train_AE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-13 09:45:24.227171: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-11-13 09:45:24.468037: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-11-13 09:45:24.504843: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/kiliakis/install/anaconda3/lib:/home/kiliakis/install/lib\n",
      "2023-11-13 09:45:24.504855: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2023-11-13 09:45:25.283217: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/kiliakis/install/anaconda3/lib:/home/kiliakis/install/lib\n",
      "2023-11-13 09:45:25.283402: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/kiliakis/install/anaconda3/lib:/home/kiliakis/install/lib\n",
      "2023-11-13 09:45:25.283409: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "# Train the ML model\n",
    "from utils import sample_files, load_encoder_data, plot_loss\n",
    "from utils import fast_tensor_load\n",
    "\n",
    "import time\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import yaml\n",
    "import os\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using timestamp:  2023_11_13_09-45-27\n"
     ]
    }
   ],
   "source": [
    "IMG_OUTPUT_SIZE = 128\n",
    "DATA_LOAD_METHOD='FAST_TENSOR' # it can be TENSOR or DATASET or FAST_TENSOR\n",
    "\n",
    "num_Turns_Case = 1\n",
    "var_names = ['phEr', 'enEr', 'bl',\n",
    "             'inten', 'Vrf', 'mu', 'VrfSPS']\n",
    "# Initialize parameters\n",
    "data_dir = './tomo_data/datasets_encoder_TF_08-11-23'\n",
    "# data_dir = './tomo_data/datasets_encoder_TF_24-03-23'\n",
    "data_type = 'float32'\n",
    "# data_dir = './tomo_data/datasets'\n",
    "timestamp = datetime.now().strftime(\"%Y_%m_%d_%H-%M-%S\")\n",
    "print('Using timestamp: ', timestamp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Train specific\n",
    "train_cfg = {\n",
    "    'dataset%': 0.5,\n",
    "    'normalization': 'minmax',\n",
    "    'img_normalize': 'off',\n",
    "    'data_type': 'float16'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No GPU available, using the CPU\n"
     ]
    }
   ],
   "source": [
    "# Initialize directories\n",
    "trial_dir = os.path.join('./trials/', timestamp)\n",
    "weights_dir = os.path.join(trial_dir, 'weights')\n",
    "plots_dir = os.path.join(trial_dir, 'plots')\n",
    "\n",
    "# Initialize GPU\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "device_to_use = 0\n",
    "\n",
    "if gpus:\n",
    "    try:\n",
    "        # Currently, memory growth needs to be the same across GPUs\n",
    "        tf.config.experimental.set_memory_growth(gpus[device_to_use], True)\n",
    "        tf.config.experimental.set_virtual_device_configuration(\n",
    "            gpus[device_to_use],\n",
    "            [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=12*1024)])\n",
    "        logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
    "        print(len(gpus), \"Physical GPUs,\", len(\n",
    "            logical_gpus), \"Logical GPUs\")\n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "else:\n",
    "    print('No GPU available, using the CPU')\n",
    "\n",
    "# Initialize train/ test / validation paths\n",
    "ML_dir = os.path.join(data_dir, 'ML_data')\n",
    "assert os.path.exists(ML_dir)\n",
    "\n",
    "TRAINING_PATH = os.path.join(ML_dir, 'TRAINING')\n",
    "VALIDATION_PATH = os.path.join(ML_dir, 'VALIDATION')\n",
    "\n",
    "# create the directory to store the results\n",
    "os.makedirs(trial_dir, exist_ok=True)\n",
    "os.makedirs(weights_dir, exist_ok=True)\n",
    "os.makedirs(plots_dir, exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading ./tomo_data/datasets_encoder_TF_08-11-23/ML_data/training-00.npz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-13 09:49:54.225796: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Training files:  15885\n",
      "Loading ./tomo_data/datasets_encoder_TF_08-11-23/ML_data/validation-00.npz\n",
      "Number of Validation files:  1403\n",
      "\n",
      "---- Input files have been read, elapsed: 13.019152879714966 ----\n",
      "\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "start_t = time.time()\n",
    "\n",
    "assert train_cfg['normalization'] == 'minmax'\n",
    "assert train_cfg['img_normalize'] == 'off'\n",
    "\n",
    "TRAINING_PATH = os.path.join(ML_dir, 'training-??.npz')\n",
    "VALIDATION_PATH = os.path.join(ML_dir, 'validation-??.npz')\n",
    "\n",
    "x_train, y_train = fast_tensor_load(TRAINING_PATH, train_cfg['dataset%'], dtype=data_type)\n",
    "print('Number of Training files: ', len(y_train))\n",
    "\n",
    "x_valid, y_valid = fast_tensor_load(\n",
    "    VALIDATION_PATH, train_cfg['dataset%'], dtype=data_type)\n",
    "print('Number of Validation files: ', len(y_valid))\n",
    "\n",
    "end_t = time.time()\n",
    "print(\n",
    "    f'\\n---- Input files have been read, elapsed: {end_t - start_t} ----\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# plot some of the outputs\n",
    "\n",
    "nrows = 3\n",
    "# Get nrows * nrows random images\n",
    "sample = np.random.choice(np.arange(len(x_train)),\n",
    "                          size=nrows * nrows, replace=False)\n",
    "\n",
    "samples_X = tf.gather(x_train, sample)\n",
    "samples_y = tf.gather(y_train, sample)\n",
    "\n",
    "# Create 3x3 grid of figures\n",
    "fig, axes = plt.subplots(ncols=nrows, nrows=nrows, figsize=(12, 12))\n",
    "axes = np.ravel(axes)\n",
    "for i in range(len(axes)):\n",
    "    ax = axes[i]\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    # show the image\n",
    "    ax.imshow(samples_X[i], cmap='jet')\n",
    "    # Set the label\n",
    "    title = ','.join([f'{num:.1f}' for num in samples_y[i]])\n",
    "    ax.set_title(f'{title}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_padding(input_shape, target_shape):\n",
    "    # Calculate the padding needed for the first two dimensions\n",
    "    padding_first_dim = (target_shape[0] - input_shape[0]) // 2\n",
    "    mod_first_dim = (target_shape[0] - input_shape[0]) % 2\n",
    "    padding_second_dim = (target_shape[1] - input_shape[1]) // 2\n",
    "    mod_second_dim = (target_shape[1] - input_shape[1]) % 2\n",
    "\n",
    "    # If the padding doesn't divide evenly, add the extra padding to one side\n",
    "    pad_first_dim_left = padding_first_dim + mod_first_dim\n",
    "    pad_second_dim_left = padding_second_dim + mod_second_dim\n",
    "\n",
    "    # Create the padding configuration for np.pad\n",
    "    padding_config = (\n",
    "        (pad_first_dim_left, padding_first_dim),  # Padding for the first dimension\n",
    "        (pad_second_dim_left, padding_second_dim),  # Padding for the second dimension\n",
    "        # (0, 0)  # Padding for the third dimension\n",
    "    )\n",
    "\n",
    "    return padding_config\n",
    "\n",
    "\n",
    "# model definition\n",
    "class AutoEncoderTranspose(keras.Model):\n",
    "    # Pooling can be None, or 'Average' or 'Max'\n",
    "    def __init__(self, output_name='autoencoder',\n",
    "                 input_shape=(128, 128, 1), dense_layers=[7],\n",
    "                 decoder_dense_layers=[],\n",
    "                 cropping=[[0, 0], [0, 0]], \n",
    "                 filters=[8, 16, 32],  kernel_size=3, conv_padding='same', \n",
    "                 strides=[2, 2], activation='relu',\n",
    "                 final_activation='linear', final_kernel_size=3,\n",
    "                 pooling=None, pooling_size=[2, 2],\n",
    "                 pooling_strides=[1, 1], pooling_padding='valid',\n",
    "                 dropout=0.0, learning_rate=0.001, loss='mse',\n",
    "                 metrics=[], use_bias=True, conv_batchnorm=False,\n",
    "                 dense_batchnorm=False, data_type='float32',\n",
    "                 **kwargs):\n",
    "        super().__init__()\n",
    "\n",
    "        self.output_name = output_name\n",
    "        self.inputShape = input_shape\n",
    "        # the kernel_size can be a single int or a list of ints\n",
    "        if isinstance(kernel_size, int):\n",
    "            kernel_size = [kernel_size] * len(filters)\n",
    "        assert len(kernel_size) == len(filters)\n",
    "\n",
    "        # the strides can be a list of two ints, or a list of two-int lists\n",
    "        if isinstance(strides[0], int):\n",
    "            strides = [strides for _ in filters]\n",
    "        assert len(strides) == len(filters)\n",
    "\n",
    "        # set the input size\n",
    "        inputs = keras.Input(shape=input_shape, name='Input', dtype=data_type)\n",
    "\n",
    "        # this is the autoencoder case\n",
    "        # crop the edges\n",
    "        cropped = keras.layers.Cropping2D(\n",
    "            cropping=cropping, name='Crop')(inputs)\n",
    "        x = cropped\n",
    "\n",
    "        # For evey Convolutional layer\n",
    "        for i, f in enumerate(filters):\n",
    "            # Add the Convolution\n",
    "            x = keras.layers.Conv2D(\n",
    "                filters=f, kernel_size=kernel_size[i], strides=strides[i],\n",
    "                use_bias=use_bias, padding=conv_padding,\n",
    "                name=f'CNN_{i+1}')(x)\n",
    "\n",
    "            # Apply batchnormalization\n",
    "            if conv_batchnorm:\n",
    "                x = tf.keras.layers.BatchNormalization()(x)\n",
    "\n",
    "            # Apply the activation function\n",
    "            x = keras.activations.get(activation)(x)\n",
    "\n",
    "            # Optional pooling after the convolution\n",
    "            if pooling == 'Max':\n",
    "                x = keras.layers.MaxPooling2D(\n",
    "                    pool_size=pooling_size, strides=pooling_strides,\n",
    "                    padding=pooling_padding, name=f'MaxPooling_{i+1}')(x)\n",
    "            elif pooling == 'Average':\n",
    "                x = keras.layers.AveragePooling2D(\n",
    "                    pool_size=pooling_size, strides=pooling_strides,\n",
    "                    padding=pooling_padding, name=f'AveragePooling_{i+1}')(x)\n",
    "\n",
    "        # we have reached the latent space\n",
    "        last_shape = x.shape[1:]\n",
    "        x = keras.layers.Flatten(name='Flatten')(x)\n",
    "        flat_shape = x.shape[1:]\n",
    "        # Now we add the dense layers\n",
    "        for i, units in enumerate(dense_layers):\n",
    "            # Add the layer\n",
    "            x = keras.layers.Dense(units=units, activation=activation,\n",
    "                                name=f'encoder_dense_{i+1}')(x)\n",
    "            \n",
    "            # Apply batchnormalization\n",
    "            if dense_batchnorm:\n",
    "                x = tf.keras.layers.BatchNormalization()(x)\n",
    "\n",
    "            # Add dropout optionally\n",
    "            if dropout > 0 and dropout < 1:\n",
    "                x = keras.layers.Dropout(dropout, name=f'dropout_{i+1}')(x)\n",
    "\n",
    "        # a dummy layer just to name it latent space\n",
    "        x = keras.layers.Lambda(lambda x: x, name='LatentSpace')(x)\n",
    "        self.encoder = keras.Model(inputs=inputs, outputs=x, name='encoder')\n",
    "\n",
    "        # Now we add the decoder dense layers\n",
    "        for i, units in enumerate(decoder_dense_layers):\n",
    "            # Add the layer\n",
    "            x = keras.layers.Dense(units=units, activation=activation,\n",
    "                                   name=f'decoder_dense_{i+1}')(x)\n",
    "\n",
    "            # Apply batchnormalization\n",
    "            if dense_batchnorm:\n",
    "                x = tf.keras.layers.BatchNormalization()(x)\n",
    "\n",
    "            # Add dropout optionally\n",
    "            if dropout > 0 and dropout < 1:\n",
    "                x = keras.layers.Dropout(dropout, name=f'dropout_{i+1}')(x)\n",
    "\n",
    "\n",
    "        # Now reshape back to last_shape\n",
    "        x = keras.layers.Dense(units=np.prod(flat_shape), activation=activation,\n",
    "                               name='decoder_dense_final')(x)\n",
    "        \n",
    "        x = keras.layers.Reshape(target_shape=last_shape, name='Reshape')(x)\n",
    "        # Now with transpose convolutions we go back to the original size\n",
    "        \n",
    "        for i, f in enumerate(filters[::-1]):\n",
    "            x = keras.layers.Conv2DTranspose(\n",
    "                filters=f, kernel_size=kernel_size[-i-1], strides=strides[-i-1],\n",
    "                use_bias=use_bias, padding=conv_padding,\n",
    "                name=f'CNN_Transpose_{i+1}')(x)\n",
    "        \n",
    "        # final convolution to get the right number of channels\n",
    "        x = keras.layers.Conv2DTranspose(filters=1, kernel_size=final_kernel_size, \n",
    "                strides=1, use_bias=use_bias, padding='same',\n",
    "                name=f'CNN_Transpose_Final')(x)\n",
    "\n",
    "        x = keras.layers.Activation(activation=final_activation, \n",
    "                    name='final_activation')(x)\n",
    "        before_padding = x\n",
    "        # Add zero padding\n",
    "        padding = calculate_padding(input_shape=before_padding.shape[1:], target_shape=input_shape)\n",
    "        outputs = keras.layers.ZeroPadding2D(padding=padding, name='Padding')(x)\n",
    "\n",
    "        model = keras.Model(inputs=inputs, outputs=outputs, name=output_name)\n",
    "    \n",
    "        # assert model.layers[-1].output_shape[1:] == input_shape\n",
    "\n",
    "        # Also initialize the optimizer and compile the model\n",
    "        optimizer = keras.optimizers.Adam(learning_rate=learning_rate)\n",
    "        model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n",
    "\n",
    "        self.model = model\n",
    "\n",
    "    def predict(self, wf_in):\n",
    "        wf_out = self.model(wf_in)\n",
    "        return wf_out\n",
    "\n",
    "    def load(self, weights_file):\n",
    "        self.model = keras.models.load_model(weights_file)\n",
    "\n",
    "    def save(self, weights_file):\n",
    "        self.model.save(weights_file)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"autoencoder\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Input (InputLayer)          [(None, 128, 128, 1)]     0         \n",
      "                                                                 \n",
      " Crop (Cropping2D)           (None, 100, 100, 1)       0         \n",
      "                                                                 \n",
      " CNN_1 (Conv2D)              (None, 50, 50, 8)         80        \n",
      "                                                                 \n",
      " tf.nn.relu_27 (TFOpLambda)  (None, 50, 50, 8)         0         \n",
      "                                                                 \n",
      " CNN_2 (Conv2D)              (None, 25, 25, 16)        1168      \n",
      "                                                                 \n",
      " tf.nn.relu_28 (TFOpLambda)  (None, 25, 25, 16)        0         \n",
      "                                                                 \n",
      " CNN_3 (Conv2D)              (None, 13, 13, 32)        4640      \n",
      "                                                                 \n",
      " tf.nn.relu_29 (TFOpLambda)  (None, 13, 13, 32)        0         \n",
      "                                                                 \n",
      " Flatten (Flatten)           (None, 5408)              0         \n",
      "                                                                 \n",
      " encoder_dense_1 (Dense)     (None, 1024)              5538816   \n",
      "                                                                 \n",
      " encoder_dense_2 (Dense)     (None, 64)                65600     \n",
      "                                                                 \n",
      " LatentSpace (Lambda)        (None, 64)                0         \n",
      "                                                                 \n",
      " decoder_dense_1 (Dense)     (None, 1024)              66560     \n",
      "                                                                 \n",
      " decoder_dense_final (Dense)  (None, 5408)             5543200   \n",
      "                                                                 \n",
      " Reshape (Reshape)           (None, 13, 13, 32)        0         \n",
      "                                                                 \n",
      " CNN_Transpose_1 (Conv2DTran  (None, 26, 26, 32)       9248      \n",
      " spose)                                                          \n",
      "                                                                 \n",
      " CNN_Transpose_2 (Conv2DTran  (None, 52, 52, 16)       4624      \n",
      " spose)                                                          \n",
      "                                                                 \n",
      " CNN_Transpose_3 (Conv2DTran  (None, 104, 104, 8)      1160      \n",
      " spose)                                                          \n",
      "                                                                 \n",
      " CNN_Transpose_Final (Conv2D  (None, 104, 104, 1)      73        \n",
      " Transpose)                                                      \n",
      "                                                                 \n",
      " final_activation (Activatio  (None, 104, 104, 1)      0         \n",
      " n)                                                              \n",
      "                                                                 \n",
      " Padding (ZeroPadding2D)     (None, 128, 128, 1)       0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 11,235,169\n",
      "Trainable params: 11,235,169\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "\n",
      "---- Models have been initialized, elapsed: 0.14526152610778809 ----\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train specific\n",
    "train_cfg = {\n",
    "    'epochs': 5,\n",
    "    'filters': [8, 16, 32],\n",
    "    'dense_layers': [512, 16],\n",
    "    'decoder_dense_layers': [512],\n",
    "    'cropping': [14, 14],\n",
    "    'kernel_size': 3,\n",
    "    'strides': [2, 2],\n",
    "    'activation': 'relu',\n",
    "    'conv_padding': 'same',\n",
    "    'pooling': None,\n",
    "    'pooling_size': [0, 0],\n",
    "    'pooling_strides': [1, 1],\n",
    "    'pooling_padding': 'valid',\n",
    "    'dropout': 0.0,\n",
    "    'loss': 'mae',\n",
    "    'lr': 1e-3,\n",
    "    'dataset%': 1,\n",
    "    'use_bias': True,\n",
    "    'normalization': 'minmax',\n",
    "    'img_normalize': 'off',\n",
    "    'conv_batchnorm': False,\n",
    "    'dense_batchnorm': False,\n",
    "    'batch_size': 128,\n",
    "    'data_type': 'float16'\n",
    "}\n",
    "\n",
    "# Model instantiation\n",
    "start_t = time.time()\n",
    "\n",
    "autoenc = AutoEncoderTranspose(input_shape=x_train.shape[1:], **train_cfg)\n",
    "print(autoenc.model.summary())\n",
    "\n",
    "print(\n",
    "    f'\\n---- Models have been initialized, elapsed: {time.time() - start_t} ----\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 128, 128, 1) (1, 128, 128, 1)\n"
     ]
    }
   ],
   "source": [
    "input_wf = x_train[0:1]\n",
    "output_wf = autoenc.predict(input_wf)\n",
    "print(input_wf.shape, output_wf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[1.3602243e-04 6.4021046e-04 0.0000000e+00 0.0000000e+00 7.9464505e-04\n",
      "  7.9532352e-04 4.5320755e-04 1.5429618e-04 8.2942349e-04 9.4197679e-04\n",
      "  6.2702916e-04 0.0000000e+00 2.6950971e-04 0.0000000e+00 1.1467917e-03\n",
      "  4.7868764e-04 5.0530513e-04 4.1871925e-04 3.3401875e-04 0.0000000e+00\n",
      "  0.0000000e+00 9.9050591e-04 0.0000000e+00 0.0000000e+00 6.1091618e-04\n",
      "  0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      "  0.0000000e+00 1.4927379e-03 0.0000000e+00 0.0000000e+00 2.9490941e-04\n",
      "  0.0000000e+00 0.0000000e+00 1.0440690e-03 7.5351767e-04 0.0000000e+00\n",
      "  1.7760609e-05 0.0000000e+00 2.1546845e-04 0.0000000e+00 4.5907067e-04\n",
      "  0.0000000e+00 2.4493673e-04 0.0000000e+00 7.2041422e-04 0.0000000e+00\n",
      "  0.0000000e+00 0.0000000e+00 0.0000000e+00 9.5091917e-04 0.0000000e+00\n",
      "  8.3531986e-04 0.0000000e+00 0.0000000e+00 5.4961769e-04 6.4068765e-05\n",
      "  8.1772590e-04 1.8122036e-04 1.5748468e-03 0.0000000e+00]], shape=(1, 64), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "print(autoenc.encoder(input_wf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kiliakis/install/anaconda3/lib/python3.9/site-packages/tensorflow/python/data/ops/structured_function.py:256: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125/125 [==============================] - 31s 246ms/step - loss: 0.0029 - val_loss: 0.0023\n",
      "Epoch 2/5\n",
      "125/125 [==============================] - 30s 242ms/step - loss: 0.0022 - val_loss: 0.0014\n",
      "Epoch 3/5\n",
      "125/125 [==============================] - 31s 248ms/step - loss: 9.4700e-04 - val_loss: 7.3692e-04\n",
      "Epoch 4/5\n",
      "125/125 [==============================] - 31s 249ms/step - loss: 5.9609e-04 - val_loss: 4.9881e-04\n",
      "Epoch 5/5\n",
      "125/125 [==============================] - 31s 247ms/step - loss: 4.2611e-04 - val_loss: 4.9236e-04\n",
      "\n",
      "---- Training complete, epochs: 5, min loss 0.0004923588712699711, total time 154.35350489616394 ----\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tf.config.run_functions_eagerly(True)\n",
    "# callbacks, save the best model, and early stop if no improvement in val_loss\n",
    "stop_early = keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                                            patience=20, restore_best_weights=True)\n",
    "save_best = keras.callbacks.ModelCheckpoint(filepath=os.path.join(weights_dir, f'autoenc-transpose-noib.h5'),\n",
    "                                            monitor='val_loss', save_best_only=True)\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "history = autoenc.model.fit(\n",
    "    x=x_train, y=x_train, \n",
    "    validation_data=(x_valid, x_valid),\n",
    "    # x=y_train, y=x_train, \n",
    "    # validation_data=(y_valid, x_valid),\n",
    "    epochs=train_cfg['epochs'],\n",
    "    callbacks=[stop_early], \n",
    "    batch_size=train_cfg['batch_size'],\n",
    "    verbose=1)\n",
    "\n",
    "total_time = time.time() - start_time\n",
    "print(\n",
    "        f'\\n---- Training complete, epochs: {len(history.history[\"loss\"])}, min loss {np.min(history.history[\"val_loss\"])}, total time {total_time} ----\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load autoencoder\n",
    "trial_dir = os.path.join('./trials/', '2023_11_13_11-52-53')\n",
    "weights_dir = os.path.join(trial_dir, 'weights')\n",
    "plots_dir = os.path.join(trial_dir, 'plots')\n",
    "autoenc = keras.models.load_model(os.path.join(weights_dir, f'autoenc_dense_512_16.h5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract only the encoder part\n",
    "encoder = keras.Model(inputs=autoenc.inputs,\n",
    "                      outputs=autoenc.get_layer('LatentSpace').output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "def elementwise_mae(y_true, y_pred):\n",
    "    # Calculate MAE for each element of the output layer\n",
    "    mae_per_element = tf.reduce_mean(tf.abs(y_true - y_pred), axis=0)\n",
    "    # Return the MAE for each element\n",
    "    return mae_per_element\n",
    "\n",
    "def mae_0(y_true, y_pred):\n",
    "    return elementwise_mae(y_true[:, 0], y_pred[:, 0]),\n",
    "\n",
    "def mae_1(y_true, y_pred):\n",
    "    return elementwise_mae(y_true[:, 1], y_pred[:, 1]),\n",
    "\n",
    "def mae_2(y_true, y_pred):\n",
    "    return elementwise_mae(y_true[:, 2], y_pred[:, 2]),\n",
    "\n",
    "def mae_3(y_true, y_pred):\n",
    "    return elementwise_mae(y_true[:, 3], y_pred[:, 3]),\n",
    "\n",
    "def mae_4(y_true, y_pred):\n",
    "    return elementwise_mae(y_true[:, 4], y_pred[:, 4]),\n",
    "\n",
    "def mae_5(y_true, y_pred):\n",
    "    return elementwise_mae(y_true[:, 5], y_pred[:, 5]),\n",
    "\n",
    "def mae_6(y_true, y_pred):\n",
    "    return elementwise_mae(y_true[:, 6], y_pred[:, 6]),\n",
    "\n",
    "\n",
    "\n",
    "# model definition\n",
    "class FeatureExtractor(keras.Model):\n",
    "    def __init__(self, input_shape, output_features=1,\n",
    "                 output_name='feature_extractor',\n",
    "                 dense_layers=[256, 64], activation='relu',\n",
    "                 final_activation='linear',\n",
    "                 dropout=0.0, learning_rate=1e-3, loss='mse',\n",
    "                 metrics=[], use_bias=True, batchnorm=False,\n",
    "                 **kwargs):\n",
    "        super().__init__()\n",
    "\n",
    "        self.output_name = output_name\n",
    "\n",
    "        # set the input size\n",
    "        inputs = keras.Input(shape=input_shape, name='Input')\n",
    "        x = inputs\n",
    "\n",
    "        # Now we add the dense layers\n",
    "        for i, units in enumerate(dense_layers):\n",
    "            # Add the layer\n",
    "            x = keras.layers.Dense(units=units, activation=activation,\n",
    "                                   name=f'dense_{i+1}', use_bias=use_bias)(x)\n",
    "\n",
    "            # Apply batchnormalization\n",
    "            if batchnorm:\n",
    "                x = tf.keras.layers.BatchNormalization()(x)\n",
    "\n",
    "            # Add dropout optionally\n",
    "            if dropout > 0 and dropout < 1:\n",
    "                x = keras.layers.Dropout(dropout, name=f'dropout_{i+1}')(x)\n",
    "\n",
    "        # Add the final layer\n",
    "        outputs = keras.layers.Dense(units=output_features,\n",
    "                                     activation=final_activation, \n",
    "                                     name='dense_final', use_bias=use_bias)(x)\n",
    "\n",
    "        model = keras.Model(inputs=inputs, outputs=outputs, name=output_name)\n",
    "\n",
    "        # Also initialize the optimizer and compile the model\n",
    "        optimizer = keras.optimizers.Adam(learning_rate=learning_rate)\n",
    "        model.compile(optimizer=optimizer, loss=loss, metrics=[])\n",
    "\n",
    "        self.model = model\n",
    "\n",
    "    def predict(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    def load(self, weights_file):\n",
    "        self.model = keras.models.load_model(weights_file)\n",
    "\n",
    "    def save(self, weights_file):\n",
    "        self.model.save(weights_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1403, 17)\n",
      "(15885, 17)\n"
     ]
    }
   ],
   "source": [
    "y_latent_valid = encoder([x_valid, y_valid[:, 3]])\n",
    "print(y_latent_valid.shape)\n",
    "y_latent_train = encoder([x_train, y_train[:, 3]])\n",
    "print(y_latent_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([7346.,  630., 1054., 1233., 1059.,  956.,  872.,  856.,  775.,\n",
       "         802.,  768.,  705.,  733.,  715.,  581.,  528.,  520.,  434.,\n",
       "         393.,  367.,  299.,  287.,  229.,  214.,  187.,  171.,  157.,\n",
       "         110.,  103.,   81.,   59.,   53.,   47.,   40.,   34.,   29.,\n",
       "          47.,   22.,   37.,   24.,   33.,   32.,   31.,   27.,   23.,\n",
       "          40.,   31.,   29.,   21.,   27.]),\n",
       " array([0.        , 0.02      , 0.04      , 0.06      , 0.08      ,\n",
       "        0.1       , 0.12      , 0.14      , 0.16      , 0.18000001,\n",
       "        0.2       , 0.22      , 0.23999999, 0.25999999, 0.28      ,\n",
       "        0.30000001, 0.31999999, 0.34      , 0.36000001, 0.38      ,\n",
       "        0.40000001, 0.41999999, 0.44      , 0.46000001, 0.47999999,\n",
       "        0.5       , 0.51999998, 0.54000002, 0.56      , 0.57999998,\n",
       "        0.60000002, 0.62      , 0.63999999, 0.66000003, 0.68000001,\n",
       "        0.69999999, 0.72000003, 0.74000001, 0.75999999, 0.77999997,\n",
       "        0.80000001, 0.81999999, 0.83999997, 0.86000001, 0.88      ,\n",
       "        0.89999998, 0.92000002, 0.94      , 0.95999998, 0.98000002,\n",
       "        1.        ]),\n",
       " <BarContainer object of 50 artists>)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y_latent_valid.numpy().flatten(), bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bxplots = plt.boxplot(y_latent_valid.numpy())\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"feature_extractor\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Input (InputLayer)          [(None, 17)]              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 256)               4608      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 32)                8224      \n",
      "                                                                 \n",
      " dense_final (Dense)         (None, 7)                 231       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 13,063\n",
      "Trainable params: 13,063\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "feature_cfg = {\n",
    "    'epochs': 50,\n",
    "    'dense_layers': [256, 32],\n",
    "    'activation': 'relu',\n",
    "    'dropout': 0.0,\n",
    "    'loss': 'mae',\n",
    "    'lr': 1e-3,\n",
    "    'dataset%': 1,\n",
    "    'use_bias': True,\n",
    "    'batchnorm': False,\n",
    "    'batch_size': 128,\n",
    "}\n",
    "\n",
    "\n",
    "feature_extractor = FeatureExtractor(input_shape=y_latent_valid.shape[1:],\n",
    "                                     output_features=7,\n",
    "                                     **feature_cfg)\n",
    "\n",
    "feature_extractor.model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 17) (1, 7)\n"
     ]
    }
   ],
   "source": [
    "input_latents = y_latent_valid[0:1]\n",
    "output_features = feature_extractor.predict(input_latents)\n",
    "print(input_latents.shape, output_features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "  8/125 [>.............................] - ETA: 1s - loss: 0.4820"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kiliakis/install/anaconda3/lib/python3.9/site-packages/tensorflow/python/data/ops/structured_function.py:256: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125/125 [==============================] - 2s 13ms/step - loss: 0.2049 - val_loss: 0.0626\n",
      "Epoch 2/50\n",
      "125/125 [==============================] - 2s 12ms/step - loss: 0.0360 - val_loss: 0.0299\n",
      "Epoch 3/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0203 - val_loss: 0.0158\n",
      "Epoch 4/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0152 - val_loss: 0.0142\n",
      "Epoch 5/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0128 - val_loss: 0.0122\n",
      "Epoch 6/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0119 - val_loss: 0.0103\n",
      "Epoch 7/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0105 - val_loss: 0.0190\n",
      "Epoch 8/50\n",
      "125/125 [==============================] - 2s 12ms/step - loss: 0.0102 - val_loss: 0.0099\n",
      "Epoch 9/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0091 - val_loss: 0.0090\n",
      "Epoch 10/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0091 - val_loss: 0.0083\n",
      "Epoch 11/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0090 - val_loss: 0.0085\n",
      "Epoch 12/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0079 - val_loss: 0.0082\n",
      "Epoch 13/50\n",
      "125/125 [==============================] - 2s 14ms/step - loss: 0.0076 - val_loss: 0.0095\n",
      "Epoch 14/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0080 - val_loss: 0.0072\n",
      "Epoch 15/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0080 - val_loss: 0.0091\n",
      "Epoch 16/50\n",
      "125/125 [==============================] - 2s 14ms/step - loss: 0.0070 - val_loss: 0.0069\n",
      "Epoch 17/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0072 - val_loss: 0.0081\n",
      "Epoch 18/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0066 - val_loss: 0.0074\n",
      "Epoch 19/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0067 - val_loss: 0.0074\n",
      "Epoch 20/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0069 - val_loss: 0.0105\n",
      "Epoch 21/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0069 - val_loss: 0.0058\n",
      "Epoch 22/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0063 - val_loss: 0.0061\n",
      "Epoch 23/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0062 - val_loss: 0.0060\n",
      "Epoch 24/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0068 - val_loss: 0.0074\n",
      "Epoch 25/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0060 - val_loss: 0.0072\n",
      "Epoch 26/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0055 - val_loss: 0.0053\n",
      "Epoch 27/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0056 - val_loss: 0.0053\n",
      "Epoch 28/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0058 - val_loss: 0.0088\n",
      "Epoch 29/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0064 - val_loss: 0.0062\n",
      "Epoch 30/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0060 - val_loss: 0.0053\n",
      "Epoch 31/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0055 - val_loss: 0.0050\n",
      "Epoch 32/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0050 - val_loss: 0.0077\n",
      "Epoch 33/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0056 - val_loss: 0.0054\n",
      "Epoch 34/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0059 - val_loss: 0.0049\n",
      "Epoch 35/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0053 - val_loss: 0.0071\n",
      "Epoch 36/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0054 - val_loss: 0.0119\n",
      "Epoch 37/50\n",
      "125/125 [==============================] - 2s 14ms/step - loss: 0.0057 - val_loss: 0.0065\n",
      "Epoch 38/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0050 - val_loss: 0.0047\n",
      "Epoch 39/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0054 - val_loss: 0.0050\n",
      "Epoch 40/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0049 - val_loss: 0.0052\n",
      "Epoch 41/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0063 - val_loss: 0.0100\n",
      "Epoch 42/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0055 - val_loss: 0.0053\n",
      "Epoch 43/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0050 - val_loss: 0.0118\n",
      "Epoch 44/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0055 - val_loss: 0.0044\n",
      "Epoch 45/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0045 - val_loss: 0.0044\n",
      "Epoch 46/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0045 - val_loss: 0.0044\n",
      "Epoch 47/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0052 - val_loss: 0.0050\n",
      "Epoch 48/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0049 - val_loss: 0.0062\n",
      "Epoch 49/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0043 - val_loss: 0.0065\n",
      "Epoch 50/50\n",
      "125/125 [==============================] - 2s 13ms/step - loss: 0.0053 - val_loss: 0.0052\n",
      "\n",
      "---- Training complete, epochs: 50, min loss 0.004379729274660349, total time 81.43741226196289 ----\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tf.config.run_functions_eagerly(True)\n",
    "# callbacks, save the best model, and early stop if no improvement in val_loss\n",
    "stop_early = keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                                           patience=20, restore_best_weights=True)\n",
    "save_best = keras.callbacks.ModelCheckpoint(filepath=os.path.join(weights_dir, f'feature-extractor.h5'),\n",
    "                                            monitor='val_loss', save_best_only=True)\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "history = feature_extractor.model.fit(\n",
    "    x=y_latent_train, y=y_train[:, 0],\n",
    "    validation_data=(y_latent_valid, y_valid[:, 0]),\n",
    "    # x=y_train, y=x_train,\n",
    "    # validation_data=(y_valid, x_valid),\n",
    "    epochs=feature_cfg['epochs'],\n",
    "    callbacks=[stop_early],\n",
    "    batch_size=feature_cfg['batch_size'],\n",
    "    verbose=1)\n",
    "\n",
    "total_time = time.time() - start_time\n",
    "print(\n",
    "    f'\\n---- Training complete, epochs: {len(history.history[\"loss\"])}, min loss {np.min(history.history[\"val_loss\"])}, total time {total_time} ----\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1403, 7)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evaluate the model\n",
    "import bisect\n",
    "from utils import unnormalize_params\n",
    "\n",
    "y_valid_pred = feature_extractor.predict(y_latent_valid)\n",
    "\n",
    "\n",
    "y_test_unnorm = unnormalize_params(\n",
    "    y_valid[:, 0], y_valid[:, 1], y_valid[:, 2],\n",
    "    y_valid[:, 3], y_valid[:, 4], y_valid[:, 5],\n",
    "    y_valid[:, 6], normalization=train_cfg['normalization'])\n",
    "\n",
    "y_pred_unnorm = unnormalize_params(\n",
    "    y_valid_pred[:, 0], y_valid_pred[:, 1], y_valid_pred[:, 2],\n",
    "    y_valid_pred[:, 3], y_valid_pred[:, 4], y_valid_pred[:, 5],\n",
    "    y_valid_pred[:, 6], normalization=train_cfg['normalization'])\n",
    "\n",
    "diffs = np.abs(np.array(y_pred_unnorm).T - np.array(y_test_unnorm).T)\n",
    "print(diffs.shape)\n",
    "\n",
    "\n",
    "# Encoder, graphical evaluation\n",
    "\n",
    "evaluation_config = {\n",
    "    0: {'xlabel': 'Phase Error [deg]',\n",
    "        'range': (0, 2.5),\n",
    "        'xticks': np.arange(0, 2.6, 0.5),\n",
    "        'desired': 2,\n",
    "        'multiplier': 1},\n",
    "    1: {'xlabel': 'Energy Error [MeV]',\n",
    "        'range': (0, 3.),\n",
    "        'xticks': np.arange(0, 3.1, 0.5),\n",
    "        'desired': 2,\n",
    "        'multiplier': 1},\n",
    "    2: {'xlabel': 'Bunch Length [ps]',\n",
    "        'range': (0, 30),\n",
    "        'xticks': np.arange(0, 30.5, 5),\n",
    "        'desired': 25,\n",
    "        'multiplier': 1e12},\n",
    "    3: {'xlabel': 'Bunch Intensity [1e9]',\n",
    "        'range': (0, 2),\n",
    "        'xticks': np.arange(0, 2.1, 0.5),\n",
    "        'desired': 1.5,\n",
    "        'multiplier': 1e-9},\n",
    "    4: {'xlabel': 'V_rf [MV]',\n",
    "        'range': (0, 0.15),\n",
    "        'xticks': np.arange(0, 0.16, 0.05),\n",
    "        'desired': 0.1,\n",
    "        'multiplier': 1},\n",
    "    5: {'xlabel': 'mu [a.u.]',\n",
    "        'range': (0, 0.4),\n",
    "        'xticks': np.arange(0, 0.41, 0.1),\n",
    "        'desired': 0.2,\n",
    "        'multiplier': 1},\n",
    "    6: {'xlabel': 'V_rf SPS [MV]',\n",
    "        'range': (0, 0.25),\n",
    "        'xticks': np.arange(0, 0.26, 0.05),\n",
    "        'desired': 0.1,\n",
    "        'multiplier': 1},\n",
    "}\n",
    "\n",
    "fig, axes = plt.subplots(ncols=3, nrows=3, sharex=False,\n",
    "                         sharey=False, figsize=(7, 6))\n",
    "axes = np.ravel(axes, order='F')\n",
    "\n",
    "for idx, ax in enumerate(axes):\n",
    "    if idx == 7:\n",
    "        break\n",
    "    plt.sca(ax)\n",
    "    config = evaluation_config[idx]\n",
    "    hist, bins, _ = plt.hist(\n",
    "        diffs[:, idx]*config['multiplier'], bins=50, range=config['range'])\n",
    "    cumsum = np.cumsum(hist) / diffs.shape[0]\n",
    "    b = bisect.bisect(cumsum, 0.95)\n",
    "    # plt.axvline(x=bins[b+1], color='tab:orange',\n",
    "    #             label=f'95% < {bins[b+1]:.2f}')\n",
    "    plt.xticks(config['xticks'])\n",
    "    plt.yticks([], [])\n",
    "    plt.xlabel(config['xlabel'], fontweight='heavy')\n",
    "    if idx < 3:\n",
    "        plt.ylabel('No. Samples')\n",
    "    plt.axvline(x=config['desired'], color='black',\n",
    "                label=f'Desired < {config[\"desired\"]:.1f}')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.gca().set_facecolor('0.85')\n",
    "\n",
    "# delete last\n",
    "fig.delaxes(axes[-1])\n",
    "fig.delaxes(axes[-2])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'{plots_dir}/enc-abs-error.png', transparent=True,\n",
    "            dpi=400, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([7])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_valid_pred = feature_extractor.predict(y_latent_valid)\n",
    "tf.reduce_mean(tf.square(y_valid_pred - y_valid), axis=0).shape\n",
    "# loss = np.mean(np.abs(y_valid_pred - y_valid), axis=0)\n",
    "# print(loss)\n",
    "\n",
    "# print(y_valid_pred.shape)\n",
    "# loss = keras.losses.mean_absolute_error(y_valid, y_valid_pred)\n",
    "# print(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---- Plotting loss ----\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Plot training and validation loss\n",
    "print('\\n---- Plotting loss ----\\n')\n",
    "\n",
    "train_loss_l = np.array(history.history['loss'])\n",
    "valid_loss_l = np.array(history.history['val_loss'])\n",
    "\n",
    "plot_loss({'Training': train_loss_l, 'Validation': valid_loss_l},\n",
    "            title='Encoder Train/Validation Loss',\n",
    "            figname=os.path.join(plots_dir, 'autoenc-noib_loss.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model\n",
    "autoenc.save(os.path.join(weights_dir, 'autoenc-transpose-noib.h5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_sample(samples, autoenc, figname=None):\n",
    "\n",
    "    ncols = len(samples)\n",
    "    # Get nrows * nrows random images\n",
    "    # sample = np.random.choice(np.arange(len(x_train)),\n",
    "    #                         size=ncols, replace=False)\n",
    "\n",
    "    samples_X = tf.gather(x_train, samples)\n",
    "    pred_samples_X = autoenc.predict(samples_X)\n",
    "    # samples_y = tf.gather(y_train, sample)\n",
    "\n",
    "    # Create 3x3 grid of figures\n",
    "    fig, axes = plt.subplots(nrows=2, ncols=ncols, figsize=(14, 7))\n",
    "    # axes = np.ravel(axes)\n",
    "    for i in range(ncols):\n",
    "        sample_X = samples_X[i, 14:-14, 14:-14]\n",
    "        pred_sample_X = pred_samples_X[i, 14:-14, 14:-14]\n",
    "        ax = axes[0, i]\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "        # show the image\n",
    "        ax.imshow(sample_X, cmap='jet')\n",
    "        # Set the label\n",
    "        ax.set_title(f'Real')\n",
    "\n",
    "        ax = axes[1, i]\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "        # show the image\n",
    "        ax.imshow(pred_sample_X, cmap='jet')\n",
    "        # Set the label\n",
    "        ax.set_title(\n",
    "            f'Pred, MAE: {np.mean(np.abs(sample_X - pred_sample_X)):.2e}')\n",
    "    \n",
    "    if figname is not None:\n",
    "        plt.savefig(figname, dpi=300)\n",
    "    else:\n",
    "        plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Qgja/hH7Pz3Go88xtEL1TmrHrfD1AHKO3wWNun/v433Z5T9v0ofd1TO773HcxPt3FQgXROyUiYgdLHMscDm3KH6+j3cqhDKjvk5DE/+e272eXHiiyXpOHSSiPzzYr60hsEwrUte1rZEobkXLIZGTE3SA0Rm1DnnTNA3RXXimh1atCniqPYZ7pgq5z0aET/neJuzAmvE8ywrZ4H9rjPv3Hbe/lLohGfu7HNB43oe053uVz7nq9vu8ikikR7y9usToPoIcedLWk8zqHjl1Z+u47mew2YTxN2AUxEbruNglsU1GmESrbWNpvg6Y8D6EwnhCREnF42JbUuI9rdSFEtvHo6vPf+iqeofA3ft0QmfI+9qXoldIduwgHe2yyzq7wvrS/vv/zIRYU4LhvJZ5f930cj4HbeW5ug7Y2tu24F8e6iPcPt/BECREmModDXwXwfR5M7xPbTJbbDKhdr8XPfZv8K7JckiUh8mQfsqI3Kb2RSDl8NAkXu/YSuC2B0kSO3IY46bICWMibpEtom9Y/QsTMY/JKiV4Pd4s47u4G95V/4xDf1V30330nT95n3LcBTzt3V0Klr+wf56KI9wO3SCzbpaNIokV2yENfoUQ+g0O28rWF0rS9j65hM23X3pZA0ciSPkTKNu/sNhNFSHkMeXBpREooJ1EUiPYL9y1QaNfrEooT6rd9y0PX09DkmUXnvc1c0DbX0DV43UNFH+H1kOeqXYPLNXe1quD7rljcdxjJrsIUbvPO+vznXbeNXYRU3rdMdChy/20Qks+6hsruAm2J2neRpP19H+8i3hdsSaJwAUySI++b1XzfB4uHFJJvQ6Bs4/XSRqA0hfrIc4XQtOpF39CnNiIl4rBxSO+xD1HSlTzpKwQ2eTHK412JFi2XED93RARHl/F3W7nmfR3b9zEPxz5hnwiU28qLcXzthz6htbtCGxm8q3HqfR3vIt4n9OypCRyTGhJoOa5Y3TaPlEP1RtGwLwn8utxDl2Sufb1BZHnbte/K+6SJVGnKidJEjoQmIO2db+sqGSIi3zeC8pDxEMKDvGabgNaVIAl5cvEy7Xpdx0DpFdGVPOl67rZcQ4Rd5MB4aGyTU2Yf5qp9QpdxfFuvlPdJsejbru6aNLnP0IS+Y99tsC1psi8hIxzvg1xzm3DYpt/0eW5yXpTn2EYGjoh4/3CLcJ4QJHHCvwPhSemxCRcPLZy2XfuhB8DQ9W9LoGieJ7TfJYxn21wxmgVmV1aZNsHisfWdQ8Y+kCdN2IYs6UKm3MYDpal9a56OHG0EY1PfeB9CfLrgoeeqh0YX63yoDW2j9L0P43Wf9vTYPE66/Pddvf++ngxd7q3r+3gfxsZdo28IbJ++0VRXM9QRuhAkTUaHKJtGPCYMAJSda/ecvbR4Ps3bRCJEpGiu1ofY4UL3/VDC6W2ueR9eKNuE8IQIlBB5whW9NmWQl2no4m0F1MPZuvy+Lw61j0S0Y5vxoq/3Ca/TRI60ESdNhIq8noa2vt50PESmhLwdJcEir//YyJRtyNtIpDg0JTsOtZFIpDh0bUe7yM1xH89wlyEq+0ae3IbA6uq595jb+jboa8S77bjc1eukSxJ3WY/qHtL8GBEhsX0f6zmC5vCFaM3rpAtCXira7R1K52wiUuj4fdzDbbALAqUNGoHS5TpdPU80r5MuJAovl/fH63axmGu/v2tEQeVhsatn36TMtl2jzZLV1A8kcaKRJn3De9rA+5IUyJvmh65tvalPhoTEJo8DWX/f0XdMiMlmDZqUw6hIhHFbL4dt2l3TnN10D9vmtGm7Tpc626Dp2XRVvLuMzdvOPW3jY5+x6BDH2q7g8ih957iNR2cfhIgVfpzfQxuZ0tSn7kM23cWcFeXn9wdt42n3vrdFOI9m4eMdSOtMbZNWFEBuj10TKE1oajZdXQq7TspaWRcCpY9VPQRNIAqFqrVNFKHjsd0fNu5i4r2LGPw2b5PQfl8ysus4FArTaQoHbepjXT3ApADY1xvy0OaqbQTY980rhdr2bb1M+raNx0R8PwSBEjrPfXqnQFzvLq59lwRK1+d+V+TQ+4Y0sN9GntzGQ1UjhduIkLZ5UsNDECm7nKd26XEWsZ/oO5a2o+evRgAyuy9Jk4HY7wMtJKhP5z0EdBFM+w40uxpAbkOgdGHO+yxh3Ba2A7avESchgiV07i5o85gK1W17n01eLV3qSTwmoXzfsU/PuUlobvLg0sr6eKe0WdVCKGDmEi2UjxMrXQiVpjbfJ4eKPIaG83Z1Y98XbCMcdrG0PzaEvBru0rX90MfsbcmTbX/X5dl2eaa7lCt3rXzdljTZhRLehKb/u8vn+hg9UsjKrcmkQLf5tIu3UGiO6qNfdVkQJHTdUEjkbfrIfc09kTB8XOhKKvN6d5YThS6kkR6SVKFTv0+NsG2Q6EqkUN22OrtAkwIi0TTgh9CW2yB0/qZJpguB0mQ5187bdH8hBU4jS+SxtnNLbEugRNwf9mlM60qgaPuyLESs8DrbenU1IdS3OPoQKU19UFrbtvEeC51v37ELhf0xjk9dxaCu7/p9I1Ka0Fehb3sXXRXrx/xMCbclT/rIQKHr38czPqQxtgvajHrbGvvaSGCg3Yggy7saHbq2g23azEMS99E75XARajdt42QaqNP9bA3IYSyIIcuMNti1DX6hsKBQZ9537GpieciBo4ncILTdnzxHm4u8PK9U2kbsu0acjNjvQ0pj0/1v8866kCVd20PXUJ84mD8M9vG5h4ToJvJEIxm1vtPk1dUmBIYgc26EhDKtT/UZV5vIGC2MaBuPA473cX56bAgpGoS+XkuPnUi5rbdEU90u6OINdp/P9LbX6fp82oiTLsRKH7Q9Z+0Zh9r+obXxuwDpT21zaF+DH0fT+LWtXhWKEuhyTOIhdZttEcmUw0AXr5OmviWdQtrRs3eSIisHQ+ma3XSJtgE31LEPSVBtwr7Em3cZDPq6EwLdFP9QQsmQJZy2kkgZKXX470PeLBqaBv02rxPaD/2+DZFA2T/s+7NuE5Y1EhKo9x+IrSRWmrxUutyH1te1UFDpURIi5TW0hZCGwkW1Mn5fjxVRmfEhZZoQmdbXa+mxEim7IFD6yhZ3QWLuSz/vQqA0jbO39UjREArVAboR3PvybPcNIY/OXXiiyN80hWRuO860zZfy3HfdDm5LEva9t0im7B+2IaDb5Nc780Tp5+YSdrUGmhVSTWE9JDTd9z4QKG1oese3JVC084es5YBPlIzYVh7TrORNJErIU0abELhVuymEjfbb2mxTjhjt3kL1InaPQ3jOXT1QeB+QfUTz4pIkJRD2UgGAox73eoN2IlKCe43IfY6u3mBavZAAKOu2zUWHpjQc6tx6l2izprYRLbu6h31+L30IlK7kSReZSLOqy/MeUv8Dbif8N5U3nbsr9klZPLT32gTSoSR5oil1ffrKXbynphB2IDxfakQKcPv3eFvCpO2cj5Hwfsy4jdeJphv2MbqHr9iCEYCncI2Hkv/JDpYCuFS2ElIh1Rpmk2C7z5CT/j6RJ9t2/j7/ocs1Qkof347hFL2B2JdKX4g4OYJR4jgkkaEN9E15F/g1mtpsE7rkF+hKskT0xyFOghqBItt8iBShPqN5okiCRXqpBAiTthmkaqr0e3mvNF9wElL2n0ulToHu768tj1Gq1OPXIeyTYrELPLb/sy24cWgbt/Wm0J67IlseAtso/fJ4Xy+WpnCALmTqfSg7u8rx0IUgaRL+tfp9cddeJn3fx2PoNxwDOJmWvkP5zsugHJPQogFCHt8hUkSiiUCR0MJkqVy7B+0eQ3Wa0Fev6ppWAOiWd6ntnBG7wy7HzTYSReqLYdzCE4VbEjUCRCqf0no/QHMjDXXYQyJSCPtEoGyL+yZQtHACUvZ4WJlWH6grffJ7SHGC8n0X4MSjhj45ZNquEdGMx/KcUrHP+6jsX7I/pdD7nOyLCnmSii3f79q8q/OGLEFcQAl5rsg5pa9HSqifb0vcH+LcFOGjiRR5n99vVwKl7/G2OndNQt33O91WEdjGGyV0zb4eddHqvjvQvKrtE7QyeUyiLaSVwMmOLnU18HExZJRoyxkFVrcPduVhBbS36a5jQ+wfd4+2vhAikpv6VdP37u2sZws+sh+6yA3qlkTNI0VDW2d+n2PW7wp37YGyLXkiLeecMHnK6mhkSkNIQUg/A+BbxjUvlYHYvytSRbu5Lh4qoboRYRzyswpNBnxf8yzhXlvSo0vz8hL9KYfjW5rIE4J05KJtoXwH4OYTPmdwL5MBgKVSrqFpTgmF8TQJf9uG9hwa3neLmiQgObrk0ZGK/m2t9/vWvrooc12UfO13oXohr0+g3fOnLwlwG3myj8yjXVer11Wx3qWy3eY9JZ/hbcbErnUfo4xPi3N0UeQgyrqCnhv3Tmkqk++7aW7cZmzqSvD0Qd9nQtAMNYSm1ANdvFL2acw+dGw753TtV/K71EuTzne6bUu0OBKnkBOd9DbhZe+74PY+Q1rApVInlT55bGbPc+Sfkm+1S3IlzsORqBDa177vEn1Dd2Lf6Y6Hfla3mWTbBGdOpnAPE963eJlGrLSQJ20kiiaPad1II1kA1OcSXkkT4NFQpvXRJiEwEinvNzThrKk9dJkDbkuk7Au6KD+hOm2KYNO5byMfHsJzbvMm6UuoyHN2ue5jDlXcV3DZF9Dfc5OyKMvugrjSyIU2klnLlwJWDmzfJ2+ppqrn6hMmSL+LRMrDYdvxsgt5otXp3ub6tc4MQGn3VUsiHZACLWEkymTn1NzDmoRa70YiGrFN574LDxRpJZcWcangPWXfxf2Qogdly6FZwNVmoyln8rvUAm87aHaOfwjcT0Q3PMQEtwvri5wwNE8u6dE1Rt3zZAbXj7hXFztdijB5EiJRJGT/Kjp+ALi5BMpWu5CcO6i+Nsf0mVtC6CJIHYICF8L7qkhJxSXkfUJ1u+TWAR5mzNkl+lgD24Tc0Pm0c4asxbd5zvf5LprG/V0pA03nakNXGfqQx7J9BXmiaO9Xs4pzaO+Y5vwrUcbfW+h9a9+b9psMEE11doFdebLIZ0HoQih20TkPfcx/KLQRwm1jZdO4GfI2kfX5/pOuN96TRMlh5NygADyCC9+RIREcXMLWclH0EXbjQN+OfSBQuAYmlbmZ2DaQJ1zRy1lZG3kCACtbbyXKAejJZ2uVOpTvAtq548B8e9zHBNdmXe1z/ZDALAkUTkYCdcKE96cZKq8TOuUU/UgUeTuad4k2R6zYlspln6z+N63mIy+m3YAGSap0mVv4ubsoaKFjhz4nvY+CoNZvNaus1nb6kE+H0jbaCJQ2zwitTJ4jhNDgsk1fvE90JU943T4KQaisr6wm22tfMvk+cAh9ZBvwORnwjRkh4oyXNZ03FAaesjpc5+KkgJwvab+tjoa7GuP6qas+pIcMQSNVNG8aeR+RSNkdQnIuP9aFZJZECJQyud801nZDv1aZAbhmv6wJwJxIkadOUR+0m9hA7VYjkdIfd0Wg9HEj5A2ce5rQh+c9kXka7L1I8iRkHW+KvNF0sq2aTZ+cJdues608Yr+wyz4jzycHe9mnNFKFttS3aMsIFNmfcjhisqmPNQ3bkrDkRIr2e15WIzU5AcLBSZEQOIEviZSm32jHpbfB+xDW04cYOHTQPBPyZmqysN6GhNtX3AWB0mRZlGjKz9HUFznuSybsMu5rz66LUtD0vSs51WRhDz3LbdrsobXxhwKfpzWlr4lIAeo5ALnhL9RXNEFY86IrRP22uTAEGYGwa2wjazURs7J+lzk+6py3R9MY1mUcDO03EStd9oE+77ZfS5/CkChcKK6FShzBWUTpQCgcgkO6YGsdnSMSKc3YF+8TOiflMXmKOnlyAqfosXtI4azkXKlr80ABdAs4lQdJFDlBSZdI2ZavWJ0r9pvbCBRd2nBbndgPwtilUtOfte53Tm2QlyvoNHl0UV+z5AknSugTIidlH9OIjyYPlJztr9jvqP+tUG+mNe9GunAoOXkTNLdkiLImZVn7kw8JzY36PhCy3D0mJHBtrcnzUHvmTaE9h2j06UqgtCl/oTJ5Pg7NWtz2PPs85yZi8C7eSZdnIglyOhYiTUJkVldIz6k2Uuoh2uq+9o1dYAZfzm1bVbINWv2Bsk9kS5O8Svsk147YPp8PU1Ff9is5n9Ixrd83kaC3QZ/5W47t2vwfIltC13ifjBB90NXrpMuYGCIjQ+SJ9rvQ9bq3v34tNYEeKqS2JSmMUufkx0NsXx+rz31BukLuK7bttLchUCQzoQlNkoHnbo1cEbToo+DJW9FujetUvZuR5nkSeg7bIHqh3C9uS6Q81BjAB36tP0lyhU0OGoGyLYkCdgyo97FQPY2I4cQKnasChdjx5y2JjibC4668ALoMIHc9V2keOvcBec1SrXVYkIKdZrwJlUtr7121uYdCH6W9TdFvEzfbFJ8mwmSfEPqfXRQCWa8LedKknGoK7mMmKfYdUsHrSJ500dQaXyud/zbkhNTnAJ1kDxkneB1Cn/vZlQdKH1JbI2L3ddzZd4TeX9sc0eZ1wsdOTko2kSdt42/3d9yvR03hVjWWbto1CyOfADSTZZPLl7QiSjbzIS08DyW8NuG+FMLQdeQEzxsw9zohKzlZyD9E1U64kscVOvJECZEn8pXLtimt27xpBSE9UKjsCuG2HMIuB9wo/OwOoUm9qd626Oq5RQix6pwcoeSxWlgcs3RRn6J+NEU3EkWSJ01yuub1xfOeyL4qcxJRWSp+CyC8AhyfW7S4Zn4BWd6m9HYpk/MQoAtwu+qz2j09lHfKYwPNU9tYLbsQK1Dq7Jsyq7Ud2de0spDyz7+Hzt8HXb0lHvI5N5FHIcuq9hxDQr527i5oI/e2UQz3qe0eEmbw8pJxyLm2ae5t4hulrOuV0XV5W+NeKtLj+hK+R0qTBwu/kPzOy7RkuF2gtfuuYTv0+75kYhevlKaX8b6TLiHCt8/4GCJNNPJEkiih86RoJi67e4T1I1EyuyXhmMe90zaHEIK1SYLKQ0ooZzvbJs2HwL4QKffZOft4oEgChVvKiUB5iirsiyt2nDCRJIpU7AC/KXCdqSmEJxV11abURoyEJgtt8tgGmvdLxN1gH/oyQWPc+SQgyRRJpLBxlsiTuyBRJIESEuZ4PX4umic0gbEmkxCRwq1c0tpF2IYQ6Yp9F4gOxVty36ApqhLanC/JOKB5nN739sPRpLl19aTgx+U+x7Z9NqQUPcRz1v5z2zPSFAStHj+HvJaGkOFRknuH1B4fCxTlTc6zXUgUBI5pXp2ap2jN45NfXPsB4NqrJJIRqN+Gvu1PM3z1meu0fsHL+4QO9sH72s9CY1bb+KiNk3yslITJIFAmz9dAnMh+dwPgIvzP5E+7gzwBpAcK2FZaIj3hgirxpY5DwklXaKzgY1c4d9Eh79IDhSt40uvkQ1SWclLkTuEUK57YEqiTKPKyBM07it9mJw8UuTqP5oVyFTje9L0Nu1L0rsQ24uHR9i66eKDIZMwDGGsW90ChpMzwvU+o/8zRn0SBsgX0MV8jJTUPMP6bXDnGCZaaoBfyAKB5BHDkfBelVuK2hL3WZx9iPopkSj8k2G4+bHrfkuTT2sA+yCpdPFC6ECYasdJH8edoCglo8/DpgtBvtnkfuyJPtDraudrQ9sy0Z3jfaHpfD90f7hossbtGnoTk3K7dR/U+YdvGfe6lMoALO+D9ka/ww9sZycd8lSD5XbvJbb1SQkpAX4QMMV0JFgSOvc8IybS8rG1s5B4mIW8TbjTsQZo09TXaXuOOSJQJ/CWOpbVfoqojHyDvZFzh1EiVEHN+3w32oQXSXSrGt/kvGhsRUvaewil7z1F5opAyR+TJKcLLFnPFDuwYvxVOnsiwAF5fkimdPFBCHjh9PFDuktSIhMn+oi+Bok0W1K9o1SoK2SFSkvrYkesrc/gkSo46iUL7GnnShbCkbQFHelB9TrBroTuh84CdSxL2tZvhkzOfMyS7r7E//Bwhi2xXy5R2Xomu9XYNGWISoYPmrF2MpX1DxUIT0X1YL/sSKF32td+GrgXUlRj+3LQ+2EQCNPXTXfe90H9rI0y40C/rNJEo8jocWjvpSpzcVTvb5nm/BwpoAjetdyVRtDm4aUoJGS5CRg1pAPdIFY1Q4eQIlY2UY2DfgXo708gUrV4XcMMJnUM+pL5tvQvBIhE69j54ozSRJ01zB69Di9JoRIncDpTjjDQJ9a1QH+OvugTwquXvilN0r02e1bwT8pUYUva99mONJOGNTmtksvG9D41xnxAKtSLIRk1Kn7SSW+WPlLc56goeKXP8MhqJwm+lSY5qkp00pS6ItvamESjbttH7JgcjHgYhK60c1bV+RZMNlR35pAj1qynC4TxdSRR+S7LP8CXuacxvmpxCExb/XijHqu7ALWXcQkbgRHzKyuQfaeqbt+1/dzk/3ebcmsdnhIEU+IA6CSL30XBsV+//vmWdJsEXgX1eZxvviZA1VyNSms6xK2+UNmjyDy9vUxJCVtOmc7SBP8MmAm8b7FoWec9ldznXhhK4h+ZgCVnO588+JArpcbLMm3upXfI2wcuaDAwhUgNo1zE4urRHSajIcjkXbmtcaSrT8Jh11z4ESohADnmTSLJEGu0FeQLoXtWhnJpSPgV65cvvR6Icw6zOwwkTzfCnddDiCI5dogbKG2+o0zVpxCGB9zEporvudH0E6aZrc6s5Ne4ZjHL3IVwIz4emjIfuzKF7ogD1FpmLMv5atSSWsrn0a+EWWugOZ+H5jbQRKPIcIfSxhkfsN7bxQOFbGid5MmZJSn6IijyZw5GRfMtJlDl8EkWG8wA6icmhjuvw+9+5rXMO1//knABxHrlP/ZoTM9XvyCrWdoN8K8mTrkpHSOhp8yYI/U7OcdvgtkQKP0+EQY66tVRa72lfW247JLQTmtzDHwLavWqCb9O+JE80AiB0LUIXjxxOpIQm8wL19wVsN3/KPqpdM6QwaMpCG3EiEyHKfXnNkNeJhNYm76rNNXkY3Mf1DwjHAIbwFbwQgdJEojRNJXKOlVttHpffgx8eVkvbG3YSzUO7i2wc+jOhuvI30pMwRN5oHlna2BGCHGt4Gb/P0At66LF/l5DjSxMJHFo1J0SU8DJJmojrkgwr+5JMDRHqa3xL6CGi9VMxU/hLHEsBmXugyJvzhGBJjmiT6WNqbPeBpkn/rsA7Cg854J4oInnsVHx48ks6JW/AOXzIpqP9JlS+MzS1y20IlK7Q/lDsJ/uFPu9CE5YJ2gTDP5Z515Ytlv0s5InSJRcKByc5UvhkhxY6R9u2siJwLHQf3sE2rxK01OmKvoNJ7JeHBS53cEhSRfNIAfoT5PuEkIZG0JR7vpUCs/xdCE3EAK/T1SOFsIuJv4scdRsCRSNU5HPUnh8v0yz4vA2HDIzab/sgGnO2xm0UPK1JaspfKvZDhm7aSuPjSuzLerw+AOelInMKEkJkpuzTTX1c69NNRIbsC00krCRqu+if+z6m3zU0IpnKtXGR6mnj4gj1MVEuSEJl4nS8P8l+lbJ9Wb+tb/V4tf007glMTB/gu3AD9c5LdaZglkrqbDw5EdiPeEOWjF5fl84706D3EF3/Z1frYx8PFL588TO7/YorO4VpJy/YdgpjGZ/Dt5JzSFJEs1RTPU0pk+fQzusd52t3h34gobHufBs6R1tblhMN/8371K4PCV3dOQl8wgD05LEf2uNP4YfFwe87RJZoZVP4fSxEotAtpQFBiJZGLFD3AOOCFtA8J8j+Jy1hBD6/0Lbq9+TVKPtraM4YQFcumjxG2vpYl/nmLj1SbovoleIwhhPqQh6w8l1rHikSTdbNrl4SuxTWt/VAkWMU4Au92u9C1yN0/U9cLgxZjJsUn6bn3PXZahZX+V+bVorQlASpTPBzyXlCaxuawqe1Xap7V+h67l2ETvVTV/YSxzCrnEoypS+J0jSFaDIu38p97XvbR6uPI6BoIgD5UsqEJlm57ZiWa6WAn5+FwB9YiHzsA6mvyrKmc3chj/cVGnmijV9NY52W84/qjEUZu55GmMgy+R1oDpnjW45N8AHU0G9UyuFIFKC5Y5EQzIXhc/oh72hy8OcTJtDNnfp9xn0K4lygok4h85+Q0jfwc58QmXLKyuaokygq062UcQWNOkafCaXXY+sysLe5G4Z+H2Lm+wzssT8cFrgQzrdyUqHvLEEztXUiQ3gfm8P1MX4sZcdkKE96A+RrAMCTtESS+sGgZWEG/OsiAew+0kE3UoTIlVyUtwmKTZOd1yV4JU3Z4qRJk3LRpITJ6zX1yT5Eym2w63PyZ/U+gvfHLtA8IzTrP68TIgD4Pdw3qdaFQOHHtbpN5EkX8TJkIeakJ68rFZe+/aAvkdKmNEhSie9r27alOLXrtildhaiTtvynu0SI8OmCR0CStCFDfRVKOdeF5j+5r6GNQKGtti8Ty7aRKFp97fwVeE4ziH2NYJF/NkSKNI2z2/YDbVySxEkX4haojzmhevsMjUimcjkujlgZjXNEjjSRKNyA2IE84VutP2kfKPsa7oxEmcC1hxT+MpS8jWhWRE6uVA/+EubhSglbnpB+o5UfAvhjfqj7v42QrAlOnDAhJc/mQkktgfICzgPlY/ieKESscIWQ51bg3iYQx9o6gERv0uQ2uG0IT4hIkeXy/Ic0IN8HtIntPq4TQsi620RG8hxD9vdz+ETJHHqOoSk77nmiGNKECJMkLZAy8oS2RJ7QflEkKIsUZZHgmv+nJhKFlwP1lXf4PCEJllBoaHVuKZDJMXag3IimXLQpt0396rZ97iEU5xDeZyKFBLmQdV+rr3miSMGbN2BN4O7jCXHbsb1JCObfaT9kRZTjVxuR0hWaC35IQeIIKTUhJUtD2702kScaKSJj+6UnivZs+XUAM75Jr0A5XshnRdcPhT8dqoxA/7FHtsd9Bc3LfJ4D2zaRKH3kXbnfhUjJxXcuh2tEila/7Tqh++tEsPAfSRk41N+1sXeAel9qQmgcD30Huve1Q+iTXYlknvdEI0+onJMmgJN3GXmiESdNYeh9wnmgbCXWgXIF/UkUkk2pHfMtv1HZEWsMJrlkX6K+JJa8tX0RNPsg9Gh3/b+6vMK+VrbQdaR1PIVLHvsUwIkp+hhGaSMSZQpHopzCt5ynAKZXQFoCqwxYHZn2cW4vy5uDJO0kqcIRGsy1AV/9cdvAph1vIlD6DJQhwkRapOREcAgD8ra47f8KebTt6lxNCFlvuYBN4TojuESyJ+44Jx6pH1Efmotj9J0+OfBkfoEkLTGeLg15kpTI7EyRoEQiBNQyMSRKiQTIgDUylGWCskiwXg1RFql5CkUKpEeufwJ6v5I5Uzi0/hgiWGSZSijKvsPLNes2R5tA1NbnDpng5ELm+wS+6hXgj/8hko3GY06otD03KYzz88vyXaILgaKNUVwQhijTPCn4ubXr9g3hkftNdftYh7U+KqH9D4340EJ2JGmixfXTd7GqhAft2ADh3BNAnaTTiBYN+zZGaYpbP3VlLzGBmd5vS6LIR6G92hCZ0kc2XoltUx2NmJH7TfcQ3Mp+wD0VuAcL1yF5H5Xj67ZkikQfIqXpOvI+9wVdyBM5FkqiWHpVy9yZ/F1CX/RAkiZT6ORJiETZhpSURvwG9BuV+M00WRHpz6xEGS8H4DMyTRNl14auCbQPQcAc6mAfst7zDsPZRCJT7Ica/Jx9uGLHj+UwVvG0wGB6ya6ewcRWwn91vBO0oYn5bv1RVzQJHffZ5jSl7hFYbCrcdTz3LgjGJmiWFaA+2ci+NYaRto7CiWK1T+2Y8Twh8mSULZFa0sR8CqSsvSQobSmRKK7TFUmCMkkrL5Uy3+B6BSAd+AQH/T05cXHSXfZnuS9/R/vBrpUibLHmysRDEfL7pqQ04X0jU9omFz5OaI2xyfovESJl7gqhd6iRHppgLGWAUDk/V9s1CSECBMp+CG3Psel4m0Ch/S/+jLQyTYHgVtcu5Ekbjtj1Q8aBtrb1kGNhG0LK277ebw94obQIkyd3SaLQViNRtO90vRB5oh1rI226HkuVYx6oH8kHksL3FtRkvS4ELZQ6TXLGvoVsbgONdG8bC+X4FwpNH4tjR+60vG9MWVmITNHK2vpRFwIFaOapBfpp+7Q8V4H6igzU4FesnLttg+1X5Atn4OS/442Nx3n2bYj31XC3IU60/ypxX8K3Ft9N98c7AlnMn9ntcwBj0+g/gdvOYbxQPoYL7ckBnN7gyXSJYb7G7PgcAJBhjQQlFuUMy/MRNqsM1+nEPBYtZEx71F3ZdW3gVp8BhyYwd0WXulob1Vh0wB/479qC+ZDgFoO7xF1eo80DhSdlnsH1reemTn5U994iUpI8vObwvVNsH0O+Rj5dYnZ8jgQFZlggRYkhNlV/SxTyhOAolgQlUmwwRIEEG2RIJgVKpLhMSxT5GqvzMZBaLzKNVAe6911NmOPkDJWf0xeuUEihiJ+IT1JAey6GPsLQbcbobfvufcwLu/Te2mccwa0wAdTbCqAr9VTnUmw1tHlKEELtYZv33ccDRcapSzKAjoU8MTRSRoPWb+5y/tIIGn6M3wuHVBb4PidFpJIwCJSJ8/dRjKVCC8Af9yT4f24jkfZFbmizevPtAeMYzom7j/LHt3JfQr7SJqKCtiESpU1+biNgmoiVJkKna/1Kt5RLL9/AjS8FXLSDHHOa+kqbp4omb4RI4a4eKQ9paOnqeQLUwxflmChJlKfsO1tZUhImkjyZok6UUB1JqGzTjzQUAJYtdRj6jUr8hgAn3EqmR2N/pGcK3WxtkuEv7RAsd/s+sPcVemV9aS2XuRvGukV8Dj/UYA4gBwbzBcbTJYbZBmPbUoc2rGCYDFHmRpFbpWMAR/V2pI0/oUmjCfciO/Rpv03CMz9XV8Uu4mGheaBIIVwqKjxeFL5rI+9bmicKy3vyZLo0fSw3fSxBiTEukaBAhg2GNmtWYttLKjyXiDgxpMqQHR8CWFdkC+VPeZKWuE4LIE8NkQK0T2JNxwrlmGzaXtkR9AGCj82ciAfCAssuldi7xkPck7zeYxxzpKVf847g7Ul6ovC+39be2o7dFTSNrIlk4WOVJBa03/YBJzTlPj+nRmR1hdavtXtNxXH+DLisKp8FH9dHSn1xejkWhqBxebX73bdxqQ9C5JVW7xGMNZRYlutFENtUlO2KRKH90PdtiZOmcJ4mUkTWk995l00DdWrhvYQmklFCGukJ/OJUj9BlHDokmbwLgSLrcTlWGwNT+KE7KTwCRXqUaJ4oU1FHEiddSBQo2yYUAEv814p+DMAEZhCgC5GXQApHkJyzs/LjgN8xKo8Vyo2iWdypAUoh5SFxF6SJxyopkIp0V3QRaDTXbbof6iCU4JJ7oHwC4MhYv1/AKHCfwnmiUPknAPIrHL94UxEnYywrizjgrOApSqRZiSQdG+t2kZjrU3vpInB0+XRCl7a264m9SVrSvLbAyvjv953Ye8yQ/UgSJzShkAcKLWP8HN6qVjzvySnbJ48uxRNlcPolsnyN+eQMQ/h9jciUIdZIlRwoBO59UiLBGkOUSCu/FArvKZECGbBJzYSwBHBdlMA014W4pj7YRVjLlfo07wCoexLIPqBJcpqyS9Cs5HxMkAKSHC/6CFCHJGw9QmQwEZDeK6D2xFk92koBmnuiSIRY/5AFE9hNe+hqVeQeJVzpp3FKWhhlna4kivxv/P/u+r9vC+2/aP9XPhuuLJC8BHiu6l0F+5CCq+abouSz2jPflZy867bYdB1eVyOvevjY7yuO4XIBtil/GtHWVcQLESmyfdG2C5miHetbBjTnV+lC0rTJ9jWZgNqONo7LOV7O6dti25xM963j9pknNHlWyrWcRKEPGwdJtpVECTcWauRJiEThdQCfeNT6jdzn4E3gIlBHQT9tawojcGiCsLxJKeRyQZhvV4DP1nNBJIU/0W47mO9iIrgPxXSXAkTbANDkTswFqwFcfgae7PLITx47hwvd+cR9n378XQzzDebJGTKsMcMCQ2yQwCW2BIDCKm8FEhRJgkG+MSuBFCk8j5QQmhS0LvWCoDYYaot3JfSFBlptkN2WZIu4O/B+JD25iJCkJLIDVAQKzx+kkSinyrH5Cvl0ifnxGTJsMMOiIk9mWFhPlKXtdy4HChEp5FnC86BsMESJpCI4iVqh8pLCfxJzP7R6z1WRAEUKFCzmv4lE4d0I8ENANcGR16t1O95nJOmuKcOawNSWLO42uAsBKfb9W4O6qjo3SDKFIOdXbVKRbSdVynaNNst+Cl1w5mQBF4r5MUkWyPM1TdKh/9y3T2jeKfw8TX23TSbSyBOge+iOsLaGFGUoW/orctpPRXntMTblVuEyy75CI994ewP8+fPAMYUejtD2QWAr509A72ZtJAptuxIYXcq03zedMyQnpEr9lTgXD/uHOAagHu4jIY/x8UNDyEtOEsNy/NmX/qjNE00kshzvJGmSwhkGhUd1F8JkVySK2m8Y+ZoG5t3CVi7Y2N0R/UYlckXTBGFZRvVyVifEFhX8VviWBGESisG+32eD3JfBu4vA3Jc9bbOck2WFljF+BuDIESeU94T2aXsK5C/e4mTyBmMsK/KElDnAhROUSDEEcIkxLskGbht70KsqpJTJY9r3GrpaOPiAeZeCMIdGpsgBfh8H6fcNXT1QiJCk/Ce0ZQTKHCLHCXzSZIrKE+XJ/ALz0zMMkzU+wFlFnHAPFO6JIhPJEsj7BAA2GAKokyuyblWeAGWeYL0CrmyIT5VsNiQEciFyxbbaJKiFgqpKxZGoRPMEEHbZBasLtPehPn3svsYIIPb9WyCDmWjodQXJFNkwB/CDp7XxmOO23rTb/lYKx/z+pbdFk0eK9Eqhc3VNkkoW4bvsF005UJogn9GY7YcUBzm2t5Ansvnwy0kChbdFXk8TB6r7v6+xpun9dX23fciTAfq9yz3GBLcjUUghDCmDGgrW4EhJDJEpTSTJLoiS0LEmUiRVjmnNLEQeeeV8DJInkZ4i/KRN44lGlnMd9T7lgC4IeZ80kSdSJ+Tjnsx7MnanlqTINFAWIlHoGO0DPsHi9aMb1y/SEk+sLEph54SE9Z2S9Q1aMOG6SIAbj4FrRD92gP4QNUxuDaSB/xx+o+cdhE8OKSs/B8yLKGBewBL1SYEapFRg+wza2zTkpke07cDeJAgFZ8kdXFdeOzSB8U4xggk5sOEG0yPTDj6F8z75BEa5s2X5J28xPz7DKd7gGV4iY8ochRNwrJGhRIIlRljbBJZZPrNKWQGXgNgiROB1JU5am4EcBEPCq6aUyee7S7dagiQRpdK4TwP2+4C2fkQTDnlxcRLlxPzsFC5/0AtlS+TJxzChOy++xHi6xCxbYI4zDLHGKd4gQYkZFhhhWfW7BCVG8FflARxJIkN4NhhiiE0VzrNBVv2W+i55pND3Mkuryapa/piIFKAuQNGj0rYE3oe1cB7AzUHBsB7+I20r+7CWAyMkCMmyhyYyeDuMhEpnTGFIlFblgEKPwQ4AOqHdV97QxnTt96E2FvJA0TxGpFAM6B4VdEwuSdmHOJGg30nvHtqX86dWp+l3bXO2hHxOMmEilcnxXCzPyYV5biEF+w5lS5ByC+1TG6RxThvSvOcSCpFq8ujW2l3X56fV66JWhKzegN/uqM4IwJMO591zUDhPqI1UH6sQWmUwqRRC8+7StG4IcfNvUjtGKNgxUiA95REAyJsUcDnO+pAo8pj2O/lZtXwPHaN9rpfKYaJ2X0cwbYtCfNQnZbeUaoKnnAjV1cql/NA2xt/lvB0iT+h7iDyRoYpcph2zMnsuTobM4ZMhtJV1enuimMUTqG9k+RoJ7ydJWRnpJUhm5cbBKky9TFAWCW6GC3zZ9Ci98/VBcqP/QhIrgLMu8oEh5HKTAoYhlQMqF3D3TTG8DZHxUMJ2iEDh37nFiSeQtULDFHry2DmsoneF+fEZZlhUnxQlpnZLeRlcboUkmOBShSRQ2vabyrZGSFih7zu/YADy2vvYTx47QsKgZslN2TGWgJBPEtOGz9xu8yuMp0uMsmXldTLCZUWUjLDEGJfIsMbIJpSlxLK8j/HwHQrcWSPDEJvqmKm/ZiE9CVse2YUFJSiAxAh3ZZECaQmkKZAqyaER2BaozwuyDq/X+l6kkislLDrG+1EXRfYQwD1wIhqRwrk8pvBXHSRUzUCSB3wuCJV1me9v09baZJG0ZZ+PVXyM0jxVGggUrU82/qUuyR+1RLNoKOOKCldgQhgEtjJkhysTItYf6OZazse0NnCOiH8Pgtqc7Pf8WdzVWNZHntUMDnJfzqmCsDpkSGWwluKgTp5w4iQJWdcT9j3zDqEsnbI4BCdZjEc4faeQXCq7LhIgJWIlNTqaRqakSpk8phErspwf4+cizxNOKPIxmh8LlRG8LsCXR+Y3q7UzKpfHQ14ocv+hcRvvEy7DagZCseIOJ0q4F0kqjkuiRCNYaiSKI0+G+brqE8PMRTfIcPVQ/r8huCHREitJgjJJcW3P1wW9SJSj6RI3I9tDeez7OVwbPIcfwkOdgF9RdgoAOAOcpYcmBN6bAL8haCwf0G9Ab4N8PLscxNushqoEdwfXlh2FkyaU7PK5Cyv4FKaB/4D9/oktm9/gxde/hTEu8RE+xxxnmOMMp3htiRM/HwMtl2qUszFKJNbmvcFliEyRg2vTftvvekO+L43AaBLo+l5DQ8jzRB7rx43uJ7hQtQvcVS6K0CTErZUfsi2FxY1NtRdwiWLncN5dOfzlwV8AmK7wPV95hQxrnMCFyRnPk0vMsECGNaZY2FV41t6qPHxSKRkVwpcxHmFpljG2dZYYVYQneaJw4nNtQ38A652SJ0jSEksAZVriGmNf+OKEiBSmpH4XErQImnBWvQty2e0LbR7pa03aJ4T6UCRXKkzgjJKyjQK++3jVxsiSSTncNPKck3hNSmCIcAm1raZxMRV1+DjKiQEiBcjqzy2MDaEq/DLyOYUgH0lNkeH3KrUygiRCAZfQl5dJ9qHtWYUssVrekxlqITtc2OdCf4g8kc9Kk02kpV3+xZX4bZVgVkISJ9rz7SM7N7VNTZ6Vz57/ef6swfab3sEQB48T+6nainlWT/JNRZiEFMOa0YKh0QDJHFMKymPGUGaWOGGeqVS3RFpZ5wFDrhTVfurIFpkLjbdfOT9rXifymOZtshL1eVmOuleKlCHUcZzaHI3l9EM5/3N0nTsfWn7Q+l+b3MrHu6aQnafuXDTezeHGRE6UyK0kUeZoCOdZVYRJZvvIMHGLI/CFEni/6GSQh+0PYJ4o9ixrXOJNpzP0lDKTtMQRJfzkp8iZkEyNmW+BAOuKeuOuSdJNCVDvU3i9Sxa8bfK6j47GLU+STIHudVJ9rpDPF/gAZxhhWREoMywqDxTKgwKYSWFj6XK3CkgWdL9S0UaI7OxxNVlwODPd5ELchUjp0r6azrNL8vAxoo203PZc9J1LybIv8T5lP9pkM4fzNpmy/bnJfTKeLqsVd0yOoXXVxyihLBEnmQ3EkavyEGQOlLI6NrQ0iT8ZpVUqWerLQ0aqmPpDAOvE9Osqp1FaAOnAPRrahpSLUJm2pf2C/c5TLGTf7WKNpZM/9r4UPVUqpHA6KDfuNLU3APU2JgVUqdyHQj/l2L2rsVzeuByjeBn/LveP/NMAvreFPBbqYhrX0XjPklnl4F4XvIwrKk3QvB1oy0mmlJUxAiVXtrKsaZzTOAyIOvx4iPeo/addCD63lTe1Zy9JLfknpZWbl9MDDIepHAz4Esf5FZ7kRi7WrOq08ALPIcit7NLC3kV+LpX+pJEnVLdEgjIxxEuBxBEujFhZr2wONY1UyY/08VQSG7zJacSi3Moy0jU5QSL/qvRgCZK5HDKFhBy/SUfVSHRe1kSk3DXkg9DIS97veJ8L5YBiMp0cA7lcO0czwZKiTrRYcvFJvlHJEzLIk2GQ9gHf6yTkgUIorV+12XfGfeOZrbkx6ehFoownl7jOjZBMrl9lkeA6HZoOQ6uo8IlWMoIkpHBhxWMXeW4UArc4ANsP8F0nB/lYmiaFPmi6dptQK4WLbRDyQCErC8W3PYfzRIGxiH8MQ5j8AJwnygvgyScX+Oi58Tz5Kj7DGEt8Lz7HDOeYYYE5vrBX8xspsYcbmyklOAEUqc8mh8iTO+XTZLvhwop2TKKLUNcVvJ1o145KUTua3oV8fm0CoRTCeXItWtHqQ/vdeqSQ5wl5m9B2Drc0+BSVJ8r04+9iPjEE5XO8whBrS1he2j7mr3zFlxDnEw4HEZcAKm8T7pdCdQCXaJb/NkEBP6bUrdhTJgkwBTZWuLoC3PzAmytXKGS/5hYl/gmVrcT5qgvIwrb+EQrN25UQFPpdq3Z5R+jq/Raq383is9eY2C2XUYC6AstfzTnt0BjQ5JFCJ5Pg8kzT7/rILCHPCn6vMiyFC8d8hQX2rjVSQCOZQn+Pl0nlyCOl+H1K1oX+S8iFnuct4Mc4mmQg/mz4M2Gu6k1u6lyRANvyZ8Wfg/z/0pLOxzStTXpN40hUlKQdfx6hECkJ7VlLK7r8Q1pjCFm/Af958+cO+GFUoj0eKo5vgPkKg3yDJC0qBZFyOGiK4dCSKXVvlHYSRQvf1cAt8XLLDS5VeWJW0QSATZbV6hLJsl4N6+TKauCTKHx7Lr5rZStRH6JMG7e5Rwx/TLy8Nu5cwrS9kEcKP1lojO6SjiLUv7aBHN94GZ8LZIJwTh4/tWUkv1L/o74IfxycwxEl9F0SI1RnCj1PytSE6eTTJdK0xGjiPKjJAFgnT9xCCXKxEvNv/f4h27/0PqE6ZtGEC3RFLyZgkG1wY5nTpEhQFiWKIsEGwHVRAsjgxcCv4CYZjUShzsEnnnOeBV9a+kMD/30l4wF6PrKG34Y6VRcL4W0Fbsk+DuCvGvKhKafGTyTKC7jliz8FBi++xEcnn1fhO0SiPMOrKlfDDAvvyjyJLIEs2r1yomikStt4dptXp55MExy0i3cVhOVvQucDdAGHyu9bEXtMaBLUmhQVORnJ5LEsp9Ac/io7RKqcwlsq/MnHxvvk2eQV5vgCY1ziOV5iiA3mOPPCeYaKJ4oMo6MQHcBMFpQ8FnDECA/ZKdjkYupwQWroETN8xZ4SqRGw7JhuBKgSKHKdEOVWJE6K5EodwFcuOIFS6zJaqEBofB2I47zONv13W4TGkp0OXuj+fx6B4tIGaldcmeWQ7ueQ33nCWVJegXqjlG1PSvuA3ta6COHyuyRONGui5qbdkO+DSAL510L8kOyP8i9KRcf7i3xpaaAeukPzX5PLvLy5kDIvvSAG8No9ybCcMAmRJ12fkTYOapZ0jUSRZdVzI4KE/19JeIQ8ZtuUtzZFr42sojL+vIF6iBlvn5Ls67qK4v4inS+QzzeVxwnN05w4qZModU8U6dkN1JVGd9yV8/mcywOAr2TKlfpCYQ+AC+mlkGDAeqQmwCYbVsaazdocW56PHamyyuClhOB9QJIinESh+kB9XEnFMa53QhyTRplanqZL+H2nzzzc5i23S4T01IGyr21DhDqVM/KEEyZtJEroGJWlNxjMF8jyNYb5BuPEeE5zD2rqE5xMASD6DpGLdRIlRJ6YfSf/chLwylt1rxm9JLMEBZ4kJilokpQo0wQJi4+7sn/Nszh2CefxSBTACSUkkPCJtOmv3LZhtj2OXQqyu7rfPufQOpNU+j60nyOn1J3C5WV4Ycryj80KPCd4jWd4iRnOcYI3yLCuFD5OotBASvuhZVYJJUtypQtZLX8zxHEAymPTEuVJgUT7sVZHayOhwbcIlIfO18QOvU/EyW364bZkFiE0KXEhnELhuHfXkZtATuGWL5Zlc7gEzadnmCXGm8uQJpeYYoExLisShS8fPscZXBLZsvJEAZwARn1MhtMV1VRUD9nJlMSyQ2ywRoYMG5RIkWGDNdzElaIEkrWZI2wSvOv0BpW3Iic/aoKMBVcmuFejVCRkmfr+6MSaVSmkwEpl4b49vXZNnPQ5t6bIb3OeA4AVWWrKrNyuAmWV8C2tfIBvBOJKKOC7g3NBHejW1poIAr6VQjMnC0aoywMdl+qVtyBvLaTcaN2NH1Pn5hHqfZO2RKZIAkXOuxqJ0kCeSG+TriRK2zMK/eeVqB86R+N0L0lj2m8KA+Y3pR0PhSi3gT9n+s6fOVfYeBlZwvlKUUfYfkWo/UGWbTCaXFfz8cgqa5I8kZZ2mfOhS/hCW3gPlweAZuUSqBMpRJiMKtOLu8sRlhWpQjLIJjMrbyZp6bxU8g2uVkMgFWQKtX1OfPDvgE6KcIOK7FuSLOd9jpd7RIpkarRnSmMOH9/5OKTJFHK8h1KnKzTCkspT1PsfjXeA7pVI+bHYgiJAOLfJ3J7iFI4cmYv6c/m7GzyZLpGkJWbzBYbJ2ltN0pEoG2YQ9EkUHqKukYoSofYNAIVow1eNXIOPXhLQGCvcIAPIgpkARcI6RVpgk5a4zhMgzetrf9M+ZxoBv/GfUzmfNCnhz5XdlwKwZoXoEhbThDYPlG0sdE0Kt3zxmkX0NpCKH7dA8SSyz+rLGP8AjAfKxwD+6ArT+QJfn/wOTvAaH+E7+AifY4wlPsLnGOGyIlGIRQSApU0eu7RDbonEmxQ4qsG6SFBb176JTNFebRchr0KTF0do0NvWUtuX1eb3oN3jYyNSglrxDs7bB3KC4iSKdIEcwBCQKYz3iQ3foYnEEpBayE7l6TW/wocfv8I4WeIZXmKMS5zgNU7xBiMscYo3ljD5osqDMsMCtLSxZPFJsPLD6RKUuGRhPC5HEfVJHrJDHiZ0HiJdxki8xLIuDIiErKxaKaAsEixByyjm/uulZis9AKaoW2vBfsOVXn4eTsqAezbySoDrg6n4zi/ShWzp2/e6eBv2Oc9doKuSdPiKDSYwq6fKdig9ULhwzreVLMOV8AGc0YdkF43V5w1bCt6aJ4GGNmujZmEkGYDGrUDSVCDsidJ2W5r+EdrnZfxYNSYQSTWAn8CGfijDeDRIpV6E6wA+caKRKNyNXZIooWfE0dTG0LCVz1sd0jTlj9qQJjN7P0a9z8tzhOTVkGyskVVSidPIPGqfIgfNDdDDy34vcTx5hwmeVOra2JIoQ0uccOs6z4nCCRUqA3TLe5e8EFq573Xqh/OY/VQ9JgkUWjQCgDXjZN6xy2SMIkmwyYyZp0SKy4sRiiLB6nwMFAlwPtBDfOhzLo6loo4s08ZtvuXNeQX4nnBaJ+R9AzD9YySOheZ3zTAT6rchNHme0HdJ6nO9T4afc48TsWSxRpjMUSdI+Hau1LfEyWy+QJKWGCfLKvycQtCJGKGVJnlZkwcK9RWOLp5WIU+UzV3lRHmCAloMdGmX10rTEsjX2KwyY3XMWY4Umni4MCInH/4ppFsVgRTdu3S1biJQbiO4NgnP2/6PLr/TBCzNhcsKUlPoiWTnwHS+wHSyqBLH0meMZZXgkrxQSJEDXA6GFCU2qMd0FhDMYLVmPft7fR+Ppus8CLQBsakdbatcPTYipSv69sk+kxNQn6D4NoUfsy1ZffgMvPzM+fYG+XyBWUJJY8+tp8k5aNniEZZVHxtijZFd3jhBAb7EMRdtAL7cG2Ufd0IXJYflW0m+aP1WJpaV9atziWUZr9MbVGGfmlKQKt9luSbPdO7fXLG4bfxxm1t8U5/sS6bcJWnSFQ82iN4dUhgShQvWVE4kCeDkFyh1vNcslUhZJtuelOghfreNMC2JlFTsc88LQSSE5LIc9f99G3SZrjxClKB5p9DJQn1KUy7Ys5Nyqvxox7oSTVD+p0ZCac9TlvGm0mu8ayN928q0cS10LTou50kpf8p2SEqoIFDoc/jRPBhigxSDKsyW5lO+ykjI4q4pjfX5nRModYKFQ+Y+45DJZXm9kPWef+fXI48Xs+qfOy/Pi1jmiU0RQflTbBuQxCo3mBA5AtRDgAG/j+WiflP/87pCkxFGThhdZe+uckdXLzCNQOFb2h+ID58TiEChPhnIAyU/JMfSWCi9VKpjJlHseLrEKHNyKnmfjNhiCJo3NffIkt7VoXAegltdkpMofqgPtdvEUi9QSJkQek2DxqvAnZxuIEGJMkkwnKyxKTNk+QbLtDRuWkVeb+ycMaR2SKwiPfQzwE2SPLSHYtT4ZJmyfcI2AnJf8qTr49MEI46mRF28ft//IzsXj4XmHigjAF8xis0LuGWM/whcMtlPb3D88Ut8I/stTLHAN/A/MccZnuOlZzHnq4QAfDnVtBow5eBdVoOvy6lQFqm1Wnf8m5oAzI9BqeO9FnmwScnidaTwxqFZEpsG2qYEVppidlslcN/RV2ns2h+71OvigcLJEu6BIsJ3PoYZ9z6GIyQ/tsc+MVsKj/sAZ3iGVza3kOtXfOWrzIbuUP4T44HCJp5yjaQokRTXSERz2+RPUKYJhklm+6PzQOGgfClmAnOJ5dbIMMS66qv8d2aVH5fxvPI0sx6LAPNISYeoVu0JKaaAy6vFDQN8zpDdiZefU6HMjQI4xYIrGJyc53W1vAKyD3ZRONrIlD7oM3XvknyRc+I+EDu3xDHMwh9cUJdbad08Z9/pU3kV8LEC0Fm+Nmslr9f0jHl71kiStlh3KzBLEiFEEPBPG6Txg29D+7xMq+eVce+UnpDEUMjrRCvTPFHano38H3ysWynH5TPhZFJIISyAsNLXJaG2vHFpIQ8959CYIIkT2S55m1TCyKbw39M1gHctf2PPcYwzjBmJMrbealoOM5kUXuZOoTKzdV4qfrmuXHbJP6gmlBUECmDmfUmokIfqJcYVgULHyCt9jQyXGIF7pyyyGUqkuJwusV5lRofMc388Pocbh3lZ0xhNdWWZ1v9oW/VDnl4C8PVPPufTsYLVWYqyNvlf9ts2vVOTa5rkVentxROJW7mV+ttUfHL4ITtUPofviXJK9V2+k+lkwQgTQ56Q0Y/C0SlyQcuDIvd5FAM3+oUgQ9JMmU8Q8ja8uisSJcE1EpaM0LnLrG2HGgLJ2jCOaQHkwFXOcqQQi8i3VM736c4Kary8IQ3g50bpY/lrQh8CpddjE/W1e7stM6n9Z3mPNBtJLxQiVI7Cyxi/AKYvXuMke4MTvMEcZ5Vid4I31UohH+DM6wyEEgkyrO0yxuHGXrJGXCGkLGlIxb4mu0rCZZumUkEqUJprniTAmtoCb2ddE789ViKli+Jwl9eT/Z9PTLRy2Ej5CG8uPgnN4S8NPgVwalwciUAxy4ObcDjer+a2b32AMyTVxLOuvL6G2GC8XlbESVoCR2rbvkaZXgM5sEmGANbVEsfOMlZUExTfGnqkqOU/GWIDk6jWt06h8kJLDd2S2RjpfI1VkQBpaohbetxcUeDCM7cwScWhSXHx+jfvk01ejE198LbYdsDp2t53RWg0Xa/J2nWgSGFIFHo9pKDyrfSI4Iosb7ugrSZ4A/UJjY/fsm5bEsKQ4CwtjlyBpS3JVaL/cYJAEg28n4Vev3arcr6lbWhf/q5QvvN6XbsU3SuXP9tIFEkqaSQK2Hd+HQ66R05+8HGO36Mc2+R/KJTfeM8glFA7tCU0yRxt/Vx6nQD6CiAhEoW1Q3oH0gPoESwENsIlRrgCkSScPAGgrqgnLfE81IeTJdIbVPdO0T1VJJpW6wHaSZShNc6kKKvcaRS6k6DE2hpTeSixW14iASbGc3WdFsZ+UiQABnpqCD4eyLGaj8mhcRyiHq9fPSJJTgLNcyGN3X0NsW0RFn0IFCmvStKStgN44Zx8DOSyKx8D52KfiJXqmFmBivKdzOBIlCkW4CE7FMHAyRMeycBJFAA1UtHs+zn/JPT2vKm1Z2q/T8Rqlk3oSaIUnoDtlsUkOsV0iDQpgamxOi6KxORIodZ6DvOQyRMFYn9qt8QaegPuJVxOFN7a+YRwl94nXYRLia55UELKtibkN3VK7Z54h6GYN7sCD74C4MgtYfwx/Fwop8DxD/w+Psm+jRO8xtfx25jhHH8An1ky5U3lgTK3Ch5ZwsvULINGgyQHNVbOUHsfWg6tCan4SCUrJPgBgTEqxe1DadqsNfKda9fTyBdZf1uF7JCwTX/b5lzaObWJSVrT+BJwbPlimnReoJ73hAiUjwFMb/DhJ59jnCyrFa5mWNQ8UTixQsljaVIaYYnxxQpJAQwuYATNEOmYAoMCSHMAuMJwYrxMNnbCIBITADaWKMnYlhLIblhC2QTcm6ystuYcw+oY0TNJ5m5sk5a4xtiQ5VLo4QqTFIpkGUHW8Y5rFlqeE6WJVZV9UfbbkDcK0Dz+98VtiMU+/aVpLpHz4yMgUSaoK7YQWymQa3MP2HYFOCKFKmhjPc9XEUpIG3rGmsVfU1K5sSSFsz6y+5/DV1ilEts0l2rQSI6mbdf90DF5TYJGSGj/rw+JIn/Lzx8iUDSiKDR28XuWJDI/HhoDvT/ODzaFj23bj7sociOxT3MnUAsbkCQKf86PQNyZ4hwTJBWJouV7kPlRuPLIc6PI0FqNRNE8UbTcKSHwnCjmu1NGSQFdMxmePFBIrl9iXJEn5Knq8iOOK9WZvFtJhxxijctsjFGW4Dw1q8Cu0rFZzWfF2gzgN2HubULHpG4giRJZJs9Z1ePEOG/v/AfcwE+dkzxSeH2OLkmxtd+m0OcAzdhHcwOfCxTyhHuU8K1WxgkW8kDJr0xI+vE5MqzVlSMrox9LIkuEIs+F0pZkGfBDeggyPE3m9OF1QiTKZQ89sNfISfF87kZ4skIjUFdsELlwz4H1amjbau43yvPqX/pxx4CfgBYj+OTJCG5CoIRt2yiVIZKkD7HS9xpAs1dCFzKlC1mkTWLkxvsUVdhBfuQntiQS5RTAD1xhenqGr2e/ja/iMzzHK3wN38YMC3xit+SZkqDA7N0KKRuXi+Qam7wEMj9hpd9wjVvfumKzzaBt1pRPwwohQSNN5PeQUOw1mSNsF3TbNOh1gWy3bZ4sbe8+JMkdGkJ9UR4PoWs/DU1UVC4nIpp4BnAr8IyA9Mh3ZTyFI1E+gTn2iSuffvxdjCaXVVLmZ3iJU7ypSJQM64pMmVtPFLPylfVEWS8wXF1jsIZJtlfAOH5QfykBby5hyt6R6ZLY5GuTGBwjGJ+UDHwFHkmYkDcZCTxcwPJX+am/Gyd8pVWy2TQtcU5k6XRQ7+dT+OE8TSQKlUk3dyoD4CdBpXfJx1m+WgpQF5D4d96+NIVX/mYXaGrz2/STrr8ZiH3eNzuGXO4zSGkDupMoVJ6K41zRXQEmtGcA/8QccvzvylJQHU1wlmTKjO0ruZo0xZU+VK8PidJEoGhl25ApTeeW9yZlAfqEyJMmEkX+TsobErJdrMQxgiRKQl5OfEyTZEpny7kmUN0m0b0cE6TcKVcAYeMwf6Z8Xz7vFI8iJ8rMkijaiiOcOJEkih/G4I4BTraW5Ii2NLKrH84hQZDWe77flFCWZPoSCWZYeEEaJRKMcGm3S6s6D5FhZiUPQ6JsMKy8V9KJ8WZJ0xJFsTTJZ5HXiRIyxtO+PCbHeNpqqqM3hrOyKoRQzvNX4seAL7uP4YiUbeVzrS9rcwDNORp5Qvn7GHlCfY/GvFP7s1PUSRRe5pEoRlcc5hvMkkVFmszgkykUzmM8US49g2BoOeP6ClV+220K55EyKi+XW96mh3dFosgbbWIyKZZ+k5ocKWWRutAePjmT8Mv36c6qu+NCSJtyxdGkcHbxMmkSJjWEZtHQtZvCkOS9tx2X5+adik9q1IGs5XyOeqjBC7M9fvEGJ9kbnMJ8TuxnBj+x7Hi9dAodu8U0B8ri2q3QwQZlsnoTccKPt0IjQ+h7KuqFXqs2cALwvZzabkK+D6Bb8jWOJtKEX4O/75Al/DGB2iz/LrFNf2z6PR9bpBeXJCOJTLEky9wemkNPzEzEyikwOP0S84nLcUJECe9XtHQxbatJqVwgKUpM3l2bpnEBR57w/seFbsB5qNgmdVQASVFaEqWsrEA8USz5GAJ+OI8U1kor1Lklj80ZyOWY+jdPOjvMNthgiEG+cYnkViweF3CKBQlAXFng5fyYVGxqZGmThxff8ofYFbvwFOsyZvQhTG5Tt80zK8WjWJ0ngz/s5+wYJ+ykottFCa7qSI+UECNBx7X3JhkCTXjmYxVtJbECPSxFEgRcTqPb1OZU2dSlsgL445Lsl1qdPgQL3/Lr831+720eKLRt84wIvUIC/4/ynrX7aivT/g+U33hEiiR7u5AlJId09YKi701tkNofU9xCzzb0vLt72O8tBtggw5NaThSuNIYIFj+5pplbm5RLP+zBKZyurJlIkaH1Mq+ETCrrDC5JlVNtiXFF+lAZPxedwZ0rre6Rcq5RxAMmwGY9NLrkNAFWg7A3Vqhc9kVqW3z85uO97Hdq32rSHeV4zU9SiDIpP2ho63dj+P2P5FU6JohMGudIZk1FmUeUQPVEeTK/wJDlPXHy6sYSJGu26IhbCGEsSBQZxgNw7yu//QPhfEAypKcLGUhl9LnpIbt11FpR3XSqjGSm0/jLBFX5UhKgtMsgL9PCLmGVO9aQC8wpXIIgztifUwORSwYCYU8ATfnk9QmpUq41fO1YF4Tq8w4E6Pff5pWiCVpUh09g1IloGeMPgXxQX8b4Yxhr+R81rOIfzv47TvAGn+K38Qm+Xe2PsMRH+A5mF+fIL+CSfVE7tvHlRykYgeLc/dbVkOqWP/MZ7BTgyxvLvxcSjKR1RipffGAMCh3yOTd5gjQNnhLaO9MIlBAZIu9Fs5I/JiKFXk6X/rcLRVGOAzyOmwRAmqA+RJWAiyaZFzDt7AX7/gKmb31iygaffIn5yRlO8bryMjHLgi/xHK8qAuU5XmKIDU7wugrhmV2cI1sDR+/gkycruFzfNGaKflh9aA6xQvxwdQ1gg01mxvQNhlV+EzMpras+6ec4cmE8G5bnyHmbpfYyjoSRRGmCsgrtqZLN5gmQ5n7iOPrkcJ6LUgkjSCWLvnsW2lRUon2NPE1RT1zeRNxpJGgXbEsGNo3/oeNN/UkTCkMWL8D1iQPHMWAT+tTbHP9Ocglveyv4FlA6RmUkz1QeKYD/DKnNUdJ8wG/YfL9JgG5KHsuWjOVCsuaJwsmDEInCb0FCdqHQ91C/7VJHOy6hkQ5NhFEbiQJ2LAWQW7eItOEmVvZ9UZuRY5ZG/EjFj5fJca2pfiU/c9mZ2gaV8+NSpu4TRgb48yWdk/YHrrrm3aOVyfeT4lHkRDFzfJ1E4cRJkzWevEtoaeS2UAfuL0L1AVQh94SkuFbvt0yf2K1VOhOfQAFgw3VIOxyCe6TwkB06NsYSBRIb2DG2nihrmLxqJqemWY1wbGUP5wG7yTIMsw0u87VJPJvOjJ7Ax1xuUGkaowvxG5IvzlkdWZ+uUwCubVN/uWT7XB6nH4Ygx/smcC8YOf4D4UTiZPQTc8AcuicKban8VBybX2EwvcR4usQ8M4scUE5MSaIYouS8CtcZeyRKWREtCbQwnnq7HlYCr08KaqgTf+HEslSvRIrzHoNNbxKlyf2Lg6Ljq6WtkhJpWmKQb3CV21lZU3SlRbG6Q3JP5IN/Ifa3Xb0mVNZmCZdoEoa12Hj+LDXCpK+CLJ8PF3y5S9dAX2r11Hymp2eYT86qMAK5nPEIS0egkDIHdruT+p25xdzMcyQvFP/YFq7hGqmiHeNl2n4vbENYyItpChcnUkLftfM+RiKFKx1A/fm1KZChem1kqVQWuUuywuLzD2fo5+IzvcFsvvD6EXmZjOwKO65/XVbHMpswNqd+dgFHohRwRAp5mmigcRVwgmgBpKXvKUYTkXSPJFpcW86YSkJJ7Xh97qVCoGSz6vLHkhhNxUcrCx1TwckRqahqpGlb/9IUXn4uWd50Txq2afNdSJNQeSr2ASesUd/oJg/sNXg7IcE6FVvAD6vQPKFWyvnoGAnolazCx/+QBh0SrOX7oU/KylJRflS/J77Pv3fpUyHIaYr/NX6cl8s62rbtEcl7kFv6n0Cz90OITPK2V6C45SepLmxfF4ltLymQH/ltQz4H7ZnJNhn6HcTvtTLVIwXwxz2gfWziN0j15cOW8iYzhOXikzaUae3yEZAoxnT4hIXt1FfikYokD/EhBTK0zCsRJ4Cz4lf1yrIiTngCeoKWiP4mNeRKkZhtmV7Z7ZOKWEmSsjKQkhFFJvmkezPkyqXNj5JWx4iA0fOzGZKFK7ybdIgsX+MqH5o8KTlrZ9oYnYtj2njGddCV+A3/HR2vnpccwLSwXtrnBAuX8SVCMoOck+X4z/dlfqwjvc9xQl1upXw7BZDfVATKKFtWOU1omWK+Ao+f98TlROFeJ86kvq6RJ9QXAHLiCHiilNZ7KdG9UHjuPvc2hCyKtHL/uIJOKmropUZS7mSt3NxU4nUMQvVgJgWG+QYLwC1/zNsaCRtccCnAEgVxtq1L9mNNoOXH+TH5nZeFzqFB1pMeM03QFOu+19SUPpFE9gVckss/AqPk/VEAnwDTT76LPzz575jjDH8YZvt1mJwoH+AMn6y/jfHFNY5ewVnBuTAJuIkPwCYf2BjHrGKjLzGqvFCWGFVxkdVUUAoyRRPk6Dp8kFx1PKadyxsQQ0QWlO9NCL2/0HuWkmYbgQLl+GMhUmgS4N85mhTFpvpan5aKB1BPzMisaadwhMkczvOEtnO4PjYFPvz096rksZRD6DleYgSXE4XKTZ4hm6j54p3z9HoHn0SRITwFfA8UutcMflvPUPWFowJIEn9y4jlRABeWSS61JMyUbCIjwY0mpKG9nEs8bgSrJUbe/EDzRpatkaQlNquhW/6YrLgkyFSWVbjmTh+pkBXK8UrJJYVCE1JC/V3b74OmcbwrMaLVbSJI+PFQmw+VSWuXJOSZYr7V89gzHMP0C3r1K2XL2xIRLfxDxzjxwi2foN9xsgOor8wDuBxwdFxbNYXqSYGZ52tS8p/wLe1z8kBTZrV5E2K/iUvTjjURIdse4/ck71klQ1B/Dp5icQOkBZ7kGyRpiSQtkKal3bfjX2LJ4tIt4U7b9cqw01erIZBmqDxr5bjF77fpP/HfrUR9Xu61NzoJtRN5gSs4eVrelKyrycw0PkhPFPjPPdTepuK4bIv8HHwOOFAc4x2eAjWCxCdRuuVJ4XNo5a3CiJLh6tqt0kfyAW8b0vgp92G9yQEMxBbJNWAJFmRXuMmBIgE2uSFX1gl5ooyqZY75do1h5YlCJInxNjFhvktcVjlTqIy2GTZIshLrzEgaRbHEKh8D53k91JePz3w8luM41zOlzsDr8zrVvMDzpJCMIL0KL8W+HOPlAMBlXwLVGbPvWsgceUwrHojU3+bwZdkpnFfKC3vsBfvNKcDznsyTM/v2llWojllBcm2NgS4XCnmbUE4UF7LjvLE4kQKIJMq2TZO3VGIfERGAVfsGgNSfK2/sI6M1SkohInEy0NQzvWrUIwFTVy29hpD7DDGQFN5DblpVDFxSYpMPkaQFVnnukgZyFhCos2QrwLjEUgvm+4AvaLQpkW0EShfyZFtSBdCJlbaEopqAz8GlBmkNIIGKLWN8Cpf/ZG62+cdv8WzyCs9saAElu3yOVzi1St7k1bV5F+/gFDoAdgkPd5uZa8CSJy/gEk9xl6rQ8lTeX9SED/mR5QjsSwWsgiQ4mnLTyBuU6KtkaIqa5mYrSZOm+zpESIm9r0eJPKbVD5EoclKywu/cFs3hJqJTuElGkigvVsinS5wkJiyH+tUcX1TJYylkh5YMn2KBD8ozDFdXyN/C9DHqayvUSZTQajwpHKlSwm/n7HGlZT3fCXkRAoDLjVJUbD71Zuqv9BsA1XhPXifm8ok9VvdEIeK9SIzltigSbABjB8gH/pzA/6ecL+iYJEtz9huviwxQf2iy7XDpqXYC+H2P6vft713GjD4koTzWxUigKUX8O98Ky/INnDB+qMhReWN5r5fPEVI5peOaggulPGf7hVzlQXoESAMKJ/20d8OJXiX/iUaUSNJAI1Ga5lCJNkmyjUgJlff5Hb8PTS5oIlG4Ap9f4Um+wTBfe6RJktTj8gll4sIdSqSeYF4WCa7ztbOY87FKjk+0pXLZnnLxOzk8aQaj6nnJZLOprCDQZnzkYwPNl2LJbN7upqi3xTnC70K2xUeQw9pY4qF6nZjjTsHUEssSeaIpmSY8F4440Qws9F3Ln6apdDI5Pd9SuHAOHF0AgxwYXBhyZTW5Qpk+QZKRyYUtOgLnSWNOU4KngJArCfEyHh6coEQ5SV2eFABIbd5NPnbT+M3/v5SZZJl2LA38rpobqH8RARJaeY1IS75AykDU14w8obGf9z9uPGcEOo3xU7ZPMiwnUTiZwr4PTr9Elq8xn7iQHfI44WE8RKrwJY2HYitX4BnjsuaFkq3t8t6WCASYpxRvz/RdjqEWR7ZsAAAJG9FsPeNpdc1IFtNui/Udkii+m3c90WzVuJmlkoRmekDFJMGmzLCar4DUsodzuEH1HObl8YkGsLlRANeAqDFJaMKt/Ltt1jd5vi7o/Tgt2mLt+X2ErFJ8MiOLFJEnz1B5oMzhcp+cosqJkn/6Fl89/gwf4Tv4BN/CDOeV98lH+BwflZ9j9u4KeAlnEZfWYSJR7KdIgGUyrvKgECu9qfh25n1in12JpLLmVOeFf96aQMGFpFVDfe1cNfA8LHxgk254Grq2GykUd0kSGyrX2s627XCfIOKpAej9t0t5E1kasrQza5rG3tNkQ/sfs+0cePLiAs+ev8QMC3wVn2FsvU7MsuCv8bxaxphW4nmJGc4xXi8dUfkWjjh5BzNxEInChSONQCHFjedBAerCVEtTIYKEcqRsbIyyJErMKWmcb8qGnrJ6TiAqkSBNSmAKpGmJdVriipLNksAt/x+fGzSCRVNgz/lJ5PxAJAifL5r6e9dxW8O2XiVdjndp9/Rd9gW+z5QiglRyrgF8jsPGBLoyK9sQt0zScU1IB+rNi7fPc1giZSZOBvZjGu+pXPPK07zlyBNl4N6RFJxpq3kGbEuidEUTAdK3nibWaTICbYMkyg2Qr/EkLTGeLpGkJvE1Jcbmy88CvtwbTLaZJEgmZeWhsl5ldva2Hina2KV95HH537W68h15dY5gFK0bUYEUsgLtizFIJY7OC//ZakSd1u40Iou/J/qQi+MBI8cSYwChkJ1QYllJpGTrjReSU5EmgBuLOInCvU5KOMWfywN8S5CyM+BkCmq/GZysYd9ZvgaQXGM4OUc2NasAZizfGq0EmGFjnwhQVkl2h9V/pTwqZmVYlxOGykoklSfr8nyETVriGmPTx7iME5ruqVyWhY7R89X62ArwvTNpTKZ8KXQy7onCvcA4qSI9UaR+yvU9Ik9SeMvXU/+Zo06inMLJsiTbUr0XrM7ULFk8Pz6rvE0oUSyF5tCKkXwFSe6Bwre+J8rall1WOXqC3lNcfg2NgwT5brQ2DACJIFlgPa2Sa5QLdEavKfEJI0bMtj7T8TIuRJsOYZyyAGCTbFCeJlikM1wXEyeonNm7Oocv+NKnajiX8GcMLkSSMKwlAG1TqOS5+jyibQVq3lPpPCElOyQ8S4FqBnUZ41MYAuUH7P4PGJbxG8f/E1/FZ/gIn+NT/A7mOKu2X734XeSfwyhyn8NX5ni4ACEzn03+pHLbu2RhO0ubMIq+y3wp7q/ewLNs0MTKBzouiIAdL8RvpHVGExCrV7CtpNjUbkJKUMGOkes23cw2eRm0ax0icnhsOoDmfqyVdVEaAwojCXJz1N0diTg5ZfufmGNPPrnAbL7ASfYGz+FIlBGWFSlpvLwceTLGJU7evcPgS5h+9QZG6HkHR6JQf7uw99fkgZLCCDQhTxT2u6MCSMsSZVL3FwNQCXgGQ6BSJOpECZdxuVWJE+lUtoFZvpDKAJOgLklKLCdjJGmJRZGYZLPIdRKFb+WxkCLiWY1oHqETaPNFkzanjdscbSSL1nbbjjWNK13bPZXxtg/UCBNNkZGKUYHDJ1FIuNRIN8C1mZzt06tfsQ9vj1QG+G0Q7Pg5CdxPUfc4vYI/B3DwOZ8EaGF95MrpHG4so3coyRP5XsG2cp68DW77+6ZzhsgTLgekAKYrPElLDPM1MhuqM0yMkkpWUukBAOje13zsMt9dcs0EpfFQsfeSpIVpErSke8g6ztuepsRR+4E4xtukPEbtViVTuAJH+xJy3DjyD9WIKehtTJInst3JtsjHHpr7DhgzXGCG606r84xs8lhKxJmWJYarKyQFMKBnsWZbSZ6ESBR+TIb48C2g93uSL+i9ZGzLyZQEGBwDgy+vgOwKq8kKZfoEWbYBLWucYYMRluAJ6auQHZRVv6Llj+m5UX4Vqp8kJZLjEpt8g2VaGu8vTJrH6NCxc1ZH1j9HM6Fe9TE+33J9lAssXCjTwjrlS5DjvjSaMyKTk5ZzuPGf+uAcTpblJArJtfkN8tMvMDs+r4gS4zn9BrR0sQznkZ4oKUpM7ZZypNA2QVkRJ+OLaxyt4Mg9QF8sQWufkgTkj4ugeVM11U3BjG3t6D2laROJlifFNXowYdqwkZQ8aJgZl8lVPnZujiTMSIsI7ReA74kS+luSSKHfheprddoU4W2hCdu8Q/E6TYI476gyO/NTVB1rDl/pY5/8xVvMj10CWcrNwD855WS4gD8w84GGbsM2wpsUWGdmSnAhPEPw7AsunMdfSs2DJiBJ5UkT8lKlXOo8wSZEbnlXYisJD6C78iMhj8lz30bafAwkCvcQ4WVgZX2IlBCTLwRCLshJ6+0cNTdHmTx2fnqGWbLAHF94yxaT2yNP1GySxxrvk8GXcH3sAo48kSSKtCjRfqi58GNNfICAG9MdNUJj/wZ+ThMKBeK/Iw8VHuozxJr1/7ISGjcYglyUS6SGtMmAYb42brq5ddHlyoVU9Eggkn2ek65UvyJSAN/zhOYLajNcMqJj/EFr/VQjVUIvZxdeVqFrNREnfF9ZAU3Ou0B9hRLqEz3a1N4iuwLSG/uqj/z5TZIqUljmkAowUA9Xnor6K8C9C/ohn1vkZEflQGPeplx8NEVXlvFjYFttbt0WfX7bJN5p33nflwp4GvY2kVZ/7hUAoDa2EXgOQMARKpRTqsr/lLgcKk/S0oQqpgO/j3EjD/8unzkd4+NeoRzj7ZR+SwoJ2G8qy/kN/DYokbL6DLL9aKQIV+pCpF1Tm6TPo0gsu8YQN16eE+mRIkN8SNlMimuzQh+XAyRRQvvS24SU1FBdoDuJIlf9k2MivasMjvgqzOu8yq4xzMzNl5YkAgxRZEzsa1s9wdj2I7MCYOIlnaX9jJEsJRIgM+FzRZFglQ+BIjVjuuwvUl6QfYY/gy59jD87T77gIT7aGE+yRZuQxuVVLhuzpLF0f7KvyTL6yLI5lZlwRiJQ3Go7ay8sZ1zFFlxW+U/Msctq5R2+Eg+F7IzLpe91wmVb/n40LxT+iOQ7Cj02vi9Dj+m4JFqW6Iytp0O+NrMW4uOSC5rGbQToy0pgruLkj9c4S0uc49RYZs7hGijhHKZhntn9Ygbf7YncoiS4Yir/Lm+Q/DvEb+TvuqJJkdVW6iHI1qBdO+RWOYBLIjt2yt4ndvsDMK5anwD4I8DgxZf4w8cuiazxRPkOvoHfMp4ob38XR28AvLKfFUyIgbw9wUTfTIDF8aBab2SJERaYWeevUZVMqvCmEZbJOy1RBcKRsCEVIdmRtMFRU7C4QMwfb87qAXCWan6BkHcT0E3x0SDfPb3LAnVyTe5L4ZrutXtm6f0FeVMB3TxMZLmsI9yO6ZCmQEjyhD4v4Nwdp3A5haZX+J7v+061ZPEMCzzDq8oTxSxjfImv4jPM8YVZxnj9CsPVNQavYAb2N3Akypcwr5IvZywtT5qgw2KUAZi2nMC1d8lTcg+V6hSBJRFt4jfAX2KOVu3hYzr9jkI7/TBPZ1HiyeLcdo1LjI0Cc2wSNlaJyNPc3C/1Ub6VigI9n3P43Ya6zDngEsLxhyi9vngZ9bk2cqQJfQiS0DzEy5v6Bt9vWC5eEiZNyvYUfh/Z4OCRHS9wPTbPqiwSs7JKkRgBHHDEChee+XyxChwjWWYltrKM2mJBho8CRqaRuVII9G5ZHgp6h3P474kLzPIYf79c4OZtAspW7jdB1mv63uWYVyaWGbarPwJAlq+9XCZSOfUV1rJGpiRsDNTAPVC494nZ33iedwCQZAXKzNx8USRYFQmQpkDKPG0BNz7RuziHP91TW+PKGj+mESjUJvnvAH+spKS3TfKKNl60jQ+p2E6VY9o5wL7Te8665ynYVxzjHea4rtohJ+8AVEoq5YVIimuTXF6SIeeokyL0PrknilRMaerm55QyNGw5T1APsZ/CyRvSAyVj2wlcuOQEGKTAhxcr3OQrLI6XWCaX1hPFyQhrDDG0Xiq04g9PMLuwfXZjw35oGWSqn01MuPF5WpowOlrBZ3VU7xe8P/HxexU4dg69rwF631zBju3cqEGyKE8sy8d5zUCrkSjsEO9X1P/m9tgp+y4Jk1P2fb7CIN/g5OR1FbpDxr65zXtyitdV/hNabYf2ZW4UIlsSlBiXSwxXV4YEpLZH7e8ceo4/+g74bRTwCVXebul5UHnbnCVlYhrb7oNE0dCUL4UwhGENTWy9sTwO8w2Qr4Ei9xsy/9ADyWHdYAFfyOVrdPNjGkIWbom+j6erB4BWL+RKrtXlAjMp3dwqNfYnrTnbzgGcAk9OTdjBHGeVhfwDm/SSOs8RKXU8seVK3ALts4+RQR1BYpY1S63IklWkSZMHypO0NMJseoNqHXhNqCsC+/y7vF9ev/UVp+juidKmFEnI86XKfhNCv99pt34gJLg9ecL7eUOogqZMcMWDEym8bA5gvsJ0bjxPxrisLQfOPVFc2Tkm765dyA5tv4RTqnjInPQAk9DacQGXLLMDQkvX+4ncTB1KEBc6ZspcyCdZZsk7kWiVoY2RpjmBvBZJoNxgaGKpc0PJXxUJKjdZ/t6k4sG7QZNCWP1lPusCfn/nD1ZL6twXbWNEW1mX3zSQJ9qz0ciTNiWJto/AOpzlV7jO1yiKBElaYLPKgLTEdVGayYyeIyfl+RbsO324kC1/Q/u8DgndkMReE4nC3rv27iRZopHF8ne8PYT6TRu0ubbPMa+sTpQA/tLCfOWcoSVRKDyHr2oiSRSezJDnP6kSdgYad90DxXnkreFCHTkRXRkcK+OQMBLJ/8+JNt5mtHfC29lKKaP2xX9biDqhpibvS46zGgkXam9T1Mk6Wdcbj8zqSNWzujr0DNYUHrth7dJ5O3meKeuNS6rJCRROkIB9XyllRKxo9SXpAujjWsqO0z7PiULHyPNEnofeKXUlG+p/BGCYXaGYmNX+xlha8oTm/g1ceM/G3uYQiTXIU1oILiuQccf8bliNBQBJ6xmCnoayr+VwoDLqIxpZyfVT3teovBrfAd04ImUPWZfLs8qy4bxfSaIyF/XkdgqT+2S6RJZvPM8S8kRx4ThuGWPukTK0SZKHNgMmD99JyxKj8yvndUJEIG9/t/FCkXlS+HdJtmjgj3ttv18G6rb8/FbgzL0UtgEXO08Cd8liAZOkAF4Ai7MZrs6fOs8TwjnMCz8Dy43y1B4kqz39666Wf+mBElLQ5O80FB3qtHmmdHkVmnWdiBObUIg6ywu4RLKf2u0PAPgYGHz6JT49+W2c4A2+gf+JE7zGN2ByojzHS3zy7ndNeMH/hVHu3sJZxfmASh87MBLrvDjOPTXSeKJMq6XNNoxMkSRKgtK5v+YbXNOARP9ddiJNeZTKJh/4+ADHhQhNEKkGK2KONVamqZ0o1l8PmkJUQF9yMESW0D1xT6wnLdc9BJBVFmj2FmtRGqm4qwA4t/tz9n0O3xPlYwDzFY5Pz3CSvam8TcZY4ntt8thTvLHJYhf4CNZLxSZnPnoDlzSWPFF4KA8nT6TAIycCKaRwAaAp3CeFx8KXSJGirEJ15FhuhBUSXLjCUNh6ZEsysc3cssQ9FilLPw/jcXNC6nmpmFtPgAkwzDdYpgVW6dhYlXDkKwz0LqmMk73nrEwbG2oeKUTKhzzB6HsXaOO65smiEYKh+tq4EWj3gE8s5cqW9wcgnKdAE756CBv7ijxb4snEttQyQWaFb3ILN/up8VI5t0kLqS3xLc0htD1n+2dsOw0cO+fnEySJeuPw3w0XnOfw3xOVyfoa+SKVZrDv2r6ESozoZAjgCBEiQwAg9crcyjjmlG48IaUT8JfF9BXVesgOL5ckCp2bg3uWcCMQT3xJ45kv9xKhYqzpZWZW7smnS7eiCAb+cHIOXwGVCpzW9uQ2dIz2AX9OaRrO+NwJNBsfeLuai2NTsc/bYgqElpQGTDu4GS7wZcNtHgKO8Q4zRpiMrcmbe58MV9cYcMVSkiFcNihFnVLUlflR6J2TYQasTHqlSLIO8L1TpPyfw3mlT+BIl4ktJ9nbjpF5CWTrFcbZCsnE5T8x4Tumv1LekzFcQlrqx+QZyz1Y6LclEiRJiXKSYJmPsVkNsV5tcJWOgCIF0iOfQKHxV5ZRX8rFMW281/odJ2Z4vysAlzelozGG9z/Z77Rxf27LTlE3CFLZqVlBcnZ8blfUcXlPaLVIMg5S0lieE4X2eTjPDIvK8yTnsiy1Tdl2pWeKJE80mY2OQTmeKt/bnilBLsTQAbcmUaTbo1wCzlkrS3tvbnJxQnSKTbIA5sDb+cw08DnMA6eXXrBtDitk8MSTXNgFwsREF+s1P5/2Wzo/IaRQQ6nTdqzp7cv75B4oA9eJqJPQ52Nb/gmAFyt8dPI5vhef4xRvqhCDj/A5PsLnOMFrDL4DM6C8glPu3tlLk5sfDbLkwpcDmABXE9iIuHEVvkMr8lAyWc65S1QiCglb+cZ6pNiBj/4+7yhygON1mj7aRMGFhepVcM+nG/EDjjbSpAnaNThpUMC1ae5xpfX2R2AeRg7YrO1+/9L2OxAnnEDRSBTJ1POJZ27LPzZl+cdvMTs+xyleVzlOTMjOEh/hO5hhgRO8rsiT53iJEZZ4+vmV6Ufv4MLjyPuEe6Jw8oRvtaGBJhnqi7Jdt+CmQz23zLHfrrLKbX1dCS9gLraaN6IXtmf/kFQ4aP5w3mpptXIPAJOBv5jU/yvtr9gW7BjgclQU7EMkizd+U6gEBx90pEea/L38HYcmPMk5JESYtIwxUtmhbZ/+0Ec5v2i+nUPAGCvcIAOwQZkk1TK1yEw4mVktboP1yqjYZj6yVk2ac+iZyTbI2yE1nXPlGNXnCjT9RvZ7OsYVV0mUzFFXZmkrFV7+zmmfrgWx3yjGsHCLgOdIUm19BRkwOfKAeqg49wyRBK/MsBYKy5FeJ7wcAPMIcKQMwVs50BIoZAQyv9HkGEeymFB2M06WsO0rT7BeAWW+McG3lLsBcO9cyjn8XazEMa3dFcpWk3maSJSm8SSkzMkxQ5IoNXL2ChSKlaRFRWLy9gAA19ny4EmUzNJuPqHnQneyNVySTRpbz1H3RAmRKJJo4b8D9HAgmXQWcCJkKKRHJpbN7XmIRJFjWAYno6fumkcFkE+Acb5Ekpj8J9wDjJY4Xlu9wemTxlDD0wKQtCBT5CMBkokhY5ewIZu0go82Rssy2Q81HUL2V63f5fCfi5wXtD7I+yrvf6F+x/vYnG2nECSKyRF1fHqGUea8pPkKPORVPcIl5viiOja0hAl5mlASWVqlZ7ZeGE9r3gZ5G6Z2Te2XvFOAelvUEsfycl63CR3l4aptr9oq9j81AOCaNdYu0MJ7KIaeN/xKoU6AxXxh2ML5wLF3Vniutue2vCIRUtRXWghBE1LTQHmX83A0XbtvqI8meNMx/p/txDuH80AhhY+TJ3Ng8MmXeH7yEh/hc2/5YlICn5cv8fSlXcaYh/FQR+Ag1nnitlcT4HI6sA5eY5v7xJAn5IFCyWRJcdKUqhRlJWARrnM7QnNBg7O8XJjlgkTTh37PH7csr3XO2xAlXaHlaihQz6BP1nLZtu7jHu8a1LcBvX/2IE6kgtgk+M3hkyinqCafJx9fYJiv8dGxIUdO8AaneI0pFvhefF4tY2xIlDcVefLs3VuTTZ9WuCLvLiJP1qgnlOXCDQlFHDxmmf57aFjmbZsLxR0gQ3cAgMJyfJiksdWygzBxyXJVSu7qTt4mTklJ7bHCmxfofIklUrJ8g3dFAuQ25Ou8ujEntHDrkjzG/78kVKr5QFbUSJOQ6y0ayrT6sqyhjctTSYWoy5jXJpDl7BNSzkkoo+d7wDBLLS6ruWhDhIqVSYhUSVI3L61XGyOMr4ZAOvDnCxLAuaLLhfAcjkjhx87hWzVDwqF8l01kFydReD3+W7DvcozQiBHvXuqDTsizhLxKANQ8SzSyBIBHcmieJ1THrajjh+zQ+fgysVRHXpfuhYOH7vC8J5q3ifwdBR24EMWhvXZS5Ucx54UJHZPyTUjJ4vIOfyVS/uFti9fPxRbQ2xlQJ1F4uwu1QY1EyZU66Q2eTJcY5muvfchQLMJ1H81mT2GWOC4csSeXKibCg5MiJH9Tmyihe5jIMi5HrMXvZX2gG4nCt0SkTOAIlBzOyJqz+nTeNTvOpr1RdoVhemU8R6zcR/ohlz94clky7qxtugDej4l0cTJFiYwS2hYJlmBkeHrkxmROhvA+w49RP6O5jx+T47eUL2S/lmO81EdoPzSHdyFRTtn3OUAr72T5BvPsrArZ0bxOKJSH8p3QsZE1kROhkqDErFwgKUpDoLyD7xXFyRSe/wRwcq9GmlCZ9oxCnighyLYonzMv6xE52ItEIXCLISdV+MREk18qBkL6Pa0XPrJZfAEzGa5PhricjvFu9cIXLs7gL3+8gs2N8hRGmeR+xXxNbu3vdgkLgDjeF9qjDZ2riZBpiInjExN1FCJOiEQ5BfBHbjCYL/CNk9/CM7zEV/EZvoH/iVO8rpLIfv3d/zEeKC8BfAa31CpnvNk68NWAOQVwbD5nx1NsMMQZ5rVwnku7rLEjU/x8KLRGT2atNSRspWmJokic62vBc2Wg/oo1oYD2afALfefbQvmuXU9DqGNq9ykRJG2k9Zqy6XM0eVEdGmbwlm2T0AT+nB3jSmITeaIphkRGViSKC92h5LEjLPEcr3CCN9UyxhnW+F5LopzijSNPqD8RiUJ9q4QLkyMyhSYcQBdu6L9S/DH9T7kP+NaihG1ZOeUvMpdxggkJIxR+w+E8UMhLJa3ocB6+SWIwXy4ZIFUis6NB5pEuJqmsWcONhwSR12KS2GVDT40wdJ7OgDx373bFtvRM6LkUog7f54JSFd7DiWu5/OcV/BA6oJ1Y7xiGI6uEiBKtrIk4kQJYW7/gAhkXzKZXGEwvMZsvcPPlAl+E/8FBYIZz3NgQSLM6lBPUAUf0l0mKzcSGb+SGRCmLBOtVZgiVqV0N4pwloiVZhSuuRLCc2bKpqNtEovD3LZVW/p64wjqHTpJ57YCF26RlLeeI29cnrlTUpxAcwPcs4SveSBJDEiU84SZfMUf+VnqW0JKa5nf1EB8pm0pPasD3PuHJY0lpI0KF1/VDFxOUuGTjpFPw6HdJViBJS2S59XKyz/B6ZdvRioUsksJGbYe3EWpbmvK2Uo5p8g3YfpMC12Xs0EiUajxxy0unaYnR5FJ9n7yt0HspH4Hb2wwXmJUbfbUd2krFc8WOaQQJ/x0pqoAL2dEIFhn+A1Tyxk0BFCWQJsCRFCXpO+kCgDOkEqFCSijNr1N7/BiOlCHPFNueBwAGGTBLFxhmrj+7pcMvPTkAMCE+AKwOWVSyA/V/CqGjpPgbDJFkJcrMjN9ViM9qaAShfODLAby/5P79VmVgW9k3Ab0fcv0D8PufhBzvu5IoJMumYCTKFfL5AuPpJeaJI0pcvr7zWjjPyK4gSaE+lGCWCJQxLl3oDhEnPIembG/Uhul/87Ysw8nkd77VPFG4YVFy+5znbvIBIdm4R8L8XtqWIT66e6JoYT0E0yGyKkkNWSXHuAQy4N10BZznfjgPH7TpU9DOADq1xxHyQpH7vK77Nz60a3QhTrp4sEhhW5An9Je5kDtnW/qcmk9++kW1jPEHNl8DJZClj7rEqmQMAZ+ZzvyPSyvk0gxR/hMXwuO8UJqQKBauMk3s8oD2ecnwntAHgX1eJuuEQApXW502aHWkANN4Hb56kFTItiX+9gmsvctiwLdytAl6QLOVPdSPclQEyizzk8fyRLG04s7YMvZTLDBfsz4l853QdykMSQ8UPslqnift3aj+3AiB35aiIhEZ8juN54U4xsvouyNhjLdKak1pJgSoqIgbwBeciWynJHKozjLEKFtik2YY5BtLruZ1oWZqt1xR5QIPJ1f4hwQeAPXwOpkAXHZa/jsOXqeDhwltuxAnfF/2AaCbBblJEeKK+NzEUI+nl5glC1wn5wdPojyxKjIRhnXlOGHt03pTJYZUKVND+LtQH+tRsGIeBbyp8O9TpSxl37clUeQ7lO+f6lRlLoHnE+4tIggT6R1q6tTJFkmecKLCT+Ba2DKf3AgRH9xAJ0N16DutBiHryFAf8wh1+dS86001/piykh0jDO2KICwMGb5RkUhk8sCuPFFoHEzMOOjy8KS4pmea2zmQK2wETbmgdqMpZ5wsXonfyHMC3efWLiRK1Q6vqpCd8fSyWjUpRJzJ5L5HHfWOfcYTFD6BQkSGnIPWrFwm3eQEiCwvxO+oXigRLZM3KG/vVQEUhRGzB+KR05DgkSvS86SA76VC36kN8ntJ/bLh6hrABpvMGWrIwMKXM6Z8KdT/aJESgLxU3DLIHBSKN0zWXp8yIT5AFVYn+1Wor8gt9b9UHOPjuRzztfNqc31fEoWX5TcVgTJOlhUJYtZKdckXaBnjxBJRlGSWJ4/NrJZHyxZT0ljPg2qFOlFXsjL6r7KNa54okjjRCBVZJp+lnEflc+bfS9QX0G1ALxKFTyrmui4mVIbuyAlMxrmSiyMtbznEBpcYoUSCJcYov5LiLJ/jKn3q2Mxz+DHtKYDXY/jKZMWuKH+vyeukrxdKV6+SLsRMBwulnKhS1JNeTlGF7uATAJ8YBfAbmfU2we9UuU/+EH4LH+AM33j7f8wqPN+C8UL50m65Uicn0Akq7xOcAFfPTDLZM8yxxLjyRLnEGAvMsEbmJZWVXihckKlW7EiGKBP7LDJgsx5WXikbWrmHu7/SM5ICKeDaDG87HE2DnTwuf3PXCF2ngBXUB3VlKoHLYXOwOPLbXZNQF1IaNWWRTzxz1ImTU7Ofv3iLLN/gJHtjSZJzL3kseZtwT5Qxhe5QH3oF54GyRj3HUIF68lhtQuCutfxZkHUkQf35kGUoZ/vkPWafyVUGlClq/VGDW3UnrY310gOFXNgB55JL+9zrhJa8NwKSEZrIYlQiwcKm4KOlCy8xxsj+JsMG62SI4ckamzLDcjrCajozubJy+C635/DL+DHaL9gxbjVaAX7i2R0QlG3ESNOxpr5AZZqwRXWmth5XcLR5pRLITOz0dL7AMN9gliyqhernOEOBJT67xaPYB8xwjhTXNfKE9wkeY09eCQBQJAk2SYYyS7A+zlCWCTaroZmnVplLRHsO057mcF4nVHbOyqQlUwrvTSQKvbvQeFeRJ+adPqlCKIpauA3gyBAOGcbHZTraNoXpkIKskSLacsOhlXRCvyNyZswWLmgKF6qTJ/DecyWPwoQZlEixxKi6nvzvbpw0V+MkAN0DVw7XyJAlJhfPMMuqHDyeh1ORwCTThpnzq3EJ9X36Tu0nVA+oty3/xbqtNs+GiDlvXDG5TvLpslo5aZwslXcnQ7h0EqXos+7onmL2bo0JeWFQPggg7G0C1GUEOsaV0xXq3ilNnigXhjS5KoDLFVCURm/0Mu2tdQ1lACBNgEFqiJYxkSEZ3FxLZQV8TxQ6IT0DPtZl5JFyjeT4HYYTl1iW+hItZ2zKXF6UzOqQpE9yD1aSKei35PG6TjKUkwTryRCbMnPj9jRzfY762xR+3yoatvw/yb4Z6n+yH2pyQBN5OWdbGvvnLu/JMNuwvCfG2ySByWMyZgZBE7Lj8p+MscSU5UuhOuP10iVA5h4oBXwjYZMHFP1vqhsiUULkiSRRuO7Xpp+F+Nj7TixLqyk0QU5cocmLBGdywxxjic10iHfTGTBlbLz8AHBWeXK5bpsZtHAeeRzKfggyL0WIvJHHeuR5CHWcOfykQXNUHigfvniDeXJWKXonNhHmCd5UyxofvYMJLaDElufwYzAlKcEVMvJAyZ9ggwzLiijJKr6yYO9WWrk5eNug0B7veMoEWnJ7Te3N5UfuPkkRalK2Q8pHG2hS4PXvmkwJ3ZcUrPnn+o7v6T6Qwe+m8n3lqP9vjUSRwt1U7NfYexOnPTs22ceJQCHPrYy5OZIXFy1jnJVrDGglK+7ZxZcxfoewwBOaSELPp4C+jDF/ZtRtONHSE1zw52E+0islfKtrcO8UUkDNdz9ZHIFI9iQwbgytIlUiNV4BeYINJWmcDlxfpQ9vG7SVwgx/3jSGhI53Rarsd9l23W/6aEKXnENC1qwcwNSEgWb5GtOJE6K4YHX1CBSbATZ4Areqg8sDRG2drzZFey6MjecNKpMEyaREWSZIbSJDQ/jn/twh5w96LySkU/uUczDYPn+HmjIrCZbA6ida+A28y9W9iLXvd0GecM+SOoni5z+hWH0tJ4r0dOH3zsky7jHrQnncArTc6k2hBRnWVsbRZVyXWNbPCWXuxRAptF8kxlJOZBaFMpfScERtRY5Z1FZS5ZgcE4HwmKaNM3zsbCVRTMgfJYpN0hLjZKm+s0Q8N05O8bZ21MfHfk8xMMvf1b1LaN7XFO5C1OdEi9xqVn9JtpSGQLlcWRKldJqTXK5AE3OvYDxUihKVZ8ooE9oLtTWeJ4XaIPdE4ftsm62BMt2gzFLbf9JqDDGGmA1KRmRS/6TjmgcrHSOZIoELF0qTEsiBhK3G5i2LLB8KJ7nlfy7YvuxzIRIFrEyO9aH+xz81WdbM3UlaYJSZ5YYpU+WILUtMpMrIlpt6l5VHyogtE1ItXbzeYHxx7efvIeJoDb198vbHc/xxTxT+TErxnT+fUCgP0E12bjq+hV7XS6zeYIjUqrhZ9a+5qyO5ajvwSVQyzvTyyIJABMrSeqSMsyWST0q8XX3FNIwz+N4EpDCfA1g9tX9nAddyNZ+cpgSyTTklJJnStjpPT7KE/yzUYTh5ksOtvjOHyX8yBfAp8OT0Ah89/9yuvHOGb+C3MMUC38D/xHOYxLKfrL+NyZtr44HyDsZa/ga+Bwq/J7t8MY5hBsYTs3/1IfA6O8E5ZjjDHGsMA7lQMk8YoQFPWmooJwopUNUtJCXWSQZkxm24LBKs7fY6HaLyyOCPWQ5GkhBK0W2wQ8N+l+99oTU/rWzFjvF2U8J4PRwyJjC+8xp5QtsQeaIJeXP4JAr1n9Ru7aQzPzmzSWNfY4zLinj8AGdq8tgTvMYH5Rme/t8rP+/JW7icQuSRQt/X8LOVl8CNtQYBRrDhGNj/nhbAUY46iCzJxD4XYHJ4xCesF8omH3gqD6AnSjSXcR4nbcQJ9Wuy6HLvFK6cbirlyuUdGGEJvlQhCVFLjDCuPFHIxXdtRILsEsPnG5RlgrN8bvIK5APnwUhKKc0ZOeoeAFSHvtPkD/hCU1v/1hRevr8NeQK4dt6HPAkp2VL4YvPLk7lJoExEIsVHJyhxgjeVIPYBzrCuHszhYoxLDFDPgULQVmeRZeRFBdgEpEmCzSTDdLJAiRSXFyMURYLV+dhZOOdw7U1rd6G2xtuDRoR579l5nYyndjnVfFMldpWr4Jj9cAMPESrcA8Vf6aa+lLAkQFwek8taGSUtHGFZyZEus5pY4cTKkJ68WZZV+ERi/1Za2jwUcKuTWd0Jm/wJyjSpyIw1htW4Z86b4ZI9J3rvI0YmUhsZw/fc1hLS0vLIRNgAwCaxyuKEeT6tzbH1auiW2zYJrYz3nZRb2izhXUgUbe4NkrN1rxNKEMu9g/j7N21lXWt70ruJcPUY1lO/gDFyyaSumpIZ8kQpUVdOeXLOC1HGfnd1YeSLxQWwtNXpqdIyBUCdRAklGBiUwNMLQ8iM1sCYksyW8A03mSjjxh9umLXbIwCT4hrD7Bw4dmMzz4lCXqx83Fhb2cAtf3zpeaNww09piZaN7X9D6xG2zoZVn6Nl7jcrxTtFkxFC/bCp7zWN8b1JFBNum+UbzDLfs4SSxNaXLN4wTxQ315MnCuVFyco1Zu+uDHnCvZxk0lgKW9dIPh7OA1bGiRX+jJo8U3hdImPaSBD5zGV9etY9Igd7kSi0Oo+5pmuIEk5Y9idYzj7TOcxNuOzpRuB2S2YWSYLFixmu8hnw2jLwZ3DJk0hY+H3ALYlK0KxkbaRJWy4UrbxtyAE6ESchQZg+czjlT5IonwCYAsef/j7mmVlt5xN8G3Oc4RN8uwo5eI6X+Kj8HJNvXTvyhLxRaIlVatCkkHGljJMpT00y2XOWRJZIFCJQyBXWWXXqz9NNmi5rOQkUJJhR/hwAVgG07bBIbCx6imrtdyJE6NXwMmm9gVK3aZDrQqZoHbhJ8ZKPpEnxon1SCGnL28ttSZx9wBR1EkXrI0A3pbGaZFAL43lyeoHZfIFRtsSpVRKf4VW1Ao/xOPmiWrL4q/isIlFO354bb67/C0eiXMCQKBQWR8sZv0Ul2NxYAenSNumisLHIbPAm56vCvvtRzkaWECFIhEkuvivPr0yf2JVHeOy/H9JA4FZ6ggztoXruOM0RjlDh5yKLPi8jckQmlqVj5O3GrUg0dyQosEky4BTYrIZ+0ll6ZoCziOVsn48RRLDw8YL6VVP/p3cR+n4bEkX7AGFSUSNMNCW7IhWd5Xh+fFZZrpyL7xkSFDjFGy/Z3IoZUw4VOZYVYW8EawfTH5wF3CU6TFlfSSphnrxqZbjaeLJEiQTL6aVNaJjh6nwELxGtJFEAv83JtiTfufeeXQJP8gTQVj3RlNY2gpSTLBoJ0yW3CZEhnCDhW6pPJApZTsnwJpXzBCWy0sXn13JNgH1nOLLPcmCVu0F2DaTXuMqusMk3GGaW2LBKGf1Ht5LTxm6HnqXcedDxcnpW9eTb3KOPQhu9lYHsfciQn8pThZMqlJQ2RJ60ESj2mQTHmByQIWHj6bJqY9yLiNqDtkKS+a9rr701kXnJYyBRNjAkCil+sp1qeUw0guVc/J6TJdIzwMobRem8T5Yw5uYr+CQKPXEiU5r89Ee27ArAqASKCyPHVHIKTz7LxzCaS6mNSVmWyTaDwix/XKYmD5Ubf93KPdQ3L70xyPVLZ7otvN+asT5zJEq1tSRKNkSRWRLTJhL3VmVbDdz7aeprWr/rQmLSVtMJaTsF+CpXlPOEe5nQqjs0n5MXNS1LbFbY8ZPH1kiUdyszrr6BI0x4GBk3vpMHNhD2SOH/e9twHl4ubTlNU5g2p8rvPRY47UWi+C5TbkVuyaw792yfYOF2HDOh+oQLz6bMY+HOTs6wSAusTj80d/wazoozhx9jXBEpUpsO/e2uYThNCJ2jA3EC6EoOF3ql8kckygv7mQP4xDCQH2VuyWK+fPEMC3yEz3GC12YZYwrh4YkviZiiR5awe+EEygTAU2B1bLyGDGlCSxrz9EMZI09cG2mydJMjbT2kZ10JHeT2yt3vNoCzPtNgzLFCfUCH+N6XRAkd08q69LQQYUJbvs/vU5IHbFXKgwW1N02Qo23T5EL7czT0oys8yTeYn55hliwqYsTEjDpPlFMbDneK1xjZshnODYFCeU7I24TCefgyxjTx2H5GrrSAI054PHI1mrDmP5DtRxInnDDhxCf1Xf5JjQW2TP1+2BRup/VZGe5QP+6TIG5ecHMGjfNu2fvUejy6OSQBW50HJYb2OGXiX2JcjRtrZCa8Z2LOv0xLXKdjAGylCyJOUvjKKn3nxEqTUtuVGG3r13IriZI2EkXOG9RHJIEoyZMcoJV2snxdrZRByxlKEkVb9vDyEbjYp3ChHk1Jz3k75/MYz58BoDIWcEKl8rhKNthMhlhPMmymS7Pc5vnYKMDneXtOFHPDBnLcs8K0WfFlbZTabFMZHzRvYKmwSk8TDVLppW3Tijua14gkRYg84d4m1fKZWFSECV+BJy1LnziRK5qE3ML5s+RjqJ1zBgWQltdIihU2k4xJu2l1/+Z/j0GeFZRWlj/TIUz74LIuT1bL246jlX2Shb+fBKVNapw4Bc96qXBS5WplqUDKpVIcheUbDepYY7xNKDkseZwkiQu70kJ1SKElrzag7q0kiTmN0EsfgddbJYdy4oPKAZ9cKcR3HvpQsmNUj5MvnJBZWxmjMEYbIk5oy0kUwIX20D7gp1TnzaYmMdj/U63ss2KVUvZfqC7PCcO3F+5344trFMk1imPKhbKs9EySFaTh3hGTvIe5iAnzP5Kq7fkhm85oQzpMkhVAhmpVnyJfY2P7VrWaluxjTcRJqA9KGYE/O0++NaGZZPgYTy8xTNZeglhabYfmcJ73ZIYFyNNPeqLQOEzfxxcrs8okJ0p4ElkqW7FyHn4mx2N6x9S1JbHCSZIm4oQ/S4UgD45vpbIvz39XJEqJtHJvpLh2aqRmn5orH/jKaistD7zBk7sVCcI0cY5th1kcz/A7n2a4fj1xL2EKR6AQO3sGYEVEypXYcnT1OOnyNG/a6zUJxU1WxDmc8kfkycds+7FZtuqrx8Y6/jXrffJVfFYL5/m+V981yt4rOMv5/4XrHHyO4uTJU1ThO3hmtl8+H+CLZI5XeI7XOMGlTSi7wRBfYI5N5ZSbVe9XI9Xkd6cobcQxP2N+idRLyHaZlijyNVaYmQEtPXKWvSZliQutfUkUud+HfOFoU6iAuis/Pye1FWonh28cNu1NIxe79BmNRJnDeZ/MVxjkG8xPzkBJtmgSoXAFvowx5T6hxLJfefXWESdEnlB/4qE7lAvFhsl9+Q74cu1bfwiaYDKCHaVsN0kLNmqR4M+JEk52TuGWHuTHc+BmAqwzmMRqcB5iPFzBXLY9rAcIu//LegQeCiGXk6VxooRv2V+L8WSBGUokFXG7tEmsNxhijCXWGGI0WeJyMsbydIzF2cwoFq9z87CJiF/BLTV7Bj+8h+aUtvEhBI004ftyTtDKmggTXsaPTVHvDx554rxOeMgOudtrliu+/CGVz3GGi9aHsP+Y4hwJY57dqjz+f5MJmPl+KMRHJqIlz8w1hthkJiHtckJlWeVCvrYCeuVhICCTwpIyK5Ou8jvRyRON/CygEaohDxTaShKlyeuEEyWkgFdCuyVKuCBP7uTD1RVyvrKZZhUlIZ+TKICusPBQR0aiYAIcTYDcLru6zobeWEbPJ7MecPSvZWJZkoWJTJMefb6KJ0kW39tJeqdUY6b1UimztFavLCnHg++5Uv2PIlFXQtSSDXNPJtm+tCWoteWo+e94u/Ffi06iPIbQQSxh2iQpfVyB5MopP6YllqVj9P1C/M5uyQPly3O7hZE/FnAECvnrt3miAG7a4aTKU/hT45KIlBQYE3FCOdzIyMNl1IKV8XwxQBXaM0iBD8oV1tkKyYT6lJEduGfqBhkowSyRLFpiWefx6mQg2udjNn0nY886GaKcmGPFsetjvH8BcAQLeYcBqFyKZY4V7QGn/pLzACryMss3GGbcC89taZ4eemOny2dGBInzQKkfq4iVi3Mz1tJnBdOAOGEix2HSJXlbBPzxmHdj3ub5M2kiUuSxAIlyExBNiFAMYZACVz0M0T1JlCe1ydUXLHSrVCIGRS3JLA2ma9b4ASN0zKyLxGy+wLsiAaa5E4Cn9iI5XCetXhIP1ynYfgg96KeuvwsRJ0BdMebKX84+U/gWdPY9ny8wPz6zyS4vvWWL+WdWLpyF/ByuY/CBuYSzyEiFlStnObBMxkwMyoSSkzKXZt/zRLdo+xOqseYQOxxW0CjuMUFhhMjCJpcsSmCV1z1PqG3k7EREqtC+VJQgjst9WW8bnaKrIsXfB79H2VZ69eo9BVcMgXDojmToZd/hz8Va3ilmlBJt8Q8Rt055XHifcbmse3DRPk0gon/dXBgL0Jdr3wLU9vfp9db84UJtg4gSUg7oWSSiHCaUR+ubGoGi9dkuFusu9aTlyK3cY/4kDwEi8WaNYZVvgCseYztuuDwDKV0E5TTBOi2wWtlEcTRH0IOmD3+2kniV40OXvh4iT2grj3edL0L9Qc4hFXFiPzbMg9zvqd2TMmu2jkDhliqyavH+8eQReKIYJdAtqRlqs34+C7lKlVOYeR0jx7jlfKn9mtUI3bxH7uTkQj6slrw1wrkGmdskRJ5Iq7/Zryuuoe+FeC7SE0XmQtGSh7rksX4Z37plNB2hVyU7pNUgSGaReaVkfD4pme5P1EHECbdGUj9nclBSXCPDBpcZpyjkcsZl9R75s6FnTXVJVxiy9kZ1+JLagO+xV4X12PbEx2p3Xd/IVCJBmdjxMUkMycJIlSYMM3+FHM27yG93dfJE8zah3/Jz15+XTvAljyFjvpxLQoqg9p1QKr+TvyldufR2LeD2udcJlcvLhUDT0hXqU9Xl2hiBqntMxL1R+Yod4545GXwFHCaPUZIAWbnGJqFQXhle6ZI4r1FPLVtWPTFl/cwfyw2ZYtqrSSuRiLHfHQPMKoFIUPWvskiq5eA5aUl9TiPFJeRS8yGinIfOuTl8XYXj0nw+rAhrt2Sx+Z07Rhocefhl3KuEkyZ8X0siy8kTrW3KtswbYKH8TiNP+HexT+RJ4Q8fFWgJ7ybcGYli4tGGcHx6WQkFKRu+aYoBUE0fgBtMuWWCexwYK6KJI55hgQVmVeLZBWYosxSLr8zwv/F1IM/9xGyASzybs/IC8JeovGN0VYRDyh8pwXO7PYUjUF7Y/Y9N8r9nz19WITtfw7cxwhKf4ndwgjf4CC6x7Pd869woef8XLuHlKzgrOW/I3Lr9od3/EMBHMIlkvxe4nA7wEs+wwAyv8BxvcIIlxpUHyhIjldXVIAU7M6muK68kKZxwpYnHPa6TzKyMcGqSzS7zsbE8n+d+nGUBZ3HmhAq1ISKU0LANHZP7gU5eg6ZccSUJYMtTok6izOGHqhz+ghnGE4WCbrsqjVPo/SgHMDfhbuPpJeaJ80AZYl2F68xxhmd4WSWPpdwotDLPh99aOa8T8kSh8J3PYdoOeaK8A66+NGE7b9bmVb2F7zJLkMGA1FS97E2pDenhbuc8TxFtcxiPMR56x0KjriYmeeI6G4LC7GRfleRKV0jlqn7cdYI6GV/v39xKRGMIeaLQcqMzLEArg11ihAKJ9UjJMMOiys20yGZYZ0Msjs+wKTOczedm+dnVkRsPaMu9UWgO4Z4onFyR/TrE1YfIlL4kSpc5RHphTY3nFa2yw0MnpAUqgR8LTaE9FM4jPVEWjyB2cIRLZFZB4+1Sel5IhTYE6T0AOCLCtWnncaW175JyFSWJl4RR3p80SFG4hJb7hMq7QvMiC+VPIWLE3IMfsqNZTKW3CbU7Ik7o+AdvVyYJLIVG8qSFtLRmiERxD80HNxTRGDqFs4JTEkwrF+UJcJNfY53x5LGp/a9u5RCjoBXesyPvE/ncqiWyUfcscdeoE9m8PpVLjyhz/sz7XUW6JPbeW4b3prYlSRRzrE6scILNLytr16F7b8rPM3gMLrbc4s6t84UoC62sI71NGpTZm5UhMy5XfhJZ2vIP4Aw8mv0QrA4llZU2RW6HSGHCltMLm2CBy8baj6kvkmzDnxFQ9c1BBqTlFXC8wCihtk1jiV0yHBtrjC/YuDr0+iklr+fjL0U9cBmoSvZMnijwQzXNbbI+nFjiMvPJdLMNG6bqhGG978jVzHgSbhprOYlSC8upQn1cjjPyRKEx10seSzkyQ2OubJPcQ5DnRAHqHim8kcl+ALGVpIlCptBy3RySKKHjGrnieV6tnTrYBb1IlGukqLPfppH4boypV8bhWHyfvaZjgL/EWYkE55hVk2yCAsenZ3h39sK8mDmcwAjUBV7AV4wlulCuQPOT4iMHxFYTiqWCHPI+4YrxHFU4z+D0S8zmC5zapJeneFMJtyd4g1O8xon9fPB25edreAPTObhFRwr+XMCYwlPILqcDLJJZtfAVt+Nvqq6cVYOPdEltUrRosOB5deqTLo9b9H9fIME4SQyhYtneK3rA5LXEvZf4IN5nK8v4xMCfpaZkaWgi20JthJ97Ct9bSeerDgss/ARA+DloCmStH11VBMosWXheJ+TSSLkeZjaUwSXgMgTKB29XbnUdnveEyt7525sL40J7WfoJ3HgGfMAM2lyeoDJAD0IEJ1LoP5OnSQ4niKTK9wQoU6BMuaDA6e9e04EHrV83Wbu54MzrSmssnY8UFHLVNXVNHS7Uk7BDrr6+oM9WvzgFFmlpcwfkfkz2OczzWsH144qQh3tZXFHrQpLK733mjNC4IMlVj1R0K2bwmGkpPHGX3gQujHaEpeqJUnlplQvclIdPonAlV5dZpELnvAjkHCaF47ongS/EA04w58I+93nwz18neXj4AydR9Htvh/xv8rs/N9eVZG4g454mZDHlllOeE4WHlY3LJUbnVzgiN3K+NLyMz+fhDjw+39y8Dxo7gfo8Sv2IK3VWWTgqYFb6SZwnCs/b5oe1+9vSPgvuaUc5UqQc0/S+S/i5Vfi7kaFGMkyyiazh9+q+11duqhMkdS8kWU8+D76qJz83lw1D80efNry3MDZC1+40TxSpJPJyTaaUx21ZURpFsihNMfdCoS3PxSaXOaYyzfzM69Bv+NRViceF8Qw4SuGUbiJJVvD7HP1wLU5GMoyd845WwDC7Mrmfsg0K8HxFrg1x422CsgrPofbPU0eQfOF70bvzpd55E9sXXZ5G88jrpLnsvxIhnShEPoY8/NrH1Q1b3viy5okyxmXl7XfEx1AeraB5nchEyBo5CNTfL4fW9hWixKvLym4KRpCwvsFJFU6caHK1LFNl7wB6Sc0bDHFjXZ0IzsU6q4QRLgCYRuS7/FLDcI2gqNxpxzYOfmQ9UoZ28qWVX84xwybLgE+Bd9MTkyH5DE45JgGSvAzIstikCKOlrA3SYqgJx9yKKJU/um9Onpyy7Rx+8tjj71Sr7ZAnyqf4bYxwiU/x28YTZc1W4PnfcPkauLLHLTZ0L3IZ4wmA5+az+hB4mTzHEiO8xgkWdlljs7Rx5uUpkJZludUmbSrXhMJQWA9nl83yZmZ/ORljPRnicj3GMh+b2PJ84izORKiQIMXbiZyQJGEiy9DwuyYiBmgm2qSyxJUkfp45HIlyin406r5iDmPC6EI4BvuRyReUpiXmk7NKKTQWzw2e4WW1nWGBD3BWrcpT5URZv8Lk1bUhS2jlHZ7/hEiUz2FCd94Y68/bC8NVXsKRKBROygfokd0O4Ge9p7IUQJoAaWqsMBW5JFfKyuG8Tp7CJz9TkwelSMySxstkXKk2gBOwNSGgCSH3frPtturHsPqdT6KYuYDmkMvq3shSP8MCBZJqCXXjdWLGHRqHXMLrEWaY2THqHBsMsUhmWJzYOWU+M8vPvv7AeKacw5EmZ/BJFBonwLZ9yVL+PUSitJHv2jghyJPpfIFhvqmy9RMJQkQJkSMkZPElDum7XAqREi7PLs6Rv4Vp3AcOE0JSD8mVgmw35bOZVOFykZSTiEQx9eoeYVIwb8pRErofeU/af9Jyf/DrSeWajpEiTcv9EmFHruXU7kjYH1nXcu6JMsQas3c2kSER1gXcONuXROGQfYrnjCrhlLUCfh+05xqurlBM1pXnNaXQB/tOMmuJ1CMuZJ4T+W7lcw8h5MGnlcn3qNUNIUSQmWN1kiSk/HGQJV27Fr+30HxxhMMPHcQaZtIjZZCTfSEyRZbLLf0eftmVVS5DYTuaaCo9UZoguUfAyTCXtuxybWSXEYAjIjBJyab+lsD02xzOk4xfRBIqtupNfo0yXRhvPTiihLenDYYVYb1GfYlj7pEyrAwwbiEMt1SyP1b7tEZ9vGwbY0NoIqjlilY8dLOenJsMJoKcZnN9lUPFrmo2oGf/zj53HqKujbWF2F+xrfQ40Yg+NBxr8j5h5AngyMKupElTG6fmdmckyhpDXGPoDYQUL8abGOA8UmifI2GNgjNo5k8kVkg2jZ4aBReM18iQZCVeft8Sv3/+/Y5EeQ1/4nsNXUEG/Kcon+g2RArgx6zLCVsTfKdsS0LwHD6J8sLun67w4iufY4zLaolVlzz2i2oZ46/jdzB/d47BZ3BhB5/BJbskAYQUbT5gkUDBlbNjAB8CF8+eYJEZ0sSQJx9ggWn1fY1h5WLPByH3SP24ag5OoFD8sHRLpuMaqPWNrFJlBkQj0IyzS4yyJTZl5izPee7c9YmQ4EqSJES6kildfqchZGluItr4uU5ReaE8mV/gyeWXWzfhvQGFpGiWd9rKZzRHpUTSiiPTibGyU4gCKYYjLKvlWk+rZYzP8BwvMbJhPLOLc+Sfw3lwUT8iMuUN3LLFr4xL4dt3hiz5EnXyhLvO8oFXhvNcseMDmDCeESdQJsqHkyhTcSwFlpMnKNPEI1Cot9E+UFfUOEJEpjveLHjz/ivHAU351FzTqfzSrsK2tASLyZ21wBpZ5ZY7wrIiWRY27IeSz3pLsk9M+M/CLj+7PB+7UB/qazRehMjWtj5u/nT9e18SRY4TNIfYbP1P8k2V72SWLao51heeXBJP8rwi8oSUWeorU+ulcoLXSFDidP0Gk3fXrv0/AsLWKPlNHih+yAGgEwnyd+67axTScklb6oecRAFc2AfhNh5jIYOGVAKGXn2Z4D1sKaU5m5Mn3LU8hSNRKvdxsCU1L965RIYkwL+EkVFomXhuACJhXyNRSMnkYgj1GVLmaP6XlvECzgBHSh+AbA2U6QabbIMNaDUeTqb4z9kYdMzbdWOYa0e38fwLgedW2VaRM+dpJka0Y/y3fEvX5/MDh5aAXCMFj3qpNnuKFcw6FJqyKAkUTbaEqKdZ+1mZDF3gXidyFR7NE6UJJKsM4IcEjcR1isKUj7luRv2KG5kLmL7ZRKLQDSfA0RqY4Qrr7AqYGC8/yjdF46bz8jP0JrU1bnglI43mBTiy/0ySJto4bm5PIzWb+3mIDJfzD40xnDzptuqZn1+KEnUnKAxhTR4mPGSHkygaYU1tjxMm0jsF7F3Sdx754B6af05ZDr9c5jwhstArY6eR7TnUxqmpicu1otcofl26gZ8WjzIX91l2P+2WnwDQ/JbXKkGJbQBnmdxggxEMQWMyLpu8KGQNMwp7gjcvTnGVz4DfP3ITIFeKaZ8fkwOS7KAp6i+6DSHBVyNTQmEIUzjlcM628ytM5wubPNaFGJjlV1/bfZvX4d05BmQh52EH3IojG2sCfwlUTqI8NdtF5tJrGhvSyCooPC2RizHUQG2na7JJ2peTr5xg+cBGg6T0dNokG2AObFZDI/enmVGSgHpboPahWZpDE5y7OV8Y4+cM9bZtSBR+33NbNl1hNl/gyfAcXwQudTAgMqDpudQ8Udyyb+PpEsPMJdLiSiO3wpv+5EJ5qrJ354aZp5Ad2Y/ewbAjtmx5YWKPyfuEh+7wOGRtQAd0UiW1+2lq3WFT+K6t5JHCiRXRj29yoEhMCA+txiO9Ojhh0UR8h1Yt0RASwukYrxc6J80nXIEtlX5OpOklRlUOrTXMsqQ8PnpjvSX5cslkVV4jQ5KU2EyGGOYbLFKTW+k6nfj9eQWfcAX6ESmATpwQJBHP55CgN+MNkK+rvCe0VHGVlJNZ/aXbL4WuaUsbjuyxDC45XUWgUF94BJ4oA2yQKHNWs/KoWwy1Ovxcmns3t3rKUDWyhPLfhOZXV6d+Db4vQzsSdh+8r9VD7PTnQc8gFfs8Vt8tdUzu506wH2KDrFwbAoVbPrnXyblSxgV76W5OfZP3LT6GluwYyT9ckVvBjKd8Dq+qOxd/Q1rQsxoyabeErzwZ7xRuJe8jB3U9rrUz830bEqW9XfNrarlNutwzldM9hq/TR7U5AIRkSC6fy7+8xSPYNfXUJMrS9TxypjAyTBXWI+Vs6m/kEcbnUyJbqB5YWWlCezIYcpMPlQmKSj7YsH4JcBknrY6b32hRFH6YDl9ltCuJ0oYQARkiUcgTxR9r9YTdZDCReajG6yWS4toRKNw4JPPscA+TQtSR70sjByHKNGjESpd6/FCAMOx7Wh7i1gW9SJTleY58PESSJiiTFMQuA35iKcBZD0lglUQKAM/taHZxjqQA0tLEnwLATQosJ28wz86wxBhjXGJhfVRmMDlBcAIsTmb4P/gG8PrIERFncJPiGXwrIhBWkPm2LzRlOKT8ccV4yj5zu31htoMXX+Lk5DU+wBk+wbcxxhKf2GWMP8G3K0+UT9e/YwTc/wW39CpZzCnpJWcI6X55MqdjmBAeu4wxPgLwIfD7z47xOT7CAjN8jo+wwRAv8RxLu6wxWXeX1jpM3ZsPBKFJXCpUqRU+XB4DudpA3drNhUPe7viKQeTCv54Yy/Pleoz1aojVfAysMuD8yA0U2qChfRAoz5U6UPbpHUglSWsrc/hthc6VAk9eXGA8XWI6MX2iXD8CEuUEwAcI96Mp33fhC0laYpS5uE8iTE7xulIKydJuvE4u8dyG88xxho/Kz41r42fwlzF+A7ccOIXzWJJyeQH83oUhSSh57BJ18oS+czmCQnc4qHwM44Eypr7JQ+0oZOcEJvHzxO6zMLybCbA4HqBIjOcGeXAQgUKxvzIJId/n/bdJiA15mXC7DQkBCQqkpRUQCtpeI7F9Iy0BnsC+TGn7xG4NIQTA+w+0T//13HotLhnhazwaU5xhXiXApvGrSoydjHB5Yvw3FqczlEWCxdnMZNU/z924wIn6JiJFY8rouywLkSheHzBkIeU7ocz9w8Rl5XfeJi6fCbX/yo1XIRWH1jOAyBQKsfjg7cokm2OJk/EOjyKJtVlbzuV2kUqcFoagkwc+sVInG3TBgnudaGSm7JdckKey0O9CHl1aSJH8PT+/fC7NuVDM8yLiLrSMcZV3h6yh5G3CyGlaHh6vUF9VkCymSsz+TWGE6jRhBDT9JfJw5EQJKWakiNE5E1QW86MVMEyusc6KyuuG5x6hFT8kiWVC3Z1XTxuh0UTCNXl8bEsyhNplk7cgl7vaQO2Ln6/JYh/6r8lj8ETZwHiiSAu9poOUyj5EvdD3HSKUBJ+6DXnOFvA9UKhOlZslBdIVcER9jM9/lhRBAv+/8MSyJdQcjkdrYFJcY5itUKYrbPIB1gkZTHgC76wKtRtj6Y2ltA/4Y64cG/mxbQgTQpvnljZ/aEnDNRKF6vKVe6hsfLFCUgADco/WvPj4lifsDpEn0hOF5COwLa8fasuh4UsjZCxopZ3QSjzyEkA4nIfabC9SpG/9skz9pdG4oFu9VqMG0z4dc/X8S1a+K0SgXLhjRxfAGCbuLUmcKygJdxtkmOMMADCYL3BVPDXK5pk9wTnqS9oCvqK7SzQJvpoyqFnVObGS32A8XVbWwjGWmIolV+kzeXftW8vP4ZKxcQKFGhtndfk98VU+JsBVBqtsDHFpt0urkNB3nrBpFwOMuT1u7akPGlJoNVt/KTLOKTuLkbUQZaiWENukJa4xdp4p5g+4rbREkwDWBM60075Wh7ZNpJtsI9Se7bmH1vpMTPNR6wK6BwD5/4Nk5A2e5BskTJF0sZ/OpZEvo0mfERPk6TNcXRmBnls7z+G7NdJnZUJ4Lle+5wkN0nwlni5DzQB+bhTKhVIpAER6ci+UhD0TXicB1hlQJIZgKFgPIsFBSzYY6r8kNPNtE3x7jhAOGIGSFGZVlMwKS0SgDxiRbrspClu3TK+RpCXKNEGSGPd1ZxV2ViZ+HwAqEocEqwTGkdd4sqwtGcOX4CzNc0yGwByGdAVQ82KTZCvgv3CNONW2tM8/gMiF4sJ2krRERm0/Ibde533nFp9fVwotCVmk3LqP81Jx3is20/96YwgU3gdo/xGQKAmua+2Zzy8yv4MkEbibNRBOstnWZ7iXSVO/5MvjSu8SZzF1ruk8STPgyJTmZ+L6Ev/fdExLOsolQDn3khDv3M+txXS98a2h3Coqv0vXcrkqxNqsSFKUTrhOU2BQGs6xIlNIMSvZh8/TXNhXBPi0LAFFvOFeKdr4w5+jRkDw51tdqybj6KScloOE129DyOuI30MXhMJB+X3zeURDyGPx0SxxPGD7GnbocJMmpv1LiZCH4Hj17TZEnGyLq0IYjLhSTcRJKso1YonqrsWx1N1nUpj5zSyDbJY83iBDZvdL1vYo0TNvr1Iucsmc/SXE/bBHh1CfC4Wy8d/Ut3IFLJ9cacqTMoRL8J2gQLbeIFtbGUuSH1KGkV4onDjR3pEEL99Vew7pUXf7007n7ozifITlKEeSlkjSApgCSaKz4CQMkHeCFOTpNwlKM5ESM8atCwVwlANPJ1d4mr3D7A8ssMzGoKUWP8dHSFDiDHMkJyXenJzgd6dfNTkvzmCEznOY3ChEqJD1MORlAPhPu+uTbyJLAH/5VfrM4bwMpnAr8EyB/OO3GE8v8Tx5iRO8wQleV3lPnCfKt/C18tt4+uYK+P9ghIn/Bd+CTpYd/v9I6eKEyTM4LxS7nPHbr+aV98krPMcCM+uBMsIbnLrlQ22+Gs7kmq0vbIZgLDgFaxeO8EjYgMCVYUms8HMBLpacO7edY4YCCc7wAS4xwjrJsDieoTxOsDiduXwIq6FJWHxuT6p5pmgeTZp7f5vFoI04oXYzt9/nJrkwAJRFiiQt8PzYJEOlsK6rx6DZnMB4W4RIFLZsK4UvuNADF75ACTHJE2WOL2xY3GWV/+Q5XpmcKO/OMfgOzHv/HH4/omWMySp6AXz5DvhybboXeaB8CX8lHrLOcCsNUBdMKInsiH2eToHBBFVIXeUpRsuO03fyRHlujl19aJYxXmZjLDAFzzhPfYHnLtLi83kfBtxkzgVgHlTghOP6gOkJBGWJ4co8iaSAUaAAXyhip+DpPgd8m16bT3IFpIZsWWfGW2Wdmf9HniaUA8UlojXfyVOFxi+TINsco3pn+MD83i6RvDk2yylv1masKIvEjBdFChRH9XGB/pOGVhLFekfk5uEM8g2StKg8T5KkZMSHG+l4wk6ZlZ8bIjKsK1Ke8gYlKDHHF6bu2pLzaziPgJfwQ9qY0eNQYeYX7oni5izuZUF1acstf7wOkVKeC3a59khDMhpxmLA7s8+9rswx501CS5NLDxbZzyVpyvMDUL/XvFPoO1+FqP7f9WfEhfYx80RxCWUvq7DK8cXKhPBQ7P2XMGMv90QhGeYtnCcKX3qzMCuhFaUNp7TjSLVk5dqOrYnJLZWm1rOPW7gBlxeF8jTIOdxawtPSKGlpUoL8q03uEyKw1vb5hb02QkpXSIHyDUZ18o6O0fOX53Xf9YFI8xBpkteaEmlKyz1Qz+sj61Fdfz6py43FnalB9whO2hHaLPGpckxqhXwOsY91kLolib25U5TRT9umKQ6SV+R5BoH6gMtbQQYSjwyR/ZFfnAxEVF968dCzyMx/HkyucZNeI5mcsTDmSyuzuNXR+HgJ1HOvmVvUPfRCBKBGxrt9/wnr+U/q/ZzKm5av98nr0ptvKuJEEtUl/ETcnJQu4YyHkrAGfI8SqffwR9DgRVIDeRuZP1lHacqPYDwNU/YKUnb+ovTla+5hwjnMqr7dbksW9iJRUKTGpdlisxoaQiVzq+twe4Rp736ZhqS4di9KuhJldj8DJuk1JpNzLJ69rM75BifIsMZLPAMALF+M8fb8K+afncN5nNB3wCdLQiQKYzi930jwv+RZDMVHU4y594n3uaqWYyWvG/qQskyfp2+ujIBBCS7fwc+FQs+SngH9FzJGkUXbep7whLK0+s658Hsx3ihkxx97SpkGik+sP7p2ipJbt7iFVQqwEnQvZr2eDcYwXipE9GRYe+FHSID1xLTn5fkYV2kJz4WJnh93VePHwKpzzxU5Wcou0OhlAb+N5FcY5BuMpzbZVZEgScsqbGVkPSyuH0e2x/ryrSmA3C3bmuUbm/fELddGSiT3RDGJDcmb6xJTsYzxDAuM10uXA0Vuz1HLK7S8MJnneQ4UCuHhoTttydq4IEICSFXGPU2on/LEsbzPsnwoRKAQiSAVKZm7SFqmQwICh/RIafNOSVCaJUKr8B2rRErLBhcwmyZeTkBYl/08A4BrjPMVigTIpmusE0OiULI5ypHC86VQDpUEJZYYwSQy33jPJ0FZeeElKFFmpu+VRYL1aojNKjNzY0rLBxz1+x+0T8SJDdd5kpaV0YK8TmiZVbLoU1Z+CqGg/sATyvEF6U1fcTlR6sfMErPji2vfI0vLV9HmkXcAkFZubvkDUJtnuIdJndR3yf3I+pcU14YsAOrtHagEx4FV9A2ubdu295WaEeQqW2GTmyTRReITJyVcKBsRJf4Sno7o3FQhJgabqh7lGjLeFJwAkORJ6BnVvZ18D5TMeqBk0rNJ80ShMiJQuGfghRGmFxdGQbss3fhLj5aSXA5KI1ynhQmRPKLnTqSJpghwudDO5+Qpx983fz7mtHWPEo6QoqURU1TmezfpHk6aPMTvQRufNRlMy2lSX3rZqW30neo5dU6fD+rPy/cgDnmiXD0GEgXQ9Yw2UZhIBBnuQq+FJ2llGKQIhjpQdVpJp4mX8c7ZcqsSPFE+AF2xZkRltU/ECsnTdFMJXF4jmS/FnvsoNaF33Gt1nThDvqlK/TettcGmsMYm+UiSIfVy96dDSWTlMVfH7138eFZakt8SJ56BiuYe7llCRAl95/mkZMgOHQPq7bUQ+xqREgK9b66Xiveo1od5vzeB4SBN/DY/QJ1I8eorZX3Qj0RZHwHnOa7TG1xb61iSFiYsIvEbA02WNIm7FGO+FQUwAnXFhpFgRtYGuksiUybA9+P3MX5mhL81MhvfPsYHODMP+lPgbD7HNSaOODmDU8TYpKh6F4SEX+1JSxKliTyRITtz1DxRnpya/BYniVlKkrxQTu12hnM8x0uzksjFd411/C1czDBZzUnpk4IADUCcMJnAeKDYz9UzYHGc4w1OcYZ5dfUljGV7g6yWbwBw3kcEHi8M1AeWQhmUCqUudwumLCekOFDcX1NiSiJ5RpaNpvaywRBnmMO4SK9NPgS+ms/ZzC6NbD1TODkit4rAVZskJKjtSPJNtpXUrLozzNeYHZ9XK1nRY6NVND6wxFr5GMzDx3D9wybOfJKWGOZrpGmJ0aQ53l4u1zrHF5YCPK88UWiVnvnFO7NcKyWRJcu7TCBrLabLCyO40wo8kkChV99EnHDyhDxPZjBOJycZMMrh8p/YFbK0vCfcO4U8UBbZzMv3AXCPwGGlXJVsauaQy5rShM6JkgJJ1b/Nf/JzE2l0am2Cpwka8ONppZDF+w4d4+GItLXhTEe5fbaTKyC7wlUGXE4XVW4Y39vErNxDeVL8lXsyjHFZlZkQRud5N06W5pzZGOvjDGWZYLMaoigSlIUJfa2MDkVA8BLuCIPcKkvW4wQAhraMQnbIKsXJE1JWKZcJ9QfKf0IkibbEcS2x7HqB4erahNbyZQ5pfpYJPg8cT1AgxZPqu6aoevl82HciqcgjgayAw9WVsQBqFr8QaUhtmEO0b7K0Ir0GMuOFdWWJU7P6llutipOHPAkzCeK8jEB5Pcy+6+vmFhThnXnf0DPjxo6KNIFLKJugMO1LhuzItiWTyPJPacbhojBk9tI+xi/t/5DKYPW9NPVHYG7tPSXopLgGMvM8XPB6UT03TnyUokyDFhrVFsZMshBXrvh3LeeU/x/EPdhhSBummryjiIDnhLOcV2Qbqyc2roeIapb5q06a2SMAPSqpjHKZU76nDDXDL4UDpwUwKusiK8knI/hTbVo/VQ3c4yS155DeKSmrG4QkhkgWljdBz4GUbmtYpzm/OsZCg8gQdZNeo0iuMUyvkEwMaUKra3EyUBKBHE2eZECd8OsWkldU30OkSXUOa4CSueMIR9p8wkkoOrZiW06YrAN1qFySI1I2axtD+fvkj4+/+1BjC7AcR7DehSRiyToBIqUN3LuqC/qRKBewit4RUOS4ghlQU8v0cYOmn1i2qKbToSVWeFwvAD/edQW3Iga/01f2+mvgxVffYf7RO5STpBJ+X+I5ACBJCrx5for/U3xiEgECLtksWR3oxZzDV37BvgPtjYODkygyNGPKtrQ/t8dPbdl8hfnpGcaJW371GV7iFG/wDC/xHK8wwwLPLImSfwZHolASWbkijxQS6J6IRCHljMJ4ngGvjj/EAjO8wjN8gXlF41zaMB5SKkhQk2E8ACxx5gteJSi+MDUxi8hQiuOAH5frvFAcDUeCGLmhc6WCrk2gyXxjCZ8NMsywsLbZUeXWx5WlBWZYJ0MMT0wSqsW7KcoixdX5yJApst1oJFxX4YzeBxAgUa7wJN9gNl9UCVNndkkMEjxodRnyTioeQzjP8Q0wX1WW+Cxfm9wPmcs8zskT2vIEmhT2N8S66k8f2JCnMZZmu14iJ+KRe3TxLY1FVqF8e+FCdy6VDwko8vVLV1oeujOz26cAZhNDBFQkCoXuEJmS2+0Upt9+aFbhOTuegkhCUviJLOEu/5xYbLOuOBGXh/NQqetnZL3koXlB93HpgUKuo9zyAdT7ERcQqpNVN+pLdUS8Z8AgBwbZlbHmT1a4yoDF8VlFqNBzmmHhjW1LjAJlhmAhUpbGlXUyxGbCPH/KpMohVhYJCqGhpIJASWxbB+B5m3DDBPfII88TTi5LEoUn8aScWqkdM/jYQSEWaVm6EB6aQ2hfKrOPJCeKecbGA0iz/ktvE3PMfOdEPq14UK0yw0JOqpBaTqJIIlHrgpIkpLmb2ndqQv4G2TWQX2M2eYd1Bowmy2rOI+8UTpzInCfUy8kTBeDzcLsnihZuW60EQbl1sKkIpoHm0RQiUcQxCt9ZXPihk9wDkHAFX0m8AjAq7JKrXDHQiK2SfQBvLOKKvxsby+rZkNdrYmuFIJ8pJ05CoWLS24kUrPHFta9MAfWlRul/aPvgnlAWVhk14yt5RV0DCXCTX6FIVihTYJOb2Y08/7SV1MxtmLAd8hSWSTul8YzLdI+CRCmga2g8lEGWS29FiDLAt+jbceGodHlICmtXG8EnUehWNLsgv4ym5xJRQgSK5k3bqGBSP+MkyQqolheXNyZvRN4UkSi0tec7Si35nABJ4dorefKZn/rEX0ge0qIpOBlibqdONjcRmzy084j/T9lnNRID4rtWp41MoTp0DT4vceMWvw+CJpOZP+xDepykyrEmfSkR9QpXdgTjaUhjF60GVRQuhKy6TgdoHGVb/e6QD3CV4TotsLZKTpk5AZ3npHATtP6pXWMN/wVSp6Dwnqfme54Cz772EkOscYKPUFgF4gvMUSLFm/kCy7TE9XTiK7qa9wB1XtlJu/r6aN4nIa8UqSjnAPIb5NMlxgkl9KPlWTeeO7YJPzjH+GLlu7hKF1iNPCEvFPJEmcIPB5gAKxvGQx8Svy/tXZCo7ienVN5jADRRciuYtIRp+wB3aTNCBLfKjm2aLD7xEnjCyA38pH8AaoqmOT6uJvZyaizMgGUzKf8BH1wAvf2E2o5U+CTxZtvEk3yDYb625IFLFEn/y1mhXYLIJ4/BPJyvMciPKvKEwhj8cAS3/lIK5z4+tsvcjtiSbtSfnHs5CfTXPnkrraO0bz9Xaz95LN+X4EOHtNzQZyQ/mSVQKDxH9E9M4Yf0ZMDVxFiineO8IwZCnic8/K6t7xL5SXC0Sr3fOoVhgxBqggL/Li0fgE+qyIlQKqD0YAEXpkjzhn0hgxT4oFwhm2ywzoaVMmzu2i1/TDHIZBAgpYX26dmYSxm7/yUf2xIXcoEMKEtHqACoCBMALLeYplA5pV5aoaXCSuOA8QRYV2MGX4ie/z8aT1OULl8NjWu8H8j5sg9RvOcwgu4TT4HzvQPc8phcYOY2TBpL0hJ+aIocR7h1T5Io5sR+GyclirdpkktYm8YElUCZFzAXsnk+/FVjhp4By3mZtUuYsm26Z1cv8+277FOUxpIqiQv5kaQG+06JY2ns9VYCsfttcfC7Bu+njmBxnUMSBO53fuJIs617nFGfdkalTZX3gNrdETeayWcLhMMoOBQSpRpbydhjjaVHpbH0m2HMPPFiYuaKxOoB0gvF/e8mYsm1M/P9EZAnXcA9UKjfc4WT78v3RL/hx2yY66A0ckVaAFelqzJCPWcKxyCwT+BeJ7VQZLZP+Yg6I0SckO5GhvAcrk3TGKoJWYysGaSuvZr5d+OFRroksj5cWV2u6UqYNJIl8r9KckLrx/yY3JfnlGPCWtSTdUL3tC2aDAR0vT5tROjlPCcKD+chMsVDy/+4W0+Uc7iJO4VRJNOB8UghK9vENLjMukuNrRv5AjNrNSvt4Dq24luCkthuTqC8gbECkxUC7LqU5PFz4PvW38X3ffhd40GB51Xisjc4BSbAYjLDZ+lXcfX6KfD7cB2QXgKxniFBEfAbaROkEswV4ykrn8N5oOQATleYzheYThaVlZySXpIHynO8xPfic8ywwPe8Oq8vv8pzopAlrBD3QKE7JzBJKCcA/oDbfvkHBnidnHjLGZtkss+qZKyvcYoSSZUbhSenI/DBJLO0BZVxxZ+s1twyJr1ZaFsJqxW5tK5yW5DCLF1cpQVkiRH48qbkmr/GsPKwmWKBc2t1PsPcKJDJGptJhuVkjMt8jfUqw1U6MmTKipEpfdoNH+xlW8mvKvJkPL3EMFlX+TzIJZ+eJQDPE+UDnOHqEazO88Hzt0g/GEPmGyAvJHoW0hPFWNRd+AJPLMtDnkZY4unLK9NXXsJ5oryCH87DPLuWF24lHgrjkSvyEMh9cMTK6JU/hfE8GcM4lIxguuRsAoyPYbxLJjCeYRlMXyVvlO+FGUs+MnVWHwJnk2MsMbJ90i3Zy5cxliqNXM0jLORT33SJnqnfmt/ZTPgYWgHEeZVRTgXvfHKS55O6VDJpcpUhEPxBS4WTP+gcThilxJHWin90DEwm15hkK3w4WQEp8OXJG5Ns2qZb3SDDG5yAks9e2mdMoT40PvDnTWPMJUaQZFVFqCT8luk5pp5CJb0eAD9kQpIoLifK0kveSZ4pVahOra+YELesXGP27soIdzxx5zkcyUgEAf88Av2GVueRJIFv9TfvgLxTvOdNy0ZKr50V2+eePIXY16IvORnPrdHkhUZtmraUhNt6r+XHQD5ZYTVZIZk4rwlHEhbVvK2tTEEeZ+Z4vU3qbZMvt+lWh+Jk93B1bUL5NGKJf0Ik1NqMwUSi0HAgCRUJORZXkGZH+bwV4T8pyEvWhUZtwJV+GkuH9pT1fB/VudiorHubUH6ZDqFiZAjQlKUuZAon6+i/Uxnfz9g2t/moJqZOtl5VCb6HNsG3uaSupHLyvRRtSxKXw8cw2HCEtC8yxpFiuRLlLGRFnQ85EUvEASmW507BJG9Y8t4ixbGpHxG4Vy15t3DihJdRQmceelFDU9vk5InmUUX/l+pxclkS0GvgKDFrj7h8U9e4Sa+8ULZSvBuSW7RyjxjRCAjeH+V/5b+BqKMp/006hXaMn5dfl3uWyPuTJL+s09QN5TvSIOU07f4zVqa9c+oHNjfOEZ2T2nbB2po9F7V7WrmNQCtHFaX7TXEDdF0MrB+JorFjAFCkuF4BRb7GZj1EkhXVoFl4W2mj8JXm6pxceCZhg4OEhxxG2QEwf3ZWhWrQ8sekbI6nS7xbDYFpbhQQwA1IK3aulX8ZryN2IVLSDh/FS2VAy1XChSqQ1dC5w7rv1TMhAYOeFT03rREn7PrcG4VZu5cJ93ehNKVjbJCZ1WyYx4amiNUvWTQca18mVTsf36b2LrgLMeULIUFEy6pNViK+2sjGhviY/2h+Q7Zb8nIpkaDMzXmurGcKUjudaO1HQyr26Z1U3533ScqWLs3A48yJDXcCcWatyoacOnxh40niPEt4WAO3vHPr3JAJ69zyPmR9ilvqs/WmLrhz10XpEWEH3qJ0QwG3gHJI6ye9cv5dxg+PMvOB/IQ8USbAzQRYTvIqnMQlleRqi/NAAUyblqsmkIDfZWUGDp4zQbM4dvJQ04jHJmuJphRISMudHMdzdpwS1CXALL3CMLuqCJcNTHJZstzTuFPAKaRuPOHkkVu9iMJZnTeA/my1vAhU7lvzfaKYiBVSXvnMyr1O+Hl4ok8aV5KidBn8+RwsLVPyuXc1MOw5NC8LCf6Oqudclk6QlmOGRgxwcuAcvhLMIRUAbpmmsB4qp20J56li5YwsBTa5SbBs2ovxvKTwE674m8u60J36M+JttKiVuzZG43Xh/SYty+Zk0toY0Ca0s580QVoWa5ZxGTZBZXzbAD7+OU8UNyaEvCokrW3KilrfHWJdeYpVBAqfr2hLbYmeaRcShbZtJAp59fExFOx4aggVs5DUtQlXTPz5hOYeF1rmLwddf66uX6aPYYnjPuAhDAT+riDKU3GMFNrCvJcUrt2Pyrr8MkKdPOHEiiznlxmIbXUsUQgU2ddoHtb+TwG9X0q9jA8AVJ+3c/lsBDFzVLhnc5Oinm+Efq6Va8QJ73eA6ze8TJtTNdIDon4f8PpNHi98nFV1fHF/HE36scYwyLmNrt2PjfCvq/z2SNyXeiuFPxfQfnqDJmfq2m10xzkMuS4FUxiPlBVmKO3KIVlmcp+YtblTjLGskhwuLJNhVkJYY5MPkKdX/kOlCYEsxLxhkvX4lS0/Ab4v+S6ef/RdJBMz8XyAM9CytmWWYPSVJd7kp7hKn5r/QQwvbbk3ihb/1SQ40n03eaLM4a/EkwM4dfkuZonLaTHC0ibCPMcJXnur8czWC5efga8YwtlFGqymdp9Ip2cwlqpnMB4oT+32GPi9Zx/iJZ7jDU7wHeuB8grPsMQYb3CCLzD3kiouMa6m/k0pPFGSuvWaYJRgbu1yAph5tHVFjtfjCgMPc+LLeZKikK1t6E5xjTI1K5aQ5ZgUT8ofQUs2z7CoPFUoKZ+x2Bpr9DDZYGNX8alW5aAlTtOjcJuR5AltU5gVZwAMppfVKhyU+4Msnm71HZPvw3h7mQ5hgq3cKhtHnVMo7S+OcYaxRxJRYkIXzkPeRzOb52HKLO90fI4vkGGDE5hEzdVSxhfwk8eSxwkts3mOWv6Hy7Vu8eRkiDagkiBC1hnugfKVxCSRHfO8J9wTZQLfE8WWffnRAMvEeEuRN9WZDWPkS/vyfEU8BEcmU5OQChNXwvlSmjKBGlcIaOz3LI8pyxrPwZUraQnhRDEP8QTayRSuAPDtFN6KRsiBo7dmhZ/8+Bynx+dYZ8BssgAtdbzECOeYYY4zLDHGDObY1HqtkHfb2pLOfKlZuZxsCJqiyl37AXgKllzimEIbM6zZCjzLyhOFE/JENI4vVm6lFK7oa0quFKL6Cj57DD7nyHbP34FHRq2uTHuWniX0IQ8UklW4p+i5+B2BnrckUMjKemLLjm3ZMfyVCGk6XhlBfwZDDpYTp7K7S3GPkxQZI+Fp7OCkQCgnCvcYlM+sRtTxvq6RKluAi44c3CpOY29KCl2TsStkLVXA5RuXF0pnfjQvi1DoDoVyp2WJ0fmVCdkJkXAkL3NPJ8AZIGkM5Q9JtjkZOkbfqT1N7dZ6nVSkvlBqBxNSRkzIRJmlrPWYtuRWg3K5emRCc3qe9LySx0CiSKIqVEeSpnyfeZlU/V0zFgDOaLo2Y8HT1Kxqcrk2nnOURwior2wlwWWekEHIk4WsbDPKrELL/z//jyGSSJKqpThOz4G3P/58eJuW58xEOdse8elZdOMj/iV0f3I/NIdq/yvUN2UZoaktCe8M776bCFU5Foeuz88fIqC18kwckyGs8l7ltbXnl4s6Cil0ZPcHJVtm2+KmgOeJcnNnJAoJWR7VyD8DXKUl1mmBZWZc8d3SkeMqSd/GBnlsYJacWicZkFz5jD8XTKwgcmUnhAEJKiWMAHEB4CMgL4GPfuBznGGOBAVe4rlVoIy7SnmS4rvnIyAdOCJFNgz5IrTGrT01TphoJErOPpZEqUI2EvL5WDJiwGUjIZLAW3aSBDEes87B41hpIKXVPI5hCBSbqHJ1DEbTzD3CZGm3bjUeQ0RUiSlZ8kSOEgnKhBQpZ6Ex7aGowr1M3ZT9zri1k7XXf8ROQeMWGiJUEpSe0DHgCldyjXF+jiIBhsdrjO3TJYsygCr0DDBxyXz5yEu4/C0JCqyzrMppsAFwXVgzIOVK4W2G2oe3dUuZPiFPJJv/Y5SxZIWWKCAliLwszHXNaER5UpyXRsfev8cYY2X9KnwBU1vKdWzDBLnyOBL9p9rSUsah5IWk2HDPlMIMsl3yn0iLZwqfRBnAhPLM7PenU7uUMYXaHSv7H8IoTk/N52oCLBLXNymcRFPgadUELYFaaMl5QFrmjbhLpB31YVPPH7S5xxc5vFf9PE1qq0TYygZF4MNJFU6sAHWLDwfvc9wFnX5HiieRzBRnvQaO1sZF/QRvsckHNh/PuOqXlEOFlACTLN21UbKwkjcLJVg0t6qNlykkaWKeb53E4mVc8fI9sVyOJOmF5eVLKddICmtVI0VLCoByPNvGInYgkKS/Kav70ZJnQKK11Qu25fs8vIfkGiJW+CX5M+dtmMg/+k6eJ4TSnovmfFvvKDPVhhMzpw1ZP+XepNSO+f+Wz6G+ikRRfdc/tk3bHAE1+UqCkysBpKlLGCgNmnJEo+9csRukVqnTiBMp27o/3yotO7KpnrRX7mueZhqZQt4nlSxDcu852+ftiIdCAXXPFHpQUkEiSAWH8kpRXVJYebujZ1b6+1kKFMU11ln9RfNnwkk9jZSn+o+CRCFoXiZ9wEN75NgBuPfBH719zEcFSzhbGKMGLUNcwJdxeN+S4XB0jMplH0tTRqBIYo6fQINy394xOk76KJ1P67Nr9ufA9lN2XN6bdh8cISKEzwMQx6RXmJxP2+bXLiSK/P9N9y3Pq8lhIXI7Cex3uT/+7Pn1NGhkTMGOSaKR1+H15PXFPR+ljFhJgaMeQ02/LkxWqiCJAiDNsAKQ5Rsgcy7jtCIKESpmO8LYJivFBK6x04RGL/HCJHR8+84sBvC8AMY0OWQwk8czc28vnr7D4qPPkKC0OVKWOMMcgBmsL09HWOZjXJ9PfPJBY3JDJEroSWokylSUVZ8bjKdLDPNNFTwz8pS+/z97/9rjSJJkCaLHw4w04yuc6e4dUZPVVd2124MZ7A6wuNhd3P//C/bLxQUGFzM9M1Wbkw+PSEaSThpJM/r9oCqqR8VEjaRHZFZ6zIrDYUZVe6rpQ+TIaxuCZ068cDhujjEtoH4+eQZJ+8W/b/092RLlW7f/8dsaaywoifJ9iH/CIMrWZ7IIoMJ+GsCTXNaJoux6ZnpsieK+SYQmhKLYFk1ktfaWnYkqxKCKwT9d/PppQrqp3OR+t3e+4ot67eKd+IfcemG8wsFb3EQQhdO5StDZQzEG5u59900VkfpGnPAItybQxG07M/OMAExawyxlEx9smCnGSnD/b74CCceNh1Gi4YwxUToaH2mWHp2dJ2Yo8eOLQUgt7LCWTy0knHe+RBrZfoQ+gNJj3pGCKO+9lmZ0h6hdFmBTLFF4e+/qfnmfWqDEGB3O6s4BftMwOnJZEM5bobjz2J1KxqWMYfee0ZLs4N0EUisUSn1cFCjKDs+lzyLBjJUmiylhwJiBFaA/F2rNqhaS9ogCgmhWBUQRQG3mgnTW5RHFuw/YV+vgCiNp3UXrLFaVolWV7Q5TjBHdIOVb5AAsWwMrWv+9+nptcDVkCzwdi6KisnQ8dShacrEAbbXWT7YF+szLV0pDrj0AYtsZoGsyl+jYMmyl0iJk7nn27S2BUwEyLy68MCKWBSzM8rcT9zSOI1e5IKDV7ICu7LAtJgkQB6DnssYuKOJ2YfVJaadYnheYA4DK/UpbpXEfk9/MdJe+TWqgfUpdEkpErbrQBHHefVs5wW4k453dJnmOsKxU6Pl0XASLuB177WAAKFo5VCDGPyn0fMdu7q3asiUK1+U04bzV8yQQrBiS76CFvBpmytmbBihroNofsFd8YIdhN55LXUpfHXH/YuK1UMAVkbVywiIDA2yFzuUtXaOLdTclMPV9ZLKPcSKOrZ9/LnwdHThWu+7ciOWnBW5Y/c0iWcvlfQrEeFxDoIf0VSCu90LimmaBDizc8zNY97HAhg59JY+OVQTjnHOypn4GC2DQgNUQWUMsB2hw/2NLqCHKPYf+5ue+v34ei1dhSzups76fJTdLPc+DV0w/14Eoor3TgENyJZf+eF0uMK73qG6dS8IaiyDsiVuPs3Y4YIspnuuPuOEFTahxAR3XT8Df4BbJj0/A5An4x0/A2xZRc/ujO/df6r9hcbfGGnN8wEO4NwDsZxXWswV+asbARsWz2Ph32sR7XwyiAH3AhEGUJaIVyrLBqD5gMotWJi4YJv9e+0w86yAA1toChZ9FJogZfRexQHnn97+FE8a+BfBn4On2TeK6I+484sbziPtgkSKCmoAn+6YKIMpJgSgCDhRlm1ipFD4VdlFErTZnh2CShbULR0SNdqqdiNlpqm6PWrR6nOJZGEpqo3oO1NURi9ufsJ29wbTaYhvsU1x/FeGIt6yFPmCMouiwn40xrg/Ybhwmf/AgErfLGwaWABRli9K306Ry1xOrE05Xyu48AhhI3BehmJNlb7bla6RbfMLMM/LyjbltJHgmuyhwsMzUzWuPRbd2gTPZDS6XtpUFID3OPGmLk7fqd4k+eCIM/dsKeHsP1x85eKyAKOLGI0Gf3wHHd8BuPsJ3xbdg97PUSswFNZUtC+5ACqS4bQpQWhpTqROrBimL9fGc6OrgQorztcSya18B46ZxTJfWEAF94EQDKHpMS7l7GHuBtEAUscyT/RpOmGWLvQpBOJg9nTCbNVi8/R6r203i7jfFNtgRTrxLlaRM3/otu/bskUoUfYB4WNji3wyeiPY6ArBsiRKtU5Kgle0pulicMxtmYVaO+4qUw0waJNBt76xQKKWsACcCYAhwoi3e2GXwF+C5ccqhnb8dZ/0SYjP5txXwVixQRDCQR31C7M86i08HTJ9O2FcnTGc7Gvucuce5iu3h+l4+YHzf1UzPHQwKXNjgkXnNAZ/+nThoYNtGN0tpI6YJgIkX8hYzD0TpeFOyXyBVdBmAyrPv+/3MgXb8N8uqCUjbjeN88VpXtF2Mf8J9i0EUbeXEa5gcz31TC3FC3FeAFERhS2aZBxi826tr0Fxx0wBFdXL9irqT1S8EfNbHXNyHXgPlwDmul3YVgdUS2HWdzAesWChhr6Wyfvo+MfJjSq4nrg0WccrYHsAr95RnB+I40u8v+1qY1sTAHStGGDxiYK+k4/h55Fh5nhyAosmy8uHfPKb2ah/IgygMZp5ztcvdX7elvIcFpFwj6VvzA5O+Bz+PnG8dwwBMoY7PUQ7o0c9ZGWWar+RyDbCcA6wG6DoQRTqhfjDeeob21Bbo2hLizsMGxtHQmDy5Kx8x2UAm2zb67u3oVh/2wNtffMEHBKuUmw/Ol31RbdChDFYwAkh0KDCa7xyjshnFRpV7iqmcbIc+MpOeIDP/bzzAIIunuKnEQJpRI5EwKTnmVg8mWeR48asR3QNuHYCyrhaJtUm0OBG7jhiic48xDp0DTtq2QNcWESRQIMoJ/vuXBTr/ru47Fs5CCfCB7lxPPYQVIJKYxkpLCOUC2QrT4W6EVGPNMW7ihQDPVM3aE/bvHDCx9QF0nRjozPJF67zDNgilEsg1CKZFiao+oGvdO/eezwBPJG2vxaSL20BOW8WC7NdI8tU5Iwm7L9hComjeD8SQ+t7cHGPgTNFi6EUNxm+iEVILFKEScV6yfIWDJhTOvHWhmXf5p/EZfvvtbj4KLjwHjENcH7Fb6xAtUERYzwnsbMrPxEys9C9x3SnAfuzumD3GELc8qRcrKXbnYf1qgTbGRbl09bGYSNlq5iS3ILKWQWtumOnh5prBCSYU7X9UANN6i6JqQ0/jdpS2l4CygOvLMZ10GcZvfL3+fKZN2d2jxVg0HFQ5nSfsEO45K4GkffsPYf/mdfIrm4KiFeSFJjYWY61/W0JM5wCU3d4BKBKPQE6R+UTmlhBvaQ8sfKyT0Fe91RRKuPEgQkKFxGr1poUP+tm3JmEQIAb97K8zg5Y5uBDAt/qV8F9sjcxjVYR2v5XUrUGQa91vrUGfiMVfqQAU7XJdIAVOWAhQ/GhXvum90pBrZP/1z7dRAtK5G+TXKfnGYhXF/Yzrci6QFnjK+5q35N98LbGOaulYAEXrrHfKrgOKPmgcb/OVTSYWcZ/6Na7D3ZCFWdCx2sqrQQRhCm+xnbG8GOkAsHIdLazzuGVhWQv4lwj73OdkHFwCDuSACC236n6tz8+BCprnYF5kn6kHbFcffq4hZQaT5mtky4CSBSwxXYMAMHjFlOvLQ/0CSAP86/cA1eXuZ4Ew/M0Y6GVlA/N71rG5+2boOhBFNB0KbU62Ym1R1jjWFdZli25ehCwjAEJgzA+4BwD8jBVWs1vct58wuqXr0AA8wsV+FMwEfn/3HXD/HfC+RrBEAYDZxxP+3f/7Pwazd/ZhX2GJ7r7AdjnFR7wDVqO4qLaIFinaEkUPKk1zxEVZW6LMXdmb5ROm8y0ms11w35GgoCISTYJecxdceSocUm2h3Mea1IBUQHvvt38BcO+CUv61+BO2mOKv+FNIZyypjDmgrDgW7Z4mzmWlGTvQpC2RxP9g8hlrTuUzTmUbGZuyw6GpggtLVzurFMAJZGwW7ILcxZg6kpuo8mbHFtNStCfbsuCT0TbSdl7jfPfUALMGk3fOemGNBSpvJVWi89ZMTmDfei9QeeYtps7Ev5qirQqMPZgCAF1bBAAFQABOrIByYnXC8Q20tYX0D4kn5LpCzLIA2ELZa6NbfMICbxJ3kgL9VK6c3vobn86YYwtJ+tZatHQb2LFQ2OqExxkAlI5JLwtg2vXX2xbREkXKBTgZwYEmo9KbkUufu0MMHjtHdLW7Q7RE+daBnY/VPVY+TtEPeI8DxsFKbEsgilig6LTGTJZ7nA7mrC29nFb6QPsu4O/UAydjjNH5bwAgWKy4+5ThHhKLoaw7FO3RAU4sCFqaFW5kS0iQuACWsMBkMXdaeBK3x4+Ic6fEmPgUf89+camRp+/+hkM9wryIadGn2GKPcRijAnSxm5UALfxNeMxa3yF+DzvI71BsFA3WB4BFYnpYbSWdnBUJBVJh3dJGvULq8AYdnGDMbmstbcfqnIJVtQzqMVDLVgIyz4g1ylO0QPkBMc2oWKKIrSG7DJZwqdXx0YEDd603l5dvI8KQVxAEIYWstkYAqm6PrnC9QNbY0g8ubX0SX5GDu7ehrc4B+Uk/L98A5Slam7BLDWjbIqYk5/YUEEQATbFM8da3WoNeFsqdgN33SrhJe45e1rNQJson4RUqx/p0JcOUBfQcm6NLYsqEMdp2Md6OdmPkvsRuPbLGaYs9DTaz5Z60MaiNZF9AEhnrLJDN6DgtB6i5tuyAzrtytUVMKyB0DoD6qhRGb9C3tgeiYGhZPrRIvxULgXyMtn6QEAlcJ/2ggS3o875mcnjLNLQO0HhNjhdigdgSuvX95RwWoPnZGIjV71DQsRwHRb8L32vIQkTzG9rljss0jwKk47BF/zufIz1egdje3EYCVlhuM2wZMnQf/ZuvNQR8DX1TefZrh7YFbnE/kLrct9PWXfp44HJ/NlzLAjFixsyV7tDCNOAGh6ZCWXY4zHwQWQ8JjH3AvSQJaf0Go+qU+lbSE2pT14+Iwst70RhS5prl0yd0s8LbWMwTS5QxDmiLAm/qA071KF1cuPNplIwXDKs1h/5rF618XEeTarFAEcsGrVUEDO2PheTq5xFNdo3ILNwCx7f9oJSyL1COaLuDpdB+HN13BEBpbuz7SlkJB7KUI4R4IG2JU+1mr30TA7N2BZsWt3SZPrPCQqDJvOiBoicuqHqZdP23XlQNDrc7dCgD8DbBDi1cRiIJlCsm8wxmhOwbhQNKus67SYgVSmGbQVtWJ9oiRWuS5ZivATCxyPW86IsvFikVbUU45DgPHEizQBv8yoMAzgypXoCtMoP0pCkWKkAKnky8D/5UmHa2PpGxKdsZojuPt0ARazEJM81ZpRhAkUCyYjHFaY31GOH+oplXDuoswmRwgwTIykSAz5h9zWWxcuNSwM69B1u4RwOOke7Ko52lRxqYF8CcFlZbE8n1LGsUYaysGAyipbHmdRFKtXAxA+onoGiP2N/GSAzxncvQhoBYqDjXCWlrIAoIhfpOWvtvxaSwLVE64zjr2DNcmrSVMJ7MMMn63wDoK+W/KnqxRQqXdej11SNZoMi/8DacMUMuIRZwI1937Fwsg6l8D+nfLCBpUNiDLUXrLAJSQV7DRAh111hZWNRrQ2aqWZiR55cxxgIYYztaA+vf66aizF+WUDFHBG5YCcVgjhYQ1KNzPJQvtfbmAIJSg/lawM7NkQzqMYDSGMfytbUQoj+7gCkVUmCF54hW7RfRAkqsUbriK0BeX0oacLjU3YLXplKVA/b8I/WW2yyvdwzIcP9gYkuw3H1y5WLZZQnsliB+afdgHl7WcNX3eiT9VkiDO3qMWbxgDtBi6y+rzBqjfAyMrSa2KNHvUap9OVa/s5AGRi+ZzixA5dy3t47hco0jnCPLEkUDRPztuO/mLJn0fHgFX3PdbCamotaLS9mcyhvgVE+x8dr4onLMpViirPANSnT4gHsssXK+5/c/uevdw1mWfPTmmL5TsluPvPMdgPf/Dbh/QjSB3wP1O+CP7z7i8dt/DcyA81v/JgAI3fsSq7LDsZ44gZ8XhyFhi0net1b/JcgSxaUzns63GBdpTAeBkqQsFRjpZjIZiSDW+HZqETUDgjpKBp45QtyFj3+p8cEHkP0rnCXKd/gWW0zxA96F+DGS9nfVLXFoxthupjGNb3OTtskQLx4Gzw111jpYqGzh3FuKWxtc6DxwATjzdZf1YhrKxM0oiAai6WJhqkMaP0HaUb4XCwrejPEP+09Y3K5RVNGVR9LoFuiCJUp8NhdoUsz2JUhfVxRoi76fr9YoW1pkne6Q+wpfj4P/OWG2wvErkGzm2GCBN6Gt7BTH0SpFUhtrS5SQpUkHjtWxT3RfVpP8Te1iERxboLQsHeCsTSY1oo+wBZ4IgHKPXtwTSWPcfAtsZzX+ux+bkjVLrMP2qEIabmftMIW4kGgYFrCZ/UsCm7I7j8unVoU+Lt+A+3tMidqG8SvkgjNHiwg3XzWohcHjuEXyPeQbWGAxMyTqOx730Xe7NeansgQmDWUNEOFJnoVdrMSFVbai/fWCxGgO/MN+g2a2wXS2C3FSxl6rP8E2ZE1yVipV8p3cq+S12doKhb+NAIhA6vaWuvrEHpElFhhZW6eBIy24shXBKybXkraLRukBsfh9PJCoM03p4ZQDZ30/PbYpgCL/oijaqcuViGCtxFaaPrn+/VbWMqAfK0i7dZTOYrOo4iwBxH7GMY543tCWE7GNOD5S6Y9Jrd1adNgXDnA9VieUpct+lbTVHnG+dBeJgpeUyztJn9Pzdk740+OcLVEEwL5FtEbjuVqAlho4Vs6api1y8CRlIlMgKZfpsXjW0iLHf7JApi1VtECnXQ38+RzMuCxcAGIAfVBZtjIHAikIpq0fWEBr3ZzbtSc3bpAH7bRLKVOXJph9ncRgHZOsczlLFF1mxYCQeIhs6ZAD2PaqXo4H7HGUex5+/ly5Pt8iC0zRdXqc63tyPdSxch3hL6z75KyBrOvqsQek40y3vbZO0dfgff0++v15jrRkGra8A6Kcw/1OAydD7cHE52vezAJ1mIfQ4JnmK3LPocmSv7VyzPpu3H+tMr7uFQlOrwNRROC0kCWrAWsAGxdodltPMalcjAkRQteYo8I+ABoV9sDbnyIo4AGRUeXMV+XFOTYK4BiL/7sD2h+B99/BCSglgO/cszx8+wEHVFhjgQfcB+DGCcRTdMsC27JF034DlGRh4YEggLa5iU3et1T7fivpjIsyatF15BERVtj3PVlwmRmQNupoH4gDhTTax38DHOo3+BHv8Yh7fMBDcAcQl50oqE1CENntZoJDUzkAZaOsdSwQRQ9yq5+0cG1cj5zWrexQeuscwFtxIGpdJbaAreEuQ+tlNYZ6smOts0xswoDKN4OLk7L8duXvVwVBUYTBnXfhcQwQa4mjAOnOS0kLQgKOpGBKLJNr65gHFkXG7vVHe5ygwRQRdGK3BQm4q4PMSllw9en2DkDZI7rxWEyl9A+rWWmRGFUIkeiPbRpYDSCfe9nOERlzGZMyr+kMPD5bVnMH/DD7B+wwxSMesMYcK3wTxuYK3+CAsQc6XQtw2nEAidCXGxdWuetXYw+UgIR0l6KX5yXLdafC3mepcQnshWRsCCAz9vco6w51dZSTHT0hvzDHh0/nH63hb85kGdg7EGXix71kE7iRuUDmOZnreL6V+4kg5gWKeg8U7Qa4jRnpxBVRwFDXXgcT7NIZe3SqT8AGVLQ4J8B7CIBKndqaN0J8Gm5rbvOOflsa59eP1/q2t19EXHnYxeeCCzrS/dT/P7euj+ZAFM3fiCuPXPYXRDAFEiNFlD+i6GoQ+6vU+ecpWlmnht/lcy0dUwChCBZoQHTFCTwSkAqGVhtqpl3mcD6HuzgLE3wfcXWaIwW4GfBmNx6/7Up4V54ytAuP20sDd+sA+ZpCOmhN3Cb6vaVcz4vanaBzsXgAJFmgWg90TPylbqS/CBFvFARRsUiR5+B5weIP4ayg2sLuU9ymQMr7utnsKwFRavqthdhSHdui7/bQquN4DQRtK1VnASn8jbRwr0kLnhZxnbZuyFmeMl0jkbZIQX9Nui63nyOrTa1xp914WlUG9EErdT6DmUAawFdTEtBXnoeVTzLXcT9hIMYqA/KWKKWxz+C0BcRoqz59zwJpTBR9/tCyo6fHcyAK30O3vfU9gehPewFdB6KI+a4Wji1hWTqM/5jHeoLdfIqy6rDx6WMl7oQDURyg8nT/BrPuFLSyIni83QNvP8Uga2KJIszGR/8I7z/BBZmVbD0A7p8+Yj9zgsePWAFwlgUFXKyLriiAORxgUI7dHdi1x/oY3Ojy7go4iaDKs8tUVB8wLgQw6Xr+6inD2/rbxIX4uXQmq6HzWWstC273wPMM+HB7iy0m+AHvscISHzyQssUUP/tYCytvnSM6/MN+nAIo2iqH319v9YDJAm4joCyxbw5o2yJJjTyuxI1gHMAu0bZKKlVJKypASlFNMas2cXLVJl5CwoTwcwtI9wnhm39TN6hmB+wrB+RIGNMSHZZYebenKmGOxjhgDcdIaW080BeEtBm+WJpYZvoWSdYiSSe7T8Ta10uupW8SVx1OccxpoHVA2QqH6MbDgIlmKi2mGxgEiSWf/IgXCT6H4+1wDBQGUe7g4hRVCPFPnr8F1rcj/Fws8SPeY4tpiE0kcZ3EEsVZ8UUQRbvvaGbeojyIggQU1IKQ9Gku01pX3fdF+JRvE+KrFB2OlY+NIou+aE06RAsQYehZc6HnZURGhAEUBlGSwzugfXKMSOubaQGvIRfARO6ptX4irLJWsXH9YokNDvUbFxvJAydiHSJtJKAXfy/nCphXfzBw4h5BQNh4DwZOUqClz5F1KJ1Lh6ScpnbpWZfo05kR+QrkmhNKtPQi4tKmheDEusun60Z56jN8zChac4miI/3rpZRJy6nhnM4HgmQhSc9vUueftew6HAqYa9QQscstu/2lx8T1UAJOl7Ju1x3GzRFlTQIAr7/MM2jhUaxVZD7XmnfNCGttKYMpBaK1SUlbnr9p/7kEDvUI+8LNuTzvuUeMc+454JrTH2u3P8D3LSs9Cvera0l1KA2gyFbmwhJ+aIvlMwNZVj/TPKGyBmCXnssfuVC/vwLSQOC5byouCEwWyKH5WZK9zDGi46EAl1uiDIEpuQmMx3bu3EvIui6D+3yMvp/BM5jH8ztYFgtcxwI8W4CxRWCLrDuPxHGSFNMAjcsuKuiYdvCKHwY99UGsHNaAqKbc+mX95n6rQRLrOvIMus9rdx6+9jkwZ4gf4TJ9LPdfbRSh+/wVFrbXgShPcKlXNDMrNEcfXAhgwgifyiX28zHGt870/gMe0KGEpCc9oMK76gc8fPsBb789OpXLJwDvfHYLD6KsEbUxorn5wW/v/wq8l8X1W/fM9bfAn/7yPQ63zhplii0JJhPHxhYd8AAcmjE25QJoKue6osAgE0Th95f3De48Dd6UnQNQqoMS9vo5i1hglu0eY4wxxnb2BuPi5BgmMTXliJakSWlugdXMgSff4VvsvOvOCks84r4nqH3AvWuPborV49Jl3lnVcQLIgShWJxWyEEg2j58DKG9wxAJHHytF0iN3bYFDOcak2gahTJiPNRboUGDpg75K/wGA42wThTJx45BJxBps7Esuk7Fn1m4AzKoT3v/5RyyqTRBO5F5bTFGgxcZbUcWsBs51TEAXixgc0a47LF5FIamvWWZhmVPZHr8CycYl7o0ZSeSf3XkEhNRuPdNum7rx6D7MDITFePNW9kkAScq1FlXMwueIIMq9LxMXHgFRZsDxTy7zzmNxH4BMGZM/4J0HOJfeOsy58Yj1iQZQtIvIOdJm5BaIIn1y51P6sjuPnC9WWnre4sw1Aj4KgBLuedui2h8w25/kofrWf9K2Oa2GPy8wI11cFzjjCej3Tk73AmgJlymlLBxgP/KpjcMaJ5pXdudh0Gfm6kZ7YFSdULz7CYd65F03p34Od4CvpHtmixSx3Bn6bhpMZXcey7nActeIzeXnjrLDvjqhKBxTFrKBaLBKmHk2OZf3f+Wk3XliFqoInnD8H1dWohMAymIoLc5qgNtiYIT7LRDBldLXiZIsKJQaYCQWKOxKoRh2+Y46eZz0F7GqtNwAbbedFpalCq9LEktJlCFF0aGdOVOZoiAwWgQRAS5EIJF9GW8aBAdtNSNsMfkaTBGwREAUAbwJYDlWzpJXABQODi1by43yEirAoDcBMWWHtjhhxM/Pgqg2jb9EoPXtE5IYktAmAltJ1ynhLVI0qE3AcbDqYdBO97sBSl0a+6AU02FQEnwlxMAd1DbXbazX1u1qCZE5MICBFK4fAkD0PSywzALRLNBBl1v3ytVZ7cS8O5MoYKx7WZrGvTpOv4+WdaSMXXhky5Yo1N4aNDmr8MlhqR0w9Va3oxKY+ONGci8tn1vAiJbfezehfUtZOLTm8VwLpGCKXEfA7E6dYz0vn3stqAic74f622t/2gG6DkSxhGZmuhqqk0ZjhripcCg7HG7H3uJhgim2IZip+I+viwPezj6mvukzpyn8xd+KgzkCsRNugTRquY9YPnoCJrcuFKO71w7OT32HnQ8YWhTOreRN2bkAqG0dOweby+ao1P/PARQoyj6DaxFrDVMtb4muLHCogdH+lE4a1Jmfb4F9Baxn8+AmJZkj5LcEpewHka1waMYOQGkqGzDJufOcWSxDvSzErGWub9z9yhantsDJc3lVvcehclosaQuBmwp0XjCpIK4MBTpnns6DXKOdsXHTNpRnEsuFAgFNnj6dAGwxqVz/AVywWQCosEDnA89GB62D93wrINmENDEwYKU41i5C/eZMNV+i9WsR3TpeM41wQIXnpJ10nIc06wi1Y9s5QcGaOK3+yky8kMXQaIHRWkxYs8n+9WKRIuV+f31bh6CxEo9IB3vehv8JxUGZBAZeYpEkmvILGXmdFUaEqQ4CWrrwvvBuPdL+krlEvoWkO5axKW54h1AKRM30OJzXYedSghcUUNwyRWUGaojhNN+xD6ZoksCd6BxjAvktcxRrlnSMJY4R4N0q6yd31eksrsYHb2Um7QsABe2zpY9FGhDRLj2Xknw7JtEQlyAgxT1UKpSDtkOm1K+INIjC5YBrL+6/AjS0RYG28JZULOjyb/73AMeNwXXJIdIPL+XhBpMIsCCjrTqyp+RZQg266kDwckw/NkoHDe4e6pGzSOmoPXJAADPP0hdzIIomC0QR5l1AFCnj+Zy2h/pNyMgT3zHNqDWUCS0+SpeUR4uUGHsnvLYE3wb6wAnPi5ZwZAnotL6VBaANXSyNt3sQ2FrZ3Lhngf1C6pJ2zFvxvH4nZfQt+WU7pPkeEiitYzQIIAC47Ms30orhc0BKDpTh+2qhlBURwlfzOL6EeL25pG8Jb8/nMrAAxPY6Bz7mrNwssNACEq8AUOTyzKsMCehHeQ448FPcfMIz6zmf35kBCyarb1rghgWg6Gvm6jQQo+VYvg8yv4W4TwM2CKfXP83LcH+U467QQ18Hoki6NC2YcgdlQVuuHnyfbnBazvBY32M6n6IqDpCMJpI69hussMYC7/7tx2ii+qO7xj//qwND5DY7xJTHa1/2EcD9jz7Y2n+Gi4Xwr67yz/VP2Nz9FRPssMIyBEYEyOqjqIJFyrbsYkDV2gdU5YlHD0B246mPQNlhXO9dzI8ipp1kCxTOzMMk7hkHjENw05+LJarigP27vU9P2aFoT+jKN9hXLm6BaKp/9jFOxAJljzH+O77Fz1higwWlSn1wMRaeljGA7MrHP9nQ99TaYQ2mAP32sAYkAyhisQMApc/kAwDlCMe6wrGcYFwfsC+i5vsAlz5U0ok6rWHrBbc1prMtJrMt7vbi+IuoOWYtN9Cf9Bg1FsGodkLF7PaEb999j2rm3M8AeCumLlg3lehC/BS2FtDxDoQ484xroj6woim1PqlCm8hZe4zRXuPQ9zulBTaY45S0CbvzOEuUNA30BFtU+wOmTycXI0BiobCWIKcdYwZbT8i6fzPjw8y3xEERwERAlDt/nHdRfL4HHu/m2GISgsf+gHfYeCBFxqZsxeVuj7G3wipjOuP9OKTTbtuX2Hn7VyJXOs4mFS2BpmE/WpVUiXWFaJwli48EU+0QXVsku5XMhgAwLvYo7j+476YZpsa3o8SzYTBW5mP/bUbGdxUARapEOC3Rjzshy9hk7yxKemlkOS22dj+aUd3cbesZUN9u8HS7xbbaUVsdEiuiJBg1cccWEKbBd3bnkXoLVMm6phRAOyvCenIAUFQnFC2cgMuMojQot/Om3+avjfYYeaiQYax+YF6OIRKsreqDyygIRAaMYxnwvmxLF49n8hRBE81SSGpjDZKMkKGcRvhCQSUvvKbggAUURBe+1s8L0pplGO/R9ccddyjGGM8O2M5aTKsGReutUrQmVywJtfuBxX9Zc7sw6oDN+Eu9xJQjtx6xQNlW/RTyMnb3Pl6UrMGAPW6lnNd1OV7irrEF3xhAV77Bc31KYzVxjAUWVGXLQmyr6uDe/8a/4qSOrgMc42vErIoWzgU4ZcG8pDo+Vrb8HAal6sWi137CO+2vRWd+jzRH7GNKaHxWLOLQci7WZHyMtjC70XMA1zfI8zetOtaqs+YY5q8tKxd2adHH5AAKoG91xVtd36mtLrsERNFrXc6CRssNOpgzgSe7fQROdv76wpfwlm8RFDuKSqRBxkcdgmvypHVZIYN1pLyLzB8vAU+4nwq/NaPyGjbQwqEnLPf3Gv1vmwOM0R8fQje6/wjlgL6cJQqXPeFiug5E0Q9rmUtpK4PEEsX9Hxo3o+6DRYrTso69EFGgw24+wuj2GIWRJ2B6C7x9itHpgX6A2R1ch30rDK+4BN0BN5+A+d2aBPBxuPvBCyiAY+pRO6HkAODUds5SQsNTGk0Ni/MzUHZ444OmFmUa2yKnOUy1PDYbJ0FUC59Gt6hi1qEORXBR2mCBFb7BzgeKFc22CGprLLygP8Ghq7BvKpw209SFKefCA7XNdWKp53ZiAEXuUar/Fq6t6xKHZgzUwLaYeueYKoBKWx/cdef7jwh4BTqgatIUhsxA5Uy9ZF8GkICGEjxyBkxmW7QoyCJlG77XljL0SJn7ro7NHLIOYDZCl5+jmGLZMV+HIY7llZDr8adE+JZybTwd0h7vDyhaL4jnFr4hRsHSVLibpmUMuDATXiJmfLil/XsEt57nW+Dnu5oCxS6TuCecHYvH8laBKC7t+BhdWwYQRehkcF9vNIelqAsgShvmrK4tgCp174na05wlnSsfB3A6BkKWccGZZEIQ1mqMom1QCyjRoR/ZvYYbizUSoeBSOqotk8lbdW4tGXVwZvWiQOB5gQWEkvZFKKujJVtX9S1NWrV/zh3LXjdSAGWIOI21O7eQi7hMYiGo5Sm2h2+UG4sJGzSFeB3kAoVGSxQt5PatDlJAKqwpvB36L7xPO2La4iNVc5NmQZNfkSyXlJw1pdQJkKJJx2eS/taig1i5lbVYD57SPsZztfwe0pbrOd3SfnI5W6sqC5TgwlONE0WIFvg1yDSUQp5joUj7ibKMy8XiqSsL59KjhQ9O8NCqLdfL8dw33Y0cJytabHpMHSz9s0gDK0lVkfSzPlhX0r4c8/wFHurvTCx0AgE8kSXbihnTlX0rOSt6li6T7GEFjRNhA24s7f2lIIoFpMhv7nfSJ4FUeakBHB6nQ+wug3ZCl/ZTfZ4FLOm6c4CRJRsxUNRF65PWAylA6lIsl5T9bAy3gdcK5O/Tln4cS6V8a27nSp/sKQegWOU8ZwIpHwSjTgM1wiMJ+WvzeADimLDGAeD6eWG8iwkqWgAe0LfKumL+uw5EyVqYoD+hW8f5Y0/lDM18ig/zHQ6Fc9eQAKHf4QO2mOJfi3/Gv/zlP2HWnFyWnRmA/wa8r4Djv7pLfVAvImawvwBYPAHTj77yRwTt8Pt3P6GcdfgB73wQ0AUh3eTzWhxQ3HbYlZ0DU8rOCSdt4Xppe9MfWPNnoGyBssOoPqAoW4zrg3MTQgwiGwXBNNNKXJSj+4oI5uyHrLWRe2/eL5YoIpyt8E3Qdu9R4Ue8C4LaI+5x6CqsVwscm7GLfyJA1wZ9EOWllii8lWMEXJsbx5RUX99gUy4xmu9Q3Isg5nzTt5iSowA8eOHqJthi/G6Pb8omMgsbf30B1wr0XTg6VcYMiS+/axtMbxt0syKAOQL8ybdw23F4TgG/3C1SZkG77riy2JAi9GgmVuKtSL8QsMHZOF03rH+P5BxXtCWKjokSAzQXaDFuTm7iFJcs0dZZIB8zpjLR8zFDkyibg+usDobLDu6A59pZn0i2HYl74lKMT0J68TUWScasLSYOJOymMd14cLe7SZ85N/6AaAqd6Rqn0jGoRz9/CegilnQS7NkBHzE2yjixrRj7/urmogr70EclIxtbXHRwmllJ7X2YVVjik1suZB2Rb8nfSCw+GKT3daPSZd4pW+DY9V9Xa3k0BiC3ncgxXsPzFj6LjfQliQUgaxtrMsiKDS1wcwvMmhOK20841COURefHqdgkHnrBZt2zpgKoRdZ6oMk6X+YVsSoKWZkKZ4EUwDOVKYStHwHgqX4GXrlwcwywXkdzr5uvWWwWYVeOHmOPbTVFMdu4bsBaOukjvC91jQPm3m4AdM5NWb6QgCpCmpWaAJj67chvS83oamaVSc1rur9pQTZnHSDHC0WQNbajuK4ViAoEmTuq4BrYYV+MURYdtn5+ETC8aGnMac22bqDcbw2ecJt4xl6Y9q50bkaAs0iWdTu6TUarkxjLaEw9JAVA++487MYXXXflNysI5HmLeo2uPKGWNmD9CPMmzM/IbWWu4v5BgtWodNZ7z17wKgvlasbnaQsevs9nUE5ZKHV8zO4isfJ3TlOX7IFBk6584wGz/nx/CZiZVc5VyK4Nhep7AJK5Xgc2lvHYs07U+wIiNOgDDTrYKluu5K53LWgiyg6LrO6T4580eLJXx/EzcmBZsqQTCxTAWaCw205uK8QKH22VmBOzAS9+tmosM5jCxGuEBkfkeA2k8BqnQRQFDiZAiXEtlzLeVQs/sa9sOSm3Bsm1rX7eV0jT3Ostb4G0bwO+f6/tW1l0nbSlUyHqK7VIzeEtdFGOaW7wsb7HYTnGeLbHxMeVkKwn3+I7TKsd/uXPf8PNf0PMZAHgj5+AXz66y++My+8ArJ+A6Sdf+AFBE1z/CNy/+4iH2QdIqmMxzRT3i60PWAoAmAFdFz/eoamcZYqAKYATZoAEQKl8OuOiSAGTbPpiT3K0CN3u1zR0KC1gC5MjaYnFhednLH0GnkmyDdruD0t0bYHTahaBE7IW6gEnFurK//o7uxfsM3MyA9SqXG+DNdMIx7Z0OZXuXfussMQYh+AqIEvvAWNMsQ2xbnD3iLu2iULYHv2+q5FlKZNPIxME9f16Dzz8+QO2heuzkp4aQLAIEuGErWM6pBYpbAI9ZKHExEcKc+ceuQjC0OErCCzroINTYK84JbROA111+xAH5Ub6ql6QtfURkPY53ex6vtbouiwQHJCwQAweewvgDjj67Fh7jMP4c1nCIogirjsMorix/A0OewecHDcTN99sbuyxlyPNFGePufHj1YlxpxJA+YymGYc5bd+MUZYdDh4Ydv28SsAUjpPAgWUl+5iAfUDUYAuccMABxaxD0W6c5YfMQQ1inCtmWBhEIYZm4ueOkf+mO9gGE1acFGZqJvAxuDwIO2nhYVO6v2YMgdTFS55t7w1o9kfgdo1xUfnZqwvuT1qQ4JgSTLbwGre5OaRvJh9t5JiS9UmNAwFY5Px19QzgZ/N+r4XcXF2EVgcAcVeDV+6kGY/G1McPONQjFO3RuaMAMQ7SnvZrROuquduXfrqmz6U1kpqVKuGBE0QgZaTXWQs0oLHfeqsjJothZQBlCMhz50QLp5J6lgZTgjsPgSgCqAgovqs6FFW8O7suAzC1jkBf8yjvCqQafmHYO29u0RZs19gPzWyBJ3Ks1GnrnRzDL+0gbebmPbEg9ZY5NH7HlZtzq70PFy/rjQBzeyrjOUiEOTa7B/o8eREzzpngCJvqW/3pEgkiaXtuPwGd+nnFWGiS8sNXkE/9OHUxCxk4yYGVlmXOOVDl0lhoet3wP+I6ogZZUfW/TrV365Yor24EUOAtAwu1quMA7RpwkXNKKjvX13K8EJ8/BLpaFjn8bFxnWaIQeMQWKOLCI3O73h6RWqWAykfIvxbQH84jtkbRBw+LFnnQMFY8WgABAABJREFUVJRFzPuKqypbBWu3njo9nsHqrnRuku6xUrA0nRvSuvR13DE5a30tXyc8UtGG/l5WsV/L9X6pn3FZTu5rQZQWrpGkQzZqy64a7KYBOg6Ilg6bGtuyw2r2Tch4ssISAPCIe0ywxeJujT98+8md70GUmw/APz453/UdIpPM4+wIROH5if4/AXUFLGZrtCiwwDoEWRUXDaftjmkO90WFqj448/Z6H0zoA5ji7/qmPviYAm0STFYECJ6wrEmsAy/epVcwiG2KjUDL4i4giqQuXvmYKNFlILoOrD/NnVDWjICVfAvk3Xjkt7Qpb4eEOf4gPLD4fK3dkDI5dw4ANziWE6zLFt28wLRwTmDB/coDKi0KzLHGHpVPYd2ivP+At0/HyGRs6B7SpIyIay0OkPoe+wlj8emIYrbGtppAfL0lq4MTJ8c0yCPTJMKRfM9LTfAtQUqYOHePqOU6fAUh2Jy29wYaZNKA0xj7wGAnbjyXAAxCzDzqVcmqY+sTtkApAbxDiH/yy71LiSlArWTAchmyBNi8T7JjRXeeCqsPS2cltqlt67DPBVB0fU8QuwFql4b82Bbo6gO5/URrBa05HOOAnZrrBCyR7FVCYlW0xsJZkGGPar5HVx5RC+jJQAX7XEudAslEmD0yGIoIjjBp81n+/Hz8zs8Ro5JiBkgGHw4sK7ECxNWnpotWLq3ruDqG9nNxlMY0X4whsZU08ZzBNDR/8LqRy6bSP8c98AHsgtBnWDqU2OGE1w6iMNMGwIPTXZjL5XuItWwHyUjlv11Roai7GBuFARPZZ0sU35emM9eXPnplzwipJYoF/L1FBFFKf2xZ0L20xUBO+EXsG1aQ8tQiwBbgrHaUvpgDUwpVxuex4iGxyiiAqjj4+WZ4rbRjy/Wfu//ufSsI5q8ABGWbBpcsod9un9J4Z+lXffdHCczdFR3G1REVKC00x+OSOVGsnoR/Eq08P06nyrWQqB+dBSjpX9zHgHRtZFJllqtKS98h17aAAzr3X4Fy6FDfYFSXPfCELZXdtm/NZIGZGryT43Kk5/FzGvxEe6++UFV5EIUsyAKgIuBJRVtZE5m/ZwUnW0sxaKGt7IA+75MDSS4ha0phYETfT1upGFsJIHtUz6NdirUFinWc5dIp7p+DxIBP7mBeG4YAFO2ywwBKTWWlusasb+knfT+C0un6o4F77uM5d8mhsAcWv8TKWV0GAGuc4FxYztN1IMoT0q9Xqy0PDJl4hZFsaZ/OOa1m+KktgD8CC6xRYY8lViFoaIcS0//X/4W33x3dO71z572tgbcfAPzVgSgf4bYSK+UI4PnJ+37+6J/Fa4fRAu++/RFjHLDCkibqChOvH9x6naNoT7fVxEVq7yp0bRHiEQAIMQks03fRlqc6jv4HF0HZdRIXqyQK505TI8+ktSASfPLgBbUURJlGIe6Hexf3ZHOTgicsnF1qdXIJiAL0Jz8G17iP8DGgY+CPa0Zomjs08wb4g4tb06HEAmtImF4B4qYe111jgV0xxb/5y3eYPjWoZSL/CBcnZwZnpaSRZ9l/omcQ8NALSDctMJud8O2332M7q4NLiQS63XlrJsmsUmEPZ5I67TEICLdIB7teQHkCkawtwvxxnzp+BRqbqXeWsixRRHs/7bYo2i5142Gm8JwFikz6slDLgs/EAbGkT3L8k1vEGCgV0LwDtrPaW5Q8BCsTtgRbYRnSF4slyqNYiX2ao3n8xsUmWiFvHXaJeoL39UyfO0bvlwBKB6acSmehcvRBs8VlsaoP2Fci/EvQ2XGIPSXBZjsU3nmlwg5bn2HIAcDO3m7q5rOiwnS2xf27j6j2fg7f0HewsuOI1aH/hqMGuCucOe3OM3QCimgwpVX7DKZIUxzhgJT2E7BoffA2uZ98F/lGzGiwRtjX1S2A6oTxbIPdfIRx4U5gxiKKmbZWZohEQIvBPIukPB+rIc8OWBrQFgWe0MH5275e2mGKGxTJHKNjALHLgUAN7EraVQWK20+uv4pAIJ1J1h3pKyQIjPbAH1vXR3/polKI+RheHidw7j8TAPeVs2a5EUsXZmytecv/d6X71qz1s+J8aJBA6Fz/Y42fbKUdxepKC2cAB0nu151XNPQzTl36vEBf8NQMvGUZYQNO59qm8OCke1YJvCvjPSpc4rXH2AMz4FDvMSmOMS20KIaAlK9iwVQUmhL4ukU2/SoAG0SR68p9uY9Z/cwQeiUopBOgUmuUw4ClDwNZzVcQgGlTLXAq4niT+HXsmm1p33N9zALvcgDMpXRO6JRxGq2CvaxTHTCpXIKFCbYouw7j5hjd8j4hrofCXwt/pt2A2HJFygr0s/7Ehx6WQXJKXus3AygMRMCo19fxViiSgUe8oi515dEgi+VyzK9kiU7HFi5bT18P07+I9S9gKVufqJhRwbqygFN0y/FA4q6zm/fdI2XcSwgK12z98S7l58aApmiVn5K1HvVBFFfn+JpfA0RpgSCfccfUW12mrVLYmqEEsKmxe5qgmLUhBogIGyss8XOxxPjuJ9R3cIPoDgEMuf8R+GUfzaGE+eg9tzILn+63OFSOuZ9gB8mSIwtZhT12PlipgCEdCnSFN3U2bEcFQGELlBzlF/wy2UraWp4E2YR0D5e9Z+MFkZgKldKi+ngKp2bsABQtlGnwxAJINFgytH9pr2JAJgemcF/ZAECN7WaCri6wrSYo0HpRzfWAZRKnpPWg3BSYAfXMX4wZWjYx1M8l+zKha004XCrTrjxgWm0DeBOzk0Sk1Q36cfLdZSI4p0kWwcdp/Uv/WHzd9HrHMwzna6A3/m0sd6dB650hQA/I9zNkyphh5QWkRIy0XyNYpGxndRiPErzZpSqeBEsTnXZcjll/mqMRkFP6uwZR9FiznvkciKKZCgs4kevXSO9TAoC3ToGr3zfOMqUrOt9Xu0Qgkm819gwjm/LvQ1nlj4nZyrazGl15wGzmNfycXli+AVt8KG3JTefNW8vo2sNanUtZcmlyYWja1q03U2kXsYrRQo1sK1Xnt6MS6MojUAOHYuyVccIkaEC1TAStHBOhhUnNUAydN1RmCZlOCBgabK+DOrwBA0i89rrfMutEAEpcfRLwoXyDtj055pW/u8wdAqgoDd7E99fW92024eahCMSg+lMAZQlnhcJm0/yvBVv/CbvyTfi2Q9YTliZQ6BqhjCnK6sK8FqGvc191fTiul9Z8fwk4IsedA2Fy1xxi4l8KMjHpwNJyH7FCFkG7wgF7b/EEnJz7DWuAgcjXuIvIRVPScVO0FYnucNJ3Zd+ydCpVvd6nfseUBwNsS59cMPPXRKz8zL2nBYpYwAoQ100ez5blmN6/ZDxoixQGUcQCNQXry+S8oujQzvbe7eeEkVjhuQd1xICz7ntsrS5bPt/qq1D1Q7we09AyZsm1+relrPsClLNEeRFlLBLNdcJaQ6z1Rbv7lanlibY6kRAEHfrhCFKrP7fl/p0DCzUN9e8cQK8tU56u+JjXgSgNYoRCbkwtQJdIO7FsRRgFlc1d+ab5B2yWS+CfnEXKGPsABBRo8ePsR/yf/8f/xym9BP36waUyfvsE3H3n4qAwgtd2HgEVTeVHBHBp9t0J47uPeLz9AYBr+C2mmGAXgAnANfAYY+z8ZFGgQ1cUKGYduq5A1xboPNwoQWRzMRws4U86RwEXX6DwXcA1UectG+RZ2qRDStpTsUARlwERzj7s77Fvxmi+v3Ntv0IUzDb0TYbAE/17CFSxSAtx3G/YDcwSBOUY0abM3X7T3qGpn9H9ocBqtguC6cK78jhIZYqlT5cdyr79DhNscXfbRGsUjpXSIBXUeKJnEz4OlgRg9umE6f1HFHddsCCSuDoVDthiFwJqcnpTGbDngvalptGu/IAqWKIAkeFyx79+S5Qx9sHOBohmzhwHJdFysMYC6PdHrR3TC64mvUiwoELxT55vnY/zaubinvyI98ESTMbiD3iHA6qQxvhnLMNxPz69c8FiH2dxfK6Qgig8Nhv0KQeUnKvT9dZ+Tb/ZirC8Aeoax7LGsX5Gs5liVB9wmI9RlJ1LYepjnTjXOpnLumChMiErlRYFKm+lkqROrvZ4+PYDJhuvgRWtvoASBVzgkjlSs2Cv+RpVPl3xk0/j+ZS6fzKQYrn6cJOINgl7d82RF2JvgNTlkcEcAWhndl29B6pZGidFRrow21Z2L2vc6zqmnKbdXaPtCYj6WtqUXISB7VegHd5hghuMAjMVmbUd2P1CYv9wzB+hA5y7ybg64KHbxBSTQGoBIONI5pLGxaSY7oGpX3+Oe+Ctt6wKfc7TFMDdre93AuByOnXJCDZDHBMC2Pj7urgUwsTG4KgxWXwK/rOWXNrC2mfSAljcTzWAXH+O8dX3HNJKskVH7jq5Z+RnPSeQajcM6zmte3If4rDGQFzv5dsI71Cgw6EaY1wdMK23mJWnCCALaCexoz7B1bH2/4n2pT/ylkmD7Aym6GDq1LdC/y5oPwhVfbCA00VLnVhcc9DeAyo0V4orv0faYI4RaqMdonAJxPk2VwbYYJOU5ywYLV4zZ5ko1HPjCcGhnVXwBNsArkjgf1nzK+xdf622mNxuUe0PmD6dYvwUmRvJzTEoKxl4bhFBQgYspF6Dg7xw89rMwAvLrpfQb6gvEIXNpQAKHxdcjhV4DqAPilTqOBnLUsegv4x5PfZ9vJPtLAaHjWO5nxa+87Luzps8cP/VAGMOrM6B/+cAbD3/shUkl+0odt85um5WstA2RsS1xUlOaNmgz5SvAGCEzcMC3azEBzw4k31s8QEPAICP39a4Q+OAEDHf/g5ABUy94CuppJIUbRKQ8Cn9H1XA4naNNRbBbqPzzLwzozwEjWpkZgVtbaG/lwZQ5NOHer+vLVQ45kWKOnfJhFZ6Bm8XMr5UIXuHgAWSznjVLbFeLZz1ycp/pxWiVptBFPl2Fmiif58rY7KQYd7XWm59nAbkpH4Fl7mnXqBtCxS30VJo4tNVS3wbJ4zvQtkeY1S3P2ImaKBM2hz9XyZt/W5sTijP7pmVm9rF2SmqDjuV6hiIQsdYlTENafdS3+GU8WUGzPWf1y/YlBRUlsdNKGu7GFF7iAF0J8YtL56sKeuQLsByTm4hqRyAsr4dYVtMQ1piTlkc3emcWw+780h2rM3jEtiMgEekIEpLWw1w9htreKvfyarjY/SxQOzv5j1uAFQ4tgW2AIqyBeYIwStlLpSFiue0ONcu0HmwsUCHKQqsvQhRFQe0t1vc7Zs4/tgaSEjWHw6+6GnUujVh4oG2EtF6kV9VjxxLFyDn7RrnTjFityJWDgBx3SkQGcY5HQ/gpgCKmWM4uyLVOso2tWArewJgDkgRKzZNllAo85X2O84xL1+Lib0D7W6S3zJ/c6BPsVSVYyTo7CGYt7v2287eYAovKLDllGxlTAsYe4vYRzwDfIdoEg5E3/pJDafRLRD7Oc9R8puZYmGSIVrCNHAs0Be0Uka2L4wBNjDH7k9CKQ8k7Qcqu97CgJ/dehaLLNegHFPNdUPr9DWuPPr543OMk+fgmDxyv8hXuucsqhbFrHHuY7J2MS/FvAvHm5D9Dv2JDei7LVDfSRSgNW0LpJZQQG/9CFk4EPuevJuU8z8fK1nMLgvz+PumPUZ49sKkvBsQg7DrODBAOgfnQJZrQBehS63JtCWwgCcCmIgihME+IK5L/L0PVQVgjaI9xaStWn7I8SYCsriLp311yEJFX/Mc/YZgiShnXnpujqU7ezDQ53dlrPOW9xlwIZ648Wsbgyci70j4gpjhLAVSdP8VuWZP4wJAMlZyih2uGyK5o3t12xql+dVAFNb0Wh/FYsB1p9YmWmwJsQE25T9gMz9i+k/b0KATbLHCEg/4gPtvP+Dfdn9z59/68z2gMn0L4MmlNwaQaoME3RSrlA+uePntCjsfgUFS1cpW7i+ma+wXDTiQoy0KdEUEVjSG5so7WAs4LyCyL50n59fFcTV2iC47Iqj9jCU2TwtsVgvgsXbv/IhoicIgirS/fBsLEBkCT5A5R+hcH+HzLFRYykSA29B+DaCt0cxr/NhUODxUmBYuuCzHRHEZl6oAkE2ww6GqsHy3wuJ2jVlxSmMriDanUc8gyDnoWBHafPkMJ0xnG3R3RYitI8IjAEwDA3kI2hYrPkoeZeUJqgroLpPrs68/AJsDMI+9cVNAsjVQRHhg2AJFGEYg9h8Byhg8AfoLh/b9rFLrE9fHJkn8EwFK2BJlh2ms65b4+P29Cxj7N8QxukIcp9LfLwUqcwzsS+u5vUrYsYwSKxUXN+XYjHAsn9G1JYpygsO8wqGIQN8Ye79YOgBYLFA6FFhj71mxsU89X9DYmaJ7t8Ky2mBkWaLIN9IxAkQr/8nFqriDA9kFbN/6w3Vgcoufa5H6KAt4UhbUD2W+ECs3ntfkeQSQo747K084VidgvsWeYqQU2bmgD4JwmbQ3M7XWOe69GBiwmRRZC7ncgSiXMxu/V9pjhIJczFJT3wgKyDotdSLkABFAOeCAourQlVsscIwaV86iwj7mbxH7MgH6o413QeOx3yFm+2Gw0IjNFFwNFQi8r4BDwsiya3BqEWC5HbjHsEEVaYkhSsHAolfWv54jK6CgFizPkbZ40cy0XM3V9UEVi4bcofT76Gvr+8b23AWeUyyYBaCSQLR7jHGY7TCe7TGtPJgi80uLmLlnA9dvhF/pEHk/jknBSiTexgdNFQvS/6QPMpiiYqe4VKZvKB4KC/3Rslr3N7aKcmDl66cGU7Q+C6gWHvvgZSoTyJh0+/YYdcfHMjkvF7vHAgjzMVHclcQSRUAU3opVyhYTVDhgjTmm2GHs+2wFHz+t6jCZbTF9apw1sViYcPBZKWPARPalf2o+ncvk2JrKNOl1W+8zDZXLtvPZrvzzli1CXJRcgNghaxOrrkQ/CLmUlQWim6e2OmHelseoXpN4fAsQr9ecGfBcu/VkO6uTPswp4fsgSox/FGM69seBjhWU9sA45+r+y/U56ssUes3vsM92GPt7XE7i1iBnMsepPxRzpHKMMJCgYzaIQIrUzUf4a/0nbN9Pg4vLB3zAAmu8ww8Y/2mPfyp+cgyDmC4CzkXjA3DzEZH5YCFYAJTCHwvgm48NdneroD0u0IVtDBw3TT5YZDYdkxtjHR3MD2JpWpghLUIn62gSSy0M5NgOJdaY+62LtSBuPEnw2NVNBE8eETXbDf0DfQ13DkQ5t89boRyIouv4PC29yDcUtx4GUbyLz2kzw8fNFKv5Fu17cQsYh3TZAsQJwCJC2rJa4ds/f+fMC4HYN+TZNBMr+zyhA4kVz80cuC8+YTrfoiuiNZNMIG6CSNO8buFiu7CPYM6MOGow3CTEjLz00dNXkJ1nhD1KPPcY25juUgEo/F2YNHAgiwiXWYi8sjpBDTzPUtcdZ23yDXaY4FGBKB9wH0CUkMa4e4/1aoHj41vge7i5T7Yrv5V/AZw1iKLfS7+b9b6X1FnHaPCEQRQgBVNkfw6gvMGxfYtjfcShqYKLT1dxYNkJJtihQxk0/GNvlSLudxF0iRZmh9sxJrdb3JVklcICIwedLREtD72gegMXy+T5yWn1J15wbeEsTHJ8khBrjbZwrheAtxCQdmMmkC8oII8wLEIe1Bu17g5F3QFVFNwtRlevDUMaxhxAq89nZr0v7PR/7z1r/NqpwRSy3sb4Z6xNjUEwJXCygH8AfB8mEAUd9sUY7d0ai6JxoJ8IqtIvBJxlIJ5N2VnA1WsOgzElHB/Erj1y/RmVz5wwe6hHEBdgB5qkAIoIrjkwxb1vH1CR8nNkHWP171jXB2x0Hx66LwMYQ0wzl7HQ6OrPAyrXaPUZaOJ7i1Wp8IAHb4lXIo07sfU8X7CYmq1RzDosqg1q6WvSf8Sth/vXW0StPrtQACmvw6+r10hLuJIyAYl9/3QpTR2Pq8ETeftD0hejoMXKov2Fbfx7pjXmKEme4Hc9N99ac7SOjwikIGnuenINayvEcotYkXJMlCl2EPBELL4FUNliGsrWwRq88mXTBAgs0GFab511irjmyFreIg+iaPlOdw+WQS1Jt8uUa22KyAJyHUu2jY3myLv7+twjAVS5lqzHGyFlx6RM0t2PSjIg4PGqLcY0r1ur32yBrdx4jjMXNHZfRKCTPSSkv4kyLK4v49A/t+H42G+HxoMVJ4XlaFd22Xpkze087x+CvfF5ug5EcU972THaVUOEAvnNPWCDyICv3LGnxxlWZYfp/RaPuAeAsP3v+IDptzv8Q7dxaY9nCKAIgDjIOPUkPxsxKjcNQhhWEbRlK6blFfbYev8tCS9X4RDWHcvKRH5zmezzgigaByYp1xOe/EdLFAogK8FjJTClCGPNwD+QB1F0ma4fOu9SYoBE3xPo95NWncMCSnmDUzPDql6iqg8YV5G5l4mbJ3Jp82m1xb7a4u6piddj81hmMPgZ5L7s3iOBAZ8A4Ijp7Za+2cQPzso/UzRbC0GLVf+xgBTpE1HASicNx5B8sVBUfzdy7jyn3mQn6YwDDWkGpJ4XQes43mb8vnnRSILBYo6dB2APHliRGD0/Yxks3LaYYPW4xGk1c6AmA5xifbKhfSBqDDWIogGUawGTofP1vvVvtaF1r3aEU1t6F58ORdmRlUUUhkR4YL/UDhLgO2b2kYWuRYHq1qVTHDWIjIt8a5nfGdwHlTUOTBFLkrekLToHolgk0fDLwqd2lGeRe8p8pQLLBqG6jGVlDXTtCUXVz0wiY52D+DHpOEoWWcEKLQ1ojnnndK9OuHn98Zf2GGFE7eU+S5x3pM+JvQm7U0krAQggoKwvJTqU8w7A0bng8Fonc5JoWwU4YbBFHgZI1z4NojBYIoyv/K7jcYc6tQaI68W4VyZAPZCCFZY1Sg7MEzoXh6HP/wxbvVjC4CUAThG+k2tU7vFSJsBYmqHJ3r6EEnfwUFagn/aZLXby94/Wzi0wA7py6+ZFnsiEj+HYEi395pgSFs8npNcGS3utXcuKvhVK+g1T17IUvKuS+q+BrzlgjBNSdx4NhnAdkAbZ1P2fLVgsV4ihuR24bOzwmeK2I/GCosVUFI7jOHPvNPX3E6BQ7svXRwVU5R5Fe0RZ+jhjrByTfgpEwKSkfU1ajrF4Qn08Wy1r2VXuoevlOaTOb8vC8RSlr2tfOGVcaonSs0Jh8CTHx1nWKkO/abwf6jcBQJG+ywAIu+EJ8Kv7qJQxj8H1Vl/XvIlr7r58JJTr2zkQReqOZywq9fd4GfEkazHePBkL8yCaF2EsN75sTvsbhGCzx8e3+K//uMD+fx7jGy9Z3OND0AL/6U9/xf9a/GcHoJQAfgDwVzgGooErZ0ZaFpASzgQcAH4E7soGh3cfsMYCFQ7YYAEJ6gogmJ3L7wIdmdWlC5r+GOzSo0l//Jz7ju5oa8whgUXXWOCwH+PT9/dAM0o12yuk7gGytTTcOfAkV5Y7l8nqXVrQkn35PvpaArCxtnuDfv95dNtmdYdmDqz/sMBiucZ95b7rBNsQ+2aFJZZY4R4fQkDaw5/+imm3xdvZMaYDBFJXH0bAOf4BB7vy/oGjOfAOHzG5deCcA1Bcn5l6k0dB9TnVl0weQpp5lYVKXLm0JcphP8bz61cO+7c+QaxPQrkHUMxYKFqYFpLTmWkE0r4oi4WYJXtB5en2DbbVNIAmbu5x1ic/Y4kPeAjuPFtM8CNZojz6su//72+d6873cGPwEW6/oe0K/XhF1ngQOgeY5MpyoEfuOGvx1ZYoJVJLFCAEgUZ9g1M9w6kEPm4mQNlht5xgXB8wLbZhzhXN1RpzLLDB1I9XCTA+xt7b/awwwQ6baoFxdcD7+Q8u6OwvcBpWcZvYw2nlP8GNVVkTZA3yARhHTy5V7HMbg8/uujT4LGDHSgmfpkPIrDIBxSiQNuG+ytoenot92Q3gtHFoXFpTRBcLdv+Mt+673mjSzEbOVz7HuABIgj0yg3SNxub3Sg2meEYFFhQAJMJClbj7SPDfqXczGEMChscA4j4wfjHF9HaLZb1ywq3EP/mE6I4sFrUCtLWI1rWchlb6lYwxBnwFNJHAsmKd4oPOHiuEeYzXjqjlH4d3YasULeABNgAn5byVeiEL8OfjLQsTq8wC+S6NicJX0CAKa9njfuThtMsPkIIhfB8meTaLv+Pryb126pkKdCHGmyRaqDwvUyAG9lxjgWm1Q1GRlcC90/BXew/wissEEF19LEsUDjZrAfiyZmrttbLcbAsXK+FAfWxvCF6p607cRrB2jN1XAKI4S5S54fpwXmjUgAmAxCVCrqGvbbpH+KQYAMK2be0xxFlHx4X7ItInC3QhEUiFg98/+MCybVjLx3DZTsd+rZfzXN0BO2xRFB0Wty4g7XS2dRbiAv4JeMHWUwI4A3H+ZF7P4hGByLdrUJv5Rq6zmoVBHAMguUGMwybWKGXrLFKOSG9nYTxAzDRbUrkAKFI/8b8nlQNQJpWLz9gDOBnc1O48lfrXwWPnCJZmza0DRdeVUwxygou0L45VXT/ArNRZoIsFpjA4aIGBss0B8Onn68vtUu7W96feOTm6EkR5tou5N1h1LVLTVbmz7sRA1L4KEFDfYPWwBG4R3Gw+4AFLz0z/8v6veFseQ8rjxHz7Cfk3ZGR+Dz9NH9T/HoXPtiIWKO4TFeE3kxWULEeWSbYmywzvAF6Qxjjsx9hupg5AGbI6ke9A2lj/IPb2krpzIMoQWTOHhR6zRhdIQRSul31vAXWqZ1gDKB46VIUzHVxgjQ5F6EcykXcosMQKKIDF7BNuhEmt6dq6/8ozFnSsaBD9dvQEVPUBh8p9MQBhK31Mg2fSv4a0d6nmJj7QYT9G1xZ4ziyGr4nC2yUAip/oru1vFrjC35FNHP3+c80M4DhYfDkhxFmAbbyFiVibROuUmL549zQBVnUESlZIrcT0lscrW2hpGgJA+F1bowxn6hh4lt817cv5Mh5yc3vveJcaebvpHMM2B4rCWVUk2ntEjXEHF2B2Cuc+IQLu1lvjrYsFMF+j7I64EQFVtg3dn7P28PDwoNpNCUw8c9Z216U/FqamFYBOGKsGaSBRbYbc+X8RWIpY54Imx7TRrglTC5Tc+jFUlvO75zllSJOpzXKPX4HroJuD+5Yo0jbRrTcCLOLWw4GvC4ibT7R0kHPH1QFduXf9NN449gPhR0QYkHqx6GUQRcYigyhshaKY4ufSu1RAwjrHtYP3u6SubxmQY1R1PxLKuaRa15AyizkeAk6G7s/Eyi22OpErdUjTtUosJvmuBfF/fC1+t1iWAzNj4N20XMctcs/HabSZn9TPxs8o7xXqKqCoWnTlAePCxxETjbvcSvod82DMW+lH5nleW6KQIkLSnPYBuLJX5m7DfbKE5nO6nPzxiuiICqcEqBQrE2uutWPEABEMSWPGpMCKBmmAyCO2bYHOS/ddW+AkPCPzjmXsc6P6gKJs0dUFuir2Y9lWGKPzwN0e4zBOmLeVNbtAat0vHG/pzy3RucDJbYMKXinByi/hNaQfSr3F20HVMc/Ox7KFCVuk8Pn6OkB/nBSq3gMpZektVuXaGeKMgEI5axQGVMTi5UbATUv5pfmzc8fyMYXjibvyTcjupvkIDaAIQCLH6uxvGijh83T/tiythtYL1/Tp+sKk41K5snjFa9KpXwmitEg/801axVsWcnnLzLfFhG/o9yOAFdA0d/j+4Q7l/9phiVUwm/8RfwUKYPnuZ/xv/8f/z2XqEQ3ML4iDQZhpZkgka8JHV/7NrMHq7mc49PQeBbqAnrnBXQUtE5ME+gL0gtb/SEC66GpmjX1lteUBT6IhE8+nOZrNNGq5pc1ESHv077ryWxHUhkAUvZ+rGzo+vnieSmNfAzN6MA9ZoqwQLZlK9/s0n+Hjwwzrf1xgOt9iV01DkOJ7fMASP2OLiU+N7GKorO9+xPvbH7D45JldtkQRDSKXS5wgAS7FvcD/nu1PmN5+CumPBYEVkr7DfsLud9on2C9QfIv3qMKiCDhNwqGp8Pz0ElTr90Vv0KLsTgE4ERIA5Ua/Ii+AllkmkM5NspVFxDOBzSwi7U6AXwRnP+eyk6YTf8R9CBqbWKI83WPz/YNzrfsvcP3ib4jj8hGpO4+MWQY6c1oUvb2krDaOsY4dOi/3b1mkbGh/TmXlDU7zGZp6hmZ+xHq+QFXvsZ1NgiZLtFlOa9fhHh+UJYpYqRyc9rXYYXn3M5Z3K0yfGtRiiXIHN15/QdT2z337fkJcAz4gWKWMGheYnIPPAi7+iXwODgZXUlnpjx917jooEU3nZT4TQVe7abAJfAeMCqArjyGWzB5jYhn67g/WVgu0zGDkzMc1YM/rjjY/d9DwBK+dnjDHEXVgohjoFhP0uC+BjiUzRYxNIdpXCWQuc4dL1z12mta7NSZ3LtXn7O4UY7qJNYAIs+zawyAK0LekYy2jaA99oNnnGfDznQv8J0DvLgGEo2twamE07J8+xLxqoZjLcmCe1GlNu+VGJlr0feMF0KaKQqCQ/l12eOOFwnG9D9r1ceW+K6dtZTWV6wd7pOJCBNMAy93n8vXXDnI46YknkvGP7y/PNfGBaJ1Vn8ucksataFFVLuCxiDEFOlTdHkXbOQspno+EZ2ZNfm4t0oJYCCQbA05KDD8WoqRPJQrBIHjFrZzjtN6vnz7hFje+PVhYlPFnAdY5y5K+4Eka/q7AoRkHsOTYjN2YaEZxPtH/QPqdyyi+H+saxxJoagD1M1C2GM13KMoWi9uNXwm2mGKJCvvQXxdY+/JdsFj5BitIOuQJtqhwwA7TYKUi7vcc66dofdpetipld0gGoIXvk/4r/ZPfz3LRBx3bqn0LfNG8pYCS1JYCWE5L//zwWf3gs691/TT2OcxSymXFnRQuFhvgLVBKxPmfeNpegFi2UpGyGR2nLVFmqQUK88UcIFZ+c7/lOrZ2bCHxUlLQcCgQranc8WsBy0BdW4bfybpgrAlCsjY4i6sWz7/8gkvpShBFf24DTBFixjH3zwCLRhK5k3pQ5buHb7F9mGJRrCGmlu/wA3aY4P27H/G+/IQbtkQRU24g1ZSKoFIgiY0y9YEOJajhFLsQLMcFQqqCFkP8opmptRbRoWBk7lFsiwNhLFLzWm8i5eOfNJtp1HILQLJBqulm9wBLy/1SECX3+1JKJmsq089T01YDcgKMyXvVVAYAS/f7iLf4NF+g+OcO08KZxcoglwlc4qWUcCaL7d0ad5+aqNl2J8VJUk/C0p+A6Prj++FNCUxnWxRViw1lforuPTs/4UySvjTUNw7ETPLEcWrGQHO5P99roiQWikU8ZxS07aie64jxey7TSONiqiiLxQ4TL4RQGnEKLOvSGbvter9wqYsfb6L7DgOcukwATh6n58gCP4YAEktTM3S+Po/LeEyW6hhrTtfjNWxHOLYljqVjC3YeMOgQs/KIWMWp5sW0XQSZg7cmA4Dd7IB37z5GoUAYBSA1ARaGrEScO9wNcQPHqIgZLuDOya1+Aqq0HUJK2nB/WXdEMIHaCqOm46VUQLUH9jihqCKrwRaRqXtEFFYviSfBwdlyprTMzLA7Dwd7bL8C7fAeFZ6DZYkCbWkOLvwXsFx0o0ZW+AMXW82Vjf02BhivqgO6+zXGzRE1K3uGQBTptzx/AdFEWxhizwA/1y7dMguqznUnCl1p3IlSaRHteA3SZvKe3K9cWQqsaKsVXTdkXWJp0ANo0npb+cbzn0N8SjnCybdXU0+BssWb+hCY5uncuaVNi5hphEE0BibYIkwHnXVPnbcE1RYs2h0ccP3kEK4l93PHC3DigLoISEkZj04gpusWcIX3J8UWZdEFcCUGbffuPxq8s6Q76ouyhnblm+DCo7XQ8Ru7rQXm9oHbr8fqbY8x3hhzreU6Z7n8aG18z02iK7DdTPrACfP9QwCKxZcDKoj8DVCmmfiqeo/9rAq8EyAxoqLcAri5UL52HANOloohEuL4GWOPYuZcukd7v/oOyS787AJscB9m6ozjX0LWeQy6+HYcwbsNe16jbB2/IG4+QKqg0Zl7dPwTCSJbFt6Fh8HMGnFdKNR+Sb/ZgkyOK6mujhYo+0rc8IpkPZE+mPZF7c4TXXfifj5jjwZPkrK9B9SbcZR72JKqLYGW1oTsd42te/JtcAJwLJ+BdX4O13QliNIAmCLlyPWlFKCiwRSgr92Uwc2MttxOBszSWxasZvjXf7cPH2SBNZZenNne/YD/6d9+744XbaRoGtm1R7YiFHsgZdpt0RUuDS4gKXIdQixM/MF/SMko4Rij2BZDAEqHvAuPpU20mNs9Kpfhoxk7CxQGTywwhSdO+T0EouQmhEvKrGM0jGqRBlEsZohN+Gpjy9ZNIqitADz4svkNPrZ/xMd5g/0fq+CCAcBrEB1q7m5Z4BusUH37X6JfptxLnkWejYNf6fgpYrUCYFadMK4a7G5XAbHl/lDApS8UhkksoJh6fUQDKDKB7F+/77CkMc4BJ8+ln2m0hgBIpyY9VRHjJy47h9qZKG6LaVgILPccCR77iIeQkYfTGB9Q4YcP73FcLRyA8jfEDDxkWZeAKCukbjzpW6rfyvJPMzxDIMoQ0JI7Vp+nx7I1Jhv0x6YGW6RsA2DuUiNv2gegbLFfjrGdTzEttsECQObbHSbYeUuUkC4RYyx8XCMpa2/dvHxffXIxKCTdMLvb+bTHAWRnTVLtmJJR66xTnlugLB3DIzFTLiJx1xGwRCwIOOuAtLEGWHwMgwrAdtZiDHhrlPziblkHyFYLqEOWKBYDwwySHOvKXn869S1qAGNEcCq2W0HzMbtyyG8H+MU0tJI5JWSf8EKslG19ZooxDtgWUxSzFovZBgVaVHsXLLns4JRBFogCpJpVIGWCPYjSzFwmHpnTmKEVZlVbBLBlgP7WFsBhlQFRMZR3G8uDJi0KHLoqaBf3jRs0QSAU0CQnEApZAn/gP50QeKodsHIsgWbe4E3ZhUxi22oawAYBbSX2CIO6Q3HwNA0p1Nz5MYAoXydtRWelEmNLtCHGhFhHsWXNGvlYL9FqxVunFB2KokVRdahmlGmyEzfarrced+Ubv/XgWtFPny39LwUL+gBL5HdTiwsBVkTp9JppgzlufMYSBqd1fAhr/J2zOtk3btycNlMnRGqlqbV9EYji9+cAyhscm7c41s/Yzrc4LH0mvsKt2XGuWfu50rmwJ4Cy35d5VRTWbt9987LogPu1i3/G7meyfj8hzosMSrPVXovobssgi1DO8qREf861hHPmh7TyyK/xN1VMXV96nqDtHF8BRCUM4MpGfE95HAs84a22RJEydvs8V1fH33sfT0vWh7w1Y8zOowEWyxrNAkyyIApZViUgejNKv0cOHORtr0H19gZ4upyvuRJEaRETQXLYPfmtjZwzTH+rDpNByUy4CBhLvz/3xy6B79v/Cbt/mWJdLVDAmXtP4bL4dO9K/Ev5N9zcI4AjKOBMuhmsEeFXgJYnYPHpCNyusSgkKNI6oKM7v2CxBsaZ6VYYAk40MYBi7VvmrKHj7cfYN2MnpDU3UavNwthG/TN48lJLlFzZOWJ091LQJTeRW0JdrfZFMCvh2mOOKLQGoa3GT5s/Y/WwxPJ+5YWwDVoU4Xu7eBePKKoOk2qLf2p/cm5f8rl0MCv9jj5oJQQF9+WjGVyAwSq6hQmT7pYS15eEaZB6pkSDk9PKXWLJ8LWQnsG0lrZKq59LBF/tQz1CWxTJIiALgw4iu8UUj7h3QIkAJngXA8ru32O7meL4t7euv32PGOT5b3Df5HvEmCiPcP3ikZ9ObB1a+i2k59YRknnWWtQvAUjOHWf9bowyHodAn+kColtPTf9StnGCTbO5QzM/YlUfsFiuUZQd1oUzU19iFdx47vEICUg7xTbUTbENZdvbCRa3G0zfbTG7PUWm6gkuAOct3LcQpky2Aq74spsOmD4Bz41jeEb+/QPj44doKcqQ1gEvaL05r7SVtjbh7HECvLJ5sg80W+0P2Fepz66Oj6IFUinLCqpKUNFCD2tDhRkCkDBKXwuI0mCKzmdPY6E4avijO4/l1iHuH3s/f7P2XwRvyc4m5uvOZN3F5hI3oMTl4q7vclFmWAuZz4B0TtPaQEk3KXMbu+4w89oPMMtWBdESxepfQHS9sQCWIZPsxD2nGSOxMmG+RfMwLBCGRlGNlIAoSOerEsC8xqkENvMZUD7jzXwb3H62sxSskFbjgIQRVEnBD028novgmDvGCngoddqyRJ5Bux5ZFij6N7v4aBCmQIuq8O9WdC5WhfHMcc7pA2R6PrGCxupYfwzoSf+8NE7V75k2mAMh7asdAyLnxpNY53igcbuZOoCxqSK4qPn9ITAlJ3AK6fXfWrtXcAHk5zN8Ws6A+ojDgwNTFtU6uN8usQrAioCA4uIjYIrULbAO+yFuYAHsbw+ouj3GzdFZS3HQWVaUMGACKpOu2xjHM7BigSDcPhz7jM8T8IbBGHYrQnwGsfQadU5hw/RMv280j8u8rQA8Mo8xQGIFiNVBZtm9h9x3UDpXULHOjqEIIiAv8QD1+sIy684rFVgJ0z/eAlFSq6oADgLDIHquT18CpHBf32aOy5x6Bck0Jr3q3LRmgClyOhMjeHyq3Gbl92X7CHya32P8TwessATg0h8X6PABH3B/94i7tgHu/fliEWClPJbn8eaLResCkYoJpwRHEtM0cd9hzUN85DyAklswpU5rbxhMkQ61b8Y4NJVDmXMT4lDHyqHP1la3zzWkJ5xLr6Gf15rIZMvfsKbzQfX8WyZ9b1VyxFusACzu1+hQYoUlOpQ+mrjzI15iiQ4Fnm4/YNaeIihX0lYmSUa05b4cONL38XFzQlfuMS7SQMZADKSp2ZAuuTjdRgcDk37xFVNXxrgoz9QHbnihU3VAbKKujMGxRGvGAkU0TUyDyK6xCIKJLBw7WkjWq4Vj/FdIg8fKv2UtloBdR/RzwpwjAa7PzLN6zFjMAVSdPo/3WRixyoWs6/B9WnVcGMdOS7yGi19Q3MYAnQDC1zp4Rp/Hh2O84vzdoURXFSjuPqEWV88akcT6RLtR6Of3gEhw0wGCOe6oTLVGSZA3/b6F2sr8wEFn1RxYtCeUZYey6NAlglULjqMlxCBKzj1CAyhdsp8CKJ1xDRF6Tl+BaNPiDd6oOTaux11g/DkNbelXdCFpT/k27ILBgqtrV9dLtTULZ2WZemvYcbFHWbjYHVXnOl9buOty0G2XQraftUNWGRFcW/rWHIci/vcDzFqpVXO/h44BLoxtMsTj8Ly58VtLkTEkDEodz1/JMTc4YYqmLRJf+a6KFsiOxmHtFip8v5C5x5XFh9Euuvp32m/cVfga7AohrStzXRvuHsEWqSvQJfupm5LLLeXeyPUxye5TIM0WJM+QIytOju4/bG0iAlQ+wHHfmuk1k3NrSYPBDrnsmAALu+xsJkiUZ8JfaMCRyz4HRGFZgusTsGKETbnwwWi7wBvL2EllqTbwvOLWw3WAKLAp2LMHDbqywxSn6HYGehZWXggfzs9fo/+uQF8JZ9VrNyDZSpvUSF11+Xnk+TRYw/Wd4h1yltYCmuittk5hsIUtUKSsNP4reCvtUeibB6/oZdBTryuJ0j8BBJnX1lm42L3Uxwh6ckqbZE2wwBNtwftSGVf3851xTIZeAKIIRMMeWhbXqW9hWKkMDdwN3IdeIVoVtLR9HOGnxz/j//rfgSV+RoEO7/FD+HDv3/2Af/8f/quzIKjgtp8QARWeFETI/QTMcML8nXPrYEsUNp0EJBp14ZWVfcsCi7g8F/RPm/Lt9tPg+3XcTJz50grR+kSEtBXiZMkWKTyZ8qR6rqO9hKzPj0zZNcfknkmEIZ4UZLtCBE4eEfuSbJfA8eEt/vM//y8YLdfY3rsAs2vMfeDZRxxQYYE12qrA8tsV3t/+hFosWkT4kr7KQdhkktVBJffAqAJGT0e0364ARB966belZ4akf0i9mPInmqy2jC48zHR+BSmONYnZcNGegtY1oUIfH89xv4tE0ODJmzWz4sazwhJ7VC7GiXfr+4AHHDAOQWQ/4MEFkF0tgL/V0epkhZjGeIN+0OcVCEDZws3YLeLcmuvwJdJ5V8pyv9VcO8QgyfZcWe63BkJqo16P0Zr2l347j+Wn5QxNOUOzvAPqI+rl2lmW+ODQYp0ywRYLbPCAR4zhgHWxVnHOWBs8zB4xnh3w/t6nROb1QLL5vPXbXxCDfUoAWnEP3XggpY2AynEfg72ZxKAJkAI18HUyV7Mlim/Tag8AR4xnTisnIoUdM2lY62+ZiA+57uiyRKPUTfF8eRD73y09PS2wr6YuPkYRLQkEpNvCBT0G0jgVwtjrQLSVt5Zijb64WrDWX8qiG0a8tliwcJpdsQgIz6HSlLE1SPpdUwsj0Q667zpV2+h6EV0NoiUBC8KujfpZRbK/vUk2gOh6wKCJpQzKgShDAiETs6Y8R+k5aQ41/9yY1ilF2flA2MIH7nvfjl193K2vHyS8zlv72iqq/yxd7xj9m61WchYs2g1I31s/FwO4rvlTqzcACXigrUBT8CCa/e8xRvtZzOnvg5xbw5LApP441XP0tpsGsDEIlZs65fctwGRjlOUUr0Cf32Y+S48Z4aN5zMzh1vEawLLGsa7xcbnAar51iR1mU2+BEoPIb3wQebFSkTpRUonFigAwYm06LvaoigP21RZl12FSHd167N1xw3uxlYooLUTmA6ICX96bAZaKjpNzQb9Bx8ma3dI+n9eqfb4GlwH9OczidwUAYcAE6GdoK5FapGjrFB+AHDO/XwDPtw5AWd/WELddCQLL1oxstc2BYjkmihzPQWelno8X66osOCh9GEjB83MgyqUybqn2r5ChrgRRuBeJFpTdeERa1GXnbm9oUBvjsJWve0QQjlcflsA9POO8x88+QsoUWzS3QN35Y2WA5axRBK1skTAvvLCUaiv7QH+hG0LNhwKwaUuULomuXfYZiUv/obZfAkTRbXju3EuAlGtII7+55xGwbIU42Uv56gbH9i1W998AgI+B4XzUV94SZY0FSnRYzNao3zbxnkCcOFkbBqQmfNyf/eCs9gcXXBAcXyfG2dmhz/ycjU7/+nkMk1Iw5E0AUkSGsEAVsTYBEMATACazEk2GJ0FgYAYumi1OwnHC9G0lOxaDl9rahEFNKccz3AdjAKXFees+PceGsKboz71sqZIJ/p27vGyR2bd+l+jP2xbwIlsenxt1TakT5q0doSmd4NLNCxRF1KzKfCrZKCQoZEUroehPpsUW7e0W33RNbBGxKhNQlMcua7CY5Fm7GDAOANoOzlfZWlk7pO8oZbT2WNubFvDGB4MCGbv3xDUms64QgBIF45iOkLXBqcWCnFvh0Izx/BUEsd7vx7jxpmpdW6AoC596W1wtYyY9QATEPSwQq6B253g+rq6FaOXE6lBbFbArERDT8IrlVbxPPF7IsgLYG9+uTerGyXe2LFFylkxWfwJE204ZoCiDQi9biBXjhHkbZpxzIMoQP8Mk85DMWSIs1Ua9UBAenXXKqY5zSlF2LoUw0mw9CFZweYuUc8R9i79xBFFSK5US/X7knqQP/klZRZYpY4zBfK24UuypXLu0pc/j3o3nIPc79gurH0n/Sftb+s/nvnY6YowjUgvYHJgdyjgWRHBzQzoeNK9h1TGYkhs/FukxAzpWr2d8bIswbrYAxvUBbSF8bhlkLECA2hh7cuz/gNR6DUjHUYEOXdGhqDsAJ8f5iBWIPIeWDaQbMV+TW1Zz5TmLFCG2dBHAw9qX52BXH30t5QYEth5hKxP5PtrapDbO0QFl5b9GLzW5+GNEC6nU8iQCfimAoq1OUve9Ku3vHmCPlicje76H2l4qBwPDfVxI2v7XA1HE5BzoM+5Sxi4/fBsp36Gf+ZofQwLFjFKhQzqlCMAbACvgWL7FTw9v8R//tzU+4D5cZYUlprMtlrMV/oiPTsso6FuL2EjckRtXL1H1xS+vRRGCi8lWFphouplxuTDKh8ATjkAsPo8u48qojzBrwcz6t5gTS3PzEgF8aNK95DiTRLjkPjV0AxUDRKpk4hTLERHU5PcSLuis3/+p/TN+Wr7D7o9TbLz/pnPvcVZJ9/jg0PFvv8Pi/Rpvi2Pq2iP/eqCKub7U+SCXs+oE3K9QVJFhiS4IkfGIsQictkiYmh5dshi+QtKB62S/aLsAnnAdEEGTVCsfGXytmV1jDvHzlOCxP/t0xmKJssbCpzYe40e8x7ab4uP398D3tQPn/gbX575HtER5RGqJsgIBKGu4Pi7BmsSVB+jPnVDlUiZzqR4rfJwcU6pjCFjJzQEMeOT2ucw6jv8tK5WcJpj3Zbus0cxrNDWwelhiXO+xvF2F9ImizZJUnyssQ8rkFb4JdVNscX/3iMXdBounTUyJLDFSJBitpDTfI6RBDikWWyQZVNi/GUDKKOlPKO0soA3P1dJGG0RGZ+++WtXt0RXREkVnOQBS0ESYT1O7eWFQN22psMcYh84BKNvN9KtIp95uJtiMZnjj094WpXunQ+3aT9LgpvEpnGUqg92pRUqXCNfMN3Ada/0ZNNd1woIKaYC9904K/Eg1/Gk8lN73HbBESdxxkAJxCS9DwEnP6gRIgROtRbSEvmstUYZAFD0n8fyzod9zVTe/AWqnYT/Oj0DZeZeFFpVk+iliP2ArkWsAlHOkFXeWdYp1fw2s6FgqrixN4c1WKnxe7toWMbgHAHqOEUsUK7U2p0RuvwLXwU+4ReetXXlM6vlVLE96Wnk9Hlb0m+UDlhMuAVGg9pmsMcP/vE4zjy3buYuX8rEZ4019wOGhwrhwVicLrDHFDpLaWGKj7MgShYPPSvpj6Z87rzA5VOPg8rgoj249FplReHQgVWaKHKgUnImViliZ6HaR32xlUqM/D2krFD6Xt3r45FhAIOUtLIBEtlLH1icCskiZtz7BLUJGt918hH0RlYXi5i48sgSTFQvuaKUyD3y1WJuJlYoZo6tbpBZWIufqPjw07w9ZoEjdNbKutPUVU82VIIo8CYMil5BO1qSFAb4WCwae+d/cRKFYABWhB1f213/8Ew73YyyxwgJrdCjxHb7FFlMs360wwymNT5F7vb2zEuiqkjCzQ6I5En89h/7HxQRIXXYs0oH/NICiJ9DQsXQnGgJKhhA6jer9Gjzwi66pwZNz3BATCYmS2mqFVNvEwp0egDLhr2p8V34LvHdM4gJrbDHBEisPmLVuoi/GmLz7HiMJPslAnFyfH5t/04Q9fTqhK7fYFhJ00AGULjCmS7ctTI34kbLPfVG2OEJpgr8iECWCJQYQSWUSIwCwXOP6mlRh3DZkhshmiweMscECqTvPNGbpeVo6C5THOnXRkS3/a5AzACi/IILS0u+5/wtdYunHHVDOgT9mZByjryF1AzFV+LAhUEX2tXZX5m7e1rTfIp3f+bglHesZtlPrXH1+bCpM51tsZ5MARgJuvMi3dYBo1HJN4FLY7zDFdjbB+29/ckHqgMhclLQvY1ZAU22h8qTeg0kDTtKecixnDJK6BhGoEWav8Nkxirj+5DL1iMASzectH3wr80N6nE4zKu6lIhifmjEgwd5eM+1HQFPhVLY+xWGHN2WHri1RlK23TumCkAz03SkkxgQQ3XpcQFlXLyAuuwWxy4S+Jguucqx2E+Hgn0AUroH47XOuwuymBdh9RM7jmBbaRSwJUNz5upzLDmBbnfA6LNshBRALhIDN6wD2WMyBKK2xtcpEcGxHQDnCsa5wLFsc6kMIRNvVru3HBccQGYfvzvQ51hU5EOVcvfy2QBE5xgXwTsGTFAjU14qqWw2oaEUigyfcP/uptdPt6TPa6vdCW0xwInc6rdCR5BEBPAGADQmWGgxh3iIHouTAyJywyWQpRTTwyGv3IK/tYp2t4GKd4dYdJpZrYnkyDYr62NfEtVJiSYksBkQLlRaFUzLM1i5Nd+mVG0J+HQ3dU95Hr+clHcvrMl+routosEUDJudAFAukkXpLQpcybYWSA1HmiC4/M9ov6LgaOM6cBcq+4HDpDjCJMbQk0PyUpOP0eOYf2KJbx0rppeLejOz+fQ5EseTb3HpwDkjRrPQFdCWIItkj+PRLwRQNpEiZJmb6d3De5hMnHK+Qgiglggb4+Le3+K4tsHy/CtYDAqLcV494+PYD3rZHd76OyMzMP1zchaKyGRoBU6IGKm1tyypFo/BRO274FXvtTWLWlANGckDKpf894pSqfAB3ky+RjUGnbmXhTwuCFumsJSzlUcwIHoRauGMmSUCUOXCqZ/gO32L/3qWknAek3KUWdAzGGtXtAYv5Gm/3xzRgUwMnVHFWmBJxkia0+6YBJsUR01u3aIg7zxRb7Lx5o6RhdgAOR9D/CoIRDJADRk4hlkmO2KpLSHLZszZea2FZyx5ccwhEWXsQZR1sGRYhTspmtXAuPCuk4EmD4cCyeIab0+QfSEEU4PLZ21phNUDC180BKqDfLwBVNEggZfoY65/ncgEhhDGzAJY5otbLl53aGTb1DNvlNGi4ZAwdMA5A6AHjYGHo6qpgvYIZUM0OeI9PLrWsMBYbxDFbIlqbiTUjM1/CRGlwmtulQP+TcdtqJk6tU7Iu9YWj/lzAZvCyZUHasn7krBA6MBwHNw+MTzMC9q8/O4/7zi47lOubJU4ATvUeR4yDtQEAlBRoFIhWKgACvyBZeoAImnBQTm11omNbsCUK8x7awkHHsRBybkN9noO/uRXUj789W6JYMXV6LmGUgjJkU9BBYoeEOIs5Psc4D4Eo1jSq5yhrjrkYRPFlc58uuS3ReE27BHwXMEX6igBw56jrrgML+LrMjw5ZrABp39T90srqs8Yi6Zt9ILEfBBfogyhAFJy5H7IFCsAWK2kcntdMjq+b9i1xCJxOhEprPPAYWCEeszHK9PFyTE5esEiv2xooAdIxI2u6xWvXwKmcoanHWAHobqO7o8yRDNrKmi3HTLHtgSiydXEqt87FrurQlVsU7Snq3AXI4LWbx7/8c6Y+/b58LdC1gDyIou9tXcOaGnIgitxPwKyce8/cH8fACVulkJXK8ww41G+wryS5QoyPxdl1OBU3Z+nhunieS8zQIo2pJpasvfg+FhB4bi0YWldeAqLgwnqiK0EU7iUMoFhaUSs95yVPJsJFSb8n7hrN29R8RwbAI5wA/DjDfyz/HXDvYltIsMEptlgXC/zpL3/FHZqoUWzQp9YF8uvKAybVNjDjYiXgzMqiP5+wpq51hl13ZGtlTBDz1+3GTaY9CxT+3xhlVmfTTEd2stRWIJqGUq0KDTHTAprwja177ei4S8E5DqPMIN0kbiWXuPQXEXrnfn/ubzl3x5xWM/z0/QzdfyiwKBwg94D7IGwvsQIATIsd/vkv/4pvbhvcfEAUuCQopTVpstatcE+8qNcoqi5EIRe/QUDMaltMPUMr1lEFOpReY3oCrh7Jv3c6oURbSAyCvD+0jCfZB6LLjuxrIUFQdDExlMw74s7jUhsvsfO/f/b7j5/u0WymMYjs9+i77HyP2L8eccYChd0jz40/y1VnyP1HS/EtYnp6+Ouxa2Vp/AbSMaXAFWsuyYEqvD/0L0JKSdsV+iCKlMncP5/h48MMqJ+x+cMC4/qA++IxuPq4OFk7bLDABDss8TO+wQpzrIOLz/ruA6Z3W9zffUT9EW6d+AExsOwTortPgxhsVixROMuPZoyY0eGxyscKAwckGb2kTNalQ+VgQJdF3XYlZQsszviQRtrvW5ukpuWRedo9TdC2hev/Au4HcPCVk/ADoZ/eeP6gBkrg2Dhrg0B+3gWQuP+USVkUnLUrkGw1+MLlOg6FfC0+XoCVc649QlbAV7ZEAlJ3x51fQ6U/aNAFQHA7HrQ6yQElUidgiGacrwFWLIaZt4A9D8kcI4DpHOlcw2VL+t3o8zyYUo/Q1FOgbNH4fhL6guonIbueQV1bqt+XAStyr345gSuqn3IflX7I7jw6yKwFouiUz5aihwGevrutnZWGtdwHx+m8anIuD4tgdS7AYzKvapCQBUsNhqzwZUCUS2hozIjSQfYbVTansgZAPUKzuUOzmWJUH7C/d0pLiRe195xu5a2Ex3AxqMSVZ4cJxAVethNsvcXD2vHR1RhF1WFabzGpjig7p7gEEN17ZJ1l0VbWcG1dwsRdu1FllpzFskCrjgf619fsG5OU6YCyQBpoVsoEOLGsU25dJsvt7A3WlQsCu0v4Y5emWPbTFMfTUOdcdiY9y5PUnafCYT+Oabk5OPJLQRQ99+dAlByYkqMrpporRS9m8i3AxMrUo88DHXuNS1ALJxiXCL6BwkwDIWjocbXA+l60x3NIgFDJVX438z2+QkQjW0R0z1PRnnzQsFSPk2p8hjULWrhjACX5Tf7DgRGRILLX/OvmypUl5Tk3GotEcLO+s7Yu0STH62+emz00vcQftkz3RSO2oWq98KwQFonV4xLdssCqWqJEF2wSAISUyGssUN52zsqJwTkxBczxPhRQ0pke7jEunAvCGAdoyydhUKI+uQ1M0Kls4cyLX9BEv2PSQepcmYp/QoyYFQaaNT7CoOmYAIK2c9CruGg4U8ZtN3WMTlMNg5rWxO+fNFrytUitrjSAMjSHDoEpVpwqJisA7eeMq4Fgtbw8WPu5f328viXfQ5g5bor6Bpvap1e8j0EdxwmDFi0FHGO2C8xYhwLj2QFFu8GoQARHZEwDbtyyFYomAUx0W5RUZ1Fr7Mt8XfoAs35dirfqu/XwGOF9GSv2tuyVhUCjXQwICr02fQ2ZwHhd5D5YS5m3UgGA8hloSzfnAjiV0d2wM4RlJ9SOe9YIOQBF6rQVrIgLQBq3QuJo6ZgWfD3dH7RFiiu3gw2ngYej644Z80SCDFuWJ9b/OYZXM8xWnf5+XMbfV88TGtzV5VJW02/hOUF11vUgVk2un5yCe5hzC9MASfg2BlBy4rIckCLX8+5oFh0JXHmjrKmqek9AT+eDKsfYS9zHpC/KLCGAivQxbWXFpGM4scsZK47k7KS/ZRmp10VHVDiC3fbdtgeg5LTsufqc8tQSQKGONekZpiWqZk20UkD25fo8NoTXrrmuxhEejC0LjH0Gsh2mcOESyrAu67lNMtW1nj8WikGSfc/xgWe79oQKSOOldLSv30vel9dsfZxcC8bxeg4q1LlWnZC2ZNf3Zl5C/9ZpjzWwQnXPJbCvgH0Vg8ayZaooWizXeFG+yNrAwWZJSonXCq5q0t+R78O5Pg/jGGstkDoYdRafpenXA1E0s2/Va2BFSLvy8NMPWSVIvAth+CdAcwd8T3FS5oipbMsb/Ff8O6z+cYlJtcU3WKHCIcQ9wDvnc/cP5cadL3EtqEPetAAqBPFK9HUSaf+AMSQdl3QrIT3ZX2KBYkbf3pDAz8JaboLMoXMWWhdI3AvkewwBKTkA5VIhTF8zB6awoHmO9DW4z7BFk/jtexBuM03jWAg6Pve/HwF8D5yaGT7NZ/j//ocxHm4/BLcOiZEigYeXxQr37x7x7ex71BJ4UvqWBJ9kP0x5XS+Y1U9A0R4xvXWmiwd/XTGhLdEFzZ8I9hUOGFcH7JuxY5DKEkGD+hWAKU77FBdxi5HijDtA6h7HliecSpHTrklgrA3FPNGWKD9jidWnpQNQvq+j1YlsH2m/QT9GCoBoffILbHce94Z9ypXlwBW9wusyy/JEjrOsVDBwPN/HKsukVQYdpoUV2Uq5lM3pt+zP/e8llcv+owv++NPDW/y0bFDPt3i4vYekP55giwfcB1DUBaI94GdvrXKPRzzcfsDkdouH+w8YNyeM7uAszCT9sQSdbXyZWKPIeBdNF499IWGQrLEqDJUAFCUSv+yiFWs0V7hDjMvlGMkYI4nXHu2y0wcSrSCyVd8ChU3MZW167fQJbinUQrUWkgEEwSKAKiOcfJ3wXcdEqPDKhdIYCF5wfaOsB8S6RUCYouxcSk9lAWCmQVbuQ+7x+zEqzGCwgS+xU62yxWzi1mVl2QGuFwhzIHTuvI26jwWi6GbX4EkNe66R+cWac3QZ13FZcBHzfaQETuUZZVNLgqvF7A8KAKOL6sRyVbZH3Sbls+uvFDi3LDuM60MInKvdydL9fswUILW+YrKso4C4jsvcdPoKLFE+7W9x+LS0XRmkT1v9nGUBLlshnY+1zGBdN5D0RYsH0eEXRAbz4xyIligih8m4qKECyyINOttQ3QZAXePT5g9AfcThYYxxfcCyWIXUxjFTVBUcr8W9XoLPioJk6pVeBWLq+APW2FYTVNUBxcz1sulTg6KFS4ssa7S0DVuT5lIcc3Ppbc5tZyh47JC4kwNRgMhLsCWKACoMnghw4sGTY+Xin2xnMY2x8MgcKFZ4BIkRqFMcbzExYwvKeeFan+Zp8NgGtsUU9+HcOiF1el0A7LUgB6IMtfk5mwCiF4hblgA9FDRW6CXCNt+DhYcdgGkchEBq3v14g/V8gdX7JUp0IeXxCktsfIyJ59kGNxxkSG7n5TRJn6rZDSnjcvst+tof2WdtDoCg5YsWKDfpB7/kXzedJcD0ABQ+8RxwoetG6H+rS861NO5cd2kslKF7aNoiFfJ8fAg9aIHEEkXcMZrHb/AIYHK7JUuUb9ChxArLwESsZxt05Razp5M7X6xNeosXka8vyxgMUxiRPZyJo1iiVJ45YYuo4NJTtugDlV8HaWsTobwQ0DcNFuZM4mOkqY2nFPgqtUTZ7ac4NBV6aYytff0PwI0zGVt6UOqBm+vfQwC0Bq31uOSyzwVCNeXiXJ2JrQLEpmAGoaRtqepLOkcEHmlj1iSxUFQCaGs0bYFV2WEyc05bkuFK+tTEZ+4RaxURAPYYo6w6TKot7vZN+qwCdMg4hyoDUsZMk7yjZZki84bci8rLDii7Lm/hpigdO7wGlcnWsk457Md5CxSrO79WauCwJw1qybfTQibQZ3P0N8yBLgm5spM691Q+O/chcgep6jEOXoiV/tmiCCCKCLYyP3Jq2oO+LfpWsRGIjpYoEbjvxz05iMsOAyg5baBmdq06qDqt+LmkTvNCVt/UcwpvZZ+/txZc9G99vFWe/BtzIV+Xn3NomyuzrpEj/cxh/waofeBccWUjQOVQu0wokjUFAKaIlivS96z0ylYwbGsN12u3HPfaad+McDyRsjQ3Vs4BiC85PiG2QJctYHcwvUiLqzyBpnqd1us3ENfpmuqkfgOgHWFbT9G2Bca3e7jYTkXgiytEYFh45Mgzt1QXwWRRdgOu/zgeunBx0PYHhLTI1njXbJYeT9qyhMcjd9Vchp+Kjtfhfpin0MdzW2uFTC7IrN8+127KPtRv0JVpLKKoeJSYNGX43SbjUlyD3XkxIHn/vJhptuzH+MzN39b/JfP+pdfS38qiXw9EyQm5erQANsBy7tpWmQ40u4tl7VunAZ7DaSEF9aydFcF/efgLVsU3WGAdTAXH2GOJFco7p8n5pm5cMEF6jecSIX0qkC4CItymZosdNGjitn2zWGZOoguPBOqrkI1eryfEXEe0OpvJ8OoJlCdS/T2sb6gn2EsEMd1jrUwkbabOOn/oOnyO9B2xamoRAhavRvGwOaJgvKKy8gbN8g7/6V8qHN5XIdvHHGt0KHCPR7z3KXGn1Rb/5i/fYfrUuNSpoqH+pB6Rh0vnTfX95C8C3RRFiMUzxRQdSkyxw86bcVfYo6t9poyyQwyMmGmmV0R7jDCGT2+MfkYrISvYoRUYkwNbCUAiSPsKy4CqswXKbj/Fp8ely8KzQbQy4e0K/cCyAqqE+XJnbHncybE5einIISRjYAhAybliWs/H84FOWS9jjY/j66ky0bxqhoX/BTSp1X+JqN2qES1RVrSdA1iOsFn9AzY1sPvHCcb1Aeti4S1RNlhjEfypp97t08VJ2cXtu0dXetdgJPFSbhHTHz+pMrFIadD3q9bjs0AKpkj3ZmbUAzTOpcebKCcwYhrk0bLa6tSq1Yaxoq1UoumtGeCc15mvwZ3nCcAb2IIwMDynWuXn5l+9jPWOTy0YTiVwrAHUnrOr93hTdpjOt85KpYogSprVp/WXtwMQpwJq3zJFhA8JepkEjRUh8FyqYos/OScYDmkfc4KkyeOcaX8WAGsqKxG15TXtc11D+3x8qa45BLacez79rLI9JwhY7WC1iwaPeBue3QMqGLnUziXQ1ADKZ6DeY1R7EMX3RW2p4i4ZZx0tCAtZfZEBPWeJ8vqD6e8/LYDRLO3XwLAGnutyZY1xDb5WIMul+Eh1QH8xBpKEDWHNp/AKoGeTNYtBkyZT1iAZY6d2hqZ2Wfc4UPzYewJIPBQB8MRi2ym+tj7zXgkXR3CHAq23M61C/yvgZL9d1YW0yEXbucDtrbJOmdG+UG5sydYCTIbmpXPd2sIO5bNoEIWBFLI6AWLgWJ1aXMaXDgzLikWxKOFYJ5zuOA0+Szz2p7mTb1cLx+vp/qrl29x8n1sncuDJ0NpwyRpxBV0pbknvYOYbsLWf1wjXMM7ncn2/X+LvjQ82+z3cdokwUD/+47fYPkzw/vYHtB6FXGCNHaY+P/kUuHvEomhi0KESwfWUWU5mTGQgpm8btUDp09sWKLZJ7E2eociBJJcgb71FVbTjQH4ihT7pC9C5wLUssA3d+xqARfoP77dw1ilTAAsAE+Dxpm8mBsT+9ACcMMNf8SesHxaoCpe5p8LBTz4l2IRwMtviofyA2aeTbdqvNdCtR8YrZ5ESUxzvPYIuWaEmAYEf44B9UaGq946xLZ8d0/P6FTY4osLBgygAyF+69Nu+FYqO9M/pOnmi54Cy4t7j3HnmWOEbHDDGypvc4rHupyzm7QrpghAAFKAPnuRAk88BSc7Nw+fOzQUF1+W5ecFaC6zj5FgLdJHzCFixQBUGT1hgYWGHyxo4EGWFALBv2n8A6mcc/jDGeubgEsek7QOTJv1E3PUmHrTcYI3d7QrLeuVcfDiC/wZunP/iX/uT3z6hD6LoZmFhyyKetloXF6WsOg/du94/FFCUARUZMyygpDGDxiFYaNeWJCwjvy69dtoj6X7mdohTulQgHirLXTMBEL27Zl3jBGAznwH1EW8ove5kFjO9acHVspqVvmEGnQ1xG8Y2eALkmVsY5ZeAJ5cwzjkQJSG2tM25lpcI2nQRZmWs1YhKFdmqcRj2WWhksGXur6nBFCDtM3I+Uw4oseYR/f7Wb8BoI/UsFoioweywvQFK5zaJEvg0d4Ab98Wxspwa4xCAOQvYG+yL+zGe99FV8dXS0yiuUeeExVydBZjkysI3lzEgvIjwwEDK/wNpRylVGStiZN+DKZJBVeYsDZzw5Ri0nKsyD6asEFMhi2WJCPUizwnAAiAEXoh8cxmAZGcDFeP7yO8x9jgUYxRFh3Hl40rtDymg4i7eJ2uMWRak1vHX0BBQzyCKlymevQzblUBXRuAktSKJGdhEcSKZdAQw4UCxnG1Hpy6OGXtiSmRRxEQlzM1lYJ/uu0PgeW5dAfprQ7btcxlpL+fJrwRR9M0soOSaTDzX3I9XmiMiQ04IV4mYFaMG8P0NmvYOP9y+R4EOS6xwjwe0KHCPB7gozi1wu0K1P2BcOJ9LMXVi5tNiQHI+noDWnqt9skBJgkqdA0EuBUwwUN5rV6ZcTJTct3xpYMqXat0vBU+GBEuu2yL2Kw/GraiIBTFByusZPjZj/PBPa2w9GCff1qHeG4w9WIcK2L9bY/GpcaOiRZyMZegYwaZiSKY2TPRiASVAnvxX2ONQjmOAWbFGeeXkGCgHomimSsrccWnQWFkoGGGXrZQLgi6+nBxlXFKwhSCyG9j/mqHhSR6ArfHhfSA/roZoKNaUFXx2CMzWAMhLSLv0WPccqd9DTFqJKNGqLEAyB4qAYgkvLHTy4gtEQWl+gw0esF9WOCwrlIVj8CXOhPSznY+lVHkX0K1nDtqqQFUdcI9PkckSqzNLsGqQuvZorRanJdTzgqZOqqNLH291GZOOK2S58cj61Po16iL30tdO0qdeCqIM0dBSmmO09bOwAMv7LZBkhKn32DdVcLkQywBx1eH+IaRdvcJcq2O1CZ+i+4MGN+Qdhpjcc0yxVhRdwv8EGorzoBtZiOaaxqjibT1Qx8/DdfxNa3XOEGl+Tt8jd8zQuTnS7zz0r4HsEo5/LeH6YumyE43mHtDzgWslto+VqUrPS+5xY1/s2gLPF2Yo+l3TE6Lb5xBgkhMktZA5BDiG780uxTmFDu9bpMfMEU4Jyev2BAC59ek+zv2HwXe5dIOkX0kq5E3ZBVdG5gEPIf5TdCkT4ORAQAuARCGp+52cE+bCqkRRtSi7Dl3p2qMyxuuNNZfnUhV/DllzBQEmABLQpCsLtEXkmXWmSuaVOWUxgyHBItUDUwy2iAo3JmlI3eMF9Nw341QJM9RP9Zx/KUByjUyckM4YO+TNMExXsgbnYlZ8CaZ8iGS0iTm8/J4A308jkAJEq5QH4L8s/xnb9xEBv8eHYJUizPKk2mJZrcKdxNcrBVLYZLoNAIplPu2uoX3O44LQtkW0QNGpjK3J8RqmI9exAEQNDQt0Q1ryIbKEoWvPY7r0GazzcwALT/46vo4RK2LzNrqFiRDUIrqLrQA8jPBfm3+PN8sn7N+P8YAP3g1kgrnXXkvAyiV+xuJ2jYf5B4ybows8yxOumgyBGIeH01rKArDFxGcTqYK1yrg6xMxO9eir0A7vMcYosP2pxQmQWqLk0hh3HlWPligpiPKzd8Fid54PHx7cuPy+du34iH7w2BX67jwCpLRAtD5hrY8FqsixObpkHFqgyqXj9xLQZcjKUEhbl+jjdBrynXHMSB0jdWyhMkFw/5G5Xhj5OSKYskI/6OyK6uYAHm5wnL/Fx+VbrP9xgareYz1b+NhZ3+AR91hg4wPRilvPFkussMLSxUC6/RFj7PFw+wHTpxNuPgF4CzeuP/hm+wURRBEQRwerk+BvQARUdBPLsXDxuuIcIdmGOIBj21uPUiEl5vnirQQOTYKcW+6lvOZ8Le48QkNAyjnSw8RisocEW2s5HRJea/qfe8uAubhcuOCgb+pDL1At0E+FK5lhem47krJYA2mXAB/nNIaX8DPn7hPokhgPGvDluYfmGjY7L9VW2nuorlZ1DDbwv34MoVy/0bycbJvMsda+vr6mHIBo9UM9/4Yy5452nDtgJQSu9VZTVn+0SPqkZLB5fvpcafR3QCu4+X4IKNH8/oa2VpmcJ/uhmZjnYKtYwOZF+DwhPSlpt3g5Xtb1idtvyE1exoZsW/o9VLcBUI+waf4BqI/o/lBgXTk33C2mkFTILsDsJKjBJBCtWIU774NtEjtq55OC8HFsMVWgda5pMx/XZ9ZXmJedCgje0hrcXhYEuSvfJMd25RvjmHQtbwtLOZ8qQoA04YLmoy9VPEZXH+eqowPLMq8d4gg+TbBvKp/GmGJPyve2+m6uz59bC3hdgSrrTRe5TLRWf9/gUnoBiKJ/58y3r7z0RfcWhl7uU8bf3OAi1KzcIafVDJv5AuuZ83UfexeMAi77iXS9nS8TsoARy43HIi3shXJiUgJZi15u8RxaVKHqAaMjfS5daroPpH3gSzxI7hrnLFS4v+jjCUBBi7gIoB90VoQxuO2pnWH1/huIBnuONVoUPv1xgQm2YeKdFjvsZ06S8laDAckWAEVPlu7pOPtGCskBZLUi6Y75NV8xxTfuxxSSeis1qywM4q4QU6zJEhrR9UPY985RXUWxiWBP7ucmdgCRedEM/TX0UjBaz8kWUAL0tUpyzNBYsawMreP0tfjYSzVdupx9sUuYQe34cN7KrUXAUbc6zhc41mNMZjuIa48DIsoAUrAWa+xzAUyxQ4UxquqArtxigaMb0+JTvUcESfZnnk+nK5RnzqVFNGjIMnKIwtgJaWs5yDnyaw/Q/8SvkfT76O/D3+nS6+l9a6uvaQ0X+e7cb7n/anZItq2LZXFqS5zqvbOs9cIrAJSG8NoGIIUAFHEztphVvj//W3VDx1/73yMNoIQ38ttLXSdl/af5hdualRNDoIiu5996/hnqW7nxdimvOHSNc2S9mwC98vzWu+TKSgAY4QSXFairnR1BQX3SohTM+wrcefZw8ZeG+Igh8FHv63NCU7L1CfMjQJ4vscaG5g30Gq6VNi2VIx0zDJzAX0p4aynj38lcM8J24yxDt9UkrHOSnYczlI09QCCgiHPhiX2M7S/lNwDvHiR8dbRYiXVKMVEoEKWI97DmVyEBQejiJrHrer/OA4wXuLlb4An/Zhd44ZU5uHinzpUt/4c0yB1bsRaXzee6P3/OupBdJ84BKHzCOWORlF4gbg0NIC7/ktwVM+p8zx1ifJSJQz+/91UNotC7BDarf8B/+g9AMWuxwhIV9iEwqLhl3OMxIJEAqIOlk7flh84DTlwNkkwH5F8c/cS8hscS1iwh7hJNTa4MQNqRzlmhXPL9+BirK+nvden1LnmGIWHs3PVlX3Okct0J0L4F/jaKh8wRwbklXNsugZ+aP+OnP7zD4x8fsMECc4q5I+mQl1iFNKr3s0dUs0NAuxnN3hcRMRbiKZAjfVTYY+pdDw44oKscs3Gsj8Dx17QG+21ohyneqIk6tzDsvJ+1dt2RbDyCoAtqLvsrLOFAr2+we5pgs1q4GCgNQmYmfI/U8oTrpJwXfGyRpjGWAfg5gEqOdN8VsoCPIRc3izT4IvcD+uNF07nr8/k6CK2u52AVI/otWuNJnEM3iIxZiWh1UiNapWz89tGXzQGsboC6xk+rPwPzIz483GM5W2GBdcjsxpYo93jEFDv8jCUqHPAOP2Ba7LC4W2N5t0LV7fH2h6MDUCQdcoMYbJZBlj36QEmJJB3huVVaM4UMvHJQR6aUCfJHtfHfFJ5za89rpydED2EYW96/ZHnRy4ze5oRc3lrPoi0apG/z/5yOrQGUN8DcxVA51WIV4FPYZt+jRNaFi789kO8Xuu6SfnQJL9Prc8+I8yyQatplm3tXndZdeBWZY7x7ggh20v4iDJZqy9+B62rY3w8YHt+6z/A+l2mAyiqzrgXkm8Z6vpq2DAoBab9L+p8ui24/APVJAMilf5a++GTlmXpl9AnOc0z35w3640KXWXV8TCCJdWJZw1q8yKW8vgAoOtnHhLa6foIQD0Oz3EukVgo1bYXXprJj8xaf5gus51vsllOMq0NIg8yWKKIM39J2gTUKxJiFO7+VtMhpevhD+M0WKkA+rEP6uw1ruhUnU2gIHElbvh8kXo7tub4p5f0QmALEzGtpEobo4sOxT6ROB5YVa+/N0wJtWzhX+E2dflvmk62+m+vfer0A+mNHrzkJWQC7Je9qPrZ3oSxdCaLIEzOoAdhpLi26NFOPdV99T30vv3huRnHwrXzVozt987jEauYsB37GEgCCZYqL5hwtUQrEsH1CQwMiHpNPxybmiaaG50v+g7Ym5Spzk2pOUBq6Zu57naNzE/u1bj65c1jQ437MrgZ3CMGyWqSa7CUCkALUWNVLPN4/YI8K32CFPaqQWQeANzN009KBpqei6EJQSw2gMKWWJ13ST8P0WIYcqANt9Dqow5sw0QvSDUQTRY2kM4LOQWVjkFmyOPH7skDsniZOy8FpjPXEfw7kBNB3lcvROabl3JjJccFDgIplnQJVZ13rnCWKJq2pupRy6wqXS7tyOTFrHJCWT5n7fWbmpU5OF4G0GWHj14b9rPJMmDP95dgSO2K8JDWyaL8OxRjFvQ8+K88i2qZOPTZ/Mh76WhNcpPVuHijCOiXzwVCAWffKw3EF2mCBUPbXFNC+lL/+qcaNX9EOy7cBhq0FrCGg115ra63RVpkGcCyhXJ5dwBMWVOa0z89XwgErknLZmjK0IM7Mqv4fqtNM8CXHW+dZbQcgDR4LpAzxkMl2jo6IcR40uILY/kB/fEofaY16UH1tlA+xlVbfyZVxudVuuX45RPx8GjjivtjSbxF8Zd7lrR5j3NSS/lm3B/eJ104NbBDlUnBxSIDs8R/XAihDY2RIMSLl2k2XAcmbfv/Z0OmlseUxI1vc4IQptmWHri1QzNpgISrEcAHHFtSWJ2ydIolBYp3jKV1Q7rGpiOi594T6vMWUziypKeeGew5I0WAK0OeVteWIHKMtUdjVh8EVjn/S9Vq3DG7AzjgAw/O/BZRf8o8zdQlp6xOgD6DkXNcupytBFH2zIUHZAkyuYa4tIV1zBEDUQniLlPbegSZAjGHwN3/IfIS/zv+E7f0ES6yw8QCKRB12bxTz3AtF9wBbyLV80AGOq1ImQdpCgLZLNS/NBccMMTZJm13DXLy07qWWSEMAykuF0tx5upzTzvLgmgCraYqMLn3VHK5LroDj5i3+87/8z5gv18BMQBNn7XCPx5CSbYtJiMfDIr64DwBIJi5tlcJTofRdEeYOZYVRfUC7665Jc/67pB0muEGM3cDBsdw2gidibaKRc0HJ2Tpl49PX7n1WnsN+7CxQNnWabecR0dpkhTTttRzDIEuYsJlx4f6kJ3MYvy+hob6u5+XcvYZA788NDJ4D1pn0tXOAjX4f/s0WKRl/bNZqzdHXdM0RvyXXrXxK5PkzDv84xrg6+BhE22BVNqWUyKLpEuuzKbZYVwuMqz2WtytMnxpUe7iYKS2c5cPe34/dfHSTVGrrwZSuTP2hNaUM42VZ5IAI8of0dNcwNq+ZGjgfAxZsYewzWe9tCak5xi9Xr8kS2kVwFWVRjVRrqwEXEbLmflsj/678DFpI4/1LmOJLeZZLgBYpC8QMMgeRvdayVvjKo/qttOkyz7SIvIAl+NWZMgYbgDTA7Dku/Fw/ygEmubGa21rEz6afmQFpBqh5n7fzgTqg39d1Gzzh9ZO8g/RnARKYv+S6Vv22xgqA1CJLB4/lMWIFXLb4EOm8+pgcby/HMh89onMmQDuK/BODjrW6RJ2pa+H60PwGx+YtjvUz9kuXnXI/G2Pq12OJieKcbveeN3T74v4jKY93mGKCLWIslJgGWfaBGLwWyFieXECpQr4fSFnvA4BW5PMxAmLwtbXlttRdYpnCIEoaa3Ac9jkmSsh2uZ+iEwsUycTDvPHQv+7L16wFem1KaMgCJWeleKnbZ0pXgih6cOSY4eseIg+45Bhx6xlk4pgAzdQJQXNEEMUzyMf5W3zfjPHNH52bxQQ7j7yVQUctUZ1lsEj9kJUJk4Ansh/9xLyvOWfiyYEll6DPlyyavc6lO5FVr+sumyTydI0FknX/c8e+hPS5MqBktt7R73ugnQDfU6wUIIIoj76srbFZ1vhP/x6Yz9ZhspGUYRJXYYwDllhh6oNdcWArAOBAqUA6+ab4bxtghg4u8n1RTvCm2r/6eI8HjHHjV1AGlHSkcQkaa+W8ZzBFgmPtvDvPHhVWH5ZOcFzVdspi61+DJ4GJYeZBm9FyX/vcsXQJyT1y0/sQ6D1kRXjNs2smbIgsyxi+FzNoVtBZ2Uq9pFycpt9NhEsRLOdIwRMGWDYA5jf41PwBqB2YMqm2wYw1B6KIebEEvtthislsi+lsh8VsjaI9of4EZ8HxhDiXWKkRPWiSCC0DTSqskZCA+0NWKj2GS2J1XWMl+drpCQ5EAfIAigYYkPltCajnhGCrLfXwsQRXWX9YOF3CBlG4jK9hgSn6fax/C0zJASwWU3wN6CLPEogBa660AOtL+AnNa/I1MmBKoy4hAl9Lh8vWAhb4W+T6nLVsnANMpK45czzUvr4fUw7ksIC9msrmqmxjHFdnrmf1x61R9troE2L3PcfvaxBFC6CBpN8LD7JFKkDqrZwjlPvw10zumn/mCVPu/daVS3p0qbJAlCZTx+1T3+DYvsWxPjoF9bzCoRiHNU/AFAEG2J1Hfo9DYPboviMgiqTkzrnE5i1R+PX6FiQ50MSyUsnJnAyA6OMs8ESury1SLItuK+isTokcXOa7CtuNA1GwqWO/le+kwUFdluv7eq3R64P+ndBQkPEhK8WX8elXgihCOW3jOeb92uudI35RduuZRDSsREQ+V/CuPTV+/uMSAEJ8FBFmpx6VFNOuEuLWE0VXd+d8h4/lRQjWByD6msuj5xirz/23micEmsq1Yw6F053pnAUKnyP9IOc+cO56mq6Z1K+9Zmk8i/hMU9wGBlAaOKZVJntftlm69KnT+2jeWGGPCYEm4nM49gGr2PcyRYdTixTdx3g6BICy7F5k3/B7IweWCIhUJOi41Os2St15HLIuVipbTEJgWclhH4LIsqDNE70FmFiCRC/WkJ60LfrSVihDx18yH5+bgy+1NrTufwldAsBbY1Tuo+ca+U1xDQQg4XlXM/pzpAxbCaC+wad6if18DNy6KnHx2XoLRmE8AEAyarl0ySUWWDsmpCpQVB0W5dq5+rCLTof+/C3ChAAplQtC3ZVviAGyAX3N0Ln5oW9mbPl4ozX6y5Dw9dpJv1uZ2ectjN9W++QE10uEWtD9Wbjk5xIhQ9agjTqnVfvS11u6Ht/HerchwT3H9OLMMS/hZ5IH04ogbYHCL3DOGuVS0lnGkAJUfIshgCQHGFhAHV/z0r50ri31s2Z5RvXc/Oyyz/2P+xifw3OrPo6Pz7WJkAauXiM1SL1ucmCkNca4LhD3+xbpGODfQ7z/S0mDjOd4GuGnXYDY5HvK+izvJ6CbzGvc14AUiGxcWu2uLdHN3RorfHEMvFqEdVnWvLFXarrLRZlP+PHIl6c8thzP66kDZvp0zrJEAyoWqGJZoljuO+fKh6xTrPTHXMfxQUM6467CoRnHLLNDc/0QOHLt/KX3ExIbfGstsOTanFvbr2qJwit4zkT8koFp3Voz8pdYo8i92K0HAN4Cj6OUeZbJeQV8P/8LVg8/o7h1jPDWD2xBJjmSAhA7nXQ295Z9RDF22DRYXwgma7nxaIQ5N3Fech6XB2Lf4aFJVX833dFyx1mU0yZb1+PrfimG51riUSmrO/cxHzyrXTj3ngZpfJQVQhBjbG5wXL7Ff/6X/wU//uER9zOXAjma++98wNmfUXmrlBAjhYQaBgZkn/scB7sKji9lhzf18dVbojxhjpNCx3MgSj8l2ySkW1tjjiSwbDfFerVwEz8HkRUg5RGubEVbqVvBfW8BVlog1QKxNogn4mv69K/B7OTOHZqDc/RSAPQcDbkbsWSSK+PsPWyRItsJ0JAWbI7hoLMPiHG1agCrGs28xvcPd/jw8Aum8y1W1TchJbJYotzjERUOWOEbVNhjCRegduJdgUp0WFRrjKsDFrdrTLstirbDuHGmECMJOstUOvBkXzkAZVtNg3uplaGKyQ6EZ/cHyc6SNC/vDzE2r5mekMZ20cPC6nbWvmbshhjAc8yhJhYsRZiYI7V0qJFap5SZMgYNtWDPlGNac8KfJfRZZefO0/uBLA0jz7UW/zBksg2jnOcV3mdrNz+fAEA7jc+rASop098JyAeZ5a3Q0HcAbKsTXdYYx18klBBZz8l9UvctaztUxufr6wMphvVaSazedP/foN/vdVkCoGj3YbZ81a47l7q3DSk7hTRPzzwOy20WH61AF7EUZeBE+GpRUGrATeYzBl1qAI1L7/6pXmD/MEZZdtjOnBWJJHoo4LJoVjiEFMfu9yLhv8fYg1Mda0uUSyxQYmudty6xXHIurbeO0VYpFshyzrUHQE/2YNf5NI1x7b7NCrGfSt/lPmz9W/Ks1ef12gDaBnqJBYq1NrS4JtjblSCKkPRqpmutSfRg1Ne6BEixrictfUTiGy8fNoAqN2jKBda3C2+BsvMCbhG0i5oslO9coL70ES8IJmu9krWfW/QGmVqr8loueOh47hf6uzFDcu11P4euyQ4kpJ/F4Boaj6avfJVM7lz2eINNuURROsGpQxG002LX1GHnA1OO0WEXJm33FNr8bni4mlrlV0ot2J3JEhaj2SH/x2CyMbCslO9ByHkujfHQJK8ZfHraPnp9CVeq6be2ITo3Ji26BmR5KeWYOX5ePianBbOkYdKCSbV8TxaARPhZIRV+SuBYLrBuC4zfHxINFwAKHH0IlmbuyQtizCQYbAsUQFF06Mo9irYDcEKpHluwja5841LVqvngnHupDrrHgWgH6ZxwZa1Zr5GueQ+rPXSZJeyyAGsJs/oZrCVI/hu1zR2nly0WUhvjOM3WXfI+Q7xMjrcZuob17j3SB15q7XfJ/KqVc0B/fmGg9ujrVSYrblcWDOVyciyDBkL8HYb4vlxfHKq75lqaePpl4Zb7Igu7TKWxz++oQT0Yv1+7Zghw7XRC7CfWHGDxHL05inmOVpXB2PK+/sjnxk9uTbf4h1zn5eNk+4wwbhiEbIyt9DHQvl6zAQA3aDZTjOoDxvUBXRHX3LF305F1U34DcQ0tw++Y6rhDtBKXMqZuYC21XHdygIeuk/KcK491jaFraz7aKhPABEhd5qOlSgxPkfDRl/Tdz/mHsX8xWf37JWuDTVeCKEcEBL63yJx7mEsDDeaEcDlO30sveLu0TtIe8yS/gmOQH0b4a/0nbN9Pg8AmrhYT7LDAOgkyxNruGCelrwVMkL22SDMe5FA3S3DLCXW5889OuJqGOo5mUC6ZeHUwSGsSHur9OdOql9A5YY41Tbl7tUi5BUH8JwDugWbi0iCvQHEU4L7FHK78DyN8evgD1v+ywHS+xXq2wARbPOBD0FLf4wMKdL3+JsT9aYcpJMmxBeAVZYfnryA7zwYLjLzAy0i45a+5U5YonH4tBJR9WjjkfDMBVqNogdIgDRy7orIG/TgpMt4AuP7wiy9k7Y+MN4uDfYl7z1Urxgto6PrX4uy/BRDEa4Nm2nhcs0XKFqkWbOJSGwN9rdYK0TplhTQQ7RLAww1O8xl+Ws2A+ojVH5ZYVM4S5QPuMcEWKywxxgGPuMcCmxCUVqxTxj64tMRFqoo9iqLDpNr2NF9AykxJn0+j6jsGx3I3Fer7dKdpG8uyizFRcvRrd8W/B4n1j/XqQ1YBubIhBjBXd456azr62nzpp7xlIWSjjs9p/a8BUjRP8iXrEpJ5ZYe+gMhza45vsQDuHPF6PwKwRl8lXiLVrvs4TA1lIZHDdHtzu2twhbe5R7O+A+g6vEbx1jruJUKJdawG6vhdc/2Ot7m24TbpaZ5fIeVSHOfkgoTfAFLLV+DzLFAuXauHrET1NbnMQmd5bRZ50odfkGoBRhg8qalubtQJz13DWaWUNT5uJkDZYbecYDLbBZd6bYky9crLCgtw3BSOj5KzROm7zXY90ENbo7zUkiRew7Yokd9cbl0zd55O3pAoLPdjdG2BfVOhawucNtM410k/3dDWKpNvyvvn+n5ufegRrwkW//2l1oY+Xcsh4zJwI3ee0OdoK/W9tNUKz+KU9ljAkxXiPoDT4wyrssP03jG5IoRpJlQGThTobH90fZ4ElA1WKPIKX/JfN09SxnlarI6kyUKuc/VWnSXcXGpNJOedu88lxPe8xBrlHDKpf1M2qJUvkkl9jjih++9xqmfYLKf44Z8PWBRrAK6vyMQlsVFifplU7aLBOnYZ+xrpgDHeKPAISN14dGo2CYAVHfEqB6x0BZkejuz4J7osB2omY0ubDebGy9DEfK5fXj+pf1m69v4vWFLOkrV2MEBrLZb6ORhcYRc96g9AHLOyz8w9f/+NHDPCJ9xjvxxjf+uYD3ENrbwVCpvIcuByAeM5uHQH9sN2N9PaMgFQOa23Zqw0lbB9trn+YuI15vXjte67VojvZIEIyJTlQAYu04JuVnB9zlWoB7uJ18oBHvqZLO2/tibICfHW9bQgbjG519bleJrEz10/1NDcOyRA5s4Brguizcd6IEVr1+XWDFjxFMVbXSdkfVvZ6nYb6ou5sh5dkt9PBQjVfUn3MW6TuT9vnjke6LfR12CJ0sLNmUMgih4rgSSuIX847dIG9Pu7xYO8lL8esk6RD2Ypt6WeJySx6AJC7DL9zgKQ6L4hdbrfSH+q/TPWI2y9UrGrC6CIFhZjWp8rn8VHYqLEjJlpvEIgVT7wmqqVFNZ6fM6K5FKXHatuKO2xdT8+L1XSeBCli+Eo9o0rO268EQUDKFbfzf22lP2XrAXZtQHIx0GRskvWBl12+fh4Icd7zsLgGkAldx0ejOfMynRDKJS2uY8axbk/5G9wjPASOLZv8UPZYny796ln95hiGphfGVBA7Ih7Eta6pCNKWmPq1GKFckmHGULncp3zLBOS8/3K1VuTn3WeRUNAyjk6x7hc08cuuacWrM6BRjobgPx+66wbgL42RQT0FsD8Bh/bP+LjvMH2jy5LzALrkHYtZuxJ46Mwui3ggEAtHPT4a6Idpjh6YVQHj3VlFQEl3k8TE7QeBA0xUT7N0bUljqsFQrBpEZpXSEGUDfpxTzTA0gJ9f2TWDAlDI1uma00IL2Xkf0/0pZ45N741oHIJmKI5MP5uXhPW3tipF/VW+kJSN0KzukOzXODwB0mJPPbR/afB4mTtfa+dRUqsY+2X/E41YHHtcdu+ya2MDwZY5NhfbX54jd3Toj0iH6YZdYu0MCr7OYF1cG0GUl9ufWEmGROkHJC5yurqBOJnLSPkVpZFin5f/T6ALfRZfIoFnlykYTwXBwVI221Iy4iBskvrdWPq8yReyk36zjyvMH8gv3Mgllbg8z5/A/l9rWDSo2dVca6tOFsaENzV+dktqxNtSWBZoGhLlCEU+LXQE87z+7wNJCmM2cpE8x26k7yk719KOZnM6lQiy8k+/+bzKfOV7tvcl3h9lroW0UJF6nzfOrUzbJox3tQHHOZjFGWHfTVGRS63Is2lligxY48oNTWQovc15dx5hixShlx09PGXWqJY95E6BksABMW//HYZZSv/PW7SNceyRMlZU+l+z/PWkEyr6xLSa6e1FshvIN//X87MXAmi6BtdOpAuBVUuTXWcW8CYqd7BWQuUAN4CzciZ78vgWiJxv2hwh//+Lx3WszVKdJhjjQPGmGONymsK3Z2jMKsZWO6YLQrfOX1a4/am38H4t9WJLgVPsp3sWRXq75KbaC26xORJAybavWeIdCc/16mv7WOXkAZScmCTBvh+AXAPrN7GR5v77SNckEqxUGkALGv8tPqzcwNYrrErHIjiYvLEZLwc1AoQ5NsBJztMEsuLr80qZYcahQdHcu480hYbbzkmwInkrt83YzSrBdCWznVDQJEVUhBlhXwaYy5r49PFccUCOWCbE14Dnrx8Mv+66NwcA1wO6sqY1a4AHCRyBLQLN09/jzT9MTP70h9qtZ0DWI7wafUHYP6M9cMc0/kO62LRA1HWWKDy4z2C9m68L7AIDF3lGbcxSQ8CqCYuo4gB3zSgn7NK4esxFWWLY9m69vgSU+proAYxxTEw/N7WUsplernNCqwsrA6Z3zMpgTUETi5Tc3gGUTRgokEUtrLSwjyTft8cHwJcZoqdA1gSuiSQrPWAQw9/TR2TXvu5oWU7QXypEj0wRXgAIG13Bg+AvEWQftyhfsf31MeY7SwHnQOhNO9NmQvDQ/v91m83VGUBJrpMt5GWu18zPSE1JrGEzARMFPAESEGUIaWN9R2BX6cBLStR677agpSP4/GklBp6TMiazIoMGX6yDkvZEnGMzUc41SNs5lOgbLFfSvDZaQBNZCuuPrIG82p7DkTRLrjWdggoYVcarhuyPsm68HR+n7LDtmG/TOpOSebYEgkYmpu/GTwBbDedHLBirREaWBmUbYH++gA6WfPjwPm5LVc2TF+ITbKADqZLBemcFcOl18wJKz61Fqc95g/vBavtxmm+t7Nojs2BiNgioA+c2ExrFzqm8fi5MosRG/rPXc+koU4yZPaU+30pDX3PLyE4XtrHNF2ietQgn254BuwmwMZPQMIMlIjxN+YIDOsRb/GxGaP4o0zgbUjNy4EoAYRJPE3rmwpLEoPnuX39YMoBFQojsJVObZxaqfhgsl2FfTPGoakceMoTtP63Jnhr8k66CDPyuvzSvmTRlxgHXzvl5hHNqFmpF3msW/OaCACjVLsvDD6fMqeyFdLpp3FByw9NBTwAXVGQO48bz9HVp/CufGWwONlhggl2vXN0CnQ5X+YAmTu0Oa/FxDGZ6Y//RwFQAPft3lxxLG912UVr8iXas0stN0V4Vyl3NVkASWnU5UAUviXf+hzPoh/VOhcw2inXRpquYZKt+foc6fmGf+fO15r2UTxcvo+8uwZq5XTGZ3KPPNTvcvXZV9YnDSnNzrUbP3iJ4Paj35+bsTZOk+3XBKLwd8gpTgPllKCWFRbf4LcCUDRdIgdq4JHPlWNEuUHxhXh8ALF/sCKD67SSQy6NG6AeodlM8UZiB85iuIbozrMPa6hYhgLjsLLKse5212XnkXX9nAuOdrPJpScOZV1xOWACAOIhAaAHmshW9jUom1P8D/Xtc/+x0S5jpZO+Dpz3uBi6zsvpBawSc4tMQ9Yk+tyh21tAim6Moawv2v2iRYxfcQc83kRhVhhhr2k+YYbNcoa//nsX4G+PKmgRxcxaiIU2cSPokv8Sh2YcET7uWNbWEuxe0ikDMROSs6gYopdMuta3uCb1tfVcl5qTWve4tHvzhK5dBPQgZQsUFtRESzABmjug8cGy5nCWKCvEYLNzRAuVZY2fHv8MzJ/x4Q/3mMx2FHDSaagBhLRrQltMQ9regGJ31VcDoqwxR4F5AE76bgsRPFljgQ4Ftt0UXVtgvVrEwFcruE+0wnlLFNlv0bdEAZCa0MqW9+UYwJ7gLfq8CfzvS9fOEV8icw+QH+PWuNXjtFXlpXGsBLvzQclZYyxjuKbtIyjorJQ5zdfHhxk+Lo8YzXdY3nNg2Zj+uPJBZgt0WGIVtGIy5ifeCrKCbc/eBxpTwFG7meaoKLs0zbE072vuopfQHqklCpOWn3kr+znhNSHLZUdrkM9ZonAKbz6G+7LX4sr8lbNEKakO6Gv99bvz+/KWNYeWlck5gdFklnMWKPKuut24LXT75TrvtXyGnCPzBfM6PLcwvyXlhsUQAycWiDX0DfTj6z5oCT+D7Qz021S34zkAih+UY9GV9HuCoOXm0xio5q2u+xrmoU9IUxzzWAik+z9bu1rjYsgC5bdGniyFY+44PW40X+1jDG1U0FkGHpe03QzUiaw392XzGqca2NQzbOYN3pQdpvMtijIGd89ZouiA7JY7jyg8gb5LT866ZKgsB5AcvJtNDxjR8Tf1p7DWrNw6Zs0nFohiya4bo25IYclloN8JWRYoep661CrLKtPlw/QZ+iYZAJouAVMuucbnMtz85anBmpFtiSL/JbBeLYAlsK0mAW3U2kBtiQKw6VURTKncbekdc4yWtcjpBVEzIbr+LF0iyFn7v/ZE/DlmVUP9Lde/Ppd4wmd1ijK1lDTIzATMaSunzgE0N9jUCzc53rpiEYJiTIQYYPJAwVOFZHJ9bi9Vq/5+6YgxuhDzJVrapCh+DPLcosChGcdMWOK7yRP3S4FKeqqUUdYA5RDDoOk1coSfOw8MgeEvJWuM6zmBGbPcNYDU3UdIxZ0AIt9naU9ZOJVjMMKxLbEuWxzqMYqqwxjjAIoe/NoiPtmV14JNkaYi5rmASWumGMjnsv+HMtQiiZEZSANIlzKhPdJxJjQDCNgMob4pUy5WnHRIch8G+sJ5rerk+WsMU44hzzHhQzzMIFnCOwuSfPNzgIm+zrVzL7eztC/zqDmAwXouskoZam+5TY6FyfXFS8p7ZLWplPN26CJDa5+eJJV1jrRDow7jtmnwdQSxbhFTHGfHAn84DcpZwCtU2e+Bt7DAlCFzIqnTiyldQ+YzVmpoKy6oMq6TZmFQGQDKCqeyxb7sUJRe3qtShYUDUwrE1Me2a4/87tSaqy1MgL5FyrngrmxZ0oWMr2dccPCFtrJvAeXXWp3A+K3vA7V/EekTruHHX74+XClh5h7yc8CU3DW0BpHLcsyDXEcft4ZDNT8CeOeQzUekKWnlNnPgVM7waT5D988FdrMVJtji4E25YtTmMgizklqV3QpCp28q17lznU4jekPWKpd20NAevDhaDFqOcftcYUl/s2vOe+n9+ZwhS6UcaWsUuealAWfFGmFC590BzVvg+1E66YsW+wEOJV9JWY1mXqNZ3uGnZYNRfcBiuUZRRM20e9IOW0yxelommuOuLV0GmqffwyL6ebTGHG98rBMOGntQrjstCmyeHPjUbKZuvG1u+pYlK6SWKK3asuUJb8PiLN+Xx1Wrfg8h40KXfBurv30pK45r6LcET79kxjZ9/aExLN9Na8G2AKaIqUunqWUSM3BifbJETI0sxzz6svkNmsc7NDXw6WGJUX3A6l4sUSSg+SGJkaJ9tTnAuWbgEq2VWKapDFYSdFbGEIMt/0NTTkDTzaKXUr2fkI4zwfOCJRzBOM4ibeXA+0bK3ZZcmOUwtk7RW7aMEBpqB95nBlu2Q7xKon0HYpvJPMs30wDKOcGf64YY5GvmuNwcwvyCvv6OjlFWKRwvRQNcumyIhoSeHl8InO+bsj9k1TC0jslDi+UUz8OGdQpb6MjplkXK69cNuZgoHTI8uw5Yz/OExWdY88Xn8NC/BvH4GOozO9rnd6Mx0xCYwvMVKyyYx2YQZY4g3yVWKnMA8xugHOE4H+FYAk19xJv6gKLsUNV7FGWHcXUYsERJ12RNuRTHEhxeB3cNGV3hFKOnxrvaWkCJZWlmARYv2ebq9Pw9NN+zsUJOxh1aG3rjA+jHA+L93DgYWh8+j74Q95RjYoHPA1O0BtHSAOSCzvIkwujmGsDCBZqcwzG5QNxvEeJXbOZLAMB+VoXBw6koAYQsPQD5tYUBUaavluvoQ//njtXXBdBPT6c7DHe+oeOurX8p5RieoTJgOFvTJcGINWnVo0VD9dyuzOAugNU0ZQ6kSiaLOW0bAE2NY13jYzPGm7LDYTkO6HiBFoeuwnYzDWZ8b8qOXMdev9Z5hwluPDjJAGUELH1slP2Y0hfXceJmECQHjuT+W9oHkGdkeNK2QMhrAJRzTM+l8+iXot+aCbPG7K9x7dx7tYiBIYG+mTHcth2lKZHZmkzmYbZUkX4k47oG0Lqx/aEtwrjeo4LERBrjgB222GISANMCbQg+azFyQD4YnXbr+X9IUQfbEsUarhctgUPmxlowsuouuZF0tJwFJhAFECPlLpCyRbItjd9D7ZDjR6w661jzokI8j+aUPBZwYV1j6PpD5zBdatkG9BtXSACiSXpNnjssYGVo2ObancvMk6x2HLJw0Bc8135aCcrXmqpzVHpobg/hmXIud6+JWjj3wd53GeIxdJn17eTigP1ddNlvqZTJ8S45GUTGxxYpAEPAo6yvPD9xv9frsxzPZSXSfibb2rninuojurZAUXbBlaYoO/dfdFmFhia2SBlyz+lZmAwFeNXz6BCYgiu2l5T9Fv/6/uaD5OYooZeMi4sW+IRewFHlmF2eLK8579x1rPMsptgyaZV9bnhxtXjrfs4RJ2lhdOfwGsURNu0D9ss1intx4xkng4bjNYQB4vNqd5yV50t1Kv2NB7/5pUzDr0Hn+oMcw5RjlHI0pMnO9QuhS7o+a/bkmpqrzL0Dn+PL2omLySOTuQj1MsFLf9wgarc3znfz02YK1Hu8KTuM6z0OTYXTahaaOfAXLYCnv4fVwpelBhM8U8aRrWe6JAuJA0/GMX0xxwFgEIVBE8vapM0cGwCUZ7jvx37JwshYK5EFoAwN0mvH5K8JNvy96dcGUoBhQUhLmHrx9YJP6118VnQK95taXUJbofntqZmFcb1bTlCUHQ5VlViijH1muAIdNpSGUYMpTDoQHQdljtYpMT06B6XO0iX48mumz363XIYTi5HTghDX8YNcOjew1pbPk9/SbxVDXhpbBvmHBHhr6svxK0M8DYBU6TMkLJ5jnL+ElnGozXkO0UDBJdfLgS9eOATStrkURBEa4hMD5VzLhi6of1+6tgnxdxHgb0t1zGcp4I/fSesGXyP1rN6GMlDprF16K3XA5X2Qy/5efAR3cOvZ1LyVgHHemkn4Nj5cjx1dV9NleKvLgqXeCKf5CCcAx/oIlB3eCIhStih9YNpCbS3itXXvLUsS0AQA2iK1tsnNmQyYaPAkx5ZaZby9tOzc3C7Pp8u1twWM3/q8hD43Ew9Tbqy8bN14oVoqhyzqHnzpeZqYIZDzLIsUvpb+rZljLQxPHDP8qC47R3S3AIDmBsfNW/zYFhjXe0znU4yLGOSTkUUAHkAZRzOsS91wzv2fu06gXFR7q1Nxh8t1vs8BXnh2uoZecs8c0p4DWnT/4H2us1wBLAELqkzOm8AMOru5iQGvNlCpUhFBlAd/u4cboK5xKoGmnsVFRBYHpie8elpjjmcsEhCl62Lck0NT+fE1ssEQCzDR7jxWnVwLQEwvyP/C3OSYGuA8Y/O5YOavzQD9lmDrl6BrljFLEBJiM1FtidLCdPEBIjgiLj0l+mNa1hQe0ys4U+K6xuahBkpg/bDw68wO0yIFUQRUKdCGILM64LRFskZxMGZx69HxUrr2mrb8H5lYorPWTr1/DljRdVw/RJZCSUj6rfRnlXI3B6Jolx7LemWIUc8x9swwJ6T5kBw/cmkb8m8ug1E3VJajS46VBpVAwPIMwg9Y4AG5+cgl9KNfq5dKKBfcmPeHgL1cm+baw9LylohuPkD63uLus0OvTRrLVOwVUpiqL0nfbYGt+nsMfYdz/fRSeexLkSWjWUBKrt8AiXsPRmnmHQFA2I1H5jZZnzdI5TxZn0tjy8fXIweqlMCp9E9YPgNlC3jw5A2BKAKoMHiSgCUAEksTLctpYIHLLpEXobZWGW9zZUPHWPez3iO3BgzVJWSNFah9DVbzw1r8uNXHXkZ/J47pEiGAB86l55y71hGRO/CN2IxSAQpq603GTpspmrZAWXboyiKYdAHAYS/IYgz8c0rSRxn/wPnvpo8/V94jq4Pp37+2wHQpI/ilyeovuT4kz3gO8LEELymzQD1hCLjc/26nEahbIQ1GKY8i5oqsIZRjWqR+hjxUzInoddEBFZ7ZwsubP4qFVwBQZOKVreWWo+u4vM38BowCa+K9dix9qb7+WzNAvxVdO89/ySWMB1JOetnSb8/gtySYypoh/UmmlDnt89jm25YuHldTOwurbl5gXB0gaRYl9WKFg08B3oU6oZxlSi7obMwGUMb4Sm35OTzFV07nwBP9Oyd4DjF41m9LGaCFEF6/+HytRFLaTt7XgHyOhhh1/m3xPQlZ7SlkzZXXtOFvSVrRZxGDJkPAg2jgDeDgxa84ZMZx7UWvEUY0D3+OjnBAdYvYDnKdr8EUBbgMQAH6fT0HaF3ye4h+baXM0P14DmOLE3lPHiuy5fgp/jrBVRYpCCxAiNwKVAf1m8uZz9Ygc/i/cfev3UmnMn6vkwAq2rrTypyjQQfeatDBOu+cfJkr422ubOgYa/245PmGjunRpRYouuyaOc1ady4fP5/JgeYYeS3VXXOuvg7oWpZFgCW8ym/dOKxp9MHX2vtojcII5dwfuoHTLjakMfSmXaP6YJpAn5qxGyzNTV94s4S2IeHv0s7Yo6FRoveFfktm5FptkPVMl/YxYFhbN3QPfa5M6jnARWtzRPMiQWd3vuweaBfA4zSCKCKALWFYoiCCKHOkk6uUwx8rct4rpjXmaPdLAM788dBUPuuOD9QsABK75az8VgDRnCWKPn6FdOwBcI3Ywn0v2ZdJWpvZyj5vhxjNL0m5a7+UKfq1QdVL6Ldg6nidyGm+jrQvW9aUynYBFytlFDVXsl0hrilLX7ak37ruAUDtgtx9mr/1492lYFws1yHAXeXdeqZ+sF/i4sOBZ0NMIYFl9uNgXnw2PeJXR5ppstaV3Hjm8T80F1yr4R8ahxq4tYQPIQ50zmXKKoW1t7KWaLcevc+Pa/UVi+lP6JyGMad1v0TrqB/QqsuVWcTXuQYYsOYQw9IiCbrK/LOyTrmacm5m/Gz6PS7pg7lzdLnmwTRPz7+1FbnExJA2WJ95ntdAwj/ofp8bA1aZ7PM19T1e8lyX0Jdal4fkNKA/ZuTdvdwWyr1VisQqY8sSOcSyTmmoTo4XmW9OdTXt90AU+fdjs/RtU8p7ZV7LAhL078/9z92Ln8Pa6v1c3dC8/9L3ScgKfm2NA2sOy/3mMuvlrPrzdCWIcg0nxQvBEF3CMOeuNXQPfV0WfDW6SdYogGmJksaucOjjEYAZxFObafHjDnVsi4aONc/lDsZ0LUBx7pyX0jXXvLRxhIb625cUzM4BKXxPPoeZp19iVTuNbiY8WesJnME9qy/JefsXvtbviI77MU5FkbruCDAp720BlOcsUVqjvDeRM3N/iZYoPPVA3RBd048vpd9au/RayVonWCgdmq/kO7G5Pvlrs2ZLqmUss4BZqzrWgtUA2hqn+hlrAON6j6o+oKtKnyWu6AEoOmuAkIAmANI4XpQZIJocD7z2V0cyti1XYOtYPmbIoiB3rdw5uWvmnoeVSyyM5Cwe+P0E1CfATEgLGkM0xFTzb5NJtt6L51qLrpljv1QnzjFeunG4zXkOsa5xSSwVnpf4XpcCKiyIWNfX+9cIJJcAKLl7nnt34DyQ+VqJ+4gWDEH7R6Msd72h31+avjRfoefdUpVZ78N9g8eiHxdsiVJStWVtArWvj+HbWQCKPlc/nqYc6HAN0JBTsEuddR/rntZW7+fqhsqse1vPOsRKhwprLdRKCt4fWpdzc5ZVfzl9CW4d+YHFi8hLzs9dKzfwcOY5uOHk9y9wsSqmIStPIqwyuCLMbQ2gdpYp4VK85c5yjWXJuc6mr63rzPcG+p2tzeyfo3OgwRBdo9XAC+9jnTOkNWIm85KFIWeGq8t5YtcMIffFHVz/uwMwcVYpQLR+Yp/MB8R+OUf6qoyYf84n+h3R+tMcz9ul+yEud3o8yb8AnEPWJhs6XpfJsQDi99qprQy6o7EF+pM4l+Uo96FyTPq1dE3f/rWZr2vpmmf/EvfSQIp1DI9d3sr5OwALty8B4mQ9YY2XgPOiNasRAdQlolZM9pcAyhuc5jM09QxNDWB+BMoO9dxZolQ+JaNkDdAWKeL6w1nlooucs0KJQCWG16Ovjq59wZzWPjcH6DX23G99jRwNzR9WP2WScgre2KpDc8KBJcPw/kU8iuZBtNAIKs/d6HMF+0vmvKHvoPlJuabFD8g8ob+F/k4CbvF5rHwB+nOi/ra5d7fazRJINA0BV0PtfEkfzgFOzMiU/phN7+zXRw2csK+BKwtUsfr3UHv/Vmu4xUd/7vUuAbCF2JJH958JEgW2rKWyDjdUxtYmzEM3qk72gZTPtubJS1g2PS9aayxbCFplct45Vx8Y+0NbvT/0e+jcobVAv09Cl1igXLqOnpsHdfnL6AVcurUgA18GSEHmGta1LCBFlw8h3zIQf/Fbb47dIh10cnl2mRCGmAcUaJ87jqX9/tx/bo6EdJT7ITqnDftSdMni/GuSxejw/a2+MkSsaeJ75NwChAw/zoRhKgHcu9+rUerjKX2KQRRQne6Lw3EmXwU9b6bAaNQfO7Ld0G8LRNEWKfqYVtW7u6KfhYcn8hzD/5J+/KXH2RCdA6l/bwAK07lnv3R9ueQ+QB9M0WvHEJjCrhMeTOGUyPrwJSKgImOYmbyNURYAmRFQjtDMa6B8RuMzdxVlh6p2pmjj2qdELygNsg+AfvAZAkKMIZ1S8eI152sgZtQuOVboEk3Ytb/1PXSdRZdo9rl/i5uEnMMMDGXwaahYd3dNOea5R7lMPFxmASowjuMbXsMkv+RbX3qsVuidA2TPlYk7C//W343vMcSH5NrsEiFEP/9Q2bl2G7ouvyu/Z4uUh3qtZPESyOxfKhT+vddv3f8+5xrnwBS9iPK5DAzTWsZVmp9m/rJWl7bcebSVi5SBfgPpMLZeQW/1WnspUPL3AFEuOW/oHXPXTKwTh8YH1HFDv3NlQ9TiGiHqhdzntYzrNcefE2b14Ll20OoGlY/j0X4WylZ0GguqMvjmvk4PKrnsuX/rOE25smx/GBLwfsvJ9qUgDvDrceqa+eDnuAZM0YzKJeYfPHvrmeQXRGZpB+AuWl/w44pApRFc1nbXgE/c8brp6SYNFHapJUo7UJa7FoAUQGEmRu8zMSoO5Medpl+rf19Lf2/m61L6rYAU614aiGchCWqfNWRABFa8dmyFlIED0mDRXCf9tDa2YsUSAJaYuetUwkzHyBRin8BnDBAAxXKVe8ma9epIv8iQZh8YFual7ByTd67MuleOhhRY+rcIGUzaMsJw8eH+qU8ZYp5N4jlVlzGdEyQvZZj0Nc/R53RqzZ8Cl7v45IjBlHNzXe6aXwpAeQkNtbkGhf7eSrdfm44ACvTHQE5YzF3D2v890Etkstz5l4wVsdISYBiI/YjiPlksOq9hGpfRrLrIepdaolwLoujnsdbYz7VEGbqvfja9f0n90DowuC7k4jbp/WsVE1ymySp/2Zz3GZynNZl/7gDS10Lmenqhyk3EFoIv9SWVc4NOgMdRHBgstAqzyxYBPKBkgPFjNuo6ljB3bvDkmNbkm+d8jPXB1gWuWTgvAQw+ZyH8Lbhyi2HO9Zkcab9zayhZg5nbXCZ/CTYr218AvIWzSnnrDmeNtUzo0u/mSAG+39u6+hL6hDT70DUgyiZTp8sCGKVTGQuo2tI+MMzoXDJx6+N+SzqnrfwtyHr3XwNc/1yy7mV971Ltj2gr7ypBPEdu25bAygupMp5L2q6ovDaOscp4W8JZqWCEU+1BFWbu9CvIlv/1WBtam74K0mDopevXufnd+n2u7Jr7C+WsD3T/hNrKPMa8ke+n4bpI+wlTDkgx6Voz7SEm1wJb9ENY5/9WvAfzotZ9j+oYvW99txaRP8hp68/xIPpZhvpo7piha7+E9PNYljby++kL3O/vTQ36GnegPx50Hf9G5vclpNuWy3J07bqam6OuPf+chZ2MD97y2JJy5eID2C47c6NO1s4NlUHtD4Eo59Zdvc2tydf+y3sMXd/a6n3r97njz5UHGhoHuvwcoH7JeqvLL3rIs/SluM6/A/ECcwl4o5kF2fJH8sJwO0qFNAZSNnQqgyd6bsoZHeh967UuPfYs/RpaBeAyIMWi36N0bzE8L3XxOUe6n2ptkGyFWZLn8jEWLJ6J+Yya9l87NQAqRGEuB6K8VABM2ogL9JjJSRB87Lmy3xv91s94bsIDfp9LkbWuXKr111wUv6ePfcTgPK8nFtNTDpTxtjbKcgydXmdy2qzsuDF+v0qStf8SHkLoEgbuUsFziMk7Vy51uTWL+Z4hoYSPkWtIh8kEMr3627OgyHSN5v2l9PdQ3gzxFkzW97Poku+XoxxIeE7o+K3JskT4ksrZvzcd4Rgb2QfScWF9j9x1LqEXS7jq2M9dnz/nG+p+IM+eGzN6/pLzyb3HWmMFPGno1JxsJ8cz6TX8nJiUAzf076F1+dyabV3/kq3ev7ZskHTA63Pg+ZcaC78O3/srcK5DTOZLbjc0+KxrDoErehBazI43D2tL4PEmFcjYEmWDVDPIqCbQB1GG3AnOMayXMLPJwRZqx3Sufqh8iK7VoP1adM29tFYnZ8nEv/ncS4AWZk4ZMJG+eKR60QQKkPI2btsJsJpGNzNtiSL/X0FMFHwAMEa6WOittu7KWaDwlgHRgIRLXCRJZSzfqTW2QPodr2VG/kegl7ynPie3VvDY1mNOcztfgqzxndP6H2m/VXVsciwmyJPotrdC37KkzpTxv9RpjZrWpPG/RXqtkm1u/PH+q6cOmUU1Q9eCKefKrOtavy95pnPCuBYs9FbWIC5jM/lrUu1azLK1P1R3qXbe+g6530L/f/b+PUyWq6obxz9zqvoyMz05nZxJziEkJJCLYEBuEi4i1xcigoDcicpVxVfRyAuC8iUq6E94BN8HkYs3BIXwgPCIF+SiXF4FLygKPIIREy4SCOSQk5xk5sx093Sf+v1RtapWrV57167q6p7unv15npmq3rXrtmvvtdf67LXXnlXl1XQLghzcA/Lfgp8r5Zvtui7PQ/d3TXe5R1GdLkrX8lH5SKt1ETFE5tnKjUZXmaH91u5RN/g1q/avZYkULtNM1rsmz6Q+zWVa4mXXU1byaStb7m0i+1GTJ4pp0EI2W76vbU0237Sm89RebXj8K5d+Uv52zVeUJtPlMS2PuzydUNvklXheUdRwtU4qMWBp6VliJ4FxTxST3i5HDqWOJvflN9XOmRhLofEWoCxZI+sHr9MmEo5DEil7MFPQ0mNFknpAXoDQNIBQ5EvmepoE5SHl1ouGPoDTKCZRTMdMnVDus1CHy5WZPYwXbDXhOhnmXa6aUJeMcelb6ibsi+4F6GSKKV4KgeTBKnSNhY2SbSPvUaY5tMg2QcRJD/n4Kh3YSRS+b5IlLv3XwoNI0Uk9UWS6a1qV0TcTZF9kisPh4p2igVcaE6GiKc8mGbqH8u9qM+rnjUCR9+S6BaB/Jz6SrukINthYUomq9dmW3wWuI8uSEJzlQNy0sIfMxVY7RjCV+34QKNo9JiFSgPJkisk7SZNtNtAAhpiqCOQ9UCQ3Q2laPzpU0rW+1kRWyH5XppXxEHUhU+T1KyEypLvUWxcSxIUst93TJp+K2l451KBpag2qLONowiSspW0kU7qEUe3iMSkSA7bXiJc+BvLMI1XukG2l0sqhKb82Y890rilPrgzKHiuLsi1v3js/ObpN7+fqlWIiUjiGSl55b/rNDS6qy7SSzw7i6QCcVV/Lz+M0ybdFAk1/NrUZjUzhXicyTfVA4Svx8FgogC54TQJey6/BRK7JPIuIaShv8ppa2ZiUMt6G64TWt5mMOS4zhiyNb6nPSfobWs2HyBRO4Hege6cUeaLIUTQoW0BXsEx/1L76yqsvHIwdqpKPYFPMiuSD7TpVjSSTDmYjETSLgeKh2DwkADfdzKYkDy3HTYpzXYTINGSV1CGK7i0HamwyxGQ4Sl12Eo8Uul/ZY0X3c53qrKFs21gEjJBfZchmJJZ5/1mQJ/J+k/Stru2F57cRKVp+ysf7XvLyZgOU2io+1O/aPFFMfxD7gLmohmK/qA/W0ky2oY1EMcLkPULQ6qBL3XPpL4v6C5m/bP9aL3lCKNkK9pDVqnmBqTHzdKn4SgWXg0b9WZ5hIwv4R0opKZJ85G+IfGOTjwN2XtkGYIW2tPEe9Mo5qSJiyme67qJ1fJKMA/ICm+eTRIqWPjT81jpIqWDyfGcgE/7EpnMPlRAYrgLbZdyt5xgnkbWpsiRKD/qqPACyILKcROGr8shejCv19HsSmEZNPHnifq/9JlNkHTD1JdQxcMOHtlTnGuw3KXkJqSJdjjlRug0zmSKPVSFS+H4RkbLwsI1ymfJq+adBnpTtl/nHlMS+CSYdiI7RyK2sz/J+pmfi95DKsUaa2PKXqXAuI5CTwkR+aJB6ADA+UFO2f6nTmOXPZcszLZi+z6L2jRr2kM1Tpt+AWZbIfQ37JYTr6ltdCRVNNy9zba5XU7vh3t7MG5T6Sepz6XZaP2qa1gPki8aVSKGtZhMCbp4p8rgRpvgk8sGgHLflM8FWr12JEls/OlsCBZi5dCoj8Mt4odgIExjStZEWSToQYxxmCu02O4U3Lv6bGpoGE4ECZctfr7AxFFWYaWLRiBKguAMwjTgXTe+RoEpRVkHi35CMLP6sRKhQPtrfwFK4ovShG2xlpvWohh4d2BN/lAax5Z1LnW1sGRTD/VTeyrTbonPqhhw1M0F6PdGWj/azkTLOw/DX4dwM90jhaZyAoXM0RY+g9UmlFbRFQhXjVTu3LgKlasHa+jXNk4HyTutD2kYXJ9EbisiLWegkZe+hySYbmeJiLBblscVykrAZ8trxqqiqCy2LsNEMVo6qeuJ+oc6+1TQYUnRvU3vROkyOXXaMvPBEfytPkySKfJS6SBTa2ogV1+k7Y7AFd4VI0x7UlKcIRdd07TNteYuOma7Hj7m/1zJo8hZIhcJGpHDQUqeUZw/AWTGRcjMypbSDvIIqFVl+a/5I3MAD29fIFROhkqLI/apOIVulMs4jNKnFIUk1DSbPE1O6CVpj5x2DlKoyTgoJf0q/HcCWw33nHLchju1SRKIMoXud8OMA8mSJ5oFi2tK5HEXK5rJjXhQ3ghSys/RK4ffj97QpyPw5aJ88UbiXWbKKD3mnDJOlGnvsdBkEj6d1xG+5D2Vfg1TMeNpSVH0KLFsGVRW4IkO/rrZlM84lqE5yLzk5QiSNFbqerR2a3r3Iy8T1t3aPujCtim0adde+lwaT0Si/s7xu2WeT93TN62HHEPmgdWXJVXmteUHdfatNf5ZtyFYOXLbxvpcv3kCedtLDmw1gUJ/L7TotoKytXy0qGhOZYup7bWSKCtOSwtpvnib3tQcuAxc5U9XDpKquXl2ezSmJMmk8FVcDVjsPyDc6alDJyD8psUSeAHnXL5kudQwbW6g1GJmuog5hWvdo1CJ3slyhNIHXMa2+2eKk0L6tvKWyJEepZV4+z3aRyz5BD+MkCpBvP0Xk41jxktXH/wCdOAE7xrcSS1DWpTANxc1kWLjC1F73wytFM2RcR5UlaDUfaSCxoHg8GB4wrsSZvCNdPVIINkXuQKGovZdR8CSmUZi8rhf1WXR/GTOO8gN6n+TidVWH94mETem25bVhlvLc9A2KvhfXDQCzRzXgJk+LSBvX/AdOGFTAHuLpPEC5+itxEMq6rB1nIhgJNn3aVP7i/ry/5fvUx5q4aMlJa4+u/XYlUWTeMUgCxUZI28iTOglV08O63LPK9acj2ytokkWCf1JUJVDkM2idDqXz+/CClS6udC59jF3ES802Yo+UDvLkCTUk6YliGqDpsa3JAHRuJKYT6kKZxrMMhmVR2ZlG+KRXiolIkdCIFf5NpWRusLQ1lraKLCrrAuM2xIs/SBLFNmVnLP4JkJEl5LZJ+2C/5RZifxYjn/OOWRGsRbLD1D9Itlq7nmmEdhqEiq2fkflkzBR6Jh4vBVC90Iq8U7St6Q/sGnxf+/RLRaJwQtUFRcpZkXE0qZIozy/SmXidKvJK0c6l+ib7IXlN07OY5Kdt9FMq9NOuaPuls2hlZzMubCSYjRgD7K5mrs9Y57kSdQ/kzSvKygfTufMIG1NQBUVESlkiV5NjJN/oWuQByvtgETdFNiuyA/lvWLZF0IgT2toIlhz4lB2bXC3Sc+vyQnHtb4rOq5JnerbqnHqiVIWNzHEhemTj4gKBRgVp3lwjv4IC14G1BgaM9xEasygfRz6KEzSlsGylLxuMbpkhSTgpiDmk0DcRKbxuDQ3H5DNoz9RAVjc52bfgoFU/JLFYRJ7kiilCRo5QZyE9UMDyEHjHApF+EFHXe9chMzSyUsK1bc4CJs8UEzQFz0X+sGXPtzHeJ3Gyv4hIcdEKrMrbooHkg4t+IFG2n500f1Ee1/otR2SBcUKkLIrOdzUeZ12p5kWXsZEgJu8UDq38q07t0a4vMcl3MumXJv1H9tGLihHbL0ugLBJc5KkrXPp8jUwp8jIxgfRpeW1qewmZQuDdh+yuZZqswryIbPafaWtsEqapOyYyxUZoaw9XRz2t0ve5PMc0ntWMKZIoJoW1zsZV9RnoGJDvkExzfPnI9VaSfkacvyeykkcKb1Q2bxTNSJTp8pwU1FA0JnG/MA/PUIQqIx42Q8ZUl8DSizxSXIgUKOlcOhNBsOPyQvON7WRrah8amZIWDW8X3ANF/pa9kqaoLUJ9nhbqVFrrLscixUoaH7bzTHnrhIlM4X2ORqDsId/G6Tm5l4qcz514p1AbkjFRILZS4YOy1VCa3J93uL5MFcWtDCk7SVuxESo2g9z2PHWizGhn1bQyRNW8yXeTXOPfTtM3ZoFJ2odHhqrkyaIJ21naegQu07Q2YztPDmzKa5n64JUsi2Y+an1pGb5eG7CYGnkybW8UiUnq/STPVJ+MqljD92NEzxWy4WoNwQRZsDxyM50P9jtxr+6xsqDRvk7y27TkMX9cjVmUxuO+jvy5GpZa+rQ6+kmvV0SkFBlbplEgG5HC0zlZwuuniUixCQxJdS+BJ8opZP1BEYmS+4x8CWMiTfbYb9MWYh8Y72AklllZnGcCxXRtE2kP1E+muHqWSMjnNbVrLmOk4sb35ZYTKonXJO+fbF4oNjJF7hNGStrCgQjWstDayH6RJ7bruRjkYHll/XOFi9Fk0yW0/s51MGHZYPIoKfPernGYpH5bBdPuC12nn807pF64jOQJR50DFK52p+aVQs9gIrBM03yGIg8frJDvppAqfAuRXUvTPrXT5y+auuNKnphsDpe+rg5UJbzLPF+9z10jTbgfrKMJZZ7FtWHKBsMN1aTh8EBDUvks8kbRtqZ8KmbVwVQhUPh+XaNss4JWP7T6xRVP27xlQhUihT8TYO6MF7nTTTBEnkQxBZbNvaokQ3iHIokTiJNdicKDgEUhULR7VfFMcWnftntVlW2uzys9VrS8cvRM09zE0o1A3hNFrign+6ylJVGqoKpSuR+y2VTPtHquGRQ8r60uuhrt/F51pB8EmOQUYPYsKiKY5XWqPFNVHORveRBRl33oWqd5XtMUH0kYF8kveb7tnSh9ZfxQZaJEg22p4rLeJ/K36eGm5ZVig0le7P8A57ywHlOArPBAvpGYvFV4fv5bspSUngSbRQgM1zKDjwgVOQdd3ppfjhuFcsTd+I4uI19VUHV0lWAjD8pcf5qNwXVaj2m0ziVNCnIXgkWCkyyap4TsEJbAE+U2AKehkyZjn4zKgXud7LA0vjV5oPDrLBkh5Yy633W/SE+Cq2dKVa8U2YareKcU5dOUPe35Gmx/VaQ12H5ybJhst4WSZ+qrTH3X6YLHXwhUWeKYYBrZ1LCfCl+RVwrYcRuJoREsk6CsZ4p2jun8IiyaIV+Hd8p+oUxZu+pliwrXsli2Mqh7oN3UHkz5TCSJjVDRZiGQN4qm24cizfQbUAkWKyK27+JBoum1Lvnk9eQx7bgp3zRQVf6XaXfubW+JSZSycBmt4YUrlXA6v5Fl5VsbNBJFnjt2Ha1BaSiqOPvlJjkv7plVOmxeVzQPlEmglYvLM3LBvwSd7xBmEiUHCh4LmMkRvpUdCUEjUGyYh7o7z5j38rGNwhflKzoHqFe+yVGyoqkWpOTZjnG3ZIcgeXJLWAoSpQrKftv9HzHL7mfrp/hxG2kiST3TAJUJRZ48S9CHTR1lyF8TiTZtVK3fy06kHFTUTaRwlJFt8llcPFMmmeqoQSsHTqxEynGwZ7IR+HKA3TTY7uIWUySXZ9WHzac8mDKJUpdhWRWywWrGLwzH+W/58UjAr2KcaUzipAwbmVcK90gBzKWukSlGw9F2gf3uuKp+c9PI07ShvXeREVTEbpdBWY8U20gh5VtwnEI2QKzWf+lRQlstDZY0sN/TGO1cFCxBnRnDrLxSbESKfI4imEbXpEKkkbicGOH7fBRM805Rjg9XzFWCF4OpK1ookNyQKONZVNb7xOWa00JRHefHqzxjGb1Pu75NDygaIZ0GtG84T+OPmpzTdFaZ12P2cGkby9gXc5QhW8vChUgBxtuJRoxIG1E+t/Z7F/k+lQ9ohNAHOMqWg82bhKe5eKmY0ni6dkweN2HaddnlGaYr8+apJ5gSbMxnWSJFNh4Y0vixlTyZAmXLH4e2hZ4stlH1WWIROuVper3YvFImYd21uCg8fkqRwF9w9GCIt6B5ndgIlKHIT7ARKAcNdcuQeSxLm3JVl1eKCZPIHxdSRTuH5+NyibuUcIWvwdK1KULCW+VAYFIFrQ4CxXaNSeV9UR2XZIrJG0oOJFQdqS1bsfZTzmg64DzA5HEyr8/rkceBEa6Yns5aNHjC89jIFNdp+xrk9UwyVHuWIpjIDc3zpAqBUkRca3lM+aaF+dExJ6jB++1lUhUuz+3qkULYxbiBRl4qq8gF9APsHimF5IkJs6jAptG6Kq6is24ERYaMRoa5Gj9S6JrcASlP0TNKskRj2+SzLZlilCNQ5LJtGnkCFMc9mdQDReb1WCxMk0gpkoF1EbkaqWJSaELLviRmNUKFe7NA7APl53TPK6p6W5hQhwwpq6hXhckrRfPg1YwLaXTw53J9tnlU0stgmiPr8h4cRdMMAbsHnuu1yqJoBN923kHFQX33aQ/+uXimFHnklb2X9k7cLgB0u6lKHdCIE3mtIqJkUs+//bI9p3FO+XeZQ8trGuSMSSmgY0CxAm36IESYcMNsFeMNioL9MTfp0t9LW8bK9GxVKrZ2jst1FoVMq+JeX8b4MeUrQ6aYiBPX+3K2bpFhinXC25mJKDERJpI8kfueQFl+uBgXk0zvsfVfVeRPEeS1pBeKyYtSI0ZM03tgSAuRBXBedNTxTVzkbl0Eiil/VZXOZkzQcaBcf1h1qmtVfWYa8rnM80/DILTdXx6zkbscrsTKJKiqSx5EHPRymbaXVNEAhzYwYfKsk7ZjUVwo7d3k9V3bqC2/jexwIUmm5X0ya5159jr6HJIo84SikUsgr1TYGo4hkN9EWFbhW3dDKOMpUyYuTNEIi/RKkc9UddTadjy0PM8ioYgc4aSIzM/ToKSjIH3SvB6LDVevFBP220tTGrxljFlqV64yZFn7oLKoS5mctDwnMeRdPK9cdCIt/oaL968Jrgr9PGDaI+t13LuK57DHdODlZx7TbD9V633RIHxoSdcIFg4t3dULpkguuhIrZa/rco2Dg2WwthxRpAy4umlrhhqNztG0HvJGoSVXuYu03BIksSIj9kk20TSqPg2X8box7y5hkkjRvr004MtCc4PmsHmkDA3nAPOrXJbFVrI1ESfavs37BMhfh2Pe2PRZ4qB2hEUKlYtXSpFHCkHeQyNUq8L0/eToGd1XthEZ7E5O9aHz+D4/Ru10kUHee2XUoTKk1Kwxyciui+dVEcFf1SjgsJWvdi2bp/CsUTS4Mot7E1ym/EhMQq4U1Q2PGAe133XBtNtPEblb5Jliyl8mZhShDlmpXYc/l2se0zvOmwfKtK5drU3OKYkyq5G8sqOLsOQ3VVjXD2PzZpD3kASKNgq/SChybdsPlPFIsUG61Gsu9mXru3y2up51nkCGjSRQiogSsHTbb1fMS330mA7q6GuKDPCie9QVL4XDRAJVnVJkegc1+vOCwsUAnaacnca1q47sunilaPlM5xGqxkjh1/dwR5XvrxmRLvnquu6yY9l0tWlhFl5dLv2/Taa5hImwDb5IQsdWN7Q4K6bnlbB5pdhQRd5OQ0ZPW+5Xb5M119D9dGV0QdHzuTCg2qieCfw6Mk4KHZcjezbYCBUNZfK4oA5lf5GUIM0jZY8dA/SylUqjRqTI+xSx40V10sZuLyJ2AASwE4Zljsl9nkfDItXTqphWPZkGKbBfcPFIMeXj0MpjFkZEGY8JSe6almfk+7tYXkzSPuap/lcd2XWt57LvshnLVcvUVp7zVNYck3gE8fPqmOI1yXNMq3yn4Qkz7xgCaO73QywQZuHVVTRAzvMQTEG0bW1Fmw1hu1bRM9juo6GMR8l+yNT99tqshnlmPGaEqiORLmSKZiyToWebO+d6b1MFmKeGseiYVIkpIk60YFQudZLXHz66vCwxUUbIr7YD2MkT22+5f5Cn7xwETCOAq8sovCmfCXV4wZSVTy55bc/F32+IeC1yjxjzLjcmIVNcRmIpL0T+STwQisp03sucMC8DjHUQO9NGGV3Y42BgVl4phDIxnGwkSJGnSdXpOy4oInTqPq/o3Eny1o36BhDnVYpOES7K8CSKsPw4u9AbJCdTyirTmkfKIigTVebWzRuqjLJrU3fk9B4tnwbbVJ5l8gDYQ+yJQvuAO3nC0yZ1OfaYT7jIy7qV8DJECpS8Guqob5MQvSbXYi3fJKNj8ww5sFEWi1YGVcgUk2FbhkyRxyZF0XXq0C3qnCo7i1H1MpjV80zqCQV4QqUMlpWEmiURWaY/sHnsAWad3kSqaHn5Oab7F2Hantfz3g9Ox9acsEbWMZK2H3AlUqDk02AiB0Kxz7cQ+ybBV7bRaKPwpjxFaRommVNf9Z77jaJpPUXzDU0BpyQ0rxTb88jgs5S2KOVqwy7ywZVtRIkreXLQp+9wLGIdmYe+xnV6jynvtFDF2JMELofmISnbG03rWRbYZMAyEdSEaZAp8nqTeKEUPYMN8yzftGdz6e+nhbLPU3RulbxF99srea+DBq1NLKp9ZoOJdJgGypJ4LlN+XK5hu1ddbaBMX1Z3u5t2PzpbOTEvlPiSQHqWcA8BOs5hi5fhSlYsk2I3z+9SlyKjeaVA+V2FdV+mALM8YGVZAkXDspSLx3zA1SvFlHcaKNP+OdnoMnVHkinUZy1TYFkb5rlvmhRV6mdRvYHhmpN6Fyw6gWLCvE2zmXUZzpuXziLB1iaWkUjZD1SRW0UeQUVefGXuVeZ5XLHocnQ2mFOJNauAey4uqDbh7uKyxe/VEPsy5oMpOGjRZzIZkpMYnUUo45GyyF4oJti8UWTZmOZFunqlUF5TPn6PZYmJsof8KHdRvZb7Wl7tHgcNi9ru5lERrOKVIvPXjSoBpl1lkHbOjuM9POYbk3ilAGbykDCLqWCT1vd5waLK6Dowb2TSvMNV/57H/rMO7Ed9KTPVxiX/PLT3SZ5h1vJ0Hsorw6Fy2bWHn/cOyYZJPobm/syhGdCcyCD3RNqX7op7Iv+QHR8qaTaYXP08ykEK6YblmAv41CstzoeJONCwbJ1kGWLQ5Roes0PdsmUR67Zsvxyucnu/ob3Dojy7RzVU/ba2+k7XnfWUlGXCsgyOlMGyf9NJ4XX4PPa7vlQJceAqN+t8N2k/zpJAmaTOzqfuMQWpLD08yrKg86Awa8/s8vHkaA6f3mPKL0cO6bdpWsck7qwu6dqUo7ob2fw1hHpgi51CkCPV2vQezVPFJdbJNFYl2S9oxBKgv5tm6BVde9mxX21sGmU77yNpLtMXbCP10zKObF4pttVTJDSPg6HYLjK4cnbQDFUNk5RFGe+UqvcouuYywlZuy/z+Q8P+QUXV/nXe+9C6MKupsyZUjQHl4t0yT/X/YHufcHiNwTovrarQ0eaSu+Yvgi2fyRtGooyHwyRYRgKliChxJZ00ckQjTcose7xMGMFOnlStW8tMoCx621p0TEqmmM6tAza5VCZo6rTma88L/HSCDJMSHkUxASa5RxVZtwyyXysfmeb7geXEpPX3oOiQ8yTDi75ZmaWUq5xfB+qSm1WuM/+ybL9r2BLD5pWiLXHLf3OU9UYgVJkCUaeSUeVai7oCgu25y3iHlPFKgfJ7GeFCngDe+2QeOptplvEiKYA2UrzoPaY5klYXkUKQU1CXDd47JY+qdbNM213m+lQHXL7BIgSYX3Yytm7UacgepPLeb8+UIlT1XDGdP69YlOcsj3muXTOG1tjqMFI1I9h0b03ATeoRI69lOuZ6DdP1XNLnvWOfBDLQLFA8tQcYVyaKpvJo9WgZO0Y+nWeS+rS8wjvGvLSpZS/nsnDxSgFmN+VBXkurN8s0HbBOTNrGlknNqjrKO6mxMAmWrT4vGpFSZrogYdn0mao4iNNj68a8Eykc8+B5Uieq1t95kV3FKBlYFlikl5tfcONQxn3gv01pZeKzlH2maeEg1JsiAVdGAJq8h0zlWBSgalHhCRQz5qVNLXs5T4J59ZCyBapcNEVt3jEv7bRuTBorbRZ1/yDLpkUxHDUc5O9GmLZn50HCssjgPfHnsd+YkpQtCi47r8yg6bnqYG7lCA73MtACY2rzXMsG7HWNG1HFUHUJ8FkFZd3K65oCVJeQdfFIAezlJ4M3Fk3pkvViGToMrZOoMpVtWTEP33jW5VzXCBpvl7MI0Fg0ej+PnillAs96FEOrW/OoA5VFXXFTgHrJu7rq7DzIWQ2uOvQ8eaSUhetU3mXDrN7zoE2pmmY/ul+YVl2ZNFbLJFgseVXBE2WamIeGbBvpn8Y99gz7Wpo2PUdOe7CxlGUIlEVAg20bmI/6A4w/xySKJVAc02axhE55uHpfAYtdnz2KMen3DQ379HvaS4m6eKYUveM02rvtnedFri4bysi1RUEd3imTtPE6R2jr/Db7OXK8DMbiQcF+1ZGD6NmwbLK3TkhbclYeMIv3TRZMuhY97rQ/QB0xUgjaCj5FXhU2L4SyxrXtPnV6oFT5Ji7lUHRsvzsEbdUeICuPoud08Urh91q2IIhVDIz9/uazwDx0MvtdzpN4pJQZwaX8dcOlrRb1NdNYgcAlXgphv+tAndhD/O6eLKoHddRNlzgZi6LM2wbK6oq3N89Y1AUDZol5KZ86bZxFwLzOijiImAfdtjwWqPbMUvEt6pzqEjTaCj50Xdv0DTgc0zCpoC5DoEyrQbiW+TyQKdozFC2RLGEjU3gefnwxhVF1zIsCsuw4iOU8L2QKUEyozIJMkc+yTHVivwyIZZbXddbNWdW1aREo08IiGIJ+eqAZ81gedZJ9845FISOXEYvf983ZdB4TylbuWTWGabmNmkYupIHs4l5VxgXLZoDPA4Fiu+c8o2h6T5WpSLY6c1Bw0FxQ97PDmbdynuR5qpTjNKf51DVVrW4C1SuVHnVhUcj9WT/jPE498pgd5q1fNWFRnnMSLIqMWhYsR1kvsZZUxwiiC8NfV3Amk1fKtFF1lZP9jM1RZsRwXkZM5XOUWQYZ0OuZKVjxfr/rtLCs7+WCRVXup4lJp/UAkxH0dX4T1ykQLiOEdY6saQHOlx0HzaV9lpjGNLQ6MI16PWsZamr389Zm/fSexXz/gyIXF8kzZZpt2/b+8yZT9g+LUEv2GbN2lSyarqH95udx1NGZzhuBUhWL0nFXfc5FcOmdBIvw7aaFeW9bHvWiTFu2EUnTkAmLvNpHWUxC0nkUY5GMlUXCQWmfi4qDrMssEpZdpy6ClyMuOAA1pA6lz7UxuTC1rp4rNs8Uk6HtQqy4wMUbYtJ71IG6vICA2RkHrh4pPI+EaTR6KLaLjCG8sjFrz65FxKSG7iSK0jRHfMuM2Nvk4DQMVdsKR8sGT6RMH/tJpixa/BOPxcYQiy9Pqi76sIjwRK+HHRVioixiZarjmafl7ukyz931vGkuRSWvtYjzB+dJwLssgzxPz+uxfFiWmDLz8g7TjJkyadyURZTX84J5qV/LjiFmW0/ngUA5qH38QX3vZcey6BQSvu/00DFnjMg0R31mFSMFKP8erstZynu7eGIUCTSXmCEagTJNVBHCrvM1bdNlXLxR6oxaXrQMMr9+mRV8PBYf89jGlhXTiCEyzdV8+H002OSBH1nzWARMs57uJ3ni++gYvv9Zbizj6j7zGM/pIE2vnQWo3rqX6ZRqgstl92u+2aQu2K6du8t0G9s5NgWYIOOmlL2f6TzTMdeVeyYRmJN2ri6EQlUixRYfpuo7uyyDTPmKyqa8APCYN3jypBrmbdrFtALQmq6p9Ucu03xM53rkUefUUQ931FlP62qH3uvEw6Mc5q1/nhTz1H96IqUa6tGFS07nmda86v1E1eeoK0jrJHldvCaqenW4ECjL6rpXBZOWg5/e4zHtpcF9W7VjWuU/i76urAddmXM98vDtaH9QdapPXVOEvAz18KgO336mh3mxp+cV0wt1UUPJL4NhV9U7ZdpspMvoV9kRSdP1bHPpXfLJPFXqxTRiuJieo4o3SpE3yKSjlWWCzsr7eSwmvOfJfGFaHpLT9kyR1/WeKdPFso2sLhIWdbn3g1pnfB/kwTHLwLSzaHPzMkXWe6Tsh6ypEFjWQ8e8GEOuoy4urNxBb5CTYJLG7OKV4uFRBK+8VsMyyD3vmeLh4eHhsUio00tg1vrPQe03bbakzcac9FvLa++PvjuhdaYxfMswDWFeGb0ysUcmGVk0Td0pg6rBdW3PIDFpkN9Jg8y63JvuUxZ1LIXsMb+Y9tSdg4ZF6mOmuTSydl2TnHQJQEvwRO44DqpnwUHBQZSjdcKX32xg6z8WVW4vagyq/fZKmaXtamrfdaXX8SzTx4J4ouwHoVGlEcyrm2mZ6Una9J1pV9Cq15902c8i1CUIp+2V0mB/HvONaS/heRAV12nU+1nK8mkrXEV1zkXG+yWSdRzE9nYQ4L+rxyKgSCYPxd8iwsdSmT/470GYQHtz9UI5aJj1qkOu3g51BsJ1HaUsGqmrYxpRmWWnUfA8GiSz67JKjun+k6zgI1fvAfRyahjSPfYP+z0y4FENs5Tl01wamTCJZ4q8hu06HosPlzhqy4ppytFJV/NbBPh+aHqYtH/Yb0+JSSB16HmWUYtczh5lUOELl62kdVXq/VgSuapr1H48a52dsyacbOVQ5n1dRjzLoozAqqODnxaRYgs+qXUe8zrtzCPGLL7NIiis05SH86I01YV5D0Arr+Plz3JM63ENGE9Y9Pc1YVby1LUsTc+zrOXvoaNuObsfNkodKGqfZQduD0rQWY9pwenLRlGU7PXgbgQSTI3fdOt5m99XVXjtV6OpQwnQ3rmoHIpGN12vM0lnMa0yryNGjO1aBNdYCUVl2gPA2+3iIHvm3X19jnowL8Gm9xPTViKmXcb7qfzMgqAoer+iOha308WWNb0arrboZFJZWbLo72vCfsjUoql2Zc/Zb0yrDA+qXjOtb72shj3Vv6H4zdNk3mljlmU9C9kwz/KnDrjrNU5fdmtrK9l7TeVH8vDwmC22trZw+PDh/X6MUshkzVX7+hweHh7uWGxZ87J9fQ4PDw93LLas8XqNh8eiwEXWrEQOVMvp06dx4403YmNjAysrK7U9oIeHR/2IoghbW1s499xzcejQgsSOTuBljYfH4sDLGg8Pj1nAyxoPD49ZoIyscSJRPDw8PDw8PDw8PDw8PDw8PA46FovO9fDw8PDw8PDw8PDw8PDw8NgneBLFw8PDw8PDw8PDw8PDw8PDwwGeRPHw8PDw8PDw8PDw8PDw8PBwgCdRPDw8PDw8PDw8PDw8PDw8PBzgSRQPDw8PDw8PDw8PDw8PDw8PB3gSxcPDw8PDw8PDw8PDw8PDw8MBnkTx8PDw8PDw8PDw8PDw8PDwcIAnUTw8PDw8PDw8PDw8PDw8PDwc4EkUDw8PDw8PDw8PDw8PDw8PDwd4EsXDw8PDw8PDw8PDw8PDw8PDAZ5E8fDw8PDw8PDw8PDw8PDw8HCAJ1E8PDw8PDw8PDw8PDw8PDw8HOBJFA8PDw8PDw8PDw8PDw8PDw8HeBLFw8PDw8PDw8PDw8PDw8PDwwGeRPHw8PDw8PDw8PDw8PDw8PBwgCdRPDw8PDw8PDw8PDw8PDw8PBzgSRQPDw8PDw8PDw8PDw8PDw8PB3gSxcPDw8PDw8PDw8PDw8PDw8MBnkTx8PDw8PDw8PDw8PDw8PDwcIAnUTw8PDw8PDw8PDw8PDw8PDwc4EkUDw8PDw8PDw8PDw8PDw8PDwd4EsXDw8PDw8PDw8PDw8PDw8PDAZ5E8fDw8PDw8PDw8PDw8PDw8HCAJ1E8PDw8PDw8PDw8PDw8PDw8HOBJFA8PDw8PDw8PDw8PDw8PDw8HeBLFw8PDw8PDw8PDw8PDw8PDwwGeRPHw8PDw8PDw8PDw8PDw8PBwgCdRPDw8PDw8PDw8PDw8PDw8PBzgSRQPDw8PDw8PDw8PDw8PDw8PB3gSxcPDw8PDw8PDw8PDw8PDw8MBnkTx8PDw8PDw8PDw8PDw8PDwcIAnUTw8PDw8PDw8PDw8PDw8PDwc4EkUDw8PDw8PDw8PDw8PDw8PDwd4EqUCLrzwQjznOc/Z78fw8PBYcnhZ4+HhMSt4eePh4TELeFnjsQxYOBLl7W9/O1ZWVtK/druNSy+9FC984Qtx00037ffjFeI5z3kOVlZWcMYZZ2B3d3fs+HXXXZe+2+te9zr1Gh/84AexsrKCc889F6dPn1bzXHjhhbly4n8/8AM/MNE7fPSjH8UjHvEIHD58GBsbG7jvfe+L97znPYXn/cEf/AEe+tCH4ujRo2i1Wrjzne+M5z73ufja1742lve2227DS1/6UlxyySVYXV3FBRdcgOc///n4+te/PtGzazh58iR+8id/EmeffTbW19fx8Ic/HP/+7/9uPefLX/4y2u02VlZW8JnPfKb2Z/LYf3hZs3+y5u///u/x+Mc/Hueffz7a7TaOHTuGH/iBH8A//MM/OJ3/pS99CS960YvwoAc9KG2nmpwBgO3tbfz8z/88zjvvPLRaLdztbnfDW97ylkrPXYR+v4+XvexlOPfcc7G6uor73//++Nu//VvrOSdPnsQ555yDlZUVvO9975vKc3nsP7y82T9587GPfQzPe97zcOmll2JtbQ13uctd8OM//uP41re+5XT+r/7qr6rP0263x/Kanv01r3lNpWe3wes2Hhq8rFlcWQPENtjDH/5wbG5uotvt4vLLL8c73vEO6zmf+tSn0me/+eabKz27DQdV1oT7/QBV8apXvQp3vvOd0ev18KlPfQpvectb8MEPfhBf+MIXsLa2tt+PZ0UYhtjZ2cFf/dVf4WlPe1ru2DXXXIN2u41er2c8/5prrsGFF16Ir33ta/j4xz+O//W//pea7173uhde/OIXj6Wfe+65lZ/9bW97G57//OfjUY96FH7jN34DQRDgS1/6Em644YbCcz/72c/izne+Mx7/+MfjzDPPxFe/+lX8wR/8AT7wgQ/g85//fPpcp0+fxqMe9Sj853/+J376p38al156Ka6//nq8+c1vxkc+8hFce+212NjYqPwOHKdPn8ZjH/tYfP7zn8cv/MIvYHNzE29+85vxsIc9DP/2b/+GSy65RD3vRS96EcIwRL/fr+U5POYXXtbMXtb893//Nw4dOoSf+qmfwrFjx3Drrbfine98Jx7ykIfgr//6rwsVmH/6p3/CG97wBnz3d3837na3u+Fzn/ucmm80GuGKK67AZz7zGfzMz/wMLrnkEnzkIx/BT//0T+PWW2/Fy1/+8krPb8JznvMcvO9978PP//zP45JLLsHb3/52/OAP/iA+8YlP4MEPfrB6zi//8i9jZ2en1ufwmF94eTN7efOyl70Mt9xyC5761KfikksuwVe+8hW88Y1vxAc+8AF87nOfw7Fjx5yu85a3vAWdTif9HQSBmu9Rj3oUnvWsZ+XS7n3ve1d6dhO8buNRBC9rFk/W/OVf/iWe+MQn4oEPfGBK3v7pn/4pnvWsZ+Hmm2/Gi170orFzTp8+jZ/92Z/F+vo6Tp06Vem5bTjQsiZaMLztbW+LAET/+q//mkv/P//n/0QAone9613Gc7e3t2t5hgsuuCB69rOfXencZz/72dH6+nr06Ec/OnriE584dvySSy6JnvzkJ0cAote+9rVjx7e3t6P19fXoDW94Q3Tve987es5znmN8xsc+9rGVntGEr371q9Hq6mr0cz/3c7Vd8zOf+UwEIHr1q1+dpv3DP/xDBCB64xvfmMv7R3/0RxGA6M/+7M9qu/973vOeCED03ve+N007fvx41O12o2c+85nqOR/+8IejZrMZveIVr1DrosdywMua/ZM1Gk6dOhUdPXo0uuKKKwrznjhxIrr99tujKIqi1772tRGA6Ktf/epYvj/90z+NAERvfetbc+lPfvKTo3a7Hd100021PHsURdGnP/3psbLe3d2NLrroouiBD3yges5//Md/RGEYRq961avG5JTHcsHLm/2TN3/3d38XjUajsTQA0f/3//1/hef/yq/8SgQg+s53vlOYF0D0Mz/zM5Wf1RVet/EwwcuaxZU1j3rUo6Jzzz036vV6adre3l500UUXRd/zPd+jnvOWt7wlOnLkSHTVVVc5y6kyOMiyZuGm85jwiEc8AgDw1a9+FUA84tfpdPDlL38ZP/iDP4iNjQ38yI/8CICYNXv961+Pyy67DO12G0ePHsULXvAC3HrrrblrRlGEX//1X8d5552HtbU1PPzhD8cXv/jFWp73yiuvxIc+9CGcPHkyTfvXf/1XXHfddbjyyiuN573//e/H7u4unvrUp+IZz3gG/uzP/szKthZhb28P//Vf/+XkSva7v/u7GI1GeNWrXgUgdoOPoqjyvYHYXQ5Arhxuv/12AMDRo0dzee9whzsAAFZXV3Ppn/70p/EDP/ADOHz4MNbW1vDQhz7U2e3/fe97H44ePYonPelJadrZZ5+Npz3tafiLv/iLMYZ0b28PV111Fa666ipcdNFFTvfwWC54WVMNZWSNhrW1NZx99tm59zDhrLPOcvJW++QnPwkAeMYznpFLf8YznoFer4e/+Iu/yKX/13/9F57ylKfgrLPOQrvdxvd+7/fiL//yL52e/33vex+CIMBP/uRPpmntdhvPf/7z8U//9E+qN99VV12FH/7hH8b3f//3O93DY/ng5U01lJE3D3nIQ3Do0KGxtLPOOgvXXnut8z2jKMLtt9/upBft7u4Wvp/XbTxmCS9rqmGWsub222/HmWeeiVarlaaFYYjNzc0x2wgAbrnlFrziFa/Aq171KnS7XeN1vayphqUhUb785S8DAI4cOZKmDYdDXHHFFTjnnHPwute9Dk9+8pMBAC94wQvwC7/wC/i+7/s+/PZv/zae+9zn4pprrsEVV1yBvb299Pxf/uVfxtVXX4173vOeeO1rX4u73OUuePSjH12LO9STnvQkrKys4M/+7M/StHe96124613vivvc5z7G86655ho8/OEPx7Fjx/CMZzwDW1tb+Ku/+is1797eHm6++eaxPz6H8Jvf/Cbudre74Zd+6ZcKn/mjH/0o7nrXu+KDH/wgzjvvPGxsbODIkSO4+uqrjXMKNZw4cQLHjx/HZz7zGTz3uc8FADzykY9Mj3/v934v1tfXcfXVV+PjH/84vvnNb+Lv/u7v8NKXvhT3u9/9cm53H//4x/GQhzwEt99+O37lV34Fv/Ebv4GTJ0/iEY94BP7lX/6l8Fk++9nP4j73uc+YULv88suxs7OD//7v/86lv/71r8ett96KV7ziFc7v67Fc8LJmHHXLGsLtt9+Om2++Gf/1X/+Fl7/85fjCF76QkxWTot/vIwgCNJvNXDq5Mv/bv/1bmvbFL34RD3jAA3DttdfiF3/xF/Fbv/VbWF9fxxOf+ES8//3vL7zXZz/7WVx66aU444wzcumXX345AIxNOXrve9+Lf/zHf8Rv/uZvVnk1jyWBlzfjmJa84dje3sb29jY2Nzedz7nLXe6Sxor70R/9UWN8ibe//e1YX1/H6uoqvvu7vxvvete7xvJ43cZj1vCyZhzzJmse9rCH4Ytf/CKuvvpqXH/99fjyl7+MX/u1X8NnPvMZvPSlLx3Lf/XVV+PYsWN4wQteYLymlzUTYB+9YCqB3NA++tGPRt/5zneiG264IXr3u98dHTlyJFpdXY2+8Y1vRFEUu3sBiH7xF38xd/4nP/nJCEB0zTXX5NI//OEP59KPHz8eNZvN6LGPfWx0+vTpNN/LX/7yCMDEbmhRFEVPecpTokc+8pFRFEXRaDSKjh07Fr3yla+MvvrVr6puaDfddFMUhmH0B3/wB2nagx70oOgJT3jC2H0uuOCCCID6x6fO0L1c3ueMM86IzjzzzKjVakVXX3119L73vS+68sor1XK2odVqpc9y5MiR6A1veMNYng984APRHe5wh9xzX3HFFdHW1laa5/Tp09Ell1wSXXHFFblvtLOzE935zneOHvWoRxU+y/r6evS85z1vLP2v//qvIwDRhz/84TTtW9/6VrSxsRH93u/9XhRFZpdIj+WAlzX7J2sIV1xxRXqtZrMZveAFL4h2d3dLlIJ9Os9v/dZvRQCiT37yk7n0X/zFX4wARI973OPStEc+8pHRPe5xj5wb7enTp6MHPehB0SWXXFL4HJdddln0iEc8Yiz9i1/8YgQg+t3f/d00bWdnJ7rTne4U/dIv/VIURVH0iU98wk/nWXJ4ebP/8obj137t1yIA0cc+9rHCvK9//eujF77whdE111wTve9974uuuuqqKAzD6JJLLoluu+22XN4HPehB0etf//roL/7iL6K3vOUt0d3vfvcIQPTmN785zeN1G49pwsuaxZU129vb0dOe9rRoZWUlfZa1tbXoz//8z8fyfv7zn4+CIIg+8pGPRFGkTzv0smYyLGxgWRkE6IILLsA111yDO97xjrn0//2//3fu93vf+14cPnwYj3rUo3IRiu973/ui0+ngE5/4BK688kp89KMfxWAwwM/+7M9iZWUlzffzP//z+I3f+I1a3uHKK6/EU5/6VHz729/GF77wBXz729+2uqC9+93vxqFDh1ImGACe+cxn4sUvfjFuvfVWnHnmmbn897///fHrv/7rY9fhQX4uvPBC5yk529vbOH36NF7zmtfgZS97GQDgyU9+Mm655Rb89m//Nl7+8pc7udB/6EMfQq/Xw7XXXot3vvOdKiN99tln4973vjde+MIX4rLLLsPnPvc5/OZv/iae+9zn4r3vfS+AeOT2uuuuwyte8QqcOHEid/4jH/lIvOMd78Dp06fH2FGO3d3dnFscgaLqc7b5ZS97WRpJ2+PgwMuaGLOUNYTXvOY1ePGLX4wbbrgBf/zHf4zBYIDhcFjqGjZceeWVeNWrXoXnPe95eNOb3oRLLrkEf/M3f4M3v/nNALL2f8stt+DjH/84XvWqV2FrawtbW1vpNa644gr8yq/8Cr75zW+O1QmOMrLmNa95Dfb29moPbOsx//DyJsZ+yBvC3//93+OVr3wlnva0p6VTHGy46qqrcr+f/OQn4/LLL8eP/MiP4M1vfjN+8Rd/MT0mXeSf97zn4b73vS9e/vKX4znPeQ5WV1e9buMxE3hZE2ORZE2r1cKll16KpzzlKXjSk56E0WiE3//938eP/uiP4m//9m/xgAc8IM37cz/3c3jMYx6DRz/60cbreVkzGRaWRHnTm96ESy+9FGEY4ujRo/iu7/qusY8chiHOO++8XNp1112H2267Deecc4563ePHjwMA/ud//gcAxqIKn3322WONrCpojuF73vMefO5zn8P97nc/XHzxxcalON/5znfi8ssvx4kTJ9LKfu973xuDwQDvfe97c3PtAWBzc9MYcboKVldXcerUKTzzmc/MpT/zmc/Ehz/8YXz2s5/FQx7ykMLrPPzhDwcAPOYxj8ETnvAE3P3ud0en08ELX/hCAMBXvvIVPPzhD8ef/MmfpILuCU94Qrqu/Ic+9CE85jGPwXXXXQcAePazn22812233Yb19XXccsstufSzzz4bQRBgdXVVjQxN8yNpjuE///M/4x3veAc+9rGPWYWJx/LBy5rZyxrCve51r3T/R3/0R3Gf+9wnXeGmDhw7dgx/+Zd/iR/7sR9LFY0zzjgDv/M7v4NnP/vZ6Uob119/PaIowtVXX42rr75avdbx48dx7NgxfOc738mln3XWWWg2m86y5mtf+xpe+9rX4k1velNupQ+PgwEvb/ZP3gBx3KMf/uEfxt3vfnf84R/+YeXrXHnllXjxi1+Mj370ozkSRaLZbOKFL3whfuqnfgr/9m//hgc/+MFet/GYCbysWTxZ88IXvhD//M//jH//939Pv9XTnvY0XHbZZbjqqqvw6U9/GgDwnve8B//4j/+IL3zhC9breVkzGRaWRLn88svxvd/7vdY8rVZr7EOdPn0a55xzDq655hr1nLPPPru2ZyxCq9XCk570JPzxH/8xvvKVr+BXf/VXjXmvu+46/Ou//iuAcYEExHP8ZOOvG+eeey6uu+66sYCvJEhlQCkXXHTRRbj3ve+Na665JiVR3v72t6PX6+Fxj3tcLu/jH/94APFIzmMe85g0DstrX/vanLHF0el08A//8A8pcUP46le/igsvvBB3uMMd1GBQlEbLmL30pS/F93//9+POd75zKpyJgf/Wt76Fr3/967jTne5U+v095h9e1uQxC1mjodls4vGPfzxe85rXYHd3Vw2iVgUPechD8JWvfAX/8R//gVOnTuGe97wnbrzxRgDApZdeCgCprHnJS16CK664Qr3OxRdfjBtuuAF3vvOdc+mf+MQn8LCHPQx3uMMd8M1vfnPsPClrfvmXfxl3vOMd8bCHPSyVNd/+9rcBAN/5znfwta99DXe6052WRgnxyMPLmzxmKW9uuOEGPPrRj8bhw4fxwQ9+0Mmz1obzzz9/zPAw5QOQ5vW6jccs4GVNHvMuawaDAd761rfipS99ae6bNBoNPOYxj8Eb3/hGDAYDNJtN/MIv/AKe+tSnotlspu2aAvDecMMNGAwGOPfcc72smRALS6JUxUUXXYSPfvSj+L7v+z6rEn7BBRcAiBvdXe5ylzT9O9/5TiWywIQrr7wSf/RHf4RDhw6NrRDBcc0116DRaOAd73gHgiDIHfvUpz6FN7zhDVOvgPe9731x3XXX4Zvf/GauTMjgqCo4d3d3cyzmTTfdhCiKMBqNcvkoWBW581NU5zPOOMPKFN/znvfE3/7t3+bSaC32e93rXvjkJz855q726U9/Gmtra6kR9fWvfx3/8z//M2YgATG5c/jwYadVQzwODrysqR+7u7uIoghbW1u1kSgAEARBToH46Ec/CiBzd6bv0mg0rLKm0WiMyZp73vOeAGJZ84lPfAK33357LrgsjRzR/b/+9a/j+uuvz9UFwk//9E8DiAlrW6R9j4MHL28mw4kTJ/DoRz8a/X4fH/vYx9LVAKsiiiJ87Wtfw73vfe/CvF/5ylcAZDqU12085hle1kyGqrLmxIkTGA6HY7YRENtHp0+fTo/dcMMNeNe73qUGrb7Pfe6De97znvjc5z7nZc2k2L9wLNXgGoSGBx7i+H//7/9FANKAfRx7e3vRrbfeGkVRHBCp0WhMNSBSFMWBkH7t134tF1RMC4h08cUXq0EJoyiKvvGNb0QrKyvRa17zmjTNdX3zwWAQXXvttdGNN95YmPf9739/BCB6+ctfnnv+Bz/4wdFZZ52VC7h4/fXXR9dff336e29vL7rlllvGrvnpT386CoIg+rEf+7E07XWve10EIHrb296Wy/v6178+AhC9+93vTu990UUXRZdcckku4Czh+PHjhe/07ne/eyxg43e+852o2+1GT3/609O0j3zkI9H73//+3N/P/uzPRgCi173uddEHPvCBwnt5LBa8rBnHrGTNTTfdNJZ26623Rueff350/vnn59L/53/+J7r22muN17IFltVw/Pjx6E53ulP0Pd/zPdFoNErTH/awh0VnnXWW+vwusuaf//mfx8q61+tFF198cXT/+98/TfvkJz85Jmso8NxLX/rS6P3vf380GAyc3sVjceDlzThmJW+2t7ejyy+/PNrY2Ig+85nPWPNq8kZr/29605siANH//b//15rv9ttvjy666KJoc3Mz6vf7URR53cZjuvCyZhyLIGuGw2HU7XajSy+9NJUVURRFW1tb0XnnnRfd9a53TdNkm37/+98fPf3pT48ARH/yJ38SffzjH4+iyMuaSXHgPFEe+tCH4gUveAFe/epX43Of+xwe/ehHo9Fo4LrrrsN73/te/PZv/zae8pSn4Oyzz8ZLXvISvPrVr8bjHvc4/OAP/iA++9nP4kMf+pC6DNWFF14IAMZ5eCYcOnSocJmnT3/607j++uvT6S4Sd7zjHXGf+9wH11xzTRrwFYiX3XrnO985lr/T6eCJT3ximudud7sbnv3sZ+Ptb3+79Tme8IQn4JGPfCRe/epX4+abb8Y973lP/Pmf/zk+9alP4fd+7/dygYVoGVIqj+3tbZx//vl4+tOfjssuuwzr6+v4j//4D7ztbW/D4cOHc3EGnvOc5+B1r3sdXvCCF+Czn/0sLrvsMvz7v/87/vAP/xCXXXYZfviHfzgtuz/8wz/EYx7zGFx22WV47nOfizve8Y745je/iU984hM444wzjMuWEZ7ylKfgAQ94AJ773OfiP//zP7G5uYk3v/nNGI1GeOUrX5nm0wIzEWP60Ic+tNAl0uPgwcua6rLmMY95DM477zzc//73xznnnIOvf/3reNvb3oYbb7wR73nPe3J5n/WsZ+Hv/u7vcoHdbrvtNvzO7/wOgCyQ4xvf+EZ0u110u93c+z30oQ/FAx/4QFx88cX49re/jd///d/H9vY2PvCBD+RGVd70pjfhwQ9+MO5xj3vgJ37iJ3CXu9wFN910E/7pn/4J3/jGN/D5z3/e+k73v//98dSnPhW/9Eu/hOPHj+Piiy/GH//xH+NrX/sa3vrWt6b5HvzgB4+dS14n97vf/dLy9PDg8PKmurz5kR/5EfzLv/wLnve85+Haa6/Ftddeq14T0OXNBRdcgKc//em4xz3ugXa7jU996lN497vfjXvd6165pUXf9KY34c///M/xQz/0Q7jTne6Eb33rW/ijP/ojfP3rX8c73vGOdKl1r9t4zDO8rNkfWRMEAV7ykpfgFa94BR7wgAfgWc96FkajEd761rfiG9/4Ru45NT3hc5/7HIBYv6Ly97JmQuwziVMakzKohN///d+P7nvf+0arq6vRxsZGdI973CN66UtfmmMSR6NR9MpXvjK6wx3uEK2urkYPe9jDoi984QvRBRdcMMagbm5uRg94wAMKn7/ouaJonEElpu7LX/6y8Zxf/dVfjQBEn//856Mosi/NdcEFF4zdy5UR3traiq666qro2LFjUbPZjO5xj3tE73znO8fyXXDBBbn79Pv96Kqrroq+53u+JzrjjDOiRqMRXXDBBdHzn/98dYT4G9/4RvS85z0vuvOd7xw1m83oDne4Q/QTP/ETuaW5CJ/97GejJz3pSdGRI0eiVqsVXXDBBdHTnvY0p+XCoiiKbrnlluj5z39+dOTIkWhtbS166EMf6rTc1qIvzeVhh5c1OmYha974xjdGD37wg6PNzc0oDMPo7LPPjn7oh34o+vu///uxvA996EMj2ZXRvYqeKYqi6EUvelF0l7vcJWq1WtHZZ58dXXnllcb3//KXvxw961nPio4dOxY1Go3ojne8Y/S4xz0uet/73lf4TlEURbu7u9FLXvKS6NixY1Gr1Yrud7/75Zb/M8Evcbz88PJGxyzkjes1o0iXNz/+4z8effd3f3e0sbERNRqN6OKLL45e9rKXRbfffnsu39/8zd9Ej3rUo1L50e12o0c/+tFGXcXrNh7TgJc1OhZB1kRRFF1zzTXR5ZdfHnW73Wh1dTW6//3v76SDaEscE7ysqYaVKKq4LpNHiv/8z//EZZddhg984AN47GMfu9+P4+HhsaTwssbDw2NW8PLGw8NjFvCyxmMR4cP714BPfOITeOADH+gbvoeHx1ThZY2Hh8es4OWNh4fHLOBljcciwnuieHh4eHh4eHh4eHh4eHh4eDjAe6J4eHh4eHh4eHh4eHh4eHh4OMCTKB4eHh4eHh4eHh4eHh4eHh4O8CSKh4eHh4eHh4eHh4eHh4eHhwM8ieLh4eHh4eHh4eHh4eHh4eHhgNAl0+nTp3HjjTdiY2MDKysr034mDw+PCRBFEba2tnDuuefi0KHF4km9rPHwWBx4WePh4TELeFnj4eExC5SRNU4kyo033ojzzz+/lofz8PCYDW644Qacd955+/0YpeBljYfH4sHLGg8Pj1nAyxoPD49ZwEXWOJEoGxsbyd7VANrJ/p7I1Sj1cHaEAIbitwlDkcf1PBdo15Jp/LcG23PJZ9fSqp5nS5u0XMrA9j5VUed3nRSyHUwT1Mb2YG9vPQC/xtrt4iB75v8fMllT5/cqgqmNTfMZTPJOa78uKCujXK43i3KQcJW5ReXneq1ZysVlQQ/Aryy4rPkFAC3Mtm4D05c1dbR927WBevQaDzOq6HxFcmwRyz4E0Afw2gWXNWRDmfTGKn03ryPy289a3/coBtfji9KK8kwbLs/g8uzzAtf3cbehnFpX5n7WBhAk+4GSsy4ixaQoa5D52mqu6sKEd1TaNVyvqeVzSbPlsQlPW3ntB4lCqPPeVa9VpxKhtQN5L03xAao/fwCXtraIbqN5WbOa7O+HMF4Vv6f9DLwu8G9bRJi55Kny7CajZz8UsjIysWxaaDnm4YrFljUtxDK1SJZPC1KeTypr6rxWEXiZkYIq0zTMo4I9b9C+46R9Ab/WoiGuV4sta8iGMsmaSfRlaRfw6/m+bf8h26VWB2SaS55pw/UZXJ59HlD07Nl3cpE1JScWFgnfuoSzZuROYvgO2bbsdUJMd0THdD/XPIs4qrCImMQQNX2jIarVSXqeRVWGyuAgvCOhgfGO1pWYXqZycpGB07inx8HEfvehfDSsTgJl1tCe3TVtv7Cf5WXDrJ9L63s8Zou65dBQ7NuuP4ku6lGMeW5bdT3bPMuQ6T3XgmiOtsc0eYi4nl/2GWyeBK4CyMXTxnV6UlkyZUE+eSFm/R6TNsKi71PFQ2heBVZd2C9lWyvXSdwTXUaGJ/mWRedWefZlkRPzinl1d/XYP0zDXbsOueVyfhmvif1wS7dh3tqiqf+peq6WR3Np95g+pKcWR12Dtdo1XAZmPeYLNrlUt8yq83rzJk+BaT5PzS1nGsLY5RFnKQCqkjXSra7MNCAXA3veheA8xUThndUkHZetYXpGfzLsx1xeFzJCQhoMRcrpIiqsi1iXTVPotDw0AjfN+lZUF+ZN6fCYPmwkg4tccZ3iYZJbrsRH0fTCSeLjFV1rmmgo+0X3dyEgFqEtLwrBtawwDaJOw+t9Fv2ary86bLLQRZ/U+gLt/KrxSMrKQNep5vNYL6bzTIu1TthCYxJBNu8EySJjEQ1Ej/nCIhIjZbCI8se3a49lR53ea5N4OlR9jkWUm/NkFHh4uGBafeE8GsrzhEWUb8uM6dTTmrVjl6BWJpgY2EUKjGSKVF/W+2RZIOdketQL15FIDzdUKc9FUCTqeLZZxoVCTfdymUIHzM4DxWXkRmKe65XHZNDkjfze2ghjnXWCP4Np1NN1ZLIO7Jc8rXq/ulek2O++3Mub6aPumI9l7lln0NlJ7L1lh0u/r7U1m/xzlc11yDKJIo/I/fQodEGZvrNcHztnnigu02FMv+cZi/SsdWHe33nahNZBI8yWCXV1APMQVHHeOrNlR1FwtbLfwyupBwfz1Fbn6VnqwrQDHy5jmXksJ+ZdP18k2OSKSSZMIivKTNWZtv6wJ7Yuz7BcOk2Nlp5LwZhGM5dlmUnXZTaXDYsmkOt63rIjT9OoC8slkPYX0xpVneY9XJ6hLuV+Vt4os1wJrQxMZen6nSed9lD3qLfHYmAaHij82rbf/P6m49PAND1SpvUOkwaCnIe+fBE8K5cJRX3QounW+w0bmVFmcKNK3Kc6+vyyMsLmDaLlsT2XSz5XQsTlvLKeL7Z7zmrl4HHUZNWVqSgyqGdVHARyYhGwH0K+av2Z9bNOu47Og9K16Ji0DKnzmqcgobITnCaR4kKsmNqBafrjLFC1be5nm6PvyIPOAXkDdxmCXh4ElJ0usx9EhvYc+wWX8qojRswkAXy1PLK9lp0u5bF8KDMYUcfARVGQ9f0I4j8pXPo6md/1ekVkSB3ttW5S1VU2lZmC5ToVuSpc5Oukzzp92TrDliO9TSYVDIvY8D3qRZml3MrUNx9r5ODApSM21YciV8ZZ16NZTR8yLccOFC/dXnSszJROl3OWCUXf1/atvaE2X3BVSk3fXFPCpyVnDmp/aIpPM4lxU3Wq30Er+4OAsit01nHduvvK/epXtLY5q6l6dQy8zeKeUl5p+qrN48YWn6vqM5muRWku06JcBodmVy9Ltqgqc6xMt9ACr3LYDOQh28o0F/e4aSndtpHVWSr68h2nEVSKX2OeUPRMsqGaBI08R0KOBntMB7MkS6flEuhynq1O1vksi4yDQpZIVFUQDmIdqQtl57hXQd3z4l1d1svKGnl9VxfrSWCr8yYCydXocPE2KSIkq7rpVy2nMnEQis73U3YWD0WDCwe1b+Qo4+Xlqu+XNepN9sWkmFYMlTquW6c8cbnGfMmtki3PJXudrOkyYdkE3TwSKEXgTGwdrr8eHnVh2iMps0IdMs7kWj9fnedscBDfedaQdXYZ2uGyoy55MMsRzFnUq6qDQq44qHJ4nrFMdkWd8PV0NjjY5Vyh9bl4M7gGS3IlZeS53MujrDE/K4Fj8v6o2xtEQ5G7fVjxnnUTJ0VzNacJLZaFCa7KzzTr1iKSVnXgICkI++eSOHtUaXeuAdJknjowjZH2Io+AIvf+Za4f84p59w6rOiXE1VPBNnWxSgwRE6rMly87f77OUVjXe84KddZFFznsUQ42vcbL9nrgEueo6FztvDpieHgsC+bYQtE8WubFkNS8Subl2aaBRXq3Wc3frmO6VpnOskqsCI/FwEFTmiZtm2UImEnda+uQI6ZnWBbvo2XAfnyHg9buFw0u04aWDWWIsGUvi1nAVN6uZTuNqSMu91yW7z5NQnBZysjDhgrTeXgDqrpEZJmASvJ3WYNRi1MyCWzxWIowx5zVvqJOEqCo069yvmugriqKeB0j6bNaetZjOljUztYlwFdRfoJrXKyyKDtaPY1vUcZ7wcc7OTiY1284r881a9Q9Fcb1HouEsn3AQYfJ+7kOr4ZJgo9O+5x5hYtnneu5mq6xTGXloaFiTJQipXCa7HWZyNMmT5a6yAybkl9lqtIsUBTQd1bPUDQVqyxBVeeobpGr5STnu2BouI+t3cx6iViPxUVVYrlM+6raTqq04TLByYsgR6Zm5dnmsf8oozgvYn2YVoyhaZbFPJRzndOUOObZwKpzKlYdzzHPZVU3XAfFZL4qg2lcDixbWbsEjS0TQLqoXFwDW3ssEyrGRLE11GlXHq4w82ep6t0yLezHqjxlsN/P5RIbpkyHUCZuSdVvUzeBYhLiJkLERaBXjXczb+Ak2zK8zzygajykeSVPbNezpbvIlWn1Y8uioB5ULItyXGc9XNRpaWWJpLrfcVIia1mMXlmui0xU1oUiOysUW5nOUdTfTeJBMc/eFkXto8xzH3SPHQ8TKixx7HLatEbD93uUvQo7LM+dRWDZRcMs3j807JtQduqBdo5GhtS1pPR+zIXdLyzbylbThm2pdVfsl1dXHd+5qD1Ma+qQCd4omH/4ODV2SGNJ89gqW24uwZWnjf3+1nUYoXUZsq4EuKuslN/W9Vub0kaO911EaH1S1XAJRdcpM4igXc82RXbZdNGq7zPL+DSzvJ+HRAWN1eWj7eeqK/MEb/iNYz+nMNlQhTQxnV9mtSqbUiI7RVPeeR4N8Jg+isjYacvgssSwra1o151FHzKLuEK+jc4/9tuoXiZUiSvget4yYpJ+fBY6gPR+OMi6fV2oWx92qQeT2GdF7dLroh4HCxViohSdIhuQxoLKeXh1rFBSN0zLEWtwMZpdVldZJNJl1qO5JpRVuFy8qejb1z01wRTp3zZSIElLU530jPTBhS3GkG2FM1nv6vC+Knstl6Crrp6PVA5lArnK51o0w8C3+3pwEI32IpQJylzU59UROPMgYFIiBY7nly17m05bZXrkQf32ZEOZvGtNU5tseVyO26Z/A/aBvLLQvNb2q3+qw8urjuvUjXl9roOHioFl5X6VaQYNsV+lMgyVffmMmhGxX9ORPOpFWVdiG4HChRIRKLKOl53GQ+dp92goafx8LR6Kq3Lk6+/yQcq3ulcdKwNXY6os8WLKV2ZkzTVm17IpH34EcP+hfYN5+C7TfAYXo087flCN6LpQJIOrrpJWBYtIPu83XAcS5ICz6zUBe0w9oFjnrAP72c7nhcjxcEcZ+3w+nA4mmM4DuI+Oy7x1TUlwCRKqRbAuc91ZYD4qgxmu5VEkxMu8Zx3BH+l+kjyxxWLgowRlR09cR2v48wxFGjDunSLz+M7gYEEji03HXVHkheLipVLUniedIqe1DY6i0TV+DQlTW6pb9vu2enDBifL9rAezvLcpdorpt8f8ooy+5qfwu8HFw1n2QVVjh3Hd0+YJVmTHVYHW7qcth4pitZQlfOvEtHT3/e5b6saktvDsCZYpTOeRKGMAzlOFqGLwu3ouzDu0Z6/yLlXfv45OuIhAsRmJWh4TQeQyoqBN2eHXLfI6kUSMnAa0jEpLFVmzbJjGd3VxIdbIxjLT1kz5Xc5zhesy4C6jc9MmUhYFrl6kywYpa2xeXza4kAeLXq5lps96wqR+TOLFV+Z7FMn7ZZsWOWtI2eKqb3Pd3HVwwdVDRdM5tfzzgCKy1vUaHC7eW/MSBFp6sy96v1LFA2XS5b1NaKBMEOuSVkoD49MeOIYYr2wmVtU1mGYRqrLlHmbsZzlVbQi2Tl8jUIpG3svebxL3WpO3yqILRo/Fg41AKauo2BQdFwKkiuJShQgwESnAfCqQHtNF1f6P6pEnDzyqYBr9/X7VRU+k6JCErRyEc5266jqlFso1bTMHTKv7zNO3rIMwmdY5HtNHXd7Y9WACEsXW4CWZUnaUp+5Rm2mTArZpHGXPO6iouxFohAmPdSJjktSBKvO9izxOeD45/Ue6Yc5TR+cxOSb5ri7nakSijTwpE+PE1r5sXlmESeqy1kaK7uFJy3Esi/fEpCjTDotGh2fh7VNHXTa9xzwYFvM+Sr6IKDs108uGauA2lOm4Cya1HYoGuXkefr/9bG+2sqmDvHY53yRbZ60/uPQji9RGXb1QtPNc++WicqA8ew55MxxyzgkACJSbFk2DkHlMirqGeeiwi3CQiZD9+j5Fldw0HcekCITiD4Z0bYoJTyvqIG3PKq/pMRkWQXa4oqgOavnluTLd5Z6AnUCRpLp2jaJnN7UZWx9SBstUD/YLy16GszJctPvOomzL3meRvvei95XzYOBMqwwX/dvsF1z69zJt2uV6rtN15+mblpmOM837ajrQrGS7R3kCeLZ3F6BRfO0yrquJaEsb20YW5mWEcJ6ExzygjCeHy/dzYRRNnh7S4KI06XWyyvKUIfM4tFgMtpgnLjGBtGOm8jB5o9Axvj0okOXn4i236Jhk+pmp/TTEvjxmKmfKZ7qHrMs2z72qI11lPLqK8tW5asG06+C05kabFEWJg+YJUCTnTSg7+mzrJ8rk0Qa5po1p3sdV//Qww6XvcP2GRaPd/tvkoQ3CueQzwVX/ljaW1ieappXzPPLZynzbsnLBxbtkv4gKV0+9aTyfq1fYLL0f9wsui5e4vnO1simpiUvD09Sw6Bj/uCRMKU2Ln8Kv412tlweuSqINLq5Y8jcnUnieVYwbfK4dA6+LLgSJ5i5pc78zGXDz4ka436jqClvkCsrzLXN5uhCHkoikNJsCqBEz0oArCgSnTVmjdH4/re/QvltdCvwiESlV4UfN8igyDiXqNhZdvkdVebcIOGgDAXWjavlNo94U6VaeaNFhkkE271COopUfbf2s6/eY5tS6WZOx81YHq3i1u9pJi6DjllmcZf/6iwqeKKsoF4uBFDypIMvrus5Xmics8go8s0LRN3MVXFrHoRl78ncIYC3ZX2XHtGkF2j7gVu9MHir0DFUEND9n3ur+fsHFEK9yrSrnzxI8lpRJEbVBI0+0NiOPmQgVeU+bd5jrM0qi3UV28LwupPx+KRGzIFImPX/RDe9J4DKyOOs6U/RNbLKwSE7OIybRpRbNEHcl7l2+3awIE5u+7yrfXLwSlxk2j00TtDxaH+4SHF0ObPPj/Bvajpm+9TQJlTIwESTzUNem1YfYrjsPOq3r0sM8n6tMd/WsKyNjp7Y6TxuxIUpESpFiSkouJ1K0W0tleJE6w3lpnNOEqyJXBF5W9I2lMiHLkjcqabxxb5NVjEN6o5yB8c7HpqiapptJUlCbzsPzVUGVZTXpvEVqPyaYSC7+vmUJk3lza6yDWOOefa6kBm8X/FhDnKe1M+1aWv4yRhzPqynYQ9i/lwuZYoNNYZTP5JFh0fpqE1wVtjKGIkeZMqqqDLuQYKbpsC75JiVlZqEj8W84LwadyYtAokyZFnkQloHLd5F9i4lMce1fl11ftqGoH3WRL3IwQ8JEppjy2Tw8tfua7u06DdZl8N12XhVMo865yNNJ5Ka8vul+demymi07qX0rv7FmA7rWh6oELG8vpjK0eVkXP1UJcGVDPoScqgORVzbYIiE7Cds9SxzkDqEMhmLrCkl6yE5IGnjaeeSBYurAXL6h9Cpx7cC0Omuqx/NWtz2mhzoU+7IKuaz7JqLK5NmltUXTtTls7Usqblz2a52enBpa9hwo+YpQVenzWCy4kB+2emOq52W8JPbLU0pCypBJdK+6dSRbGS2aPrZf5IntelXzebjBNthhqtumfEVpBBspYiJTbLMHNJgGyssYvqa+dlYkrLx3kffVtFFmAKpuzErfKTttZ9Kp1fJ6k6HkFTYQe6NoruVSeTW9rI29k5V33omUg9C5mFjPugSIzfii46YR8VUlTcsrtzb3SG0Kjc1Q479thh3BJACqTGczKZHLUi+5F5tMq+vadV931nDxPjG1IZmfxwrinl0agULXLxoV055XtgGNoNRGcGwEiq19mBRC2zfXjk3agS8bGliO8qB6bBvBLQq26HIPDhevlzKwjVaW7bPr8JYAzCQtYVJdYl4IJxvkd6gyMq15+NVlDExipBXp+GVwUGTrWvJX5DlW1H7r0vE075OigUJ+rgmyz9ZgewebPVDkJSNh0iXK3LvqQAq/n2u7qEv+loXpHYtIDtNUHVNZlfEWpLroOuhts+1s+WUbc//OJVsiV7w1VxzTMa0Sl5nrdBCE6zxiVkpKkTA1jaBLggQs7yrLq3mrFCl4EiZW3tb5HxTFYJqos+PQCJn9wDTqhI1A4XkkgSI7pyJSkmNN5NGexdVokFOTbF4mNk8U+RxFREhZIsVj+SDbiomcrrPdlr3eotVDl/51v+TvIsBUfmWNBHl+WXgZWC+CZGvSRV08HkwDJ9r0c3lNmV8b7OPEh21ausk+m8agF79uFYK5yLNnEev4PDzzLOwbjUSWmLQvqX5+SRKFYqJw5RWYLP6DVIYJ81BBDjpc52dPogyaBCMfETd5oMhAsdooue2Y6bkkI0/Pp7HqMm1o2DdBK2PNG4YfO0htQxIfk7573WXn6nZad+wkTZGSxqAkQ2R70PIT+bgqjkkSRbuHBi1WEQ9ODuTLxuZ1osFFcZSYxCD2xOhywuQ5aJvnP8kgQ9WYV675SG7Ouq8oOzihnePqTs/vUcVjYlYoO3Lu0k/YRuqrwFbOtv5XK1tPvJhBeqvJE6Wsd1JRGvWfZUbjXe9LumhRfa3bA60Kiu7lWmf3k/yu41pF+mqR90iZa5eBrVxJPyxLoGgDifKYy3XsKKnVk5JNL1VVWLp08F4Q7x9cyt5m6Fe5HzBupFF904y/BuLpZfR7DXmE4hpVAhJRByGFftn3pfxD8VsjSOQ5puO2Y8tm5BW5vs4aRd/B1CG4uD5XGWXROgvZVjQPLUmUrCr5NU8UeWyl5DNH7Fyu5HFvRm30QSPa+XGbQu/7k2KY2lmVKQiLBF7nTKOqtjo2DdRRV23T26YBG4FSRtUsYyDye7m46M+6b3SR7zaFnrfJOggqDs2tvsiocvHuM+XziMuK/kww6ZlV2q38DnTfXYdztSk9pmcoM5gxqfzRBi1d5Ys2g4Kglbtrfa8LRf1vVRQRFPNgM1R5DpNs5MddCBOZx72/KkmikEHregnb/Fx+vhYLxQvg+URdjU2rR3J0m8gRaRCSscc9UkzXCxEbeUWCWxP+3AADplsnXQmSsuTKIkPzDrJBK4c6vD/Klm9VDyQXFI1AynbEl/fmx00kivTckiRKWcJEA79GAzGpQvt7yNoeJx01L0WehxCy/NNS7KsaZLOQI3WjyMNh2WSPyavQRtaVrVe2Mptm3ZgmAVYXgVLlntJbdF5QdmSf0FD2NS/AMtesms8TJPXD1QupLu+RMs/A5XuZ+mHyNinqKyeVDWXP14xl23Qk+j0rIqUOz+tp6Kuzgo1AL/I44bB5SruQKu7lXrIGkjHKG0bVj2xiWYvmI3vhPX2YyriOxqYZZ5qBR+TJGSK9kaQB2XLbdRh3dD5/rl3ojUsz3ggmtzObF4qJJJnEtW7RUWb6iysBRXAZndsvuI4+mUgT6X2iTXnjBCS/lswLqO0qFFu5D5gdcdTi5SQnJ9HJ7ZkH4jNNAwI7xpVAOR1IHtNezLUOzMsITp1wIY2XsQ/WFFebkV7VgDfVl0UuU5OBX8bwd0WRflhUd/erzdo8UMoo+WVU9knKXPNesBGHLnk84pAINqOwrJdFUX0wTefhfac2iC3bUVn5JuVp0XPa6mpZD7qynuMu/b52rarnFaVVbTPLoItUJYi1fkezNU3XkfeN4IqKNKA0OIdiC4w3HJubrOaNQsc85huTfCMu3DVDT/M+0dIsBIpWwwtlDdVHzQiTeeqEr+8ZpjV6yVHWFXTWcFUapLdIg+3ztgKMT9mRTAjlE20qxHgxaWn8WKVptCuWi5ZpHzalz0aklLkOYO/jXK7pDY39h0mh0jxPXOrVQcKsZeeylLFtxFWiiECZhofRspTzPKLIS0vzhKgCE3mhDShokGSKiZSwkct03HSeC8q+v4snyaxBzzAtb8BFI1Am9SAimAhokyeKicCs3ubKvck6gBHYiCL3TOEGCX8APqpouu1+V3CPPFyEjsu0EhsDyA0+im3CpxOsKsd4fBR2OZOBx/f5o/WUR06PryAfs4EfdCH36nKlqzPmjEcei9bhAOYKLqfgSMJRTn2TJCRdayV/Wb6VbUzuc8j2pIXqGbK/UKQBsE+/Myl9k5CdriNWdWJaypRHecgORPNqMBEpsn65kG6yPs6q3mkjn1VhG8lzGfkrC80oA+zfgR/fD7gYTpLINnknFHktVIE2cFmmLs+DcbpoID2Ww/T9taDsMp+t/HkA96GSLuNhSG9Qm5yDw/EizIqA1aYbcUgCSJ7LYdIxtLwSLs9QFoumy7qQhqb8No89E2kCjE9b1/LIPqtleSbzU7jlbiBTeFNjlEYPtZeyCWpZuf3cy/lEUUN1cWczMYTcoNOMvjX2exW5EfJ28scvz2EiUtrQjbgURKTQ805LULkQJTLdZT79ognWgwYXmebieijbkvQ+4ftabCHRkYQYb0+SRCkiUCQpIvl10zQf9biJoSHwtskVv0nrf1nD2HSex+KgaDSV0qdFpCwLigiUquSGqczmXUe0kR+20c8yniqu9wX06QjTxLx/n1lDkmY8nW+LMGsPMDk9ltJciGaC7ZmLCIaicqnbW5SfO6926TL0Iabvqum8RQRMaMin2aC26zcQe4u4oVxLbAE4hPxIImi7gvzKPXTANIeVtq7TeLQG7DF9uDRU03fUmD9e0YkoOYP9lmm0ZSPlbbHlhp3tcYcYf0yZlmIFugDlCFFPPXSJj+J6/jLBpbOqoyOZt85I61hkR8Cn53CikdJk0GW+kpXidULtqS3SNDKFnysx1i9gvI312O9ecq2eOAdA1gZJQQuRxUehzFqfIUfS+MNOe1pelevNi1J2UCEHgEwjk7wuFY3U2r7pPCjlVV3LTQoqPyZllcyn/Zaw6Y2A3sbmnZwqGjm1lZfNK8HlntLbALCXsQtZ6FEebeiEiYlYM8FF4eX55AqVnPSQ7aqBfB8K5bfLdFjX+lJkhtqOa/VawkYcyoH8ophLNsy6fSxSe3TpN0zHKU8RMc/z2GxPl+tNi0TpADiNcUU5VYr5fHZb5dZ8vYHxgECLUkGWFabyL1JwgHHFgAtzEtJnIO99cgbGjb7kOpw4IUOvg3Hjjk/V4a8wV1WpSLmuMpVqWbHIpGkVQ8XUeUgli9K4hwkp2mewY6tsn10yRNx+6DeQta0Q4+3KRKDIflz2DRqpIsnLITLvsDG9gKb2yLIkJU4jT2zg1yqa211VaEwy8jUPmObc7XmE4pWVQo7AlokHUIYcmQaRUvS8ZWEy8l3IE03VHIpjkoTicCU/Xb6Bdq6rUVoVpjJyVeptqrpLH6nJJE/ezh68n6bfwPRJFAJvc3KQQco4WT+mQahNo5+RcoXgShpq+cvkW8Q+n8Nl2qe01TUUkSOuxIpNZvI8mp6s6c9S1mrXdv+G5UiUALEnCj+b160ekJ/aY+voTK6ZwHhnqlV+3wFMH/w7lREONmaQG39a3BNuACJPnEjjT/NEgXhMTrabCJayr6dewAVV6uuiC+SDDFMH4yq7tE6GMxnSo0v+5qtXJdDISL7lbQoYb1+SQAHGvUx4m5NEStErqu2QZyKlTzNep6nMHLT+5iC9q6ZQSUNTEilaHtO1560sJzVcXEfQbaSKFtvIRj7aiIIqZazJh7rkhRSMRWq2JlSB8t47rt49rnXXo36YyDIb2ciDvEfit+2b89h+tg64Sh0wxSYrcz5/9rIySTPibbFNTHkJmjzwbaPeAZUikoSn2zxRTAOKpn0XEkVee1okymHklWJt9DAlUtaQxZUYIl4uVsZsMDGFcoTRxA75Sl4/eMWUgpIrkgRNmPGKS+l8RHxV/J2FjExJOgjyMqEtN/DkloO8orQpBbTVAsta2w430ngdtsUgkV4lciuPu3qhHBSYWOppTccwjU7OisQqUqxkh8Cn8PCpb5w8YdfsYrztcPKEHzORKKbeQpIolCYJlJ4hnR+DOB/AuIejaXTMJJdMo/FFBvGsv79v+/uDALpSpX0P2f/JNDmyq12rvKJWD6oowyY5rCm7JkXWdC0NJkPI1D7p2KRESt0Eitwv8kDRjAR+rmnklFDm+TWZR+kaXGWilu5lWh7UycpvbVlhMgfXfDKvHMyMMK7D7rEtectQmux/TaSyrEum+lJUn4ugydCiemyCyZOQrll2cGZWesO07jGpLmLqM/ixKl4na5ZjNl1Z1n3Sm+UzmmS3HeVqrim3VLhzyi9/cK5gNDCuNRd9vFkqtQcRVZQsG2PI03ilpj+5ikgi9E2ESZGxx2U9h0yfeTXydXbxMMkoyzQhOwdtn3mfSBKkLfYBdxIFyBeLRkC2Ya7upnNdB2zHwPuQokY96XGP5YeL8mgi3Mpcw/VZ4HAtUz45GFIFNmXYlNeVQLEZV7OY1sSfA4ZnKQuTANMIOq28bOcWCUcXUqWKQSjB83uCpDw4IRbCiRSpqopYPy2fMcAH8PjNNE9PrZ7JAVdpy7m8QFVPFBfiQ2v/RXJcPlsVglY+V93YT53FVCYufYYLgWIalJf5II6tirSGcq4LielOVpZrnqRk0yghjR5KRXgIYJtOkp0EKb6alwMdB/KV4yApt2WESV2dmOmeMr2o0XCvE9pShT0jSaOpOxsAjiA3Yt5J/trIe6KYYjVIw47XOz7aTfW0LfLwc+VIeHoR7Tcf5d5laXJUy/X72DoAjzw0b4Miy17mMUHLo3VU2v2qosgDBcjHEZKrVZEnCrWphIyUxGMX+TRt6k4HehvTxDQw7k0ivUxk+yPwYLJ8+g/1JWrxyhXgZB9iMiK10TKwtCJDWCqGBNc2WqauVLm+x+QoMmRNkHVC1i2t7rjOs4clTYMtX1kDRSsL2wgh/TYdq/IMBFs7dG23/P51kym2spJ5TMq8LDetT3AJLFukR2gyzMUQKyPr5LXKeAfMwvDcb2heRRjXZU19rgtXJiH76TSNDEV6Jgo+S7osXwp5j/2mC2gjIBqpUgZaH1hGdtrIFRM070J+TF6/jGyxta86dH1bGbu2I1u7tcFGkvPjsh8o6ic4ySG9R0wENCdPpGy1kJbysel3BKCvvJKCcrW8hUyXJeWXX4VGNnvILyObeqSYDBzaly7WQLGgXxajcxIvkKrvX+WepirDKzTfktFHlZkvXZxM3yHjrohE0ZZf5VveWXADTmsoLroDgMnr1iQjN8usTLig6CMVESra76rPAcOzTKL4FY3U8o5Edgoy7gntCwJFEibUrngbA/Ln2EgUDk05I8KET5nj3incK8zUDq1FKjvesoqLpuxpJEsRpj06xa8/qZyvgmXpV11hIz6AfP2Q9cVG0gHl6tU8wkag2MgUfr42IGMapDGVo+mYLb/EtOu1bUBKKy+TwSCvV0ZXsxmWRf2prR7dek0AAQAASURBVGwWuQ7PKWRfKNP4Vp4zFMdsXZ7WT4+dI71TtGmyk8A00MFh09dsz1FU503yRyNF5PO4Fqyp/eyX/LcpbjLPJHqsJu/lMdd+guShPE56rswHwzmccFG8Sky6LaWdxpRIlE7yPHzEUBKS/Kq5UX9esLJAhzB/RFsnasqziNCUCtfzJrlnGUjByg07YLySE1Ei4zYky7KSEddlW06e8GNaJyMfjR/TRrUleFWyyhCTYNHKzzXNlk4wCd9lqO+TwhSvoA4UaSZ13Mc26iwVbLmildwmnihU37sYJ026GCdXuOcJoK92RcSlBhlbSBIl0hOFi3lOtIfifLouPUd6H9kZ8o6Ublw0CmYavTYZxvuJ/SBPCPv97rOECL7sjCIiZRGhGfA83TQSqBErUPJp0MhCjezkz+JKpEzze2hlpb3nKsbLh5cb15uA8TLl5xHKeLnK8itLHrsYm3VgkduNI/hANMGk35rIExNspIo2UDFU/iC9UzTPDvJKoX3uhb0qfnPYvKlsSr0GE0li8lwrai+mqUz8HlCu5Uqk2FBHP1+17WgGfBG0b2UaEDSRJ1rfoZEgYPn5zAbtWvx8pivy9uTS1ohEuUV5TQXlNId1xKvzcIWYK84Q+1wpHgLZy++KDPINAH0O20FS7FxQtTyKRml5HupsZQfKKzifvgNkAYA2EAeNXUVu6g4ZdceQGXrSE0VOQ9Aek9czGbSySC44y40G8uQRYU/Z19xnqxj7LoJ4Hoy8/YDLO0uFsSykNmK7t9YB0reh65jyEEydiuZtQtPhiIxk3lxdxO2Gtlo74iRlJ7mNLSYKoIuFITKChadtI98OtUCzdC9OpLTFcSDvwTL2MNS2TJ23RqRMy9itYtx4zA8osGyR8W0i4OaV7Dbd2yQTi0gB0wigpgybrucKk2eh7fvMksAqMhTkb6nscxlfRETx42WmK7h4T5kIFpeylOeWGWzicP1mS0CyrANoIl+8vO/l0FQQafTZVHk5sMHP28Z43yx/Axhv0xEy+42wy06Q035c4dqHaqOeGvnKjxURgFyP0LxvikhdCe246f7SuWA/+gtTPydldpE8L5J/fF/KQJmfEyacRJFES4GXielPPrJsayPl9QwoR6K0ETM08mxSiEOxT3ly8pi/BX2sXKtN0GDHtMdeRIFqaiRVDL1pwKY0SkJAdvSaYkCj5MzY42QJJ09C6CSK7FykjJQy1dQoKsNGnnAUKdxV77modX1eUNXDq+w9ysBk+NMx3pnwP41MWRn3LNG8TeTWFKjZNGWOQ3qMaO2Q0FaOUZWWHRpE2lDkB6DHRiFw+aoZAyaDwoSqo0pVPczqfA4Pd0hPCVfyQVPSy04vmSZshoGEzWLTjHspILTRRHkuYPYW4ce0Z+fpZdPq7kNN72cjU6TxINP4edrKEdq+qa6VlXP7iUlHwBcMbcQkCjBuI5n0XBu5YisS/tllMXMPUznlfSj+clhBvn7yG3A5aGq/JriSCa7yVBtEMbUNflyzO2ctX/YbNoJEO26T9ybi3SYDJYlCeSSJonibcP1V02n3nUQ5jGyAlY8ebif72+yht1k+uksPyBohZy7542jppkfnhn3ZRjsryAo3D4SJlJgmmAgsIF+huYFHSxYTeZJMNSByZBPxfjfZJ8OujTyB0sF4Bee6FhfwMlClPMdUy23HAJgJlCoG0CT10iSktbo0b/W/bthGv0zvXsYzRVPobdDuaRpFdVWyaSunwPFpcYm3VxdZu+mKLZGSIezTeeQqPRxyOo9sd3wUayjymDxR+LXkMd5fSNIlBV/1jXe2OxjvFzRlygUu399Ul6ZhONvq7bK3+WljFbGfPSnXJiPU5FVoIu/4cS3vrFCGPHEZReT7UiG2XZPvT+JFq5FZBFnumqFj+wYm1ti1nDQSiZeT9Nq1GRI2yDpWpjxNRJaJjHGFTU/hulMZeezKGCwIjiB2fOOQxp1NX3VJAwwDEGxf9t3AeD9s6rOHK8JThdfVSNyozikqdC3NfpS6ubRxypKLlMfWJrgsk/czybl5JVw0m9BEnFSRhSbiRG65fJSyk92G9FbZbtriN9dvgfE2ZrIBS3yiclIpSP56Il2yQFrQWXrYITBe6GXYykVSGOeBMJEo24lp0Iw/YHz6wRlZXk6U8L9QbLmBR9CMKdqXRleZV5tHWeZRAov8AU0jutIwIZKAdzJJXpO3iRZUVk7vMXmiSJ1VtidNqZKBxLlipimHMk3aQLKda/mApDxMI0ZyxHbWWLS+6iCDrBqtg9HqlFSi54EoqROS5KV9E5minSuPcYPflOZi3LgY6i7pZWB6JxO0gTMqMykMbQYHUOxJafK8K/Pe066v8tp16KALipb4LW2nIgLFhWCRxas1Gd5fA7peTf24qenKcwFk3gGz/L6yjdjqv0mu20hVF/J1mWD7dkXyXRLClGYiSuSW6738GPI6alUSBco52uuW+MzlajoZttQA5egjf0FqhB2RrwdgyDuJXeQ7EK3FAnqFn1dBPAvyZFKG1+V8rXx5JU8CxKb7FET2vDgfjYp3EHudtBHHQeFpJhJFi7dAIANt2/DI2jkmhr2H8aoGIM+oc0ZaXnTPkFYGspfThLWpXUhIw3LZMGknprllcpjKro7OUzNCZAfSQH4ZcPJIEcsXU3vqImtHtO3C7Iki21iuY4qAMHn/MPNnPBSOcHoYAMPE2ByG8WgUtR2+BbK+gCtz1BdobZL3J7Ljkwpher5JqySPFKDYC0Uavxwau1NXu6rjOgdBmZsVGtArnBzBLJqrX0SkhCKfbTrLrOHigcLT1lh+myeKRihMMsVEc8EvIg4a4jz+DeS1bTC9nywDGSh2VTkmPVFMxJQWjFN7Z42ocJVtRel1okxdn1f9fgJII4+2NqNOHjN9OoLWbdl0YZMXqUay8DRtOm+6TciU4SS2kDyX6+Q8gC2PwaLZhzK/lEGufakc2SlL9E4Lmq1cttxtxKZJNkkdlss7rU/gA+wyTZInK1kW0gf5oB8wvjCCRrJAOS7TJIYABkq6AeWkVAuZjWZz0wbG46RwhTk1fhvsQIOlyUcbsryUZ56USJPr1rQw7ftQg7KN5lLHz6fx0LSDRma4HUM2jUeSKF3oJApvAFo9o/rEDTUut4rsJtvWepKt3CWZMi3MWkDvJ2zvaSJZizo6WTlkh1OmA5LaigbNiOBti2+19kRkSmK0UBshgqSL/DQejUTh3ip0LATQTkiThDBptAcIwuydQkakDIcBRsP4Hfd6yaTusBUrSyF0hYqTIoC9M+PpJiUy9ylXkClVgG7ASuMJLI8E5XGJt+CCeeqf6oCLcF1UNBC3L9vIJcFknEqCZKikz1t8CqkYa4qyRhIUESyaok2QZcFRhhAx/Zb3LaMz2vJocpzfx0YmkXHASRRZlqbycxkt5zDVQShpddfDeanXcwxtpByGNH4OP8Z1VhdxrNlnXJ+WRIsrkUL37onj8p5yazJcbb8B5FcOipAvDC43pAwpajsmHcGkR5gG4urwhCvTzjXI6U3a9cvAlJ/LKq3/ME1X5HH9OAnDr4OsrnOiRHpO88F2upScmu5KVBKoWKdGorSRcRlag5Z5eSNTmU9JHfFH4q2QW8vzLKin7YEyqWLuQgbI+3EjjzcIHjCW9o/Gx7vI/ohE4VsiVLrIN5Rco0gMpDARnLwOpSRcwWvSvithYqxaXCg7sS4oX9aybtsEs4kgWHbY4o8A4x9eg6Z5yI5WI1Zs1+TH+LWlMaEZJLJz4WQki39C7WMzSdpkv7viGP3WSJQOgPYeDrUHaLb78ZOFIwT0F+QjagXJew1GsR/yaBigHw4xGoZxqQxpZRNWDJzs5Gm2KT6cdJednVHv57FReCYTEWYyKkyGmWYAy2stO0za7TKRKVxZMY3sE4reW5aPjRjYz8EgG4HClVuephn9cqSRX4tfvyw4GcAhjRrNiKJ8Jtj6T244mc7TyogbApoBIUkUWzlq96R90+ABf14pC2eFuvRzrY01UCra47yihXz1cCFRpIkkUYWU6LGttOPkQAjEcSh5NNXLVe82Pa/1fE6ogL2EaXobz2u7uYn85rJa6gNl67ytndRBoFSBTWbb5LzsG4C8twnfJrrs2Mo7wutE2oTcNtTIFE1npONQjvE02V3T71NwRjlN6DDcljgOkW8E/C58/nyPB5mVI7P8RKqwLpVkWoqJrUOfJup+l7LX24MeRJbIEwoiuwF0kpVCzkOeROkkadzo6yTbMALafRxKDbm40oyGIUbDAKfbyTSCsJEFjtVkkEbqmfbleVbWW9Y7fsKk30bW1bJKyEEgUzQ3Ze3YUMlLkG1UE0zy3KIRDJuhrmlBUmnmSnUD2XQdTqIw75MudBKli3yw5jYMJEoPh8IR1jo7aLYHCIIRWugjwAhBopwGGCJkiuqQRcAbBbtxziBAEMbPNeg1MRwG6G2vAWErIzw1BYxIFZL/QL4v4MVG57dFeijyp4my3bi0B97ncILUtf+oy2AwQRrbpmPTRJHWuyxECrU7LheK6sEu3JThouvMmkjRlGWbYgyM9//ymHaNIpjeWyNPNCJKGx02KQbyutzdX9OibWSQJFLo/deQ1480mc/lvWJEOEGTdxC/eZlwaAMDddc/F7lYRm5OW7+eMdaRVRWbcSe3Mh8s+Vy6Qm1QUiNSbORJGaJFOw7LMXmuvM/YtWhAxdSmbfVSTgeShoQkVk16Ak+T5I1pamcZ2PJXbcOyQmkDf3xfyj+NWJfTcxrIBgSFxwlgXzGSp3XElrcH6a0l/7RX1YqB66qOKKcFhYiX59IMVD51J0eUiP0xQUBLVspOihuvRRV2v7BoBIorbIoWHzHfYNsV81SDjp52qBvTfTQi3mpnPlSj4QjDYYBBrxWvqh2GsZEmGwZBE7pAseA2yqXIkF4k+Oqe1uM6srbMREoRqr67Rqa4wGWU04aG+OOdC48xhHxH0mH7vJ3xthVCkCcxSdnu7CAMR1hd303JkyYST5QckZKRKE0Ao4RIGWGEIQIM0EIz6GOEEAGbBrRHRCdVRVMnpqVDyaMVqVrN+bQefnPNaJIXtTGq0+pf6min89D3LRO4/iFhmnZiU5g1gg7IyxutHtQhxzXPBBtkI5QKMb+Wdkwq1RDnyGeTZIXJuDCN/GpEii2PCzSZ7UKgNMQx6W3iEjCxDIFC+bluQnoy15cXDaY+Uxp0ZfvpOQQ3CiG2RURKWRKliOeWRImW5kKQSM5BHtd0cBMHaDu/DV085tLIniQUTRskNDB+cRNp4iqn69TLZ6Hfa3KOfksnBy7/pEzjRDJnDJOYfgQbUaKRKdoxsMvb/jhc2oTJ/FNQTiodRuyORiwmryP0YtxbgLuMQZwzZNdJR4FCxCM8lEYnUYcpW5VUTuoEV8D3gw2v2iFqhkHZfBpxQo3gCLLgsUmshk3EFZy8Ti5ENkJ+jB1rA4fOO4WN7haarQHWkgCQgXDTHCHAqBVgiADb4UY82g0grnxseTVqIMMSfzCkGUGZuIu3aQqJhM0lvKyxVkSmaBbpsqOMouxqXPDKVbUNmth8Ph+UtmclecSS4CHy3iZd5GMLUZvrsv00fzZdZ62ziyAYYQ07aKKPNWQkCoCcJ0r8e4gRqz8ZiRKgjyZG2MUWNgAAg1YT/VYLQTjC1jDA6XAEDNvjnRdn9yXJwhUjlzarKYup4lSmXcn6INvxNLxRXPPulzF0UIlYGsVsINaeuKJC7XdPpEkFSBvlJ5jIWhORUpRHg2t9BXTlmHuUcO8KOiYJFs0DRco9gq1NUhmaPEls0HQzSaTwNFjS+Tvw64bKvjQgOIHC97WRWUGaFHXbmgE6ZijKTFy2DTGZPClLSpWVISbiTaurS4AjiL1RADOBYiNKZH0pMgjlvuyThzB7omj78lxTHhP5oqXJY7Zrc9tR3ifNy6f6uMp08rjX6niZum/KO62BjzLXlLLS1MZM8g7IEybaDAV+jP6YzOtgnCDh5EgZEsWFSAHLY4OsizumjOMoJ5naiFcD5PPXgfFGzr1RetBfmLZppZcXo1V7tIopO9uynW8RGob9eYdWBtLyKILWgEgRkCvxrI17nrSR9zjp8mMRNrpb2GhtoYmMRBmxaQPxE8cj3gDQbA+AXjNeISQcIh3tlh2I7fObimUh4Uehy2NP7NfVpuW1bJQ3V7y5As6VcBaUWXqbmPa7yHujtCM0OrtotftotgdYC3YQgEiUQboNWAMIhRfKSJnSM0ALLQzQB9BCH0MECBCghT5GYZB6pZyWfQHnNkz9hZamiS2ZFxBECgWb4/0BZZau/9rIk7z4fk3rcWnjLh5qrlhYYTgFSANVgvQRTf8wGZy83mnYb5nOG5b0OuHpUiegfEWGrkZgEExxBoo8TIrKlN9Tpss0/q78GTX5Tfl5OUhCxUQ+reQvp21NGGuiZT1Y9hNF+jvl4cd52pIQKIBu5Ml0KMegnGNKI3DDUF5XIztMZIktTZIewPinNZEmdB5/phD5a/LfHNxANt1PPUGTQVymS5tT6hLTDikxq76gLIEij5tIYq7XNlC4yg5PK/JIKYqJYiJRXMSHrJunHc4pcfkM68jrC3yfyJLtZL/DtlIgkLcKnxuPVWTECZBVZl4adNxFwaxSEeeJMCn7DlxymAxFbaqJqfEAmRJwBrJlVo8iJVS6iD1MjiHePw95T5RjAM6LpxMc3jyJZmuATdyMLk7mphMQYTJKxsX7aCFMpg8MgwBoA32a1kOvSmx06s2EvPDVhLsJrvkmguv3NCl/8lqEZSdUpLHIy6euNi4tdlexWKT4yU5IBo8NEXuiGOKfJN5bOTJyUxzbTP46e+hsnkQQjrDR2sIqdtBKSJMAI6xiByFGaGIw5omigXugDBFghF0M0ESTtc1djNBHC8MgwFonQL/XRK/dBIbMYNCUIR7XRBNHIfLKESfjpdKUO18GmwPsU0LLKEim9jstweHaF9VBpkyDCFogtBDHrMwRctRmqfJSDBQgG7Xk5aZ5Y4Dl58ekIl9H2dvqga2P54ovoHtUSBkGjC9xzO+jQSOX+LPb0l3qp3Tb59cz9SFgv+V344aDzTOHk+BryJebuCbJL9o3KfnS+JUyM3dBIF9PtQvxPPz9ZB6I41VkiulbmeSZzYjTfi84DiNvE5kMviLCRI6uu5BwofhNf9ssjR+Tadox2Y+b8mt9vdTd+bannMfTeIgIeZ+eOE/VC+RvLj+4R4o2+GID7w+qeteZrmnLY4MLQakRJVzOaX2C9KqWJDLG451wj5KiY4CbJ4rWLlxJFFN9LUEFlJNMrWic2+A6KL+qrfHxgkuPcZdaysTJE+2GRZ2EqWMmaB3tPKFKR8Ybv01R0z69VIi4wUeBZJN9Mt40EuUYHYtw1oU3ohn0sYEttDDAEZzABrYAZCPaI+ym0wUGaDEDL8AAAyAAgnA1K4kimSTrnobSdoP8Ftp3qZN9tilE8txJ7n2Q4NLGbSLRNCVIO0cy85JE4W2rkXUcnETpIu/VRW2LT+fZBBqbt6PV7qO7HpOTXZxEC32sYjclUbI4KDGJAmCMSJHESbwfR0wZoJnSKQAwQDOd9hNPv4v3B+0BTg/ZtB6u7PCOTqZpfYnMZ1Isx5qGizHn2vhtgmKeiIdJ5UBZgbgkRg0Qvwo5oIwp37IPpfqzIy5AW82Y5efzfHVA9vMm2SSVZEkQSGXZNtJoGqG0vfckI6zSkDEpnqbzJNFlIlOA8dVzgOJpTSGy5eiprBi47CKdV3plE3j10QxPQNRPHg9qEh1A+z7THBUvS57MaoR+yliHO4nC7SOZl5MoQ4yfXwROOmjkCgq2ktwokw/st0bK0HtIAoancQKGriXtTvlc6YW17VhGAW5ZS6JEXt9EpGjyZ5Y6hI0wkbYfT5NyDsjLQhoQZFN3TJ4lHbbfRZ4I6Sj5NU8UOibbQ/rYEZAsUIIwP0h4KBzh9JDNfOi18kU0XCn9ScppQu0+0IjyNzIxjXz+u8zHp/vwxjH2cbnSIgkPzaXTpVJyQ2gWBqgLDVYnqGw0ZcpGKknlgBpMiJhAoVV4zoiTiDjRSJTzAGzu4fCxEzgS3IwWBujiJJro40ycxCp2c9MJBmhhhABBoqyMEKKPpv56pnon62BZe8CaX8tQhWnm9diV9OPPAMu96h7ZnAdoHVJV2AgUEwnCYWpTMr/sgLgRwomTM5BTuLvIOphNjMc96UL1RGls3o7ukZNYww42sIUm+tjAdkKi7GANu4JE6YMHkpUgIiWmS0IM0EQ8dWeEQdIm+2iimWwDBGiiH+/zaT3tpLxIvlMnyD1K4htlCg9XDCXpwokU7ROoRIppVH7okA8obuOzVoKmjSIZI/MtCVqI3Xfla6e/ebunUUpN3mrlRvXHNlWF38NEprvIfQmpKNM+V5j5livLkvwF8ivQaAq5aYqJrb64ynWNSCGYSBqpJ0oyRT6jHLGVhJFWNlQmpCspgRNNf8D4axSJlLFjRPTJOimJIlcvOxeZVtQv2wxTWRdcRsSpzEtEe5xXEIkCjNcD7TeHRrDk0kX5hJbvOExO6q3o3p1FBArfN+WXf5L4oX5d5qd34roBoHiYyHdi58omn55D7ZPicMqLmTxBNNnD2xyXQZPoBS7y0FVm2jxQaF8jTyhdW3FMeiyyhUXoEjavE9JxTR4pWpokUXLbhDAJRziU6J5Buo2/QciIlCAcYZSQKMNhgFF7kP4GEBMswwCI3G2NUtrQ+uFtDJrteOnZXhMIG/lKGiKbYkFpMPwOkWcm0/pMlXsH45JFq7CAuRPQOssi75S6sJ+KpmwUHCbJrBl8tPrOeYhJlEYWKPZiZMTJecn+xQC6EQ6fdxO6rZPo4iSO4iY0ExKlhT66OIlV7ORGsbewkZAo8Rj4AC0Mksk+OWMvFfwYd/fjf7ZjJtnmZIwRsWcayTaRfSZoRAqh7FzvZYPWJovIVGDcKLFBkiBFiqYtXbYjSZqEyBGRckUraldd5IkSIk66YEuFR2hv3opWe4AjrRPo4taUqAwwSskUbToPn0ZH4G2RtkSi7GIVFKOIUkfplQKEqWfKAGEwwiiZ1jMA4kCzaI8rUZrCxhU5mVczbqnvUPV1k/suUM64cMG8ESnTHrFdMgIFiNsVkSiyHwHYAI+UN7ys+RQfCo4PxIqoNo1WQuosthHOIki5pskmLqOA/BKUpAdo5An31HAFb4+y/9pTjlG5uspyU0DIItiMDI08kWR4A0iCbANs5cAi8sREoNBWM0SlvMtVhTI6gRxNl8cmKUfbVDLTOVrdBNvn9aJEoIJ5RRfZYD6vC9IzSasnnCRJVrQ8FI7GjEYgMxzpGBmKgRiZ7/ea8QqYZDwCmZ4NIJ4hQPsw108tDch7jGh2oIlE4Tp8T9nKAXhOukhRqbWjnJehtBmLooryi7sSKSb5raW7Eil0f9Mxfg9KN9l7PK/0vOPy7gyRxuQe6bKc9OgoWyJIuuI8TpTQMS5TO/G7HmoPEIQjBOEQLdoPyKs6IU5yiyUIj+tWkNNxAWA0SnTfYYDRMEDU3MIppWQ1lNKI6MEBAO1BLNKGYdzQpMu1HD106VBSZYUqIw8ua3sFSarMm2Jrgstz1qkUa0YobXmnJZWF5Dc1gi7yI+TpX4RDnR1stLawgeyviQE2sIUAw3R0fISsIrcSk6+JfjJNYJRUb2W03CQci0iS2jBp3ZLfwHS9sgTMQYVpBNIGKfZ4O3CZ9yrPMxkpJiU8RGpY8A7E1OHk/uKVd9Y6u2gG/cQDJfY8IcKEgsfSlJ4AQ7QwyHmkcFAgWT6FB2giTFpikLTG+Pco7aiCtBWPEnIl9kQJwxFG5DYZRkCorKhlMyjGlEdLfmszcDUu+He3xVCZVBYvYpu1qQhFffOCIAQwgHl8odBg3WNpskx4xZeEuat3AL93UXnbjFbeaIrIFf4nZdok4AGgNWj6ibSMNJ2Pn8PbsslIIWjkCaXLuf4aiSJGYF1IFH5bE6SB6CTvqkC7oItOMqnxR9eQpAn/HpJYmcZg54wRYPx72vo2w9SERnsQXy4cjhEm6TZg/byYtZD24eyao4Q8GQ2D/JQHIE+wFM1E4L9N5IZsypr+LolD2rYtx7S0ISztR55IcoXAf9vahIlIASbXI2yN3tYm+LtpJKWWV8p7udIY12nZ1B2Se7ZpPCkZohwznZfyCXtodOLBiVZCIDbbAzSDfvL05tUmszfM9NwRQjSB1Lt6GCS6bxBiFAaIBoPpkCjt1g5aZ8StcdBvot9rYjQMsReuAr1GxixSBSd2lXucbGPcE6UDxkByhm0P+YBuppFFWfk1d09CnYJYVu4qCobpOTlsrGOVZ5GKAjDONq4ijhCbzPXdRPx3IWLC5K5IAscmf90ezrvjDVjFDs7Ft9DFrdjECRzBiXRUnC+xGgemXGMVepB7woDRLCmZQsK7J/44Sw1UI1fU41JpkOQHYDbgh+I3z6sRKdpDmOq7PE9iUkV3niDbhywTLc31/bXyNXUyputrSiB1MtSOaBljCsq8Mh489hjGlwqnY5sAusCh7imcc/Sm1OukhT6OJG2shX5CVI5S4rKVRBniJErGymd1jXuWELGZTbEbJdN2Rklg2UEub3x+kOQdYhgECNazTmxvGADtRl5ZouKiYuwhC0ROx3oir3QF5ooRpcmRrtz3oj5B++Y2I8JEPBe1X5OipGl22v2LYBNomswpg6L7L4Exw9EC0MT4iCk3YtP6KIPOkteJRnjsIjPAJUz6iqaruJDumkyidJtirBEEIbKV+LiOoKwuI/clpNEkr5MrSyBf6LIP5XldSE6NUOHXk89gIlA0MkUYEB3kjQl+rIg8kWXEpy/wEXc6n4zIMWOQ1yFZtyShJPUX0+COZlBKbz5X/Vorfx6TRwYrllvDFO9FQhdxcNkceZInSmhaQjMxFjlJQqPuXC+WI+18NJ4GNwgy7yBoYucwTaMfXyWTMEKI0ShIPVpGwyCeDjEM86QLbcmOk17hPE16m8j8tL+dHNs2HBsi3yakCJG6B79fTp4DolEh7+Um5eoQ1fpXF1tVyn4pOEz9v0k3pd+anirln9YnkAfKWnZJIj26yW/aclKko+TpWPInU3Qa3a2UIGy2BwiCUTpISPWbdFoAaXvQpqmPDxpmemufLWqSpgUtnA62cet44aooZW2FOI1DiQHcbGUuYnvhKOk4Gjxzts0xSkqa7GhyH50rKtQZaEyfSbmYFvgL1nXfOq8lrQztXlw50ZSqpBF1kA962UU2xaALHNo8hbVOHJNhFTvo4lZ0cRIb2EqmGGRGHI2SZ4Epm2ihj34yXSDMmXJMiA9Dc9FI8mRq4PWuiDyxnS/3qzzDQYTWPmSai0hzZe9drmPqoGQAWcbe8w6mI/a7yC8VnqQd6p6KlwdPAjRn8U8oLfY6oVgoRFpSu6NYKEBekSI1C0DaoRA7H8cmipc2HiFk7ZKz/sOUUgmZ91g6whWOgLCRL1qb/Jd/0p4Cu4a0ByYSn9TXzKp9SStUvozr+UUvbKrrVd9RKpKlVIj5RIjMOYIUcqp3RKBQWk75lorLHjuR9BSpFEuygI6VGeGX315TnCmdyyj6rY0syt98f4KleSXU6sqXlZZGhSlNe3db2WrECVh+zevBRDBZvE/4aKqq2yowdWFc/slXzYEHmKWMZUa/TXXRFWUErzTsZP8JjAf35fVxwdECqx96PAfy9m+l3ib56Qq0yh79BqAakNzgJIzrANlxTpoQcsHjgwAIknwtYDBqpdMfhim5kuRvDxJP1FY2+Cn185Cl8SokCQ/64zK4LY5RGg3Q0/VJfvN78nunkNN7NPDGKIkVW/uxtY+i9lY0GGM7j+fT5JuUe7TV4p6ELB2614kkTmze1dwjpYuUPDnU2UEQjrCWbJtBP63va8k0K6q72iqTfKp6rLNm7YPAp/EEqe7bZOeMMCqc0pWhlFRqYwdIggmOMALW+VyiMCZTwhZS920+Mkh36iFmD2XlpjzbQFyh2QdLPVKA8dEFwF4Rp2VVO/VuJa+nYc8hj5bXdg2NgaRRcxZENlzL4p7QSPldkfNEaR+7BUcPH8cGtnA+bsAadnA+bkhX4aE4DXmCZIQdrKYBKqWwHyUj4Tu0vshoDXu9pp0kKSoa3gdr17HqAaa6JhU4l1FuzjAXsc3a+Qcd9KFMTH7VaxJs5KOJMOFpNIJLHiiriD1QwlgukmfJJuL985D3ROki74nS6eHw5kl0W3Hw2HNwPBdb6EycTII2Z1Pm1rCb8/6Kn3wESZ4QqANJp+RgiIxgiVfN4jFQyJOsxUYG6Xqx50u2Wk8/HKI3DIBhCLRXxtueTYxyZanN8lNek/GRpq2IRFfjoKyc1fLPiuh0JVMkirxJTCSxNNiXQD6tJ1t6FasnClgd5noKHeTlxj1RtjCup5Ack+Qdr9haPXIh3OR30hRlThBsYFwvYEtVAmIFBHELjcvg+6aBjlxbJTInSg7QKDAfSOPyfpddRDIO/OJcl+TgRsWqsuV/hsCJGnnSYZc3lZOpXDQSD4at0QjkusqsZJOtLnLZIeWIFm9HWwVpSTxRDu8B3d7YdBwadGi2BrmRdgC5PhxAblBEGpEcXK+W5Akh7t+3033pjcLzyRX7iFTpt1q5kXyADcjwGQu95Pv1EmJF8zrhW9qn9rCtHAPGm3+opEmyJhT3AzDukUJ1b89wUR77ygZpO8jGW8a7y3W6uWxrWh+g9QN8X/E+6bAtkSFd5MmQDsa9Tbosb45EybxOWu0BVltx7D4ew4+moa8m5S1tSUqLHzFPlmjkIo+HYlqRcug8maek9RE36n76AAFGcUPqAINeE0E4xCAc4XTYjN25OBNIlXgbWQFzkoW7QPaAfPRkrmgQbISBi2Ln0pG4VFZXaNcqq9QX5XdlGHhjkoFkz0Bs9K1lht2FSFbcQRpQ9tBdT6G7eRJHgpuxmRAm5+LGlEw5ghPpiiGagB8hQAuDNHgsH8UG4tHweEJCCzvbq8AwyCtgGiFien0rQWLKx4PhSaOd0jQ2WtZR2cToBpKYAfTvW9VIWhZopAb/UCZSi58voeXj15H3lG3X1ilRMGaxAg91JB1kRAkRKkSYEImSECud876D1fVddHFruqoVBWrexM1pwGbyROlgK9cBNRMSRQuyxTsSIkxCjBKPsHiffEwomGw/WX48Y/0H6bUGyf5Oogi30EfQiss+Xfa43daNKKngSAOiLdKhbOW+Cm5UuBIlsk3O0gvMRuhJuAi5ovbBZY1tYEJ6Nyw4yOiV9ZAgp4nllG8iUqg/CJGXIwRJ/moafZFHStH35d+F/5ZGqkxrIDNm+VSfBKZpKaYqII0bntYT+Xn7T0GEAHcPAvJT8iQZZSNRTIYHvS/YlpeDIE9I+ZfkCTcu6Bidw7caqC6FYkvnababPJZ7H3pX7r0t9RKuu9QJk74csuMyMLHNiJN97OKvzhN2dtHuns5NzQHyHibSSMxNaQfG+vWxwJmJMagNXkpw4oSID3lc7ksDlFbwi/OEubRBq4VRK542Meg3E6+VnYxUkYSKRqIQecLbCK/7WvvQyEnZ9Wt5cvJcXlTWb1cdQtoHk8ycKLJJ+bPSvvQskWT6qvKbdNlE/nWRl3NdFJMo3Ntkk/3uxh5Y7e4WwnCE1fXddOo5j+sn6/9q4h3SSr2w9PgnpLtmJZKv97mAskodBoC9aXmiBDgNJA+UKdlhHJSlDQRDeiDgdKeRZxLpTxIm9DsUf0PuLgu2r81Pk5a1TJ8GTEUnK3kVV61JIV2JCbKBceWJCJSz4jSq+N1kS0beeQA6wJGj8aogR3EcR3AzNrCNc3Aca9hJDbs17KCDrdwTUOVtJq5YkjmkvzEMRXnbbGPNLjDl1XQuNbM02uWJ0q2Pjttc+7SRI1eXW+3ay4iiD6gRV2W8VDSCxJYOuHVKREqujk/f6SLrcDbZNm1v8UhVdz32NiGSkjy7mglhQtN6aMrcRkqi5IPJAnm3XQIpQuSJMkiUKIqJEp83SltlKxkRiwNA5wmVbMSAiNFsNYBs2eMIOdde7p4rCRO+z5uUdOPV2nwORIaWUVp4PyMxLZmt1dOyBEUVQkMzPjXDS1POplUWM0YLgLQveL2S9U4eTz2eSPmW3g2Arvxq8SpkmRaRxKbrN9iWyyd6robYKvE+SOXi+prsN22PpDW5tpIm86bH5SpbPJO8sWZFSa8HWUbceNAM+ARkCGgkSkfk4bosfzQJXgbc8wRiaypvfixXntqgj032ycEcOV1eO08bAOKQLywHGoBxr5NVliaX0qbzpqnPzwZr67torZ8eGznn5Anfz46ZyZY43eSJMk7C8Px8MGU1WfBBg7ZynyktHiDtJ3pEHEOtiQEGrSaGrSTfKMCg10S/N4jJFJr2w5sxkSZAvtnzem8iRzSCRE71AfJBanNyx2RLFsUk5DJd2gQSNp3eZpi49ANc3st2xOUcb3/Kcu0k0zh5UpZEafNjsedJq91PyRNt+rlOopg9UQianivrfh/jnlOSgNzLTZOxo5TWtYodrCBMG0yT3bQVDDAKAjRbLQzaA2whWXM5bI8zhtsYX96KKjh9rJPxHeODkr3mblQ2A7RQwy4JV+LE9Zh27TLPqrEFfARVKhFasDRy4z2KlEAhY+5iZN4nFwI4BnTu9R101rdwZ3wNXZzEubgRd0g8UI7iOJroYxMnQKvwbGCLNYU8W72buInx+CdxQMswSR+l08XGXpsLSnpFUxpEmqbgDLX8vEw15WxSTyVNgJquaXIDNOVddPBOwAZb51OkvRZZAPz6PE0aIbzjSQIx05LgnCjh03TOQzadh9I2gcbm7Th65CasYgdHGSlJxKQMInsEJwxxh2LCgzoHHpkc4EpRmI0aYZCMB7TSY6TAka8YELP2tHJPrCD10+sROUrnD4MAa5143vQ2LaM4TMqUSHYqVu5aK9uznPppG1mSBq+VFOGQSo2cNlo3JPkn08rC1dXXhIbY5+8t++AQmdK14FjH+BLHQFYdSNHmfcQ2Mr2lB+RHMIFYR9lDVoakrHLdZYw1wPhoJYeN2KMHlvvcOJUGK3nMiVFHIK84a38Ek61hez25lf32UEsnAyBCvo0Cee9jkwcZXVgjkwR5BOTfv4hEkeSJLCPtMbQ/lUjCeFlB3GPM+KPvbTLkitqsabq8jaACyyvTtYCxcjq51EnpfdipEcbJzgXDWa1b0EhYSW16DvW33FDUpu7wNNNACR+xJ0hShRZ44Pm0QLRch8/FSWF6/SAxhYHMUO0nsQ9jEzkbrBkFAXbW19Bfj4/vnlrFcBjEyy33mvmFSkjW8mk9nGzhcjgUaXzbhh6kVrvWWABx3jilR5x2EQDie4yj7FR0U98eijyh2JdLFnO7T7MJkZd3nBTpsq2WpuUP45h+zXYfa51dbARbuUVGaECQpu1wTyse64QPCgJI8/NBd6rzNmgBZdNVetLfUyJRspMyJZm/yCAJDhqEQTb6yJk/zrhrv8c6IM4IyooiGUES7HURJhLTUhbrUtKL3puzkJyV5A1oLWs8/K+b/XXW88sXb2ArmWqwkwa7JAIlbhB9UNjJAONzLjUGnTDGiktlqwymqutLhcOmsGhKsjTcbNCYIQ937xwX4kReUxIqsu2EYpsc4x4ovHORTH3Sxg514wDN1H7WsJO2KwranC0VHnubrGEnZe0p0BZN5wG0yP1ZbBMAGObSs2WMw+R/gCGyOCkx2cljqnBvMq6c8S31BYfCEU4DQNgYl/UuxprMLz9Z5TZualOTyOaic02eAzaMWU0VrqFdy3Se9oxSQdNHLhcKIWISRUuX+9o2rT4y2Kw2kCEVcal4a3WR6zfSS0V7WDpmU675X4gxAsHUHtvIQ962avdUdN4YUQCMr5zh4nVB59FWvD9/TzllhxMokjxxmcZTVC6cUCbI1+FVplDekS4ijbUimcbrmVb3tBtrdVGmFY2I0z5fvQlZ+UZAiVAFc4l4kKEhPDjzq+dRP64di8/haeOrkshrEyQRE2CYDpjwPNpUCG50ZtN/smCc/PqShBmgmdIvfPAmG9AZYdQO8jMZAKT1RxKq0huVkyHc082mIxTJdlXe0EHu6S+3MOQl2LwOTZD9sK39hcp+qPyWW0ag2OQdl3sd5diYJ8oekASKbbYHWAt2clN2mgn1RmnkYbWW6LfxU49SvTYrgYxA5JGSbJ4ofBCf6iKl0e/MMaSYjMmuXwJNDLCSspvxjTJlOgviEgYjjJLRxx0gZhaHjayi8/luQNYIiHUM2b46kkKjPLZXMsVGcenli5RRaTTz+xahjLIuR1hctBOtwXHlQQt6uQrgiL6M8SaAuwONi+MR8kvxJaxhFxfiq+jiJI7iOI7ipnSUnFYM4auCcOZ6CxtAIlQpjbbkDjhAEztYiznJXhODXgvoreSZaao/WpFIoavpqhDHnGHSaviIj6kuSAGqPbhM1+KmHBQihZejLFdtFF+ew6F9eBs0AkUq33JEV8Q/6SK/VPEx5KfHdRC3tU6Ew+fdhG4r9jg5gpuxlsQ/oek8XeQDy9JKPDx4cxN9hKMRmr09BIbqMQqBUXgIozCIp2EC2BWKEwBksVCy9klxT5pJt8VjoqxiJ+kOB+BeZQDiiP5BgFEnjuLfA5AGIAfGlR5CB/m+Aci3balAqUoQkA+6CJZJTqcwyWB50arKjwaTULJdrwq5Y1LEXEbLNOOHRrOWINhjB8AhjNcpPt1Mc/vm9W4IERSfkxQyRopN9hQZvFqdIiWYHyddSS5VTN+N0hMPFEkecBKBt6siEgUo9qaQ26K0sT8+zYeXB/dSMUEQRkCm+APjRkKIcSPBRrDYwN+rx7ZSF26LfNIwlEZfzvOOe6OYRrp5GblMN7fpLPL6UkaA7cvYC3IQgi0bTVtexqex8CTKBrYRIGIj7HEfSiuPkGEJ5AkWGSBem85jI1W0ALPUv/cdZPiIfWdOovBpPeTFKj1QaICd9nexhiECrCU6/gghVoMdjIIQg1YT/cNx/JSd7bV4+eST6/l2sY1MHnPdvifSuCeK9CTkeWSaPD8X9BrI2oXWD2vGRl26ehFpTumyDcp2F0L1QCQ512VbkoOUtsm2JA95WjJl51BnB812HxuHt3Oe0zQYGCb7Mo2m7MjljItWpCJ91WVQXnpUAXlPlBFC9NgqP0UoRaIEGGFFeUjO/qTz5lvxMrZBOALaA5xuN/SYKPyPN5T0yagCU6KmVLgolHJUSDvHpPRqxputQtvgagDvGfar3iNkf6vjf9RQusjHQtkEsBnhyJGb0cVJnImT6GArjdNAUw1Wk2kHcWyGbbRGfXbnrM7sBKswKd55l8FY8A5pDXqT8USwkSlFcLUnctBGa/jFTNNvbB4o2neTHhYuI27LSLBobdBWXgTT6FmRgcvJExNrL+OfiOWLuxh3d+R/HQDdPTQ6u9hobaVTdiiIbJd5ePE/IipDjNDBFjZGWwiGIzR7pxGOgBVZ79mrhyNgODyNURgHtxuFAQtuR60vyI1YcdafAkCPRN64jcfrbWVnjFjn10SzPQB6zdgjJRwC7RBpjBTuikv9Q489P//TPhU3ZgGlakjLw0bCm9pXFSLFdA/bbw1VPFeK7u16DZNixonFBUYLsUON7Fuo/pHCLqeJcQIP/Li2/LHUPbSRS4JWr2ydFNdJeD2n3/Kb8RXEDKvOcOMVGH8dXo1k9ZXlIvc1g0U7xttyT0kf87jWyFID+PuYPEukUSEJFmC8rLSyoC29B503hF6mWhnLZ7cdH1uVTHsgPoJOsMkzkzwkyDrI66IWNFaSKILIk2UaYuGn8gBAAwOEiSeKNmVHm8qQjw+RESuAHjy2JQzAvBfL+HSeZjIYrhmgWoyUbOpunkRpoondlBxqpQOifbQgvVTypMx4QFwaf+n3muh1kmnAaIy3ec0WAMblByddILamYxpxjlWMr8rjErOK8lE7stkCNsj+hKeb2iCX/7L9MQJFksbSe7rIq7oLoB3rs2udHTRbA3RxK4gwocCxRJjQQgh0TMZEAezBkaWnVVwK495QBB63h5xACDSbho4fmqYnSgM76QPK1R0ytTr+3Wr1EYQjDHrN2CMF61kd2kZ+pHE7uQkp0HR8mx6TuRvlWkiRQkLg52rpGlwMr1JFaDhHa1SAPShp0TVXWRqVHa0YsoHYAyX53UYa8wTHkMVCuTuAzQjHLvoq7oyv4QhO4EJ8LV2BJx41P4FN3JySKK3+AM3eaTT6QBTGsm8UAoN2Ix35TgMSM7KE5lDKY6NhchETecIFn+QxTMXFi0pT+MageR5JBUS7yaRkhiRggHydMD10lTq5CNC8vkyWNT/uel3tPpr7I80npba0kil8m8jY+S5iGXYesvgnXbAAzT0cu+ONaGGAc3FjSqKcg5vQwgDn4CasYRdHcHNKrNCqPCmLf2ob7dsQK5jU4WvKZhA/30oINAKgEQJ7rZhMGa73c20uvkSQrMYzRBOxO28WWDbzQBlfUWuQKkW8kwswxDAIEKwn7sHhKFnyMPEy4wYbQZLuQGbccoOWEzD0Jw201LDgisYQ+RElbYRJtjuTMSGFj/ztChciX8tfZuoQT+PvrEF6omiBIBcc68gb6JoxL419+cn5sW1gfPlj2bdraUWQ+W1yiq8yI+M10QpizHAlRZiTKB12ef4H2B97qOzLre2YJKqAcU80mSavrUG+BzfUTQZEiIxEMXnp8OtwaO9Debj3ifYOJNO4nJPvzb9FKK8pF2YAsvrD9cIqRHBD2efGHZcVdB+qk4YAxlq94949dahTc4B4mvvpMeNwNfVE2R0zGjmpQh4p2nQeSaJo03rklB4+Pcc0is/tvfh3/K3l6H1sBm+Be6CQ1wn3RIk9UFpYww52kmC2O1hDPJ0i1kWa6GO3tYZmq4UwHMUerOFavJrP9kpW3+MHiEGkxzby7YnrA1I3oOtox/h+eq8VxPU4QkymaOxNmByjbfb19TAURTB5kcp2aNJXZftjtrQkRbi86yI/+CcHBOmcTQDtPbS7W2i1B9hoxZ4lLa6nIouDQt5W+W0/9bgyxQbi09niUhifwkb7GokC6IGRtfTdEnKxlIa3hl2sIHODIbpEMjxZzJR4lLO/Hje27WEAhInSLBsBCU7emVHlHRL7R0I5hK6g2JRbCVdl1VSBXa5lg8kTpmxMB84ASO8IyfZzAuWOAFayKQYXIgt4eVcAm0D7rrdg83BMnJyLG3EObsL5uAEb2ErJlCO4OSVP1m87DfSRCqCVEGi0YoNtFI5il36EqZAdpFFTMh4yG8VOjLphAAzDcUVCCkJ65aIaXSS3xgwvWlbPRp64BHSkC2vf2BRE0FXILomGYYTm9eUyMl/G20xem3dEQL7zIaNkLVMAu8jIkw4yskQjUY5FOOvCG9EM+jgXN6ZTdzISJZ6yQ6tdxcFjb8Va4p3SGvWxcdte7HFyCsBtySNS29Neq4WsfQTx8zQAhCNgFA4wCkcYBM1ECRvkFKU+YqaeOh2a40xKFSdf+PQ96pYA5Nr9qE0r9wwxCEc4HSbTPckI4EoRkSjc+OAjRFT+QF5RMjaJEONEiZZR8wCj37wt8zR5HxNcR5u0/KZzq16TP7s0iKhANfKE0sTUiEUE6RwaiQLkvSCAfN8D5Vj6J5c/5t9olx2rKrtNhqv2vaj/J2Ua4+SBC4nCbyv3tdewkSim41paW0kfGyVWriuflcsL+a4mEkUrG0miaLqHrE+y3phIFHpf+R4hO8ZHzqX3dk5/4XWuiG0qkiG20W/NgCMdnRN6a/nTeXnLbyHLeFrxvWeIDrbRVuKfuASW1ZY/1ggWSapo037i/WHOI0SLJ0Hg/qg8kGx8LEvnpElMjuyCpvfQsVbi+dJHCxvYQh+tHHlCsdhoWdvWej/2ZglH8Wo+4Sqw3Rjv93mb0GR1G/nlkm3EyhB5uc/ThkCeJOeNWnqXaDYp73tNHik8Pwd5hMpjWpwnSaifkZ3LPaa55zQnUTZhJlFIt+1k5Em3dTIlSjhpEmAY662pJ4q+Kg9ZhLJeAxir3wR+vAhygFCm0XaIADsl+uRSJAqBGp/24AHiQELUGOIRzAEG7QEa7UEsBzvtvODkjDPfUp5tPifN1fOkLGZFnvBzbaOblKfoPaUCzBuTdN2iZYzPALCSsYjc6NtESqxsHj6RTteJPU5O4AibxhNPL9jGxm09NPqIDboh4pFw2fknkOI9NqxaydzI8WPxVJ4VczFotosmEK2G1aSwkV9Si+NEiskYk3kAt3qwTEQK72zoN0Ra2VF72zHZMWmdEl8OfCXrcDSGntoS7TPvlPbmregGJ9FCP4l1sptbgYeWB+/i1pTNj9N2sXFqG60+sHIKcYd+Chl5wr14qc7TNAVK44pHEKsCwTAO4xYEI1AgWQCsFY63zLiksiC0UllrIh+sFsimeg6DAGgDwTDxNKPpnvRcGonCR2ZdPFG43MkZFcC4XOUZpceiRqZIgrOI/K7iwWjzHnG9RtlzV8UxSaJQHj6itQRoIasS/A/IikCrd0B+6hk3anPKNhmQu8qNAPN3LCLqNANWDjRxPYAdl4arzctCI1C0R9a6Kq0f5q+nGT2mrfZd5P1M4O8giRBJmJjSOuw6bYyXiywTjUQxeZHINO2Zh47n0b1zGfkg5B7bmj6IC+QgA09rsD9O6CWQRElH7PNj9BpLEMO6mfhvmILH8ikNRGpw7xTyArURK7qHSnwtbVpEvr8fb0zm1Xgyj1QiTigwLcVZI3uQ9AjpzcKfgTxbBoktEO+3UluTBl9GwwCnh2FsF0h5DIy3OUrj8hvsN2CWOxqRkh7XlmDnD0Fp1P4gjrvYj9pgIdeNQyWd2hq1vw22j3GZJj1RZFqHpXX5sWQqOot7wkkU8kCJp+dkK/GsMRKF4qDYyMT4kcdjolB6nGYnUbjXCdVLns7r5QgBommRKAFGOJQ2jkxhjt2+szlG45GYAyAAgiMj7J5ajT1Seo0siGz8FvHvbYy7ZPWAeFlMk3usSYm1KSnaMZui6TKCXQZSQeL3kZH7ObTo/LJR8Ya0hjiIbIh4KPwsIGxkS6teiCyI7IVxlva9bsHG4W1cmCxj/F34Ei7C9djECVyELycBLm+KyZPbEY+ESyOOwIVWgn7i1kceKDE3GXOU5JmygzUM+s3Yfc/k+mpSKKTipykgNkUvBznSavvWnOTTbsTz0DEX7yPTfTh4e6hSH+cN/ANqxIn2jhrpohnLLu1cc0FORtHI0Ogiz8yTtwknJTtZWvu8uF1t4mbcgXmg0HLG2TLG+cCyG9iOp+2cAnA8eSUiT3rI2h0p6IQ20mk86LNtixXLCGiFQB+n0Wz1czI7ni+dLVlIkzWJIF9N5moTAkGpSHdJAKnSNwgGGAXxKm6jYYAtJAHIaeUe6ZXCjVT5aflvKTZJeUqrgSYzwdI0SFmtEZxlCD3XEV95bhUyRp4n24DsT3j/MUTmlqxMBWkDiBq63F8kHEbM+klDdzvZ9pD3gOL1jHSVNvJ1r8eOpS7g3LCUxievtA22LVoRhb4XXeeMZCunHDIlmivFGnlg8rYguHYvWp/tmsdkFMGylZDPrA3QmTxRbEY9v5aty6X6w+uVSdcYinRJHvF9rZx4vcvJPNJfVpGvY/ICmuDUSBb+srL+cfJEGnIwlzOVp0yTf+5hCuYWh3EbVhEi8x4Z5EgUWuLVNE1HEi1yqWMeL6WZ5AcyYzO99miEYJgYkmHSN/fyOuUoPDT2/BSQXnqm0LR8mpZDWx5YdgdrGKCJNezmF49AC6vYwQghdrCa/m5iA4PES2WAFoJghMF6HFttp92Pl0MO12L5ymUuVWXS+8mm5PKbb/l5UI5JvYKfAyAv1/eQESYhS2uIdEC3QbWBCdnv81ANklSR8fpC5AI2d8S2y353RVoXYsoOgM0ICIfobJ5EZ30rJUxoQJCmmsspO5SHVp6MiRW5So9e9wHk6nX8206ecKKFk4C0TcNFCHlIebbV5fp0VLa28i41w7FjI/byQNyg+0lwwUPtQfyI7UZW2fmoI1VW/jcE+wFkIzqq5WuBVIB5evYG5mMSVYvQZMnT/UwrRkgDWmoKsgOjfQp+2cgtq5oFuERq+HUPn8wFjY2Nu+3cKPnGbT00TiE25sgDRZMJQ+RGELKKHLv38ykBPB2IGWcn5OoI8vUGKFc9Sp/D64ZGimhKizaaDfbbNtot80AcXyZMQp5ov4uMU5tyyDo3OWrLOyBT2+r0sHE4a0OpZwm2sJpss7/tXIez1t+JCRTe1mhLRAqQGbNcGWgjHyOFSBVRv2k1HzlKlZ02BPc8ydJ5lzQc6wtI7gNI3Hgzcp0UIwBZAPJeI2+oSA8U2bZlPwGxHesi6CB5BQBZeyLDAeyYqR0WGbgucO1XXOv+pNfidb4BYAd544jawUr2TSIsPokiSQJNF+H1D2IrvVS0ejoE8jKcypmT7hpBB3aMQxvxD0WaEn/C5mlS9Mffm8PUZ8o+mUPa7vI6UoewkSime8ttnSRKCCCkqb4AQvEAwxBoCw9aPqDEZZMmr6Ack1uZJs+lNAD5YLNchyDwOidloDyuDdjxesfrt1g6W/MCkt/CVD+XYOZgTICchkaM0Gh73ksl8yrhBqYkX+T0H6I5mjnDc4hWf4BgeBrBEHGQ+QCgBYXToPSI4xmiP25IDoPTGIVx/aA4h1koh/zoPqWNECS6Qzz4TluOUZKT5yVPl0ESlS31GgiC1CulNwzigdY2qxxSXrRFOjDejngeKTMkwWm0L3iCFEhSqJWNhxWKfUmeaHKfNRrexjT9tSN+a54oIYB2H432AKvru2l8EyJBaHoOBY9tiTQeE4WTKHJaDzBOAKoxUUaKnjpUgiOHSRDlgC9xnCdUJIbTIlECjNBgS1gGacPISJM4fSiOZZ4r/aAFbAKDXhPbw03gZPKRSfHYZlsCGQk9YtNJ+eCVSI6s2ZTLhpJPUzShHDPlmRRFq7Noz9NQ9nncBho1TILIho1sus7FiI072t4dwIVxoMvvwn+ji1uT7UlcjOtTr5SLb/kGVnoAbsH4dAIuYFr53/1WPCcybiY0A45WCY9d9ih9hACjEavYJiW3J47LkUJXBaSQMJFKh2nlHakA24gNfk0ufG3kiO1huTJed93cD9DILTDeNk1ttaidFl1HjhCzUTQ+UraJcaLkvCTPMeSmxh3qnsI5R29KPUtoKfDYy2Q3iYlCcU9OJvs3x4Flb7slJipvRywPTyHzRKE2R1PnJBcHxG2QPFBkH47sGitJoNlWf4BRKxsliy+RuewOkuCysTKTjSgRoy89UfJuk2HaTcaKXhyENsAIoyAAusgCkIdrcdwsIOsXuGcKV3j4+3IZBCXfEMhWTpFR9nnQRU6m0ImTkJRF/YmpvWpGi+3apu5cy6PdU05h20BWoCyOBvfEOo0sJs+i4jCy/or3K6ZRS65Q87RtcS7t07EhMO6RQnWKSD3y/JH6DA8CyuU81WUt/hmLQREiv8oMV5RD5IkCzYiFsnVBUd/qetxkl8h9W5+vkSim988Z9fHo66H2AEE4QhAOEYb6COhwSFMOwjhwNnld83rFn5WLHPm+BHkeJ/PoOrJOQtlPY6RQO6c6t4esfg3FPoGPkHMZIusd6zO5XsbrXYhxsop/CxOJko78Ly462MYZSWRJPrpOBqUpLoQkXWj1EoomCGTBZlujfrpaX4MIbvqUXG/oxf1++jlZ+Zr4qkaYnAOg3doDwr10YGavBex2YmKF4p/sMs+U1SQ+SuaJspvGRNnCBkYIsJoco7IYoJnGUNnCAH0042UoWoPYpmgP4hV82iLorElG8zR5zEXeA5mdIfMPqX1JPWIofvMPYorLZtIRqI3xtsbTRMBmk9yntE32m45tsm1yzqHuKTTbfXQPx7FNyGuaVopsYYAjOJEM/u2mpAlfRTIjUwYpocK9T4h0AWJdFGBTzRPSj0g+Y7/BxTIVe5AQIgn5F7GipYVPgMzzahQGCEaMIC9Ame4Qh5RRSs2tJmN5spV7eP6dYDURnP04PkoP+cCytOUjOm0kijWvPFpJmtK1fIC7QaYpnVWNVTm/nu9z41rLx+/LlS0+J5WUKlKkzgKwMr50cRdZsMvz9nD2HY/jKI7jXNyIDWzhKG7CEZzAOTieBpBdOYFsRFxOJyDhTI+WfIYojEedh6w7GLLxa2A8VkqKcBiTP2DXtZEiMl0qV1COlYZptJATLUUwMdHyGto15Qi4JHiWAeQyQdC8RbTfHDbShNJsnRPGFT3e4XQxHvfkWPa7fYym75xIiRKarnMkTbs5XYGHvFMoUHPjFmSBY0+xfb4SDxEqHC22TyRLwPJzRYJ5qgTD0+m5XJ7zEa5BSoIMEE/3QdqOaQWfbNW28ek8mpdLH000gyaC9SQK/zCIX6K9km+nVP5DtjV5p8CQljMmbFZZFWgymlDUn9gIElMdd6378npF5CQpYvSbKWXS6FmCZUfRRp5E4dCUNm7M8jTetqjo+EhmatSaBnwobkWI8Tn0EHmlvJLxz1jMGpvh6uoJAGVbBpM2Lc2Alt/GpAPIvyISJS2PPSAcodEepMRJQH9BflQ09Z4dBRgNA4xoqsQwiMc0e43xEXH5/LyuSN2XvyMZcZQuDeG2ck1+n1xAY6pDdNCka0po9S7EmKwA8mUsy7uL8W8hyS76W4KYKLGBiZwmzGNAkPHJ0/iqeDzgKsBX6cnIk7VTp2Njk3up8sEWOsbrCEReCSp73q5IRUv+GutAo7+HvdYewk68kESIEfoJKULxUQCkRMkO1sY8WPJx2YY5e2Fs+nArjL1Y08dvAVjJk9kEKd+1fZu8l+1WHiMyJQ06yzOuIfbslPYd/61B66M18oS2isehbH+cROmy33w/TUvinnS30Az6ODOZskMDfmvYQX6pYh4TZYgNbCfbrZxnChEscf3OyL9mb2+cMKHylYSgxEg5xuVGUpQr7Fiq6QcAwtPJirKnsbdtuIeCUt1htlylXXOiBhMr1k3weUdxYTcxCAYYdJvYCUc43VvPPurJJGMH2TLHvCNIRxBlD19EUQHFSqiJUNF+T4Iq19I+FXVgfJ/mP5+V/J0RGyMdxITJZrK9EDlPlGMX3IBzcSPOxbfSFXjOxw04ghNx+qlvo30LgBuRJ084cUJBLGkbAmgB/RbSuY/Zajyx98kwrSN5t6rRMEhHdRBG8TtI5cGmWAyV35JcMRlZKsEi6xkJwar1wjSybSPTCEUa6TJ4okiCg6e7tNsiwkS7llACSbHrsH3O0G8iH/eknWy7PRzePIkjrZgoORc3pnFP8p4ocRonWDb6W1g/kaxyRW1tGxlp6bKcMQcpSJTPgTcg5S7vSRj7mIyQD/ZFW77sMZ1F14rTw1wbzy8oxzxYOgHCcISd7RFOD9fHjVMg75XC2zW1+S7yRggnnFK5wevCLruI1iZVgWDIJ1GWmOf5bCSKjUCxETQaAU+/V/I/pUHEDXBqFwDwH+pLLA7WUdxPEHFHytU2+82rR44sSaB5EqCBeJCD6tsOy0TfRo5SysC/WhB5sQKPRhRImaaRCNyY1cgJ7fdQOS7TeDMqo33K84qao4k8AcaJozaAzri3SSvZbwbZSD+g67/pYFCQxIxoxS83HAYYADgdjuKRcqzoxpiUUfLdNdJF7ss0btjl6iUfMecVnE8vI+8UDZK8U/pNacRJb5OuckySJvJ30/A4C4QOtrGKQznyg0/LoZF6GduET+GhQJ08tknqdcIJEtLTuXFJxygdyIxMzQiV4EZpm/0OkdprjQBonNpD1N7DWquHQbuBnWAtHYQhDxPySKCBmSxuWhPZwE0zebR4mj+fSkw6xyBoAh1kegPWYntBk9FSVlM9GyIv27l9oOkfJrPT2MZ2xUW49xe/AC9ogvQ8VAb7CG3lT/bZpMOG0FfgSdIObZ7CWmcHq+u76SIHcdyTfm7aOY+FQgFm5Qo8+Wk9mSdKgCHWTvUQDBHXXz6rAcjrrbKsNd1X2oWyKDkRG4pjYeaV3dhSrm1AKRIlxAiHLK0sU4i558ka+Bw4cj3ro4X+ehNBOMJtwwAYtrMK30ZMpnSQKS9I0nLLBpKRSa/CXbKLXldTNOVInencOsEVJmlAQ+zLZ+FTDqgzOyvZP4J4Cg+yYJcXIyNR7op4pZC73oLu4ZO4GF/G+YiJlItxPTrYwqX4Ero4iTvd8h2s3AjgBDLDjpMnQJ6VZt4ovXVgZ72dBJaKefjdJMAUn85DZFvqoZISKMnFwkYmtKk45CgLH6nWBB7fmpRBTWjmljrm0lPzbtAgzwHMU4JMK/TwfFz4aliCIZux1T80w1K2U7lCAIdGvijtXBoVXYwH4TrGtptgwWMjHD7vJmy0YqKEvEz4lB0iUXjaGnZw5NQtMUl5CnE76ydb6fFlCuLMlRtpuLRF3iHyRCf9FuCTckh5o+k8WWyT5ljUdKJH6BoA0j5ghDBx6o1HIHawliNgECDtF9Jgs8PGuCFnIlG4oUTKEz+eMyZkPdHanCaLJbmh9YlaX6PVSRN4/TbVW5le5hkUh205csUvw9sDV8jcpw7PL6h9U93gI/xA3m2bH+uxraZo90Q+Xo16iOv1mDwjbxRyAzcR7UB+CgXJSzHtqoP8N+NbjUTRjFiC1tUVdX/cGKHf8jwXdU07z/Yc8l34+7SBeGCmj0PhCM12H2udXQRBNl1CegkAWVyo+HG4l12YbHmEqBDB+hCDES3N2sIoTAy84Ur+nfjzS5nG8/E6pslDiON8S/WX19fU0Gsg1m+kMDUVstJ/qsQUsrqlGXSawad9M7r1EpAoXdyGDlbAvTul14kMrplbApZN1UlH60kfkIOb3CAlff2U+A3YSTler7iOzPV92l9Prp/sr4RAex1ot/aw0b4NW4d3MAyCdOrOFjawlkzxl2TKVtLmVkHeLLGLLJEv3M4cIUAQjFK9YScJVj8WdHaIcVnNSUb+fiTr5Xlg6YDe1kJ+XJIpQH6qnCRRTDqCwT6V7aTDfsu2xvuCzSQf6a8d5MgTmrpDMUyIPDmTESYxWbKTepnEnijjgWUD5qWSxj7pD8Y9pmhwkOqmrJeyvg4xXl/pPK4PS0g7MECeUAxRKs6biwUoTjAPfWpBCXlwmLgzypTuFuI5+I32AHvtdl6B4UKVKy5DIKuUvGXz+Z389WSPo7+VezpXWKt6Itg8EPg7cIVe5qVGRfs8iFwSD4U6sC7bdpGbzrN5OJtiwAPJ8uVWV25DfkoBHwkHxm185j41Cg+x8eYwFXxyOo9EEI4yTxS6h/ycsjFoadox+czy+VVwIkVeTEvjxpY8Lo01gqk+aVHyJRnDsQyeKLbpPDajVCp6EGkhVAOSK4B8nzoYTqJQWhesbcVuj91W1m6IracOiNpUF7eyZYxPojXqoy3bWC/5zRWjPsaDONOzcwKBF6GcpiDFt0jnrrTZZTKvEh5ALj85j6++E+ck+oWm+MS3GeVkAJ3dSrpYItlHrRDNdj8exeXLH3OShP/mZCkdozSTDACQd70tIie1FXlM0/JcvEbKnidJHxOJq93HEpWRyqeT/OZkNP2WBhHV/0UPKgsArT2gHcUKLzc6gKyOcaUaGG9rnGDhx/h1euLYNu3I78X7f9k5SVnGCRRL8GvbHycYTAasvD0K0giSRNEgj5nuV4aQke8SAjK2Savdjz1NWpmyrwXx1Kaskz0fy7JBMmVhmBAqzeQRRkDQxygM0Gr30e+1cLrdB4ZBQqAh386kjLIdk3oxpfdEPikL+bGczsOn72lo6Pv8Plw+0L7cmsg7SqNr8WvSfZZg6mDcyx0a80AxB5YdpquZ8OkOVq8TriPwPNoxYDyOBN8S+HcQ03jGVDWlnq0AWA32MGiPMGpRLLXdlIAcJN4nNC24mXgxxIOsPMBsthwy6Q18FcFRK0y92HvDIN/WpPGtyWapU7RFXuoDePvj1+YyPqenEZlCNoTUHUwFTnmVPpzLbROJKbcyrcu2HQDtPax1dtBsD1IChZMiHUaY0MIHFPdECywrg8iujXbQ7O2h1QdWqC5y7yg5QG8iTDSvKX5eH+MkSsiOhchWqpR5qJ04oohdyCFIokpnvyVhYmLqg3QSR+ay1c+U5iPAiWGA0+21ONBsiNjIp0LaTp70ZPK7R4rDEDF5MkR+5IazfJohJl9fc/WX+V20B27YloHJA8H0PJo7JcU/uSOAlXy8hguRLWN8LP7duft30F0/ie/Gf6KLk7gUX0qn83wXvoQNbOGOx2+JDbkbAHwd2eg4r6xSoAphOwrjrmAnXcK4hX4ylSe/HGpGtABAs5WMTlOANvJEAcYFIIErulwJlEKTilk+b46o01AmPDwJTK3eSM1I+83rEvfIkHPl5fWHWI7hYe6iTuAEiolU4Yal4XtpCjYpdFLh62KMqU/jnpwHYHMP7e4Wjh4+niz9fTwlJWmqTjydJwss28VJnDu6Ec3eHtrk2XUCGYlyO+I6TMGbuaJEHQ9/F77ijjR++ojbKl2DB3we5c+L56KOMAoyw4HkeRzcLUxjocRpfQQYookBdlPShJa9p7nPg9TLbJDI/2yqZwthch7Alj+mYHmHgVFnFycBnB4GQNiOn5eMT664QKTJviPE+OhROgrFR4t2WQbu1s49BPjNTC7vBN7HyDSZbsqneZ7wazjIJc0IkwazNHAImtJFJAov+wVF2NnF6dV4Os3pVPEO88GNqf/YRqbw0XZbHAtZuuxX6FiPpW1zb1KpzwD6MtRAVvfWssP07brIk70h9CkU3IjlW9k35gpMK0VDuss1bAaYlhaKoH+0Mk7itUreJQDSOCbNlr6yieYJwA3Y+LZ5PTe/kmC8n8k4TqbEecLWCP1WK16adXs11mmAuI6FzCuFyyhOAFPdAsanGlBeLvt7lmOy7tK9c3IQGJdLvLzZ9UzGm62+aaQsvxY9OydR6PeCo4NtdNPVefJLG1O9ozpJcU/U6Q5yWk4P+ZX7gGzgheejY/Sb6xK875RTfQj8+5ABup6krbP9FuLvtZ7srwONw0AjPI21w9vot4DV9dgqiIPO7qRTfXawmrZVCkRPMVQosCxNCSL9gfI20UdrPR602W0PMBoG2GmvJR6tYRx4logSKhfeHqSM5+2Hb3vQ+wJ5Ld6ee0BGpgDWNqZBk9EaiWIiSjoY12U3AXQiHOrsoLt50hj3pMvSsik7GYkST/HJryopp+y0+ogXJaGB+G1kdZe2PFYP1UNpj2mxUTR7ECjuk0z12xSSzHK5UjDFROHTeajjGaVMfrxMFTUUJI1hQI2i3Y+ZQ+6Rwv9kxc8RCbzkSPHlZIRWWW3KbRF5IvO4HDMte0vPrhEppuvyudAh8iNRK/nG0tX/OutbuWWMzxReKGujnWxUfBvZ6Lis5Nxg46+XVM54CTRJlOTHsG3eKECsFJ0OI6RLaxYZBFz5kPkgjvN8tUMzbohYMX1jDu3BeAGYsAzTebiXVVGaNCoKRt2loWAzJDviTxAr7e5WunQxsfbyr8OWM07/btuLOxTyNrkNGXlCU3aozVFnIjt17d2AfPtssf1AHJf7BZBGBQ8cm8XBih+CIupTtBMaZQLAXHeHqefKbuKjwlcDaqGPftCKPVJ6LZxuR7FhS9+IGxo8zUSkUjqQb0ZpGZBXitaD8j6FfmvCQ36Yov7F5p2ikSf0uyJxQltpqEiDmhss3Njh2w6WQtQEwRBhYnRnpP0IQCs2LHm9IR0EyIzPNtsn/aSt/KYt2PmcTMlNrwDcPFGTY5pSLY1Tl2NSHnK4kin8uOkcK0lCW7GEMNMHOCiGCYCx4K9yWoS+PGy22gmNmAJmPTdbOCEjgIFYjDeRxUfhcjLAEAiQruqzJ6cpc3nFSRDeTofiN0Sa7ZiUffyaEkUiTdYjkg8mUkQ71hHHXEiUqelps0Mz8cHgdRLI+k+a8hBglMY7afWhT3sA8qSKJFj49AjuQU6GKV0DGI89IQdJuS5N+j3/HrK/oGvKGBTt2JBuD4FROEhjB/F4KLGnyWDMAyWb9hvmBl747AayMwLEcYlGYZDFJRqOgGFbN7ilTOe6Bb2fPMbToKRx/ULKfXl/E3jbdiFRtDbGf/O/EHEsqHYfG90trAXjSxWvJUEY5FLFRJRQHh4UmS9jTNPP2pzg07yoZP3m30J6SmnlJ8+hcutjXH5xLxXZR1H+aZEoo3R9niBt9AQedJbPV1vDDvh8eBIQI9YImhgAh4Gdzi5u6Z0bs4W8EhPrR9sQwM0NxPE/6G1vx7gHiPytKahcIZH5XMgUDjnVx3RvjmFBHt5C6fobyZbmQNMUHmTxT44hW3nnrkiXMW6cdzvOPXJjsmTxrbgUX8KZqSfKt3AUN+HYDbfFlf1GxAbdjcgvrQpkwpW2CduMFuIlI9eB6AiwhY1kaeNm6pGyhQ3sJLFySCDSUqnxm8bM/DAIsNYJ0O/FIjKdT8wbkgRXIGQxcgOUNx7+myvA24Z7TAQyfrS6JVkfzVqWSraGElGR5hYbAM4UadzzimCYmqMpliYjgSt9XeSNxU1kpMlmsj0vZu6PHD2BzWRVnTvgxnQZ4w62sIkTOAfx0sbn44aUzU8Dx96IuI2Rt8lxZB0MdTgnMB5Vn0aS+Pu1xDtr4FWKiBU5rxkx6cmRxTQZMaMg85nkq/Dw2ABZANkR+Mgseau00tnNg7RPWcUOdrGGJvNQaWKA4PAQg04LW+EIe+Fq7IlIbZVveful7TaEoYq8McGJ1B4Q16c1ZDEC5BKFHDtsX7ZVWxuV3ivaeTzdYSoODFsp67R0agukWHWRN2CoHfD20cXSTOfpHD6FQ2ccylZWafcxHAYYJSOZqXdKrzE+0tgW+zytzdJOIutTSI9pi7QekC6H6zJKafp2/Bt2lGNS0QbbanVDu6/pebTny/1OiBFBitCACV8+OGBbvhqOnGLDyV0tngn/rcWhkMfoWhL5UNhBore0cvfmcRoApMfjtBFG7QBBMk150GvFPqNhiJxHCn0fXs+47iKJOpmH6hQ33nqGfPwPYp++Wfrtkn2NfLMRJV3lWJedL/vmdqI7h6Nc3Yhaty88j9LBNjaU+san7IwFiiUChL4dn5bDp0TQMdJb+Xl9kZ/r8tJYNRUy1T+q0vS9SO9vY9wDpcd+9/Np68PTaK5vY9A+hKAVe5mEqW6QDzpL0+VoEJ48VgGa6ZAFq6V22EQfoyBE83Afg04Lg14TfZLp24ktobUZ3sZ6lmO8jcl21VHStLZmg2x7ss1R2wEy+d3FeBvbRNYO6fhmTJ4c3jyJ1VY2+MeDx8aeKNngXxN9bOIEKMYJTfHp4iQCDBMvlSRoLHlPcZ22hzzBtw3dEwXIdDRtWo8WbBYsv2Y2EbRVprRjnGAsQCkShZa0BChQYNaxxVt9qk9Gu2Qu39RMqMIP0EQQjLDV3YoV5W4jq4Rd5IUxVcyTNHLDaSM+DUbz/pDK7ao4zo/Z0rQWUKo42XVNK0IQqFYQ4XMG8nOhG+NG33nIpvEk287F38E568dxFDfhKG5CFydxFMfj6QX4Fs7FjThy223ATYgrPW1pqgGHFuW4xf7WgZ31Q4xAyZQP+vbcQ2UI3XiL5y7HwrI3DIBesvQpb1SSBSclViqCmoLIDSlZ7Jrd5AJbNVCvR0aSfAiZmT+sVkcJ7muczy/aGDc2OYGkwPR96XKa4cg7ow7GO55jGCNROsduxur6broEeLwceDx15w7J8uBdnMRmeixeiefMW3rxPNCbkAVoJhLlFuSDxpLCIz2/qJORozwQ+Wx1kAV+5mU0KhBf2ggtudHmR10zmp0eeMSOczdd2l/FbjrilE0JaqVpYTACujE9uBcmI0qy7QJ5Yix7yKxceso+KUGUPgTybrd0EZMw0LwduYeU5m1i80ApmIbG0WbH5NZkDGvHOHHYxfho1zFxrAtgcw/YLaFtzCnWsIsITSCIScRB2EITwKAX173hMIgN3nAEhIl3Cq97fJ+qiOxfwPL0lGPUZ3FDFxhX5vj3bSt/XWS6Epdr2pYbv1o94m1I3l/DWB1jhAmQGsVA5j0ShiM0k/5dEiXxfn5qDQ3C8Xx8QM82TUIe4yugSPJF6iNARohQPAZaLSTvjZIt8z5IvK9JDg7QSt8xDEcAxXzqAWiH2QARME6aAPljlM7zcAPPRIrw/Z7IoxklWn3gxAevP/Rbq3emY6mciePVyCWlAeaRfGgbt2KxsYZdtBCN1cW10U4+YCw3OIn44AbnNvLBZE1eJ3yUH+xadA7/5to0CYI2fZ97qbSRkSTcEOb6zBDZgE9yjwaAcHQao3AHQZCPuUYzGvoJOcJX56FjNC04frTswePrrKVkJgIgWI8JudEwwA5oinArI1P4u0vZrh3j+1QGst3ydmpqY/y3lK+yjwZ07y3e/ng/zvuCbvLXiaegt9oDdFsnUw/qjiBRzsRJrDIShabxUMDY8ek8W2j1BxkByMkTrsvy+gqMe6cAOokyZGlaOQL5QR2+6hT/Lfspvp1FYFmaf2Z2dRz3TgHyo5Q0b5QUae7dstHdwk44RK97VkagEMvGt2lHEmJ8JQ8+pYdeUxpgsiRNrtMyr3S1hXJMS7PFSaGWJu/J70GkSQPZMsZJ/g4yJfc8ZCTKMWSr8XT3cM768STuyY04Hzegi5Pp9ihuwtFbbsPKcWSj5McRV6bbERt4XHByNpqnsc6x34oj4cQr8sTxUCguii2wLFeKmkHMJo+GARrtQRzhnpY+1ewaMoikAkj1hY5LI4IarRSOYL81aC3IpVVZyRR+IUnyDZU0eX4V5mfesIF0epoGKfyAvJInjUl1xAt55Y5IFG4sUhvqZFHLz1nP4p4QUUJeJ3wZ403cjA62cM5tt8SMPF99h9oWTec5gfxKPFwZkh0IME782T55gLzy04LeoVgvkQWc5TKevAyJPMnSw+RY3ksFQCr3+4w0ya6TebzQVE+6f+qZFg5jQjVsjCs09NdDpmxss+PcgO0hb7hCnA8gk7MRywDkYxVpAWl5f6IRLI7gBIjc52lanrYlj2wrJhKF2s2x5HfSFtCJl/E+tHNy4Q2bBvpAakwHCINksGc9qYf9JsIk0PkgHOWVbxOJQvVKU8JNBMs2S2uz63Dw78iVaRNRwpVpzZjVyGX5jBpkXBIgI0sAlTABkA6I8Ok2eSIjT4xw8gTI4ibFjzjueSLjSvAAsc30Gn1IT4CseDPdg+QdSaVWGkB2lAwCjU9rJASIpyTQ4vCZHIyXQEYbmUcKkqkGNH2Myl8jRHi9MZEoUm+R50kZwvsXjUTh8kTrO011i9K67JxcWrZCkgupdrq/u/Cypo0drCIaq3+5gLGSPJHTHbhuwEkROWWHkxiaAct1C6lnmIiUAPl+hab1EIFCZAodp/NkHWb6+Uo/DjrbDPcwWs9ippFNGMdQzOzG+JJBVnZpHJVMdxiy9gbEdWiAFoLWMLf8+Kg9iKdvtoM4NpX2nFyOh8jrELyN9mD3DtNIFN4naND01SISU5Iom/Q79qBe6+ygs54RJnwaehMDHMHNueCxcjlj8kShqTvkibJxWy+rv0TgUf2UA4QmEkUe00iUoUiHkm6DjUyhYwOH6yiXKcQeWqknyviF8p0eVWbqxLgnAu+4yF0rwAir2EU/aGLt8Bq+sbkBDJNKTZXgZHISkSjbALaTaSypd0YDsdXPYRoV5ApuWJBPf+txyHnM3ONFjljaiJU18XsVsWHJFDdu9B1Lfl+MuLyOIQl8CZx1929iI9jCd+FLOAc34XzcgIvxZXRxEt+VLGN87IbbYvLkJgDXIR/gklYOCRG75XEjFcjc+DrJ8cPA3hlIlzPewRpuRRcDtJKpPKvJMsetVNjJ+iNX9MA60GwPMOg1Uyf61CWPQAKOK7LbyJRSEnBDlib3ucJQxEVIJVOrDjYyRnZYY/eTo+FQfivPE2EJ3OxXkCNQpCIHZcs797ZI452Nxt5Tp0OyhrbHgMbm7Vjr7KSsPXmbHMVx5okSe5ucjxvS+EJHTt0Sr7pzAzJvE0mibCMjUzSlSXa6hDbygWH5Mm28zOQfKTo2A0kgQD5obDbSykObZ3O7gfxyn5wk4Xn4dB4AWEM83Y8MFboOES4hRghbIzRb8X1HwxB74QbSOCnU/oH8VB86Jg1c+iMZwRUfbmAASIOM5kh0zeNLs0IKpuPI37Z6bar7UtnSztPyUN2X7aAt/o4BaEdob96Ktc4u1oJ4etqov/ijwxvYBpnwQBYUOV05otXEsBWr6IPEHXw43Mnip/TYaCbvS6huUR3k222Wj7bbyBN+Wr8gv6E0WDvK7y7bN8nA9G98ug1Bi0WS/53ofjnyZDw2iWl6DfcYaTEDSSNIAE6scFJExjvJCBTNK0Vi3AgLU6/ZHaxhDUHqIUfTDWnUmz9jH800L5DVKbpmGIwwDAIE4ShH0AHIlmbVjDFulJmOcWOO5+WykW9NfUxcIGb5YSJRtLqY1tM9IByh3dnJxa9pBv2czQDkY3DFfcg2bjQ85qJgA6dw5miAYJi0keHp8dV2JImiTdnh5Akf9ZdECTdopRHL8/JjAhFLW6HvzwdlTiGzASiw7JBtSecgWUdkDD1XC2gMgUY7XgJ60G4gDLJ2BGRxUeLAstmUHfIEayUhAkh3GCFI47LRVB/ebin47AghBv0mRsMA/U4rnupzcj3fZkg2A5n85sdsbVO2QbAylnJdA29zkjCRcp63Od6Pd/dwqD3ARncLG62tZArOrTkSpcOm8xzBiZzXCSdYyCOFB48985ZeHLPnNox7SW0jX1+5TssJE+4FxQlAYJzsA/sty9Fm99D5nAg0reBTYm2YUiQKkHQAyLtdmcA7sXxakFZyIFamm4nbI60C0ejsYq/TyIQyNUBeSdoAtkNkcRLoz+QqbZoCoREoMk+J0UNjXjmViAgXl3nzbFSeGkwXerTl3F8P3eBkOsXgTBZEltI2+ltZEFn648J3hMxg05T21vjfKIwFHgmu/DLH+VV5gMywiutWVva5KWNBTKQMhwFGwzBeLpBP7+kZns9kcPA0iN9Dw74G07W0PC4oIm9M96X3WgoSBXZD0fQtTYYk73ikAthFJluEIXmoeyoNukWRyaWbI7Uj7ha5MdqKCRRqT9vIvE5OIR88to+88iRHjSS44CclxYRA7MuyCkR6BRT1A1lwcRoZimOpkKwHwOY00/SfMOkTwlxAudUkYNmoE2DQa2LUCXA6bMaEOydS5Pt0kBkXErxDlnnoGJcFKTRyxDGWBd/ydJusaluOudR9LY0rYl3k2wNvJ51EETu8nQaT28AWhmXWApxThBghwjAd7MmCG1NcHqRUYNy3xXEtRokhFK+0EgBo5OsRkDcmtDYm8/N0zbg1kSiSTOG/NeV7jERJplMAkF4kGgLmdaLFMAEyTwK54o0WmwQY9xDRPEvktJz4vHHixLbqSX76T/wemR6S/SbTPf6d+cmlZZDTX8gbL97mI6fQ1INRLi8AjILY84K8UlK9hmLw8DbOjQZpQGgGhaxDoTgeYryO8by05bLDhUQxEXvtPba89GDMG0mbphX/pjq4+KsOHsIQwXCUkidp89L6fklw9MRxKPmHyrVGStpwPE/UA4aiue8J+dMYZWJipY1xPZPqFBErPZZOeVvsN5+e3ANafQDYQ3N9wGxCIlRNukE8HCNXzaL2KleKDRIbI0CAPoBmItOHSRs83d5DPMOBxSmirWwf8t258Q/2/lwn0dqqdi3achIFGPdAtLW/doRGZxdrnR2stvjSw7R6Dg1p76KVBpXtp7FKeSDZEGypYjr/VC+eeibrmKyvml6r1MFcGi9z6c0Elm5Kk5C6HDD+HencaZEo1C30k5UVqCOQjL4WAV16sHCGsIUB4rlv2wCAHazFyx53juC2zhEAjdgLhUZwuGJyMwUCPII4Ngp5oQyT30RSmIiUVZa+KvKULiILTMr1hsjDGi9HB4p7FliwS+Q8UQ6ddwrnHr0RXcTLGG9gC5fiS9jECZyLG3ERvowNbOHYdbflg8fegmyUnAehAvKkCS1ndhbiol/PtqcOH8JWayOla3awii10MMiFjMq8UKj+cKHJp/mQOBwk89aDwyOMRgF2tldj9+qwGZcd1QkpyIBxFlgKPN7ApBGmKbNAXtDJtCJo15TKTNF1NeUmRCwIZAybRcM68s1WEiiaASkNRM1wkAZHjrFHSky2j92CVnuAc1o34UycTIPFrmIH5+JbWMNOLiZKvIzxDs499e2YPKF2tI3ME+V2ZATKCeTbGM0dla6MvB5wsoPqyrpyXJIjNEpEZdESx0MgCoFReChHcNoQJr1mNm0n79pOBkN+tFYaKAFThuLtDlYxQAtrSWfOYwzsJkGpRwiwFuxguB5ga30Dg1ELO91V9E5uxIYHESbbyAfyJIKkp6RxuSFHlIBxWQGMyxS9oOxkCQz7vD6b6rtrO5DtgR8jI5u3g00A7djVvtEeoNXuo7seL3FIU9TIjXeAXfyr5fUXAW3sIkyC5gPIbXkwUSDxKAiAQdACWkB/vYnBKB7BjD1UAvS212Jyv8e8U7rJlupcD/lgs/yYNJKl4UvfWyrOnAjjx1QSZQ88cKdc3UYDTyeCBMh7DMS/i71NOIGS9zoZpUp8Xn/sjxEj2vLEJs8VTtZwfZVGsvl0c9rSNIIdrOZk1C5WU92Vn6MZ/k30sSuIGz6C3sQAg6CZeqUAcSweGihKAxtLbydNNvFj2+KYlG9AsTGnyRWtT9XIlNTAi6cQNNt9tNoDrLZ2cvaB9FjX6hHtLwNhu3FbH+t8xTxJbvDtNsZH8SmNviFN4dkW+3Jaj+LlGg1j0mS3F5Mlw1FmOxYFKmgACAOgEQKrp4AGDaAeRqaDkrfJenL/w0keOkb1ksqjFVs+7TYQDLeBw1kMJNIf4sCy/VQ34MFmKRCt1Cko7wBNrCatkY6l8Y2SALQjhNilwOLkaThMpvoAefndhbmtyT9Nf7BB69c1Oa6RKJ3M8yQIR+gGJ1PvET4thzxSKK4JBYo9gpsRYpTmoYFCCh4bjkbx6pI83skQsX4rdVmTdxQnRugYlQ2vp0VEiSxTgzdVLo8sWw65GpUDKpEo2e9MyJmguU1mHVe2Qg+tiU4d3Rp2MGg1sdNZiz1ShsgzcdsQbtpco5RECSdQJJnC0zVPlTIeKDZoRS3JGmX6Au/ApNLUZX9EpmwC2NxDdzOZUoATOIITqRcK/71xajtbWpUMPBmXQSpxfFUeHguF/fVbWdDYzAuFs8N5Zhi5y2cR7mXdIgONVu4ZhM04Rko4AtpJ+fHyIgOUfnNWXBogvNwpr0vr0BpjqVY1wXXpt0aiLDpMhqJmWEolT+5ryh21H25odAB0kyXfDm+nLo0d5m1CAbT48uB8GeM2tSeaAse9u+RvHniL2hyQsvDkRpujVEneSe8Trc7Ru2seKEr+URiMyXgNvI3K37b+gNxr43yx8hOnUU+Svy4ZRwBSObKKHQzQSrcj7MZBZzs0vWeENOisLJsQ4+2aylN2xjaSUyM7eQfN07Xv0lbS5TNpdV9L1/JQG6H+0kQq8rqfaw97aHR20Wr3EYSjNHp/E4O0LWwgXtZ7NJ0lzGaK2KMhVsjjEcp8fSaPFO6lkgsoGiCO2xXGXiujYZj0S02kcXsAXYnTyH1TfZTnaiSKZszyY0DOI4DiUGgr4WjQdDkqJ82TQHqGmGKVcHKElHkTYUJpNDrKCRfSH7XpQNxQl8ivopNNwxmmb5GXUeShlAW1zBNHlJYdyzxVJMjTCUEyyp54poyGo8yYA5DzdtJkE9UdjdztseNcRhURKLSfqz/Q5YhMT4i6tc4Omu0BmsE4EUZlRL9lucXb+CGjJVBsGgNkU2+5kch1bc1rRI7syyk4mier5dhePyFOhsBuPyZNiDgZYpxEkdVkD7FXyi77JA0g1mfIFgiT+1FdaiHTXegYeRzwbQiEI6A16sfe58xzFSA7NL4oJzI5AWrSKeJ3ydo0kHkZpmnrSKb4JKTnMIh9oIYhcjGLJDHCZTcnOMGOaYWpQevXTTKdt79OLx34IMKSvEk2sJ3zLInjnsR6LgWKbaVD3X32O5tTQIFjV7jeyj1MhsjXU0rj35f25WAhJ0d6Ih1KXp5G5dpDHlxkDFma7E/57xFK2VClzL0hG62Jf+eDBpJ7I5nLfBqPnPNKaeQ+tIad2AMFsSfKEEHsOnRkhP/pNYFue9wTBQC+naRtryFuxkORQcLmiRIa9ieBfI6C+fHa6CE1ks0k/RjbHkuOXQigC5z9XV/HJm7GOYiDyFLcE/JEOYrjODq6CWd8fS826r6KLIjsCWQGHq+o9Ax8+bLDyd9ZyJY0PhyvyEPLGm9hA320sIvV1PuEj/iMRNmQyyspQgAJvNj1jhRXiqMzSBTBfjjMPFJ6jbw7vlQY6J22Dcc0Q8umbPB7mH5r6TYjTebXjDFuLMm6EgH4rOV5FwGHEWuXZUiUDtu3KXmUb5NtOxEa3S0cORIH1TqKm9BMtkSUHMEJJSbKzdjANs4+vp1fFvx48tdD5tlF3idEsLA5zlEvVmY0NEZAGCSSgxQQsO3/n7u/bXJbSbKEwZMCCIBvSkqpkm6p6vZTPdO9z+zOPGb7bf//P1jbXZu1Z6fHpqar5taVSqnLFJkkQQLM/RBxIk44AszM6u6ZmwozGsAACAKBeHE/ftzdBo3VdIN15hiPN+63J+9+Z9MbW+us+6u8K6elxSv1Vmd+II5pgioaN0X9l2NdGwCUmfd7nvk1YuvnmGmxw+xmhxY1NouFy6ayng8ZKRzfrOuQZ6JY664qJp35HTA+P+TGbpM5x577FLDEzgFAvu/nLMc6LlbuOwMn02WHgtMMO9zgFkx7uBQmymHA5355pcEOEwz7O/smzDHty5pBqitcv2Y/PLYV2kPllODt1AnhZKeoLMOt1o2BeTkQpcFwbtM6r8wy64l1pXCXta4Tw6LHLrMGlIGSMkoiKBLBEFLEqyCwD48pYGJBFAr7BFuAaMEu0KHs+8SFgoXZyPrylaPzF5F1xLsgU5pzno3XxHNTw1AZZGHtQyoPa4Yfy8rriwJ9UYQ4PH1fhExR7aFO026zTwGp4maNYDmj2JhcY9eXsTU1UeacO1h012kdGFTEd5hjGMW+M8zIZPvaKUkp/0LLPVK0Qg0nCpZYBkqHVDZv5XpUWp8QQ4Xgyf7gAJATYm5TbnMgCksuXPqpBWYtMK2BZefjpvSIoMkBrp+wDkiNQJRRWHcArjpgiROq+uRADT/GKlRhxHDenXrdQD0bNDYKx+0Ue+wxhYuhcgTB0nhtp3fsMMWxrtHXBbp5gWNfB3bYoZml4y43tsY+wPiYYxkbe7lx12AQY2g63wtosgEZJQX6EChWM+q8MWwTrvEaWLbGEct247LukG2i8qtloqgRnuwoCwYqm8rG/lMWla3PtaGOBwVi9PhYOxeZOvbfJ5ZngSj0L4udMIIn7nhE/WwGHwVQZn4ypOWHA8DRsZjOyv1HhwKbH5bYrJc4/f61o8Cq8LHyT7EFYuYaDewKDKUQnqtTwQTDKeKx5rlCGlzwAkAyVvTFqRCkCiG/K3iyQMzAswImf/iG5WqDv8d/w4132fktfsIbrPEH/BFLbPD3+CM+fv2Kq1ukyh7dD+4QBTm9Px3EnPAIoiwQXHt281fY1bMAoNB1p0OkxqaCad7qTYHK7bupUMEU99syZO5hOQI4k7WkCDD89wVS9LhEfqIrkVeaxsq/Noii5+UUKNYv/L4FUV6+67DrUwoAWBClydQtMK5ArjB04/Egyqt39y5w7HwdgBJu3wuIYt153uMTPtz/1bFPmLL4n+EWk1sMQZQ7JCDKQ+eAk66LdFotJYeGf75Jrl9ZAGVsX88rkTDJ+jJ15cnR29O/TOd5W8d6a8Xn9SogmQvisTIIQjEOSlQuai/ytKLYMEXyDDtsqOxcd+ivS2ya1rlWNE0c60AU6hqkFlpVZg9SB+SVEJ0jkNkHhuMWeDqIcgkwfC6Ioh9dXxYIzJPVzVqovq6tKXQRVCTtl0ys/XOch3+lZYYDXqEEpM8ByI4De0zdOriu7TFzTCkviLeocVzsXPDCQx2D0W6NEL6Vfbv+5NaErDLLutQdqyh7VHVkaShzAsmlH2cW2/1cDAvLNqDMZ8GQ6Nx7FH/7XVKvdaSlK7NkiS3qvk2AkhCo07pHSpmEOfEMFGegPAVA+dhMsCvSQJUaXJtBYxUsRvibwss8lW/vY3IO+0luHsx+LwpUc8+s8YGN6ToWXA6AqNx1V2mAYuBxcE5LToEDMvNKzK5TNW3IrMMgsfqeXDuk/WGoH3RJe7Ge5TQwM7/AsoOTz9g3VbkEUuu+jSGRqxsDUYAETGG8E7rufOsdaNIhBVEU3xkr7A4atXEPYOoBmmkDzFSGpmWfLjwl0jmNySrMsasSaGqgL3eoS7JSqjBHceylbLAuHKPR1ekTlTe+Op2TAe05f9O1zl2nQ4+9uPrssZs7l766OUZgnOOOrnZPBU/+xSBKnNtni10SmDkBPrBBYUAUzqMMLDsMHpsCK7N+h+pwcjIuwwTkQBTbFy1g8hQQhW2TY2nB1MHUtWkAZJWlB3F9SoTxVop4e6Vt/gwd6lkgygkVJkGoiEJzj3R1ItACDBddUoNYIqXKbflSo6WnwqpYo7pp8fOhcgIxhY0tojV56z8ds9rsMUx1bB9dgZOpNMeF2CSD8kzg5JKCbEEUVY4XSJkoBFV+74Jf/njzp2Alf4db/NanMl5hjY/4ySl9d19dCmMqeMwUQl9LTrq5e6YFez7yuQY29dLHP1n60IOzYNHJWWzi5SOtjvEPONm5euLJkULNfnNEhaJ2N12WPba00NigbGOTkypEnMQPct4YiGIFW2S+X6rX/9Zrjt2rgga2bmG+v3zjcArYPfWTA1HseFrIvo9avnq3xrJIA8Qyzds7ur4JE+UdvmCKHd61t2gIPjLzjoKSHFtfkS4898DuHoFKC6R02sB96+MkX9phQyCkNPtsN2Wj2Hr/m4fGDZW+fBVc8NTdThXGXLGslBxoHm+3T66lKZA1eCOVFrX2Vt6Vh3dHQYoCEtOoq3WqRQ1cuzmhbY4xiw8FULbJQbY69lXQ063OBQ2Gc4MVlCyIYhUTrbfHx5QYPc8CqFSogdT9NQeiNDHlYdXEyPsqiDHwHNdlDaDswJaXD6K8EkUv7c+xbwJp33VgPgXxGBsIQOibFNSn2GFfz9DVBabzfWCoBKvmQdyVrbJr9UbtDwsM+8HiMKrUKnCSY55cYqHEc2JnzwEn1pVHmScFOsywD1sqOlS2KfQTuIvn81jmd32P6fbkUsiPWTet/zzbLwMsT+ZOyC66E7DYoSjcHEfXQ/f+i6CQMTaUm7dimnYAHvClXBPdFuPcFl3HtK/lABWez6w+qP0cKiyVJI5K6eVguh9wjtN2yAF0bB87z/AaJi2xuvzl3bQ04G/sH3TfHyadSBlN6W19B0yUA5zKYJVEVTJzwV+pfB7M7w5yLKfA+2vRWEPXnT0iiEINSUGUS0wURpncSx21qb0fg9PaK6WURQvEeChknNDVh+tuZ475387uz2jrM6p5GmCe7ry5OSy6+0SAJRp2on7BPkljPoueozrLsa5Q1bVhhx2H7DAFVSDb3L6VD+y2RDLuOLeTdaLzrF27qwCUtAkDhZl4ciDKwjNYZv3Oza0E6Cw7yoImWwxZJAoOsm7MlU0BExtY1s7jPM//hvF9gAiadF36PTS5tHfp9yclorzdAQ+5xIsj5VkgihMMKi+41qHDcpKnn6hD+mqoawYj/boXHv2KecwF/dlJ5+5CJwCcdaf8XY/dDzN8xe/inf+MmL1nC89UmcEFm6VqotMFiwVPlHnyNzBKWFT51u9WIB5TAoOCh1Q4WiEGj/2D+/7q9/d4/8EFvmSg2D/gj1jhF/yIP4V0q39o/4j53dm57nyCU/RoOb9FRBgVRCFwwi1deBhE9j0CG+X0FthcN/iE99hj5oPJuq1jpNTYYSZLZBqMEoiTXBrIL4JpahU8BtrdLChedX1EXxchDfK2XALbZqgkUVjlgkRFKGcNtIrTJQSZxY4oXic30i5dz06s7Bddps4qSS9fr3F9yyqIVlkcA1FyltkVYlutnN/o6sYphARJCJTUaPFb/IQZ9viIn3CDL5hhH7Yf258wuz87QPIT3Nj5E9z4+WdEdx3GGfJgyuk+Umm/9emMpK+VXUYdCa/0OSmQWMCEQWbniAwxnjM359VAWzsAZVMvg7DeY1yAtwCoBUpY57ZRUbMgjHWNABBcOZXmPsUOmmK0RR0Cz3LsM+gjY8w72u4b9CgcmDufYj+fYbfauHlhsXJK6xbDoLPccr9DqtTyM6aM2Dq+UC1PAVGAy33/0jjQvq9rRxgXjv67WG1QNUfMil1YmylQMV23CmIEFQkkcrzsxWr8UotTzKPUdAk8zDFT7Pk0Guhalrhv1AXausLxug79OgRKP9R5YRyQPpK6Tmhck2ntFE36tVsmAPA4WGJZZVpvfz8W/wRIs+ZE4COCI6Sd54ASVQAUPJndH1C3cBkhNK4UBXZVMoE0aGG86XQu5ZzJbeMCZU7aE071Cf11pP+7tnFBKx1oW6MAAbPIqmamMZVrqNCp+6L2pdxca7fK6AXgXJDm6fkdChzfuf/KAiw+Cwk6844lcPCrJE5OFwA5m65aAS1A3agup6nOrRPaz2wfA4Dj98JEOWK4Tqjbg7rvqPGly2zvTd0BKQPFAyeb+6gJnQBsZJ9yiIIoLFZEDWAJUlVn6rdLeCDlzimmrwE3ptTwQDBF/0THpBoBGWy2dMFmj80rVHUMIstxRJYa2WHUIckoacMsVCfzMflwKvNEgCbGRnLxv4RRXxRo535/LnN979cAcb9j6e14yxQ75gDHPgMQwHBl8UVXyDZZr7lGx3m1H6QqJouP2fa45q/atXPd0flV+6L2OxrgCZ7YmD3KOtE6YNiXgRRUydUJaAKkwAkZ3ay3fTl870W29n1t6l/NpHTAyv7fCkRRoZrfc+dcWqQVqecAiIt7gZmPnrzEBgA8VXsFwHXkoujxdXVwMVJWiADKwv9BIugSGMlpq1ZqBZ4Mnly6pGpB9m+sAN3IVgVg+1GAZQEfTPbB5f02AS5X+CWkMWYq4/ntOQa2tAFkbQCgseflfag120+Ix+YVnP+wI9y3gW8UWShWKdPyFAuYFtJgY/aOiFC3RY2qOWLSHHHSQGx8L3YdttZkvpOc0jT23rU8phzZY0/pmlZZ4n0SILDgwR4vv+QURJi6MQUyp0iybnFCs9ihNpZ3fjR9K4V5LjSh7v6MKwIlGkj2PvPZuu2DB0/UEgSkQAojOnHLEtx4xAUnUQDsfinnWjaPrw/ZeEoFKVMAhcXGLrpUNFbC48patPRoykKmHqT1VllpPYogdFsFw12zT+YcOYBi3jt3CgafBdL5WudtIBV01dKXO85PaY7lmq65cMz2Z2C4TjwGonAtXGidU7wniz2KssN0vg+uEZFxsktceNxxt52FbRS4ptjh6jtAbHNg4OO/yQWhTZkoBAD5H0D07VflukCPvihQXLs4X3T7AZAqvUiVW1K5AQTlduZnFhuQNVVcn8I4yZ9zmYmSZ6HQcKZ1GqwwBC0UxYB9kceCT76CJqSY2+DclgKuFszcvKnjUefazgnUqrA5CcY9J9+/Pn/MWNmHeUrd3S04YPtQ7pj7bTyuYEuFGFxzwFhBgWLuMhm6gMdFUOQIqoT/k+xM7rvZFun7TBkmw8DB2t/S38U6tlP63P2gj8XzXj5gm/RF/W6VR/3oMcjvc+uP/A+t810Xzck5Xv7YsafO7AyeoOIslduHzsU3QSnPQZCEMVMKpKAnt8pK6Ryw2Zdn1GWLY8GwzgwXEF2AOR4oP9gSvSRUxkndjukmnBubMbSAO+buwgPqRZGMO81o9hiIkmQ/k5hVNoOVzosaF4ogdQwe6wBrgi7qEqlzbdTUfAAGzrOWAaXvT3VHC/5pf+W7tn3YslIsyyRXlwFQLOsk1ONxQFDLxHST7t8KRDmiwpVnmGiKKdeZogieA1fiH6YTLRDBmR5lQPym2GGLZaB17jHDDHtssETxux4/LX6LQ/nWMVHW/knWAL4gMg/W8Jac13CxS1RlAR512xkTci8dUyE6B5hY4ThR8GDSTMrnHRwDZQFc/8PPWNYb/Ig/BZed/wv+T8ywxz/gn7DCGj/iT/jd569O0PgvSINeqquBRQopYADRkn0NBykzDgrTGb8Fvt1M8KW4SeKguMCy06CaKoCiVjogtXqlQl66YFrLHhUvpiwjslygQ1+UKG56HBc7R5veetp04wGVLdJAkx1SRsohU2cXLVu07pLi9BgIw99b8G0h+/ysZKt95R4vv1zDmTXGlMYcmLJCOp6SNjqgWewwW+xDyjeNf6JMlBl2MWUx/hIYKO++bl1UcrJN6Br3DW5s6RjzoOWpBT7dDflwOZxLp4xBOGsFLglmklWiH+tyx5SD1/HYyQOfuzoyuXozNnMxUS4B5DbQJBf8VNFIO7/+xywzvq3VdYdZYlVyTJRZ2PL4Ehu0qLD0QWd3mIYAtC7oZ4X9uxl225nza143rvHXuBz0084FfBwFoO32qaDqGNieWy9y42GRqVtF4ISBHmeFs1xFWm8MIsuAchwDSvslQ4tgPdMlbvAMaeNXWpzCHp2gx1gYOUU2V8aYXEAEUXQtpLXziMq51dUljvOoGFsQM8f+AChbSUBVo7jaa+TA0Uuymx3HFkSxQV1tzJPLbJM+gNoE8Sq0ePP14JQwcYUcWEMvZDjLrrcNInjCeZVzJ9f9FkAPXPXAsjg4cKt2Zj4yraMSVaH279UCvBrDTd/VJbaJltRoOfzNpcDH4VgRY6ugzl879z5Z1D2Hx6zLziWWSa6f5sATWyyg8iqhKLzQwuw1VlFUOdxa6A/yPRcnhcckJgoZKAwgqwwU677DeKDKSrFFl7NcDlMFYBgkoeyByT0w7b2S6oHJBG0BUsY7wRYguv50CP22aYF6fkIxX6MviwAW1jiG9Z/jkgZ51qu8oLKFslMAZI/lvlt5RcdkjxIo4IJEc5zZjIqm6BjgXKpXt5nNFERJ4kP5edey+GgE1HmY6/sUu8jyo3fCFrHP6Zyrc6/2U01IYoEVrbdMlBz7JMNOseyTvb+OxhNUMPAprCrA9dVvrPN9dTN8PaPlWSDK2XSUuECkVMLCnzc2UaYTslvMieTxu7oI7TANg6RAhzVWWF1P8fO7N8DKAyAL/ycUgjlQKRzjCvlgsRkApRzZz323x+xvL31UAFYQxdY1vv6d82Ff1WSdrEO6Yga9dCmMv+Dm/msan+EeMXgsP9qhxyiv6jpglLZTDewKikNRwclNMCoM2sVbwbTHrGRq5dM4Kc4CXYH0vR4FiroL6G7flThZejQQmUxAyjjJASdjihJ/c0lxGlOecuCL7SfAEDTQvrFACqI8a1T/Sgv7HJAHT/RZbbvoOd4CT/cFxj4hA6XyCH1MW/xLyDwylfrl3cEtLnSBozD/Vfa59fu7e++6g1RoyU3wUwzDYbOEeCh8To7NHLCSaythjzkGCpw7QXAxiGOVCp0V6HWOH87rKYCiIz49L/+d/28DLbLoPi0rOx/WjgITn4KCUBW2Ma0pY21VqIJA05YdDuo60WAYlFEFP11XupG6sTK2rthxr/tjIArrFhiOB7995ZkKs8UOVR2DcuZovxS6eIxsAApl6kvNdN7L+y0evgPA1gIN40ChnlOMnmvlIbcfQZTIUnEyEOnkzqW5CPt6LR0DClTqPVh2AM+1bIb4Pb13GiEulVwGFQvaWNcddeexaYxtFqjgu++ZJyGVZg5EsZkirNAODMcjlTRavw+I40bHcgEn5xyAcg703RlFzTeasqnHmD6RdXEU43ua5n2MbcLiQ1f6Y6l7s6uPE4kqcnYe12OXSu4ZNOBrDrRz9xmVvvi7lImSu3763+nLsnF7Hr4XJopVNNlvgaHS2WV+Z39rjpGBsj8gscpboGSs3sonE+SBFRaNo6JBaqdwQE5ZAhNVoDVDD+Oi6PMDkUmL4bGrAqiKM/rSxUkpTN+uMuOkQJd1p1O2id3qMQ0kHg1PKWvXGp8ugaO5ko65IRCpIIqNJ0XwnLJtZdZ4zqta5+bh6CbZqHfCAdHFbIs8aGcN8MpWsYCJ9tM+U2dBRbvPXYIlXdyOASh26s/14XKk/jn82mepW65TeZ8v3yE1W0JUnNNFgTkfAFosIpLm6qJdlp2v9lYyZgHaefGCPvAFOvT/vsRf//h3HiSB21KY3CIyUlQpPlxgneQU3X8JiAJcpmQvZBuAEr//A1IF+R3w9g8/YVbs8CP+FOKfMGYDM/D8e/yTs978M2KmkH9GDHpJFwMKILbQkt2YrbJR5sDpNbC+XgS2yQ4xpbH7PvXvagioAHkXAQ04HJvU9aWcxYXb3CRHv8a2qDC79ulQm9bT+ZeuHywQJwhlnWwRLVKsswubtt0luZOC2VOYKGMKlPaVBCBA7DcBWDngan//8u3DZDzZdrjEROHYaQBGMA+uO/UmgCLW4k4mygq/4CP+ghot3uNTiH8y/+zd4T7Bbf8C10eYxpgMlNZ93905H+RvcJPxZwyFE3XhofuOIuMlHHgyKSUeiqYo1rFJK+pCjmXYKaca2C8maAun1micotwY7VAMxh+ZhipwsB4YCsdjmRbseSpo8H5qIzS32GAPN5aXwYq/wRE1Fv6Yi4niYqlssMUeUz8nbXFEhRl8GmRssK9naOsam+st+r5wGeAOlYulxDnAbgnSw2zHANdcGVtnnguiDID4NFbGcuXcXy8JVktsgxsbs/K8wTrQgmNAWRfNf4V1DDj3FTg+x2TzKy2V59gSoAdif7blEsifU4atAG3jpGhdLu3tJRAlr7jng8bm7seyHPS4npcbtxakKWBjYKSpbSnsM2ihYzPtBaCLARCXdwfnj0/AhJZQrbsEogDjY1DBZs6Z/C3rdc3ugKu5O1TNW//eHCDp5qgI1lLx4jFrPIKXlbW9bVs/Vsben14zB5gooDcWq2qMIaJgCOu4zdWl2zTmySUQxRbr1vPqewBRDoikeFUic4poTmG9JIdKCUrmyG3oUnWJgWKvofscJtTeSsR4KVNfN4UDcyalY6UE4ARIWe9jLBUCnlxr565u0vvsWtiiL1+hqHscBeSjzkiwuvIGVhpf0mQX5WCf44V6bo6lksZvzM+ddo59LkitoKW6aubmWH7nvKouPsrwy8aYsnFP2OcsoKKMXAtcj7FPgPy83CPfhw2AMua+QwBFXXcu9efc36gIRjl8Kn//lPIsEKXHq7BIuO8lWrgX3iIGiC3QBbRPSwxdGK0TKjJooRLsBgQXJ1K26vD/f/3Dj8DaswvW8kRbRPCEW370BefAk5yQmytjAEtOEM4Jv4+BKAsE4Xjxwxd8KD5hiU1w4aE7zw2+4Ef8CSus8fa/HJx1/CdE8OSfERS8hPqq96pUM9JcuZ0jAig37rsDUBY+6soKLWqssUKHQtIb14lFZChUuPIYzTNHPc4pXUyD2qHADDOQ9r/z7JRy3qOd19iU3dDFZ42oLCn4dglE0ZXIFq3LgWu5wnNyytIK+f7TAFjFLBvT+R7nbxvcPuHvftWF6bNzlncga3l3YFJsCwZaDMpfklXk6LPsuPStrGdQzQ/4jFm/w/ync2R0MWXxJ6RpjO/jsdtPLmjsBlHGv0UeHS+lPtdFGOQqmT/UZYfPrsBJjei6cx3rDvM0bacLzpoKFDnrphb1F7aMlNy4HVpo/Tjv5Xed7p/Rl6/cf5UF6sKCKFUQJKhw7mWcE1CZ+fHuAJPKu4K6fa5R9lhfFN7Vp8b+forddobzoQK24gKoAgS3wHB+gNm3JQfSj60ZQNrP9XsCxB9COlsGgMy5R1CIWmKLAl2ImzUAShCz8tD1Lavgkgf7gosDlc5+P5Yxy7g7lrOk59cqW3JrIevsemmtnDnWl72nMYDSbnNue7l5YMx9h8esK4cK9CrYkz5OMIVxdhaeAbW6v3PWUAueHBAZf/ZYh1SmueTGUyK68RSIAIq68XCO1fHWOr5y3R7R16V3I3by6T60WBeenW3J+E4a14nfnhvf4xJbJQfUDc+53Cdtv7L/m53HM/3hKdd6ahle8zsAUY5wIIoFRFQRVUDFAiyqrGpzCijDJTVsMVQuL8VIwUjdWOmQZuoB0vTJE8+KKQuJj8ItFXcGm6Wupu1CsKVE2la1G6IPzRnAzjHPk7HGdMaO4XcUgFNZgJR/6FLM7+7ZIsDtzo/HdB533y8Do1Zm0pLTlXPzKpDGRomBZWP2Mxt0lu6SISZK32K6PaHsPXjCOZTzKBkpCpRo8G67JRtVgT7KRmOsEyDtZCPHx7LuWADFgiZPje+jxkwtzwkr+UwQxVIJleLdyfdxRNz9qXYM7z/bt6lA7ZHFPgjKrhMvsQkMhx2muP79J9w1K2c9JHOAQi8QXyZBFVWMbQv8a4Enum8F35zyR7BkhRgs9x2AhVMIq6bFar4OlvP3+ORdeL7gAz7hBrf40H/C8u4U453cInXjUUosn39MgLcW7+t0+zCHJ3U7BgrjDgxR2vxkk2/SVOFi4cQxZhkE4kRnhURFcTl5VjjG1KeH2g2iXBaEMlx8OLEDKU3NTga8hu5bJkquj+WUpsTSLPdG0K0EJqtNoOwvscEZ25cPouiz5yzv2TZyKYuZsrUq2iC0R/eETYjpQOVRA8uGeA/txmW0Uvc3CvTfTJ1fgE73KYCyl08OGQcysU+kvix9UFm61NkgsTpOG9kvkFpbSwegdEVq3wAQxk0O3OTxv7VYSwrBE87zRXdGYaS1onPKbF+egWaXnF/VVVgTVFmN2TDqIBxRYGE6ZACYIrIoyWrk+kMhaYodirlzA+y7ArvGgynlZOgqyvVGx3oOZIUcAy6D9jkw5eIacgLKPjCu6LYThaYhjVf9pRe+v7v0h9F1J9Z5l7d+h+pwwuQboqDFzwsvTgCN5ZLyl/Tn0fOO2d8BQ5q3roljMYms3HWJaZIrQ7mNwZzToKQxbgfPywM0ORYK69U/X7cxoCzNK9G9bIY96r6NdHINfH9n6jjnqmA/xkSB7HMs0Y2H8yOL/k6FeSoSpZ+v6m7wXkukhkALNtjy2HvL/W6MPZIrz52zLeMjPZYzbF0GUS5d/1LR+87d0yUZ8MWUDsArpGtEP7LNldyx5yAeI2WMbZIrHE6XrtMhugGdAJRe6Z1wrSTzhBdTNlgD95zKbCCIxCIuzlc9MMMZRXdA2fRAMdRNj6hDf+3hGLY09GuIgB4xE1daF/WHMfDEGnbHigVE80D4cI5NA3W7fRsThcYSBVg0+GzdHmPmnR5xDqVso+48Y0yTHOMEyLviAPn+qefm9CfzGwugjJXcIcue0lKac/4WafdZv3HC5wSa054vVDupO+rSvdG6MsXeLLo+qE17dNkuKIz5p5zXWzw0wPJ6g2nhUH/nLuLo2u88BX9bL7H53RJ/Wv2I7ZcV8G7iAIg13PYA4M9IgwYeEF2AHmsFFZBt/WNgyiWlTz/vEAGU38MFj/3Dz6jq6J9+g9uQvvh/x/8PN/iCv8cf8SP+hJv7r2j+n3DCBANb/gWpG4+CKFSuWjhwBIjKl7oBvHX3gvcAPgK4Br59dNbsz/gQUhjzvZCBwvgoDtWNCK4t6msLIPj5xeN5S7Zr2ngeJ7MjYnoz0vZpoXYpU6OFejef4TivsFvNXOrTZgl0hbM8s49sEQE5FaqsomQHvp0YVBHKFQu+WZZSieiyExSrB0xWG9QeYKMwOsMOHe7xxwt/9yLKNYA3GFrmc6DS6oBXZY/VuzWqIl1AVlijQB9YJyv84i3tjoHClK4aY2h5v0XzJ8TxdIvIRGkRg8j6FMcP98D/+OrAkk9wIMoOETyxnnMlhsHZ9PjUH5/WwFWD6LrDfevOcy37tKQu3PY0h4uBUkT6asyDkQa2VIXtMUHfCrQELfg7JcWWfY+i6wNowiD0V6r0sPiGmJRAU5x4caAD5vUBD80BXeEC4wJIguMyvvzOg+4rv9WYTW+wBl0ON1iEdYUBsVvU2GONXT1DW1fYz70r4P3Sga7bqUuRbAHVp4KsaSMOt2Mgin73rJOi7DBb7EPmjPTNxsw66jrBmCjqpkPwRBkpITNVv0F1OKFh3ydouPXb3cizvaDi5ou8oggMlcWcoAtojJLLQV3HBGwruFuQZZwhNhTetS5nZLAGj6Osz2MgTy54aC6NbU6gz6XbJLAdgsdSTtFsZ7dIwZQ7qbPxUlSYz0nLtGgz2Lau3/xOObRGamEFUM+BvjziWB9FZonuPNrOGnchB1LnFaghOJfrP7nfPOYqkAMg8srb+Dh4yjVZxlyN4vfHGTXD8fOcSAW/0sIUx0AK1uVioowF3hwDXjJFlUMFOOw5Y7a9McMPh4zGcdOlbqCU9sBU1nYAcXz1GLJNeEFOS8IKC6wVAgClkyWakkFnN+jLAlUR45eo+w6ARPZJtdLh/GvnzhwozWOXipWRtA647PqmwIkmZmFqcTJMNUtWcN25P6DoEFMWKwBtXcY4xyrzSRl+Oifm+qHVieKDpn0XI9/9ec/JwKN/Zd16tORmMovfsPsdMueOlWcDL5pVxdZ10hkfYx6EDtKd3QJqJ5POIYzT4oTl9TZYGGcosMYKe8yw8IpPgR6/zFcAgO32NykLZYuolGpRK+JjZYw18BQQxQrDlonCe9NPcwpuCBrQz322idV81a7R2ICxainXwdH7lGNA6o8IuR97nybGQltE1ommM6YCk/MFtswl9/epdWG4eOctX/aY26YWOstEUWGGdFtaolEAxbxH1xU4HpijYTIUsixY8txRNtaHct9zbKWEmeHifZB9kqYk3aH7HnIc2+cfUyibh6BQEkCJKdtiGs1pUKX3QUHUlK78zNqds4hSSVR6o7WUbt33fZuyTnZIGSi5Cd0yUEpTXxY+FgrHaY1U0FDmiT2P3wsHoPTlK5mXy2SbY4yNFY4jFaztdz03xMEKzJN4/Co3prTo3CwL7VXvfKHL/gyXMXCHtq6SJ+L/cxuV3NSaBAB7/0yMx1QhUsZZ16NEPy9RlD12gE+RXCAw2IDhfKHPMa6HPL5mKADLmCdlj7ppUZQ9qiIGm7NAilJ9Y90+CF+1YQTEoLP74LIw3Z5iukOOBbVYfQcMezc6hlGkcrFHLgXQzFGwx1gruRgn+dhhl93sLGiSE+w1ADsAQ2uPeUQfd/kYPlf+2btklqHbdqoM+Kft+wimqjCvllFuOSe3GFpOBURRCnho484D0kCMgdYiyoG2ZNb9q84z5Yw9SJ85DRQb98fbNC/b2LYNz5E57srjsafGABHrRv3YPefuf+iinfbLnNHr8Wunz//q4kT6wsolRslja8YTS1nE1K0njLNew/n/8r8cLSdEJXgC5MEg1nMtLWT/gFQGYvu08hsAV4cYdBYN0BVjAHOc73TeVYJAjAE6ZKYAzvWzR3TNewxEYbkEjI4B9AqeMB4K13QAstbHc2btDkV3djFPdK608y3bNwfa6T7MNvfuLgB6/9JSlimQ8mspzxo3OzSoMEMhC3JUUlNrDRkIOx8IkMwACrBH1Kjb2+gDS2TsK+KgKIDJHPjNhy1QAA/NV7Q1MJ1HJgpT606xw3a+xE//8SM+/fABp/US+Ker6MbzBW5/jXSgsmhHsdCqIqR6jv3YFr3kutPI9geEeCiL3/8V0/nexWTwLgdMvfq/4/+MwWOxxh/aP2L+387O/P1fEOOf0ErOYJe+bSlclPAKGu+xhmOdMIXxezjQxLNP8AE4vXdxUH7Cb7HHDLe4SZwgjqhCTJSjAVbYJ8aKKmdj6KsKbjEg8dAavsRmIIwSbWa6ZWXMrLFydddLtNcOINq3M5caeb10ihJj7hB9VZaKftT6rGWsfyBTzz4yYCu5FL1l2TvlqY4xDJR+v8Iax+8BRLmB65MUfBMg0oFIjAGxmMdghVQetT00nTEZJyHuiXePW2KL1d0WEwaN/RNiwFhlorDuHjh9Bb7eOczyExxo8hURQCFKroHX9FEmENYJXNihJYC3c8dCCc9Pd7q51L2FG7c35jzW+Tgom/ki6ft5JkoMdmgtpFbwZqBvxh8qvOKgJYKWJereBx7szs4HVxduBc1zoKQuzmw4L0RdlT5YXXHGvDwANfDQOGxjc92AgeUsO+UN1mDcJqY95hxAtoqy6xhzZYMF+rrErp6Gdtv1M/RdgfZQOQC2KyK4crjKg6+22PlgIXWlV+wb10ga84SpijXuhE0n6wwMXUhtqCmLyQjg+qKBZVdYx2j9ygBQJgoBlS1efKnRokR1QfmMSrIyLuKxdH2iBVABFcuytP9hyxDgjKCIAiYWdNF0ycoyUSXgCBfglGzheM5Q0bUuFsPnzmePYJ8ccyNbYotZu8Ps/pzvY9/g+hZZJ8pIUVefDni4c0C2Ur0HIEoBTFs/ZwCRkasM6ELq7auRsasGIGbeYRuokUbbUrOPwb8hvVaube1xIGbI0Trei/1N7vd/a5BXW1RhtECfjgg+f/rboeu1PS/HSP5XQRb+V5cOaUJQVTpz/U6LPU6XmFK2ACa16+9dFy32egkGfbXB7cfiQ9hb4O/5l5rzlN+1BEMSQc6DBzT5SgukwKYWZalAzrHP3SMYkCZ+nDf1CQ/lCbv5EbOyQFfYuXA4N7q/TBl7ts6CJY+FKbDlqSCKBVZtiuMCw3AY1SEjZynr5IAhy0S3PaK7pMpplmkyxpLK9V87bOlSqbJPL8eKOEQmJUJ2yuAO5mXFEg4k1MCyub8bu5Uc+MG+/DQ4bPw6o0VTD49FJU7pjKn1k+c4a6BH9a1AvUX6ItjYJXBVO8rWu7+7BQoH0Lz3gSA3WIIZgrobl4llu/6NG3RfkCq4B8TUtp2p11YZa3EFVhQgsedaJoEyChREWQBYAa9W91jMN8EXPQVRvvgQrusQDHB+e46xTyh4kIXCut7FamDhRDbh5FXIfVFRs4EpXzvFZIcpNlgm9PijiO4EK5j1g/XWauVeKzHfEtacqUJErEuFNde8wxFrhb8jYraOCNY4kI8oLutclqkWdX1EW1dYA56dMnNKUYNh+mwFUC4BJWPH9LguFFSm/LZZ7LC83oa2YB9Raj4hreOzaDK/0kKXFNt2i2EMCCotyizRdK1UHgmiaLwHV+cBFMYTovJoA2hKGuOHe+Dbdhj7xAIodoLno3CynshnCmAGceNRtom62TWZrQabrV0mnmMzEV4O3evqMCfnsoGwxPFZmHEb63KsxFyhG8+VHTO6ABuLEoBUoBJgPekPMn9deeHpTe9cfupFGzIROaUu+kJTAdphFp6XAD/nn9bHU5liB8ZgqdGGea4oevRFgaKcoW6O6LsCXVe4dOrbKRKmiq41Y6VEYJvQ5+mVB00BhGDJVdEGQcpG4Nf4E5rGMCqzaeR+TWccmCgKoJDam/OdpjL7wkuB82DNySmfVJKBNL2wBUzY7qRXAy7WTi4mkNsO76n3ffzYOJWEFlWVpZSarjF9OLY1CCLP07XWxWzkdYcAT480O5d1x9Z20VkkCvuRfh4Ff9cmdd+6FMaWbWK3uq9MQPbF3gEoGy/jjHmXzfpoxZwocMugsnbusQCoKWwf2wY6X2p/4rwTf58CIMB4n9JrqVHJ/oemEtbr6H+MHVfg296frcvJ/gre2TVF24r3wvPtMXuPep9Pja3yqy49oj8Mvz+lKDuDxeonmbmYbBQb+DV3aXtMXXVYJpnz9Dp6nr1uAmzq+MoZrTm+CZpYpVvbI6eb+ba56ly8lLY+u3XUs8goB2m/zwGDGgPFXT7nhpZXo617js6hw3PHGSjcUu/RVON1e4xskzHgw4IoY+CJlc/UyJWT23IAynOH6BgCIdeiwb+E60Ol/37qPMDS5/sqyyXg5DF21lPKs0CUw/0UD/MafTFEy2oZwTtMsfS9tULMzZ1D8QBEIa1FDCDAJ6cwVyBYpV93J0zf/wxcIzAiAGCNVVjAf5mv8Mc/1I6Rsr1yT7r2T6z+67nBDP+dQIu2liq6VvnN7S8yW4InK4SYKK/e3WP1bu3TS+5wgy+YYR/Akw/4jA/4hAU2eI/PWN1tnUX8J0TWCU3iPnPI7iuy9KdpTjFj9p33SJkoc+Drxwaf8QEbLLHGCkdvvVVQRRW1sfesdSpwWEUtnhOvpgis+v3ZyWbMStfDsaG43WEWXKU0NTJZTXvMUF73aK9r7Bc7Fw/hUAGH2gEquX6Ui4PQmb5ii+0v7CMBaPPxPq7XiYXTxS5Yo0QfmBV09+q+h2iPN/CBc1Ol0gbQtOlaaXnXIJk2eCzbi1lIEgCFIAq3nzAI0Hz65qLNf+sjiMJpS0EUIL+mKIAy9Z8lt3OXUjOJf0KGmAKcNiZKHc8/zIHdvAlgpwKbdHhiHXDZZcAK8yr8xkwmVdi/CKjYBVgppLrluSpo2vlZXZvK9PvV3Fuc5yegPuFUA9Nrp1xSyZz5dIcum88ujH2Xwcf1G6ZCPqLG0qdIJuSgcZemxQ59UeJYRwVi37QBUOm7IrJUxorv33RNAxBYZwFEKSJlN4IhKXiibJRc/AkdI8zEQ2DxTb9G0fVoBCyEBpKlArvFdwWiAEO3HCAVZCm0UtFVv3Qr3BKwUiG3oSALjPuHS2HcgBAXqDwl/fyhdN2pLx3QojGPaGFlr+Aat08U6GGAVJ0HAIIuafvkwJOUpSPWUW9icYAlgT3nTlYdjJuYuoupi46ynrgls/beCdWbeweeELS2ZQIPrrTRij6YplQBsJO2vK+iY9tFMyFlDtbRVarAEBzQWIJ2O3QHG8bW0X6n5/Aa7IsRdEmBGQCjYB6AkB3tUmnrKvQNBfRsqlgFVGJTDq34BKhzhjMg9rXuewBRtBAEsH2RzUXgoDPnkIGi5wLRlbd3lvxp40QnHIBTn/7EtmRu7OTOy6k6OYMQMueF/+pdXSDk2HnQ2gBzABLku5ULWDyL5aoHmgOA8oyqdhc/Nkf0np1ijUjDxCl55tSlkgM53fduUOceYWxe6MJ4rQ7uDTGu3MANEkhjlbAdratkznilspmVyXKyGDJbLbk6BcsuNSOP8xzfXFeI6+KkdH2b+u2sH4+YRJbVpf5sz38OuPIsEGV3P0W/rVE3R6BOFwXbuSi0116Y4KI+8xawBFXWRZIZMNQyybWcI/IATO6Af/d3P2P9/r9igyUqHLHGKvh5L3CD/qbE5maJv27/LrItgHy6Su0oyiqAOWbBk0sAijIKlH2yQgKiTH74huVqg1WxDkJtdDX4jBV+wTvc4j0+Y4kNfvN56xS6P8G573xGdD/45Npu9xn4H16XpuAwAQI1KtwPlbMbRADFgyen3zoGyl/wET/hI1pUWONNcN3ZYYYNFth7l61o5X5aQLzUwp1OUla4UAsrgykx8wqFt9x1+jBtxbzxBEkY8JjACvvnziufBFZCENp+ht126tgp5TyfMhuZOra1LZadoi5fTZpphgBAFNyPeOMDQRJwI3Pp8B0EKri6uUf5BgOFkrGClNqYBi10VHFN10oXBbYhXXtu8AV1e8REUxj/Be790a2H4ApTGrfOhWfnD6n7joIoQF4oocBBN57XiK48r2tgZgPF8ruOUwIrhRyrgdNbp1St59dBgSJQEMGU6I5iqap2H4CoCVFY72R8O0WhFYXhOLhGUiyAYumlKkyNASoqPHHs6LymAXZrYNIAb78dgAIuvlYzwbLYgO46DjCpsPTZxpbYYu/jPtlg2cxIRhBqGEi7DnMG27jvC/QeQCFbRUspQEnYFpH1QIVJGSYLPxfkmCjWxYf9Xt15yDoJ7jz9Dq9vT66dmc1N+nySEUUV3pc/1fj2ffD7Q8VVrf8WMGGbK7i/bDfROkjwhNucsKr92xYrU3hj0lXh+vWkBprGgYUPJbCbv0JfFoNU5pryk89sY4i5/XJ0/FpmxJjLkrYLWU8xU0+Luj1GN24LmJCNrExArrMSq+rh4MCTfRuB7Fy6VgrOU8QsIQ+dt26qEtAhslKGMfDTa/Y9isK2BdvTJVmI8kgKDgzlm3HgRF3H8oBVpPQDHmDxQbyrw9nfq1G2gHQutaU4ZyqlMQHM5wec6qjp9qUD7W062Z2XPMcCJbt7JoN93DqvbfziiwIgl+pYr4+sSqh+t/U+dojK+/v7NJh9DoPZZ+ov6cIKnthrQc6xxwKDgOOOBknOcTlQSY/p7/TiLWLQ2UK2dNHzcyYATNozHsozuuIUWH8EEAmu2GLnRqtzpP01ApaF8S3k2OT/DoLts3Bf5SALYlh36DEXnLE1R68NpKxTK3fBXE9/p+VSXx4DUtgpD4gGsZE1cYIYeBZAiJVS+vuaZH7O72OxCO25zwFGngWiPHjLWutfWFErCp7GsyC9lFYRRfnG0mmGF+3WoviS+aLZ6BSSF8CH958www57TFGjDUIu4JgpBTr89d17oGviSyoRgZQco6BB/g1wX0ES1tlj9rwLn7ppMSsirZrWRArGFHpJvQ6Chlpu+GyGUqWT3LR2HS5k/Bj7eIVtcx2t2VQUqFw4h4ipF5Nq/56HmXguKVSF9AkGZcu56MTzlXgX2Sk5f3OWNGhUzB9PKj8Bvh6F2NOV3hqtTCgALJzSsz1UrjHLq2H/EbAvvIQciAKkIza4fJ0wWexRlB2mcx9hG/uEgUMrn6Yzm3pGxtV3EBOlqltMmjJY4p3Q2iVRyGM7tGCATCqROmbYTgyWGQLO+sxgiQBvEXsFdHs3eZ8wDBrLrRbrZ2wtNmShlACmhWOhQNMVK9tEP4Wcx7raZaxp6xhWd+fD6TJLls3Ic5T+nrMQjo2p6Dvcy/fUNY4um11RoLqUVSG30F/aZykRhSMgrg3WX7pO6xoAzf0J1fwOu3kTFJCZvwABWWUd2LhOBZyrDxAZmJwvlLbPDB1tUQGFb6caCagCIPRvdx0vfF1QnGjdZ9Qpq7RyjqjC7BzTy6YsgTZ8iq5PhSxtc6tsjr2TF14s64p1tqhKqOtPUGwVQFFXFG07nVcuCaXavxukSgG3fvxfNY663hVnYLELfYpKqhv36h4RGaBM68nCvpy2z5CtU0g/1XbhOXp+gd4rFefLfSo37WTqLqW6fHYpR/YvFDJShsBzyk5J48o8dZz3YZ17KnhSHU7RSm0tzmqZvgiiPK1dJqLElj3Ql0f0ZQ8UUa5T+ckWNb5aZTS9ne8AONHSI01qoXqFVSwhx3LXseeU5phnpEx6J19ofBQ9fYZhhh7uU3ew8VJy4InKOHrcKq4T+zxjwCXbw4IJ6sKTc/XRm8oZuX256jyjwbdX1xHcOKPCKcPKcuyVsZJjdilAwv/k84R2MTqbuxlzbAzQeAxEAYZzQE6+0v+067q9zqUyBvTxXfYj9fb3MOebclUC5UjUAmJWKn+zTz+W4IF1/2ZMFNw1OE2WODVtoCkzyCUXC1rkrCXCpY6cocYRTIfsOuk5AgAKCtASYTtPAWd6eAvgK/C77itw/RWrH9fYYIklNniHL/iCd5hihzVWwO+Az+8+4O7dDbCaRLeeLVI2wRbxXsY6iw7GMdBEPwu/Xcl2Ae/G45Tl1XydxDtZYBMC597gFius8Q63If1qcDH4ijRWg1Bjuy5OZARPZgSfyDi5RmCd4GOsP/zoLAt/wUfsMMNP+IjPeA8GYWxR4RY3AVghfVMtD7ZYocGddwkwoUAbt9Hi5frbwltVSW8HYvYE/pc6BLFvkoWyxxS/YIUepXdTikEl6e5zRIVfvAvTDlNsiiWO8xpVc8TxUGG3nTmqPt18tP80sl1In2HhQtr4AJLebWWx2oRgqWoxpoWZY8sGTaWF+nuIiXJ9fYdqfhoImLS8K4DEDF3q1uMs7q5tKrR4h1vnsuDTGM/6HeafzzEI8y1iDIh7+cj42t15Nx5EFx5m4skBKVaYIGCyhASRhRNwbm4QWSVv4cbke7h+8xaOgcK6EjGY7Gvg/saBJ2ng5Ah0tmKNZmtydgbGhdcj8hZnBVg4Lhk/JPrNF94qKROpXYz1Y0Hz3LGx6cLSeklrJhil8WL8trkGmvoANAfcvL7DsXmFVe3mtuiiWCfzgqZLblFhiyU6FFj49iYrxb0DplccsvP6okRfDBUvtqd7lDhPqpKuoGkh84MNLEvWSYkeCx9YljGByNoKWd/uDs61Ql12dKugogIC30lxb+ecgAZjrAB12VG2TwFJJUkXKGXwMGC+9vMt8rRrWziRsF/PzffXcPNEDVxde1e21yc8NCfs5jts6mMA/WJfcpIYFX722yIALanyD8Q1me3jrpemNgYiS4dxURLg7nBCrcwTcc8ZuPdZ44T/3cPBCcqa5tLOv7n4Dq+1wgKydl9lOBN3QePTxLYZMiUITF0KLpkDTxQIta7Lob0FNJncYwjMXaL5q7Kkz22/89n5nYYggv3+nKsCmB/OQHnGYX7CsZmgKPogf+0xA7NDaZvRlYdtmQOj4n6H0/cGqACXNTDKh/rY6saamys6Oc8XMl5PnVM86U5DQ9A3fx6/2zhutoyBIyUiwzboHrJfFjFQ6FUG1Ej6rS18XpUR2DYEWtgn7dbuF3BzDvsvXF1wFeH/IcfKOueVezumLgET1gV2DLCwWwu2XAJDbB1M3aX7PWD8+WyxcydBkTFQi++J92H1Idu2ZKYAEWgja6eI2acmXUycouA63X1OHQbBxoHY16dSN0Ea+/mx8jwQpQPQXQFdgdOhClT7viy8pS2yTMhGuJRnG0CKQtkXaaneLLTI3MIpQAdg9fYOxdzFh9h44XaJDQDn9rGvZ2hXFQ7rt1Gx1XJAVHo5edmOYwf+JfCkMVtb1wCvmJo1sQpSHD8GqzoFtbpvowBywR2JVKeADJd+guBkIvETEmu2V/Y1nkIESobJY611+xIVGABUwQIQlQrfqFX+Z4NrUOiI1OFhtGqemwY4iwoKhRGl4BNkiakgK68getAPBXrsUcJZl5kaue9Kv+jUSAJJjjFRygd3Xun3PXjCIJJVk1qMCxlZadyDSCXXvnN5CXwZpcQ5COWqvFg2DoETFdg18fZAiGevPZyGqTRVebeKfIcQ7V7XQztFXAq6RmGCwsUEDkCZqrLPD4E3Hat6zP/moYk+6rEfp0+vbMB0jn7KiBuW6BJAdpe7HtlkuXOfeOEhuKKsRGDY4CwlomCl87e+LNapMtC7lMmT9oz2/c4I7o5RGWMcpMcIzHJOYNEsdO6WXfvQQkslle2TU065n4sBpdb/OD8oO68N82NkqygIHVN/l33vLGVjwpUKWdqW32nJWb5z78RuXewTpG3JtTnHbKPBqJdjumZosYoA+zfBFBVaD/HcKwBFfUZRu36ylz4WmSidr4tr8yU2aPz9kJGi31mnrNGLxQr7Tyw5F55cGczLOSXuX7HkGCqu3rJ7hu46OWZPkHV8Fo5sMEnLpgSGgST/FhCllN8psKJzqX+sonMW+aLownxnn5lrgg22O1ZybLAXX8ZAkDGllCX3ncVa+eVdlx1Cxp5JCZza+NMp4tRuXXQuWe7t8FELvmWkTOABlLExRzBEi67b3FqQaKwvCxsn+Q/7GwWkeHzMvWqsPAZ82LpLIMolECbHWMm5PFtwJXefOYDF3nPu/nJF30vuHel/6/rF+ktTwKU5WvV1uPWuhO/nBFdKDx6WSLK32fsKrCp/L+UD8hjaM29xWO79jR8mQDnB4VADZRdcD45Nhap2KTCZ6m3rg75SIacls0XtwJf+HB+GCwEtwLdSp0LJVziLLa071y4LZHOzRf2P/xnLYoMveAcyDLY+c091fcRPf4BLXXuYxJTHVKTWGAIouQGnLUeFpjR1ApYkmXhWAFYu3sXNh1vUaLHCL4GFwvgW7/AFU5+i1bFRfsHy7oQray1Xdyciq42zchM8mbz2xxhPQeKe4CMcTP13rv7wHviMD9hhhk94jw2WuPU8GE0JvA7sjBk0naJ7lVSyImjBotYWzaBTiPCmx9x1ohBWog/pOmmF1aCiwRqYQbSZAnW/mASf8TdYQ2MfrDwnaIcZptiBgScZM4EsFVrw6muXrePYOlZKEkTyMAng3KvVPXLFuat0KD14AgDLYhMsxhrXgM/NZ175mCgMpEpL86vvQOi4xhrOu5quO3S7SC3AmmmEzKQYA2IYN2aFtbO8M3Amt/wwHsQWibX0wfvf75BaPm3RaUAZKIAbkzM4Esl7v3/DMfkBMbizsk6Y1pgxUT648x+ugbZ28U/YdwkeM3WvMiNiYNkYAFDHbPoMqmwNqeTumILiZdgHHBjKuAAFisA4nFjhREGOMVeqHjEuAuT3Y0qXCv4E2wlSqSLK9mR8mRp4e38A6gNO8zvsF7fYFTOfMrzyDK/aj/9p2I/BZtP4KAxWy/egYC1dhy6l9NT2z1mmOQ7sPKEMUI6HWXDs2gUGSphDt6dhhhTrHjpGbCvxZEHj11xeoUOJSN22iqurGzIjAwuSaw7lFGVYMMYbZRWm8VWWira3Xbc4mSi7iv2Who8tRDZDwoBsegBwcYD6IqYzZ1FXHxvfTsd4bJcU7FMAIPbTCNqx7UL/bTHM0mXnhJyyIO1xJUoSlTXbXCxqDS89YH2l84GCBdZa/Yh0nAJGETRwj5EHECzYpCDopXFOl53p1gTk7RHXKstuYl+0oPRTiKraDg3SNmEgcwXyOlc3AVD2Z1SHA/aLPrB2OP/BMxaB6MozZnjT9iUo/OJLgXGl0SqjQIzxYd+hb+/wm4P5vQJevctsig54XYqRtY1W+A5DNpcdgidcdnMgYGK3ZKFQH8myUIAU+MgBC3areg/nPc6h2haWkQL5DqRsq7Exn5uT7f1YgNKCKJdAzEtAzNg5up8DZi6xZS6pCDxemjq9B5YcwKX1LARb2W/t9XP3aJkq9j70Ofzzc21RV6mJ/PbBPHeOnQIAeECkaD1SngeiEGwIne0KKCc4lX3iK7arp4GJQqscbcAqdIeSs0BSMNCF4c4HBdPFYe4fdu7qXtcn/Pbvf0KBHp/xHqT00za9u5463GT11v23vswFhh3NImcWSMm5aiiwspCtr58s9i4OireiO6r13gu6e58tYR+UQaZhvVLgRK0OvD92us4JClNI/JMSTkmzgSrfxu3hfVTKHGDyBnufgYd1dBkg3V0VNV0MuTjSYqv1rnlj9HpNm0qQTZkj+lsVMpSJ4lImnlxmCWvd8+1zVXtQqXUxEY7NBHXhqM5MXRr/I9LxAQRXJX22qDQesaunIc5Byk6ZAIsDqqZF6Z0jNbCkgidV0XqQaBeeMaYi3Q8UpUjlZ1BJV38ezKgvrzgl72FAo7cME46fwrdRjTbEh3HtxzrPrWp3TtnRjyqQGsdA6gmg5OR+lpwwr8LEDDEOymsyUFQh0phEVO7tePWA6ObaZeRghixV4OluokEl1Z1HU6PmSswgQYWRgRiqMGajQOuCyrbeQSAd135bFtFPOLcoWkHDUtI5/2vD5xQtC3QrcDL3dQRWDnBz8hzp/FkDk2s3R0zrOxTXMb5XnB86HJHGgKnQYu/BVZ7PFInsrbF9I+ikCqputf0ZSJv9PGWgpcBJGliWqb/j/BDd4drIQiHbx7KvcusyixWavoOSY1OwfhgrxUsyfR9Be647nEO2sq9bdVnuEGUZ1gN5ZYp999rXv0UqPLf+OLdeHmhqoOhOaK/ZZ0tEtmXMtuX+pk36Ko/p+qxtZIGSFIDqsv06C5TkhP4xAbeMlvWTF5I7pD7wcmpU5lRpsgrTmCQ8Uk9mqhYLCFwCCHiNHANFmWehzmd5SgAUlQXZfw6mzs6lj4FUfGZtIyqlnE+BuG7p+X7/CkDZIMaICLF5utCLlPk0xlTRdvouSy5GhBYqnurSwHP5207OJTgmWXoCoCqg4FUJTP3/ThFdHSjfTJEHUtTlARi6zpUYGo6m/r4nZXTlGVWeS7PVY0BkiPBZ2TetfmavCQx0gazC3yCNu/IUdpyVSbTOAiZWhrGyj24v/fcYsJK7R1s3BqzkAJPH1vfnzJkWbLn0jvU8O30qgEXguzfnsy5z7Std2gzAomXyDOPQ80AUAhqcXEMnbXAuH3DwymNR9tgVUzgLqLMma8aTPeuKGZbzO/dguqhphzsg5A/9ducG/OsemFD4lUCouIezKOKA+uOfsK5XmGKHj/gpPEKPEvX1Ef/93Rug9C4V+kxq8bSfEul9EkSxTJRS6iwTZXFwGVfq6MceHWfiR10Sptih7ts0fosOUP1vLm6t8xkLGXgapBZuxkTx29N74Hb+Fl9wk7BOHBPlxsdCqROrt7ry5IIFW0sC66wAEhWR6LsdrRV7RH9aJbqmAm3ia61KMWCAP9cGzRxo6hOmcxcPofZ+43ySmVdcqBApRX7q469EP3Pvpzx393nsa/Rd4VgLZY9mscNsEYO9qi2QAVNt4EiyL9IAkMPgqdYNzLkevHyho8Eec8SYKJqZiW2Vy0JCpVEBlUqAldn9eQigKJCiAqcH4ix6rcWuPVagKOHAE+o8U7hh+PbapzImE4VsE45J1vEcP8dp/BOyIcgOI8BpwRSNV6RBvnNMlNStpAp9sveAQBR+ew8YDBUuICY9LtCjKloXQLv1rIcSw7k0V8YAFv2es85YEKVE9IHm7V77ujkG1nu0rn5SA+/6LboCKK77ALYq4Mq5a4dpGLN0A+S7aH070i1oLGWiVdLdNnXnsZmpdAxYgCVmlYlzSnCv8DEVrtjPx8ASBZhg9oGhUPaCi1XociwC655SdH0MHKjAvV2HLIhyD8e0VYPQvVwj/qkryqrqpZ5yEPz/UIBkP/eywARwMkTh1i23gg5BEbtvn1+P51zQcr8JwEp/obOoIk5Fy7JFaME8OOPQhOTOfmjrUmWOceGWc28Fz7lP1qae7W3mKmbUiOM4TmCXAJMcOKp1KTdQZQIGjT27fqYAio0lkwNRLJjyGIhigaZGzp3LeZwr2ddUmYEDUqrijGMDCZodnzmNh2LBqO8MndWSA+/s49o6fQeqI6myq01IAICAqr5zf2xSuPUNiLEkpl730fgRCko+5iSu7jwh/om/30kZ/28wrvhcl9w6OjnHrk8WVLCgiv62RPpfChKOdTurawFp2+eAjDH20FOAE1ufu68cGJI771/b2GFddGy/yxUrJBeZephzOsT5XkufOZZj94y1pd23YIuCbl9G7s+U54EoWzj6LjsigYIODpBYNDh1BTYAqpsWPfbY+0w5G2x9KuI2uEPsMMXmeofX81NKn1RB5B74dusswbe++tMdML0DPtwBsw5OIAacsuEXmvn9Gf/+P/1XrLDGLd6FCdzRqXe4/eEG22bpIHN1u1Fa3BbDl0B0WNHLBdJJQT8rv1084NVih9lih2m9C4wTKn9TT7VmANGpULBn2GO6PaXuOzpoSqQWVSAupnTfaeBcd6ikcf/fAafXwJ+uf8BnfMAaK/wkAWUZfHWNFfbCRKHrgM28RB9/d1t5gZQKAQt/Y/2pAQUxYoyB6NFNBcHThA+IdGlLY9VCYalxYNykPmN2c4e2BmZz9142WKJA74GVHjvMMMMeC2y8xdll9Sn9MWZR6uHSS/aFC7p8PFQuRXGxkedNFSdVigAkTIpL7jwRhIssFafEvXzNZoEtFkFxj0LlWEpXdenStgoBNPvN0B1O+4oF4BRYAWKAKn9/HN6Brurr2dXIOFE6+Qe4NMavOQYbRJcdgpoa7JnHbtwY3S8m+KVwbnQM8Lz1LDG68Wjg5DQmSjWqwBNISd0X+nAOAU4NKKuudyw9mGXLpbbndY44oi96TIsTysZnLXkMRCFDQgUQpbDnhBP9rbu5KDA1Uge4d08m0Dckbj2aNvrq3gmAP7R3ONXA5nqDhQ88SzDFjUXX5jMfRJ1gCrc6V2qwWctEYbFW/1xQZbLWUtr/MdknU0uZagW6EJRysJ5cmjrUeAJcFnpfUDmjRIcrAe7jGqaKbfwun+4c41JYlzSdW6jg6txD10F/zu7epe0FInWeBN9pI4rIPaKixP7aIcohzOAFJIpwNT8BDXAsHLDKBADRQOHif9mAqNE1JWcUSV2fckCTAk6hUN6jAqMgnQIm6o4HJHPBcu6UvckhKn5qHVdFbuqD7ob24X9oPDj9zvuT7UMJ9OWrZA4dKxofKgc+pf2oC8eVwVPhOGSg5IASsm/vkIJ1PSJAapnLLHa8WwWXAAnfFR/bg3MAouGxQTInlA2Awxl92aItnKzEYOUaD8XOfU+Jk/JiS46JocKDBbFYby3snZzP/cZcg2NIXf45jqQvXHmL/ETXVkTjkQ3WCUR5KDyCPFNgnMCDlnxGC2I8BUBRpdsqzTmghH22NXWWfWaBFN3m7mFMmYfZWnnEglh8H2PXzzEo7NaCEnpPLDmg4pK2r4BIbk7IXeMp17bvNU9+fjooxDnNlhx48pQ2zf2/BeoeKc8DURyjOx2wekMHAJigb1xcFNrJqQD3iEEPaZvrigIoT+mdaMPLwzB6NMfM7N59ALgFpIRDWl4DqIHV3Rb9dRGCzTJ7T4sKi7lTareHGiEWrwIl6mOXazHdKqgE8z3styZoaPQTVgqnCiKuKWjFwbBjqVBrO6tacxaIAoK69Lx2ytnmugnuOnTZYWaPXXA2mgk3JqY1VhZK30flrChUiIg3rTRi9Y11WxW/CKswEWOq8OnvpCIVFLgo2Heoyhnc8avS0Z77coe+dtfdY4YKdaDpa5l5cJBUe96bBlUuit6986INoJET0PNWKWWkWEGrHNla65Vt75daam/Pt8E0cyld4zkdrJtXiP9AqzsFClXS2Vfs+Mo0owVM7ClqASWIsvTb17W48FBwp+Kurjyv02MP8xjHh0BJyjZJEz7H+Cd1cKFk2ncLeGpdL32Q4IkGWSaDyoEBfTheyLV4PsFHBQr6Em4eU4Fc569LQgy3FkBRy31ugVTrPBdHBesh91Ca44bJMumAWXlAPy8HzwiossT2U1A4znFsJ1XGhiBKGmzWjvmhIjacC3QmTcBI74IS1hTbZlr0/bAN9T29fLz2by7ZeTYH7ulH5x+vDD/cO0Vlf0BYTTo4eSek37x3ism0A2Zc1yn7UKnmu+KYUGCngAT97JP+lX8265oyBFD+5pITvtmvOD9YkJUKYY2wnl81QHlwc2rZOcXO5zoI4EnIBkJZTEGBZuS/YL6PgL2PASnusf62tkoMULrQ5NarPvNd+lfod0ZJzo75Us6x45zfNRaFvS9hPFx1+no7wDN9NbCsu/Tz1JAXXVRmt8pwDkzR39n3ZUEE6/bDa2nhPM41GIjysboIdd71gQBLZxi5NZJ4Epr5NxvzRA0ZFkB5ZJwlazeQbydtnzGmg+3XuXeQK33mmFXSx77nGCkHOc9ey96LlW2Ax5kzPJ9juTB1Wp6zlufaOvee7W+0vR+7tp6X6wsqi9h63Vo5hfW6jgD59n2sbU153uy1lT9VNxayNxbuc8Yc67LHbLHDsnZpFgliFOg9/bwMCjrmB6dAtEj90vyDnDonXDCjL62/3wDs/uTiC9z0cPR30hw/u3N++HCHf/gP/+SpzY6WztTHm/kSt384YrNe4vTFJ8BTd56tfFe0nS3HhXiFtLOoK8/CZV+hS8esiMH9lIGi8U+mSeyLFnV7dAqgdhwKBGp1ypUG0S3g7xDTpf6dA1D+eP17bLDET/htCCBLJgrdeugykASq7F0Q1b4rQoyP48GDB17yY8BUACHuR1cUQRFmoYVb6Z012uCgAgCFBzKiYlYmimCgpVvKtLAJwsDg+ykQrVJfXXvOv54xf3uH0/wOs+s9dphihl1wb2KblIhZe3Yebqq8iwVZKgV6HOsqvGP3bMMhl7rzpDFRGDhyGtgmwzxJqkLXaHH+DrLzXGONKaqEpaMW91zcGDK7gusO9njX37pYObT6kokyRrHnOKPw6UspQkdCW/VbnkrWCUGUGZzFdFICk7eIQWSVgcKAsa/hxuZHd97hI3BsJtgUMSbRLW7ANLw9SmywCAAn457sMQWZD3TnIaMrB6IAQ4o5t5Wn/tPVzgEs+zBW28C1aAP7xI5vgsXHxkUtaJQWrqwUFdRZVPm0wKgqD1ZwcX8cX5INKKf0fVqoFbzivgbsnAPNN6B5vcVDs8Xs7T64Nu4kfGuLKgSl3mCJKXawcWkU8tCSC8ptQRId75raOGbjac1W5gdv2S57P2c+Vi4JXYALwPYdlFzcLlsUpC4Q46FA157W7F9ionxz6Xo/f3XyzQYxXTqdPxWULXtgeg/c3DtmxesOQ3cegrQ0chUIuX0nLQCcUdVtMG7Z8Z6LiZJvi2EbjYEKIeB8WaArzphQmKewyvHFv+X8QMWJY51zsMicV517ronOARaYsUFQ6fK8kH0bm8q6+JRAW7tn6M3c+ZySA6DyhpHex3kTNx62hZVvGFvnDsP+pjKQMvqAOF+qcsR2I6uEILTK+ex3/I1152E3aN37qWrHgHLgHftbMdpfcm2V8pRfcCkQWSW2nsXEbQAwVBLZ32vZnyNd/7g2qiyTc4e166b97utCQGhfJrlp8pJLxxi4AuQBllyxnYB9MsfQUdDfzgkwdbbYZxszxFrghOfmgBRbb6+lz5djT1hhc6ydxhg2ubbl2B4DWMauZeUp+7ux/7t03yw5UEnLGKil20vn5MaUPfcZKtTzVoIWMY8QO6/ehAyUvivQHiq0dUz3yJCQTqw7BlXwVPugX5xgtEhnUkDv5P9qA4eI3tz5kygAlwjslBXW+AUrLD35vUWFGfZO4Sh26BcF7g4VsBbXHm1YLvTaYuUTP02LV2WPujmiKKIySP95VRBTCnfcD/fA9mA7KZAzJkBYa/fCfVcGin4ImrSoRDGYBjF8hxn6vsDx4IMn+jg4mpXm7NP19l2B3mefwaFCUfboi9Kvy+qe426a7JQCXRS8suLFhcVXBQWrgOk5iozrxOCFpgmA2WKHougjIIIqxJZwsX4cxZ62XwabdAprG+6z9O/b/XWa1s8qrhTQLTMptSwrV2fYQt9DKXBOFENNY8ytxo0h/TnQoDm7MFaO9oNc/BP7yRQLnJSZYwqivPZ08pnSxym4q+tILXUSYNamGmcQ2R7OZYfbnewrAyV1ISGIUvqtVd4pMcfvDrDkUzKobOzXZKf0/jxHVDyiBTzkaQAD7+L2UJ5Ta5UqVJdKb/atkGLA96xA0kMfM7VQqJWO59oYE3DnXfXAbL5DUXdI2XGlnytiWnS2lds/BmDLtXE6n+VcIOnmR9cpbVn2eRtTgefZeaPozig6AVBsfy9G9oFUWNW6F16ewihgyc6vtkpZbmP91NcxoKOCJwqi8NUwiwbg2Sot8JqAAhknHEMEcdh35R5Iu1fXCo51toWyBZ7cBo8UpkAHnFvMlcpIVtFpTZ2e02Ho2oPMMdYpm4zAwBPjnyRKhHSRKLc8vd88pYyt31faf8z7TD56TNlIPGaZudongXQe5ruwBqjcf9u5O6coIs5btt3G0kGr++h3UyyY8JhCzLbNWd/tuACG74J6mmUPcW5gX9HvqvzyWoU5B+a43tPYc1wCWPgfjwEoueP67GMMBS3KbLFtl/uNHSe5Ou3vYy48um/vb6xdL8lEYwCFgkjAeLvpOU9Zxy+BKvp7PSdXb69py1P6QS37eh1tt5z8Yq+hbTMmBz1SnicCbeWP1P9OJ1//OTdzHJoKm+stAGCNVXBpWHhrqqs7YnW9xtubg/stGSm0phTAvo/Chabh2sCxUWYA8Bl4ewvc8AW0cFbeFvj49Su6t24KP3or4S3eYYMlarTY1EtUvzvir3gPbJsUJDqkzzUQ/JWRQ6U8fD+hWexQlj2m9S4wBdL0rNFfnYEAVUEuveAbii72VL6AGBdGzwMiA6X22xvg/v0r/FR/xA5T/Ak/Yocp/oKP+IJ3WGOFT3iPPWb4gptgTd30Sxcs1abxBYCuBLor6dATl/myfMAZwKnsAA+sMEvNsTm6uCFwjA5rZ4jK8zEIdmSj7DDz33YejCmjYKaFAoPGR+G745ZCFpXZHiHg5Ov2BNQn1B9a7IpZsOzuPYBC6/4Mex/vxwGDylLRNLzuluJNqvCqASTTLDwxoG2equ9+7zKBfD9lgQ2WeAW1vDMuRARP3HghW0fTt5KN0qi1jtY7tRJz37r4SLkqndWFQAmzQEyRdil12Zkobdxm22FMogaBdRJiFF0D3z66zDuf8T6Amgzo/AU3cAyUGER2gwWU6UCF3brcAeMgihuH1QCMYx+1LDAKvvwPF1/hiL0/g6Ahr937431doui2aDgeOd9zUaxlny6V9O8fsxh5weTBC/w5H2710wZ8XBb+F5U23odnnQzcOhs4gN6zU+Y4Y14fUF8fsav3gUlINx/GRCErhYwUC3ABgGWksFjeimuiNDuVBqXm2GCsKDtvJMUK3laB4vyo8Q+skP6vq0f+LymudTXFMccKx0AJ+HXGummZC8Wi6w2V2UO63d07F5493IdM2w4RRAHi3KIugh2A5b2XfXTMaEBRyiRUhFs3l9WtG4fOFW/qPbSjWxmDfo6BBLpu5UqX9OkqXKstahRNDxx8unONz6DyI8d6a47rXM1nVAUFGK7xCqIoI0XdnDk3q6slz/Fgy0PjWIFtUYfRxLZQM4Ztg8faSmPvqNGFpcwphWwjq5ipYUAZUVYOssBeTjHiPFBnzqEbmZ6rczaNfP5dRDcy17Mos2k72f1LdS+6sE1ZxlxPdIrJuR50F+py7zj3O6vU29/bukvHLq3PLI8p0WPaqP3dJWBB/1fPsy4wCtjy+yUjzFg7dGafx1pzzLJQ9N6eykTR+wbG21PBNdapkZ3HLdhir2+vCbPNgV+2Tr9fur4tl97xGDCmx7Sfjv029964PeLJ5XkgikXQLIiiZQugm2B/PwXmwNIH5CzRY4slAGCDJRY+iOdbuvQsEDu8b3gKGWTY8DvghA4KF9964PVfgAnBjM9uc/UZeDe/xbZ2bikA8AGfUHmrKi19+9UUbXPEqXs9XFAsnSsHouinOeFVc3QMlDLGZ6hEAaSwaxXiAdPAdghOxHR9shMzzwHSQJUfgfvrV/hUvw9AyS1usMESn3xQWeey8g5HVFjjjaOrt0vstjOcDhVwqFPABIj7B/nvEgixZsqJA1Z8BqeJd+0p5u6ham/DVuGB70QDy5YgGySN7+Cs3JLCkPekwoVaYPQelVGl9GGCU3PgdelSIvdzKost1InG/bQP90slivUM/sp7zSmw7hmjpZluK5cYJlr3lGB3L6k4x4grr5DHscLgsRobJaaBjtmuXCaewzADjwaRVaVGF7cRdL4s/HQggArr6YM/I1BCplMJ50LHLV13PiAGfeb22o3PX4qVBzE5DlcJS2wsnbFVzrkP5PudLQ6wjMp2jMkRXX14DSqaCpZQMK7QhjHgQE+XqY1A4rGZoOhOjoKvbDBdT5QRYpV7LfLe9n596roIonS9ez9787Np64NNksWntGIqKFS6qDRY6rSfg+fdGdV8i/1igqLoA+uHDDbn3udmBk07TTZeLrMZSy42ioIlpanjfgRe07mjGBNOVJjiVp+XbW2tRN+JO48tOabWkxgYVpnRj1F694doIOJng5SJwvIaUf755vffHryboDJRBDBJmHcCUhTdGUUd+wjgxrrGqvjXUF7JPkmCWZcF+vLsAkxTgKXsQsOcNrOm0iWYRzDAGO4Gr8eyS+y8rG476urTyMcDKM5mlOOGxq175mG7WVZfifx6nt56nwbiHStjrKeDOab9Qc596MT4NKY4A6mRkP1JlVJVQJUxAwcE9d0ZZdmjL9JnutTXrLvpd1Eq5PUlIFWKcwo/v9eyP6Y82nkIcr5V/C9dyzZ7a87PnQMMrwn87YD72Lpv5QKt03LInHuJsZJTrMeAJjv/2H2NO2Nj0Eh72wyQOUMQC/WcQRwaXpNGIY5XK1dR7lIgBeZcYPi+LMMkB5SozGaPj133eSjEOMDyGEvKvmMgfRcWBHtied7tHxCFJlo4OLlaQGXrTtttfXae+TIohszO4/zFV3iDNR7mf3XR01/7awm1/a2nss2Qf+d7OMGiBPBt61175nBBZgHgEzCfn3Hz4y1+8SDKCmuQdg045Xcx36BqjvhKsABX+YHKllMgRTuLB1CKsg+BRWmd1dTF6pJgl2YgXUSglhsKA1QA6bLD83h/tLb4uAt/fb8IcT3IxPmED9hhGgAUKmz09T+2FTbrJc7bmQNPqHDqC7ATju1VYQG+AlDj5GOpAHAZbOBcfQpZYAlwMZ0w3Who4S3Qge5FU+xwbF5hcn/OUxFzqLylDOt9UwAQX+2mA1blGl3tLClUYtlW7meFj6Eyg9JWmbLa3VJUmKJCGgVZB7oQTInuOnrcUlwdENiG/Z7g1QsulW9VZfOogpjGgFB3HhOvQEGSMbCEhf1FKbJ+IZrABXXMlZA1g0AJqeIUzMlEuUGaYpzgydylGKd73S3ehTG5wzRk4LEgCpkoykBRECWnoI8BKbRAO1CEAVOpvBM+SRX69PcxswSQuuZFgLFGjxJVcUTR9Cj7swteB6SKUmPqclYVIBnfIZOAgClUOk8CegVX14M7d8pjymBk/+A71XlXAWNhaEx6oOxPwHV0AdQ5wGYq0WxJFSJYnCt5Jkrs6zonWEA+VxLrdq49dR7n2j5GsX2VqXthpcerZFxo0GT33W59zy4KPOo8becMVWzllA4p05YgCvvtHs5QtJfz973LqDGxjAQLLuh816dAmj4nvz8XjNcsNOyN7nsMDB/YKUXh4mN0p5jyVAFTbS9rged4IzvCy5iJYqdF13adVyg/ETAheML+XsvHAyjH5pWMrDQemy2233BfWaN05ItAUyoLAA5wcukwn1GsUttl6gRAAeI2kRp0LuBjUta0fYr9jS4nerxw7khFEdkolrGTrk/ja9X3INckCqit1y3XHy0KnnAMaLFyzWMgCuR822dy7BX7+xwQY4u99lh5yjlarBIIJOynJ/1f7hq6r+2QAT6SOVfPZ51tJ2WjIKaWBsTo06XfAcBiqTTaAYgppAlYdgKoqIE4/DjzvMBlNx0FRNT1kcc1PsqlY3pO7p7s/ljJvWPV9Z5znroF6bu1pIkL5Xkgyh1CkLIENMl1SD/Znps5tj7Na104xZhxST7hAwBgiS2+vP2MVbHFhDT3f4YDQe6Bmxtgcgd8alMrDfcZZBYA/kcPTH7yQdeu3e/x39yxH4o7tB//hBn2uMVNYKVQAd1hhraogB+A46HCtlwB28lQAdOWC0wUF0AWZY9Jc0Tts/HMijQQ5tQHIa09O4EBbyu/LWWhdfEGivh/jfyvTqJUzLTDiyL39aOLrfAn/Ig1VviCd8FVwLnzREaKO36DY1/j6883DkzaCnhCwUUHY67D2YER+soVUE5wbibYeiDllZfq+1WBvnYCl2OdHAOYAsQFlkwU51bTYYYZdvUe1Xwb2ShWeNCJjROZDmjG1LlBzGDEdvZtPG/PqN7+FZvrBi2qEI8CgMTjaAPPiPU1Wix8YFlVcG1J4+WkWWmGSutQ2KV7RXtBgXopZYktZigCeEKlceaDmjKFMRkoyt6Z9TtMtydM7uH6K62XNoCsUS6SYpXG0mfF4DFguDA0iGwTpsmlgG5BFM8OO3x0sU8cgPkGO0zxGR/Qogpgysan1CWIQrCOLjwa98RGxzgOJDF9xPShXc+hAkQwJPZjTbULELxjUu3I6CMDI3UjKrDx/bpAh74u0Jc7vD74zGzqPqlAJxDZIWTeZRZHZjdhjAkgKqYTxDWD22nvwIRZC0wPThiZUUG7l3dm666ljvfmmWtX18Dr7oRTfQKu4cFe114u09cx1Nl3pSCrLRa40neh84QFVlJHAwcsZgGU3JqmwMklIfm5VqRfYbFtbl3fqqRO3ThK9CWca8qYIJhTUA5RgCZwQiZKh5Rpq6KVZaIs4QCU5QG40vEDDNc6bg9AOYcDmYsU2LvkwmPLkE3ZJUABo1/EwPAuGhgA97+LHfryhKL2ADSVRo4tC5gQwFRQnAyxMUXNWkh1nqaAry4+dNcTt55T7TKjdUUx4BIDEQSwa7oaSmKbECjpZJ/uU+kaz+OdZz8m8MHYmGP7WEXCKr5dmr5W/tRlMtLf6v/x2mxPVerZUQ9I5w1/rCwdG2VsOVLwzYJP3B6frWn/Cov2OSDto1rm5vtjj55THnPAwCXAROtyAIm9RmuOj92DvffceM1dwz7DpfLYeVZHvfSbHEOH86ptg5xekWubFji1PhW7ACUBRPGnWQaiQvSB/exllwkYLNwVZUQHw57qOvaZO6T9zPZJBUwUgAZS5u7c/E7nWwuiKENG//cxdkqXOZbrV7n63DX0fQFDudK+iAvleSIQFeYcgsdGs4v5AUA5wfFQoZ27ILNUPJlDY4MFdpiiXrSYzE8xJooEW1x2noItf6UdjALzCc4S+ZqLLZUmHw1/+XGDI2ossQmC/wwxfCMAB3zMe+y2M5y7Ejhcpc+nLVciAVBe+UCqZKEMhdk0C4a7RF7pjRRYN2iC1ZYdkYIF20rBCg+gHOYIStgGS/yCVUhdzE/87nOatDO0ZOMcrvIgUm6S1L6Qm7SA1JpxqIGyc/FVALSHClVdeyWDDKE6aS9t0RauPwXBpnmFSX2OwBYprJZxYgQHeRFxcLH/ANG617pJq26PmNVulFGscot8BWWFMNAs2RJ0iBgram1+rFBBzVn/Xj6EAkzgIuXwbVsmCseTDcpMGnRJYGTMUtKbbc7Srt8t1VZ/w/FWYhAcNoxT+t2/RgRY5sBmvgiMJrJOoqvONKQXZ7BnZt7Z+7hA6h6i40KFz0vxNsYKLaWtP8+mOGa/4/iM8Q/cPx5RIWbYcuMgurl4wLFo8dCcnNCuQAnjlVjlp0D6nuT9qHXGPhaBlJN856kM0ll23r2H19Wxr1udR+7lWIkQh2LSAfWiRVFogOwy7Me6PlHC2O7u2NAybdeMsWOXSle4teTBP8OV0lZz/Z2W5pw7K49/ByUHHui7IGMgO5b82jDom1Y4tOuQucRTCmWcAbMKGFeIdGvKc5knfP4hGzIyKggcaN8PoID/HRkp1cE9QVl69554waGwrIaPsTV8+IDwNzycq7lVo5MwUU61k72cG8+QfdKb72yfYer4dCzbtnJ1aVr5Ar373/IU08Jzy76mz6fPaBWOTPtclUMXgtHC8ywAYhW03O/8/18RJHtieS4b6sUUq0Ta/mnPBdKxcKnY92SBibG5wLouWBAFSOcWHrcAux6vzTn2HvX15mRw1l967udias/p7zmwhN/1HAtS24+vVwCF4InKI7yk7o/dfq5JJoiATKkndIgMHdsvLg2xXL8sMvWFOa7rXyFb/Y1dFzPy3ODecnNHjo0FPA6i5NhEtp8+g/T2PBDlHpFdqA2iN0EFGUgy3WwXS0zn+2DZZPyCvQdUahyxKtb4P/6v/x+XjpSpJX/yz/QV+Md74NYPeDJQINsOzqf4FkB5C9x8hpsg/huChfNtd8Cbj3/G+q1za2GMCwBgmlC6jPTvCmzKPo0Foq5LANA8YLLaDNL5FgVjOcTUs6nrgbr1MJlvGl+gh7NY7uYN+vKIGc5xMSLQ9BbOZedtXPT78hV29SwoZUxZ/BN+izXeBLcdMlH2mOKLd+/Z3C1w+Pmta68tIoDC/c48P6TOltJ84O81gHGOlcJjByxxPNTYL6bo59GyvXNckwBykclEy677qx6ogfmHr+4/gBRcUr9I+ieOCZ1kKvi0x+Fa/jnn7RkfPn7CrpjhiDq491SIKYmpGGuwTgpJNlMKMPT5paBFpTQqw1VyvgZoI3vn/tmry6+vLHGPBa4EKCELIjJRCKwwJgrTgc/uz7jKBZLNWQiA1JXDCjOWqqlCkArhZJ28R7RoMljhWzl2DZzeAp+v34a02WSd0H2HjBTGJtoIsEImSprYOrqG9H0RUo8DCOnHWUqhIjAVOc+dzvfBbc4GNNV4G9w/yhhkcGgNEt2ixgy7cJ6LA+Lipczgxk7/do26b/G6PLl3xdu1QRB1gc4wyq7g2CU4OGorhRFVNjtEA8PGX45BOssewJ0DYqatBAYm6EurOIFZZaop6O8BoBCU+iYGnbVxbGwQYCCCxK7L5RXWnIufAozunBixIbhUeKWsl/7d6HxeIBVMyLIBhpZRvo8tXnw5YIYK0SWNoDjA9o2ThWv3NgKCufg+VCCaTJ03bly1HvjrYx+ku06JmIlHy8R8p9C9b4HZU8CsRxRZjcTGZx1zvcil4tYSoRQF+iJbpUOBsuhRzD2bqu9R1ScUncR40bXYuiwBKWuNz2eLCvM5AZ/7C4R5/KEEdvNXaGs3RhnTSYFryySz37W92EY6npWh4gxBziBkQbqy6QGc0FB2YT/qkCZhKJDOSzQC5ejqokSURlkJrgBj7KqnYhtWCR8pwzyDRdIH9bz9dyDXhHXCKpE5YEWP2/2nNIWeMzY/5GT3HGBiARWOTSAPNGSAhIvAhAVarGzema2935wm+xwFW0su/onVF+w8ZJ+zFbZhF1myZB1a4MTe1iUn0Qmi7FLaOi8Ddf5AWfgxbef9HODP79a1uhjZlqbOAis5oKXGcC7O3U+uXJp7xsbFWB8Y69Msz5BrngeiBOXX1PFKqqRS+Nr6+kONY1sBNYIisMECM+yCUl+gx2a+QNFtMXkLp3yQRt0By7nrkNM+ChzakRJqLNkELRwLZe63t47RsXwbUx0vfXBbKul7YaQcGycsncirPEzkeR+Apg0ASmCfFHFZGKaqjQJwumRYy46GnC3Rlz264owSstBR0J+7gJR96SLgA8DG5zfZe0XNKWxvQswTWr43WARL9+ZugcN6CazlPer71I6mwNljExPPJZih/UU/W5fVpy17tHMXBZ+sJbaXg56cJVwBL8ZHeZh/dSAiQQ8VJNSaDKnLAUKF3CspqhRMSm9Ba3aYFTuxsDtLvHMriVZovmcqS39r2j5lm7jvMT5MfJgKx+8gUIF7o68GirsyUhRcKdCjbo8ourOjtusizmKFSFs44VsLiP1uLZlkmZRIM/HYNMY3LrX4+nrhwZM6gCgxeOw0jUkkIErvx4NuW1Q49nUATphyPNy6AVH6BETxQFxXoig7HNsKRVmgKPog/DMfiXtsMkqi8h6uZc7Lx0RJg0TvMHXuekWBaX3nlESyUKiEEsTgfK6MFWW1+ccsCYYhXVe1G1gBheft+Th+3E/0YC3HyJKxLBWez3vvgFlzRl+2QAEw6wnbS2MpqftTzAKT76hx/RiuG3pcS3Q/eYWiO6Mv3RwR0k2rIKp93RoN9Dmt9eYFFwUL4vwaAQMyBSIHrgRwjHFRcusZQSkrIxlLnMVt1f2M31VY1tINdp5W3H0/4/wRCVZBAstAYeF67fYj4NJL/+2L3sdK6QGcHTNFKfSBvSr7Y3EhtORAlJxl1Cu2p9rFP2nrKrhJaqBujYvCZ45tMQ44WaOHnhfjoyjzya9yPi08ivO40sN9BVj0HK5fRfjTUXZKuB5LzmKs5QlAyXOLdadz+wX658aH+TWWCqk7D8slJbLI1KtSnOvzuXpbLENFr5dTMMeYKDnGSg5A0e80PvA6jzEfIedyPyerjT1nbvq6JIZbkCcHrIwBRX0KoHR+f+cPazgKXsp6VWjJuvT48yzYroyUHBA/KJf6ms6ddmvlX8i2zJzD6zbmOnoPUveQeafdhSWrN+ePBdAvyYrL9dtwsfH/yV7zWWeTHcJfKXqak1hpifGsg7vyBq+aI/aLKabzvbdSupafYYcNllhig5vrL/h3f/dz7LgEPz4DN3PgD39y1RsAf0baCffyCZZNuvPwMwdufNRZZgqiwu7SUBbBwtrNC1TNEbuyQ9+V6Jsjzl3h3Xac6w4z8FRFGsdCU9QyDgqVQLXs2qKuIapodNeuQWb1Iekkm+sGf8HHxCqtMRSYxpjBZBlctkWFn/ARx77G+ssK5/XcgV5rpCDKFkMQRYXoxybrEmn658bUse8sAHQTnLoSawB106KeH4XPUwXQYOaTqe69MkmlZPl2g+V8g3l3jgNXLVv8L1qxcouRR3PDb4E0rV8LNDXQ1Ce8//gZFY7YYBmAHWd5F4YMolWvR8zuU4QeMLQ4q2DqtvEYBXqeFwXUCi7N7CPBDl9A0ew8CjZqbBQdW2XfozqcoxXT+jjaRY/HOHFzQjVKeVbgIbtJ6d8ESpgJiyBKAzy8B9oauJ1H9okbf3UYhxybZKIwnTHHcQQKfRDZtkLfFWgPtWPKAW6VIYDS5fmI5zKmUzmVsYFOAI4hGPaQVdf6GVEZD52fp2Ia8h4zxDgskSlTBxc3dX1T9lRx3WPW7ty4LSHgO+I41YVchShfJj1QHlyME7JROBI4VVmrj64bJzggZdo76/609iHAFCzh4kvwVX2FM8ryFWKclHrROqUIaZyUnCX7KW5YdmxYYMtS4gsUPjtKrG/rc5gCg3ChwJEVKETIeihd5pKXXnaY+tcVgUCWwvdRlioA1VWYe4/NEcA5BkpVEJD7ylzy/eb1wgWG/eb7F2Pq6+zN/SlimuMZYkbCKVw/HQiiVkCVOhVGL7lN2H54KQOcBV37TJ9UphRjhQEIadGronXslFoYcB4Yp7wz0TGYkzt0DCKzb/ov2+PYRCNUAKeRxpq6FLjbftf2u+Qy1aMM04sNzu3qHMN21hyGYGfOSNT7j7Id9Pll3rwy82fSPiqb0dJcZL5bxgrkd+ZYX76CneO0rRNGJYaMnu9BrsECUX5gkbZ6MGO0t+1XFknWpt5SiS6UHHBa9hkQvuvDmKPNZaB4dsize9UQwe8H87tLTBVlnlrQpjb/kStjx1ivYM2Y3nIw51iZkfU5l9/OASj7Fonrzg4peKLzvN3XYgEUxa4s+K6/LRGD004w8kO7ZnCr4AjHOtevBSL7xIIoTeYaysREev5D6fo5+zj7NxD76ligaTufPpZNLtWtzJrU96HPAw6AOX3LXmbk2s8plkVwMFsg7WwdnEIeFGcfUHQxw7ZpsfzdBi6Aahvozu/xCS0qfPj4GfO7s7s2g6Z6qvzv7oHTV+dtcYvYCW10+4eDXyiUiXIPFxvlfgvMgTVWQRlYY40aLfY+e1CIP1D06JsCwNHFCgGyzJMc5V23VVgu2uRF5gpp8XT04WJTosdxHi07jtHjXHbaYL2OVH+CKFTKNlhi67PytKhwe/vOKWFfGqe45EAU684DRCAkh+Tlip6v1iRgOBN0VziVU/SdA7CqIgoxdOPZwgWopJVr5sGvNVZo6wrV279G1JYuHVv/XV17FE0HooJUI8Q4AJAq0/z9HFjNtyiuuyD4VKH/zECLpVqhHEhXhnfsHlmtoPl+0YHplSPllwI/ATcnrH0fwoYLwHyVCN6WiRLGVnt0AMo94vuzVG9guBiqgkGlmJYNPcei5nPZeiZYAFH+Tr77mESf5r/B0QMmBDYVPCGw4phjU/yCFY6epRJcQPoZ+q7AZu1A37PLuemYcWqx1a2WMMau4vcytWufuxLnssPJx3YCgK4rwlzX1+4ifAcAAkCiyoLOTaRpV37uootevK0ezFTW1wWK6zu37lKB5zgkcMJ3KnGKwju9dymLp16AUYaIVUwpkACp1Z9rRwl3nfLeB2njf/JHnAMI7qgQyDoKFQAmDVD2J3SFCzrr5oU+gPUKslJpUKaKliGbMT/5KtDK7wNBunHspKI7oyBzgqV2QrQK9GSwUOHclA9IEeaXVxw4fxUUf4CgN8Fpt8bEQM1t2C/Qo6pdfqVJ7S3lZEvVss8t+0yNALpMfT+dwgEptFhqIYCinxLAlHRtS6fOFSN1q5JqyyXARH+b+10d5oMiaVPGTOJ/HuW7m9sr07c71PUx9MkEMBTlL5cK+JJyqQK6sj8sQNIiD5ZQtgIiu24sFoqCAXwGPhtZKADDnES+k2Y8AoDd3K9xVEqoVAJRNqG8xuNWEeW2lOM2HgaQ9iHbt6xbwGP9zT3EwLKsa0Pq0hPnPXVxPKLC/hFF6UWUJbLBPNUV3yqSANK+ZUDQcZZYflwnpdDdLtQV9RDSBxBkLUCs+ipvKcACjLtSW+DkIOeovNXJd+27uZIxrGRLDkDpzHHeew7gAbLP8ECXnc657wDOKEPvCDXi5LYABpK73haPMUvtFMNhXbL+qWwKlWutWw7HdoN0/KvxMAeijLj6EDQ5Nk6GaOs0rMEQtC+QA1FyAMpYpjQtOdfTAl5/L7qkz2+uHhDT1VwuzwNRtNOw0TukCnFgnvjvVL6poB8AF1ukwfqHFYqiC24mAHCLdwCAdb3C/P1XN6g+++t9cNurO+BDC5e5B1HoOMF1ML7vfeszahyQZubYAs0d0Jc7LOuNBxs2wZ2H1kHi4wDQ1kNnYsYUsELsJXcNi6apgMuFWy06LtZGZLi4/3NdjR2H7lAtauwxDRZrWrHXWHmQ6E1Q0jb9EsdDhdN66YLH8v3wowon3xuQjlrIOWNFJ74caDJWmgnOhwk2ZY+6qdDPoyVo7Z9sijdBaJthhw4FFrjBDDPMrvdYllunkN0jshM0CCQVNJ309VnsDMV+hPi7yTWwxAG4XoOAydbHRbElLknRMkVBAkhp+Gmg2AIM6pmTeXpETkuJ/rsQNgqcUaMb9PnI8vIASt+iOpxjINncIq3KNycHC9sr0wFI+6pdWAie1HDIfI2Y2vgGwGvgNHcMMbrT7QUo2WDpMmAFJkqduPNssRRXH+cGuds6ECWkGldl3o5N7ufGWWm2yT5jFJWOOF12OAI4AqiaFn3nqeV1nPMq6ZFpPI50UugRAUBraSWw3MMF9Aa2aOjGWSC6SvEd87k5JiF1nkESfILl3RMssS4RLJ0cZ7fZH9y1ysILjJw7gDhPqm84L6QgENx9XcFlcpmVBxybSRBgNWaCm0MKz3eIbWcVsTGglcfH0obabE29FyBQOwE51HtBvuh69GUxsAwR1N/hjJcOotik8TmmH1026arGYMmOAXmMbAF3UuwDut8hzTIzdwDd661jTtnlUYewMlH4mQGYKquTwq3OWbr1F+9Lo5Qh9p2cUhvr80KsBVCp/KbMlGhMYEyQKrShqz8KiMI6slRY3HzRpor7M5e7VAZL2+FShrMxcIXXsW10yTpKAC66RhbJv1pXyKqu0Jc9yt4H4ub6xjlQ46Gowtkj31aWpWLXQzUeURGyFmplpY/JdAralcM268y7GCpR8TePWZtfRCkje0/B6WMz8XVDJg6Qjk8tuex7ud/FYxrEPK+n6DgdgCj1EVXtxm3du7V/uj2hnHv3O/ZLAiZUpFUnzJ0HDI2a/D0Q5zALCiLzW2Wb2KJ1Ob1lzBBlf6f6jIArmn2HMVB42iUA5VJAWVs0UH6JVJ4Zk22SogCJXR+AdLwrMJKbA3gM+WMPDQLbhAAhQ06kAHSR9M1UP0qBlMdAlDGAX/vxkM3bJf19izP+bUCULYb8aJitdma7VbisAb42H7F7N0V/7RbYN1hjiQ3WWGGJLdqP/4wf5z+7oG23cLLaa/f72Rz4+5+AzWcnHH/zl18iZmHeeNlu9tVXzOEAGQD4yQUIff/3n70g5OISVFhigwXIcgCQZL+o6zZ5abbxU0UvzzhRC0SIeZIIH/F6qqSM+dWuscKf8GNC999gEUCTT/jgmSguZer6boXDlzcOPPkZkaVxkP0OqQtPDkTJocKXUGIgTqZchO05ylIpgdPhNU4NsG1OeNUcMVvs0M8dcNKhwAyrMGjI8mAwy9V8jZu/v8Vv6m0ETRgjh4DaZ0RFyE6MpMdSGGE9GQ9+/ZpcA2+/HTB7+2fs5u6hqDQzCxQQLZqcPLg/Fs0fAHZe+KQoClSBKVVghgKdD5IZ+8whC7W8rLLABlM8DCY7AooVjh5AOUUGykG2ulArgKLBY1UB19gFWiwiT6W+QZqF58Zd4/7jK2zqpYAiM8M6mQbQmOwwZZ2EmCh0sTtUkW3Cj/ZTtT4i8wwWLCmRn/VlXnZgCoBygrMP/HwoZ0Dj+tWkOaIoO8wWexTFDAyQrYFlHSPKPc/MA8FUnI6osccOO0yDC5rrzc7FZTefYjbf4125jXERyApTZgq7ORVIIIIVnQM+9q3b34u1n1ahsSZT5fWbB25OnVN4l50XFjmP8R7ISOL8xTp+l2NN61wBT/Po4kOAie6Jrr1SZQ7IWBJBVksEYtQlRcFYKrq5UqDH0QvHiWJd6Lw0tNxv0QPeNfalliMm2GESWjrOtci2ZXRNq6SuQnntgqM2nGOoJDSyVRkIAA5u6ng4AJOvsX+qlfGECKKUCCIQlnMnB+E14jxUm/1GPh4odm4VDgSL7IuowFq3FffsKaiSK2MCKvftOVY2sqxD3cZzhoarp1ghc/dpf5cDimz2LG0HrcsJ/Tnwszf3rzHz4jlunuS1ajJNix64Xrs+RtDfAreUa3iM2zukcWV0vcwpjrl1byHfa3O8lDoF7/y1AmBg5jOti5/I+NG2Pj0nZcavtNy/vgKuXcMQMAEgzzoEUXLudLljLLl+ydIiD7rYcQjEMerq4psLfbbwwf6vd6Bhi0Gip1uXVepKmSiazY5zozJQdMv+ac8nSGINs5aBMgam5Pq67l8yxOm5Ils+kHViXHgUJNmP7FuQRYsdjtzm4qdwfRgtdizrWLXj17JNKL+oQcsel2sTOGFsKQCJNwWBP02wYecFBa51m1uDLo2VXL92zZHq7LruVDjiHj1CVptHyvNWH0uDGgNRgNRSSCRyi5TBsr7CoZlhd+2sr7XEllhjhRV+wfp6i99cb935r/11viIICkt5iD0yGXt0QHBA38ftrN1hV09DzJJoZ2IMdif8U2nPIb+5hV1pwZfo1lw0QwYFM+mp1QvI07uZmrj1CjbdejR1MVOhbu+XOGxnwPYqVczsB0gBlNy71m2uXNJUDhiW0hxn/+kAIAad3c1dtHzGs6F13+0v0IMMniOm2OFwvXXR7SlgqJakdHz9P9ZBjimYZJW70sVJAQ6YzZ3Nu/VcCkCVGfemKSTxreeVo3HLCwWt2IeiT/oRD6O/eynFPUscW1Rckum260cDSAEYB0W44Nomz82GykJRNx4uLHTjmQMPc2BTLwN4kgsUu/OOSmnw2Dqcf0QVUowHAIUAZ4foktaZT+65y8x+DkThMWsxYl0HoLkCU5KfDhX6Mrr5RLp9EZYutdRzgeJ4qARc3nu3N2UBMvBsW7tLXy38/ajQRaCC1tdO9v39XDWS8aRLM6FoM2mxrj5hSzZLC8yoFNu5QEEVrXMNkQIucKBMX55cQM0i9nM3F5CH4sY6Y9AMGT5lslUQgNunWHBzggnrrQKp9cdEQn2ZxSobVHT1WTX2DzOyDQjvDAJanyNoYpko7DsUVOG2V/CB872iocI1AwSWiCyUCSQWyqUPrY36vKU+q7Xy5ZUxq/Q+tdi+GK+ZCq8A/LyRAidPZfle6uOlvE8bo2zsOjkg5Cmsk0uKbq64rhBTwMeYKNHFj8aXCq2z4jZwGaE4N7Mf0OjDC3NOZH9TxVInOMtKAdJ1r3jCVq+ha4w8unVP0f5GACECCcN4M8/pd7/WQlaf7V/KgGJ9m1EkL/VPPRcYAib8n8E9ydrBcslSz/PVrZd9NYB9ix2KrncZRXVdZP/UV0n9TJECNaZ2GMxhSVEghd9LU2+njZwB+Kkl87sx95kcE2XsvJFLD/76KUp74s1ox7c1rNltbtwX5liD0flAY0yxT7ceCKZWDURgJTevjvV1AIM+nJtz+XvruqP7tl+zvkfxrExgzwNRDkDQ57UT5kAUnUgt40D/+UuDn7f/Dsd/qPGleIcCPVZYo0Dvg6C+wf/xn/5fePP1gCsavWhpuQb+vgMe7oGvd87/bI9Ic5rWnu5K9gF/18GlPX4NzOdn4OMt9vUMa3zCFLskwKy71Qh07NEnk7sCKNaKkiuqVADpxJaj2o0d1462xhvPRKm8hbsOwMIRFT71H7DbTh37ZOtdd774duA2B6Ko1dsCCpdKrv9pv+DW9gtOnKrMKXOlnOC0mODn7RSvmiPaD443tPdxI6bYY4Mlpp6JcoNbrLHCdr7EdL7Dx/nPqFvg6hauP1zLf3xFDNKmaLjeG9F0FYwJtvh2a+bAx7/7Cbt6hhrHEKuFAJz60ruYN9H6m0uTCEThyhY7Kbhth2MWoXpZZYYDpjgnYhSAYOmoDpl0mMo8sc2lC2wj+2Nz5QWKIlknDB57mLugsS0qfMaHELyZ7jif8D5hm2ywCOmLGf9k0y/dGN3OgHXjQWZE4IT9zzJRLj3DY4umHcfKDssqZd7dp3GA5nY7A8oOr5ojqqZFWfbYzR07igyTDRZYYuv7JVMi1x7a3WMHx2Rx49exU1aYYoY92nmF2XyPm+LOsY0WiGmEdY6g6w/HLsf0PXB1D8y8i8++dUoqly6691gLEIUdNg8B+bIHOq/kTmtJHXhAZJpo/2rggH+uQWSvtXFLVko1vwup6dVi49YYB7IWGArTtqhSwnWLClkO0Nd1rJB4/nYesimYeQ87dAD+v9l7eSlljxmASVjDcyCUtjlbQOfoyrfxsT66uD4UKoHIXCTIRzmE/dT3hVkNzO6BUwt820ZrJssETjh+e+37nmb/UlYcARqCvU2sc1bCYeBUxtXStSqnyAJ5a2DOUDQmwMY2HBdU7Xp3CYjIxW/JyWBjPvFPLU9RYnOxUVgUIFImCg0gdGfi2GUw/QJ9MndOix1210csmw2K7uyMN3TnoaGICujcHKMMAwwt/EAqpykQokaEWr6zTvpYsmaWEu8DZdK30k8Z2k6ZUCoTHS70gZdStsUSCiCpYnjJjUzP0To7Rp96zLpRsYzGjUBUOCukmRJn2IW4ZmSpzIodiqLHrN4lrOGiQ8ocpjGCehn7KNfT3hyz8nmX2VcjWc6gBOT1GGs8tSUna/lmLIsYyBVIXXT4U923x3Mlp2I/5q5T+nMmpf+YsRjGspVrdb0g62SBOJ41+ySPFfG3TAtPdx1NcAIgySY5xjQb66+2z9pxMRaPSvu4DbrPMjTOun69x3Fw7qU2f3o5QA1kQ0VXLfWlHOO5W7/PDrz23xfAerUC3gG3xQ16FLjFDRjrYo0V8HaNt+8P7sV9hvPfaQHcOIvjWzgh+eu9eByVHpFTkIBMlDt/0j0wuz9jWu98xpdoFVVAZYcZom+vE6Ees35EIKREkYQvhK9XBC61fnFL4SwHphCt1pTFdOHZ+XgoO0yjW8DaxD7JMVAUNBljoFhAxJbcZGW3QJ6NoqVBmqUj/J4Bipfo5yVKuGwXLWq4zDcOXFHFu0WN2XyPat7ibSd/vEWcmO39W2Rb75fna/BZv53fnVHVWxyvq8GCSKtTHYTWMtxniQicub/V/pFv7KH1wAVJfOllghY1HkTodC2p2RrKHDMutwBy4VBrh7r42GIXG6UrckHx2/trp/iSfaJxTzRAbAqiLENqY7rubNbLND4R58uD7HfmA+SfV5/D7l8CUexvxz5hDDtQ5dyVOHQFJo1nn5Q9yroP8xRnqxgTJc51Cq7wXDcbMkBlieq6xaw8oFGKr6WXAnGO4DHpH1edjyPhFYi9/GyMRpub6k6IQeNmnB9tOxMsIZ1ejynIwt91QF0CXefSIacW23EL7NDfPaWv6vEOkSWk52iWJAVp+BsVeGyds9i8/CDWPV6ZNo4BT9kHXTr5Ojy5umTynJCVqnEe6w3BMiD2ScucKhGBP9/ZJqXD3tS/nmXaAFeqzOaUVyq1nLfEitgVQ4ufawNlAUQgbsylzPZJ2/d4HkueUTK0io+VnNJHcDE3RnKgjd7HY25D6W+78L9jyugYg8cWC9CRiRJhuc7vpcY5gisJ27meoag7FN0BE52DKPPyo8fG5LExEMV+Z9+ylulL34HgQmYBJ7aJggppv4vv9/iM/vJrLR1eJSCSBY60j43NuU+x3FvlM6d4slwCRC0jRV13NSC6g2RjBj+9TofCZRedtz4gtHf1yTEkFAwB0v6n67/9XU6XyLGNtVySndwDXD7PAjMjf3Ey+2Pgyr9WIQulzLWvgvu5scr1gutKbi0x8jBB0rZOE4AoCKrAqHUXzTHOLgEjY/OwBbZ57NIcX3iQR0NvHFHh8G8GomgnHhOulV1gJ2J1E+FxD6ycyzm+bmf46d9vsMESMw9ckMq4wjplpFBY3sJZHGtncZzdR0psSDmo908QhWj8rfvtm+s1lsUGALAEt9vAGogdItLsbBmz9qX+u0MrIq+Zs8xsAOmYMSCPdkhm+1CXgF+wwvZ+id12hvPPc9f2XxAVsjWitTsHoGDku5Yckjv2/SlKHIEbDloFT/ihMtkA2+432C5OOP5QoauLkPaYINgGS7zBGi0qzLAP1u79+1ssbzZ4feunMw02S4DNWvzt8/L4HWK/6uK1JnPgpvyKan4M76jy7lbqSw8oCyX2DUvl1HPTMhQsTs8c1r/GUuOECuco9PpsDAqgXKkFwgIqQFwA9DgXDIycz+blQkE0nvNNDTzcuJTF6/l1AEYY98QyUeKxNDvPBksc2wqb9dIDnJM4FtnH10gB4ByIos9hX3tuzF3a5lADVfgbU8cx2lwBhwanssFpccCrske/cJm1ZsUuKOkUtlxMlKkHTmI8D7q4tZ6p4s537pTT+Q5vmjVe16cIXCoTpZY6ILpTzOPxqxZYtkB5cK49jDvBIG1PMUCVQFgHpz4bULIm0pJWIKUwt3IOFWB5l1elCzoLnHBsoosUs5Ex2CzCJdmmqqhG0D5XtK21zikoKSU7J9zkWAnfA4iyxwwPqILCSiUBSMGFGAulRYFZso7HIKlToIB36fSBzdWAA8T+CsRsFARcPHNgsnV9dNApCZzIfKTZwALziSyBBcIcdqqB/WLiHeYcK9IyJPmsjNU1Zrx5iovYY4UuaiyXXFovARMM9JorKjhbtjA/PMZ7sr+N4yoPimgZA5qGoE78P8qYLuVzFcapApxKTT/iGJSUAh36a8f0W94dMOF8p/I45RRr7c/Fn8i5WwBxzgLSWAjKeirlexO/MzaCBUXcs1Smz0XlikycKOu+fIbtPZY4YjoYR4yF9dicm1MwraEup2w+lkXKuj4Aaf/XuVHj0hXoMMM+gCszuPgoe0xRoMcSm6BOT+HYKe31HjSIzZqzk+OU3W0ZUuyv7mZSlokFAAupe8zQxGvZwjoL4NhCA03p0hoTyDx1wl6VU3NiJ2WPsVLKebrP7yViwPEJXLDxSQmUpZdNLCjCsWm/j7HKeIzy1SIee2icHHxsJugKlyWW45fgCWN0MtOtBVaAPAsr17+f4kaZA1W05MB8HVns1+0z5prnaVsHhAyZiRJsheucIH6AewFUCnhc99dX+NPiRyxXGxR1H9gVALDCL6jRYvV2jf/wH/57tOowSGiDoARPthgCPhRItnC/vfPfv7rjr69PWL1fh4EPRAqSTgqcqHTSyi3U9sVoUUuPLa05hwvPERV2gRo1DR2UGXgYPJaxUdZ3Kxz+/Na1wc9IAZMtopWbdZeUMwuqjPWaS7DqmOKmv+PgVuDkYH6jSt0BQDPB3eEH9L8vMJ1HEKVFjRXW2OILOhQpiIIZlsUGq/dr/A5fo9BBAKX2W7qK6LPr/fZwjKh7OCGWE3/h6hoAzXyL++tXKOo+vK8KNSyIon3LThR6nvv74QSh553+FQTc/9Vlghaz9hRyt7PU/n1caX9VZYN1utjqggvk3QuBFJFX4MQvJA/zCJ6Q9UVgREEUMlIIonzGe4QMPHTbWS9dvJO1v7cvSBkoHVL2ySUQRUtp9i8BJ3YcL5COPSDOoY3Z57aUbQng0OBcAttDhVfNEcdFhXbuhH0KapynYirUyDw5eqDRMcsYdNaxA/uiQPd+jdn9wblLPAaiFIgCPl0mxLVnf0DwZWbslJxrj1Jo9/BN3/ugtT3BDymtbNWaRaWGAgr7LkHcgoLYGUBM7eraqPOXjAwI1g/jWgzHP9coa83NKcR7DxDkGAuqsPYonmWx+bWWIyo8+PWdFn8CVIwxM0aXZ30MnNwGwNCxTrbOhZTyCA0BHDPi2hUAvwNiYoAciK9zE7fq2tMgBVV8f9svJmiLOsgITjGtE8E3tRSO063j8z9vrcmdP+b7rudadzIt6uYwVsYAlJxPvK1jfW4/91xjymn6O7rpxP/U9R2IYAkZfARTag+DEXwJoMv1HhVaNz+SCcc5XfuWKqdq+ed85G5WH27INNE+SCVN10sqWqWzUKsRkuCduve0olzFmAlVmG/cE6eywEssWyxQer1C+8kuA6JcGn8KigAxvgTBJ17XjlkCVfa3tlhwUQMg02rPuW6BDcgGn8EBJHTh5Tpf4RjY4Xvs3Jutj9jVztVnWZ5i9jvK0QekgAb7HddV7bsqDxwwNJbxnMemq6foMJnzrgRomZRRrlAwhWEmnqd0D8ET1imYQiClLByAMmWoAQuKWJe8HJPRxv6rTV3tXNgJnlBe2Mu6wrHsYlSmTBTqr5rKPAeWABgdA0C6XlwCCVns99wawH59zHiOPPZ+nlY6IOspoK4WyjbJ9Rg9Z+u3DUJsjvPPc9wdKtz+bxsU6FDhiFvcokeBn/ARLWq8//gJb+8PrtN+RARE7hCRNxVgbVHBVYPM+lgaU2Ey7DBFDRdvA0AQltxlhtaFXOyK9K+LrPCbOw+IwRhVuLGdlvfJ2ArHQ+WUNFXK7Jb71sqNzDZXp+CGPSdXLGqsfUOvydLIcd0SDGrS79tmhfZQo75pQ1BZlgpH7LFDjTYAURyI0/c7J3Swv6hAoX7ruWdkX26QAi4UTjw4M7s/o/XaP/sWFUYqkTmbQ06YfMzH1QkmLz+wLJknAAbBYwOAMjbU2C/YbI/ROhVc4cLDBcUrJ0xZ3KMMrjiagUfdeRREobvdDjOs2xV22xlO2ynwZZKyTXRM6thUEAVI59fHnicHmIyBKCqE5M63+1pHcEDBqtLFTdn50+jio4oq+74jf0ZAxT0eAZdjcqxAj+O8xk1355iGnMeB6EajcXI4x3N/7mjEjP7RdS4GhQo4AShBCqAoDnfyvwU8Y0CZJpw7Dhi6+x3MuRTsZB5x/3lGX/ZA0YbZgSPfPWqM16FuDbbk5pD4PMXgdxTCo0Aeg8ABQ7/87yFOQYsKV0kbRPfJAhH4c9+ZALmXcyKrwIH4+3BuMe/Ql0cXe43riwJr7KcqL3Hd4Tmq2JbmPLLllJWigrGfyxgLJY7AFCDTOlVwH2Mi2bXpsZID/3NrWA6wGuuvjxkVVCEcxNcatEJ8lzYF81NiqeSEfBbrox8B5DSj1vCapb/fY+h3fL6o5Lbh//u5Q+oKWsTVzUst/ZyrbGyJsaL9T63bul5yvyGAolZi3Q7ddXj/fOYueSvPB+x+jaVFjbOAScr2smOSsmouEKc1uGldLraenqPjbWzdACK45/7bjYXKy61u3Xax+gjs8Xrsi7pWcf1SY0BPRbUAinnv4vu4g7FwLeWCXJhj7o+GRrKn6CJa1L07Zyzm2ND/6uW3HZJgrqU/X115xtQqyhiUQWzRZCkKqDDIOIedxkK50nUiJ9OprJv76DFhlREY7ctX6IqUWaIACvVVyyprTd1Y37fg4JhRx/bhXPxQljEQBUjdeWpUz2LzPw9E0QlWO1mZqdMeoxZLKgc8tkBUIFasm+B5NG9nAACrtklEQVTP63/Ep99/wO2NC8C48m4Zb7DGERX+/T/+Ez5+/BkNWSX/Bc568wkxSCjzHttUWkAKvNQAXgM37537BeOJ8CXHicLF1ijhgn5xAYw+5Xk/2w7FwOmCSJzm53FNGX+ji7Ci8paJssESf7n/iPZQ4/TldXTXWSMGkbXK2RZReYN5J7kJyNY1mWP23edKTjmzwMrY4Of/UtlbIcZN2bqgs39eL4Gmxe53UyyxwRusscYKM+xC0FmmHl5hjV+wwnK+wY//4U+ODvsXuD6k/uq3SLNxqFILpEIIQf06nnt1D7ztDjjVB/TXBXYG5STbiotPJxPJPiyk+UUzZ23on5Yt/lddqkOH6hVc3BNfEvBE55dcf9O+SNZajqYJ5IPHzp3isZu7mCeOeZLGNrFsE8cIe48Nltj6mCh7zPDp9gNOh8oFjOVc50HjLIhykLou83msjIEnOSBFj5EJdumc3KfJ1HmW2LmZYNvMgeaE3WKGouywWSwxK1xOsQ2WcIG8XNruFdY+khOPHX2a5L2A2kfsrt12db3G/O4c6epq0X/tt9/gxjBdOO+Bqzsf06QFynsHiOzbyDzhCD2ZrZZvrQ9sDC+8aCYMSFsS3OH8cCff9Z228ZzJ3P3rsenR1zGoLAAUMta5NuUUkTELv+5TuNH6nHAzJuh8DxT7PaZ4EFapskm4OleeFZAqvn0QDJ1MEAOAu/Md06OoO6x+XKNuj66v0m10Afe+3/rtV6TBP+1cB6QCrs5bC7996+tuEBgqp7lzqaDxhYFkU+t/KuQy0x+Qrj1qPWQdy1i/0zIGjvB8K0SPuTLo7y07SstQJsszhW2wTHvMXoOxzcaeT9vBvbacP356b7Vv2334/1m4HyquZETzLRXogwsFmQA1WmzmCxTosbzehJSzIQg7LfwqE1swRYtVKDmvFbJvXV792ulYKFXS546IgYxTd548Kyr+5uXHervDNWoDhuTmV52XLag9ZsW38ScuMQl1rOaYKEAKorD/MoupzpN02anQYulZKQtswGyrdPPhes5AtHsfSL7GEbt6irLusWw2qA4nx95jP6XMz63twxxaBE+o41lWKosCJfpd2ctAqu+W5jwgHSeF4xcEsdPvTITpav+eW8oWVmq3avUksyWI8tqzWJdzceOxLnf2u2dYB/Bd2SaF1JHZWEQGyq6IrBPqo3TnoUGfQGCP6OrTGiaKzt92372C8bhBed1nWGeLztE6z+sVrwIV9PHyPBDFltxka/epcJbmO6SeSj2QxOo4Na+xXuywqZf+0Cpsb/EOs/keP9zcuWt8lWvROmnpXlZx7+T81lm8677FrHD++HWY+o9hMY0UdIet2mIXxrEytuizDCNz17LwxEWmhXfhOdTOwm2BksOFz5iC9hiIUiL/rse+50oOcLPHciAKEIPN8j6o/HUAyiugabBdLYG5m/ynXiwhg0AFGhd7ocQKa+AaePvtkGZRKBGVWfYpe8+cWJXGDwz84CeIcR84YNm3ehnAGkhOS06QzKO0L59ib8sV29sCuU8t2l+AlO6ZoTae5kBfIgAoe8w886sOAZuZWpwxTsg62frve8yw6z3zhK47B0RwszNbC3BeGqNPfVYdP52p12KvmwNfeF5jtnre2JjtJi6oWtm71Mjz3v8sKqkV2gAoAgy4WGLmlTknTi/RY48KS8dSqY/A9Q7z7uwWembq0bGqrp0CbrJMWieAnMR6lFsYx3yXyUgpDz5Ynq49/G+yELhGwbcfWSw8r46/L3ug784o6nizpP3nmIy5tSRn1XePP7QCW2HFWj5z8833kE6dwluMg8J2jQFm3TcEZTbO051fq1vfRn2whu0wDQr3HjMHhs230f7wGulaQQFemb3sQ0DsPwrUm3nLWg1RxHSTEWwrk30VH5Uh4P4y9p20n1wG7PTYY0DLc4CTHJASFNB+xKJejLkmpGutAme5Y+wLlbw2C46MCe+WtcRz9fdpH0wzP6Z8jHQ+0GPaxqGVfMrZvjyhqL37IV10qKSyr2mmnnjz/DM+dLp2AtH4ILESGGgyZTald8y2Getbeu5TmE6/9tLjVQKYjAXZtDEQLymb9jsVV23jHEvF3c+lGI/DjKPu/vayDkXQeCbjxAHNpbkeXc/IoIxjyh3rUPkU3sAJNbzcRwCFMgeLykO1qQeGYIjKP/a4AiS5xZ/r+SW5qo9ASumPaepje9kxOYNlIufkAJRS9kMcFAXZH/sUI+cSHLV1ZWSgaN9T2YCxtIaAXgQGFTgEUpcd9nU7N1DftefnwBZ+1+to0X45Hn7j6bHe/kYQ5QGJXw87pQVLSjiFgS+CA4FCpAoKBzhrChAtNF+Au+0P+M//CVjWG3Qo8Man9GHwRvyn/zeW7cbRZb8ipB3FPVwWnw7RQqkdhguGBBOdfAPK/oTVWxcbhTR8wL04Kk+Vt44ecQyTFnAZOEnRrwiMuO9DzcgKEtFS5IIyEtHbYInt/RKnn4WBwu0XpHFPrKLGd8b3BbN/CTixSuljxU5cek17rBzZ5/et2a4R+9kKLujs+jfYvvsNbt99w+ZmGSzbM+xwi1vPIYhBZ3sULl3yjz/h/euvmHzz15MYJ+GjdEMWVYRs+7A/t8Cq3qJetDgW0Xq28Ei+W5RmoGVwzDXMCpRW2Tl/B77DSfBYq0iw5AABXQzJANAyl321oon1rC8LbIolOhQBECHzpEWFW7wLMVHo0sO4Jz/ht9hjhvX9Ctv1Etg2wJ+Bi4GdFUxR8CTnzpMbb3YWt+NHn3VsjPGcsfNyY1J/Z8/jWtDA9f8SwHYClBNstw22iwMmzRGbxRJVfQy+02vssMQ2BBav0GKPKabY+/b+BTPscYOFZ6nMMKt3WH7c4Ob6q7Ng0ZXmGo71QZaKgvaSDnTi16Wb2gWI27fA5j5G0qe1iI+rtNoT4Ki7cNYnMlOgoMmYxQuI6xMVXzn/qgCaDujLI/qyRx+El1LmBtJXU2EhR+/W83LzB9cjtRTlLJ/6+9MzfId/rWWLBWo0YbWlMku2gVLYGbQ8sk/qYNKIMSpcbJSZB14r308LdFjONy5LHFos3x5cf2FwcgY0V9ajKrU5tx7uE0hhtqrXcT7b1M4KSPdfgsI0EXWJMlclssaYomaZKBZkcXXDPpcTdCMYks9aEo71BfquQHvwfb4r0XeFC8xN0283wlYoH8JAfdUcUZQ9irJD7ferIg2WyRawLBXXH46JguleSdzPgZnx+DAQvDW8WZeiCNQBe8864f2wT9IgYwN8hm2xQzX3z3YdmQQ2290V+53OTzmAnROg738PJdAVGkSWQSWj0Y9GCO1b+j2mQdXU29FgePoO5Jo7rPAK84EMR5aYzV4CIAFFcnN2HJPDTCgJ6NgXOPqx0x5qN3a6Ajh4IanMCBdlj1dlj8JTguumxXS+D2OEfYzsEsdKaeN85xkpU886KdGHoPKMfVYjxks5okZVtKjnx9Bfl/XB9U26oqlLGnVKxvuhzGddb/ho6j5pGc26LqtuwuHMLf8f5lx/zlXpmSfSrJM+DWBP8AO4nK2HJqUxIGVqGSiWcXKJbcJtI8eU1Xgdf0c2I9cSysS6Xuh31VE5FxzN2KfezKCzl9gmNsi07dd959cYv2X/BuD6uBb9Lv27KDuUZY+qOeKhn+Gp5ZkgygMkgTAGAVJsD1CmbyPnHDLH+Z3uGU383d3qA3arGVY3axzhAoZyAN/gFqv6F/y7jz9H37kO0V1H/T9ZFOih4usFl6vCLZJTPykwS8QGjg1Df2culC6xVwuLuuaKU5IjgqvCBQvrc3Q9dlIGhiOAsl0vh0Fj+VHLtoIoqqCx2Pf31O8WBHkOsJIDUXQ/B6LoxKhWFKX9eWXpdHiNTwBmix1KH9wViOj+EhscUXulbYoKLYrrHqvyDs09YjvaZ+ZErW3A+2Kgy3sMJuBJC/TlCdU8spxUWHfveQoynWw/UWRVJxvdP2eEuJdWEgCF5RKAoIsmi1oV7FYVkTrmud/Vs2RB2AiIEtOIR3ceBpZl/JM13mB/P8X2y8pl3NkigicKonBMrnE5/omts21hn8vWWVB7bFzZcgnQtP/ZmWN6XMfFQn/T4NTU2HQFirJHvypQFe1AeWJGFJ0H916RrRCp7j0KYA5M5zuXwpxxifQ+FTSnJZZgB4GLLo2X8i0DmNpmo7Wp7IDS03rDXMQAbxozSYEWFcx4jO3mz2Nw5aKIwWRzyliOuea2qcXGWiit8L0XZcYK9DaA28vPzcNnqpFapdzzqYHDWmVVcWZ71p5NRbCLgIo7v/P/52UGZlWpPZiiykGOrm7HvAVTRGCm0Lur02B/DCYb61KB1LIF1GJo92Pbxf6gxxRU4bGj/H6MYZJYJlvP6tnOotLXFQ40IWDCuVTLQPF36dgB4NxMcC6BU3PCwQvRs8XOgSk1QYsjGPNhih0YF4dM0hqMEuEKARd9Vss6GStDMIapY/NBjgmAqBzJOiqoBP24ZYBaBV8qtCjqHnUdmTl1612XOOd0AgzDrZPhmfwj9WUMMplT4jm/RJAuBe9UWUo/kRV1RIWTtNNLLU62GwbupryRA0hGlUdE0DMLovROkXTAo5u/Tx5EwaF24yeRLYxDSQkwttm5BFA+4NRUaA81irLDbLEPaxKNkQDA7JMEHWlsdpeMzBU+4yys54z5456PfbZc9Ci63rFNFTgW4CKAHZoIglsLkOhQVBceZYh25ryxrjdixLvyx8oyPTTpXZw4a1vRy42Vqd0WLuX9pMwAKATW6dbD72rw4pphA8sa95+HJoKjMUNtdM+LmXdSAEVZJ5QpqLsq60SDKg9ZKrHP0710189Cv+66IoDpgAAm7N/AUHZmQ2v/bh5wKh2SPGmOeNg8nWH7TBBlj3Sg2Z9fjfcCtaTynK18pxKsHyoRzRVOq9f4Y/MHrOZrVHDCdgsXK2WDJWbv97ip7zAhItkgCq4qNMPva0YWKrx37vCs3QG1S3XMDsKBTneeOAFG68ElICUueKVvhpQiqudZNJ4djn5lG3greQBQmmG2HVXUuD2YD9+HbiHfrQXVHrf1uXf/FMu5VZZzCqF+F5Ak6Sd6rINT2tYOSLlbvEb7+wp1c8SmXmKJjQBjLhPIEhvf1jU28yV+/PHPDkgBXFwF3z9Cv8q1nQJ3W8Q+x/vyWXtm8z32AaBb+EeLPYuuDEAqtKf06jIz+dQ44+W781wpQGWtA7bk+sglhcN/d8EW4wJxRB3G2BordH7LDFg2VfEtbkLcky+enXL76Qbn7Qz4cjUETyyIogCnBUy6zEeLPl/u+e2xHMCRA2By1/1bQBQdjzpeddyWVzgv5jiXwNdDhUlzxH4xw7LehPdACz9BTsZHoSLj3H12WGLjWCnYoX//GXXfunTIZKNwju8Qs7kxsxvv95u7t6vSZfF58HPkqQP2vg9eAg26zul2JbxFt0QERVRIs2w1ngdExowIPy6wsnPrIbB6ifUIQBTiKGy7V5UK7zkLT7QIp9RbBbei0v3q4n28hOIENvehcgnAg9uR/m7nXwr8qujuvAAZ2SlUZh1LhcyUGm2IEbC53gVlvW6PqA5np7jSyNPJPpDKMe5GQ1851d4Ncd6A/uq0crsxVQbhNgrCKciyCwIvDQ6Pu3WxTXJME3ss9/sg83hrOYXj06Fyk/RhMgSU9fslph6QzltBkZi4uE0lsF3MgLLDZLEPDBUCKspMIghBCIXj0BrG3LHLxgz+5hjOT8e0gnqRkRINLjS+EORRhgqt/mQE1FgGoIfnkaXCt1EigkhFndqCH3uOFHhL4yjp/KKASmoAUpbFkBXl3Hle/lyzxQKTjNL42Jxr3R3yrMF0/BwPdQQddfzoR8eOLQN5wbnKn5oGpxI4LA54VfbYL6aomiP2xS4AjUfPSCFIwhiTtdfdaj83TrELawr72VHm4SOOLvBs0QPXGzc36loORFlD11rVT3QepWGjNPVauP5aOadGKh/ZrQI27oFAPsOp9e7CAErf3p13HdZ4KWPyhWbmmdYOnJmU3mhTIg0yrvGJngiUJGmPJdUxM1Lu6pmXIzRuURqzyBr5VZ7eCYii6Y5VBsnpvYmc0lYBEAwMRMAJXQcPmORk5jH5OenfHmQvJzg1TWo4f6Q8E0Q5IHYLC8EB8VUriiNslWSwPrgHV0VY2CfBvWeLYMXcdr/BdvEbdP/R0ey3WIbgoTPs0F5X+Pj3f3VpIm/9NQiOUKgtpR6IGX3uENIKzu7P6MsWy2ITBvjWK9y0RLRIfaOdeBuBlHQx7YLwpSVnscgtQKRCKnjSdYXLwGOt3WvZWkWN360golvI95xCZcGOS9DppWPadXICUA5oYVGktUHaT/jbra9/h5DN5/DzWxwWwN3vV7h+t8a6fhMm+Y3vS3vvsvUGa/TzAsv5xqVB/oo4OdpUgXYyZ19jH+M9A4EhNbve4VjTd945FK2xCgJTdO9RkG5oaY4TWhUsDw/t0Er94solYdgez81i1n/b0I7VekZqO8eYi22yCIsA3eZu4YJcf8IHB5jgXQBUPt29d0LLn+dxPH5BfmxuEd14dDzmwBNgfBHQ58Mj26fs2zF/6Rpap3Nr7tNktvopAWwbnMoGd6ulCzy72OE4d3OsYx62WHhe0Az7GJDOAycERZnefFbssPu4xvJm44J50n2H45LCAvdbxHXnG4Ctow+/Lp2Lz+beCUGT/jKQQkFpou59FqRSqxgL5wfOGQKsTDIu6ylDLY1j4epSodta+ZUqbgUYCjw56i3XI7dfo0fm5l5YOWCGIxZBlaOrjrrnxFgos7DOO6W2Cko2twyS7Fx69olSq+E1UyXW0+LrHYraswTQB8W9QJdkLAOA6nBC0UXGCRDlDtKnOa/RNZEWPQquOSE3ZxEH4rvPxiNBBEseA1gSIVlcDHbb2VDp0/kxBzIDqVxj5y/Idztv6fzTXAGNC05/aoBDcwpuP3vPUpkVu/BeI7jRQlkqqdzXJzKhFruW8/y43/lbjQE9+T3laziAJAFCkLoiKe8o3lefnMPfMgulDbCbNmXe+JeLYwBA5poYcNIagDQLpVXOojvPy2eibLGEpjh+ypyr7g78XRiv3irfHqoImtA9Z3s1HDM5GYN1tuTGjK7ji8YBkCsXOH6z2GO3mqEq2rA+x4hyuzCH0k2Xbrtk+3P8aChhGq0rHNHXBaa1B5prDzRTbqELpAIsORDFuvrYuJm6Pqu+al1w9bzCXEfvy6/rk0NkdE1rD6D4Nu9M2586B5AMXkcZfx+y7yhgwq1lpOTcea7lWIOsq8/DPAaHJrsyuufMAuNEjfuXQBTVZR8HEKP7JoGT03YaAZNcHwZSXdLK0LoNjeq3jXxvkOpuj5RngijAZRFSART2Sn5X158Hf3wST7WoKLeqcPjt+m6F5fUGC2ywxhsALthshRaz6z1+823rzmdHuUbaeIr/FKbeuxFUhxOKecTICZ7QP1qZAnvMApCSKxS47PFoxbDCbypsHP0CQ4oeUeaBoPHcDzJbPLG+vHBO7vxL5TElUc9RpS13HvfXcr5lNzUN7rAC3gGzeoc9pphhhw4FFtgEgeXGTwCr67WjEhI4AVLAb2xt7+XD9+Qn2OpwRlFHIUWBE7rzcBGxgb9UAEusP34xfTi8/Ow8SckpnTkhucvU+6Yj66QvXXCsvizQFhH11qCxvSgaGlSWQmAUC6ZgJq/Ddubin2wx7k5nWSidHLOCDktubMAct/tW8OlkH0jbSYUHmN/ba46BKI99usxvrcKz5flXOGOGrXfzqTzVnGA2wPk0WlI5Hrg/xT4IbKgBXG8wb8+pix3bmFRgfq+l3o/1qxZYwsVK6TpkXccfLfq8KnDppzDnSruVPVD2PY6k0CONP6FFVSyek/M3HrMAqzCvddZC5PZfPmDbosIEkVFCTgEFeACpMC/nsXCfjJQ6LBTRhYVMlqO31fLaVGLZpmSlANF9uECHouiDUg0A1dwxEVRhVfAsF39iaO3LhS3N9xFrHcz5rrv2zLt+JX3NuBoksU1USNb50cqIes6YIsiSm7PMGEvkmW6Cc1fi3LTYwcWCQANURewH0T0ruh3yHXAuKqRdbNF5y36P4FPq6qPMZ2VKARH4YB8rfD+K7IDYDx1AR4t/BFEIBEbQ8DKIkt5rKpdYF8AUOEmTJKjMawG578lN+YgJzoZZou2hcWEsuD0AXTwA2R5qx9hSFx0dK08BUYC8gjk2bgYyhgscvys79E2Bsk4Dy3JMcNbgHMrCfmvZXIAmY3A32NclgB368owmJ/NZ/U7BEWAog/CcA1J9sJHz9Nn1mnYOse3H402svip9XBOROZIAtBkQhemTA3jCe6gz21ydAsa2nt/NsbYG+pJxeiIDhfJBOo6rZK2JKY6VVZa6kOoxy1TLsk4uAevsu7m6x/RU26c7wC+/TyrPBFH25idjGmzuGMPhdIgJJP3x7irSZ9jhtogBOUvZXwCH8i3+6x9qHD/UPsjgF5TocYOPWOMN/vDjf8Obj2u8Lk/R+vgWkZVSIg1MpIPl4M5puuhy4fz9UqGGpUCX+HQ9Vjo5j3RM+9seZeJXxu2+naE9VI6BwmwfBJnWSAUKVdYuWbv5PTcR6b59rU8pTz03Ryd87Lc6qefovGu4PvOz/yzgGAHcrhrcvfsBm39wVu/NfIkV1kFhXsHF31lig64usPq4xse5ZzkRpSSYo8i1pQdKn1IgZzIHVuUdinkf0FsXXCv+eI/eAyouGJc66aiA0aHAsa1c39jOgLuXnzEjFKvgXxpiZpEk6wQA9ouhz7b109wnsU1WwVLLeChknTCd8RqrGED2j417z3/GMLDzz/4Z1ohj84C8kJMt+j4zko4NpvgUYQgYBpHlfdjpPAee2O1TPlvznSyyUrZrAAtHG77b/gCUD1ivNijKDsvrrWei7LCCc+t8h1tMPRPFpUfeh7SKa6wce6Xe4N2Pt6hwxG/mWzd+rxFTyt745/4Kty4wGC0tWz1wtXUuPugQxvhDh2BR6jokFNvsMsm1iLRgO4fxHB6TGCpXHVDM+6DEucumyi6Qo9Yb4cT0/ZxFyAaIc7eQBnukQN/13wET5X6KtnBM04JB5ooI3DGQPK32gFLOowKqQUmjojoM/jl2PhkD+ruU9ZC6afKcnPygwMeYxS9nFRyry7kVACkTJdcvFTQBcNnV4IDxeXGMiWKBFphtTjTlvMc5pzH7/CyugLLBaeXcF+CDYddNi6o5oij6wbscCzZriwVQxgqPK4uE27ivbj+tf1T33br65FyE8n03zVoR/zPN2GLZMzlXLvZNdQXjfOKYn2k645SFHV0Fuu8gE9gWSzzgtTwf01rPwrx8yQ2KymVgbW2bccDEjqnngChWH6ACXsq+rtkNgMUEh9VbHBpgs1qialosr7eo0YbEDjVa7LFGhWNgf1c4BrYp3R1poqLbjzOk1KFH7Oop6vqIZblB0Z2d232LyKzoMAwiq0ZQldV5XOVH1tXmWpBjCqB0SOMkamwWnj+XOviYf374TKTtsyZQyg36bkq5pnXdIbuE59iAs0owUJZKA5xeO2PjZr4IYy/HNtG4gXEsO5mBMrWyH7nVdSgEne3rAAhmAfVL/XsMREFmH7Kfk3OtPPaE8kwQBUizWisrpUSepWIl+hPS4LQlkmw/BzkEhLgWQQnx2/N6jvVqhU29RIEOv3hXiLXPu1IWPV5ff3XXoJ85G84OGL1VYQ8oDl7hGFB6XVwowj5WYmAw94fqnqF+xO7cuMh0sui0hwp9V7rOZSfCsQnyqZ9cYb0qss8BUZ56/nOvyd9YRUSvtZVzOak28hu47fnLHNtDhc18DQAhxk6BPpkcSvTYXG9cCmQgTl4NIpXQ3kOHaOlWNgrcb+oW2Hm2E8WHHgX2iDFRnGUz7W/HcPlInwXg+sahBrpc+qAXWHKzU045NccY/E6ZJwqgcPLWLQWVSCTlG4nf1edzB5e+eEcGSg4csZ9L4EnyLFZY7HCZAWgbKgOqaL/XhV+/HzAcI/b83NaeN/ZB5jvMMXut5gonLHFqWp8a2c2bDKy4EYs+xwyFNmUDUERfXm/REAjlOqACkPpa8zwNTk4BqYjpDFkCgMJiKcAsOSaK1hWZ/cIFeyzLfgAkjlm63d9aZmPMqpLzS7YxCXJuQOH8tkJ3+Fsm719X6bpXuPIxOBgPAwDagilnK9BGCji5QNPRpsciI8UqyJGd0CfXOCKyrWLsiwjgULlVtgMQgRxg2AfUlcICHepSYQGPY+gfqZRj3cAiWyDPTOE90GVnlHXSXeUBk6eAKFxLc0aUkbVhMG81SMe7/a03erg5q8bJPwdAwK1AXzBYZsoqti457tLpeyL7ydbxd+wvNpNjygYpAhtGwRntb8qcsa5Iepx90cq5OfBEWSruWGSl2dg43MZ5RxlulvWUfleA+KWXHq/QCZik87KCmIABrr2SGQDIoGQiZbUC+bGzNXVAOp6A4ZhhsWu3kaGT3/rj53KGQ1e49UoyIrq1OzJLCnETZV+1gDD7aSv9i32/qisnF3QnBz5wvSzDibGosZzj3h4XYCMp6gKkspO2g1W8rf40xoSx95krBK/0XAVWLPukyNQpwMJ6GnzK+Du6ukegQwF5N241wGzQTw3zMQaTrtO+bGQJBVBCDKyth5JsH9a6p+i8tp1h9m0//rcHUayZUssJDli5NBLJ4uA5e6Q9fgJ0kzQjCvcXSNkpX4C79Q/4z/+P/xtW+AUA8A63QdFZ4zOqH/8zVq+3mNCiSJTyzl9LB0tG0V3eb3Gcx7gVBTqXVhmqwMZF0AnxdhIY36fg1SMG/uO1LR13fz91LINDDayvYqdaY5i6mFs7cfK7FUD0leSKVcDssaeUf7GsTfDNBDbu4NqD/YX/E6xJiEj51m9X/vPO1y0m+PP2H4HVAZvfOcbBDW6xwxRLf2GCK7/98SfHcpqfomsA3QTGYqWo0kg5eOtSsS6LA/bXG1SosPFp3lREofUxihMx2wP7Xd87Nx5H6bwC2pE0jy+p5FBipNkBclPRJZedHqlVZ+iek6YxJspONgrTGH/BDfbtDHdfVsDPjetDPyOyTjguv0jdGIgSioLLndTB1OUaytouZD4N+1ePLyZlpu6x7VhdDjihENBkzlvIlmN35etWziK8XTXYLt7h1WKHzbslqqLFG6w9E2WLNVaoPQNlhh3WeOPf3sbX7XGc15jOd3h3fYv57dmN2c+IVqwWLp7WN8R4KR3cmsEg5Rzf9w5ImdyLJSknvOXAJroL6aJN4Q5Is/n4axcdUHSOJcHCOQFAAnqkzIMUIFGh3cZEUbBQfxfYCd4Ngz74D/cv3zrc3i2Bs2OinMregdHwLhwYslNUgdzLbJ3GkUgVURfvyiqmkWUwxjrJHeO1yRoYK2r1BzAQXtVlwKaXTd2A0mwKeg0b58T9bzkIEJukU1XgROWSS0qfBVaAvFyDzBZIx98lq7rOP6zb8piLm3JuJiEQLeOmMLsPi7JUnltUflTgQosCGCzaH8b62hg4YhlQ9hiApA/yHixD5lJRQCTHjooMqHStJkvl/B3kArvFW7zyxjkbRJZBnRl3guBjyEyVc2nIyRS5MaPjCUgVzUs6QG5db8x2hTheqCJuHZt0u22w9Qyu5WqDooiZopjieIY9GHSWQPLeB5InE4VG7BiwO6ZELooOs2uXNr5uj5g1Z8fy4Dqt8jhZ5JoFzTJX9HxlrgBRZ9TfUUaYZ36nc5E1qOTqbLtrqc2W70AZKAqaWCYKz6kRA9BKTBQGkd3MF3AGqsg6GSQ18awTysgdXIIMytVkVDGYuR4LLMh2FuOd0B1te5X2SWuEtP02p88+BUCxMpmVY5+Rm+OZIEpOqC9x2Uqa+23uGAEYZboA+OK3FC65yPlG+OsPv8X+3RTL+UYGpYtv8RE/ob8u8MPNXeys3CoSaRvcf+rWpczs4FLhAggBktzgdvQyLiBHpIuJWmUuFQo3+rvElad1KN2AumcnUFtvJ9Ix5O45ZUwJ+1ctGleH5QQHutkMUQAwdeBbOO8EHGbps1NBpGWJbUTBCQAWDf6MH9H9rgjuNXsfuJJBvgr02BczVO//u6MQMmMPr6+3rClNlRbIuntgUgDT69RVbOcpjeRDEKEnK4VsFEXtHUOpSCeU76jkUiuy9KX97oJhWQuqpcfqRB+DyM5CIFkHxq5MiuMZtvdLx0D50qQudQcMXewOstVPKARPOtmy3pZcnQWudT42ADVKjAb6VtADI/UjwNZFMEXrKHQd5LiCNzxGJcbWHQAsXEafr12BSXNEf1MGGvAeU0y9WjvFHi7j1jKA7BtE14pjXWP1cY3l/dYtLQJuhkJLDcEODUxr14zHpnm7aCvzhMArz9M4LcJmKXug72IspVLmhByrIFWVYr8HVKGOzKy0zgZ3dIHedttpzJpyqIHtv9ki8D+v3E+AyQQoH4Aupm3ufVyeouyctbPs0ZdFoiCrQmkBlpzl3n0f/jbnBgQgAVPGFF1gXNlWoCO64KhbRcrKs4CaAibWYj6Is9N7BdkHig3CMYBBsMungii5Y1Zw3sp+DkCxYy8H4nJO4rlUCDkP6bYBAJfN4dyVOJcddgAKCZZUlj2q5hhYKpcK2y2pE7YLgAQ4ZbEgyj7Tp2y/sOAeENlOYy5pOZelMbAwVyxbyoJvETCxmZ+GrJSXXg6YAZJRh89GAGmHaaJcDlwbLGCyNfu5cTE2fp4LogAmGDOGcjX3IccOLqPPuitQNS1wrVlO3Tt2gLRzHWXcKTf3RAAPcOsW40XFLGpu26NAX5foyxZF12N278GUnGxuZQ52LY13ogFiNXgszHcFWmrzOwIeNiittjevY+UvGwvQxi7hcxRw8xXBFAuq5MAUDTbbRADl2EyCTJAGiU2Z2YwXqIGgVa6mIeZojoXsb70BB7vSrQ05oMSCKI/15Zw+r+VSP+f+M/DaZ4IoHSKb5Jk/Ta7BooAJlWNeV76vr9LAoCvEQfzHCbbb3+Dzf4wxLGo/CP+Ez86H/u92jjmgSJ2mQi4QOxUwjGuBVBAiKsogs7pA2QXCXW5I4QSi4KOLB48rAyVEJrbAiJ1QxzpWrjPlhI3Hyr85aJL7E1UIOdPYHq6a2B4RlJtEJVYFJiAuHGpxWgBoGnwuP6D/UAYrtvOJjyDKEhtU8xbL+Ra/ud/GialAtFRr39L/5YQs0cTr9gjUCHF39h6o61EEYIURzGMrRWAFcAJcaJW/dWj+ispDkQdOcoBJ3Pfjpxj682tmAJuZgpO+gidE05WJssESu37mYqBsmwiUbDFkgGldFkBhn7cAijJSgOHq+tggtGwt/c59A6bwsvbSKmhoHbc54CR3TPu+VV4siNIgD6KUcPN+WEydYPbXQ4VmsUN7XQdLFYBg1doHpkWZxLUKQsJ8ig8f/4q69UHbtogCSGvubY6YLtkeU0BqTPDRWCm6X8rvVOjTusLHRSn488hA4XctupaoMmKzYvRImQk6PiwTckgl/05Yb2FcXvl34QSBc9nhDMdOeUU2igAqAAJDRd1zLbihAIo7nh6Lv8mDJ1HuSLOpqKLL6465hlhwzQIrufgnwwCAabwKAAFco7tOwjxRlx0LmKiskhOOc8rimIxjBW9gOJeljeKKgikw2wQwkf/UuhI+bsoEp67EqYnuVq/KHh3dGZ5ZOmslAMJ1eMxeV/tjqMuwYSyYAkD6URovxYJ1Y8CKu974/3Cr8nCOxWRZcsqUImT80ssOU5y95T6XbWffzobK5Rh4MjZWcuNIxwqQjpfngCg8PweidHKugil+/4w5DqV332kqF1AVkSnHuW8q/L6YSjy6/bDt6gH4742OReFBxx2K7oyiRkwZr4VrMMEKBU9s8NgxZonKRxy21FO18Hx7DSCVF8YKgRHXUOl7of5ayjEFUWw8FAOiPDQ+jfG8CSCHAigax4SZoiLbpA7xO3Mx1TSbj5UlgiGGcU8oMwP5vsxj9hztwzn9174HZOr/54EoKuBb951S6k6m3tYZtkm4DeOqwXO7185H6s+IrAEqKA2AFfDn8h/x5YevwLVb2JkudoU1+qLAzftbfLz+CfPmHNkD9/I3khub5arjnXARaYMgHheE0oMpwwwqFhjh+fG3fSK46CKz62c4HioXQGo7cx2NE2dOYbOTqJ08c+jc/1QAxWZpYoYmlrFey3MIjlw67yTndXAZ2EvgMEVwZ6DCs0Jst4VsO+B8mOOvX+bo/0OBZbFBhwJvsPYBk3z6Y3h20t//V6zebjH5ipgy1cZIsUWDXN0D87szqvqA4/U6ME/YF1x8lpg2cydZ5ddekaqKFseycqbqcvJdgCiMOQhE4GQMMLG+1xrtP07i04CKWxDlF6xG3XnWWIXjX+5unEsdXXi+IE1jrNs14thcw4y3B0RXRguecF/LYyw+nWv1fM6nutLrfuxLrlzlWUyPgSi23h6328bs2y2PE9xk3RqRibj1x941ODQNDou3WL/7hrppsZvPUMG5+jAtsgs224IpkV2gYHdsO1+imh/x4foTptuTG8vv4dYJuvhwewfn7tP7bYcIqgBDYYlCjraJ+i3bsdrJNUbm3DRsbKqw59iPaZDZYVBlTa9JASnQcr1VNAlkqOvK3eD2Xl6hHMB3cfBrFOfScoIzgHP54EZW2QEeWAGQBVdCfREBDi055kieJTCMW6GBaLXegjK2pPOje1gbIHYMPBmwUyRQLPAI62QM8LgEoowBK88BUcaUQjsPETzVOYfzDBUirVtI3cpvG+e+wHIugcPCt8ffAKQM6Jah3t/8WIow/ldXhD5ajPw/+6zb7xNAcIZ90qfI4FPQLrKj8sFmrdtx8hgGREjBE9cXqbBFJtQzOPa/0rLFEr2AKGE+1nl2O0NItjEGiqguYMeEBVjsNeyYzCn8wHB9tx+OH+phlKdL2W7hxsvC7zdXOBze4tA8YLeaYbbYYVrHDFJcn+nm49bsaTCMu7TxM+yxAwPQsm7m64KsXE9R1j64ct+jOpxQN16v47juEVniVOHINNV1WNd1tpEyUDpEdx5tw0suPLZc0rGs3KDvRJkoep4CKgRaCKR4ffcwd+yTtojMEnXjse48BFY2WA5cdiKYknPnWQYm62E7c/PTesQ1Lddfx9aEXF8e69fazmP6Gdv0GfruM9WtMcmOYMoYhGMBFL2GAjEq4LOI+wZTtyp4sPanrYFDs8T6euV941tssPSHHKCyrDeYv/ZSH0ETFu2gItyWvQvkl4tUzhKDf0ZrlBYbZMuek41u7606LoJ9Rhi5BI6MFTvAR0suLfW/pDyM7LM8JfZD7nxVErndm3Omvm7qhGNO8IpqdoixdtYIgtRmvQRWwLZwgWV/wQpTf/0b37c2WKBetJi0JxfpGoiRr7Vo1y6kzn/K0gZs6wOJDoBnP7n0lwzMpe49hReYzk9ovZdULICi4AlgQcmoXmrwRGttH2YBUJpiminALSTueIhJZK0/uU9ukgcQwcPHABQ7DspH6u0xC2rnir3WSGrssSGZm67H8BotXMA4FnPXVWuvvQdet5EtgFO5xKmpUDVHVEWVCO81jphih7WmP/aFwtes2KG73uFNf4gzHwHRDqlwxXpehgtvKedYkMQKQJDvl4pet45r0mNF1xmOjUvnRsVGWCvJOlTkBZaXXvSZxvpzCbcOlwC6CVA6Nw4AOHs3n96DKSxlwg5IU0FbxgAwZKgU6JM5n/u9X4N6FOGcHCtlrGhwWGUluccdslV0Xu2kX1jmiesfJQZpVnMyy1MF4UvnqwXdWtMvCdA696hS2JnvLAdTp+dsMewz4ZzoIvasvOidXNBmXgv1I3N1ModPcC4fQj+15WSAQCDGAern5QBEYX9jvbpWWJ4TEOcfy8S6xEjRtVrl5u/FneeICTSwLJlcTBrh2CdXeTkiJ2OMKZwdhtcAxsdjrouwLjc2csYSIDI5LZii12kAdFc4lVO0ZYei7NEWNToUgY3C0qMAA8aT9cQZyfVHt7XuZOw9PbdF7+WEE4pCRonViQiq2HWc9x//YNhWuXN5Xc3IZ/8bcg4y5/F61oWH+xZgaWRrwRTPRHlo3HRNAIUuNyl4rnKyAzdjHB9ltsbgyNZ1J7it+RhZIewAY/s8dT3gBxfOQaY+185PWe+fgXs/E0QZYwLoaFPquP3t2NPoubkgid/glOEPjlXwx6to6aU1YAFgPcGfD/+I3T/McFvcoMIxxDJ5j0/O6vbxn7H6uMZbHFL/dzZagaAEP5RAdTihmiuN0dFqC0xBdoqmPXSXKgJmHgNppUH/7GJjI9nvtjNn2Xlq5o8xIWRswh1Mmrk4JP+WJdcf9FiuziqYnRzj929+61kowX1hBmDphI8vk5jumJP+Vr6vAbwDTt1rfF28xj/934FVsXbxMOBSIQMIfWtTLLF6v8aH+V/RKMvpgDQGilLySsRYKa2Dq2btDqiBIzahj6xC8NOZ/1lcLKbYo4DLIjStd2ibCgcNhf6Ci4sQHoETBU3cNgpVmomC362LAtF1pR+usUKPAmusgkWeqY2/4AZHuCCyIQbKz34s/owhE+VnuGNrpABvomjuEOc7Bfs6DIFmFs7qtu+Xcq4dR7nzc66TFoxU1x9efyR98tjQLTPbx+pyykmJyDps5PsCJugsonV45azBX7/8DmiA23ffsFxtsCw2JujsHh/wKbBUGIh2g6VLn/x2jdXbNWbtDvPrsxunn+DG9B3cFHNAZKdsEQPBck1RWdCusiLMJGCQFczUZuG31L1LLyDGv1AFJsbYshR465YR/Z1jYEcyUfb308hAWTfD9WQLeB3qZZc7xOVvrL8Ojl0BIOtvgnPp2Aen0l+ou3IK9FhR5ZjnqbLrX/SkOQbGANPqatpky0KxSi0wjEnBklNgtY6sR5uR6WKWHSDdXlLwcooe+9VTzrPHgHS8PGWOsmAulT7KA6oQ8jvnodJsOV9BrlkKq8n+P8slkasb2dfvVmFLiu+n///23nbJcSXJEjt5iSSZTOat7K6a6aueWds225HtH72bHkEvKpNpTWO9PVJVD6uTN5NMgln6AThw4HSPCIAAvwrHjAYwEPgKRHi4n/DwsFB6WAEothmwK/WT9RxFvSSvKwmeKx4r2hNFahAHmxV92Quw6y1/XKQ1Y/jcQmDZNZ6wK0flJUD3Ps+wWz2h8j7Rev0ah8bkurqgLZctOyBkpDK4HnkykPst0aGlbXCb4Xa01tt7bNaFV8r7b1NMsj3eZ7PSs+St8kgRklhIOznGK6a+kifKUxl0VjxSxGtlMskxK225xf4Vk3yP6eajnuoj/a0eLJFjuoxydR7n03qS5X8QavcWiaKnAHOoAO19YqXNgN1joVu/Ps4rGb9HPaWdYwXydJ06eOwTeDGGLWZ4wfJgGlDl1bJ/su1ZqcO6Xus6zMelzocIdYto0WVt9Q26vFuMRrckUbyeSc/Bh/FfkyX6uiGIwfFS5M1/bQZvBOq4F8/A6uszpn/a4hs+l2c9lUryS7V87R8fSXLI7fXIRInmOEyRWfi3+nhTQWnOAc2qLbPwgorRo7nFe2vkL/UHZz/Kj1hGnEB3xKEOzSPSrHvF8oXO02lskLJmwZJH/t/VwjxHPeWCFaSvRdrq6zMmfypWWJBAwiv8AeKmNsU7MuyxeHwDsMb8kW4ntxTo0WmaVznJPzDLtphMeNnjetlWnjNcPEdR7+RYlu1bt+hLxb5UPvcZtxPb64SJFWvuvrDrh7EehDWXgFgPlfCXDuJ9P8N2Myun1KGbFwqAum5qT6kczXrLCDEWrEF7nlkeQt4pGlyhEjzT+JG5uXE7yFSaR6LAyAPUHaUQkmy8zOvtLitpzmcUU95KbxSZgingFQB4lbT9bILpH7/jXghRfgaeh8zPbhW99e5yDUtZ0lDvr1fo8WB5nmhShUeU2BPlfTutPVA2s7Ayfu2Q97Hqo97qentQp++oTDwvAp2gDO2SmAGA3XyGXWnICqHyPi8Mjv1kcjDFpx6giXulsDyVes/TDA6WL96z94l4J5UEiqWDxEYMQ4M/lnLMCrVO83QfC/zNRF5sKC303fV/S2ZZefieXMdSnpP3rbTYud4x/cyAWkGtiPeCrCAJ1/mkmh40mxdTiKezuv4BAMcJlJojdVLgeaXUqzvVdY7/fxjy7NqwxRR5SURWMabE29zTIXTd98gR3kfgGiG7QYPrimytegM6LvV7rvJt6BjrBbjD63qB6XyL19miqhN6mqoMVhfH9pBlktkWA5pLv4uWWOyTZ9Rkj8kkR7EcyAeyrJzmM1HPCLUvZaJjpnBfLv/hpEEds6q1R6LwMSFIgKb3iTyLSts9FgOT+8yOh1brwzLAUuvL9eIM9XnseVLr2FOwN4rYstXSxTG536ZP8OpxrG7zNaQcdT0fjkQJeaKIMm8Z0F5MFO8e1rXz8jrl0Nfm12L09xk12/mlyPaRP+Jvm/+Cp/9ajzK+lqNsWTlyP/sv71jsX/Gw3iHbA3cb1HOjy4pZLJf6S6V4ZtiXRu1bpXQW13zGHrZSq+cZc+eQlYKA14CvgrLJvMg2xppVabQBclCp2ANFG3NMnFhSVn+rVCNOzm2jgVsj9rr68nFeySdDczntHQpPlQfg611dvYRQ2aAm55bAR1bESHn7ywO2j9NqmWvxRPmCr5U3w9PjC/7yv9LKPeL2L0urCURZoVA6soTpbFKIoD0mVYyUh3LFqcLlsRl0tuoksj2Q/QBm17/s6D6bYDurg8nZilUdlI4Vfk2SiCcKB8nalvFOZA5oETz2oUr7TzzjfT/D6uszPr4+1p4nGzTjn6zQ9DpZ45A5rzxQ3lAP3eu6nCofQ/DahZcvFDtFr4LF1/SWVAYahqOl+Et2z1Dl43Paz9Ac8ZL9JZqjw3PU3inrO+yeC2+yl9+eqngpBzFR8IQHvBUeXXjFF3zDM1ZFLJVP37D49IrPn/5et+m/o2jPHCdFlj/+hFqGWKNXwOFcZVGQ5H1lVSCDZCn6pKabvJ5qqolFJkokrXa5nR4oQ2Isv29mOFiS1lLorx2/o1CaQuQJb2Xf+291a5YiZ7UL/Z8M2d18jl0GbOYAsh9YLV8xnW8xK5fZnUw4bkV+UC8ETKpY8lXL1fetxD2Z1kaftVSxpwBz+johzdJvrPNaE3o/cCCfREYzmZKhuTqPpPF/TpPtGk15lav/jBTNW8tPS7ez8st7hK7pPYf817I3A7CcF7GB5sCuSiv6kPtloYvM5tvKY8pa1Qfwg80Wj9eUaLzy0499h9gyF4b190fspk/19EiZ1iB6g2y5znPdt9qDtgnkWIqRmqqCcz5dR0R3lrYhenSGui/eoNl+WNdeFjbbZv6I980M0/kWT58Kg51XpxR9eFFOsS6OFdPOtphVK6fyUsgT5FXeDLWnnqS9zfY1Ebh9LwYy56iXSJY4J5aHiezroLOGB6lZjjz7QUP3LUBzOo9s2RuF9YgJT9kpiBOZtqNjoslWxw98JS94XuKYPbvFg5tjDFZp26nvgeLVZasv0DaZJtFDdrBVx716z2XewoRqSaJw7RBjFLBjorypYzGj2fN+kOvmKPyoiaTZfK7d6JdoKtn5Pf7nlz/j7XGBz/gGYS1lTfInvGAxecPTpxcs9q+YznZ1TNmyVPZZcyQcqJffeq8Yt3fFrDeLtJ7G0+wUJC8vGynL2hVKCi0FGGKmectoU3ka3yeV3PA0Ra9KaSks92oDb8TeOm4FPuZnKUkU/Ipiig+a0cTZSCvr1hr/hP/5F+DlsVgBql6556FyQeWVe54e14Xh9Tua0cst5YqKZ4J6BShRQpi8ExbYUkww3wJvqT3j5eJ9MkNGCj67/Uoae55w25LpOiLkRbBzkCwR+u/ltJ7XcolcSVv//lR4oHx9bE7R0YQJ/3QHUUEIPJ7OI9/I8tQLabptv61uk1570CS4vlfoGTyCRdITVwLS/1mRB5or94jx8ozmdDxt9JTpu82v2M2B/F8nWCzfsJ1M8YQlnsqOX7xQiu2sWuJclLPt4xSLxzc8P34v4kduUK+yJYFlpa3naC6DDNTBpOU9AHseM0/zSeidLS9IPZWHvbbqx5mAp/Owr+X7ZlrUfR4h9Yzj5hT268QGdRX1DN5YWqyJWgaxl9e6h9T7ql0US31v5o/YZD+A+Rb383e8zR8wyWrDwJrWw/2Gris87ZjJk2pZa/E+sYLGWvUkRXfxCJNQPhjbBkLBuY0pi3INVjcrXVKl5SqNj+l+PYVE0cdyY1+2m0Ae/q/LJJTfei6LRDHTiiltu2W5nRd1kVezEpJvOqmn9WhiT6CJE9GJ9/kEPzZT85xrwvb3BfC+aOr3XOdZfwiRi0L+rZxjXtu0bIXoVH7Vj0s2IQ3luqIvyz3ktCUO2wyTKNQvFmTKAvs8w/uy8HQCUHk6iSep9Nnipc3Eb0ESPJTT3XPISj4cSxBA1d9X8QVnGSazHPvsvbmqD78zT92R/3OVJ0fTVPZgqVdcbrKVbnuOpqxgAqXMI8RJMR3+HvmkXj5b+nweOLFW1JGAzuKNXUzdqVflaRIti0rf5iWM37dTbDfTYlXZPLPrdQpJyOkWURIiTzxZyd/P0rEyDEmiMPgpuIPKEK89gG0IW94r1j1zVKuwrMsOcVUe+opqvvz66zMA4NvjZxRziN/whBe8Y1pui9gT28kUT48vmOSbRnBzBiurVgfQnPIzoa2MDhLrvidltvQ+qSLbVy6ysJUR68fFE0oHjM+TwydOQt/xGC8UuWcbWNe16scucFxG1/WUintUU8S4RXxFUe5C1GXAev4F2+cZFp/fKkNLvusMRSTxBV6LpVUfp3h4fMXi9w3mf8fhdwEOhCBjgrwS8jydTOYdy1SfhicKgE6rAVwYQp4nesRU4sXwdCcR9rziiAj45pSdadURyGzbLWZFLIjNtCn4Vzhk07lz0My4G0TW0no9AsVL42NajMem99wbaRY0qZKST/cLCaSK9YisdGUqTStpGWpFLVPHNnRsCWyyP2Iz32H/2wTvs6IOAPXyirz6msSEeMES4g22fZziD/NVsYqPECMZinogSwnuaSvvJTGR5HmZPAHsILQJvbO17Ke4P9v5bZd4ns5TLbEamk7Kdf3asUWcRAEOv4+1D9h1OWVrwTJiZSuGS7kyzC6bY7csNMf7+Ttm820jhgVgL0V74AWgliyupxwEAgJaCm0KiZKalqL/AGguHS/wPkiGWj7d14e07gQqc8vw0QaQ5BV9QpMqvNX7+jFztW/VmZAeqN8jVueYRJH/c0rT9ZHrYoaqLn5kwMf8B3ZZjveSRJmV09Cq5ZidKYm6/gHAPs/wY3sDjO1mBuwdzz5uM17biR2LtUmzvWi9WVdyrU9kaBAq8u35FKAe3MjRrE9SXxhzuk5+hx2e8IrCPpo85tVgtfaoLDxN6mlgCzSDzLJ3ZjEgUujShUf3O8RqkzQmU7L9HvusePdJWSTZvl61tdG38/8YgWIdC5GsTKLw/6yI2ymkyT77pYof2Fz6WQUyBnugNEkR9uRuxkipvVXfmSyhWRaVd2tJoLxvZqiW6LbkOpMcVr0GDus8YNdpi1jPVZpV/m3VbQMtSRRvOo/Oo//ngWOCUEcneFP//w6s/9QMCiod3RrAl3usV/+E//N/+zf85+S5rChTPGOFCfZ4wgs+41tp9K6AT/+BbL/HJC8bHnmhsC8JaM6nHJOtrJgiEJcz8VTYI6uWBQTQXC8bQDW/mBk5T3Bayqyn4LgVQ5MOIlA9ksQ6T/6nGFze+Sn5c/iEDxCua2xhST3SQTbvgdWvtWeBKAo5CmJuA2BTTBH4v/7tv2H5/AI8Av+J52rJr4JUybDAKz7jWzFR5PEFnx+/Itvv6+lj9E1+lMJRVqJhd2yJ3fAicU+IQpA5ohJocDIp5s3nb/s2ROpF4hVzAIdzpC0iRS+FyK6JIuTZ/VD2i+WMxROl8EJZfX8ujIavj8X3/oo69tJX1GkbSmcyZQWqni8o6hVP5wHsoK9evR0C0ha0JyHQlK+crmWvbutvKj1z0q17eI9532zqbJQsy+0KzaB2GeqgsyvUnmSrMu0rgPk9vn/9Dd+f/4T75xf85+ci2KxM5/mGz/iGz9W0nyIg7R+KoLNY4WnygqdPL/jz8q+YbnYFQfoddeDZHHVAOt5K0GntgsuYo57Kw3ml5MquRdqA7Ncly/2UnU8TKdWKVvtZk9Tf3B/2Pbo/0grKNUIUfSCdKAlpTjHjNWQQ6/My9WPDdWmkPc9Lj4Bi6g+UZwBwuLQtQ6+405i6o5VgwFd8LcMO6DadR1/T1GnYGGSjkDNZ05RFD5BjD6g8UyqjDk2iYKP2mdDSJJf+fhahoqFJY3l/nabrFNR/q87p83jL99ZGr6R5ddHazoupaB/LIqbKbg5gXpZzWe9+UfWP62NV/wQ34ImCv9/VcTe0nq/lK7cLXf+tNuO1owY0adK2rXDaQ3kaLVXLZGOD6DXSNqj7Z33s+Q67deE9uv0yw2y+xfZxWnmAS9wT8ciWKTziFV7MOlhUW9GRX/EAmdYjXt4yBVJ0bYkvNZ1skT02p0RWcVrITpzkH+W27p/vuCgFntlSkiGNrFTteYVKvciC9OWygo6e1g4cLmrCxAeTKe8HaRwotkhrTuNpEjCNILKVB8pdUz/W+7ruW/2DRRjqflTLx6jda1lIUgm94zZakigW9CikvqT7Fi3z6GCh5bK1Ms9VOqw1/VZF2uv6AYtPEtN5AVmPfFI2pqIxlnPCy4BD2X5fVVYLrJwCgOWm6LksAqhH+xqJToC2UBoCeaLQ8VAEMS8hvmkWyRMKMqsf9JjqaJErKQbpG21zAA9FpyCNHbRdgQIZz7HOJ1g9PkMCq0mMFFn1pQ7+Wgr0yR5Y1lHBs/J1mUkG7NFint+uV+JgLxWgUESSq8CF4oM6iBCBwh1EM5hsplhyDnpV7Nfsesmw72cFgcIeKFqBCQn3RtvjtsXtyyJQYOxD5RO0aSPcPrWXn3VMkynWOdYxOa7vw8/chhzScuW+eUgOW4at9AH6uCj2K9SkCooRr/V8i/f5DIvJK/Y0klV4n/yhjPq/bbTLPSZ4mLxi8fiGSb4unpC7J5nOI4ohgMbCWRkOg9FBpaUYXASRB0V2ZRw7F5BAso007pu8/uaWsEXzO1hFpdMtw1P3x3obM2pD12bjJFfpkjZHcyBpjkKnwKyMZVEuX1suxwygsSQzgOa04so7VhEoqbpJyjFLIW5zvQZCAy3ewJBX+OSZwmTIXP33iBFNNsi12JiM1bVYmaBF2sbJx1t+fn42nSbvxHWRt/oY6FhermhVKj96+eUPWna5WvkJALIcB+7h14gcNY9hkSNax/COxchFs41oohHGPox9TtO6g1QINdWH6x1oX7cV7rPnaL5XmW+3KQKUTufvwASlv8m+Ik1meMcb2V+F14r04zwQLsse76utnKODb0v/KUsk1/ZdqZNO9uWzECGd77EXQkWRIJMcjfiHDCZJ6jQ10DFp6r8AwF7aPKW9SMsOyBOZ7tRceW1anc8r9lmxPEVXluWPJU/l6dJYFCWDac+myPUu/UEo/QCsm2cqoyUUw2hptcY8UaxOSRsPkk9gNWANbrQ6fxnb4m+Len68ZH0G8BXYZH/Ev//2BPzXYk6dTOeRQEQvJbMpVW2KLSaTuurV5mtdITnOiexJMFDZvqJeQ9tCpbDI3HNLYDJr5wlMS/DGKuJB+ct+aFoBI/Zdretb5/O5b+q/vo5Wjqz/stXvwe8m93oo03XQ2V+B9a/Av6NWCJaoy/q53C7v8T/y/45vv33G6vEP1fQe2f4J/1F5o6zwB0yQ4w+TFSaTPR5mrxXjrSGEXj1/cwvxPDmsicKmF/FS3pavyN/fW4iAy8Qr5vih4p8AUKSIuB8WAp3ncrIrojDowp5zLJQiwOwf8Pb7A9arJ+DrvOlt8hWHwWO/0n9pnytQVdyhDmz8imrqYaMOCiz5Z3091qp1eoi8DNUEa9pPiHTR92W8qf/3Rh79bLwfeocHdc/7ehqnJC9xOEL/FXWg2WfUJMocRRDyZwDLO6y//hOwBF5+e8Ji+Yrn2aoKLFusyPWOz/iKBd7wjBWe8Z+V58oU7/jy6SuWn16K6aK/rzHbAnffULj1rlEvf8yjJXvU3ibs/ssxUWb0vyRXvGmmWo5YK/MUt+KpG5OD9IpAabMy3LXjdzTHaBDYT4FW6oDmt+djWgnU19AkSobDem7V/Tn/yuDp8zmQoVqOGUC9JLMYs6L8Ws9oEcihY5a+sjby6cEKz7iUbQPaMGRvP0+myj57192rNJFfD8CGykMTKbzV3hgb9T9EusD4z/XCGmmFsW/lB3wSRadZ9Z7roGz1e3GaVT8P8pd1MrtX97yv6mZTtiiZf634BuAX2G1Atlaa1vN1HjkGUEB7wPbQYj3E06EF2pNUtxkmVe7R8Eph2cTkobQVbePMabumczfF9LC/b6a4X77hbfmK91nTE+UJa8ywxUO5iIjEv5RIHS/lkNlbOWj+hodKd+Yl4wFUsVTEUwVorhRU/CcCppzrM5nsgdnhAIaApxYxcqsfpkbB3iZyTJMpepn6Ir+9VDh7m/CKPO9KnxYPE46dchA8FlO87hfY5xO8rMqAyRJE1qrXGxzW61D99ghEbj/AoXxsQMf8Cc1mEGh91kdb9UDd3FO4Q+dyAwyNvFrXtu5bvmy+OAzwuCqzfQWQ3ePltye8zRale/ZzVSkKN6+30kulXuNeJrvpShpC0xOlpYaZqrS2VW7Nx2B3pZALLGB/AwtdPVEsQ9CaHqSFvfViqV40Gqw8lQrUalGPWueohbrsbwB8vcMaXzD51z0Ws4KUE3JOlkMWom1WRgiviblXxXLz6hp1/B2JiVL8r2PvyPE6VkqOSbbHjxuIiVJET/c7CimFpvC3l2vj+ZtvVdyTaZ32+wNe14tC+LP82Dg/VnQ0gYkfqBV4rajEvKVayosgQtPxPBnrkSWnmGKky0bua7kT8/PfNTvijLZLdUlW4uUypLztsl/xfTMFvhTLG4vLsLj7SlBjoI7uvygVrmo62eME08ct/phvmo8phAhQT/EBPYcOHif5M9hLIRNENnjKm4ZLsHgjvV5fYiosV4gNasMGaA5O6YEqUDqcY5aR66VZ/yVNG9RSd7mec3/ERq++b6auUf3U8sr6uYCwq3Ts5w3ueANCsfs04E3hsQZRgG56zBtQxk3i0fEoKWKlWz8tivU7hsrB+j6yDX0zGPsaFpki7y/1iIkjrmOSLvUTOKx/Aj7O99PtzmuH14YcxUpgXjvQxiJUesjIPGgjIQLF80DxClkLJA39Ae+a7yDg+qIvncNuE+vyvPV9NTla4jzJqjsZ6thOQn5IGgdVBQ5XhxL9XB9rBqKdljoAquNMrKTMQKin2DbLUQ9mNEmVrErXHtl8juWtXRTtpNJbGkHDaZUeySPeKRInTggU0afZS0WmD0kg+rwx9ROHdXnInyDYRwD2FDYP6cKmJYmSQ9yFD2/UVtmWvNJqtMEcmovH5/0DlffAhlZZyVEvf/xc/P++/A3/z39/qZaoXeC1IlK2mIKnZYjyLEykXsFAIMYxgEYQ0Pp4XUY5Jtjv6znHjWj3llDVP0vIRhWN6uaJiTGXPvdiCiESLHQNFtahZ7GMQmv03RoRV8HkGtcXMZ0BWNRL2bICJQbbsvz/5Q7f179h+5cpnj6tscW0Gqn+Vi6XWoxab/EF3yiE6WvDSJN6I8wv0BSwmpST2ihLb7/jvZjHOXvFP3DdKMqg6YnCHYU3h1O2EjGcl2QroowvqlgoL3jC+3ZaeKCs54eeJbIVclbSNmgStjzK19BMPaLP05hDmqJO5/aQIsYtojulHXvtTst+C971Ob/H+LPrsOUVI8cfiuvld83lSrWyrxV/+bbPqJX9NYDlHN9Xv+HlS+GVwksiS0yUZ6yKFXtQzNEugkkXQcufscIMW7z8c7mC1+cXLH7/qJdMzNFcuSdHc+UeMSBmtD8HMAF2s2K5wu1kVrUDgVYCNXiqD9CcT61RxSHwjC7L8L1mbFAYNoBtPHrwmqtnwHpplhGsYXkzLGm7NtKk7vB+ZlxLv6ulmLJ+Is+sdRBt/IV0FstY9K5lGWPREXZ+kVTvWuvlWfek5WjlEG91+Uqa6A5yDGiSB1ad00SYNdLq6X46P9Dso7xrWbDIDX4X/V5LStd1MTOOeUSTdf/fnWe8JnxHUZV0/V7Tf63z62M6jbcA6gFS8bC2yJMYyWj17/ocqaTcV5f9MccWkudb0nNy/dZE2zxwbHOP3ea+8EqZv2P/PMF0sm0scSxBY2WqDw+nvVdkyBQy60A8wsVjg+OlzBqD6jXMOClG32v1x54nivY+4ensnE/0Xp3G5/E1LZJFdGUOPsvTeAA0AsfKtB8hXapgs9tiJb9qGWOpm568jxGBIXJQyy1LpjVgxf8BDnVaS/ilKzYdSJSQUi1ESqrbXcgQ5sfTRi/neSnP/TuAhyIwqDTCv6FouM/l/yXw7/O/YPllhcljbbyK29MCb9XcdyFPiidrVl5mNCXSwgR7vGAJ9ioQhpSxp6Bt4LljUgQplU9XLt0Rep1rA9a3FKRUspDXiXdOyjXk3DZV08rrGaoamkhho+6hOL66bzZSUYxEwK8ArIHN+o/YPP8Rb395wPLxBa94wArP+IxveMe0Wp5thi2WeKncDXlKGCCRxJsGD4OXrhRhPyVx93ESz4Fh8Y4pfiG2W3cGzJJLgKva1fChEvIvWBZBr2hZ4//Ec5H/+7KIIL6aH07ZWTu/DWwvlQa4Ifb1LVKuoxUfq+63Jbtj9wDayXzJr6HboecVw0rbG+1n9Va738s3ymi7RtPLbE5pa+Bj/Yj18hGvXxaYzrd4/VSQKSs84xkrPOAVOSZY4A3bcnroU1nHJthXRPzzbIWnWbE/228xyfd4/P7RXBp4gzroLBsSs3r7IysIlH3WdNll6Kl+nO6hMaJFq2FUsQikHGWr+5Trd3orjLMPlaaNOQ9W/+oZtkBT8dPKoT5XnoOfh39cl+doxkRZUppca1luJR04NOY9fdLTO0LEB6dbhEnoWkH9RSvH1mCLVpg5v6WneDoOT2Mo9QGQPsBGnyZR5D2ZbNC3s4gszmsZDMBhWfLWq3+6vmmixSuGGJECHJIoTB6t0SwXOQY0ywZoXlOXy6vxbNeG39H8tqnGpkW6AM02A6DZNphEkbr/RplTPc9DMw2kk+X78BSfB1ShCkBZWT3X39siUWRblUWxGtkqn2A63+J9OcP7ZFrpzW8o4l2+oRk8VnTtmkSpj9XBaqekV2/LRzvsT1NIFM6niQ+BNXVHe2BzmhAffK4QH1Z+IUf4fJ7aoz1RhJBhooUD0W4xLeMHlgSKBJGNkSEeiaLPsX4x27cBHetT13fg8MRQnQ+jJYligTsaaR1tleoUWNeT+0jjLV9HhPaqfKQVikb4FcWKPXjG6rE5nWeB19IYq5e+0sonV/CQS5Ygpwqqz/lgAiWmPHhKbKizPHwYp8K1dWsK5U+d1pUCT6lJAU8V86o4K2BssHGBSodDgWZXaCqnX+k2Zdp6+YztZobZ55qIK1z/F5Vwlnoh8zDrmCf5wSixCLJaOGZl/czR9Ay7HbxjijsS/jomCnui1C6J04Ml2ZrHyoWhyw5gs14A+SQ8dccjMU0hLlN5AN+lXP5n6v+poO/NiJEb1rWA44mZ1GfxjrEilyHofr+mfXn8r2gGfyQD6AOP2MwXWM3fsZ3VyssWMzxhrVxfC+WDiVEA1f5i8lrMm/70gummsNqzPXDHAef4ectpPLuZBJ6eYDup+xLZyiiR9tgCWIHr0NVbxlXM4LpGiGKmm2QqiaL3easNVYtE8Yxd4LD+Ak0jfe6cw/V7jeZ1+P1yNK/tvaOl1EL9j+knbX6uomwFx9RKco52CjNgrzbGhSikbd7Mr8kNLmdL/mikkiihOmLl8QiVFB3TekaGJoUsw5jros6j2wyTMlDn8r3fcf3w6nmKsekRjxWkbchAoLQDazqx9pKNDZxmTj4PWtDQ4h9MMAqECF7TKbyv6055nY9sgY0Q/0sAE1Q9owyG131hPZdA+uQ9HeM0mUa/V/0o79fLxU8P8oTAur3lhSJ5LK8SPuZ5m+i8mmDhuCdyHk+L5yk/OsisJlCqILIbZcdaddfrJ7w0GOkH/YH+r+OfCHR/EAv7cFIShW/IDGTIyyDFwJZHkxgV1v0YIjhegfWibrBLNBsnAHy5x1/nf8Zi+YrJ475aqecJL3gojVrxROFGxHEXGFzxOGJyUflm5Uog9frZ1dzzDc3n18JTnjfG7nE+0DuGKt3B8k1cqXQPJ0jxTgHShK2Oh+PlbevZwpBglNZ5XAetAmIDOEM1CiXLHy9RjzBt0Aw+ukQxd3N+j//xlyd8++0zvj1+q+LtFIEqi2k9z1hhii3+QEtue4JYXOvYlU8IGV6Z51bwhge847FBKLEb4pYIEg4em2OCdRlEdosp1mWaBJN92T/hZfVUuB9KENkVak+Tr6jT9DFum1qhqeoMj/4Add2y2o833Uejv/mb3fLHrpUhHL/I0o41Uj1t9LUecBjPSJYtz4D8oVhmT7KLQSlK2wpNBW6J5tLIvwGY3+H76jd8XwLfvvwDL5+LILNF3KMivtZDOZ3nM75ihvdqWo8EqJURryneC8+UWV619QXe8PQoQ4uHUfpllTip76z46KW8eTnC6hqNUa3DedOMfZ6hWtKWi9n6FKHj14QtbI8aq5qmlkfIWI0Zs1aXlOHQ2FzicIRf0ljvsfLNjWu2IVLYm0ErwpZSLNV7HcmndZ0GUoLIWhUzZTqPdUz6fjlfPFHY+60caV/Bns6jy5tH1kMECj+WVT+Aw2+QUscsA8V7fQ9MRDOZJ1t5N653umyejfMyNMtIl0WDMLhSSDvwdPq12o/ZABWYLHlDs01YOolHMvIAeGwqfqbySoXnNvMr6mm35XS4FQ7bR67SNijqiOzPabsEreZzB8zn2Czn2Cw3eJk/4fV5gelkS0scv2Fd0gRvpa0n3id1INrmtHoJ7RDzRGEvFPYw8bxS9DRc4DAGYii+idV3W7FSrJgo1rQeJlM4AC3Q9FgR8uR1/VCHohA9Qeopb3Ud5rqcQhJaZKHVXxglXEDXdU8PB6VznnRh05FEsRRkfdy7dCg9JsmZWNHPIp4oOYAfaKx2I4aP/ObAx+oRrwBeHwvjVmIoiDsXUIwu8vScZiW0GUQPB/liQfyCFcU5FuoQD9LazImMGTjaG0nglU3oelx3+vBo4mdgYsQrWK5H2l24XP5YFFOgGYfhGXUnMQewusM6e8Yk2+Np9lKaN2+YlbEUCu+T2nVQpoc1n75mxNmA4vokdVTEq/ZMv0YU8Sbq+Zn8/rx6Ec/llP85OOq4rOZTsuibad0BaIGuhbhOCym0SYRImzY1JNq0q5AsD71PiAANlVNMnsrzsBeit1SpECzlChuikGnjRTp4Vt4FKzSmP+yyYknk/WNWLX0sgWWBwgvlvWzL7I0ipDzna8yrfsyR7fcVYcI4XBHu0FNNr7YTQ3TULNS/3BravFeob7W23g9oGsHW+d49pP5KXQYOY29wHbaMddkXA0a2Ok/ofeDse+/rGe2hcyrIwA+Pqsv/0Kh6Wzmr87NlJ/fQsi1DNdLO32FOWfhb8aW08cjfN1TGiPzv+i1SIedzXWTo+qbl6xqH9YzroOSzyuyasUdRlT0CUX6x442y0NMXLD0+NkjKebxRem82ANAcONeVF5SuQhfMaT8z0qQ9gbZcL6ScMgDZDDsA75sp9tkE2azogTUBwh4pk4buXE+Tn0APlNfeJ9KH8xLKsnCyzq89WTQpUudvNgY9ZUd7plj55Zre9B/Lk6WeJXFI3Ig3d44J3vczIxRFYAnjNjIoVLctOeXKAe4jrBNS+gXpS9KFTUsSRV+8IwcDwG6Q3oo9fC8aAUCOpuua/C9ZT1GOv6LZsa2KMz6eH/F/4y/45z89IMO+jKTwWgUGlaVqJ6iDx/JIOLs88coNXOEZUgk/NlMcBJO1DDadrpnpNhXURJteyRO41v82gSu9ezGRIlt9Pav+PRhpGjT16+C+vJVlavmcByB/AP797nD0elXuf6Xtl3t8/+03vPxbEajy6+MXLPCKz/hWLZUqo9af8Q0SbVxqkNS75sozzYCqEkFc2PT3FstzXSq+4xOmNKJez3XlwFZ6BZ46NorEQhEPnpftE17Xi2L+psS4+YpaPgjBukLzmPyXYzkOvb8O5iGzILaUfEZMO+xKtoSuGZoa2fZaKcdTztXt3VsunQkU+f9G+fXomHim/Fp+kvKYjGQt0fREETJ0We7/hnoq6BLA8x3Wf/snrJfA6l+fqyWRn8pa9w2fK0+zaemR8gQhUEtPFLw0AtqJNxomh6NcWiE6VH6y8q33leKm0VQU69GzSeP/tI6HYhW79YlvwagBijgF/OohtUa/s6XYhfrljdp3YUxH1d5BrNNoYkW2Ur9ln/PP1bkW0WK9Ez+75Q1h7WvvBy+PWddSgsgCvpJsKdEePD2HR9plyyRuGVBT4jEJpFy1J4b3g9qXRwoZGF1/DaQsA6orxj0aMf2AppeK9kjhOrcs8+u6CDTJFv41x5euE98RXp1n7aSxPdAAe8Cyrmq1Fxj7jNR+3hIQ2oNF2sg/UNtqAmor/J5c/+VdMxx6pCxByx/TseUdsJxjvZkB8y22yylm83e8zhaVlijeJm9lX/yGReXNvSg9UniAgxcNERxO5+ESmphpDKtPr481p61bU3Rk+i5fP2Wqj5VuLZtcTefZTqs4aUKefKwXqEJQeHWX09flQ4YGJ2OeKLpfMKupNc0TiE/bieWN4xgWxIDlkaCHPoBDBZnJE2vFFKhjvD4534s7PAr+tabtCrXhtAE+vjxiNX/G06eXatnjRRkAVDAj6f2uKl1R2ZuNoniigGuXDiYr22M6QU/RNWFl7LNihUalj4GuIyx5Q/cIMegMbcjpesWE3a9oTPuQrFo5KL/Lx7wIVDn5yx6vs4Lo2WOCt2r6WCG4OTK4zNOUvE0PqKZQBGqjaHpWL4d+sMMMIAad2XH2RpFpPQ1vkwa5Uk5zWC/KCOL3trC3iEpLyB+0P15CrfkG6W3HI1L6/o4WUaINj77aarIwUvm5DWq3YsvjhM8XgoXlAl+HiZa7ZruVvoJHwdjo1AruEsWSyMsH4DdgO5P5w3VQ8hlNCX3FA55KrzPJU9N/0/KpyDOFFDhLCbO8T3hanybwdfykIm+650oDbT/tpSNH3Yzbjnhb/a43yMHHgmDiVfdvajRXfnM0jRDeynFJ4/eU/dj5+n7WL0SUeNfw8lSwPFC0fsL/LcWoLTRJG5I5vBViRRG1FjnlkSlyTN+Gy9a7fVfd8WAZUClnDZ0mA5agrRqd5vrGslUX7ZLSNDmIcnsLMVE2qJdTD7Uj3TYOCBQvgCxon9M8nZbTNKw64IVs0P+9a/EHXTTrSYbDNsODG5pA4vMabeYOyOfYALXnxCMgcQTFHmMdWvpq6Z+l/5V99j4BRNeeHFRJ9j7lNOu/F+tEjnkECE/pF8gUHL6G9lqxPFnMILUWebIpdQdNfKUQHzGZH5JNlow7wA8jQ1cPxPb9Rc8kigVWBEKw5uFZCr8QLtJo2cDN0AjhvVk03cOE7X5GPfXiK7CZ/wHflm/4NvmGVzw0GhDQrGhW8B1hBnkKgaweUhl+1dLGtHykxbBZgrRNhbTyNqBd/7jMdefZvWI14X3T2Khz6D+nswD3jMDYu3jpGQ4jmi9QKUwbtXKPEHbSEYjhBQBL4Hv+G74vd3j7bYHV7BlPeKnipbxhgQe8YkaMucToYaFXe17U5EFzqs8vuHa8YIn7Mmhnc2m2wttEAsvKctDimSP/Zdnyt+0C28208EARAmWF4nOuUBNhTKxoUkUz5412JW1GK/m6UTPORXKlECSe4hSSy9b5be9lke/WSLPXJ/C5+rik82o+tDTyCk3dTstSUeBkKwQLAMzv8X3zG74vN1g/v+DtsRjZkngpxco9T5hhi3UZG0Xau8y5Fq9HJk4mJZFqgRUlHSCOFax9Qybo0a+ssQ8AWbYvYqJYYIOnLdFw6dig8ERhVUV7AljwFLyQchhVAoHaMJJ9PlEMddRbqasWCZKjGcOLPQO0oavP9UiU0DvKs3h5LEVb52+Ab+gZhp6B6P0PQQ3EBWWKFBIbiFSQEi9gjsNy5tOZ4OJjjBiJ0ubXgEWg6BvxezPEy48fWjzFHTKF9XF+d5apVrncConyOwoOVLcVHrH3Rugr/EBd7noZY+BQF9HoSqAwdCXVerh3TWfQZo1m/Re5JY/Hgxxsz4HOk/5ZHmUzx24+x26+w3YzwyTL8b6cYTLZV1NxpQ+WmQgSC4XjojSm3ZYPxGSKbJsDGs3pPN4AuyY42BOF07UHyTuaC0pYJIoVJ6VBquwn5eyIelW+ijTRq8daMtvyRLHyWVu9nyKz3L5TEOsPQt6Juv9N19GPJFEsaW8puvyAsVt6CrtupFK6b5QvQ7HkMZppq7tmpyUC+xmFR0p2h7/PP+Ov//JnPJXnL/BWNYRFuZwlYJMobMwyqZKjnlcm5MlH17lksZHxUOWswOw1VyzL/U+gKxt/H08Ah75xbISjjfFleaQw0aYRazgexFWSFSvZf0IRdPauqSDI9hn1VJGlbO/x/etv+P7lM+bPL3j59IQp3rHGU+XuL9PIhEwRsDt/vaTvoiJXrGlk14g1lsjwCAAH7W2LWRXDSJYt5ulNL3jC636B980U69VT0Rl8pRWWhBhZqS0f80iVRpuS1Xi4fnD70e3Dq2Mpyn1bhCzdNkRH6HxGV4JGH7PaNBkmB8aNRaDmxvlaLixQEynliNhX1P3DM5ojY9K2hYQTBU/SlnOsn+dYf3nC/fwdr58fMMN76YGyxgzbKsCsTN/jZck5TdSm5qhXUymTfT3SxH0Pe25ZbsH6egAwyXLssrwoKz1CrsV6httZ4lh4Z01AaOg0S8FLMlz5JMu7Qk/J5OMPlE8CKN81qz1v2WCVNK7TlsFqdeH8rno/pLfIFka6W06xKTyWrE0lT0JEcUwWWx5yfA2tG9zV7y6yhb8Pp4VIFF0+ut55ZRkkqGIeKJbxAXXcIr8lrayn+X39zeU9l2jWNZG1XE/171xjD31ijSaJotvDWu0ffDetr3t6hyZVYOTR6FrAIcLRgzwLLRvO78/9K7eHOQ5JSda5GyRKuV3eY7e+x27+A5v1Avfzd7wvp5hke0wnNZHyqqbzyJA4oKe+NjEz2L0UEsXzEPG8zS3vEUk3yZa9ECP1VrxL5H9FlgCADKCE7NOQ7emRf7ouW/I/hVABbRvQ/YKXxhdoawP6OJJE8WARKW0QMoLZWNZkgGxFAXkofpv7ulHpUebKaJpXjUgCyr5iUS1BWz/BoZsV0KzwbgDAPCsqbKySpFQkT/nQ+0G5FhN6XQWtdV5qVQtd39IurGPH9rY8suSx9nIvqmvi6iZCfUVZJW2JWnnFPTb5H7DK9pjO3zGZFJHEgaI+xUaixfuJR5vbBpi8VBTG4OF8zTom0awyQWU0vt7WS7BVEcQtQiRGTHpCvoLVwGKjpRqpikeKp15bpLaTS9NetfKu231KTCbJ+1DukyHK5IlW1BiiGDQGoOfYzWdYZTkWyzdMJnvskTWWMi/6mGk1lee9rMmS1gxwVwebZvKDl0LnPohjJulpQBaYoJ1k+zouiidmB9IYzoocOHDe86qOTveMWb1fwfIE1car/s/wdCM1+g80YwZwM2Gj3tpKXs1h8n6qvpKq3xzAupHAG3Hk/dhgTegYG4VaB+D/3PCtl5DzjZWumFBgo5CNP76FV+9439MFo3qgJo9iBkgMXNnkpdQUNIskAZryZU7/Lfl7jRBxG9Iv3LYhgzY5Dr8Z6L9FhMUKr40u4HmNhqYKC1h+NTpOVPILqNsAtxP2xuE03QS9+pTfAZhhl0/wCmA23xb1alKvpqqXOpZ99kA5nK6TmeSKHKv3bULFW45Yztdp3nQeJk20d4mkf1SEiawQS96Msg3J6hQSJVafrWP8DPo5YBwHYK8yq5Eqv3S+9NGhHlQiFph9IuZNIPvsiWLVhAWAPxau/EDdbpfl7wuqRvft+TPelg+YPW6raT2FMbutPFQElpsUL0PLxmyO2m2qWFMbh5WPSR3rWKzSJglgT/haZRtD2x6taw+oDaTcSLMMqq7g8tGdBY38VfsyKvgrgCcgvwf+Ri6Kz/CXUX0GsLzD+rd/ApbA6rciUOXy8QWLMsjxc7n8MUMMqHpJsjrI8Ttm+Lg4o7c91ljiFywP3Bf11J2tCigry4hv1gtgPQ97m3CabFcoPrek6fYHoG5L7IHCbs0ekcJo0x68INyC2LCld27XeuKdbxki1rEQuE1bvajV9mPyTCtr/HyvQDltrPIqW5Mb/hK1sib/5ZcbaV8AzO+w+fJHbObA3798xvL5BQ+PxRLIs2qZ8/fKpXiBN7yUU36YOBFX4kU1pa+O9C8ywXLrlfZQE611EFodQ0kjy/ZAtgeyDMjuaiNPDHIpwlsiU7aobDwAdnPS8IxVL39wpEzLCv4vkODJvJw312mOTUGksR615e+ZGVv+tuKdYjVD652lfvDWU5z1/wbEWBRw2aR4oOiH5WtYxxip3he6UFk30MHtH+pj+V3ztkx0WaRCyiOz4Wl9F9f40H0Vl5VVpqGA9fLAVoBven/Zru+ap3reJ/xTMXuvEutyq/V01vtNj1f5Rjx9x/PS4u+o67+uDH3oitwGGJyWow4wK+1mh3pqvKQvmuWRqS33Rdwv87byQKFttX8HZPfYLe+xywAsN7ifv2OS5ZjN3yvvFIlLeLCKXll+lpeoBe05WpREeiyU2PQbwJiCAyAYdzM1zfrPst2T9zEvlVB+fR+3jwBl4HpuyXuvznttg+VfHAOqQsd6o6Rc3+poWermKJRkIxq0NDQylj7WC2yzPbaP9VLHoqS+qcZisYnsRq3zHax8oJWw2E/DquCWYtdAyK3pUg1vt/UQUupaynXaPAvvZyjqGRtr981I9SvU03mkU5DLlML/Y0l+T4/ijbKtBDYghk9NngB6itkEHzfgiVIsN17P85Q21lwVa1YRR3tMqoBY75UHCnwBHVLqreON6sNKjCWDzg2uh+fEMeXhtWluPMc8A5fPK2rDSIIl3h2Ogskpcvs1pfGxhmE6L7LlE+BTQXoISbLHpPR9zA48UXhkDGh6nnDsFJkLLXhXMVIsrzRrRCwJt0aeCHLEDbRYH+zlAXAYHNXa5611MT3aq+s1PwRZnBbxJTKO1STOY4386lt5756iqAdFkw4SqAkU4FDp1QiRu4LQQ+hytqaUi7ywPFbY6tP3UmyAfgzRBzyE6qH+n2R86JNC5JF3PDaVw5LjVA5SH4Xw4y1o/xamDupvYxmLrr7BiR7ZasmXoRDTu3V7CZEsgh848NzirZZnmoQMEZImUVcsi7zPysCx2R6YA/sJ69w8tbZe0lgfO3z7tFV5QsFfhTgBcBDsFUDtWbIp9YBceXzp8tN1i2W8zh+qm6nER8ie1eaUddztW60FHbSMCunmMXmXjp5UIq1dCrxG1oZg0S/pxVvRtUP2JVr6Dsg/18sdywjiM50yv8Nu9Sv+mv0vWCzfsJ9MqiWwihgV9fW1YctTKvZk8NVrbGd1PBR+RO/nVVK9TXYDlESPtRZYyl0XpLr3xZBCjmjFJvRMfJ73THq0W19Dyo8CVFblW7LpALD5Z+Bvd4eeKHMUI9ZfUHulzAGs7/CxfMT6+RHr5T8Byw1e/2WhhPhhfAQtdH/sr1/b+I5P+AVPAJrBM2WUnWMRvWyfsM8neF0viijivALPCsVnkXa/xuGxFZreYNy2uK01PFCAQyU/1L76UmhS5WaMSLkUwkd7k1nHoPJIO9N55DiPmHoWBo8cW95l5W9Nxqi0X2nD3I/MUQcs53a+AvA8x2Y+x+b5j8B8h9WXFR4e3zDDFg94rTxRimUW3xpzsiUNqBU2Swbwf02uiifK4apVzfhdAAqyh/spbQsOrZefAxu090QJpQPwA3byvlDmKcaRNtD5PCEY7nHYL901lVvtiSKXsAwMz5i3lF/eD+kyehSyAS8OCtS+fnerLw+ledBETRuPPf1NZF97qait9kzRhmAKPGPJhDY+dF3r4s3A+r/k4WXnpRw4ILJa1Se/bwZXBQ7j9Hx473RF+B1NdVx+rHdUYH2D24Q38q7bjKRD5dXpbaHrS9t4KHwN1qW5Hj3AjfNkedeJ7sb9sfZW0d4pGYB5sSzyRwZs5o9A9gOYb/FLtsd0vq28UyYTmzSxPFHYTrSIEyuwLFB4mryXq+A0VsbhIK8AqmXULdmbus//reasz9PpWr4Dh7Ldkvch70TvdwDuJ6D2tT7OafwyGvp66fW5JYnShXPRxnSOcOcUUqZD0zbYE4XziBAq88i0nlV5aIXmqj0bYPP8B7xvZpj+qRjte6jms+flHQ4jMwNozEWvG03NGppIqUSxih6teJbwLcuj2uqT+hCyHgPd5VqWcmI9s55C4I1my/HQs4VIQH4OnV91BptFYcSLUsAjLMtyK2lL1NOAlnN8yz5jOt8WDDmKuAUT2Z/sD+ZBAsCPTdPF/xrxhgfclSQJs/NvWEAC624xxfu+WL64WsN+c5e+0o71y9V+Q7HRwtoSuloY8zYVlgHVp1ffJRAoodHilHflNuZdM1b+WhPT7TlHIzjkBoftda7S9bGcjm8AzO+xxjNe1wsslq94nS/wNnmtPEze8VJ5nwmJ8lb6q0i650LM3ipAPfXNmtZjTe8J9lMWMqB0iLlu6KJs21wPYHlMePucL/UhQv3SAs22cV/nFaNCLs/GiGTXo7ceLAVbtlrBTtJRYoFk+R2tcrP0hGMNSIvgTTUapRD1oAtfd4fK640fM4f/Daw071scwCP2QGkerPz6HEvX58rlfTfREYVQKctjjWZ91CEQrhFctYNtQlbg0W3Bqv8x4lVubKUPjVB7sdKkvpBHKJfNXGXldiL9rOyzjFvC7uqXah93wFxIlSn2y3IAI8uRse6dEZEyOZzqIwPrejoO0Az02igN8aIG6iWF8+yQNOHyYIJcb7193mpvw1BefT+tH1skSko/EGsLB9VEH7Ts2tT6djx68kQRWIotYCvIXkOONXDdGXF+bVjLvjC5DwA+16OJQJNE+YpS0S08Av5f/An7PxVBAcW1mgMLiXo6wxY56pURWEltNBwrqKx+1FCl8iqidV60DK1yThWu3g2OuWYK+NvrwvO8R2L3j8Vt8IgZ2UpHJworP9c/6v+y3DZQdwRr+CRKeewDjwVDPi81iCzHL/Mi4CwLcsE+n+DH78MIi1OiIFEeoOeIVjFRytV3tptZsXxxnhVeA5osWaEoX0lbq/0czXM4jTsIALVwflP/tdEE2ApjKtoSj57cDV1fcEz75HbA/0PoW4Gz3l13qG1Hyv6Bw3ZekilSP1ipk3oidUa6ISZYhKSfA8jv8TG/x3r5CCw3eJk/4fV5gelkW8VGkZgpTJ7oOdperCQhUeqYSbOD9mMRKkAdhM6EZWRf/8zBnhALGMvywCJZGDFyP6M8etrJK6U9qHPualko9ZY9UvT35W7VQkwxt4hoUz/RI4ueUuyRJm0IlC7Q+iuTK6FrszdGCOKdYpApgC/aNIKvqQkU0L4u2zxwTJ+voctKXytz8nCFo/JI0mevCL+j8KixdPsKTJbsYOsbulwF+htbsuUU0Dq511bkuz/QcT5XkcD6VCHatKzSxIocX9Ix7qeZUBayeX6PXdkf7ubFaMEvRKQABbkCoDHIydtQkFdBI9irXlI41dbz/qNFGiLHYs8QI1H0MYuQ8d6hgjU1lmH1EfxSXrrXXuLomUQRxHrfPkZWLcGhIUHYgKab9qLp5s+PKg1uCXzkj/j/1gvcP79g+3naGB3kEUGJZFErqHXcBoCXklJlktoQpMJZ+zqfWelCzJw3euvBO+4J5757QOs+loAODdnweXxdz7i0lAhRprhsMxwakuyK+QCsKOjsEs3VejhNH5vflQpvYYABwEdD6a1JFtyAJ8p3fMIPfAJQRyivp8YVU3ca5EkOOyispPExTZisKP+a8jWglZkQoWIpONZIJEPLQ62sd4HlrWXd91giJRXHKnChfsPrJL37smJnDVHJ9WRaBJEpG/JMEcVMFLEVmtN5ntEkUTI03Y6Xc+yyOf7+/CuQ/cD98wsm5co+T5PmMud66cXGyjq07825rpdBr9tTRajsKVhdnvniVBvaA2kQlw0eEveUMN32Y8cZZkfuQOqrrstMsmRqe384xcf6WWpcRufxVj966NeAFfDeKrcYaRJTkq1jXrp+ce7ftV4QkkeePOFz+fspAsF7hdbg+qqJEV1uXp3USHkoLjf9X95biCZ5d9D2QaXfgCvKBsA7Au1B9AyPRAEOv58lW/SxEI7VM1gfFmiPbG8AFDisJ7pt0DLI7BHK1YiJEN30eDrPRm2XdEy2Gf0XL5B5Mcr5kZV6NwBkP4o3LokUELnyixrg/NhM0VhCmF9b23G8L/Ye59sY51o2oXdN696xtNRr6t86cMzqF5L7CkGIBLb+90muF7gAFagLoZJ6jhYusprGPRrLHotSKx+ct/M77LIHvC0XxRKQk4lBphTXrxVTCjJrBZS1KjNgV0zr3Fhahdj811j6JeHY5ws1mC7XtkYB9bXEJZFHpEoFiVn1FepOQEOOLVF3BtIBsLDPROm6B37vc+rHefCOGX5QYFkx9N43U+T5BLvNtGjD0snorZ6io9NzdU5UeFuKiTVyaimhOr+FEImXCk9pvTaE5Lsul1DcpVAZai8aj9iy+hAj8KzA42OW5XEmWqSuSbvP7rDDUzXqhWU5Za+a6lMEnuWAsXrVAA0dZLYZAL08VkX9V1NPg33LLeEH2htoVjvntNhx75hO9+5rgeuyNvgZZKTrOqhvJXXYewRPH4nKVKA5qgi1b5WNdZEQYXpM5eXGmwI92u7JlZjKnaMmWhSZ0gmWh5RldHt6oUYKCSWwjOY2XoHcjwnhcuXYw2kT3BbYMLR0DEu+WPLmlIjJLJZHnme3HMvVPus1FKOMiX2GR/J7ZDHU1krX1wBqnTsj8q8czPwQYoWJE4/ASCUk5NhQJIrOAyNP7FmtZw+lWdd0+4tQx6PhDZylnp+OAUkUq/cF7A4+Rop483+10SD3te6dAXhBPQXjX4oR7L+h6ea/RK1UyEj28z2+r38DMuCX598xyfZYLF8xnRVEiqyQwCQKgGq51Wq1EB7B1BVGp2nDMPbTrm5VWch2h6ZQljSvMt1HjgO+4OxaQTWTrdM8xrwrCRI7ljr1jL1SgGaHJkaX1PF/FP/zX5sBUJkxf0Y9gl15oqDJuGfGVn6/h977OvB9/wkf22cAhSfXdlN4oFSM/vqu6TUiW/Yo0Z4omlxZGedxB3WwtKAeIdJbgTXKl4KUepaCLm12KHS9X4wsSb0+lwX3Cayc6lhbvGU3Y6D2Ziw9G/MMWFEQ1iVqb5MMdTvmY8849FKptsWc7M1yjk113ga/lH3OJNtjOnuvvB+FRNFLMTKs5RLZs0uIybrI7tL6HV28V4vUaRcMfvGYUZo7eXThtR1Btp7JMjyVF0p17B7IyTPS8kQBms3G42e1fuvVlwqWB4plPHp5ALtMrQeKlaV3XuglY6AyNvVRljEs3zW5m9Ex/g8cEiyWhxQQLkvrOIw81jE+bt3XAlcqPbik3/FNpb9Ern0FWEPFYNLtQHQMT79oU/fbEF6c3scgnNbl+bunyDbLdpQ2YwSdZT1ZblVNy6GtyLkl/ff0ac4fIlcaaXflVpWhJgr0Md2fWrZdLM2Stfra+t6W3eg9o/Uu1jPzPk/j0Xm8dzARWuHOsr30vvcyHtmS3v92IFHasPNtkKo0ewgp2WwMi2Aujdv1XXNEH2h6okgJlQrtR7bAR5ZXcfUn2QTZhALL7puB+vYWEwn4iqhVtKnF3cinme22F+xKoKTA+tahEaUu9/NY72NhKUS6w+BRKe48uPETey31bAWKGl4eFjJvTvtLuhxv5bwbCPZYLFUs0+GywvMknzS9T0IeKLrDCf03hbil5AtC5GFXAuVaESPAz3Ufb2TVI/YBf1qgEO+arJfjFACPlS72btQjXaxASFtnhYMVRMzxkf3AOp/gfv5e9CvzCSaT2gtSvFWsJReDSyg25mvLtFOjiIIKzrVDv5ynFlkFoOVDTKGLjZCFnlGezTNQ4OTR0BaAci/Xt/QexUu3DIEGvOUprQewyGnrpl31gz6hR9X1d+hTd85ge095fZUuy9h5+twu4Dor1/OmdOj7egOnV44ggcIDnfy9TkGgHItQ/2wNVFiQZ9d1hNsTYAadFd1YskkfK6dYxxmaHNEiUpMooXMsvjS07xEfnm6q9VdJ42MMi8DIcHgN67li72G9Q2qa9T8I3Re0kUtt+th26ECitIH1UKm31B0ScNhIMyPNuxY3ZBlVzoHNr4XxKhCDdUn/ZaRb5sjN77Hb3OP7Zopfsj1e5w/Isn1zNA/K+GMDLmTEITHNNf4Ae+6Yrs2hkfOugjbUEYdgPUuI/IgRbpo46xOh+us9s5B2b2iW9a/FsfxXIM+K5ZDF3X+Fep9HsZc4XK6NvVPkVleOf3z7BDwUSxxXEcql/Yixyl4nOWyPlBWa5Arn1+kNVp4j48t/a2ljVoJAaQLL48H6H0NfRIUe6ZRn6foMbdrpMbCmzlnQ99TEh1bs+fqW3GiwGQRuy+yZUpJ8X9Fsr9KnZGiSpnLsWeVfqrSsWIpxN59jV42W/cAvy4LOn5ZTgGbzd0xnZeBpJY8l0Dmv5LXdzIo+CkAd2A6BvuUWkWKsW+fovG2NndjghlXwui56pAx7QIh88oI3AuZKGLqphLq+FOMAgL88JadZo+9WWXr6iiV/EUkTeK42HnQhsf6iPZzkmfkeTKzIvkU26OfijxHSday6GDLOuzb20HmhY/wM8k46vo9gjdtAaCUqnQbY35D/c5pOD6V5sHTwGGJ5Uwk0Oc6jg8BhmxHQ1Fo5LFkszxLpgyUP69OZkZ/3+TGsrUfM6Ff2SARO5/fp4oliyWBLHntp+nk0rDw6jfV165iVFrxhqM9FIM1rJ/r63dATidKl4zn21ppgsa7HSgRQCym2NH8UBtqKTlnSqVIRdOMCICOEGyJPOKDQRz4ppvHkhnISqvj8+HDSg0qu12KPFa4evO8eu3bImOv6XJ7R2hb3xn7omaz3YNJOoOu+1MV7IP/n5goz7IUiwn9J+yJIdUdgTu26Mvw+B/Zl+bEw1iSKpFnkyBphgkVfs4Lnai7bkALP0MqqJdzPEb9G39PyBGuDoeSKd82u1/ZGgnV5cB7dr+hvf6+2JaEiZIr2IuMRsrVxTNr2s5HG7XwOICtWkUMGbLJHIPuBTZbjvlyWcVYSK7xSAK8SUHmelMsqmv2UbM1+5lYgI5ttYNVFz9jx8nn/tTGrB5J0fk9bt2B5BfC1yTNlo06xRmFbK+AiV73MFoGiLx5q/6fQd/g6HtmhZYP1jpnaZ/DxPqZxese9Mu1rYI3PDcVDkfe1SCfBLSg2OxwOxsjWIlWAdvKkz353aP1E22cp+b1nUmQKyyy25ay0ZZnO3t8yLUjysVeLXKMNkRIS+bE+d6OOeZ4olk1o/feeQz+LdTx2rr6O5QWj/0f1Ch0YO3RSat8Qy5uOlkyGp0l10a6scyyF1UOsAEQhsO7LIwR5sc9G2BLNfi1Hk5WU42sUq6Ys52UD+lEHFAJwsMa33oYaD4z0pDlk1twxr/KEFJTQNz228vVBlujrtRX2nhIZupYOgJVyD8mrOzqJj1KOYGOHYs5vOZotxr4YTDlttTEmxyTvDUznOajr8l+TITnCniWaKMmdtOrz7HCo0PAxS2HJnePXhK5EyhBkyRCwvkuoDVsjX6nflpW9+8PpObKd0w84VO60AsftXM5vEPx3alnGwsOkQexXXiflO+mAd15b8X6HIViuFMfU/ZhM0HkkPUWpSyFe+Tqpo70MnnoqK6IAB9N8Yk0hSRn2ymeHw3e15OqpRuO7Qp7BMg61d5CVTxMsHvHAH4PPjT0Xl7NVhm2Qcl6qrucNVPF7XktfkwLpI2SrCRTOB/h1O6WdD11umuRNfQ4rr24fOn9O+0Bty8l5yiuFB78FFrECY5/74BBh4h23EPu0su95osgxrbfGSBS9r++LSB5v/CmUFrJTTdtVQwfFDvUd+mGOlW3p6OAOkvpQXmPSjcA65rHy+hks5SGEHM257TICVU6rkGCVrKCu0Qw2K1t20f5SnrO8A7J7vzF6v6BR5xzTjaOR2eq4PAXFQuhY6vf3RjaOgfVcVr3g/7HpBhSbpLH1nrcLM88CQHu4iCcKS3aZGvAr8Lf7emSaiTzuHOTYsvy9d3jES8N3NKfYWJ4o2svEIk9CHihyrIKMEsVGh2LCuk1dTyXlhh4VkmeIEdd9xBji8kvtgtrey7P4JC21LLkOaGWeNTHZ8ve8B/BUbPOs9Ea/a3qgSBuWfmdJW0l7pjza9ZjzV0SqBLotyP1qKfRgv0FpVt9k/T/w4LpW8DcGwnVSv7DVr6aQK5zGdTFF+QsRKZauxEaG6DySX1aPk3yyzWh77HK73iqBkvZK6VDH2hIoXfWO1BdLuX7I40LO4W/C+zD2rfuxrLbqFD8Lp/M5ofLzrhkqe+s5U9PlmC47NqhvYJ7yQblJ3be8U2Ds87mha1v/2yIU48Q73lZP8d7DmqIv7YL3F6jLTAYky3NZd+RBi0xtJZ+k5SpPiDDRx608+jW9/lf3zbq/9gbRczTfVV9LnxN6Bu9YiESxuk39XEniNRQYm29k6dqh9uDdnGWo94JxdCBR+kKosWlFIPSYx76CVnzRrJCiyOqRcIYoskBzqcBQowg1JP2/N0VVd5xt0ZVAOQV0B3Ppoxf8UUVx0MErS0OMDX55tczYF3wM99QnQ45a99bCOGbUef+9NgigHiX1SEed1pU0uXQcO7XnmtAHKRUTzqIck3Gaq9EyVtZA+2scKmk8PQiUX67FssBS8lKUKE1cWn1Wb33SJUDWHRW0fbmQJgocEq8aMWUvdA7D8kIRo8OKM6GnonmQytNluV3tiu2RAtb/WLkJrqUyejqv/j6g/dj3SSG+9X5M6fRwbDmnECi6jCwL7ZqRo/7W+ntYBAqM/Tak1SUjhXDZ4dA65zbCRjDnJfKXSQjdP4L25bhHlAD1QLomW7TI1SQKP7615eNeXxuyJducF7q/d8xr+l6ekI1rgr1OGKmy7Vh0l20tJZOu0MdCdxBaeHr36qr88vNLoQkTLOkPwGpR7G5QjAKKAcueKEvUo39yuvYSkNtwhfKmG2jFlI1AToOTdgAWMLHKpi9i0afeNWKVr81IkBaW/DzWfaz60aYx6FF17SXStpFqJYBHgTxlMcdhOxA2/R8ovKTugc0DsBHmHc1RaB7ZXuJ6dMoQ/gPAFM12A4Sn83CatK21ysNtrgKPRMv3YOPXU24sRagruRIa3ekbMdbdq/t9ESypI/3H9DUeCZ8yhYeR6p5sMRgSjFhGm2VkX6bskXEqbVn6kBUOl0bmPHMnTXurhRQ6fnQrzfqtjbSrhyWfY/D6RktG6HuFruXlkXRrhPY+cJyVBott01vLc4WnRsv1Y23TKh8tLz3Zycesa1j/OU3vpz5nDF1lH5e99V56mo+28uSYNR0oVS/ivKGyi9VrQZu+IKXO8zt5fc/GSL82yDvouq63cgxo1gmNPg3JVC/TUJ/Yh3eKQPepqedIfCvZ3h/2V+yJovtLJlP0AAfbe1aaPhZ7VP3fIz6sHx/fOOnetaxnsOzLFHj6g4vYUuzeMUs+xPpbD/21mxPTu54S4B3XhrWFLDGfvqdmM/k5KEjRukzSxpgeHdTMpPyPNQbArtje/9YVvQ8td2gPFEvRs463veYQ1durv5o40ed4ng3svir/2YCX818p7xOQ09QzoKiDy3L/FuIUbFF41ITaj+d5slH7XkfTAMsBlgepSqXGpYwMeW2grXJyDdDGB0O3S/2/q6yQe4Zc+C0Cjo3Th2IqqR7lyuGPfC3RVNq4jsux0Iia9Qq6SoQUuFurOgDCfYb1wp488GR9yjVjCPUjfDzkis91lY3X2H3bGkJe+aQqzhqhY5cibxn8LbRs4mP87PI9QtNarDTLe8N7Jn08Vl9P0dhj+loXovMSsQMgC1EwYaIRI1A8kvHSwIZvVyKFYbUD7VGnRw0ymPGddPOy+ku9b+XfUJqXX59rPYNOa0ukhEiUEIFiPYNGijkVFRMp5EkbWXMZ/UFLzTGmPfHluo6s6vNinb1VEzKVxs/FGinQnA8M2n8ANsRgMjki8+iWqA1X67j1eNrYs4xBj0DxXr3CDysXvRefmKronRptG1HKqLaXR9c1Sxn1zrHSY3NIQ/eUd9FGloxmy9x1QQbki7r+ZKjj99zCdJ7vsDsbITEtLxM+5rWxxmAWl7UeDQKa7cTqfULs9xDTYtooIimivS2Rcmr5kNJzM7oSIanneWwDkF42ui+S7a/Numl5m/BUng2leX2PHjVLHSGzHs+b4nML8Zeql0klE/S51jEtI6z8oWu3bWtWv5ICvQyvnBuTC5a+FcrD17VGFDXRGJKxnleKPh7C0MRAzDiOkSkab5RP6wohAld/G6uepxo0KURg6Fu0qVMaJx7vHQxi5HveJiEZMHT/y/pFzAMzRddtc89Qu0htq1pfYFtPPLlUyAa277QnCk/TsQiTGPHC29AjW/89EsT738YTRad5zxJ71iToeFht5Qvn9fTtWH8QwvH9wAkkU1cGsotXCudNuT7QDOqJcv8f5fFfgfy+cK1mRXWJukKywqpdxKxHytW5mjwJkSlB6FV5PEXISgsZU6EON+V7tFXqzgGLRJN9IL3+tsmfQtiw8H9Bs4NZNI/nWb088sS61pVBvGy0sA+RKCFPlMYUHhHsopgKidKGQBkKXeVlV/TtkZLazi2FR6DZs3Mq0Ckjvdx3xN5fG4h83dIrRbwgtQ64RJNICZEoWskLKXch7xTZ9343QaLwKHfs+1ltJYUoSSVPjoV3rZCRnqN2fbfqZMijwjrHeyaLQAkZjl2UY8/Q92RNKtp8I8/4DcX64HNChiMTKF316hRdzzN89LOEjncdYfbuNXTfewrsYdd9S8c4x/vqeuWRs13qn6cf87W0HhAadEy9J3ekcn7pnSL6o9VfcpXzpux4fWuIRIl5ouhtLE3rtyGyxdoeDbE979R/LdtjpEmsjwydb8mi07SfDp4oWtinQr9cH4px10LijihTacBhw72vK+nSuBR7poRsEqtyh4gTOGkuYhljnWGba7XBuQmUUxtiqe+b2kFwfeU0gSxL+YDGkqXXjC3sTiOFKNHHAFUmljLP8ISzpVjeQmGfA7o9hrSNS4Vuv5acsdquPs79kHgGqHnccniu0qwtKC8QVvj0vveIVh91K3bNQWBZC17HnqIkphitKekMbWzEEBooSPGaC3k+pJCHMdkZMyhD19PX7Rt9XdcyPLXMiJG2lidKH89l3Usfi53r4SaERM+I1d9jvFCswbmug5mpMcGGgGWTWQRi2+fI0YiXIkmy1YFi+VFiJAr3xUBYrQmZXSHChI9rjxqo/0cRKKGZDQy5mH6xVOLDO+6ln9uebKKDlsoFZimR1mV1AegRDV2Ymcqr752K2HMw9LPzMzwA60WdlQPLLlE3miXqUUJmN/VttEGYI80rJUi4WK0jZXQtT8jD0FKCYSljKdc9ZjQ8lSBpo3T23Ui1MPHqJZefbNmNF+V1xND6B6qpZ8hQBKF9wKUJmU74jjqwLNd19jrRgWU9MqUCe6DwN3mlfR1QVvYZqcbPUFN65Noe+iAf2j57n++Z2qZTESP7U+4Xej/PuNRp2vjRbVvcjbPmNr+v6zN7nPA2o/98DOp4iEgJKXcWGcl271WDPVEA2xMx9LJWn6vTreOhNO/6XY5reG2b24Fm5zTxYl0r5uLveZZYpElI5rbVcfQ5fcDSdS054pEkgK83S31MnVLh6d3eeRZSyJPUMmwrFELv2sXj4ZLxhuZ8a8vgPIZAaYO2HtfHPkcqIROagRCrg1yOLMt0gHe5nyzikAF56UmxLg9JfyuX0n1nyDsFKi0VFnlj2Xs6zQofYe0H0XaFHN2PaBntDSJ07QNDnittPFA4r6X00LSvBHTwROl7dNCj4zwiZQho4cVbpRizMqvnpevGkxvpVqPQlb3v/v6kGMJw7BtD1OMuzwAcKlhW+XFaiNrmOCpXjhy1rmGRh6F4QmYb4ulunuD3PFBGhDGWkY+UgYCYe7K077u6rwHiyluujqWSKBpeX8Xbm0IbI8ZT5GLXTMFQhcv9DNc9vS95gUPPB0s/S3n/Y+Vq2zI5ZwVNUebbxp7QujFwSKYAh9+nq5GReo6HLvfSx26NSAHOLzjvnf1LgPe9Q/XAIzAFIttCbSFDYxl30S81ryz/+VjoUdqYGyESBYGtd66VfoAuq+Vwf2CRVikEyiXrjenPdoQlGSI6QrWm6zzOrud615LrWbXwDc0aSIqEeKTkqIkUeV1pVEscBiCSfatBWIagFygoSLRwp9NWSLNwCSk6/EAp1ecUMVHkWdq8sycQPOHtHesCXeeskSovD7c1YdE58Kz8rhzfUfRnuv6nTOeR/QrigbJDs/HI8uZ8zBLyfTDebZBS30J5+iAJU57dG5FuA6sMT01w9nU/b3Q4pVwkX47DUTMhR0uPM1mZS07TP+2dEiJRPFKFH8vqb26KRDlm5CJmcKZ6rYWucSw0WeLVSZYbOWqXd74G/4dxXQ+WTqHTPOW7axm2gSVPU66f8p1S5bG+lvedPGLW0iMsgja13FLP6/IdLKIy1t/dgrDZoQhax98n5IWSek0LVnkeo8MeO0DqtbEYYZLal7Kc020hV3k0ISw6tDxfSajoroH7S+2lAhz2rZyWAu0Yoe9v2Y5WelKXpj1PuspeKe+Q/diWmNV1JdT+2+jkKc/Qro4PqK32ocgP8Qxeh2914JxHGhYF/gNls1y79JbtfN0wQgTJLfQdFw2rnqQoL0Pd3xNibNGIkSXKLhN/a1w9NjgkUSRdtla7OWg/VsBloNnWvW+dkpZyrCssj6SfCX32IalkridsU58jRoB519cVXdr6DnUQaUknd9NcHfI8UKR/SgmQx/D6rNjrXCVS5btXh1KMzqEGE2L1Thsl3gAYp8WeNZUg1ND9W8iAHFKunlKeerIsNroO1OXsETwhTxTrfinPNRSBkpr/kkep+8Sx7xlqM30OOg/RVmKeRqHB+dg51iCpRSi+ou5PtVwqbT0Gq9/WSj2ahwaafW0Mlu0XIlF0nuTBDSv4q9XmUzt4T14cO+jYhthJQf9ypUeWQ1fqcxMogK8VCiymbIfDViDn3aNYJeUByO/rWAzSoGQEkBuaZi01QoZgEpvYF/oaRQb8KSld73NKhAS3HAfCnYr1jp6EBdIUnp1x3Vf1XzqL3wPPdiWQV9DMuvys4LGNFXhAJ+5Qe+fIfyZVvI4kZvAyhq7zbeeMH0tAtB116lN+DCH0QiOfui2mtE3rPC9PipdcyEjhkR4dM0VGz+6abUP3PZpMAaV5fZTH6/Lr7COvdTWwlL5UHEuehOqfBWuUjo95xnaX0d3Yc3hyKRR3RT+Xlyc1vWveU5Jcx4DbP6Nr/9JmlO6Y8ugqxy/xG/QFXp1HYLWJrvUXGJbw6OtaqXEnYkSKwLPrNGJTZ+VaYuvJvqTf1Y/hkSTWoIS1H1MfBidPgPBgYoqsluNen9OF/PBeom2f0XXAbNCYKN5DpF6mTeO2jABv1KTtteX6Gh6LCTRH+t8APAFYFArr1zLbEjaJIiRLSM+WrR5hh9qPImX0KHSxmAJjSYA2iLnLpiiRnFeepU/Dy2NmrTxt6t0xzc1i1qW+/gPNstDkyhXidzTlvUeiAA55YgWRlW/GwWNBxznNM4yOrWddXGJjdSx1BN2D1+ZCz9pVqWpTfn0Q8ceWS1dYBmuozGTKjtyXSVM5XweNlik+QqhI2n1TJEo7kaCzDO2J4nmkcH5BjhshUXYoolh3QUxJbKvItVE6u7TN0AiuIGRotJU1MYIpVR700b/3ZQh6z9JVXqToEpau3df79O0d0sUI+hnhtfVjCJS+cYmDn5Zt6A1WeF4oO+M8bofc3rhvfqB0IlSgTrG8T3RaiESxqoBl5iSbPrGpO5bXieVkYIENXr6mdV6qHE+pb8fImf7sxZZSfwffpSKGtsqs9Wh9z/FjeAWvGQ2uKGXjZGXVU0ot5VTXuRizGPzufQm5EIlxCd5FjD6JE0swpxzrWu5eObd9J+4gtPF1xdjgkESRdItUaYBjoOgR5i5CPaU8+1ZE9Tld5Fyo3up8bXHs/OgUtCXpLYTKzjqmBa6n8VjaTArpr8HvaCl7DM4j0HPA9XOSO/IGh48p3TmT/jC23qf4wI2gz75EENIpzglN8KXWW+5jNDg9JQ7HucugK2IDUDHPtdDx2Ai6d3/rmvIN+VueQi8YCZQwhLBNIVBSy2zovrjva7fVZUIkozVYwWCZFYrftKP00PtarEhgyg/fLqTOaNHQhkQJQgeNtQYDu5L1+johmR970L6Ij9PLmQ7aaYpiqC9r5Q9Rb30Y66lu2EDY7VSupb0euGN6QCPYHzcidqPuSqJ4QWZNhAQqH2sreNt8ky7C39PW+1S29GibRkxBAvolklLp51gadxQ5biaw7Afs9mCSJ0ycgPZDSxbzFB9OZ/RBoPSh5MRGK4/1RukCzyvw0qENi5QR4FiapHsGqdfHaKXG6gflGrq+6lEyoI6PlKn08n+eoVrKEWgu48i3lH1L3LFovgkSRbvYt52qmZKnCzk+JDwiJVRfu1yPkWJge8p9zEM15ZmPlcPnJH70c+spDhqWMThEnerbsLmWPqQrRHE5ZoTemm4wVN+f0qZC5/XpRWsRJjHSQxDKZxGNWifUsk17jkL9v0sfAE81KZKqiOjBDEu+Wu+ZQvrr8rD6zZhXS+j6XdHVSy5UJ9JwQa4FF/QoFZhO5EYrnXGm8qn5cjmaI31Ql4FzLESWmPXth5UYQJspM+fAKepCitJsdQSx0aRTQo/8MZFy5dgA+AWHbUJHIwfgEygCi0DpovwdU66n8Nw4N67hHUNEWWiaQ6qi5xEpqbDatHUNHlWLGa0sFwx3ZL2MIyiL7r9019G267lIaEU+ZLDGrmPhUuVxqgFj1cNYWwm9s+cd0bacThG3qS361g+868XkUkh29IUh6vU19CHH4lbeb0jipu9ra8OLPVM0waI9VywCJtVj5S6QRz1a6FKdmlobXddDCvHdNi127WPhOWS0IXbS0VHap4zeaXauh9u2alyhzizVpVIrVjyCLf9Z6Jfz0zf3Tf4FOFwVgdNCg53WyHuw7lkVw+uYYiPJfF4b5aCN+6h+NstdZ+hRvFi9sthoDcurpG0915ZMbLRJ0KexfyH4HXX/43peiQWng8by/s5IZ3iNL7VMU+uaZzgMjWOV+rZK7RBK8LHvkNpv9PXc3vPGRm+sZ7EMUy0TWaGz6q7ETuHlz5lMeSgvZ7glb3CIIZwELxpdvE/6HqEfAlb/wgZE6nt3MXiOIU8Yp5xWOPQ5XRDS6yxj4tSN9tx1/NKwQzE6lJLPwqm8TY/9bqnP2WVqjye3NLiud/Fg0QSK9MGePeCNRHBe61kjRIvbZC2vE8DWdb3+SesZXfTclHNS7Ze+vQWP1dvjuJQh9RawRo2GFixc4OLGpZkNcbcuG15uuHPpeeZwjlvHrP1WiI0Ohc7rQqT0iVMoACkjzqE8XDZ9PmeIuNE4RVs4EbaxDLoDkc6NOwftlRPqVKw81v8+oDuKNh1HW88IYCRSLFxbW2njrqo9JEP13pqsrdySrVvcFHiAhNFlWs+tFI7nneNNAQIdDxk2+txTIfS9vNHKS1GP25aXJdtOWS+7yP57Y/8WCZiUSNx9DwIeg1N/Czbqu05dDhELXacDWffXRMQ9paeEq+hKrFjLE3vTkThNp2u0JRy6kieh8tbnnlIG87OmR8wf6AmHenGr4fQhPGIMBo/OoNzXjMgbitE9yb8okjcqG29DBKbAiocSfA8PxwrBcw8/trnv0KNTnhA/ddlYHZxnENwKdAdieZ/A2OoOz0uDcbwPHBtnwTvHm4YQU6KHiHGUkv/SpxL2Aa9s25aj983YC1Ln00qhRZrco17BS8j/exzKeI6rwu+U4KJ8Ezj2e/V57SHgGduhKWSWZxSMc2KDNm11laFkRkgOdr1nn8/adsRNYH0775g+Hrtm332ZnHfL5EkqjiWg+kIf0xuHhOeRknIeYPedGpYXStfrx9qx1bZio+pWPx/Sc3W6Ps8jzvU1PIS8Mbv2pTFC6lgcf62WbEdfU3PO5dFgIfSxNOPI4CWPWdm0BE7pmcK3k9Nka5ErgLN8K0NG5NmA7AqLfOirQ+vaQZ7D/TSFhBmyYwnN4/OOX1Kb6hOxde2154nOm0qexAgVD5eq+KWM1qQQGn0SKMfimDp+7HOljvZZREQbtDE8rXtYRNoL6j6KiX6gnt4jx/R1LSKHiZWbCIoSQEqd67sNdXmGPuHVXc/QtQgVr3/sUhZtR0hvGbHRNE/+tNVnYnnbfs/UOnzr33TipF/qe18igSJI7ZM92y4FXvuxBjEsvVx7qWh4xEPqdKPYdaxjIb23jReJZSfpc0LvkGrbec8R8lSyMIyu3qFn9piyS2pcghTBnUXyhdg5wHZf3aEZvXmHWkGlUbyuAzUnRV9eHV2vcY6Ra0+QxGLphEb1joVXDrdInACFccZTdmIESYiJ198zJvi9NA9dR4+GVpxSiJRz4FK8UUKeO17+WL4279VXOYSMXiBtigU/U4qCKPluYCUwFykE9dAEyqXB84DzRoVTjXgPlyAnLgXHGB19o8331MRPX5561whrmsAlv7foKZdo3w0F7t8sxGRalylCIYRmAYS89kIeqRba2ispenRXhLzeQu/ShtTtBy0tsB2a6yGmnqML4tSjKimKIxCfgxcytlghfih/MmonLtNyTFyn1Wo+1iO539qaF+fBOh5S4GPHgg82ANrcs0/h5Y38Anadshq+92HbQjPZ/J5DurudCy8o2ocnqEPESczTJLUziqEPz69jrxVTcE6tAKWy/cfKka6eHh5Sy8kahT2mfPXzt1WYQsctIsQqNz1lh/9b03nuabuOPNM1wOqArTyp6MPz5FTQnpcxMoTPE1gxnqxrtEHfROspSGuB14aPkVVdCRT9DWPXOdaTTs4NybG+Zfc1I6VOppL9Q/X1p9Yf2npFyXne8dBUHMuLMzZ1x3u+kEcKt73Q+8l78CC8hte2Qh4nVv6UPLF3DSHm1ZJynz4Ik6750zBKsaPgNTqenyP5yhgpB/mc+eXBOqenOPxsuJSRbA99GFcC7111Z3PumDVDIER4aE8UqLyhtEvEMUr+pREpQ8Or46ea9hBy7b0kaDnkebJZilRqOV5ymzoHhhjx63pOKlKfWRCa5sPXO6ZNtO3HrqEenko+he7fNk/M+1UbjtrrJHavn9kEOabO9qljXira9KtW3q6y2PLEtJAa/iElnooVGyWFmLbeMeSd4uXpa2DRQlcCpat8iJ3Xr43UowTTFS/2Em1ufaygCHUE3AmkuBBZ72hVyJzySCA/HtXjY/xMmlThOeehhuCNwndhEo+pZEMoMykdu6CLIE25p3e8a8yDNue1IY00gXetsNZWBZr1PMXzxDrPu2YqLlFhj41WeV4K1rG+nmMIxGRBzFsshK6KaUrsmT7Q52h6bIROSEqLnOU0r53+bOjju7T1hhlCzqe0AasuWPInVibH6HVtXdVPiVsayEjFMR6Ft6CvtEWbOmu1k9S0a0eqFw7ntWyAFHmmB76Pnbor/atXx0O2p34mS39rY9ul5A3VyWOIkK6IedhbZK71f1j5ckbpdW7hmeKuldKILHcwMWS1kiqGn6Q/4PBjy1Qf6znbVtRTKRfHjKDHcKxCMrQb7zFkyrH1X89XvTRl8hjsYAdhs7xPPFY9Nf1ScEqX80vxnrhFDyqNrsSpPh/oVkeskTXtgSJ9kkfY6kEDyX/LMVFScS55MiSpkiofYiO5sXuE4A2I9BmX6Bi0MfAEpyDCToW+ytnzZrnmshnRDbHpGyEPjZRrtH2WlP7W8hIJDcr36R2dYsem5O/yHEMOmKeEKAjpK6FrC7rI70OcSEpdisIegqcMAGFXVY/xauM5oGEZxrHG0nYUK/ZMbfJ3MeIv1ZAVtC2DLspkX0TKLWKHcNwZT6jqfNYxCyy8z2XQ99n5A+kjN33hmOtdM5ly6v7NU+z6mBKmSX++9pAuv+dG2xHPrsQ1T3fooi/Ert03kQK0H0zicwVd2odXtpfihXKqNn+NMnHE8LgGu6pvHDtg28UjJfXafH2LTPHkMz+btie9fr7NgGBXL5LQuZfS76fIxhS9pXtbOrLHPYU3ySUICauArbRcbVkxZSNBDLXQFKE2bKU1Gm8RKymEjGBIY/LUDfCcnhqproSMrsr1LXmkbBB2gUyZuhM7ZuVpy+xfMrpOT0m97hCIyZ0uIw+nmDrah9HowXrntsFtU5SEFGVKe6NYq01cG3LEp7dpgqrPNqAV52P63Vjb4XzHxL0Jja6GDAULp9LxTunpl4qYjOpaF7xpeJdI1vF12/y/VvRRB3dqO3QbGvo+x5L/fT5fm2kjOp/n3cnfy3pGizDx8h7TDrpO2RlCblrXDE0fHhrd6tDoL5cMj0gRWEqYNAyrMbWF1bCvsVO5hJGjoZ7BU4jaspzX+F2HQAoL7pGNtzxq3hbHjlidesSrb9l27qmjfSAUYLoLupbJz+pqfwrZceoyvZR20UV59b5HTG5cIpEC/LztasTxuISB5nOiqwd8myl4Ke1S59PenaGZDh5x05fulTIQ3/VcfZ2+Zdhl20Mt37ZNYB9v5IoR6zhOLRxSRlO8Z7IUAe26q92AdXpO+7GK67kUx1xg+5yPdwnTd7pMA7glj41bxRvCMVEEqdN2Yh4o50aXuth2NLnNiI01KnJKMGnAbbzrNwuNLjGGJIwuzVDqWpYcMO+WYqIMTSroKYOnrg+xKZCClOcJeV71dc22OIU8954xpBe2HUDhsupKKKd+665oe62fcepJCrrqokN6hLQNzNoH+tLJ2zxrm7xdvV9DRIk3tTN0XkqsECtfKK913y7QMzGsa8e+8yXo5Du0KYdfhnsQC0Mr4pbin0JGtDGwYtcUckTny9Xx3NjX9+j67Hw/UJ42o/Z9IuW7d3kGrWy0xdDGEmMkbNojp58FXYe9cr90AuVUiMmtlHynxK2sNNUXUsrCk2nHeKykTIkbEUYqiXcJGFI2HkOExq57KzjmXS5RZo7yojtSbYG+7sVbvX/q5zmFB8bQXunWfSwZGNJfrXwxOarzWc/T10BZiky36lbfOGXdrHEGaTsUU4iEvG0LNzQyFXMLS52+w/n4frHAtbFr6meEk3YJBArgv1sbT5MuozZDufeG2GW57wgfEmshJnzbEFZDzSU9F44R39aowCW+I+OapjCmjtS3/YZdy6DLlAmGfuZrqTNt0Ic6ZMl9bwSya4ySS0Ssflll4I2Wti2Hc8uES+rLtbfTOREz0i6p3E4J1jlj8lPnbRsLKwWWXurpqqf8ZqfylD+2LL3zvT4zVMZ9ttlQ+IlQWp/P0eaeQPcFBkKyZtg620Ov3aXTO7VrX0pj8p4pxeXWahypkZa954gZ4Xw/7zpt3YVDrqpdBNpQCnbfyuY55kmndJ6xc29ZCfE8uby8MaScHwsuOeL8OIVxcMr+qYuHwrkNpEubltQ3ur5f1ykUIbfua4MlN1OJFZ0+VFn0OZ03Jif6IC1juKSVzdqU6antgEvCMTpnW++K1Ok5qWn6Pl2+4ZDf/thy7SM2kwbLdq3XWgSLPpdxrD3noQ/5YRF9Q+L8dmkPvVQsdkiKYhDz+OjaSE8Nbgj6uVkxsEgWRI63jdHCiCkrMZy/otaGQ6wu9Wlg9LFSQtcmppnUEKN7i4pIqtv3MUy6xV6foyxHwiYdl2AodEEbWZ1KpOjzYui7bue4jdV5gDABYq3OEFqx4dg+4xJwCYRO7BlO7Vno9cExQ3PoPsUrp1ORrWP/1R5tvSRS/6cgFr9np9Ji53XBJeusKW06Jd3yTImt9JNCQsfa9LlJ1VPKg/MHCj+yl2wzveRYIgVoF9j2nLCEkQdNlnieKCFPmVSFOjSynzrqf06k1rfUJcnaXKvrdfoYVWsz8nDuut8XxBMlVXZcK87ZARx772v2SIjVqyHaVGr/pMs1pZzZYBpCBlxK33oOdPUw6XLdS4I3yNMWxxAJfTyDvn9fHih6SkaK+/xQ8AbchiZSPKOvK0kQutatkDWpHunHwKvvbWyoGGETm/pzrkGpUP1LfSYvX1tPK34mIN6XWH1+6j2PnRKUaq/qvEN4noTsrsvSRY4MLMtuqMfOYeLrHYtLFrbybFbcEp2my1VPcdDn6X0Pl6689YlUhSvWII81FH+mMu8LudrKfu4c6wNeG+vz+reOW6vrfX+zn6EOXBNurb4OiWPlriVfh5a5Q+EylPg4rpHkHtEdQ0xHuTZcS9vU0DZfGzkbkqNtp4C1uce1yu9+0FNMFAvWdJZjbpfSKIZ07ek7vog3nUeTLHpp5FQcM83hWFxrg4oxqinf/hTle80eAKnYIM7xxuKcXGKME92OTzl/tE/EOvrYVMSuOJer6rEjL7FR6thc6DZTfLrEU/DuK7hWpTQVlzW6dfloGxQ3ZRpCW0+tVPQlc9vGjEitS32P2J9yKtaxo/VyDUse3qJxxl7qfZRdCrrcp4u3jNfOumII/S31mU7hVZaiAwC+N56nH4SIlHNN02zrURiS8W3rxbAeUQOX5iXMF71U8LtbjUFP62EFwzKiUypTaArPLaNL/IC+7y0Mc4rguGVypCtCddXr8H5m+TIkvOkmPxPays7Uedanqq9dVgUCbvNb73DY745Iwy3Wh3NgCCIFqOv1uYNRt8HP0v4uUdacP8aEjXM9V99T/6z/fK/U6VDHxLm0YrOk2kjXtnLa8DixtaY/lq4g3uO0ETRDN7aQoZsycsgNw1KcLQ8UzysldaRSTwkKIYVoufWGMZR3QOqob+i73nrZd8EQ8StG+BCFPLUuXkKQyj7RVS6kECWe4tSVYO3LeBrlzgjB0J5nQxuXl2QkXtKz9IW2OniKl9KtYQgvlGPtpGPbXJ/t1vP6auv9eWxfnXqf0DmWfhp7NmsgMKQ/tO2fQ/nb6HWAL+9j1zmlTjFcn3JGzTaVQDnm2n2PFjKOURpCUw284EApLq6Wi9cpCZSfofMLQZeNDvDW9ZqeIeR5JN2awdOms2+jkF3KVJpz3f8YJaOtcX5JRMqlPIeH1BGolJXKeNunXNip7TXj3tk/Bm0Uy2v38tGyINQPpuISR+kZlkETeuaU6aVdvdFS6s+11q2fBUMTKKG6FZqG07d8jw14xXSSLjrbMV6eoftZ5XYMaeNN87J02pCc8Z4v9A4hO8OCJ+/7Xh3VukdXDKOrnEmbbMuYXgO6BNYNsdFt2UYrxkpbI6cNbkF5jqGtUGzTnIb8NreA0HzovhTsS6nDl+pC6+ES6uM1uadrHOORIkglUHRaX2TWtdXZVJw6PsWlE3opGKodXjKRMlR8lBEjToWYh8NQ8Vli1+2zrfTtGXNsmXgybUjiKgRvZoOHU3uXDDVo31/f0rIHz2Cz3tZl2jzgEIrEKSui5yXQNtiahzbLXDGshp9Sya9xCk8bpb6NAXaMsTCkcgkcssg/EyzGPWW04Ny4pKlHpzaC+47503UE/1LaS6y+Hjsy3faYzmP18Zx2j/PX4SHQV3yanLYeqTUijD7l5SWQfkM8w+iBMiKmD7VtO0NOWT2mHYvxG/Pk7wttje0UsmWn9rs8c8r3Sf2Gnpfcufv2oWVXP0TKEUscjwpAHEMb0fpe1nScNgSKN/o/1Hu0iaswIoyxHK8Pl6DUnwN919Vb6osuqU7cUrmeGqM8rnGustDGyogRl4ixbh6He9wuoX8qDFUHd8avr+tdBjpqSd60EWu1mL5GD9pep4sHQZ+NsI/gaqHreuUS8xBKKZMuSs8xsRX6QJv7t32/rq587PFyiukHlzLCfmpcjkANI9V75mfAuVaiuob2kTIydKryu4byOhY7HAa8Z0WtD70gFoz11tFXnBSg3bSDS5OvKXrpJU9jSsXP3r+dE6F4O4DvxdHntzpGdobq/znaRSj0wrHP0/X8IabTWrFX5F7HXMc7firvqC7XTb/XEZ4op8ap54ldqvCPeZvovPwLvRPn09fX8BrbCBs6wOOQ+Jm8e1KE8Dnb8q22iz7rsZZRI64Hl9xPDoWf8Z0vDdf4DS5pGucp0de73lr/kOLxbb1zXwE2z9GG+pyCcikY+nktL5tTy48204LalMc55biU6fH376ANn8sV+xQVp88PemqvgDaddOo33Klt6J5tcK5R6HOg73e8tk5maLQNVBbyBulbxoRG4675Ow6lzB7TVq450Oy14Gcp31Db/JkM4CHRp2fKsfc+BbqMuo51rcCtyh39jXUbuLU4SkPU53PG7zj1PU+5ilLovn1jyG+Y4oF1HHpqkccGlg3hmOsc41LYlwvvEEIvRkKEPFM8tC2jvgknIFxWxywp7V0nBt24+2AuU4Nh6nuHDPGfTdnyOpM+5lseq+xqoX3NZEkMQy3NeqzcvPYlYwG7Lqa29yFI6hQPxVvDzyZXz4m++vfUe5wLKYT/z9C2UnEJ36xv/CyxelgvSx3IkrRTDnZ1QWxlvT6v3yeh2ve10PP1vGt1KdOuMybaeb1d0XSeU6Nv4XaMO2IXUqTNtU8pyPX64t5646ku/m3mmneJhXIugT0GyvLR1h3Vyzt0vb9lBWkI3JrL9rFIqT9jeY24NQxRp6+lnYx9Ro1r+WZt8DN931Qdra2xewlleErPj3PbATHd+VqmV6bYVO1lTgfKXwfLHBrndGsMjQYC/gi4FQDQ+t+m/Dy3pLYfve2ctWPRZfnRWJr1X48+D9EB9yUoujzbudaRv3QcW5+PDab1M44cnsLT4xhvlFtSvr0+KKUPGLqfvhblqStCU2RDff41IvU7dvWOaguvTsfqch+B3C9lKg0/xyWNwp8CtyTDRxwXEqCN3mahjzYztCd4XwFqU8vzUoK6DnmfUHDii5jOEzr1lgR93y5GjBQl9xi37rYYwvvmlBjyfpdkLPxMhvvPuuLQiBEjLgeXYlifG+fud1L7g6F1j2skNa6lDo8EyohzoI82fW75mIprlF+XiY7WSVejJiVw0hA41ug8x3SXlLJpw74ewwRbiHnU3BKBAvRLXPQRBPPWhd81BR/+Wb2ELI+US4hHcg115li0UYJCstr6TjFS/2fFtRih58Apy0b3DX3Kmh1t+wgGe0l15lKeI4YxSPiIPhHyKLTy9HGfU6Ptva9FFnSFVR4xb51uMmeMiXIRiE1B6eI2vVO/WHrs2VJwa0uV6jLqU/D8DIbesThXPQp1tD+zEenhkuryJT3LuRCqoylLSuvjtyLPPVwSoZ1Sfy99xTdLTg4hO4+ZrjPEO/PWSxsxIoZbl7e3hmvXC4d69jZ2ZmqeY64V04u64YQap+etMCq9Bby4H1w+x7pgxSpgSkBWa5TgloT+KQOQjiMucZzDIyU2eujNVb/mjvSScU1eSUOgr1HxLggR+7eE1DqW0j8MPco3hOfFENc6hRdGl9X2rP+W/PZGMy2iJHZeX57QXct0qMGgU2GH29CVhJz+WfuycyMUHuGacKxsHZI48dJZdh7jvR3rb0O6eP/vfWRLjgW96ktQDE0e9I2hlYch3ie1g7qEjqyv+XyXIDy9NnIJ5XxOXPr7twlsdqsY+huFAkhb0KTkLSmqqTKvD4MrFbfidahXhTu23ly6wXpKWRWTk6cqn9g7p5bJOXRJL62rMXhJU4x+NmjvPkvW9L00fV/XuxXyJ1UWnLKNxORiH8RsKL83MHjMNUN5vfex7tnle8UGNfVx1h3bEbZJLeLHjx/l3kYd0QJB3zjFQOy6Qk0bnEPR66uzPZXQuiZlmCt7H9e5VIRWKAg9+xsAbrfXg/qZ39Cs+6fuvLsI858NXjyNIdtV6uocKXmvEbHlD4c2UnmKQo7bkDUbDCdr2raFodvPpcmwU/XBl/befYDLru37nVL36VL2FoFe2B/XLWtOrdf06cX5s3mEnpNoHMJbJnaNoUmUFByjd7cpM4tkYR4jXa9Jag0vLy/l3v+Rkn3EiBEXgJeXF3z69Oncj9EKtaz538/6HCNGjEjHdcuaUa8ZMeJacN2yZtRrRoy4FqTImrsfCVTLx8cH/vrXv+Lp6Ql3d3e9PeCIESP6x48fP/Dy8oI///nP+OWX64odPcqaESOuB6OsGTFixCkwypoRI0acAm1kTRKJMmLEiBEjRowYMWLEiBEjRowY8bPjuujcESNGjBgxYsSIESNGjBgxYsSIM2EkUUaMGDFixIgRI0aMGDFixIgRIxIwkigjRowYMWLEiBEjRowYMWLEiBEJGEmUESNGjBgxYsSIESNGjBgxYsSIBIwkyogRI0aMGDFixIgRI0aMGDFiRAJGEmXEiBEjRowYMWLEiBEjRowYMSIBI4kyYsSIESNGjBgxYsSIESNGjBiRgP8fd7GCdcPYBgkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1400x700 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample([0,1,2,3], autoenc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_padding(input_shape, target_shape):\n",
    "    # Calculate the padding needed for the first two dimensions\n",
    "    padding_first_dim = (target_shape[0] - input_shape[0]) // 2\n",
    "    mod_first_dim = (target_shape[0] - input_shape[0]) % 2\n",
    "    padding_second_dim = (target_shape[1] - input_shape[1]) // 2\n",
    "    mod_second_dim = (target_shape[1] - input_shape[1]) % 2\n",
    "\n",
    "    # If the padding doesn't divide evenly, add the extra padding to one side\n",
    "    pad_first_dim_left = padding_first_dim + mod_first_dim\n",
    "    pad_second_dim_left = padding_second_dim + mod_second_dim\n",
    "\n",
    "    # Create the padding configuration for np.pad\n",
    "    padding_config = (\n",
    "        # Padding for the first dimension\n",
    "        (pad_first_dim_left, padding_first_dim),\n",
    "        # Padding for the second dimension\n",
    "        (pad_second_dim_left, padding_second_dim),\n",
    "        # (0, 0)  # Padding for the third dimension\n",
    "    )\n",
    "\n",
    "    return padding_config\n",
    "\n",
    "\n",
    "# model definition\n",
    "class AutoEncoderTransposeIB(keras.Model):\n",
    "    # Pooling can be None, or 'Average' or 'Max'\n",
    "    def __init__(self, output_name='autoencoder',\n",
    "                 input_shape=(128, 128, 1), dense_layers=[7],\n",
    "                 decoder_dense_layers=[],\n",
    "                 cropping=[[0, 0], [0, 0]],\n",
    "                 filters=[8, 16, 32],  kernel_size=3, conv_padding='same',\n",
    "                 strides=[2, 2], activation='relu',\n",
    "                 final_activation='linear', final_kernel_size=3,\n",
    "                 pooling=None, pooling_size=[2, 2],\n",
    "                 pooling_strides=[1, 1], pooling_padding='valid',\n",
    "                 dropout=0.0, learning_rate=0.001, loss='mse',\n",
    "                 metrics=[], use_bias=True, conv_batchnorm=False,\n",
    "                 dense_batchnorm=False, data_type='float32',\n",
    "                 **kwargs):\n",
    "        super().__init__()\n",
    "\n",
    "        self.output_name = output_name\n",
    "        self.inputShape = input_shape\n",
    "        # img_input = \n",
    "        # the kernel_size can be a single int or a list of ints\n",
    "        if isinstance(kernel_size, int):\n",
    "            kernel_size = [kernel_size] * len(filters)\n",
    "        assert len(kernel_size) == len(filters)\n",
    "\n",
    "        # the strides can be a list of two ints, or a list of two-int lists\n",
    "        if isinstance(strides[0], int):\n",
    "            strides = [strides for _ in filters]\n",
    "        assert len(strides) == len(filters)\n",
    "\n",
    "        # set the input size\n",
    "        img_input = keras.Input(shape=input_shape, name='Input', dtype=data_type)\n",
    "        scalar_input = keras.Input(shape=(1,), name='Scalar_input')\n",
    "        # this is the autoencoder case\n",
    "        # crop the edges\n",
    "        cropped = keras.layers.Cropping2D(\n",
    "            cropping=cropping, name='Crop')(img_input)\n",
    "        x = cropped\n",
    "\n",
    "        # For evey Convolutional layer\n",
    "        for i, f in enumerate(filters):\n",
    "            # Add the Convolution\n",
    "            x = keras.layers.Conv2D(\n",
    "                filters=f, kernel_size=kernel_size[i], strides=strides[i],\n",
    "                use_bias=use_bias, padding=conv_padding,\n",
    "                name=f'CNN_{i+1}')(x)\n",
    "\n",
    "            # Apply batchnormalization\n",
    "            if conv_batchnorm:\n",
    "                x = tf.keras.layers.BatchNormalization()(x)\n",
    "\n",
    "            # Apply the activation function\n",
    "            x = keras.activations.get(activation)(x)\n",
    "\n",
    "            # Optional pooling after the convolution\n",
    "            if pooling == 'Max':\n",
    "                x = keras.layers.MaxPooling2D(\n",
    "                    pool_size=pooling_size, strides=pooling_strides,\n",
    "                    padding=pooling_padding, name=f'MaxPooling_{i+1}')(x)\n",
    "            elif pooling == 'Average':\n",
    "                x = keras.layers.AveragePooling2D(\n",
    "                    pool_size=pooling_size, strides=pooling_strides,\n",
    "                    padding=pooling_padding, name=f'AveragePooling_{i+1}')(x)\n",
    "\n",
    "        # we have reached the latent space\n",
    "        last_shape = x.shape[1:]\n",
    "        x = keras.layers.Flatten(name='Flatten')(x)\n",
    "        flat_shape = x.shape[1:]\n",
    "        # Now we add the dense layers\n",
    "        for i, units in enumerate(dense_layers):\n",
    "            # Add the layer\n",
    "            x = keras.layers.Dense(units=units, activation=activation,\n",
    "                                   name=f'encoder_dense_{i+1}')(x)\n",
    "\n",
    "            # Apply batchnormalization\n",
    "            if dense_batchnorm:\n",
    "                x = tf.keras.layers.BatchNormalization()(x)\n",
    "\n",
    "            # Add dropout optionally\n",
    "            if dropout > 0 and dropout < 1:\n",
    "                x = keras.layers.Dropout(dropout, name=f'dropout_{i+1}')(x)\n",
    "\n",
    "        # append the intensity to x\n",
    "        x = keras.layers.Concatenate(\n",
    "            axis=-1, name='Concatenate')([x, scalar_input])\n",
    "\n",
    "        # a dummy layer just to name it latent space\n",
    "        x = keras.layers.Lambda(lambda x: x, name='LatentSpace')(x)\n",
    "\n",
    "        # Now we add the decoder dense layers\n",
    "        for i, units in enumerate(decoder_dense_layers):\n",
    "            # Add the layer\n",
    "            x = keras.layers.Dense(units=units, activation=activation,\n",
    "                                   name=f'decoder_dense_{i+1}')(x)\n",
    "\n",
    "            # Apply batchnormalization\n",
    "            if dense_batchnorm:\n",
    "                x = tf.keras.layers.BatchNormalization()(x)\n",
    "\n",
    "            # Add dropout optionally\n",
    "            if dropout > 0 and dropout < 1:\n",
    "                x = keras.layers.Dropout(dropout, name=f'dropout_{i+1}')(x)\n",
    "\n",
    "        # Now reshape back to last_shape\n",
    "        x = keras.layers.Dense(units=np.prod(flat_shape), activation=activation,\n",
    "                               name='decoder_dense_final')(x)\n",
    "\n",
    "        x = keras.layers.Reshape(target_shape=last_shape, name='Reshape')(x)\n",
    "        # Now with transpose convolutions we go back to the original size\n",
    "\n",
    "        for i, f in enumerate(filters[::-1]):\n",
    "            x = keras.layers.Conv2DTranspose(\n",
    "                filters=f, kernel_size=kernel_size[-i -\n",
    "                                                   1], strides=strides[-i-1],\n",
    "                use_bias=use_bias, padding=conv_padding,\n",
    "                name=f'CNN_Transpose_{i+1}')(x)\n",
    "\n",
    "        # final convolution to get the right number of channels\n",
    "        x = keras.layers.Conv2DTranspose(filters=1, kernel_size=final_kernel_size,\n",
    "                                         strides=1, use_bias=use_bias, padding='same',\n",
    "                                         name=f'CNN_Transpose_Final')(x)\n",
    "\n",
    "        x = keras.layers.Activation(activation=final_activation,\n",
    "                                    name='final_activation')(x)\n",
    "        before_padding = x\n",
    "        # Add zero padding\n",
    "        padding = calculate_padding(\n",
    "            input_shape=before_padding.shape[1:], target_shape=input_shape)\n",
    "        outputs = keras.layers.ZeroPadding2D(\n",
    "            padding=padding, name='Padding')(x)\n",
    "\n",
    "        model = keras.Model(inputs=[img_input, scalar_input], outputs=outputs, name=output_name)\n",
    "\n",
    "        # assert model.layers[-1].output_shape[1:] == input_shape\n",
    "\n",
    "        # Also initialize the optimizer and compile the model\n",
    "        optimizer = keras.optimizers.Adam(learning_rate=learning_rate)\n",
    "        model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n",
    "\n",
    "        self.model = model\n",
    "\n",
    "    def predict(self, wf_in):\n",
    "        wf_out = self.model(wf_in)\n",
    "        return wf_out\n",
    "\n",
    "    def load(self, weights_file):\n",
    "        self.model = keras.models.load_model(weights_file)\n",
    "\n",
    "    def save(self, weights_file):\n",
    "        self.model.save(weights_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"autoencoder\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " Input (InputLayer)             [(None, 128, 128, 1  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " Crop (Cropping2D)              (None, 100, 100, 1)  0           ['Input[0][0]']                  \n",
      "                                                                                                  \n",
      " CNN_1 (Conv2D)                 (None, 50, 50, 8)    80          ['Crop[0][0]']                   \n",
      "                                                                                                  \n",
      " tf.nn.relu_24 (TFOpLambda)     (None, 50, 50, 8)    0           ['CNN_1[0][0]']                  \n",
      "                                                                                                  \n",
      " CNN_2 (Conv2D)                 (None, 25, 25, 16)   1168        ['tf.nn.relu_24[0][0]']          \n",
      "                                                                                                  \n",
      " tf.nn.relu_25 (TFOpLambda)     (None, 25, 25, 16)   0           ['CNN_2[0][0]']                  \n",
      "                                                                                                  \n",
      " CNN_3 (Conv2D)                 (None, 13, 13, 32)   4640        ['tf.nn.relu_25[0][0]']          \n",
      "                                                                                                  \n",
      " tf.nn.relu_26 (TFOpLambda)     (None, 13, 13, 32)   0           ['CNN_3[0][0]']                  \n",
      "                                                                                                  \n",
      " Flatten (Flatten)              (None, 5408)         0           ['tf.nn.relu_26[0][0]']          \n",
      "                                                                                                  \n",
      " encoder_dense_1 (Dense)        (None, 512)          2769408     ['Flatten[0][0]']                \n",
      "                                                                                                  \n",
      " encoder_dense_2 (Dense)        (None, 16)           8208        ['encoder_dense_1[0][0]']        \n",
      "                                                                                                  \n",
      " Scalar_input (InputLayer)      [(None, 1)]          0           []                               \n",
      "                                                                                                  \n",
      " Concatenate (Concatenate)      (None, 17)           0           ['encoder_dense_2[0][0]',        \n",
      "                                                                  'Scalar_input[0][0]']           \n",
      "                                                                                                  \n",
      " LatentSpace (Lambda)           (None, 17)           0           ['Concatenate[0][0]']            \n",
      "                                                                                                  \n",
      " decoder_dense_1 (Dense)        (None, 512)          9216        ['LatentSpace[0][0]']            \n",
      "                                                                                                  \n",
      " decoder_dense_final (Dense)    (None, 5408)         2774304     ['decoder_dense_1[0][0]']        \n",
      "                                                                                                  \n",
      " Reshape (Reshape)              (None, 13, 13, 32)   0           ['decoder_dense_final[0][0]']    \n",
      "                                                                                                  \n",
      " CNN_Transpose_1 (Conv2DTranspo  (None, 26, 26, 32)  9248        ['Reshape[0][0]']                \n",
      " se)                                                                                              \n",
      "                                                                                                  \n",
      " CNN_Transpose_2 (Conv2DTranspo  (None, 52, 52, 16)  4624        ['CNN_Transpose_1[0][0]']        \n",
      " se)                                                                                              \n",
      "                                                                                                  \n",
      " CNN_Transpose_3 (Conv2DTranspo  (None, 104, 104, 8)  1160       ['CNN_Transpose_2[0][0]']        \n",
      " se)                                                                                              \n",
      "                                                                                                  \n",
      " CNN_Transpose_Final (Conv2DTra  (None, 104, 104, 1)  73         ['CNN_Transpose_3[0][0]']        \n",
      " nspose)                                                                                          \n",
      "                                                                                                  \n",
      " final_activation (Activation)  (None, 104, 104, 1)  0           ['CNN_Transpose_Final[0][0]']    \n",
      "                                                                                                  \n",
      " Padding (ZeroPadding2D)        (None, 128, 128, 1)  0           ['final_activation[0][0]']       \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 5,582,129\n",
      "Trainable params: 5,582,129\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "\n",
      "---- Models have been initialized, elapsed: 0.149796724319458 ----\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train specific\n",
    "train_cfg = {\n",
    "    'epochs': 5,\n",
    "    'filters': [8, 16, 32],\n",
    "    'dense_layers': [512, 16],\n",
    "    'decoder_dense_layers': [512],\n",
    "    'cropping': [14, 14],\n",
    "    'kernel_size': 3,\n",
    "    'strides': [2, 2],\n",
    "    'activation': 'relu',\n",
    "    'conv_padding': 'same',\n",
    "    'pooling': None,\n",
    "    'pooling_size': [0, 0],\n",
    "    'pooling_strides': [1, 1],\n",
    "    'pooling_padding': 'valid',\n",
    "    'dropout': 0.0,\n",
    "    'loss': 'mae',\n",
    "    'lr': 1e-3,\n",
    "    'dataset%': 1,\n",
    "    'use_bias': True,\n",
    "    'normalization': 'minmax',\n",
    "    'img_normalize': 'off',\n",
    "    'conv_batchnorm': False,\n",
    "    'dense_batchnorm': False,\n",
    "    'batch_size': 128,\n",
    "    'data_type': 'float16'\n",
    "}\n",
    "\n",
    "# Model instantiation\n",
    "start_t = time.time()\n",
    "\n",
    "autoenc = AutoEncoderTransposeIB(input_shape=x_train.shape[1:], **train_cfg)\n",
    "print(autoenc.model.summary())\n",
    "\n",
    "print(\n",
    "    f'\\n---- Models have been initialized, elapsed: {time.time() - start_t} ----\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15885, 7)\n",
      "(1, 128, 128, 1) (1, 128, 128, 1)\n"
     ]
    }
   ],
   "source": [
    "input_wf = x_train[0:1]\n",
    "# print(y_train.shape)\n",
    "input_inten = y_train[0:1, 3]\n",
    "output_wf = autoenc.predict([input_wf, input_inten])\n",
    "print(input_wf.shape, output_wf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model definition\n",
    "class AutoEncoderReshape(keras.Model):\n",
    "    # Pooling can be None, or 'Average' or 'Max'\n",
    "    def __init__(self, output_name='autoencoder',\n",
    "                 input_shape=(128, 128, 1), dense_layers=[1024, 256],\n",
    "                 dense_layers_decode=[1024],\n",
    "                 cropping=[[0, 0], [0, 0]],\n",
    "                 filters=[8, 16, 32],  kernel_size=3, conv_padding='same',\n",
    "                 strides=[2, 2], activation='relu',\n",
    "                 pooling=None, pooling_size=[2, 2],\n",
    "                 pooling_strides=[1, 1], pooling_padding='valid',\n",
    "                 dropout=0.0, learning_rate=0.001, loss='mse',\n",
    "                 metrics=[], use_bias=True, batchnorm=False,\n",
    "                 **kwargs):\n",
    "        super().__init__()\n",
    "\n",
    "        self.output_name = output_name\n",
    "        self.inputShape = input_shape\n",
    "        # the kernel_size can be a single int or a list of ints\n",
    "        if isinstance(kernel_size, int):\n",
    "            kernel_size = [kernel_size] * len(filters)\n",
    "        assert len(kernel_size) == len(filters)\n",
    "\n",
    "        # the strides can be a list of two ints, or a list of two-int lists\n",
    "        if isinstance(strides[0], int):\n",
    "            strides = [strides for _ in filters]\n",
    "        assert len(strides) == len(filters)\n",
    "\n",
    "        # set the input size\n",
    "        inputs = keras.Input(shape=input_shape, name='Input', dtype='float32')\n",
    "        # this is the autoencoder case\n",
    "        # crop the edges\n",
    "        cropped = keras.layers.Cropping2D(\n",
    "            cropping=cropping, name='Crop')(inputs)\n",
    "        x = cropped\n",
    "        cropped_shape = x.shape[1:]\n",
    "\n",
    "        # For evey Convolutional layer\n",
    "        for i, f in enumerate(filters):\n",
    "            # Add the Convolution\n",
    "            x = keras.layers.Conv2D(\n",
    "                filters=f, kernel_size=kernel_size[i], strides=strides[i],\n",
    "                use_bias=use_bias, padding=conv_padding,\n",
    "                name=f'CNN_{i+1}')(x)\n",
    "\n",
    "            # Apply batchnormalization\n",
    "            # if batchnorm:\n",
    "            #     x = tf.keras.layers.BatchNormalization()(x)\n",
    "\n",
    "            # Apply the activation function\n",
    "            x = keras.activations.get(activation)(x)\n",
    "\n",
    "            # Optional pooling after the convolution\n",
    "            if pooling == 'Max':\n",
    "                x = keras.layers.MaxPooling2D(\n",
    "                    pool_size=pooling_size, strides=pooling_strides,\n",
    "                    padding=pooling_padding, name=f'MaxPooling_{i+1}')(x)\n",
    "            elif pooling == 'Average':\n",
    "                x = keras.layers.AveragePooling2D(\n",
    "                    pool_size=pooling_size, strides=pooling_strides,\n",
    "                    padding=pooling_padding, name=f'AveragePooling_{i+1}')(x)\n",
    "\n",
    "        # we have reached the latent space\n",
    "        # last_shape = x.shape[1:]\n",
    "        x = keras.layers.Flatten(name='Flatten')(x)\n",
    "\n",
    "        # flat_shape = x.shape[1:]\n",
    "        # Now we add the dense layers\n",
    "        for i, units in enumerate(dense_layers):\n",
    "            # Add the layer\n",
    "            x = keras.layers.Dense(units=units, activation=activation,\n",
    "                                   name=f'dense_{i+1}')(x)\n",
    "\n",
    "            # Apply batchnormalization\n",
    "            if batchnorm:\n",
    "                x = tf.keras.layers.BatchNormalization()(x)\n",
    "\n",
    "            # Add dropout optionally\n",
    "            if dropout > 0 and dropout < 1:\n",
    "                x = keras.layers.Dropout(dropout, name=f'dropout_{i+1}')(x)\n",
    "\n",
    "\n",
    "        # Then some more dense layers\n",
    "        for i, units in enumerate(dense_layers_decode):\n",
    "            # Add the layer\n",
    "            x = keras.layers.Dense(units=units, activation=activation,\n",
    "                                   name=f'dense_decode_{i+1}')(x)\n",
    "\n",
    "            # Apply batchnormalization\n",
    "            if batchnorm:\n",
    "                x = tf.keras.layers.BatchNormalization()(x)\n",
    "\n",
    "            # Add dropout optionally\n",
    "            if dropout > 0 and dropout < 1:\n",
    "                x = keras.layers.Dropout(\n",
    "                    dropout, name=f'dropout_decode_{i+1}')(x)\n",
    "\n",
    "        # add the last dense layer\n",
    "        x = keras.layers.Dense(units=np.prod(cropped_shape), activation=activation,\n",
    "                               name='dense_final')(x)\n",
    "\n",
    "        x = keras.layers.Reshape(\n",
    "            target_shape=cropped_shape, name='Reshape')(x)\n",
    "        # decoder = keras.Model(inputs=inputs, outputs=x, name='decoder')\n",
    "\n",
    "        # Add padding\n",
    "        outputs = keras.layers.ZeroPadding2D(\n",
    "            padding=cropping, name='ZeroPadding')(x)\n",
    "\n",
    "        # Now with transpose convolutions we go back to the original size\n",
    "\n",
    "        model = keras.Model(inputs=inputs, outputs=outputs)\n",
    "\n",
    "        # assert model.layers[-1].output_shape[1:] == input_shape\n",
    "\n",
    "        # Also initialize the optimizer and compile the model\n",
    "        optimizer = keras.optimizers.Adam(learning_rate=learning_rate)\n",
    "        model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n",
    "\n",
    "        self.model = model\n",
    "\n",
    "    def predict(self, wf_in):\n",
    "        wf_out = self.model(wf_in)\n",
    "        return wf_out\n",
    "\n",
    "    def load(self, weights_file):\n",
    "        self.model = keras.models.load_model(weights_file)\n",
    "\n",
    "    def save(self, weights_file):\n",
    "        self.model.save(weights_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_9\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Input (InputLayer)          [(None, 128, 128, 1)]     0         \n",
      "                                                                 \n",
      " Crop (Cropping2D)           (None, 100, 100, 1)       0         \n",
      "                                                                 \n",
      " CNN_1 (Conv2D)              (None, 50, 50, 8)         80        \n",
      "                                                                 \n",
      " tf.nn.relu_24 (TFOpLambda)  (None, 50, 50, 8)         0         \n",
      "                                                                 \n",
      " CNN_2 (Conv2D)              (None, 25, 25, 16)        1168      \n",
      "                                                                 \n",
      " tf.nn.relu_25 (TFOpLambda)  (None, 25, 25, 16)        0         \n",
      "                                                                 \n",
      " CNN_3 (Conv2D)              (None, 13, 13, 32)        4640      \n",
      "                                                                 \n",
      " tf.nn.relu_26 (TFOpLambda)  (None, 13, 13, 32)        0         \n",
      "                                                                 \n",
      " Flatten (Flatten)           (None, 5408)              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 512)               2769408   \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 256)               131328    \n",
      "                                                                 \n",
      " dense_decode_1 (Dense)      (None, 256)               65792     \n",
      "                                                                 \n",
      " dense_decode_2 (Dense)      (None, 512)               131584    \n",
      "                                                                 \n",
      " dense_final (Dense)         (None, 10000)             5130000   \n",
      "                                                                 \n",
      " Reshape (Reshape)           (None, 100, 100, 1)       0         \n",
      "                                                                 \n",
      " ZeroPadding (ZeroPadding2D)  (None, 128, 128, 1)      0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 8,234,000\n",
      "Trainable params: 8,234,000\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "\n",
      "---- Models have been initialized, elapsed: 0.10493922233581543 ----\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train specific\n",
    "train_cfg = {\n",
    "    'epochs': 50,\n",
    "    'filters': [8, 16, 32],\n",
    "    'dense_layers': [512, 256],\n",
    "    'dense_layers_decode': [256, 512],\n",
    "    'cropping': [14, 14],\n",
    "    'kernel_size': 3,\n",
    "    'strides': [2, 2],\n",
    "    'activation': 'relu',\n",
    "    'conv_padding': 'same',\n",
    "    'pooling': None,\n",
    "    'pooling_size': [0, 0],\n",
    "    'pooling_strides': [1, 1],\n",
    "    'pooling_padding': 'valid',\n",
    "    'dropout': 0.0,\n",
    "    'loss': 'mse',\n",
    "    'lr': 1e-3,\n",
    "    'dataset%': 1,\n",
    "    'use_bias': True,\n",
    "    'normalization': 'minmax',\n",
    "    'img_normalize': 'off',\n",
    "    'batchnorm': False,\n",
    "    'batch_size': 128\n",
    "}\n",
    "\n",
    "# Model instantiation\n",
    "start_t = time.time()\n",
    "\n",
    "autoenc2 = AutoEncoderReshape(input_shape=x_train.shape[1:], **train_cfg)\n",
    "print(autoenc2.model.summary())\n",
    "\n",
    "print(\n",
    "    f'\\n---- Models have been initialized, elapsed: {time.time() - start_t} ----\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 128, 128, 1) (1, 128, 128, 1)\n"
     ]
    }
   ],
   "source": [
    "input_wf = x_train[0:1]\n",
    "output_wf = autoenc2.predict(input_wf)\n",
    "print(input_wf.shape, output_wf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kiliakis/install/anaconda3/lib/python3.9/site-packages/tensorflow/python/data/ops/structured_function.py:256: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 2s 146ms/step - loss: 0.0046 - val_loss: 0.0026\n",
      "Epoch 2/50\n",
      "13/13 [==============================] - 2s 147ms/step - loss: 0.0027 - val_loss: 0.0024\n",
      "Epoch 3/50\n",
      "13/13 [==============================] - 2s 153ms/step - loss: 0.0024 - val_loss: 0.0023\n",
      "Epoch 4/50\n",
      "13/13 [==============================] - 2s 162ms/step - loss: 0.0023 - val_loss: 0.0021\n",
      "Epoch 5/50\n",
      "13/13 [==============================] - 2s 147ms/step - loss: 0.0022 - val_loss: 0.0020\n",
      "Epoch 6/50\n",
      "13/13 [==============================] - 2s 148ms/step - loss: 0.0020 - val_loss: 0.0019\n",
      "Epoch 7/50\n",
      "13/13 [==============================] - 2s 145ms/step - loss: 0.0019 - val_loss: 0.0017\n",
      "Epoch 8/50\n",
      "13/13 [==============================] - 2s 146ms/step - loss: 0.0015 - val_loss: 0.0012\n",
      "Epoch 9/50\n",
      "13/13 [==============================] - 2s 150ms/step - loss: 0.0010 - val_loss: 8.4926e-04\n",
      "Epoch 10/50\n",
      "13/13 [==============================] - 2s 145ms/step - loss: 8.1673e-04 - val_loss: 7.4596e-04\n",
      "Epoch 11/50\n",
      "13/13 [==============================] - 2s 146ms/step - loss: 7.0972e-04 - val_loss: 6.6043e-04\n",
      "Epoch 12/50\n",
      "13/13 [==============================] - 2s 144ms/step - loss: 6.3386e-04 - val_loss: 5.9706e-04\n",
      "Epoch 13/50\n",
      "13/13 [==============================] - 2s 147ms/step - loss: 5.7550e-04 - val_loss: 5.4823e-04\n",
      "Epoch 14/50\n",
      "13/13 [==============================] - 2s 146ms/step - loss: 5.2400e-04 - val_loss: 4.9865e-04\n",
      "Epoch 15/50\n",
      "13/13 [==============================] - 2s 143ms/step - loss: 4.7954e-04 - val_loss: 4.5903e-04\n",
      "Epoch 16/50\n",
      "13/13 [==============================] - 2s 144ms/step - loss: 4.4002e-04 - val_loss: 4.1917e-04\n",
      "Epoch 17/50\n",
      "13/13 [==============================] - 2s 148ms/step - loss: 3.9377e-04 - val_loss: 3.6302e-04\n",
      "Epoch 18/50\n",
      "13/13 [==============================] - 2s 149ms/step - loss: 3.3983e-04 - val_loss: 3.1393e-04\n",
      "Epoch 19/50\n",
      "13/13 [==============================] - 2s 147ms/step - loss: 2.9799e-04 - val_loss: 2.7843e-04\n",
      "Epoch 20/50\n",
      "13/13 [==============================] - 2s 145ms/step - loss: 2.6900e-04 - val_loss: 2.5202e-04\n",
      "Epoch 21/50\n",
      "13/13 [==============================] - 2s 146ms/step - loss: 2.4018e-04 - val_loss: 2.2706e-04\n",
      "Epoch 22/50\n",
      "13/13 [==============================] - 2s 151ms/step - loss: 2.1753e-04 - val_loss: 2.0639e-04\n",
      "Epoch 23/50\n",
      "13/13 [==============================] - 2s 149ms/step - loss: 1.9639e-04 - val_loss: 1.9014e-04\n",
      "Epoch 24/50\n",
      "13/13 [==============================] - 2s 144ms/step - loss: 1.8352e-04 - val_loss: 1.7203e-04\n",
      "Epoch 25/50\n",
      "13/13 [==============================] - 2s 150ms/step - loss: 1.6460e-04 - val_loss: 1.6054e-04\n",
      "Epoch 26/50\n",
      "13/13 [==============================] - 2s 150ms/step - loss: 1.4727e-04 - val_loss: 1.3983e-04\n",
      "Epoch 27/50\n",
      "13/13 [==============================] - 2s 168ms/step - loss: 1.3364e-04 - val_loss: 1.3206e-04\n",
      "Epoch 28/50\n",
      "13/13 [==============================] - 2s 153ms/step - loss: 1.2743e-04 - val_loss: 1.2105e-04\n",
      "Epoch 29/50\n",
      "13/13 [==============================] - 2s 154ms/step - loss: 1.2011e-04 - val_loss: 1.1553e-04\n",
      "Epoch 30/50\n",
      "13/13 [==============================] - 2s 152ms/step - loss: 1.1650e-04 - val_loss: 1.1925e-04\n",
      "Epoch 31/50\n",
      "13/13 [==============================] - 2s 155ms/step - loss: 1.2202e-04 - val_loss: 1.1275e-04\n",
      "Epoch 32/50\n",
      "13/13 [==============================] - 2s 152ms/step - loss: 1.1351e-04 - val_loss: 1.0788e-04\n",
      "Epoch 33/50\n",
      "13/13 [==============================] - 2s 154ms/step - loss: 1.0586e-04 - val_loss: 1.0132e-04\n",
      "Epoch 34/50\n",
      "13/13 [==============================] - 2s 154ms/step - loss: 1.0116e-04 - val_loss: 9.7788e-05\n",
      "Epoch 35/50\n",
      "13/13 [==============================] - 2s 153ms/step - loss: 9.8119e-05 - val_loss: 9.5531e-05\n",
      "Epoch 36/50\n",
      "13/13 [==============================] - 2s 154ms/step - loss: 9.9086e-05 - val_loss: 9.5536e-05\n",
      "Epoch 37/50\n",
      "13/13 [==============================] - 2s 152ms/step - loss: 9.4725e-05 - val_loss: 9.2015e-05\n",
      "Epoch 38/50\n",
      "13/13 [==============================] - 2s 158ms/step - loss: 9.1847e-05 - val_loss: 9.0587e-05\n",
      "Epoch 39/50\n",
      "13/13 [==============================] - 2s 159ms/step - loss: 9.2933e-05 - val_loss: 9.1881e-05\n",
      "Epoch 40/50\n",
      "13/13 [==============================] - 2s 161ms/step - loss: 8.9684e-05 - val_loss: 9.2595e-05\n",
      "Epoch 41/50\n",
      "13/13 [==============================] - 2s 176ms/step - loss: 8.8744e-05 - val_loss: 8.5290e-05\n",
      "Epoch 42/50\n",
      "13/13 [==============================] - 2s 154ms/step - loss: 8.5751e-05 - val_loss: 8.4400e-05\n",
      "Epoch 43/50\n",
      "13/13 [==============================] - 2s 163ms/step - loss: 8.5673e-05 - val_loss: 1.0794e-04\n",
      "Epoch 44/50\n",
      "13/13 [==============================] - 2s 159ms/step - loss: 8.8820e-05 - val_loss: 8.3827e-05\n",
      "Epoch 45/50\n",
      "13/13 [==============================] - 2s 167ms/step - loss: 8.4455e-05 - val_loss: 8.1244e-05\n",
      "Epoch 46/50\n",
      "13/13 [==============================] - 2s 162ms/step - loss: 8.3893e-05 - val_loss: 8.0802e-05\n",
      "Epoch 47/50\n",
      "13/13 [==============================] - 2s 161ms/step - loss: 8.1664e-05 - val_loss: 7.9800e-05\n",
      "Epoch 48/50\n",
      "13/13 [==============================] - 2s 160ms/step - loss: 8.0192e-05 - val_loss: 7.8830e-05\n",
      "Epoch 49/50\n",
      "13/13 [==============================] - 2s 161ms/step - loss: 8.0352e-05 - val_loss: 7.8579e-05\n",
      "Epoch 50/50\n",
      "13/13 [==============================] - 2s 166ms/step - loss: 8.2742e-05 - val_loss: 8.8091e-05\n",
      "\n",
      "---- Training complete, epochs: 50, min loss 7.857894524931908e-05, total time 99.61077523231506 ----\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tf.config.run_functions_eagerly(True)\n",
    "# callbacks, save the best model, and early stop if no improvement in val_loss\n",
    "stop_early = keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                                           patience=20, restore_best_weights=True)\n",
    "save_best = keras.callbacks.ModelCheckpoint(filepath=os.path.join(weights_dir, f'encoder.h5'),\n",
    "                                            monitor='val_loss', save_best_only=True)\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "history = autoenc2.model.fit(\n",
    "    x=x_train, y=x_train,\n",
    "    validation_data=(x_valid, x_valid),\n",
    "    # x=y_train, y=x_train,\n",
    "    # validation_data=(y_valid, x_valid),\n",
    "    epochs=train_cfg['epochs'],\n",
    "    callbacks=[stop_early],\n",
    "    batch_size=train_cfg['batch_size'],\n",
    "    verbose=1)\n",
    "\n",
    "total_time = time.time() - start_time\n",
    "print(\n",
    "    f'\\n---- Training complete, epochs: {len(history.history[\"loss\"])}, min loss {np.min(history.history[\"val_loss\"])}, total time {total_time} ----\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---- Plotting loss ----\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Plot training and validation loss\n",
    "print('\\n---- Plotting loss ----\\n')\n",
    "\n",
    "train_loss_l = np.array(history.history['loss'])\n",
    "valid_loss_l = np.array(history.history['val_loss'])\n",
    "\n",
    "plot_loss({'Training': train_loss_l, 'Validation': valid_loss_l},\n",
    "            title='Encoder Train/Validation Loss')\n",
    "            # figname=os.path.join(plots_dir, 'encoder_train_valid_loss.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model\n",
    "autoenc2.save(os.path.join(weights_dir, 'autoenc-reshape.h5'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_dict = {}\n",
    "config_dict['model'] = train_cfg.copy()\n",
    "\n",
    "# callbacks, save the best model, and early stop if no improvement in val_loss\n",
    "stop_early = keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                                            patience=20, restore_best_weights=True)\n",
    "# save_best = keras.callbacks.ModelCheckpoint(filepath=os.path.join(weights_dir, f'encoder.h5'),\n",
    "#                                             monitor='val_loss', save_best_only=True)\n",
    "\n",
    "for dense_units in [1, 2, 3, 4, 5, 6, 7, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192]:\n",
    "# for dense_units in [1, 2]:\n",
    "\n",
    "    train_cfg['dense_layers'] = [dense_units]\n",
    "    autoenc = AutoEncoder(**train_cfg)\n",
    "\n",
    "    print(\"----- Model Initialized, training with dense units: \", dense_units)\n",
    "    start_time = time.time()\n",
    "\n",
    "    history = autoenc.model.fit(\n",
    "        x=x_train, y=x_train, \n",
    "        epochs=train_cfg['epochs'],\n",
    "        validation_data=(x_valid, x_valid),\n",
    "        callbacks=[stop_early], \n",
    "        batch_size=train_cfg['batch_size'],\n",
    "        verbose=0)\n",
    "\n",
    "    total_time = time.time() - start_time\n",
    "    print(\n",
    "            f'\\n---- {dense_units}: Training complete, epochs: {len(history.history[\"loss\"])}, min loss {np.min(history.history[\"val_loss\"])}, total time {total_time} ----\\n')\n",
    "\n",
    "    # Plot training and validation loss\n",
    "    print('\\n---- Plotting loss ----\\n')\n",
    "\n",
    "    train_loss_l = np.array(history.history['loss'])\n",
    "    valid_loss_l = np.array(history.history['val_loss'])\n",
    "\n",
    "    plot_loss({'Training': train_loss_l, 'Validation': valid_loss_l},\n",
    "                title='Encoder Train/Validation Loss',\n",
    "                figname=os.path.join(plots_dir, f'units{dense_units}_train_valid_loss.png'))\n",
    "\n",
    "    # Plot some samples\n",
    "    plot_sample([0, 1, 2, 3], autoenc,\n",
    "                figname=os.path.join(plots_dir, f'units{dense_units}_samples.png'))\n",
    "\n",
    "    # save file with experiment configuration\n",
    "    config_dict[f'{dense_units}'] = {\n",
    "        'epochs': len(history.history[\"loss\"]),\n",
    "        'min_train_loss': float(np.min(train_loss_l)),\n",
    "        'min_valid_loss': float(np.min(valid_loss_l)),\n",
    "        'total_train_time': total_time,\n",
    "        'used_gpus': len(gpus)\n",
    "    }\n",
    "\n",
    "# save config_dict\n",
    "with open(os.path.join(trial_dir, 'autoencoder-summary.yml'), 'w') as configfile:\n",
    "    yaml.dump(config_dict, configfile, default_flow_style=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---- Plotting loss ----\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Plot training and validation loss\n",
    "print('\\n---- Plotting loss ----\\n')\n",
    "\n",
    "train_loss_l = np.array(history.history['loss'])\n",
    "valid_loss_l = np.array(history.history['val_loss'])\n",
    "\n",
    "plot_loss({'Training': train_loss_l, 'Validation': valid_loss_l},\n",
    "            title='Encoder Train/Validation Loss')\n",
    "            # figname=os.path.join(plots_dir, 'encoder_train_valid_loss.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ncols = 4\n",
    "# Get nrows * nrows random images\n",
    "sample = np.random.choice(np.arange(len(x_train)),\n",
    "                          size=ncols, replace=False)\n",
    "\n",
    "samples_X = tf.gather(x_train, sample)\n",
    "pred_samples_X = autoenc.predict(samples_X)\n",
    "# samples_y = tf.gather(y_train, sample)\n",
    "\n",
    "# Create 3x3 grid of figures\n",
    "fig, axes = plt.subplots(nrows=2, ncols=ncols, figsize=(14, 7))\n",
    "# axes = np.ravel(axes)\n",
    "for i in range(ncols):\n",
    "    ax = axes[0, i]\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    # show the image\n",
    "    ax.imshow(samples_X[i], cmap='jet')\n",
    "    # Set the label\n",
    "    ax.set_title(f'Real')\n",
    "\n",
    "    ax = axes[1, i]\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    # show the image\n",
    "    ax.imshow(pred_samples_X[i], cmap='jet')\n",
    "    # Set the label\n",
    "    ax.set_title(f'Pred, MSE: {np.mean((samples_X[i] - pred_samples_X[i])**2):.2e}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save file with experiment configuration\n",
    "config_dict = {}\n",
    "config_dict['encoder'] = train_cfg.copy()\n",
    "config_dict['encoder'].update({\n",
    "    'epochs': len(history.history[\"loss\"]),\n",
    "    'min_train_loss': float(np.min(train_loss_l)),\n",
    "    'min_valid_loss': float(np.min(valid_loss_l)),\n",
    "    'total_train_time': total_time,\n",
    "    'used_gpus': len(gpus)\n",
    "})\n",
    "\n",
    "# save config_dict\n",
    "with open(os.path.join(trial_dir, 'encoder-summary.yml'), 'w') as configfile:\n",
    "    yaml.dump(config_dict, configfile, default_flow_style=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is used to plot the performance of the models with increasingly larger parameter space. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read the yml config file\n",
    "plt.style.use('seaborn-v0_8-ticks')\n",
    "\n",
    "file_name = os.path.join('trials/best_AE', 'autoencoder-summary.yml')\n",
    "with open(file_name, 'r') as configfile:\n",
    "    config_dict = yaml.load(configfile, Loader=yaml.FullLoader)\n",
    "\n",
    "\n",
    "# get the keys and times\n",
    "x_val = []\n",
    "y_val = []\n",
    "for key, val in config_dict.items():\n",
    "    if (key not in ['model', 'timestamp']):\n",
    "        if '-' in key:\n",
    "            x_val.append(int(key.split('-')[0]) + 0.5)\n",
    "        else: \n",
    "            x_val.append(int(key))\n",
    "        # if (isinstance(key, int)):\n",
    "        #     x_val.append(key)\n",
    "        # else:\n",
    "        y_val.append(val['min_valid_loss'])\n",
    "\n",
    "# sort the values based on the keys\n",
    "x_val = np.array(x_val)\n",
    "y_val = np.array(y_val)\n",
    "idx = np.argsort(x_val)\n",
    "x_val = x_val[idx]\n",
    "y_val = y_val[idx]\n",
    "\n",
    "# plot the values in a log y scale\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "# add grid\n",
    "ax.grid(which='both', axis='both')\n",
    "# add y axis minor tick labels\n",
    "# ax.set_yticks([1e-4, 1e-3, 1e-2, 1e-1, 1])\n",
    "\n",
    "xticks = np.arange(len(x_val))\n",
    "\n",
    "ax.semilogy(xticks, np.sqrt(y_val), 'o-', label='Non Interpretable Latent Space')\n",
    "# get index of 7.5\n",
    "idx = np.where(x_val == 7.5)[0][0]\n",
    "ax.semilogy(xticks[idx], np.sqrt(y_val[idx]), 'ro', markersize=10, label='Interpretable Latent Space')\n",
    "# annotate the points with the y values\n",
    "# for i, (x, y) in enumerate(zip(xticks, y_val)):\n",
    "#     ax.annotate(f'{y:.1e}', (x, y), textcoords=\"offset points\", xytext=(0, 10), ha='center')\n",
    "\n",
    "x_val = [int(x) if x!= 7.5 else '7+' for x in x_val]\n",
    "\n",
    "ax.set_xticks(xticks, x_val, rotation=60)\n",
    "plt.legend(frameon=True, loc='upper right', fancybox=True, framealpha=0.8)\n",
    "# set the y-axis limits from 10-6 to 10-3\n",
    "# ax.set_ylim([1e-6, 1e-3])\n",
    "ax.set_ylim([1e-3, 0.5e-1])\n",
    "\n",
    "ax.set_xlabel('Latent Space Size')\n",
    "ax.set_ylabel('Validation Loss (Root MSE)')\n",
    "ax.set_title('Validation Loss vs Latent Space Size')\n",
    "plt.savefig('trials/best_AE/encoder_loss_vs_dense_units.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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