMOLFy8YjUb4vs+LFy/Y2tp6A5hbkJRlGffu3ePRo0fUdc3du3cR4iyz5/d+7/e4cOGCAx2e5zGdTt0UzdLsFosFOzs7zOdz94w+OTlx+SWz2QwpJWVZvjG5CoKAy5cvU5Ylp6enLuTVThp83+c73/mOA2P23rdhpADj8Zhnz57x8ccfOyH3+vo6jx49Io5j98w9PDx0blyffPKJ0xjZoMpz587R7/eZz+esr69zcHDAwcGBo1c9f/6cra0tl1j+T23fGGiUec0QidcqdC1oeuAHASaQNJ5PGnqodo42Gozk1CxQcUQsIK1gGcncS5jJhlZ7xJ4kaDzyaYFMEiojkNQMIo9c+wyUoCgFfhxQpDlea/DDEKF81DRnXimGvSVIppRFzaJQeMUKlAtEFtA3HtFCYwYBrd8STSGOI06zQyYTnyCWlK1H0NP40qcNlsilYRRG9KMQE9QYIUlHExZ1QeBL8pMC40X4wsfMS+Syxowi5oenBEVLMB4iyynz6ZxUNSglaFuD8DQDPyL3aqIgQuWCUMLywOcP/+X32H014MsXr9l78oK5OmVxcERzXDDzDVUtePDqNf0SdGOI6wY/DMEPgTmtyhAmQmUlC1FT6xq0RumastH42icPGpTw0WGDrj1U3eB7AhIPzyyQnk/Um9DzSwLpM1jykdGEZDQgTEOWoxWWByPGmwOW1ob4dYjveXhhSC5DQjSVmlNUAXWkSAcJPfosry6ddfvLFiE9jFEs+X1CLTAoRlFMUzfQ89DpiFII9LBlngnMLCGocqJaUqQ1/UEIpoZEouIBsslo84JSB/h9HyT0qxjZGAovwjQVcSiZl6cMg4haLUAaqtYnSkJOdcNgvIK/MiJqE1qjKLyGRlUMyjHFiWK4vERRzEiUoMw0cVAzm+VEOkJ4FZ4nqKcL4tUhx4sS2UjGUUKWFGgDZdXgxSnGrxEl+JWgJUeqikCGLESF8mO8rMFvIrTJCQcJjQbdBIhGEytD208oVUnrKfBSavXt1mjA18nc3RcSfA0QuhOJLpiwhXK3eLeFlx05d2lNdrMFbZ7nnJ6eMhwOXbKwLfDseLvLX7cPeAtGuvvXdcGy+2YLzm7X3u6LdTSyIMF2vy11Z3NzkyAIePXqleMd24IiSRKOj4+5desWFy9eZG1t7Q29he3k+b7vLBDbtnUFRdu2lGVJ27ZvuHJZd6Xui9tu9nhtF9wCF8ClkncBiN2XLtAAnIDQFjTGnInOz58/z6VLlzh//rz7nm5gn/3Zqqqo69oJ6uu6dsVAURTumOw5nUwmvP/++1y8eNHRQOxkwlrZLhYLl+zdneJY+pf9PLs+4etJVneC0V0r9u/7/b5b5xaEWNBxcHDAX/7lXwJnRYEt9q34u6v/6Yrn7T7aznY3iK+rE7L7asFKd9pn97E7Kfk2blZHYVOWbWFuu74PHz50DkQWnNnr+vTpU3Z2djDGsLy8TF3XXL16lUePHjkRuAVox8fH/0hvYR2u7H2wsrJCEAT8wz/8g1v7V65cccDbFvmWguN5ZxbLFy5ccMDi+PiY6XSK1tqlS9+6dcuJki1QtdNAq+to25Z79+7h+2fJ13mec3x8zLlz54Cz5+bz58+dFezm5lnMa9u2XL9+neXlZZ4+fcp8PieKIj755BP+5m/+hqqquHHjBpPJxAm7u3TXo6OjNyYhdoI4GAxI09TRpOzPfPDBB9y8eZM7d+440L6yssLp6SnXrl2jLEtu3brlJrN5nrvC2TacpJQ8evToDXfBtm0dFfIv/uIvnEnDgwcP8H2fjY0N9/vnzp0jDEOnvbB6ndFoRBiGZFnGrVu3nGOZdQczxvBv/+2/dSAtDEP6/b47p9ZAwNp97+3tOTBrKU+LxcIlb1uLY3s/WwpclmX87Gc/47vf/S55nrO2tuaC/9I05eTkhDRNXRK7nYTeuXOHp0+fsr6+zvHxMdevX3cAK8syJwq39OHBYOAcy+xky2bPfPjhh46yZW3Mp9Mpy8vLTtPUtq17f66vr7vQyn9q+8ZAY0VqCDwyv2JtsgxlhYoWHC1aQp0SJyFVBnXcIAKPxEvwtEclJafllEEUQhAQeBFah7S+pi1bApNjhGBEnzrULJoFsmnIRY9g2AeVUYkWVc5ZqmLCyZmrDz2DlBGeCkH28FVBu9DoXkQrJKIVeK2gyTwIYpSvaIqMvt+jbCpMGiMLgUhbGiGJFQzjAYnu46uUNp8jdEsrUtpWIhpJGQiCpqEWFUvjZdqmRWQCI2K0bmmOG+oipNUtoVS0QYTnxWTHFXgtvQD0NELFY6bqhKgXYEyDFy/xzkfnubx9geki5/TwiPmrQ57PXlE1gupgRqsW5EKTNTlt2yBDja9akgZE0NLqHNNoer0+aEE2N/jSIHwP4Rla5RHqEF8GSF9iPEV/kFJkLSYN8eKUtdEWMkqIU0nq+WyuL5HEEUmY0J9sEJsQY0KQNZ7XY5Y3yKjCTz105BF6HqPxOUZhTN7mZGqKV3lUqiVJJJExxHGL0QFqkSDnNSIWJOM+TSNojERUHlHPYJoFog9pPCYSFTIoSSuJp2ERgOenyGyBMhKigGZRIhclsYioJcT0kLOMwWiEaiRpFGDqjMprKOMYf+yTrq3SNmAKQTOIaEwJNWhV4scRr5opcVhiZIskQpoeiW+opgtMoBkmPiQS0/pEqaQOCnQUoJBEKsPTPrpsaGOD70s8PF6HEQrFUCYII/F9D9mWeM0MMZa0piUM+/QiQ324YIqk8AxpEBEogyxOWB7Jf/qG/We62cAxW8B33XG6xal9qb1NXbH0B8AVy92f7/5jC4aus9FoNOL169ccHBy47rmdCNgi0uo8unSIt92v7IvCgocuNcUWcrYAsF3mrji66/xji9Kqqpz47/nz565Isbzffr9PlmXcv3+fV69ecf78+Tf41HafAFdInj9/ntFo5LjZVVWRZZnrOr5NNbOAwBb8dpoBOGvEbpHRPXf2/Nit1+u5a2NdZ/r9Ppubm+zs7LC8vOzOn6V72a6lBR5ZlrkCXgjBV199xYMHD5jP52/8ri3iRqMR7777rissLHC0blsWwHWPuwuO7JqxhaIFwfa47L50gZm95l1rX3teLGfdXpvbt2/z4sULxuMxvV7P/Yw1JOiaG3TXsS32uiC2qyfp6kG6a9feX12qVFcY/m3cdnd32dnZcQLbfr9PnudujWxsbFCWJRsbG8xmM9bX1zk9PUXrs3wNO8UbDAa0bevcih48eMDa2pqzwbbdddul393dRUpJnuccHh6ysbHB//yf/5OrV6+67IF79+7x8ccfM51OSZKE4XDIbDZz07O9vT22t7cxxrC3t8dkMmFjY4OVlRXu3LnjJr2WOnNycuJsat977z2nuXjvvff49NNPHd1yNBq5FHKrCdja2iLLMpaWltx66QadLi0tsb29zd7eHg8fPuRXv/qVS0N/+fIljx49cpoT+zyzgvbLly/z9OlT16homoYsy7h48SK/+MUv3phyPHjwAM/z+Hf/7t/xF3/xF1RVxa1btzDGcHBw4J6nX3zxxRtTOPucsWv4o48+oigK1zixzQd7X9t7zj5r1tbW0FozHo/dO8QaYFjdTa/XYzqd8vDhQ1eA37t3z+k4BoMB586dc+cuTVPqunZugEdHR4xGI2fRa8Pu7P7ZdWR1RFmWucmKEGehrQ8ePODChQu88847VFXFy5cvnd5ic3OT58+fI+VZxsVHH31EGIZu0vYXf/EXLnV8bW2N4+Nj53Rm83lWVlb43ve+x+PHjwmCgMPDQ/r9vnvmWTBx//59Njc3HQC2FLn79+9z/vx5nj59SlEUpGnK0dERX3zxxTemX37zHI1jjRgU+JOYUsyJ4xZV1ozDmEZDEywYLvfwipJFsCBVKaI2DKKIermmNYaqkgTBgDA7QcmUMvTQ+izArlIaRYPSEkKPdt7g6woRFAz8FBFGhEXLYl5gjIfXNCi/JIj7NDQkowmhqSnbikUNIgrI/JqBCRG+TyXnxAIMHmioF4o0HuI3NaGvGcZ9RukSQT+hLFqyWtDvBQR1Tj+FPK8IoxAxy9GBIa9qalUzMwaJj1AteZ1RFSVeG9I2mqgXIFpDmC5TlTm6rWgHEYXW1EYj54JWRjTGZ6xbwp7H6tIlsuVVuFTxnVLxZH7C7NGXzBfH5Ikk2zuiMppiUVAuGirPx8xa0iBFSEOdVbSej0x7+AK8wEMVBUULsZfQ6w2R7Qwv0UjTY2VjFaVaJqMevaTP8toG1SBlYyVlOQ0oZjWTgY9OQvJXhtBPiSZ9ZkdTZCTpJ2dC8X46YdLr0Zg+s2qGkj5+3lKqEhW2ZMrDbySqbKlNQhJL1DCgFYpc+aS+QKcG77UipKTCUOsQozW9nk+WlwR1SlyXjAOBiiXeyCdrQVWSsAzRvRbhga7BTyJUXVAXChl6BBKytqAfLyMLj8nqkIEHeEBpUFmN59UYP0KR4EUtSQix9jASmkrThktkeo6/UhM2EblQxNJHLxRBX+J7IVpokiiknlfowBAoQ7mQKAy9MCIOQXkteSUJ0oQiX5CkAXGwRJZnNDoBE5DVR/iBQcuItBWUs5wgllRtQSQG3/S2/We32QLSdnptkWqLe2O+9mK3LxyttXMnsl1k+FrcaX+3KzC3BWEURQ7c+P5ZyrK1Rj04OHDOTV2wYwsW+z32ZdYt5izIsUVqt/i2xSvgpg62qySEcGDJah5scSqldA46NmvDvlTtsVnr08PDQ46Pj5lMJqytrbkXR5f+Zb/XBhdaHrrVqliqkXXusdMgOy2ynUpbqFsLWMuLtw5JNo3cnid77MPhkLW1NZaXl1ldXWU4HLruc9M0HB0dkWWZK9atWN6eTzvhyPOc/f19vvzyS05OTt7QT1inKJswfPHiRTY3Nx2w6wJWK+oWQrjj6E4kuuvNAh77890QPQs2u9MDS+fqOkHZ47Rg4cmTJ/zyl78kiiIHEO01s+vSfk9X82PPf5fzbdeL/X5bNHTpZXbd2GOz99fbNLJv26bUma2nPX+2m7y2tsajR49YXl5258MmRdd17XQW9+7d491332U8HtPv9x0FS+uzFOS///u/58MPP+TJkyfs7u46i1QhBFeuXHFGAWVZ8s477zgQeP/+fY6OjvjNb37D2toaAPv7+24SMZlMXLCepaG0bcuLFy+cBuLo6IiLFy9y//595zIlhHBBcWEYsrW1xaNHj2iaxrmT/c7v/A7//b//d2azGXVds7Ozg+d5XL9+ncePHzu3v/39fXZ2dhy9MIoifvd3f5fRaMTdu3fRWjuLUyEE7777Lr/+9a8JgoCPPvrI2eG+fv3anXt7nxVF4aYJW1tb7O/vMx6P+cM//EP+x//4H/ziF78AcGvTJrNvb287INjv93n58iVSSlZXV4njmMPDQ7Is48GDBywWCyaTiXtuBEHghO5ZlvGd73yHDz/8kKOjI3cPWapXWZb81V/9FX/4h39Ir9cjDEP29/e5ffs20+n0DUB3//59Ll26xPXr19nc3HTPXEu5XFpaYmdnh+l06oTl9pl+enrqbMLtZNSmzNu1OBwO3XNuMBhwenqKUoo4jnnnnXd4/fo1vn8WPGqtimezmXtXLC8v81/+y39xVs15nrtnyo0bN5hOp+R57pyjDg8P+e53v8vjx4/dNATO9E6bm5su78Q6Ai4WC3e/WTqWpQFbE5Isy9z07J/avjHQkEFKbUoiAqrTlrDX0nohQV3QjyYslMKIijbySf0eTV0TphEnTUmfGKULAhnRKkUQCXqRz75uMEGAbj2UqWlnAWEY0ExrQt8ja0oGfoo0LUV1SpgGREbSGJ+wkfitj56XjKMUXUl07EMe0CaaKDSouqAVklC2eKWPjFpaUeO3ISkeSi2YrC7hBZJ+b4CiplKavJ1jejFNGxK1BcbTtAR4sxCjG7wAtChpRYtqFtCO2Ds6QXoFvvZQNFRBQV1DVAkEglEa0RuvUImSg/0F3pGPMBqlYbk/Jq9qVLhCVIL0PUIxwY8qrox7eEsDVG14ZWpeP33IrMzhRFKrKVVoaI6BSmAkZHJK2/rEsUAVGTJJUP0egwCW+z2SWuAnKwQjQU9skYRjhMoYbvYYLHv4ZUiV9hGmJo8lRibkrcJbCEwiOGmOQPdopabfj4n9lIHx6S9PaGc1x8evidYiRLbAzzTCnBX5cynxohBZa8K+Ty+SkLeUi5YwKFFKU8wCpJGouaT1NC0FqtYo5WG8iCpU9NIepvCpipZkeZnK5Ohpjpd7BEsJxngEnqE0rwn7HpXyGCJpjcIfDVBezM7qKonsI1qJ8nKSCOqiJewPyLKaUtYYUzHSE9QiQ4Y+CIPwS/oVePSYa43QHoYaOfBoqoY4imkzhdA+OlhGqgWKhrD1yOMK7RckcsJRWSArReQHeIRUpgHRw/cCYnzK5swHPW9qFBnL8qw4y3VAI2Ia9e3sRAKuM2a7XfC1C47lj8KbFCpbZHXpRJZ2BbzBW+8WgZbvbIt5+1nLy8vs7+9zcHDAaDR6g4ZkC7IgCNzn2wKtS7Gx32/33R5HV79g/9ty9+2LqOvtb91G7FRjPB4zn8/xPM9ZHR4dHb0RcmfPD8CzZ894/PgxOzs7vPvuuw5YGWPesH2Er2lraZq6jqmlctmunz1HNizPgpWmaTg5OXFALk1TF3bW7eJZMLe2tsa5c+dcInjbts4j3uYZdAv1rhjfAgylziwxP/30U/b29tzP2rVhwactvra2tpwewxb/9vpZGpQFFl2anb2+FtC+DT7s9bSTjTzP3XWw19j+TBfsdXU7WZbxd3/3d2RZ9oYA3K7RJEneoGV1JytdmqH9vC7Fx9L/7H50pywWTLytqwG+tdQpW8DZgti6OzVNw2QycceZJInTNVg91Pnz5x2IsLQl++wIgoD79+/zwx/+0GkZfvazn7nudffe3dvbI45jVldXybIMOMtvuH79urs/nj9/znw+dwYU8LUFtgXy1rrYBqrZsLnJZMLBwQFpmnL16lVev37tuvFZlrGxscEnn3zC6ekpP/7xj/n5z3/O5uYme3t7zGYz7t69y/7+vnNDst33tbU19+y14mtLl/n444+Zz+cuEDCKImenWpYl586dc2njVkhvp6ZWDG0zLO7fvw9AlmX85//8n/+RKYF9BthnoM0fsfeFpSjt7u5y8eJF9vb2yLLMXWM7ZbENk9XVVUeL2t/fd5au9n6w6e4fffSRE98fHBw4wbq9hoPBgAcPHrC9vc2///f/HiklaZry5MkTXr16xQ9/+ENHXTs8PGRnZ4fj42P3Xnn27Jl7Htpp8NbWlpteGGNcVokVo8/nczY2NlhaWnJgoq5rlxVidTg20LGuazexsLo4O83d3Nzk4cOHfPLJJ+R5zmKxcGF/Dx8+5Pj42DW8ptMpaZpycHDgxPhLS0uMx2MXDvj69Wv3DrEgaTAYIKVkb2/vG+u8vjHQGA5gWtQkjU8bRYg0pTnNWBkMUK0iFRGRr3l5lJMECYQe2aLFG8TQhlSLFhFqojikzGIaMwc/wpcGoxYEYYqXRJT5MUkY4g999LyhnnuEQ0UcBuRNjt9LiWXC4atXRKMNBp5P6BXUMiBTGmMUYTun1Jqk16c6mFFLRdzroT0PUQjipIdvSuQgIR1GaAwGQdPUeDokCCa0pqChxetFeHGIrxdEIsBLE+blAkIPH48BPk1eIQLNQpR4JkUXDcHQR7cFrTH4foBGsJ/POLeUYJZTplIyOzkCUyEazSQOOS5KghaqqKGs5wR+gJ9BTw9gGNHWC5avvY/UPbLpnLrJaUJFKALq/IS8aOn3+pxmLSE5RV1AMkEKhfFLlI4YasnKcEI6ScjrFFPleFGK50kCryXtCWZVgZYlvSJhVs3xB0PaaQMyIA181Dzj/NoWwTAhDT1q5TMvS6bzY7w4gYMFxkDe9wnakDquqFSLj0fg9wgaRaEa+sSsCIM0gkWrqIygIQAJofRpwylxOEaRE8cBSRkidUMbx0R1g8pLotSwMGCGEdlcM0wDgrBG1yDxkKHHtMmJwwFh0GcpSQmTkKwVyErRFz7zcoYOI6TSCF+SxAlGhZxWc1TocXx4xIWtZVRbESUGoyqCMCAoPYSKKIXCpBGNakE3eEWA53vkZU0gW3r9EC0imkWOCFt6QURQtlQLRa0qer0BucmQvkQKzkTiRoEQGF9QeYoWRdEs8H1JW1T/9A37z3SzD+Bup7xL3elSoexL2W5WQNedGNjirjtZsMWenSLYsCnbARTiLIX36OiIo6MjlpeX/9Hv2w5zV9DYLVTt99uft8DCajbsMdj96IqlbTFp/65Ls9FaO8ciS3sIgoCTkxOn5bCFpN0vYwwvXrxgPp/z3nvvceXKFZcuvFgs3gBR9njatmU4HL7B77cUMGvF2RXtW8qI1TlYHUKapm/oGmyRZEGVFWp3r6udONnzZsFJkiROsHtycsLdu3e5c+eOKzBskdKltNkwv+XlZUdFssWKBWN2etHNxHhbU9IVsVstip2Y2f2219UWRrbAt8CmC3RtEWf39+bNmy7IazAYuGvY6/Uc99pa2VoKlb1f7Drr7oPdV7sWrUNPF9B2tR32uLrTj+699W3atD4LURuNRo7qaJ2f4AxYWqC8tLTkdBZWhGtDDbuaIVsAPn36lAcPHjg61Pe+9z0ODg749NNP+e3f/m3yPGc6nbqsiPl8ThzHPH36FK3PkrCLouCrr77i9PSUk5MT+v2+o0Naa97BYOCoo5cuXeLp06eUZemeVePx2DmcWde5yWTixN12jQsh2Nzc5LPPPmM2mzEcDvnggw+4e/cuR0dHXLp0idlsRhCchWfaotOCL/tcsQ2Bfr/PlStX3JTw0aNHnD9/nv39feI4Zn9/n16vx+XLl50u5eXLl5w7d845NFnXqiAISNOU09NTgiBgaWnJCZ/fffddlxVknbdsmJ09N9ZF6vnz544WFARnQZcfffQRp6enRFHEzs4OaZry9OlT9xyyINACurIsmUwmeJ7HV199xd27d92UqEuFHY/HXLp0yWVcWAH1hQsXXHMmjmPm87nLwwjD0AXv2eeaXVeDwYD5fO6m1YCbVF29evUN22QryH716hXr6+ucP3/eWXZbcNXv992EwhjDeDx297TNCfrbv/1bfv7zn7O8vOymuFKeBQ2urKy4d8V4PHbGBfbZnuc5cRyzvb3N8fExa2trbzR2Ll++7PRL165d+8b37DcGGoenin7SBxkzbw4JM4n2Ig6NQLWGOBaU8xYZRtS1ZBgmmHGDrGuUAOUZkqCCWtDWiolviAjIo4iFOWVAhdckeDJlXhvMXBHEoFDUbQqipPACwqaFoGG4sUw1V2fd4EATBC2yrskixaA3wVto4tBnMDTMF6foNiDxIpR/tgjbQtPvj/H8iFE/oFjUBAhqI9C1T1JJdCqpZEpW1CRhimorSCSJF2O8BF+3UNdUskZ4LWFhqMuMPGzxfA9TtaB95mVO6vuUs4KjtiDyGpJ+RJgOaZuC6mgORQGRjyIniEJS5VMvJFoqVNJHNCWxp2ilwisEQX9EUQm8oU/qVzRqCaVagmBAmjUwG+APYhZaYtqWJAoJhU8rC9JC4/k+ZBVBA/HSkEV1Vpz7Qw9VevSCHuVegR9KatMgwpR+krDR9+kP1zFtRS09ykahTMn8dcG80YRpxYA+eB7CCLTXEDNkrEuIJd7cQ6uCcBgSphG1Z1jUzVlSduhRzabIVKGCCU1hqJqzzAm/qlFlDxlUVL2MXrCgOQwxYR9P96Ep8NuAomhp/QgPj2KRIdIWmfYQIiVsY0a9EaYIaZVh4Fe0scYfhIgwolUCTw/J64ymrkn9EbLIIQ3RoqRUEQKJF/okGFBQlAJpfIxqUbrB9FqaxWsaHePFHn1S/EhTZguEDMHTCNFQmAIv7eNlGq8tUWWDSGOqckEcJ5jKIxnFzPIFiJDWCEQikWVLJKN/+ob9Z7pZ8a2lrViaii2E3xYC25eALfK7hXaXytIVhXedlux0wj4sbZG/vLwMwHQ6dXoH4I0ivls8djvgbwu/7WfaCYDlLdsi1dLELGXHChm7QnLLGbfFd5ceZD375/O5Eyu+rZWwndFnz57x+vVrNjc3HaXKpu9a/QXwhnbAnmPLP7ZFhlJfW8Xajm93v+056XbvrJNVV4Bor4X9s64exhbUlmf94sULHj9+zOnpqTtXdt/tFgQBo9GIjY0NV7h3tTRCCHfurFjVTj8saOlSoOw1At4wBLBTMXuN7LoF3DmwwMDaFtvPtTqYNE159OiRE4D3ej23Fu01s05Vb6fH26lIV4Nj152dxNjOtKXzdado3bVr/7tra/xtpU5JeRZ2CWd6jUuXLvHs2TO2trYcqDg9PeXx48dMJhO2t7ddjo5dc7PZzNE/6rp2U6q6rvmzP/szrl+/7hzednd3+e53v8tXX33FwcEBKysrjqpowYalz9jO+9HRkWsSWIemOI4py5If/OAH3Llzh8ePH3Pjxg1Hg5LyLMPDUqtscffixQs2Nzc5PT1FyjM77NPTUw4ODhxQGQwG3Lx5000TLMh99OgRT548cS5Z1rkoyzIXcjgcDt1asqAtDENms5nLibDFtHW8WywWzgo3DEMHluyzsAto7TTOPrcAbt++DZzdy3fv3iWKIleQK6W4dOkS+/v7XLp0iSdPnjjBeq/Xc3k44/HYTT/G4zGA0+ecnJxw/vx57ty54/58b2+P27dv8/jxY6fxyrKM//gf/yNffPEFn3/+Oe+++667558/f87m5qabVtgm0WKxoNfr8Zvf/Ibt7W1OTk7cFHYwGLhnkDUjKIrC2Slfv34dKc9S7Pf29tw1raqK/f19rl27xtWrV5lOp26ide7cORaLhXuu7e/v86d/+qcodRY+aS2L7fq3VrTnzp3j3r17XLhwgdlsBuCoWtbCeX19ndXVVRfoaCcd3WmspY4uLS3x1VdfubVkG3rfZPvGQKO/FCDqHn4zw0slOogJTEDjgScCMmHwBh6yhiABiabRBQJFG2sSNF7lY4QiHKZU0hCJBk/FLHwfmpJ4sUCHIbon8MwckoTC9wky6PsClftoDU2iiYdDvEAzP54zln2axYKV/oDQi9BRTeIJlJpRRREwwosNipQ0DfHjlnA4oBeGGMIzsXbfI/Qksq5pdYNA4fuSRlUkVY2IDMYXaK/FC0HPMsqyojo9JZeKcjajmlfkOqDqhTCroYpoi5J4ALOipC08Crng9LRG9Ax1tWB7vETsxyy9t0pWSZ48+g2iLmm9FhkZglgglSLzDUXTEiU9aGqm+3O8vg9tga4VXuCj2xRPGSZ9n2o1JCgFaq8m7AlqNCoUGOmf0YtEjzjIEDQ0ixqpMgyS9iAniQPKIsEbSAZLPdLBmGGSkCYRy4MVstmCvMqYLwSRFyONJvCg1yhCrRknirkWaCDIffyyJOiltGWLF1ZUbY0wPvN5ie4ZTFERqBDfKMbLAVmdEzcZBoNPQChCWpNhBiDqIYGpyeMVBDXz0wVRkJBqUH2FVgIjcgoZMuwnRKJHXkvWNoekvo+IY+qmYS0N0JFP1pQUBzn9ZQ8ZBzRejWgLol5MUEzxfImM+vheSCVLQiMolI+oNFlT4SURUVPh1QItY1oxI+j3SQloPYnwPOrpCSJvCJeXyOqKRPgQSoz0SYYxYRIRTxO8VjIcCJq6oi1jCq8lwUeWFY3WpOEQETbQfjv97wEXyNbVW3Q7td0ua1e0awsq+/e2SLQvNAsKurz2Lq3FFhj2ASqEcIFbu7u7ju5jf///RoGyBVx3swWdpbLYY+kWefYzu2DIWs3afbcagm63OUkS15213TZrd9j1+bf7VVWVc0F5+fIlu7u7bsKwvr7OcDh03GF7Xrs0hm6X205BuufYds5tUWTTd+2x2t+zBbOlAHXPnf27OI6d/mI6nfL8+XN2d3ffmHgAb1xzO2na2dlhY2OD4XDoBOfdgrq7WY60FcFbXUo3CK9Ln7LTjC7Fyr7gu2vC/r3tLMObjmr2POzu7vLjH/+YxWLBysqKA50WSABvTCC6a8oCTru23qZ3Aa5j2wU9b++vvcfsn9tjsFSsb9s2Go34/PPP+eijj+j1euzu7jKZTLh69So//elPXXr8jRs3HPh//vy5E8Lu7Oxw+fJlZ69qpw5t23L58mWGwyFfffUV+/v7vP/++1y5csVpJj788EPu3btHEAROI2XPfRzHvHz5Eq21o+DYJoEFn71ej8ePH2OMYXt725kYpGnqHNRsQ6Hf71PXNR9//DGnp6euEZBlGWmaOvtcm7eQpikPHjzg4cOHDsxeuXKFe/fuMZ1OnbvRL37xC6ffsIWmnVa8evWKCxcuOKG6nQQ0TeNEzLb7/eGHH/Lw4UN+8IMfONCyvr5OlmVcu3aN+/fvM5vNSNPUZXJYO+koijh37hxPnz4lTVMuXbrktDNpmnL//n2CIHDaGwvClpeXGQ6HNE3DysoKT58+dRlJaZo6W28pJffv33d2so8fP+bJkyfcuXOHqqp45513ePbsGWtra/zX//pfUUpx4cIFXrx4wfXr12nb1k0jbJFvmzH9fp80TRkOh+zv7wPw1Vdf8f7773N0dOSmaVmW8cEHH7hJ88nJiaOA9no9lFJOQG+NDezz1T6LrP7INrSePHnC4eGhu3en0ymTyYQgCLhw4QJ//Md/DHz9/hsMBtR17aYllr4VxzFSnjn0WftgCzLt9N0Y46Z3vV6PZ8+esby8zI0bN9jd3XX5M99k+8ZAw0wPMUFIZjR9RsgsQFFjwgStJbLKCfyaxgyQQUUgCpo8QAF5k4NK8fs+ng4gyyCQLKoaYdqzcD3hMRWCRFaERoOvidoCn4QFBuKQtJTUwiCjgOKoQqPpjWOypkH2BeiCYRCwyEIWukWICBF4BN4A2dQoM6cvl+gFHqUnMOOI7Pg1oYnxTYRYZEjfIJXA60WYqkJKQ40B1WCyHl4TUjGnVTU1ASelhmqBiAzKQBoqoKH1CtKBpJSGupAok4NpqVV0FszXlqhWsWtOiXQP9eKYpX6fnSsXWW1bmkXD6WyG0gWi9AlES1x6mJOawCuIlxW9NMFoSVFLGjz8WDDwEqrikDqvKYIlvEGC8CtMUWPqmsHQo/VaGiOQfo6nDKrN8T0P4oSYAYP+COGHmFSyubGJVpq6nNMsar4sn1MWFXLeoiKFFC0xhqEPhedTNAEZGmkKdAgyMjRGolSJ58e0yqctPAbBgsLrEQUxMzUnSULqzCBKQdROwBMopYmkQjYCFU8IfA/V1rS5oqkhSROW+ym6gbaYUrc+6AQRNPheCV6Kagu2Jj1GfkRvKMjLBY1vmJc1gzjCU4LhyjKND1orVNUwigWlqakDqGqIzYRSCrSCMO5hipxKV6RBiO8l6LYmkBIZC6RJiL0BrV9QqpLaH1GZBPohxig8DGVRESQjmqzBmxhOqkMGgyXmU8ADE9bUhcTUMYEQeKFgJELKRU3VagL97fS/Bxz9x1KR7GSjq28AXAHYFfTajpzdbOHUdZiyHeKuvsLSabogxQoPbRdyb2+Pra0tR4exVrGWQ2wBQ7cAt5OYrnuRFURbkGQLyiRJ0Fq/EWpnj9sKNW2Bar/XFtzWwtGKx21H6vXr15yenroXkxVYdjviVVWxt7fHixcvSJKEyWTCZDJhZWXFuZV0hdNdcacd6XcnD1a7Yvfb/rwtbCwlDHBpu116mJ2K2PG/5YPD1ynsdhphO6CWTra0tMTm5qbrUFvaVpfOZn/Xrh/74rZhVNY2s0tRs1Oo7jXtAgp7LSwwfBsI2GtpAaXdiqLgJz/5iXtBd51e7DW2FD273rrgtGs48PYx2rVoJ3iAu6+6YAN4A8R0AUZZfjttsqfTKefPn3eCV8uJf/z4sSsWV1ZWABwgsNodm6sxn8/58ssvuXTpEkEQ8Pr1a+ceJITg1atXjs5nge3jx4/dWrKdYSnPQuyOj4/d1KwsS46Pj1ldXQVw3Xq7Vm0SuA2/nEwmPHnyxE31rCOZfV48ffrU0SyllEynU6f7WCwWLmejbVv+9b/+16yurnLz5k22t7d5/vy5m+bkeU6apqyvr7uO+TvvvOPW/YULF/B9n1u3bnF8fMzW1hbz+dxNfa29qy1grT7j9u3bLrtDCOFCN1dWVvjVr37l7uGjoyPnFmazLc6dO8cHH3xAURSsr6+7Av6TTz5xxe3KygpZlvH+++9zeHjoxPVdJ7+PP/6Yn/70p054bQ0qHj9+zC9/+UuXxN62rbOItU0b64S3ublJv9/n8uXLvH792rkuTSYTHjx44KhrBwcHrslg78PLly8zm83Y2Njgyy+/ZGlpyWVi2OmMfXZfunSJW7dukee525fNzU0nELdUt25jAc6adH/zN3/DF198wXg8dm5m6+vrvPvuu9y8eZNnz56RJAlVVTGbzSiKwgFMG/K4vLzsNHlWG7azs8P+/j6Hh4dvUFQvX77MwcGBo3TFccyDBw9QSvHy5UvXXPmntm8ONNJtAnVElPq0C0M2m9NLQmTZMJOK1BhA0AZzhFSUQtL4Ab0gwDQlfmrIRI4xIT3foHRDqwSiFxEphQhLwlThxT3McUHTBihhoDhlHKaYxKduWsgCTFYSVj5mYGhjRdJCe9qiZIDoSWQd4tct4VBgqobMNwRhQOxLwuDMhSqOPU6nBcIfE6FRWYYUAbR9GLQUnKFJZM3pyZR+2scPSzwTUzUeSgQs8hlllNOcQFvHxCOo2xN84SMwZCrDS3pEhHiFoBk2hEiaVFJohcKnLKE2e7RFTH2aUKQ1SS9hIMb0dy7StCWe0izaBfP5gOLohEBERAmUs5JFpIEzxyiljnk1PaYvQwIN83aO1B5CxaTKox8ZytMSPx3TNj4y92jDkHAQsnphg56/RLtoGW/EzOYFMveYv5pzsniBlhW+TMhaCLSGVhLHYxYqQwQS3caEiY+qM1pvgtGK1OsjdUVjJEnk4Xklqo0gCCE2BImirXPi3hB8QTCdEbUJepAQ+w3Vwofcp6d9FsUpYRzQFIo46hPULYgMpYY0psKPQeQaKRWyjRiEAQEBoj8mSnq0aGZziZGGuCdIZEOVe5haEkSCps1hpgiTmKOiJdQeqUyIY01LjpY+KTHzWYafKtAekdZo2WICgWoVkpqybjE6Itc1iVchmgZfhfSXNJVsEGWIl4bIuqHya1pVEoiWtj4m9kfokwJ/SaCHkvrkjGJGUCGKClUHNEaz4NtJeYCv8zPsS7lLS7Fi4G5XuusM1NVi2G5wt2tri7K3AYkFGlaIbH8evu7cHx8f8/jxY7773e++QV+xxd/bHe3uP129gT1GS+vqTlC62Qdd6pHtXNmt28G3x2kBjOXRLi8vs7S0xNHRkUsD7/Lx7ed0Bb9KKU5OThytxBa5k8nkDatVC3xs9/ttRymr5bAalu55sOesW7DbIKyDgwPXtbXFuj1X3UmUPV9W4JimKZPJhKWlJSaTiXPfsZ76XZ69tS61HUHrlGUBrgVz9ue6qdz2hQ5fa0m60x577ew56VKb7HF3aSK//vWv+c1vfkO/32dpacmBWLs+7fmyQMOuu+73dHVMb69te5xW+2M/w6657j3UndrYa9M93m/TNhqNXI6Bpe5YYCalZG1tzU0ZpJR89dVXjlpjizDAdeZtlkFVVTx9+pT333/fBdXZTBqbX/P69WsuXrzo3KKWlpacDe3R0ZGbXljb2KIo2NnZcaBOa+1EvzZUsKoq3nvvPQ4ODhgMBjx+/NhZ8trnk6XrTCYTDg8PGY1GLBYLZ3NqbWI9z+PDDz9keXmZBw8e0Ov1KMuSra0tNjc3uXnzJqPRyAXNGXOW2r21teU0IlmWOQG4TdW2gYGPHz9+Qz9nC/T9/X22trZc4b60tMTr1685f/48y8vLrKys8NVXX9Hr9Tg9PXWWvGtra1y8eJEvv/ySDz/80LlQGWP48ssvuXz5Mr/7u7/Lj3/8Yx48eOCocYPBgBcvXhCGIQcHB3z22WdEUeT0JtahbjqdOtvw4+NjoihiZWWFk5MTer0e586d49y5c3znO99xDk+3bt1ylFPrXtXr9Tg+PiZJEucqtba25p5BVi9h6U4WoNnARSs2z/Ocx48fs1gsCIKAtbU19vf3yfOc4XDIw4cP3X1qz4Ntlty/f99ZIFtbXoDr16+zsrLCj3/8Y+I4dkDCgsKjoyM+/PBD8jx3z7TXr1/jeZ5Lpz88PGR7e5vZbEaWZe499/LlSwAXIGgNBaz17v/fJxptowiDkOlRTS+OSDYETSlRbYmKPbxAQCsYtjVFJTnxYRgGlKct0pcEske0mBIow2mmzqxjYwXiGC/0yGsIGkMUgQoFSkl8LfDjiGKhEO2CIAwIIwikR5MYRBQwN4qqLQm1JJIhSjaE6yn50xoOc4Khz8ryEFFmxL2YkB6mAa0E/VZSe5ppXTCSITqOOM3PpOGjXkA2qxGFTzIa0+SKUmiiVOEfgcka6plC1x5tXJIpTVU3hEGKNhFtXVIUAq8x+L2MMBIkZkKee7QaSq9AaYXwhjRlyHI04OXBDHOkkXHJUTRHHQRMxiP6WpAsL9EEPmtLS7ComdcF0VZMpWqUgsxvMKWgH2p6xqduFZFu8Mhp/YSmEoyWE/pCEMiEvDCMVrdJkxAdtkhPYERDUVQc3XnEbL5gKV3hNDsiDiRlHOCpkuHK2tmUpW1odUvUi8izDK/VmDhGComvNZEwUOcUTcNwdQnRCkxlWDQhTAQn1YK40XhZfqYdGXqYYYiRMZWGuFTEGuJ+SlMXRD2BSkCKBlWe4ktDNFliSoFqNaGX0MY1xjeEOkCrgP5oTG99iCcMRghms5KgL+gbn1ppkIrQ12ivRukS2e9jQoGsPXy/j2ihFS0NDaEqaHV0ZoerFaIFHQwp64wwEMT9CLMQBFVC7Sl6niBMPUxQg4ZZLtBS0RqFV+UEQUEUJgSmR11XZLqGaka8NIZ2QaBzylCwkBWJ8WibklwJvHgDESbf9Lb9Z7dZgGHtVLuBaLbgtIUWfO3k1C2+u11ku9mCF96kX3U/x4ovLWCx4GZ1dZW2bTk8POTOnTtcvXrVdRS7dqEWrNgC2j60bee7W/C+PT3pTiwsV9tOW+yxdKkztiC1PFhbhNpjtABsfX2dzc1Nl0hsj89qCbogyAofARaLBdPplKZpnHWhdUCy4uSuBqYrsrYdctuFr6qKxWLh3LJmsxmnp6csFgvXwe1OPGwXzdoa24LXUoisiNS6yPR6PQdqrHDcFuG2q2ivixUH22mG1ZjYfe5qSeBrkbVdE11TALt/9tx0QaO9ZvY82Wtiv+fBgwf8zd/8DZ7nOa5+F7i+7RLV/ezuBMUCgi7gttfwbbrb/6919zYt6//bffJt2KbTqaN/WABhi0FrD/3y5Uu2trYYj8e8++67PHz4EK01Fy5ccBqmTz75xHVmfd9nPp+zurrqCr/Lly+7v7t48SJHR0c8fvyYtbU1ptOp0//Y55ANcQzD0BXzQghHe7IFXdu29Pt997tVVfH48WM3mbET1fX1dZfK/fr1azeZGA6HLtXb932m0ylZlrG9vc10OqXf7zMej/mDP/gDHj58yD/8wz+80bSwE4bxeOyct7a2tvjyyy+p65r33nsPKc/So1dXV51drbX4FUIwHA558eIFFy5c4OnTpwghXEdfiLPAwe3tbRdC+tVXXzEYDJzmzN7PYRjy7Nkzd68BjjIl5Zn5x3w+d9S2NE0py9JR4UajEUmS8OrVK7Is4/DwkMPDwzdC72zRfnR0xOXLl50YfmVlhXPnzrkmzzvvvOMStG23fzweO/DU7/ddYGNVVdy/f5+lpSU2NjZQ6sw6OMsyNzE+PDx0U6yLFy+6JtHh4aFL4LY0v6OjIy5cuOCaRfaZZU1Bnjx5wt/93d+R5znvvvuuCwS0jlTz+RyA7e1t946xzlHGGDfZsODaPnfsBNkYw/7+Pmma0uv1XBPq6dOnTixeFAU3btwgDEOeP3/OeDzmiy+++Eb37De3t21a5qYlCRPwW7LslMoPSMOIuJYUbY3xKrSpoYiJkxBRNLSBoVICgcb4IRmQ7AygrvBUjvEUMvDxEx+TS0SpodaEfoBuarQx9KOE2lvQSkFbp9BCODBgavy8pIkC9DIMwgC/rGmyjGAoke0YP/VoTjWTYR/pQ1YZIi1oZyeESR/hK6KeoMokpmnw+ynGhJSnGb20j4oXqBKIA9pSUp7WHC7mqHxKo3JaUrzQZ6I8ZtMWIwymrvA9n1RE9JOQQjUYsaBZGA6PDxgupfi6T1QVGFlT1gGv64wgKqkKhZITwlZggprT2R4VIXl1DFnD60RjCo9Bv0+ylhLpgHaRn3XJkzHR5jpBnpEQctLU1Eqhm4ZagWxbVNSiWh/hVZRVxtHJC05nBaNhTImGWUgYNxAHzJqKXn+MNILAE/i+QdcLRGDwYp8wgHouiIJ1PDVH5ppBOCb2PETbUPoteqFopjl11Ac/QCmNzkAogRf7+NGQWlRM+iHZHIRo0bOChTEQ+qhAUWqD0CkcVkTE+LGmRtHqOVqf0cVMPqMWEUJCPAxpvYi4l5L4AmkaqjZEejWhCWjykjYYo7yaxFNkWYs/GrFoWqKyYhSnnJxWMEnxGoFHiteA8QoaSlgkyDQkVw2i1QTGo/YzvH6IDDWervBDDVpQFjVoQYPPIImYUSKrEGUCpNdDzTNEJQlXU0xT0RaneEGA50EaGfymR0yPmTilP06QoiEvDPDNOgn/3DZb+HQnGV13J9tJgX8MHrrFmBDijSA0+/C0VJ2u444FLrYr3wUF1q3HjpKtiLNL47Hd6+44uzvRsJMD2822xYM9Nru9TYfqgoi2bV131k4ubDHe7WDbQtnSY6w97srKiqNYnZyccHx87ALnLGCw3vFdW2DbDbdFdjc/Q+uvc066QKerLQDeoIxYMNUVg1pAZacllnJlKW55nr8hOlxdXXU2nN3rbM+fnYTYyYk9N5bLbCcK9uXY1VTY9WCLcKt/sZxlW9jb7+3SkN6eRtmO4dsTmqdPn/LHf/zHnJycsLa29gZofNuYoEvFssVFF/RY8NUV4b89XelSrrr3QReMd3VB9v+/rVue587i1RZ4tviT8syO9Pr16zx58oTf/OY3BEHAlStX6Pf7vHr1is3NTZT6OmzNcv9tdsTR0ZFzdxoMBvz0pz/lo48+cpMPm0NjAUFRFAwGA7a2tnj8+LHTdBweHtLr9fA8j+l06mh7e3t7Tksl5dduRPP5nL29PXZ2dlyI29LSkpuCbG9vU5YlX3zxhVvX1mrVPr8sHc9Svz744AOWl5dp25a/+Iu/cDSh3/u93+PWrVu8++673Llzh1/96ldu6mWpoVbTlaapyx2y1LLxeMzp6SlfffWVExBvb2+/YZdtdSt2imSfE12KZfeZbp9LlqJmp6H/63/9L2cBXZal6/5bYGHdpz777DMH/u3+2u/78ssv3TPhX/yLf+HonePxmOPjY3Z3dzk6OnrDSU9K6SZWVlxvn6NN03Du3DlGoxFRFHF0dATgKLI27X17e9sBLatpi6KIDz74wD1DbBDrkydPWF9fd890++4py5Lbt287Af90OnXUNPv8un37ttPCdLVmURQ5c47T01POnTtHXdecnJy4d9mLFy9YXl7G8zz3LLYTuLIs3QTHTmzs2rBNpm+yfWOggdYQezR5jZACL4nxDKQypdWKth4g/AQ/zkGEpIFH5YHX18hFALlESo0aZsigRbQ+qhUkfkhee8jIEMUBC10SyJbYeDQoQs+nrQymPyAOIMtymiBkUPhoU5I1DUlviGwbTuZHiEqiQ0kpFb6QGB3TUlEREQU9YtPiozFhSBUrotTQ5i2IEX7i0zYFrS9pdUpTtch+j4nUnCwKjNaIOicWDVkkqPMFppHUbUucGnTUYESLNiWeGiKQ5E2NaAxBANN6QQCYYobwe1QYjGypAk3ZlEyaAt+v8YYxqgZ0TS19ZBKj50eoMiPLoKkM8+NjDl89R/YMZgbeoEeSgO/N8IuaaibJgpa8gDDx0FQUc00aeqA1dV6CCBCxwBcl0/mcxgsROqQqNL1xH10JvLpPS4M0OZ4nqHoC0UiCaMCMip6GVPkUrWC4lLDIS4rQJyEkGoTUpsAb+0TmzL52HPiYQqP9kNw31EVLP0k5Pc7QmaJJFUb6SD9AjCOKtkVGkkobxuMe1cGctlD4SwOK2ez/0J4KQj1gOIhQoSFKE0bjkIiWVgvK4EzbEf8fPUcQ+UQGsrYh9xVBL6SoW9IQvLbFKEMY+ESmxoQNeV5giCj1jNrz6ElN2yyIwgFiMKDQikR6KH0GsGLPJ0oCNIpwAdrT+EEA85pJT2LykjyMqWVLv58QRw2e8ajiFBGHSJmgTEVfaeZZgU4VkSdopECIBrxvJ7cacCnOb9NlbCHZLaRswWT5yV2Oepcu1Z162N+1RbIt7mwnvktv6XazB4OzEMSDgwOeP39OFEUMh0P3AuxmadjC34Kit3Ub3aIPeKOLbV+69sXVdWjp6lLssXc1G5b+Y3/WBr/Zol3Ks8RbW5TYNHBbYFqXo+75soDMdvC7xXRXOAy4iULXickeo+d5TvNiz0e3iLZgw05BuvbB1uK11+sxGAxcsF+XfmXBWzfs0AJM+zmAA2xFUbxhWdudmnWdl+x5s+fVuppZDUqXfmb3x35OF3jac3h4eMif/umfsru7+4Y7UXca1Z0eWVBmz2sXxHVBU1er9H+j1tl9tvvT1Q7Ze8luFtB8W7d+v491aUuSxF2jtm1dkJxdB8vLy1RVRb/fd1aiQRA4gau1JL13757rZNuwvTRN3YTvzp07rmC3eorJZOIKT2s2UJYljx49Qinlkp6zLHPXJE1TR9uxU0CbHP3RRx/xk5/8hOfPnzsr0Z/97GduwvfgwQMnzrb0xoODA5aWlqiq6g2b1OXlZXzf58mTJ2xubqK15oc//CFHR0c8f/6cn/3sZ1y4cIG7d++654wVA+/s7PDTn/4UwE135/M5v/u7v8vJyQnPnj0jjmMnwF5eXmZnZ8dZhR8cHFBVFa9evXKuSVmWsbu767RQvu+zvr7OycmJmzpcuHCBIAgcFTSOY86dO8dvfvMb3n33XV68eOHAjlKKX//6126i+eLFCwdwbty4wU9/+lNOT09ZXV1175vr16+ztbXF5cuX+fzzz52w3T4HAK5cucKzZ88cuKrr2tkeWxByeHjI+fPnXUPITjIGg4ET8c/nc65evcq9e/fY3Nzk/fffd82fzc1Nbt++zfvvv8+dO3ccYLaNnl6vR5Zl+L5Pv9/nL//yLx01y2r8ptMpu7u7bG5uuvUexzGTyYTpdMrTp09ZX193z7XRaOQCI60D2vr6Oi9evABwwnDLMrDv2+7UzQJbq/VQSjkd0j+1fWOgkfQyhJeih4Iqq/BCTaAVXllitEB5hjqWeFLSGomsagwe7dwjMC2RPyQ/LhgmEVoJGhOQesuEcY3xfFTjo7WHEh5BUIBR9MMRlW7QfoGKJCbx6GkPk8Tki5ywDQjDBLOoGSQhxguohxEBhp6OCeuGfJ4zxnD6ukH6AXEvxNQ5UeyDrsmOItJwgExbpK6hD3VZoo3E1JpmfoYOfXFmU1sEHnqhKU5ywiCmLVo8FdMIQ64q5rqlH8WIeoZpPLT08YRGeT4mrZG1R1ZOITZ4dUgcRJgggsDDALHw0GVJ1ZQM4j5N41OUGl9LmramLQ11HBCahNYoEu0RCQ+vDlgMNbKukaVHIXOKPCMuQUQRutIEQ8nq+pCn9/aQMsH3DU2ZE/UUbRuTmPjMPtf3EYsIrRumYYHxW2Tko+sGzxgi47PIMurQ4OuWeX1MlMa0QUPla8I4Qs8l81lF0Is4WbR4ngStKRKNLxKMkUQLH9+rGEiF7w2oW0WdtJhWINqSeR4yTHroaoanF1RaI/oryKzBFw2RlPhhRCs0ftAiGlhe3SDxRvR6CfOsIQkFYVtgUERS4sU95gsQiSSqPfJyehY2eBrQlx469akDgQlK2sBHVS2eamlkxGR0jqo21M0JSRTTThvaukL2YhoJ4cjQCE1dhOgywPgeQpckfsBJ29JPfNppRuT38WnQTYsKYG4Uw1YgIp/jWhFIQ0hN6yvEOZDaI5Y+pqhopUfCt9MtBnAcfVvUvs0p73Zau6I0C0K6f26L5m7X1naDuw48wBvgwr7sul1izzvLNLABXIeHh67Q6Ha0bVH7f6NydacyXVteO7mw32dfbPblbruQQginHej+09WbAO5FYYtdW7TbDqvt6nmex/r6uptsWPBjudldfYTlXXc1Al1Nit1sgWyvQ/f37Xnuamm6XUwLGsfjMZPJ5A0htO3y28+3HccuHcmeN2uDaa+dnXzYF6OlTMGbyez2mLoTqe55sefVUrK669IW+XZyZcXx9jzAWbjVn/3Zn/HgwYM3QEZ3HdtzZqdEVm9ir10XJNjj7wIGC1gseLVbF7jba9ud9tj9h6+nit9WsJGmKVprl8lg77XhcOgKsTRNeeeddzg+PgZwGQQPHjygaRp+8Ytf8PHHH6O1Zjabuet1enrKq1evHNVEKeUsXa3tp3XkSdOUn/zkJ26a0ratK/qPj49dE2EymTgKjqW7vHjxgsFgQNu23Lhxg9PTU/b3992EdWVlhc8//5zBYMDHH3/MP/zDP7jjsiLvsixdiFsQBGxsbAA4ilPTNKyvr+N5Hnt7e/T7fX7nd36HP/mTP3HF7KVLl/jJT37yRsr1l19+6QrLL774AqUUKysr3Lx5EyGEy+kYDAYurM/eu0+ePCGKIpffUZalW+P2edylggKsr6/Ttq2jrG1tbTlRs72nf/aznzGdTt01sODn6OjIHaulqf3iF7/g/Pnzbnp14cIFF7QYx7EDF/bZORqN2NnZ4enTp9y5c4f19XWiKOLly5eMx2Pu37/PYDAgjmPnNHXlyhWn9VJKMRwOnUC+qipevHjBxYsX6ff7Tkfn+77LW9nc3HRWvXmeo5RiZ2eHqqrY3t7m9u3bPH/+nOfPn7O/v48xxuXAwNk9bg097Jq4efOm069Yu2U7mbMi9mfPnjEej9nY2ODg4OANOqfVAdlnlgUydkJnM1Es/eratWvf2FDiGwMNPV6izBQir4g9TdQbks9ymvAslM2EOVWoaWqDQRNGIdQKTypQLbP2Jf6SoRGQVAl5MacOA7wpyOgsL0JjGLcxdVGjgpY80EjRYggw2iOqBD5TyqmiTRp02xCXIcIznGTH6H6fNB6z2H8NXonv+chhQtY2oAzTrKCoNYPxCH9R05YtifFIPUHj17T4tKok9vs045bQaGQVkFOStzNkqqgPG+p5S1uHiCSl8qYo0+AvauI6wAQ98nlB2ItoFSQqojJHFLXGq2OkD4IBrdR4IqJG0i5eE4Y95scL/IngdLFgfTzBF4JAapSfoY40LEZ4XkY/iqlf54T9Hm1l8ESNDBQyE5SlYTFvaf2WyIsoI1C5IMxahBTsvXhNWbYEQYUqFRhFECYoItSiJu2H6DojMQVz34ekps1K0tanrWq8OKaWFWnQQGYQwYAw8KkGZ+BLklKZFtHXUMeU2icRLXFQQRhSmRQZgpe1yMbHRIKiKlFeiRcYPC9mnpUMBxFLfoyoWqrMZzga0fiv8Zoa+jWpAC8JKWSACgxVKxmtj4nHMb06oTlSKBmRy4aoqNEDn0y06GoOQmMaHyM9ev115EwxCkMQEhkHeI2iOZ1iRgmq6RH3PGQDs8Mar5Gkwz7qaIrfizFhytATtKahXpRoZUBN8Cg4zAuCXogWCqk9ZGio5x75rEL1GmQDJQodG2QbEJiI1BQYOUOZBOEpmllDIEOqqkRpRdXziEz2zd/K/8w2W3Ta4t4WCXbaAG9qNbo2sLaD3S3q7WaLKctrtT9vizhbXHUToi3QsV1OW+SnafqPusjdYrlbTHenDxaQdMGH/W87tbGTle7f2S66LeC7lJeu8NIepy2eu8LnbrFvX+h2Xy1dyfKFm6bh8PDQvbjtPnQpOF3gYI+pS8fpXoPuebD7aQtxCxpsB3o8HrO9vc3KyoqjjHXD8WwB8rYuB85E9jYMzG5vT6e6FAzbIXybjtSluUkpXVHVBa2WAmLXaPeadoskey6m0yk/+9nP+Pzzzx1H214vC6rfpitZ3UvXateuBXu89jzb9dcFFG8DmO5Ur3v+utOct6cd38bNFtJ7e3uuY7uzs0Oe52xsbLjphS3+bTiZ7aIvFgt++MMfkmWZC6MrioJ79+7x4Ycfuq65daKzk6vj42NOTk548eIFeZ67rrUt2uu6dl39wWDgCndbGFtaI8CFCxcc7er09NSJpYfDoXuG7ezscOfOHb766iu3xvr9vpsCCCF4/fo1JycnXL16lV/84hfcuHHDOVlZwfXjx49dkvfr16/5vd/7PUajEcYYPvvsM3Z2dhxF5+7du/yH//Af+M53vuOyGnzfZzabce/ePc6dO8d/+k//yT1j//7v/95NljY2NvA8zwEqG5T3V3/1V84pCs7W9ebmJru7u7x48YJr166R5znPnz9nsVi4JsKdO3e4desWQgjOnz/vtBsAn3zyCQ8fPnQGHjdv3nxDi7a2tsbq6irnzp3j/fff59NPP2UwGFBVFV9++eUbU+Qsy5wtsNVcLRYLF7T4W7/1W7x69cplUcRxzK9//WvnkGX1JTYA0D7jrNOfBVpRFDnHPN/3efHihZu+Xrp0id3dXWfRGwQBWZbx2WefOWrd9773PZ48ecKPfvQjHj586EJTj46OkFLy8ccfk2WZA2NLS0tuWmS/xybXG3MWztg0jdMLHh0d8erVK46Pj/n93/99er2eo3IVRcHFixddovnq6qp7B32T7RsDjdPpCY2MYaGhL/HyBk8mlHJK6Q/QoUQUApVnpJMeJ4uCvhBIJWl0TRsa2lYz9sacVJo4GlJRElSCVCnqsaEkJD89IO4HzJqaNJwTtYK2CNGiZr7QTNI+nh8hjMAIg5YRJpJ4RuPnIU1b4/VimiKnCWNUm1A1mkmkkbkgP54yNSXL0RDZT5AqoNYVAo8ygygcoJqGVIYILyDULZVs8KTEm5WEVUPrKUQ/pqoXQEnpa4Yt+NMGk2hGUlKomsk45vTVKb7WDMcRZhYyzRX9JOCozlnoBVFl6AWKhTlFDGN0VLE+3qKZlmRRjBY1ntBIDFBRehqpWvpjwey0RvcNmSxZHgwITwSlF9BgGCZ9dHGMiGIKs0DMW8RJhFpqSFdipElQlYcqTzmaNYg0IakMpZej0hjVzvCCEflpQTJeJVickMSCJi/pjWOk9AmWfLRO8F5rEu1RyJBQGlTdEAWCaqEgFAxki45CWhPQLkr80BD3G5qmJIliqFuknzIVDf08I+mn5CZiECuMqZFegDLQ5j289MyKuC5myJ7Bi1tiQpa8SyxvD5maBWXaok4MIj3TYBhvQJ0rVATxEGg0/cyj8DzSCJTxKKsCTxr8WUxdQzpaQiuN8TVtYjBG0VsS6MJQIwiSCBMZJIZFY9ClxguGlFrRNqf0W02gfPyyIfZKhN/HzGvCSZ8ibJFZTRS05BhM3MNUPqop8YoW42uMjNGVJkGQqRIRePS1R9m2GDH5prftP7vNJirbIt8Wzm8H2MHXHfMwDB1v3r6EbCHddSmym+04WwBh+dC2c2S59V0evB3n2052WZYMBgNnDdjtKttuu+VE22Oxhb8tUO3L2BaathNqJyMWWHW59YDTjsRx7Ipl232z3ThbLNrz1nWusgV0t7C2+2qpRmtra84f37pCWW5ulmVuCmA/y+6X3Z/uZot0+28LLrrall6vR7/fZzQauXNlj9l21u1ndyl1wBtJ3V0AZq0q7Tmw06qum5cFB11qmD0uW2xY+kV37dhjtHSprsbCAg/7ws7znJ///Of86le/otfrveFG050SdacbXb2K7SJ2i//utOFtcNEFs9113wXx3YyMt4FI9z75Nm6TyYTZbOYmAlZzYQtEmz0zGAzY399HCOHsZ9fX110StRCCg4MDN/E7f/48Silu3LjB/v6+c+6xzY7333+fH//4x24dHB4evkHzs25Nw+GQS5cu8bd/+7copXjy5AnXrl2jrmsX3HZ0dORAic13sc+2JEkIw5D79++/QYf76KOPeP78OVVVuWdB0zRcv36d4+NjxuMxe3t7xHHM1taWayTYNWiPKQgCnj17RpZlXLhwwYnM7fP45OSE27dv893vfpe7d++6LBIp5RthbhbgW17/YDDg4cOHVFXFnTt3+PDDD7l16xZBEHD+/Hknqn727Jlzufvwww8dNcgGwj19+tR9n70v9vb2aJqGS5cu8eDBA3Z3d10OSpZlTlc3GAxYXV11AKBpGh48eMC1a9d4/vy5m6YsFgu2trZ49eoVk8mE5eVlfvrTn/Luu++ys7PjAvmKoqDX6zlzAEv/sgDMWgYHQUCe54RhyO3bt/nOd77jJhCPHj3i+vXr7pisgBvOng8XL17k8ePH+L7PysoK8/mcw8NDbt++7a4fnBl4LC0tOXoVwMnJifvug4MDAJaXl925297eduDs6tWrLqjQUkPtzw+HQ0ed2tjY4P79++zs7LC7u0uWZS6RXCnlktGtC+A32b4x0Jj0hxRVg78U0JQLvDigyXKoPUTU4BtJXrSEMkEtTglFcAYIGkOlYqSviWSOzks8nRJJQyh8KqlYVB6yiKGtEZUkTw1eEqAWNV47ploskH5M6Gm0d0Yt8muNqiI8GVDMjhCTBqlatOhT1A2pF6BfG4JBi0Dj1T6N8PETgxQe0ypj1IsJ4hAqgVeHSC+gMQbjQ101eBoaryRvBflxybw80ykU1RyQTKIBC79GxAHtYQFjj7ataPKItk6ZCY3yBL6JmO7XpGlNz88xeoRQkkEkEVLg1zHBrIFIE+mE4PjM5lfFhiCccHI8RaoG4yskHrNTTb+f4A98lCnp94boUhD4GtG0rI09FvN9lJ9S7Z8wHKXoJY9F1iClQBaKqs0ZmR5yNKEtS5Atfj/gOPQIfMgqn1HjM9FQHh9SCYmWa0jtc3qYE/YHDBPoD0IKGpq2RAwNofap65YmGKKDjJ6MyVWGlAN8oehFNW0Zc5xqQlMj64pQx+jcYzgSjMUEIeBwdopmiXoxw2s1MlMEoSYwBi0EKuzRNJD8f6j7sx9LrizLD/6dwcY7+ewecwQZHJJDJjOzurrVXYns+rokodH6M/UmQI8qFFqAqjsbXcnKZGUmySAZDMY8evh8B5vtnO/Bax9auAoqCtBD0YBARLjfa8M5x8zW3nuttQvN9beuMHYZ7UlHNNHEbYlPLI4UW3a4WDG2KX1rUL2ijg7p4pi+aem6jD4F31uSXlN1Ch8nVLpGRYaFd5hFxXo8o+tWpHFHVzfY8YR51aBVDV2DNgmuB0eFyR2rHjaylHl5jJ16dAS+HFGUEW1sSBLouoZcKerTGp/2NFbRj1P6XpH0PdZVxC7HR5auz9DqlFnUc1D/cGnVv7RNQJKAu6E4VgDSkFMuVCD57kU61UW9xEXe+7CHxrAyYK0NQFQCA/m3BAxKKbIsC1SjoQBXQKEEKVJCH4JAeUEIVUwychIAyMtqKLIe6hmG1zqsgsh1DzUqkgmX8wFC4CDAVsTSkuWXzyilmEwmIau5t7cXXuDDAPAiRWoYFMp5iCC9bdsgPl+tVkEkKaJQsdGUa5f+H0P7UZkrCRyGVK4h2JE5GVZ9hpUgockN9zvUVgz3I1UFmR8JDrXWgd42pOMtFgt++9vf8nd/93fBrnI2m4Wq0hAoDis2EohJrwap6sm1DHU+F4NjCZyGOh5ZuzKOFyliF3UdQ33Nj22ThIAEGt99913QVGVZxvPnz0nTNNiyioWszJ/c92VZ8uLFi+DGs7a2xrfffsvly5cDDUY4+dIg7+rVqyil+PLLL5nNZmEfb7/9djgHaab5q1/9ii+++CKIh5VS/PSnP+XZs2dorfnwww/p+57nz58HzdZ4PObKlStsbm7ym9/8BqXOe3oopfjmm28wxvD2228D8OjRo1DlEAtXYwyLxYKvv/46PEeEovf++++HOZ9MJqEB32w2Y29vL/TN2Nzc5OTkJPStefr0Kaenp8xmM9q25dWrV+FZLAGS954//vGPXLp0KVx/URRBvP3VV18xGo1CQ7uiKPj444/59NNPOTs74/bt2+zv74f9/s//8//Mp59+GoKJvb09vvnmm+CQ9/jxY6IoYmNjg1u3bjEej9na2grWx0MDjePj4xAEeO958eIFn3zyCc+fPw/He/r0KR9++GEI+u/cucPh4SEbGxs8e/aM9957j2fPngW7X9HDzOfzkFjZ2dkJHepPT09R6tyVTITrZVlycnISKiRD2pt0PV9bW+Px48d8+umnnJycoJQKJgRw/i55+vRpoFrK+0motMOkT9M0fPnll/z617/m8ePHQbO0v78fzBCMOe+FIuctBgnffPNNcPTa3NwM1Tl5d8RxHJoZ/qB79ofe3NWqxjgPiSPWFpXGuLZnUmWApqpKJikYpni1pK4rnM3wdFRJzyw25Gcpp6UjTWr6XGOSEYtmgYmWKN1gjMMlMb5QjJ0BFVHWMM3GGF9QeEVXt3gHFSvaJGYtm2IXmkZbijzB90vGEbTOQtxCpNGu42hZkk3WiBOLr2p8bai7EiYG3RhcXKNcj+sNReuY6hzXrVhVntUioalblmVLs9+QjSN8UeGaE4xqMZ1imbjzzuNdTO2g0wpbqX98cZ7incf1ijQbc3biyLKY3LQUbcJRF+O8IlMt4Kj8KY51VJ8RVXOyek7hl7TeEPeKWT+i8Z7er7CVwdqeeGTxfYfRC1xj8LVimowp0gZnoHUNazfGrIoOz4S86tG6Ydk1HHcKe3bGpk0Zlzmdq5hFG3S+J6JhzVi6ZEbTexw9aTLBKEe36jltajIVozpLUiXUrUNHii5WWBOBW0AW0fWO6mRBlozxHeRmjTrKSKIxvu3o0oLIGdq5p9WOyCh81ZDnWxTFnMr0jEc5J6sKY3uSkWai19ib7pDYnF43+POiDy5OUbanbmt61WHMiE4roEHVkIx3qJICFfcUrsFGGhMbYp3Quph6sUS1PalpyeIOn1n6esXYWHSfwLTixNUo3TPreyrnqJoKZWFrPcVRUZclq+UJdmRYLGIi2+Oahkm+w9l8H2JH1ViaqmESZXTtEWk6Zt5McP0KZu05nS0C36wwHlY+whhFlCf/zzfrv+BNQKOA32FWfwgih3SXIWiXv4fZXAlWgADgBNDJi1a+I2VgeVgK312y1JLtFivbBw8e8OLFC5RS3Lx5M7xghvQh+L6L+TB4kGrAReqTfFeoMcPjD3n4Q+H2MNiS3yVJ8kZnagErw3MZ9i0ZAnSpBEgQJp8TioN8VsBWnucBuMj1SpVBxkyOPwyOpCojPxNxoWTWZI7gzWBBKRX2J9c9BNTyMzkHyV7KuQz3N7S2vahVkP3IdQztcqWqIYHdcD+SPf/tb3/Lf//v/504jrl27dobuhMRkw/PSeZaqBTSKV3WvhxPxk+qfXLfyP+Hony5FplnGWsZM7nXJMCQQGtYDfkxbQcHB+HfMjYffPAB8/k8CL8lyK7rOjwPJBsrlsqTyYQoirh9+zaHh4cB0C6Xy9D4TaoLR0dHoSKyXC7Z3t5mNBpx9+5dtNasra3x9ttvE8dxEOI+f/48fFbmSfoSyLPGOReMG0ajEW+99RZ/+MMfQtY/jmNevXrFf/gP/4HHjx/z8OFDHj9+HLL40oxT1m1RFNy+fTvw78WNqigK9vf3Q5dtIGS1F4sFcRwznU6DlfSf/dmfURQF8/mc7e1t/sf/8X/kxYsX/PGPfwyAs21bLl++zMHBQVhjv/jFL/jNb37DfD7nD3/4wxvPHKH5yPr705/+FMbl7t27OOfY29vj8PCQuq75i7/4C/76r/+avu95+PBhoMFubGwEpzCpEn300Ue8fPkyJJveeecdyrLk2bNnwTBDqpPr6+s8fvw4rJ/Xr18HKtd8PmdjY4PDw0PeeustjDG8evWKZ8+evfE8FIcxsfY+Pj7m5s2bwbr8+PiY69evhwZ58mwXsXySJJycnASrb7E2vn//Pl9//TXPnz/n5z//eUh0iJ3sxsZG0J/Ie2RjY4OdnR12d3eDRbH0E5GfHRwcMB6PQ/8M6eo9Ho8pioLxeMx8Pufx48dcunSJnZ0dyrLkwYMHvPfee6yvr4cO6UCgf21sbPyge/aHBxq9Y2I1yucslw2GirZecVx4JtE6LSuy8RbdsqTWNfk0IkWzXMEoHRF34H3PJLXU6YI4S2hPC8ZrhqSJaTNLXWhU1JH6GLdooe3Jt3OatqEmozGGzDdY3ZElY0xk6JsltVWUJqVqYVZqIpvQmYpm0hE3CU3v2diZUVUlRZFg64bYlPS1ZlG0bF7ZpGsNXbVCTzMSDFEFx8uWYtXSdqe0q4K1uOUorQFPbh2td5T0uLKlbHqUXhFFE3TX0CcO2pKzTpEay/omFMuaQq+RxC1eOarOMmdFZ2vySQJFQ2tbfJIRW4WrgY2YzI5YuiV93dKbnK7vSHRORUE/MmTZiGX7GtNaVKtoWo/qLKtXx/TGEAO2s6yenmLGU6qqofM9zju0d0ySKREpyq7QXURmW5LVGSsbE0828G1J7VaMYkNNTty2dBG4pgeXkEUdxBpVN5DZc+H5qsFGYIxHa0XZ1GRbE6g90XpE1UW4xP8jR7AknWpsaujLFrwjHWWoSLPoW9xkTFQ76rrF4JipMXGak9ocG2VUTYNLaqLxNu1pSawjXO/IRglV19M1FTQ9bnRe9Tk9aUiMRUc1tC2mGKGTjqWraVoYmwLXO0bjKWO7orMxUQzdSU0TWSrX0rmWUVJR1Jpe5ShtiZoI5TW6zxjnE0oKMpvQ9QpMR5t3NM0x07WYvlfEUYrWJRVL7FixLFvGqcXoKVVXgbaUJiaOz+lXi7LDdTCJf5zcavi+8iDgWYDfEKQCb4DBf4p6MnTTGWZyhtWOIf9dji0NuoS+I5x7ofkIOI/jODQvAkLH1CtXrgTwPMyGC1VnSAcaZpvhTbE6fC/0vQgGh0BUAqaLmhQZF7kuAZbCS5f/y/UMaV5SHZBzFo3CRUoRnINe6Wgrvv1y/kI5aZomVD+GtrZildu2LcvlMgRcw2y9CKDFFEAqHhepQrIm5LtDep2cw7BqIBk9qWQMqyIyJ8MgQNbQsOeEnIcEAkPth2SNP/30U6y1oauwBBgC2IZBGPDGHAsNT8ZgSA2U9QNvUtOGlZeLQeiQQjYM9kTcLtchcyj7/7Ftct9JRWNjY4NXr14BBOehR48eMZlMSJKE09NTrly5EuxjNzc3GY1GgRv//PnzN5y/hGcvgm4R3m5sbHBycsLBwQGj0SiIl0ejEffu3ePGjRtBoL1cLnn58mXQKkj1ToDZ5cuXefbsWRCxn56e0nUdDx48eEP7tLOzQ9d1fPPNN+Ec5vM56+vraK1566233mjW+etf/5pPP/2Utm2DbarWOjQ4lIZyQmV8+fIlly5d4vHjx6FiYa1lfX2dJEnY39/n9u3bPHv2jL4/7x7+Z3/2Z+zv7/Po0SOUOu8e/cUXX2CM4Y9//CNVVfG//C//C3/7t38bKnJd1/HkyZOw7oeVZFmL8qwxxvDf//t/55NPPmFzczPYXQtNaDweo5Ti2rVroYeEuEIJ9evbb79lPp+HKlaWZaGvhbXnvZNevnwZ1oG4LwmlSZ5FQ/epS5cucXh4GIIW6Y/y4MGDMFY3btxgf3+fyWQSqmJ7e3s8ffo0UGy//fZbnHOhE7sIqtu25Y9//GOwQv7888+DFa4YHRhjuHfvHsYYrl69Guhxi8WCFy9ehIBD9CRPnz7l+vXr7O7uBvquWORWVRW6p8uav3r1Kuvr61y5ciXoQA4PDzk4OAgic6UUz58/RykVKi3/3PaDA43Id7Q+wtIQrWm6lUK1G2Sxpj0uyKcJqmmIdY9Va1SuoKmPsXlCkkxQvmNe1+jYMY4z6uOO1I/p5yUu6kl9ijaeptHoPEIpqM8UZVlg1mIK19GdtKRZRILGVy1J19Pzj3SGrmMycvSVp3AKjydNPHm/oCWidQrvNVEXk4+hVoq+cuB6Dp6/IjUj1na2KPsWo2oKTvHZgkV1iPfQWUvdabxWGBQq0hSdZlVDu6jJJhHj3lLR0cca1zrSaY6qV6hyxOlhwXhjRve64bwxR4PLMlLfk9mOpVvR94Yoj2lUQqZT7CilcR3zpcOkayRdxamt0aOIpGuZOoefWcpVRKzWULrEt5bJ1iar0zNcXBJFCX3f0hEzMhOaypGgMbnCzxt6RuBbqqijaDyRrkiswTegowQbjyj6jsg5XB2DdrQJJInFlSsyG+GcIsk1VVKiVz25jjCto7Y9Zd+TW4sqOzCWJm3x7oyybVhLU7R22NZApamihun2GF0pFl2Jbs4F+qmNgBadJozclL3tTdTGhFglqLYj9T1V01I0c2waYViilaatGlI1oXQNo+mMti3x1JhRx4bSVKWmbWOUcajUszysiE1EHBtqXXK6WBJPoas86WjEwldYXTKzGVXhiE2CNxHaWpQzNDi86VC1pi4bTNRRlxE6iujcubjb2VNaNcXEMQZL5DXoMU2XElvoy4oKRxR1dEmPsgm6m9A0DbH2dOWS2P54qVMCdgUMCbCTLPGwWnAx6BgGIvJ/AYhDYDzk0g+PKSBPAHOe52/QcQRUCOd4SFGSUnPbtly/fj1kdIaOSLJJVvmicHxIQRpenwQB8hLIsozxeByAz5CmJPuVrOBFEb2MowQ9/xTdZjgX0ldjCNiHegWZI6lEDIM8Of7F6olQKgRIyznLMaSKNBRKy3eHvVFEczIUvAtN7aJj1pBuJnM4pIcNqxFSCZDgVrjKch5DswAZRxGuK6WCSPOrr74iyzLW19eDMHMYbIiupm3b4Iwl4H5YYZA5kTmV9T+kUw2rXcM1MaSUDatLcgwZc1l/Mg8Cen6M23Q6ZT6fhyZlQsURjrkxJjgZSR8E0ehIL4DLly8HUJ3nOfP5PFSwyrIMfRuk6Z7MgwhkxT5Xgsiqqvj973/Pr371KzY2Nrh37x5bW1s8f/48WIuKO9GzZ8/4P/6P/4MPP/yQxWIR7hOh4ly+fDloLZbLZaA1vnz5krqugzueaAdEX7W/v8+nn37KZDIJXP6HDx8GO2CpfkpFV0Tqn332GdevXw+Oa8vlkrY9b1j3H//jf+Tzzz9nMpmwXC65dOkSs9mMw8ND/tN/+k/cu3eP09NT3n33XeC8F8i/+3f/jqqq2N7e5uzs7A3XvLW1Nbz3/PKXv+T3v/89P//5z/nNb34TkgPvv/9+SFg45/jX//pfh4pU0zThWqIo4ubNm+R5zt27d8N9dXp6Gprjyf1c13Xosr5YLPjJT34S7jHRwon73wcffMC7777L3bt3OTo6ouu60B9EGtpJlUwaQW5uboZkiXQOz/Oc5XLJ3t5eCIx3d3c5ODjgrbfe4tWrV3z11VfhGtq25fXr13z77bf0fR80LPIsl+STUipUyMRaNs9z+r7n8uXLrK+v81//63/l8uXLHB4ehj4+QkFO05TVasXly5cBgq7Fex/OV1zE0jTl1q1boRKWJAnPnz8PFC+tv+8c/s9tPxix5MR0kzHlomWaeEod0aoeIsuyN5hI07dLEh9he4sZZUTW0JU1flmy8p6qbjBKkbc5cb6G8i2+VzgT0ZyekI83UZnCNx15qohcTBoZqromaXsmmcIbw2l3iusgchFRpmhjTVEpJn1B6Q29L5kkGWblqXtHn3u08xht6ano2548nqBSTWs7rCtp6xX7c8VaPiKNLauyoi4a4qZnsYDTbol3LXEyoq9boqTBuoZRFONHEV29okkUrothLcbVJ9StQ/mWXnWgG6qyJxuPsJXiqKpovULXioXx+EgzTVNWdUkcGVQ8xhnLYv6SWDV0dLSuZxKnNKuSXif0LiUuE9K4w1jNWZOQzkbMmwpvI3rrWItTirliWcwxY0uceCJtKcuakcmomg7XNagsp7EJTXOGaqf0cY/TS4rjBh9Nabse5SvicUw0UlA3RKUmz0ZYZ6hdSVGXjFxEozQZGZEBZ1oSbUjWoW4a+jIm20jI05ikdDh13ntCxxpXJJy1MIkzVNWQR56yL7GlxinDeDNj4lLW99Y5qyravsX4mNZ7IjcGD3F7Xn1o+xqjUzrbYc2YsuhRvaJLLGkScfpsTj5K0AZUpHAnnqxv6dSCvouJfU7ZaArXY7RjdXJGFGcYBd3xgnGmqaqILM3QpqXPa1pjWcznRF3OeLJBd9xTZS1tMeesUGzvTkhUjMay9IauqIn7nhSHdtBnLWQG0zQktWYaOwq3gHiNkZrQnrXM1jZpmx/Gi/yXuA0pQxctRIcgWH5+MYAQ8DQUtUolYljRGDrrCD1J6FACrkXXIGBVXvYCEiXTNAT4i8WCZ8+eBRGl/HxIB5NNQP8wGy7nK+B4GERJZWXIqxauuWSjBDRKBUaA6VAXIud70UVK9i/AWY4rAET4/QLmZT9yDJm3oWZAQL9UdeS6hpoAuQ6ZXwneZF4k+BgGoENQLucpAYAcS+Z9eO0S/Ml1it5G1p6Mj4zhRYqSVFSk0iU/k3lZrVZ8/vnnfPXVV0FULzoLoU2Jy8yw0iYAX65feolcdKSS9SBjKEHpMFCT6x5SqoYCeAHFMjayJmXfwyD8x7iJuLVpmpCpHjbh297eDoHr48eP0VozGo3C54WWd+XKFT7//HNu374dngFCOTk9PQ3c+aH9p/DS33nnHU5PT1mtVrRty+bmJkqdi8t/8pOf8P777/PNN98Ei1nvfTBfiOOYDz/8EGNMAMDi/APw+PFjrLXcunWLg4OD0D9mMplwcnLCjRs3ODk5YWtri9/+9rdsb2+/EQAURcHJyQl5nnPt2jUODg6CPkvu+5cvX3Ljxo03gOZoNKLrupA57/ueO3fuvNGPRNy4Njc3uXfvHtIMcX9/P2gwJLCbzWbs7u4ymUzY3NwMlR4g0KwODw/51a9+FTLmURTxs5/9jKIomM1mPHjwgGvXrvHLX/6Stm2ZzWZhjIUSdOPGjdApu2kalstlANPGmCBiFkH/69evg6BbqmGz2Qwg0JhE+yCUTHmWiKBaAhQxBpF7Xah70pVbKHrHx8csl0uSJOHzzz9nd3eX7e1tFosFp6en3L9/n2+++SasQanGJUnCcrnkF7/4BU+fPmU0GrGxsUGapty7dy/oK7IsYz6f4/15v43ZbMbW1hben1vcZlnG48ePefToEf/m3/yboCEZPpe8P3c2GzoBfv7558G1LU3T0NjynXfe4cmTJ2xu/rDGwT/4aeNtTLMqsC6GXmFtzaop6DqwswSVgqk6+uK82Vk77+jaEh1rorUIXWt01zGOYuoWjC1RXYdOI2rdkvmEsmvQvaVsW+Ztw1Y0oewKaDRdO2KVV9hmSRslGJ3gfYeKFF3vmKiMrIMo6mk7j6o6ktYCMYt+SRwnzEaKA1djlKGeH1DpBp1P6OuSJMlQ1Yqny2M2RinUPdVpy1nT0yUR5Ib29ZzUG5xROJfQFCW1PyE1Ka3JaNIMtWrpmmMqChIUyhm6yjCdKvqiwCcFXevxdo1u1THJDZ0zmBZOuhVrow0y1eOaGoqKFENnKlSxJNqb0Bz1RF2CS0tqa/FuiekcaTSmyUbUPiJyK2pV0PcRxZnGV56ra1tU/RmsOvwsx/eeRdERRzXRmqWlZ5I2LLueyhdYq2ibhjhPmKSeRV1iO42KIuZnc5QxbGysM2870gZ659jcXKN2mlgbitrSzzsaBTqD1HiM80S5oSpqCl2xUYxQfUw6yWiqFTmKMtKU9QLf19Ta42JDHo/Y2Ngk3kmZFDHVvCTNFMa1+N5QVZDEI1JrcBX0boYzBUUxJ17L/rFpI/SRJW0siY8obEHnIfMW1XqUVYwiz7yERmlis4ajwhcladRBBK1vyJqISPWsqg6inF57TBxRL6ErWqI8JUoTyrzFlRrjS2aTNSapRdue5UqjMzgrS7JThe0Vauax4wSDBavwOsNEEFtHVy7pxzl0hixfp7UNqbryQ2/bf3HbUBcw1FkIyBTANBT7ystRAJIAs6HgVfYt2g7ZlwA+AWvC35bSuIBn+D6oEbA6pK5IVkgsLh89esSVK1dYW1sLwPVihUCua6gxkYf7xf4Skp0eVkLkgS/XOhyvYTAhIGn4XRkPGR9xQZLAQoCE/Gwoqh/S0oYUq6ETkozhxWqLnJu1Nrygh03VpHI0BMMXaXEyjlKBkDkYBjTDKoW87GV/Q9qUVGkksJHzk6rDkIIkgZzQOIbrQjQZv/vd7/j666+ZTqdBfCqAI8/zUC2TisUwuJHrH/ZAES2PrFepoMi/hyBguI6G1RwgdGWXAHoY4AznZnj//FipUzL/EjRI1ng6nbKzs8Pz58+J45iiKIJFrTjOSZCilArUmQcPHgTOfVVVrK2tMZ/PwxxL5WR/f5/d3d3gIHTjxg0eP34cAHbXdSFTfenSpRDwPH78mI8++oiyLHn9+nVosjd0elpfXw+9HiQIkoZ43333XaiK5HnO69evWV9f5+nTp/R9z6tXryiKgizLuHz5crCAlh4N//pf/+sgJBea1a1bt9Bah0rFcrkM9qyy7qRaGscxu7u7wQHp5OSEt956Kwi/JbCQtShWvqJlUEoFECzgWRyWpGo0m80YjUbBDEISL1K5EaqUVD0///xztre3gyNfnuesra3x4sULsizj6OiIsix5/PgxN2/exBgT7FulX4RzjrIs36hoWGv567/+a9bW1hiPx+F4fd+zublJWZZ88MEHfPvtt2/QReXZJTRUoRilacp3333H9vY2cRwzm824e/cuq9XqDcvjJ0+eBDrXeDwOVQPR2NR1zQcffBCu4/79+1y9ejVoQaqq4uDggL29PS5duhQCrGvXrpEkCUVRMJlM+OCDD8jznJcvX+L9uVOYPJPl+SHP0ziOgw6o73tevHjBfD5nc3OTp0+fsrW1FSha/9z2gwONlT8mcxugOryNaZsVeZ7RFT0pnqi3lGjGuWXpKvLJNt2ipWkrVFnTLGtiZdCNpWCBbTqoDX1sGWcZSsGyrYhxqBrS6YTFWYWNI+LYoXxFU9fYdITtNL5tMOOeSsU0Tcu20axWHXrm0EqjtKNrHWMiEjthlBp8uSRyBYkeEacpzrUkdMTWopSjbzu6JKXoY7q+ZhQnNMpQzFt80zCKN1Ddkqau6EgZ5SPitsM1higyFE1JToIqNLqKyLMY71qaqsdnPXaywarr6PQKbVJM1FJR4ZuSJInJJhld3+PUCm08talAaRZHjqKJiWrojEInmrhVxDaC2FIrQ+Z6kkjTLQuq+ARlHEk1RcVg6FhYBy3gWlRxXjImq8jziGXU4bwmjyIYjTmpDLFXaAW9Maz6FX1kaJwmVQatRsS9oayW5GsRbb2kpcUdKbw1uIlFFw04TTqJyBJYVpokhZ4V2hk2lcG6mnSW0ugIC+j4DFcqXGuYro3x8ZixiRlFGVvTCa5QVE2NXVtjVZ6SNICucLEjGRt6NE3XoxrQWUw02aBtKnyuMUmHqSqsj2h7RTae0bclbe+gM3QUxFvrTKyhjho6NK5PoOswI0VX10QpdHWPNiOafkXfVjRdTWYT+khjWxibCaUqaDpLOh7hi55GeXTWUNJh4pQ4NmzlKdk4pTlqcHFLHHtMb1Gdom1Koil03jHduErfJyjvUWmHNgmm+fFmI4fl6mEme6jJGALXIYgectWHVqdDW88hVUiONxS/SqAh5Wh5uErGfyhGlr+HPxMQKR12lVKsra2F4Mk5FyonwBuZ46FGQc5NrkNechIUCYgXOseQ6zw8LzkfAdhDYCkBhQAzOZ8hlWw4xkNKFHzPU5ZzFQAuoHioxZDzHQrvhe8u3xP3nSHVa3hcATcCMgRwDPnc8nKXgEOqYrIWJGAYBgnDNSGVILl2oWhIgCpjK3QWGauzszN+//vf8+zZM65evRo89kX7kKbpGyBKrv8iyJfK2jCQk03WvlyfjKmsexmb4XnKPA2rMRKcylwMXcgEOA0zmT/G7eDggJ2dnaClunTpUnAAmkwmtG1LnufcunWLJ0+e0DQNH330EY8ePeL4+Jjt7W0+++wzPvnkE+I45v79+/z0pz/l8PCQBw8ecOXKlUAngfMxvnr1KnD+vKmqiul0yjvvvMM333wTKHOyb/mZtefdnR8/fhzuQ3GHGo1GvP322/T9eUd5oa4odW7H673n4OCA2WzGBx98wN27d3n77be5c+dOoHpNp9MgyP75z3/OV199FeiX0kdBumAD/6iJrMK9cO/ePbquY2tr6w3qpHOOs7Mz3n//fb777jvu37/PzZs3ef36NVmWcf36df7P//P/DH0WpDHcdDpluVwSxzE//elPg7X4dDrl6dOnwaVKdExFUQAExymZvzzPg64F4N69e6yvr/Pll1+GDtxAoIylacqTJ08ChU7WuYjkHzx4wHg8DuJxocsdHx+zu7vL/v4+Wmtu3rzJu+++y61bt/jtb38bKtxd1/Hee+/x3XffcffuXebzOZ988gmfffYZq9UqNM0Tkbkx57a1Z2dnbG5u8vbbb4fEh5yT9G45Pj4OAcx8PudnP/sZr1+/ZmdnJ5hzvHz5kqtXr4bq18nJCdeuXWN7e5uDgwMuX76M1pr5fE5Zluzv73P9+nWstdy9e5c///M/5/HjxxweHjKbzXj33XdDp/W9vT1OTk5CVUeCcGmq+qtf/Yp79+7RNA0bGxskSRLm9YduPxix2DhjEkXMFwv6pQM1wgOx7aldQ112qGSCrzumSU5VnmLzjKr255+3GpfEzOuOdKKhd2ijYdmiuoheL8g3c1ztydMYnKGNDE5rtFGo3JKWHX2/JFcZdQ9Ro4j7Hlt19NkZravJ/QZ9f0RkLL1tedmD9QnFoqKxhnScsvQQ14Y4nkB3Pgpl35PmhjztWDaGyGvMpGHKCG0cttPMXxcsV5okjvG6AW8oVzF9o2jjGps0NK6n7xX5JKZfLeh7h1YxlYopVivS3qOnOVHraLqGsqiwscNnNbpwaF9SR4a+9SzahkmeYcmwcYo/PCN2ESbNcKYkTzROa7puRZ8AXUbqW6JI4WOL0x6/KFFxjxtlLFaaJIvpupK18QbtAsqup1EKr+D4uMJnGT01URpT1S1rfUtLineWWCVo1zPLRvRFiyshcSUmazhTilVkQXWkdcJoHAEFC1XQupx4nMNI0ZUFU5+hVp5sFNMu5tAqXLKiTccktaZtG9JRTJ6OyOIxaa7oov7cJMBaVvOWzXydJG1YcMLYrtEUPat2RVIbzCRF9QrwJEWGn6+IdhylBTpD1K/ofYxNIpSOMbWmtRFt06CWnj72EBnWrKVOIvquJ8bgqhKnM4quQ9mIzFckPqJpPN5UTKcJxhc43dAeVGTJJpGLaVWO0Q3RymONJa0roiRm6VfoWYqz4CKoUCR9zGickCmF8pamScgyRWc7vLGkUYae/L+6x/9FbZIlExtZydQOwdMQeAoYuggChyBVgO9F8fWQOiW/kwqHuNBI4yvZt2SehxoN4S4L+FtbWwPOBeJPnjwJ4EE+NxTkyrkI31Yy2PLvqqreyL7Ld4RiNtRVSJAhAFboFkN603As5bjD65cM6lCrIGNalmUA12VZvjHmcn5DitTFYEVeoBezY5Ihuyi6Hzp4yTEEaAt1CwjjcxF8yzXLXA/B+3AMZP3I8SUIGPbPGJ6zBCCyn6OjI+7cucPr168Zj8chyBiuDwnqJMi4qE2R65djys+GAdIwOJKqjwRx/5Q4XOZG1r+Mjax7WWcXBeTDOfixbru7u+Gahbf/hz/8gdu3b1NVFTdu3AigWigqT58+5dKlS6xWK5Ik4S/+4i8C0O26jpOTE7ruvNmdjKsEubKOhTr1+eef884774TuyN988w2j0Yi9vT2+/PJLVqsVd+/e5de//jXPnj3j+vXrvHz5kt3d3eDy1HUdR0dHoYKaZVlwSzLGMJvN+N3vfofW53arTdME0bOsVQHPIhIW4L29vc3Tp085ODgITQBlLUhGent7G+dc0LEINfDmzZvhOodBzWKxCNqBZ8+eBbcsoY8J9altW1arFQ8fPuQnP/kJVVWxWCyCPuHw8JDT01N+//vfs7a2xv7+Pk3T8O677xJFUXCmStOU+XzO7u4up6eniH2z957VasXp6WmwEz44OGBtbY3Lly+HYEUqRK9fvw6dsgG2trbCc2Jvb48oitjd3SXLMp48eUKSJJRlyWKxCBSqt956i6OjI0ajEcvlkl//+tchGBRa6MHBAXmec3R0BMA333wTLGifP3/O3t4e1lpu377N8+fP+fTTTzk6Ogoi/7IsWVtbC0G0WOnK8/vhw4dsbm7S9z3/7t/9O+7evcsHH3zAN998E6i+MtdvvfVWeKadnp6GjufiJiWmKJKYmk6nbG9vc3h4yP7+Pm+99VZ4Lr969SrMgVBDpcLxQ5MVPzjQKJWlbgucNnhiEh3T6SVEELWOJB3TNZq+V6iiI08t8+MalUeMIovVK+beUXtNvmrw/YqFz7CzjKaA0k5I5gs8GU1e0DaeyCagavwEorIlV7BqDT7yuJHFEWG7jo3RhMLU2A0o25JcjWnPljjV4H2DP85xSUJnc+quwmc9Oo4w9T82s2p6OmUoKo0uF/jMolVPksaU2pJSkPQZiYtpbEzTn7Dsj+mqjla11KOENJmh3YLOOOpmRdtF6HyESzapuwO6viSNLUndMC9q2l6h25rc9pBm2LYkjR2rviN2lhUtIz3CNR2+XpKOU5SOqAuH0TFO9/jSE6UdaTyj7lvUwoPz1CfgG422PU4ZVsoTFQ3T3Zzu5IQ4ianqOaprqCtQowRsg86gPi3Qo4SzVYM3LSpy+M7TuwZGNftFycSNcK4lG0eUEZhSkfcJ3jX43YS6XjKxE5zqmZgxtTE0tSaKFKnJsbT4UUbhPONok7J5RetjIjdhNovIL0WM1jexaoJJPMWqwGHJs5huucBEEY6WronQ+QbONeRpjHJgNyzL1WvSJMKOIpyKoDXYk5Z8GnG4XJH0kK/HODpc2WAjyzRXNIuOPkpx2qN8i4o0SZygmjkmzsBntLFCRwrlW6xJiHyFQdORUakFE6dwC0+5OGOcZrg8Iotaosiikhi8wTQJ3jt0OsKiSK3G9YrNKMJF5lwL4h3FaQW6QZkMl1gir+hWFX0aMfmROtwOBa4CDgX8ChgUACkgbSj4FnA8tKMdZn3h+yz2sHog2XYBo0NAODyvi9oO2YcAdaESrK2thczjgwcP2NvbY3d3N2SRh1n2i+BXzknE30MR/D9lZSrXJ+BctiH4lvG7WCEaBi1DOtBQqCzUIhmLoWBYAgmxBB2O/dCeVq51KHoeUnOGmXyZNwkuqqp6Y/6G4muhgQz1IQKcpTom5zp0vJLsvQQvAvIvVhMksBgGuEOa2osXL/jTn/5EXdesra0FJ5lhhW0o3pZ/y9gM+2kMA5GhQFvOdTj/w20YNF0MyId0QQng4U19kFSdZF0M5/DHuIkFZ1mWXLlyBeccRVGwu7vLzZs3OTo6Is9ztNZ8/fXXb1QqxTHKe8+3334bRNDvvfdesGkVfvyLFy+YzWYodd7LQMTZzjlu374dMuuz2YzxeIzWmtevX7+hATo6OuLhw4esVit+/vOfs1wuA/jXWvP8+XN++ctfMh6POT4+5o9//CNRFHH58mW++OILrly5wv7+PteuXePrr7/mgw8+4NmzZ0HcfevWLf74xz+GXj+S9JhOpxwdHYXeHk+ePOHKlSvBOevXv/41z58/Z3t7m7quA8C/desWT58+ZbFYUFUV6+vrnJ2dAQTbVxGMS++R27dv891339H3faCziTOT2PRmWcbNmzf527/9WzY2NhiPx1y6dCk8X5RSLBaLkHyRZ8qlS5eo6zpQoTY2NgKgFsOJW7dusVgsaJomuCctFgsmkwmvX78O94SI/LuuY3t7G2MMOzs7XL58mT/96U8B5E+n0xCQHh8fc/nyZZqmCa5Zxpy7Yg17iOR5zmw2I0kStra2QuDz3XffhYqJNCt9+fIlR0dHb9A8T05OKIqCf/Wv/lU45pMnTwBCl/LxeEzf9/zkJz/h3r17AGFtSPAC32sWd3Z2WC6XXL9+PVCuRCdzcHDA+++/HxohbmxshAq9iN+lmaQEdhLQSnJOGrD+kO0HBxqjM00/0YzjGBUrvK44Pq5IR2OyvoHTJfEspesTqr5l2bdk6xbX9bQs6fIMznLGyjBO4XDeE2U5y7YlykbocY/zlqjrMPEY6oa2MqyPE1gVuLaiVzCejekLT1+VmCSjqDt67amzEa7TaEpIM1Sfg8vI6pZWNWQ2o+1WeO1ZNxnLVYvvEjAKbyCeppwt5kT1BL9SdC6m8CVx7vHTbaKuRJeHxLqnUVNOD89QbYXNFF4poqLFJgmeFclUs3jlyJOYxcE+UabIsoS+0VTOEXcO2iWrrEf3I/pFg92c0sYRzbzC+Z5MG3zTUrqaHoMpPGmSUyenFP0ZXR3RFjF529OPVqgoJR45tIkZlVD0PfEkYlUscMuEleuxbYH2Eaa2tGrB+iiFYkHhDUk0pu+XqKQl3ZzRnxh8CWclWNWRmJiq6diNU6qoJZnMcK2hiS1plOIry0jn+E7hTUljK1TZojPFOMo4LEq+/f1jfFzy85+9g8lS+pOe0sxh3TCdrBOvbZPNIjYjC7UjpqPpFVpPcFVN0R6iEoMpOla2IVMZsY+gMZzNF6jc4lNwfUdTjPCxRtHjbUpja2IP8ThllGS0VUnrlyTJCGpFimK6NaEoYNHU9KVDpxqdRBiboiJDbxxRYmmKCuUcvc1oU3OuU+oNebaDrjx54plc2iSdpfRRTdQqfN/g0jHeGiZ5RutrXNXRVIp4FuGbBhU5TK8BRWcMLs+JI4uiQ2tH3xt04mh/nPgA+B4oCS1nCOSH1CQBbRcz1QICBWQJwB4CKAG5AuyGYthhlUSyuwJkBSQKQJZs8vCzkj2WlwucvwhevHhB0zTcunWLLMvCdQ4tHIeAU5xFhhoR+fkwizX83bDiMex1MaycDIH1EOzLPiXLLdcn1yjnIYHDxWqFjCt8b2sr2S3JcElQIHMyDNSGoHdYhRoGY8O5kW7Ew+/LdyVTN6QBDSsCEjxJVURA2ZA2NqzyyDZcJ8YYHj16FLoz7+zshF4iAtJlfck+pVon4zdck7JJ0DisTgxF98N7RM5xeF8MKyEydhfXxkVdhqwLGedhgP9j3K5evRo6cC+XyzfWhdyLaZpycHAQnI92dnZYrVbM53P29vYw5rzx3fHxMb/73e/46U9/ynw+5+joiBs3buC9DwLYqqqYz+fBTUgsbeu65uzsjBs3brC9vc2f/vQnjo+PQ0Xk+fPnoTnf1tYWp6enbG9vc3x8zHw+D80dnz59Cpx3aLb2vJGoWONK8LNcLtnZ2eHRo0dorbl8+TLPnz/n7/7u75jNZiEIXl9f5/Xr1zx48CDcZxLk3L9/n/F4zP7+PtPpNIDxw8NDXrx4wYcffshyuQx9GKIoCgB5tVrx/Plzrl69ynfffUee54zHY8qy5N69e6GKJL0WJAgqioLlchmsaff29qjrmqIo2NjY4MWLF7z77rv8wz/8A7PZLND/RPt0cnISup53Xcd4PKaqqiDab5qGR48eEUUR6+vrIeuutQ6agvfee49Xr1694ZYkvViOj48D6Af49a9/zZ07d0KfiizL2N/fD3TfNE156623uHv3LkVRvFEV/fzzz/nLv/xLHj58yGg04unTp6GyINqcxWLBF198ESieo9GI/+F/+B/4/e9/z3Q6DZ3XDw4O2N3d5cmTJ7z99tuhp8mVK+f6zDzPieOYn/zkJyFAkB4kQuUU+9qyLLHWhu7heZ6HKsvu7i5FUYRn1tnZGbu7u5RlycHBAfv7+3zyyScAwZRAgsg8z/+/d52KRjHOd5ycHrM2GVP3mjU9IeoUbZbTRzll39N0C0ajCca2FDWoZkqjTrHLhjRLSFVP1VboNIHesJaOOGlWJLXFdoqu8/iuR2NZH4ExPSt/hkln6NZSdR5Mj/UaHyX0uqVvG6LG03U1xid4m2JVQ02D2pgRuTmu00yNxScdvWuJ8xbdGvA5TVPSLit6E9PPDFnUkyxqVksDqaJzPaQJzDbJVYxaFbwz+YCnr5+hix69WmAoWRYFSZLjVES8EdOvWmZJi6JntYgY5xVsasojh1lLsa5Gz6HC8Hq5IGLGxCbotKcuzui0pvOW9bWE1bLiaOGIXU4c9ZwuOmbrE/LYYcY9R8uGpYJctdSrFqs09XyJo8OnGbaJiZc1RhtOilOiyZTTWFGPDBZF27SkyZg0hsqBUXNGzEgajx/1VKpBOYdLUlAQ6xwzSVBuQRNplo1i6lums579VUekLD6L2BhNmbaKT7/+7/y3P35NU5b4esm//cu/IBlHZOtTVBSzk6/TW0tUtkyijCLyNGZJG1uKvmCa53idYesIN7IY3dK4ivnimFm0RZrlxLrlZHVGT4KJPRPX4zx4DY2KqZ0nH0Uk/RJrI3qX0TuHW4s5VRUj1dN5xXQtJ95O6VfQmA7FBBpHlJckStPZhLLraKOYzs7IVUOfVNQdjGLLTEfYKKVaVsTxCBNVRDahrix2ltA2Gh3FxFGDT8DisNbgVYyPQcXg8cSxom9bvIrRraP3HmcjnPvx9tEQEDoUR0vQMARSFwOMIcAX0ClgW0q8Q9AqzZ2GGXzJZg8rFheBtGR/h9x5AfYXwffFisdyueTu3buhU+0w2yz7GbosSZZZHJyG2WfgjWzfMMst5ynuVPL/oWPRENQL0B4GJBJYSNAgXXwleJLeE3IMCYCGFKdhXwmZBwHW8rnh3EhQB9/3QhAqk4zTRTcp+dzQklYoRSKSlPU0rJRJxUT2L8eW8ZB9D6slAubLsuQf/uEfePnyJXmeBwA4/O6QMiXHkABF5kgE5/C96F2uXdbgxbkbztEwSB3SAodif7mm4dzLfuWeGbp0iWbmxxxoAKHho1gge+/DPXfz5s0wJzKGa2tr9H3Py5cviaKIR48esbu7i7WWjz76KFQAdnd3mU6nPH/+nLfeeivQgNI05fHjx7z//vucnp4GpyWZZwkwf/GLX/Dtt99y6dIlvvrqq6Dx+O6779jZ2eGzzz5ja2sLY0xwWpL5KcvyDY2EdKmWwKZpGtbX14OoXNbf5uYmBwcHQYsQRREnJych+Oi6jvv374fM9FAj8vz5c6qq4he/+EXQLhhj2NzcJIoiHj58yKVLl3j+/DlFUXB0dMTa2ho3b97kzp07QesgtLLT01MODg7IsowvvviC1WrFZDIJYDnPc549e0ae5yRJEmxjZa2KOFtoUaKRUOrcFU/uoZs3b4YO4Fprtre3mc/ntG0bdApSbZIGgMaYYFcs1Kohde53v/sdv/jFL0JXc611cNqSZ+hsNuOrr74KFdo4jtnc3GRjYyNUt2/dusXa2hovX77k5OQkrNflcsm3334bzluc5+7cucP169eJooiyLEMAJZUbMSjo+54HDx6wvb0dnnmfffYZo9GI3/3ud4zHY27dukVVVcFNS6x9r1y5QhRFoZ+MGCA0TUMcx0wmk9DwcLFYhEBwfX0d7z2LxSIIweUdXBTFG26K/0/b/4tAYw3XHJKvjak6T2QVXdfSNSm9glKd+yyN4pxlt2SsDdN4wvFyiVYeZS2Nd/g8wrsYnKaPoFtWjPOIdtnQpxmLRcHmtqXpHLWvieoWFU85qzumOqIpoDYdiXMkq4r1yNEsVyid0EURtVboSpNpQ1M1JK2HRtHbEq1iXFPTa09relJfYSc7dPMe4zJmFipKpnj6uCHNDY2zRP68edp4bNEluDyjbjv21q7QRhX93hYv9h+j+obIW9TZHN22dKojyjQq2yQtHKv6NbqzOAVdX2HinKJoSG2CQmOswWNxfUZRNTh6VquGyGT0qqLpPVnvyFqFncSUtqVrDaMuI1/racoOhSdJoes0Ra+wscFFDaNWs+w7sihmNpnRVg2NUbh0itcKW3f4pqGYVygbkZLg6Ji7BVSeUTalw9IpS+dbTv0pcZsycg2GFEtJklhMNCZJFabQTMcpTdXx3eEBi9clb0+u8OXhnzh+fcSoyYkzzWS0wXQ6w9QdKzxJMqboahapYWzPe6bkKmJpHKlSZLahti2pNphYMe4ndF2DSTQuSmH/CB0pqswzcxZ6S28burLBeo8yEY1vcd6xMV6j0RUmUrhe0UQ9ySglNhFer2BimdgRVWvQqoeqgl7BJCPtDbmLqVpHmhlc7zGZpbcpVAqje0zaERmLJcWqCAzQ9CitqfsWrTpUV2PyGTqJ6RYdPj5vcth2DUpnxJ1Hxx2t8XjnsRqm5scr4hzqJIaAC3iD0jF0CRIgP6RYwffdkIHw4JXPSWAgxxTnoyG9Regqw32I25Fk/mUfAiYEwA3pOUqpQKs4PT3lm2++YW9vLzzcBWTK54ecewGRw2z+sJojYmU5NwE0Q4qUfO+f0msMAa68yCVoyfM8eOqvVqsQdAhn/GKgN+wDMnQGGwYHAt4luBCgO6QQDa/5oruUVJaG4FrGSwIfOZYEpxfnQgK1YQVpWA0bCtDlO3Ier1+/DpaOk8kkZFZlXETfIEGEzAm82aFegImcqxxrOB/DoGS4LoeVjYvrY1i5kDU/vDbZ3z9VpZN7Y1iV+TFuw2QEELLum5ubrK+v85//83/mL//yLzk6OkIpFXqq5HkeALXW52LcyWRClmUB4EkQube3x97eHqenp6yvr/Ppp58ym80oyzJYhUqwLxqAd999l52dHUajEaenpwHsf/bZZ2xubvJf/st/YTabhbUr+ort7W1ev34dtBlDrRJ8H5iK7uLFixfcunUrNMHs+z64Qm1ubvKTn/yEv//7vw+dnt955x3W19cDven69euhGWGWZdy4cYOXL19y+/btQCkTzcU777wTGtGJ09Lm5iZ3794NndPFeeno6ChQyFarFVEU8eGHH3J8fBzA8v7+Pu+8807QfEj2Xao+8/k8JBKkWao8t6SPRhRFnJ6eBnqaAHCpekigsLm5GaiOzjnu3bvH1atX39D2yf3VNE1w9JJqyPXr18O7arlcYq0NDmTiXiY2sa9fvw7PUWkWeOPGDebzefjz+PFjyrIMJgZ1XfPzn/882DNLPxbp0i5rQd6VH330EV9++SV1XWOt5ZNPPuGLL74ACDS0w8PDUI2RpIk4dy0Wi9B4djKZBNczce6SHidSURKTk+l0yv3790PX9OGzczqd/qB79odrNMpDIu9xNTjVsOgquniCSjNUc0SWGlS1wLoeGk1sxvQeVOJIs4yobFhaTa/cOW/eNlhb0WcxWRThfEObtIwmPUpXqERTeE3eOvRKk6YJyli8as9BZdGdN2k76/FRRmNysiwhVg7qExatxeoJka1pbITrG4pqiWsqpuOYvlbY0TpdWZPEBsolOENuY9JGo/yUljkVLREWXWpy51n1Nc2qJRvlTDZSmjLibKXYvW44K07oT+Y0PmEyHbFolng6TOpZFKeMtaVvE5q+JW0yGq+ZbOc0y46EjBNXoLqYRFvStRl9dYZqFKu6xOiaaZYytgl0/+iPnvU0haPoOqbKoJuO+lRjtKH3nixTpPEI3ccY2+ByTaMhR9ObFtNH6DbGtBVOt0zimNpE+D5h0XcYo+lqzcTEVFVLayqsmdGVliwyoFs6axmplDiusRGsTmo0YKcJxbxlSUk2WWNn6xJPv/yUq29/xF/86i+4emsLiojx+hptuURHI1Tn8JHB+QxXrahwxFFEoh1W14zGI3zZ4V1MVXdERuEWBa4AG3sak2HHU9Aes+xRaxm9gsR7dLZJq8+wao7WI6rIUfsV2iRoPKNYU/UKYkvbK/reYlKNp0QZDSolHm+i6pJKx1hjiesOE4PVilWriZME10WkaNCgcuitxtUOry2L+oy1NAOnia1C6QjVeVzT0mtDbz2ZiqDsiKKMVd9jtKLrGiIb0UeaxnsU7p+9X/+lbsPKwTB7KwGEACr59z+VjR/arML3GfUhVWTIs4fvM8oXQb/8PcwUiz5gGBAM+e/i1y6BgLy0pGFbWZY8ffqU5XLJ7du3A29ZqEpDIfIQiMq5ysNfghJ4M9iSLK787mJm/CLIl4BDwJXoEuTFNjyHYSA3pKhJ1lXORY4t4H8oXB9qOIZVDwkMZB9AABlSrRjOicz5MOgUsC6gHwg0KgFoMk7D/UiAIPuT65af1XXNt99+y/3791FKBXtLCQYEnMufYed2ATvDa5LA6mJQdlFjMXQDkuPIepKxHK5zme/hdcq1Ce1kGKRdpB0OKzdD2tiPaRNbUCCAHalu7e/v89FHH1FVFZ988glHR0c45zg5OQnmCXEch94C3vuQAReAK0LpV69eBerMe++9x3K5DB3IL126xM7OTgCaq9WK2WwWBMHvvvtusKB9++23OTg4oKoquq5jsVjw8ccfs7a2xh/+8Ifw7BHwKta88nxQSnF0dMTe3h5N0/Czn/0sZPyVUmxsbAQazPHxcdAPfPLJJ9y/fx/nzh2k1tbWODs745tvvnmjIiABWZqmQeBrreXp06d47wMVVPoyiJ3s0dERZ2dnoV+HdCwXHUdVVRwfH4fO2icnJ4HeI/dmWZZ89tlnoXeJgN/ZbBbGZXd3l/v37zOdToPjkVS0JJEjAvnFYsGHH35IVVWhi/erV6+4dOkSBwcHQYR/+fJl3nvvPV6+fBlskPM8D5W+mzdvslwuQ2NWucelEibBy2g04t133w2C7J2dHV69ekWSJBwfH/PTn/6Uzz//PAQhWuswF9ZaXrx4wfvvv8/z5885PT0N+ou6rkMVSN4dQuMcj8ckScKXX35JnufhuSzJKLHzjaIojNmTJ0/e0K7s7OyEa93c3GRnZ4dvvvkmBHpAoPd2XfcGLU4pxdbWFvfu3eO99977QffsDw40KrNP5q6jnWFpOqIsp7I9I1NhbI6ul4yKJX2Ukk3GuIOKPo+gN6yqM9KqxWbruBaaqsPQYOMZaQqOBq00Se+po5a+b8j8lM57nG4w44SRS/DGo6067z4d5yxiTTKN6b0mUh7fnLtNmVFEt/B4BYWDui+gV0zUBJ9Pzt2YTEvRW5SzZFFEbVccHyxZXzfMk4q+1BidYDvI0hzXNxzWDdk4Z9wZbKOIqg4TGWoibGZJspQ2G7E23qJdFfSFgnlD/fqM2Gh0mmPmhmblUOmIkfd0Y/BphCsKbG/pfIeuPVo5dKJotCfxNdr2LJo5Z0ygUYxzQ9S3ZKMI6pqmX6fL1T92ZgdlEparFb1TmLYH24GrGel1al/TrWe0VQOLQ2ycURtL0XToHNoONApMQT6yYNcobcVoLSMuHFFs0CYGSrxJ6b2l60t0bDHWENNT9QvW9Qi1e5ne1/z5/+/f8vOPPiDb2+bm5QlaAetjiDxO5RhyIs6oq5ooGpHEFh/3jKzCGs980f0jwI6JnKMjoi8MfeTJxgbjW4q2Jp5k+LKl7TWLVUOvFfiYlhVNdcZ4PQJdEZEySy1F6bFJB5FmrCdYl0Ic42mh1+i+INOe0tV0LkI7RW6h1w0qgdZ5Om9Isglg0ErhTIf3Dl9anHFESuPoyCZjMBG+dCgUZdOQZTlGOTrvUPb8b6siaHtGCRQ6omscketJbUrtW3r1w8qV/xI3yfIKmBJQKoBJgKQALQkWJFsvYAt4A4wOOfPDPwLAjDEhYykPbsneDGlbcqxhw7UheJdMsdjZSkZbtAJAEDSenJzw2WefcePGDXZ2dt4QAw//DGk9AqiFByv7k0qGBAVCO5CATbjdsg/5rIyXjIG89GW/AkoFzEsmUihJsu+hCFvA75DaI/8X7YZk04ZOThfnc1jVkmMMqW3DsrwA6OEYSaAxrOpIgDm8flkHErjJOcp3nzx5wt27dzk5OSHLspDJGwL5YaAn9qFibzvUVEhHaTmfYY8MuWZ5wcs9IN+VMRpWYS5S/YYVmWEQJRWfoZmBgMeLAZg4KMkY/Ng2Y0zoqO39ueuPdKCWNTSfz8M4FkUR1ujJyQnb29vs7OxwfHwMwB//+Ef+7M/+LIyHNCETMbhoPe7fv08URVy9epWu6/ibv/mb0NlZxn+1WrG2tvYGeBcwV9d1cGg6OztjY2ODjY2NcI/8/Oc/53e/+x3Xr18P3PdhokQc7o6OjkL17OzsLGT/+77npz/9KZ999lkAylEUcXh4GDLat2/fpq5rXr58yWQyYTKZ8OTJE6Io4u/+7u9IkoSqqhiPx7z//vtMJhO+/vrr0NRQulIvl0suX75MXdcBaGdZxrfffkuappRlyXg8DoGQ0JakkZ1UHaTCIvz/6XQa9CDXrl0D4OnTp6Hh34MHDzg6OuKtt94KgaGA8bZtuXbtGjs7O/yX//JfAk0qiiKePHkSNBxRFPH1119zcHAQul3fvHkzUOGEKiXB0ubmJnt7e3z99ddYa6mqitlsFiq1v/3tb4FzC9/PPvuM3d1dxuMxr1+/5n/9X//X8DmtdUhGyXPDWsujR48AQgNQ0S++fv06VKl2dnbeSJAcHh6GIGJ3dzdUMaSSv1wuuXbtWniviUPW3t4e8/mcra2tIPIvioLvvvsOpVTYZ9u27Ozs0DRNsNqVcZT33M2bN3n48OEPumd/eGdwlUPXEuWWqAMbW9KqQLWavoDMGcz6Ost5Q2cN0WbGqjqldZ4oSVj1itSl2HJBlmk0ntg6SgNJB6ZN8NoTK0VORKs8setI0dRELJnj6hyjItZShW4qTmnpXYZLDbaqSHWL0jF1r2l0R9/OSZtzN6LOaAyeVdPgOk8+ylidVTjV0/XnDe/WN3KKviAqIY0tsV2R/WMGfbWqMSanrCJOypJYtewkCaZuiXOP0pBUmitbeyyigiKeko13abIDKtPy7HhFbAx1s8TZDlRB08akKoZkRHtq0OmS0cSxvshZFgvqyJBkCdYZuq0xWduyaBzjPKctKmIPudU08Yi68ygyjv2SkeppDpfYrkZNHb1zaNWivUHFFarTmLYjVjFq7NAjx0SP6Jeepl8wNy17ZUQXabRfEruUrlWo+YqN9TEHpzVL78lGY/SZp3cF49mIeenII+icY7q2wUY+wY8n6EnMLE4wNy6TpZZRC6dtT53U4ICqo6pW5BsTPI5R0rNCATFFZYlcxyhZxyrDqm2ITItVCQunMQ6yzNPUDZGOUAuF1TGjtSml8ZRtjWtakiTGZlvUdUecpFiXUjeOOOmxLsHVBXFsca4BralpyVyMbnK8rbCpQteeKIrplcVbR0uDbSwRKXQVUazAKIqqI4kSsBCrhNb1qLrFGkW57NHGEcWexDm09lS+PheBqwxtPc53UHi0N/SrgnRi0UrhXXNuihD/ODOR8L3FpoDOISUK3rT5HOoWhtlh2c+QqjOsYAxpKPLZi/SkYfVAwJyU4mUfAvDld8OgZGhTKi+Si5URAQBffPEF6+vrvPPOO6ytrYVzke/LC2fYY2LYtVzOfzh2w7G6+H8B6UPqhdhCDsfwInAfVjQkcBkKxS822ZO/pdfGkB4l4P5iZWZoGyvVIvnOUNMyDEaHAFzA5EUx+UWxvbhqyTwCodO7nNvZ2RlfffUV9+7do+97JpMJo9EoXMewL4bsV5rySafwi78f0qeGYyfrbBhwyNzIGpX/D4OMId2v67o31qdci8yjVMLk2PL7oR5F7rVh9e/Htsl1DN3Hhmtarv3169dcvXqVuq5Dn4i33nqLk5MTvvvuO9bX15nNZvybf/NvgO+NFJxzodOyuAhZa5nNZty4cQOtz/sVfPzxx8B58CIdm40577R99+7d0Blb7E4lKPz888/59ttvKcuS1WrFlStXKMuSO3fuEMcxjx8/pmmaIFZXSnHjxg3u3r3L7du3Q0XgZz/7WXCHevr0KfP5nJcvX4Znh1RzptMpt27d4t69e5ycnAT3ptPT00DxPDs74/Lly9y5cye4G/V9HwTNL168CF2vsyxjNpvx8uVLjDl3YHrnnXcAgnuWuHNJfwdrLTdv3qQsS168eMHR0VGgNx0dHbFarYI+Ru7/tbU1Xr16FbRjEmDt7OwE2pnQt4SuJSLmvu955513OD4+DsmUPM+5evUqL1684Pj4ONBFJbiaTqdBpK+UCkHJ48ePQ3Ipz/MAxnd2doJWRahoH330EdZavv76a46Pj3nvvff4zW9+AxBoeqPRKKwped7K81CqaKKHk/4s0oVe9GKnp6dhja6trfHuu+/y+PHjELBduXIlVM+cc8E9Td5P8/mc5XLJzZs3w7tNLITlWSr6IrG9hfMg/OzsLBgYDLWD/0/bDw40lvMRjYGyOGI9H2FXNRENPk6wrsN3PfNlzdim+Kamq2ts59nczThzLUWsUU5jnUb7HhVPKI4q7FpMs2rpjCLTPfNihYrHVO0SXzvyWQ51R5pomrYhzQxlu8Q7WMtmVIuervcYp0myEUXfoU4b0khBYomIcF1O3c6pooLOWKzxKLOAvCd2Y7IkpvIt7apjupYTqZKTvqY3oLXBdI7R2KFqje9qptOepPc476hWEeOxRs88hhjX1YxdQmwtNgWXzCD1ZOMpfeVZuBLrpvjjBdGapXAlcRMRzwzxyuBXKX3vIbMUqwWRtbTripW3pKR43dClhmS5Ohew1RrVxmRJTe1r8lZBnJGlLaN4nZNyQTL2LIuG0dTSrAry0Q7NKZS+RmWOJJvSnLWgG/JEQRLRuJ4onmGaGKYxaWdAFZSqwkUt1k2Iqp7et1RKsZ7E5Fazk0/IxgnMYiYmIY7XiFFgNdY0qETh9YjM9Jje0tqKtWlGmSo63bFmHU1TYHwKaUyDp608cZRwdtah645F35GujYg7j+ksTWPQ+Tq2atCuI09mlFVJ6VeMyKm6mChylE5jo9k/upPVpH1M5hLm1jGKUnogTgxEjt5FOAe1AY9GuXN5RqxjrPYoFLUyRDiUruj7Dt9bfNcTRdD0PcooIuVx2uG1I1Ye2/cYr1C1I8kyOqUxKkPj0aqn7luM62mNwRnHaKyJlKLta1QUo43FqB+viFO2IW0FeCPrPXTFkZfUMEAYguthhlwytcPPiGhXfjbUAFx8SA4pRsPgRwCw6BaGlQnh0Q7pRULlyfM8vLDFceTKlSu89dZbbwBaOZ5kn6VSMASRQ7A9pHP9U1SnoWZBaDvDzO5Fq1MBy8PxHQLdIS1KQDC86dIkYG8o4Jb9yUtUAjX5vVQZRJR8Uew/nMNh0CSgW/Yla0WOJdxzyVAPKwrSo+P+/fvcuXOHk5MTjDnvWSBOLjJesqakCiCBxZCWJHoNWavDIGNoTiB/S7AhQEKuSdaABCdyLTKPcv7DIONixUnGYXg/DT8n5ze8z36MW13XPHr0KNBT3n33Xba2tqjrOjRUFCqeiJ8l6Lxz5w5pmnL58mUODw8D0FdKhUBU1pAAWuccd+7cYWNjg7quOTw8DBlm6WWxt7cXgPOrV69CNj+OYz755BOWyyU/+9nPuHPnTnDLOjs74/T0lD/96U98+OGH1HUdqiNFUfDw4cM3LJlF47Bardje3ubk5ITnz59zeHjIxx9/zPHxcbBBFcG5AG3RgEjF7fLly1RVxWeffRY6anddx3/4D/+BBw8ecHh4yNHRERsbG2HNSVNE7z13794NVV2p1qyvrwPn1YrpdMqNGzeCkPzu3btYa0MPokuXLjEej3n27BlwDsKHGqZr166xv7/PZDIJ1WmhSB0cHASrVoDr16/jvQ8BgfSvkPkXXUxRFKEJ4LCXhmiwvPd8+OGHjEaj0GFcwL08N6w9b8AodsIvX75kb2+P58+fh/t6Z2cHOA90q6ri9u3bPHv2jHfffZfj42OstWGuh53YxW68bVuyLAv9RiT4kj4+ANeuXSPP8+B81rZt0ImIuYhUYn7/+9+HCpOco6zPtm3DepXqmzGGy5cv89lnn7G+vs63337L5uZmeLaVZcmNGzfouu7/e43GepZTFw1bucG7AjuK0XWEisc0cXH+4KVhXvWkDbTWYu2E6rREaUVmC4z1KAumUzS6wKyDTzLKPiGLOihq8ukWfaSIXYklhpOWOouJtSNVmrKzNAuHyXJs5ehdR99VYCfUzuEaT+8q+s4T+zGFNfiRYz1Zw9dLRkmPbzh3ScpmRG3MQVnTq5bRqKcoaibrGlMrXKOJ0hzTNThnYNLS1B1x70hsS7XqIclZJpoEiCIwqkdFkOoEHzXojRENmsT2oBXLTHFWLJhe2eT4aMHpiaPRDWpxSppvUrWOLmohsiSFwncJnTsjrRpUsk5Seaaa84ZvdU+vEqK4QU88sdIUhw2eDp+WzE1HU/RM2jFp74n6irrvWS5fkZgZvjM0ynJ20KJr0H2CyzqsBz1L6dsC5xKc7VC6wdTQ1p6qi5hlEWtxjMpiJpcztFPsrY0YTcekSYI2ELmIvu4gSolsh2oaXDelTxOc64iWEY6aThWoOAPlKbXB2wm5HtFWFdZrTJxiVU/UNjhyslhT1wrPnMQYOuXo8CS5ZnUCUdzQqZ7UpUysI6kdcWRZizKKo5o+TmFqMapD9REUmt4BeEy+otUZ2uS0fUsUaYyymNbT6JaV70irliiN6FtLZB2t64hIaBYN0cSjVUdEhoJzKlhq6GOL0Q6ta5yOaTqF6jT0LS0eG1m8b4mMom0SbKTwkQMFbatQLsE7DcqgOwPxD71z/2VtAuAE+MD3mXShuQyBs/wtwEhAo3h6X8ygw5uaDQFqw2ZvQyA8/I5kkyXgGFJyhgHG8PyHWgjhYIsWQ16Msp+6rnnw4EEAR1euXAmVEQmosix7Q7Q9tKsdAnE5Pwk6JACQMZMxkjGTgGhIHxoKviVIkOuVgEmufVhdkW1I4REa2tBxahhYyPhkWQYQfiZBiPw9BMtD2oicr8zjcA6B8B1rLaPRKARPct5CeXj69Clff/01L1++DBnDocmAfHZIL5IARICjjPeQnjesyA11NXIOMs7D31+sxsnvLq7FYaAtYzpc+8MMv5yfgMChgH+oa/kxb1VVcfPmzTcojOvr65ycnHD16lVev34dggs4t5/e2Nhga2srUA7he4rUb37zGz788EO6rgsAUxIB8/mcLMv48z//c05PT8OxxJpUNBnL5ZLDw0OyLAt9MF6+fMna2lqoMAhNSCoPQu96/fo1+/v7bGxscP369aAPunz5cshSLxaL4DYUxzFVVQUXK+kpslwuef78OdPplP39/RBsjsdjbty4EeiQm5ubvH79Gu89o9GInZ2dQL+5c+dOED0L3SrLspDdXi6XAVxev36d2WzGH/7wB6qqCh3KZd3O53P29/fJsoxf/vKXPHr0KDynxL66LMvgNCW9IETkfv369aAx0VozHo958eIFN27cCJatAK9evUKp8z4cYl/bdV3oHi7OXuvr69y/f5/t7W1u3LjBnTt3gmbBOReaKlprmUwm4d4S6lyapmxvbwdq0dbWFvv7+2xtbTEejzk4OODw8JB79+6Fe7ZtW05OTtjY2AiULNHM7O7u8t5774XqhwitF4sFH330UTAKkKTET37yk+AwJhWdnZ0dkiQJQZlQ2/q+5/nz5+zv77O3twecN5hdW1sL47m7uwsQrHC7ruPKlSu8evUq9Ira2toKz98sy3j48CG7u7tB9yT7+Oe2Hxxo9K4gn6VEXUpvW+p5Qx/l5K47B011zSTqOUs02uRQncLEoQuI5h02STG7Oc3JitOVZ7w+wtNQ6Zq6syTaYnxCutBUmaOMPNZD0VbYOAJnUEqj2o5RnBIrQ5VC6zpib/DzHj92mF7hpzlVCagIozus9SyKFpUo8tjDyhBlKWh93pzNdUTZmN60lF1HXhumqkUpS1+nFH2Dt4q6gaatWfcpPh6BBq8a2kjjT5dsphMaC8ukIO5TVGEg2sKVB5gkxY5SemcYX91G6wLW1tCLDm97bLHF0/0eY1bgGlZ9S0uPMRGNN4ySit6dQALlaUEbjzGTFOsjkqxjcXyMblNir2mbljzpWFQtvtfUZKTqvMFhFDk629EXBcp6RpGldoq+q4l0ROJjfNbh6oRieYpdN0RKs4ZH2Yx8vM300hYj1zKejckvbbI26TAuRZFCWRKNExQRaEWmO2oPvl/Hq5xWx1g6qGp01ZFMIsgc/fKYLI7pTE5ZtuBLVAUmP89kLJVDrae4yoNfMbbr9G5M1yoavUK1Hp2NUZGnLE5QeUbiIvqmQZmY2mh612LXUkDjVw15lkMckSlHGsX0ONrWE+kYHOgWEqDTFmc8ibb4nnPnKueJI43yjsjEqN6gY4fWPR0xvuhpVEftDI0yeF2gW09kDFGq6JWm0CtUDSpJsCgqF2N9T196jALje1rjcN6S2IhWNeA12vx47W2H4FSytPLzi/QmAW/D0vKwsgDfg9EhgBsKcwXIie5gSK8RUHcxKBlm9eFNAbscQ74nFQgBeBLU/FPVEgE5ZVkGjvDbb7/N3t7e/023IcBdHvJyzGFGXKoBQwApYyh9JOB7G9Sh1a6MmYyFjNewMd7w2DIOw21YORFO8/C6ZV/DLPqwGgW8ETDK+Q3pYjIOAk5EayLgWq7fe/+G5eRQ4yI0kjt37vDdd98FKkae5+EapFohf49GIyaTSfCcF9G6AHYJTqQSJetuGOwNBeBDmt1w3GQ+h8HaUHQvoE3WlawtmdOhtmhYIROQKeMwrGjI/oZr4ce0LRaLNzocP3jwILgN/ft//+9DYCjXvre39wY9JM/zkDVu25YPPviAsiwDrej27dtvZICBAFplTgCuXLkSuO9S2ZjP50RRxPPnzwOXHr43LEiShF/96lfs7+/zzTff8Pr1a+q6ZmNjg2+//ZbFYsEvfvGLYNU6Ho+DLSwQQK5zjk8//ZS1tbUAEmezGffv3yfP80Aj+vDDD9nf3+e//bf/xs7ODnVdc+fOndDkLo5jnj59GnonSOXigw8+CA3jfve737G+vh6c1sTx6fT0lPv37zOZTKiqigcPHoTAYTqdkmVZyJw3TcO1a9eYTCbcv38/uDxJIKTUua5mWC1eLBbMZrPgCibBjojQJ5NJqEArpcK53bp1K3QzFz3JT3/6U/70pz9x/fr1YN+6ubkZniPz+Tw0d/y3//bfcnBwgHPnjRnv3r0bKlbfffdd0IJ8+OGH4VrOzs64d+9eeJ4JXUkcn371q19x9+5dtre3KcuS9fV12rYN/TPEZUqeK9JHZTweh4TGtWvXePHiBVevXgUIAvOTk5Mw3ovFgkuXLvHs2TNOTk7Y3NwMzRWNMZyenobxWltb409/+hPL5TLYNp+enjKbzYL7lfQfkS7tH3/8MV9++WVojvj/ecO+vvcslz1968hyRZd5nK9J2hjtS3yaUFcN2SRGeUfXd6i6QiUxPs0p3BJXzlHGYiJNX9ScmQ6VjEloMbpETxJWrxta61FFQhRH2Kyl6+e0ZETJOZfFRgmdOiVJRtgelI/o10e0VNCviN2ESjuM16y5lgNf0rWeNB3T1hVpB7Q1q7bBMCbNeyp3ilFbjNKC6nRFsjHmtCiIopxlpkmaFanOSPMxqldo22F8RNtZWLXEyQRlPb2zWJXSuQYzntAWJTZeo3BLzKrAWkMWx7Qo1vKUSWo5rXvSTU2ULNC65uykJO+OKJMx5XzB2I/oXQYqotANI5tSNY7F6RHORFg1oYvHuLLAGkXqYyyexLaoiaGuK/Q4xvQ9nYnBxFRdQ5JNsDahr0pm6ymtbuh9Tz6aYOOOnbVt8nyGX/Xsvr0L3RrjcUI01Xhi4s2U3nhGZUxNQ3Omma5PIG1pXYVatXhzTptyjQUVkZmepi2wUUSfd0TpJvQL4umYHkhdRz4aMa9XxOkEqzWqqYjSjMrXdH1JtKwxGy1V50mNoe4VUZ5S9jXTaA3jFEtTY5oOrc4F45aOyEWkRuMtmHRCh8M7B8qhrDrvzaIy+rKCJMMYjXegnYO0x6/ARTEu1ig8vm+ocSTRuYi/1uA6g8NB4/GzmNR7MB7P6JxyFbU4A6kz6K5FpxnWdPR1h+0tURoRjTpc3OKdQ/UJUQLGeXyfo/r2XNfy49RxvgHwBbhepAkNM71DByMB1wJsBfQNs/hDYDzsjyAvpKG150XgJUBxSO0ZcuQlmyjnCt9zwocgfkjpkaBBax2Av3zn+PiYsiyDfaS8zAWISwAhzfakkiHBjVQJpGGeOKMI8JHM7RDsy3mJYHlYmZH5GLokyfkO9TRDfczFqsaQFjTU0wypXsAbFrTD48i8SSVoWCUSaovoZIYgW8ZejiE0ttPTUx4+fBisOcXGUSogco7DoCHPcyaTSeCjx3FMnuchqBmOx1A7M6R8SRVpWAka0sCkWiT9SiToGNLUhnNwUbg/HLuLa1k6rctYC61sGGwMg+Yf2za0MBX6SVmW7O3tBXcdqWwtFovgSDSfz9+4x+WP1jroKYTKIveWgLP9/X3efffdcA5aa+7fvx8a1X3xxRdcv34d51xYQ2dnZ+EYUiF79eoVZVlyenpKHMf81V/9FZ999hl3794N1yEZZbHcvXHjBicnJ0H0v1qtQrVG+lR4f95g8J133gnPgslkEqx1d3Z2uH37dgDYQiWSJnkCbAXsv/322+GeuHTpEpPJhK2tLR4+fIhzjn/1r/5V6N0hY3h4eMjz588ZjUYURUFZlmxtbQWb6CdPngTnryRJePz4MYvFgv/0n/4Tr1+/Dpa6ItCW5IkAc0nsyLPt9evXAKytrYX7KE3TsK+PP/447OfRo0f0fc/BwQE7Ozuhr4mIyaUaKA5Nt2/fpmkaHj9+TJ7nVFVFURQhqRDHMX/84x/5+uuvefr0KcfHx0H0v1qtQlX15s2bnJychM7joqPy/tx4Y9jpXN5LzrlQmXDOhcBNaHNVVfHdd9+FZ5S1NgTCWp87C4pzorw7oigKXc8lmDk4OODq1asYY5jP57z99tvEcczJyQnWWh4+fMirV6/48z//8/AuevXqVaCPNU0TDBX+ue0HBxpxo+hVTZ9GGJug+ghnLaX3jGdrzOsCZWMW3Yo4zrBxinIRTRZRdC2ph80o59BpzNjRlSWzOKI9rJmsGZZlh7YpTinisiPXKatVSZeOUJQUVcE436SNSopqxVo2xbeaWi9wfUo0rvFliU4V6Ja1OMZ1Pc5GmKZjhuKs8ChlsKmiNh2m7TC6QrcQNS2qPkXrjiiNmS/nmF6jzT59WZDn61Ct0JmlbHr0yuC1xY4NbtXQ24yVr+j1hL5dYouWdNZgYgWxYbo24vCwIcoimkVDPM2pxz16v2RnfQvLKTu7ls5O2NnOiObHtJWnXC04rgt8AqsmZhHVdEcHTHrNqCxI8pxuaUh9RjdJaenJ+oxYlUSJo0sryiUkjHAmQnmPUT3l9hpGgZ1lbGUJ2qToLGE0i4mwrM8mOAUb+TpZYuhtT0lMHI9RnUb1BbG1rBYN2vdMbUK9pum1B6XQvcN7hTEpsdN01lLMz+1aTeSIWk8TGZqqIG8ty7RFT8e0hytGkUd7hckVZdlgIkXkFKZU5KOExMZ4fU5HG+kMa3r82FA1C5yu6L0mzkC5MUXTMsk8qdU0pcEYh9MeTIzJRtS+gVbjPNTzU6I4IZvOqGlxdPTdeXdw11W43tHbCIfDNR3OgIksdVNg2xirWjqtiFyMSzTG+/OKGgbnO7yJqWpPhKXvWrSJ6b1DdS0qSbGtwdHQNXPiJGfVRWh6qD0Yg/c9SvV48+PMRMo21FQMQeiQvz/c5GUtwGlIKYLvaTPDTPwwQBF6hYDToTB5CAYvaguGQdEQpAH/N4csIBxnGNQIuB86R0kgIFnWJ0+e8OrVK7a2trhy5UoohwugHTb0k2NK0CUvX7l+2YaOVcMAbBgkyDUNgyYB9UMQL0GJAO2LAZgcQwKpoRPVMJC4WNkYZvWH+5RARQINCVqGWo0hbWhIn5PqxvHxMV9//TWPHz8OnW/lZS7nNqSlDZ2khOcuVQ/RaAgda3htwzU0rIoNtSwyzsNq3fBzF0X2UuWQMZO1edFRalj5GY7JMHC/qMsYBvI/1kAjSRLu3r3L5cuXAzge0uBOT0/fqKZJf5utrS12d3d59uwZGxsbfPfdd4zH42DQIGBSkiCSPNje3g79EyQ7f3p6ys7ODv/xP/7HkPGXPgPCZ7937x67u7vUdc329jbL5TI0PBP60mKxYG9vLzRr++qrrwI95/e//z37+/tsbm5y+fJl8jwP67lt29A74fLly6Eb+NnZWbDi7bqO9957j/F4zHfffcfr16/p+z7Qwra2tgKYH41GwPlzTWxigVB12N3d5fDwMATWd+7cCeMjVaKqqkKDt6tXr3J0dBT6X5ycnIT7WZ59xhhu3rzJ559/Hqg/ly9f5tatW5RlyWw2C7bEly5dYrFY8OrVq0DD2traCp3HZT1LkPT555/z+PFjAGazGdZabty4wT/8wz+Ee+HFixdorQP1TYIwrTV/8zd/w9bWFtvb25yenvLOO+/wv//v/zvb29sYY/j8889DNSrLMowxvPXWWxwdHTGdTplMJsG2Vu476UwvjmOj0YirV6/y7bffhnH13vPxxx8HK17p9C6VMQksP/nkk0Dzm81mocP51tZWCMTX19eJooizszN2dnYoiiLY0u7u7qKUCtULCTzg+35W29vbITiS8ZaATKpbP/QZ8oMDjSbuiCLFyMbsn9Uom5JUPX5cU/cOG/V0ZU1swPgKnTqS3LJc1awlhtW84bBpsVFOug4YQ98oMpdQni1AjWi8wSZg0oTGl9B0rFYVycRgJ2Pmy4peOeJ8dF76VBG53mDlzoHrvOiJohw8dKeL8wzdZIRSMXneYZynqgyt0XjlyJI16qYmTyw2UZRtwSxZp246fDJFrVpYduzkGbbxuGlHYRL6LsFUJZFfsiodWWyhrFAxmHENOLo+R2UKThcYFbOqCqLY4nWLURHVoqPRjiSx1M0pfdTjknPr2nSkSZs1Ku9JJxNS4+gjh/KGQq3oL68z7ke07TW6CJoqJi4SVqZBxR3z/SVpZFmyIM4csZqhjixtXtN3ms085qRfoKwhW1sjWS2ZbKyzKGpmbpM+KjFGE21GNPNTehWRZ1N0FINSuNOOKElxNSRRjLeWJOqYz0+IohG6UdDE9GVD2S6IZ+uwtGTGc+oLdnQKDbg8xnctddNCC0rFqElEQ01qLa5dkOU7dF1LddySjVKc8vjE03jQukGzItf6vIt7b2nVEqWAbszId0zGmjZOWFQ9yvZobWCisG2Hagtsq4hMgtEeleQYc+48pVHoKMI3LbSOSMc0ZkWiO7rO4uOURrWsGY+3EbVviZMY10JrIhhB1Fc419FohfL9eaBgDEpB7w3aR1jj8cpQrxqU1sTK47qIrlUYBcrXGJtQug6tPUpFdFr/WAsab1QUhjapIrQbgrGho9Awoy96B3nQDYGjbEMK1DC7DN9XUIYCaMkWDnUJwyrHkEokmb6huBjOH9B5nr8BBofgT7JM8keuW9xBzs7OePz4MTs7O9y6dYvt7e03Kj1AqOzIOQ7BrYzRkOc/tDaF76lKsr+hLkCuf1h5kXMXID6kpQ3nTgIUmZuhM5cELjKOojcZBlFDIbnQhIAQLIp9sOxf1oZUHORz+/v7PHnyhEePHoUAQ6g0Itwe0oZkbLIsC65TeZ4zGo0CNUteqmIqIBWfYZA5nKfh+F6stMgcDgMHGethpeKinkPmR879n9IkDdfysNpxMfD9pyomP6ZNAJqIpGezGVEUBbBYVRWTySQEWMaYAKSVUty6dYuiKALNTubx888/58MPP2Q2m2GMYX9/P8yDOBHBORhfLpdcunSJe/fuhSTBMAAVGtzr16/Z2NgIlQ65N16+fMkHH3zAF198wdraWqC9FEXB3//93wNw9epVyrLkr//6r9nY2AgBsIivX7x4wXQ65dmzZ2xvb4dO469evcIYw3Q6Dd2h4zgOug2hBSql+Ku/+iv+9m//lidPnoQu42tra6Gp4bNnz5hOp2xsbPDZZ5+F595yuWRvby88K0TrUZYlDx8+DPe2JA3k+eqc4+bNm6HaM51OefHiBXEc8/bbb9N1HQ8ePAgVD2tt2OfGxkawJe778y7vVVWxvb0NfF+J/eqrr/j444959epVuN7nz59z+/Zt3nrrLZ4+fUqe54zH4xA8ee+ZTCaBopUkSeju3fc9v/3tb7l9+zZPnjwJuou1tTUePnwYAh1Jhkjwenp6Sl3XoUojyZCtra1AnTo9PeXGjRvAeeXm2bNnaP19rw1Z36PRiMePH3Pp0iVOT0+DSF6pc23KdDoNXeWHtDSZr6Iogrj9xo0boary4MGDoFUqiiJY7+7t7fHNN99wdnbGjRs3uHnzJl3X8fXXX/PLX/6Ssiw5PDwMBgD/3PaDAw1LTK8UpoORjylp6ZKYvp7j6InxjDHMTX/eW8F2lKsTZkzpqh6fGnQ8pm1r+kbhXY1PYoqmZmQUOiqIooy2X1GZEZNoTNvUqKhB1Z5ExfRJR6wNru/pFhDPDEVVMVlfxy2XzNKUsk/pXIfNHNpAVXaMc0OtW2xVkquE2lrMKKGe1yxWBZ1JyaY5NHNs5FicKoqkYDPWeKvwXlHWK/xE0Vc9Y6UZbY/pGuhXNVZ3ONuiG0NXVXRdhVqzVA2Q5ZQuIml78lpRdgatOW+CV1dENkV1FRU1tW8YpRl1XxJt5HCo6RNDul7TLRaYJGPqPFG+TVE0dCqibh0TD/EsZ5Q1+Mhz9fpVOD2iNlOS6QTrLOqyYmUMHk2kSvb6MSrLKLuEaGcboxOU6YidIp+mnC5LzApm4w0aF+MTUEbRnngmazGFKXG1xyaas5MGUsvIOmgLnM/oTEK6FnO8iLBtRG17+uWcJoF56zBRQt3GzOIJejynXrXo0zlqlFC3FUWnyOMR2lXEWYTvHGns8NazqqDBE9kpy6Yg9Z5IRaSjTTrdYMue+Uqh45TedHSrGmcgijwKg+scihZfa9AZCk/R9iRZgvcNHocmonMeG1toezrn0VFKT49vzru643qaXqNUD97Qd4qmqFGRphufi70jchrXgrdo5XHK0fagvKFtKoh66M/5+21ToUxKlCU43REpT+ssfduSmBw0tLQ0rWIU/eBb91/UJqVnAZfy7yH9Bb53ChJANgw+hHs+BKYCzoZgbyjwle93XReoJEPdxZD2IiBxePyL2hABzMPvCWCXa5RNKhmSLZUXopzLYrEIzdakYZj4pEuHYqkqSLZJmlUN3bEu6kJkXARcD0FyVVWBZzukZMm1DLUCQ0rVUDshx5NAQMTdWp/7xUtwMQxk6roOVR3Z73COZO4uVgCEeiYVCJlr586tGg8ODnj48CEHBweBOjDUTwjwEd6+BLvyMhbRtwQUQ5H38M8weJWxHAq+gTfWlazrYQ8U+b9UpWS/spZknQ3pbLJvWbfDCtDFe2NosSvjOQTB8jtZNz+2bX19PYC7KIoCkPvss8+Ce444LhljGI/HzGYzvv32W+I45qc//SllWfKLX/yCe/fu8Q//8A9Mp1Nu374dKFbGGJ4/f87m5iZpmrK/v8+1a9cCYPvwww95/fo1eZ4HQezDhw8D5UVExpubm6H53zDAE2GtUirQZ6y1vPvuu+zu7rK/v0/TNIGqUpYlSin+p//pf+L4+Ji1tTV++ctf8vd///dsbW3x6tWrkH0WIPrRRx/xn//zfw7zL+cuncG7ruP/+r/+L05PT0OVQxyRxCFKa82rV69CfxCxW10ul0EzJcmC09NTXr16xccffxyaAW5tbQWbXUk4aK1ZX1/n0aNHXL9+nfF4zNnZGffv32dzczMA+cViwdtvvx0SDyJMF/2AJIzERtwYE87r7t27/Nmf/Rlffvkljx49ChWM/f39IMp+9913w3Nrb28vJIpevHjB5uYmT58+Dfs9Pj7m4OCAoii4fPkyaZry7bffAt/b1gpg39jYCFQ+uRen0yld17G3t8d3333HxsZG2N/JyQkAW1tbgbImSQ+lzps1Nk0T+rdIMCH0uaEtrTw7h88q+c7e3h4vX74MznkyD2KcINa6YpohFZD/7X/73/j1r3+Nc47333+ftm0py5K33nqL09PTH3TP/mC04pWhWNXnACqOWLOWZdmQdxY1siSVhkhB56mKmsk4w0QZxQqMGZ1TROKUFIWKoK86Ep+Sac2qbYjHCXFRk0eWquroqwIbj1lEQFPi6p7xTKNqTd3E2NywKgu6SUKxKBjXGqVKUC0bo5y2tczLikb19KsWl1lGdYQ1Pa6YY9IRRkXsrI1Ae3y5Yme0wWLZ0JqczCt8McekMbXW+GyNuLT4tqKlofIpXbckTTxdHLFyPVPdnWeuo5RiVbOhR/SmJVctqupQasQYsFGDa2PiZETVL8nHHn02Ja3B6gjrDL5qaOKK3ieopieyKdYbqqKnNS21XxFZRx5lZMmYOlNkxlMpTdxprM0Y2Zg2TymPF3SqpYumpJ3FGkvtNVHZnXcpyXO6siHuHT0Nq14RjSz6rMXHMUws4GkPSyzpuUMVMJ0mUFb0yoKzxNNtXp8coaOY2EU0lNikwRrLZDSmKBWzJKKtOiJlGKcG62qWXqNn2yStQ5UOohhjRlhl8XWBTzQ28tQtQIbqVuTrOcXSob3Fb3h804DKiVyKd45cN7i6I8ITmRSdgtLtuQ1y0eB9TEdElCV4rcDVOGtBWawDpRXGKPAtve5wvcGqiL7TxBaMU8Q2pvUe3/dY1YP2JNn5DW7RKO3pO4dBoYzHOOgjj/cQOYdrHN7EdKWitYbWxCTa0CUtVre4BiIV4yJHt+qwcU8U92B/rPWMN61Qh4LWf4rnPqQYwZs2ncOMLnwPqOT38tKEN6sTAuAuVh3EoWmYTR46XA07Lg9B3LCiIoBdwKSA2iHobJomZLckWBi+EIYCcGns9fDhQzY3N0O28aKd6pACJQGQjKmcz9AOVqopMh/iACMVmrIsQ2AA31OMpGR+kcYj+x9Sp0QnIeM0rLRI5k0oBzKeFylqMi5yPcPKVN/3zOdzvv32W549exaCONFTDF2gJHMtQcQwyy90qCF9StaWHHcYpML3gdXF3g1S/RhWDoAQ9AxBvlSWZK1e1BYNGx0OqyPDcRkGe7LJ2hiOk+h5ZP0OA5Uf4/bw4UO2tra4evUqjx494uDgAKVUoCdlWRa49yLo1lrz7rvvhvtYnIEkaDg+PubmzZv81//6X7l9+zbee27cuMHDhw+5du0a165dC+Lxzc1Nfv/73wd3Ilnbb731Fkqp0ONAfn94ePhGEsU5x0cffRQMCo6OjpjNZiHTffnyZT799FO++uqrN54hXXfeJNBay1/8xV/w9OnT0GNha2uLLMt4+fIlJycn1HXNH/7wB3Z3d2mahuvXr/PZZ5/hnAsuTWmakiQJJycn7OzscO3aNV69esU777zD559/HmhZGxsbwcRCqjBSEYJzofx0OqXve27cuMGDBw+CU5WAV+n5Udc1x8fHvHjxgtFoFMTjRVGwtrYWXKvE1lfsYC9dukRd16H/gzg0SXVThPN/9Vd/xd/8zd+glAqCfAmc5J6x1rKxscHvfve7QB0T8bw0sbPWsrm5SVVV4TqkN8j9+/eB8+ec2GJPJhOK/z91b9pjSXKd9/9iyfXeW3tVd/XePUvPwiFHHFGUREqGbFgwDMPQh/RXMCDDhizKtEhL5HA45Ozd7Om9u7rWu+UWy//FZcRElyhz/oBfcBIYVE/VvZmRkZGZ5znneZ6zXDKZTCjLkps3byKE4Msvv2Rvb4/t7W0eP37MixcvePfdd/npT3/KpUuXuHbtWjQCCO+1cJ+H9Xvp0qV4z08mE/b391lbW+Pg4IDj4+Nofxsoo6+88gp1XXNwcBCBnvc+Oo0FAPXqq6/GpNZoNGI0GvHgwYMIcKSUbGxssLu7y89+9jP+43/8j/E5+pvf/OYlfeTv275+RaPoqf0IKSRnrkO3Y0oUjXWI3GNzGAlPZnNkJRGD5EzmKK0RM4/H4tZb8lIxnQ50xpP1HVVWAAXKjjB6wIslqpUMviOrjhmpYtUtW2kGQFU5vlBIB0W7hmmn+NzT6xzZw3qZY7OWRS+RODZkj9Mak3t83wIjSpHRzw0y1zRzxZD1jLMMqyRMRmh68q7HuTGqHGi7hrWFJjMLZOURaxLfLbAaenFGKbYprOakaxmViqxxmLLGK03hHLnvMVQsS0NWeVrbY5oFygmU7BlEjsrBdy3d4GmkYaM01K6ltZDJGstArjzeCFQJWm9Q2J6l6jF6gSNDa4vuLYiCpdRUvoSZp9YVU9NRa4NFYYSiyzIyUTASNe2sY7CKqsppzzpMrymKGf2aw896Oq3IKw/S4BUM3qB6gT/19LYkLwU4wfRpR15vI7yi6Ac0BV44mqEn7wfGVUknBX4EohcM7QKT5SyFp1YzeqMRAjwNVSspJhOGsmLpPeV4QjH0ZIDrQcwN4xx6aVmqkkI6BmHQg8OWqw7lBkfXrXpjLE97svVyFaxnNd4owKGEwVoHSuC9wZPTu99adPY9I+GwxpKJkqEzuFzTGagKiW1bsqrE9haTSxzgnV6BgcFhtAQ89D0ik3inwXgylaGEwRc5VilY91jlsGagHzy9NXgNOstwFqx3DKMM50F7RWb9N9beNg1MQ4YrAISU7hRoPGnWPO1jkFYvwn5DpjgEfOd58+G/89nzNAAPlJ+wzzToC8F46gSUVgrSykxKgwnjAv5F4BoAQwjsAwBJxxxEieElsLu7y8WLF9nZ2Yl0nvQ8QmCScvfPVyrCfIWfIfBIrXlTzUQAJylQBOJLPs3unwdx8BVITNdBeOmnblbh5RXGFbQq4VjT6ZTHjx/z7NkzDg8PsdZS13X050/XTgCxIaCs6/pfaF/SzGjahyUFavAVtSkAwLRTeRg3fAUqwljCZ87TpcKaClsKsIJgNOhv0kpTCszSqkpalQsUsXC8cJ+Fa5gC32/iFrK9f/d3f8f29jbr6+sYs2p2FqxXQ4B58eLFWDFYLpdxHoMO4NNPP+XGjRux4dxbb73F/fv3Y3D36quvkuc5Dx48YG9vj6qq+Pjjj3nzzTeB1XULGXYpZfx3WZaxl8VvfvObaE27u7sbxcp5nseeH/AV0P7oo49iY7jXXnuN//W//lek9D148ICqqvi7v/s71tbWogA9WPIqpdjc3IyUxrCOnj59yvr6OuPxmKdPn3Lz5k26ruPzzz9nNBphrY2VkkBZOjs7i9azoelgSHo8efIk3idbW1vUdc3Ozg4ffPBBXKOBnhXWaQjalVLs9iRi6gAAoKlJREFU7+/H58Pe3l68LteuXePq1at88MEHHB8fs7m5GUFOVVXcvHmTs7Oz6K50dnYWK1jee37yk59EWtGTJ0947bXXYnO56XTK22+/zdnZWbxPvfeRjnTt2rU4/4G+dP/+fU5OTmK1Y29vLzoxhXfSzs4OFy9ejLqKy5cvR6H1lStXWF9f5+HDhzHJ8eGHH8ZnUegi3/c9z58/59KlS1FT8uLFC8qyjO5YobHhixcvuHr1Kn3fs7GxEZNDIVESrve9e/di0iU8h0KTxidPnqCU4vLly+zs7PD06VOGYWBrayu6fE0mEwDeffddjo6OODw8xDkX7YK/+OKL6AT2+7avDTQ6XwGGeVOi8gJvGyg0ak3T2J6BgZKC8VzSTiQ27ykXOarIYOxwc4vzLS+mlpHaQesM2y0gG5ionPnQYn2HF4pcuN9y+iSF8UgcsuqYDyN0BV0/ZVNNcI2hzS3OKbzSDAb84Ry9kYGvUbnEDitdxUhZ8nKEkhbvQfscPYDTA2WhMZ2jW1icGBCiIxt6uskEYzy4gmWV461lIgTN2ZIMibZj8myLbumotCWra7JeYq2nazWusix9z6wfyOuCpRqozzTGaGw9QS6njKp8VSGpC7KyorESo6DvBdJmiFGG7QS9KBnsgBwLymzAngmWeYZtPD2KzEu6vqSoPNYLutJTjiTuzCKy1b5zrVdd0pcKLUsa69HSYlyO6QuGTLMcBkbdkiwTiKJGbwlcBn7e/TaIKLBlgVZLZt2UoVcolZN5BZOVnexGOUZbQdcahDfIKkeJEjUMaCxKS/qlorM90kuEaVFKMOgJ1ZAh2URkDt+CHwa0cCsAIgxSOdZqxQyFyT11uYawHmEN5OB9j3KWrC5YCo+WFbJpWC88XmoMA23ryLRCOIdvLEKUlEogc0snewahUJ1DFgqBQaiKwSlwnlz02ExhZU9fZ5jeUvgCxKpHjJGOkhxvPZ0XuL5DCYs0mlaCRsHgaZ1HFB6lLd46sl6gdb6iVjlD30nWMofINdJ5VlDTg3C0zlFSf91b9w9qCy/V8449aTB8nm6TUpaAmKE8HzCl1YzQ/Cw8YMO/Q2AcXhIpxSeMKxXZpj9DQB2OF4LptFoSfhcCzxSMhCA8FSGHzH/QdgRRauiVEPYR9h1esk+ePGFra4v9/X22trYifSRk9VOQFQLmoI0I+w3XIAQjYc5CxUUIEft5hJdVCJjT807PryiK+MJLLX/DsVMtRsjyw790TgoASwgRG5s9e/aM4+PjSDUIfOiQgQMiCAgAIx1TqGz8LkpTqISEikRKlUorWOfBUgqm0t4bAfylACOA6wDc0sZ8KbhJKxfBAem84P93gewUBIexp0L1ADLT4O+buKXiXljpAx4+fMjJyQkXL16MBhCz2UqnGbprh/kKFYCnT5/Gxo5933N4eBgFyc45Hj9+HHUSr776Kh9//DGXL1/m0qVLzGYzXrx4weXLl+m6LgKHtbU1jo+POT4+5sKFC7Rty+XLl2M/jvl8Tl3XnJyccHJyEimgIQHx4YcfsrOzw2w248KFC9R1zR//8R9Hh59AXVosFhwfH3N2dsb6+jrOOd57773YwTvcd/P5nFdffTXqM/I857333uPk5CQ2Ldzb2+Pk5ITpdBqrmaFRXGgGd/XqVTY3N1FKcffuXfb39zk4OKCqKq5cucLDhw/Z2dlhe3ubjz76iCtXrrCxscG3v/1tHj58iDGGra2t+Fy7ffs2P/vZz15yYQI4Pj6m7/toyxrm5fDwkP39fWBlb6u1jp3C67qO37l3717UZSil+PTTT+P94JyL+hHvV4L1w8NDHj16FOcsOGF9+eWXzGazl6rFgX727rvvcunSJU5PT6MObGtrKwr7Dw4OYiPC0OQxUDN3d3fjmgsaGGNMpFYZY2J1IQAmpVauUFevXmU6nTIajWJPFCll7GAejAcCze7GjRvRbjk8w65evRobVAaHrAC8Uv1kcPI6PT19KREXQN/Z2Rl93/+/76NRuIFGSMzxjLwomZewqTXKS0alYeYK5p2glhY6yIDeO4S2eNWjvEb1IIeaxi1YL3KyvQzVO5jP2MjWMCPJIqvwsmNhWnRdIk5bhNX404zxfsmwMPiF40y0iLpYCaYL6HTDJK/xg6JbNEgtOO1aduqcChBDxWAMnW0QQ0YrNEsM1aZiuRwQVUnbS3J6aqGR3iOGHnxOrUf43OJ7gRaW0pd4rWnPpuRCo4sa2cKmlHSjHONacmFBdWAt0ljk3DLOFe2gUXlOUReYdgLe0wwd1JJi4fBmSVnWKFvhS0fTzmibnt2tLbqlpFsoxkVJM1+dv84FjozW94zKHGtWDY3KvKUbMqaNYkd0KDdgK4FXC7RTaAq6wiIxVFWBX8xx1Rrrryg49Xif4+czhjJDDxqNwg9gak+vDcu2p9I5+UaG6Xuk6Vkoz1ae4VVHnnf0jWOEZtl5TGlwpaCTko1S0zpF0dSUuWJeC4ZekXUKWXmUdeTrI7pmQb2pWUqPcQt0VtAMkrwokUODsHoV4KPQuQQEy0pRZRLb5OSywZgOJTSoJUM3J5caq3MWix7loahyvPZY16EpkINkJCXSDbjZQFdaelkglaITlokTlEqDXTlsWSsZrKFf9uS5R+oMMxiEcGSdw+YZ0ki89eTZb7s3Nw6hczwGaxTaOoQVNMOKTlGJDDOALHOwHVpIeutprUdlK1rcN3VL9RbhAZg6boRgNtB5gH9RPQj7SYPSkJEOwCHly6dZ5PBCM8ZEgWLoiBooNAEQBPpMECKnoCgFHOcD7jTIg1Ug2LbtvwAk4dzS6oEQIlpbnhcHh8+H3wUagRArZ6iNjQ0mkwlbW1vRfz6Ah3C8VLgejpvOY1qVCfOR/j0FSueBWBh7WnUKYw5/Py9EDtc9UBgCZSA0MTs9PaVpmghQpJRsbW3Fa5xSgAJNKHD3QxUj/AxrJK1mhRd5oFWlVrBhjsI5hmOktr3pXKXXNaydUEWy1kZOe6iOpftODRLCeYbjh6AkrUikcxjGm1b60vsmDTRSOl1aUfkmbcH+2HvPo0ePuHnzZgSUbduyvr7O3t4et2/fjnan0+mU3d3dCHiDSDnYjwZ60MnJSbwmr7/+Ot57lsslZ2dnXLp0icVi8dKamk6nGLNqDheqC977aI0cPhs0OfP5nNlsFo0h3nnnHe7cuRMpQKG6sLOzg5SrpmpSSq5cucKdO3eiFuCXv/xlzLC///77HB8f81/+y3/h2bNnXL16lYsXL1IUBdeuXWOxWABw+/Ztzs7O+Oyzz6LmIehPQkU07Vi/s7PDr3/962jVG+xYwz3z2muv8ejRozg3X375Jc+fP49A8PDwkLt377JYLOKzJAj10wRQ6GECxI7lDx8+jPdAXdexr8nnn38eXZJCFr+ua2azGScnJ+zv73N0dESe51y/fp1hGLh37x5XrlyJFLsA9sN9Y63l6OiI733ve/zoRz+KgboQIuqBQi+d3d1dPv3005dor1mW8fTpU6SUca7ruo7i9Tt37vDKK6/Q9z2z2QylFD/84Q/56U9/ynQ65a/+6q/4b//tv/G9732Pjz76KOoxTk5OomYozEVZliyXS+Cr+z6A64ODA6RcNUoMlbzgUnb16lXatmU2m8Xn95MnT/jiiy/47ne/izGG8XhM0zTxGRUarD59+pTXXnuNk5MTvvjiC27duhWrceF98vu2r99HQ2ukadnZrlhMOwo0LOc43eFzGPsaZQ0iX9Iq0ENBJRqkKlmcCVqXUeucqsqwY8m8B9Va1jKD2Cpp5kt0WdB2LQMOVRZ4K5F5TpYXiKHHLgaEUIhuQBQbLDrPaDzGLaaoSU3fgbMdIld03jIqc8zSUvoClbeIXGCp6EWHzqCdDZh5hdQTer9gvCFx0wwlNO1uRtMu2CpqVG9Zzk9wBhaFpukkVSGptrfovGXR9eRuTtePQHWIwUPvcZlgaHrWZMWyc4zI6bzFFZCrY7LtEru0KKPol55pC6Kw1CIn945j29L0UCnNtJ1hhYJKYLIc6cAVhqGSZHaVuUd62qbBObBLifQW4wcGVeHIsZ2llSOU7qjcgCkr8mod2Z5iJgrXe2g7MiR9O1DkY7yRFHkHUjKUGc4sKHuBKyukBdNaln3LdlkwdAPkGZ0bYbxA0OMMDAq21zSu7dG2xkwVftqiN0u87dlUBV4avG2o9TpeZKi+pygLMB2VzBBFhRPgvWZwHmkMSucMvUUpTW8syJX72eA0fWcppEdVBd4IKArUADiPcxImGWrokRlYIch0hXcDWkicH1BSooqMppeIQkDfUiqP1ivRdystfe/IMo2pJXrwZEKCymh7Q61BCwkYEB6PpnAKYSSDHXCDZVAgMo+QjkxqKpnhnKTQhryEtjMUsgCGlcWvkViXITL7e+7WP9ztfNAcAsXUmSgFHOeD9zQQTv9LxeFhS4PytPoRsjPhd0DM5Ieg73wFJOwnDbbPB8/h+OG/MLYQ4AZwkh43AKGUJhbmJQ0sU53DeYqW9z4GMNbaaHm4vr7O9vb2S83oUgCQioTD/6fBMxBdeeCrbuMBaIU5SYPjNGBOQVt4Iae0pXCM0HTq9PSUk5MTjo+PY+AVKA6TyeRfCLLPV1bC/49GI7TWETyEClfgfaffDdc7rWKEaxAAbJiTFOyGOUr55CFTGSoaIaAP85ZSoNJ7IayPlMaWzm16vdM1lFZDwphTABn2Ec4prO8QSHxTt77vuXfvXsz6BlrUm2++yf3792M1rG1bnj59ynw+59atW9HRp6qqKGLt+z72W3j//ff5oz/6o9hXous6XnvtNXZ2djg5OYlBanA5+su//Et+/OMfRyvWwJHf3t6OiQznVmYFYU0EC+VgjzufzxmGgePjYy5evMjly5dxzkXjg2Bxur29HTUA1lr+5m/+hs8///ylqtQnn3xC13UsFgvu37/PzZs3ee+995hOp3jvo4tW6JAenNnquo7z8vTp0+i6FmxY08TLbDbj6OiI27dvR5AVOoI759ja2uLx48cMwxB7dARAHZr2PXv2LAqiAb71rW/x/vvvM5vN4rodj8cxOROenS9evMB7H4XrAXA8f/48gqSjoyOUUiwWC54/fx6f6y9evHipUae1ls3NTe7cuYNSiocPH/Jf/+t/ZW9vL1LHAsXr8PCQYRhYLpc8fvyY/f39CATOzs5ioB+E6GVZRpvfTz75JNojS7lqQjgej/n0008py5Jvf/vb/PM//zNvvvlmrGSsra1FzUjozRG0N4vFItIDw7Pk0aNH3L59O66Bd955BylXfV42NzcjVSpQt4JVcFEU8f4J9srp5r2nqiouXrzIr371K65evcp7773HvXv3YhUwPMN/3/a1gcZitgSpsJkg26qQzYDpF/RCYRcSjcFbRVFMsGpAdxKrS5TMEGZAeo31M+TaBN/PQGgGY1CjNZbtCbIQnM7mCApKryhkhm2OcEOGKMa4wiI6UPMl62OBcQuqzLLlas7mYPsBm2X4MscuoZQduc9ohaLLOgpjUCbHux6sJR95sklGLucI7IqqpA3eLmi0xbqccqI4m87JjUHUOabVFBNHbjV60FivEVowkprRtkLZikW7pCo1jXC0JsfLjIwcLRo6pXFmIO8txmicFygrUPkE2bUgM7wpEAtPkw/UVUYpSjI/IAvPoumxpaZdvKAuxnTCs5y36LxCdRbdWbJcYkxJ30NWKCplkVR40yNUy1q+SdtaSjvF9j1nzlEZiT+z9LpHa6Cs8Z1jupxTrdW05IjBU+YwciP64w6jDNVWxWAttcwQnUa7DJPlDEcLJmPFshJMjV3Z8jYtw2Igy/qVyFkVZIuVZkcLSz84xNgj8g5nB3ohkEZTFDW+9XRth8gUVSnwmcdKDT5jlGls2+K0RQqJNx5VgJg4tHMgHVYJvNXIfkXDq8aS3ju81Ew7Q1lkNE2Hyi1SZzjTw6CxCgo7IJ3Be8Wys7i8QPVLrLTURuLNgBA5Vmd0vkMJT1GAR+JZ3YjWWKT2tF3LSE0wpoOqRDQtudLkIsMriW86fF1ixSrIyTOBG3p0Bp01KDVCNC2ZVP8/7tw/rC21OU2DoBCQBYARuOdp0JXy+kMwe14PEILvAATS4DANzFM6TOqmFKoX8JUNbgiQw/ECKAgv3zC+lC4VziEFHOl4QsAdQFIKUMK8hAA6HCuMN5xrCPhTTYUQIoomg+98OD+tdexCq7VmfX39pXkLwCZk9tOKThhHGEOYr0A7CjSpELCkAXzYb/h5enpK27YcHBzQNE1sxBdoSwGIBIAQKiWpPiEN2tOqAhApUaG6EtykQiAegrOUJheuTaB2BGCUAoYANsLfQqUgVNHCf+Ez4fqloCytsIW5C2A7HDes/bTqFQKfIIoPf091HyngDd8L6ygV0wewktLAvklb0OUAXLhwIQbLqXtPCJ4C4DbGsL+/z/379zk9PeXq1avAytLzgw8+YHd3l+9973uRmleWJfP5nM3NzQgyYNUcLqyHzz77jK2trXj9Pv74Y65fv87h4SEXL16MOgchVjqOsFaDduHp06copWIPivX19RiwKqW4cuUKZ2dn7O/vv2TlGlx/wvlcunSJo6MjXnnlFe7cuUPTNLGJYHB0CveTc47FYoFzjitXrnD37l1ef/31eMy+78myjMVigRAiit8PDg7iZ3Z2dnj+/DlHR0dsb29H8fdiseCNN96ImoblchmbZIb79c6dOzjneP3112MjxNBdPVQz19bWuHLlCp999llcp6PRKNLGTk9P471y7949Ll68GPt/BCqWECs3r/RZmZo8HB4e8uGHH9I0DVtbW+R5zt7eXrxfv/vd7/LP//zPHB8fx/UUQIBSq54TVVVx+fLl+LwKeomgbQhAJQTzoQ9FURSxu3qY72EYuHPnDhsbG3i/ci07Pj5mfX09VkImkwmLxQIpVw58n3zyCf/hP/wHrl27Fnu7vPPOO2RZFkFL2vC16zr29vZ48uRJTOy8/fbbcawBEAaXtCzL+M1vfsPe3h5SSra3t5lOp1y5coXnz5+jtf5/b29LWYDqUKWlHQxVplBmfcX7NxLb92RZg2kFBkNdbjBvlgy6wK5L5HEPXlLKAWElvdDUYzDdKUoL3FAxQZErh/UtwncUdcbpGajWgyjxokeNCzrbUBQZXW9Y9lPMSLDUklGeIc5aMjWiGEmyswGrJboydEtFnsPcAtmYPPNkSHxvyLTjzHvyTmM9OJlhpo56s0DUhn7mVxQvC9pWKCWQ/QwrDGQ1TW/JhMaLBZ0bGJqeUTFiOh8odE2XdSzVQFYoCinIco9dzMgKT28saigYT8bMhgHtNcoa5s2Molhj6VqE92RnglauFmkmMvqFwYwLNtb8qqKhKlrRo7KCoWnRYxjJjD4rOD06YjuvyUvJrJmSz8dIaoqxZOk62gJkbtlaUzTdwNngUM5RixInSjwLqnoMc4OSBrVWUizn9L2lt4KdyYR+KRBOszydsi0L7GKByzN81aFdiZEZVity57B+AROBbMEODpEP+HxM2y3wRYu2NbJUTOdzSj/BZWrVy0NmyMEhlcDqkl5K2qFBZ45C5nSnPfWoAuPQyoACjEBlgr7vyKRAeAXeIuSArEao376kda3IKGDosUtFpnO88LhKQukRVlDrHCscupBkvsDKJcoX2EEhWrcC4bkDrTEDkFVkg2AwDQqJtIIuM+RlgTMCUY1xytAuG1SZIZXH2JbGK+o8x3UWkUtwjspLer/qk+JtD5T/9/v1D3QLwV7I8IXgP7zUQnXjd7k8pdWB8D0gficE92mwlwaXabAcgv0AFMLLKQCNEGQGSkbq3JNmnlNqUgqGUl59etwQlKTi8QBmAn0oz/OX3KfSikKYo2DbGl5UIXOYVntSyowxhuVyGfUd8FXH6AAswn5DZj5UXs4HsmHc6biWy2WkD81ms6iDCQFHcGwJ1yoEwXmexy66gd4UyvYpoAkB+fnsfVpZCPOcUpFChSHMQ3odwjoKoCDQKYKeI9BswudDVjZ1egpBzPlqUciwpoAgHD+tNqR/C7qNMO8hEAlrPKVaBUAZaB6BEpZW2dKKTABS6ZjD2v8mbkGUGtZSuHeuXbvGyckJfd9HoepoNOLFixfMZjNu3LjB/v4+jx494vDwMAafZ2dn3Lx5kzt37kQb0dCELwVlR0dHUQ+VZvqFEPzZn/0Zh4eHsTN5APmz2Sza3gIRZC+XS7a2tqLrUzA7CG5MgZsf+hqEhmrHx8exghb49uPxGKUU165d4969e3zxxRcMw8A//dM/Udc1i8WCTz/9lH/7b/8td+7cYXNzk93dXYwx3LlzByA+5wLAD/McePrhmRCsbr/97W/Hnhbr6+vM53N+9atfRcvbQPsry5K9vb3oVBSe/QHIhYqCUipquwJtKDzbjDEcHR1RliU3btzg0qVL/PjHP2YymTCbzXj99de5d+9e7K8Ursl4PI6mG0+fPuVXv/oVt27divqbjz/+OHbV/vzzz6nrGu89X3zxBa+88gqz2Yxvf/vbPHjwgLOzs2gNnFpnD8MQLYaNMdGdLMzlMAxcvnyZ58+fx+oRrLRCu7u7jMdjYPVsePToUQQnb7zxBs+fP6coCl599VVgpflYLpccHx/zp3/6p5GKFUDo0dERe3t7LzWY3NnZoe/7l7qHBzpf3/cRSLVty4sXL+J6EUJE56rNzU3u3btHVVVsbm5y+fLleH2+zva1gYYpS7KZpVn2qEm1AhbK43SJn7bYbFW1KDNgbjgThwiV4+YeVRrKLUXbeRimLFsJZU5VKoSUDO2A6g3eCXTdYQbL0FZoOaL3DYaG2hT0jWIoDL5WCGnpvQYxZu48KnMUokcVkq7tmDUCLQ0iq5D9wLzvKOocr0fIQWFtQ+8shZzgnUJ1grZbkI08SitsNmJ5umBSdOSjkoySRrQ0iw43eCYjSW8HjD/D64FebSCFoKhzZssZlR+zluU4ZTl1HarKqesRYujoh556MsY7ST+yCKBpFhihUUL91uFrg2bp0doge/BZSaYkNRZVb2J0i2agaDWdtciRQBQ52cyRjwzIAdsZqs0CnwtM5yn6mrL0GGVwKHorYNTjzCqYxgiMcayvlXRqYIOCFgG6ojl+TqYLmnYOow101SMGTa5rzqZz5ChnpDTrjPDW0bQ9mpLMraO0YLaYkkuP1J5qtM3Md5RS4/E4KRnlFucLFs3AZqXxXiJ8TlHldKZFyZKyLOiagcqC7GHQHdp5irIG5/AVUHqkdTiroCixZqAiw4oeWzl863BW4GyDlZ5RNgYhGKzDGYsaBDk5VgLKIzKJ8A5hNcJ35G2Gy1ZOWFICZkCKHJEVlLqg9z14QWbdyjbZQ14V2K4nyzS969GixPQWpxxKSESd09qBLCvQyuCXEmd7hFHgc7xvcboj0yMEiuEbmokEXgrKz2dVQzY3DdrT6kXaeC18LwRfYb9pZj+l8px3UgpB1jAMLzlNBYpLyrkPv0srESFQS4O48Pnwkgmc/DCWYGka5iGMP2SYU2F6WgFIg9W0GgTEF3GYu/ASC4F0StM6XykJ5xK69wZxcsr1TwXR4fMBLIVANr2uaTY/PWZaZQjVgBCcp8FwWBMhcAzUh2AXGv6eBujwVWM/7/1L2p9AF0qpQun4QxUj9AAJcxb2GX6mgDeAYGttBEbn/xZoNulaTuly4TvpNU2rV6HPCXzlxJXeMwFEhTV0/t5KaYPp+IOYM6zTb+L27NkzYNWTJ9wLbdty8eJFAB49esRPf/pTvv/97zOZTKIQOVCRnj59inOOy5cvxx4MYW6uXbsW3Z0++OAD/t2/+3dkWRYzuKnYP6xHKWUUlgexd9CQhOTA3bt3o+4iz3Nef/11Hj9+HOmO0+k0AtzQ3XmxWHDt2jWMMfzkJz/h9u3bfPnll9y+fTtSnAKFZXd3N2odwnkJIaKoO3Tv/uCDD5hOp9y/f5+Dg4Po4rRYLHj8+DHf+ta34toO1ZsQdB4fHzMajWIzuqOjI87Ozrhx4wYPHjxAax2pQ8H9KMz7yclJbCIXAMNisaBt2+g6FZ79wZo1aHGePXvGyckJ3/rWtzg5OeHx48cRCF67do319XV+9atfRT2K9yt3pfF4zK9+9SueP3/Oq6++GvUL77//Pnme8/DhQzY2Nvirv/orPvroo0hNknIlsL5y5QoA9+/f58aNGzx58oSHDx9GC9vQgDGtOASwtlwuefr0KdevX48albR5X9/3sVIQNChKKS5duoRSq94vR0dHEfgAsZLlnItdyINAPojTA60vPIObpok6m1BxffDgARcvXqQsS4Zh4OjoKH5ve3s7NgBsmob33nvvJaphuMbhuRWeUb9v+9pAo1pqhFojy1pwGusUlJZq8PRSkYmBYXCIDMrcMYwzEBK1cFRZwbJf0rmWiZBMtsa0c4PvLDNpWc9L/GBZaoHRBb5wKDOGqWOrGtGrgTrP6fxAJVq6JSzbHlF5UDUj4ZHqlM7USBSilKtuzyV0ylHajEIqihykMCzPGiot6ArNIDTWS1TV0AoPRY6zCouhHk3w1iCdpF30NK0jq3MmlaJliRWajUIxWINhiZA9voVKSVoaXGYZiwo5WUNmguasIcsFdiY4HTrEbx8stfCY3CGNZegMbbHGsBiw2iJ7iWsEed6iswKE4EwN6NLjmgV+USJHGSfLF9AUZHWGAHwzkKuaZZ/TOs1kpFkseiwFVjfITjCyEoei8RnCDjRGM9YldlhgdU5vLF1vwHhGoy1mbYOrfqvFKBWlKyh0wVArZnaGyCVmqHFDi8o2WHqP7R0bKmPkHcJrrDbYpqesDJmWmA60HdH1LZ2FilVfCy81mTKYtsNlgvF4gBawjqN5y2RtA+2gxKIHR+chzyQOj/GCHIs1Ggm0sxmqymikQmaAF9TVBn6wDIselWdoL1AFeKWQosN4AUKivSDzglYIvMyxgMcjsDj/23KsBYtk1rRkY4nxLYUXWJthM4lwHp1nSAyFz3EIspGkF+B6QUaG8h5vLDKzdMrhiwrXOEQ/rCpBeoyTDtc68Bnf1NbgabCVBk2/CySkQW6oMoSA63wwmGbQ06x+4PmfBwlpkJDy8tPKRggYQwD4r3H7gQgKwndDVSbt4g0v047O06QCdSgNUFINRQBeKb2mbdu4j1BFSYFQOHYIxMN+w4soBNmB055qEICXXJlCsBzmPIw57CsE1qmmJDg9pdSilK6UBtBhrOEzYZ2kbk6pFW44Tri24fNhzOfF0alIP6U2hWsf6EWhqWA4dhhTCqTCmgt/D44x6b5SkJVWLcLYw89QhQn/ToFtyoEOx021JGEfKUUwnb903Z0HZ183SPhD20KAE4LJQM+Zz+d0XcfZ2Rl/8zd/w3w+j+d8cHCAtZZPPvmEa9euAatOzEFEe+PGjdhvwVrLm2++GelMi8WCGzdusFgsePDgAevr6wBR2Nw0DXVdc3h4yPXr15nP55GC8uLFC7a3t9nc3OTw8DDei8fHx+R5zocffsjGxgaXL1+OBhDB7jQEs4HKo7Xm1q1bvPPOOxwfH8cA8dvf/jaPHz+maRpu3rzJp59+St/3rK2tcXR0FPn53/72t6PLkveeu3fvcv/+/Zfuq4ODA959911+9atfRarlhQsX+OCDD2Lm++nTp7Fh4enpKcMwcP36dU5PT3ny5EmkCt2+fTt2Kd/c3OTs7AxrbazehPMNXdVDMiNkybe3tzk9PY2g7c6dO1y8eDGKvrXW/PrXv+bmzZt8+eWXHBwcRLAQ7qFQof7iiy/Y29tjbW2Nuq7Jsow333yTp0+f8rOf/Yzbt2+zWCwYj8dcuXKFGzduUFUVjx49Yj6fc3BwQFEUkYYW7LTH43Gsziil+Iu/+Av+8R//kZ2dHW7evBnNOjY2NqK7U3iW7u3tRQraZDKJlYNPPvkkzkEwtXj69ClZlrG7u8va2hpFUbC7u8vJyQnL5TL28whViNC/I+xzY2ODw8NDDg8P2d3d5cGDB2xsbLC+vs5kMuEf/uEfuHHjBtvb2xRFEXU+r7zySnzHwCqpEXQqQSj/dbavDTScH5CiR2WOWgqcylh4g+gGqMZI68mKlkUjsErRmZ6cjMo6/MytbFfHFV0P/cnA4CVetxRGgwctcwo8bT/HFjUOgZGLlaNRvUXet6i8IReQLwsWtUKrHu0sFpCmoNcObA9OMvLgpgt6D52uUFmB9IL+rEVkgp6WbF4xiJ7GCzIp0VVN3y2ppMcKixwp2hnUDWSVQoslmVdk0rCQS0Rb0fYDupiQe4nQOYOybIxKzgZLt4RJ0SMXLS0aUayaFAogH49wStNbxdKcUdUDXTsgxZjWegYPvVPklUAJg5ZgvMF7zeZIMJ0NeFkhtEN7Q1ZkmK7CLi0IhWRMawtyb7CyQ5ChpED0HaO6QKgC43v6Fx2ojhKYixklNSO9hls2mHaO9asmgvQa6gkiGzHJYTBL9FiDXcAsZ6QqhtLyfDjhwqTCtILe9SAki35A6pK+7cArBtezJ0f4oWHuHRNTIGXB2BkyYRnaAasGRnqCd45Br66xdlCORgytxgMSyyBACgeDobUObT1KAt5hmgaXS5QQaKPRUtMLS60sw4lHZjkyA6Ecyim8c1g/4GWO0grnHUJIzDAgpKG3gjwTyKUHLH70W/vKpSWrM/IyQztP7jKEdiy7JX4xMFrfxFqPUBI/eJS0WHryrGSQYHuH9gKNw9sSrzpwS1Su0VLRKsPSKbTxaKexyv1f7tQ/7C2AhJSykgZ5KVUlZKTDZ8Pv00A2rYKk9JI0+3s+yxse9oHyEgL/oCdI9xm2VNtxvhqTBqIhSxyC2/PnmY4n5eWHIDzNMocxhkA0pQWF/QfrxLZt44s7DThDVSIEnwGIBHARzi21XU0BUKp1Sc8nne/A/00D17QaFeY9FfuHfYUqTgjG04A+rItAfwoUrzCe870pQk+RdK2kQX66js7/Ll0rqbg8DejP7xt4CdilICRUO1KKXQAuge4TgFk6R6GylK6hdBxpZSTs+3zFJXw+teqVUkYtTPhMCmK+SZsxJoqmQ1+JsF6n0ylvvPFGzF5vbm4ihODy5cuRPhSCeOccJycnsTHbkydPuHXrVuyI/dprr8X+DWHdhWOFitQXX3zBu+++y3K5RCkVe3UYY/jNb34TqyhnZ2c8ePCAmzdvYq1lbW0tWrOG9VtVFc45nj59GisUZ2dn7O7uxrW5t7fH//yf/5Ou63jllVeYTqecnZ2h9aqreOgLkud5bGJ5cnJCVVU8efIkdiAP6yhUeoNJwGw247PPPqOuax4/fszGxgZffPEFxhhu3LgRq2FPnjxhOp3y3e9+l08//RStNQcHB6ytrUUxPHwFCvf39ymKIiZPjo6OGIaB3d1dnj9//tJz5fHjx+zu7rK9vc3h4SEnJyfcvn07ZvmrquLOnTvcvXsXKWW8tqHS4b3nlVde4e7du5FOFtbHzs4OdV1zenrKL37xCy5fvsx4PObw8JCyLLl8+XLUSTx9+pTFYsHm5mZMdITM/y9+8Qv+7M/+LM7ZpUuXmEwm/OQnP4mJkbW1NZ4/f87GxgbT6TRe6/Cc/eKLL16y5+66LjaSdM5xcHAQq1MBlAQh+MHBQaSdBS3FhQsXogvXt771rZcSckGnsbW1hbWWW7dusVgsYjO/K1euRBpcqJSGeQ3A+smTJ1RVxf7+fgScQSv1+7av3xncP0PJMZkcMaNhUD2SDD80KD+nLAqwOV2V4cQ2sj9FOklnDLlfoJSnGEb44wwhoV4Hp7aQuqPXFt07RFHj/fqKZtK3UGuUVdi5w60LZFNgdEdWWDplMcJQDAWylMxMgR1ADz21ylGZQ2aCTVVycqQYo2mGBUp7mr4jKyqcKMgFyGWHa2GUO0ayYrY0VAXIRU9WrDP4HmUMxVivxuAVuJUNbiYKGicwTc6wtIhyQCOwXqLzjC7vyMYZ45nALCtaO8UUFQZN1zRUxRrZeESvDLIyZG1BhsHYlpIM2baUqibLPH0n8Y2gtWdMSzBtRllnDFJhnCAvPViNGARkBX1mmc87dO7ACForWRvVNMsljbRIb5gU20zbBV3myMct3SBwfoksLe3goK6phMMNA1LndO1AqXoEgm4qEOMxg7Wsa0dvLD7LWLYL6CSlg6zI0MrQ2ZIimzAMhkmeY1qDlwZZj1majKIfqBQI7ZF6wSDGzDBsFxV+GHCqo+96PIJKgnc9pRQgwQlLXkpcb8iFxqkcvEKXnmHoyIoCrxTaAVYgHKhcQ5HhBVhnEB3I2uFUhxQZ3rRIJZkZjxIZpR/IJORS4JREiAzvNJIeWSgG16B0ies1UoEZIM80mZD4wZIpQWd6eimRWYmzObnrsR5UpshkjjLQdQOToqRrPCaTeLNai5X0LK3GdoJlN2VjPPq6t+4f1Ba0AGFLKwwhQAovqRAohaDvvFg3ZJFTu9UQmAfAEPYLX2V302OmVrhpkJ4eD17uW5BuKec+DWRD8JgKy9OMeBpMp1noUM4OAUr4TNALpBzn8FJIgUYQh58XJIfgNMxnCEBD5i/wr8NLMA1e02A1BQXpvAUwE+b5vJ4gvOjDSzjoUtJmW+m8hc+HzwRaSaq1SEWmaXUjbOE4abUh3dLKRgquUlpXuqWfSzOwQXicnkcKiMPcpVW1tKqUnlO4RwK/O+0jk66t89WVsC7C78NchwA5gLhw7cNxvonbzZs3+dnPfsarr77K1atX4/3+/PnzmCkPmelQLQxzEqgpr7/+Ovfv32exWDCfz8myjBs3bjAej2Nwvlwu2d7ejrQXKWXsWRG6Tu/u7rJYLCiKgtFoRJZlUZMUgu8bN25w+fJl3njjDQ4PDzk4OIii7tBhOwC/3d3dl54B3vtoqRvsrIdhiL0rgpX16ekpL1684Pr169EdKWg7Qlb6F7/4BcMwcPHiRaqq4vj4OFZi//qv/5r/8T/+B0IITk5OontT0LEE4e+nn35KnudMJpOYxTfGxH4QOzs77OzsMJ/Po4i+rms+//zzqCUImpi9vT3yPI9Z/+Aidnp6yoMHD/jwww/54Q9/yIMHD/j44485ODjgrbfe4qOPPuLmzZvMZjOWyyX/+3//b77//e/z4x//OFoHn52d8Z3vfIfDw8OXKo1BhB7odLdv3+azzz6LVLrwLJzP55RlGUGAtTb2ZQFixes73/kOP/rRj6JdcOi4HZ5vo9HqPR3WZOiZ9OLFi6hFU0rFfhR1XTMej2NH9aBbGY1GsbIxnU7Z29vj9ddfJ8sy7t+/H10bQ1VdCBGrcVprdnd3I1h68eJFNCm4ceMG3q/6M+3s7OD9ysEwgJtAzwtalWBgEFy1/p9Tp7zYACVoOot3BpP3iEWBYqASoFtB6wRKN/hcgoBmZhnVGUNWkQvBIDR+YlDWIYXHecvgezI/YFCYowV5OUL4Dt90OF1RjLcR2qGGBaLRtNkUsVYiRYkFhqZDzxrqiWDwBSqvwfa0U0NbZHjZMalqlFoynRrWtnNyr3DNgHcWpXLcuKFwE1hMyaoNRmhMAdoXFN7QdAuKvGCYl5iFx65leCPp64xclNQdHOseX/Rk5EiVoweD7+bILmPmNetGoFqH2tjEeU/uPePdTU6XS9Z1hnKA9HSloCwdRQeniyWTvTWWJz3leoZQS+p8TDN4tqhpMoNSlhbw2sPUU9ceqx1T42g6Qz4pcDiazjMIaBsBQiD7AaHHLHIo7IomJCiwokHnFUMLZa1wpeSsO0WiGaMprCMTEqEES7UgUx49MUihEbqmJkdZiZTQeoPTPV2WU2QaBbipwWmLK1vmPRS9RzAgaekySZspStawEvQwp10aXKVxjcDolkkJptNINyDReKtwrB4Etc4xwrF0PUJK1DBQuBKPREiLzhzagjUwSItGILzB5gKhMlYkpglDuwStyZCs4ehbC2PIjAUj0QVY7/Fe0QwSmSlKNcZ1Dary9MKhekHuc3QmMd7iW4MqNTkdxilKNNoWFNLSOc/CGQopyHOQQpLXFtVDn4MjIzOW2g34XCDEN7QtOF9ZqoYgNBVvBwFhCP4DKEmddtJAPQ3k4CtHpkA3CPtK6U3h9yHICpzpUDEIxwjBS6oHOQ80UkehVJieVhHSgDsEE2m15HxVJGznA9dULxCCyhR4hQB+uVxGkWL4ewiQU4eqUNEIguW0pJ/OQQgUQpB//rxSmlfIxqfgKYCiUJFIG+cFIJDSm1JqU6ANBGF24H2nupVU85KCwfPzGa5NCgZTi92UQhXWStr9O2hGUoAV/pYG/uepZanuJq1UhLUYQFw4drheKUBNxx+uS7ouQwCR9gBJKx3nQUmYtxSUfZO2IFDd3t6OWdgAsm/evElRFHz22We88sorkXoTNFOBI991HVVVxUB9Op3inOPRo0dx7oKT0qNHj6JwOqyD27dvx3X//vvvs729HRvFaa2jfei1a9coy5I333yTf/7nfybP8+gWNR6P+fjjj3n99ddjZjjYn85mM5qm4Z133uHzzz9nPp8zHo/xftU9OwSxIVAPz7HFYoG1NnZ4Xl9fj5WM/f198jxnOp2yvb3NgwcPIpD59NNPeeutt6I2Yj6fx/ssrKEvv/wyWtouFgtee+013n//fbTWUYNxdnbGBx98wLvvvhu7TyulOD4+jlqPoGUJTepCNSE8g3Z3d/nwww9fcsEL/XR++tOf8t3vfpe7d+/G7u7OOX7zm9/w9ttv8/jx4/i8CaLtVGv22WefxSrCZDKJjmVBdxE0Mi9evEBKybVr13j+/DnT6ZRvfetbeO/5zW9+E7UVH3/8cRR4b25u4pzj7OzspeSDlJJXX30VIQSLxYKu6+K1ODk5ic+A4Er13//7f+eNN97gwoUL8RkUqlvb29vR6SyI+Pu+5/XXX+f58+exL8z29jY///nP2d/fpyzLCDwDOHz69Cm3bt3ixYsXjMfjeO0CHWp/fz9qTe7evUvbtrzxxhvkeR6pU1JK3nzzza91z35toKEax0jmTBdT6twiRYkZ1YjFGWac4XuNWHrQlqabMylKmAyMRiULoZl3DrfwrE1gZgZEq3CloLAw9IbMWYpRzenQMK43kWaKGAr04NB2znw+RRU1pahQU0suZ1gUvXKUKsMtLKJqELZgmAvKeqUJkBT4tqGvwGUFZ7M525Mc21i8MmTFJloWNIuOPu8Y2QXYVUO8uqzo5y0y65Gqwg4wTCCrPGU/olSOk9mUwRSQNRQOrAchNagFdm2MkiUjudJYzPsG5xW51tRCs2hP0M7g7BjfCYZlzyB7TpWgKDI0noVRDOWIfpijcQjZo0WO6S2ZzGiHHmvmTCbrtM5w2M0Z1SPQPbUxiNZRkdM3Dl21NEsYVZIa8HnOvDtFdT26UiwHSSFKVOs5nQ4gPVJ4hKlZ9h3V2KJ9Ttd2IHt0rVGDxDSGrpTMzwRrWz2YBdQZYqrQS43MwRcLtJRI6xhkgbIlY69xrkOMNYXLmBpLUSskGePGMF+0LEuB0hJhW6pK0y8ABaoscI1FSEWhJFYJUArXGarMY4cGrxUyMwifIYRiGDq8lxjjcNrgKChdRp5JvARnHcILpKpx3oIXeClw3qPtCly5wSCUQLmBfujIRjnWC4bGk/sSY3psphHK4EQPosBKi7Ia7wRSWWoEw+BwXoKyeCFACsDgvGMwA94LEJA5iUJitMAOHp2VKNv+nrv1D3dLwUKqFwhBVgABaUY9tQcNgW0IRFOaUxr4pmAlgIoQbJ/XgqQZ9BBgntcanP9sSidKO4anYCZko9JjpcHd+Wx6GH/qIBTGHM49zc6d17kEoJbOU0qTCIFtAEgBkAUaSXre4TNBZxHmMD2PtCIQKBFpQBsAUPh36KsRAEbYzle4QnCSukKF9RI+n1Zo0qA5pQukayX8LmQawzyH74ZKTVoVS2lM4fNpJeo8BTAFdOdBUErNCusvjCWAXK01VVW95DR1nqIHL1fezlO/wjpOK3ypeD1ci/Pz/k3agmOSc6vO103TcOXKFa5cucL6+jrL5ZLr16+T5zkHBwdcuXKF09NTfvnLX/KDH/yACxcuxIx7XdfRhhXg8PCQmzdvRoeepmm4dOkSZ2dnXL58mb29PZ4+fRrpU8652Ksg/Pvhw4fRQjpUCT/77LPYaE0pxcWLF1ksFnzrW9+KwV6oACyXSz788EP++I//mNCn4vnz5zRNw/3796Pm48KFCzEAv337NkdHR3zwwQdcunQpWpROp1N2dnai/e10OmV/f5/5fM7rr78en2n/9E//xOHhYWzSlmUZOzs7rK2txSz68+fPX3pWhapIoPcopRiNRjjn+MlPfhKPFe7NUF0IQDzoZl599VUODg548eIFSikeP34MrNbnRx99xPXr1zk7O4vH/Oyzz8jznBcvXsT+OABPnz6NFUzvPZubm1RVFbtqh/PL8zw2FQw0tqZpWFtbYzQaRWF/aBw4Ho+5desWP/3pT3nnnXfY3t5+qVIcDCdCp/CyLNnZ2YkA8cKFC+zt7XH16lUePHjwkinGeDzm8ePHOOdi9egv//IvyfM8OkcFR7+dnZ2XnktbW1ucnZ3Fa7RcLnny5AlZlnFwcMBkMkHKr3p3hO7qwzCwubkZBfVra2tsbW3Fxo3B7vjg4ID19fUIAoPe8OLFi/Eafvrpp/z7f//vf+89+7WBxiAVXijKagtfSkrf0zuHlBVaKCwCtMflJZka0FlP6TNEL8i8w+UOUWYscTSNYlRoapfjrGGzWsM1sNAVVhq8aVkAWdkztWdMTIXO17DCkWUjMj/Q06K8QWYV2vQMHtyyIDNQFiVeGSqgkZahsMhcsLmRIYYKjMXYDGsyMtngS7BylTlubUdRrjFRHqOnYDS6K5irVQ8FUQHtMaOiwC9KFB22bqnKgrztAYn1HeiSYbqgXZN08yVog80dznqkNDjvyQpLaRWtgyJ35LpEO83CLbCqA3J8a/B5T98uGWXr9H2ONUvqrGfRONx6zbjQWOsoSkGlLE7Buh5zdtKStQYtNUZImqGh2oCmz5DZmFIONLIl26pZtw63XEJRs8gy6nGFP+uh89EyjtahT6bI7W1a0TPC0hlFJjKGZqVTaNuBjJwhE5hxycQ6bDPQ945hkuO1o/YS27QYq3DVGmbRoaUnWxsxDAYxAuTAWlmwmEtqDy73eKsgz8lyRWckVV7ghUNIjx/ADh15luF0BnJE5zwOKBWredcjOmdQLCjyCodAW0XrPaaxiLahnOQYIVEqo3cAHkporaQWGpdpWt8BGZnyZNaRZzk2d5i+w4qCUmT4Qq50Rt7iBoM3jiyvcKrG2wE/GFyuEArwnpHLUSgGN6OzCgpJJjy2NwgHtjfYrEJ0A7PHLxivj7/urfsHtf2uwDHNnKXBWEpfCRlh7/1LrkXwlQf4eV58CFjTQDQFOSHgCxax58FNeJmEKsJ5u9gAMMLDP812pxnwNGMdgFUIVEKwklZe0uOfpwPBy+LuEAinQvkAltL5DJWPdB9pJeS8tWoaUKfC6jAX4dzCtUqrQamGJMxnGsSHc0vHHMaT2tIGjU5a9UgrHmEM6fycrwCl+w7zFAKT1MXqfFUlgKcw9lAJSvebVi5SelYASOlaTKtEYYwpvS8Fw8BLbmUBTKVAMzU+SOfk/P2VcuVTsJHOyTdtE0LEoPAXv/gFr7/+Og8fPuTGjRvcvXuX1157jc3NTR48eBDX43g85gc/+AFFUfDgwQPm83nsfB3cjIZhYG9vj42NDba3t/n444/Z3NwkyzI2Nzf5+OOPo0YBvrpWo9GI4+NjNjY2ODg4AFaOWG3bYozh4sWLUUTsvadtW549e8Zbb73FYrHg8uXLkXM/DAPPnz/nL/7iLzg7O+Pw8JC9vb0ozN7a2oo9PmAlSB+Px0ynU5qmiTz94NJ34cIFnj9/Hg0OQo8MIFKwPv/8c9566y0ePnwYA/HJZMJ3v/td/vZv/5a3336bTz/9NDbRC2Di008/jY00wzOhaRoePHgQXauKoojc/o2NDSaTCV9++SXXr1+PDfVCcHzjxg2ePXvGq6++GitLSq06gP/gBz/g6OiIk5MT5vM5f/Znf8aXX34Z77s8z/nP//k/c+/ePfI858svv4xgaWNjg7/8y7/kb//2b9nY2HipR9DNmzf57LPPYqU06Cq2trbic+j69evcv38/6nMArl69yqNHj8iyjAsXLiDEyuEruD9VVcVrr70Wk1eLxYLPP/+cO3fu8L3vfY+Dg4NI0Qvfh5eTEuF3QaQeEmcpKA0d7be2ttjd3eX4+DiK54NWJlybuq757LPP2N/fjzqkdP2sr6/HpolCCHZ3dyO9dn19HSll1JG8/fbbSCmjKP73bV8baPSqp2kbstzTdmsYMTAeCZpe4s56wEAt6AZLRY53DdZq6rFmsXD0yyOkWEMLTSEceIkXLY10mF5QZ2PcdMZa5vDGIqQn1xldVjPtPFnvKNdK6MCKAT3UNFaAdchRhrcDG8JjmyWDlWSjdXrXI2WOWCxRGJwb8EuB8N1v3SIsVrR0nWNDbdA5RyEFLtcMbqDxPb5tqIsxWZ4zX7RUfUWmYGo9lhY/9HivcOT0FFjdUa2P4AwKt6RswWqJEzlqnCGHAUdPO5T4RqK0I88y8B1iPKZyHmksnbHoicJ2HrPsEVnNyWAoJLg8Z+k6MiVAeJwwVMLSUmKHCbYraIYlovQgM/pMM7AgzxS9bXFK0PeGLFsF/a7paPscI0uGvmIr0wz9EuE1zkiqNY0rM7xxDKVjaFuKSYm1A9YLXLdqoHQmHZkTLEzPRb3BQT9jqXNsL3F9RtFATokzSyZrGc1C46RA4Jl2S8R8YD3PMYdTbA5+opH1EplprNV4LINcoHyBdqPVueMYLEjvQUqcsXglcEKjlEHg8a5AeIu0hlJIvKoQ5Ahn8QJ826OdIBuVdHYgQ4GBRWOpakXuBE568CC8xPscgcRJvxKMDz1SKHwFprcr0T2Ced+RScikZig0rTdou8ow5lqTKYkxiqKULBc9WjjUAEUh6USGMyCMQzqN1jVdM9DN5xwXsP91b9w/sC3QGEKAmgZuQMxIhWxzqqH4XcLaFJik2bI0EEw1HmngNwxD5LCGCkQIxoJWIRwvFdqmgWWayU8Dv1TcmPLkU4FyyO6HY6fBZBhjCHRDZj78O+Vwpy+l8Pnwckn1GWFe0upEAHDBnSUNuFNuewiShBAxkA4uVmm2PlzD8NmU3naeJhQAVdr8LnwuZP1SMJCOOc3opwYAIbA8fw1SwJheo3TtpVqIMAfptQjnEfQTqZtZqgsK4DME+eE7AZimDR/DXKaALowpZGbDvAVQct6qN+zjX6NJpYFx0PsEi81v4raxsRFtOV9//XXW19dRSkXR8yeffBLX9SuvvBI7LT98+JC33noL7320Db1y5QpZlvHWW2/x2Wef4b2naRq6rotBe1EUTKdTXnnllcirH4/HdF0XmwEuFouYsQ8UlzzPYyfq5XJJnuccHh5G2uPh4WHUQQRg+OzZM65du8ZyuWQ0GsWqY1VVAJHn/+LFi7iWQ++Nuq5jMzVjDLu7u4xGI959910++uijl5yMhBA8evQoCqrfeeedSN0Jncp/9rOfsb+/z3K5jH06guj50aNHsV+O956LFy/yrW99K4IY7z2TyYQrV66wu7uLlJLHjx/jvee99957qSp4eHjI1tZWrAK98sorL2X0pZQcHR1x+fLlOPcPHjyIlYmQ6Pjxj39MURQ0TcOrr74a6Wt7e3v86Ec/eomeFaqrocv57u4uP/nJT2I/ijzPuXbtWqSdCSG4detWHA+sAOYnn3wSAVR4H5VlGcFU0PCE/hZXr17l008/jQmub3/729y7dw+A9fV1/s//+T+8+uqr/OVf/mVsWHjr1i2Oj485PDxkbW2Ny5cvU5ZlNEII79QAWk9PT/Hes7OzE93N0mdzeO6EZ8XR0RGffvopVVVx9erVaGgAq/dHsGwOc33z5s34XD08PPxa9+zXBhqjWqGdY/CO3Ba0xiOcRg4GjaNQGYvumNFoAicZIivoHHTP55jRGC8r0D2DFwid48iZZy1WOYSuGAZHXZQsxRwlYFJmDIPC+5Xw1zoDXYYxCutaqrLEOUFrLW1RIztN7zrWJ9s0veN4bpCjDLvoKVDoDlSpaHMBJuNs2VPKmoyc5uyQQc+wo4JZbxnMCUqN0ZMNxAXPcsjYmA8Y36N7TbE2wuEZbIdyJXnlkJnGtL8NKIyB3lDVOVr2jFyOLysW0zM2KkHnC7zIkJVAjDXD1MKgaKaeTp4xLkAMnrZbUpfbdDRktYSlXzUyFCOWLse7jNoLOl8yyIxB9JSjkvasp/aapjA4L0AoRF1QDg2Nhy5r6OipRcHmpGKYN1gaxlXFtG1orMVlCo+mzDzdMOAFSF2ylDXSttQLi5QgtKCpYTY05LJiUhQ0bcf00YzRlkQ6B0rjsgzlO7zvWQqLGARZ0SNEhy5grVrDWQduBRLm3qKnHaOsou8M3kvK3JBLiTY5Vnu8a1E6R/YCITqMyqCTaO1Wze20QAoJDCjpcFZi3YAQoPyAdJredkjnKMqKgQbnBN4PdNrjKsegR3SDQSlQWpFjUU7SGY8Q+aqJpFS0xpLpilJ6rBlwBSgpUVikcFgPvVcUDnRe03cN0oNUGqxB5B1aZChZgl51OJde4nNJ43p05hDCI7oM8w3lVgPxJXs+23q+u3UIDtPGaSFQSoOxNAgPOoIAONIKQ/rd8/SVlHKVBmZhS8FFmj0OQXtw8wnfDQ/zNGscAEYaKAe6UxhbGqyeBwEpGAFeAmlArBKE/QahdAhiw3fSADy8oNPsewiKU9vbsAUAECoCIcOVVkDCZ0KFJs3gh0A5nFtawQnnll6j81tKS4KXNRLnKweppW74LLxcaUq1Fb8L/J6n2IVzCIA0rWaF44bzCXN7vvKSnkNaHUuraQF0pUYH4dihAhUCvFS8HihtYTxhXs5T84LX/zcVaKyvr/PLX/4y6hpOTk64f/8+b731VgQg4TzX19cjveTChQsxc3t2dob3K71DCLaqquLo6IirV69ycHDAgwcPePvtt6MGIgT0JycnkSIVqv15nsf+FxsbG3z88cdcuXIlOif1fR+D1+3t7Xjtnz9/HkFC13Vsb28DxB4M29vbnJ2dRave5XKJtZYLFy7gnItVjiCMXiwWKKV4/vx51Fz88pe/jE0Kt7e3efjwIXmex8rBkydP+PDDD7lx40as+ATdT9CcVFXF9vY21n7V8yOAn6AhODo64saNG1E8f3Z2xsnJSXS6CtWPvb09jo6O4rPiypUrbG5ucnBwgFKK2WyGMasO296vHKQ++OADXrx4ESmMo9EIKWVsrnh6espisYjAMHQPf/bsGZcuXYrz5L2PwX5I9AC8ePGCvb09Ll++zHK5jP0ywnjKsuTw8DAG4sGq9+bNm7H7dtBIeO9jI74XL15w8eJF7t69y3w+Z2trK4quHz58GMHXZDLh+Pg4aox++tOfsre3hzGG6XSK956NjY1omZxlGdvb2zx79oy+77lw4QLr6+svVUJPT0959dVXo/sZECtXVVVRVVUEzN/73vdic8qwhefphQsXsNZyfHzM8fExXdfFJoNftxfP16dOKUlhJXWxSd9LZC+wx4Yql/gip505GK/R9RqpBtQg8LknrzLKwjPtVxatuSgwZLgiw2uB7gxDb1h6SY1j8ILWebJly9B5kBMKtUlRLLCmAe2osjFZ7WjODlF5QWctmDG6sfQjx9TOKeoREsWw7lDLBS01wg141UOtwBrm3SmjrkbmaywXPcJ6jPJkQpJLjWksg+gw3rNwPTIr8L5ncQooCa5BVGtkVU7uQbsZ3hvaNkN6iTSCRT9lY3ePWdNTSc1y2dKblTB6MvHMl3Pwkg5LIRzSWQZdMPMDVb6HmbVUWY71PVb3jGxFt5yT14qlt9B7lBC0ylD0Hcv5gM4rdL7qdN71BvIC6WvaYUlmDevjdTpfYHrH0vfkWcVESZaDIffQTj12LBltOKrKw1xhF5DVPaJWjHSOXy4Qtket5VhrUKbCGc/x2YLdbIJb83TKodwMqQBKhk6j1xSl8ZhGQGZQFjJR07gFvfKooUSX0PklMqtpbM5ElfR+Sas9/SKjzjJGpcf2HdorjDOYDOwgUVKudCUCENmq6uEa8GBkibMgpEFJDdIjUKAlnXd4pykliMGgMkmpJV7YVRM9rxh6g8wkRlq01AxnLfmao7cGnQuU8AzO4L1FWgG02F4iKo1SlgpH5ySiNWRZAd7ijQMhkTrHGoF3AuU83lk6mQGGqtAY4xAFDL5jU35Dm2hADKyHYYgPuUAnOs89D9t5WtF5ykr4TiqGDUFeGpyHQC+tAqQBWBrcpgFlWj0I+wqgJoj+0gx0GGc4ZkrlSkHE+WM756INYQhcw3+pTuP899PjpUAqzRqmNLKQ2UwD2RSUhepG2E/Yb2oLHI6ZitxDpj9QN9IeG+l1TXtPhC0EyWGezvc+SUFF2NL5SceTUsJSZ6VU2Ar/UqMQzut8FShdD6m25Hx1IuwzgIzz1Z50HgLoSit26fmm1yvVKIWf4T5Kr2e6hgIwDeNN5yWAjK9rTfmHuG1sbCDEqj9BaDh5dHREnudcuXIlgovQSO309DRSmVKtz8OHDyOd6LXXXgPg1q1b/N3f/R2XLl2KXZJnsxn7+/uxCpJWhkajEWdnZyyXy3iNgtD5008/Jcsyrl27hpQrwXDTNGRZRtM0USsRhNI7OzsxoA1VDyA2cBRCcHZ2xjAMXLhwIdqMnp2d4Zzj2rVrFEXBO++8w3K55OLFiyyXS46OjiINMdzroQnb1tYWQgju3r0b+yqsra1x8+ZN7t27F12wlsslGxsbUZsRnrMhUTGfz3n48CE7Ozt897vf5ac//Sm7u7vxGRkavx0cHETQFYL+nZ2daCU8m83Y2dnh0qVLkVq1ubmJ1pof/vCH3L17N2oWgk1uaGKY3tOB8pO6ZD1//pwvv/wyuo8Fwf39+/fZ2dnhH//xH7l582asgmm96vA+mUy4ePFipFcFulG4bkIILl26xObmJs+ePePo6IjlchnNLII9rbWWq1ev8rd/+7cxkfPOO+/QNA3T6ZQbN26glIouZ+H7s9ksOp3B6hkQrHfPzs5in5BLly5FLYVzK5H8w4cPuXz5MrDSyYTqx87ODsvlkqZpEGLVRT1UM7z3LzlNdV0XE2rXr1+PupKdnZ2vdb9+fTH4UqKUw/YrRwJZ1IhioHWnaF+QV2BQeGvRBeS+5IWd4jNL53ucGuNmHW7sUHXD/PSMtXrVgE7OHDYTdNmAwFJk67i5ZX8TZs0cITO0lLAE3BJjMwZZ4tnAOoucWyYKhjrjYHHEhfWCfjnFLDXjSc7cC/pxhmg10nrWrGMYJF1mEZOKrDUsPOQ9KC3pC0VmPFJB7g2bmaHJehZWUFhJVZc42dK2eewT4XyHxSGLAqOhEB0sBDof8cLNEKWjPJ6S722gTc2y67G+pDAlpphT+IymtRSVpj+xSONh3NB4hxEZuhuwpqfPcpYqJ88Nc9EhfMko08zbDicVm9k6J4PnmT1jXOcIKVfibFNAVdNLTT8bkF2LViN6L3B2SWZynAaZd+TVBp1adWo/aBbkpaefO0ZZhS7WUF4y+A6bG4aZZZRXdLLCjgW1GrM0Dc4a2kwgesVmK8hq6HXB4vQAMVojVwpDR28KcuXI8oI8r5n/Vr+QyTGDleSlxLYWj8TJDLSkF56ysyub4cwzVBLnNQiB1w5vwWea1i4RVqOdRAkL0qyqBYPFSYNwEun8SqytJM47kIpeCTKlkDLHWIO1CucFqpQY7/G2x7Ugpcb20AsQHXRYyDUKiew8oijASaRQOCdBWDIlkUi8dlgjwHukF0iZ0wmHsorcWawHg2WUgXQ9eV7SWxCyRXUz4MrXvXX/oLYQQMJXNKkQAIbM7nmqz3nAEALOlHMebFBT6lH4bMhMB3F2CFrDFvaTZnNCEB4CvzCWtDIRKC3hu6GSkYKhsKVOQangOIwxDfRT/UgYWxrMptSolLIV9hXGlJ5XqmtIs+fpfIY5DXabgV6TBsWpUD1QzEI1Knwu0N5CoJtSvYLuQoivOmeHc07HEwBKyLqn1wZe1uWklZL0mqZrKOw/gMc0+P/XAE96PcI1D8AtbXJ4nvIW9pEC6HT/od/H71onQQ+S0gZTWmAKWsM8pFSv1Nkq/D4FoeF7gZLzTdzu378f76dHjx5RVRXWWq5cucLh4SHDMFBVFVeuXOHZs2cvgdzzdLnlcsnVq1dZW1uL98s//MM/8MMf/jDSA0ODtkePHkU6049+9CP+6I/+CClX7kPBdcpay4cffsjrr7+O9yuevBCCJ0+esL+/z+effx6rAWVZcv/+fb788svIhz86OnrJvQiIblZt2/LixQvW19cZj8cx2A7i98PDw7gOnjx5EkXvGxsbkZMfKiWB8lmWZRzb7u5udCXq+56PPvooCqWFWHVfD0mIxWLBrVu3uHv3bnS5C1S1J0+e8Mknn3B8fExVVfE+2Nvbe4kiKoSIoGptbY1PPvmEtbU1yrLke9/7Hr/+9a/jdQsC9c8//5y1tbWXtAPhORGeK+HaBgAeAE5odhcqWiE5FWhlBwcH7O/vx2djoNZeunSJxWLB8+fPuXPnDn/yJ38Sn12hinR6esq1a9dih++NjQ2UUhHsGmPY399nNBrx85//nK2tLZxzXL16NVZ8AjWv6zpu377N3t4ed+/ejffw9evXXxLzh/fn1tZWXHtffPFF7JURrtn6+jo3b97kww8/jA5TAbhtbGy8RJUKP7e2tnjw4AHT6ZSiKJhMJlhrI8gMdrzPnz//Wvfs1wYareqp8jHSS4bcoSrDkPfkxtPIEu0tvZPkwynaKCyOSb6OnR6xeWGTk2NPUY9ofIsyHWMpVs34hEFXI4wbcN2SXJZYYRBVB8NAyYxOSxZWkVMgZIFUnm4x4Jym0AWZt7hyhspHZIsx88ZiVI7ICtxiSWbWWR97mm4GZQF9gbYtY0Asp6hWU+sStQWFn7HwEmFXnZ8LI+mOzxA6pygV0nqsdCz7gXpcMVs4WlomaxKhNd4I5HyJt2dYv0ZLzkhkSFVQXBzTt4J+0WHVjMe65WK2R9aBbTpK77F4ZA+jPKNyA056rPcUeUUpK3Ld0BuNnObsuB4vFO18jl8Dm+fMTk8Z5SNavwq2dZUj+zkMC2aDQckJeZkjncVbgW46VJ/RVoAuWLoFpV4yzjViOmALQY+kmNTYrkCInrkYUMWY3pbUuUP0AmvmSOXJ/SbWO/yoIGNBUWvICpoldK5FC8nYSPpe0ZclvRqQTjI0BnxH5gZyUbKQllw5/MIwpSUvckqrKTtPq5Y0HvIamqZHygKPQguBH1p8JtDOI3qDsgppQRSrKpO3ParMcc7ijUDl4JEYKcFI3NDjc01vNLk14C1SSLIyw1mLlg43eFrTI8oSPOTKgRZ0gyPz/YoO5RXC+lV1xgK+Q5kcJy3oDOkGvO+wg0BWOcgO34pVNcYvyUSGzgt8Z1Y6Fi/Qy5WwvlXfzAABiLSN8KJQSsUXUcjSpnqJYRhihjxk40LgFqg6IfAOYvDzVY000EqpOyFIDlSTVD+R0mhSCkoaPKZ0mLQiE7Y0uA/jDcFMmlkMQfv5gD/sKwUOqctSoB+lwXNa4Qj/TkXJ5y1lz9vLhkAg0NbS0nigUKRUs0DjCdv5/hPnA7xU76G1fklknc53SmdKgVU4VkqvSzuqB81Hes3SKlDabT29zuf3GX4X5i4AkvCdUM0I+wkUt3T9hTUW9h0oZal1cXqssK+0mhaCjHD+aSAVrun5ikjYT1rxCZ9Jxfnn7X+/KVue5zx79iz6/Icg7PT0lDzPadv2JQBbFEXsVxGojiEpcevWrRikBsHr3bt3GYYhVh1CVjzQTQLl6vj4ONJxwpZlWeTQh14DwU727t27UVw8m81ioHx8fMz+/n7sP3F0dMTTp09ZW1uLmgEpZQz48jzn+fPnFEXBwcHBv6C75Hke+x2UZYmUkqZpkHLV3G4+n/P48WNu3bqFtZa/+qu/4uTkhGfPnvGDH/yABw8evERhGoYh6gOOj4/Z3d0lyzJevHgRG8R572MPkTfffJO///u/58///M95/PgxWmsuXLhA13VMp9NYqXBu1YNGShl7ltR1zc7ODn//93/P97//febz+Ut2srdu3YqWsLPZjO985ztRXyDEqjHj3bt3mc1mrK2tcfHixVh9Cc/6jz/+mMlkEhvznZyccO3atejAF55/x8fHzGazWB3N85w/+ZM/YTwes7W1xYsXL2KX+d3dXabTKePxOPbBCGvz6OiIN998k88//5ydnR0uXrxI13WMRiOMMRHgBK2Vcy66jAkhogtWoIjVdc3e3l40Cgg9PI6OjhiPx2xubsbq3meffcZ3vvMd4Kv3SGjweO/eveiWBvDo0aOXNEWvvPIKd+7cYXNzk9FoFC3Kwzq4efPm175nv/aTZjI4BuuxvWVS1tBZTFvTK4/r57jJCCMFqqrJ0dAIxODIygKzNIxEgWvPWF8vWRpNPrErZ6LlbxurnXbkzYBeG+MbgasUrVCoTJB1HWW5QTtuGJSjGwQZsLYhMf2cpWnpe0u1qOgl2FKRkzM0LR0LNootxEKSI3BLi61Bjyr6Zz3VRDGnQzUDKi9oZYFth1UX8a7DFxX5xhpFZ+idoMlgvffkomC6HCjLDcqhJxskSnukd5x2A+NqF6Fh0bVoJ2iXS5bKghT02RgrpqwhcY1n4TxFtcCJDO8L/MiRe0U/0/g8wzmPEEv63K2qMtqhMfhM45ykyGusG5gfOdT6COFbqtbgB0lRLJllFiUUupGUugJr6MUS3xvKQtKXFc6urIZV4+kyaHyPHZZs6QlnbY7tp+T5yoHJ/7bvRqYk8+WccVHhsxEDA7UdMLrDeYloPEs3ZSlyKlMiTM/QSnwBUg1gW8aTCts2IDOWyjEWDaZfMGKDoTcMfoHuFZkrMM6jipWlrZAZQimkkrhBUQiJEQNCK8gstI6aGqEFDoNXis71oDwGSykznJS0pmekJc72WLWyj3VeMEhPbzxGQqkd2jpMJ/HFgNYZ2itsCdIrlLcgBgaVo7XCdi3OgBEKJxQDrKx9nUMhcFg8gM9RUmBdh5cKVSmUHfAuR8kcY0FqifAe0wwrWl6mcdk303EKeMmxKDgh1XX9EgUlAJC04VoI8AJNKa1qhL+HjNa/lrEP2fRQPQkUklTUHKoSaYbnfHUjUHJCoPiv2e+mNKqU/hOOm44xBQ9ppSaM4byGIQ0cUzpQCK6Bl+YjfDbw+cP4Um3H+YqDMSb61IdjVlUVQV0ABmF/4ZgpMAJeCqZDE6r0eGG8qT4lvXZh3aQVmjTYDiDl/OfC+kqBRlgvYQzngWUKBgMA897HTGVYO2nFIowzpbSF9RX2H4Byuq7DPsI8paDgdwGGFGyn85OCsvD/aRXwPCUsrKOgtfmmbSGYCtevbdvIGzfG0LYtn376aXRHevr0KaPRiMViwf3795lMJlHEvbGxwfHxMcMw8LOf/YzXXnstBo9BjxGE3Kenp7zzzjvcvXuXzc3NGMxqrWOVKjTZVErx5MkT1tfXo8Xp+vo6n3/+Oevr63FNbWxsRPvcAEqDkFupVafzEOhfv36dpmmYTCaRMhOy86PRCKUU8/mc0WjEtWvXYjUjZLX7vufp06cURcHt27dRatXJ/Pj4mNdffx1jDHfv3qWqKpbLJWtra3z55ZcRHLVty8WLF1lbW3sJvIW1ubGxESugoQpw69YtdnZ2+OKLL2KFp2kaNjc3AWIDu+fPn7O/v0/XdZycnHDjxo1oq1tVFXme82/+zb/hww8/BFYVm2vXrnFwcEBZlhwfH3Pjxg3W1tbo+z5WGHZ2dphOpzx8+JCiKKiqijfeeCMmAUIlLFAJ0+rvjRs3YiO7+/fvs7m5Ga/R8fExX375JW+88QbDMLCzsxNduMIzOlR8g15lb2+Px48fR9ewQFV744034lqq65r79++jlOL999/n+vXrXLhwgcVigfeey5cvI6WMY3/w4EF83mitI8iAle6k67poIxyE+oHWt7Gx8VJiZDqdxm71QQAeNCGHh4c8f/6cvb09bt26RdM0PH/+POp0ft/2/yOlodAauixDOg+dR+mB0WQDe7pEzSSZ7pClpuvnjEZrNINZ0UrwKBoG2WOFwY/HLKY9YqERfU7nl+hqRLUusd1A7TRk68y7ZzSyQ7SajUzgLAyZoqxBmZZcdVhVYKmQtsFkJwhbMvIK5eZIJXAb6ywOWiqRUYwMy4Vjbuc0Z5JRlSM19INlbRO641Nctc6h6dnNRygvEb2m1QYnDIWVlJScHZ9Q707YEBknR4dsrRcoIxBDRj8s6XTBdJFTVD3jMgfjqMp1vByY+TkOyVq1RdGfYKszim5ADiV+vUScObJaIa3ltDX4IaPMOpZFh3MD3uTIoqLLJaLOUc7BQlA0Fp/XDPMWM7IUokBnjsWiQ9UFbiFQUtGIU/SyQhUboNpVAJxbFpml7wxjkXOmCrQdKLRmkD3lJGMkMvojx7xpyWRO2/QUEiZrY/reMghAenqt6e2IwbCqDtQ5I1fTNw2jnZopjvlCkUtDLtbJpgXeA96ihwYxGkM+B+/xKAqbobIK4RWqG1jqnlJopHHgxig3II3AFD1LD2taY9sFslpDdh4nBJ3WlGpV2ciQWDNgvEGogkx4vAAUCDdgWgNO0MuVfa62GrtY4AqxapanBYMD7VeNDr1zCOWRUlEJwDlML3G+Idc11kns4Fedy71j1cxc0JqekhrrHFIYhAfZOYQRCDK88Yg6Q2qDMQNeSszSYDqPFN9MW0rgpWA7zViXZflSZQG+CiLTykEIToNYtmkaYPVyDi+987SqkPmFrwTJIfgLNKEUNJyvNIRjpyLzsO+U/pQGlWHMv0sfEv4LGfEAANLg+Tyt7HxGOxWRp1WM8KJM5zbsOw10YcVhDvMY/h6OHX6fOjil55P2okib9KVjCwAuBOQhmx8AS+hmm1YmArAIL/vzPVLC2MIYnHMvgb4UvKUAKr2uafCeAp30OqWBwvkKVwCeIahKXcXCmFLwGa5BuI6pAUFKB0zHlwLdtIIX5iatXoT1Hz4TxhSOHYAhvFwR+qZ2Bj8+PgZgOp3GSkOojoW+De+++y4vXryIaz2YFgS60cnJCS9evGA0GsVsf2jQd3BwwLe//W0eP37MwcFBBGSXLl3iyZMnCCFi74yDg4MY0AdqyWg0ioHs6elprMqura3x3nvvxWsfwMmLFy/Y2toCVtf76OiInZ0dhBBcvXqV3d3d2LwtBIRBL9J1Hd/61reiKD0EpEKISKEJjkFCCLa3t/nss884PT1lZ2eHhw8fcuXKFX7+85+jtebWrVsRfGxubrK1tRXF1+HeCE5Np6enfPLJJ9F699q1a7Eh4PXr12P38vAMmEwmvPbaa3jvY++Qo6OjCLwuX74cBd9h+/73v8+vf/1rpJTcv38/PiP//M//PFJ+2raNWoFgF7u3t0dZljx//hwpJRcuXCBQnZ4/f87Ozk4EbMvl8qV3hJQyVpPee+89Hj9+zI0bN6jrmi+//DI+C5umiVWNvu9jpSeYBwSxdRDHl2UZG0Vub29z7dq16N4lpYxi60Bfu3nzJqenp3Rdx+bm5kvvngcPHsR1DXDhwgWapuH4+JjJZIIQIvbyUGrV48M5xxdffMGNGzew1rK+vs4//dM/RT3QZDKhbdsoCg/i9WEYmE6nvPXWW7ECM5lMePjw4b+gtP5r29fvDJ5PkHmBymfYsyXleIysoOtnZKKg7zu0y+iVISPnrOvpWkvmM2SdMegzBl1TLyuqrsWaAj8M9OVAPVnDLFqaocMYjVCe6dkZDqgm67CecWQB4chzh2oKilrh9BLpBnIhcKLG+xmltRRlzdJ30OSIVtMYR1E4JD3OSzKfU+c5g+lxMuPiJKNtGygnSJdRj0c44an7AtMI+qxDiAH1WzFxtrVJpy257Ritaxw9y6XCrAuqyrHeVHQ4elWR2ZYC6BZTRCZR0lIVcyaFQgCNGcBoRKfxS0+rLLmtODubUZYVPYJcCLA1QmpOfUfBQJYXSD9gl6DsCIND2DmiWAfXkQtNh2KQGa7zSARa5mRW4q0kc4JOKbquodSOic/pRIEpQTQd827BzvYWxsxpu46yzFc6l6zFI6h9gWlbZvOWbKxRjaVZHqJHWzhfIQtLKQVWFAg3kG+NmFPQz07JtYcsx7uczgzUYlVRkHLCwiuMtKxXCtc2dBYUS8a5YpAduRIor+hNh81rlHFInWFFh8sFQmmUrVAiw+eCoR3ItcQOBgUoFFJIrJf01lIh8V5hsUiXobTCO0+lHNoJRL+imBnrEULStxalLL4tER6cHhD0eJHjTIN3Aq1KVF0gTI+UDldZBjS58ehc0FuLKEYMnUOWHikL2saRKUVrZqsysLXkSuM6i5YZLYbjZo4dBgr7zRVxhoAvAIK0uVl4oQeQkGaG0sxsat0KLwugw7/Dyxa+4uqGfaUdn1PufaBxpVWSMJ5URBp0DGlQmAbD8FWAmL4c0hdoGkCmXvApJSocKwTp8HKjvvD/ISAP/x9+pvsJ+wjAIQS4qSVwCmjSzHe6v7RCkI4n/Dud199Fy0qF/UGYnFrrhmOHALEoinhuAUimNLHzlae0ahE+k4KCVBMRvp9WSNIxnKc2he8GABLGHb6XZdlL/S1Cdec8+AnnE9ZECn7Cv9PKSbpeUiAW9pEaIqTrIz2v32UmkAKVb9LmvWdvby826zPG8OTJE65fvx4pOIvFIlJ6fvzjH/Paa68xmUx49uxZnP8QFFZVFSsGbdty9erVaI36p3/6p7HfA6wqcoEC9fjxY3Z3d2P/AiEEFy5c4Pbt2zx8+JDt7W3atuXKlSux4hK0Fz//+c/Z3d3l+9//fqy2OLdykbp06VIM9l955RWklNy5c4fLly+zv7/PZ599xr1797h9+zaPHz/m0aNHHB0dsb29zXg8pmma6LbU9z2PHj3i+vXrcf1sbGywtrZGnue89957aK2jXe5yueT+/ftsbW2xvb3NyclJ7KQegFlwTZpMJvz1X/81H3/8McvlMtrhSinZ39+nbdsoQO/7nosXL1LXNUdHR7RtG7UjgUoaKiAAR0dHdF3HL3/5y1itDpWlAJCCbuD69eucnJzEebp48SKbm5sx6XF6ehrv9aDRCPqFkKiaTqe8+eabsXLh3MqJTErJs2fPYgO/4BhmjOHy5ctsb2+jtY7g9/Hjx6yvr7O9vR3fC0FAH57zofoUxrG/v8/R0RGHh4dIKSPwEkLEak14ngRRe6hehfsAVn0yUk3N3t4e3/3ud7l48SJN01DXdQTBoaq7t7cXnxt1Xcf58N7HaloYx/r6egQiga4YKlO/b/vaQEMXiulyjvMNI+UxtkcOOWbRI5zFVYJ6rMkMMCim8x7ROtbLEe3gsdkuamhQQ4+zM7wdIzNFpUqOmw4hLOtr2+izgd4vcL5jUo2RZwXLsscMjlp4siGjo1/131h4xnWFUA572jPKNjGuoR00XbGGHltEO6eqNO1iQY+nyifM+x5vB5SrWJxmuPFiRaFwOTmwZTyZhMXC4hzkfgRuxacf2lMMOZmQCNUzOMUwSLLSQd/jtMJ3hkx6jLPMzZJBKwoJmFUzOp9pDmdn5N6TeZC2xxmN1o66K3GNY5xXWDFGNg12bJFZjTkTbExKRLlALgzDtKRSCmN70JqqLHDCkVnLXEInHEVeo3pJbxvyzJJ5ics9g7dkxiDzAptJ5mdzNDlCV2jRU9US0fQI71jLc4TxaGnI+hzkCEWFEZ5y1DP4M4zSqFFBY1cgp5QKaz3MG/JM0h+BmNSsjdfIxBw/DLTNDFGNaApJ2wgKIzB9C+MMMZOMqnXO5i/YXKux3mOGHCcsqpKQlxg/4Lyksoo8r8j8At9Nkbqiazq0VGgBmBakxuuC3hqQGcpBITQeh8ehjcIZgxMW5wakl+AWLBtPtV6xcA7vLRhJrmv63GGcIc8Uwlf4HhAC4TMyLRi8RyqHE24FVLqBUVbiM7DLFus7CiHItKJxBq8yvDXUozFCOvqqZPAelWvaZb+iENZjlk1HJb+Z3GrgpQA+ZONT3ngITFPtQxpQApELHzLJQTCcBrrwFa3o/ybSDp9LA+m0P8b5akoanKWBaxh3SksJAeD5zswhAx+OeV43kAa2YX6CwDocMw1I0ypPSgUKL5OUjpRWW8ILJK2chPM4v6+0SpRm4tNKRgpazou00zGmQf35DH743XmABMTqSRhfulbC78P40mpR+FsKQs/PezjfUMVKHblSCli4Puk6SylYac+L9PwDOAn7CZbN6TmGOQxgJK2uhH2Gf5+nW6X3VLjW56mFYZzf9E2IlWNSsECdTqdMJpNYQWuahrfeeos7d+5gjOE73/lONDG4ePEiQGzE9+zZMy5evMhsNuPnP/85t2/fZnNzM1qK/upXv4oBoRCCjz76iDfeeCPqQYQQsSFeKmg+PT3l6tWrSCmjNW26Xv7Tf/pPkY7y5MkTbt68iTGG2WwWaXoXLlyIFKzwM2iQqqri4cOHcU5C5vznP/85Sq36Xdy7d4+rV68ynU6ZzWYxmx7uw/l8HkXKwW0q8P43Njbouo7Lly8znU7RWnP16lWapom9RIIu6fT0lIsXL7Kzs8Pdu3fZ29uj7/uXeofMZjPOzs44PT1lc3MzAoxgOxtsU09PT+P6TbVXwX1rb28PWFWXQhIgAHrnHHVd07Yt9+7d4+23347V083NzVgtmU6nnJycxI7ju7u7sfHdaDSK66uu61hpun//PpcuXYrPrtlsFu/HFy9ecP/+fa5fv87W1lZ8Nj59+pStra1o5au1Zn9/P1Lenjx5wq1bt/jNb34THcpCt/bwbLh8+XI0MDk4OOD09JT19XXW1ta4evUq8/mc5XLJfD6PIDOAmWBrO5/PWVtbiz+DTmk0GkWXMCEEH3zwAbdv347Pud3dXZqmiQ0XA8VsPB7HZ0ugaf2+7WtHLKJvqXODVBrfdmTekWuPyQv6vsAJaLueYTFCjRxCduhCsuxf0HUau7G14vbnmqWoweYI3zNqPTr/bVbu9JTcFXRNRykL5MxQVoIhnyOdhsyAsIhMUhp4ulyipF5lm3vLwraoTOF7ifQZXlhElyNcRrapWC5nqMJDqZHNgJOCRaboXMGo0hS9RjiPmTd4qch8gasc/eIEm2mqMiMfZ0zsGOEGemexfY+va7w31PkI2wzMrWecFawLSVM7GtsgqhrXeJyTCKNBbmL9klwoTO8pRtDMHSq3WN3jTUZPi5t0GDugGkddjWj7lVD4pFmS4yhLhRM5w+CRU4WULY1p8Jf26RcnjIVF+WyVFTdTZsoxqtbwc08tJWKtYN7OUc5Slp4KxVQY/CBQuSWrMhatoWgc2WJJvrtBKxuUk0gszeBQmaL04LMRpodiAtoP2IXAFQIzqrDdCYYKbTOsMQgy1nLB1C+YdQ7nQfmWtdzSdZZCKEwPm5MdMtdjvSLPBEiH8pJSOuZ9j9MFVhlwCus0uRE4p3F4rFZ4v3JBA0mvLEZ4FB5hJXZpsJUhLzKkB+cEwmmEzvFypbvIcxBaY4YBAVRZhjOGIpcMv6XRt3a1zxwQUuKcpXcCk2kK4ZFSMMoV4BmcwGeK0hqkFXggwyG9QUux6vdhGqQuGFSOw5LVAoylWyxQa5ZJ8fV4kX+IWxokhcD3vEg7rXSEz4UHW6A/hJ8h2ExdltJg93dVGdIANQ1aw3eA6I2eUpBSwBEyPSmNJxwnBDtpQJpWZ05OTuLDGr7Kop8PuFOaTaB5pRWJNFBPg3zgJfAQtvR4Kf0mncM0630+E5/SydJ5ToPe3zXn4bqHMaRVgEArOl81SAP2MJ602hLmNYC49JqGfYfrdP6apeAxjDHVnYR5TelxaWUjreiELew/XK+04pACw7CWw7jDFkDGeZpUuv7TY6VVthSUpxWwFHCGLayf8y5d36Tt7Ows0kMC7zzY0Aax9osXL7DWRu5/oCg55yKlSmvNpUuXyPOc3d1dXn/9dZ48efISKA5VhhCYGWP45JNPqOsaa21s6gerrHgIdkOAHixwAwVPCMHBwUEU4Sql2N/fjxWno6MjFosFk8kkdnYGoitWcMAK+5ZSRq3EeDzm1q1bCCEYjUa8/fbb3L17lwsXLkRb3dD1OgTg3vv4rAqUGaVW7lenp6fxuIG7v729zdOnT/nyyy957733uHfvHuvr61y7do3Dw8NInwo0pSB8vnr1Kn/6p39K0zR89NFH8Vl2dHSEMYa9vT3G4zG/+c1v4hqGVRUzfDYkb7xf2RLv7+/HuU17eywWC/b29jg5OSHLMt5//3329/dj75FQYXjx4gV1XfPJJ5/wJ3/yJxwfH8eGf6FDdlmWjMdjXn/9ddq2xVobdSH2/2vvTZ7kOq6z719Od6ix0QOAxkyQICmKkiU55AhPEV7Yi9cbe+PwP+m9lw6FB4VM26JkUhRBAQQxEEMDPVXVnTPzW5QzmSjztfnGp4UYUSeig0R31b15M2/ee55znuccayOYunnzJsfHx7z//vvxuHfu3OGf//mf+fGPf0xVVXRdF5voVVUFfEVlfPnyJe+8807U4wSQ+rOf/YyDgwNGoxHOrUtXB0F+ALDj8Rj4qht93/cRrAohqKrqtbLp8/mcuq55+vRpLNX8/PnzWII5fD4Ety5fvhzfuUEvEyh6oeTt/2bfPDQqLDIDydqpxnr8MDB0UGSGs5Mz8rFE1hVD7pFaYvWANBkMBauTE+bjkrYdcKJFGTAmx9qBsbBIM6YbWvpSoIeMTGSI1jNYh+sr8vIKp33FVevQosT6llFhUI3A6IJONogio5CGprZYpxnpnLbryJXDeoEQOXUPzg9cmAmq8yViNSAnE7zKqZsaVudMRgb0ZF0RpevosQg30PgeZWa0tkerit5YbD4jUwKcpGs7jBMUmaF3jq6XaKMpvSbrPHU2AiuwrJDe4DpNrQRZMac0Dt1rGnOKyAR2ObArJjQqo2lPEUOPKjukUgxSojND5ixNb3GNwBlFWxjIIOsLquW6b4fsFY3zjC9o9KJgJNd0oFGhWHUC4RSqL7GUNE3NkJ0zmezQIGjkwNDX9AgKRrjSUw8Wr0DIjiJXWKuRukTR4WuQRmLPK2QpaKSmyA1VD6KYMR55VCswasa562HhcBMHmeDCeES1bKi7mrGbcdo26FyQeYfJHa4ZUF5hJPRtS9uXKA1FIajbBiM0spY4YZFG4JWjdQNGObxQuEECA5mXGOWwRuIbx0SvG5tZAxKFyRV134FSSK8whaanpdQKYQVCdAiv8KzQZFgPIpcI6fGtwytL33uwisJk+KqJTkerB9BgpabIctqmJxeawfU0g2UsDNAxCAHektPhB3CSdeUx4ZCyResVMP/GW/d3yYLTnGYLUs56SjmCrzIFwfEMx4CvHPM0sp9mDgIvO9BaUucyRD9T5z6lYKVRYSDSb9LPp0J1+O+lZsMYg6WRujSLkv4tjCMAoOBMBBF3SsdJdR1p1D2NxqcOeEpjSseUOp6boGHTUQ7ZnjCnmxmUdH7C+NJI+tfRvkJ/jnRdQ5WXNBsTvhdKy6a6g0CvSOdiM3qfrk16r6UUqRRQhTlI9Q/p/RXui8lkEvuqhHVNrz2MJ3C1wzjSbE4KfDevNwW5KSAK1xHul/R3gUYY1jj9bMiQfVuBxh/8wR9w9+7d19YPvioDW5YlJycnMYMR5vTq1at88cUXHBwcRNBaVRWPHz/mypUrALz11luRhhOoJaGKVYiIh8+GSkchoxroeaHfxMnJCV3XcXJywmq1iqVTQ9WpICIPfRKGYd1h/Ac/+EHsci6EiJmHUEAi9Jv4+OOPuXHjBtPplC+++CJWotrd3UVr/ZpQONxTwdkO1a4CbWs2myHluslb0K2kWT9jDPP5nNVqFSsuhXK6z54947333nuNehnE248fP8Z7z507d2LVp6IoGI/H8XlW13XsZh0qfQXn+ezsjDfeeIOu65hOp5Fi9p3vfCcCgbquOTg44OHDhxHknZ6exnkOVZeCGP7GjRsURcGbb75JVVVcvXo1gobQ2ylkvoQQfPDBB7z//vsMw8Dx8THXrq1Ly+/u7vLFF1/w0Ucf8f3vf5+rV69GjcRqtSLLMr773e9GMTbAgwcP+OKLL3jjjTeYTqecnZ3x/PnzKAQP1KiQad7f32c2m/Hq1SsODw9j9q6ua/b393n8+HFsFJnnOU3TcOXKFZ4/fx6fC6enp+zs7MT98+LFi9jpPjybnzx5wmKxYG9vj+l0ysnJCUB8XwF8+OGHkXoVKod9U/vGQGNlK0Z2TiMlrvdcGuWc9gCSjhPUnmDpO6a2oO0bWlrMeEohJSMH08sj6sWKrtfkucJXFU2jyDJLoQdss2LV1Qhf48yUykkyqdCN5cKlXU5P7LqZXN2z1A4xEShTUp1Khtozyqb4XtA68GaJyuF82bOjpwzLHis9WZnTA5kYcb46Qw2WXEiUHmibnrnO8VqQjQwv+xW6cKjcYP2KsbQMjcWfvsRqjdcTii7HmQFqj5juIE8qKv+K/fEODWsA1px3iMkcJytkZml6Qd8smZQ7LLuObDpm0vWsGstgPF4abOsxzoDs4bxCCgNZSa1AS03dHJNVCk2OKzwuF+tmcKpBDT3OOEwrKPOSqm8R5bqSluj8ugpT0+JHlrrpGbcZfVcj5yV0hl5JKmVo5TkCQ91K8kLRNhV+Zhj3Yg2WxHLtjOcldqnwKmPwLZm2lN2UatkzLseI5gxahWW2ppbZmtNCMd8dsZI1MMI0kuHsOTtqhMsu4GyN7SpsLvGM6JuWUabopaDvBFKCyC3KgXIKrz3KSXSmccaBtQi/opAK0WlcZxCZQluL6HowisF4+sIweEW7rFAF6CzDoRBO4RqwQO89LlMooRAOnHIoP+CsXes9MoUaBkwvGUTJaugZZZ5cZvQMFEUOtsUJC77EO4vEYXtH23Q4sy4hPGiNtQLfghiNkWINCNuhB+NYLc9o5JKxG6MpvvEG/12zTfpHGj1MI87BSQu6hZTzn4KQNOsBROpTShcJTmka3U+jx6kDm1JWwoswVLIJf0/PnZ4jHCOAojR7E/4dSgQCrzm24W/BwQz0mpSKtSkmDudJ6TzBUhAUMkThmjeBWRhLOi8plSs4xaFCV4jChdKZ6d/D3ITfp+ML8xMsXFfqoIffh/naBCab98ImJSyNeobrSWlV6ffScYfvpJqH8P/hHCkgSMcZytamVLiUipUCBSFE7M0Q5mqT/hSuI4wvXdN07tIsWbiPwv2Z6jaCRimMI9xX/y+Owu+Sffrpp+R5TpZlsWJRyDwEStSnn37KbDaL38nznMePH0eQHzJKZ2dnXLlyJXLwHz58GKtBBUcvVDgK/PWmabhw4UKMip+dnXF4eMjFixdjlu3Ro0dRJB2c8fF4zJMnT2IBiqCNuHHjBru7u4zHYx4+fMjx8XEE+EVRUBQFv/rVr1gsFvzFX/wFv/rVr3jzzTdjF+qzs7PIm7fWcvny5bjWQBQVB+d7uVxGMOXcWtT82WefcefOHbRed/BOC1/cu3ePK1euxEwKwPvvvx+v4c6dOzEY1HUde3t75HnOP/7jP3Lp0iWMMRwcHPDrX/+a09NTrl69GsFQoKA9e/YsOvt5nkcqFRDLsC6XS7quixH/yWSCEIInT54gxFo4Hzp0BxpXnuexO3voCh/21dHREbPZLIrKg3alLEuWyyWnp6eUZcmVK1diM7sf/vCHsTHjarWKQm1YZyWm0yllWTKfz3n58iV1XXPz5k2GYYh0tvfeew8pJScnJ3i/FvSHeX/58iXe+6jnuX37Nv/5n/8ZdSaBvhZAYeiP4ty6OV8odDCfz7HW8uTJEy5evBiDZ4eHh3zwwQdMp1M+/PBD3n//fRaLBZPJhMPDQ/b29mLZ5dCTZn9/PwLkUKkqzHmaLf2f7BsDjbEesbSWzEtm05yhHZC5YOhapC9QoqPzhpPcofwUsfDkwmOKFQwKtTS0RmBW6+7PGEk+ktjMUC/PcIuevVFG7XOaPqduOkSWk8+h63LMaCAbDHVv6ZXHLHpEWdC0K8ppTj2MKH0NWY5tM5TtUdqvOfI7I2QxIB20ywrrcnqbYYoCl+VUXU2Rgx86lIa2GJhKTScMbdfRW0+d7SKdpexaBg/Ow9C1eLfAFIahsuSFphkmnHmBkysy21AYw/niBcNoCs6S+4wsu0guHHYKmRLUrWWxgmy/xlYDo2xE2zZgLBaBFQbbNJSVp3UlUg/k84x+2SN8QSc9pcoweMpy3SVcypJ+WEdgs5Wnmwj8SCPtgB2PqVzDpFRkXuNcibM51reUJqddWYbOYHYUeuhxvQfpcYNnmefkTmPaDI8jpwPvqVvLpJQM3YDH4npLPdEwHjF0Da4ekHlGv1iiqpJe9khtyXJBXzv8MKI1AiRUXYYtegrVkHnJYDVdZxgZiVQtTkla6yhHc1bVuslh3zswDtd6Oj2gfLGmNzkwWcbQW6S3OAPSGHIh8a6j9ZYsn2DdwCAtnR2QWYb0HuvkOrPgPb4HNwwY7XE6QyiDYx0V10LA0NHr9QtcyR4lHG4QdBI678iloO868kGjjYBMYYaeXEuc8igGhDBIp7DOMlQO6UGYDoRgaC1gMPkOKvv2isE3ezhsClzTaLLWmtFo9FrZ05QeEhzZ1AFLswHhJwUS4dghCp46janDt3kerdc9JEKkD77Sm6Qv5fQ6A7AJY0zpGKnTGhzlUDY3vf6UVpZGysO/A7BKAVdwZsN8ps5nWtlrM2sTgEQADGmE3DkXuxOHqPxoNIoUlCzL/pvWI81MhGMEoBEAU0otS68zde43qWcprSvQrgKQSo8DRFpIeqywvin1Kxw3XHegzaXUuRQkByCaZqfS+y89ZgB96ffT7Fg4fli/dHzp/ZxW5Urv27De6bk26YabNLp0zN82C9TEhw8fxms8ODiITvdoNIqR5bD2bdvGykSnp6ccHh6yWq0Yj8dIKSNXPfQsKIoi7v8Qvb158yYfffQR4/EYa22kj/R9z8cff8zu7i7z+TzuvVBCV4h1BaC+7zk8POTRo0dcv34day3379/n2rVrvHr1iuPj4whWQuO+58+fo7Vmd3eXt99+m4cPH8Y+DcYY9vf3OT09ZW9vD6XWDeKCnmA8HnN8fBw7gIeqVEdHRxwcHHDt2jVevHjBarXixo0bCLEWT//iF7/g4sWLXL9+PVKslFJRwB3ux9lsxmeffcazZ8+4cOECRVHEsrjGGN59910+++wz3n77bT799FPKsuS73/0uR0dHrFarGMg5PDyMQCBUDZtMJnGOX758yf3796nrmnfffTf2FRFiXf3rz//8z3ny5Al3796NlbMePnzIMAzs7e1Fzc3ly5dfC2ycnp7GYEko5BD6X2itY9nZcL8dHBzEIFDoMTSZTOJ9JYSIGpfQMTwAuZ2dHf793/+dvb09AJ49e8Z8PufRo0fcvn0bYwz37t0D1pmyhw8fxoIDVVVx584dpJQx41GWJS9evIilgoO+JM9z3nzzTb744gseP37MpUuXePbsGVprJpNJpFp9//vfj5oZay3T6RT4qqLb/v5+fLY+e/Ys/vvJkycRfP3qV7/i4ODgG+3Zbww0OpGzi4ZK0klHryxC9msHaiWwXjAqoBl5lANmE3rX4XNNIzVZN2BzjxkXjClZdRbValTFWrzrHR2auuqwo4EdOvLRiMXinFqO0IUgsxmFFixtQ+YNqnaMswJtBsjOEKqgO10wlSXQo3OLESuq3GI7zavTYyZijwkSO5a0YkApzVA5RrZGZ1MapVHtgBKK9sUJo+kO7SinHnqknDCZjTDnFU1do0eOPC+xQtF1K9x8jK8smRGcVz39dIauB+bzGVUlsCuP9o6h7HGDhYWHkYRcM8kttpZrUJYvEXLgdDBMTIFHI43F1CDzHucydJZhZprGWoq+g7yhO+3pM0NdKya1QGu/blRXOYZOI9QIfE8pelxl6csxD9U5KjeMrcdYRXu2wpdTJq7E9BpZaITx2KbB+nVFsMY2rLXPHnmyoszHFGWOQEHhaYa1rmAQK2SXrdeiLFB2oC8c2sFqccbu3HC6WNBJwEB+YYptLHnW0owKJkIjB40xPSPtWL7yFHsjRGvJC0k39Ni2oxQlvbI4QBlJoTUKQe+hU3LdANI78ODRtE6je4uxEj3x4BzCW7xvUU4gHfRaMjhPaSSDbXGdIxcK3zv6qodMk48Fvm/pfc4gLYKMQmqk6xj6CqGmCG/RXuM7h9YdSlq8LbDOImyoq2tRHrzwrNqWTGfkpcb5HuMyag91JhCtxgmoreXbWZjydbFqysVNwQJ8VQ0pRHbhdTFuSj1Jo8wphSeNMgdHNHwnRLFTMLHpeKUOXhh7cJaDQx7GE0BToABtOqQpV37TwQwv3OC4huh8iJaHSGVwFENUOlh67M3oeZifENkPnNpwvlR0nUbvw7ylGoAwzwEsBGFquJYwB2kUPp3XlGIV1iulYYXvp+dPe6qE76YA9esE5ilACi/LIApPMzIBuKSUqhSgpvdI+Hw4Z5ZlkfYSxh7oeOHcAQCFjFKYh8CNT+/jAI7CvRbu17TnSQrKU5Ca3vtpJi0FcelnQiGFb6udnJxE6styuYwZonv37vGnf/qn3Lt3jzt37tA0DZ999hnf+c534l69dOlSLEsrhIilX8O8vv/++8BXgDHsK601jx494sKFC69lRLTWscty13Xs7u7Giki//OUvYzQ8iKbfeuutqMkYjUb86Ec/iiLwyWTCxYsXefXqFd77WF50GIbYryFQi87OzuJ/P/nkE3784x/HTOnu7i55nnNycsJHH33ErVu3ODo6imAgNMYL5WKvX7/O/fv3oyMaMguffPIJb775JsfHxzjnuHXrVgxCPHnyBCllrGYVxMy3bt2KkXpr1+V+QzYndEIPDniI2rdtG534xWLBrVu3YuPCkOl+44034rMnPLMvXrzIRx99xGg0oigKXr58yfe///34/AwNCYdh4NKlS7FR3ZdffhmzWAH4BWrqq1evIvXt/PycFy9eRIG3cy5qeJ49e8Z0Oo2gNWRETk5O+PWvf83ly5cjUBJircsJ2ZK+72PZ2FA+OGg3wnlCtamQIQl7+PHjx5yenmKMiTqR0EgyPEdCR+8gup9MJq8JwrXW8f7vui6WGvfec//+fS5fvszR0RF1XceSvKHR7rVr16jrmpOTE4bhq/Ly/5t9Y6AxasE1XzLkGa0XmH6EtBmdaHFZjRI51msMHtNbVsJTFCX9UKEzgckU0nt0LxEs0HbN8184C5nBFIJKKfq2okD9F/+/oRQTusbgzbAuHpT3zMsx/dkSOSrxosN3PaUs6FtHlgm8UjjhyHpBazta1TNI0EKSqwVVZdGlwQhNuzrF2I4uEygv8IzwdoGbKvT+DC/GmNzihKU5P2GYzDEjQ9u1tCUoJWlbQS8KWGRk+ZLV0IEpaBYdunf0g2CoOnqzZCIPMb7BFtAPnspLfAfF1KNcTlkMnImGQQkKBLKXlFbT2Yo+8xQTSbfK6CrIM4HWksaXtL1AKENrW9AGUYDxILSgHSmEcgyLM7I8RwwOUc6p8pppLhGNRNCR9SXIhs429E1F1Siy0QST9WAGfJujlwNm5KBxiF5giwltKejqmlE2xvfghcN6y6AVblhgTIF2HnfSkHtLn/UU5YyqeoVhhJYj9Gig6yy+PWY+mwKOXgmMchg50NQVqtih9w6yjJEROOfBeCwdRnhoDUoL/NBjjWEQGmU9mgGkpvfghhblDU0/oHO9Bg5DRVYU4D1ZpnGdIO8dZJ7WOpw2UHh8XTMajVFC4IXADxZahS402mjcYLFWItUYbwZa/LrCVe5QvcOLdQfy3gs0DlMq+sEhhcFYjxMwms4Rfbdu0ucsfpCYrsO9OkGPRmRZQS8bYPxNt+7vlKVaiRDVTh3GlNKTOorBydqk+oTIcfh9eIkFpzzl4qfVnzaj9oFOkmZOUsc+HDtYcOC+zrFOMxjBid4EGamDmVKkUlrTZiYkONrhesNLOETgU5pRmpWx1kbaV+o4bdKbwpqklLSUahQcrPC9EPkNGY1AIdt0noOlmph0XKkeIgU04Xgp0Ao0uJSetKlVCeuVZhHCNaXUra9z9AO4+b9lQMLvvw6UpnOfUq/gK/5+CrTC/R32wNeJ3YH/tk4BTKVgMuydAMhSulp6T6ZUs3Rtvm0WOPw7Ozs8e/aMDz/8kPfee4+maWLJzmEYeOedd+J8BgpkoKcEkPzq1SuOjo7oui52Cr906RLHx8dkWcbR0REPHjzg4sWL7O3tMZvNIof+6OgIKb8qSbqzs0Nd13RdFyv4tG2LlJK333476iJCdaCwFlrrSGm6cOECN27ciGvcNE2MPPd9H5v5ObeuRPRXf/VXvHjxgrt378beDefn55yfn/M3f/M3dF0Xsz/WWnZ2diL95/PPP+fGjRuxQd61a9fwft1Yb7lcslqtkFKys7MTu6m/fPmSq1ev4pyLWZ29vb3oPAdKU1ifxWLBu+++y/3792PJ3PPzc6qqou97Ll++HK/xzTffpG1buq6LJYqvXbsWKymFwFNd1zx69Ij5fE5VVYxGI/7wD/+QW7duxd4gYQzB2e+6ji+//JK33noLKSX7+/sxSJTnOS9fvoyVvkK2KzQF1Frz/PlzmqaJZZNDdgGIzv/3v/996rqO1aCUUjx+/BhjDIeHhzEbcXx8zJMnT+KeffXqVWwo+OrVq3gOpRTHx8fMZrNYMvnOnTucnJxQ13WswvXy5UuMMREATKdTdnd3efr0KavVivl8zmeffcZ4PObs7IybN2/y8uXLWOQgpTFfv36dk5MTdnd3qaqK69evx+xaoMaF/fPll19+o/36/6DRAFHsM5t0LM57dJEziAXCevLZiEY0ONdhO0UxCISx0DkKDSdthXc5mSxYWAfjgqHpsCNB4zRqyKn9gJAelRmyuuDMdKAGjB+QeY7JGjpnWbYeoT1mqrB9R2EVos8RxoOskE2LKxpslmOznFdnDWJYc+9nxRije5wcgx/oG491grwcIQDhLKsmA72DrS191XFwoWXZ18jekk/m1FYxMUuQEoFBSoWSEtMKjHdovYPaGWBxwiBLZD7GqAY1VoyyOauVxy9qcjNBFBVFn1NkgqZXOFrsUCG9oLBTpqKnrV4gLtxkNDqgPz9naAZkrmnQNLTQeYyVTMYjhFOcG0kzLHBesmg8IzXDNhZFQzECa1u8kNSqwzOsxy0lVikGa9eai+MF0/kOfWfBKKxa0Jz1TPMML1qMbOh1hhuX9BXI4xYzNiy7FUbvQFORlRqcpXMlpZuh8RRzw+npEi/miGWDnmnIJaXMOK8NXgj0KMfbNUhSZCzaFabQyMGCq5GdQY8nnC6OKcYanY9pVg2FLtGZoXcVUgr6fkBJj/AK2Wswgr5vyXOBrivKPKfX/1W+WLg1KFMS6x2DyhDOgRbowSGcXFOYzIi+VZgCfD/gmw4pFHXbUORjnPgv8a9w1MKTYaH35FIzSE/bWybSI52gazrEqCDXEus8Sgi0FNRNj848wmmMzhlcx+q0pixnSNUwlopBfXurTm3ShYKjF5zFNBoLa1FhcC7TyHoq3v26hnDhxbJJkwkv/lQPskl72gQL4fOp05kK09MKVKm2YLM6Vho5T53D8JNme4IzGuYqOKh930fHJT1HaH6XOtxpBialHIVxbzqam851+vIJ6xWyKel8pwAxnevUGU/nN9UXBKc5AIewRuE7KdAQQkRAleocwnwFEJECgZSCFa756363mWXa1Nhs3hNhjgOQSMsPbwKmNMsWrnUTXKdAKzgfKZgOQCYFomFOU7CU9mJJ6Vyh1HF6vd9W6tSVK1e4cOECn3/+eQTpIRPxySefMJlMIqUjOIRt28Y99ejRoxgBvnz5Mt579vf34/0E8NOf/pQsy5hOp4zH4xgBDyVEQyT38ePHkQYlpYxOYuibEahLgR4Vsh6ffPIJ165dwxjDdDqN2bX/+I//4I/+6I8Yj8dkWcbJyQm/+MUv+M53vsNoNMIYE3UKgdb18uXL2DAubUz3zjvv8G//9m9RszGfzzk5OYl8fyFE7CAetAx7e3vcvXuX8/Pz2OfinXfeYbFYxM8FbQwQy9GGBn/h3srznPv37/P+++/jnOPjjz+O2eGQwT04OODZs2dxn4fO4OkzryzL2Kl6f3+f1WoVnwWr1YpLly4hxFqnceXKFb744ovXtB1BExdK9u7u7nJ4eBgzDGdnZ8D6mROqbp2fn3PlyhUWi0Usb3v//v0IFAJ9dLlcxs+Hufnoo48oy5I333yTR48eRUd+uVzG/RwyBaHPye3bt7l79y7WWl6+fBm7kYdnQNDFlGUZsyjps8x7z+7u7mtZ5bShqjGG3/zmN1EzFN6bR0dH3L59O1ZuU0px48YNzs7O/puOrK5rzs/PyfOcTz75hEuXLvHkyZNvXHXqGz9plqMaUY45qzylFeS2BZGhhzGy8shWoIaCrFbUqxw15CAGhHSYpqBzkl7BIC3nw0AjDUNVoKqWzFTkM4nWjnxiaHtATXEofCEZtKF1mrKYkUmNMgrrBqRdkNkzGnqELEHv4s0caw1eKFbOM5vuwGKFaM4oiwErHJ3qGGyN6Swm96hM0YuOE9FQZCvGwlM2FRfyDJXnaGtwgDWGphuwvWE8nqDliMYOlMqxO+tRsmFoKqoVGD1DiAyhO6Rr6YVDeI9VNWiNrxQTJCN/jmiXaNui8gypLftZyRiB8QYvDI1Y8vysYikAL2iNwrYdupbYGgrhWZ03rNoVsq3IlWGWzXGiofYnCDWQZR0ig6EZEJmkM4oyK5mNx0ipMGrAlQUrY8jKCyybgT5vQbTU3cDkwgWE6SlNTtNb/GCpuwoxeBgyhLSMco3vWjQZeEEue+SwBFcx+I4jFrSXS7psoB052nxC7zJs45BC0TUW149pVgJVLdHVCZkwdNbgrSbrNX0j8MsGrUYIUdI2DVoVOJ1hAdtLBgtGgFYe2zeI3kI9oJTEeAVSgQKLwxsBeBDQdpa2H2iFw5YK4R1CKbp2YFh24B3CuXV5XiEhA2tAmRxhLUpKNJ5hUaN7g+4EufdrLYdzlEpAbxG2RxYSnUn6pkdYz9D39K5Byg4tPIPoGAYBNqOpLavH52RugvcC4b+dDgLwmnMU0r+bFJTwMgvlCgMdKTh2QWuQVo4Kke3wYN103NPMQfq31PFMo9bh3yntKETaglORZVl8kaVlGMNPCji+Ttyc0mtCpuLrxMhpBDxce/hpmiY6UZufS4+ZAqRNkLEJ3P5vTmyYw831SgFO6rBvApaUnpSK2uErYXwq5A9OeZj7pmniTwB/6TylWZtAe0p/0sxXcPBT0JNqYtLrTdc4VGsKY0v7CaQZkq/LFqQAOsxt6vin8x3GEShO6TWmP+F46b7ZvMdT/U+q0/i2Ao2wB/M8j52fh2Hg3r17MTobSs1+/vnnPH/+nOPjY6SULBYLbt68yVtvvcWNGzeA9ZwHrUao1PR7v/d7UcB7584ddnd347Mq0OqapuGdd95hd3eXFy9e4NxaXP7ixQuAuFdu374dS+MGvn/4bKj69PjxY4qi4G//9m8pioL79+9HCs8f//Ef03UdL168YD6f8+GHH7K/vx+PGfZDyFQEQfTR0VEED23bslgsOD09fY3u0vc9o9GIZ8+eMZvN+M1vfgMQO2d3Xcf9+/ejQ/n06VOOjo44Pj6m7/sIQIwxTCaTeNxHjx5x8eJFPv/881juNVCLQvT96dOn7OzsYIyJ+/uHP/wheZ7HpoFBoxf2V9AMFEXB4eEhDx48IMsyDg8P6fueBw8ecH5+zuPHj3nrrbe4dOkSDx48YLFYAMRsknNr8XSoQBWoYd57VqtV3Eu/+c1vyPOct956K1KYdnZ2YuWx0MDv2rVrzOdzdnd3uX37Nvfu3aPrOm7dusXOzg7f+973ePjwYXxWLpdLxuMxe3t7PH78mGvXrmGtZX9/n5s3b1LXdXy2aK2Zz+dcunQpaimUUjHDA+vyzqH3y8cffxw1P2Gf/PCHP4zZqitXrnB0dMT+/j6vXr16LZjy6tUrzs7OePjwIV3XMZvNInCtqir2ZAnHuXXr1jfas9/4STPrWuywRJUTLswKssFCNaLtCkymKHtPd+SYlzv00pBLTWYMTQNjO1B0Dt+3FN5geii8p9AKNXQYanIsfV2T5Z5RCWXdMc3BSY93ltZbzupXFCPwXY1ucnouUI9HtNpSdytk3eFEjsx3aRqPdTmuUhzMpmSFRo1KfO7IeouXBmY5fb+kPT8B6ZnlU6QcYTKJyQasWtJWp2TlCKFGqNbjW+jLHXolyZ1CVo5+sLQDiNzQGw1DyemxwK5aOtvgTY47XzLOLXNp6WROrQRqNMIXM/piTJYJjO2hFhSqYFKUeD3AaErnHeN8QCiBHXJsNTAZOYbTM/JSIegwvaCYjFFmim0yTlbnZHpMWQtU1eOEpOtriqzEdgrVDtgKzo9WuA66RcX58wWqbvHdisGuEGNFI0D5nFVdU/GSho6x3GciCiZ6hKdBFh0y9yg6sn7AiR6XC3o/Qaod+qVH9CMGP0PLCdNiDOviVfjG0vYWM9GMCw2tRfUOM87wxqEZKPqWfGyQomOaK1S/QmYtbVMhjcZoha16aAewAik1XecR3qFFQ0+LV6B7w7CUdCKjtwIpJL3rccpQDz3WGaTXKAZ025I7gRwsqsgRRQESXFbjXEdTd0CORqHNmn5lvcNJTz4aYWyPbSxd24IX60pYKDwFXmdI7aGzqFxidY8XPc4OOAFd39N2PW7oaNoFi65DlxOUnOG8omqW33Tb/s5ZcOZSIFEURXSE0opKqcOeUl/CizUtcZoCgpRXH2zTeUv1GykVaTMKnzrJm5FvpVTkx04mk8gVDi+pzWsK40zFwmG8IUq26UiH34frDQBrE5ikWYDUaQ1OdABhARClQGGTugT/vfFbWJPgbIXr3ARRm1HzsBZhHOn5w3jScXwdYEmBZVjjMC/pvIX52ZzHVOSezn2qvQj3RVpGOM9zxuNxLJkanMwUjKX3xSbFL1xL6iilcxq+m2Y+wjFSMBHWJFD+wvWlmpBN4JQC6HS/hXXbLKLwbbOQVbh+/Tp5nrOzsxMjsHfv3uXRo0cIIaKDGMSswfkKUeswv/fu3Yu0H4Dlcsl8Pme5XHLv3r2YNXz69CnOOX7zm9+wWCx4/vx5dBgDkMuyjAsXLvDLX/4yjhVgPp/Hcf7gBz+IlKDpdMpsNnvt/KEqU+iNUBRFFO9evXqV+/fvx14eZVlGMbiUksuXL8dSug8ePODnP/85Dx484OjoKGa2wj11fHzM7du3mc/nka4kpYwlVIdh4MmTJzFbOB6P2d3dxTnHvXv3mM/nGGNYLBYcHx9HoLVYLJBSMh6POTw8ZHd3N9KGJpNJbDoYsjb7+/tIKfm7v/u7mLX83ve+x8HBQdQChGu4detWrOAVyuSGviOhmeJkMuHWrVsYY/jrv/5r5vN53NOhB8nu7i7/8i//EvdCoHLt7e3FQNjBwQF93/Pq1Sum0yk3b97k4sWL7O/vx8+HrEfQDj1//hxjDDdu3IjjD40hw7OoLNeshAAcfv3rX8fMRci0hGpUly9fjkD32bNnVFXF22+/jdY6Ng4sy5KzszOapon6kVDStmkalsslV65cQUoZGw8KIeKczGYz8jzn7bffjtdZlmVs1lgUBbu7uxE87+zs8PTpU549e/aN9us37wzOCCsAHO2gkNaRCU+nKmoPXkJhPJVcoHegHRpc1SPVGCsrun5FJvZxlnUJUinQqmZcTjFK0p5ZhFcszzvEALr26HygLHYYkJR+xEkxprU9hXAoNItmoBSKsvEordFCUlmwXUeWGaT05CajfnmKKRTLoxWtyWgbweyigd4zKmeIQuGdwq4Eiw6MbhjteUZWwLmlcj3S9QzdwFwVaH2M9ZZ2BaozdMpgdsaorCZb9sjOMh6V9MLTKoXSMNvb4eTsnMGD0SM66RC9wjbrlGmd9zi3dkVPqo4sk9TKgFjP82ykqVY9Do9UgqqukDLHth39uIA+p1o0CCcwrgEaXDairjVal5h6ACHpRxrROMbCcdpZpLPkY4kyGTOrkecD5WhEbVtcJ9EYlAYvPcNQMZ6VuJWlygx26Mn0QDGU2HOPEx0tHb7W6LzArjqcU0jjkFnHxBbYlWUwgqxUoMHToosRy9NTBtuwk4+omxbbCqxW2N6Sjca02qNLiesaxhnYoWesx7SDxZYe1TiElYhC0CqLyzS1HVAmw4kRg5ZkWNTQoRQ4NLZX62Z9rKtEedZN9BAaIS3eCfASTU/vLV4ZcqlwDmyv/4s+56HtEMbgO4vMBIPv0UpiZc/COYquR0joUAx6QDUO7zx6NGAHB1ojlUEqSd11SFlS5gI79Lg8R5kSmUFtawpZovLsf96sv+MWHlYhUxFAQFo6djOyu0lXCS/M1PkPTkPg86cAIjh/8JVjmQKV1PEM/w4c6pS6ktJpQiYlXEuIHgWnPjiR4Xtp1D7lbqeR/9RxDS+7QIdJgUkYU+D3h/FuZmvC9YbxBgcjrW6VaifC31OtTHBYQzp+c23ScW/Sh8J54atKU+H4qdA5jDGMOQVQ6dgClSrcK2E9ggMe7pGUOhecpFSnk4r2U3C5ST9KQUT472YWLPwurG/4e3oNYQwBCKSZpxTghgxG+H6aBdsEbyE6mgK8ML/p+cL3w3kCwPu6rMu3wUL09o033ohN2ULfiaIo+N73vhfn6pNPPuHw8JDJZBJpLOEeqKoqVhOaTqd89NFHMcuR7rPgJIceDnt7exwcHHBwcMDe3h6//OUvOTw8jM3inj9/jrWWvb09zs/PY1+PsGZBo/DGG2/EffajH/2In/70p/zsZz9jb28vZqu+/PJL5vM5T58+ZTQa8cEHH/DWW2+9pjm5du0aJycnsRJSKNcaunAH7v8nn3zCG2+8EQM8QW+gtY7du998802MMZyenlJVFWVZcvXqVabTadSltG0b9+6jR49iB+nvfe97/P3f/32Mvo/HY6qq4uTkJEbBf/GLX3D9+nX29va4ePEiwzDEkrahydxoNOKNN96IfUECRezRo0eMx2OapuHu3btIKbl48SJVVcW9AsSqV6Fp4y9/+cuYbYA18A/ZsIsXL0ahfygJGwDB2dkZxhieP3/Od7/73biPXr58iZQyitkDSAt0qizLKMsyCubDWga6WciIP3/+nOl0yoULFzg8POTJkyeRtjSbzV57hofAU3huffrpp7F3SNM0zGYzrly5glKKH/zgB2RZ9poeDYg6kNDoL1gAdjs7O7x8+ZIsy1gul7G/iRCCd955h52dHZbLJYvFgmfPnnHnzp3Ysf1/s28MNIasw/QlorOctRXT2YzMNqxWZ/guQw4W7wqGXtHLdTR2wFOWmn5V4EfrB2SZWzoj6JYtbdVBuYN0gpKeuZGcSIMWazGzrTVCOfzQ4PqMnZHAVRmD6unlwHiUoaylnI1ogUXfM5YjhhraxmFERa8MGM2MnM6CL3u6vKdxPTpz2E7izjxCV1CP1xF0KRi1mmX1CmsO6NuMPBdkosWvLIiW8ShDjErEXCIrhbcZSMt45mlWFW02YNHYQVDZisyOycyIvHQ4I2mf1SzrHjMpUINl6DW5AT8IlB9YDWfo8S6+6On8wOJ8RTaacDackrNuQFiMJN1/8ftlPpD7gWookMUYschZtT2zWU5jHV55MidYVSDRZKZlvl+wXFk6NyBUgZtYstbQDAuMVJhqoM+haVeMZYlZQpXVCD2h7VZIPyDljN43tO0A8wsgKiQas1yRTTK8V6yqU7q2YlweUp829LZix3ToStMXElH0KEBRoNUSk5UMZkRjW3IlyboBk8+plMTsNNhBIIoxw9Ihc1B5hsk87bKjqxwi82Rmrb8ZSoHyDpxkwCGkQntBpy3KO3zt0VquQQCWSaFpO49zmiKX9K7FdxYvHV5lWJGjlSUvBMPQYKRBqgLpPZi18N97QYvDGM1M5thuhcolTioQHiMEqlV4C5nK8Qisb9cVx6xCGotXGmMyzo7Pqf1LZFmwU+7jpUeIb69GI1RVSpu0peVZN504+KoR3qZt/j2NCKcC7E0aVQAf4TOBXhJe3ikoSR3hTdCxST0JznMARptjDsAjRKrruo7nTulNAXxs0qw29RbBUr1BqpdIx7B5nK+jYqUR9xR0bEbvU51HmP/0+KlTHObUua8E+ilFKR176lAHAJSuweY8AK81SwtambScr3PrsrzhmsNnhBCvAd107lN9RhhHuL5NcfymeD7NpqUam1QvEe6jzfUIgCn9XUpN2xzr5vfT+UvXLXw+zHvI2HxbTQjB8+fPgTWV5+bNm1RVhdaap0+fAkQH9c/+7M94+vRpjNCHdc+yLHZY9n7dtyA4fcYYzs/PuXXrVlyT4+NjhmGIvRKCgPnVq1d8+OGHNE3DrVu3YrT6o48+4q233nqtwzIQqyudn5/zk5/8hB/96Ed4v24GGDqYh+zFF198waVLl6JjHAS+3vvYUK6qqujshczJ6enpa3syOJCXLl2KmZcwj957/vVf/5Xr169zfHzM48ePuXr1KlevXo2gPsxROG8AwaGK1Xw+j5Sa69evc3h4GB1dIQSz2YxPP/2Ut99+m//zf/4Pd+/e5fnz51y+fDmCgrOzM6SU3LhxI87zZDKJIvSzs7PYaNFaGys9hfkKJbf39vaoqgrnHJ9++mksJ3v16lX+4R/+gT/6oz/i888/j40LpZQ0TRMbO166dCnOzXQ65fj4mMuXLzMajTg5OeGLL74gz3Nu3boVSwUHgJY+rwMtKWQN4CuAs7+/z6NHj3DOxYaGt27dYnd3N2bbqqpiPB5HUfbms+vo6Ig7d+7EJpSBhhf0bKHp3snJCQcHBzFzdPHiRc7Ozrh48SK//vWvY/WxP/mTP+HLL79EKfXaeylkygKYa9uWsiy5efMm9+/fj1mY/82+MXVqimBSn5DvaPSkYBAtrV4h5ITCXcT0E+rBYVvPyDumOzOmkzFtvUKjycSIUlWs6hVSCMYmY3QwwfRg2oGqPcZaSVXVFJSY1jLRYLykcz1OdoimJWuWZINnPHj06picFdO9FWZcMzKeulpwPjwlm/XonQG9I8l3S16qntOqw7eO6WzEGEFWOgrXo31GoRWMe3bGOWN5jpQl3uwgnWImJcVSoESBLSVMxjxbOlZtxaJ/ichX5LJCDAP1qwE7CDTjdY8RCZUd4ZuBfDQjdxNoBqZjyzTLkJ2k6x2uN9i2IzcSxn7dQM/WNN0p47JnPMk4P17gZUGpCya9QliHNx67OsEPNfXKYitHy4AbSXS9wrw8ZaYcnc4YMs+FsqVgSeegX7RMVElnPer0nLxa0Q09wgnIWiZZxlyOIOvQuYBixiAGhuUxplEMjcX5nF6DGM7plg2CgWxUkXvB4riHrmeczRFWMaws2WTKyOTUsqQSEtdl1FXPpCiZzHI6o9Eqw1pN6TLcsmckMrpqge5G7KhdBDl9XWPzAqFy+mGgZYGbWlSWIXuF6gaMGhBYBi+wrcN4j0Xh+wzleqzo6DJFZQeUMOQqw/oGKUGT4QU4v8BYjTpXmNbRdev5cUOPU4oOTydrar+iZcD3Da1W9LnASkndDzizhlFZ5Sk7hbKG1rPOhoiOQQ7gBM725CMD1tF3Fa53CG2gLLGLMUZNcWogG76djbbgdSczFcOmD2l4vft3iAgHxxO+ctpTalEaPQ4RoNSBD8dLna9AJwnOQEqlgq/AQ+rEpVSbNFocjr8JlFLnG16neX0dLSql4KTZjhQgpOMIPyk1KgC6Tb1IcHhCdD9QjAL4SWlcKUUnpRuleofUgQ/fCXOf6iU21yjM7ddpWQIILYoizvFm9a3NOQnzks5tqqvZHNcmte7r1u7rdBMpkEwzRin1K52rkJUK65JmPlIAHcadzvnm/KWlPdO9kWZ+0nsxBe0pJTGd02+jTSYT3nzzTaSUMVsRosiwzniEpmk///nPaZqGn/70p5ycnDCZTGL2NNAwV6sVfd9z+/Ztsiyjrmtms1ns7lzXNVevXkVrzY9//OPX+sK0bctf/uVfcnh4GHvsKKV4++23uX79euyRIITg4cOHPH/+nLqumc/n/P7v/z5ZlrFYLKIDFzQJs9mM999/Pzr64RqttRwfH8dz7ezs8Itf/ILd3d14Xzx79ownT54wn8+Zz+c8efIkVora29uLDm+glllrOTs7YzKZMB6PefDgAY8ePWK1WsVGf2HcYe+8evUqlsMNGZKQQQmle0MX9tAUb29vj7quuXPnDrdv3450x6IouHDhQtR9BL2Kcy5qGaqqigGEg4MDdnZ2YsYi9DFZLpccHR1RFEUUjyulmEwmPH/+nFu3bvFP//RPEUhdu3YNKSWPHz9mNpu91qcmUMKstRwcHMSyxnfu3IkZp4sXL3LlyhXeffddjo6OqKoqitM/+OCD2Jgx0IuUUnGdAm0vy7IILEKjxFevXsVSu2+//Tbe+0iNCoGV0WgU9RU3btzgvffe4+joiLIscW7dIPLTTz/l7Ows9iI5OjqKz/+gIwr6jaOjI54+fYpSioODA/b39yPFMmSyHz16FPfOT37yE+q6fq2Awv9kwn9dqHBrW9va1ra2ta1tbWtb29rW/n/Yt7PsxNa2trWtbW1rW9va1ra2td9p2wKNrW1ta1vb2ta2trWtbW1rv3XbAo2tbW1rW9va1ra2ta1tbWu/ddsCja1tbWtb29rWtra1rW1ta7912wKNrW1ta1vb2ta2trWtbW1rv3XbAo2tbW1rW9va1ra2ta1tbWu/ddsCja1tbWtb29rWtra1rW1ta7912wKNrW1ta1vb2ta2trWtbW1rv3XbAo2tbW1rW9va1ra2ta1tbWu/dfv/ANVGyCUCHVPUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "p = data[data['labels']==CLASSES[0]]['images'][data[data['labels']==CLASSES[0]]['images'].index[random.randrange(0,data[data['labels']==CLASSES[0]].__len__()-1)]]\n",
    "s = data[data['labels']==CLASSES[1]]['images'][data[data['labels']==CLASSES[1]]['images'].index[random.randrange(0,data[data['labels']==CLASSES[1]].__len__()-1)]]\n",
    "g = data[data['labels']==CLASSES[2]]['images'][data[data['labels']==CLASSES[2]]['images'].index[random.randrange(0,data[data['labels']==CLASSES[2]].__len__()-1)]]\n",
    "\n",
    "p_result = np.argmax(model.predict([load_test_image(p)]), axis=1)\n",
    "s_result = np.argmax(model.predict([load_test_image(s)]), axis=1)\n",
    "g_result = np.argmax(model.predict([load_test_image(g)]), axis=1)\n",
    "\n",
    "plt_figure(\n",
    "    [cv2.imread(p),cv2.imread(s),cv2.imread(g)],\n",
    "    [f'{CLASSES[p_result[0]]}' , f'{CLASSES[s_result[0]]}', f'{CLASSES[g_result[0]]}'], \n",
    "    columns=3,\n",
    "    w=10\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('./content/models/VGG16_Noise_Classification_Gen.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inception ResNet V2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications.inception_resnet_v2 import InceptionResNetV2\n",
    "from keras.applications.inception_resnet_v2 import preprocess_input, decode_predictions\n",
    "\n",
    "pretrained_model = InceptionResNetV2(\n",
    "    input_shape=(224,224, 3),\n",
    "    include_top=False,\n",
    "    weights='imagenet',\n",
    "    pooling='avg'\n",
    ")\n",
    "pretrained_model.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_3 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 111, 111, 32  864         ['input_3[0][0]']                \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 111, 111, 32  96         ['conv2d[0][0]']                 \n",
      " alization)                     )                                                                 \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 111, 111, 32  0           ['batch_normalization[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 109, 109, 32  9216        ['activation[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 109, 109, 32  96         ['conv2d_1[0][0]']               \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 109, 109, 32  0           ['batch_normalization_1[0][0]']  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 109, 109, 64  18432       ['activation_1[0][0]']           \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 109, 109, 64  192        ['conv2d_2[0][0]']               \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 109, 109, 64  0           ['batch_normalization_2[0][0]']  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 54, 54, 64)   0           ['activation_2[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 54, 54, 80)   5120        ['max_pooling2d[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 54, 54, 80)  240         ['conv2d_3[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, 54, 54, 80)   0           ['batch_normalization_3[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 52, 52, 192)  138240      ['activation_3[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, 52, 52, 192)  576        ['conv2d_4[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_4 (Activation)      (None, 52, 52, 192)  0           ['batch_normalization_4[0][0]']  \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 25, 25, 192)  0          ['activation_4[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_8 (Conv2D)              (None, 25, 25, 64)   12288       ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_8 (BatchNo  (None, 25, 25, 64)  192         ['conv2d_8[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_8 (Activation)      (None, 25, 25, 64)   0           ['batch_normalization_8[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, 25, 25, 48)   9216        ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_9 (Conv2D)              (None, 25, 25, 96)   55296       ['activation_8[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, 25, 25, 48)  144         ['conv2d_6[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_9 (BatchNo  (None, 25, 25, 96)  288         ['conv2d_9[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_6 (Activation)      (None, 25, 25, 48)   0           ['batch_normalization_6[0][0]']  \n",
      "                                                                                                  \n",
      " activation_9 (Activation)      (None, 25, 25, 96)   0           ['batch_normalization_9[0][0]']  \n",
      "                                                                                                  \n",
      " average_pooling2d (AveragePool  (None, 25, 25, 192)  0          ['max_pooling2d_1[0][0]']        \n",
      " ing2D)                                                                                           \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 25, 25, 96)   18432       ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)              (None, 25, 25, 64)   76800       ['activation_6[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_10 (Conv2D)             (None, 25, 25, 96)   82944       ['activation_9[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_11 (Conv2D)             (None, 25, 25, 64)   12288       ['average_pooling2d[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_5 (BatchNo  (None, 25, 25, 96)  288         ['conv2d_5[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_7 (BatchNo  (None, 25, 25, 64)  192         ['conv2d_7[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_10 (BatchN  (None, 25, 25, 96)  288         ['conv2d_10[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_11 (BatchN  (None, 25, 25, 64)  192         ['conv2d_11[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_5 (Activation)      (None, 25, 25, 96)   0           ['batch_normalization_5[0][0]']  \n",
      "                                                                                                  \n",
      " activation_7 (Activation)      (None, 25, 25, 64)   0           ['batch_normalization_7[0][0]']  \n",
      "                                                                                                  \n",
      " activation_10 (Activation)     (None, 25, 25, 96)   0           ['batch_normalization_10[0][0]'] \n",
      "                                                                                                  \n",
      " activation_11 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_11[0][0]'] \n",
      "                                                                                                  \n",
      " mixed_5b (Concatenate)         (None, 25, 25, 320)  0           ['activation_5[0][0]',           \n",
      "                                                                  'activation_7[0][0]',           \n",
      "                                                                  'activation_10[0][0]',          \n",
      "                                                                  'activation_11[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_15 (Conv2D)             (None, 25, 25, 32)   10240       ['mixed_5b[0][0]']               \n",
      "                                                                                                  \n",
      " batch_normalization_15 (BatchN  (None, 25, 25, 32)  96          ['conv2d_15[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_15 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_15[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_13 (Conv2D)             (None, 25, 25, 32)   10240       ['mixed_5b[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_16 (Conv2D)             (None, 25, 25, 48)   13824       ['activation_15[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_13 (BatchN  (None, 25, 25, 32)  96          ['conv2d_13[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_16 (BatchN  (None, 25, 25, 48)  144         ['conv2d_16[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_13 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_13[0][0]'] \n",
      "                                                                                                  \n",
      " activation_16 (Activation)     (None, 25, 25, 48)   0           ['batch_normalization_16[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_12 (Conv2D)             (None, 25, 25, 32)   10240       ['mixed_5b[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_14 (Conv2D)             (None, 25, 25, 32)   9216        ['activation_13[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_17 (Conv2D)             (None, 25, 25, 64)   27648       ['activation_16[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_12 (BatchN  (None, 25, 25, 32)  96          ['conv2d_12[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_14 (BatchN  (None, 25, 25, 32)  96          ['conv2d_14[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_17 (BatchN  (None, 25, 25, 64)  192         ['conv2d_17[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_12 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_12[0][0]'] \n",
      "                                                                                                  \n",
      " activation_14 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_14[0][0]'] \n",
      "                                                                                                  \n",
      " activation_17 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_17[0][0]'] \n",
      "                                                                                                  \n",
      " block35_1_mixed (Concatenate)  (None, 25, 25, 128)  0           ['activation_12[0][0]',          \n",
      "                                                                  'activation_14[0][0]',          \n",
      "                                                                  'activation_17[0][0]']          \n",
      "                                                                                                  \n",
      " block35_1_conv (Conv2D)        (None, 25, 25, 320)  41280       ['block35_1_mixed[0][0]']        \n",
      "                                                                                                  \n",
      " block35_1 (Lambda)             (None, 25, 25, 320)  0           ['mixed_5b[0][0]',               \n",
      "                                                                  'block35_1_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block35_1_ac (Activation)      (None, 25, 25, 320)  0           ['block35_1[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_21 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_1_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_21 (BatchN  (None, 25, 25, 32)  96          ['conv2d_21[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_21 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_21[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_19 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_1_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_22 (Conv2D)             (None, 25, 25, 48)   13824       ['activation_21[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_19 (BatchN  (None, 25, 25, 32)  96          ['conv2d_19[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_22 (BatchN  (None, 25, 25, 48)  144         ['conv2d_22[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_19 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_19[0][0]'] \n",
      "                                                                                                  \n",
      " activation_22 (Activation)     (None, 25, 25, 48)   0           ['batch_normalization_22[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_18 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_1_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_20 (Conv2D)             (None, 25, 25, 32)   9216        ['activation_19[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_23 (Conv2D)             (None, 25, 25, 64)   27648       ['activation_22[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_18 (BatchN  (None, 25, 25, 32)  96          ['conv2d_18[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_20 (BatchN  (None, 25, 25, 32)  96          ['conv2d_20[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_23 (BatchN  (None, 25, 25, 64)  192         ['conv2d_23[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_18 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_18[0][0]'] \n",
      "                                                                                                  \n",
      " activation_20 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_20[0][0]'] \n",
      "                                                                                                  \n",
      " activation_23 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_23[0][0]'] \n",
      "                                                                                                  \n",
      " block35_2_mixed (Concatenate)  (None, 25, 25, 128)  0           ['activation_18[0][0]',          \n",
      "                                                                  'activation_20[0][0]',          \n",
      "                                                                  'activation_23[0][0]']          \n",
      "                                                                                                  \n",
      " block35_2_conv (Conv2D)        (None, 25, 25, 320)  41280       ['block35_2_mixed[0][0]']        \n",
      "                                                                                                  \n",
      " block35_2 (Lambda)             (None, 25, 25, 320)  0           ['block35_1_ac[0][0]',           \n",
      "                                                                  'block35_2_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block35_2_ac (Activation)      (None, 25, 25, 320)  0           ['block35_2[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_27 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_2_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_27 (BatchN  (None, 25, 25, 32)  96          ['conv2d_27[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_27 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_27[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_25 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_2_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_28 (Conv2D)             (None, 25, 25, 48)   13824       ['activation_27[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_25 (BatchN  (None, 25, 25, 32)  96          ['conv2d_25[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_28 (BatchN  (None, 25, 25, 48)  144         ['conv2d_28[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_25 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_25[0][0]'] \n",
      "                                                                                                  \n",
      " activation_28 (Activation)     (None, 25, 25, 48)   0           ['batch_normalization_28[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_24 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_2_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_26 (Conv2D)             (None, 25, 25, 32)   9216        ['activation_25[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_29 (Conv2D)             (None, 25, 25, 64)   27648       ['activation_28[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_24 (BatchN  (None, 25, 25, 32)  96          ['conv2d_24[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_26 (BatchN  (None, 25, 25, 32)  96          ['conv2d_26[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_29 (BatchN  (None, 25, 25, 64)  192         ['conv2d_29[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_24 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_24[0][0]'] \n",
      "                                                                                                  \n",
      " activation_26 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_26[0][0]'] \n",
      "                                                                                                  \n",
      " activation_29 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_29[0][0]'] \n",
      "                                                                                                  \n",
      " block35_3_mixed (Concatenate)  (None, 25, 25, 128)  0           ['activation_24[0][0]',          \n",
      "                                                                  'activation_26[0][0]',          \n",
      "                                                                  'activation_29[0][0]']          \n",
      "                                                                                                  \n",
      " block35_3_conv (Conv2D)        (None, 25, 25, 320)  41280       ['block35_3_mixed[0][0]']        \n",
      "                                                                                                  \n",
      " block35_3 (Lambda)             (None, 25, 25, 320)  0           ['block35_2_ac[0][0]',           \n",
      "                                                                  'block35_3_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block35_3_ac (Activation)      (None, 25, 25, 320)  0           ['block35_3[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_33 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_3_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_33 (BatchN  (None, 25, 25, 32)  96          ['conv2d_33[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_33 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_33[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_31 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_3_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_34 (Conv2D)             (None, 25, 25, 48)   13824       ['activation_33[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_31 (BatchN  (None, 25, 25, 32)  96          ['conv2d_31[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_34 (BatchN  (None, 25, 25, 48)  144         ['conv2d_34[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_31 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_31[0][0]'] \n",
      "                                                                                                  \n",
      " activation_34 (Activation)     (None, 25, 25, 48)   0           ['batch_normalization_34[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_30 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_3_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_32 (Conv2D)             (None, 25, 25, 32)   9216        ['activation_31[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_35 (Conv2D)             (None, 25, 25, 64)   27648       ['activation_34[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_30 (BatchN  (None, 25, 25, 32)  96          ['conv2d_30[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_32 (BatchN  (None, 25, 25, 32)  96          ['conv2d_32[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_35 (BatchN  (None, 25, 25, 64)  192         ['conv2d_35[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_30 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_30[0][0]'] \n",
      "                                                                                                  \n",
      " activation_32 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_32[0][0]'] \n",
      "                                                                                                  \n",
      " activation_35 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_35[0][0]'] \n",
      "                                                                                                  \n",
      " block35_4_mixed (Concatenate)  (None, 25, 25, 128)  0           ['activation_30[0][0]',          \n",
      "                                                                  'activation_32[0][0]',          \n",
      "                                                                  'activation_35[0][0]']          \n",
      "                                                                                                  \n",
      " block35_4_conv (Conv2D)        (None, 25, 25, 320)  41280       ['block35_4_mixed[0][0]']        \n",
      "                                                                                                  \n",
      " block35_4 (Lambda)             (None, 25, 25, 320)  0           ['block35_3_ac[0][0]',           \n",
      "                                                                  'block35_4_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block35_4_ac (Activation)      (None, 25, 25, 320)  0           ['block35_4[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_39 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_4_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_39 (BatchN  (None, 25, 25, 32)  96          ['conv2d_39[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_39 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_39[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_37 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_4_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_40 (Conv2D)             (None, 25, 25, 48)   13824       ['activation_39[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_37 (BatchN  (None, 25, 25, 32)  96          ['conv2d_37[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_40 (BatchN  (None, 25, 25, 48)  144         ['conv2d_40[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_37 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_37[0][0]'] \n",
      "                                                                                                  \n",
      " activation_40 (Activation)     (None, 25, 25, 48)   0           ['batch_normalization_40[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_36 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_4_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_38 (Conv2D)             (None, 25, 25, 32)   9216        ['activation_37[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_41 (Conv2D)             (None, 25, 25, 64)   27648       ['activation_40[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_36 (BatchN  (None, 25, 25, 32)  96          ['conv2d_36[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_38 (BatchN  (None, 25, 25, 32)  96          ['conv2d_38[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_41 (BatchN  (None, 25, 25, 64)  192         ['conv2d_41[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_36 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_36[0][0]'] \n",
      "                                                                                                  \n",
      " activation_38 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_38[0][0]'] \n",
      "                                                                                                  \n",
      " activation_41 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_41[0][0]'] \n",
      "                                                                                                  \n",
      " block35_5_mixed (Concatenate)  (None, 25, 25, 128)  0           ['activation_36[0][0]',          \n",
      "                                                                  'activation_38[0][0]',          \n",
      "                                                                  'activation_41[0][0]']          \n",
      "                                                                                                  \n",
      " block35_5_conv (Conv2D)        (None, 25, 25, 320)  41280       ['block35_5_mixed[0][0]']        \n",
      "                                                                                                  \n",
      " block35_5 (Lambda)             (None, 25, 25, 320)  0           ['block35_4_ac[0][0]',           \n",
      "                                                                  'block35_5_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block35_5_ac (Activation)      (None, 25, 25, 320)  0           ['block35_5[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_45 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_5_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_45 (BatchN  (None, 25, 25, 32)  96          ['conv2d_45[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_45 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_45[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_43 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_5_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_46 (Conv2D)             (None, 25, 25, 48)   13824       ['activation_45[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_43 (BatchN  (None, 25, 25, 32)  96          ['conv2d_43[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_46 (BatchN  (None, 25, 25, 48)  144         ['conv2d_46[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_43 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_43[0][0]'] \n",
      "                                                                                                  \n",
      " activation_46 (Activation)     (None, 25, 25, 48)   0           ['batch_normalization_46[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_42 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_5_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_44 (Conv2D)             (None, 25, 25, 32)   9216        ['activation_43[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_47 (Conv2D)             (None, 25, 25, 64)   27648       ['activation_46[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_42 (BatchN  (None, 25, 25, 32)  96          ['conv2d_42[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_44 (BatchN  (None, 25, 25, 32)  96          ['conv2d_44[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_47 (BatchN  (None, 25, 25, 64)  192         ['conv2d_47[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_42 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_42[0][0]'] \n",
      "                                                                                                  \n",
      " activation_44 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_44[0][0]'] \n",
      "                                                                                                  \n",
      " activation_47 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_47[0][0]'] \n",
      "                                                                                                  \n",
      " block35_6_mixed (Concatenate)  (None, 25, 25, 128)  0           ['activation_42[0][0]',          \n",
      "                                                                  'activation_44[0][0]',          \n",
      "                                                                  'activation_47[0][0]']          \n",
      "                                                                                                  \n",
      " block35_6_conv (Conv2D)        (None, 25, 25, 320)  41280       ['block35_6_mixed[0][0]']        \n",
      "                                                                                                  \n",
      " block35_6 (Lambda)             (None, 25, 25, 320)  0           ['block35_5_ac[0][0]',           \n",
      "                                                                  'block35_6_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block35_6_ac (Activation)      (None, 25, 25, 320)  0           ['block35_6[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_51 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_6_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_51 (BatchN  (None, 25, 25, 32)  96          ['conv2d_51[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_51 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_51[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_49 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_6_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_52 (Conv2D)             (None, 25, 25, 48)   13824       ['activation_51[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_49 (BatchN  (None, 25, 25, 32)  96          ['conv2d_49[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_52 (BatchN  (None, 25, 25, 48)  144         ['conv2d_52[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_49 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_49[0][0]'] \n",
      "                                                                                                  \n",
      " activation_52 (Activation)     (None, 25, 25, 48)   0           ['batch_normalization_52[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_48 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_6_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_50 (Conv2D)             (None, 25, 25, 32)   9216        ['activation_49[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_53 (Conv2D)             (None, 25, 25, 64)   27648       ['activation_52[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_48 (BatchN  (None, 25, 25, 32)  96          ['conv2d_48[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_50 (BatchN  (None, 25, 25, 32)  96          ['conv2d_50[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_53 (BatchN  (None, 25, 25, 64)  192         ['conv2d_53[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_48 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_48[0][0]'] \n",
      "                                                                                                  \n",
      " activation_50 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_50[0][0]'] \n",
      "                                                                                                  \n",
      " activation_53 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_53[0][0]'] \n",
      "                                                                                                  \n",
      " block35_7_mixed (Concatenate)  (None, 25, 25, 128)  0           ['activation_48[0][0]',          \n",
      "                                                                  'activation_50[0][0]',          \n",
      "                                                                  'activation_53[0][0]']          \n",
      "                                                                                                  \n",
      " block35_7_conv (Conv2D)        (None, 25, 25, 320)  41280       ['block35_7_mixed[0][0]']        \n",
      "                                                                                                  \n",
      " block35_7 (Lambda)             (None, 25, 25, 320)  0           ['block35_6_ac[0][0]',           \n",
      "                                                                  'block35_7_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block35_7_ac (Activation)      (None, 25, 25, 320)  0           ['block35_7[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_57 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_7_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_57 (BatchN  (None, 25, 25, 32)  96          ['conv2d_57[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_57 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_57[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_55 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_7_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_58 (Conv2D)             (None, 25, 25, 48)   13824       ['activation_57[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_55 (BatchN  (None, 25, 25, 32)  96          ['conv2d_55[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_58 (BatchN  (None, 25, 25, 48)  144         ['conv2d_58[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_55 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_55[0][0]'] \n",
      "                                                                                                  \n",
      " activation_58 (Activation)     (None, 25, 25, 48)   0           ['batch_normalization_58[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_54 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_7_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_56 (Conv2D)             (None, 25, 25, 32)   9216        ['activation_55[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_59 (Conv2D)             (None, 25, 25, 64)   27648       ['activation_58[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_54 (BatchN  (None, 25, 25, 32)  96          ['conv2d_54[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_56 (BatchN  (None, 25, 25, 32)  96          ['conv2d_56[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_59 (BatchN  (None, 25, 25, 64)  192         ['conv2d_59[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_54 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_54[0][0]'] \n",
      "                                                                                                  \n",
      " activation_56 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_56[0][0]'] \n",
      "                                                                                                  \n",
      " activation_59 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_59[0][0]'] \n",
      "                                                                                                  \n",
      " block35_8_mixed (Concatenate)  (None, 25, 25, 128)  0           ['activation_54[0][0]',          \n",
      "                                                                  'activation_56[0][0]',          \n",
      "                                                                  'activation_59[0][0]']          \n",
      "                                                                                                  \n",
      " block35_8_conv (Conv2D)        (None, 25, 25, 320)  41280       ['block35_8_mixed[0][0]']        \n",
      "                                                                                                  \n",
      " block35_8 (Lambda)             (None, 25, 25, 320)  0           ['block35_7_ac[0][0]',           \n",
      "                                                                  'block35_8_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block35_8_ac (Activation)      (None, 25, 25, 320)  0           ['block35_8[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_63 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_8_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_63 (BatchN  (None, 25, 25, 32)  96          ['conv2d_63[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_63 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_63[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_61 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_8_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_64 (Conv2D)             (None, 25, 25, 48)   13824       ['activation_63[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_61 (BatchN  (None, 25, 25, 32)  96          ['conv2d_61[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_64 (BatchN  (None, 25, 25, 48)  144         ['conv2d_64[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_61 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_61[0][0]'] \n",
      "                                                                                                  \n",
      " activation_64 (Activation)     (None, 25, 25, 48)   0           ['batch_normalization_64[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_60 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_8_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_62 (Conv2D)             (None, 25, 25, 32)   9216        ['activation_61[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_65 (Conv2D)             (None, 25, 25, 64)   27648       ['activation_64[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_60 (BatchN  (None, 25, 25, 32)  96          ['conv2d_60[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_62 (BatchN  (None, 25, 25, 32)  96          ['conv2d_62[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_65 (BatchN  (None, 25, 25, 64)  192         ['conv2d_65[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_60 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_60[0][0]'] \n",
      "                                                                                                  \n",
      " activation_62 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_62[0][0]'] \n",
      "                                                                                                  \n",
      " activation_65 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_65[0][0]'] \n",
      "                                                                                                  \n",
      " block35_9_mixed (Concatenate)  (None, 25, 25, 128)  0           ['activation_60[0][0]',          \n",
      "                                                                  'activation_62[0][0]',          \n",
      "                                                                  'activation_65[0][0]']          \n",
      "                                                                                                  \n",
      " block35_9_conv (Conv2D)        (None, 25, 25, 320)  41280       ['block35_9_mixed[0][0]']        \n",
      "                                                                                                  \n",
      " block35_9 (Lambda)             (None, 25, 25, 320)  0           ['block35_8_ac[0][0]',           \n",
      "                                                                  'block35_9_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block35_9_ac (Activation)      (None, 25, 25, 320)  0           ['block35_9[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_69 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_9_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_69 (BatchN  (None, 25, 25, 32)  96          ['conv2d_69[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_69 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_69[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_67 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_9_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_70 (Conv2D)             (None, 25, 25, 48)   13824       ['activation_69[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_67 (BatchN  (None, 25, 25, 32)  96          ['conv2d_67[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_70 (BatchN  (None, 25, 25, 48)  144         ['conv2d_70[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_67 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_67[0][0]'] \n",
      "                                                                                                  \n",
      " activation_70 (Activation)     (None, 25, 25, 48)   0           ['batch_normalization_70[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_66 (Conv2D)             (None, 25, 25, 32)   10240       ['block35_9_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_68 (Conv2D)             (None, 25, 25, 32)   9216        ['activation_67[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_71 (Conv2D)             (None, 25, 25, 64)   27648       ['activation_70[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_66 (BatchN  (None, 25, 25, 32)  96          ['conv2d_66[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_68 (BatchN  (None, 25, 25, 32)  96          ['conv2d_68[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_71 (BatchN  (None, 25, 25, 64)  192         ['conv2d_71[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_66 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_66[0][0]'] \n",
      "                                                                                                  \n",
      " activation_68 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_68[0][0]'] \n",
      "                                                                                                  \n",
      " activation_71 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_71[0][0]'] \n",
      "                                                                                                  \n",
      " block35_10_mixed (Concatenate)  (None, 25, 25, 128)  0          ['activation_66[0][0]',          \n",
      "                                                                  'activation_68[0][0]',          \n",
      "                                                                  'activation_71[0][0]']          \n",
      "                                                                                                  \n",
      " block35_10_conv (Conv2D)       (None, 25, 25, 320)  41280       ['block35_10_mixed[0][0]']       \n",
      "                                                                                                  \n",
      " block35_10 (Lambda)            (None, 25, 25, 320)  0           ['block35_9_ac[0][0]',           \n",
      "                                                                  'block35_10_conv[0][0]']        \n",
      "                                                                                                  \n",
      " block35_10_ac (Activation)     (None, 25, 25, 320)  0           ['block35_10[0][0]']             \n",
      "                                                                                                  \n",
      " conv2d_73 (Conv2D)             (None, 25, 25, 256)  81920       ['block35_10_ac[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_73 (BatchN  (None, 25, 25, 256)  768        ['conv2d_73[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_73 (Activation)     (None, 25, 25, 256)  0           ['batch_normalization_73[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_74 (Conv2D)             (None, 25, 25, 256)  589824      ['activation_73[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_74 (BatchN  (None, 25, 25, 256)  768        ['conv2d_74[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_74 (Activation)     (None, 25, 25, 256)  0           ['batch_normalization_74[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_72 (Conv2D)             (None, 12, 12, 384)  1105920     ['block35_10_ac[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_75 (Conv2D)             (None, 12, 12, 384)  884736      ['activation_74[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_72 (BatchN  (None, 12, 12, 384)  1152       ['conv2d_72[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_75 (BatchN  (None, 12, 12, 384)  1152       ['conv2d_75[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_72 (Activation)     (None, 12, 12, 384)  0           ['batch_normalization_72[0][0]'] \n",
      "                                                                                                  \n",
      " activation_75 (Activation)     (None, 12, 12, 384)  0           ['batch_normalization_75[0][0]'] \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 12, 12, 320)  0          ['block35_10_ac[0][0]']          \n",
      "                                                                                                  \n",
      " mixed_6a (Concatenate)         (None, 12, 12, 1088  0           ['activation_72[0][0]',          \n",
      "                                )                                 'activation_75[0][0]',          \n",
      "                                                                  'max_pooling2d_2[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_77 (Conv2D)             (None, 12, 12, 128)  139264      ['mixed_6a[0][0]']               \n",
      "                                                                                                  \n",
      " batch_normalization_77 (BatchN  (None, 12, 12, 128)  384        ['conv2d_77[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_77 (Activation)     (None, 12, 12, 128)  0           ['batch_normalization_77[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_78 (Conv2D)             (None, 12, 12, 160)  143360      ['activation_77[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_78 (BatchN  (None, 12, 12, 160)  480        ['conv2d_78[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_78 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_78[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_76 (Conv2D)             (None, 12, 12, 192)  208896      ['mixed_6a[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_79 (Conv2D)             (None, 12, 12, 192)  215040      ['activation_78[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_76 (BatchN  (None, 12, 12, 192)  576        ['conv2d_76[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_79 (BatchN  (None, 12, 12, 192)  576        ['conv2d_79[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_76 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_76[0][0]'] \n",
      "                                                                                                  \n",
      " activation_79 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_79[0][0]'] \n",
      "                                                                                                  \n",
      " block17_1_mixed (Concatenate)  (None, 12, 12, 384)  0           ['activation_76[0][0]',          \n",
      "                                                                  'activation_79[0][0]']          \n",
      "                                                                                                  \n",
      " block17_1_conv (Conv2D)        (None, 12, 12, 1088  418880      ['block17_1_mixed[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_1 (Lambda)             (None, 12, 12, 1088  0           ['mixed_6a[0][0]',               \n",
      "                                )                                 'block17_1_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block17_1_ac (Activation)      (None, 12, 12, 1088  0           ['block17_1[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_81 (Conv2D)             (None, 12, 12, 128)  139264      ['block17_1_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_81 (BatchN  (None, 12, 12, 128)  384        ['conv2d_81[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_81 (Activation)     (None, 12, 12, 128)  0           ['batch_normalization_81[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_82 (Conv2D)             (None, 12, 12, 160)  143360      ['activation_81[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_82 (BatchN  (None, 12, 12, 160)  480        ['conv2d_82[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_82 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_82[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_80 (Conv2D)             (None, 12, 12, 192)  208896      ['block17_1_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_83 (Conv2D)             (None, 12, 12, 192)  215040      ['activation_82[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_80 (BatchN  (None, 12, 12, 192)  576        ['conv2d_80[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_83 (BatchN  (None, 12, 12, 192)  576        ['conv2d_83[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_80 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_80[0][0]'] \n",
      "                                                                                                  \n",
      " activation_83 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_83[0][0]'] \n",
      "                                                                                                  \n",
      " block17_2_mixed (Concatenate)  (None, 12, 12, 384)  0           ['activation_80[0][0]',          \n",
      "                                                                  'activation_83[0][0]']          \n",
      "                                                                                                  \n",
      " block17_2_conv (Conv2D)        (None, 12, 12, 1088  418880      ['block17_2_mixed[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_2 (Lambda)             (None, 12, 12, 1088  0           ['block17_1_ac[0][0]',           \n",
      "                                )                                 'block17_2_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block17_2_ac (Activation)      (None, 12, 12, 1088  0           ['block17_2[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_85 (Conv2D)             (None, 12, 12, 128)  139264      ['block17_2_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_85 (BatchN  (None, 12, 12, 128)  384        ['conv2d_85[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_85 (Activation)     (None, 12, 12, 128)  0           ['batch_normalization_85[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_86 (Conv2D)             (None, 12, 12, 160)  143360      ['activation_85[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_86 (BatchN  (None, 12, 12, 160)  480        ['conv2d_86[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_86 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_86[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_84 (Conv2D)             (None, 12, 12, 192)  208896      ['block17_2_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_87 (Conv2D)             (None, 12, 12, 192)  215040      ['activation_86[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_84 (BatchN  (None, 12, 12, 192)  576        ['conv2d_84[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_87 (BatchN  (None, 12, 12, 192)  576        ['conv2d_87[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_84 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_84[0][0]'] \n",
      "                                                                                                  \n",
      " activation_87 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_87[0][0]'] \n",
      "                                                                                                  \n",
      " block17_3_mixed (Concatenate)  (None, 12, 12, 384)  0           ['activation_84[0][0]',          \n",
      "                                                                  'activation_87[0][0]']          \n",
      "                                                                                                  \n",
      " block17_3_conv (Conv2D)        (None, 12, 12, 1088  418880      ['block17_3_mixed[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_3 (Lambda)             (None, 12, 12, 1088  0           ['block17_2_ac[0][0]',           \n",
      "                                )                                 'block17_3_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block17_3_ac (Activation)      (None, 12, 12, 1088  0           ['block17_3[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_89 (Conv2D)             (None, 12, 12, 128)  139264      ['block17_3_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_89 (BatchN  (None, 12, 12, 128)  384        ['conv2d_89[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_89 (Activation)     (None, 12, 12, 128)  0           ['batch_normalization_89[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_90 (Conv2D)             (None, 12, 12, 160)  143360      ['activation_89[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_90 (BatchN  (None, 12, 12, 160)  480        ['conv2d_90[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_90 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_90[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_88 (Conv2D)             (None, 12, 12, 192)  208896      ['block17_3_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_91 (Conv2D)             (None, 12, 12, 192)  215040      ['activation_90[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_88 (BatchN  (None, 12, 12, 192)  576        ['conv2d_88[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_91 (BatchN  (None, 12, 12, 192)  576        ['conv2d_91[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_88 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_88[0][0]'] \n",
      "                                                                                                  \n",
      " activation_91 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_91[0][0]'] \n",
      "                                                                                                  \n",
      " block17_4_mixed (Concatenate)  (None, 12, 12, 384)  0           ['activation_88[0][0]',          \n",
      "                                                                  'activation_91[0][0]']          \n",
      "                                                                                                  \n",
      " block17_4_conv (Conv2D)        (None, 12, 12, 1088  418880      ['block17_4_mixed[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_4 (Lambda)             (None, 12, 12, 1088  0           ['block17_3_ac[0][0]',           \n",
      "                                )                                 'block17_4_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block17_4_ac (Activation)      (None, 12, 12, 1088  0           ['block17_4[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_93 (Conv2D)             (None, 12, 12, 128)  139264      ['block17_4_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_93 (BatchN  (None, 12, 12, 128)  384        ['conv2d_93[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_93 (Activation)     (None, 12, 12, 128)  0           ['batch_normalization_93[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_94 (Conv2D)             (None, 12, 12, 160)  143360      ['activation_93[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_94 (BatchN  (None, 12, 12, 160)  480        ['conv2d_94[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_94 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_94[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_92 (Conv2D)             (None, 12, 12, 192)  208896      ['block17_4_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_95 (Conv2D)             (None, 12, 12, 192)  215040      ['activation_94[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_92 (BatchN  (None, 12, 12, 192)  576        ['conv2d_92[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_95 (BatchN  (None, 12, 12, 192)  576        ['conv2d_95[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_92 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_92[0][0]'] \n",
      "                                                                                                  \n",
      " activation_95 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_95[0][0]'] \n",
      "                                                                                                  \n",
      " block17_5_mixed (Concatenate)  (None, 12, 12, 384)  0           ['activation_92[0][0]',          \n",
      "                                                                  'activation_95[0][0]']          \n",
      "                                                                                                  \n",
      " block17_5_conv (Conv2D)        (None, 12, 12, 1088  418880      ['block17_5_mixed[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_5 (Lambda)             (None, 12, 12, 1088  0           ['block17_4_ac[0][0]',           \n",
      "                                )                                 'block17_5_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block17_5_ac (Activation)      (None, 12, 12, 1088  0           ['block17_5[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_97 (Conv2D)             (None, 12, 12, 128)  139264      ['block17_5_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_97 (BatchN  (None, 12, 12, 128)  384        ['conv2d_97[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_97 (Activation)     (None, 12, 12, 128)  0           ['batch_normalization_97[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_98 (Conv2D)             (None, 12, 12, 160)  143360      ['activation_97[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_98 (BatchN  (None, 12, 12, 160)  480        ['conv2d_98[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_98 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_98[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_96 (Conv2D)             (None, 12, 12, 192)  208896      ['block17_5_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_99 (Conv2D)             (None, 12, 12, 192)  215040      ['activation_98[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_96 (BatchN  (None, 12, 12, 192)  576        ['conv2d_96[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_99 (BatchN  (None, 12, 12, 192)  576        ['conv2d_99[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_96 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_96[0][0]'] \n",
      "                                                                                                  \n",
      " activation_99 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_99[0][0]'] \n",
      "                                                                                                  \n",
      " block17_6_mixed (Concatenate)  (None, 12, 12, 384)  0           ['activation_96[0][0]',          \n",
      "                                                                  'activation_99[0][0]']          \n",
      "                                                                                                  \n",
      " block17_6_conv (Conv2D)        (None, 12, 12, 1088  418880      ['block17_6_mixed[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_6 (Lambda)             (None, 12, 12, 1088  0           ['block17_5_ac[0][0]',           \n",
      "                                )                                 'block17_6_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block17_6_ac (Activation)      (None, 12, 12, 1088  0           ['block17_6[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_101 (Conv2D)            (None, 12, 12, 128)  139264      ['block17_6_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_101 (Batch  (None, 12, 12, 128)  384        ['conv2d_101[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_101 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_101[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_102 (Conv2D)            (None, 12, 12, 160)  143360      ['activation_101[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_102 (Batch  (None, 12, 12, 160)  480        ['conv2d_102[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_102 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_102[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_100 (Conv2D)            (None, 12, 12, 192)  208896      ['block17_6_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_103 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_102[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_100 (Batch  (None, 12, 12, 192)  576        ['conv2d_100[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_103 (Batch  (None, 12, 12, 192)  576        ['conv2d_103[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_100 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_100[0][0]']\n",
      "                                                                                                  \n",
      " activation_103 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_103[0][0]']\n",
      "                                                                                                  \n",
      " block17_7_mixed (Concatenate)  (None, 12, 12, 384)  0           ['activation_100[0][0]',         \n",
      "                                                                  'activation_103[0][0]']         \n",
      "                                                                                                  \n",
      " block17_7_conv (Conv2D)        (None, 12, 12, 1088  418880      ['block17_7_mixed[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_7 (Lambda)             (None, 12, 12, 1088  0           ['block17_6_ac[0][0]',           \n",
      "                                )                                 'block17_7_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block17_7_ac (Activation)      (None, 12, 12, 1088  0           ['block17_7[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_105 (Conv2D)            (None, 12, 12, 128)  139264      ['block17_7_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_105 (Batch  (None, 12, 12, 128)  384        ['conv2d_105[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_105 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_105[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_106 (Conv2D)            (None, 12, 12, 160)  143360      ['activation_105[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_106 (Batch  (None, 12, 12, 160)  480        ['conv2d_106[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_106 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_106[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_104 (Conv2D)            (None, 12, 12, 192)  208896      ['block17_7_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_107 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_106[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_104 (Batch  (None, 12, 12, 192)  576        ['conv2d_104[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_107 (Batch  (None, 12, 12, 192)  576        ['conv2d_107[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_104 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_104[0][0]']\n",
      "                                                                                                  \n",
      " activation_107 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_107[0][0]']\n",
      "                                                                                                  \n",
      " block17_8_mixed (Concatenate)  (None, 12, 12, 384)  0           ['activation_104[0][0]',         \n",
      "                                                                  'activation_107[0][0]']         \n",
      "                                                                                                  \n",
      " block17_8_conv (Conv2D)        (None, 12, 12, 1088  418880      ['block17_8_mixed[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_8 (Lambda)             (None, 12, 12, 1088  0           ['block17_7_ac[0][0]',           \n",
      "                                )                                 'block17_8_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block17_8_ac (Activation)      (None, 12, 12, 1088  0           ['block17_8[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_109 (Conv2D)            (None, 12, 12, 128)  139264      ['block17_8_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_109 (Batch  (None, 12, 12, 128)  384        ['conv2d_109[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_109 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_109[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_110 (Conv2D)            (None, 12, 12, 160)  143360      ['activation_109[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_110 (Batch  (None, 12, 12, 160)  480        ['conv2d_110[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_110 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_110[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_108 (Conv2D)            (None, 12, 12, 192)  208896      ['block17_8_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_111 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_110[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_108 (Batch  (None, 12, 12, 192)  576        ['conv2d_108[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_111 (Batch  (None, 12, 12, 192)  576        ['conv2d_111[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_108 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_108[0][0]']\n",
      "                                                                                                  \n",
      " activation_111 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_111[0][0]']\n",
      "                                                                                                  \n",
      " block17_9_mixed (Concatenate)  (None, 12, 12, 384)  0           ['activation_108[0][0]',         \n",
      "                                                                  'activation_111[0][0]']         \n",
      "                                                                                                  \n",
      " block17_9_conv (Conv2D)        (None, 12, 12, 1088  418880      ['block17_9_mixed[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_9 (Lambda)             (None, 12, 12, 1088  0           ['block17_8_ac[0][0]',           \n",
      "                                )                                 'block17_9_conv[0][0]']         \n",
      "                                                                                                  \n",
      " block17_9_ac (Activation)      (None, 12, 12, 1088  0           ['block17_9[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_113 (Conv2D)            (None, 12, 12, 128)  139264      ['block17_9_ac[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_113 (Batch  (None, 12, 12, 128)  384        ['conv2d_113[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_113 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_113[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_114 (Conv2D)            (None, 12, 12, 160)  143360      ['activation_113[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_114 (Batch  (None, 12, 12, 160)  480        ['conv2d_114[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_114 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_114[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_112 (Conv2D)            (None, 12, 12, 192)  208896      ['block17_9_ac[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_115 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_114[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_112 (Batch  (None, 12, 12, 192)  576        ['conv2d_112[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_115 (Batch  (None, 12, 12, 192)  576        ['conv2d_115[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_112 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_112[0][0]']\n",
      "                                                                                                  \n",
      " activation_115 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_115[0][0]']\n",
      "                                                                                                  \n",
      " block17_10_mixed (Concatenate)  (None, 12, 12, 384)  0          ['activation_112[0][0]',         \n",
      "                                                                  'activation_115[0][0]']         \n",
      "                                                                                                  \n",
      " block17_10_conv (Conv2D)       (None, 12, 12, 1088  418880      ['block17_10_mixed[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_10 (Lambda)            (None, 12, 12, 1088  0           ['block17_9_ac[0][0]',           \n",
      "                                )                                 'block17_10_conv[0][0]']        \n",
      "                                                                                                  \n",
      " block17_10_ac (Activation)     (None, 12, 12, 1088  0           ['block17_10[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_117 (Conv2D)            (None, 12, 12, 128)  139264      ['block17_10_ac[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_117 (Batch  (None, 12, 12, 128)  384        ['conv2d_117[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_117 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_117[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_118 (Conv2D)            (None, 12, 12, 160)  143360      ['activation_117[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_118 (Batch  (None, 12, 12, 160)  480        ['conv2d_118[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_118 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_118[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_116 (Conv2D)            (None, 12, 12, 192)  208896      ['block17_10_ac[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_119 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_118[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_116 (Batch  (None, 12, 12, 192)  576        ['conv2d_116[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_119 (Batch  (None, 12, 12, 192)  576        ['conv2d_119[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_116 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_116[0][0]']\n",
      "                                                                                                  \n",
      " activation_119 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_119[0][0]']\n",
      "                                                                                                  \n",
      " block17_11_mixed (Concatenate)  (None, 12, 12, 384)  0          ['activation_116[0][0]',         \n",
      "                                                                  'activation_119[0][0]']         \n",
      "                                                                                                  \n",
      " block17_11_conv (Conv2D)       (None, 12, 12, 1088  418880      ['block17_11_mixed[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_11 (Lambda)            (None, 12, 12, 1088  0           ['block17_10_ac[0][0]',          \n",
      "                                )                                 'block17_11_conv[0][0]']        \n",
      "                                                                                                  \n",
      " block17_11_ac (Activation)     (None, 12, 12, 1088  0           ['block17_11[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_121 (Conv2D)            (None, 12, 12, 128)  139264      ['block17_11_ac[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_121 (Batch  (None, 12, 12, 128)  384        ['conv2d_121[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_121 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_121[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_122 (Conv2D)            (None, 12, 12, 160)  143360      ['activation_121[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_122 (Batch  (None, 12, 12, 160)  480        ['conv2d_122[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_122 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_122[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_120 (Conv2D)            (None, 12, 12, 192)  208896      ['block17_11_ac[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_123 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_122[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_120 (Batch  (None, 12, 12, 192)  576        ['conv2d_120[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_123 (Batch  (None, 12, 12, 192)  576        ['conv2d_123[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_120 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_120[0][0]']\n",
      "                                                                                                  \n",
      " activation_123 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_123[0][0]']\n",
      "                                                                                                  \n",
      " block17_12_mixed (Concatenate)  (None, 12, 12, 384)  0          ['activation_120[0][0]',         \n",
      "                                                                  'activation_123[0][0]']         \n",
      "                                                                                                  \n",
      " block17_12_conv (Conv2D)       (None, 12, 12, 1088  418880      ['block17_12_mixed[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_12 (Lambda)            (None, 12, 12, 1088  0           ['block17_11_ac[0][0]',          \n",
      "                                )                                 'block17_12_conv[0][0]']        \n",
      "                                                                                                  \n",
      " block17_12_ac (Activation)     (None, 12, 12, 1088  0           ['block17_12[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_125 (Conv2D)            (None, 12, 12, 128)  139264      ['block17_12_ac[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_125 (Batch  (None, 12, 12, 128)  384        ['conv2d_125[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_125 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_125[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_126 (Conv2D)            (None, 12, 12, 160)  143360      ['activation_125[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_126 (Batch  (None, 12, 12, 160)  480        ['conv2d_126[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_126 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_126[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_124 (Conv2D)            (None, 12, 12, 192)  208896      ['block17_12_ac[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_127 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_126[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_124 (Batch  (None, 12, 12, 192)  576        ['conv2d_124[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_127 (Batch  (None, 12, 12, 192)  576        ['conv2d_127[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_124 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_124[0][0]']\n",
      "                                                                                                  \n",
      " activation_127 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_127[0][0]']\n",
      "                                                                                                  \n",
      " block17_13_mixed (Concatenate)  (None, 12, 12, 384)  0          ['activation_124[0][0]',         \n",
      "                                                                  'activation_127[0][0]']         \n",
      "                                                                                                  \n",
      " block17_13_conv (Conv2D)       (None, 12, 12, 1088  418880      ['block17_13_mixed[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_13 (Lambda)            (None, 12, 12, 1088  0           ['block17_12_ac[0][0]',          \n",
      "                                )                                 'block17_13_conv[0][0]']        \n",
      "                                                                                                  \n",
      " block17_13_ac (Activation)     (None, 12, 12, 1088  0           ['block17_13[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_129 (Conv2D)            (None, 12, 12, 128)  139264      ['block17_13_ac[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_129 (Batch  (None, 12, 12, 128)  384        ['conv2d_129[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_129 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_129[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_130 (Conv2D)            (None, 12, 12, 160)  143360      ['activation_129[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_130 (Batch  (None, 12, 12, 160)  480        ['conv2d_130[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_130 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_130[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_128 (Conv2D)            (None, 12, 12, 192)  208896      ['block17_13_ac[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_131 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_130[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_128 (Batch  (None, 12, 12, 192)  576        ['conv2d_128[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_131 (Batch  (None, 12, 12, 192)  576        ['conv2d_131[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_128 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_128[0][0]']\n",
      "                                                                                                  \n",
      " activation_131 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_131[0][0]']\n",
      "                                                                                                  \n",
      " block17_14_mixed (Concatenate)  (None, 12, 12, 384)  0          ['activation_128[0][0]',         \n",
      "                                                                  'activation_131[0][0]']         \n",
      "                                                                                                  \n",
      " block17_14_conv (Conv2D)       (None, 12, 12, 1088  418880      ['block17_14_mixed[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_14 (Lambda)            (None, 12, 12, 1088  0           ['block17_13_ac[0][0]',          \n",
      "                                )                                 'block17_14_conv[0][0]']        \n",
      "                                                                                                  \n",
      " block17_14_ac (Activation)     (None, 12, 12, 1088  0           ['block17_14[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_133 (Conv2D)            (None, 12, 12, 128)  139264      ['block17_14_ac[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_133 (Batch  (None, 12, 12, 128)  384        ['conv2d_133[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_133 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_133[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_134 (Conv2D)            (None, 12, 12, 160)  143360      ['activation_133[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_134 (Batch  (None, 12, 12, 160)  480        ['conv2d_134[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_134 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_134[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_132 (Conv2D)            (None, 12, 12, 192)  208896      ['block17_14_ac[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_135 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_134[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_132 (Batch  (None, 12, 12, 192)  576        ['conv2d_132[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_135 (Batch  (None, 12, 12, 192)  576        ['conv2d_135[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_132 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_132[0][0]']\n",
      "                                                                                                  \n",
      " activation_135 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_135[0][0]']\n",
      "                                                                                                  \n",
      " block17_15_mixed (Concatenate)  (None, 12, 12, 384)  0          ['activation_132[0][0]',         \n",
      "                                                                  'activation_135[0][0]']         \n",
      "                                                                                                  \n",
      " block17_15_conv (Conv2D)       (None, 12, 12, 1088  418880      ['block17_15_mixed[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_15 (Lambda)            (None, 12, 12, 1088  0           ['block17_14_ac[0][0]',          \n",
      "                                )                                 'block17_15_conv[0][0]']        \n",
      "                                                                                                  \n",
      " block17_15_ac (Activation)     (None, 12, 12, 1088  0           ['block17_15[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_137 (Conv2D)            (None, 12, 12, 128)  139264      ['block17_15_ac[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_137 (Batch  (None, 12, 12, 128)  384        ['conv2d_137[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_137 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_137[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_138 (Conv2D)            (None, 12, 12, 160)  143360      ['activation_137[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_138 (Batch  (None, 12, 12, 160)  480        ['conv2d_138[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_138 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_138[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_136 (Conv2D)            (None, 12, 12, 192)  208896      ['block17_15_ac[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_139 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_138[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_136 (Batch  (None, 12, 12, 192)  576        ['conv2d_136[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_139 (Batch  (None, 12, 12, 192)  576        ['conv2d_139[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_136 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_136[0][0]']\n",
      "                                                                                                  \n",
      " activation_139 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_139[0][0]']\n",
      "                                                                                                  \n",
      " block17_16_mixed (Concatenate)  (None, 12, 12, 384)  0          ['activation_136[0][0]',         \n",
      "                                                                  'activation_139[0][0]']         \n",
      "                                                                                                  \n",
      " block17_16_conv (Conv2D)       (None, 12, 12, 1088  418880      ['block17_16_mixed[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_16 (Lambda)            (None, 12, 12, 1088  0           ['block17_15_ac[0][0]',          \n",
      "                                )                                 'block17_16_conv[0][0]']        \n",
      "                                                                                                  \n",
      " block17_16_ac (Activation)     (None, 12, 12, 1088  0           ['block17_16[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_141 (Conv2D)            (None, 12, 12, 128)  139264      ['block17_16_ac[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_141 (Batch  (None, 12, 12, 128)  384        ['conv2d_141[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_141 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_141[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_142 (Conv2D)            (None, 12, 12, 160)  143360      ['activation_141[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_142 (Batch  (None, 12, 12, 160)  480        ['conv2d_142[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_142 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_142[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_140 (Conv2D)            (None, 12, 12, 192)  208896      ['block17_16_ac[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_143 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_142[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_140 (Batch  (None, 12, 12, 192)  576        ['conv2d_140[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_143 (Batch  (None, 12, 12, 192)  576        ['conv2d_143[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_140 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_140[0][0]']\n",
      "                                                                                                  \n",
      " activation_143 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_143[0][0]']\n",
      "                                                                                                  \n",
      " block17_17_mixed (Concatenate)  (None, 12, 12, 384)  0          ['activation_140[0][0]',         \n",
      "                                                                  'activation_143[0][0]']         \n",
      "                                                                                                  \n",
      " block17_17_conv (Conv2D)       (None, 12, 12, 1088  418880      ['block17_17_mixed[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_17 (Lambda)            (None, 12, 12, 1088  0           ['block17_16_ac[0][0]',          \n",
      "                                )                                 'block17_17_conv[0][0]']        \n",
      "                                                                                                  \n",
      " block17_17_ac (Activation)     (None, 12, 12, 1088  0           ['block17_17[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_145 (Conv2D)            (None, 12, 12, 128)  139264      ['block17_17_ac[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_145 (Batch  (None, 12, 12, 128)  384        ['conv2d_145[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_145 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_145[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_146 (Conv2D)            (None, 12, 12, 160)  143360      ['activation_145[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_146 (Batch  (None, 12, 12, 160)  480        ['conv2d_146[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_146 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_146[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_144 (Conv2D)            (None, 12, 12, 192)  208896      ['block17_17_ac[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_147 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_146[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_144 (Batch  (None, 12, 12, 192)  576        ['conv2d_144[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_147 (Batch  (None, 12, 12, 192)  576        ['conv2d_147[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_144 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_144[0][0]']\n",
      "                                                                                                  \n",
      " activation_147 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_147[0][0]']\n",
      "                                                                                                  \n",
      " block17_18_mixed (Concatenate)  (None, 12, 12, 384)  0          ['activation_144[0][0]',         \n",
      "                                                                  'activation_147[0][0]']         \n",
      "                                                                                                  \n",
      " block17_18_conv (Conv2D)       (None, 12, 12, 1088  418880      ['block17_18_mixed[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_18 (Lambda)            (None, 12, 12, 1088  0           ['block17_17_ac[0][0]',          \n",
      "                                )                                 'block17_18_conv[0][0]']        \n",
      "                                                                                                  \n",
      " block17_18_ac (Activation)     (None, 12, 12, 1088  0           ['block17_18[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_149 (Conv2D)            (None, 12, 12, 128)  139264      ['block17_18_ac[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_149 (Batch  (None, 12, 12, 128)  384        ['conv2d_149[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_149 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_149[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_150 (Conv2D)            (None, 12, 12, 160)  143360      ['activation_149[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_150 (Batch  (None, 12, 12, 160)  480        ['conv2d_150[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_150 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_150[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_148 (Conv2D)            (None, 12, 12, 192)  208896      ['block17_18_ac[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_151 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_150[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_148 (Batch  (None, 12, 12, 192)  576        ['conv2d_148[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_151 (Batch  (None, 12, 12, 192)  576        ['conv2d_151[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_148 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_148[0][0]']\n",
      "                                                                                                  \n",
      " activation_151 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_151[0][0]']\n",
      "                                                                                                  \n",
      " block17_19_mixed (Concatenate)  (None, 12, 12, 384)  0          ['activation_148[0][0]',         \n",
      "                                                                  'activation_151[0][0]']         \n",
      "                                                                                                  \n",
      " block17_19_conv (Conv2D)       (None, 12, 12, 1088  418880      ['block17_19_mixed[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_19 (Lambda)            (None, 12, 12, 1088  0           ['block17_18_ac[0][0]',          \n",
      "                                )                                 'block17_19_conv[0][0]']        \n",
      "                                                                                                  \n",
      " block17_19_ac (Activation)     (None, 12, 12, 1088  0           ['block17_19[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_153 (Conv2D)            (None, 12, 12, 128)  139264      ['block17_19_ac[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_153 (Batch  (None, 12, 12, 128)  384        ['conv2d_153[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_153 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_153[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_154 (Conv2D)            (None, 12, 12, 160)  143360      ['activation_153[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_154 (Batch  (None, 12, 12, 160)  480        ['conv2d_154[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_154 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_154[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_152 (Conv2D)            (None, 12, 12, 192)  208896      ['block17_19_ac[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_155 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_154[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_152 (Batch  (None, 12, 12, 192)  576        ['conv2d_152[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_155 (Batch  (None, 12, 12, 192)  576        ['conv2d_155[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_152 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_152[0][0]']\n",
      "                                                                                                  \n",
      " activation_155 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_155[0][0]']\n",
      "                                                                                                  \n",
      " block17_20_mixed (Concatenate)  (None, 12, 12, 384)  0          ['activation_152[0][0]',         \n",
      "                                                                  'activation_155[0][0]']         \n",
      "                                                                                                  \n",
      " block17_20_conv (Conv2D)       (None, 12, 12, 1088  418880      ['block17_20_mixed[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block17_20 (Lambda)            (None, 12, 12, 1088  0           ['block17_19_ac[0][0]',          \n",
      "                                )                                 'block17_20_conv[0][0]']        \n",
      "                                                                                                  \n",
      " block17_20_ac (Activation)     (None, 12, 12, 1088  0           ['block17_20[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_160 (Conv2D)            (None, 12, 12, 256)  278528      ['block17_20_ac[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_160 (Batch  (None, 12, 12, 256)  768        ['conv2d_160[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_160 (Activation)    (None, 12, 12, 256)  0           ['batch_normalization_160[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_156 (Conv2D)            (None, 12, 12, 256)  278528      ['block17_20_ac[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_158 (Conv2D)            (None, 12, 12, 256)  278528      ['block17_20_ac[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_161 (Conv2D)            (None, 12, 12, 288)  663552      ['activation_160[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_156 (Batch  (None, 12, 12, 256)  768        ['conv2d_156[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_158 (Batch  (None, 12, 12, 256)  768        ['conv2d_158[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_161 (Batch  (None, 12, 12, 288)  864        ['conv2d_161[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_156 (Activation)    (None, 12, 12, 256)  0           ['batch_normalization_156[0][0]']\n",
      "                                                                                                  \n",
      " activation_158 (Activation)    (None, 12, 12, 256)  0           ['batch_normalization_158[0][0]']\n",
      "                                                                                                  \n",
      " activation_161 (Activation)    (None, 12, 12, 288)  0           ['batch_normalization_161[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_157 (Conv2D)            (None, 5, 5, 384)    884736      ['activation_156[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_159 (Conv2D)            (None, 5, 5, 288)    663552      ['activation_158[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_162 (Conv2D)            (None, 5, 5, 320)    829440      ['activation_161[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_157 (Batch  (None, 5, 5, 384)   1152        ['conv2d_157[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_159 (Batch  (None, 5, 5, 288)   864         ['conv2d_159[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_162 (Batch  (None, 5, 5, 320)   960         ['conv2d_162[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_157 (Activation)    (None, 5, 5, 384)    0           ['batch_normalization_157[0][0]']\n",
      "                                                                                                  \n",
      " activation_159 (Activation)    (None, 5, 5, 288)    0           ['batch_normalization_159[0][0]']\n",
      "                                                                                                  \n",
      " activation_162 (Activation)    (None, 5, 5, 320)    0           ['batch_normalization_162[0][0]']\n",
      "                                                                                                  \n",
      " max_pooling2d_3 (MaxPooling2D)  (None, 5, 5, 1088)  0           ['block17_20_ac[0][0]']          \n",
      "                                                                                                  \n",
      " mixed_7a (Concatenate)         (None, 5, 5, 2080)   0           ['activation_157[0][0]',         \n",
      "                                                                  'activation_159[0][0]',         \n",
      "                                                                  'activation_162[0][0]',         \n",
      "                                                                  'max_pooling2d_3[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_164 (Conv2D)            (None, 5, 5, 192)    399360      ['mixed_7a[0][0]']               \n",
      "                                                                                                  \n",
      " batch_normalization_164 (Batch  (None, 5, 5, 192)   576         ['conv2d_164[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_164 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_164[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_165 (Conv2D)            (None, 5, 5, 224)    129024      ['activation_164[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_165 (Batch  (None, 5, 5, 224)   672         ['conv2d_165[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_165 (Activation)    (None, 5, 5, 224)    0           ['batch_normalization_165[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_163 (Conv2D)            (None, 5, 5, 192)    399360      ['mixed_7a[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_166 (Conv2D)            (None, 5, 5, 256)    172032      ['activation_165[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_163 (Batch  (None, 5, 5, 192)   576         ['conv2d_163[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_166 (Batch  (None, 5, 5, 256)   768         ['conv2d_166[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_163 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_163[0][0]']\n",
      "                                                                                                  \n",
      " activation_166 (Activation)    (None, 5, 5, 256)    0           ['batch_normalization_166[0][0]']\n",
      "                                                                                                  \n",
      " block8_1_mixed (Concatenate)   (None, 5, 5, 448)    0           ['activation_163[0][0]',         \n",
      "                                                                  'activation_166[0][0]']         \n",
      "                                                                                                  \n",
      " block8_1_conv (Conv2D)         (None, 5, 5, 2080)   933920      ['block8_1_mixed[0][0]']         \n",
      "                                                                                                  \n",
      " block8_1 (Lambda)              (None, 5, 5, 2080)   0           ['mixed_7a[0][0]',               \n",
      "                                                                  'block8_1_conv[0][0]']          \n",
      "                                                                                                  \n",
      " block8_1_ac (Activation)       (None, 5, 5, 2080)   0           ['block8_1[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_168 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_1_ac[0][0]']            \n",
      "                                                                                                  \n",
      " batch_normalization_168 (Batch  (None, 5, 5, 192)   576         ['conv2d_168[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_168 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_168[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_169 (Conv2D)            (None, 5, 5, 224)    129024      ['activation_168[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_169 (Batch  (None, 5, 5, 224)   672         ['conv2d_169[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_169 (Activation)    (None, 5, 5, 224)    0           ['batch_normalization_169[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_167 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_1_ac[0][0]']            \n",
      "                                                                                                  \n",
      " conv2d_170 (Conv2D)            (None, 5, 5, 256)    172032      ['activation_169[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_167 (Batch  (None, 5, 5, 192)   576         ['conv2d_167[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_170 (Batch  (None, 5, 5, 256)   768         ['conv2d_170[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_167 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_167[0][0]']\n",
      "                                                                                                  \n",
      " activation_170 (Activation)    (None, 5, 5, 256)    0           ['batch_normalization_170[0][0]']\n",
      "                                                                                                  \n",
      " block8_2_mixed (Concatenate)   (None, 5, 5, 448)    0           ['activation_167[0][0]',         \n",
      "                                                                  'activation_170[0][0]']         \n",
      "                                                                                                  \n",
      " block8_2_conv (Conv2D)         (None, 5, 5, 2080)   933920      ['block8_2_mixed[0][0]']         \n",
      "                                                                                                  \n",
      " block8_2 (Lambda)              (None, 5, 5, 2080)   0           ['block8_1_ac[0][0]',            \n",
      "                                                                  'block8_2_conv[0][0]']          \n",
      "                                                                                                  \n",
      " block8_2_ac (Activation)       (None, 5, 5, 2080)   0           ['block8_2[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_172 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_2_ac[0][0]']            \n",
      "                                                                                                  \n",
      " batch_normalization_172 (Batch  (None, 5, 5, 192)   576         ['conv2d_172[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_172 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_172[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_173 (Conv2D)            (None, 5, 5, 224)    129024      ['activation_172[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_173 (Batch  (None, 5, 5, 224)   672         ['conv2d_173[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_173 (Activation)    (None, 5, 5, 224)    0           ['batch_normalization_173[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_171 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_2_ac[0][0]']            \n",
      "                                                                                                  \n",
      " conv2d_174 (Conv2D)            (None, 5, 5, 256)    172032      ['activation_173[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_171 (Batch  (None, 5, 5, 192)   576         ['conv2d_171[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_174 (Batch  (None, 5, 5, 256)   768         ['conv2d_174[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_171 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_171[0][0]']\n",
      "                                                                                                  \n",
      " activation_174 (Activation)    (None, 5, 5, 256)    0           ['batch_normalization_174[0][0]']\n",
      "                                                                                                  \n",
      " block8_3_mixed (Concatenate)   (None, 5, 5, 448)    0           ['activation_171[0][0]',         \n",
      "                                                                  'activation_174[0][0]']         \n",
      "                                                                                                  \n",
      " block8_3_conv (Conv2D)         (None, 5, 5, 2080)   933920      ['block8_3_mixed[0][0]']         \n",
      "                                                                                                  \n",
      " block8_3 (Lambda)              (None, 5, 5, 2080)   0           ['block8_2_ac[0][0]',            \n",
      "                                                                  'block8_3_conv[0][0]']          \n",
      "                                                                                                  \n",
      " block8_3_ac (Activation)       (None, 5, 5, 2080)   0           ['block8_3[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_176 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_3_ac[0][0]']            \n",
      "                                                                                                  \n",
      " batch_normalization_176 (Batch  (None, 5, 5, 192)   576         ['conv2d_176[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_176 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_176[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_177 (Conv2D)            (None, 5, 5, 224)    129024      ['activation_176[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_177 (Batch  (None, 5, 5, 224)   672         ['conv2d_177[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_177 (Activation)    (None, 5, 5, 224)    0           ['batch_normalization_177[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_175 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_3_ac[0][0]']            \n",
      "                                                                                                  \n",
      " conv2d_178 (Conv2D)            (None, 5, 5, 256)    172032      ['activation_177[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_175 (Batch  (None, 5, 5, 192)   576         ['conv2d_175[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_178 (Batch  (None, 5, 5, 256)   768         ['conv2d_178[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_175 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_175[0][0]']\n",
      "                                                                                                  \n",
      " activation_178 (Activation)    (None, 5, 5, 256)    0           ['batch_normalization_178[0][0]']\n",
      "                                                                                                  \n",
      " block8_4_mixed (Concatenate)   (None, 5, 5, 448)    0           ['activation_175[0][0]',         \n",
      "                                                                  'activation_178[0][0]']         \n",
      "                                                                                                  \n",
      " block8_4_conv (Conv2D)         (None, 5, 5, 2080)   933920      ['block8_4_mixed[0][0]']         \n",
      "                                                                                                  \n",
      " block8_4 (Lambda)              (None, 5, 5, 2080)   0           ['block8_3_ac[0][0]',            \n",
      "                                                                  'block8_4_conv[0][0]']          \n",
      "                                                                                                  \n",
      " block8_4_ac (Activation)       (None, 5, 5, 2080)   0           ['block8_4[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_180 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_4_ac[0][0]']            \n",
      "                                                                                                  \n",
      " batch_normalization_180 (Batch  (None, 5, 5, 192)   576         ['conv2d_180[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_180 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_180[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_181 (Conv2D)            (None, 5, 5, 224)    129024      ['activation_180[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_181 (Batch  (None, 5, 5, 224)   672         ['conv2d_181[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_181 (Activation)    (None, 5, 5, 224)    0           ['batch_normalization_181[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_179 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_4_ac[0][0]']            \n",
      "                                                                                                  \n",
      " conv2d_182 (Conv2D)            (None, 5, 5, 256)    172032      ['activation_181[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_179 (Batch  (None, 5, 5, 192)   576         ['conv2d_179[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_182 (Batch  (None, 5, 5, 256)   768         ['conv2d_182[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_179 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_179[0][0]']\n",
      "                                                                                                  \n",
      " activation_182 (Activation)    (None, 5, 5, 256)    0           ['batch_normalization_182[0][0]']\n",
      "                                                                                                  \n",
      " block8_5_mixed (Concatenate)   (None, 5, 5, 448)    0           ['activation_179[0][0]',         \n",
      "                                                                  'activation_182[0][0]']         \n",
      "                                                                                                  \n",
      " block8_5_conv (Conv2D)         (None, 5, 5, 2080)   933920      ['block8_5_mixed[0][0]']         \n",
      "                                                                                                  \n",
      " block8_5 (Lambda)              (None, 5, 5, 2080)   0           ['block8_4_ac[0][0]',            \n",
      "                                                                  'block8_5_conv[0][0]']          \n",
      "                                                                                                  \n",
      " block8_5_ac (Activation)       (None, 5, 5, 2080)   0           ['block8_5[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_184 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_5_ac[0][0]']            \n",
      "                                                                                                  \n",
      " batch_normalization_184 (Batch  (None, 5, 5, 192)   576         ['conv2d_184[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_184 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_184[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_185 (Conv2D)            (None, 5, 5, 224)    129024      ['activation_184[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_185 (Batch  (None, 5, 5, 224)   672         ['conv2d_185[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_185 (Activation)    (None, 5, 5, 224)    0           ['batch_normalization_185[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_183 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_5_ac[0][0]']            \n",
      "                                                                                                  \n",
      " conv2d_186 (Conv2D)            (None, 5, 5, 256)    172032      ['activation_185[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_183 (Batch  (None, 5, 5, 192)   576         ['conv2d_183[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_186 (Batch  (None, 5, 5, 256)   768         ['conv2d_186[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_183 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_183[0][0]']\n",
      "                                                                                                  \n",
      " activation_186 (Activation)    (None, 5, 5, 256)    0           ['batch_normalization_186[0][0]']\n",
      "                                                                                                  \n",
      " block8_6_mixed (Concatenate)   (None, 5, 5, 448)    0           ['activation_183[0][0]',         \n",
      "                                                                  'activation_186[0][0]']         \n",
      "                                                                                                  \n",
      " block8_6_conv (Conv2D)         (None, 5, 5, 2080)   933920      ['block8_6_mixed[0][0]']         \n",
      "                                                                                                  \n",
      " block8_6 (Lambda)              (None, 5, 5, 2080)   0           ['block8_5_ac[0][0]',            \n",
      "                                                                  'block8_6_conv[0][0]']          \n",
      "                                                                                                  \n",
      " block8_6_ac (Activation)       (None, 5, 5, 2080)   0           ['block8_6[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_188 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_6_ac[0][0]']            \n",
      "                                                                                                  \n",
      " batch_normalization_188 (Batch  (None, 5, 5, 192)   576         ['conv2d_188[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_188 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_188[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_189 (Conv2D)            (None, 5, 5, 224)    129024      ['activation_188[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_189 (Batch  (None, 5, 5, 224)   672         ['conv2d_189[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_189 (Activation)    (None, 5, 5, 224)    0           ['batch_normalization_189[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_187 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_6_ac[0][0]']            \n",
      "                                                                                                  \n",
      " conv2d_190 (Conv2D)            (None, 5, 5, 256)    172032      ['activation_189[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_187 (Batch  (None, 5, 5, 192)   576         ['conv2d_187[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_190 (Batch  (None, 5, 5, 256)   768         ['conv2d_190[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_187 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_187[0][0]']\n",
      "                                                                                                  \n",
      " activation_190 (Activation)    (None, 5, 5, 256)    0           ['batch_normalization_190[0][0]']\n",
      "                                                                                                  \n",
      " block8_7_mixed (Concatenate)   (None, 5, 5, 448)    0           ['activation_187[0][0]',         \n",
      "                                                                  'activation_190[0][0]']         \n",
      "                                                                                                  \n",
      " block8_7_conv (Conv2D)         (None, 5, 5, 2080)   933920      ['block8_7_mixed[0][0]']         \n",
      "                                                                                                  \n",
      " block8_7 (Lambda)              (None, 5, 5, 2080)   0           ['block8_6_ac[0][0]',            \n",
      "                                                                  'block8_7_conv[0][0]']          \n",
      "                                                                                                  \n",
      " block8_7_ac (Activation)       (None, 5, 5, 2080)   0           ['block8_7[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_192 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_7_ac[0][0]']            \n",
      "                                                                                                  \n",
      " batch_normalization_192 (Batch  (None, 5, 5, 192)   576         ['conv2d_192[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_192 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_192[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_193 (Conv2D)            (None, 5, 5, 224)    129024      ['activation_192[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_193 (Batch  (None, 5, 5, 224)   672         ['conv2d_193[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_193 (Activation)    (None, 5, 5, 224)    0           ['batch_normalization_193[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_191 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_7_ac[0][0]']            \n",
      "                                                                                                  \n",
      " conv2d_194 (Conv2D)            (None, 5, 5, 256)    172032      ['activation_193[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_191 (Batch  (None, 5, 5, 192)   576         ['conv2d_191[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_194 (Batch  (None, 5, 5, 256)   768         ['conv2d_194[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_191 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_191[0][0]']\n",
      "                                                                                                  \n",
      " activation_194 (Activation)    (None, 5, 5, 256)    0           ['batch_normalization_194[0][0]']\n",
      "                                                                                                  \n",
      " block8_8_mixed (Concatenate)   (None, 5, 5, 448)    0           ['activation_191[0][0]',         \n",
      "                                                                  'activation_194[0][0]']         \n",
      "                                                                                                  \n",
      " block8_8_conv (Conv2D)         (None, 5, 5, 2080)   933920      ['block8_8_mixed[0][0]']         \n",
      "                                                                                                  \n",
      " block8_8 (Lambda)              (None, 5, 5, 2080)   0           ['block8_7_ac[0][0]',            \n",
      "                                                                  'block8_8_conv[0][0]']          \n",
      "                                                                                                  \n",
      " block8_8_ac (Activation)       (None, 5, 5, 2080)   0           ['block8_8[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_196 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_8_ac[0][0]']            \n",
      "                                                                                                  \n",
      " batch_normalization_196 (Batch  (None, 5, 5, 192)   576         ['conv2d_196[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_196 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_196[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_197 (Conv2D)            (None, 5, 5, 224)    129024      ['activation_196[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_197 (Batch  (None, 5, 5, 224)   672         ['conv2d_197[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_197 (Activation)    (None, 5, 5, 224)    0           ['batch_normalization_197[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_195 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_8_ac[0][0]']            \n",
      "                                                                                                  \n",
      " conv2d_198 (Conv2D)            (None, 5, 5, 256)    172032      ['activation_197[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_195 (Batch  (None, 5, 5, 192)   576         ['conv2d_195[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_198 (Batch  (None, 5, 5, 256)   768         ['conv2d_198[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_195 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_195[0][0]']\n",
      "                                                                                                  \n",
      " activation_198 (Activation)    (None, 5, 5, 256)    0           ['batch_normalization_198[0][0]']\n",
      "                                                                                                  \n",
      " block8_9_mixed (Concatenate)   (None, 5, 5, 448)    0           ['activation_195[0][0]',         \n",
      "                                                                  'activation_198[0][0]']         \n",
      "                                                                                                  \n",
      " block8_9_conv (Conv2D)         (None, 5, 5, 2080)   933920      ['block8_9_mixed[0][0]']         \n",
      "                                                                                                  \n",
      " block8_9 (Lambda)              (None, 5, 5, 2080)   0           ['block8_8_ac[0][0]',            \n",
      "                                                                  'block8_9_conv[0][0]']          \n",
      "                                                                                                  \n",
      " block8_9_ac (Activation)       (None, 5, 5, 2080)   0           ['block8_9[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_200 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_9_ac[0][0]']            \n",
      "                                                                                                  \n",
      " batch_normalization_200 (Batch  (None, 5, 5, 192)   576         ['conv2d_200[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_200 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_200[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_201 (Conv2D)            (None, 5, 5, 224)    129024      ['activation_200[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_201 (Batch  (None, 5, 5, 224)   672         ['conv2d_201[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_201 (Activation)    (None, 5, 5, 224)    0           ['batch_normalization_201[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_199 (Conv2D)            (None, 5, 5, 192)    399360      ['block8_9_ac[0][0]']            \n",
      "                                                                                                  \n",
      " conv2d_202 (Conv2D)            (None, 5, 5, 256)    172032      ['activation_201[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_199 (Batch  (None, 5, 5, 192)   576         ['conv2d_199[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_202 (Batch  (None, 5, 5, 256)   768         ['conv2d_202[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_199 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_199[0][0]']\n",
      "                                                                                                  \n",
      " activation_202 (Activation)    (None, 5, 5, 256)    0           ['batch_normalization_202[0][0]']\n",
      "                                                                                                  \n",
      " block8_10_mixed (Concatenate)  (None, 5, 5, 448)    0           ['activation_199[0][0]',         \n",
      "                                                                  'activation_202[0][0]']         \n",
      "                                                                                                  \n",
      " block8_10_conv (Conv2D)        (None, 5, 5, 2080)   933920      ['block8_10_mixed[0][0]']        \n",
      "                                                                                                  \n",
      " block8_10 (Lambda)             (None, 5, 5, 2080)   0           ['block8_9_ac[0][0]',            \n",
      "                                                                  'block8_10_conv[0][0]']         \n",
      "                                                                                                  \n",
      " conv_7b (Conv2D)               (None, 5, 5, 1536)   3194880     ['block8_10[0][0]']              \n",
      "                                                                                                  \n",
      " conv_7b_bn (BatchNormalization  (None, 5, 5, 1536)  4608        ['conv_7b[0][0]']                \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " conv_7b_ac (Activation)        (None, 5, 5, 1536)   0           ['conv_7b_bn[0][0]']             \n",
      "                                                                                                  \n",
      " global_average_pooling2d (Glob  (None, 1536)        0           ['conv_7b_ac[0][0]']             \n",
      " alAveragePooling2D)                                                                              \n",
      "                                                                                                  \n",
      " flatten_2 (Flatten)            (None, 1536)         0           ['global_average_pooling2d[0][0]'\n",
      "                                                                 ]                                \n",
      "                                                                                                  \n",
      " dropout_4 (Dropout)            (None, 1536)         0           ['flatten_2[0][0]']              \n",
      "                                                                                                  \n",
      " dense_4 (Dense)                (None, 512)          786944      ['dropout_4[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_5 (Dropout)            (None, 512)          0           ['dense_4[0][0]']                \n",
      "                                                                                                  \n",
      " dense_5 (Dense)                (None, 3)            1539        ['dropout_5[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 55,125,219\n",
      "Trainable params: 788,483\n",
      "Non-trainable params: 54,336,736\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Dense\n",
    "from keras import Model\n",
    "\n",
    "x = pretrained_model.output\n",
    "x = Flatten()(x)\n",
    "x = Dropout(0.25)(x)\n",
    "x = Dense(512, activation='relu')(x)\n",
    "x = Dropout(0.5)(x)\n",
    "predictions = Dense(NUM_CLASSES, activation='softmax')(x)\n",
    "\n",
    "model = Model(inputs=pretrained_model.input, outputs=predictions)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss='categorical_crossentropy',\n",
    "    optimizer=Adam(learning_rate=LEARNING_RATE),\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "32/32 [==============================] - 26s 521ms/step - loss: 4.5994 - accuracy: 0.8220 - val_loss: 0.2147 - val_accuracy: 0.9800 - lr: 0.0100\n",
      "Epoch 2/25\n",
      "32/32 [==============================] - 11s 332ms/step - loss: 0.1583 - accuracy: 0.9820 - val_loss: 0.1764 - val_accuracy: 0.9840 - lr: 0.0100\n",
      "Epoch 3/25\n",
      "32/32 [==============================] - 11s 332ms/step - loss: 0.0643 - accuracy: 0.9880 - val_loss: 2.9147e-04 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 4/25\n",
      "32/32 [==============================] - 11s 332ms/step - loss: 0.0088 - accuracy: 0.9960 - val_loss: 0.0314 - val_accuracy: 0.9920 - lr: 0.0100\n",
      "Epoch 5/25\n",
      "32/32 [==============================] - 11s 333ms/step - loss: 0.0041 - accuracy: 0.9990 - val_loss: 1.0563e-04 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 6/25\n",
      "32/32 [==============================] - 11s 333ms/step - loss: 0.0062 - accuracy: 0.9970 - val_loss: 0.0092 - val_accuracy: 0.9960 - lr: 0.0100\n",
      "Epoch 7/25\n",
      "32/32 [==============================] - 11s 349ms/step - loss: 0.0019 - accuracy: 0.9990 - val_loss: 3.7699e-05 - val_accuracy: 1.0000 - lr: 0.0100\n",
      "Epoch 8/25\n",
      "32/32 [==============================] - 11s 334ms/step - loss: 0.0411 - accuracy: 0.9900 - val_loss: 0.0167 - val_accuracy: 0.9920 - lr: 0.0100\n",
      "Epoch 9/25\n",
      "32/32 [==============================] - 11s 334ms/step - loss: 0.0214 - accuracy: 0.9940 - val_loss: 4.2457e-04 - val_accuracy: 1.0000 - lr: 0.0020\n",
      "Epoch 10/25\n",
      "32/32 [==============================] - 11s 336ms/step - loss: 0.0028 - accuracy: 0.9990 - val_loss: 1.5247e-05 - val_accuracy: 1.0000 - lr: 0.0020\n",
      "Epoch 11/25\n",
      "32/32 [==============================] - 11s 334ms/step - loss: 0.0103 - accuracy: 0.9970 - val_loss: 0.0108 - val_accuracy: 0.9960 - lr: 0.0020\n",
      "Epoch 12/25\n",
      "32/32 [==============================] - 11s 334ms/step - loss: 0.0207 - accuracy: 0.9970 - val_loss: 6.6920e-04 - val_accuracy: 1.0000 - lr: 4.0000e-04\n",
      "Epoch 13/25\n",
      "32/32 [==============================] - 11s 334ms/step - loss: 0.0122 - accuracy: 0.9970 - val_loss: 3.0558e-05 - val_accuracy: 1.0000 - lr: 4.0000e-04\n",
      "Epoch 14/25\n",
      "32/32 [==============================] - 11s 337ms/step - loss: 0.0030 - accuracy: 0.9990 - val_loss: 2.3049e-04 - val_accuracy: 1.0000 - lr: 4.0000e-04\n",
      "Epoch 15/25\n",
      "32/32 [==============================] - 11s 334ms/step - loss: 0.0044 - accuracy: 0.9980 - val_loss: 3.8557e-04 - val_accuracy: 1.0000 - lr: 8.0000e-05\n"
     ]
    }
   ],
   "source": [
    "history = 0\n",
    "with tf.device('/GPU:0'):\n",
    "    history = model.fit(\n",
    "        train_gen,\n",
    "        epochs=EPOCHS,\n",
    "        batch_size=BATCH_SIZE,\n",
    "        validation_data=val_gen,\n",
    "        callbacks=my_callbacks,\n",
    "        workers=1\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('./content/models/InceptionResNetV2_Noise_Classification_Gen.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/8 [==============================] - 5s 266ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        78\n",
      "           1       1.00      1.00      1.00       102\n",
      "           2       1.00      1.00      1.00        70\n",
      "\n",
      "    accuracy                           1.00       250\n",
      "   macro avg       1.00      1.00      1.00       250\n",
      "weighted avg       1.00      1.00      1.00       250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "predict = model.predict(val_gen)\n",
    "print(classification_report(val_gen.labels, np.argmax(predict, axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Calculating model accuracy\n",
      "8/8 [==============================] - 2s 262ms/step - loss: 3.1518e-05 - accuracy: 1.0000\n",
      "Test Accuracy: 100.0\n"
     ]
    }
   ],
   "source": [
    "print(\"[INFO] Calculating model accuracy\")\n",
    "scores = model.evaluate(val_gen)\n",
    "print(f\"Test Accuracy: {scores[1]*100}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(history.history)[['accuracy','val_accuracy']].plot()\n",
    "plt.title(\"Accuracy\")\n",
    "plt.show()\n",
    "\n",
    "pd.DataFrame(history.history)[['loss','val_loss']].plot()\n",
    "plt.title(\"Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 4s 4s/step\n",
      "Periodic\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "p = data[data['labels']==CLASSES[0]]['images'][data[data['labels']==CLASSES[0]]['images'].index[random.randrange(0,data[data['labels']==CLASSES[0]].__len__()-1)]]\n",
    "s = data[data['labels']==CLASSES[1]]['images'][data[data['labels']==CLASSES[1]]['images'].index[random.randrange(0,data[data['labels']==CLASSES[1]].__len__()-1)]]\n",
    "g = data[data['labels']==CLASSES[2]]['images'][data[data['labels']==CLASSES[2]]['images'].index[random.randrange(0,data[data['labels']==CLASSES[2]].__len__()-1)]]\n",
    "\n",
    "p_result = np.argmax(model.predict([load_test_image(p)]), axis=1)\n",
    "s_result = np.argmax(model.predict([load_test_image(s)]), axis=1)\n",
    "g_result = np.argmax(model.predict([load_test_image(g)]), axis=1)\n",
    "\n",
    "plt_figure(\n",
    "    [cv2.imread(p),cv2.imread(s),cv2.imread(g)],\n",
    "    [f'{CLASSES[p_result[0]]}' , f'{CLASSES[s_result[0]]}', f'{CLASSES[g_result[0]]}'], \n",
    "    columns=3,\n",
    "    w=10\n",
    "    )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}