Testing_file_SAR_CVNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from imageio import imread\n",
    "from pathlib import Path\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# from IPython.core.display import SVG\n",
    "# SVG(filename='tuto_imgs/nn.svg')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Layout\n",
    "\n",
    "- Create a complex-valued neural network using cvnn toolbox\n",
    "- Apply the cvnn on PolSAR data (using `dataset_reader`)\n",
    "- (Optional) Testing\n",
    "- (Optional) Documentation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Installing CVNN\n",
    "\n",
    "- Using [PIP](https://pypi.org/project/cvnn/): `pip install cvnn`\n",
    "- Using [Anaconda](https://anaconda.org/negu93/cvnn) `conda install -c negu93 cvnn`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Training CVNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-28 11:06:41.419932: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-06-28 11:06:41.700863: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-06-28 11:06:41.702254: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-06-28 11:06:43.661973: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import datasets\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()\n",
    "train_images, test_images = train_images.astype(dtype=np.complex64) / 255.0, test_images.astype(dtype=np.complex64) / 255.0\n",
    "train_labels = to_categorical(train_labels, 10)\n",
    "test_labels = to_categorical(test_labels, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Sequential API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 30, 30, 32)        896       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 15, 15, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 13, 13, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 6, 6, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 4, 4, 64)          36928     \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1024)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                65600     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                650       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 122,570\n",
      "Trainable params: 122,570\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import models\n",
    "import tensorflow.keras.layers as layers\n",
    "\n",
    "def get_tf_sequential_model():\n",
    "    model = models.Sequential()\n",
    "    model.add(layers.Input(shape=(32, 32, 3)))   # Always use ComplexInput at the start\n",
    "    model.add(layers.Conv2D(32, (3, 3), activation=\"relu\"))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    model.add(layers.Conv2D(64, (3, 3), activation=\"relu\"))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    model.add(layers.Conv2D(64, (3, 3), activation=\"relu\"))\n",
    "    model.add(layers.Flatten())\n",
    "    model.add(layers.Dense(64, activation=\"relu\"))\n",
    "    model.add(layers.Dense(10, activation='softmax'))\n",
    "    return model\n",
    "\n",
    "get_tf_sequential_model().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " complex_conv2d (ComplexConv  (None, 30, 30, 32)       1792      \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " complex_max_pooling2d (Comp  (None, 15, 15, 32)       0         \n",
      " lexMaxPooling2D)                                                \n",
      "                                                                 \n",
      " complex_conv2d_1 (ComplexCo  (None, 13, 13, 64)       36992     \n",
      " nv2D)                                                           \n",
      "                                                                 \n",
      " complex_max_pooling2d_1 (Co  (None, 6, 6, 64)         0         \n",
      " mplexMaxPooling2D)                                              \n",
      "                                                                 \n",
      " complex_conv2d_2 (ComplexCo  (None, 4, 4, 64)         73856     \n",
      " nv2D)                                                           \n",
      "                                                                 \n",
      " complex_flatten (ComplexFla  (None, 1024)             0         \n",
      " tten)                                                           \n",
      "                                                                 \n",
      " complex_dense (ComplexDense  (None, 64)               131200    \n",
      " )                                                               \n",
      "                                                                 \n",
      " complex_dense_1 (ComplexDen  (None, 10)               1300      \n",
      " se)                                                             \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 245,140\n",
      "Trainable params: 245,140\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import cvnn.layers as complex_layers\n",
    "\n",
    "def get_cvnn_sequential_model():\n",
    "    model = models.Sequential()\n",
    "    model.add(complex_layers.ComplexInput(input_shape=(32, 32, 3)))   # Always use ComplexInput at the start\n",
    "    model.add(complex_layers.ComplexConv2D(32, (3, 3), activation=\"cart_relu\"))\n",
    "    model.add(complex_layers.ComplexMaxPooling2D((2, 2)))\n",
    "    model.add(complex_layers.ComplexConv2D(64, (3, 3), activation=\"cart_relu\"))\n",
    "    model.add(complex_layers.ComplexMaxPooling2D((2, 2)))\n",
    "    model.add(complex_layers.ComplexConv2D(64, (3, 3), activation=\"cart_relu\"))\n",
    "    model.add(complex_layers.ComplexFlatten())\n",
    "    model.add(complex_layers.ComplexDense(64, activation=\"cart_relu\"))\n",
    "    model.add(complex_layers.ComplexDense(10, activation='softmax_real_with_abs'))\n",
    "    return model\n",
    "\n",
    "get_cvnn_sequential_model().summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Functional API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_3 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 30, 30, 32)        896       \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 15, 15, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 13, 13, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 6, 6, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 4, 4, 64)          36928     \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 1024)              0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 64)                65600     \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 10)                650       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 122,570\n",
      "Trainable params: 122,570\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import Model\n",
    "\n",
    "def get_tf_functional_model():\n",
    "    inputs = layers.Input(shape=(32, 32, 3))\n",
    "    conv1 = layers.Conv2D(32, (3, 3), activation='relu')(inputs)\n",
    "    pool1 = layers.MaxPooling2D((2, 2))(conv1)\n",
    "    conv2 = layers.Conv2D(64, (3, 3), activation=\"relu\")(pool1)\n",
    "    pool2 = layers.MaxPooling2D((2, 2))(conv2)\n",
    "    conv3 = layers.Conv2D(64, (3, 3), activation=\"relu\")(pool2)\n",
    "    flat = layers.Flatten()(conv3)\n",
    "    dense = layers.Dense(64, activation=\"relu\")(flat)\n",
    "    out = layers.Dense(10, activation='softmax')(dense)\n",
    "    return Model(inputs, out)\n",
    "\n",
    "get_tf_functional_model().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_4 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " conv2d_6 (Conv2D)           (None, 30, 30, 32)        896       \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPooling  (None, 15, 15, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 13, 13, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPooling  (None, 6, 6, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_8 (Conv2D)           (None, 4, 4, 64)          36928     \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 1024)              0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 64)                65600     \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 10)                650       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 122,570\n",
      "Trainable params: 122,570\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def get_cvnn_functional_model():\n",
    "    inputs = complex_layers.complex_input(shape=(32, 32, 3))\n",
    "    conv1 = complex_layers.ComplexConv2D(32, (3, 3), activation='cart_relu')(inputs)\n",
    "    pool1 = complex_layers.ComplexMaxPooling2D((2, 2))(conv1)\n",
    "    conv2 = complex_layers.ComplexConv2D(64, (3, 3), activation=\"cart_relu\")(pool1)\n",
    "    pool2 = complex_layers.ComplexMaxPooling2D((2, 2))(conv2)\n",
    "    conv3 = complex_layers.ComplexConv2D(64, (3, 3), activation=\"cart_relu\")(pool2)\n",
    "    flat = complex_layers.ComplexFlatten()(conv3)\n",
    "    dense = complex_layers.ComplexDense(64, activation=\"cart_relu\")(flat)\n",
    "    out = complex_layers.ComplexDense(10, activation='softmax_real_with_abs')(dense)\n",
    "    return Model(inputs, out)\n",
    "\n",
    "get_tf_functional_model().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-28 11:07:02.100270: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 1228800000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  64/1563 [>.............................] - ETA: 3:59 - loss: 2.2922 - accuracy: 0.1162"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[13], line 6\u001b[0m\n\u001b[1;32m      3\u001b[0m model \u001b[39m=\u001b[39m get_cvnn_sequential_model()\n\u001b[1;32m      4\u001b[0m model\u001b[39m.\u001b[39mcompile(optimizer\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39msgd\u001b[39m\u001b[39m'\u001b[39m, loss\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mcategorical_crossentropy\u001b[39m\u001b[39m'\u001b[39m, metrics\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39maccuracy\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m history \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mfit(train_images, train_labels, epochs\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m, validation_data\u001b[39m=\u001b[39;49m(test_images, test_labels),batch_size\u001b[39m=\u001b[39;49m\u001b[39m32\u001b[39;49m)\n",
      "File \u001b[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/keras/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m     \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m     66\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/keras/engine/training.py:1685\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1677\u001b[0m \u001b[39mwith\u001b[39;00m tf\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mexperimental\u001b[39m.\u001b[39mTrace(\n\u001b[1;32m   1678\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m   1679\u001b[0m     epoch_num\u001b[39m=\u001b[39mepoch,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1682\u001b[0m     _r\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,\n\u001b[1;32m   1683\u001b[0m ):\n\u001b[1;32m   1684\u001b[0m     callbacks\u001b[39m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1685\u001b[0m     tmp_logs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrain_function(iterator)\n\u001b[1;32m   1686\u001b[0m     \u001b[39mif\u001b[39;00m data_handler\u001b[39m.\u001b[39mshould_sync:\n\u001b[1;32m   1687\u001b[0m         context\u001b[39m.\u001b[39masync_wait()\n",
      "File \u001b[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    151\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:894\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    891\u001b[0m compiler \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mxla\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mnonXla\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    893\u001b[0m \u001b[39mwith\u001b[39;00m OptionalXlaContext(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 894\u001b[0m   result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m    896\u001b[0m new_tracing_count \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    897\u001b[0m without_tracing \u001b[39m=\u001b[39m (tracing_count \u001b[39m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:926\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    923\u001b[0m   \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n\u001b[1;32m    924\u001b[0m   \u001b[39m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m    925\u001b[0m   \u001b[39m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 926\u001b[0m   \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_no_variable_creation_fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)  \u001b[39m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m    927\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_variable_creation_fn \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    928\u001b[0m   \u001b[39m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m    929\u001b[0m   \u001b[39m# in parallel.\u001b[39;00m\n\u001b[1;32m    930\u001b[0m   \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n",
      "File \u001b[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:143\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n\u001b[1;32m    141\u001b[0m   (concrete_function,\n\u001b[1;32m    142\u001b[0m    filtered_flat_args) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m--> 143\u001b[0m \u001b[39mreturn\u001b[39;00m concrete_function\u001b[39m.\u001b[39;49m_call_flat(\n\u001b[1;32m    144\u001b[0m     filtered_flat_args, captured_inputs\u001b[39m=\u001b[39;49mconcrete_function\u001b[39m.\u001b[39;49mcaptured_inputs)\n",
      "File \u001b[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1757\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1753\u001b[0m possible_gradient_type \u001b[39m=\u001b[39m gradients_util\u001b[39m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1754\u001b[0m \u001b[39mif\u001b[39;00m (possible_gradient_type \u001b[39m==\u001b[39m gradients_util\u001b[39m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1755\u001b[0m     \u001b[39mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1756\u001b[0m   \u001b[39m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1757\u001b[0m   \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_build_call_outputs(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_inference_function\u001b[39m.\u001b[39;49mcall(\n\u001b[1;32m   1758\u001b[0m       ctx, args, cancellation_manager\u001b[39m=\u001b[39;49mcancellation_manager))\n\u001b[1;32m   1759\u001b[0m forward_backward \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1760\u001b[0m     args,\n\u001b[1;32m   1761\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1762\u001b[0m     executing_eagerly)\n\u001b[1;32m   1763\u001b[0m forward_function, args_with_tangents \u001b[39m=\u001b[39m forward_backward\u001b[39m.\u001b[39mforward()\n",
      "File \u001b[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:381\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    379\u001b[0m \u001b[39mwith\u001b[39;00m _InterpolateFunctionError(\u001b[39mself\u001b[39m):\n\u001b[1;32m    380\u001b[0m   \u001b[39mif\u001b[39;00m cancellation_manager \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 381\u001b[0m     outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39;49mexecute(\n\u001b[1;32m    382\u001b[0m         \u001b[39mstr\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msignature\u001b[39m.\u001b[39;49mname),\n\u001b[1;32m    383\u001b[0m         num_outputs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_num_outputs,\n\u001b[1;32m    384\u001b[0m         inputs\u001b[39m=\u001b[39;49margs,\n\u001b[1;32m    385\u001b[0m         attrs\u001b[39m=\u001b[39;49mattrs,\n\u001b[1;32m    386\u001b[0m         ctx\u001b[39m=\u001b[39;49mctx)\n\u001b[1;32m    387\u001b[0m   \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    388\u001b[0m     outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m    389\u001b[0m         \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msignature\u001b[39m.\u001b[39mname),\n\u001b[1;32m    390\u001b[0m         num_outputs\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    393\u001b[0m         ctx\u001b[39m=\u001b[39mctx,\n\u001b[1;32m    394\u001b[0m         cancellation_manager\u001b[39m=\u001b[39mcancellation_manager)\n",
      "File \u001b[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:52\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m   ctx\u001b[39m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 52\u001b[0m   tensors \u001b[39m=\u001b[39m pywrap_tfe\u001b[39m.\u001b[39;49mTFE_Py_Execute(ctx\u001b[39m.\u001b[39;49m_handle, device_name, op_name,\n\u001b[1;32m     53\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[1;32m     54\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m     55\u001b[0m   \u001b[39mif\u001b[39;00m name \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from tensorflow import losses, metrics\n",
    "\n",
    "model = get_cvnn_sequential_model()\n",
    "model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics='accuracy')\n",
    "    \n",
    "history = model.fit(train_images, train_labels, epochs=1, validation_data=(test_images, test_labels),batch_size=32)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Dataset Reader (PolSAR)\n",
    "\n",
    "To use this mudule you must inherit implement two methods:\n",
    "\n",
    "- `get_image`: Must open the image. It must be:\n",
    "    - numpy array\n",
    "    - Data type `np.complex`\n",
    "    - Shape (Width, Height, channels), with `channels = 3` if `self.mode = 'k'` or `'s'` and `channels = 6` if `self.mode = 't'`.\n",
    "        S format: (s_11, s_12, s_22) or equivalently (HH, HV, VV)\n",
    "        T format:\n",
    "        \n",
    "    - *:return:* `np.ndarray` The opened numpy image.\n",
    "\n",
    "- `get_sparse_labels`: Must open the labels in sparse mode (last dimension is a number from 0 to `num_classes`).\n",
    "    - **ATTENTION**: Class 0 is considered as an unlabeled pixel.\n",
    "    - *:return:* Numpy array with the sparse labels\n",
    "\n",
    "**ATTENTION**: An internal variable `self.azimuth` is advised to be defined in the constructor to tell the class the azimuth direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/saumya/miniconda3/envs/tf/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from dataset_reader import PolsarDatasetHandler\n",
    "\n",
    "root_path = \"/media/barrachina/data/datasets/PolSar/San Francisco/PolSF/SF-AIRSAR/\"\n",
    "\n",
    "class MyDatasetReader(PolsarDatasetHandler):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super(MyDatasetReader, self).__init__(root_path=os.path.dirname(root_path),\n",
    "                                              name=\"AIRSAR\", *args, **kwargs)\n",
    "        self.azimuth = \"vertical\"\n",
    "\n",
    "    def get_image(self):\n",
    "        if self.mode == \"s\":\n",
    "            data = self.open_s_dataset(str(Path(root_path) / \"SAN_FRANCISCO_AIRSAR\" / \"S2\"))\n",
    "        elif self.mode == \"t\":\n",
    "            data = self.open_t_dataset_t3(str(Path(root_path) / \"SAN_FRANCISCO_AIRSAR\" / \"T3\"))\n",
    "        elif self.mode == \"k\":\n",
    "            mat = self.open_s_dataset(str(Path(root_path) / \"SAN_FRANCISCO_AIRSAR\" / \"S2\"))\n",
    "            data = self._get_k_vector(HH=mat[:, :, 0], VV=mat[:, :, 2], HV=mat[:, :, 1])\n",
    "        else:\n",
    "            raise ValueError(f\"Mode {self.mode} not supported.\")\n",
    "        return data[0:900, 0:1024]\n",
    "\n",
    "    def get_sparse_labels(self):\n",
    "        return imread(str(Path(root_path) / \"SF-AIRSAR-label2d.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  Using cached widgetsnbextension-4.0.7-py3-none-any.whl (2.1 MB)\n",
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      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /home/saumya/miniconda3/envs/tf/lib/python3.9/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.3)\n",
      "Requirement already satisfied: entrypoints in /home/saumya/miniconda3/envs/tf/lib/python3.9/site-packages (from jupyter-client>=6.1.12->ipykernel>=4.5.1->ipywidgets) (0.4)\n",
      "Requirement already satisfied: jupyter-core>=4.6.0 in /home/saumya/miniconda3/envs/tf/lib/python3.9/site-packages (from jupyter-client>=6.1.12->ipykernel>=4.5.1->ipywidgets) (5.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/saumya/miniconda3/envs/tf/lib/python3.9/site-packages (from jupyter-client>=6.1.12->ipykernel>=4.5.1->ipywidgets) (2.8.2)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /home/saumya/miniconda3/envs/tf/lib/python3.9/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n",
      "Requirement already satisfied: wcwidth in /home/saumya/miniconda3/envs/tf/lib/python3.9/site-packages (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython>=6.1.0->ipywidgets) (0.2.6)\n",
      "Requirement already satisfied: executing>=1.2.0 in /home/saumya/miniconda3/envs/tf/lib/python3.9/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (1.2.0)\n",
      "Requirement already satisfied: asttokens>=2.1.0 in /home/saumya/miniconda3/envs/tf/lib/python3.9/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.2.1)\n",
      "Requirement already satisfied: pure-eval in /home/saumya/miniconda3/envs/tf/lib/python3.9/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n",
      "Requirement already satisfied: six in /home/saumya/miniconda3/envs/tf/lib/python3.9/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n",
      "Requirement already satisfied: platformdirs>=2.5 in /home/saumya/miniconda3/envs/tf/lib/python3.9/site-packages (from jupyter-core>=4.6.0->jupyter-client>=6.1.12->ipykernel>=4.5.1->ipywidgets) (3.5.3)\n",
      "Installing collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets\n",
      "Successfully installed ipywidgets-8.0.6 jupyterlab-widgets-3.0.7 widgetsnbextension-4.0.7\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "dataset_handler = MyDatasetReader(mode='t')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image shape (900, 1024, 6)\n",
      "Image dype complex64\n"
     ]
    }
   ],
   "source": [
    "image = dataset_handler.image\n",
    "print(f\"Image shape {image.shape}\")\n",
    "print(f\"Image dype {image.dtype}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels shape (900, 1024, 5)\n",
      "[0. 1. 0. 0. 0.]\n",
      "[0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "# One Hot Encoded labels\n",
    "labels = dataset_handler.labels\n",
    "print(f\"Labels shape {labels.shape}\")\n",
    "print(labels[0][0])        # Class 2\n",
    "print(labels[800][510])    # No label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sparse labels shape (900, 1024)\n",
      "Sparse labels min 0\n",
      "Sparse labels max 5\n",
      "**Note:** Value 0 is used for unlabeled data and 1-5 for the classes.\n",
      "2\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "sparse_labels = dataset_handler.sparse_labels\n",
    "print(f\"Sparse labels shape {sparse_labels.shape}\")\n",
    "print(f\"Sparse labels min {np.min(sparse_labels)}\")\n",
    "print(f\"Sparse labels max {np.max(sparse_labels)}\")\n",
    "print(\"**Note:** Value 0 is used for unlabeled data and 1-5 for the classes.\")\n",
    "print(sparse_labels[0][0])        # Class 2\n",
    "print(sparse_labels[800][510])    # No label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.          4.57857091 24.05415663 25.01970659  3.90548135]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-19 13:21:32.347703: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-19 13:21:32.353892: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-19 13:21:32.354354: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-19 13:21:32.355121: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-04-19 13:21:32.355637: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-19 13:21:32.356229: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-19 13:21:32.356626: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-19 13:21:32.702407: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-19 13:21:32.702724: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-19 13:21:32.703001: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-19 13:21:32.703262: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 8998 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5\n"
     ]
    }
   ],
   "source": [
    "print(dataset_handler.labels_occurrences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13701, 62731, 329566, 342795, 53509]\n"
     ]
    }
   ],
   "source": [
    "print(dataset_handler.get_occurrences(normalized=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.          4.57857091 24.05415663 25.01970659  3.90548135]\n"
     ]
    }
   ],
   "source": [
    "print(dataset_handler.get_occurrences(normalized=True))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Methods\n",
    "\n",
    "### Plotters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/barrachina/Documents/onera/PolSar/dataset_reader.py:669: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  diagonal = coh_matrix[:, :, :3].astype(np.float32)\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = dataset_handler.print_image_png(showfig=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Plotly dataset colors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rgb_map = dataset_handler.print_ground_truth(showfig=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Plotly dataset colors\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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NSIMneo8hhBqYph3NwRIdu11jMCEmY7gKxNqoeWKOiq2G8w2/GVilUA8nSvPEcUetAVSRCmG3Ia0ZHx1zivhVGENgfKWsfqVIoHjQuVFC4ZwKV+6Wspw5vs9caeRf3C98Lw28OyvuKlGaMvlAM7i9ukKCIMERvKfUStFGCA7nAj7QLx46+NxqJQ4Rb8qaGz6A3zrkn53YvnWc/sKRaj3Lqapka4CjmhKd691MlGoKtYAJ5noHcs6ZYUhUGiqGWSWMnvCLA9Pv22BeMFX8EJnX0jET6ZmGXbAtxJFrwQdPcI6lFATXP4JXhECuBfEQYmDaJOzK4GPjynboUimqtGrYURnuM9agHhsUxe0ijEJwoEdhvB6ZdkK8jUjqqU8tlTBEHEZRAzOqKR5jGBMtGjoIcRt6Q8E5RoPaKsEH1Bo+CE1759peeq7+5T3BefKpYL53d9MuMaSIYeilGdFyZUx9G5vNBvtEEYTWDP8avPWy2wy893gTDueZFEbslWf7q1vEgf/3fzZG0jciMIHx8jWU7DAVHAGksWSH7ipbLcyTJ7x5TXi4x04QT2dmAlwNTCu0ZaWuM29PzxzXJz66/YxwI+x3V3z8YuK4HKlt4Gk5MrZCChuCDMSykhcljkYNAQ2gDsQaPnpwI/sXewIOF4yYG6Eu5NORx8M7nmY414acwQ0r37t7x+bFlu0u8ryeEFNyBb/1JFM2LnDSQpBnnttIyzOLy3hGNt6op8TTzcQ+eMYw8SYIadzTOGLM5HUDIXNsypZAGyObuCG7A+tOIBTWw5F2VnwKtE2kPj9wKo+4NDDHFQuB9dw4nxspJeS54Kk8lYH9rbKWkbE2Dg+NtBsYose5Ey54rq8dX7hnjvPMZjtRQ8CnAZKShqGn9sHhvcPsgjN4h9W+LUQotRIEljzTqrKZJlSVZophhJee4eNIfVdZ10IIgVwKPoBr7ZJtGUbHe/JacZftukvpZFovwcczfZyYfnXLcJPIueCcw4deigbfO2LWFHOGopjCNHpqAxGj0mjWaDXjY7i05wtVK64ZBZBa6KFSaK318qk2/BBwW4/7bAtVEedZciYNqXcfMQYbiSHSaqO2nj3N80yMEdVK1tZb803xQ+8+L3npmZN2fG0aB3KtX9MtclkvwLRd6B6GhV6+grC9GllzoZRCHD1NG8456tpv0uaMWjukVc0wg+Ad4vt+qBhrbhdagKc4B96oVgjBIa5f1/p3kCZ+Z+sbEZicF5wYKVZq9oRaiUDxKz5F6loYknH64kdsLfHRJy+52wfkPlNlwzDMpNB4VEjSwdPvf/F9xrcRHRy//NkvY3GlqceLx/KZqo2zLHhZCUMgWuV8L2hauN7tiNsdu2lCmcn3Jx6WJ6Yw4t3C0oQfHR/IDyf2m5dcx5nH+oxtXzKFyP2X71ndxCc3bzj7Z7IIPgzEXJnXlfO54RelcCQM2rtaG7hT4+bjkd0mMLFlkzIP9wvcPVNZ0XGh6h1T2tJOjbtQCMEIbuSskV1duHl5w4uPd6xfHijLguZGeVjY3iYWaQQyctxwKhkdjMOpEKtSHARXmeeIhAfSZsfxtFI/f+Jmv+XqdkJ3kX9JHb/x+Rn55RvG7cRmO+F9wDvfOVjWUGmkSzcJjFIqQ/I0aVhT6iVbaSu44Kmt41Ai/aI2McY/siV/leEnnvwuX7plShwitdQOxLoPJ70iwUFw5FJQGqso/k1k+mcGhlcbzDmqKg0j5xW5ZBE+emrOhNBLunq5QOecewDMpfOSvMPEaNqAhjbfOV0eci2EFDovTgW1zoXDCeuy9Owm157pmSHJ0ayx1taD64XX5kPPmKs2iNKDgymK4pxHTTr47Hqbvhk4D+IcWSveXXh0zuODw5yRa2NIiZYhxR62Sm2oGblVYghUM2ou+OA6kSkIDoc6enCPgbwWqnb+INKJbOI8VTO1KE0V733HBcVYlrWD8/weLuXUlC+fnhjqiG0qQ4nstyNOhVMpvBwHTnPjdFo5B+F243j95iWpHalOEN3h5pHFea52jqcnZfdi4nC/cF4O/GaZ2d++ZNoZgxXC3rHkGdciURRh5laEQ1qp4xWH8yPHxwNCI+bClAZuXk+YNh5OM1/9+InsCx7Hu8OBV2/2vL7tbfzZFfxqNP/MYXZc7RMg5GWhzNJxr7xyLJUyNYZmhGsjpQmvkYnIejpx/7zyN54faRR2k+fm9QteTjcUu8JboaZGyE+0AvFqx4um5LNyh3JdhBdvbnhYPc9PT9gOUjTMTdTskckTd1fY3crAgdlV/DjgLVFCILpGPlc2k9JEuDs8sK6PvLz9hF/6DP61KPzFVzdsh8BaMiZCjIGqHTMy63fxqsoQA945FFjOSwewBeqpZ0JcCKJWAC/UmgkughOG74z4jzzxKVJ+kFnfZup5Rbl0yXLFh0DVjDCgmgnXjvGzkfHNiAUjjakHilIw79FLMBDf2/tlLRfCq6Ctg7xqSkebPPmSoUXxqBecCLUoLngUJfqAWsUupVYphaKFJCMxRsw6ptRQVC84lzhKbWAQkqeVhmijuV7iOnchrDrfKRdVyDmTPoDlpeK9x6wR4kCpiqqipYKXrzEsxNG0sZS1v48pcy44A1wHrdXAEXAecs6YQUyhP78p4hwlZ6BnTLkazgtLXnGX/dFSCMHjvEdN0UvWJ2r8nTTT39n6RgQmUWF79ixRqYtjN8HD6ZmbTSLnHe+fMq9KZHw58dW7e+6/OmK84NXrVzyenpjvT8gWXu63rCpsdpnl8cRaCjHCaV45P3/OZn/i5mZk3F8jGTbbEc4H2jgwiyeumbw+kuKGN1dbxiFgSyWIofXM+e4dz01xW2O0SLLC0ynz5eOZlyGwe51wbx+ZBk81eFrPlKGnuiKBGiOnVgjR4y0xusJ2grjfE9IWXYT39wdaKJR2IvqVbdrw5s0N5SHw1fGBdbljMwnFHBvn8DcTMc88n84cq1HeH7HRw0eRwd2wv9oxvhjQ+xWJjnC1QULiSgvP60rWibgUGCLJC2U7MbkdV9ZLnPOQyQlcnrh/Lvzw/Mib3Z7ff+/53vWRNmzZpURrypqXr+nAcmF8L7XgnO9tbNfZ+c47vPc0a7gLmCsiaFGqGuPYOzqoYqqk20R6kUiHlXbfaA+V5ZRJEglXkRASecmML0eGVwNyYbBXhbXW3kpyvbzEdVZgrfWSoVi/868zPgS05guzWjBToneUVlFtgJFLxvne2i9VoVRwQm0ZJx4XPP6SWeS8Qm2ogQZHFKVW64F8HBHtGZqPvuNuTfGhBxMfAq02XAQwUgyUkgkxdZ4T/T20NYJI53k5OkE1OFQ7Qdi5zi0yL/27wCG+dy1jjKBGrQU+4HaXY17M8PTnlZoJ3pNLo2nDrOOhuZbLFdwzumY9E6slo2Y45/hZtbjfiMBkBhKuiJwp55nid4zOYeKxU2abYN0vPL49kr3iZuOrr07Mm8Kr3TXrJxvWwzsGidTWWDCmaWCfoeHJwYGPrJL5/L7yyjmS3zGIMl4PtFZZTGnjyF4U2+xJcUs+3PH27o7ldMYGz9Rgxdj4AScNJ56xLOwCnNsB/1x5+eIFx7xi1tCzsq4NZwXZjYglJj/hnBGuPVdT4npYabZwv544aWN6EXGnyH4fGaXhTHj43gNHWTBtxJsNZfHsBiGwoTyfeX9+5O4k7HNmNcf92sjb12ziAW0LT2XldhiYF4+2wv42QrliepFIyxOy3TGvC5wzEhZ02JDLkbY2MhV7LjwKhNGjBR4//4pfmk/8sn2HP7ee+dH1CXEN8ZEYAhL81xlESqFfuEUppfSMY82kYQTXweyU4uWEN5zBeZkJPnwNxlrNOO9hdPBaiW8ig59wPtBKRmLAWsdpAAxjXdZ+UYiy1MJ+s+3AtjZaK4zjxLqshOBBKy72S6GTNQURR86lB1BtSOvgsmrPfLQ1EKFqxpowDAlUWZYV8w4nxhgTxZQQHXWtqHiGMVKLolq5iJzAKnOuiCoh9Qy71oICVo3gfcetLvjcmCK5Vkq50ANQfPCoXsBx68dNnPs6cxLnCEbHyMyIIaLWjw2mpBAxeplm9MBagFprD26mFFWcGcd5YRwT3l/oBYAE31UENSPBE0W+li/9LOsbEZgQw4fKCizLzDBu2G8jh+LYjoW1Cg9neF4anoKkgYBwPj3wI3nmu1e/wE6ueXecSWmgjEaKYPsJnSHkB9x6osqAROH8fCDdJJrA87IwaqKGxOn0xKFU9PAFJWdCfsKKJ0tj465wZlxPFXFGOSonW6g+M2tjQ+LwvLAunl+43fL9uydkN5F8w3JgUo9oYUGZxolpaKhALo5yirjryEeDY10rQXun6eF0puUzxcPm1YZ1LlxPW2RzjTsfOegzx6/O1GhEBtzGYyc4HA6sdwfCxx6Kso9XlOWMR/j83RPt8YnrzY6dbJkS5KaMcSJ9MpB1ZUoTrTjy1uNKYVgf8EF5no9c+wP+peHDxO74Jf8jF/mPfxP+cppYx0B6eYv4gHNdp9jR0d6Nc04QJ2hz6AXQbSbouSCh313NII1Dl4Wogu8XWGtG09oJnd6jZrS6UKsRLmUSdNa29+4io+icGy9CbYVaG8F5EGEtK2adde0E6lwwgVoLTvpzDMWJ78GLrj1z/qIbVKVdyI+5NFopuBgIQ0TEoaas9QOHy2PBelZRSt9+LngfCTFQS+7YTAyoaeczXS4NHwJrztgF+/IhsJaVhvXyD09TZUiJNWdiCJRWOjk1ROZlIcVA1UbDKOuC957SCq1UvOvk2NNSwZRxnFiWBXEOMwWB4DquB73kFSeUmnsmTO9mns+V4D1mRjHFO0e64HY/y/pGBCYRoSUjFePV9TVzKxQ83jLrZNhcyWslBSO6TpdXqex2E4+nyl//3o/59MWefdow18oYEucAkitxclzfvOb4/MQ4TbhcmQ3i2JiXzEYr7zSz/OTM7c4xDNd8eV5xesZSYjdsiNqQaeC4Hkne453hbhLvPy/IfKZdJ9I0EsPI410mbmBz6ylLJiVP3Wyw3RVJT2yks43FKWWt1KuRm+sthERrZwYnMHmW2fFi/4LTfM+8nPEFtj5xf35muT8zlgXvA5L6BYgpJQoaVuKn18yPjcPzkZe3r2Fy1OeIa8qblxvuDzPH+zOnduC7r7ZcXX+M2wrP9Ymx7Alz7il9VIaSSR99hJ2P7HaG6QtkgNOhcrYn8hD4F26v+GM18f058x/9hR9y+v2vSZuhSydE8SEyxND1YCqogPl+J4/I1+3pDy2l5XQmpUTJa9fZOUexig8RaeWirzOcCzgqYoJzhvcXRrYq67rife9A9c5RB3Fr7phSzr0MiRIo2jGaGCLiOqhdW+tNiQv2VGsjpoA4odUCznd92YdAeCm1xqF3/j5cwGIGl7a95tyzx3qRu1ghrysSQqdPqOHiQK6ZYRh68Ct92951GkBpDe88rWk/DqoYSlsa1oylNbzvASzn3Amp80waRkoptNYYXKMaPWiWhk8Bq6VztEqh1k7ilCAE7zidzqTgMXE07cL2TpbtZap4R10zBSENgSBdcN7lOz9bTPhGBCYVo7rGkEBTwLGyYFxtBqLAV199hffKOCY24y2HU+ZkShTP7e2WL96/58v3K9fba1JKLBIZWkXGip0DMUykzQ7vAgvClAYARpQ5Rzbzme1mh8VCsTOuLky7AadgrNQw0dYjlhuzC7zaRurpnpuUOKWX5Jx5r4VdCHiOnJ43jPst++DYXO/wcUOdBjYrnNtKbQsubQk+k9eIOcfGeY5lz36qOAo76/KAcXzBuSVsnvni4YhVY1oq69YRCyQP6wJgDNlYz43tkrGWOT1FqA9sbzZc77fY8czTKRGHzMvrK97OR+rq+MkXv814O7Ed9lQfGKOj2QoukXbXLOuKRY8LShp2/OiLL1lPsN0sbNkS6koZvuSXrq/4X195/rL8Cv/fcMS2tQtPqxKmoStFL9iTqYE31ITW6kW8210V8I5aay9rpItLTRsuRITe7TM6qBx9pJZCM7DcCavWicyI9MDggqeUBlR8dGhpFwGq0mq74F1Ks+4+UVvXkllVmhZKzvh4yYToGZuI9M6gvwSuXLqC4XBC6dwmRMilklKkNiXFzlBfl0KMAR8iPcu7kFVbw3thHEZa63SHoplhmHqGaaClges6O/GuY2fiOhziL5KQGHqAUSWI4JxnWVYEI4iQa+80okLyQi2V2ipDHMmlZ1sSPaCU1vE/kY4FOhHEB0qtHUOqlcE7iL7fCMxo9dKUqB2X+1nWNyIwYdKFhiqIS+xS6BhOK720UiU4mKaEScWssFlnjB326YYXuw3Ls3GoR5LtCLHCtMMtEbOZqGeiFQiB7faWsBWsZBoOvx+5fnPF+f6Bd09KeXrGfKa0K168iNx9ueA3AT8vLJrZjo1zMebVsegCxQjeoA3YVInDFbEFIiObqw3RlDluGKry4uYlL6oy64EyvSAf7kj5zGk5sByP4Co/+TGc6oEkC8fsuL0KqBvQnNl4h8oWeTXSlieUEzElgnncuXL2gtuD5srV9YYwRPCN54d7DufKL7/ac7WN1J9U1jLjznAcKmsUjs8nDl64Ho7I+JK6daz3R+7WA6aVl1Nik3bUdiA5cDuhWWCxRCKzKKwPd+juij+8/5IfHV7wdpMZ40QLjZrXnuE41+031HCtoXQKgfOO2lrHMxBKa0SU4D21KBKEw+mAIF9bdvgQWJYFj1JLRb2nqeHoMpVlXhBtpHHshNYQgJ7haG29y5QLUnpQca0yDSPmG+t57RiK9A5ZLZlSC+Mw9s6Tda6U953X5JwQo6c6YfCB0hq02nlQrdG0a0JdcITYgeaynAkxXThfijhYl4UQ04XX1dnXOa9oVQg9C6sXykJwvmNRIpfgIT2Qrwu1dUG0hfD1zSA66dhWuJRpCNW6PlC8cF4XaO3SYeMixuuvzSX3Dl21vq/ekWvGi2OuDe8c53VlShGES5DXn5Ut8M0ITKqGHGF3a4SXN5TTM/lBqfs9gzuzm+BYGnMpeCaaQnWBuhbk3RM3ccNDPaBzoKWV3ToybAo5eM4PCw9LJsaBSQpuZ2wlUPxAmw80Fu5PSjmdsKyQenv7PAvz2aE+Mg6Okw5o8fBUqTeJ/XUinZTnw5nb3cDiAqMMyD4xkhm8Y2kLmsDfHznlIz9+esfVmFieK0d/ZLtPvNh9jNhKNjiejrTyFmisdYRy5HR0hFi42SVsETQ2JBzYvAo831/xcHcEGtvbG+LpLasE8n7LaI3aZgYX2A9wOM18v514+eIVtx+94uHtE1LeEW4+5YUVsi5kMx4PB6KL3L644bwZiI9vOc7KV6vwqc80D6+2W169+JgvTs+0ZQYnLE8z291ACLDe/XVe8Ad453eUWvrdFWjSM5swJMS6BstQnA84JzQ1aqkdb3G9nZ9rBQyrnVGuTdFLueIQihjWjBACTXsnT1CsOVIKnM8Fd7m7V23I5VqpWrr63wnOBC+CWKOVtZdxvivmRbW/zl0Y4MvytVcXZozDyHrpQlkRQuhOC1YrZvTOY+v4jgup83+8A4w4DNRcLqxtYV17eSlkjP7ZA0LT1ikMqxFS6MHZPHkpXaIjgqp+HZxUDWddXaCldBANKBcBcAqh27xoI15MucQLVgrOe3JVdF3ADCdCLoUQQ+9qmtEUxuDxHzy6oIur6aV00U6yiv1j/kzrGxGYQhBcGngqM8P7Z0QrYZcYkrEGI22viYeZ0e/QurKJwmyJ6cLLCBJh2jD4BSFi0cAZuaycrbDRI5tc8duJ/S6gc2FeF95/cY93nmEUKI6WIqkF1nUluhkZbxhVSctMs4CGAGPiscAuzL378Oktu+pITLDzxI3gTx7xe6Z2xzRtcKmycbcczwd+67cfEKmEySMzLIczop5jVZanO6IJ+61nPhd88eTamENGlsRGdoQrYa2e+lSYpkAmYhYxycRhYK8Rf5qxYWKdZyxUGDwuGJMIXx1m3myM69c3TBvIEa6nF6zHI56Vu/UaScKylG6hcRXZhoACj89nhs3AumTQtwwmHLyxqPDy5S3eKe/XRj0ZLX2MtWfWVlCtxBi6wr9ql3xcWuBqDhFhXXMvlWrFfOgyHusmfd3gT/+mMZl0T6BSK0K/KOd1xZwnXcob8UItjXEcadYw7a3sKK53zSzgXKXWhrvsW28iOWotvbsoPbMLoUsvcN3xoNZKih4Vz7zMNDVijDixThbFMYxDx4GyoU6Ykkfo2NCH7mDwDtNGqZ0lbt4DxlIbcgG3XRqotTGNiVoa53m9AMseky761dYND3GO4C7lsDgEBdGvhbfeOZzAMi/dJcF5clsxFaRJzxBrpTQjBg/SzeXchw5pXnExgkFeKz64Cym2Er3vnKYGQTo+ZfqPEBP+UYPKP57l2PtG1khIO8abG+rDI5ILWwvI0DGA1JRHE7xLxEkYqmO2mTsObDdbzG+QdsSVBdNAKgOb2yui7tmNlRc3e75898CZyPr0QJiUVgaGcmaIt8xOeVpXkg9cbXbdLsIZLexo50d88kSXSM4Tww0B4fVui8SBrRtZYmEcN3j3RFkUa2BsOOQjX739PnltCA1bhGm/4aie3dqIN8ZNCGS55vHpiUNR0m7i4X3GDUcGSRSvIAszO4bQiC2iz0dyOxFdAI3kw4k0TPiN5+iUXUrMprTi2U6OUwmUVpmlMU4H0ICFgbmuTOPI5uY1kxrOD7jTkenKs0ue+/rEMSx4g6E2HlMilzPObxl9w52NxRTJK9WUOVzhwg5rj4gY/uuA1DlMreUOHqt14mAMODPyuuJdb4FXVbSWzjFq3TzOu04qdK5jPZ5e/lXr2UJw8nX5I3kF17EQQy/4je/2HQZNu5VHcmCt4uOA0SkN4oWslRBSx7Lo5ELneoPfp4heun347v3UasfJvHhyKzjt8pM4jF0oW8qFh9T5SYph2mit4U1wTrHWsx8vjpgiuXT8STBKyajCMMTemKDzobx3iEIuhTEEWtNLqQeeS1vUlM1woSkIeKyzui942gey5ro2xnGAi5Yw596188ERvad5QcxhreCSXG4UXV9YW8fNEKGUhqv1Z3YWgG9IYDLnyFNjM2zI9czTg7HfCLZGmihjNYbXVzwdD2xXo2ahFuFMxYUtkgKDdO7I6ib2VzcUt2XaVa43I+KE653Q1sD21cTNIXN+4bF15pAFP10BI359YL+/wlxg3HeRavKRrVWCvyYOkauXV8RThUFxbWQbGzOOd+8eeDrcY35ktEcWHyiHmX26w7aBjGM3KnMZGEYDEuMUuiLfGk+HxjCNvBlvecwn1mXleq+UJRB2Rloz83UkWKZJQo5nvigZKyCxg+CWAifnuBo908kTr4TBCed55TRM3D2dSe3E+WUCB1WUsSlOAxVjvn/g83dHNpMxTo5Qrhm2kVevNoxm3C/ww/wdfuI/4fOp4eIdh7/ynu8MlT96fWKKA+/sj3Dwf4B801Aqy6Uk8DEi3rHmQnC+Ozvm3LOdZYXW7YP9ELtEohSGGC8BqnZ86AKQO+sXVdbSFRTekWsvK6IIKl25H/jAXlac95znlSDSMRTnGby/tPAbVSspRrT1rl4KDqzhguuGax8M6VTxLlDLCqGLZ2utl+zFwHf3zYBAq1htrPlCyvTu69JN7KKrUzjNM7vNSIpddJ1b7bACDi6ukXldexeWzuBul0BrzvVGgnbGdSu5g/NmID3wmSrz0qkY3QK685Sc83hrF0Ln0INTaT17xHCXrMtL93LS2jlZaez0gb7/fbum4KzABwzwUjr/nga/Ywy83Gz44vHIfDxjLlHuEy9f3va7yOBJZeLl1HjeRNz7lSgLT37LmKDicKWynSKfTFMvnxzMtiXNM+uwYRsTRylsaJRjZa8Vf7XthmR1RHXFwgbXQv9ic+Z1jEhKDG6L7E6ckmc5LTzPC/s6MMuJH/zkiVwE0QWjkaYuot9NK6fJs02R5+pJeOJ2z8ur13znZk8Jwtkc7ulLfvy9L7kDNDzz6vpjXt3uOJwfuL9vVHvsTYG46xmYNyYHp6HQnhZqbWiCzW6kHhQHrOfKfuOox0DaGDqNrI8zVs80Xbg7jIRww+3kOmM4eJKvLAdDYuH5uHJeBhr3pGFlH3YcZODXb3+Vn3x0w6pzL6PcDZYTv/7nfsIffjnx105/jHenW65+8cTblz9hLR0fcqGXCA5PSh6t3cgspADNOknPJ7zqxR/c8L6XRU0Nc76TMdXQ2oi+W5BE18FbV3pn7Fgru5ioreAu0ow8L8Q0UFsHapsq0h3lulVH6/wkaVCl4byQXMB76WWZddM25zxzLmxTd05w3oN48rp0JrbvzpJGFxon11X9pTRS9NTSW/ToJThIt3ATM8YUunatdnsVcf11QwLoujXvA7l0M7eOr3XHTm9czOs6BiQuUtbSMSnXtXfGxRJGe5bmYuo+9GaodL98p6VngRffqmLGGCPzmsF7RCvGhT/WwHlPyZkmRsDjQ9fgiQlpGjFTNmNC3M/JXUBE/j3gfwC8NbN/9vLYC+A/BH4J+D7wb5jZg4gI8L8H/jXgDPzPzOwv/oO2sa6ZLw+ZOmtXXTvFl5Xffv8VL4drrm4uRlqbK67min1cWB8SzMZtGjGUeDWQ3Mhcz+SHB3CO+/UdmwD5Ed4tgW1U7M1rbl5veV4jRxfRWrkaA+YCN6vjWRWhcsWEDBPHS4fn8LDyfLfi18ywLTRvXfwJTK8CdZ7Y+Mi8nRgXR8mF243i3IaPrq653l7holKPR3744yfM166mt8q9a3hnEArv795x1SbGNPHyFpbBeD4eWEJiTMK4jZRnR04jw/SMrYJKIo1bNksm+0RKXcPW6srWX7GcYD4thDGwPkaGduRhVdpHE9/95Iq1OJb2jGyvSK6BJKw2xt1Iflh4q5WH0fj1X8jk8kB9PnY5hAnxE4//Q7dEyVzbmS8m44c3Z9Y1E6IHSaw1IwJrq4TSwdfWGrVxKV3oARbDu3ChE4D1WrjzcqKnrJmYIuu8fO3uKM6h1ogpErRRP8hHzPA+9la9NlIM3XHAeVzsGjS7SEeaGWOPAheHg46PqHayY3Mep0Ywo9Tuhmm+85lSTORWaO3SoTMll0wvcoSqiriASc88JDrKBy90666X2rQ7HcQuMnciqHfdkE1gnmfSkEhDROjC4BAuQy4cxBRRVY7n8wfb9K7q14rr3ArEjJgipVxkMNZ6RuQCIpWmnaKB9Q6pF8daK8PQZS/iYseYXBdPi/N9f5EuwXEwxP75+5AQaD97wvQ7ypj+z8D/AfhTP/XYnwT+IzP7dy4DLf8k8L8B/lXgVy7//jjwf+TvMiHlb1+GEZvgB0PjhM6V6XpgqsaxdgOu69B4epy5Xx55c7Xlyg2cYqWsJ6bhimkcsaVyrpUosBsDn2pkGVZGU3JYmGVkvntLK1e0YSQ8H7HNyO31yLE4khkbtxKm15zX2jsuxxP70FiT8Xras57OSLxit0ndbO2mdBzgZkPcOL7rInl+Zl4yw1Y5Fagl8/ndF6zHlR2NrAGrZ2S7JdTCJm7BzYDnXCtvDw9sQ+bmo9e8uv0lfnh4i9w/cXV7i85Qh0oS41Y+YXl84u5Q0U0h6Z7tcKYOE1Uc2xeR55PyND9CCHgRNtFRdxvERw7Ple/VlduPb9hPnzGfZ+LVK/T5GTd4xBxBofoDf+7djqdPH4m7HQ3wptgQiN5x88+95D/76j3z5oEDJxBhGCbWpXS5hCk4TxLXhxKY4YJQm6KtMm6mD/ZsPVMYAu3CiUG7a2Vdu0RjzTNpTAxDopSKomhWvBOW2m1fOyUg9FJHe6vfh+65vZRMct3bxkyQEPBcgol0615r/WYUfWCpK048TXpJVVqnh9RquEtm0S6t+Syl275cpCPOdTM/Jx3I11axepHqhAG72AK72PdBnKcCegG/c1Wq6+VaKSthGLsIeIgE4SIEdhfnzIZZw3JBQpe19KlB3elAAL3cIKidzNrEQW2ENPRgfgks4C43hcuwBZOv2efBGUoH6x1GqY1sXRAcnFDo34W1Sorys8alf3BgMrP/VER+6W97+E8A/53Lz/8X4D+hB6Y/Afwp68q9PysiNyLyiZl98ffdhhrnpZLzgOJpfuDp/YqPHueEx9PKu+bwsTJF36cmXb3gOymydWeqThxtZW6ZsUaKM85rZTkf2MYd9ToRypZUZ8bdFdebK2S4Yg5fUqkUV/g47vnxUZjXSrx/x3M54ceB5XjkyRn7acLtdsSQmG4mSobQjG3YcL2F2e8pz1+QGQhT4Hq/p3Hm6tD4qj4QClhdYPLo+czkIJ8yp1BZzjPjZmBQ2NSFEpRH89iP3vG0u2ez3RFev2B+OhGl40Zu9bRyhM2OT/eVMAs2rYQ0YVnZ3+x5eveOL744sP9sRy6ZOEOdGlGUFDNuLCxzpX5+4vF6xzi84GovuO3Ecn8iWOThE+XP/qbx8Eu/wO3La7woh7bS1BirYRIRMz6fNlgFCZ3NvZw/+Ak5FhXCRdDpvUOtTzcx7e6TXXfm+oSOwfcuWe22rz5cKAJiOFWQoZ8vp7kbl5WCE0chk9fScRrxLEvBu4oKnY1dO49tGsavBxqAdSX8BdguVQmhd/NEL46V4sm5Z2omDkwRvXSjtPYArb200VoJIWIIzvuOJ13kM6gQhwBdtsyy5G743+wiyoVWaj8Wzvdgbhd2uxeW3KA0slW8d9TSGey0QqkFQiSGRG6dhqEGvlWaE5pmnAkudhb9B0Y7pnjn+8g0g0ECZp2uYdKB/lpLp2XULmiez3OnRDjfuUqtgjlCCmhZSOPUhc4X2c7vNsb05qeCzZfAm8vPf7eZcp8Bf9/A1Kn9oMko+dQZx3okxsRt2jHutoRxQxg8edhys02MNVFq4aATgzuyGW5wSeB04tEpYmeWcMPzXLnNwngNB4vU58wPHn9EkMCLqx3x5W2XGWBYaEgcUMvsj5HiB9IEZao8l8g1sBtg1cL5+YHP3z4itZHijuoLZh6xGVxiu91SliNORyRVaJ5hCNRyZJAtlYyvC24WcqtoK8huR/EetUBWZXst1Psnasnc7K7Yj9e8f3jPRM8GWhwZ9ZrtdUHfCO1hSxyNsxrtWFhkYb/fcVWNM5G5LLgE9XkmbPfIxpMmg83I8XDi3dPCq7Ll5tULxqsr9AR/5nuV/8868Ps/u+1+P6UQXXdv1NbbzeK613ejXwxrtd66jl0UGlPqnTTr5vtdBK8spRLGgeADy7zSYiA5T5BuNOYE1nntmZWVziCPkeA8ayudN6OGcz3ABOf6oAeE4AOtdJfM1hQvgc0wUcuKuY6rgHTxKV33NkRPzkvXyhnghNaUzTjgvO9AvioF3y1a1owfEl7oU02aARX3YQhAx+MJ4mh0yYhqvQSoSssVNWUaEms1ov+gJexYUrbOh3JOiN7Bxc7Ea58o4+OIHwKUQCmVvK48n2aSc/iYOpYkRooT/kOc8A5PFwaf1wuJ1Dp9IeeL2Z0owTvquiJBaPjup1UryQfEh67Tu/CmOuHVcMOImJJi7BKaov/k3AXMzETkH3rrPz3w8vXtyO1OOJnx8cuXbLaBJK8YViPPmeyU5/pEfix4J5x3Iyldcb0LXA8jRW7JrTANDpEdkxktBz7egG4qLYw8nO8p5yeOc5+7Ne4SBzI3uqBsOFtke3vL0ARqpgwr9nRPnY0ge6brSDwtzH7h6YsDT+8fuqA2eR7PM6NL3G4c96eB0AqSjwy1ch4WXHUMwwgBysnhFqVtHEMITAkYE/lw5HyqbNLAWjOjnbAlsR8n5nrm8QnqLvJqO/CwwhQX3OIRyRznBVkibrsjI6xtwaaBOG+YdjC4PlJo3L3gVFfYNm5Gz3saH6UJTmfK+xMny7T3wnI3s7uKLPWW7+cXfPZHBkIEadDKSmt03ZdPlNYYY+hBCYPYWfspDWirqAnjFCmld4GsFpyHpnLBZLpS34duGaKtkueu8/PB9ykmzTrf6NKB0la6p1EpWM2o9Orka6JTDOC6cHU5ZqYh4ULnHPURTe3iweQwrWTlouA3wHfOlYCVepnB1rBLduGcx4XO7E7DgBmsuTCm2JnerktwXOhi5HaRewS4uENWPKHLUJxQVZgvchGzLhYuNSMudIB56RSDKkbygHqqZHq5pdR1pbTSGw1VmXykqGJFES+EIJAXHs8L07TBi2NplXgRVLvgaVy4YaF3S0U7ibUYBBUcSnAD1SlZjdAqQVrPFJ3vxn0+dd/1WnFB0Nb5T/K7bBT31YcSTUQ+Ad5eHv8dz5T76YGXv/+7t/bRy48gJcpiiJypCxA9ciVI9cQSUIUQBVmUZT0zz89IGVhyotYFrLJ7ERmGK/b7a4SAixGvhdvNSE4fE0tjciPPb7/kdH5iPs/s4sTEyL0eCUYnEBZHqCtzXvFlZn9sPKWJ+XBgcIpPE34dCfuZ6XqkzY11bugycLLM+dzYb0GqY9oOnTV8OHCcC+OrK8JygqvA8e2MErl5uWc99K6QZqFWYdYz47AnnLekWlievyC+esOnn73E+zP5aBzXM3pyJO9w7swgI81lSmm8enWLzgcO92eCG7m5GgiPz+TNQHErWzewrivzfeHhnFltRlMkH+5o+08Rd2Y5KN4P3H35DoLSBHCJMY44L13mcAFMnRNw0rGdC8+mG4dVvHTOTEpdQyXdnAlxkLUPWrTaOJ8XgihWVqqN9A326SbR+z6A4SIBEYzSOvhL6B1JFxOmXYgqXojOYw5C9L1cKxmH67KXUmm19dltIlgrvbSpGXERa33Sil1wK+fApHfD1nkm+Q/DOJXzWgjiWee1m78prOsC2nqJVVbMPFUc81rAGs2NPTPJK00cYg11jv1uT54zrbaLsycEFy5BWVmdMnqhLQvlbmadO07bJlBZECtUjWQHeKMRCAon7QM5CQGtMI6BZa1UVZLQS73QjzFOCWmi5IITu9j4GikmtBaaCE4apUJKvncem126h7UP+Az2u+4u8P8E/qfAv3P5///xU4//2yLyH9BB76d/EL4Evfvy/umeNO54OQjFRcwvJIQiIyuZVhLD7YCoY13OVBoyQ34+gW9ImZnX3nX58vGZyX2O7QauNwMqgTh4pAnH+cRdrYTVGG+3nE4rXy3PjJsrlvXAjkrYvcSHikwTcY2YK7w/FTaDZz8lmt+wWY/oFHjOkRhPpMlzlmsGPaGzYhuHDY6NDkTgfHigFmO/GwmakHGgPD5xLAv63Njsr5n2Sn1ciRiESJs9TB4nGb8RavE8/+hHuLKwnRyOEZ8ipgUmhTvjYM8ImWmcEAfLUrl7mBlv4e4+swmGOxbczQ4tjfK8cFczQ2gsc6AqKGe++PEX7Lefsj0b9UtP+OUNz/OMiGccho5zWO3aKh8pq/abhnSXSFDKZXJGa9qZ2yKsuWKtkYbUeTnOIbVSlpWlrt1UzfdpKyEktNTexg7h4orZ+vQO6y6WMQkpxa7y10IaAqVcmOIu9EzkInUxU0IcvuYF2cVRcs25X3A4ghq1KkiXYTgB5wIl1wumYxgV70CdwxPIde4mcSn2kn0MlNKHY4JD1frcNjPM9wBQ1oJPyrJUcIHgXfdcavq1l1RrijqgKD7JpdsHwXcXhNJmnv+zezbJOC6gW4//dGT7CzsKFasraLrwyAaadLmIu1gBl2aYla8Jlqfjggtjt+5tFW0ZFWOIHtPCNHRfKC5Aee2GmXjxlFw55kwK8aIfbLTacbKfZf1O6AL/VzrQ/UpEfgz8b+kB6f8mIv8L4AfAv3F5+p+mUwV+i04X+J//TnaiXRjArG95ut6wWwdOcaANnqoOJdBKQZ2AHTlko64rg9PuexwzzhpuhuPj0rkyzwsywvnsiLIga+PLw4mgA5UDwUZujpWoyrAJ6FwJS+S8dcjDV/g8wIsdIRk35sjR9dbrAC2v7NLA/XrGrDK6EfYJe28sciZKoKkjpV0fJbQUsjmyb+xi4HB+7lKRVcnFqPMdy25g2gy0KbENkfncWP0Kd9q7j4+PaJzw4xVVlNxGJm3oI9TkcGv3xV6dZ/SB06Fx/PJA8xWZAk8m7Frm7twIqXB9GHm4e8S0IRFWcxA8pS3EGUpeyDxQph2PP3zm1X/lBS9fbLthWNPObi614xOtD6hsOJLzaIAhDFgpJNf5QE56e1td746BsJwXaqtwkX0EJxASOWeC64DwnFeGEKDVC8fHoQLifecieUFcn7nnXeB0XpiG1DtJtRG97zyq0vEcb42ajZorIYTuqtk60xmEufauYPDdDrg3sXrGt5bWO7AmLE+FoRnHxzPpWohvAmhFzJjPFbtkck0N0drb6r7P/VtbxvtIrV08HFxAakbo5oi1lN6QrIrEzt6el0waQwfY3cXPqXmmP3rF3eE9+ddn2hcnxuU1u/ce/3JCX1whtzCIZ10zowsMY59MHGMkpoF5OdMnzAjOXyQo0n2yguuDCNT68T2d8kUoLKQ+BwFPH00VfGCbOqalYpznFWfy87M9MbN/8+/xp3/l7/JcA/5X/7A7IShNJ5YA5fDEoZ64jq84bzJ7H9lfvyB+5zX3+kh9KIS957QNtPVETY1YFR3mzoZVIaVr2k2muMz5+ciIY9x62tMBi8KTOrY7R3x4JE0BCVuSWzhYJb89kQdhGCP7uuDHidJWjrHgK4xq+ASH6kkitAgaE6ElnJ3IObC98iT2YBEPzNb5JWl3w/PTkcN54WoKnMduhD+kyE+++ILw2Se82m1QMfwY8BlOKXN3XwhRSQZtfuRgE+MnwnFWzhi1bsmnEyeJBCoPTnhqA04zt96hrEirzOWENiExUfyOGGdmVVwS7PlMbEbwI0PL1BC5zQOf2Z5zO7DmGed6x6jpSmiA6xhRBYJ1VX9bK4TLhRQDoGgp5IuWtDXlpMpmM9Cs9ZM6BDBo5nClMYSI5sxp7uTFPq8MgjTW2qU+YYyE4C/TRSq5GUPopeV6niH04QjzvDCkQIgRKqylIVaJ0SPuQ3ADLhNyXeoykFxrt/comdoaguvAsXNYg/OvnTg+NFo2po8Cso2Ejac0vbDDHTl3/251XYjrJIADT5+sW3MF57vTQGk46bYiXdfXg0dupfOgaLTcy08xQ0tGwkj8eMOrT254HBKnv2wsz+/40Xtj/P414YVy9dGI/cEN037fITgnl20otWSmafza78nUqDWjOnQpkfOETkbvtipOiZeGAQipU/BpptRWwAKl9jFXlILE+HvbWlcQJDXSYqhEDrkwhsZoE6tvnJ+ecEF5OJ0IdeF4mqmMkLoJlzQIwx7vZ6osnPKRpsKYAqeWWXXh8c7B7MEKpTk0LJRpz3k+sz7d89KNyOCYxbi9GpCieM3kQySO0ifIusisQjiDVti9GslzoC2F4AyLkZtpw25q6GyECTAYasQPEwsrp1gZrwdUHO14JjWlRrAl8sPP3zF98ilhv+J1Ig5AFfavhHVOrEUwO3N6nzlYV4aPjNy8ukaGV/wnT42nILibgI0BZ8IVRvnqyOb9HR/dfpe6+Q6fXwe+akeu3nyHX7aRzVe/1svdcUulcXwsbK+FL7944KvHB47MlHeJzWcjrSmtGcn77oVNZyIHH/AxUNZKEI9Ioy6daInzF4taJY0JaqPkhgM2Q+oYRoMxJXJr/Yy4XChhSJh0qCl5JdDQeJF3rGdqbYwpItqorbeeJEY8/TsbYugdqVrJeSbGsbOjW+lDOX2/q7fWuoas9fl1rRkulg6U1+6BnVynOdSc2f7zG8qPCsffOHP+csH9OWHzh3eEnaNdHAHWUrjebcg5UyvE0P2L1F90dtI9tWnS/2auO21eSsyuQ4NSVpwznK9U6xa5iOtAeowkd82L7/YO85e/8WPkc2N4Uk7PD4y7K9pfy4z/3B4zI3hHipEqhhfDW88Oq/bscq1CCiDmuye6KdYqzqybqKf+t1y7M2b3Znc0rLutamMcN0zbXZcQ/byY378by7xjR2ZKW47WuBVBxopKo+UVSRVtwq2f+oBDV1kx1lMgjZ7DnBm04QZh/rJRhq78zk/gln7Sl3ImjL0TMWTf7SSc0bY7RlcRP1ECODY8lwJZ+eHyHSLwav0eG0aOY+8qjd6RklJz5vBVYbj1lEPhcJz59BdfUw8Zi4G6nqEZQ9jSQuPxecVJZ1avayaUiSgLD0u3JVXv+fHd57yIL2hyx+SvGG6uGOYz2yAc15kvzytSjfzuPdcvXrHsf4W/xB/l+8NPuPulO3BGqUZyhazCWTz+u3vuPt3xg1YRV4gDLMXzzowfsHDrfpHXi7Juhc/PwvWLN3xyPiIvfp2ggatFWErleJ67t3VRclHCGGmlsYmBtfTOkGDE6DERioJdukshevJ6sSvRTsLzl5EpWitijVqU2irjOHV+1AXn8F6YpJMivReCSKcu+Eiz3rHzFxV9rQasl1JPsFwYU6DkgsQukWilobXwgccUh9hHEzkuYHQ37b+YMdGsUxEwZT4vDMPIdjTyr3jCbeD5L50xr30SSoWUHEUd0cGyLMTgmaIg2sm4ZS3oMHTpjRktFzKVIQ4E6+6ZuTkoGVwgJU/LhaUp4xh72amuB6e84mJgGCY2mw3DFFk/e+Tpz36Jr5G7u/cMNaLPHcDebEZc8H0SjQinecYuNITBBxJdqkPNVLMLVcH3LuTFqSFGR3SRnGvPCqXgYiJGTyDggiD+Upr/jOsbEZgcgMsciDQF9WCtoPkZLTPr4FgXuJUdu1cjixobJ3ifmQaHGyaYFw67T7n71Q3fe+rA3EGN8XXker/QbGEct0iOMDtkcgzbysMR9nXlqUhn45Zn7ptjVE99/kW+6ypWTth64rWeOTblamgQPIc7YdHKeTXaqryZBtpsWEhYqzyeztz4wCoL33+4Yxwa+AlK9xvaD5XjwZC5IaIElLl6nt4deH19zePhxMvXe+oJLEJeJ5IoZRMgbXj74lO+t93x1v9FXDS0eMwbesGBhEApjSKV4AAzYhqwZkhrJFkobuKdb7y/GgGljo3ZfcX7G8/T4Zbb9ZmtRLa3W9YorKczaRPAGvPauivomvHe0NK1XufjmTH2O7Hn4n1RjUCjFele0zg09LZ5aQZrHyrgY2TNhTqvhODQtRuhVRqqhThtACPXQqvWZ6a1iy2vQQWmlPqcN81I/dC6voxuUkFQRA0fuhslGGKV2gwsUKswpj5iqdvyuj6PsHUs7Ou5cc2IbwZu/pgwbh3ZKTF2I7yEkinQPHh36bCBF9d9QaxneNtRepMgC7iA6Yz3ERC0VaRlLAxUcRev8U5obLX24yP08pYOVO9CYProBelfvya/nRluHMEPrE2ZkqfVzi3KZcUVoeWKE6F5R26FFKDmFb0EUZwnWAGXOiNdjMNpYRxiPzbaiENCfOd8DUPsx1ob59P68wO/fzeWE0fc3uCcI5+VKc+sEinOI5pJ5sijsLaVeFy52e9p58owwXNeMDO+bx/zF2pguQnoEHtwq4UyKId0Rc5bmjVqBEkZiRH8ANuVB9kxnxaIUMqIUHlUI776ET++fsFvn34f5XEmrZlmjo+nwmYp3Crc8BWUzMZ51o0jcOjBYBVubgbMR96/O/cvvHSZx9YvlPWR4q45iOCTQ9zA2QwWwdqZTQzcjAPzXJjwHE+Zw+NCfvmv8O7lx/xw8wOW1DB5pGZlHEdop8uMe2jicTiCq904jEizbtrf1egNwojVTn2Ajp2kBCmNVIN021i/yNzExLof+kQOCZeTzxDrjO4QIqLKRhtNlLpWnAtoq5QLWdG0YzZmijYjRGHJ3T5DrXWzOJGvdWPTJuG1sYq/TDsRmjqsFCIVL6ETJekNkG4Q59kMiZozrWZwDteMy6xcWq5IArR17yDrGFMuHdT1rgcMo7FmQ6wTOltZqc4TA4h2h02s6+B0PhCvRvBgRVETqla0disSj6KiRD+QW8VJ67P1rJMtz6V7Q9Vq3Z4ljvia0eqYRiga0Lp2HVrnZHenz+iJCWrz5FZBPOtSetBU2O23uF/cADDPC161a+a80dYVZ0o14TQvbMYBCQPeWr/JSLd1SeNlOkoBH7ogWy/UjW54p1zd7jt1w/oEmVpLn1WnvQv6c5Ok/K4sEZyLlEUJ28DgR0rt5LLqEkFhNwpShYdTJsgzMQspXDGY8L6u/Bc3I6fJCC6SQiS3SnOedS2s6wnvAiEEvDOqBlwRLFfUOapWJHjEarfRcAPqepZxPj/SiqHXgbUM+OD4rQUkKG4/MLk/wCfnAzfvnrg5NW6Pj+wHx/7NC7Zx5JQL06ayLo0UuqL8XDzEa8YGY3DMukXkTMgeHxQXEk/nM6qwuQGycaqNdvtHybf/Lb56+WtkJ0Q/dumB79YWIU1gfcxQns8Q48WaouJThKqUZcGFiAsJJ71E8a6LUtNmQ17mfmHhiUkYvJKXPi46jEIc/AU7CGxcF1iodaFoVSgKWhRs5rxkhpgo2ii1sR2nfjc2MPW9pZwNs8bShF2I+NERNVKXSl4yLm0YNoHlfGZI7mIyFzkVZTMNzOeFpoXoPMHpZR8q0Tscyjyv7PYbzAxJnQiK6iVIuk6gFEcKES25z5ILDnxAS5+x1mUmlWUuOJ/wvneRvQhqHq2wNsWhaF3JTTvO5gew7t6p2jWDtWgnnKoRksd5IRiE0I3m1qX0UdwCuXZJR26N5EIfER77hNx2AcEldueBkhviDeciUpXT4cAQI81FpC44CZ206RzgWXIhCkzjSFUIa0Vd96WS2qcyaykMU6LiLuOsVgTpU41NiGlD8EIrejkeirNuX2Nq3Qv897LtSS2Zh8OBfem8nBwjm1o43mzw86kLQefAtI9Ms/BQC2Ge+eH6xKefvuQ//fENx082+NYnj1jsfj6mndmLNMQNzLW3nJ1UJO36MSsV8cZqldgKwQ14L1xttpeRQAHn5z6Z5cJk9alheHCR5ozP45bv70ZcNq7uNvzBUvnV+8px+YKcV3waub4daZqZHwvDOGIpY8fazeWbsLgRj5CCgTfO+eLw+O6B290O//q/y3P+g9yNP+HAe5bzQvS9e9JtZXuQiENgrZU4Tr2EcYp4j0Np0jMTaw1C7G1jUWIaKM1oy4IAYl0Q6pPxFCdSq9jhmTFuadKN3rSki9kZlNZlIJix1opzipPA+ph5/vKJsBkYfCa/6RM1vBNKU0Iyok+YBZrMBCcE833YxJdHLAZeftIZ3uMUO7dmbSAwiHB+euruA8NA8OAlUKyTBZevZtzOE6Kw5oZDCLFfRMF1/ZpqzwC8Kcvp2C/w2n2ZpGbWUhlTJyGKg+ADTjona9XC4BKbaQIzTsvCGHq5l3zsOEtRgjjWCuILQSCOvvOlnFFy7Ra+RfuIJ1PSELsMBYCAFyP5gGsfzN0EaZ2n5fzQxbTSx6zjuo4y14y2xqE0xFbGob93brn7jdMu9i1dekK7sOpNaMU65iSK90op3WqaSydT1XrGLJCXmeYELdDqSpX+uaycaS6wzsvv7Skp81L4/m99zk3a8zLfcPvRhiU41mWB40IMGw6c0cOOMEaGkmnDhhsm/tIXnq9KIpmRrXKaF0Lr3A3DyBWqCpbaRXgIMSY0F5Y1I9GTnBG9sZluEe9oOYNaT2G9oq6n89L7Nrg4Eq1xyo3dbkNeT4g5cmh8ftjxg7vMr+v3+Zc+VghbrB4ZDzuCH0jREyMkd8UhHqgLuMnx0bDj0b2ijZ8SzOGej7Srn1COR3L8w/yW/D4+/+jXudc7KP7SYhemMbGUgqOSS+Ay/asbKzbjeF4YEzgJON+JggBRFaIwLzPLmolp6uC1KVEEfPc7X1bjxk/MzpFroy4zbphY13JRoHvKRXMVQyBER5fHArvI9gpcqLj9hiZGCo6GJ2JYcazrQinasRscqxasKls/sj416s2ZLA5LAZWOB9XaSZ0xJcT6xBMvEa0LwfWyyO0jpD5bbSndYH+ZS6cfBHcZZVSpTQmihGEA+l3e0B64rPOtYvA94/F9Pl23rvUUNWzNxDTgzHcBuA99Oq4phkMlEnwF6QZrWjPVur93HALzWggXnMsLUBtrrv1YOmGtirNGxXDaXQ9CAhbADJP+nXQuUs+0t5uR5JTkXR8+4AFzoNJnIq7la1KnF0dr3SddXKSUFR+6+V0+N/xcOojtfW8eaGMhItZHuTt6IDQ11Lo9sIjixEjJ/96mCyzq+P+99/z1XPlf3sKLAOV+ZgTaKLx/usdZYPH37MpAzkrY3zCr5zfmiem/umfVxrrOiKuIjBiQGz1V9o7cINHZwuKAVgnJEaQSwkAYxi6yrA0urN7aVtQcta60Zkg0tpsd4gO6LAwRrK2IJMJg+GlkHZ9Z58L7ccfuJvD26QvWImzdketWcOOIxS3PDwd2ty/Juz33/r/Nj2yA3ZYqvXxYx8xnm19j3u/4fnLc7f8KThRZPWkQaBHBOJ9nximSs+BR1qWb4G+noYsuYwTLLEslJaFodzBUgyFETPsIoVq7Ar2WjEnEa0OGQHQzua20w4AmQ+niTq8wayW6RvCepS1Uq4RsnHJlGiPb5NFfmPAf1PxNybUzh2vJxOjI2m1fFcN5JYaEGyfythLrZYpK7sC6c32skPMB9Z54scjNpVLWLvpuZphAGB3NhDlXxhgp2nDWZ7Itaxf1eluREEkhXbRwRqMLfIdxQFzpXtkeYhRqXfAyXEzmur2OGmjJuACiEUd3vRQTwsU+pFvyOtbScOZQ7Z1GVHAu9eGcZghKabUbFXp/8ady5Gokaf0GW1t3qXS9o4mC+J7BqDbSxRYX6d3P4KBYt5wxbwgBfOjNEPrIc7u4VWLK6D3ZulDah0y17hdeyoor/TyZRqW2RoyZIfRpOR+GSFQ1UnLkeWYI/mdUyn1TAlMY+Y3rz/jqN+85VM+7dyfsuHJajDY64rjheOrK7qfzkSkm9IXnz6SBp+uRZT6Rz2dcHEgh4ZFuPKYFcxHvI/vBodItJ8qykoaJFD1mFTXfnQBDlwWYBVpVfAgk18uAYg3zkbye8aHrhVKMmCibTaSsjdxg9+mEv+9t8j/1m4/88Zcf8cYvsJlIY+L6ypHXSr5Rhr3H1Sv+wvgDSAUZuxNgrY3TlPkbVNq0gncMeMAxjIJ3/cTrhmmekgtdqdG6hOIyQNEwUgBtA3H0GI4xdmN+006GXJeCj0KoivmIdrkbWRv5lDnmE7fra/JByVdg3uPqAfMjUXow/KC1sqakYewDBCrkWnDOeoDHI6G3uEPLzMXADfiUiNOG87yQLiXnciq95HAOawL1hMUN3nedXa0ZwVO0US82HSIBdxHaSgw90OK6dEZ71mF4siomPRNeLVJa61YnpuRKB9dT14sJQtaCw+NV0BbwoeNYgrC2Hkixipij1T6N15lgLWNeWNfKfhu6Na8TNJcLnygAmeQTgkPEWGu7WPUqXhrJda/yzgtzeLkEGoUhOEwVqZlWA8UgSidxhgB5afgE4hOJ7mulF1rANEScOPJSqbXgRTqOJb05EqV3OeNm2215c+VqGClVkWo0LwwhIdJlR0P0eDxlqVQT8nHutIt/BNPvb0RgGnae/R9/yX/t04mX6ztKNsLOU61yzo4/837k7F4w/GqiPc4cf9Bw+wDTwvH5TM4rThwjStXEQGfXRueJqfs3hZRoVhECUQzzrqfO1gdu5os5l7torOIHk3rnyeIZUqJKQ6ti1mg4ijWoDucV846gDncjyB8PXE2veDje8ldj5De9ELXxQifOj8+cf/zIL8UNv5p/wItPPPP8ltMh8ubTz1hOudvGhoDIQNCMaB9xpc4TzHpJcpmmGrRRFFAIoQ+IXNYVL405G9GthDBeBKT9Tmm1UIReanjrJnsG3ocuILZOchzGAXvhuXs02ruZ60+7VlElMg6RtnS3zz4dRIgxcVoq4xipDVoxGspahcF7rCi+U8YZksMnT27GPK84jLpesgWU+ZxZW2O8SCdKacjF61pxiHRHAlv7EMvgYlfLXzy48zzj3IB3DsUxpIC5zl9yBJYLtWEMfYzR4AMEj4WANKWUtfsqiZBix4C8S5i1fgxFQBpaF6p5xtFh2omZwUEnRAgpOowArLS1y6/GNPbgkbuSv3smFxKBqtJvQa4/97gWYhy620Hr+jnnPGae9bwyDkPPND9M+BXtlAhnqAqulS7jqaX7UGlBq+Cl/x3p57u27l0lrge8OEZyqXjviSnhY5fjmnYqQEgBF1IX+ZoSXMCHlYKgLiAx0rRcpD7/8OsbEZhEert8m56Q5xNznnj98ZYvg/Kfjy/4oSrbG8+pzTy7GfedFdcy62OfvJHiRFu7mZivZ4rb4QcHdO8Y6JqrKQ14b5RWCfThfQBryQwpoc16e7tVTutMir3zFbywlj6bzkfj+XhkiEPHB9LAuAnoWhjSQLPAZjCaNK5u9rR15ZgL22ngB2Wl3Sb06iV/+UeZH73/lJfvV05ixF9JPB9OQCONgRAEqzNPc8WbR1wljRMSu2d2ZylXDqsxDgOIclrPRGlAuGSKFRdGaulZQReP9PFFISaCGttNwlajmrIeHjEcuXXAPGjGFLxWbrYDZylY7pM0zoel6+SsMZfc282hseRM9d2psswrm01X0M/rjGp3XxzjZaoJpU/yqD1Dqs2htnKaM8F1NraFyPH4THSJmBKtZUqzyyy20EdvW/dHEqkXDK3ry3xwxA+jujVTtGNtwffsseYuxnXSEImscyFKo6LdGyn27HtpjYARY/f/rq3TJIpWzHVweF7OTOOWtTZKE4oak+8m3+eldADbWW8saKZqoKJIy33ktzik9eDhAWuOXBrOgJYx1wXWyRcMT11nRDN57b5IRfvoJh+h5N5VRRq1dE3bcHHVpCrTbkcp3eHAiUd8vBArG7juspCzkVKC1mfaLQsXtrpnTBOtVcp5xomS7TIkQSIigV3ocx+bdprHz7K+EYGpNsVWx7u3gUcBH4XHw8Kf+e0d5z8ycvNGkNIYYiSEPa4cOJwXbjYjzTx+iISto7XS8RfXRxB9MLHqY3IUa5najOC6yNacQ7UwpLGT2VSJXnmeC7tNwoDDce5z76SgFbxL7Pc3aFOGITFMYzeWt4sDoutMj/NcGdPFvtQLy5pZcoUmffLGLzgePv2I3/6tJ+Zfe8vV6z3xE/Bxg2nl6fmA10oTR/R9BDQGuSxYaWRgbQ2H7945WhA1ijhi6MMZm/TJtKqGWKBpA2v4lJCmDLlyLkI+z32oogjmDHDkZl0kJULjzHo/4ErCBUdZG0stCF1DFtOIiWJN2G4i0DjPmXFM4LslyxgCxxWmMRGC0HKmZMXEWOrFJzrs+oRcAVMlAcv53I+vc8x5oYiwCYE4xs58Lisld0Z9Xldi7N7owtS/X3ozI0ZFpHtmt6rglGKNpr30sdKnkDjvaafOpqY2XOiBpywZmvaSKShr6Q6QIuDxuDTR8hnvR3LOOG+o9GPjVBG76M1iBFFaWQkYi0IlEsUoCv4yxMBKH3+k8mHAZUERkEBwjopDZYM0ZQzxMoFY+8ACobt+muGlj39Sp6h6RAItNzydkycuYfTsexpGvHfM1ukBrdCbGc0hrSIx4LSiVnF0GxRi7CPKuygFUeNcA8H1ce0/K8j0jQhMDmP+ovC4DvzVN3+IX7GF737a+Oej8h+fZ7abiYLHh5HNpMiYwJ9xXijtMmLGNZr1js8YYvcHki4mNWn8/6n7k1hbszQ9D3u+b3X/v/c+59w2IjIyK7Myq4rVsBHJYgmiScmEbIMwYFgTq4EBwQMBnnhi2DNP7IEnntgwYAOGAQ8I2wBtyNaEgAe2JZCQIVAUVWKnIqViVpdddLc5Z+/9/6v9PFg7kiWBoqqCLCG5gUDEvXFx7rnnnr3+tb71vs/jxc8jzNA5b9B2A9mDqRKdMJh1gNM6r2FznelX6xGv4caWmX/ZzjnaKDy+fYvIzXdGg3TExM/Wdcn0MZPOVeY3HV5wVhjqwUdOPxd49uJEV2VdEiU3cut4DXQchzizNoan1kqtO1stODPGgDEc1Eoz5vFPjdwzo+utgza37204vAMsU4oSnPFIJ5+vtBuIflnCZJ0H8M7YesBSJL0vlLjRv1s5fOeerQ9W7zECczw9U9DDdkwhJsfD8YRapcscmMfjiYPLiAilDI4xcNk7NON4PNyOGO3G4U70vTJ6ve2KAuAYQwhshBBpZadVwfnAGubV+9xHMlPSToDBNZcph7Q4d2lO2HLHuqAu0GvBASKOcj3D4YgPaR5vgOu+T0CcgI+JkjcgEhW2OnEnwzoueUqd2akQlJB0moXbDDYiMm/fZF48iFtuA2SZSJWoeDNEPYwJRBx01uBpYxaS1zD9dC5OykK/xSD2vd1w5f3GyRtzHGDz+02kUeskNHSn1DFmdkvG/BpLvLkJB9dtR4dhaT5wbSi97ah6aIaKw+vcjYVTINdO6R0vChbZ285Mcio2/7hf6fUTsTCZCDwT9LjwvVeRl+8yfWv8/CL8f36r0u7jvOLvDTrUtk8dss1vBEPYcyWEdRpRvRC8UEqm9E5wsNWKX+di5YKjDDdZMtZut25zaz1McTdh4JIcfSwTUKdzR4FVZAxq29lKp+cNies8eoifwLMxi61hncdDDUpnTN6yjzOdbcZ176TgsQ8WRs6c9ytUw4VIKZUghdETxQZ1P7OmFVGdV9Kl0ftMAp/zNMNezzshTWusJLk19h0+RGrZ8cmzNz9ZP6MgI3MtHUOpQ7AKhxRwTjBzuGi8m4kHHrzn6d1gG8DovC+DoIFnx7lbKm0O3JcY6SK0/Uw3ZVkPqAz2p32GW7Vio1DaARGbPrvrFUYl98HhcKDWbc7+VOf3hhlxDYSScXLAxGhDQB2qU1mtqnM+SKfkuQjUlrFJNUbawLmAOXB0nA9T0pj89KLZwMcEBuq/jCV8ySoSQlooZSf4uTM3mwbaaaOdwckus6QbYyRvneCn381kIOapvQCV1iEEB7XjDyeIYxINuuJ6nt/XzhALOO+xvrGXRooLyGSP99YmhYHZcGkMogS2vXLJRrzpoXqtQEQdDGugYQoQDFTjj9sAKoAqIbgfz8nUhG07I97T+sxBxTiPaLUUxAWsGaIzRS5ACgvilLbvyHD/dO+YBGF5iKBGL8Z3gvLJ977g7rDw+mr86PPE+qrSro0lHqbiyTlKbrTacWNQxXDRo2yM5rju88kVgzKjh3XuXmYSEbV+g5rtqHPkNtO2XuauKBeQOtO0h+jIreB0hvygs1fDUecVtpulShkdWmWMK6PJBOAD6mc6lwa9FPztKTn51BMejzj8MDKe1UFcAqULrQtRHfgj3RxqjcU7mlsIrWGi7PuFNhQf5rU2t3Z/qVP26GS20q/XwhIjrT9y2YXTKRF0fg7alNW5H8P/zQ88gdOrlfybDbecCAikBVXj5JTW9IYymfOHyY3OjF4QGyzrkVEyj9cdj2M5rTg1xC1s1wshOBYf5zHbOcZQWrX5tfCeoLPjduPmU03Zy/wzBB+otTL6PHpCZ5jgcXOneLOU4Pt0sI0B0rEb8M0wSu3UXljiHLB7mchftQ2ThGNynESEQaYOxZmfV/E6OKzrrbrmSFEo2VCJ9NYmJ6wbMgbNKuuijAFOA200St7xLtFKYdxAcHqLLPjg5uehQil97miWxGgb4qd9xrlZPBadM0Vv01xCn5cETY1dAgcfGHiUTloTtU7DsXlHhfkg62Me5evcGQ7VGf5UnWgVr/QBTmdk4cswJwgmDW0d5yEP5ZDctKnEQO5fuSr3k7EwDTNcN8wvjO9/xneDo9/9Mq+e/g6/8EHnB+GChgdOaQoqey3kvRJj4piUXDPeAjKMIY5eZ3fIcATtc+jqGh29aXlmg7y0qc9O6jAaoyneGS5G1JVJKcRR2u18znShtduRQERpzMQ0bmY/rnvGOYcTP9EdOFoT3IBmnV53StYZQchXRtZZIxClDUGs8vjYEBl0G/SWOddM74JaoNdH/HpEwpH9NrQP0iiApgLdIf6O0gXV+U1d6xxmu6HYANFEcoMR47QMx0DKGe8DbXhEK81PUuKdDbKAVWP56BkSGxoCa4g8Xi60W4DPhQPPlsh+vfK0G+vxRO+DEANLNzCl9kpvlV6MkG7ZFzdv7hYPqomRd5Z4YzXVivbBsEDvc4q7rgsll6nstoG5iGdGJVIItD53rMEr3veZeROlitFHQ9UwmcC1JSquz6vzXgYaB3146ghsOXNYAjiPdehjHmFm0n7uKPuw2+5ysGdDepkDchGCQHCCWWfUQd4aTmf3L8VIU0fZKupnGr+XqYISUWwvVBPwntYqd2mhloyon+ZbcVhtk9/k0jwV2ETkOqe4Sca6yTCnT28ywx1RK81F5gVepmWPcwbWqDukxc0d4VCQ2Xkb2UA7zi/0clvghyJuIH3MI2vtmA72Oo97P5aKfsU14fdCsPwpplPuQ+Zp+f9oZv/bf6LSSwNrO7VvZBH+6mnBP1z4s58841di47uXyvffviW9/GA+mc3dyqmN3jK9O7zOW4VR+0SzmlCK4fysoMT1MKsBOtvYaYlQbKZw6/RliZ87jX27gAv0IQiV0Xe6TeniMCXI7DC11tmus9vUemMf14m7DY01DawvE7x/vdLTLEa2linVz8/VwNipeUOTR0xw0rjmMassyqQttFu4yF3pEvGlo+OJfTe0VXSBFw/3uPU513eZ0aeRQ52bqWVxxBBp/mZilYCP0JUZAtRIiG4etapgMRAdpBigKWkxwt3CWAYjJGxc2badNXhY7mlbpu2FIgZ9I4jSyz6PanmqtPMYjEuZb/Ybf+mwRvI+8SN7Hng/UB/YynTIheDxOnevi18wZqVovTXY91oZ5YykEyp+zlhMCaqMvLH1iAxh0GZKfSiEAAJhMM0kUbERsFGgN85bZY2w+BlAHWI3y0qfqX+ZPrnSHIxOHnORleEIqtyi/HRrLKoUGYgH721iX8JN323G4TgH2204xBujziNSXAJhDLpVhEauGWVH9DgT6H0wdMYiqA3DU/qAkWe5eRi9Z9J6YK+ThW46kNGQKjMi4gO9TKLDmINWVAetTYqlaKfXjoYwRQWSaX3u5MGRb2nz4KGZw8SRpLJ50N7wAiXnr7YqcSOO/Je8GvA/NbNfAv454H8kIr/EP5Be/hzw/739GP6z0sv/IVN6+Y98icwip0NwL6GEjY8b/PLPvKbazh8ZjnqdnvfRobXMtj+x1zlcbq3Se+dyOVP7IIkAFXTOiprNHIwDPB7vItQp5IOBChiCWWHPFcKt7FsrrXX2rjMtTSCqI9yOzilEDotgznNIyum4cjitrA/3+BQwBr01gpup3JEz2jtJZ+8oBsWlE25NLDEQY4LuCc5RKhMn6xUJhumUOS4BzBrBlIfFc1hX1vUlwUfq+abUBpzC8bASgkf7dKXZ6PTWbscaY4wKreDa3IGID8ToWZ1n8eE2FPa4ccK7Snyb6U8bIivBR7ZSuL67kK+NUWaAcQw/bwWdohqpbdBzIfTGmmaJNPlBcpV6w7qiHtUZNGQUsM4aPaMLowuiO+qVGBf2vdPGoI9KUuFwON7su8I0h0wErsaAZ8z5TzdMdBaQrU9udXTzGDPmzC14z9CVNQWGBHAODRPh67wn+ETrQh8ODQsmisaFlCZZYYwBeDQseOmI82yt4hxoOMzcXFzng8qMNgZ1KFtpjLbTe5tONy9ctsq+Zc5bYXyZfvDrfIDbLXLQ6yRFWEBKpeXLLVU+DbvhcERUiVJQNYJb0a5TPKpC7Z0QJr5miGLW2GwabLz3M/OkczbaZS6aNgq5zTFGlzRv7sbsEPqb5mnFEdy8uInp/g8uLnCTCfzw9t9PIvJrTFfcvwT8udsv+wv840gvTTAfppZ6XShn4/vffeJvflToj57eMoe60Hoh709gA+8WMEO9Bw3kfcMHR0xTXzMbzhnGzLps5w0XAiJ16nm04epckFKcAz/nbt8DnikSdIJpIHSl1IyjziOaGLUOUlzw6Y4lCN47rltDbtH+PgzrFbHZRBeXSMEhstC7kRtMPCmwLATn6b1xWO5mkl3uiWrUvXJ3t7J15RAHQd0MVsq8hcIpyQtlO+P9icVPVHHrg1IFH+w2tGf+4xwyOsMiQQPVV3rp+FZZ1wP+FkZFdaqOENyzSGgL5jxCpuwb+97I3pjkoID6Ti3GpgunKJC3mb9aj9Qx8R/BT7ml6SQoGlPv7Wk4TVQmsiPGiW5Jce4mkrub4cqhjN7wMRL8oLZBa3MhN8DEoBecV7yLlDHtusE3VBy52jziyZjX6dUQB+o8pZfbMXe+cZcwr95L3kg+sTPzUKVUWh8zjDoygtK7INow87TRqHtDg6O2K92dEAb4gFgn547QCGEh5x0bmUakt87qGt4v0DoS9LZQd1rvrM7T+o6TMP/+U2KUjFOjCqQwRZsijmaGM255L49PHlrBnM4dk5Rbqt7mjWeft8yjD2qfWJepKp+Uy3xjtVt1OOt0c3QBJ7NpIDopoMuyUkcjl0ZvNgv0/1V05W5G3j8B/FX+MaWXv9srt7444DQhyVCpeArnI/zlwwuKzvKsPxT2804pmeWw0m3qf0a5oD4RvTIpnrMe0PYN54C4IgxC1PmFr1NbPVom+MSyplsnad7CtAqlO5bgCW6+qX2IuDh3a6JGJyF2ZrTrbLbfwn+rNGrvWJ/ZpaACrdLDQgoOtZ2tBbwzxCAlQZ0iA9TNgqe6BY1HnrlJCuxrYzkE7tqgdQVzWJ2F5BjjbPuPKY9U2znE+XnXphN9OwSTyUBC55uwGYgrYIP1eGQMRSTcQoNTeV3b5PJ0M3wvkIT7DR5/eGHcD4iRBTfZSlo5XzdwC2l1lEuZT13anJ/12a1qrbBERXyCXlF3e1O5OIff2xeE9Gy++WrFbGJ8pXXMVWq2GTIV4/Eyh9vBK9UG3nYkJFCPc4EhHhcmN0llIHC7gZsXH6PuoMu83YoTLleGketAeyX3KYKcggCdKqe643yktoyqmxYXM0waDHBSsTEttVvOpHCci4sZ2js5T9lmCjPsGsVRNSDmMHZ6dYxRcTKT7IXBGj2lFGofk9G0DvbccKUT0wpj0G0exZZlPjR7n7bjvQmBRhyzBBxN2V1DzePstmvydsvaOYJML5zTziCQy3yYmE5hRHfMnakDSqEUY6hw1Mn8zqVA8NQ2sNqw8V8Bj0lETsD/A/gfm9mj/K4t2leRXv5ur9yLn35p3nvU6rR1LJFn3w6EFPA1sPUCV1iPK+MGji9lIDLrDWsc2Eh0ZlMcB+vxDu9sVkdKx0kgxhkY621geJzrbHthDYHSmezluGI6qKJkXzmI4szhXaTK7H0tflCios6Ty8BoiEwpYlJFvSPXaVT1zk9EcK3U0Yk+gQuksCPzngv9kjft3Bzs3rRBMTi6myXcYYJzg1ZAQycoc5B7Q8Qej4nRdsCz1zznSMwjQwgOcYUmC7lUDssyJYwBchl4g9J3hirSCk/N2KicJKLYdLFpYKwBc55+ANcUxHDR0W0GMb3vRNsYKeIWZZSOjFnh2AbQlVYMGZklzb6h+jkHkdFRvYPWaZznLkxkfmzvaWaIDKKD0m4DbBuIzlQ2HIjeMXq+CROMUipminfC/TFgdLIJgse6BzezY3tpWJ+q70SjlonbNeuIj5S6gxni/DxKj9mFK13xt0iAaqD3QnAGznF0nto6g8owQcf02vkQaHljWZQWlNSEJo7EJCWYTq16EyNgbK0x6+cNp5VadRbRuQWH62ARxcI8KTjpOBO2IixBCDGwj47VjgWjZkiHQAAKzNCvKd6Ucx9Qb1QABiaNgUdypwc/Nz/a5u5vTKKl6tShl73MkbtW1HT6+eQPmC4gIoG5KP1fzez/efvpf2zp5ZevYSB9DjwPDupt8bhul1tiNqPDI6NylyaKYwkTYnYtSs4D73e8TzgxSpdpRHVj0st0MoscHecceci0V0xHDrkOcgevxuLqbSDZcOU25B4FVBhtqpW72Y0oGNjaha7Tc5/rmJgJUTSG2VkKHr21v1ufRdbRKr1N08e8uWNeH4uhwbGXDe8TNqCVjRgDjEYZgxgSbfgJ1xOPi0buY4o5xwxONvz8mlLwbp2/n83bFsdEYexBSd3RdRDorMFx3fu8nvaClDITvQ6usRPiER8W+idfkF8sHIYDOuecuVsCh8MBRqfk6/Sm1UpAKXUQF+VOBVE/ESk6hZAiAeuT3IDMSsVolb001jCgTbtJ7Y3gDJfcTOxjk7/EzHOFlFBp1FJZ4ry1NYmoREwM9TotLW7eUFmfbKZS5qKLQGlzx4Mw8b6tY9yY3H0SFEJy9A4pJUqdt5G9ldsw326ww8rQOcfcaiZ45RCMSzPS1NzSzLjkhteBD/4WE4GozKt4ZnxhtAp5dtdMHV7ng+YQVuZ0tOKiYX3GWFz0qEWcDWIQ/DLZ3ic6m0TyMGQ1zhW83tDT5jmkgFGgzcuDax1EMawMauyzb9ptPmSiUcog1EoTh2uN7VIYGNGEYTPA6VSQm1n4q7x+L7dyAvyfgF8zs//17/pf/+SklwallwlNW4/zmlE6p+S5lox2xZyf4PNus1zrIoM4I/w2WUFJOs5F6DOxrRhBPaoDS57SFW2whjYLpm1eo9cKJo0QAtslY2rk3EhxnvOD93gRTBy17NjNBjvqBRcmpxyZaIlzs8mTHjOAdskzWDiAlDzSOoJHdGBWJoe6wXJylCzs1wnpQgq5wl7yJBeujmS30uWYyNxSJmUgeEdIK+1Wq4iUeQSLgSE6A3gWUT+xuiqeVeeKKCNQ88a1bviQCOJwJqzeoSGyFyPsjc+uP+QhvCAvgpSGrFDb/HiX62A5LAQfCMtKUOHaKtGErAWrk9AIhl+XeUQyo7RJ2/R+LmC1FhyDFCOY0EdlDFj8irg5wPbS5kxyfNnxMEq5kqKbXG4UZDKrg5u67shAJM02vgjmjFzbfCCMPvNtbaC0uSDImBcQHWrfJlu7dnBG7u7GC1fUJq+7DgjW6LfPxXnPsID3EJhp/BSgl4Hr4KOHnkEWcmnTqRdX9rxjteLZKX1Q3KyWHJzhXKIMB30umEaBMHnnGiOue5LctOgjz9uyvTFsdhobnnw7gh6B3TvK9oRYJGinl0oNDtqFgaeFhbgG2hD2MtDROSxzDmpq9BFxfUOcUPx0AFaZDLNiGVfk9vfxB9eV+zPAvw78LRH5j24/9z/jn6D0UoS5AITpHWNUmnhwM2KvfnZvrAnGpPyVOlvSIa1TtRSn/nleezrEKr1zy5R0ap5drl4HPoDThpWdYQHGTHl/OYvqbR4/ZDSazdJmiszHjCraHT55RphP/XzdeSoD7wutRbQVVn9CxTBXGa3iXMKa3hTbxjBPRKDPYOieoe77FCDeWDy1K8dl3n7U+S4hpjgpnV0Q16E19upvmNbZ19u3TIyOaoFhhb0NvBscolBFUWnY3skd3HC0UcnFKNsTKUWa6Cz89orFwDjA5XGwWifunW5gbbBXR28ZFyrWwmRJmYFXbpg+dEwc7lN9xDQRbZaIRy300knrXPRDdKg4+vAYiguB1mfvbtiYIUSnVDreLUiQudOqMwrQ+0TYYI4YEkoCBt4rfVR0dKw3ep8dtGmzFGRMqsS6CnuZRxBRQV2HPHBizA9rGB4ds+5hbu6yFhXS7Rhea8elwyzrMjt0XoxtH1TfZ2CzzT+Huoj0zpYLo1Va8zgCTTodQW44kmWNlA6BjjNlIHMk4IxmxnbN1Ot8iF5an+MBrVTzhLZR3EIXo7crKQTcdSBJyGMnRePyxQXzmeYc0QbmEn001npmZGPrynpc6T6w1THDyL0QamfQkAr7GrHcWDzkFXod7H3ghRmN+Aqv38ut3L/Lf3FO6p+I9HKm4R3D3AyN+flkEI2ECNt14xBnnmKiWMftLC8gc/cSVendYx5olYxxWtK8+cFjbcOJ4pJjGHTzdINWCl4j12tGfSAlh48JnxJ1O0/0qM3j3agdGQMXPGXPmNUJnA8C1uatVjwhVinXC+U6wWbLsuLTHXazTJRSUevASncD65VaKyqCaGCvhVVWDskTo96uouduoNv8y57ZGpsJ4Nqx1m5gvJk2BiW3jnODeGP59O7oo7LvhWGV6jypJcRNMMd+c9wzD0u43vCuoy8Db3/UOLULRSvBJ7YOpeysasjW2Ktyuot09ey1EPykhWqMODN8X6ftpA+0NVLydB8Jyd/Y2dCqEJLSaoeWWWOgj4nBhWmRMXUMKyyasN7REGnVMZjKoPnnZM6a6Dc4/5gLkgo34zbRO4Q6h7sDag947dTR52wsLYRl0jwvBXob7FZmkdyMXjvBzRlakwV342mPfplHmOgYRbiOTL7hdmnGzgbVYf2JJayID/OYaQ3n3Q1pe6S7CfUfvbBaolmjm8xgbpu7PCeO6Fcs72g3ap3ZgnBc6GXnXDxh/VLpHalWkdWTDfpWGdp5dh8p2hEzah8cnSPYAVJhb7NMTe+45Nj2nWAzgU8/I22l+EarV0yXGZLen/AoyS8wbr25r/D6iUh+TyLgAJsBRxPPGuscLrqVxRfGmC6xqZSBbhPHYab0DqXM25fZRws3g2pDnZ9DUZaZGs8FrOGsUb2DPhjW5k1O72xbnvMbmyHF9ZBmWXX0qdexTiuzxlBa4HAUahEeHk4/fjNeL7P4Gdc5C3LeI5YxjbOHFObTnDFopbFdrnQFRyCtnsMSiU6x0bler4g1qq04HCkZRKM3g2pUNrxERmMWl0dHxdj2neN6wPlEo1E702DRO1UcyTuoO5jHcPjQWM2hNq0j122wHIUooKeEhI6tMxyZmyA6yL1QayWK0PIbHHccHl4iYQoSbaR5jImDNhLOJ0otOOrsg0lhCZ7tOhAHA2N0w7k4h/tOaTYmPtcmelQxeqvkLgxtmCw4oNeM85E2YPSNLoHg5m1cyYOUHMFHYmQicL2fhtnb95X0AlYhHggKdZTbjdrszkljIkVyYTPj/hin2aTMwKaXybha0oE+BuROYKa0/TJln+oMb/PCIqVpEFbvqL0S19mBszIfrq6DRk+/QkwduQlDbxn/eUytFe8dTQKjVyw4rG30LVPGLdpSK0cLbL9ROQZ4uHvGsr3n19gZrxZKH4zmCX7upLMFxDKXeXBhSZHD52fO393Y3585fHDHi9Md1/UZpgNvg0sX2mLoUPzxAS8ergUZ8Z9u7AnMkKWj432i1ynbG6VyvFvoYcFs9uKCzqtQFw9EJ5Q2iFpRN4OOITikwzJ9hyiN3I3g9MbqaTj1E4bWp2ZHvaPlK94fcD7inbFvZ3BG6Y6os+tmCPGYqHWaJqKftxpmk+G0tUYK+mM1tovLjAxQ6E1AMtYFG5XaJ68o10LrwnKIKI7jMmiVOWcaSs+KCytSOpo8tW7TaTYcxEAv53mD45XoPdvWCTGgfUK9Wu3g/0FzvfSGlEo/rbh9usqu1zMuekqZlhkUKjtqATXHuTVKubIud9zJM76/ZS7xghM/v77qOJhnV0XKhdO6YvSpyzLBroXgPR0hbwWXAosOhEBtQvD246Cl9I64xuPeWNTddjdTZhnjNNw47ynVcJKmydYGTv2cIVnDu0j07tbo7/PhII4Y/W1orex5YD3j19PslhGobRBnKYy2dS524eBODMlUdSx7Q4aiYR6tQwi0caGIR4phJVPciWHGmgQrgyaOvuWZIdKGcwu5bxgLw0/d+dEJu83zpSmwN4Yo6w3tfO2RUPPEl5QJGIlrIkgl7eC/gMdQyFvl4QeNy9cC6cWKD0bOIEPonzSqdX5Uvs+fP5z42XHkPyzv+eSlR13EtkGJiawD70BLw0tEtTLeRMqbRw7DU9407HyGNxvnp4YLhfTzB+yVp4qQ0jPUG6VWqNx2+7//10/EwiQwuUaMOQtSGLJisVH6jvlEUk9uF3o1YgrQK07CrKYEoctgAWw0eg+3a+LJ41EzvArhGMAitfYZQosOXQPBBXY500qllUwegz6EcMsjqZtXqjFG1K94P0A8ezZCnH2lXivqGj4dcbe2dm2FPG5Y05GphTnbGBUn/eaMn4TA0UECXC/zitn65DDPUYgwpDFGoDVHHZkUItrcvMaWRu/CbobTwbCB84ngHVvdsN3dGvAdEeHghLJlhp/l5DUG8i2A59wczncX6HunWsBbpIWVzWWCX2l/pxH/yEJaC6PesbiZJ3JuYE8/4l29B/MkZ/hlnRC54Hja88TD1IxLC9UatXKzdLh5bHDKaGWaOURRq5i5iaUVNzGzKN7tDPrEu6DEZRZfZch8CKgStLO1afpt0il0GBDczBY5F+exvw4yho4pGyBFusI6HOIG/ibrHLbR40JrDbEAtmHq6Q2GGuoinsnQaiYQVtp2xrtO96ADRr3iotLFCDhMKsNBkDZnfa2xpCO0xvsxcGR0qwxx1FFRAsTA5fGK/PBzukSCi6zeo2dP/aKwIPAw2PrOUGH3Hvf1jf2HjWfpwK8/bfw37xPf/o0Hfu37G7/2auPTVwJJWLPOmW7olMcLKSuffPfMpRWCc7xKDuuNgucwKhfxyHcb64cBuhCfQPtGbk+TAWb/FC9MBtBlJq6XSCSQayMg+LSSujGccLITj09PlC0T0kJvHaeKIQQxfEzk/ES+oTmszl+HS4w2UDNUBmsyvD/cpIx19seOB/ShzPZ0mazosm+oyGz2l8G+Ddwy5xKIEL0S15W9GktaGKLk6wYS6HWfiV+m8qYzoO2YKbXO7pSGyKiTm+2c3KoGgdorLg6c3hPcYPSMV/fjm6jDknA6tec2po7HhQNOM90UG541OWovtzfxtL52c2gv1NEYLmJ9n/UcjDYghICnUWueoVEXGKOxLIlx8DxdGg/hCdrG6TIY955GwtMx2/HBk52Sm8cacALZC1XrzA6VWypZPE+P14nNkEFTCH0QbNCps6s1lDIyzgW6dZIXnI/kmpExuVtJBtWMOhquOqDOeVGb+qk505q3nkafgMCS2U1I6rEgYBshJKQLIcnEpPSN6ISzzav+YZCtoMNYaVNY2WRW7wT8aGhT/Ooo14HzRqm3HmfrXKojeiUl45KFpGBu4kgW9eyUuXu8VWPEKl46dzSazUgE0hhLJwHXyxMXS4xYkGPkoBtHifQPI/b8yPHd4PL3Ku51pC1PlGMgvPaMD+8o/2HlvXnqXWCJ8Ivd88c+h3//++/5tZdX3j9byAV4k9k/fWJrHn/srCwcDwkumWwHoiZqKsScqV2xs1BX4+tvE+l8R3m9ceGK/FN9lDPjcn7Eq6ONK6YeYbAeFqyOqa5RI4XA3erZ947TgeptXhCFWgelVQZ+/rh40roiOudX58uZmoXDEkirI2+dIXOoKVI4Hhei97OQmAJBjZw89DLJlN4oRdjOnbtTwKjQMipHvFR6nQ4tVY8CrY1borwjONreUeb1bun5ln4Gcx1xgeteCTroDnKprBrIvEdx+OAxmXMqdYJamsRC1dstpuG0za1+HzAK+5bAR4bNvqDIBN6Lj0RWaq80pywxUveCuoI3u918LYg1+hBinJSEh28E8t99T2kNl4T+NtEUrFT2o1JioW2V2ITTvXGuV65vHfEhouN2R9efsJEow4gaGXToGS3GcJ7iDI+/qbkb3AQD0S00gbJV6EodnZQifVwRhagyqx3miKFTG9DLjCjI/DqZCr1seBO4IWKGKV4WCo7uOoMpMBh9oLoQo5B6JQ84poDJgh8d6Y3i3KyiSEO8J++NIrPOcSm3jNGotK7EVgh6Qs04uEq7gIuBvWRGSvQhMCq9gmyOvjwRauKuOj734OJkSqkal9po3eHaE0TPgw84DWxbpccrxxVaUvLmiL/R0GcOfW3E5LFr5/I+s7nCX/30zH/nax9Aes4o7/hz6zf5M1X51TdP/CaVH5ZK+vg7WL5y0p1zF9682bg/DFQyEgLjciK+VLanK+FHQv72ke+/+YS7LeJfKsu6/p7KuP+w10/GwgSozV2Sdxma0UbnPISDV/AD5w/U0TkdV9Y7z/50ncPySVhFb3ULFyYedPF9Lg75OgN9ZeY/Llvjuk0tec7z+h0dPD5uc0gtswSrplNGaDP7MgxCisQESzD2pnifkN4pXei9UhpEN+h4nDeQjpqjlMniHjb1SssKrQ6CVLpM19saHb0CDFJI+O7pzgjq2OuYcQX8zXd2e9O5hLqdXAN5u1Br4Hi/THi9QRmCx6ZPrs9qiYSIE0PU4eVE369UjBDvkN4JfqHKIKqniyGaEHbKJbM9Gf6u4UKkv7+i1xeQjeVkHJYHOAbYO0uBdDTay0m3dK1wuRraGsUZx+TJ11szvQ5qV3roHLUzmkdUbgPwQK2D677dZAqZJSwEE0adgVNwlJJZvWc44TImlsRnP7Nq4ohu4LxjK47eylzAgUHj3V5Z/Vw4TRRqx8nseJXc0CSEdcEq0CpnjKieZJ2zGHutLFYmSqQ3IpHgjGUNXJ9m0HEXYYkDzWeGHeAwUcmHk6OOQRwbrsJQz8MXxmcfnzhcNtIXz9FwRX9q8sX1TSX/DsRvRtDO6zXy3lV8L6TTgprDHQTflLtfdPS//Zb6qce/82xPFx4+fGD54CXBFz4vnrdb4IVdiDwQ/MpTecefffGcP61Ql2d83owf1Myv5sKFjcPhjpOPtNIhdK5rx299wufSyn0905KnfBrIfqOKnyCtr/D6iViYzIw8ZvFPWPGeOdi2CXU/hIWRO+IH2RLsRlqPqBnLyXF52ohxuutNQEelW6ffRHy15hm41DCVQq1zqVckHPE903YFNWIac/CdG0uaUC0zRW+ZmQTEMHg8d2IIXGubR0Sd1Rhngqqj5cK1lJu5NNNKudlg9fbrF1wVugopRC5lXmXHNbDnRnCOLoXRlX3A6RgYA0IK7KXcbh0r3oyGkpKjF53BPvFUK4zqiGGSDUs1tlJY4/TGTWZzwPmC6WDV23HBlNYrSqURUJ1vttAd736rsBxW3nPlee68+6wQDm9pGO7sJu6VRm2VexfQZ3ecvm40q/S1IauQ3YbzJ/YnQ8JciIeflhp3uxofBTROIYKLUEvHvCPqmBLT0chZGKMxRsb7I2ik24xyRMD6oHBDJ+dOd4JrjTDmzkjd7ZjYPUHavL0K/aZfighHXL/yLCVy6HOXeNloMWGaaWGGfX1VkjnssRHOO5tl7OToD46ugu+dPTiOTrBrpT3cUbad1XeGecbZCJ903r298s3xmuvPFb744oKtgfLyjrs3xvG55/35PfGdQ94eOW0bSwm8j4H310JYA6gnbwNWGHXlfiiVQQNM4cXxgKbIOu4gQbtCiJG/GZQ/crnwNe/oT8bqEmaNdcx529eG48OX93wrB/7+Y+NvjY1P6pWwCdUaZc/zIedg3QP98BI7XYhx0OxAe3pk7f8UH+Vme0nwWm5M5OlxT1IxlJwb1pk3NjKB7LUVopsq5y/VNGYd5yttKLnWWY60mYOpJsShBA1IjDemUsH5E6aDuHh0eKJXcJD8bT5Bv4XwhF47l9yJbjCkMpqhTujNI7SblCDQx453wqiVWgeXbUN1HjEtzhmTyk5ajnjXuQsekwC0yUCi0breDK1jcnfE2PNkSZkYvVeC76gd8avhgkctQW9I69TWsLYzLFHzDPX1PsuWpSj3h4YRMYkwFBtXXJzYE/q0z9Yx8MvkP8WHe1K+8PL0Tf7kR79C+9mNv/6f/mWu1ws5P3HNysPDPW4Vni47py8q51xplwd2njj+1D13v/SKzx/fEMoAjqQ2QCPa2sQaiyNaxVgRHDIcuex4M6650OPCwWfG7nBxdum+xNao92iAvJep3woRa+CXijhlK8wohXpanXNA8wWrxtU17oaneqHuO10ip9VPGWQteO108aTLhjErG+mp466FVJWnp458MJAPGsVlwrgjbzuDE0kCYbxjB/rmeaXP0M+e4G3Gb8YPP3ukXCo/OGTuf+EbhHWjXwr5/oIsz3j4+0aPSs0Zc4E/9bUP+eXa+EufZn7nmwdcuBDOK+2k2L5Twpnr8Fw/a7jvZV6/OrLo4OPXJ1KLrJed9XAkh8ArySx43mvjWXDIubF5eG9g5lmeGc9U2DfPL0Tl5+5O/NV3G589v7JXBRf47PFMfDN4/+vf57SfEBLnkTm4O1w6cuhfrZbyE7EwjTFh8tHJ7ZbpZgX50hNWd4ZNdK3zRh8XnFspLYNMcHu5VTvEJnNG6VzzTu3TZtqaEMMMsamb3qsJ7K8z5LY3ug3OZ2NJnfM2e1rIoJWCaJxyRCdYXLB2pTeH3rjTNQ/EdWzfaR2QTm+DUca8qncTgSpukv96nTu70iJoxfs6DRY3e67RGaPjnOfxaedwF9GWCZoQM7ybJl5nlVoc0gItz4oMTKeeyDo5Uq4gfrKHnAQOSbBbCTjaoI2GxImbzbcOlw7BioIUdPEsB+Wf+eBP88Grb/Hi/hXX3PjlMPi3/u3/G8MCH377OeNSuHNH6l3is7eP2GPnw4dBKgfe/b0LD+HI8vVALk8kDVyHcXkq3AeHix3fIMZEuVbydWdfBItG2woxLkjPXIdDaYhVUlK2uhNDwtlAxZPNoGe6RlaUJSS2nBlFGaFipeIWh/dKNm6UyMbWoPjG/t4Y7zcengeOj9Bx/OaPzvjHjQcRkr8n9cY7u7BvO8d4IvnDvOiwqXyS2tDiiTXT3ly4WsNdHXr5lHZ8wK6Dk1s59wvH5cD58crlbef19xtpHGnfK3xwl/jkzQ/Q7Hl6t9NdZb0PfEshWuC/65/xf/n7b3n/c4l0mgHl5j0eo39vR7/7nmfugV8qyi8fVr4WE8UC9aMFyReu+5lu4HHkDudq1G3nUTvrYtyFFZOFLQXinVK3nZ9OBx70xN/af8h/YIXHS2ZtEb1z3J0e+PTthcVXmgj900c+PHj+4/5P8VFOlIn4sEFrwtHPEF3pYSJu/eQZqw9Yn9aJOsq0bayRsl1pQ25WkE7ySs0Nr/rjaoCLbS5uAnWrU6uMUMo+t/yj4jVirtOqsi7KtU4RodOAVyH6QMt5sswIbNs2Fw+/oOJwTnm8ZJzsDPO4G+rXLZHDssz0MUJ0iohnLwCdGGZ7fLTZsr9cwWwm4L2fi6GakRbo2E1U4JDo8Fp5fCpEtyDikT7DhyaQFsWZzWvrsuMUTDydzrB5rJxZBp22FZ16cXWN1oVhjeQSjI2PXnzAQ/hpBCWfCyE2xD3n29/6aX77d75g+3TDHzxdCwcR9PCcp+2Jfu04OuvHd9j3LqQ2CD+1cL3u5KvgtFGbZ98dTit9K1yGEK/KYUkgiZxmQTro/HyDAyVM7blvmE2wW8mdGAaNhPZC6YWa76j7lcsGx1VpFxhbY4zB5THjeiD0QHGB8W7j8tuPLKd7Ho+CTxla4fX1QHCBrRi77OgjPH914hoO3G2BoZ3zZ4Pym1fCSclPxvhipwRl64VdHffhMNlHZcN1YbeN4Q8cD8LrcOD95R3X7z3i1pVmjd/5j96Qws6O8igG14G6wWhvubx+zuHk+PP2ir/x3U95/03lugJPcPl04+ET45cPD/zh4x0fHE4cFuHhEHhflbJf8L1zp0rPkbQ4nt0biOf92eFouNaoMUAttDxYY8DfJ1oyTofBny7PeM7g//3pW7pOltp1P+OqJ8TI+6dHnv3OPX/82ZG/rF9tifnJWJiQCeuv4G9D4mbz6NP7zdjh5i6gtjoVzZax4imugCaWpLS6o3icE5yr7HuFIVQtOBWSE0rjZrGd+ukYVkoFUcVJxUflUnTG/nvn/mBc8/TSjT6Iy7RrXEvn/nRkWWHfM8OEsk8X27Iu9GpzNqGVskNToDt8mhUSQfE3uPuwyiidEKH3yR0XiTfao84S8Jcp5ThI5ugFrk8ZHyf83QXoVlHvcGa0kXF6oNfMclhoOnEhIoXkPa0r0ufip6o3++4kTtYRaK3Dl6FUXXg9XrKug67K9e2ZZx8kkIix8sHrA2/3R+rThXv3clpJ0s7zwwLnwWf+yt02S7af/r3v8zp/iHyo+AXKr+0488RfOPHFuysjNPz9AV54XBLelAthQFrBqaI14v2gqhL9gtWCc8LIMsFwPqHWsD7ll6O9R9Szv98pv75Tv+jk6jmEREjG4dhY0wEfC8EC+eWJ/v5z9r4S4mEiZGpjVcUt9xwPwsVz20VXfuv8hv5Zpd9F0rUxPim8PDzw8fN73CHRhmO0ws+9/jovQ+W7n33G3/riUz4R4fhscOTA4VnA7AX5Cj+oGzEUmijtPFncIQTWpMRy4fsBnl3fUzXxdef41vFrUDx/++z40XnjW+tzfvEP33G3V9LRc1Zl0Q6SCL5y0MS7L86MRTg9CBIcrRVcqyzrkdUHLniUA+X8GTE0OpPucLle6cvsgH5LjX/lYeVvusqvtg18xz8/4ZfKvZ34mgxeb/l2zfD7f/1ELExmg5KnJFAEar4BrIYgNisGjIXSO4c1Tv308MQYZlZHjLZn8rbTesfFedPldSJKeqkzicuEpjsfCYCFyTpyIuQys0YhKtYqe2ssacEsMSxjGLg5CA3uwBJmJODxcafkQYyGsk7ldBvkvVDrZEOFxWOu4PyCaSH5QN47pQk+1HkLqAtiDuc73k1LsPpKl4VWK84rLiTOjxewmX5HHW7YpF8Om0VWZpHYe6XlgoXA4zVzCoLVDG6h1cHT+53TydFK57gc6BXMe1Isc1YXhKCNfXhczdxbJO8ZaZ5+NC61skrD42nLlTs98u277/CuXVhernwQvsmzkHgfhfA7/wHnz78gPxNe/tR3KN+/EL7oFJfZftRY/2jCnMdHRY/Qi+DSoGyZ6Bq9RiRUzr1wH5/T2hzej35FR6Xaimej7jtFFWnC0/sd/+TI39+ItXLZCqe44hTWcSHdzZa9u145RIduK+/LRu4bORcebmXgJ0k8v08cYkCtsj9BChCXRN2Nw/HAzzwc+KmPHvhWUUYU1hi404UUC9eyUK/vuHtIhJH4uW+v/NGHxF//9A1/tw8GjZYjehdJsXMpZ8JQzm/PNBqOgntKpG/eUdyB32wb37kGogb0NDg5jwn8N2ThaXGk553RheLhvTXEImw7i/eEh8RBHUEcdr3QVXGaSHeJdslcRiZfO2qVtAg7nsKNaLAnwquFU1YKZ7rzHI5H/lw0/qRr/NUvnvg7759wQbk7OP6FF895KXazqfz+Xz8RC9PUMRi52kSV+IFfDvjR6N1PhlIeDBH23BAGZp5LeZruoDFT3FMqMFjioO2ZECNJA7UvxKRcrzvOC9Cp3ZDWGDYIptPUap7ahcPiwGbg8LJvCAONafa0hrLVK6XOJ2wzQ5k1k67T/6W98HTpxLSwLgtLSLS+TZd77xQDk0qXRpBZcYlBCIsn12mdPayd6xVKvhLjnLnlrYNGxAQZlRAgpWnoGNbmNXlYESeoBEwFJ56kggs6dVRjws8eXkxQmwxHrnVeKPRys37MqgkiEIAsjPU5toIrYyJAouPZEnhx8Pzg85VSM9/+mT+E+QMtOPzdiaM3XgXhxeklf+Pv/jU+2397KrdbJl09Bxc4vTpw0YqewI+FLjs+Kck3BEcciXooFDPyI7w5f4p/fkJrJ6Yr9Zo5bxeOl53xvvD+olCm3bfthZ9+/pzrcMQ2cM+PHEKhViWVwboc2B88zQXW6HlWjnx/P1OjR5PiTomP0om6X5GD8jIsfFg9f/L1a/xo3HmldEWotFbxcRZtgwhZBi0Ydw7e1YC5yHVcCM7z8Ucv+DA94zuffc5fev+WFgLt4nH7zn0avDkLRWAJgbSstAs8/agQXgjX04HzduF0cFxF4XJl+/SJR/2CHhPPX91zeerc+YC1gumFXTxPb8/4ZytNhCKNeBo8XQvl3c4LCQQ6R5sJ9E0bRZQYlePDio65Oy/bE60IvQzCQUinDr7zQgP/8sev+fkQ+UTm2OEbquyPZdqcv8LrJ2JhMjNaM4IK4oDReTxv00BrBiOjfsX6IA/DRWX0HelKaxutKdLdBPyrY988PsgEz9vO6IfJctLpnnc6Kwt9dGLw1L0T3eFmO9lmUrtncmv44EjpiPfTDJFLw8lAJU52cneIn3qfnht0T26Du0NCgyMGR+sVacqQGWNoAk4cp9WjEhGZmvTrtmMM3BC6OLAvQW+zpnJYPcFFWsm328lKKQ67CRLUBfa9Yjr5RzKEMQqMQm2O1gfjlpiWMVv84iaM/rh4ZG/UPojebl+nzjGtHPYX/HR6xnXMhHg6BLwa+55geeCp/Trv3z9RcbS8E4fRBfR+QfXAQueXvvVLPF4e+P7738ae75TPN4JFRhmcfrBxjonTRwkLkTUr0d/jNiFfHXHfefvpI+GN8tkWULtAe4upJ7hO3RqX5YDfBJeMvWTipvPNqRMhExJIq+TRydfKs9f3xAW2d8KbcOXZ88DeKi+d5/S1j+Fd5tnhjtfR8fXDwrfXA69CYn+7YXmHEAnXGWjt8XZ8lsh+3WmHzhoi64hcS2aoseeN0xIYW8PCkSUV/sTHH3EoJ/69d4PP7wafPH7G03lj+IjJoOed9f4FLkSeakO68e5y5fPV8XEyrA4qyusXdzOIrHB92+mXwX5nkHdsBGrKlBjwdsLIjNIpcWVdMlEcb948Ia6y3C2wO2iNLMJombV0jsszLtZIW+RyuXB89ZzsMsqG7kKjIMvCt7/2wM+2R1xf+OL9jkvrZPJ/hddPyMIEMq4YAZEFY8r/BoOchcULuZQJ15eO6w0b7qa3iahTkgPn+THcvo+OuDiHuH3iK1yIVLvNdzwzO2ON4JVmhVqMUhqH9UBKC+rDbNuPwvlpZ8sVDYqzMm26GrGxgSToymhX6tg5He9pY3rUXOmYdFxSkiyon+ngVirZwI0xCYk6bR2iHlNojWneHYHWQf2YHaV2mwn1dlNeMwFl3QgMvPeoCLlBDGBuZty2x4z3E5PypfDreEi3xapRWwNVnA16NZAvpY2NZ0+vufjClnc8kX0TLt7x4CKH+BHXd1+gUfjR9jv8zLOfoRsQhW7G+/xE8p1np1fcpcgnl8+J8TPqwSBtcLkniDK+H6mfXtARiY+OXB5Rb2yXTqgNCx3JA1ceacUxjh3qQJKylIVlV/Z8IT1PVPU0X3CHE5++eyJFz+vwDO8TzUXW10oJYLmynOb3wL6/p42dRQ+IZj66H/y3Xxw4+gOp79NfVyZF08Kg74NxMDRH+jEhrfJSO+9GxI9MDEIrMxi6eHB+Zd8rLji8NZoXBp6vc+Rf+SjwRYPfPDj+yq//Br9TZR7PdYaKZS88PA9w3Xh5eAANLGnhoCucB917Fn+A+p6QFHMb1QJo5Hm44119wwiNbb8ivsFtZ2fFgb5lfXXkZM8IIfLpOPNw/5x9COWpwF55J1fUCePaSA8reduxmAk1EdZAHlD3QdkekapYuyA+4KLyVc1yvxeC5QL8FSDdfv2/aWb/cxH5NvAXgZfAXwf+dTMrIpKYHrpfBr4A/lUz+81/9O8ywNZ5POoZHOT2HulHGI5LFZxOQwiyMSTidPp1MTdj31aoZUPDMsWB0/WAGpjW6ZrT6XyPQbBm1OEIXsk2He3BG8t6pI9G6R0tQh87vRZ6C4TkGK0SDidGybTRMb1ds18L3UXuDgFxRtk3op+hT+cdvXUIju3a5pO7DXxwII14iAwGSwi0NhG/ohP6tvpKqYKaQ4MyRkZxDJ/mV06nMTf3wbAbPraNaWXlwraDj0e8Y2atRkXdQmuGczuqDkeklsxQTwiBMc6MEXk0kM8KvxBeMIJnFMea4lT+kPAy+Vk/+iRzn4Tf5Ff5+Bde8rWf+ogSI9u1ks+FfN05PHRaazyEjzi7HxCOgTdvPmexJyLPWK7G6lZ+9O4Nn7z7TbY+2VCLCL/04XfwuSF+9snepjfk5km9Qx+U4Li2zPFrifv0nPvjgdg756cL+TA4+gsv08eMnrmGzvndBecWrgeQKgidVx99QLjr9Ked92UjsHByjsEjF+/wfaC+EXFcLnB1BXeutLURips4FOkco5C7sV0f8UNgcThz7HXncBRaXCnvMg9XR/WPIIF1FE6ifP0UqceFH5wfUbfwz75c+en0mm9ExxKE+NrjkkdzYyZJI+X+zPbmkRQuLObY8yMpTKHG3UOAHjjEBaVj7ULNFS8JSUK6GzydE0fxrHcLbcsoVx7HM1otHO4cowOXxl06cn6mWKn40KEJ70fhrhrihRGM3j1tDEyV++QQPfyBduUy8C+a2fnG/v53ReT/BfxPgP+Nmf1FEfk/AP8G0yH3bwBvzexnReRfA/5XwL/6j/4thOuW0XDL8mQPHIlx4jNqbfTcCSfF7ISqUYoSo2B93BahOBvoprdrZU8dG2jCi0ATQvJY2+lDaWaYFnqpxHCPMCFh5brjglD3io+dbZvqH5+mqqiJUq5XbCpTKGPQ9yspKTEmohdaN4JkYnzGwLCu9LrxtF3wfkVFiSnidBCXAwpTM87MPNVd0DjLuaVNYedsyId5U9YGIfY54F8WzvtEVaTo2PZCCDNSMMqkby5OsFEYTfApzB2Ndlof9D4QnfxqLDB6o/bphvvgqMj+Gi8PM+m8JmQ3thTI76/cf30lXDp3H7zgtRnhzpFH54dvMu7ZrP/cnQJVHMMXXAt846Nv0rny7vKfgDxwiY1Lm/60z7aNujWW07N587jvOEm8Gzt7PLO+foV83vH1xIcpsL5O3B8X2jmz5x19WFmXE7qcePr+G56C8XJEaPB+bJwvT1wQVicclnuu9UyuBe8d6+XIXt7xbnR++NufIh88cH75jFd3zxgtw264JdFpVF/JTxfeivE8HSnlivRKPXXOzXEcHiuDx2DEx/doW2lp49CPaBv4ZPTUZ4ewOp6K8PnlLZ9140fHxMenD/mvn4780fWBIxEsU1ok9IZI5GJGyIMtbCwdih046JHHtiGiuOQJdmF/9GyusO87Pi08HJXTuoAYtQYujyDiyD6Szxkho/uB1XlG6lQi0XvqqJxtZ+yOLg0LEe0BvV9ojxe6VcaTn7aaVbhcriztjHsuXxXH9HsiWBpwvv0w3P4x4F8E/vu3n/8LwP+CuTD9S7f/Bvg3gf+diIj9I3QJZswrSw0kFba64Zyn1itjzFSvC51ucSabxzRE1N2RkjBGnQRDtel0M9AJg8FpwQfH1gZj26hdEJRWGz56vEuYGKVXvMxSZmvQqqEyjaIqRu1nrB/p7UofkYFwiI7IgOM6gfFjUOvA+ZW4RK6XDTC880gMRBx+CUTvUA3kUhEN9J7xZVIPjUodg4WZMp5W1IoRkVEYOvkZvRtDFFXhcAqMltlLRhRKaRyXw8wwpQ7SwUW8OAYd7zu9gxIx6VN7ZUK+PHE4RLyb6JTKwrf9t3jtV876jrEvWFRi6vhXRw7rkbfvf8gqjswZ5yJ+PTHKxnjbiPcL7W1mxEaxRCJyp4Off/mL/Kej8Mn+d7mXAyEFDh8HdnkJ50qUAc4xLkrtEMLgvK8s8cTVPuP13QlZDpht7D3z8uUd6Rx4d4Pg1zdv+c43XvC9i2PLj9ytB/KbwnupyHblZ3/pn+Onv/EvMMLO9Ysf8Hn9jDre8/hmgs/GcfK6XBVy3dGQ8G7qipoTYocXL19Mxx2eHjqX5kmj4WUQTgvjnZHSxiYnHkLni+vG332383n+Pg3lTfMQdxqBbPCFXRll0NeFr9fAHwsralcaxrEHNp4IY8GPwSko295xa2RxkcfrGT10Xh1WLm8rNWckeUoMlOuAxWj7heXwjFSM92VQ6xuiKI1JfjiIYgEW19ikwNPGOQuLZPwqvHvXOa4rL14Y53PnfQ442yluJxkU11hTZKnGY67k6En5aZbMv8Lr92pJcczj2s8C/3vg7wPvzKzdfsmX7jj4XV45M2si8p553Pv8P/cxf+yVWx4WDjECDdFpl229Qe/EGKdKJnhGveLdMudDfr6xbThgYkrMpufKS6NmJm1xGxjupliKpOCJIfB0nQgUzLheGt57qmS26yyGuuBpo7AkeDrviAv4NEjpwPvrQGVgLuBFEVEu25W0OHIzXMuglbQGvEuoGMMpXgLq58yLPliXWR6dUYWGG3MnFOKYFhFT7BYNcF5AJ6FTZOqJHIXHy4Z3V0bOIAdcmgbcYe2G4O2IdMSvE4iW+5z/1Ea9sb0TnjqUlA5zkVKHGYTqeC6vuT49UqrDpyc6d5OpnS/89qe/zW+9+x6PfcNvlV/86Dt89OIV1+ZZfEbWyB4am1TWHBm+Ul2i6iPffvXH0XHPr373r+BypdYTdQSOD/DOQXvqrDKwGGkY6eURtSuyHPjg+TPeaSfsR8Q1Ohce68a6BO6d573b+J3rZ7zZKkc1ct14+/ZzxDwfvfo2v/TNX2bbr5xEOC33xHjHb336a9yvj3z+thC78FR3LqVwz2BclWqVJQg7g3AMGDt6rVzuHboVDuGIywZ3B9p+5bFU0EE6RjYU2078+/4d5/M+1e5B0Ky0+p5P+mC0wrPTc2IXPvjaivPCwXvKpbG3QXy9MB6BTfFLZ3JlVkw99yZcroX7PSCyEg9fcsEV9YVzUZ6rQr6wLZG7NaLHB1Q953qGOtCwEnzHnkVyruhy5MWhsL8rlHNgicLejMtFGTVz9IpbYdSXMwKzv0eCUPfMq3TkcX+iJ/2DxZ6YWQf+uIg8A/4t4Be+0u/2n/2YP/bKPXz9wVxUcgFHwvp0VJlExpeeBUukNcwrcxexegEie+l4FTod9VPF7RTKPgghsx4PcyC4r8wFzGitIDrwbu5W2qiM1nASUS8TR2LTxtFaxoWJnD0/VuIKQxpqDnik7o5SZznU20xQx2iYnWCUGXhUoeVB8zOXpQP6mFfztTd8mEcqs0GtgdIGrVaOx4gXoWu4HeU82sZs2lsHqUS/su8bwS84FCcKvkN1zN2aTNyKyfwzuYBQUVFwSvCJvBW8GiyeOqYgoenAbQ4pitXBQQ+4EJHh8FsBW7HtHe/ef0E9P1Ks8+r0DdYxSNFBOvHi4QU/ePeWy/lzLs5j18zBLRwP9/R84duvf4a318/o8kiRTq2Zp3IBdTSdGiNXnvHxxx9ykud8Wn7I176xsgr4yyN7iCDK49Y4540yjCGPjNGIdmSUJ2pQPv/kSu3K65fP+MMf/zLPwz2H00ZhJVFw8QWncOTcfsgSfo0R4G449LBybpVj71RV3tlGAHp1YInuHQnhbMLq3vFeI/fXxpMp+9H4MAVCE65j59XRUz/pPL65cK6Zh4fIw/Ge8z5dhz4ooQtHX/nFoxLcPcM1/GIcIuzVscULZUw4YZIFRbhQsajsCqaNxd7xox/urCHgDgkbnfueOKcrH6fAxa14N4g4Hj/PXDzYMfCKig8nmhd0vMV2xzClH+9xohyo1DbIl0qKgbLtSPZYfKIcPeH0nOCVyz4oVJyP8I7JwP4Kr9/XrZyZvRORfwf408AzEfG3XdPvdsd96ZX7noh44IE5BP8vfH3ZjYshzGOHKtIcSAVLqChdxuwBKSCDQZwGjjEY2hijY9eOSZjQNe9wesScI5dpW+m5Y34C/9UmlD9nm1fOVVBf6NtgWSOtDtQFaodaK5etEnVQzh7EobFyrUp7Uo53EZ8CmLAsY0Y3ZMLxVeZi6f1UUnUVnFeke2rP0KeKu9mMHZRRCXEl+sic7XecgbpKb4LEAy7cbte6UutgWVZEYPROyYa5gRsOCY19K7i40seFNQRabzfCpWeYUGuljSkp0KVRirGmBbcMth8Ao+ElEZOyd/j6eqLtnoejwx08z9JL3uknZL/iSXy+7Tw/vSAYPL1/YshGXBU7V+zZCZcmYVRGwnznn/n6n+I7H3zEZ+OJH/zwB2z+iSCOJd3R68JpecHx0Nmvna/xIb/12W/wG9/7G5zzheYWxDySd3yb+u7jKnzn2R/hGx98i7/9/b/GF2++y8Nh4RHhpz/+aV598DUudcN84OA6mz+C7BzjyrP0U7zNG/7pQtSE5EZqibYafh+IP+DyhVzl5rkL9JLnbjYshO06j3dh5T4NqnTatbIcT4xqfBNlvHzB9d2n9NrgEPi6v+NXDgsuelYSH6vjG1Vxq6Nus7f4eRnUp5304Ig+cdkrOWee+0HdK4eUuOYn/GGhmtK8cJ3wXp6vyhf5CWeO3/l843gQWljp3nAJTjc70Nank+6YOvvV4ReBuOJypbVB8J5ruRIPic+3C6NlHtYT62LsSyQ+ZbZWUIkcpFCdJ9ydQP6AjnIi8hqot0VpBf5bzIH2vwP895g3c/95r9z/APj3bv//3/5HzZeAG7PFUDe139YN7zomDpNOaxWNjtocqGB1niBLL/O6u3e6zFubJTpantfo4oVaKr0EWt8JwU076qi00qYPziqVQfALhhEXR+kNo/C0vZ3mWzdobeDdAbFAXKbm27pDD52wzG7ZpC020rKQ947TiDEHjE4HuWSiW6e22watCyoOvWmH1Ad8M7DClq+4cYf30xrVB4h6BJs3kb2wX/UG1TfEDWwoSJtBUITRjbSujBvXtbXG6gPcdo21yg3t6vCmWFGSTunheFd4uCTkmbIQ0NSIufLuaePl/SsKwsKRn/rWz/ONhw/YJXEQj0Yhuyt7MbofeNe4P53QVXnKhq+dYYX3l4aPDucDTzpwCK9ffJ3KE94deL/tjH6hlDN34R6pjvX5gZ/SxLvH75F/+IjcDT7/9DP29437Zydc23h1/wG/8p0/w3555Ntf+1O8efsFH318gs8+57R+HQ0Bc0wNUp47XA2z8D1q44P0LX6j/Bqv1idSSCgboztiFC7bGRcTTh1t64Tk0bqwcMa5BVkSm4vE4pCxEcQj9wfaGLB3/uz6jJ97JvQXDxxC4llyPE8J168MccgQNpuRFd3ODDWeqrEsgXpsuC2x+QYjIS5SyuDZvWNDYYu8a52DjzxEpa4JlzvNDVYn9HakaqMnWNeB78JTOPDsZIyxk4aS/GDrgVNcqb2g5mgnxZ13kl85rivb9Wmaobtw7Y6jCPEMxkLzyp1VpHucBkZu2B/gUe5rwF+4zZkU+L+b2V8Skf8Y+Isi8r8EfpUpxeT27/+ziPw68Ab41/7LfgMzo7ZO0hkCdN5o7eb0cpFKx7bGYfFczpeJsR1265s5xHu8X+ijQveo7lOfHfxErrrOoFNL57pdCM4R0uRshxBpo4MVnh7r9KmRKSXgg7v17uCwLDA8MQqqCRsVl4SBo3aQUWkMvDlK7ajrmChtG2gMeJloWLGBc34qut3UiOdc6b0xJE6yYq8c1yOYkotn+I5Tjwh0HTgmYM1GxRiMmgjaQafYkNGxPhHAozHJBSrUHtnbldYGSZc5g5JGVJ0WkiBcLhvH5Fi+f+DEa+TuwNMlE4gwQJfIVTY+ve7gGr/0rT/B8m2jV7g8bmy1YeEJJ4EtJeKupDo/dlga9TKoC7x0d7R+RsbgfNl4JNOfMhIalt/xWITYNzhO/MkxRa7V2N4UUn6BHZ64vHlL+/TCw6sj95r4vF0RAr1cGB6e3X3Ar/yxP8/LD55h5841D8QP9l0J0ri0jtZG0cC+Lmz1kfuD5w9//b9G/tH/j4hwbQ0rCTsN1AJudcjTIC8VSY7ghPoUqE9KXYUoET+uZPN4Fnq9YMGTV3hxNJ5LJD4IrnuezoMuGSMybD4scYJUwcb8vvngXnkaV1Z3BOYurWw7aRibFFbnMHcAb7xcDyQa123q1Ef0PJqyqhBs0Lyjf5F5t15Y/KSgWlgYUsG52QldO65kasnE08rRO/arEe6E4+LIduQbkpDnnUuDeHeHe/cWk05Z77nmK7oesDYQH79iiun3div3N4E/8Q/5+e8C/+w/5Od34F/+/X0aMt+4biqSSmlYVdCCkwUnMhO8/bZz6sa6RFrLlHGzbEikD0+zjNFRjdRLAXP/YDDOlyHMwfV8oQ/h3KHRWR2EKOzd4XjGulQ0etbgKAXEj1n+rQ4ns/5RW0V0mmtFFROHxgnV924WZdNd47INAuBcoLYLDGG/VtajnzUbHA/HKfusuaOmPF42kk8sR8dAJwBfhd4ySECJ+LChJIYCBHLZUZ23jVP26RDXGOanLglD4gFp23wT6HS11QzdOr417g53yFUon298/PM/xd3JsdfA+fEJ3SouvuTzvbKdC8cYGakzdONph0pnWZab3rxyzINqhZwDa2/QhLVWqt5TU8cuFQice8GbJ0rl83d57pxNWNMDsi68L0oIHmeZ49o5HzqXxyvaMiUa0Tk+y1dePfs2v/jxr/Be4OiVk3bi8SX57ODxQneZdxpAHHe2sNd3PKwHHghUaWx3g6dSSa8OrNxRnZ+2EhX6l+jf3mhiOITcG61H0vM7tDZ6biTf2Dfh4RR4bJW+d2wz1uNK7p0UjKd3mbNXXkSHeGOpnp1KXIW0eLb3jhYbNTneP51J6YjJRquZYY7VK3jPvgfEe9J7ZdvPjMXNWdMp8q4aixZiNq6deWHUr0hccEtg9Myqwt4a0SUqnbZV9LGgK1wMzAmpdx47XEaeFa52pJiSbUdl5/GzhtcDFzrH0tl0Qa5TJrE9PvIVL+V+MpLfyEwvj+ZudMWCS47WE3UUrDdUpmdtTQ7M4xzkLIh0SgEk08vgdDT2K5zLlP/V69PkV+PRMLXO6pUUOrUE8J4l6JRBtkEIlZgUGxEjYcN4/tApdczPL3S8KqUVRBLeD4ImzGRSJ0cHc1OwoMp+bZNZXe2G2w3U1pAU8FGRFni9TlBbbyAqBPHAtHj0rixHj1RoeUecAzqtZsQlei+shwOtK84vDPLcjcmAPtXgvVeWJSIysLKBdcQvOGsIK3F1tJHx8QEXPfwn77Djwmm5m1SUUlE3OBwPVHti1QW9P5FcI6tiPbJKw2nm2enEpexInCqmpgP6hUuZdpq+Ks/HzqU5fFi4vq885p3n65F9a3DesbQSDwF5dsBcZGmZIZ3rtnOtmbhfcFQuZWd1Rr9Unr/6Jv/8L/zzvJCXvFmeuO7GiypcqqAnIRwHq77g3f6Iw6BecN2zLIHz+51qFbJDkkH13KePeR0bb/YvuLTCfTvR+5lRAk9NOcTAlq+sY6f7latl1ihcnzrv3l8YZUFkzF2jBNIqtOIo50HwB+4QmgySBnY3kNzJa6J1gyTQNwIRv0SqRdwRNAjXLNzboKeIDHjaIsd1EJd7tmthVfCHyNEd8KNAKKTmGNapGpFRGTWhbcWdhM/zjrsai9/w7sTlWgkVXFS0X7mWQdcr6ImwBKpkUjvgJRL8ha1e6OnAya3ky4XWr+ybYknwarivCP3+iViYzCZhYM9nmg2SU9ow1K2z06ZTVphCozWjZbhcC60XNCi93pC6buPxqZE3IXfFMR30Lhy4P/kbUkToJjgbBDfm7qIWNHjUVZwmRCqZwHHx9NbZ90JcVoiDXDvDKj4KguHFzyt5Ny2c0m0OvUdHNOPTggC1Qi2FmI5AwUWjlcqojWrC0+VCDMLowqVl1tWjQcjbNou7dAYOJzpFiTdQHM5RbwPKaRz3qE6NtdqY9AJzlLqTZKCuISRKbfP2sjbWoBPQr8bhENjV8+r+26zLM/ZPd+p+IR0P7I+Vd2/e8cFHL7lbj3xRO6k1+iJUjAdW7LJxPESeulLYcBxw7kLoFQmOunceUbxr7CPCofLMC0vLGBUNnvv7lXD3gCq4ZLRlpT6e55FiPfL2/TvG1inxwCv/mm9+/Q/x9dff5hhPNClo7QQPOe/IeiRaZQ0nSkkE9cgYbEM4xUbeNyxAZ5AlocfAwYxVjmR9A9mx9s77utN0ZYzCsg7MB3ij9KBYmfGPukY0HPng4+f02rDzFUfi7l4ZFVQ63cHhMNhE2N5WDh8ookY2RxxCk0aLDZcXUmvQV+rSOPlAlUIdnY5H7MK9LFj+Yj5MGvRNGXfGdds4xYhdCxe30eOBAwt+b1zUoX1woPHmcdaOlgBWK0/bE9Lq9A/i2b4oZIk8c3eEOitWiwpFC5BRjISD1jmfdw5Ho2dD14HowmW/3lyOv//XT8TCBND2wn4tLGtiqKA+Ih5GMZx3nM8b2RdEFmpu7LmgWqjZwShIb3SpDIGgnjUEUox4g7A6TJQUwCNc88A0AoVhQjokzDy5gOWCBDfVz71Pv1ZXnA6sC8kFoJNbR7WxN/Bj4H0npgPOVUpumBuzXGx11j8k0oHSr/iQECc4c5jLlGak+MDob8ACPgSGeNq14LyD0SZlkghjfm1K6VPoORo+LbOP5z1xNJyb8yjnB8M6wSnOC6U4tCtIR+i06udTfS+MGDkkT3m/86PfecfP/vwr3v3oh1Q/SJpIIRJC5bOY6Fdj65dpOWkVHYrzcIkVrYnx7pHGSrr3jN0j/o6LbbRWSc0YSXnajYWdEISDPyIuMNKBD+8Kbg3E+4WnDl4ztmXsWnh4dkTrhfTiRPOP/OH1F/nGyz/EN45HSh+ID3x+/YSUFsbdQlkj4VKQ4tgeAvcHT/1ipZ0ad9eIuMz7S8VTKKq8eHbH+144+kjmNaO94UGM3Tk+vE9cyhRO+mVhuXkKQ9uRuFDOmVE9MTVauRJo1OBR6/TdsDiQuFLbW576ER/mx9lbZBk7ziLb3riLHtIV5I4aHbrtpKy8zVdSilgzDv1AU+G6b2jqZE3IuqBlg9DJeoffM+Ygl0Dq4GxeuMTeqItnt8Jok00VJWHugTAy4gLNOq02ul9wbeBPflptZLCukYtrrD2gqyf7I04Hq1vY2ifIcKS0c6kDlYD/ilumn4yFyQxR4f5+xcSzpDjlfmaM0Wg7WM/k4liWK+oC3nWUqT4afhDU0/aAT18quIUYB6NB3hs+RWwYW50EfJ8qzerczXRHaxnvPaZ+Ik+0UHZBDopTx+P1aYLvnTDyrZfmp8RgjIH1NIf43VELmHPUNgmLKgH1E706uU6ZXB2jFlJUZMzdT+13DNdwI6C4GbSUQe0O5xwiFbVE64VSKkuE0qYq2znPEjxtGPveUR14JkmhC4xbtELEGDINqaKN1hwhKsUG76875a+9oTXB1wN27AxdkCTs28Ci50O5xx+Uaspx9wxnsB1w93XOvpIQ1ufs7zbCuWFRcEmRfiR5w0Yn44jLIKULRQOjB841TxNJWkluYPXKclFaNMqls4jnzWfvccH49gd/kpfrh9yv3+bkJnFTE5gXPvggUoqHNhAnbG6go+N75gt2JHTCcsTHDS7C3TIwd0+IQnMCeaOeBw9pMLbCedvIpnhLyDJ3qq7XeatJJh4OiAT8sxPlEdJRcXljcytugZNk6uamKj3DcIHoGkUPs5jdlK4B007kVnO6LFxd5X40nuaolaQe1wN9z9Rj5v35SjBj4YBLCamw9Z1WEqs7Y25lDSeaNtrTlX4409vg6Bce907RheQGgqBWuTaHOMO3yrnNgvsShVUWuDTetp2B44u3GZd2cIljb7SrcfFK8IX7ZeGxXqjvIs+eRa7S5kniK7x+IhYmVeEQE2F1OL+wX6a7ykaY/vphBL/MsmBt5J6JXiltFnJjWlEU/wCtFkCmSLHNoam4jnMT5aE4vE63mHMBMbtppTtlF1Rm9MLE42+MptYqSpzapBFhGGF11L3O0qw1rvuYv5fqVFa7hHRDe6RrJmfDx0D0CSd5ih/V+P9T92e9lm1peh72fKOdzWr23hFxuqysllmUSyJlV5kuUTIMC5IFWxakO9/4xv/AgGFY0g8wYN9ZP8PwT7ANEeULQxJZKlKkqlhtVmaePCdONHuvZs45+uGLGZR1YUl0okhkTiCA2DfR7L3WWGOM732fJ+eGNUKrBZEC4jFjRrKgBmi1UVrbayzaUVumBhisQYn55NfLdDSlBlqTnamEomGoecO6DnR6S3S321C0EXoVlE60psjdsV5X9L3y+vVfZ36YkbLszO+72o8XzjC7Aast15bpeqNtDn+ALSmSz3uNB8fh0dEvjQC4S8SOAypWPhDQIe0wv+pwKERHfO2EfKNKY81PpJw5uB0lM2nNNhfiEpiM5ftfnFBPn2GHM9ftxhI2vK7kodL0gRrulA61d4x23LRGffvC08OJbgz9Q2AdMyZUUjOYSdBJuIbKAUP2hqVX7i1zPDwSl5WXNeDrTLGZqDNdoGWPSYl6EkarqV6z5oz2R7QG3wyTmnkO9/0DViJeZuxgqFIxUWNJLF3zgGbZNto8oyucdKBbmKUjttNDYstCUxulqz04bIWyCfUSSc2Ru+JpeCDVC1uKRJVpUTH0lS0alHGs4QYyUsTvH2hDhT5xLJVNdTbrOAwKlzNuGAkt01pC31b0+AqZOvM4o02mh8BhnpAC4+OBoXVSip8aBhY77j3Wn+X5uViYEEAqKVV67CiloVtybUhLIJCzRquO9cLRjFD3HEmrZq+WECix08RQi8ZoQcjEWEHvwc1WE61q9DjQeyXmvVqiUaAM1gq1VkrbdzxCp+WAkgzGk3NCu2lv16f2adrX6Hr/NraWKaUiohn7Pk3rRrBqX7ycDeBG4MBkhRgyVjdK4b8yx45WoHWyAlMtSgZmv2GdY4sNazTdV5SGbQ2M4wGnE7lWWtV74VcU0OgUjFXULhjyzmwSv4flYtgdbtJQ1jN0oSwrpo18/jjvvrN6RF1fsIeJNlniVqhjYdsqfhjYpLG6bV/ssiMlUCIs9U5zivOgsGpE1EpCuHqFZaTIXuG71sxdTVg8n8+GwTa+fUk8jZ0iQqESmsPozuQe0L/0wCAKQyUOiqFfMUERciN6Rblnco2sq7CVldPJMhnNlDRJvWINBWUN86cdtnoaoWoCCSpoq3DRcBw8y3qidU0r133XpSYsF2oGUxXJdczoWbbMhFBfQO4Z97qT7YZaG80qQje0VBGvcWL34+8G3lfM6YT0O+fs2Lhj+nk39fSFD2vDJaGYjqSKGQxjV8z6wJoNk9N0LWw6MB8NbhWMsZT0wqwH/GEglMCDGwhqwVahGgPi0fWTnt5mRBvEdLIq5C2jleE0j6QAbXtBH1/h7JmtvsVZR5LIy9bwtXAeTugDpFtGwj6t7OaEPm7UbrgtG73/AhMsewfEQW8YEcIayNIRMi1XRBm0KLRlr4yUDsqi9V7alN5p3dNV2p1iOVPrToQ0SlHrRuuNhqCsodVC14I2lrAWtEsYsbTU6cZifSVteXehRbVPxnzF2wlKp0mnSMeNBmkGpysFUHpG4g1tPb0oaImaM9o7TscB0btRd5cJgLeW2hq1V6wb9h+GdMImWOcwxqCNIDKQUqR1SEGhjKJ1GIddKRXrHocoqtDbzjc3xu1I3sGRQgJrqM3Qc8V6S11XtBRibhixiL2jxbK2REgDSwkE07AHwR8PGNW4s/HxcmVUT8S6sMWMvja2fkOZCZ0SyjRkGFAhsyXN4XBH/IFeNIO2tC3gR0dTHZ0brC8oP/BiLU4c81xYJTBicDJSB0F36MVgrcLLymWrhBrp1vOuZayCc3HoufDx3Y4amdSRz40Fb7iVgD0ZSgK/RtTBEUWoKbBFh9EG44Rz73jtabqijw6v36DjwHi5Il0RlGFSsLHv3HW5ovTMyXa6AzUrpjFAn1lVxanKgGVxjbpuBKe4i6G2jMmNki9UBUNbiTZjTMPbSsHjTMOqwoO1LL2ylIIaDCKOmO6cjFCCMNsDWhJKeUyJNNl40RbXBKvPKDpXDJOu1BBRJtOHA29mT6yQCpgkVNcwOCBydk9semTzC+fBAwsMdh9iqJHcN8z5gdwCTY48+A2ZCj0ZRucIoTEOnlrULzbzu7XOtia0VSQSjZ0npK1FWU1tjcFXYhR0L9TqsK7S6x6yjLliXadVTa0d64BSUYzUVuhdo4pBVAPZL/x63a0pyglaHEUsym04a6lKo6fKaAQtlpyBLuimKQZEPINPaBTdarZcOE9CyXtgxOo9xb3VjCmyj6elkJJBZEMh5ApOOrmUPQluNa2Ac3B+mOl1z3T1VmitU7JC9T1OoJWwxYSRPSDaJENpdHF4Z6gUtpTxZicNeNOofefmSG9sy4Yxnd4Vg3Gs6U5sCyxnTtbxZvoMowW2jHUHtFGoCjPCas5cLxemhyNmHmlyY711juOGsTMbwqlAtSPFBhY6pm2Yu+ZiGy5HdIuEXEiiOU1ntBpIt49ISkynJ7S1LCFTcmUeDGPfxZF1XVhImFthHAQ1GF51QUKjmr3rOD9ZTtWgfeexCZvxcPuAf/1qP5aHyId4x6+d4ZVlZlfQ6wWCU2yuIcXQsNyr59DuPNC5cIfsib6gkmMYDK00ak7c8shaVgyFdLFou5BL5uMGD34gxMahWxSZSUWqdWjvsFlY1kIxMHZD1Y2S7jgZGL0nZLhvEfGaoRROw4GlRo5Ynu+FKSmiDWytcjIrs/EsF8GdV9rg4Ra414YdzoTcObrAUA7EnmirYJ0n68iaKofiGJUj9sw3z89UU5i6JtJJrVJCQfwBWxvzDCFknNUoiUQ8vgZSjyy57iiaDKr+gu+YoBPKhlcjTcBojWGvfeyEyR1BO7hGy3sTX5LdDSGyT6k6CtEd2ypdCmqw1JwR1XaJJYLSZi/52krHYPTOupG2K7Fbd7TW9rE/lS0qaIX2qYARemZwn6yvXYFEtOw6ppg9owHbBK09rXR6qogbUM6Ra6HVTkoV4/ZoQVCNnBJaO1re6ZMilt4KKWUq+27PKoWz+6SttX3EOw172tq6Sima0gXyBkojgNWWWiJVd5zRSFf/FX8pcEdRcEYRsmAHxxot7pb57On7fO/NZ5Q1YiaDnQ97husW6EojOjAMu+FF5/3Y6AeQJ0srlePd00QoqqJrQtmZcquIrcxDITXhXSnoBhK+4Ycfv+ZH97fcfvrnfPblr/E7P/hXOTNgi0IfFbZVSnKI3oiXG5vfMcLee1gjl8sdVy2qgbEG0/Y3eK6JlzzjtUK8xd2v3PqnhLUYtOqspcCmyBYO3iG1EpeGmkH3QFATznxHOx8Y78LoFAlNspWShSiaySqstXwxDsSXK5uBlgRmxzl2etP4qXBXhrPZUM0SL4lI49AdnYqzJ4xqlJS5tUqUxFEaZU0EUzimkVzhQ3pPSQ3fI9Y/IsZwUAHvLUr2Zpc9CQfrid1waRma5tEHVgotWD76jFUBXRLlYtBOyAKhBfzQmZVmGmdiDCitdzKl9tjBMaaNMky0ckb3DxgGunRsWDDmQBHHUPf0f+kDYgH5Bb5jEmCeLPSKdo6wFowGowSlBra0ISh67mgtgOCHhhZF/GSp1dpAKyRVKXG/EB68BzGI3jVQrRcEhRKLdewK8LZ77VNIe7lWC1Ypci6A0KRxOBygRmIze7Uid6rRoCas6XgVCFtiWwolF+7LncGBHzTdFbakMQRaUZRYyKUjfUHrA0a5XaPEvptrTeimoJUhhUiXTjENYzqp7ELQ2gu9DjvzuzZU73QBbYVYOq3vSXo/GRSNlhMogzaQk+bgJ1qu5CQ7FbJ03Jap3wXe/MYTW9ujCGlVnCawtXFNmU0Cx1cTqacdJRMyh9FQh9dstwujsZjR8Zzu2DrixwlP571NXG4by49+TD11fvzuz3j/7gMn7zHzREiQdeQP//wPyLXwP/j+v8Srz99QbiP9ybHVjetSEOs4HA+k4Y6tjqqEkJ65FsWTcyxrZp4FyY2hWlZdGLvDacN3lxVjdqpjjJHYB9yWcCVR2sxmK+PQOCpNVYq0NbbSuN0v1DxhuqPpjlegSiEbiysrW/HUl4XhPJMqhB6x485VzyKE5pmyIdIoEnBqJI+KLpWXWFil4bb3vHkYaV1jo2L2CYXBDQcelSHEimoBV0fqWOiiKT2iyNw3TY2BaRgpvXN5DhyfNMbNvDkW7ilB00xOc9sKZqvMLrPgaUfDEAO2HZjnSo6Zu+m0lllSZzCREiP+6LivnrpWPJE+JGrXbFvA2xFtPWXVFJ9RRji6icv6wpLjzypJ+TlZmERQWpCmaHXfHSgFSwjMdsDqnYSXe0FE4+xu+GhSyCnT6WjxKKUYBkeshd4KonY9OFKwzrCtBWsc2hRSqiiEUCuj1ijdUSi0UYQto/SE8vFT6RW0e6DmxuSE6ixeEq1odM3c70ItjS4K7Mj5UaNNx9nCPSRU3SdhJUeM33cWyLgfBWvFGI02UNqOJlG9IXpnJss/6bF16KXindDaiKjMuu0X9LZnnP1k7KiFcdA73aB1RDWUDKypkmqmR0HZjsoRMYdPSJYb+WPGZXiav8RRuW2dp1cPPB0LPQpFDUiEughYQQ+droV7dowtUz1steFcxsqAiitdHP/g63/EP/iLP0SJ5X/y2/8z0vKeb3/0I1wfiLOm3zd6iUTVefVm5oc//WP+7I/+Ib/9r/6P+Zd/9Xe4bRUdC0PKMIy0mnHKkmMl+MxnjyeWVRMk8JrK1jWpK86lMUpB6oFh8ny8R87WUYzCrRWs4tImTjVS8xXJndQ8qUfO4641+m59y28MB2zNmO5ZlZC2O0pVKHfU4cgYOq3U/XWoE3ZzKD+ibSYXheqFdaz40LhrT7otnJ1GVc+shclZrqsnVAPXjZsR/Or2+1Q6H/NC7/uU7O7AGoN2nhQMh7NnsJqoroyzEG7Cq8cz/jBzC501pz0+UnehbGNDd02sI1YqvglRg8+R7aJpRiCuxHHi9WzJ8U6tA7Eb/Ozods+riTRyHxBJ5OuClIHuFnzT0BqlJOynXN0v9FRO/smbSICyonSnZPNpipHIBaRntPGUlEltZRr03sMRtUcA9EarjZz3Rr/SHq2BvOc0WjbA/o0TOt4achacEkppzKPek9Vak5zgTMf6I0rqftfehMFPeHOjVghlpORIpdF6p5UORojbld6FefCUtbNLpiNiPDVWBqV2xXgvezRBK9wnSJ5q4A3E7lC9Y43/hEyprKnSSqMZQetEKQbvE0YKa7BQFEYVNoHRQimVdYPuLL4JoQml7x67JJYVw4OplLxSsqH+ZWQ6PBGz4JfGeR74wo589/5C0WDmM7JBWW7YQRBR4DUlJKTDthjK+oHn+IG4Lfs9g3P8wz/9T3gz/ib/o9/+V/jRN3/Jt2//GESTWmPsiXvYlUavzxPX5wuH0wH/cOQf/+UfcBDDr3/x32OeT7xTI325M4wGZQv32HANepmZHx2tGZoYJsk7A6h3bikjvtCc4c3DhDb7tHSdHL53jjlTtGeUAi5RBNzhwJZWWuv85fU9/9YvP1K6pnXwoql6RqldPd+SYNbKMxtljbih04YOdSOsdbfCxcasCmYeyRke3ijoK/UZLtEjk3Drifo+8XQeUFHAGQYxBFex0hDADDMtr2AmVEvMWrEtL5SXTpiEYcg8nE5croY/e/+eV+rI4AbCZaOeK8OSKEHQcydsgcPDkUykCqArxgspBlIRXLxxCYI7OQKBU8sMolitI9xX3BFOQ6fGBPNAj4atpN0WZDtNNV7PR14bu/P4f4bn52Jhaq0To+DcLhCotdKl4aymYT8xPxK1RsQK3ggpF0Q80iOtZbZt/4RpFaoI4yiEpQKgskW7vJ/DRVEqmLJnnQaniSFR60Bre6ZnHjRddhV1r4pRq51xRKPJkd73CkhLCWMNIjuBQGthOlruW+N6i5yPFq32F3UrK9a7HRnMvgiPbqAURasdrfWeb2rs2BF2R561llobuoEbO8aYT4vxTgzo1WIsWNvJW8eYTBCHFhBbabUTpdE38BOsVfHQhNgUrXdK67SXik4LP/itf5Nfev0lQiTTeS+FVe8OttqekdY4nh8pstFq5WEc2epA7pEl/DG//8e/T0wb93hlxOCnM5/NX/I3futv8gd/9H/nXX6LBfLtxnx+4P3HlfFxJpXK87s7zjn0TRHUlTrO/N4f/B7tNw0/+LW/zuQMwVS0dO6xog2M44nmDbYWsggSFZOdkHxhSRq1brTpHW1JDOfPuG8bS4h8uP0hT9MPME2QEbR31GfBTIKNgV4bOUVyhvfXK3Zp+LGSUsFZR94UkYIvlewqh5QRM9Gtx7WBrvdduqu7sceLhgZl7Wi/EFZQFOzRggi+NrSGtRSUCCVWLqawJo8bZ6ZUueWOijCYyGG2JHGU+4J/rTDdcv0oXMyGlI3JDliz697vujB3TZoreEOLhuNBs20rFY1URwmZUuHhpPFHS1s36nFGbOZ0nEgVyrLgZouIZuiKNUKrI1MeqKkipRGlslWFFmFDM47lZ+XE/XwsTCIweEtvnaYStI6RT0jdmilZMKbvgDfVEQKqN0qz9GowFoS+77TQKNmgKrTWbKniXN2LsbLvJEZvKA1ayeRq8dO+SBlvEAq1CEoyqQVS3L+z2h3w40quds8JpYUUEj3LnhiKiS0kBuPRujIfRqzXpLyhlaFVj3f70Uu1hij2rpxmX0yBcbTELSBE/OhJCxifKG2/R+toejd0GlbZHZnSoJW0t+C9IUVLq4nY+z7ivgaG2TBPELOA0yTVsHYXAAiG/l3hi8cf8OWbXyG2goodTgppCZ0ievbYUrCjw3nFNTiUKG4vhb5s9B75x//oP6OoO9Y4+k3YmvDmfOZv/Obf4u/9wf+Ln1z/lPNXr1iXlYxmng48DYr3H28srXA6WxSGlcCgDhiJnO3E3/vJf8qr84HP33zJ+ajpaUIGYbuvO6M8TfRR4+J+N/Kxb1gRrFf8+Y9/xMTE69NXhO9e+C+++WM+XP+C1w+v+dXPT4SwMKuJuN4xs0ZT96lncLRS+fz8BSH/FFFCuTuaHwjrhdL28OeSYBgtSXe8CC5taG8RtTcTjEzc7i8kbxExaKspCsZJIyGxloLUPb913TrEjKqG0zSi1YpUkCXxsoT9BSKasTvuYeXjhyuHWdA1UazQx5kvTx1nRv7xj99z58CoLjweD+BHYkj7Byh7N9MOihIF3SpOFbSvhFqgNLQesMmQwp0ojZYbkgvdrNxD4bOT5zg6Xt5fKEaRrMJRsePE1BqtbqjpgYPdsUQ/y/NzsTAhuw9O6LQGNWpK2/aJmhWcBcGCqN0eqw1dNE4JXe26nFYMrUdOx0KuM6IqynUGpdCqU2iIOIyuxNQwVnZOkurQM+iZ1sDoivOGGDpxi7SQsJMi3O9kLGAYyPQW0ApiXam5UNEoPZD6jkoJmyKWjmUg5DuCp9TKMALd46yGpqgtklE0xb5LstO+CEtD2U7N+zQR1WhSiK3uUYpP4H2vOlFZWhdCgJQ7RSyD1YgIx2NhqwO9JeiZebP4YwY7cC0J9XFF/bTz67/9W5xc4yU33EHz1199H1GeD/aZ95dv0Mcjpsi+G/WBcu3gGsUE/tGPf58PMaGqpp8Tr1898eXDb/I7P/jb/Mm3f8q76485nAZYAqkqTp8daK8My/s7moxOms6EJM3TqSFZ4d0D313f4U+G//gf/B7/+m//e7wqA/ao8KPFO83z+4Vtu0PQJC+cquwTL3tgFs2bN5/xn/zh7/G9p28JdeEPfvRfol4U//1/7bfRaqMqRWkB0zysiVI31IOnjZauLSoZTMo7WmZuhBQ5H85st40tC3WKJF0gKe7XDTcopnQlKI9vkaTMp9dwZTSdyzUyBEuZFLoZnG24YXd7nP2JJQWGurFuK2Ob6EYYBsPXL8+QNadh5Pl2pSlH1ZH7NjHOE6SK7jduCVI4YrVA2cj2QNMb+r5RkiLajLTKWjRj6CgH/vEVb5cXniSTK7SkOEywlMKbwxNv40K+f5p0r4lcC9dw5OVyIW6CySvDpHZBx8uGnR2xzsTvLuTpCP2f8VHuEyju7wJf997/nb9Kr1xvnZwjwzCgUqcrzTDuHbWcN4zeQ2mtNrQeySXuKjnVdzKlthT2oGWOEGJimA1K9ikfbe+lGZepXaNbI/eOVhpEU5tF2h5OTE1jjd4jCrVQBkvOieW2cAoPuz3kUdPUfjlttWZZC5aAdY5x1PvI3gpOZe7XjlUj2miM3VlNJTdqbCilUVZwGlLtlKjIW8ZOCtU0IgXjhFQqBYWqez0HrfFekUsnpIwUQY+OZiuFTO2e2AK6FDww284tZuaHgSVkrHjWHIjJ0r/tlNq4fLztnrU5IQleTODV48A0jhzTmcHBco0ELVSrML2RXyrfxm/56dufoFrh3iq/8/nv8oX/daoq3MvCD3/y+5zfHLiXwLLurCWfPW//4sd88dX3sXakqCv32wcen87UmHn5sHAaRx4eR57v75mN8PL+PZN8jpILrc5I7bih83JNDGj0ItyHA940vAYZMmZ8oBr40/d/n5wqj3bi9W/9Czy+/pxUDdYWStUY3Ug1kpsm3i98/PA1hyq8ru+oo6F5tbfuQ+ZjrZhUke5RWNYouFxY2oZKRxY9UNqddVtIqwJT+KXDCSkg1wwPI9ISRWem4shtoUtgTQE7eSY70duNPChK0NRUsWmkSSbHTiKibecwOmZdmVSmeEXZwCTDcAK7zlijedc9Tx02lbHqQm2eojRTa4yHzuUeuCdBlUI1Dec0W+x8jJnJWW633anobGMc3hCukacHjS43hteP9LKxhcqraabVyIc8MM4T8RaYveX6cqe2+s92YQL+t8AfAqdPX/+f+avyyolg7cC2VaQXROs9/iAVLeN+mQ3EVMBHlHSg0ZvB2gHVCtp2jJ3wkujV4HTb7bRKdlCaVBCN9EyVhlYWOohkvDf0JjtOwjlQFqkJMxh6EWiOs5lpsbJtG74fqHSM1YyDwpgTOc9gyj5mj9D1QowTtSSUFmoBpx2lR4TdepurwkrH+oaqQmsdxoQy436xny0pJEYPSqudT64qIRpiL+RucGbCWAgpodVu9t0ImLyzx/PBE0NAR2G93fY8lo7kssLdkb5NHGfPmy8+R58eUT3iFbAK38Z3+EnTdKL0EecVOVacFtJk2WzhT//gH7DEG6INvzL/Kr9y+g2Ug//33/89PsaFLldOyuK0wdr9e9vMwiwzb79+x5vH1/za5088368s75cdrjeOPN/ulDVyODzy8ePCN+c/wR5mij8gKnFAk2Lk8dWMabuau9rEaB2SIi1X1lL44uEH/PD5znG0hNL561/9OsppRmu5tE7Y3vP1tz/m7fUnVF2wUviNavkbR8+sGkLDV0+fB3RKWBSHB0POiiV2jl4oWA7r3meM6k5bIrdQ8UZwBt7+NELNfMzwSzSUhm46+Z6RWmijY+gDW4d1iFh7oFw2vGrIMFLVM14UoWxIEh5cIt47W228Gvfp1xob0QzUj4UeE+RC3zbe6Q1pA4cyUXvjahpeF8KtIpNDa83hDkUrelox5oHUKm9OXzCJ4Xr/Kcuy8t09Qbmzvuz3TGe9UkNmy4XLh8ygGtiJ9XYl+05MmlsutJr//1hi/r/PP62+6ZeA/yXwfwT+d7I7Wf4KvXL7m2j00HAYLYQi7MhJsE5RQkdcIfe+j/DVQE0FmiBKGIwip0bqBuOFkg071mQPSGoraLsfFXsYgILT+yVzrhWjLN7tuuNado6TOLefkQXerR8I653YFPYWKHXDmkKZFG5sKAVGHKUCrUPRqL7ivLClhnew5pfdkqI8tSfGweyUAuWRfqUbj2uOeCs0rbHS6aoR0n6R2UpDi8a7jBaNbYpQKyEGxAmiDK17HnRHJLA4h0r74hxNpZkJ64W8RqQ5+otBrDBbz8kN2DFRIrw6fM6r04EXvdHLwtAG4jUQkgKnSM3Rwgs/fP+f87z8mKpmVLnz5WdfogfDD7/7Y97WD+RUcIMipYy2nVx3MsPp/IC2GbWt9JxJW2c8jJzzA3ed6B9uyPlMXQKXlyumaT7+5B+DZKL6mxzuB0oz+KGQXzTWVU6zZ8kJo6FowXrNF6Viv3rk27eBb293TGrIrxkMhZePP+Qffv2HXMsNUeCV4mmG31Uzn2eDagmnHbE1/FmTckNpje2VD6thzI1u4XovaKn0utMu9g65Z+uZY+4waaQqSoHmK6EV9FoxvpD8Abd29D2T+g2THumzJm1XrJ25Vo2Peb+3q4qH0eBmw1dfjnQGPrzP+xRW3TicNLovOH+kG8vHpZNGy9lB7cLtNtP7jdItsxhKv1I+NJCMTJpqZH/9xo+oovnmRz/kzWnm/fM7mh/w6hlvHIuBEDNz2JDYGKcZlzsynlGlEYcVXR3+oXHAovQ/2zum/wvwfwCOn75+xV+hV84fPb1XlG6sm8LZCjXS1L5b6qWjzICh0JVHW0VOhaY6pWksGlLFiKE7AzRiE0Qy0jtxByFhxaNQDN6hxRNSptVd6S06IMri1G52ED3Q24UYYX25stxXqt6nZLGttLoD4tYc8Ou2Z6jGmYanlso8DqRQKGXD6D1yoIumtI6YlXkYCKkyDJkQO11m6tbZakKLoEsn1MY0GfqnCZpqmm1LtJOlpYLpmTBqetMMVdHVXk5lGilrxjahDQpZKt3NeNsJbR8WWOm05Ya1A3hHGApuSeRY6K8iH64XPqg9gS5ERm3oumHMmXp5y5999yP+6C//PhoPqnMYT7x5+pK321v+7o/+ABXu2KqxfaQoh9L74u2acNmupFjpLWCHEx9F8PeVVCIBjXrwhA83xBsejgPLx4X7NHH78BP+4kdv+Zd+61/hN3/tVzHmSGuVrTVSKNxo9FaxHkqC1j0/+e5P8Xbkbz5pft1N/Hr/mtff/TFR4A2NMJ/pBh6q4rU1PNjKWjVRGtkUqiguLxfIA3ncEbgnvddKKo1jc4grJKtozeAlsc6e+VJZuqLdGq5muu08OkGpdY+9VAM1ELxlmh7p64LrF+4fGmuNfHacaCZQ7wWlNOfJoJRHZaGuifchM1sPUrhVx9Ru5BWMHOjBUgjYGLmWCSOJ0VR62ZsSRQyaAe+gzWr35a0rsdSd7aVmsrnxzbJgTgODaqg2sjqNVEENETtWUjG4ZCAkLO+4thnTHd6dSOkDS9izgT/L809jSfl3gO96739PRP6nP9Pf8v/j+a975U5fHbuxHbpH00gBBmcprSG2EeI+JtdqL/q2yg5Mo9KkUDqI0phht8qWsI/PS7b7QkBkHBtdVWqzaFMJSbBud63XEmj6hHGeBLje9/PzNbFcN5btitIeqYph8KASQSq1K0wv5LvHPXi2a0Yc2GECVnoXNJau0h6oHECVSlFA6yjjgU7TGUlCV5XRGJSupC1h6YRY6NJR3e0JdxtoQVC1U3rHsUPfW62ItchQKS3QtWBUIXaNaMVkG2lNDAbufmSOF16+rQw2oVSnFkO1jYLl3eU9Kijs60beMklVqm6sJWHKvth/fP+eIRsens58+NGd/jhxsEfefvyavlSGcaJ5IVwrZ6tpWaF7wzn45jmwroHPTo7n92/pbiLHhVpgnAypOa4R7D1RVMIMjuVtwT4larrzB3/0d7ikv8nf+t5vMR0fuKWM7oWQLiyieFUdx/ZC/OlbflfDZ7/yKxy9x9bOZXtHV5oTnT52jgNUN6NaATqbFKIqNG04TIYWMpM7spkdS2PVAc+d0Bu0SsqZdC9oazAuYm3FtYyb/c6JHxzejzhlca6zXG47vM94DJHEjf4SyVEIojCHgWOuZFuZ2sDqP+6I5FqoRbGwEG4e2OtMHYU6afAP1LSitmd6Hik28dX0OW91w9ZEihkxhhADgzWMk2XZGtIrDGfkuuDHgQfjuRWLtpaqBKsrQ/X70TQ3mBzSHHU7kFxh21b8cUD6ARMS3cGaF2ToKDE/Y7zyn27H9K8B/66I/NvAwH7H9B/xV+iV21PNQiZgB42qjtIV2gS01oS6H8XojYwjdzBmL+eOVtNLJfNpiqb6J3yqxjtADejeyOzdtdGM+3h0bFDtLr60E+IUrXVGlYklQwrkNbPcI001dOuM4w6yd3rCqkapjfs2MFohp4bWekevyi4DwDQUhZxGFLuG6V7KnolRmlrvSHXEDgfT6FXhjGOpK85WinXYFGhGEVtDZ2jTSM4GN2r6spGrZZBIVWbPO8VOGRRdK4o0rFIwe1IpTB5WZRlukfBnCnGCEgNu2u+6XGUcH5BaMMdMyjCcG/p5QDtFxqPaHTXMTGeDyZ7H05f8+r/4OfL0muHkCd++xfuMnTVSO/Opsy53zOxhVHz85gqpMI2O2/NG05qT0zQLS8iYpgm5oEzZJ6ayR0iMG9hC5DwNQOXD+z/lP778mN/48lcJy4baVr7/4PkXq+YLo4n3wvEkOBlZamHQgU0GkE7VGqs7x+qRbunhTl8ttTf80VFVpmXNNQQkdbZ2R1ZYJ9DcmNnftL5lrtpxmjwxLgxqwORCUYo0H9Cm0mImlAy+0LKmFU83ChUat2Swk+OlrMzDAXd6TboF2iGRUuG+ZKzpuCjIPOLsSG6OQ28U1VnWTPYb832g9bKTMJzHs1FXzUd/Y2udLSXOaO6+QtrL5uI6rmv6tXKL7zi7B971ji6ZyorWeS+NZ8t3pTB7x/w4osqGFTi4wojjxVhO8wGvhLvPqBJoOPp2ovWKUv+Mkt+99/8Q+A8BPu2Y/ve99/+1iPzf+KvyytFpvdLEoarGqILoSimGXs0OfesGeqHSkbbHCxCo1WKUwkolxYYfFYqKskJvGm0ivTq6NLRizwSVAtLoNPxgaXRCAUVijRuiOvd74Xb7ADim+RGt2dlJzYKN9AJKV05uoJQdSWpbpSpHjhllQewuudTiUFWoJtK14o7HNY2TwroknB2QEapuvFwWFAYzdLAQkqXkimqNnkFJR0miicfqTJBOUAa75B3zMgLT3jFcc0UpSyuduVfsZmjfCMvLgtaKh9MDMlVsgbdvv0U//DLnhz2LVGMgX3eo/LUn7NpwSkjWM5bG48NrWkr8+vf/Zd6YL1nalf/8j/8z/vzrP2E6HuhKMfhGNIFuBhyK1GQ3jAyWVhLVDjjdsQ8j5bYx9UbbGg/Tge5gSw01HbF54fD0GaFt/Io786HfeJNnjOn8C/dnvvfKcgwDw6OnB4WkSi6wRFj0jWw1g4Mh3VHVcM0LSTtygfFkUS2j1cgtRV7uH9FimJKFUYPfx+o5wpwqw2TAVow+kWJh6opUCs1VUo8Uvf95TVnQFqcMXV0ZnKHaIzlc8d4gg2fOla5GBjEUEdZ3bzHuiOBot8hnh4HNOTZpNFbMfU/6v2gY/EjTEWkgsZGMRpmG0YnJCj0u9Jhxr16hjCZcG4MtuOmBa3gmNYcMcKuRvq7c3a5BC2ycp4lmj9SUUaZzHgy31MjLyknZnV7RFamvjEYwapcxDKqTxPJxScy6MjhHq//86QL/Pn9FXjkQSq9YswcJ47ZvOzVQdcLpPTyZi0YZQZpQmsOyj9q7EhTCMO7n/KISBkdvu5/LqB1fompGbKNUj3WZ1h1b7hhZ6K0hGHI1hHUh3SPz4QFj6w6fo+0pdElIs3QJCA7rNbpNuEHQAywvjdYNXW8oc6CVRk6CGww5BJwolHT2PVzZy7Uu0ZZASRN1qJgYed4GdCiM04ApgZ46ZQLpnblo1nWnYDoTqU1ofkJOUJrZAWMqcQodr/cF38mA/aj4MkdGd+bP74FtjIT7bj7+ix//PsfDFxwtLDWxrBtmtCxrQTVHtI5B71aaTQrjeOL7Xx1x3fLnX/9dfnj5M759+1NSgIm6Z6SmmUtdefQTWSJxKYzW47zw+PTIrXZOobLmxmt34PELh88VdxA8nvng2EJj8mcmmSAJn4eEfnxgFofgGZ2mtUCl066FXPed72QVJk80FXeqqLKIWpHjwLBMBL+ht87z8sIBjW2FoQutjgyTo5iISoosKxWBwaMjXJeKsQrHlaY6qiS0MXg9UXPH9oYyBp0c2laMd6xmYu3AmlDOkPLGFleOPJD1Ru0N3RTGWI7D7qQrh0b0HbUtTMagYgWvqEXQwFobQ3OoSXjz4Ondc4uJ2jPh7sB1SmgcKtThwBLeI7ERyntU6RhTkT7uHDDncG5gnBT3uyL2Qrt8pDNTdaWHCT3sF+Z1mkkfKzrBpBylZH78k+84PhoeJ4PRA98bjiAdU9M/Hx5T7/3vAH/n0+//yrxyIjtet+WGshUlDVEKawduW8RpTa875VGaofaO9bv1BLWHLlNuTJ/Kq6oPGC3k5qjbgp4EpwDnUUqTYkZ1i/RMaBrvHYRCt6BFQQE3GKzTaDXQa6J3i84BqzTJQll3U7AyDa0tWkdEHEEqTgIxJZxRDFpAC/dlRUiIBa88qmR62cDvaqdYhXtPzL2jdMWw9+FSyLRmGPpeI1FNUXtBbKf0GeHO7A21JN4czvyyfMYX7zr2tEcQHlJlrBZzb7xyA+EE97zysv0Zb+8OK4kwKO55I7aPbBfBVEdOYCRR8sDJN/xUiUFTqRyLpQXH1Qv1msntymf2keJXPpS3hFTBVUxdsUrR7J0SPKVl7KExioa6MZv9InaqG4M/8G88nTh5y8BMrYXJzdxfviEWhVeeaizjg2VtDSMjTd2JxaHVgCGi7MRhNtzXG1tTjKeNvMHMRrgYwtZRece2pFZodsLXxtg1H2LG6jtaW1LOUAV/ssAD5IKaGk73nYd+GBjtjFleiMbhvaNugVwixSjUJlx84xgq2I34Igwj2LCSh5GXMPDKaMwoiIKGQa2J5mCjE+6B3hRFBVw3yDiSa0d0YlKfE9KCrpbkC2VdkDQzucq13jlaRdSWGjXJGGxLtBhxUfCjxemZF7tRmsfUjDEDxlQoG+nuoVqaM8wPj2zhBT2MtOIZ0oI6ztzXimkFezjx1ZPn3ccVtX6zI1W2TkpXzNPnuLJyT4r/rrPSf9Pz85H8RrDa0iiornaNsxJSquhuMJ9S0eNgiLmilQAKrwug8X7H8ZbSsF6QZsixgoFxFLpUQgTXDa0HWikUtRstfFPcryAIqRdiSCgDFkstHUxDG8dEoWqPUkIKHxntI83uQLumO7I5tKoMY8HYig6OzEbpiWo1B2to5UB1jUUbXK8YJpwZ2J4jVSpiDXVbuIkgGCTnfcTudxRHo+L0DrQXOjYl7DyR6aikePyh4t+8rTyND6zvF9Jdo58M27agphkplc+cRzUHxpEUKONgeUFthf/iz3+Pf1iFqVrseObh8Su6eeAHD9//FIPYkKy4u4wZhEdlKR7e/vgnXLdEcoFRJkrrPN8XZjdQQmXrDl0XRMC6Ezat3JNgEjSdyXLibz9+xpvDHpcgZZoZCK3xcP41lAjP60921rW33J8vTKcTl9r3EG0GxUYKio1O6QsqDZjTRMiNrj1bWahWM82W9eVGseAnz9Y2atkNvaqPHI0HFdlaAAacq7ieydmSSqJnR3q+ssrGfLRIt1hGXrihCUgbKblickEcKPXArF6QNFImwejGq9SJTvBjo8WKWRVtqxjvsVzgcCSJhi2TSsf1BSMWcmatiVYTlAgFzgeDHSNNLEeZUH3g0RRububQM8PRcVsX9DDgp5mwXFAxw+BpFUJe6eqAxCumNnq5MfOINIW0jm4FtV3IpnJuB0IPmMOZWRvyPdOdJnVDe84UcRif+dw56piIH9Re9v4Znp+LhUmEfedTZ8R84mmLUOtOFDBGQDVq1Ti7N/ZrAXG7b00bswsnEWICpTNGJ6rsNo8cb5Q0odRA7n4/IlbFFga8gS4drzpb7PSUEWUpajdidC3EVtmjWwUSmDTQVWE+DKySKdpRWoLu8U2j+4F7W6hrQlRlkA09nVDFMIjCe01bEzo7HqrDkHmxkZe6oO2EVxdiVTRlQAq9GwYlLMbRXMOKRVfN1e3ZGvpGHBR/8c2dDyZgaudkHqgPnVwz7uipy51BnlikkVQmqwqlkddK3YRXw8i376+47knHQr9svMsvpPcvfPOnr3n66q/xS/Mb3HTEJ0uZJkwRvI4c5q/46Yf/ki8eT5jJ8Edvf4gthbvq1AiuBfpkOBuLlEQ1nnLL6LHRWudRC7/TFTOChIXsJrpSHESI/YbrA3UouM1wLRucKkZXtpvw+pXlfl8QZ+lWo7MCXZiU5faSUUlxb5m0doZJqCTEaayeadcb1jlwgYYn3VferQprC60L7bowPA4szTKHzDBoaq5kbSmms26NKBFlYB4PJBkZfEcpwaeB5/EDa7iQrce6gulgVCN0OC1CVQZJmuoK1c70LeHUjL45NpOwLaEGQ76D6IVxFOqyYI+K1/PEujVaUhjfyaWx+Ymp37leDWXI5LSR+omowciN6z0TQ6H2juqWGx1bC40b2hqat/TmiOME+c5SMm/mielh4MNaeXe58HiYedAzf375CWV2LMZx1IZ1VLSlktWRdx+uDKMFFXd63c/w/FwsTL0LRhe00oTUMbZjB6F1B2RSE6hgjCcnzXhoaKd3/bKCbY0opam14IyhdIv2M9ItYQu0ZrHO0lTD9IbCo0xlLTBIZ/JmB8bJp0JkSdAUojKa3dBb80pohp1a77GzY62VIpHJaF6UQ/1lxD97Hl47TnNB+UdQnfKcOd1OuGLYlMPXyto7rhXS9QOMmoGJL793JuobOe9OsNQ1eLOPmvtA7wElO7pkUaCqot73s758W9huiYs1fCGam9yoWkNOzG5ks55FBaQqWlwZ5Izr31Fzw3lPMI3HB4c3I1o1bqpTXnbd0TW85f6jZ36iRn7wxW/y6vTXeOiarWXGSfH56x/wR1//ER9ioKnCaCc2M9JLpbRMKhY3GMQodK/EFDDeEYpBJPE0OKx3FCxXm3iwhrlWUut8/O5GzS+8/rzjx4FuK5Zf4nq/czo4ag6YWdNSwpUNqoByxGmkLC/YwTIeOrpoRp0Ish91nOmkLGinMBXSPZF6ovVOd0IOneMwMPkjxxJIorEOiodeK20BcYLpCtPuxKxY74m8GM6z5Z6fMc2yAb01ei9QNSl7VkCZymAK16LQVuNuia1knq8dMxcOtrBEz6Qc3WUoipAMze3vmZ++7P9WGrhyZjpW1vXKN8+FX388U+fE5X2gFDjakT51tmdhPMzkPKAkcbZCsAO1FNzscTRu9yuzCdiD43x7Qpph1Qnnd8RzCI3v/DeM1vJuS+QWOVjw7oFh6JwHTciBdq8oB+oXeWFCOin2PehI3UuseAbfqE2RSwJ2f5gVoGs0iYSgeiJXjdOaYgdGx6c3hBC3RM4BO2pGL3QJpKT2ZHgRht6IW2fwnbQ2jIKuNMX+kz6UpaOwvhCLodWEHT3O7dWWxo5zLTTG1Ck/uRF159sfvfDqsydEeWxPjP4Mz8/c5ci6fsM3LxsVzdODo0wD7SUwSGZIjmmybJ+/prkXVBRKtQxtRv3ZyvTZxPDYOIQBv2TuLxl924i3QCLwwMQ8amZ35LJ+wBzOWGV3qFfbZQZ5yLgGf7sP/KfqyLf2Tk9CbprWOq11btJoXXE8CS2MdCV4pblX4UfPf8K36Scc18/43uOvMuTPGefA4eToJlOLhd6ZTpZDW7jKhDWN3huEjGD4zDqaM7wyE6cU+c3zibFbdAStjhAMTnZmuTp7tqBROZOSwmKoKeDUzirXg2WIDWOO3PPC+XTGThbZOulg2Kpwv2To+656uVtKXjk8QNIFv2xckoeuqMrzcDpglCCHSlo7W3umSUKUZ7lWuu4UtV83jHi6jlhl0VjU08gWM96DM4qWHamvGGvYPk1tB60xgyGmj2ybsN4ST8CCwouljne6Gjgqi/adW8+MayPYStuEPVOZcPOMFShUsqzo7BiJJNv5Ji7oGlChkVTChMj12hlPR/IaOEbFIitmqFg8GoPNlZAiVmsu1w1CIWXPGK4wjOAHnEyo2RHyAqHjiuZ0PkC782Y6MRjLy3bh1TBx14IaB362QsrPy8IEoDwNg7WdWqGEjDE7Nc+5id4yWjq5Kra84rzBW0MoCjcI4+joWyV3QWnFljIlrygUDkNOu/vdm0hujaYcSsAoTb5nKpm4xV1aqTQ4D61T44r51OQ/vT5SGNguV8xBSFk4iEdHYXzreJ5fUbZnvB253QuzzbuZ937jngupJmLZO1DOnUB2caXOEGsgx862Zs4yclSvYYmsvSEfPvB+rXyWGo/fWQYVGFrjmDYkD9Rp3ic4odKHA8rApsHEC04P5PtG7w4zKyRntLH88ih8pj4D/augrmiEZa2E2XPNiWFIXHLjEBRv1JGPbeNDCWQDvVquLnNa/4Qlv6fxzPdE0cVxvSY+9MIXXfE///L7HB6Ep3HmVu54NYNKvOmwVMvBGcbWGOcjtxD3e8SuyCbS07R3/uyB0awc7EhuCv8ph1YReonkWGjWITZhzEQI8PJyQ0wiLCOnY0WKQXpHN2FmoHaht8LgCuHWMT1RFo2bK9MA776r3OMzg/Z8/uYLTuPCy8fA8XEkpYUeNZIL2WVsjxRG4i3TTEHrkdQ1pWfuMaCs5zB52rpwmEZuccGHI4YzmsQXZ0vtGqPuiIOyGSyR52whVSanMIdG6mBV2dVh0qm3Tu0L0cs+ORNHyo4Hl3j15ffQTvPup18TS0Y48vp1Y8sVf7KoaujXjTYdkZyptRHTLh8yW2N6mKFBNyuGkYEDyQwIBZsUT+rI9lAIpZAl0pXlw8dv2WKn1UyaXkGveNTPepL7+VmYBiuk2GnaoKQjBtDC6CutNUrcSZMpBZSuiBrIVXY9N428FZw41rXsNoyUQPQue2yFDFjl6M6i+t5t0sZSl0I2jboJojWtZlQ3dBLWgWhLWDu9FyZtCPeVnisuWRzC3AwYgduKC4J2lt5Ax5XLZWOJkeIa662SwkYzK9du+fxz4ePW6U1htkipCtSd0jPxPmPVjCURSqRpIfbOxw+Rf+v7n/Prp5m5Cz95/1PG6YDr8FIWxAjuvvGuf6AlyFbhDpYdoV9xOZKLRdTIKAtoy+nQUfkNulXuQyDbyrl3zsbS+8gwD/S68cvDgUs7YkTz05cLT/OM5ES6f0BOA4cvf4BRnf+HfMO764VfqZa/5V8TRThYRdIz1SVSU3BfMUUzTZ2X2MllZTO7OfnahZE97hCUYi6V1DxrSzRGtIVUGoMWim6gOzXdua6V7j2zFFxvzONMbyOqd/y8EpdMzwplF+50XMwoJ0h3HI4T7+NCzgvf3BuhFsbRMB+FLb/nnjRGLCUIkh1VO8Z2J1Xo1VCAQTYMFnKlloSoEajYlimXjp0UH0LkgKdPcFAZNXgu1w1JgUHNbG0l9QZ5wk+dwVs0neo9X2jFy8eNnkbUqBG10fTAa2W4S6LYjGOkVU1MQt8EYw3P1zvHVwr1SUbZu8WNlqg9bak8aIcTyHHgePzIMlrwlp6umKy45k7KHZNW7peFzXYOGux4IsXMuCsZOesJmTNd+R2REjO97Nyxn+X5uViY9ghWp6uK0QZB01omLAstKGQo5NyxRmGd3bUzVVFCwbpGiIre6q4Er5BSxGjBGdmPXUrTq0EhaJ2x0oh6oGYBs2M+OhlnKrXYvc7SNSKdHgLOO1IJvF+gVaHrQquO0XuWl0Z/NqhesCicd4QPC9Iy8zSw3gpbjNxaxz890d8nzg+f4WdLui5cPtywxvNoNC9LIObEMTf8qZKE3e47DMzjwst1odQ7X9SJ92njaB0zmnbUPN4CZlDUMkKH+UnvxedusAGqbSQ8YjNhfd7vfEKg2plrujOVxEphsopTtujiqSwMzrD1TiqKKTRuunD2Gh8UQTQP/khpO3mx6sij8dS4cD99wbJdMfO0d8bEY64N+yj0xwN9CYg/4HrgmtK+yGyeoTWaiaiuOamKuWpkymzaIjWQUyKFhIgi1sDjeCJ0x+noqGWhdYs1ChUNvWUWBy4bHr3h3f3OYDxJCqkIkxYWGzCbxc2eZRlJHwMyaPyjQTeLDxr1ysNdyFJRxjLYSsViiiAz1GiIttDNiOqKeTgiZWO+F6oY1uuKL47aIut54tGdWD8801PB55FgV4ooanLMvpNjxOX97ul2rUxa0IcTt/qMVZ6jaLY8omPgRW+UFLh/FJ4eLYXG9bu/IMiIWIWxmpMZqB3ChzuPR4V2FaVPMFfC842mK84slE2jnp54+fCBx9fHPbcWGufpxG27Mw8WlRvq3BnngTk0mpuoPZCbYyyNriv3DYruiLG/2FM5ELoe0DoTYoTcqK2gpdOVJ8eI9mdEVZooYtr/s7HubrfBCaIHKhZbA0LDGI1SjVxBtf2yEWOIacD73X4rtSHNImSUypSsqFohZHS2VISiDVJXwl0jakUpxxBnRvHktxlZJxQGLXvJ9X6NaKDpB2LYuMWCe5hx777D9Mr05jWjdww5U6pi0w4xjSCdWhT6MFCqpihP3BIt3vBlxb3WEBpBOZQcWLeVkzesIXJORxSWuA00iajTSClpd3o973kqF0eSayh9QrsLD/3Ey8OCx3IjI9YzGk1UiY5BHx01BBYv3EWYbSX1TN0UURJUy6AT9/NEfU6Y1pExMM4Z0wrJ3BnnB6LJ6GwwJZB8JOsjphXwldw0yiukOwZTsGZA14Jrma2o3cJxsHx3aTweDSmuRH8iTZrHwXB+iYgx2NKQKvRiUcbSrNCaJqmKR1PDQuweYxUvpaCLAjtxyfuuOSZDJ9PGzmxHHJ5wKUxzRA8TqmeyEXJTmPrJ2KMctS6k5nG50lQgZMuwKWJ6wftHmlIop3FfPXLSwJZQttPKhWZ2K3Sule2y8eaoCbVx7RqpmVwmbrWhQ2FTjVt8yzAIOax8uz5jqsU7YRPPKc+IV7hB0cVQ87ovZpPju58GUj0T7hvaeb4r4C6ZIRWq3T/UWigk5Rntfn81YRiT5r5ElC7QL9RWad5weHrk9s2PWe/PnEbFaCvPdaToG04G2npn1CvKGdZeUL/IzG8EWo70LoRtwzPSSsd4i9IWVw/0LkhV9J5pLVByYxoM3jiu15XDsVN7gy4oUTsHpjW0GVDGYM2eA4LCtnqUVJQSchZKFkIx6B7AFNYwcjQFbRxjGVioSCnIaOnBsP7BHf1G8cXjK/KYSbeCckL1Fp80yzCAARMa8VjoNfL0vddYPSAO6ocr/emR8eCxsTPOnRQ2zk8HjJtZbu9pt4C14OYDvRveLxeOKN7dAmGMTN6TpGJOB+6l7Sxt7oSceMXMlhU+BNZZsVA5usgmnUcgxisvFm5LJaiKnfZdZYkJk4RbCahiMc1SYsOVzvn8iqAL9/yR0XpuJWJE0XsgjRVKRZQiLkLd4P11Q37FMJZGU4pqEs463Br3n0vWMFWMaOZSyDkSe6Bnw83tgduiBw7K8ngWbHNUW7Cp4WSflhaEoeyL5TxprvrTa6CMVFk4Wbcf/0fPRsZqTy+ZaVvQDOjWucWN5iaMrvyyfSSOFRYhq8S2KUrMvDlZ+qLw7Phlozz3bxvHR89zbejRYtYzg+uoJ0XZztRFcX4wXK4JMwjf3ROqO9K6EczKMY0MQ0GbRp9G9DjRy512jQw28vyyMuhKlZGtrczO0XNneDxwkJG6JUpTvBJHtIWxFy4fO97vID89wlwCYW28/e4bXr850+6fuN5mYEi78RqZqa6zecXl7YLqmegdqiZqd1TbeVk6Shu2mjhJ5+qFpCLWfcXgOn5JGOUJvZB64zAMjO7IervS2y/wVK63xrrcaNbijWcaOqVYqvZghK4bkgLdOqRXlGiUlk+UgIJW4845ymU/749CLQNKG3LrqAaZhrQR3QKNlVIUxg8oJaje0LKgusYrjcidJCOz6kQrjM3TB6iDQqGIBvTxgVSfWeuIMgmVFPNpYHGCTSttrUTRjI+PHIYDDUiSKc83qms83+40K2yXDbrCmZGybrRUqGsgzInP+pnxlWG9FU7mALcK24Wtfk5WjZMdWPPGbA0hNsajQsrIZXnH5A5Up7DVEVkxamJKG+Iy4h95OLxii98xTIq2btxsIhfF6VjxxdDTBcWE0ZmYDTG/EDfh6GZCembYGuVQuF92EkQKgfGkGGXDHCxz8Gwh064VPwO54Y4GtkxXiXWc0KqTW2HyCr1o+mnCKYvkZ6I4elv26lBJlNzpztGbpupMTp0+DNxTQTuHs5nj9MjLEikhktGouuedcjOkYGDymHylxpGFO2rbP2xG30kCL5c7scJJLNL3QuwsnkmdaKeN8hzpjZ0iOllqhrktNCUEMzLYHaVT0kq0kdtdqL3QbsLysnHylXbUHE9nhrVQU2GLiZ4r39xuLKEhUyWjyMFgHg673mt0HN1AZcGeD6glcRFLJ6KVxmfHPb0wTgOHUYEaeHw4U57vmAHEQTMwW88a7qSyEYYnNqkYbuh2py0HrHlC7J2aCkP1yNkQY2Vwiu9a4TTNbClweHpNqY1rfseHdw3azKgSL1vhiy9P9Fr5+v1HDtOAcz/bEvNzsTDtAssZxQbWkGvDuZmcGoWG6oIyQMl7fqlodK9oEZpReK8oOaBNoscBBEQXpknRlCWve57G+EbJHS2FYx+J6wtqcLTcseuR5hNVKlqNtMGQy25jnbtlMgOtasa75vE3nghdKN2QbhuUQvPjPvHaCkV1nHP0Z42lkZWhceVUJ97fIu7g+PjhhhiFqEi+AUfL8OZAfHdHDo3T+cB37z9y+PqJ6WFkUhtrKwTdWMo7jJy5xIV6zfQ3E2ttSHHQMj4PiDWEAk9PirJ5cm/cXQKZadudLJaiC8Z4mnNUrSn5jglHUl45o2klItpjTObysdOr0JpBi0W8wRso+Yp1I3PsaDnyOCsmf0VrQZeBw5sMpROMUKjcZ40KA752SiwYnbnZEYMjXzZ4FDY90pTFdEMRT1FQR8cpZ6oPsESSERRQ0rrr2LMmXu7YrnEB7q5jlKHdCg+Hie+Gd0gp2F6503jSbzCHyKIDKig2VrQf8HfFRsE6YbSRwzzy/uM7MJrcPB39iRoRiCXizRGxmSlWtMpo2bjSOYwzJSZiTETZyIPiDjyUge3DM5et453h+DDgqsK4E3NYSfcbpWm++r7l4jwtZHTfYNCkINzefwvrjJ+FdIdv+ltenV9BgrUG0kUxUbD/RAEvmkZmjWBaodMZnaOLIdeK5cToZ7Yt0Aeha83cd1tz2xrYI18//xTfLHLs3KrmUTqzODZlEOm8XD7SDgN2gu9eNr56/cTRR0KrlFL+29/6/w3Pz8XCJCJ4ZyhpRClPJ5BLpaeEmYfdJls1tSdIAWmOEBvD4LDSoWRazXQctWfuF4MbPCFXlBOSVmiJqDbQqqHLhFINGR0lQ6aAzagqiO1kqzCh0avmaArjougcGKLHCzyHBSmZpjpzU/u0yA1weY+fjhjXCKHj5kbXjRov+MlTRke5eKY2krZnnr460s+OWSku94AOmsc3B26r4+UWGAaPqop127BJUAcY5okHfWRpjXE+UPp1n444R5lmxpZYeuToBdM1aXtG5/2+YQ6WKWqy0cS24HXDlIHQKsYmlPW8iObBHZDawXVSdPSp7AonE7mKMFH3djuVrEceRLg0A3mFBnlL9KFzbRuCRlJhE4NXjoNvRJUwSVGOCmMPcAmMQ2WthjEKm3QyF5RqqBaZk0PrPSXessV4TzKKXjPnV0+oe6N7gaQpRPzjiImJVBPBQe53Ssvcrzes9QS1olqhGI0Sy60WvFXEFlDd0BBKhyqeSy989upE7itKW0pvWNfoHwLdzNi5sxbZA8GDoGOkWM8lX+hbJqXK62HkbCxl7rvz7u6YZON4OhC7EK539JiJrZNiQ1RiYWDuCmUWnqPltghNNLU33NBoKWAmg5eZuBbWSeOkcXITS7zzeTsgkwVfOA2Wp+nMdQqEq0dMQZc7x8FRcuCSDWOFrhOTvOLePtBihmlgSzdeP03kqCmu8UZBN57aFNo/MOQ7p9kyG8Wz7khKBInkphDbyPUX+Cgnnyi6WE3NhZgV3lTGaR8hl6joSpA+UsuG1pbzo0IQStWkdEV3QZTCSMVMUGumVIOOAakO7Sw0h2qdkDPdd7xyNJUZ3QkzLKQgbLHi587tLzL1m8TT9Ig7jyRbuX68oE1CiyZ/t5B0o00jXjK1GvToKTXR6bx+NXO7vMLaGx/fv6ctmXTfEAe3NeGtsFwy+osT5Mjrzz8ndlhy5mBGpqNm+ZBZ8sajc1zCBlHxrRM+//7nfLy9cLcLYkeeBkcrjSSZpAz+yeAirDZw0o67KK6p7nznOiI6EgHTG5E7l9IYuxBrQOtOaY1ePTIaFn3Hb51WCr45/FNHJ42uLywYFIqPKWJsQGlDyZoBy9IVxRqkC8ppTh1KzIja8y06Z9alk13DWajNkErnagI5Fs4e8k2hZ896i7S5UOjMQbBOQZ/3ekiplLzHSrKrmKWTdaLWQFeCzmlXr1fDg9NEY3nFgJTOqqCowGY0RxkYx4Ipmvlh4ONN4aviHiIvW+D+0tBqJbrEVA3KVlou5MURUiK5jimOW3KEsDA+HWDyDNPeslezI77cqDXtH4DziKTE5DtbqxD2i+RqMr036ra7C02eUPqCTpXN6v3+NJa9gOAU58lSbUEqqHHg+ZYZteN5Xamb5nx84OXte2y/kDUQGlvX6K0RbWewHUmVcu+oR1DGcJ4nLkyY2hgmkO7JvTLnkUxG90w3A1bgR+szXx0mqnRYLUY63/7lR4yPqOx3dv/P8PyMKYO/2qftSi2MrjtgrXSU7F64uO5HCJUMvSWUUjhdd/+bgLYBLZZm9I5+1QaFRgk4NWC0Q1GJqXMLhVwLys7kOHC9rtQlwxKJayX2O914VJ54+FXH03lkHD3XtPLuT7/m27/8EXkT7pfG4iyzPdByIZkNWVYOzfB0dKwfDR+v0Mud8iK48Yx2hqI0BsEPlWw73ljmRWGK4/ndlbou1DVy+fjM/ZopTTNox0vsKKuxWlG8cKuRQCZmIbqZ55K5S9vHzDFhGmiTccXhtMcpKEsj9sBHd+Wl3jmeHaImmpp4GCCRSHTO84HmMvqVsNWCKKHRGM8T86uRgiP0yoUduLY76kD1iYxBAZurRFO4x30x3TbN/VJQqRLviXvI1NPEMDrO0aNzJwVHo+6JfAXn4UQ+esQZ3CC8cjODWPzJkmvDhoo2jkMf0blSQ2TsmWZ2FVYfZ0wcKDjcOKBMQTuPtRqMIswG43YZwIO2ZN3pCa7tzk8+fKS371jDFb0W7vcr49QYesLnyCBHUm3cqzAeFK8+f8Pp8AWqC1+8ecVpPuNK5sFZdFgIwZA3eByOiPNcc+TyfN3ZXh81twQlJKwbeXp4xBhPSIEUCvdl5boqMsJgMofWeDho6sGRdUOaY02RcRjwyTK2iC6CrkJtsJQL2o+YLsx+5PXZcTIG//Sak35F7SOiO/U0klKi6MRGprxEpMN9gy1FzhpKyZQ+IlmjiFxS4HyeWZXmfak8Pew6Mz+PxGj2tP0vsiKc3lG9sa2FXCpOWWIxTLbTeqO3PekkUlBaUZumt71ZHlOjVoWVhNYDcduFmIPWpHxD8ojgGFUmW0NaO50NpQxNC0p7WoPSdkedqIbOCXcYMd/LLD+JbCmCMzSjUXG/r6pG89yuqCwoZajiuFfD9ccX3rx+zVgT718ictDo3ohRECkM1kEIPA0PyHm3AOuckKR3PpMo7COY0bOoxqwVw9ZYlOY4W1oPbHEhtZFH47jcN1598QWdxvPzW0qr3F8uVOcR1VhjYokJIzO0Az0mhnRie1+5svEwWkIRzu7IocK1XjhUz+3DQu4O0wrTkPmuwlfdcsiR1Rjq1nk9C9/EwKBHNpMYjMMfHJ97xzdbgbXiHw+7NFN38jBj+0YgkHMm0hmnB0rI6BGOaWSNG6uu3LYr3npaL1QRomSsn3Y0yEvHzJWjuvHyvrHmwjjO1CDcEnw+Ofo9YX3D1krIeidEKEUZOs9tYMiQYqZ5T1wievRISZzdkWw6KlaGU6agMdGjmqV60HdHrwWlDwyqsdxW+kFxiIq6rVylciqKrXeCFLwdwYLoyLUGSvdk1fGlcukLrhcUnTxYPl6vnOqIapXqI3bybFpDzOgpo5rQTeF67xRTyHnCrlfGk+G2BZ70A/5pIhTPlgqTmVC1cTN3Nj1QLy8c/C7HRGVsK7y9bxwHR1+vDN5wyR9x1TIeDc4J42xpSbNtHXtUuFGQWGkYXjnHlcKHy0oxlo/dcXjQrO/fMXbNZ59/hTX2Z1oS/mktKT8Ebuw+0NJ7/x+KyBPwfwV+Ffgh8L/qvT9/Mqj8R8C/DazA/6b3/vv/XX+H1pm2aPzkd/mlKsQoaDqZgIJPhVoY3SfPXM/0KIgqhCjY2rEW7lkgO9zUQRpFOko0utW9nJvvGD8gVoFttDgwTJb4deT+Rxt2hDhacLCVjdwaGsN4PNOHxFFplmvjXYlM0yuGnjBlxaojyRu+ffkOvTXEdPKt73ksA6ruJmDlH1BTZCyV0SmUsrz66hUffvKOohqTtfRueN1X6j0xnRSHXqgLmKipX3RqWbirGS2V+PKB2jutd462siXNoDrLXVDGk61iUJq+XhE3glF0NgaB2O5oGQlrJ7cMdJZUUaPjxEjLmbQa/KxIvmGU56EKNyW8KE9JG9XBiKaiqUHTm2HNVzatWFOhX+74sTAPnRob335coBuKH7jajSqBpgSThGgCRxn2PNWqcMVT2o0xj9QaqFUxWMO9R8ylkpTjq8/PvMsrQ9GcpDN1Q40r08NEyJFeG3WtRJ9YF0PaCrPVeD8xOEOxG4vuPJ4eKSFSWuP9dWGqI2vYGJ1imhuD60znM4f5iS/VxLZ8QxDPXe5UOzLNZ7I1hG0lxUTWnq46o8mUrmh5vyc9WMdn58+4EVBd7Zfp2qBi4rI+485ClwOXLswHj++FLoZ+LRRXuW27JceUC5faWJfIwXiWp5nwEjEuoO8F6wcolbg1VL1Ru+JZRn7jzSuq0uSxcaiVh0HRBTZdOOsBFRNZKm1otGgpa6R6x3oNnMLAljIPnw8sa6LExuvDTJ4ct9tC0ZGTOxJ74ruffk3+5+CV+9d77/9108l/APw/e+//JxH5Dz59/e8D/wvgB59+/S670ul3/9v+YBGh90Z3jVDzftwx+9ElZYM1sCyF0Qt0qCygCjVpiiiogvlUrFUyMLQKLpOLxTj2Aiia3je0HUl5wnRNlYITQ6ahVlg/FsrLyjVovNXES0a8QrrgvKNvC/emkL4jV4+t08IzsU+ISWgsf+34hnz9GtGVaizKW4bueDIak/8/6UBFjAAA451JREFU1P1ZrKxtep6HXc87fkMNa+3pH3tkk82WmmqR1BhKjAIpko0gsGE4QQ4zIEeJkcMAOcxRTn0YIECQAAmSIIDjE0OOBQeOJUUKRarJJkU2m93s7n/Y/x7WUFXf9M45qJ+OjCiJ3GYL5Ass7NpVhV1VC7ue732f577vS1NtYXw6kmvhefUUrdgrfdVtfa27Qjx1Is3wzotbSrT0tkeVBYswiSPXhtMa41as3V29Tlu4vq/RMcTGsTpsV1BOUdaNvnNUs2ffW0qtSOyYrcIUS3ELTmuU6omxILZgdaGXzKt1wlSh5Y51LqQw4YeeSynscr7GxWC46Rr38YyqPXETcjTMJnPwK/cd5KI5XRZG9wzXV+paONiBvbbcFY+ERBgae+3Z3kzUOOBaIu4V5D2TZHTb6LqemhREhbRKXRbSTuFNozVQi+HtH75kGhyfvfmI3o9MTfBPFKyaoXeIB0krfhCm04R1Bhsjp0vETZ7JBvqxYluhQ2PTI7W+S7xLDE8SNZyYykyxlXXZCLni3cacLIOtMBQIA4NEgu4xRWFmsKYxtYg5HK7yFSU8pBNl63C60IJhGBXvHHpezpFhhEMuiBa62BO7DCoizKQG2fdUL8hqWOIC8z167Bmto/YJrxWXsLIphd4/o1OZVoVPXr1FNyHYq00p6muCxuMqvNMbTsuJZ+P72HDG+J76ZOTNfGboHfP2iDkcePXmnuhhdA4bDTmsMGjKQ2GqjSzC/Tly7c38F1//ZY5y/wbwNz6//b/hmmz5P/38/v/t5znf/0hEbkTkvdbay/9v/1Brjfa5QrRTmkQhVUE5RQuZdY0YzFXNbTVr3nA4SgalMkZrlDZodVV0676/RlikhLUKVKGWCkohdaHUyuWx0u0Ml2boVaSkhv9iT/h4QTWFWytBDLoCQV2neCKUuSA28tx0uN1zvtw/4T3f+OBokGh4ZjzucCCsK67zzI8bZtfThUC+vU6qLI5UVw524yEpBmuwWXAD+O6IUprNJGKKSKdQViB3NK2pUlFKKJdIM1fyy2kGawomRO5fRYZxINnKGhvpnGgpMOm312lkUKQUGbhOP7u20eo1tdFoPie/OEquTDpiO0XD0Cg45ylRE7aCWEFvGXEWlQoPDsYK1MzQX5HtoSVSzkQEXx2g0UWjT47d0z1LfE20GgkOJY3LEsEq9K6nw7DlFbbGtMVroqYO3JbGYCx6s3TDB6zbhfPauL3Zg86kNCHm2h/MNLa5x5uZF1bRpLIEkJIIEshB6JziIa8Y3dO1RtoVXL2lW++wZiS3NyzFciiB3BRvp4n+cgE9kEpFRvDK4awh6Ui2DtGVkvxVD9RXllxxXvHpJWFcYDCWliu0zI19SrXtupt/atj5nq4Kft4oeuG+KWKC6ickF5I9gG7UZc9OZSoDXVuY9nv6fiQ0iOtGDBOH/YF9t2eb3iL3FX9bseJ5+/AxvdxA50g1YtjhxTGqE6Vojsdn7Hc3fPzZW3yZcN6jjbBNKzfPe+4vC2r0jNNKMcKrlBlYOahbogiFif3YMXTQflr4pj+qHcD/VUQa8L/8HL30zj9XbD4D3vn89n/Glft8/RFz7j9XmP55rlx37MhJoXuFlIZJGlojRUVtBfO5Rw4l5HxNtVxnwfhKywqsoSRAKwIbTnfonK9ftJquhNqmSJvCDAZbIqUULku8ihPbhmo9Thne+9J77EzHejlx/9krYnUYySjr2XtFKIYvpMK/9TNfZLAd/ZzYWmDfOmC8Jvy1hr4Z2OYLVWmy3BPcgNEgbaC2SIqF4HpizBh1DXGhjrz97MzgDXhDlCu251A7LmnBJ2FwhoQQfGJQHYaG7DwtTtxroV8UoY/c0whlx2iv+TiiBqQUZiJWj9yHCV0U2Va60ZMfoJrIaDXGFBZpDOIZOsuaFXlLNBGs9jirkKrYdRs8XPsMbrnaNmIL3BRwVVHfFi5WCCmwr46wJu66e7Jemef1GrI29fgQ6Xp4MQ6I1eRtxdmGjwOla6i1YvYW040QJ86pw/iMihe6zrO1ibswEeaG0hP2uUfVQiwOvcuoBzhLJjpwShia4qSOqBA4LYVGQnmH7kYqBRMmNlXZ4gPVWLoKWQK5P3AgElFUtVKNY0yJLJVl1TRTsWohl0oLETt4UnEYVnwnfHlwtLxn2xJzjlhtqGXl8RJYJPOzL27IsvHoGgyKlDt622PMBEYwSvBO0XfPKU9vYH5DDYpLLOhF0T9P4CxrKkQ78HqJGBMZrSWWBT8cUSGyYAlUyJmxGxkGy/2amO8WLirjDkIKH19tKEooMRJLoI43XE53uK5nWwqd8Uys7M1IMIZ1OtPMgNbP8Dvhch8Q+ekCL/9aa+0TEXkB/Eci8nv//IOttfZ50fqXXv85rtx7h0YGbTX5mi9JrZEWA1YZmoBRBVU8OQsxR9BQckYrgzFCrJEmGi8aSsabq4m1tUqOgc5cqbdhbigFhxvLHArFRWpWaGnU00Z8nZnqibvX95xc451hx7zAkgPzqeD2I9uu46Y15hav+O1qeWiKkF/zbDeg9Mbdm4jeeZoPdPVIlYxJGVMarVdsj5VdZ9h1FdxGCRBiwhdNFCAstAw3XrFpxdFYtFU8hkCfN0ZRZFUo+QjpzHHXscY71pzQ28CznadsG34/ImrEactFBaz2IAldBg5YPppO3JoVPVRU9mxbZVRClZU5NS5bZSyAVld5xJbYSoPQuO+EOV8YrcEnS61n+rHn5tgYSsIeFckaRtVfPXGl4zhoXNkxNGGpDWUC+VGoNkOKGF2R4ZZ6DpzSTGcUbu9wTlND4FI0XV/Q3Y4+Zx6nEzkrCJFWKs002iVy0Y1WCr4YsmnMs+HZrnHrLH6zjGKZq9CejKTkgInSuCaY1oGQIJ0Dt89uSAaWbaKGlegsYyuYQw9lRYyHPGP8SCoN1RVMdlS7wQLdLhNk5HxZ6YaGioalzbh+oGqhhITqFnb0pKix1tKFwpIrOyt84WlPSh2fTvdUvUOWjFKVsrwB77E+8HTYo3THRsbEja7fk8vKZXqg8ztQFiOBls88boGW4tXYPm+8Xa/asGI8brhhGISQhDVGQnL4FV6FwJPnt6h4Iq0b8V4h3cydOJ6pGxapHAfo1cCDVM5v73g1Q7d36J9mukBr7ZPP/3wtIv8eVwjBqz86oonIe8Drz5/+R1y5P1r/PHPuX7gEcD6jRWGMIpWAaR2lrawh0dnr28yyIcYj2YBMKORzQWZGtNCaQloDXWjYqx0gNbTRiBnIvuH6RkuRm5uBeBeY88xOC5QN/6zj9cvEB9uOk1s4qITSib4HsmFznrYmHkQx3a8oLaxJGI+F9TLTRPGDuzM31rAVcFOiKJjmC8ptvLO7pW0XSgHbK3IHNihG1/MYryrmVcxVsbsEJDvMzpJJtJa5RE2ZAncl82zsWeNCq41tm5mCxzd4cfuUKSXO5wtlK8xSeB0jow447/CmsIVAFk1SA6MY9FqZ1kbXQSeZLB0lO2z22GHmoC2xKPZ+pLiMqsJFMsUVxPV0/sg0nXBi6YIiFcNSNxZV6ShMMlOzo20QTSVrTaiG2FZMsYRR0IxYeeScKhIfuHl6w9O6p5zObFpxKgmbeoSNLQhDylxyQtHY94VZhSsefElErXFVY7oBozNRQ4kTj28zdZyhdOzc9TgVF+gppN6i8ejxQDhvqM5x0wmTV9hq8L6nKs/4uQ9z1JWtGrZlo1rH5VQYlWWwmmVu2LKj7j1rPeFMZdg1TMnghfPiGIwmBHBZsTMjbvRk47jME7e+4xAdl8eJT4BUEtt+4GYnrNURTeS27Gg5sdLTaGws6Gjobx0Pry4oq3gyXlFm1Tfmx0B6zIBHlCFvKy8+fM7j3R1xzfijw5uOt6czo2SaLrRNeIhnXnzxC0zLiUah0x31uWNdwayRdTihtOXxlFh1YkkFxCKDYj/oa0DgT7D+ZUi8I6Baa5fPb/9t4H/O/5sf97/g/5Mr9z8Wkf8D16b36f9Xf+n6IhCDYHzBqHjtX6iAKo6qMqbzrJdM1yecqYSa6UdHDoZaAmtMV/Jra4g2145IrqBg6DWgSFWwrTAvF3o3Egv0N45yHwhrI6tIMhVBUZSQTaCIQ2OpKhEJeDzZ95TWGAbPeNgxxESnAnePkXrToYJjb/aYekZZg8KSDzO6dfSl42SmayxHsdTZonsLQYitQSiMrrFbG9JeUNVMSprRQJGEVyP7p5q8erpB83d/9AdMdWYTRU4PfGEY+G/dDtS0sO8HxFWMtsTLmTF3mKMmPURujjvu5wl8YXSJmgecmnlytFy2RCqNhrnSZHwkVYii6E3m07sz2WqWUDk4w75YvE6It1ziNSjNmR0peGadsb7BxwG9awTTkZTF4kDPWCx9KLQA5jjhZYcVWKeFemmk/ECJgh0Km2hkZ2jBcOMslwJGd5wuiTFG5rjgiEgBu9uxbgXNRlkt3lceCuydZ75PYB6JbYRY0d1IjJlygbKrdAgvnh15mBemy4qeK0NfWNUfeRgDsg2EN5WkEwbDz3zplnNSpBAxXccTc+EcPBSw+yN5nciqv6K5ao+LkZ1UTCec6dh3DqdHLmllkI5FDL4mkk/UmrD9gCorZXb4GCnZsO0Dl1DwseHkc+S7LszB0t/02EVzXjLvHnfE3lAvZ/Yi5P1TRg3TJbO+nVBJWEWI54mtJDopdN7STEe1kdx3zPMdF2toc8aYHSltiFSyafStUEvP7uk7TPefYUxHrxOWyJIEflrAS669o3/vqgLAAP/71trfFZFfA/5PIvI/AH4E/Lc/f/5/wFUq8Adc5QL/vf9/LyB/9FaSoEYLURB9davbciWHNt0+z6zJ2EHRqmBsIRXDodfkWFHWkFsmp0wuBmuFdamIrvQ2Ij5z04brczZY10qaC2IhFkV/5zCvV16WM/v9QBcd2lVivvqkbg+WNWee9z0Xa2nLxt3dxu7QQCn2nUNywzfNJSrq2BhiwWMRt8NLx14foXeYWOjYs7iZlCN+ywzHjjdlQ/LG3i2sl0wphWA0vfSEFtjPluBObEk4jSNER4kzn12EWjPrXjFHQWuhbCvWZXZDY65CSQujzrTgoTiqtKv1R0XyAKdcCE3Yd0KKibATlHyuONYjFY2xjrAoDq6ynDOub+SyMYlwKZDCA8e10fvEFitVF+qxQ5kO3wquFDYyjUTJid4ekXiBKGwSWJLDS6O2Cd/DskQOfs+yFdJSqIMQTuX6u9XCoBrJFjQ9270jSOU2W+wAzVhqKbDNqKIIRuEGjWaAqinKsWyP+NIRW6M3BVUzd48b07RdcVNDz7QoMAM3Q6HUPcOho9ZMkozuNJ+8fLw2kbWl1kwrjhRnWtrIxsD6+X/0OkJulFIwTui9YY09fa3kJTHealqNzFl4PGrqZaCYkbw5Wh/xJZNsQ6qi5cY+NdroKOUa1VOLRbYN20A3MF7z6uEN5QQ5aqLZE+odTimaZGoc2aon+42DE3wWiuzox8YyBbQVHpaA2oQ6bvSpcqcL435Pu2RGrWmAahv3r37Ezu+o1eF8JCwaVUDJTwl4+Tk/7lv/gvvvgL/5L7i/Af+j/0LvQtrniQDCOges72kJSsyI8kjbsEYjTSHKoOsEDWrrqLUSq8I4RVMKTaUUuXLlQ0IrS+cUOWYwVxtLTYklKsiVqi0tBciFt997y17fcvukw9GgV1zOj8S00fmRIJa6BFbdmJeAH3tkdIxWkw+JkhLbtBH0StpnrBkI08RuHEg1cteW6xfeNlrV2O1My4pZVTopTKUgOrMlT64byle67KhNsamAywZlGqFeo113qfL9+Z67acI4sDgmd40+6TZFFot0mi4bWrswLAO9MizFkD8P1ZuqoFBY7NXeoRWhFWRdud0ZQmg0VcnLA6fBcikeu9vYqqZVKLUQq2bvK/Fc6Z7dovMD3cWgYiVlwXWWmgu0ypw1KkWc91R7HfEPN++TwhuiSnT7PaTP9V7FIV3krgFJgEd4o9DvPeXmONCVzHb/hhAbIcyokimiCVuHLSA20kKmqSfU/MD5LjGOlphmrGkMuuOp23Exml4qeW2c1YQSz423hLmCM7RWUK7Q1ob2jS1utMHSWseGJ4eE6xx7oxh6RW2NOvSERxjHgSaKtUZqu7YoRt1xWhXiK/1w4e5e8c7esIRMZKONR/SUyUvG7TJuLJyUIquO7bIxmgDaMqmNfqt4cyRZy/FWsyyJ5RLZ7Uf6eUO/GLBmx93Dxjpnwq5eG+nVcdERo4WmNpTaE/uGPlVefTrT3XpiqSSraTUwxkYbjgwIsiw443EallipJPa7EepGlMbl9czO7JBDpZQ/xcrv1qDphkZDuyKWY4Gu9+S0Ykwl52tRWlPEa3v1dIWC769FJyeNRiMmYa3hCpCo1OQILUITjBjCCl0/0GiEpVBPEWcg1Ym6elrTNAcvPztxe7Tsx5GIQbbAQy1UBaklTCqYNdLrwrlu2NrT2kZqUHuLbpa6rUgoPJaZYoV+FNADKp7JSWGUEFNB64Wx82xaOLQKbYfTkWxWctLUJnRRkSOcbCLWFaMVlA2rJrrOshsLlcS8FvY7h1NCwVNS4FFpZBWwlUvpsLbSSsVWj8NhTMM8XigHR5sbujPU6sgyskqmK5bONFyDvYosRmOapVaFsSDKEVPi4CFVMKWxNqEWe/XPSWFbJ4wSlB3A9AxlY4uWmYVn3oDAUhrDduYioAtMEhHxqFbp3UBpE/GomNZHRiWoTdNUwarKU3dE7TVFKquOaOlILVBNRSgo0xiaQ/vM8/4ZxjVaZyE3dmtFjCNuD6wBvA1kLSwG9hukVgnbxlgNcW1kDz5mrNUIGjtYfI2cV01OgDvTdwdC16HnmbIJ06WR9YoyG3ku7LsOVR3F9dwcM1GEtiaUtXjd48fKaa2oIVDVDerxwjJm6qAoyUFIjLbDlR3FFLyphFRBX3FOw9gwuqMQidtE5zXnNyuFK7K+asXTJxbjLeGxoYYdT91IkIU1XRMo86JwJKp4ql7RIcLB48VwubuH/VOSZJpoZFmxTtPOCaUNU84cQkL4V48I/2NbV2SbkFtFN886B5TJBHWNnzBiiXlDKnidIGsalsyCrj1Kg1KKnAOaRlwbiJCaorOBshqGvSVJRrxmWyrp5YXwsmDdkXzZmE6BtmbqlyzS9kjY+PjNid3YYQdDjontktHNcsmKh6cJO1TG2jFNmcIjQ79DHyJOKUJO5AYYR7MVxUKYuyuHrRu5oxB1RWWDlj2naDE1I1YwkjANpqp51znOcWG2BlsyjQ6VK+PYoT/L7G9e8EW7I4VELpGx18wB8Jq8zqgSkFZRccSPN2xpZre7QS4rWiKHVlF9x6ntcGZgyBO5Nlq/Q1fDzmS8UqS645IDuYELQtoi9rCnxUfyFtiahrXR1YlWIaTC47KgtxWjLNJ16JLwrWAxrE2h7YqwI5tKmQ06G5ZW6HaKLQc6D7Ez9K2itjsW38Pm6MaG2ipK4KZ/QpGFEBRNV+I5kVyj6cS8bLjs0W5DvGe/v6H3DfLKKeZrXvxSkCGRU4c2ghao9Qq5PIyO5g1d0ZS1EUZhvjQ0GV8zYc10u0JLmtgqMUaUGuizXJvOayb1lqYdZqeofYeu0KnC7mZHLQvbegUXnPOFvR65nFbs+UT/VKP2hm2p7PfC/thxjpmOGd3tKCHA4QmDc6zhgrI9oSxM9xNaRx4+HcAkHrcLu6cDt2WgPll59vQpQk9ez3S3t4SYKaPD2sqlBqRbsa5nyxslJUwT9M6immVKDX+JZJOp1bIuCayld5ViNNTKfnCcYs9x11PthZ/wJPcnozC1JogojKo0yWgqRgtFCqUqpCi8PUBThBzJOdNKowTBDtfA85wqRidEKmITlD2djahmUFqhRFNjQauZFjfks4LC07pCrJl3bl9w0Y+0F4qkMtuPE3XeWJJjvxg6X0iuQzZN7hpSIrEeyJKoLtBVf40KEUU2iRAD2jb2oyemSO4P6AhNK5ofsOWMiKFTiloNbtDk2HAipNioMtIrT6qC3jyuq0Rt6H2lHx2ihb3XfGe642094dvIYIRpOtMJlDagrKKsnuo2Sh9ZHx84i6DzW3KqJApZ2lWLk7hGshrLQ064ZSINlkkSA8JaCp1SLHnDe4/QUFLZnMWrihdLdhPFeo59pjOFRx2Y8nVQcb4U9KDICDm9JojlKCNiNEuOxNJoQ0Zl6HJiIuLNjmZ6fGy0pnk+DNypCe931BgIbMzLhrYKtpUkCm+EXXXUHiIOXwstOFxtsJx5Gw2jTnhl2amOKS/07mpMtcOO+/Mj08NGqpnuWYdeE7otV6hqEQ47jfae+LCgh57WIq5otBko+40SNVF3pCmwbld6iZSEURa7ZHJNbJtieRXoPYRUsVL54HmPtx3DoWc9F0q6RicTNtaU2O07BnXP/NgIo6KvI3rWTNs9ViXm1MhB4Wrh+fMvIpthVTPPbIftGpdYCa0hMnDezqhO0SvDeXogTgvHfEDfWn78cuJ407FXjbQfCeURLZopZHaHIzUEtDbIoTE9rHht8EGI1lPqSlgKu2cdTg3QhPbT6jH9q1hKCUhDaGx5g2yoyhCXgjEVM1hCXlFiUM2hJFNa/NwgmMmf45WU0oC/AivbRouKpCJadYTTmSrgnKU7GMq3hIPOJNVAegZTsdMeNVgiju4rR9rvaZYwM+jKGhXJNQajIAtvHxe+evMecVvQ3EDL1zhbK9iuoyXFbbnaNZqLtLwiaqCFSKFis2ZnHatubOWRJ2lgkQhaYbuO+bxQbKbuO6RPONsTUuASAoOAGzTj2Hj7uwutN+zNPZ27oaQXFA2LgJENKxZTGtLDYWfpU8EkmJtmbsIWAwrBSoJiSWhGbzCpsKkIl4zIgreGYKH6DtGV7EHHC1kryiXRdQ6DxVTBWEfn9qxrvmKkHq+TyA6HLT25QtOCWyOzDqiuYQdhVInH6DD+GQe5sEsWloU2GgqCDTDWgZQ2XD8w+j2YNywPlbto0McOs2R0L4SmkBAI1nC+3KO84UZuOCBUXdBRMZ1n5PiEMM+kuvAORw7N8BAKuje0aaU/dnTdQK4KtiuVZRcda65QGilrVrmazdNjxN4M5BAoJaJjIGRBTKPojTUrnBUWHhntkYlMCBEtCnUZcHahuoEgF6QOmJK4VMXoDEWdaaXiR4ftG6opbLdxKjvMdkFaJObIeLvjca5QAnbf44eF+7cbuUScSoyusd1H1HGEtNHCzI2x6L3mzcOn9AgPH72B0cDektbETdcjSjAmEqOQakA9rDw5PmcpC0kbemNJ2rA+nOAuwmGl0/onjj35E1GYWoOSG7UoaCC6QgPnBSVQy3y9ItdGzBVtNVRFigEaGKOpVRFTwejKdVe5kmtHK/Ya2zFwhTGmhu071hKgelTe2O0dcW4U0+GLYbA95QtfptU7wnd+i4e3F+xxQI2edVvotUVuwLVCqILZKhcCqzOorInbhtTGuWSSjqRkIAvWXQPdl1qpylP1zKAESuWcE3PXc2sL8xR5OvZsNSOlMueCaQkIeKsYXGZeG9oVzBAp1ZAvjnVfrqTWXtiV7mq9KBOuCaY+Y9k2prhx0x3I5wV50nGcKyZFqgSMFVCCTh1ZMvUxc+gNynt6DJd2QiXPohy7LpJmRcMxmkosK9I8Sjoacn0ds6eXnnNL1zTJuTCwEEulqgquMDjDqwZadWzRUAoEl2gXx0ZkQaPzgtgdpzjR74/XlNFiWEpEmz3L/oGBcKUouwyq4aWgjWC0vzbs+0rbVUQ86XziFDdUB/bceP89dwWrrvcYrXn/6YHSC3XKVInECEaeo+zGqlfmAqmP2E6zrBZZJ9rR4fsr7l1boSiL7Z6Q1pmshCIZcuEcT6gA8ZiwKFoRAhVvNYfRE0phPOxQNbMoQacrnky3G2K8Z/AKYxXZCnUW+l0iDYYwF2zXYVvH3mS2TjBpI2+JJ94zi6UqCKI5PjnwcIm8yjNr0uyf7XnYVkKzHIfr92UuG8+6xmgOhDkyHvbEdDVRi8qobgTZMC3RPCwlU9TA+HRHrhvh8YTajbSfsDL9iShMtHrlwGmuokl7BeXVKOhOUbKQm1yvLAq0VFRzNGUwKiHSyCWjxTEvAasU2lhQBScWaiVdLNEoTMtsIVOz4TracyzriqJDgKgK+SRs6xPUkw95/pe+zOm3/z4l3jPUQnOGzjkaiqWcWSJXQoa6ctuzCAelQPWc1T2gkLJRWmPRjrZkrGjsvlDC9Xhmq2VRQtxWcqcpNpFMI66VvAWsA+1AVw8hE2bh9mbHzWPkl59/CdPv2VXhcX68quSDQbJCdx518OQlQz5fAZOlQ5pi2jle4Am+YrTjFISbJlzqmUPfo6ziYIcrnjoIzYCUAcmwB3Z25G47YaIi6atqP2wb0gpjrfQ4CoVwXhmPmjAHjO7JnLjZHzhfZuYq9LPnxRNHPlWMXrhkxdAMF5Owg+IYPTU01r3HrLBJxtSr9ag5g8tX7Hu2O9gq0VsW5dC2YuOGLIX33vWEboermQ9efIE/zIG9V9z2Pedt5vwQiCqQtMLKhgyafC7YrrGeK6pv5PrqagdpFlVXQtUo5bjpIs0PbKHhpOKpbMGiO4W2lbhGvB2ZpKIeG+XBE19E2pbhxlKbIa8ztTbOW6KiSSFg6KBXmBbxeqPNG924A13QaiMXT82KPlkewwohkaqm7Q6kcqLuHJICaemINaJJkB2ffvaS58dn3HZ7frg8wLDnIW+4EK7xQn1iZz3rZJlWwbbMsNuznBd0SwwFqtPI6CiXBdcfiFtiXU/sdwarLS0KsQysMVH+FaQL/NRWQ2iiMAiVds0uavnqHq8GZRQpNNCZljOlKZo0WskUKhp7TRZQCqM0hULNoG2jpUpFrp+0VnKFsgWUNMKmyCTWLBzsRMDhs6ZsB9qUEUlweMqzX/4bXH7jP2DJGS0W2xRKaXYIre9xXYfbGlNdSceBtzUzlIXWHE2uJmXbGgZF9Bs1aVqqmKQ5pWt++TBanFbIYtmbgqqwhEfeGZ6w6IkmlrTN2Ggo1pKxvCsD/9rBMhxGhlrZjj39bkfUFU6B2DqUBPbdHqHhXWUylaIrwyoEHcle0FWhpJFSJsqVgFLqhpaCpRGap8WEFsHpwuTAtoHdbs+aVqovuHwdpW2i0L5jBO5DYEoFszZirbSaGF1HJZCsRmMpkmmrENWGHnp8r9Aoaq2cY0CHGcRS5oBzkXyaSLZj9nvMmiEFOl1JdWNVBq8aRV89ldFoxieWrTVGYxDp6A8dvDLM64xxllwiFY3YkZ0ydOKvx+nniqAyvhV0r1lsw8yKZ2juuO6ySQtKe+7XFW81RRyd0Wy5oVXFtpWpKGRrjEbQt5Y1VcJZIU6orxPH90aOo0UtGX009E4xJ83+4FGxMg87wgDKJFTeqGFgUgrvGrEpTKmY/kicTjij0GUiqIquja1UAoq9s1hluA+VLSqMuuGhnjg8uUEtD3zywzORyu7JiF40oRVG3xMpSDcyZ0UZLDJt6NZjRKONogw7li0Sy8aTFy+4v2Se6Av4gXfGG6btNdL+FPeYgOsHaBqloDcC1bLlgnNXnHMmouTaJG9SkVZprUCz1DUjYshlZUsbzneIcqxbwElGnNCKgRZQVJy2UDRBLZA0pkamqrAtkspAyh25OzKqSt4Sx6fv8yAjwkJbMsp0vC2VWXmabqxhIy+C2gvu4UIbOwyVqhVH+5Q5vIXOo1UmS6Mi3PqRNZx4Oh7Z1IWhNvIMnTO8jZFDPyDScWpX6qkKGeP2rKzkUthO9yg30K0zZdpI6gg6c7l/zaMxdHmjyEZrCq1mYga2Kxg0ZI1bPM15wrqwms8LixnROrORYI0w7LA5oG2ktD1WX/ltcZ2ZykS2I6FVTAg05clKM2wb677Q9wabNnSv0SVBa/ih4pQm6oLvwU+aiwSeWkVcQbbENEXiLrGthXetIqqRnC1qutAGz4amb3AIG2JuEF25LB2QkSDUKlTrsTVj9nC6n+m8J+VEDWdeHzzaGnpR9J3ntrfEVqkK8lJYTUFCIznBNGFxBYNHxYzYwFoNzSjsqinKo5XhmXvC3C441ZFbgxZJa2ONEVyH2A6X4FwudDeGvnX0Ygk589Q6bC88nh1PiqXGDSWKdFrJVZN0Jqk9+y5zXiNu9FjJlJTwOC4FfJzpVEarESsjLQWqAqs9MkKfM2Y/0EXN+f5HvHz8mP7dJ3ywc7xeJ+yup9Z03e0pQxNHTommepzWlKgp+g3quCdMFmszKVRazqh2QZWBNUwcq+Yhm2v6RTyxFP2nWy4A9crXylBLoDlDCQ1UZVsqqIoylhQSoiJh1mgd0eIoJSFaQCUogjIO0yDliMISU4HPr2DDqClNEeaI9Q6nHIvKDEdF3jRbVohJ5MuO/diorTCXTEgJPTxHLt9HDp7OKXxzdKeRx90FVSxp33hudlySoFqm9R2WQlguGNEsaUMx048dWQameUW8x9eEboLvBi5zJNRE5xNpmznsGs2stItiKRe2bcQODWVWljleG4tiKLniMAQT2ExgX2dycNimySIs6koxNuIx1tJZzVsb6GK+HqPFowxs+YxvBSOW0vesObIayzFWjI3E1PCDQRVHyGA2eH6j2BZP7zzLVsm90GqlpkxnPGlTjE+ekGphbys1bLB2aDOwqgI2UZeB3b5R80JvNg6t521vwDma7QmnRzYPSl9THHXNXFTkCHgpHA+eZYokD6IaUjf+IGTqZ4oPOk2vEmYxrFbxePeaNRlQhunthXuVaSYi2rCvmiU1sMKToaFjpoYNLzvEBIouzGVAKzB9wYphuSx0e6FOFUMAFNVu9MWh+44SI60oioCLnk0SaQ3UCqsLmNcVxhGtJ07KQIC6Tljn8IcRpRuHkmhNY6RDNxg7h7Ydb7e3hHw1ZOeDZWcjs1xwZWF0zwnzCVcd92uhra+JqbL3nnc++JA3r37IR4+Gth8ZhhVfLCVXvDPYnKnac7kknMmUfIEqxNJQOhLzCnpAE1HsEB9oqqL1joOzvL2fr/gsW6+52T/B+hNRmFqDWhSNTKv1mlbZaVLQIELJBdQV2+TGgm4ZwVyPcaKo7XoUyanRD0JLBiFgdAUp5FrprCKtmn4sZKlMp0R/sHRG00JE8nVSVEtCu4m17FAGnGqs2xnfP2e/fcQzY/lw3PFz+wNNbQxao1PGlSsrfn/UpEtiqhtWR2JNmN5Qc0Wi8Jhmxp0iqCtKajc6HvPKcirEWvD0TCHynI5gM17BSqCZPeIWhmo5i8NJJVxOTOqW214x5RM5R2KAur9yvhgG9JbIxYPSiG0sKdCSoV8K47GjdQUlDV0UVjoakJdAf9sxz/fsNvC7kakWNJVjGmjiODwbuYsz3jrscaVU4dAbCp5mAWdJOI7v7NEUhrZj0YVe7zltD/Q7z+wu2AukPsCsMIcnlLAR9IYtHp0bcVvIodCPmUTDDpqyZlTsiAOERzilhTs583tb5NUqJJN4W4R9Fv5WP3BjB9KwIjWSa4frMkVlalO4pjHNYJTQtOMQEkWNHOyeolZsK2xpwZSAKIvWMwnHuip0iehekzZNp6B2mpNO6DCgXSAHhd/1SKucLwFtLE1rtJErlqlA8po+XEXBx74wr4Y4dkQt6GUh2MpSKi+e9/TRUkLAlMg5CrEDNyvmJqQ5UQ89z1zPgwTWt2+QruKTRrsN3IhugZwbYZ5prePN+UKOwtOjYThlHp3FRI1fKrME8J7lHOh9TxkHBhKxFAY0r99OdH2HJMf+0LOalVdzZSiBJ6qSTKGY7jqo+gnWn4jCRBNyFlrRSMvEqBANuV4tGsYYakuI6skBrE0sW8ZrRw4JZQwpajoXSVm4hMDoCk0VjNF0xVBLw5mrN6xirurnthKDkJqm1Ay5YWpjK5G2FYoFbRVFFIfnT/kfPv8zqGnm9vkTXp3OJO+Ia8B0jSVl3CKcTEY30K3Q2Y5YM6EKnkZUHaMvrI8ZkUzzlnVNxBCvX7imcDpgque8ZkpqFCWIE8j1unWnYQTyOPJEGUDwyrHtAyzC2mCvPUlHnu0G3pqVPmuS3jiWjkE7mhViLaxbw2hH50eUBM4torvMqAqmZkZt2HeWEAvHHUhWV4e8jiitcTmw5kDnFMMgfPrjBW8VbRCeieFHdeMhbhxDZeOMM5ZAT+8U/XpCRBG3SumFsVMoXdGup+qKo7HFRowLrrNsWNazYhk0mzpw0gZdPTKOfDR9yrcf3lBDwnYWUzSXmill5Z+ukS/Qcd4uPN+9SzIRYy1NyXWHvTSoGqmZpCZs31HOG/elY/UzWw4cBoWuA2utSMlYJRivrkZw27E7dtw/nsm5kh+huY1thdhfsfM5R4wH4kI+a4YRCgqxmjVtTIAvI6lLKBTbo2Hr4jVhI10YnNBSQouhZEWzFusFZ8CNml3ZCGdY54U5fS4mrpp+GFl0JS2OFrerZ9Nn7j57IM5vGTrFQ85sZcfxnZGbS6SS6HaG0zrjloyylkuE5yWQbLsSWmg89T13uXAzKOoIaoFeAkU17uOJd/df4NUyUf80N79RoE2mVRDTUWqlloZWmkalbIVCw7iVGAoma7Rcc4ptX4FEk0aoBi0G1yJKgeir4bFWjUgmiVyFmdmibSFtiioZJRUjhtYsdzlh/VtM+wBTGlVpQlEMTfH21Wc8vVXYvOOgCmcC4hQEYYdQrcPESJYG4kihEIrFN2HL17H/LgvjMPDWrpiYmVoiJqErjdFmzkXREWlW0fx1GhmTYHMk2w6TBUjsdGOr1/C8Xins0nMcHOvphA+GbnxKrzqe6w5bN2bVMF4Tgrtu1a1gasO4yhIWtG0UE0nnlc1YnnTCrd/TqUYTh85n7uMA/vp+iFe7gThNZOLydoOh0JTjiRWMs5TTiYdT4cOhQU247jottEWz5ZVSPGkcMVtlVoXRC4tYNmN4fIC1U3y8Nu5TYO0zvXnKF5/+EmhwxSJ7oMKzJzcct5nl7XeJNbNKR1cTaVP8WGl+WE685zuWy3UXMOmJUQs6K+ZYaC3RjGevOkzUpH5jawFvLE73aKm43Y6WN6IM1ByJJFQUTktCp0y8RMLo0VIR7zjnwiFWqhiMXZEKWQ70x0yTht5Wggr0csCUjSCRrSlUCuwOFlxhW/egIbZCnUBrQVVoWXAHzTgJkkHMgL3JLJ9MuLEhi2KZAqXM7LuemO5pQWhdRy6ZxzxxPNzw9ImiPEbkEnhdA8+0JwyK14+B8HmcDKVRe89jaOhYCeeIuvHMujB4e7UaTQaRQGdHHu5O5NkSbjVlDuT0p/goJwjWeJSt1AoSFNJduVreOpoW1i1Qi0LqFaKnXLu21ZShJKFJpBaNcZp+sCCQZtC+XrfOVaGzprWCVhHrGqKEFi2taLxLbJI4pEipC1ILWIUBpFSqcQS9p7WrGr0bdwRWVNFoDE1VtlbwnWdswqYSJUPnNTVGTLfDrhprEmu8MKodum74TmOiw+tGEstN6Oi1Q+NYSiZjEL1BK9hk0W5jbgajFaYJZd2Yx4bWlanM9EPPQkLHyqdpIuXMi8ExzRW9F6Zyx6HrydtGmyzmhUdaIgFNEgfn2JQha0tpiVwztW6E5NmpiDI9fWlsFOyoURXWcuTFrSOVwlxO13F5BmMNWi08v/kKf/DqNUlGLnXFp4zdPSXrzBZv+FgJyt1gnt7w6k7RDUL7oiIOHc+XhXdKIoQN0R3SOgZbqL0jLRtJKpINv/Dlb/FxVHx090OSOtGsobjGpjQ3ww1ffn7DJ/cTTTb66hitJ7TEh+/tqS1zdylYqay5MaiRjYCshSkm2n1k98STUSxxwWtFsdcLxCwe8sq2Rnbe01RhnhtFKYbbG3TMxGhRckORB6Y1IdaRzA7rDARBuacYs6IapM8tVPGkaP4VKRZ6K4Ci+khtkRIdedVIcSitwFf21nMZAk4Zcmv09BjtuXvzgOwOmJ2QQ6BW4ZAcyigeHzMvbo7EeE/xI0lpKAn/dI9Pj8QmlM1w2xRiG1tc6Y8erTuMPNDRsUjlYVrQ1uFkpqBRg5DSG/rDDuv+FB/lRIQYA0brK0qJRlkz2ipiAIVCG4UCil0R6XFOo5VQFZ9rmyxVLEpnwgbea7S/WlJaC+TcIQpSWVBKyMXQEgRJOGOIa6G6RLQW30c20ejQYdtGVQqpBre3dH3P3DJMlm5v6d3IGhcQg82FPg/MakGNhiqefU6EkCk2X5u90dB3Pc4aymqRlMm14POOkM5Er9mwOM501jHVyOV0zSH33UKWAfJGWa+TzDQkAkLnGrZUGhV8Y5uvDLp+ENrW09V0Naqe8tVQ7G7IY2KJla4Xlqmy15atVVyr5O2aCHkYR+6XM9YKd1rYx431VLjoa0O2eMPrbUNMwOrGuTSsbci7T/kr9hssxvO7h3e58x9y1hqlFH0dCKniukK+1n+2UFAx43cGkxsPonCrotZG1ymatjSnaevG4xwopmG8RwE+VQ4c2P3Zv8a7p6/x2x/9Ez6aP2PnhWdUXvQH1mlipzRzErSHWSq6t5zPE496Q+UejIHUuMSA1orJZaJUWvXodaHOcg05sIkqjuoEHc+0bkQdG7UlylbYCTS/sDZHlogp4MwjSSn6nRCbIhFxYSXoBrGRWkFNM0PfkcJKGjQ2VW4OjRoNdB0PKdLlAZqntBVpM7c7kDpCrtjq2KQgY4UayRuYwZDahtk8XQbdGR5VoRfL4q8gVffOANOZrVgWCq5EjOkxZsUdR7RE5jcJ1VlKEvwIJnuwiZw0Xgm1FAa/o6QzrT+SUwZn+AmHcn8yClOtFVolbIJXFiXXkPWSNxSFrVw1OE0UJM+w6+h6TYoZiUKsCaULNRmMFpxu5FSuGUvdtbmuVEGLUOwB1kCrDmVWai6sMdIpiFVjDEjS6BIRfw2js1Wx9R43PsOdN6RtiAqQLE4X1DhSW6VKorHS5x2uWE7LhLjKWgaamrGpomyjxEpumhwFaZHmOsiVZetQFOgDKjVI0GlF8xWqwlnDFgvJj0jcqNaQVeS5NE5LY3i+o4aVFDyMkZ00Wnb0rnLRG/1suB07DtqwFM0DG0fdcTdFZiKPq8YZy8vlTNyAsnKJF4iWeTvB+ATjNZiVg36XTQl+eMpub4i2ocJIdg0tgk+N0RZsv+OxCusw4Bro9kDTlZwCSg6kfOEf/tZ/zC988y/jOdByJNZGboYWHhAjVITkDD/83rd5OD2Q6yOVhlNP+dKXf44vHF9QAY2lP77Lt+y/xs3Lf8Anb3/IC+e4nF4zNY2Sa4NbtCanjdYyEj3adUiDtRbEJrY50leHbhnVCfExkPcOZQtdc9RiUK4xWo3qCq15XLtaqwbrSTrT9VftWGs9q7qguh5fMqcgUAaauuc7qeJKR18C+y7TdQNPtCW0iG2GoB3z6cTRQFgqS1JQA63OHHeOlmE6JbQJ0PcsrjKIcFmgC4q8naBzFK9ZQqGrmTRdG/7iCikUjDakc8OYnlgzQ+4xQCyBZA1HI0yXDdM1qhZMg/PdA89Ux7xUxqcesUfU8pbL40Q7KmqpjDcj83n+SYdy/9JcuRvgfwV8kyuY4L8PfJc/Rq5czddPkCpoo9BacLYn5wVjBIWlUOmGAWs6Sirk3IghIrYRV4XSmRgLZEe1QqeEmvP1gxqhFkMJBeU9cZ1QSSNOs4qAaARFSytZJ7xdqHpk2xLoa6C8cs85PnvkzWcXRqdRuuGsJpRyvTJWzWNO6HaiMzcsMV9BjzZhqLSgmWgYo3HKsDKx0xoh8uT5FzBL4LS+oWUQo7iQcUnjXUdNBceAqSAm0eh4khUX7xFjOXTgk+VRCk/6I/fLxHp4gnSaH1Th3nyIH3uW0lHmDXVQXHLj1t/yWYJON5at4ruVJ9JxLpC2yD6cMEfPV+wB5XasISM6YnAUk9FNA8L8mNAHRVPQm8jTBqe1Z0kF6y1KFlrWpGYYW8FTcLWR9YElTOw7g/GOTSu21+u1+JPJRXEuD3z7975LuXwEIbFcEskb9rsTD7/zY17tn/Kz3/wViJ6uWtzQ880v/WW6dsOXlx8SykZ36PG15zKtpEult0KykcUIXa6EosilILkySKWWSFKWojWYxlF3pCcNp6DzI2lqJKPBDbhpoYweI5GNDldnwiaYIXI4WORhR4kJax03/cgWFpK64Z+VwOvPAsZkdmtjcJlWhBcCRsMXjKWvcIkbT6PhWZeuUT6iGbtbWoo8LBtp3BPXCc0VABsfM6U1ej8S0gVbRpJEilRiyBAC913jnf1IFIVumVdT4cbv6LuIFIM/7FlOE+e3D3SlQqdJRYMUvLNcloXsOoiVG7sh+z0xrmyxYJWwbivmePjpBcV9vv5d4O+21v5tEXHAAPzP+GPjykFpmlYLdhCsuvZlKoqSFEoZYm1YLeSSoBqULqScEKNIIZFqo3f2quI1ERCKFlISvFZYaSQiplfEJqCglUInGpRClUDWnoqja4XSoGaLUhuFTIgdb3jgFwaHM5rUD1jJtEFhSybcV3RV1CqMux0qzXg7k9xIiplRecQUbAe1VLSt+J2jGMHHyJIfaRYclgXBqky0e1JY2fkjU58paN6GivLv0LrCIwNBDI9sDN07PNKhzEp3eI6qgbUp1JygM7BVqveUCPbQ6IC8vOG1ZIxxV+uARNatoFQGPZIHoTO3dMZdkxdNwLrKYwC/XcAVjBpYU2EvFms9k05cVkUvci3Ga0YZx5vpU+bLys+9/2fZ3IboyunNHd978z3m9MhaCv1aUc2wf77DCJQWSefEx59+glpWsj2y1YnJK0yJrCVhUuAP08z9twt/+ef/OqM6ko3w6gwhTfzD1/f8otc8041WM9ZUWBQfdRvzKePGDSuGGBpva6ErYP0e1MZuGHkmjbR1nIxm3yqhVFpVdJ1Q5plaCnVtBFWZmtBJoF48uxeCHQ0hN8zRIAFShC1HsI2cI/N8LUrDAOf7I0sOKBHuraFsE98xEypAK42duTCehedDx0jh9seZXSuYTeND4OnNnlOIpC3SacW+Dlit0UaT9QT6SKszvRroreMRTZQ9LZyY7t9yM7xg7wJhjUhvSDbziOHYCxIza1iv9KEBshSU3xFyQhLMU0a1hZoNDo1tgRdmZDI99acVFCciR+BXgf8uQGstAlFE/ti4ctSGUoLvLUplYtSIWREcbqegNkwrtKxoNVFyISyC7jRGF5QZ0C3SgsaPglIdSOX0uGB6T5HCtBSGoWPnFTEm5qaIPhBmRfUVSsWS0FqRsLTU4QAlmlIsynjOeEStrEqTLveMxl6D18PG7jiwPSQOrtFbQ+0NEi22OtqwYDWIEpIr3F805znRm8yhdbxuPR9Lz6VZgqm4/YfEmKhaaPsDOgqLiaicuTsuzNuF29sPea57UJVBFKolrNfU2DM9rqRikNTQkq9RK63SL2e2qrEeps2g7I7Wg07CHst68KwhE5eFUQqhQSFS9Ug+TVxawaGvOzpnGbRjLRZRmYe8MYRCzIF5eeDTx8QXvvCcnB/5T3/3x4S7R37hl/8apW3U0vHdj/8ZP/zet/loueeoNh5OE+M7z4it4jXMLwv2MKB74Ztf/ht88+uNUlfW6YEtVM7bHQ+v7ujNSGDl47vv8vuvvsPX3yvI5Hjz8gf89nd/neGo+HvV4OZCjSdyhpud40UwfNEfEGOx2rLziS/qDh0DDCODmTFt4FZpLstModFiJStDnU5kZbAKYIRDw2yNZA1q2Sh+5bIaiI6JjUF5VNMoH3F9x/S48KAyqQnG7BET2d9uxCTEEDFpRVJmMULf9VjVeDs17rzhsw06hMxEvEQ+fKYYuh277Nimjfdz4C+++4yGRrC4NjCUwON5wvTX1ErnDXoLqHzmHGZMt0P0wuUcQHn6+8i6KD58ccClM2/OGaM6VK9ICEYbVoSjGyinhe5Wc44rpjuwE8NyObEpxfPuFlE/Pa7cV4A3wP9aRL4F/DrwP+GPkyt3cORc6IbrY61t1LUj07B9Q6SRVkVIga5XKGMwNpE2IW4NpVa63hIoqCY0gKyQVpAiV66caMIWqAV0Umjv8VSyVp+DNBVbTTQpKBxkRTIFLRZxFiea1TqMg++8fMVjEn71xS3GCUt2yGPkk/mBF7unvL1ccGrPCWHRCZ/2HIxw7m+ZrYWnlkEG3vLIP/zdf0AJlT/37Fd4d3wPlRrmsEeobHVlekz8k+/+Ix7ffEqUO6ZlRUnjK8+/zl/7S/8NLut6ne6JQprClsYgjYemUYODkulKIhYhtkqrlhQLpoIfoC2VRTJnl9geDL7r8fsd6xYxXmGDIW4bzTlu9j2xrTwPmgVLUjDUjA3CVDy/9+nv8YPv/zrbfIcaPb/5seZoj3zl+Ve5+epfYl9vefPqNT/48a/xO2+/h1GVTl+N2XG7p8T3sOqqQFbv7TAPgdACuAaz0HmH+Hc5do7bZx/ypWOk73ouzNiPBr732bf5+NXvI4xspzNBEqN0jN6z6zVf7t7ha0fPzhiOqdAdOlRnSQ8rzu/IKzTX2GpCNqHPldg1im1kRmQI1LQxGk9KkQuaLS4sZb0er85gb/bsvWOWqwH2Z/Z7ppjJq8H3ilQsh9sn/Gh+w1wjfdlQypDEUGPESweOq7ZqnRGxKO/Y5UA9Cjkr4sOKHkFcZdv2nGohTm9JU2F6NvDnsTyLT7m5rbxdCrubLzDNn/Hm1Rveuz1wWSvLtqBrZXCeSSnCw0w8J3Z7DVT2KnP3MnJ8KvgX7yEvf4wxjktpnJPm8BhZx0iz1+DG9UGhdeTmxiDV8ck5cEoP1J9i89sAvwT8O621fywi/y7XY9t/tv7LcuWO7+9bN2hiEpRqaOOoCXIpqKIpecXqDm9AmkY10MqTVcJ7QdTV9KmN0HSj1My6bGivaLKQksJZhxWhpkxRDS3QjZ5pquR1g6HDlWt+k2mVXAtWAslZSszUvPDjlvhPf/DbfPLi69we3uOH8ULyjR8/PmCfHrn0X+bd4wFnBzzCNvb4HEgPmd+Lj7w7vGDUkXNJ1EOHyc95NnyZ3/zo7+P0/4Obb/wdnPSEdSXpa4BY7xTGVU7TDyjFsesPfP1nf4Gvvvtn+N2Xv8kPPvkeLa90urI/vsuHL36ep/YFeSv420CJjc1uiHi2JZPazPDiHdQUWHBgF5TpOT08oJWn3s+YLzyjU4alLaSikQA3t0fWacZmhUhjrBNxZ9lQrJeMetfw5tPf4+78Ccfxlq+99y1GPfK1r7yP7t/B141TLrz89If8/qvfxliLsQ2dDU9unvHB0y8zCpQiLENFuYXYEsUYDmNP2Qq5JlYqMW90RfPB7QdM8khNirH3tH5A54DRmfVBsTseiTWjbhQuOX71trsG81lHUYU2PxLWG/YirEvGeyGKY5+E1WcyM1p1WA01zddMpup4LCv3U+E/evua1ViyAicFqzq+IfBzyvDus4EaGpOslKmRZKaogWoSNW7oqfKUxuosaXEUEfZ2Y6mJTjWM6Tj3jVosYasYY1BbT0fm7DMH3XF79ChlyWkiiLDfeeZmeT1FCC+51x2qNVSYcHbisDeo28Yxd8jDBeKEjnt8EcbeMdWMzpbxmSLlglTNtlrq9kgWh90ShA4hkHpDjQsOz/QYqKaw1kQJC2YQfNiTpuWnigj/GPi4tfaPP//7/5lrYfpj48rRIOUNwdBKQ3dQTaLliqhyDYGzibIJc4m4Kp+L3zzGXPHf1VgkR2JoKIHOO2rLKCx0BWqlGcix0EpBdY5lEbJJWNexTQubNLQxFNHUViAmcrHMlxNtvOXHW+H/+PiSvez54ru/wNun79OnhT7t6J484Z26J9aZWjUrlWELNNPz8Oa7/KPv/j2+/DO/yNd/9pdwzVKXBi3wwfEbpK9N/Lkv/yUQzyf3b2l15cnzDzkawRjFn//iX2UnPb///d/hV771rzO+eMKPPv0Ov/+H/4RgGjfmyONl5s3r3+Sjh8/4xrvf4gP3LjoMODlguyOpNMryhqVN3MxP+eztHzIJ3I7PUHohp5EUVj585wXnh5lcIqqu9N0tk46U0xnthcfLxrMPdrx9hMtnbxhsx013pG6G20fN7qu/wp/56i9yPI7UUtg2RT9dyNZipPHOu1/i9uUz5rTQmub2+XO+9f4vU+TIZjsIF5a3iTVuOAw4S37buO08XfKYfWFrmtE7shJu7Lvcl1d88tkPcFLJyhO3DXs7oGpDe43dFKMWjNthQua8NEY/XuGjbiNrQ6WQs9AdDdvDSk2NkjydEk7WMYdIpyLHoaPGgf+w3vPjlNlbjTcV6TqScvyzduE37wJ/cTvwVz94xu7mhnoIPL58y8Fnxt2Ot4+Oz+prnO0R3/E4V3Y1kJ0m1wbm2v/si72CRku69lPn18jhFo+jb4IYT5ZGOglfMzu+6Aw/VAtFHKqPtNwYrWPbIpdQOa2J6RPNTV+pyoGxWFUZ/MDb85kXX3jBw92Fc2xoU7gRzf20YHWkQ1i3gqQz/V4Ta0V8xyUsdMMTyBt6WcinhL89QDyhvb+Oyn6C9S9DSflMRD4Ska+31r7LlYzyzz7/+WPhytV2lQsYDTlr0haR1GimXYPXgVg2lNFoQKqmJEO/v2JpahG0MpQaUVyzhxsVaz0KTdoaeh+uWiU8VimMgDYJlMDWELGQz1Q90qJH+VtaziQiWmlCDSglnDsHy2cEVna1kq1h6Xo++8F36HrLl49/DkOkHoS4WbRN3L7zBY4v3+X3fvQP+Pn3fxbvn2Jd5fUS+Y2X/ze+svsKS1n5jW//PT6bXrOR+ZVv/C3Mux/ipcMayze+8gt86enPcfveF3n95rv8+h/8Q9SW+OKzn+dXf+lvs9XIx48/5nF7SwwBtY883b2P1sJkDfUMOzfwuy9/i9//+Dc4byfiVBj1gb/1K/9N+lvw7YalRiStKGNQek/ThratbEMCrVnDHf/PH/4Wy6uXvJregO35S1/9FZ4fvsRXf+lvsu+eUig8XiKd1oyjIc/XJIZj1dhux1//8/86H/34B7SbA1957yvolFi3xG5QJL3DqLcYYJArBNL4xKor0Sv2qeDyjlgmJqV4OWp++PaH/OHLj3H6GhTnD0e6ktDZU/NEiJqHCi+PM+8ojWmKpVZ853lcKl1X0fjrl20SrHhGVZlqIbSGs5Bnxe3gcYvmLi6IGPy4R1FYpGO3KTaJ2DiTOsuvp4V6uuO/PihSaoSm+Pht4mabKRpeFuFUDTZsdGYglplWKsZa3GbJLiGb4qZXPJpG60bWuHJoGvLIq8d7slo5An/TPeVne8ORjl/ejZyCcN5mWqxMU6TzK7lz7Juguh0xz0istKiog+O0nbntRi6niaZ7bkRhTWFGY2chkJDBo2Um+Y5WG0oiaYrsvLuSiA+aoR94uChub25ZzpklJuSnPJX7d4D/3ecTuR9wZcUp/pi4cjS5UjDqhhaDNpaqhVoLGoXohJaBmq/CtzhluhG2eO0ntSTkPCOqXbUpSkM1NCpNXRlrcRNEDBYQI4RV0PpK/s2lYXRE5cagIYaMUpmqFS5lJgM73ZBcsHrgyfEpecoYO+C8sDeJ3335Pe7WR8o3Br54+yU0oExDdQ06z4ff+EUef+sVxQdCyKwpELeFtmY2O/Gf/MY/5uXdPS0lmjb82vf+ATX/FX7+qz93bWTmgd2NoabAKDf88lf/Krpqfv79n2OxlZZm3n/yHl8bvoV1C1o6WlUsjxs5TZyr5qPPfp83y49oRRGjIYSZKU58dvdjPvjg60jn2DVFcIpcEyYpioabceQPPvqnvFYnPvro99nfDhzGA8+fvIeExEef/iafbj9iPT8wtlvCUtm5W37+a3+G4oUFweXMXBNUcLcv+JI8Ydx31+Czc8EeDWtc0CozDk/o1Aqf95u8MohqqHI1Qzs9MbUBPxT044/4nd/6eyxhw+0HxuOBlDXaAi2gm6HmhYsIH8fIF28G4mPE7xx+TXxhPzBLpWaFcoUtRnbectoS0V+tQjFtyNAxo1l0o409y3zG6oZ1jVAapW3Eqqiu0lVhboVff32PQfMtt2f0goQ9YXnDx/3AxgWfLGlZmetCIOEy7F0kVg8IHsMWE7om4lywtfK43NOPFqs8XzSRf/PpM25lT9geKU5QWjjshM49QbKi4rhsE05VFtW4dSuBRGmFWjTJJHTsrsdJd8ZYixo1W95j2obfJWSuTCGxc5qcC25t6MFROmGTjBNFHy2rF6ormPtA2SuIP31E+LeBv/AveOiPhSunlCLkhtYabSylBEQc3jdyWJGqyOGK+rbNoPuGqEaOFd8ptlqon4+9tQh5s+Aiecl0naI0hVGGVjVKeVJYEdHENYJcd2utKmo/YI0wdxqzZKzqWEMCU6i1koriq+99jb9y8wu0nWLZKsqAHXr+q3/tv8PLT3/A6PeYnbleKXSDGAnG8rNPfob3f/HfhpLJNHYNfNfzsx/8Et/5/t/nfnq4qmSrxTnD8+4GGRuXJUFnUdOCOTraNvPsec/w4i9zudzzW28/4ZP7l8zyyNf2X+Ab7+9ZcsJUjWShtMj9+TXf/sE/4dP1BxjlGGMjV+HJ4Rn72wO7p7eM3Q21wfm0kPPMZio7dvjkiDby8fQpF3PCmcKtM9zHiaqFcTLI0pjNG2p+zf38CpsOfPPL38R4SyueL95cLyaPRmNiogRN3yroTBCF7zr2O0OaM+GiSD6iVWLLCYmKdBgQvyGxok3PRuQ8v+I3v/t7/OHpD3h4+5rRGJzztFMB48gVNiWUGUbfo2p/JQtvV9DFOgU0hctU6L0lbRO1eVqLxBSRoUeKxWoh5kQ3CiFe+46y9ZR1xfnCFkF8o5wzVRoWxVvA1g1047fXyOMy8f7QeNfBb05wl2fG0vNXnj5jfJ5Y5kxwhsFpkkpIEOKWiC3zh6bxdnY82Qtby1yYOdBzC/wbH77Ps6agaGYZcL4j6ECnDSH2vLMTWr3m5WcKoxoQUax1Q1UDTdGSIQ+WWlY6N1CixekdRk3EB82xH0mHzI1xhO0VUyhEbdBcI349Fm8CD1FoD8Luacf9/YXlFHHN85N2v/9EKL+hYQxo8dcCkDXGJNKssUqjlFD1RC6WBjhj6QZFLEJIV/Ov6zytNWoRrC9o3ZFspeZMihVfDX7vyOlqDoYAXhFzREUHTXi6F7ZY0KFHiSOnQiBSk6IAWM2hP1AHoSShxoD1Cm8GOuV5fvMO6/qWmBK9dphtI3qHThkxG+88f5eUVj45nXGmR8aOn+lecHvzd/iPf+PfR2fD+1//Oj/zzlfY2Vuia7ixR0dheD7wcFlAW7738mO+F1/zprulSY9+8VVyTvzBdMeHeuaJ9myPFz46v+SHn36HVx//mGA2bvYH3n3yPl975+d50r3AvvOUvXGczsK6TOTYiC5z6BykxFoT9AG9BkZ34PRwT1rgbhdZakCdFW3n8XuLpI2Se87LxN/55n+Fw9OnzKHxRE28jYb8WDgfew7uanFospGnjrETmt44XQRnLLGdMFZzWTQ7p1lIjEBJe7AbVmdU6vmnv/N/QbqO9158jZGe7FfIKw+vM4vZGI3hWXeg7lZED9QlgB6oyoCrWMmYJLQKKI2318gW6a65Q6UKo9rI0mPHEaRiNshi+b9/8oq7vGKkx6uGtEbsFHVV3E8Fsy941eGNQivD99PG9x80Vm/85RvDf80ahic3iNJMaubJOFJSY1WZvXGcc6LuPOfUeJMe+awo3jyutJ2iac1XR8Mv9wc6PRJaJiqLpiCup2Bo4WpMPyWQkNn1hTV4immooq+nBBG+Or5LconZGMI08ThtDC6zXgxDZ1jILGnChMTarjTsGzcwhQQFTFTsuj1vTqAk89QapDTufKXPHbWB0n+KC1OlkeI1oCtuV/qJMQJNcFYo8RqVaqrD7+w1mztWSjPY2oj6amux5opqyqXQ0JQtUrdMswqMENcNMthBiKtFZYU2heYDvbYQBlxJxPUdWqnkEjBi2ARKg05ptqDYQkZnhfZC2AzGOB5OJ7Lc8Y9/9++Sa8eL2+f82Z/5qxjjoG3EdkN+vNDpTN85rFaorpFnjbIdf/uX/g5OPaPrNBJBBs1Ow2XKSM3MD8Kn88f8zquPmW+fULr3MAVqXVgwdFTabqSljf/wO/8J2+mB0+WE9x1f+sVv8pXdh3z43lfpnaGgMFsh58y5QA2wlTO622OC4tIJxo5olRGX6TrDk6e3vF0/JuuOfElYk1AYtreB6lbGXY+eCyobMA2aRteVIIqSRpK58A6RNu5QuRG7RsgWlSN2Z1nvHvmHn3yb9PYtc3vgw3d+hq+/86uM+yNiDC0+okvH5mCzhb/4tb/O0/0L2tD45Pmn/P1v//sstdJ5w+2gMOLIayK0Std5dG+oRUMR5GCRU2XZK/SqWFPCux5rMyIOLYE5Jy5BcdNVtkukyxsnpfn9u41fj4Fqe5RXlBLY7hpCQY89pteMGLRqXKSjrhudXC+mrRSOdaRG4XG654W/oY0doVSc1YiCy4OQtUaRybbwJTnw+/qONSb6qeebneFvvN/TTom6FRYB4onNCOoy4a1HY7g0wfnGRsA0KB76tEd2wrg+cpmEOxt44oU4zxij+PD5kZROmNqhrMEOhe3xjrU01gw3tyND77HcYFOkqok5nDjurixI8QomxYDjsmwsupJL/Ylqwp+IwiRwBQrkhHOOVBp5syjdrvSUWui7gVavhSTnhm0dtExpmZLr9WgXKuN+oLVMLAHrCqUaTK9RTZFSvfZrCjjXUGOBYKhNUxBizigNw+6RcPcOWSmgQ7uMiRkhs+kdve348fnHhPPKh8cXlFg43nim9JT9k6/w5tPv88O773OJE7/6i38bt/UU9cjIDXKjebdrTA8nzi8FURolmt49Q/yItMIkC5TGTfb0KvHpm0/5tU9+m+n4HvXpl6lV0OG6W6tKqGQkKWq84w9Pj2z3L/nw/ff5C3/uL/BsfMGL3Q2ZPUUiaSqsZAZzjT1xRpF2hrE9R1lhzffYpOi94XFN2OqIfuDQPaHXmapHqi2sjwmasHWVmhR5g1IaSRVO2yPPyx3Hm/dYtsiWCjtjuKjEq+9/D60jx/EZnSngIMfMy8++z8c/+h6lOew7X+LH3QtUvOMb3FLSjlgUWjeU6uhtZfjgAxqay+Mjnz0+MA4jriacrkjaIRqe3Rx4mV5h1oVWwKiOPAWaA7ENzhldFNsonKLmubXUuzN58EQcQy5stUD1hFj5rDR+LZwo9upVzEaBaqBWUlaUsPHU9ZzTxh6N1RMORciZ7CLH5jlajz2MSM1MLNj/F3V/8mtbt97nYc94RzXnXMUuzvmKW/OyFiVKjKjYDqOEdhLHMBw4QIAESSfpJX9DOmnn70gnCWDADSNpCAiMQJIpK5JpUZRomRR5eXnvV51zdrHWmsUo3zTmF8dROuI1KfCO1j4bBwd7nzXXWGO87+99nvEes1RScvuozxBZzYa3ws+IcNo8/7ERqm9UNvzJ8SEJ3z8eGJvjqS2M4YisV+LUqRksDVHBtgPRHqm9MCwbXTf8DYZwRA5XptZY1bFU4ZvxyHZNHI53VLti3Ru8whoCZd7wdw5CJJojc6pYVzm7idwy4+BISwci136hu449DRxroOtPMfZEzN4lQ5R8a6h3GJPpraI2UICDCyiF1qHkRpKZgKe1xjhFehe6zpRWsW7As9LXgJVOK4r1inEdMRFMgaZsV4eJhd4E6wxNCwGhk/CD51CVFpXna2KuhjfHiD8IzW78Y1auuVLnxF/5eKRbYQief/2X/02++Ohn+OGXf8DzV3/CbX7FaSB2z3wvxNtKWjtt6Yix2CkwCHhRnCtsuXAaAq+b8uN3P+T+8Y5//NXv8/rRd3H+jjEpN2kUW4h+xNkBChwnw1+9/z4fn97w7e9/j5+/+wYJR8uVuUVuecXZiva9blJvndU1rmnjMEWu7z/wz//kH9PY+NVf/A2Uji2G48GyrjfG4Z5f+4V/Gx8Gclp4erugsrKVRH0tFN9p58pwHlid4Zo2ynijrZVx9Kwa6Lnye3/wt7m0D3xy/Dl+46/+91E/YmpHbcR/8nOEu+/BdARj+Odf/lM+4rt8cv8zaBbUdax2ylZxfaD2RtcbXz7/HsUUvBsZiYz3jg/PV0qvO9guJKQeyX5D7s60kilqiSNoAWsdg+ust5V4CPRVGJhpw4gvigwGkzoadhSL+7Byc53DkqjSkXVvjBir9NbwdmRphqOAxAFZE7oK90c4xoBsleKVW+48pitrPVBCwqnwsYA6h/NH0rYwBvg+kffXwOEs/PGceOs6b5dXDo8fUcvGxUamGAkCNm44uefBWibfMVlw04FkMh+AQMeow49H+qUyds9JM+/SO0rySPT0zZP1xrrN2G4QMxF7wbsjt3Lhoz5SiufHlyeMGmwMxOCQ3rgb71i5UXJmOkfkp5kusG+qgncOxkIXJaeODx5IWOPZtkyMjrY0VA3DOGIwO5lycLRV6G7CqAWzUVJnXTfGSUCFbbW4Uems0By977QCzWZPnGslVMipYUziNlekF2rw+DGwLY3GCkVoagicSWbmOTaqZU8sd0evK59+/Jf4yH+HP5z+AcfjW46nt6Q1EdVQR4tvhnA/carKu7TyPiUOxyNHO9Kk809/8Hv85//075P1yv/kv/M/Y3z4HlqFFIUUwRm/G2Fa59vO85fffMpBHbf2Jb/1D//2frz+jf8po9Gdf94LYZ3ZciW7hpMjUR33NtBpRJv5e7//t/jy9cK/85v/c4YQqSrEYSRthibKaCayLUynkTi+oay/zRfbwiF8zLe/cSY+vKWgdHUYu9JpuIuSEMzN40/QaPzqX/9NpHW++PEP+J0/+W3+0vf+CtPhxOMv/gzyJ484oDbhfjL8wie/xEdvHzHWchqOdJ9gEXocaFpY1i/4rf/8/8nF34jWMF+uXNcrj+eB9Jr4/PMPuE/e4MOIHyDGhhqLDB6Lo68boziWAqCY1tjo+wbuLCYGtmXjURq30Pi0Ct90hnzopOvKlg39rPiTI6vj1DpdM9Y5SstkExAXCa7jaubteGZblW0YuT8I9+dOzYKRhbMuXxftLWuuBOkoyiFF/p1Pzvzi0TCGge84z7rCU5uZtOIZcK1SUkXFUTclxxtmi5Ro0O2Z168MgzhWc2V7GIjF0qpyOihiBsQ+45OjkBkP95h85bZCaxVzvmOUl51Z1irfPL3hw7ygziObQaZAoSG6IPHI4WB5636Op9uPWNb055dj+lexVBUjjm0zaJfd5ioRYzqtO0RgXTqproxeCGbAyI5ltcHt+FIpXye7lSJQihLiTq/0Frq1e1fOd2rdeU11ARsda92w1aB0nNtfEOc3mhnRdUMHwUQlG7e3i61npCNW0ejI14R4JWtkmEa+en/j/uHArz3+dZIa8uuVKAemWJi3QHaNYC5Uf2bUSOvKyY1ozthg+d7bX+E7/91vc2sLYfgGH01PfP7hBTFK3q6cdOWTGPjFj36Gw/FAKIbmO3//d/8TUnvhl3/uX0eaYIeJVTJ2XTHDiPeJSSOrM3gS1UQGFLbItz/5df7yz448hEc2UcJs8a1C7LAJQ+x0Dtyuyh89/x7/6Ef/gNfPXmjnyCenB/6N6X/A0b3FdIf0/VAqYSDWTmuFCsRi+eT4Cd1YHqfvQZmpBMZl5KN64/tu4wcSkZb41fEjPj4JzSnWBmyCjf1UfJsh3T7wu3/4d3laP8dkS5ss3lpWe+GrywreIaK81cLB3nF3b/lGTjQtpKZISzjvuOWKMxPWFnLPbAuEo6GWzhQ30M5WG84FjIHD1ZKaMpwHnj/bIDWqKDFGLrXiWsKXTogDQ26ortzyDWfdflUaLRdTuCVhMoUwBRZfOJk7nrbCq87QM6FC9Qs/ennmMJ34hUMk+ri/Lr2SjJJaQrOnhYKi3B8mbupA2o79uVRMgPPZfz3MHniIRy7thmQlF4M7VEqMmLEiGd6/vmOUQNGAc51SGkO4ZzgFeL3xJ2VmXgzONRDhLNCTkqMgac8tXcofsPRCznuJ5SdZfyE2JhTStqLdEgZDaTCOlpLt1zgUg3YlOoc2j4xQtaCmU9NeF2oVmnqaZuKoBOew3lIrGGfx1uBwVCmE6HA270nwahisRVE0e7oFlgesvcMFg+ZGnjulGYzNJIVyLLzMC+JgflpoB2HxEfmaIf7xXaSWTtET8c4jJYMmbq3RjFJaxtgzzjiCTzyEEyxwsQtHFUbvaD4icofOmW+NE+/rZ0ztHcPg+Kb/Hg+ffEKTCmIwFJTA3/j53+D+5yeSOEIvVGbGDapAWVaCC6Sj44FG6iOlJ3rvmNb42Z/5Pt2CGQwH7TzfZpIYhuj21K8IwcPf++3/B/P2Y0wqDIcTv/KNX+RbH/8yd+4N2TnM2hHXGcKIugFxGbWdOTfaaCEbnCakWNwk9ApPUpjGe35pfMDOX3Hijk+8ZdbMsTo0X1iykKTxfHnm5eWH/Mm7f46rlV87fsIpNP5ksnyYN442UG/wyduP+Xlmvns48O3zibfe8+VxZX1KtEkYtKPN4SfIxtHnGW0zw/BAVYd1CbM0vOzjGdhCF8uPa8IfG2kZ+OjbHmg8L4W+rkSZGGJEe8dmYcMw9gviJo4PEyYEzsMJmsX4C7Z6fOsYGdHhzGT2vNHz60oyhfm1MATIQ+ZgR7oqr3ZDQsWmitiBZhJbMygrf/T+xsmd6LnQzEazd7uW3QWG88Tl6SvWOeMElr7HHG63C5NEuu7Y3svLE8ePvsn5+IamkQ+fvVKGxHkJlFPEXnfS5rLc6Ksh3A1889vfQ1++xA4G8R7azLEdeGcy/afZK2dk5yHlWglYanb0UTC6tz1Nt3ivOLeHJjGKBUpXxCrGdnoWLH03+arQRXFO0K+j9GMQstnQKkADjbihI1opFUoy+7/TLLfXb2P9hpYR9R63rZxMx4xntjnjsmUcE0sJLE0p9yfO3eD8yLztg8CtV5ybqNeESON1a8TRc+iZ27JRuHG6O5MU3gTHohl5rsxO6WFjNCOThTYqpUT+xrd+FdUdgdGMoQGWgdZnDErZFDEnXhVM3wvc4wa1Jq5r53C2xPGA5MZiLUJnskK2jmHoLCi+d5aSscbz5vHI64eV+l5xR0tH6bmxlFcwkb/6C/8a3/7GN2H4mG0z0BqxF7oYjLPQPeIzGEN3gWM1zALWCOEh02+OKhBHQ708ExfD6+0zTp/9IY+Pn/Lm/CmBDZ9XwhAZtCCp8j498fYwYe++QWiBSQJeDD/84h0/frjnd1Ph9Kbz733nkVN65HJpnNdGkl0IYAwwK/EMQy2IMbTlihiPTI+Yahlbw04TxVSCE/IGn8+Vf7Q+cwuNMh+RukAR1jlhB0MJnslPFF1wVRjvCsv1iBsaLheu72Z+Pyz8lx/DRwbq3OijULsipbC1C2INRxdIrvDadpFrHgTvLflJ0OkV4y3enqktcTAb1QhraQTrsYzcDZBsRNeByyKUY+COREpPqGvEDrUUrBzYuvJ4nGhpI2eDS4KI8vn1K8L2Fber8DgOdCMk7cjceHOMtFbYzIT0Ti7Ky9OPeKFzWJQqbR+An5SYG+ifH13gz30ZDNZUzoeB2sFKo2272NJF2feRtrO2xSjaLTk3jFHEOLaLol4J0VBWpVZFWyezyzInG8mpYiTh3EBvhVo69uCpS6f3Toh7psWWjj38AQP/GlUcNnTaFGjFAUJlQ+rGw/Qt1nRF/IH7w0fQZ+q87IRDt7ed82iRSyNQ2FInhJF8skwuML/O1GSgNy5poeIZ70eSwkmgWUs2jbYaYhAkN17rxmmaMC7gW8Hnyi06NtPRWGhXCJKIdiRh6TSGeGYaHaYF7JgJw8C2rXSvLKVhUuGpeKKOXPXKUAVzrGTg7qM75rWQ1w0njcE5fv27v8n05p7JGG71Sv3i9/H1gDtNkFdUDdvLB/zDiVMb+dgJ716fKF75do3ECJcvL3yEx2jHb5339R1nc+avmgSffMQmK2/lHVv+gHdHtr6y5oXgKg/WIv0CbU/IRwq9w5uHgTdj4K9sHXk19Hcr67CxjQPeFEwrDKvn6g2DF0ie4X6g6Iql4yfDWqHkhRgGVCouN9aq/NMt8wf5xu8vN2yNiHREHMoNF++Io+UlX6jbB+ZVGG2lfiaMH1dah60L9rGRtshvXTv/47sjb+4spRes6bhw4MFlUlPmaqliuRM43D1Sh4W1HHH3hmP8mJehUedOuB6I9kQYYSlXbAl0a9nqTBbPcByYxgJBWZxCMWiO1KEz543xHBlqw5MwEtAYmHWhFKhrQpOFpsw28sZ7xHhueSElJR8dY3NsY2dslWsCO/qd5jElTHG8PRz5w9rpf7rZ/v9q/YXYmJRO7waMEkdDUkvXhBFDXg1xEox6rLWYbjDG0lvFOsWKEKcKTSil4oZO25SaZEeYBKFNBmsE5yPWCNUrtjfK0neYuxVazrQGqoESnjDzgh0bQsdqpVhhQOgPZ2Qa+fV65FenR1prHOvKnDq27VmUKAMZyHOjsgs5Pz2ONAPu1skK5/OR18uF6RBRPRFMg1qJ0VHSSquKOrBiKF6BzMF0uiZiamwlcGFFyp5ivrvzDGeHWzq3NhFDw4aMGwbMpqxW0Vun1o1OIibLNVbOxnFyDmMC3R/ZXi84aTA38nrBYohiWZavWNKG74b41RdMeeWbNC7lyrfHN0whYPsLSSxWwd1eMevGSSIPpWIHy6wNyQarHWNAq8UOI/fe81gbKVWyD1hVLrUQs6XZlUlGqoHJn9guM3Y40soLxjua7FKJ4EcuumFs4/Qw4phYc+cwOgbptCwsDqIBVysb8LKuuH5Dwx3OdHqZoXWKHLEvGT1FSsr87nXhC4RhOLPOnaMkXlOnuYiPeyB40oHclVI3yqJIKFzeVY5RsD5ibpY3IVJi43fzlX9rfEQ2i7A/f1fg9XXBNUFHCKPnZRZMMxxC4v2SWG4D48eZ19zooTPPkZUCVLo2pggl7c/I1UfcJXE2nRYmeoFeC7Uqp/GRYgqXuXB4HDDG0D4sqFNcCxRWqml7ZskKwTq6VA6DcEkba1+x+sBDWzmezlzUcb8oZkyY8S2ne+Xgj4TXBffnPCv357sUMB4c9ApiFHFCyw1tQMuIU9C9G9Vyx3RwWOgAAecNXRutDxhTkNCwXhALphXSlunVYEPHD9Cyo5MxziJbpRWH80I3lofYeX2pqA8oiaux2LVQZcG3geQz0zgxushKxbSGC4FqG/l5Yd0i/s5wuJuorbFclFYLJsKSFWkrsQQ+eTzw0gxdhaOrtGthfq6Ew4guNzR01DoOCD0eKT7hk6FtFZsrkx/ph84wCNUatm45A3ZcKa1Bs6wvV/raqO0V7StleyW0I+Zw4OcGR8gr5575slc6K+Zl5TuHkbxtNNcxtfPRNDD6hKyJH95WvvXRPQe7c747B3xeWLYbqQveWMQWRrvxYg1FM9lP3NHoDtQbQm8M3VBiJnXFtEpxSjx0vsorRydI6VQzEUqlS6GExmXdPW5WQKaRKhmGO/y20VLj/mGkPAt3MfJqn6CANrgGYYyVuG68WuHAAbUd1xJuPKBV2dYCw4luO9kqpionGmtv/Px44MPtAsNAqAuLdloQcJ4qnb4+sxXPUDo2gEHItXEIQhyOpPV1nw5AuC5X/sFm+bZavmMt7uyRdOM5V27BM6rjzXFga9BcpnXHqIKRjnEb63PEhA3tBhcX3HPBnSK+ZXqyHOPIUJS7cMflsTMvK30OjNOJWL+iRc+1Gt4yUE3l5blgSEwnQWfP4leiGZBw5G48cilXXo3Da8bRITiiNrbXz7htlkUT1g18UYXlXeWjvjDzwp08Ym+C/nkRLP9VLGXnfOcKwh4HIAmGHfZWuyGKIyXBxMZWEz4Ipe2KJ6RSesAgiKuIa5gWUAzOKSUbQohIbJR5Z9uUtO3FcG044+ii1FJobBgTkIPDOkjVc/CCdR0/CVoTr6bzWHU3VTjlWhubrwyuc7gPtBLYbKe2jXUTTlPE0WmtknsibYGbMdSUKdni+3uWaNDjAQ+Eg6cVT8PgjdJqptRKKYndg11BL0RrSbeMXr/iONzht8apr2i98ul4hz8K9emJRy9sWyJ0h2kvNHmlL45jstReOfiRY08kk3HHwCAL69hptn/NsepI84gX3g6KBM+HDx17LNyPAZ0qrYCsgoZK2QrFHen6wnpTWtznpqIYpMHaLVksrWcmMZToUBO4LBeEgsrEGiuxFvRupNDp1TFpY94SHxFJvZGswZTPCOHI5gowgCjLsrFG5XyGXt2u9rJK7IFYFWP3E0ZRR7qtiHRSclgNODJG2h5ZEUPhyK984vjD80guhS9fn6itYRVatxgbyMajB8/lyTCMBXRXGMnXtZkiB+ql82HaOPiBWgv/pBUeg8MXRzCN23ZD7ITrG8tSaDIwcSX7R/J8JVjPFBt5tizlTCezXA324ChB2LYBWkcvkS0k5u2VnhtGIjYXLvoeUxofhRM3hdvLlYdwYq0bpW7ENeKmE9UJLhgygSwFkyxWhL5llt7xd0fS80I3A7dDI+bO2/uRk3SenoXjYWBdBl6r0IaE+Qk1KX8hNiaMYozgbafOgoy6jw+YjW4sbel0azGUnZNU98R0L51eDWGybHWfJLfOgXf0XjFiSAms62jdC75aoSSLmLA7yValDUqwHZ0Nm4eDj+R1pa+RZpTObl7dmkG3Rr2DPCSGMuF7Zh09J420lrhVxbYbQxnRpoTiWLQyhIql0GJgMoa0JNQI9+MGzVO3hSW/py4j8/Udp6bc6j6e8Ka/8q0xEHImnByxFcaycV0TdzLxul2gjYRe0RZ44JWxJPL7jbs+gU04GmqFFcsYOrl2erCktTAER1srmhz50Eh5Q3PDTSMhLzQfMdlTo6c04bQtyOA4D7vEc0kQLSQZOcWVhmc83BHiiJ0s5fqyF1BL2029a0FZ9j02Q+0zT2VFnMfXgVobqRcOHDBzZxoNWwMvGw9nQy+dGE9c5oXLYeC3/cJjEc4vC1OzvGuJUAfaKsSjZ3mtrHPDupGmggkQUF6XnYoZ1FBF6H1X1Zsp0teZWgtxclzfv/DF8xNSLabtUwJaCoMbGAaDlXGXqoaF0i1NKtEe6TnR6oYtijmdaEUJrrNuGz8USxLHtTVqgck6FHAhMjihV4sdzhzKSjoe0ZcVuuEYhClOXMsLNka8sYxbRwbDU9uQ2LAycB6V1yz7ZIHb+OjRUV4fee5Xtlw5xoHP7Y2z2Tu0/dYo8YqUzhQj1sGXtyuHOnE0jhw8vXhEPUYDWlamwSDiub2+Eu1IkESdBecGQhCadPhpnpXTrvS2jzgYZ1huC+M44Jwj9wrGYFyhlMr2YonDQO0Z5zziGr3a3S1nLdZ0KOx3QgeIgxpopTOePNWuCJYmnUkFNUJbE4sGomvYJZBjRuWEP0yorrSt0jA8PH7E/MU7Nt27gC8eQlHiXGgC3mRui8VEoeQbLTXKdiMW4cl84Jsc6HHhbquUVviEE/X6jmAdLnuqm4mDQ1oiGMcfvn7Jf/CjF/63v/Q9rCuMYWBsA0Y8xQmihqiWUpXjGDHZgIdSJiRnJFrmmpiS3btlwWGeN3K0iCq17dorOxdEKqc7Q0qNRYTJOUSVzsDhMJAUpmlgvt4I8hF35sZWM7Y3tBe8jZT1lR53qegt31iXGwe9Y6Ny7AM1bWTxxPrAi7/itWHdkSEH3NiwItwMHH3nxEQtmSUa5qIQBmadcRguuhHF8lv5hd/ZFkw22POZ8+OZ89PMm3nmUhLu0pkuK99weQeioQQ5EhBOw4C0Qk+ZokIbR9p2w64Fh6W7iq6Vd5vyD12itoWqA0uFYTT0UUAb2wLTwZIV4l1k2YRWVsyWsEPEjJ3ba+foIqZltpzRYSLcnZjGI9MwMH/xJR8PB5p1rNXgB1g2i5ZKFMPBDtz8C7c2ML9+jjseaC1zv3Vy75Qp8GgPzJeEOkPWhfU6ELxyPETG8Y7LdaP3C7YrQ29sNFxziFOmEFn0wmo6UQxxiMyvG846MoWVJ7bLQhXDiCWc7jhikaac7ibmkrHFsHZlPJ5I1xvleOPLvFFK/Yn2hL8QG5MRA3b/BYoqxoOaTukW7w3SAqUlrHGYoUGviICLnpQcYgtBhE5GW8R5i5qKl4CxSisO2xfWm2EKA70XVAVio5vEfAEJGfUNNzq6VXp6xubIKVRmDM4EpF0xp8hXdeM7PSH1hbfVMgWD5AveK/f5wmMfKNsHPrKGnit9PKLLC2NYuOaERiWUyNBfyBauqRBsxdVCTJ3ZCpKU1gM3X7m1whsdKckyOEO+rni/oFeYjwvWBeyysJIhOCYRnPf0rWJTBuvxLmIt9Aj3JvA0VCbv6EvgPA4srzeMFzatfDJGysXsLeJsuL0PlHgjX4Fh4jLPWD/SesUEZewDHuHu0DBqIAoTmdF52i2TXaJME745qlb0dNlB/kuiRM/iDI+m0+xCs5FuoPQNH5VqhFGhibIUg1H40brx/3p6z8vRsPWJ092JKBtK5IdW+cfze7bbxhg7qsuOelk7XjJ/7RT4ZTtw0pn82gkB7AShFnILmKGjg6Kb8l9Ww9++vXJhQ52nfDUzTQPBem6vic004smxLjOH+0/oYnFi0ZfGcDI4MdThno8k4Yywtk7qiamf+OSy8JwMLVyQhze01ZJpmLAxXzxzvTKvM64q3/7uA8ftwHa0xPGO0Rl0ctTXysPbbzH3jevtxhDizg0LE9u1gR25pEZJCZGFpRqcnMA3TPOc7oT6TmlTRazDGYFYQDdigOGm/Hhb+K59w/HsGQfP/JIw6ki03SuoCYwhaSMb4bq9Y4sDJ7sim2Dsn58l5ZfY/XH/n/WzwP8B+D/xZ+WV092mWzNAxbvd8VZyxnih9RUR8CHsie7Rkq6gYSMGSzdCb/sbOcTOlgRhoGpDiyGOjTAN+wtkCk4gVaU12Sfth85a94JrE4fRhLMJc95HV8Zm8TZAqQSrmCnyc+/+GZ+eAvLhwkhEQ+diC2+MYF0l14aVQFGhlPfERUlBaaYRXizxDWjqrKVjzMLhODGvncvWsWEH4N3FgvOw1MYDgnydeVEgErEm0TXS84XzeEcpBtuhmQ1r9pZ2PVlojro2AoaDNNIGb6bIiHLpjZda0MHTNpDoyGZAzo2xKxo3Dux2YKLjNq/MpXHQitwt1ORp3mJFMU4J1jPXxMVuSOkc78/Ua+ZGozkYJmHRjlbDGD1GDFNSpjjxVBu2Nqo19K7cnU483zYa+1VnToX/8MsbX8XOwQv9klnXFXO8kvyB9fIjpMBl2B2Cx7MhXRKZGf1aYvF3v/oCHk78zU++Tx7fIwePXivj1BjGSM5Kqo5sDP/w+o6b3LgsgrFCPDrsOKJDxWXB4YgqtGNgWS9MpuGw2OMdr/mKlIXJRQ6l87Oj5xuHt/zM2XG9NUwMTNwY5UTuV5bDkWPozBsE00nGoOsNYwLX51dybcSueyzEHiGvVKnc1ldq6bRcKX2m+SOpNYyRXVFvGxobazKIh14yxzqiUqhPFheVsxt5pfEQPJeceJcy03ak18onU6fnG9JH1HX848D2stLUEGVgwOHaQooj/ZL5sDXklPDpSCwv/IT70r8UWvefAb8GYIyx7Pzu/5Cd+/1n4pUDId066hy5R8YoaOuYLjizu9qIe1tYxJJSQV3D4OgZuus7TH7wpNTRlnEhomKpKbGVgi0GVUunE91EbxtbMUxhf3OcjoWyNISZtQLpHdRPSRKxVuhrAgs+ToyHM4f+KcPyY9rRIlmpdmPqluY8ozHkbpCyK5Jdb6gPzCYzMBDPytY7sTd6XQlTJNOQxXEXhB5h6JHP50TvltrAHU/o83scb8DNIIFD33NcchjJ3nL2Z1ZjsDchmBEb9vGbMHxtkImFkg5E37ikgt8cMhZupaJZsN5xKJlUb2y97kf+HokxM5wjfq14hMeHyGMM3HrneUuINrY4cWqVeVyoy4pbBmqpiE98880nLK+FmwZSSrg+4+PAeTjxtN52O65RaAPD2ClrQrXz+XUhpARE/tmHH/Mf/eCZYoUwWb4Qy8PoGQ28fHnBTDMPjw9Ua7gTKCjpqeJCp0VLmcv+81TH31lvjJ99xi+cLJMdSQ8BCqyv71iMw0yeH75c+KoUqiqmbrjqeHu+52YLUjvSB7x05gwtC2I21mFiNAWvFZkcj9nz88HxnfHAWyfYYDh0z9iEr16u+IcjagsC9K2QzMa2NpQTEPlkekPRggbFJ+Ws8LpG1GSaD/z912e+Xy0/d3S40x23W0fCgMrEuXe2PnOUI7mCGkvwwoe2QL1hxjOHY6NVx9oSrToWm8hqkZr5vF2I04oGR+gja2lsTxs9dHKrDM5ivZLKgLX7B64Xsxf33V7Mz7XxEwa//9RXuf8h8M9V9Y//LL1yxihx8BSFSTtaO2ob4ix1FcKh0RBqyTjj6ZLpzVPo1NqouYKF1sFYR7TgIjQtWAJlK4iz+3G6GrqpCAbnlTU7hkG45AWQ3RAbDGpmZDOMEWQtNAVVS50z+U3lB4cj30iWbgLVVEoVDqKsvbNsiguGbWnkMHFuljY1BquwFWpwtF4QGxjPD2i/YXG0sdL3HxwzCifvOYjFiycsnc4RpVCapdjAFjNjFIJ7pM4NbwteNnToxMPG85z2nkiJXJrwsYksaUa1I5MhHJTbtZFaxYfAZiomKYu5UkVoDDhvyR2WZQ+unraV08OZcRCerkrtwliVJpUSK74Icwt4Ucw0kBN8Jo23dEZR5qWghwN1Vt693JCh4p3y1cuGPATStlEJuCVzN02UxdFs57deN/zxwEfTwI/fvycH5X1z2GgJbyNGFV2V2hLRGtzXmR5TPcFDqh3RghsbTUZ+RyvfHwY+LHvzobWVMjrMKuTXhe4dNjiO8Vt8+jazXG+IE0Qiohk/KQbHWYRQPDd12BscDp7vTwN/WR55M0G4M+RNiKNBEjxfVjQ6vus+xk8GNcoJy2w2mnng7djZfMNrxx/viLlSQ6PNkdcl8aN2412I/NH1xj97XcF7vmkHZN1YaqHLC60F8gglKS+9ASstBpZkycYyHM+IubAtkduaeXwzMBmhjkJ7hcn6HclSI30r9MngbCGeHTJ6bAp89v4LjMu8OXRSMSiR3gs27u+pmhcCe87wJ1l/2o3pfwn8n7/++r+RV+7/Zyn0ujCMI8UoW6lMzoGvbF3JxaNiIQsSdk+cmr3VXkoljBY/OKBRlkZSg0pHjYee8aPb61S1YI1BNbO1ioiAFsrVMdlA6kKNCaxDpbKoMvSRRW+ECMZ06JaWC89dcc3TlveIHtHgSM5iXjtxHOi9k0c4mEwvhubbPnysmVJhjI7W9mvibYE0BVpQjuJ2TdF2o4uwNUepSukz4gxLFXIqFF8ZRoO3lbTNmOnIrcBdN1yd4fKhoN5g1JNDx3Wl9UT2jTgdsNVw2TquVz45HPjQCluDKECNDNo4hIANBpMqxo3clieKGOanz7DhDb3DeRT6bUMloTJhjjCpYVqPxMEz54S/3VA30sicpgFrM6on8v2A4RU2yzlubLWTNSDGICGw2QsS4aVaHg/wcs388fOVOhqcFrYbmJQZo+7X5kGxNdAilOsGHkzOmGsnmID1gsOTBV69YsvIwRnUb9g6csk3aJmDjHyuiZOz+ARLUoxGni+Jdai4gyf2xsO4++V8t9yXxq9/45GfHeHOWA7xRN0qeV3BVm6LEq3w5pNIyx5tgZeWsBU+LM9o9Bx1obGwXQKLrozbcZ8DLUqJV77aEv+XLz7D+ANRI9IUH80eKxGH5BHfV+TYWbPFrB7OjTkZ7jRjvaNUx9kHFnOkr4BZoQWe1w1X8w5YbJVoOjdTuA8nrAriPLl0jsbS9Ia3QIHt0lG/EqJiRrt34dKK7wNlarQ/71m5r0UE/z7wv///21d+Aq/cvyi8XLtHE7TSsKahOMR6fMgYC2nNWAK5FVx15A2aJsJgMHgiFrwlm4IxgtqGqbsiuRmltSs2BGiG1hu2KuZgCMOZvCpbucAUka6YDPSCqDCvV1zah3xtsKg3rL2ztgNOLS9x3F1rrtB1xB4qpTacs0wYGg0ryjYbDB0xYH3FiuNFK0LfPXbrTI+WW74QuqVFR9RXvCu8U8Nf6Y6SE3Lw2CAUCnVVHJ2u4JYrVg50M3I0mWIbviyk414rcjZwdzdyWxujdjYiU+ls1mBTJrpG+bpofvCdUsAFqMuENQv5kgjVo0fH++eCTA6rA5Nkns8RYzxzthx64WAaeqwQPdE4pjVgKGQt1KZo7xRzwZpXcoKPemaWA+iKhjtqKlhbWC5t11znyvf7xGdSWKVgayWNEZc2whapYmjB0TeDwTCpJdyfWF5vwAZmRFUoLhGlM8mIrYViK957arYsJAZ3ZO4L/aqoCvkKN2Ykdg7Rc5S9ZBDEEptjLpl7N/LLx8gvHo6cG2zdcq0VKRdm57HW4UZgWcHbvYvXAnN7ZiCzpZEweFY3EdSipnM4g8/3JIl4Gi2vVAPfOn3Kz2LpoWPywGcvbX/yZ6WdlRgMzR0Y5UDuG106a8nEUJmbxdQCdqIWYe4VWxQfhccwUM2MDEfadmFSjxnvKSmR+g3dYDw6kgR4rdw9OEw6cSBS0sIYT7xuGecKOTu23NhK4ZtvvoX8hECmP82J6d8F/jNV/fLrP/838sr914WXp08PKma3xqIW4w3t6wdY3Ii1UOmUvOEiO6TeR0yqaB+wQanNUOrCMB0Q04E9Q9ERWlupqvjWkQ4SLMdhYt4WBMVY5VAj2VhKHzBRiMZjnu2OJfUF1NGyUHunb0J+9Dz9uHJ842hmpRfL1l9pFUzf8O6IbhvJCyFnzHjG5T3J4FrglhPQWLeGNxOiC7oJ4TSirdFNo4fAFDPNv2LNyLN1jN7gusMU6LZj3AnzYUYOjsMtYx8D1e8O+ZQK/UXpwTD3FdyG3wybr7TW4OGBUKCq4d4a8vWCDwNUUFe4ZWEKUFpDe8TcjwzWsow3Nn2BkrmJxVSl+xtiRsqtk1HGo+H960LQK9MU+CJZfI1IaAzVEr3ga+O6VNJ4ghhxvtHnhO8dnSxuWdm4x99VPpkbx1Z53zu1C3prmKDo1NHmabpgHZSbJ4sjnAZEC5oCTIL2Fb15bnTMo2FynVDADsowTPjuuL5eiaPhpmCrhxVsT5y8Z9ZKMIGTdMrXVMtD7PyPTpZvuk67RXKdySr4aWK6e8O76yt3fcObHedjfcC3ylpnDJaDm+hzpgyOoCuXWeg2M62OZhKhCx/SlYPtBBzXd1/xxeWZXguqlo1Ge3PAh4mXLTOtCR8HGjOSDT0IJxOhOawZqD3t9AxRgolsx4LdOs/bjWY7z+8uVL1gwyPSDa53rqZwdzfRBN6o4SLK0woTG84MWB92msLhQDQd22ae8oa1HmHhT3le+a/Wn+YC+L/i/3uNg90f97/5+ut/0Sv3vzb7+jf4l/DKiRF6L4gFcQaPR5ul5koumZorrTa6XehdscFjTMPYQPAG62FLhZaFvhqsCWjzdPpeT+kOwe5AOhHKUlGzMUmHtWEbdBMYmhKy7uMnzUAXuli2tZN6Ye0dHQWvmfcK80dH7NbR1VNqhWZwoTIMbzCr5RDe8FE/YNqAkBG/cLtu5OTwOeCyMIghqcGHI+I6XTJihPt4xllLV2F57cxNmOaE2RopXXHSCA1agXzncWHEnAaCGTg3S26JbgeYBox3HKKgDFxDoYujAM+vT7xvV5p2rteNa8n4tPCaOvMNaprx9pUbFa2N2hulvjJlS1OLMwEngnfKow10LeRVyVmpa2Vyfof5deW4VSYp9FzAdwzKtjkKjmw7LmXYHKEnhtlSc6frwOgLgxReeuf9vFFeHSZ7rAdvD/Ru6SR6aqQOkHD5Rrou+/PkPU0MJjoIjT4YNCc+7RbXC2RI/calX7Ex0jXgpPGj7ZmlZhZXuKaGXoVLLcg0Yl2FeePXT3d8Z7hjGu4IYyQ+OkxsHHxhnZ84a8eNHpci4g7Y5Ng86MFSBkPhQI+OOyvYS+IU4X40nM4jkx84RM937+55uLvjeIgQFBkmUhMwHi2B3h1JBJsqWM/7VrnWTO0JGwMvVGY/s9rM/XBH2QxPlwvXpaBLQmqn1Zmu8K3HA/fhRGqV7GbWWuiq5E3Y2sgVg90shy1i+gGkYydP841mLEu6cSmNFcHaAbsJ5s9zJMUYcwD+beB/91/79v+RPyOvnDE7GrYWGEYhJaU1cF+3jVGDd3FPeheLVYeYjh8czhkUg3eKOEvaCtINxlvavNFFMR28WHLer2+2etJasB28LbjoeV0zDthcw/pIbTu6xPtIOPi9/bo0xGdMr/j+gHZDlU4Ie60hSuVK3FGxdSPFQhwcKx6rFtGZ6T6SpWKxGG3c+QhzZTCGWYWheWoFtVeO1WFL5DQK0S1sbuDYT8SxgXjWktB8o1jDPEDsjXeXAu7GtighOuZbox89c2/ELTOdAmmprAXGQ4NXz6u7EuzAyVmaySQDpgnBeZaychILQ+GyVIJUpDWOashady66axRxDG2EMHM+TKxrodPZeiCskDGMveDGAZZMfhW20AmxMFXB9o76AdYOk6X5jg+GXjv/2cXxd9orL2vD5o4OhrKsJNPwzqA946wjisdqJTuPm6DNDTWK1UxJK9ItQuTBBf5SmDCjZfaVZkGWSjAnGgt/+/XCf/r5C8N5RJ4772PG1YU2V9phJLrEz3z7Lb9wJ0grrH0guMY0jBz1QLUGGyq2KpMc0dIZguBCJImjtQJb4u4U0XmlN/DjQD84UjVQZQcWpo0yJpyO0IS1GwYC9TTRt0wbK9buzaL748TTfEGLcjgPtFzJ6YpvBo0HZK68Cy+s0ri3Hb0U7H1km79mR0nhwMR4d8CmF8QKzRruxHO73DglYb0raDcsW8eLYTiOzH3jYD25rvQwsL7eiFsFXbkMe0r+J1n/sl65GXjzL3zvA39GXrl98xFQQ84FsX5HIBohiMUPhrJZ1E9Y02ipI9JRs9cCgjOstaJ4rCvka8ENdhcQlkZ0Ha2CWqWkwmF0+yd7qzQRYq8EDVS7YRqIrYTSwCi5VDCCFojO041Bm+FaN8SDWyymB8Yp4pIhHM7kpxtXD00VNSNDvSJ+JG2VkUI2BqmFxv4iC1BXgxwcsa6AQ+fdK3Z/aDyFgg2Qj0JxN9beMddCt4FXe8OLZ6gwOM9LnnHd0bRAHIhmpvTM0ZzRVqgJpuTQcGFrw94ZUgOyUdXuwLDQcV5I10J3kcE4xBdYC0XOBPvC5jbWnBjljlbAmcjxFBjsAeMr1678/Vvir2lgmISpQVXBSMAMSqh9T6m7SAodWRsmCRrgnVmIq6W3wt9aL/zRi/I+3Pjud77JvFjevf6YAWHJF2zedVpyGCnV0o8dW5WxVl5LwwWhpsLVGA5qEOf4mePIt9ltItEYmvdYe+LzfOX//sNnfq/PuzwSdrLDKER7x/X5hteGs5H7snIqE6l67LjSsKyrgDqqFnoSvA+8LheiVEqxHCRSCZSSaNIZ8pUWE2gnBeEghrgYskIPheYNkz2gEbyvxGvh9fUdX3UIl4a8FTRnhjiS1sSJQD/st4+5N8QqE4ZrV5gSNQ3IYFnXTjlHjieHjkpqlYMJXMtMSA7jIDrHp0xcxbAuF4Zzx5sIYaV6j9JpBVQC6iImPbN2obfK3BtDMrhS0J9muoAqNNMw4uhd966AG3Chk7YCyVJrxXrdoVmbYGonHvfJ/KIJCWA0Y/reUSurEseOGQwILEvHtEJtwiEGTLdY6fvfLYI6x7YIzTXKZnG6405cbqSiRBvRkzKXiBODkUbaNpZbYrWFoQr94AjXF7aunM8B1khuG+NU2fTCMUY0QF47cUqYZSJI4V1tTKNgl8oLhWDOvEbPUpW+GGaxPDPQyDTnIAu5r6iduEtHVIRuoZSC8R6LIqKUlFhL5yEEeq9ozGzJMJ0FKyOhKQ8x8FwronCoYEzk7DvXBi1UvM+UYvDV4WLnIML16hjtgftuiNVyizvjetk2hM5nJfMf51d+fIO/h+U3NfBr/pGP4sC1Jc7N86V7pRthKCO9esSbnRhhC/fd83lJ/KfPL/wwGC7+FZbIl+9f+N7dHfEbb8jbgnk5kduK1cJ6uzDjsE1xxnKdE0O4Q0sHe+I+3JgT3G+NX7MnrMCWM+PhjpYLH3jH/+2y8AdyZZIKfaDjsBgeg+E6X5CujPJAlZXswJkzIXi8yZjYsEaY7crBHkjPCxIqOEuMB25VkVlQl8mtY43lqg7yAR1WagIrGRkCS1vZeuNohJQ8PVdKr2CPmLgQ8oY97uDBrI5UDMepsb5OdHvB5IFYd8yPmM45WSRMRD1AvVJHh2sbEyeaZrw7MbBzqJZ55rU7dHzEmgt5zZzuzzwOI7lWNm8IWfEhMBsLTpm1kOgEo2z+xPA1RYK50nv7ifaEvxAbkzFgu6eVHYNrVSg9kTewTehWqAYGs1sopqOhtUDdQH1Byo5JMWJIRRH7NVBOLU0zJjmcyG5jsY2misSGS4G5KUOu+DHwYhMBiLlhjqA50+wZd3CkqkjOHMQw9IhvQggHWnzFyEK3nn6tLKJs2XBdG6PrmLVT+oYdJp7rE75EGpb2ovSUMGdlaJ5SGnUVHoYzfRxx1sBcGEsjp0b0nl5mtt4YtoaNleqVuCrSDe9T4uNxYrJKHJRt2TgkS2iWqEeSm3HF7aaQfGSYItItpihRE95a+iaMZ6E1iFSqDlh/Y1OD7UppjlozX20dv9y4mUIEXCpYDfRi+Tw983/dFiQW8sXw7AP/aDpylzPrdWU8TXyImcyBuHR0bFhrUGd2G7N2xsHwD59nfmfNPGhH2oQfGstt5qte0LcOI43TMHFTy+cvKyZVTseM2gk1Ga8BqyvN7DqwXoSjWqYgPJw6sq0Mcs9SwZrK57fCJS0MZqRq5BQ2aqqkUbFeOJw8pndUoHbH908fc3SGJXXC5FlVeHGFEwP51kihM3lHXS0pK3rLJMA6Q4jCvHSGY+HyWploqA3gLM/VY43haCKD24hm4svrje5haE/cj5lrD2ANdlHuThvRvmVtDT06qJGyOpoJVN+R64pMnpY7VhO27ELPUhu9XFnnlcNDZskZMTMlQy5CN39MNBPv37/wEAeKVjZ1NPUcp4ncVuryJcPp7c7pV0cfDCbdcKZSJLFi0P6TeeV+QrP4n+3SDqkkcm+Y4hALVjwGixsNhoozsNXKeuvkJLRcaLo/cJ1dILC+ZIL1GAtdOjV16LtVtRela0Ms9Kr02pGTcn9Wll6hzExBsbXSWTAlMi8WEIYqTK1BO4F0ck5stfKcZ5QVN0V0NQyDEHVEp46hYulYnxEGSELVgDTBTYHNCqtv5Gq5rQlbDRwttSfWemVcM8eDJQ+WPlw5F3Bmgs3Sjo7SHT5FFipradwZjxEYhoAXtzvojyPD6EmnjMrX160+UfpMTgvvtxeethuXK2zds7kLLy8L11XRzfGqN15XwdCp1WApzHHjTgytW7odcL2wiZBbotqN/6TAkhbWDMMgHL2y1MLfLStpVHQrsBpOW+Xu/kBrloPdKaUnE7mUwpyVsgpp3XitBv8wkUpCdOGqyud/8hVPn115eX0iP105dIM3Ac07Y9w2T/Mjl7LSbgvWKVP35OCY/IhvkcNwxntlXB311vijqmzjQLcL1ndu3bE9eDCJ9VoJ1dNroffEUTJjA+smrrcbnz8/UbfGvRdMBxsNm/EIfv/AtHB6O/D9n/9ZhnAi9sCdBOKSmCQhOnAygXXbg7+LzVzNK18tN75M75HiCWlBxZCeDbpcseuNS8nE6UQ5bl+TNjZWrZS4p7BjiKznAT/tPHvjjjgJnG2nlEKsnbdvLem5EsaAGw0f3Y988zHyMJwgJb51ONDjREHxvnEWx5wW+lbwXuj5hby+EFvj1AXTBHf05M1QfPhJJSl/QU5Mwr47+UraFBfszkK6NWwQfHCw7eZdG6F3CL7jqqGZRq2dEB0yKrVuVK3kNTGMlt4mnFOahd6g9EY0nc1kyBteB9wARVZitVytZ0AQsVgZYOkUn1FTUF/JaqimEp3lbpi4y3es1wVF0C50Wffh1xZwq7KNcDe5fSody2Oc+JKV051ju3bceMBwxd95dOl7hks7S++42e7wvGXicknInXLAs1wK6pXNbiCQdMW2wm27xy0LJXh6g24yc5sZZ8H6gdYXQrMEa7gbHU8KtIa6xtI6ZXUc7wcoV5zxHApIVPys2JMhGccYB27nC5u5YdRxaUqwIyqJP0k3fjSvLLfGnRtIrsNmUHllHQc0BBThOIyUNeE0cB9gLANbf0FN5iSR6C1vHhz+BrgBvSTOdyMv7zNVNya54/r8jjYGgnpidORYKNljN4upmTDUHQh3hJL3kZt4FN6eDOuSOU6RrSrb7Su+6MKXwWNy5f40kV826tDo3bO5e9R0sAPf/Xji86+uqIHWE5e68YEN93ShPGa+Ee4YEeZWCabso1NlQ3RCa2GNH8imsZlCGzKSKuM48aEaoipGIqavlBfBnQTBY0dobcF4aA1OB8ePMtAHvhMHvmMn7sWxhhkQTOl0KoM2hnYiVMtLeiHZkYNT6JVxchzKXptLm2MMBVsatTtUIskkRGCMZ4YgtLIw2GmfxujstRcxOOOxOmNlwIqlP92+FoB6Tq6TE+i/ghzTn+syJkBJe/crNbBKDB3bDK2CBGXUSK6VOMByFUpaiMeIqKUtex6pasPLQMueYhohGjoKuu1AOt0B8KoeI0qpSqqdeIwc3hxpLyttUdIaUFWa69gYKXRMbhhb0RRot8olZT72laSdQxde0x7fn7IwfU04uMez1crgB8o10w5KXAybDCAb+vyKNQUpA2XdWUAxGbbQqWbXcLe+oSFRlonNKH6A3idGk8lrRAJcN/goLFh/4KE03r9smHNhqmB9IRZDrpE1VpJmWvf4pPgGNka0VrIzGJ+oTTCDoy8NmtAoODfiXwq5KkMf9rzMrZOkQ24Ub7mFgSKZkwlsuXDUxkakV8NHbuStPKBmRZdGGzyvFKxWhJUtVaLxxLEhzWO1s22Vw/FG6nCaRlQcURw4IcSB7j3BeBLQ64CmVxyVTz79mNd04QUwvQCd0ZyJ45n/ImW8XHjTIq/LxpdXw8V1aqxoKRgnSLjHpSt6NzHnwtltuNnyru2J/W6VL14r3+zvKK3ANJL7xLt5xovQZMAnYXMV3xuYhVBhfn3B5sidFdY6sZqZ52qYHKSWOKjHlUoIA1tPtJS41QmDw9hHpF9xNuPtA//tEf7G+YjXypocaTac34y8S1fO04gY5dITtd0YOTKeAtEaevU8LcI4RkabiYeJ1/o5wQTkODCOsH5wxAK1Ny6rIbWGt53VLJjekOzIHqopTNETJ+V1bRgZOGyRS79hh4LbHNJ/igmWsJsb6MIw7pkgEbsrp7Oh5IyPgveN2jd6Mng74O8crW3kVLHR4fC4aqEXDm8DEjypCH3o+LXRtj2YthMuHc5kUksM3pFvFTNeKXVvheYi9KqIV+q6wKRoGHDxQHYFFxrlqhgv2KtBjwbbEuP6lovZcBiSdDQ3ug+UtePdwDVXXnNjCo1NO3IwkI+0zaCukvNKU0/dhHisPKrjB8axGY8zhZY2unM0E5nMgeY2XBdiLzQ8pRRsVvKhMcmJKBe2fsCkwuEOihpSiTgiU4RTtFyLwagj2oTdBFkF//EJt4CYRvMGym5LufSND8uNexkY44mhXMjaWZpw1YL1mTY4zLpyqTA4SHS+5z1TrLx0Q6MTVBG1zNLQ4OmtU8tGlxHthbQZjEC1Br8lBjPgBdLtCsXyZjBcu0OihS0ziGG+LWzR8sPPnxDXWOYFmw3xAXIMyNxhEP7Rc0eGjbxtoA2/Newq9OCQbJnbhZohTBtvnSUvnqwFroVtghhHfrc98YOL8OAiHzfPz68Lq0KXwiCO3DITSrEBp0IxleIsgxYWY9FcKM0wDBbrMtoSNWV6G+k0RjEk80iMkBWG/kJ3lr/53Xs+Lpafd5Ffebzn+voF905YPx4YNPKYV+SUCbETdORHYvg4ZrQavHp6CaxtQ6k81YWjqyx+oJdO7s+YNNJxrPGIcmU0B2RVtl7pmhh1oMWKt55VO1kaffOoiSz9RnCOFB2+TARTfrqvcl11Fwwo+8YQDbUVbFB6F7wXylb35Kp6VAUJhXXZj6U17hko5xpGLM3sXaa6WoIz1GtDvCWGXXjQtWFapaplcI5tE+rQaVeH5Ey3AR8qNTdMBXECy47SZVuJUchUxtD2n2sc8G2F4FF9pfTGtgT00WMGj8mdXhrrELCvBeMc3ijFNWyz+O75xv2Jp+eNGhq+B4o2puGeKh9IudJS4+4kvDRFfOBgKiCEpsQpIDpxdJFVLHfH425gCZXL1XD0ndY8rh84+BOv648Yo+KKpQ4TUjPiLePc8McJ+9oJq2OaHIuDlg3aNrJJvB3fMF9vHHqjxBfmsiNrolHMIqw3QxgEUUc3hdxXHs093xKD2MCdd1ib2bbK6AXNltdlQXzF2sh2qdgJvIOKYpaMGyy3DKfzxPYSiKOD9kpUZS5CMBbRxOGjj8EWXpJncA2v0M4GYcWrpTlHLTdohqcfX2h14W4Y6CeH5sLLj69M/oCdOv4wkD5csW8mrH9EpRLPSmqZ1DLGRj5fG587+J3rwj+RxG/YMz/jR7rP+HDPJp2+VKz3e40xeJIzDDYDHsmdVjsSYOqBNXsmU9iignM0vaFbAIFtimRVvmVWPrYj52b47IuF2jrrWBjNRrq8smqnbXek1giSOW1ws4J1R27zxiSVaarcqqNchemh0KWSg4Wk5GxYtkZwV/yWWIeZXjOmWcL5AGV3LGp12BYwxkCBEoTBOebnyrk3rJ0o/oa1P+X6Ju9h3So2yD4ugSGvgrWGsjWsU2oRlIpRx21umAHWuRKjpTswCrntLdo8F6apA0Kp0Hvdp8OzgwaVhu2FYix2FCpQy4rHUVpnyw0NlUEHkEy3BraO+oBUpc+GVwu/MgSabKTNYK1jk0TPQhoN2q44e+QmDWc7fl359DufcumFcn3iQMCK8sHOfCiFVW704vZBSNNZrzOPYvgvdE9566o0hCOdVjqDBqpVRALm0BjkjB6FrBs5TgwK/t7iciBaeNHEfW+kYFnUMJcr5qq0kjE9MRvDqIZun3HrmTRW/OZRHG0ATY5ehUIl1zOhVdiUNLY9riCOHCxVEz3svCmfPPZeiCXSiuU6K8fJs/aGZs9UlewrqVhuujH4O3K+EXvneBrIxmG8J710pjEy+U7VTg4PrCyYksil0EKlm8Bp8Ayr3+GlsqENJnfCB0+oFn96w4+/+BHzy27BFe30ZsgS9rZ4GrnaJ1rtaLPcPtwIg+5J9W6gwvk+shVlsIpeLvjDidnBf7Rd+Gs58Js64UhsLqNFqNFwDZXYG7U2Wus058m1IlSWlwIu0k3BGWVynq0uOInYwaDWUqmMPXGOD6zWYaRzao7L5rk7Csk5ag0YbXwcPKlBLRE5eibXKa3TJSDqyEDIjZMEJnlkyc9047AouECXjdSUHkaGIkQtuCC0ZlFRhnZktsp0CwRrITQGlLOxxLMwTQN348T75HHG/kR7wl+Ijan3znJNe5q5Zmzv1K5YY8kJjKukZPDOUVXpdMIITcE7h7VCFEM3nXAQSjZYW/e4QVCsKloMxWSCj0gwuK2Ti8XhkKHjUqITKMWgtmLjjGwXjHugG4Mz0HODoKAWayJP999g/er3yWUPKYoB6ZHJbpTeMfWOVl8wVbEhsCHclo3eHc0PLHXmPhqOzfD8vLEZ4dygWo83CurwztM0UgelpJGojmOoJNO55kZuBmQma+dVLtzWxuBXbn3ADwahE+4PPH32HmsbVx1QGnpLzNFx6gmbF9RMhMHRa0cJPMfOWA3FNTRVymYJNvJVuXAmYILSgmEQCwPUJXPaCt5mnKlcnzLl1nAPkc2tvHOZB7PhQuPkjkQGUEttiaF6jgQSMylVavAcJ8Owvcca2U/IslFlRH0As+y25izc8oIxwm11TKGT1h3p4ozZleId2jGjq1DuhEdxuGYYfSUeIu0YmbJQT/tV76vywvL8yvTmjBwNh3Dg9lporJTNwDDyaCf6JdFPd2Sr2Ka8PM+c7iL/JK28E+W/p5Z7J9ij8lQ7zuwjOwcTuEghHBoGT/CZuo2M1vD6bqU8RtQo43Ckb7DMBTexY4AV3i9XegsEW+kmY1Lh9WXfdLCQfeVaXrH2jlFgTQHrPFYauCdyE/rVooPQNdNK59jBqadq5Xw+MTml54KzgVYazQaKi2i1uKngreEontm84P0ZmxrGeXLsZDGk+cYfr5X7vIeMf5L1FyIuIGJw3qCy0WpnSxXToNe234c7uKAYB87vWiZ1igRBHPSmpG1jXjIGENvBZyodI42SG10MwQ5oVlpO9Nb2kZRg4NLRrCS3o01GMQwmITzR64q5JmS1FIQd7j2QWuOHPTI3w2ET7jZPW17wtSHZ4+2GLxdCDIhxnNzIOQqpr7zfZnJSYrHMcmaRiBwCh/OB2zFgJkucLOY4Y6WxaGXJnRwqxqxc1kCxR+LQCH5FeqZunVvtdIHu7gjR7own51nqFTk0MIHB933WyhuGXBnDQMMTcGivX6t7LHfdko3QfWGchOHg2VrjYJRpOKMl45qQizJUj3PCxw8DWhJzVuRokDdnfBy5fTD8/mXD4jhGz1NbqDKT7MwTnZfY2Mw+ZhPPoF44tEpdO6ElWi5YF2lrw5lO8xEnCaMWNZbkKo/3Hh8q25aoVVjKgjZB4ggbeA+BhvcT0+mbyCGAWGIYaSHjWyOmjo5gnaM3GG1AtxUFShVqbxhnuKRO9wa9vKcn6AqUTluFmpUfvCz8reuPMQJvW8PXG3l95fWycJlfMOlKXtadPHkBq4kyOxRLvxZMEWSr9LpwbUoviWIMvQp4oYmA2fN/a2tMJuBXCzYT0oaGB9LWudmMPRRUOrdlpedAMCfEND49HHHDQO4LtVYWnnkxnQ/vLnyZlMXCdVtZnjbWcmHeLtiSqdWBNJzm3Wi03Lj2TJOZWjZk66xp49Nx5M55zE9YZfoLcWJCoTSDuIATC07oydJN2a9WvSE9Mk4KLcLQaFeQWNBmSXVFxOJtZJsrLlha3fMBObHXTwbPMhdsTBg1SHM4IM+d1Ru8WEJrtO5ZtEO1GPfC9UPFIUwuQ6tIn6jaIVc2cyR1i9gr9nSizCc8I9VecXKgykZtjulrIWfoHVplGoTjark43Q0TzuHWQh4qPgtLTyRNSDQELE4NgT1RUVxA5sJaFxBPnTPuPCCnyFA7rQujt7hiKE7pW+Grz2+00Gmj8mmMJGl73gbYemG3pgdOgyPmgg+G1YDNcCcnnsnUrXI+WbQ4VtNw6vE+kMJC010ceucNv3SK/LPXxGoMA/uQ7BQGfvv2wrsfFI7B8m+9mbiTEdvAHSuhd+p4oqcNTco0NGKyjM1w2RIyOrQ6LtcZc+i4NWG8cGsJ5xXfJtZrZlKPO3jIia3fY9Iz8ThijaezUtKJL5cnSsiM44jzR9a6IkkwzxWZ9kCvDI66KvcPD3x1uxDsihWhMXL3ZmR7f0MHYTEbw3Dkepvp0bGRONYVOxnuZeJ4sGxVyJrptWFCxI8TbuiUOdLKDfEeI53kMyYqGxv91jENNp84MLEkweg+e6e10lzn5WrIZqFj+Ox23WULm6Ooo97AbTM6edK6s9OTXgnOcbCROtzzaq4stxthnLiR+VROmL7RqoJAaR7vKnwaiBLYrhv1ZHAYbvNGwdBSwk8e3xymJoZBMJoZzImH0fFUN6z7Kb7KYRQfhDgG8rztYyUdmlGsdaAWJx5spzXFN4+RihWLMxbnA7WDoWNU6JJxWEoy9KliNqWYTu8J2wJQKUYxsu/orm8YM5JtAcnI1uEwYrkx3BdcP1FrgWkff/E0vNmtKNGCYPDlymgttV4YrMVkxWwDqhv13pHSSjTCOnckKCbum+MYNk4G8jnsznd34TEOlDJR0oZmRUmoL7gWqdeCHkfi1tG3lpQ80VfM3Ih3Ry7bhSqnvZuzrAzhyNtvHbn6zu3lCYwljJFm9qQ2LmFbQcYVyoC4w247jp3eCxeXMEW5C56v1sahGNanlfsxkGwlRsXYTF0Cg/f8t/TEH7PsKJIhsnVF4u5Y+1HZYPW8B/4X54HHITPUgaQZX68kGid34Do3nspCDZH80nf4vmRi6Kx4iFBbRp2SK7vdxo7k5jnICuMIWuheMNIYgzI/Z9bLlzBZcnGMh0h2K/ayUW3gfGykugsUSu/kbvjhl19Qto1mHX60fHQ+YXJhHDz2UjCTUBelVcHait8q6MhHbuRvns9MTfYB61Q4MOJrxNqK6W8o9bI/S20/yZrNElpnlZGj27Ewr6biWenDgN8CJV8I54AjoeeBMQPacTVwFcN8mRmHQLcvHM+e4Abs5LmkFZMNqymcZaPcFlx3xEHwQ8UReLc0ptFSzIy3AZMSq4mcj8ogp1311Qs9173zBmQdyLrjic7tiBODyMaVmfdfNVrKP93CS4NgWqTmiioY2YdWLZEYPa1Xau3kudNVcdESosM5dqFlNejXhXFsABG62egWXC+MD3doM9gmtFS+Ti1v9MFjquJk2nlNxqBSiMdOAlr1mDaj4QFrd0hdKQtuDPt09faCP93IiyVVg9dE0MiHeYdm9SgcxohdFa+CdgXZiDLSNDGYSFszyUKZDGImjIn0VfFOGELEWkOvmVQzZw00uzEaw+IctgEuEdd71kGw1eHHwKIK2VAPDt86SRditTCdsOKoL1/gTw8ks2DWfVDVtQtQyTrvm3wqUCs1BzgMLFkYbSZEz3CcuPZG6I1kDIYRdQVW5ZPhjn9/Ev6DH7zDDYFKIr2uHN4aRD3F3PiQPL9XhV/fjqg6lEYeBJMsXRpzvvGy3qhlwx8sW52ZEKw4Qsqktu0n5MHTE6g2sDuO5rpUDgfDoQvPbS/oXpPyuihbrZiXQheDt4WuQpCJaiwt912dJRBuDbRgrWOewv7/3JRl3hiWQvWOJQYCStkunMWR0oh6CMeR75wmvj9MNCy2bshTpA2NPiaGBtuyknpB+xkxjmpgdJ1sGyTlJV+Q40jdLF0LU37GcKRP7Los6Ri1YEeuTx8YJsvYHU1OMADdkmdlSQtxPLFuHW8EbZ65d7xzGH/EbDNtO9BuiVMwPK+Nj/0Dya8YGzm4gJJxdv8gj77h5MRFE/fqueUbww54oh6FXldSLbhW2Iqgc6PpT/NIiirGJ8Qo1jvUWEQs0PchQKMYozgr+MGhrQOGvAitK7k2uql0BR86umVa3SMEmiJ1yZSaCMNAiAPD6PA+7uE7U7Cm40TQRaCfactENIoLlSIvtNJRU3DsM32sO4Y2XT3SBlzpOLEs1VBRxinQx0jUiK0V9YqplS3d8NYh1rBl4Uan2UhSwawwBUEyWIm8bjeqE+6GgWKP3F496gvGw9xuZLNhtpW7aSD5ylozX9Un6jURr0oLwroasjauXlHjWELD+g1nldUo58lzPpw4NmHQR5qYvVhMxvkBjgMtVHRVkEq1AXWecPIYVYZm6CVic+CaOhINc888tsBffzgxuJWj9xQrbNvI0irBGFJtOCvoOOCmFWsPjHrEdI8JQpwG/k42NNeRTRir8JQ6aU5MvmMWS+idj4cjroKzliidUvs+ypQzl1axNGwxjKFzPERM27t4gyr3hzOjOaOjxXvQOHKrM+9uG6s1TIOhATEILXtGf7eziZqjByFIJawGpxPbcEcYhNP5gVE6v6aRkjuLZF77yvlk6QfIzbAWwyW/oG2DuCG2c7hlzGSZTSMa6M5zFk90Awd3hvHMZi3O3ZGa4yrD/7u9P4vVbWvz+6DfM9rZvO9619r7NF9brV0VVeykbBdROY4i5DjGKgVyYxQHBA44N3ABhAtki4uAxE1QRCcQToRBURRSSRwLLCNkSGwULlBBOcZO2eVK9VVfc5q991pvM+cc/eBirq/4XHGV6zt2+Zwj7b+0tOc75txrjjHmu545xtP8/wgKbzN9tDA15oPlODcGLLYL7mAZBkdKEJznxfvvY7tlMh7XFSUbQjVYJ3hviTUyYjiXRrtmagts5calZT58/YqWnsih0/OKvjTWZec/S/GOqjyl1D3yN3eCCKevvGB698XOXPEJ8JlYMYEgIsRcsc9bNlGKFgs5dwY/0FoEXZBq2eKKtQNaWWKpOOv2OjsDjY62DqRiJ0ttGdRu1KoSUt41s7SCphQmKC4pIl1hxSAmEXxnVOAkkebXqFsFC94PJFUQMeQq3HQhZaFYw6QawXuOFZZWGLeGsUdqaUyDo08W3nTSAHFtsEWSGZitQzzkpystKbQVxFRm5TB1YnIBpSttKJyUZnWd3BWWRO2GdulUvYBmj2CZRrGZMVXUKvg5obIlyJW+FfrksO7Im6yYraG3SsGgdMMpx0ZBF40pA9Qrpc3M9y+xcWUYIKSA8RNiVs6iYV0YpwOhdyorB5XwZuQfn1/w4fqab2ShS0XaFWU6ehywq+aQDTYW2sOAYNBdqJNi9I6fvVyJyyPeH1iOwnZTqJK4omlVY4+evm1cY6CrREqyKwc7i6mWIpWYBJ0bNV5Qq8M4x/F4omjNNAlKg/OJmISjUlSXqJunxBXiwjftgFMavVWOdxNm0Dgcae1ISxzsnkRZc8fGQrYwKssfOhz4Lt/YUsBvloP13GaLNQ59OzONd5QJxtiIemA2mtukMT1x7zquHImlsK2B2izKG2LuTN7iPAQ/8cJDuyaseHTtHOcZnRW4B1J6RKxj2wy9XlA6MIiinC3z4MAP5H7j+vRNNgN128UpxkPbVaevlmtqDK6h/EzLjekwkbdMjYX1CuZOSC3h3YgqkV6ujIPGOM/WKtpYyquKtYb628kuICL/EvAvAh34T9jJ374I/Dg7T9NfAf5rvfckIp5dc+73Aa+Bf673/ku/+Q06WnWaQKNhEGrU1NYRgbQVioqU2tEdBIcSTe8do3Z/VAwwTm3ndu5xr92RjNRMWjv6qCmr4AcFqlKzRveGuICI0JqiesHSOVRFyZWgPMYW/JBJ+UCUgqhG1pX7pskUesyICKlUnGQWMTSnaWiaK/RFuF4zw2DQhxNOXVntTs1qjHBtibusScMANhPILH3C5IwxG+et0VyFrfDx1NiaQnVLqwFLo9wNmNAYuyLXRi0FrVfWbjm+HNkeM9nB+3WmtTOSGtqNfNFqlhwxFpacGMaZbCJJOQatWfIeeLBAX89M2mNE82qLZBVJzfLuvaFohXGNae20JORxpjYNNfHDhxMfrI+ENaOc4qThtnbGWPG9op3mPs08piuhZaRBb56fzxljLedrwMwDWjVUHalsrK9WDvcW3Qq9aub5wMkIqVbqpcLQcEpjXGI6DKylo73iEgrj7JDZUZrhzRaZSuVOOVw39NYJVRNaQ6ymR6GNFmMVB32g3TLRRrpoahzJqoMI00nwoTGYgd+F43cd75iVYF3mMHmUsoTXr2niWF9MfBwXTm2mYmntxuOiiLJi2kBsBd9uWJvhMMGSWbbOREGUZzSKnBtlK8SQeROvVITHc0KGmUOrPKXIrCLdnTBtooUrp9MdyTauT5njFcQZ9Gh4YYQi90z9ym3LjNPA4W7XWkwxYJWglGNsflepsJkyFZJ39FshLU/Y8QHpDumWsI4443DLG7L9iMF8Cas/2drntyJ4+WXgvwP8UO99E5F/l10t5ceA/3nv/cdF5E8Df4JdQ+5PAI+9998hIn8M+FeAf+43vweUKtTWMbIXCCqfGZrZl/emQHVotSeK9WKARK4agmU8aI4P0LLZfTJRMGiWGvHW4GeBBqVlaqo7T01vsHVqV1gU2QZaPhBK2x3HuVKGxjg2nl4teBzUxjRoehOi0jQs4o+8SAtpdGznSnB7fdBkOllBkf3NcYtPHKzBbYLVhqY6yShMGSh5wQ2G6EC3ieM8EZaNsK4Y2dCLpwwDB1PpSWguMlhLlRGrC0oZXBGwmhd1JIZMmy1tqZgGMQae/EBtimTuSHklps79aOlFcz6OFP8Co2+83/YkvZICwWq6VuSaOduGXjaUU/ReGVQkeEUvlh4KeexkccilIM5ibeG7ULzrLdfQyLmjtMGpTPX3ZGMRbzjnC40Kg6emyMVttJrxd0eul43tdsFPnjwn5PVGqbBujuluJOdAen1jmmc22+h5wegjvW6k1smXTPG7/1INChMsbRPc2BncQFGV2BpN7XTDfhwZKeg40YlYLTthnolkGqoYYtgYVUAPhrV11DRwOGgOyvLDd8IaI+cgmAKXkGlh4U3POJ2YuiFUT8gbwzQzhI65dxgUooX2xuEOhqEPlFCwWhFtgKbQupMUmA0qgWxG3ru/I29XlNnTJwoDRzR20Bgagy1s44mtz3i7oe8d0g3L+XFfxcedQqhsipfjO1zrx2SBrQ44NTO4mdv5RvIbPTmGY2cIinUDLYJ6cULXO5BElkASTd+TCHgYXrCRPyF/5W99K2eAUUQyMLFLMf1B4L/yfP7fAP5H7Ibpn30+BvizwP9aROSZ2fI3gNDaXs3djexO8KaIOTFoUKohXWhNM40DfYAUdgqTTqFVhTaV1oXWZOcyomJKpxcwzoIRpjGitNAjoDpl7DiZiUvCuwGpcFWOURaUGOzWAcFUwZqI8h6kUZTFSuNx8pQAS2/oXneunabABpZi6CZj3K6TpqOQh4abKt4bXoeEi7sx0WZgKxXBMUwdUR1Xdi2vnBUH1xEVKJwwOuLsTFyXvZxlKDQZMCbz3uElGwGplTs/Ey8bZQbXBJsLGEvfMq5XFtXQq8e4xrt6ptUrra0UyWgvwIrLipYtxmW2JSMRmu74FxOldlyGx00z9Ij3E0aEMu2Kr2qb8D3yAwIf3g3UGJDkKc5S6sbfyoWvrmZX2qByctOerV0FlRR1S9w/GL55q9Q3TxzfOzK8fOD8urLeLmjvsE6j7xVxDRRRHNw9boSUPWtXtOWRdWmMfkOcIaqB+WSwHRg8st5QztCx5BwRI4zjQGgXvHUYD+70Dto3DpcbLU94NzJMmrpthCUwpIqdPP/Y/R2nwSK54e4SOI/bKqcvvMA9RkJ7TdgqQQqDTlzXTqNgPhzRd4lSMlIm3oQCyWDaBT3ONBH0bEmAjpGr6RzthFcVcUJhYKozL3vgcROKFeKa0RNca2VSE2l9Q8qd2RhCga4UNIdyhRxv1EGT0xXnJ0IvaBGKbIQzuF7ItWOVoTVFLnGn6eiOa13QrTEiSBWkrYyHE2/GwqNE7GCov13sAr33r4vIvwr8CrAB/zf2rdtT7708X/Yt7Tj4Nl253nsRkTP7du/Vb3IPahM8GqUTtRqUEqyAsrtoYeyBUvbUeGmC92bPf8qVECrWKsJSGKZd/VaqYEVRpZK2ih0FsQLdginQDPKtHCjd6cpC2aWBMKCMpTcNZWMcb6z5hM47LeysAvrmqCJ8EK+8WxVeNVqplBSxdwJac1kiR7E4HJiGLY3mBupTZnJCmzyHaOGkCK8yRilM0kyj5dI3LH7PEO4FEY0YBXFCzMDhKPQgJHtAxU6tA62vlLTQJ0WSTHKKIA5dAsFWWBttGGnR44ZKaZ3UVlo3eKvRWaHHxHYppNLxR83BdGrXxG1jGDVy07gl8bjCdDVot78cRA+cBsWaO0utiM2k2nlv8IxxI00HWut7JnpfeYz3yMGhx4EYF1J6gkUzTLuIZdksA3A/PfBqe8Xy8ZnVOXQfEDpP5yvTbNGpUWJhMiPDbGiq7syTyyNy8LxPI2+KUsGPmlBWFAfE3HhxN6Gb57xFRI+sr9+QTUQ9ZTh5WnBoFzHZoN1M64X7aUCZjmmevjQO6p7jOPC+tlyWxFwU6qggZ/LSuZUnshKMf8GsF0qsrGFG14wxikkLwzCwVSFJQ8fM4DuiJoI2eBLWWXyzFIHTGOhGQdUYY7DxSkqRSx+xQ6GbCd0SnQFbEo2MCh2sJT5l5vcaeM3T1lEqYpUQU2YJBdM9WjLadPqWEd9ZWuSYB5JZ0XVP/5DBclkrLhtsF9og5KbwdEromDRwHCZCjshvF7uAiDywr4K+F3gC/j3gj3yiu/3tv/fXdOX8wUFs9CHR2q4NJmYPcZZU0TRi6hwOnhgXnB3QsK9qnEGk07bGcFBIFZTve/0d5jnru+4RGxRszyyWppO7ME+WLWVUqyQleyg+alRLeza6VYS8S9lUfWbgSImWMFSGPiCHA+XDDxjNQB0VohrOO7Q58E43nA6ZEBMrmlQ6/RaZrZBDZvIvWfoV9aoRBsshWy69chTDvZnRR0e8PWKd5WkT7FqZtOYpb8TQUWOjts4olTRorhfIB6GXhE4NVQUvmWtQHN1IlYXqCg6hdUNqAUmdaRyILZKMRdbIwRxZ0oq6FLbZME4DEoVhmtnkglIjJW/U4cggG8UoUtgIveOKZRJDN52QPF8dJx7WjavJKOtJtUEzXHPh9XplUI0XHroeeFqu1DYT68pl3TBHx7vOUb70gvXrj5iPC+09zXAEgjA4h/VC0jAMltEKAaHWhBmmXVreKhSR2XTGwXBeK+PcGMeX2KZIOaNcQa6WSttr7wxw3uinSr4ZDqbRjEG7SkLTWqYXKALKK36wd441Mg4jBkXTGZduxMFzsxWnR5TJXJLnoOD+aNFRKGpBRLFsCRkE74XDdCRtC6syeFvYWkdyp+fCYDzbpePHhDl4ztcrNSiU6ahxpWWN2xRPS+JeNlIbWAeNTBCXK00aunq6FqSf0R3a4nEmUkfFy7mTg6LVjhqFwd1ReEWpmVzBVI2pGvpAb8KLOXGNG304kB4r012kL1d011AVh6rRn5Dz+7eSLvCHgF/svX/ce8/AnwP+AHAvIt8ybN+uHfdrunLP50/sTvC/Db33f733/iO99x9xs0WNuzBlinGv5SqdZnYFktobo1M7C2V09GyIqRFTRomhS4dR9glRhZYbOXVKqfQse86RNUg2tB4oRqMFnAJVRozVZBJWddzYECPgLWXYnY2q32iqY4xD68D6vF1MLfFRzryc70Fpxi5MVqHrjAoJ1RtPbVdFVQLNDSijGaxl0rsxwHp0Bxs3LAtjizzdrmy9cX16Ir1ZUSjMITNOlUVnfAOvIu26YW+RmDuXpbNoR87syihYkutkXfEj2NyoovG5osJeLCoUNqNY40quhSVckSzE1vECXk0czANDO2KArUa2uYJ1GG0ZxoYeAqO1DOPA5Dy0QmoQRWjN0FTmC3d3RDUSOrzsE8qNZF355RJRUcEZfIx47fjyy5dYM+AGIa6dK4r3xgPj3Ql5OPCV4x3vvfwSL777fWZzoFbDi3ePHN85kiQQut+3PXqvoUQJ2VnuTu9QqmYeNK1Y+jWypcYNMKGizIocHXHtYPbsb9PBkYhkoiQGHE/rwu0x8LTdWClIC3zJGyqWVAtBr1zylUdjCEkRQ6PmQFwD67bRUqb1wM0krlWI9UalIBeDToXXH78mMuC6g2LQEYQRslCIhFQYmycHmK1hnC25NGK2lNJoXhP9gfF04uV44AHDIPD+OwNaF1rtOKURY3F9RvtGMBY9DNxyZSkF6xvnC5xDRuRAV5pxtVBXqjc4Kgev6GrCuXc4VOHlbKC9xEwd3Su9Jjal9r/NT4Dfio/pV4AfFZGJfSv3TwE/Cfxl4I+yR+Z+va7cHwf+X8/n/9Jv7l/aQ33WaKQqipJdLaW13dpKRkzDi6PQKTrulA5eiCXRU8eIxThNa6DGuvuVBFKGWAIuepQStFNoMdRU6caQKgQdcOKQGwSfsQpU00y2s5WKNo00rQwr9GJpveF9pTuDjo3lqTPcWQZlKT4R484OUBtor/hoiXyXOqBSZHCWogqlBYJXkG/clOHFoBgipFbp1WIFXqWFwUD2jpo1H2bN01K59c6oDNpNzIMClbleG9OxgtmQMtFdQ1SB3PF2ppAZRiH0yv+zdH7Rdgidoh+YR8WsjqgXE6Fpvvfrf4MfqRY9zqjaCXrhWjRlbqgeqHpgLRkzW5REzBYoMjA4Qxstx3ng+rSQ14wYix4972wL23VheGfgvJ4J3SBR8aHphBZ2PTd2nvZXT1eWrWK0JXfNGqG3zDvv3+OHQjcw2Mrh7iWBFe0HzjEySCQFi7S4M3RO91iXWGKkL4aiMtk2nPUMulBE4YCiC0q/oKQP6OdEd5lRebJolFWMdmbNCUShN81kC0veSBrenz0HNzK3TipxL7geJ466szVFchqvhUk0W90YvOJoZgalWcqC0yfMKdC2gfHlyFZW1nBjrhPdZfyguUqlpYVVGrZ0/Kh47A2TNa57aluIcWOcZlx1WG3QfeXDNeCyYxDPmzLxIAHvLIME1jhRY6QMDtMaY+7ktKHGI1kckcz9ODC6TmmaVQ4wV7p1zAW08WzrxtU4Us70FOjSSW1juJtRD5XlmmiTxnxC7/dvxcf0EyLyZ4H/GCjAX2VX0P2/AD8uIv+T57Y/8/xf/gzwb4rIzwFv2CN4f7ebQG80Kt57lltGq92WhdIxriLK0vu+ZG+bxfa2CwAmj/KNUjJVtZ3JsglOlX3paneqUa3tzmLZPZmMLwlTGkUqvU3oo0PnSq8C3VMkMrRGFYe0J4rqaG8pJaKKYfMaVSq/XG5cr4o0dppquNqgOJaq6K3iVAdbCR9u+ANg0v62iomKYQaadFLT2NDoRLLZoxupVZRpHI3C2IbTnpNreBpFr2zBogTWdONhPHK9RuZB6LHj+0yVwGGcyD1grGLIA/+xfZ94/2VOOrBcNY9WWJbK8SmjRsdLNaBtp6jKpiun4gi6ozbQs9C3jeLO2E0RTooYFdVvKOOo15U8O155g4uFISZ0dmTV8E0huROq5agr4zzinaaMnq00ugh9XflwvfFaInpS2NIZnhJmVNzCQtgiiKJQeBUyh37A1IL3CTscMb6jsuZuEN5598v8/IffxBTH3fsObeCd1glrJPYzJMemI++qgVt7w3bdyE2BdqxlV+2ZygTDQFOar94daGsiZ0gpoSVTteZwTQzD7kC3DqpOJDngY8OYhcEcMbVj3IlTSpSY2aziVjtar+TXEZMTAwstRboozuVKVR297dqD3s57DljueNtp7OkQl7Y7mI914v7ecXm1ItYzu4WX4x1Bdi6n92bD5U1ndG6nzr3e8HeOSqCExsuXA08RutJMNZPXxA1IRe+7FZ8oRmMTbAgh3HDSUanRfcdWQ1UGlzW3S6a2CLVy12bab7Ou3L8M/Mu/rvkXgH/s73BtAP7L31EvuhBiQ+uGthWrBaU1oSZUV0Cj611OqUdLDAntFA5HLpUqu0oKrTE4Q4mdRKMVjdUDtRdai6Qm6L6vXKiW7redbVBFVFek3smEPX+KTsbTVSbbCmUPvdMqQubIPdkG0thZ1442ndgXpL6kjUJPZ0rT2LXxkRRuVnjHVJzxuGfJad8razeEqhkm4TAMPOWGXTLjw0jaAk+tU3NErZbzQ+XYhRIEUYLovbzgcG85jHt/VW90Z6g+Ybqit8x1FeqxIc1Qzgt+jtw+hqobJhomJaTmSNVQs6XwSGHGaGi+ccrwdZWYGOg14brmKXTclCke6J4YNrz3qNjxbzaMdMyoCGWhdkcZb/S8J1gSEn0TvjTYfZUREnXrbAKP1tKqJS8bg92jsLVXRirzXefjVxeuJTHngXzQbNZzMDMxC0kStkbW1fELX/sFdOuYuWNXS3CZUi0Vh2WGcUB1dsfxmgm3jlhN22Oj9LiiTo02wn1RXNLCFjq6RO7uRs7VkKzicHAIGj8IUhX5sTFPu2P7/BhwYydMAzVs9D4w3DuqdI5VkEulGoM+DByGA7UG1NiILXDXDMUU1rYTy+kwoaeG6YVbTuQr+Kky1IFzbrTXBaUUekk8RUXfMlMz1G2j9YZRJ0J7QrhyNx1Yb1cODzP1tDN3bqlwcoqUNg4inNPKmA1OW0zX5FioqeOHAbKm24TKnbs+UdxCuEY0BqdBeU3tB5rYT1wr95koSRHAVAtoWmtop0A03tu9h3VgWwslgdYKe4KuG1VlsAVRGgWoVVNjfeZKt6i+l1IoZekoBuvRNFCJTsRohbJ7EmdNBSkCt5Exg1S98z41xeQbuUekJqxYpI603ChNsfYRNXu6Vdz5O3rO2BZpzeF6oY4NqwuzqYxmgE0Ag+qGabqjrY2hNx5fXXl6XGjLxmMO3LaF87Lys6+uvAGSbZz0AH0l6MCWFWkJVBqlKpyaObQBqzLKCM4JgY0oN2zN2FAZRCM1ULJjVZaMkHskt0TLe3nPkCIid3STmKyBIoShoJ3QqyKLw/UJP400JVArUhItJT4uV9g6wzzQDp7mDBjNx/UCraB7BYncwgWXC++XGZsjB68YENQA73jDv/CV7+K/eNyJ6PJU0CqwJeFcPLGAQ3MJhleXK7lcqaXiQ+akDK4LsRtsdbQlsF0qTVZGY6jAwVRMafQl0UPC0NG1UQDSymQnxskjQyPGQN6u3Nudmmd6d8bNM6k0fFEcjw/40XC4M5iuSKVwejFQpeJVZz7do90dkz7g/IHTaWIaHJKFoVvkOFCGlzQGbkvhGgORGy0Vrn7hMSnipbHcNuJ4Zd0C53Wlxw0liRAzr64brm2sErlJQ3VFj6C3SiuRasAP4GXDq45KmkLG373DKA+7RuOt7X7ZWFEKxCnuxgFtNdpXtuoweqTakbu7mWI3RAU4dEK6MdSBwTjmccB5R85Hzq2St5X2CeWbPhMlKbuDLNJ7pbY9pK+qofWC0RploMdOrcJWFlCabhxiKsYJJWdSjohYtDIYVdiWjnSFlr3+iqZo0skCThy5dJrKhNoxfRcnKKXijKBPCvqGC0LxHkLaa7FCZ1NwZzd6rLjcKPoOPS6EoHhv9Hx4iMyTZ90eMaOwPmruDwYZNMFWYlHE5Yy1Ay0ajFVsLWNnyyuZebMGJt+4rI2fOcNfq1cu8YmyWVgXtLvDqcZiArEb7rsmSeTV0w3fJzbnSeVK0Z7R78rFy93umMdUbLpx8C8w9UaJG/U5Kc6aBgFkvkeHM7FUyn3h3CPvbHcYk9CiufOd0c60GtBKSFrIW+DOQquONHbM2thCJ1vHpWe+8ZTZlMVtgaYd4+nAl82Bu8kgZLQpZDSlNUYa0le+4g7oFBGnsc3hh4junTY4lq0Bldk2tJroweEm9ax9pvEOntYrVPCHQpMTKgKqUHNExOEMXFsiZEcbHLxeiapzZyZ6ATc70lMh68bHAvPkd5XiwaDzSKobkxhUWjhHg0jDtcp6q3jRnHPHieLpsjKdAgj0LdCNZSkKmgaV2G4f8+69wk+Oq0r0WPGnfaVydAMXpdgkMEZFxjJL5yYW5Tq1dLxVpAJZC141REaMGJIyWNUICUQnFr2iLWh1RHIjvY6s9w09dFrX+NKZiLwBPAqNoaSAKZ68bSyuc18Hyio8qIEkR3SvtL7SyVAE8Xv2f1sSwVSWVzf6Jyzi/UwYJumCGTT6mbQ/ZzCmUci7Nn03aDOiakWbnZir106hoWR3mjo3UjqIqtRecbNQWyI3QRWgaSqFrmBbIt5oEoXWDIXMVhKMnVZ39sgcDUkv6LYLcPYq5NLx3lDNgClCmGDpE4TIO63TkkatI+dYuRtmLBM8RFLeKNbsdKyqYIHL9Yq6K9Qe+Y/WzM9ez1R9Q42G6SZsqVB+x+9B8b1898//Va5xY7kqeKfiawfR3Nu9snvsmovT5LghCDl0mjKk2rhzivu4pyf0yXPymrMOVPaE0N5H/NTQTaPiSp4BiejkiCKoNZJ8BGOQpMg28dhln5MEYg1162S7z0lqESsTxhVaSmxx4RWRrg+ErZHfJCIb3zhYZLzHTZqwaPxooQ282iJLT/xEWlFjxwwenTQ1FIIuKO8YesGKoklBXQPnQ2fbGipZxhcj4RZpKeJp2HzAzZpeOzGAeAOpogbHe+NLUmiM18D4/gEdE5NvbCXgzBFtVuIl8eb8AfLlL9BEQw34wZGyIUih64GxdayytMFRTKS2kZgKdkr4e4OZBBthK4JJhXeOHmMstIk6Kdb0yK1F/OGEUZWSM02EVUcsUP1MqZ26JTIFNPSe0bIXXJfQacAwNK7thjoqTFNYa5gHzegN85PDU/jmdoUwYn1CJ8XxaDHunrN6IieHNwblYL70PWKbhNu64dBso6IkId4Cg+roSQgRuuxqObl0agJ/53DhSvZ7NccnwWfCMDU6YOhdIaYxKLVvo8xEiwXlCl0XWtQYp/Z8pLoX+96eGnY06FrRXkPdCyZLj9DLvhOs4Fyni0Z6J5eMDODYmQITGcFBgOorPWgMnV7GndnAK2poYCqidqdf1Q5TFe7geDw3mg/YJLShscVOC4ppitgQCa4S6l4LtuHo3uCLww0wlIE3qXC5BU4Hg3cTt/WG/eo/zDi9S2mBf/KdL/Azb77O6dRYiucsCwc90KxwnBWXm+U4e7a4YC8eqyYuvTG3zDlbxixk3bmkBVc73CK1NxqN3htpEXRNhEMjrI51K8QOL6LjNnmqaNq6EAbB6kYuliFpNpeoeWcBgEaoGTtYtrIxek3cMr+k9qxu1Rr6xT2qFK6r5pVu/JX2ikHuaDVwfdJ8o114tUSq08ylgdprtra1YFrh3jjMaSbfV7a4IUVTY0LrhtWW7DstG4YXI2yaEgsOj5tnjtqSXv0KRRvU7Hh5eEFYItp5dBW4VebBs4RK9BYbG5w0LkApjut5QyvLcKe4rSvQKZdAn4RrtxzfNajQMXVGuYDXM4LD2MrrteFKw4qlnSvaZrYk3MIVVYXTZNBpIsfGGivO7RzfNkAbBJJwUJVSFaPvhFy5iEMFGMdK0nDnGkY3FAK149QBpTI6K8R7yn1j7Q1VC2qsBGYGk/nVx4LlG3Dy5Lhxsg/cnlbE7hQz2ja2wXL0Ax9m4d506myIreHUBLLRi8JKYCoHHq0i9YW5aLZDQ30ynrjPhmESOrVltHhKTWix9OrofUO0IVdQkqjWcFsCfp5wRtOrYTpWlIFcOiUmejcY5ejNIR16TVjXaN2hitBoNNtprewrrMGhCpgI1lVU02ATa7SUCmI1EtkLfjt0OqUqZh3p6sDrCst84uV6QawjP9fnZWDLhU32RMwDG10LzRqGCIs0QsiM84F3uvDx7Q3WCrVU3v3BH4F3foCPLgtfOn/AMSSQzqv1wjt3jpgqkY26Vi7tjqcVhrLRlGAsWJVpudCLo0kmT4V87WjpmFroaUMXQ1MOYxKuadbe8XmgTg7NHcfUCSrhN7izinWYGVqjcKKrTMgXIgONxDB2smgG7TE9sFF2Zdme+GEn/L7xC3zzzZW/8Zj44KDRXnGskb+2CKpZ0hLpJrGGBQnQomGpBqcKhIqbRlJQ3C9qLyF5MKSnG54GfsTWSGHD9IlhAnJBquAmSy0NJBCN4O2JdbtR2hmM5n070sbKOhdM6NS6UVLHlRE1G5wUxAnZO3RpdJ1IUShr4GE6Ir3xi7byAy8c3zWOMBnOry48rgt3w4FiNap3HlxCeYUUwb6jwWiWrBnGkUOG6fAuLSzUNqL5GDVbwrUxKoVkxyKB1hI4IRhLDIVjLdRR6M3gdUPFSr1k8gPUemTRt53grgXW28J56xzuD2htKBqOEoi1MFmDT41+a2x55ON6wU8zFVDKcjp4Xscb4dp5MAkzGUITRutQVAwR2xvRergbUI9n4qCRFkDmncbjE+AzYZh27slGWgtNGbQ0qgSMtdSS6BUqisE6Cu3XxAW6qRirEBSRsq9ueqGUnarXD5ArhNQxPSJeo7owKEPOidwMpmWkmt2H1BtaDEsSrLcYCksC8QLlhtgvIr1hVOYWHP7QMeL4JWc43RRH1XDZorRDWkKmgk2WLo3e9i1TSgtKTxwszHbiqjW3dOEL3/87CfO73MqRxb7koVvulhu/9/JLjN4wNs3oRlQ1HOxM3gKLUpzTSuod/SYjs+epXOgd7CBcunD0A9532lLJJpHyRugFuqOZZ0GGQTEhWOtI9weG7CCDcpaWIkoV5paRSShroZvO0b1Hrm8QJ9wWheOKjCcchX7trLPsjAXN4LTw/fOJ+2Plxz/4gHga8NExbppLfcJ2WJZIayPKbqQuONPJa0ZpsK3TB02YwfZILJbh5UR7nbB9JaoVmmKm0FPkZCfC4UTbMvqQuaQbXK7kuNOLLHHDfHjh8q6CPDCNL+jliSc3YP2Nri0HLIM1rGbinWnG2kpchTUsPPpEl87D6Pn4aeE/EMUvPq7cm847uXDJlUd54sNXmqyuHI5H/pFm6LKwSmdIM7SNLpXbufLqowtJOmW22CqokugB6kHofaXUjlcTw1BJa8OZAVUbZEOLnqYiWYOaGzo3RK3kBqYOqJgoOFIu9CSEeGbmJXlwKJvws5Bumpotq164E4uII6krcd2Yg2UYJ1K7YZtBmmesjpmONho9v2RsmtlADB3nHa8/fKLyHDz5hJnfnw3DJB1jNKFkdNmTEDuNHDNNgTMGQagUtFZ0UUS5IVXRxDG4vdykVU0rmUTFiaMX6N3T2kLTnZwVtndCrYyDQimo9D0E7gQrntYCnkKJFTVMDBKRZDBsNLnRuqepTvYFY0d6THzz4cv87jePWJvoqeBt5ZwzXxjfY1WJEAIxV1Q21Kshz0Kvwrk2qBuPa2N55wc4fvHEdx9fsL7KXNITXxoL/7DTfG0tbG8C+qsarYVBBh5Gxyt9A23JJPLJglEckqPeKtl2rERKvpGr5XAUyI0p7qU7uSrQifno0Ur4cr3xbnzii+eMUkCLtFun18pjNRg9UFrCoHAWLj2Tn56Yv/gORAUSuYQIVtAI+RpYakPHDlbYlMJRmQ4WdMeZzgf9Ro+VWgrWKuyU0SHvX/Ik9KZRvbLmgm9CEUVVncEUhtORu+mOX/zlb4J7ASojyvPdp9/BWjaGnGA+kF3jev6ArRRC36jXlSqaSV9Jj43mT8T4BKrR+64oMosnNU17vSKnwvV2RQ+Ko9qZI+zkESznunKOK8vF8Kv1ka6FaYNoGyYGVE2M1vJgCz98/Cq2TSz6Rm6JHBregb1zvLj3wIHYC0+3hcM40N1KuXn8VAlD4w5P1CtNb6SzIAPYmClD4FoFEwonJTA1DJrSCz4UluJQOjD7gh4c0zaQXGcIjdnD+jrRlCPpzr0esHWkhSt3k2bLluIyL5pDP3iuS+HY77j2C7lrtusFYzxLWQjJ0AeQLeMnu1NcZw31c+z87k1IpdB1p7eKiMJqQ20dqzvSLTnsenFGyV52Uhp2MNAby7VgRoM1+0opBqG6TAvQTUIpzeBGxMBySUiGrPZ7ZakYo8nd0HKjO0+oendOlgWDoRgwyWLXgjl4tBfUBXSLZAzFHwnvvc/w5jXRbnRj8ZJJ24VeHKVkdE20qME6lnJlbCPGK9ZSiObEVgb668yrX/0VcobBFd53iePD+zxwYXtyRJUR35B1ZTxMUCCVAiUyMdAOmlFgiQFweAtdHcglo/1MIfGjx4z0gnGR75JH3HrhS9uZsRVMsjAajHjuxns+jo9EcUgpbNsZyQO9ZrbUcTnRh5ltqSRT9uhaqqyzZ9SFtBaEjpsUYa3cZTB3I9NwIKUb+Jn+FMgpEJXiemkcxhvucKJpg9Z7kfGMZlkykczoLR3PdS2YkKhzJZwN/lDIixDftTRxvHhhaGpfHZ/zjWn0nD/asFS8hy0JoVhW2/jB04mnzXC+RIwUjDWEuDJOwvVVw40VBFzOMFRaf0KSI4yV5aJxdyP32u7GSyViP7GVyqB2xZ7WDA9YQl0pbaAGqGi2VfBZ+LopHG+ZIX3EzU74LZDpaA2bC/RWuGwR4zthS2ypU3OlWbNzqZ87L6RSj45pVuA1s7HY1x3GO17MGaUmnp5u6OjYeuHd05FLbFxujaou5FXwB2gtcBscNWcuH91wVXOvDpRSUPrAkq7MzqEdpJZRVbOZimiLa+Cc41WMjKoTSkWchd/OBMvfbogILXRq7fgRUJ22CcYZtnVB+0rvFqMTvdg9s9sPtA3Q7OkEGYoqWGcoudF6I5OR7Gm9EHpF2cYw79LfvemdvaFpVBXMqWOK4RoCXhlqbuA0XSDX57CoaHxtmKshh7iH+6fEFhtLdKwmsLxeyZNBVKTgeVpvODFksZy85xJAZ0OqFSWJmyhEKsNs0JNCbYEsjSJCCo03yxO4G6MXalewBXJvYIR6FZTR3I8vOJiB13UDBtydoec9crO1lZM98Oq8wqh5qT3/zONfJ26OaczUJsRFIQfF6YVHumHLhn/rl3+Oj3rjiOfHjhO5d3TMNA1WK8zLAULl2CqrCFaNJK2IwWA05MMN3+9RrXDyEW2E1jt2TUxJc3d/4OG9O7bzIx+GhWV6pvHImUMbKHrGkWlaYSQQloxqCmsSMXeKKYwXwU6RrQmt7NudWq98/Wml5Nv+gjvNLGvH2wGRhIjeucqfNlTQ/Hz7WXKCOzYe2wjNIrURbg11msjTgFw33CRcQ4TmYbBcl8QX5pFbSLzq36AxYvOMN4lBLNIa/QZ374/846d3GTtcU8G0xjD3vSTKFN5RheNsqP3AF/TMsihKTbhhVwyqqqLXStMdM8PBDEwBcr0RRRO0Z/C7IOwaN1QVUlc7zQyV+ehI27irHKcL3mjOrxNrWag1oo3FTYU1CcbDrBSH4z1Gj1yW1xjjMFIhb2gypQXULaEOlnzYdlqVa2aZIq/OinmcsT7SqiLVYRdv/QT4TBim3jutC+PoSKlQ666+0ZXg7F6hnHRDidB1pXdFT4pmG1YLxJGt5p2VoAuma6zyNFPIOSBqJ3eTtGuDea0Jte2Ft16TS6I8dfqxoq2mxkbTlto63mhMVDQdaashiTD5geb3vKAsirgsvJ4NP7AJd7Onqk5nhFSxk8JVTeh7xIXaaC2A8Zh5YnmKWGcZyoYSz82saDcwOM/d5Ql7HFjLXpKTvOc9HB/fKrpNzDYRFMStUsoZ5fawe792uDf0Iiy5sjVwdGLIlDWh58ZghC17NonMTlNLZQkbaYXXPfIT59dop/lH3/9eci34cSQ4hYmVYjL0iisd5wfWnvGmszbBdmENET058q1wcB5z1KQk/OTjB/zc+kTH0LMjhISxHTUOmHBhjZoxO+IJtCwc7mba2nF+wraRrBs5XhHt0ALKOLRVzNJ59Jmvv/4ICjTVKCozWUc8W9a4UDMoLejBMnRI3rFOBVMKzng2ZbgfGm/eNB4Gx9I6RnXupolvXj8iPxVsvcPcDeTrwrvHE9u1gjROL2eKWOqTJuqI9iBm4jAZftdB8T1klCgGbSn6gqQjRyfYeiS1jXXZC4ezUnx8beg5oFKmiDCtitrgIhqv7P6ybYmmPWrQDL3TS4SqkINhdp4YK7E2vO6c14IxkZo6RRxH0dQhY4PivWkmjyO6d+5UQgeHJdBkwKrGi2nm4BSiDxgd6dxzePESfxLWvpLakYmRm97o5RvgPa0lVNcUpVBNweeZ81sETvcDcSt4p8mpY7A0KqIbMXdqrag0oJUh5A3HQCuV7MF7zRB3me+YAi0LhYCg0VohNIxmJ7hSiqYFNyraFcQ2lBKaz7QsxBoZlEXrQERTaqV5sOFCdgHVHWtd6VZQTaHJGFtxDQZd+agF3lEzb1rGuo7ESlWGqW1kPMZ7Gp6SLbcIXzSOf/TpI6bbT1POjfu18Kvf+6MwKs4fPbGtH6NPBtszLVT0SThlh8GjtMZJZ+sLdRgRJbzuK/bYqCXSIqjeiGQO4nGD4VoutJopyqI63ClDk10JQ9oL7FhYbhuTTdydvkDpAZmh6hkpGTcYypIJkjnHijaVkgUtEybdMEZoSlGDYhRIsmFkYFORn4wbWwiEBss1o6VQ7ix3xxHRlZM1lBq5XW9oM0AOIIaDGHI4E8jcm5GkKjp/zLm9oJiB9x7eJZtf5vqm8HoJ3E2dWgxb26mJISNYaqnoAsfJcU4VsuAcNG1ZnzJxWShFkbUjukzcGvVrHzD7A9xBR/NgLX144Lye+crpju+fDnzv8cQv5yvXMfK1J2HNnde3G9/3cOSHuobYUU3TpBGj4EyionkMZ6xnjzweNN9Ir9lywiWP2IYl7/6q6pi1pqnGcR5ozYMaedwe8XSi3hVbTnok9Iwbhp3Xauhsl8zoZzCWw6lyfbxQsmI1+/eS0hgohEUYRotVBRUiEivaKc460zjTlozunbFe+XCL5KzQIrxpK2YEMLRgqD7Q+4zWgjaZXn8bOb//QWC9BZSyVNnQ3hK3Qk3gnKbXhlWaVqD0jhsdBsg36BqarVRpFOlIVYhSWK3oNHRvaD1AVTuNaiq0JBgXaMNEDPuqqzqDStuuNSeW1gu9e5R0RpMxQ6CXFeUnqA6MRklGy8yaFethJOeCTUL1kXEV+iwchjtQ0IphUBVKIfUBbQQvnkDhP3f/Aocl5Mz3qMK///TLfGN7ny+akUkb3gQBNBIqwQSq97wpTyxSUMNAWRqahBND7AapGWLmIjDnQj85lpaYjMIPhXBVpB4xdpf0Uc0Sm8HmC2o6sW2O48N3MR8cQQdKGnhvmChmL18RNzGsnmD2LUlOhVUEFQecV9gAmE7xjTMKfS68KpnXS6A1jaPuJUAHy5g88RY4aIsox2pWSIIrGV0rVUZSXCi9Ijw7wFtF3IBGMYdCbZnjeId5P2LKxho6OVcqwLQxuAF/NNiieO/uPZ4+fIUvE4pHHs8XYrH4uzvKzaB8ox8W3Gb2/LXuiT0wbxU/zyjV+cHpJe8dX/APDUdK/giXFO/rgaYV9ZiZ1D0f6cxgr3gxmGS4qb0Q1vsjNE0pIPZMyzvhoWwBZGGWgeV85XRnyG1EbCIK+OWJ0CxRKbYCs3tCK0H3zFYtW4uwVSiK6jMpGd51BtUsT6VyWa58391XOKtHzvGJsY/krewvqDERikfOH3MzI7OGFleWpTK8N+COI7YNLHXhzeNCUYryCOZloLOyLQNRa7qNeGksrxe8tlTd6Z/nqFzvnS0WjAajLc0IeuiY2mlN4cxeUd57w8ouKmAfhAFNiQIFtIYUG947hLaPrMmunV4SFIsbNb1lltjQeEQ1VBMEA7Fi5I61RLKshNjxqlEHjYq7BruoJ9r6kpxujLNmU45ZFbJbkQS2N46nI+SCngpWT1QVCEXt4gISWZshq85QM7EIslXOBxj7gncWR6Tcnqi8y6oHqgyouqJyIRjYlOJQBpwvhKgwa8DXSrGatWWsCMtgGSrMPsFiKE2gB3K2hDeWly8tl5wRGShtZXSOmBtJObzuvJqFNH2Fr795RX+6IO/eYVSnosmqknvg4SDIuWHWzkhGpYZSnSetaKbucuwmM5pdhfa95nlfndj6hbu7maupnB5G1mVF7MAWFvqgIRnunhVsbqEQ9BXXE1k094cJ1RNbyhyHgVwTYoU3y0fothui4+GO5IXh6YYQeRMqtQraW5xyPD2tbDGijKFfDT15jNL024bWhlF1vHOk1kAXZq85tC8QdOR9dcDUhT9w9JTLjYGIPr1PLRuPbzbs/QOlLwwvB74/D+R+IMeN4iJVhGoyaQE3Tbgx4fMLrueEuiuoplDlBGXFe8diZnSqHExDgjD447N6csBLRXWPnTXgsdVzXp+Y7ya4VVqGpawoe4+bAtPhSEgrl9uFp2apbiL7I6OqmA1UiEwUTN0LjMtdp5+OmKeIrp143pBm6RkimS1AGxV+23CDI0tjqB3jB3pZKKOwhLi7vT/PJSkIGKfRTYM0lKrkqGlNYb0QW8ZIQxtHLpGuoGwDWgt22NXRxQqjt9RNUF3RYkNcgwpbzFit6SWjs8XqSskaWkSJJvadwyeVglNQ68DRKNTQiWLJY8UuneF0IzwJxjpUUwzagA6IslwPJ9TXFUo2onsgLR+xrJHhqGhNoUehhQnMxgCIMbS2cfAjZh3geCGfBassE08ceyFjyLozdjh5Rb4lVu/pdsEvmrRV8qywdsAajRJFUJkHazmrztwHFjLaa2KdeGecaIdMqyPaBByNgKb1ATVstBao50Z172He+0fox0fc/StenX+RYY3oYXdsr7Xz1CqpN662kmLCmAeiX2mlErtBqcSYFcUe0AMoWfhd9zMfxhuvXl85asvJzgQt9HUjFjiQcO8cSbd1f/kbzXzQtK6ZwkB7DFy8YZgOrMCxFLLt6BTpSTEcZ7bbihiPHxQy3DMuhSCgEpiDZbmcCb3T6krSV9zJoYcRVQqUCZkMxlfaltDJUq8rt8nifOF1jfzhhwN3WnjjDMVVtrLQlivaanqrmCZ89PqG6xozeg56xMwK+/qCMlADlGHFZ8slnykK9KPQD4rUM3bW5KpRPSAWSvck3Wit45smO0de2y4zFnZ+MbHr/mJfA83sZDo3Mvr1K+w0kj9+JD+eyS87LjSmw5HLdYF7h/jCNQ541XDHA6MtOOkELfjRMAyeqBxHUeiYeFdrvvHmNav2OK/pZaPTQSluFdgsqlWUg6z2Ve4nwWfCMAm7OsxuQIRc9mptXMONQluEmsGNmlIzWjRCptRderpSMXmktkwhYnFgKp1dtbe0Tu6dQTRVN5w2iOn0NlB7IwWhkVA0jPE006iiCDljdECioVpNvnyBeBGsFxgMVQLemH1pe71R3EgzG5NaWXj2keBAF66pclSOeu07J3SwdOtYeiWoC7M2uzgCM4ePX/HRy0QyM2544HzrXJPD33e+3xtC7cikcNGgq6ZOUHOiqIETDvtcL6d0ReWMWRxC5hwS6jixnm9YZ2mm0jgQU0QPQqsz2lVKjbzZLtSg6TLRbKFhKUVwQTCiEV2YvMXmTKmGmAIZy2ATtldaStyCR5Rw1JWbgx/44jv8zS0wv/eAKjtN7Ze/9FXKsnCl8vTRa/r5xtYbphnsMwWxliPukHmdGicaYUvYu5ELjdE0puld8nLl9fXKO25CioEHT3haKcYypgwj2NGQj452fo2wCw+QOtkY3nt4oN4syhScbIw9E7KQNdieCVVhXST50+74p7CsG6Y5snL8agjQbrwKGYzwQdI89M4/dK/46lZBdTITMu2kcy+8Z82dqq586cULir5jlMQHr65of2PE0pJiaUJXjVoLao5QZZc7q4oBdqog5bmoM6cwcD3ApDMyPnBNZw5aCFvFnixP15VmIWvwRhAaveyiDuOUaaswsOsi+ptQpZFCJesrt5AoTZGnI9Z47nolnTsVdp/mrdHQWKehCBlFzY72m3NE/ob4TBgmEHJRKNvoHZBCeS6+Xa4RLXteUctg1QC602iUVEA8SkEfIgpBMnQX92LbnqmhI02hjMLoDiqTc9/lnBBM7wyiSJtlcEKqZS8dCVe6Nkgf0B3quvF6e8PRvIuqmloKXaC3Ql8z5W7ct1K1knLCSqOOhRANgxFab+h5F9XMJTHOmiqCZI0ymlFGTP2IefoilcD0zf+UhxdfIteIDg1nHEbBtgXawVG6IbiNwSmGMlEtZJ1YboHeLfmgMEEoEYwvWDvQWwHZI30hmD2JkYClImeQKZOageUR0Z3RKlzKXGrhcH9HkUyzA+p1ZRontnQmuQntN17YmTcfLdRqKU7ROogTermSrKPlyvs3+CEz8B9dnogqMpuBUtLuC+qacZyQsJHXiJsnwlY4Tgeyybx5s3J/f+ByuzHZGZ0aLBHYRSSs85wGRV465lCIa6NYwSkQUdxenUlvMsUXXpxeEqXz+OZjxHjeHQ7E28r68Y2tF04vPYTOcJiJtSMtI0azhc5fOn+Dn3MzEjcexglU5ucvZ37+Eri3IxZBlsJwf+KWr3ztqviB5viRCTyR6hTHaWCpHacbdRh5LBu9wWV7w5IU79u2EwfOE8e6cQsLw2mi25HT5qm+0/KK+AM5rnsisR3ItjMxEEpC07FrZGsLKjaiEvJgODRFSQqdK1yh6MxRZ1g91pa9rrR14qppd8J0uENtkeQ0LVauaySFjvcFRmil0FaN+IZNihg6raxoXZA2f76VeFvrlF52RVZVaUVoAhihZ7WTxsWyl4Nog7YGpCFKQ2t0U9CMoDvKK3rp9L5LN7WmIAMtURCUU3sCWxNoDYXGuQ5Wcw2Fzg01GFTf6VBqXShlwCrD1BM0IdvMqCd6i3Q7smW4lsqkLOu6koJm8AkZR5xt6OQR57gsG/hp9xU4S1punOxE74KtjWs9UlTmn5jf551B4+vC3dp5BMSs2DrQRoVohbVCKcAt82gzY7GkQ90jj2fNOA3UtuKOI61nTBNGL2zFkrVBjwrWSqkNkUYvnfzGskyV3zkOHJzhK+8YvvJhJMqBZDQhZzwOZzu3LSPJYp2mps5VAC34wWNsonqPlEqrM/dq4ONQKHR+9+lAE/jp/sgHNEwVRjdDDnQzYA6ObiNZFR7uRqo0wpuNwzOV8YsXJ0qMxO3GeHzJ6DWqZpLdyyQufePY9+chciDGlRcvT5xDRmuYX1q2Tci3BS+GIJ3LdmGwGpkVJmhsVnycI/rVyv1XjmwhMlRF36C0wK+Ohtutkj7+AJxGaqOqkduWOA6R0hxpvVKsxeeBn54K37xlfv984ktW+PAxcJgr15QoW2KxirQkzKAZj5rqPbNqbGvBDZ6YGq+XxuA6uhW22PaXdF/p0ilVE0tBuwOq7uwAbjFc9EbvCeePqJbpz9HpkivdZUJQONcooXFZEy+1w9x5eleM70RytajYCSUzNE0Qw+FOY4YF516QQkAPnZoSLg4kMjY3gtY4Gr1f2XkPvnN8JgyTCIzOkBM0BK0NLWe8GWl5d6JZrUCELWRG3YGMHTTlokALa0lY09HNQauktgtjKttpNoI2NCWEraFMpXSLaZqmCpcI2WdQESOK2hXiO7U3dGyIqrRW0Hce+6ZwGy2yVZwfKKlRU+bd+ogpHXWc0eqGUgOmbtQ2UGRFNY1pClUESkWCoW0zqjXEaZJWLD0whYqxe5FkbmeCTWileGEr6I5FuOW4S0lVy01dqM/5WP4WiGKZjppuFKNoFqXQGdYWKUXRlULnTJU9I/iuV+xhYlCOJ7khg/DVAr/7o7/B04eBljInfcTpyuhGNLB6i+sWumO1hd5mqsqoF5ZNDPEpYg8RuoGauPa2/9F74T47/sj9u/zwcuJXc+Gnlo1fqQvkXejUDgYVN2LqVGlISbjJY8eJbV2Yk0Jqww8nVEtI9mTTWMOGUQ7VCyFohtERUqKfPG+WR7rsklTpw4HZeLyzLK2iQ2Zo0GNA/EAPio8eAzltGLshwTGNh52e+a5zXbZdtskJqQtmEgZ3j3s8szw76cUUclOUYEEVJtU4u5U//3Hm980PvH9naKIpWuP8S9ZlxUlkaBbvPXGptPsBM1iqbwwPA+8PE7VWvFT0MNLYv+cZIS6WB+XQ1rOFiGqJtVSiFrR33HRHQmNyA6o90vVIsZYSNVioKtKZefPmiaYqp4cJpzNHNfG0XpCe0Xribu64bLi1Ss4bY4pErUl9pBpF64nltqGHiRANuQRU/RyvmHrvXJYVUz1Od7ayQVUsYaU2yL3RWkZhSS3SN4c2O91u0xWqkFsmbZnaN8QarFbYqrjdCl0npBZiNYgStOycN8pqVG7E2smp4bxiqR2dFM0kSlUYrajhXVL8AsfyQFaF4dpoxtJ1pWCp7UPmy9e4jDPxVqlZcNMVoyZexcChWp54ZLCenC9YO1KWSMgLH2jDcE3c1AgIpmimspACrDbQ7chKZk47cV5YKq0JwV/pbeFcPJbKls8MDaiahOG8fkzTI2q5MY8z55B5GBWpFSR1Loz4fKZaDapTqYRrx6bOxyXSeMUaK3UYuD8qDnmg98KxVlCZ5QJfzw3XIjOdajtNThwOlUu+AAO1VhyKFALlYnjzkOlRuKuFajJfyAlRwi88LqTWuJ9GFBPKaJxsbEvEuhGH8PTBwvxCWB8XhjtHjQU7GtwK9cFw0Ip+C2Q78vhUeDhuuFq5XhVGGg/vfZH8+pF1/Qh194Kadp71SMFlvUfzlkJOV7Z14+E4gOpst4zKe8qJpTOZgao0jci0VkKrvMofITWijaNx5N44Hm+Bq36kXAXz7sghCDUv/D96Y/i4kbnyB4/v8f090iUwH0akC+F8oWZNUJXRV26h4mLlGyaQ2fnfRWtqbszWkkpkqYpr2sgfB8yLIzZ2zssjZTK03jnWlWwb6w2moaMz2KrZikK2ROgzSORaArYk6mKJbWTtibgsDOPEqoTLq5V2veFJqAfNUjuxFBwLMSVCmki1Mm3rzmOfoH/CzG/5uwiY/AOBiFyBn/m0+/H3iHf4TUQ9Pwd42/9PF5/3/sPfeQzf3Xt/9zv9RZ+JFRPwM733H/m0O/H3AhH5yc/zGN72/9PF573/8Pd3DJ8JMYK3eIu3eItvx1vD9BZv8RafOXxWDNO//ml34O8DPu9jeNv/Txef9/7D38cxfCac32/xFm/xFt+Oz8qK6S3e4i3e4tfw1jC9xVu8xWcOn7phEpE/IiI/IyI/JyJ/8tPuz98JIvJVEfnLIvI3ReRviMh/97n9hYj830XkZ5//fXhuFxH5Xz2P6a+LyO/9dEewQ0S0iPxVEfkLz5+/V0R+4rmf/46IuOd2//z5557Pf8+n2vG9T/ci8mdF5G+JyE+LyO//HM7/v/T8/fkpEfm3RWT4LD8DEfnfi8hHIvJT39b2Hc+5iPzx5+t/VkT++G/p5r33T+0H0MDPA98HOOCvAT/0afbpN+jnF4Hf+3x8BP5T4IeA/ynwJ5/b/yTwrzwf/xjwf2UnTvhR4Cc+7TE89+u/D/wfgb/w/PnfBf7Y8/GfBv5bz8f/beBPPx//MeDf+Qz0/d8A/sXnYwfcf57mH/gy8IvA+G1z/y98lp8B8E8Cvxf4qW9r+47mHHgB/MLzvw/Pxw9/13t/yg/r9wN/8ds+/yngT33aX6LfQr//z8A/zZ6t/sXnti+yJ4oC/GvAP/9t1//adZ9in78C/IfAHwT+wvMX6BVgfv2zAP4i8Pufj83zdfIp9v30/Ectv6798zT/XwZ+9fkP1Dw/g//CZ/0ZAN/z6wzTdzTnwD8P/Gvf1v63Xfcb/XzaW7lvPaxv4WvPbZ9ZPC+pfw/wE8D7vfdvPp/6AHj/+fizOK7/BfA/gF8r934JPPXey/Pnb+/jr/X/+fz5+fpPC98LfAz8H563ov87EZn5HM1/7/3rwL8K/ArwTfY5/St8fp7Bt/CdzvknehaftmH6XEFEDsC/D/z3eu+Xbz/X99fBZzL3QkT+GeCj3vtf+bT78glh2LcU/9ve++8BFvZtxK/hszz/AM++mH+W3ch+CZiBP/KpdurvEb+dc/5pG6avA1/9ts9feW77zEFELLtR+rd673/uuflDEfni8/kvAh89t3/WxvUHgP+SiPwS8OPs27n/JXAvIt+ql/z2Pv5a/5/Pn4DX/yA7/OvwNeBrvfefeP78Z9kN1edl/gH+EPCLvfePe+8Z+HPsz+Xz8gy+he90zj/Rs/i0DdP/B/idz5EJx+7k+/Ofcp/+MxARAf4M8NO99//Zt53688C3ogx/nN339K32//pzpOJHgfO3LX//gaP3/qd671/pvX8P+xz/pd77fxX4y8Affb7s1/f/W+P6o8/Xf2qrkd77B8CvisgPPjf9U8Df5HMy/8/4FeBHRWR6/j59awyfi2fwbfhO5/wvAn9YRB6eV41/+LntN8en6RB8nucfY49y/TzwP/y0+/Mb9PGfYF+y/nXg//v882Pse/7/EPhZ4D8AXjxfL8D/5nlM/wnwI5/2GL5tLP95/v9Rue8D/t/AzwH/HuCf24fnzz/3fP77PgP9/mHgJ5+fwf+JPcLzuZp/4H8M/C3gp4B/E/Cf5WcA/Nvs/rDMvmr9E59kzoH/5vM4fg74b/xW7v22JOUt3uItPnP4tLdyb/EWb/EW/xm8NUxv8RZv8ZnDW8P0Fm/xFp85vDVMb/EWb/GZw1vD9BZv8RafObw1TG/xFm/xmcNbw/QWb/EWnzn8/wCVBZODzz9LQAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rgb_map = dataset_handler.print_ground_truth(showfig=True, transparent_image=.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Getters\n",
    "\n",
    "- `get_scattering_vector`\n",
    "- `get_pauli_vector`\n",
    "- `get_coherency_matrix`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "It is not possible to obtain the scattering vector from the coherency matrix",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_343700/3189154597.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_scattering_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Documents/onera/PolSar/dataset_reader.py\u001b[0m in \u001b[0;36mget_scattering_vector\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    530\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m't'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 532\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"It is not possible to obtain the scattering vector from the coherency matrix\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    533\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Mode {self.mode} not supported. Supported modes: {SUPPORTED_MODES}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: It is not possible to obtain the scattering vector from the coherency matrix"
     ]
    }
   ],
   "source": [
    "s = dataset_handler.get_scattering_vector()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "dataset_handler.mode = 's'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 1024, 3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_handler.get_scattering_vector().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 1024, 6)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_handler.get_coherency_matrix().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(900, 1024, 3)\n",
      "complex64\n"
     ]
    }
   ],
   "source": [
    "complex_image = dataset_handler.image\n",
    "print(complex_image.shape)\n",
    "print(complex_image.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(900, 1024, 6)\n",
      "float32\n"
     ]
    }
   ],
   "source": [
    "real_image = dataset_handler.get_real_image()\n",
    "print(real_image.shape)\n",
    "print(real_image.dtype)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "`get_real_image` has an optional parameter `mode` that accepts:\n",
    "- 'real_image': real and imaginary part\n",
    "- 'amplitude_phase': amplitude and phase\n",
    "- 'amplitude_only': only the amplitude\n",
    "- 'real_only': only the real part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(900, 1024, 3)\n",
      "float32\n"
     ]
    }
   ],
   "source": [
    "real_image = dataset_handler.get_real_image('amplitude_only')\n",
    "print(real_image.shape)\n",
    "print(real_image.dtype)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Get dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing dataset seg_airsar_s_window(6, 6)_stride6_pad((0, 0), (0, 0))_patches.npy\n",
      "Computation done in 0.07188360200962052 seconds\n"
     ]
    }
   ],
   "source": [
    "train_ds, val_ds, test_ds = dataset_handler.get_dataset(method=\"random\", percentage=(0.08, 0.02, 0.9),\n",
    "                                                        size=6, stride=6, pad=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "x_train, y_train = train_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1839, 6, 6, 3)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20735, 6, 6, 3)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ds[0].shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "`method` parameter allows you to divide the image using the `azimuth` parameter.\n",
    "\n",
    "<table><tr>\n",
    "    <td><img src=\"tuto_imgs/train_ground_truth.png\" width=\"350\"/></td>\n",
    "    <td><img src=\"tuto_imgs/val_ground_truth.png\" width=\"75\"/></td>\n",
    "    <td><img src=\"tuto_imgs/test_ground_truth.png\" width=\"75\"/></td>\n",
    "</tr></table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing dataset cls_airsar_s_window(12, 12)_stride1_pad((6, 5), (6, 5))_patches.npy\n",
      "Computation done in 5.301745194999967 seconds\n"
     ]
    }
   ],
   "source": [
    "train_ds, val_ds, test_ds = dataset_handler.get_dataset(method=\"random\", percentage=(0.09, 0.01),\n",
    "                                                        size=12, stride=1, pad='same', classification=True, \n",
    "                                                        complex_mode=True, real_mode=\"real_imag\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "x_train, y_train = train_ds"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Training CVNN on PolSAR dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Activation\n",
    "\n",
    "in1 = complex_layers.complex_input(shape=(12, 12, 3))\n",
    "c1 = complex_layers.ComplexConv2D(filters=6, kernel_size=3, strides=1, padding='valid', activation='linear')(in1)\n",
    "conv = Activation('cart_relu')(c1)\n",
    "p1 = complex_layers.ComplexAvgPooling2D(pool_size=2)(conv)\n",
    "c2 = complex_layers.ComplexConv2D(filters=12, kernel_size=3, strides=1, padding='valid', activation='linear')(p1)\n",
    "conv = Activation('cart_relu')(c2)\n",
    "flat = complex_layers.ComplexFlatten()(conv)\n",
    "out = complex_layers.ComplexDense(5, activation='softmax_real_with_abs')(flat)\n",
    "model = Model(inputs=in1, outputs=out)\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/6\n",
      "723/723 [==============================] - 6s 8ms/step - loss: 0.3164 - accuracy: 0.9036 - val_loss: 0.1955 - val_accuracy: 0.9305\n",
      "Epoch 2/6\n",
      "723/723 [==============================] - 7s 10ms/step - loss: 0.1804 - accuracy: 0.9370 - val_loss: 0.1683 - val_accuracy: 0.9389\n",
      "Epoch 3/6\n",
      "723/723 [==============================] - 7s 10ms/step - loss: 0.1614 - accuracy: 0.9424 - val_loss: 0.1586 - val_accuracy: 0.9405\n",
      "Epoch 4/6\n",
      "723/723 [==============================] - 7s 10ms/step - loss: 0.1491 - accuracy: 0.9461 - val_loss: 0.1541 - val_accuracy: 0.9396\n",
      "Epoch 5/6\n",
      "723/723 [==============================] - 7s 9ms/step - loss: 0.1397 - accuracy: 0.9489 - val_loss: 0.1410 - val_accuracy: 0.9459\n",
      "Epoch 6/6\n",
      "723/723 [==============================] - 6s 9ms/step - loss: 0.1340 - accuracy: 0.9509 - val_loss: 0.1351 - val_accuracy: 0.9477\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, epochs=6, validation_data=val_ds, batch_size=100)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Good Practices\n",
    "(and what I learned)\n",
    "\n",
    "- Use a VC (Git)\n",
    "    - GitHub\n",
    "    - GitLab\n",
    "- [PEP8](https://peps.python.org/pep-0008/)\n",
    "\n",
    "IDEs:\n",
    "\n",
    "- Pycharm\n",
    "- VSCode (less heavy and for all programming languages)\n",
    "- Jupyter (For fast scripting)\n",
    "- Spyder\n",
    "\n",
    "Books:\n",
    "\n",
    "- Cracking the coding interview\n",
    "- Effective Python\n",
    "- The clean coder"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Testing Module\n",
    "\n",
    "### Assetions\n",
    "\n",
    "When do we use `try..except..` or `if` conditions and when do we use `assert`? [source](https://realpython.com/python-assert-statement/) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Test Driven Development (TDD)\n",
    "\n",
    "- Unit Test\n",
    "- Integration Test\n",
    "- End2end\n",
    "\n",
    "Example scenario, create a function that sorts a list. What would you test?\n",
    "\n",
    "- [Typing](https://docs.python.org/3/library/typing.html)\n",
    "- [PyTest](https://github.com/NEGU93/cvnn)\n",
    "\n",
    "Steps:\n",
    "\n",
    "1. Add a test\n",
    "2. Run all tests. The new test should fail\n",
    "3. Write simplest code that passes the new test\n",
    "4. All tests should pass\n",
    "5. Refactor as needed. Ensuring tests still pass."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Documentation\n",
    "\n",
    "- Markdown\n",
    "    - Used in this jupyter notebook!\n",
    "    - GitHub default `Readme.md` file\n",
    "    - Easy to create and read (No compilation needed)\n",
    "- ReStructuredText\n",
    "    - Optimized for documenting code\n",
    "\n",
    "CVNN [documentation](https://complex-valued-neural-networks.readthedocs.io/en/latest/index.html)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
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