tf-keras-can-classify-paddy-disease.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/cbeaud/tf-keras-can-classify-paddy-disease?scriptVersionId=108228211\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"cell_type":"markdown","id":"4bb9b02e","metadata":{"papermill":{"duration":0.024306,"end_time":"2022-10-16T07:40:22.648848","exception":false,"start_time":"2022-10-16T07:40:22.624542","status":"completed"},"tags":[]},"source":["<h1 style=\"color: #2c3f51\"> Paddy Doctor: Paddy Disease Classification </h1>\n","\n","Learn to use modern deep-learning networks to build an **image classifier** with Keras."]},{"cell_type":"markdown","id":"5ac26b29","metadata":{"papermill":{"duration":0.022182,"end_time":"2022-10-16T07:40:22.694632","exception":false,"start_time":"2022-10-16T07:40:22.67245","status":"completed"},"tags":[]},"source":["### Problem Statement\n","Rice (Oryza sativa) is one of the staple foods worldwide. Paddy, the raw grain before removal of husk, is cultivated in tropical climates, mainly in Asian countries. Paddy cultivation requires consistent supervision because several diseases and pests might affect the paddy crops, leading to up to 70% yield loss. Expert supervision is usually necessary to mitigate these diseases and prevent crop loss. With the limited availability of crop protection experts, manual disease diagnosis is tedious and expensive. Thus, it is increasingly important to automate the disease identification process by leveraging computer vision-based techniques that achieved promising results in various domains.\n","\n","### Objective\n","The main objective of this competition is to develop a machine or deep learning-based model to classify the given paddy leaf images accurately. We provide a training dataset of 10,407 (75%) labeled images across ten classes (nine disease categories and normal leaf). Moreover, we also provide additional metadata for each image, such as the paddy variety and age. Your task is to classify each paddy image in the given test dataset of 3,469 (25%) images into one of the nine disease categories or a normal leaf.\n","\n","Source : [competition website](https://www.kaggle.com/competitions/paddy-disease-classification/overview/description)"]},{"cell_type":"markdown","id":"f33b3c47","metadata":{"papermill":{"duration":0.021913,"end_time":"2022-10-16T07:40:22.738575","exception":false,"start_time":"2022-10-16T07:40:22.716662","status":"completed"},"tags":[]},"source":["<center>\n","    <h2 style=\"color: #2c3f51\"> 🏃🏻 Let's identify the type of disease present in paddy leaf images </h2>"]},{"cell_type":"markdown","id":"3d1df54c","metadata":{"papermill":{"duration":0.022659,"end_time":"2022-10-16T07:40:22.787078","exception":false,"start_time":"2022-10-16T07:40:22.764419","status":"completed"},"tags":[]},"source":["### Imports"]},{"cell_type":"code","execution_count":1,"id":"998b5906","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2022-10-16T07:40:22.834716Z","iopub.status.busy":"2022-10-16T07:40:22.834335Z","iopub.status.idle":"2022-10-16T07:40:27.232265Z","shell.execute_reply":"2022-10-16T07:40:27.231466Z"},"papermill":{"duration":4.424439,"end_time":"2022-10-16T07:40:27.234531","exception":false,"start_time":"2022-10-16T07:40:22.810092","status":"completed"},"tags":[]},"outputs":[],"source":["import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","\n","import tensorflow as tf\n","from tensorflow import keras\n","\n","import matplotlib.pyplot as plt\n","\n","import os, warnings"]},{"cell_type":"markdown","id":"c813a319","metadata":{"papermill":{"duration":0.022343,"end_time":"2022-10-16T07:40:27.283573","exception":false,"start_time":"2022-10-16T07:40:27.26123","status":"completed"},"tags":[]},"source":["# Config"]},{"cell_type":"code","execution_count":2,"id":"cb636705","metadata":{"execution":{"iopub.execute_input":"2022-10-16T07:40:27.33114Z","iopub.status.busy":"2022-10-16T07:40:27.330454Z","iopub.status.idle":"2022-10-16T07:40:27.335149Z","shell.execute_reply":"2022-10-16T07:40:27.334312Z"},"papermill":{"duration":0.02992,"end_time":"2022-10-16T07:40:27.336906","exception":false,"start_time":"2022-10-16T07:40:27.306986","status":"completed"},"tags":[]},"outputs":[],"source":["batch_size = 16\n","img_dim = 128\n","RESCALE = 1.0/255\n","\n","VALIDATION_SPLIT = 0.1\n","SEED = 42\n","\n","warnings.filterwarnings(\"ignore\") # to clean up output cells"]},{"cell_type":"markdown","id":"9d419e19","metadata":{"papermill":{"duration":0.022669,"end_time":"2022-10-16T07:40:27.382862","exception":false,"start_time":"2022-10-16T07:40:27.360193","status":"completed"},"tags":[]},"source":["# Load Data"]},{"cell_type":"code","execution_count":3,"id":"da034d83","metadata":{"execution":{"iopub.execute_input":"2022-10-16T07:40:27.429089Z","iopub.status.busy":"2022-10-16T07:40:27.428852Z","iopub.status.idle":"2022-10-16T07:40:27.432096Z","shell.execute_reply":"2022-10-16T07:40:27.431427Z"},"papermill":{"duration":0.028349,"end_time":"2022-10-16T07:40:27.433849","exception":false,"start_time":"2022-10-16T07:40:27.4055","status":"completed"},"tags":[]},"outputs":[],"source":["# Get training data path\n","train_dir = '../input/paddy-disease-classification/train_images/'"]},{"cell_type":"markdown","id":"58628de3","metadata":{"papermill":{"duration":0.023039,"end_time":"2022-10-16T07:40:27.479937","exception":false,"start_time":"2022-10-16T07:40:27.456898","status":"completed"},"tags":[]},"source":["Let's going to do image preprocessing with **ImageDataGenerator** \n","\n","[Doc here](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator)"]},{"cell_type":"code","execution_count":4,"id":"34de16c3","metadata":{"execution":{"iopub.execute_input":"2022-10-16T07:40:27.526001Z","iopub.status.busy":"2022-10-16T07:40:27.52577Z","iopub.status.idle":"2022-10-16T07:40:28.456966Z","shell.execute_reply":"2022-10-16T07:40:28.45622Z"},"papermill":{"duration":0.956582,"end_time":"2022-10-16T07:40:28.459164","exception":false,"start_time":"2022-10-16T07:40:27.502582","status":"completed"},"tags":[]},"outputs":[],"source":["img_datagen = keras.preprocessing.image.ImageDataGenerator(\n","    rescale=RESCALE,                    # we multiply the data by the value provided (after applying all other transformations).\n","    validation_split=VALIDATION_SPLIT,  # Float. Fraction of images reserved for validation (strictly between 0 and 1).\n","    rotation_range=5,                   # Int. Degree range for random rotations.\n","    shear_range=0.3,                    # Float. Shear Intensity (Shear angle in counter-clockwise direction in degrees)\n","    zoom_range=0.3,                     # Float or [lower, upper]. Range for random zoom.\n","    width_shift_range=0.05,             # Float, 1-D array-like or int\n","    height_shift_range=0.05,            # Float, 1-D array-like or int\n","    horizontal_flip=True,               # Boolean. Randomly flip inputs horizontally.\n","    vertical_flip=True,                 # Boolean. Randomly flip inputs vertically.\n",")"]},{"cell_type":"code","execution_count":5,"id":"efb0d3b3","metadata":{"execution":{"iopub.execute_input":"2022-10-16T07:40:28.505995Z","iopub.status.busy":"2022-10-16T07:40:28.505387Z","iopub.status.idle":"2022-10-16T07:40:32.441324Z","shell.execute_reply":"2022-10-16T07:40:32.440458Z"},"papermill":{"duration":3.964008,"end_time":"2022-10-16T07:40:32.446134","exception":false,"start_time":"2022-10-16T07:40:28.482126","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Found 9371 images belonging to 10 classes.\n"]}],"source":["# Define train data\n","ds_train = img_datagen.flow_from_directory(\n","    train_dir,\n","    subset=\"training\",\n","    seed=SEED,\n","    shuffle=True,                    # Set True if you want to shuffle the order of the image that is being yielded, else set False.\n","    target_size=(img_dim, img_dim),  # Size of your input images, every image will be resized to this size.\n","    batch_size=batch_size,           # No. of images to be yielded from the generator per batch.\n","    class_mode=\"categorical\",        # Set “binary” if you have only two classes to predict, if not set to“categorical”\n",")"]},{"cell_type":"code","execution_count":6,"id":"bf91fda0","metadata":{"execution":{"iopub.execute_input":"2022-10-16T07:40:32.501296Z","iopub.status.busy":"2022-10-16T07:40:32.500974Z","iopub.status.idle":"2022-10-16T07:40:32.859999Z","shell.execute_reply":"2022-10-16T07:40:32.859151Z"},"papermill":{"duration":0.386277,"end_time":"2022-10-16T07:40:32.862139","exception":false,"start_time":"2022-10-16T07:40:32.475862","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Found 1036 images belonging to 10 classes.\n"]}],"source":["# Define validation data\n","ds_valid = img_datagen.flow_from_directory(\n","    train_dir,\n","    subset=\"validation\",\n","    seed=SEED,\n","    shuffle=False,\n","    target_size=(img_dim, img_dim),\n","    batch_size=batch_size,\n","    class_mode=\"categorical\",\n",")"]},{"cell_type":"code","execution_count":7,"id":"69a61165","metadata":{"execution":{"iopub.execute_input":"2022-10-16T07:40:32.916743Z","iopub.status.busy":"2022-10-16T07:40:32.916135Z","iopub.status.idle":"2022-10-16T07:40:32.921371Z","shell.execute_reply":"2022-10-16T07:40:32.920684Z"},"papermill":{"duration":0.03457,"end_time":"2022-10-16T07:40:32.923728","exception":false,"start_time":"2022-10-16T07:40:32.889158","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["{'bacterial_leaf_blight': 0, 'bacterial_leaf_streak': 1, 'bacterial_panicle_blight': 2, 'blast': 3, 'brown_spot': 4, 'dead_heart': 5, 'downy_mildew': 6, 'hispa': 7, 'normal': 8, 'tungro': 9}\n","\n"," We have 10  differents class\n"]}],"source":["print(ds_train.class_indices)\n","OUTPUT_SIZE = len(ds_train.class_indices)\n","\n","print(\"\\n We have\", OUTPUT_SIZE, \" differents class\")"]},{"cell_type":"code","execution_count":null,"id":"e6a5d1c0","metadata":{"papermill":{"duration":0.027243,"end_time":"2022-10-16T07:40:32.978123","exception":false,"start_time":"2022-10-16T07:40:32.95088","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"markdown","id":"e4d42d41","metadata":{"papermill":{"duration":0.024314,"end_time":"2022-10-16T07:40:33.027204","exception":false,"start_time":"2022-10-16T07:40:33.00289","status":"completed"},"tags":[]},"source":["# Modeling (with keras)\n","\n","Now, we are going to build our model. Let's use :\n","\n","- The **Conv2D** : to create a convolution kernel that is convolved with the layer input to produce a tensor of outputs. [more](https://keras.io/api/layers/convolution_layers/convolution2d/)\n","\n","- The **MaxPooling2D** : downsamples the input along its spatial dimensions (height and width). [more](https://keras.io/api/layers/pooling_layers/max_pooling2d/)\n","\n","- The **Flatten** layer : to transform the two dimensional outputs of the base into the one dimensional inputs needed by the head. [more](https://www.kaggle.com/code/ryanholbrook/the-convolutional-classifier)"]},{"cell_type":"code","execution_count":8,"id":"cd0b4c0e","metadata":{"execution":{"iopub.execute_input":"2022-10-16T07:40:33.078844Z","iopub.status.busy":"2022-10-16T07:40:33.07838Z","iopub.status.idle":"2022-10-16T07:40:36.202159Z","shell.execute_reply":"2022-10-16T07:40:36.201237Z"},"papermill":{"duration":3.152023,"end_time":"2022-10-16T07:40:36.204389","exception":false,"start_time":"2022-10-16T07:40:33.052366","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["2022-10-16 07:40:33.186867: 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","2022-10-16 07:40:33.387795: 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","2022-10-16 07:40:33.388652: 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","2022-10-16 07:40:33.389956: 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 AVX512F FMA\n","To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n","2022-10-16 07:40:33.390228: 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","2022-10-16 07:40:33.391002: 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","2022-10-16 07:40:33.391644: 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","2022-10-16 07:40:35.727306: 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","2022-10-16 07:40:35.728377: 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","2022-10-16 07:40:35.729299: 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","2022-10-16 07:40:35.730605: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n"]},{"name":"stdout","output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d (Conv2D)              (None, 126, 126, 16)      448       \n","_________________________________________________________________\n","max_pooling2d (MaxPooling2D) (None, 63, 63, 16)        0         \n","_________________________________________________________________\n","conv2d_1 (Conv2D)            (None, 61, 61, 32)        4640      \n","_________________________________________________________________\n","max_pooling2d_1 (MaxPooling2 (None, 30, 30, 32)        0         \n","_________________________________________________________________\n","conv2d_2 (Conv2D)            (None, 28, 28, 64)        18496     \n","_________________________________________________________________\n","max_pooling2d_2 (MaxPooling2 (None, 14, 14, 64)        0         \n","_________________________________________________________________\n","conv2d_3 (Conv2D)            (None, 12, 12, 128)       73856     \n","_________________________________________________________________\n","max_pooling2d_3 (MaxPooling2 (None, 6, 6, 128)         0         \n","_________________________________________________________________\n","flatten (Flatten)            (None, 4608)              0         \n","_________________________________________________________________\n","dense (Dense)                (None, 2048)              9439232   \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 1024)              2098176   \n","_________________________________________________________________\n","dense_2 (Dense)              (None, 256)               262400    \n","_________________________________________________________________\n","dense_3 (Dense)              (None, 128)               32896     \n","_________________________________________________________________\n","dense_4 (Dense)              (None, 10)                1290      \n","=================================================================\n","Total params: 11,931,434\n","Trainable params: 11,931,434\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["from tensorflow.keras import layers\n","\n","model = keras.Sequential([\n","    layers.InputLayer(input_shape=(img_dim, img_dim, 3)),\n","    layers.Conv2D(16, (3,3), activation='selu' ),\n","    layers.MaxPooling2D(2, 2),\n","    layers.Conv2D(32, (3,3), activation='selu'),\n","    layers.MaxPooling2D(2,2),\n","    layers.Conv2D(64, (3,3), activation='selu'),\n","    layers.MaxPooling2D(2,2),\n","    layers.Conv2D(128, (3,3), activation='selu'),\n","    layers.MaxPooling2D(2,2),\n","    layers.Flatten(),\n","    layers.Dense(2048, activation='selu'),\n","    layers.Dense(1024, activation='selu'),\n","    layers.Dense(256, activation='selu'),\n","    layers.Dense(128, activation='selu'),\n","    layers.Dense(OUTPUT_SIZE, activation='softmax')\n","])\n","\n","# Prints a string summary of the network.\n","model.summary()"]},{"cell_type":"code","execution_count":9,"id":"d3d9846b","metadata":{"execution":{"iopub.execute_input":"2022-10-16T07:40:36.254225Z","iopub.status.busy":"2022-10-16T07:40:36.254017Z","iopub.status.idle":"2022-10-16T07:40:37.226554Z","shell.execute_reply":"2022-10-16T07:40:37.225714Z"},"papermill":{"duration":1.00035,"end_time":"2022-10-16T07:40:37.229398","exception":false,"start_time":"2022-10-16T07:40:36.229048","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAoQAAAZXCAYAAAD+UmHdAAAABmJLR0QA/wD/AP+gvaeTAAAgAElEQVR4nOzdeVxTZ9o38F+AJISAAUEWgVZckLpFBq0yhdcFK7WgVh4RHey0z0i1tkoVta61nap1tLTVqbvYTbSV2o92qEsfpXV8RJxGq1gXXLCuLLLIYgSEcr1/+Cav4RyEhECAXN/PJ3943/e5z5UTCJfn3IuEiAiMMcYYY8xq2Vg6AMYYY4wxZlmcEDLGGGOMWTlOCBljjDHGrBwnhIwxxhhjVs7O0gG0tIyMDHz88ceWDoMxxhhjrVRCQgKCg4MtHUaLsro7hLdu3cLu3bstHQZjbd6JEydw4sQJS4dhFXbv3o3bt29bOgzGrMLu3btx69YtS4fR4qzuDqHOt99+a+kQGGvToqOjAfDvUkuQSCSYPXs2JkyYYOlQGGv3JBKJpUOwCKu7Q8gYY4wxxgxxQsgYY4wxZuU4IWSMMcYYs3KcEDLGGGOMWTlOCBljrJ1JTk6GRCLRvxwdHUXb3bhxA2PGjEFZWRkKCwsNjgkMDERlZaXgmLrtJBIJBgwY0NxvqcXs378f/v7+sLOrf87lvXv3sGnTJgwfPhwdO3aEQqFAjx49EBsbi8zMTNFjampqsG3bNjz77LNwdXWFi4sLgoKCsG7dOjx8+NCq4ycipKen480334S/vz/kcjnc3d0REhKC5ORkEJFB+wULFmDXrl2ifS1YsMDgZ3Pw4MFNis2qkJXZtWsXWeHbZszsxo8fT+PHjzdLX+Xl5dS9e3eKiIgwS3/tDQDatWtXo9tv376dANDGjRvrbXP69Glyc3OjTz/91KBco9EQAAJA06ZNq/f4jIwMcnV1bXRMrd3Vq1dp9OjR1K9fP+rQoQPZ2trW23bKlClkZ2dHa9asodzcXNJqtXT06FHq1asX2dra0p49ewTHTJ48mQDQwoULKT8/nwoLC2nVqlUEgCIjI606/osXLxIAGjFiBGVmZlJFRQVlZ2fTpEmTCADNmTNH8F79/PxoyZIlT+zX1taWBg0aZHQ8xv6+tRdWlxlxQsiYeZgzISwrK6OuXbvSqFGjzNJfc1IqlfTcc8+16DnNnRCWlpaSj4+PaMKn0WhILpeTq6srAaCdO3eK9tHeEsJJkybRypUrqbq6mry9vRtMqKZOnSooP3PmDAGgHj16GJRnZ2cTAAoMDBQc8/zzzxMA+uWXX6w2/osXL5KdnR0VFxcblFdVVZGrqyvJ5XKqrKwUxCqRSJ74e8EJoXGsdh1Cxljr4eTkhOzsbEuHYTVWr16NvLw8LF26VLTe3t4eO3bswIsvvohp06YhKCgI/v7+LRxly9q2bRsUCkWj2iYlJYmWq9VqKBQKZGdng4j069npFjl+5plnBMcEBATg0KFDuHnzJgYOHGhi9G07/oCAAFRXVwvKZTIZfH19cebMGVRWVkIulxvEOn78eMyZMwdRUVFPfETOGofHEDLGmBUhIiQlJWHQoEHo3Llzve3Cw8OxZMkSlJeXIzo6WnQ8YXvS2GTqSbRaLSoqKtCnTx+DxY0DAgIglUqRlZUlOCYrKwsSiQR9+/Zt0rnbevxiSkpKcOXKFQQGBkKlUgnqx40bh9u3b2Pfvn1mP7c14oSQMWZRe/fuNRgErks86pZfv34dMTExcHZ2hqurKyIjIw3uKiYmJurb+vj4QKPRICwsDE5OTnBwcMCwYcOQnp6ub798+XJ9+5CQEH35wYMH9eVubm6C/rVaLdLT0/Vt2tqdiczMTOTn50OtVjfY9t1338XIkSNx9uxZzJw5s1H9FxUVISEhAd26dYNMJoOLiwtGjRqFn3/+Wd/G2M9Wp6CgAPHx8ejSpQtkMhk6deqEqKgonDlzpvEXoBnpdu1ZvHixQbmHhwcSExORmZmJRYsWoaCgAMXFxVi9ejUOHz6MpUuXtoo7sK0l/rKyMqSnp2PMmDHw9PTEV199Jdquf//+AIAff/zRbOe2ahZ+ZN3ieAwhY+ZhzjGERERjx44lAFRRUSFaPnbsWDp+/Djdv3+fDh06RAqFggYOHCjoR61Wk1KppODgYH17jUZD/fr1I5lMRkeOHDFoX9+YwKCgINExcg2NIRw2bBh17NiRMjIyGvvWGwQzjiHU1X3wwQeix2o0GlKpVPp/FxQUkK+vLwGg5ORkfbnYGMLc3Fzy8/MjDw8PSk1NpdLSUrp06RJFRUWRRCKhrVu3GrQ35rPNycmhp59+mjw8PGjfvn1UXl5O586doyFDhpC9vT0dP3680denIQ2NwROTl5dHHh4eFBcXV2+blJQU8vHx0U/acXNzo23btjU1XIG2HP+yZcv0/Q8dOpTOnj1bb9vS0lICQKGhoaL1PIbQOHyHkDHWJsTFxSE4OBhKpRIjRoxAREQENBoNCgsLBW21Wi02bNigbz9gwAAkJyfj4cOHeOutt5o1ztraWtCjCXvNeh5T5ebmAoDoIzgxbm5uSElJgVQqxbRp00QfG+osXLgQv//+O9asWYPIyEh06NAB/v7+2LlzJ7y8vBAfH4/8/HzBcY35bBcuXIgbN27g448/xosvvghHR0f07t0b33zzDYio0Xcwm0NRURFeeOEFDB06FJs2bRLUExGmTp2K2NhYJCQkIC8vDwUFBVixYgVmzJiBiRMnoqamxgKRP9Ka4l+yZAmqqqpw8eJFBAQEIDAwEMuWLRNt26FDB0gkEv3PNGsaTggZY21C3QHrvr6+AICcnBxBW6VSqX+cpNO3b1907twZmZmZzfoH5MiRIyguLkZwcHCznaMpdI/kpVJpo48ZPHgwEhMTodVqER0djYqKCtF2e/bsAQBEREQYlMvlcoSFhaGiokL08V5jPtu9e/fCxsYGkZGRBm09PT3Ru3dvnDp1Crdv3270ezIXrVaL8PBw9OrVCzt27ICtra2gzfbt27F161a8/vrrmD17Njw8PODm5oapU6fq19Rbt25di8cOtM74ZTIZAgICsHHjRowZMwZLly7F4cOHRdva2dnV+/PIjMMJIWOsTah7R0smkwF4dEeuLmdnZ9E+3N3dAQB37941c3Rth729PQCIzup8kvj4eMTExODcuXOYMWOGoL6qqgqlpaWwt7eHk5OToN7DwwMAkJeXJ6hr6LPV9V1bWwuVSiVYGPvXX38FAFy5csWo99RUNTU1iI6Ohre3N7788kvRZAp4NC4VAEaMGCGoCwsLAwAcOHCg+QKtR1uIf/To0QCAH374QbS+pqbGLBNqGCeEjLF2qKioSPSRrS4R1CWGAGBjYyO600JJSYlo34/PvmyLvLy8AAClpaVGH5uUlISePXvis88+w/bt2w3q5HI5VCoVKisrUV5eLjhW96jY09PT6PPK5XI4OzvDzs4O1dXV+kfydV/Dhg0zuu+mmDZtGqqqqpCSkmIwuah79+44ceKE/t9arbbBvu7fv98sMT5JW4hft9RMcXGxoK6srAxEpP+ZZk3DCSFjrN2prKyERqMxKPvtt9+Qk5MDtVpt8AfEy8sLd+7cMWibl5eHmzdvivbt4OBgkED27NkTW7ZsMWP0zatPnz4AYNLjVUdHR3z33XdQKpXYsGGDoH7cuHEAIFgGpKqqCmlpaVAoFAgPDzchaiAqKgo1NTUGM8V1Vq1ahaeeeqpFx+G99957OH/+PL7//nuD9fHEDBo0CACQlpYmqPvpp58AoMW3WGtN8c+dOxeTJ08WrdPdeRRb41D3e6v7mWZNwwkhY6zdUalUWLRoETIyMqDVanHy5ElMnjwZMpkMa9euNWg7cuRI5OTkYN26dbh//z6ys7Px1ltvGdxFfNyf/vQnXL58Gbdu3UJGRgauXbuG0NBQff3w4cPh6upqcIelNVGr1XB3d693z9qG9O7dG5s3bxatW7lyJfz8/DBr1iz88MMPKC8vx+XLl/GXv/wFubm5WLt2rf7RsbFWrlyJbt264W9/+xsOHDiA0tJSFBcXY/PmzXj//feRmJhocJdr8uTJkEgk+P33300635N88cUX+Pvf/47//Oc/cHJyEjzCrrtkzhtvvIEePXpg48aN+Oc//4m7d++iqKgI27Ztwz/+8Q94e3tj7ty5BsdYW/w7d+7E+++/j+vXr6OqqgrXr1/H/PnzkZycjKCgIMTFxQmO0S03NHLkSBOvBDNgkbnNFsTLzjBmHuZadmbPnj36ZSZ0r9jYWMrIyBCUL168mIhIUP74HshqtZq8vb3pwoULFB4eTk5OTqRQKGjIkCF07NgxwflLSkooLi6OvLy8SKFQUEhICGk0GgoKCtL3P3/+fH37rKwsCg0NJaVSSb6+vrR+/XqD/kJDQ8nFxcWsy6DAzFvXLVq0iOzs7OjOnTv6soKCAsF1DQoKqvcc06dPF12Wp7CwkGbNmkV+fn4klUpJpVJReHg4paWl6duY+tkWFRVRQkICde3alaRSKXXq1IlGjhxJhw4dEsQxfPhwcnR0pJqamoYvGBGlpqYKzq171V0uJyIiot62ulfdZYeKi4tp3rx5FBAQQHK5nGQyGXXr1o1mzJhBeXl5Vh1/aWkpJSUlUXh4OHXp0oVkMhk5OjpSUFAQrVy5kh48eCB6XHR0NHl7e9PDhw9F63nZGeNYXWbECSFj5mHudQjNRZcQtifmTghLSkrI29tbdC/j9uDevXukUCieuKZea8bxN0y3l/HXX39dbxtOCI3Dj4wZY8zKqFQqpKamYvfu3Vi/fr2lwzErIkJ8fDw6dOhQ7/p1rRnH37Br164hKioKCxcuxMSJE5vlHNaIE0JWr/3798Pf39+sW3M5OjoKxqskJiaarf+W1J7eC2ufpk+fDolEAkdHR0FdYGAgTp48iQMHDqCsrMwC0TWP/Px8XLt2DWlpaSbNaLY0jr9hmzdvxooVK7BixQpB3YIFC/Tfx3/88UeznL/dsvAdyhZnyiPj8vJy6t69u8FYlvbs6tWrNHr0aOrXrx916NDB6C2QGnL69Gn9dlVtXXt6L8ZqbY+MP/zww3rHpbV1sNJHWIxZgrX+vvEdwkYgItTW1oougNvaODo6IiQkpEl9vPPOO/jzn/+MU6dOiS4wa03McT1Zy5g7d65gXbrly5dbOizGGGsTzPcssB1zcnISTMNvz7Zt28YrvzPGGGNWhO8QMgFOBhljjDHrwglhA/bu3WswaUC3MXzd8uvXryMmJgbOzs5wdXVFZGSkwV3FxMREfVsfHx9oNBqEhYXByckJDg4OGDZsmMEK/MuXL9e3f/yR5cGDB/Xlbm5ugv61Wi3S09P1bcw5IaQ5WcP1rKmpwa5du/D888/D09MTCoUCffv2xdq1a/XDEUpKSgQTVXSPPWtqagzKx48fr++7oKAA8fHx6NKlC2QyGTp16oSoqCj9wq1i1/jSpUuYMGECXF1d9WWFhYUmvz/GGGNtmEVHMFqAqesQjh07lgBQRUWFaPnYsWPp+PHjdP/+fTp06BApFAoaOHCgoB+1Wk1KpZKCg4P17TUaDfXr149kMhkdOXLEoL1SqaTnnntO0E9QUJDoorD1tTeVt7d3g5NKhg0bRh07dhQsZFqfJ03EaGvX05hJJbqFYz/44AMqLi6mgoIC+uc//0k2NjY0d+5cg7bh4eFkY2NDV69eFfQTHBxMO3bs0P87JyeHnn76afLw8KB9+/ZReXk5nTt3joYMGUL29vaCBZJ113jIkCH0888/k1arpRMnTpCtrS0VFBQ0+D50WtukkvYMVjrInTFLsNbfN75DaCZxcXEIDg6GUqnEiBEjEBERAY1GI3rHRavVYsOGDfr2AwYMQHJyMh4+fIi33nrLAtE3TW1trX4Qv7m01+s5dOhQLFy4EC4uLnBzc8PMmTPxl7/8BWvXrjVY+iMhIQG1tbX4+OOPDY5PT0/HzZs3ER0drS9buHAhbty4gY8//hgvvvgiHB0d0bt3b3zzzTcgIsycOVM0lvnz52Po0KFwcHDAoEGDUFNTY3CXlDHGmPXghNBM6m687evrCwDIyckRtFUqlejfv79BWd++fdG5c2dkZmYiNze3+QJtBkeOHEFxcTGCg4PN1md7vJ6RkZH4+eefBeVqtRrV1dU4f/68vmzkyJHo27cvvvjiCxQVFenLP/zwQ8ycORNSqVRftnfvXtjY2CAyMtKgX09PT/Tu3RunTp3C7du3Bed99tlnm/yedu/eLXjEzS/zvwAgJibG4nHwi1/W8LJWbWOAWRugUqkM/i2TyQBAdKkaZ2dn0T7c3d2Rk5ODu3fvwsvLy/xBtiHt8XqWlpbio48+wp49e3D79m2UlJQY1D948MDg37NmzcKUKVOwYcMGvPPOO7h8+TJ++uknfP755/o2VVVVKC0tBSC8Zo+7cuUKfHx8DMqUSmVT3xIGDx6M2bNnN7kf9mQxMTGYNWuWWf/TxRgTFxMTY+kQLIITQgsoKioCEQn+J3L37l0AjxIZHRsbGzx8+FDQR91kQsca/3fTVq7n6NGj8b//+79Yu3YtJk2aBDc3N0gkEqxZswazZ88WPHKPjY3FokWLsG7dOrz99tv46KOP8Morr8DFxUXfRi6Xw9nZGffv30dFRUWLTyLy8fHBhAkTWvSc1igmJgbBwcF8rRlrAdaaEPIjYwuorKyERqMxKPvtt9+Qk5MDtVptcDfLy8sLd+7cMWibl5eHmzdvivbt4OBgkPD07NkTW7ZsMWP0rU9rv552dnY4f/480tPT4enpifj4eHTq1EmfbFZUVIgeJ5fL8cYbb+Du3bv46KOPsGPHDtExkVFRUaipqTGYVa2zatUqPPXUU6ipqTEqZsYYY9aFE0ILUKlUWLRoETIyMqDVanHy5ElMnjwZMpkMa9euNWg7cuRI5OTkYN26dbh//z6ys7Px1ltvGdz1etyf/vQnXL58Gbdu3UJGRgauXbuG0NDQZn0/w4cPh6urK06cONGs56lPW7ietra2GDp0KPLy8vDhhx+isLAQFRUV+Pnnn7Fp06Z6j3vjjTegUCiwZMkSjBgxAt27dxe0WblyJbp164a//e1vOHDgAEpLS1FcXIzNmzfj/fffR2JiYptZfogxxpiFWHKKsyUYu+zMnj17BPujxsbGUkZGRr37ptYtf3wPZLVaTd7e3nThwgUKDw8nJycnUigUNGTIEDp27Jjg/CUlJRQXF0deXl6kUCgoJCSENBoNBQUF6fufP3++vn1WVhaFhoaSUqkkX19fWr9+vdHXSLc8ithr69atgvahoaHk4uIiWN5EjFKpFPT54YcftsnrKfZe6ntdvHiRCgoKaNq0aeTr60tSqZQ8PDzo1VdfpQULFujbBQUFCWJ+7bXXCAD9+9//rve6FhUVUUJCAnXt2pWkUil16tSJRo4cSYcOHdK3EbvGTfkK4GVnWg6sdBkMxizBWn/fJERmXCukDUhJSUFMTIxZl0gxRv/+/VFYWCg665MZzxqu5+eff47169fj5MmTlg7FgG7pm2+//dbCkbR/EokEu3bt4jGEjLUAa/1940fGjLVymzZtQkJCgqXDYG1IcnKywTIajo6Oou1u3LiBMWPGoKysDIWFhQbHBAYG6ndmelzddhKJBAMGDGjut9Ri9u/fD39//ycOs7h37x42bdqE4cOHo2PHjlAoFOjRowdiY2ORmZkpekxNTQ22bduGZ599Fq6urnBxcUFQUBDWrVsnOtHNmuInIqSnp+PNN9+Ev78/5HI53N3dERISguTkZMENnAULFmDXrl2ifS1YsMDgZ3Pw4MFNis2qWPT+pAWYulOJuegecTLzaI/Xc+vWrfTSSy9ReXk5bdy4kXr06EHV1dWWDkuAHxm3HBj5CGv79u0EgDZu3Fhvm9OnT5Obmxt9+umnBuUajUY/nGDatGn1Hp+RkSG6u09bdfXqVRo9ejT169ePOnTo8MQdmqZMmUJ2dna0Zs0ays3NJa1WS0ePHqVevXqRra0t7dmzR3DM5MmTCQAtXLiQ8vPzqbCwkFatWkUAKDIy0qrjv3jxIgGgESNGUGZmJlVUVFB2djZNmjSJANCcOXME79XPz4+WLFnyxH5tbW1p0KBBRsdj7O9be8EJYQv58MMP6x0j1xLqnlvs9e6777ZYPE1l6evZnLZu3UoAyM7Ojvr160enTp2ydEiiWmNCaO6tG1vL+c2dEJaWlpKPj49owqfRaEgul5OrqysBoJ07d4r20d4SwkmTJtHKlSupurq6wS07p0yZQlOnThWUnzlzhgBQjx49DMqzs7MJAAUGBgqOef755wkA/fLLL1Yb/8WLF8nOzo6Ki4sNyquqqsjV1ZXkcjlVVlYKYpVIJE/8veCE0Dg89bCFzJ07F3PnzrXY+amdDRW19PVsTnFxcYiLi7N0GKwdW716NfLy8rB06VLRent7e+zYsQMvvvgipk2bhqCgIPj7+7dwlC1r27ZtUCgUjWqblJQkWq5Wq6FQKJCdnW2wNuqtW7cAAM8884zgmICAABw6dAg3b94U7NBkjLYcf0BAAKqrqwXlMpkMvr6+OHPmDCorKyGXyw1iHT9+PObMmYOoqCheScEMeAwhY4xZESJCUlISBg0ahM6dO9fbLjw8HEuWLEF5eTmio6NFxxO2J41Npp5Eq9WioqICffr0MVjUPiAgAFKpFFlZWYJjsrKyIJFI0Ldv3yadu63HL6akpARXrlxBYGCg6E5M48aNw+3bt7Fv3z6zn9sacULIGGtRRUVFSEhIQLdu3SCTyeDi4oJRo0YZ7PO8fPly/aDwkJAQffnBgwf15W5ubvryxMRESCQSaLVapKen69vo7hro6iUSCXx8fKDRaBAWFgYnJyc4ODhg2LBhBgt7m/v8rUlmZiby8/OhVqsbbPvuu+9i5MiROHv2LGbOnNmo/hvz+e7du9dg4P/169cRExMDZ2dnuLq6IjIyEtnZ2YK+CwoKEB8fjy5dukAmk6FTp06IiorCmTNnGn8BmpFuxv3ixYsNyj08PJCYmIjMzEwsWrQIBQUFKC4uxurVq3H48GEsXbq0VdyBbS3xl5WVIT09HWPGjIGnpye++uor0Xa6Pex//PFHs53bqln2iXXLs/SkEsbaC1PGEObm5pKfnx95eHhQamoqlZaW0qVLlygqKookEolgncv6xuQFBQWJjl9raAyfWq0mpVJJwcHBdPz4cbp//z5pNBrq168fyWQyOnLkSLOef9iwYdSxY0fKyMiot40YmHEMoa7ugw8+ED1Wo9GQSqXS/7ugoIB8fX0JACUnJ+vLxcYQGvv5jh07lgDQ2LFj9Z/HoUOHSKFQ0MCBAw3a5uTk0NNPP00eHh60b98+Ki8vp3PnztGQIUPI3t6+UeugNlZDY/DE5OXlkYeHB8XFxdXbJiUlhXx8fPTjnt3c3Gjbtm1NDVegLce/bNkyff9Dhw6ls2fP1tu2tLSUAFBoaKhoPY8hNA7fIWSMtZiFCxfi999/x5o1axAZGYkOHTrA398fO3fuhJeXF+Lj45Gfn9+sMWi1WmzYsAHBwcFQKpUYMGAAkpOT8fDhQ9GtAc2ptrYW9GgyX7Oe50lyc3MBQPQRnBg3NzekpKRAKpVi2rRpoo8NdUz9fOPi4vSfx4gRIxAREQGNRoPCwkKDvm/cuIGPP/4YL774IhwdHdG7d2988803IKJG38FsDkVFRXjhhRcwdOhQ0Z2HiAhTp05FbGwsEhISkJeXh4KCAqxYsQIzZszAxIkTLbq9ZGuKf8mSJaiqqsLFixcREBCAwMBALFu2TLRthw4dIJFI9D/TrGk4IWSMtZg9e/YAACIiIgzK5XI5wsLCUFFR0eyPf5RKpf5Rk07fvn3RuXNnZGZmNusflyNHjqC4uBjBwcHNdo6G6MYCSqXSRh8zePBgJCYmQqvVIjo6ut79t039fOtORvD19QUA5OTk6Mv27t0LGxsbREZGGrT19PRE7969cerUKYssUK/VahEeHo5evXphx44dsLW1FbTZvn07tm7ditdffx2zZ8+Gh4cH3NzcMHXqVP2aeuvWrWvx2IHWGb9MJkNAQAA2btyIMWPGYOnSpTh8+LBoWzs7u3p/HplxOCFkjLWIqqoqlJaWwt7eHk5OToJ6Dw8PAEBeXl6zxuHs7CxartvP+u7du816fkuzt7cHANFZnU8SHx+PmJgYnDt3DjNmzBDUN+XzrXu3UiaTAXh0R/Xxvmtra6FSqQQLY//6668AgCtXrhj1npqqpqYG0dHR8Pb2xpdffimaTAGPxp4CwIgRIwR1YWFhAIADBw40X6D1aAvxjx49GgDwww8/iNbX1NSYZUIN44SQMdZC5HI5VCoVKisrUV5eLqjXPUr09PTUl9nY2IjuglBSUiJ6jsdnRtanqKhI9JGtLhHUJYbNdX5L8/LyAgCUlpYafWxSUhJ69uyJzz77DNu3bzeoM+XzbSy5XA5nZ2fY2dmhurpa/9i97mvYsGFG990U06ZNQ1VVFVJSUgwmEHXv3h0nTpzQ/1ur1TbY1/3795slxidpC/HrlpopLi4W1JWVlYGI9D/TrGk4IWSMtZhx48YBgGCZiKqqKqSlpUGhUCA8PFxf7uXlhTt37hi0zcvLw82bN0X7d3BwMEjgevbsiS1bthi0qayshEajMSj77bffkJOTA7VabfDHpTnOb2l9+vQBAJMerzo6OuK7776DUqnEhg0bBPXGfr7GiIqKQk1NjcFscJ1Vq1bhqaeeatFxeO+99x7Onz+P77//3mB9PDGDBg0CAKSlpQnqfvrpJwBo8S3WWlP8c+fOxeTJk0XrdHcexdY41P1u6n6mWRNZZCqLBfEsY8bMwxyzjMvKygxmoW7ZssWg/YwZMwgAffrpp1ReXk5Xr16lCRMmkLe3t+gs3xdeeIFUKhXdvHmTjh8/TnZ2dnThwgV9vVqtJpVKRWFhYY2aZWzu87eGWca1tbXk7u5e72zourOMxSQnJxOABmcZN/T56mYZV1RUGJTPnz+fANDp06f1Zfn5+dStWzfq2rUr7d+/n0pKSqioqIg2bdpEDg4OgusTGxtLAOjatWtPfC9iGpql+/nnnze489Pjn2WhKXUAACAASURBVPG9e/eoR48eJJVKae3atfqt35KSksjBwYG8vb0pJyfHauOfM2cOSSQS+vvf/06///47VVZW0u+//05vv/02AaCgoCB68OCB4LidO3cSANGt9oh4lrGxrC4z4oSQMfMwdeu6wsJCmjVrFvn5+ZFUKiWVSkXh4eGUlpYmaFtSUkJxcXHk5eVFCoWCQkJCSKPRUFBQkP4P1/z58/Xts7KyKDQ0lJRKJfn6+tL69esN+tPtfX3hwgUKDw8nJycnUigUNGTIEDp27Fiznz80NJRcXFyMXiLFnAkhEdGiRYvIzs6O7ty5oy8rKCgQJAVBQUH1nmP69OmiSXFjPt+MjIx6t56sWx4REaE/rqioiBISEqhr164klUqpU6dONHLkSDp06JAgjuHDh5OjoyPV1NQ0fMGIKDU1td7kqO5yOREREUYlVERExcXFNG/ePAoICCC5XE4ymYy6detGM2bMoLy8PKuOv7S0lJKSkig8PJy6dOlCMpmMHB0dKSgoiFauXCmaDBIRRUdHk7e3Nz18+FC0nhNC41hdZsQJIWPm0Rr3Mm6ILiFsa8ydEJaUlJC3t7foXsbtwb1790ihUDxxTb3WjONvmG4v46+//rreNpwQGofHEDLGmJVRqVRITU3F7t27sX79ekuHY1ZEhPj4eHTo0KHe9etaM46/YdeuXUNUVBQWLlyIiRMnNss5rBEnhIwx1k5Nnz4dEokEjo6OgrrAwECcPHkSBw4cQFlZmQWiax75+fm4du0a0tLSTJrRbGkcf8M2b96MFStWYMWKFYK6BQsW6Jcj+uOPP5rl/O2VhMiCS+ZbQEpKCmJiYiy6UwBj7UF0dDSA/7//aWuWmJiIefPmGZQtXrwYy5cvt1BExpFIJNi1axcmTJhg6VAYa/es9fet9e28zhhjZjZ37lzMnTvX0mEwxlirxY+MGWOMMcasHCeEjDHGGGNWjhNCxhhjjDErxwkhY4wxxpiVs9pJJSkpKZYOgbE2TbcXLv8utYyMjAxLh8AYa8esdtkZxhhjjDEx1rjsjNUlhIwx9jjdlz7f6WSMWTMeQ8gYY4wxZuU4IWSMMcYYs3KcEDLGGGOMWTlOCBljjDHGrBwnhIwxxhhjVo4TQsYYY4wxK8cJIWOMMcaYleOEkDHGGGPMynFCyBhjjDFm5TghZIwxxhizcpwQMsYYY4xZOU4IGWOMMcasHCeEjDHGGGNWjhNCxhhjjDErxwkhY4wxxpiV44SQMcYYY8zKcULIGGOMMWblOCFkjDHGGLNynBAyxhhjjFk5TggZY4wxxqwcJ4SMMcYYY1aOE0LGGGOMMSvHCSFjjDHGmJXjhJAxxhhjzMpxQsgYY4wxZuU4IWSMMcYYs3KcEDLGGGOMWTlOCBljjDHGrBwnhIwxxhhjVo4TQsYYY4wxK8cJIWOMMcaYleOEkDHGGGPMynFCyBhjjDFm5ewsHQBjjLWUo0ePIiMjw6AsKysLALBq1SqD8uDgYPyf//N/Wiw2xhizJAkRkaWDYIyxlpCWloYRI0ZAKpXCxkb8AUltbS2qq6tx+PBhhIWFtXCEjDFmGZwQMsasRm1tLTw9PVFQUPDEdm5ubsjLy4OtrW0LRcYYY5bFYwgZY1bDxsYGsbGxkMlk9baRyWSYPHkyJ4OMMavCCSFjzKpMmjQJDx8+rLf+4cOHmDRpUgtGxBhjlsePjBljVqdLly64ceOGaJ2vry9u3LgBiUTSwlExxpjl8B1CxpjVefnllyGVSgXlUqkUr776KieDjDGrw3cIGWNWJysrC88884xo3blz59C7d+8WjogxxiyL7xAyxqxOQEAAevfuLbgT2KtXL04GGWNWiRNCxphV+utf/2owk1gqleKVV16xYESMMWY5/MiYMWaVbt26haeffhq6r0CJRIJr166hS5culg2MMcYsgO8QMsaskq+vLwYNGgQbGxvY2Nhg0KBBnAwyxqwWJ4SMMav18ssvQyKRwMbGBi+//LKlw2GMMYvhR8aMMatVWFgIT09PAEBOTg7c3d0tHBFjjFkGJ4Qm4nXKGGOMsdaH0xrT2Fk6gLZs1qxZCA4OtnQYjJnkk08+AQDMnj3bwpFY1tGjRyGRSBAaGtps54iJieHvC8aaWUZGBtasWWPpMNosvkNoIolEgl27dmHChAmWDoUxk0RHRwMAvv32WwtHYlnl5eUAACcnp2Y7B39fMNb8UlJSEBMTw3cITcR3CBljVq05E0HGGGsreJYxY4wxxpiV44SQMcYYY8zKcULIGGOMMWblOCFkjLE26MaNGxgzZgzKyspQWFgIiUSifwUGBqKyslJwTN12EokEAwYMsED0zWP//v3w9/eHnV39w+Pv3buHTZs2Yfjw4ejYsSMUCgV69OiB2NhYZGZmih5TU1ODbdu24dlnn4WrqytcXFwQFBSEdevW4eHDh1YdPxEhPT0db775Jvz9/SGXy+Hu7o6QkBAkJycLJngsWLAAu3btatI5WTMhZhIAtGvXLkuHwZjJxo8fT+PHjzdLX+Xl5dS9e3eKiIgwS3/tjbm/L06fPk1ubm706aefGpRrNBoCQABo2rRp9R6fkZFBrq6uZovH0q5evUqjR4+mfv36UYcOHcjW1rbetlOmTCE7Oztas2YN5ebmklarpaNHj1KvXr3I1taW9uzZIzhm8uTJBIAWLlxI+fn5VFhYSKtWrSIAFBkZadXxX7x4kQDQiBEjKDMzkyoqKig7O5smTZpEAGjOnDmC9+rn50dLlixp0nnF7Nq1izitMR1fORNxQsjaOnMmhGVlZdS1a1caNWqUWfprTkqlkp577rkWPac5vy9KS0vJx8dHNOHTaDQkl8vJ1dWVANDOnTtF+2hvCeGkSZNo5cqVVF1dTd7e3g0mVFOnThWUnzlzhgBQjx49DMqzs7MJAAUGBgqOef755wkA/fLLL1Yb/8WLF8nOzo6Ki4sNyquqqsjV1ZXkcjlVVlYKYpVIJGb/G8oJYdPwsjOMsSZzcnJCdna2pcOwCqtXr0ZeXh6WLl0qWm9vb48dO3bgxRdfxLRp0xAUFAR/f/8WjrJlbdu2DQqFolFtk5KSRMvVajUUCgWys7NBRPrdqG7dugUAeOaZZwTHBAQE4NChQ7h58yYGDhxoYvRtO/6AgABUV1cLymUyGXx9fXHmzBlUVlZCLpcbxDp+/HjMmTMHUVFRT3xEzloOjyFkjLE2goiQlJSEQYMGoXPnzvW2Cw8Px5IlS1BeXo7o6GjR8YTtSWOTqSfRarWoqKhAnz59DLYmDQgIgFQqRVZWluCYrKwsSCQS9O3bt0nnbuvxiykpKcGVK1cQGBgIlUolqB83bhxu376Nffv2mf3czDScEDLGmmTv3r0GkxR0yUfd8uvXryMmJgbOzs5wdXVFZGSkwV3FxMREfVsfHx9oNBqEhYXByckJDg4OGDZsGNLT0/Xtly9frm8fEhKiLz948KC+3M3NTdC/VqtFenq6vk1bujuRmZmJ/Px8qNXqBtu+++67GDlyJM6ePYuZM2c2qv+ioiIkJCSgW7dukMlkcHFxwahRo/Dzzz/r2xj7ueoUFBQgPj4eXbp0gUwmQ6dOnRAVFYUzZ840/gI0I92OPYsXLzYo9/DwQGJiIjIzM7Fo0SIUFBSguLgYq1evxuHDh7F06dJWcQe2tcRfVlaG9PR0jBkzBp6envjqq69E2/Xv3x8A8OOPP5rt3KyJLPzIus0CjyFkbZw5xxASEY0dO5YAUEVFhWj52LFj6fjx43T//n06dOgQKRQKGjhwoKAftVpNSqWSgoOD9e01Gg3169ePZDIZHTlyxKB9fWMCg4KCRMfJNTSGcNiwYdSxY0fKyMho7FtvkLm+L7Zv304A6IMPPhCt12g0pFKp9P8uKCggX19fAkDJycn6crExhLm5ueTn50ceHh6UmppKpaWldOnSJYqKiiKJREJbt241aG/M55qTk0NPP/00eXh40L59+6i8vJzOnTtHQ4YMIXt7ezp+/HhTL41eQ2PwxOTl5ZGHhwfFxcXV2yYlJYV8fHz0k3bc3Nxo27ZtTQ1XoC3Hv2zZMn3/Q4cOpbNnz9bbtrS0lABQaGio2c7PYwibhq+ciTghZG1dSyeEqampgvMDoIKCAoNytVpNAOj06dMG5WfPniUApFarDcrNnRAOGTKEXFxczJqkmOv7YvXq1QSA1q9fL1pfNyEkepT8SaVSUiqVdPHiRX1Z3Wvz6quvEgD6+uuvDcorKyupc+fOpFAoKC8vT19uzOf6yiuvEADasWOHQdvc3FySy+UUFBTUyCvQMGMTqsLCQurfvz/FxMRQTU2NoL62tpZee+01kkql9PHHH1NeXh4VFBTQ5s2bSaFQUExMDFVXV3P8/09VVRVdvHiRXn/9dbK1taX333+/3rYSiYS6d+9utnNzQtg0/MiYMdYi6g5a9/X1BQDk5OQI2iqVSv0jJZ2+ffuic+fOyMzMRG5ubrPFeeTIERQXFyM4OLjZzmEq3eN4qVTa6GMGDx6MxMREaLVaREdHo6KiQrTdnj17AAAREREG5XK5HGFhYaioqBB9vNeYz3Xv3r2wsbFBZGSkQVtPT0/07t0bp06dwu3btxv9nsxFq9UiPDwcvXr1wo4dO2Braytos337dmzduhWvv/46Zs+eDQ8PD7i5uWHq1Kn6NfXWrVvX4rEDrTN+mUyGgIAAbNy4EWPGjMHSpUtx+PBh0bZ2dnb1/jyylscJIWOsRdQdWC6TyQAAtbW1grbOzs6ifbi7uwMA7t69a+bo2gZ7e3sAEJ3V+STx8fGIiYnBuXPnMGPGDEF9VVUVSktLYW9vDycnJ0G9h4cHACAvL09Q19Dnquu7trYWKpVKsDD2r7/+CgC4cuWKUe+pqWpqahAdHQ1vb298+eWXoskU8GhMKgCMGDFCUBcWFgYAOHDgQPMFWo+2EP/o0aMBAD/88INofU1NjVkm1DDz4ISQMdbqFBUVCXY4AP5/IqhLDAHAxsZGdLeFkpIS0b4fn4HZ1nh5eQEASktLjT42KSkJPXv2xGeffYbt27cb1MnlcqhUKlRWVqK8vFxwbH5+PoBHd/SMJZfL4ezsDDs7O1RXV4MeDVUSvIYNG2Z0300xbdo0VFVVISUlxWBiUffu3XHixAn9v7VabYN93b9/v1lifJK2EL9uqZni4mJBXVlZGYhI/zPNLI8TQsZYq1NZWQmNRmNQ9ttvvyEnJwdqtdrgj4iXlxfu3Llj0DYvLw83b94U7dvBwcEggezZsye2bNlixuibT58+fQDApMerjo6O+O6776BUKrFhwwZB/bhx4wBAsAxIVVUV0tLSoFAoEB4ebkLUQFRUFGpqagxmieusWrUKTz31FGpqakzq2xTvvfcezp8/j++//95gfTwxgwYNAgCkpaUJ6n766ScAjx7Lt6TWFP/cuXMxefJk0TrdnUexNQ51v7O6n2nWClhu+GLbBp5Uwtq4lp5UUrd8/vz5opNH1Go1qVQqCgsLa9Qs4xkzZhAA+vTTT6m8vJyuXr1KEyZMIG9vb9FJJS+88AKpVCq6efMmHT9+nOzs7OjChQv6+tY8y7i2tpbc3d3rnRQjNqmkruTkZALQ4CzjsrIyg1nGW7ZsMWhvzOean59P3bp1o65du9L+/fuppKSEioqKaNOmTeTg4CC4NrGxsQSArl271uA1qauhSRmff/65fiZsfa/HP/t79+5Rjx49SCqV0tq1a/VbvyUlJZGDgwN5e3tTTk6O1cY/Z84ckkgk9Pe//51+//13qqyspN9//53efvttAkBBQUH04MEDwXE7d+4kAKJb7ZmKJ5U0DV85E3FCyNo6cyWEe/bsEfxBio2NpYyMDEH54sWLiYgE5Y/vgaxWq8nb25suXLhA4eHh5OTkRAqFgoYMGULHjh0TnL+kpITi4uLIy8uLFAoFhYSEkEajoaCgIH3/8+fP17fPysqi0NBQUiqV5OvrK5ixGxoa2mpnGRMRLVq0iOzs7OjOnTv6soKCAsE1fdLM3enTp4smy4WFhTRr1izy8/MjqVRKKpWKwsPDKS0tTd/G1M+1qKiIEhISqGvXriSVSqlTp040cuRIOnTokCCO4cOHk6Ojo+isWTGpqan1Jkd1l8uJiIgwKqEiIiouLqZ58+ZRQEAAyeVykslk1K1bN5oxY4bBzGtrjL+0tJSSkpIoPDycunTpQjKZjBwdHSkoKIhWrlwpmgwSEUVHR5O3tzc9fPiwUdeoMTghbBq+cibihJC1dea+Q2guuoSwPTHn90VJSQl5e3uL7mXcHty7d48UCsUT19RrzTj+hun2Mq67xFFTcULYNDyGkIm6d+8eNm3ahOHDh6Njx45QKBTo0aMHYmNjkZmZ2ag+vvnmG/1MQt3sSHPQaDR49dVX4efnB4VCgY4dO6JPnz74r//6L2zcuLFV7qlr7PV0dHQUzMa0sbGBi4sL1Go13njjDZw6dcoC74RZmkqlQmpqKnbv3o3169dbOhyzIiLEx8ejQ4cOWLZsmaXDMRrH37Br164hKioKCxcuxMSJE5vlHMw0nBAyUfPmzcPMmTMxduxYXLhwAUVFRfjss89w5swZBAUFYe/evQ32MXHiRBCRfmmDpqqtrcW8efPw5z//Ge7u7jhw4ABKSkpw8eJFfPLJJygrK8Mbb7yB7t27t+gA9cYw9nrev38fp0+fBgCMHTsWRITq6mpkZWXh/fffR1ZWFgYMGID//u//xoMHDyzxlpgFBQYG4uTJkzhw4ADKysosHY7Z5Ofn49q1a0hLSzNpRrOlcfwN27x5M1asWIEVK1Y0S/+sCSx6f7INQzt/ZDxlyhSaOnWqoPzMmTMEgHr06NHovsLCwkgulzc5pkWLFhEAweB2nZqaGho1ahQBMOvK++ZgyvU8ffq0fmswMbpB22PGjKHa2lqjY2ptj4w//PDDesemtXXt/fuCsdaAHxk3TdvZ1Z21qKSkJNFytVoNhUKB7OxsEFGLremWlZWFf/zjHwgKCsJrr70m2sbW1hbvvPOORRaJbUhzXM9//OMf+Pe//41//etf+OabbzBp0iRzhWsRc+fOxdy5cy0dBmOMWSV+ZMyMotVqUVFRgT59+rToAr9btmxBbW0toqOjn9guODgYRGSwUGtr1pTrKZFI9LtOiK0rxxhjjDUWJ4QtqKioCAkJCejWrRvkcjl8fHwwYsQIfPHFFwb7OT7eTiaTwcXFBaNGjcLPP/+sb7N3716DCQfXr19HTEwMnJ2d4erqisjISP3kipKSEsEEheXLlwN4tHXQ4+Xjx49/4nv49ttvAQCLFy8W1GVlZeGll16CSqWCUqlEaGgojh071uTrBgBHjx4FAPTr18/oY9vq9WyMkJAQAMCJEyeM3s6MMcYY07PwI+s2C0aOCdIt+urp6alf9DUvL4+WLVtGAOiTTz4xaKdbHLa0tNRgcdi6a1LpFocdO3asfhHfQ4cOkUKhoIEDBxq0feGFF8jGxoauXr0qiC84OJh27tz5xPeQl5dHHh4eossRXLlyhZydncnb25v+53/+h8rLy+ns2bM0cuRI6tKli+gYQmMWAPby8iIA9J///KfBto9rq9eTqOExhEREFRUV+vF2dReXbUhrG0PYnhn7fcEYMx6PIWwavnImMvYL/tVXX633mBdeeEGfEOra1V2fqbKykjp37kwKhcJgIVFdApOammrQfvz48QSACgoK9GWHDx8mAPTGG28YtD127Bg99dRTT5yIUVhYSP3796eYmBjRxUqjo6MJAO3evdug/M6dOySXy0UTwiFDhjR6AWBdQvjLL7802PZxbfV6EjUuIXzw4AEnhG0AJ4SMNT9OCJumbQy0agf27NkDABg1apSg7vFJELp2ERERBm3kcjnCwsKwfft2/Pjjj/jrX/9qUF93r0hfX18AQE5ODtzc3AAAYWFhCAwMxBdffIH3338frq6uAIAPP/wQs2bNqnfcnVarRXh4OHr16oWvvvoKtra2gjYHDx4EAMFep507d4a/vz8uX74sOObIkSOi5xPTuXNn5ObmorCwsNHHAG33ejZWbm4uAEAqlerjMsbt27eRkpJi8vlZ42VkZFg6BMbaNf4daxpOCFtAVVUVSktLYW9vDycnJ5PbeXh4AADy8vIEdSqVyuDfMpkMwKO1+x43Z84cTJ48GRs2bMA777yDy5cv4+jRo9i+fbtoTDU1NYiOjoa3tze+/PJL0eSlqqoK5eXlsLe3h6Ojo6De3d1dNCE0xpAhQ3Dq1CmcPXtWNKkW01avpzF0YzSDg4MhlUqNPv7EiROIiYlpUgyscdasWYM1a9ZYOgzGGBPFk0pagFwuh0qlQmVlJcrLy01ul5+fDwBNWjA0JiYGvr6+WLduHaqqqvDRRx/htddeqzdRnTZtGqqqqpCSkmJwx6t79+44ceKEPm4nJydUVlbi/v37gj6Ki4tNjvfxOOzs7LB79+4ntnv77bdhY2ODrKysNns9G6u2tla/U8Wbb75pUvzjx48HPRo6wq9mfAHArl27LB4Hv/jVnl+7du0y6XuQPcIJYQsZN24cAGD//v2CusDAQMyePdug3b59+wzaVFVVIS0tDQqFQvBY1hh2dnZ46623cPfuXXz00Uf45ptvEB8fL9r2vffew/nz5/H9999DLpc/sV/dXTvdo2OdwsJCXLp0yeR4dfz9/fHuu+/i5MmT+Oyzz0TbXLp0CZs3b8aECRMQEBAAoO1ez8ZYuHAhfvnlF4wbN67B5XgYY4yxJyJmEpg4y9jLy4t++OEHKisro1u3btH06dPJw8ODbty4YdBONyu2rKzMYFZs3V06dJMgKioqDMrnz59PAOj06dOCWMrKykilUpFEIqG//vWvovF+/vnngl0j6r4enx189epV6tixo8Es4/Pnz1N4eDi5u7s3eZaxzoIFC0gqldL8+fPp0qVLVFVVRbdv36akpCTy8vKikJAQun//vuC6t7XrSSScVPLHH39Qfn4+7d27l4YPH04A6G9/+xs9ePCg0dfvcTyppOUY+33BGDMeTyppGr5yJjLlC76wsJBmzZpFfn5+JJVKycvLiyZOnEiXL19+YjuVSkXh4eGUlpamb5ORkVHvNl91yyMiIgSxzJs3jwBQZmamaKwRERFGJzCXLl2il156iTp06KBfpuWHH36gsLAw/TFTpkzRtw8NDW30LOPH/fLLL/Tyyy+Tr68vSaVScnJyosGDB9PatWupqqpK0L4tXk+lUimol0gkpFKpqG/fvjR9+nQ6deqUUdetLk4IWw4nhIw1P04Im0ZCRNS0e4zWSSKRYNeuXZgwYYKlQ2HMJLrHzLrFsVnz4e8LxppfSkoKYmJiwGmNaXgMIWOMMcaYleOEkDHGWrEbN25gzJgxKCsrQ2FhocHWiIGBgaisrBQcU7edRCLBgAEDLBB989i/fz/8/f2fuGf5vXv3sGnTJgwfPhwdO3aEQqFAjx49EBsbi8zMzHqPq66uxieffIKgoCA4OTnB3d0do0aNQmpqqtnuPFlD/MbGs2DBAp4lbGmWfF7dloHHBLE2jscQthxTvy9Onz5Nbm5u9OmnnxqUazQa/djSadOm1Xt8RkYGubq6Gn3e1urq1as0evRo6tevH3Xo0IFsbW3rbTtlyhSys7OjNWvWUG5uLmm1Wjp69Cj16tWLbG1tac+ePYJj7t+/TyEhIdSvXz/697//TQ8ePKAbN27odyr67bffOP5Gxm9sPFevXiU/Pz9asmSJyfHxGMKm4StnIk4IWVvXGhNCpVJJzz33XLs7vynfF6WlpeTj4yOa8Gk0GpLL5eTq6koA6t03u70lhJMmTaKVK1dSdXU1eXt7N5hQTZ06VVB+5swZAkA9evQQ1E2fPp06dOhgsJ0l0aPERi6XNzmhsqb4TYnnzJkzJJFITP7byglh0/CVMxEnhKyt44Sw5c5vyvfF4sWLyc7Oju7cuSOo02g0pFKp6ODBg2RjY0NOTk506dIlQbv2lhA+vsRSYxKS+igUCrKxsaHa2lp9WV5eHtna2tL06dObHGd9rCl+U+OJjo4mHx+fJ+4FXx9OCJuGxxAyxlgrQ0RISkrCoEGD0Llz53rbhYeHY8mSJSgvL0d0dLToeML2RKFQNLkPrVaLiooK9OnTBxKJRF/+r3/9C3/88QdCQkKafI76WFP8psYzbtw43L59W7CZAGt+nBAyxoxSVFSEhIQEdOvWDTKZDC4uLhg1ahR+/vlnfZvly5frJzM8/gfh4MGD+nI3Nzd9eWJiIiQSCbRaLdLT0/VtdIPWdfUSiQQ+Pj7QaDQICwuDk5MTHBwcMGzYMKSnpzfb+VtaZmYm8vPzoVarG2z77rvvYuTIkTh79ixmzpzZqP4b8xnu3bvXYFLK9evXERMTA2dnZ7i6uiIyMhLZ2dmCvgsKChAfH48uXbpAJpOhU6dOiIqKwpkzZxp/AZqRbpmlxYsXG5T/+uuvAAAXFxfMmTMHvr6+kMlkePrppxEfH2+WLTjNoa3Eb2o8/fv3BwD8+OOPLRYr+38sfYuyrQI/MmZtnCmPjOvu/FJaWmqw88vWrVsN2tf3CDYoKEj0UWZDj2zVajUplUoKDg6m48eP0/3790mj0VC/fv1IJpPRkSNHmvX8puyuQ2T898X27dsJAH3wwQei9bpHxjoFBQXk6+tLACg5OVlfLvbI2NjPULd7z9ixY/XX/NChQ/rF5x+Xk5NDTz/9NHl4eNC+ffuovLyczp07R0OGDCF7e3ujF6F/ElMeuebl5ZGHhwfFxcUJ6nTv09PTk2JjYyk7O5vu3btHX375JSmVSvL396eSkhJzhd/u4zc1ntLSUgJAoaGhRsfEj4ybhq+ciTghZG2dKQnhq6++SgDo66+/NiivrKykzp07k0KhMBhA3hwJIUS2EDx79iwBILVa3aj+TD3/kCFDTNpdx9jvi9WrVxMAWr9+vWh93YSQ6FHyJ5VKSalU0sWLF/Vldd+nhnTUHQAAIABJREFUsZ+h7g97amqqQXvdTNGCggJ92SuvvEIAaMeOHQZtc3NzSS6XU1BQUCOvQMOMTagKCwupf//+FBMTQzU1NYL68PBwAkB+fn6C8WvLly8nAPTOO+80OW6d9h5/U+KRSCTUvXt3o2PihLBp+JExY6zR9uzZAwCIiIgwKJfL5QgLC0NFRUWzP+pRKpX6x0o6ffv2RefOnZGZmYnc3NxmO/eRI0dQXFyM4ODgZjsHAP1YQKlU2uhjBg8ejMTERGi1WkRHR6OiokK0namf4cCBAw3+7evrCwDIycnRl+3duxc2NjaIjIw0aOvp6YnevXvj1KlTuH37dqPfk7lotVqEh4ejV69e2LFjB2xtbQVtlEolAGDEiBGCoQKjR48GYLnHmG0x/qbEY2dnV+/PL2s+nBAyxhqlqqoKpaWlsLe3h5OTk6Dew8MDAJCXl9escTg7O4uWu7u7AwDu3r3brOdvCfb29gAeLeprjPj4eMTExODcuXOYMWOGoL4pn6FKpTL4t0wmAwDU1tYa9F1bWwuVSiVYGFs3puzKlStGvaemqqmpQXR0NLy9vfHll1+KJlMA0KVLFwCAq6uroE73s1VQUNBscdanrcbflHhqamrMMgGHGYcTQsZYo8jlcqhUKlRWVqK8vFxQn5+fD+DR3SAdGxsbPHz4UNC2pKRE9ByPz5qsT1FRkeiOC7pEUPfHprnO3xK8vLwAAKWlpUYfm5SUhJ49e+Kzzz7D9u3bDepM+QwbSy6Xw9nZGXZ2dqiurgY9GpIkeA0bNszovpti2rRpqKqqQkpKisGdqu7du+PEiRP6f+smH4ndYdb9bOkS5pbUVuM3NZ6ysjIQkf53gLUcTggZY402btw4ABAsCVFVVYW0tDQoFAqEh4fry728vHDnzh2Dtnl5ebh586Zo/w4ODgYJXM+ePbFlyxaDNpWVldBoNAZlv/32G3JycqBWqw3+kDTH+VtCnz59AMCkx6uOjo747rvvoFQqsWHDBkG9sZ+hMaKiolBTU2Mw41tn1apVeOqpp1BTU2NS36Z47733cP78eXz//feQy+VPbPviiy/C29sbBw8eFCzfk5qaCgB46aWXmi1WMW05flPj0f2+6n4HWAuy3PDFtg08qYS1ceaYZVxWVmYwQ3XLli0G7WfMmEEA6NNPP6Xy8nK6evUqTZgwgby9vUUndbzwwgukUqno5s2bdPz4cbKzs6MLFy7o69VqNalUKgoLC2vULGNzn7+lZhnX1taSu7t7vRNcxCaV1JWcnEwAGpxl3NBnqJtUUlFRYVA+f/58wQSf/Px86tatG3Xt2pX2799PJSUlVFRURJs2bSIHBwfBNYiNjSUAdO3atQavSV0NTWr4/PPP9dv71feq+zkeOHCA7OzsaOzYsXT58mW6d+8effXVV6RUKmnQoEEGCzNz/A1PijE2HiKinTt3EgDRrfkawpNKmoavnIk4IWRtnak7lRQWFtKsWbPIz8+PpFIpqVQqCg8Pp7S0NEHbkpISiouLIy8vL1IoFBQSEkIajYaCgoL0f9Tmz5+vb5+VlUWhoaGkVCrJ19dXMMtWrVaTt7c3XbhwgcLDw8nJyYkUCgUNGTKEjh071uznDw0NbZFZxkREixYtEuxUUlBQIEgKnjRzd/r06aKJb2M+w4yMDMG5Fi9erH8/j78iIiL0xxUVFVFCQgJ17dqVpFIpderUiUaOHEmHDh0SxDF8+HBydHQUnTUrJjU1td7kqO5yOREREUYnVEREx48fp/DwcFKpVCSTySggIIDee+890eSF468/flPiIXq0U4m3tzc9fPiwUTE9jhPCpuErZyJOCFlb1xq3rmuILiFsa0z5vigpKSFvb2/RvYzbg3v37pFCoRBdU68t4PjNT7eXcd0lkRqLE8Km4TGEjDHWCqlUKqSmpmL37t1Yv369pcMxKyJCfHw8OnTogGXLllk6HKNx/OZ37do1REVFYeHChZg4caKlw7FKnBAyxlgrFRgYiJMnT+LAgQMoKyuzdDhmk5+fj2vXriEtLc2kGc2WxvGb3+bNm7FixQqsWLHC0qFYLcts1MkYY0ZITEzEvHnz9P+WSCRYvHgxli9fbsGoWkaXLl3www8/WDoMs/L09MSxY8csHYbJOH7zW7VqlaVDsHqcEDLGWr25c+di7ty5lg6DMcbaLX5kzBhjjDFm5TghZIwxxhizcpwQMsYYY4xZOU4IGWOMMcasnIRIZJd41iCJRILBgwfDx8fH0qEwZpITJ04AAAYPHmzhSNq/3bt38/cFY83s9u3bOHHiBDitMQ0nhCaKjo62dAiMMTP47bffAAB9+/a1cCSMMXP49ttvLR1Cm8QJIWPMqk2YMAEAkJKSYuFIGGPMcngMIWOMMcaYleOEkDHGGGPMynFCyBhjjDFm5TghZIwxxhizcpwQMsYYY4xZOU4IGWOMMcasHCeEjDHGGGNWjhNCxhhjjDErxwkhY4wxxv4ve/ceFmWd/g/8PcDMMAwwIMpRFLSQKw8ji36VDdYElwlBEL4gGVhuWawdyDxGZrZpWa6lbVuZUatpFmZfSTy15matiBuagEdQPJRykIMMBwFB7t8f/maWYQaZGQYGmPt1XVxXfp7P83lu5nli7uv5nJiF44SQMcYYY8zCcULIGGOMMWbhOCFkjDHGGLNwnBAyxhhjjFk4TggZY4wxxiwcJ4SMMcYYYxaOE0LGGGOMMQvHCSFjjDHGmIXjhJAxxhhjzMJxQsgYY4wxZuE4IWSMMcYYs3CcEDLGGGOMWThOCBljjDHGLBwnhIwxxhhjFo4TQsYYY4wxC8cJIWOMMcaYheOEkDHGGGPMwnFCyBhjjDFm4TghZIwxxhizcJwQMsYYY4xZOE4IGWOMMcYsHCeEjDHGGGMWjhNCxhhjjDELxwkhY4wxxpiFExARmTsIxhjrDZ9//jneffdd3LlzR11WWVkJABg8eLC6zNraGgsXLsRjjz3W6zEyxpg5cELIGLMYRUVFGDVqlF51CwsL4efn18MRMcZY38Bdxowxi+Hn5we5XA6BQNBpHYFAALlczskgY8yicELIGLMojz32GKytrTs9bmNjg8cff7wXI2KMMfPjLmPGmEUpKSmBt7c32tradB4XCAT47bff4OXl1cuRMcaY+fAbQsaYRfH09MTvf/97WFlp//mzsrLCgw8+yMkgY8zicELIGLM4c+bM0VkuEAh4ZjFjzCJxlzFjzOLcvHkTbm5uaGlp0Si3sbFBWVkZXFxczBQZY4yZB78hZIxZHGdnZ/zxj3/UmFxibW0NhULBySBjzCJxQsgYs0jJyckaE0uICMnJyWaMiDHGzIe7jBljFunWrVtwcXFBU1MTAMDW1haVlZWQSqVmjowxxnofvyFkjFkkOzs7xMbGQigUQigUIjY2lpNBxpjF4oSQMWaxHn30UbS0tKClpQWPPvqoucNhjDGzsdGnUk5ODn777beejoUxxnrVnTt3YGdnByJCbW0tduzYYe6QGGPMpLy9vREUFNRlPb3GECYkJGDnzp0mCYwxxhhjjPWO+Ph4fP31113W0+sNoSENMsZYf/Ljjz9CIBDgD3/4AwQCATIyMjBr1ixzhzWg7dixA4mJieA5jYz1rISEBL3r6p0QMsbYQBQSEmLuEBhjzOw4IWSMWTRdexozxpil4b+EjDHGGGMWjhNCxhhjjDELxwkhY4wxxpiF44SQMcZYv3L16lVER0ejtrYWlZWVEAgE6p+AgAD1doTtdawnEAgwYcIEM0TfM/bt2wc/Pz/Y2HQ+NeDmzZvYuHEjQkNDMWjQIEgkEtx///1ISkpCfn5+p+e1tLRg/fr1CAwMhIODA1xdXREREYGsrCyTzRS3hPgNjeell15CRkaGSeLTC+khPj6e4uPj9anKGGP9FgDKyMjodjt1dXV03333UWRkpAmiGngyMjJIz68fLSdPnqTBgwfT+++/r1Gem5tLAAgApaSkdHp+Tk4Oubi4GHXtvujixYs0Y8YMGjduHDk6OpK1tXWndZ988kmysbGhDRs2UGlpKTU0NNBPP/1EDzzwAFlbW9OuXbu0zqmvr6fg4GAaN24c/fjjj3Tr1i26evUqxcfHEwA6deoUx69n/IbGc/HiRfL19aVXXnnF6PgMyd84IWSMsf/PVAlhbW0tjRgxgiIiIkwQVc+SSqX04IMP9uo1jU0IlUolDR06VGfCl5ubS2KxmFxcXAgAbd++XWcbAy0hnD17Nq1Zs4ZaWlrIy8ury4Tq6aef1irPy8sjAHT//fdrHZs/fz45OjpSWVmZRnl9fT2JxeJuJ1SWFL8x8eTl5ZFAIDD67xInhIwxZgRTJYT9SX9KCJcvX042NjZ0/fp1rWO5ubkkk8nowIEDZGVlRQ4ODlRYWKhVb6AlhLdu3VL/tz4JSWckEglZWVlRW1ubuqysrIysra1p/vz53Y6zM5YUv7HxJCQk0NChQ6mlpcXg+AzJ33gMIWOMsT6PiJCeno5JkybB09Oz03oKhQKvvPIK6urqkJCQoHM84UAikUi63UZDQwMaGxsxZswYCAQCdfnu3btx584dBAcHd/sanbGk+I2NJzY2FteuXcPevXsNDc8gnBAyxpgJZWZmakxcUCUkHcuvXLmCxMREODk5wcXFBVFRUSguLla3s27dOnXdoUOHIjc3F2FhYXBwcICdnR2mTp2K7Oxsdf3Vq1er67f/wjlw4IC6fPDgwVrtNzQ0IDs7W11Hn0Hx5pCfn4/y8nLI5fIu665cuRLh4eEoKCjA888/r1f7VVVVWLhwIUaOHAmRSARnZ2dERETghx9+UNcx9B6qVFRUIDU1FT4+PhCJRBgyZAji4uKQl5en/wfQg1Tb0i5fvlyj/JdffgEAODs7Y9GiRfD29oZIJMLw4cORmpqK6urqXo9Vl/4Sv7HxjB8/HgDw3Xff9WyApn7lyBhj/RVM2GUcExNDAKixsVFneUxMDB09epTq6+vp4MGDJJFIaOLEiVrtyOVykkqlFBQUpK6fm5tL48aNI5FIRIcPH9ao31kXcGBgoM6u0q66jKdOnUqDBg2inJwcfX/1LhnTZbx161YCQG+++abO46ouY5WKigry9vYmALRt2zZ1ua4u49LSUvL19SU3NzfKysoipVJJhYWFFBcXRwKBgD755BON+obcw5KSEho+fDi5ubnR3r17qa6ujk6fPk1TpkwhW1tbOnr0qEGfw70Y0+VaVlZGbm5uNG/ePK1jqt/T3d2dkpKSqLi4mG7evElbtmwhqVRKfn5+VFNTY6rwB3z8xsajVCoJAIWEhBgcE3cZM8ZYHzdv3jwEBQVBKpVi2rRpiIyMRG5uLiorK7XqNjQ04MMPP1TXnzBhArZt24bbt2/jhRde6NE429raQHfHm/fodbpSWloKAJDJZHrVHzx4MHbs2AGhUIiUlBScP3++07ppaWm4fPkyNmzYgKioKDg6OsLPzw/bt2+Hh4cHUlNTUV5ernWePvcwLS0NV69exbvvvovp06fD3t4eo0ePxldffQUi0vsNZk+oqqrCww8/jIceeggbN27UOq56uy2RSLB582aMGDECTk5OeOyxx5CWloaioiK88847vR22Wn+L39h4HB0dIRAI1P8P9BROCBljzAwmTpyo8W9vb28AQElJiVZdqVSq7jZSGTt2LDw9PZGfn9+jXxSHDx9GdXU1goKCeuwa+lB9mQqFQr3PmTx5MtatW4eGhgYkJCSgsbFRZ71du3YBACIjIzXKxWIxwsLC0NjYqLO7Tp97mJmZCSsrK0RFRWnUdXd3x+jRo3HixAlcu3ZN79/JVBoaGqBQKPDAAw/giy++gLW1tVYdqVQKAJg2bZrWUIIZM2YA6IVuzE70x/i7E4+NjU2nz6+pcELIGGNm0PFNl0gkAnD3jVxHTk5OOttwdXUFANy4ccPE0fU9tra2AO4u6muI1NRUJCYm4vTp03juuee0jjc3N0OpVMLW1hYODg5ax93c3AAAZWVlWse6uoeqttva2iCTybQWxlaNKbtw4YJBv1N3tba2IiEhAV5eXtiyZYvOZAoAfHx8AAAuLi5ax1TPXkVFRY/F2Zn+Gn934mltbTXJBJx74YSQMcb6uKqqKp1dtqpEUPVlAgBWVla4ffu2Vt2amhqdbbefldmXeXh4AACUSqXB56anp2PUqFH47LPPsHXrVo1jYrEYMpkMTU1NqKur0zpX1VXs7u5u8HXFYjGcnJxgY2ODlpYWddd7x5+pU6ca3HZ3pKSkoLm5GTt27NB4U3Xffffh2LFj6n+rJifpegOtevZUCXNv6q/xGxtPbW0tiEj9/0BP4YSQMcb6uKamJuTm5mqUnTp1CiUlJZDL5RpfFB4eHrh+/bpG3bKyMvz6668627azs9NIIEeNGoVNmzaZMHrTGDNmDAAY1b1qb2+Pb775BlKpFB9++KHW8djYWADQWtajubkZhw4dgkQigUKhMCJqIC4uDq2trRozwlXefvttDBs2DK2trUa1bYzXXnsNZ86cwbfffguxWHzPutOnT4eXlxcOHDigtXxPVlYWAGDmzJk9Fqsu/Tl+Y+NR/f+s+n+gp3BCyBhjfZxMJsPLL7+MnJwcNDQ04Pjx40hOToZIJMJ7772nUTc8PBwlJSX4+9//jvr6ehQXF+OFF17QeIvY3u9+9zsUFRXht99+Q05ODi5duoSQkBD18dDQULi4uGi8eTEHuVwOV1fXe+5Zey+jR4/Gxx9/rPPYmjVr4OvriwULFmDPnj2oq6tDUVERHn30UZSWluK9994z+k3SmjVrMHLkSDzxxBPYv38/lEolqqur8fHHH+P111/HunXrNN5yJScnQyAQ4PLly0Zd7142b96Mv/zlL/jPf/4DBwcHrS7sjkvmiMVipKeno6qqCo888gguXLiAmpoabN26FWvWrMGkSZOQmpqqcQ7H3zlj4gGgXp4oPDzc5DFpMPW0ZcYY669ggmVndu3apd5TV/WTlJREOTk5WuXLly9XX7f9T/s9kOVyOXl5edHZs2dJoVCQg4MDSSQSmjJlCh05ckTr+jU1NTRv3jzy8PAgiURCwcHBlJubS4GBger2ly1bpq5//vx5CgkJIalUSt7e3vTBBx9otBcSEkLOzs4mXR7F2J1KXn75Za2dSioqKrQ+v8DAwE7bmD9/vs7ldyorK2nBggXk6+tLQqGQZDIZKRQKOnTokLqOsfewqqqKFi5cSCNGjCChUEhDhgyh8PBwOnjwoFYcoaGhZG9vT62trXp9JllZWVrXVv10XC4nMjKy07qqH13LCx09epQUCgXJZDISiUTk7+9Pr732msYuHRx/1/EbEw/R3Z1KvLy86Pbt23rF1J4h+ZuAqOu1BBISEgD8d/FHxhgbiAQCATIyMjBr1ixzh6I2fvx4VFZWmmUmak/ZsWMHEhMTDV7KRqlUYvTo0YiKitK5zEh/V1NTA09PTyQlJeGTTz4xdzgG4/hNLz8/HwEBAdi+fTseeeQRg883JH/jLuM+ruNuBX3BzZs3sXHjRoSGhmLQoEGQSCS4//77kZSUpHd3zldffaX+vVSzB41RX1+v1W2Qk5PT5XlLlizROGf16tVGx6APe3t7rTgFAgGsrKwwZMgQzJw5U2uMmKkNhGdJ1+doZWUFZ2dnyOVyPPPMMzhx4oQZfhPWG2QyGbKysrBz50588MEH5g7HpIgIqampcHR0xKpVq8wdjsE4ftO7dOkS4uLikJaWZlQyaDBTv3JkPUPVbdReXV0d3XfffRpdE73hySefJBsbG9qwYQOVlpZSQ0MD/fTTT/TAAw+QtbU17dq1S++2wsLCSCwWdzumkydPql/TR0RE3LNuZWUl2dvbq7vyeosqxpiYGHVZTU0N/d///R+5urqSUCjU2YVkav39Wer4Oba2tlJZWRllZmbS1KlTCQDNnTuXGhoaDI4HJtypxFR03a/+ztguY5XLly9TZGQkKZVKE0ZlXqWlpfTggw/S6dOnzR2KUTh+01u6dCl9+eWX3WqDdyqxEESEtrY2neuW9bQnnngCL7zwAtzd3WFnZ4eQkBBs374dd+7cwdKlS3s9HuDu6u/Dhw/H/v37cfz48U7rrV+/Xr2ArLnJZDLExsbi3XffRUtLCxYsWGCWOPrzs2RtbQ03NzfExMTgX//6F5YuXYrNmzdj9uzZZt9doztUb3Tz8/Nx/fp1CAQCvPLKK+YOq0/w8fHBnj174OjoaO5QTMbd3R1HjhzB6NGjzR2KUTh+03v77bd7583g/8cJYT/m4OCA4uJi7Nu3r1evm56ernO2nlwuh0QiQXFxsVm+iK2srPDSSy8BQKddwDU1Nfjoo4+wbNmy3gytS6p1yM6cOdPpenE9aSA9S2+99RYmTZqE3bt346uvvjJVqL1u8eLFWuvV9fTQBsaY5eKEkJlMQ0MDGhsbMWbMGLMtdvunP/0JXl5e2L17NwoKCrSO/+1vf8P06dMxcuRIM0TXufZJT39ZKLgndedZEggE6h0pdK05xxhjTFuPJISZmZkag76vXr2KxMREODg4wMXFBXPmzMHNmzdx5coVzJgxAw4ODvDw8MBTTz2ltVJ8a2srMjIy8Mc//hHu7u6QSCQYO3Ys3nvvPY3ureDgYI1rJicnA7i7Z2D7cn3fvnQcgJ+bm4uwsDA4ODjAzs4OU6dO1bnQaFVVFRYuXIiRI0dCJBLB2dkZERER+OGHH7pVt6vPWLXIZcfyK1euIDExEU5OTnBxcUFUVJTWWk0AcP78ecycORMymQx2dnb4n//5H+zZs0fj85s3b949Y1LNYlq+fPk925dKpQgJCcGRI0e6/D0NJRaLsWTJEhAR3njjDY1j9fX1eP/99/Hyyy93er65nrfDhw8DuLtWmmo7LH6WdD9L+lDtCHDs2DGDtzpjjDGLZOpBie3FxMQQAIqLi6Pjx49TfX09ff755+qB/zExMXTy5Emqq6ujjRs3EgB68cUXNdpQrfHz5ptvUnV1NVVUVNDf/vY3srKyosWLF2vUzcvLI6lUSnK5nOrr64mIqKmpiSZNmmT0wEy5XE5SqZSCgoLo6NGjVF9fT7m5uTRu3DgSiUR0+PBhdd3S0lLy9fUlNzc3ysrKIqVSSYWFhRQXF0cCgUBjTSJD6qri0DWwXPUZNzY26iyPiYlRx33w4EGSSCQ0ceJEjboXLlwgJycn8vLyon/+859UV1dHp0+fpmnTptGQIUP0mvRRVlZGbm5uNG/ePK1jutovKCig8PBw8vHx0dn+1KlTadCgQTrXlNLl5MmTJJVKiYjo1q1b5ObmRlZWVnT27Fl1nbfeeotmzZpFRET//ve/dU4q6cnnTdekEqVSqXNSCT9Lup+lzj7HjhobG9WTjEpKSrq8pgr64KSSgai7k0oYY/oxJH/rlYRw7969GuWjR48mAPTjjz9qlPv6+tKoUaM0yrKysuihhx7Sajs5OZmEQqHWLLMdO3aok9C2tjZ6/PHH6eWXXzY4dhW5XE4A6OTJkxrlBQUFBIDkcrm6bO7cuQRAKxloamoiT09PkkgkVFZWZnBdVRzGfIlnZWVplMfHxxMAqqioUJclJCQQANq5c6dG3Rs3bpCdnV2XX+KVlZU0fvx4SkxM1LmYZ2ftX79+ncRisc72p0yZYtBiuO0TQiKit99+mwBQcnIyERE1NDSQm5sb5efnE9G9E8Keet7az4RW/QgEAnJxcaHo6Gj6+eef1XX5WdL9LBHplxDeunWLE8I+jBNCxnqHIfnbf/fL6UETJkzQ+LenpyfOnDmjVe7l5aW19lhUVBSioqK02pTL5di2bRvOnDmDoKAgdXlCQgKWL1+ON954A8HBwRg0aBA+++yzbsUvlUoxfvx4jbKxY8fC09MT+fn5KC0thYeHB3bt2gUAiIyM1KgrFosRFhaGrVu34rvvvsNjjz1mUN3umDhxosa/VbNrS0pKMHjwYADAgQMHAEBrr84hQ4bA398fZ86c6bT9hoYGKBQKPPDAA/j8889hbW2tVaez9j09PeHn54eioiKtc1RdqMZ65plnsHbtWnz55ZdYuXIlsrKyMHnyZIwbN+6e5/XG8xYTE4PMzMx71uFnSfezpC/V5vFCoVAdm77Wr1/Pi/D3MNUi26pFcxljPePYsWOYPHmyXnV7ZVJJx6UBrKysYG1tDTs7O41ya2trrWUvlEolXn31VYwdOxbOzs7qMUhLliwBANy6dUvreqtWrcKkSZNw9OhRJCQkwMqqe7+mk5OTznLV3qA3btxAc3MzlEolbG1t4eDgoFVXtQ9mWVmZQXW7SzUeTUUkEgGA+nNubm5GXV0dbG1tYW9vr3W+s7Nzp223trYiISEBXl5e2LJli84v8K7a72x/1e6yt7fHggULcOfOHaxcuRLr1q3Ta8mOvvC88bOk+1kyhGp8alBQEIRCYbfaYowxS9Arbwi7Y8aMGfj3v/+N9957D7Nnz8bgwYMhEAiwYcMGvPjiizqXpDh8+DCUSiXGjh2LZ555BnK5HHK53OgYqqqqQERasx1v3LgB4G5SIxaLIZPJoFQqUVdXp/XlXF5eDuDuWkeG1O1pYrEYDg4OqKurQ319vdYXuep31CUlJQXNzc3YtWuXxubs9913H7Zt24bJkyd32X51dbVpf6F2nn/+eaxbtw7bt29HRESE1htpXfrC88bPku5nSV9tbW3qXSyeffZZA38L4MUXX+xTW9cNRKqt6/hNLGM9y5C38H162Zk7d+4gOzsb7u7uSE1NxZAhQ9RJWWNjo85zLl++jCeffBLffPMNdu/eDYlEgpiYGFRUVBgdR1NTk9a2YqdOnUJJSQnkcjk8PDwAALGxsQCAvXv3atRtbm7GoUOHIJFI1F1phtTtaREREQD+292nUlZWprM7FwBee+01nDlzBt9++y3EYrFR7VdWVqKwsNDYsLskk8mwcOFCyGQyvd4O9pXnDeBnqTvS0tLw888/IzY2lrskGWNMX6agRmatAAAgAElEQVQelNheZ4PUFQoFWVtba9WfMmWKxsQAIqLQ0FACQGvXrqWKigq6desW/etf/6Jhw4YRAI2tvurq6mjcuHH07bffqssOHz5MQqGQ/vCHP9Dt27cN/h3kcjnJZDIKCwszeJZxbW2txmzPTZs2GVVXFYcxEwE6li9btkxrkszFixdp0KBBGjNDT506RQ8//DANHz5cayLAP/7xD63JER1/2s8O1tX+mTNnSKFQkKurq8lnGeujs0klPfm86TMZQoWfJd3Pkq7P8c6dO1ReXk6ZmZnq+/fEE0/QrVu3uvycOwJPKukVPKmEsd5h9lnGOTk5Wn/Uly9fTrm5uVrla9asUX85t/9ZuXIlERFVVFRQSkoKeXt7k1AoJDc3N5o7dy699NJL6rqBgYH07LPPapx/6tQpqqio0Gp31apVev8eRP/98jx79iwpFApycHAgiURCU6ZMoSNHjmjVr6yspAULFpCvry8JhUKSyWSkUCjo0KFDRtX961//qvOz3LVrl1Z5UlJSp589EWmVt9+3trCwkGbOnEmOjo5kZ2dHv//97+nHH3+khx56iOzs7DTijoyMNPhLvH37quVK9uzZQ2FhYepznnzySXX9kJAQvWcZS6VSjWsrFIp71tcV7/vvv09EPfe8dYwRgNaM+o74WdJ+lnR9jgKBgGQyGY0dO5bmz59PJ06cuOfnei+cEPYOTggZ6x2G5G8Coq73hVJ1u1jieI/x48ejsrJSPSvO0vj7+6OxsRFXr141dyisn+sPz5JAIEBGRgaPIexhqjGEenz9MMa6wZD8rU+PIWS9o6ysDIMGDdLa0eHKlSsoLi5GaGiomSJj/Q0/S6wvunr1KqKjo1FbW4vKykqNHXgCAgLUu/O017GeQCDQa2Jaf9DS0oL169cjMDAQDg4OcHV1RUREBLKysjSSdCJCdnY2nn32Wfj5+UEsFsPV1RXBwcHYtm2byRL6vhYPAOzbtw9+fn4ak9y6G/9LL72EjIwMk8VoapwQMgDAzZs3kZKSgt9++w23bt3Czz//jMTERDg6OmLFihXmDo/1I/wssb4kLy8PEyZMQHh4OBwdHTF48GAQkXqiYF5eHhYsWKB1nqpeTk4OXFxcQEQ4fvx4b4dvcg0NDQgNDcXmzZuxfv163LhxA8ePH4e9vT2io6M11gotLCxEcHAwioqKsHPnTiiVShw7dgzDhg3DnDlz1MtxDaR4iouLER0djbS0NPUqDaaK/6mnnkJaWlrf/Tto6j7o/gBdjFkCoHOskmr81ED0/fffU2xsLPn4+JBIJCI3NzdKSkqiixcvmjs01s/052cJfWwMoVQqpQcffHDAXb+3xhAqlUoaOnQopaSkaB3Lzc0lsVhMLi4uBIC2b9+us42cnBxycXHp6VB7zfz588nR0VFj9yIiovr6ehKLxXTq1Cl12blz58jGxoaqq6s16jY3N5OLiwuJxWJqamoaUPHMnj2b1qxZQy0tLeTl5aVzAqyx8RPd3fJUIBD02t+ZPrdTSV9DPG5FS1hYGMLCwswdBhsA+FlifcXatWtRVlaGV199VedxW1tbfPHFF5g+fTpSUlIQGBgIPz+/Xo6y95SXl2PTpk14+umn1YvWq0ilUq2uc39/f63hH8DdRem9vb2Rl5eHpqYmo5eL6mvxAMCnn34KiUTSI/EDd3e9io+Px6JFixAXF6dXl3Rv4S5jxhhjAw4RIT09HZMmTYKnp2en9RQKBV555RXU1dUhISFB55f4QLF7927cuXMHwcHB3WqnpqYGFy5cQEBAgNYORv05HgB6J4OA8fHHxsbi2rVrWmvHmhsnhIwx1g1VVVVYuHAhRo4cCZFIBGdnZ0REROCHH35Q11m9erV6YkL7L48DBw6oy9vvubxu3ToIBAI0NDQgOztbXUf1NkF1XCAQYOjQocjNzUVYWBgcHBxgZ2eHqVOnIjs7u8eu3x/k5+ejvLxcr12DVq5cifDwcBQUFOD555/Xq3197ntmZqbGpJQrV64gMTERTk5OcHFxQVRUFIqLi7XarqioQGpqKnx8fCASiTBkyBDExcUhLy9P/w9Ah19++QXA3W0kFy1aBG9vb4hEIgwfPhypqald7hxVW1uL7OxsREdHw93dHZ9//vmAisdQxsY/fvx4AMB3333Xa7HqxdR90Iwx1l/BwDGEHRcFVyqVGouCf/LJJxr1OxuTFxgYqHOcWldj+ORyOUmlUgoKCupy4fyeuL6hC8ir9MYYwq1btxIAevPNN3Uez83NJZlMpv53RUUFeXt7EwDatm2bulzXGEJD77tqcfeYmBj1fTp48KB6Tdb2SkpKaPjw4eTm5kZ79+6luro6On36NE2ZMoVsbW31Wpu1M6o43N3dKSkpiYqLi+nmzZu0ZcsWkkql5OfnRzU1NTrPXbVqlXo8/UMPPUQFBQVGx9FX4+moqzGExsavVCoJAIWEhJg85o7MvjA1Y4z1R4YmhHPnziUA9OWXX2qUNzU1kaenJ0kkEo3B5j2REAKau8UQERUUFBAAksvlerVn7PWnTJmi9wLy7fVGQrh27VoCQB988IHO4x0TQqK7yZ9QKCSpVErnzp1Tl3X8bAy976rEISsrS6N+fHw8AaCKigp12eOPP04A6IsvvtCoW1paSmKxmAIDA/X8BLQpFAoCQL6+vtTS0qJxbPXq1QSAVqxY0en5zc3NdO7cOfrzn/9M1tbW9PrrrxsdS1+Mp6OuEsLuxC8QCOi+++4zaby6GJK/cZcxY4wZadeuXQCAyMhIjXKxWIywsDA0Njb2eLeQVCpVd0GpjB07Fp6ensjPz0dpaWmPXfvw4cOorq5GUFBQj13DWKqxgEKhUO9zJk+ejHXr1qGhoQEJCQmd7mFu7H2fOHGixr+9vb0BACUlJeqyzMxMWFlZISoqSqOuu7s7Ro8ejRMnThi9UYJUKgUATJs2Tav7f8aMGQDu3Y0pEong7++Pjz76CNHR0Xj11Vfx/fffGxVLX4zHUN2J38bGptPny1w4IWSMMSM0NzdDqVTC1tYWDg4OWsdVsw7Lysp6NA4nJyed5a6urgCAGzdu9Oj1+ypbW1sA0Dkr9V5SU1ORmJiI06dP47nnntM63p373nHCg0gkAgC0tbVptN3W1gaZTKa1MLZqzNqFCxcM+p1UfHx8AAAuLi5ax1TPS0VFhV5tqRKePXv2GBVLX4zHUN2Jv7W11aAJLL2BE0LGGDOCWCyGTCZDU1MT6urqtI6rFrV1d3dXl1lZWeH27dtadWtqanReQyAQdBlHVVWVzqW0VImg6oupp67fV3l4eAAAlEqlweemp6dj1KhR+Oyzz7B161aNY8bcd32JxWI4OTnBxsYGLS0toLvDurR+pk6danDbANQTinS9NVY9Lx2XT7lXrAC6nPjRn+IxlLHx19bWgojUz2hfwQkhY4wZKTY2FgC0lo9obm7GoUOHIJFIoFAo1OUeHh64fv26Rt2ysjL8+uuvOtu3s7PTSOBGjRqFTZs2adRpampS77qhcurUKZSUlEAul2t86fTE9fuqMWPGAIBR3av29vb45ptvIJVK8eGHH2odN/S+GyIuLg6tra0as8RV3n77bQwbNgytra1GtT19+nR4eXnhwIEDWsvrZGVlAQBmzpypLlu8eDGSk5N1trV//34A2t3g/TkeQxkav4rq/0HVM9pnmHpQImOM9Vfo5izj2tpajdmmmzZt0qj/3HPPEQB6//33qa6uji5evEizZs0iLy8vnZM6Hn74YZLJZPTrr7/S0aNHycbGhs6ePas+LpfLSSaTUVhYmF6zjE19/b48y7itrY1cXV07nRSja1JJR9u2bSMAXc4y7uq+qyaVNDY2apQvW7ZMa1JQeXk5jRw5kkaMGEH79u2jmpoaqqqqoo0bN5KdnZ3W85mUlEQA6NKlS11+JkRE+/fvJxsbG4qJiaGioiK6efMmff755ySVSmnSpEl069Ytdd1FixaRQCCgv/zlL3T58mVqamqiy5cv09KlSwkABQYGatQfCPG0p89OJYbEr7J9+3YCQLt27TI4JkPxLGPGGDOCoQkhEVFlZSUtWLCAfH19SSgUkkwmI4VCQYcOHdKqW1NTQ/PmzSMPDw+SSCQUHBxMubm5FBgYqF5CY9myZer658+fp5CQEJJKpeTt7a01Y1Yul5OXlxedPXuWFAoFOTg4kEQioSlTptCRI0d6/PohISF9dpYxEdHLL79MNjY2dP36dXVZRUWF1rak95q5O3/+fJ3Jsj73PScnp9MtUDuWR0ZGqs+rqqqihQsX0ogRI0goFNKQIUMoPDycDh48qBVHaGgo2dvbU2trq96fy9GjR0mhUJBMJiORSET+/v702muvaSUvSqWS0tPTSaFQqLeitLe3p8DAQFqzZo3OZKe/x5OVldXplrYdlxMyNH6VhIQE8vLyotu3b+sVU3cYkr8JiLrexy0hIQEA8PXXX3fnZSRjjPVpAoEAGRkZmDVrlrlD0cv48eNRWVlp9KxTc9mxYwcSExN7fBtRpVKJ0aNHIyoqChs3buzRa5lDTU0NPD09kZSUhE8++cTc4XA8esjPz0dAQAC2b9+ORx55pMevZ0j+xmMIGWOMDUgymQxZWVnYuXMnPvjgA3OHY1JEhNTUVDg6OmLVqlXmDofj0cOlS5cQFxeHtLS0XkkGDcUJIWOMsQErICAAx48fx/79+1FbW2vucEymvLwcly5dwqFDh4ya0czx9L6PP/4Yb7zxBt544w1zh6JT/9mYkjHGGIC7ew0vWbJE/W+BQIDly5dj9erVZoyq7/Lx8enV9el6g7u7O44cOWLuMNQ4nq69/fbb5g7hnjghZIyxfmbx4sVYvHixucNgjA0g3GXMGGOMMWbhOCFkjDHGGLNwnBAyxhhjjFk4TggZY4wxxiwcJ4SMMcYYYxZO751Kdu7c2RvxMMYYY4wxE4mPj9drpxK9EsKcnBz89ttvJgmMMcb6kvXr1wMAXnzxRTNHwhhjpuft7Y2goKAu6+mVEDLG2ECl2rd4x44dZo6EMcbMh8cQMsYYY4xZOE4IGWOMMcYsHCeEjDHGGGMWjhNCxhhjjDELxwkhY4wxxpiF44SQMcYYY8zCcULIGGOMMWbhOCFkjDHGGLNwnBAyxhhjjFk4TggZY4wxxiwcJ4SMMcYYYxaOE0LGGGOMMQvHCSFjjDHGmIXjhJAxxhhjzMJxQsgYY4wxZuE4IWSMMcYYs3CcEDLGGGOMWThOCBljjDHGLBwnhIwxxhhjFo4TQsYYY4wxC8cJIWOMMcaYheOEkDHGGGPMwnFCyBhjjDFm4TghZIwxxhizcJwQMsYYY4xZOE4IGWOMMcYsHCeEjDHGGGMWjhNCxhhjjDELxwkhY4wxxpiF44SQMcYYY8zCcULIGGOMMWbhOCFkjDHGGLNwNuYOgDHGektlZSVqa2s1yhoaGgAAly5d0ih3dHTE4MGDey02xhgzJwERkbmDYIyx3vCPf/wDTzzxhF51P/vsM/zpT3/q4YgYY6xv4ISQMWYxlEolhgwZgpaWlnvWEwqFqKiogEwm66XIGGPMvHgMIWPMYshkMkyfPh02Np2PlrGxsUFkZCQng4wxi8IJIWPMoiQnJ+POnTudHm9ra0NycnIvRsQYY+bHXcaMMYvS1NSEwYMHqyeTdGRnZ4fKykpIJJJejowxxsyH3xAyxiyKra0t4uLiIBQKtY4JhULEx8dzMsgYszicEDLGLM6jjz6qc2JJS0sLHn30UTNExBhj5sVdxowxi9Pa2go3NzdUV1drlDs5OaGiouKek04YY2wg4jeEjDGLY2Njg9mzZ2t0GwuFQiQnJ3MyyBizSJwQMsYs0uzZszW6jVtaWjB79mwzRsQYY+bDXcaMMYtERPD29sb169cBAB4eHrh+/ToEAoGZI2OMsd7HbwgZYxZJIBBgzpw5EIlEEIlEePzxxzkZZIxZLH5DyBizWAUFBZDL5er/Hjt2rJkjYowx8+DR093w7rvvIicnx9xhMMa6wd7eHgDw+uuvmzkSxlh3BAUFYeHCheYOo9/iLuNuyMnJwbFjx8wdBmN9zs6dO3Ht2jVzh6GX4cOHw8fHx9xhGOXatWvYuXOnucNgzOyOHTvGL2i6id8QdtPkyZPx9ddfmzsMxvoUgUCAF198EbNmzTJ3KF26dOkSAGDEiBFmjsRwO3bsQGJiIv8NYhYvISHB3CH0e5wQMsYsWn9MBBljzNS4y5gxxhhjzMJxQsgYY4wxZuE4IWSMMcYYs3CcEDLGmIW6evUqoqOjUVtbi8rKSggEAvVPQEAAmpqatM7pWE8gEGDChAlmiN70WlpasH79egQGBsLBwQGurq6IiIhAVlYW2i/ZS0TIzs7Gs88+Cz8/P4jFYri6uiI4OBjbtm2DqZb37WvxAMC+ffvg5+en157f+sb/0ksvISMjw2QxMuNwQsgY67Pq6+tx//33IyoqytyhDDh5eXmYMGECwsPD4ejoiMGDB4OIkJubqz6+YMECrfNU9XJycuDi4gIiwvHjx3s7fJNraGhAaGgoNm/ejPXr1+PGjRs4fvw47O3tER0djTNnzqjrFhYWIjg4GEVFRdi5cyeUSiWOHTuGYcOGYc6cOViyZMmAi6e4uBjR0dFIS0tDeXm5SeN/6qmnkJaWhhUrVnQ7TtYNxIwWHx9P8fHx5g6DsT4HAGVkZHS7ndraWhoxYgRFRESYIKqeJZVK6cEHH+zVa2ZkZJAxf8aVSiUNHTqUUlJStI7l5uaSWCwmFxcXAkDbt2/X2UZOTg65uLgYfO2+av78+eTo6EhlZWUa5fX19SQWi+nUqVPqsnPnzpGNjQ1VV1dr1G1ubiYXFxcSi8XU1NQ0oOKZPXs2rVmzhlpaWsjLy4usra1NFj8RUV5eHgkEAqP/bvD3cffxsjOMsT7LwcEBxcXF5g5jwFm7di3Kysrw6quv6jxua2uLL774AtOnT0dKSgoCAwPh5+fXy1H2nvLycmzatAlPP/003NzcNI5JpVKtrnN/f3+0tLRotSMSieDt7Y28vDw0NTVBLBYPiHgA4NNPP4VEIumR+AFALpcjPj4eixYtQlxcnF5d0sy0uMuYMcYsCBEhPT0dkyZNgqenZ6f1FAoFXnnlFdTV1SEhIUHnl/hAsXv3bty5cwfBwcHdaqempgYXLlxAQEAAZDLZgIkHgN7JIGB8/LGxsbh27Rr27t1raHjMBDghZIz1SZmZmRoTF1QJScfyK1euIDExEU5OTnBxcUFUVJTGW8V169ap6w4dOhS5ubkICwuDg4MD7OzsMHXqVGRnZ6vrr169Wl2//RfagQMH1OWDBw/War+hoQHZ2dnqOn31DUd+fj7Ky8shl8u7rLty5UqEh4ejoKAAzz//vF7tV1VVYeHChRg5ciREIhGcnZ0RERGBH374QV3H0HuoUlFRgdTUVPj4+EAkEmHIkCGIi4tDXl6e/h+ADr/88gsAwNnZGYsWLYK3tzdEIhGGDx+O1NRUVFdX3/P82tpaZGdnIzo6Gu7u7vj8888HVDyGMjb+8ePHAwC+++67XouVtWPuPuv+jMcsMKYbTDSGkIgoJiaGAFBjY6PO8piYGDp69CjV19fTwYMHSSKR0MSJE7XakcvlJJVKKSgoSF0/NzeXxo0bRyKRiA4fPqxRv7MxgYGBgTrHznU1hnDq1Kk0aNAgysnJ0fdX75IxYwi3bt1KAOjNN9/UeTw3N5dkMpn63xUVFeTt7U0AaNu2bepyXWMIS0tLydfXl9zc3CgrK4uUSiUVFhZSXFwcCQQC+uSTTzTqG3IPS0pKaPjw4eTm5kZ79+6luro6On36NE2ZMoVsbW3p6NGjBn0OuuJwd3enpKQkKi4upps3b9KWLVtIKpWSn58f1dTU6Dx31apVBIAA0EMPPUQFBQVGx9FX4+moqzGExsavVCoJAIWEhBgcE38fdx8nhN3ADyBjuvVmQpiVlaVRHh8fTwCooqJCo1wulxMAOnnypEZ5QUEBASC5XK5RbuqEcMqUKeTs7NytxKUjYxLCtWvXEgD64IMPdB7vmBAS3U3+hEIhSaVSOnfunLqs4+cwd+5cAkBffvmlRnlTUxN5enqSRCLRmGRgyD18/PHHCQB98cUXGnVLS0tJLBZTYGCgnp+ANoVCQQDI19eXWlpaNI6tXr2aANCKFSs6Pb+5uZnOnTtHf/7zn8na2ppef/11o2Ppi/F01FVC2J34BQIB3XfffQbHxN/H3cddxoyxfm3ixIka//b29gYAlJSUaNWVSqXqbimVsWPHwtPTE/n5+SgtLe2xOA8fPozq6moEBQX12DX0oep6FwqFep8zefJkrFu3Dg0NDUhISEBjY6POert27QIAREZGapSLxWKEhYWhsbFRZ3egPvcwMzMTVlZWWksQubu7Y/To0Thx4gSuXbum9+/UnlQqBQBMmzZNq6t/xowZAO7djSkSieDv74+PPvoI0dHRePXVV/H9998bFUtfjMdQ3Ynfxsam0+eL9SxOCBlj/VrHwfIikQgA0NbWplXXyclJZxuurq4AgBs3bpg4ur7H1tYWAHTOSr2X1NRUJCYm4vTp03juuee0jjc3N0OpVMLW1hYODg5ax1WzTcvKyrSOdXUPVW23tbVBJpNpLYytGrN24cIFg34nFR8fHwCAi4uL1jHVs1FRUaFXW6qEZ8+ePUbF0hfjMVR34m9tbTVoAgszHU4IGWMWo6qqSueuDapEUPVlBQBWVla4ffu2Vt2amhqdbQsEAhNF2bM8PDwAAEql0uBz09PTMWrUKHz22WfYunWrxjGxWAyZTIampibU1dVpnatazNjd3d3g64rFYjg5OcHGxgYtLS2gu8OdtH6mTp1qcNsA1JOHdL0hVj0bHZdPuVesALqc+NGf4jGUsfHX1taCiNTPKOtdnBAyxixGU1OTeicOlVOnTqGkpARyuVzji8jDwwPXr1/XqFtWVoZff/1VZ9t2dnYaCeSoUaOwadMmE0ZvGmPGjAEAo7pX7e3t8c0330AqleLDDz/UOh4bGwsAWsuGNDc349ChQ5BIJFAoFEZEDcTFxaG1tVVjRrjK22+/jWHDhqG1tdWotqdPnw4vLy8cOHBAa3mdrKwsAMDMmTPVZYsXL0ZycrLOtvbv3w9Auxu8P8djKEPjV1H9/6Z6Rlnv4oSQMWYxZDIZXn75ZeTk5KChoQHHjx9HcnIyRCIR3nvvPY264eHhKCkpwd///nfU19ejuLgYL7zwgsZbxPZ+97vfoaioCL/99htycnJw6dIlhISEqI+HhobCxcUFx44d69HfsStyuRyurq7Iz8836vzRo0fj448/1nlszZo18PX1xYIFC7Bnzx7U1dWhqKgIjz76KEpLS/Hee+/p/WZLV9sjR47EE088gf3790OpVKK6uhoff/wxXn/9daxbt05jvFpycjIEAgEuX77cZdtisRjp6emoqqrCI488ggsXLqCmpgZbt27FmjVrMGnSJKSmpmqcs337drz++uu4cuUKmpubceXKFSxbtgzbtm1DYGAg5s2bp1G/P8djKGPiB6BePig8PNzkMTE9mG8+S//Hs5oY0w0mmGW8a9cu9fIZqp+kpCTKycnRKl++fLn6uu1/IiMj1e3J5XLy8vKis2fPkkKhIAcHB5JIJDRlyhQ6cuSI1vVrampo3rx55OHhQRKJhIKDgyk3N5cCAwPV7S9btkxd//z58xQSEkJSqZS8vb21ZvGGhIT0iVnGREQvv/wy2djY0PXr19VlFRUVWp/fvWbuzp8/X+ds68rKSlqwYAH5+vqSUCgkmUxGCoWCDh06pK5j7D2sqqqihQsX0ogRI0goFNKQIUMoPDycDh48qBVHaGgo2dvbU2trq96fy9GjR0mhUJBMJiORSET+/v702muv0a1btzTqKZVKSk9PJ4VCQT4+PiQSicje3p4CAwNpzZo1WvUHQjxZWVla90b103E5IUPjV0lISCAvLy+6ffu2XjG1x9/H3Scg0jGghuklISEBAPD111+bORLG+haBQICMjAzMmjXL3KGojR8/HpWVlUbPRO2LduzYgcTERJ3jIu9FqVRi9OjRiIqKwsaNG3soOvOpqamBp6cnkpKS8Mknn5g7HI5HD/n5+QgICMD27dvxyCOPGHw+fx93H3cZM8aYhZHJZMjKysLOnTvxwQcfmDsckyIipKamwtHREatWrTJ3OByPHi5duoS4uDikpaUZlQwy0+CEkN3TzZs3sXHjRoSGhmLQoEGQSCS4//77kZSUpPcYpK+++kq9PIRqyQtj7du3D35+fj22LVhubi7mzp0LX19fSCQSDBo0CGPGjMH//u//4qOPPtK5nZa5GXqP7O3ttZbtsLKygrOzM+RyOZ555hmcOHHCDL8J600BAQE4fvw49u/fj9raWnOHYzLl5eW4dOkSDh06ZNSMZo6n93388cd444038MYbb5g7FMtmzv7q/s4Sxiw8+eSTZGNjQxs2bKDS0lJqaGign376iR544AGytramXbt26d1WWFgYicVio+K4ePEizZgxg8aNG0eOjo73XCXfGHfu3KHFixeTjY0NLVmyhM6dO0dNTU1UVlZG//znP2natGnq8TIdV943N2Pu0cmTJ9VbhhERtba2UllZGWVmZtLUqVMJAM2dO5caGhqMigkm3Kmku/761792Ol6tvzN2DCFjA40lfB/3NH5DyLr0xBNP4IUXXoC7uzvs7OwQEhKC7du3486dO1i6dGmvxLBixQr8/ve/x4kTJ3QuemuK9tetW4cPP/wQa9euhb+/P8RiMdzc3PDHP/4RBw4cQEREhMmvayrdvUfW1tZwc3NDTEwM/vWvf2Hp0qXYvHkzZs+ebfD4tL5m8eLFWuvVrV692txhMcZYn9Iz/W5swEhPT9dZLpfLIZFIUFxcDCLq8UV5P/300x5bvf78+fN46623EBgYiKeeekpnHWtra6xYsUK9pldf0hP36K233sKPP9o3WxEAACAASURBVP6I3bt346uvvsLs2bNNFS5jjLE+iN8QMqM0NDSgsbERY8aM6ZUdGnpyK6NNmzahra1NPUutM0FBQSCiHhu/aGrduUcCgUC9PZmuBYgZY4wNLJwQmkFVVRUWLlyIkSNHQiwWY+jQoZg2bRo2b96ssal3+3oikQjOzs6IiIjADz/8oK6TmZmpMTngypUrSExMhJOTE1xcXBAVFaWeCFFTU6M1mUDVddba2qpRHh8ff8/fQTW1f/ny5VrHzp8/j5kzZ0Imk0EqlSIkJARHjhzp9ufWU3766ScAwLhx4ww+t7/eI32otp86duyYwfveMsYY62fMN3yx/zNmEGtpaSn5+vqSu7s7ZWVlUW1tLZWVldGqVasIAK1fv16jnpubG2VlZZFSqaTCwkKKi4sjgUCgtRBoTEyMepLA0aNHqb6+ng4ePEgSiYQmTpyoUffhhx8mKysrunjxolZ8QUFBtH379nv+DmVlZeTm5kbz5s3TOnbhwgVycnIiLy8v+uc//0l1dXVUUFBA4eHh5OPjY/Skkva8vLy6nFQydepUGjRoEOXk5HTZnoeHBwGg//znPwbF0V/vEZH2pBJdGhsb1ZMwSkpK7nm9jtCHJpUMZDyphLG7eFJJ9/Ffkm4w5gGcO3dup1+WDz/8sDohVNX78ssvNeo0NTWRp6cnSSQSKisrU5erko2srCytGAFQRUWFuuz7778nAPTMM89o1D1y5AgNGzbsnrNoKysrafz48ZSYmKhzhfuEhAQCQDt37tQov379OonF4l5LCKdMmaL3rhCqhPDnn382KI7+eo+I9EsIb926xQlhH8cJIWN3cULYffyXpBuMeQBlMhkBoNraWqPrzZkzhwDQli1b1GWqZKN9AkJE9OKLLxIAys/P1ygPCAggOzs7qqys1Gjj3Xff7TSm+vp6CgwMpEcffbTTRMPBwYEAUF1dndaxsWPH9lpCaAjVVmT79u0z6Lz+eo+I9EsIi4uLCQAJhUKDt5JSJZL8wz/8wz+99cMJYff0j9HxA0RzczOUSiVsbW3vuXRKV/VUm8OXlZVpHZPJZBr/FolEAIC2tjaN8kWLFiE5ORkffvghVqxYgaKiIvz000/YunWrzphaW1uRkJAALy8vbNmyBdbW1jrjrqurg62tLezt7bWOu7q6oqioqJPf2nymTJmCEydOoKCgQO+lZfrrPTKEatxnUFAQhEKhwecvWLAAQUFB3YqB3VtOTg42bNiAjIwMc4fCmFmtX7/e3CH0e5wQ9iKxWAyZTAalUom6urpOk8Ku6pWXlwNAt1aZT0xMRFpaGv7+979j6dKleOedd/DUU091GlNKSgqam5uxa9cujVm29913H7Zt24bJkydDLBbDwcEBdXV1qK+v10oKq6urjY63J6WkpOBvf/sbdu7ciWXLlnVab+nSpVi3bh3Onj0Lf3//fnmP9NXW1qbe0uzZZ581Kv6goKA+tZfxQLVhwwb+nJnF4z2Mu49nGfey2NhYAHe3YOsoICAAL774oka9vXv3atRpbm7GoUOHIJFIoFAojI7DxsYGL7zwAm7cuIF33nkHX331FVJTU3XWfe2113DmzBl8++23EIvF92xX9YbtwIEDGuWVlZUoLCw0Ot6e5Ofnh5UrV+L48eP47LPPdNYpLCzExx9/jFmzZsHf3x9A/71H+khLS8PPP/+M2NjYLpfjYYwxNgCYu8+6P+vOLGMPDw/as2cP1dbW0m+//Ubz588nNzc3unr1qkY91QzW2tpajRmsmzZt0mhXNT6tsbFRo3zZsmUEgE6ePKkVS21tLclkMhIIBPTYY4/pjPcf//hHl+M22s/kvXjxIg0aNEhjlvGZM2dIoVCQq6trn5xlrPLSSy+RUCikZcuWUWFhITU3N9O1a9coPT2dPDw8KDg4mOrr69X1++s9ItIeQ3jnzh0qLy+nzMxMCg0NJQD0xBNP0K1bt/T+/NoDeFJJb+BJJYzdxZNKuo//knSDsQ9gZWUlLViwgHx9fUkoFJKHhwc98sgjVFRUdM96MpmMFAoFHTp0SF0nJydH68tftU9rx/LIyEitWJYsWUKA9oQGlcjISIOTjcLCQpo5cyY5Ojqql1TZs2cPhYWFqc958sknDfrMsrKyOr1+x+VdiIhCQkL0nmXc3s8//0xz5swhb29vEgqF5ODgQJMnT6b33nuPmpubter3x3sklUq1jgsEApLJZDR27FiaP38+nThxwqDPrSNOCHsHJ4SM3cUJYfcJiPr5RqVmpOpK47ELjGkSCATIyMjgsW09bMeOHUhMTOz3+00z1l38fdx9PIaQMcYs1NWrVxEdHY3a2lpUVlZq7IQTEBCApqYmrXM61hMIBJgwYYIZoje9lpYWrF+/HoGBgXBwcICrqysiIiKQlZXVadK9b98++Pn59ciWln0lHiJCdnY2nn32Wfj5+UEsFsPV1RXBwcHYtm2bViw3b97Exo0bERoaikGDBkEikeD+++9HUlIS8vPztdp/6aWXeKZ8H8AJIWOMWaC8vDxMmDAB4eHhcHR0xODBg0FEyM3NVR9fsGCB1nmqejk5OXBxcQER4fjx470dvsk1NDQgNDQUmzdvxvr163Hjxg0cP34c9vb2iI6OxpkzZzTqFxcXIzo6GmlpaepVBQZqPIWFhQgODkZRURF27twJpVKJY8eOYdiwYZgzZw6WLFmiUX/JkiV4/vnnERMTg7Nnz6KqqgqfffYZ8vLyEBgYiMzMTI36Tz31FNLS0rBixQqTxs0MZK6+6oGAxyx0D/RYaHTlypXmDpMZAX1sDKFUKqUHH3xwwF3f2DGESqWShg4dSikpKVrHcnNzSSwWk4uLCwHodJvEnJwccnFxMfjafdX8+fPJ0dFRa+H4+vp6EovFdOrUKY3y2bNn05o1a6ilpcXki+X3tXjOnTtHNjY2VF1drVHe3NxMLi4uJBaLqampSV3+5JNP0tNPP63VTl5eHgGg+++/X+cxgUBg9N8N/j7uPl6HkJkN8bgnxsxi7dq1KCsrw6uvvqrzuK2tLb744gtMnz4dKSkpCAwMhJ+fXy9H2XvKy8uxadMmPP300+pF5VWkUqnOrvNPP/0UEonEIuLx9/dHS0uLVrlIJIK3tzfy8vLQ1NSkXvIqPT1dZztyuRwSiQTFxcUgIggEAo1j8fHxWLRoEeLi4nqkC57dG3cZM8aYBSEipKenY9KkSfD09Oy0nkKhwCuvvIK6ujokJCToTEIGit27d+POnTsIDg7W+5yeSr76YjydqampwYULFxAQEKC1A5MuDQ0NaGxsxJgxYzSSQZXY2Fhcu3ZNa21X1js4IWSM9QlVVVVYuHAhRo4cCZFIBGdnZ0REROCHH35Q11m9erV6IkP7L8sDBw6oywcPHqwuX7duHQQCARoaGpCdna2uo3r7oDouEAgwdOhQ5ObmIiwsDA4ODrCzs8PUqVORnZ3dY9c3h/z8fJSXl0Mul3dZd+XKlQgPD0dBQQGef/55vdrX5z5mZmZqTEq5cuUKEhMT4eTkBBcXF0RFRaG4uFir7YqKCqSmpsLHxwcikQhDhgxBXFwc8vLy9P8AdPjll18AAM7Ozli0aBG8vb0hEokwfPhwpKam9vouS30tno5qa2uRnZ2N6OhouLu74/PPP9frPNUM4OXLl+s8Pn78eADAd999Z5pAmWHM3GXdr/GYBcZ0g4FjCDsu8q1UKjUW+e641mRnY/ICAwN1jmvragyfXC4nqVRKQUFBdPToUaqvr6fc3FwaN24ciUQiOnz4cI9e35iF1ImMG0O4detWAkBvvvmmzuO5ubkkk8nU/66oqCBvb28CQNu2bVOX6xpDaOh9VC3WHhMTo/7cDx48qF6/tL2SkhIaPnw4ubm50d69e6muro5Onz5NU6ZMIVtbW4PXHNUVh7u7OyUlJVFxcTHdvHmTtmzZQlKplPz8/KimpqbT8009Zq+vxdPeqlWr1GO8H3roISooKNDrvLKyMnJzc6N58+Z1WkepVBIACgkJMTgu/j7uPn5DyBgzu7S0NFy+fBkbNmxAVFQUHB0d4efnh+3bt8PDwwOpqak9MpOzvYaGBnz44YcICgqCVCrFhAkTsG3bNty+fRsvvPBCj167ra0NdHejgB69DgCUlpYCgF5dfMDdWcU7duyAUChESkoKzp8/32ldY+/jvHnz1J/7tGnTEBkZidzcXFRWVmq0ffXqVbz77ruYPn067O3tMXr0aHz11VcgIr3fYOqi6g6XSCTYvHkzRowYAScnJzz22GNIS0tDUVER3nnnHaPb7+/xtPfKK6+gubkZ586dg7+/PwICArBq1ap7nlNVVYWHH34YDz30EDZu3NhpPUdHRwgEAvUzynoXJ4SMMbPbtWsXACAyMlKjXCwWIywsDI2NjT3ejSSVStVdVipjx46Fp6cn8vPze/RL6vDhw6iurkZQUFCPXUNFlWwIhUK9z5k8eTLWrVuHhoYGJCQkoLGxUWc9Y+/jxIkTNf7t7e0NACgpKVGXZWZmwsrKClFRURp13d3dMXr0aJw4cQLXrl3T+3dqTyqVAgCmTZum1Z0/Y8YMAL3bjdnX4ulIJBLB398fH330EaKjo/Hqq6/i+++/11m3oaEBCoUCDzzwAL744gtYW1vfs20bG5tOny/WszghZIyZVXNzM5RKJWxtbeHg4KB1XDXLsqysrEfjcHJy0lnu6uoKALhx40aPXr+32NraAoDOWaP3kpqaisTERJw+fRrPPfec1vHu3MeObytFIhGAu29O27fd1tYGmUymtTC2aszdhQsXDPqdVHx8fAAALi4uWsdU97+iosKotgdCPPeiSlD37Nmjday1tRUJCQnw8vLCli1bukwGVeeYY4IM44SQMWZmYrEY/4+9e4+Lqtr7B/4ZhIHhNiDKRcS8JPKIOhL2GAWpYI6GSXJELLXTKYtXF9FjXlK7WGoeO56s07HUOOYtU449amhaRlk/FQtNvCvmLRVRQBkQAUG+vz98Zh6HGWSG24Dzeb9e8wdrr732d/ZsZ77utddaarUaZWVlKC4uNtmu72L09/c3lDk4OODmzZsmdQsLC80ew9yIxuoKCgrMdtnqE0H9D3FjHb+pBAQEAAB0Op3V+6akpKBbt25YtmwZVq1aZbStLp+jpZydneHl5QVHR0dUVFQYutervwYMGGB12wAMA4TM3QXWf/7Vp39pTM0tnrvRTzVjbqBLUlISysvLkZqaanSn8/7778eePXtM6hcVFUFEDNcoNS0mhERkc8OHDwcAk+kmysvLkZ6eDpVKBa1WaygPCAjAxYsXjerm5ubijz/+MNu+q6urUQLXrVs3LF261KhOWVmZYZUOvUOHDiEnJwcajcboR6oxjt9UevToAQB16l51d3fHV199BTc3N3zyyScm2639HK0RHx+PyspKo1HfevPnz0eHDh1QWVlZp7Yff/xxBAYGYtu2bSbT66SlpQEAnnzyyTq1fS/EM3nyZIwZM8bstq1btwIw7fafNWsWjhw5gk2bNhmSxtro/03pr1FqYjYazHJP4KgmIvNQz1HGRUVFRqNTly5dalT/1VdfFQDy8ccfS3Fxsfz+++8ycuRICQwMNDvKd/DgwaJWq+WPP/6Q3bt3i6Ojoxw9etSwXaPRiFqtlpiYGItGGTf08ZtylHFVVZX4+vrWOOq5+ihjc1avXi0Aah1lXNvnqB9NW1paalQ+bdo0ASD79+83lF2+fFm6dOkinTt3lm+++UYKCwuloKBAFi9eLK6uribX2+jRowWAnD59utZzIiKydetWcXR0lLi4OMnOzpZr167JypUrxc3NTfr27Ss3btyocV9LRvW25Hhee+01USgU8s4778iZM2ekrKxMzpw5I1OnThUAEh4ebhTP559/XusqVOau9TVr1ggA2bBhQ60xVcff4/pjQlgPvACJzLM2IRQRyc/Pl4kTJ0qnTp3EyclJ1Gq1aLVaSU9PN6lbWFgo48aNk4CAAFGpVBIZGSmZmZkSHh5u+MGZNm2aof7x48clKipK3NzcJCgoSBYtWmTUnkajkcDAQDl69KhotVrx8PAQlUol/fr1k507dzb68aOiosTb29vqqVPqunTdjBkzxNHRUS5evGgoy8vLM/nRDg8Pr7GNl156yWzya8nnmJGRYXKsmTNniojpkpaxsbGG/QoKCmTSpEnSuXNncXJykrZt28qgQYNk+/btJnFER0eLu7u7VFZWWnxedu/eLVqtVtRqtSiVSgkJCZFZs2aZTb7S0tJqTHaqT6/T0uPR6XSSkpIiWq1WOnbsKEqlUtzd3SU8PFzmzZtnEk9sbGydEsKEhAQJDAyUmzdvWnyO9Ph7XH8KEa4fVlcJCQkA/m+yTSK6TaFQYN26dRg5cqStQ7FI7969kZ+fX+dRqraSmpqKxMREq6er0el0CA0NxdChQ+86DUhLVVhYiHbt2mH06NH47LPPbB0O47HAgQMHEBYWhjVr1mDUqFFW78/f4/rjM4RERHZGrVYjLS0N69evx6JFi2wdToMSESQnJ8PT07PW+fEYj+3jAYDTp08jPj4e06dPr1MySA2DCSERkR0KCwvD3r17sXXrVhQVFdk6nAZz+fJlnD59Gunp6XUa0cx4mt6SJUswd+5czJ0719ah2DXbLahJRGRjCxYswJQpUwx/KxQKzJw5E3PmzLFhVE2nY8eOZuePa8n8/f2xc+dOW4dhwHhqN3/+fFuHQGBCSER2bPLkyZg8ebKtwyAisjl2GRMRERHZOSaERERERHaOCSERERGRnWNCSERERGTnOKikni5cuIDU1FRbh0HU7GRkZNg6hHue/hzzO4js3YULF9C+fXtbh9GicaWSekhISMD69ettHQYREZHdGzFiBFcqqQcmhERk1/TL6/EuGxHZMz5DSERERGTnmBASERER2TkmhERERER2jgkhERERkZ1jQkhERERk55gQEhEREdk5JoREREREdo4JIREREZGdY0JIREREZOeYEBIRERHZOSaERERERHaOCSERERGRnWNCSERERGTnmBASERER2TkmhERERER2jgkhERERkZ1jQkhERERk55gQEhEREdk5JoREREREdo4JIREREZGdY0JIREREZOeYEBIRERHZOSaERERERHaOCSERERGRnWNCSERERGTnmBASERER2TkmhERERER2jgkhERERkZ1jQkhERERk55gQEhEREdk5JoREREREdo4JIREREZGdY0JIREREZOccbR0AEVFT+fnnn5GRkWFUdvz4cQDA/PnzjcojIiLw6KOPNllsRES2pBARsXUQRERNIT09HQMHDoSTkxMcHMx3kFRVVaGiogLff/89YmJimjhCIiLbYEJIRHajqqoK/v7+yMvLu2u9Nm3aIDc3F61atWqiyIiIbIvPEBKR3XBwcMDo0aOhVCprrKNUKjFmzBgmg0RkV5gQEpFdeeqpp3Dz5s0at9+8eRNPPfVUE0ZERGR77DImIrvTsWNHnDt3zuy2oKAgnDt3DgqFoomjIiKyHd4hJCK7M3bsWDg5OZmUOzk54dlnn2UySER2h3cIicjuHD9+HP/1X/9ldtvhw4cRGhraxBEREdkW7xASkd0JCQlBaGioyZ3A7t27MxkkIrvEhJCI7NIzzzxjNJLYyckJf/7zn20YERGR7bDLmIjs0vnz53HfffdB/xWoUChw+vRpdOzY0baBERHZAO8QEpFdCgoKQt++feHg4AAHBwf07duXySAR2S0mhERkt8aOHQuFQgEHBweMHTvW1uEQEdkMu4yJyG7l5+fD398fAJCTkwNfX18bR0REZBsWJ4SpqalITExs7HiIiIiIqAGsW7cOI0eOtKiuY10aJyK6V/z8889QKBSIiopqtGNkZGTgww8/5PdnE1i4cCEA4K9//auNIyGyLWtv4lmdEFqaaRIRtQRDhgwBAHh4eDTqcT788EN+fzaB//znPwD4W0XU6AkhEdG9pLETQSKiloCjjImIiIjsHBNCIiIiIjvHhJCIiIjIzjEhJCIi+l/nzp3DsGHDUFRUhPz8fCgUCsMrLCwMZWVlJvtUr6dQKNCnTx8bRN/wKioqsHDhQoSHh8PDwwO+vr4YMmQI0tLSUNOsdd988w2Cg4Ph6NjwwxSaSzwigl27duGVV15BcHAwnJ2d4evri8jISKxevdoklmvXrmHx4sWIjo5G69atoVKp0LVrV4wePRoHDhwwaf/1119v8lkJmBASEbUg169fR9euXTF06FBbh3LPycrKQp8+fTBo0CB4enqiTZs2EBFkZmYatk+cONFkP329jIwM+Pj4QESwd+/epg6/wZWUlCA6OhrLly/HwoULceXKFezduxfu7u4YNmwYjhw5YlT/1KlTGDZsGKZPn47Lly/f0/GcOHECkZGRyM7Oxvr166HT6bBnzx506NABY8eOxZQpU4zqT5kyBePHj0dcXByOHj2KgoICLFu2DFlZWQgPD8fGjRuN6r/wwguYPn063nzzzQaN+26YEBIRtSAigqqqKlRVVdk6lFq5u7sjMjLS1mFYpKioCE888QT+9Kc/4dVXXzXZ7uzsDB8fHyxZsgRffvmlDSJselOmTMHBgwfx3Xff4dFHH4VKpUKHDh2wfPlyODs7m9R/88038fDDD2Pfvn2NMnq/ucXj6OiI1NRU9OrVCy4uLujcuTOWL18OHx8f/Otf/0J5eblR/eeeew4TJkyAv78/XF1dERUVhTVr1uDWrVuYOnWqUd0uXbpgw4YNmDt3LlJTUxs8drPvp0mOQkREDcLDwwOnTp2ydRj3nPfffx+5ubl46623zG53cXHBF198gccffxxJSUkIDw9HcHBwE0fZdC5fvoylS5fixRdfhJ+fn9E2Nzc3s13n//73v6FSqewinpCQEFRUVJiUK5VKBAUFISsrC2VlZYZENSUlxWw7Go0GKpUKp06dgohAoVAYbRsxYgRee+01xMfHN0oX/J14h5CIiOyaiCAlJQV9+/ZFu3btaqyn1WrxxhtvoLi4GAkJCWaTkHvF119/jVu3bll1h7exkq/mGE9NCgsLcfLkSYSFhUGtVtdav6SkBKWlpejRo4dRMqg3fPhwXLhwAVu2bGmMcI0wISQiaiE2btxoNHBBn5BULz979iwSExPh5eUFHx8fDB061Oiu4oIFCwx127dvj8zMTMTExMDDwwOurq4YMGAAdu3aZag/Z84cQ/07f5C3bdtmKG/Tpo1J+yUlJdi1a5ehTmPf4airAwcO4PLly9BoNLXWffvttzFo0CAcPHgQ48ePt6j9goICTJo0CV26dIFSqYS3tzeGDBmCH3/80VDH2s9QLy8vD8nJyejYsSOUSiXatm2L+Ph4ZGVlWX4CzPjtt98AAN7e3njttdcQFBQEpVKJ++67D8nJybh69Wq92m/p8VRXVFSEXbt2YdiwYfD398fKlSst2k+/ss7MmTPNbu/duzcA4Ntvv22YQO9GLLRu3TqxojoREf2vhv7+jIuLEwBSWlpqtjwuLk52794t169fl+3bt4tKpZIHH3zQpB2NRiNubm4SERFhqJ+ZmSm9evUSpVIpO3bsMKrv5uYmjzzyiEk74eHh4uPjY1JeU329AQMGSOvWrSUjI8PSt16rESNGyIgRI6zaZ9WqVQJA3nvvPbPbMzMzRa1WG/7Oy8uToKAgASCrV682lGdkZJich0uXLkmnTp3Ez89P0tLSRKfTyYkTJyQ+Pl4UCoV89tlnRvWt+QxzcnLkvvvuEz8/P9myZYsUFxfL4cOHpV+/fuLi4iK7d++26jyYi8Pf319Gjx4tp06dkmvXrsmKFSvEzc1NgoODpbCwsMb9AwMDpVWrVnU+fnOP506zZ88WAAJA+vfvLwcPHrRov9zcXPHz85Nx48bVWEen0wkAiYqKsjouALJu3TqL6/MOIRHRPWbcuHGIiIiAm5sbBg4ciNjYWGRmZiI/P9+kbklJCT755BND/T59+mD16tW4efMmJkyY0KhxVlVVQURqnC6kqVy6dAkALOriA26PKk5NTYWTkxOSkpJw/PjxGutOnz4dZ86cwYcffoihQ4fC09MTwcHBWLNmDQICApCcnGx2BKwln+H06dNx7tw5fPDBB3j88cfh7u6O0NBQrF27FiJi8R1Mc/R3n1UqFZYvX47OnTvDy8sLzzzzDKZPn47s7Gz84x//qHP7LT2eO73xxhsoLy/HsWPHEBISgrCwMMyePfuu+xQUFGDw4MHo378/Fi9eXGM9T09PKBQKwzXamJgQEhHdYx588EGjv4OCggAAOTk5JnXd3NwM3VJ6PXv2RLt27XDgwIFG/SHasWMHrl69ioiIiEY7hiX0yYaTk5PF+zz00ENYsGABSkpKkJCQgNLSUrP1NmzYAACIjY01Knd2dkZMTAxKS0vNdgda8hlu3LgRDg4OJlMQ+fv7IzQ0FPv27cOFCxcsfk93cnNzAwAMHDjQpKv/iSeeANBE3ZjNNJ7qlEolQkJC8Omnn2LYsGF466238P3335utW1JSAq1Wi+7du+OLL75Aq1at7tq2o6NjjddXQ2JCSER0j6l+p0upVAKA2alqvLy8zLbh6+sLALhy5UoDR9f8uLi4AIDZUaN3k5ycjMTERBw+fNjsVDXl5eXQ6XRwcXExO+2JfrRsbm6uybbaPkN921VVVVCr1SYTY+ufuTt58qRV70mvY8eOAAAfHx+TbfprIy8vr05t3wvx3I0+Qd28ebPJtsrKSiQkJCAwMBArVqyoNRnU79MUA2SYEBIR2bGCggKzXbb6RFD/YwsADg4OuHnzpkndwsJCs22bGzXZHAUEBAAAdDqd1fumpKSgW7duWLZsGVatWmW0zdnZGWq1GmVlZSguLjbZV99V7O/vb/VxnZ2d4eXlBUdHR1RUVBi63qu/BgwYYHXbAAyDh8zdIdZfG9Wnf2lMzS2eu9FPNWNuoEtSUhLKy8uRmppqdKfz/vvvx549e0zqFxUVu7zLHAAAIABJREFUQUQM12hjYkJIRGTHysrKDCtx6B06dAg5OTnQaDRGP0QBAQG4ePGiUd3c3Fz88ccfZtt2dXU1SiC7deuGpUuXNmD0DaNHjx4AUKfuVXd3d3z11Vdwc3PDJ598YrJ9+PDhAGAybUh5eTnS09OhUqmg1WrrEDUQHx+PyspKoxHhevPnz0eHDh1QWVlZp7Yff/xxBAYGYtu2bSbT66SlpQEAnnzyyTq1fS/EM3nyZIwZM8bstq1btwIw7fafNWsWjhw5gk2bNpmdSNsc/b83/TXamJgQEhHZMbVajRkzZiAjIwMlJSXYu3cvxowZA6VSiY8++sio7qBBg5CTk4N//etfuH79Ok6dOoUJEyYY3UW80wMPPIDs7GycP38eGRkZOH36NKKiogzbo6Oj4ePjY/bOSFPSaDTw9fU1u6asJUJDQ7FkyRKz2+bNm4dOnTph4sSJ2Lx5M4qLi5GdnY2nn34aly5dwkcffVTnO1vz5s1Dly5d8Nxzz2Hr1q3Q6XS4evUqlixZgnfffRcLFiwwugs1ZswYKBQKnDlzpta2nZ2dkZKSgoKCAowaNQonT55EYWEhVq1ahXnz5qFv375ITk6uU9z3QjwAsGbNGrz77rs4e/YsysvLcfbsWUybNg2rV69GeHg4xo0bZ6i7fPlyvPPOO/jll1/g4eFh0sVf02Tz+umDBg0aVK/3ZhFLhyNz2hkiorppqO/PDRs2GKa30L9Gjx4tGRkZJuUzZ84UETEpj42NNbSn0WgkMDBQjh49KlqtVjw8PESlUkm/fv1k586dJscvLCyUcePGSUBAgKhUKomMjJTMzEwJDw83tD9t2jRD/ePHj0tUVJS4ublJUFCQLFq0yKi9qKgo8fb2rtf0KNXVZdoZEZEZM2aIo6OjXLx40VCWl5dncv7Cw8NrbOOll14yO/1Ofn6+TJw4UTp16iROTk6iVqtFq9VKenq6oU5dP8OCggKZNGmSdO7cWZycnKRt27YyaNAg2b59u0kc0dHR4u7uLpWVlRafl927d4tWqxW1Wi1KpVJCQkJk1qxZcuPGDZO6aWlpJrHqX9Wn12np8eh0OklJSRGtVisdO3YUpVIp7u7uEh4eLvPmzTOJJzY2tsZY9C9z0y8lJCRIYGCg3Lx50+JzpAcrp51hQkhE1Mia6/enPiG8l9Q1ISwsLJTAwEBJSkpqhKhs79q1a6JSqe46511TYjy1y8rKEoVCIV9++WWd9rc2IWSXcQtQfVWB5uDatWtYvHgxoqOj0bp1a6hUKnTt2hWjR4+2uNtl7dq1hvelH+VXV9988w2Cg4MbbCWE69evm9zSz8jIqHW/KVOmGO0zZ86cBomnJu7u7iZxKhQKODg4oG3btnjyySdNng9raPfC9WnuPDo4OMDb2xsajQYvv/wy9u3bZ4N3Qk1FrVYjLS0N69evx6JFi2wdToMSESQnJ8PT07PW+fEYj+3jAYDTp08jPj4e06dPx6hRo5rmoJZmjs31f7j2xNz/5ouLi+X+++836kJoCs8//7w4OjrKhx9+KJcuXZKSkhL5+eefpXv37tKqVSvZsGGDxW3FxMSIs7NzneL4/fff5YknnpBevXqJp6dng89Ev3//fsPt/CFDhty1bn5+vri7uxu68ZqKPsa4uDhDWWFhofzP//yP+Pr6ipOTk9nuo4bW0q/P6uexsrJScnNzZePGjTJgwAABIM8++6yUlJRYHU9z/f7kHUJTZ86ckdjYWNHpdA0YlW1dunRJHnnkETl8+LCtQxERxmOJqVOn1vnOoB54h9C+iAiqqqrMzi/W2J577jlMmDAB/v7+cHV1RVRUFNasWYNbt25h6tSpTRLDm2++iYcffhj79u0zO89XQ1CpVLjvvvuwdetW7N27t8Z6CxcuNEwea2tqtRrDhw/HBx98gIqKCkycONEmcbTk67NVq1bw8/NDXFwcfvjhB0ydOhXLly/HU089ZfOVNepLf1f3wIEDuHjxIhQKBd544w1bh9UsdOzYEZs3b4anp6etQ2kw/v7+2LlzJ0JDQ20dCgDGY4n58+c33Z3B/9U8Vxoni3l4eNQ4OqkxpaSkmC3XaDRQqVQ4deoURKTR5yH797//3egTdjo4OOD111/HSy+9hDlz5mDjxo0mdQoLC/Hpp5/igw8+wLPPPtuo8VhDPwfZkSNHUFhYWOMkxI3lXro+//a3v+Gnn37C119/jbVr1+Kpp55qqHCb3OTJkzF58mRbh0FEzQjvEFKDKikpQWlpKXr06NEkk9I2xeztAPCXv/wFgYGB+Prrr3Hw4EGT7f/85z/x+OOPo0uXLk0Sj6XuvJPVUiYJbkz1uT4VCoVhNQpz880REbVkjZYQbty40egB7XPnziExMREeHh7w8fHB2LFjce3aNZw9exZPPPEEPDw8EBAQgBdeeMFkRvfKykqsW7cOjz32GPz9/aFSqdCzZ0989NFHRl1RkZGRRsfUTxo5cOBAo/KaZtWvrvrD8pmZmYiJiYGHhwdcXV0xYMAAsxOCFhQUYNKkSejSpQuUSiW8vb0xZMgQ/Pjjj/WqW9s51k/WWb387NmzSExMhJeXF3x8fDB06FCzd22OHz+OJ598Emq1Gq6urvjv//5vbN682ej83Tmvkjn/+c9/AAAzZ868a/tubm6IiorCzp07a32fzYGzszOmTJkCEcHcuXONtl2/fh0ff/wxZsyYUeP+trqGd+zYAeD2PGn6pbB4fZq/Pi2hXy1hz549Vi9zRkTUrFn6sGFdH4qOi4sTABIfHy979+6V69evy8qVKw0P6cfFxcn+/fuluLhYFi9eLADkr3/9q1Eb+rmE3nvvPbl69ark5eXJP//5T3FwcJDJkycb1c3KyhI3NzfRaDRy/fp1EREpKyuTvn371vkBTY1GI25ubhIRESG7d++W69evS2ZmpvTq1UuUSqXs2LHDUPfSpUvSqVMn8fPzk7S0NNHpdHLixAmJj48XhUJhNPeRNXX1cZh7AFx/jktLS82Wx8XFGeLevn27qFQqefDBB43qnjx5Ury8vCQwMFC+++47KS4ulsOHD8vAgQOlbdu2Fg36yM3NFT8/P7PD9s21f/DgQRk0aJB07NixzoNK7hQYGFjroJIBAwZI69atzc73ZM7+/fvFzc1NRERu3Lghfn5+4uDgIEePHjXU+dvf/iYjR44UEZH/9//+n9lBJY15DZsbVKLT6cwOKuH1af76rOk8VldaWmoYZJSTk1PrMfWa66CSe1F9B5UQ3SvQ3OYh1H/pb9myxag8NDRUAMhPP/1kVN6pUyfp1q2bUVlaWpr079/fpO0xY8aIk5OTyWiw1NRUQxJaVVUlf/7zn2XGjBlWx66n0WgEgOzfv9+o/ODBgwJANBqNoezZZ58VACY/3GVlZdKuXTtRqVSSm5trdV19HHX5wU1LSzMqHzFihACQvLw8Q1lCQoIAkPXr1xvVvXLliri6utb6g5ufny+9e/eWxMREs5N61tT+xYsXxdnZuckSwn79+lk1Ee6dCaGIyPz58wWAjBkzRkRESkpKxM/PTw4cOCAid08IG+savnMktP6lUCjEx8dHhg0bJr/++quhLq9P89eniGUJ4Y0bN5gQNnNMCIluszYhbLJBJX369DH6u127djhy5IhJeWBgoMk8YUOHDsXQoUNN2tRoNFi9ejWOHDmCiIgIQ3lCQgJmzpyJuXPnIjIyEq1bt8ayZcvqFb+bmxt69+5tVNazZ0+0a9cOBw4cwKVLlxAQEIANGzYAAGJjY43qOjs7IyYmBqtWrcK3336LZ555xqq69VF9PUX9SNicnBy0adMGALBt2zYAMFlTs23btggJCcGRI0dqbL+kpARarRbdu3fHypUr0apVK5M6NbXfrl07BAcHIzs728p3VTf6LtS6evnll/H+++/jyy+/xNtvv420tDQ89NBD6NWr1133a4prOC4uzuyAlzvx+jR/fVrq0qVLAAAnJydDbNZITU2t87HJMvr1iHmuiazTZAlh9SH8Dg4OaNWqFVxdXY3KW7VqZTJFhU6nwz/+8Q9s2LABFy5cMHl+6saNGybHmz17Nr7//nvs3r0bK1asgIND/R6XrGl0pq+vL3JycnDlyhW0bt0aOp0OLi4uZqdA0a9XmZubi/Lycovr1pf+2TE9pVIJAIbzXF5ejuLiYri4uMDd3d1kf29v7xrbrqysREJCAgIDA7FixQqzP7a1te/r69tkCWF9ubu7Y+LEiXjzzTfx9ttvY8eOHdi0aVOt+zWHa9iaa86erk9r6J95jYiIgJOTk9X7JyYm1uv4ZDmeayLrtIhRxk888QRmz56NF154AdnZ2aiqqoKIYOHChQBgdk6wHTt2QKfToWfPnnj55ZfrvGi5XkFBgdnjXLlyBcDtpMbZ2RlqtRplZWUmA2MA4PLlywBuz3lkTd3G5uzsDA8PD5SVleH69esm2/Xv0ZykpCSUl5cjNTXVaJWQ+++/37BgfW3tX716tQHeRdMZP3481Go11qxZA41GY3KX25zmcA3z+jR/fVqqqqrKsILFK6+8YtW+enL7MR2+GvE1YsQIjBgxwuZx8MWXrV/WavYJ4a1bt7Br1y74+/sjOTkZbdu2NUwXUVpaanafM2fO4Pnnn8dXX32Fr7/+GiqVCnFxccjLy6tzHGVlZSZLgB06dAg5OTnQaDQICAgAAAwfPhwAsGXLFqO65eXlSE9Ph0qlMnR7WVO3sQ0ZMgTA/3XN6eXm5tZ4927WrFk4cuQINm3aBGdn5zq1n5+fjxMnTtQ1bJtQq9WYNGkS1Gq1RZP5NpdrGOD1WR/Tp0/Hr7/+iuHDhyMhIaHe7RERNStiofoOKqn+QLlWqzU7AKBfv35GD/GLiERHRwsAef/99yUvL09u3LghP/zwg3To0EEAGC3LVVxcLL169ZJNmzYZynbs2CFOTk7y6KOPys2bN61+DxqNRtRqtcTExFg9yrioqMhoZObSpUvrVFcfR10e2q9ePm3aNJNBMr///ru0bt3aaBTnoUOHZPDgwXLfffeZPLT/+eefmwxkqP66cySvufaPHDkiWq1WfH19W8QoY0vUNKikMa9hSwZD6PH6NH99mjuPt27dksuXL8vGjRsNn99zzz0nN27cqPU8V8dBJU2Hg0qIbkNzGWWckZFh8gU8c+ZMyczMNCmfN2+e4Yf0ztfbb78tIiJ5eXmSlJQkQUFB4uTkJH5+fvLss8/K66+/bqgbHh4ur7zyitH+hw4dkry8PJN2Z8+ebfH7EPm/H7qjR4+KVqsVDw8PUalU0q9fP9m5c6dJ/fz8fJk4caJ06tRJnJycRK1Wi1arlfT09DrV/fvf/272XG7YsMGkfPTo0TWeexExKb9zjdkTJ07Ik08+KZ6enuLq6ioPP/yw/PTTT9K/f39xdXU1ijs2NtbqH9w729dPLbJ582aJiYkx7PP8889b9dnop3Mx96o+LYqISFRUlMWjjN3c3Iza02q1d61vLoaPP/5YRBrvGq4eIwCTUfrV8fo0vT7NnUeFQiFqtVp69uwpL730kuzbt++u5/VumBA2HSaERLdZmxAq/nenWqWmpiIxMbFO/dItXe/evZGfn28YvWZvQkJCUFpainPnztk6FCITLeH6tOfvz6am787XT0JOZK8UCgXWrVuHkSNHWlS/2T9DSE0jNzcXrVu3Nll94ezZszh16hSio6NtFBkRr08iosbGhJAMrl27hqSkJJw/fx43btzAr7/+isTERHh6euLNN9+0dXhk53h9UlM4d+4chg0bhqKiIuTn5xstsxgWFmZYgvFO1espFAqLZh9ozkQEu3btwiuvvILg4GA4OzvD19cXkZGRWL16dY13u7OyshAbGwsvLy94eHhg4MCBZpd4tbd4rl27hsWLFyM6OhqtW7eGSqVC165dMXr0aLMzSLz++utYt25dveO0ht0mhNX/8Zp7ubu7Q6FQ4MCBA7h48SIUCoVFo0pbIn9/f3z//fcoLCzEo48+Cm9vbwwbNgxdu3bFr7/+is6dOzdZLJZ8NrNmzWqyeMj2mtP1SfeurKws9OnTB4MGDYKnpyfatGkDETHMMJGVlYWJEyea7Kevl5GRAR8fH4gI9u7d29ThN6gTJ04gMjIS2dnZWL9+PXQ6Hfbs2YMOHTpg7NixmDJlisk+v/zyCx5++GF4eHjg2LFjOHPmDDp37oz+/fvju+++s+t4pkyZgvHjxyMuLg5Hjx5FQUEBli1bhqysLISHh5ssKvDCCy9g+vTpTfufXUsfNuRD0UREddMcvz/d3NzkkUceueeOX9dBJTqdTtq3by9JSUkm2zIzM8XZ2Vl8fHwEgKxZs8ZsGxkZGeLj42P1sZujY8eOiaOjo1y9etWovLy8XHx8fMTZ2VnKysoM5bdu3ZLQ0FAJCAgwGolfWVkp3bp1k6CgIKP69hbP888/Ly+++KJJO1lZWQJAunbtanabQqGwamDInWDloBK7vUNIRESk9/777yM3NxdvvfWW2e0uLi744osv4ODggKSkpBazulJdhYSEoKKiwmQlIKVSiaCgIJSXlxt1n//88884cuQIRowYAZVKZShv1aoVnnrqKZw/fx6bN2+223hSUlKwZMkSk3Y0Gg1UKhVOnTpl0s2s0WgwYsQIvPbaa6isrKxzrJZiQkhERHZNRJCSkoK+ffuiXbt2NdbTarV44403UFxcjISEBLPPE97rCgsLcfLkSYSFhRktO/nDDz8AgNlnJ/Vl6enpdhtPTUpKSlBaWooePXoYFiy40/Dhw3HhwgWTBQIaAxNCIqJmqqCgAJMmTUKXLl2gVCrh7e2NIUOG4McffzTUmTNnjuHZ2sjISEP5tm3bDOVt2rQxlC9YsAAKhQIlJSXYtWuXoY5+aT/9doVCgfbt2yMzMxMxMTHw8PCAq6srBgwYYPRQfkMf3xYOHDiAy5cvQ6PR1Fr37bffxqBBg3Dw4EGMHz/eovYt+Rw3btxo9Jz02bNnkZiYCC8vL/j4+GDo0KE4deqUSdt5eXlITk5Gx44doVQq0bZtW8THxyMrK8vyE2CBoqIi7Nq1C8OGDYO/vz9WrlxptP348eMAgPbt25vsGxgYCAANele1pcVTE/30SDNnzjS7vXfv3gCAb7/9tmECvRtL+5ab4zMwREQtQV2+P6uvFKPT6YxWiqk+8XpNz+SFh4ebfa6ttmf4NBqNuLm5SURERK0rNDXG8a1dVUivLs8Qrlq1SgDIe++9Z3Z7ZmamqNVqw995eXkSFBQkAGT16tWGcnPPEFr7OepX8ImLizOc9+3btxsm879TTk6O3HfffeLn5ydbtmyR4uJiOXz4sPTr109cXFwsmoDfErNnzzZM2N6/f385ePCgSZ3HHntMAMiePXtMtp08eVIAyAMPPGC38ZiTm5srfn5+Mm7cuBrr6HQ6ASBRUVFWxwU+Q0hE1PJNnz4dZ86cwYcffoihQ4fC09MTwcHBWLNmDQICApCcnIzLly83agwlJSX45JNPEBERATc3N/Tp0werV6/GzZs3MWHChEY9dlVVFeT2alqNehwAuHTpEgBY1MUH3B5VnJqaCicnJyQlJRnuRplT189x3LhxhvM+cOBAxMbGIjMzE/n5+UZtnzt3Dh988AEef/xxuLu7IzQ0FGvXroWIWHwHszZvvPEGysvLcezYMYSEhCAsLAyzZ8+2eH/9Z2iuS9Re4ykoKMDgwYPRv39/LF68uMZ6np6eUCgUhmu0MTEhJCJqhjZs2AAAiI2NNSp3dnZGTEwMSktLG70byc3NzdBlpdezZ0+0a9cOBw4caNQfqR07duDq1auIiIhotGPo6Z8FdHJysnifhx56CAsWLEBJSQkSEhJQWlpqtl5dP8cHH3zQ6O+goCAAQE5OjqFs48aNcHBwwNChQ43q+vv7IzQ0FPv27WuwFbaUSiVCQkLw6aefYtiwYXjrrbfw/fffG7Z7eXkBuP2fiOr0Zfo69hhP9eNrtVp0794dX3zxBVq1anXXth0dHWu8vhoSE0IiomamvLwcOp0OLi4u8PDwMNnu5+cH4PYKLo2pph9MX19fAMCVK1ca9fhNxcXFBQBMVsKpTXJyMhITE3H48GG8+uqrJtvr8zlWv1upVCoB3L5zemfbVVVVUKvVJnO1/vbbbwCAkydPWvWeLPHEE08AgNEo3ZCQEAAwm4BevHgRABAcHNzgsbSUePQqKyuRkJCAwMBArFixotZkUL/PnSOlGwsTQiKiZsbZ2RlqtRplZWUoLi422a7vYvT39zeUOTg44ObNmyZ1CwsLzR7Dku6ygoICs122+kRQnxg21vGbSkBAAABAp9NZvW9KSgq6deuGZcuWYdWqVUbb6vI5WsrZ2RleXl5wdHRERUWFoXu9+mvAgAFWt23JsQHg6tWrhjL9cfbt22dSX18WExPT4LG0lHj0kpKSUF5ejtTUVKOBVPfffz/27NljUr+oqAgiYrhGGxMTQiKiZmj48OEAYDLdRHl5OdLT06FSqaDVag3lAQEBhjsferm5ufjjjz/Mtu/q6mqUwHXr1g1Lly41qlNWVmZYpUPv0KFDyMnJgUajMfqRaozjN5UePXoAMH83qTbu7u746quv4Obmhk8++cRku7WfozXi4+NRWVlpdim2+fPno0OHDnWev27y5MkYM2aM2W1bt24FYNyt3a9fP3Tv3h3r1683mo7n1q1bWLt2LYKCgky6ze0pHgCYNWsWjhw5gk2bNhmSxtro/03pr9FGZenoE44yJiKqm4YYZVxUVGQ0OnXp0qVG9V999VUBIB9//LEUFxfL77//LiNHjpTAwECzo3wHDx4sarVa/vjjD9m9e7c4OjrK0aNHDds1Go2o1WqJiYmxaJRxQx+/KUcZV1VVia+vb42jnquPMjZn9erVAqDWUca1fY76UcalpaVG5dOmTRMAsn//fkPZ5cuXpUuXLtK5c2f55ptvpLCwUAoKCmTx4sXi6upqMsJ09OjRAkBOnz5d6zl57bXXRKFQyDvvvCNnzpyRsrIyOXPmjEydOlUASHh4uNEKICK3R1m7uLjIqFGj5NKlS5Kfny9JSUni6Ogo27ZtMzmGPcXz+eefG0Yi1/Qyd62vWbNGAMiGDRtqjak6WDnKmAkhEVEjq+v3Z35+vkycOFE6deokTk5OolarRavVSnp6ukndwsJCGTdunAQEBIhKpZLIyEjJzMyU8PBwww/OtGnTDPWPHz8uUVFR4ubmJkFBQbJo0SKj9jQajQQGBsrRo0dFq9WKh4eHqFQq6devn+zcubPRjx8VFSXe3t5WT51S16XrZsyYIY6OjnLx4kVDWV5ensmPdnh4eI1tvPTSS2aTX0s+x4yMDJNjzZw5U0TEpDw2NtawX0FBgUyaNEk6d+4sTk5O0rZtWxk0aJBs377dJI7o6Ghxd3eXysrKWs+HTqeTlJQU0Wq10rFjR1EqleLu7i7h4eEyb948k+RL77fffpMhQ4aIp6enuLu7S3R0tNnrxd7iiY2NrVNCmJCQIIGBgXLz5s1aY6qOCSERUTPTEr8/9QlhS1PXhLCwsFACAwPNrmV8L7h27ZqoVKq7znnXlBhP7fRrGX/55Zd12t/ahJDPEBIRkd1Tq9VIS0vD+vXrsWjRIluH06BEBMnJyfD09LRqvj7GYzunT59GfHw8pk+fjlGjRjXJMZkQEhERAQgLC8PevXuxdetWFBUV2TqcBnP58mWcPn0a6enpdRrRzHia3pIlSzB37lzMnTu3yY5pu8UjiYio2VmwYAGmTJli+FuhUGDmzJmYM2eODaNqOh07djQ7f1xL5u/vj507d9o6DAPGU7v58+c3+TGZEBIRkcHkyZMxefJkW4dBRE2MXcZEREREdo4JIREREZGdY0JIREREZOeYEBIRERHZOasHlSQkJDRGHERE9yz9Grn8/mx8e/bsAcBzTWQtxf/OZl2rjIwMfPDBB40dDxFRkzp06BAAoGfPnjaOhIioYU2aNAkREREW1bU4ISQiuheNHDkSAJCammrjSIiIbIfPEBIRERHZOSaERERERHaOCSERERGRnWNCSERERGTnmBASERER2TkmhERERER2jgkhERERkZ1jQkhERERk55gQEhEREdk5JoREREREdo4JIREREZGdY0JIREREZOeYEBIRERHZOSaERERERHaOCSERERGRnWNCSERERGTnmBASERER2TkmhERERER2jgkhERERkZ1jQkhERERk55gQEhEREdk5JoREREREdo4JIREREZGdY0JIREREZOeYEBIRERHZOSaERERERHaOCSERERGRnWNCSERERGTnmBASERER2TkmhERERER2jgkhERERkZ1jQkhERERk55gQEhEREdk5hYiIrYMgImoKK1euxAcffIBbt24ZyvLz8wEAbdq0MZS1atUKkyZNwjPPPNPkMRIR2QITQiKyG9nZ2ejWrZtFdU+cOIHg4OBGjoiIqHlglzER2Y3g4GBoNBooFIoa6ygUCmg0GiaDRGRXmBASkV155pln0KpVqxq3Ozo64s9//nMTRkREZHvsMiYiu5KTk4OgoCBUVVWZ3a5QKHD+/HkEBgY2cWRERLbDO4REZFfatWuHhx9+GA4Opl9/Dg4OeOSRR5gMEpHdYUJIRHZn7NixZssVCgVHFhORXWKXMRHZnWvXrsHPzw8VFRVG5Y6OjsjNzYWPj4+NIiMisg3eISQiu+Pt7Y3HHnvMaHBJq1atoNVqmQwSkV1iQkhEdmnMmDFGA0tEBGPGjLFhREREtsMuYyKySzdu3ICPjw/KysoAAC4uLsjPz4ebm5uNIyMianq8Q0hEdsnV1RXDhw+Hk5MTnJycMHz4cCaDRGS3mBASkd16+umnUVFRgYqKCjz99NO2DoeIyGYcbR1AS5aRkYHz58/bOgwiqqNbt27B1dUVIoKioiKkpqbaOiQiqqOgoCBERETYOowWi88Q1kNCQgLWr19v6zCIiIjs3ogRI/AvkgVdAAAgAElEQVSf//zH1mG0WLxDWE+8AIlMKRQKrFu3DiNHjrR1KLX66aefoFAo8Oijj9o6FKulpqYiMTER/H892buEhARbh9DiMSEkIrsWFRVl6xCIiGyOCSER2TVzaxoTEdkbfhMSERER2TkmhERERER2jgkhERERkZ1jQkhEZKfOnTuHYcOGoaioCPn5+VAoFIZXWFiYYVm/O1Wvp1Ao0KdPHxtE33BEBLt27cIrr7yC4OBgODs7w9fXF5GRkVi9enWNo7izsrIQGxsLLy8veHh4YODAgdi1a5fdx3Pt2jUsXrwY0dHRaN26NVQqFbp27YrRo0fjwIEDJu2//vrrWLduXb3jpPphQkhEzdb169fRtWtXDB061Nah3HOysrLQp08fDBo0CJ6enmjTpg1EBJmZmYbtEydONNlPXy8jIwM+Pj4QEezdu7epw29QJ06cQGRkJLKzs7F+/XrodDrs2bMHHTp0wNixYzFlyhSTfX755Rc8/PDD8PDwwLFjx3DmzBl07twZ/fv3x3fffWfX8UyZMgXjx49HXFwcjh49ioKCAixbtgxZWVkIDw/Hxo0bjeq/8MILmD59Ot588816xUn1JFRnI0aMkBEjRtg6DKJmB4CsW7eu3u0UFRVJ586dZciQIQ0QVeNyc3OTRx55pEmPuW7dOqnL17hOp5P27dtLUlKSybbMzExxdnYWHx8fASBr1qwx20ZGRob4+PhYfezm6NixY+Lo6ChXr141Ki8vLxcfHx9xdnaWsrIyQ/mtW7ckNDRUAgIC5MaNG4byyspK6datmwQFBRnVt7d4nn/+eXnxxRdN2snKyhIA0rVrV7PbFApFnb83+Htcf7xDSETNloeHB06dOoVvvvnG1qHcU95//33k5ubirbfeMrvdxcUFX3zxBRwcHJCUlITs7OwmjrBphYSEoKKiAt7e3kblSqUSQUFBKC8vN+o+//nnn3HkyBGMGDECKpXKUN6qVSs89dRTOH/+PDZv3my38aSkpGDJkiUm7Wg0GqhUKpw6dcqkm1mj0WDEiBF47bXXUFlZWedYqe6YEBIR2RERQUpKCvr27Yt27drVWE+r1eKNN95AcXExEhISzD5PeK8rLCzEyZMnERYWBrVabSj/4YcfAMDss5P6svT0dLuNpyYlJSUoLS1Fjx49oFAoTLYPHz4cFy5cwJYtWxo8VqodE0IiapY2btxoNHBBn5BULz979iwSExPh5eUFHx8fDB06FKdOnTK0s2DBAkPd9u3bIzMzEzExMfDw8ICrqysGDBhg9OD9nDlzDPUjIyMN5du2bTOUt2nTxqT9kpIS7Nq1y1DH0bF5zvt/4MABXL58GRqNpta6b7/9NgYNGoSDBw9i/PjxFrVfUFCASZMmoUuXLlAqlfD29saQIUPw448/GupY+xnq5eXlITk5GR07doRSqUTbtm0RHx+PrKwsy0+ABYqKirBr1y4MGzYM/v7+WLlypdH248ePAwDat29vsm9gYCAANOhd1ZYWT030y7zOnDnT7PbevXsDAL799tuGCZSsY+s+65aMzywQmYcGeoZQRCQuLk4ASGlpqdnyuLg42b17t1y/fl22b98uKpVKHnzwQZN2NBqNuLm5SUREhKF+Zmam9OrVS5RKpezYscOofk3PBIaHh5t9dq62ZwgHDBggrVu3loyMDEvfeq3q8gzhqlWrBIC89957ZrdnZmaKWq02/J2XlydBQUECQFavXm0oN/cM4aVLl6RTp07i5+cnaWlpotPp5MSJExIfHy8KhUI+++wzo/rWfIY5OTly3333iZ+fn2zZskWKi4vl8OHD0q9fP3FxcZHdu3dbdR5qMnv2bAEgAKR///5y8OBBkzqPPfaYAJA9e/aYbDt58qQAkAceeMBu4zEnNzdX/Pz8ZNy4cTXW0el0AkCioqKsjou/x/XHO4RE1KKNGzcOERERcHNzw8CBAxEbG4vMzEzk5+eb1C0pKcEnn3xiqN+nTx+sXr0aN2/exIQJExo1zqqqKohIjVOGNJVLly4BgEVdfMDtUcWpqalwcnJCUlKS4W6UOdOnT8eZM2fw4YcfYujQofD09ERwcDDWrFmDgIAAJCcn4/Llyyb7WfIZTp8+HefOncMHH3yAxx9/HO7u7ggNDcXatWshIhbfwazNG2+8gfLychw7dgwhISEICwvD7NmzLd5f//ma6xK113gKCgowePBg9O/fH4sXL66xnqenJxQKheEapabFhJCIWrQHH3zQ6O+goCAAQE5OjkldNzc3Q7eUXs+ePdGuXTscOHCgUX+IduzYgatXryIiIqLRjmEJfde7k5OTxfs89NBDWLBgAUpKSpCQkIDS0lKz9TZs2AAAiI2NNSp3dnZGTEwMSktLzXYHWvIZbty4EQ4ODiZTEPn7+yM0NBT79u3DhQsXLH5Pd6NUKhESEoJPP/0Uw4YNw1tvvYXvv//esN3LywvA7f9gVKcv09exx3iqH1+r1aJ79+744osv0KpVq7u27ejoWOP1RY2LCSERtWjV73QplUoAt+/IVVfTj6Kvry8A4MqVKw0cXfPj4uICAKioqLBqv+TkZCQmJuLw4cN49dVXTbaXl5dDp9PBxcUFHh4eJtv9/PwAALm5uSbbavsM9W1XVVVBrVabTIz922+/AQBOnjxp1XuyxBNPPAEARqN0Q0JCAMBsAnrx4kUAQHBwcIPH0lLi0ausrERCQgICAwOxYsWKWpNB/T53jpSmpsOEkIjsRkFBgdkuW30iqE8MAcDBwQE3b940qVtYWGi27YbqkmtsAQEBAACdTmf1vikpKejWrRuWLVuGVatWGW1zdnaGWq1GWVkZiouLTfbVdxX7+/tbfVxnZ2d4eXnB0dERFRUVhq736q8BAwZY3bYlxwaAq1evGsr0x9m3b59JfX1ZTExMg8fSUuLRS0pKQnl5OVJTU40GWd1///3Ys2ePSf2ioiKIiOEapabFhJCI7EZZWZlhJQ69Q4cOIScnBxqNxuiHKCAgwHB3RS83Nxd//PGH2bZdXV2NEshu3bph6dKlDRh9w+jRowcA83eTauPu7o6vvvoKbm5u+OSTT0y2Dx8+HABMpg0pLy9Heno6VCoVtFptHaIG4uPjUVlZaXYptvnz56NDhw51nr9u8uTJGDNmjNltW7duBWDcrd2vXz90794d69evN5qO59atW1i7di2CgoJMus3tKR4AmDVrFo4cOYJNmzYZksba6P+96a9RalpMCInIbqjVasyYMQMZGRkoKSnB3r17MWbMGCiVSnz00UdGdQcNGoScnBz861//wvXr13Hq1ClMmDDB6C7inR544AFkZ2fj/PnzyMjIwOnTpxEVFWXYHh0dDR8fH7N3RpqSRqOBr6+v2TVlLREaGmp20mEAmDdvHjp16oSJEydi8+bNKC4uRnZ2Np5++mlcunQJH330kaHr2Frz5s1Dly5d8Nxzz2Hr1q3Q6XS4evUqlixZgnfffRcLFiwwugs1ZswYKBQKnDlzxqL216xZg3fffRdnz55FeXk5zp49i2nTpmH16tUIDw/HuHHjDHUdHBzw73//G1evXsVf/vIX5ObmoqCgAK+88gpOnjyJzz77zNA1b4/xLF++HO+88w5++eUXeHh4mHTxm5tSCIBh+qBBgwZZFBM1MJuMbb5HcJg7kXlogGlnNmzYYJjeQv8aPXq0ZGRkmJTPnDnTcNw7X7GxsYb2NBqNBAYGytGjR0Wr1YqHh4eoVCrp16+f7Ny50+T4hYWFMm7cOAkICBCVSiWRkZGSmZkp4eHhhvanTZtmqH/8+HGJiooSNzc3CQoKkkWLFhm1FxUVJd7e3g02PYpI3ZeumzFjhjg6OsrFixcNZXl5eSbnLzw8vMY2XnrpJbPT7+Tn58vEiROlU6dO4uTkJGq1WrRaraSnpxvq1PUzLCgokEmTJknnzp3FyclJ2rZtK4MGDZLt27ebxBEdHS3u7u5SWVlZ6/nQ6XSSkpIiWq1WOnbsKEqlUtzd3SU8PFzmzZtntBzcnX777TcZMmSIeHp6iru7u0RHR5u9luwtntjYWJPPsfrL3PRLCQkJEhgYKDdv3qw1pur4e1x/ChEbz4HQgiUkJAD4v8k2ieg2hUKBdevWYeTIkbYOxaB3797Iz89vsJGozUFqaioSExOtnspGp9MhNDQUQ4cOves0IC1VYWEh2rVrh9GjR+Ozzz6zdTiMxwIHDhxAWFgY1qxZg1GjRlm9P3+P649dxnRX165dw+LFixEdHY3WrVtDpVKha9euGD16tMVdTmvXrjV0FVTvtmiqGCyVmZmJZ599Fp06dYJKpULr1q3Ro0cP/OlPf8Knn35aY1eHLVl7ftzd3U26cBwcHODt7Q2NRoOXX37Z7MPpdO9Qq9VIS0vD+vXrsWjRIluH06BEBMnJyfD09LRqvj7GYzunT59GfHw8pk+fXqdkkBoGE0K6qylTpmD8+PGIi4vD0aNHUVBQgGXLliErKwvh4eHYuHFjrW2MGjUKIlLnUW4NEUNtqqqqMGXKFDz88MPw9fXF1q1bUVhYiGPHjmHhwoUoKirCyy+/jPvvv7/ZLbxu7fm5fv069u/fDwCIi4uDiKCiogLHjx/Hu+++i+PHj6NPnz74y1/+ghs3btjiLVETCAsLw969e7F161YUFRXZOpwGc/nyZZw+fRrp6el1GtHMeJrekiVLMHfuXMydO9fWodg32/VWt3z28MzC888/Ly+++KJJeVZWlgCQrl27WtxWTEyMODs72zSGmsyYMUMAyNKlS81ur6yslCFDhggAqaioqPfxGlJdzs/+/fsNS4aZM3XqVAEgw4YNk6qqKqtjQgMuXVdff//732t8Xq2lq+szhET3Gnv4PW5sfIawHuz9mQVXV1eUl5ejsrLSojnYBg4ciJ07dxpNi9DUMZhz/PhxhIaGGu6Y1CQjIwMPP/wwKioqjEYzNmc1nZ+srCyEhYUhLi7O7B1WEUFERAR++eUXrFmzBk899ZRVx22OzxDei+r6DCHRvcbef48bAruMqU5KSkpQWlqKHj162GxC3oaKYenSpaiqqjJ8odQkIiICItJiksH6nB+FQmFYjcLcfHNERHRvYUJoAwUFBZg0aRK6dOkCZ2dntG/fHgMHDsTy5cuN1nC8s55SqYS3tzeGDBmCH3/80VBn48aNRoMDzp49i8TERHh5ecHHxwdDhw41DIQoLCw0GUwwZ84cADDcQdK/RowYcdf3oP9f2MyZM022HT9+HE8++STUajXc3NwQFRWFnTt31vu8WRODNX7++WcAQK9evazet6V+RpaIjIwEAOzZs8fqZc6IiKiFsWmHdQtXl2cWLl26JJ06dRJ/f39JS0uToqIiyc3NldmzZwsAWbhwoVE9Pz8/SUtLE51OJydOnJD4+HhRKBTy2WefGbUbFxdneCZs9+7dcv36ddm+fbuoVCp58MEHjeoOHjxYHBwc5PfffzeJLyIiQtasWXPX95Cbmyt+fn4ybtw4k20nT54ULy8vCQwMlO+++06Ki4vl4MGDMmjQIOnYsWOdniG0NgYRkQEDBkjr1q3NznVVXUBAgACQX375xaoYWupnJFL7M4QiIqWlpYZn7nJycu56vOrQjJ4hvJfxGUKi2/gMYf3xm6Qe6nIBPvvsszX+WA4ePNiQEOrrffnll0Z1ysrKpF27dqJSqSQ3N9dQrk820tLSTGIEIHl5eYay77//XgDIyy+/bFR3586d0qFDh7sOmsjPz5fevXtLYmKi2QlNExISBICsX7/eqPzixYvi7OzcIAlhbTGIiPTr18/iSYD1CeGvv/5qVRwt9TMSsSwhvHHjBhPCZo4JIdFtTAjrr2U8DHUP2bBhAwBgyJAhJtv0a0LeWa/6+pPOzs6IiYnBqlWr8O233+KZZ54x2l59PcmgoCAAQE5ODtq0aQPg9iLnYWFhWL58Od599134+PgAAP7+979j4sSJNT4jV1JSAq1Wi+7du2PlypVo1aqVSZ1t27YBgMl6pe3atUNwcDCys7PNtm0pS2IAgB07dljcZrt27XDp0iXk5+dbFUtL/YwsdenSJQCAk5OTIS5rLFy4kA94NzL9JNu1Pf9KdK/bs2cPHnroIVuH0aLxGcImVF5eDp1OBxcXF3h4eNS5nn4t0NzcXJNtarXa6G+lUgng9jx7d3rttddw48YNw4CB7Oxs/Pzzz0brUd6psrISCQkJCAwMxIoVK8wmGuXl5SguLoaLiwvc3d1Ntte0BqylLImhLvr16wcAOHjwoMX7tNTPyBr65z4jIiLg5ORUr7aIiKiZs/UtypasLreo1Wq1AJCioqI61xs7dqwAkBUrVhjK9N2RpaWlRnWnTZsmAGT//v1G5RUVFRIUFCS+vr5SVlYmL774okydOrXGeJ577jmJjo6WsrIyo/IuXboYPafn4eEhAKS4uNikjbCwsHp1GVsag7VOnDghjo6O0qdPn7vWmzJliigUCjl27JiItNzPSKT2LuNbt27Jf//3f9e567eu+5F12GVMdBu7jOuPdwib2PDhwwEA33zzjcm2sLAw/PWvfzWqt2XLFqM65eXlSE9Ph0qlMumWtYajoyMmTJiAK1eu4B//+AfWrl2L5ORks3VnzZqFI0eOYNOmTXB2dr5ru/qucH3XsV5+fj5OnDhR53iticFawcHBePvtt7F3714sW7bMbJ0TJ05gyZIlGDlyJEJCQgC03M/IEtOnT8evv/6K4cOHszuSiMge2DojbcnqM8o4ICBANm/eLEVFRXL+/Hl56aWXxM/PT86dO2dUTz+CtaioyGgEa/UVNay9+yQiUlRUJGq1WhQKhTzzzDNm4/38889NVnmo/rrz7tPvv/8urVu3NhplfOTIEdFqteLr61unO4TWxiBi3Shjvddff12cnJxk2rRpcuLECSkvL5cLFy5ISkqKBAQESGRkpFy/ft1Qv6V+RiKmdwhv3bolly9flo0bN0p0dLQAkOeee05u3Lhh8fm7E3iHsEnwDiHRbbxDWH/8JqmHul6A+fn5MnHiROnUqZM4OTlJQECAjBo1SrKzs+9aT61Wi1arlfT0dEOdjIyMGpflql4eGxtrEsuUKVMEgBw4cMBsrLGx/5+9e4+Lqs7/B/4aZBiGAQYEuYh4v23qji66RiuZYEzeL4lk6HbTWEsJL3xLzfKRum1+2cwtXS9ElpeS7KdFpW1R1qa4oQXeMhXLCwhykauiIu/fH35n1nEGmeE2yLyej8c8HvI5n3POm3MOc96e87mMsjnZ+OWXX2T8+PHi6elpHFLl008/lYiICOM6Tz31lNXHqz4xhIWFWd3L+FY//PCDTJs2TYKDg0WpVIqHh4fce++9smrVKrl69apZ/bvxHGk0GrPlCoVCtFqt9OvXT2bOnCkHDx606bjdjglh82BCSHQTE8KG49R1DcCpcogs49R1zYNT1xHdxPtxw7ENIRGRgzpz5gzGjh2LsrIyFBYWmsyEM2DAAIvzjt9eT6FQYODAgXaIvvFcunQJa9euRXh4ONq2bQu1Wo0ePXogJiYGWVlZFteprq7G22+/jT/+8Y/w8fGBt7c3QkJC8NZbb+HatWutKh6D69evY+XKlQgJCYGHhwf8/PwwYsQIpKam1vmfkrFjx5rMvHSrF154Adu2bWuUGKn+mBASETmgzMxMDBw4EJGRkfD09ISvry9EBBkZGcbl8fHxZusZ6qWnp8PHxwciggMHDjR3+I0qISEBs2fPxrhx43Ds2DEUFRUhOTkZmZmZCAkJwc6dO83WeeKJJzB9+nQMHz4cP//8M06dOoXo6GjMnj0bDz/8cKuKB7g5xml4eDg2btyIlStX4uLFizhw4ADc3d0xduxYHD16tNZ133vvPaSmpta6fMaMGViwYAEWL17c4DipAez4uvquxzYLDYM62r0BkJdfftneYVI9oIW1IdRoNPKnP/2p1e2/vm0IS0tLpUOHDhIbG2u2LCMjQ1Qqlfj4+AiAWqdJTE9PFx8fH5v33RI99dRT8vTTT5uVZ2ZmCgDp0aOHSXl2drYAkAEDBpit8+CDD9Zr5qOWHI+IyMyZM8XT09Nk9iURkYqKClGpVHL48GGL6+Xk5Ii3t7dxKK6lS5darJeZmSkKhaLe3xu8HzccnxCS3cjNTk13/CxZssTeYRK1OitWrEBeXh5eeukli8tdXV2xZcsWODk5ITY2tsEzDLV0SUlJWLdunVm5TqeDWq1Gdna2ySvRc+fOAQB+97vfma1jGJbq7NmzrSae/Px8rF+/HjExMcZB9w00Gg2qqqrQt29fi+vOmDEDUVFRiIyMvOM+dDodJk2ahHnz5qG6urresVL9MSEkInIgIoKkpCQMHjwY7du3r7WeXq/Hiy++iPLyckRFRVlsT9jaVVZW4sqVK+jbty8UCoWxvHfv3lAqlTh+/LjZOsePH4dCoUC/fv1aTTyffPIJbty4gSFDhti0XnJyMo4ePYrExESr6k+YMAHnz583G9uVmgcTQiJqEYqKijB37lx069YNLi4u8Pb2xogRI/DNN98Y6yxbtszYkeHWm9Pu3buN5bfOu5yYmAiFQoHKykrs3bvXWMcwF7RhuUKhQIcOHZCRkYGIiAh4eHjAzc0Nw4YNw969e5ts//aQlZWF/Px86HS6Ouu+/PLLiIyMxKFDhzB79myrtm/Nedy5c6dJp5TffvsN0dHR8PLygo+PD0aPHo3s7GyzbRcUFCAuLg6dO3eGi4sL2rVrh4kTJyIzM9P6A2ADQ4/VRYsWmZT7+/sjMTERWVlZWLhwIQoKClBcXIwVK1bgq6++wksvvYSePXu2mnh+/PFHAIC3tzfmzZuH4OBguLi4oFOnToiLi0NxcbHZOufPn8e8efOQnJx8x6lab9W/f38AwBdffFHvWKkB7POmunVgmwUiy2BjG8LbB/kuLS01GeR7w4YNJvVra5MXEhJisV1bXW34dDqdaDQaCQ0NlX379klFRYVkZGTI73//e3FxcZE9e/Y06f7rM5C6SP3aEG7atEkAyF//+leLyzMyMkSr1Rp/LigokODgYAEgmzdvNpZbakNo63k0DNY+btw443H/8ssvjeOX3io3N1c6deok/v7+8tlnn0l5ebkcOXJEhg4dKq6urjaPOVqXvLw88ff3l+nTp9daJyUlRTp06GBs8+zr6ytvv/12o8bREuIxnKeAgACJiYmR7OxsuXTpkrz77rui0WikZ8+eUlJSYrKOXq+XZ555xviz4bqrrQ2hyM22rQAkLCzM5hh5P244JoQNwAuQyDJbE8LHH39cAMj7779vUl5VVSXt27cXtVpt0pi9KRJCWJgt5tChQwJAdDqdVdur7/6HDh1ar4HU65MQrlixQgDI6tWrLS6/PSEUuZn8KZVK0Wg0xrm8LSWEtp5HQ6KRmppqUn/SpEkCQAoKCoxljz32mACQLVu2mNS9cOGCqFQqCQkJsfII1K2wsFD69+8v0dHRUl1dbba8pqZGZsyYIUqlUl5//XXJy8uTgoICWbdunajVaomOjpbr16+3mnj0er0AkC5duphtZ9myZQJAFi9ebCxbv369dO3a1WRmJ2sSQhERhUIh3bt3tzlG3o8bjq+MicjuduzYAQAYNWqUSblKpUJERASuXLnS5K+RNBqN8ZWVQb9+/dC+fXtkZWXhwoULTbbvPXv2oLi4GKGhoU22DwNDW0ClUmn1Ovfeey8SExNRWVmJqKgoXLlyxWK9+p7HQYMGmfwcHBwMAMjNzTWW7dy5E05OThg9erRJ3YCAAPTp0wcHDx7E+fPnrf6dalNZWQm9Xo977rkHW7ZsQZs2bczqbNq0CRs2bMBf/vIXzJkzB/7+/vD19cXTTz9tHFPvrbfeanAsLSUejUYDABg+fLhZc4cxY8YA+O9r3rNnzyIhIQHJycnG9Wzh7Oxc6/VFTYsJIRHZ1dWrV1FaWgpXV1eLbY0MvRrz8vKaNA4vLy+L5X5+fgCAixcvNun+m4urqyuAm4MM2yIuLg7R0dE4cuQIZs2aZba8IedRq9Wa/Ozi4gIAqKmpMdl2TU0NtFqt2cDYhjZuJ0+etOl3ul11dTWioqIQFBSEd99912LyBdxsMwrcTJBuFxERAQDYtWtXg2JpSfF07twZAODj42O2zPD3UVBQAABITU1FaWkpHnjgAZNzNG3aNADA4sWLjWWnTp0y2151dTXUanW9Y6X6Y0JIRHalUqmg1WpRVVWF8vJys+X5+fkAbj4JMnBycrI4+0JJSYnFfdzaI7M2RUVFFmdbMCSChhtfU+2/uQQGBgIASktLbV43KSkJvXr1QnJyMjZt2mSyrD7n0VoqlQpeXl5wdnbG9evXax2matiwYTZv+1axsbG4evUqUlJSTJ6Ede/eHfv37zf+XFlZWee2KioqGhRLS4rH0IHK0lNyw9+HIeF/9tlnLZ4bw/WydOlSY1n37t1NtlVWVgYRMV6j1LyYEBKR3U2YMAEAzIabuHr1KtLS0qBWq6HX643lgYGByMnJMambl5dX61hrbm5uJglcr169sH79epM6VVVVxlk6DA4fPozc3FzodDqTm1RT7L+5GMaLq8/rVXd3d3z00UfQaDRYs2aN2XJbz6MtJk6ciOrqapNe3wavvfYaOnbs2KDx65YsWYKjR4/i448/hkqlumPdwYMHAwDS0tLMln399dcAbr5mb4iWFM/IkSMRFBSE3bt3mw0/ZJiBZPz48fXevoHhb6q2MQ2pidmh3WKrwUasRJahgb2My8rKTHqnrl+/3qT+rFmzBIC8+eabUl5eLqdOnZLJkydLUFCQxU4dDz30kGi1Wjl79qzs27dPnJ2d5dixY8blOp1OtFqtREREWNXLuLH335y9jGtqasTPz6/WTi6WOpXcbvPmzQKgzl7GdZ1HQ6eSK1eumJQ///zzZp188vPzpVu3btK1a1f5/PPPpaSkRIqKimTt2rXi5uZmdr3FxMQIADl9+nSdx+Sdd96pc9akW44VGYYAACAASURBVM/NpUuXpEePHqJUKmXVqlWSn58vhYWFkpSUJG5ubhIUFCS5ubmtJh4RkV27domzs7OMGzdOTpw4IZcuXZL33ntPNBqNDB48WC5fvnzH9a3pVLJ161YBIDt27LAqplvxftxwTAgbgBcgkWW2JoQiN3tSxsfHS5cuXUSpVIpWqxW9Xi9paWlmdUtKSmT69OkSGBgoarVahgwZIhkZGRISEmK8YT7//PPG+sePH5ewsDDRaDQSHBxs1sNWp9NJUFCQHDt2TPR6vXh4eIharZahQ4fK999/3+T7DwsLa7ZexiIiCxcuFGdnZ8nJyTGWFRQUmCUdd+q5O3PmTIvJrzXnMT093WxfixYtEhHzKS1HjRplXK+oqEjmzp0rXbt2FaVSKe3atZPIyEj58ssvzeIIDw8Xd3d3i71ybzdq1CibEjARkeLiYklISJDevXuLSqUSFxcX6datm8yaNctsere7PR6Dffv2iV6vF61WKy4uLtK7d29ZsmTJHZPB2NhYi/Hr9XqzulFRURIUFCTXrl2zOiYD3o8bTiFiodEMWSUqKgrAfwcLJaKbFAoFtm3bhsmTJ9s7FKv0798fhYWFjdJLtTmlpKQgOjraYtvHOyktLUWfPn0wevRorF27tomis5+SkhK0b98eMTEx2LBhg73DYTxWyMrKwoABA7B161Y88sgjNq/P+3HDsQ0hEZGD0Wq1SE1Nxfbt27F69Wp7h9OoRARxcXHw9PTE0qVL7R0O47HC6dOnMXHiRCxYsKBeySA1DiaEREQOaMCAAThw4AB27dqFsrIye4fTaPLz83H69GmkpaXVq0cz42l+69atw/Lly7F8+XJ7h+LQ7DehJhGRnSUmJiIhIcH4s0KhwKJFi7Bs2TI7RtV8OnfujE8//dTeYTSqgIAAfP/99/YOw4jx1O21116zdwgEJoRE5MDmz5+P+fPn2zsMIiK74ytjIiIiIgfHhJCIiIjIwTEhJCIiInJwTAiJiIiIHBwTQiIiIiIHx5lKGiAqKgrbt2+3dxhEREQOb9KkSZyppAGYEDZAeno6zp07Z+8wiKgBVq5cCQCYM2eOnSMhooYIDg5GaGiovcO4azEhJCKHZphvOSUlxc6REBHZD9sQEhERETk4JoREREREDo4JIREREZGDY0JIRERE5OCYEBIRERE5OCaERERERA6OCSERERGRg2NCSEREROTgmBASEREROTgmhEREREQOjgkhERERkYNjQkhERETk4JgQEhERETk4JoREREREDo4JIREREZGDY0JIRERE5OCYEBIRERE5OCaERERERA6OCSERERGRg2NCSEREROTgmBASEREROTgmhEREREQOjgkhERERkYNjQkhERETk4JgQEhERETk4JoREREREDo4JIREREZGDY0JIRERE5OCYEBIRERE5OCaERERERA6OCSERERGRg2NCSEREROTgnO0dABFRcyksLERZWZlJWWVlJQDg9OnTJuWenp7w9fVtttiIiOxJISJi7yCIiJrDO++8gyeffNKqusnJyXjiiSeaOCIiopaBCSEROYzS0lK0a9cO169fv2M9pVKJgoICaLXaZoqMiMi+2IaQiByGVqvFyJEj4exce2sZZ2dnjBo1iskgETkUJoRE5FCmTp2KGzdu1Lq8pqYGU6dObcaIiIjsj6+MicihVFVVwdfX19iZ5HZubm4oLCyEWq1u5siIiOyHTwiJyKG4urpi4sSJUCqVZsuUSiUmTZrEZJCIHA4TQiJyOI8++qjFjiXXr1/Ho48+aoeIiIjsi6+MicjhVFdXw9/fH8XFxSblXl5eKCgouGOnEyKi1ohPCInI4Tg7O2PKlCkmr42VSiWmTp3KZJCIHBITQiJySFOmTDF5bXz9+nVMmTLFjhEREdkPXxkTkUMSEQQHByMnJwcAEBgYiJycHCgUCjtHRkTU/PiEkIgckkKhwLRp0+Di4gIXFxc89thjTAaJyGHxCSEROaxDhw5Bp9MZ/92vXz87R0REZB9Wt55OT0/H66+/3pSxEBE1O3d3dwDAK6+8YudIiIga19y5cxEaGmpVXatfGZ87dw7bt2+vd1BERC1Rp06d0Llz5ybdx/nz5/n92Uz279+P/fv32zsMIrvbvn07zp07Z3V9m8dX+PDDD21dhYioxTp9+jQAoGvXrk22j5SUFERHR/P7sxlERUUB4L2KyNY20Rxwi4gcWlMmgkREdwv2MiYiIiJycEwIiYiIiBwcE0IiIiIiB8eEkIiI6P+cOXMGY8eORVlZGQoLC6FQKIyfAQMGoKqqymyd2+spFAoMHDjQDtE3nkuXLmHt2rUIDw9H27ZtoVar0aNHD8TExCArK8viOtXV1Xj77bfxxz/+ET4+PvD29kZISAjeeustXLt2rVXFY3D9+nWsXLkSISEh8PDwgJ+fH0aMGIHU1FTUNczz2LFjoVAosGzZMrNlL7zwArZt29YoMVqLCSER0V2koqICPXr0wOjRo+0dSquTmZmJgQMHIjIyEp6envD19YWIICMjw7g8Pj7ebD1DvfT0dPj4+EBEcODAgeYOv1ElJCRg9uzZGDduHI4dO4aioiIkJycjMzMTISEh2Llzp9k6TzzxBKZPn47hw4fj559/xqlTpxAdHY3Zs2fj4YcfblXxAEBlZSXCw8OxceNGrFy5EhcvXsSBAwfg7u6OsWPH4ujRo7Wu+9577yE1NbXW5TNmzMCCBQuwePHiBsdpNbHStm3bxIbqRET0fxrz+7OsrEy6du0qI0aMaJTtNSWNRiN/+tOfmnWfkyZNkkmTJtm8XmlpqXTo0EFiY2PNlmVkZIhKpRIfHx8BIFu3brW4jfT0dPHx8bF53y3RU089JU8//bRZeWZmpgCQHj16mJRnZ2cLABkwYIDZOg8++KAAkB9++KHVxCMiMnPmTPH09JS8vDyT8oqKClGpVHL48GGL6+Xk5Ii3t7dMmzZNAMjSpUst1svMzBSFQiHbtm2rV3wAbFqXTwiJiO4iHh4eyM7Oxueff27vUFqVFStWIC8vDy+99JLF5a6urtiyZQucnJwQGxuLEydONHOEzSspKQnr1q0zK9fpdFCr1cjOzjZ5JWoYAPl3v/ud2Tq9e/cGAJw9e7bVxJOfn4/169cjJiYG/v7+Jss0Gg2qqqrQt29fi+vOmDEDUVFRiIyMvOM+dDodJk2ahHnz5qG6urresVqLCSERETk0EUFSUhIGDx6M9u3b11pPr9fjxRdfRHl5OaKioiy2J2ztKisrceXKFfTt29dk4OPevXtDqVTi+PHjZuscP34cCoWiSeYKt1c8n3zyCW7cuIEhQ4bYtF5ycjKOHj2KxMREq+pPmDAB58+fx2effVafMG3ChJCI6C6xc+dOk44LhoTk9vLffvsN0dHR8PLygo+PD0aPHo3s7GzjdhITE411O3TogIyMDERERMDDwwNubm4YNmwY9u7da6y/bNkyY/1bb4C7d+82lvv6+pptv7KyEnv37jXWcXZumXMhZGVlIT8/Hzqdrs66L7/8MiIjI3Ho0CHMnj3bqu0XFRVh7ty56NatG1xcXODt7Y0RI0bgm2++Mdax9RwaFBQUIC4uDp07d4aLiwvatWuHiRMnIjMz0/oDYAPDDDCLFi0yKff390diYiKysrKwcOFCFBQUoLi4GCtWrMBXX32Fl156CT179mw18fz4448AAG9vb8ybNw/BwcFwcXFBp06dEBcXh+LiYrN1zp8/j3nz5iE5ORkeHh5W7ad///4AgC+++KLesVrN2nfLbENIRFQ/jf39OW7cOAEgV65csVg+btw42bdvn1RUVMiXX34parVaBg0aZLYdnU4nGo1GQkNDjfUzMjLk97//vbi4uMiePXtM6tfWJjAkJMRi27m62hAOGzZM2rZtK+np6db+6nWqTxvCTZs2CQD561//anF5RkaGaLVa488FBQUSHBwsAGTz5s3GckttCC9cuCBdunQRf39/SU1NldLSUvnll19k4sSJolAoZMOGDSb1bTmHubm50qlTJ/H395fPPvtMysvL5ciRIzJ06FBxdXWVffv22XQc6pKXlyf+/v4yffr0WuukpKRIhw4dBIAAEF9fX3n77bcbNY6WEI/hPAUEBEhMTIxkZ2fLpUuX5N133xWNRiM9e/aUkpISk3X0er0888wzxp8N111tbQhFbrZtBSBhYWE2xwgb2xAyISQiamLNnRCmpqaalE+aNEkASEFBgUm5TqcTAPLTTz+ZlB86dEgAiE6nMylv7IRw6NCh4u3t3aiJS30SwhUrVggAWb16tcXltyeEIjeTP6VSKRqNRn7++Wdj2e3H4fHHHxcA8v7775uUV1VVSfv27UWtVpt0SrDlHD722GMCQLZs2WJS98KFC6JSqSQkJMTKI1C3wsJC6d+/v0RHR0t1dbXZ8pqaGpkxY4YolUp5/fXXJS8vTwoKCmTdunWiVqslOjparl+/3mri0ev1AkC6dOlitp1ly5YJAFm8eLGxbP369dK1a1epqKgwllmTEIqIKBQK6d69u80x2poQ8pUxEVErM2jQIJOfg4ODAQC5ublmdTUajfG1lEG/fv3Qvn17ZGVl4cKFC00W5549e1BcXIzQ0NAm24c1DK/elUql1evce++9SExMRGVlJaKionDlyhWL9Xbs2AEAGDVqlEm5SqVCREQErly5YvF1oDXncOfOnXBycjIbgiggIAB9+vTBwYMHcf78eat/p9pUVlZCr9fjnnvuwZYtW9CmTRuzOps2bcKGDRvwl7/8BXPmzIG/vz98fX3x9NNPG8fUe+uttxocS0uJR6PRAACGDx9u1hRizJgxAP77mvfs2bNISEhAcnKycT1bODs713p9NSYmhERErYxWqzX52cXFBQBQU1NjVtfLy8viNvz8/AAAFy9ebOToWh5XV1cANwcZtkVcXByio6Nx5MgRzJo1y2z51atXUVpaCldXV4ttxgy9U/Py8syW1XUODduuqamBVqs1Gxjb0Mbt5MmTNv1Ot6uurkZUVBSCgoLw7rvvWky+gJvtSYGbCdLtIiIiAAC7du1qUCwtKZ7OnTsDAHx8fMyWGf52CgoKAACpqakoLS3FAw88YHKOpk2bBgBYvHixsezUqVNm26uuroZara53rNZiQkhE5MCKiooszqhgSAQNNzcAcHJysjjDQ0lJicVt39rrsyULDAwEAJSWltq8blJSEnr16oXk5GRs2rTJZJlKpYJWq0VVVRXKy8vN1s3Pzwdw84merVQqFby8vODs7Izr169DbjYBM/sMGzbM5m3fKjY2FlevXkVKSorJk7Du3btj//79xp8rKyvr3FZFRUWDYmlJ8Rg6V1l6gm742zEk/M8++6zFc2O4XpYuXWos6969u8m2ysrKICLGa7QpMSEkInJgVVVVxpk4DA4fPozc3FzodDqTG1FgYCBycnJM6ubl5dU6npubm5tJAtmrVy+sX7++EaNvHIbx4urzetXd3R0fffQRNBoN1qxZY7Z8woQJAGA2bMjVq1eRlpYGtVoNvV5fj6iBiRMnorq62qRHuMFrr72Gjh07Nmj8uiVLluDo0aP4+OOPoVKp7lh38ODBAIC0tDSzZV9//TWAm6/ZG6IlxTNy5EgEBQVh9+7dZsMPGWYgGT9+fL23b2D4e6ttTMNGZW1jQ3YqISKqn+buVHJ7+fPPP2+x84hOpxOtVisRERFW9TKeNWuWAJA333xTysvL5dSpUzJ58mQJCgqy2KnkoYceEq1WK2fPnpV9+/aJs7OzHDt2zLi8pfQyrqmpET8/v1o7wFjqVHK7zZs3C4A6exmXlZWZ9DJev369SX1bzmF+fr5069ZNunbtKp9//rmUlJRIUVGRrF27Vtzc3Mw6FMTExAgAOX36dJ3H5J133jH2zK3tc+t5u3TpkvTo0UOUSqWsWrVK8vPzpbCwUJKSksTNzU2CgoIkNze31cQjIrJr1y5xdnaWcePGyYkTJ+TSpUvy3nvviUajkcGDB8vly5fvuL41nUq2bt0qAGTHjh1WxXQrsJcxEVHL0ljfnzt27DC7CcbExEh6erpZ+aJFi0REzMpHjRpl3J5Op5OgoCA5duyY6PV68fDwELVaLUOHDpXvv//ebP8lJSUyffp0CQwMFLVaLUOGDJGMjAwJCQkxbv/555831j9+/LiEhYWJRqOR4OBgs168YWFhLaKXsYjIwoULxdnZWXJycoxlBQUFZsfvTj13Z86caTExLiwslPj4eOnSpYsolUrRarWi1+slLS3NWKe+57CoqEjmzp0rXbt2FaVSKe3atZPIyEj58ssvzeIIDw8Xd3d3i71ybzdq1CibEjARkeLiYklISJDevXuLSqUSFxcX6datm8yaNctsere7PR6Dffv2iV6vF61WKy4uLtK7d29ZsmTJHZPB2NhYi/Hr9XqzulFRURIUFCTXrl2zOiYDJoRERC1MS/3+NCSErUl9E8KSkhIJCgqyOJdxa3Dp0iVRq9V3HLOvOTGeuhnmMr59yCJr2ZoQsg0hERE5PK1Wi9TUVGzfvh2rV6+2dziNSkQQFxcHT09PLF261N7hMB4rnD59GhMnTsSCBQvwyCOPNMs+mRDeBW6fZqoluHTpEtauXYvw8HC0bdsWarUaPXr0QExMDLKysqzaxgcffGD8vQzDPjR3DLWpqKgwG8YhPT29zvUSEhJM1lm2bFmD4qiLu7u7WZwKhQJOTk5o164dxo8fb9ZhoLG1huvT0nF0cnKCt7c3dDodnnnmGRw8eNAOvwk1pwEDBuDAgQPYtWsXysrK7B1Oo8nPz8fp06eRlpZWrx7NjKf5rVu3DsuXL8fy5cubb6fWPkpsqa88HIml1zvl5eXSvXt3kzYlzeGpp54SZ2dneeONN+TChQtSWVkp3333ndxzzz3Spk0bmxrARkREiEqlsmsMtfnpp5+M7TtGjBhxx7qFhYXi7u5ubNfVXAwxjhs3zlhWUlIi/+///T/x8/MTpVJpsT1RY7vbr8/bj2N1dbXk5eXJzp07ZdiwYQJAHn/8camsrLQ5npb2/fm///u/tbZXu9vV95UxUWsDvjJ2LCKCmpoaiwPONrUnn3wSzz33HAICAuDm5oawsDBs3boVN27cwP/8z/+0mhjUajU6deqEXbt24cCBA7XWW7lypXE2AXvTarWYMGECXn/9dVy/fh3x8fF2ieNuvj7btGkDf39/jBs3Dl9//TX+53/+Bxs3bsSUKVMsjtt3N5k/f77ZmGhN/TSbiFo257qrUEvm4eGB7OzsZt9vUlKSxXKdTge1Wo3s7GyISJMOTNtcMTg5OeGFF17AzJkzsWzZMuzcudOsTklJCf75z3/i9ddfx+OPP96g/TUmw6C0R48eRUlJSa2zUjSV1nR9/u1vf8O3336LTz75BB988AGmTJnSWOESEdkdnxBSo6qsrMSVK1fQt29fu81S0BQxPPHEEwgKCsInn3yCQ4cOmS3/xz/+gZEjR6Jbt26Nsr/GcuuTrLtl1oim1JBrQ6FQGKcnszQAMRHR3azJEsKdO3eaNNA+c+YMoqOj4eHhAR8fH0ybNg2XLl3Cb7/9hjFjxsDDwwOBgYGYMWOG2RQ/1dXV2LZtGx588EEEBARArVajX79+WLVqlcmrqCFDhpjsc+rUqQBuzmV4a3lt0yzd7vbG8hkZGYiIiICHhwfc3NwwbNgwiyPEFxUVYe7cuejWrRtcXFzg7e2NESNG4JtvvmlQ3bqOsWG09NvLf/vtN0RHR8PLyws+Pj4YPXq0xac2x48fx/jx46HVauHm5oY//vGP+PTTT02O3/Tp0+8Y04cffggAWLRo0R23r9FoEBYWhu+//77O39NWd4qhvlQqFRISEiAiZo18Kyoq8Oabb2LhwoW1rm+va3jPnj0AgD59+hjnRuX1Wf9rwzBd1f79+22e95aIqEWztrFhfRtFG0Zdnzhxohw4cEAqKirkvffeMzbSHzdunPz0009SXl4ua9euFQAyZ84ck22kpqYKAPnrX/8qxcXFUlBQIP/4xz/EyclJ5s+fb1I3MzNTNBqN6HQ6qaioEBGRqqoqGTx4cL3H8tHpdKLRaCQ0NLTO0fxvH5W+tLTUZFT6DRs21KuuIQ5LY4bVNWvBuHHjjHF/+eWXolarZdCgQSZ1T548KV5eXhIUFCT/+te/pLy8XI4cOSLDhw+Xdu3aWdXpIy8vT/z9/S2O42Rp+4cOHZLIyEjp3LlzvTqV2BqDiO0zI/z000+i0WhEROTy5cvi7+8vTk5OJrMt/O1vf5PJkyeLiMi///1vi51KmvIattSppLS01GKnEl6ftV8blo7j7a5cuWLshHH7LAd30tI6lbRm7FRCdBNa2sDUhi/9zz77zKS8T58+AkC+/fZbk/IuXbpIr169TMpSU1PlgQceMNv21KlTRalUSmlpqUl5SkqKMQmtqamRxx57TBYuXGhz7AY6nc7itE+HDh0SAKLT6Yxljz/+uAAwu3FXVVVJ+/btRa1WG0dIt6WuIY763HBTU1NNyidNmiQApKCgwFgWFRUlAGT79u0mdS9evChubm513nALCwulf//+Eh0dbXGU99q2n5OTIyqVqlESwrpiEBEZOnSoTTMj3JoQioi89tprAkCmTp0qIiKVlZXi7+8vWVlZInLnhLCpruFbe0IbPgqFQnx8fGTs2LHyww8/GOvy+qz92rAmIbx8+TITwhaOCSHRTS02IczPzzcpf/DBBwWA2RAOQ4YMEQ8PD6u2bRg6wdLNfdGiRQJA7rvvPhk9erTcuHHD5tgNDE8ILWnfvr3JzUGr1QoAKSsrM6s7bdo0ASDvvvuuzXUNcdTnhnv7FD1z5swRAMYkRkTEw8NDAEh5ebnZ9v/whz/c8YZbUVEhISEh8uijj9Z6s73T9vv169fghNCaGOrj9oSwvLxcfHx8pE2bNnLy5El5/fXXTRKI2hLC2jTGNWxNImPA67P2a8Oa45idnS0ARKlU2jSVlOH7kx9++OGnOT+2JITN1svY09PT5GcnJye0adMGbm5uJuVt2rQxG6KitLQUf//737Fjxw6cP3/erP3U5cuXzfa3dOlSfPXVV9i3bx/effddODk1rLlkbb0z/fz8kJubi4sXL6Jt27YoLS2Fq6srPDw8zOr6+/sDAPLy8nD16lWr6zaUoe2YgYuLCwAYj/PVq1dRXl4OV1dXuLu7m63v7e1d67arq6sRFRWFoKAgvPvuu2jTpo1Znbq27+fnhxMnTtj0O9kaQ2Nxd3dHfHw8Fi9ejJdffhl79uzBxx9/XOd6LeEatuWac6Tr0xaGNq+hoaFQKpU2r79t27YG7Z/qtnLlSgDAnDlz7BwJkX1FR0fbVP+uGHZmzJgx+Pe//41Vq1ZhypQp8PX1hUKhwBtvvIE5c+ZYHBNsz549KC0tRb9+/fDMM89Ap9NBp9PVO4aioiKLw1RcvHgRwM2kRqVSQavVorS0FOXl5WY30vz8fABAQECATXWbmkqlgoeHB8rLy1FRUWF20zX8jpbExsbi6tWr2LFjB5yd/3s5de/eHZs3b8a9995b5/aLi4sbFL81MTSm2bNnIzExEVu3bsWIESMwcODAOtdpCdcwr8+GXRs1NTXGKc2effZZG3+LmyZPnlyv9ch6ho5DPNbk6GxNCFv8sDM3btzA3r17ERAQgLi4OLRr186YlF25csXiOr/++iueeuopfPTRR/jkk0+gVqsxbtw4FBQU1DuOqqoqsynADh8+jNzcXOh0OgQGBgIAJkyYAAD47LPPTOpevXoVaWlpUKvV0Ov1NtdtaiNGjAAA7N6926Q8Ly+v1qd3S5YswdGjR/Hxxx9DpVLVa/uFhYX45Zdf6hu2TTE0Fq1Wi7lz50Kr1eLFF1+ss35LuYYBXp8NsWDBAvzwww+YMGECoqKiGrw9IqIWxdY2MLaqrf2QXq+XNm3amNUfOnSoWXu98PBwASArVqyQgoICuXz5snz99dfSsWNHAWAyLVd5ebn8/ve/l48//thYtmfPHlEqlXL//ffb1O7HQKfTiVarlYiICJt7GZeVlZn0zFy/fn296hriqE8brdvLn3/+eQFMO8mcOnVK2rZta9KL8/Dhw/LQQw9Jp06dzNpovfPOO3W2Xbi1J6+l7R89elT0er34+fnVqw2hrTGINKyXsTVqa0PYlNewLW0IeX3Wfm3cfhxv3Lgh+fn5snPnTuP5e/LJJ+Xy5ct1HufbsVNJ82GnEqKbYGMbwiZLCNPT082+gBctWiQZGRlm5a+++qrxRnrr5+WXXxYRkYKCAomNjZXg4GBRKpXi7+8vjz/+uLzwwgvGuiEhIfLss8+arH/48GEpKCgw2+7SpUut/j1E/nujO3bsmOj1evHw8BC1Wi1Dhw6V77//3qx+YWGhxMfHS5cuXUSpVIpWqxW9Xi9paWn1qlvbvKM7duwwK4+Jian12IuIWfmtc8z+8ssvMn78ePH09BQ3Nze577775Ntvv5UHHnhA3NzcTOIeNWqUzTfcW7dvGFrk008/lYiICOM6Tz31lNXnpT4xhIWFWd3LWKPRmGxLr9ffsb6l/b/55psi0nTX8O0xAjDrpX87Xp/m14al46hQKESr1Uq/fv1k5syZcvDgwTse1zthQth8mBAS3WRrQqj4v5XqlJKSgujo6Lt+Ds/66N+/PwoLC3H+/Hl7h2IXvXv3xpUrV3DmzBl7h0Jk5m64Ph35+7O5GV7nG9oSEjkqhUKBbdu2Wd2etsW3IaTmkZeXh7Zt25rNvvDbb78hOzsb4eHhdoqMiNcnNZ8zZ85g7NixKCsrQ2FhocmsOgMGDDDOuHOr2+spFAqrOpvdLT7//HP07NnTpGOWNcaOHQuFQoFly5a12niuX7+OlStXIiQkBB4eHvDz88OIESOQmppa538A7xTPCy+80OyjEjAhJKNLly4hNjYW586dw+XLl/HDDz8gOjoanp6eWLx4sb3DIwfH65OaWmZmCYOhIAAAIABJREFUJgYOHIjIyEh4enrC19cXImLsUJiZmYn4+Hiz9Qz10tPT4ePjAxHBgQMHmjv8RpednY2xY8diwYIFxpEFrPXee+8hNTW1VcdTWVmJ8PBwbNy4EStXrsTFixdx4MABuLu7Y+zYsTh69Gi945kxYwYWLFjQrN9tDpsQ3v6/OUsfd3d3KBQKZGVlIScnBwqFwqpepXejgIAAfPXVVygpKcH9998Pb29vjB07Fj169MAPP/yArl27Nlss1pybJUuWNFs8ZH8t6fpsLdzd3Y1zMzvi/m9XVlaGMWPG4OGHH8asWbPMlqtUKvj4+GDdunV4//337RBh81u8eDHuu+8+HDx40OJ4pLXJzc1FfHw8pk2b1qrjSUhIwKFDh/Cvf/0L999/P9RqNTp27IiNGzfecWQDa+Lp1q0bduzYgeXLlyMlJaVR467NXTEOYVNgWx5zERERiIiIsHcYPDdkUUu5Pql1WrFiBfLy8vDSSy9ZXO7q6ootW7Zg5MiRiI2NRUhICHr27NnMUTavt99+G2q12ub1ZsyYgaioKISFhWHTpk2tMp78/HysX78eTz/9tHGgfgONRmOxaYGt8eh0OkyaNAnz5s3DxIkTbX5FbiuHfUJIREQE3PxPaFJSEgYPHoz27dvXWk+v1+PFF19EeXk5oqKi7njTbw3qk3wlJyfj6NGjSExMbNXxfPLJJ7hx44bNT7ltjWfChAk4f/682XiwTYEJIRFRC1VUVIS5c+eiW7ducHFxgbe3N0aMGIFvvvnGWGfZsmXGphS33px2795tLPf19TWWJyYmQqFQoLKyEnv37jXWMTx9MCxXKBTo0KEDMjIyEBERAQ8PD7i5uWHYsGHYu3dvk+3fHrKyspCfn2/VTEAvv/wyIiMjcejQIcyePduq7VtzHnfu3GnSLOa3335DdHQ0vLy84OPjg9GjRyM7O9ts2wUFBYiLi0Pnzp3h4uKCdu3aYeLEicjMzLT+ADSS8+fPY968eUhOTrbple7dGM+PP/4I4ObUmfPmzUNwcDBcXFzQqVMnxMXFWZyBqz7x9O/fHwDwxRdfNF7wtWBCSETUAuXl5WHQoEHYunUrVq1ahcLCQvznP/+Bm5sbIiIikJSUBAB48cUXISLQaDQm6z/00EMQEYSEhJiUz58/31j/T3/6E+TmeLSorq42Wa7T6VBSUoLnnnsOy5YtQ15eHr777jsUFxcjPDwc3377bZPs3yA8PBw+Pj7Yv39/ww9mHY4cOQIA6NChQ511nZycsGXLFgQHByMpKQlbtmy5Y31rz+P48eMhIhg3bhwAID4+HvHx8cjJycG2bdvw9ddfY8qUKSbbvnDhAgYNGoSUlBSsWbMGxcXF2LNnD4qLixEaGor09PT6HI56mz59Oh599NEW0+u/KeO5cOECAODJJ59Efn4+vv32W1y8eBFLly5FcnIyQkNDUVpa2uB4goKCAPz3Gm1KTAiJiFqgBQsW4Ndff8Ubb7yB0aNHw9PTEz179sTWrVsRGBiIuLg4m3ta2qqyshJr1qxBaGgoNBoNBg4ciM2bN+PatWt47rnnmnTfNTU1xmSxqRlu7lqt1qr6vr6+SElJgVKpRGxsLI4fP15r3fqex+nTpxuP+/DhwzFq1ChkZGSgsLDQZNtnzpzB66+/jpEjR8Ld3R19+vTBBx98ABGx+glmY9iwYQNOnjyJFStWNNs+76Sp4zE0F1Cr1di4cSO6du0KLy8v/PnPf8aCBQtw4sQJ/P3vf29wPJ6enlAoFMZrtCkxISQiaoF27NgBABg1apRJuUqlQkREBK5cudLkr5E0Go3xlZVBv3790L59e2RlZTXpTerWJ11NzXBzVyqVVq9z7733IjExEZWVlYiKiqp1XvL6nsdBgwaZ/BwcHAzgZg9Vg507d8LJyQmjR482qRsQEIA+ffrg4MGDzTKhwtmzZ5GQkIDk5GSzJ8X20BzxGLY7fPhws+YOY8aMAfDf17wNjcfZ2bnW66sxMSEkImphrl69itLSUri6ulpsa2To1ZiXl9ekcXh5eVks9/PzAwBcvHixSfffXFxdXQHAbODzusTFxSE6OhpHjhyxOFRNQ87j7U8rXVxcANx8cnrrtmtqaqDVas2G5jK0cTt58qRNv1N9pKamorS0FA888IBJDIZhVRYvXmwsO3XqVKuIp3PnzgAAHx8fs2WGv4+CgoJGiae6urpeHWpsxYSQiKiFUalU0Gq1qKqqQnl5udlywyvGgIAAY5mTkxOuXbtmVrekpMTiPhQKRZ1xFBUVWXxla0gEDTe+ptp/cwkMDAQAszZf1khKSkKvXr2QnJxsNoRIfc6jtVQqFby8vODs7Izr168bX6/f/hk2bJjN27bVs88+a3HfhuOxdOlSY1n37t1bRTyGDlSWnpIb/j4MCX9D4ikrK4OIGK/RpsSEkIioBZowYQIAmA03cfXqVaSlpUGtVkOv1xvLAwMDkZOTY1I3Ly8PZ8+etbh9Nzc3kwSuV69eWL9+vUmdqqoq4ywdBocPH0Zubi50Op3JTaop9t9c+vbtCwD1er3q7u6Ojz76CBqNBmvWrDFbbut5tMXEiRNRXV1t0uvb4LXXXkPHjh3NOutQ4xg5ciSCgoKwe/dus+GHDDOQjB8/vsH7MfxNGa7RpsSEkIioBXr11VfRpUsXxMfH49NPP0V5eTlOnDiBRx99FBcuXMCqVatMBsSNjIxEbm4u3nrrLVRUVCA7OxvPPfecyVO8W/3hD3/AiRMncO7cOaSnp+P06dMICwszqaPVarFw4UKkp6ejsrISBw4cwNSpU+Hi4oJVq1aZ1G3s/TdnL2OdTgc/Pz9kZWXVa/0+ffpg3bp1FpfZeh5t8eqrr6Jbt2548sknsWvXLpSWlqK4uBjr1q3DK6+8gsTERJP2bVOnToVCocCvv/5ar/01trs5HpVKhaSkJBQVFeGRRx7ByZMnUVJSgk2bNuHVV1/F4MGDERcX1+CYDMMHRUZGNnhbdRIrbdu2TWyoTkRE/6e+35+FhYUSHx8vXbp0EaVSKVqtVvR6vaSlpZnVLSkpkenTp0tgYKCo1WoZMmSIZGRkSEhIiAAQAPL8888b6x8/flzCwsJEo9FIcHCwrF692mR7Op1OgoKC5NixY6LX68XDw0PUarUMHTpUvv/++ybff1hYmHh7e8u+fftsOmaTJk2SSZMm2bSOiMjChQvF2dlZcnJyjGUFBQXG2A2fkJCQWrcxc+ZM8fHxMSu35jymp6eb7WvRokUiImblo0aNMq5XVFQkc+fOla5du4pSqZR27dpJZGSkfPnll2ZxhIeHi7u7u1RXV1t1TFJTU832bfhs2LCh1vViY2MtrqPX61tVPCIi+/btE71eL1qtVlxcXKR3796yZMkSuXz5coPjERGJioqSoKAguXbtmtUxGQCQbdu2WV/f2opMCImI6udu/P40JIR3m/omhCUlJRIUFCSxsbFNEJX9Xbp0SdRqtUyfPt3eoYgI47FGZmamKBQKef/99+u1vq0JIV8ZExGRw9NqtUhNTcX27duxevVqe4fTqEQEcXFx8PT0xNKlS+0dDuOxwunTpzFx4kQsWLAAjzzySLPskwkhERERgAEDBuDAgQPYtWsXysrK7B1Oo8nPz8fp06eRlpZWrx7NjKf5rVu3DsuXL8fy5cubbZ/2mzySiIhanMTERCQkJBh/VigUWLRoEZYtW2bHqJpP586d8emnn9o7jEYVEBCA77//3t5hGDGeur322mvNvk8mhEREZDR//nzMnz/f3mEQUTPjK2MiIiIiB8eEkIiIiMjBMSEkIiIicnBMCImIiIgcnM2dSlJSUpoiDiKiVis9PR0Avz+bg2E+Yh5rItvYnBBGR0c3RRxERK0evz+bD481kW0U/ze9CRGRQ5o8eTIAPlEiIsfGNoREREREDo4JIREREZGDY0JIRERE5OCYEBIRERE5OCaERERERA6OCSERERGRg2NCSEREROTgmBASEREROTgmhEREREQOjgkhERERkYNjQkhERETk4JgQEhERETk4JoREREREDo4JIREREZGDY0JIRERE5OCYEBIRERE5OCaERERERA6OCSERERGRg2NCSEREROTgmBASEREROTgmhEREREQOjgkhERERkYNjQkhERETk4JgQEhERETk4JoREREREDo4JIREREZGDY0JIRERE5OCYEBIRERE5OCaERERERA6OCSERERGRg2NCSEREROTgmBASEREROTgmhEREREQOztneARARNZfvvvsO6enpJmXHjx8HALz22msm5aGhobj//vubLTYiIntSiIjYOwgiouaQlpaG4cOHQ6lUwsnJ8guSmpoaXL9+HV999RUiIiKaOUIiIvtgQkhEDqOmpgYBAQEoKCi4Yz1fX1/k5eWhTZs2zRQZEZF9sQ0hETkMJycnxMTEwMXFpdY6Li4umDp1KpNBInIoTAiJyKFMmTIF165dq3X5tWvXMGXKlGaMiIjI/vjKmIgcTufOnXHmzBmLy4KDg3HmzBkoFIpmjoqIyH74hJCIHM60adOgVCrNypVKJR5//HEmg0TkcPiEkIgczvHjx/G73/3O4rIjR46gT58+zRwREZF98QkhETmc3r17o0+fPmZPAu+55x4mg0TkkJgQEpFD+vOf/2zSk1ipVOKxxx6zY0RERPbDV8ZE5JDOnTuHTp06wfAVqFAocPr0aXTu3Nm+gRER2QGfEBKRQwoODsbgwYPh5OQEJycnDB48mMkgETksJoRE5LCmTZsGhUIBJycnTJs2zd7hEBHZDV8ZE5HDKiwsREBAAAAgNzcXfn5+do6IiMg+mBBaISoqCtu3b7d3GERERGSjSZMm4cMPP7R3GC2es70DuFvce++9mDNnjr3DIGpRoqOjER8fj9DQUHuHUm/fffcdFAoFwsLC7B1KrdLT0/HGG29g27Zt9g6F6K6ycuVKe4dw12BCaKUOHTpg8uTJ9g6DqEWJjo5GaGjoXf23MWLECACAh4eHnSO5szfeeOOuPs5E9sAng9ZjQkhEDq2lJ4JERM2BvYyJiIiIHBwTQiIiIiIHx4SQiIiIyMExISQiclBnzpzB2LFjUVZWhsLCQigUCuNnwIABqKqqMlvn9noKhQIDBw60Q/RN4/PPP0fPnj3h7GxbE/uxY8dCoVBg2bJlrTae69evY+XKlQgJCYGHhwf8/PwwYsQIpKamoq4R7O4UzwsvvMAe9C0AE0IisruKigr06NEDo0ePtncoDiMzMxMDBw5EZGQkPD094evrCxFBRkaGcXl8fLzZeoZ66enp8PHxgYjgwIEDzR1+o8vOzsbYsWOxYMEC5Ofn27Tue++9h9TU1FYdT2VlJcLDw7Fx40asXLkSFy9exIEDB+Du7o6xY8fi6NGj9Y5nxowZWLBgARYvXtyoMZNtmBASkd2JCGpqalBTU2PvUOrk7u6OIUOG2DuMBikrK8OYMWPw8MMPY9asWWbLVSoVfHx8sG7dOrz//vt2iLD5LV68GPfddx8OHjxoU8/z3NxcxMfHN/rUhy0tnoSEBBw6dAj/+te/cP/990OtVqNjx47YuHEjVCpVg+Lp1q0bduzYgeXLlyMlJaVR4ybrMSEkIrvz8PBAdnY2Pv/8c3uH4hBWrFiBvLw8vPTSSxaXu7q6YsuWLXByckJsbCxOnDjRzBE2v7fffhsvvPCCza9mZ8yYgaioKERGRrbaePLz87F+/XrExMTA39/fZJlGo0FVVRX69u3boHh0Oh0mTZqEefPmobq6utFiJ+sxISQiciAigqSkJAwePBjt27evtZ5er8eLL76I8vJyREVFWWxP2Jqo1Wqb10lOTsbRo0eRmJjYquP55JNPcOPGDZufjNsaz4QJE3D+/Hl89tln9QmTGogJIRHZ1c6dO006KBgSj9vLf/vtN0RHR8PLyws+Pj4YPXo0srOzjdtJTEw01u3QoQMyMjIQEREBDw8PuLm5YdiwYdi7d6+x/rJly4z1b73R7d6921ju6+trtv3Kykrs3bvXWMfWJzj2lpWVhfz8fOh0ujrrvvzyy4iMjMShQ4cwe/Zsq7ZfVFSEuXPnolu3bnBxcYG3tzdGjBiBb775xljH1nNrUFBQgLi4OHTu3BkuLi5o164dJk6ciMzMTOsPQCM5f/485s2bh+Tk5BYxuHlTxvPjjz8CALy9vTFv3jwEBwfDxcUFnTp1QlxcHIqLixslnv79+wMAvvjii8YLnqzGhJCI7Gr8+PEQEYwbN+6O5fHx8YiPj0dOTg62bduGr7/+GlOmTDHWnz9/PkQEOp0OJSUleO6557Bs2TLk5eXhu+++Q3FxMcLDw/Htt98CAF588UWICDQajcl+H3roIYgIQkJCTMoN29doNPjTn/4EEYGImL3eCg8Ph4+PD/bv399ox6gxHTlyBMDN6Tjr4uTkhC1btiA4OBhJSUnYsmXLHevn5eVh0KBB2Lp1K1atWoXCwkL85z//gZubGyIiIpCUlATA9nMLABcuXMCgQYOQkpKCNWvWoLi4GHv27EFxcTFCQ0ORnp5en8NRb9OnT8ejjz6K8PDwZt1vbZoyngsXLgAAnnzySeTn5+Pbb7/FxYsXsXTpUiQnJyM0NBSlpaUNjicoKAjAf69Ral5MCInorjB9+nSEhoZCo9Fg+PDhGDVqFDIyMlBYWGhWt7KyEmvWrDHWHzhwIDZv3oxr167hueeea9I4a2pqjMliS2S4uWu1Wqvq+/r6IiUlBUqlErGxsTh+/HitdRcsWIBff/0Vb7zxBkaPHg1PT0/07NkTW7duRWBgIOLi4iz2mLXm3C5YsABnzpzB66+/jpEjR8Ld3R19+vTBBx98ABGx+glmY9iwYQNOnjyJFStWNNs+76Sp4zE8tVer1di4cSO6du0KLy8v/PnPf8aCBQtw4sQJ/P3vf29wPJ6enlAoFMZrlJoXE0IiuisMGjTI5Ofg4GAAN3sx3k6j0RhfPxn069cP7du3R1ZWVpPecG59atUSGW7uSqXS6nXuvfdeJCYmorKyElFRUbhy5YrFejt27AAAjBo1yqRcpVIhIiICV65csfg60Jpzu3PnTjg5OZkNTRQQEIA+ffrg4MGDOH/+vNW/U32dPXsWCQkJSE5ONnu6bA/NEY9hu8OHDzdrIjFmzBgA/33N29B4nJ2da72+qGkxISSiu8LtT7RcXFwAwOJQNV5eXha34efnBwC4ePFiI0d393B1dQVwc5BhW8TFxSE6OhpHjhyxOFTN1atXUVpaCldXV4ttxgy9U/Py8syW1XVuDduuqamBVqs1Gxjb0Mbt5MmTNv1O9ZGamorS0lI88MADJjEYhlVZvHixsezUqVOtIp7OnTsDAHx8fMyWGf6mCgoKGiWe6urqenWooYZjQkhErU5RUZHFV7aGRNBwEwNutpO7du2aWd2SkhKL21YoFI0UpX0EBgYCgFmbL2skJSWhV69eSE5OxqZNm0yWqVQqaLVaVFVVoby83Gxdw6vigIAAm/erUqng5eUFZ2dnXL9+3fhK/vbPsGHDbN62rZ599lmL+zYcj6VLlxrLunfv3iriMXS6svRk3fA3ZUj4GxJPWVkZRMR4jVLzYkJIRK1OVVWVccYNg8OHDyM3Nxc6nc7khhMYGIicnByTunl5eTh79qzFbbu5uZkkkL169cL69esbMfqmZRgvrj6vV93d3fHRRx9Bo9FgzZo1ZssnTJgAAGbDhly9ehVpaWlQq9XQ6/X1iBqYOHEiqqurTXqKG7z22mvo2LEjx69rIiNHjkRQUBB2795tNvyQYQaS8ePHN3g/hr/D2sY0pKbFhJCIWh2tVouFCxciPT0dlZWVOHDgAKZOnQoXFxesWrXKpG5kZCRyc3Px1ltvoaKiAtnZ2XjuuedMniLe6g9/+ANOnDiBc+fOIT09HadPn0ZYWJhxeUvvZazT6eDn54esrKx6rd+nTx+sW7fO4rJXX30VXbp0QXx8PD799FOUl5fjxIkTePTRR3HhwgWsWrXKbGBja7366qvo1q0bnnzySezatQulpaUoLi7GunXr8MorryAxMdGkfdvUqVOhUCjw66+/1mt/je1ujkelUiEpKQlFRUV45JFHcPLkSZSUlGDTpk149dVXMXjwYMTFxTU4JsPwQY09yDdZSahOkyZNkkmTJtk7DKIWB4Bs27atQdvYsWOHADD5xMTESHp6uln5okWLjPu99TNq1Cjj9nQ6nQQFBcmxY8dEr9eLh4eHqNVqGTp0qHz//fdm+y8pKZHp06dLYGCgqNVqGTJkiGRkZEhISIhx+88//7yx/vHjxyUsLEw0Go0EBwfL6tWrTbYXFhYm3t7esm/fvgYdl1tt27ZNGvPreuHCheLs7Cw5OTnGsoKCArPjGhISUus2Zs6cKT4+PmblhYWFEh8fL126dBGlUilarVb0er2kpaUZ69T33BYVFcncuXOla9euolQqpV27dhIZGSlffvmlWRzh4eHi7u4u1dXVVh2T1NRUs30bPhs2bKh1vdjYWIvr6PX6VhWPiMi+fftEr9eLVqsVFxcX6d27tyxZskQuX77c4HhERKKioiQoKEiuXbtmdUx14f3begqRFjo2QgsSFRUFAPjwww/tHAlRy6JQKLBt2zZMnjzZ3qEY9e/fH4WFhc3S47S5pKSkIDo6utGGsiktLUWfPn0wevRorF27tlG22ZKUlJSgffv2iImJwYYNG+wdDuOxQlZWFgYMGICtW7fikUceabTt8v5tPb4yJgDApUuXsHbtWoSHh6Nt27ZQq9Xo0aMHYmJirH619MEHHxh7jxl6MtpCRLB37148++yz6NmzJ1QqFfz8/DBkyBBs3ry5Ucd1y8jIwOOPP44uXbpArVajbdu26Nu3Lx5++GH885//tDhLgr3Zeo7c3d3NemM6OTnB29sbOp0OzzzzDA4ePGiH34TsTavVIjU1Fdu3b8fq1avtHU6jEhHExcXB09MTS5cutXc4jMcKp0+fxsSJE7FgwYJGTQbJNkwICQCQkJCA2bNnY9y4cTh27BiKioqQnJyMzMxMhISEYOfOnXVu45FHHoGIICIiol4x/PLLLxgyZAhOnDiB7du3o7S0FPv370fHjh0xbdo0JCQk1Gu7t6qpqUFCQgLuu+8++Pn5YdeuXSgpKcHPP/+MlStXoqysDM888wy6d+/e4hqo23qOKioq8NNPPwEAxo0bBxHB9evXcfz4cbzyyis4fvw4Bg4ciCeeeAKXL1+2x69EdjRgwAAcOHAAu3btQllZmb3DaTT5+fk4ffo00v4/e3ceFmW5/w/8PcDMMAwwICiblEsSuY3+0IySrykmKSTJEcm0VZMWJXPJ1NJOaabH03KyMsM2zZJTXy007ZhlHRVPWIKmaYmmKYsssgoI8vn94XfmOMwgw7AMOO/Xdc11xf3cz/18Zp7HmU/Pcy87d9o0opnxtL133nkHS5cuxdKlS+0dimOz17PqjsQR+iBMmTJFpk2bZlaekZEhAKRXr15WtxUZGSlqtbrJMfz666/i4uIiRUVFJuXV1dXi4+MjarVaqqqqmtzulRYsWCAAZM2aNRa319bWyujRowWA1NTUNOtYLc2Wc3TgwAEBILGxsRbbfPrppwWAjB07Vurq6pocE1qgD2FL+dvf/tZgv7SOrqX7EBI5Ckf4/W4pHWtVdmo1hjVG69Pr9dBoNMjKyoKItOocbKGhoRYny1WpVAgODkZGRgaqqqqgVqttav/o0aN4+eWXERYWhkceecRiHWdnZzz33HPYtm2bTcdoTa1xjl5++WV8//33+PLLL/Hpp5+arR/bkcyZMwdz5syxdxhERB0SHxnTVVVUVKCyshJ9+/a124S8xcXF+P333zFw4ECr11+1ZM2aNairqzN2Mm5IeHg4RMRsiab2qjnnSKFQGFedsDSvHBEROQYmhK2osLAQs2bNQs+ePaFWq9G1a1eMHDkSH3zwgclajVfWU6lU8Pb2xujRo/Hdd98Z62zevNlkcMAff/yBhIQEeHl5wcfHBzExMcaBEMXFxWaDCZYsWQLg8rJAV5aPHz/+qu/BMDJr4cKFZtuOHj2Ku+++GzqdDlqtFhEREdi9e3ezPzeD0tJS7NmzB2PHjoW/vz8++uijZrX3ww8/AAD69+/f5H076jmyhmEVgn379jV5OTMiIrpG2PmRdYdgSx+EnJwc6d69u/j7+0tqaqqUlpZKbm6uvPjiiwJAXn31VZN6fn5+kpqaKiUlJXLs2DGJi4sThUJhNt9UbGyssU/Y3r17pby8XHbs2CEajUYGDx5sUvfOO+8UJycnOX78uFl84eHhsmHDhqu+h9zcXPHz85OpU6eabfv999/Fy8tLgoKC5F//+peUlZXJwYMHZdSoUdKtWzeb+hBeyfA5AZDbb79dDh48aLHe8OHDpVOnTpKWltZomwEBAQJA/vOf/zQplo56jkQa70MoIlJZWWn8rLOzs696vPrQjvoQXsvYh5DINuxDaD1+w1jBlgvqwQcfbPDH8s477zQmhIZ6n3zyiUmdqqoqCQwMFI1GI7m5ucZyQ7KRmppqFiMAyc/PN5Z98803AkAef/xxk7q7d++W66677qqDJgoKCmTAgAGSkJBgceLS+Ph4ASCfffaZSfnZs2dFrVY3OyEUuTyY5Ndff5VHH31UnJ2d5YUXXjCrM2zYMKsnATYkhD/++GOT4uio50jEuoTwwoULTAjbOSaERLZhQmi9jtFJqgPatGkTAGD06NFm264csGCoFx0dbVJHrVYjMjIS69atw9dff43777/fZPvgwYNN/g4ODgYAZGdnw9fXFwAQGRmJgQMH4oMPPsALL7wAHx8fAMDf/vY3zJw5s8E+chUVFYiKikLv3r3x0UcfwdnZ2azO9u3bAcBsXdLAwECEhITgt99+s9h2U6hUKoSGhuLtt99GXl4eFi1ahPDwcIwcOdJYZ9euXVa3FxgYiJycHBQUFDQpjo56jqw2g6K1AAAgAElEQVRlWLBeqVQa42qKtLQ0m49N1jF8xikpKXaOhKhjOXPmDLp27WrvMDoEJoStoLq6GiUlJXB1dYWHh4fN9Qxrfubm5pptqz+4QqVSAbg8z96VZs+ejcmTJ+Ott97Cc889h99++w0//PAD1q1bZzGm2tpaxMfHIygoCB9++KHFRKO6uhplZWVwdXWFu7u72fYuXbq0SEJ4pbvuugubNm3Cli1bTBLCphg2bBh++uknHDx40GKibklHPUdNYej3GR4eDqVS2eT9X3vtNbz22mvNioGsk5CQYO8QiDqcxvph02UcVNIK1Go1dDodqqqqUFZWZnO9vLw8AGjW5KEJCQkIDg7GqlWrUF1djb///e945JFHGkxUExMTUV1djZSUFJO7UzfccAP27dtnjNvDwwNVVVUoLy83a6OoqMjmeBtimGqmOW0nJibCxcUFn3322VXrPf3003BycsLRo0c77DmyVl1dnXGliieeeMKm+Ddu3Ai53P2Er1Z6bdy4EQDsHgdffHW0F5NB6zEhbCXjxo0DAHz11Vdm2wYOHIinnnrKpN7WrVtN6lRXV2Pnzp3QaDRmj2WbwsXFBU8++STOnTuHv//97/j000+RlJRkse7zzz+Pw4cP44svvmh0rj/DHTbDo2ODgoICHDt2zKZY58yZg8mTJ1vcZnjMXv8xbFOEhIRg8eLF2L9/P9577z2LdY4dO4Z33nkHEyZMQGhoKICOe46sMX/+fPz4448YN25co9PxEBHRNUyoUc0ZZRwQECBbtmyR0tJS+fPPP+Wxxx4TPz8/OXXqlEk9wwjW0tJSkxGs9VfUMAxYqKysNCmfN2+eAJADBw6YxVJaWio6nU4UCoXcf//9FuN9//33zVZ5qP+6ciTv8ePHpVOnTiajjA8fPixRUVHSpUsXmwaVzJ49WxQKhfz1r3+VkydPSlVVlZw8edK4mkZYWJhcuHDBZJ+mjDI2eOaZZ0SpVMq8efPk2LFjUl1dLWfOnJHk5GQJCAiQoUOHSnl5ubF+Rz1HIuaDSi5duiR5eXmyefNmGTFihACQhx9+2OxztRY4qKRNcFAJkW04qMR6/Iaxgq0XVEFBgcycOVO6d+8uSqVSAgIC5J577pHffvvtqvV0Op1ERUXJzp07jXXS0tIaXJarfnl0dLRZLHPnzhUAkpmZaTHW6OjoJicbx44dk7vvvls8PT2NU6ps2bJFIiMjjftMmTLF6s+rpKREkpOTJSoqSrp16yYqlUrc3d0lLCxMli1bZjFpiYiIsHqU8ZV+/PFHue+++yQ4OFiUSqV4eHjILbfcIq+//rpUV1eb1e+I50ir1ZptVygUotPppF+/fvLYY4/JTz/91KTPrT4mhG2DCSGRbZgQWk8hItLMm4zXPMOjNMMEwER0mUKhwMaNGzFhwgR7h3JNS0lJQUJCAvh1TdQ0/P22HvsQEhERETk4JoRERNe4U6dOYezYsSgtLUVBQYHJ0ogDBw5EVVWV2T716ykUCgwaNMgO0beOr776CiEhIVdds/z8+fNYvXo1RowYgU6dOkGj0aBXr16YNGkSMjMzGY8N8dTW1mLt2rW4+eab4ePjA29vb4SFhWHVqlW4ePGiSd1nnnnGOMKe2oCdH1l3COyD0DxopN8bAFm8eLG9wyQbgH0I20Rz+hAeOHBAfH195Y033jApT09PN/77S0xMbHD/tLQ08fHxsenY7dHx48flrrvukv79+4unp6c4Ozs3WHfKlCni4uIir732muTk5EhFRYX88MMP0rt3b3F2dpZNmzYxnibGM3nyZAEg8+fPl7y8PCkoKJDly5cLAImJiTGLpXv37vLss8/a/H74+209JoRW4AVFZFl7Swi1Wq3cdttt19zxbU0IS0pKpGvXrhYTvvT0dFGr1eLj4yMAGlw3+1pLCCdOnCjLli2TmpoaCQoKajThmTZtmll5RkaGAJBevXoxnibEk5WVJQBk4MCBZvvccccdFpcWzcjIEIVCYfP3DH+/rceVSoiIrlErVqxAbm4uFi1aZHG7q6srPv74Y4wZMwaJiYkICwtDSEhIG0fZttauXQuNRmNV3eTkZIvler0eGo0GWVlZEBEoFArGY0U8f/75JwDgpptuMtsnNDQUO3bswOnTp03mm9Xr9Rg/fjxmz56NuLi4qz7CpuZhH0IiomuQiCA5ORlDhgxBYGBgg/WioqLw7LPPoqysDPHx8Rb7E15LrE12rqaiogKVlZXo27dvs5IvR4snNDQUSqUSR48eNdvn6NGjUCgU6Nevn9m2cePG4cyZM2aLA1DLYkJIRG2qsLAQs2bNQs+ePaFSqeDt7Y3Ro0fju+++M9ZZsmSJcSDD0KFDjeXbt283lvv6+hrLV65cCYVCgYqKCuzZs8dYx3A3wbBdoVCga9euSE9PR2RkJDw8PODm5obhw4djz549rXZ8e8jMzEReXh70en2jdRcvXoxRo0bh4MGDmDFjhlXtW3MeN2/ebDIo5Y8//kBCQgK8vLzg4+ODmJgYZGVlmbWdn5+PpKQkdOvWDSqVCp07d0ZcXBwyMjKs/wBakWEKk4ULF9o5kss6Sjx+fn5YuXIlMjMzsWDBAuTn56OoqAgrVqzAN998g0WLFlm8Qz1gwAAAwNdff936wTsy+z6x7hjYB4HIMjSxD2H9VV9KSkpMVn159913Teo31CcvLCzMYr+2xvrw6fV60Wq1Eh4eLnv37pXy8nJJT0+X/v37i0qlkl27drXq8W1ZWUfEtj6E69atEwDy0ksvWdyenp4uOp3O+Hd+fr4EBwcLAFm/fr2x3FIfwqaeR8PqPbGxscbPfceOHcYJ7a+UnZ0t119/vfj5+cnWrVulrKxMfvnlFxk2bJi4uro2eRL6q2msj5wlubm54ufnJ1OnTm2xOBwtnpSUFOnatatxUJOvr6+sXbu2wfolJSUCQCIiIpoUiwh/v5uCdwiJqM3Mnz8fJ0+exGuvvYaYmBh4enoiJCQEGzZsQEBAAJKSkpCXl9eqMVRUVOCtt95CeHg4tFotBg0ahPXr1+PixYt48sknW/XYdXV1kMuD+Vr1OACQk5MDANDpdFbV9/X1RUpKCpRKJRITEy0+1jOw9TxOnTrV+LmPHDkS0dHRSE9PR0FBgUnbp06dwiuvvIIxY8bA3d0dffr0waeffgoRsfoOZmsoLCzEnXfeidtvvx2rV6+2WxwdNR4RwbRp0zBp0iTMmjULubm5yM/Px9KlSzF9+nTcc889qK2tNdvP09MTCoXCeE1T62BCSERtZtOmTQCA6Ohok3K1Wo3IyEhUVla2+mMhrVZrfARl0K9fPwQGBiIzM7NVf3R27dqFoqIihIeHt9oxDAx9AZVKpdX73HLLLVi5ciUqKioQHx+PyspKi/VsPY9XDhYAgODgYABAdna2sWzz5s1wcnJCTEyMSV1/f3/06dMHP/30E86cOWP1e2opFRUViIqKQu/evfHxxx/D2dm5zWPo6PGsW7cO7777Lh599FE89dRT8PPzg6+vL6ZNm2acc3DVqlUW23dxcWnweqSWwYSQiNpEdXU1SkpK4OrqCg8PD7Ptfn5+AIDc3NxWjcPLy8tieZcuXQAA586da9XjtxVXV1cAQE1NTZP2S0pKQkJCAn755RdMnz7dbHtzzmP9u5UqlQrA5TunV7ZdV1cHnU5nNjH2zz//DAD4/fffm/Semqu2thbx8fEICgrChx9+aPfkq6PGs337dgDAyJEjzbZFRkYCALZt29bgMVpiwAs1jAkhEbUJtVoNnU6HqqoqlJWVmW03PGL09/c3ljk5OZmtXgAAxcXFFo9hzQjLwsJCi49sDYmgITFsreO3lYCAAABASUlJk/dNTk7GjTfeiPfeew/r1q0z2WbLebSWWq2Gl5cXXFxcUFNTY3y8Xv81fPjwJrfdHImJiaiurkZKSorJQKEbbrgB+/bta9NYOnI8FRUVjbZVXl5uVlZaWgoRMV7T1DqYEBJRmxk3bhwAmE0fUV1djZ07d0Kj0SAqKspYHhAQgLNnz5rUzc3NxenTpy227+bmZpLA3XjjjVizZo1JnaqqKqSnp5uUHTp0CNnZ2dDr9SY/Oq1x/LbSt29fALDp8aq7uzs+//xzaLVavPXWW2bbm3oemyIuLg61tbUmo74Nli9fjuuuu85iP7PW8vzzz+Pw4cP44osvoFar2+y412I8Q4YMAQDs3LnTbNu3334L4HK3hfoM/wYN1zS1EvuMZelYOEqJyDI0c5RxaWmpyejUNWvWmNSfPn26AJA33nhDysrK5Pjx4zJhwgQJCgqyOMr3zjvvFJ1OJ6dPn5a9e/eKi4uLHDlyxLhdr9eLTqeTyMhIq0YZt/Tx23KUcV1dnXTp0qXBUc/1Rxlbsn79egHQ6Cjjxs6jYZRxZWWlSfm8efMEgBw4cMBYlpeXJz179pQePXrIV199JcXFxVJYWCirV68WNzc3s+tt0qRJAkBOnDjR6GdSX2OjaN9///1Gl92sfy4ZT8PxnD9/Xnr16iVKpVJef/1149J1ycnJ4ubmJkFBQZKdnW12nA0bNggAm5bm4++39ZgQWoEXFJFlTU0IRUQKCgpk5syZ0r17d1EqlaLT6SQqKkp27txpVre4uFimTp0qAQEBotFoZOjQoZKeni5hYWHGH5x58+YZ6x89elQiIiJEq9VKcHCwvPnmmybt6fV6CQoKkiNHjkhUVJR4eHiIRqORYcOGye7du1v9+BEREeLt7d3kqVNsXbpuwYIF4uLiImfPnjWW5efnm/1oh4WFNdjGY489ZjH5teY8pqWlmR1r4cKFImK+xnl0dLRxv8LCQpk1a5b06NFDlEqldO7cWUaNGiU7duwwi2PEiBHi7u4utbW1Vn0mqampDSYv9afLiY6ObnICxniuHk9RUZHMnTtXQkNDRa1Wi0qlkp49e8r06dMlNzfXYkzx8fESFBQkFy9etOo9XIm/39ZTiLTB/AcdXHx8PID/TrZJRJcpFAps3LgREyZMsHcoVhkwYAAKCgrsMkq1OVJSUpCQkNDk6WpKSkrQp08fxMTEtItpSVpacXExAgMDMWnSJLz77rv2DofxtILMzEwMHDgQGzZswD333NPk/fn7bT32ISQiukbpdDqkpqbis88+w5tvvmnvcFqUiCApKQmenp548cUX7R0O42kFJ06cQFxcHObPn29TMkhNw4SQiOgaNnDgQOzfvx/btm1DaWmpvcNpMXl5eThx4gR27txp04hmxtP+vfPOO1i6dCmWLl1q71Acgv0W2iQiaiMrV67E3LlzjX8rFAosXLgQS5YssWNUbadbt27YsmWLvcNoUf7+/ti9e7e9wzBiPC1v+fLl9g7BoTAhJKJr3pw5czBnzhx7h0FE1G7xkTERERGRg2NCSEREROTgmBASEREROTgmhEREREQOjoNKrLRv3z7jBJdE9F+vvvoqJ31tZYaJtPkdRNQ0+/bts7g+MpnjSiVWeOWVV5CWlmbvMIioFRw6dAgA0K9fPztHQkStITw8HLNmzbJ3GO0eE0IicmiGZfdSUlLsHAkRkf2wDyERERGRg2NCSEREROTgmBASEREROTgmhEREREQOjgkhERERkYNjQkhERETk4JgQEhERETk4JoREREREDo4JIREREZGDY0JIRERE5OCYEBIRERE5OCaERERERA6OCSERERGRg2NCSEREROTgmBASEREROTgmhEREREQOjgkhERERkYNjQkhERETk4JgQEhERETk4JoREREREDo4JIREREZGDY0JIRERE5OCYEBIRERE5OCaERERERA6OCSERERGRg2NCSEREROTgmBASEREROTgmhEREREQOjgkhERERkYNjQkhERETk4JgQEhERETk4JoREREREDo4JIREREZGDU4iI2DsIIqK28NFHH+GVV17BpUuXjGUFBQUAAF9fX2OZs7MzZs2ahfvvv7/NYyQisgcmhETkMH777TfceOONVtU9duwYQkJCWjkiIqL2gY+MichhhISEQK/XQ6FQNFhHoVBAr9czGSQih8KEkIgcyv333w9nZ+cGt7u4uOCBBx5ow4iIiOyPj4yJyKFkZ2cjODgYdXV1FrcrFAr8+eefCAoKauPIiIjsh3cIicihBAYG4tZbb4WTk/nXn5OTE2677TYmg0TkcJgQEpHDue+++yyWKxQKjiwmIofER8ZE5HDOnz8PPz8/1NTUmJS7uLggNzcXPj4+doqMiMg+eIeQiByOt7c37rjjDpPBJc7OzoiKimIySEQOiQkhETmkyZMnmwwsERFMnjzZjhEREdkPHxkTkUO6cOECfHx8UFVVBQBwdXVFQUEBtFqtnSMjImp7vENIRA7Jzc0N48aNg1KphFKpxLhx45gMEpHDYkJIRA7r3nvvRU1NDWpqanDvvffaOxwiIrtxqV9w5swZ7N271x6xEBG1qUuXLsHNzQ0igtLSUqSkpNg7JCKiVnfrrbeia9euJmVmfQhTUlKQkJDQpoERERERUdvYuHEjJkyYYFJmdofQgGNNiMgRfP/991AoFPif//mfZrdl+B9qfn+2vvj4eADAP//5TztHQtSxKBQKi+UNJoRERI4gIiLC3iEQEdkdE0IicmiW1jQmInI0/CYkIiIicnBMCImIiIgcHBNCIiIiIgfHhJCIiBzOqVOnMHbsWJSWlqKgoAAKhcL4GjhwoHFJwyvVr6dQKDBo0CA7RN86vvrqK4SEhMDFpeHhBefPn8fq1asxYsQIdOrUCRqNBr169cKkSZOQmZnJeGyIp7a2FmvXrsXNN98MHx8feHt7IywsDKtWrcLFixdN6j7zzDPYuHFji74vI6ln48aNYqGYiIga0ZLfn2VlZXLDDTdIdHR0i7R3rRk/fryMHz/epn0PHDggvr6+8sYbb5iUp6enCwABIImJiQ3un5aWJj4+PjYduz06fvy43HXXXdK/f3/x9PQUZ2fnButOmTJFXFxc5LXXXpOcnBypqKiQH374QXr37i3Ozs6yadMmxtPEeCZPniwAZP78+ZKXlycFBQWyfPlyASAxMTFmsXTv3l2effZZm98PANm4caN5ef0CJoRERLZpye/P0tJS6dGjh4wePbpF2mtNWq1WbrvttjY9pq0JYUlJiXTt2tViwpeeni5qtVp8fHwEgGzYsMFiG9daQjhx4kRZtmyZ1NTUSFBQUKMJz7Rp08zKMzIyBID06tWL8TQhnqysLAEgAwcONNvnjjvuEADy448/mrWlUCgsJnXWaCgh5LQzRETtkIeHB7KysuwdxjVnxYoVyM3NxaJFiyxud3V1xccff4wxY8YgMTERYWFhCAkJaeMo29batWuh0WisqpucnGyxXK/XQ6PRICsrCyLS4OTHjMc0nj///BMAcNNNN5ntExoaih07duD06dMYPHiwSVvjx4/H7NmzERcXd9VH2E3BPoREROQQRATJyckYMmQIAgMDG6wXFRWFZ599FmVlZYiPj7fYn/BaYm2yczUVFRWorKxE3759m5V8OVo8oaGhUCqVOHr0qNk+R48ehUKhQL9+/cy2jRs3DmfOnMHWrVubHZsBE0IionZm8+bNJgMXDAlJ/fI//vgDCQkJ8PLygo+PD2JiYkzuKq5cudJYt2vXrkhPT0dkZCQ8PDzg5uaG4cOHY8+ePcb6S5YsMdYfOnSosXz79u3Gcl9fX7P2KyoqsGfPHmOdlrpj0dIyMzORl5cHvV7faN3Fixdj1KhROHjwIGbMmGFV+4WFhZg1axZ69uwJlUoFb29vjB49Gt99952xTlPPoUF+fj6SkpLQrVs3qFQqdO7cGXFxccjIyLD+A2hFhiUEFy5caOdILuso8fj5+WHlypXIzMzEggULkJ+fj6KiIqxYsQLffPMNFi1aZPEO9YABAwAAX3/9dcsFWf8ZMvsQEhHZpqW/P2NjYwWAVFZWWiyPjY2VvXv3Snl5uezYsUM0Go0MHjzYrB29Xi9arVbCw8ON9dPT06V///6iUqlk165dJvUb6hMYFhZmse9cY30Ihw8fLp06dZK0tDRr33qjbOlDuG7dOgEgL730ksXt6enpotPpjH/n5+dLcHCwAJD169cbyy31IczJyZHu3buLn5+fpKamSklJiRw7dkzi4uJEoVDIu+++a1K/KecwOztbrr/+evHz85OtW7dKWVmZ/PLLLzJs2DBxdXWVvXv3NulzuJrG+shZkpubK35+fjJ16tQWi8PR4klJSZGuXbsaBzX5+vrK2rVrG6xfUlIiACQiIqJJsYg03IeQdwiJiDqoqVOnIjw8HFqtFiNHjkR0dDTS09NRUFBgVreiogJvvfWWsf6gQYOwfv16XLx4EU8++WSrxllXVwe5PIixVY/TmJycHACATqezqr6vry9SUlKgVCqRmJho8bGewfz583Hy5Em89tpriImJgaenJ0JCQrBhwwYEBAQgKSkJeXl5ZvtZcw7nz5+PU6dO4ZVXXsGYMWPg7u6OPn364NNPP4WIWH0HszUUFhbizjvvxO23347Vq1fbLY6OGo+IYNq0aZg0aRJmzZqF3Nxc5OfnY+nSpZg+fTruuece1NbWmu3n6ekJhUJhvKZbAhNCIqIO6sqO5gAQHBwMAMjOzjarq9VqjY+ZDPr164fAwEBkZma26A9Lfbt27UJRURHCw8Nb7RjWMDx6VyqVVu9zyy23YOXKlaioqEB8fDwqKyst1tu0aRMAIDo62qRcrVYjMjISlZWVFh/vWXMON2/eDCcnJ8TExJjU9ff3R58+ffDTTz/hzJkzVr+nllJRUYGoqCj07t0bH3/8MZydnds8ho4ez7p16/Duu+/i0UcfxVNPPQU/Pz/4+vpi2rRpxjkHV61aZbF9FxeXBq9HWzAhJCLqoOrf6VKpVAAu35Grz8vLy2IbXbp0AQCcO3euhaNrf1xdXQEANTU1TdovKSkJCQkJ+OWXXzB9+nSz7dXV1SgpKYGrqys8PDzMtvv5+QEAcnNzzbY1dg4NbdfV1UGn05lNjP3zzz8DAH7//fcmvafmqq2tRXx8PIKCgvDhhx/aPfnqqPFs374dADBy5EizbZGRkQCAbdu2NXiMlhjwYsCEkIjIARQWFlp8ZGtIBA2JIQA4OTmZrZAAAMXFxRbbbu4ozrYSEBAAACgpKWnyvsnJybjxxhvx3nvvYd26dSbb1Go1dDodqqqqUFZWZrav4VGxv79/k4+rVqvh5eUFFxcX1NTUGB+9138NHz68yW03R2JiIqqrq5GSkmIyiOiGG27Avn372jSWjhxPRUVFo22Vl5eblZWWlkJEjNd0S2BCSETkAKqqqpCenm5SdujQIWRnZ0Ov15v8sAQEBODs2bMmdXNzc3H69GmLbbu5uZkkkDfeeCPWrFnTgtG3jL59+wKATY9X3d3d8fnnn0Or1eKtt94y2z5u3DgAMJsGpLq6Gjt37oRGo0FUVJQNUQNxcXGora01GRFusHz5clx33XUW+5m1lueffx6HDx/GF198AbVa3WbHvRbjGTJkCABg586dZtu+/fZbAJe7LdRn+PdpuKZbAhNCIiIHoNPpsGDBAqSlpaGiogL79+/H5MmToVKp8Prrr5vUHTVqFLKzs7Fq1SqUl5cjKysLTz75pMldxCv9v//3//Dbb7/hzz//RFpaGk6cOIGIiAjj9hEjRsDHx8cud2qupNfr0aVLF5vXuO3Tpw/eeecdi9uWLVuG7t27Y+bMmdiyZQvKysrw22+/4d5770VOTg5ef/1146Pjplq2bBl69uyJhx9+GNu2bUNJSQmKiorwzjvv4IUXXsDKlStN7kJNnjwZCoUCJ0+etOl4V/PBBx/gr3/9K/7zn//Aw8PD7BG2pSlzGE/D8Tz++OPo1asX3n77bfzjH//AuXPnUFhYiLVr1+Lll19GUFAQ5syZY3Ycw3RDo0aNarng6w875rQzRES2aanvz02bNhmnnzC8Jk2aJGlpaWblCxcuFBExK79yDWS9Xi9BQUFy5MgRiYqKEg8PD9FoNDJs2DDZvXu32fGLi4tl6tSpEhAQIBqNRoYOHSrp6ekSFhZmbH/evHnG+kePHpWIiAjRarUSHBwsb775pkl7ERER4u3t3aLTo9i6dN2CBQvExcVFzp49ayzLz883+/zCwsIabOOxxx6zOP1OQUGBzJw5U7p37y5KpVJ0Op1ERUXJzp07jXVsPYeFhYUya9Ys6dGjhyiVSuncubOMGjVKduzYYRbHiBEjxN3dXWpra636TFJTU82ObXjVny4nOjq6wbqGV/3phRjP1eMpKiqSuXPnSmhoqKjValGpVNKzZ0+ZPn265ObmWowpPj5egoKC5OLFi1a9hyuBaxkTEbWu9vr9aUgIryW2JoTFxcUSFBRkcS3ja8H58+dFo9G0yhx8tmA8Lc+wlvEnn3xi0/4NJYR8ZNyO1F9VoD04f/48Vq9ejREjRqBTp07QaDTo1asXJk2aZPVjl08//dT4vgyj/JpCRLBnzx488cQTCAkJgVqtRpcuXTB06FCsX7++WXOblZeXm93ST0tLa3S/uXPnmuyzZMkSm2Owhru7u1mcCoUCTk5O6Ny5M+6++26z/mEt7Vq4Pi19jk5OTvD29oZer8fjjz+On376yQ7vhNqKTqdDamoqPvvsM7z55pv2DqdFiQiSkpLg6emJF1980d7hMJ5WcOLECcTFxWH+/Pm45557Wrbx+hlie/0/XEdi6f/my8rK5IYbbjB5hNAWpkyZIi4uLvLaa69JTk6OVFRUyA8//CC9e/cWZ2dn2bRpk9VtRUZGilqtbnIMv/76qwCQkSNHSmZmplRWVkpWVpZMnDhRAMjs2bOb3GZ9Bw4cMN7OHz169FXrFhQUiLu7u/ExXlsxxBgbG2ssKy4ulv/93/+VLl26iFKptPj4qKV19Ouz/udYW1srubm5snnzZhk+fLgAkAcffFAqKiqaHE97/f7kHUJzJ0+elOjoaCkpKWnBqOwrJydHbrvtNl6RfbEAACAASURBVPnll1/sHYqIMJ7W8PTTT9t8Z9AAvEPYsYkI6urqLM4v1toefvhhPPnkk/D394ebmxsiIiKwYcMGXLp0CU8//XSbxODi4oKUlBT0798frq6u6NGjBz744AP4+Phg1apVqK6ubvYxNBoNrr/+emzbtg379+9vsN6rr75qnDzW3nQ6HcaNG4dXXnkFNTU1mDlzpl3i6MjXp7OzM/z8/BAbG4tvv/0WTz/9ND744ANMnDjR7itrNJfhrm5mZibOnj0LhUKBZ5991t5htQvdunXDli1b4Onpae9QWoy/vz92796NPn362DsUAIynNSxfvrzl7wz+HyaEHYSHhweysrLw1Vdftelxk5OTLY6q0+v10Gg0yMrKavUfzdDQUNTU1MDb29ukXKVSITg4GNXV1cYVCJrDyckJzzzzDAA0+Ai4uLgYb7/9NubNm9fs47Ukwxxkhw8fbnCuuNZ0LV2fL7/8MoYMGYIvv/wSn376aUuFahdz5swxm6+utbs3EFHHxISQbFJRUYHKykr07dvXbpPSFhcX4/fff8fAgQOtXpu0MQ899BCCgoLw5Zdf4uDBg2bb//GPf2DMmDHo2bNnixyvpVyZ9HSUSYJbU3OuT4VCYVyNwtJ8c0RE16JmJ4SbN2826aB96tQpJCQkwMPDAz4+Prjvvvtw/vx5/PHHH7jrrrvg4eGBgIAAPPLII2YzutfW1mLjxo2444474O/vD41Gg379+uH11183eRQ1dOhQk2NOnjwZwOWlX64st/ZOSf3O8unp6YiMjISHhwfc3NwwfPhwixOCFhYWYtasWejZsydUKhW8vb0xevRofPfdd82q29hnbLgbVr/8jz/+QEJCAry8vODj44OYmBiLczAdPXoUd999N3Q6Hdzc3HDzzTdjy5YtJp/f1KlTrxrTP//5TwDAwoULr9q+VqtFREQEdu/e3ej7tFZpaSn27NmDsWPHwt/fHx999FGLta1WqzF37lyICJYuXWqyrby8HG+88QYWLFjQ4P72uoZ37doF4PI8aYbkmNen5evTGkOHDgUA7Nu3r8nLnBERdUj1OxXa2ik6NjZWAEhcXJzs379fysvL5aOPPjJ20o+NjZUDBw5IWVmZrF69WgDIU089ZdKGYa6fl156SYqKiiQ/P1/+8Y9/iJOTk8yZM8ekbkZGhmi1WtHr9VJeXi4iIlVVVTJkyBCbO1zq9XrRarUSHh4ue/fulfLycklPT5f+/fuLSqWSXbt2Gevm5ORI9+7dxc/PT1JTU6WkpESOHTsmcXFxolAoTOYmakpdQxyWOoAbPuPKykqL5bGxsca4d+zYIRqNRgYPHmxS9/fffxcvLy8JCgqSf/3rX1JWVia//PKLjBw5Ujp37mzVoI/c3Fzx8/OzOGzfUvsHDx6UUaNGSbdu3WwaVHKlF1980Tj44/bbb5eDBw9arDd8+HDp1KmT2XxPDTlw4IBotVoREblw4YL4+fmJk5OTHDlyxFjn5ZdflgkTJoiIyL///W+Lg0pa8xq2NKikpKTE4qASXp+Wr8+GPsf6KisrjddZdnZ2o8c0aK+DSq5FzR1UQuSo0NrzEBq+9Ldu3WpS3qdPHwEg33//vUl59+7d5cYbbzQpS01Nldtvv92s7cmTJ4tSqTQbDZaSkmJMQuvq6uSBBx6QBQsWNDl2A71eLwDkwIEDJuUHDx4UAKLX641lDz74oAAw++GuqqqSwMBA0Wg0xgklm1LXEIctP7ipqakm5ePHjxcAkp+fbyyLj48XAPLZZ5+Z1D137py4ubk1+oNbUFAgAwYMkISEBIuTejbU/tmzZ0WtVjc7IRQRqa6ull9//VUeffRRcXZ2lhdeeMGszrBhw5o0Ee6VCaGIyPLlywWATJ48WUREKioqxM/PTzIzM0Xk6glha13DV46ENrwUCoX4+PjI2LFj5ccffzTW5fVp+foUsS4hvHDhAhPCdo4JIZFtGkoI/7vWTQsZNGiQyd+BgYE4fPiwWXlQUJDZPGExMTGIiYkxa1Ov12P9+vU4fPgwwsPDjeXx8fFYuHAhli5diqFDh6JTp0547733mhW/VqvFgAEDTMr69euHwMBAZGZmIicnBwEBAdi0aRMAIDo62qSuWq1GZGQk1q1bh6+//hr3339/k+o2x+DBg03+NoyEzc7Ohq+vLwBg+/btAGC2pmbnzp0RGhqKw4cPN9h+RUUFoqKi0Lt3b3z00UdwdnY2q9NQ+4GBgQgJCcFvv/3WxHdlTqVSITQ0FG+//Tby8vKwaNEihIeHY+TIkcY6hkeotnr88cexYsUKfPLJJ1i8eDFSU1Nxyy23oH///lfdry2u4djYWGzevPmqdXh9Wr4+rZWTkwMAUCqVxtiaIj4+3uZjk3UMy+DxsyZqGS0+qKT+EH4nJyc4OzvDzc3NpNzZ2dlsioqSkhIsWrQI/fr1g7e3t7G/0Ny5cwEAFy5cMDveiy++iCFDhmDv3r2Ij4+Hk1Pz3pKXl5fFcsManufOnUN1dTVKSkrg6uoKDw8Ps7qG9Spzc3ObVLe56g+sUKlUAGD8nKurq1FWVgZXV1e4u7ub7V9/FO+VamtrER8fj6CgIHz44YcWf2wba7+hdVCb46677gIAbNmypUXbdXd3x8yZM3Hp0iUsXrwYK1eutGq6jvZwDfP6tHx9NoWhz2t4eDiUSmWz2iIi6gha/A5hc9x1113497//jddffx0TJ06Er68vFAoFXnvtNTz11FMWp4/YtWsXSkpK0K9fPzz++OPQ6/XQ6/U2x1BYWAgRMRuZeO7cOQCXkxq1Wg2dToeSkhKUlZWZ/ZDm5eUBuDznUVPqtja1Wg0PDw+UlZWhvLzc7EfX8B4tSUxMRHV1NTZt2mSyiPoNN9yA9evX45Zbbmm0/aKiopZ9Q7j8nlqr7RkzZmDlypXYsGEDRo8ebXaX25L2cA3z+rR8fVqrrq7OuILFE0880cR3cZlhUAu1HsOdQX7WRE3T0MwL7WbamUuXLmHPnj3w9/dHUlISOnfubAy6srLS4j4nT57ElClT8Pnnn+PLL7+ERqNBbGws8vPzbY6jqqrKbAmwQ4cOITs7G3q9HgEBAQCAcePGAQC2bt1qUre6uho7d+6ERqMxPvZqSt3WNnr0aAD/fTRnkJub2+Dj3Oeffx6HDx/GF198YUzAmtp+QUEBjh07ZlPMc+bMMY7CrW/btm0AzB9HtgSdTodZs2ZBp9NZdXewvVzDAK/P5pg/fz5+/PFHjBs3jo8jichx1O9U2NxBJfU7lEdFRYmzs7NZ/WHDhpl04hcRGTFihACQFStWSH5+vly4cEG+/fZbue666wSAybJcZWVl0r9/f/niiy+MZbt27RKlUin/8z//IxcvXmzye9Dr9aLT6SQyMrLJo4xLS0tNRmauWbPGprqGOGzptF+/fN68eWaDZI4fPy6dOnUyGcV56NAhufPOO+X6668367T//vvvmw1kqP+6ciSvpfYPHz4sUVFR0qVLF5sGlcyePVsUCoX89a9/lZMnT0pVVZWcPHlSnn76aQEgYWFhcuHCBZN9mjPK2BoNDSppzWvYmsEQBrw+LV+flj7HS5cuSV5enmzevNl4/h5++GGza8oaHFTSdjiohMg2aK1RxmlpaWZfwAsXLpT09HSz8mXLlhl/SK98LV68WERE8vPzJTExUYKDg0WpVIqfn588+OCD8swzzxjrhoWFyRNPPGGy/6FDhyQ/P9+s3RdffLFJH5Lhh+7IkSMSFRUlHh4eotFoZNiwYbJ7926z+gUFBTJz5kzp3r27KJVK0el0EhUVJTt37rSp7t/+9jeLn+WmTZvMyidNmtTgZy8iZuVXrjF77Ngxufvuu8XT01Pc3Nzk1ltvle+//15uv/12cXNzM4k7Ojq6yT+4V7ZvmFpky5YtEhkZadxnypQpVp+XkpISSU5OlqioKOnWrZuoVCpxd3eXsLAwWbZsmcUf7oiICKtHGWu1WpP3ExUVddX6lj6DN954Q0Ra7xquHyMAs1H69fH6NL8+LX2OCoVCdDqd9OvXTx577DH56aefrvq5Xg0TwrbDhJDINg0lhIr/22iUkpKChISEDr+Gpy0GDBiAgoICnDlzxt6h2EVoaCgqKytx6tQpe4dCZKYjXJ+O/P3Z1tiHkMg2CoUCGzduxIQJE0zK200fQmobubm56NSpk9nqC3/88QeysrIwYsQIO0VGxOuT2q9Tp05h7NixKC0tRUFBgckqPAMHDrS4nnr9egqFwqrBaR1BTU0NXn31VYSFhcHDwwNdunTB6NGjkZqa2iL/Q9Ta7QPAV199hZCQEJOBaPWdP38eq1evxogRI9CpUydoNBr06tULkyZNMps6z6C2thZr167FzTffDB8fH3h7eyMsLAyrVq3CxYsXTeo+88wz2LhxY4u8n+ZiQuiAzp8/j8TERPz555+4cOECfvzxRyQkJMDT0xPPPfecvcMjB8frk9qbjIwMDBo0CKNGjYKnpyd8fX0hIsYBiBkZGZg5c6bZfoZ6aWlp8PHxgYhg//79bR1+i6uoqMCIESPwwQcf4NVXX8W5c+ewf/9+uLu7Y+zYsVedL7Q9tJ+VlYWxY8di/vz5xpkUGjJ37lzMmDEDsbGxOHLkCAoLC/Hee+8hIyMDYWFhFueEfeihhzB16lSMHDkSv/76K44fP46EhATMmDEDf/nLX0zqPvLII5g/f377+G6r/wz5WusDg0b6FwGw2K/I0NfpWvTNN9/IuHHjjP3x/Pz8ZNKkSXL8+PE2jcOac2PoX0qOo71cn7Zoj9+fWq1Wbrvttmvu+G3Vh7CkpES6du0qiYmJZtvS09NFrVaLj4+PAJANGzZYbCMtLU18fHxaO9Q289hjj4mnp6fJCkYiIuXl5aJWq+XQoUPtuv2JEyfKsmXLpKamRoKCgiwOfDWYMmWKTJs2zaw8IyNDAEivXr1MyrOysgSADBw40GyfO+64QwCYrCplaEuhUFjs19ca0EAfwnY1D2FrEPblMRMZGYnIyEh7h8FzQxa1l+uTCABWrFiB3NxcLFq0yOJ2V1dXfPzxxxgzZgwSExMRFhaGkJCQNo6y7eTl5WHNmjWYNm2aceJ6A61Wa/HReXtqHwDWrl0LjUZjVd3k5GSL5Xq9HhqNBllZWSZzF//5558AgJtuuslsn9DQUOzYsQOnT582mSpNr9dj/PjxmD17NuLi4q76CLs18ZExERGRBSKC5ORkDBkyBIGBgQ3Wi4qKwrPPPouysjLEx8e3SNLSXn355Ze4dOkShg4d2iHbB2B1Mng1FRUVqKysRN++fU0meg4NDYVSqcTRo0fN9jl69CgUCgX69etntm3cuHE4c+aM2XywbYkJIRGRnRUWFmLWrFno2bMnVCoVvL29MXr0aHz33XfGOkuWLDEOTLjyx3L79u3G8ivXXV65ciUUCgUqKiqwZ88eYx3D3QfDdoVCga5duyI9PR2RkZHw8PCAm5sbhg8fjj179rTa8TuCzMxM5OXlWbVy0OLFizFq1CgcPHgQM2bMsKp9a8775s2bTQal/PHHH0hISICXlxd8fHwQExODrKwss7bz8/ORlJSEbt26QaVSoXPnzoiLi0NGRob1H4AFP//8M4DLS0nOnj0bwcHBUKlUuP7665GUlNTsVaNau/2WYhjdvnDhQpNyPz8/rFy5EpmZmViwYAHy8/NRVFSEFStW4JtvvsGiRYss3kEeMGAAAODrr79u/eAbUv8ZcnvsA0NE1BHY8v1Zf2LwkpISk4nB3333XZP6DfXJCwsLs9hPrbE+fHq9XrRarYSHhzc6IX9rHL+pk8gbtEUfwnXr1gkAeemllyxuT09PF51OZ/w7Pz9fgoODBYCsX7/eWG6pD2FTz7thgvfY2FjjedqxY4dxrtcrZWdny/XXXy9+fn6ydetWKSsrk19++UWGDRsmrq6uVs3P2hBDHP7+/jJp0iTJysqS8+fPy4cffiharVZCQkKkuLi43bZfX2N9CC3Jzc0VPz8/mTp1aoN1UlJSpGvXrsb+8L6+vrJ27doG65eUlAgAiYiIaFIstkADfQiZEBIRtRBbvj8ffPBBASCffPKJSXlVVZUEBgaKRqMx6VzfGgkhYLpijIjIwYMHBYDo9Xqr2rP1+MOGDbN6EvkrtUVCuGLFCgEgb775psXt9RNCkcvJn1KpFK1WK7/++quxrP5n09TzbkiUUlNTTeqPHz9eAEh+fr6x7IEHHhAA8vHHH5vUzcnJEbVaLWFhYVZ+AuaioqIEgHTv3l1qampMti1ZskQAyHPPPddu26+vqQlhQUGBDBgwQBISEqS2ttZse11dnTzyyCOiVCrllVdekdzcXMnPz5d33nlHNBqNJCQkmL0vA4VCITfccIPN78VaDSWEfGRMRGRHmzZtAgBER0eblKvVakRGRqKysrLVHyNptVrjIyuDfv36ITAwEJmZmcjJyWm1Y+/atQtFRUUIDw9vtWPYytAXUKlUWr3PLbfcgpUrV6KiogLx8fENrmNu63mvv257cHAwACA7O9tYtnnzZjg5OSEmJsakrr+/P/r06YOffvrJ5gUYtFotAGDkyJFmj//vuusuAM177Nna7TdHRUUFoqKi0Lt3b3z88cdwdnY2q7Nu3Tq8++67ePTRR/HUU0/Bz88Pvr6+mDZtmnHOwVWrVlls38XFpcHrpS0wISQispPq6mqUlJTA1dUVHh4eZtsNoyxzc3NbNQ4vLy+L5V26dAEAnDt3rlWP3165uroCgNlE6Y1JSkpCQkICfvnlF0yfPt1se3POu06nM/lbpVIBAOrq6kzarqurg06nM5sY29BH7/fff2/SezLo1q0bAMDHx8dsm+F6yc/Pt6nttmjfVrW1tYiPj0dQUBA+/PBDi8kgcLlPLXA5oa3PMHvCtm3bGjxGSwx4sRUTQiIiO1Gr1dDpdKiqqkJZWZnZdsOkuf7+/sYyJycns9UOAKC4uNjiMa4cAdmQwsJCi9NAGRJBww9xax2/vQoICAAAlJSUNHnf5ORk3HjjjXjvvfewbt06k222nHdrqdVqeHl5wcXFBTU1NZDLXcPMXsOHD29y2wCMA4os3TU2XC/1p4tpT+3bKjExEdXV1UhJSTG5c3nDDTdg3759xr8rKioabau8vNysrLS0FCJivObsgQkhEZEdjRs3DgDMppuorq7Gzp07odFoEBUVZSwPCAjA2bNnTerm5ubi9OnTFtt3c3MzSeBuvPFGrFmzxqROVVWVcdUNg0OHDiE7Oxt6vd7kR6o1jt9e9e3bFwBserzq7u6Ozz//HFqtFm+99ZbZ9qae96aIi4tDbW2tyShxg+XLl+O6665DbW2tTW2PGTMGQUFB2L59u9n0OqmpqQCAu+++26a226J9Wzz//PM4fPgwvvjiC6jV6qvWHTJkCABg586dZtu+/fZbAJe7FdRn+DdluObson6nQg4qISKyTUuMMi4tLTUZbbpmzRqT+tOnTxcA8sYbb0hZWZkcP35cJkyYIEFBQRYHddx5552i0+nk9OnTsnfvXnFxcZEjR44Yt+v1etHpdBIZGWnVKOOWPn57HmVcV1cnXbp0aXBQjKVBJfWtX79eADQ6yrix824YVFJZWWlSPm/ePLNBQXl5edKzZ0/p0aOHfPXVV1JcXCyFhYWyevVqcXNzMxtQMGnSJAEgJ06caPQzERHZtm2buLi4SGxsrPz2229y/vx5+eijj0Sr1cqQIUPkwoUL7br9KzU2qOT9999vdEWtK6/d8+fPS69evUSpVMrrr78ueXl5UlBQIMnJyeLm5iZBQUGSnZ1tdpwNGzYIANm0aVOT30NTgaOMiYhal63fnwUFBTJz5kzp3r27KJVK0el0EhUVJTt37jSrW1xcLFOnTpWAgADRaDQydOhQSU9Pl7CwMOMP1Lx584z1jx49KhEREaLVaiU4ONhsxKxer5egoCA5cuSIREVFiYeHh2g0Ghk2bJjs3r271Y8fERHRbkcZi4gsWLBAXFxc5OzZs8ay/Px8s6TgaiN3H3vsMYvJsjXnPS0trcGlVeuXR0dHG/crLCyUWbNmSY8ePUSpVErnzp1l1KhRsmPHDrM4RowYIe7u7hZHzTZk7969EhUVJTqdTlQqlYSGhsrzzz9vlqy1x/ZTU1MbTO7qT/cTHR3dpIRQRKSoqEjmzp0roaGholarRaVSSc+ePWX69Olmy/EZxMfHS1BQkFy8eNHKT8h2TAiJiFpZR/z+NCSEHU1bJYTFxcUSFBRkcS3ja8H58+dFo9FcdU49R26/LRjWMq4/BVFraSghZB9CIiKiBuh0OqSmpuKzzz7Dm2++ae9wWpSIICkpCZ6ennjxxRfZvh2cOHECcXFxmD9/Pu655x67xsKEkIiI6CoGDhyI/fv3Y9u2bSgtLbV3OC0mLy8PJ06cwM6dO20a0Xytt98W3nnnHSxduhRLly61dyjoOItKEhFRi1m5ciXmzp1r/FuhUGDhwoVYsmSJHaNqv7p164YtW7bYO4wW5e/vj927d7N9O1q+fLm9QzBiQkhE5IDmzJmDOXPm2DsMImon+MiYiIiIyMExISQiIiJycEwIiYiIiBwcE0IiIiIiB8eEkIiIiMjBNTjKWKFQtGUcRETXDH5/th1+1kQtQ/F/y5gYnTlzBnv37rVXPEREberVV18FADz11FN2joSIqG3ceuut6Nq1q0mZWUJIRORIJkyYAABISUmxcyRERPbDPoREREREDo4JIREREZGDY0JIRERE5OCYEBIRERE5OCaERERERA6OCSERERGRg2NCSEREROTgmBASEREROTgmhEREREQOjgkhERERkYNjQkhERETk4JgQEhERETk4JoREREREDo4JIREREZGDY0JIRERE5OCYEBIRERE5OCaERERERA6OCSERERGRg2NCSEREROTgmBASEREROTgmhEREREQOjgkhERERkYNjQkhERETk4JgQEhERETk4JoREREREDo4JIREREZGDY0JIRERE5OCYEBIRERE5OCaERERERA6OCSERERGRg2NCSEREROTgmBASEREROTgXewdARNRWCgoKUFpaalJWUVEBADhx4oRJuaenJ3x9fdssNiIie1KIiNg7CCKitvD+++/j4Ycftqrue++9h4ceeqiVIyIiah+YEBKRwygpKUHnzp1RU1Nz1XpKpRL5+fnQ6XRtFBkRkX2xDyEROQydTocxY8bAxaXh3jIuLi6Ijo5mMkhEDoUJIRE5lMmTJ+PSpUsNbq+rq8PkyZPbMCIiIvvjI2MicihVVVXw9fU1Diapz83NDQUFBdBoNG0cGRGR/fAOIRE5FFdXV8TFxUGpVJptUyqVGD9+PJNBInI4TAiJyOHce++9FgeW1NTU4N5777VDRERE9sVHxkTkcGpra+Hn54eioiKTci8vL+Tn51910AkR0bWIdwiJyOG4uLhg4sSJJo+NlUolJk+ezGSQiBwSE0IickgTJ040eWxcU1ODiRMn2jEiIiL74SNjInJIIoLg4GCcPXsWABAQEICzZ89CoVDYOTIiorbHO4RE5JAUCgXuu+8+qFQqqFQqPPDAA0wGichh8Q4hETmsgwcPQq/XG/+7X79+do6IiMg+2Hu6nYqPj7d3CEQOwd3dHQDwwgsv2DkSIsfwz3/+094hkAV8ZNxOffbZZzhz5oy9wyAy2rdvH/bt22fvMFrc9ddfj27dutk7DBP890/XojNnzuCzzz6zdxjUAD4ybqcUCgU2btyICRMm2DsUIgD/vWt9rf3f/YkTJwAAPXr0sHMk/8V//3QtSklJQUJCAph2tE98ZExEDq09JYJERPbCR8ZEREREDo4JIREREZGDY0JIRERE5OCYEBIRXUNOnTqFsWPHorS0FAUFBVAoFMbXwIEDUVVVZbZP/XoKhQKDBg2yQ/Qtr6amBq+++irCwsLg4eGBLl26YPTo0UhNTW2RwQ2t3T4AfPXVVwgJCbnqOtvnz5/H6tWrMWLECHTq1AkajQa9evXCpEmTkJmZaXGf2tparF27FjfffDN8fHzg7e2NsLAwrFq1ChcvXjSp+8wzz2Djxo0t8n6ofWJCSERtrry8HL169UJMTIy9Q7mmZGRkYNCgQRg1ahQ8PT3h6+sLEUF6erpx+8yZM832M9RLS0uDj48PRAT79+9v6/BbXEVFBUaMGIEPPvgAr776Ks6dO4f9+/fD3d0dY8eOxeHDh9t1+1lZWRg7dizmz5+PvLy8q9adO3cuZsyYgdjYWBw5cgSFhYV47733kJGRgbCwMGzevNlsn4ceeghTp07FyJEj8euvv+L48eNISEjAjBkz8Je//MWk7iOPPIL58+fjueeea9Z7onZMqF0CIBs3brR3GERG48ePl/Hjx7dIW6WlpdKjRw8ZPXp0i7TXmrRardx2221tekxb/v2XlJRI165dJTEx0Wxbenq6qNVq8fHxEQCyYcMGi22kpaWJj4+PTTG3R4899ph4enpKbm6uSXl5ebmo1Wo5dOhQu25/4sSJsmzZMqmpqZGgoCBxdnZusO6UKVNk2rRpZuUZGRkCQHr16mVSnpWVJQBk4MCBZvvccccdAkB+/PFHs7YUCoXNv00bN24Uph3tF6edIaI25+HhgaysLHuHcU1ZsWIFcnNzsWjRIovbXV1d8fHHH2PMmDFITExEWFgYQkJC2jjKtpOXl4c1a9Zg2rRp8PPzM9mm1WotPjpvT+0DwNq1a6HRaKyqm5ycbLFcr9dDo9EgKysLImJcr/vPP/8EANx0001m+4SGhmLHjh04ffo0Bg8ebNLW+PHjMXv2bMTFxV31ETZ1PHxkTETUwYkIkpOTMWTIEAQGBjZYLyoqCs8++yzKysoQHx/fIklLe/Xll1/i0qVLGDp0aIdsH4DVyeDVVFRUoLKyEn379jUmg8DlpE+pVOLo0aNm+xw9ehQKhcLi2t7jxo3DmTNnsHXr1mbHRu0LE0IialObN282GbxgSErqnCe2JAAAIABJREFUl//xxx9ISEiAl5cXfHx8EBMTY3JXceXKlca6Xbt2RXp6OiIjI+Hh4QE3NzcMHz4ce/bsMdZfsmSJsf6VP+Lbt283lvv6+pq1X1FRgT179hjrtMe7IpmZmcjLy4Ner2+07uLFizFq1CgcPHgQM2bMsKr9wsJCzJo1Cz179oRKpYK3tzdGjx6N7777zlinqefPID8/H0lJSejWrRtUKhU6d+6MuLg4ZGRkWP8BWPDzzz8DALy9vTF79mwEBwdDpVLh+uuvR1JSEoqKitp1+y3FsLLQwoULTcr9/PywcuVKZGZmYsGCBcjPz0dRURFWrFiBb775BosWLbJ4B3nAgAEAgK+//rr1g6e2Ze9n1mQZ2IeQ2pmW7EMoIhIbGysApLKy0mJ5bGys7N27V8rLy2XHjh2i0Whk8ODBZu3o9XrRarUSHh5urJ+eni79+/cXlUolu3btMqnfUJ/AsLAwi/3nGutDOHz4cOnUqZOkpaVZ+9Yb1dR//+vWrRMA8tJLL1ncnp6eLjqdzvh3fn6+BAcHCwBZv369sdxSH8KcnBzp3r27+Pn5SWpqqpSUlMixY8fk/7N373FR1fn/wF8HmRlggAFBARFv5KXIJUNTWlkUCdYgSRKx0FpdlC6K5mULy7ZNzXTZ0r5aKerX8rJK9tBCVzej/PZTsUULyguJl7whykWuCoq8f3/4mFnHGRQQGGRez8dj/uDz+ZzPeZ8zM87bcz6fz4mOjhZFUSQlJcWofUPev7y8POnatat4eHjI9u3bpby8XA4dOiTBwcFiZ2cn+/btq/c5uJ0+Dk9PT4mLi5MTJ07I5cuX5dNPPxWtViu9evWSkpKSVtv/7e42htCc/Px88fDwkPj4+DrbpKamSufOnQWAABB3d3dZtWpVne1LS0sFgAQFBTUoFhGOIWzt+M60UkwIqbVp6YQwLS3NZP8ApKCgwKjc399fAMhPP/1kVP7zzz8LAPH39zcqb+qEMDg4WFxdXe8pebldQ7//ixYtEgCybNkys/W3J4QiN5M/lUolWq1Wjh49aii7/Rz86U9/EgDyz3/+06i8qqpKOnXqJPb29kaTKhry/r3wwgsCQNavX2/U9sKFC6LRaCQgIKCeZ8BUeHi4AJDu3bvL9evXjermzZsnAGTOnDmttv/bNTQhLCwslEceeURiY2OlpqbGpL62tlYmTpwoKpVK3n//fcnPz5eCggJZvny52NvbS2xsrMlx6SmKIg888ECDj4EJYevGW8ZE1CrdOpgdAHx8fAAAeXl5Jm21Wq3hVpZe37590alTJ2RnZ+PChQvNFufu3btRXFyMwMDAZtvH3ehvu6tUqnpvM2jQICQnJ6OyshIxMTG4evWq2XZbtmwBAERERBiVazQaDBs2DFevXjV7+7A+79/WrVthY2NjsvyQp6cn/Pz8cPDgQZw7d67ex3QrrVYLAAgNDTW5zf/UU08BuLfbns3d/72orKxEeHg4HnroIaxfvx7t2rUzabN27VqkpKTgxRdfxKuvvgoPDw+4u7tj0qRJhjUHly5darZ/W1vbOj8vdP9iQkhErZJOpzP6W61WAwBqa2tN2rq4uJjto2PHjgCAS5cuNXF0rYudnR2Am4skN0RiYiJiY2Nx6NAhTJ482aS+uroapaWlsLOzg5OTk0m9fnZtfn6+Sd3d3j9937W1tdDpdCYLY+vH6OXm5jbomPS6desGAHBzczOp038uCgoKGtV3S/TfWDU1NYiJiYG3tzc+/fRTs8kgcHPsLHAzob3dsGHDAAA7duyocx9NMeGFWhcmhER03ysqKjL7VAh9Iqj/gQYAGxsbk6cwAEBJSYnZvm+dmdlaeXl5AQBKS0sbvO3KlSvRu3dvrF69GmvXrjWq02g00Ol0qKqqQnl5ucm2+sWSPT09G7xfjUYDFxcX2Nra4vr165CbQ5hMXkOHDm1w3wAME4fMXR3Wfy5uXy6mNfXfWAkJCaiurkZqaqrRlcsHHngA+/fvN/xdWVl5174qKipMysrKyiAihs8ctR1MCInovldVVWV4GofeL7/8gry8PPj7+xv9eHl5eeH8+fNGbfPz83HmzBmzfTs4OBglkL1798aKFSuaMPp79/DDDwNAo26vOjo64osvvoBWq8VHH31kUj9y5EgAMFlmpLq6Gunp6bC3t0d4eHgjogaio6NRU1NjNBtcb+HChejSpQtqamoa1feTTz4Jb29v7Ny502R5nbS0NADA008/3ai+W6L/xnj77bdx+PBhfPnll9BoNHdsO3DgQABAenq6Sd23334L4Oawgtvpvzv6zxy1HUwIiei+p9PpMHv2bGRkZKCyshIHDhzA2LFjoVarsWTJEqO2YWFhyMvLw9KlS1FRUYETJ05g6tSpRlcRb/Xoo4/i2LFjOHv2LDIyMnDy5EkEBQUZ6kNCQuDm5mZ09aWl+fv7o2PHjnU+s/Zu/Pz8sHz5crN1CxYsQPfu3TFt2jRs27YN5eXlOHbsGJ577jlcuHABS5YsafSVsAULFsDX1xcTJkzAjh07UFpaiuLiYixfvhzvvPMOkpOTja5yjR07Foqi4NSpU3ftW6PRYOXKlSgqKsKYMWOQm5uLkpISrF27FgsWLMDAgQORmJhotE1r6r+h1qxZg7/97W/44Ycf4OTkZHIL/vYlf15++WX07NkTH3/8MT788ENcunQJRUVFWLVqFd577z14e3tj5syZJvvRLwcUFhbW5MdAFma5+Sx0J+AsY2plmmqW8ZYtWwxLXOhfcXFxkpGRYVL+xhtviIiYlEdERBj68/f3F29vbzly5IiEh4eLk5OT2NvbS3BwsOzZs8dk/yUlJRIfHy9eXl5ib28vgwcPlszMTAkICDD0/9prrxna5+TkSFBQkGi1WvHx8TGZyRsUFGTxWcYiIrNnzxZbW1s5f/68oaygoMDk3N1p5u5LL71kdqZ1YWGhTJs2Tbp37y4qlUp0Op2Eh4dLenq6oU1j37+ioiKZPn269OjRQ1QqlXTo0EHCwsJk165dJnGEhISIo6Oj2Vmzddm3b5+Eh4eLTqcTtVotffr0kbfffluuXLnS6vtPS0szOXf61+3L/URERNTZVv+6fWmk4uJimTVrlvTp00c0Go2o1Wrx9fWVyZMnmzyOTy8mJka8vb3l2rVr9TxD/8VZxq0b35lWigkhtTZNvexMU9EnhG1JY77/JSUl4u3tbfZZxm3B5cuXxd7e/o5r6llz/y1B/yzj25cgqi8mhK0bbxkTEbUBOp0OaWlp2Lx5M5YtW2bpcJqUiCAxMRHOzs6YO3cu+7eAkydPIjo6GklJSRgzZoylw6FmwISwjTl9+jTGjx+PLl26QK1WG40hmTdvnqXDszhHR0eTsTV1vVauXGnyeDSi1qxfv344cOAAduzYgbKyMkuH02QuXryIkydPIj09vVEzmtt6/y1h+fLlmD9/PubPn2/pUKiZMCFsQwoKCjBo0CD8+OOPSE1NRUlJCUQEGRkZlg6t1aioqMBPP/0EAIiKiqpzqYvg4GAAwMyZMyEi9XpGLLUsfbKenZ2N8+fPQ1EUvPnmm5YOy+K6deuGbdu2wdnZ2dKhNBlPT0/s2bMHfn5+7N9CFi5cyCuDbRwTwjZk5cqVyM/PxwcffIBBgwbBwcGhyfp2dHQ0rLvVmHriOWxq+mT91hevghMRNY7t3ZvQ/eKXX34BcPORXXRvdu/ebekQiIiIWgyvELYhV65cAQCzj5ii+pk8eTKmTZtm6TCIiIhaFBPCNmDr1q1QFAVffvklAMDe3h6Kotzx9mNNTQ02bdqEJ554Ap6enrC3t0ffvn2xZMkSo2fF6sdpVVZWYu/evYYJFvrFYu9Wr1dQUIDExER069YNarUaHTp0QHR0tGGR01uPQ//67bffEBsbCxcXF7i5uSEyMtJkcVVL4jkkIqI2wwJL3VA9oBHrkEVFRQkAuXr1qlG5fsHYuXPnGsr0C56+++67UlxcLAUFBfLhhx+KjY2NzJw506RvrVYrv//97+vc953q8/LypGvXruLh4SHbt2+X8vJyOXTokAQHB4udnZ3Jgr7644iKipJ9+/ZJRUWF7Nq1S+zt7WXAgAEm/Q8dOlTat29vsuhqXX766ac7Lt46depUk23MrXXXls5hfbTWdQjbosZ8/4laO65D2LrxCqEVGzJkCJKSkuDq6gp3d3dMmTIFzz33HJYsWdKkS1YkJSXh9OnTeP/99/Hkk0/C0dERfn5+2LhxI0QEU6ZMMbtdfHw8AgMDodVqERoaioiICGRmZqKwsNCoXW1trWFSQUOYm2X8yiuvNKiPtnIOiYjIujEhtFKRkZH47rvvTMr9/f1x/fp1HD58uMn2tXXrVtjY2CAyMtKo3NPTE35+fjh48CDOnTtnst2AAQOM/vbx8QEA5OXlGZXv3r0bxcXFCAwMbLKY66MtncP62rx5c73XceSr8S8AiI2NtXgcfPHVlK/Y2NhG/btDLYOzjK1UaWkp/vGPf2DLli04d+4cSkpKjOr1E1TuVXV1NUpLSwHcfJJCXXJzc00Wfr69vVqtBgCj8XlNbenSpfVua43ncNCgQXj11VcbtS3VX2xsLKZNm9bi/8khak4ZGRlYvHixpcOgOjAhtFJPPfUU/t//+39YsmQJnn32Wbi7u0NRFCxevBivvvqqye1XRVHu2F9d9RqNBi4uLqioqMDVq1dNJkrcz6zxHHbu3BmjR4+22P6tRWxsLAIDA3muqc1hQth68ZaxFbpx4wb27t0LT09PJCYmokOHDoZk5OrVq2a3cXBwwLVr1wx/9+7dGytWrKhXfXR0NGpqarB3716TfhcuXIguXbqgpqamSY6tpfAcEhFRW8KE0Aq1a9cOQ4YMQX5+Pv7+97+jsLAQV69exXfffYdPPvnE7DaPPvoojh07hrNnzyIjIwMnT55EUFBQveoXLFgAX19fTJgwATt27EBpaSmKi4uxfPlyvPPOO0hOTr6nq14hISFwc3PD/v37G91HQ7W1c0hERFbOUtOb6c7QgGUntmzZYrJ0SlxcnIiI+Pr6mtSdPXtWCgoKJCEhQXx8fESlUomHh4f86U9/ktdff93QLiAgwLCPnJwcCQoKEq1WKz4+PrJs2TKjGO5WX1RUJNOnT5cePXqISqWSDh06SFhYmOzatcvQRr88zq2vN954w3A+bn1FREQYtgsKChJXV1eTpVfM0Wq1Jn15eHjU2f7vf/97nTG1pXNYH1x2puU05PtPdL/gsjOtmyLSwLU6qEUoioJNmzZxDBG1GjExMQCAzz//3MKRtH38/lNblJqaitjY2AYvEUYtg7eMiYjIxOnTpzFixAiUlZWhsLDQaPmQfv36oaqqymSb29spioL+/ftbIPrmNWLECCiKgnnz5tXZ5vr16/jggw8QEBAAJycndOzYEcOHD0daWprZhCgrKwsRERFwcXGBk5MTQkNDzY4ZBm4+JWnVqlV47LHH4ObmBldXVwQEBGDp0qVG45AB4PXXX8emTZvu7YDJKjAhJCIiI1lZWejfvz/CwsLg7OwMd3d3iAgyMzMN9eae+a1vl5GRATc3N4gIDhw40NLhN6vPPvsMaWlpd2xTWVmJkJAQrFmzBh988AEuXbqEAwcOwNHRESNGjDBZo/SHH37A448/DicnJxw9ehSnTp1Cjx49MGTIEHz99dcm/Y8fPx7x8fEIDQ3F0aNHcfz4ccTGxmLKlCl45plnjNpOnDgRSUlJmDNnzr0fPLVtFrxdTXcAjiGiVqY1jiG82+MA79f9W/L7X1paKp07d5aEhASTuszMTNFoNOLm5iYAZMOGDWb7yMjIEDc3t+YOtcWdP39eXF1dZdy4cSaPA73VSy+9JM7OzpKfn29UXlFRIRqNRn755RdD2Y0bN8TPz0+8vLzkypUrhvKamhrp3bu3+Pj4SFVVlaH8xIkTAkD69etnst8nnnhCAMh//vMfo/KsrCxRFMXivykcQ9i68QohEREZLFq0CPn5+XjrrbfM1tvZ2WH9+vWwsbFBQkICjh071sIRWs7EiRMRExODsLCwOttcvHgRK1asQFxcHDw8PIzqtFotqqqq8PDDDxvKvv/+exw+fBijRo2Cvb29obxdu3Z49tlncfbsWWzbts1QfvbsWQDAgw8+aLLvPn36AADOnDljVO7v749Ro0ZhxowZXJ6K6sSEkIiIAAAigpUrV2LgwIHo1KlTne3Cw8Px5ptvory8HDExMWbHE7Y1q1evxuHDh5GcnHzHdl999RVu3LiBwYMH16vfb7/9FgDMjrXUl6WnpxvK+vTpA5VKhZycHJP2OTk5UBQFffv2NakbOXIkzp07h+3bt9crLrI+TAiJqFkVFRVh+vTp8PX1hVqthqurK4YPH270HOh58+YZJiHc+kO6c+dOQ7m7u7uhPDk5GYqioLKyEnv37jW00a/FqK9XFAWdO3dGZmYmhg0bBicnJzg4OGDo0KFGA/abev/3q+zsbFy8eBH+/v53bfvXv/4VYWFh+PnnnzFlypR69V+fz8LWrVuNJqX89ttviI2NhYuLC9zc3BAZGYkTJ06Y9F1QUIDExER069YNarUaHTp0QHR0NLKysup/Aupw7tw5zJgxA6tXr4aTk9Md2/74448AAFdXV8yYMQM+Pj5Qq9Xo2rUrEhMTUVxcbNRen9jd/thJAPD29gYAo6uwHh4eSE5ORnZ2NmbPno2CggIUFxdj0aJF+Oabb/DWW2+hV69eJn098sgjAIB///vfDThysiqWvmdN5oFjCKmVacwYwgsXLkj37t3Fw8ND0tLSpLS0VH799VeJjo4WRVEkJSXFqH1dY/ICAgLMjkm72xg+f39/0Wq1EhgYKPv27ZOKigrJzMyU3/3ud6JWq2X37t3Nuv+hQ4dK+/btJSMjo8425ljq+7927VoBIO+++67Z+szMTNHpdIa/CwoKxMfHRwDIunXrDOXmxhA29LMQFRUlACQqKsrw3u3atUvs7e1lwIABRm3z8vKka9eu4uHhIdu3b5fy8nI5dOiQBAcHi52dXb3WKL2T8PBwefnllw1/68+TuTGE+rg9PT0lLi5OTpw4IZcvX5ZPP/1UtFqt9OrVS0pKSgzt9eP+9u/fb9JXbm6uAJBHH33UpC41NVU6d+5sWFfU3d1dVq1aVecxlJaWCgAJCgpq6OE3GY4hbN14hZCImk1SUhJOnTqFxYsXIzIyEs7OzujVqxc2bNgALy8vJCYm4uLFi80aQ2VlJT766CMEBgZCq9Wif//+WLduHa5du4apU6c2675ra2shIvfNumsXLlwAAOh0unq1d3d3R2pqKlQqFRISEszextRr7GchPj7e8N6FhoYiIiICmZmZKCwsNOr79OnTeP/99/Hkk0/C0dERfn5+2LhxI0Sk3lcwzUlJSUFubi4WLVpUr/b62+f29vZYs2YNevToARcXFzz//PNISkrCsWPH8I9//KNefek/N7c+51xEMGnSJMTFxWH69OnIz89HQUEB5s+fj8mTJ2PMmDFmxwk6OztDURTDe0x0OyaERNRstmzZAgCIiIgwKtdoNBg2bBiuXr3a7LewtFqt4XaZXt++fdGpUydkZ2c36w/k7t27UVxcjMDAwGbbR1PSJzMqlare2wwaNAjJycmorKxETExMnc/ybuxnYcCAAUZ/+/j4AADy8vIMZVu3boWNjQ0iIyON2np6esLPzw8HDx7EuXPn6n1MemfOnMGsWbOwevVqaLXaem2jbxcaGmoyhOCpp54CYHzb1sXFBcDN/7jcTl+mbwMAa9euRUpKCl588UW8+uqr8PDwgLu7OyZNmmRYc3Dp0qVmY7O1ta3z/SFiQkhEzaK6uhqlpaWws7MzO+5KPwMzPz+/WeO49cf0Vh07dgQAXLp0qVn3fz+xs7MDcHNR5YZITExEbGwsDh06hMmTJ5vU38tn4farlWq1GsDNq6+39l1bWwudTmeyMLZ+TF9ubm6DjgkA0tLSUFpaiiFDhhj1OW7cOADAnDlzDGXHjx8HAHTr1g0A4ObmZtKf/jNXUFBgKNPPDDaXsJ4/fx4AjMYE7ty5E8DNhPN2w4YNAwDs2LHD7PHU1NQYzWQmuhUTQiJqFhqNBjqdDlVVVSgvLzep198e9PT0NJTZ2NiYPGkBAEpKSszu49ZbaXUpKioye8tWnwjqf6Sba//3Ey8vLwBAaWlpg7dduXIlevfujdWrV2Pt2rVGdY35LNSXRqOBi4sLbG1tcf36dcMt+ttfQ4cObXDfr7zyitm+9Mc3d+5cQ9kDDzwAAIZJSeauPOs/c7cuR6OP6+DBgybt9WX6RA8wfyXxdhUVFSZlZWVlEBHDe0x0OyaERNRsRo4cCQAmS11UV1cjPT0d9vb2CA8PN5R7eXkZroro5efnm6yrpufg4GCUwPXu3RsrVqwwalNVVWV4wobeL7/8gry8PPj7+xv9QDbH/u8n+vXxGnN71dHREV988QW0Wi0++ugjk/qGfhYaIjo6GjU1NWYf9bZw4UJ06dKlxdbfe/LJJ+Ht7Y2dO3eaLMejf8LJ008/bSgLDg7GQw89hM2bNxu1v3HjBjZu3AgfHx+j2+wDBw4EYLwUjZ5+CZtBgwaZ1Ok/17eugUhkpGXnsFB9gbOMqZVpilnGZWVlRjNLV6xYYdR+8uTJAkD+53/+R8rLy+X48eMyevRo8fb2NjvL949//KPodDo5c+aM7Nu3T2xtbeXIkSOGen9/f9HpdDJs2LB6zTJu6v3fb7OMa2trpWPHjnXOnL59lrE569atEwB3nWV8t8+Cfrbu1atXjcpfe+01ASA//fSToezixYvi6+srPXr0kH/9619SUlIiRUVF8sknn4iDg4PJuYyLixMAcvLkybueE3PuNMtYRGTHjh1ia2srUVFRcuzYMbl8+bJ89tlnotVqZeDAgUZPJBG5OSvbzs5OxowZIxcuXJDCwkJJSEgQW1tb2blzp1Hby5cvS8+ePUWlUsmSJUvk4sWLUlhYKCtXrhQHBwfx9vaWvLw8k5g2bNggAGTLli2NOuamwFnGrRvfmVaKCSG1No19dF1hYaFMmzZNunfvLiqVSnQ6nYSHh0t6erpJ25KSEomPjxcvLy+xt7eXwYMHS2ZmpgQEBBiW13jttdcM7XNyciQoKEi0Wq34+PjIsmXLjPrz9/cXb29vOXLkiISHh4uTk5PY29tLcHCw7Nmzp9n3HxQUJK6urg1e9sSS3//Zs2eLra2tnD9/3lBWUFBgOH79KyAgoM4+XnrpJbMJdH0+CxkZGSb7euONN0RETMojIiIM2xUVFcn06dOlR48eolKppEOHDhIWFia7du0yiSMkJEQcHR2lpqamQecmISHBJAYAEh4ebtJ23759Eh4eLjqdTtRqtfTp00fefvttk2RQ78cff5Thw4eLs7OzODo6SkhIiNnPqIhIcXGxzJo1S/r06SMajUbUarX4+vrK5MmTTR6XpxcTEyPe3t5y7dq1Bh1zU2JC2LopIvfJeghWRlEUbNq0CaNHj7Z0KEQAgJiYGADA559/buFI6u+RRx5BYWFho26BWpIlv/+lpaXw8/NDZGQkPvnkkxbff3MrKSlBp06dEBcXh5SUFEuH0yKys7PRr18/bNiwAWPGjLFYHKmpqYiNjb1vlmGyNhxDSEREBjqdDmlpadi8eTOWLVtm6XCalIggMTERzs7OmDt3rqXDaREnT55EdHQ0kpKSLJoMUuvHhJCIiIz069cPBw4cwI4dO1BWVmbpcJrMxYsXcfLkSaSnpzdqRvP9aPny5Zg/fz7mz59v6VColWNCSERtjv5Zw9nZ2Th//jwURcGbb75p6bDuK926dcO2bdvg7Oxs6VCajKenJ/bs2QM/Pz9Lh9JiFi5cyCuDVC/395PYiYjMmDlzJmbOnGnpMIiI7hu8QkhERERk5ZgQEhEREVk5JoREREREVo4JIREREZGV46SSViwjI8PSIRAZ6Bd3Tk1NtXAk1oHff2pr+Jlu3fikklZKURRLh0BERNTkmHa0TrxC2ErxC0PUMvSPh+OVTyKyZhxDSERERGTlmBASERERWTkmhERERERWjgkhERERkZVjQkhERERk5ZgQEhEREVk5JoREREREVo4JIREREZGVY0JIREREZOWYEBIRERFZOSaERERERFaOCSERERGRlWNCSERERGTlmBASERERWTkmhERERERWjgkhERERkZVjQkhERERk5ZgQEhEREVk5JoREREREVo4JIREREZGVY0JIREREZOWYEBIRERFZOSaERERERFaOCSERERGRlWNCSERERGTlmBASERERWTkmhERERERWjgkhERERkZVjQkhERERk5ZgQEhEREVk5JoREREREVo4JIREREZGVY0JIREREZOVsLR0AEVFL+f7775GRkWFUlpOTAwBYuHChUXlgYCD+8Ic/tFhsRESWpIiIWDoIIqKWkJ6ejtDQUKhUKtjYmL9BUltbi+vXr+Obb77BsGHDWjhCIiLLYEJIRFajtrYWnp6eKCgouGM7d3d35Ofno127di0UGRGRZXEMIRFZDRsbG8TFxUGtVtfZRq1WY+zYsUwGiciqMCEkIqvy7LPP4tq1a3XWX7t2Dc8++2wLRkREZHm8ZUxEVqdbt244ffq02TofHx+cPn0aiqK0cFRERJbDK4REZHXGjRsHlUplUq5SqfCnP/2JySARWR1eISQiq5OTk4MHH3zQbN2hQ4fg5+fXwhEREVkWrxASkdXp06cP/Pz8TK4EPvTQQ0wGicgqMSEkIqv0/PPPG80kVqlUeOGFFywYERGR5fCWMRFZpbNnz6Jr167Q/xOoKApOnjyJbt26WTYwIiIL4BVCIrJKPj4+GDhwIGxsbGBjY4OBAwcyGSQiq8WEkIis1rhx46AoCmxsbDBu3DhLh0NEZDG8ZUxEVquwsBCenp4AgLy8PHTs2NHCERERWQYTQivH9daIiAgAmA5YN1tLB0CWN23aNAT+8c73AAAgAElEQVQGBlo6DGojPvjgAwDAq6++auFI6uf777+HoigICgqydCgNFhsby+8v3bOMjAwsXrzY0mGQhfEKoZVTFAWbNm3C6NGjLR0KtRExMTEAgM8//9zCkdRPeXk5AMDJycnCkTQcv7/UFFJTUxEbG8srhFaOVwiJyKrdj4kgEVFT4yxjIiIiIivHhJCIiIjIyjEhJCIiIrJyTAiJiKzQ6dOnMWLECJSVlaGwsBCKohhe/fr1Q1VVlck2t7dTFAX9+/e3QPTNa8SIEVAUBfPmzauzzfXr1/HBBx8gICAATk5O6NixI4YPH460tDSzkzOysrIQEREBFxcXODk5ITQ0FHv37jXbd01NDVatWoXHHnsMbm5ucHV1RUBAAJYuXYpr164ZtX399dexadOmeztgIjAhJKJWrKKiAj179kRkZKSlQ2lTsrKy0L9/f4SFhcHZ2Rnu7u4QEWRmZhrqp02bZrKdvl1GRgbc3NwgIjhw4EBLh9+sPvvsM6Slpd2xTWVlJUJCQrBmzRp88MEHuHTpEg4cOABHR0eMGDEChw8fNmr/ww8/4PHHH4eTkxOOHj2KU6dOoUePHhgyZAi+/vprk/7Hjx+P+Ph4hIaG4ujRozh+/DhiY2MxZcoUPPPMM0ZtJ06ciKSkJMyZM+feD56sGhNCImq1RAS1tbWora21dCh35ejoiMGDB1s6jLsqKyvDU089hWeeeQaTJ082qddoNHBzc8Py5cvxz3/+0wIRWk5eXh6mTZt218cYzpo1Cz///DO+/vpr/OEPf4C9vT26dOmCNWvWQKPRGLWtra3Fn//8Z7i4uOB///d/4eXlBXd3d3z88cfw9fVFfHw8qqurDe1PnjyJdevWoV+/fnj33XfRsWNHuLm54S9/+QueeOIJbNu2zZC4A4Cvry+2bNmC+fPnIzU1tWlPCFkVJoRE1Go5OTnhxIkT+Ne//mXpUNqMRYsWIT8/H2+99ZbZejs7O6xfvx42NjZISEjAsWPHWjhCy5k4cSJiYmIQFhZWZ5uLFy9ixYoViIuLg4eHh1GdVqtFVVUVHn74YUPZ999/j8OHD2PUqFGwt7c3lLdr1w7PPvsszp49i23bthnKz549CwB48MEHTfbdp08fAMCZM2eMyv39/TFq1CjMmDEDNTU1DThiov9iQkhEZCVEBCtXrsTAgQPRqVOnOtuFh4fjzTffRHl5OWJiYsyOJ2xrVq9ejcOHDyM5OfmO7b766ivcuHGj3leDv/32WwAwO9ZSX5aenm4o69OnD1QqFXJyckza5+TkQFEU9O3b16Ru5MiROHfuHLZv316vuIhux4SQiFqlrVu3Gk1e0Cclt5f/9ttviI2NhYuLC9zc3BAZGYkTJ04Y+klOTja07dy5MzIzMzFs2DA4OTnBwcEBQ4cONRrcP2/ePEP7W3/0d+7caSh3d3c36b+yshJ79+41tLG1bX3r/mdnZ+PixYvw9/e/a9u//vWvCAsLw88//4wpU6bUq/+ioiJMnz4dvr6+UKvVcHV1xfDhw/Hdd98Z2jT0/dMrKChAYmIiunXrBrVajQ4dOiA6OhpZWVn1PwF1OHfuHGbMmIHVq1ffdaHyH3/8EQDg6uqKGTNmwMfHB2q1Gl27dkViYiKKi4uN2usTu86dO5v05e3tDQBGV2E9PDyQnJyM7OxszJ49GwUFBSguLsaiRYvwzTff4K233kKvXr1M+nrkkUcAAP/+978bcOREtxCyagBk06ZNlg6D2pBRo0bJqFGjmqy/qKgoASBXr141Wx4VFSX79u2TiooK2bVrl9jb28uAAQNM+vH39xetViuBgYGG9pmZmfK73/1O1Gq17N6926i9VquV3//+9yb9BAQEiJubm0l5Xe31hg4dKu3bt5eMjIz6HvpdNfT7u3btWgEg7777rtn6zMxM0el0hr8LCgrEx8dHAMi6desM5RkZGSbn4MKFC9K9e3fx8PCQtLQ0KS0tlV9//VWio6NFURRJSUkxat+Q9y8vL0+6du0qHh4esn37dikvL5dDhw5JcHCw2NnZyb59++p9DswJDw+Xl19+2fC3/jzNnTvXpK0+bk9PT4mLi5MTJ07I5cuX5dNPPxWtViu9evWSkpISQ/snnnhCAMj+/ftN+srNzRUA8uijj5rUpaamSufOnQWAABB3d3dZtWpVncdQWloqACQoKKihhy+bNm0SpgPEK4REdF+Lj49HYGAgtFotQkNDERERgczMTBQWFpq0raysxEcffWRo379/f6xbtw7Xrl3D1KlTmzXO2tpaiIhFnxd74cIFAIBOp6tXe3d3d6SmpkKlUiEhIcHsbUy9pKQknDp1CosXL0ZkZCScnZ3Rq1cvbNiwAV5eXkhMTMTFixdNtqvP+5eUlITTp0/j/fffx5NPPglHR0f4+flh48aNEJF6X8E0JyUlBbm5uVi0aFG92uuvVNvb22PNmjXo0aMHXFxc8PzzzyMpKQnHjh3DP/7xj3r1pf8sKIpiVDZp0iTExcVh+vTpyM/PR0FBAebPn4/JkydjzJgxZscJOjs7Q1EUw3tM1FBMCInovjZgwACjv318fADcnDF6O61Wa7i1pte3b1906tQJ2dnZzfpjunv3bhQXFyMwMLDZ9nE3+mRGpVLVe5tBgwYhOTkZlZWViImJwdWrV82227JlCwAgIiLCqFyj0WDYsGG4evWq2duZ9Xn/tm7dChsbG5Plhzw9PeHn54eDBw/i3Llz9T4mvTNnzmDWrFlYvXo1tFptvbbRtwsNDTUZFvDUU08BML5t6+LiAuDmf0Zupy/TtwGAtWvXIiUlBS+++CJeffVVeHh4wN3dHZMmTTKsObh06VKzsdna2tb5/hDdDRNCIrqv3X61S61WA4DZpWpu/eG9VceOHQEAly5dauLoWhc7OzsANxdVbojExETExsbi0KFDZpeqqa6uRmlpKezs7MyOwdPPxs3Pzzepu9v7p++7trYWOp3OZGFs/Zi+3NzcBh0TAKSlpaG0tBRDhgwx6lO/7MycOXMMZcePHwcAdOvWDQDg5uZm0p/+c1RQUGAo088MNpewnj9/HgCMxgTu3LkTwM2E83bDhg0DAOzYscPs8dTU1BjNZCZqCCaERGQ1ioqKzN6y1SeC+h90ALCxsTF5KgQAlJSUmO371tt+rZWXlxcAoLS0tMHbrly5Er1798bq1auxdu1aozqNRgOdToeqqiqUl5ebbKu/Vezp6dng/Wo0Gri4uMDW1hbXr1833Ha//TV06NAG9/3KK6+Y7Ut/fHPnzjWUPfDAAwBgmGhk7mqy/nN063I0+rgOHjxo0l5fpk/0APNXEm9XUVFhUlZWVgYRMbzHRA3FhJCIrEZVVZXRor4A8MsvvyAvLw/+/v5GP6ZeXl6GKzh6+fn5JmvA6Tk4OBglkL1798aKFSuaMPp7p18frzG3Vx0dHfHFF19Aq9Xio48+MqkfOXIkAJgse1JdXY309HTY29sjPDy8EVED0dHRqKmpMfuot4ULF6JLly4ttv7ek08+CW9vb+zcudNkOR79E06efvppQ1lwcDAeeughbN682aj9jRs3sHHjRvj4+BjdZh84cCAA46Vo9PRL2AwaNMikTv9ZvXUNRKKGYEJIRFZDp9Nh9uzZyMjIQGVlJQ4cOICxY8dCrVZjyZIlRm3DwsKQl5eHpUuXoqKiAidOnMDUqVONriLe6tFHH8WxY8dw9uxZZGRk4OTJkwgKCjLUh4SEwM3NDfv372/WY7wTf39/dOzYEdnZ2Y3a3s/PD8uXLzdbt2DBAnTv3h3Tpk3Dtm3bUF5ejmPHjuG5557DhQsXsGTJEpOFnOtrwYIF8PX1xYQJE7Bjxw6UlpaiuLgYy5cvxzvvvIPk5GSj8Xxjx46Foig4depUo/Z3JxqNBitXrkRRURHGjBmD3NxclJSUYO3atViwYAEGDhyIxMREQ3sbGxusWrUKxcXFGD9+PPLz81FUVIRXXnkFubm5SElJMdzKB4CXX34ZPXv2xMcff4wPP/wQly5dQlFREVatWoX33nsP3t7emDlzpklc+uV37rSoNtEdteykZmptwGVnqIk11bIzW7ZsMSy5oX/FxcVJRkaGSfkbb7whImJSHhERYejP399fvL295ciRIxIeHi5OTk5ib28vwcHBsmfPHpP9l5SUSHx8vHh5eYm9vb0MHjxYMjMzJSAgwND/a6+9Zmifk5MjQUFBotVqxcfHR5YtW2bUX1BQkLi6ut7zEim3asz3d/bs2WJrayvnz583lBUUFJicu4CAgDr7eOmll8wuvVNYWCjTpk2T7t27i0qlEp1OJ+Hh4ZKenm5o09j3r6ioSKZPny49evQQlUolHTp0kLCwMNm1a5dJHCEhIeLo6Cg1NTUNOjcJCQkmMQCQ8PBwk7b79u2T8PBw0el0olarpU+fPvL222/LlStXzPb9448/yvDhw8XZ2VkcHR0lJCTE7OdORKS4uFhmzZolffr0EY1GI2q1Wnx9fWXy5MmSn59vdpuYmBjx9vaWa9euNeiYRbjsDN2kiFhwDQSyOEVRsGnTJowePdrSoVAbERMTAwD4/PPPLRyJsUceeQSFhYWNul3aWjXm+1taWgo/Pz9ERkbik08+acboLKOkpASdOnVCXFwcUlJSLB1Oi8jOzka/fv2wYcMGjBkzpsHbp6amIjY21qJLIpHl8ZYx3bONGzcaZuLdeuvDGjg6OprMerSxsYGrqyv8/f3x8ssvmx1MTmQpOp0OaWlp2Lx5M5YtW2bpcJqUiCAxMRHOzs6YO3eupcNpESdPnkR0dDSSkpIalQwS6TEhpHs2ZswYiIjRTDlrUVFRgZ9++gkAEBUVBRHB9evXkZOTg3feeQc5OTno378/xo8fjytXrlg4WqKb+vXrhwMHDmDHjh0oKyuzdDhN5uLFizh58iTS09MbNaP5frR8+XLMnz8f8+fPt3QodJ9jQkjUxNq1awcPDw9ERUXh22+/xV/+8hesWbMGzz77LG/JWID+WcPZ2dk4f/48FEXBm2++aemwLK5bt27Ytm0bnJ2dLR1Kk/H09MSePXvg5+dn6VBazMKFC3llkJoEE0KiZvbee+9h4MCB+Oqrr7Bx40ZLh2N1Zs6cabLO3Lx58ywdFhFRq8KEkKiZKYpieLqDufXbiIiILI0JITVYTk4Onn76aeh0Omi1WgQFBWHPnj11ti8oKEBiYiK6desGtVqNDh06IDo62rBuFnDzWaW3Tsz47bffEBsbCxcXF7i5uSEyMhInTpww6re6uhpvvfUW+vTpAwcHB7Rv3x5PPfUUvvrqK9y4caPBMTQn/dMN9u/fb/TYMJ4bIiJqFSy03A21EmjgOma5ubni4uIi3t7e8vXXX0t5ebn8/PPPEhYWJt26dRONRmPUPi8vT7p27SoeHh6yfft2KS8vl0OHDklwcLDY2dmZrMkWFRUlACQqKkr27dsnFRUVsmvXLrG3t5cBAwYYtY2PjxedTidff/21XLlyRfLz82XmzJkCQL777rtGxzB06FBp3769ZGRk1Ouc/PTTT4aY63L16lXDmmZ5eXn37bmpj6Zah5DurqHfXyJzuA4hidwcT0NWrKE/KDExMQJANm/ebFR+/vx50Wg0JgnhCy+8IABk/fr1RuUXLlwQjUZjsvitPulJS0szKh81apQAkIKCAkNZ9+7d5fHHHzeJsVevXkZJT0NjCA4ObtACwvVJCK9cuWKSEN6P56Y+mBC2HCaE1BSYEJKIyH+f9UNUDzt37gQAk2eSdurUCb169cKxY8eMyrdu3QobGxtERkYalXt6esLPzw8HDx7EuXPn0LlzZ6P6AQMGGP3t4+MDAMjLy4O7uzsA4I9//CM+/vhjTJo0CRMmTMCAAQPQrl07/Prrr/cUw+7du+t7OurtwoULAACVSmWI/348N/V17tw5pKamNmgbapyMjAxLh0D3OX6GCACYEFK9VVdXo7y8HHZ2dnB0dDSp79ixo1FCWF1djdLSUgA3F8OtS25urknCcXt7tVoNAKitrTWULVu2DIGBgfj0008NayAGBQUhISEBI0eOvOcYmpJ+jGVgYCBUKlWbPzf79+9HbGxsg7ahxlm8eDEWL15s6TCI6D7HSSVUbxqNBk5OTqiqqkJFRYVJfXFxsUl7FxcX2Nra4vr16yZLf+hfQ4cObVQ8iqJg3Lhx+Oabb1BSUoKtW7dCRBAdHY3333+/RWKoj9raWsMTIV555ZUWicvS52bUqFF19sdX070AYNOmTRaPg6/7+7Vp06ZG/TtDbQsTQmqQ4cOHA/jvrWO9wsJCk9uRABAdHY2amhrs3bvXpG7hwoXo0qULampqGhWLi4sLcnJyANy8FfvEE08YZuRu3769RWKoj6SkJPznP//ByJEjDc/5be647pdzQ0RErQMTQmqQd999F+3bt8e0adOwa9cuVFRU4MiRIxg7dqzZ28gLFiyAr68vJkyYgB07dqC0tBTFxcVYvnw53nnnHSQnJ8PWtvEjF1588UX8/PPPqK6uxqVLl7Bo0SKICEJCQhodQ0hICNzc3LB///5GxVRbW4tLly7hyy+/xLBhw7Bo0SJMmDAB69evh6Io9/W5ISKiNkrIqqERsxR//fVXefrpp8XZ2dmw5Mm2bdtk2LBhhpm0f/7znw3ti4qKZPr06dKjRw9RqVTSoUMHCQsLk127dhnaZGRkGLbVv9544w1DjLe+IiIiREQkKytLEhIS5MEHHxQHBwdp3769DBo0SFJSUqS2ttYo5vrEoBcUFFTvWcZardYkPkVRRKfTSd++feWll16SgwcP1rn9/XZu6oOzjFtOY76/RLfjLGMSEVFEhA9XtWKKomDTpk0YPXq0pUOhNkJ/W/zzzz+3cCRtH7+/1BRSU1MRGxsLpgPWjbeMiYiIiKwcE0IiIit0+vRpjBgxAmVlZSgsLDR6PGK/fv1QVVVlss3t7RRFQf/+/S0QfdO5fPkyPvnkE4SEhKB9+/awt7dHz549ERcXh+zs7Dq3y8rKQkREBFxcXODk5ITQ0FCzk7PMGTFiBBRFwbx588zW19TUYNWqVXjsscfg5uYGV1dXBAQEYOnSpbh27ZpR29dff52zhKlJMCEkIrIyWVlZ6N+/P8LCwuDs7Ax3d3eICDIzMw3106ZNM9lO3y4jIwNubm4QERw4cKClw29Ss2bNwpQpUxAVFYUjR46gqKgIq1evRlZWFgICArB161aTbX744Qc8/vjjcHJywtGjR3Hq1Cn06NEDQ4YMwddff33H/X322WdIS0u7Y5vx48cjPj4eoaGhOHr0KI4fP47Y2FhMmTIFzzzzjFHbiRMnIikpCXPmzGn4wRPdypIDGMnywEHp1MRa46QSrVYrv//979vc/hvz/S0tLZXOnTtLQkKCSV1mZqZoNBpxc3MTALJhwwazfWRkZIibm1ujYm5t/vznP8ukSZNMyrOysgSA9OzZ06j8xo0b4ufnJ15eXnLlyhVDeU1NjfTu3Vt8fHykqqrK7L7Onz8vrq6uMm7cOAEgc+fONWlz4sQJASD9+vUzqXviiScEgPznP/8xiVVRlEb/W85JJSQiwiuERERWZNGiRcjPz8dbb71ltt7Ozg7r16+HjY0NEhISTB5H2dasXLkSy5cvNyn39/eHvb09Tpw4YTTZ4vvvv8fhw4cxatQo2NvbG8rbtWuHZ599FmfPnsW2bdvM7mvixImIiYlBWFhYnfGcPXsWAPDggw+a1PXp0wcAcObMGZNYR40ahRkzZnDdUGo0JoRERFZCRLBy5UoMHDgQnTp1qrNdeHg43nzzTZSXlyMmJsbseMK2rrKyElevXsXDDz9stH7ot99+CwBmx07qy9LT003qVq9ejcOHDyM5OfmO++3Tpw9UKpVhYflb5eTkQFEU9O3b16Ru5MiROHfunNHC80QNwYSQiFqFoqIiTJ8+Hb6+vlCr1XB1dcXw4cPx3XffGdrMmzfPMJlh8ODBhvKdO3cayt3d3Q3lycnJUBQFlZWV2Lt3r6GNfrFtfb2iKOjcuTMyMzMxbNgwODk5wcHBAUOHDjWaKNDU+29p2dnZuHjxIvz9/e/a9q9//SvCwsLw888/Y8qUKfXqvz7vof6JOfrXb7/9htjYWLi4uMDNzQ2RkZE4ceKESd8FBQVITExEt27doFar0aFDB0RHRyMrK6v+J6AB9MsmvfHGG0bl+kTN3PO9vb29AcDkquq5c+cwY8YMrF69Gk5OTnfcr4eHB5KTk5GdnY3Zs2ejoKAAxcXFWLRoEb755hu89dZb6NWrl8l2jzzyCADg3//+dz2PkOg2lr5nTZYFjiGkJtaYMYQXLlyQ7t27i4eHh6SlpUlpaan8+uuvEh0dLYqiSEpKilH7usbkBQQEmB3bdrcxfP7+/qLVaiUwMFD27dsnFRUVkpmZKb/73e9ErVbL7t27m3X/Q4cOlfbt20tGRkadbcxp6Pd37dq1AkDeffdds/WZmZmi0+kMfxcUFIiPj48AkHXr1hnKzY0hbOh7GBUVJQAkKirKcM537dplWOz+Vnl5edK1a1fx8PCQ7du3S3l5uRw6dEiCg4PFzs6uXovIN0R+fr54eHhIfHy8SZ1+HN/+/ftN6nJzcwWAPProo0bl4eHh8vLLLxv+1r8P5sYQ6qWmpkrnzp0Ni867u7vLqlWr6mxfWloqACQoKKg+h2iEYwhJhGMIiagVSEpKwqlTp7B48WJERkbC2dkZvXr1woYNG+Dl5YXExERcvHixWWOorKzERx99hMDAQGi1WvTv3x/r1q3DtWvXMHXq1Gbdd21tLUSk2RcGvnDhAgBAp9PVq727uztSU1OhUqmQkJBg9jamXmPfw/j4eMM5Dw0NRUREBDIzM1FYWGjU9+nTp/H+++/jySefhKOjI/z8/LBx40aISL2vYNZHUVER/vjHP2LIkCH45JNPGrSt/v279RZzSkoKcnNzsWjRonr3MWnSJMTFxWH69OnIz89HQUEB5s+fj8mTJ2PMmDFmxwk6OztDURTDe0zUUEwIicjitmzZAgCIiIgwKtdoNBg2bBiuXr3a7LfCtFqt4babXt++fdGpUydkZ2c36w/t7t27UVxcjMDAwGbbBwDDWECVSlXvbQYNGoTk5GRUVlYiJiYGV69eNduuse/hgAEDjP728fEBAOTl5RnKtm7dChsbG0RGRhq19fT0hJ+fHw4ePIhz587V+5jqUllZifDwcDz00ENYv3492rVrZ9LGxcXF0Nbc9re2OXPmDGbNmoXVq1dDq9XWK4a1a9ciJSUFL774Il599VV4eHjA3d0dkyZNMqw5uHTpUrPb2tra1vn+EN0NE0Iisqjq6mqUlpbCzs7O7PgqDw8PAEB+fn6zxqH/Eb9dx44dAQCXLl1q1v23BDs7OwDA9evXG7RdYmIiYmNjcejQIUyePNmk/l7ew9uvVqrVagA3r5re2ndtbS10Op3Jwtg//vgjACA3N7dBx3S7mpoaxMTEwNvbG59++qnZZBD470xfcwno+fPnAcAwxi8tLQ2lpaUYMmSIUczjxo0DAMyZM8dQdvz4cQA3x6MCQGhoqEn/w4YNAwDs2LGjzmO4deYzUUMwISQii9JoNNDpdKiqqkJ5eblJvf42o6enp6HMxsbG5IkNAFBSUmJ2H7fewqtLUVGR2Vu2+kRQnxg21/5bgpeXFwCgtLS0wduuXLkSvXv3xurVq7F27Vqjusa8h/Wl0Wjg4uICW1tbXL9+3XBr/fbX0KFDG9z3rRISElBdXY3U1FSjST8PPPAA9u/fb/hbv5+DBw+a9KEv0ydur7zyitlY9edv7ty5hrIHHngAgPkrj7erqKgwKSsrK4OIGN5jooZiQkhEFjdy5EgAMFkyo7q6Gunp6bC3t0d4eLih3MvLy3A1Ri8/P99kfTY9BwcHowSud+/eWLFihVGbqqoqw5M69H755Rfk5eXB39/f6Ie2OfbfEh5++GEA5q9u3Y2joyO++OILaLVafPTRRyb1DX0PGyI6Oho1NTVmHw23cOFCdOnS5Z7W33v77bdx+PBhfPnll9BoNHdsGxwcjIceegibN282Wo7nxo0b2LhxI3x8fExumzfEwIEDAZhfuka/5M2gQYNM6vSfR/17TNRQTAiJyOIWLFiA7t27Y9q0adi2bRvKy8tx7NgxPPfcc7hw4QKWLFliuO0IAGFhYcjLy8PSpUtRUVGBEydOYOrUqUZX8W716KOP4tixYzh79iwyMjJw8uRJBAUFGbXR6XSYPXs2MjIyUFlZiQMHDmDs2LFQq9VYsmSJUdum3n9ISAjc3NyMrkQ1B39/f3Ts2PGOz+i9Ez8/P7OLOAMNfw8bYsGCBfD19cWECROwY8cOlJaWori4GMuXL8c777yD5ORko6t6Y8eOhaIoOHXq1F37XrNmDf72t7/hhx9+gJOTk8kt6duXwLGxscGqVatQXFyM8ePHIz8/H0VFRXjllVeQm5uLlJQUw635xnj55ZfRs2dPfPzxx/jwww9x6dIlFBUVYdWqVXjvvffg7e2NmTNnmmynX37nToteE91Ry05qptYGXHaGmlhjH11XWFgo06ZNk+7du4tKpRKdTifh4eGSnp5u0rakpETi4+PFy8tL7O3tZfDgwZKZmSkBAQGGZTpee+01Q/ucnBwJCgoSrVYrPj4+smzZMqP+/P39xdvbW44cOSLh4eHi5OQk9vb2EhwcLHv27Gn2/QcFBYmrq2uDl09pzPd39uzZYmtrK+fPnzeUFRQUGOLWvwICAurs46WXXjK7vE593sOMjAyTfb3xxhuG47n1FRERYdiuqKhIpk+fLj169BCVSiUdOnSQsLAw2bVrl0kcISEh4ujoKDU1NXc9HxERESb7vf1lbjmgH3/8UYYPHy7Ozs7i6OgoISEhZj8rt0pISDDbf3h4uFG74uJimTVrlvTp00c0Go2o1Wrx9fWVyZMnS35+vtm+Y2JixNvbW65du3bXY74dl50hERFFpEjmyeUAACAASURBVJnXOaBWTVEUbNq0CaNHj7Z0KNRGxMTEAPjvwr73g0ceeQSFhYVNMlO1JTXm+1taWgo/Pz9ERkY2eFmV+0FJSQk6deqEuLg4pKSkWDqcFpGdnY1+/fphw4YNGDNmTIO3T01NRWxsbLMve0StG28ZExFZEZ1Oh7S0NGzevBnLli2zdDhNSkSQmJgIZ2dnzJ0719LhtIiTJ08iOjoaSUlJjUoGifSYEBIRWZl+/frhwIED2LFjB8rKyiwdTpO5ePEiTp48ifT09EbNaL4fLV++HPPnz8f8+fMtHQrd55gQEpHV0j9rODs7G+fPn4eiKHjzzTctHVaL6NatG7Zt2wZnZ2dLh9JkPD09sWfPHvj5+Vk6lBazcOFCXhmkJmGZJ6wTEbUCM2fONDtjk4jI2vAKIREREZGVY0JIREREZOWYEBIRERFZOSaERERERFaOC1NbOUVRMGjQIHTu3NnSoVAboX/8mrnnrVLT2rx5M7+/dM/OnTuH/fv3c2FqK8eE0MrpnypBZK1++eUXAEDfvn0tHAmRZd1PTxeipseEkIismv6xb6mpqRaOhIjIcjiGkIiIiMjKMSEkIiIisnJMCImIiIisHBNCIiIiIivHhJCIiIjIyjEhJCIiIrJyTAiJiIiIrBwTQiIiIiIrx4SQiIiIyMoxISQiIiKyckwIiYiIiKwcE0IiIiIiK8eEkIiIiMjKMSEkIiIisnJMCImIiIisHBNCIiIiIivHhJCIiIjIyjEhJCIiIrJyTAiJiIiIrBwTQiIiIiIrx4SQiIiIyMoxISQiIiKyckwIiYiIiKwcE0IiIiIiK8eEkIiIiMjKMSEkIiIisnJMCImIiIisHBNCIiIiIivHhJCIiIjIyjEhJCIiIrJyTAiJiIiIrBwTQiIiIiIrx4SQiIiIyMopIiKWDoKIqCV89tlneP/993Hjxg1DWWFhIQDA3d3dUNauXTtMnz4dzz//fIvHSERkCUwIichqHDt2DL17965X219//RW9evVq5oiIiFoH3jImIqvRq1cv+Pv7Q1GUOtsoigJ/f38mg0RkVZgQEpFVef7559GuXbs6621tbfHCCy+0YERERJbHW8ZEZFXy8vLg4+OD2tpas/WKouDs2bPw9vZu4ciIiCyHVwiJyKp06tQJjz/+OGxsTP/5s7Gxwe9//3smg0RkdZgQEpHVGTdunNlyRVE4s5iIrBJvGROR1bl8+TI8PDxw/fp1o3JbW1vk5+fDzc3NQpEREVkGrxASkdVxdXXFE088YTS5pF27dggPD2cySERWiQkhEVmlsWPHGk0sERGMHTvWghEREVkObxkTkVW6cuUK3NzcUFVVBQCws7NDYWEhtFqthSMjImp5vEJIRFbJwcEBI0eOhEqlgkqlwsiRI5kMEpHVYkJIRFbrueeew/Xr13H9+nU899xzlg6HiMhibC0dADW9jIwMnD171tJhELV6N27cgIODA0QEZWVlSE1NtXRIRK2ej48PAgMDLR0GNTGOIWyDYmJisHnzZkuHQUREbdCoUaPw+eefWzoMamK8QthG8QtL9wNFUbBp0yaMHj3aYjH83//9HxRFwR/+8AeLxdDcUlNTERsbC/7/n+5VTEyMpUOgZsKEkIisWlBQkKVDICKyOCaERGTVzD3TmIjI2vBfQiIiIiIrx4SQiIiIyMoxISQiIiKyckwIiYjojk6fPo0RI0agrKwMhYWFUBTF8OrXr5/h8X+3ur2doijo37+/BaJvOpcvX8Ynn3yCkJAQtG/fHvb29ujZsyfi4uKQnZ1d53ZZWVmIiIiAi4sLnJycEBoair1799ZrnyNGjICiKJg3b57Z+pqaGqxatQqPPfYY3Nzc4OrqioCAACxduhTXrl0zavv6669j06ZN9T9gsipMCInovldRUYGePXsiMjLS0qG0OVlZWejfvz/CwsLg7OwMd3d3iAgyMzMN9dOmTTPZTt8uIyMDbm5uEBEcOHCgpcNvUrNmzcKUKVMQFRWFI0eOoKioCKtXr0ZWVhYCAgKwdetWk21++OEHPP7443BycsLRo0dx6tQp9OjRA0OGDMHXX399x/199tlnSEtLu2Ob8ePHIz4+HqGhoTh69CiOHz+O2NhYTJkyBc8884xR24kTJyIpKQlz5sxp+MFTm8eEkIjueyKC2tpa1NbWWjqUu3J0dMTgwYMtHUa9lJWV4amnnsIzzzyDyZMnm9RrNBq4ublh+fLl+Oc//2mBCFvehAkTMHXqVHh6esLBwQFBQUHYsGEDbvx/9u4+Kqpq/x/4e5BhGAYYFBQQKRSfEr2joT+l5KuCQQZJciF8/HYzilsmkk+JD2U+5NXFLf2uNAkku4YJ15YWmpWXspY5elEDUy9JYCZPykMgECDI/v3hmrkOZ5AHgVHm/Vpr1oq99znnc85B5tPZZ+996xaWL19u0LapqQkvvPACHBwc8OGHH8LV1RVOTk54//334enpicjISNTX1xs9TmFhIWJiYjBv3rwWY8nLy8PHH3+MMWPG4O2330a/fv3g6OiI5cuX44knnsChQ4f0iTsAeHp64sCBA9i4cSNX5SEJJoRE9MCzs7NDbm4uvvjiC1OH0qNs2bIFxcXFeOONN4zWW1tbIzk5GRYWFoiKisKlS5e6OcLulZiYiPj4eEm5RqOBUqlEbm6uweTf33//PS5cuICwsDAolUp9ea9evTBr1ixcvXoVhw4dMnqsF198EeHh4QgICGgxHt0SpY888oikbvjw4QCA3377TRJrWFgYlixZgsbGxrucLZkbJoRERCQhhEBiYiLGjx+P/v37t9guMDAQq1evRlVVFcLDw42+T9jT1dTUoLa2FiNHjoRMJtOXf/PNNwBg9N1JXVl6erqkLikpCRcuXEBcXNxdjzt8+HDI5XJkZ2dL6rKzsyGTyTBq1ChJ3YwZM5Cfn4/Dhw/f/cTIrDAhJKIH2sGDBw0GLugSkublv/76KyIiIuDg4ABHR0cEBwcjNzdXv5+4uDh92wEDBiAjIwP+/v6ws7ODjY0NpkyZYjAQYMOGDfr2d3YBf/nll/pyJycnyf5ramrwww8/6NtYWt6f6wNkZWXh2rVr0Gg0rbZ98803ERAQgHPnzmHhwoVt2n9ZWRkWL14MT09PWFlZoXfv3pg2bRq+/fZbfZv23kOdkpISREdHw8PDA1ZWVujbty9CQ0ORmZnZ9gvQDrplQletWmVQrkvUBgwYINnGzc0NACRPVfPz87FkyRIkJSXBzs7ursd1dnZGXFwcsrKysHLlSpSUlKC8vBxbtmzBv/71L7zxxhsYOnSoZLvRo0cDAL766qs2niGZBUE9TlhYmAgLCzN1GEStAiBSUlI6ZV8hISECgKitrTVaHhISIk6cOCGqq6vF0aNHhVKpFOPGjZPsR6PRCJVKJXx8fPTtMzIyxJ/+9CdhZWUljh07ZtBepVKJxx9/XLIfb29v4ejoKClvqb3OlClTRJ8+fYRWq23rqbcqJSVFtPfP/Z49ewQA8fbbbxutz8jIEGq1Wv9zSUmJcHd3FwDExx9/rC/XarWS61BUVCQGDhwonJ2dRVpamqisrBQ///yzCA0NFTKZTCQkJBi0b889LCwsFA8//LBwdnYWhw8fFlVVVeL8+fNi0qRJwtraWpw4caJd16E1xcXFwtnZWURGRkrqnnjiCQFAnDx5UlKXk5MjAIhHH33UoDwwMFC88sor+p9192H9+vUtxpCamioGDBggAAgAwsnJSezatavF9pWVlQKA8PX1bcspGuD3S8/FJ4REZBYiIyPh4+MDlUqFqVOnIigoCBkZGSgtLZW0rampwY4dO/Ttx44di48//hg3b97EokWLujTOpqYmCCEM3kUzhaKiIgCAWq1uU3snJyekpqZCLpcjKirKaDemTmxsLC5fvoytW7ciODgY9vb2GDp0KPbu3QtXV1dER0fj2rVrku3acg9jY2Nx5coVvPPOO3jqqadga2sLLy8v7Nu3D0KINj/BbIuysjI8+eSTmDx5Mnbu3NmubXX3984u5oSEBOTk5GDLli1t3sdLL72EOXPmYPHixSguLkZJSQk2btyIV199FTNnzjT6nqC9vT1kMpn+HhMB7DImIjMxbtw4g5/d3d0B3B7N2ZxKpdJ3q+mMGjUK/fv3R1ZWVpd+kR47dgzl5eXw8fHpsmO0ha7rXS6Xt3mbCRMmIC4uDjU1NQgPD0dtba3RdgcOHAAABAUFGZQrFAr4+/ujtrbWaHdmW+7hwYMHYWFhIZmCyMXFBV5eXjhz5gzy8/PbfE4tqampQWBgIEaMGIHk5GT06tVL0sbBwUHf1tj2d7b57bffsGzZMiQlJUGlUrUphj179iAhIQF//etf8dprr8HZ2RlOTk546aWX9HMOvvfee0a3tbS0bPH+kHliQkhEZqH5ky4rKysAMDpVje5Lurl+/foBAK5fv97J0d1/rK2tAQANDQ3t2i46OhoRERE4f/680alq6uvrUVlZCWtra6PvyDk7OwMAiouLJXWt3UPdvpuamqBWqyUTY589exYAkJOT065zaq6xsRHh4eFwc3PDRx99ZDQZBP470tdYAlpQUAAA+nf80tLSUFlZicmTJxvErJt2Zs2aNfqyX375BcDt91UBYOrUqZL9+/v7AwCOHDnS4jncOfKZiAkhEVEzZWVlRrtsdYmgLjEEAAsLC8mKEABQUVFhdN93dhHez1xdXQEAlZWV7d42MTERw4YNQ1JSEvbs2WNQp1AooFarUVdXh6qqKsm2uq5iFxeXdh9XoVDAwcEBlpaWaGho0He9N/9MmTKl3fu+U1RUFOrr65GammowKGjw4ME4efKk/mfdcc6cOSPZh65Ml7gtWLDAaKy667d+/Xp92eDBgwEYf/LYXHV1taTsxo0bEELo7zERwISQiEiirq7OYEJfAPjpp59QWFgIjUZj8EXq6uqqf9qjU1xcLJn/TcfGxsYggRw2bBg++OCDToy+c4wcORKA8adbrbG1tcWnn34KlUqFHTt2SOpnzJgBAJJpT+rr65Geng6lUonAwMAORA2EhoaisbHR6NJwmzdvxkMPPXRP8++tXbsWFy5cwGeffQaFQnHXtpMmTcKIESOwf/9+g+l4bt26hX379sHd3V3Sbd4e48ePB2B86hrdlDcTJkyQ1Ol+X3X3mAhgQkhEJKFWq7Fy5UpotVrU1NTg9OnTmDt3LqysrLBt2zaDtgEBASgsLMR7772H6upq5ObmYtGiRQZPEe/06KOP4tKlS7h69Sq0Wi3y8vLg6+urr/fz84Ojo6PBkyZT0Gg06Nev313X6L0bLy8vo5M4A8CmTZswcOBAxMTE4NChQ6iqqsKlS5cwe/ZsFBUVYdu2bfqu4/batGkTPD09MX/+fBw5cgSVlZUoLy9HfHw81q1bh7i4OIOnenPnzoVMJsPly5db3ffu3bvx1ltv4dSpU7Czs5N0STefAsfCwgK7du1CeXk5nn/+eRQXF6OsrAwLFixATk4OEhIS9F3zHfHKK69gyJAheP/99/F///d/uH79OsrKyrBr1y787W9/g5ubG5YuXSrZTjf9zt0mvSYz1L2Dmqk7cFoAelCgE6adOXDggH66Dd1nzpw5QqvVSspXrVqlP+6dn6CgIP3+NBqNcHNzExcvXhSBgYHCzs5OKJVKMWnSJHH8+HHJ8SsqKkRkZKRwdXUVSqVSTJw4UWRkZAhvb2/9/l9//XV9++zsbOHr6ytUKpVwd3cX27dvN9ifr6+v6N27d6dOj9KRaWeEEGLlypXC0tJSFBQU6MtKSkok18/b27vFfbz88stGp98pLS0VMTExYuDAgUIulwu1Wi0CAwNFenq6vk1H72FZWZlYvHixGDRokJDL5aJv374iICBAHD16VBKHn5+fsLW1FY2Nja1ej6CgIMlxm3+MTRd09uxZMW3aNGFvby9sbW2Fn5+f0d+lO0VFRRndf2BgoEG78vJysWzZMjF8+HChUCiElZWV8PT0FK+++qooLi42uu/w8HDh5uYmbt682eo5N8fvl55LJoSJ5zagThceHg7gv5OlEt2vZDIZUlJS8Oyzz5o6FL3Ro0ejtLS0U0ai3i9SU1MRERHR7qlsKisr4eXlheDg4HZPq/IgqKioQP/+/TFnzhwkJCSYOpxukZWVhTFjxmDv3r2YOXNmu7fn90vPxS5jatG+ffv0XSH30q3xIPriiy8wdOjQTl1FwtbWVtLFZGFhgd69e0Oj0eCVV14x+vI5kamo1WqkpaVh//792L59u6nD6VRCCERHR8Pe3h7r1683dTjdIi8vD6GhoYiNje1QMkg9GxNCatHMmTMhhNCPgjMHubm5mD59OmJjY41OjHsvqqur8eOPPwIAQkJCIIRAQ0MDsrOzsW7dOmRnZ2Ps2LF4/vnn8ccff3TqsYk6asyYMTh9+jSOHDmCGzdumDqcTnPt2jXk5eUhPT29QyOaH0Tx8fHYuHEjNm7caOpQ6D7EhJDoDmvWrMFjjz2GM2fOtLqOaGfo1asXnJ2dERISgm+++QbLly/H7t27MWvWLJOvVGFudGsNZ2VloaCgADKZDKtXrzZ1WPcFDw8PHDp0CPb29qYOpdO4uLjg+PHj8PLyMnUo3Wbz5s18Mkgtuj9XVScykV27dpl0sta//e1v+O677/D5559j3759mDVrlsliMTdLly41OiKTiMgc8Akh0R1MPXO/TCbTr+5gbP42IiKirsCEkPSys7PxzDPPQK1WQ6VSwdfXF8ePH2+xfUlJCaKjo+Hh4QErKyv07dsXoaGh+jmugNvrit45iOLXX39FREQEHBwc4OjoiODgYMncXfX19XjjjTcwfPhw2NjYoE+fPnj66afx+eef49atW+2O4UEzceJEAMDJkycNlg3j9SYioi5jwilvqIt0ZJ6onJwc4eDgINzc3MTXX38tqqqqxLlz50RAQIDw8PAQCoXCoH1hYaF4+OGHhbOzszh8+LCoqqoS58+fF5MmTRLW1taSOdRCQkIEABESEiJOnDghqqurxdGjR4VSqRTjxo0zaBsZGSnUarX4+uuvxR9//CGKi4vF0qVLBQDx7bffdjiG9nJzcxO9evW6a5spU6aIPn36GJ17zJgff/xRfx1aUltbq59zrLCwUAjRc683OmEeQmpdR+chJGqO8xD2XPwL0QN15B9seHi4ACD2799vUF5QUCAUCoUkIXzuuecEAJGcnGxQXlRUJBQKhWSiWl2CkpaWJokVgCgpKdGXDRw4UDz22GOSGIcOHWqQoLQ3hvZqS0I4adKkdk0i3JaE8I8//pAkhD31ejMh7B5MCKmzMCHsuTiohAAAX375JQBI1g/t378/hg4dikuXLhmUHzx4EBYWFggODjYod3FxgZeXF86cOYP8/HwMGDDAoH7cuHEGP7u7uwMACgsL4eTkBAB48skn8f777+Oll17C/PnzMW7cOPTq1Qs///xzp8TQmY4dO9bp+ywqKgIAyOVy/TXpydf73Xff5SS3XUw3ybZuUmGijjp58qTR9ZHpwcd3CAn19fWoqqqCtbU1bG1tJfXN12Str69HZWUlmpqaoFarJZMtnz17FgCQk5Mj2ZdarTb42crKCgDQ1NSkL9u+fTv+8Y9/IC8vD/7+/rC3t8eTTz6JAwcOdEoM9zvde5s+Pj6Qy+W83kRE1OX4hJCgUChgZ2eHqqoqVFdXS5LC8vJySXsHBwdUV1ejtra2U1fzAG6PtJ03bx7mzZuHhoYGHDt2DHFxcQgNDcXf//53LF68uMtjMJWmpib9ihALFiwA0POv92uvvXZfLV3XE+mWruOTWLpXfMrcc/EJIQEApk2bBuC/Xcc6paWlkq5DAAgNDUVjYyN++OEHSd3mzZvx0EMPobGxsUOxODg4IDs7G8DtbtMnnnhCP3r28OHD3RKDqcTGxuLf//43ZsyYYfCHl9ebiIi6EhNCAgC8/fbb6NOnD2JiYnD06FFUV1fj4sWLmDt3rtFu5E2bNsHT0xPz58/HkSNHUFlZifLycsTHx2PdunWIi4u7p6dIf/3rX3Hu3DnU19fj+vXr2LJlC4QQ8PPz67YY2sLPzw+Ojo44efJkh7ZvamrC9evX8dlnn8Hf3x9btmzB/PnzkZycDJlMpm/H601ERF3K1KNaqPN1dBTYzz//LJ555hlhb2+vn57k0KFDwt/fXz/q9YUXXtC3LysrE4sXLxaDBg0Scrlc9O3bVwQEBIijR4/q22i1Wv22us+qVauEEEJSHhQUJIQQIjMzU0RFRYlHHnlE2NjYiD59+ogJEyaIhIQE0dTUZBBzW2Joj7S0NElcuk9CQoKkva+vb5tHGatUKsk+ZTKZUKvVYtSoUeLll18WZ86caXH7nni9wVHG3YKjjKmzcJRxzyUTggum9jS6rka+L0T3O5lMhpSUFL5D2MV07xDyzz3dK36/9FzsMiYioru6cuUKpk+fjhs3bqC0tNRghPmYMWNQV1cn2aZ5O5lMhrFjx5og+q7xxRdfYOjQoW16TSIzMxNBQUFwcHCAnZ0dpk6davRd3N9//x07d+6En58f+vTpA6VSiSFDhmDOnDnIyspqU1zTp0+HTCbDhg0bJHUrVqxASkpKm/ZD5ocJIRERtSgzMxNjx45FQEAA7O3t4eTkBCEEMjIy9PUxMTGS7XTttFotHB0dIYTA6dOnuzv8Tpebm4vp06cjNjYW165da7X9qVOn8Nhjj8HOzg7/+c9/cPnyZQwaNAiTJ0/G119/bdB22bJlWLhwIUJCQnDx4kWUlZUhKSkJmZmZ8Pb2xsGDB+96rH/84x9IS0trsf7FF19EbGws1qxZ07aTJbPChJB6vOZPKYx91q5da+ow6T5ga2urX0vaHI/f3I0bN/D000/jz3/+M1599VVJvUKhgKOjI+Lj4/HJJ5+YIMLut2bNGjz22GM4c+YM7Ozs7tq2qakJL7zwAhwcHPDhhx/C1dUVTk5OeP/99+Hp6YnIyEjU19cbbDN//nwsWrQILi4usLGxga+vL/bu3Ytbt25h+fLlLR6rsLAQMTExmDdvXottPD09ceDAAWzcuBGpqantO3Hq8ZgQUo8nbi/ReNcPE0IiqS1btqC4uBhvvPGG0Xpra2skJyfDwsICUVFRkhWNeqJdu3ZhxYoVbeoq/v7773HhwgWEhYVBqVTqy3v16oVZs2bh6tWrOHTokL48MTER8fHxkv1oNBoolUrk5ua2+B7oiy++iPDwcAQEBNw1Jo1Gg7CwMCxZsoTTRJEBJoRERCQhhEBiYiLGjx+P/v37t9guMDAQq1evRlVVFcLDw42+T9iT3JnYteabb74BAKPvTurK0tPTW91PTU0NamtrMXLkSIPpqHSSkpJw4cIFxMXFtSmuGTNmID8/32CeUSImhET0QCkrK8PixYvh6ekJKysr9O7dG9OmTcO3336rb7Nhwwb96wB3dsF++eWX+nLdWs4AEBcXB5lMhpqaGvzwww/6NrqnQLp6mUyGAQMGICMjA/7+/rCzs4ONjQ2mTJliMEigs49vCllZWbh27Ro0Gk2rbd98800EBATg3LlzWLhwYZv235b7qJsgXff59ddfERERAQcHBzg6OiI4OBi5ubmSfZeUlCA6OhoeHh6wsrJC3759ERoaiszMzLZfgE6gm/Dd2Prebm5uANCmp6q6Eb2rVq2S1OXn52PJkiVISkpqtQtbZ/To0QCAr776qk3tyTwwISSiB0ZxcTHGjRuHvXv3Ytu2bSgtLcWpU6dgY2MDf39/JCYmAgBWr14NIQRUKpXB9k8++SSEEPD29jYoX7p0qb79448/rn+VQNelpqvXaDSoqKjAokWLsGHDBhQXF+P7779HeXk5/Pz88N1333XJ8XXudSL09jh//jwA48lMcxYWFkhOToa7uzsSExORnJx81/ZtvY/PPPMMhBAICQkBAMTExCAmJgYFBQVISUnBN998g1mzZhnsu6ioCOPGjUNqaip27NiB8vJyHDt2DOXl5fDx8YFWq+3I5eiQiooKAJD8HgDQT/j/+++/33Uf165dw4oVKxAZGWl0eqbIyEjMnj3bYBL51uiSUd09JgKYEBLRAyQ2NhaXL1/G1q1bERwcDHt7ewwdOhR79+6Fq6sroqOj2zTy817U1NRgx44d8PHxgUqlwtixY/Hxxx/j5s2bWLRoUZceu6mpSZ8sdrWioiIAgFqtblN7JycnpKamQi6XIyoqSv90zJiO3sfIyEj9dZ86dSqCgoKQkZGB0tJSg31fuXIF77zzDp566inY2trCy8sL+/btgxCizU8wu5ruHhrrAtYpKyvDk08+icmTJ2Pnzp2S+oSEBOTk5GDLli3tOra9vT1kMpn+HhMBTAiJ6AFy4MABAEBQUJBBuUKhgL+/P2pra7u8G0ylUum73HRGjRqF/v37Iysrq0u/ZO980tXVdO8CyuXyNm8zYcIExMXFoaamBuHh4aitrTXarqP3cdy4cQY/u7u7A7g9wlbn4MGDsLCwQHBwsEFbFxcXeHl54cyZM8jPz2/zOd0LBwcHALf/J6I5XZmujbH6wMBAjBgxAsnJyejVq5dB/W+//YZly5YhKSnJ6BPI1lhaWrZ4f8g8MSEkogdCfX09KisrYW1tbfRdKWdnZwC3uyO7Uktf4P369QMAXL9+vUuP312sra0BAA0NDe3aLjo6GhERETh//rzRqWru5T42f1ppZWUF4PaT0zv33dTUBLVaLZle6uzZswCAnJycdp1TRw0fPhwAjCagBQUFAIChQ4dK6hobGxEeHg43Nzd89NFHkmQQANLS0lBZWYnJkycbnKNu2pk1a9boy3755Rejx2jPABnq+ZgQEtEDQaFQQK1Wo66uDlVVVZJ6XReji4uLvszCwgI3b96UtNW929Xc3brvdMrKyox22eoSQV1i2FXH7y6urq4AgMrKynZvm5iYiGHDhiEpKQl79uwxqOvIfWwrhUIBBwcHWFpaoqGhocVp45WDOwAAIABJREFUpqZMmdLufXeE7jhnzpyR1OnK/P39JXVRUVGor69HamqqwcCiwYMH698fXbBggdFz013v9evX68sGDx5ssP8bN25ACKG/x0QAE0IieoDMmDEDACTTZdTX1yM9PR1KpRKBgYH6cldXV/2TGJ3i4mL89ttvRvdvY2NjkMANGzYMH3zwgUGburo6/SodOj/99BMKCwuh0WgMvmS74vjdZeTIkQCMP91qja2tLT799FOoVCrs2LFDUt/e+9geoaGhaGxsNLo03ObNm/HQQw912/x7kyZNwogRI7B//36D6Xhu3bqFffv2wd3dXdJtvnbtWly4cAGfffYZFApFl8Sl+53U3WMigAkhET1ANm3ahIEDByImJgaHDh1CVVUVLl26hNmzZ6OoqAjbtm3TdzkCQEBAAAoLC/Hee++huroaubm5WLRokcFTvDs9+uijuHTpEq5evQqtVou8vDz4+voatFGr1Vi5ciW0Wi1qampw+vRpzJ07F1ZWVti2bZtB284+fneOMtZoNOjXr1+b19BtzsvLy+gky0D772N7bNq0CZ6enpg/fz6OHDmCyspKlJeXIz4+HuvWrUNcXJzBU7e5c+dCJpPh8uXLHTre3VhYWGDXrl0oLy/H888/j+LiYpSVlWHBggXIyclBQkKCvmseAHbv3o233noLp06dgp2dnaTL29gUOx2hm36ntUmsycwI6nHCwsJEWFiYqcMgahUAkZKS0q5tSktLRUxMjBg4cKCQy+VCrVaLwMBAkZ6eLmlbUVEhIiMjhaurq1AqlWLixIkiIyNDeHt7CwACgHj99df17bOzs4Wvr69QqVTC3d1dbN++3WB/Go1GuLm5iYsXL4rAwEBhZ2cnlEqlmDRpkjh+/HiXH9/X11f07t1bnDhxol3XLCUlRXTkz/3KlSuFpaWlKCgo0JeVlJToY9d9vL29W9zHyy+/LBwdHSXlbbmPWq1WcqxVq1YJIYSkPCgoSL9dWVmZWLx4sRg0aJCQy+Wib9++IiAgQBw9elQSh5+fn7C1tRWNjY1tuiZpaWmSY+s+CQkJRrc5e/asmDZtmrC3txe2trbCz8/P6O9LUFBQi/vWfbRardFjREVFGW0fGBgoaRseHi7c3NzEzZs323TOd+L3S88lE6Ib5i+gbhUeHg7gv5OZEt2vZDIZUlJSjM6vdj8aPXo0SktLu22UamdJTU1FREREu6erqayshJeXF4KDg41Oe/Kgq6ioQP/+/TFnzhwkJCSYOpxukZWVhTFjxmDv3r2YOXNmu7fn90vPxS5jIiIySq1WIy0tDfv378f27dtNHU6nEkIgOjoa9vb2WL9+vanD6RZ5eXkIDQ1FbGxsh5JB6tmYEBIRUYvGjBmD06dP48iRI7hx44apw+k0165dQ15eHtLT0zs0ovlBFB8fj40bN2Ljxo2mDoXuQ0wIiYhaoVtrOCsrCwUFBZDJZFi9erWpw+o2Hh4eOHToEOzt7U0dSqdxcXHB8ePH4eXlZepQus3mzZv5ZJBaZLqV04mIHhBLly7F0qVLTR0GEVGX4RNCIiIiIjPHhJCIiIjIzDEhJCIiIjJzTAiJiIiIzBwTQiIiIiIzx5VKeqDw8HDs37/f1GEQEVEPFBYWxpVKeiAmhD2QVqvF1atXTR0G0QPh3XffBQC89tprJo6E6MHg7u4OHx8fU4dBnYwJIRGZNd06yqmpqSaOhIjIdPgOIREREZGZY0JIREREZOaYEBIRERGZOSaERERERGaOCSERERGRmWNCSERERGTmmBASERERmTkmhERERERmjgkhERERkZljQkhERERk5pgQEhEREZk5JoREREREZo4JIREREZGZY0JIREREZOaYEBIRERGZOSaERERERGaOCSERERGRmWNCSERERGTmmBASERERmTkmhERERERmjgkhERERkZljQkhERERk5pgQEhEREZk5JoREREREZo4JIREREZGZY0JIREREZOaYEBIRERGZOSaERERERGaOCSERERGRmWNCSERERGTmmBASERERmTkmhERERERmztLUARARdZfS0lLcuHHDoKympgYAkJeXZ1Bub28PJyenbouNiMiUZEIIYeogiIi6w4cffoj58+e3qW1SUhKef/75Lo6IiOj+wISQiMxGZWUl+vbti4aGhru2k8vlKCkpgVqt7qbIiIhMi+8QEpHZUKvVeOqpp2Bp2fLbMpaWlggKCmIySERmhQkhEZmVuXPn4tatWy3WNzU1Ye7cud0YERGR6bHLmIjMSl1dHZycnPSDSZqzsbFBaWkplEplN0dGRGQ6fEJIRGbF2toaoaGhkMvlkjq5XI6wsDAmg0RkdpgQEpHZmT17ttGBJQ0NDZg9e7YJIiIiMi12GROR2WlsbISzszPKy8sNyh0cHFBSUnLXQSdERD0RnxASkdmxtLTErFmzDLqN5XI55s6dy2SQiMwSE0IiMkuzZs0y6DZuaGjArFmzTBgREZHpsMuYiMySEALu7u4oKCgAALi6uqKgoAAymczEkRERdT8+ISQisySTyTBv3jxYWVnBysoKzz33HJNBIjJbfEJIRGbr3Llz0Gg0+v8eNWqUiSMiIjINvj3dA73zzjvQarWmDoPogWBrawsAWLdunYkjIXow+Pj4YPHixaYOgzoZu4x7IK1Wi5MnT5o6DKJW7d+/H/n5+SaN4eGHH4aHh4dJY+hq+fn52L9/v6nDoB7g5MmTfODQQ/EJYQ81YcIE/POf/zR1GER3JZPJ8Nprr+HZZ581WQx5eXkAgEGDBpkshq6WmpqKiIgI/k2gexYeHm7qEKiLMCEkIrPWkxNBIqK2YpcxERERkZljQkhERERk5pgQEhEREZk5JoRERHRXV65cwfTp03Hjxg2UlpZCJpPpP2PGjEFdXZ1km+btZDIZxo4da4Lou8YXX3yBoUOHtmnt68zMTAQFBcHBwQF2dnaYOnUqfvjhB0m733//HTt37oSfnx/69OkDpVKJIUOGYM6cOcjKympTXNOnT4dMJsOGDRskdStWrEBKSkqb9kPmhwkhET3wqqurMWTIEAQHB5s6lB4nMzMTY8eORUBAAOzt7eHk5AQhBDIyMvT1MTExku107bRaLRwdHSGEwOnTp7s7/E6Xm5uL6dOnIzY2FteuXWu1/alTp/DYY4/Bzs4O//nPf3D58mUMGjQIkydPxtdff23QdtmyZVi4cCFCQkJw8eJFlJWVISkpCZmZmfD29sbBgwfveqx//OMfSEtLa7H+xRdfRGxsLNasWdO2kyWzwoSQiB54Qgg0NTWhqanJ1KG0ytbWFhMnTjR1GG1y48YNPP300/jzn/+MV199VVKvUCjg6OiI+Ph4fPLJJyaIsPutWbMGjz32GM6cOQM7O7u7tm1qasILL7wABwcHfPjhh3B1dYWTkxPef/99eHp6IjIyEvX19QbbzJ8/H4sWLYKLiwtsbGzg6+uLvXv34tatW1i+fHmLxyosLERMTAzmzZvXYhtPT08cOHAAGzduRGpqavtOnHo8JoRE9MCzs7NDbm4uvvjiC1OH0qNs2bIFxcXFeOONN4zWW1tbIzk5GRYWFoiKisKlS5e6OcLut2vXLqxYsaJNXcXff/89Lly4gLCwMCiVSn15r169MGvWLFy9ehWHDh3SlycmJiI+Pl6yH41GA6VSidzcXLS02uyLL76I8PBwBAQE3DUmjUaDsLAwLFmyBI2Nja2eA5kPJoRERCQhhEBiYiLGjx+P/v37t9guMDAQq1evRlVVFcLDw42+T9iT3JnYteabb74BAKPvTurK0tPTW91PTU0NamtrMXLkSMhkMkl9UlISLly4gLi4uDbFNWPGDOTn5+Pw4cNtak/mgQkhET3QDh48aDBwQZeQNC//9ddfERERAQcHBzg6OiI4OBi5ubn6/cTFxenbDhgwABkZGfD394ednR1sbGwwZcoUg4EAGzZs0Le/swv4yy+/1Jc7OTlJ9l9TU4MffvhB36YtT5pMISsrC9euXYNGo2m17ZtvvomAgACcO3cOCxcubNP+y8rKsHjxYnh6esLKygq9e/fGtGnT8O233+rbtPce6pSUlCA6OhoeHh6wsrJC3759ERoaiszMzLZfgE6QnZ0NABgwYICkzs3NDQDa9FRVt8LMqlWrJHX5+flYsmQJkpKSWu3C1hk9ejQA4KuvvmpTezIPTAiJ6IH2zDPPQAiBkJCQu5bHxMQgJiYGBQUFSElJwTfffINZs2bp2y9duhRCCGg0GlRUVGDRokXYsGEDiouL8f3336O8vBx+fn747rvvAACrV6+GEAIqlcrguE8++SSEEPD29jYo1+1fpVLh8ccfhxACQghJt52fnx8cHR1Nvh75+fPnARhPZpqzsLBAcnIy3N3dkZiYiOTk5Lu2Ly4uxrhx47B3715s27YNpaWlOHXqFGxsbODv74/ExEQA7b+HAFBUVIRx48YhNTUVO3bsQHl5OY4dO4by8nL4+Ph06zq8FRUVACD5HQFuv0sK3B5ZfDfXrl3DihUrEBkZaXSJx8jISMyePRt+fn5tjkuXjOruMRHAhJCIzERkZCR8fHygUqkwdepUBAUFISMjA6WlpZK2NTU12LFjh7792LFj8fHHH+PmzZtYtGhRl8bZ1NSkTxZNqaioCACgVqvb1N7JyQmpqamQy+WIiorSPx0zJjY2FpcvX8bWrVsRHBwMe3t7DB06FHv37oWrqyuio6ONjuBtyz2MjY3FlStX8M477+Cpp56Cra0tvLy8sG/fPggh2vwEs6vp7q+xLmCdsrIyPPnkk5g8eTJ27twpqU9ISEBOTg62bNnSrmPb29tDJpPp7zERwISQiMzEuHHjDH52d3cHcHt0ZnMqlUrfraYzatQo9O/fH1lZWV36RXrn0yxT0nW9y+XyNm8zYcIExMXFoaamBuHh4aitrTXa7sCBAwCAoKAgg3KFQgF/f3/U1tYa7c5syz08ePAgLCwsJFMQubi4wMvLC2fOnEF+fn6bz+leODg4ALj9PxjN6cp0bYzVBwYGYsSIEUhOTkavXr0M6n/77TcsW7YMSUlJRp9AtsbS0rLF+0PmiQkhEZmF5k+6rKysAMDoVDUtfUn369cPAHD9+vVOju7+Y21tDQBoaGho13bR0dGIiIjA+fPnjU5VU19fj8rKSlhbWxt9583Z2RnA7W7l5lq7h7p9NzU1Qa1WSybGPnv2LAAgJyenXefUUcOHDwcAowloQUEBAGDo0KGSusbGRoSHh8PNzQ0fffSRJBkEgLS0NFRWVmLy5MkG56ibdmbNmjX6sl9++cXoMdozQIZ6PiaERETNlJWVGe2y1SWCusQQuP3+3M2bNyVtde+PNXe3LsL7iaurKwCgsrKy3dsmJiZi2LBhSEpKwp49ewzqFAoF1Go16urqUFVVJdlW11Xs4uLS7uMqFAo4ODjA0tISDQ0N+q735p8pU6a0e98doTvOmTNnJHW6Mn9/f0ldVFQU6uvrkZqaajDoaPDgwfp3SxcsWGD03HTXe/369fqywYMHG+z/xo0bEELo7zERwISQiEiirq5OvxKHzk8//YTCwkJoNBqDL1JXV1f90x6d4uJi/Pbbb0b3bWNjY5BADhs2DB988EEnRt85Ro4cCcD4063W2Nra4tNPP4VKpcKOHTsk9TNmzAAAybQn9fX1SE9Ph1KpRGBgYAeiBkJDQ9HY2Gh0abjNmzfjoYce6rb59yZNmoQRI0Zg//79BtPx3Lp1C/v27YO7u7uk23zt2rW4cOECPvvsMygUii6JS/f7qrvHRAATQiIiCbVajZUrV0Kr1aKmpganT5/G3LlzYWVlhW3bthm0DQgIQGFhId577z1UV1cjNzcXixYtMniKeKdHH30Uly5dwtWrV6HVapGXlwdfX199/f0yylij0aBfv35tXkO3OS8vL6OTLAPApk2bMHDgQMTExODQoUOoqqrCpUuXMHv2bBQVFWHbtm36ruP22rRpEzw9PTF//nwcOXIElZWVKC8vR3x8PNatW4e4uDiDp25z586FTCbD5cuXO3S8u7GwsMCuXbtQXl6O559/HsXFxSgrK8OCBQuQk5ODhIQEfdc8AOzevRtvvfUWTp06BTs7O0mXt7EpdjpCN/1Oa5NYk5kR1OOEhYWJsLAwU4dB1CoAIiUl5Z72ceDAAQHA4DNnzhyh1Wol5atWrdIf985PUFCQfn8ajUa4ubmJixcvisDAQGFnZyeUSqWYNGmSOH78uOT4FRUVIjIyUri6ugqlUikmTpwoMjIyhLe3t37/r7/+ur59dna28PX1FSqVSri7u4vt27cb7M/X11f07t1bnDhx4p6uy51SUlJER/7cr1y5UlhaWoqCggJ9WUlJieT6eXt7t7iPl19+WTg6OkrKS0tLRUxMjBg4cKCQy+VCrVaLwMBAkZ6erm/T0XtYVlYmFi9eLAYNGiTkcrno27evCAgIEEePHpXE4efnJ2xtbUVjY2ObrklaWprk2LpPQkKC0W3Onj0rpk2bJuzt7YWtra3w8/Mz+rsUFBTU4r51H61Wa/QYUVFRRtsHBgZK2oaHhws3Nzdx8+bNNp3znfj90nPJhDDx3AbU6cLDwwH8dzJTovuVTCZDSkqK0fnVTGX06NEoLS3ttpGo3SE1NRURERHtnsqmsrISXl5eCA4ONjrtyYOuoqIC/fv3x5w5c5CQkGDqcLpFVlYWxowZg71792LmzJnt3p7fLz0Xu4yJiMgotVqNtLQ07N+/H9u3bzd1OJ1KCIHo6GjY29tj/fr1pg6nW+Tl5SE0NBSxsbEdSgapZ2NCSC3at2+f/t2VO99z6al+//137Ny5E35+fujTpw+USiWGDBmCOXPmdPg9qjvZ2tpK3gmysLBA7969odFo8MorrxgdjUhkSmPGjMHp06dx5MgR3Lhxw9ThdJpr164hLy8P6enpHRrR/CCKj4/Hxo0bsXHjRlOHQvchJoTUopkzZ0IIYXRahJ5o2bJlWLhwIUJCQnDx4kWUlZUhKSkJmZmZ8Pb2xsGDB+9p/9XV1fjxxx8BACEhIRBCoKGhAdnZ2Vi3bh2ys7MxduxYPP/88/jjjz8645SoHXRrDWdlZaGgoAAymQyrV682dVj3BQ8PDxw6dAj29vamDqXTuLi44Pjx4/Dy8jJ1KN1m8+bNfDJILWJCSHSH+fPnY9GiRXBxcYGNjQ18fX2xd+9e3Lp1C8uXL+/04/Xq1QvOzs4ICQnBN998g+XLl2P37t2YNWuWyZcuMze6tYbv/GzYsMHUYRERdQvL1psQmYfExESj5RqNBkqlErm5uRBCdOnEwn/729/w3Xff4fPPP8e+ffswa9asLjsWERGRDp8QErWipqYGtbW1GDlyZJevMiGTyfTLfRmb0JeIiKgrMCEkvezsbDzzzDNQq9VQqVTw9fXF8ePHW2xfUlKC6OhoeHh4wMrKCn379kVoaKh+0lPg9kLzdw6i+PXXXxEREQEHBwc4OjoiODhYMtlqfX093njjDQwfPhw2Njbo06cPnn76aXz++ee4detWu2O4V7rpFVatWtVp+7ybiRMnAgBOnjxpsI6suVxvIiIyAdNMf0hdqSMTh+bk5AgHBwfh5uYmvv76a1FVVSXOnTsnAgIChIeHh1AoFAbtCwsLxcMPPyycnZ3F4cOHRVVVlTh//ryYNGmSsLa2lkyqGxISIgCIkJAQceLECVFdXS2OHj0qlEqlGDdunEHbyMhIoVarxddffy3++OMPUVxcLJYuXSoAiG+//bbDMXREcXGxcHZ2FpGRkUbrp0yZIvr06dPiZLHN/fjjj/rr0JLa2lr9pLKFhYVCiJ57vdEJE1NT6zo6MTVRc5yYuufiX4geqCP/YMPDwwUAsX//foPygoICoVAoJAnhc889JwCI5ORkg/KioiKhUCgkKxfoEpS0tDRJrABESUmJvmzgwIHisccek8Q4dOhQgwSlvTG0V2lpqRg9erSIiIhocRWDSZMmtWtVibYkhH/88YckIeyp15sJYfdgQkidhQlhz8W/ED1QR/7B2tnZCQCiqqpKUjdq1ChJQqhWq4WFhYWorKyUtH/00UcFAHH16lV9mS5BKS4uNmj72muvCQAiKytLX/byyy8LAOLFF18UWq22xWSsvTG0R3V1tfD29hazZ89u85JWbdGWhDA3N1cAEHK5XL+0VE+93rrElx9++HlwPkwIeyaOMibU19ejqqoK1tbWsLW1ldT369cPly5dMmhfWVkJ4PZKBi3JycnBgAEDDMqat7eysgIANDU16cu2b98OHx8ffPTRR/o5EH19fREVFYUZM2bccwytaWxsRHh4ONzc3PDRRx+hV69e7dr+Xune2/Tx8YFcLu/x1zsmJgY+Pj7t2obaR6vVYuvWrUhJSTF1KPSAe/fdd00dAnURJoQEhUIBOzs7VFVVobq6WpIUlpeXS9o7ODiguroatbW1sLTs3F8jmUyGefPmYd68eWhoaMCxY8cQFxeH0NBQ/P3vf8fixYu7NIaoqCjU19fjwIEDBvsdPHgwPv74Y0yYMKHTjtVcU1OTfomwBQsWAOj519vHx+e+Wsu4p9q6dSuvM90zrmHcc3GUMQEApk2bBgD48ssvDcpLS0vx888/S9qHhoaisbERP/zwg6Ru8+bNeOihh9DY2NihWBwcHJCdnQ0AkMvleOKJJ/SjZw8fPtylMaxduxYXLlzAZ599BoVC0aH470VsbCz+/e9/Y8aMGfpF5IGee72JiOj+wISQAABvv/02+vTpg5iYGBw9ehTV1dW4ePEi5s6da7QbedOmTfD09MT8+fNx5MgRVFZWory8HPHx8Vi3bh3i4uLu6SnSX//6V5w7dw719fW4fv06tmzZAiEE/Pz8uiyG3bt346233sKpU6dgZ2cnWXe4+XQtAODn5wdHR0ecPHmyQ+fZ1NSE69ev47PPPoO/vz+2bNmC+fPnIzk52WDOw554vYmI6D5i6pcYqfN1dBTYzz//LJ555hlhb2+vn57k0KFDwt/fX/8y8QsvvKBvX1ZWJhYvXiwGDRok5HK56Nu3rwgICBBHjx7Vt9FqtZIXkletWiWEkA4oCAoKEkIIkZmZKaKiosQjjzwibGxsRJ8+fcSECRNEQkKCaGpqMoi5LTG0VVBQUKsvUzefXsbX17fNo4xVKpVkfzKZTKjVajFq1Cjx8ssvizNnzrS4fU+73rqYOMq463GUMXUWjjLuuWRCcMHUnkbX1ch3Peh+J5PJkJKSwnfbulhqaioiIiK4PjbdM36/9FzsMiYiog65cuUKpk+fjhs3bqC0tNTgFYsxY8agrq5Osk3zdjKZDGPHjjVB9J3n999/x86dO+Hn54c+ffpAqVRiyJAhmDNnDrKysoxuM3HiRMl10H1iYmKMbtPQ0IB3330X3t7esLOzQ79+/TBt2jSkpaUZJPsrVqzgiHJqNyaERETUbpmZmRg7diwCAgJgb28PJycnCCGQkZGhrzeW2OjaabVaODo6QgiB06dPd3f4nWrZsmVYuHAhQkJCcPHiRZSVlSEpKQmZmZnw9vbGwYMH7/kYNTU18PPzw+7du/Huu+/i+vXrOH36NGxtbTF9+nRcuHBB3/bFF19EbGws1qxZc8/HJfPBhJB6vJb+L/zOz9q1a00dJt0HbG1t9WtJm+Px2+rGjRt4+umn8ec//xmvvvqqpF6hUMDR0RHx8fH45JNPTBBh95s/fz4WLVoEFxcX2NjYwNfXF3v37sWtW7ewfPlyo9tkZGRA3F4gwuCzdetWSdtly5bh3Llz+Prrr/E///M/UCqVeOihh7B7927JjAienp44cOAANm7ciNTU1C45X+p5OCSQejy+N0XUubZs2YLi4mK88cYbRuutra2RnJyMp556ClFRUfD29sbQoUO7Ocruk5iYaLRco9FAqVQiNzcXQgiDmQPa49q1a/jggw/w0ksvwdnZ2aBOpVIZ7ZrXaDQICwvDkiVLEBoayhkAqFV8QkhERG0mhEBiYiLGjx+P/v37t9guMDAQq1evRlVVFcLDw40mLT1dTU0NamtrMXLkyA4ngwDw+eef49atW+1+ejxjxgzk5+cbzCdK1BImhET0QCkrK8PixYvh6ekJKysr9O7dG9OmTcO3336rb7Nhwwb96wB3fol++eWX+nInJyd9eVxcHGQyGWpqavDDDz/o2+iequjqZTIZBgwYgIyMDPj7+8POzg42NjaYMmWKwYTdnX38+0lWVhauXbsGjUbTats333wTAQEBOHfuHBYuXNim/bfl/uomTtd9fv31V0RERMDBwQGOjo4IDg42Om9oSUkJoqOj4eHhASsrK/Tt2xehoaHIzMxs+wVoB91I3FWrVhmt37NnD0aPHg2VSgW1Wq3vZm7u7NmzAIDevXtjyZIlcHd3h5WVFR5++GFER0dLVpPSGT16NADgq6++6ozToZ7OBFPdUBfjPFH0oEA75yEsKioSAwcOFM7OziItLU1UVlaKn3/+WYSGhgqZTCYSEhIM2qtUKvH4449L9uPt7S0cHR0l5S2119FoNEKlUgkfHx9x4sQJUV1dLTIyMsSf/vQnYWVlJY4dO9alx58yZYro06ePZD7M1nTmPIR79uwRAMTbb79ttD4jI0Oo1Wr9zyUlJcLd3V0AEB9//LG+XKvVSq5Be+9vSEiIACBCQkL09+Po0aP6eVTvVFhYKB5++GHh7OwsDh8+LKqqqsT58+fFpEmThLW1dZvmEm2P4uJi4ezsLCIjI43WP/7442LevHnizJkzorq6WmRnZ4t58+YJAGLhwoVGz9PFxUXMmTNH5Obmit9//1189NFHQqVSiaFDh4qKigrJMSorKwUA4evr22nnxe+XnosJYQ/Ef7D0oGhvQviXv/xFABCffPKJQXldXZ3o37+/UCqVori4WF/eFQkhAPHjjz8alJ87d04AEBqNpk376+jxJ02a1OaJ0O/UmQnhli1bBACxfft2o/XNE0Ihbicx7fVHAAAgAElEQVR/crlcqFQq8Z///Edf1vwatPf+6hKltLQ0g/ZhYWECgCgpKdGXPffccwKASE5ONmhbVFQkFAqF8Pb2buMVaF1paakYPXq0iIiIEI2Nje3a9v/9v/8nAIiTJ0/qywIDAwUAMXDgQNHQ0GDQfsOGDQKAWLNmjdH9yWQyMXjw4PafRAv4/dJzscuYiB4YBw4cAAAEBQUZlCsUCvj7+6O2trbLu8dUKpW+K05n1KhR6N+/P7KyslBUVNRlxz527BjKy8vh4+PTZcdoje5dQLlc3uZtJkyYgLi4ONTU1CA8PBy1tbVG23X0/o4bN87gZ3d3dwBAYWGhvuzgwYOwsLBAcHCwQVsXFxd4eXnhzJkzyM/Pb/M5taSmpgaBgYEYMWIEkpOT0atXr3ZtHxYWBgBIS0vTl6lUKgDA1KlTJa8RPP300wBa7ha2tLRs8XoT3YkJIRE9EOrr61FZWQlra2vY2dlJ6nWjL4uLi7s0DgcHB6Pl/fr1AwBcv369S49vatbW1gBuT5LcHtHR0YiIiMD58+eNTlVzL/dXrVYb/GxlZQXg9lrhd+67qakJarVaMu2U7h29nJycdp1Tc42NjQgPD4ebmxs++uijdieDAODq6grA8PfIw8MDAODo6Chpr/u9KykpaTEmpVLZ7jjI/DAhJKIHgkKhgFqtRl1dHaqqqiT1165dA3D7iY+OhYUFbt68KWlbUVFh9BhtGQlaVlZmdCoj3Re47gu6q45varqEpbKyst3bJiYmYtiwYUhKSsKePXsM6jpyf9tKoVDAwcEBlpaWaGhoMDr3nxACU6ZMafe+7xQVFYX6+nqkpqYaPMkbPHgwTp482aZ96J5q3vl7pBuYZOzps+73rvl0NMDt+SKFEPp7RnQ3TAiJ6IExY8YMAJBMo1FfX4/09HQolUoEBgbqy11dXVFQUGDQtri4GL/99pvR/dvY2BgkcMOGDcMHH3xg0Kaurk6/GofOTz/9hMLCQmg0GoMv3644vqmNHDkSADrUvWpra4tPP/0UKpUKO3bskNS39/62R2hoKBobGw1Gg+ts3rwZDz30EBobGzu0bwBYu3YtLly4gM8++0wyUXRziYmJ8Pb2lpQLIfQTSeu6ggHgqaeegpubG7788kvJ9D26ruVnnnlGsj/d757unhHdDRNCInpgbNq0CQMHDkRMTAwOHTqEqqoqXLp0CbNnz0ZRURG2bdtm8KQkICAAhYWFeO+991BdXY3c3FwsWrTI4OnLnR599FFcunQJV69ehVarRV5eHnx9fQ3aqNVqrFy5ElqtFjU1NTh9+jTmzp0LKysrbNu2zaBtZx/fz88Pjo6ObX7a1BU0Gg369evX4hq9rfHy8kJ8fLzRuvbe3/bYtGkTPD09MX/+fBw5cgSVlZUoLy9HfHw81q1bh7i4OIOnenPnzoVMJsPly5db3ffu3bvx1ltv4dSpU7Czs5N0SRubAufs2bNYsGABfvnlF9TV1eHnn3/GvHnzcObMGSxcuBDjx4/Xt1UoFEhMTERZWRlmzpyJnJwcVFRUYM+ePdi0aRPGjx+P6OhoyTF00+kEBAR05JKRuTHZcBbqMhwFRg8KtHOUsRC3R3DGxMSIgQMHCrlcLtRqtQgMDBTp6emSthUVFSIyMlK4uroKpVIpJk6cKDIyMoS3t7cAIACI119/Xd8+Oztb+Pr6CpVKJdzd3SUjaTUajXBzcxMXL14UgYGBws7OTiiVSjFp0iRx/PjxLj++r6+vyUcZCyHEypUrhaWlpSgoKNCXlZSU6M9J97nbyN2XX37Z6EjrttxfrVYrOdaqVauEEEJSHhQUpN+urKxMLF68WAwaNEjI5XLRt29fERAQII4ePSqJw8/PT9ja2rZplHBQUJDkuM0/d04VVFdXJ/75z3+KGTNmCE9PT6FQKIRarRaTJ08We/fubfE4J06cEIGBgUKtVgsrKysxfPhwsXbtWvHHH38YbR8eHi7c3NzEzZs3Wz2HtuL3S88lE4LrevU04eHhAP47KSrR/UomkyElJQXPPvusqUNpk9GjR6O0tLRTRqN2p9TUVERERHTaMo6VlZXw8vJCcHAwdu7c2Sn7vJ9UVFSgf//+mDNnDhISEkwdTodkZWVhzJgx2Lt3L2bOnNlp++X3S8/FLmMiImoXtVqNtLQ07N+/H9u3bzd1OJ1KCIHo6GjY29tj/fr1pg6nQ/Ly8hAaGorY2NhOTQapZ2NCSERE7TZmzBicPn0aR44cwY0bN0wdTqe5du0a8vLykJ6e3qERzfeD+Ph4bNy4ERs3bjR1KPQAYUJIRNQK3VrDWVlZKCgogEwmw+rVq00dlsl5eHjg0KFDsLe3N3UoncbFxQXHjx+Hl5eXqUPpsM2bN/PJILXb/bdyOhHRfWbp0qVYunSpqcMgIuoyfEJIREREZOaYEBIRERGZOSaERERERGaOCSERERGRmeOgkh4qPz9fvyYm0f1Mq9WaOoQeT3eN+TeB7lV+fj4GDBhg6jCoC3Clkh4oPDwc+/fvN3UYRETUA4WFhXGlkh6ICSERmTXdsnl8ekZE5ozvEBIRERGZOSaERERERGaOCSERERGRmWNCSERERGTmmBASERERmTkmhERERERmjgkhERERkZljQkhERERk5pgQEhEREZk5JoREREREZo4JIREREZGZY0JIREREZOaYEBIRERGZOSaERERERGaOCSERERGRmWNCSERERGTmmBASERERmTkmhERERERmjgkhERERkZljQkhERERk5pgQEhEREZk5JoREREREZo4JIREREZGZY0JIREREZOaYEBIRERGZOSaERERERGaOCSERERGRmWNCSERERGTmmBASERERmTkmhERERERmjgkhERERkZljQkhERERk5pgQEhEREZk5S1MHQETUXb7//ntotVqDsuzsbADA5s2bDcp9fHzwP//zP90WGxGRKcmEEMLUQRARdYf09HRMnToVcrkcFhbGO0iamprQ0NCAf/3rX/D39+/mCImITIMJIRGZjaamJri4uKCkpOSu7ZycnFBcXIxevXp1U2RERKbFdwiJyGxYWFhgzpw5sLKyarGNlZUV5s6dy2SQiMwKE0IiMiuzZs3CzZs3W6y/efMmZs2a1Y0RERGZHruMicjseHh44MqVK0br3N3dceXKFchksm6OiojIdPiEkIjMzrx58yCXyyXlcrkcf/nLX5gMEpHZ4RNCIjI72dnZeOSRR4zWnT9/Hl5eXt0cERGRafEJIRGZneHDh8PLy0vyJHDEiBFMBonILDEhJCKz9L//+78GI4nlcjmee+45E0ZERGQ67DImIrN09epVPPzww9D9CZTJZMjLy4OHh4dpAyMiMgE+ISQis+Tu7o7x48fDwsICFhYWGD9+PJNBIjJbTAiJyGzNmzcPMpkMFhYWmDdvnqnDISIyGXYZE5HZKi0thYuLCwCgsLAQ/fr1M3FERESmwYTQjISHh2P//v2mDoOIiB4AYWFh+Oc//2nqMKibWJo6AOpeEyZMwGuvvWbqMIjuKiIiAjExMfDx8enyY33//feQyWTw9fXt8mPdb7RaLbZu3YqUlBRTh0L3mXfffdfUIVA3Y0JoZgYMGIBnn33W1GEQ3VVERAR8fHy65Xd12rRpAAA7O7suP9b9aOvWrfybQBJ8Mmh+mBASkVkz10SQiOhOHGVMREREZOaYEBIRERGZOSaERERERGaOCSEREXXIlStXMH36dNy4cQOlpaWQyWT6z5gxY1BXVyfZpnk7mUyGsWPHmiD6zvP7779j586d8PPzQ58+faBUKjFkyBDMmTMHWVlZRreZOHGi5DroPjExMUa3aWhowLvvvgtvb2/Y2dmhX79+mDZtGtLS0nDnDHIrVqzgyHFqNyaERNRjVVdXY8iQIQgODjZ1KD1OZmYmxo4di4CA/9/evUdFVa5/AP8OlxlggAEH5SYGUmihjYSkmBwTDDJQgkAs9fTLQ9JFkUNq4a2OiaaLXyfPUotEf5ZiQrawwDQ9lOcsdewgBmaGGmgpN7nEcBER5Pn90Zo5jnvQmREYZZ7PWvMH7373u5+9NzKP+9372eFwdHSEi4sLiAhFRUWa5boSG3U/pVIJuVwOIsKJEyf6O/xetXjxYixYsADR0dE4c+YMGhoasG3bNpSUlCAwMBB79+696220tbUhNDQU27dvx9///ndcuXIFJ06cgL29PaZPn46ffvpJ0/fll19GWloaVqxYcdfbZeaDE0LG2IBFROju7kZ3d7epQ7kje3t7TJw40dRh6KW5uRnTpk3Dc889h/nz5wuWSyQSyOVyZGZm4rPPPjNBhP1v7ty5WLhwIdzc3GBnZ4eQkBDs2rULN27cwJIlS3SuU1RUBCISfD744ANB38WLF+PUqVM4ePAg/vSnP8HW1hbDhg3D9u3bIZFItPr6+voiLy8P6enpyM3N7ZP9ZQMPl51hjA1YDg4OKC8vN3UYA8769etRU1ODlStX6lxuY2OD7OxsPPPMM0hKSkJgYCD8/Pz6Ocr+k5WVpbNdoVDA1tYW5eXlICKIRCKjxq+trcXHH3+MefPmwdXVVWuZVCrVOTWvUCgQFxeHN954A7GxsbCy4q97dnt8hZAxxpjeiAhZWVkYN24cPDw8euwXERGB5cuXo6WlBfHx8TqTloGura0N7e3tGDVqlNHJIAB89dVXuHHjhsFXkGNiYnD58mXs27fP6G0z88EJIWNsQNq7d6/WjfrqhOTW9osXLyIhIQFOTk6Qy+WIiorSuqqYkZGh6Tt06FAUFRUhLCwMDg4OsLOzw+TJk3H06FFN/9WrV2v63/wFfuDAAU27i4uLYPy2tjYcPXpU0+devaJTWlqK2tpaKBSKO/Z9++23ER4ejlOnTmHBggV6jd/Q0IDU1FT4+vpCLBbD2dkZU6dOxXfffafpY+g5VKurq0NycjK8vb0hFosxePBgxMbGoqSkRP8DYAD12z6WLVumc/mOHTswZswYSKVSyGQyzTTzrU6ePAkAcHZ2xhtvvAEvLy+IxWI88MADSE5ORmNjo87xx4wZAwD45ptvemN32EBHzGzExcVRXFycqcNg7I4AUE5OTq+MFR0dTQCovb1dZ3t0dDQdO3aMWltb6dChQ2Rra0tBQUGCcRQKBUmlUgoODtb0LyoqokcffZTEYjEdPnxYq79UKqUnnnhCME5gYCDJ5XJBe0/91SZPnkyDBg0ipVKp767fUU5ODhn6NbBjxw4CQGvWrNG5vKioiGQymebnuro68vLyIgC0c+dOTbtSqRQch+rqavLx8SFXV1fKz88nlUpFZ8+epdjYWBKJRLRlyxat/oacw6qqKnrggQfI1dWV9u3bRy0tLXT69GmaNGkS2djY0LFjxww6DndSU1NDrq6ulJiYqHP5E088QXPmzKHi4mJqbW2lsrIymjNnDgGgBQsW6NxPNzc3mjVrFpWXl9Pvv/9On3zyCUmlUvLz86OmpibBNlQqFQGgkJAQg+Pn7wvzwwmhGeF/4Ox+0Z8JYX5+vlZ7XFwcAaC6ujqtdoVCQQDohx9+0Go/deoUASCFQqHV3tsJ4aRJk8jZ2blXExdjEsL169cTANq0aZPO5bcmhER/JH/W1tYklUrp559/1rTdehz+53/+hwDQZ599ptV+7do18vDwIFtbW6qpqdG0G3IOX3zxRQJA2dnZWn2rq6tJIpFQYGCgnkfgzurr62nMmDGUkJBAXV1dBq37+OOPEwA6fvy4pi0iIoIAkI+PD3V2dmr1X716NQGgFStW6BxPJBLRgw8+aPA+8PeF+eEpY8aYWQsKCtL62cvLCwBQVVUl6CuVSjXTcGqjR4+Gh4cHSktLUV1d3WdxHj58GI2NjQgODu6zbehDPfVubW2t9zrjx49HRkYG2traEB8fj/b2dp398vLyAACRkZFa7RKJBGFhYWhvb9c5/anPOdy7dy8sLCwEJYjc3Nzg7++P4uJiXL58We996klbWxsiIiLwyCOPIDs7G5aWlgatHxcXBwDIz8/XtEmlUgDAlClTBLcSTJs2DUDP08JWVlY9Hm/GbsYJIWPMrMlkMq2fxWIxAOgsVePk5KRzjCFDhgAArly50svR3XtsbGwA/FEk2RDJyclISEjA6dOndZaq6ejogEqlgo2NDRwcHATL1U/X1tTUCJbd6Ryqx+7u7oZMJhMUglbfo3f+/HmD9ulWXV1diI+Ph6enJz755BODk0EAcHd3B6D9u+Tt7Q0AkMvlgv7q3726uroeY7K1tTU4DmZ+OCFkjDE9NTQ0aL0RQk395a3+cgYACwsLXL9+XdC3qalJ59h38xRqf1InLCqVyuB1s7KyMGLECGzbtg07duzQWiaRSCCTyXDt2jW0tLQI1q2trQXwxxU9Q0kkEjg5OcHKygqdnZ06a/8RESZPnmzw2DdLSkpCR0cHcnNzta7kPfjggzh+/LheY6ivat78u6R+OEnXFWj1796t5WiAP+pFEpHmnDF2O5wQMsaYnq5du6Z5E4fajz/+iKqqKigUCq0vXnd3d1RWVmr1rampwW+//aZzbDs7O60EcsSIEfj44497MfreMWrUKAAwanrV3t4eX3zxBaRSKTZv3ixYHhMTAwCCMikdHR0oLCyEra0tIiIijIgaiI2NRVdXl9YT4Wrr1q3DsGHD0NXVZdTYAPDOO+/gp59+wpdffikoFH2rrKwsBAYGCtqJSFNIWj0VDADPPPMMPD09ceDAAUH5HvXU8rPPPisYT/37pz5njN0OJ4SMMaYnmUyGpUuXQqlUoq2tDSdOnMDs2bMhFouxYcMGrb7h4eGoqqrCxo0b0draivLycixcuFDrys/NHnvsMZw7dw6XLl2CUqlERUUFQkJCNMtDQ0Mhl8v1vtLUVxQKBYYMGdLjO3rvxN/fH5mZmTqXrV27Fj4+PkhJSUFBQQFaWlpw7tw5vPDCC6iursaGDRt0XgnTx9q1a+Hr64u5c+di//79UKlUaGxsRGZmJlatWoWMjAytq3qzZ8+GSCTChQsX7jj29u3b8be//Q3ff/89HBwcBFPSukrgnDx5Eq+//jp++eUXXLt2DWfPnsWcOXNQXFyMBQsWYNy4cZq+EokEWVlZaGhowMyZM3H+/Hk0NTVhx44dWLt2LcaNG4fk5GTBNtTldMLDw405ZMzcmOxxFtbv+Kkxdr9ALzxlnJeXRwC0PrNmzSKlUiloX7ZsmWa7N38iIyM14ykUCvL09KQzZ85QREQEOTg4kK2tLU2aNImOHDki2H5TUxMlJiaSu7s72dra0sSJE6moqIgCAwM147/55pua/mVlZRQSEkJSqZS8vLwET/GGhITcE08ZExEtXbqUrKysqLKyUtNWV1cnOH63e3L31Vdf1fm0dX19PaWkpJCPjw9ZW1uTTCajiIgIKiws1PQx9hw2NDRQamoqDR8+nKytrWnw4MEUHh5Ohw4dEsQRGhpK9vb2ej0lHBkZKdjurZ+bywVdu3aNPv/8c4qJiSFfX1+SSCQkk8noySefpF27dvW4nWPHjlFERATJZDISi8U0cuRIeuedd+jq1as6+8fHx5Onpyddv379jvtwK/6+MD8iIh03xLABKT4+HsB/i6Uydq8SiUTIycnBjBkzTB2KxpgxY1BfX98rT6LeK3Jzc5GQkKDzvsjbUalU8Pf3R1RUFD766KM+is50mpqa4OHhgVmzZmHLli2mDscopaWlCAgIwK5duzBz5kyD1+fvC/PDU8bMYLt379ZMhaifOBzIiAhHjx7F66+/Dj8/P0gkEgwZMgQTJ07Ezp07Df4yvZW9vb1gisnCwgLOzs5QKBR47bXXUFxc3Et7w9jdk8lkyM/Px549e7Bp0yZTh9OriAjJyclwdHTEu+++a+pwjFJRUYHY2FikpaUZlQwy88QJITPYzJkzQUQICwszdSj94uzZs5g4cSLOnTuHPXv2QKVS4fjx4xg2bBjmzJmDxYsX39X4ra2t+OGHHwAA0dHRICJ0dnairKwMq1atQllZGcaOHYuXXnoJV69e7Y1dYuyuBQQE4MSJE9i/fz+am5tNHU6vqa2tRUVFBQoLC416ovlekJmZifT0dKSnp5s6FHYf4YSQMT1YWVkhNzcXjz76KGxsbDB8+HBs374dcrkcGzduREdHR69uz9LSEq6uroiOjsa3336LJUuWYPv27Xj++efv+ookM4z6XcOlpaWorKyESCTC8uXLTR3WPcHb2xsFBQVwdHQ0dSi9xs3NDUeOHIG/v7+pQzHaunXr+MogMxgnhIzdwciRI9HZ2QlnZ2etdrFYDC8vL3R0dAhKQfS29957D+PGjcNXX32F3bt39+m2mLZFixYJ6tWtXr3a1GExxliv4oSQMSM1NTXh/PnzCAgIELwpobeJRCLN2x101W9jjDHG7gYnhOyOysrK8Oyzz0Imk0EqlSIkJARHjhzpsX9dXR2Sk5Ph7e0NsViMwYMHIzY2VlMTC/jjvaI3P0Rx8eJFJCQkwMnJCXK5HFFRUYLaXR0dHVi5ciVGjhwJOzs7DBo0CNOmTcNXX32FGzduGByDsZqbm3H06FFMnz4dbm5u+PTTT+96TH2o31Zw/PhxrdeGDfTjzRhjrB+YpNgNMwlj6kqdP3+enJycyNPTkw4ePEgtLS106tQpCg8PJ29vb5JIJFr9q6qq6IEHHiBXV1fat28ftbS00OnTp2nSpElkY2MjqKEWHR1NACg6OpqOHTtGra2tdOjQIbK1taWgoCCtvomJiSSTyejgwYN09epVqqmpoUWLFhEA+u6774yOwRDvvvuupq7Yk08+SadOndLZb/LkyTRo0CCt2mO388MPP2iOQ0/a29s1266qqiKigXu80Qt1CNmdGVuHkA18XIfQ/PBfAjNizD/w+Ph4AkB79uzRaq+srCSJRCJICF988UUCQNnZ2Vrt1dXVJJFIBIVq1QlKfn6+IFYAVFdXp2nz8fGhCRMmCGL08/PTSlAMjcFQHR0d9PPPP9Mrr7xClpaWtGrVKkGfSZMmGVREWJ+E8OrVq4KEcKAeb04I+wcnhKwnnBCan/++p4cxHQ4cOAAAgveHenh4wM/PD+fOndNq37t3LywsLBAVFaXV7ubmBn9/fxQXF+Py5csYOnSo1vKgoCCtn728vAD88aJ3FxcXAMDTTz+NDz/8EPPmzcPcuXMRFBQES0tLnD17tldi0JdYLMbIkSPx4Ycfora2FitXrkRwcDCmTJmi6XP48GGjxr4d9Yvtra2tNcdkIB9vpVJpUH9mOPUxVr8/lzG1u/kbye5PnBCyHnV0dKClpQU2Njawt7cXLB8yZIhWQtjR0QGVSgUAt33I4vz584I/NLf2F4vFAIDu7m5N26ZNmxAcHIxPPvlEUwMxJCQESUlJiImJuesYjDFt2jTk5eWhoKBAKyHsC+r7NoODg2FtbT3gj/cHH3yADz74wKB1mHESEhJMHQK7B8XFxZk6BNaP+KES1iOJRAIHBwdcu3YNra2tguWNjY2C/k5OTrCyskJnZ6egVIf6M3nyZKPiEYlEmDNnDv75z3+iqakJe/fuBREhNjYW77//fr/EcCuJRAJAeCx6W3d3t+aNEK+//rpm2wP5eOfk5PQ4Hn9655OTkwMAJo+DP/feh5NB88MJIbutqVOnAvjv1LFafX29YOoQAGJjY9HV1YWjR48Klq1btw7Dhg1DV1eXUbE4OTmhrKwMwB/Tpk899ZTm6dl9+/b1WQyLFi3C7NmzdS7bv38/AOEUbG9LS0vDf/7zH8TExGjeMQoMzOPNGGOs/3FCyG5rzZo1GDRoEFJSUnDo0CG0trbizJkzmD17ts5p5LVr18LX1xdz587F/v37oVKp0NjYiMzMTKxatQoZGRmwsjL+ToVXXnkFp06dQkdHB65cuYL169eDiBAaGtqnMezatQurVq3CxYsX0dHRgYsXL+LNN9/Ezp07ERgYiMTERK3+oaGhkMvlOH78uFH72d3djStXruDLL79EWFgY1q9fj7lz5yI7OxsikahP9/VmpjrejDHG+hkxs2HsU2Nnz56lZ599lhwdHTXlSQoKCigsLEzz1Otf/vIXTf+GhgZKTU2l4cOHk7W1NQ0ePJjCw8Pp0KFDmj5KpVKzrvqzbNkyIiJBe2RkJBERlZSUUFJSEj388MNkZ2dHgwYNovHjx9OWLVuou7tbK2Z9YtCXSqWirKwsioiIIG9vbxKLxWRvb0+BgYG0du1aunr1qmCdkJAQvZ8ylkqlgn0WiUQkk8lo9OjR9Oqrr1JxcXGP6w+0462OiZ8y7nv8lDHrCT9lbH5ERMQvRjUT6qnGzz//3MSRMHZ7IpEIOTk5mDFjhqlDGdByc3ORkJAA/hpgt+LvC/PDU8aMMcYYY2aOE0LGGGNG+fXXXzF9+nQ0Nzejvr5e6/WIAQEBuHbtmmCdW/uJRCKMHTvWBNH3ja+//hp+fn63vW/2999/x0cffYTQ0FAMGjQItra2eOihhzBr1iyUlpbqXKerqwtbt27F448/DrlcDmdnZwQGBmLjxo24fv26Vt+33npL8wQ5Y/rihJCZrVu/lHR93nnnHVOHydg9qaSkBGPHjkV4eDgcHR3h4uICIkJRUZFmeUpKimA9dT+lUgm5XA4iwokTJ/o7/F5XXl6O6dOnIy0tDbW1tbftu3jxYixYsADR0dE4c+YMGhoasG3bNpSUlCAwMBB79+4VrPPSSy8hMTERU6ZMwc8//4xffvkFCQkJWLBgAZ577jmtvi+//DLS0tKwYsWKXt1HNrBxQsjMFulRi4sTQgYA9vb2mDhxotlu/1bNzc2YNm0annvuOcyfP1+wXCKRQC6XIzMzE5999pkJIux/K1aswIQJE1BcXAwHB4c79p87dy4WLlwINzc32NnZISQkBLt27cKNGzewZMkSrb4VFRXYuXMnAgICsGbNGgwZMgRyuRxLlizBU089hYKCAk0iDgC+vr7Iy8tDeno6v4WG6Y0TQvuBw1sAACAASURBVMYYYwZZv349ampqsHLlSp3LbWxskJ2dDQsLCyQlJQlecTkQbd26FW+99ZZeJZaysrKQmZkpaFcoFLC1tUV5ebnWgz6XLl0CADz88MOCdUaOHAkA+O233wRjxcXF4Y033uA6oEwvnBAyxhjTGxEhKysL48aNg4eHR4/9IiIisHz5crS0tCA+Pl7n/YQDia2t7V2P0dbWhvb2dowaNUqr3ujIkSNhbW2tKRR/s7KyMohEIowePVqwLCYmBpcvX9YqJM9YTzghZIwNCA0NDUhNTYWvry/EYjGcnZ0xdepUfPfdd5o+q1ev1twfevMU7IEDBzTtLi4umvaMjAyIRCK0tbXh6NGjmj7qq0Dq5SKRCEOHDkVRURHCwsLg4OAAOzs7TJ48WesNLr29fVMoLS1FbW0tFArFHfu+/fbbCA8Px6lTp7BgwQK9xtfnPKrfmKP+XLx4EQkJCXBycoJcLkdUVBTKy8sFY9fV1SE5ORne3t4Qi8UYPHgwYmNjUVJSov8B6EPqEi/Lli3Tand1dUVGRgZKS0uxdOlS1NXVobGxEevXr8c///lPrFy5En5+foLxxowZAwD45ptv+j54dv/r98qHzGS40Ci7X8DAwtTV1dXk4+NDrq6ulJ+fTyqVis6ePUuxsbEkEoloy5YtWv2lUik98cQTgnECAwNJLpcL2nvqr6ZQKEgqlVJwcDAdO3aMWltbqaioiB599FESi8V0+PDhPt3+5MmTadCgQaRUKnvso4sxhal37NhBAGjNmjU6lxcVFZFMJtP8XFdXR15eXgSAdu7cqWlXKpWCfTX0PEZHRxMAio6O1hz3Q4cOaQro36yqqooeeOABcnV1pX379lFLSwudPn2aJk2aRDY2NnoVkdeXp6cnWVpaGrROTU0Nubq6UmJiYo99cnNzaejQoZoi8i4uLrR169Ye+6tUKgJAISEhBsVCxN8X5oivEDLG7ntpaWm4cOECPvjgA0RFRcHR0RF+fn7YtWsX3N3dkZycfMcnP+9WW1sbNm/ejODgYEilUowdOxY7d+7E9evXsXDhwj7ddnd3t+ZBqL5WXV0NAJDJZHr1d3FxQW5uLqytrZGUlKRz2lPN2POYmJioOe5TpkxBZGQkioqKUF9frzX2r7/+ivfffx/PPPMM7O3t4e/vj927d4OI9L6C2RcaGhrw9NNP48knn8RHH30kWE5EmDdvHmbNmoXU1FTU1NSgrq4O6enpmD9/PmbOnKnzPkFHR0eIRCLNOWPsdjghZIzd9/Ly8gAAkZGRWu0SiQRhYWFob2/v82kzqVSqmaJTGz16NDw8PFBaWtqnX8qHDx9GY2MjgoOD+2wbaup7Aa2trfVeZ/z48cjIyEBbWxvi4+PR3t6us5+x5zEoKEjrZy8vLwBAVVWVpm3v3r2wsLBAVFSUVl83Nzf4+/ujuLgYly9f1nufektbWxsiIiLwyCOPIDs7G5aWloI+O3bswJYtW/DKK6/gr3/9K1xdXeHi4oJ58+Zpag5u3LhR5/hWVlY9Hm/GbsYJIWPsvtbR0QGVSgUbGxud5T5cXV0BADU1NX0ah5OTk872IUOGAACuXLnSp9vvLzY2NgCAzs5Og9ZLTk5GQkICTp8+rbNUzd2cx1uvVorFYgB/XDm9eezu7m7IZDJBvdGTJ08CAM6fP2/QPt2trq4uxMfHw9PTE5988onOZBD44x5TAJgyZYpgWVhYGABg//79PW6jNx54YQOf6e5MZoyxXiCRSCCTyaBSqdDS0iJIJtRTjG5ubpo2CwsLwdsdAKCpqUnnNm5+4rMnDQ0NICJBX3UiqE4M+2r7/cXd3R0AoFKpDF43KysLJSUl2LZtmyaxVDPmPOpLIpHAyckJra2taG9vN+lDOTdLSkpCR0cH8vLytGJ68MEHsXPnTowfPx7AH1cR76S1tVXQ1tzcDCLSnDPGboevEDLG7nsxMTEAICiv0dHRgcLCQtja2iIiIkLT7u7ujsrKSq2+NTU1glpuanZ2dloJ3IgRI/Dxxx9r9bl27ZpWcWAA+PHHH1FVVQWFQqH1pdwX2+8vo0aNAgCjplft7e3xxRdfQCqVYvPmzYLlhp5HQ8TGxqKrq0vrqW+1devWYdiwYf1ar++dd97BTz/9hC+//BISieS2fceNGwcAKCwsFCz79ttvAUCTPN5M/TumPmeM3Q4nhIyx+97atWvh4+ODlJQUFBQUoKWlBefOncMLL7yA6upqbNiwQTPlCADh4eGoqqrCxo0b0draivLycixcuFDrKt7NHnvsMZw7dw6XLl2CUqlERUUFQkJCtPrIZDIsXboUSqUSbW1tOHHiBGbPng2xWIwNGzZo9e3t7YeGhkIul+P48ePGHkK9KRQKDBkypMd37t6Jv7+/zqLMgOHn0RBr166Fr68v5s6di/3790OlUqGxsRGZmZlYtWoVMjIytK7SzZ49GyKRCBcuXDBqe7ezfft2/O1vf8P3338PBwcHwRT2rSVzXnvtNTz00EP48MMP8Y9//ANXrlxBQ0MDtm7divfeew+enp5YtGiRYDvqcjrh4eG9vg9sADLlI86sf3EZAXa/gIFlZ4iI6uvrKSUlhXx8fMja2ppkMhlFRERQYWGhoG9TUxMlJiaSu7s72dra0sSJE6moqIgCAwM1JT3efPNNTf+ysjIKCQkhqVRKXl5etGnTJq3xFAoFeXp60pkzZygiIoIcHBzI1taWJk2aREeOHOnz7YeEhJCzs7PBpVOMKTtDRLR06VKysrKiyspKTVtdXZ0mdvUnMDCwxzFeffVVnSV29DmPSqVSsK1ly5YREQnaIyMjNes1NDRQamoqDR8+nKytrWnw4MEUHh5Ohw4dEsQRGhpK9vb21NXVpdcxyc/PF2xb/bm1XE5kZGSPfdWfW0sINTY20uLFi2nkyJEkkUhILBaTr68vzZ8/n2pqanTGFB8fT56ennT9+nW99uFm/H1hfkRE/VCngN0T4uPjAfy3+Clj9yqRSIScnBzMmDHD1KHoZcyYMaivrzfJU6p3Izc3FwkJCQaXq1GpVPD390dUVJTOMin3u6amJnh4eGDWrFnYsmWLqcMxSmlpKQICArBr1y7MnDnT4PX5+8L88JQxY4wxg8hkMuTn52PPnj3YtGmTqcPpVUSE5ORkODo64t133zV1OEapqKhAbGws0tLSjEoGmXnihJAxxpjBAgICcOLECezfvx/Nzc2mDqfX1NbWoqKiAoWFhUY90XwvyMzMRHp6OtLT000dCruPcELIGGNGUr9ruLS0FJWVlRCJRFi+fLmpw+o33t7eKCgogKOjo6lD6TVubm44cuQI/P39TR2K0datW8dXBpnB7o1iTIwxdh9atGiRzqc7GWPsfsNXCBljjDHGzBwnhIwxxhhjZo4TQsYYY4wxM8cJIWOMMcaYmeOHSszM8ePHNQVHGbuX/f3vf+eiuH1MXUib/yawWx0/flzn+5HZwMVvKjEj77//PpRKpanDYOye8uOPPwIARo8ebeJIGLu3BAcHIzU11dRhsH7CCSFjzKypX4+Xm5tr4kgYY8x0+B5CxhhjjDEzxwkhY4wxxpiZ44SQMcYYY8zMcULIGGOMMWbmOCFkjDHGGDNznBAyxhhjjJk5TggZY4wxxswcJ4SMMcYYY2aOE0LGGGOMMTPHCSFjjDHGmJnjhJAxxhhjzMxxQsgYY4wxZuY4IWSMMcYYM3OcEDLGGGOMmTlOCBljjDHGzBwnhIwxxhhjZo4TQsYYY4wxM8cJIWOMMcaYmeOEkDHGGGPMzHFCyBhjjDFm5jghZIwxxhgzc5wQMsYYY4yZOU4IGWOMMcbMHCeEjDHGGGNmjhNCxhhjjDEzxwkhY4wxxpiZ44SQMcYYY8zMcULIGGOMMWbmOCFkjDHGGDNznBAyxhhjjJk5TggZY4wxxswcJ4SMMcYYY2aOE0LGGGOMMTMnIiIydRCMMdYfPv30U7z//vu4ceOGpq2+vh4A4OLiommztLREamoq/vznP/d7jIwxZgqcEDLGzMa5c+cwYsQIvfqePXsWfn5+fRwRY4zdG3jKmDFmNvz8/KBQKCASiXrsIxKJoFAoOBlkjJkVTggZY2blz3/+MywtLXtcbmVlhRdffLEfI2KMMdPjKWPGmFmpqqqCl5cXuru7dS4XiUS4dOkSPD09+zkyxhgzHb5CyBgzKx4eHpgwYQIsLIR//iwsLPDEE09wMsgYMzucEDLGzM6cOXN0totEIn6ymDFmlnjKmDFmdn7//Xe4urqis7NTq93Kygo1NTWQy+UmiowxxkyDrxAyxsyOs7MznnrqKa2HSywtLREREcHJIGPMLHFCyBgzS7Nnz9Z6sISIMHv2bBNGxBhjpsNTxowxs3T16lXI5XJcu3YNAGBjY4P6+npIpVITR8YYY/2PrxAyxsySnZ0dYmJiYG1tDWtra8TExHAyyBgzW5wQMsbM1gsvvIDOzk50dnbihRdeMHU4jDFmMlamDoD1H6VSiUuXLpk6DMbuGTdu3ICdnR2ICM3NzcjNzTV1SIzdM7y8vBAcHGzqMFg/4XsIzUh8fDz27Nlj6jAYY4zdB+Li4vD555+bOgzWT/gKoZnhf+DsfiASiZCTk4MZM2b0+bb+9a9/QSQS4U9/+lOfb+tek5ubi4SEBPB1AXar+Ph4U4fA+hknhIwxsxYSEmLqEBhjzOQ4IWSMmTVd7zRmjDFzw38JGWOMMcbMHCeEjDHGGGNmjhNCxhhjjDEzxwkhY4wxo/z666+YPn06mpubUV9fD5FIpPkEBARoXgt4s1v7iUQijB071gTR942vv/4afn5+sLLq+Rb933//HR999BFCQ0MxaNAg2Nra4qGHHsKsWbNQWlqqc52uri5s3boVjz/+OORyOZydnREYGIiNGzfi+vXrWn3feust5OTk9Op+sYGPE0LG2IDV2tqKhx56CFFRUaYOZcApKSnB2LFjER4eDkdHR7i4uICIUFRUpFmekpIiWE/dT6lUQi6Xg4hw4sSJ/g6/15WXl2P69OlIS0tDbW3tbfsuXrwYCxYsQHR0NM6cOYOGhgZs27YNJSUlCAwMxN69ewXrvPTSS0hMTMSUKVPw888/45dffkFCQgIWLFiA5557Tqvvyy+/jLS0NKxYsaJX95ENbJwQMsYGLCJCd3c3uru7TR3KHdnb22PixImmDkMvzc3NmDZtGp577jnMnz9fsFwikUAulyMzMxOfffaZCSLsfytWrMCECRNQXFwMBweHO/afO3cuFi5cCDc3N9jZ2SEkJAS7du3CjRs3sGTJEq2+FRUV2LlzJwICArBmzRoMGTIEcrkcS5YswVNPPYWCggJNIg4Avr6+yMvLQ3p6Or99h+mNE0LG2IDl4OCA8vJyfP3116YOZUBZv349ampqsHLlSp3LbWxskJ2dDQsLCyQlJeHcuXP9HGH/27p1K956663bThWrZWVlITMzU9CuUChga2uL8vJyrWLh6leOPvzww4J1Ro4cCQD47bffBGPFxcXhjTfeQFdXl0H7wswTJ4SMMcb0RkTIysrCuHHj4OHh0WO/iIgILF++HC0tLYiPj9d5P+FAYmtre9djtLW1ob29HaNGjYJIJNK0jxw5EtbW1igrKxOsU1ZWBpFIhNGjRwuWxcTE4PLly9i3b99dx8YGPk4IGWMD0t69e7UeXFAnJLe2X7x4EQkJCXBycoJcLkdUVBTKy8s142RkZGj6Dh06FEVFRQgLC4ODgwPs7OwwefJkHD16VNN/9erVmv43TwEfOHBA0+7i4iIYv62tDUePHtX00edKkymUlpaitrYWCoXijn3ffvtthIeH49SpU1iwYIFe4zc0NCA1NRW+vr4Qi8VwdnbG1KlT8d1332n6GHoO1erq6pCcnAxvb2+IxWIMHjwYsbGxKCkp0f8A9CH1a0WXLVum1e7q6oqMjAyUlpZi6dKlqKurQ2NjI9avX49//vOfWLlyJfz8/ATjjRkzBgDwzTff9H3w7P5HzGzExcVRXFycqcNg7I4AUE5OTq+MFR0dTQCovb1dZ3t0dDQdO3aMWltb6dChQ2Rra0tBQUGCcRQKBUmlUgoODtb0LyoqokcffZTEYjEdPnxYq79UKqUnnnhCME5gYCDJ5XJBe0/91SZPnkyDBg0ipVKp767fUU5ODhn6NbBjxw4CQGvWrNG5vKioiGQymebnuro68vLyIgC0c+dOTbtSqRQch+rqavLx8SFXV1fKz88nlUpFZ8+epdjYWBKJRLRlyxat/oacw6qqKnrggQfI1dWV9u3bRy0tLXT69GmaNGkS2djY0LFjxww6Drfj6elJlpaWBq1TU1NDrq6ulJiY2GOf3NxcGjp0KAEgAOTi4kJbt27tsb9KpSIAFBISYlAsRPx9YY74CiFjzKwlJiYiODgYUqkUU6ZMQWRkJIqKilBfXy/o29bWhs2bN2v6jx07Fjt37sT169excOHCPo2zu7sbRKR1b5kpVFdXAwBkMple/V1cXJCbmwtra2skJSXpnPZUS0tLw4ULF/DBBx8gKioKjo6O8PPzw65du+Du7o7k5GSdT/Dqcw7T0tLw66+/4v3338czzzwDe3t7+Pv7Y/fu3SAiva9g9oWGhgY8/fTTePLJJ/HRRx8JlhMR5s2bh1mzZiE1NRU1NTWoq6tDeno65s+fj5kzZ+q8T9DR0REikUhzzhi7HU4IGWNmLSgoSOtnLy8vAEBVVZWgr1Qq1UzDqY0ePRoeHh4oLS3t0y/ew4cPo7GxEcHBwX22DX2op96tra31Xmf8+PHIyMhAW1sb4uPj0d7errNfXl4eACAyMlKrXSKRICwsDO3t7TqnP/U5h3v37oWFhYWgBJGbmxv8/f1RXFyMy5cv671PvaWtrQ0RERF45JFHkJ2dDUtLS0GfHTt2YMuWLXjllVfw17/+Fa6urnBxccG8efM0NQc3btyoc3wrK6sejzdjN+OEkDFm1m690iUWiwFAZ6kaJycnnWMMGTIEAHDlypVeju7eY2NjAwDo7Ow0aL3k5GQkJCTg9OnTOkvVdHR0QKVSwcbGRmfZFldXVwBATU2NYNmdzqF67O7ubshkMkFh7JMnTwIAzp8/b9A+3a2uri7Ex8fD09MTn3zyic5kEPjj/lMAmDJlimBZWFgYAGD//v09bqM3HnhhA9+9edcyY4zdgxoaGkBEWk+AAv9NBNWJIQBYWFgI3iABAE1NTTrHvnXMe5W7uzsAQKVSGbxuVlYWSkpKsG3bNk1iqSaRSCCTyaBSqdDS0iJICtVTxW5ubgZvVyKRwMnJCa2trWhvb79nHthJSkpCR0cH8vLytGJ68MEHsXPnTowfPx7AH1cR76S1tVXQ1tzcDCLSnDPGboevEDLGmJ6uXbumVQAYAH788UdUVVVBoVBoffG6u7ujsrJSq29NTY2gXpyanZ2dVgI5YsQIfPzxx70Yfe8YNWoUABg1vWpvb48vvvgCUqkUmzdvFiyPiYkBAEGZlI6ODhQWFsLW1hYRERFGRA3Exsaiq6tL64lwtXXr1mHYsGH9Wq/vnXfewU8//YQvv/wSEonktn3HjRsHACgsLBQs+/bbbwFAkzzeTP37pz5njN0OJ4SMMaYnmUyGpUuXQqlUoq2tDSdOnMDs2bMhFouxYcMGrb7h4eGoqqrCxo0b0draivLycixcuFDrKuLNHnvsMZw7dw6XLl2CUqlERUUFQkJCNMtDQ0Mhl8tx/PjxPt3HO1EoFBgyZEiP79y9E39/f51FmQFg7dq18PHxQUpKCgoKCtDS0oJz587hhRdeQHV1NTZs2KCZOjbU2rVr4evri7lz52L//v1QqVRobGxEZmYmVq1ahYyMDK2rdLNnz4ZIJMKFCxeM2t7tbN++HX/729/w/fffw8HBQTCFfWvJnNdeew0PPfQQPvzwQ/zjH//AlStX0NDQgK1bt+K9996Dp6cnFi1aJNiOupxOeHh4r+8DG4BM+Ygz619cRoDdL9ALZWfy8vI05TnUn1mzZpFSqRS0L1u2TLPdmz+RkZGa8RQKBXl6etKZM2coIiKCHBwcyNbWliZNmkRHjhwRbL+pqYkSExPJ3d2dbG1taeLEiVRUVESBgYGa8d98801N/7KyMgoJCSGpVEpeXl60adMmrfFCQkLI2dm5V8ujGFN2hoho6dKlZGVlRZWVlZq2uro6wfELDAzscYxXX31VZ/md+vp6SklJIR8fH7K2tiaZTEYRERFUWFio6WPsOWxoaKDU1FQaPnw4WVtb0+DBgyk8PJwOHTokiCM0NJTs7e2pq6tLr2OSn58v2Lb6c2u5nMjIyB77qj+3lhdqbGykxYsX08iRI0kikZBYLCZfX1+aP38+1dTU6IwpPj6ePD096fr163rtw834+8L8iIhMXMOA9Zv4+HgA/y1+yti9SiQSIScnBzNmzDB1KBpjxoxBfX29SZ5E7Su5ublISEgwuJSNSqWCv78/oqKidJZJud81NTXBw8MDs2bNwpYtW0wdjlFKS0sREBCAXbt2YebMmQavz98X5oenjJnBdu/erZnauPXGcHMxffp0iEQirF69+q7Hsre3F0wZWVhYwNnZGQqFAq+99hqKi4t7IWrGeodMJkN+fj727NmDTZs2mTqcXkVESE5OhqOjI959911Th2OUiooKxMbGIi0tzahkkJknTgiZwWbOnAki0pQ7MDeffvop8vPze2281tZW/PDDDwCA6OhoEBE6OztRVlaGVatWoaysDGPHjsVLL72Eq1ev9tp2GbsbAQEBOHHiBPbv34/m5mZTh9NramtrUVFRgcLCQqOeaL4XZGZmIj09Henp6aYOhd1HOCFkzABVVVVISUnBnDlz+nQ7lpaWcHV1RXR0NL799lssWbIE27dvx/PPP2/yN1WYG/W7hktLS1FZWQmRSITly5ebOqx7gre3NwoKCuDo6GjqUHqNm5sbjhw5An9/f1OHYrR169bxlUFmME4IGTPAyy+/jPj4+H5/au+9997DuHHj8NVXX2H37t39um1zt2jRIs0r49Sf3rhVgDHG7iWcEDKmp23btuGnn35CRkZGv29bJBJp3u6gq34bY4wxdjc4IWR3VFZWhmeffRYymQxSqRQhISE4cuRIj/3r6uqQnJwMb29viMViDB48GLGxsZqaWMAf7xW9+SGKixcvIiEhAU5OTpDL5YiKihLU4uro6MDKlSsxcuRI2NnZYdCgQZg2bRq++uor3Lhxw+AYDHH58mW88cYb2LZtm87XavWHiRMnAgCOHz+u9dqwgXi8GWOM9TMTlbthJmBMXanz58+Tk5MTeXp60sGDB6mlpYVOnTpF4eHh5O3tTRKJRKt/VVUVPfDAA+Tq6kr79u2jlpYWOn36NE2aNIlsbGwENdSio6MJAEVHR9OxY8eotbWVDh06RLa2thQUFKTVNzExkWQyGR08eJCuXr1KNTU1tGjRIgJA3333ndEx6CMiIoJee+01zc87duwgAPTuu+/q7D958mQaNGiQoJZYT3744QfNcehJe3u7pkZZVVUVEQ3c441eqEPI7szYOoRs4OM6hOaH/xKYEWP+gcfHxxMA2rNnj1Z7ZWUlSSQSQUL44osvEgDKzs7Waq+uriaJRCIoVKtOUPLz8wWxAqC6ujpNm4+PD02YMEEQo5+fn1aCYmgMd/Lxxx/T8OHDqbW1VdN2p4Rw0qRJBhUR1ichvHr1qiAhHIjHm4gTwv7CCSHrCSeE5ufeeMM3u2cdOHAAAATvD/Xw8ICfnx/OnTun1b53715YWFggKipKq93NzQ3+/v4oLi7G5cuXMXToUK3lQUFBWj97eXkB+OOpXhcXFwDA008/jQ8//BDz5s3D3LlzERQUBEtLS5w9e7ZXYtDlt99+w+LFi/Hll19CKpXesb/a4cOH9e6rr+rqagCAtbW15pgMtON9s7///e9cFLePqYtsq4sQM6Z2/Phxne9HZgMX30PIetTR0YGWlhbY2NjA3t5esPzWd7J2dHRApVKhu7sbMplMUGz55MmTAIDz588LxpLJZFo/i8ViAEB3d7embdOmTfj0009RUVGBsLAwODo64umnn0ZeXl6vxKBLfn4+VCoVnnzySa1x1GVnVqxYoWn75Zdf9BrTWOr7NoODg2FtbT0gjzdjjDETMfUlStZ/jJkCcHBwIADU0tIiWBYQECCYMnZyciIrKyvq7OzUa3z1FGZ7e7tW+5tvvkkA6IcfftC53vXr1+ngwYMUHh5OAOh///d/jY7BGHeaMjbUnaaMb9y4QY8//rhgKnWgHu9b95P1DZ4yZj3hKWPzw1cI2W1NnToVwH+njtXq6+sFU4cAEBsbi66uLhw9elSwbN26dRg2bBi6urqMisXJyQllZWUA/pg2feqppzRPz+7bt69fYjCVtLQ0/Oc//0FMTIzW9B4fb8YYY72BE0J2W2vWrMGgQYOQkpKCQ4cOobW1FWfOnMHs2bN1TiOvXbsWvr6+mDt3Lvbv3w+VSoXGxkZkZmZi1apVyMjIgJWV8beuvvLKKzh16hQ6Ojpw5coVrF+/HkSE0NDQfotBH6GhoZDL5Th+/LhR63d3d+PKlSv48ssvERYWhvXr12Pu3LnIzs6GSCTS9OPjzRhjrFeY+hIl6z/GTgGcPXuWnn32WXJ0dNSUJykoKKCwsDDNU69/+ctfNP0bGhooNTWVhg8fTtbW1jR48GAKDw+nQ4cOafoolUrNuurPsmXLiIgE7ZGRkUREVFJSQklJSfTwww+TnZ0dDRo0iMaPH09btmyh7u5urZj1icEYSUlJgvgAUEREhFa/kJAQvZ8ylkqlgvFEIhHJZDIaPXo0vfrqq1RcXNzj+gPxeIOnjPsFTxmznvCUsfkREfGLUc2FeqqRn9xk9zqRSIScnBzMmDHD1KEMaLm5uUhISOD3YzMB/r4wPzxlzBhjrFf9+uuvmD59Opqbm1FfX6/15HlAQACuXbsmWOfWfiKRCGPHjjVB9H3j3J/q3gAACqZJREFU66+/hp+fn163T5SUlCAyMhJOTk5wcHDAlClTdN6j+9ZbbyEnJ6cvwmVmiBNCxhhjvaakpARjx45FeHg4HB0d4eLiAiJCUVGRZnlKSopgPXU/pVIJuVwOIsKJEyf6O/xeV15ejunTpyMtLQ21tbV37P/9999jwoQJcHBwwM8//4wLFy5g+PDhePLJJ3Hw4EGtvi+//DLS0tKwYsWKvgqfmRFOCJnZuvVqhK7PO++8Y+ow2T3A3t5e8y5pc9y+vpqbmzFt2jQ899xzmD9/vmC5RCKBXC5HZmYmPvvsMxNE2P9WrFiBCRMmoLi4+I7vQe/u7sZf/vIXODk54f/+7//g7u4OFxcXfPjhh/D19UViYiI6Ojo0/X19fZGXl4f09HTk5ub29a6wAY4TQma26I9XN972wwkhY/pbv349ampqsHLlSp3LbWxskJ2dDQsLCyQlJQnedDQQbd26FW+99ZZeU8X//ve/8dNPPyEuLg62traadktLSzz//PO4dOkSCgoKtNZRKBSIi4vDG2+8weWd2F3hhJAxxthdIyJkZWVh3Lhx8PDw6LFfREQEli9fjpaWFsTHx+u8n3AguTmxu5Nvv/0WAHTeO6luKywsFCyLiYnB5cuXteqDMmYoTggZYwNCQ0MDUlNT4evrC7FYDGdnZ0ydOhXfffedps/q1as1twPcPAV74MABTbv6Xc4AkJGRAZFIhLa2Nhw9elTTR321R71cJBJh6NChKCoqQlhYGBwcHGBnZ4fJkydrPQzQ29u/l5SWlqK2thYKheKOfd9++22Eh4fj1KlTWLBggV7j63N+1YXT1Z+LFy8iISEBTk5OkMvliIqKQnl5uWDsuro6JCcnw9vbG2KxGIMHD0ZsbCxKSkr0PwC9QF0IXtd7vz09PQFA51XVMWPGAAC++eabPoyODXScEDLG7ns1NTUICgrCrl27sGHDBtTX1+P777+HnZ0dwsLCkJWVBQBYvnw5iAhSqVRr/aeffhpEhMDAQK32RYsWafo/8cQTmlsJ1FNz6uUKhQJNTU1YuHAhVq9ejZqaGvz73/9GY2MjQkND8a9//atPtq92t4XQe8Pp06cB6E5mbmVhYYHs7Gx4eXkhKysL2dnZt+2v7/l99tlnQUSIjo4GAKSkpCAlJQWVlZXIycnBt99+i+eff15r7OrqagQFBSE3NxebN29GY2MjDh8+jMbGRgQHB0OpVBpzOIzS1NQEAILfDwCaFwH8/vvvgmXqZFF9DhgzBieEjLH7XlpaGi5cuIAPPvgAUVFRcHR0hJ+fH3bt2gV3d3ckJyfr9YTn3Whra8PmzZsRHBwMqVSKsWPHYufOnbh+/ToWLlzYp9vu7u7WJIumUl1dDQCQyWR69XdxcUFubi6sra2RlJSkuTqmi7HnNzExUXM+pkyZgsjISBQVFaG+vl5r7F9//RXvv/8+nnnmGdjb28Pf3x+7d+8GEel9BbOvqc/tzW8qUnN0dIRIJNKcA8aMwQkhY+y+l5eXBwCIjIzUapdIJAgLC0N7e3ufT6dJpVLN1J3a6NGj4eHhgdLS0j79sr75ipapqO8FtLa21nud8ePHIyMjA21tbYiPj0d7e7vOfsae36CgIK2fvby8AABVVVWatr1798LCwgJRUVFafd3c3ODv74/i4mJcvnxZ7326G05OTgD++M/FrdRt6j63srKy6vH4MaYPTggZY/e1jo4OqFQq2NjY6Czr4erqCuCPace+1NMX9ZAhQwAAV65c6dPtm5qNjQ0AoLOz06D1kpOTkZCQgNOnT+ssVXM35/fWq5VisRjAH1dUbx67u7sbMplMUHbq5MmTAIDz588btE/GGjlyJADoTEArKysBAH5+fjrX7erqMugBFsZuxQkhY+y+JpFIIJPJcO3aNbS0tAiWq6cS3dzcNG0WFha4fv26oK/6Hq5b6Zqmu1VDQ4POKVt1IqhODPtq+6bm7u4OAFCpVAavm5WVhREjRmDbtm3YsWOH1jJjzq++JBIJnJycYGVlhc7Ozh7LT02ePNngsY2h3k5xcbFgmbotLCxMsKy5uRlEpDkHjBmDE0LG2H0vJiYGAARlNzo6OlBYWAhbW1tERERo2t3d3TVXXNRqamrw22+/6Rzfzs5OK4EbMWIEPv74Y60+165d07yNQ+3HH39EVVUVFAqF1pd1X2zf1EaNGgVA99WtO7G3t8cXX3wBqVSKzZs3C5Yben4NERsbi66uLp2vhlu3bh2GDRvWb/X9Jk2ahEceeQR79uzRKsdz48YN7N69G15eXoJpc+C/Vw/V54AxY3BCyBi7761duxY+Pj5ISUlBQUEBWlpacO7cObzwwguorq7Ghg0bNFOLABAeHo6qqips3LgRra2tKC8vx8KFC7Wu4t3ssccew7lz53Dp0iUolUpUVFQgJCREq49MJsPSpUuhVCrR1taGEydOYPbs2RCLxdiwYYNW397e/r3wlLFCocCQIUNQWlpq1Pr+/v7IzMzUuczQ82uItWvXwtfXF3PnzsX+/fuhUqnQ2NiIzMxMrFq1ChkZGVplfmbPng2RSIQLFy4Ytb3bsbCwwNatW9HY2IiXXnoJNTU1aGhowOuvv47z589jy5Ytmqn5m6nL44SHh/d6TMyMEDMbcXFxFBcXZ+owGLsjAJSTk2PQOvX19ZSSkkI+Pj5kbW1NMpmMIiIiqLCwUNC3qamJEhMTyd3dnWxtbWnixIlUVFREgYGBBIAA0JtvvqnpX1ZWRiEhISSVSsnLy4s2bdqkNZ5CoSBPT086c+YMRUREkIODA9na2tKkSZPoyJEjfb79kJAQcnZ2pmPHjhl0zHJycqg3vwaWLl1KVlZWVFlZqWmrq6vT7JP6ExgY2OMYr776KsnlckG7PudXqVQKtrVs2TIiIkF7ZGSkZr2GhgZKTU2l4cOHk7W1NQ0ePJjCw8Pp0KFDgjhCQ0PJ3t6eurq69Dom+fn5gm2rP1u2bNG5zsmTJ2nq1Knk6OhI9vb2FBoaqvP3SC0+Pp48PT3p+vXresWkD/6+MD8iIhPWKWD9Kj4+HgDw+eefmzgSxm5PJBIhJycHM2bMMHUoehkzZgzq6+v77WnU3pKbm4uEhIReK1ejUqng7++PqKgofPTRR70y5r2kqakJHh4emDVrFrZs2WLqcAD8URA8ICAAu3btwsyZM3ttXP6+MD88ZcwYY6xXyGQy5OfnY8+ePdi0aZOpw+lVRITk5GQ4Ojri3XffNXU4AICKigrExsYiLS2tV5NBZp44IWSMMdZrAgICcOLECezfvx/Nzc2mDqfX1NbWoqKiAoWFhUY90dwXMjMzkZ6ejvT0dFOHwgYATggZY8xI6ncNl5aWorKyEiKRCMuXLzd1WCbn7e2NgoICODo6mjqUXuPm5oYjR47A39/f1KForFu3jq8Msl5z770hnTHG7hOLFi3CokWLTB0GY4zdNb5CyBhjjDFm5jghZIwxxhgzc5wQMsYYY4yZOU4IGWOMMcbMHCeEjDHGGGNmjt9UYkbi4+OxZ88eU4fBGGPsPhAXF8dvKjEjnBCaEaVSiUuXLpk6DMYYY/cBLy8vBAcHmzoM1k84IWSMMcYYM3N8DyFjjDHGmJnjhJAxxhhjzMxxQsgYY4wxZuasAPAjRIwxxhhjZuz/Ab8QZ+SHI8OuAAAAAElFTkSuQmCC\n","text/plain":["<IPython.core.display.Image object>"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["# To plot the model architecture\n","keras.utils.plot_model(model, to_file='model.png', expand_nested=True, show_shapes=True)"]},{"cell_type":"markdown","id":"b57031ed","metadata":{"papermill":{"duration":0.028144,"end_time":"2022-10-16T07:40:37.286078","exception":false,"start_time":"2022-10-16T07:40:37.257934","status":"completed"},"tags":[]},"source":["### Callbacks\n","\n","Let's define the callbacks : EarlyStopping & ReduceLROnPlateau.\n","\n","- The **EarlyStopping** : stop training when a monitored metric has stopped improving. [more](https://keras.io/api/callbacks/early_stopping/)\n","\n","- The **ReduceLROnPlateau** : reduce learning rate when a metric has stopped improving. [more](https://keras.io/api/callbacks/reduce_lr_on_plateau/)\n"]},{"cell_type":"code","execution_count":10,"id":"407728e9","metadata":{"execution":{"iopub.execute_input":"2022-10-16T07:40:37.344386Z","iopub.status.busy":"2022-10-16T07:40:37.344101Z","iopub.status.idle":"2022-10-16T07:40:37.34942Z","shell.execute_reply":"2022-10-16T07:40:37.348697Z"},"papermill":{"duration":0.036627,"end_time":"2022-10-16T07:40:37.351201","exception":false,"start_time":"2022-10-16T07:40:37.314574","status":"completed"},"tags":[]},"outputs":[],"source":["early_stopping_callback = keras.callbacks.EarlyStopping(monitor='val_loss', #Quantity to be monitored.\n","                            min_delta=0, #Minimum change in the monitored quantity to qualify as an improvement\n","                            patience=5,  #Number of epochs with no improvement after which training will be stopped.\n","                            verbose=0,   #Verbosity mode 0 is silent, and mode 1 displays messages\n","                            mode='auto') # in \"auto\" mode, the direction is automatically inferred from the name of the monitored quantity.\n","\n","lr_reducer = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', #quantity to be monitored.\n","                            factor=0.7, #factor by which the learning rate will be reduced. new_lr = lr * factor.\n","                            patience=5) #number of epochs with no improvement after which learning rate will be reduced.\n","\n","callbacks = [early_stopping_callback, lr_reducer]"]},{"cell_type":"markdown","id":"2775641f","metadata":{"papermill":{"duration":0.028085,"end_time":"2022-10-16T07:40:37.407306","exception":false,"start_time":"2022-10-16T07:40:37.379221","status":"completed"},"tags":[]},"source":["### Training\n","Finally, let's train the model. Since this is a ten class problem, we'll use the *Categorical* versions of crossentropy and accuracy. The adam optimizer generally performs well, so we'll choose it as well.\n","\n","We are going to try to use GPU."]},{"cell_type":"code","execution_count":11,"id":"8effa94e","metadata":{"_kg_hide-output":true,"execution":{"iopub.execute_input":"2022-10-16T07:40:37.463407Z","iopub.status.busy":"2022-10-16T07:40:37.463189Z","iopub.status.idle":"2022-10-16T08:44:31.928998Z","shell.execute_reply":"2022-10-16T08:44:31.928208Z"},"papermill":{"duration":3834.496273,"end_time":"2022-10-16T08:44:31.930988","exception":false,"start_time":"2022-10-16T07:40:37.434715","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["2022-10-16 07:40:37.466156: 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","2022-10-16 07:40:37.466735: 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","2022-10-16 07:40:37.467102: 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","2022-10-16 07:40:37.467504: 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","2022-10-16 07:40:37.467865: 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","2022-10-16 07:40:37.468157: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /device:GPU:0 with 15403 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n","2022-10-16 07:40:37.782190: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"]},{"name":"stdout","output_type":"stream","text":["Epoch 1/180\n"]},{"name":"stderr","output_type":"stream","text":["2022-10-16 07:40:39.453541: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005\n"]},{"name":"stdout","output_type":"stream","text":["586/586 [==============================] - 148s 240ms/step - loss: 2.0637 - accuracy: 0.0000e+00 - val_loss: 2.0465 - val_accuracy: 0.0000e+00\n","Epoch 2/180\n","586/586 [==============================] - 99s 169ms/step - loss: 1.7519 - accuracy: 0.0000e+00 - val_loss: 1.6100 - val_accuracy: 0.0000e+00\n","Epoch 3/180\n","586/586 [==============================] - 100s 171ms/step - loss: 1.5671 - accuracy: 0.0000e+00 - val_loss: 1.4473 - val_accuracy: 0.0000e+00\n","Epoch 4/180\n","586/586 [==============================] - 100s 170ms/step - loss: 1.4076 - accuracy: 0.0000e+00 - val_loss: 1.3816 - val_accuracy: 0.0000e+00\n","Epoch 5/180\n","586/586 [==============================] - 100s 171ms/step - loss: 1.3122 - accuracy: 0.0000e+00 - val_loss: 1.2700 - val_accuracy: 0.0000e+00\n","Epoch 6/180\n","586/586 [==============================] - 101s 172ms/step - loss: 1.2194 - accuracy: 0.0000e+00 - val_loss: 1.1775 - val_accuracy: 0.0000e+00\n","Epoch 7/180\n","586/586 [==============================] - 101s 173ms/step - loss: 1.1384 - accuracy: 0.0000e+00 - val_loss: 1.2412 - val_accuracy: 0.0000e+00\n","Epoch 8/180\n","586/586 [==============================] - 101s 172ms/step - loss: 1.0703 - accuracy: 0.0000e+00 - val_loss: 1.1015 - val_accuracy: 0.0000e+00\n","Epoch 9/180\n","586/586 [==============================] - 103s 175ms/step - loss: 0.9966 - accuracy: 0.0000e+00 - val_loss: 0.9377 - val_accuracy: 0.0000e+00\n","Epoch 10/180\n","586/586 [==============================] - 101s 173ms/step - loss: 0.9532 - accuracy: 0.0000e+00 - val_loss: 0.9973 - val_accuracy: 0.0000e+00\n","Epoch 11/180\n","586/586 [==============================] - 102s 174ms/step - loss: 0.8871 - accuracy: 0.0000e+00 - val_loss: 0.9209 - val_accuracy: 0.0000e+00\n","Epoch 12/180\n","586/586 [==============================] - 103s 175ms/step - loss: 0.8366 - accuracy: 0.0000e+00 - val_loss: 0.8204 - val_accuracy: 0.0000e+00\n","Epoch 13/180\n","586/586 [==============================] - 102s 173ms/step - loss: 0.7824 - accuracy: 1.0671e-05 - val_loss: 0.9107 - val_accuracy: 0.0000e+00\n","Epoch 14/180\n","586/586 [==============================] - 104s 177ms/step - loss: 0.7488 - accuracy: 0.0000e+00 - val_loss: 0.9486 - val_accuracy: 0.0000e+00\n","Epoch 15/180\n","586/586 [==============================] - 104s 177ms/step - loss: 0.7464 - accuracy: 0.0000e+00 - val_loss: 0.7826 - val_accuracy: 0.0000e+00\n","Epoch 16/180\n","586/586 [==============================] - 104s 177ms/step - loss: 0.6854 - accuracy: 2.1342e-05 - val_loss: 0.8568 - val_accuracy: 0.0000e+00\n","Epoch 17/180\n","586/586 [==============================] - 104s 178ms/step - loss: 0.6636 - accuracy: 1.0671e-05 - val_loss: 0.7406 - val_accuracy: 0.0000e+00\n","Epoch 18/180\n","586/586 [==============================] - 106s 180ms/step - loss: 0.6239 - accuracy: 0.0000e+00 - val_loss: 0.7474 - val_accuracy: 0.0000e+00\n","Epoch 19/180\n","586/586 [==============================] - 105s 179ms/step - loss: 0.6150 - accuracy: 3.2014e-05 - val_loss: 0.6818 - val_accuracy: 0.0000e+00\n","Epoch 20/180\n","586/586 [==============================] - 105s 179ms/step - loss: 0.5609 - accuracy: 3.2014e-05 - val_loss: 0.7027 - val_accuracy: 9.6525e-05\n","Epoch 21/180\n","586/586 [==============================] - 104s 177ms/step - loss: 0.5249 - accuracy: 7.4699e-05 - val_loss: 0.6506 - val_accuracy: 0.0000e+00\n","Epoch 22/180\n","586/586 [==============================] - 104s 178ms/step - loss: 0.5241 - accuracy: 2.1342e-05 - val_loss: 0.6368 - val_accuracy: 0.0000e+00\n","Epoch 23/180\n","586/586 [==============================] - 104s 177ms/step - loss: 0.5030 - accuracy: 1.0671e-05 - val_loss: 0.6959 - val_accuracy: 0.0000e+00\n","Epoch 24/180\n","586/586 [==============================] - 106s 182ms/step - loss: 0.4927 - accuracy: 6.4027e-05 - val_loss: 0.6021 - val_accuracy: 0.0000e+00\n","Epoch 25/180\n","586/586 [==============================] - 106s 180ms/step - loss: 0.4713 - accuracy: 8.5370e-05 - val_loss: 0.6168 - val_accuracy: 2.8958e-04\n","Epoch 26/180\n","586/586 [==============================] - 103s 176ms/step - loss: 0.4289 - accuracy: 1.4940e-04 - val_loss: 0.6618 - val_accuracy: 3.8610e-04\n","Epoch 27/180\n","586/586 [==============================] - 104s 178ms/step - loss: 0.4512 - accuracy: 2.1342e-04 - val_loss: 0.6319 - val_accuracy: 9.6525e-05\n","Epoch 28/180\n","586/586 [==============================] - 101s 172ms/step - loss: 0.4066 - accuracy: 3.0947e-04 - val_loss: 0.5396 - val_accuracy: 4.8263e-04\n","Epoch 29/180\n","586/586 [==============================] - 100s 171ms/step - loss: 0.4019 - accuracy: 3.9484e-04 - val_loss: 0.5695 - val_accuracy: 9.6525e-05\n","Epoch 30/180\n","586/586 [==============================] - 104s 178ms/step - loss: 0.4004 - accuracy: 3.6282e-04 - val_loss: 0.5715 - val_accuracy: 2.8958e-04\n","Epoch 31/180\n","586/586 [==============================] - 101s 173ms/step - loss: 0.3761 - accuracy: 3.2014e-04 - val_loss: 0.5514 - val_accuracy: 9.6525e-05\n","Epoch 32/180\n","586/586 [==============================] - 103s 176ms/step - loss: 0.3765 - accuracy: 5.9759e-04 - val_loss: 0.6769 - val_accuracy: 0.0000e+00\n","Epoch 33/180\n","586/586 [==============================] - 102s 174ms/step - loss: 0.3486 - accuracy: 3.9484e-04 - val_loss: 0.5899 - val_accuracy: 0.0000e+00\n"]}],"source":["EPOCHS = 180\n","\n","with tf.device(tf.test.gpu_device_name()):\n","    model.compile(\n","        optimizer=keras.optimizers.Adam(learning_rate=135e-6), # Optimizer\n","        # Loss function to minimize\n","        loss=keras.losses.CategoricalCrossentropy(),\n","        # List of metrics to monitor\n","        metrics=[keras.metrics.Accuracy()]\n","    )\n","    \n","    history = model.fit(\n","        ds_train,\n","        epochs = EPOCHS,\n","        validation_data = ds_valid,\n","        callbacks = callbacks,\n","        verbose = 1,\n","    )"]},{"cell_type":"markdown","id":"edf2e513","metadata":{"papermill":{"duration":5.667416,"end_time":"2022-10-16T08:44:44.05539","exception":false,"start_time":"2022-10-16T08:44:38.387974","status":"completed"},"tags":[]},"source":["Now, we are going to examine the loss and metric plots."]},{"cell_type":"code","execution_count":12,"id":"1d858d6a","metadata":{"execution":{"iopub.execute_input":"2022-10-16T08:44:55.606998Z","iopub.status.busy":"2022-10-16T08:44:55.606703Z","iopub.status.idle":"2022-10-16T08:44:55.86291Z","shell.execute_reply":"2022-10-16T08:44:55.86223Z"},"papermill":{"duration":5.937874,"end_time":"2022-10-16T08:44:55.864762","exception":false,"start_time":"2022-10-16T08:44:49.926888","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 576x360 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["pd.DataFrame(history.history).plot(figsize=(8,5))\n","plt.show()"]},{"cell_type":"markdown","id":"56c69596","metadata":{"papermill":{"duration":6.310202,"end_time":"2022-10-16T08:45:08.043201","exception":false,"start_time":"2022-10-16T08:45:01.732999","status":"completed"},"tags":[]},"source":["# Test Predictions"]},{"cell_type":"code","execution_count":13,"id":"1adfd670","metadata":{"execution":{"iopub.execute_input":"2022-10-16T08:45:19.592875Z","iopub.status.busy":"2022-10-16T08:45:19.592571Z","iopub.status.idle":"2022-10-16T08:45:21.697664Z","shell.execute_reply":"2022-10-16T08:45:21.696927Z"},"papermill":{"duration":7.972881,"end_time":"2022-10-16T08:45:21.700469","exception":false,"start_time":"2022-10-16T08:45:13.727588","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Found 3469 images belonging to 1 classes.\n"]}],"source":["# Define test data\n","test_path = '../input/paddy-disease-classification/test_images'\n","ds_test = keras.preprocessing.image.ImageDataGenerator(rescale=RESCALE).flow_from_directory(    \n","    directory=test_path,\n","    target_size=(img_dim, img_dim),\n","    batch_size=batch_size,\n","    classes=['.'],\n","    shuffle=False,\n",")"]},{"cell_type":"code","execution_count":14,"id":"443413c0","metadata":{"execution":{"iopub.execute_input":"2022-10-16T08:45:33.365479Z","iopub.status.busy":"2022-10-16T08:45:33.36521Z","iopub.status.idle":"2022-10-16T08:46:07.580903Z","shell.execute_reply":"2022-10-16T08:46:07.580203Z"},"papermill":{"duration":40.192613,"end_time":"2022-10-16T08:46:07.582728","exception":false,"start_time":"2022-10-16T08:45:27.390115","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["217/217 [==============================] - 34s 157ms/step\n"]}],"source":["# Prediction \n","predict = model.predict(ds_test, verbose=1)"]},{"cell_type":"code","execution_count":15,"id":"507cca3f","metadata":{"execution":{"iopub.execute_input":"2022-10-16T08:46:19.794445Z","iopub.status.busy":"2022-10-16T08:46:19.79418Z","iopub.status.idle":"2022-10-16T08:46:19.800649Z","shell.execute_reply":"2022-10-16T08:46:19.799966Z"},"papermill":{"duration":5.72422,"end_time":"2022-10-16T08:46:19.80295","exception":false,"start_time":"2022-10-16T08:46:14.07873","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}\n"]}],"source":["predicted_class_indices = np.argmax(predict,axis=1)\n","print(set(predicted_class_indices))\n","\n","inv_map = {v:k for k,v in ds_train.class_indices.items()}\n","\n","predictions = [inv_map[k] for k in predicted_class_indices]"]},{"cell_type":"markdown","id":"cfc343a4","metadata":{"papermill":{"duration":5.710266,"end_time":"2022-10-16T08:46:31.523012","exception":false,"start_time":"2022-10-16T08:46:25.812746","status":"completed"},"tags":[]},"source":["# Submission"]},{"cell_type":"code","execution_count":16,"id":"57a4c8e1","metadata":{"execution":{"iopub.execute_input":"2022-10-16T08:46:43.666156Z","iopub.status.busy":"2022-10-16T08:46:43.665887Z","iopub.status.idle":"2022-10-16T08:46:43.690481Z","shell.execute_reply":"2022-10-16T08:46:43.689828Z"},"papermill":{"duration":6.162474,"end_time":"2022-10-16T08:46:43.692325","exception":false,"start_time":"2022-10-16T08:46:37.529851","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>image_id</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>200001.jpg</td>\n","      <td>blast</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>200002.jpg</td>\n","      <td>normal</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>200003.jpg</td>\n","      <td>blast</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>200004.jpg</td>\n","      <td>blast</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>200005.jpg</td>\n","      <td>blast</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     image_id   label\n","0  200001.jpg   blast\n","1  200002.jpg  normal\n","2  200003.jpg   blast\n","3  200004.jpg   blast\n","4  200005.jpg   blast"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["filenames = ds_test.filenames\n","\n","submission = pd.DataFrame({ \"image_id\":filenames, \"label\":predictions })\n","\n","submission.image_id = submission.image_id.str.replace('./', '')\n","submission.to_csv(\"submission.csv\",index=False)\n","submission.head()"]},{"cell_type":"code","execution_count":17,"id":"e0c8d3bd","metadata":{"execution":{"iopub.execute_input":"2022-10-16T08:46:55.427796Z","iopub.status.busy":"2022-10-16T08:46:55.42751Z","iopub.status.idle":"2022-10-16T08:46:55.435378Z","shell.execute_reply":"2022-10-16T08:46:55.434671Z"},"papermill":{"duration":5.760971,"end_time":"2022-10-16T08:46:55.437473","exception":false,"start_time":"2022-10-16T08:46:49.676502","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["blast                       989\n","normal                      798\n","dead_heart                  415\n","tungro                      309\n","bacterial_leaf_streak       261\n","hispa                       231\n","brown_spot                  230\n","bacterial_leaf_blight        90\n","downy_mildew                 74\n","bacterial_panicle_blight     72\n","Name: label, dtype: int64"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["submission['label'].value_counts()"]},{"cell_type":"code","execution_count":null,"id":"2b7a9c79","metadata":{"papermill":{"duration":5.865513,"end_time":"2022-10-16T08:47:07.24656","exception":false,"start_time":"2022-10-16T08:47:01.381047","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"markdown","id":"30054f57","metadata":{"papermill":{"duration":5.993629,"end_time":"2022-10-16T08:47:19.676207","exception":false,"start_time":"2022-10-16T08:47:13.682578","status":"completed"},"tags":[]},"source":["# Acknowledgements\n","\n","We acknowledge the [Paddy Doctor](https://paddydoc.github.io/) project team at the Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India, and its collaborators."]},{"cell_type":"markdown","id":"01b16bc9","metadata":{"papermill":{"duration":5.980479,"end_time":"2022-10-16T08:47:31.519497","exception":false,"start_time":"2022-10-16T08:47:25.539018","status":"completed"},"tags":[]},"source":["# Ressources \n","\n","\n","- Kaggle course on [computer vision](https://www.kaggle.com/learn/computer-vision)\n","- keras [documentation](https://keras.io/)\n","- ImageDataGenerator [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator)\n","- @maifeeulasad [work](https://www.kaggle.com/code/maifeeulasad/paddy-doctor-paddy-disease-classification)\n","\n"]},{"cell_type":"markdown","id":"d80cc20e","metadata":{"papermill":{"duration":6.282458,"end_time":"2022-10-16T08:47:43.653608","exception":false,"start_time":"2022-10-16T08:47:37.37115","status":"completed"},"tags":[]},"source":["<h5 style=\"color: #c03100\"> If you find this notebook useful, you can support with an upvote 🤗"]},{"cell_type":"markdown","id":"6bfa72a5","metadata":{"papermill":{"duration":5.994907,"end_time":"2022-10-16T08:47:55.465978","exception":false,"start_time":"2022-10-16T08:47:49.471071","status":"completed"},"tags":[]},"source":["<center>\n","    <h2 style=\"color: #2c3f51\"> Thanks for reading 🙂 </h2>"]},{"cell_type":"code","execution_count":null,"id":"c41bdf21","metadata":{"papermill":{"duration":5.95976,"end_time":"2022-10-16T08:48:07.173044","exception":false,"start_time":"2022-10-16T08:48:01.213284","status":"completed"},"tags":[]},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.12"},"papermill":{"default_parameters":{},"duration":4082.144956,"end_time":"2022-10-16T08:48:16.543355","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2022-10-16T07:40:14.398399","version":"2.3.4"}},"nbformat":4,"nbformat_minor":5}