From 6fb2e47a30052db23b7aedfb21e65f9677d6801a Mon Sep 17 00:00:00 2001
From: Abdulrahman <abdulrahman2.ali@live.uwe.ac.uk>
Date: Sun, 12 May 2024 21:06:39 +0300
Subject: [PATCH] Uploaded final files for mlmodel training
---
.../FINAL_car_prediction_cnn_BRAND.ipynb | 8755 +++++++++++++++++
...PPED_car_prediction_cnn_3_CATEGORIES.ipynb | 436 +
mlmodel/Final/SCRAPPED_model_in_py.py | 176 +
3 files changed, 9367 insertions(+)
create mode 100644 mlmodel/Final/FINAL_car_prediction_cnn_BRAND.ipynb
create mode 100644 mlmodel/Final/SCRAPPED_car_prediction_cnn_3_CATEGORIES.ipynb
create mode 100644 mlmodel/Final/SCRAPPED_model_in_py.py
diff --git a/mlmodel/Final/FINAL_car_prediction_cnn_BRAND.ipynb b/mlmodel/Final/FINAL_car_prediction_cnn_BRAND.ipynb
new file mode 100644
index 0000000..3c99422
--- /dev/null
+++ b/mlmodel/Final/FINAL_car_prediction_cnn_BRAND.ipynb
@@ -0,0 +1,8755 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5b1dfb48-778b-418b-90e8-4a712e3352ca",
+ "metadata": {},
+ "source": [
+ "# Prediction a Car's Brand, Model, and Date of Production Using a CNN"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c9f2a57a-c0ea-4864-9dcb-4bb2cea4943c",
+ "metadata": {},
+ "source": [
+ "## Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "0a368a66-ef22-4d5e-ab31-3c8244d7a938",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import glob\n",
+ "import cv2\n",
+ "\n",
+ "import keras\n",
+ "from keras import models, layers\n",
+ "# from keras.utils import np_utils\n",
+ "from sklearn.model_selection import train_test_split"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "424e3d32-86a5-46c1-88ff-e4d9c14b1d9f",
+ "metadata": {},
+ "source": [
+ "## Preprocessing the Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "c64f1212-e88c-46a2-a323-defdabbe75ac",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "## Filtering the dataset to keep only the brands with more than 1000 samples and reduce the number of samples to 1000 for each brand (24 brands in total) \n",
+ "\n",
+ "images = glob.glob('../Dataset/*.jpg')\n",
+ "data = pd.DataFrame(images, columns=['src'])\n",
+ "data['brand'] = data['src'].apply(lambda x : x.split('_')[0].split('\\\\')[-1])\n",
+ "\n",
+ "brand_counts = data['brand'].value_counts()\n",
+ "brands_to_remove = brand_counts[brand_counts < 1000].index\n",
+ "data_filtered = data[~data['brand'].isin(brands_to_remove)]\n",
+ "\n",
+ "# Reduce the number of samples for each brand to 1000\n",
+ "data_balanced = pd.DataFrame(columns=data.columns)\n",
+ "for brand in data_filtered['brand'].unique():\n",
+ " samples_brand = data_filtered[data_filtered['brand'] == brand]\n",
+ " if len(samples_brand) > 1000:\n",
+ " samples_to_keep = samples_brand.sample(n=1000, random_state=42)\n",
+ " data_balanced = pd.concat([data_balanced, samples_to_keep], ignore_index=True)\n",
+ " else:\n",
+ " data_balanced = pd.concat([data_balanced, samples_brand], ignore_index=True)\n",
+ " \n",
+ "data = data_balanced"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "f9ff0b13-86db-4de5-b2a8-599a66ccc27b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
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+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[214, 215, 216, ..., 214, 212, 209],\n",
+ " [214, 215, 216, ..., 214, 212, 209],\n",
+ " [214, 215, 216, ..., 214, 212, 209],\n",
+ " ...,\n",
+ " [156, 158, 163, ..., 132, 115, 120],\n",
+ " [152, 154, 151, ..., 137, 125, 126],\n",
+ " [134, 140, 133, ..., 134, 128, 126]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[254, 253, 239, ..., 235, 235, 235],\n",
+ " [254, 253, 238, ..., 235, 235, 235],\n",
+ " [254, 253, 237, ..., 235, 235, 235],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 63, 61, 64],\n",
+ " [255, 255, 255, ..., 79, 81, 80],\n",
+ " [255, 255, 255, ..., 90, 95, 153]], dtype=uint8),\n",
+ " array([[85, 85, 84, ..., 85, 85, 85],\n",
+ " [85, 85, 84, ..., 85, 85, 85],\n",
+ " [85, 85, 84, ..., 85, 85, 85],\n",
+ " ...,\n",
+ " [34, 31, 31, ..., 48, 42, 48],\n",
+ " [40, 37, 34, ..., 39, 41, 47],\n",
+ " [42, 40, 34, ..., 35, 42, 48]], dtype=uint8),\n",
+ " array([[ 95, 90, 89, ..., 20, 23, 22],\n",
+ " [ 93, 91, 101, ..., 23, 23, 20],\n",
+ " [108, 104, 124, ..., 22, 20, 19],\n",
+ " ...,\n",
+ " [126, 125, 129, ..., 18, 16, 12],\n",
+ " [125, 126, 131, ..., 20, 18, 15],\n",
+ " [125, 126, 128, ..., 22, 19, 16]], dtype=uint8),\n",
+ " array([[178, 184, 187, ..., 195, 192, 208],\n",
+ " [179, 184, 188, ..., 195, 192, 209],\n",
+ " [181, 186, 189, ..., 196, 192, 209],\n",
+ " ...,\n",
+ " [234, 235, 236, ..., 232, 232, 238],\n",
+ " [234, 235, 236, ..., 233, 232, 238],\n",
+ " [234, 235, 236, ..., 233, 233, 239]], dtype=uint8),\n",
+ " array([[189, 194, 196, ..., 208, 206, 205],\n",
+ " [194, 196, 195, ..., 209, 210, 206],\n",
+ " [197, 196, 194, ..., 212, 211, 207],\n",
+ " ...,\n",
+ " [ 59, 60, 61, ..., 64, 63, 62],\n",
+ " [ 59, 60, 61, ..., 64, 63, 62],\n",
+ " [ 59, 60, 62, ..., 64, 63, 62]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 3, 4, 2, ..., 2, 3, 2],\n",
+ " [ 2, 3, 4, ..., 2, 3, 2],\n",
+ " [ 5, 4, 3, ..., 2, 3, 2],\n",
+ " ...,\n",
+ " [21, 22, 24, ..., 41, 36, 38],\n",
+ " [18, 23, 26, ..., 34, 34, 36],\n",
+ " [20, 22, 24, ..., 31, 34, 37]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 83, 136, 157, ..., 152, 150, 147],\n",
+ " [ 55, 69, 150, ..., 153, 151, 148],\n",
+ " [ 49, 64, 149, ..., 154, 153, 150],\n",
+ " ...,\n",
+ " [ 58, 58, 58, ..., 57, 59, 57],\n",
+ " [ 58, 59, 59, ..., 57, 58, 57],\n",
+ " [ 59, 60, 59, ..., 57, 58, 56]], dtype=uint8),\n",
+ " array([[219, 222, 222, ..., 46, 45, 46],\n",
+ " [219, 222, 222, ..., 45, 45, 46],\n",
+ " [219, 222, 222, ..., 45, 44, 45],\n",
+ " ...,\n",
+ " [191, 188, 187, ..., 154, 152, 151],\n",
+ " [190, 186, 186, ..., 153, 152, 151],\n",
+ " [190, 185, 186, ..., 152, 151, 150]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 76, 77, 78, ..., 106, 105, 101],\n",
+ " [ 72, 78, 78, ..., 105, 113, 110],\n",
+ " [ 67, 81, 79, ..., 113, 111, 114],\n",
+ " ...,\n",
+ " [ 66, 65, 66, ..., 77, 79, 79],\n",
+ " [ 69, 67, 68, ..., 82, 81, 80],\n",
+ " [ 71, 71, 68, ..., 78, 77, 76]], dtype=uint8),\n",
+ " array([[ 36, 37, 40, ..., 57, 49, 51],\n",
+ " [ 28, 32, 39, ..., 36, 40, 47],\n",
+ " [ 27, 26, 31, ..., 21, 22, 41],\n",
+ " ...,\n",
+ " [254, 254, 254, ..., 254, 254, 254],\n",
+ " [254, 254, 254, ..., 253, 254, 254],\n",
+ " [254, 254, 254, ..., 254, 254, 254]], dtype=uint8),\n",
+ " array([[137, 149, 159, ..., 208, 206, 210],\n",
+ " [156, 161, 170, ..., 207, 206, 208],\n",
+ " [156, 164, 170, ..., 213, 214, 219],\n",
+ " ...,\n",
+ " [229, 231, 231, ..., 226, 223, 223],\n",
+ " [225, 229, 229, ..., 223, 218, 216],\n",
+ " [231, 231, 230, ..., 220, 214, 202]], dtype=uint8),\n",
+ " array([[ 3, 3, 3, ..., 7, 12, 148],\n",
+ " [ 3, 3, 3, ..., 7, 4, 16],\n",
+ " [ 3, 3, 3, ..., 6, 7, 10],\n",
+ " ...,\n",
+ " [172, 174, 175, ..., 84, 83, 76],\n",
+ " [171, 173, 176, ..., 85, 84, 80],\n",
+ " [171, 172, 174, ..., 84, 83, 88]], dtype=uint8),\n",
+ " array([[ 47, 43, 53, ..., 78, 95, 102],\n",
+ " [ 54, 49, 54, ..., 78, 88, 95],\n",
+ " [ 57, 61, 63, ..., 83, 87, 86],\n",
+ " ...,\n",
+ " [161, 161, 162, ..., 184, 183, 182],\n",
+ " [161, 161, 162, ..., 183, 183, 182],\n",
+ " [160, 161, 162, ..., 183, 182, 181]], dtype=uint8),\n",
+ " array([[217, 218, 220, ..., 244, 243, 243],\n",
+ " [218, 219, 220, ..., 244, 243, 243],\n",
+ " [220, 221, 222, ..., 244, 244, 244],\n",
+ " ...,\n",
+ " [122, 126, 113, ..., 92, 109, 96],\n",
+ " [116, 122, 115, ..., 86, 109, 77],\n",
+ " [103, 115, 115, ..., 74, 94, 84]], dtype=uint8),\n",
+ " array([[200, 199, 191, ..., 13, 31, 32],\n",
+ " [199, 200, 197, ..., 20, 56, 25],\n",
+ " [205, 202, 197, ..., 85, 107, 16],\n",
+ " ...,\n",
+ " [245, 248, 249, ..., 153, 145, 139],\n",
+ " [245, 248, 249, ..., 151, 144, 139],\n",
+ " [245, 248, 249, ..., 150, 143, 138]], dtype=uint8),\n",
+ " array([[ 55, 53, 51, ..., 47, 41, 40],\n",
+ " [ 55, 52, 50, ..., 46, 40, 40],\n",
+ " [ 53, 50, 48, ..., 45, 39, 40],\n",
+ " ...,\n",
+ " [234, 234, 234, ..., 156, 155, 154],\n",
+ " [234, 234, 234, ..., 154, 153, 152],\n",
+ " [234, 234, 234, ..., 153, 151, 150]], dtype=uint8),\n",
+ " array([[189, 182, 181, ..., 255, 255, 255],\n",
+ " [194, 186, 183, ..., 255, 255, 255],\n",
+ " [200, 193, 186, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [241, 241, 242, ..., 202, 201, 201],\n",
+ " [241, 241, 242, ..., 201, 200, 200],\n",
+ " [241, 241, 242, ..., 200, 200, 200]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[119, 134, 138, ..., 155, 141, 144],\n",
+ " [117, 130, 137, ..., 146, 139, 141],\n",
+ " [114, 130, 136, ..., 142, 139, 133],\n",
+ " ...,\n",
+ " [176, 182, 187, ..., 159, 127, 146],\n",
+ " [177, 182, 186, ..., 160, 140, 127],\n",
+ " [178, 183, 183, ..., 160, 146, 117]], dtype=uint8),\n",
+ " array([[46, 52, 56, ..., 38, 30, 19],\n",
+ " [47, 52, 57, ..., 38, 30, 20],\n",
+ " [48, 54, 59, ..., 38, 30, 22],\n",
+ " ...,\n",
+ " [43, 45, 45, ..., 3, 1, 0],\n",
+ " [40, 42, 43, ..., 2, 1, 0],\n",
+ " [38, 40, 42, ..., 2, 0, 0]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[107, 105, 98, ..., 84, 78, 83],\n",
+ " [141, 141, 137, ..., 81, 83, 81],\n",
+ " [152, 154, 152, ..., 83, 83, 64],\n",
+ " ...,\n",
+ " [137, 139, 138, ..., 124, 126, 124],\n",
+ " [133, 137, 139, ..., 126, 126, 124],\n",
+ " [133, 138, 139, ..., 132, 130, 131]], dtype=uint8),\n",
+ " array([[ 87, 97, 113, ..., 197, 196, 199],\n",
+ " [100, 112, 117, ..., 199, 200, 205],\n",
+ " [ 93, 91, 123, ..., 202, 202, 203],\n",
+ " ...,\n",
+ " [ 86, 87, 87, ..., 65, 64, 63],\n",
+ " [ 86, 87, 88, ..., 65, 64, 63],\n",
+ " [ 86, 87, 88, ..., 64, 63, 62]], dtype=uint8),\n",
+ " array([[151, 154, 145, ..., 210, 154, 165],\n",
+ " [135, 142, 147, ..., 206, 144, 174],\n",
+ " [ 71, 118, 143, ..., 196, 134, 188],\n",
+ " ...,\n",
+ " [216, 216, 217, ..., 194, 191, 190],\n",
+ " [216, 216, 216, ..., 195, 192, 190],\n",
+ " [216, 216, 216, ..., 195, 192, 191]], dtype=uint8),\n",
+ " array([[228, 229, 229, ..., 228, 227, 226],\n",
+ " [227, 228, 228, ..., 227, 226, 224],\n",
+ " [224, 225, 226, ..., 224, 222, 221],\n",
+ " ...,\n",
+ " [ 36, 34, 34, ..., 31, 32, 33],\n",
+ " [ 36, 35, 34, ..., 30, 32, 33],\n",
+ " [ 37, 36, 34, ..., 31, 32, 33]], dtype=uint8),\n",
+ " array([[ 24, 23, 27, ..., 23, 30, 174],\n",
+ " [ 23, 22, 24, ..., 30, 28, 172],\n",
+ " [ 22, 22, 24, ..., 28, 28, 171],\n",
+ " ...,\n",
+ " [225, 226, 227, ..., 225, 226, 243],\n",
+ " [225, 226, 226, ..., 225, 226, 243],\n",
+ " [225, 226, 226, ..., 226, 226, 243]], dtype=uint8),\n",
+ " array([[ 49, 43, 43, ..., 103, 105, 109],\n",
+ " [ 23, 16, 16, ..., 90, 94, 97],\n",
+ " [ 30, 23, 21, ..., 92, 96, 99],\n",
+ " ...,\n",
+ " [191, 190, 190, ..., 190, 190, 190],\n",
+ " [192, 191, 191, ..., 190, 190, 190],\n",
+ " [193, 191, 191, ..., 190, 190, 190]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[181, 181, 181, ..., 68, 66, 65],\n",
+ " [181, 181, 181, ..., 51, 51, 50],\n",
+ " [182, 182, 182, ..., 41, 42, 42],\n",
+ " ...,\n",
+ " [146, 146, 146, ..., 136, 136, 136],\n",
+ " [145, 145, 145, ..., 136, 135, 135],\n",
+ " [145, 145, 145, ..., 136, 135, 135]], dtype=uint8),\n",
+ " array([[220, 217, 216, ..., 184, 186, 207],\n",
+ " [220, 217, 216, ..., 185, 187, 207],\n",
+ " [219, 217, 217, ..., 187, 189, 208],\n",
+ " ...,\n",
+ " [ 76, 74, 74, ..., 75, 68, 126],\n",
+ " [ 74, 76, 73, ..., 74, 74, 128],\n",
+ " [ 70, 76, 71, ..., 75, 79, 130]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[185, 185, 184, ..., 144, 152, 158],\n",
+ " [180, 182, 182, ..., 155, 152, 149],\n",
+ " [175, 178, 180, ..., 147, 153, 143],\n",
+ " ...,\n",
+ " [132, 131, 134, ..., 128, 128, 130],\n",
+ " [132, 132, 135, ..., 126, 129, 125],\n",
+ " [132, 133, 134, ..., 128, 131, 123]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[168, 172, 172, ..., 226, 212, 146],\n",
+ " [167, 169, 173, ..., 226, 214, 149],\n",
+ " [164, 164, 165, ..., 227, 215, 152],\n",
+ " ...,\n",
+ " [119, 124, 130, ..., 104, 94, 93],\n",
+ " [122, 126, 132, ..., 107, 100, 96],\n",
+ " [123, 128, 134, ..., 107, 103, 102]], dtype=uint8),\n",
+ " array([[197, 183, 180, ..., 56, 69, 77],\n",
+ " [181, 178, 178, ..., 75, 103, 144],\n",
+ " [141, 152, 169, ..., 150, 150, 135],\n",
+ " ...,\n",
+ " [209, 210, 212, ..., 168, 165, 163],\n",
+ " [208, 210, 211, ..., 167, 164, 163],\n",
+ " [207, 209, 210, ..., 167, 164, 163]], dtype=uint8),\n",
+ " array([[185, 186, 187, ..., 137, 138, 139],\n",
+ " [188, 189, 190, ..., 137, 141, 143],\n",
+ " [192, 194, 194, ..., 141, 145, 145],\n",
+ " ...,\n",
+ " [ 81, 79, 81, ..., 76, 74, 71],\n",
+ " [ 80, 82, 79, ..., 75, 72, 70],\n",
+ " [ 81, 81, 82, ..., 73, 71, 69]], dtype=uint8),\n",
+ " array([[145, 148, 129, ..., 117, 109, 50],\n",
+ " [134, 147, 138, ..., 127, 91, 48],\n",
+ " [131, 144, 144, ..., 106, 66, 46],\n",
+ " ...,\n",
+ " [ 20, 24, 25, ..., 82, 78, 72],\n",
+ " [ 20, 24, 24, ..., 78, 74, 70],\n",
+ " [ 19, 23, 24, ..., 79, 73, 65]], dtype=uint8),\n",
+ " array([[ 44, 29, 24, ..., 47, 52, 76],\n",
+ " [ 44, 34, 26, ..., 39, 54, 66],\n",
+ " [ 41, 39, 28, ..., 36, 61, 61],\n",
+ " ...,\n",
+ " [ 91, 93, 96, ..., 182, 199, 203],\n",
+ " [102, 98, 91, ..., 189, 181, 175],\n",
+ " [ 84, 84, 77, ..., 189, 198, 190]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[113, 105, 105, ..., 39, 40, 41],\n",
+ " [ 77, 81, 90, ..., 59, 60, 62],\n",
+ " [ 62, 57, 66, ..., 67, 67, 67],\n",
+ " ...,\n",
+ " [130, 133, 133, ..., 157, 158, 156],\n",
+ " [127, 131, 133, ..., 154, 156, 156],\n",
+ " [119, 119, 122, ..., 150, 150, 152]], dtype=uint8),\n",
+ " array([[ 55, 53, 51, ..., 47, 40, 40],\n",
+ " [ 55, 52, 50, ..., 46, 40, 40],\n",
+ " [ 53, 50, 48, ..., 45, 40, 39],\n",
+ " ...,\n",
+ " [234, 234, 234, ..., 156, 155, 154],\n",
+ " [234, 234, 234, ..., 154, 153, 152],\n",
+ " [234, 234, 234, ..., 153, 151, 150]], dtype=uint8),\n",
+ " array([[ 34, 49, 42, ..., 183, 182, 234],\n",
+ " [ 34, 43, 37, ..., 180, 171, 229],\n",
+ " [ 41, 43, 37, ..., 167, 175, 234],\n",
+ " ...,\n",
+ " [135, 139, 142, ..., 23, 22, 167],\n",
+ " [139, 146, 138, ..., 27, 25, 167],\n",
+ " [144, 143, 140, ..., 31, 32, 170]], dtype=uint8),\n",
+ " array([[ 78, 75, 75, ..., 138, 144, 138],\n",
+ " [ 81, 84, 81, ..., 197, 126, 128],\n",
+ " [ 83, 91, 90, ..., 159, 141, 152],\n",
+ " ...,\n",
+ " [255, 255, 254, ..., 171, 204, 207],\n",
+ " [255, 255, 254, ..., 123, 152, 195],\n",
+ " [255, 255, 252, ..., 120, 121, 135]], dtype=uint8),\n",
+ " array([[ 61, 68, 47, ..., 75, 105, 120],\n",
+ " [ 53, 54, 69, ..., 70, 77, 81],\n",
+ " [ 51, 62, 56, ..., 55, 59, 59],\n",
+ " ...,\n",
+ " [193, 192, 190, ..., 192, 196, 199],\n",
+ " [193, 193, 191, ..., 192, 196, 199],\n",
+ " [195, 196, 194, ..., 192, 195, 198]], dtype=uint8),\n",
+ " array([[254, 254, 254, ..., 254, 252, 253],\n",
+ " [254, 254, 254, ..., 254, 253, 253],\n",
+ " [254, 254, 254, ..., 255, 253, 254],\n",
+ " ...,\n",
+ " [120, 125, 125, ..., 122, 119, 161],\n",
+ " [121, 124, 124, ..., 120, 116, 161],\n",
+ " [122, 123, 124, ..., 116, 113, 161]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[251, 251, 251, ..., 69, 69, 69],\n",
+ " [251, 251, 251, ..., 69, 69, 69],\n",
+ " [251, 251, 251, ..., 68, 68, 68],\n",
+ " ...,\n",
+ " [120, 128, 139, ..., 242, 242, 242],\n",
+ " [158, 170, 179, ..., 243, 243, 243],\n",
+ " [185, 185, 182, ..., 244, 244, 244]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[181, 183, 186, ..., 180, 177, 175],\n",
+ " [182, 184, 186, ..., 181, 178, 176],\n",
+ " [183, 185, 187, ..., 182, 179, 177],\n",
+ " ...,\n",
+ " [ 41, 41, 39, ..., 45, 45, 45],\n",
+ " [ 38, 40, 38, ..., 46, 48, 46],\n",
+ " [ 36, 39, 39, ..., 45, 47, 45]], dtype=uint8),\n",
+ " array([[ 73, 72, 71, ..., 108, 109, 110],\n",
+ " [ 73, 71, 71, ..., 108, 106, 104],\n",
+ " [ 78, 71, 71, ..., 140, 102, 105],\n",
+ " ...,\n",
+ " [ 93, 92, 90, ..., 60, 60, 60],\n",
+ " [ 91, 90, 90, ..., 60, 61, 61],\n",
+ " [ 93, 92, 91, ..., 61, 61, 61]], dtype=uint8),\n",
+ " array([[155, 174, 167, ..., 201, 200, 199],\n",
+ " [142, 169, 171, ..., 196, 191, 196],\n",
+ " [152, 165, 178, ..., 188, 191, 186],\n",
+ " ...,\n",
+ " [120, 133, 141, ..., 171, 179, 180],\n",
+ " [151, 158, 171, ..., 167, 167, 164],\n",
+ " [170, 167, 178, ..., 158, 157, 159]], dtype=uint8),\n",
+ " array([[206, 209, 211, ..., 203, 199, 232],\n",
+ " [207, 209, 211, ..., 203, 200, 232],\n",
+ " [207, 209, 211, ..., 205, 201, 233],\n",
+ " ...,\n",
+ " [203, 205, 206, ..., 185, 183, 227],\n",
+ " [201, 203, 204, ..., 182, 180, 225],\n",
+ " [199, 201, 202, ..., 179, 178, 224]], dtype=uint8),\n",
+ " array([[236, 239, 241, ..., 233, 231, 228],\n",
+ " [237, 239, 241, ..., 233, 231, 229],\n",
+ " [237, 239, 241, ..., 234, 231, 229],\n",
+ " ...,\n",
+ " [ 98, 101, 103, ..., 90, 88, 88],\n",
+ " [ 98, 98, 96, ..., 89, 86, 84],\n",
+ " [ 92, 93, 89, ..., 87, 90, 81]], dtype=uint8),\n",
+ " array([[254, 254, 254, ..., 110, 126, 136],\n",
+ " [254, 254, 254, ..., 107, 151, 142],\n",
+ " [254, 254, 254, ..., 112, 152, 108],\n",
+ " ...,\n",
+ " [224, 226, 218, ..., 200, 202, 205],\n",
+ " [223, 225, 217, ..., 199, 201, 204],\n",
+ " [220, 225, 218, ..., 199, 200, 202]], dtype=uint8),\n",
+ " array([[164, 171, 175, ..., 206, 206, 206],\n",
+ " [151, 169, 171, ..., 206, 207, 207],\n",
+ " [127, 155, 168, ..., 208, 208, 208],\n",
+ " ...,\n",
+ " [ 14, 14, 14, ..., 13, 15, 12],\n",
+ " [ 14, 14, 14, ..., 13, 15, 12],\n",
+ " [ 14, 13, 13, ..., 12, 15, 11]], dtype=uint8),\n",
+ " array([[ 27, 23, 22, ..., 68, 68, 68],\n",
+ " [ 25, 21, 22, ..., 68, 68, 68],\n",
+ " [ 27, 23, 22, ..., 68, 68, 68],\n",
+ " ...,\n",
+ " [217, 216, 127, ..., 62, 61, 61],\n",
+ " [214, 213, 118, ..., 62, 61, 61],\n",
+ " [210, 209, 111, ..., 62, 61, 61]], dtype=uint8),\n",
+ " array([[ 86, 82, 76, ..., 64, 60, 56],\n",
+ " [ 65, 66, 56, ..., 70, 71, 67],\n",
+ " [ 60, 59, 49, ..., 68, 86, 72],\n",
+ " ...,\n",
+ " [141, 140, 140, ..., 155, 152, 151],\n",
+ " [139, 138, 139, ..., 154, 153, 151],\n",
+ " [138, 137, 139, ..., 153, 153, 152]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [149, 152, 153, ..., 147, 146, 180],\n",
+ " [150, 151, 151, ..., 146, 146, 180],\n",
+ " [150, 150, 149, ..., 144, 143, 178]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [254, 254, 254, ..., 254, 254, 254],\n",
+ " ...,\n",
+ " [136, 140, 140, ..., 130, 130, 139],\n",
+ " [136, 140, 142, ..., 136, 133, 138],\n",
+ " [135, 137, 142, ..., 134, 127, 139]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[223, 223, 223, ..., 230, 229, 229],\n",
+ " [223, 223, 223, ..., 230, 229, 229],\n",
+ " [223, 223, 223, ..., 230, 230, 230],\n",
+ " ...,\n",
+ " [ 85, 87, 92, ..., 113, 115, 117],\n",
+ " [ 96, 94, 88, ..., 107, 108, 110],\n",
+ " [126, 126, 121, ..., 109, 108, 108]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[159, 162, 164, ..., 162, 162, 160],\n",
+ " [159, 162, 164, ..., 162, 162, 160],\n",
+ " [159, 162, 164, ..., 162, 162, 160],\n",
+ " ...,\n",
+ " [ 33, 37, 42, ..., 45, 44, 43],\n",
+ " [ 35, 39, 41, ..., 45, 44, 43],\n",
+ " [ 32, 36, 38, ..., 45, 43, 42]], dtype=uint8),\n",
+ " array([[120, 121, 118, ..., 128, 127, 127],\n",
+ " [120, 121, 118, ..., 128, 126, 126],\n",
+ " [120, 121, 118, ..., 128, 126, 125],\n",
+ " ...,\n",
+ " [ 98, 94, 95, ..., 113, 119, 112],\n",
+ " [100, 92, 94, ..., 115, 120, 111],\n",
+ " [101, 90, 94, ..., 116, 120, 109]], dtype=uint8),\n",
+ " array([[208, 191, 174, ..., 44, 44, 44],\n",
+ " [252, 247, 240, ..., 44, 44, 44],\n",
+ " [252, 253, 253, ..., 44, 44, 44],\n",
+ " ...,\n",
+ " [ 87, 83, 169, ..., 37, 41, 36],\n",
+ " [ 81, 84, 163, ..., 37, 43, 46],\n",
+ " [ 80, 104, 160, ..., 40, 61, 69]], dtype=uint8),\n",
+ " array([[244, 244, 244, ..., 244, 241, 240],\n",
+ " [244, 244, 244, ..., 244, 242, 241],\n",
+ " [244, 244, 244, ..., 244, 242, 241],\n",
+ " ...,\n",
+ " [ 9, 10, 17, ..., 18, 134, 17],\n",
+ " [ 9, 10, 14, ..., 21, 134, 15],\n",
+ " [ 9, 11, 12, ..., 23, 134, 13]], dtype=uint8),\n",
+ " array([[196, 199, 200, ..., 195, 192, 190],\n",
+ " [197, 199, 201, ..., 195, 193, 191],\n",
+ " [197, 199, 201, ..., 196, 194, 192],\n",
+ " ...,\n",
+ " [145, 144, 145, ..., 143, 139, 138],\n",
+ " [143, 143, 144, ..., 142, 137, 135],\n",
+ " [142, 141, 141, ..., 140, 135, 133]], dtype=uint8),\n",
+ " array([[ 10, 12, 11, ..., 4, 12, 17],\n",
+ " [ 10, 12, 11, ..., 14, 24, 31],\n",
+ " [ 11, 13, 12, ..., 22, 25, 16],\n",
+ " ...,\n",
+ " [187, 188, 189, ..., 86, 47, 35],\n",
+ " [187, 188, 188, ..., 46, 39, 39],\n",
+ " [186, 187, 187, ..., 49, 45, 40]], dtype=uint8),\n",
+ " array([[ 55, 69, 76, ..., 100, 90, 99],\n",
+ " [ 63, 70, 77, ..., 103, 100, 101],\n",
+ " [ 64, 67, 76, ..., 106, 108, 103],\n",
+ " ...,\n",
+ " [188, 193, 189, ..., 201, 198, 198],\n",
+ " [188, 192, 190, ..., 200, 196, 198],\n",
+ " [186, 191, 192, ..., 200, 195, 194]], dtype=uint8),\n",
+ " array([[110, 93, 129, ..., 177, 212, 239],\n",
+ " [113, 118, 126, ..., 174, 180, 201],\n",
+ " [ 72, 88, 110, ..., 174, 174, 179],\n",
+ " ...,\n",
+ " [114, 115, 119, ..., 209, 207, 208],\n",
+ " [132, 130, 132, ..., 212, 210, 208],\n",
+ " [150, 145, 145, ..., 213, 212, 209]], dtype=uint8),\n",
+ " array([[ 36, 32, 38, ..., 205, 204, 201],\n",
+ " [ 27, 26, 26, ..., 202, 203, 202],\n",
+ " [ 18, 16, 17, ..., 205, 204, 201],\n",
+ " ...,\n",
+ " [ 97, 100, 100, ..., 95, 93, 90],\n",
+ " [ 94, 96, 98, ..., 97, 95, 92],\n",
+ " [ 92, 94, 98, ..., 98, 96, 93]], dtype=uint8),\n",
+ " array([[ 55, 53, 51, ..., 47, 41, 40],\n",
+ " [ 55, 52, 50, ..., 46, 40, 40],\n",
+ " [ 53, 50, 48, ..., 45, 39, 40],\n",
+ " ...,\n",
+ " [234, 234, 234, ..., 156, 155, 154],\n",
+ " [234, 234, 234, ..., 154, 153, 152],\n",
+ " [234, 234, 234, ..., 153, 151, 150]], dtype=uint8),\n",
+ " array([[109, 113, 109, ..., 24, 30, 43],\n",
+ " [100, 106, 112, ..., 22, 26, 39],\n",
+ " [105, 102, 104, ..., 27, 23, 35],\n",
+ " ...,\n",
+ " [138, 139, 138, ..., 186, 238, 210],\n",
+ " [135, 135, 137, ..., 181, 217, 192],\n",
+ " [132, 134, 137, ..., 157, 191, 196]], dtype=uint8),\n",
+ " array([[241, 238, 236, ..., 255, 255, 255],\n",
+ " [240, 238, 236, ..., 255, 255, 255],\n",
+ " [240, 238, 235, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 29, 31, 31, ..., 38, 32, 26],\n",
+ " [ 29, 30, 31, ..., 37, 32, 28],\n",
+ " [ 26, 27, 28, ..., 35, 33, 31],\n",
+ " ...,\n",
+ " [193, 196, 197, ..., 93, 90, 88],\n",
+ " [193, 196, 197, ..., 92, 90, 88],\n",
+ " [193, 196, 197, ..., 91, 89, 87]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 75, 77, 94],\n",
+ " [255, 255, 255, ..., 76, 78, 104],\n",
+ " [255, 255, 255, ..., 78, 81, 113]], dtype=uint8),\n",
+ " array([[ 36, 28, 36, ..., 244, 236, 227],\n",
+ " [ 51, 43, 45, ..., 244, 243, 242],\n",
+ " [ 58, 55, 49, ..., 244, 243, 243],\n",
+ " ...,\n",
+ " [ 75, 75, 76, ..., 26, 29, 30],\n",
+ " [ 73, 73, 71, ..., 27, 29, 30],\n",
+ " [ 70, 70, 70, ..., 27, 29, 29]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[147, 150, 152, ..., 192, 189, 177],\n",
+ " [144, 151, 150, ..., 188, 191, 148],\n",
+ " [148, 144, 148, ..., 183, 187, 110],\n",
+ " ...,\n",
+ " [ 30, 30, 30, ..., 174, 177, 178],\n",
+ " [ 30, 30, 30, ..., 175, 177, 178],\n",
+ " [ 30, 30, 30, ..., 177, 176, 178]], dtype=uint8),\n",
+ " array([[ 72, 75, 77, ..., 182, 180, 177],\n",
+ " [ 73, 75, 78, ..., 182, 180, 178],\n",
+ " [ 73, 74, 75, ..., 182, 180, 178],\n",
+ " ...,\n",
+ " [ 26, 27, 28, ..., 100, 99, 96],\n",
+ " [ 26, 27, 28, ..., 100, 99, 96],\n",
+ " [ 26, 27, 28, ..., 100, 99, 96]], dtype=uint8),\n",
+ " array([[ 61, 73, 81, ..., 249, 248, 248],\n",
+ " [ 76, 74, 85, ..., 248, 247, 250],\n",
+ " [ 89, 79, 90, ..., 237, 245, 249],\n",
+ " ...,\n",
+ " [195, 194, 196, ..., 203, 200, 191],\n",
+ " [197, 196, 194, ..., 205, 195, 194],\n",
+ " [194, 195, 193, ..., 200, 187, 186]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[197, 200, 201, ..., 164, 163, 162],\n",
+ " [198, 200, 202, ..., 166, 165, 164],\n",
+ " [198, 200, 202, ..., 168, 166, 165],\n",
+ " ...,\n",
+ " [164, 167, 168, ..., 158, 153, 150],\n",
+ " [169, 166, 170, ..., 162, 166, 159],\n",
+ " [167, 160, 173, ..., 137, 143, 141]], dtype=uint8),\n",
+ " array([[166, 227, 254, ..., 254, 254, 254],\n",
+ " [160, 213, 254, ..., 254, 254, 254],\n",
+ " [157, 194, 254, ..., 254, 254, 254],\n",
+ " ...,\n",
+ " [ 7, 11, 26, ..., 112, 84, 84],\n",
+ " [ 8, 12, 27, ..., 114, 96, 74],\n",
+ " [ 8, 12, 28, ..., 114, 103, 71]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[197, 200, 202, ..., 187, 183, 179],\n",
+ " [197, 200, 202, ..., 186, 183, 180],\n",
+ " [197, 200, 200, ..., 186, 184, 180],\n",
+ " ...,\n",
+ " [101, 106, 109, ..., 104, 103, 98],\n",
+ " [102, 112, 113, ..., 100, 99, 92],\n",
+ " [ 98, 111, 113, ..., 98, 100, 90]], dtype=uint8),\n",
+ " array([[214, 215, 216, ..., 215, 213, 211],\n",
+ " [214, 215, 216, ..., 215, 213, 211],\n",
+ " [214, 215, 216, ..., 214, 213, 210],\n",
+ " ...,\n",
+ " [155, 158, 161, ..., 133, 116, 118],\n",
+ " [151, 153, 152, ..., 137, 126, 125],\n",
+ " [135, 140, 136, ..., 134, 128, 126]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 98, 147, 126, ..., 222, 222, 222],\n",
+ " [ 92, 115, 110, ..., 222, 222, 222],\n",
+ " [ 84, 78, 109, ..., 223, 223, 223],\n",
+ " ...,\n",
+ " [ 81, 80, 79, ..., 91, 90, 90],\n",
+ " [ 76, 71, 70, ..., 92, 91, 91],\n",
+ " [ 67, 63, 67, ..., 93, 93, 94]], dtype=uint8),\n",
+ " array([[160, 172, 145, ..., 145, 139, 136],\n",
+ " [115, 179, 157, ..., 143, 140, 133],\n",
+ " [115, 146, 173, ..., 142, 139, 127],\n",
+ " ...,\n",
+ " [ 88, 153, 165, ..., 191, 194, 196],\n",
+ " [106, 154, 197, ..., 191, 192, 194],\n",
+ " [119, 177, 242, ..., 190, 192, 192]], dtype=uint8),\n",
+ " array([[ 4, 1, 3, ..., 8, 16, 102],\n",
+ " [ 10, 6, 6, ..., 9, 16, 104],\n",
+ " [ 9, 9, 11, ..., 7, 17, 107],\n",
+ " ...,\n",
+ " [226, 234, 237, ..., 155, 152, 148],\n",
+ " [233, 231, 236, ..., 155, 152, 148],\n",
+ " [229, 231, 235, ..., 155, 152, 148]], dtype=uint8),\n",
+ " array([[ 50, 49, 48, ..., 39, 39, 39],\n",
+ " [ 47, 46, 45, ..., 39, 39, 39],\n",
+ " [ 45, 44, 44, ..., 38, 38, 38],\n",
+ " ...,\n",
+ " [233, 232, 230, ..., 145, 144, 143],\n",
+ " [233, 232, 232, ..., 143, 142, 141],\n",
+ " [234, 233, 231, ..., 141, 140, 139]], dtype=uint8),\n",
+ " array([[184, 187, 189, ..., 176, 171, 166],\n",
+ " [185, 189, 191, ..., 174, 170, 164],\n",
+ " [187, 190, 194, ..., 172, 168, 163],\n",
+ " ...,\n",
+ " [ 97, 96, 95, ..., 93, 98, 89],\n",
+ " [ 94, 94, 96, ..., 92, 96, 90],\n",
+ " [ 91, 95, 97, ..., 88, 93, 87]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[226, 227, 228, ..., 245, 244, 247],\n",
+ " [227, 228, 228, ..., 245, 244, 247],\n",
+ " [227, 228, 229, ..., 245, 245, 247],\n",
+ " ...,\n",
+ " [ 75, 75, 75, ..., 74, 73, 130],\n",
+ " [ 75, 75, 75, ..., 74, 73, 130],\n",
+ " [ 75, 75, 75, ..., 74, 73, 130]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[195, 198, 199, ..., 79, 82, 83],\n",
+ " [196, 198, 200, ..., 84, 81, 83],\n",
+ " [197, 200, 201, ..., 107, 80, 81],\n",
+ " ...,\n",
+ " [207, 208, 210, ..., 217, 217, 217],\n",
+ " [207, 208, 209, ..., 216, 216, 216],\n",
+ " [206, 207, 209, ..., 216, 216, 216]], dtype=uint8),\n",
+ " array([[ 31, 48, 44, ..., 206, 160, 202],\n",
+ " [ 33, 43, 37, ..., 197, 163, 185],\n",
+ " [ 40, 42, 36, ..., 191, 147, 198],\n",
+ " ...,\n",
+ " [136, 138, 140, ..., 22, 21, 22],\n",
+ " [139, 146, 138, ..., 26, 27, 25],\n",
+ " [146, 144, 142, ..., 29, 33, 32]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 19, 20, 20, ..., 9, 8, 9],\n",
+ " [ 20, 21, 21, ..., 7, 9, 8],\n",
+ " [ 20, 21, 21, ..., 8, 9, 10],\n",
+ " ...,\n",
+ " [ 52, 55, 59, ..., 131, 131, 132],\n",
+ " [ 59, 62, 65, ..., 134, 134, 131],\n",
+ " [ 60, 63, 65, ..., 126, 129, 130]], dtype=uint8),\n",
+ " array([[194, 194, 67, ..., 24, 27, 31],\n",
+ " [194, 198, 76, ..., 24, 27, 32],\n",
+ " [194, 200, 86, ..., 24, 27, 32],\n",
+ " ...,\n",
+ " [ 11, 13, 98, ..., 178, 176, 172],\n",
+ " [ 12, 15, 114, ..., 177, 175, 172],\n",
+ " [ 11, 19, 122, ..., 177, 175, 172]], dtype=uint8),\n",
+ " array([[ 33, 35, 38, ..., 154, 152, 149],\n",
+ " [ 29, 31, 31, ..., 146, 145, 142],\n",
+ " [ 27, 27, 27, ..., 137, 136, 133],\n",
+ " ...,\n",
+ " [ 57, 59, 59, ..., 203, 201, 199],\n",
+ " [ 56, 57, 58, ..., 202, 201, 199],\n",
+ " [ 55, 56, 58, ..., 202, 200, 198]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[226, 226, 227, ..., 226, 224, 221],\n",
+ " [226, 226, 227, ..., 226, 224, 222],\n",
+ " [227, 227, 228, ..., 226, 224, 222],\n",
+ " ...,\n",
+ " [214, 214, 214, ..., 208, 207, 206],\n",
+ " [214, 214, 214, ..., 208, 207, 206],\n",
+ " [214, 214, 214, ..., 208, 207, 206]], dtype=uint8),\n",
+ " array([[156, 159, 163, ..., 217, 212, 222],\n",
+ " [156, 159, 164, ..., 219, 213, 223],\n",
+ " [157, 160, 165, ..., 218, 213, 222],\n",
+ " ...,\n",
+ " [216, 222, 225, ..., 224, 223, 230],\n",
+ " [216, 222, 225, ..., 224, 223, 230],\n",
+ " [217, 221, 224, ..., 224, 223, 230]], dtype=uint8),\n",
+ " array([[163, 164, 165, ..., 185, 185, 185],\n",
+ " [165, 166, 166, ..., 188, 188, 188],\n",
+ " [166, 167, 168, ..., 192, 191, 191],\n",
+ " ...,\n",
+ " [142, 154, 144, ..., 221, 220, 183],\n",
+ " [151, 148, 142, ..., 219, 219, 207],\n",
+ " [147, 144, 145, ..., 218, 220, 217]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[175, 86, 134, ..., 222, 222, 222],\n",
+ " [164, 109, 121, ..., 222, 222, 222],\n",
+ " [151, 139, 102, ..., 222, 222, 222],\n",
+ " ...,\n",
+ " [ 84, 88, 90, ..., 210, 204, 146],\n",
+ " [ 84, 87, 87, ..., 208, 209, 197],\n",
+ " [ 81, 88, 86, ..., 209, 206, 201]], dtype=uint8),\n",
+ " array([[187, 175, 157, ..., 156, 173, 219],\n",
+ " [185, 192, 182, ..., 194, 225, 233],\n",
+ " [197, 194, 191, ..., 209, 197, 211],\n",
+ " ...,\n",
+ " [ 87, 94, 97, ..., 152, 166, 151],\n",
+ " [ 84, 88, 89, ..., 116, 140, 187],\n",
+ " [ 85, 84, 78, ..., 110, 112, 163]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[228, 229, 231, ..., 210, 207, 235],\n",
+ " [228, 229, 231, ..., 210, 210, 235],\n",
+ " [227, 229, 231, ..., 213, 212, 235],\n",
+ " ...,\n",
+ " [ 26, 25, 25, ..., 27, 24, 165],\n",
+ " [ 33, 33, 34, ..., 16, 15, 162],\n",
+ " [192, 192, 192, ..., 187, 187, 230]], dtype=uint8),\n",
+ " array([[ 93, 79, 29, ..., 227, 227, 157],\n",
+ " [ 87, 43, 30, ..., 230, 227, 149],\n",
+ " [ 63, 23, 43, ..., 230, 224, 141],\n",
+ " ...,\n",
+ " [ 10, 10, 9, ..., 66, 70, 76],\n",
+ " [ 9, 8, 8, ..., 66, 71, 75],\n",
+ " [ 10, 9, 9, ..., 67, 72, 76]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[216, 221, 245, ..., 237, 243, 203],\n",
+ " [206, 241, 240, ..., 235, 199, 103],\n",
+ " [197, 237, 237, ..., 167, 93, 157],\n",
+ " ...,\n",
+ " [ 2, 2, 3, ..., 34, 35, 34],\n",
+ " [ 2, 2, 3, ..., 34, 35, 34],\n",
+ " [ 2, 2, 2, ..., 34, 35, 34]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[254, 254, 254, ..., 252, 251, 251],\n",
+ " [252, 252, 252, ..., 252, 251, 251],\n",
+ " [252, 252, 252, ..., 252, 252, 252],\n",
+ " ...,\n",
+ " [ 66, 93, 77, ..., 70, 72, 68],\n",
+ " [ 89, 100, 106, ..., 58, 56, 57],\n",
+ " [107, 92, 102, ..., 49, 46, 45]], dtype=uint8),\n",
+ " array([[207, 207, 207, ..., 189, 188, 227],\n",
+ " [207, 207, 207, ..., 189, 188, 227],\n",
+ " [208, 208, 208, ..., 190, 189, 228],\n",
+ " ...,\n",
+ " [132, 130, 128, ..., 102, 99, 194],\n",
+ " [126, 124, 123, ..., 95, 93, 193],\n",
+ " [123, 122, 122, ..., 89, 90, 193]], dtype=uint8),\n",
+ " array([[215, 217, 220, ..., 106, 92, 177],\n",
+ " [216, 218, 220, ..., 137, 99, 204],\n",
+ " [217, 219, 221, ..., 185, 120, 213],\n",
+ " ...,\n",
+ " [152, 160, 162, ..., 164, 156, 217],\n",
+ " [157, 161, 164, ..., 163, 153, 213],\n",
+ " [224, 226, 226, ..., 225, 220, 235]], dtype=uint8),\n",
+ " array([[ 51, 44, 36, ..., 89, 98, 104],\n",
+ " [ 71, 70, 66, ..., 84, 96, 107],\n",
+ " [ 43, 48, 49, ..., 88, 96, 107],\n",
+ " ...,\n",
+ " [194, 194, 195, ..., 203, 204, 204],\n",
+ " [195, 195, 196, ..., 202, 203, 203],\n",
+ " [196, 196, 196, ..., 203, 204, 204]], dtype=uint8),\n",
+ " array([[239, 231, 247, ..., 33, 35, 105],\n",
+ " [224, 218, 242, ..., 37, 38, 106],\n",
+ " [165, 185, 228, ..., 40, 40, 106],\n",
+ " ...,\n",
+ " [228, 229, 230, ..., 145, 145, 176],\n",
+ " [228, 229, 230, ..., 146, 145, 176],\n",
+ " [228, 229, 230, ..., 148, 145, 177]], dtype=uint8),\n",
+ " array([[139, 141, 144, ..., 120, 118, 116],\n",
+ " [140, 142, 144, ..., 121, 119, 117],\n",
+ " [141, 143, 145, ..., 122, 120, 118],\n",
+ " ...,\n",
+ " [ 68, 57, 50, ..., 21, 20, 16],\n",
+ " [ 59, 42, 61, ..., 20, 20, 18],\n",
+ " [ 56, 69, 101, ..., 23, 21, 18]], dtype=uint8),\n",
+ " array([[ 71, 83, 92, ..., 234, 226, 222],\n",
+ " [ 71, 69, 69, ..., 235, 227, 222],\n",
+ " [ 82, 79, 73, ..., 238, 226, 222],\n",
+ " ...,\n",
+ " [ 35, 20, 21, ..., 187, 184, 183],\n",
+ " [ 33, 30, 15, ..., 192, 187, 184],\n",
+ " [ 30, 35, 16, ..., 195, 189, 185]], dtype=uint8),\n",
+ " array([[ 9, 7, 9, ..., 13, 12, 6],\n",
+ " [ 10, 7, 9, ..., 15, 12, 7],\n",
+ " [ 8, 7, 8, ..., 11, 15, 6],\n",
+ " ...,\n",
+ " [176, 177, 179, ..., 195, 196, 197],\n",
+ " [177, 178, 178, ..., 195, 193, 197],\n",
+ " [177, 179, 178, ..., 196, 192, 194]], dtype=uint8),\n",
+ " array([[163, 162, 160, ..., 100, 99, 98],\n",
+ " [163, 162, 161, ..., 103, 102, 101],\n",
+ " [163, 162, 162, ..., 106, 105, 104],\n",
+ " ...,\n",
+ " [161, 163, 165, ..., 172, 168, 165],\n",
+ " [160, 162, 162, ..., 171, 168, 165],\n",
+ " [161, 163, 162, ..., 169, 168, 164]], dtype=uint8),\n",
+ " array([[132, 133, 137, ..., 116, 117, 115],\n",
+ " [137, 133, 136, ..., 115, 116, 114],\n",
+ " [141, 137, 137, ..., 113, 114, 112],\n",
+ " ...,\n",
+ " [127, 129, 130, ..., 132, 126, 127],\n",
+ " [127, 129, 130, ..., 129, 124, 125],\n",
+ " [126, 128, 130, ..., 127, 123, 124]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[212, 211, 123, ..., 12, 11, 10],\n",
+ " [211, 210, 128, ..., 12, 11, 10],\n",
+ " [209, 207, 130, ..., 12, 11, 10],\n",
+ " ...,\n",
+ " [ 51, 47, 46, ..., 46, 46, 46],\n",
+ " [ 58, 54, 53, ..., 40, 40, 38],\n",
+ " [ 57, 54, 53, ..., 41, 41, 38]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[203, 203, 202, ..., 128, 125, 124],\n",
+ " [204, 204, 203, ..., 129, 126, 125],\n",
+ " [205, 205, 205, ..., 130, 129, 128],\n",
+ " ...,\n",
+ " [160, 161, 163, ..., 63, 62, 63],\n",
+ " [163, 159, 160, ..., 64, 61, 63],\n",
+ " [156, 161, 164, ..., 65, 60, 63]], dtype=uint8),\n",
+ " array([[182, 174, 175, ..., 148, 148, 148],\n",
+ " [210, 189, 181, ..., 149, 148, 148],\n",
+ " [241, 228, 202, ..., 149, 149, 149],\n",
+ " ...,\n",
+ " [ 58, 57, 57, ..., 60, 56, 58],\n",
+ " [ 60, 59, 60, ..., 60, 56, 56],\n",
+ " [ 56, 56, 59, ..., 59, 59, 55]], dtype=uint8),\n",
+ " array([[249, 249, 250, ..., 77, 85, 94],\n",
+ " [247, 237, 205, ..., 92, 97, 103],\n",
+ " [156, 90, 41, ..., 104, 106, 108],\n",
+ " ...,\n",
+ " [ 52, 51, 50, ..., 50, 49, 51],\n",
+ " [ 52, 51, 50, ..., 49, 49, 51],\n",
+ " [ 52, 51, 50, ..., 49, 49, 51]], dtype=uint8),\n",
+ " array([[202, 199, 188, ..., 210, 121, 42],\n",
+ " [198, 203, 204, ..., 210, 121, 41],\n",
+ " [201, 204, 206, ..., 211, 122, 41],\n",
+ " ...,\n",
+ " [139, 144, 147, ..., 59, 57, 56],\n",
+ " [137, 141, 144, ..., 59, 58, 57],\n",
+ " [135, 139, 136, ..., 59, 58, 57]], dtype=uint8),\n",
+ " array([[201, 202, 203, ..., 194, 193, 192],\n",
+ " [202, 203, 203, ..., 195, 194, 193],\n",
+ " [202, 203, 204, ..., 195, 194, 193],\n",
+ " ...,\n",
+ " [ 44, 44, 45, ..., 42, 42, 42],\n",
+ " [ 45, 44, 45, ..., 42, 42, 42],\n",
+ " [ 45, 44, 46, ..., 42, 42, 42]], dtype=uint8),\n",
+ " array([[219, 222, 222, ..., 46, 45, 46],\n",
+ " [219, 222, 222, ..., 45, 45, 46],\n",
+ " [219, 222, 222, ..., 45, 44, 45],\n",
+ " ...,\n",
+ " [191, 188, 187, ..., 154, 152, 151],\n",
+ " [190, 186, 186, ..., 153, 152, 151],\n",
+ " [190, 185, 186, ..., 152, 151, 150]], dtype=uint8),\n",
+ " array([[ 45, 131, 91, ..., 78, 78, 78],\n",
+ " [ 20, 117, 103, ..., 72, 72, 72],\n",
+ " [ 31, 81, 128, ..., 72, 72, 72],\n",
+ " ...,\n",
+ " [ 6, 11, 23, ..., 60, 51, 53],\n",
+ " [ 12, 20, 26, ..., 65, 53, 52],\n",
+ " [ 19, 24, 25, ..., 67, 54, 52]], dtype=uint8),\n",
+ " array([[147, 152, 156, ..., 202, 199, 197],\n",
+ " [149, 157, 158, ..., 205, 199, 197],\n",
+ " [152, 158, 163, ..., 204, 200, 197],\n",
+ " ...,\n",
+ " [ 58, 58, 45, ..., 177, 174, 170],\n",
+ " [ 51, 57, 53, ..., 174, 171, 167],\n",
+ " [ 45, 51, 55, ..., 172, 169, 165]], dtype=uint8),\n",
+ " array([[210, 210, 210, ..., 52, 50, 51],\n",
+ " [210, 210, 210, ..., 54, 52, 53],\n",
+ " [211, 211, 210, ..., 55, 53, 54],\n",
+ " ...,\n",
+ " [218, 217, 219, ..., 138, 137, 137],\n",
+ " [218, 217, 219, ..., 138, 137, 138],\n",
+ " [218, 217, 219, ..., 138, 137, 138]], dtype=uint8),\n",
+ " array([[219, 149, 121, ..., 254, 254, 254],\n",
+ " [247, 193, 129, ..., 254, 254, 254],\n",
+ " [249, 240, 170, ..., 254, 254, 254],\n",
+ " ...,\n",
+ " [142, 143, 144, ..., 4, 3, 2],\n",
+ " [142, 143, 144, ..., 4, 3, 2],\n",
+ " [141, 142, 142, ..., 4, 3, 2]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 19, 19, 24, ..., 122, 118, 117],\n",
+ " [ 19, 19, 18, ..., 118, 119, 113],\n",
+ " [ 18, 18, 19, ..., 116, 111, 111],\n",
+ " ...,\n",
+ " [ 91, 89, 87, ..., 92, 88, 83],\n",
+ " [ 83, 86, 89, ..., 85, 86, 78],\n",
+ " [ 81, 82, 85, ..., 80, 88, 81]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 87, 88, 88, ..., 147, 147, 147],\n",
+ " [ 88, 89, 89, ..., 147, 147, 147],\n",
+ " [ 90, 91, 91, ..., 147, 147, 147],\n",
+ " ...,\n",
+ " [ 55, 55, 54, ..., 40, 40, 40],\n",
+ " [ 55, 54, 53, ..., 40, 40, 40],\n",
+ " [ 55, 54, 53, ..., 40, 40, 40]], dtype=uint8),\n",
+ " array([[123, 124, 128, ..., 230, 228, 224],\n",
+ " [123, 125, 129, ..., 149, 134, 119],\n",
+ " [124, 126, 129, ..., 68, 69, 66],\n",
+ " ...,\n",
+ " [136, 140, 140, ..., 221, 219, 219],\n",
+ " [135, 138, 139, ..., 224, 223, 222],\n",
+ " [134, 137, 138, ..., 227, 226, 225]], dtype=uint8),\n",
+ " array([[153, 151, 153, ..., 255, 255, 255],\n",
+ " [152, 152, 152, ..., 255, 255, 255],\n",
+ " [155, 151, 153, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 253, 255, 255]], dtype=uint8),\n",
+ " array([[161, 158, 156, ..., 162, 163, 163],\n",
+ " [158, 156, 155, ..., 161, 162, 162],\n",
+ " [155, 153, 152, ..., 160, 161, 161],\n",
+ " ...,\n",
+ " [ 57, 58, 51, ..., 129, 112, 109],\n",
+ " [ 61, 65, 57, ..., 104, 107, 81],\n",
+ " [ 70, 67, 68, ..., 84, 102, 84]], dtype=uint8),\n",
+ " array([[ 81, 177, 188, ..., 173, 236, 240],\n",
+ " [207, 225, 208, ..., 174, 236, 241],\n",
+ " [224, 224, 222, ..., 174, 237, 241],\n",
+ " ...,\n",
+ " [212, 211, 211, ..., 198, 197, 197],\n",
+ " [239, 238, 237, ..., 230, 229, 229],\n",
+ " [250, 249, 248, ..., 248, 247, 247]], dtype=uint8),\n",
+ " array([[103, 103, 102, ..., 162, 161, 160],\n",
+ " [103, 103, 102, ..., 162, 161, 160],\n",
+ " [104, 104, 103, ..., 162, 161, 160],\n",
+ " ...,\n",
+ " [ 25, 33, 36, ..., 59, 53, 51],\n",
+ " [ 31, 33, 37, ..., 57, 49, 52],\n",
+ " [ 32, 35, 39, ..., 50, 45, 50]], dtype=uint8),\n",
+ " array([[ 40, 47, 30, ..., 99, 90, 109],\n",
+ " [ 39, 41, 33, ..., 88, 91, 99],\n",
+ " [ 37, 43, 43, ..., 81, 92, 87],\n",
+ " ...,\n",
+ " [166, 169, 171, ..., 177, 181, 184],\n",
+ " [167, 171, 172, ..., 177, 187, 185],\n",
+ " [168, 173, 173, ..., 178, 189, 185]], dtype=uint8),\n",
+ " array([[ 55, 43, 40, ..., 165, 170, 171],\n",
+ " [ 42, 32, 15, ..., 157, 164, 170],\n",
+ " [ 25, 11, 7, ..., 145, 153, 163],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 253, 255, 255]], dtype=uint8),\n",
+ " array([[ 28, 27, 28, ..., 155, 142, 153],\n",
+ " [ 28, 26, 26, ..., 155, 148, 159],\n",
+ " [ 26, 24, 26, ..., 155, 150, 159],\n",
+ " ...,\n",
+ " [ 58, 48, 41, ..., 87, 91, 76],\n",
+ " [ 57, 47, 41, ..., 86, 83, 80],\n",
+ " [ 57, 47, 41, ..., 91, 83, 82]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 91, 88, 89, ..., 122, 179, 215],\n",
+ " [ 91, 87, 89, ..., 68, 35, 166],\n",
+ " [ 90, 87, 89, ..., 25, 35, 174],\n",
+ " ...,\n",
+ " [140, 142, 140, ..., 172, 170, 223],\n",
+ " [142, 139, 141, ..., 162, 165, 222],\n",
+ " [141, 141, 143, ..., 160, 167, 219]], dtype=uint8),\n",
+ " array([[ 6, 16, 8, ..., 198, 192, 130],\n",
+ " [ 4, 7, 9, ..., 197, 193, 129],\n",
+ " [ 8, 18, 11, ..., 197, 194, 128],\n",
+ " ...,\n",
+ " [ 81, 84, 86, ..., 163, 159, 155],\n",
+ " [ 77, 80, 82, ..., 166, 162, 158],\n",
+ " [ 77, 80, 80, ..., 169, 165, 160]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 77, 79, 85, ..., 196, 192, 190],\n",
+ " [ 73, 76, 85, ..., 201, 200, 193],\n",
+ " [ 67, 70, 80, ..., 206, 204, 193],\n",
+ " ...,\n",
+ " [ 57, 59, 61, ..., 126, 123, 115],\n",
+ " [ 56, 58, 60, ..., 124, 121, 113],\n",
+ " [ 56, 58, 60, ..., 123, 120, 112]], dtype=uint8),\n",
+ " array([[236, 237, 238, ..., 230, 228, 227],\n",
+ " [236, 237, 238, ..., 230, 228, 227],\n",
+ " [236, 237, 238, ..., 230, 228, 227],\n",
+ " ...,\n",
+ " [210, 211, 212, ..., 202, 200, 199],\n",
+ " [210, 211, 211, ..., 202, 200, 199],\n",
+ " [209, 210, 211, ..., 202, 200, 199]], dtype=uint8),\n",
+ " array([[245, 244, 243, ..., 252, 252, 254],\n",
+ " [245, 244, 243, ..., 252, 252, 254],\n",
+ " [245, 244, 243, ..., 252, 252, 254],\n",
+ " ...,\n",
+ " [ 48, 43, 40, ..., 47, 50, 57],\n",
+ " [ 37, 35, 35, ..., 50, 49, 57],\n",
+ " [ 34, 33, 33, ..., 71, 81, 60]], dtype=uint8),\n",
+ " array([[210, 210, 210, ..., 185, 183, 225],\n",
+ " [210, 210, 210, ..., 185, 184, 226],\n",
+ " [210, 210, 210, ..., 185, 185, 226],\n",
+ " ...,\n",
+ " [160, 162, 165, ..., 111, 110, 199],\n",
+ " [148, 154, 154, ..., 107, 104, 195],\n",
+ " [141, 148, 147, ..., 105, 108, 196]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[149, 155, 155, ..., 214, 212, 209],\n",
+ " [152, 153, 155, ..., 216, 214, 210],\n",
+ " [144, 150, 156, ..., 213, 210, 205],\n",
+ " ...,\n",
+ " [ 72, 75, 77, ..., 67, 67, 68],\n",
+ " [ 74, 75, 79, ..., 66, 66, 69],\n",
+ " [ 76, 76, 78, ..., 68, 75, 106]], dtype=uint8),\n",
+ " array([[215, 225, 239, ..., 219, 216, 214],\n",
+ " [213, 218, 224, ..., 220, 217, 215],\n",
+ " [211, 218, 220, ..., 221, 218, 216],\n",
+ " ...,\n",
+ " [ 84, 81, 61, ..., 166, 166, 172],\n",
+ " [ 78, 79, 60, ..., 164, 166, 167],\n",
+ " [ 74, 77, 62, ..., 163, 167, 164]], dtype=uint8),\n",
+ " array([[236, 237, 239, ..., 240, 238, 236],\n",
+ " [237, 238, 239, ..., 240, 238, 237],\n",
+ " [237, 238, 239, ..., 241, 239, 237],\n",
+ " ...,\n",
+ " [151, 150, 148, ..., 233, 232, 233],\n",
+ " [148, 149, 147, ..., 231, 230, 231],\n",
+ " [146, 148, 148, ..., 230, 229, 230]], dtype=uint8),\n",
+ " array([[210, 213, 215, ..., 167, 67, 62],\n",
+ " [211, 214, 215, ..., 165, 69, 58],\n",
+ " [211, 214, 216, ..., 157, 51, 48],\n",
+ " ...,\n",
+ " [184, 186, 188, ..., 177, 166, 159],\n",
+ " [184, 186, 188, ..., 174, 164, 157],\n",
+ " [183, 185, 188, ..., 173, 162, 155]], dtype=uint8),\n",
+ " array([[ 33, 40, 45, ..., 241, 238, 234],\n",
+ " [ 26, 40, 46, ..., 240, 239, 236],\n",
+ " [ 53, 80, 66, ..., 243, 240, 238],\n",
+ " ...,\n",
+ " [ 70, 73, 75, ..., 192, 177, 171],\n",
+ " [ 47, 51, 54, ..., 223, 219, 207],\n",
+ " [ 63, 67, 69, ..., 217, 220, 201]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[154, 153, 152, ..., 72, 70, 82],\n",
+ " [155, 154, 153, ..., 72, 70, 68],\n",
+ " [157, 156, 155, ..., 72, 72, 68],\n",
+ " ...,\n",
+ " [ 74, 69, 63, ..., 37, 92, 119],\n",
+ " [ 66, 58, 55, ..., 37, 84, 120],\n",
+ " [ 68, 53, 49, ..., 37, 77, 120]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 99, 101, 103, ..., 140, 139, 138],\n",
+ " [101, 103, 105, ..., 140, 140, 139],\n",
+ " [102, 105, 106, ..., 141, 141, 140],\n",
+ " ...,\n",
+ " [ 17, 18, 17, ..., 24, 24, 24],\n",
+ " [ 17, 18, 17, ..., 25, 24, 24],\n",
+ " [ 17, 18, 17, ..., 25, 25, 25]], dtype=uint8),\n",
+ " array([[206, 206, 207, ..., 184, 183, 203],\n",
+ " [208, 208, 208, ..., 183, 182, 204],\n",
+ " [208, 208, 208, ..., 182, 181, 204],\n",
+ " ...,\n",
+ " [159, 159, 159, ..., 141, 140, 175],\n",
+ " [157, 157, 158, ..., 139, 138, 172],\n",
+ " [158, 158, 158, ..., 137, 136, 170]], dtype=uint8),\n",
+ " array([[113, 91, 81, ..., 81, 96, 110],\n",
+ " [109, 89, 82, ..., 80, 93, 110],\n",
+ " [109, 89, 84, ..., 78, 89, 109],\n",
+ " ...,\n",
+ " [ 14, 14, 15, ..., 14, 14, 17],\n",
+ " [ 16, 16, 17, ..., 16, 14, 14],\n",
+ " [ 16, 16, 16, ..., 16, 15, 16]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 90, 121, 134, ..., 175, 172, 165],\n",
+ " [ 94, 111, 140, ..., 173, 168, 159],\n",
+ " [ 76, 85, 118, ..., 175, 168, 154],\n",
+ " ...,\n",
+ " [148, 149, 150, ..., 37, 34, 30],\n",
+ " [148, 149, 149, ..., 39, 34, 28],\n",
+ " [146, 150, 149, ..., 38, 30, 29]], dtype=uint8),\n",
+ " array([[228, 229, 230, ..., 228, 227, 226],\n",
+ " [227, 228, 228, ..., 227, 226, 224],\n",
+ " [224, 225, 226, ..., 224, 222, 221],\n",
+ " ...,\n",
+ " [ 36, 34, 34, ..., 31, 32, 33],\n",
+ " [ 36, 35, 34, ..., 30, 32, 33],\n",
+ " [ 37, 36, 34, ..., 31, 32, 33]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[240, 240, 240, ..., 253, 252, 252],\n",
+ " [239, 239, 240, ..., 253, 251, 251],\n",
+ " [238, 238, 238, ..., 251, 250, 250],\n",
+ " ...,\n",
+ " [139, 136, 140, ..., 131, 134, 131],\n",
+ " [142, 141, 143, ..., 127, 123, 129],\n",
+ " [138, 139, 140, ..., 124, 116, 122]], dtype=uint8),\n",
+ " array([[223, 227, 233, ..., 103, 191, 166],\n",
+ " [222, 226, 232, ..., 98, 183, 175],\n",
+ " [221, 225, 230, ..., 97, 177, 171],\n",
+ " ...,\n",
+ " [ 67, 67, 67, ..., 31, 30, 33],\n",
+ " [ 67, 67, 66, ..., 28, 33, 46],\n",
+ " [ 66, 66, 66, ..., 28, 44, 55]], dtype=uint8),\n",
+ " array([[178, 182, 182, ..., 218, 219, 215],\n",
+ " [183, 186, 186, ..., 219, 219, 216],\n",
+ " [188, 190, 190, ..., 221, 220, 218],\n",
+ " ...,\n",
+ " [ 95, 65, 51, ..., 40, 39, 38],\n",
+ " [111, 73, 56, ..., 40, 39, 38],\n",
+ " [123, 79, 59, ..., 39, 38, 37]], dtype=uint8),\n",
+ " array([[ 57, 78, 77, ..., 12, 6, 5],\n",
+ " [ 91, 66, 66, ..., 9, 8, 11],\n",
+ " [146, 86, 31, ..., 8, 10, 11],\n",
+ " ...,\n",
+ " [187, 189, 191, ..., 191, 189, 187],\n",
+ " [185, 187, 190, ..., 191, 189, 187],\n",
+ " [184, 186, 189, ..., 190, 188, 186]], dtype=uint8),\n",
+ " array([[229, 230, 230, ..., 222, 213, 208],\n",
+ " [229, 230, 231, ..., 220, 210, 213],\n",
+ " [228, 229, 231, ..., 211, 213, 204],\n",
+ " ...,\n",
+ " [180, 185, 186, ..., 170, 170, 168],\n",
+ " [179, 184, 186, ..., 169, 168, 167],\n",
+ " [179, 183, 185, ..., 168, 167, 166]], dtype=uint8),\n",
+ " array([[ 45, 45, 43, ..., 255, 255, 255],\n",
+ " [ 42, 42, 44, ..., 255, 255, 255],\n",
+ " [ 41, 40, 41, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [167, 170, 172, ..., 255, 255, 255],\n",
+ " [167, 169, 173, ..., 255, 255, 255],\n",
+ " [166, 169, 173, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 61, 68, 70, ..., 62, 81, 59],\n",
+ " [ 85, 50, 72, ..., 71, 73, 61],\n",
+ " [127, 112, 66, ..., 87, 76, 42],\n",
+ " ...,\n",
+ " [165, 163, 155, ..., 154, 157, 155],\n",
+ " [160, 161, 152, ..., 155, 156, 154],\n",
+ " [149, 150, 156, ..., 154, 155, 153]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[213, 147, 201, ..., 237, 238, 238],\n",
+ " [212, 185, 143, ..., 237, 238, 238],\n",
+ " [213, 209, 169, ..., 237, 238, 238],\n",
+ " ...,\n",
+ " [204, 203, 203, ..., 192, 192, 192],\n",
+ " [234, 233, 233, ..., 224, 223, 223],\n",
+ " [247, 247, 248, ..., 245, 245, 245]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[225, 179, 89, ..., 86, 84, 79],\n",
+ " [224, 126, 84, ..., 70, 64, 64],\n",
+ " [193, 93, 76, ..., 60, 56, 55],\n",
+ " ...,\n",
+ " [118, 141, 142, ..., 23, 29, 19],\n",
+ " [132, 137, 138, ..., 24, 31, 24],\n",
+ " [125, 132, 136, ..., 26, 29, 29]], dtype=uint8),\n",
+ " array([[221, 222, 224, ..., 220, 218, 215],\n",
+ " [221, 222, 223, ..., 220, 218, 216],\n",
+ " [221, 222, 223, ..., 220, 218, 216],\n",
+ " ...,\n",
+ " [155, 155, 157, ..., 151, 153, 149],\n",
+ " [153, 154, 157, ..., 157, 156, 153],\n",
+ " [149, 150, 153, ..., 151, 149, 147]], dtype=uint8),\n",
+ " array([[171, 176, 180, ..., 239, 239, 229],\n",
+ " [176, 179, 183, ..., 239, 239, 230],\n",
+ " [175, 179, 180, ..., 239, 239, 231],\n",
+ " ...,\n",
+ " [131, 133, 136, ..., 94, 94, 96],\n",
+ " [127, 129, 129, ..., 96, 91, 89],\n",
+ " [126, 128, 130, ..., 94, 95, 91]], dtype=uint8),\n",
+ " array([[205, 207, 207, ..., 184, 184, 181],\n",
+ " [207, 208, 209, ..., 184, 183, 182],\n",
+ " [207, 208, 209, ..., 183, 181, 181],\n",
+ " ...,\n",
+ " [159, 159, 159, ..., 142, 141, 138],\n",
+ " [157, 157, 158, ..., 139, 138, 135],\n",
+ " [158, 158, 158, ..., 137, 136, 133]], dtype=uint8),\n",
+ " array([[153, 169, 175, ..., 170, 166, 152],\n",
+ " [159, 171, 173, ..., 173, 170, 157],\n",
+ " [167, 177, 174, ..., 175, 172, 162],\n",
+ " ...,\n",
+ " [ 67, 70, 72, ..., 9, 7, 5],\n",
+ " [ 62, 68, 70, ..., 9, 7, 5],\n",
+ " [ 59, 65, 68, ..., 8, 6, 4]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 20, 19, 19, ..., 188, 188, 188],\n",
+ " [ 20, 20, 19, ..., 191, 191, 191],\n",
+ " [ 16, 17, 17, ..., 195, 195, 195],\n",
+ " ...,\n",
+ " [180, 181, 183, ..., 158, 157, 154],\n",
+ " [179, 180, 181, ..., 152, 150, 146],\n",
+ " [171, 172, 174, ..., 150, 147, 143]], dtype=uint8),\n",
+ " array([[ 6, 26, 49, ..., 189, 188, 187],\n",
+ " [ 9, 25, 43, ..., 190, 191, 191],\n",
+ " [ 11, 28, 49, ..., 188, 188, 188],\n",
+ " ...,\n",
+ " [ 18, 20, 22, ..., 46, 42, 32],\n",
+ " [ 18, 20, 21, ..., 44, 44, 36],\n",
+ " [ 18, 19, 20, ..., 43, 44, 39]], dtype=uint8),\n",
+ " array([[ 43, 39, 36, ..., 190, 190, 192],\n",
+ " [ 55, 50, 34, ..., 190, 191, 192],\n",
+ " [ 67, 47, 31, ..., 195, 196, 197],\n",
+ " ...,\n",
+ " [ 76, 78, 81, ..., 162, 169, 166],\n",
+ " [ 76, 78, 80, ..., 172, 167, 165],\n",
+ " [ 79, 81, 80, ..., 169, 163, 161]], dtype=uint8),\n",
+ " array([[191, 192, 193, ..., 190, 189, 188],\n",
+ " [192, 193, 194, ..., 191, 190, 189],\n",
+ " [193, 194, 195, ..., 192, 191, 190],\n",
+ " ...,\n",
+ " [138, 139, 139, ..., 123, 124, 123],\n",
+ " [134, 137, 136, ..., 123, 122, 118],\n",
+ " [134, 139, 139, ..., 123, 123, 116]], dtype=uint8),\n",
+ " array([[145, 145, 152, ..., 56, 60, 59],\n",
+ " [144, 142, 148, ..., 57, 60, 59],\n",
+ " [142, 144, 145, ..., 60, 61, 60],\n",
+ " ...,\n",
+ " [110, 112, 114, ..., 64, 62, 55],\n",
+ " [106, 112, 118, ..., 57, 56, 56],\n",
+ " [111, 112, 120, ..., 55, 54, 55]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 75, 25, 149, ..., 3, 3, 3],\n",
+ " [ 84, 59, 179, ..., 3, 4, 2],\n",
+ " [ 65, 121, 213, ..., 4, 1, 5],\n",
+ " ...,\n",
+ " [ 75, 73, 74, ..., 56, 60, 65],\n",
+ " [ 75, 73, 74, ..., 52, 55, 55],\n",
+ " [ 76, 73, 74, ..., 59, 57, 54]], dtype=uint8),\n",
+ " array([[188, 194, 197, ..., 122, 120, 117],\n",
+ " [191, 197, 200, ..., 124, 121, 118],\n",
+ " [196, 202, 204, ..., 127, 124, 121],\n",
+ " ...,\n",
+ " [123, 145, 152, ..., 113, 116, 109],\n",
+ " [120, 145, 143, ..., 114, 110, 108],\n",
+ " [120, 137, 136, ..., 120, 106, 104]], dtype=uint8),\n",
+ " array([[217, 164, 60, ..., 30, 29, 15],\n",
+ " [218, 203, 58, ..., 30, 27, 27],\n",
+ " [220, 212, 88, ..., 6, 5, 8],\n",
+ " ...,\n",
+ " [186, 187, 188, ..., 59, 58, 58],\n",
+ " [185, 186, 187, ..., 57, 59, 59],\n",
+ " [186, 187, 187, ..., 59, 59, 59]], dtype=uint8),\n",
+ " array([[195, 198, 199, ..., 79, 82, 83],\n",
+ " [196, 198, 200, ..., 84, 81, 83],\n",
+ " [197, 200, 201, ..., 107, 80, 81],\n",
+ " ...,\n",
+ " [207, 208, 210, ..., 217, 217, 217],\n",
+ " [207, 208, 209, ..., 216, 216, 216],\n",
+ " [206, 207, 209, ..., 216, 216, 216]], dtype=uint8),\n",
+ " array([[168, 187, 193, ..., 254, 254, 254],\n",
+ " [148, 179, 190, ..., 254, 254, 254],\n",
+ " [127, 161, 186, ..., 254, 254, 254],\n",
+ " ...,\n",
+ " [106, 107, 107, ..., 16, 11, 7],\n",
+ " [104, 105, 106, ..., 13, 14, 7],\n",
+ " [103, 104, 104, ..., 13, 13, 15]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 39, 39, 40, ..., 30, 28, 167],\n",
+ " [ 39, 39, 40, ..., 30, 28, 167],\n",
+ " [ 38, 38, 39, ..., 30, 29, 168],\n",
+ " ...,\n",
+ " [213, 214, 216, ..., 191, 188, 229],\n",
+ " [213, 214, 216, ..., 191, 188, 229],\n",
+ " [213, 214, 216, ..., 191, 188, 229]], dtype=uint8),\n",
+ " array([[ 6, 4, 3, ..., 58, 58, 58],\n",
+ " [ 6, 4, 3, ..., 58, 58, 58],\n",
+ " [ 5, 4, 12, ..., 59, 59, 59],\n",
+ " ...,\n",
+ " [ 99, 101, 101, ..., 129, 130, 126],\n",
+ " [ 96, 98, 97, ..., 123, 123, 122],\n",
+ " [ 92, 96, 96, ..., 117, 117, 119]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[192, 193, 194, ..., 188, 185, 183],\n",
+ " [193, 194, 194, ..., 188, 186, 184],\n",
+ " [193, 194, 195, ..., 189, 187, 185],\n",
+ " ...,\n",
+ " [ 41, 42, 41, ..., 41, 41, 40],\n",
+ " [ 40, 41, 42, ..., 41, 40, 40],\n",
+ " [ 42, 43, 43, ..., 40, 40, 40]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 22, 22, 22, ..., 149, 152, 215],\n",
+ " [ 22, 22, 22, ..., 149, 154, 216],\n",
+ " [ 22, 22, 22, ..., 151, 157, 216],\n",
+ " ...,\n",
+ " [ 21, 22, 24, ..., 55, 54, 179],\n",
+ " [ 21, 22, 24, ..., 52, 52, 178],\n",
+ " [ 21, 22, 24, ..., 52, 52, 178]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[224, 227, 230, ..., 206, 200, 216],\n",
+ " [225, 228, 230, ..., 206, 201, 216],\n",
+ " [227, 229, 232, ..., 207, 202, 216],\n",
+ " ...,\n",
+ " [244, 245, 246, ..., 244, 243, 245],\n",
+ " [244, 245, 246, ..., 244, 243, 245],\n",
+ " [243, 244, 246, ..., 244, 243, 245]], dtype=uint8),\n",
+ " array([[216, 219, 223, ..., 207, 204, 202],\n",
+ " [213, 218, 221, ..., 208, 208, 205],\n",
+ " [211, 215, 217, ..., 213, 213, 210],\n",
+ " ...,\n",
+ " [151, 154, 157, ..., 154, 152, 151],\n",
+ " [155, 156, 157, ..., 149, 147, 145],\n",
+ " [157, 156, 156, ..., 145, 143, 140]], dtype=uint8),\n",
+ " array([[ 62, 78, 88, ..., 41, 14, 14],\n",
+ " [ 52, 77, 85, ..., 42, 14, 14],\n",
+ " [ 52, 72, 85, ..., 42, 14, 14],\n",
+ " ...,\n",
+ " [ 29, 28, 30, ..., 116, 103, 92],\n",
+ " [ 30, 29, 29, ..., 119, 115, 109],\n",
+ " [ 34, 33, 29, ..., 115, 119, 112]], dtype=uint8),\n",
+ " array([[168, 169, 171, ..., 105, 101, 97],\n",
+ " [169, 170, 172, ..., 106, 103, 99],\n",
+ " [170, 171, 173, ..., 109, 106, 101],\n",
+ " ...,\n",
+ " [ 52, 54, 53, ..., 38, 37, 37],\n",
+ " [ 52, 54, 54, ..., 38, 37, 36],\n",
+ " [ 52, 54, 54, ..., 38, 35, 34]], dtype=uint8),\n",
+ " array([[100, 107, 78, ..., 68, 57, 98],\n",
+ " [104, 111, 108, ..., 63, 39, 65],\n",
+ " [114, 115, 120, ..., 34, 54, 70],\n",
+ " ...,\n",
+ " [216, 209, 210, ..., 203, 191, 182],\n",
+ " [206, 213, 208, ..., 206, 198, 187],\n",
+ " [194, 218, 209, ..., 208, 200, 188]], dtype=uint8),\n",
+ " array([[129, 134, 166, ..., 186, 185, 182],\n",
+ " [ 96, 103, 161, ..., 188, 187, 185],\n",
+ " [146, 122, 144, ..., 188, 190, 189],\n",
+ " ...,\n",
+ " [ 62, 64, 65, ..., 52, 52, 52],\n",
+ " [ 64, 63, 63, ..., 52, 52, 52],\n",
+ " [ 63, 61, 62, ..., 52, 52, 52]], dtype=uint8),\n",
+ " array([[208, 222, 248, ..., 250, 249, 203],\n",
+ " [216, 204, 250, ..., 252, 249, 137],\n",
+ " [212, 208, 228, ..., 250, 214, 148],\n",
+ " ...,\n",
+ " [143, 143, 143, ..., 169, 169, 167],\n",
+ " [142, 142, 142, ..., 169, 169, 166],\n",
+ " [141, 141, 141, ..., 168, 167, 165]], dtype=uint8),\n",
+ " array([[192, 194, 195, ..., 195, 194, 193],\n",
+ " [193, 195, 197, ..., 196, 195, 194],\n",
+ " [195, 197, 199, ..., 197, 196, 195],\n",
+ " ...,\n",
+ " [163, 165, 163, ..., 141, 136, 137],\n",
+ " [167, 166, 165, ..., 153, 141, 140],\n",
+ " [152, 155, 161, ..., 160, 155, 140]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[152, 168, 173, ..., 168, 165, 150],\n",
+ " [159, 171, 174, ..., 173, 169, 156],\n",
+ " [164, 176, 173, ..., 175, 173, 163],\n",
+ " ...,\n",
+ " [ 67, 71, 72, ..., 9, 7, 5],\n",
+ " [ 61, 67, 69, ..., 9, 7, 5],\n",
+ " [ 57, 63, 67, ..., 8, 6, 4]], dtype=uint8),\n",
+ " array([[162, 162, 162, ..., 70, 68, 82],\n",
+ " [162, 162, 162, ..., 64, 62, 69],\n",
+ " [163, 163, 163, ..., 59, 61, 66],\n",
+ " ...,\n",
+ " [ 42, 142, 167, ..., 61, 59, 60],\n",
+ " [109, 165, 169, ..., 60, 59, 60],\n",
+ " [154, 171, 164, ..., 57, 57, 58]], dtype=uint8),\n",
+ " array([[125, 126, 129, ..., 148, 147, 146],\n",
+ " [131, 132, 131, ..., 147, 145, 143],\n",
+ " [131, 131, 131, ..., 144, 142, 140],\n",
+ " ...,\n",
+ " [ 96, 96, 96, ..., 130, 112, 129],\n",
+ " [ 97, 97, 98, ..., 141, 121, 125],\n",
+ " [ 98, 98, 99, ..., 133, 108, 130]], dtype=uint8),\n",
+ " array([[ 10, 11, 11, ..., 54, 50, 56],\n",
+ " [ 18, 18, 17, ..., 54, 55, 58],\n",
+ " [ 24, 28, 33, ..., 62, 64, 65],\n",
+ " ...,\n",
+ " [169, 174, 175, ..., 53, 52, 51],\n",
+ " [169, 174, 175, ..., 53, 52, 51],\n",
+ " [169, 174, 176, ..., 53, 51, 50]], dtype=uint8),\n",
+ " array([[201, 202, 204, ..., 210, 207, 205],\n",
+ " [202, 203, 204, ..., 210, 208, 206],\n",
+ " [202, 203, 204, ..., 211, 209, 207],\n",
+ " ...,\n",
+ " [172, 178, 175, ..., 166, 164, 162],\n",
+ " [170, 177, 179, ..., 165, 164, 160],\n",
+ " [160, 168, 177, ..., 168, 165, 163]], dtype=uint8),\n",
+ " array([[233, 233, 233, ..., 208, 207, 236],\n",
+ " [233, 233, 233, ..., 208, 207, 236],\n",
+ " [232, 232, 233, ..., 207, 207, 236],\n",
+ " ...,\n",
+ " [ 36, 23, 58, ..., 9, 6, 163],\n",
+ " [ 18, 22, 37, ..., 25, 13, 162],\n",
+ " [ 16, 19, 36, ..., 42, 29, 165]], dtype=uint8),\n",
+ " array([[ 35, 37, 66, ..., 192, 190, 187],\n",
+ " [ 35, 37, 47, ..., 193, 190, 188],\n",
+ " [ 35, 36, 60, ..., 193, 190, 188],\n",
+ " ...,\n",
+ " [ 47, 50, 49, ..., 135, 133, 129],\n",
+ " [ 48, 49, 49, ..., 138, 135, 132],\n",
+ " [ 48, 48, 48, ..., 139, 136, 133]], dtype=uint8),\n",
+ " array([[ 99, 47, 45, ..., 97, 147, 119],\n",
+ " [ 85, 46, 37, ..., 107, 131, 113],\n",
+ " [ 62, 60, 35, ..., 117, 117, 112],\n",
+ " ...,\n",
+ " [ 20, 20, 21, ..., 24, 24, 24],\n",
+ " [ 21, 21, 22, ..., 24, 24, 24],\n",
+ " [ 23, 23, 22, ..., 24, 24, 24]], dtype=uint8),\n",
+ " array([[215, 216, 217, ..., 106, 124, 175],\n",
+ " [195, 196, 197, ..., 89, 152, 149],\n",
+ " [195, 196, 197, ..., 159, 122, 111],\n",
+ " ...,\n",
+ " [179, 179, 180, ..., 255, 255, 255],\n",
+ " [179, 179, 180, ..., 255, 255, 255],\n",
+ " [179, 179, 180, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[199, 197, 199, ..., 194, 194, 194],\n",
+ " [202, 202, 203, ..., 197, 197, 197],\n",
+ " [203, 203, 203, ..., 199, 200, 200],\n",
+ " ...,\n",
+ " [174, 174, 174, ..., 185, 184, 183],\n",
+ " [162, 162, 163, ..., 169, 168, 167],\n",
+ " [144, 144, 144, ..., 144, 143, 142]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[160, 161, 161, ..., 163, 162, 190],\n",
+ " [161, 162, 163, ..., 167, 166, 193],\n",
+ " [162, 163, 164, ..., 171, 169, 195],\n",
+ " ...,\n",
+ " [145, 147, 149, ..., 98, 93, 147],\n",
+ " [144, 146, 148, ..., 98, 96, 147],\n",
+ " [142, 145, 146, ..., 104, 99, 147]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[193, 193, 193, ..., 193, 193, 193],\n",
+ " [193, 193, 193, ..., 193, 193, 193],\n",
+ " [193, 193, 193, ..., 193, 193, 193],\n",
+ " ...,\n",
+ " [233, 233, 233, ..., 234, 234, 234],\n",
+ " [233, 233, 233, ..., 234, 234, 234],\n",
+ " [234, 234, 234, ..., 234, 234, 234]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 25, 25, 24, ..., 224, 227, 231],\n",
+ " [ 25, 25, 25, ..., 228, 228, 229],\n",
+ " [ 23, 23, 23, ..., 227, 223, 223],\n",
+ " ...,\n",
+ " [164, 163, 164, ..., 35, 36, 34],\n",
+ " [162, 163, 167, ..., 35, 36, 34],\n",
+ " [160, 163, 168, ..., 36, 36, 35]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[211, 209, 205, ..., 204, 204, 201],\n",
+ " [209, 208, 205, ..., 202, 201, 198],\n",
+ " [207, 207, 205, ..., 202, 200, 199],\n",
+ " ...,\n",
+ " [139, 136, 138, ..., 122, 123, 119],\n",
+ " [137, 137, 137, ..., 119, 121, 119],\n",
+ " [135, 138, 136, ..., 117, 119, 119]], dtype=uint8),\n",
+ " array([[191, 170, 81, ..., 236, 236, 236],\n",
+ " [183, 135, 83, ..., 236, 236, 236],\n",
+ " [172, 122, 94, ..., 236, 236, 236],\n",
+ " ...,\n",
+ " [176, 175, 174, ..., 161, 161, 159],\n",
+ " [177, 177, 176, ..., 160, 160, 158],\n",
+ " [179, 179, 179, ..., 158, 159, 157]], dtype=uint8),\n",
+ " array([[230, 232, 235, ..., 58, 40, 43],\n",
+ " [228, 229, 232, ..., 57, 61, 40],\n",
+ " [233, 233, 233, ..., 87, 107, 92],\n",
+ " ...,\n",
+ " [157, 155, 153, ..., 177, 179, 180],\n",
+ " [158, 158, 156, ..., 177, 177, 178],\n",
+ " [159, 160, 159, ..., 179, 178, 178]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[237, 237, 240, ..., 85, 85, 82],\n",
+ " [239, 240, 242, ..., 87, 86, 84],\n",
+ " [243, 244, 245, ..., 90, 88, 87],\n",
+ " ...,\n",
+ " [ 73, 67, 48, ..., 68, 69, 77],\n",
+ " [ 69, 69, 54, ..., 74, 68, 69],\n",
+ " [ 68, 72, 66, ..., 71, 70, 70]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 74, 67, 59, ..., 45, 48, 52],\n",
+ " [ 86, 83, 82, ..., 74, 81, 91],\n",
+ " [ 97, 97, 95, ..., 109, 111, 112],\n",
+ " ...,\n",
+ " [254, 254, 254, ..., 253, 253, 253],\n",
+ " [254, 254, 254, ..., 253, 253, 253],\n",
+ " [254, 254, 254, ..., 253, 253, 253]], dtype=uint8),\n",
+ " array([[213, 214, 216, ..., 214, 212, 209],\n",
+ " [214, 215, 216, ..., 214, 212, 210],\n",
+ " [214, 215, 216, ..., 214, 212, 210],\n",
+ " ...,\n",
+ " [146, 143, 147, ..., 156, 156, 155],\n",
+ " [145, 142, 145, ..., 157, 158, 158],\n",
+ " [144, 143, 141, ..., 159, 159, 160]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[236, 236, 236, ..., 242, 242, 242],\n",
+ " [236, 236, 236, ..., 242, 242, 242],\n",
+ " [236, 236, 236, ..., 242, 242, 242],\n",
+ " ...,\n",
+ " [178, 174, 166, ..., 194, 196, 193],\n",
+ " [171, 171, 164, ..., 200, 196, 193],\n",
+ " [168, 169, 168, ..., 200, 198, 194]], dtype=uint8),\n",
+ " array([[ 24, 16, 13, ..., 27, 30, 45],\n",
+ " [ 25, 28, 14, ..., 32, 35, 51],\n",
+ " [ 17, 25, 16, ..., 33, 41, 56],\n",
+ " ...,\n",
+ " [175, 197, 225, ..., 116, 131, 77],\n",
+ " [196, 190, 177, ..., 101, 102, 49],\n",
+ " [178, 167, 170, ..., 51, 67, 46]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 7, 8, 9, ..., 2, 3, 3],\n",
+ " [ 7, 8, 11, ..., 4, 1, 4],\n",
+ " [ 6, 7, 9, ..., 6, 6, 5],\n",
+ " ...,\n",
+ " [197, 201, 203, ..., 162, 158, 154],\n",
+ " [197, 200, 203, ..., 162, 158, 153],\n",
+ " [197, 200, 203, ..., 161, 157, 153]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[253, 253, 253, ..., 253, 253, 253],\n",
+ " [253, 253, 253, ..., 253, 253, 253],\n",
+ " [253, 253, 253, ..., 253, 253, 253],\n",
+ " ...,\n",
+ " [253, 253, 253, ..., 253, 253, 253],\n",
+ " [253, 253, 253, ..., 253, 253, 253],\n",
+ " [253, 253, 253, ..., 253, 253, 253]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[134, 123, 110, ..., 126, 159, 165],\n",
+ " [133, 121, 108, ..., 163, 162, 164],\n",
+ " [130, 119, 108, ..., 162, 164, 164],\n",
+ " ...,\n",
+ " [172, 168, 166, ..., 179, 179, 179],\n",
+ " [178, 167, 165, ..., 178, 179, 179],\n",
+ " [176, 168, 166, ..., 178, 179, 179]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[205, 220, 221, ..., 200, 197, 195],\n",
+ " [209, 225, 227, ..., 201, 199, 196],\n",
+ " [222, 228, 228, ..., 202, 200, 198],\n",
+ " ...,\n",
+ " [108, 112, 115, ..., 107, 107, 105],\n",
+ " [111, 114, 116, ..., 106, 105, 102],\n",
+ " [114, 116, 118, ..., 106, 104, 101]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[213, 214, 216, ..., 214, 212, 209],\n",
+ " [214, 215, 216, ..., 214, 212, 210],\n",
+ " [214, 215, 216, ..., 214, 212, 210],\n",
+ " ...,\n",
+ " [145, 143, 146, ..., 157, 156, 156],\n",
+ " [144, 141, 143, ..., 157, 157, 157],\n",
+ " [143, 142, 140, ..., 158, 158, 159]], dtype=uint8),\n",
+ " array([[ 45, 128, 169, ..., 21, 35, 37],\n",
+ " [ 38, 124, 172, ..., 25, 32, 37],\n",
+ " [ 29, 120, 173, ..., 18, 28, 36],\n",
+ " ...,\n",
+ " [131, 125, 125, ..., 155, 153, 152],\n",
+ " [137, 126, 124, ..., 153, 153, 153],\n",
+ " [136, 126, 125, ..., 151, 152, 149]], dtype=uint8),\n",
+ " array([[128, 128, 130, ..., 14, 15, 17],\n",
+ " [128, 130, 132, ..., 15, 16, 26],\n",
+ " [123, 126, 129, ..., 19, 21, 34],\n",
+ " ...,\n",
+ " [184, 181, 187, ..., 35, 32, 33],\n",
+ " [184, 183, 182, ..., 35, 32, 33],\n",
+ " [183, 182, 185, ..., 35, 33, 32]], dtype=uint8),\n",
+ " array([[ 32, 31, 30, ..., 15, 15, 14],\n",
+ " [ 34, 33, 33, ..., 15, 16, 16],\n",
+ " [ 43, 40, 41, ..., 14, 15, 14],\n",
+ " ...,\n",
+ " [219, 217, 216, ..., 189, 185, 186],\n",
+ " [200, 198, 196, ..., 170, 164, 160],\n",
+ " [195, 193, 190, ..., 154, 144, 137]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[190, 192, 196, ..., 192, 190, 230],\n",
+ " [191, 193, 196, ..., 192, 190, 230],\n",
+ " [192, 194, 197, ..., 193, 191, 230],\n",
+ " ...,\n",
+ " [ 93, 86, 105, ..., 87, 75, 180],\n",
+ " [ 94, 81, 110, ..., 87, 80, 185],\n",
+ " [203, 198, 206, ..., 203, 197, 227]], dtype=uint8),\n",
+ " array([[227, 155, 121, ..., 246, 144, 103],\n",
+ " [248, 175, 123, ..., 252, 213, 124],\n",
+ " [250, 165, 129, ..., 253, 249, 173],\n",
+ " ...,\n",
+ " [ 83, 105, 86, ..., 238, 238, 238],\n",
+ " [ 81, 111, 89, ..., 238, 238, 238],\n",
+ " [ 88, 113, 91, ..., 238, 238, 238]], dtype=uint8),\n",
+ " array([[154, 153, 151, ..., 128, 127, 130],\n",
+ " [145, 152, 153, ..., 129, 131, 133],\n",
+ " [ 82, 95, 108, ..., 136, 137, 139],\n",
+ " ...,\n",
+ " [ 47, 47, 47, ..., 44, 44, 43],\n",
+ " [ 48, 48, 48, ..., 46, 46, 44],\n",
+ " [ 50, 50, 50, ..., 47, 47, 46]], dtype=uint8),\n",
+ " array([[239, 212, 243, ..., 176, 214, 194],\n",
+ " [221, 217, 240, ..., 206, 224, 173],\n",
+ " [214, 213, 223, ..., 210, 150, 113],\n",
+ " ...,\n",
+ " [135, 134, 139, ..., 75, 27, 25],\n",
+ " [139, 134, 133, ..., 83, 33, 38],\n",
+ " [135, 132, 129, ..., 88, 40, 74]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[ 12, 12, 12, ..., 48, 20, 20],\n",
+ " [ 12, 12, 12, ..., 78, 20, 23],\n",
+ " [ 13, 13, 13, ..., 34, 20, 22],\n",
+ " ...,\n",
+ " [111, 175, 175, ..., 169, 167, 164],\n",
+ " [ 84, 164, 176, ..., 170, 167, 165],\n",
+ " [ 80, 126, 177, ..., 170, 167, 165]], dtype=uint8),\n",
+ " array([[163, 162, 164, ..., 97, 97, 100],\n",
+ " [161, 164, 167, ..., 95, 98, 101],\n",
+ " [162, 165, 167, ..., 97, 97, 105],\n",
+ " ...,\n",
+ " [112, 117, 119, ..., 110, 111, 110],\n",
+ " [114, 119, 120, ..., 110, 111, 112],\n",
+ " [115, 119, 121, ..., 119, 115, 113]], dtype=uint8),\n",
+ " array([[220, 221, 222, ..., 220, 220, 228],\n",
+ " [220, 221, 222, ..., 220, 220, 228],\n",
+ " [220, 221, 222, ..., 221, 220, 228],\n",
+ " ...,\n",
+ " [ 77, 79, 78, ..., 76, 74, 131],\n",
+ " [ 76, 79, 78, ..., 76, 74, 130],\n",
+ " [ 78, 81, 78, ..., 78, 78, 132]], dtype=uint8),\n",
+ " array([[184, 187, 188, ..., 184, 180, 178],\n",
+ " [185, 187, 189, ..., 184, 181, 179],\n",
+ " [185, 187, 189, ..., 185, 182, 180],\n",
+ " ...,\n",
+ " [173, 171, 172, ..., 163, 162, 159],\n",
+ " [175, 174, 174, ..., 160, 162, 160],\n",
+ " [174, 174, 173, ..., 160, 161, 164]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 52, 62, 46, ..., 30, 32, 31],\n",
+ " [ 64, 50, 48, ..., 30, 31, 34],\n",
+ " [ 49, 41, 64, ..., 28, 30, 31],\n",
+ " ...,\n",
+ " [224, 227, 228, ..., 227, 228, 228],\n",
+ " [224, 227, 228, ..., 227, 228, 227],\n",
+ " [224, 227, 228, ..., 226, 228, 227]], dtype=uint8),\n",
+ " array([[227, 227, 226, ..., 198, 198, 214],\n",
+ " [227, 227, 226, ..., 198, 198, 214],\n",
+ " [228, 228, 227, ..., 198, 198, 214],\n",
+ " ...,\n",
+ " [ 71, 73, 73, ..., 81, 79, 134],\n",
+ " [ 68, 77, 75, ..., 80, 78, 133],\n",
+ " [ 68, 74, 68, ..., 77, 74, 131]], dtype=uint8),\n",
+ " array([[222, 195, 223, ..., 227, 224, 223],\n",
+ " [222, 207, 213, ..., 227, 224, 223],\n",
+ " [217, 221, 200, ..., 227, 224, 223],\n",
+ " ...,\n",
+ " [201, 205, 208, ..., 217, 213, 206],\n",
+ " [200, 205, 208, ..., 216, 212, 207],\n",
+ " [200, 204, 207, ..., 216, 210, 207]], dtype=uint8),\n",
+ " array([[236, 239, 239, ..., 84, 84, 83],\n",
+ " [239, 242, 241, ..., 87, 87, 86],\n",
+ " [243, 245, 245, ..., 90, 88, 87],\n",
+ " ...,\n",
+ " [ 73, 68, 51, ..., 70, 70, 77],\n",
+ " [ 69, 70, 55, ..., 74, 69, 68],\n",
+ " [ 69, 74, 67, ..., 70, 71, 67]], dtype=uint8),\n",
+ " array([[213, 147, 201, ..., 238, 240, 240],\n",
+ " [212, 185, 143, ..., 238, 240, 240],\n",
+ " [213, 209, 169, ..., 238, 239, 239],\n",
+ " ...,\n",
+ " [204, 203, 203, ..., 192, 192, 192],\n",
+ " [234, 233, 233, ..., 224, 223, 223],\n",
+ " [247, 247, 248, ..., 245, 245, 245]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[196, 197, 198, ..., 184, 183, 180],\n",
+ " [197, 198, 198, ..., 184, 183, 180],\n",
+ " [197, 198, 199, ..., 184, 183, 180],\n",
+ " ...,\n",
+ " [177, 178, 176, ..., 172, 172, 172],\n",
+ " [175, 176, 176, ..., 175, 174, 171],\n",
+ " [175, 175, 175, ..., 176, 173, 169]], dtype=uint8),\n",
+ " array([[ 65, 65, 64, ..., 12, 3, 7],\n",
+ " [ 67, 67, 66, ..., 19, 3, 7],\n",
+ " [ 69, 69, 68, ..., 23, 5, 9],\n",
+ " ...,\n",
+ " [192, 193, 192, ..., 178, 179, 181],\n",
+ " [192, 192, 192, ..., 179, 181, 183],\n",
+ " [192, 192, 192, ..., 180, 183, 185]], dtype=uint8),\n",
+ " array([[ 46, 38, 31, ..., 68, 57, 46],\n",
+ " [ 45, 37, 27, ..., 67, 60, 62],\n",
+ " [ 40, 32, 24, ..., 66, 51, 63],\n",
+ " ...,\n",
+ " [140, 120, 134, ..., 97, 86, 75],\n",
+ " [146, 131, 148, ..., 86, 87, 88],\n",
+ " [150, 129, 151, ..., 76, 89, 90]], dtype=uint8),\n",
+ " array([[ 34, 37, 39, ..., 70, 68, 183],\n",
+ " [ 37, 40, 42, ..., 71, 69, 184],\n",
+ " [ 40, 43, 45, ..., 75, 73, 186],\n",
+ " ...,\n",
+ " [ 69, 71, 74, ..., 149, 150, 214],\n",
+ " [ 69, 71, 74, ..., 146, 148, 213],\n",
+ " [ 68, 70, 72, ..., 145, 148, 214]], dtype=uint8),\n",
+ " array([[215, 216, 217, ..., 71, 79, 41],\n",
+ " [216, 217, 217, ..., 91, 81, 41],\n",
+ " [216, 217, 218, ..., 102, 102, 110],\n",
+ " ...,\n",
+ " [213, 215, 217, ..., 194, 198, 198],\n",
+ " [190, 181, 175, ..., 201, 202, 202],\n",
+ " [162, 179, 186, ..., 209, 208, 208]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 87, 98, 113, ..., 197, 196, 199],\n",
+ " [ 99, 111, 117, ..., 199, 200, 205],\n",
+ " [ 94, 89, 121, ..., 202, 202, 203],\n",
+ " ...,\n",
+ " [ 85, 86, 86, ..., 66, 65, 64],\n",
+ " [ 86, 87, 87, ..., 65, 65, 64],\n",
+ " [ 86, 87, 88, ..., 65, 64, 63]], dtype=uint8),\n",
+ " array([[81, 74, 61, ..., 61, 61, 54],\n",
+ " [75, 73, 66, ..., 55, 55, 48],\n",
+ " [70, 73, 71, ..., 53, 54, 49],\n",
+ " ...,\n",
+ " [47, 44, 44, ..., 78, 79, 82],\n",
+ " [47, 44, 44, ..., 79, 79, 82],\n",
+ " [52, 49, 48, ..., 83, 82, 81]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 252, 254, 253],\n",
+ " [255, 255, 255, ..., 231, 248, 254],\n",
+ " [255, 255, 255, ..., 214, 214, 238],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 8, 12, 11, ..., 156, 150, 146],\n",
+ " [ 7, 10, 11, ..., 152, 151, 142],\n",
+ " [ 11, 12, 13, ..., 152, 146, 120],\n",
+ " ...,\n",
+ " [250, 241, 240, ..., 63, 69, 47],\n",
+ " [251, 238, 243, ..., 63, 47, 33],\n",
+ " [251, 238, 243, ..., 47, 28, 21]], dtype=uint8),\n",
+ " array([[ 36, 32, 38, ..., 205, 204, 201],\n",
+ " [ 27, 26, 26, ..., 202, 203, 202],\n",
+ " [ 18, 16, 17, ..., 205, 204, 201],\n",
+ " ...,\n",
+ " [ 97, 100, 100, ..., 95, 93, 90],\n",
+ " [ 94, 96, 98, ..., 97, 95, 92],\n",
+ " [ 92, 94, 98, ..., 98, 96, 93]], dtype=uint8),\n",
+ " array([[226, 226, 226, ..., 243, 241, 240],\n",
+ " [226, 226, 226, ..., 243, 242, 241],\n",
+ " [226, 226, 226, ..., 243, 242, 241],\n",
+ " ...,\n",
+ " [199, 198, 197, ..., 215, 215, 215],\n",
+ " [198, 197, 196, ..., 215, 215, 215],\n",
+ " [209, 207, 204, ..., 215, 215, 215]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 60, 67, 73, ..., 91, 79, 43],\n",
+ " [ 71, 63, 74, ..., 60, 56, 46],\n",
+ " [ 83, 74, 80, ..., 47, 62, 29],\n",
+ " ...,\n",
+ " [162, 164, 163, ..., 160, 157, 157],\n",
+ " [162, 164, 162, ..., 159, 158, 163],\n",
+ " [160, 161, 160, ..., 159, 162, 163]], dtype=uint8),\n",
+ " array([[128, 133, 137, ..., 180, 180, 180],\n",
+ " [124, 131, 139, ..., 177, 176, 176],\n",
+ " [117, 125, 134, ..., 173, 171, 171],\n",
+ " ...,\n",
+ " [ 34, 35, 34, ..., 38, 39, 32],\n",
+ " [ 34, 33, 31, ..., 40, 38, 33],\n",
+ " [ 32, 30, 29, ..., 42, 37, 34]], dtype=uint8),\n",
+ " array([[148, 130, 115, ..., 15, 15, 20],\n",
+ " [150, 151, 162, ..., 15, 18, 27],\n",
+ " [151, 146, 157, ..., 20, 20, 21],\n",
+ " ...,\n",
+ " [ 60, 55, 55, ..., 59, 58, 52],\n",
+ " [ 63, 60, 61, ..., 57, 60, 58],\n",
+ " [ 84, 88, 96, ..., 50, 54, 61]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[220, 221, 224, ..., 202, 200, 189],\n",
+ " [214, 218, 223, ..., 215, 213, 207],\n",
+ " [200, 200, 200, ..., 223, 224, 224],\n",
+ " ...,\n",
+ " [186, 192, 193, ..., 178, 182, 183],\n",
+ " [186, 193, 193, ..., 152, 157, 174],\n",
+ " [190, 192, 194, ..., 151, 147, 149]], dtype=uint8),\n",
+ " array([[ 97, 93, 89, ..., 154, 154, 161],\n",
+ " [ 94, 94, 97, ..., 147, 151, 157],\n",
+ " [ 90, 93, 103, ..., 140, 145, 152],\n",
+ " ...,\n",
+ " [124, 125, 119, ..., 162, 134, 137],\n",
+ " [125, 125, 119, ..., 171, 134, 138],\n",
+ " [125, 125, 119, ..., 178, 134, 138]], dtype=uint8),\n",
+ " array([[107, 93, 106, ..., 93, 102, 83],\n",
+ " [ 94, 92, 126, ..., 86, 117, 63],\n",
+ " [ 81, 101, 112, ..., 70, 90, 66],\n",
+ " ...,\n",
+ " [188, 195, 199, ..., 215, 217, 219],\n",
+ " [187, 189, 191, ..., 215, 217, 217],\n",
+ " [188, 187, 193, ..., 213, 212, 215]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[249, 249, 250, ..., 250, 250, 248],\n",
+ " [249, 249, 249, ..., 250, 250, 248],\n",
+ " [249, 249, 250, ..., 250, 250, 248],\n",
+ " ...,\n",
+ " [121, 122, 124, ..., 130, 124, 120],\n",
+ " [120, 121, 123, ..., 130, 128, 125],\n",
+ " [120, 121, 121, ..., 128, 127, 126]], dtype=uint8),\n",
+ " array([[156, 157, 118, ..., 16, 16, 16],\n",
+ " [158, 167, 156, ..., 19, 18, 18],\n",
+ " [159, 169, 164, ..., 18, 14, 14],\n",
+ " ...,\n",
+ " [ 71, 67, 68, ..., 52, 52, 47],\n",
+ " [ 88, 89, 92, ..., 59, 52, 47],\n",
+ " [143, 137, 140, ..., 66, 60, 52]], dtype=uint8),\n",
+ " array([[231, 230, 232, ..., 202, 202, 203],\n",
+ " [225, 224, 227, ..., 205, 205, 206],\n",
+ " [222, 222, 224, ..., 208, 209, 209],\n",
+ " ...,\n",
+ " [ 11, 15, 16, ..., 31, 33, 36],\n",
+ " [ 9, 13, 14, ..., 32, 33, 35],\n",
+ " [ 8, 11, 12, ..., 32, 34, 35]], dtype=uint8),\n",
+ " array([[ 4, 7, 7, ..., 62, 21, 31],\n",
+ " [ 6, 4, 5, ..., 11, 7, 42],\n",
+ " [ 9, 5, 7, ..., 8, 8, 37],\n",
+ " ...,\n",
+ " [239, 240, 242, ..., 233, 232, 231],\n",
+ " [239, 240, 241, ..., 233, 232, 231],\n",
+ " [239, 240, 242, ..., 233, 232, 231]], dtype=uint8),\n",
+ " array([[175, 175, 174, ..., 46, 60, 82],\n",
+ " [182, 182, 180, ..., 40, 52, 72],\n",
+ " [183, 183, 183, ..., 32, 43, 60],\n",
+ " ...,\n",
+ " [ 89, 91, 91, ..., 186, 188, 188],\n",
+ " [ 88, 88, 88, ..., 186, 188, 188],\n",
+ " [ 87, 86, 85, ..., 186, 187, 187]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[250, 250, 250, ..., 141, 185, 213],\n",
+ " [250, 250, 250, ..., 214, 227, 226],\n",
+ " [251, 251, 251, ..., 230, 237, 245],\n",
+ " ...,\n",
+ " [ 29, 27, 27, ..., 142, 150, 159],\n",
+ " [ 28, 27, 23, ..., 140, 148, 156],\n",
+ " [ 29, 29, 25, ..., 138, 147, 155]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 15, 12, 6, ..., 10, 8, 10],\n",
+ " [ 13, 6, 6, ..., 16, 8, 10],\n",
+ " [ 8, 2, 4, ..., 25, 7, 10],\n",
+ " ...,\n",
+ " [181, 182, 174, ..., 193, 194, 196],\n",
+ " [180, 181, 178, ..., 194, 193, 189],\n",
+ " [179, 181, 181, ..., 195, 193, 189]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[105, 116, 114, ..., 134, 138, 137],\n",
+ " [105, 116, 117, ..., 137, 139, 128],\n",
+ " [109, 119, 121, ..., 138, 140, 155],\n",
+ " ...,\n",
+ " [ 91, 85, 89, ..., 60, 77, 47],\n",
+ " [ 96, 88, 91, ..., 52, 61, 47],\n",
+ " [108, 84, 96, ..., 56, 82, 44]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[212, 217, 220, ..., 139, 133, 126],\n",
+ " [214, 219, 221, ..., 140, 134, 127],\n",
+ " [216, 220, 222, ..., 142, 136, 130],\n",
+ " ...,\n",
+ " [ 94, 97, 100, ..., 75, 77, 95],\n",
+ " [ 92, 96, 99, ..., 77, 86, 101],\n",
+ " [ 92, 95, 100, ..., 75, 89, 102]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[159, 162, 168, ..., 106, 105, 196],\n",
+ " [159, 162, 168, ..., 106, 105, 196],\n",
+ " [159, 162, 167, ..., 105, 104, 195],\n",
+ " ...,\n",
+ " [ 8, 8, 8, ..., 26, 17, 166],\n",
+ " [ 7, 6, 7, ..., 27, 20, 166],\n",
+ " [ 8, 6, 7, ..., 27, 21, 166]], dtype=uint8),\n",
+ " array([[194, 194, 194, ..., 72, 57, 57],\n",
+ " [192, 193, 194, ..., 67, 50, 54],\n",
+ " [192, 195, 196, ..., 105, 73, 57],\n",
+ " ...,\n",
+ " [215, 220, 221, ..., 28, 29, 36],\n",
+ " [215, 219, 217, ..., 29, 30, 47],\n",
+ " [195, 198, 195, ..., 29, 31, 56]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 95, 92, 85, ..., 8, 28, 83],\n",
+ " [ 90, 98, 98, ..., 3, 6, 81],\n",
+ " [ 92, 100, 100, ..., 5, 2, 81],\n",
+ " ...,\n",
+ " [250, 250, 250, ..., 249, 250, 251],\n",
+ " [250, 250, 250, ..., 249, 250, 251],\n",
+ " [250, 250, 250, ..., 249, 250, 251]], dtype=uint8),\n",
+ " array([[254, 254, 254, ..., 254, 254, 254],\n",
+ " [254, 254, 254, ..., 254, 254, 254],\n",
+ " [254, 254, 254, ..., 254, 254, 254],\n",
+ " ...,\n",
+ " [ 71, 76, 90, ..., 101, 102, 103],\n",
+ " [ 62, 65, 67, ..., 102, 102, 98],\n",
+ " [ 52, 57, 60, ..., 101, 85, 79]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 14, 16, 20, ..., 113, 114, 69],\n",
+ " [ 15, 16, 20, ..., 122, 101, 25],\n",
+ " [ 16, 17, 20, ..., 117, 50, 15],\n",
+ " ...,\n",
+ " [ 80, 84, 86, ..., 84, 83, 81],\n",
+ " [ 79, 83, 83, ..., 82, 81, 78],\n",
+ " [ 77, 81, 80, ..., 80, 79, 75]], dtype=uint8),\n",
+ " array([[129, 125, 125, ..., 68, 66, 63],\n",
+ " [132, 127, 126, ..., 69, 67, 64],\n",
+ " [132, 130, 130, ..., 70, 68, 66],\n",
+ " ...,\n",
+ " [ 38, 41, 32, ..., 37, 36, 33],\n",
+ " [ 40, 40, 32, ..., 37, 36, 33],\n",
+ " [ 41, 41, 32, ..., 37, 36, 33]], dtype=uint8),\n",
+ " array([[184, 187, 200, ..., 177, 178, 175],\n",
+ " [184, 186, 195, ..., 179, 179, 175],\n",
+ " [187, 187, 191, ..., 181, 182, 173],\n",
+ " ...,\n",
+ " [160, 133, 158, ..., 169, 157, 115],\n",
+ " [137, 143, 157, ..., 168, 161, 112],\n",
+ " [122, 145, 158, ..., 167, 161, 116]], dtype=uint8),\n",
+ " array([[146, 149, 151, ..., 165, 163, 161],\n",
+ " [147, 150, 153, ..., 166, 164, 162],\n",
+ " [150, 153, 155, ..., 169, 167, 165],\n",
+ " ...,\n",
+ " [ 62, 62, 61, ..., 91, 93, 93],\n",
+ " [ 62, 62, 66, ..., 95, 95, 93],\n",
+ " [ 58, 59, 59, ..., 95, 96, 96]], dtype=uint8),\n",
+ " array([[204, 207, 209, ..., 212, 207, 205],\n",
+ " [205, 207, 209, ..., 212, 208, 206],\n",
+ " [205, 207, 210, ..., 213, 208, 207],\n",
+ " ...,\n",
+ " [126, 128, 128, ..., 140, 141, 135],\n",
+ " [126, 125, 124, ..., 145, 145, 139],\n",
+ " [126, 126, 129, ..., 148, 147, 143]], dtype=uint8),\n",
+ " array([[ 44, 29, 24, ..., 47, 52, 76],\n",
+ " [ 44, 34, 26, ..., 39, 54, 66],\n",
+ " [ 41, 39, 28, ..., 36, 61, 61],\n",
+ " ...,\n",
+ " [ 91, 93, 96, ..., 182, 199, 203],\n",
+ " [102, 98, 91, ..., 189, 181, 175],\n",
+ " [ 84, 84, 77, ..., 189, 198, 190]], dtype=uint8),\n",
+ " array([[104, 124, 138, ..., 130, 135, 104],\n",
+ " [108, 109, 138, ..., 137, 123, 95],\n",
+ " [134, 101, 129, ..., 144, 99, 108],\n",
+ " ...,\n",
+ " [ 22, 20, 21, ..., 21, 22, 21],\n",
+ " [ 18, 17, 17, ..., 19, 19, 21],\n",
+ " [ 17, 17, 28, ..., 32, 24, 21]], dtype=uint8),\n",
+ " array([[224, 224, 224, ..., 222, 222, 222],\n",
+ " [224, 224, 224, ..., 222, 222, 222],\n",
+ " [225, 225, 225, ..., 222, 222, 222],\n",
+ " ...,\n",
+ " [109, 109, 108, ..., 56, 55, 55],\n",
+ " [108, 108, 107, ..., 59, 58, 58],\n",
+ " [106, 106, 104, ..., 62, 60, 60]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[223, 186, 166, ..., 98, 78, 119],\n",
+ " [229, 190, 173, ..., 77, 78, 91],\n",
+ " [229, 197, 196, ..., 77, 84, 95],\n",
+ " ...,\n",
+ " [129, 135, 140, ..., 249, 252, 252],\n",
+ " [135, 142, 145, ..., 250, 251, 252],\n",
+ " [139, 147, 147, ..., 251, 250, 251]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 51, 43, 68, ..., 193, 191, 187],\n",
+ " [ 42, 56, 64, ..., 193, 192, 184],\n",
+ " [ 43, 66, 58, ..., 183, 170, 153],\n",
+ " ...,\n",
+ " [ 17, 16, 17, ..., 21, 21, 21],\n",
+ " [ 20, 15, 16, ..., 21, 21, 21],\n",
+ " [ 22, 22, 17, ..., 20, 21, 21]], dtype=uint8),\n",
+ " array([[177, 181, 184, ..., 176, 172, 168],\n",
+ " [176, 181, 183, ..., 176, 172, 169],\n",
+ " [175, 180, 182, ..., 176, 173, 169],\n",
+ " ...,\n",
+ " [184, 189, 189, ..., 188, 184, 183],\n",
+ " [183, 185, 185, ..., 188, 185, 183],\n",
+ " [178, 178, 178, ..., 188, 186, 183]], dtype=uint8),\n",
+ " array([[ 14, 9, 23, ..., 164, 160, 155],\n",
+ " [ 13, 14, 23, ..., 157, 156, 156],\n",
+ " [ 11, 19, 22, ..., 161, 158, 152],\n",
+ " ...,\n",
+ " [ 49, 64, 74, ..., 140, 145, 112],\n",
+ " [ 50, 66, 72, ..., 80, 48, 136],\n",
+ " [ 50, 66, 74, ..., 84, 46, 120]], dtype=uint8),\n",
+ " array([[ 48, 41, 41, ..., 29, 76, 99],\n",
+ " [ 26, 19, 14, ..., 31, 77, 100],\n",
+ " [ 11, 14, 19, ..., 34, 79, 100],\n",
+ " ...,\n",
+ " [200, 199, 198, ..., 14, 14, 14],\n",
+ " [197, 196, 195, ..., 14, 14, 14],\n",
+ " [196, 195, 194, ..., 14, 14, 14]], dtype=uint8),\n",
+ " array([[213, 213, 213, ..., 114, 111, 108],\n",
+ " [213, 213, 213, ..., 116, 112, 109],\n",
+ " [214, 214, 214, ..., 119, 115, 112],\n",
+ " ...,\n",
+ " [110, 116, 110, ..., 35, 32, 33],\n",
+ " [106, 103, 109, ..., 35, 31, 33],\n",
+ " [103, 105, 116, ..., 36, 31, 33]], dtype=uint8),\n",
+ " array([[191, 170, 81, ..., 236, 236, 236],\n",
+ " [183, 135, 83, ..., 236, 236, 236],\n",
+ " [172, 122, 94, ..., 236, 236, 236],\n",
+ " ...,\n",
+ " [176, 175, 174, ..., 161, 161, 159],\n",
+ " [177, 177, 176, ..., 160, 160, 158],\n",
+ " [179, 179, 179, ..., 158, 159, 157]], dtype=uint8),\n",
+ " array([[ 54, 67, 68, ..., 181, 180, 183],\n",
+ " [ 47, 66, 76, ..., 177, 176, 176],\n",
+ " [ 58, 60, 72, ..., 172, 170, 167],\n",
+ " ...,\n",
+ " [170, 172, 173, ..., 177, 175, 174],\n",
+ " [169, 172, 175, ..., 176, 173, 172],\n",
+ " [169, 173, 177, ..., 175, 172, 170]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[226, 228, 229, ..., 218, 213, 212],\n",
+ " [224, 227, 228, ..., 220, 219, 218],\n",
+ " [214, 219, 223, ..., 222, 221, 221],\n",
+ " ...,\n",
+ " [161, 155, 157, ..., 181, 179, 176],\n",
+ " [159, 152, 154, ..., 185, 183, 182],\n",
+ " [157, 160, 160, ..., 186, 186, 186]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[166, 167, 169, ..., 212, 212, 212],\n",
+ " [167, 168, 170, ..., 214, 214, 214],\n",
+ " [168, 169, 171, ..., 214, 214, 214],\n",
+ " ...,\n",
+ " [ 78, 81, 80, ..., 77, 75, 76],\n",
+ " [ 80, 78, 78, ..., 76, 76, 76],\n",
+ " [ 76, 76, 79, ..., 76, 76, 76]], dtype=uint8),\n",
+ " array([[236, 214, 107, ..., 52, 177, 247],\n",
+ " [236, 216, 104, ..., 22, 51, 158],\n",
+ " [237, 220, 99, ..., 29, 23, 35],\n",
+ " ...,\n",
+ " [162, 164, 165, ..., 25, 11, 10],\n",
+ " [158, 159, 161, ..., 13, 7, 9],\n",
+ " [155, 156, 158, ..., 8, 10, 7]], dtype=uint8),\n",
+ " array([[217, 218, 220, ..., 217, 216, 214],\n",
+ " [219, 220, 222, ..., 217, 216, 213],\n",
+ " [222, 223, 224, ..., 214, 213, 210],\n",
+ " ...,\n",
+ " [138, 145, 146, ..., 184, 183, 181],\n",
+ " [149, 150, 156, ..., 183, 183, 181],\n",
+ " [155, 162, 161, ..., 183, 183, 182]], dtype=uint8),\n",
+ " array([[188, 213, 221, ..., 217, 203, 177],\n",
+ " [184, 201, 224, ..., 218, 189, 171],\n",
+ " [181, 188, 216, ..., 207, 175, 166],\n",
+ " ...,\n",
+ " [174, 178, 180, ..., 157, 157, 155],\n",
+ " [174, 177, 180, ..., 155, 155, 153],\n",
+ " [174, 177, 179, ..., 154, 153, 152]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[221, 199, 53, ..., 25, 25, 28],\n",
+ " [220, 218, 116, ..., 27, 25, 26],\n",
+ " [221, 217, 195, ..., 20, 18, 21],\n",
+ " ...,\n",
+ " [192, 191, 193, ..., 86, 84, 84],\n",
+ " [192, 191, 193, ..., 82, 83, 88],\n",
+ " [191, 190, 192, ..., 114, 115, 118]], dtype=uint8),\n",
+ " array([[238, 240, 243, ..., 251, 250, 251],\n",
+ " [239, 241, 243, ..., 251, 251, 251],\n",
+ " [240, 242, 244, ..., 252, 251, 252],\n",
+ " ...,\n",
+ " [ 80, 81, 85, ..., 91, 90, 142],\n",
+ " [ 80, 81, 85, ..., 90, 89, 141],\n",
+ " [ 80, 81, 84, ..., 89, 89, 141]], dtype=uint8),\n",
+ " array([[217, 221, 223, ..., 2, 1, 0],\n",
+ " [218, 220, 224, ..., 2, 1, 0],\n",
+ " [219, 223, 224, ..., 1, 1, 0],\n",
+ " ...,\n",
+ " [214, 213, 213, ..., 178, 176, 173],\n",
+ " [208, 209, 214, ..., 175, 173, 171],\n",
+ " [206, 214, 218, ..., 174, 171, 169]], dtype=uint8),\n",
+ " array([[199, 199, 198, ..., 230, 230, 230],\n",
+ " [199, 199, 198, ..., 231, 231, 231],\n",
+ " [199, 199, 198, ..., 232, 232, 232],\n",
+ " ...,\n",
+ " [ 34, 30, 30, ..., 76, 76, 72],\n",
+ " [ 33, 30, 31, ..., 74, 73, 71],\n",
+ " [ 31, 32, 33, ..., 76, 75, 77]], dtype=uint8),\n",
+ " array([[ 41, 71, 28, ..., 40, 44, 69],\n",
+ " [ 15, 28, 12, ..., 43, 31, 48],\n",
+ " [ 19, 13, 15, ..., 72, 24, 22],\n",
+ " ...,\n",
+ " [143, 134, 133, ..., 118, 115, 115],\n",
+ " [141, 131, 138, ..., 110, 117, 117],\n",
+ " [143, 139, 140, ..., 110, 118, 117]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[118, 118, 122, ..., 238, 237, 237],\n",
+ " [116, 118, 121, ..., 238, 237, 237],\n",
+ " [114, 115, 106, ..., 238, 237, 237],\n",
+ " ...,\n",
+ " [ 70, 66, 65, ..., 103, 104, 105],\n",
+ " [ 58, 65, 51, ..., 103, 103, 104],\n",
+ " [ 61, 68, 52, ..., 103, 103, 103]], dtype=uint8),\n",
+ " array([[193, 193, 193, ..., 202, 202, 202],\n",
+ " [193, 193, 193, ..., 202, 202, 202],\n",
+ " [193, 193, 193, ..., 202, 202, 202],\n",
+ " ...,\n",
+ " [ 96, 96, 96, ..., 92, 89, 92],\n",
+ " [ 96, 96, 96, ..., 92, 91, 95],\n",
+ " [ 96, 96, 96, ..., 90, 90, 94]], dtype=uint8),\n",
+ " array([[101, 106, 110, ..., 202, 201, 198],\n",
+ " [ 93, 108, 110, ..., 201, 200, 198],\n",
+ " [ 71, 106, 107, ..., 200, 198, 196],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 210, 212, 208],\n",
+ " [255, 255, 255, ..., 210, 209, 211],\n",
+ " [254, 254, 254, ..., 209, 205, 207]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[254, 254, 254, ..., 255, 254, 253],\n",
+ " [254, 254, 254, ..., 255, 254, 253],\n",
+ " [254, 254, 254, ..., 255, 254, 253],\n",
+ " ...,\n",
+ " [140, 142, 142, ..., 133, 134, 130],\n",
+ " [138, 136, 135, ..., 134, 131, 127],\n",
+ " [131, 130, 132, ..., 132, 129, 128]], dtype=uint8),\n",
+ " array([[ 73, 72, 71, ..., 109, 108, 111],\n",
+ " [ 72, 70, 70, ..., 110, 108, 107],\n",
+ " [ 76, 71, 71, ..., 134, 102, 103],\n",
+ " ...,\n",
+ " [ 91, 89, 89, ..., 60, 60, 60],\n",
+ " [ 91, 90, 89, ..., 60, 61, 61],\n",
+ " [ 92, 91, 91, ..., 61, 61, 61]], dtype=uint8),\n",
+ " array([[181, 183, 186, ..., 180, 177, 175],\n",
+ " [182, 184, 186, ..., 181, 178, 176],\n",
+ " [183, 185, 187, ..., 182, 179, 177],\n",
+ " ...,\n",
+ " [ 41, 41, 39, ..., 46, 45, 44],\n",
+ " [ 39, 39, 38, ..., 46, 48, 45],\n",
+ " [ 38, 38, 39, ..., 45, 48, 44]], dtype=uint8),\n",
+ " array([[100, 96, 92, ..., 210, 208, 205],\n",
+ " [139, 139, 139, ..., 211, 208, 206],\n",
+ " [146, 148, 149, ..., 211, 209, 207],\n",
+ " ...,\n",
+ " [ 37, 34, 34, ..., 106, 109, 107],\n",
+ " [ 31, 34, 34, ..., 108, 111, 110],\n",
+ " [ 35, 33, 34, ..., 108, 111, 110]], dtype=uint8),\n",
+ " array([[132, 133, 134, ..., 163, 162, 161],\n",
+ " [128, 130, 132, ..., 163, 162, 161],\n",
+ " [125, 127, 130, ..., 162, 161, 160],\n",
+ " ...,\n",
+ " [ 74, 76, 75, ..., 126, 121, 122],\n",
+ " [ 74, 75, 78, ..., 127, 127, 127],\n",
+ " [ 76, 77, 83, ..., 122, 121, 121]], dtype=uint8),\n",
+ " array([[225, 226, 227, ..., 11, 32, 184],\n",
+ " [226, 227, 227, ..., 12, 16, 177],\n",
+ " [226, 227, 227, ..., 20, 12, 167],\n",
+ " ...,\n",
+ " [231, 233, 232, ..., 236, 235, 247],\n",
+ " [231, 233, 232, ..., 236, 235, 247],\n",
+ " [231, 233, 232, ..., 236, 235, 247]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[168, 171, 174, ..., 180, 184, 160],\n",
+ " [168, 171, 175, ..., 181, 185, 161],\n",
+ " [169, 172, 176, ..., 183, 187, 163],\n",
+ " ...,\n",
+ " [132, 135, 135, ..., 150, 150, 148],\n",
+ " [136, 142, 142, ..., 157, 151, 150],\n",
+ " [139, 144, 144, ..., 160, 154, 151]], dtype=uint8),\n",
+ " array([[152, 167, 172, ..., 169, 164, 150],\n",
+ " [158, 172, 175, ..., 173, 169, 157],\n",
+ " [164, 177, 174, ..., 176, 173, 163],\n",
+ " ...,\n",
+ " [ 67, 70, 72, ..., 10, 8, 6],\n",
+ " [ 62, 67, 70, ..., 9, 8, 6],\n",
+ " [ 58, 65, 68, ..., 9, 7, 5]], dtype=uint8),\n",
+ " array([[218, 212, 197, ..., 74, 98, 120],\n",
+ " [219, 213, 200, ..., 112, 130, 132],\n",
+ " [222, 215, 203, ..., 136, 134, 128],\n",
+ " ...,\n",
+ " [150, 151, 154, ..., 195, 179, 120],\n",
+ " [148, 149, 151, ..., 112, 68, 63],\n",
+ " [147, 148, 149, ..., 60, 58, 63]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 146, 150, 154],\n",
+ " [255, 255, 255, ..., 144, 151, 155],\n",
+ " [255, 255, 255, ..., 144, 152, 156],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 57, 41, 31],\n",
+ " [255, 255, 255, ..., 144, 143, 144],\n",
+ " [255, 255, 255, ..., 155, 159, 163]], dtype=uint8),\n",
+ " array([[237, 237, 236, ..., 229, 228, 236],\n",
+ " [235, 235, 235, ..., 229, 228, 236],\n",
+ " [235, 235, 234, ..., 229, 228, 236],\n",
+ " ...,\n",
+ " [212, 212, 211, ..., 201, 202, 217],\n",
+ " [212, 212, 212, ..., 201, 202, 218],\n",
+ " [212, 212, 212, ..., 202, 203, 218]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 105, 212, 253],\n",
+ " [255, 255, 255, ..., 132, 229, 253],\n",
+ " [255, 255, 255, ..., 162, 242, 254]], dtype=uint8),\n",
+ " array([[195, 176, 24, ..., 134, 142, 149],\n",
+ " [199, 180, 25, ..., 133, 141, 149],\n",
+ " [205, 188, 30, ..., 134, 147, 155],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 237, 236, 233],\n",
+ " [255, 255, 255, ..., 237, 236, 233],\n",
+ " [254, 254, 254, ..., 236, 236, 233]], dtype=uint8),\n",
+ " array([[83, 83, 83, ..., 83, 83, 83],\n",
+ " [83, 83, 83, ..., 83, 83, 83],\n",
+ " [83, 83, 83, ..., 83, 83, 83],\n",
+ " ...,\n",
+ " [35, 32, 32, ..., 48, 43, 48],\n",
+ " [40, 39, 35, ..., 38, 42, 47],\n",
+ " [42, 42, 34, ..., 34, 43, 48]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[227, 227, 228, ..., 227, 226, 226],\n",
+ " [227, 227, 227, ..., 227, 227, 227],\n",
+ " [227, 227, 227, ..., 228, 228, 228],\n",
+ " ...,\n",
+ " [136, 140, 146, ..., 137, 139, 142],\n",
+ " [141, 137, 143, ..., 132, 142, 141],\n",
+ " [139, 141, 139, ..., 135, 139, 139]], dtype=uint8),\n",
+ " array([[194, 194, 194, ..., 72, 57, 57],\n",
+ " [192, 193, 194, ..., 67, 50, 54],\n",
+ " [192, 195, 196, ..., 105, 73, 57],\n",
+ " ...,\n",
+ " [215, 220, 221, ..., 28, 29, 36],\n",
+ " [215, 219, 217, ..., 29, 30, 47],\n",
+ " [195, 198, 195, ..., 29, 31, 56]], dtype=uint8),\n",
+ " array([[147, 148, 149, ..., 118, 115, 109],\n",
+ " [151, 152, 153, ..., 105, 103, 98],\n",
+ " [154, 155, 156, ..., 116, 114, 110],\n",
+ " ...,\n",
+ " [ 30, 30, 30, ..., 223, 224, 225],\n",
+ " [ 30, 30, 30, ..., 223, 224, 225],\n",
+ " [ 30, 30, 30, ..., 224, 225, 226]], dtype=uint8),\n",
+ " array([[188, 189, 188, ..., 207, 206, 204],\n",
+ " [191, 190, 191, ..., 212, 213, 212],\n",
+ " [190, 188, 189, ..., 219, 222, 220],\n",
+ " ...,\n",
+ " [143, 143, 139, ..., 120, 133, 108],\n",
+ " [136, 145, 142, ..., 125, 124, 134],\n",
+ " [138, 153, 150, ..., 147, 138, 140]], dtype=uint8),\n",
+ " array([[140, 143, 144, ..., 145, 144, 139],\n",
+ " [140, 142, 143, ..., 146, 145, 141],\n",
+ " [138, 140, 142, ..., 145, 145, 140],\n",
+ " ...,\n",
+ " [169, 160, 157, ..., 147, 148, 148],\n",
+ " [155, 148, 155, ..., 137, 145, 142],\n",
+ " [157, 149, 149, ..., 135, 133, 136]], dtype=uint8),\n",
+ " array([[171, 171, 172, ..., 130, 138, 140],\n",
+ " [173, 173, 173, ..., 124, 133, 134],\n",
+ " [175, 175, 176, ..., 114, 124, 127],\n",
+ " ...,\n",
+ " [ 12, 12, 13, ..., 32, 41, 47],\n",
+ " [ 13, 12, 12, ..., 28, 44, 45],\n",
+ " [ 16, 14, 14, ..., 30, 49, 57]], dtype=uint8),\n",
+ " array([[185, 186, 187, ..., 186, 184, 183],\n",
+ " [186, 187, 188, ..., 186, 185, 184],\n",
+ " [187, 188, 189, ..., 187, 186, 185],\n",
+ " ...,\n",
+ " [ 25, 24, 23, ..., 27, 25, 26],\n",
+ " [ 23, 23, 23, ..., 27, 26, 25],\n",
+ " [ 22, 21, 24, ..., 27, 26, 25]], dtype=uint8),\n",
+ " array([[201, 202, 203, ..., 194, 193, 192],\n",
+ " [202, 203, 203, ..., 195, 194, 193],\n",
+ " [202, 203, 204, ..., 195, 194, 193],\n",
+ " ...,\n",
+ " [ 44, 44, 44, ..., 42, 42, 42],\n",
+ " [ 44, 44, 45, ..., 42, 42, 42],\n",
+ " [ 45, 45, 46, ..., 42, 42, 42]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 11, 11, 11],\n",
+ " [255, 255, 255, ..., 11, 12, 12],\n",
+ " [255, 255, 255, ..., 11, 12, 12],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 51, 51, 51],\n",
+ " [255, 255, 255, ..., 51, 51, 51],\n",
+ " [255, 255, 255, ..., 51, 51, 51]], dtype=uint8),\n",
+ " array([[217, 217, 216, ..., 26, 28, 29],\n",
+ " [216, 216, 218, ..., 25, 25, 26],\n",
+ " [218, 218, 218, ..., 24, 24, 26],\n",
+ " ...,\n",
+ " [196, 197, 198, ..., 154, 152, 154],\n",
+ " [196, 197, 198, ..., 153, 151, 151],\n",
+ " [196, 197, 198, ..., 153, 151, 149]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[133, 66, 57, ..., 127, 131, 209],\n",
+ " [131, 70, 69, ..., 123, 127, 212],\n",
+ " [126, 68, 88, ..., 126, 141, 215],\n",
+ " ...,\n",
+ " [114, 136, 190, ..., 126, 129, 209],\n",
+ " [116, 113, 131, ..., 124, 125, 204],\n",
+ " [116, 121, 126, ..., 124, 128, 206]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[251, 251, 251, ..., 69, 69, 69],\n",
+ " [251, 251, 251, ..., 69, 69, 69],\n",
+ " [251, 251, 251, ..., 68, 68, 68],\n",
+ " ...,\n",
+ " [120, 128, 139, ..., 242, 242, 242],\n",
+ " [158, 170, 179, ..., 243, 243, 243],\n",
+ " [185, 184, 182, ..., 244, 244, 244]], dtype=uint8),\n",
+ " array([[230, 233, 234, ..., 221, 221, 221],\n",
+ " [231, 233, 234, ..., 221, 221, 221],\n",
+ " [231, 233, 235, ..., 221, 221, 221],\n",
+ " ...,\n",
+ " [232, 233, 234, ..., 222, 222, 222],\n",
+ " [232, 233, 234, ..., 222, 222, 222],\n",
+ " [232, 233, 234, ..., 222, 222, 222]], dtype=uint8),\n",
+ " array([[ 86, 77, 65, ..., 27, 30, 29],\n",
+ " [ 95, 85, 71, ..., 28, 28, 30],\n",
+ " [102, 92, 70, ..., 30, 31, 33],\n",
+ " ...,\n",
+ " [151, 145, 91, ..., 86, 63, 54],\n",
+ " [148, 141, 82, ..., 93, 63, 54],\n",
+ " [145, 137, 76, ..., 96, 63, 54]], dtype=uint8),\n",
+ " array([[ 7, 7, 6, ..., 4, 3, 3],\n",
+ " [ 4, 4, 4, ..., 2, 2, 2],\n",
+ " [ 2, 2, 1, ..., 4, 4, 4],\n",
+ " ...,\n",
+ " [66, 71, 78, ..., 28, 28, 28],\n",
+ " [63, 69, 75, ..., 28, 28, 28],\n",
+ " [59, 65, 71, ..., 27, 26, 27]], dtype=uint8),\n",
+ " array([[ 65, 82, 67, ..., 71, 70, 66],\n",
+ " [ 60, 69, 67, ..., 70, 66, 64],\n",
+ " [ 57, 60, 66, ..., 67, 62, 63],\n",
+ " ...,\n",
+ " [158, 162, 156, ..., 155, 153, 150],\n",
+ " [154, 158, 156, ..., 151, 150, 151],\n",
+ " [152, 147, 153, ..., 160, 153, 149]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 53, 57, 80, ..., 243, 238, 232],\n",
+ " [ 55, 63, 75, ..., 248, 240, 234],\n",
+ " [ 59, 55, 39, ..., 252, 247, 238],\n",
+ " ...,\n",
+ " [254, 254, 254, ..., 47, 49, 45],\n",
+ " [254, 254, 253, ..., 53, 54, 50],\n",
+ " [253, 253, 253, ..., 57, 58, 53]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[227, 228, 230, ..., 224, 223, 220],\n",
+ " [228, 229, 230, ..., 224, 223, 220],\n",
+ " [228, 229, 231, ..., 224, 223, 220],\n",
+ " ...,\n",
+ " [177, 179, 183, ..., 118, 117, 116],\n",
+ " [172, 176, 177, ..., 108, 112, 116],\n",
+ " [170, 175, 175, ..., 106, 114, 115]], dtype=uint8),\n",
+ " array([[ 94, 96, 147, ..., 204, 205, 205],\n",
+ " [113, 94, 116, ..., 203, 204, 204],\n",
+ " [ 59, 60, 85, ..., 202, 202, 201],\n",
+ " ...,\n",
+ " [152, 155, 157, ..., 150, 152, 150],\n",
+ " [153, 155, 157, ..., 151, 151, 150],\n",
+ " [153, 156, 158, ..., 151, 150, 149]], dtype=uint8),\n",
+ " array([[ 55, 43, 40, ..., 165, 170, 171],\n",
+ " [ 42, 32, 15, ..., 157, 164, 170],\n",
+ " [ 25, 11, 7, ..., 145, 153, 163],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 253, 255, 255]], dtype=uint8),\n",
+ " array([[ 11, 8, 15, ..., 189, 189, 184],\n",
+ " [ 11, 8, 16, ..., 190, 189, 185],\n",
+ " [ 11, 8, 16, ..., 192, 190, 187],\n",
+ " ...,\n",
+ " [234, 236, 236, ..., 139, 140, 140],\n",
+ " [229, 235, 236, ..., 138, 138, 137],\n",
+ " [229, 234, 233, ..., 135, 135, 137]], dtype=uint8),\n",
+ " array([[163, 166, 170, ..., 173, 169, 166],\n",
+ " [163, 166, 170, ..., 170, 167, 163],\n",
+ " [164, 167, 170, ..., 169, 165, 162],\n",
+ " ...,\n",
+ " [101, 97, 95, ..., 113, 109, 108],\n",
+ " [ 98, 95, 93, ..., 109, 106, 107],\n",
+ " [ 96, 94, 93, ..., 104, 104, 107]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [ 66, 58, 64, ..., 63, 67, 60],\n",
+ " [ 59, 65, 82, ..., 61, 68, 65],\n",
+ " [ 52, 54, 80, ..., 60, 59, 64]], dtype=uint8),\n",
+ " array([[233, 234, 236, ..., 19, 17, 16],\n",
+ " [234, 235, 236, ..., 18, 16, 15],\n",
+ " [234, 235, 236, ..., 17, 16, 15],\n",
+ " ...,\n",
+ " [ 35, 31, 23, ..., 129, 177, 207],\n",
+ " [ 35, 32, 24, ..., 133, 197, 168],\n",
+ " [ 35, 32, 25, ..., 150, 191, 133]], dtype=uint8),\n",
+ " array([[ 50, 52, 128, ..., 135, 125, 119],\n",
+ " [ 37, 46, 49, ..., 131, 125, 119],\n",
+ " [ 25, 32, 38, ..., 130, 127, 123],\n",
+ " ...,\n",
+ " [ 56, 67, 74, ..., 197, 196, 190],\n",
+ " [ 60, 71, 72, ..., 203, 201, 195],\n",
+ " [ 61, 74, 75, ..., 205, 206, 205]], dtype=uint8),\n",
+ " array([[106, 112, 114, ..., 237, 236, 233],\n",
+ " [111, 117, 116, ..., 237, 236, 234],\n",
+ " [118, 121, 117, ..., 238, 236, 234],\n",
+ " ...,\n",
+ " [129, 134, 136, ..., 135, 132, 128],\n",
+ " [127, 131, 134, ..., 134, 130, 126],\n",
+ " [128, 132, 132, ..., 129, 125, 126]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 6, 6, 6, ..., 33, 39, 24],\n",
+ " [ 6, 6, 6, ..., 42, 45, 26],\n",
+ " [ 6, 6, 6, ..., 42, 39, 23],\n",
+ " ...,\n",
+ " [ 53, 55, 57, ..., 167, 166, 171],\n",
+ " [ 54, 55, 54, ..., 169, 164, 166],\n",
+ " [ 54, 54, 52, ..., 167, 161, 160]], dtype=uint8),\n",
+ " array([[153, 171, 175, ..., 121, 120, 119],\n",
+ " [126, 135, 163, ..., 122, 121, 120],\n",
+ " [149, 159, 149, ..., 124, 123, 122],\n",
+ " ...,\n",
+ " [136, 138, 140, ..., 101, 98, 104],\n",
+ " [135, 136, 140, ..., 101, 100, 103],\n",
+ " [136, 138, 137, ..., 100, 100, 100]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 146, 150, 154],\n",
+ " [255, 255, 255, ..., 144, 151, 155],\n",
+ " [255, 255, 255, ..., 144, 152, 156],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 57, 41, 31],\n",
+ " [255, 255, 255, ..., 144, 143, 144],\n",
+ " [255, 255, 255, ..., 155, 159, 163]], dtype=uint8),\n",
+ " array([[242, 243, 245, ..., 237, 236, 244],\n",
+ " [243, 244, 245, ..., 235, 234, 243],\n",
+ " [243, 244, 245, ..., 234, 233, 242],\n",
+ " ...,\n",
+ " [115, 116, 116, ..., 160, 183, 166],\n",
+ " [111, 108, 108, ..., 139, 137, 167],\n",
+ " [104, 102, 106, ..., 99, 99, 165]], dtype=uint8),\n",
+ " array([[141, 127, 142, ..., 32, 31, 26],\n",
+ " [143, 130, 144, ..., 33, 33, 28],\n",
+ " [143, 131, 146, ..., 31, 32, 27],\n",
+ " ...,\n",
+ " [161, 161, 161, ..., 170, 170, 170],\n",
+ " [163, 164, 164, ..., 169, 167, 167],\n",
+ " [165, 165, 166, ..., 172, 171, 170]], dtype=uint8),\n",
+ " array([[ 48, 41, 41, ..., 29, 76, 99],\n",
+ " [ 26, 19, 14, ..., 31, 77, 100],\n",
+ " [ 11, 14, 19, ..., 34, 79, 100],\n",
+ " ...,\n",
+ " [200, 199, 198, ..., 14, 14, 14],\n",
+ " [197, 196, 195, ..., 14, 14, 14],\n",
+ " [196, 195, 194, ..., 14, 14, 14]], dtype=uint8),\n",
+ " array([[223, 223, 223, ..., 44, 43, 42],\n",
+ " [223, 223, 223, ..., 44, 43, 42],\n",
+ " [223, 223, 223, ..., 43, 42, 41],\n",
+ " ...,\n",
+ " [131, 138, 147, ..., 246, 245, 248],\n",
+ " [163, 177, 187, ..., 247, 249, 234],\n",
+ " [192, 193, 193, ..., 238, 206, 178]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 246, 255, 255],\n",
+ " [255, 255, 255, ..., 246, 255, 255],\n",
+ " [255, 255, 255, ..., 246, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 74, 93, 94, ..., 39, 36, 36],\n",
+ " [ 60, 90, 99, ..., 45, 39, 40],\n",
+ " [ 74, 62, 76, ..., 48, 39, 41],\n",
+ " ...,\n",
+ " [213, 209, 203, ..., 200, 199, 197],\n",
+ " [212, 209, 204, ..., 199, 199, 196],\n",
+ " [211, 208, 204, ..., 198, 197, 195]], dtype=uint8),\n",
+ " array([[175, 164, 153, ..., 211, 210, 209],\n",
+ " [165, 159, 161, ..., 215, 207, 204],\n",
+ " [161, 161, 168, ..., 220, 204, 192],\n",
+ " ...,\n",
+ " [209, 211, 214, ..., 220, 218, 213],\n",
+ " [210, 214, 213, ..., 219, 219, 213],\n",
+ " [204, 211, 209, ..., 216, 216, 219]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 50, 50, 76, ..., 206, 204, 200],\n",
+ " [ 44, 38, 61, ..., 211, 207, 205],\n",
+ " [ 38, 29, 41, ..., 215, 211, 207],\n",
+ " ...,\n",
+ " [110, 113, 110, ..., 104, 110, 112],\n",
+ " [112, 117, 116, ..., 110, 106, 105],\n",
+ " [105, 112, 115, ..., 113, 108, 105]], dtype=uint8),\n",
+ " array([[116, 41, 30, ..., 41, 38, 35],\n",
+ " [121, 45, 31, ..., 40, 35, 32],\n",
+ " [129, 48, 32, ..., 38, 33, 32],\n",
+ " ...,\n",
+ " [194, 213, 202, ..., 24, 27, 25],\n",
+ " [194, 214, 201, ..., 22, 26, 25],\n",
+ " [195, 214, 200, ..., 25, 26, 25]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[236, 238, 240, ..., 85, 84, 82],\n",
+ " [239, 241, 242, ..., 88, 86, 85],\n",
+ " [243, 245, 245, ..., 90, 88, 87],\n",
+ " ...,\n",
+ " [ 73, 66, 50, ..., 70, 70, 77],\n",
+ " [ 69, 69, 55, ..., 74, 68, 68],\n",
+ " [ 68, 75, 67, ..., 71, 70, 67]], dtype=uint8),\n",
+ " array([[248, 248, 247, ..., 149, 119, 159],\n",
+ " [248, 248, 248, ..., 166, 153, 177],\n",
+ " [248, 248, 249, ..., 205, 193, 184],\n",
+ " ...,\n",
+ " [202, 199, 200, ..., 113, 100, 91],\n",
+ " [202, 198, 195, ..., 173, 165, 153],\n",
+ " [145, 155, 159, ..., 205, 206, 200]], dtype=uint8),\n",
+ " array([[ 8, 10, 10, ..., 52, 66, 65],\n",
+ " [ 13, 11, 13, ..., 50, 50, 37],\n",
+ " [ 20, 18, 19, ..., 33, 34, 27],\n",
+ " ...,\n",
+ " [159, 163, 156, ..., 169, 164, 169],\n",
+ " [161, 161, 157, ..., 169, 166, 169],\n",
+ " [154, 158, 160, ..., 165, 170, 169]], dtype=uint8),\n",
+ " array([[189, 189, 189, ..., 187, 186, 186],\n",
+ " [189, 189, 189, ..., 187, 186, 186],\n",
+ " [189, 189, 189, ..., 187, 186, 186],\n",
+ " ...,\n",
+ " [ 92, 92, 93, ..., 88, 88, 88],\n",
+ " [ 93, 92, 91, ..., 90, 90, 90],\n",
+ " [ 94, 92, 91, ..., 92, 92, 92]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 45, 38, 38, ..., 103, 106, 108],\n",
+ " [ 21, 14, 13, ..., 89, 93, 96],\n",
+ " [ 30, 22, 19, ..., 90, 94, 98],\n",
+ " ...,\n",
+ " [191, 189, 190, ..., 189, 189, 189],\n",
+ " [191, 189, 190, ..., 189, 189, 189],\n",
+ " [191, 189, 190, ..., 189, 189, 189]], dtype=uint8),\n",
+ " array([[192, 193, 194, ..., 188, 185, 183],\n",
+ " [193, 194, 194, ..., 188, 186, 184],\n",
+ " [193, 194, 195, ..., 189, 187, 185],\n",
+ " ...,\n",
+ " [ 41, 42, 41, ..., 41, 41, 40],\n",
+ " [ 40, 41, 41, ..., 41, 40, 40],\n",
+ " [ 42, 43, 42, ..., 40, 40, 40]], dtype=uint8),\n",
+ " array([[109, 114, 122, ..., 13, 15, 171],\n",
+ " [116, 121, 129, ..., 14, 14, 168],\n",
+ " [124, 129, 136, ..., 14, 13, 166],\n",
+ " ...,\n",
+ " [ 41, 46, 48, ..., 35, 33, 171],\n",
+ " [ 42, 46, 50, ..., 34, 33, 171],\n",
+ " [ 46, 47, 50, ..., 34, 33, 171]], dtype=uint8),\n",
+ " array([[210, 209, 208, ..., 178, 179, 180],\n",
+ " [213, 212, 211, ..., 179, 179, 180],\n",
+ " [215, 215, 214, ..., 180, 180, 182],\n",
+ " ...,\n",
+ " [243, 243, 244, ..., 168, 165, 116],\n",
+ " [242, 242, 241, ..., 166, 163, 103],\n",
+ " [242, 242, 235, ..., 165, 162, 91]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 253, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 253, 255, 255]], dtype=uint8),\n",
+ " array([[176, 180, 185, ..., 186, 182, 178],\n",
+ " [178, 181, 187, ..., 187, 183, 179],\n",
+ " [179, 183, 189, ..., 188, 185, 182],\n",
+ " ...,\n",
+ " [204, 205, 208, ..., 204, 202, 198],\n",
+ " [201, 203, 211, ..., 205, 204, 200],\n",
+ " [203, 203, 210, ..., 204, 200, 201]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 253, 255, 255]], dtype=uint8),\n",
+ " array([[210, 214, 216, ..., 78, 31, 66],\n",
+ " [211, 214, 216, ..., 114, 70, 116],\n",
+ " [211, 215, 216, ..., 157, 119, 138],\n",
+ " ...,\n",
+ " [ 29, 31, 34, ..., 27, 25, 3],\n",
+ " [ 33, 36, 38, ..., 10, 8, 2],\n",
+ " [ 36, 40, 42, ..., 8, 6, 2]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 57, 59, 58, ..., 5, 5, 6],\n",
+ " [ 57, 60, 59, ..., 5, 5, 6],\n",
+ " [ 57, 60, 60, ..., 5, 5, 6],\n",
+ " ...,\n",
+ " [174, 171, 183, ..., 179, 183, 185],\n",
+ " [177, 178, 189, ..., 177, 182, 187],\n",
+ " [183, 186, 192, ..., 179, 183, 186]], dtype=uint8),\n",
+ " array([[157, 164, 166, ..., 107, 103, 98],\n",
+ " [166, 167, 167, ..., 106, 103, 99],\n",
+ " [174, 176, 176, ..., 104, 102, 100],\n",
+ " ...,\n",
+ " [218, 218, 218, ..., 167, 158, 155],\n",
+ " [218, 218, 218, ..., 167, 158, 157],\n",
+ " [218, 218, 218, ..., 168, 161, 154]], dtype=uint8),\n",
+ " array([[ 27, 33, 44, ..., 107, 86, 43],\n",
+ " [ 36, 36, 47, ..., 140, 129, 73],\n",
+ " [ 59, 43, 50, ..., 146, 148, 132],\n",
+ " ...,\n",
+ " [227, 230, 233, ..., 12, 13, 12],\n",
+ " [224, 227, 230, ..., 11, 13, 12],\n",
+ " [221, 224, 228, ..., 12, 14, 13]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[197, 200, 202, ..., 198, 195, 192],\n",
+ " [198, 200, 202, ..., 198, 195, 192],\n",
+ " [198, 200, 203, ..., 199, 196, 193],\n",
+ " ...,\n",
+ " [162, 162, 170, ..., 176, 177, 175],\n",
+ " [161, 166, 167, ..., 176, 177, 175],\n",
+ " [157, 165, 162, ..., 176, 177, 175]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[223, 223, 223, ..., 227, 226, 226],\n",
+ " [226, 226, 227, ..., 229, 229, 229],\n",
+ " [227, 227, 228, ..., 229, 229, 229],\n",
+ " ...,\n",
+ " [ 72, 77, 76, ..., 30, 26, 21],\n",
+ " [ 26, 30, 34, ..., 12, 14, 21],\n",
+ " [ 17, 16, 15, ..., 10, 13, 16]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 253, 255, 255]], dtype=uint8),\n",
+ " array([[ 5, 5, 5, ..., 76, 75, 75],\n",
+ " [ 4, 5, 9, ..., 75, 74, 74],\n",
+ " [ 5, 5, 4, ..., 75, 74, 74],\n",
+ " ...,\n",
+ " [ 33, 34, 37, ..., 229, 113, 33],\n",
+ " [ 35, 35, 37, ..., 233, 104, 46],\n",
+ " [ 36, 36, 37, ..., 232, 105, 64]], dtype=uint8),\n",
+ " array([[234, 234, 234, ..., 119, 117, 116],\n",
+ " [235, 235, 235, ..., 119, 118, 117],\n",
+ " [236, 236, 236, ..., 120, 119, 118],\n",
+ " ...,\n",
+ " [ 95, 96, 91, ..., 14, 15, 16],\n",
+ " [ 96, 93, 92, ..., 17, 16, 17],\n",
+ " [ 94, 91, 93, ..., 21, 20, 19]], dtype=uint8),\n",
+ " array([[185, 185, 187, ..., 183, 183, 184],\n",
+ " [188, 186, 186, ..., 186, 186, 186],\n",
+ " [188, 187, 188, ..., 186, 186, 187],\n",
+ " ...,\n",
+ " [ 95, 94, 89, ..., 95, 96, 96],\n",
+ " [ 95, 93, 88, ..., 93, 95, 95],\n",
+ " [ 94, 92, 87, ..., 92, 94, 94]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[198, 200, 202, ..., 205, 203, 201],\n",
+ " [191, 194, 196, ..., 199, 197, 194],\n",
+ " [184, 187, 188, ..., 192, 190, 187],\n",
+ " ...,\n",
+ " [ 13, 33, 48, ..., 43, 47, 53],\n",
+ " [ 14, 41, 48, ..., 45, 49, 55],\n",
+ " [ 16, 49, 50, ..., 47, 51, 54]], dtype=uint8),\n",
+ " array([[192, 171, 81, ..., 237, 237, 237],\n",
+ " [184, 136, 83, ..., 237, 237, 237],\n",
+ " [171, 123, 92, ..., 237, 237, 237],\n",
+ " ...,\n",
+ " [176, 175, 174, ..., 162, 162, 160],\n",
+ " [179, 176, 174, ..., 160, 161, 159],\n",
+ " [179, 179, 180, ..., 159, 160, 158]], dtype=uint8),\n",
+ " array([[155, 158, 160, ..., 162, 161, 158],\n",
+ " [155, 158, 160, ..., 162, 161, 158],\n",
+ " [155, 158, 160, ..., 162, 161, 158],\n",
+ " ...,\n",
+ " [ 20, 23, 23, ..., 46, 46, 46],\n",
+ " [ 21, 21, 21, ..., 47, 46, 44],\n",
+ " [ 21, 21, 21, ..., 47, 46, 43]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 81, 178, 188, ..., 173, 236, 240],\n",
+ " [207, 224, 208, ..., 173, 236, 241],\n",
+ " [224, 223, 223, ..., 173, 237, 241],\n",
+ " ...,\n",
+ " [212, 211, 211, ..., 198, 197, 197],\n",
+ " [239, 238, 237, ..., 230, 229, 229],\n",
+ " [250, 249, 248, ..., 248, 247, 247]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[175, 175, 175, ..., 197, 199, 202],\n",
+ " [175, 175, 175, ..., 197, 200, 202],\n",
+ " [175, 175, 175, ..., 199, 201, 202],\n",
+ " ...,\n",
+ " [ 48, 47, 47, ..., 41, 40, 41],\n",
+ " [ 47, 46, 46, ..., 41, 40, 41],\n",
+ " [ 47, 46, 46, ..., 41, 40, 41]], dtype=uint8),\n",
+ " array([[217, 217, 216, ..., 177, 179, 182],\n",
+ " [216, 216, 215, ..., 178, 180, 183],\n",
+ " [215, 215, 214, ..., 181, 183, 185],\n",
+ " ...,\n",
+ " [ 59, 61, 65, ..., 29, 28, 29],\n",
+ " [ 60, 64, 64, ..., 29, 28, 29],\n",
+ " [ 63, 64, 64, ..., 29, 28, 29]], dtype=uint8),\n",
+ " array([[ 11, 11, 11, ..., 23, 28, 31],\n",
+ " [ 11, 11, 11, ..., 23, 29, 31],\n",
+ " [ 11, 11, 11, ..., 23, 30, 31],\n",
+ " ...,\n",
+ " [ 17, 17, 19, ..., 37, 15, 17],\n",
+ " [ 18, 21, 21, ..., 85, 21, 20],\n",
+ " [ 23, 21, 15, ..., 132, 66, 44]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 5, 4, 2, ..., 179, 177, 175],\n",
+ " [ 8, 3, 6, ..., 181, 178, 176],\n",
+ " [ 11, 4, 8, ..., 184, 182, 179],\n",
+ " ...,\n",
+ " [165, 171, 174, ..., 18, 16, 15],\n",
+ " [165, 170, 174, ..., 18, 17, 16],\n",
+ " [165, 170, 174, ..., 18, 17, 16]], dtype=uint8),\n",
+ " array([[193, 197, 199, ..., 166, 171, 177],\n",
+ " [186, 189, 194, ..., 36, 38, 41],\n",
+ " [176, 182, 186, ..., 47, 45, 41],\n",
+ " ...,\n",
+ " [ 15, 14, 12, ..., 9, 9, 9],\n",
+ " [ 15, 15, 11, ..., 11, 11, 10],\n",
+ " [ 16, 15, 13, ..., 14, 12, 11]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 253, 255, 255]], dtype=uint8),\n",
+ " array([[ 39, 47, 62, ..., 35, 34, 31],\n",
+ " [ 40, 48, 53, ..., 35, 34, 31],\n",
+ " [ 40, 48, 47, ..., 34, 33, 32],\n",
+ " ...,\n",
+ " [ 58, 59, 59, ..., 139, 144, 146],\n",
+ " [ 59, 61, 61, ..., 143, 142, 147],\n",
+ " [ 60, 62, 61, ..., 145, 142, 145]], dtype=uint8),\n",
+ " array([[175, 86, 134, ..., 222, 222, 222],\n",
+ " [164, 109, 121, ..., 222, 222, 222],\n",
+ " [151, 139, 102, ..., 222, 222, 222],\n",
+ " ...,\n",
+ " [ 84, 88, 90, ..., 210, 204, 146],\n",
+ " [ 84, 87, 87, ..., 208, 209, 197],\n",
+ " [ 81, 88, 86, ..., 209, 206, 201]], dtype=uint8),\n",
+ " array([[195, 196, 194, ..., 116, 115, 115],\n",
+ " [199, 200, 198, ..., 116, 116, 116],\n",
+ " [202, 202, 202, ..., 117, 117, 117],\n",
+ " ...,\n",
+ " [103, 104, 99, ..., 55, 54, 56],\n",
+ " [102, 100, 101, ..., 55, 54, 56],\n",
+ " [ 98, 99, 104, ..., 55, 54, 55]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[250, 250, 250, ..., 142, 181, 213],\n",
+ " [250, 250, 250, ..., 214, 225, 227],\n",
+ " [251, 251, 250, ..., 229, 235, 244],\n",
+ " ...,\n",
+ " [ 30, 29, 27, ..., 142, 150, 159],\n",
+ " [ 29, 29, 24, ..., 140, 148, 156],\n",
+ " [ 28, 29, 23, ..., 138, 147, 155]], dtype=uint8),\n",
+ " array([[112, 118, 114, ..., 80, 116, 136],\n",
+ " [108, 113, 110, ..., 86, 131, 134],\n",
+ " [106, 109, 108, ..., 82, 124, 152],\n",
+ " ...,\n",
+ " [158, 151, 149, ..., 225, 224, 230],\n",
+ " [155, 152, 151, ..., 224, 225, 227],\n",
+ " [150, 145, 145, ..., 222, 223, 223]], dtype=uint8),\n",
+ " array([[196, 198, 209, ..., 194, 193, 191],\n",
+ " [198, 199, 204, ..., 196, 196, 191],\n",
+ " [200, 200, 201, ..., 200, 199, 185],\n",
+ " ...,\n",
+ " [143, 142, 162, ..., 168, 155, 112],\n",
+ " [125, 146, 160, ..., 166, 157, 110],\n",
+ " [114, 150, 158, ..., 164, 158, 113]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[218, 225, 229, ..., 116, 115, 115],\n",
+ " [215, 223, 228, ..., 116, 114, 114],\n",
+ " [215, 220, 224, ..., 116, 114, 113],\n",
+ " ...,\n",
+ " [ 9, 10, 10, ..., 44, 41, 46],\n",
+ " [ 3, 6, 7, ..., 44, 45, 45],\n",
+ " [ 4, 6, 10, ..., 42, 49, 43]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[219, 220, 221, ..., 221, 219, 218],\n",
+ " [219, 220, 221, ..., 220, 219, 218],\n",
+ " [219, 220, 221, ..., 220, 219, 218],\n",
+ " ...,\n",
+ " [165, 163, 163, ..., 181, 177, 176],\n",
+ " [167, 165, 164, ..., 179, 176, 175],\n",
+ " [162, 166, 167, ..., 177, 175, 174]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[130, 135, 167, ..., 187, 186, 183],\n",
+ " [ 96, 103, 161, ..., 187, 188, 186],\n",
+ " [146, 122, 145, ..., 189, 190, 190],\n",
+ " ...,\n",
+ " [ 61, 64, 66, ..., 52, 52, 52],\n",
+ " [ 63, 64, 65, ..., 52, 52, 52],\n",
+ " [ 62, 63, 63, ..., 52, 52, 52]], dtype=uint8),\n",
+ " array([[141, 141, 140, ..., 225, 221, 242],\n",
+ " [141, 141, 140, ..., 225, 221, 242],\n",
+ " [140, 140, 140, ..., 225, 221, 242],\n",
+ " ...,\n",
+ " [132, 124, 137, ..., 141, 132, 206],\n",
+ " [134, 134, 132, ..., 139, 132, 210],\n",
+ " [134, 133, 134, ..., 133, 129, 209]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[161, 162, 164, ..., 166, 164, 161],\n",
+ " [162, 163, 164, ..., 166, 164, 162],\n",
+ " [162, 163, 164, ..., 166, 164, 162],\n",
+ " ...,\n",
+ " [ 41, 41, 40, ..., 44, 43, 42],\n",
+ " [ 41, 41, 40, ..., 42, 42, 41],\n",
+ " [ 40, 40, 40, ..., 41, 41, 40]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[225, 225, 225, ..., 202, 198, 191],\n",
+ " [225, 225, 225, ..., 203, 199, 192],\n",
+ " [225, 225, 225, ..., 203, 200, 195],\n",
+ " ...,\n",
+ " [126, 129, 130, ..., 113, 99, 80],\n",
+ " [125, 130, 125, ..., 104, 97, 109],\n",
+ " [108, 128, 122, ..., 112, 81, 98]], dtype=uint8),\n",
+ " array([[200, 187, 178, ..., 192, 190, 192],\n",
+ " [197, 194, 187, ..., 194, 193, 193],\n",
+ " [198, 194, 191, ..., 120, 128, 141],\n",
+ " ...,\n",
+ " [ 16, 13, 11, ..., 13, 13, 15],\n",
+ " [ 13, 12, 11, ..., 13, 13, 15],\n",
+ " [ 10, 11, 11, ..., 13, 13, 15]], dtype=uint8),\n",
+ " array([[ 2, 3, 4, ..., 17, 32, 32],\n",
+ " [ 2, 3, 4, ..., 24, 25, 11],\n",
+ " [ 2, 3, 4, ..., 10, 5, 4],\n",
+ " ...,\n",
+ " [184, 187, 187, ..., 189, 191, 189],\n",
+ " [184, 187, 189, ..., 190, 191, 187],\n",
+ " [185, 188, 190, ..., 187, 188, 186]], dtype=uint8),\n",
+ " array([[ 81, 177, 188, ..., 173, 236, 240],\n",
+ " [207, 225, 208, ..., 174, 236, 241],\n",
+ " [224, 224, 222, ..., 174, 237, 241],\n",
+ " ...,\n",
+ " [212, 211, 211, ..., 198, 197, 197],\n",
+ " [239, 238, 237, ..., 230, 229, 229],\n",
+ " [250, 249, 248, ..., 248, 247, 247]], dtype=uint8),\n",
+ " array([[134, 123, 110, ..., 126, 159, 165],\n",
+ " [133, 121, 108, ..., 163, 162, 164],\n",
+ " [130, 119, 108, ..., 162, 164, 164],\n",
+ " ...,\n",
+ " [172, 168, 166, ..., 179, 179, 179],\n",
+ " [178, 167, 165, ..., 178, 179, 179],\n",
+ " [176, 168, 166, ..., 178, 179, 179]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 47, 47, 48, ..., 138, 124, 100],\n",
+ " [ 48, 48, 49, ..., 137, 116, 94],\n",
+ " [ 49, 49, 50, ..., 134, 109, 92],\n",
+ " ...,\n",
+ " [ 31, 28, 30, ..., 158, 152, 151],\n",
+ " [ 27, 23, 28, ..., 148, 143, 141],\n",
+ " [ 27, 22, 31, ..., 143, 138, 137]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 69, 77, 86, ..., 81, 80, 77],\n",
+ " [ 73, 80, 88, ..., 84, 84, 80],\n",
+ " [ 78, 83, 89, ..., 86, 86, 81],\n",
+ " ...,\n",
+ " [101, 101, 100, ..., 67, 65, 62],\n",
+ " [100, 100, 99, ..., 65, 63, 58],\n",
+ " [ 98, 98, 96, ..., 64, 62, 59]], dtype=uint8),\n",
+ " array([[181, 182, 184, ..., 178, 178, 178],\n",
+ " [181, 182, 184, ..., 177, 178, 178],\n",
+ " [180, 181, 183, ..., 178, 178, 178],\n",
+ " ...,\n",
+ " [146, 152, 155, ..., 148, 146, 148],\n",
+ " [140, 144, 145, ..., 147, 146, 149],\n",
+ " [132, 128, 131, ..., 149, 149, 149]], dtype=uint8),\n",
+ " array([[216, 217, 218, ..., 220, 218, 215],\n",
+ " [216, 217, 218, ..., 220, 218, 216],\n",
+ " [216, 217, 218, ..., 220, 218, 216],\n",
+ " ...,\n",
+ " [139, 137, 141, ..., 149, 151, 148],\n",
+ " [137, 135, 138, ..., 150, 149, 150],\n",
+ " [135, 133, 136, ..., 146, 144, 143]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 253, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 42, 40, 39, ..., 30, 34, 31],\n",
+ " [ 41, 39, 37, ..., 38, 40, 36],\n",
+ " [ 39, 37, 37, ..., 38, 39, 35],\n",
+ " ...,\n",
+ " [171, 174, 152, ..., 178, 182, 179],\n",
+ " [162, 158, 143, ..., 174, 178, 168],\n",
+ " [157, 154, 150, ..., 159, 167, 165]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 20, 16, 16, ..., 34, 36, 61],\n",
+ " [ 6, 14, 15, ..., 38, 41, 61],\n",
+ " [ 8, 15, 16, ..., 39, 54, 54],\n",
+ " ...,\n",
+ " [ 51, 48, 49, ..., 142, 137, 131],\n",
+ " [ 51, 47, 51, ..., 134, 132, 136],\n",
+ " [ 50, 50, 49, ..., 127, 133, 130]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 253, 255, 255]], dtype=uint8),\n",
+ " array([[ 12, 11, 12, ..., 13, 54, 26],\n",
+ " [ 12, 11, 13, ..., 7, 41, 54],\n",
+ " [ 12, 12, 12, ..., 24, 43, 34],\n",
+ " ...,\n",
+ " [237, 236, 239, ..., 236, 235, 232],\n",
+ " [238, 234, 239, ..., 230, 232, 229],\n",
+ " [250, 237, 235, ..., 226, 230, 227]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[253, 253, 240, ..., 236, 236, 236],\n",
+ " [254, 253, 240, ..., 236, 236, 236],\n",
+ " [254, 253, 238, ..., 236, 236, 236],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 64, 62, 65],\n",
+ " [255, 255, 255, ..., 81, 82, 81],\n",
+ " [255, 255, 255, ..., 92, 96, 154]], dtype=uint8),\n",
+ " array([[163, 165, 165, ..., 148, 114, 119],\n",
+ " [160, 165, 168, ..., 111, 110, 125],\n",
+ " [158, 171, 178, ..., 193, 212, 224],\n",
+ " ...,\n",
+ " [ 45, 46, 48, ..., 50, 51, 51],\n",
+ " [ 44, 45, 48, ..., 50, 51, 51],\n",
+ " [ 45, 45, 47, ..., 50, 50, 50]], dtype=uint8),\n",
+ " array([[254, 254, 254, ..., 255, 255, 255],\n",
+ " [254, 254, 254, ..., 255, 255, 255],\n",
+ " [254, 254, 254, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [124, 124, 126, ..., 124, 127, 111],\n",
+ " [121, 121, 123, ..., 130, 124, 97],\n",
+ " [120, 119, 120, ..., 126, 129, 103]], dtype=uint8),\n",
+ " array([[ 90, 92, 95, ..., 149, 147, 145],\n",
+ " [ 90, 92, 95, ..., 150, 146, 145],\n",
+ " [ 91, 93, 95, ..., 152, 148, 147],\n",
+ " ...,\n",
+ " [ 68, 70, 70, ..., 74, 72, 69],\n",
+ " [ 64, 67, 71, ..., 75, 69, 70],\n",
+ " [ 64, 65, 71, ..., 68, 69, 70]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[252, 217, 95, ..., 204, 209, 158],\n",
+ " [253, 252, 234, ..., 206, 209, 186],\n",
+ " [255, 255, 252, ..., 206, 210, 208],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 22, 17, 17],\n",
+ " [255, 255, 255, ..., 21, 16, 17],\n",
+ " [255, 255, 255, ..., 20, 16, 17]], dtype=uint8),\n",
+ " array([[138, 151, 160, ..., 208, 204, 207],\n",
+ " [159, 163, 171, ..., 208, 207, 209],\n",
+ " [157, 163, 168, ..., 213, 214, 219],\n",
+ " ...,\n",
+ " [230, 232, 231, ..., 226, 225, 224],\n",
+ " [226, 230, 230, ..., 224, 220, 216],\n",
+ " [232, 232, 231, ..., 220, 214, 204]], dtype=uint8),\n",
+ " array([[ 52, 51, 127, ..., 135, 126, 118],\n",
+ " [ 37, 46, 47, ..., 131, 125, 121],\n",
+ " [ 25, 32, 38, ..., 131, 126, 123],\n",
+ " ...,\n",
+ " [ 54, 66, 76, ..., 197, 196, 190],\n",
+ " [ 60, 70, 71, ..., 200, 199, 195],\n",
+ " [ 61, 73, 75, ..., 205, 207, 205]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[185, 186, 187, ..., 185, 184, 181],\n",
+ " [186, 187, 187, ..., 185, 184, 182],\n",
+ " [186, 187, 188, ..., 186, 184, 182],\n",
+ " ...,\n",
+ " [124, 126, 124, ..., 114, 113, 113],\n",
+ " [125, 125, 125, ..., 115, 113, 110],\n",
+ " [123, 123, 125, ..., 114, 116, 107]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[212, 210, 206, ..., 204, 203, 202],\n",
+ " [210, 208, 206, ..., 203, 201, 198],\n",
+ " [206, 206, 205, ..., 202, 200, 199],\n",
+ " ...,\n",
+ " [139, 136, 137, ..., 122, 122, 119],\n",
+ " [137, 136, 137, ..., 120, 120, 120],\n",
+ " [136, 137, 137, ..., 117, 118, 119]], dtype=uint8),\n",
+ " array([[ 19, 19, 20, ..., 146, 151, 156],\n",
+ " [ 19, 19, 20, ..., 147, 152, 157],\n",
+ " [ 19, 19, 20, ..., 149, 154, 160],\n",
+ " ...,\n",
+ " [ 18, 18, 20, ..., 52, 53, 52],\n",
+ " [ 19, 19, 20, ..., 49, 50, 50],\n",
+ " [ 19, 19, 20, ..., 47, 49, 48]], dtype=uint8),\n",
+ " array([[ 84, 109, 130, ..., 60, 67, 131],\n",
+ " [ 69, 89, 92, ..., 64, 66, 108],\n",
+ " [ 73, 98, 114, ..., 66, 65, 93],\n",
+ " ...,\n",
+ " [ 21, 21, 21, ..., 51, 46, 48],\n",
+ " [ 22, 18, 18, ..., 52, 53, 59],\n",
+ " [ 25, 18, 17, ..., 50, 49, 52]], dtype=uint8),\n",
+ " array([[128, 101, 79, ..., 96, 99, 100],\n",
+ " [121, 95, 75, ..., 96, 99, 99],\n",
+ " [114, 90, 73, ..., 96, 99, 99],\n",
+ " ...,\n",
+ " [ 27, 30, 31, ..., 126, 123, 119],\n",
+ " [ 27, 30, 32, ..., 134, 130, 121],\n",
+ " [ 27, 29, 32, ..., 139, 134, 127]], dtype=uint8),\n",
+ " array([[62, 68, 71, ..., 13, 13, 30],\n",
+ " [65, 70, 72, ..., 7, 19, 29],\n",
+ " [69, 73, 74, ..., 6, 22, 28],\n",
+ " ...,\n",
+ " [60, 65, 66, ..., 25, 10, 15],\n",
+ " [56, 64, 66, ..., 50, 16, 17],\n",
+ " [53, 62, 67, ..., 75, 21, 17]], dtype=uint8),\n",
+ " array([[183, 177, 168, ..., 255, 255, 255],\n",
+ " [186, 182, 177, ..., 255, 255, 255],\n",
+ " [188, 187, 184, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [235, 236, 237, ..., 185, 185, 185],\n",
+ " [235, 236, 237, ..., 184, 184, 184],\n",
+ " [235, 236, 237, ..., 183, 183, 183]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[220, 221, 223, ..., 216, 217, 217],\n",
+ " [220, 221, 223, ..., 216, 216, 216],\n",
+ " [220, 221, 223, ..., 217, 216, 216],\n",
+ " ...,\n",
+ " [ 78, 72, 70, ..., 80, 80, 75],\n",
+ " [ 73, 74, 74, ..., 77, 77, 74],\n",
+ " [ 68, 78, 78, ..., 75, 76, 73]], dtype=uint8),\n",
+ " array([[214, 219, 220, ..., 230, 230, 231],\n",
+ " [216, 222, 222, ..., 224, 224, 225],\n",
+ " [222, 216, 203, ..., 225, 225, 226],\n",
+ " ...,\n",
+ " [213, 213, 213, ..., 230, 230, 228],\n",
+ " [212, 212, 212, ..., 233, 232, 231],\n",
+ " [211, 211, 211, ..., 234, 235, 234]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[153, 153, 153, ..., 129, 129, 128],\n",
+ " [154, 154, 154, ..., 131, 131, 130],\n",
+ " [155, 155, 155, ..., 132, 131, 130],\n",
+ " ...,\n",
+ " [104, 107, 122, ..., 76, 76, 75],\n",
+ " [104, 105, 116, ..., 76, 76, 75],\n",
+ " [104, 103, 111, ..., 76, 75, 74]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[198, 199, 199, ..., 195, 194, 194],\n",
+ " [202, 201, 202, ..., 198, 197, 197],\n",
+ " [204, 204, 204, ..., 200, 200, 200],\n",
+ " ...,\n",
+ " [174, 174, 174, ..., 185, 184, 183],\n",
+ " [162, 162, 163, ..., 169, 168, 167],\n",
+ " [144, 144, 144, ..., 144, 143, 142]], dtype=uint8),\n",
+ " array([[129, 134, 137, ..., 180, 180, 180],\n",
+ " [124, 131, 138, ..., 177, 176, 176],\n",
+ " [115, 124, 133, ..., 173, 171, 171],\n",
+ " ...,\n",
+ " [ 34, 33, 35, ..., 37, 39, 31],\n",
+ " [ 34, 33, 33, ..., 39, 37, 32],\n",
+ " [ 32, 31, 30, ..., 41, 36, 33]], dtype=uint8),\n",
+ " array([[ 99, 155, 146, ..., 233, 233, 233],\n",
+ " [178, 194, 166, ..., 233, 233, 233],\n",
+ " [157, 143, 195, ..., 233, 233, 233],\n",
+ " ...,\n",
+ " [141, 143, 145, ..., 134, 135, 135],\n",
+ " [141, 142, 144, ..., 133, 133, 133],\n",
+ " [140, 141, 141, ..., 131, 132, 133]], dtype=uint8),\n",
+ " array([[188, 188, 186, ..., 166, 166, 171],\n",
+ " [188, 188, 186, ..., 166, 167, 170],\n",
+ " [188, 189, 187, ..., 166, 167, 168],\n",
+ " ...,\n",
+ " [ 56, 56, 56, ..., 60, 61, 61],\n",
+ " [ 56, 56, 56, ..., 60, 61, 61],\n",
+ " [ 56, 56, 56, ..., 60, 61, 61]], dtype=uint8),\n",
+ " array([[164, 166, 168, ..., 168, 166, 164],\n",
+ " [165, 168, 169, ..., 169, 168, 165],\n",
+ " [167, 169, 170, ..., 171, 169, 167],\n",
+ " ...,\n",
+ " [ 55, 53, 52, ..., 52, 52, 32],\n",
+ " [ 49, 48, 46, ..., 39, 44, 37],\n",
+ " [ 44, 48, 45, ..., 22, 22, 29]], dtype=uint8),\n",
+ " array([[ 72, 72, 72, ..., 122, 122, 113],\n",
+ " [ 73, 73, 73, ..., 120, 124, 113],\n",
+ " [ 74, 74, 74, ..., 125, 120, 116],\n",
+ " ...,\n",
+ " [140, 141, 145, ..., 169, 167, 163],\n",
+ " [167, 169, 172, ..., 173, 176, 169],\n",
+ " [187, 188, 191, ..., 167, 176, 172]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[185, 185, 187, ..., 183, 183, 184],\n",
+ " [188, 186, 186, ..., 186, 186, 186],\n",
+ " [188, 187, 188, ..., 186, 186, 187],\n",
+ " ...,\n",
+ " [ 95, 94, 89, ..., 95, 96, 96],\n",
+ " [ 95, 93, 88, ..., 93, 95, 95],\n",
+ " [ 94, 92, 87, ..., 92, 94, 94]], dtype=uint8),\n",
+ " array([[ 55, 53, 51, ..., 47, 41, 40],\n",
+ " [ 55, 52, 50, ..., 46, 40, 40],\n",
+ " [ 53, 50, 48, ..., 45, 39, 40],\n",
+ " ...,\n",
+ " [234, 234, 234, ..., 156, 155, 154],\n",
+ " [234, 234, 234, ..., 154, 153, 152],\n",
+ " [234, 234, 234, ..., 153, 151, 150]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[128, 128, 128, ..., 128, 128, 162],\n",
+ " [129, 129, 129, ..., 128, 129, 163],\n",
+ " [130, 130, 130, ..., 129, 129, 164],\n",
+ " ...,\n",
+ " [191, 193, 194, ..., 213, 209, 217],\n",
+ " [189, 191, 193, ..., 216, 211, 222],\n",
+ " [187, 189, 192, ..., 180, 178, 201]], dtype=uint8),\n",
+ " array([[197, 198, 200, ..., 198, 197, 194],\n",
+ " [197, 198, 200, ..., 198, 197, 195],\n",
+ " [197, 198, 199, ..., 199, 197, 195],\n",
+ " ...,\n",
+ " [123, 126, 132, ..., 143, 142, 141],\n",
+ " [124, 129, 129, ..., 143, 145, 144],\n",
+ " [118, 126, 127, ..., 146, 147, 147]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[195, 188, 188, ..., 176, 178, 182],\n",
+ " [191, 187, 183, ..., 188, 186, 188],\n",
+ " [188, 182, 182, ..., 194, 200, 209],\n",
+ " ...,\n",
+ " [171, 170, 168, ..., 255, 255, 255],\n",
+ " [171, 171, 170, ..., 255, 255, 255],\n",
+ " [171, 171, 170, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[207, 207, 208, ..., 64, 64, 73],\n",
+ " [208, 208, 208, ..., 65, 62, 78],\n",
+ " [209, 209, 209, ..., 64, 70, 77],\n",
+ " ...,\n",
+ " [184, 183, 184, ..., 171, 170, 166],\n",
+ " [183, 181, 182, ..., 171, 171, 168],\n",
+ " [149, 142, 134, ..., 178, 176, 174]], dtype=uint8),\n",
+ " array([[165, 165, 165, ..., 176, 175, 175],\n",
+ " [165, 165, 165, ..., 176, 176, 176],\n",
+ " [166, 166, 165, ..., 177, 177, 177],\n",
+ " ...,\n",
+ " [160, 158, 162, ..., 43, 42, 40],\n",
+ " [161, 159, 163, ..., 40, 42, 41],\n",
+ " [161, 160, 164, ..., 32, 37, 39]], dtype=uint8),\n",
+ " array([[204, 208, 211, ..., 203, 199, 193],\n",
+ " [205, 208, 211, ..., 203, 199, 194],\n",
+ " [207, 209, 211, ..., 204, 200, 195],\n",
+ " ...,\n",
+ " [ 69, 68, 68, ..., 73, 71, 70],\n",
+ " [ 71, 70, 70, ..., 75, 71, 70],\n",
+ " [ 74, 74, 73, ..., 75, 77, 72]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 99, 78, 16, ..., 109, 106, 104],\n",
+ " [ 98, 97, 25, ..., 107, 102, 99],\n",
+ " [ 96, 96, 55, ..., 105, 103, 72],\n",
+ " ...,\n",
+ " [ 79, 82, 80, ..., 70, 67, 66],\n",
+ " [ 73, 77, 76, ..., 73, 78, 69],\n",
+ " [ 71, 75, 74, ..., 108, 114, 108]], dtype=uint8),\n",
+ " array([[ 45, 45, 43, ..., 255, 255, 255],\n",
+ " [ 42, 42, 44, ..., 255, 255, 255],\n",
+ " [ 41, 40, 41, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [167, 170, 172, ..., 255, 255, 255],\n",
+ " [167, 169, 173, ..., 255, 255, 255],\n",
+ " [166, 169, 173, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[168, 164, 163, ..., 106, 105, 114],\n",
+ " [164, 160, 159, ..., 102, 102, 109],\n",
+ " [164, 160, 159, ..., 103, 103, 110],\n",
+ " ...,\n",
+ " [163, 162, 164, ..., 171, 168, 168],\n",
+ " [162, 161, 162, ..., 170, 168, 168],\n",
+ " [163, 162, 161, ..., 168, 168, 168]], dtype=uint8),\n",
+ " array([[123, 133, 151, ..., 175, 152, 142],\n",
+ " [129, 131, 146, ..., 173, 151, 145],\n",
+ " [142, 139, 147, ..., 168, 150, 148],\n",
+ " ...,\n",
+ " [189, 188, 192, ..., 196, 199, 195],\n",
+ " [193, 194, 191, ..., 192, 198, 198],\n",
+ " [193, 196, 174, ..., 192, 197, 198]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[220, 221, 222, ..., 222, 220, 219],\n",
+ " [220, 221, 222, ..., 222, 221, 220],\n",
+ " [220, 221, 222, ..., 222, 221, 220],\n",
+ " ...,\n",
+ " [ 82, 83, 84, ..., 85, 83, 82],\n",
+ " [ 80, 81, 83, ..., 86, 85, 79],\n",
+ " [ 79, 80, 81, ..., 85, 85, 80]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[185, 186, 190, ..., 201, 200, 195],\n",
+ " [166, 188, 199, ..., 193, 199, 199],\n",
+ " [166, 180, 189, ..., 195, 198, 209],\n",
+ " ...,\n",
+ " [188, 188, 191, ..., 177, 176, 176],\n",
+ " [187, 186, 188, ..., 177, 176, 176],\n",
+ " [191, 189, 189, ..., 176, 176, 175]], dtype=uint8),\n",
+ " array([[131, 135, 125, ..., 12, 6, 5],\n",
+ " [139, 145, 132, ..., 22, 11, 8],\n",
+ " [147, 158, 136, ..., 48, 35, 5],\n",
+ " ...,\n",
+ " [215, 218, 220, ..., 230, 229, 226],\n",
+ " [215, 218, 219, ..., 230, 229, 226],\n",
+ " [215, 218, 219, ..., 230, 229, 226]], dtype=uint8),\n",
+ " array([[168, 165, 163, ..., 106, 106, 112],\n",
+ " [166, 163, 161, ..., 103, 103, 108],\n",
+ " [164, 161, 160, ..., 105, 104, 108],\n",
+ " ...,\n",
+ " [164, 162, 164, ..., 173, 169, 168],\n",
+ " [163, 161, 162, ..., 172, 169, 167],\n",
+ " [164, 163, 163, ..., 169, 167, 168]], dtype=uint8),\n",
+ " array([[189, 190, 219, ..., 205, 208, 203],\n",
+ " [195, 185, 216, ..., 207, 212, 205],\n",
+ " [196, 199, 207, ..., 201, 204, 199],\n",
+ " ...,\n",
+ " [173, 179, 183, ..., 162, 159, 156],\n",
+ " [148, 155, 161, ..., 160, 157, 154],\n",
+ " [ 60, 67, 73, ..., 159, 156, 152]], dtype=uint8),\n",
+ " array([[ 12, 14, 11, ..., 17, 19, 22],\n",
+ " [ 12, 11, 12, ..., 16, 16, 14],\n",
+ " [ 11, 16, 10, ..., 15, 13, 14],\n",
+ " ...,\n",
+ " [190, 191, 189, ..., 120, 123, 126],\n",
+ " [176, 184, 185, ..., 139, 145, 155],\n",
+ " [168, 176, 178, ..., 156, 157, 161]], dtype=uint8),\n",
+ " array([[ 43, 43, 44, ..., 173, 168, 163],\n",
+ " [ 43, 43, 44, ..., 170, 165, 160],\n",
+ " [ 44, 44, 44, ..., 164, 163, 166],\n",
+ " ...,\n",
+ " [ 41, 41, 43, ..., 154, 143, 129],\n",
+ " [ 40, 41, 41, ..., 151, 140, 127],\n",
+ " [ 37, 38, 79, ..., 149, 138, 126]], dtype=uint8),\n",
+ " array([[ 19, 20, 22, ..., 145, 150, 155],\n",
+ " [ 20, 21, 22, ..., 146, 151, 156],\n",
+ " [ 20, 21, 22, ..., 147, 153, 159],\n",
+ " ...,\n",
+ " [ 21, 22, 24, ..., 54, 55, 54],\n",
+ " [ 20, 21, 23, ..., 51, 52, 52],\n",
+ " [ 20, 21, 23, ..., 49, 51, 50]], dtype=uint8),\n",
+ " array([[ 31, 39, 44, ..., 241, 238, 234],\n",
+ " [ 26, 40, 46, ..., 240, 239, 236],\n",
+ " [ 53, 80, 66, ..., 243, 240, 238],\n",
+ " ...,\n",
+ " [ 70, 73, 75, ..., 192, 176, 170],\n",
+ " [ 47, 51, 53, ..., 223, 218, 207],\n",
+ " [ 63, 67, 70, ..., 217, 219, 201]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[193, 229, 213, ..., 1, 0, 0],\n",
+ " [218, 229, 179, ..., 0, 0, 0],\n",
+ " [228, 225, 133, ..., 1, 0, 0],\n",
+ " ...,\n",
+ " [180, 178, 176, ..., 155, 149, 141],\n",
+ " [171, 177, 180, ..., 127, 113, 94],\n",
+ " [179, 178, 179, ..., 85, 82, 82]], dtype=uint8),\n",
+ " array([[192, 171, 81, ..., 237, 237, 237],\n",
+ " [184, 136, 83, ..., 237, 237, 237],\n",
+ " [171, 123, 92, ..., 237, 237, 237],\n",
+ " ...,\n",
+ " [176, 175, 174, ..., 162, 162, 160],\n",
+ " [179, 176, 174, ..., 160, 161, 159],\n",
+ " [179, 179, 180, ..., 159, 160, 158]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 93, 91, 111, ..., 5, 3, 2],\n",
+ " [ 94, 99, 152, ..., 5, 3, 2],\n",
+ " [ 97, 99, 168, ..., 4, 2, 1],\n",
+ " ...,\n",
+ " [ 30, 40, 18, ..., 68, 66, 61],\n",
+ " [ 34, 31, 16, ..., 66, 62, 53],\n",
+ " [ 35, 22, 19, ..., 63, 57, 44]], dtype=uint8),\n",
+ " array([[154, 156, 158, ..., 82, 32, 28],\n",
+ " [154, 160, 162, ..., 101, 34, 30],\n",
+ " [155, 159, 159, ..., 123, 44, 33],\n",
+ " ...,\n",
+ " [182, 195, 191, ..., 40, 43, 50],\n",
+ " [169, 166, 179, ..., 33, 36, 43],\n",
+ " [147, 152, 170, ..., 47, 39, 35]], dtype=uint8),\n",
+ " array([[117, 104, 143, ..., 125, 96, 119],\n",
+ " [163, 113, 113, ..., 101, 121, 179],\n",
+ " [227, 182, 122, ..., 133, 195, 235],\n",
+ " ...,\n",
+ " [ 13, 12, 30, ..., 30, 15, 16],\n",
+ " [ 4, 4, 45, ..., 51, 5, 6],\n",
+ " [ 4, 5, 45, ..., 54, 5, 5]], dtype=uint8),\n",
+ " array([[122, 144, 161, ..., 81, 80, 78],\n",
+ " [131, 154, 152, ..., 83, 79, 78],\n",
+ " [140, 139, 137, ..., 76, 82, 77],\n",
+ " ...,\n",
+ " [ 50, 55, 54, ..., 8, 8, 6],\n",
+ " [ 54, 59, 58, ..., 35, 33, 29],\n",
+ " [ 57, 57, 59, ..., 54, 59, 50]], dtype=uint8),\n",
+ " array([[156, 159, 172, ..., 189, 189, 230],\n",
+ " [157, 167, 169, ..., 183, 181, 226],\n",
+ " [161, 170, 159, ..., 179, 178, 226],\n",
+ " ...,\n",
+ " [142, 139, 138, ..., 117, 119, 204],\n",
+ " [141, 138, 137, ..., 116, 118, 203],\n",
+ " [140, 137, 137, ..., 116, 117, 203]], dtype=uint8),\n",
+ " array([[175, 175, 175, ..., 196, 199, 203],\n",
+ " [175, 175, 175, ..., 198, 200, 202],\n",
+ " [175, 175, 175, ..., 200, 201, 201],\n",
+ " ...,\n",
+ " [ 48, 47, 47, ..., 41, 41, 41],\n",
+ " [ 47, 46, 46, ..., 41, 41, 41],\n",
+ " [ 47, 46, 46, ..., 41, 41, 41]], dtype=uint8),\n",
+ " array([[ 56, 75, 63, ..., 51, 50, 50],\n",
+ " [ 59, 64, 56, ..., 60, 50, 50],\n",
+ " [ 67, 46, 43, ..., 61, 53, 52],\n",
+ " ...,\n",
+ " [200, 202, 201, ..., 206, 206, 200],\n",
+ " [201, 202, 202, ..., 208, 208, 203],\n",
+ " [190, 191, 190, ..., 193, 193, 189]], dtype=uint8),\n",
+ " array([[132, 132, 132, ..., 106, 102, 103],\n",
+ " [133, 133, 132, ..., 105, 103, 103],\n",
+ " [133, 133, 132, ..., 106, 104, 104],\n",
+ " ...,\n",
+ " [166, 166, 167, ..., 103, 101, 101],\n",
+ " [166, 166, 166, ..., 101, 98, 99],\n",
+ " [166, 166, 167, ..., 102, 97, 97]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 30, 32, 35, ..., 249, 250, 152],\n",
+ " [ 31, 33, 35, ..., 251, 249, 122],\n",
+ " [ 32, 34, 36, ..., 246, 238, 98],\n",
+ " ...,\n",
+ " [ 12, 28, 32, ..., 20, 22, 21],\n",
+ " [ 13, 18, 30, ..., 20, 19, 18],\n",
+ " [ 28, 10, 29, ..., 21, 20, 18]], dtype=uint8),\n",
+ " array([[232, 232, 232, ..., 181, 180, 179],\n",
+ " [233, 233, 233, ..., 182, 181, 180],\n",
+ " [234, 234, 234, ..., 184, 183, 182],\n",
+ " ...,\n",
+ " [ 94, 95, 95, ..., 102, 103, 103],\n",
+ " [ 94, 95, 95, ..., 100, 101, 101],\n",
+ " [ 94, 95, 96, ..., 98, 99, 99]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[103, 104, 106, ..., 158, 154, 152],\n",
+ " [103, 104, 105, ..., 158, 155, 153],\n",
+ " [103, 104, 105, ..., 159, 156, 154],\n",
+ " ...,\n",
+ " [135, 137, 139, ..., 148, 146, 143],\n",
+ " [131, 134, 135, ..., 147, 144, 141],\n",
+ " [131, 134, 134, ..., 145, 143, 139]], dtype=uint8),\n",
+ " array([[223, 223, 225, ..., 231, 232, 233],\n",
+ " [224, 220, 208, ..., 224, 226, 224],\n",
+ " [215, 197, 188, ..., 224, 224, 215],\n",
+ " ...,\n",
+ " [217, 217, 217, ..., 235, 233, 230],\n",
+ " [217, 217, 217, ..., 232, 232, 232],\n",
+ " [217, 217, 217, ..., 231, 231, 231]], dtype=uint8),\n",
+ " array([[248, 240, 201, ..., 181, 183, 188],\n",
+ " [236, 246, 233, ..., 183, 181, 177],\n",
+ " [227, 239, 240, ..., 181, 176, 174],\n",
+ " ...,\n",
+ " [ 9, 10, 12, ..., 16, 12, 14],\n",
+ " [ 9, 10, 11, ..., 14, 11, 12],\n",
+ " [ 8, 9, 11, ..., 12, 10, 10]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 37, 29, 36, ..., 243, 238, 227],\n",
+ " [ 51, 44, 46, ..., 243, 244, 242],\n",
+ " [ 58, 56, 50, ..., 243, 242, 243],\n",
+ " ...,\n",
+ " [ 75, 75, 77, ..., 26, 29, 30],\n",
+ " [ 73, 71, 72, ..., 27, 29, 30],\n",
+ " [ 70, 69, 70, ..., 27, 29, 29]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[201, 203, 206, ..., 196, 193, 190],\n",
+ " [202, 204, 207, ..., 196, 193, 190],\n",
+ " [203, 205, 208, ..., 197, 194, 191],\n",
+ " ...,\n",
+ " [127, 130, 134, ..., 127, 124, 120],\n",
+ " [129, 131, 135, ..., 124, 122, 117],\n",
+ " [134, 136, 137, ..., 122, 120, 115]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [ 78, 82, 86, ..., 150, 149, 146],\n",
+ " [ 78, 82, 86, ..., 151, 149, 147],\n",
+ " [ 78, 82, 86, ..., 152, 149, 147]], dtype=uint8),\n",
+ " array([[221, 220, 226, ..., 228, 229, 231],\n",
+ " [146, 146, 158, ..., 174, 176, 179],\n",
+ " [136, 138, 149, ..., 167, 171, 176],\n",
+ " ...,\n",
+ " [196, 201, 195, ..., 198, 197, 197],\n",
+ " [195, 190, 196, ..., 197, 196, 196],\n",
+ " [197, 201, 198, ..., 197, 196, 196]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[135, 135, 139, ..., 136, 136, 136],\n",
+ " [138, 138, 141, ..., 136, 136, 136],\n",
+ " [142, 143, 145, ..., 136, 136, 136],\n",
+ " ...,\n",
+ " [ 11, 11, 11, ..., 6, 6, 6],\n",
+ " [ 10, 10, 11, ..., 6, 6, 6],\n",
+ " [ 12, 12, 11, ..., 6, 6, 6]], dtype=uint8),\n",
+ " array([[ 24, 15, 9, ..., 42, 40, 33],\n",
+ " [ 26, 21, 15, ..., 44, 42, 34],\n",
+ " [ 19, 22, 23, ..., 47, 43, 35],\n",
+ " ...,\n",
+ " [155, 157, 159, ..., 171, 170, 170],\n",
+ " [158, 159, 161, ..., 171, 171, 171],\n",
+ " [160, 160, 162, ..., 173, 173, 172]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 71, 83, 92, ..., 234, 226, 222],\n",
+ " [ 71, 69, 69, ..., 235, 227, 222],\n",
+ " [ 82, 79, 73, ..., 238, 226, 222],\n",
+ " ...,\n",
+ " [ 35, 20, 21, ..., 187, 184, 183],\n",
+ " [ 33, 30, 15, ..., 192, 187, 184],\n",
+ " [ 30, 35, 16, ..., 195, 189, 185]], dtype=uint8),\n",
+ " array([[ 16, 9, 19, ..., 53, 87, 108],\n",
+ " [ 58, 13, 50, ..., 56, 119, 115],\n",
+ " [ 77, 58, 56, ..., 66, 139, 149],\n",
+ " ...,\n",
+ " [177, 175, 177, ..., 194, 193, 195],\n",
+ " [178, 177, 178, ..., 194, 192, 194],\n",
+ " [178, 178, 178, ..., 194, 192, 193]], dtype=uint8),\n",
+ " array([[170, 171, 169, ..., 153, 159, 157],\n",
+ " [171, 172, 166, ..., 152, 158, 156],\n",
+ " [173, 177, 172, ..., 152, 156, 156],\n",
+ " ...,\n",
+ " [123, 128, 130, ..., 118, 117, 116],\n",
+ " [120, 123, 124, ..., 115, 114, 113],\n",
+ " [119, 118, 124, ..., 114, 116, 113]], dtype=uint8),\n",
+ " array([[253, 253, 253, ..., 251, 250, 251],\n",
+ " [251, 251, 251, ..., 251, 250, 251],\n",
+ " [249, 249, 249, ..., 251, 250, 251],\n",
+ " ...,\n",
+ " [ 40, 48, 31, ..., 79, 113, 185],\n",
+ " [ 9, 19, 50, ..., 57, 108, 186],\n",
+ " [ 3, 4, 7, ..., 6, 115, 186]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[243, 243, 244, ..., 244, 244, 244],\n",
+ " [243, 243, 244, ..., 244, 244, 244],\n",
+ " [244, 244, 244, ..., 244, 244, 244],\n",
+ " ...,\n",
+ " [117, 114, 97, ..., 62, 60, 59],\n",
+ " [123, 128, 131, ..., 62, 60, 58],\n",
+ " [123, 129, 131, ..., 62, 59, 57]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[254, 254, 254, ..., 254, 254, 254],\n",
+ " [254, 254, 254, ..., 254, 254, 254],\n",
+ " [254, 254, 254, ..., 254, 254, 254],\n",
+ " ...,\n",
+ " [248, 248, 248, ..., 248, 248, 248],\n",
+ " [248, 248, 248, ..., 248, 248, 248],\n",
+ " [248, 248, 248, ..., 248, 248, 248]], dtype=uint8),\n",
+ " array([[104, 106, 109, ..., 220, 218, 238],\n",
+ " [108, 103, 102, ..., 222, 220, 237],\n",
+ " [118, 118, 119, ..., 227, 223, 238],\n",
+ " ...,\n",
+ " [204, 200, 211, ..., 204, 197, 234],\n",
+ " [220, 215, 216, ..., 197, 197, 234],\n",
+ " [221, 216, 218, ..., 199, 208, 237]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[163, 162, 164, ..., 97, 97, 100],\n",
+ " [161, 164, 167, ..., 95, 98, 101],\n",
+ " [162, 165, 167, ..., 97, 97, 105],\n",
+ " ...,\n",
+ " [112, 117, 119, ..., 110, 111, 110],\n",
+ " [114, 119, 120, ..., 110, 111, 112],\n",
+ " [115, 119, 121, ..., 119, 115, 113]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 12, 12, 10, ..., 146, 143, 145],\n",
+ " [ 25, 19, 16, ..., 146, 143, 146],\n",
+ " [ 55, 51, 45, ..., 147, 145, 147],\n",
+ " ...,\n",
+ " [176, 176, 177, ..., 139, 143, 152],\n",
+ " [175, 176, 177, ..., 139, 145, 152],\n",
+ " [175, 176, 178, ..., 138, 146, 152]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[254, 254, 254, ..., 254, 254, 254],\n",
+ " [254, 254, 254, ..., 254, 254, 254],\n",
+ " [254, 254, 254, ..., 254, 254, 254],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 74, 45, 33, ..., 182, 181, 180],\n",
+ " [ 54, 64, 33, ..., 183, 182, 181],\n",
+ " [ 62, 76, 51, ..., 183, 183, 182],\n",
+ " ...,\n",
+ " [156, 155, 155, ..., 136, 142, 144],\n",
+ " [154, 156, 157, ..., 138, 141, 137],\n",
+ " [155, 159, 159, ..., 133, 137, 129]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[183, 185, 187, ..., 188, 187, 184],\n",
+ " [185, 186, 188, ..., 190, 188, 186],\n",
+ " [186, 187, 189, ..., 192, 190, 188],\n",
+ " ...,\n",
+ " [125, 130, 121, ..., 114, 104, 109],\n",
+ " [124, 133, 116, ..., 119, 107, 119],\n",
+ " [120, 124, 114, ..., 119, 113, 115]], dtype=uint8),\n",
+ " array([[176, 177, 178, ..., 176, 176, 176],\n",
+ " [177, 178, 179, ..., 177, 177, 177],\n",
+ " [178, 179, 180, ..., 178, 178, 178],\n",
+ " ...,\n",
+ " [ 97, 99, 100, ..., 88, 89, 87],\n",
+ " [ 96, 98, 101, ..., 85, 88, 84],\n",
+ " [ 98, 100, 103, ..., 90, 87, 86]], dtype=uint8),\n",
+ " array([[193, 193, 193, ..., 192, 192, 192],\n",
+ " [193, 193, 192, ..., 192, 192, 192],\n",
+ " [193, 193, 193, ..., 192, 192, 192],\n",
+ " ...,\n",
+ " [234, 234, 234, ..., 233, 233, 233],\n",
+ " [234, 234, 234, ..., 233, 233, 233],\n",
+ " [234, 234, 234, ..., 233, 233, 233]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[217, 218, 219, ..., 218, 216, 215],\n",
+ " [217, 218, 220, ..., 218, 216, 215],\n",
+ " [217, 218, 220, ..., 218, 216, 215],\n",
+ " ...,\n",
+ " [133, 131, 123, ..., 122, 118, 110],\n",
+ " [124, 121, 118, ..., 124, 123, 115],\n",
+ " [115, 121, 127, ..., 116, 118, 111]], dtype=uint8),\n",
+ " array([[ 75, 21, 13, ..., 0, 0, 0],\n",
+ " [ 95, 27, 14, ..., 0, 0, 0],\n",
+ " [108, 38, 18, ..., 0, 0, 0],\n",
+ " ...,\n",
+ " [107, 71, 63, ..., 208, 208, 208],\n",
+ " [121, 74, 67, ..., 208, 207, 202],\n",
+ " [131, 76, 70, ..., 207, 205, 198]], dtype=uint8),\n",
+ " array([[173, 178, 201, ..., 214, 182, 175],\n",
+ " [174, 176, 186, ..., 193, 177, 173],\n",
+ " [170, 175, 180, ..., 181, 176, 170],\n",
+ " ...,\n",
+ " [136, 149, 172, ..., 150, 158, 158],\n",
+ " [135, 168, 168, ..., 153, 158, 157],\n",
+ " [154, 167, 161, ..., 161, 160, 158]], dtype=uint8),\n",
+ " array([[224, 227, 228, ..., 218, 172, 147],\n",
+ " [225, 227, 229, ..., 194, 140, 214],\n",
+ " [225, 227, 229, ..., 144, 208, 220],\n",
+ " ...,\n",
+ " [208, 208, 208, ..., 136, 67, 32],\n",
+ " [206, 206, 207, ..., 84, 67, 16],\n",
+ " [204, 204, 206, ..., 73, 58, 7]], dtype=uint8),\n",
+ " array([[156, 157, 155, ..., 146, 145, 145],\n",
+ " [158, 159, 156, ..., 149, 147, 148],\n",
+ " [158, 159, 157, ..., 150, 147, 149],\n",
+ " ...,\n",
+ " [101, 112, 118, ..., 200, 200, 199],\n",
+ " [110, 114, 113, ..., 200, 200, 199],\n",
+ " [115, 121, 119, ..., 200, 199, 198]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 24, 23, 24, ..., 12, 14, 12],\n",
+ " [ 22, 24, 25, ..., 13, 12, 13],\n",
+ " [ 23, 25, 26, ..., 15, 12, 13],\n",
+ " ...,\n",
+ " [163, 167, 169, ..., 220, 219, 218],\n",
+ " [163, 167, 170, ..., 219, 217, 216],\n",
+ " [163, 167, 170, ..., 218, 216, 215]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[219, 219, 219, ..., 224, 224, 234],\n",
+ " [219, 219, 219, ..., 224, 224, 234],\n",
+ " [219, 219, 219, ..., 224, 224, 234],\n",
+ " ...,\n",
+ " [ 95, 95, 96, ..., 90, 89, 140],\n",
+ " [ 95, 95, 96, ..., 90, 89, 141],\n",
+ " [ 97, 97, 97, ..., 92, 91, 142]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[180, 183, 187, ..., 159, 156, 152],\n",
+ " [180, 183, 186, ..., 159, 156, 153],\n",
+ " [180, 183, 185, ..., 159, 157, 153],\n",
+ " ...,\n",
+ " [ 82, 82, 88, ..., 86, 84, 83],\n",
+ " [ 82, 83, 89, ..., 86, 84, 83],\n",
+ " [ 82, 83, 88, ..., 86, 85, 84]], dtype=uint8),\n",
+ " array([[195, 193, 195, ..., 197, 198, 191],\n",
+ " [204, 203, 202, ..., 203, 202, 196],\n",
+ " [202, 202, 202, ..., 202, 201, 196],\n",
+ " ...,\n",
+ " [168, 169, 170, ..., 162, 161, 160],\n",
+ " [168, 169, 170, ..., 162, 161, 160],\n",
+ " [168, 169, 170, ..., 162, 160, 159]], dtype=uint8),\n",
+ " array([[254, 254, 253, ..., 253, 253, 253],\n",
+ " [253, 254, 253, ..., 254, 253, 253],\n",
+ " [254, 254, 253, ..., 254, 253, 253],\n",
+ " ...,\n",
+ " [ 99, 114, 112, ..., 111, 112, 130],\n",
+ " [ 97, 116, 119, ..., 118, 128, 136],\n",
+ " [ 87, 106, 104, ..., 125, 128, 136]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 253, 255, 255]], dtype=uint8),\n",
+ " array([[ 35, 52, 95, ..., 47, 53, 50],\n",
+ " [ 42, 73, 74, ..., 46, 45, 48],\n",
+ " [ 67, 83, 123, ..., 35, 36, 37],\n",
+ " ...,\n",
+ " [163, 153, 153, ..., 205, 199, 199],\n",
+ " [158, 175, 148, ..., 204, 202, 202],\n",
+ " [156, 202, 151, ..., 197, 197, 197]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[147, 147, 147, ..., 163, 163, 163],\n",
+ " [148, 148, 148, ..., 163, 163, 163],\n",
+ " [152, 152, 152, ..., 164, 164, 164],\n",
+ " ...,\n",
+ " [242, 242, 242, ..., 208, 208, 208],\n",
+ " [241, 241, 241, ..., 208, 208, 208],\n",
+ " [242, 242, 241, ..., 208, 208, 208]], dtype=uint8),\n",
+ " array([[229, 229, 230, ..., 113, 110, 109],\n",
+ " [231, 231, 230, ..., 114, 112, 111],\n",
+ " [232, 232, 231, ..., 116, 114, 113],\n",
+ " ...,\n",
+ " [132, 113, 88, ..., 20, 20, 20],\n",
+ " [118, 96, 86, ..., 20, 19, 19],\n",
+ " [110, 83, 92, ..., 19, 18, 18]], dtype=uint8),\n",
+ " array([[162, 163, 164, ..., 166, 165, 162],\n",
+ " [163, 164, 165, ..., 167, 165, 163],\n",
+ " [163, 164, 165, ..., 167, 165, 163],\n",
+ " ...,\n",
+ " [ 38, 38, 38, ..., 42, 41, 40],\n",
+ " [ 39, 39, 39, ..., 41, 41, 40],\n",
+ " [ 40, 40, 40, ..., 42, 40, 39]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 8, 8, 16, ..., 2, 2, 2],\n",
+ " [ 8, 4, 7, ..., 2, 2, 2],\n",
+ " [ 8, 6, 10, ..., 2, 2, 2],\n",
+ " ...,\n",
+ " [153, 158, 162, ..., 164, 161, 167],\n",
+ " [154, 158, 162, ..., 164, 161, 163],\n",
+ " [157, 161, 163, ..., 164, 160, 160]], dtype=uint8),\n",
+ " array([[148, 145, 173, ..., 222, 222, 221],\n",
+ " [153, 143, 146, ..., 220, 220, 219],\n",
+ " [133, 139, 149, ..., 223, 221, 220],\n",
+ " ...,\n",
+ " [152, 155, 157, ..., 172, 171, 171],\n",
+ " [154, 152, 153, ..., 171, 171, 171],\n",
+ " [155, 154, 155, ..., 174, 175, 171]], dtype=uint8),\n",
+ " array([[ 98, 98, 93, ..., 89, 89, 82],\n",
+ " [ 96, 96, 92, ..., 80, 81, 82],\n",
+ " [ 90, 92, 93, ..., 74, 73, 80],\n",
+ " ...,\n",
+ " [232, 232, 232, ..., 143, 145, 142],\n",
+ " [232, 232, 232, ..., 143, 142, 140],\n",
+ " [232, 232, 232, ..., 145, 142, 141]], dtype=uint8),\n",
+ " array([[161, 174, 169, ..., 110, 108, 108],\n",
+ " [162, 169, 164, ..., 110, 108, 108],\n",
+ " [163, 164, 160, ..., 110, 108, 108],\n",
+ " ...,\n",
+ " [ 30, 30, 35, ..., 53, 57, 67],\n",
+ " [ 32, 32, 37, ..., 52, 60, 71],\n",
+ " [ 37, 30, 28, ..., 50, 64, 69]], dtype=uint8),\n",
+ " array([[251, 251, 251, ..., 250, 249, 248],\n",
+ " [251, 251, 251, ..., 250, 249, 248],\n",
+ " [251, 251, 251, ..., 250, 249, 248],\n",
+ " ...,\n",
+ " [ 92, 93, 93, ..., 88, 88, 88],\n",
+ " [ 91, 92, 92, ..., 87, 88, 87],\n",
+ " [ 90, 91, 92, ..., 88, 88, 87]], dtype=uint8),\n",
+ " array([[212, 211, 210, ..., 184, 185, 186],\n",
+ " [213, 212, 212, ..., 184, 185, 186],\n",
+ " [215, 215, 216, ..., 185, 186, 187],\n",
+ " ...,\n",
+ " [246, 246, 247, ..., 175, 171, 121],\n",
+ " [245, 245, 245, ..., 174, 172, 109],\n",
+ " [244, 243, 239, ..., 174, 172, 98]], dtype=uint8),\n",
+ " array([[47, 46, 43, ..., 31, 30, 29],\n",
+ " [47, 46, 45, ..., 32, 29, 28],\n",
+ " [47, 46, 46, ..., 33, 34, 34],\n",
+ " ...,\n",
+ " [82, 81, 79, ..., 64, 65, 65],\n",
+ " [87, 83, 81, ..., 58, 57, 57],\n",
+ " [83, 80, 77, ..., 56, 56, 55]], dtype=uint8),\n",
+ " array([[223, 226, 228, ..., 142, 145, 136],\n",
+ " [218, 220, 221, ..., 137, 138, 127],\n",
+ " [218, 220, 220, ..., 134, 134, 125],\n",
+ " ...,\n",
+ " [142, 146, 149, ..., 155, 154, 155],\n",
+ " [160, 160, 159, ..., 152, 149, 149],\n",
+ " [153, 156, 156, ..., 158, 156, 156]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[140, 140, 141, ..., 139, 138, 137],\n",
+ " [139, 141, 142, ..., 139, 138, 137],\n",
+ " [138, 142, 143, ..., 139, 138, 137],\n",
+ " ...,\n",
+ " [ 92, 93, 93, ..., 63, 61, 63],\n",
+ " [ 91, 89, 89, ..., 63, 62, 62],\n",
+ " [ 90, 87, 86, ..., 64, 63, 61]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 81, 177, 188, ..., 173, 236, 240],\n",
+ " [207, 225, 208, ..., 174, 236, 241],\n",
+ " [224, 224, 222, ..., 174, 237, 241],\n",
+ " ...,\n",
+ " [212, 211, 211, ..., 198, 197, 197],\n",
+ " [239, 238, 237, ..., 230, 229, 229],\n",
+ " [250, 249, 248, ..., 248, 247, 247]], dtype=uint8),\n",
+ " array([[186, 203, 238, ..., 232, 229, 228],\n",
+ " [188, 193, 218, ..., 235, 232, 234],\n",
+ " [213, 219, 221, ..., 238, 237, 239],\n",
+ " ...,\n",
+ " [176, 177, 181, ..., 151, 151, 151],\n",
+ " [177, 176, 179, ..., 151, 151, 151],\n",
+ " [179, 178, 178, ..., 151, 151, 151]], dtype=uint8),\n",
+ " array([[178, 189, 195, ..., 32, 32, 170],\n",
+ " [170, 177, 185, ..., 33, 33, 170],\n",
+ " [176, 179, 181, ..., 35, 34, 170],\n",
+ " ...,\n",
+ " [ 10, 10, 11, ..., 34, 30, 171],\n",
+ " [ 9, 9, 9, ..., 33, 28, 170],\n",
+ " [ 7, 9, 9, ..., 32, 28, 170]], dtype=uint8),\n",
+ " array([[156, 169, 176, ..., 174, 170, 156],\n",
+ " [159, 171, 174, ..., 176, 172, 159],\n",
+ " [165, 176, 174, ..., 177, 174, 165],\n",
+ " ...,\n",
+ " [ 69, 73, 73, ..., 19, 17, 15],\n",
+ " [ 64, 70, 72, ..., 18, 17, 15],\n",
+ " [ 61, 68, 71, ..., 18, 16, 14]], dtype=uint8),\n",
+ " array([[248, 249, 241, ..., 153, 151, 151],\n",
+ " [250, 246, 238, ..., 152, 150, 151],\n",
+ " [251, 249, 239, ..., 151, 150, 152],\n",
+ " ...,\n",
+ " [ 66, 66, 66, ..., 67, 67, 67],\n",
+ " [ 66, 66, 67, ..., 66, 67, 66],\n",
+ " [ 66, 66, 67, ..., 65, 64, 62]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[161, 161, 162, ..., 178, 178, 178],\n",
+ " [161, 161, 161, ..., 179, 178, 178],\n",
+ " [161, 161, 162, ..., 179, 178, 178],\n",
+ " ...,\n",
+ " [198, 195, 192, ..., 162, 165, 168],\n",
+ " [191, 191, 189, ..., 165, 168, 171],\n",
+ " [191, 187, 185, ..., 166, 169, 172]], dtype=uint8),\n",
+ " array([[182, 183, 184, ..., 182, 179, 226],\n",
+ " [183, 184, 185, ..., 182, 179, 226],\n",
+ " [184, 185, 186, ..., 182, 180, 226],\n",
+ " ...,\n",
+ " [ 94, 95, 94, ..., 83, 80, 176],\n",
+ " [ 91, 93, 93, ..., 77, 66, 175],\n",
+ " [203, 204, 204, ..., 196, 195, 225]], dtype=uint8),\n",
+ " array([[108, 93, 130, ..., 177, 213, 238],\n",
+ " [115, 116, 128, ..., 174, 180, 200],\n",
+ " [ 71, 89, 111, ..., 175, 175, 180],\n",
+ " ...,\n",
+ " [113, 115, 120, ..., 207, 207, 209],\n",
+ " [131, 130, 133, ..., 212, 211, 210],\n",
+ " [150, 145, 146, ..., 213, 212, 208]], dtype=uint8),\n",
+ " array([[124, 125, 126, ..., 157, 158, 157],\n",
+ " [125, 126, 127, ..., 159, 158, 157],\n",
+ " [126, 127, 128, ..., 160, 159, 158],\n",
+ " ...,\n",
+ " [ 63, 63, 64, ..., 56, 54, 53],\n",
+ " [ 63, 63, 63, ..., 56, 54, 53],\n",
+ " [ 63, 63, 63, ..., 56, 54, 53]], dtype=uint8),\n",
+ " array([[ 54, 63, 74, ..., 125, 191, 235],\n",
+ " [ 56, 67, 77, ..., 112, 161, 234],\n",
+ " [ 58, 70, 80, ..., 120, 143, 222],\n",
+ " ...,\n",
+ " [124, 109, 82, ..., 224, 203, 189],\n",
+ " [125, 111, 87, ..., 219, 209, 181],\n",
+ " [124, 110, 91, ..., 216, 203, 176]], dtype=uint8),\n",
+ " array([[189, 183, 182, ..., 255, 255, 255],\n",
+ " [194, 186, 182, ..., 255, 255, 255],\n",
+ " [201, 192, 185, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [241, 241, 242, ..., 202, 201, 201],\n",
+ " [241, 241, 242, ..., 201, 200, 200],\n",
+ " [241, 241, 242, ..., 200, 200, 200]], dtype=uint8),\n",
+ " array([[153, 153, 152, ..., 129, 131, 132],\n",
+ " [147, 150, 153, ..., 132, 134, 135],\n",
+ " [ 92, 102, 112, ..., 138, 140, 141],\n",
+ " ...,\n",
+ " [ 48, 48, 49, ..., 44, 43, 42],\n",
+ " [ 46, 47, 49, ..., 46, 45, 43],\n",
+ " [ 48, 48, 50, ..., 46, 46, 45]], dtype=uint8),\n",
+ " array([[167, 167, 168, ..., 183, 183, 183],\n",
+ " [169, 169, 169, ..., 185, 185, 185],\n",
+ " [171, 171, 172, ..., 187, 187, 187],\n",
+ " ...,\n",
+ " [ 91, 73, 90, ..., 119, 118, 115],\n",
+ " [ 82, 78, 91, ..., 118, 117, 116],\n",
+ " [ 76, 81, 94, ..., 118, 117, 116]], dtype=uint8),\n",
+ " array([[114, 114, 115, ..., 104, 83, 71],\n",
+ " [117, 117, 117, ..., 85, 69, 56],\n",
+ " [119, 119, 119, ..., 80, 63, 70],\n",
+ " ...,\n",
+ " [ 61, 62, 62, ..., 76, 76, 74],\n",
+ " [ 61, 62, 62, ..., 75, 75, 73],\n",
+ " [ 59, 61, 61, ..., 73, 74, 73]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[120, 113, 48, ..., 254, 252, 251],\n",
+ " [121, 121, 57, ..., 254, 253, 252],\n",
+ " [125, 114, 73, ..., 254, 253, 252],\n",
+ " ...,\n",
+ " [186, 189, 189, ..., 94, 32, 43],\n",
+ " [186, 189, 187, ..., 122, 46, 23],\n",
+ " [187, 189, 185, ..., 135, 60, 22]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[221, 221, 221, ..., 228, 228, 226],\n",
+ " [220, 220, 220, ..., 228, 228, 227],\n",
+ " [218, 218, 218, ..., 228, 229, 228],\n",
+ " ...,\n",
+ " [ 68, 71, 65, ..., 63, 63, 63],\n",
+ " [ 65, 65, 63, ..., 72, 69, 59],\n",
+ " [ 63, 65, 68, ..., 73, 69, 57]], dtype=uint8),\n",
+ " array([[ 42, 44, 46, ..., 20, 31, 39],\n",
+ " [ 42, 44, 46, ..., 19, 34, 53],\n",
+ " [ 44, 46, 47, ..., 18, 20, 23],\n",
+ " ...,\n",
+ " [215, 213, 213, ..., 203, 204, 204],\n",
+ " [213, 211, 212, ..., 201, 202, 200],\n",
+ " [212, 210, 211, ..., 201, 198, 198]], dtype=uint8),\n",
+ " array([[230, 230, 230, ..., 178, 176, 175],\n",
+ " [231, 231, 231, ..., 179, 177, 176],\n",
+ " [232, 232, 232, ..., 180, 179, 178],\n",
+ " ...,\n",
+ " [ 78, 79, 78, ..., 89, 88, 87],\n",
+ " [ 78, 79, 78, ..., 91, 90, 89],\n",
+ " [ 78, 79, 78, ..., 92, 92, 91]], dtype=uint8),\n",
+ " array([[184, 193, 199, ..., 200, 196, 191],\n",
+ " [184, 200, 199, ..., 200, 197, 192],\n",
+ " [188, 211, 209, ..., 198, 195, 191],\n",
+ " ...,\n",
+ " [120, 119, 120, ..., 116, 116, 113],\n",
+ " [117, 117, 117, ..., 112, 113, 110],\n",
+ " [118, 118, 118, ..., 109, 111, 108]], dtype=uint8),\n",
+ " array([[149, 152, 140, ..., 209, 151, 164],\n",
+ " [137, 144, 150, ..., 205, 145, 174],\n",
+ " [ 70, 115, 141, ..., 195, 137, 188],\n",
+ " ...,\n",
+ " [217, 217, 217, ..., 194, 191, 190],\n",
+ " [217, 217, 217, ..., 195, 192, 190],\n",
+ " [217, 217, 217, ..., 195, 192, 191]], dtype=uint8),\n",
+ " array([[ 46, 64, 55, ..., 129, 115, 201],\n",
+ " [ 46, 63, 64, ..., 125, 128, 211],\n",
+ " [ 44, 55, 64, ..., 161, 144, 214],\n",
+ " ...,\n",
+ " [217, 220, 222, ..., 207, 206, 234],\n",
+ " [212, 214, 216, ..., 207, 206, 234],\n",
+ " [207, 210, 212, ..., 206, 206, 234]], dtype=uint8),\n",
+ " array([[194, 203, 212, ..., 58, 59, 59],\n",
+ " [214, 219, 224, ..., 58, 61, 65],\n",
+ " [225, 231, 236, ..., 63, 69, 77],\n",
+ " ...,\n",
+ " [ 51, 51, 51, ..., 49, 49, 49],\n",
+ " [ 51, 51, 51, ..., 49, 50, 50],\n",
+ " [ 53, 53, 53, ..., 50, 50, 50]], dtype=uint8),\n",
+ " array([[ 21, 20, 19, ..., 51, 48, 174],\n",
+ " [ 21, 20, 19, ..., 44, 46, 173],\n",
+ " [ 21, 20, 19, ..., 53, 49, 172],\n",
+ " ...,\n",
+ " [141, 110, 50, ..., 47, 47, 175],\n",
+ " [228, 121, 49, ..., 47, 48, 174],\n",
+ " [187, 60, 45, ..., 47, 49, 176]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[162, 160, 159, ..., 146, 136, 130],\n",
+ " [160, 159, 160, ..., 139, 132, 130],\n",
+ " [163, 164, 164, ..., 136, 132, 131],\n",
+ " ...,\n",
+ " [ 84, 84, 84, ..., 68, 66, 67],\n",
+ " [ 82, 83, 83, ..., 64, 64, 65],\n",
+ " [ 81, 82, 83, ..., 61, 63, 63]], dtype=uint8),\n",
+ " array([[ 61, 63, 65, ..., 254, 254, 254],\n",
+ " [ 61, 67, 69, ..., 254, 254, 254],\n",
+ " [ 64, 70, 79, ..., 254, 254, 254],\n",
+ " ...,\n",
+ " [211, 211, 211, ..., 250, 249, 249],\n",
+ " [211, 211, 212, ..., 250, 249, 249],\n",
+ " [209, 209, 211, ..., 250, 249, 249]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 56, 140, 93, ..., 78, 79, 80],\n",
+ " [ 19, 122, 108, ..., 73, 74, 76],\n",
+ " [ 34, 84, 132, ..., 74, 75, 76],\n",
+ " ...,\n",
+ " [ 7, 12, 24, ..., 61, 54, 55],\n",
+ " [ 13, 22, 28, ..., 67, 56, 55],\n",
+ " [ 21, 26, 27, ..., 70, 58, 55]], dtype=uint8),\n",
+ " array([[177, 82, 20, ..., 15, 15, 12],\n",
+ " [ 67, 54, 28, ..., 21, 17, 15],\n",
+ " [ 47, 34, 26, ..., 16, 10, 13],\n",
+ " ...,\n",
+ " [184, 185, 186, ..., 195, 194, 193],\n",
+ " [183, 184, 185, ..., 192, 193, 193],\n",
+ " [182, 183, 185, ..., 192, 192, 192]], dtype=uint8),\n",
+ " array([[127, 129, 130, ..., 248, 250, 249],\n",
+ " [127, 135, 128, ..., 236, 242, 225],\n",
+ " [118, 132, 129, ..., 229, 189, 186],\n",
+ " ...,\n",
+ " [ 88, 86, 88, ..., 140, 138, 145],\n",
+ " [ 86, 87, 89, ..., 140, 138, 145],\n",
+ " [ 87, 90, 92, ..., 140, 137, 145]], dtype=uint8),\n",
+ " array([[ 85, 85, 86, ..., 86, 86, 86],\n",
+ " [ 86, 86, 86, ..., 86, 86, 86],\n",
+ " [ 87, 87, 86, ..., 85, 85, 85],\n",
+ " ...,\n",
+ " [209, 211, 211, ..., 196, 195, 182],\n",
+ " [207, 207, 210, ..., 191, 180, 171],\n",
+ " [205, 204, 209, ..., 180, 177, 177]], dtype=uint8),\n",
+ " array([[ 64, 63, 65, ..., 98, 98, 192],\n",
+ " [ 63, 62, 66, ..., 97, 97, 193],\n",
+ " [ 63, 61, 67, ..., 101, 101, 196],\n",
+ " ...,\n",
+ " [157, 163, 164, ..., 159, 159, 217],\n",
+ " [158, 159, 161, ..., 151, 156, 217],\n",
+ " [159, 154, 156, ..., 155, 155, 216]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[225, 199, 195, ..., 43, 44, 43],\n",
+ " [214, 194, 195, ..., 48, 49, 47],\n",
+ " [201, 191, 192, ..., 92, 84, 75],\n",
+ " ...,\n",
+ " [ 29, 31, 25, ..., 148, 147, 150],\n",
+ " [ 28, 31, 25, ..., 149, 147, 150],\n",
+ " [ 30, 33, 26, ..., 149, 148, 150]], dtype=uint8),\n",
+ " array([[177, 178, 179, ..., 168, 167, 164],\n",
+ " [178, 179, 179, ..., 169, 168, 165],\n",
+ " [178, 179, 180, ..., 171, 169, 167],\n",
+ " ...,\n",
+ " [144, 144, 144, ..., 148, 148, 145],\n",
+ " [140, 143, 145, ..., 149, 149, 144],\n",
+ " [138, 142, 145, ..., 148, 146, 141]], dtype=uint8),\n",
+ " array([[ 4, 4, 4, ..., 66, 45, 25],\n",
+ " [ 4, 4, 4, ..., 51, 20, 28],\n",
+ " [ 4, 4, 4, ..., 36, 20, 21],\n",
+ " ...,\n",
+ " [194, 195, 189, ..., 193, 193, 192],\n",
+ " [193, 192, 190, ..., 193, 194, 193],\n",
+ " [192, 189, 191, ..., 193, 194, 193]], dtype=uint8),\n",
+ " array([[148, 149, 150, ..., 126, 125, 122],\n",
+ " [149, 150, 151, ..., 126, 125, 123],\n",
+ " [150, 151, 152, ..., 127, 125, 123],\n",
+ " ...,\n",
+ " [ 40, 40, 39, ..., 37, 37, 37],\n",
+ " [ 39, 40, 39, ..., 38, 37, 37],\n",
+ " [ 38, 40, 39, ..., 38, 37, 37]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[153, 151, 153, ..., 255, 255, 255],\n",
+ " [152, 152, 152, ..., 255, 255, 255],\n",
+ " [155, 151, 153, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 253, 255, 255]], dtype=uint8),\n",
+ " array([[232, 236, 235, ..., 33, 41, 48],\n",
+ " [232, 236, 235, ..., 39, 44, 47],\n",
+ " [232, 236, 235, ..., 44, 49, 46],\n",
+ " ...,\n",
+ " [203, 203, 203, ..., 8, 7, 8],\n",
+ " [201, 201, 201, ..., 9, 6, 9],\n",
+ " [199, 199, 200, ..., 10, 5, 9]], dtype=uint8),\n",
+ " array([[221, 224, 225, ..., 233, 233, 234],\n",
+ " [225, 219, 210, ..., 224, 227, 224],\n",
+ " [214, 199, 188, ..., 225, 224, 215],\n",
+ " ...,\n",
+ " [216, 216, 217, ..., 233, 232, 232],\n",
+ " [217, 217, 217, ..., 233, 233, 230],\n",
+ " [218, 217, 218, ..., 233, 232, 231]], dtype=uint8),\n",
+ " array([[250, 250, 250, ..., 250, 249, 251],\n",
+ " [250, 250, 250, ..., 250, 249, 251],\n",
+ " [250, 250, 250, ..., 250, 249, 251],\n",
+ " ...,\n",
+ " [250, 250, 250, ..., 250, 249, 251],\n",
+ " [250, 250, 250, ..., 250, 249, 251],\n",
+ " [250, 250, 250, ..., 250, 249, 251]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 70, 82, 90, ..., 238, 229, 229],\n",
+ " [ 70, 68, 68, ..., 240, 229, 228],\n",
+ " [ 82, 79, 73, ..., 241, 230, 227],\n",
+ " ...,\n",
+ " [ 37, 20, 23, ..., 191, 188, 186],\n",
+ " [ 35, 30, 16, ..., 196, 192, 187],\n",
+ " [ 32, 36, 16, ..., 199, 194, 188]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[223, 222, 220, ..., 228, 226, 224],\n",
+ " [223, 222, 220, ..., 228, 227, 225],\n",
+ " [223, 222, 220, ..., 229, 228, 226],\n",
+ " ...,\n",
+ " [218, 217, 216, ..., 213, 213, 213],\n",
+ " [218, 217, 216, ..., 214, 214, 214],\n",
+ " [218, 217, 216, ..., 215, 215, 215]], dtype=uint8),\n",
+ " array([[128, 128, 128, ..., 128, 128, 128],\n",
+ " [129, 129, 129, ..., 129, 129, 129],\n",
+ " [130, 130, 130, ..., 130, 130, 130],\n",
+ " ...,\n",
+ " [191, 193, 193, ..., 216, 215, 207],\n",
+ " [189, 190, 193, ..., 214, 210, 208],\n",
+ " [187, 189, 192, ..., 182, 175, 179]], dtype=uint8),\n",
+ " array([[204, 205, 206, ..., 192, 191, 190],\n",
+ " [205, 206, 207, ..., 192, 192, 191],\n",
+ " [206, 207, 208, ..., 193, 192, 191],\n",
+ " ...,\n",
+ " [ 77, 77, 77, ..., 83, 81, 78],\n",
+ " [ 89, 89, 84, ..., 84, 80, 75],\n",
+ " [ 91, 92, 83, ..., 76, 77, 72]], dtype=uint8),\n",
+ " array([[ 4, 3, 4, ..., 6, 5, 6],\n",
+ " [ 4, 4, 4, ..., 5, 8, 12],\n",
+ " [ 9, 5, 5, ..., 14, 17, 19],\n",
+ " ...,\n",
+ " [107, 110, 109, ..., 71, 68, 63],\n",
+ " [103, 107, 107, ..., 70, 67, 63],\n",
+ " [ 94, 99, 99, ..., 60, 56, 55]], dtype=uint8),\n",
+ " array([[234, 235, 236, ..., 114, 110, 97],\n",
+ " [234, 235, 236, ..., 117, 110, 97],\n",
+ " [234, 235, 236, ..., 105, 103, 96],\n",
+ " ...,\n",
+ " [130, 123, 104, ..., 126, 125, 123],\n",
+ " [114, 102, 114, ..., 122, 122, 115],\n",
+ " [115, 93, 112, ..., 119, 121, 116]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 105, 212, 253],\n",
+ " [255, 255, 255, ..., 132, 229, 253],\n",
+ " [255, 255, 255, ..., 162, 242, 254]], dtype=uint8),\n",
+ " array([[ 3, 4, 5, ..., 6, 4, 8],\n",
+ " [ 2, 3, 4, ..., 6, 4, 3],\n",
+ " [ 6, 6, 5, ..., 4, 7, 5],\n",
+ " ...,\n",
+ " [129, 152, 172, ..., 96, 109, 120],\n",
+ " [105, 109, 114, ..., 132, 152, 173],\n",
+ " [ 99, 108, 113, ..., 174, 176, 170]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[233, 232, 231, ..., 102, 102, 103],\n",
+ " [233, 232, 231, ..., 102, 103, 104],\n",
+ " [234, 233, 231, ..., 104, 104, 105],\n",
+ " ...,\n",
+ " [ 68, 75, 66, ..., 36, 36, 39],\n",
+ " [ 75, 70, 61, ..., 36, 36, 38],\n",
+ " [ 71, 60, 63, ..., 35, 35, 38]], dtype=uint8),\n",
+ " array([[162, 166, 170, ..., 189, 188, 187],\n",
+ " [162, 167, 170, ..., 191, 189, 188],\n",
+ " [163, 168, 171, ..., 192, 190, 189],\n",
+ " ...,\n",
+ " [234, 235, 237, ..., 20, 20, 19],\n",
+ " [234, 235, 236, ..., 22, 22, 21],\n",
+ " [234, 235, 236, ..., 24, 24, 23]], dtype=uint8),\n",
+ " array([[ 39, 48, 34, ..., 141, 138, 134],\n",
+ " [ 33, 32, 30, ..., 140, 136, 132],\n",
+ " [ 30, 20, 22, ..., 136, 133, 129],\n",
+ " ...,\n",
+ " [134, 134, 135, ..., 18, 17, 17],\n",
+ " [139, 140, 135, ..., 21, 20, 19],\n",
+ " [136, 130, 131, ..., 25, 23, 21]], dtype=uint8),\n",
+ " array([[179, 185, 189, ..., 117, 112, 106],\n",
+ " [179, 184, 189, ..., 117, 112, 107],\n",
+ " [180, 185, 189, ..., 118, 114, 108],\n",
+ " ...,\n",
+ " [ 75, 60, 66, ..., 49, 46, 44],\n",
+ " [ 61, 56, 61, ..., 52, 52, 52],\n",
+ " [ 57, 61, 61, ..., 56, 53, 57]], dtype=uint8),\n",
+ " array([[ 17, 17, 18, ..., 188, 188, 188],\n",
+ " [ 18, 18, 18, ..., 191, 191, 191],\n",
+ " [ 16, 15, 16, ..., 195, 195, 195],\n",
+ " ...,\n",
+ " [180, 181, 184, ..., 157, 157, 155],\n",
+ " [179, 179, 181, ..., 153, 151, 146],\n",
+ " [171, 172, 174, ..., 151, 148, 143]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[22, 28, 36, ..., 1, 0, 0],\n",
+ " [29, 29, 34, ..., 0, 0, 0],\n",
+ " [35, 37, 39, ..., 0, 0, 0],\n",
+ " ...,\n",
+ " [77, 84, 90, ..., 90, 84, 79],\n",
+ " [75, 81, 86, ..., 89, 84, 77],\n",
+ " [73, 79, 84, ..., 83, 68, 48]], dtype=uint8),\n",
+ " array([[ 15, 71, 22, ..., 24, 23, 33],\n",
+ " [ 9, 38, 10, ..., 29, 21, 12],\n",
+ " [ 11, 5, 5, ..., 16, 7, 18],\n",
+ " ...,\n",
+ " [187, 189, 191, ..., 174, 172, 170],\n",
+ " [185, 187, 189, ..., 172, 170, 168],\n",
+ " [183, 185, 187, ..., 170, 168, 166]], dtype=uint8),\n",
+ " array([[218, 188, 172, ..., 191, 191, 191],\n",
+ " [219, 189, 172, ..., 193, 193, 193],\n",
+ " [220, 191, 174, ..., 195, 195, 195],\n",
+ " ...,\n",
+ " [142, 144, 144, ..., 183, 131, 46],\n",
+ " [143, 146, 145, ..., 181, 115, 71],\n",
+ " [144, 149, 147, ..., 184, 107, 102]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[123, 126, 128, ..., 231, 227, 222],\n",
+ " [124, 126, 128, ..., 150, 135, 122],\n",
+ " [124, 126, 129, ..., 67, 68, 63],\n",
+ " ...,\n",
+ " [136, 138, 140, ..., 224, 222, 221],\n",
+ " [136, 138, 139, ..., 226, 224, 223],\n",
+ " [134, 138, 139, ..., 228, 227, 226]], dtype=uint8),\n",
+ " array([[ 94, 90, 84, ..., 9, 10, 9],\n",
+ " [ 96, 92, 87, ..., 14, 13, 9],\n",
+ " [ 99, 94, 89, ..., 12, 11, 11],\n",
+ " ...,\n",
+ " [206, 208, 210, ..., 248, 247, 245],\n",
+ " [209, 208, 207, ..., 248, 245, 243],\n",
+ " [208, 205, 206, ..., 248, 244, 242]], dtype=uint8),\n",
+ " array([[238, 238, 238, ..., 239, 238, 237],\n",
+ " [238, 238, 238, ..., 239, 238, 237],\n",
+ " [238, 238, 238, ..., 239, 238, 237],\n",
+ " ...,\n",
+ " [113, 113, 115, ..., 109, 110, 108],\n",
+ " [112, 113, 115, ..., 109, 108, 107],\n",
+ " [111, 113, 115, ..., 108, 106, 106]], dtype=uint8),\n",
+ " array([[195, 198, 200, ..., 194, 192, 190],\n",
+ " [196, 198, 200, ..., 195, 193, 191],\n",
+ " [196, 198, 200, ..., 196, 194, 192],\n",
+ " ...,\n",
+ " [139, 141, 142, ..., 131, 133, 137],\n",
+ " [141, 141, 141, ..., 136, 138, 142],\n",
+ " [141, 143, 142, ..., 140, 142, 146]], dtype=uint8),\n",
+ " array([[ 38, 152, 245, ..., 129, 130, 59],\n",
+ " [ 34, 68, 229, ..., 133, 98, 32],\n",
+ " [ 33, 37, 147, ..., 122, 45, 29],\n",
+ " ...,\n",
+ " [187, 190, 191, ..., 192, 189, 189],\n",
+ " [187, 190, 192, ..., 192, 189, 189],\n",
+ " [187, 190, 192, ..., 192, 191, 190]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[131, 146, 148, ..., 163, 169, 174],\n",
+ " [131, 146, 147, ..., 190, 189, 189],\n",
+ " [131, 146, 147, ..., 199, 197, 195],\n",
+ " ...,\n",
+ " [155, 157, 156, ..., 164, 162, 159],\n",
+ " [155, 157, 156, ..., 161, 161, 161],\n",
+ " [155, 157, 156, ..., 160, 160, 161]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[111, 109, 107, ..., 167, 166, 166],\n",
+ " [109, 108, 107, ..., 170, 169, 169],\n",
+ " [108, 107, 106, ..., 173, 172, 172],\n",
+ " ...,\n",
+ " [107, 106, 105, ..., 56, 59, 62],\n",
+ " [107, 106, 105, ..., 56, 58, 61],\n",
+ " [108, 108, 107, ..., 55, 57, 61]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[219, 219, 218, ..., 108, 108, 105],\n",
+ " [220, 220, 221, ..., 106, 108, 105],\n",
+ " [221, 221, 222, ..., 108, 111, 110],\n",
+ " ...,\n",
+ " [ 25, 25, 26, ..., 85, 66, 64],\n",
+ " [ 25, 25, 26, ..., 79, 45, 114],\n",
+ " [ 25, 25, 26, ..., 60, 66, 183]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 253, 255, 255]], dtype=uint8),\n",
+ " array([[224, 159, 48, ..., 149, 176, 173],\n",
+ " [212, 93, 29, ..., 198, 176, 174],\n",
+ " [152, 46, 29, ..., 211, 178, 174],\n",
+ " ...,\n",
+ " [ 22, 23, 22, ..., 28, 24, 22],\n",
+ " [ 22, 24, 29, ..., 23, 26, 12],\n",
+ " [ 36, 25, 37, ..., 22, 17, 8]], dtype=uint8),\n",
+ " array([[212, 210, 210, ..., 184, 185, 186],\n",
+ " [213, 212, 213, ..., 184, 185, 186],\n",
+ " [215, 214, 216, ..., 185, 186, 187],\n",
+ " ...,\n",
+ " [246, 246, 247, ..., 175, 171, 121],\n",
+ " [245, 245, 246, ..., 173, 172, 109],\n",
+ " [244, 243, 239, ..., 173, 172, 98]], dtype=uint8),\n",
+ " array([[163, 166, 168, ..., 172, 170, 167],\n",
+ " [164, 166, 168, ..., 173, 170, 167],\n",
+ " [164, 166, 169, ..., 174, 171, 168],\n",
+ " ...,\n",
+ " [113, 139, 147, ..., 134, 141, 154],\n",
+ " [127, 128, 134, ..., 155, 153, 157],\n",
+ " [112, 132, 146, ..., 168, 171, 166]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[198, 199, 199, ..., 195, 194, 194],\n",
+ " [202, 201, 202, ..., 198, 197, 197],\n",
+ " [204, 204, 204, ..., 200, 200, 200],\n",
+ " ...,\n",
+ " [174, 174, 174, ..., 185, 184, 183],\n",
+ " [162, 162, 163, ..., 169, 168, 167],\n",
+ " [144, 144, 144, ..., 144, 143, 142]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 75, 77, 94],\n",
+ " [255, 255, 255, ..., 76, 78, 104],\n",
+ " [255, 255, 255, ..., 78, 81, 113]], dtype=uint8),\n",
+ " array([[216, 217, 218, ..., 220, 218, 215],\n",
+ " [216, 217, 218, ..., 220, 218, 216],\n",
+ " [216, 217, 218, ..., 220, 218, 216],\n",
+ " ...,\n",
+ " [137, 135, 141, ..., 149, 151, 148],\n",
+ " [136, 134, 139, ..., 150, 149, 150],\n",
+ " [136, 133, 136, ..., 146, 144, 143]], dtype=uint8),\n",
+ " array([[107, 148, 43, ..., 95, 84, 138],\n",
+ " [100, 159, 45, ..., 88, 77, 135],\n",
+ " [ 88, 170, 46, ..., 86, 77, 131],\n",
+ " ...,\n",
+ " [140, 141, 149, ..., 113, 117, 158],\n",
+ " [153, 137, 151, ..., 116, 113, 158],\n",
+ " [218, 154, 120, ..., 116, 112, 157]], dtype=uint8),\n",
+ " array([[174, 238, 241, ..., 234, 246, 245],\n",
+ " [199, 235, 245, ..., 236, 245, 235],\n",
+ " [201, 239, 247, ..., 249, 237, 222],\n",
+ " ...,\n",
+ " [127, 128, 128, ..., 123, 125, 126],\n",
+ " [124, 125, 127, ..., 123, 125, 126],\n",
+ " [123, 126, 127, ..., 122, 125, 126]], dtype=uint8),\n",
+ " array([[188, 188, 186, ..., 166, 166, 171],\n",
+ " [188, 188, 186, ..., 166, 167, 170],\n",
+ " [188, 189, 187, ..., 166, 167, 168],\n",
+ " ...,\n",
+ " [ 56, 56, 56, ..., 60, 61, 61],\n",
+ " [ 56, 56, 56, ..., 60, 61, 61],\n",
+ " [ 56, 56, 56, ..., 60, 61, 61]], dtype=uint8),\n",
+ " array([[127, 129, 130, ..., 247, 250, 249],\n",
+ " [127, 135, 127, ..., 234, 242, 224],\n",
+ " [118, 132, 129, ..., 228, 189, 186],\n",
+ " ...,\n",
+ " [ 88, 87, 86, ..., 140, 139, 146],\n",
+ " [ 87, 89, 89, ..., 140, 137, 144],\n",
+ " [ 87, 90, 91, ..., 140, 136, 143]], dtype=uint8),\n",
+ " array([[17, 14, 2, ..., 6, 4, 3],\n",
+ " [15, 13, 2, ..., 4, 4, 3],\n",
+ " [10, 9, 1, ..., 5, 4, 3],\n",
+ " ...,\n",
+ " [15, 40, 54, ..., 47, 46, 46],\n",
+ " [17, 26, 52, ..., 43, 44, 44],\n",
+ " [ 9, 17, 51, ..., 41, 42, 42]], dtype=uint8),\n",
+ " array([[105, 116, 114, ..., 134, 138, 137],\n",
+ " [105, 116, 117, ..., 137, 139, 128],\n",
+ " [109, 119, 121, ..., 138, 140, 155],\n",
+ " ...,\n",
+ " [ 91, 85, 89, ..., 60, 77, 47],\n",
+ " [ 96, 88, 91, ..., 52, 61, 47],\n",
+ " [108, 84, 96, ..., 56, 82, 44]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[104, 106, 109, ..., 213, 212, 236],\n",
+ " [108, 103, 101, ..., 220, 219, 238],\n",
+ " [118, 118, 118, ..., 226, 222, 239],\n",
+ " ...,\n",
+ " [202, 201, 213, ..., 205, 199, 234],\n",
+ " [221, 216, 215, ..., 196, 197, 234],\n",
+ " [222, 217, 216, ..., 199, 209, 238]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[204, 207, 209, ..., 212, 208, 204],\n",
+ " [205, 207, 209, ..., 212, 209, 205],\n",
+ " [205, 207, 210, ..., 213, 210, 206],\n",
+ " ...,\n",
+ " [126, 128, 128, ..., 139, 141, 134],\n",
+ " [126, 125, 124, ..., 145, 145, 139],\n",
+ " [126, 126, 128, ..., 149, 147, 142]], dtype=uint8),\n",
+ " array([[195, 188, 188, ..., 176, 178, 182],\n",
+ " [191, 187, 183, ..., 188, 186, 188],\n",
+ " [188, 182, 182, ..., 194, 200, 209],\n",
+ " ...,\n",
+ " [171, 170, 168, ..., 255, 255, 255],\n",
+ " [171, 171, 170, ..., 255, 255, 255],\n",
+ " [171, 171, 170, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[213, 224, 226, ..., 210, 203, 207],\n",
+ " [194, 222, 227, ..., 210, 207, 211],\n",
+ " [198, 214, 226, ..., 208, 209, 212],\n",
+ " ...,\n",
+ " [ 81, 87, 87, ..., 117, 118, 114],\n",
+ " [ 81, 84, 88, ..., 112, 113, 110],\n",
+ " [ 81, 82, 89, ..., 112, 109, 107]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 19, 17, 28, ..., 220, 222, 223],\n",
+ " [ 36, 25, 23, ..., 220, 222, 222],\n",
+ " [ 36, 20, 18, ..., 219, 220, 221],\n",
+ " ...,\n",
+ " [104, 91, 73, ..., 92, 90, 90],\n",
+ " [ 90, 79, 74, ..., 86, 83, 79],\n",
+ " [ 72, 71, 82, ..., 84, 84, 79]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[187, 192, 193, ..., 4, 5, 6],\n",
+ " [188, 192, 194, ..., 4, 3, 3],\n",
+ " [191, 194, 197, ..., 3, 2, 1],\n",
+ " ...,\n",
+ " [201, 205, 208, ..., 166, 165, 164],\n",
+ " [201, 204, 208, ..., 166, 165, 164],\n",
+ " [201, 204, 207, ..., 166, 165, 164]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[116, 105, 143, ..., 125, 96, 118],\n",
+ " [165, 111, 113, ..., 102, 121, 179],\n",
+ " [227, 182, 122, ..., 134, 194, 235],\n",
+ " ...,\n",
+ " [ 13, 12, 30, ..., 30, 15, 16],\n",
+ " [ 4, 4, 45, ..., 51, 6, 6],\n",
+ " [ 4, 5, 44, ..., 54, 4, 4]], dtype=uint8),\n",
+ " array([[254, 254, 254, ..., 255, 253, 254],\n",
+ " [254, 254, 254, ..., 255, 253, 254],\n",
+ " [254, 254, 254, ..., 255, 253, 254],\n",
+ " ...,\n",
+ " [141, 143, 143, ..., 133, 132, 168],\n",
+ " [138, 136, 135, ..., 134, 132, 167],\n",
+ " [132, 131, 134, ..., 132, 130, 169]], dtype=uint8),\n",
+ " array([[155, 158, 159, ..., 154, 151, 148],\n",
+ " [156, 159, 160, ..., 154, 151, 148],\n",
+ " [157, 160, 161, ..., 155, 152, 149],\n",
+ " ...,\n",
+ " [ 46, 46, 47, ..., 53, 52, 51],\n",
+ " [ 47, 49, 49, ..., 48, 46, 48],\n",
+ " [ 48, 50, 50, ..., 44, 42, 45]], dtype=uint8),\n",
+ " array([[169, 173, 175, ..., 43, 75, 31],\n",
+ " [171, 174, 176, ..., 34, 49, 30],\n",
+ " [174, 176, 178, ..., 39, 29, 32],\n",
+ " ...,\n",
+ " [120, 122, 129, ..., 130, 128, 125],\n",
+ " [124, 124, 129, ..., 129, 126, 124],\n",
+ " [122, 122, 126, ..., 125, 122, 121]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[252, 253, 254, ..., 253, 253, 253],\n",
+ " [252, 253, 254, ..., 253, 253, 253],\n",
+ " [252, 253, 254, ..., 253, 253, 253],\n",
+ " ...,\n",
+ " [247, 250, 251, ..., 251, 250, 249],\n",
+ " [247, 250, 252, ..., 252, 250, 249],\n",
+ " [247, 250, 252, ..., 252, 250, 249]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[224, 231, 235, ..., 118, 117, 117],\n",
+ " [221, 229, 234, ..., 118, 116, 116],\n",
+ " [221, 226, 230, ..., 118, 116, 115],\n",
+ " ...,\n",
+ " [ 9, 8, 8, ..., 44, 41, 46],\n",
+ " [ 4, 4, 5, ..., 44, 45, 45],\n",
+ " [ 5, 4, 8, ..., 42, 49, 43]], dtype=uint8),\n",
+ " array([[189, 183, 182, ..., 255, 255, 255],\n",
+ " [194, 186, 182, ..., 255, 255, 255],\n",
+ " [201, 192, 185, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [241, 241, 242, ..., 202, 201, 201],\n",
+ " [241, 241, 242, ..., 201, 200, 200],\n",
+ " [241, 241, 242, ..., 200, 200, 200]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[220, 221, 222, ..., 222, 221, 231],\n",
+ " [220, 221, 222, ..., 222, 221, 231],\n",
+ " [220, 221, 222, ..., 222, 221, 231],\n",
+ " ...,\n",
+ " [ 82, 84, 84, ..., 85, 82, 135],\n",
+ " [ 80, 82, 82, ..., 85, 82, 135],\n",
+ " [ 79, 80, 80, ..., 84, 83, 135]], dtype=uint8),\n",
+ " array([[164, 165, 166, ..., 142, 141, 138],\n",
+ " [165, 166, 166, ..., 143, 141, 139],\n",
+ " [165, 166, 167, ..., 143, 141, 139],\n",
+ " ...,\n",
+ " [ 51, 54, 54, ..., 51, 50, 48],\n",
+ " [ 51, 54, 55, ..., 51, 50, 49],\n",
+ " [ 50, 53, 54, ..., 51, 50, 49]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[206, 206, 207, ..., 184, 183, 203],\n",
+ " [208, 208, 208, ..., 183, 182, 204],\n",
+ " [208, 208, 208, ..., 182, 181, 204],\n",
+ " ...,\n",
+ " [159, 159, 159, ..., 141, 140, 175],\n",
+ " [157, 157, 158, ..., 139, 138, 172],\n",
+ " [158, 158, 158, ..., 137, 136, 170]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 56, 75, 63, ..., 51, 50, 50],\n",
+ " [ 59, 64, 56, ..., 60, 50, 50],\n",
+ " [ 67, 46, 43, ..., 61, 53, 52],\n",
+ " ...,\n",
+ " [200, 202, 201, ..., 206, 206, 200],\n",
+ " [201, 202, 202, ..., 208, 208, 203],\n",
+ " [190, 191, 190, ..., 193, 193, 189]], dtype=uint8),\n",
+ " array([[188, 190, 192, ..., 185, 182, 180],\n",
+ " [189, 191, 193, ..., 186, 183, 181],\n",
+ " [190, 192, 194, ..., 187, 184, 182],\n",
+ " ...,\n",
+ " [129, 132, 132, ..., 126, 126, 121],\n",
+ " [125, 131, 133, ..., 123, 121, 117],\n",
+ " [125, 132, 134, ..., 122, 120, 117]], dtype=uint8),\n",
+ " array([[ 89, 90, 92, ..., 22, 22, 21],\n",
+ " [ 91, 92, 94, ..., 22, 22, 21],\n",
+ " [ 94, 95, 97, ..., 23, 22, 21],\n",
+ " ...,\n",
+ " [159, 153, 152, ..., 190, 189, 188],\n",
+ " [157, 159, 152, ..., 190, 189, 188],\n",
+ " [157, 160, 154, ..., 189, 189, 188]], dtype=uint8),\n",
+ " array([[ 19, 19, 19, ..., 15, 13, 15],\n",
+ " [ 19, 19, 19, ..., 12, 13, 17],\n",
+ " [ 18, 18, 19, ..., 12, 11, 16],\n",
+ " ...,\n",
+ " [193, 197, 199, ..., 130, 131, 128],\n",
+ " [193, 196, 199, ..., 141, 133, 132],\n",
+ " [193, 196, 198, ..., 174, 155, 135]], dtype=uint8),\n",
+ " array([[113, 107, 106, ..., 39, 40, 41],\n",
+ " [ 77, 82, 91, ..., 60, 61, 61],\n",
+ " [ 63, 58, 66, ..., 66, 67, 67],\n",
+ " ...,\n",
+ " [131, 133, 133, ..., 156, 158, 156],\n",
+ " [129, 130, 132, ..., 154, 155, 155],\n",
+ " [119, 120, 122, ..., 151, 149, 151]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[135, 124, 86, ..., 139, 172, 206],\n",
+ " [136, 126, 86, ..., 140, 173, 204],\n",
+ " [137, 130, 86, ..., 141, 173, 204],\n",
+ " ...,\n",
+ " [108, 108, 110, ..., 105, 109, 111],\n",
+ " [117, 110, 107, ..., 106, 110, 115],\n",
+ " [122, 114, 108, ..., 108, 114, 121]], dtype=uint8),\n",
+ " array([[151, 154, 145, ..., 210, 154, 165],\n",
+ " [135, 142, 147, ..., 206, 144, 174],\n",
+ " [ 71, 118, 143, ..., 196, 134, 188],\n",
+ " ...,\n",
+ " [216, 216, 217, ..., 194, 191, 190],\n",
+ " [216, 216, 216, ..., 195, 192, 190],\n",
+ " [216, 216, 216, ..., 195, 192, 191]], dtype=uint8),\n",
+ " array([[195, 196, 198, ..., 73, 77, 40],\n",
+ " [193, 194, 195, ..., 69, 63, 53],\n",
+ " [190, 191, 192, ..., 65, 52, 47],\n",
+ " ...,\n",
+ " [ 22, 23, 24, ..., 33, 43, 90],\n",
+ " [ 22, 23, 24, ..., 32, 42, 87],\n",
+ " [ 22, 23, 24, ..., 31, 40, 86]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[140, 144, 149, ..., 104, 103, 102],\n",
+ " [142, 146, 150, ..., 105, 104, 103],\n",
+ " [145, 150, 152, ..., 106, 105, 104],\n",
+ " ...,\n",
+ " [170, 171, 169, ..., 146, 155, 166],\n",
+ " [164, 166, 167, ..., 170, 171, 171],\n",
+ " [162, 164, 168, ..., 172, 174, 170]], dtype=uint8),\n",
+ " array([[212, 211, 210, ..., 184, 185, 186],\n",
+ " [213, 212, 212, ..., 184, 185, 186],\n",
+ " [215, 215, 216, ..., 185, 186, 187],\n",
+ " ...,\n",
+ " [246, 246, 247, ..., 175, 171, 121],\n",
+ " [245, 245, 245, ..., 174, 172, 109],\n",
+ " [244, 243, 239, ..., 174, 172, 98]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 42, 46, 44, ..., 52, 39, 46],\n",
+ " [ 42, 45, 47, ..., 40, 41, 49],\n",
+ " [ 47, 38, 49, ..., 24, 28, 49],\n",
+ " ...,\n",
+ " [182, 174, 149, ..., 221, 218, 220],\n",
+ " [181, 170, 150, ..., 221, 222, 222],\n",
+ " [178, 151, 145, ..., 218, 222, 222]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[ 18, 17, 16, ..., 35, 37, 61],\n",
+ " [ 6, 16, 17, ..., 38, 41, 63],\n",
+ " [ 6, 15, 17, ..., 39, 55, 55],\n",
+ " ...,\n",
+ " [ 51, 48, 49, ..., 143, 139, 130],\n",
+ " [ 50, 48, 50, ..., 136, 133, 136],\n",
+ " [ 50, 50, 48, ..., 130, 131, 131]], dtype=uint8),\n",
+ " array([[246, 247, 248, ..., 247, 246, 248],\n",
+ " [246, 247, 248, ..., 247, 246, 248],\n",
+ " [246, 247, 248, ..., 247, 246, 248],\n",
+ " ...,\n",
+ " [126, 125, 124, ..., 122, 120, 160],\n",
+ " [124, 123, 124, ..., 121, 120, 161],\n",
+ " [125, 125, 126, ..., 118, 118, 159]], dtype=uint8),\n",
+ " array([[254, 254, 254, ..., 254, 254, 254],\n",
+ " [254, 254, 254, ..., 254, 254, 254],\n",
+ " [254, 254, 254, ..., 254, 254, 254],\n",
+ " ...,\n",
+ " [ 68, 73, 83, ..., 90, 87, 93],\n",
+ " [ 63, 70, 78, ..., 92, 104, 96],\n",
+ " [ 81, 71, 76, ..., 90, 88, 109]], dtype=uint8),\n",
+ " array([[ 66, 35, 41, ..., 250, 249, 249],\n",
+ " [ 76, 40, 39, ..., 249, 249, 249],\n",
+ " [ 88, 48, 33, ..., 250, 249, 249],\n",
+ " ...,\n",
+ " [ 42, 29, 26, ..., 28, 27, 27],\n",
+ " [ 37, 27, 25, ..., 28, 27, 27],\n",
+ " [ 33, 26, 25, ..., 28, 27, 27]], dtype=uint8),\n",
+ " array([[103, 104, 106, ..., 156, 154, 152],\n",
+ " [103, 104, 106, ..., 157, 155, 153],\n",
+ " [103, 104, 106, ..., 159, 156, 154],\n",
+ " ...,\n",
+ " [135, 137, 139, ..., 149, 147, 144],\n",
+ " [132, 134, 135, ..., 147, 144, 141],\n",
+ " [132, 133, 133, ..., 145, 143, 139]], dtype=uint8),\n",
+ " array([[140, 143, 144, ..., 145, 144, 139],\n",
+ " [140, 142, 143, ..., 146, 145, 141],\n",
+ " [138, 140, 142, ..., 145, 145, 140],\n",
+ " ...,\n",
+ " [169, 160, 157, ..., 147, 148, 148],\n",
+ " [155, 148, 155, ..., 137, 145, 142],\n",
+ " [157, 149, 149, ..., 135, 133, 136]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[202, 204, 205, ..., 35, 35, 35],\n",
+ " [202, 204, 207, ..., 35, 35, 35],\n",
+ " [198, 202, 207, ..., 36, 36, 36],\n",
+ " ...,\n",
+ " [ 43, 44, 46, ..., 13, 12, 12],\n",
+ " [ 43, 43, 44, ..., 11, 10, 9],\n",
+ " [ 42, 43, 45, ..., 10, 9, 8]], dtype=uint8),\n",
+ " array([[202, 205, 208, ..., 129, 125, 121],\n",
+ " [203, 206, 209, ..., 130, 127, 122],\n",
+ " [205, 208, 211, ..., 133, 129, 125],\n",
+ " ...,\n",
+ " [121, 123, 126, ..., 39, 40, 37],\n",
+ " [121, 124, 123, ..., 40, 38, 35],\n",
+ " [120, 124, 127, ..., 39, 38, 36]], dtype=uint8),\n",
+ " array([[192, 194, 196, ..., 186, 184, 181],\n",
+ " [192, 194, 196, ..., 187, 184, 181],\n",
+ " [192, 194, 197, ..., 188, 185, 182],\n",
+ " ...,\n",
+ " [147, 123, 140, ..., 137, 132, 136],\n",
+ " [119, 123, 130, ..., 148, 130, 115],\n",
+ " [144, 145, 144, ..., 153, 145, 129]], dtype=uint8),\n",
+ " array([[ 23, 14, 27, ..., 20, 55, 26],\n",
+ " [ 27, 11, 24, ..., 11, 27, 20],\n",
+ " [ 27, 15, 31, ..., 20, 19, 23],\n",
+ " ...,\n",
+ " [250, 249, 247, ..., 246, 244, 243],\n",
+ " [249, 248, 247, ..., 244, 244, 243],\n",
+ " [249, 248, 246, ..., 243, 243, 242]], dtype=uint8),\n",
+ " array([[157, 158, 158, ..., 200, 200, 200],\n",
+ " [152, 153, 155, ..., 201, 201, 201],\n",
+ " [149, 150, 153, ..., 202, 202, 202],\n",
+ " ...,\n",
+ " [ 48, 51, 52, ..., 62, 62, 62],\n",
+ " [ 48, 52, 51, ..., 65, 66, 66],\n",
+ " [ 48, 48, 47, ..., 65, 65, 65]], dtype=uint8),\n",
+ " array([[194, 194, 194, ..., 71, 56, 57],\n",
+ " [192, 193, 194, ..., 68, 50, 53],\n",
+ " [192, 195, 196, ..., 104, 75, 56],\n",
+ " ...,\n",
+ " [215, 220, 222, ..., 28, 29, 36],\n",
+ " [215, 218, 218, ..., 29, 30, 47],\n",
+ " [196, 197, 196, ..., 29, 31, 56]], dtype=uint8),\n",
+ " array([[186, 193, 199, ..., 9, 9, 9],\n",
+ " [180, 182, 193, ..., 12, 12, 12],\n",
+ " [177, 174, 189, ..., 11, 12, 12],\n",
+ " ...,\n",
+ " [ 9, 9, 9, ..., 44, 42, 41],\n",
+ " [ 8, 8, 8, ..., 45, 42, 42],\n",
+ " [ 8, 8, 8, ..., 46, 45, 45]], dtype=uint8),\n",
+ " array([[221, 224, 225, ..., 214, 170, 143],\n",
+ " [222, 224, 225, ..., 192, 135, 206],\n",
+ " [222, 224, 226, ..., 138, 200, 219],\n",
+ " ...,\n",
+ " [202, 204, 204, ..., 136, 67, 33],\n",
+ " [201, 202, 203, ..., 77, 63, 15],\n",
+ " [200, 201, 202, ..., 69, 56, 8]], dtype=uint8),\n",
+ " array([[206, 213, 218, ..., 213, 208, 201],\n",
+ " [202, 210, 214, ..., 214, 204, 197],\n",
+ " [200, 207, 209, ..., 211, 201, 205],\n",
+ " ...,\n",
+ " [153, 154, 157, ..., 144, 144, 142],\n",
+ " [163, 165, 167, ..., 141, 142, 140],\n",
+ " [162, 160, 162, ..., 146, 143, 137]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[148, 157, 167, ..., 232, 186, 167],\n",
+ " [129, 158, 147, ..., 227, 174, 161],\n",
+ " [107, 148, 151, ..., 202, 164, 149],\n",
+ " ...,\n",
+ " [159, 161, 160, ..., 159, 159, 159],\n",
+ " [159, 161, 160, ..., 157, 157, 157],\n",
+ " [159, 161, 159, ..., 155, 155, 155]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [254, 254, 254, ..., 254, 254, 254],\n",
+ " [254, 254, 254, ..., 254, 254, 254],\n",
+ " [254, 254, 254, ..., 254, 254, 254]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 47, 45, 53, ..., 78, 95, 103],\n",
+ " [ 53, 49, 54, ..., 78, 87, 94],\n",
+ " [ 55, 61, 63, ..., 84, 88, 87],\n",
+ " ...,\n",
+ " [162, 163, 162, ..., 184, 183, 182],\n",
+ " [162, 163, 162, ..., 184, 183, 182],\n",
+ " [162, 163, 162, ..., 183, 182, 181]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[106, 106, 109, ..., 120, 99, 94],\n",
+ " [104, 107, 108, ..., 122, 103, 97],\n",
+ " [ 96, 101, 100, ..., 120, 109, 104],\n",
+ " ...,\n",
+ " [126, 123, 129, ..., 54, 58, 58],\n",
+ " [122, 118, 126, ..., 61, 60, 66],\n",
+ " [117, 117, 122, ..., 65, 67, 65]], dtype=uint8),\n",
+ " array([[132, 133, 134, ..., 122, 120, 119],\n",
+ " [133, 134, 134, ..., 122, 121, 120],\n",
+ " [133, 134, 135, ..., 122, 121, 120],\n",
+ " ...,\n",
+ " [ 32, 32, 32, ..., 31, 31, 34],\n",
+ " [ 32, 32, 31, ..., 32, 29, 31],\n",
+ " [ 32, 32, 31, ..., 33, 30, 30]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[196, 199, 200, ..., 213, 211, 212],\n",
+ " [197, 199, 201, ..., 213, 212, 213],\n",
+ " [197, 199, 201, ..., 215, 214, 215],\n",
+ " ...,\n",
+ " [126, 131, 128, ..., 139, 139, 137],\n",
+ " [130, 133, 130, ..., 140, 139, 134],\n",
+ " [129, 135, 131, ..., 140, 139, 133]], dtype=uint8),\n",
+ " array([[254, 254, 254, ..., 254, 254, 254],\n",
+ " [252, 253, 253, ..., 253, 253, 253],\n",
+ " [253, 253, 253, ..., 252, 252, 252],\n",
+ " ...,\n",
+ " [ 15, 11, 10, ..., 23, 21, 22],\n",
+ " [ 15, 11, 9, ..., 23, 21, 22],\n",
+ " [ 15, 11, 10, ..., 23, 21, 22]], dtype=uint8),\n",
+ " array([[185, 186, 185, ..., 144, 152, 158],\n",
+ " [179, 181, 183, ..., 155, 152, 149],\n",
+ " [174, 177, 180, ..., 148, 153, 143],\n",
+ " ...,\n",
+ " [133, 131, 133, ..., 128, 128, 130],\n",
+ " [132, 133, 135, ..., 126, 129, 125],\n",
+ " [131, 134, 135, ..., 129, 131, 123]], dtype=uint8),\n",
+ " array([[ 28, 62, 19, ..., 15, 12, 13],\n",
+ " [ 18, 40, 27, ..., 14, 13, 12],\n",
+ " [ 25, 16, 22, ..., 13, 11, 10],\n",
+ " ...,\n",
+ " [211, 213, 213, ..., 181, 186, 185],\n",
+ " [206, 211, 212, ..., 175, 180, 181],\n",
+ " [203, 211, 211, ..., 173, 175, 177]], dtype=uint8),\n",
+ " array([[177, 178, 179, ..., 128, 124, 202],\n",
+ " [178, 179, 180, ..., 130, 127, 203],\n",
+ " [179, 181, 181, ..., 132, 128, 204],\n",
+ " ...,\n",
+ " [ 58, 59, 57, ..., 28, 28, 169],\n",
+ " [ 56, 57, 58, ..., 28, 28, 168],\n",
+ " [ 57, 58, 58, ..., 28, 28, 168]], dtype=uint8),\n",
+ " array([[209, 208, 208, ..., 178, 178, 179],\n",
+ " [212, 211, 211, ..., 179, 179, 180],\n",
+ " [214, 214, 214, ..., 180, 182, 183],\n",
+ " ...,\n",
+ " [243, 243, 244, ..., 168, 165, 116],\n",
+ " [242, 242, 240, ..., 165, 163, 103],\n",
+ " [242, 242, 235, ..., 164, 162, 91]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[225, 225, 225, ..., 106, 106, 102],\n",
+ " [225, 225, 225, ..., 108, 106, 103],\n",
+ " [225, 225, 225, ..., 110, 107, 105],\n",
+ " ...,\n",
+ " [ 63, 65, 65, ..., 43, 46, 43],\n",
+ " [ 63, 64, 64, ..., 43, 46, 43],\n",
+ " [ 63, 62, 64, ..., 43, 46, 43]], dtype=uint8),\n",
+ " array([[ 59, 45, 52, ..., 174, 166, 158],\n",
+ " [ 49, 48, 51, ..., 174, 169, 162],\n",
+ " [ 42, 53, 39, ..., 177, 172, 162],\n",
+ " ...,\n",
+ " [131, 140, 155, ..., 20, 15, 11],\n",
+ " [122, 139, 154, ..., 21, 12, 5],\n",
+ " [115, 138, 154, ..., 18, 13, 5]], dtype=uint8),\n",
+ " array([[174, 174, 174, ..., 25, 25, 26],\n",
+ " [174, 174, 174, ..., 24, 23, 25],\n",
+ " [174, 174, 174, ..., 24, 23, 25],\n",
+ " ...,\n",
+ " [186, 184, 181, ..., 150, 150, 150],\n",
+ " [183, 181, 179, ..., 149, 149, 149],\n",
+ " [179, 177, 176, ..., 148, 148, 148]], dtype=uint8),\n",
+ " array([[184, 184, 184, ..., 178, 178, 178],\n",
+ " [183, 183, 184, ..., 178, 178, 178],\n",
+ " [182, 182, 183, ..., 178, 178, 178],\n",
+ " ...,\n",
+ " [146, 152, 154, ..., 149, 146, 149],\n",
+ " [140, 145, 144, ..., 147, 145, 149],\n",
+ " [131, 129, 131, ..., 149, 149, 149]], dtype=uint8),\n",
+ " array([[105, 108, 110, ..., 216, 213, 236],\n",
+ " [108, 104, 101, ..., 219, 217, 234],\n",
+ " [118, 118, 117, ..., 226, 223, 237],\n",
+ " ...,\n",
+ " [202, 202, 212, ..., 204, 198, 234],\n",
+ " [221, 216, 215, ..., 197, 198, 234],\n",
+ " [222, 217, 217, ..., 199, 210, 237]], dtype=uint8),\n",
+ " array([[ 77, 65, 57, ..., 75, 95, 98],\n",
+ " [ 72, 63, 29, ..., 67, 84, 89],\n",
+ " [ 59, 43, 24, ..., 75, 79, 75],\n",
+ " ...,\n",
+ " [106, 118, 108, ..., 88, 77, 84],\n",
+ " [116, 124, 109, ..., 82, 71, 74],\n",
+ " [ 97, 111, 113, ..., 74, 84, 77]], dtype=uint8),\n",
+ " array([[ 50, 50, 47, ..., 39, 39, 39],\n",
+ " [ 47, 46, 44, ..., 39, 39, 39],\n",
+ " [ 45, 45, 43, ..., 38, 38, 38],\n",
+ " ...,\n",
+ " [232, 231, 231, ..., 145, 144, 143],\n",
+ " [232, 231, 232, ..., 143, 142, 141],\n",
+ " [233, 232, 230, ..., 141, 140, 139]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 35, 39, 41, ..., 30, 30, 30],\n",
+ " [ 37, 39, 41, ..., 30, 30, 30],\n",
+ " [ 38, 39, 41, ..., 29, 30, 31],\n",
+ " ...,\n",
+ " [ 82, 99, 101, ..., 70, 70, 73],\n",
+ " [ 96, 85, 81, ..., 70, 69, 72],\n",
+ " [ 79, 82, 82, ..., 69, 70, 70]], dtype=uint8),\n",
+ " array([[196, 193, 208, ..., 210, 208, 205],\n",
+ " [200, 185, 210, ..., 207, 207, 204],\n",
+ " [198, 189, 204, ..., 207, 205, 203],\n",
+ " ...,\n",
+ " [171, 175, 182, ..., 134, 129, 122],\n",
+ " [168, 173, 179, ..., 133, 126, 117],\n",
+ " [167, 172, 178, ..., 145, 158, 168]], dtype=uint8),\n",
+ " array([[201, 203, 205, ..., 210, 207, 205],\n",
+ " [203, 205, 206, ..., 210, 208, 206],\n",
+ " [203, 205, 206, ..., 211, 209, 207],\n",
+ " ...,\n",
+ " [171, 177, 175, ..., 166, 164, 162],\n",
+ " [169, 176, 179, ..., 164, 164, 160],\n",
+ " [159, 167, 177, ..., 169, 165, 163]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[128, 128, 127, ..., 127, 127, 127],\n",
+ " [128, 130, 129, ..., 128, 128, 128],\n",
+ " [130, 130, 130, ..., 129, 129, 129],\n",
+ " ...,\n",
+ " [230, 232, 231, ..., 74, 68, 70],\n",
+ " [232, 231, 229, ..., 74, 68, 70],\n",
+ " [231, 228, 226, ..., 66, 81, 76]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[182, 183, 184, ..., 205, 204, 201],\n",
+ " [182, 183, 184, ..., 204, 203, 202],\n",
+ " [181, 182, 183, ..., 203, 202, 203],\n",
+ " ...,\n",
+ " [189, 188, 187, ..., 178, 179, 178],\n",
+ " [186, 185, 185, ..., 177, 178, 177],\n",
+ " [181, 184, 185, ..., 179, 180, 178]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255],\n",
+ " [255, 255, 255, ..., 254, 255, 255]], dtype=uint8),\n",
+ " array([[ 39, 47, 36, ..., 222, 220, 216],\n",
+ " [ 50, 68, 54, ..., 223, 220, 217],\n",
+ " [ 11, 29, 41, ..., 223, 221, 217],\n",
+ " ...,\n",
+ " [ 42, 42, 41, ..., 31, 43, 40],\n",
+ " [ 44, 45, 41, ..., 33, 38, 41],\n",
+ " [ 42, 43, 42, ..., 31, 34, 45]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[201, 203, 206, ..., 196, 193, 190],\n",
+ " [202, 204, 207, ..., 196, 193, 190],\n",
+ " [203, 205, 208, ..., 197, 194, 191],\n",
+ " ...,\n",
+ " [127, 130, 134, ..., 127, 124, 120],\n",
+ " [128, 132, 135, ..., 124, 122, 117],\n",
+ " [134, 136, 137, ..., 122, 120, 115]], dtype=uint8),\n",
+ " array([[253, 251, 248, ..., 17, 39, 49],\n",
+ " [253, 250, 246, ..., 20, 16, 15],\n",
+ " [252, 246, 243, ..., 21, 22, 18],\n",
+ " ...,\n",
+ " [247, 228, 245, ..., 255, 255, 255],\n",
+ " [237, 246, 241, ..., 255, 255, 255],\n",
+ " [191, 246, 246, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[153, 165, 187, ..., 243, 247, 245],\n",
+ " [159, 166, 181, ..., 243, 246, 245],\n",
+ " [163, 168, 175, ..., 241, 245, 243],\n",
+ " ...,\n",
+ " [ 41, 53, 56, ..., 111, 109, 107],\n",
+ " [ 42, 54, 55, ..., 113, 109, 104],\n",
+ " [ 45, 53, 54, ..., 103, 111, 106]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 5, 2, 1, ..., 153, 151, 149],\n",
+ " [ 9, 0, 2, ..., 155, 152, 149],\n",
+ " [ 12, 8, 3, ..., 157, 154, 151],\n",
+ " ...,\n",
+ " [135, 134, 143, ..., 21, 19, 16],\n",
+ " [141, 138, 143, ..., 19, 20, 15],\n",
+ " [145, 135, 143, ..., 15, 19, 17]], dtype=uint8),\n",
+ " array([[ 90, 77, 88, ..., 200, 196, 192],\n",
+ " [ 65, 69, 81, ..., 201, 197, 192],\n",
+ " [ 70, 67, 114, ..., 202, 198, 193],\n",
+ " ...,\n",
+ " [172, 174, 177, ..., 125, 103, 119],\n",
+ " [171, 177, 179, ..., 108, 93, 94],\n",
+ " [171, 176, 177, ..., 92, 77, 89]], dtype=uint8),\n",
+ " array([[163, 166, 168, ..., 172, 170, 167],\n",
+ " [164, 166, 168, ..., 173, 170, 167],\n",
+ " [164, 166, 169, ..., 174, 171, 168],\n",
+ " ...,\n",
+ " [113, 139, 147, ..., 134, 141, 154],\n",
+ " [127, 128, 134, ..., 155, 153, 157],\n",
+ " [112, 132, 146, ..., 168, 171, 166]], dtype=uint8),\n",
+ " array([[189, 183, 182, ..., 255, 255, 255],\n",
+ " [194, 186, 182, ..., 255, 255, 255],\n",
+ " [201, 192, 185, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [241, 241, 242, ..., 202, 201, 201],\n",
+ " [241, 241, 242, ..., 201, 200, 200],\n",
+ " [241, 241, 242, ..., 200, 200, 200]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 47, 48, 47, ..., 49, 48, 48],\n",
+ " [ 48, 48, 45, ..., 46, 48, 50],\n",
+ " [ 47, 50, 44, ..., 44, 49, 49],\n",
+ " ...,\n",
+ " [232, 235, 237, ..., 236, 236, 236],\n",
+ " [231, 235, 237, ..., 234, 234, 234],\n",
+ " [230, 233, 234, ..., 230, 231, 230]], dtype=uint8),\n",
+ " array([[201, 202, 203, ..., 194, 193, 192],\n",
+ " [202, 203, 203, ..., 195, 194, 193],\n",
+ " [202, 203, 204, ..., 195, 194, 193],\n",
+ " ...,\n",
+ " [ 44, 44, 44, ..., 42, 42, 42],\n",
+ " [ 44, 44, 45, ..., 42, 42, 42],\n",
+ " [ 45, 45, 46, ..., 42, 42, 42]], dtype=uint8),\n",
+ " array([[203, 205, 207, ..., 207, 204, 203],\n",
+ " [196, 199, 201, ..., 201, 198, 197],\n",
+ " [189, 192, 194, ..., 194, 191, 190],\n",
+ " ...,\n",
+ " [ 23, 50, 46, ..., 45, 51, 25],\n",
+ " [ 29, 49, 46, ..., 46, 51, 36],\n",
+ " [ 33, 49, 45, ..., 46, 51, 47]], dtype=uint8),\n",
+ " array([[ 5, 8, 8, ..., 190, 183, 160],\n",
+ " [ 11, 9, 49, ..., 180, 129, 54],\n",
+ " [ 35, 19, 105, ..., 75, 30, 15],\n",
+ " ...,\n",
+ " [164, 167, 170, ..., 192, 190, 188],\n",
+ " [161, 164, 167, ..., 192, 190, 188],\n",
+ " [159, 163, 166, ..., 192, 189, 187]], dtype=uint8),\n",
+ " array([[ 5, 5, 5, ..., 9, 5, 8],\n",
+ " [ 5, 5, 5, ..., 18, 14, 11],\n",
+ " [ 5, 5, 5, ..., 16, 12, 13],\n",
+ " ...,\n",
+ " [177, 179, 176, ..., 177, 179, 179],\n",
+ " [177, 178, 176, ..., 179, 181, 178],\n",
+ " [175, 177, 174, ..., 178, 180, 178]], dtype=uint8),\n",
+ " array([[139, 139, 140, ..., 161, 161, 161],\n",
+ " [141, 141, 142, ..., 162, 163, 163],\n",
+ " [145, 145, 146, ..., 166, 166, 166],\n",
+ " ...,\n",
+ " [ 76, 85, 97, ..., 121, 119, 116],\n",
+ " [ 69, 87, 98, ..., 120, 118, 116],\n",
+ " [ 66, 87, 98, ..., 119, 118, 115]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[39, 38, 32, ..., 43, 45, 47],\n",
+ " [39, 34, 37, ..., 42, 45, 60],\n",
+ " [36, 35, 51, ..., 44, 44, 76],\n",
+ " ...,\n",
+ " [14, 56, 71, ..., 16, 14, 13],\n",
+ " [13, 17, 37, ..., 16, 14, 13],\n",
+ " [13, 22, 26, ..., 15, 14, 13]], dtype=uint8),\n",
+ " array([[253, 253, 253, ..., 211, 207, 234],\n",
+ " [253, 253, 253, ..., 212, 208, 235],\n",
+ " [253, 253, 253, ..., 214, 211, 235],\n",
+ " ...,\n",
+ " [124, 132, 138, ..., 31, 31, 171],\n",
+ " [127, 143, 145, ..., 51, 51, 178],\n",
+ " [127, 128, 133, ..., 75, 76, 183]], dtype=uint8),\n",
+ " array([[171, 172, 174, ..., 174, 175, 175],\n",
+ " [171, 172, 174, ..., 175, 175, 175],\n",
+ " [171, 172, 173, ..., 177, 176, 176],\n",
+ " ...,\n",
+ " [145, 150, 150, ..., 134, 141, 146],\n",
+ " [141, 129, 132, ..., 146, 139, 135],\n",
+ " [122, 131, 132, ..., 151, 132, 131]], dtype=uint8),\n",
+ " array([[170, 170, 168, ..., 65, 75, 89],\n",
+ " [170, 170, 168, ..., 64, 71, 85],\n",
+ " [170, 170, 169, ..., 62, 66, 78],\n",
+ " ...,\n",
+ " [113, 114, 115, ..., 183, 70, 88],\n",
+ " [113, 114, 115, ..., 184, 71, 86],\n",
+ " [113, 114, 115, ..., 184, 72, 85]], dtype=uint8),\n",
+ " array([[121, 124, 128, ..., 129, 124, 121],\n",
+ " [121, 125, 129, ..., 129, 125, 121],\n",
+ " [122, 128, 132, ..., 132, 128, 122],\n",
+ " ...,\n",
+ " [138, 146, 147, ..., 148, 148, 138],\n",
+ " [135, 141, 144, ..., 145, 142, 136],\n",
+ " [138, 135, 138, ..., 139, 135, 140]], dtype=uint8),\n",
+ " array([[214, 215, 216, ..., 212, 210, 209],\n",
+ " [214, 215, 216, ..., 212, 210, 209],\n",
+ " [214, 215, 216, ..., 212, 210, 209],\n",
+ " ...,\n",
+ " [126, 134, 132, ..., 131, 128, 130],\n",
+ " [128, 133, 131, ..., 134, 133, 131],\n",
+ " [128, 132, 130, ..., 134, 134, 130]], dtype=uint8),\n",
+ " array([[183, 178, 167, ..., 255, 255, 255],\n",
+ " [186, 183, 177, ..., 255, 255, 255],\n",
+ " [188, 187, 185, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [235, 236, 237, ..., 185, 185, 185],\n",
+ " [235, 236, 237, ..., 184, 184, 184],\n",
+ " [235, 236, 237, ..., 184, 184, 184]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[124, 80, 78, ..., 218, 217, 216],\n",
+ " [155, 94, 84, ..., 217, 215, 214],\n",
+ " [156, 128, 96, ..., 216, 215, 214],\n",
+ " ...,\n",
+ " [145, 143, 154, ..., 179, 181, 183],\n",
+ " [144, 137, 139, ..., 180, 182, 186],\n",
+ " [147, 130, 127, ..., 176, 177, 184]], dtype=uint8),\n",
+ " array([[254, 254, 254, ..., 254, 254, 254],\n",
+ " [254, 254, 254, ..., 254, 254, 254],\n",
+ " [254, 254, 254, ..., 254, 254, 254],\n",
+ " ...,\n",
+ " [ 63, 45, 56, ..., 64, 74, 70],\n",
+ " [ 80, 54, 65, ..., 57, 74, 67],\n",
+ " [ 73, 73, 66, ..., 55, 76, 71]], dtype=uint8),\n",
+ " array([[255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " ...,\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255],\n",
+ " [255, 255, 255, ..., 255, 255, 255]], dtype=uint8),\n",
+ " array([[ 21, 23, 23, ..., 144, 112, 57],\n",
+ " [ 21, 23, 23, ..., 168, 149, 114],\n",
+ " [ 20, 22, 22, ..., 182, 175, 155],\n",
+ " ...,\n",
+ " [190, 181, 167, ..., 255, 255, 255],\n",
+ " [189, 179, 164, ..., 255, 255, 255],\n",
+ " [186, 175, 160, ..., 255, 255, 255]], dtype=uint8),\n",
+ " ...],\n",
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+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " 'Acura',\n",
+ " ...])"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import os\n",
+ "\n",
+ "resized_dir = '../ResizedImages'\n",
+ "os.makedirs(resized_dir, exist_ok=True)\n",
+ "\n",
+ "# Function to preprocess and resize images\n",
+ "def preprocess_and_resize(src):\n",
+ " img = cv2.imread(src, cv2.IMREAD_COLOR)\n",
+ " dst = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
+ " resized_image = cv2.resize(dst, dsize=(100, 150), interpolation=cv2.INTER_AREA)\n",
+ " return resized_image\n",
+ "\n",
+ "X, y = [], []\n",
+ "\n",
+ "# Preprocess images, resize them, and save to the resized directory\n",
+ "for i in range(len(data)):\n",
+ " src = data.loc[i, 'src']\n",
+ " resized_path = os.path.join(resized_dir, os.path.basename(src))\n",
+ " if not os.path.exists(resized_path):\n",
+ " resized_image = preprocess_and_resize(src)\n",
+ " X.append(resized_image)\n",
+ " y.append(data.loc[i, 'brand'])\n",
+ " cv2.imwrite(resized_path, resized_image)\n",
+ "\n",
+ "# Create a DataFrame containing the paths to resized images and their labels\n",
+ "resized_data = pd.DataFrame({\n",
+ " 'src': [os.path.join(resized_dir, os.path.basename(src)) for src in data['src']],\n",
+ " 'brand': data['brand']\n",
+ "})\n",
+ "\n",
+ "X, y\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "606f8c60-ccf7-4046-8a27-2ba361d9f099",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 2500x1000 with 10 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, axes = plt.subplots(1, 10, figsize=(25, 10))\n",
+ "for i in range(10):\n",
+ " axes[i].imshow(X[i])\n",
+ " axes[i].set_title(y[i])\n",
+ " axes[i].axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "25a5601b-d6ff-4ca7-a906-8e585e7881dc",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((24000, 150, 100, 1), (24000, 24))"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Preprocessing the input and output data:\n",
+ "# 1. Convert X and y into NumPy arrays for efficient computation.\n",
+ "# 2. Change the data type of X to 'float32' for memory efficiency and faster computation.\n",
+ "# 3. Normalize the data in X by dividing each element by 255 (common practice for image data).\n",
+ "# 4. Reshape X into a 4D array, suitable for feeding into a Convolutional Neural Network (CNN).\n",
+ "# 5. Convert y into a pandas DataFrame and perform one-hot encoding on the column labeled '0' (common practice for categorical target variables).\n",
+ "X = np.array(X)\n",
+ "X = X.astype('float32')\n",
+ "X = X / 255.0\n",
+ "X = X.reshape(-1, 150, 100, 1)\n",
+ "y = np.array(pd.get_dummies(pd.DataFrame(y),columns=[0]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e682fb1d",
+ "metadata": {},
+ "source": [
+ "## Train and Test Split"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "66c9455e-52ab-4ccf-89c9-0af023e9e807",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((19200, 150, 100, 1), (4800, 150, 100, 1), (19200, 24), (4800, 24))"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Splitting the data into training and testing sets\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=.2)\n",
+ "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e23d77e8",
+ "metadata": {},
+ "source": [
+ "## Testing an already trained model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "a18065ff",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 22ms/step\n",
+ "\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 22ms/step - accuracy: 0.5280 - loss: 1.7315\n",
+ "Test Accuracy: 0.5266666412353516\n",
+ "Accuracy from predictions: 0.5266666666666666\n",
+ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
+ "test_imgs\\TEST_Audi_A4_2012_2.jpg is predicted to be from the Audi brand.\n",
+ "Classification Report:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.48 0.64 0.55 174\n",
+ " 1 0.35 0.61 0.44 196\n",
+ " 2 0.47 0.46 0.46 226\n",
+ " 3 0.48 0.67 0.56 192\n",
+ " 4 0.55 0.47 0.50 231\n",
+ " 5 0.59 0.69 0.64 213\n",
+ " 6 0.43 0.34 0.38 181\n",
+ " 7 0.59 0.67 0.63 204\n",
+ " 8 0.56 0.37 0.45 200\n",
+ " 9 0.51 0.31 0.39 197\n",
+ " 10 0.66 0.45 0.54 196\n",
+ " 11 0.75 0.81 0.78 234\n",
+ " 12 0.46 0.47 0.46 221\n",
+ " 13 0.51 0.56 0.53 196\n",
+ " 14 0.49 0.56 0.52 185\n",
+ " 15 0.61 0.84 0.70 198\n",
+ " 16 0.68 0.43 0.52 187\n",
+ " 17 0.42 0.47 0.45 181\n",
+ " 18 0.53 0.36 0.43 187\n",
+ " 19 0.50 0.52 0.51 208\n",
+ " 20 0.54 0.39 0.45 185\n",
+ " 21 0.42 0.35 0.39 193\n",
+ " 22 0.55 0.58 0.56 198\n",
+ " 23 0.64 0.55 0.59 217\n",
+ "\n",
+ " accuracy 0.53 4800\n",
+ " macro avg 0.53 0.52 0.52 4800\n",
+ "weighted avg 0.53 0.53 0.52 4800\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from tensorflow.keras.models import load_model\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "from tensorflow.keras.preprocessing import image\n",
+ "\n",
+ "# Load the saved model\n",
+ "model = load_model('model1.h5') # Update with your model file path\n",
+ "y_pred = model.predict(X_test)\n",
+ "y_pred_classes = np.argmax(y_pred, axis=1)\n",
+ "y_true_classes = np.argmax(y_test, axis=1)\n",
+ "\n",
+ "# Evaluate the model on the test dataset\n",
+ "test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
+ "\n",
+ "print(f'Test Accuracy: {test_accuracy}')\n",
+ "\n",
+ "# Alternatively, if you have predictions on the test set, you can calculate accuracy using sklearn\n",
+ "# Assuming you have predictions stored in y_pred\n",
+ "accuracy = accuracy_score(y_true_classes, y_pred_classes)\n",
+ "\n",
+ "print(f'Accuracy from predictions: {accuracy}')\n",
+ "\n",
+ "img_path = r'test_imgs\\TEST_Audi_A4_2012_2.jpg' # Update with your image file path\n",
+ "img = image.load_img(img_path, target_size=(150, 100), color_mode='grayscale') # Assuming input shape is (150, 100, 1)\n",
+ "img_array = image.img_to_array(img)\n",
+ "img_array /= 255.0 # Normalize pixel values (assuming your model was trained with normalized data)\n",
+ "\n",
+ "# Expand dimensions to match the input shape expected by the model\n",
+ "img_array = np.expand_dims(img_array, axis=0)\n",
+ "\n",
+ "# Make predictions on your image\n",
+ "predictions = model.predict(img_array)\n",
+ "\n",
+ "# Assuming your model predicts class probabilities, get the class label with the highest probability\n",
+ "predicted_class_index = np.argmax(predictions)\n",
+ "\n",
+ "class_to_brand = {\n",
+ " 0: 'Acura',\n",
+ " 1: 'Audi',\n",
+ " 2: 'Volkswagen',\n",
+ " 3: 'Toyota',\n",
+ " 4: 'Subaru',\n",
+ " 5: 'Porsche',\n",
+ " 6: 'Nissan',\n",
+ " 7: 'MINI',\n",
+ " 8: 'Mercedes-Benz',\n",
+ " 9: 'Mazda',\n",
+ " 10: 'Lincoln',\n",
+ " 11: 'Lexus',\n",
+ " 12: 'Kia',\n",
+ " 13: 'Jeep',\n",
+ " 14: 'Jaguar',\n",
+ " 15: 'Hyundai',\n",
+ " 16: 'Honda',\n",
+ " 17: 'GMC',\n",
+ " 18: 'Ford',\n",
+ " 19: 'Dodge',\n",
+ " 20: 'Chevrolet',\n",
+ " 21: 'Cadillac',\n",
+ " 22: 'BMW',\n",
+ " 23: 'Volvo',\n",
+ "}\n",
+ "\n",
+ "# Get the predicted brand name\n",
+ "predicted_brand = class_to_brand.get(predicted_class_index, 'Unknown')\n",
+ "\n",
+ "# Print the predicted brand name\n",
+ "print(f\"{img_path} is predicted to be from the {predicted_brand} brand.\")\n",
+ "\n",
+ "from sklearn.metrics import classification_report\n",
+ "\n",
+ "# Generate a classification report\n",
+ "report = classification_report(y_true_classes, y_pred_classes)\n",
+ "\n",
+ "# Print the classification report\n",
+ "print(\"Classification Report:\")\n",
+ "print(report)\n",
+ "# Print the predicted class"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3e406686",
+ "metadata": {},
+ "source": [
+ "## Data Visualisation Plotting "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "485bdde7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1200x1000 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import confusion_matrix\n",
+ "import seaborn as sns\n",
+ "\n",
+ "# Generating confusion matrix\n",
+ "conf_matrix = confusion_matrix(y_true_classes, y_pred_classes)\n",
+ "\n",
+ "# Plotting confusion matrix with legend\n",
+ "plt.figure(figsize=(12, 10))\n",
+ "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
+ "\n",
+ "# Adding legend to the side\n",
+ "class_names = [class_to_brand[i] for i in range(len(class_to_brand))]\n",
+ "plt.yticks(np.arange(len(class_names)) + 0.5, class_names, rotation=0)\n",
+ "plt.xticks(np.arange(len(class_names)) + 0.5, class_names, rotation=45)\n",
+ "\n",
+ "plt.title('Confusion Matrix')\n",
+ "plt.xlabel('Predicted Labels')\n",
+ "plt.ylabel('True Labels')\n",
+ "plt.show()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aa2a4102",
+ "metadata": {},
+ "source": [
+ "## Training the Model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "036b9b64-1703-48dd-9e88-8ef99569b0f1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\nawar\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+ " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/100\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\nawar\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
+ " self._warn_if_super_not_called()\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 564ms/step - accuracy: 0.0413 - loss: 3.2123 - val_accuracy: 0.0385 - val_loss: 3.1749\n",
+ "Epoch 2/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 595ms/step - accuracy: 0.0466 - loss: 3.1717 - val_accuracy: 0.0671 - val_loss: 3.1496\n",
+ "Epoch 3/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 544ms/step - accuracy: 0.0612 - loss: 3.1538 - val_accuracy: 0.0752 - val_loss: 3.1242\n",
+ "Epoch 4/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 541ms/step - accuracy: 0.0744 - loss: 3.1308 - val_accuracy: 0.0904 - val_loss: 3.0942\n",
+ "Epoch 5/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 538ms/step - accuracy: 0.0825 - loss: 3.1018 - val_accuracy: 0.1040 - val_loss: 3.0517\n",
+ "Epoch 6/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 524ms/step - accuracy: 0.0936 - loss: 3.0826 - val_accuracy: 0.1044 - val_loss: 3.0999\n",
+ "Epoch 7/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 513ms/step - accuracy: 0.1003 - loss: 3.0520 - val_accuracy: 0.1108 - val_loss: 3.0666\n",
+ "Epoch 8/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 527ms/step - accuracy: 0.1078 - loss: 3.0290 - val_accuracy: 0.1219 - val_loss: 2.9888\n",
+ "Epoch 9/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 522ms/step - accuracy: 0.1219 - loss: 2.9953 - val_accuracy: 0.1379 - val_loss: 2.9650\n",
+ "Epoch 10/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 531ms/step - accuracy: 0.1312 - loss: 2.9642 - val_accuracy: 0.1481 - val_loss: 2.9304\n",
+ "Epoch 11/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 522ms/step - accuracy: 0.1385 - loss: 2.9324 - val_accuracy: 0.1579 - val_loss: 2.9032\n",
+ "Epoch 12/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 506ms/step - accuracy: 0.1449 - loss: 2.9259 - val_accuracy: 0.1477 - val_loss: 3.0334\n",
+ "Epoch 13/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 510ms/step - accuracy: 0.1496 - loss: 2.8977 - val_accuracy: 0.1804 - val_loss: 2.8538\n",
+ "Epoch 14/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 506ms/step - accuracy: 0.1622 - loss: 2.8501 - val_accuracy: 0.1796 - val_loss: 2.8589\n",
+ "Epoch 15/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 511ms/step - accuracy: 0.1713 - loss: 2.8389 - val_accuracy: 0.1985 - val_loss: 2.7986\n",
+ "Epoch 16/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 510ms/step - accuracy: 0.1819 - loss: 2.8049 - val_accuracy: 0.1915 - val_loss: 2.8290\n",
+ "Epoch 17/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 519ms/step - accuracy: 0.1865 - loss: 2.7816 - val_accuracy: 0.2144 - val_loss: 2.7366\n",
+ "Epoch 18/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 520ms/step - accuracy: 0.1943 - loss: 2.7621 - val_accuracy: 0.1979 - val_loss: 2.8266\n",
+ "Epoch 19/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 514ms/step - accuracy: 0.2033 - loss: 2.7345 - val_accuracy: 0.2015 - val_loss: 2.7807\n",
+ "Epoch 20/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 514ms/step - accuracy: 0.2082 - loss: 2.7085 - val_accuracy: 0.2138 - val_loss: 2.7931\n",
+ "Epoch 21/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 511ms/step - accuracy: 0.2204 - loss: 2.6846 - val_accuracy: 0.2367 - val_loss: 2.6906\n",
+ "Epoch 22/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 510ms/step - accuracy: 0.2280 - loss: 2.6670 - val_accuracy: 0.2285 - val_loss: 2.7203\n",
+ "Epoch 23/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 510ms/step - accuracy: 0.2348 - loss: 2.6235 - val_accuracy: 0.2406 - val_loss: 2.7130\n",
+ "Epoch 24/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 513ms/step - accuracy: 0.2319 - loss: 2.6223 - val_accuracy: 0.2325 - val_loss: 2.7484\n",
+ "Epoch 25/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 511ms/step - accuracy: 0.2407 - loss: 2.6005 - val_accuracy: 0.2688 - val_loss: 2.5695\n",
+ "Epoch 26/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 505ms/step - accuracy: 0.2455 - loss: 2.5914 - val_accuracy: 0.2512 - val_loss: 2.6383\n",
+ "Epoch 27/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 505ms/step - accuracy: 0.2637 - loss: 2.5417 - val_accuracy: 0.2473 - val_loss: 2.7048\n",
+ "Epoch 28/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 519ms/step - accuracy: 0.2596 - loss: 2.5317 - val_accuracy: 0.2738 - val_loss: 2.5787\n",
+ "Epoch 29/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 617ms/step - accuracy: 0.2614 - loss: 2.5288 - val_accuracy: 0.2756 - val_loss: 2.6149\n",
+ "Epoch 30/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 587ms/step - accuracy: 0.2716 - loss: 2.4846 - val_accuracy: 0.2760 - val_loss: 2.6052\n",
+ "Epoch 31/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 516ms/step - accuracy: 0.2736 - loss: 2.4810 - val_accuracy: 0.2867 - val_loss: 2.5673\n",
+ "Epoch 32/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 510ms/step - accuracy: 0.2795 - loss: 2.4563 - val_accuracy: 0.2788 - val_loss: 2.6061\n",
+ "Epoch 33/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 515ms/step - accuracy: 0.2922 - loss: 2.4344 - val_accuracy: 0.2862 - val_loss: 2.5584\n",
+ "Epoch 34/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 513ms/step - accuracy: 0.2944 - loss: 2.4097 - val_accuracy: 0.2875 - val_loss: 2.5689\n",
+ "Epoch 35/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 515ms/step - accuracy: 0.2893 - loss: 2.4270 - val_accuracy: 0.2917 - val_loss: 2.5490\n",
+ "Epoch 36/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 515ms/step - accuracy: 0.2964 - loss: 2.4055 - val_accuracy: 0.2854 - val_loss: 2.5999\n",
+ "Epoch 37/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 523ms/step - accuracy: 0.3042 - loss: 2.3827 - val_accuracy: 0.3065 - val_loss: 2.5007\n",
+ "Epoch 38/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 515ms/step - accuracy: 0.3061 - loss: 2.3769 - val_accuracy: 0.3117 - val_loss: 2.4767\n",
+ "Epoch 39/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 510ms/step - accuracy: 0.3151 - loss: 2.3432 - val_accuracy: 0.2952 - val_loss: 2.6218\n",
+ "Epoch 40/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 513ms/step - accuracy: 0.3242 - loss: 2.3221 - val_accuracy: 0.3140 - val_loss: 2.5185\n",
+ "Epoch 41/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 514ms/step - accuracy: 0.3260 - loss: 2.3250 - val_accuracy: 0.3156 - val_loss: 2.5023\n",
+ "Epoch 42/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 534ms/step - accuracy: 0.3199 - loss: 2.3194 - val_accuracy: 0.2971 - val_loss: 2.6454\n",
+ "Epoch 43/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 523ms/step - accuracy: 0.3317 - loss: 2.2827 - val_accuracy: 0.3113 - val_loss: 2.5772\n",
+ "Epoch 44/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 527ms/step - accuracy: 0.3323 - loss: 2.2851 - val_accuracy: 0.3396 - val_loss: 2.4352\n",
+ "Epoch 45/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 513ms/step - accuracy: 0.3482 - loss: 2.2498 - val_accuracy: 0.3290 - val_loss: 2.4878\n",
+ "Epoch 46/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 514ms/step - accuracy: 0.3436 - loss: 2.2462 - val_accuracy: 0.3221 - val_loss: 2.5048\n",
+ "Epoch 47/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 512ms/step - accuracy: 0.3481 - loss: 2.2372 - val_accuracy: 0.3346 - val_loss: 2.4628\n",
+ "Epoch 48/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 510ms/step - accuracy: 0.3499 - loss: 2.2181 - val_accuracy: 0.3348 - val_loss: 2.4649\n",
+ "Epoch 49/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 507ms/step - accuracy: 0.3582 - loss: 2.2124 - val_accuracy: 0.3321 - val_loss: 2.4876\n",
+ "Epoch 50/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 514ms/step - accuracy: 0.3487 - loss: 2.2150 - val_accuracy: 0.3540 - val_loss: 2.3784\n",
+ "Epoch 51/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 513ms/step - accuracy: 0.3615 - loss: 2.1907 - val_accuracy: 0.3408 - val_loss: 2.4816\n",
+ "Epoch 52/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 513ms/step - accuracy: 0.3604 - loss: 2.1832 - val_accuracy: 0.3669 - val_loss: 2.3406\n",
+ "Epoch 53/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 505ms/step - accuracy: 0.3618 - loss: 2.1717 - val_accuracy: 0.3585 - val_loss: 2.3936\n",
+ "Epoch 54/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 507ms/step - accuracy: 0.3692 - loss: 2.1590 - val_accuracy: 0.3392 - val_loss: 2.5267\n",
+ "Epoch 55/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 517ms/step - accuracy: 0.3721 - loss: 2.1560 - val_accuracy: 0.3540 - val_loss: 2.4226\n",
+ "Epoch 56/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 508ms/step - accuracy: 0.3748 - loss: 2.1251 - val_accuracy: 0.3556 - val_loss: 2.4330\n",
+ "Epoch 57/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 509ms/step - accuracy: 0.3733 - loss: 2.1224 - val_accuracy: 0.3550 - val_loss: 2.4210\n",
+ "Epoch 58/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 507ms/step - accuracy: 0.3829 - loss: 2.0993 - val_accuracy: 0.3417 - val_loss: 2.5081\n",
+ "Epoch 59/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 505ms/step - accuracy: 0.3772 - loss: 2.1397 - val_accuracy: 0.3729 - val_loss: 2.3572\n",
+ "Epoch 60/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 507ms/step - accuracy: 0.3919 - loss: 2.0931 - val_accuracy: 0.3571 - val_loss: 2.4433\n",
+ "Epoch 61/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 511ms/step - accuracy: 0.3948 - loss: 2.0755 - val_accuracy: 0.3771 - val_loss: 2.3251\n",
+ "Epoch 62/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 513ms/step - accuracy: 0.3883 - loss: 2.0896 - val_accuracy: 0.3867 - val_loss: 2.3000\n",
+ "Epoch 63/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 507ms/step - accuracy: 0.4003 - loss: 2.0521 - val_accuracy: 0.3675 - val_loss: 2.4012\n",
+ "Epoch 64/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 509ms/step - accuracy: 0.3973 - loss: 2.0641 - val_accuracy: 0.3606 - val_loss: 2.4360\n",
+ "Epoch 65/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 520ms/step - accuracy: 0.3962 - loss: 2.0488 - val_accuracy: 0.3556 - val_loss: 2.4952\n",
+ "Epoch 66/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 509ms/step - accuracy: 0.4054 - loss: 2.0345 - val_accuracy: 0.3617 - val_loss: 2.4601\n",
+ "Epoch 67/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 509ms/step - accuracy: 0.4102 - loss: 2.0121 - val_accuracy: 0.3754 - val_loss: 2.3713\n",
+ "Epoch 68/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 512ms/step - accuracy: 0.4135 - loss: 1.9951 - val_accuracy: 0.3748 - val_loss: 2.4237\n",
+ "Epoch 69/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 509ms/step - accuracy: 0.4063 - loss: 2.0093 - val_accuracy: 0.3915 - val_loss: 2.3754\n",
+ "Epoch 70/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 510ms/step - accuracy: 0.4182 - loss: 2.0142 - val_accuracy: 0.3781 - val_loss: 2.3964\n",
+ "Epoch 71/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 509ms/step - accuracy: 0.4100 - loss: 2.0199 - val_accuracy: 0.3873 - val_loss: 2.3804\n",
+ "Epoch 72/100\n",
+ "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 510ms/step - accuracy: 0.4177 - loss: 1.9764 - val_accuracy: 0.3896 - val_loss: 2.3839\n"
+ ]
+ }
+ ],
+ "source": [
+ "# import tensorflow as tf\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
+ "\n",
+ "datagen = ImageDataGenerator(\n",
+ " rotation_range=15,\n",
+ " width_shift_range=0.1,\n",
+ " height_shift_range=0.1,\n",
+ " shear_range=0.1,\n",
+ " zoom_range=0.1,\n",
+ " horizontal_flip=True,\n",
+ " vertical_flip=True,\n",
+ " fill_mode='nearest'\n",
+ ")\n",
+ "\n",
+ "batch_size = 256\n",
+ "\n",
+ "input_shape = X_train.shape[1:]\n",
+ "model = models.Sequential([\n",
+ " layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),\n",
+ " layers.MaxPooling2D((2, 2)),\n",
+ " layers.Conv2D(64, (3, 3), activation='relu'),\n",
+ " layers.MaxPooling2D((2, 2)),\n",
+ " layers.Conv2D(128, (3, 3), activation='relu'),\n",
+ " layers.MaxPooling2D((2, 2)),\n",
+ " layers.Flatten(),\n",
+ " layers.Dropout(0.5),\n",
+ " layers.Dense(512, activation='relu'),\n",
+ " layers.Dense(24, activation='softmax')\n",
+ "])\n",
+ "model.compile(optimizer='adam',\n",
+ " loss='categorical_crossentropy',\n",
+ " metrics=['accuracy'])\n",
+ "\n",
+ "checkpoint_callback = ModelCheckpoint('model2.keras', monitor='val_loss', save_best_only=True)\n",
+ "early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
+ "\n",
+ "history = model.fit(\n",
+ " datagen.flow(X_train, y_train, batch_size=batch_size),\n",
+ " epochs=100,\n",
+ " validation_data=(X_test, y_test),\n",
+ " verbose=1,\n",
+ " callbacks=[checkpoint_callback, early_stopping],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "45f4b04a",
+ "metadata": {},
+ "source": [
+ "Epoch 1/100\n",
+ "75/75 ━━━━━━━━━━━━━━━━━━━━ 44s 564ms/step - accuracy: 0.0413 - loss: 5.2123 - val_accuracy: 0.0285 - val_loss: 3.1749\n",
+ "Epoch 2/100\n",
+ "75/75 ━━━━━━━━━━━━━━━━━━━━ 47s 595ms/step - accuracy: 0.0466 - loss: 5.1717 - val_accuracy: 0.0271 - val_loss: 8.1496\n",
+ "Epoch 3/100\n",
+ "75/75 ━━━━━━━━━━━━━━━━━━━━ 42s 544ms/step - accuracy: 0.0312 - loss: 6.1538 - val_accuracy: 0.0252 - val_loss: 128.1242\n",
+ "Epoch 4/100\n",
+ "75/75 ━━━━━━━━━━━━━━━━━━━━ 42s 541ms/step - accuracy: 0.0344 - loss: 6.1308 - val_accuracy: 0.0204 - val_loss: 1824.0942"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e2625de2",
+ "metadata": {},
+ "source": [
+ "## Plotting Accuracy and Loss"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "739da885",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plotting training and validation loss over epochs\n",
+ "plt.plot(history.history['loss'], label='Training Loss')\n",
+ "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
+ "plt.title('Training and Validation Loss')\n",
+ "plt.xlabel('Epochs')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "da966dc4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plotting training and validation accuracy over epochs\n",
+ "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
+ "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
+ "plt.title('Training and Validation Accuracy')\n",
+ "plt.xlabel('Epochs')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "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": "3.10.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/mlmodel/Final/SCRAPPED_car_prediction_cnn_3_CATEGORIES.ipynb b/mlmodel/Final/SCRAPPED_car_prediction_cnn_3_CATEGORIES.ipynb
new file mode 100644
index 0000000..a63ebd7
--- /dev/null
+++ b/mlmodel/Final/SCRAPPED_car_prediction_cnn_3_CATEGORIES.ipynb
@@ -0,0 +1,436 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5b1dfb48-778b-418b-90e8-4a712e3352ca",
+ "metadata": {},
+ "source": [
+ "# Prediction a Car's Brand, Model, and Date of Production Using a CNN"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c9f2a57a-c0ea-4864-9dcb-4bb2cea4943c",
+ "metadata": {},
+ "source": [
+ "## Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0a368a66-ef22-4d5e-ab31-3c8244d7a938",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import glob\n",
+ "import cv2\n",
+ "\n",
+ "import keras\n",
+ "from keras import models, layers\n",
+ "# from keras.utils import np_utils\n",
+ "from sklearn.model_selection import train_test_split"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "424e3d32-86a5-46c1-88ff-e4d9c14b1d9f",
+ "metadata": {},
+ "source": [
+ "## Preprocessing the Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "c64f1212-e88c-46a2-a323-defdabbe75ac",
+ "metadata": {
+ "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>src</th>\n",
+ " <th>brand</th>\n",
+ " <th>model</th>\n",
+ " <th>date</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>../Dataset\\Acura_ILX_2013_28_16_110_15_4_70_55...</td>\n",
+ " <td>Acura</td>\n",
+ " <td>ILX</td>\n",
+ " <td>2013</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>../Dataset\\Acura_ILX_2013_28_16_110_15_4_70_55...</td>\n",
+ " <td>Acura</td>\n",
+ " <td>ILX</td>\n",
+ " <td>2013</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>../Dataset\\Acura_ILX_2013_28_16_110_15_4_70_55...</td>\n",
+ " <td>Acura</td>\n",
+ " <td>ILX</td>\n",
+ " <td>2013</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>../Dataset\\Acura_ILX_2013_28_16_110_15_4_70_55...</td>\n",
+ " <td>Acura</td>\n",
+ " <td>ILX</td>\n",
+ " <td>2013</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>../Dataset\\Acura_ILX_2013_28_16_110_15_4_70_55...</td>\n",
+ " <td>Acura</td>\n",
+ " <td>ILX</td>\n",
+ " <td>2013</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>64462</th>\n",
+ " <td>../Dataset\\Volvo_XC90_2020_50_19_250_20_4_79_6...</td>\n",
+ " <td>Volvo</td>\n",
+ " <td>XC90</td>\n",
+ " <td>2020</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>64463</th>\n",
+ " <td>../Dataset\\Volvo_XC90_2020_50_19_250_20_4_79_6...</td>\n",
+ " <td>Volvo</td>\n",
+ " <td>XC90</td>\n",
+ " <td>2020</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>64464</th>\n",
+ " <td>../Dataset\\Volvo_XC90_2020_50_19_250_20_4_79_6...</td>\n",
+ " <td>Volvo</td>\n",
+ " <td>XC90</td>\n",
+ " <td>2020</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>64465</th>\n",
+ " <td>../Dataset\\Volvo_XC90_2020_50_19_250_20_4_79_6...</td>\n",
+ " <td>Volvo</td>\n",
+ " <td>XC90</td>\n",
+ " <td>2020</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>64466</th>\n",
+ " <td>../Dataset\\Volvo_XC90_2020_50_19_250_20_4_79_6...</td>\n",
+ " <td>Volvo</td>\n",
+ " <td>XC90</td>\n",
+ " <td>2020</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>64467 rows × 4 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " src brand model date\n",
+ "0 ../Dataset\\Acura_ILX_2013_28_16_110_15_4_70_55... Acura ILX 2013\n",
+ "1 ../Dataset\\Acura_ILX_2013_28_16_110_15_4_70_55... Acura ILX 2013\n",
+ "2 ../Dataset\\Acura_ILX_2013_28_16_110_15_4_70_55... Acura ILX 2013\n",
+ "3 ../Dataset\\Acura_ILX_2013_28_16_110_15_4_70_55... Acura ILX 2013\n",
+ "4 ../Dataset\\Acura_ILX_2013_28_16_110_15_4_70_55... Acura ILX 2013\n",
+ "... ... ... ... ...\n",
+ "64462 ../Dataset\\Volvo_XC90_2020_50_19_250_20_4_79_6... Volvo XC90 2020\n",
+ "64463 ../Dataset\\Volvo_XC90_2020_50_19_250_20_4_79_6... Volvo XC90 2020\n",
+ "64464 ../Dataset\\Volvo_XC90_2020_50_19_250_20_4_79_6... Volvo XC90 2020\n",
+ "64465 ../Dataset\\Volvo_XC90_2020_50_19_250_20_4_79_6... Volvo XC90 2020\n",
+ "64466 ../Dataset\\Volvo_XC90_2020_50_19_250_20_4_79_6... Volvo XC90 2020\n",
+ "\n",
+ "[64467 rows x 4 columns]"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "images = glob.glob('../Dataset/*.jpg')\n",
+ "data = pd.DataFrame(images, columns=['src'])\n",
+ "data['brand'] = data['src'].apply(lambda x : x.split('_')[0].split('\\\\')[-1])\n",
+ "data['model'] = data['src'].apply(lambda x : x.split('_')[1].split('\\\\')[-1])\n",
+ "data['date'] = data['src'].apply(lambda x : x.split('_')[2].split('\\\\')[-1])\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "f9ff0b13-86db-4de5-b2a8-599a66ccc27b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[11], line 8\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(data)):\n\u001b[0;32m 7\u001b[0m src \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mloc[i, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msrc\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m----> 8\u001b[0m src \u001b[38;5;241m=\u001b[39m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimread\u001b[49m\u001b[43m(\u001b[49m\u001b[43msrc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mIMREAD_COLOR\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 9\u001b[0m dst \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mcvtColor(src, cv2\u001b[38;5;241m.\u001b[39mCOLOR_BGR2GRAY)\n\u001b[0;32m 10\u001b[0m resized_image \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mresize(dst, dsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m100\u001b[39m, \u001b[38;5;241m150\u001b[39m), interpolation\u001b[38;5;241m=\u001b[39mcv2\u001b[38;5;241m.\u001b[39mINTER_AREA)\n",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "X = []\n",
+ "y_brand = []\n",
+ "y_model = []\n",
+ "y_date = []\n",
+ "\n",
+ "for i in range(len(data)):\n",
+ " src = data.loc[i, 'src']\n",
+ " src = cv2.imread(src, cv2.IMREAD_COLOR)\n",
+ " dst = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)\n",
+ " resized_image = cv2.resize(dst, dsize=(100, 150), interpolation=cv2.INTER_AREA)\n",
+ " X.append(resized_image)\n",
+ " brand, model, date = data.loc[i, 'brand'], data.loc[i, 'model'], data.loc[i, 'date']\n",
+ " y_brand.append(brand)\n",
+ " y_model.append(model)\n",
+ " y_date.append(date)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "606f8c60-ccf7-4046-8a27-2ba361d9f099",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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/LPAx4fAx4WOixMeEw8eEj4kSHxMOHxM+Jkp8TDh8TPiYKPEx4fAx4WOixMeEw8eEj4kSHxMOHxM+Jkp8TDh8TPiYKPEx4fAx8TzFxNVWvb/lW77FNhoN2+v1dlznH/yDf2DDMLRLS0vWWmuXl5ft29/+dnv8+HEbRZE9ceKE/YEf+IHqfWutffTRR+0b3/hGG8exPXz4sP35n/95+5GPfGTfsy6sdTM+Xv3qV9tms2mPHTtmf+Znfsb+4R/+4bZ9/P7v/74F7Pve9749z5cdZl3s9N9v/dZv2W/7tm+z7XbbPvnkk1P3+c3f/M220+nY06dPW2ut/fznP28B+3M/93O7tuVjH/vYrsd+xzveMba+1tr+23/7b+31119voyiyd955p/3t3/7tK9rv61//+mq9T3ziE/Y7v/M77XXXXWfjOLbtdtu+/OUvt+9617ue9iySLwd8TPiY8DExjo8JHxM+JsbxMeFjwsfEOD4mfEz4mBjHx4SPCR8T4/iY8DHhY2IcHxM+JnxMjONjwseEj4lxfEz4mHg+Y0JYO2WagWeMn/mZn+H9738/jzzyCHEcP9/N4dd//df5mZ/5GR599FEOHz78fDfHcw3iY8LjGcfHhMczjo8Jj2ccHxMezzg+JjyecXxMeDzj+JjweMbxMeHxjONj4m8v8vluwN8GPvaxj/G//W//2wviyw2uPT/xEz/hv9ye5w0fEx7POD4mPJ5xfEx4POP4mPB4xvEx4fGM42PC4xnHx4THM46PCY9nHB8Tf3vxTmmPx+PxeDwej8fj8Xg8Ho/H4/F4PB6Px+PxXDW8U9rj8Xg8Ho/H4/F4PB6Px+PxeDwej8fj8Xg8Vw0vSns8Ho/H4/F4PB6Px+PxeDwej8fj8Xg8Ho/nquFFaY/H4/F4PB6Px+PxeDwej8fj8Xg8Ho/H4/FcNbwo7fF4PB6Px+PxeDwej8fj8Xg8Ho/H4/F4PJ6rhhelPR6Px+PxeDwej8fj8Xg8Ho/H4/F4PB6Px3PVCPa74u3v+PeYAKyyIED1Bd0zFqEtwdAy88AKYmtA78VHSWcUrYspIjckByLCDc35r4oZnMqYuT+kfdGQdgXRhiXsG/oHFemMwCqIVy3tizk6lsjMEi8nqI0EkWagJDZUICXkBgLJ4Gib5ZeEDA5aggFkHYuZz4jbKc04Y22lzcxnYwaHLMJC3raYyCBaGrEa0n1MEq9ZgsSSzAgWvtDHRpKsHaBjyfIdirAPBz8zxEpB1lUs3RkwuC4jWAsItwSD4zkAspNh+gHhSkC8LOgfM6ijfXSusFowc1+DwWHLd77lz/nM2gkevXwA81CHg582qNRiQsHGdYqNu1LUaoDIBIfuM0gNWUsQDC29Q4reSUvQF4gcEBD04dB9A9Qgw8QBp7+hRXjXGsMvzRKtuus6PGgIeoJwU7gPVEDrgmXlJZbWqQ3SNEDnCoRFCkueKhBw9NAa13dX+dLqQTY+vUjrvEA3oH/UEq0LZh917bv8ckFw8yaDlSbHr1tmaaPNgZkey395hOSAxjYMaEGwqWgsCcJNC0AyJ3j/j7wLgH9/4U185r+8hM4FTbyakTcVWVuSdiTDgwI1gN5xi71ugDzd5JGf+alnNxqukDdf/8/BGAgUK199nPnPrJAdbPPYt8WYlkEOJDIRnHjFOb7+8EOEQiOFYSnrspY1mQsHXBcvEwpNWyacy+ZITEhHDZlTfbpyiBKGUOTMyT4LckgkDABh8TFqCwZIrcQg0AgyK+nZiL6JWdMthjZkJe+wqRskJkAjyY0kswoAY93cFIMgMwpjBX/yZy/h+j9IufTyGBNB3rTkHYvpaESsYTPEBgaUZfHjIY1V166Zz1wCpTj/qwG//dL/TM8GPJEdoCEyDqlNpDBc1l1+8YvfSu+vD9BYtsjUnYsJwSoBAkwAMoOjf3wZsdFzMS8FaMPy609ipaB9PiUYaMLTSwDYzU3MYIgQAsIQEbjbm81zt0wpbJKAMdg8B6WQzYbbRrlrgRCIZoPs9pMEG0PkpVXscIjpDRCh25+44STm4cfd+tYU/9jqeyGkAOHaK4IAoRQ2dSdptUEoCUqBtdgsBymQ3Q6i24Fh4tq5tYXpDdz+a/vCWlAK0WoiGjEY676DWmO14X9e/PVn+Vt+ZZgLtzyvx/d4JpFHvvS8Hv/v3P5zmNkWspcgBglkOYjiBi6Lf42F4v5iZXGvCgNQAivlaPqglJhAkrcD0tmAZEZiJQgDwoLMLcl3rvF/3fVb3BJkVRtkebwdMNbu+r5m+/tm2nrFfnRtHV1sqsTo92o/aHZvM0BmJZmVfC45zl3xWUIx2rtGoLBoBJd1m3/98Fu5tDzDW1/0Wb5x9vM0pLs+QxOSoTiTLtJVQ7QVLOUzrORtLqcdVtMWlwcd1vpN+sMIrd2HIaVBCJDSkg4DTC658eRlfuy6e5lTPULhroLCMLQh2kraMmFowx3OxX3+QxuynHc4nS6ynHZYSttsZTFbaUyiVXEtLYE0hEojsRgEEouS7vwj6Z416sji2Q7gd1/zvn1+CleHU7/+a+4PAVZM+e5NfvT115Pri8l/XR/F/WcRsnhtJnY67bjFfkS5j/rivb6Owo43U9jqGKLYp5TjxxQTbbBWVMuFcD/32w4j6uuPltX3Vf49rclS7N6G/SCErW5dovhelfuZ3L+xAmvF2PvGClSxnbGi+ne/LVG1Y1bbWoGxAl38W6e8ruV6AEqaaj1rBfd90/9+xdfh2eQbmt87vsBc4edizcRLu20ZYpd56OWz5i5Uz7bgnkmVcs+jQrjfsuJZ1X0ha/sq26ENWIPV7pkVUzw/W+vO15qxZ+k921Hud4/zssa6tkpR9QWQbhshyu9AcVxTu2bFc/W26zh5zGnX/goQSu29j+J9EYTFNdSj96ad/5Q2V/2cKfsXcvvd4n+l79+r6VeVX3vgGwH3W56ZgKT4jdRWkpigit/Je05JeW8p31fF00dmFZlVKAxB7T5Qrj+J67vbbe8rsf0ayz3uYq5Pr8eWlb/VCosUpuqb15eNn5fctn25bNpzk5poU7l+ZhXaSpQwaCsJZV7tPxS6er86brHv8pmjji7GICYpz7W8LgYxts/J/YZCu7EIq6rXk9tNHkdiq3MYO+/iupXvl+deHXdifV1cE1V8BtXxrEAjMVbwf7zsv247x+cS38f2vNB4vvvYPiY8LzR8THg84+wnJvYtSmPAPRsKrLRuENSU/wJCYOMQlRqCoUBYi0o0w1kFFqSGYDlg5owmawpkBmHPkLck67dAvAJYyNoCHUt0obpZJRFVZ9Fii06VkGADiW5IMKAS0A0w8xmNTopSBiEszW5C3oxRqSA5qCEX0NSIwIwGrgCjIG/WR3tAZu78ZAoyNdhAIFM3QCxiAxaiNdCRIj2omekOWBt2CNcFrYuWrCOxx9y+sIKgb4mXBX9+6SaePH2A5pMRc09Y4tXcXUtrESYkb0bopiXrWrKWRKXFAJMGG0DeNVgliVeEa1sGWItIc/K5GKss/a0YlUO0Dr0TFhsahFXkLUswEKgEso4AYUnToBowskZgJUTNjDwLGGYBK0mLrUGMVZC33HVWQ3fNhQUdQdATDC+0ERbWBw26rYQkDwi3IJt1QrYwAqFB5BAMnPCYty1PZAv88cYdfPbSMZCgQ4HIDAGQtyQyh3jFkrcEJrYIK7DTRtuea8LADWAId81sIEm7IcfvvMgts5d5dOMAy70Wh1ubxDJDYWnJBBWOOshl57hvIp4cHKCnIwJhaAcJLZkSy5zZoM/BYJNhsMai7BMKg8YQ1jqakTBoBCHQFjldMlA9hsE6mZVshg16NmJTN+mZmL6J2dQNtnTMlo7p5TEDHZKaYtA7pTgnN6HBxYHAhoZWJ6GXSdrzA6IgRw0XsFIgc+sGowLF0ZkNNIJN0+DBwXFaKkE1DArLe858Hfn/7wDXfXwduZWA1ghtsFExWC8FSIlVAjFMIQqr64zWLL0cWjetcfnsDMc/GhBcjhH9IaLbRYZRNbhmh0MAhJJuYCnPi/1LRBRhtcammRsME26gTEQhLM4zOBIzs9LH1oRjlHKC9ulz7vXkoE5twAdwIrKU7jjFvkXozkVEETZJEEoiGjEijt35KYVNUrd+GIyObawbILMW2e3A3AwmDNy9SBfC9JUOYno8nquOjULS2YgoN6iBu9+VA+LVD5kaDY4JbbAUjyZaIMp1TTmoJpBZhEoMwSAg7OVknYC8Ibl4t+T/vPO/c0uQVUK0QkwVla8GSohKmIZCSxcjYfpqEArDjBrSsyFzIhktx1SDl+XAZvhYgz9o3MGNd1zm9vgcABmKzAbFf4qWTLg+WuLG+BIbjQZDG7Gum5xN5rk07DLUAf08op9FbA5j0lyhAoPJFKcvLfDgoWO8ofMAodBEGJZNC4UllBk9E08dyK7TlQPCMKerBmSNgJW8w8VshrPDOVaTFoM83DYoG6tiYmTxHBfIyUHv6YLh80bR/EqQnvY8N3VZIRbb2jqTgnVdkC7f2+9pbxN3pxx/5023Cbx1sXhSON5xP7V2TwrTk+0ZrecE2vrbpWhrJx6W92rDftooa+dSF4d322f5vmB8ggQ4gVjWxORtovIe+5TCOrnJimr/4yuPhPDJ6/GCYQcRcid2Ei33zaSgu4cgPYasfUH3fbzJz0SMhGFjsFf6G3UF5zsmuMop5ykF5W3ZSjkuTD8Npgm8VyJUT51Q4HY8/ne5zjRxfj8TDGrtslpf2XfgOUIjMIXgWYqhmVVjgvOkyAtMCLumEpXLbd2+ZTVLrhQdEx24SS+FGBwKjS6utUYiGR2rFC3rv+llO6tjC7P99ZRngLr4PCki70VdXL3Sbcv21AVdWRNlJ0XknZ5flDDUe8Cl2A3jQr3E9bdLAXhyv/Vnm/qyzCoyo7bd25Uwo2s8cR5lGwzCDWhMsJOQXhejgWoiv/bJJT0ej8fj8XiedfYtSjvh2f0nrKjGZqR27hwbKogChLaoxGICicoNwsLG9QE6hvZZQbSe0zsUEa87FzQiJm8LGkuqECkhj53IhxDkLUW4VDwElwO3gQRrMaFCx85Zmbctt7z0DF9/6Is0RO5coTbgwc0j3HewQ+dJSTorMN0cIS3WCIKBE0mtArQTXJPFGJkZpLYEGxkyD7AKbCCwUpDMKvKmxQ4UpmHRsaB9FqxSrAUdmo9HdM8YWpdyhosRPS2d0JsLgoFFaDj/N0eZfwralzRBT+OcoQJhBOGG5uBnLRvXBazdYUlnJUHPiYQ6KoRpZTFHh2RJg9ZFgRo6x7rIDdmMu9biUky8Jsi6kB3IiS8EyEQUTnEIes6ZapoaoyUq0FgDKtRkAyd3Gi3ItaKfRSSXWsSpwISgG4Vb3ml+mEDQumBpXpSkc4It1eFVL3+AP33sZhoKdNs5akndGLywEAwtaVuQzRr+49nX89Bfn0K3DK02NJaLscXUVN+FsGedSCoBA3o+f3Yi4BmgFzqI3CAyjQmKARYBLz9whr87/3EuLXZZ0R0eGR4mFJquHNKWiXNjIdjSDRoiQwpLZhWB1DTJSHTAStpihRax1KwGLbbCBpu6wWa4TkskNGRGW6Q0hC5mARcONevioeyYtkWOFoK5wo6cBpIMSd+ETqQ2TS7lM5xP57icOge3sQKZClRmCDchnXOu/MayYHB9xqAXgbQcn13n3MYM7Z4pvr/F4EaoONjYQmH51OAUH185xVw0IDFOdH7wkeMcv6QxcYAcZIgsd5NPhokT+AOF0MYNVMURIims1IVLWM/mvPLIGXoHIz6R3Mr8wiEOfGYTG0g2r2+RNwSNdU33E085ITfPEWEhUtddHVpVLgmME4FFs+FeBgKx0cMOhm77oJiAICV2MEDOzmC2emDcYIWQwjmgKZtq3fehdGVbg2g2EN0O5tKSE6YKRzdh5P7OclAS0Wpgez0QxT6lxGSJGziLIkSriW7FIItJO8aCtqPJOx6P5wWDbQTkbUV82RT3Aw02q1xlQkoI1GjijbWIXI+JFFbW7q+BQg4yZC8h2AjcfTRWLL9S8fe+8U/4msY6fWuR1tISTppVe7mO99IWJm4tGrtteK4+VFk8Ul1VtC0FZ0NLJPRMzIIcVoOMSlgy6waaF+WA1xx6nP+vPIS4b4b/o/8mXn7DaW7qLFUDjxeTLk2VERaDtFJYUuMekSOZkxvFXDRAW0Evj1kCMiPRVlB60vONiI9evI2WTDkcrnNTeIk5OaBno8qBs/d5SRSWRbVFQ2TcHp1n0zR4ND7ExWyWC+kMa1mTfh4x1CHGCiKZjwl0kwO3QW0gXO0iHj5XjLmjJ4Xlaezkjt7pfXAOaXcw9+8eLuXJ/YvJ9fdqYs0Z7V6PC8xSmm1O6UlGzueRoLxdiJ6+j7oYK2r/Tq4/qZdNamVqjzaO9rPdHT1tDsDke6U4VP4tC9e/sQJRLp8Qjs2E+1lJs+17XncxTora4L73FiqB2tYc2i8Expyv8DRE5n18bpOnai1uxrlESLG3cFoTLcWVCNKTuxFTXPFSuOfpZ/uXoxRvS5d0IYaPtV9IkG5i57a2SfGMm3SlzulddlTdz9znJa/oe/KsteM5ohQttZUjgbFYFkgnGJe/qZMu2RIpLBqJtlQZwkwltoqxvrMpnheMcAKxQtBUWSVqY83Y/aIUKacJ4ONt2C5kb0OYSjiddDtrq6ZtMUbpbt6JsX0Wx6mL46omiNfXrQvTqmhjeT57idXV+xPCvMSiGd2zS1G5LuCHQrvti2Wh0GQoJt3qeuK86+0Y+95QTnhSY+9Pc3zXXdflZAWPx+PxeDwez9Vh/05pqM0kdv0iK0aWBRsFWCWQiUbkhrwdkM/GdE8nZK0YrEA3YeP6CIRz6a7e3iLeNLTPKKx0jl8buHEdKyDtCqwIaD4l3chJliOFIG81UZnGxIreYcnWbSlvfun9/N3Fv+ZUsEUI9C3EAqIFwX+efRnv+/M3oLYUncU+Wyst0IL2WTfQm8wJwh4k85allwS0LjoBtHPOOXwHhyytizEmhMGidOm/U4lpOKd39ymLMILgwZDumSFqmGOFIOiF6H6ACA1qNSTqaWxf0P5LgwkFKrGo1CCyYjC6EFylNjTWitTKgRP+hbVkLSfQykTSPJwy6MTEX7Q01jQydSlBhbaYhkEmzo0+XLA0nwyZf1ijI0HeFCy/KiOZVxz4lGDzZoGQxqUYt4Jue8jKIESvRRBYkkbAloxQmxKZulThKhFsXW+YW4doU6MSSfOiU6h1MyDYivmLpZew+JCldxxEQ2OHCpGLyuUSDA1ZU9G4oPji4mHidUGeSXTsBEFbzuJWAhNA77gT52UKeiskmE2fje//M0I33CQMWTqlxUg4CIXmSLDOIbXJ5bzrRGk1ICo6Ty2ZkpgQKSwNkaIIaBXCsbaCrEgRmltJbhR9HbEuWsW2CTNqyFAkzKm+c2HhXMihMJUwXe7LFE65SBja0qCtoa1yZm2CUVucDFY4G8xxOZphRbfZ0g3uC28HIF5zrnwstC4bNpeaBBsSM6dZHTbprTdZHBhsWxYZASwmUjRVxqaJ+Ivlm3lyZZ6VZot+HhGpHNXKufiVDYJhm2itTbhlUSnE626SRuPsBujUibplWj9V/B0GiIHi88tHWWj2Wbh1haX2LHlzBmGgf8ySHsjBCo7G15G1BNGWoXk5Izq/gegPsVs9AESknDhcpCB0C6VbNjl2Y+24K6Qc3KkG0gomUgJW7cc5p22r4dJ2a+2c1WGAiKPRhJs0g2ajEsAJR6lehRDITtvtQ4m6TQoU2KutAnk8nivGxAEmLOzC2oApBGdZPOiUA+WlKF3aI0uRQozEGhuoKu5tFLiJaAcjNq4LOfyac/zD+Y8ztM6tXDqlNXZvUXoPpBBjKb6nua+Lx5faOqOxfHWVx/TaMmHNtFCFyKQLZ4wqnFWhMNzdfZQP3XgX0adbNL/Y4D59PU8eXEBJg5KGfhIRBrpKgQ2F8FY/x5oQlmhFkgUYIzFGOld7Jnjy7AHuGX4lB1p9XnPgMb6m81BxPban5pxGhkJhi99zzZxM6RapxudUn4Vgi6W8y2rWYi1rMdDj6cBH4vRIhA5E/ZyemQvwWWU/gvRO65fpBEp39Ng6z0B8EWwTgqeuNsWN65aPxOhxcXj/+6xcz5PO6x22mxSjy7+nGDa3i9STqVD36eaeJjaX6IlBfFV9JwtXdS1N+bTj1WMQ3EToetRMm3hR7W+Hc8iNrEz2pbO6EqZ3PdvniElxcV8isxj/+ypMTJx0/VaTL6c5jveLLJyLxrg2S1n93k1zGT+t/ZcY60T3mjN7x3XrGZCe4bW0xu56LuUkgGnrCCl2fZ7fUVzeLV37DuK1UKpYb2/h87mmTOFcUoqM4PrYpRhZdz/DuLNV2dFvrqmJkOU9qnRfu3uUoanGJ7yribvDpEg5KVgaMe6UhnH377TU1xLrBNIdfpv3+s2up+7ej1N6p9Iok9uWYq6pTQ6gWDYpru+WknvqsYQhlvmYID3m1saSMbrvSyyh1GPidWKD6tpNC5fStV4XmuvXXtvtgvM2Jza1sgZXOlHI4/F4PB6Px7MnVyRKY51Tl8IQIEzh0C1Fl0wjE13kbGyQzYQ0LvaZfVwyPBDRP2qQiSTasK5+bAg6EsRrFqPARMJpP8al0h4cEG55K0Ku910TQkXeVJiowcptMf27+3zXiz7D13e/wKIcuDSRYlRzVwLfNfNp4tdl/J+ffgNJEiCUJbgcurrGAnQMyaxAdzWpkAR95zC20rmkdUez+qKillFkUYXgG1wKaCxb0q5L/RVtFamGAudeDHsWMXSCe2NZEGzpqtMrc4FKtBuoVsKN5paivxg1PutCtOGcwiqFwSFBuC7Rn5tl9qKlfSEl3HD1u60SxMspcw806Z2AwUGLblk6TznBDyCdUcieIugJ2hdzVtcDmAcVaMIoZ3OrSWduwNagA7kgHbgBT6nd5yK1ey5vXJa0LmUEvZxACdRG4lIJG8vMacXMGQh6GhNG9I+GoCwiE4QbgsaKRSaWaMsgnpKsLDQYnNDIviToO0c4EvKGQmhLPIRkUSC1c2ebhkBnz38aJZkWn6cs3PaBxASCzazBmmmyrDuA6+jNqT4zclht2xDZtv1lVpEbhRKWuKgJ6f7OXGdMGDaNc/IqYd2XW0MkNA2RuZnFuJrSytpC8NYuZVnRkU6LgRYlnLu6LQxdNAvyEsNwib4NWDNNfrv1WnSsCIaWaN0ic0u0rln8lIuJ9ZsUS48tIHKBzDJEIcwKbSpR+tHsEF84e5R8M2TYiljvNWk1Eu44cZ7rbl2tUow+sbXI6fU5ltZbqLMNbvxvDdQwdem8jcE2IuccxIkyaiDZ7DeQwjLXHLBwc5/1kw3SXJFvtHjR8Yv8v458in839w189anHuTjo8vB919F94hDxmqF7JiG83HMC8PIaouj12izHDoeIA3NuYkwjgkGASNNRyu0ixZ0dDEa16CSutIC1CGNG9eq0HrlfRJFCPMtHbu0oRMQxthmPBPitHrSbLn03VHWxnYAdYmc6mKioPVsoPdZYhLPHezyeFxgmKsqRKFHU0bRl/lu3QilEi9qyKQPitsia4Ab0LflMA5nkZN2A1dcP+bWbPsysVKwbTVdIQiHJ0BhrUXsoYs9miu9pKbyv1lBeOeDZEHk1oCkLp09mJS2ZoxH0TcDxYJVvuvUL/I9Lr6DzpKT5aMzKeogNivImucQq67K6lIJbWJR5MQKr3WQvERhkMHJDaS0xqYLcZRiR5yK2ziyyES7y+KkFnji1yFfNPspd8RnWTItQ7C/Liy5SVvaKOpoNkXEw2GBO9TgWrrIWtVjXbVbyNqt5y6UeNQF5keKyLkpPGwh/3rnSR7hSkC5nrU4TpOVIGK4UyCsRqPcQpMcE3R2E5rogPZ6Ke39ib9GMqa7fnYTY8t+6vrWT4LtTHeuSSUF4x7ZOtGnStVxv86Sr2kzZrn6+05z+9XZXNaLZfm12an3pii6/FkpYbLHs+e9NPA0m6zbDeCzsR0Cp7WOa0DlWQxooS+tU2z5dSkdymSq7/L2YnOS5E7udW+mILlet726aS7rcX5mJpCaS2ysVpusptffgSlzpVRurt2qu9vKYE+tvF7undBCEdJMMan2WFxKlCFqKiZlRTmjGVLWH68LiSHwWJCacECBFMUGrrHEskYUwWtYMVhiaKhurIVwun0z9vRv1dUp3b53JutT1esvTRNxnOpFsmghtrNx1v84pPj4hYNrf1f52eb6oO55LykkFZYp0oMqmVrWhFn8GQSzy8fTnZlRreqfJflk18aicjLD/u33120SR0dA7pj0ej8fj8Xieda4ofXc5FgRQlG8hGBiCvnaijbaI1IltSilM5Do6QS9HDaNqHzJ3HUVhXKpuowqXtASRu3TgeVNW6+hGgOw5h5ANFUhB/0DI6t0pf/+O+3jzzGedCwU75sgxQCgkB5Xgbd37efi2I3z8wnWsrDVoXnSu1iCxYJ3YK/uScKtIs22d8Cw0IC3pjCHoCXTD1WQOtgTd0xaVWTZPSloXLOFmMdioBHKY01jVNM6H5F1J+6wT9mSSOzFJCCckB0VNo8KJ6ep0F7W6c0FyQCOMm8kcbVpUAvGapXXZEK9mqF6GSEe1coOtlHitQe+4wIZUEwnKyQMyl8x8SdFcMYSbGY2lkMF1kk5rQBRoLi3N0G45gVkOJUYpTJyjEkHQg6BvsQqay5ZopRBZpashbqXrFMTLiauPrS0zT1hMENM7JhEa2uctzeUcmRmEdfWyZx8W9I9K8qat0sRbITBROTvVogbuA1VDgelJ8ubzP4wUXN5wzrXATTzQsUINDR8/dz1HG+vMBn1CoYllxpzq0ZUDhtZ1usY7oqMOlSzSk5WCtMSlNizXbcm06My5z3xoQzKUmyFtLT0bVZ2utkzojgnhery2FbZw1bnOeiQMkUirVN8lUluEhcGBAJVa4jVDY1WQNwQyH435ytw6F3AgSU3AvWu3o1djZCqKWl0SrQUPD2IekQcwRtCMMwJlaEYZp65/igfiI+hWgFRugET0BtCIR2mqw4B8LqcZ5iRZQDPMiKTmSHuT3EpuXljiQNzj4eERji1s8JLOWb7nwBmyk4qz2QJ9E/FHl1/EQ399inhV0D5/hPaFjOjyANlPEBtbsLrB3CdSGAwxw6JGaVEb2onHBmRx6zSmGFyjcHTXBsNKAcp9sNh+H5HnbhAojt3AWBhAFGKiADlMYWGOwU2LNJdWsMPECd1CFHWnI7erYYpVCtuJXEaByiHjO8wezwsNHSuX/SNUWGMgcr8BVSpRpYr03LU03XXXW93dVYtztZWSz8Vc+CrJr9z9Qe6INrmo4ZgK0Fj6hdWqJRRyD9nlaaX4nhg7LVN6G0bCdOmWvprCdFY4opUwDK2qBlI1gqFVhBj6BIRC8zUzD/OZF53gfHKE5iWBXZPoyKIbo+tfv1QiB6zAhs65agWgLEYUxnZduoFAJgKV4EpfDEElYC51+IvHXswnX3SSf3b7H3Mqurzn+ZTpM7UNyWxAz8SAm3wWipxIaNoqZU71GZp1hlHIpmnSMzF9E7Get9jIG8U1GLnKygH2FwR7/VRtcyGX/5UitB1fXl+11kepv7fd2TzlsOUxprw9TVSevgykNNuc0vuhLvSW6bvrAsZObajXeK5vP41p9ZantWE3pgnR9eMFE8J2KXRvT2trq3SrtphoUF+2W5uNda5AW2QD2nHdAluI42Uq8LrT2+7z83nBsZszdi+miZ571SGuT6SCK3NL1zKrjTmSa/verwhcCrLbRPOphx1v65ggXQq3dUG6zFL0dKgLx2X2paK8z2Tb92JSVLbGpVhHKSfqlzFWupwnHe3Fs8UYE+dVlTIqSomQvbD6EGUt4FJ47BvXB4plTmYViRkNYZXCZvk7J6tU0+46RXJ67elJh3VigjFRuj5Zpi7klvuK5fgks2m/sdsE09pXoKyJXabG1mxPhW32KaSactBs2vJ6GyfSd9fPq7xu5d8hoxTZlUN6nyJ5Od5QH++Yll47I6iWl+74UOY0ZFYJzplVLoV3cS1VIWYbq6r7/06TBkJ0Ma5itx1/p98LNfE5ezwej8fj8XiuDldWU9rWHAg4ESrs5QSrAycWgxs0tRaZ5qhEYyKFMNa5oUNZiNlO9LXK7VelzmmqUidKqoHBBIp4FRqrBhNKTNMN5FoBKtH0D0a87aWf4nWdL9IzMTNySGIVylrCQpwOgb7RzMqIrlT8i0N/xP8Tv4Lf/PzXEa+6FN1CWzrnc3QjRObCpageQPdsBgaShRCRSjpnJO3zBh0JhouujnPnXEo6E5C3IUgsUltkZqr6006UdQ7mmSeGTujNNFYLdCtEJu49YWuDoUXJKGEsaiDJj6cMZzKyTkz7KUnroqG5nBNsZc6pi3OPVzVlraWxkhNuRaQzFmlE1QGy0rm1Zx+3ROspwljmHtNs3RzRWtggVJq4mdEbRKiZFLYaqE1FFkaEGcTrlsZq0RFbSpzAHgWQaawQ5J0ImRvUICtSWRtkpJh/aIjQDRDQOZsRDDVZKyBrSsKBoXXZIjNJ76gYpXFXAh050VNq5xTPW4J4FZpLsNa4MpP/VWGYuIGHKHTpu0NJvJKQ3z/DPeuv4saTl+mGCa+af4Iw0oWTuXRzmW0DaaHQSGVJzKiWJRSdYVyHuCEyGjIrUnvmZDZAYYr0VNs7TxfyWbeOMDRExoLaoiGc3T0sRO9wolNmcAPrQltM6D4Hod3YpQ4E/YPOQR0M3HfeKjexRFXp/QWXhh1WkxZWWmzTItsZUlmyjRgKxxkGEtmsjnsuWqD5WER4qRi4L9zEZY1pK52wE80mtOOUVlg6yJ0r7GDssiksJW0uDZ1L/d7lW1ma6XB78xyLaouXNZbpHh7yP16Tc6K1xqWky988eormF2dpn7XMPdIheORcIRiHzh2dJIgoHA2iNZtuAC2fcLyVg0LlwFp98MnYora0RbSaro5sOUBkjIvdLMd2mvQPBbQ6HUxvgJAaggBRuqqNQSQ5NOPRvgVYL0h7PC9IrHLPSiI3bqJNmZJfSra5pGXhadV2NPAPVTkLAITAxCEEkpXbYr7zTX/BK+OzgGBBOgfQ0GZoawmfiZvtCpl0W086pq8WpQvITbJStEWOwpJRCNbClbVQVjAn+7zl2P38XxfnSXI3YUrmYHPhnr3i8t5dTMKUALYSp4WwkAusAJm538XSeqlS9/w4SisN0aZFaEEvnOXz153glc0nuKzbe56Pc4ZJNIZIuOeFtJisBqZ6jmjIjDYJi2rLidI2phfEXM67xaB9OFaXE14gbp96E6YNCm9ThKe8ntxswsXsHqRL8Wq6eLxb83ZKz73X9pNCdJmKez/1i6fVhJ627/FtxretrzfVKb1rC3YWdaetVx6ndC5X205tp3vedNuM3I31bUSt1vNubZp2HbdtU/uCGAQUdaunpfd+QcTElTL13nplU38qx60t3cp7C9NPmyntnVpbet/7M1xReqCd0nfDSAw326/f02pjKfJC5fyu1wwvBeddBepJ53OR/s2l2za4AQVZ3YiEmhCmldr9JjflGITTV3u+mCaAlum063Xmy7rR5d/ul1Ogha1qPJf330mBODeFu7q4M+a175RBEFCK3bJwMo+yP0wrIzCZSNpY8YzLl0y6e7dP2jGV8LxvAbsYUyj3v5cbuxSENaO6y2NtmBIldTF6WnvrYyD1FOHaNXC7OF9zO2dWVRMIZeGSyQrheawNtRrf29ozsf96tp3dzsvj8Xg8Ho/H8+yxf1G6eM62ksp9Gwwt4YVN15EzESJJRx1Pa1G9FN2OkElO+6JGaEW8YVCpZTinyEs9qhj8K8rmEQw1zcsBYd8Qr+akswHLd85iImhessw+0qd/3HI8Xi0G7gQpiqEJ6dmcUGgaQtMWObGALZsRC8lhFfCP5j/FX3/1DZx56kbCnhOmZWqY+1KKjqUT2QSo1GCFc0vHy4r5L2bEqwkmkIT92DmZtWU4J8natnCJFtcqK2ZYbuUEfe3E20C6FJCBdIJ1bpzDuHBFidwUbmOBQBD0DeGmQjYy0n6ECd2ATetShurnCG1GndFMj3Wm40sDDn3SsnldxOCQG5gzoUSmBpVowq3iQheu7MbigHaYkuiAViNhkEQcmtng9OUjhFsCVgM6Zy3tcynCghrkqL5z04okw4aK7ECLvK1onOtDbhCF4C+0QWjonHeCXDDU5A1F3nL1oxGS9oUMlRh0UXszb8LZ18XkbUPnSUkwsJgA8gaEW5aZJzKCwQug9xxHkLtr70R0SWAt7bPQOh9x4UsnuJxA+s2Kr+s8UNSJNDREjhK2mv1bUs7SLjvJgdDEMieWeVUXqSGzyiWtrUQy2WG3aJz7emhDlDD0jWJNt9BILuVd2jJlTvVoi5SuTJ1IXSBxqe+FcbW984arQ46FeMNgY0HWLkSAzDpHe+D+E5bqO/nk+gJJrpDdjJnugMPdTVYGLTYfPsiRv0qJVhP3/dfuuymMxYSKcGXZ1V0tBo9sXKTuthakwjRDZjsDFpt9WkFKQ2XMBEmV9kwKw2w4YD1rEkjDRtrgM2sn+OLmYY41N7ircwaAr1l4lK9rP0goDF88eJgzr1ggs4qPr97A6i9eT7Q8YHCsTeN8H3mu5m5LEmSjidncGhvkEnV3hTHbB7eKATQRx84ZmWtEJEEbxDCFXLuYPnuZxX6CWVpGhIGrPR0EiEbDpfkWAoaJS/EtRhNOxHMg/ng8nivHSkHYN4itAeQ5BMG4g0nJKlPKtkFkISr3tFVuANoGEpRg+SUdbv7eh/mHC39JQ0AsJArBls3oiBAjDBLJiklZkNFze9JXgck6teBS8CZGFAPRhr6JaShNiCEUhhBDUvuNPaS2+IrmE7z+1iPcO7yd6KIrR2ECJx67LC04rcOCHArCnkANwISQt0C3LKZhsJFADiTBwLmkywmWMqWYxGXdhC4LCMuBcIsLeXfbQOu2cyoGWBWGsDbAXWZUyYpB89IRXjqG5lSfRXpoBKfCywxtyJpu0zMRK7qDse5Zeb+D1s8JV+JQrVzS5evyv7oYy0iQLpdJu/9yu2L/07t2qhMthEXKkWBbCtKwPTW2npKquEz7DdtTa08y6ZCujr+D2xu2C7L7FaEnj1Fua6zY0Qk9dhxG5+VEBIpntlF7J13MdaalMa9EqT3S05fHMcUHVm5niixB8m+jKP1MsWZ63eJJx3T9dU0EpUy9DYUAKssv5Gg72D5Js9pXkTK7lirbZjkiDLa5qMfaQumQLid3Tf/sbP24RTav6j2tR05mVeQIMXaUTrw4r3IdEUWun2et6zeHQfVaiKL+c80lbbVG2O1OZTEhGgtVupVr17W4tmXpnur8CuezO96U35D91uIuJyEw3ubqGC8gJgXdUcpnd98pMzIYK8iNrCZ3AyCo+tjagjGuvEVqgkpoLCc0138TQ6mrFN9BcRx3vzGVcFqmDw+l3laXuKx7XJ3DxDUtayLXqdfO3knI3Y3ymcHUam6PvT9F3C3dz2N1nLdNALBFZTlROaWn1ZOedozS7Vzux30Otessyon6omr35JhI2a8vneRle40QY/XFy+M15PayaPXrPa3W99i5VCVPtsfXXs9tHo/H4/F4PJ6nx5U5pUt3QuEiEdoisrxIrWucqFJgtUFkhdBiQCWGeFPQXEqx0oldJpAgigG8DJdeOoNgK6O5ogjXnRtYaoOwITqSTvwVkC249E09ExfCW4zEOJcJLp1jiiQUxrl1in7BrIz4seMf45+8+DoOflyhUuuOkRlUUsyYjWRV1zles8RrTmAWuUECjeWsEOYt6ZwoxFdbicoy0W5czDrntLsgbr/S2MotXolKgkK0dttjLcFA01gKWEsCRGAwLUPaleiGRPWpritBbWCg/KysJb7Qc6nPo6gaaJWpRuaFCwswgWT9hoDFbo84yDE4l0IQaI60Nzg9u4gZRjQvSJqXM4KBJpmPENoSrDshFgMiN06U1E6cE8bZvW2RxtwqQdDLsYFAhxIbjAZw0xlB6yKooaGxalm7STA8YpCLCVGUky/NEG2ACUo3vZswMPPkkOediYEBGwh0MySZEyQLFnuyT/RgiydX5tk42WBO9WmLjP97+Wv4ys7jY3WQMqtQuIHolICBDukGbmZyphUdlTAb9BmaECS0VY/MBsWMZTdBIC3iQePSGjaEJS1SXilhCHGpPzOrWNNthjIkRdEQWSGYW2KhiXCp6lVqivruIHMYLEp6Jw3RukDmgrytaF/Q5C1J3igmWhiD1IbVjRa3Hr1ENL/CTZ3LHInXed9H3sSpP02IT684IbZIHaeUGokyWe5qOdcHvMr3jCFZjGmFG3TChLZKaQcJTZXRVe77UHZAQ6FJTEBbpWS1TufpZBFwgxh/MbiZI8E6DZnyssZpDqoeL2k8xT/9hls48ceCp/5+jjURdv0G2qcV8w/ndB5ew6YZMgyxaYrtD9yOlXL12GqDUCIMixqyBpu7mtREIaLVdMvCADFMsRubiNkZ7MYmtteHvnN8iyBwA1NKuePMdd0kiFYT0wiwgURmtVHFfaYF9Hg8zy1qaBC5xgrpMiZoU6Txj9x9QLv7AeAm4dRqSwtjsXGIVc4ZYhohW6faxN91kZ89/gfEgm01o03pJsLQEIIMjULsmcb7SpCVwDP9viOBbWqgcHqvHhmSK6bVPZxkm/BkR8JYQ2ZsmgYL9Ku3E6vG9quwzMgh3754H5svirnP3EiwrlCpcz+jC7e0FjQuSuYf1kSb7vlQRxLdlOQNyXBeMTjkyqtkoSDYgnDTPde4/4pnrBCyDnBsyNFwDVXUxxw/J7nt/EsnkptWYIrUnlOusShc03Y0MA2uxnZD5LSLZ4OeXSWzAUMbku5Qe/F5oXQzT4rNk5TrbHNMl47i8dfI4jlbuH/LSzcWJnUxe3K3k67r2rai9rq+3rZa0sXyuvtZGzn2elr95sl9ltQzDZditJKuzEtJ/StSX15n8ph1gdeW2QD2oC4a18vLTLa5aldtv2VN6Xq90rqoU+5v3EW3i3u6vJ47CMuTwv6kK3ov4f9547kWCCeOVwmolYhcE6NhVBO6/t5O+7V2W6rsMQphuErhPbluIZ6Ope0uxfJaiZySsTTgEzWmYSQ2V8cQEhQI6yZ7W63Hr0dZX7pM853l1fGr23lNyBehcM/vSjnhWtWFr+Lvclkh5otJF7ctJ6bUYqvIsCSK5dtqY49tvkscX0Ht6+cTjURa16dztYRtlYLZWEVuZOWQdlkQRiJymcq5dOTq2gSWOuUk8OqY1aw0ajWN3bUKCiG1bFv9Aaa8H5VO31Jcnkz/PE2Ufroposta2+Xf+1kPRunEh/UazgayiSHBTKiqfXUXsaxdh1LYncxMYRBjNaLL90sx2dRqQ8vib2UNYTFTpnxWKv/VVhKXonOt1ni5LuydN2Hy+XHyushCPJ+K7157PB6Px+PxXBWuLAeyHf/bpfQuBCPt0n9VtV+1dh2tRLsay5kh3AS1kaLbYZWW2QRuTEFmLnV3tKkRSUbQD1HDHCsFaiulvTrAKoWeicg7IWE3xdjCDWoNqXDiWgMnsGkhyJAMrQVhyawhAVrAy+MV7rzjDBfuO1XUbjaQG9cBEW5w0rTcbO3GqibczCvxR+QG1XMPxqYZEK05h40aFp3mclqvLUbCik6sHDp3szCu5q4VYjQSVtRfhmpMkTySpLOC2dk+R7qbrCcNzsXzLGcxC18UxKsC1Uuxudlx8CJcHdI5r8hazgEuUz2qVwmYhmLzJs2C0lzqdapBMyUsTZWhIo0w0Dnr6k9bKUhmJToOCVdqTpQ0J9xIqxTuo4EKWZybu25WKUzsnOIqNQQDGC4o9/kn2p1TGkE3Jwg0YaAZzFkal913JF61RFtOzA+2kiv66l4VssyJkNa568sJBcJAPqt5xcmn+NTqTcgk5G/6N3FdtMRN0SU+t3Kck42VqsOsRMaWaVSpp5pFTedeHtNUGe0gITEBiQlpyRSFYWgiQpFXM5ENklDk1axjVXUYR+nCwXX+ytTf5exlhcEU6Q8Sq1gxDcItl0Eg7Bl0rEjnLUaBXswYNoJigoggXhPoUKAbxdigdB31ZjPl7oUnOBBucjxc5c82buXgfdB47LITVrVLS11+F62SbqJFOKrVXIkZQjiXIJDMKGZUTiRzYpXTVBktmTKrBoQirwQKbSWJDWiqrJpBH8scJQyZcbXITieLJCako4bMyIHLuGAV3RetcHl1ke978b101ZCVvM3968f47G0n6T5wAJFDtGGZf3hAcGkD0R860bgQ2W3Z/rLt1iLi2KXtDgJsMyY72MYEkvjMGvT62F4fO0xGA35R5PalDUJrbJoWkz1slcpX1Ab3rBBXLeuix+N5+pTpu8uBeZukCCWxMq4ynlTpucv7npSVM7p0UQlr0Y2I9VvbLH0F/MKpP+WwSp3obC1ZkXpVwlga7VCUoqd9VisKT6br3nv94hRh5H6yV5pwdhxTTMBy/5rKtVS6kyaF7vL1kWCd1y98ibPXz3Lh9ALhauBqSlsQmSDckMw9auicLjK/WEsQSEwcYCJJMAyQqUKmiuSAIe8Uz4EJ1XOyCYVzV3csxxbXWQy2qkwmk+gpA8v1khzT0mWOb+9qv+iypnb1O6+LCWo5aem0ti+A0ifTmHA980zFwt02rzmJdxVha27ouhg9/npcpN5v/ej6tjshxfb1lBgJy2oifexkSuxpjuPdnNLTshFM1ofetj/Gz3naMfVEu8q/t70ust24/dZS0U75LHdyVE+u47lCpj1I1t26UImhoxrTEy7pbfsshOm6yFz+XZ9ULSVk+ZTtnYg65jSuC6ulo3mnFN31Uhh1MddY59guqdo38b0qhODqR6w8npLbz70UmYNgtN3ktSmvRf26Th6zGFMRxriyPfVzE+KaKdjjYtj9/qlyglZBvX5zVR5BSLR1YvOkCGmKGsK73RfcPafsv1G5pmWRCnysXUKOLSuXS2FHqcf3+O12+57ch7yi93cTpGFcjK2vOzbxZ9o3aqLtpSANIyHYWDVVZJ90Rm+rQz1R17p8bqmf6+R5ZUVZs8m27uQs3y17Rv0Y1blMeQ7zDmmPx+PxeDyeq8v+R6cKRy/W/SdsIebW0nWXrp5SmCsFFRNHiNwSZDkyzdGdCFGsK7RA5i79tcosQb/oAKQGE7maSCIr6qiGoPo5G9c16LQ36ZvIOaXLToGi6rgYKxniOgiZMGihMUAqcrpS8eZD9/MbzRswgROS3EiuRegcKUA3nFgabuUEhQhddcgLYRlt6Z5JyboBQlt0KFGZwYYKMcyqdGFCG5DU0njbSsDC2iqVt5UCjCWbCVm9LSJ5xRbfdPIhjkZrnBku8KVmn/vVMYRt0DkraT8FwcZw+0CAKdpgDK2zfdKFBmqgXVpxJSphK5kLaRzpsT5osHp+BtnOiRsZQlhOb82TDwOaPUG8mrvza6kqnXMnCpCD4rrkGrU5dE4uVVzL+ldHiGLwXWFC4b4/OYQ9Q9B3acWFtqjE0Lpo6R8LyQJDM87IDmbYx0PidUu0qSuX+QuCwr2LcbPldSyQqaaxZEkWFac35rGtHKzgoxdv42TnEGZe0okSVrM2mVX0TUQss6oTlJiAlbRNbiVtlbKWNZHCkJiAQ9GGc1JbxYZpciRYA6gEahil2yoFVlmrPwmuMxvJjLZMULUBwBKNYFl3CAbuGqvEohuW9FiKigyh0ohWRtqIUOsBedOJ0iZ2Y8g2UAwORbzm+Bd5feeLtGSCsZIPfvrl3PGXF9yAU1ErGm2qVN3lZ2oD5RwE1YBMIcIW3/G0K2gGGZ0gpRsMmVUDWiphIdiqrgG4WptDE7JpGiQmJJYZDZFXqdrWdRNtJX0TsakbLIkuy0WK0684dJYHXp9zOFxHCsv10RLfOvspPnHwRv7w5js51V7msa0DfOGR44RLh4mXBbOPa2YeWEUMU8RgiB0m2MEA0YixeY5otWCmg41CsJatYzGrt0vmvnSQhT/cgMGwmuAjlCpSfOeQZe6Wqw1ykFQOcpelopA3yvp0+03f5/F4njPyWBJuandPM4VrKoyc60kXpQlCN4Bthfu3GsQWLuOIDRXDwy2W7wwxr17n9cefZE45R7ATpKGsCaqe4+FqKcSYW7o2pPucUK8pbZAuQ84eLTBW8tLmk4TX5/xe/DIeeuIoci1AZoJwS7DwgGHmoXXQFtsIMIHCKokJ3X8ys7SWNWFfsqUlyYIlmzWoRBLlYJRAx84lnR3I+KpDj3NQbdAzcTVRbLL9ZR3p8fcM1QOVHQ2Q1ge8KwRjA+RlxpT635HIQGxPcfm8UXdAV85Xiv5G3a3MSJjclq56YvmkkCvG1xFj29pt+5vc97hzefL1uGhdrx89vo4tfqZNtWw/P9d1d/HY38X702qalsvr2+9EKQQbCoF3igC9t6Cy9/N4Pb13XRQaO37xPDq+P/e8Ni3t6qQYv2M76sezpVO7lia8mIzroRKkt7mkqQnQJfUv8K5O6Zp4LIX7zSuwxbjB1P1OOqDLz2jsWKpab6ogvZNIvlN7pSjaO3GPiaOR+EwxabT4TRgru1G+1sY9j8vavuqZtYzLnDJG/ZjGun2V51ZO/r8CKtf5tHMs3eEvcLf0eP3o7Q7WSUE4t2Gh9deFzWISflVzeULQ3DZJZ5R9okz7Xx5rmqDtMvOZsXtQ5dY2ilDqsd9pDSS1iWHT3NRXJGLvQ/RmUhyeImJPFbYLR/pOKcfrlPfwMlMZ7CwWTzummdIGg4Cam3mai9lllBH7Snk+6Yye1o6n61r3eDwej8fj8Tw99p++247GiZwTEFRmKiFlW8eyFKlzgykctDLVboA1EMjMohJLlBmCfjFTsp+jBpkTqTLthNVMI4zBdGJsqDCBZPVFcHN3k76O6JuIhsjoF2m8FdbV+RHuYTYVMDQBQ6FdKm80GM1N0SWSWeiesYhEu9Tboaqe1YUBlWjXDkGVmtxEgVtXCNQwRxaCMhRuKFwaZxuqoiZ1sW9VDDCHCitrzighIJDYUCJSw/BQzIW7Fbd/9WN895FPcCq8zJppYawkljnBKc0X4qMMnmyTtZrMPiYJ1pNRvaxS2CocVyLThGuJmwBgLbY2yLB5UjHTGnLx9ALdhwOShYDBEQXK8thSi2gpIF4FJIW7G8K+JZ0RmEghNwbYZoRttd3nlGSux2UsphWPOtLF9iZ0LmmZWjcWmRnalzRqmCMSjQ1cfens4ZC1KELM9BHK0li1qNSghsbV+x7mlbP8eaUUVqUAAVmzSCWVWtRAsrbRQkiLHihOX1jgUqvDUIfMRYNKKO7riAPBJuA6Qz0bc3nQYSuLuGP+Iqc353ncLvDKg6dpyRSNRNuIhkjZNM1iuxyFZE3HY+k8EVR1KCOhkcK4fzHOLSXysSFwjSAqZqILAzpWJHOSvGMJmxlhqGlEGZu9BijrHIApYCzprGDreEQwmCHtSD59+QRKWG5oXmYp63LgL0PsZs8N7ihZpLsuswtIl9q2TGdnLAQKctxwpbFY4b5LeUvQCDKaMnUO6aBPVw5oy4RGbbC9ITL6InYztqUqanHrStQPRU5mAzLratGrQqze1A2ua67QDhIeTw7SVUPnwCs6si+eOcddrTN82/yn6B2PWdEdnkwO8Adn7+DcHxzCSpfyf+Z0QnRuA9EbIJVCRCFWSndP2xoQb2iSQ4bBauDqzCoFaVoNIpWDgmODSvVa28pNahHaYo1140vXjHfC4/nbg1VUGUpEHLkMETA2MY2iVrQNJLYZolsBWScgayuGc4LhoqB/Y8ZX3/kAbz/yUdZMi88PT3BTuExDaOIxfeC5uw9MuqXL18+GxDPp2pw6mDhlWWaVm4S4bfBYFE4pQ2YVc3LIK5uP0zqW8EfNO/jk2evIv9SlfdbSfXTT5RhX7nMxsXtu05HERNK5oAOBVRAvW4ItQbIoSObdREtXOgWyGcOp6y/zqvZjrm1TEkyWA6IGOZauEnDp1qvBYFkIdC6tN5YqTShQDN5KEHnlmDaALn7bysHW8IXi/JkmIoqJf/fYZj+C9LTd79qGaYed2O+OKbyZcANXYuz49kru77iTtagnxehJ0VkJW6XK3kmknSpi12qsTlv/SutOTxN+SgG6PJFJV/TOx5ryfZ1Mu7qD+3FyuSiuT/le2dYrcbd/2bDf1DoT6a9FUdt4TNitu4R3PJbZLvYK4SYz1zIjiSAofhNHNZsBKMYRJp3V03NP1NokZSFYi0pYFmFIPQsJdXFZCNf/qBO4CfK27M9KWf3typkV5biKfr5I8yI7mCtrVV5HYCRO1/vs9fbWa2GXfejyGk07v52Ykt57m0g9+Xm9QEVqTa1usxktG1tO7RlhUtwsMqjUJ8WU9aQnGRekzWj/O4j4xgoMqnBHj4vXZYrvyXtavc1lOvLx98e/f3LifMoMaa69e39makLErqe+ro45xX1crqOnvF9PnV2lvRaGsHA7yz3EX3DPanVBeZrQHdac2WW68Wl1t3dyRU86w6eJ7+VnpTDbaoB7PB6Px+PxeK4uV5THT5R9JNdfROS2JsoVabemzswtti8GZk1Ri1hllmg9J7q4VQnQCIFpBFWqa5EbNzgY4MSvuQh93ZCZaMhARyQmRElb1ZduiIwMNSa6aYQbGQYaQpNYkBjSBYMJBTaUo46iKZwNxqVkJlRFFsFCWM8NtnAv6SAsnDMCoQVaufMLejm66S5t3g5IFkLaZwajmttCFDW5zSiFV2bI2wFnXy/5xtd+mu9a+BvmipTCbZGyEGy5wdSOgqPwoDzCet5BpRGz/QwyMxKmYTSgGhXXsiri6NKFCgT9oxaZK5pPBUTrtqjJFpLNGETuPiOZWTcAG7kU4M3LGVaGzsUeKPK5BslcSLySEaa5+w5EoRPZ+04kFDjRXUeycNnbKpVpsFXMqO0nLk2ZajLzZE7WDdmYb0IvIBgYto4qWlLQWE6rdOvPO7XPE+EGoUVmyBsC3bC8+Pg5ntqcY/0zB5BpRNps8ImLbeaObzBcCDjWXEcqy0KwxVLeZUvHLIQ9ormchzcOsZnHXFrvkPYjbrnuE5xP5zgQbrKgtjgSrHMmWyQUOQcLx9yabRXp1w2hdPWjeyYgtYoZNXTpvtFkKIY2rNxT4+nQBEMTIXNAwHBBYuYS5tpDMq0wRmK0AiOQqSDaMiBg8wbJykssyXyL+Ycz+r9zgHtPHOSP7uyR9UJuv28N0W1jpXD1pJMUWk33WhtsM3bXMc2qOvUU9dOsGA3a5E0IhCGWOS2V0JYJM2pIQ2RVSnKNcOnKJYUAERAKdz10IUoo4e4ZqQ2IC8E6EjlDG7j04yojMQELgWZdt7iYzbIQbNFSKZ/rn+Rruw8CLg3s3Y0n+LaZT/PPw+/idYce4fPrx/jc39zE3IMHOfRnlxBlrdg0Q2z1IY6ILw+Z+0KXw3+9UTmiUQpRDqCVDoliMMpKxlL9WVWk9zVFWQG9u+PL4/E8PwQDixrkLn5bTffbl+WgJLbdxAYS3YoKR7QkmQ8ZLEq2TkJyJOfUDZf41kNf4sXNpzgVLnFMpVzPgE/2b+AzyQle33ySxjMcSNtfrenx39xSHoWREL5Tfemny7aB0F0m3riBUHe/NzYjnXJOKYpG0eZhIdzeFZ9l8eAWTZXxR2deSmPFYuIAosB9Ns0iwwsuJbcuBGkdC/KG22+8YehcsJx/jSDrQtAX6KbFxIbXHXqEI8EaQxtua8/oPMfTdpe/zaX4rIQZX1ZsIxn/7Xa/f7KaSFU6pVOUE7GFmZqi+TmnLhqL4nX5d0n9qyRG20ytC72LGC3Gtt3+/dwtdOp1okvkhKA8mbp6Wh3oyuXM7oLxtOOXzujJlNzT9mNqgvSOojQ7t38ae6Xv3g+TqbkpnsMmRaJy3Um2i9xT3H77/FobnGP6Sre7VhhzSZeC9LSU2ACyEJF2qhddbO/qMZcKbul4NuPrl6KrGhemKmG6fL8sZ1EI40LE422Aok9Wa79y/dWyLIbL6iVdqSlJfdaJE5e3nef4OmU/2i0zkNtiInghVuuij2smJuzXz7e8pvX3y7+lcGWOqnMR49elKBu1K5MidnEdq5rdL5SMY7swXhtaVJO5pbAkJqjqQY/SLpef0fb73Oj+4rKP1B3R245bu1flhVMYDAa1zUk7KTDX72PVsMtV/s2dJq7vxrSU29Pc03WmibmlODzpVi5d6fV2ZSaY6nSusr4wXfiuu7SryQRTnNv7abOs6g7ufl5X8r7H4/F4PB6P55nx9IrLlcJ0pquOkQ3keMex3rEzzj1cuoHKQT6hrdtXb+A6mODEKSmLOn7F7OlAVmJx2pV0OkOMFSS1h1xtZSEyjR56S8eoG6TTDG1Aw2pKP6WeyzEqIO9ESG0wgUTHEhMJ0o4snDHuWd2EAmEsOhLYwAmQJgQkmNCCca9lBtFmiA7dNTIBJIc1c59vIQwEAyfOyxyCxDjXsHGa+fKLQr76q+7n78x9jtQqejZEI9kwDeegwdJRCQ2Vs9Dtce5QTG8lYuYxV4/XGlEJ0zZUriNdpgcv3Owid45w3QrIZjWryx3ml53rVQ0s8YrASoluWbKZQqi2YAKBSizhVkbUVuhYYqOAvKGK2eAGKyUiUJimG3yt2gKYwF3XMv276/xTiP/lDHKLCQThRkbnKcXWjREyEYBbbiWI1KVFeyHUz7WDAQCi1UIm7vNODrcwIWQHM2ajIQ/2G8w9BIt/eR5R1krThoEO+OKNd7J+Y5PfuftuTtxyidcdfgSAU40lbm+eZynvctttF9FWsp63WMtaPNFfZCYc0g2GvKh5jjnVZ820uJzPsKi26Fk3QDM0ISt5hznVr9zRm7rJgtpiTvarges6xroh7jPZAgDpbEAyB2jBVr+BtTDbGXJkcZ1+GrJqZsha7haSLmpuvfUcG3fGqG/O2FqbYbDUovW5NgcfNdgowLQaiDTDLM6AtcjNQTURhbSozx0GmGZU3T9Epp2AEwZYKclmLO0gIZY5DZHTKhzSdVEaYIhzP7dxacpTqwoXGaSFGKEwY+5qjWRB9dgUTmlQgeFwuE7PxBwLV7mcz/C5jeN88pFTvN+8ijfe+SBvmr+fDMkFPcPJzir/YO7jXJ6J+Q/h15N/leKx4W0s/vET2IVZl2VBa+TKJmpzyNGPDhEbPYwxiE4bOxiOBo0ylzHCao0QxcBeq4FIMpfivLw2AEUd66mDaR6P53mleXGIWukB7vnGRAGmGTA8GDNYVGzcBOnRjOtPLPGyxad4Wfs0c6pfTSJSwrAo+xxWGWtGck5HHFQp39z9LL92/ht5ybGzXB9YQhTZTq6xZ5m6iL2TW/q5wg1Wu3ufEqYQfwdT1y1LuwBsFs9VDZlxUG3yPYsf5+TXr/L/bryOA59ogYXhoiBvgwlcdheZCFRG4Y4bPQfqpmD+oyucGHY5/RZJNufWvf7mS7yh8yCZDarU3ZODnHVndOnm1jXBv/zbleIY3650T+P+KhxQ7rfODQgXr4usQVi17Xf/eaEuQNcF6VIksGKbWCi2vW8nxOfJ16P1BSBq4mpdWJ4Unbc1dUIM3+aWLtar3MwTbuhSqJ5MW72fX+u6wFG6fMWE8DEmvgjGxIFp57VbTekd27FLPE+m3DZTPrvy/VLgNoXjMCjFhuLi1tPllvvZWaQ2E6+nrUP13FdP2V29Z93EjWmuvmuBsRrN42+4f+puYqAqLSGFE6RrLupK7Jy2O+H6bxjp5ndbi/tDUyl3pdBaiLUiCFwm/7rjV5YCc1DVbLZxWPVvgVEpoML9XE5wddmFin9do4oU22J75q1pk57rzmbDqL70xDpVv7cUvOvfzVptCxuosX0KPXHMaRnongnlGEBdmK7zAhSpVeFUNqiJMlTlfbSeJUSghB2bdOVESFBCo617X2IxtUky+RShdBpllpX9tLksSVBva9WmCWf0ftJOT2vLs82k2D5NQK5TF6SlMJRT7spxhEkyq0hMLW15cR3qjuy9iGU+5pLeT83oK6F0tte50iwhHo/H4/F4PJ4r48pqSk/8Wz2rFSm4bNm5m5wBbC1Guc6gQJLMuL/jdY2J5PjM42J9YUzVWbWhrI6jI0Ec5lPTtZkifaG2Ei0EQxNXdWYjoYt0vU6EasiMhUMbbJw6wNaJGBNC3sS5WwKLaRlQFqRFhAYZONE3CDVxnBEXgytRoAmkIdWKULmH+kEaIor6V9IKXrR4mc+0r0MEBptLbC4glaiBdIOciUvxld/Z47rmKo+lh+jKISfDZTIUK7rDum4xNCFdNSQ1ilaYEc8kpHNhreauHV1LJVzq8bGaVtalFwPSbgihJTwfEfQh6wrUwKISCDddWsp8MYPC3SO0RWiLHOaEm8XnBqjUEPQ1aitxQnhQnHdaqxsWKnQkqSbEitGYYkVR11tlBjXMaaxo1JZCZm4yQLRlCfqmqM89ZRDheUB02thhAkAwHE0uGBwWLBze4Hx/Bv2lDu1z6agOdq6dMxYILm2wuNqndXmW4cJhfv/gUTZv0hy79TJvOvpFXtQ8y0reAYrva7BFQ2akNmA1b/Nna7cSq5yXdU5zPFzlUj7D57ZOIoXhttYFjoerletqaEMu5zM0ZMac6LMghgAkVo05yhSWLd1ARzBYlOQtN9s/G4TISGMspFoRhznHTy6zeuIIKgFiw+qwiTaCWGnajZS0G9K/HiBg61iXeM1ipUtv3ljWNC4pRKJxGUeNq8MWFC6IclCoXh5Agm7YUXq1KvWjE5dlkb6/pBQqQqGRNnQD81YRFbcNJUw1icUUk1qUcPvqE6OtZFM3ORktcymf4f858wrWPnaE41/S6FDwiU+/lI9d/xLaN6/z2uOP8fDqIT6xeJxDapOXz5zm//PIqznwVII+tkj/pHOJr96iaF6exwoIEsvM423CpxR2mLi603mO0BobFl38sv5ekfobbarUgtX4vLHFANwz+z57PJ5nH9VL0XMtNm88yMb1ksFRg5nJmVnY5ECnx/Ew4XBzgyPxBgeCLeZUn5ZIKkdsOVi4WQyqzomUoRWEwvCPDv8pf7D5Et4282kWKhHsym8Eaj/bXMFA6LMhTBvENmf0NPGpvk45ASmtibV1QqHpG1fmoryuQxNihBtw/srW43zmxSd4+JFbiVcsMnPPLCYG3TDYpiFoZwShdtqElmS9kKSvuHz3PPNfHNI432R4WKMXMt589AtoBL2ivExmgu1u7+I32p1fIU7bwulna67QyVO3E+m7a+m9mdimfA4uU56+YNghzfaeWT/qKbP3cD+L2jpjy7eJ3rsJ0+Pvl7WjYXzQul4relrq7WrZPge664K228lIjAYnaEy2W9rpLur6Pqe93kkc2UuI2U96753Td5cPMZNORteiHW8hYrtDcRpOhB4JV+V2kyL6NZm++5kyJa33ZHpoUau3LMBl79LFC2tcdqDSAVy4m0WZSrtwM4sym1gtvbYNFCYKi9JXxcTo3IwmYWvXX6BMs10XoifvNUVWtOorME0IrgvEBpfVqxTra+e4q5A87b36svoNydpRTekys9hO636ZUzljraleVxOrisswclNv/50sBeJp99xpzwi6nFRTZnOolxnYx33CZeMajX9MG6u6klrLAJPu3jK9d+VS3sez2eR9fLIN21KIV+7z8efQnd6vt30nsTiU2ydN7lYPOhS6aldZ2mSsvRPH2islOYyfd/l+3dU++XwU1iauezwej8fj8XiefZ6WU7p6LsyN6/RJ6cTIKBxLOSWMdbOctetUikxj4gATCnpHBVYoGoCNQkRaPPhZW9VjqjpiGnccYzARBEUnJZCaoQmZVYPqYXVNtwiFZk710VZyIZ9jUW2xbEPOZgv0TURfx/RNxNHuJme+StGMMrpxwmw0IJCGYR5WD6upVjRUTiBdjcLUBERFPZ/ybyksQx2SG0krSKEFQ+1EJW0kx5ob9E5epBlkRMpt288jEh2QG8kwD0iygCPdTe5fP8aJ1honGyus6DaJCVnKOqQmoK2SanBHCUMU5WzNGEykXC1uJbAIRM7IgQpVvjqhNVa62dnDeYUYGJqXC3e1BZVC3oZoA0CQz0qiLYtMLSozrkZWIAk3U9LZCHJDuDJApDnZoQ5WCoK1BDHM3XiSEOhujG4FmNilvFSprU1sKGpLWwvG1b8OVgeI3BBsBYRboatZXKwfbmRu4CA3o+/Z84htN51IXqRn1hGkHUXwsjVedvAcH3v4VhYfgWg9Hbl9wwCWeohuFwYJdmMLdahDY9ky+9CAuUfb9D9zmHtuOcwbvv4zvGX+s8ypPkMTMiOHXNJd2jJhM2jy306/jJX1NivXt/ja+Yf409Vb+auHbkJsKf76phXeevJzHAg3acsEhaFvIucYthIlLSFO4JUTM6IvpV03KaEhyOdyZCt3kymMYOXSDACdhT63LF7m4uxhonWBauQsr3YwvYDhgT4zrSGNZspQWLKuIu/AcBFM5IT7aC3EyllMaOk+CbOPp6h+jlXCfZdFMalCKYgCRJq7mvLdnEjmVX3oMv24FIaIcceOLNKXljU0y3qeGlPVlC5FeykMsj7oIQwdNSQUmtQq/tOjXwMfXOTYgz3k0AnH3cdD5h+OGd43y191X46OBT/78rcxM9+n14+ZvbdJsLbG+otmWb1NIlM48vVPEaucQR7SDDK+dP4QC//zBDKztC5lRBd7yM0e9IdgNCIKi1S/rkY91jrhXgpXS7qYHFTdLz0ezwuKpVfMkTcEW9dZ9JGE7lyfI91NTnWX6aiExbDHbNCvSgj0TIwWspogozCkSIa6QUPkdKV7VmoJzc3hBh8HHs3miaNV2kJirH1awvTToUzhrar8yc9+Cu8ra48hs/G+BmkVrvREQ2RkNqjqFb5x8UE+/6pj2E90CAYgU7DSYhsG1cppNFMW230aQVYNOK8nDS6Ei1jRINyE4UHBrddd5Gi4ytCGZDZAiXTX9OMl00RjV4JmvN60E/bqr81Eym89ug4vNA1DsF2QnhCO618jMW39yf3Vtq07pOv72Mv1PLWpxXaTzmg5sW0pSNf3paQZW79e13Q/CGErd/G0tNtTXcTsnHJ793rSZqqAslf67v04r8cE6IljKmHJjNqWTtfY6YJOtf02o+d2R2J9+Y6TO15olGmgTC198+R7ML3+75QUUnU3tDV23B1dX7+egruW9looVYnNIggQjSJddiPGNiI3Cd1axDB1l9bakUtZCCeuSllNOC1FVlGkuLZSjpWeMjUBeerNAKoxBxOMnM9CimryrxB2VPNZjN8chB2Zl4Ut2itrx7GjydujA9rRfhTY4h5txaid1RblBHEYuaJF0c+uv1c/3iSVMxwnsNevRf393X5rpRxlrquL2X8L+wn1Wr9lzWaJHcv64fp324XKaam9K+G5vnwCU/zmluw1yWdsokstc0ux1vZ919lDTJ30LJcCrak9F+xF6fSeTIm9E6MJRG6beh1rgMSMxskma2CXbZykLmyX4nV5HCXM1OQDddf5pDBef3+/buvJaxsKXWXRmJZB7uk42T0ej8fj8Xg8++eKRWlRaIrCFGmqis6cFWCjALIAkWvnmjYWtEFmGpkqhHY1obOOIFk0WCUJhtKJekladZjGXLDFa1dXVmAC6MYJTZURy3wsxdDQuJS9a7pFVw54JDnCg72j3NBcIrOKi+kMW3lEKAyB1BxvrXG4sTl2fgZBbiSpCZBYWkFaDSIF0hApXaWc64ikGvSLpC6Ea0lqXA3fvHBLB1LTDp2jNtWjS94MMgLhXMflQ7FBsJS2Wcua9PMQKSwNlTvRTeakOmA2dOnLu42ErbmMzetiWpclQU8TbKWuNqVSxfV3HWwxzCqHsQ0h60CwJZEZGOXqXgrjOudB3xL2BLIvUYklGGpE7j5vEyrkMEclhftjkGJmmqze0qB7JiPMnPPV1eyCbMbV3baySN9mqVz2ovi7/JwxIBLX8ZGpRg1d2/qHAlevuRhAeMEIcFmOTVJoN8nagnQOVr4y5x/c+Bn+fOkmmg82aC4XDqUj86jL69iZDkIdJJ9voZa3EEDWCUi7kubplPZDA1pnYvLWPEfiDY4E63RFxv+99tX87t+8kmhZkc5rTt1ykWOdDdY+f4AHP347nzp8G9mChsjQfVSx1j/AR+PbeNn8U9zSvMjBYJOOGtKWCRumwYZpcEht0ZUZbZFXg+W6SItvhUtd3z7Uo7fUIroUkLcsYSoINwQ9Zfls7wSNdYHMoNHISB6Z4fCnLGlnhq25WfIZi2iASgVGWXQD1BDa59z3MOsINm/L2bptyOrZNqapUDMpnOvSOi+ZeVLTOp8ghxmIEN0MaS8MOBBuFUK0HR+kZzQAWTqm9cTAQokuXNOZVVVHtEz9Zqzr4PZNRGYC3vvw6wg+NM/BP7voJhYU5QTUVkJzZYvWlyykGXamzcKDXfJOl/nM0npsiYuvO0DaFZjIol66wc/f8D+q458K17h8fZPvv/RjdE6tQ5zy2Nl5mk8s0DpvmXkypXFuE7G+5QaXMu1qUodBNXBojUVkBlnWc/d4PC8oekcFumHJD6d0Zgd0GglKGgY6RAnLlnZxuxBsFdkeDEqYyt1aUjl7raIhNEq4wcqvaD7BZ4fXcUhtEQW5M4hdZeWldFbrF8LvMIyl79aFw1pNGUwc2pBQ5Ghk8bcu3MPloGfOyWiZv3f7J/kv2d20P9VEZqCGwj2nxYI8V/TSCCEsi40e3TDhaGuDXCvW1hcxJwbcdHSJNx+5n8Vgy7mksS5997SB06peonMxaysKYbmov2pHLqftTqDpdaXHrgm2ejaedk2eF+oCcyE4jwnKVmw3A9aEZFt3t+4hSIviYbMuSG9Lwb1DuOyUqnsnQZra+5Nu6roYPZnOeyfq7uKqLnVtP7vt40rSjtbrO08S7EPw2M+x66JzPZXu5HulyBBKXQhG22PG2HF3nUHseb6Va7omWk0K188rk4LyHhOLhNpf2mF3Dyn6a1JQ1lp2hxBU6bmldGVjVCFGF5NtCSMn+AYBtt1Ez7bIOyFbxyJ6xyQ6gmgTmpcNYd8g83KQgFqqajcpGQFqaJCpW68UpoVlLH10JVIb1y+tjwvUnc+iTKVdTcCurVMK0oUYPCZEw9jEzj2veb0QuRBuEn69/rOEbZUzilTdVonxlMCyeF0Xul2rR/uvnf+u34Pd3itToU/Wlt4JKVx69RcYZZmKEO3SPwuqGtNKFOUtbD2eTSWSOrHSOahHGRJM8Xp6Fgj3Gz3uxDZWIYvPRE2ZpFNlwKBIN14cv3QG18VUgyAzaur24+c8YqoDeEKY3ou6o9ogtgnA9bG08TrOaqobuxSVde3aqloGmsl9AlPTd9fPd0f3tpXOm1K0Y1LQ3i9lW0tCqSvHtRQWrNn2WTzbKcI9Ho/H4/F4POM8vZrSJZOzl5VABEWKWahmS4vcIDODboUMDrhDilyQt91sd5fWG9fBMta9njLYaUNF3hSVIFXV0iudkFbRkBl9E/FQcpT71q/nqc05EjNK0esO7gZAAqErIWqgw6rjEkhDJBPiwtVcurLLQZSmSquH63qdoabKyI1kYCIkloEOCaSmJVM6oROwQ6nJjBpLIVeK0eXfwzwkLdaJlC4GiyQraYvcqGrdxWYfDsG5Vx0gXAsIN0MaKzHtCxqhIRhowo0UEu3SAMugqquVtwRqyMiF3Hf1soEqZaVKnDApk2KSAVR1r2SmXeq0IKZ3XZt0RlRCtTMgaXQ7drXEKSYtCEb1vorBSCGKAYSJzrUwFjUAPQ/9IwKZQjoTEi9r991QL4DOcxggOi36N8yzcZsmWBzy5psf4rbGee41t2BiSDuSqB2iG4rW5XWXyr4Vo5sBKlDYRkQw0Mhs5P7evHmGjbsHfEP387RFzoc3X8rvfuoV3PC7mmi1R3KwxcVXHOfSy9fJu4ZDf5AicsvKHU1WXioYHHGDQktbbR4ND3I8XkOzVdVQbouU0/kCK7rDkWCdBdWnITQNYclwcSVzV0+8EWX0B4rGkiCdEWRdV3+c9ZB4SRKvuXrfRhqCLUHnyX5RY9ySLkQkswodQd6AbEYQbloOfG6ADQQ6VggTkb+2z/ztl3nlwdN82/yn+OjGnfz5pZs4c3aR6FyT1vkW8ZpBWDjYXeXWxnkyGzhxwUo2bZNIaJQcEgpDZiWhMKS1Op3DIn13ifMfOhGgHLDXVhKKvKhJCpfSGT6xdD39++e57ktDiEJ3bpkuUmlbRJKNnB+9Ac0vDajKFwjBxg3QOg/dx2DleIP/uvwqOirh5Z0nOR5ssCCHmE7Od9/4Kb555rP89akb+aPbX8QT6ws8/uQ8C59dJF5foHk5I9xIURuj1N2mmPgjrEUMEjexx+PxvKCQGvIQZGAqYUkXE9+0zciKyTGuvIgmKgb1XK1T41y2WKIqBXOR1to6IexUuMZDyTGeyBc5oC7QEILM1tzLsM05XdYrllMEn/0wKUZPpup+Oqm7FXZHJ/Feabvr+yjrN8/I4ZT03yNXkbZyJOAWvw0KS4jm5sZFXnfzI/zJ+h00LiiCvsCEkrwjKRK7VIKgLjPoBDnRLRv8nVMPcrKxwpFgvRgEdbJxatXUFKL1AedSkNa2TLOdgygHYevis0RZU0xikNWgcZW6eyKVd+menkwD+rwhJv7d9v7EdaoJ0gBioib05HZjgnRtnf0kEJhM0z3Z5LqwPBKKR0LzTuLxZC3o3SjF6Prx5ZT9TttXPR331HrPU45V/3fSGe2Ot/P3Zr+pY+vf/TJ7zUjMGbW5jipEpCl7HH855XLuLFSXIoSp0v2+YOqG1t3PE65oEYTj69TE5W31gKWolgnlpilZaxFKFbWYa+J0ECDKMk/CvbZx6CZgRoHLbtYMyBuKrKvIWpL+YcHWTTknb7hIK0x5+Mxhhk/GNC4HqMSVPZAaJ1Az6u+BczkrKRCDoi9nSuGYSswup3OIaiLz9M+n7DtuLwdV+7se9IUQjXQu7mr/RRvd3PcpN4m6ID1xna1wrvJ6aIlSEJe2+teampt6p9/HmohOOem//GxLMfsaZjJO64L06N+R8FoK0jsxOYakhN21tPa0+2A9nXh9kpBLCV0Tjss2V8ecfqBpDuZty6oyH+W+9Lb1p6XVnnxdicHCICdeQzG+NfGMNHbu2Op5Y3K/pdFiJ+o1qXeifszyGUfXz31ist40AXk3B3XpGt9NeN6vA9vj8Xg8Ho/H8/R4+qJ0+TxtivrPspitKWVRW9o5oUvBJp0JyduSdMZ13loXnes52sxdR0sp1xHWtXRTVWfMvTaBJFlwy0Kpi0EFUXUISpfMatbmYjLDua1ZBllQCblBsf5mHrOeNQoXclZsa1HCzcwvndRh/YG96Hgshj1aKmEp65KYYGyG7GwwoG8iYpNXKRhjmZGYgE6QMtAhs+GA1Ixf9lKkBvdQ3VBZtY7EspXHSCx9InIjq0Gphso43NqkdVtGPwvJtGJrELP2VAfVF4SbATNPhMw82kMVNXsRAhM5oTAYOvFXalCJJWu6NgSJJZ0RyFRgAotMXdp1KERpSVGHS5DPxvQPSKINS9DLQOLc2AhsJBGaUcccKPtOojYQUH3ejP4WuSFedxMUZA7JHCTzimhdwvCFMYCUzzaxB9tc/MqQv3P3p7iheZlj4RoHgw2+6ej9vP/lDZZbC6QzMUEfwo15ZKoRmUb1MkSSOVG6lxGcXQElyY4vcO4N8K5X/w5HVJ8LusX7/vprOf4/Jc3HLpIdniVcTzn2F5rNMzNkb+6zeaLJwscvcmilR7y2wPlvTrD9gK7SrCVNLmYzxDJjPW9BDMeCAWtmyJ9u3sZD8ii3Nc5zKlzi+mBABlweurSlwlrWNlrY0JC1paut2XCib+u8pPOUIW+4iQsC1z9M5yJ0Q9I+vUXzbEbrMZeK2zRDkoMtlz1Ru06sUJaFL2ZcjmfZerVlPuzzkmiDkwt/xd+f/zit2zVndIffuvzV/OkTN5Gux3zHwhleEZ+lZwPWTIMV3WFDN9B0GdoQiSESmq4cjokcmVVoKyvBZ9SxdYI11nXAYyldTW0rebK/wJkHjtBeEQwORyAhWE9wqfALF0KgYDDE6qJbK6VLu2gtZtGlOm9fMHQf79FYa/FXn/8KeicNf3Hrjbz5+APMBn3EUHE4XEdhuSs+wyuOP8HB61Iu3BHzMzd/JxvDmNX7Fjn6V4LGRhMbBS5ubBFHxrhJJ9dQnTmP528TOrIICcZItKllRikdGjiRNbOK1CqkNcW9zJUbSF3OUEKhkdjKaWiAlrDcFp/joeQYp4JlTgSleO3e3805va9a0rtgMGMCtLH2GdWSDrFkpYtxwvFbn8RXF5s1Ytvg8pppFaK0E2dL17kUTuSHmqu6GtCUpNYtn1M93jj/AJt3xdzXup74dJGyNndO6VILSk2AsRmpVsw3Brz52AO8uPkU2koyq9g0zbH0oOXnVmen9KEaCTYAcjd5cmzgV1Qu6qp+NNSEnfG6vKWb+gUzwFqpxna7qFwXd8rXdrTd5PrVI2Sx29IZ7bYf7Wf87+3v14857ROZFKPrQnS5XE5xR9dF6m2u49qByj5AIMxY2u5p7ag7jHdap96GOjulua5eT8RvKPW2ZfVBfDUlpWr9O113RJcEE7VF5S41V6WYJhpMCufbmlBNMi43HReMdPXvC8IlDdvScY+n3XYCsqvbPFpeptiuJkKWy0rRWUknQivlvuxRiI3Dog6zwsYKU6TVlkmODSQ2lOTNAB0L8qZ0z/ixK+ejm65MUXJYc/zUEl9/5CEaMuP8xgy9VoRuCIR1k8qtnWLgtLisWcr17awWCGyRWY2p4rMVVKKvy862x3WsC7v1ZfXrKep/My7+XgG2utaFAD0ZjuWE650yipTLrR1ll5s8D5j+Bd8vV+KWfoExqgftnmRikY+JvqVjuvydr6fqnhSjxyfymOr+YBAExbJyIk89a0PJ5D2rpF73uCwrsJe7VgpbOYrr65Yi7eTv9OT9U9a2nVrDuVbqw0w8T5V/l25pWWZQKdJzq+IaZFYVY1/u3JKivElJ5XJmelrt8jwnKbcrn2X3i3vuHRef9xLe99OmMYe42Dutucfj8Xg8Ho/n2eWZOaVLah0roOpMicL1jLX0jgT0jwpUAr0ThpMf0WChcamPMAZbzMoWg9SJ3LkedehKlCCbN8zFA9oqITHBmOO4JVM2dYPLaZez/Vl6aUgjdDWfU61AugfbqBC0AXKjCOQoJXcgRoMwTsjWlSAtheVAuMmMHBQP6A2AqlM0NgPWOmFbI+nlMe0gIdEBCkNTjjsao2JAoRykjmVGU2Vs6ZjNrEFTudqFvTyqtsmtrNKMh1IzG2taQYrpCqKj5zBWcHZrlnM3LZDMdjjwWUmw1nfbNhUujVrpiLZYCSZ0orFKLKZIl+3SbrvP0AoxNilWGFeTMdp09XBlL8E0Q9IDDXQssYFzT1vh9uHGCifSpcnRoFH9eyTSnO6ZlNYlSbiZc/b1TdJusR995YMHVwM1yMjiBum8+0xbMkUXqZ9f0niKG2+/zPItHe7vHeeh9cM8/PAx4suK1nloLltakUJYkGkhYs60OPc1LX7gtffylfEFzumYf3fmmzjxB5Lu5y6BMZx7fZvZRzWzD6zRDgT5x1os3wVSH6b91JDWpZTZjzfZvMFyuLPFjZ1l/uipWzk+s8GJ1hqvaVvWTMCC2qJvIk4PFnhw6whfMXOG17e/SFemnF6fozUwzH+xT97osnGLJm9bonXB4qckrSWNCQUysyRzARu35xyOMnIN6YxCpZa1F3XpH5K0LhrCgSVeyWhc6GGFwDQC0pmQrCMxoWDuUc1Sa44vHDjKJ9qLHFfrhMKgBCzKAT995H/x00fcQMOisqwZQYihLVIitc4Rtc7D6WE+sXUja1mLY401DoUbLKqtKjX3gtrCINkwIZFwbsSz2Qzns7nCIa3Z0jGh1KxmLS4lXR5aOoTp5mxdF9A/Ijn2FyFykCOK1H1ymGMDhWjEbhJOv49oNEBr7PwMK3fNoVKItjQiM3Qe26JzWiL7KSaK+V+3vI7NE5L5Lctv3PTVPHVsASUMN8cX0dElGkJzx/wFDkcb/JfHXkd8YQuWVuDEYWyoUFuJu2dqS354jmSx8XyHhMfjmcAEYAOL1eOOnpLcSDKhqsGx0GqUtURCoykcLFi0MEgE4bYjwO3RKiu6w5/1b+Vt3S/QkorMGlJradSeoUyR8LlEW/uMhWmFGBOit70WYsxZXUuO+ayjcGnPL+czHA9WidBImVXZL8qBaj0xqGmKFN4aVa0TipzvPfLX3D3/OP/j+Et4amkOhgFYwSANCYpSLrmV3NRZ4pbmRW6NLnBJd10ZF6u2pdfeDyOXtBOmp33edUzhGFLloG0h4o5Sme8zze9zzT4FaSFqmrRgzBJZidBQpPweic2TQnSZ8rsSieV4nWhTvl/se9qA+m6CtBLbU3TX91MXqEtk/aWgygK1V93p3ZzLo3Wm72MvEXra+3ttM0mwxy1lcmKG2cupbCeFcLvjZI6qjWVnpdi2Xoe63m8sXz/fVOm4p6TXrv9bpeCOQic4B8pN6g4K4VlJV3NZCWyoMJHChhKjJDqW6IZER25SuI4FpSYTbbrJliYU5LEToq0SmND9hpkQrAITlc5JQVcNiWVWOwn3jy3mqJsqzXbxtnbjAsIU69TScFtbF+Jt8X8nWtfdxiVWjP9bCc+T4nLNxVyl/i5fI0apwIs5PtPEaSHHxS4318Htp8oCJoRT6Er3d9mmqm2FW7p0QJe1omvjJjXz53ZBul6Per+u6Sstr/FMxO+rQJmRrj4ha9IZXf49TQieFtf1+seTKaCdAD4+6WevTBCmXmKgFLZ3ncRjxsTVsv9Zdw4/G5PHxjOw1L9YIzezLjJGaASZCQqHMzClRvReTJ5zLPKxVOflxPCd1p/G5Ge623V9utRFaVc65xnv0uPxeDwej8dzBTxtUVpYKhc02riOXPlaCCfW1GpBxZuGzRskQgsWb10m/L0O4eXeSHiud4YKR2+FdR4IKwW2ldNWKaHQ9Oz2Gn2X0y6raZP1pIGSlqPtDWI5qs04OQs2DrbP8KzS3jFKzRRIV+sxMSFD4Wo8l7NDMxTaQmJHlzOWOWGRursfRBwKNzgtF6v3FWbkpiYfS0N1KNrg+miJp9JFTrPgZpQKW9VYy40is9J1gEnIjWKoA1ITkBtJP4+IlKYVZhw/scL5xizCtDj4CXcdrHLissxcmjWVWfKGq/nlRGmDCVWxLq6Gbr0vYIpBPikJVwbMbmWINMdKyeBom43rAkwE0Yale1oXKbqLPlGtwz1VXC4nM2Q50fLAOaU3hwT9ZtE2/YKZ9d0/2UFHgtY5yeW0w8nGCpumicldJ7MtE06Fl5mb6XFj8zKvWDjNctbm4fVDnFuZJV1p0LgQMPO4pTMTkTcVw5f2+ZrOQxjcYPP9TxzjpuXUXZMwIFq3NJZzyDWrt8a0L2kGhxUX3zKk8UCLeM2SdUF3NE+tz5IZxcq5WdbW25y4bY0N0yAUOZ8fniQ1AbHM6adNPrtxgswq7mqedoO3hWth5kxOuKUI+xaVGoKBQeTGuSwkGAVHTi2jjUSlEK9rTCDQoWR40JLNSExg6T4Z07oUEPQ0wjrX+3BeYhV0z2jmHzJ8pnMz/+aOWf7edZ/kjsZTrJmMnom5JVwlFNAHesYWM7MtkKNkRkNY1swmL2ufZk23OJ0s8nvnX8bSVpvcSBbbfW6bu8iNzSU6akhiQqQoYtmEbGnnguvlMbHMuTDscnpjnjQLaM0NyNqKfLnpJlcoiQ0FMtUujkJXf1vkGtHtYKMQlCQ5OkMyJ5h7yBAvJ6j13uj+mOWoMGDmAU37TEywtMn66kH+681fy+Co5rrbL/LW45/hjvgsl4Ydbmhexs6nmEZI0O1gQoXQBtMMkcOcfKbB5Ze3Mc/OFCOPx/MsYkLcb7URGOOcbrlxk8qMde7owLr7EdINkilrnGO6rHVfyJshztlnhCCt0jO745wKl7h/cIIn8ya3hQlAkcrbEtYep8zEaPYzEaZdC0b7k0JgdhGgFdtLb14Ju7mkSxoic9kukKjiaPUyK9W2UwZ+MxtA4aqOhEZbybFwjW899jkemz/IY1uLJNpNBFyI+xxtrHMkXmdWDZiRA87m88W+R2m4t42bTjgizYT1r3wWLCdUmVpN2EmqdeqDzoWo7SYgvDCelaYxWQN6MkX3uEA9vu5Ih7Ljy8Ycz1Mc0FMc0aU4WTqtYbqAXE/VPbnOTmm6S+ru5u3XwS1XhfgbyJFjbzdBZHfRemdRey8xYDLV9vZzefa/U3vvU461e5uovKM4MRKm3V5GwvRkqt3nG9lpuz+UAqmcM1pKCAInNJficxhUgrMNpHsmlcI5j6XAhAIbFP1lhSuvUwixpnht1cjFXIrHOgIQbp0IVwda4SaGWJAadPl1NDDfGHBb4xwKS6eR0CuEaCupxNqJOSTVhJkSW006EbV+Yt3lPCHgyvp71G8E4wLslHz9lTtaUE2Sdu2xzsFdZvMqMoHV2VaPulxWpg9X7pdu/FyL1N61cRGkHe1MydFx6rWvZe0aTLq+t6Xz3uN7W6UB/9vplp6WnrscNynvAeVk/mqbCSF6J2GzvCeX954yM96o3nxZJm7vNo5+nyWy5px27dl+Dy/TW5c1jhPrnilCoaekF5jOboLsNCfxTkJ3+Wy0lxi+l1Cu2TnNeJ3dnMiT57TtmBPpup8NdnJ579gGj8fj8Xg8Hs+zyjNP3w1OSCzNCtKl5QVcGlsAY2g/NSBtt9k6CSe6a6w2ZgmpzZQ2uI6nneiM1cg6Ad35TdpBwnzYYzlrV7McM6vY1A2W0jZDHTJMQ+ZaA25oL7OStmvpmlznUZUp4ewoLVNYpCoKhR5LnVefxbqum2RWkRR1aEOhUda4+n7FRYllSkNmtGTCsXCVzAYcCdYIheZ8OucGnYVBFudX/S3G6yLVhW+ASLrUVbHMx1JVlfWrs+L8cisZ5iGxyulnIbMzfdZfD/F6l5nHeq7Os3WfoUptNVCBdEJ1KRYL7WbIuzTBtkhVBiI3rixbqCCziEGGsNa5pGck6ZzbJtpwn6sohGaEGP2ra47pcoJDDaENJBmECtNtEAycgC6y8jv1/A8iDecUJoTWBcNG2uRYuEpDOFdWZgO+lBx2NSqFZk71OdjaRFvB1889wMrxDhrJUtZlOWvzRG+Ry/0277r5D7gjXGfFKDZMA9sLCFY3sBubcGiRwUHBzGkJWiOMZbAoWbxfc/EOg4lh7kspMrekDwVYMcNAznAkElz8RkNmFOeyedoy4f7eCR7dOMBSv8Xhzhb9POKDyy/lT9q3sLnVpKst2UxMvOxcENFaDsLFYP9ohFUQbVnCnmXro4cJhtC9qImXBgwPN+mezYg3FHlD0DsuUUNLMqvYPB6QzIOJLfFqUWu6LWhdyDjyVwFrlw/zG8lbSGcgvXnAK244zXcc/BQvbzxVpT5NrUQJS4YkMYKhsBxUm1wfrBIJw9lGh1e1H+PfP/ZGtv7HEVaiOT62eJQ/TVymhv4JzeKpVa6fXSE3irWkSaxyDIJMK86vzqBzRRQX5x5pWl9URBuJc47ECoxFGlW5V5zDQtC/eYH+oYBo0xBuWeYe2kKu98Fa9EIHkWnkRh+GCWKrR7gUYLOMufsMs1+IsFKSHFnkd+a/kbWbFemc5b6DpwiWQmzo7kki04g0R7djdDNk67omG7cYWk/5zrPH80LDxBYr3cC3MaKYv+cGUjMrSUxAKDRajtyxVS1iIanLuMZKtLDIwuHjXC3O7HUiGPDG7hf4k97tHJ75NAtKYazFCJdWu15XWmPHak7vh2nCdemALvdVOqS3vRZibP2n45aedD1N1osuX8/IIaHQDAuR39TSTtYd0nUxuJ7Gu0Ri2DAu+8TN8QVuji+w0u3QMzGZVTRERlcNaYgMJdwkgsvZDF01ACAtJylOPKrUXV/TXruJCsW2Ytw5DbVBXVtOWJDut7EQqEcDt6qqN/1CQ8hCTB4Tnidfs80VXa3H6L1tgnXhkBYTr0ddiynCNiAKkaO+XR1Z27YuPpfCthQWJUc1nMtsS/Xa0NtSade2BZe6uxRDnCBit33vJ2tVT2M34XkyDe2ka3kyxfikYDwtHfcke6avndx+z1vR5Hd44llnp+YIqrJMMKqROhKeLEbsXvv0uUJ0Ok6olE5oLt3PpfjsxE+BCSQ2kEWfrchkRSmSuj61E53FyMVrx53JWNe/sy7xAyaErA0qGQnLlbhcf13+rSw3dpa5I1zCACe7a1xQh8bPZ3QrGmO0b6dQW1G4oUuRHDEaUyhdzmL02u18l89rQsit0nMLxra32CobWJU2uxSnp9glhbWuHvUk5XVSErQpHNTus0KIolz26PtqFVUZK4pMcttEcCWKMkdTzrU+gX83Z/MO6dB3Q7zASgDV43Lyd7J8vxwLmRSj65kSxpePUnRXBgQxqgtdTlre70QV59JWhGI0YXDfwnIxKVEKi7TFfXVK0Ox2P93p932/Ymrpli7/K+s2X6kL2ZWk2dlhXbrA627wvUToyfcV+xPa90vZnvrxdtvnC+F3wuPxeDwej+fLjWfmlC6x1s3ClRIC6xwHuqhzWqQUU5sJcw8LBgfbLA06RKmpOtoU9VlF2YHSmqpGVjnL2IAJnSjVUQkHg00etkfIq9qwkrPDOdaSJgBJFrDY6HF78zx/nt4y1vZY6so9XQ4a1cXoMlV3+P9n789iZUnS/E7sZ2a+xXbWu+Td8uae2VlZWUt3VTd7YbObDbJ7xBHBBkFAEDDQSCNAgB70IkiYp3mQHoQRJPBlBEEDaIM0GMy0yCGbW5Psrt7Za61ZlVm538y733v2ExG+mZkezMzdw0+cu2RmVd4exh84iBPh7uZLeLj79/2///8TupkeiOncxFRWNWSxFIZUGGLbvo99T9uz0SGXon1yG7Elc85Fh9yvJhhE008n/B97VU6F4qAeYqx0SVVo+w9JGlK6soq5jql9EJCqmrSzj4ciawKra/kmr16+xfd+6WlUOXTOTD5RoUqoM9mQ1MKA0I4AxjpSWtamTWZICVa3Kufw/F5o0BHpgUaWElVAemhcII9olPXC0iQDhO9L3a0kF13Ffe1SyNXW0BPjtEG2/jR6q88GlRc2WClYS+Y8FR2wLedUVlKGRLGwbKtjchujrSQnxljJhpoxlAVfG7xPbmNmGymxqLmojtg1ConlSnTA2at76PGQSLjfSjyFaighTVj/oGJ2LiaeaqI3RpgEshuHiOM5aaSYvXSWck0RzwyX/knEN+69zu/q10FAveHVYBsFz27sclylbI9m3NhbR72fYSLjLLo16ESy/0KKTgXRzBIV7jtIDjXJEax9ZJGlU+yJsiY+rIkO5qS3waYxo1sJqtDMz6Xc/6rAJIbsToSswExg/0WJjhPGt2o234ZqIIhmAlkO+O77L/Md+RLVlmH98gFfv3CNf7D9Z5xVU2IMiS8e0UIwMxFHNmZbzvnYbrP/jae48o1dzDDm7f/JgPj8lOm9EaIS7B2MUNJw7+0zjD+QFNvWJeY2NVuX9qm0cgR4rbAWhncNsjTOBjGWyEhirdf91cKFztJZI1YjQXIEgz1NtZYSRRIrBDKvXUEHODWMtVifkLLTGaKsEEoy2DtkkMRMPphg0gidKXRSE909hKrGxgqdRc72XTplxegjyfqHn/9vYoUVVliEUSCMK1wxWlJpRWUkpVHerUJTWeWKYkxEJTQKQ27jprc0OCVl6ZVmCkvl/UYTYaisYCIsL8eH/KFV/Gl+hV8eXgcgFdKTW470WySMF+28PwkepJZWXhoXpipPGHzaK9VphHRlI6QwDGXBh9VZvpBeZ2ZTMlG11t09Qrrs9hT0W1rZCG0layonNzG7eoy2kkxWPBUd+Pu5IDcxU1IyUaKEbQjpbmKzeogV5klSWjYK8NALXHWOb/P82RPKGeuPg1+fK7w0D13/5wJPQD8qEe2muYfAfh/ongPuEoXz4rTTekYHq9FGuSwXx+krowMZ3SWim3mbYgzTzH9af+cuiR1JfWK+B6muH2bxrYSlMovff/yQIoX4lN6pAdFDlGWwaJF70h785PHqqx376PeVflgf63ZcuXCMjQi23x215RJ78s8DdjJslbfCKaFRwqmhpWil/MITrJ6QDmR0O1AgPa0jLS0d++oOKUzncwU6E8jS/R6taMddeA1Ec2x5bnCPLaVQCF6b3ORPkxewQjZEeCC0gcUCZNp1C98aKpDB4kEk6rJe0Z8UvtbLdo/n41pd0x6/VpHs8hnW261bFS7SZsEZzBHxdBTUuDzIo+JBfao/zbxPGJrfrRUL98AThV3++tEvoGmI6d5nfUL6s0C4L7sishYPKwqLhW5axoU806clW/vLn0q++m2rTETsxQ6fhJBehsftGf0oqKz6zMfsQrL82bTtW74ipVdYYYUVVlhhhRU+a3xiUnrZs5lVAisUErBRiih872QfRKpZyfoHGff/8ALP3N3xPYp1E4SHgFRo4x47ez2ls7sFt9/a5N8Nn+V4M+XGbJ2NZN5Um96ZT6iMcg+QWrKZzNlQM5JAQIdegb6ndCxacjrYIKayXugzlIqaWNbeWs8wVnkvQRgt2OzFwvWwnqg559QRE1mhOpnDTFbE1MTCJ6J9IFWZqLF9qqxiZhIkjoBvek16kjwEaGNVIIWlMFETWA1liRKGkRpwvxyznU65odZ488ZT/PrX/oJ/evDTbL3hEuWBdDaxU0xjHcElPSktDDTO58ZCJBpLNoxBTEtIk4ZElsdzhh9UzM5uIytLfKRdD+FE+u+1d/74hEoTrAcyOgTQcYSVslkevBJfyraK/HPE8TOgcoGJLOfTI0aidGp7LCmac9ERmag4q6bsG1cykNuYqXFJ8g+rs7wxvwLAz47eYSJzKiSxV/LHWH7xwrv80dM/zea1GFtrNt6rkYWFqmbw0QEm2USWhq0fana+oKDW2NnMJ0LOYqUgOahIP7hHNbxEPRAUW4LpQCILSZkYZnXCjYN1rm7u8dLZe7xxbczxJUW6J4mKCFlbrBSowjK+WRFNa1doMq8csap8gm8QoScZJpaYLEaWTtGb7DgiFZGSXj4mjjRH9RrCKIK/XzV2toOjj6bMLg8p1iNGt6xThl+zqNJyeGWLf/0Ta9x4dYOXJ3e4lO7zXHqXs+qQDVkQC+N6eiO4WW1w9jsVXLuBunCO6HCMOStJt+YIAXFck0U12V3J2e/kzM/GlBNJPYg43N92xP2ZCploTC05MzX++iYcsSx9YklKUMbZKyYR0cww2PEqnMqiMwm+K6iNJDaWKG28e4DBGuOs34xtCi1sniOSGHV7DyUEcRxRXtyASGGHWVO8YSOJrAzpoUaVluzeYq/6FVZY4QmAV0lj3b3bmEBMK0qtqJWk9v32jHDPMgpLbHWjltY4JW4CaGEokWS45wGNcCQ1MBGS1wcf8X55jg/ruzwTnbwm6F5Sz2BQj9B3uE9e923AfxzoktGnkVjGk8fXijO8nn5M7O24A/kc+kcvLOP3TeISwtoTw9oqSv/dhGez0BsxJCmlsOQ2oTIuKa16x2WZwuth+9gQyVZiOpbDDWz7zLqABXLK4Lxon0A8gDz2k/1ngW22JwjnLjm9sKyfx/aClL59N5wkdUO/6GXTup8JIJKmUUuf1n+6T0af1hMz8i16ug5Np27DIxDSXWK5+3+fgO1bdS/bxjDf46A/xgl19onz1tnengZHHnffn1TU9fcjTKFDJrT23aFYAGrzZBRtmGGyQFwKa7EaBMbFSlHHplsIRG3dvSWSHeJYtOpm4ey3rfKv0rfl8fx2UD2HOhYLTa9nYS3CCtcaufvb8sR4PC55Ib1NjEIJwdPJfYhCsXGrzF74BXam0SG6BcsJ6cYy28eKjYV3wEKv5ZZgDj2oG4U04biIhXGXkrW+6OtUBMts/yraW3sQfnuynfZ76pqdLLMZ71bUBJLaWm8xvoSIfxSCuW8LDsstvMO++P+fVKfiLjkN/voh2t+1Es5m+3RiehG6OfFazxaDaq4rwVHvcQjr0+YNzw4PWiaoh8P8y8bo47NwQAnjanDCj46TzOOO37fB7u5TGLPfw/thyuhl6I7xaQn8Zb22l863IqRXWGGFFVZYYYUVfmR4ZFI6PG+HeNJZeUlnN+Z7KlklQTpVTLmZMihr0KaxsUJbxu8fs/ZW7ce0PviSCK2d9ZU2DcnVwFiEMKhpxfk/S7mhL/LxM5uYUvHaszfQSHarEfM6ZhSXfLy/gZSWnWLINw5+gtqoxrIplfWCIrrp4wOk0tn1KiyprBrFSlAyu2mmSXIaJBORN5+DI51zE6OwaAQbEs4rw31Pol6M95wCCkNlo9Z+XERN8BTWCzTEdbd6VWK9hXiEsaIh1w2Cl7NbSGG4Xm77sQQvbt3nLw6v8sb+RS595RZ3Dy82h3a+LUkOXQAstXUBdW1DW0XGN2pMopxtdjfmCn2/y6pRtYtaQ60Z3qmpJsqTycbZzOFIOoxtqv+NdIR0sOQW2tD0KPdWaDaNiY4r1j4WPkjvEdefI6rtmuzsMdOjDCksmbcJDMTyhpwhhSETmrNqTuwVY1Prvrf/9M2/x/G3t6lHln/92k9w72jET178mP/0wr8CYGYV/4P17/BPXv8Z1t9cB2sZvXuIjRVi5s67ZG+EKjST3Rm7L29Rn50Q7e5j65rsxjHpXeX6Gdea8c2Sg2dSjp+vEAONuJWS3Ex4c36Z+EBx41XDhbVDTAQqt6SHGllZsrtzBnci1LREXL/jFO9x7HvfSWwaY0dZc1ziwv8+EtcXz8QKnUpmZyVPb+2xOx9yXAtEDdm+RZZQbLlriihrsnslOpGkuxUmTkgOarI7Mwa3I9KDER+9+yzvD5+l2DZEF2c8c2aX1zZu8tzgHpfiPbbUMW8dXyC7eYRQknp7zORD0Lcn1AMotg352DBfSxnOIN6bI7Tl8JkBk48Ma9cMR5cVszyhuFQiphFgGps/pwr357AAoWJII2ysULlmcNdQrkeYyClPpDaYSFKux6jS9YN27oUC5u57RAqscb9wMRwye/kcwzduYidDqrMjis0YldeNmkJWhnoUo7OIZL8iAaecXmGFFZ4sGJ8Y1gKrXcGcNq5VQKkiCh0RCdPYeFcmQklvy+ytvCWyeQbRSJR1KljllX/uFXat4avpXW7XG/yguMRF9X5Had0ldBftvB+GZWpqiUvoaWs70w2aduygmO7bdave+8fBaSrpQP5qBBtqxvd1yr4ZkonKEb04sr9ZLiSgw3K+/3MgpIOFtkESixolLEe+V3X3u8AaNLJRSvWVyQ9SgC7bn7bNDCCMU0ufSN4GfXq7rAwMSTPuk9tXWngV8gnr7fC2Q0aH+ZaRz6erom2H61kkcfv9ovu9ohtb7iVks+iMI0Tb/9mtTyxd16KyuUeY0Cp1l/WdPtWeewmZvDju6erfvhL6YcqzPqEMD7cx7e5n4/T0oPkf4p1wwuZVLNqaS075nYnl9t0S0Vj+RlI/9Df644AeJsiiRlTeYSrYP3cI6UbVK1ggXRuES4WfRziRbuNAYPvz41XRCkTdqqitd+Bp5/Gfe7vvNKvYVsfEQmEwbKgZSHtStS1azse6S2Wr7g722YJWSR0sp5seypy0thbtdi/O5wZqC57DsVokrZv1sDit2Yag3u7uRnA4ki7XIYIq2lrQftn+z8g4S+aFscK+KaDuxbKBeO84h1klW2vxPnpF+0vjYinafe30lhbgXJqecCjMQlGX7P/uRdvzudui7bTrY9dNIRQw9++tQUUdiOmHIVxnTxTaPGiZ5n7ghQY96+vHUQN32xF00bh0POJYhY2asdw1sn8venh82SehuyTvJ1Ffn1bE9aB1Pi76TiIntqFzHq2I6RVWWGGFFVZYYYXPHp/KvtsK0QSMwjjbbisFQgqiXKPHKWpaYpVypDMgKqeetYMYeTjHxhF2ECOmnoisaoS2J4lpHImtE0E9NkTSoHXEOCqYmYT9csAoLklkTVFGRJFpVAepciRZ5MnbYJeUipqhKpqExFAWjfI5ExVKuirMWNQkXWJamCbdB05d061yjYUmkyW5jdk3c8Cwb9Kmd0+MS1x2k5sTlaOtIBGa0ioMsulJWHgb72DpGHpShyrRYM00Mwn3atfT8Ln0LrHQXC83OZNMWZvM+PD+Fl+8eJNrz5YM30/QqfA9tXzAbiGe43pOA0KDKs2CKrl5PvcV2KLWIDqFBGXF4NYU5AgTCUwsm0SGVYAUbX8z6XpLY+1y5bOSTW+v+LB2BQyRBOPU0p83ZFbzy1fe4Zv3r5CFggZhCb0ew3dbWknmz49UANT8SXGV8vfOcPWPp1STmJ0bFxkcWv7g6y/zPz33h1yMjryF9yFP/eRtru9dILtvWfuoJD4o3LEBoqPCfw+GbMdy78sjLn40cN/P7XuIssKmKUJJ4r2cwXpEtBdhZgpZO6X36KOIaAqH6Qb6xhZPXTdsfPc+3L7frEeCKxaQAltW2LJCZJlLehxPEWITBjHRx/exszkkMWI0xKwNfTGBJJrDu7fPYnZSNn8oiObWn3OGcl0RTTX15gA5r5l8MAXgzLdLd67ECqEt2Z5m8nGNmlYUZzMOr4z48MqI96Mr6PMlT1/c4dm1Hb55+zLnxwnRxfNU6wkI2HivJruXowcR+XbM8YUhw7u+EELC7JIhORSUY8X+FwyyFMTjkqqWlGuSeFov2NYbJSGWWAkmlk2/9JBbMJFTphgl0WlwC3DXNjNMkVIgat30qRNSQpZSPbXBvS/FPH1tyOGrm0zPKybXa3ftlJLjp4ccX1KMb7qiAakNstBQP3nkwwor/PsOqX1qUwvXS9oIaq2ojaT2Nt6RUU2hmVLG3+uFJzprFNIrfFvjaY1oSK3cKoZCN6rpL2fX+NPZC1yvB1yN5sRPQK/IR0vxPhr6JFJfjbyljomF5p3iKV5Ob7pjZ6UjqJb0DWwUQx1COpD94Ejgyrh5QvJZeVLYqWhk01Myt/EC+d3HMhJ62fSWNLcLap4wvyPW/P/COW4YVJO4l8K6fX4CSekFLFErL75vCel+v+f+aX1y+qJVd79ndLdPdJdUbubtbWq/n3O3X/RCP+nuOA+wiZW2ndbv5ezGWf7dhfPyNMKh3Y4Hk9fL1rGMoH2Qde6ydXfnP01J/qlgzULb334hiGwIGaitao6BEprKFymHCmttxWMrwX8kCPGNFOALdR3J2FRLNOrbhcUe89LetfLu1bEsqKcXEOb1n7uibtu4ZeQ2biY2fayXbZ/ovH5Wt6TgWvRpbX0fZHPdVVwvQVcpfRoWLLu76+xCSZdT6RLTkpaYflgv7TD+g3pNh3n/CpDS4Vpj7GLf965Libv3nXQ/gdbWO6B7PewWqizCdE7ex8szSGEfWWEc8jbBSrurUn4UAjjkmh64PZ0T8kSx3Ck/wL7C+XHQJ6GXksT++1q2/v78/eMQpj/K8fmsYJrzRDy2680KK6ywwgorrLDCCg/HJyalAUcYBkLRq2xRAmslojToYYzMK5cZ0EDkA4wsohrHZLMSM4zRo5i4qFw1sjEPDLx0KrDjGqUsOtWMopJr8zMcVhnDqEQKS1VEDNI5kTBUVqKEZSCdjaWxglTVbEVThrJAI5mZpHlg34qOOdIZuY0xRjZEdWWjTuVpsHtyxLHypHRQPwcl9ZF1/a0rGzE1KUcmc32Fbcyxdv+vRzPfh8gR0kNZMPT7emQGxKKmkk5RXQZltfH9ejq24RrR2Ese6QFb6rixKD+bHPHC1n3+/K1nOa5SrlzZ4c7tp5CVQFZ4Us0lz+Opcf1v/fcpS7No8eV2vK3ANm666JxKcv8YtT3AjBUm6VXc++ryhUSFbgPwrtWYlU6FL4xFFiU2VkyvjBDWku4MHukU/VHiP3z1e7w4uMMvPPs2G2rK1EZkaEaibsiBobd+r3wANhJwZAS/u/8KG+9prJKkd+eMb0QcXpVEo4pMVFRWsu4VKv/7F/8xv7H9Nf75W69R/dmA9Q8Uw7JG7h4h8go7SKg3M7bfmPPufxxx/t9toPaPYZ47Ank6dQmRqiIbXOHCn6TMziiOn4b55QpRS9Z/oDjzLcH2X95HTOfYWd5+56Uj3K21iChyZHRdIyLV2MzZYcrs4oC1uwnUNWiNPThETGfEUhKNBqTXFTo5y/yswArL7JxAZzD+WDK+aVC5dvnppi8bqGmJGcTNMY+PHSEtZxXJrmQcCUCx9VbBwbMpt164wPX0KUxmGTwnGN6N0ZlAJ87uUOaOWB7mmuQwRhWOMZLzmuH1AemBYfdVwfaze+wdjBhkFXqkKNYzBndFUyQhrCOdrRSYRFBn0tve24aAtsLNUw+cvaKqLKYSmEQhpcCaCBEphEixZQlKYZWk3EqYXzDoScbOa4pi07D+vsUmEVYJ7n1V8vLPv8+1f/Qcm29X6Fg6UnoVL6+wwpMHg1NxGQtGYI1LsNZGUhlFbZwbShmU0lYhjaUSERnBscWihUBa6VxYvIJWWUElnDNHbiXbyrJvBM9EJe9Ex7xVXuC8ep9YONtuSdvv2Vjrejx/CkgkuqNyPPFeCHQn+d11Mv2sERxmtJWMRMlmPOV6uclz6Z1mnjDdPSu5ZxbdSWgbG9TpIUEsKL2ziVvekbyVVVTe8lx1ChRnvk0Hj5jUNUuS6QvOOMGOtPMQ1cxvpX8U89ObRHpw3Alk1hNMTHcVy/QI6Z4yuvn4FBI72HX3Cem+9WtAIKSb6Z15++uARXK1q6buTjvNpvs0BBXyo5K2XYL5QWroQLIuI6SX23N3PzNLielmGzjdFj6cZ30b8tPmC0n+viLys0RfSdiNm4Kd97J+1D9uuAJh90yptG1j4aXqZrwauCWLu4Tpg8hT96NgkRT2X39j572EPO4fotzGQIG2lt16DLpfIdIS311Cu7uNtl/4ERTP4X9YzvY2JPGSnewer2Uq6WX4NCRtZ9kTdt0PQGNZ3u+V3a3eCuP6fVpQTff3p28DHnCaWjoQ3uEY/RWPH4KFd8Bpv+lIeGeEZvLJgq9PCneP6SuvT7Hupr1XBMV03076NEvvh5HGj0sqd+2wAyn9KOvUVhLLmsoEV5lFe+7TtuPT9qw+Tc39WTxbLrsPrRTSK6ywwgorrLDCCj86fKqe0iZWqK5llO8NTQSi9MkbbQEvFRK2UXUKAyaJqMcJNhJeTe3H0Qai5ZY61VAQDyrXEzapMFbwzuFZCh0xjgtXUVso4kizlczYiGdkslp46B7KklRWVFZxv56wVw0pTMwoKoiF5ka+wb18zGGRURmJNs5u0xhJXsYLSTCtBVJapGwTVMO0YpSUFFqxlhTESlPoiEorYuX6RlsrGEQVz4x3SGVNbSTjqOBSssfzyR1HXouYTFTs65HbdgyJrLkS72CQHOqMAz1qkpKprChMjEZwr15jZhLX41AYXhjd4y/iq1zf3+CnL17j46e2UXmCLKEaC6QGayzRzPe7rXEWY5LWrszttIu5QvAUosCqdt9ZEoMQyNqAUI64E20w7SroRVOhv5AL6wTSjc2ccecKBuZPZdz9KYnOLONrnz8p/bXxB0yk61v+fnmOicx5KtpHSUssDKnQDIWlslBaZ9G1bwwzG7FXDrj1cwK9FpHcddbXL//C+/zdc9/mrJpTWknhD8cz0TH/m3Pf4D858weYnxP8ZX6V37z7Jb7z/atsfVuR7VuEsYw+mnHm/Ix3/9cjxMcXufQHNYPf/h7EMTYvAIi+/wHjtyMmaxO2Lm5y/ZeHFJuGtY9qhtcO0WsZ0e6BW7HWrs9YIKO9faBQElSCnc8RWeZU09fvMLlxF9Ym6KvnkXmFTSJEXrtzQ0nY2ef879Qcf+EcwljKsWLvFcn0oiDbAS5nxFODMJZopp2NoYkxsXLnZKWJ9gvXQy+LkJVmeH3K8CMQxrB9XJIejRAahjdmFGczdl9JyLfBKkt5W6GKAfFhQXSYE+3PGlVCfX7EmTcqyokkmklmf3yGYQ7Fz9RcOHtA+XeOuV9FHH+0RnZPMbjrbMdV5Uh5o0DWApFZjFpM8ljfv13WgjoTqLEinhpUFiEmKSqv3XHy/d7qgWT7WwJZap7+F0fOJjxRUBtsHJG8esDPbb3HR/Y54lmNnNcIbRb6Ea6wwgpPBoQFWYFRAltJTCypa0UhI5R0LUQiqYlljMQ5bAyjsikw01I2hW5KFmgM2vpCHYF7BhMQYzgylkxAaeGL6Q3+m/2v8VR0wFfTXeLQEgRLKgIp414r69J5sVALnz8IQSXXJ7ZbAteivDVlbs2CWjskD1WHb1CuFqlpfWIQVJ2iu/CqsI3i2fTI2pBIPTQZL6Z3+MHxRXYGYy7Fe+TEfrslCZq8R0Z3oYRtCgGDQqa11G7J5MYq1LdUaY/B4nYvPX4ddbbbLv/9dHpYSyzGkzT9BGzVbKtx2+BVtxVqQUGl7adPAv8o4Lgmx1otENIdVXTXjnvxNUxfTlCHve2SyGqBRHbK5i4pHfWW6RPU/c+VVzgvI6NP9jW1C9az4f0ydXRYPljzu3W5BPyJ3sziZGK+SdifMvbD1HWxfJiy2pJSN+9diyFBKutGKfgwQsMV4i62JOoq606QAA/pjR6jm99DFwvkM+7YOKLeOK6uY+f9eSP0ixbWYiKJrDsTH0Codi2zuyrnZSRwfzcDhya0d/RQoiWsRat6bhdw97N5EfNRtcWXko/QWK6XW4hatCR2Z5uWbeNCX2eJJ2U5wT0HBXQbD3ZJ2c5+wULFU3dfT/BJy06ljp13iFFPHKfOOl3vaEHnAuWnhcB2yfcVtq/ZXotFNMJcqwTNT/MUW5EeZ7lgx7705/6YRLt4wHn2eaCrkA4FYf3pXXQtvLvzBEW1Epa6pxjuFu4ooYlOuT6GdXfvA91imvBahft14xhx8lqohFl6T1bCELbuQYrl/jXc9PYpPD9019F11nPbGS+0pgvzuG1/tF7NlVVkVFR+2bAd3f3ob1P4jiqrFp6ZwnHpj78MrpCwPQ7h3tEl8R+VSO6T0Kct55xRVi2yVlhhhRVWWGGFFT5rfCqltE06D3PBYkr6QEkJooPCE1IxZhhj0sj1U53XJDsz3ycLouMKmZedYM2pY63qBYaRpB6CkC6JNUpL9soBe/mAUVJSG8msTgDYGswYRY6Iu1tO+HC6zb3ZiKKKOJ6lVNMEKukCwtpF3jY1oCwil8hcogpQc2fBKww+AUxbAU7Lz1rlFcfKsp/CTmaQleCuAJMZZCmRpcDEFqssKLCp5gejp6hLBVagEo0QlsGgxAJVFRHHNWtZwVqas5XO2EqmDFTFupqzFR0zUXNyE5PJiomaY6zk0Aw40E5vncmKmUkYq4Lt7WN27k+ILmtefu4W7+49TboryM/B6LoLpmXlHuqFBSOhHkTEVckJI8OmN5Y/B0I/aCFcj2EpXHDeWJD5qmxflOBOGYn0/dOa6u6+TZkBoTV6kjHfVJRPVSTjknJv/GlO3c8E7+bn+crwQxSGIz3g/37t53ht6xZ/a+MNzqpDRqLCGOEU1EIz8cTCji8ysOcL0rSmIEOkmmdGO3wx+xhok8hDAbmFqZWMRE2O4muDD3n+8l3+ePNF/uK1p7l2sMne3pjNPxyzeyeFQsK65uhyxCCOQWvk+gQ7z7HWIuMYMxqAtaR7EB9LBreniFnhFBpKQVGCkIjJwCmijXVqXnCK3rqGOIE0cU4JRQGDDBtH6GHM9MqQeuCKVKYXBfExnPnOEDWvkJWlXFPI2rLxjkFWFhMJ5mckB89KhIXB3Yj00BAfRURTR7yaJHLFDl1VgU9q2RrQlsEdt435uQEq12y/UTA/F3N0RVJsQLGeEM9ixjdKp7jOK0RRN+eqqiyjG5YoN8ga8v0x08GE2UVLfTUnuzhl8GzJvEioa0k1jyFXREeKdFcgS4GsafZJWFwiT1tUCbL27mXKXydjSZmlQNoUaBgliApLuTVAaoOaVt4toEKPEyJpuFWuIytLnSlib7Hfv16usMIKTwAsiNBX2johqzGun6n2aunSRMy1IyFTf89ORQ2yTcxVVnt1GsSibnpLq25iDuEtgeGsrPn58dv8xexZXk12iIVTSZfW+jYSeI3vZyuRCt2OwRHTbns9+evneVhP6dDvsd9DsiGsF8joJcoW/1kkDG/NL7KmciZyzr4eoRGUVrle0zZu1NFdBGK4S0gHQryrbnZEuGoIsUBSL2zLKVbhyxKp4bVRXHVV0qepnzrTtXV9s4ueZekTwLktIPSUPqH0PYWQXuwBvfiZ6Cwb3suF+ReXl53lZW+M0/pO9y2oF/pALyGku+rsvuX30v3uoGsxG4uWCHlUi+kuIf2Z22YvW19n/8K5/ChKPdWzcZXepv6T9AU9fdsWVd8PUk1/3giEtCvslr6O27akq7Vt72IjsFhXydMnTE+BsPZEHBdITVk5Uhp6RHR/TOEuN+Vhyh8dvsgr6S0qq3j7+JxrT6H8PEEFfYKc9syzWDJ2F113LRGU1uJkbcJJ//7l6ukucX0KFvpKn9ie9jtw83ZW4gnkQGZ3rctPrqP9joWUvsi7/V4a9XR/u7uF/z7Xsqw/+Ike0yGufkBv6RPreMIQngGCQwm4e6pBtKrnhywfrqmP06u5P0bAAmkpTpKf/Wtu/1r4KNsQina6hTtd5CZeeL+MzC1M1BQJnegPjWUoS5R304O2gC60HjG9MWcmaYomgzo6FprcxM17iWWoimYbYNH5xVgFHaI5tD0Dlu7rJ7URX2GFFVZYYYUVVljhrw4+OSktwERtMCB06AssnVLagpy5h1OrFHoYU40jTCyIj2qSndxZNEeOmKSqXaDUkJGmJTw9bOT6EwPUWlLUEQflACUNqaqJpGFWJVC7h+Dv7F3i2s4W+c4AOZfIQqAKQVxAVnihb0QTaOtEepYZTyTREtL4YD3yOUAJVlpHZkvb9k2OLCb2r/44ETu1qfGEttACUQLzCH0cISuBVRYdRUQzyZyhW38NuYJ5BLdii00sNnLEuRrWTEY564OccVKwkcy5NNhnK5qSygopDFtqBrgAR2F4dmOH+3fWOKgGfGnzBu9dOku5O3SKz8JZeAvj+uNaBSYWVCNJfOQTCYFkhkVCOvxvrSOnhfA9dh0JZ5Uj6MAF084aXDjbb2Oh1icD4kA2xhKhNdU4oh44orsqIrKjT3zmfmaYGVcAMRIVX8w+5p3tc7x7dJb/69Ev8qXNG/zNyffZUDMqq5iZlJmoWJcFG2rGr515gy+u3UQJw81ig8vpHj81fJ+nVEFundJFIziyUHk1x5GJea86y7Y65mJ0xN9Z+w4/O3qH/GJMbmP+i0u/xO618xBZ5KCm2IyQ45H7ndU1YuzJ8O0NiotjZGlYu1Yjaosoa0gTR0Ybg1gbY0cDp1Ke5nBwjJ3NkGe2OPipi0zeO0LuHHLnVy5z/t9ehyzFpjHV2THHlxOOrkjmFzU206TrObN5zPTSAFUMQUCxrbGxbX5cItNsbx9zYXTMcZmyP8+4vzNi+F7C2e/4c8e6a45JFbLUYEEPIlReI63FxApZ1AhjSaxFlhpZasr1iI13NSo36IHr/Xx8McFEKdm+Jt0p0QOJTiQ6EcQzQ7pfI0vD8EZNPU5ID2LK6xn1cMDheYt4esrrl25ycXCAsZLdcsid+YT7xyOOjjLMUUyyo1C5IMpdcUs9dEmiWLvfkkmkL2jxhE3kbMbD73B+LiaeGhKvgFa436YQlrcOzhPNQRXGW5JrbPLpujGssMIKPwJ0iAQsWCt8y0hPSmtFKRWJlFTSK0iMQkqLsq3NdGkV0hpiUS+18e4qfgzuevFyvMN3xNN8uzjHL2T3mdqTCpYfF7rpxWV6EwmNWs5ZcYZnDU4o6AyLKuRAIrfju2mjqOB6vsHXx7CjxyRCo3BJXSPkAiHdtfvUwcYbSV+N3WyDV2QFYjkkVRfTxSetNxcUoeHhM/zfna9LTENLPvfRIa6lMCfU2YGofpLQJO49cdV9rHwQId11JOqTz4EcUD3raNVT/obp3Z7RD1JH90nnE5bdPXX0aQR0t9d3WCaWy5VXYXpQRp/WF/o0RXRDErO8l/WD8LD5TyitbYc4X6LuW0Yyd8nhvl1sV3EX0Lf37hernLbd3Xm6BHS/r+yPg7x/KMJ1ToCNBMKIkzyhL472J/9yRW4Yaxns4p+wFmtdISXtT/LE1SIQtiasu5R8eLTND9Yuoa1krxguzPsgdKcHArd9FU3Bim0vAn6iPUnadvd3ycfLbLuXWZsLaUG323baPrQK6t5BCrbjQfH9AIT1Nz2mmwk9Mjqc7qcopk/sV2inZjpFu49BNAvxgPPpc8Ky6/vjFJF0C3we5B7RXdejKmzNA64d/evuJyXDgYXCtUeFm791WwluFlK4/4NCunttrqxy8/rl+yrlVNQL25HJitzEKGEoOiT5TKfuOci3mHsQuveSsJ0n96PFZ1mwtMIKK6ywwgorrLDCk4FPbt8thSMafRAjKo2IJFYKZ/0cgiElsbH7vB44khMRER+6h0vrg02hjXtsl0F5ezJZY2VbBV1VilIZijpCCUuiNOfSI64dbhLvKt69dh4KSXYrYn3PLWPixUpo6/OBwfJMWBBFSzqH52FXpe3/D9Xksp3WxKeiHSf0xbKxhchgDY16WBh33EQNshRNIoLaHzto5hMGqByp5RvuOhJbJszVgGkE9ZrBpgY1rEmzks3RnHPDIy4P93kqOeSF7Dbb6pgvTG7xzexpbk/X+Mn1a7x88Q5v3r7K+CPZEPNSG8ph7O3ZoRxLslgiS90mA/pxRle5isUMYkzsE4SF8X2lnfpdBEI6zK6t+661cUmWzlhWKUwaIYuaeqSoJiAii60l8fHnn0Q6Hx+S25jbeo0tdcyvb/4lb4+e4r38HPvVgH+2/2XOJUesqzkzk3A+PuDV9AbbsuBvDN9FDmFmFfsmZUMWxMIwNS6xnQlNGZLcwpAJy0xHnI0OURhyn2h+Lj6kspBbxf/u2f+O/zz+VTJVc2W4xx+efY6Dd64yvjZtjj8A1pLs5iAE0ZErHKk3MtRhiZrOHYmdpZg0RgZCOs8RWUp5ZZtqKKnHCZEZE88t5TNnOL6UcviMZP6UZnDpiMsbB3xt+xoTlTeB5L+5+ApSWF5Yu8dXxh+RyYqhLMhE5e2+3Pbt6DEjWTA1Kb/5ypf41uBltn6gyHZrolxjEonOFLI0mEQijMKkClG7hJAFVKEdgZ0JorkhPiwdkY1rOxCNIkwqyTcVxdqAfFtQDyDKYe2aJppWrmiiMsR7ORsHOXqYYBLJ9ELMwWzM7Y01/s7Z73Ix2mNN5gDc1uvcqDa5W61xfb7JvWLM3emY/eMB5e0h6a4iu6sY7ApUYRd6+LlrqiOnTeR+f8WaQm0NkNoyGMfMzyjWsoKPdjdZL3wrBK0RlX7iEkorrLCCv49bi9BgtcDWEqMUdW0phSVSmsooch0jhWUu9WJCM9gtWk1iNZV1zzzaq5yVtWjhLGilz+7nRpAIw7oU/Mr4+/zTw6+woWZ8MZ6hcYSvbNTLn0yNskxhHcaSSCpbI4Vw67FgTqh1wgFyjwHhM41X6wjTEsWhVrFJBDsitq+adtsgG5XR89k9Pjx+hTfml3k2veetv902Tn3/Z21Fs0wgxyqr0EifzF1uxR0I6UYR1CGbH4QugW6saIjJyii/nlZ9FaMbgi6onvowPplscD3Hl+JJy+X2iGcQS8noQFIH9BXLC4ppK7z6mYXp/flbVW9LFp+2Dok9MUaXjH4UIjqMI4X/jh6i3GuXXU6oLCh/xXLSOZAirYXso+NhBG1feWfEIrkc/m/H00vVfkEZ3R03BFZ9sl5buWCdaqygawxToRpyfAFi0Qe525+9a+X9RCA8nof4JxKgOzFrICoDMdxdVLTXydP6SUMnXg0EdFD4BhePEKN2Y1raGLh7ahxXCffqCQCVVt5GLKyI5rm2iQx7/1tfg75ASPeVyEt25UH7t7izvfk+zTVwWc/ppdvWJa4fjgViujt+V+Uc1t+dZxmB3Ok/faL3dJ/wfkJV0X001y4vkY/R6FDcQ3st7LsffBo82MWiLSwK75ddX/sK5U+j+F3WbkE+pAXDUJYLymPjff3CWLHQjaK5aXHQ28a0977rUlOZiApYj2bs1SOGsmTdF+Dv1uO2XUKn0EhxklR+WJuHx53+KFjmrLPCCiussMIKK6ywwueHT6WUDgo/qyTUGlFJSFRjwWzTyNlaRRKTSIp1STUSCCPJPNlIqGT2xHSwel72zB3sqoSw6DoCKrRXCEVC8/LwNn9UP8v4I+BjV7kptMXETn1oped/Q6U4jmRuAvXOOoUGlbugXWg/vwSjwEZOYR22J2yviXz1vvHjKR/ZFwqhfdCkoVFXxwJVuPFl3QZVNvLLW9vmbAJJbcHEFlEL1FyggHRPAQqhY6wasC/Xubt5nm9taEbnpvz1K+/x19d+yMvZLc5vH3DvaMRMp7y+foPvr19CFTF15nM1xq0/2JVXI4FOnZq9SSpIsD7R3gS3wRIsjtDDBJ1IZOVIaFH7RFzpqriNEtjIKVYdUW2cMl52qmSFwMYSnUqUdApWEwHSQq7IDp6MRFJlIzSC3MZsyDk/M3ifV9KbfC+/wl498hXJNUMJE5k3gZ2DJROaDVk0auhucF3hLLslUFn8vMfkNiSv3by5VXxcb3A12uOXtt9GCsPPDd7l58Zv87/62/9jrvxzp05Wc4OwEO8XRPcOMeujNhFkQJZ1832KokSVFfZ4BkYj0gQRucvF+IazyJ5dXaMaCD7+5QHllkZtz4iV+15uHKyzl7/CudExv3jmbb6YXecb8iXuHI358uZ1nkvusm+G7NZjJsoRuo0Nve/TDfDa5CbffPEy+3pMdj8h2zHEc2f5XQ8VsjLo1GW3pDDgWwroAcjSYK1F5dqdS9L3OgeSvdwRuUyoB5KqkNRDQZ2BToS7pglnlyinBWiDSSOskFghkAUM4ootdUziq85jYbgo9rgS7aIHArVmOTIZt+t1btfr/O75l/noYIO9+xOO78dEx65voGsT4PpTi9r9/nQsMClUY18sUgmKdUl+Fl6a7HLj/gY6FW2S0Fi/PyussMKTBKFxbSgMCC2wWmC0QGuBVpJaKyrfW7rUEXUkqY2iEsqTkpIK1Sh3tZWUXu+ssJQCJAZlBZWQYF0vwdQ/ilxUJRfifb6XX+GKepORdM8Nxqu61Gfo7eyoI3cdkkKgEK2Ft//fkc7uUcgQeh5DYYMdtqAiWGe7beuSz6clnQOh7PpRu3nPx/usJXP+4N4LvHD5DhpBZWJ/HFsy2q1DLlh2B0L6tL7T/aTvgkXlA9Dtd6iRDScWyOiw/pPH9qQ61B0P64+lWEpyqifIoriPRnXs1ZGnvj+FQO6S0AbbWHMHhPkXiGkWieelpDPteqKe8jpqiGizMM5JG+9+D2nTbGvXlnsZuoT0smkBTo122nynEyYPwsMIlL4aOpANywjp7jL9dXQJhfDsu6wf9aOgX6wQfkcKw0nPZ9P0kg7EtF7yu/pxo7HqFjQEscBz7dYSWmPZ0yyql+BB8wXiORDSiM42hGV93NvA+D9Xx+wsfGVNrHQb78ICIX3CFvtxD7U8Uffsxzk5UBOjnrKOBxLagaxdRtp2ldC99YVxRVNVYE9uW3/jOyroLhm/VAXump8vjnMaqfwwAvqvECF9GsJvun8vPO03HHJEXVeSPpyLgnjotdHNu0hIf9rr6TLkvt9zWP7TKISD5XbfkQIerDrvT5t5VXR33MLErKs5APfrCWOVN1beXeeLcM8Kz0gNaf0ZPnuusMIKK6ywwgorrPBXE49MSi8871tH9hYbilGkqM+vow48yWMBKRDaOFWsciSziQXlmiCaW4p1QbUWkey5Pqj1KEbdwxGTgPAl1KLW2DhqFLTCWEziEla6lNgRWCsYpzmbyZz/z7WvM/3mGQYKT1r5hFDl/qzyBHCJI0Mtja102K96CPk5A0YwuunUjM5qVyC0JSo7sys3brAB16lAp3jyWngLbLAy9ALzpLKyvn8YyMJVvwfiux66DG1DnNs2oBfWk+haLBT3h/2yXt0taxjeEIjrEfVwnX/71lf4l2e+yNVn7jFJCmZFghKGVNY8d/UuNz++TJ1Z1t4HPVDumFh3nKS26IEkmrbxcOgJTVW3QW7tvc6T2Kk3jUVnoiHbo5lGlQZRGVQFJm2PvY18WrzWECnsMHNq64H/7v35l1/QYARyJomPaj5vfPPwab669hHPx3eRwpAKzU09YWpSfmbwHhdVyb6RJ5JelZWN0nkiK4ZCc2DdfEEtHIjnWLik/dRGjETNvrcMj30QfU+H9zWJMLyY3uajapuJrPhicpf/7K//E/5Pb/99Nt+tEdqCFJhBhB2kmMStQ+QV0cHcWXeD6xc9m7tsmJCItbH7nouSaD+neGrEwfMDDp+H8mrB0xd2uXMwobgzJLqrSHZBFRY1tVzfPst/+bPn+JUXfsi1u1vIt0f8o92v8sEL20yrlFtHE7LYJbXGccn54SFfnlwH4N/ee4XjMiVJNPMX5hRnEupriuEdgawt+aZElRYrPLFbWqR2v8VqJBje047kVeE36gobZA2ytiR7JcleSXazZngjYno5Y35GUmeCYitBVtYprK3FjFPKjQSdSeoBlOuWnz3zPufUkS9KiMitIxZGonT9v33f0heTOzwT3+drV97n6NKAD8szvDM/z418g518xMc7GxS7A6IDRTQT7lolwSSWcstgY4MoJcWmRF6e8cXJDf5EPsP8jGCwkxAn0vXHLj7/38QKK6ywiO59u1FLa4HRCq0t2veXrowikoZCRwxUhbGC3MSkokZ6O8XKKhTGJRcxlFaR4JWCAjJ0k/RTQGktiRB8dfAhvzd9hR9W23zJ95f+tJB95XCjChJoaxuyWyE8YeieK4wAbS2VhQpB5YnfyquOSxbJYJfI9IQ0xhcB1Q1x21U/Vh2SGSARmsvZPt+5c4k384u8PviYilbdHGy6oVVhVzZqp3sy/EEW3H0ExXNznHpJ6wVCuvNaG78PngyXwlChFohFgzoxnrYuUW+sWqqmNj1r8ycBXQX04v/25HtacnmZEjpAieWq5+5ZehoZ3VdGd+ft2m27z83CcsvI6DAftD3GF1XPy61kVW+sdpx23O750Cdjm3Fol+mTN49COj8ID1Lu9fujP2h9S3tKAyyx734YTpDvov1NB+vvRdVlzxL/SeBGDAjp7LS7yuXQa9oVJtuWdbXWkZVKNKSyexgOxKdjaC0WYR1l2vSLDq8essa78/hl1CJ/F5TNeGcxUQvmVcTMJEyCIlTatqe07PCwouWRu7xgU+wdFOC+SMpI2SqmQyG6gbaS3C3TmGgEUj2cAp2Y0f3TYco7L12EYoC2yNPnHALP7AsDmv7WwTpdtssiaZ3O8PMpH6tL4eOvDrnsCw0CMY3ygYovEnZ5Aeveqw6ZHGxFQvssY31Rt2z21wrb5Ewa9O3AfV/pdoOffMK6f6+GRyM2F4jpZbBy6fU4oEs+91sj/Cgspg2iscDu22w/Cqomf9BaaDdFfZ6c7j5jLaz7YT26/fKprJjplDSq+ObhVW7N1/jq5sdMVN6sPyikG5V1eG5acp8I8/8osVJJr7DCCiussMIKKzx5+HT23QrswFkg2lhhUchKU48TZAgqkwg8QVuNcZZkBqqhJNkXrpdzpharlLsJ0xB4y1ZBba1AHEUc2RFnn77LRjrnvcMz7Hz7HHEO5RrEx6IhVWXpyGETOztsVdqmDZ8y1iuV3eqkhnJToBMXYEbzUOntAt/Qe8sFphDP2sBPx64nbBPwi6CobnamsUGzgQw3oLN2vBAxu16zjtC2Amf5HeLZHvcU1NzCK7Txy6Ld5/GxQOiIj+ZPYVMDqUY/I9mKpqwnc96/UBHvRp6wN+gkcmrtXuJCGPtgpzv/Hcq8RtaJL0jwu5lIVGmQtXG9yK0jBrEgau0IaSmwkXLnUqyo/XlhBhHVWGCz2sX+lSDKP39V6F9cv8L+uQHnLxzwTHyPGZa3iou8Ob3Ic4N7rKsZEzXna+kNYmGpEA3ZfFaVVBbu6QFDWXli09uAeoXYhiyZGkkiDE8pzdQ4Mlrhk6QCMmGYWcVFdURpJVejPYbS2XLdMynv5ueZXzBc/P05an8GkcIMYvRahsxrMK54hLICKbHTGdS1S5BEKSJJsFmKXh9QbaTsPx9z/NdnvHThJvZ4wv0b69z60wuc/bZBp4JyYik2BLMLkN2XbP6wYuPdhH/33FfhimV007L5w4j3vvMi9cidX4eJU//fSSzvZJY/yF6CWhIdeNLcQDoVpHswuG9IjjV3firGfuGI1y/e5KAYIITlvdtnsTcGmNSQXpiye2eIzF0feQxILdCp+z0lB4LBfcXwrkZWCen9nNH1nOEtp9Av1yOKoULlinpzSLGV8vGvCuJ9d92qz5b8tdG75DZuerdvyJqpkVTI5rvMRE0qNEpWHJmYTB1xdnDEc8ldbo/WqWxEfiFmZhLuVxPuFhNuTteJlWY/H3Dj1qY72SqJHmk2J3MKExNFhuPna45ftkS7Kdm9jMH9JzuhtMIK/z7C3ZtbRwonD27V0pVWqNqRZ5EwlEox17EnaSoqqVDWJRFLGyExxFY7+2zhk6NIr6p2yddEGHaNIvOZ/+eiktvpLb45e4aX4x1i0ZLGmvb/R0WfkA6fdYlphXLkM9qppS1UWKbG3Qv3TeKJ9ojSKmY2JX+AillhfPGVS9Q6e0xH0Mdend1PsuYm5pnsPlc39/jNa69x6cU91uScqU0bZVRQJodkrLPubsnokMju2nsDC5+H/yujTiQ+de+y3PbGFM1+tSpp6RO3TmUurcUI93nTX3iJTXHXQvk0NfWThKXk8yOQ0YFI7iqQH5WEDuMuEMq06wufdZcLttnL7LuhJZjbaYsKZ+UVau1yLUF9GvnbXV9XvbdMlbdMDX2SKFkkiB9GOj9snq7y7TT0py/raR5+q4qgVJTeptueIGL6FuBKPJj8kWhXqBNgTXPeLKqne+T05wnrKnaEtJx2+IPSt6to7kL4MZrdacjqU9YpTvl/YaUPmIb77oSwS+cJxdUNuStAmvb9sm1bqhZehkAKB+LaVzzZ7oCPWXy10LNaLBnLE9OnLYuwCMSiOnyxiqYlfnvE9OL2PuAYhHkWCOeWkH7oONIT5d1cS3+bzI+WHHwcdB1Ewj0SFsnobtF39/obPj/NdWIZHtTuoHu9fdR76ifpCd2uL9wvHs/WGiuJZU3mk0WxqJHCMjNJc/zyvvK546ii/fJdrEcuzu2S1gbBP731Jd75/iVsYtl6fcZX1z6iEicL4brtTgLh3kf3M1cw+Nmj/3z0pD0brbDCCiussMIKK/z7hk9u341X8w2dUtNkTtWqpqVTSEuBySJ0FmGUs7ytJpbijH+grxTD2z6pl4YK5l60tyzo1qCNID6QVNI9UEbC8NHtLdZvCMqJI6B1CtmuJcodAV2qxUA9KIsxXkld+8SPFERTgVXWK5BdssxqQa8FmtseY1ty1UBUeDJeuCrpQE6Hz4QJBLRtplUD0VSQy8qtxyowiSO6rWrJcDgZ6AdSPVSeW9WpdPdktzCC+BBMojCJ5Hfvvsjfv/RNLg4OeWO9QNyPCPZtJnLKdivxxLS3CTYWYczyiupOcCsq7ar+/XkQxmzQrfwP1t3gAmvlCGkTK+qhIpob9CCi2BSogUYfR8RHAjX7/FWhz5zZ5Wx23CTWQwD4xt4F/s37L5OlFV84e5tnLt5nIipv7ekI530TYaxgahMwUKLIqJqxE68NG0nDro65oVMuqWNPMtCoyzIM2ivjDIJ9k7ImCo5MzDeOX+X/++c/w+a7gmojReZ+fCE6FvsxVBpRa0ReYOY5YjhERAqiCLM+pjw/4vhiwvSiYPpcxa+/9D2uzbY4/PY2KoX6mZz59YyjZ13BRLYDZiaIZhZZGpKDknSvYHc2Ijk2DO+WjG+AjqVTbiuBSYVXMgvqNMYLwl1hSG5R3j6+GknW3s95+rcKbs4nvPvzZ/ibl99mM5rxU1sf8cali7xz/wwAw4vHXFo/QEnDhztblNfGDG5J0gNLNHMkutCgU4lVTp2hDgtsEmFiSXykqUeK8qmEo6clr33xfb731hWS/Zgrl3fYUsfMTErlLyy3dYqxrm9XSMJqnBXtzLjvy5XuWDJZsaFmGCS5iRnJmInMOR8fcinb53x8yHePL3PzzgZWS/97d7+hu+UEYwRiVGNrSb1RMx1L5hceLwG3wgor/BgQnhu8fbcwAmuES6AbidaSWro2FYWOSE1EoTWRJ38K4x7TpDBIa1BEVDYoE13vwFg4m+/QWxoWCdrKWp6KDvguT/PDapuvpruNhbd8zMT9o8KR5obKc/Eza5gayczGTG3Cvh42quTSKnKbNGrwbh/nkMxUwjTKH+XJwlhoElGTiQopnIq6C41kJAt+bus9/l/3fpp/de8L/NL226SyovIEP9CohpqEd4eQ7pLQXQVn1+q7q35eRgp3UXWKz4yVDaHcJaTd+O65MKyzNsoljlmixO4Q1f3pQEPaPykQAqS3xe73kYaWRFhGSAthG5r1NGvugC5p3Ve60Rtj0Wp7kZBeZu+9OOZJsvlk/9HFHtHd5broktChb/Jp276MqO7aaC8jmJeRAcuWPw3h97cM4RnnUdElQsK6u7avzbierA4IVt+nIYyxrAdsVz39JENYi6c4/Qcs5yqDUjegO49gIY4Wp/Hdj3sLsK44PEAK61TAoZC5y+X6//1trCkApxMbN8OKTj/kB5Hp1jHlrdW4OwZC2tZmO+x3V43cxLNLBvbrs8IXBkA7Vpc47m1vMya2IbKFFNgOadwQ1paWDO5ahYdTMfw0pZuvKYiHk4R2F12L78501196ybHrzt8lobtq6ydANd29VwaEa0a/3dUyLLpTnN7GIlzn3XPUg8dsrhu+0KXbjqMdzzaKYIl9bEXwQk/mpY4aD14+lnXTOiy3MUf1mJlJOKyzxoUlvIb7QV+FvoysTWVNJitSn+t4e3qOD//kCus3BUfPuufVoSyIhSa30WKhn23HD/e1vh14fx/73++T5viywgorrLDCCiussMKnx6cipRGgxwk6ls4eNxbIWYWaV+hB7HoLD7yNT2EwiWD7uT20Ecx3txu1sInEQpC1NJAiEMBgtCQ7EpTnDJVWlEYR3UwZ3DOUE9nYaifHFlUaP97imNITpqq0qCqsFx+UtkrnEMR38zhW0vSWrrRE6lZ5XXuCXWra4LpDJlvlY1FPTgsLydQ6ZbSkUTw7+16QSUtKCxPIbppxA0Ed1N+BhNap76Otwlg+YaAs8ZHg4+9c4JuTqzw/vMcgq8jB9232gUM3rxmShsaelP2c+JKED6j99ioQpQvKdSKRpXTJedMJxKX06m7pLecENhLoVBBPHaGdb/nkonF9uMN2fp740uYNhrIkE5VPqkdsq2Mujg4AUNKwkczJhHbWYVYw8aromYmaBJ5GcGQy1pRTOKf+ZJtZxbrQ/MbBT/Ebb3+Z/8NX/jHPRDuAS/7FwnBgYoaybkjqs2rOzET8P3d/jt/6za/z9F/WHF4RJDtzrFLYVCHnFUIIxPHcvdbaJSGOp8jJGAYZNlJU59e4//qQ46vuO8924MyfRvzm2hep9lLO/RDqAeS7A3QKVljG12Gwo7n7NYkqBaoyqLv7ICXbtcFGEnVUYJUgEsKdT5GE2iCq2tm3CwGRbJJHJouwUnDzF0bonzpi6/sQ3djlyj8+pvyzDX7jH3yNZDtHSssXnrrF33v+u6xHM2Y65TsHl/jux5dJ3hxw5kNDtlOhCsPeSwl7X9YMP4qQFWS7EkqDnmRN6wFZaOqBYnSjYPrUAICrz97jo/I8/9Gl75IJzUQdUyHJhCa3iorWgl36i4fCokSr5AtJ0TXpemlr6XqST02KriUvZbf4cnad+/UYGVms0pjaFatsDOYUJsJaQRRrTGRc0U9t0erz/02ssMIKy9Hk+Pw9u3EdNYLaSCIjqcOfDX9OeWsQVCai8irhLoxX+WphiHFJvxLJhqyRuF7NsYArquD1wUd8c/YMryY7SHGyn7S2FvUZk9TGWkpr2dUxUxtzZAYcmoyZSdFWktt4oX9zZaIFFbLbR7FAUHRJ6lRWZKJmKAs2vGvF4j4Jrib3eXprj2+9fZWnv7LHK4NbC7aWXQJfI5YS0l3F0wlCWrRqrj6W2XcHAho839F5H7YJaO4b3c/7RGZYv7PwdgRcZZcQ108QlDILqmhrRfvez3Oyd/Tie3g8ZXQzneXTu3bd7r058RksqqP71tzd9Sy+b4ni7vvTlunbxQZ13qMSqd2+zw8joR8XfdK5+zsKxSJ9LCODwrNvd5zPsvd5n3juW5l3e9M+TPn9Y4MnWxc+WlDvwkLPZ7lY+BzmaZdtY9llymrrP7diufJ62Xh9KKzruy5csfUJkhya9lZWuljW1CCVC7YFtu0FvbBx7XYvkM3dQyS8Ujp8Fk7NEIcG9E6r/r72SepGkR7WG8YwneW69ubSH5zQSksIhFxCgHfX2z/eXv0ulvWk7i7TENi+QDxYiYfCpPCZV0PbYAPu1wE4QrpPOndJciEeu07hR4VuwVdDoPrfcrdIbNm9EVpC2l3Hl5POrtjNnrhGLIzTca8AcF/wyeKW7n34kyIUE51Gwvav6f37vfJuE5WNOKiHvHn8FB8fbTArEowVJJEmiWpXROULt7RpSWNtRfM+IFGajWzOhcEh59NDtqIpf/Tu81z4piHfks55UNim0DATNRWqKTQM96RY6sYN4+SzzOI6+0T1k1Vat8IKK6ywwgorrLDCZ4FPbt/t+yUXmzE6kQhtXf9lQM5KTKLQnkzCWuqhxI5qvnz2Bt+8e4nkAEwsG/KSoMB9AGRRI2uoCwUWJuePGScFt6drjK4L4pnm3Lc0sjJUE0duycoRvsmxI7R1EghjTyIPRGOxbRJHzIILLMsJ1Fn7kCw84WulI6SN8sSqj6i7ymQ9NNjMBepogSgFonY9lmUFsvCv3mLb2Yu7ZaMpjc136FMl6pZ4bshpSVPBHRW287lj003SJgG0dcS0VW57196F3+M1Jr/0Fzy7ucv3huuuv/dALQb44c8T0k7hvKzKXDSvJovb3mRCIIxBWEs9UghtSQ5KqA02VYjKBSU2Us0YotJAjI4F0XGFlYJ6XRILizCC4R1LfOfg8U/azxix0AxVwdSkC8GVFJYsqhhGJc8O7rnkmw9kSyspPIFdekXT1KTEaEpPbh7ZmA1ZorD8oNzkv/kXP8/l3yn53/7Pf53/9qf/bxjrCGmAiayY2ogYQywMt/WI//yjX+PaP3uWK388RVaG+Dh2yYlYIGqDHiVE+3OnUgcwBpvEiMEAuz6m2hxw/0tDDl7R2KRi9H7M6KYlnmuKNYm5OSCq3fc7+bhmcgOKdYUVkrWPK+qBpF7TxB8o1GHprMABeTBzxHNVI4SAusZai5By4ZwSSmHr2hUuWEM0GFC8cI5qYtEfjoFj7HwOR0ck9/d4wT7DtV8b8exPX2MzmfPbt17izvtnSO4rBnfhzL4h269QuVNbm1iQ7VvW3oqIZpYoh3ISO2WKFIjaIiuDSRXRXCO0YevNktv7zzI/KxAXDDOdcrNe52J0wMzE5EIxERWZ1K4/LC5B283shL6pCksm6lbd6GeakjKRc9ZUjsJyXKdIpTFagbTITDOJc+Y6Joo0WksiaamFomb5z3KFFVb4fCGsa09QrRvUzLkexGsldaUwtcRIg7XC32Jdb+lcx0TCEHWU0khAu4TjFEMmKzCQyZLcxm2RkzBgHdkdC0OMJRMCCfxMdo8380v8zuw5/oPRB4BFW0sBpL7XamUtMaEnoHCK51NgcMpsJSTaGq/SlhgMeybnyFh2TMqRychtTG4SpiZpVNGFcYR07l+7yqhugribKO6qkbvJ5NB/caJyxipn5NtYGCS5lfzq+e9z63CNf/LnX4WvfZNnsvtNEnRmElJZnbDsdsUArSV3P/kbiOi+oqtrMRr563x4D62tqCsm6Cq0xYIyvj5N2cXJvsSLKt3FZG9zDj0hiJVLL3fV0QH9ntHLekA7Pu5kP+UT/aA79txd4vsEAb1EFd2qpd372NunP0glHXqXdufro9sjum8F+1AVc29+1SO7++9jWT9UuZybeGG+vgW+6pxTXdI5PLuoZt0d69XeGKeRD6FgLxDS3f8Xlu/tQ79gpIuFftYLVt1LijNsS0x/7lgW/y4hlLubaqU4OV12lw3TaV/DtM74y1TLD4PtFOFEQkPH/St8XaGIuh4KqlG7n7K22LJDSPt4V5zk5HuEe0uaNv2cLYt9pX0P5gXi+RRSvtmXhgzGx/CiUap3jSccSb1826znhpG4YgHrtrcxsfDq6Sa2b4bo7pOfuIzI7qqkO25X3di7UUH3l21suTvktLWuILxr4x35ndUnyd0nAe19dtFVRAlL3O9r5tFerxbvzWFauOaGe3mXZDZWkHZsrGPRtgUIywWT6Udqe/AYLhIBwUq7MlFzfQ/PKjOTMJQlUhj26lHT9uDjcos3D87zwc0z2EIhs5rBqOTyxj6vrN/hUrrX7IMjsOvmeauy7lmh+6yjERzUQ+6VY3aKEe8eOjcycTelnAiqkfvx3plP0BNX5OPiXdU4+UhhqEzUFBKG4xeKkwoTP/axCcdD+X1pHTI0iPY+0W2VElTay/qTu2UXv6PayFOdSlZYYYUVVlhhhRVW+PT45JmqTmAVqnBVaRBaY6VE1AapjVdxuCB0/FbC7+6+zvCmYOPdurH5CrZXIegSvorXKtkGT0L6gBMoFNXEspFUZKrihzvnOXfXkO6WiEpjBhHJvm2qqOuhkyfL2tlhm9ips2tvmy1Lt16r2odToSHKIT5qFciBlIYu+RsW8MS2tw4vkWjllMmiFsgqkNIgK09Ie/WzqyIHndlmjL4Fm8oFKvfLl+4YBYJa1n4s7Y5jnbb7IJRXdXe2G2GxSrD2Hvz+S8/zlXM3sFslfJBhAilv2nUjcOrR0whpWLQD80Gx7byqucHEEhNLdKpQtXGK3bL21mFeQl5rT1L6oeY1ZhiDcklFNXUVufmz2w8/R3/EuFOsAbCu5oxk4XpgIsl1xEGR+aRjqwYJfaP/5dHrfPfwEpOo4Ktr13gxue2Wty6ZH1BZyb84+BLr70L24S7xNy8w+1rMRJbNPEchkBOuP/V/9t7f5eafXOTKX+ZE944w60Oi48pVvlfuOxRSQFlhB6n7rRrjLNPXh+y/PObgeUlxToOF5H5EfATlumB2QVEPfBHFxZw9mzH+N4Z4PyeapUR5TLJXopOM5J5ifEsjZwUUJRiDSGIoSmzlrAls6RTbNo6cNXyaOAJbCNDa/V8abFly+HRKeb5m85sRcub331isrYnfuMbGsy/zwfPb/LA4T/x+xvaHMNipSQ5dQsEKZ0mvM+ms6QWk+/43pC3lmnT7FblzP567ghZZWUyiiKY1m2+VjG6nHBxH/L/P/jQ/fO48/+Dcn7MhZ2yIOQZBYRSp0GRCU1lJTGvZGQMxhgpJ2QTPrjrG2dBqkI7SqaykMDFJoikLgRYKpQzjuGBWO2/zJKmJpKFSznZBy5O2rSussMLnCytA1u7e5ay8BVpL1xIEsEZijKHWCiUtlVakqqa20l1PjCKVtUuuCdEk+5R1PZaDTWRMaAMRVJUC6Umy3N+3h0LwlcGH/P7xK3ycXudq5K7FEqealkg0GuP7VD8MgZAOqNFUVpNbzY4WzGzMzKRMTUplI0dM26QhoyurmJmEyigKEy0QziFJ2u9hCB1SV7TX0UJEKByJH5K1qawaIm8oS75+4SN+v3ief/XeT/Crz7/Ji4M7KEyTgA2EdHjfJaSXEVfdJPZp29pVQeteYryr9gpJ9nY9csGRtTnmwjTkibGqkyh1iWDVfVBttvPkOJ83TiOj+z2il8172ljL+kWfhmWEdHdad/k+qXrSMtsuvD7cLnZ5L+jHQd/C9bT3D1Oj9XuHLkPfslv5K0SXsD5t/EfF4yqlg9V3sPENr10L8Edf9xPyAwnqWmcC7VXMLBKNHicuR2Lxf7vkfSiqPjH/svfNihanCf9ZuPZJYYikQYQi7d5yVkE1hmLTIit3/zPKXcCW2WgHgjhYYzeW2l0b87At3WLWbi/ohow9hYzuf2RZJKYFjpAWi+uwdKy9O9vr1r9kWzvrC1bsC9+nCSrxxaKDzrveOKI9Rgv5EdGqo409cb44kj3M0yGz++24ipInDQ9SLz8K+iR0/3p4mvtEl8Bsx3IfdIuH+u0S4PFtph/WMzrMk5uYWGhmJiEWmrHKOdYZB/WAM/ExP5yd5+2Dc0TSMIgqXn36FgAbyZwz6TEK91u9X00W7j+prDkTH6EwzTPTSBaUvjVZbmMmSc56NONsMsSM3TPS+k/lfO/cReTNDAQcFBkf5mdQGDbj2UKRnMKivFOcsbJpVjbzzjiprBaI90fBj8POu3tveCKKl1ZYYYUVVlhhhRX+e4ZPLZ+QpUVp4+x4jytEbbBJ5HsQ+5msRWjB2keG8U3IdiqiXFNnqrH9tko6FeWD1NKRswkXlaDccA/Xxgr0vYx01z3iCmMRpXHzaoNVEjtWrr9z9/lVOPJJlS5Qds/OLiCtJq6Hcrrje1L7YLNb4ez+t40lp5Vumomc3XZ2H0ysGpKrsdcG8DbkwUJcJ06xrTO3bLAGbxIJkcV64jr0ixZGOAW1dtttalDB3sxFwAt/UvugWDuVtlWQHMHO25tw7gbrGzN0nGGUC8Jd4sAFs1aGgLtTZb3se/KqeFHpRv2NBSSo3Fkim0RiUoUqNMw11P4vid2yxmJr3SQChNZU4wEiMehaooCjq4Ljy+kjnZ8/Sryx+xR/NH2Wp7f2+GvbH/BCdgeAc9kxpWmrjStPGoR+0nfLCTeO19FGMlAVV+IdRrR9mDJRo61gxwz4/RvPE1dgJkPOvFHxdnme19MbSGHJfTI7wfCD8in+j+/+Lep/dJar358h8xo7yrCR65esjgtEXoGSqLKGNOHuz2wxvK9JDmqOrqQcXRXMLzqFcnQkiQ8F9dhy+JLBbpUkWUWxn6EOIs6dOWT7ym0+OHiW7R8o0p2S0cczxLwiU5ILfxIx/PAQMZ1jp1MAhJTY2idAtQvqrf/OURKy1FXoRwoRj910n0BJpobBhzHb35shpnPIMkxROlI7L5h8XHLv3oCN70esf+BU0VI7a2s9kFRDR0bXWeuMYJXwxR3u9yIryLdd0crkI39tqEGmzgkimmui44qtNzXRLOM7F3+Cb//kJf6TV/6YXx69Se4V8LmNmMiSyvoKa3vS4rNRF2EpBSQWhqIAUhLhVPMGwSCpsBZ0LYmTmq1kxm4xwlrBJCswVhB5K9RiFS+vsMITB2FBFhBXAj2wWA16HjVJexMbtJZIaam1pFbStSUREZFwBSq1kSjpyJbKqo66Rzpi1rb2lm1vaXeN0QhirHNLEfBqsscb0ZR/efQ6/7ONv0ADmRBo7CPQ0IvoEtIGy8xUTK3hyEjumSG5iT0RHVPZiKlJmZkeKa2dajrcM7VdVA67sT1R7YmnQO7WHdVpgBSGw3pAJDWb0cz1QJQVGRVfGN9g58KQb733NL93/QU2n5kxVAWprJjp1NmIe3VNVynUteZeZsfdfBcdOiHsR1DYnrZfXaLaWNGOYZeTZW4M0zIXzSzhu3Ake7eHsX7Ckqn9XtFBxRysREO/2tN6RvfV012COXzeHfO0ntHu/YMV0uE4LvssoEtCy4Vz8iTx3Fc7L/vsYQiqu1OV0o2CzC4QKEt7lPYS+8vsuZeRz+7zk/a1odDugegT6L54s1n2YcRE77fWfJe0fWb7/aPD6zIb7yeiv3RQ/nYITH/CdxTOvs/wQv9gmqJo6BRLh/ixR06HeW13fY97ebCLtGs4b5etzyhBtWYxZ0v0TBHNoxMEemNL7eO+QAgvtfUWYmEdXQJ6gTDur+NB8OR3Z/ca8r0lncM+ntKTOgxle0Rzd9394gJPEnfzCkiwxq/Du6O4iS0h3X1/YryFjRGL/8sQzNOqoYVw8RdAsGwOluCfM5wF9enFtg9zpViGh11ju9firmV3aJPRKHyXXHsfpc/1yX04SZT331cmorARlVEM48KpiiUUJkYKw0414tv7l6mMYhA5scbFwSEvDO90FMmG3XrMXj2kMNFC0Z9BcKdcI5F10zd6M5r65axXPkuGsiSONROZo5F8efQRP7V5lj+58izHZcqrm7dJZc1RnVGV7jl1HBXEOGcw7S8+Ve85VmKbZ6NPQzSfILSbe4mzWu9+98HR/kSBWeee3b9frEjpFVZYYYUVVlhhhc8en9y+2we1srZER5XrFTurFmyfHBEsXM9lbUkOakwiiWYaG4k2Nop9sARt5e+yCmrlyCNRC+xmRRrVlCYiORCoXCPzqu1LDE2QaaUjWLHO5joQzLIE5dWQrv+xI2DTXUdMy9oSFT7ANLSBq2jHDQjPwqrqVFVL60he40lhn0xYeG4WlmgOqpCOwPbEdz0QmCTMIxpr8C4JblLcs/Zc+B7VjvwP29PdZlG7f4QGVdL0yJ68Lyl+NuLc+Jjr6RYmcsR1t8rdKdo734exnCgh9tuJtYhCI2tL7RMeJnaV8QvW4wKEEgvjBoV8v7dWsRERD2bUlUIPDPWGQcw/f1XonTsbrP1lysfjNd6+eJnNZ/a4ur7Hdjrj9fUbHNYZZ6IjgIaQTjD8yvr3+dnJu3xrdpVMVuzrIUoYtqULAmNhKKxiXw/Z3x+xviYozg9J9iu+OX2Gl5I7ZNQYK8lEzV/mV/mHb/0y2T9d5+y3DpD7x+iz61SjGJXXyKJ2inRroaxAKfTmkOOnBfUowkQRx1/KeenKHd757hXW3xJML0P+hTnPXbjPIKq4fTzh/s6E5E5Edl9wNz6Lfek+5Rdn3M2GjD9WTK7XJLsQ7c1Jrs8hd/apKIXIMve/Nj7h4RLrwidDRJa5a4A27vsfZk41n8RgDOt/eoO176awu0/93EVml4ZM/uBd7GyOGGTMzscgDOm+IT5yxHc9VFRjRTWSVCNXMBIs8pvfBq64Q9aAsOgB6MQVi5hQGOKLQLCgpEDWhuH9mvX3KnZ3JvwX+S9y69V1fnX9u4xEyaHJmNqETFTEaKpg/WpcEJ4JvWg1Z6EUOLtZg7Pl9RjEFdoITCaZDAo2oxmFjogjzTCuqLQCb4Wq9ROQWF1hhRUWICuaQhhZC0wNopCu/6ayWC0wUqKlpZaOgC6NQhlDZBxZW3SILuMTeqWNkBikNShcv2NljU+OCpQVGNHa68bC94wGfmX8A/5/Bz/Jnxfn+HJ6F4DKGmfH3UtGL1NNB9tuAG0Nha0pbM2+MRyYmH3fNzqobML2zkzCTKcUNiI3MbVxjhCFiShMx5rTk7P9ZGA3SSyFJed0VWwkNbVRDFTJWBVUyt2Pvrh+k73LQz786Cz/TL3G37j4zoJFZ7Dj7lo+Lii4O+syVlA3KuueUtrPXzX71RLOpxHRJ8jvU4hpaQWmbx8diDk/nrTCPXgCtfn8n5e6UHKRVA4PtuF963i7vGf0iV7QLNqAh4LV7jLded3/p5PR/V7REksku+q45VbpfWvuZY/J/W0K/aMf1961O3+f2AhEgmJRka2E8RWv3XH884NXsC2zEA8E9MJyfrslS/q0Pix3/4Ae01IY9Ime6EtI6iXkdVBOt597J4XA7DV9d2VzDXlSiIYuQdwSorSuXNAeVx9rLVVLd8noEKt2Pv9EJPRD0Pz++uN6krUeG86cPWT/cIi9G3mrbns6wRu2UYqGPeoS8/3xm2V6G/Agy+5HQo+s7r5/2NgLRLPwC/aWFcFuu6tePq2/dffzLnENi4XiXZvu7jhS+LjHryNSPl8iHSktBDZWWCndMZWffzzRvV5r2xaahOKV4JDwICyQxKGgqzN+/7pWefeRxpbbtwCoQ89l3HVDW/fM1RaRNVeZh+LkNj0csdDESnOsM0cOC839esgbBxc5qlIqrTgzOObp0R6prFEYDuphs0/hmaZacFdx21LoiIPKubtFnXtYKmsmcc56NHeEuCrRVnLAkFhojmXGZjTlf3Thz7yKu+ZAj7gvx43LTG5itJUUvjgwtFiprHIFk56srnxR4uOQ+v15T3vKUWLxXhwsxft27SeW6xQsddu0rLDCCiussMIKK6zw2eHRSenwQOfJSqkDsWuQtUEcFYigfvRPf0J7JbFxAdP8bITQkOxDdFw5K+ckJZ76nrbzog2uvGISIdyrD7RUaYkPJdHVnElckOuIwR1HFIHbJiNA+PdI0Kkgyq3rY9UJ7oV2RKisA/HkVNPZnutBrXyfZqSfP7Tg9cXGsrKNEjqQyd34wrnxikZFLetFQjoElMYrNqUWjRW6iQHRvg+EbiB1w/aYmIZE1okj0oTubId1+yYroBQL/bSNguTI8pc3rvDzT7/Pe1uXsR95gq5zjKzyiYEQAMtO8BsC4FBFPnTkY3xYIkxMNVYYJajHClk6takstCPnK42otdtP/xoC5nLdKcl2X98g3xJkfzJGzS3lhmB2QTC48/kHzBxFbP2wJJrWVJOYo8vbfP/qNub5OV+8fIMvrd9gQ83Imx5NLkGfCE2mjvmJwU20leQ24UgPGImSTDhCMrcRu3qMvJMyPwcQc+a7NW8dnEdvCkokWyrn28VF/h/XfpbhP1pn+0/vghCY9RE6izCJROU4QrpbDR8pMJb19ywHzwuiLx7w966+SWFidr/9NNVY8BO/8B5/48wP+c7RFfaKIdM8IbqZcu5bhuSgZng3Zn/3HPq8Rm9pDhNJNYkZ3VSsvXcM93P/241c8mmQOoVzSMBI4azjkhiRZdhhhvCW4qL0xS1KYkcDV+gwLxBHU0xRcvTciFu/aHj122PM0TEySSjHgvUfRAx23PEr12PKNUk1cjb99aAtpOk7H4gIyN15n923vojEXS+kDvNar2QBURlibws+2DWof5vxG+VP8spfu8WleI+hLFwgLiQx2ieIra8MN2TChbndVIiz9TZoKZtzAGAYl5RaoU3NKHGJiNpIBklFLF2PaWMlsdJo82QkV1dYYYUWQoNNoZq0riyiEhAcG4zAGIHWAqUklZZEUhFJQ22Us7YWEZHUFCYikwppDYWJUdKgrAVv462RVESNA0PI1zYm1AIqC1eU4euj9/iz6fO8GN9nokBjvVraE6S4BwX1gOR7IKRntuLIWI5stEBIh2RoUEwXJmZmEuY69mppSWmcAqg0qiFlg3qnfS8WiDzoKE17JGOAQbBfSQoT+f7VEWNVMFY5X9q6wd3DMfevb/C94UWem+ywFU8XkpRN/0rUogq6lwMPZHSX3Frod4ldbtu9hIiGlkBeSsTRITR9kl6GsTskdVBoB+Xu46q3ftTo2m337bpF5zV8tszGu/v99+27F6afIJ1bMnrRwtsXb/R6R3fHCCR11CFulxUNBMJ2Gbr9o7vKrkclpPt2sX1rbbeOtv9zl2TWSOQSclkTHo6WW35392e5hW07r/ZNAE6DWSp/9ej1qe6i26daPsS6+zQr76562nQojD4x9XmgW+TsYjzRPKtifOzaKRYO7PUJctT2uFSxODa0z75hRln764dwLlrLN7DzIt3vM7Q+kMIiVSBW6ZDnfgEtqGqFriWRduuzQiz8np0rWC93oG1bjB7U1Kd8VY1tdjhGS1TgYXUm7hTB+8+Fto1COxSthmMk68V9C1cn61XsTYwsfcyuXLskV1Aecg4+vu20ubIhFsG7hEnh9hPbWmsrv2ylF8lnJTBxpw+vEo0zljuW0hXaK2+XLjvnSjef00HINyw7Z55EPG6PZtO1FPCfnLTolsheYU63OE4LibbhmtEWt2g+Wd9o95zTK8LpjaGEYSgr90xiXCuSO9Ua707Pcm1/k/VBzmtbt5BYal/8XNiEe+WE4OrRuMv0CtQqfzwSqTEIav8sM6sTpLDsFCOGUckoKkllTSQ1cx0z9kV+kTQcqCGxrJt7wmY0RWGZmYS75YT71ZhIavf8FRWciY5dnOwLFo0n+MO1Orw+jpX3o+DTtHh4Itw0VlhhhRVWWGGFFf57iEcnpX2g17wVEE+9/XSqiI9zp2zUBpkrTBJ5VbAL1Ewk0IkL4HQqiQ4qhIjbIAhcwJXE1GcmRPePEFXt+s1WjgASFgb3DfmW4vzaEZHU6DohPTCOhDZuJhFIJGOxyEbpLOu2stwqVwgsa4ss7cmg3QT1ZGf//WsQTASb6yauFm3QS+ezrkK66ZXl5xEGpLXIWmCtC6idosoHiD0iDQuq9vNUThntCDQ3VjluEz5WBXLbvVelJ6fxhLx1yu58N2P0XIE5V2JF6hRcve/aHU9aYrpf3e5JTztIsEnkLKML7Sy7I+GSDdYReu2xtCcrvIWAJEan0lucG+aRIjl0CyX7FqElG+89ak3yjw5Pv3yHw+9c4Pxv3yPKEtI7CdtvKEys2B9c5Z889wL/1RUwzzqS+qc3P+SF9A6ZLMltypY6ZlePyb0FV27da+UTc+/m59n+LszPwfy8ZX4+YW9vg43nXDD4W8ev8g9/69d4/jfmxLdvuWTEMKVeT5GFRuVOIR16jSME1faI4kzCzqsR9quHVPOY8b9c49p/tEUiNfe/Ytn8AXz3u8+QfKXmh/fPUX5rk423DaoyTM9Ljp9KSY4sZ79boxNJOXYW9NUa3P+yYOeLE859c0R2r3RK7eMcUVTYqkIkibeQE9jpDIDZFy9x52sxV37rCPXWNRgNIY4ACca468BogJjOEUKw9o+/xeYfbGNnc9SF8+z+tYtsvzHDJAqdKaaXUuZbkmrSsbyXHVLZJ5zUXBAfu+8yPraM7mqiqUZqg6gMstSgLcL4gpfa997OIuqhc2SwAqLccvU3BP/nd36dr//d7/G3N99gKAtmOmUi50xkTmJdclhhOfJKQoBY1At9x11w7gjsSZRzZbRPoSOEsEziAiUM0zJhPctZS3K2khnaCg6qAUX9qbsxrLDCCp8xrHRK6XpDEx0q38eeNvmvXeLYGonWFqOcKicopkvjrJhjE4HE3S+wxEo3vaUVhoqIRLiO0P3e0kcmJhaGdaFRAnJreDW+z+/Ur/LH8+f49fH7xEJS2UdPAhosldUc24p9A0cmYWrdXyi+ym1M6YuxZtpZdx/rlLmOF8joUkeUnWSpNpK6Q+B2rZgD+grZMC2ShsgX75RWUVs31lSkyNQSW8W5+Ii/efVtfvPgS7z95mXWvpSzFU8bRXSXGIeTKuiF4+AJ5r5Fdkj+mmCT2Xmo6hPR7WftfofigH6C2j1Sm45qrEfOhyS5FX4M2ai1nxQo2aqxukdhmdV2n4juquf658SyefrFCpFsSV33PtixLlp0t+N17bvbsU6z3O5PX4auMvqTEBldMrr72kzvkMiq9/lSWG8HbyVpx6llYZ0E9XhnG7yPQn++ByX8HT/WP6c7ZNkSBfSy316zX83vJRBqLTHdfd8eh0WV9JOkloYOYRxwmjPVo4yzbEI/RuXE4V6YtztOI4a27hodCCVYLBgBT2pqV0ieHAj2748RU4Wau6JPNw+Na1dYPCikrbFt4Sg0iuhQ9L6gAu8s32w3HZLZLs4rS9Nyk1IgarsYm+Nj9u6+NMfgZL4A4WJcUZl2/tDzuRvfhu32hQbhvVMs+/VL6/4PxLl0PbLNRtbMbxoCGnQim3ZEVoR4h/a7k+32y9o2eYdAxIfcSMjxCOO2bSE38zmhW5QWsKCW5vF62C+7zvav35HUrXvFkutlS0I7S+juZ58VlimAQ5HcS9kt3pxf4rdvvcTd+2v83Avv8fRgjw9n20hhGEclU50296u5doULSljmOiavY2rfWqp7jxxGVXOsa6PIVMWsTjisMu6YMetpDsCZbNqMm0qNFIaPZ5sMVMVAVYyigqEsmZmE3XLEXjnguEo5LlMssJnNeW58n+cG9zxx7eLWTFbNdfuTZnf6951lxXjd+wJIHuU077ZoWWGFFVZYYYUVVljhs8UjsxjO3pqFCuh46noES20QhUtk2DjCJBFEEp0qTOwDvqDmHQiqtYj0Xrd8mSboLq5uce/1jIu/XSGK0k23FtKEehyz/4JidrXmwvDQPzzLlpT2szYBVScIFLYbdLlAURhQuUWGQNJXFzdks3GWv90gvhknbHogi2H558IifEDYtd8SxtItxg3bBGB8Rbz0imfhA0Vh2nWEoDIQ0lFukJWlziJ04tffEOIu8JeBlJadINRCvBtRmojzZw8oB+eIct+zq1uIYEy7/Qv9zESzUTaOMGnkLMCU8McxbIh7kaVXSdemUdD3Uycmi9AxjG67c6r+CYUsBdHMojRUAsrx5x8cnBsecf2XN6iHl3jq3x0h96dwDCqJUcOErbcMkxsR87cHvHvmRb5z5XnE2YIvP/0xz4/vcz4+bHpm5SZmTebkJiaRjrx84+AiANNLBlkJiomkLGI+rDf5sDzLP/w3v8aV39aoWYWNFHaYooexI1ILjSxr168bIFLkF8YcXk3Y/ZLh4ku32RrMeOd3niOeugD8udF9/mT9OapxyvCG4o07L7viBQm7rzmJQHZPsPlOxeDGFLlziNle4/jZibNr3xFMc0WxBdVAYC6kyDpBVgOG1w6RszlIib64jag0HByC1uy+HPPq33qbN/VLXIqeRR0XWKWQs8L1hc9SzDhF5QVIgRwOnW33xhrVxU3Sfd0Q0vmmYn5GUo2hHllM4ghpYdo+dLKGaCZIDmF025AcamRpiI5L6klCOYkZfniIPJo6xbbWEEVOzV1rVFEhZyVIiZUjinWFLA3n/7zgj4evMfm1nJ9fe5uRLJoerznSW5W5a81QFmSitb3MbcS+GVJaRSxqKhuRyYqzyRG34jUSqTmbHTfn3iTOOZNMGUVFk0QYJ8WP7dxfYYUVHg3C+JYZpbfPNCBqgVCAV4w5C2+nlq6NJDKur3QkDaV2/f8K3xqlkrVX/sYoaV1Rm0+ellYhcSRVhXLKTgsTWTG1ETMryIRlZiEV8B9sfIc/PH6ZH1Q3eTXOUQhHTAuJQjRqacSihbfBoK3l2FbsaMGBSRsyempSKhthrGRqUo51xswkHOmMuY6Z1o6UznVEbRWldmpwbeRCAlp7MrpPGHXtmwWLSlpwhGckDMpbOJfGkNeu4GuvHHB1uMtalLMZz/jaSx/w5+88w1+8f5X6GclTg6NGBWsQD7Te7mKZUrpr991PrHdJ75AkDctU2q2zS7h2l42ks2eXtk0oh+MSXqWvbjSevHySSDdot1WwSAo03y2cKDYI/4dChP733p3u/l9uz9383yGb+58tjNkhfANR0d8m1RSAyAeqpGFRTRdadZymij91jB4Z3SfIWwL55Lh9oiX0zcRb/59GXPfHCtecvvIaYAkXsIj+4fF22ir0SveFmc26gr3TwjKmUdg1+9Kz7+6+76rk+mTEZ0kofeY4TbHaicUXDk1v9gf+9Dtx4rJll85vQHh3j8oqd4wblvPk/MJAsi+wKvZxHIgHsE7dr6IpIpc0febbaeGBvkP6hlOhIaodMR0I2xBjB/I4FJYK0Y4XSO/G6b0b4/v1Nv2lEQ3HHCzTjZKYWCBlm3MIuQNLZ6zOOgPRbUWnKCGosKXAOb7Jxo2tS0DrRLRttkT72vkZNPthlHdL09a3FBONCrx1ZbOI+sn4PRSesHyQSjU4g5w6Hbswb/+33r83xkIvXOcLE524dzfwPab7/aUfBFfA1m6v6rlZgCs87O+DtpKhKvj9g1f4V+/8BJtrM/7DV7/HUJZ8c+8K2kgmSU5potZq3D9nlP75KtxTbe9ZBWCvGDbTjBUMY5d/G0QVUkfcm44oqpijYcokKUiUpqhdwfQwKrl+vMH+PGM2S9GzCJErJ7JQFiLriy3g3rBkb2PA/cmYS4N9nk53GcqCI5OdUDA/TM18oripd2y7x7i7r2GeYGm+ANEjtjsq+ce9T6+wwgorrLDCCius8HA8OintFa8BUoMqjLOlnteOuFHedrCs0WlKuR5RjQTx1LZW1ZEjFG2sPBHrgi7r+xodX0o4fK3iwh9FiKp2lr5xRLU15N6XM+qfPOLsKCeVNZV1idvsqF6sSIZGhWuFD/Q66mY33SmNZWkcKe2XNxasEV517Z9PhWgDvU7Qa4XwpGsYUrRV0CcOoGgV07ZVTYflRIdDb5XNtiGmXYDpKsd1LJp5ArEsarc/Lgjw+2s8se1V36p06zWIJlh1PbQFd/MxV9f2+O7Z8wxvt8eogcHZiXWsurt22+6Ld9+rjZyFmVVORe8CZoFJ3H6KvNNPt2c9Z6XAJBE6EST3Z+QXxswv1c4GPLakO5L5syXTpz9/5c+VwR6bL8z4hniJ6cdD1u4fuj7KaYKsNcmsJN5XDG4rqknC+gcROkn57pdf5C/XniM9M+cXn3mXK9keu3rMOd9/Gpy67a0751jz1vHD24JqDHo35XcOXuW/+72vc+n3DIOPj5yCN4uxkcQkyimkjT+nI4UeJcwuDdl9RaG/csQvPv0hlVF8+/YlRjfcd3dxcMBPDG7y2rM3eOPgGaKpwEagBwabGpK7EeOPYOsHM5IP72EODt1PYX2Mle43HRWWyceawX1BcmQoJ5J8QyIsZHdiZJqAEEyvDBncKYiS2L83/McX/pD/8m8rvnP5Kuf+ZMjatRyZl74HtkQezLCzHOt7Yosso3hmm3I9ItmvqcYRO69FCAN1BvXYYhKLjf25qZ2rgMoF0bFgcM8y2DGMbsxRh64CXBzNUAcZ8Th1BTHGgtZYY9z3ejx1duNJAlmC3hyhCk1yBNGsRtSGC38i+c2zX2HtZ3JeG1wnRpP4avLY/6AOber6TWLJbcRIVEhhGsvbkSzQVjJWOUpZElkjhWWgSo50hpKGcVwwUCXrat6QGaEifoUVVnhyEArC4kO52H6jdq09iIRTS/vnIGMEtZYo6Z5vaq+cNqpNjlVWEVvtX2uMkIDGIKlQJGg0LbEj8S0CrEShiYW38Y4Omaict4qLXIneZV0q9LIenz1oazuW3a06OpDRlVVMrVNGB0J6WqcUJmKqE/LakdLaSoo6QlvRkNIuKeqSo11SOuT3G5JRGkfKC9sQ0ADKSLR0/bWlcD27ayEbgvLabIvtdMpYFbwwusfdSxOuvXeOH9x+iuFlp/SJhelYbi8S0ssS4MuU0AHdBLF7bRXYp9l4u57Qy51q+3bmwfpT2pY8bcYRAinkycTr54ywNcusuaVY7Cm9QAD3COm+Stwtc1LB3O0XHXAaId0tEjiNkF4Yp9uzuUNI94kG0/wWF6cvs99+HPRJ8AcR0g/CMmV1QEjiS1rSvluc0SXylxVtPOr6u+dp95iEXtMB4TfYJ6ROs+9uvmf/+WnrfKLQJYtPwYJ1d5dY7Sx7qmK6GaQ31qMcDgtaS9dewoYiGm+mFYqhO2PFxxZh3QzxLFhV07aK6qLXD9nKTnxs27i5r2xuEGLzzvRASIdjYmKnYhbWHx8lXDG79eRsCHE790Ir/P1aCvDbbSNnjR2IbOP7MJtIYGvL0n7Q/kZmYtES3NaTzz5/0JLMomk7FJ4bjPJtulzdESaiUUIvrMcT2G6fwrH0+YDubCG34PdfVsYJCZb0pf5xo09Gh1YDfTcNeDAx/TjrkGh/X3akamjpsawY6bNCn0jtX4NjWTOUJR8V2/zuxy/w1NYhT0/2mOuYu/kEbSRKGtdqq0zQ/plDSUMW1QvPR+BcDrR/jgjPWMG9JBDWpR6QKN0sW2uF1pJ5GSOAWGmmZcKsSJjPEsw0QhSS6FiSzASqpH2A8e3irAKdxdweD7m1tcHl83uYM4Jnsp2m1QicTi4/7nFs91kufNbtE/2w63+Y/4kuXFphhRVWWGGFFVb4K4xHJqVl7axwCQRp7XquqrlG7U1dRKo1xBF6lFCPYqqRYH5GUo5dL0WTODtpnboxRaWbAFBUGhtHRHNLtOc2S+QFdjxk/7VN9l6W6KGl2h2QrE2d5Z6Bg3nGKK99wOlJ0lARLVyw6FTGtrExbshh7VTSIfhyfbp8FTG+qrjyCTLVVlRDJ0A1LTHdVDiLRZExdCrZRee9FS1BbVzCJBDocQHJkTs+bcAJWItKnG1XncrW0iwCW7n9MkqitDvmwrqgVVYsWoYKb6EmIN233Dhe5ytnbpC/lJPtpgTLs1C5HSyMT4WSECm3rd7muw12XV/vfF2S7CckReW+C2OwSvqg3DqrdulskeuBQOQV06ciJk/tc5wNQVj0pZpIS7Yv7j/qqfsjwzgqiKThV1/8Ad944Wus/YXGzufOxr5wSlrhiVc5TUh2lFOI202kFhRrY/7tL7/C3//Ct5jphGeS+2zIGRrh+nFeH1FsuiyFlVCctQxuKH7rv/0Znv3TnOT6PsIree36CJNG3i67RBiDTSKqzYz951P2fjHna89dY1Yn/N4PX4SjGBsbtu5rZtuK9WiOwvD1zQ85ejXlME85nmXYj4esfy9i8+2C9NoOdm8fU3p7R6WwWUS+ISm2hS+McAkorCTd1+g4Isot6mAOgFkbMjsjGb9XuCTQ+hgzsNyuNvhfXPpd/mLzWf7xldd598YGw2tneerP1sg+2MHu7mG1wWqNjCLqFy5SjSOiY009UBxdVszPG1d0kVhs1rGJN8LZ3JdO6T28YxjdqkjuThF5weylMxxfiBje33C/xdqSJBGydERzY+Fd+esMOMX07jHICeAKc4QxpDsFT//zjP/6+Oe4+uWbPDfZ4ScnH3Il2WFDznin3OYbB6+wVw4bsuLVtduciY+orGJLTZvAektNUcIwqxMOisxVr0clUri+YXeKCVN/MZ3rmOM6/XGd+iussMIjIuSy4mPhEnMRGOmcUmTlrTj9s4oxgrpWSGmdWtorphNZUxn32lhaBqLUPwRoJNpKYuFJHEI7AM2uSdiQZfM5QCxgJOBXxj/gv977Oi+mt/lSMkchUAg0FrWEoTAYKrQjpE3MzCujtXV22WE77lcTChsx0wlTnVLoiLmOmdUJ8zp2hLuRlFqhjUt6gq+z88lSCPWF7XspDc7lVGCMIymFCElYixXOuloZSSTNAokZS81xlVIbRZnOGKiKVzdvM72SsPPeFtc3Nnh2bQelqkb93LXmfhgh3VXVLL76JOiS+Uxn37oKa0dQCE6qgRbX3SRLBRirkHaReIPFHshPAk4jo5e+cnK+k/2gzYnlFtZFWKYlVbskdd+iGwCfJO+qj8N6u0R0sOLu9sFcahOLPTG9b739KMRKmFd19lthev2p2z6iJ+y1e4RHs91+jL7y2SCJRe2X7ey7kEsJaOVV1w9EfzG7qNg+OX25cnoZWX0aMb1AUHv3gL8KfUK7Ct+l03u70P9KHotzX0KEL13eF28XJuqQ0nbxewtjeYvu+Nif87X18XmPfPbLBsK0S6qHdZ5wKsO6U22Jmjz0lIagAu60CpAtCQ00vZebVXmCOMTeJnHxvlEtcewI4nae7n7L2qIKi4l97G56++PjY6MEwroi7ig3zTZrJVzfba+2Dk5PwTlNatCJ/+59PqNbEB+E63hC2rX38lbd2m2fa/9lfQsz5zbXkNO16VTJf36ofeuS7jU3ENPwaMRwX926qHpecj8PjnV+Wmjv0F9fuB+4e7jrq645WYz0MOgl7Qr6949M1Lw/P8tvvvMaG5M5L2/cxSDYK4fuecZKjouEso6otXtmDPfL/eMBxjjlfhxrYqVR0mIsGN8mxlqBlGbhOUtJw5yYqlYUeezOO6Up64iyjpjuDaCSyLkk3ZEkh04YoQp/PvrfSFD0h+IJqwRmT1Dfz7h59zz/9OI6X7pynZ/dfN8VIgmD9MfkYcR0/15WmGj5Nd0XKFZWNXH3svkUJ11luvM9qCXHCiussMIKK6ywwgqfDI9FSncrrmUNalYT7eeIvHRBXllhNyccXx26B0/l2FkT7KV8j2OdCEwaOYs+i6uMlmAmQ5IjTbLvyE07zLBKkBxpNn/oLKoPno146guHpLLGWMnx7hBR5yc3ONhhebsqWVlfge17NVkfHHf6Q4WqZRNI4BC0CjC9xIDrsdySyo0vV++B1lln23a8rgud6JDbPoB0Aa0jpKOZdhXY3V5UbuXUsWwU0U0AAKiqDdyldvsiK6+SDnGCtU3bNqFB5bB3NCQ6q7l6cYed4aWm97QLeMP/wpHgwRo9bJeS2CRue31JnPLdI1igCwsmkY541q7/uFWBVfeJCp8sABBlRTVySXoOI7a+J9n9UszTL93h8nj/5Hf+OcBYwXODe/yL877KWBvkOMFsTJD7R5AXzL9wCVUY4rtHDeE+uG9Y2y05vJZx7dktJlHBO8V5Xk5vMZIFI1GS3XN9kc1azfEzisFNxeimZXyjRM19qf5s7no1xxFSKeQ0d4r2NKE4M+DuT6bMvpDzN196mxuzdd763hUmHyjqAehUkRzkHF2OOKgH5DZ2duJasXt7nezjmK13DJvfvg/3djFFiRACEUV+X3VzThgF9bojWuqhT7QY93tP7mvE8cyptieZO8cPZ1hrQBtG1xTf2HuF/+GZbwGwe2ud6EBRjy0HzyZk7xmsNmAMIkkwL16hnsQuqWIh31bkZwRWGczQIFKD8MkcWyrnGFBIsh3B+KZhcK8imlWU50bIasDRlYh6IDAHoukxL5uCGe/yoA1ECh/JYyYD5OEMUWmUsQitEfMSISXDyY8AZQABAABJREFUj444/6drfKgucvPyOm9OzrOW5szrmA+vnyH7IEVWNH3gv7/xgrMaHxoGZ2a8cu4Or0zu8NLgNpmo+PD+FuIHE3Zji4ndb/bW8Jz7/iPr7NEMCC3gF37sP4EVVljhARC1RYZnjNTfqp1DtksAahDC94WUFi0lda0ohCNc60hSmojEakoTMffEWGj7IIVxdo/S9ajXVpLjXRN8oloKQ24VsTAureafNUpr2ZA1rw8/5rePvsAzm3/KxPeWlsIR0+FObjBUVjOzLSF9aFNKqyitIrcxuSepcxtzoAfURlKYmMqoRiF9VKbMq5hKu2SoNtL1J60U1rikaLeozz1atRl94TWUAlCRJklqZ2Lj50mimlgZauus0IMCKJaG2jjiS3u18kYyI5Wal7fu8pfzlGvvn0O9YDg/OCJVdWPfLYUh13EnCb1ILrv/l6s3A0HcV0wH6I71d5eg1rBg4d282rDc8vnCOAvvH0BqfR7o9pR2RVYtyeBIw0UyesGCe0HhfFK5HPUT/KeQ0bEnG7pjLS7je02zSHifZs8dyIi+lesCWdwhlLvLhGkGu0C+9ntFt5+3RE0gjPsI58dSe+0wTp/A56R9ecIDekyHYpD+OsTJhH+XwA7bFAjzLqkfiOkuGdC1727cH4RB20XHpPDb6/abbi27299iZTyJZMNynz8B18ejqqSF6RQbQ6OgXXh/yhiNmjgUU4vecqK3vA1Fyu4aU5jW/vbUS4xfBt/CK4TJgrY4G1yLHVjcl1BAjncY694Y+nbbTd/t7jxKNLbYbUG4c4FyziTtq4m8wlm6wvl2ens8uvsENAXv4TPpHcqEd1prvodmftuSxQbi3BDNNWpWu3ZLxu2HHsRUY5+e8dtuIkFQSlvl4vqwjU2cbtt1hGJ3Z9VtETW+h7QjokNRPsYiAwmtW1e7JwGBEO62R4BQzCVd6qhblLKsiK53jQjXoe79oLtsJNqe1aEwbVmriLAdOvSox12PK5Qff7GtQLdVRxfh2hfahQyVs82emYSJyhmrvCGkhYD1LOeoTpnVCYWOGqeZvIqovJq5rpVL0RiJ0cK5/wko5+65UEiLUNbrN2yzLw2sQCpNXUTYQrmHDmUxSlAdpohCkhxKomPXBiuauTZyUjthhYmE/1+4+pMqXGcsOgFtfE5IS+p8wF/OnuHg2QG/ePYd5xAmLTOTYBDEQpObmMoqd7/r3LfDcQvHuHuudM+X/v49iGiuTtxT/Pdqnb/ZCiussMIKK6ywwgqfLR7DvpsFWy5HZtbIWe6IRKVcRGYtqrTUmU8c+B7GXZgYyo0YNVeYYPWcxhTbGfVQuorKSEJVI4wl3S1I77tg6uC5NbbTKXOduB24H4OdL64gBICd539n0Y0PTA1It94mkDU0QetC1bXx5HPtHt6FaS25ZFAD+MDU6k6vKdmOJ7xNV6hMDtF7Eyhbp7a2yiWprXGVy2GefixkJRRrvop37oOB2gWiqjQIn0gVxu9qhzgXut0uK31lawnFzgD5vOXS6ICbmxfJ7ovFJEUgjRuLN+FUuuHzSDXzWemtRqXAxAKdSlRuSI68Kt04gnFhzEBya9e7WpUWm8SU6+6YDG4rtt6YovIhH423ufzSPp83JJahKslEBRdzbJYghMCujdh/fYPJtYz4+g7Xfzkhmgue+lOFTiW7r1nW3lVsf78iuyv41vXLPHNml8J/b+ejA56Od0n3LPNzAjWoUeOS6N0xsrbkZ2Lm52Ky9YTh2xrqGnE8Q80L51YwyJhfGLPzxRTztUN+5sIN3jk4y50/usjl79QUa5b5GcHm2xpZaOoh3M7XuB5v8XG+ya23znH59yzjD/Yd0XpwhLUWoWRLDqs2OJMVRDMo1yGqBDqFamIRtcTEML5F46JQTWJUDsxdIYk9nnLmOyXf+9kLPD+6xz/76Au88n+ZIu/sMv3yFWbnI/C95UWWwvkzHD43ZnC/os4U87Mx06ckxZbBDjXRsEYqg9E+GNcCWTpCeu0DzfB2iUkkxXbK3osxwzvuurD1Zsng/V3EvIDaK6Jr7fbZWmcjDhBFiEhRXt4gSiKoDep4jo0jRFkhjyw2Sxlfm7E9GrNrRtytxuwdCNI9y8t/foi6ewebJbB3gA1E/2iIObvB/MqE9156kW+9+CzPvHiHC8NDzPtjnv69HJ22x7ybZDOJC/CtBP6XP+KTfoUVVngsNMnhkJz3r9a2n1l/r7ZaemJaEEUCbRxpG2y8K6GIhKGSrg8zEjJbURERW9e/3ghXdFcReeWJS+JVTXbddBKxDl9Mb/LG/DLfK8/wtXQHCUgExtrmOqOtpbKGI2PZNwm5dSq53Mbk1pPRJuZAD8lN3BDScx1zVKccFANqK5lXMWXtFCtaS8oiRlcSW0lX/Rfap0CnEK9zQGODiFzhkbURRjuPUindXyV8H2hPdEbKE1LKHYdYun7T8zomkQmlNEyigqvbu7x95zIfXD/L5vOzhe8w18kCeRqS4gFdBbTsJcb7JHYgurvTgYaMDusA1z9aW4FoEuV2UU1NS+Rqu5hgt8HmVjx5FsUnlc59cnSRkH4YGd1XVXcV0ADxgpJ6URXdVz4v21Y3hl5431XghvfdbWjei27v58X9PEEoizZcCnamy0jwrqq5TyI/in12WMapDhfn7x+Lk0rDhyvHln7eUVYHFXd/mQUF5BJi2liJ6hD34f9wXPv70kdQTwd1e2PT+4Tj1K+0Q3wGcrmZtxtnPio65HRY7sTy1n1otaA8TSnd1hCd6Gsc1tFd38J2BxI5TPexuYG2qLszT1NUDohO7B56L5vIF8V3yGediJaEVn782P0FZWc4tp3bZof4dX+yAlmJtjjc4ONxnyMpbWdZe7KIPDfEB7lzmwuOYZFCVBk2GizYkDepisgR7Ylt9zEcgDB2Q0zr7ntPOnsl9IIiOtwrvIPZk4K+6lgvXurcfbdj+7wM/etM1ya674zRtFho7hWLhUnda1SXIO8ikKecIEQlmpNOGssIa4NgKEtSWVFZxb/5+GXqIuLpC7tILDeP15lX0YLSWWuJ1sIR0Z70NbV/prI0z1ZCC9d+XfnfqwoPoYv7oU2EqKXL/SXWWdYXkngqiI8E8TEkh5Z4bhf6mNcjQbkmqEYCPbDogcXEfh3GOQDIEtTc9ZePjwX2bsy74jyDqOLrmx8yVjmx0Ggrm3tsLLRTQnd/kLRkdL8Qz32ny1XyfaeT/vQuwrxPdKuHFVZYYYUVVlhhhb/CeHRS2vho0j94CoOz3JaiqXAmjkAI0p0SuR5TD6KFwM5KXJ8ZC+VEEUUtUatTxexshE5coOcCOINVClG4wMBkEfm2JZU1+9WAWBjSPeECK/ymWZrgq1sfLirjqoStRQZFLrjeUoCwxge8YpEI9v2sXAWxX4H0wXZYrwGpTdsfCjBKtoG2l0KrwriKZLoJBLEYZPv3wrrgsxMvNdtjlXvoj+YWeextuHzlswt6/bzGVa524zVZtQG3UQKpHKGd3nWnwkBVlFuabCfyAW8vyRGI5KY3t3XKZ99QTBjbqHJcEkA0vbuT0rTK8fqkxZWodaOcViWYSUa5ZlEWJh8Zov0ZW9+r0Ok6fz54Gn7m9PP1x4GtaNoEjb/0/Dt8ePElotv3kHuHzM6eoRoO2NabvP4L7/Ar22/yX/381zkzOOaV7Jh/vfkFsv30/8/en8fakmXnndhv7x3DGe/85pwqq7KKNbAGsjiIg6QmJaoly23ZbslQW5bbrYYsdcNwA+6/DBsy4IYhGLDRsC0BBjx0y5agbrklUWJTajXFQSJZxaHImqfMyull5pvvdKaY9t7+Y+0dEefc+/K9LFZVvize9fBw7z0nTkScGHbstb71fR/TNyzmV8a8+NEhw4/XfOH0aSr3HIlyNGOFHXnsaYY1nvHPPuDu8Ri/MpjThPwoY290lfGrc8z9E2ga7LUDfG5442czBh8+YnV7yud//8OM7nq2lw6XKNKFY/p6DQqW1wfYFL546zpfuXuV5StbXP4dmH7zWMDnupGGk7JqAWmgLXi7ROMVJIWX5FLQDGEMKLkWTdFdgOWOkSQ2AM1kKV4rlsucz95/H+nf30O9+jVcVTP8ta8wShIZA5Si/sizPPjYgL2vlzRDw2o/YXlFsbzhUJdLhrkAx5e35ozSiq+9ch1lFYM7moMv1Qxfm9HsD7n/g7kUpFLY+eoJeI++d4xvLL4OXtJa4YsS5z1KKUhTYfd7B4MpurDtWOBGOSQaX6aookKVNcqN2P0qlNtTslPPwe8cYrcH6Fdv0bzwFHaYkC8LObYB8NYnC4bfusnglxpuTCews8Xt59/P7lV48JEB5W4cG0OzjwJTiNKDSzjfn+8iLuIi3v3o1cqkMUwFUBXa+qWX56oPiIALrOHaaZQyVC5pWZ+pS9piWaGEJS0e0lEmWiPUtE7Kux+OVqAGo2BPW35u60v885OPs69/nw+kBaV3pCgcDueh8Jal9yw2wOjIkF64vPWPnluRyD5tcuZ1zqwecFIMsE5RNQllmVCvUlgZ0mPDcK5IVqAaKeTHcc5HsCBR8jOHeuKppx43dKA9NhRWzaBBa4e1mlonAmj2zDMbq0mMSFSawNA+8qOWeTNJS6ZPnbJ4cYeX9/Z5bueQLADZyyZjkpY0PUD5YVLcm4XRTXlu6zbPxvo6PF2BtQkS5C17+hyGdpxJnSeHrc8pNj8J0QeYlfIk4Vp+mJR3ZD9HACHpMa3hLBjdZ1Od5xd9nk/02+1rf5l1+W279tomEN2tY12mO8YZqdf+bqge+zqA0OsS4WcB6ceJNQluzrKl4/6ufWZtAHtnoNW657M/w5jus6WjjLfsQwdMd5LhnAtMb+77ebK8Gt/eK+vs9ScYbDhHGvoRuPuZeDum9JokxWN8vs/ydVY/0pO1bWLvN33H7RJyUC9jVKtupLv8GEK+6pUwO6OdVe849H2lPfJ6BKFdolovW5eodt5vo3pbIipPwkRmjaWN6oFtMfV1YV+CJHasa6gAQkdpbJmje1TRAfXKSc1A2665PdYQvNGSa/nuPbNqaCXGvZdG/fh9tTSFR9sP2UA8IH69kT0wzJUPTfGNE2Wn2sokoA9KNzY0jb/zceW7EY97X74TP+n1Z0N/7JeOhygZvq5icRa43GwMA9bGqjPM3CAhvSnXHUHXkSnX1DZ2kwXOK/7ezR9l/sYWT71wl6284LgYcv90TF0m0sRoFdTh+4d7qJVy96Eu0wgYLfWX8Bq9caHX3AK0PuRyH3lUodElpAuFWUF+4kkXrr3OXKYotxTVjmL5lMUPLcmwYTCsmAxKBklDEpRqFlXG8emIYpahao1eatKZQt1O+ZK+gX7e86mdm0xNgTaOk2ZEqiwjUwIdkzmqXTzsnLTH/Q8Q51mBXMRFXMRFXMRFXMRFXMR3Nt6ZfLcDF5KwpOgSGUASGaXwicaUlqZJ2iTPa8RP2kBSSCLYDBSmUvgw77cDw+qyFB5FfkzRMnCNorg8pJoaqhsVpUtonCFPLNmJ7IeyMWkTVrLStGwbXYs81Zocr9YCSK/NzHtd170u7NDkitOBMe2Fed0mg1FuOnQfoxTaOYjSYNYHb2sBj2Xhnp+V7kBelwt7fG1u3e8gj0WKUGs0pccUtn1NNV4SjpBA+1BE8CaC5/KaS+K25f3RLU/lEraSFYNLK+xrUwG+HJg6AM/xfIdz3R00Lwxn6wDxtRXZtNCIUAbfbO/RpUiSRx9pjIamS6qV85T7OfmppdwfYCcO98aY7ZeEuaQWBZc+68hPd+DPP+xq/d5E7Q054j35w9NX+fzzH+fSl1N8VXH1MzO80SyvD/mzey9yNT3hz934QldI+7jnX771KXa/Dls3G2yesvhIhlaO1052yRLL/BMFz1w9JDcNjdccrwb40pDfTtn9hmN0uyI9KdBFJVL3acLqxhibK+rLNfWDMYP9FSs1wCcpyVKhK4IfmcLm0kE9fd1TzLeYvWC58eue4Z1SgNLYXFDV+KaR8x5l2QPTXXlPtvDYCpqhsKSVBxXYA7ICZGxIE5pcMbrbtB3MKkkwlSP5+oiX7jzFC1+dyet5jq8qYWhbi3/uOifPDxg+kGuwmhqKfUVxyeP3K/K8ZpDVeK84GM7ZzVZ8zV8nOdFMX3cM7qzQswX1s1NQMLzvOfow+NRgDue4+ULA5+EAVgW+qlFZJkSNskTVtYDyxqC0QjdOGnLq0KRS1jJWaSWe4qsSNco4+FKJWTWoW3dJ3vT4qiK5eZ8kMfjFoj2+WIvSE2Gge49frmBVMFyVDF4ZQJbihinFpSEuV9hMU00UxYFi/lzD+OoCr58chsNFXMRFbESvoI8VVpdKQtOYC8xWq7piemDB1FacncsmacGwNIBgUcbbeEfpUjLVUDixYagwpKpBozHeU6lQiA1IQcsu9OIv/UIy5zPJis+u3s9TyZcBGGnDMkzSFs5z4lIWPqX2CYUXIDpKds/tQCQOvaJxhuN6yGE5orSJMIQbw2w+xJ6mpEeGrTuK7NQzPLSkM0t2uJJxtG6kQG9daw1CmmAnOc00o9pOKLY11U5CtQ3VtsMNHVYZnDKoxGGME8UMp1qgUykPVmOdIjEKF1jIJQL2j5KKp3eO+eZTGcev7XArbbg0WqDxZNpS2YSmz47eKIR6H/2nzz/9EcBpzgGlIxAd1yPnJez7xvKbq98sjqvw09LJZD9pofFrAHTcy02Z1M4P2q29v8my7oOoHQu6e33TL7rPjn6YNCv0GHI9Zl0EoFtW3Tlg9Nr+9P5+lN/o2nfHnmFER9nrPtP5TJwjnX3+tmLH60Ne7+3/5t+PYiRvrk+AZHcuSHBGyntDqvc85vbDpPLPWyZG6bt0V+PPyLS+29FKaXc4Jt55MOqMJHfb1BwUuVpg1se/e//7ql0RswqNUNKA7bvm8X4+rLp1ET4jn5WFvIfCiqxuYdOWndnuR9yXhGB/FbcXmMVWtes1te8URSLbN+S47Wu9Ru64v2eatnt5uzMEz2fa5m+xEZM6Q6xNxNdbCey4Hqe68xC21TZ9+wBAV9JElaxCM1VD8NV1mMKhq3C/h0bu6NlM/AnoVY3PM3nmKSUqSt6jV3VI1sMqYr0hrm8zYk4dcm2ck2doeK9VNot2RH0LLugY0t7LXOQJAKbPk1J+VPSbXozy5zfdnNOmIet2NN6Elj5NquyZZrM+ELoJQvf377yxLi6z6ZWs8ZQuZaSrdvxduowvzZ7ijW9e5vL7HzBKK+4uJsyLnKZOcJWBSqNqhVnptaYP3zNmb9UK1pj+qvs93qvnhIwdUjdI53KN5yed4l89VFRTRXEA5YHF7Jc8tX/CvdMJxdGAxYOccr5NslRtHcoOPM3Iw7hh95kjjo/H1DoXFvVJylfevEaiLZ/cfoOJKRjoGus1R/UYrTylS85YZUBngfJOgehNIPsCeL6Ii7iIi7iIi3hvh/UO8zb5sN1QUmmwaDQa9bafe6dRe0uqzuabm/tX+9gwuT4n+U7uy5Me74gpHRNjZSGbW+wkRxuDOVkQZacAok+zS2g7jSMwTbR/9qCDtBVB5rmegDcel0EzSUm9D4zalKMXUuop7B3MaJzBIX5W6YJ2m7KjHVsZgJ5/Uj9pI0y2o+TWZjd4C073pNFi97VuPEQ/JyPFZe+UeEfWEQmnLTDoRopuKvg3CeFcEX2y/OYFF7YVj3efqewVKOtboFdXDt3I8VZevKO07cDmmHj0fav7EmQxKclmnvvlmA9PZ+xMlhwPpy3zUvy4XQtMq8YGD2mN6gPU0WNYgQ8SY22BJDDBVCPL96W/ve4ATK8Vp88k7LxYsbyS4hPH1jcM5nCBz1KROq5qpi/OHvfS/a7F0mXSwatLpmbF/M/MufwrE+ybt9FfexW9t8Nrf/Yp9pI5t+tt/tmdj/LSy1ehUaQ7Je75gvqTC5K0YXm4RdkklM2EG1unTJKSK6M591djXnv9Ksn9lOxU8cwXG4ZvnqAXBaqq8UGdAEBVNcPbS5ppxs7nBpT7sHoOzGnC6JYnm4snVLUtTPvhfcf0WzP06RKco7m6gzlZ4VMj5zpKrNdBPjtKdmuFtxZfN6jGSXNEDePbDlMJqF1PU4pdQ7EvTSq+sbitIfVUkb1Ud4XTLCVZ1Nz4155mZNDzFTiPb0q8teJf/dQ17v/wLtnCM7pVsrqSsbiqWV73uGsFO9tLRlmNVp5FlXJ7scUX37iBPkmYvgajew16WeF2pyQLy97XYHB7QbG3w/y5MVvfehOVJKjtKW5ngj5ZwHIF0zGkCfrNO7hVIUB6XUsDRm1xw1TuaaXEIzvPpLhkLFQ1+mRJVjSoshJgv6rQeY69ex89HgLg5gthSzuHf3CIt05kyq2VxpkHh/i78poeDRnfTnE7U6orYyCl2lLoUrNaZuzvzr/Hd8BFXMRFPCr67LJQ95Q5QCwG+jAXsKEZzct8wrkgyWgUVisar2m8RjtN6RISbUm8ofaG1FssispLwU7jMGjqAMQIsCUFuFoJa0fmveIZvfSwrRU/Nf4GvzT7GPdtyrNJYFp7ATgLr1kEZrT1sm4Bp7Oen3TGyqYsbMa8zqmdobaGokk4ORlhbudM7yjGbzmmry0xpyX6ZI4vSlRQSfHW4V2vmF8YlNYkJ5rEOQbDAZOtEdX+kMW1lMV1TbGnsFOFT+UgO63wTZcEGOPQGppe4dgH0Ndox7LOWmD62cuHvPr6U9y+vcPucyucV0zSkmUjljGbRcwIRp/3XnytZUpH4Pmc9zdBaRBQ2feK4P6c9cf3IrirAxtVAQQP7Sct+rLc/Ug2QPRkQzI70X2W8rpXNHQgtby/6SPdYzuzLv/9MLZ0nxW9CUzHq2vTJ/p8lrSsP9uQ694EJyII3X2fDoSW987s4lnp2sc43Wv7+CgQeuN9G5jK7yh6IHHL1N6Q8+7GpO64bDIUI7AdPab7/tLA2mub+2y9gEzx+5QuWVv3ux0tsNwDggnN2WuXSQB7oXfqjN8AqtUaYB1zsIjxrUlfqwBiqd56Y7q8mZp6yf91DWqR8NK9A27Pp6yqlPJkQOoEhLZDUbWQlfe+UwC9156JDeha8mpdS+O6trTqZG0+72nVyFQTcl/rUI3HpRqXaWyuW1sqAeRiLcJ3+bPy0hBvumOyqVjdekOHfWxB6WiVFZjSphL7K1M6dBXy71r2STUOXdu2NrEpjd0qrRUVfjKAohRFpsaii+oxLhh/9qfr/d5vIo9AM0jzbbsKv/Z3tNNSnD33T3pIQ9jj3ccP83c+s5yP40u3bAtCK8WmPPTasj37gU2ljbXlw/Ok9gaLorDS2L90Gb/8+x+B3PHB3bu8fHJAWScURYqtNNQaVWl0JaAxgA4s6PObFuRH/96L92JsuFhb3NDWjJKVWMWlCy+1v0xjcyE3HH+04Sc/+U2+dPcap29Nuf3qVbJTxTDt1iGy+cFirgyNN7OU5RsH+EuWp3/wNq/f3iN7ZUCZZXzJXOfyYM77hvdEjc4lrS3LZlOafCkp6p3XaPQkjO0X8c6jDxjEonx87Ukq0m8CG+8knqTvcREXcREX8V6LR42/j3rfKN0uo1ulQb82t3P4NaD4YeN2H2TefFYtXUWuEhyeuSsZ6ZQEQ+nrdtsahcPL9jkLTv9hiMcHpT2dH3JIdJfXBqQLx3AZvGx7oHTLEo5MaSOAM16ha+mM1rVrmcjNQFNtS1HCbTeU24aB0a182ez9Fj+xPD+dkWiLtp55nZOsXNtJ3IJzkaoSr5vN3EPTS9oCMO29dGo2IicnSbzqJekeGrUu7xX8kwkd7hoEnO5RZVTsXLYR2Pf4IIO+Bp4b1RYP2tVv7APQgs8xr4lJu4/Jv5P3nOmUnM67rjfrb0npeXO+zSe33mB3sOLulsOs9FkZsNDN3a1ItZLb7TFRYDPddf4/qiga1+ccpBmLG7D/ZcfyihTPx3cCYyo1eG3ApbjhY1+637UoXEqua3Bwq96hfn0M6hS9PRV56qpm/Ab8jV/48+hSkc4V+3fEO7wZjrFDWOZDji5bhlfnfHzvLRY243feeobi5SnD25rhfc/Tx5bh7QXFwYB03qCs5fiHLpGsHOPX5nilcKOUci9j/K1jskXJ5UXNG39iyvQrGVuvWbKTRgompUU56cJ3ecLpC1NGtzLSL79CUlY0z15GWY++ewRJOMZKo6J0XFWhTCZAamxCQIpA2UlDeioSW/myJjvUTF/TpLeO8c7iUyMy02XsdlDSaFBb0lVNdtehqhrXNAJGG4N65jp3fnKP4aEjO26opwnz64bFUx5/rWB/d840L0mUY1ELaPBgNqY+ypm+qclPHPmdhRRayprsjRWkCc3uiJ2XGxaXDdvbU/xsDmWFvn+Cn88hzSDPsNOc5GgIZQk2ACZFAWyLFL9GFBqsQy1W+GEu/tLOoeYCVLvtEWY5xd69j1suwRj8s9dRqwpWBWZ3R15brqCq8IUcQ+WcsLOhBW08oI9n5NaSHmYsD7YxhaIuDCfz4ffkur+Ii7iIdxCe0PwWmq/0OU1isb7pkHmG1tjAdqmNQyuwxlJbmYAkTiS8UyX+0qVPME6AJoNr5z6patBet/7SNnTfRWC6D+jNnOdD6Yp7o9f518sPcjD9Cs47Rkpxzxpqr1u57tonFK4Doud2wGkz4LgeMWtyTqsBRZMyKzOOjibk3xpw7auW0a0V6d0ZHB6DdfimwTmHMj2YSytpWrK2LaR7pJjuvYejY7ityfOcwSBnbzqmfGqH4xcy5k9r6l2NTWUuqTKLs4pGQ5I2aO3x2suh9x6tpZi5dJraaRYmY5jU5C+c4l6bcns25fJkzv3VhNRYmh7IG2V/N0HlzehLcgNngGXrdFBRDfNX5VvAHKc3QO3uEPVDbYB38fOex/MY/l5Hot16YTns4rqc6tkC9CYQvelHvQlSd8use0dr5dc8ojd9PuGsJPfaNjYA6EfJckcwevN1zfkJ86bE9nl+1HEfN8M+BtjSAezSvPKofXK9ZTZ9sB36DLj+0O2yDtScB0w7dAD9Ra48shGN6mRwIzBtvVk79ptgdf/7DrQk/9FeAGCkKwyO5g8o8/qdDFHmUmeZqgFAiuxm3yantD8F+O1UqvqezXHdfVZxzNEiI7k/VOgoaLQGkst+6FqhnKYqJ9xPJgAkpTzfbOZxae+7bIRyag0Y07UA2a5RGO3RNjRYN7Ssad3EZJZgCWRJZiW6aKCx+EGKyxPUJMOlKqiZxeOggky3wiSRMS0NuiLrfRaAbUHp8L+V5G6k6Tv6Q+vKiRJb7UQpzQa1NCsKT61ns/OdupQLTeTRwsoGFTdj8IMMVdUCUD/qOvHr14cKgHK/QR8TGozjJbABVqpoxyUr7K9svT7xhIYNqi8PA5itV2vPg/5Y41A4b0Q+PoRWjqzngxTZ0npjPJZl/ZnfDZ3st1OqXfdZZu96aDzbZsXMDgA4SGf83a99GmUVP/rxl/jm0WWWVUqxyrDLBD1PhCFdgm7CvNIhf1u6uWX42W9Ige66XgOqzzRm+PYeTQpPfizX9vJSgkvhwQ9Z/js/8gW+fHSNz/3zjzC67dlTsDpQ2EFodPGqbcY0lRBVbB6bNIJy20px+zPX8Zcbtj59jwcv7lMdDvi15P2UNxKu5KeMTMWV9JTCJ5QubX2422Mdmgwiez3GdwKQrr3hSWzs+8MSF8DtRVzERVzERTwqNgHht1suLhN/ChjsWmB6c7n++t/J9q13gXkdsBIUWims99RUpMq0gHQ//jAC0vAOQGmgZc56A8WeoZoqRvcUwyRIzgYmpbJd17a8IP/jpFjktCXBs5kwZZuBwu3W6MSxu72k3NlbS6D0fsXWdMlevsB5TaIti2JMunRnk6fe3y1oqzYm3nGCbn3HWNZSOD43v/ERVO4BzFa+q0zug6dz49rtKIL/1eZ13O9Sj6vvHa8o74VWHajc26c+i1tA/fUEspU6C99/Uy1SBVZW/zOm9Nw+mTB6umQvX+K2Gvy9TL5jlBwPEt0+TToWbZAF81HWOSzqEunabSXKgo/WuXP7mEwDfphRb8t2ikseVWnywxo3yPBpAKYV+PTdn6iubAqMuG1TbhXbuNRz9KPXGN8uyW4egXPsfrNifEdAytWeIT+RYnkzUJhSDsb8xLCcbfHPi4/w7JUH7IxW3DJTdl9qGL2xFBm3qmY0r7DbA+wkZ3FVky4U41c9yjl0AclC7kNVVBjn2Xp1THbqGNxdUe4PQHvSowJ9PMfPF5Q/+jxv/azn8m8M2b93SQrnqYEctPeoJsjJNY2w2JTuChxKQZJgU42pPaYKUnXBt13VFl+Ha6xuUGmKmZUM743Qi9jEkodrCfncssDPg6S19+iDPY4/soOuIT9ssEPN/FpCsQ/uSsnlvRkHo0VbRDitpNBYlQnZoUE1kB8Jm9tlCeZkgZsMwMHiqSE2A5sr3NZIigdKQVEGAFh8tNUwhTTtQBNrWw/oNRl77/GrldzCWYpPDIoMVZT4QYLb3UIdHYNzqCyj2h2SpEa2M8jxwxyVpTATtrNvGgGktRIWd5oIUN40eOdRicHMV+x8a0gzyqgvQ1M9WXKUF3ERF9EVAH1oTuvLoUYZVWwoLEZGm1N4qwTwsRqrXestrZR4DdfOUOsg8Y2hVsKajnLeEOSAFGSInLegu7YFpg1B1hjxll56z/PpfV6tDvjN4gY/PniTE+cpfFx30gLSC5czcwOWNmdpM1Yuow7V/WWdcftoin1rxPR1ze6LNaNXTlBHp9J0Y600HcXCeZpIwxsEMMauM7sCUK28w7tQaLcWv1jCasVgVbK/2ic7HTJ/2lAcaJoti7e6ZYlZZSCxeK8CC9C3TGKtPNaJ33OiHdNhwWIy4ujOFrujVXsuve/YWC5MZqzTawzozVTCPgK0tq57v/95rXxgSfdA5rid3kRKKR86bGnZ8NapQLJ8uDT1uxmbstxrr2+C0uewn/ts6egRGiNRFheK1v1t9AHpGP3PnsdgS3VzrmdxH5Turzuuf92z2T0UfM42QOoIwG6C0OetIwLF/dcjoPs4oSMre+PyOI8ZvbnttWXOu+g3IoLH50lt90GjPjDdbsf3tucJQHZYnnVv6f7vm0eh9oaTZsjN1S6VNVwdzkiVfaSk+vcqtHXByimMAx6R71a0D41WeSyqaTlaBa+WDR2B57hcv4c4glSqe/44E6SzKw9JL2/vXxdxmmtAB/ajrhVmJct3Vl0esbpa/9yGNISIZNkuj3UJmM0T5glezcJA7vyVERC4tmKVU0WmARij0bWM+SjJvVsGeWSPx2NlxI85ek33o2OP9vP8oLrmPKoWgE7XTub0pRXVsB4ArRrZv3ZfvG8VkJRz+L538yDDT0a4SY65fyp2SZth1uf3LcM5/tQb95bW680N0RKtPb5+/fNR6ltvrPc9EJ1SyUaDjXJnmrJkmchi8WdY01Gyu1vH+gAZQc9NFvUmQKrbjkPCts42PvXfi+s4SGf849ufZHlvzNMv3KUJcxLnNE2ZoAdW5PArjfIm2MIpTB0UDNpmCt/JzfcbS+jGgbcL3cgyphb/aIB6rHEZPPixmr0rp/ziVz6GvpcxOe7qa0kBDeAGgAkPh9iMaXy7Ly730jiSiKQ3wL27W5A5dKFZngz5xvgy7Mj+nNQDnhodM9IVqX68cXvz3J03B9uU+95cxnkdynHfPiP2Ir79eJQE60VcxEVcxEVcRD8iu/ntnh2bDGjd5tTrn3vUes57RvW3b70jwbDyFUOVMVAJbzUlNYqnjO4VAsEhy54HnP9heA5+W3RTr2B5RXyZslOFG6YC6iRaQCzVYxT3O7NjDhRA3QgUewMuVeTjimFeM8krHhzEJE6ST39vgtlekGtL6QxGeRZ1RlZuTBR7Hkyxq1vohTHR3wBvQbqbfTgasbt87fsqFCFxL11IWH3wlo3S4AQGsz8nmVfr1BZPlxCGRFFZkSBDd0xr77zkha3WmqzbG4VukG5037GvlfetX9fa8Q671O63l4KbVwodz0PjqY4GTHXBVlqQTSu8zlAlmMqhSpGK9nna+UdHb6rAkJckSIA0r5UA/jG5ryNYHyneIVmOgHRgXNc7A8xSY4cJ9X5DdichWUqC7lODy/QamP9uRu0NdWN4fbHH/dWY4Y05t/7kkPT+gPEb1xk8cAwfNIxvLlFVw/Jgh2qqGd+qUdaIxJyCyZuW8S1F+fKA1z9wA55a4fcrHnxkQJNPmLy+Ynl9h60v3cfMoNkaMLorUtlqVYHRqKImP13RepIpxc5XTgBQRY3aG8h11TgpimxNKXYNo0szlle32RtmqFVFMitptnORrl6VAhaczvDOodJECiHWynVqMrwRuTyba0wu14EpmrZhAQtkqXiH3z1k9wsKDk/w1qIj87dqAiC9xJ2cooZD1NVLLF7YpxlopjcrXKpYXE5Y3FCUT1XcuHzM9ckJ46SidoaddMWtxRarMsMfCWMaBdlRicsSlLVU13dYXssZPGiY39CsLnuyE0Br3O4UbxTmSAswbC1qvkTnmQDGeQ6lSIq7shQGRPB/jpL0uABMNw1qNBTGdN2gFyV2e0iyNcUF0DmZlehlJYWpupHtbw8x4Rjrkxn2wZHI1Q8HcOVAGCFv3UGPU6KU/vD1E8aX9pl/QOO0P3uRXsRFXMS7GmtFwsASa9VqA1iNjr+rdg7jnRTRo7e0dRqrPLUyNN7QeEvjDLUWtk+Uf7R0UrUCKGlsUHIBsKhzJHohBY6dZqoarqdHfHH5DM8khwJmI8+7KvwvvPhIL23OPPw/rQcsm5SjcsRb93cwLw+49A3P1rcWpG8d4pcFvgwMsCyFfuHddgVGH/0woSusq07ae634bgx4hz85JVmt2D3eI5vtcvpcwvxpg50ofOYgcSK3iUFph/e6Y2Frj9cOZw1Ge+ZVxiBpyLcL6jfG3J1NuLo1a4uVLVu6J8nteTgTumNSn399WKfDlHBdnttCYHKH62hTRqcXWnUezXGdm689SbEppw3nANIPAaMfBlDH12Q9dg00OE+uO9V2jQl9hsXcY0H3Ac/+cq3n9AYI3X2Hjrl9nv/zmcJ6KH5nQYK/DzybHlBtozLSxvYiu/hxol3fI+S7H+v9t7nM3EbRP4LufQApVQ0usqLj4Mj5ILvBiVRvv6P9MdjhMzvgzdUOb8x2ANjOCjLdPDGgtAtzaZ/IT4IFUvQ7js3HXncMYOgD1bIeYUWqM6/Lm5J/6/js0aDDJaAbAahbhnV4TrXbiKsI++OMwmUCKBPT7nhZxtzTdfl/KxfsQAV2Z/Rl1g3oypMuIV0K+9gUrgOBm86DWXbCy3MgMfio0qYUuratutC5sdkIHgDqTV181QQQPDCfW8azlSZgGivAc2RAx3y4v3994FmrTunDOZTWKK1BebHsqWrspW1cnmCUNKJ2O6Pkuz4MJG6bttTZ15VqG/B9fxnfHc94HSndgfn+vPW9C/FOGqrOUwRxXp/T+NQB0uc/O9afQ5vj9iYY3T4DetvpA92PIxOeKrFfmZqCN6tdvvaNp9Djmue2HnBcjWhCl0c6rBnkNX4CRZFS+wF4E8gDHZCsA1kigtLrrOhYEHrETnm5P00p92IzNqz2NUc/3LB1sODw7hb5G5kA4RZsqtq6Xhya5d6SjYlSotzvLhM1P1T47DAcu0rLPMcDpebeyYRxWtF4zb35mIFpuDY4QXvPxBTULgmy7bo7J32W0h9QASM2t8mX+f4vCF/ERVzERVzERbzXw+FwgPNxHqbWgF4TbezgXLns0tfYtTn9+duweExIfCKovekf7dq83XPqCv7jN39OrFmAP/3pL/K/vfJLHJihsKgxODyNr88wqP8wNGg9PijtZdIJkqhGeS6XgB2mAkAahUu04DNRJqsnI9Z6M4XEGKRQ61LdzvecV8zLjNV1K4BYY1FVw9Y3Nc37NKm2nNQDxknFoswY1h1jt5XjjrLRvX0HxAO5BwirJjARg3y3chqXnHPCY0HA+ta/WYD3LvHHKLm+M7P2GRTt93OZ6ZjUhI72RLcFgNY/ejNZ8KFDPADKTgnbPH7HNaZ0SCTa/Cg2PQdwPnaAA1KNCO8pD+mR7HumG7bGBbNsgppDMzTCaA3Hdw1Yjkl+ZE3HpCQWNUIDQvTzajvG+zd7TICNodxLmdxUFHsJeliRzlPoF2MiA/xhFd7vYeynC5YuY5DU5EnD1qhga1Sgn/KcfmjA7aMR6d2U8c2MwaGj2lYkCzmH6cKK/5mB/MTitcLUmmyuKF8ZMn8Gls/VNMOE1cGY1RXP8P4W6f0lybxi+2s1yva68I1ISGM03hiRj266e2Nwa47XQVJ9mOOGKcP7DeWvbGNWHn2ywM+X6NGAJADXfjzEj3J0WeFXK3zdtNtTGNBSlCm3NC6FZpCSzQzJ0qBLi1nVqKYG74UdV5bw2puhaBXWU9XiuXwkQDWA3t5i+fwe1dSw9WqBHRpWlxKWVxXlJcvV60c8Mz1iJ1sJWxDDyqZUjaEqU9JTTb3t2PtqKDrlBrW0nL5vSLUt11ozgPSDpxQvb7F8aiKsh8qRlxaVZcJ2jtKG2yP0vcOuSOO8sDMSjQ/FLL81FvZDWeLrGrVSqABoUzfoosHv70ifjHWYO8f4ohD52tMZXN2luDQgHSYo58mqWtjRSqHSlPLSmPT+UtjUedZJp2tNuvQkxwZ77Rx2xUVcxEW8q9GB0huFeUWQGPXCInGhqOcBJ77S3nmsVWitqXoeyZU2JMqgSclNI57SXgfvPRsAaYVVipTOB9YqgYhsKJpGeUsD1ECmRCb3B7I7fGX1FP/V8af58cm3GOiq9ZFuWdJ2EKS7cxZNznE15M58yv1b22x9NeXS5wuyN45Rs4VYHijd2T40jTxPtJZivXWsGXv2gGdvXWAJ9575znXPEZB1eo968w6T2ZLhrR3y4ynHLxjqbYUbgms0DBsUHXtaa0/TqJaE5nzasou3xgX3RkOWr25RfmSF0Y4mAMh9Ns0mE/osKN29dh5jOoLWnQVoT37SCUNa8AXfsqXPSIDTvS+HQ6995kmLTQDAeUWyJsO8DjwDG+870ihBvfG5CCJEnCluYxM8WAMkzmFtp8quMaLjWekzrqMH9CaALNvvgxYh+d0obG/KTLdA+AaLOTKqO8ay7k3k33n0Ae6UR4GyZ+Ww+8xpi9n8wJllzTnrOLNc+L4tMA1E1nS7PXXWh7sf5zGkQT5TuoQHxZiT1YBhVlPaBJeoPzBw8Z2K5ZW0BXvbvBlaYFk5ye2gA4lbxmM8tL7Lt89gdF6YlEnpSQrxQLa5xqUKm8n7aUlruRU/04+Ywwsw7nGVLG9zYRu7NFh29fxo1wDusA/t/8YHCWJRT0sXDlOIPZeuXJtTrvkxxxywEululJLGU+9RVRPkgjdqAHC+olqfxdyPullvvG7za7/+nArPkVY6O4YxogSitSwfQOVWLrtlN3dqYqoQiyXJ40y3XMyPN0HiPtM52mht+kP3rbX6Mt3eCws/7APQ/d177b0QmxLdm9G4bnzqK2tsjvlnmovC82LzeR2Xi01NEbRc84b2GkvXMOQiO0NF5ktfItK1Y+O2WfB//eIfRzWKH3zmLZZNxqzKmRc53sPe1gKjfHtb1cMU6xTKatK5CnNL8Vxvh1Adm058V/+JNaXefbkWSkBjU3l05XGZFkD60zVXrh9z5/U9Rq8nXY3PSK0nSuV7A9oqnA5NLnhaFaD4M/HhD/BZAMoDkURZhXKKapFx0+ywNSq4NFkwTkp2kyWlT2Su680aKB3PTxsbMp7uEc8q5xXNhi917YJFxHvnlvi+jU051Yu4iIu4iIu4iM1IwrO+EbMnQEDdyGCuvcXhOHEVxw6OXUbhU2ZuwMwOKbzUuWLOblHn/+47Va+Yv490iVaeTDUMVM2l5BSDZ6Dgb978M7z+95/nw796H5+n/LezT/HX/sKvsmfW2dLQgeR/mDymHxuUnj1lqLa7TszoP+UTAV1VAFNEpluf62eF7xjUOkpCO4/NJSmOOVPjNMmlFW4yEKlf70kXHhJLorqEYllkgY3bSxijP9Yae5i2K9olWrqfrQsS2/Q+j0iPm5gEyudc0vOdJiS3Mb9LVFg+fL+EtlM0drS73jLKmrUE2MekNEqL+S6ZiNEHpH1gW+kmFh8iUBaW1RGg3wDlYyE8FsbpGgRiZMeKwqeMdMXOcMXR2JEfaeY3DLBH/qAkOVm1+9rKhPeB5jXp9N73TGIVRXVM9n5S7T1oRTXRjO5YqqkWyUoLNhcf6QgAiqzbu19ofTa/z7EdYRAfqnmdM6sHGOXYHy6ptk9Innc8+MSIu/e3SF8eMr7lqccGUziyowqfaAH9E0V+WJMrSBcJpjQURynNEE4+ZPGZZ3E1Z+wgOS3QZS0FGa07n+80wRsl7GnvpalDKfwwE7Z0YkTVYFWjqob87orLR6UAt0aj8kx8lcsKjMHtbmFHKVzeRR+ncDrHr1YCWPsgW+88LoFyR9EMFfVQMTxUmEq359ocneJPTlHjEayKruACqKrGL5adXJ0xuJ0pLtWM7sj3qMeGckezumYZXZ9zZTTn8mCGwVHaEYVNePV0j2WR411Ippea6WsrXKKxA4Map9Rjea/cUmgLV3dOeWlvyK2fTBneVeSHnnRek4yG+PEQVVa4QUIzScniSQ9dSqqq8YNU9jc12EFC6j3qVAmAX1WoQS4FJq1Ri4Lm8hZ6kKBPV8JCtw6Uxhcl3miKHYPNFIMHNf50JtsZ5MIqVKAXK5hO5Jwen+KrCt3skCy3SBfmkaXli7iIi3gXovf8JbBXIDz2+mzpUMhXNswFWmBaY62nMRrjPLXy1M5QuYREO2onAHXiXfBJ1CLhTULmLU5pkQEP8E6KsHKMVzilAutSJLzz8IgdKMuHB2/x927/GAfpnA/kdzDKMXODAPCklD5hZVNWNmPW5NxdTLh/c4etbyZc/t0V2St3ZSxUiugRjQ5ssarp5LtBgOn+MYuF/PB7v5GpDWtb32nVY5e5o2P0asWu9Xi1zewZTXHF41OPKw06szgd5oZK5Lyd0yKL3miSxFI1CUY70u0SDkfcOZ5yZWfWAtAAmbHUgcHeZ0K7Tb+U+JX8+neMktwCIHdgs++B2B1JzuOcwp8DyPUB6nXgOk7PnrxEZtNTugOo+4zj9fdhneXWj03W2nmvPYwR3fp9boAUHUt6HZzogxd9f+U+MP2w2AS+N0HqVFkBjENya3r72gLVEECELkHux+PKd7+T6DORHyZF/rafVwIHVL31mWAjsLb+HjD96H1aP97x2ErBQRLNPmDhvKJyBucUdWOYNzkH+ZzGad4sdt7xd/pOx/KK7sDkHqgcT6duWLOM6jMhocvl2r831q+8x1SQnTSkpzVmVeNTQzNJKfZSXKJISteTu+6v23fAVciL+6C5zZQwp9NOyrvdlz7w5cFUEYQONl61gM66dpiy580cGM+qWW+wlYOhUSuZN5OlkGjUokStyi5Ph/XnxWYztNv4e+1grQO4UQ1MWM9awN4g1a3gLFNZ6y63P28breKUlu4Z71FFiarCuvsS2hF0TszZdfQA6xaE79cFwnJ9gF71gXVH170TrJfaw/UEPDceZnkRGazd+Ona5TfZ1fG5sv48WVfp6IPRUS77YdF/xqztU0Bdrde9/dZdXhY6Olr/Y1SrZgMy9v/rkw/h3hyy/wOHXBrMubnYYVFJ9pkYR6pFjjyqq+jU4bJQzOzvUgCVm7GAvunct/ddWwsK97SyvVpNj8xgVDdXLaeao4949q6ccuf1PfK7Bt2AzQk1P1r7mXMjAtEBjG4tBpAaogwQCqw0u0h90UOtKBYZSWJ5anrM0NRS/PVy/EzLdHEULu0k1M807fWY1Oe8349NZvuFbPeTFd9Lptg78RO9iIu4iIu4iHcv+ixo6x0NlgRDg2XuahbecegSCm84tiNmbsixHXFox9yvpyyanNMmZ9lkFDZl1aTU1oglWpx3hVpPrNe48F8pyQWMdmSJlZ/GkpuGy8MZQ1Nza7XNa//wea5/5hiQOXZ+qPiF00/w6vAmO2bJvl5xxTgGyqCV7gHTDv2Iprrvh3hsULr+6VPqMiV9cYiqkclrnFgGIFo1Tdf120tCgTYxbZPVxreJVz3U2KEiSxsmgxLrNNNBiR1sCyitNfVYcTAo2kllbhrqVYqyTQB3emAnhIRxfeIpklSIPJBFmI2xqKFppadbFq4Knd86TuJ7YLcNxb9Mt5NyoPX4arvdjXSQi0SW6ZKCwPwFScpdKmB6sorJYuhqbXdePqMA7x26iQzrdUD+DBs6ngtHkA/lLGiNvJedeJYuY2JK9gcLXpo4vNHMnoXVQcLotmHnZUP25on4ZdFtv02+HWsJcdt0GnKOc6PHQK1HMLpjWVw1+EbLefG+ZZS3iVTz7icKhUvZM3Oem9znj09LZm5I7Q33mi2WLmNmB6xsSr7d8Fvpc9z90lOkC0c91qA0SaFxRq43s3REVn122pA/qCgfZCwvG7JTQ3HgKf/SA47LDPNbu1z5nUKYs4XcH/SZ7Erh86xlUqvG4Ubit9w2cWgDGnTZ4LXG7YxRzRC1qsQH+XQOxyekuzuUH7jC6kPbpAvL6JsPUPOlyHknCdVuRn7iaUaKZgT1VFFvGXRpGN/RbD1YCHM4ywR8rRuwFp3n+LrGzxfBw1lDmqKfvk5xfcLgfoFLNPV2yumzhtn7HNvPnvDhgztcG5ywmy45qkc4FMsm42Q1oKkN3ima/YZn/xE0Yxne7n0yQ1dQ7HuGd0Xl4an/5oj5S9dRfxzs0wXmo0umoxVH/98bHMxKkYofD6i35biprQkcnUDq8dbiVwV+fwqAWdVU+0NmP7DL9OtAXcOqwJ/OUIOBMM7HA8xJgZ4vcfcewCCHJEFvCchcZ4Z05aRgeFzgF0thbFuLvX2X5K07+OeegixFPTiWcWqQg1IMXzlievkSq+vflhvDRVzERXwXI8p2iweoRzfi/6uCJ6hqlDSTxXBKit9WKoPeK5wVb+kmgI61NS2Ql0Xmj/IMtHg/4yA1ti3GVeFBLEzCAGYpH1jUAkxnyrVTqMIbXsjukCjL506e4alLhwyoQxOWp3Bp6yO9sil3l1PuvbXD3ucNB1+YY156syvt2Yi6I/MapSBJWPO7jMfKGAGtre2A6cg2g/UCf599BnhrUcagjBGm26tvsr8oMPVlHgw09Y50LDntUSawjzcYYRHQbaz4CE4nK462BnB7xGpcCIgcPKAT7SjrZA0UPg949l61MtwR/O6Y0Yqm0Wgt++PCnNV7ZI6mQSknhD2rcTZq4Z4PpnQgv1/DVZ606K5ZtwYwRwC6DwBvAtWPYrj1X9uU6I4F5/bvCHLjyXV9LuAQ151tABV9aW44Kx/bMaq7bZzxa8avgcqpalrp7oeByx1YLc0l+h3Iip4Hmj8KxN48tptM5fMkwDeXiUB2poJqQ295G8EFaAGGDgDtJNbeLkT5QYCd2ps1sL8FgWJnu9UsqoTT4YDVION2PeCLb11/5Da+21GPwy+xkbvfzISAN6oRdQ1lxesVYr7XAdVnGpt7rEhTetJZTXK8FEsmo0nrATbT+ESJTLank5yO5t0xZ42DSh8091EZLaiHhWloVPDq94CID7MPXs3xv+u8mWsbGrdsm9erupHxf4MZTGNhNJBmZecEkC7K9ju38hd9u4dHgdAxzmMYQ+wDEeWwuL5N9nPvcz4NzzjnzgDFLbAd9y+AxT5L2/y6ZT8r1W2zvy8tSzrUP84BpCE2ynfgsxQnesfTe/ls/zn77qfYZ2KTxRpj7X4PwHS/gSnTzbnPiPPXFdRMet71/YjAZ2RD98OyDniug58yttXOrLG16wBOz+2AX/r9j6Iyz09ee5k3ljss64yiTsiShjxtcF5RW9M20qVZQ+1B12lnS+glx3WpYnFd7r/RbUW68F39qQcgR8s55ZH70Hevu0xjM83ykuYDH3+db905YPxqsgYqu9RjM4WuBQiPX9mZro7WVwFs5b21F1WFeIgSB7VuP6O8wlvwhWFhBryW7LGdFjivGJqaga7FX1pZiiDd2G9K6Dc2xXPal2aP58bgsOjenGP9OknPXgIX8R6LTdnT8/6GDng+j43d/8wfBhnVi7iIi7iI92JY71j5imPXcN+m3LUTXqyu8ma5y0uLSyybjHmVs6xTqeEg06EssW2Np7aGpjE0jcY7LaQAK0QRQCYujlCvU2ECRftTGYdJHUlquZNNSYzj9Av7vO/XT9D3T/CjAcpakgX84lsf5bPD9zFJSg7yOU8PDrmUzHguvc8VM+eqgZFOcfjve1j6sVGM/9VHf4lfOfoBfuvmh0nrkHiGZNRmWpLXmMR6jynXk+T4P3ZLahtlnhXFvqI48FwZFuwNlgBUzlCzDQGQLA5gGiakIJN5Xxh0U63vqIrZPO0EHe9xufgRA1BaTGnXkzcnhTyszOxl0r0uo9Ym4dYL87dfPIAWnHaJAMzCkO79beJCHUIrUugKG8DvZEXHijYB/HabfloiqxQ/77USP2/a1bYAro/b6R+ifrGjl6Rnc88rq0t8eHSLaVJitipcmlDvWuodaCaGbJGRvaW6ZNzJinxiUD6AyFFurbfh+LeqbZvYt8lwPA/W4jJFOm9IVikUWiTeCiuHLTFyHDY6ut+tuFNvM9A1M1cw1lIM0ThGuiTXNQfJjKXL2UvmfOb++8hOOj84mwqzIIY0L6hQGFJoD/lxjW485ZZB15rj0R5up2b04ye8/NyEa7+6zeS1JWZWtOtRZdMWcnyatMda9YoiPktaOfq2qN+EgtMgFVZAlqKLCpwjf+Ue6eEYO8mpr2+TnAzk87VFN57xWyXKZZTbmnqigtd5kAubLfFVLdLc0YtaKWFGN2FfsxQ1yHHPXaPaHpCeVnilqLdSFlcSVlc96dUlz+4ccWN4zG6yZOkEEFk0GasmbWVOfa0xxwkuDdeMUqSnMhalA0WyCgBGopl+5QFPV3ucPjukmg5Z/NG7rC4rFu/bphkqspljdWDIjx3NtV3MfBHAEi/73qltyFhooLw2Ja+bbpkgSe4TLedAawHoA2NcJQkYRfbGIemDvD0/+toV/PEpbrlEJYkA1N6jThdyHpNEJHDzFLxn67WCajL87l3sF3ERF/FtRbTHaBVmYpNbbHRzoKxC2yAFqXxgSYNvFC6woZrGCHhpPZUyLfuzMiIvqK0wRTQebRylS8lU0zJIokxk9JS2KGqvWxlkAbg8S284dgOWLufnDr7KL9z9OJ9fPMO17ISRLrnfTDmqR8xtzlE14kEx5uZXr3LtM7Dz+Xtw/7Adn6SlVNbvN5/ZtV1//sfoy6O6d1AZ31y/83B0wu5vWfKjS9z5kYzVjQZWRqQjM0fjFToJFPYowxkaBHRkRF9e0rw24f7dLfYundJYg9GOWZGjlT8DRm4ypQVr0O3vffA5SmsLsO27HsO4Dgd1HU1BVbtsf4sdq9rj8RuvdfLfT1o8DJCW9/yZZVqPzocA0mc/2wHQffZTX2ZrbV96/tBGOSqf9KTA3RkQejNEBt+uyXW37wVAepNlrJVbK3pHQPqdxBrA+wiA+tthOT+uR/V3Oxz6bfeldCknbgQESVyvGemK2huZL9qUyhqqZYa3igeDETfvfhB1J2dwV8O//b36JueHikNQj6249l6XNrYSvboJrGMb5tsti9p3oHbIgVGqfd2nRkDdTK6+ZBngNU3wTQ7jiFYdwB0bgiM4HffPQ+K75bt9Dh/s5a7KhxwQZGyPAHQTGmjPk8yO247PguivnCa4UdaB2Y3tVJica32mI2u53aeoqBaB5/OeQREo7jOW+yDu2n55AZg3ZcDb5Zw0AffzXaXwKpHv3mM6Kx+U2srwWaPxkU29uX5Yfy9+nX5eGc9Tv1Fcr9cTWkss66S45kP+/jZj3ZMaZxqKQhNT4ww9jFJUFt6GLfvdCBfQ2Fw33Kum7KRLTtyQg3TGUTPmH7z4KYZvJHzs3/wGt4st7q8mzMsM7xVZIvdM4zSN1dKUqB1JAko12EyIDQBKK0wpgK/dER3vepFhjxTaSq3OxJpSGCuicqHYr6lQy5L1LS8b7B874cU3LpO/MsCloJoAQAdSiksBBz7rXTabYHQAotHgjccnnmbkBczeFdUM3wjj2ytQtQrqQQo3T7lfT/ls+SyjvGacVYzSiucmh+wkS1JtGegaELnti/j2gNN3C2z9g253E1TefH3z9/h3lHIFcHicX28z6culOrpcZvPzp67gbx39EL979Cyf2HmD/3Dvt9nXw7V9etg+xvfO2/cL4PsiLuIivpNRe8vvV45fmn0Moxy7yYIb6SHPJ4dcTxTb+vw68ndiPHrcdbzdWLkZfWlrh+eOXfF75WU+O/8AXz29xjfuXqZcpXCUkSwVyVJ1mGS/sW4As4GQA9HgcwtGanHUWuZKjUK77vPKdvmRD012MU/ziadJPU1uqXOZk2y/BnpeSm5yeIwej0jnu9w/HXM4H9HUpp2XZlnDtZ1TboyP+cHpm3wov8WN5JjrZsWeyclV+o6O03slHhuUvlXvsJctsBNHuugmfeUOnDyfsv2yAI8ai2o8ycIGqWrVJduWVqJa105kv2vH4imHfmrJKK3ITMM4qTgqRzRBvstOB5QHlsxYShckGlHoQouvFEgi5X1XZgqgm7BsFYv9jHos4O/onmXraCXeu325qnhxhQRPvLFCkuYk4XZGo52j2k6oxlqkveMFHgDqCMq5NKxDy0WsLaB8SD6lW9UDmA7MRiHgebhhNB2gG5PHFlwnAOcprax697q85lXvRiFM9onAcUyGBSxNVp7fvfs0n3z+dXLdsLu15HQylBuvUZhSkRRWbqiyd3FYKQD4UARQjW+7+GWHYwLkITKcex3ZPvhKxu7w5HjF3lc8LhkxfGDRVYMbpPIdeh3273a8UewyNNVa1+5IV+S6JlMNU11glGOgamprRII+hEjchWsnSM7Ha8XmGpdpTGnRtWd4rxav5oVh/kzOai9FeTh5XrO8MmV6c8j41Zlcp3WDH2ZSdArAsYD4rruus8CqbuTYi0qAF+UArfEDDYMEnaVQVqiyRt05JH2Q4LfGsu48Ffa685iiYXhPkS4Nq72EZiRFtHRuoe4AaW+dMKJBXnMeZTRqPMI9d43yYEg6l/vZDhNWBwmLGwr71IoPXb7PC9O7XM5OASibESubUtiUoknaor8qDelMtUl2umjYuikJe7rUDI7Ev1uVFnU6J5tNmb1P8+w/qzk6vsTBK1U4tsJSb8ZI0pwOmX7LBOaeAmtR1uK1kfs5SNnPr2e4ZI/RV0p8XQlLvKrBDiRpT4wA8LNZ56/qHH5VoIYDAPR0IgWzeIyyTMD8ULgjSVB1LaxzpXBbQ9K7c4ZX8+/VpX8RF3ER7yTWgGkvY0gLSIMyhMY4+em9/MSG7sxQtI5Sz8Z5ygCOVjYRr8TgZ1i6hIGusUqkaytMKxNp0dQEyp3XWNUVZVKgRrHwCUsnY8mOWbKXL/jS8XXSXcvlzDGzAwGA6iF3l1Nef2uf/S8otr9yBIcngRnm11nQzrcNOt1rGyCDtTJNiZLcPZuHM5+Jn+u/Hmwl1mJV4MuKkVLsbl3BDg31jhPAPyQXXqlOMQdw3tMgw7PWHmMc1chhjhKqXTnWTZBxErxnfR83dyECzvF3a1WQ4hbAGK/wCHuaVsJbxcsGnBYsQ/u2RHZuQT2A1Wv7o/xZb9knIM4Hm8+Czg8DpM+T646v9+W6N5nRsC7f3Qen+4A0QLYhzW3OYWSf971kv3wLjMbtnwcIZ2qzCNqB4OdFH0g/9/3vU6nRR8miR7a2UY5UWRnvUMztgMKl1N4wTkq5bxYJZqlZrKYkc012rKQZ+F0OXfeeETGnjM8IF/LrHhBtyg35a+sD07kDjyPoBCH3cyEnzlPUqhJQ13vMqu4GLheaWK3vmp1jNO7h3sx249pzLuR97uzya8v588Hc/thvNNDzWXahEbq2ko8420lsx+bbHss4qnJ43dtWT5L7DAs5fO+YM8XjAqyzkSOjeBM03jyWBAAZuuPnPV6bNYazR8lxj3mS1pDooBQWvlfcdE/mu20G0D2QOTYK9P+OzQ3hOLWf81Jw8Emcn/iz5+k9Eg+zeGiCN7BWXnzric14m8oP370x1HlFjWE/m3ce1C7hdrlF+foE9ZElB/mCl2f7LKqMxmm06iw5aqvb16C79dp5pJWxw+YKm4OeG8xKkc6R2lIijWrayrxDqb63dACmo1S/h2orYf6MYppXlC9uYVaxLhbrFbQ2ftp6rIpzqh4gHZcxAZAOwDQK7MhhlhqlfTsfUzY2SYR6YaPQlcJXimU1YjW0zAY146EUoZ6feK7nx5Q+aZ+3b2f/0DLhv08jFv43QdQ+AAvr/sx9uVP8epH7ewGOxn3tA7h9L1DnbbvvcZk6AMipMu06YmU6fr7wDTNnWXrF0iUsfEodpDwKLw1ZFkXhsrX5WlSviWQTgBRLpiypakiVJcOxZ2puNiP+N9/6i5z+/66z95UVP/9D7+eD/+Ft/vvjWxilMP3cYoPpFo9//P7xHDVES5nvH7DhIi7iIt69iGPN0lf8H9/47/LNf/xBlBOFpmrX4a+UXDs44YcObvJDk9f4Y8OXeSYZtZ/vN+24aAUXxt7HBUfj86UPJK89e6Ad8wHoSXD3n1dxW9IY5LDe81rT8PdPfoR/+PInWH1ri/FNzeiu49LSoSuPck2HG2khBXqjqMeixCzTRk09Utgh1GMTGu48Npc5VouzeVAufiZG16grALXYErtKYUsDqePkQ56t13cZff2OLDdfML1Zcf9oAGmYd9UaVSsKcl6+N+Jlc4Vfz14gG1Xc2Dvhw9t3+PD4LT45eI0PpSu29WDt+RjPVf95WXvbnqsnPR4blL5Z7NI4YZj0Qc5m7CmuOkb3DOkyMGOCP5QOE2Tle3hpAGPlpxfW6Jbl+u4M7xWVTRiYmlWToivpIl5dzvETS6abkFgIO0WXIYGDttPYK1BGZH+UFSmwZqApdjXlLtihF79bJ0W+VlZr03MJxCMrIUhvSkenTxRWa+qhZvaMlq7R4OvVl812YdJu844xbgcC7LYJQACyxT9H1p3NdXecnBQZxNO6d8Gr+H7424gcqI5ybrZ3jMM22jl4v8m7xb7luJnKc3QyxuBItGWSlxxOPLoUdq9ZgS7lZtuc0ivnOUNi6OeEzqMDIK36BQylJOnWCoZDdCWgfXK44Mpna9lWafGjTMDEuL16o8D9LsTNxQ4D0zBIajLdMDQ1E1O2IPW2kUrXsR1xshowrWMnsiT/LtPoKhQsVFc8sAONzRWm1OjGkywtydIyfdMxPNIU24ZyT1FPYDn1FHsJq70dxrdFPj8/LHGZFGwUgAZnhJHro3+0FWn2yMj3Ka1vW3zdDVPc9gDC+WBZoI5nAhZkKW40wAzExzqZVyQzT3acYgcGXTnS2yf4Unyh18ACrVvWNGkKVw4oLo9IVhZd1FS7A1aXUmZPK1bvq/jQ9bv84M5bXEkFkJ7ZAafNgEWTsagzikqY0k2ZYOa6a/jQkJyWZHcbmq0B2YnBLGrxqytKfF2T3p5x6XM5+Rde5eoXNTiL2t5Cv/8axz/gMSvxfJD7Nly/xgjAXjYChCiFahzNQLG8oih3M/IHu+hXC2EzzxfowD73gxRV56AE0PcE4Bl5QHnv0VXdMlj0zjZohT85BW1Q4yFYhy8KXFmiE8P82THTbzTUo+/fRPsiLuI9G+F5r1yYn+iukOid/N0+s3UEpJXUuDVglYAyCggFyr6Md5NoKmvItKZxhloZCpeilaPwqRQ+o3x3ZISi6bvQG2ROVXrD0uWt3PfC5bx/dJ8v37vG15Mr6C1P6RJOm5yjYsRrb+6z8zs5l37zLtx9IPYCgxy/6qE8GxLdZyKw2VrQOjSptbLdPW/ptTjv700QO/h2+vuH7Py2xaU3ePBxTb1toVaQCDjdrimcH6yAzklisVajdyrMqwOWi5zJpKBpDMY4rD2bhG2C1KLiEcADH1jSPeBZ3uhA6BYs9+EaaFEIuutj85CG5t01/+hQyFZPYAG2k+HuA8pnmdPxdfl5FojelO7ug9HxZ6qbNTD3rKd00663z4iODPgIRj+Ot2RfolsjXddvJ8Xd96TeXM/jbO+8EkD9EInbx12v83pNUrxfrD1/HyKq1otztrEJ0Dt05wn6DuJhgEOUYHdeM7MDai/NONvJkqXNOazGnC4HJKea/EihGtM2EtsnoJ8vWbIuww0CEIS8QcefzTorOkruautalrPqNQX1rZVUbTsAFainGdnhCrUMXsxad0jXOQxiYdP2zu0mazjm0pvz/bcJnz3i/G8ylHt2DsKQdtIgmqXrywaFkdh4G4eJFqztAb0u690zEehtwXHWGOMbPT9rr7XDl+vUvLzTa8u1mHDf8qofGvCSV8Rz5Y3qGKxx/4KllfxBq8LVbst3+49HAO9gIRKbw6Ux2tPKD6qeV/gT0N/iNsaGTVWSzXjYMwRoPZ+hA6Yfbx82nucP9SF7dFh0AN9gZEpOmhFGO/7FVz6CcfAT73uZ42rIvMpprMYoT5pYtPLUTuNCM5xITGrq2mBrQ74U+WxlBRx2RpEsYfyGJll4TOXbOhOBAKHxMrf0YKw/e39oxfyaYfLp+9y7vc3oONbDBIR2xhNZRTaXepaLFcUoZRlY0eFkCEPaxPc8fuRgoaXxMljSqSYcXyc1vgi4oxSu1LhaU9ZiZVJUKdZpLh3MNry8//BGLNZvAgaRSeZwJJgW1NWoDniOgEEPIP5uAdJxm9a7toC+zmz2rVBF9NPsg9a1txilqD2UvmbpLQvnmfmEpUt54KbM7JAHdsLcDjhsxhzXQ1Y2pXGGeSMP/coalnVGnjSUTSA99exjjHYkWqxgMm0ZJRXTtGBoam7kR/z9V36Y5L/c59LXZqjGsf1Kxn9z+FGeTh9wSS+Z6pKpNgxUEvZd9QAV334/wqyoP5e6YEtfxEVcxHciIjg5UAmvHO+x/YrFrFxnv5OkVJPL/Mq1q/zC0z/E5Q884Kevfos/ufVlfmIwY6IH1N7ikLF32AM5NwHjh0WuUuoegBpB6odFg8V6Kej3G3TimOjwnLiK/+L0I/ydV36M2ecO2P2GY+9UcgJTOWnYrV03R+8rNzWOATL/96nG5oZiz2CXiuwYogqMHUiTnzMQFZhjGaFt6gs4oMuQ+o4GnYGrNDYDbzQu9zz4SMrwpXDsrCW/tyS7vyNYoiMoYCmieJ9LPC6FOk95+WjAa5N9fm30fp7e+UF+bO9VPj1+mZ/ID8lVglEKvfkceYzz8iTFY4PSs3rAssnQhW7leFAem3vMfkmTD1sGMICubeclFbtzQ3Jk6ghIK+zIoEc1eSLFmcZLYXXVpIwbh0/EzyYbr8i1pfG69QczZQCeoZPZCgwktCfWXLWVrnIBlgKLUiOd3FFGPEhrR69ilyjqkYDOpvLYVKEa+T0yXJuJxw6EcRzl1bzuADE38JgrK6oiwRymuEw6PuXiVe0xUbbrOs1OdMvyNDVhed8CyyqA+Up3EzY70OjKoxvbFcAjIE2oFYRT4R8GUBuFrj32MCdVDRNTsp0VuEnD4PUMO/Rt0eTcIoNzwdWtCx0loUEA6XNY0iiFTzTaeewoE8ZCoqEGffsB5Jl48hqFrmzLfn8SYlblFDolb1JSY0mU4yQA1Lm27KRLtpMVS5sxPx6yFQZDO6CVd9cV4D2KwLrS4dpJwvu1SMDrymMKR36/whQppjYUpWZ1RVFvOR58EmbHKVsve9LTGjswGO/xVos/cm5wofteClwCkOgmMqgFsG7lu6zDp4ZqJ6OeGIapJj0WdoVarGC5Qlc1ad3gtoZSIAF02cj/RQlHJ/iylGsjFDu8953vpTHovR3q3RGmdJiioZnmVDsJy6ua4qpl92DGB6b3uZYdM1A1MzdgbnNWNmXZZBRNIqw1p/CVRjfQDD3ltiFdOpppTraq0VXTypHpVS3AR5Lg3rrD3nyJW67wdYMeDmC5YnLLcvTDDoqUYl8xOPSgBCzxIN+pqvF5gtIa5TzJypGsjDRWJBo1HOKXS2ga1KrEGwNZgk8T9HiIWxVyLIwAMN5akfPWSqRvtUKNBvhlIf7R3kVUQ1jSAFqz2tdMBgn63e/TuIiLuIiNiEoxynX1enmGyxPT6VhMVFjjQwemD3OJwOL1Mk9xCholDF7tnADJNnT+W0eurUgCq9hpHwC14IdrAkvA4KkCYIbXpKqh9IZjN+TUDXBo3qx3SZVlZEoujRd89fZVMiPNgXeWW3zrzgG7v51x5TPHItntHZSlPMfqpgOXHxbRE9oYef644AdtjMh/O4uvRJJRBdbbWnOTc3jriJYQ585Lgq+o916kvD+X49UBhx8zNBOHT6w8O1zw9Q5zVKelWF97SBKHNlY8qY8zmmHVMtYfB5TuYzveC0gRwedNTAfA226S1nKjVd+HmvMTuTAnj7/HFj7fW/eTEhGQPs8vul1GuTNAdB/E7jOi++uNf6fKtkzodUa0Owtk4zpwOgLdsSj7EED6PNZyH5A2ve0Y/LlM6c11PA4Qvf75dRly2+vWflhkG06o1TkuVZv7ZXpAvt3YgPV67bs9jNVsgld0fD8C048bj5IRr70RSwKXUPqkVS26X0/5vaOneeX+PuVbY4anClPImOtSiCpe73YI0Nzlx8ohsty1b0FoAZ+lWVnXLiheSR7bymE71zaYEkFk71sQ1CcGtzXE7U9E8rdxqGWwAHqYl3L/tbXGl8C27b/eH+w213lOeHP2+jsvx2tBYk1rE9YuqzU+7TywW4ZzaBrts519WEdkGsf8BwLgq3vAb1/KuN9c1aIlfg1w7quhtN8hstU3mrO86QGA5x1rrdZVLqIsd6g39UHoWCNAS37St89q6zVxv60cAOU3cmmlOjD9UY1k75HoA8g2ysT5rkPhYb7S362Iz4PCpdTOsJsu+Jd3f4DRiznmR4+YJBUvz/YpmwSlPIlxGO2k+TCwpL1XFJXM45QCVyQYmSaJrZ4V73hVQJ+8ILUT2gYGl8gjTntwqYzhKowtXivKvZT5M56pU2S3UrmW0pCGKrl+IdxP8RZW8YW4TEdWaMFohWxUe1QEtr1CLQ1+0qBPDaqR8dlUUpwV9jd4LSQJ5wx1ranTlFeqhGcmR1wfHOO8aZsN+v7SMf6wgNYNXRHfKE3p5QLR4Z9RmsbXWO/JVVcGFmD6ewOEbgKzqTJrzOFUmZaRJ1enbwvsDZYTVzHzikM74PXmCq+Ul7lbTblTbjGvc+6vxlivSLUjNRbrNKU1NOFeKqqUpjbtXNwHb9B2SIxeoQDGo4xDaY9JLYNBzSirWVYvYH5pl/3P3kZVNW46Jjuq+NxbT5Oqn+Ign7OXLLiWHnE1OeGymXNgBKROMW3jQOGbFrSOTQMrX6GV/r73EL2Ii7iI7350zT+a09mI3aNaVG2LBpdodOMYGsX0VU3zpZRi94BffO4yv/jpj/Dvfuiz/PemX+R9yYBcpS2b+dtpmonsaKBtgtKoMMqHJlE8tbcsfc2hg7eaKQ/spG0wqr1pm69fWR7wK5/7KHu/p7n+Wil2Rj0QGsL8pgpWmka383HlAedITmt8ajBGk9/3uNRQ7WWs9hN0A4NDRz1S1FOZN8V5Tr+BWDkgsKlVQ8/mKHynROGivUmeQWtr6hm/EY5N77PRhsklimaAKOimGpelVMMB3xxu8bXRU/zD65/gr3/wX/Hjw5f5QBob8lUApuWIv1dY0vAOQOnTekBpE8yqVyQLgKqKEtH9XLVxQnWHlq0bw1QhOdZgM4UxMulOjaW2hqXKWFQpY+9xo4xyTzHIa3LdsKpF777xGl2FRDGCnMZIoqYEYI1d38ncMl05Rvc0yoEphX0T2bbK+04us+e3VG0LGzU/DJN8H4oB3pOdWLKjlHrak+8GXCaFBZcAByV/4cO/x51yi1/+zA9K12csfngpOLQT+HDNlLsilZ3NRQJbOR+klmLN0beTdOelQaDJFYkHH46HM6r1+4kMbk93DqJMuNeE7lo5f3rpyO+l0p2rLFvZinyrJCkydK1ICh+kyM9J5GJnfT8cKLobXzx1gSJ47RqDjyzpUHQf3W9QiwLSBD8dd8USJfupe3Lt73Ysy4zEOOpEY6JctLHkpiHTlpVNqXPDyqYk9zJMbfFGUU1DUSwWVWxI1ogFHmHpl7uKdOFxK01SxAsA0llNsmpIlynNKMFrxfD9J+z+wIrZ3Wu43FBtJ2ShONKMUpqRwaUKU3adQ0AHRDsCME1X8FYKlyrqoSIbGNQkQ41STJqgZwv8soD5HK0v4yYDmnGKasL6lUIliRScopcyyCAcpFnVeIzd28LmhvS0xKWGci9leUmzvOrJriz5wN59nh4csmfmFD7DeU3pEiqXUFtDbQ3OBTmuwES0A8/iqmb6hqe+mpHM0u6eyQymCT5ueYa//0CG7iilHb53eirLKAvL6w6XaFSWihd2FXzsG9sWiJTzDB40aAumEI87P8hQTYNfrQRcCQ8plSbCxnaBQV7XoJQA4knS7osvK5Hsriu4fCAy4GHfBcgRxojLFHaYMr5dfw+u+ou4iIt4xxGL1f0iYQCr29dt19CmAqbQjsU2sKWcFFCs1TTKYI14IzZOCpalM6RaJLy18i1jOvUWozyVN2gcFYYMqDGgLDWawicsfEbtE2pvcF5go9KlXBmd8srdfV4+3ue57UPePNkm/eaI/a+uULfuyzzMGHxRrqGwEURWm2AGyDPB6KCTbQRA0QIQqMTgG1AmjMnGhMqkXfs8vSansMG1TagkkTE7Ah93H7Dz9Zxqa4vT5zU2czILDnMkZaKMtjxvvdM458VnelKj7+aUZSpz1uD1vPm1zkxN+mBy77X+8pEd3S4bi2HnNBBuEhHXXg9zBEWY07bdiGeXfzejD0j3wehNVvQmqxnWweg+CA0dKL0JRvcZyZvAdp8d3QegWyntTdC3F/319sHWuE8RjH5cWe0+WP64TL71z8dE5jsXZ4DyDSb1w/djPeJn4vHoM6e/kzE1BYPgI/312VW+eXyJt27too9TBofSuNhKz8Yc6QloJjelANNJ6dG1J52HJtw4p66aFnxW1kPddJYuMQdufZxdBxRvsoe9xk4yFtdyxrdKycsicBytJULDULRk6nayYxevMXkf0nx0LrhsNpY9ZwD1QK+msr6MD/WG824PFfMa3du33vsRSAt5Z2QbK+elaTcA0M6oNt+K3zWqm635ZredRKz9VL4DoiUH9733Qg5vXXvcvA7HsmdL5RPdSW4DLhEmo1J+bd9a5bXY690WzGJTgAqHxqPq2Ljg20aF/rmLamZPSmyykjeZ04+KTWZ1H5jWyp0LXH43I25rpAus1xg83/jGDcye49OX7nC3mFA0KdYJG9qEZsI+IB1LLVp7ucUrLSp+QZ1P171pQ6z/xOslzj/XmhEigKzk/DuPz0RdsLlScXR/yrBULQvapdI42d5/kQHd+q+vA9LiO+3ppLvD9Wu6fVDGoUqF33ECRhfC/DYh1Y6N+sqBqhWqVvja43JFrT2lM2sNbW8Xfen27/ew3vH5quHzxTNMzYrLZsaOXnFgVlwxQ3J9tli9KTH93dqvDoxQ7WstA87bVnI0MrwBHrgVN5uUr5TX+ReHH+ON+Q63j6eUpzl6nqDDdQPhujdCUnBDB5mTJggdxnGrRS0pyFeoKszfwhSqPyx4DT7xOONxaYKtDUWaUs9ynn2lRtUNVDV6WaC2Byzvjfksz1KVKUp5srxhMih5ZuuID07u8vHR67w/vccVs2JPZ0xUToN851yJSt5ED77LZ+EiLuIi/rBE6RtSJQoZthK5Y28ULpU5fTMRi9T0uCA5LcgfpIzuZhQvTfg7z/4pfvFPfYz/5P3/iE9lVdvIFCW9U2UeC5yOY/qa3Hd4Fix9RRqePC81jpvNDr96+mE+c/d9vHV3B303I3+gyU4hWfl2zqMbz9UaTO1oxoZkFZrrrMMOkoCBGNTAyDhuBPMwKwerBuVFPVaXDXpV4xMtlqSLksHdlHJ/QLFnyBae8d2Gemwop0qYzV5A5Ng8bKqgSmNB1w5TOVHD9b7FWuwwwY4zTKjzK2vZ+VbdNh0LadCJKhXgciO4YyFkOpdowXh2ElZ7hnJnl//z1/8trv/QLf537/8nfCpf4LxiqLI1ZZH3iuLGY4PSx8WQ1NiQtMUKnvwfDitsPuqSIC2gtKlkobZTMqhNmcq3xVlT+ZZZUjbBr89r5ssBV5qSaien2vZcGoi3R+M1iXKUNulAb+fx500yvYfAas4eFMGP2ApQ5uWG7CftyntsluKMeEWffsCx9b5jTr+xS/b7ClM4zKpBN45kVrH3DcXJ+5K2Q1om/ipMhGB//5S/tvcZZk7zS4OPYkqNKeT46BrSmRQgXAI2VRT7itMfKlHGMf3tIcMHTvzCmg4oj/uqK9qEMik0JshAo0PxOhGpgXgZth2y4ZyJvxitXFz8/vmhdPGmyrKTrrixd8JbwynZiQwEKsiznwnn2431CxHaRik55HOa1jt67VSlCXaUkp424oucJqjG4ptGfI6VEpm3ApRz2HF2/oX6PYxVKE6XjcEoj/OKLLHkSUOeNDgUx9WQ42LI6JY0Sqz2Dc0QslOPLrsue1zwhVNQ55pyT1H8+Jz5rRFbL2nc3LO8pEnnkK4c6cwyuFdx5bhhfiPj7vYU/5TCb8HieoYzCl0ZTBkkmmqH8op0JjeNLuogJ5GgmgZT1uI1HYpZPniw5Yc1g/uVsPQyg8s09TTFlCOSRY0+WaKOTtH3Dsl2tnDbI1ye0OwMUZMcMyvg9n18AHJVIkOOnk5wl3fxucGsGuw4pZqmrPY1s+dAPbPkg1fu8eHpbXaTBbVPmNsBJ82QWTNgXucs6oy6MVircU2QnkrBp55yD/LjWGgjKCN4TC1e0j6wK1Se4xZLlFby+3KJ1orjFzL0qSc7VTRPlaymGj8eihz3cinrNbq75gNTwRROvCRGGXpZCcisNNQVqqwE2E40fnssMt2NlfUc7OGmI8y9Y5G+VQqsxZ3OAKie3iFZ1JgTkcVV3kNRoGZLslNPPUkYvfTgu3m5X8RFXMS3EbEpLMpkdszpMFG14e+goCLPWCUy3/hWxocASDuncc5hnaZqDFkAXBtvaJyhtAmZbgRYRrVdpcYLc9MpjUNjcaRIoXfmDQuXs3A5tU+Y2QG5rjlqxixtJsofW0vu359SN4bypS1u/FZN9tKd0HDjwGlUmgh7GTqgJHppbjLitGpZ0SpJRB0CiHLeyniRdNRKQGvsmXWoJJGx3Nq2+enccKGsZS361Vsc5An1ZMwyMVKw6hf6VedviIKmTsjymiSzOO2xpxl6p8AFb8dH9cc5r/r4QQc8+9jLpzp2dGxE6IPSqgP5I3h+HibYAtIKYVjH79Ru+MmJVEfpyHW2dF+O+2HMaKD1Dj4DSm8wolNl1z+3ARBHMDq+1yoMBNC0z3wWAUk2PttjetP/3a8tEwHyTVBlE/BNle0t83iF8zWpbfwjfac340yB/u3x5nY73fKboPXZ5SNgvykF/rhs6b5X6Nux3eL5u1Vt87sPnuG1Nw/QD1IGM42qBfgVSbT1/XwbxfPvWRz89n2wTvKjvhXBZsNvm2er7u/YCNT6KMs8u/Ma1mcZyx7MvBIAdzKCullfj9a4bL1EsMbsjUzkwCJek7KOTOO4bH+zm6D0Rkiu6luwdfPzm0B3BIyV9fg0ANE9uetN0FussnTLNPZJp/4Wm4J9ZHTG/VfywY790FtZf58i8BwtvZyCxkkOG5qvpUnbI2zl7jNet6etqyl41bFQ2+X6IHL43qHQpppY16BjjcfvaQLw3QfVg1KcHI9uG+15eI+G9WptrN30mY5S3o+jTLE53thHFPceBnjq0OwPMk5tmxX/9Z2PMXo94fLPvMl2uuLl2QHLOsV7hdHy/Kutwa4B0gqtPc5qbKNJTgWUdkl7OdLZedACbPE+aUkO8drr37dhrtmMDPOnFMNpSf3StL0f2vvE+N4941tQu12PCiB50tWFvAkbDYxpFeqIXhNFSXDLhHyhSFZIrSzci1Ha0qxk3PEqCEMkoBMntjU9lnT/3PXnEN9Oo9d7MRJMC/z+5d/7n5H8+jY2h3rqqfYtW1dn/OSNV/jk5HU+nt/kY1nNUJ1fU/tuFLSN0jhvWx/RuM8xHG6N3XXfrviH8w/zT259gm++eJ3tryYM7znSpeNaKY1b3jTtWOiNotoyojhoFC6Rxtd6pKi3pLHCpaHRwdKqzIntYad8KS8GUDo2WSSiTllnHhLHg49kjF4dwmKFv/eAJE/J74xYjXJ8paHRNM2AJRPuqn1+J3mevz/5NNPJig8d3OVTWzf5xPB1nk8PuW5MK5E7JGsZ4xdxERdxEX+QiA0+AMwTVFOhywYzL7FbA279+IjywJEsckZvweSWZfT6guyeZXIz4+TWNf4nP/HX+fd/+lf593d/j109WAOYH3cfrHesfEWu0latwyjFGw38wuyj/Mbh+/nS155h8q2E6U1HunA84zyqaQQPi3hd4wVQB7EuApphQj2R8bKeZBTbmmpHFGmzU8GiklLm8PXYgE8wlWdwvxJ1vNSIkqpzUDlUUTOcFwxuJRTXJlRTwdoGxwqXKEztSRZir2pWtVimFkJAc+MBdiujnqY0I42ynmYoOAWXUrbq6+i37rF8fof5taRt+tM1pCtPfmJJT2psbqguGbwetKpZeDCFZ+vVqsUGy9++wl/5qb/KX/yZ3+A/2v8smoaUrqnrvfIUeWxQurKG0yJH1bQTzggsZ4mlzjcSzUYA1bX5eZjUyuuBYq+lvHGyGrRSRYl2VIsM3IpqqmmmjmlWUjqR6k2Mo/Ea1UBfdhjngr+Wb2W4o8+0dAWHLmEtSbxPTZiwh7+jp2+uqaaa5z72Jv/zZ36Nv6n+TaqX9hg+UJ2nloP8QclwEjut5StKR6fQ7W/f2uULLxywcDnpoSE/9iLXrISmn59YkpXDZpp6oplPFP/eD/0G1mv+3mt/jJ2XOd+nOX6fWsQd01lv+0kA2c8r3Pi2WbY9d33WlleKbOY5tGNGpkQrzzCpgy+2wlS8vXx2OI4tgK5k4FBNl5jItWG7RoDGobScM1M0MrlMpGmAqhaJ5UHSrct7XJ6wvP7udxE2jZHE0DhhUoWwTphrzity03C0GDI+9NhMU+xJwSApQjdMPFWhUBBlW20GP/v8N/na3hXu3r0RuoMkOSsTaZrwWpEUlsGhZedrCcd+SjryFHsas/LYXGFzGYqSpQUNZlmBUrg8od7KZB8aLf7RqcGsalwqDHbxijI0I/FQj1JxXsu1opuMpBgxONwivb+EssLcP0XnGW48EK/w1AjgEEHpLBNv5ekYO86lyGMU9SSh2DUsLyua/YobuzOeGh23vtxLlzOznXR3aRMqa+Q4u1jUJ7S5StJRTxX5scMNEuxIOqbMssLXNWowgCR0KkWJ17JsGXWTNy26NuQnFhiI52CaoFLpYlXG4NOkZesRimLRlkDZAJCkiQDTZQllJX6rSRZ87gR4YbLD6rk9Zs9kTN4aMXhrhp4Xkqk7G7qpxOvbjTJ0IdK4BCAnPw2KD6vyu3m5X8RFXMS3E95LN2a/CNh7/ioHWDpv6VAAVJowt5G/o8Scd6EgqaDRmtrJw7VUCYkS/8GhMxgctTZijaE1lU9IlaXyhlQ1pETWiGXmhixcLrKSPqH2CdYrTpohpUtY2ZTL4zn3Xttl+foul7/oGH/1Nn4ZvKOdB+3AmKCI4TqAA8S2YRMUUQKgqDgW5lm7bMvyM4ENbSIPpufBG8fjusYXTrax4TPtm0ZAiqYJVgkGvypIXnyLg63nuD1OKY3HZ3IO1qVOZa7oPTSNFIXtyJEcG+xEPKUfSw7SK3wojPZlAtei/1p7jQRkopXw7h+7jW30EKFNexaleu8/QbHJbFr3l/ZrQHJ8bU2a+xGAdJSu7wPNst11gCKyo+NnI3M6Hr8WfN44hJsM375E9zpYHRnb7sx52wQwTBgYXGDRbcajGMrxu79dbALjm9vZbBl9p+zEdyu0cixtztcXV/it15/D3hwxONb0yfZ98LkdVwML792OVhUqRp/hfB5rud+A430PkD6HuZxIY69yDqzHLBvGt8NHcyPvhfcjy9hrHT7X36fwI+QsrXS3Duzm2NAK5zKZvZJizlro9b9bxkA/l+yxO6OlQQteqwDW9UDorkG8t27VLdMCaBGwPQeMjvvbNnGHbfd9m2U/1u+f/u3nlRz7tby4/71DPh2tzyK45xNpVuus0sJhSKS5OTYXKyeFuXqaUI91y0zt+2G7oGLXsVb7xzrspw41nQDGCHDOezIiIB2fzQ5FQjcuiqerb4HpdVV2xyN6Jr6tiIB0VPeY2QFfX1zl1V9/hvLphk/vv84ri30apynqhNRYVGh0t051RHyvcE5RVwlulpKeGPIHSmojWon8o+0BbXEICU0e2vbOcfScD40SOpIflKLcNpTvL9BlSlqo1hc6Xo9Ric9H+e1470ivhSzb948GAaMDKK10998nHt8oGHoGb6XkxwRlCAEAXSI2J6YCnNRAXB4a0IeWNLO8fLLPOKnYyxZyzr16KAveeZlcf7+ypfsgcuNriptTnv1SCVoUIOxAY7NtPrv7Kf7lU5/Cf2DBTz/3Mj+z+zV+fPAa14zMwyMY+t0qaGsUqUpb8NzhaXxN7S0z11Ajl85Xq13+9pt/mq9+5nmmr8BT9xymqElWVpQfQgOSXtl2nFXWkx2HxqNU44yi3kowhSZdhGd+Jmw3l4KVsk6rLNCC0v3rOVrQJaArLYD3QFPteBbPbzM5mQv54HTB6PYBLs+FRVerIMkax3eDyxNOhzm/dWfK706e4dLux/nI7h1+bPtlfnz4Mh9KDQ22lcm9iIu4iIv4g0QEpEvfkB5pktNC6vzec+dHJ/zZv/Cb/Oz0K7zV7PJLhx/hN196nsGL24ze8my/UrH94oL8ZMj/e/4znPzckP9o/9e5bEatV/XjNs/0G20K3/C1KuOfnHyKf/rqx7C/u8PotufGiSOd1yTzGh2Uolpcr3H43LQYXwzVOJJANLWTnGakyU8dD34YBrcNuy/WeAXVlqEeaZoReK3RtafcHjI4smTHolpKI57UqrbSKFzUDN6YkU0yqu0MbUR1Nj2t0KcrwQSzFDfKqPeGMicfG+qxoskVzUi1zXXCqgZnpmwNEoodw9FHPW5kwSrMwpAfaYZ3FFu1zF9sqmiGqrMKDkBeJJWmSzlG134T/tG9n+b4fzDif3/1V0iVofQNI/3ukzgfNx47FX9wNMEtEwZW4cJEVKx5FGVjUKZLhJT1qLoRRnQ/QdNSnG2BzV42UAeACWAyKFFLScDrsYZpyVZW0IREI8pV6rq/biXyj+FiamV1rYdM4wapAJ/LqO2iiPK3re57zEYCKPhzV77Gnxze4hcu3eILw/1gBh+6mzXoyjJ5q2w7UH1IgmU5RfXVjP/D9T/DqkrZ+hZMbjXSGZoIgJYuGlTt8DpFOWgGnv/h1u9Ro/nPDn6q9RRGs85OjolGOH6mDAC8UWDpEpNeAaGdm4ccQhKUnrQYCFi68tyttnj/4C4GxyipsKPgm219kH97RIEzTOhis0BMdNqwVgrI3kMjvavKOszhovte85Wwn5Kk9SuODQE+zVhceff7PlxlcNqjrBaPTxO6dZSmahJqq/n4pUNee7DH6G5DuWtYXvOk8wDwx0lvkIvzSfCPbiBdQKIsb9zbZbSQJDDKEXkFRa4odhOGDwz5Yc3uNyumNw31RDN7SrG4Dv5VjfKJ+DWXNjDVNTSO5bUBi6uG6RsNunIUeynNUJMfGXwi17BLFNVUs7qsWF1x2L2a8XZBnso9JNLZmtUywx/uMH3ZcO03ZuiX38QspGnANw3RBwjvBUg92MUNU2kwSA3NOKHcNiyvKYorlp1Lc57fvs9T+RETU+C8sP1Kl1C6hKJJW48tazXeaWEWuq4g73JPua3Ij6G4nFOPNPmxJVuW+MVSmHfDHLRGZxl2vkCl3XA4+fybpB+4QjM27H85JOSJRiXie6qyVMD1RMvDMfq+J4p00eC1wo4ztFLouhHGf1mJ79Akl/VNRnB8CvMls2cyzL99j1OrufV7B1z97ZrRK+I7oRYrsrtzXJ7iUw1lBXUtAL9WZMcNurL4YqOoeREXcRFPRsRCh5I5EI4gHRukEAFlIkitguxn5y3ddpN5FQqT4ovjnKaxplWYabzB+YbaaxI0tRNblCjFbb3CoLFeU5Ng8FjlBJD2KQ5N4aVCc7fawqJZuUzW7TSq1ux+3bP95UP8XJ7XWCvNNzZ4QAdrjj5zOQLCbUSZ2OghbTQk8sxQzolSShk6VuP8IUh7t5EkkKYdw/q8wx6bjqyF4VAAbO9hsWD0lVtkH3qGeqyxqQ3AcATEu8eWTh1NlaATB6nDVIamNKTTBlcnbw/4hmYERY8dvcmKDvPotRppfJD1EaG4mZZ6v7H8JvgcXvN41BoS8eQA1Jty3X3v6D4YDbQFfWANkG5ZcBuA9Jon9ZrXc/f9N/2iW0AazgDMjyMzfR4r+u1A4nN9qXvA9PdTGPw6q3uDNX1ebMrvPqwJxKJY2pwXl5f5nTeehZfGDE4j8ChFAJ+wBii2IB2caTh4N8JnaffHGus5fI/ITj7PCiG+34/AZJb3CCBo10RpFvUaE1oYlLqV5W7lrc/ZRuu93N+mCiBv3Bbrw1f8Xpug9CZLXVjGvpUZjsziPqu4VezSag2A3dxXr6H1Zlbn7FMEgTXr6wjjq/Jh9PRdnhxBv7hcnz3eynTH8bkF61SrcNHWSbyHtGN0uzTYD+VSyOr2L4AoqexnduLZWtrWN6+eJMxuGIp9he9dQu137RUC1oZ+H75HfE13f/dtRt7LsSn//cjlvWati+U7tR9etVLcpU94s9jh177xAvuvwrM/+Rb3qwmzasC8zIVoEBreIkM6Tv+cU9IMf5qS30vIj4Qx0zY3xoaCMMXQsUm6bYD0a2Nd/6u2nvOpppoorlw+4far+yTat3UH5QMgbSAqsUR57lYRsd9IoWkbxaNstwpe0kpBklhRUK4MZI7BPUOy8vg4rQr2gNp3TRsuBZuJJLMeNjirufdgyoPJSQtKX4SA04e2JD3RJEtpztTLOsw3YUtD9fKQ1VdHfG7/4/zyJ36An/v4V/irl36ND6QVE3KM6nw+v9P7VvqGQ1vxRjPkzWaXYzvifjNlZgeUTiyEVjbl128+T/bL21x5S0g8ybzGrMQD1CsVhlrfNgB5B9o1mEUl17MxYBTpacUgT6inCdXU4FK5F2wmXp82U+t2HnEMD7VSXXdzBhmTxd4OoJrEZ7XcBMP7Dl1pseIIahYxXKKwmcIOFM0opR6l3N8a8ivjA35t9wV+/H2v8Fev/iqfziqSh9hiXMRFXMRFfDtx6BzZTKGWJQwz6ssTmj9+wl/d+3WmWvFceswnr93k9GrOiz9ylf/06z/D7d/eYe/rCcPbBU/9csovnPwEz/ylB/ylrW8yIQ/+xY+PySQYXm+W/PLyA/ztl/4oxW8esPWqIz+phXW8rIPdjDQa2UlOtZ3RjDSm8qTzhuzuAhqL3R117OYkQS1LktoyPilYPTtFNYbRbc/gtWOag0nLanaZYXV1wOmzhsUNKI4Spm8ohnfArGqpyQQbI1VUqGWBskPyxuHyBDMv0bMVVDVuZ0p1eUwzNhQ7hmpbreWWUQ0xCSKrLoPFdY3XI5LCkSw0tRFLYiF/ho8qSApLfqowdS+Xi6vW8iwpt4zM9RTsftPyK//wh/lbf/GEf3fnd7lk8u9P+e7klQHeyGRQOVCNTA5RnqJIGUKX0DknMom17x7uxMRYHvixm9qU0vc8HRacLIbt9sxC47OEeqzIhjWZttTOtIUa53WXNPVkzlQtGu2yUGAsYqgnCd4oBrUA1xjVekp7ozoZMiUXRFJIMWnmHcsmE6DXIV13cbONwyyb0P0m3eXKKnTpcZlh+1XLyS9fxaxgeqdpQWiXGdlWACSVk4lLutDcbLbZ0gUkwiInSl8HmfEI5nsjoH3/AlXeCyhd+3Z/CefFswFS00tS6IozpvLcrya8MLxDoh3baYHfrdB13ibOZ0DpPrOpt85WQqwn694evMBsV3WD98ICbTvhEwMnp6ANStk1GbK4/83ozCX6PQ9faWEIaGm4sFpjjZckyjiU8ryx2EF9cYpyFfVQ4XKPOlEiwQDtPSNSbQQmgSSa//T3PwlWUe5BugRdhsEqJIjeKJYHinIrI5t5slPL8G5NdqpZXElYXVGsLhlGtzXbQXo+NAlz/IIhOxbvOpsL21/XnuWVlNWBorjsqa9UPP/0PX585xaXsxmX01NupIcsXM7/642f4tX7eySJ4y/94G/zTPaAV8pL/LOf/TDD//wFtn/3Fu7BkYwF3qOyFLWzjdse44Yp3gQ/9JGh2DMsr2iWVx3J5RXv233A+0YP2E6WAZBOWLqM02bIST1k0WQsqoyqSWgajbPhIIYuIh/AaR8kGl2iGN6rWVzPGN4a4K1DNY1cp0mCWwUwN94LhTCOTbGPHRh8KqoIqhJZeZTCrQrMg2PU5V1hMWcJzcBgc40uHdprOZ+5w09HwhRfFVCU4CY02zlpbWG5Qm9vkS48y392GWVhqKEZaJpduchtvsf9H8y59pszknun+PkSt1jIMZ1McIminuakw278vIiLuIgnI3QjzwdvVPsoh65xTIopBB8aec+FcVqkd8BnYcEw/rtGEZMApeSZk2rHos4YmJpFk2N6cpGFT9DKUfgMqNA+xShH5Q3HbsSxlbGm9oa5HbSS36VLKG3CcTnk5tEOO19X7P3WbTg6kR2NyK21a02GSimZowAqSVCjYdtNKs2DDQxyfJ5hJzmRYada6W/QaQV12jajqbLCB/sHjJGmHCfrUsaICkVVr1lFKO/xASRXSth/7TT1wSHXf3XKm39il/mzSo5xlO3W8t8HphKAcwaVeJqxxxym+Ilsx1slihfQZgztFMnTym627/dBaae6i8DTNihEZnVbje5HKyd7zsXWLwj3Xlxb33ehyPhOY6jl2G16R58Fkbv3+17RIEDkeb7REdA+D4hOVUOmbCsb3QeeNwHiVDWPJYXdgeIb6zgX3FhHejYB6z5oe57c93n78yhm9JnPPApt2kAJ+77Zso8bye0msLz5nVrpbTlfNRrnu2Mft1H7HkAavvvS5Qx03Z5LGZcScl1TupTaG7Ry3Kp2+KU3Psj8i/tkx6q9xM+yWsPrCnzaAS1PAmmuvrpNy/TaZNUSAN+WmbzxXl9SGtbA0jVv5o3PSM4ogK/PDd71gF4tjbLnbccFGeg2N4sgdly8BWTV2uXhtVq7vNYBYvlD5OmiJHe3QATUlJX8NjaJr7F/13Ja1e7DJgu6D8a2TGnWgedOMcOHZ3UPfLZ+HeCVD3fjclinC43GzggrT16TW8ymKkgcC1O/fT332KHIy6I32KbA4FZCNk/JZhaXKBbXDPPnoJnatiEcr9Ya06UW0gH7anOYijLkhraI9iSC0ptNKYm2G++vy/snG41LGr/2mX7zUwzhJ3Yy2y5cl/EZtTn+bX5+k51rlOOkGXI5O+VmscdXTq7xrd9+hquf99z6EzV/cu91vj67wkk5YFWlTAYlmbHMihwbAGnrNHVtqOYZyf2U6R3xztW1gGUxf1dOzp2p5F6J174z4lGtnPhGC3Mz1pZAVy4QJBTVNOX0A7DrNNmhkbEy3D/xZ1f3AIwP89twDbfXcjgOqZPrL3F4J3OlNGtwTmGMo47XZKkZHjoBB6U/BlN182ebw/Kqotq3+KGVfVgmsDK4seXWYosXpvcY6JrCdd0ZkewSz9UmEz6ez/55a1n2Xr/jxoZ3O4zSLfv4LZuRzgClaIaGtLa4PKHaTlHOkx1V7L45Y3uQsPeNIb/zuU/ya//GB/i7P/L/5ENpxUQNvqPFbOsdc19y31q+Xh/wd+/8EX7rledIXhswvKNI516aErSM7ygYI0ywKE+vrKeZ5kFVUdjQuna4TGMzLYSh2qCrFF07dFGjigZdNxjvSe/AcJBSHYxZXU4xC09y37HaM9gB7fhnankW6QYBw5eWZCEJWnzuuUTqxs0kxQ9z/OERHOxias/krUbGbS/EoVZJz4SxuBHfUFG7MNRbKYurA7548BH+yg8/x//pR/5LfmZ4yES9+6qQF3ERF/HejgZLguFms4UpwOcZKMWDDw/4jz/yX5MrqLxnqmAnabhOwwvJS/ypH36Jk08Z/txv/nUu/ZMhk9dWPP0vCv7T7T/LJ//83+LT+eM18UVgVKO4a5f87Qc/zS/+gz/CwZca9g8X0vxTNOhgJVrvDmiGUlNfXtYsbkC170iPDJc+r8hfKyEQ28zhAh4cSe60NeHkE5cotjVHH/P41JPN4fiTBxx/QJ5lphoyvOeZvFVz/ZdPabYGvPEzQ+5e1ux/KWd8W5HMa6BB1RY/yCBPUUWFWZUYrcXuyGj89oTF+7dYHhiSUjC6yVsuEPvCd09VO19qxop6CnhYXlGM7mhu/Ks6NJ3KXGNx2WAHisXVlGwugDMEzI6u8U81nqSUbTW5PC+LHc3ui5b/4u/8DCf/zpC/cfkzJO8hC4jHFy3TvdqZ6QBmgHqVMl14mfjWtgVRTWnxJm0TIVMomrEnWQWZ5ghMzVPmw5w8bVBhYpidKJppTnEJjJFi1LIRxk5hExyBxh6YLaqxeKMDSzokj6lpi53pSYUums5TOsqB2SCtmXaVPF2Jjv3f/lc/y6995IN8/Xee4/IDMSxXlUwkdNVvMQ3F1OhPHaS48iPY+1rQsV9YVPB9VjEpCMUH1Ug3686Lir/2z/49vPHsfd5gKikGtRJkMckwqt1/FXya5TgAqRaaf3hPkvde8VuptVqZzTXJSi5qMVj33CsmmG2RPWy8Zmd3ASoX2XVPV1W3vWTMCfgv5z9ZS+J11aCLJkzGQoE4smarWoDpXCbJ3mjUqsQ7jxoIG9WluivAajF5fxI84FrJTeOlNh96COT+15RFyltui8lNT7K02EEG3mNKSSBhvSgTpaxtqrC5YnAzpd524TqXibCKRZ5wT7kc7ECWb4aKwZElnVvGtxuUT1hdUiyvKuwgZ3w7YXhfisF4yE/lGil3ExbXDKvL4jeU7hbkecPEiBTs791/mmemR3z88utcMjN++eQjvPjmZbiXUw4cL165zINswt1yQmoc9z6laQbX2ftcDm/egSxFDYf48RCXJ4GBoXCJphkZyi1FuetxuzWXtpZcHc7YNitSZVtwpHAppesku/ss6ehJr0JRP7IsvPG4VOEsmNqx/dIStSzxXppmqBsBLEBY0uF3b0Ep8YCoJ6E7KQ0PoaiwAMJ8jg8L7zGVw+WBXWKtdHVnCd5okkWOL0t8Y9FljWqiDLiGpkE3nvzEs/VagUs0unEtG2V5JWX2QcvBlzPMLWGeqyRB7Wzjj0/I76+ww7dnDF7EkxW1t2jUQxP+R/m0iNyZa3243k6iJa4rTkrjNs/bhsO3HiTCrPXkKnnPdNk9iaGCnUa/MSwWhtsyWK9YLIVFJTYjoUnON6pd3luZjHnnO09ir1oZ72WTodOS0oqcdyzQGWSukCkBhazXWHTLkgba8fakGbKwOcfVkNN6wJ3ZlOqlLa58q0bNlwJeQAdERynIJOkIvcYEZRWHX67wZYm+fICfjnCDDDtOwShs8AVK5zWqiutzIpvU90tNkvXyYBM9ULXg886LLYO1eGtFsjuC0dYJWL3hOa3vHLL39Qn1JKXcc/jcdSfGqsDuCaCGB2UcduzIHhiqWYYZWmEyhZPbiYKoFhxYA6ShA77jMnTzu5YJGIHk8zDR9gCf997bodU85L3vfWxKdcfYBKP7LOVNeW5NB7imqjkr970BFvRB5ijH3TKb/4Co5ONKgb5Tv+dvJ97pNurv0WS6f5zjz+gnvXB5C8BbdCuxO9JlC1YXPhUwWzmZE/qEl5aX+cL9G9x5fY/sviE7Do2JAVxrm4l1uO16wGQ793agH6+u8l2NxY28bRQWdhUduOylQO4jyBoiSliv4WBRhSLOg1PaHHwTuMYjOZfzqD4A7Wi9OVup7njcjDDEYo4Xwdf+cfWB0d0WT1yvdtAyKnvNNaortOimA8zjucMHy6PGo4zICRe7pt2uD9vYFIrojhGtly6AiQCeC4Bzbx4fVU1aH2YVgDatA1NTtd+xBZ2DzHCUFY6S2eJb6ok+uCL/GlmlPbC5N/a3TUXBc1fVnfe011BvOR581JAsBUSptgW41is5EP3Gi/b7h2JWJAmYUmHzACym4RqyhCYpzjRzvFdC1DHOf8+odfWMh0V/LD9PmeGdyj4XLmViSu5WW0ySkm988waXvwqnz2r+9Ce+zL1qykk1pGpMK9tdNAlaO3Cask5YzXLULCE/1qRzAaRb9nM8v73/beNBv1Ew1rwg1IfCG3HOEQCzeqwZfvCYe3e2yaBt7u5WFGoXgSEdGdHR0q9tjNBA6qRclMgFaDKH1k7sZ6ymqRIm7zthtcyZfHGEV64F110qc99qqqininLH00xkjqZShzYenzrUtIZZyp272ywOctLUtkD0oxoI3i7eq0olMa/TKF6srog/96xAlwlYz70fG9L8GycsZzn5K2PGb4wY3bOM3lxy5e6S1Wtj/p0X/5f8W3/6s/wvDv41N0zHAIn54WY+GPNZoJVzjfsAcORWvFQP+LXFD/B3v/Vpmt/dZedFRza3PFs6vKlF8VFBMqs6S0YVn0EKlxuaoSjrzZ7JWF5RNCNPslSkC1G3TJaQnzrShcdtG5LCk50qtGnQBUH+1aLnBfmyJL+TUV0eU+ynDB/Y9lo3hSM/LNHzAlVU+MTQXJqKst9I6sp2oGhyLUzrBJrhAVt1Q31pyvH7kzWmXH7kyeae4b2aajuh3BZSlW6k5mpqj1k5xrdrtl+28Fvwn7z/f8rP//Uv8b++9s95Ksk3PLe7PP3Cc/oinrR4u/rVebWkd+JLfFGL+vYiV1Lj+Xp5jfzYByws4ejTNZ/Mb2IQzoMFbJgnxLMy1ZZ//pP/N37+4x/n//6P/xTXfrPhfT+/5C/n/wH/lz/7n/EnhjNqb6m9xSiF9TIuubCGwjeU3pErzc/Pn+Zv/Maf48YvGm7cWoiF6KpGOUd1ZcLyypTlZc3pC5af+OFv8Jcuf4ZPZA8A+FuHf4R/8E9/iumLpwBUz+yR3jqVetHlfbh9X4gOqWL2HKQzRT2BaqwYHjmu/K7U7KvthHJLc/9jGc2PZAzvea7+Vk091tz7pGb27ID9ryQM7hWiKFyK7alPE7HJjPZ0jeAIp88mlDuw/xUneVpo2NUBL6tHMH8Gso8f8x988F/xI8NX+B//zl8h/1dTqi2FS1OSlTwH8HD9L7/C/+P5/4r/dvkM/583f5yXfv9pRm9qeY4suqZlFTFBZH5nc0UzViwzzfSm41/853+EW/+jbf7mU7/AFbNOWus/x58kJvVjg9JrHdCbbwSGotDHg1Cg1tjctN2V5zE4oncSDqoqofSQJI48bWjGnmZkaEaerazGeU0V2g4ab1g1aScnHQEcJYl4f+LtFZTbwgpV1tLKdNsw4RmEB3rLtg4AU+mZvJrwjdlzjG+JfryubNvZ1o/IUIboS+0FtI4+Uh75bM+gvTt8Uu00hSU7ga0XZeAYPrDo0q3LhvlwLENy4RXyXUJyHxOCcktTTxTj2w6XSpdem1xrKWBEibSeFbL4stSOo2JI7Q1GOTLdkKcNy1GQgLK9InGU1LQB5HPxWBJkRxXNyGBWSsBnLWCd+J/12+V9y5BS8WbXqmXZQtjXlXSt6MZhngClYtUovA4VldC+4oNfuQ1+Sc08Z3fm0LUN50KRnXiZgG8WiZCOGpdIQja+5SkLjSmka7MFLGLiGS4In0A9gXqqqEcJ2cwwPGyY3qxIlynz65rikngplFsD8lPH9HXH8IEwp0/fZ5h/sMKMGrRT1KuU5v4QM9csy33SBfz2lcvs/NEVmW74+c99itGrKbqGZqT5zBdfwCw1yTyAJQksrmrUJ/eZ7I9J5pV0mxsljShatV7V5bam2FeUlxp29+c8s3XE9fyYiSnIVEPp0tZLetFkFDalskZk/J2AMVJtlAK/rmNBS5LkclexShTpPGX82W8JkGGMyMcHKW5PvX4SejKxyoGuPM3AYLZH6HkpUrRJQkQgfPDrM6uGZmikYOc0xvnQSZugl2PUfCGsvmWByVN8lqAu7eNu32Py6pzVtTHp3TncP0YNcuyVHewgwSUKs+hkD1We442hemqP9PgE9dot/v/s/XfUbdlV3gn/1lo7nfTmm6vqVt3KkkoCBVBACIQkhEg2JgjbGNM2bpzt7vbwN2y36XZ/HhjobpvPNsZtQ4NJtslBIAllQBHFykFVN8c3n7TTWuv7Y+69zz7n3lJVIaG6gjvHqHrvOWeffXZce835zOd5giAQsP1GfEnEc5FEK6kT/HkptViFTfJ/rcS0PdE3SmNaBZr6e9faFncNUOOZQPQb8SyiAqebYktVaK5B61oto+7U10rhlEgva+XFctgBpRK1DKfwTuOco7QapQxGeTIbEOmSSOvG8sBUQI72jtSFhMZW3tGGsYvJKuA6dSETGzG1EVMbMikj9rOE3St9Nh6FzhOb+CyXsW/xWmhVg3393A5mjBW9vkZxZBWXSGHHBQqde3ThMIWrGtpcVWB11b/r+YCGtqdqpcbjrROZWxAQOzAyNgO+KPG1Q64WYFotgNJuf0j3iR26Nx2g7CnKsN6PdmW/QYrlPCUWvEHvB9CxzalVSs5J/b0GjJ7zilZzr5Wr5wyIrHuzHKh2dX2uKCyvvfINE6N5v97mxcL75wKyn6dYlOiu5bnnfKJbIHTNioYZa62W+66jZki3faPbntFGuRk43QKkryUl/VxY0s92+eca12JLL35+NcN7HlR5Nj7UX4xos9MXj7fzumlChNl5M4pK3QESVRDqkqHrcC5b5YH9ozx45gh+K6azqQnGMudzEQ2wWDMHG89oPX97zPlHPs9RxkpkQsvqfNUgTxs9h5YfsrxbqyvIm7N/1gCr12ru3zXoWLOWdekJJk6aowNF2dHYWDd5+2Lu78wC27kCpKg3Vc3ev2q71Gz7299v/l0B4M3CVYOBLiHZdiTbkje5SJEPpBlXWxqGdHM+2wB1Vfxf3AdVqbZRA+UtEF1YeTN2Qy39XoPSwnqmqWnUy9Vs5zaIXG+DokrZqCVnmetJwi+8l6u571Ll/97QYlJXDRVWzbH951TRapDSzRrSVYk0ISg/O89mfi5yPdwTi/GMgLLyzTMDmGO5No1OzwBM1r9Rew9/YbbLsRyknEnX6D8Z0LlScOUNlqPxLg8MjzIpQpTyxGFZWbGIRUuah6T7MWYnINzXhCPJceU6nykRzgHTdZOKn1037fGO6r2GQOFkDqaso1iKSVcVRwZDJk8sNyxp1brOZ399pQggP9CMsfXv1vLeHsKkxBYGbeQCLNOQIC7pDTJ2zy3ROx3Q2XRNA0otO25jRdFX5EtQrDh86FGRxQSOILTkWYDdi4i2DfkGFC2m/FWKHtV7z3T+240IDjV3PX2pxZlijXjXoaZSVJ/cMuDot53kf7v1Nxi6hK1X9fnA3j284/F7ie5fYumUo38m46Z3O37LvpLtN/X4oWPvoFvll7EKKbFzuSTAxOd0VUSoTMXSlif9yGc8nEf8p8tfz3sfuZulT8T0Lzui/YJwKExiPS2b5iqsxyWB1FnLau4fCIAb7E6IlMJ1I1YnJdEw4fLLNL3zntWHR7jIUPZDsZzbkBqozpB62GZJvFVdktNCcgbnoSiJzu+j0x62IxdwtD0VaVujcd0Iv9yh7IXkSwF5X2MTKHpqTtJeF5AvG5Q/hC48+RJkx3Io5Ti507LuZAuS7RwbxfIMURWQkGhYMvKcWgvQpaezZfnYf38xP/CNq/xvt/0GL4lyLJ5EzSYzz8XL9Ub82YzFOtD1GIvb9VxA6hvx7MN6x6VimWDqwHlcHHDo6C4DXTzjd2MF39B/gOJbDT+x9rXc8rsxx97r+Me3/AV+42X/kSNmRoppe0YnKsB5T+o9/+Li1/B773wpxz9YEm2n6LyUZrjYsH/7Cpdfqlh50RY/cOIPuTO+yAEz5oAuST3cnx/k5z/ySk68V2wZ8qMrhOf3IAopNrqYSYmJI9KbV9j61gl3HNrksT86TjBRxPuOeLvATArSgx2Ug8G5gv4FRbZiSFcU2/eEhEPP4BRka7D1woDBqS5Ln/WYoiJ81SrM3s/+7Rw2guilO0wvrdC9XE/2RbFmckCz87qUH/jyD/C63iMcNRmRUrz59od598e+gnhXcjMXqqZZ9sJwCes9r++c4qUnzvD7R+7gnZsv4BOfPU7yREz/rCcauUYxtw6vBa+wsWJySBHtez796y/gB7+t5MduelfTmADX71jw7JnS7VhMVkstIGcpEwjxFNSN38aidFYNGkkXnIB7xVTauZ317HphVI+PhJRLliiwOK+YliGxKdF4Qm1nXlNlVRjUWiYbdTXQe3Tpqo7LSjolUJV8tm46wRcTZhtpYape8QQjhclkwm27gchtlx7ti6azVL6rqLv62v5XqnBNAtF0sCPb4xUQzpJ/5TydK7K8ySuZl9LjjEZp3zBCGwnGOtGoDN+lfiqgYNmFsqMoO9C72PKDpi7WzMDe9v7rwrM/TSh8IMdZObphwV6/uuALP+u+NVrkjL1HjUoBp3V9LGbH0we6YSgpu/CwqXzT6m792u+szUht9rk61ip3RHvXQaJQm87XMlZVYug9oBU6FhZVmQgrWOcQDBXRyGGmVhjgrWhL3ZnUE+17dD47X766RpRVTZLY9rlxBrLVCpzuBSQ7nmhkWX3UMj4SMD6iGN2syPcNyydLubY61fVXasxnO3QvKMKJr+SL5CK2kcIZzQfO3M54q8vSQyHhSK4bkyri7UC6fHKPDYW57SIY3axxQYfBGfFdrhsVpONVky6LD1q27ghXU44u7XNzZ4fVYEyichy6kpONGRYJozJmWobkZSBeW66S+Wr5cepcivxeybhSJpCtO8qTSsDcvEAFATgnHn5RiB+NUZi5AoQKQ4a39Sg6imgsJ9l2I/GuCAMBt2sPrkDPwBIkyQCNKqUpxQUKO4gJrgTyW2mGKrr4IKQ4uky4vQuPnKR/foB3Dj8eQ5Gjuwm2N8BGsPw4BBOLLwpUKA8WH2iRRL94GcJQfK5vxJdE1BJrdVwFILc+DzBNZzTMWNLWKzSKEtt0Jj7dby1G3WVdr69eT7sbm9prA5rk90b6+9xD2VnxwsNs3qJVU0ism81UxdpzIHMCJUUxp5QocNhWYdBqnPaUhZyV2rdQW0NuAlIrssWZC8Snty7KVp1oqQ8Zu5g92yFzIdYLiD2yMVMbspN1yZ1ha9Rl8HDExid2cRcvixS3BdqJY8XM876U8dXoZjxVRuPXV9h94SpFVxFUHojRviXcywn2U1FHMa3r1FfzulrqOwrngWlbAdha0UiU4PD1ezVDOi9mIEfd6NYOY+DyFquPLpGtJrhI4+L6hIBM4ur5TDUHUwIKRPuabE2LjKSV+VjNYp+xoGkxo5m9X4EEUsBtf878b18r6nmfUnh8A2b7mrWkmAHf12mEyl7lFX1NUPoqELqW656B0VFLXrqW6m57RZtm3W6OGa1ZkO1e0Kutf+eZog2W/0kA0+YZPE4Xf3OR3feM27SwfrNoz3NVLBwT9cysMotCM5MnX/TN1qoCqlFoHIUPsGhSF+K8IlSW1IdslgM+vncLnzp7DHuxS3JJz263FnCIngHRWkmTJ1RgXH146nE0uD7wNxeBLwUYdWZ2OdZAkqvmmTMm8uy7MwC59aaaMXZloQo8br3vtSKYeBKlMLnDxpp0RQr60AJVa3CqZhC3T3cbrLrGsLMIcF51qSiufQLq91VlH10odGEIMicMtY7CRuBLuYR12Wr0qgC7ppHbq3l8sWLEzcBj1exHs3/t15XvdHOdtSdCNRDuRViDYvHcLNRBFvZ/kdFdH6PmdbWdDcPczA6WKhTkC4fNPc16/UwRQBeyD1bPbao8k1rs2+c7FgHBRauC2pahvfzcMq3xRf4+tzu9dPqq7zzXdQxMivWawgWMy5jeOcf2C0JeefsTnM9WuDLtU1pDEpZiwZJHTPOQySiG/ZBoTxPuz3sNSj2rrg1U77XqOnXMmNO+eT13SL0o9elClAazlYCdl5WkOyuYTM3GixYrulFKNBUgrT0YaZLQJdX8A/AKE5dir+UVYVxSFgHaWFbWRuzu9hg/tMrqKehuWvKeFlaSleb8oquYHhQ2rIvABx4ih9KeILR04px0P2bwREC24ueeQ21Auv081MpfBVYvPi8bi8DrpJnrjxslllPTDeI9J0qSSKH9n9/yDo6aDExGEu3zwugi37D6aR568TF+4bOvIH3HGmuPZtz87pQ/2nkxP/PXLvDtg08z0Oppc8yuish80eSVGs2mnfJb47v4Nw++nvAPljhyztK9PMVMZvM12w3INhLygWlsDqKxo/fUCL0zxA166DxHZUVTG3QrHVxkyFZk8Ir3HcVSRLYqdaho6OhsFpQdw/DmgNHNUg9b0gnRbiEKlF4DDgqLKi3BNuhujPIevbUPRYE7tEZ6qEu2Yih6Clc1R+Eg2hM5eV2KF3W+IvONnTsCBmcdS096issRygpAUDP/8+WAZLvA5H52rwC6dHL9l/JsLhONCz2Ds47Lv30z/+wtf54fuv1XeVFUVG19rjre1yewcCNuxI24/sLhOZ8tE46kTuJDw12rl+k+y0fdinZ8+9InufeN5/ifR9/LrW9Lid65xE/c+lp+8OCH0OimhlhiCTFoNF0d8p937uH9v/QyjjxQEO3mmEmODw3To12inZzz31Dwg6/6Le6MLnJnOMV6oRSkHibe8NMXXsOR9xris9vYtR7R2W3s+kCwo8aW17B/POJv3fduElXww0vHGNwfEo4c4c6Uyc0DxocNLoRwpBs1pnjfY6eQbijKLnQue6Ybir0Tmng3oZNb1GgqNSmjRTW1nlNNMzYeKDh5x4D+kiLeFXC5ZkxvvdzyH171C7wk2iJUqnpWlrx+6SHetv4KaTR0YANE1aT0bO/2mHjYMIYNE7GiH+O1nSc4c2SZ/3bPV/KeT72ApYelka9+jiiHYJVhbRkkGMjglOODb38xv/3dD/INvfOY6qFzvTYzPTdQui54Na8l41OZbpIdGxuCvCUju5iMNgmyb06qGWusC/ChMEytDaFr2T8RoHsFWokPUOmkWK+NF+ZQoKTiUIGaOAfFjHXrOzE48SihdLhOSNmPyFbDCihUjbR1HbrwMymyqrjhAyh6AmIHU41JHZF1qLIApfGBFkZk5W+iaoa0Ax9IoqxBipe6AuSdgOreebT32Oq7JpfJddnR6FCYRN7M142U9TOJN+dxcd2mDrpwxLsipW4yP5PydrJAzZKGhcQaSTRM6Uinkfi/KUegLZ2gIF9xFbPJzgHHviO+BM2ksTq3LhDZdFOKJLfrx+L3neVVMVkmUz4MpDDsPR43k1GrgEN5UYExociDKy8SBs93iO9YVRhG/rYZ967QqMCRLWsGShHviQeOycVPhvDqCWUtNxhOZLIbTuVY22jWva6qAahhB1glt4GHMoEi8diOIl9RLJ2EaGjpnysIpgG7d2rGNzlMZkgSDR5WH7OE44Bg7OnslGRLhrILZrfeJhno0ss9lh8K6F6WDp0g9bj9lpydUZgCsnWYHHUklzTxvkUXrin6uEg8IspEUwwU+bLHDSzL3YwDyYilICVRBUZ5aWSt5GSnFUO6sAbrZixpXxf86+NXyOTfB7NjaabCQPF5AZ2k8iuvmmesE3CoLYmoFIQh2/dosnVH75yhe8HTuTiB0qKSBLc/EuDDtq5loyt1BhlbbKwJ0uo+NwoVRXg/EsaetZBBuZ4QLQ0AYe2htUjgWoeepNhwmXxZ0btQKTTkRQPKBMMM34llvdbK/t2IL8m4FnDc9gZrL2OAwsPIZ4QYYhUQKOaS1DboXIOHi8D24qSkrNjTNThdS+8Iy/pLu0DzvIZHxoWKJe21agrZjeyoEr8/XxWDhXVbP2OQk+4RsNFVlXrAa42tAFNjHKUVtnRhDaXWFM5QeoPzJYU3hNU5tl5R+ICRTZjYeG6cHZcxw6oBaFxETC71uOWxEn1xq1K8MAIWOy+KE5VCjbdWxq8klue59yitcQdW2L9nhdFRTTD1xPueaLck3EvRexPUeIovLSqOZqzn2grFCdCsSguhAq2bcZYgEEa1tTOgo7ZjqL2ntZ55TBtzFSitjMGXJfFTV+jfdIxsRRqqZueuer6oFjjtwXYd4b7GpwbdK3BWz5adYzw/DSDdfr9dKG7Lr9Z+058j2izJGtxuwOmrFua6AatDXas/zMBoeX/egqINRsvrz+0b/XRgNFwt1R01Ptb1OubjWoD05/J0fjaA9OIyny+L+QsBgl8Fan/ea7w6rJcCsjS7utl79W96jW1tRy3fvWe7AkyjeGx8kEe2D7F5ZYnodEQ8VLOGHtO67Uw1B2xL71c5VAPY1aCjAdfIKT+/If6sAhR6czVbtZHJbhi7zMthL4LFXJ3j1czG+ljpEoKJQnmNshoXQLquyJc82lYNnq116rIGxWXDtK3Zxf4qQLpmeTcYYb0v1Rg6x+ZtwOTWctVr5aXAUgyk6dtM5RjYaPb9ujFYFKTkWPkWOO2e7vz61rO4xXyub4IZy3i2f76cZ8nN7Ve92ms0DcydixrAV7PP2yC6rxnQfrYdLqyPW/1Mmn0+X1uZ347m2Na5v5c8SQeyIXMAeH1uFsHLL6F4ugaZGkz+fCSdn93vq7nfa35HOc6OV1AO0pePWYsm7BYdxnnUuK9NipDhJCYbR+i9kGCoCCYKk1E92+W6aGS7/eyaaTzk2+Ghlvdv5Orr3a3mWNpK3cX2AtI1xR0nLvLE40dEcKYZU+sLsQaiW/MMjViYGaCcXctoj1KeIHQ4JwxmE8gzd2dzQHwmYvkJWW820AJqT2R/8r5iekBRLPvZ+FwpygAEgSUwIuNdJnJvqFxTej2nwGK5uqngWZ1DFHaOBPKlFUZpSm+5nPUxqcWHgdQ/XzrkBeFetYxi4j3L2vKSaJP7ok2+/iUP8vt33smP/eY3cfT9JUfet8dPDb6eu7/nAq/vbAPSoNxmXIEUuOvGZoA9l/Nvtr6Kt/+XV3Po4YJgOiXYTRuCijeKsheyfU/M3l2O2154njcdehjrNf/pQ1/NXT8pDVjZ4R7JaWmGRSn00oDJkYTdO2UwtYmc5/3jkTzLDyiyVUCHdC96lp8q2PhkxsXXDLj4FSGHPwxxYVGBQ48yISWEASor0EPbEJxIYsqVhHTNEO86wrGMnWVH2PteK8zIY0NFugHlPRPKYcjgsZC8r4iGnnAigEHZUVx8rePmE1e4+MnDLD8qNh2m8ASpp0xkfdHYieJeLvXb6brGa+iftez++jF+5LvezM+deFvTIN5R5pp1gxtxI9rxpX6NfKlv//UStYLFpekS0X5VK/aeA9Gokdz+XFH4hhfIPdEV/vY3/i4/ufkWjr5vyNt+7VX8rb/++xwN4qbuqNFYbFWLhB//6Ndy1wdG4Dxmb4pb6rB7V49Lr3Ece0/Cq+56jG/tnaTAs2UVRklumnvNh6e38ejv3snxT1/BrvUILu/jex18aDB7U/ZfuEa8XaKHMZPDipd2TvLu4Qsx/ZKlk6J07EPD7u0B2bqn6HuiXY0uRGlWWQhHML7JwUZG0U9Y+qwnX1JcfnnITe8OCbKiUdeow3sPZUn3U6e5JTzO7okqJ7Ogc5gc1vzAa97FS+Nthg42Kqxg6B3rZkR5sMCdixp2s9hWONSlmIu2y4bJRIVEGwZacdjs8Ypj7+Shgx/gX9z5LZx99y0MTgmR1UZ1TVDm9zaAYiA1okMfK/inK9/Fzd/y49wdTukuPL+vp3vsuYHSC0llXUALpkpkRzQU3RAzLVBpIV4ZubCTpRu5tY56olxCOFIEU1Ml2rKe8khGdgTCwBFqKTjFpqRwhswGpGUgE+waDFVKWDR61lvrA2Hfds+n6EmGW+pgE/EzDjKkBYPZPjmDSOVWfr9lLMCuNBUKAFyzq12gUaHBhaZhAzceXA4BuvwsKbTGVJ7KItUNdTKtcFpTM6VRIr9ddqULvO6AqAsqTZd8K2TbZBlTdU3XnRr98+LRI+dMLtoaONNZlVzUXeJG4UuFLZVIfVZHMtIl7kBO3pcu3/r8ycoULg5QnbgCnEGVoqtfdmS9Njb4QKEz8XKZkz6odfqLcibd7aqqfA32VcdGpyU+0OQrsZi6P89RF0CgKoDIP+WoefDW0Ds0Zfe+kM5WTLJrCTLxjlmUsJP1+aagZnJPtqrR2wI6o+ReqSXWVCUP6k2rQFLKYIQSvzCbwOWXQ7wdsvK4pXOlIN7VFH3NlZcqdl7iiDYNqw/DyhPip5CuGMZHpSh14JMio5UOpINz6eGA/jkr3gWR+MrZCMZHNfmSp1gS2YreacOx9zmi3VyuhUmB7Yk3uO1o8oEhW1ak65CvO7prEw4PhhyMhywHE4BGVrbwhtwFpDagcAbrFaXTM+nuVjGf6rjrYlat8QY6V5TIEyYx9vIVVBShD6yDdfggEFC3Bi6CAO89fjhk/SHLuTc69Kv3uHy5T768xKE/9JjxVLygrRVApPI8Uq46t5UsYtHX+EDUDgzg1gao6VQkvEdT3OFVwr2c8b0H6ZzuwFNn8KkkSliL290jORlyKFvBpCX6qfPgHT5NBZApLOVaj6BYRw3HAljfiC+pqNnQtQ+XwzXS2rEKr8lOrr/TVzFTnzPyGV11tZ90W5a7ZlfX0EvNtG5/ppGmr6nLOWVzHi/WsV6TqIJXxBNWW95iN+LZRwNItwvvzOYGvlV8n2s+q1hV3kvLU104V65qgELGndqSxDlFaTVGS3Gu9IbcucZbupbHFT/WiMyFTFzExEU4rypQeibbnTvDlb0+/ScDep8V2W4VRZUFh2skuuf2NQrFHqEQuw53cJXR7Uvs3i7Sk/GeJ7mSE26OUKMpPs3wzoI2woiup6RmYZJTeUw3TV9KyTJaGnSUUvggAGflwFXNiirqNB7TV7GkoQHS3eY2S0+usX+8h+2JZ6ny9XGGRsa7BopDh4s8ZqRxHQU1ONM+JM8GkK5Bkfb3akBEw1VIz8LqZ7qcNKBC0yB31ReYgS7Pc1yLGb0o0Q2LLOQZO/rZyHRfBT4vgtOt9Rr8MwLEi9vWfr/++0zrWCyQ289xfv80hVGO1EWkPqSnM+wCEKQrlvRseTmf22WPC+kyV7I+j5w/hD/TJdlXRPtUHlrINV8dVqfBJqJspQDnq/lzfZtUOZnMs30jt3w9gNJiPVPJefZnTO86an/imr3YgLH1ONCmxtZg8OIw0N5PXx2rEOrERRqwJXdwlYpW+wq1Zn6FrgKKrvIgXgDUrwWYNh/5+eUb8LYer6pLxYbgloCl2baqsvUcNTXw1v6dhfG1Bc61t6fxFG89g+s8TlXHTVWvvfc07iftg1Ntb2NhVn0Pqvwsby1H65pTNajYWpWe7Uf9vs5bn1X725bnbrbHt45p+xhD0/Rd54k+n2fTz/UEfYmC0ouxOGYvMmf/JKOxTcFjveaxJ46wsqZ5zW1PUnjNpAzJiqDxQB5PY7LdBLNvCMYak1XXZnVetZXagOxIC5imNa9cnIfQukb87Nr3Shg6qpS6VtkxTA8pVuMJZixMzaZZo1GgqGtGNSDtwXiUEV9zWSky5lbqfu1N0dqTbicsPxjSuSIbnC+JFH+8J2OJCyBbUaQHqh2qf9vUOwqhsRjt6PYzxjcHhNsGM9JkVgbN5rwuyMBeS9K7Hc7reUD6SzBqeW2NZlTEaOshMEwPJ/yDF72NiZf+zq7SpN4SKUWIYuwdzive3HuYr3nrY3zXXX+dwS8NOP67Q/7BxvfwU9/4n3hVkl2TnTtyKR0VUXjLfxveyr9855/j5nc5Du5PMaMcPUwpN/pMDyXsHzcsf8MF/sntv8NXxjsUeMbOc952+Z7f/+vc+TM5ZmdMdusGnYcv4FcGTL7uRfQ+ehK/MmDvNsP49oKVT4cC+iaKlSczwp2U7GAXM7WgYfdEwsWviFAuYvlJR+cKnP4GzeoDfdYeFv8/nZZSN7FWGjxLK3Vk75keiDj8/U/x2d++ncFp10jKm0yewZtfpvgrb3kvf3Xlj1jTER/NEv72qR/AJopcQTiW+pnJPCtH9/m5e3+W7gsUZ8uAv/fYWzn34CGWH9d0tuRaLbqzh4VywporOzC8xZBsebb/1a183d/9bn71hT/Dso4wSuE+h0LajbgRz0fckN6+PqMGi69MeyyN80rpEwL97M5XomDXaQ4YjwO+tf8A7q9ofiZ/M7f+yiZvvPfv8PGv/g/VskFVT1ScsxP+5cU3cuQdAcpmUDqyY8uc/dqIf/6d/53H0sO87ZNfzV859EEuWhhoz82BIVSGPZfzUN7nh3/tz3Pnz57CrS8RXNzFLfew/Zjwwi4X3nSEzp+7xPSnDxJfisjWHSs65dHRIewwJN7JKTuG3Xv6lF1Q944IPejjjiIPKLNACACp5t57z/L/3P7f+cX9l/AfPv46ug8lBBO48OoeN72rEFJaNXduiBMAxtB/4BLx9irpgQgzdeTLAf5bt3hd7xHOlwHHA0uiAiauYFkbCCZsHNzHT9fRFgozazztXNR8Nj/IK+PLODx7LsWh6OuYic85Hkz4+bv+Gx85vs4/f/hb8G9bJ973FB3V5EC6kLl9vqzY0yG3/UbKX83+Nj/5Hf+Bl0f5053m5z2eNSjdzPGaN2YTXZNWoHTNDA5nD8lmMlx1WM7lBxWj2Eyrk6FkMlt2PX4SgPFo4+hHWQVISddjJxD29DRQwoau2YOumjArmRj70FD2IwGR4yVAmMSgmy7PGSALSs2Yyl6BNgpVzMCtGmxygbA9lYuaZM7XHfmtugDMEk9lq+K0VrAkniHeiEwRSjWs55otXSeHqpoI1bJGqpq018xUZ5gVKYwUaWqZJxcogmnNzvZzibjCU/Y0wdjNEufaq6zQjGwisqFe/Cy7/Yx8KaGR7q5uSFU6VAQ+CVF50XTr6sxBIhLuuhRGq1cKH8tyviikCO29fKf2jvRemFFK0faUlmPq8Vqkn6/ZFfxFDuWkEF2nXY0Mu698Io3HOcUdd13gzJWbOPgJRbQrJ9ZFV08kXaAYHRMAOBx7golv/JycUXPd0PXAYzJpMPC1HJ2bB6aVE4B58yWazuWY3gWLyT1rD3qKrgEFRV9RdgOSHUf/bE40Eu+ceM8K8ByLF1nNvK+LHkWssAnoDHrnIHhCmHDRfkE4mslbiNqAxkXiVVd0FUWvus+7Jb0kZyWaMjDCkgbxNs1cSOGlCaX0Buu0yHb7iiW9UOyvE1ZlaeT5XAg2gXyg6cQhOo5xdx/n7GsGrDxR0vvYSXxRCgLgnXiUAj4vWHpoB5Ot4PWA8gUB+RK4JMBo3QDZqtU1paxDW4WyQlswCMOcQKFtIMByEODLVEDw0hFMcq68rIcLlxhMDuDOX5y/KPZGhEPxoqYoK1UBB2XJ5PgSNlIs7YT4Q2uobJ5ldiO+NMIhTQvix6XQSpP5gj2XUnhxPE+rB8vEBez7mKFL6Kmco8EUg2fsCwaqxKiKuIAoLBukIx4gRKGVwqCksNCANSK5s+9STpWG3x5+Gf/vp1/F4KMd4l3P9IDiL3zv+/gnG/c3CbD17rrqrruuoxqX66a8GcNFQM6muclVQGT1WpRUmDWl1VLeVP7TeAFDtcZ7YaNoXVBYTWENuZZiaKYtgbONZHLhDXu2w8TG7JcJhRc2dWYDhmXMpIwYFxHTIoQnehz8RAZXdsSruSzx01RYx+0wBhWFqDDET6egFNm9N7Fzd0y2IkyflScKksuTOXY03kmDTT3JbxjSfgYsa900qDWy3ovPf6VQUYgv1Zzvj+okME1FScK6OUUMAPICFco0OHzyIoPbT2C7WhqsfAXwNszj6lljAK+abttsYGbgRLsgXD+f6mug9v2sgYO2rHd7V1rMwjab7qprSsl+N0XoRh9etvuqqNbl/4QL8c8mksrD6unkueu4FhANM2b0bBk3Bz4vsqLnZLpbv9tmyT0T6zhsd4y0ov6ero//c4jPl+ksrLDZOiyKooWwyvFc+I2Fl6mfTwOvBbzPf39+3P/c4uIShQ84X6yyXfY4FO5xIBgSqrKR6jZ4Uh9WKjlyrq6UAx7aP8yDF45QXOwS72iCEVVTpABoupCcpwbWGpDV00jIQjX+Guket5FvWMboqxm+z1eYXFhTLpb988Fs+GiYyG2ACGbPE6VmFPeWEkcbsPRmJpQwB4BWdk8oAb0b9bDZ0NWsQy0cq6bRXDOTzl68pBWt3Kj1Xr3e1r54Fl7Xv+vlt+rPlWXe17rdr+Rmv9OAvW62HrmNW+NydW2I6tRs+2Yrb+1/69nc3rc207gZX1WFiVXjdJOz+vntWxyqvar8rm37JLUOS1UPcNVk7yrGdjW3WGxKqPfdq/re8VglVlCLTOvrhSm9OBaFzMsqL8p7X1Ny2etnHtOe9vevPgiLcs+LcS3f4uVgwn986LXc/DuKs38+YyMa8dR4nbPDFaZZSJ6GjAGGIeFQY1LVsNqpyAjG0ti+KT9/ztsMaEXr/da11lzfrftD5+Il7Y2m7Gmy21I+c/7ovHy98c28Zw6MroBiFTh04JommqZRwqpGulsZUfLxD/c59KhHOUfRVUwOS03NZKBKqYFMDymmhxyu60Sevqod1uC31p4oEGu05e6USSdGWYMPoGOKOfuPa8XTyXU3569u1q1lvK+D++CPExpFZgOpeRYl01XDfckZuqqSRHWWrlKk3jP2nkTB0aAk9xAry4+95L/yr5bfwu5PHeOW37X8zSN/id/5iv/AAXN12ThUhpHP+MX9O/iRd38Tx97nSS5NMeMcO4ixaz227uty6K2n+IGjH+FwsMuJYK/hBe25kH956ps49PYIs7+LXe0RXdjHHlnDB5p4M0MFAcO7Vzn2llN87YHH+KlLb6B7XhNkFjMuGN/aJ1syBGk1n594Vh915H3N6JjGG1j7jGd8TGGTDhv3K6KhgNM1WxqATDxLO5dyPnPyGHG3ej4UEJSOoqO59Er4h2/4Hd7Qe5iBNox8wc1BzuR4QfzJQJQ9ekrG2dKzv9+h5qbdFjp+6M5fYff2Lj976dV89ON3MnjS0L3sCCeVNUWkmmY5VUrjhjMB/tcO8teS7+TfnPglbjLhVenMjbgRz3csWtM9m7gBZH9xwnrPKI1ZLqomfQXLZiqfPcN3Uw8D7Ug9RErR04pv6D/Af33jy5g8usL67wb8mxe+jL+39kcEVZ3QAR9Oj/Ge93wZt52ZoApLttHh1FtC/sYb38UrktP8X4++gWJZccAMWdGOUCnOW0uiSp4slvj/PPpt3PLODL/UQ+Ulri/kmPDJi2x/7a3c8t1PEpmSs/6g/OZGLvlkGZJcDPBVrSFd00xuLukoT3apx/Ijhu7YMzquSA+VhKsZ962cxwHfNvg05mWe/9x7NfYzS+gSxsf79B/Jxb7TSg1K5lqiwqqsJTy3DaxR9OUZsH1liX/wyHehlOdlG2f5mwfexyEjipVdpfjKQ6d4/8oG3cu+Ift5rQhHntP5BiN/pmo8ErXKic8xKAZaju+r423+rxf8Mj/UfQtn3ncL3fMyV/NVeVcjOV/Zg9HRiI1Pe77v2PfxX179k3xFLCz2+n69Xuq5z54pPcuzmjyy/qtzefCq0lWgrcZo1cj61oDprIu3rqjJJDtIoaRaJgAfelSu8R1LGFoiXZJa6SaNTcmBZMRO1uVyAtnhvkhYG0XZM/KbWsDasqPkYR4AumJCN5JnVWEinCX7PvBzMkWYUmbvC93lTfe2opXlu1Z2WC+rpLvTA4WWZMCCKpQUJOvj4kG3JNB1JpMZPASpwky9+GmVdaGhYpkXHl20ftLPPnOBao61VxWT088SF69heJNh+SlhVEPF8tYKCs3ERnQr2nWgHYNOyv5gebZvrS4RF2o0LTkArTCFazT1BcxX+EjjowAVBihrxf+xOVa+KUo3Ber2b0GLjS7S0c97VIWQJgmrC8RK5Lzxiukw4dabTvLE4cN4rTGFw8ZGpNCLhS7eSJGue5JtuTZ0y5tMeSrPJkU7D9eFeIQ6P5NhqyVg66K2Tzy+hPExyJcNg1OezmZJvKsoO3KfukAxOmKI+prB6ZzOFVddNwabIJIXA7Bbmu5FRzRyhBMpkACY1KHzqmBczvzQMdKkYmMBpctYfrPsgu05gtjSi3J6Qd6AJhZN4QNSF1bgdEBuDWUFSMPVmATIvrpKtpuqWKZzORY2Uvh+BxUeYvsFffrnLL3Ht1HdDmoyrZh4Fl+WM8/pU+fo7+zji4LOpWPYTkCwn+L7HdjbF6ZgzU5WQNVAAjKumcxTdrWMTV1NuK8hiSFNwVp0muPjkHAocveunwi4Ewpbm0KaN1S/J0BQt9PsqM8L9m8JMLlnoDV2kFAceW7CFzfi+Y+apTxxlgLLtrXsuZDLdsCZYp3T+TpX8gGjMiK1IaNCZJWt06wlE16wdIHVcMxe2aXwpgFxQmUxyhEqS1fnhKpkxUwYmCkDLc0fKzonUeInGSvN/UWXv3//W+H31rjtwZTo8hZqmlEcWmbyPVczsW/Ec4j6WUz13PQz6dnZX9+aT1TP+hqsrieYKJSuDCPqYr8FW2qUdvSigrQMKJywRrTy5M4QuoBMOQItzJ3UJkxtyNRFWK/IXcC4jBgVcaNEs7nbZ+VJiM/sSFOMbgG+0DCPvRZOvgoCAY+dx955jM0XJ+QDiHdg5cmC+NIEszeW78URSpfyb2NkvUEgLAXn5pvfgqABqJvfLe3cHIQKuFYtpZw6caDaNl9eo2nHO1lfGOInU5afnJJudCm7qgJc6mc5M3DaVvO8QCq+KtP4uJ5Y0UKM6tfIfM+qubnaHKNtsbhUIc31PHsx2vhnDSD5ppPhGuurfxNQz1BU/2KFXgCK237Rs2VmUpxPJ9XdgMLXXK69rmcufDzTMk8HIjfg8DMc2mcCNK613ueyPbVX83P7jfnlv9AS4w7Nvk04na1xbrrCpBsxMOkcm33sIvZsj4GZonF8NjvEJ3dv5pELBykvdUkua0ytONeSabbR7HWtIqHL2m9V7tu64dnrih0dSq43x969DtKJWqK5TLgmc7tuUmry0RY4rTwNg3HGsl1oTVkEuJFlnAE6cgCa361B0wUGdAMS14uo1n/MhuR2/0YjAV4P1083PrVXXNcKKsC4FnlpUuyqnlDXUZSrFMr8wqmcA2qrZ6pu7Vfd9OVAWXmm6hoMbu3rnJx3fZwUc0N9s831d6uUVpTrq+fQ4vivZt9tGtjxYvm18Lhq8kxqZTT5MVeB82055/ZvzK9E2LGm8Li8Aj7c7Lsg69Xl/Dm8nmIRiH424WoPWZ55TK0b8etlnw0I/blC4/nw7gmS9w+wsePLTjzF1EZsTvuUVpNPInxqUKVCT3WTs9bXXFvGXfJLj1oUxarvlTrvbYHXc00VjZS3B+elBuE8rqOZrmk6/YzJXodGpKVV/2pqY7WPtAZlBJA2xmEDuXGb67DU+MKztD5m/0qf5U8kJNtCxhgf1aQbQpQIxopkW7YrW1VMD3hsr5pbhq3f9AptHKo6N0p5JlmEDjxlz+P6VdOlM3Pn7JnOz7ViBkhfH3Ol5xKaGSiUlgGDTG7koq9IVElSdb4WQFI1z1MB1SHCgguBO8M9fvTEL/Od3/z9HPn5hOT9A3789tfyjw/8Af1r/O4l6/iRP/p6bnqXp3Nhig805XLC1ou69C5Zdl6V8++P/xZ3hGn1DcXQeQZa8TvDF3P6Hbdy0+P7+CTA7E7wnUhqyloxOtFl9SnLdNXw5w4+TFdnlH3P6iOecL/E9kJ2bw8o+jKO18ScYCK1qOkhT7lakm0FdM9LE9buiZj1cRezN6VtJ1mTYsKLe2y89xDpWkUKqUhVw1s0f+G1H+Kb+g+yZoyABig2jObWE5fZ++RRdDkbz03mUJdjLtmQ20KHQXF3OCVUGbceexuf2riJ/3jyq7n0R4cZPGWIRjWBSDUNVjaWmm7niufM227lX3zbN/KjN/02B2+ol92IL/G4AUh/8aLAkuUBuLSanMLApM8o3Q2irhEpxdh5Jh5WNPS04x/f+Q7+l29+Kyd+JeenP/RVvO6Nj/CapKDwlgs250cf+3oOf9Rhxjkoxc5dMd/3hvfwnUufpKcVOxeWMIcdXVWZP3jPQAmZ5m17L2Hy7oOsnz6P6yeoVBT4mOTYw+tcfkvGDx57Hz/81JsJUpHoPnpol10XMyxiksvgIlHOy1bg5tuv8I1HH+A/jr6a1ccs0XZK2VlC2YBi33DqljWSDUWi4NuXPkP3BRk/mr6J7qc7jI4YOpe6mM0hymuZk1ShlJLnRpYTXthFr/YIxiFH3pnggg2cUrzjRQd5w7c9yPFgE4sAzd+0+inefuTL6V5SlU2q5DvR0PPZyQFYA60UIUbOXXWv5N43cuo3B/v87Vvew49/7ddy5veO07niG+EsXT0HbQzpuqZ3wbLxjoT/4+g381/u/O+s6uQqJc7nO54zKA1V7qVmyVfddatLYZf4UOONabwyhNUrE585ecrKF1nnHl3JXpc930h4q8gRBZKZTUopigfa0TM5O3QZ35dy6mCMSxy+awk6OUFoxW9GixRNYGY/aLTHOvHfNdo1sgVaCZsgNLZJSIxypGVIbErKanbRTlAKW3WCVMtr5ZvJMoCvZIYbH6PqezW7o6hYn6GRkk/pNEr5ig0qTNAosOSlwWhPXhqK0mCtxjuFLTVuGqAKjU4VOleEQ4VJIRxVx7oEbwzh2M15YwHYRDO8zdG7qIn25Ri5sALUCsXYxg0zROPphgXbXfGHloUUtTeMiw14movbawUVi9fGVTEoUHhj0J0QNQ1QpUU5NwPMAR8YAaXLCuBugda+BVILm/z5f5CpukBk1azwoBFwurovrDFcSfugvXSmxwYbaZT1uFCLt3RrfcqDSaWhougpwiFEpcjg46XBQttZMaQpSNSM+hxUzZwvVOP/5CKPDx3lMqQHFd1zIYPTjmjoUBOHjYWCkQ8Ue7dF9M+Xwni2nqWTmmhX9Os625ZorxRf87JFT4BGpUCVDhcbYYyrqkkk0pSJwkbCrrYd2Z4gnN2rhTdkLmykHDMfkFXS3dZrrFeiOPJ0iaL2Umws5H5oF77KrmJyUx9dOHoXC7qfPoMbT1AbawL2rizB5S38aCwStWGAG43xq/K+vv+zmChCrSzhkwqgc8Lqx3kwuvK+kx8NxyXOaBnbkMTIhQa33EPtDfHTFJ0V2OUOvUslV14S0jsXSTEgL8A52Q6tWsC3RsWRyHc7R7YmCVh+qIeynq0X3QAOv1Si9ooWGW3PE6XjkfwwHxnezqPDQ5zZXWE8ibET0SFVwYwGoQOH1o7NoMfDFw9hS4OzCp9reW5qqUzqUIpFUVQSGstyJ2U5Sjna3WMlmNA3GV2TsWImrJgJP/jgNzP42SWWP3Fuds1ZC2qZl/WempMDvxHPIdpzpxozbP5WQPXi8lVRshJwQFXNSKpWm1EtgMKBzzU+0CzFKdZ3KOxMWaJ04i1d6Orf2jSA9NSGaDxTG5LakMwGZGVAmof4Swmrj6VwZUu2q1KNUWHQKErMbXblL616XbZe0idbg2gPlk6XM0A6LyAwwkzwHkyErztPQYDlomRWka3nGTLvUKUVGW9rG/lwVVt9VMs2eIvzwnqothm4GpiuUY2iwAPBY+fo3Ho7kyOiNNKcD0Uj560Kha8+cyGYVFNGbeRmds4bqe4W+NEw5ltMzhma0XqvBUxfKxoARrVft4Dpp4vr4DZelOpeBKSfjh0NM2nnuvFmEYyuGdJtdvQi2A1SrG6z2v44bLrZdl4HqGYVzxVUjq46Ll/4C2ToOpybrnB6uArAvZ3zQCXr7RXbts+FfIW1ICJUlndduJvzp9YJdgLitFKTcMKoa5p5gbaXcJ3j1I3QJpcmRRfN5LxtVM0RW3PIBkR8vsNLDubC2eurTkUbBF4AQOXzipFbJejOzA8rc2NEBTp5hXgu17/n1BxTem4TzecYXqpnkqq2r1aR8hphfdMCydpAdp3P1OBXCxivGdKN84iXbZ3JUdfLtr1yq78OaUyg+mGvpIlbza+/DQgDuMqKp/l9qsdEdVgbdvjsEM41B8zJKDfofesw1d9tXatNPaW9XC34Ua8bZs/+lgKbKWfraL5bN823vl835gt7r7L60tLAoYvK43Qqdl8mmzUZfynFtZjTjkphxmsCbZ/RQ9pWboh1c2e2oCRxLZDzc0mCbxU9/uhDd3Fw03HxVfCy7i5PjjYonJZ5Vq5RhcJM9Yzt7CTPr6+BuXnD0zQL1NdmzbBu7hHPHEsf76vGA4cqbAV6GYa3wnJcMMl68lvB7Eb3df6haUBpZTxKe7SWPKMM5q/t+OCElf6EnY8eYuOUJxyLR+P+CSj7Fp1qon1FMKVS9oPpIY9dKufGOCGIzI6p8wrnFWFVN7CpESEdRVOra5+nPy6wXH/vmuz76zhCZch8QYAhLQIGpYPASGHaB6Q+J1SqUuESJS5bFbjrYbao/h4yBf/65f+df3Dy+zj+jpRf/71X8v1v/QM2FirZ1ns+lt7CyodiuueGwmhLQk6/ZcC9r3+cJ37jTm6/+QJrJsUB46oOuaI1287xk595NXe+e4jXiuDCDm5VYG+Vl5z6tmVcCKsf7ZCtKQ6FezyVHQAPybbFpJbh8YRsxVPcnBPEJWlh8GN56OhUE98y4h+98D386oUv58kP30LnsiI9oBie6LHyicnMS5oqnwDYH7HxB57JnRsUA4MuRPluet+UH1j/fdYqtahYyfhQYHnVxlO8LT7WWCbaSGS8wz3NZdvnrnDMxBeEShNiOB44bgrO8sp7fpa333wv/+cfvJkDHwzkmahbTSa5KP5lK4ql045P/M4L+L2//ADfPbh03YEKN+JGPNdwref209WZric255diaBSFd5TFzCJNF5ZQSU2kVld8unDAllX0tKenBBjVwEvj8/zrN/4CP/L+v8yxd3n+3Qtfz5ff9lskKuDdk7so375B9+wQlKJcjhl/9YjvXP44AFesJrkQkp1IiZQjqsZe6z1nypBf+cxLufv3tnHdBH15B7exih5Pcb0OZ79+mf/15b/MzcEuW+MuB0YWHxpevH6ei+UK4zyis+Uo+gaTefJVx0s3zgBw4D0RnUfPQmk58r4cF4f4UPOx+C7OHA05EZTEWvP1vUfZe0mX/2fz61h5SJGtx3S3x03tjtZ/KggkuZhMpWe2SBicFIzLxQZTRPzya1/OV938NhywrCNeEm3hDuQoH6Gsaki14dhzYbIEQFbVinVls7HtHI8UG1wpl3gqO8CojDkc7/GPjr+dv3Ho++mfU9gYdF6RA6t5pE1guqFZeTzn0i8f58w/ClmOHFxnT5DnJN/drp81hboqobGRAu8bdmztVexC6UqziRQHkksaMxFpX2804V7G0mnN7u0heVc8NHSusANLEFmWkozlKCVPAzpBwZHOPqcnq+ymHW47uklxyMwKQ1pYYhoBiO01uh2fboLaBpxl0mtQys9NSGUiLLdt/bcGpOuIdIlWnkC7a3b0ll43/kFNN2b1Gxo/N6mu1xsoOycxpJVrvG/q7zXbiEzY6+3IXcDWtMvepMNkP4FxQLCvCcaKcN8zXRdA1RnJik0mCdKojLgp3mG/7BCbkl6Ykx8qyVcjgl0txWWtUWlBuDXBG4NdHQgoUnU3qtJBPH/Bu9igBkmT1rlBgh6mqKwQsC8MYJpRe0v7MBYmdi6sJxeK97iZXgcJs5qvN9QymjVYXXc+n9lfhkJLsj+12FgTDgtsN5BBoy68IPeSLnxTxLaJYryk6F5ylUSXmhU0Wl5gsgHzyasupRjklIK641h59FrBOAooegHdCwHdK5Zg6ulslZhc/J537wiJdwMGZzM6mznxjvgje0XjiQ40LAYfKFThUJUMHiCgrHX4UAsTuCq41T5+7dw+cwETG6HxJLogdSGFM5TONPd0fT80QgueqltJNWNSzYBoijfVk9ZGimxFOoWSxy5ht3bQnUQ8IoIA248xexH68AYqzfH7Q1QQsP+CVfrdCHNhGz8Wlp/KirnzrnxFcHQOtGK6EVQPQkc4tCjvKfoBtmMI9kElsch35wUqs+jcyXmf5M3OqSQWWdtBH7+zi7cWncT4OIQsR4UBNvaEQ9l3m5irPAhvxBcvah/oWt669ocOMBil2XNTuipqCgZDl3O2DPjp7dfwgXO3M5rEFHsxemLwxgvzMvCQa+IrAd0Lnt5lR7KVi3d8atHTYmZd4T1qmsn4Wb32UQhG45II2+/ioj57gWKzfyvpiibdUBRLnvxASf/AmKNL+2z+Vcvuncc48odTopNX8GVJMQg5HOyR+XLOu7rw9pr7W8eNBEJCuap6XfkOa4t03VsZvGsWNKpKzswMFKiZZbNGflWBFzSzN3l2iFXEub1lenFOViUezitUNUfRytExBTtFl2GZkNmAvBpjc2cahvQki9jd7HPwk0quAVmR/JhW83qGWqOSRP5tLX6pT3FoiaKn6J3zLD+ZE18cocZTAZPDAN+Jxc6h3xGgGmTMK0oo8+bZjzHiG50X0IlR1slnFau6sf3QWmTF+92Zykop8lRtEFolMX6/qJATjTJaKnHVfgCQZax/6CLDW46SHpRJb93YRXX45dmu5TwEEI6U+FDXkpaeOWnu5pntqAAfWe+c5Gr7Qd4Gberff7prq14eGqsPmZhfveyczPfzHKGyVwHGT8eKltfVHL96vSil/WwA6bpIXXtIG2zz+dy68NcEmRfBhnoZ25qHL875iwUA4+nW8ScZV7Gp68bYZzBStiic182xrl/ryie1qMAai8J63cinW69xaLo6I8Ky5cWGZb9I2NzvYb1ie7lPogqs0jiveXB0jNOjVTYnXbYvLRFsh0RZNbcKxePYTOWe0QWocibbXc/3ak9hk8s4axPf+EejoYxnSOBV+ex1EC6Qhs06H/DGz0DLOj8oF3KOemyol2nd4z6gNR4tfO7by1brNFRyuZVvdYVi17/dbAvMgV7NZ1S/44VxXDcSeFM1zyrxmK0B8atS8WuNWZr5H2oAZzVjOtfjbMs7fIbEt2SNXWV9EdCwURsfajsDqF1QAbazQzCTUmYGOtf5RgNSwxyY0AYYm3/XY/XCrac8ULPrGqZ7DSjW6/BzrFla169s47w9Wn2+RTWgIgAUnmBcoicFqrLHUoU0eqminKmU1M/Z5zkWpZXrsbpd/3i2IXUTReml8fnpGNMG1wCfZWv8rsHpxe8t1oBCZZnakI4pKJzhtz725dz5q1PylQhzJOPsZIXNSY+9cYd0FKEqUFo5Gdea+UJ1rptD4ITl3tzrdR5cRX0t65bsuzS6+6oeUKviyXWtM4sPNHqcMzkQ4I6nZKWByM0PiaGjVv2rG15VNe6EUUmvkwEwjRzKafIVR3hsTLbVIX3fgPUrDq9h74RmesziA48ZaeIdRTCmUQgcHoey64k2A/LDRZXfy37q0MJWjO1DNJj5ITqvUKnkTCrXnB6tshGNsGjyZ5kQX8s/Wq4P+fezXc/1EoW3lZ+nJitCVGGlPteBA2basNE0Uvyvc4+ZlkBrXcB90Sb/6tt+nn+a/yVu/e2U73vJ9/C++34JjcLhKbzlqdLyz97zF7j7j0bgPemRPie/C37n9f8n7xrfyyPJnZwYbLKm4bw1rOiSrlLsOsePXflabvm5AHxBcHkPe3BZaiFFyWe/7zD/+7f/V/7Fz303PjBkq547o4u8Z+de4m1NtDMFBaObNeXA4QsNF7r0r0hhPj3oUIcyvuG2h3hj9zG+484n+NUjJ/j/fvCbWHogYvteTffiMtG5XckHKgWmmmygRhN6D13EbiwxPdojXdUcWBvybze/BucV37v+h9wcFHSVwaD4uqUH+eVDryHZhiKSumqZaDqXPR8Y3cOrk49hUHRVhMORVcSbQybgLy89xte86TF+8N5v5pHfvoveed88e5QHX0gtfXxIs/aw5Ud++jvp/Q8/xxu6l+hWft5dPU9MaOfoN+JPf9TAbU16EEu4CtRCzdVk2sSIWIVY7yixc9fMxBWMvav70SlQhPgmbbZevIZNBZjVrM5QmWZ8AHDVyBIwvz0jn/HfhrfzQ7//jRx+r0FbOP96x699/b/lrlBRYFlWnav270Y89zBKsmk7CWSc8x4XdivVKnVNtvTike7pGVN3tl54dXKJe//hA5z5h7fz2K/dxTt+4DBf373I/33/13HTAyk6Fbxo++6E/9/Lf4pEeayHR/JDJFfg6FdtsqxV8/xJPfzE5a/lll/S6FGK39nDH95AT4Th/dR3rPGPvvNX+bL4DIXXDC8MODzNcIHmTSsP8MD0JsZZxGrmUBaKvsb1LXd2LvETj76WQ6czmec6h5qkmDQHa1n7dJ+Pvek2DvUfpYdmzRj+0vIn6b8p5ccm38TqY9VkxZhZz3BZqfy158vTFGU0Kg7BgM4tncsFf3j/nZw6EnI8KKrx2hDGJRBhMhqmtEk9j50+THanY1lHnCpz/oeH/zJ77z5MsiUkRV83zwLTg5rL3/Mh3KBk986Q/hlfqXX4Zl7oQlGp3T8esfZIxvf9u3/Af/47P8ZXxFBicX42Rjyf99mzB6Vdq9ClRE7ahZJcyoGs23KFheuSULrUK+k0AB944l0PpZu72jundhkd3WByWJJuFyiIHHFSMIgyNJ7CGpbjKTt5h2EeNyCyrf6rw9UJQnXPPNsuyadbbvH9q1iaC/JOuQuqbbNNojIPil+dTC3KRV3V7buQmNWv6+WCFkBeR72eQDuCrmU1mVIsG8rKl9t5RSco2Jz0GKYxRWHIJyHhxQh9eEqshYWyEk7YzHs4r0hWUvZv6dM5H4r/SlEKQ7SwqMLiuhEuClDWSfGoYsF7UydcTma9tXR7adGTHFWx8lRpq4KHF7lQEJ/e3M6S+UBVifbzX0lqOuFbp3OuEOEhGCu2NweYiUYXBcF+StkLCK+MUOs9yr7QImrZZ10V1JTzhEPxcB4ddyirSbbcXMLZ9quUAaoFynrAgipF7lVnGu88LnLY1BAu5bCSsXsgoXwyoH/WEaSeeNdics3WCwzZusIFMYNzBSazODTlkkEXBl0z5erWWpDJvFIQtq5X63GxabHl55Ns5xRZGTAqYraCHhbNxEXic+o0hdcESiS7jPKVekAgqgQKtPbY1nmo5fBrL57mUFVy/sG4wO3utT6Q4oueFnjvyY4uEZ/fh60SvbHG9r2G6dqA5ZMJnSc2ucrnvNYrrNqMvVZMDopcfbKp6OeSWBRd8TSKtIZOAqMxPsvRkwy1ErP2aIGaZCIB0uuilgb4/SG+E8NEABm3NpB7zWjor6BLRbLp0dYxXQ5Jtp7/e+LParS7O6UooNAtgLarIjJfcLqc8nixzv3pTTw1PcCTw3X2h13suHoUK+ieMyydVCQ7JeHuFDPKpIGhtJDlkjBnWePxC4gEX6uQqKKQmmarAW0MKqwoWEHAcmDwvQ7lUkKxHFH0B2yuLTM9qCiXPGfemNC5fAvLTxaUXc1D6THuCx/HeJm8xiqYS3br/b2RMDxN1IXzRsJ79r7yVbG+Bi5V1djXFKNbRci6gF4DDEqBRaC4wjMaJ43iSmnlWW+9FOgiZymdofCaqQ3FEsEbcmtEicJVXtSlIbwS0j+Xiz/0NUIp8bJWtbS2tagkYXznOvu3BARTz+B0QbQ5Qe2PQSsZy0CuZRWhiqq4aK0AzrVXdGDwqRTIVdgRVk5R4ici+6eqZgtAaHNFS8uy7lgyWpKO+np08kAUlvfTF9t9WcJ4Qu+8p1hS2E49kZztd/t81rKSNXu6AUbq8+3UDDhx9TP6WcxJ28DD081h51AhGoD66bDGOSDueY5rAdLNZ4sg8VUgwLUbEp8rwPvH9RmdWwf+C8osvlYj6/XG1CoqmnJz/LzGKNs0yxrliLBMXMxuy/g3swF5GjKJIrbLHifiy+zaLp8YHef+rSNcvrIknqr7GpNXzTeVx3H1MwJwVte4jcHXAKOZfe4rn+hmrqllPW3qYK0/cB30ZzSRD5TIkdf2UnV23gJwa1/l5h5vXxr1cq08Xb6kKiDLz2yjKrBXuTrfqI5bq6YtTT/zv1MrnqEl126ab+rft6qRla7HORf5GevSLoxDdaNp6Bs7BOwCYl1bYEG186oqKFVz7mrZGihrN/u0G4KUVTPweQFgrptr689dIMtqW9thzYDi5nh4GpC4uTbDqrG4Jd+qrZ/PC93sPNVgc7NvzN7TpRx/bYXZWv/XnJ/6e67e74WLubLyonTyWSnygiovpEFMKWnmquRqAVFA0UokEvWNudyzibrhyXnVNPDElfb62XSF5YcCwlNn2XzJcYxJObm7Rl4GFHkApRxj1b4e2/dcfa262WdXKQIwu6bnrtF6mfq6rj2p60Z4pdC5Jd/oUvSg10sZjxPI9IwRXd3ryli5XrXHV2NIGJU4p+hFBcM0RjnF+EUyR3NP9Fk5q+hesUzXNdNDivRwCaFDDwNpMKruNWU9+UBRLDkGT2mWT5YMj0WkG5DemkGpURps4uTfdVOVFeVArGwjFvbTpHk+/VkOjRKQqdQom+ONmZsX2oW/MLvsFv9q4NXJeV76dY9w+jN3Mf31Q/zaiTVe3zlPqDTOe779Y/8jR96nwTlcHHDqmw0/+OpfY914Tqbr+ADu7V2g8J5EWRIlAMR7Jnfwnl96BTdf3EVNMtygh9kZ4+OQC28+wiu+9iEOB3vE24BSlH3Pis65MF0i2qUhdhR9z7d99UdYDSb8zG++nsMfmbB7Z0ckxLOEj20chw0IleaNvSe49Irf5z/xWgYPRoyPREQXtKg3gdjswOymzHL03oSOUsTbAaP9DT4cbOCN4ve++R5+4yt+gth4DIqjZkjvhTvw8Kqw/yNZTbzn+czeMfTGx+lqw6adkihNqDQWz64ryTyESvE3j76Xn/wWy/2/fi/Rvm+e67WdnwshXdWsPmb5X971Vn7tG/4td4QFoTJMXE5XR40k8g11sz9b0a6/OByZF8DZUBEnWqD11Ev+bfFcshm7LuDJYoPHs0Ocy1bZKzoMi5jUhuxnCeM8JDSO1x95DKBqTDXEumwwDq08y2ZKqEu6OmegpyyZlMNmj4HO0RQt32DD993/vQS/tM7d9++jR1MIDJdftkGsLEaFOK/IvOT3Gn3jev48w3oPpZJ6y+Jnz2E97W9XKRbfvP4p/vFb7uPgJy0//OjX84oX/xTxhwaEOzv40KAyS9lRrJkRiVJYPGeKNZIdx4nBFqZichfA/flB3vsH93HX41fwe/twcB1vDIqC3Zcf5o7XPcXLklPEyjL0IWZk0IWjWIo4bPb43fQ+Rpf6LAeKaFQyPhQRL0+4PbrM6FKfg8YKgcd7SDOx1QT6F0oeGN/Et/YfbfavpzRf032MX//Ks2yfvImDO5E0ckJVY6owAS3Pv7qpSaU5ahrhlmIhaTpPcj7kol3i7nAXgBBDHBc4owTbUWKlGhaW4MIsGfv53a9g8yOHWTvjmsZCKisWALft+eDl2wh7OfkBzc6qYuVBsYJRJdJwrKopf1eRrQWsP1jw1t/8u/z6t/4b7gikE1YjpCmNbsaKL3Y8e1DatupjFQCmam89D2WicJHIOHsj//ZaiWxXJb3jA195x3hJTqsT66OAsqPwgSRvLnaY2BIFZcP4jSsZ72GeUFhDaGxTRAUZDOvharFzdW4/Ps8qRLkIELv5QbIuKuXKPCs/onqZoGJ5u2voeF1LkqoduQuaZerC0GIEyhGFpYDUyuFQLIdTDiQjQApGqQ24fGTAwe6QWJekLqSrcyJdAjHLvSnbx/ssne7R25+I/2MtuT3NUMZId4gH78WvKFBQ9IKW5Jmf3R1Vlwp5Iayoorz6keNck0iBdB4K2+z5ryYpZvhkPZmdq3I5YVHpkzHRbpUQBhqTWdjaIZyk6I1lyuWYsiPHrXvJU3QVWUcRDj2dbcd4oti/2xJ/WDcFpVkhSX5cW/ElBqi7rL1qJaN1cukUvhT59yCyrB3bhWNwaWtAdCpm5TFFZ7Pk8EctO3dEbL3UUXYiepct0b4l3irIV6Sdx6SWYFLiAoPOHWVH2ME+UE3xRLnZuZNOfZHQ0zmoTJNPQraCLoXTTMuQ1WRCz+TNvZA5YfNp5VmKUyJdcoElhj4BSsoiQAVVh6yWAph4ZHuMUw0wbSPAKyZHOixdXsddvCyHqigE2Dt7EZ/nxJ85jdvfRw/6jF58hOnNBe6+lP0n+xxcOczyp64Iu39rp/l+LcGunHSEd654ir4iX1ZsrwgQaKaQ7MDkeI/Bp0dSBHK2ARF15nCDLspI1dWu9VGXrqC2dmXdhzZwYXWvJTHF0VUGT3mC1LN7e4ILIRw///fEn+WoH+B1h2g9gZ64nCdKx5PFBk9mBzmZrvPo3iF20g7DSYLdD+mcC+hc9gzOlCSXh5jtEUxT/GQqIJrWAkIbLeBykuCtQ3U6TVFRVXJidePEnMRyWeKGMtYrYwRI3N4lMIYgCulGEUtJhFvpkW4kTDcCyq5n586QYgAfH97K0XCHO8NNDhjpwHUVO1q3OsxulIOujlaPXAUeVp7Q9ftVLb79uv29hmlVDe9aKdBeFDBqVpsCck0JjHRMEDhKpzFOGvqMcpRGk7mAzBnSMqT0FQjtKqlvr8iKgHQSsXwSac5ZTF5aLGll5JpUWoNSFDdvsH1vSL4EG5+xJBdGqLQQQLpm8FeTdjWVRgvfiUEp7Gqfsh/iqoJpfH4fSkt2bAUbi8pItDVB708kicgrJnVggFAm6BOxNSAKwRgBrxGg2ZclFBbVSQTgLssGnFb1dmmNtwIxrjw2YXK4xzRpATv1/reKxSJNK5LeRMwyO38NJl/rvNbXwrVw1Hr1LC7fWsA3k/F5YJqFt6697usjsX86hrRupbym9dnsO/4q2e6aIV2/rpeFGbt69vfpD1CbJX2tHGLxuw71jOv7XPFs/FGfq4fqnwSI3Qbea8nzCCvvN7eGbljTRsln8nRwbBYDhlmMzwylFfb1Vtnn/bt38eGTt1HsR4RbAcFk9js1w7mRpKVi3Fbgn6vl9RGAwwdgYz8H2HpTgbu6AjGrZsaZvM31E8WSzF+9oQHVZ0BzVZyu88OawdzaBa8A4+eUFcQ+oALplZ+vJqlWbu6pFCFaAGkNONffVbPvNdtTM3rrZeuGgHolqgKcdfUw05W7chs81x6VWHTg8E7hS93u+2zYoN41F9rMMskqsDOwvT4OjSpFtX0iDz4PorcZ0I2vdA3qVce2zrV0wZw0t1xTrcPZahrT1mOyGatZPM5905jdZq8q71FFBRA3jFaRVZZGYyf5RaX8MRe+8h6tZVS0vmbDU6Mw4n1jfUH1zG6ek1rPntFG44IboPRzjfp5UXjDwKSkLmQz7TM4JwXQnRdb1DQk3UlQkRO7nXJ2PXqqa7cCWNuqX81/7RujuY7nGfLXitpuyyukWaHxTfeUHUO6rugFlv1hKE0goRfLIA/eapTxaO3xgDEOU/tIW82V/T7GOPRahncKczph4zMyuRkdNUyOecoDmcyJhgHRnm68DgFsrJgcFXWoA5/OiU9uEQ432L0jIjvhqS1Hdb/A5YYwtBjtKK0R0NUpYUqXitE0pnSGWBeNwuD11tT1Jx3t/NOWVV1N66vqis826j6JHzjyXr73zbdz6y87/tknvpVfe+V/5KYA3p8epPuuPv3TE1RmGd054I1f8Wle330S6+HUZA3b8dwZX8QCg0qSYssqfvjTb+LWD4zRe2MBha00pu6/YA31pi3+xuH3kaiCeM/h4wC3XFB4zeakR7IttnNF35CvWb555ZP8709+C0c+WBKevMxaukr/bII3ikvjI3z6xGHe0NlkWRu+e/njnH/xCm/f+XKSbdWyEzKzB461s9x6PMUUJWp1wNKTFhdoXGwYfWLAI192gLXkCg7F0UDx0kNneciuoKzCxrKucOJ4anuN4nZL5jwWOG8Vj+QHOVOssVP22Cs7DIsEh+IbNz7Dh2+5i/gzeqaAUT04fQBFT5HswJEPaP7e7W/lZ+75WY4G89l3iZ1TLrsRf3bCKD2XB9ZNCiWWqcspvOOihUeLgzyeHeKh0VHOj5e5OBwwnUbYUmxCfaGh0LN5k4NfPPlKlFNCeLIyB2vm2aayxzEe3SlJOjkrvSmryZRDyZCNeMTApNyVXOCXL7+c7A82OPDQPubKbnPf+cCzol0FQle52A1A+vMOWwG+qpjVO75Q4YD7oou88HVPcO6xO7Dv3OCnb/lKDn14jB5OsesDVJGJTQyizqGV4rHJYaJ9x8sGJxsricLDD3/2zdz0Hgvbu/IDgUGPJuQ3r3P+DZa/f/SDDHSBRTG0CeFI9mm6ISo45yYrJBcDnHGYwpFuKI6t7XHY7BNdMbjA4eNQnjdW4QODso5oO+XB3cNwSJo1NAKeHzKev3vLu/mf7vxeDnxcS84BeKdRTs/m1jUwDfg8F6nxbojviH9U76zn8ewwr0t20WgyX2KUgMsmB51XeYL1dC8IAWjoct55/h5WHnMEU4evMLBZXUmY07vjDoNeyvBkjzd83Sd5/7mX0r1Y4TB1ruEr1dglTbxdctN7FH/r7r/IL7zgv3DEdDBKM3U5sdJfAkzputimZv/JRHg2qda5xWSOIjbCjqxku+rOb5Snc6WYFSYLh48N+3f0SdcBX7GuuxZjHN2oQCtP5gz9IONK2mdahA3oXHjxXnYetPHYinFcSy8thrCHF/brOYLUi+tts5yBhomsEXmCxW1ZBMxrX8Q2GF3Le9dRe18/3TaAFKJk382cDLlWvgGhnTc4q3EVs7p0ppFFLpRnNRKQ2ihPoC07ZZflYMpaNGE375IEJeXRjN07EjrnOqhpLslsoFFjJ9IKGpEYlg1AZxZdXQtzzxRTJb3TVIC9ekIITbLcXCfeY6MAU8lNAajyuZ23P5Hws1pCfT94PQOmdQnhCMIhdLYcJi1Ba9l26/Bphp5k6CRAOTFkivcElC47EO1Kkto7p9hbgnRNEe37Rt5vsbta47GRmpeLaxdbrGoYBq4w+MDhvaIf5xy45SKX1/psxWusPRSSbFuWnyoI0kBktfqG5SfBRFpk8AJFuh6SaIXOHS7SAkhXALTXUjTSWWs7LJjcE0wVPlC4QFMSMEHkX8dZxH4e0w0LOkFBUO1EDZoEQUliZBCPw4Ju7NkfJxQ+qPZFPM69rhUaxIeBqqjkDaQrmu7GALO5LQ+NQR96XfzOHiqK8JOJbLB1xNs5vc92Gd/lUV1HtqRxg4RiOSEGKeZUEiA4cJ2wSSC6ly1FT1P0RUYKJQ8Dk1cPwF4XNxqj0xzlPJOjkch9dxL8NMVs7uNrSRAl94rZGuIGHXxg0GlB90pIumqwUcXMUDcmbc9XtB/gRmkylxOrgKnPeSAP+ZGz38i4iDHacWXcZzSNycYRyWdjjjzmWHpyiLmyh9vakW7zal3S6GMEaFtdxndjikFM2Q8peiKLbyptpbwviYjJPUEqExiTWcwoR48y9P5IZOOLUgDtOJJxaDgSwHsSoHf26J0N6fa7EIWMb1tmeJPhvR94MR+98xbecPOjfMfqx7g3yklU0CQLC4J/N2IhZAIpY2/jy+lbzDU/GyObLyjVeE3OvDORZ40SWW68EkK8qSrvpcYWpgFRFRAYg9GO3AaMVcSkjJi2QOnCaUprsE6RZiHqUszqYzlc2W6239dS2dAkM0oZVGBkDrDUY/tFXfJlT+8c9M5MUFkp6if9rhTG8gIfVNfyNMOtDBjevYw3inRFka3KzsY7ngN7MXpaMDoWMT4iXovxbkRne0DnYorZHoskeJZDHMmxmkwFYC6l6O47sUiSBgHKyxWqokiu1BZbup6DNe9lGcET5+mfuJ1sXWGrZ20z7apvzurkuEC8bl2iaDxMW2D0nFT3AhCtmH899/7TAMyNfYW6FsDsnwmXvi5S+9rHqgak50HmawPRddTLLkp215/NLYu7ChheXN+14ukA6efKZH6ugPIXIp6pifW5Rg1IN3ZDXgrclhkQDczsktBYHCt6QuEDThYbfGLnZnZHHVSuSacRJ6fr/MHm7Tzx1CHCyyGRFdltYadKE6GLvPi61/cPAjzL/M43vtIu9BBVuWY4YxirBmhd2CE1+7y+H68HxnTR8/iwunfrMR0aKW/RV603vgKta69VXy1jfMU4Root7a5Z5WdKX761rgYwBhVWBRWn8HWTrQJlfANY1zlzPeXxHgGSS3n+1OA0xqMCL/J0yuOcxmYGCj37XHuCyBInBYF2ZEVAnoWzMQ4aUKrW0/U1k9r5SiZbtVSr5Dh5L42vvmK9SQ408yZv/i6A0k0to/AtNnd1PtxsvNblbNkauDY5RCOHmTrCUSkAtK3+K50UsqxIZUuzdY1wu1m+C83nTdFw8flUA8rNAao+D5AmhCp8lWM3eUH9nfrgBkbypdDgjcZ1AmxsKm/iWT3ieoprNdz/ScfTERzqKLxpnmOhFrLExEZYNJG2TNc0+kWH2bh5l62TqwQjje05adCuQOnm2qtPVZvxvDA+eQW6Pb+g9ReuOW8Q6y8aWXldOHRhRdEwVExvLch2e3Jt98rZufegtJVLRzuM9gSBw2jHZBKjlNzD1hrsXsTaJw2dLUc+0AyPK9Jjhayr0AS70nRkUpp7yBtINzzm1hH+VJ/o/DZ2YwkQoosOPLa6n41xxEsFSTiTQLOlaeZjJldk44jCa+JnPq1/asMoXTUMi6WPKi0+pmlWeibv0GtF6hXHzIgfeu2v8KMfeyurbwv4+bu/kn9y4KP830++kZXHc8w4g0Czc7fmz699gm0bcjQouTwZUPYdh80+DkgqP+t3jF/I6tt6hBfOyY8oJblpYDj3Zse/vvdtrOspYx8QDR2uG9FfmfLZYp39ccLhXYvXium6IjkwJcJy6tNHufP8PgQGc3GHziUZ8zaSo3x0dII3dbbZdTkDrfibB97H5qt6PHryHnwnlPzEVNCX8TQ2QrWc92gsSksrPdnc0rH8pOPnLr2KVx//zUa6+KbODg/W9fHqUWVKz2iry56zdJVi2wV8z6e+j/SBFRkH8lnjk42g920567ftUDy+gbIQTH2lwiEWFN7A+Ihh5fGc/V85zL/662/kXx99P6Ya603FYC+xmKsmQDfiT3O0pXczXxJXMI+uJjLnbc6v7X8Z77x0Lxd2l8jzAFfIJMaXCjU1RNuGZBPiXU80dpjMNfaT9XxG55VFljFgxAvXG41LjPjohgbb6ZGuLHEhgjM9RbrmyQ+V6G5Jr58yeUHKhXyJQx8xBI+eAaVwsWegZ9CURjcy5Dfi84vCg85bE7vPc45Xjyyp9wy04n++6R18z1ee4NZfL/nZ972We574LPQ6Mo5aS2fLMXQJrjK2enz/ALr0vKLzFGPnCRWcLJcZvvMwN330cZkLr68I4SDNuPSKDn/r1e/g1nCT1BtC5ThTrBPty3g8PqLZtn22pl2STdk2rxTZuufEYEsIrjuiZOrr2mqsxFaOElU4zm0tM/EwUOC8byoRr06uEJ/Yr/ApJZB1rSjkWo2lWt73eQHTFDOKcZ0AZxT9iyWPTQ5TLD0EOD6RD9jd7nF06ggnDmcMJncVQdEx9IrCaS5eWOWWfSG96WKW+8oOSs6cZSFHV/ZxZxV/deP3+f2vOEH57hWCseTETRMuwpaeHgzpXsrZ/vVD/MShV/ODBz9O4SFW4fPaAPLcQOk68auj4u2XPUjXFbYTCthkFGXHzMuuVVdvuJ/JJDKQE5utxuzerkUm0UM5sJiOJYpKDnaHJKYgtSGjImZ32iEwlW+Zm5VetJLXgbENEFvPyz8XU1m3k/SnicVhcHF59TRs6EWwevGz2Qqq1w7Kal3W/fEmEW0J8M/1+7VHzl6RUDpDEhR0TMHUhmBDHIqNaMzUhuwVHY7EeyyFKftZwmB5yv6dITpf4dCH99B7Y9xKHx+FqKJEFRbbl5Sg7gIPRgVlP2xY9niP1xqqThWZBBrxlDZGZGpbibQqBfR0oZ55gLnPnSg+L1FvkgKcQucKk/qqoC4S0a5Tsbq6HXw3QWU54aUS1DJlN8AvKZJtKRq4CNQIkh1HdsGQrUvWGqReGMDthLRVaPO0iiwKka3TvirCVCCIUTinycuA1DiO9vd44fIF7DHNx77sFnZ+7zAHPpmx8njK4LRm7/aIrRcZVAlrD1viXUkMi54hsh5dCkO6TGRQrcFpVbE0dOkrrzZFNBS/A50ryrEhLxRT4/GFZhR2CEIpUkVBSRTM0qfYBpROk5UBvahgLZngvWLPanykpJAWVkWp0GNrT71KChcl49T4li5LpxJUJ5GijdGoJMbt7OLLEhVFuMmE4MGnuGXzAPv3rbN/3BANq0KRAqIQZRIpFFXXo54W2MMJl19XEPYKlt7VZeP+KePDMemqwgUwXdf0V3qYSSqduPtDdLZOtqyJl0OiJMHtDXGXrkAYypDb6+C9x48mqChEWYc5t0nHrhPthphxLsyK6EvL++pPa9SePBfshN8Z38XPnf5KzpzeILocgINiydM7qzl8f0HniQuws4efpriWl7g/vE56pM/wWMD4JkV20OKTSq7EgZpqdKpRFsKhxhRQVtY7olCisInGmwAXhRB00aMNwqGmcxniXcfSyRSzl6I3d3DjiShWWItXKaqSbe5dvEK/32Pj6Bo7967wm/d8Je+79w5ec+QpXrf0KK/vXGTVdClryZcb8fSxAEzWRUUBpGXMrxUtcDQewQ3zS9GwBqVIKKsVz0glbDarcLXcofXCZnEizV16TVopomTVWFo6TWF1I4VYZAG9y5ro8lgUJIJA7FYWQiklrLfSooynXO0yPaCIt2H10RQzlPEN70W6yfumSE5eUBxaZnhbhysvpZG8Vs43sr1ma4gfTTDFqgBRCDvABYZsqUdnM6ZzeojeHQpzOo5Q3Sr5Ka38V0t4g8wrquaeOSPGa+yXTzMoSnoXCnbvikU20qkZixDmgDI04glZqBljrj7HlcQsPD0AfVUBuQbA6+f6wuYqaLzGm11psCbV/P+qn3kmtPqLGNdiRy/6RusW6FwvBzSAdMNoXmRMtxjS7de1H3Kzvqc5GLJtV3+2CPYa5RvrII2/SmYcru1XubiOL3Q8029+XutGNcezLZNqW2BRqEoMnsIH7LuEByfHOLO7Qj6J0FOFsxF/ePI2it2EaFPUtUwmzYRUGIMLhfXsIo/OZlKvDUBbTelQFVBtwAcVS6Np2KiiHnPbLODrMGwCDQO6xiP1jHlCKAUSVYrPvI8dhLN9UqEjCC1BaFHKU5bShAwCKGntK7EKhbUaVwgbRhkv9jihJUkKvJfPy8LgvUIb17ATW6mG5NlKrCKK0lDkgYAg2hOGliQqCAP5Xl4a0jwkB4htZb8zOxGhscRhKfm0Uzir8b5mVcvcHqek5hMK49PnMwbPTJWi2jqnKuaBauS0dVEV/ktPOKaSxaZRc2qkvJUAYspJ3lJ73urSYzKHKj0uEvUOncuXlBNlMJ0W6GGKynJos5jbgHBtuRIEswHc0BS0COvCiXyvYT+3n1tazywsaoaRUfPL1HZKevaatsJYXUgOjeRunYCiV10v1+k90g6Nvy5YsHWtJdS2GQd3yy4aTz/M2HmhZ3IoYDUQsNfk4EOFzhS6mF139dxeF7P8fa4ZsQ1e13OKpztPav47s7mKSHeqQuZmNjZM1zW99RHTkwNIHH43mjXFGE/QKwhCi7WKOCoprZbmReUpc8P6+oitz65x8OOKYOpmct1HC0yvxO6FmIkmGKlGwa3mWRRdyA+UbHQz8vMDCAzFUoSZlqQbEVo7rKnHLY1SJdYpwMjtVWrpqfGgSvCZHH+RU//C5CPPR2PZFyq8VY3a0R/3cGigqzyFh9d3z/ILf/Eppv/sCL/yttfw5/7Sxzn/wCHuGI5RlVVbvuo4YIZsmILcw940Qa/mdFWJ9TD0noeKDf7d776Zuz9wFr+zB0cOioKR0Zx7y2F+6Kt/kRdEl5i4gI9NTxDtlZS9kNvWLvP+/XvIdhN05rCJIV9W3H3wMluuR+eiEBNknl93K2i65yZ8bPs4m+sfZKA0sQo4ZAr+12Nv4zu+5ij5YwnJOJN8BVBVU5AKZCxWgA8CkWTNY7RSOG1Itgo+9shthLdqMu+YeMu9yXl+a6AIpszm5Uqh9w2Fh64O+fuPfwfB76ywsSu1s0Y1o3oevf8rbufbT3yKnz/6OorVktVPBpUlonzuQoVNYHhzyPqDUz76M1/Oz/2dU/zFwZMElY2WAHnP//h4I7740faUBth3KZ/IB3xofCeX8wGbWZ/9NJG54CSg+2RE/6xncCYj3JmgxqkAgdaK4l4NCFf2HvX7KgjESo7ZvNCAvB8GoA39spR6aRjikwg36FKsJqTrS4RrmulBOPWWLvoN99A/46Gfc95ajgem8qI2cxLeN7j/f/zIvcZk1Xzx2diIPYdIved4MOF7XvsHvP0jX81dP70v9jBhgE5LVJqz9OSYR7Ij3BHuEynFyUvrbKyaymMZtp3h73/6rdzy9k18mgpxJgwgmzJ5yc3c9M0neWPvIWJlmfiAUDkenh4l2hMlmPHNjkeyI+yOuiztC4O47BnK9YK7ehc5U67I/MvIvNhrhUJD3TxaWMorfcY+YA1HgSdEMfaORCm+6uYnOanvgNCglANvhFxSUs31Z02fKgrxWYba2cd0QtxKgpk6To7WuL/o8lc/8NdY/VDEsU1Hsl2gSo+ZukZFKdkqeLJYQ+MIL4WosgSj0KWox/iKcEf1s1o7burtsulupvCGf/qC3+WfnPlONj6hhZRXQRfhxGNDRbakMHnIwY/s85vLX8W3/I+f5JWJoaga2p6v++xZoxiVRc7VnnQa8iVPugFFP8BkTky4A2EGwmxuoEo96+oFbC9kdDQQoNFKYUG8pHNecug8kzJsijb7WdJ0aE+LgCQsKxZy00CN9+pzMp+fCaS+5n4vvH4mZvW1gOBFr+drhTCZr83wXgSpF7dhcZ01+3lx3bXUuVKeyMzAvrQMG3Z2pC0dU5C5AIdiK+uh8ayEU3ajDpMiZDgo2bsnJJgus/FHVnxxy5YkmPwILlAEaYkZF/hQzyXXqk64w/rhpfGJME1rnyucF9/paYEqY5GHR4oD1+Vcq91d7IU9Vfva2VgKBi406ELYYzsv3aBzpaDzxBWCzRGB0cAS0vKuyZfEt12XEE5gtOGxMYRDRTiEZEfOsbJS0Ko74pWXOcScF6mVQpYUtACnsLnGRuI9enG8RKRLbkp2+cajD3L2red5+30v5MC7E/rncwanS/rnFemKYetFhrKriXYV8Y5ncjDCpNC7WBAOC1wsEtQmtVX3nMh5B6kkurpQBKkimIgvoC41KRFmOvOcnsSeUeIgcijjUNqjjIAs3inKrm6KXSiPDhwuEhmZsqsbWVWoCplK4arCZrqqWVpZgstbcs36Sr7OOkF6nMOsrUIS485fYnlvSLJ5jGCYofenRNNCJJQrieRacsp1RCFARY7X3vZZzv2lZR4/f5ClD2mWT8qkquhqyn7YDPi+LDH7KcsnY9JVQ3d9CZ2m0qSxtw9G46OQ7KYVpi87yNKjQ0yW4ydT9N4YlceonX1JoL4oF/mNuFbUPtJ1h+pHM8Vv7n0Vbz91L8NTy3Qua1FNmHjiPRg8voe5IBMvbx16fQ17cJm9OwYMj2umBx3KKcJ9RbQLvbOaeKgIJg6TWqCUJDV3mJE0JZQrCS422EisAFQpCYTXMhYXPcj74pU7PqbZuacDdEi21+hccZKQXBzC5raM43mBTyegFOFTOQcvxKx/ZontF63z21++xsWXLTHcuJ9Xd57ikNH0VdwA8jfi2tF4SkMDxKpqclmzpanZW6oNAcg/G7aLBa2V+HpahcqR2ZyRQp0zHqU01mqsUxX4bCisYVqGDTu6tCLbXZaGPAthP2T5SYve2a86oI1M2JWf9Vwp6SwVZkMO/RUmRxJ0AWsP50TndqXxyjpcTzolfBSIsoqzFEeXufiKDpMvm3LHkSs8fuYQPtOo1CAWDJWcXhRSxtLMo8uqa3skihWjowHT9VU6WwN6nz6H3x9JR61SjQKLqrpz8Q60rLvNNlPGXOUvXTMLFRCf3ibeOUKxrOae7YvM55p9ZDJF2W1JadY+4e3p2TWmf1dNCf38zzUb1P68mfO2V1SD4erq+ZFqAdXXQY01qpnSFRjdBqKvxYpuv66Z0XPA9FVS3f4qANqqeWnup1NTqpcJr6GJuljkM1UX4AxQb+U3z+KJ/Fx9sJ8pvpD+1u1oF/gNjsIHWK+JKi/pej8sisIHXLT9ZvnPjjYYbXdRo4BwXxqpuNQndDXITGMzA1B2PbbrW/LczK7ZqknHBRVDWreA28Wo575tmW7PTNa5vd7rIHxQAdI16F6xfgkdOrJo4ymVF6BBQ5CUmKBiMSpPHJbEYUlkBAgurGnyOlUpdBnlya1hmodkeYAtjTAgjaffyVjpTMU2pwzISikPGO2IjCWsmr4X82zrNGkZkAWWsgKT623phAWxKdn1HabVvRMnhTyXrMI7TRBavFfkpSEvZZucVbjcVPLGSp5xhUJbaQAymcJkwoY2lW+aKeqivlxLphCguPFozj0mFwBAW1c16VZMuHZTb7tYt+DT7ANdefJKfupCRTEwpMsaXcLSKU2UllKoKsrZ943YmamqWcqHAW65i8psZftT3V/1beYAo2bfX3y9AD7DNUDpxeWgqhgv7K+TopoqnSg5VdO3L3TR8k9rtBusxB4lYFRGmAqofsNrPs2l6RLbaRcfOuJtM5M+bQHHjVx3NQ4qW4NQVNfs7PoGZmNXa5ooTRwzIFrVEu+0QEkv170PDWXPkG4oOlGB29bYRNG5JIXPYiBevdmGQq+n9Do5pdNo7clHEYeO7LI/SbBvW+eWUyVFTzM6Zti/L0dHFtIAtx1J02wmzSEgrB5VQtmF9Iiluz5hmoesnrTYQUx8Zpfi0BLlbSmBnp9AZVmI94okkpX5Qlf1DNCFAqvmntvXQ9PC8xWFt/jUNGNGff6fK0vaIQy7sDqUP3jLb/IXv/7vc+BTjv/p5d/FykMKvTPCrfQwm/sE4z6pDxhoy7a1TNOQ1eUxiXL0tGLXwQ898RZu/r1SahydpFGJ2Hn5QV77lz/OndElCq9Z1gW/e/lFmLRkcqTDvd0dPjs6QHQ5wIUl3kgz9q39LR5Nj2JScEkojGGj8XEkNcRJzhOX1knvUGzogMzL/POAcfztF7yfnz72TcQXRSob7UXNrjkAVeOR1vjxBG005ZFVyo4oTQRbIUNnWdEBEyxXyiW0FcluF2pM7qWhKVdctF0OmZyznzzK0QuVfWVrvJV6nWI6jXhV73F+1ryOv/LKD/KLW1/NyqOzc6gLuemLnmJ6KGbliZwf/c1v5da/8J/5uk7WnP8bVaE/W1H7wdb2apfslA+mx3gsPcJj44N8dm+DURoznsRwIWH5UcXhcyXJpX2xjMtysbYajeWx0hEVS4xBBYHYYFUN9DoJoNuZqb9UddA6xDIrl+Y7a/FZBiNQlzaJ44gkjkFr3NqA6U19pmsBRR98avjfzn4Tbz34Ee6JrnCTkSaSG/H5R4FGZzKPFOuXz299DgiZqV8AfMvSJ/nt73whPBDiyxJtHT7LoSzROyN+58p9fNvgMXLvcZcThrfMMvyf3n41nd9egrMPAqCSBPbHuPUlTr854N/f/G4AEuWAEusVT43XxbqyKHHrBU9OD5DuxaxmXprkNgxBJ+V4tMm5Ym2OJCuNoq6l3lwSbxkmLiQOLIUv0EqRoHDAUpA2JDdvVJVLapnvez8jQVSEDhUE+DwX5VOjsb2Q7WmXd+7fx+oHI9YeTgUPqubapqzkuQNRB3w8O8yd8UWCiUIXDqxClR6baBR+jjXb72a8dHCaT7sXcaZY5yuTM6yf2IFPrBOkHhurxtLVK4+rZLzD9YSjvz/mL77w+/mtr/73HA9UVcN9fuq4zx6UriaUM5ms2eTZr0qBwGSOcJgDEcXA4Ou114zNXAqGype4QJMvhWRrddeGdMbryNJLct609gC/fOnlTG1IasOmGOSrpLiwIkkZGtd0a7fj2dxrn4tN/IWI5wqAOy9+bH/czsx6f+qJeN1JHCiLQ+P0jIWt8eQuaIoWtVd2akMSV5CYsilObNHjUGefpTBlx3QI4pJyBbZfFBGNVlh6ZFcAjMrTpJZ3rqW2dV6i8xAXVfLKjcSzRtWAdC2nUBYNIC3tIB41zTBFFxu3QOngOmHl1QnhwinzqpL0DAWQtbHGRQYXaoJhho8D9k5o8kFEcqFTeWt7gnGJ7ZiqiCId++HEUybyO7ZbDVpGS1Gmui9d0CqUL1S0lUPYXK3ua+8BqygKgzEBmS3ZzztMognH4h3uSC6y9mVjftG8nPxDHZafKog3M3Qe4nXI9KBCl1D0FWVXfiecBjJZ9uBi3XhguVBhI0U4cdJRpHwjXeZSwGuU1QTpTO5fOkG1eARWLBgXgKs8S/aygPEkxlo9d+yV8diuFJx87HBaiwfPcuUfWsp1Nr1tlW6aQWlF3irL5GHjKi/eKBT51yTBjSfEp7ZomAx5dY2WpUzEylmapwuPn0hDxz3Ll9hIxnw0uIXJ4R7Ln4XOpsVGLTaEMahJSrTXZXIgIDvUIxkvoaYZPgxBacoDA7bviSn7EA27dLwX6drSQiVPixYQ8UY8PyHeyoY9N+WJwvDftl/Du0/dRfbUgO6mJtnyxLuO7uWc8MI+7Ozj0hS9vERx6wG2XtQhW1O4EIIJrD6oRAoy94Qji5mU6LRE5yVqNMEnMSrNJImoOuGjLQOdBNdNINCNJ2EtG+m6ES4JRcUk1JRdTT7Q5AOYbmjGRzvookP34gbdywXx2T30aCJJSpqiAHNhm41pQffKEvfv3M0jX3aQJ259gDct3c994YRYBQ0435aR+jMflVR3DWDWdFhf/bO2X/C+KqRVeIQ3LTZQNedS9Vy0BO2l2Uh7mTR7vMi2lhqnPWWpKQKD0R6tZB6VFgHO6UaVxVqNLbV0TJ839E4P8Wkqv1mWsCibpavit3Wo5SUmdx9kuqFZfbykc3bYjJOul8gzvrCoosQud9h6cZ+tl1uCpQmBcfTCDBNZ3G4oTOMZ8o3vxCI75ORZ2r1S0nlym34UUhzoMjoasXdriNc30T29j7qwJYX/JBZJb+vk3ihLAde1kUTZik+cN0buD2ZgNNaiokjG9a1d+ucOMj2osUkltV4BXfKl5jTitYDSNq6aBPzs86uwzWo+PA90zy/SLlJfxXBpPedVdV3UjPvWpbLwnVri9vpA4Z4OjDatg7UIPNfvwbVZ0fLXz6+vBULjZw2cpo38t6It0V17X9Xh4G0ldAYAAQAASURBVJrfmf9uO679G/PH4QsdV//mc81DFqMGpC0K29riGpCGWVOA9YZd2+VsvsZN0TahKrkwXsJsh1ViPWviqCMcgavkt10sgDQe9FQ3gIwLKxBaVWNiSCNdPSfRvTgHVjSypY0iw8L9dp3cEviwZnpXG6SBwGFiUQ4LAktmpPExDC1xWBAFFlMBzt0wJzElgbYE2uG8IrctGUTlcF6TO8MkiBiaiKyo1mcsS0nKgWRE6XVj8VBbURnt5qygcmsoXFW0WLCXqgHmwhrGWYT3iukkwk0CVKGZ2BhVzliiroQJoEuRMe1OJK8xuW9YozUgp0sp/uii/s+JZY73YovU8mVuey0DM4m9ejsDeTbZbtTkpS4USxSbiOKbC6ka+lTF3q/+Szw2qa7H9ZyjB3cZZxGX/lAYo+F2is5boHQNHCN2PUQh5SAmUDneegGm65zWgVICGlYHdHZwq5xq8f1rWveoazUnSe7Y3B9V42J1gczWpxXuOuwtdKiZz6RyOP8nv5GL4+dizSisCmG1J+zUyj3VCVO5N/Ec7e7Jwsc3OZsdJBhpwqEi2odgLIweF6rq+lJzUtt18yLQNOU07Of6Umg1GTR/a4C7PdaVcs/UDfJlR5OteManV1gaQ/cCxEMnzRsXoIw1NjKMbu6TfvmQbBqSdHMG62P2P3SQwUlPkHmGNwUMT0BxKMPEFrsfYUaacKhRZQWut8bmsgPZusNHjuk4xqeG9cJTdkNGN2+wd8KQdPYoKisaVakyUDXfdKOC3BqZ61Z+0nU9MtCW0hkcCuvV561G8idZI/yTiFrmduIL9LR1f3wek436q7n3rOicl3zNYzx56i6K3z3M4fuH1IqGTFPCfcXFcoXjwXmGPqBMQw4c2sQoCFF8eHoL239wmNseOi2X5vIAnKM4vMzlt2T8+bU/IlEWjadA8fCZw9xpS7IlTS/I2E67JFtKmle1ohg4bom32Sz6BJN6TqBQXkhQXiuplVxIsF6R+ZJd5xhoRYjiNZ0n+PHDkg+gqmzLCzjdsN+aAyF1Fj3KCbTCxYZoT3HJRmw7x3/e/Bre/vgLOLDriIbSEKVzJ6D0VLHrukz8lMGTNPelLqpt9PI8USW43HA4GKItfOPSp/j1F7yY8vQK4aRy3fBS61JOifpe6jj2vpJ/dPe381sv/U8cM11cwzO9kYP/WQnrPVrByKU8VBj+y+bX8cELtzKaxBjjydMAdSmmf0qz8kRB99QeansPP5lKI3gYisXVbWvY5YR8WWziykRTdqQuK4qXcvEW/cousgSTCYEqmMrzLBw7TOqItqeorEANJ2LVaS1+muKzXGzIRmN6ewN6WlMeXMKkPT5+5R4+ec9NvPrmp/jW9U9wX3SZo0FMgLlRV/o8ovDCmsX5qkHxj7+uqhcMqHsmFbn3HDI5/+iu3+PHj38HvccE8FVOaiwqzbn/4VvghHwnvqIZHy+x3vN42eeXP/ky7v3ItiiWxrHUyZ3jyitWef2rP8OJcFv2A0WkHGMfcG60TDiRLekupZwer6GH4uGsc0e6FhLFBbeGm7w3OzjLD6u8wBuxJPVVTT7agy3XA/abkTNRhtRb7t89KhiBoiKTLPynlagR1apIFanDT1LMzhgfDNgbd/ij7Vvon5cJi6staevtMlLY8YHi4ckRBmYqKjBGzepBSub82vrKmsVTWEOoSmyi+MD+3RwNd5hkISsTP5P91hUhtJLNszHkKwHRbsaxX0v4O0feyi/d8wtoNAXPD8Ho2ct3l8yKYLQmzArKnkOt5RSDkGirJByCjZPm4NX/6UySPu0cLjKkK+KHStUt75ZLkqSkGxa8MD7P7wQ5oyJmXESN52EUWIwW2W2jPLpKlosKmPZVcfbpoj29/OPcj88k973IpLgWs/pak1xJfKRQvNjdedXyV23D/B7X4HTNBsl90CRWRjsC7dDKkduQwhmMEolP1ZIz18qTVslVagP2ig4dUxBqR6eTM/EKdzhl64UdOpe7hKWFwMwmgVWSXwMjygrzzxndsGflgFUJtdZys+SF3OxG4S1yY+cFKne4ruh46uKZi31fjFCWRhIBaMznfVU480rhQ1BaYSMB0l2kUdMcN0hIj1iUN9hBTDDNsOsDfKAJhyVFNyIce8qOouiKVE+4p8Vnr+PJlyVrjXdkO1wkhfvZxlV/6mJ1u0huhXnglcblhjIQpsK4jNjOu1wOlgiV5WsHD3HXKy7w/x5+DRc+dJTDH4Fwr2D14Yz+uYhiEFAmimxZUXZlO83AEEydTGQ6Gm092ZJh/1bF0lOa3qWCWt5Al1I0CaaeZFcGWBeo6j+EJRdSFYvkPVFgAB8YXBhB4hs5x7pgWbPFMB7vBcRXnZlPVmZg+56Qon+U/qkJen+KynJ0HAsAV5QC8IYBLPfRRuN3dlH9Pn6p6hzMCwGylRLWhUd8VjKHnoZsZT2mNiTSJW++42GePLzBg4dvovtkSPeSJ7nUhytbKKMFWNQyuUvXApKzIa6XYI8fINhLKfoBxZKw4fduC3Fhn/5o2vi01mB648l+I77oEWDIfMkDecyv7rycR/cOMbnSo1cB0p1NR/fchOD05QbwU7cc5eJr1hieAJMqkiviRZ5sl0SXxuhJig8MdqVLtpEwXe+inGfl0RCzNZTJTpbL+ddGGiWMZnLrElsvDJoxId4RQLyzWRDsZiSX96sigsV3YorVDsUgJF8y5EuK0c2anXtigulBBqccKw/toS/vyI46h9reo7c75Kb9Q2xeXuUXXvkKLty1zDevf4rXJZcJlbkhsdQO51Fazcsn1+MxFSBasWpr1gwwa/5T1SS3drWo6iU1SKZLAW1k4l9Nhq14gnqtxcvTKdIyoChn7LlGxtUpbKlRU033opeO6VqCpqgkcrQgr6rWDVcaDJSHVhjeFKAsdM9OIMuFhaNUA0gDlGs9Lr+8x/7LMnpLaSPTupd3ZM6WOIytJoyOGYBQF2AsmNQ27LPosmcpswyPd8hWNN4s0c9K2N2X+yKJpGhTlvi6ut4G17VCofBWlDGUEv9VXzHXvfeQZXQvZIR3dBvWpq8B5fZ5rCR3RWZYzQrJ9eft8wmNxUb93lWgdTORmC1/rT7FubfUtd5cCMU15o7PT9SAdFui++lY0TAPRtfLC/A8e12zhK8FSLdB6Kfzhl7cvmuVPa7JbH4GoPpPW9Qy6ACpC0l9iFGOnsoBkU6duIgn0kNsFT0unl8lGVWAdCW1rXKa4oALBZAuexXQp8FMZ8CMNCn6GcBcJ+W1F/Jcs8XCyVgYb6+pXnCdhA88RNX170EFDh064rigGxcYLY0cYWDpVgzk0FgiXRJoR2IKQuUaKWGNp/AiY1vnf5kLKL2eNWdoT2l11SCU0wvypkG5aXKuvltYw9SHZGXAMI3J8oCyED9CPzXoqcbkAiy7UppWhcEMK5MZwGxyX0ljOxnXMwGWxbdQvAqlkWm+4XMO4K09l+u5r5Yc0gdaWMdaik0+FJ9ksX9SlB1NGYtqjO0odAbZuvh5u9jjOg6VWKJuThhaOlFBNyzohjmRtkSmpBfkDIKUY/Euj4wPc3v3Ct80+DQWxfer72Fve43V3In6R+Eq6wp5runS4rFNLtgUiepCVnPyq+JWJUXrTQX6tMaThl1t1NXHCBqPR2gB2TD/rKjnJar2hJwB0tcrU7oNTH8xwi48CfQ1uKYTF7FXdJjaEOcVvSCnYwqcV4zLmI4p6AY5Zazp37zPeJjgLiYEY0U49QSpQ+dSOyk7/3/2/jvItiw77wN/2xxzXd40L58v21VdVW3QBu3QMA00GmwCAxKiDZEcDsHRUJqgxBjOKDQchjQKTYwUo5AY5IhDaUjFEByCEkFPaGAIT6AbaN/Vprq8e/XquXzpM685bu89f6x9zr2Z71U7NNDV6N4R72Xem+fee+4xe6+1vvV9n9RH6n48H3eLEeA11/pFQ1xYxJPRU1pyVfkcn2rJs1cdo+ctyVGgt+dJjxppnp82pFHiXYWE248k3Hdxl6u31uGZFVaueJJZYHLRcPygx29WKMAdpCRHBjNTXaM5ClreiE9krtO1wt62gBUrr56jWkmohorZvY58CWlX2uNqg9JQ1pYsabpGMwyoUnVrgcHTYOQaeX1ewr8v49gLENqqA7WXy9fjKb08MgX/p4u/wp953wM8/P+pMa9swWjQ2fzku4GpTwHYc31CqVlJCxLgOHj+2a13c/4TJX53D72+JrG21tx634D/8B2/xKaZogmkynPsE8yrOTChGovi07xOSI6kVqSbgB87ztgjnphcIpnH+qNSwnYuKkJP7ATTfc1xSNigYhDntprAGVNTj0LnE4oJC/Zbl//EPCEq5OndA0Kygc8MuoEX603+p5vfw0u/+CBrNwL5boWuHHYuNU8VAqaE3WaIS3fI9xfNIXJyYg6oYgNKjFmDhiIk/MWHP87/+0s/yuqzdLlFK+Xd5Ipi3bDy0pzePx/zrx5+C//++DkyZfF/AOPP74zXHpmysQ6V8Pdu/yAfeeEh9I0cO1GgYXAMqy80DJ/Zgdu7Uu90Dr22Sn3fJsf39Zid05Trcj2agkVjdczD2ybxNo7zUQrZp1Knrgdtk5tYuHqbiYXlfqC36+ltlaQ3Dgg7AjDiHOHwGILHHB6xuTNm9fkxk6cHfOTtb+XFt5/hRy88yY+PvshDCd+R8P46h485sm7o1B9+t9NDEn8WIZC3fvbAW7MbVAPNMLGxHhU/r2kYP2mpfixQBEV2APl7DzFK8fHpw2x8PCFcvYGyFjXoQ93gzq1y+MNzfnLzo12TWZtX+6DYn/Q5PxcFonF/zvXjMWauaDLQtadcg35ac96UzHwalXajgmvbtOqaLodIjgPX63XqfJ9EtSROjcfx/I2zPETz5Q+KUlKLj2QIZTShruFogulnlNM+t7Mh49pHZF/un+DBJ7qrLwUFT+2fZ2BKaSRQSmKoiJEAqErWkWSm8J9a5b+5/uNcuO74tV9/B/9m9bsYP2np3S66eK/bxQis+0QAeddLGD1xm+1/fJGf/2sP8GdG179pKhtfg3z3qatXSQcxXgLMcxtHVKNN+m23b3tgI/Mx2IBLArpyBGMIiaYai2+uywPNiiMdVtjoQZUoz6X8gE9P7qNyhrIxWO2pnSa1jioWV20EpRWckBa7G/DbAsbt3wN3ZxG81n3aemiBFH+NXoC57XMta/vi8JCDskftzYnE3nlNHRSJcTivWckK5lE+u+1CJ+6X1V7kkr5CBaX1MQoh4Je2bWIy1Rah2/3zMfixcf/b15vIrG68pmo9J4MwUXeLAWvZjNVszrRKKa0DDNUb59w+6nHhoxX6uIDYddIksWBglDCTSofSakFoMNFnMhEwj9JFprSL3SUOZQ2td5YKIjega1kIm/43f1nq/J10rPV2khByvddDkaryRiS3Ce0948V/PfEoZ3A9ix71OHh0iPIwuFmR79b4VFNgaHLxp0kmUigo1xXViqdaFclqXS261VomSifl6QU/EN9jFYvuRMZwIHiF98JsmOmEg6rPtq3IVMOGmXBvssffeuif8d+vfJBfH7+Vix/R9G8UmNoT5p7eVsUgNdQjy+yMoRwbYclHKSKR9FJsfu9NtpLzZEcaO3WoJhAd41A+dN8ttBOuQhjFiijXKAm6i+C0T8UeoFpTqNZawLTNAHIuaiUyacEGaBIBrAeNdGef1bjMUI6GqDBkcGuNbGcuxbAQCC6A9yI767wAx3kaz68SaaiWVddKdyDzpJ0pjkoBrych46jqMUxK3vbGq9y4uMLOy+ukkxXG13LpFoyXjW5EbkTNS7a+7yIuU6w/bWkGhtFVT7mimF0E1Rj6r/QxewIuUkoxGPsdiZvfr3Has6cMDV+sDL89fYQnDy9wezJEF5r0MDC45Rg8twe7+yL3Phqx84P3sP1uj64D42cVqy+WpNtTVNkwv3+Va394ndllz/D+Qx45c4Oz2YTz2SF1MPzKf/39rO8cLe1MK0ls2fqhczz2k0/z761/iRVTcORy9tyQnXrEzWLMdjnkpd0Nptt9+lcSxi95Vl6ckOzN6NcNJJbywohqbJlcNOy+VXHrAyN619a4/G9nJFduy+c5R/rqLhf2eoxfWeFj3/1dXP/BMc+ffYYfHz7BQ0nWBTXL0ubf6XJlCWwUKe+2iBjxzSgBpLq5T6ZvKVQrxwKo1UAjTU/CJJFirY8MqVbG2zmNNxrvpaDnveq8RX0EpX1pSKaa1RcL2D8Uv8oQRN5aaxRKAGmQ9dhoyHKm9/Yp1xRnHy8xO0d03aFWWMi+n9CMUl79UMrmO24x2R2T/MaYg7fW9M/McF6TpA0uSHrTraMRcGjxWfHvFIkPVdVQ1SSzgtVJSbOSM7uQMX14nf4VC9t7i9ih30PNxfua4GXfQlhUSI2h7WpV3kMSJcriSK9s03vsXlH0OeVJu6yu3koQ65oFmzom822j2gIgUyjCl88H4zbAHUT102zoDo5TJx+fHt1+vA5GC0gv+0W3zOjFNuHu0t2v4RnNKc9oWAKkT7znax/5097Qy3jP3V7W7t2dLOmvjphkTp0Pd5fPOL3Nlx3hd1dw/mqGVp4URxESbjVjtpoxuWq4L90mVY4D1+e3th7m6q11wtSS37LoSu4RlwVRyLF090nwAkj7TABnXSjMXOF6gaYfwIbuQLc+p92FrMNdboTFviqvoFFdA3U72nnldTUyh7bxiyow1pGmjmFednZLPihGWck4nXcvS43DKsmZW5UNF1TXpOGDiopjlmls8p6WKUWVUM4SQqPBK7bNmCcahSo0ZiZy1LpS8acUwQVoDqQl9MvQKXeY0mOKBlN5UXkqXQTCgsTUjReA+bT8dAiRSREWDVBGCxCrY3FHK0Jqcf0UnxvqgaXpKcqxpukpXE/Yy80giA957kXyPHekWUOeFuRpTW4bhlr8rFfSgoGtePHwDG8e7XMhP2RoSnJdk+maVTMjUQ6DJ1HuhCz+cgPNueSQvUak6h9L4G+++Z/zv93/SfL9lP7NeC5b8EGfvOBUEwiJqGJJYSw2XGgIbXNWu60LizyjLSgmZgnIjse0ZUdrtWTntARUL23fPt96ahMCQenFvfF6uz++SeOOGtGph6W37FV9jqoeVjvO5hMAqijjDTCtBhSNANaXx4fMBzP2Rn2ON/tM9hN6W5b1pxuGzx+gZgVh0KO4NKIcmwiy0clOtrn16fkrEok7hbIWmFZLgLSqfTzfmnpoKNY1K09rVq46vIX0sMEUDelujU8NzTBlejGh6SnMrYzrNy+y9iL0dh3limbnuzTFPRWmJ1LR+tiSHCtRxWhYkiZe1CXQwsgebHnsLN77PqBd4OjejPmmIvQcPtoutHNiOBD2dWVztjZyBqvzWGMMC+a4WpA4rHZU/ts3Jy6W/UPjfe+Cwi3HN6/x2ha4ruPS21fyuJ2S7rNz/vQ7P8NvPfg+1p8uUb0cNZkBsPpCyc16jVy9wvVmDVVqzudHJErxW7PLvPSrD/DAl14kZBmkCewfcvSDD3HvH32ZDw2fYlU3HMdG0utuTL4rNbNqDKVPmMwzxoWwMYtVw/rZA96Q3uYfz94LxMK+NbKemLYerch3A9tuxJuSA4rgOvbbSBuSSWy+UFG9qolzdiTadKAKCIhWVZjtA3x+Bl3BVr3Kk4/fz72fr7DTppPGVY0n9GRNTo7gWrXBdn5NWKTRSiIYhct0p4agPOSDStbzJPD4/H4+PHiKv732YUDUYzrLk+hlWq5oppdyVp865qd+6se4+B/s8ycG+2hUl3O3DHqjNHWQeGLZcuw741tvtOfR47EY5qHi56cX+PvXvo8XXjhP79WE7ADSw9ApjbG9h5/NhITzyH3svXnEwWMSE6cHiuwgsP5UwBYOXcq8bEqRGLaTWkg8RSnEnV6OH/W6HNpnFp8JEBkSTZMbfKpock01VEwvGPYf6ePTPtneRUbXHMMXj1DXtoB4zx5PSQ4nrL+csPLSGfaevsDf/f4zfO6Re/jg+jP88eHzjHXe1d5O15ROW+l9ZyyGD1F9qq15hCDs2hA6i4Z2TTDdmhG6uV8DqVIdCF2EwBeqM3xm+iBX5+vUQfPOlav8xPBL5AdOatIxLlVKEZqGlasNnywuMtAldh54YH2HAw//4On38cAn9gnzOfrcWYll85RrPzLmP3nHz7Jp5hTBYJB4PCEwDQnFJMPOHWpeorDMK1Hlkjk4UI888zLlv9v+AX75lcfIqxiPJKarBwW7iKWTaeB6tUZrslmGBqMVhQ+sfKwHOpI2IvGCaC3aSXjjI3U8Hsk25vYBfXuf5NoqkzxjrVnE395oQqIW9gwuYGeew398gd9IL3D2xZLksMQnYhkREo2PxM+mL5aN45c8K1fke68+A0EbdB2YXE4JSgmZL9qjto0l6cST7zXosgGl2PzFF/kb5/4kW3/uV/mr60/hQzjBlq6Dow6uk9P3BDTqRC38dzu+eqb0qcJY0FFKy4MKintGBzx1+Rzj57SwYtsES9GBdAQkQa1qqpUVinVhWfuegEVpKknptEp5vLiX68Uq8zrBLXmCNc6QGE8IirK2OOMJtukKST4QD1Lcz6WEogOjg8j63M2bud16mFRs9iZcOVrv/u7j67Lo2VU2Fhuly7QKuAiKG+25r7+HDxtsz4fdaztQewkc79uKsrFLf1vsy/LnLo/Tj+1d5NNcEL/JljHdyna7oGNkuWBPt8C3Rryn5fgtPK5DUJReY1TOMCkZZiWN01TG473m+GHHytUhK5+bEFSCb3233KJrUZc1GIVXaiFFJQeh/VIoHRaJdYhem+3r5zW6ymKyHDrJg2/qaIvLLfDLouZCZH7YuZIu/DSCEBZQMpG0LLd6aAm6z/E9UZK7SRlcm8MMXJ5jC0kiTUVkZ0eP2FGgHoXIMhDmcXfZnC4o+KUCeZw328K5d4aytphYNKm9oQ6GqU/JdcqIgr909jf57h+5wt86+qPcs2dE2s6KfFFQwmQOFqq+ZM/JzKMCzM5qirOeP3Thaf7++TM0PWFSq5ZdHBYFR2mzjsVcH6L0WCzMa0FnxC83UPe1eOSuCmDfDvHOlvdSjREWeRalv9NA6INKvDALA0zuURQXG+YvJQyvyfdyKQy2HMm0QTUB6wLae0KWSiCmFCrPUbM5agkI9plFuUB6qDguU5QK0kgTT0ZqGh5c3aX/xprbty6y+sk+YTYHrcUX2GfktyXY23t3Q/5qQrCa+YZm5ZWafBcm9yc0A0W9lmP2jnBnVtDTElXWSxffd8bv5VhO6Fzw3HYznqrHfGTyKDOXspIWPLtzgXxfM7rWMHhhH3b35fyc3eDaHz7L8cOO0YuG9adrsp05zSjlxg+vU7xvwlsvXuGPrL3Em/LrvC3dZaTtiST6l+wPEPo56nAS5WKQeXN9zN67Gv7ryz/Puk7v2O9EGTyerQdKnqjO8FJ5jivFBl86uMjTL13g3v9FMXhxn+zqPlli6d3qMX4x5eDhhKNHHM//7wwrn7+f85+cYPamqOMpSikGT5fcu7vCzcP7+IcfWOXVe9f58dXP8/78mB6yH55AE2psDPTaY/jt6D+tIugcWvmkuKapVmKZuFaEBetWmNGyRrfMmTaAVU6Aae0WftUt08nXGmel6SgERdNoQtukBtKUVGvU3JBvK9Kre6IU0doLtBJHETBQedYtdn48oBpoetuB7NqhJJRaE3JL660zu9Tn2ocD733rs3z65ftY/WjO5uPHTC4P4QzM6oQ0aSi9Ai+eofmel8YHbePxioDIXKS3ReZOgXfo/QnJrGQQxhRnUorLI3pVLZ3Xo4HM1SDdqmUj+65apD+g0oRQlNJg1CZOztNKmYbJhPFLFUcPpvi2J2m5YN+ePwXBLkl4t9iSi7He6QW5BZ0DdyDFCxBb0IHlMDUsfV4Hjp96XnGXpaBtZFgC0r+Zo5U8XWZHL7Ogl8dreUefBKgDRrk72dHL73Pq8Vdi23n4fQNmXFgk/V/LaCXU6iDy2i1DXCMSZ3f9rLhd2yzb/myfXwbgitgs0gLRjsiOJqHG8IXpvWxXQy7kRxTB8sr8DB+7eT/718dkt62wY50kwz4VKxYfAiSIfLOjs4QSBR+5Rl0/iLpQ4jugrtstBd2JWe72aNUm2s2cinLHS5suvc3rLVoy2eLIh6DQWpTAQMCEqpGCi/OK4zKjcZrGixKG95KnNbX4MQevCNGTWdUqgssqNs4IQ9jWkFVtw48wmr0BWwSSmcOUkizoRkBm5QK6XgKWPeInGH/Hami8NPfEOBmQJiIfCL2MkCaERJrSxZ9Z0/SFyexSsfkpV0UuuxlCPfS4YQSZM4dNGqwtSa1jkFX0EmGjWuUZZ3NSLZLymXasp1PGZt7NK0mUm6+DwRDo65JxIsnDG/LbXc6dqoa+XshO1UsS0XqpEFKEhHUzoQqW35g+xvrKF3lTWvMfvPOj/PQzP0J6aKWgO60FGHRBQBLvUaUAFy0buQOL20lALVUHWxnydtxNqrvdNALSdz6vlu4f1W3bgpd3zR1eB/lEfYd3RRyKr0m627eo7vJzr9WhdaqYrVWg8ebEuW+fn7uE2+WIoknQyot8vnI0wVA6SxPBtVmTUjnT2bNZ7Rn3ClLrmI0SpqMeLk2p++uMnzvGXN8hrxvs5ohqnOByjTftXBYWIcOJcynNC8KCkXhQvGfjcJGdCTSjhCaXWtDGcxW69jS5ESa1ahXuQmR7WrIDQcKSqch1T89qju+H5lyJth43s+hjQ3qkUbUUPJdPT4jxka4gOZZ4rhhrGC+YQbqBdOpZeUURbEp4cwlBvOXV7YzejtRHgoWqSpi2jfZqyWu7rQN2tawl+5vuMNx53v1rXWd8mevkdTpaduwsSDMYIaDKChUgVb4DnNMl0OH0KII0w7UghYOOCQfCwv7RlS/y8/e+n/UklTxhXhCahuyVXZ44vgSrn+datY4uNfdme2w7xX/11I9yz68e44+O0etrsk5srnPj+xX/+eVfA2DqNX3l8MCr1YbUrWqHSwO3yyHlPAEFyaTh4EHLfStHnDczam86Kx20XjT7eGlGTY8DV6pN3pvtUoRAogJ1CGy7QLYfurhbaUTlSfkOvAHk93a9U4own2N3J2QHfV4sNsl2NbZwsfatBGQgNpEokTeeuIxXm1VR/oveod5K/UwsKGT/57f7/OfX/gjJseKnnv8efmPtUYYvGXo7dacc2IILLlWiJAPU6zmXfv2A//Tcn+XRP/23uM8GEgwZ9jsg3bf4cEvdyW1jgcfjInB02834maPv4hduvYWXnjtP74aldzuQHQWGV+ckNw8IB0eEssR/96Pcfluf6UWwM8X4uUBv15EeNR3wTAiEPBFiTtVIzFLVhMmM4GPTYQgoo3EbQ4rNHJcpsv0GM29Ibk/JZqIKGIzGj/s0KxlNz1APDeWK4viyYfdNa9j5GoObnrXP78HuASpNwHvMC9c5uzVkeGOTL7z3UZ797rPcvH+VPzn+LA8lFo1Go7pjUYSGvrqz9vWVjuu3w72h40KrGmQeswblAtvNCo7rrMZazzR46rBkhRVfnyBAdRECfaX4T29+iF95/K3YI4Mu6bC+3x6+ib+9+kEefX5vEbu2NRul6d+Y88+338WPrD9FUPDQYJv/+eA9ZB8bobZfgDSVZqCqZvrIRVZ/8BbfnV/h2Ces6woPVEFzEFJ+6vYPkF3JwE+hcdzaWydNG3QtgGszTPBpoP/LIz42eQ/9TNQ1JBxU3ee0dnQYTW+34Weeehcv37/R2a+MkoK5S1i52giupU4c2Lt3gi+tHSpNCY0jzOec+7RjiwGmmAsYHALmMNqLtk2nUU589TmPyy1N31CuDWStMHRqsnSNipG4Z+ksUJWLf1eyVrie4CAtMK0dqEZjyhzIMfMR/a3A6guOv/eRD/KOD1/hA71ZZ3kBosTQAtINrqvnwjfuPvrq2wjv0pVJLCDouaJwCcUZkeU2RYjSzSdf2xboAKbnjTA9cw9JIEmbzgdrViX8yu6buXK4jvPC3FUsGL8tmOyCAqcxWpPYBbulBaZfa7RBpl56r+Wv6YMisw3nsiOusH7i7y14rCPgdLdhtWdkCnIjHq8tIOzj60MsAlntSXXTve8yEN0WisLX0dLfeUoH7gq+h7AAnJulYLyTbYsB/envXXtD5S2DpKLODFpb5mWK3Ziz+6Yhva017N5UCsGhe6H8rGqU1tLdHRezE8n2Ukdj21WDFvk15QNqVpJM8k5u4PUwWq+65SqXCkCtYKMmuZ4t9P81+ERAT4yA9mgpBrlMUfcT5o8WmJsZyURRjvsMrzcMXzqmPNPDFsJYr1YMve2ArkRpQDdQboik92zFY4816X6UXfHtZBUZ1Ev7qDzoUuM1OB1wicYHOCpzrHIk2jEyBR7Nqp4xUDUfHDyH/pP/C3/D/gTj52DlakWyX1Cv5RBgeL3pujhBJsmjRx16reQf/psfYrAXPW7bjtC2G7U99T76wkFcxOL91XaHK4WZe1BgioC3luJSTbNnaVYd2ZZl7RkBw70FO5XgHWRS9qnCb+edP2GTQ3oMYcviDWT7jukFy/ycIj9UNH1hQXMhQ9djkuMGO43evkajigKVJITESpdt7SAxZPuBw+N+bFAJWOM4qjKMCvSSmmFacvXhAndmBbWzF5MmhakC9nCOO7vGOx65wtVPPQTAxhNTDt44EEb5oaIewvRigvKb3Pj+HFPC2nMNvVvF7+0F/53RjXZ92fVzHq/O8KX5PUyaDId0hdMohlcD/VeOYHsPlKZ55BKvfqhP0w+sf06z+pKcrxsfWKF494Q/9IbP8h9u/lsuWkWCwSiFCyke38nIfLLcIDt2+Cz28S3JKTdrfe65b4d1ncbXnpwnZ6EiwXDB9DiTH/GB/ADGUJx1fOzeTf6Trb9A+N4zjF6G9afm2OOS/vYx6eGIfK/H/iMpR4821KMhm5/PGTyvxNtcKey1XS40ntvlCj/3nrcxf3NKvfFZ3p/tkShNphKRXYrfI1GmA6a/rcYShnLHaMHOFjxZbtYhqm4QosxQfKwjKG2Q5iiQtdMHkQVyCu81dQ0h2puEIGA0INtUwowb3vCEg8jAj8WXTsq6Y+PHDzIJzUqO8jB6tUTNCjpPHS3+bD6z7D1q+Osf+Fk+e3wfKx/rcfbje+ijGXY2AqCoLYlxUlj0kO0qhlfnqLohpK04FOBBz0qo6wW7GaTgVlTYrUMyu0qxkVJdHJOVFZQ1IUskjmiaRSyiNARhTqskh7oRGbPYCKfaWMR7Qt2QvbKHnVygWl+AzSfPmRT8fRJEwjtdnOOueeBuhc0vU+w8wWpekrF8zXEKbG7B57u+4nUQPn01zOh2OzgJRnfbLjOtl8BogOTLmvi07/3lD4R4KC9t/xps6K9FStaoBRu6ZUBr6OaFZAmb6v629PjE/gWol/c1XodfSTLcL0ug3eUSNATqICBMEmVoFsfZMwsZRy7Ho3lxssnOfMDt+YgQFFd21vFXBvQmIuPcAdKZxGRBQUjl+6om5u32ZFNFy6jGhJiktT+XdlItHZTl+yiAasFptwBsTrOkOyWh0L7myx6y35fhd7IFOF8rXICZglmcDMSfUtILUUaKILMD4wEPaU0n66lrBOyJXsy6DjH29xHECSc9a5eOg7y+pacjRXircEaBUZIvJBqfSXEdoO5rXCrvkcyi53Mj//AwP2OpRmpxLcTiSHu+fSKqaaHXoHsNg1HB5dGEga2w2pGbhkQv7kirfGd3ZZWnZ0QxqAlGmrKDYubTk6B0BKmE/aw5k0x4ZnIe3V8A0qfnoOXGF1jMXakSX/Wz9ojb9Qq/MXuQHx+8zJ9e+Rz/7P3v4GB/g/GVBgKYIvpLp1YAk7qR/McsgR6nxwk2OV2BSS3//csA1O1oAWmRYV5aThTSxKa/uvf5ZozTYOHCu17292vxC25lv0+Dy1/N0FEWvwWZ5XfNXjVgtxiQaMdqWjGwJaW3+KDlJ+K/LoQGaSIJQWpaTbRV0TqQrZRUDzu2xxnleIX1lYxkv8BMK7LaUa3nND05FkIGaSUoQ7d2dPdyV1tB2Pg6sqRdgMYLg61vBEA49KR7ErvpaiGdj/eowqGqhkHl0LMa5UdUI8PxJcPkfg8XCrRX+OMEM9XYqcbMkTlsqQ4AcQ2wQRpkGqhWFNU4Fkkj69vMFStXNL2dBpcodh9MCE5jDiyDq5rsMNb9MiAogk5ohmIL17FGtZwbq9zCZmLpGjoNUJ+4Ptq64OthMfhdDI9HoymCjcpKMscoB0UwaBxGiT90y4TOTh2WRMEsKFa1ANAAh14zDZY6GHLVcOTzeM35hXoDECYzPvXKfXAPvDA7i65h3U54tj6L/s1VzHNPQpZ1c87Rm9f5ofd+iXvsISD3dB2ZcFfLDaknpxafwX7Zh4Oki2Hn5wMrScG263Ftd5U1hdSejVqK9+X3ZBp4fn6OZOU56uAogydXmi+Ul+jt+EVDj1cxUPsyc2KUH1THU/q3G67NViHE+VkR52yxqwS5vvNDz8984n38TPYeHjySdUHVXlRkrOrUAgA2PpvxxKuPsnIzUO6v8WJvjfF1adDzqcK1oHQ8DrYIJBMfC9ie+36x5C888pP8T2/7BzyUCJOt9BWJMt2/dny7gHLfqmMZjG4b00WpT+5zh+NaM+e35g/ya9uPcm13FV1o8p3A6HpD7/oUvXNImM5Qa2MO3n2e/UeEMTu8FhhsNfRfnRKMZna5z96jGdOLQ+p1j14ryfMa98V1HvjnuwtVxnYYw/TRM1z9X8GDD91iYBoOy5xJkTGZ9AmHq2TbhvQA+rc9/a2a/OaMXu3ww5R6JWW2aZmf0Rzdpzl8wxmGVzcYv1SQ3DxANSnUDf3ntrnneJW962v89A+9l91HB/zY6hd5b7bPULeqfJoEc4LJ+dUeV3dKjuwP4v3gCVSI3UBoHCoXjG7mU2lsJuBCwCPzfxpriMsKGW1F5tgHfvUT38X6kzpiGyp6jss63v9ihjqeSuzpl+tHCn0w5ZPPPIh+NHQJ7v/85Lt58NMzwmyOXhmJqlKacPtdlr9+/0dwKHzQbLken54/yM/feivPvXKewXMp42seXTWEssIdpVQrMCggmTqKdYudKc58foJ+4RrukXs4eqAPSKNEMEsKgHG9sMc12eMDPvnSm9sDJ7GMhvsO5tJ425IiNbJegDRAtY1Mbbc4dPUqUUZMGT61Q3q0it0+BqPxg4xmNcenBpfruH6BN2Ll0mSiKC0KtCzILCylvmoBTrcKi6RtfiUxYLASg/ksiHJ1qzqmA9oK0XdSGOyB+Hj/nes/jLn8q3xfXsTD4GlwZEquAh9Cd58YoAw15mtuq79zfP1M6aVfsj3NEy9cJp0rmoGlGYo/ZZsALR84lMKt9pmdU/i+A+tRNmCMvKMPiqYxPLu3SVEl5GmNP+V3vDx8UCKJHQP8hUT1Xb5EZAO0scppwFaImapjQW+VKye6KrUSab/aGVxkF/v4BVugWce/79f9Tjqo3fdlgLn9Lm3S1YLEp7+XD4rkVJB8GjDuJL6VfMf2757oLR1f3kmDo2jlK5eBb+c1JjYGtM+33zMxjtoZSmXJbENmGypnSKzDWsfxAxW7t3tsfrbuWEhyyOUzVWRR6epUd/OSlMRyYAsIWyuxInlbN8Im7Seo2qHr1wHD7tTlqHy83g2gpZsm25eAM98Xb2NhA3tcLu01ppLjVK4q+qOSqVdMmhQ7VajG4tOhsGenUJzLsIWnHJko76CwRcvGFhm7ehAo1wPZfpS1DmBnsli0nUzdhGYCyYHBJ5oyKEb9Ujyw6owynXPscs7YI3bdEGdmJMHz5uwaq9+1w2S6SW/PkOxJR7Wd+xPyQ9LJqci2Dc0aNKsNoysi6+JTjZ7HRSoG7ydGlBzrJlcfWWVLmxnnGF73mF9P8Elgvmmx00A68fhEdf51Tb6YrH2z8KX2dlHky/blOB3fa/FGkR7Ia5PjRmQJZzW6cpRn+8wv5HirSI4d6WofXTV0rPM4say8UlN9ps/+YwnZuMAYj1JQlTIfbK4dgwI3TLEhiN9ECN38OLt3wO2DdY7eCJPLCRc/6hler7jxfeLNUm441POKyeWU3lagHiq23m1ID4a/60v6O+PuYzloTZTpGNLPN0OeL8+z1wyw2pPRcK1YpXctYf3pCermLoTA7D33c+MHLC71nPmcYv2LB+y8c5W9DxZ8+NHHBYw2Aa0UBoXHU4cWCBCQ2uP571/9INluO8cuzZda4XqW+1d2XxPwbd+3DF4SUxIOfUGuDO/OblNdqvhDb36Kw7rHpz7/MBd+K2f8hR102bD69DHD6xm7b86Z3BO4+X2GjZUzrH12B1VW4Dxmb8K5325IJ6v8+vQtlO8ymM1P8pZ0lwsm7Tr42yT424klfUcctDSW5bvbxyFutwA1WRQe21hoCVBRS6wRKUaqrojtG5FCDV5eEyJY3c67qtakh4rh1ZnMYUFxRwBlDMpEQNp5yFJ8ZhjcrMiu7sl3bO0NGkcY5hy9YcDqB26xoud89Ofewb2fPETNhIGWTKGoDUpBmlWgIdtXbH6hJH11l1YaqS1sageqrMSPPUmlk3oZmJ7OSW4Z8CPmZ1OCPkv2xFU51GkiTXHDAaEoaaWXUOIb14LR4S7gQGga1PGU4fXA/lgtGtFOb7f84FQY8zXXOCM4J6fy5MVyVwnuFpA+/TmhBR5ef6MFpE9IbS+B1MvMaLN0QE8+vwCkO4/cpff7SoDF1+r3/I0uUyznrQmQa0V16ho8zZ5eBqk9bZE5MIqKLEUQ5lm1BDzXnBxaCRu6ZSHmynXXT4VmoBqmISUNMFB1l+cUweLQFD7hanWGmUvZmQ+4uTPGTy2qMNh5lG5ta1dq6ZqNsXEwAo66DHyUFPPpye7zbg7UMXGGxYUcVIy51OIAdkCzkmZpr+68T06Pdt506o579psx1r+oIyhNx/zTrmUBxuaMhrhNZEG2x2RpjWgtcFp5Zm/UMi4jxY0UvBEQOVhpjPVJPCdJPEde4c3S9agXQLJPpLAR0khFdwo1KNEm4GuN3k1Ijg12BmYuQNr0MtRjJ6ppQCe9rqUwouLvSdpEewnNYZFTJ4bEOArTkOqmy5ltBKgrb9EEhklJol0EpNvmN0+mazLd4JSOgLJ8fh0MYzNnFmWVRe1MnwAtW6luFzSnVR0MXmQEVcO96Q5fmN7LW7NrPJbAX3vkl/lrW3+K/o5BVx5vNdqHBbPcaJQLHWjRnUM4CUbfBSzuWNWn/vaa13sEpFHRNKJ7PYtYRC+2/bLv9W04WpZ0phsM8rMOhpvFmK35CK0CuRGP9xa0roOmcJbKW8pGfNx9UFI7CorGGYraUlWWpjZdk2DYqNkfGA7elDC4mrPyimNwvSA5rFA+oemLN7qdenQIscEfdOW7eA9A176zw9K1sPUJAazGDUSOu8lhdM0Jg7oo0XWDmswIS3ZQOs9wlzdoVnPmG5bDh6G8XJH0K+p5gj5MSAol/tEzlhrS5WfQsdGk7fPLgshz69Bt28a/PgvsPwrTw4TRVU96JQcVpFHxhiM99th5Q9OzTM+LD/XkfoXP4tzoQCUit98oTRn0l2VA/0EdLXvJoWNjUogypXJdJtot6uV3iRsXzOhAX1l+aX6Zf3LzPWxNhkxmOcErhgNh+fe2g6jFNdHmx0q9zj414PD9geuzsSwPQfNTN76Pi791gJ8X6NUx1DVhfczN9yv+5rl/2zWN5Er8Rj3wzPE50qO2lijKSr1bBlM5XCbf82OfeYSPmUfItozkP3CyYSf+bmeOL+xdYuuMsMUrr8iV59cP3kS+2yxijqUhjTxLk6HWUcVJ4oxQC8j2hVcv058SpfVVpypi5nXXVGSnhnt/Mcdbg/I1Tc90n9ECDa0CoS0Dg5ty/6QHATuDYlUx30i6GlYnh+/BFpBMNLoOVKtjBq/OSP7VGv/VmR/jv7/3FxjqrPsKp0HoP4gA3B/UIUC0XDcWQ4Njy1V8rLiPl8tNnNdU05T8SNPb8/SuTVDXb+PnBTxwD9d/eJ3JfZ7+DVh9yTF86ZjiXJ/rPzxm8kDD2qUD3rixzfeuvshb81d5KDliVVt+wP4Fml/OSY6msQkl3ilZyvUPaP7Oh/6/fCA/wBHoKyFS7LmSGy7lo7M38sLsHE8enOfq9hrh+gqjVxSbj0/pvbBD/mpCfXbI5GLG0QOa/cdg/7Gc1WfPs/H5I8zOIRQlydUdzu7k6OYsP3/wTnbeMaQ4+2m+L9+irxIyZTs1wNNy3nerNbXbfLs0ZXg8PiTRCkYaiVSQZs0E8YXWSLOzRKl35qJ1CCTAq67P4KqhGkG9Err8zfU8ulac/ZywglWe0zVdhgCjASFLGX8h5ZNHj3CmhF969THyL/axWzdF7jvPCIfHNG+6j/F7bnN/usOn5w8y8ym/svUmXnjpPKNnEh54osIeTyk3MvlO3kHicZOE9Fhio2qkSI4UeloSmgY9r5cIk9BacQLdOqGcZ3DDkx7KvG/acKiIFnLtd1leX7Rk88FEupI/1XDqAyQJKgGKimRvTnVpTJMb6pFYtLpoSRqWUNkTtifLj4k5sl7KrU8B1D4JXYwfuucgJAFSj9IBZb0ochlPkjiytZr0HkdRJbyyv8Y/SL4fzn2Ud2WTruGjVWaARV38G3n/fN2gdHtAAPKdgJ2n2GmgWjE0PS1BqZdigxt4Aek8BKs5fnBAecZD4lGJRxuRKavdYuKYFRla+wWA7BZf2vlFx6kPirKJxCB/8sDcjSHc7Xz3Xiezr/bzDsucwzI/8R5tUqFVwGhP7fWJwldbGKu9MAgKZ0/4Sbfv0Y7GayZ11jGUF0C16gBizYIhfsf3WBrLp+d0x+dyd2gn1X3q2EihavHaOiZXbbJulacMlnmTSKe5dqRG/h0VGflKyf5bDfn+kKyVIjhlkKcahy4akW8zUhRXLsrBOZkkg9EorxGtqqXk3Hvp4A02spFfJyy75cmgPU06EBrpRMsOPD7V5Dv14ngokakjCKOZAMW6YqVXYLTnKCjcnsVUGp8YmqxHdiBM2OmlHi6D9FgCVZcJUyc9AlcIw6Ja85Tr4mOtK7l3XS8s2CgB2ge9LYVyiuZ2ymSUddd7HTRb1QqJcqSZI1c1U1VTB8vF4RGHH6i4PrjI5WlOclSRNAGXmdiZI0U+U3l6tzRv/JGraAJPfekxTCykBa1QcbuuOttVXNXJ43rH8ZZkO2hFvlsLQF8kQlhzAVsHvI0yHUF3HtU+do1LUC8yGCg6H26Xth1JcJQYersKOw/YnonMkUBvq4QQ0PMGN0zxWHnvTHwdbOGw04b1ZxTDG4bZ5oijhx36TEn2RJ/eTmD7DX3CRkO5qrBG5EN00WAKD1VN3dfsb62Q3DOlPM6YnUsYvzhj40nLwRsMdrPApX1m5xX1UHz03EaNy759/bN+r8eyH7ILnutuxufLs7xQnufQ9brtdusBH//Im3nDzx+iX70FznPw4Ue4+UOOZA/u+yWRhH/hz63yPd//JP/lpV9kpDSJ0ugIPGs0Hk+uRJ6oDo4axyuN4sqv38/9W1undk6jtMZlmhVbxqD8TqZ0T6U0uC6wcDj6sevtpUa6B39o/Ax/YrjDc5d+gb94//+Gw394hpXnjgiJwR6VbH7WMX4pZf+NCbtvgSbf5Myn9tHTOdQNejJj/ZMVvZ0NPr37Fra+f4U/duFzfHjwLJvGvqYP0Jcb3+pJgzQjLSMup362D5fAaQlsY1DZxihtETk2P6lYG2n7ulQHzERQup0rGy2xcTvvB+LaG+OEUpHvBJKrOwvc59S1o4wRcDdKW/thDkB+c0I4nqKyVLxx+jnKOXxquPX9nveODvjrH/0T3Pu5Bj0pZSfLKjZMKZyTOM4eGVZfcOQvx27sJFl8Zy+gBnVDqOoF/ho94FCK4L0A07fBZWOaviG57xzm+o40t62OoKhQWrhyOOLBi4C0MQvA4PSYF6y8UnL0QE68Te48byzWK91EBlNkrn5dYwmYXjx16olT+9Ct792Tix+vAyXW1xx3k+9eBqNPS0rfjSF9enw1DLqvZ0b5mvydv4r3aPfBIcXg09jookAQt2+vMaSbPVOuKyKDyHMC3bkv7iJxK0xofcfvy49bL90D38OhyFXdHevtZoVX5htsF0Nubo9hJ0PpyNBt1ZxTcGnovmDLoAUWLLok4NtObhsWDGfiVGXDArhsQeh2HnOxWHyaUu64a+B4upljmSX9ehmbnz4Qj7J2vfA+NucofGrE/shqkfK1SpR/jMJl0k1fD0VFpxkEXB7wOYTMo/MakzgpOiSN/DOOzEhzcW5qBraiZ2oGpqQOooh1VOc0kd25nLe2+aON11rlDftln1FSUjjL3rzPthsDlqC0AE5OUQ89IY967U4tmGgW8IrgZH5zWhZB50SWvKytyHWnFbnVHUCcRhCjijm5gICmUyGrvMUrTx00c59G2WRHppvOPmBs5ljtuVmvcjY5iizqhjpYsQg4ZTOQKndivmrl7c/bQw7zXX75+K2Mxo/z4f4tDr73F/lvd3+CSx/RC1Zb5UQFJLGoEEHpWNBqrTIAliUPW69TGn9HrnTHpd56SS8/366RpmVKt6wNdWdxDZbik28+Mn03VvMy0Nien2/kuFMlznfPt5LdB3Wfm7MVKmcYZwWpERZ+HTSNNzRBd4B06exCmc5r5lVC3RjqyuJqTaj1ySZBL8lvsRmo1jQHDw9YedmTzH3MnSWmVI0wK1WraOBAe9/NaeLt7mM8KE3xPjU0fSOqBlmcTzUS1x1OcEdHqDQVxZjhAD8ecvDGPsk8sPdmaM5VmMxRH2bYQ4uZC/PZtCJdbcmmbV5JY0N8iA01QXVWfrq+SwBsoB5JMXn0SsBl0Ntx9G9X2N05+nBCag3J0SoHD/eZtgQsF5vwtah8uKXV/cuxo7+q6+F3+fpvxpiHCsiltuE8OIduWouOk81wibpTfdTEf682nv/7/+9Pke6LTVq/kbW8zAZUHu55/EDmiaaBJJEmTqPZ/FzDTx+8l2uHYwBeLM7y8s8/yOUXn0APB93nbH3/Bn/xQ/+WNO5Rqjwv1Sv8s7338CvPP4p5sceFgwY9q0gPFIezHv1bAVMHyrHBThX3/GpD/twtJu+4RDnWtA2tSusT81t6WHH1ty/x4St/dRFPWE+ynXD/fEbLJj8h193+M3pRpzQG5aXFWmmN2Tnk7L++RP+WSCSjtdhUZAafJ11dbNmiongg62KiZQact5zID1spVpBaujSdBZFwjfFV0EvxU2zIs5MhplR8/KmH+Afjt/DHRl/kgWRIGWo8dIy218qtT0tFf2e8PsayXG6D49na8Yn5G3mxOEuiHJu9Cc85xfCVwOjFY9SNHZS1FN/zKDe/LyPowJnPKda+dMj0gSHP//kVzr35Nv+XB36L9+WvcMGcVNkzKsOFwPGkx8paQnKjTUAiVtHP+a73vBgl8aGvkk6Rb6xT+trx4MpT6PHT9C+m1MHxpVrxC0dv52d+4QcYXBuw8mrD4MUD1nZmrD6lOXjzmMMHNDvvaTh6aIVznxow+uKWYAOzgs2P3mR48wyP7z7Gs+/c5C+94Xf48OBZ1o0jVxaL+ZpqRt9O13cVjICsSoMxeKvZb2Q+LoIiV4FcLewd2nCzbZZun0+Uw+VQbHoY1+jUYYwnSxzzqyN6rx4TihK1Ol7YujUNpAnNas7aczXjl+QaKn91g/GWpzk3hrMr4l2+OmR+NuP2ixl/4dpfQk8MulaMrsC91xy9W8foq1s0b7jQfTeVJCSDiuZ2j+xI6lT1QGGnoPYO8VWFLuoOy/RWLIRUvYh/Q1T59VYxPxfVKBqZp9ee9bK9axWkFsljWFb3JU7hJ5pKY3zfOEIvY3r/iMlFQ9MTELoNY0UBK+KtLQai6BRlWsC5+3mq3tMyo71d5LwtSbJlSGMFkLZZg46EOTl3dVdfGGQVWgVePNzg7/kf5MrGk3x48AKbJuvmhtZLWpodAvob1Lb/daMYbSd3UNJJ0PTBzuW5ZOpxqaIaxcJolBgyc43PLMeXNb5XS3f0km+WXwKIvZeTLF6IAsT6KHXUON0VM0+DxgG6zjbzGvLaPtD5556W+VYRRG5Z16k9Ga4Z7WkieG6075jUYenzAQ7KHqUzGBW658ypyoj3muM6WzCbleqA70AMDCNz+fQ+vtbogOglgPvE8YkdiP5UwqkRoN0tMaiX36OT9g6KshG2tI3AvNWeYB3NRsn+o33WnwY79ZgmynRr1clwUzfiPaZ07JBHKm/tDWzFo1hpYXd1HpKxaCyyVdLc8M0eyx0sbQCpXAwsSwGlTRWkaWMmcm3Ki1+CSxQ0ChMl9coNT2YcOi/xa4qJ6lFUKUGrTt67n2pmm5rJB2bwcp/x8yKT542wHAQ4VehGU60GqrFHN4rkUGEK1fkqd8BFZIh4CytXPDcfTUmTht29IZUzPLS6Q5lajnyPXNeshIKfO3g7F/uH/OTF3+E31h7jF3rfzcWPWPLtCl05QqJxbfEDyA8Cn3zmQVTiOXPUeoyLvL8PuruWlQ4LVr2OBZO281vR+fIs+6UFDb5naDTRp5oTjMRWOjAYoP2+bZ3UBQzLnUaKagWKM558WzO9LIxuOwPlNcW6ol4JpAeK4bXAypWCjt0c5NgHqyCyH5JpEzvSLMlUMz/bp1wPNH1YeQGmswTlFpYDAKbwqKJi5cUp5eqQ8R+/jVvT3HrrefL9jN52xd6jPc6vHzFL+pRrgWazRhkPjcaNXyeNGn9ARwtIX21mPFEJIL0XA7pc11ybr/GbH3sLb/jZGfrabdRwwO0fusTO+xqGLyRsfq6kWrXc+PGG//b9/zMf7N2ir2WRN0p17GajFD60vsuxyzMYPjp7A+vPOPFohHj9LTVr5Zp7cmGt3s0nbB4qNAuGcvt5dfBcbzaxmeOeZJcdN+cBm/IfveHf8jfP/2lGLxuaQYI9LNFVQ+9GTXKUcvu7++y8q2HlyoDsljRUYAw4R+/FXe45HHHz6F7+n+9bY/amjA8Nn+LNyQLY/1qB6fYcfMsNpRYSsmppTut+LoUFXUS7eCzewyK7jY9vo+K6GmIg6+mk+pQJC2nvoAjNouC5YFcvKlG6UvS3PX7/AJUu+TG1THwtCYycW49KEpphJj6jB8dxnXaIUSxQOo4eyHnoket88ukHufjLht71CSGzqNoRqioy/gLOKaZlSu+WYvTcIczm0i0br9+uK9TTgTRyc0j7ajAGlSSSEISAmszIb2iKyytM7x2wsnsMzhFW+qh5KWwOIPh4MIOP7HC9kJhaPnXGEEIgvXFEepSz1H8in3+i+B9/RMnir0MldHGOlt7vjn0Kr4GlnQLdlkHq1wG+cGIY5UlOlULvxo7ufKMjg/EEE/ouDGl96u/f2H3+3b1+meXcvSeLgrCLXegmFgMAig58X/h5uSD7ohGJzSJ65ObKdV7S7XefBU1y2gu1PW6BE37SifLdcwNqMuU4iBdVEv26bzcjnp2d55M37+X49pBkx4oiTX/BfAMBpF2+yP2CluY/aZYRYMInAkLINmGR4wcWuUELSPsWkJaYVXm1KCzDya4L1TZgfpkTFlhI3Lcy4d/kcfDmMS5RnapRk0vBosnB5VBtOBg2ZIOCXlZhjScxjpWkYpQWjJOC9XTK2fSIsZmzaqas6IK+LslVTYpIECd4jAokLWMYxcxbimC55cbcqsdcq9YBmLuEEtuxPk83SHcs+sZilacJS6pd7VDSPBvStpjDIh6PDxedRgo/iQ1Jiaf0isKnaBOYpBlJInmnNZ7UOKz2GO2xylO4ZCn/lbtqeT5oZb4HtiTTDXOViBStqXl+chaG0NcViXIUsTEj1zUJTSd17/CxUBf/rhY6BA9nt3iquMSvTh/lJ4ZP82dHL/GlH/4Mv1y/i3Ofdpha7M20t4QkNn1HoFDVnMht8HHtVhAiIKLa7rN4n92RD7VgdJzsQ8zzQrT76GKO5W2JAHWMUV7vDOnTzNe7+QN/2aFYBBWvMU4D4YlylD6h9JZEOfbqATemY46KjFFW0beVSMmjaLyh8kZ+OtM1SbhINChrS1lZ6tLiGw2N7pozlI/+76XCZzI/trnrwSMaMxdPZTsP9EAY0IvLQeorLqAbjyprmR+NwmeWkBrQiqYnpJGmB/UIyVlb390gdiX4gBoOcOdW2X7niGJDsfmFhuacRyced5xg9y3JsaiNtfNoWwhtRzCyDgQFup2zA6ha5LbbhbArwGq5JptBYH5OMboSSGaBfK/BHhSorV38dAreYycz8rMPoBsj62eMf5UOXRONVv7rYkp/q3lI320UwYkFRy2er8xLlBdVFBNj5Roow6LBrR0OqIM8VwbD5uOBg4cV5ZrUj3wiVgsrL2jU9W2p2YUlTZ88I78956c/9r2ku4b0UPFPnngXD/32dGGNA/gzazQ/esAfXfk8M5/wXL3OP9t6N599/n5GX0q5/ExN0A47c6iiwlQwm6UMaincN2NRxMuu7uG2d7DT85QrWuaxRC+AZJC8qHSsP+2pX1kuexuyQ48uYi1Gh5MB2mmQoTtIUqMkPr/68Wu4c6tU96zR9A3V0AjrLTZmBIX4SLcfHckRsm/xbfTS/NvmhVE2NugQAesYTyWBkAWC9WBDR+5CBbKsJrVO5p0y4RduvYU6GP7o6AucN3SsaZGADksw593Ht3pT+B+k0Z6HQz/n5VrzifkbuFatkyjHUSNNhNmNhPUnj9Gv3AKl2f/AA9x6P+S34eInKpQPPP/nRzz89lf5e/f9Eu9Mj6NCn5AGZqHCoMhUQh0cn6lS7LN9kqPZonGujSMyw7tWr5IrIwQKBBju9heLi8BVW296SxJYXf0UP/PAuzj3vh1KZ3nh0xe59JGG3kt7rH7xgNHLGQeP9Dm+T3HrvYb5xkXOfPYA5UQBpPf8Nvcdjdl7ZYO/+Yd+mOO35Hxg8Axvu4ud9LeTIt+XGxrNse+RH3hhFdcN9nDOP/rN7+dX3vgoj67d5tHBLd7Ru8I99pBc+U7KO4/n20U29aquCBaS8zPOjCfktmGQVBxXGfsvjVHXbwvj2RpRUtQamga1d4hNDCHR6Ermx/VnhXnf9ES1VbtAM4iqop/SgMYngnWkx45su8Bc2yY4Rz1KMJVHzSvC2gpZ1hBmGlM4TCnA+eBGwE9n0mhXlKIwBV1sHfSpuS3I+tYMxH9bSwUuWiD5GFfdcXDlDbUCtMTXsbFU+RCVSkK3hmR7NS5VTM8bOi5uW7NpG5GWaoPLst3LbOlgJEaUXAB8VKHSjaLJghAk7VI+FkCVBkpw+wmhFmJkExRFPwig3YsLYCqk4aNZzm4xYOvsmB8bfZHHUvnwVspbLED8iYaZ3834+kHpKEmilHQVgJw0W3jxhR22mZMEo8EHzFxRnEmpVgMkAkgHr/BKn5Dobs+d94q6iRInAZE40iIz3bKitQ4dKyJAJ0Wt2t/vMgTgjt/jDoB3IVmtVGBeJXe+HlkcVJS8boc1rnv/In6XFvxugfB2n9rv2nZ5W+2pWrA7dqJXzpDcBZR+Lc+bNpZaBp/b7Zffo2WYs7R9Yhw4kTRrgfAQQXYXFHpJrtwFxbyW41I7gzVSJACRG57uJgxv0nmSBWMEZG4P+rKX9CmZnZBaAa0bs9hWKYLy0Djp+I0SOd/00U4M0AG9wUAYNlBq8W7QimTu8ZnB1rHTOXoGqEaSOm8VbrXpjnsvran6lmrd4BNDtifHaGoN9VDYZdlcMbsg3caDGx7lpJCpfICJQnlFsSnSWaoGO1cLILoFghthBpcbAeU0ykjDh58mzEcJT26f56Dq0d+oWNFzCp3w2d17+ROXPkeiGj44fprb7x7x+PwRNr6YMbhZoZogsnaZIWhhK4+/kIoM0cQtaog6NpTEYtRCkm65uLL4XdjOqpt8g2qZIkjH0QBGr3jSadtNvvA1l+tl0XXqk7YIsLiPlAuUG4GNR3aZ7G527GkpqAkg3YwdzQh8ZknmqUio1R7TXudKUY8SkWZXUPclaertOWG9W00zDBQbCp8E7NwJYALooympUYSixLx0g5WNB5lWKW/euMXOQwP8J/q4xlBsSiOMruXc0sTuruTrRUC+M77aUQfHjpvzbL3BS9VZZj4l11KUvFGu8huPv4kHf74mefkWDHrsvf8iO+915DcSzn264Oj+jPlPHPI/vPVf8gP5MYnKKEN9osPMEdBRHqUITdeBlijDS/NNTBkIaSJsm+UkWUsX9uV0T5jQBJJTQYJ4OquOiS0y5MKY3qpXSdKGXDUYpTBKCSuoliYLn2qZ77TGG9CNJ5kGyH1sxojBQAhdkcvuTjj/cdidDPl/Hf4wN797zJ9f/zj32cBQZV9VQvwHYUjQuLT+WhZNNmrpdxZzzsniRDtfxccRUAle2CfBQ1s58YDSMqkqraBBCjJN+wGxYUiHzosvOVIMrkzk784RnDvB2pIOUNUGMYTRgJBoklvHwozIM/CBkFj0pKA+P2bvx2fsfvYyD/1Cgd2foIpafKcTS/CB7NAzjUzp6sUB9zxeoG/vLwBppcDI3CaeqAgbOoLEVI0UUo2R/UoTlDUSIxxOyLQmXB4xf3iT3rNb6P1jwqCHKko5JtoLau+jqovRBBLxrF4eJl6hu/sMr52h2DwphdsyLrtat4rFUae6ps07rG++ivpOe37vYNW3SMPdXrPcXRsfh9Ovf52MBHcHM7odhjvXsmSJEbcMRrePYQFAtbLVX+kwfz0g8zeyNGdO/UyU4tgv9j1VijIsPrd9vg19fXxuVTfkSlEEmHpNjRTlM+Xo6+YEE1peF/O1eJyMCrhgRHI2WGq/SAuv12vUwXLo+jx+dC/P7W+yfWtMcjuhN4kgwrBthpR4VArXLBLioDBz1X0RnwZ87kWWu5WHbs/nMvgMJxiE7ZdWJ5QjFjFkN7rHoZvvus9YenyCOf3ap+n3bRz8sWkHNs+rpFPk6kdm85nehLP5hPVkypnkmFUzY6QLNsyEVT0nUZ6+cgy0IkGhl9Ycg6IOIvG+7CVaBc1xSNhzQ458zl4zZK8ZMnEZtY/ezKi7AtIgilptA7NSAe/F/kqnDp8YKaYb8ZGmVY9oz3F7GqOqB+181/7eGHwpsbTTAWcNpUlRRgrwaVZjjADsWnt6SdNZTSXaUXnfgYUtSzU3jVgUKZlzjpoePmhuzlaw2rGZToRpGTSJcuS6JtO1KAUoUQ9IlIDUiTLd8wB9XXI53eXJ+WW+VG3w3myf/2jzNzn+UM5njt7K+tOQuiBMi8jKCEbhMrOIg6IXsKx97XPxZ2RNtxPAckwhf2eRFysl99rpvAqW1hIWjGnFyZtAqRP3x7fzcGisdhzXOftVj63ZiEmR0UtrVrP54ppqUhovDOnaGXysJblINJhXCcU8pZlbqDVdc4aX+Gy5AGmPY8wXc3bVEKWwARSzTakvmNKgm0Ay8wJAujYOyTuFMDP32MLhEk0zNNQ9Tb2iKNelVjc/36M/r1GrK1hrcbv7qPEIs3vMud+Yosqa/e+9DKVC7SVk04XKzqIZEtJjYdG6XAA3n8V1uZZ1obMkqJfiJtc2Tgh41/QEcPRJoB7A4KWGdHuKPprhZzOCcyIRfXzM8Nl9sjdu4lLd2RqkmcwBpbN3NGPB19HE8C06EqXZdUNsx2CXa+jAZ4z0DBdB5+Quh8MgU3QdoK8b5uua+aUGModu6wyNprebQFlCNopNDVF1KEtBK85+3JDMPC5R2HkOeoZ/4LIoHmooLo44PnT8+c//RebzlHqa0H8x5cLLnpUXj9HTkvk9K+jSEayhXA/4wuCt1BabnthDsH8oX9GHRQxs9MnaIkRwF+ZnpP6mIyOtv+VlHlwOCJcZ0rThxBLw3ua6bU406DG5f8h8Q3fst04dAJbuS2gbOFoiyx35XlQZUEHug+WG5rZRJdgISCcBkzlUrFe3zLc8rQlB0c8qDuY5/3b7jbig+fDoCR60JUOdxWb31yBuLQVUp4lb3xm/f+O013EdHBNf8kpj+GJ5iZ1mhA+KvqnY9kM+9dmHue/jNealG6g8Z+sP38v+mwP9G5ozX6w5fDDl4AcL/h/v/hl+sHeDvjJoLD7mXHXXzCc1okmo+bs3P8zGkw57VEBR0hEhtCIYzcV0v6th+ZaoszTETo6TxAsES/jzlz7Bj/Sv8nOX38B/ufFHeOR/zEEpdO1Yf+KIwc0e+4+kHD0QgFXOfBb0kaga2K1DzswqlF/jf5j/ILe+a8zB+AnenR3S1+I7f7qZ4tu9wWLXDendKqQB3xpUUXHfLzYcP3GWT54/x2+vvIVmtcEMa8ajGeeGEx4c7vJQf4sH09tcsgdsmoqZT2j6gcvrh7x17YY0ebqU39x9iPNPFOJfPhqhmlYJ10mNKATUtECt5IREoV2AJqDLWIeJDZI+k4YqqZsr6r6OtUhFvZqhq/WO1YwXPKk6t8JkTzPaVgJUV17A7Gm8h4whTGdkB7WA340QAk/IbAOEQDKVe0ZXQmzzFkwparAqlZqTauf/lgGgYyDTYqNR4Te0tVHnwAgp0UwqejuimlEPlTQgmyXwOSzFRTaqzWjwEYReVqSRNSF0Kkqm0NipIt1XmErsk0wZsKXEh+331rUQ+ezciZWQVdQjK4RBo0DpKCuesdMb8w83L/H3738/f+SxJ/jja5/hbamQnIQx/Y0jDH3doHRQCu3l4OsQsHO6YkCb9OsYSIvvFJhSMTtraPoBnToBtBuFdwrnFIvGM0XwMikuGg5iEKxaUHqRBNcY2naDzntahTsC0NbXWa4VfYdl00L6fvFZy+ztdqyO5uS2Yed4IH5AQaG0R2srTO5Gd5T4xDq0it2bS57XEvMsbgQbWd1ae9b7cxLtuHU8Ys7J7WDB8l48lg74dk/1UtVFq9CV+JblwfELUN3F7VpQ1HAncK2UFK989NPuntNikG61hxSq1ZLpZUt2pLDz2DFiY7kwaGFFK/FcCa2smNYo1QLYCpTpgj6RvI7dKa0cz+nOlm/SaMHo7sB78L1Ab1ww3+53iaQuA01fvMXabX0iyWZQ0PQVvdWCnpWCuAuKQa/EOY1LLSUJwcoEpjz0vtRj5Ypn960K++gR04+PyXcDppRJ25tAcixganHWC3jbLBLIIBVOYbj0oBl5plaKRU1jQAWmswz1ao9n7sl5ZLTF2Mw48j32Zz3uSXcpfEquK35k/SluffcKt6qLaJeKnHYj/tKqAW1gcNOJMlf0z25zxTYJaMHh0BVb4vHsgJsobxQ7qYOBcmSYXpRJd3Y+4C6U2GkuhdEok+617hJ9ly46VYOVxxB9pRtJhF3PcXl0wBOjMyRT1UlqoGSbbMtSbTiKCzXVyzaC7hpXLTyw66Gm6cVEJgLxpghkjefs5xzl2HB8j0I3So7TYECYzXC3t9GTKaGqCE0jUlO7I+yZGyTWkR421EMLqxXb+yNWgZB5VCX3VtIvqfbyb+Tl/Z2xNOrg2HJznq3HXKnOMHNZLFA6jl3OLz31Ju79RciefBW0Zv99F7n1fZ7stuHC75Tsvikn+/Hb/Ms3/0PusyntROCWwJNEGbKYDADkylLjus8/qPvx3tV0RqFLEsZNplg1U+rgSe4SILQdox7fMamz+Bk3qzF5WtNXDWOdotG8VJ4lmYDPTGT0WEKqUZVHVw3Ty4pkKyE5mKCqWuSVnCcktgPz7N6Us5+s6G+v8C9n74Xvhz+x+hkeS0tyZXFBvaa3z+mE7G7bfCsMb082UXWdjUsM6rC0jixjSCfY1MtfOxBVQ1T8XeZ2DYQ4x6sWzA06BsnLe6UkdqsV6SGYgwkhTUVi6fTQi+KOyjJ8P0PPG9Q0spqdF4ls73HjATe/d0A9rbj0KY/dnS8sOpomBnPS0OAajS8Nq89AeuMw7tapQlL7XZ28PsRAX/ZL2M2hquTQad3FBnoyI9s2zO4Z4M6soK9tQz+P20jxDKMIwUuSrZUcz9Pf3bmOId7bqdFVistObtIyl7uaf1uobU9puHP7rziWsTa19Dj+p0696entOoZ095qv4jN/H8eXA6RPy3WfeN1dAOkT7OgItP5ejN+LWaeV7U6UwqAYaGKjkEimZXKb4sKCveSWAMVUeeoAB96Q4BloUaCYYSiDYaTqOzyll4+tW2JV18Gw7VaoguGS3WfbrfCZyQNcmW5wezpke3eEn1n01EjcOpKGx4AUST1qkTTHHKUFLzq2TyJxS+tp1Sb0IarYnACj27EEYMqbQlChU8RqfSTVKTWIE+Nul4SKsahXX909+Xs8fvLRT3Ah2cejeXJ2iVfna8BCtWpSZ1Teco1VclOTm5qhrdhMjzmbHHEp2eceu4enIlGBHEXWrq2nDoBHpN13fY+Zz9h1Q45dzk4zYibBMYl2lBF0XrZ8OiHlTejAN5C8MTWOwbDguDK4Wo6tS4HEC9rRSq/H89oBXK1dW1SckjqPWjRqRda91yL73iQWFdlhygQmiUgIai15aZY0JMZJ3hqbqQvjKJzt8u1UC4hVNJbb8xGNN+zVA2z0EG79qPum6uK9RDn6ugREIWegyw6oHuiSC8kBj8/u57w54rFU859d+Df8hQ9usqvPs/4MZPtKinAxl3GZ7hq4u/pJCAKsgIDYLkSPYL1YaPypi3Z57dTgzUI2vOtleo3r/ETT3OsIgzjNdL2Txfq1zsqe11haFuPUZzZBZOKPm4yt2YijeY5WIcp2CyBdeUPhpFG/dNLA4bwWtnRjqJ0RQLoQQFo16oTig46Pu0U/xm/Kqw5oa1nJLgOUwrtoi+Ch7ptu19tcWTeB7DBgJy42cWtcqglGlA1df+maq2phV1uLObdJyFLKy2OKjYRiTTE7pzDHClOCLqVRsGukNNDbDqw9PaM8kzE7Y2gGimJdYlfVIH7P7U/XzrsLIFGUZaJcppem63zfY49r9ERUyfTqmDCbE0q597i9S75zhmokx8hlkCYLaf62+eprBaJPy3V/KwLZubJcqzbIjrwAAYll9Krnv3j5J3j72jXuz3d4e/4K99gZCXfKd2sABTOvKddg/dIBRgdS40iM45Vnz7P6pQN8WWLCUFjDzhGcl4ZprVh5RRBxlxmyI2nYD+NEiuIuUA80408n6DpjbUa8XmvS/Qpzc48w6BGMEh/PxFJvNKi5kVpy5alWxXYoRBacnVSiRBj3PRjVWeaFWHP0BqrV0DWN6krey+c2+pJ6iVW85AfK0QHTC3vBpcKxEsDBDdIFoGzj2tUsrWXtlKIEiG7v29YbumO+RbuSEO0vgxXQoZ23QxLEE1S+FFQKV0vtTlVaVBdTz7SXi2JWbPo7nubCYjzb50dWvsQ7swPW9CnZp++M1/XwBMpQ80pjeLK6yM1a4sNEOwqf8GvPPcoDP9uQPXEVlSRsf+hedt/l6L1qufCxObtvyTn7p67yz9/wT9k0Vjzn4/qZRRu3OiyYznVwVCHwyZfv56GrM1QR/dFjLUNpkT/etEeRCS11p7a21Nax2vzMIveui3kNQfFweou+MvzJ0cvsvP83+bX/6ftkrq8cqg5keyWbn3Nsv6PH7ts8qy/mpIdTUTDQCjUrOfOpXWyxzr+evJvr71yFcx/l/fkxWukTpIe2nvStWEP6RgyNYr8ZkNw6xDsv85ox9F7cJT0cMX45iexhRbAJxdo6N9bOcGX1fn5p1eNWGwZrcy6NDwX3iVPgWjLjbHJEHQz/5so7yJ67KqFMnkkTAwgwC1J3cR5TNChvOua7mccstfGEXKxtVO2wE01IDGmqhRyTKFymKc4PRKW5FKwBL++z8fFESGClE3DVIkTZTNYFZS3J7oyQJYI/NB5Vnax5qdoxvFaSHVhcT+OSBWDcDGSd0K0KsAsLcltgAVRrJcq4jZc1xSMqIc4TUovPLQTIDxymNpQrgtc0vSUFDZD1Il1qRtJ07GiCYKrpQVTQmUIyFUDdFo58p8JMK9RkjprMJHaK96YyGrIMlSyRbkOglyYLjC3mIBIXGtz6kPmFAb/2lvfwi29/E3/5rR/hf73y5Dd8HfmqQWmXx0CyLRB4oFYd6u4TRZNDqlUsGERE3kEyqvBe43JLsaEI1vPopS3WsxkvH62zdzygro3EA9rjvUbrBSDto8eOsR6lBPT1TqPiNk0jYFqbkMpr1Qkwux3eK7wTpnXwLCTA282WgenIzAa6bbQOnB8ec29/n9+cPkRTm3hMNEoH0qzBGJGiy9OajcEMTeD2ZEjpbPfe3iuM8VGZ2i9Y116TmUY8nGtL0xj0KRBaRyC4BbdNlDBTKqCXHleNxRrXFROWj0Sb3LXvXC35ebd+1svZ6bJUug+qA6Nbb20Xf0/ThsmFivJGRm/HyaTTJtIhdOBz976JkRt3qfC92EmgETapahxhIBd/uNu234yhWzAhLB5nUgwpaiUgbFs/sIp6aGV7D01PiWQbgXKsuHd9n/VsykRnaJUL8wCg0rj1mvmKwhwbelvSYV0NFBtPBP7jP/Vz/K3Bhzj61fP0bwXsXCoQLodsD8p1Rb4jfkBBQT2URceZgM88rg80ApKaayn1PQFzbBg+mRAMVA86tqshPVPzka2HOD865nazwnl7AMCeG/CmtVvsflef29mY8fMp+YEnmXqSSYOrNTpriysnwZWgY+AfFqDMotOfJdagOpFsB43MI+85ZFqkJM/2SJ/OyQ6CsJrNyUReCj+q6+CWxwGfSRKgI4sw3TN86WMPERLxsjJzFcFsYgeSFF+HZ6cEPcYlkcGdS4Gtt9ugvMJbRTqRxdIniuSoiiC9p3+lZv3jBaGf4wYp4cIGej/Db+/gJ1MIHr22ht6dMvjSWX6dRxh+ISe9dovpu87CcYIvU2bn4v2bCDBd3+6x8YVvv0Dr92OUoWbLlTxTrXGjWePQ9UXWUTkyXfM/PvW93PtPDYMnboBSTN9xDzc/XJPcSrnwOzX7j2R8z08+zn91/jfIVIILImekCRGYDdQhpghtTT4GD8tS2x0YEzsDZY2LCbIRSb4ER6bsCUZ0+3kedUfC4COjerceMO4VDLRn5muK4PlHz76HS9dK6qEVdYGjyMoHivN9ygcKzv1yipmUUkCNLNgTKhjEIO/FQx44GvCzxfv44rsu8R/f98u8MztgrPMFY/BUorAMVn8rD5eeXK/uJtN2msl0Rx1Mndx28WZxjtN0c54UQwS9Ui4WVk4Xo2OBU5eK3q4nzOZ33/lWujsCt2QpPrOYaUWoa5H7bhpCnoLWTB4cMnm0YuWJlNFze3HfFaqoOml3gOS4wTcaNbWsPj9FHU/jfnnIUrH5CAtJYOWR57rjIfFEaMHudqRRytt7zP4Uc6ZHtZbT2+tBGRsnWhlwoLMRUUqA9aZZgN7t/sTPS7em2FmGy5YO5qnjuiw3HmLn6tc87gaofTXhTrvd6wBg+0rjblLd7fNa+Q5qWPaObpm9y2OZHd1t1772Kxyzr3W1/L1QdfAII1pAQk+uFI6FLHcdNHUwTEPK1GfMfIZDnquC5BMTl1MHw33pDg+nW/RVQ6L8lwf2CdRojmMRN1EOh2K3GVIHw6qe8VJ5lsd37uHm9hg/STAzjW7lJG2ARnU9mQEW1jBRKYB4W3kbcLlfvC5zAiYC+Ggv0Jw6G8us2S9zTYfl3O3rufbDAuz5Zg+tPEVImbicvWpA5QxNMB3ABZKnlc5yFHKKRs5/L6kZJBWr6Zwz2YQL6SGX0z3ekNxm08ylABiHXGdybbU5YKIaclUzVRmaQKYbuea8dN9Y5clNTaN0B7y5ENn4pmFeJV2ebbQn0Y7N4ZTMOg57PcrDDEzAZk48dBvV+aEprxY5VATgzCzGtjqe2nZ6iA1Ybd4VtDTQtsyz2gaq9nrQAZKAThzahC53NdaTpzVZlAFPogS485rd2aBTLbPKdT7BWskx0ZFdnSjPajLDKE+mGvqmJFGOgS7p65J1O+HlcpNfnryF0crnuGBS/odH/zH/Rf+P8MXNNzB+Nmd0vcEUXopsqUJ504E1hEBQsejqW4RSEWzreReLYafUwoJWC7ZfzJ+CXbTshBDuuM5PhFahzbm+w5JeHi4o5j5htxhwOOsRAgx6FYOkFEA6erDXsYZSO7lnK2coaktVWerK4ucWVWmZN32cd7pmnMiqbEPnwInYrVNhi/OVT4CkjQcX+XELAidTsJPYFF16fKql2BzvO29jbBgC+dYcdSRKOeWjl9h/Y4bLFdVI1LhcT4Dj9KgF0uO/eHyChsGtBvvSTXRzjqB7TBODqdTCI9FFxnTTNistzbvxupO/KeqB5Nq9nVrYeSHgV4e4YYY5LFA3bxPmwsYa3GwoNpJYMIZMtXmTF7l1/e1naeURda0nJxfp3SyEoWY0a08csvuPLvMLl++h3PCE9YrReM7Z0YQL/UPu7e3zQLbNpWSf8+aIc6YmV45qzfOuM7dIlGeUFBg85UcvwIuvorPYodnG7PWccAx6fYhXCpcbzNxhZlK8D0Z3gVd62KCbIDLXywCyUYRRn/rMEGCpsK/o3RAGHQHqYWD0SugK7WbniHw1i3LySoCrroEVCCIHn+1LEV/eW+Tum36CmTfSLOUDqq03GSRf0Fqaa4kAS5vjWgPeo2c1/esF3vQoHTQDUSEEuvcCFl6fFnyMh0IEodt1sGVCK6fQhSI5kv3VldxDpgxCpIj3kKnErimZOlQjBBhvLd6qri7vrWKSDfmF4Xn+xQPfzdve8Cp/6dJv8YH8gKHKTwB1LvgT7OhvRwDv9TBaefX2XEx8yQ2neLK6xFY9Fnl+AjOX8rPPvI17fsaSPXEFxkNu/fB59t7hWP+cYfWFkmsf7PFjf/QT/Gdnf5tE2SU4WkYdoqw/i9qTRlMHYCdDV6XIMBuzyLeNIRjNg3aPRCVdvQmEFW0wJ2zkGlz3uFWA2tAlYClbwLhnsNNGZJUjycEUDfmu5/BNXmrBWdrl5apxhBBY/dwO+c6YL+w+yn/5PWP+m4f/BQ8mBWfNgDq4ExLeX+l6/oMKWnsCH997kHAsNgqqrMEIYUDPawEDfeg8k/MbArKGxAjAmxhcr8fx2oimp1jTcKO4yE+fOYce1qR5w4WPBfzBoXxgIoom3UhSqRnlGXpaomqxc2uVc0Mksuh5LbUwoxZ1qkJqNz5tC0LSZKedxxxVKOfJbh5x9paiWe2hfKBazWQ+VYpw6ayo7IKo9pWN1LOaaIe43OAZAmlZY1f6NKOM6aVM4ouexlQSo/uI5SofLULvEi8rD9qJGo2qxXpWzyqJyVONy02M+wLJFEwlc7rrSQzUrgnBxjqYh2QqVqx2BulxID32ZPs1yUGB3j0iTKaE+XxB3Iykz4AA0XowpHjkAi7X5DdnmFu7HYEo9HNpKMtSqdOpSDLKU1AKPa/p3Zpx6bZi9myfv3PtD7P/IwP+yvqnSJTGYr4h981XDUrXA9UxbwCh0nsACXRdJkh920UWYnEi31FMLhmyfk2x4SSRHNf87y//Jm/PbvNEdYZ/sfNufufKA50kd/AKbUMH3i6P1ktamwWzWIDogHM6+k4LiJxEsLdNSBPjxf+YBShrYhKq4k+jfJS+jgl7DGrbRNVqz8XeIfdme+yd6zPZkKBMx2Jabhp6pqYOmtVkzqXsgEQ5rq+uMnEZjTeU3nbd50UEqvtWPBILlzBOpMNwb9hnXidUzclymIss7xApMD4yy9vxps0dNIHn9zcpa9sBykYvjoXz6gSAPamyrhveB4WO3fniry0FiCQysnUE0NtCxPJjpQJJr2Z6OWX0qorMOYO30mGoWlnlRiRznNXYOCGF7OT3VK3Mt1IEKxmZikzr14MH3AlAWtFNHEfHPXQZZScGkMzarsj43eP9oht5ucthNZsztFVXXJ3VCTZxhJUKN5fZyY0d0yxIkFophjcaPjV9kHeeeZVfT8/jMrkXTSVd9dZJgWb1hci0HRlmmybey+IT7nqekHnqXiDdtpibGdmB3OvHD3rOj6bMmpQXpptcu7XGDz3yHAAvVWe5mOzT1xWTJmWtP+fVzT7zoxSXGZKZJpkZ6SpybZAj++Xtwses/dkVUU4B00CU3l6St4hd5O5LY9IakolsWw9bWXQWxcyYk/gILrd+fbpRwryIMnm6AVOoKNstIL4wqEPsOpdjO3zJ4q6tSoIVJW21k/mw6enOTzRocNF7oV5JsfMGEpFjZyXHHJeY/SmqbhZBnlbSslyWsLPH+Y8NmV7N8dbjhz1sKXKY3imaQcBMNMqLZ3hvJ7By9TQn6jvjdzvq4LjRlLzUjNl2KxQ+YWgkMT/2OT/13Pdw7qd7DL5wDbRm/pbLvPohw+CZhM0v1Gy/I+VH//TH+c/OfoxkabltU4IWME6iJ0/7N60WwLEw56QwuizfSASI44JH01cMdEmmLGVoqKk7ye5WINIoFX1h5LNa+fDDusdaNougSODvH7yL4b8ZkmzvU6yPF+oGsfC59d0JHBjGz02InVV3Mndg0VXrAunWlDf8i4qtV+7l//BD/y5/+U0f4d29l7jPTjhjel3y9VpgdCcpFpO1bxWvIJfeLWI9CULDXYDqO7ZnqREqPh3lN0XGKD4X50eIhZ7Tp6UFjgLYOfRvVwIsl6WcR62l4SH+JMp3K6Xw/ZxgNWoyWyiXxKDWrfXZe9RgtzXnPi2d1X6Qy5w2ny+ksH0g3S3A90gPFXbrsEt2Q9MI66Lt1oxAk66DyIrDoiAEi8YM5whlKZtbK1LedUN+fUJxaUh1eZ305dvQy7qCFYAyYcGWjgXe0yM4J0H5zj7p4Rrl6l1P82LNYalR4C63RLjLcyc3oPveirtcC3f76LaxaxmYXn6Pr/wWv6/jtG/0yefDiedP29W0MIvv5os7v13rufzlxtcze5i7XB9fy3Dh9HeR52YBtl2PXT/g2PW4Ua+xUw+5Ol/noOpxUPSYlilVY+LlK7LFSgWs8YzzglFSME4L3jm6ygcHz5BHoPm1RhGMMGR9j0t2n1zVHLo+T08v8OnwAJ+7fYn9rRXMoSFp5WVN9AiNwEpIhPkDdEzWVgFIJCaXPK0UkESGtI/s6Fqj6qV5UC2/1wJwVq3cbRydPylLJ3r50H6liz60gEiMPb/Mpr9fwwdN6RMKn2C15JqNMyfYoSHmjW0TsA+KaZUyKTNuhhFwlkFac6Y34WLviIvZAQ9mt7k/2WGkJcd0KGY+YRpSDIGpzyhCQqoaEt1QO0OmG1bsnLlPqb0RaWI0qXY0UXnDKMmj53VCagXkbfPHRDvWezOGWcl0Je1yw3mVUGYW1xhCowmFXM80S2z12MzTKk4o31oP0c1ri7VwYUcRliWv23zCSCNwa/3VJIEiDZ0UrU1cp5zmG03VGHppLXlszI0z03RzkFYBqzy3zRCtAj1T0zN1l//3TN1Zunz64D4AfnT0BA9a+LsP/Cw/u/kwP/3m9/HKlTP0rmVR8h76t4R9aqoga11kQrSN/V2+IRcByoUTthAqLP29vVas7nKtVsrDK9WpOnUAd1gcV5TqJGVfD6M5xVrW34Q7tWdqbs7HbB2NqCrDeFgwzoqFn7pLRB4+SPNIGdXkitpSFAn1LIVCo0sdr+ElL+Z4ncOCLd2prLTnhQhE2zitaZasqAJhqYpnp6J8M9hyZAcNZtaAAp/YzmbP1IHebU1vy5AcFugrtwhn1zl6dJWdt2mq+0qUCfhJgp5pKYLOJTfu7r8IbgctMWS2V8L6GELAFJ5gDKaM928jn9kypQmiZtaC222juuyfyEhm+wEza1CTGX48YPrAiPmaIZnnjI1CvXKDUJT0rx4xubxOuaaoRwHVGCZLUjbfiiznb8QoQ80zB2dZ3TokBC8yqtOCM5/eY/xyH290VLAYMBuPeXp4mc+vKcq1QL3iUWsVKytzLqwcoWvxSj/XO+bebI9f3n4Tax+5gqtq1PpgEbN39nwefTSHUS71kcZ39b4u5zFiEWWn0uwarNgYeKuoVxJcvkIwClPG96wbNj5jGGw1+CTOW4AtAmZtNeYAnvzVQ/yKkFdU1YiKE3RB9+DaDDvPCRrqgTDv6r6Na4+VGmXHgBNgrJtvne9821uAugPSc0uwWu652lAVmrovKog+iey3FphuvdZbGW4tilWmADvTAlCUIYIODjt12EmFOZyjpnPCdCbqUL0cleeEshImYGu7ZKPSpFqsF+3vIU9p1gfcuv8B/urbHuQN77rK//X+n+MdaUOC6XLqP4ig3LfyKEPDS43l+eocB65PHQx9XbHXDPiXX3oH9/xzy+DxqzDsc+PDFzh4e836ZyzDG46X/pTh//aD/4wfG7zSMaLvNk7nJh7PoU+EsW8UIbECTINca2mKyzTnzd3rWCDln2WLurb+5FBo48iUqPj0leHnrr+VzAWqlYRkJjVRFSWTDx/WpDuKZOfo5PWc2M46Lr9+xP3/2rK1dZ6f/KGf5K+85Tf5scHTrBtDn7T7Tk1wHYD2WoSHu6n2favfE3VwfOn6Bd4YXhWFO62EyWwMqnZxTY4NN25xXFr7NqMUVmuyLSVAtVasPWvEqtMaXJYw/PQVXFmisgycw0/nEDzKWlSSdsxcVVQd8aD7nJZ0oJTUb4IGC6EhJvRSz2rXEVu7yEqW66BZ67P3WJ8zX5SaZNPT0VYq0IwyYWfXDlQiQHTtOiud7pJdrg1VTWyQzfBGmnuCiYp6ba0oSP0fWmxioX7YxTlO1DiUAzuRYK3JDU1PLBddFhVcVWw6qkLnt23KgKmk/pXMgwDQuzP03jHh6PiEcmAL6qtUrvUQiUsC9Mma4DdW2H1LhstguDpirZIYq10vwjJTOtYAAbBGlDK1ElJK6Vl7yvLT/e/luz70Kh/o3WRORY+FqfvXe7981aB024XpkQPXSoy4rpuMjjHdBtUuVSTTQPpKRnmvTCLeQn9UUgQxyD5vjkRCSweMiQmiDni3AD2VjoUMIEkcqW3Ik4Z+UjOwFSvpvPOPabuarXYLGZ/XCEwdmsabO2R+fNCSYAR14n1bgHqnHHJYS+CzkhSd/KBRgblLqIP4Cu1VA6ZNhtWOoSkZmpJSJWSmYWSLDpwGWLEFua5xQXdJ7YqdU/rkrnJC7X4v/976Zr1v9SVmLmO3GDCt084LrPOabpNwoHEaa1qWtcieKaB05k6WeSyIpMZRNLYD/FtZdGkEcKgcjs9XTM+l9G9ofCYTl649ZEZ8hyeVdBg2sfspMYQkavWrIHwXC6hEJqTIfmqD2jv8tL4ZY6lo1jKgkyODKyT5rIcClOZ7Ch3abA5CYnAp6EomNZ/CwFRY5RgnwpLfL/to7QFDvlJSVxY3N4S+o8o8QVvO/07Br117Iz948YUu2XMpVCsaUwZ6Ww26tPRuzVHOk+42DK6AzyzzCz1mm4bJvZryfAM2UJ0VlLxZU6hao+dy3UzqjJtHK4S5ZTWZsd8MOJcccqNeow6GnolgqBfvZZ8oprE7s7etyXeks9MWoWP0LXxUpSjozclu7+6YEpPvdIktHeWPdCWnoBovEvtlCbQu2XeSCPg0doh7Og/E0EokGbnUlAc7k/dGQTqJjRD7bUIdMJVITy0XyEQKRBZMW0j3r6nDQi4tFyZEyESGxOYGM09RpUPPStR0JqBKmuLnBebMRZLbx6xd25PASynKlRUu3b/F9uEQvTMkORJwurcdOPvJgztkSL4zvv7hgqfBcegrbrg+t5pVppGllquaIiT8k5e/m+RfrTF88gYAxRvPcf2HUrJdxYWPzdl5a4/3/jtf5K+c+SjJ0mJ9euhT8EkLRrdDvJ9g7pKTwFlbCIiBVNMTxlsZE4Tl91+W7G6BlSLI9dJTlqJJ2MwnZErz8WKVv/87P8DDz4r0sk8UyaRFPGFyb079yJwzv5qj57V8vlESyJ7yZpEbJLQvRU0Kzj6u2J8N+Zv7f4g3v/EaHzzzLH9y5YuMtZHEeAkuEka5+JaId8nrAT742oZPFsXyZfxs+bkvC0jH50+yqjlZOA6Lf22xrwOf27rMKdBUeem4tAdl9PzxkqAsr61aCWDrA6QWbJQabbumQxCGsVJML/Vw/cDGEwEzKRfXQstqns2leKIV5nAKvo8plKihtEWUEMTX2dqO2SOsgHDy2vJ+IUXUJhLex8KMAiKD4XBCMsqo1lLSNJHiXHdM42cuB88tIL70OXLwZP+zg8DkXtWdg6WetEVhn6VzsPTciXP1lcKX5fda6itQ7Xlc/nCWtjt9Tdzluns9jNOA9EkJ72WQ+iQ7Wp/6fRmQ/lplu09zqL4akNqFcAej4G6PNVAvbds+bj/TIA4M2y7l2Odcb9a4Vq3zuaN72C0G3D4eis/iQYaeCzBgKplHWiDC24Dve/SwphhathBm0X7Zx6F4e36Vi/Y4fld14vjUaIroJbxVj8lFtocX5mf57NZlpvOM+nYPU+gFg0+DqpUIC3hhRneNFwEBo434b2F9bPqL6G+j4+/y+aFRUEWgplH4zMd7SooMndf08lh+6oTP9NIf2vixA9pOeeaefo/lv3+Tx14zIEtr+qbsruuvBMKdztEAplXCvF7jlcM1UnuZe0YHvHF4m3uzXS4le2yaY3LVUISEKhjqJVRrbCTnLL2lZ+oo8Si+0CAgofYaHYvX8ybBGkc/qUl1Q7WkPeeBzDRkPQGsU90wrTPmTcK8TgS0S1NcBKd9FWPqqBLV1hQ6VmU7n7IA51Rb/AnqtOoy3ZahnRtVJ4carBH2QyeFCtopjiaWo9SDCR1onWfCrAaiX/UiX7bKk9mmO09pVDmzWpRtPrl/PzOf8udWP8V9NuXPjl7iPY+8zGfvu4/fOngjW7MVbh2P2Lo9RE8NdiZ+cLqSPMRUkk+0PrzKL0Dq9nioKCEoDYssAYexAdgvDsXyNRP0UiPA0mW2vN58u4y21nO61mK1Y+4StmcDykIaxQdpRWabru5Se2FGh6CovaZxhsZpiiKhKRLU3KAL8Y3u4oWWAR3jMNWmbmoRu50eOsoAN/3Q1d9az1k7U9iZyBkPtqQJXTW+uw4AVAjdXD687rAzj64d4dIm2+8ac/BowJ8tCbUmzDRmKkVdUy08rZdjGp/I70nMkf0gkzgpgt+6JDKJiP/Cwi7Lh65oC/FadVIzsLNAfugEzATqjT6zDUM9UtQjRTIZMdwdEqoadTQlmawxP6PwNlCVCU1Yyh/uPil8TeMb8R6/n8PjOfYNN2+vsjqX3BTnhSCilFjvGI1uPDYE0kPdsalbgLjuW6rxKjc21xlPA59Wj/KJkceslSRP9rm/eFrYdloTipIwLyQfMAZlLVQ1em5QpROgo5VvbRtnovSv9lp+dwpVeUIaFSJqUVhswWc1nbP+tKg5udzgkkX9xZ9bR80rqQlqhZ6W8n3LahHLR4lX0zh6s4p6vU+5GsGGgdTrTGwEop1nw6JO1Voq6FrmWl17AdyLBuW9sAgz3V37pvXvdNDkimYg66HIdQdMJXO8riQHy4486bEnPWxI9mbowylhMu2UpVo/1tCytvMMf995Jg8MGVybY3aOBejpchrV1QlaGwg5kAozKVl5OZAfpFzfvo//4g//BH/7Df+UBxNFGbzILH9nfNNGC5QapSF45qHihnO82gggnSjX5Uv/8vm3c+6XUoZP3ABr2f7ARY4e9oyfSMj3HFf/Hc9/9wP/mO/Pd0TOWqk7wOfTo81ZOlXiZAECg+TbQWlR5xsaErVgSC+zpdu61mnygOyDJNDtX56qDbufOM99R8dUl/uYUliwQSmKjZTifMOZT5oFaHq3+r9SqKLm7Gcm7M2H/I3jD3P9nWv8u2uf4pFkkV+K6qDGB3eCCLEMUL+Wat+38vB4fK0X9Q4fpKEIwHn0MlDcNrgsN7dohYrseGWFvBCUwtglm9aiiNvGmpFW4LUApLCYk5aINSfqLcufB6dqMeFEqYZ2jjaa0M/YfvuA4wc9Zz9W4PsppvTkO5psayZvlWipYYVA5yPtFuS4jgDZ7mNmmN47pFiN8ZNX+CrmuS0ROURMx8TmbC0YZ1gi40jMFe+FuelsVuu+wmV06q8gGImdBPq3HXbuSI4q9KRATeZQ1wsChvNyPjh5fpaHUkae06b7W0gMdhaoViQ3mN27Qv/FhpZ4ciKXbtcNFW2DrDDVg1VdA3C2Y/ito0f4QO+mvOT3kyndMgxVNNwOS55fQcmJaHIoV0wXiIIEsf0b4JMUn0pQ2jSaX91/C68MznC7WuGLuxejHHcQlnTLbE4b8rRmJS9ZyQpW0xkrtmRgS3Jds2anrJoZq2aGxpMqh8aLxzR0cndTnzFxOccuZ+4SSi/A8bTJOKh6XUJROsusTnDR+9ktyaa1SagPijr6SGsdOm/r5aY0HRnJPvo2A/SyqmMmp8bRTypmdUrP1gySkr6tOzA9000XCGvl2UymJ87F2M5O/L2OQbgPOsqLOUam4JHxVrddHTRzl9B4QxM0jdfMmpTS2S5JrX3LUJPvriPobFomdAStjVp4ScsxEta1VkHk0ZIGP1Yc35+y9lwiMjI2LmRKoYzHzEBVbZCFXPCJXDvBB5QN0vEA8QYQ3wHlvXS7pK+DRaItcsWClykVo5dgvqmpx3Ktt7L3LXNKNwGfWeqVgJ1K101xThLfC/khfV1xqBs2ezmv3l5HG0c/LwlZxSxJqUtLcBpdgxskHDw/4NW1NexUChjeKhotYKpPFMOrimo1JZk04s3jPea4YFA2DF5RrD2bMT+bcnzZML8QqNcadF+6q11imRQZzmuObg/JN+ZMXMbQlxy7Hn1dsVOPADn3KvFxLpBiZbhQsPG2fTLTUDrLq7fX8KUBrzD9RhbIqY0VkHCyKJl4eb7WkHhMvxHGeL1EP2u96II6ybRgUbMPNsh2NqALBV5Fjx+RRzIl+DgRKxeB68heUF6KcTpIl1LLXGgbb5peu6jI9umxRke/CV2HzsO66ekI6skirWv5bkEpSDR+lKP3MpjN4qHwhOMpRz/wIMnU07t6SLCaaqSoyxSlAvd/8AoPDnfZq/o8df0x9Nbe3QO174yva3gCh77iSpNyq1mlDobSJ2S6ZuYzfnXnMfwvn+Hcx25BVeMurHPzfRk+CVz6dIVqPLOLgb987jc4Z7LXTASks1R1zOZ2tMF9+3ejFDfnKyJdtpx0th1t1uB6gRTPLDT0VUKiTPT6cdSIV9By0pArSxEaytAwbxJ6pmLHOf6Pn/vT3PfzAXNcgNaSiLsQARHN3mMKN7Osf+kYqlpkerSW7sPlIFOpRWDaSvQkBnM4Z+MzFfnBCi+9+gDPvXmT4dsKHs1usKHn9FVFokTWVgOJ0iQY6dTEnACmvxU6WJc7LIE7Gqp00859LIqR4SSQ2NXF2gL76QC0jakDhJZto6T5ppOSPnWYdAnZQcAcTqPHkAC5AVAtON2xmx0kvQj0Rp+gqkb1xcc+9DMOHjbk27D63ET8dFxcq0EsOKKnDsYQjqcQNmWO916SI6WEcdEmQy3buIlqG8uy2u3wbaNT7BZtfauVkuKYc9i9KdV6Sn1hleTFm9DLF/dPCCij5XVNg7I2kspU935tMhVCYHCrYv+xTOLhFgBoC1lB1pWgpeDUZdwtSHAaZF4613cdS6d4GXBurwsB004h0e2bv75via5htB2nZbpPN5Imyt91+3ZovrJc99c6TrOiq3Zei2CzQwqNMRelavcVMCjqFnA/9bgOAki/6ob8ytFbeW5ylhf3N5jMcqr9HHtgyPcU/bl0SOv4QQL4KlxP4gqfKJpS4aea40mCXalYGc14fucMz2yd5QP3v8C/v/lb9FUjzAREsrsOGoei8AnXqnVemW+w1wy4Wazwyav3U+3LPa00iH1RjLOIl7oOhCzGWG7RhNwViXMPWXyy0RJ/ZW6RIzUaGo2Za3TZSsrGa7mN/dr3C+08IHPkwos43AWAlmPUsqi7YcPCdxoIXsl3iyCPCmHhff1NHI3XzFxGrgXgtcp319SXG22jcZuPGe2pnKZxmrJOeLY8y5XDdUZZyWPjLR7p3+Lh7BbrZoLzOatmytRn7Lkhuaro6woXZeMNkp+NtGNgS6ZNxtwlTBrTWT6tpCWDpOxUtDq1rSV2cd9WnX9zZhuGaYnzmmkvpXKGeZUwn6e4mY21BbUA7JYA6WWp9bAM3oVFs3w32m3j64DY1Kru+L19f39kxZ6hrWlkgeMkcJQ5acowHm0Dxop/tbWOzDps9K5OjCPVTvJ87TiuM357+w30dcWHhk9yn3U8lqY8kLzMnxy9zLF3HHrDq80qj8/u50a5yo35CnvFgJv7K9SFJcwtqpTGFAExFLZAmKdNwJSS+y2zatXSPXHioLRxRQv2eLUAq5ePpV9s880cp5sy2mvLo7DKvybhoJVah5Ogon8NT43GG3pJ3V237fv2dcUT+xfZ3h+hVWB1OGOYljReY7WncuIX3Tbxl7XFBUVZJtRHGXpm0FHGmuXrOCyB0e15C4vfF40U0LH+NbieNLF3voKI3H22p+hveUbXSgH0TgFRqvHoUqPLaK1XeZq+4fjBIftv1FRvmkFQhJlFzQxmrqKcpFooj7G0P9ECSztFdhSvpaIh5JbpORvVxiIw14LRrgWphTUtDRYLUFqlCpNqVFDoKqCP5vjVIdPzKc1AdTXF6QVD/8oIdXQM3pPvOw4e0di5oioNA1MydRlaeRp/J8C23Higl+KN0w0J38qjBtROSihKYcXFuFhZg57V0fvSS0MocKKAqRSJNeRZwsqLGm81q89rfKqpVjJGz+3iZzNUBKFDVeHnBTpNUMMBJJFtVdUSR4ewYKQRcxetUbH8LGuwWqgeIcX80CyRUFZHHD2QM7hRY48r5hd66EauE9dP0ZHRpcsa6qaToF3kyQACqFA3Cx9QBDRWDlGxa8eS1UFLmGibfNsaj8y/aZQbVvhML9hvkUSha7BemoqArskqmXqyQ0e+XWB2jyUfil7pIQR8XcsxslaUmlrVqiSW7NfGzO4ZsPUezUbWZ2Q16Ss7MRdfAnyWjwGIlKuODHAPw+uelx+/zK+df4x/b/y8HEMVvgNLf5PG6XpGGRpuOMfz9Qa3mxWx7vGadTPln9x4N8NfHLL+seuE6Zzp+x5g592ewcuG8csNr/5hxT/4wZ/iHdmUXCVYDM0drbgnh49Jt1GKRBkyVUc2f7yGjJGcOE1Aa8qRsOtbpjTQWdE5HB5/gp3dbnMcEpQO5Eqz4xz/5+f/DBc/WkotwBDZqIpmJeHoHoNqHKsvFR0r+o7RsnuNRk8K1r8UyI4G/NOj97P9/hF/auNT3GMPeTBZ1MVcCF19De4Eptvv8q2iyPeVhiNI7tUqw2kl9TtrZS2oG1kvQmwias95vB5V9KBW1kDjBJxWCirVzfOhKAk+oI2QBJQxBB9rOxHI7kZbK/wKdWsVQqxtKcGGvADcwWoBlY2mOJcxuxSk8W9WoFJLelAxdgE9mRP6GS5LO5sIlZiF8gUy53axSCTZ1OOM+RlN01edsqBPiGztk/vY1vpbVYwOL13OjxHrJBXxBZ8JkK2d2JXYeaC34+ndLkluHsBcrEtCWFIJNKZjoXe1rvbyPH0c2xpeWyfWGp8neAvFwwWjqxmzc5b+FfHtVi6cqBuFdt2MDWTBKHxqcD3NwYMWn4nix7OH56jPBvrfoDrs1+QpHWL3ussEzDmdDzQDkSpRXmGKQG/XU47F1yLbh2IDmhVHogOf377EZ7cuUzuD94rVlRmzMiW1DZuDKYOk5HzvmHPpEQ9kt7mUiLxcohx1MBQh4cXqHFWw3KrHzHzGlWKDaZMxdakAro2lCZpZlMBunIk1UB1lwOPBD+I/vUCyBJRelGNARZnwFozWreS3DSeS79ZXKDEOcB0obXTAeUUaJcEOih6100yrhNt+2LGOtYIyynUruKNAp6Ncn1oCiIdpSWocqW5ItWM1nXM52+eR/q3O56oF7I3yseBgmfqUIqQcuh779YCZT7uiw6xJOp+kykvi5aKcNwgrvAlKknAjXmctgN/up79/zuxcTnboUC5gSt/5ZKm2Q8UFKTop5AbUKtaZVAT/fDd5BaUElF6aTL7Zo/M91iL/nB94ZucVLvfoxuCbpe3iT58ZXM+THsbZZKVesI3j0CpgkwYbQX4FKBWYBEXwHjPPCEqR7Wpe2DsjwFFMZG0RyA4cxbrB5TA7a+kZOZa6bCQ5LmrQiqSsMZOUZNKjvGUp1izFmYTigoPEMy+kWKRKTZ7WHNc5Q1PigmbdTqmD6RI/Zb0EMJHFonXgbP+Yt69cY2xnPLl2ib2qz6xJ6duKoypnazIUD67GnPB7B/CNkkXUCQimCoOqYhHJnZL0awEClh63sjMmEJzCzBTaqa4msWAanCxMgYDTqpHua5AFp/XDasFpXUvCgmWp2CPFItWIVL2u45yZK4IRgM+U0u0XUi1gelFL13Hrz2Qt1BX5bo2uPW4lZ34up1qBoXUc3Fjh6clFVh+Zc09/n6cC+OkM1e99vZfxt/WoY8fksmz0LFTc+v+z95+xumXpnR/2W2GHN5x4U+Wqrk5sNtnMkzQjzAwljiVbgA0BNgwL8hcPbBj6ZMAfBX8wYAlwgAwbMKAvtiAL8GDGYw1tTRDJiSSHnGY3QzfZuaq6bt18T3zTDiv4w7PW3vuce6u7ms3uruJwAfeec9787r32Ws/z/MPjDQ/dAed+DsCxXbPQLX/36c/yzi+/ySu/8hi12hBuH/HOv7ePWwZe+TUJHtobFftvwR+2L/ET5YNnGKo5GA5EAnGw2J6qpvs8F4msQse7Z0fcbrNVi0aFxEY0wloNJcy1G5ir7ZT5ieIidDQx8sSXnPgFAc2JX3Lqlrzz9JgnmwWff/IaN/7WnPnbZ+ADsbJU58lKKUS2ty3dDc/L/1CjzzdDkKm8l/4+1gwFgWx9Rm65AKjeExNbcfHNS6qnNaePFvzvwy/xyq0zPnv4kJeqc+4UFxyaLbfsJfuqldYYynNLK5a6GhKJwIc/gR7aCmR132TrUhGCH29/Bny8VpycFtjlQc++n0qPiWH8fQrcQC6uwOypg+f0k44JrFVFIaxMa4llIb1xto3cb8XZJFaW9Zv7hAJu/kEv1n0Au0YKhyQA21qpdgctBf4mzXXvx+AZJEGyhjCz4mZhFeVp88EPeAhiBa6E0aualvpxQ3Orxi7n0kupLORxMTFnJ4Dj1BL8+ijOGuympt8T4CIf0isF5eeN+JxTNQ0w/2wMgDSMCunraujp+FHj7gul6YlsQqRWsKcNIUaaGAYhbxdj6hkdOQ2GJ37Bb24+yW+evslX79/BtwZ9XlA91dRrWDYJZGqDXMOGoaWJL0GZiN0k55hW1hRfg+4Nrqs4bSyz/QZjAr/69R/j8w9f43/xyX/BT9ffZk87nvg59/sjvta8yIHd8muPPs390wO6TYnapZW08kIwdJrgLGYHTLkP2SfeJIC6SCBJEaEMKBsY7LlVhAJxuoqjZbdudFJWJ7VfkcipKoqCWY+xFiB/R0UkDvGdikrAaki9ieO4zk2nTEQY5WFSJEjAtorPFg5+VMPqQButOC4pfwVY+yBjcBbLpGEk1+16S9dbNk3J0/WC361f5uXlBZ/de8DPL97CEDEqcGzWfK19EYA909CEAqMCTVJOhyiEhgzeaBWpTcfcdmgV2bpisLEO6MG9K/9zQazIrRIQN2id8mR4qhaEoOgAv02W3lmlxpiayxeVH5mPcB2IHvbJyd43WCIHiNk9JOVtue8tGkIrcb9N242rUzHfmsEyPBgBBl0RaarIqgioNO9t4SlLUYbXZU9tHZ03/Df3f4KvH9zhLx18k1+YvcOrJlIozS1TccvAp4otf2325eFr9NGzCo7zoDkNNedhzr3+mDO34EF3wNN2yXvrQy6bistdRd9YWBeoVmFalQBsUmshrlgvC8lLDX1+p2MgyDGJVz6k4/0A6e9l5L3FGMfGVVTaAWJDv/MF5/2Mu08PISpm85Z50aNVxAVN6yyttzTO0vYWFzTOGdpdQdwZ9Cb1Vc6qaK+ugNKDRX2O17IwJQHYJCJhROpvoYyEUj5vsGLVXZ8o6pPI/FGP3XlUlxSpSWXMpM+47qXQ7CtNc1xw/ilN83KPrlvCZYnqFKbVyS5yot5mvE5kvUbI3oDqoH7ao3rP5Y8fikrIQLGJohYNjFbdGczz8vnku4+gNEpcOUwbxA7ce9pbB/RLhWkju5uK5T15jfaFBfWjitj12I1DtwXVqSJ8TC70PpgPtHbmNWpq8/1+8cZHZViMEOOa9J0SKTg6JwSsrkft2sFZSHoki8Uq1or9s/PSD3O9RS9mhEWNAcqTiNpKn2rsCGRngJqkoARG0CH/DlcA0ziN+ZUaxCfKh3HOOvl99dljnvw8VL9qKE8cuo/MH0Sq0xbdOUJtx9wmuSYN1qO59mZEaennJdsXKtr9RNg1arCaH4dsPMGK42cmocYMTCdCUNRQrO3QG7vbT23mkuq6WEfKdaS+J6o3+/hSLLidh76DEAmJIKAKK0CC92Bnw7nT81myaU3XXFngDxeYXcDtyfc6+/SMO+9paXM4Vc5NgWkQ8c/MEgpNvxShQ3Wm+G8e/QT/4/0/4kDXf2pAuI/auA6IbkPHe77nrf6Yu90NPJqFbjn3c766e5HHv/wqr/zqu+Aczc+8zr1/07D/NcXtL2x576/P+c9+6f/Bz1dbVsETVKT6AOvaIFrIYjY0qkskhgRsKaVkjegd/UKeY1USViQQV/pRezyRbewwqKGV3Cp0bMMeB8sdnsh/+vBvsP7bL3L7vSe4G4sEAEaC0fiZZvNK5OhLGnuyS2pOniWoZ4zAymfU247ltzwff1TxG09/in/xU2/yV15/i//VnV9hTwcWSlMpSyCwDj1NDALEoyiUpqL403kdRFK9Q4gug4I5r9NJ2TzkXzCC2NeGCul4mwRqto7oBYjGWgFNlUIVQqyJXYeaz7nu5PDsC7/PfRMAWzUOKpN6jyt2Ny39fuDga4bYtCg3x1y22AsJ5KMWh9JQKLHSDkqs0mJM5NU4tJhQUVxfdjcLgoHyMg5tOWUvyC2DxpgNpCWrr1KcVox4qZrU90Ip8bXuFcWl4KKmiyweeqqTdtwfQkjXmR0tuDPmldwxVSZ/TY/bdGRxhxkV774Sx5nZssUXFe3xeC6i4cp+kUHpmM6vT+11d8cW84snXJ4sISqebuesosLEfrDJ/37GBwelZ9lHPQpbczceAJWS/WhBVUi9opITp50E1FlRaA86ZpXYH0XgyO44KHcsC2GJHZdbfnx+n0OzYRsqVn7G2+1tvrR9lfN+jkuK360rsSmK6IIkByBs88bZAYSGfB2qoec05DVMbrsyohqTXRhA5ZFwkPtUC/A8ZYQrFamKnhDEEtvoMAC186LHBc1Lywteml3w+SevAdAn++yszg4I29roSNvb4fMP5yEq8NLjWfpBRzZtidaB0oq12AO9z7vFkYDUxrNnW/btjhvFhmO75tiuOdRbXrZn7OmOWklvh1op+hhZRcUTP2MVZpz7OSd+yeN+n7N+zsZVtMHyZLdk3ZfyOSbK6T6BihE42NuyenUufVa6gGmcWC4kK53BQgFQPgjRweRgdVJMF0SWWOgESk960/yoh2JQZ1SnYivd70ViKTaG2XIrA6ihULi5IS482hmiURwcbDkstoSo2Aa5qDWRm/sbIrBXtqNN37zBeYNaL9A+MH8YOfvWETcbZNEzUK4ixWXH6acXtH9+zeqsZvGOZXHfMH/UU6x67EUkzEtUK+rz+sGa2XuRUFv6w5rNiwXtYUG3VxBK8AeBedXhUt87qwMrX9MGO9i+ax2F+dPLAt61hrN2Tq17Xi7O6OeGB/aQp+2Slavog6G0Xr6Pijhn8a0RNXRQqE5RbPSQ1OvM2o7g9mQzqc7VcwtW0ltbjkco5NgVG8aez6nAJL2sni0IBHGklWQEOa/Zykl6dkN1GbGNGnp1EXOfrFwojdhdYlEZhS8kCdJzjS8U/VynuTFjfnvO7P4atetkE3Ge4lR6PfRHM/qFZvdmx08cnHL55CY3/0Dxu+9+hn/5esuLT4JcT4lp+2fjextTQLqPnkd+x7edWHavQj04UeypHb+z+Rhf/K9/gtf+3gOxy7KWt//9Y9pbjtf+Aczurbn/V4/wM6ifRE79kia6K5bUIH12nv9Z4gBg98hk1QqeeMXmbDYmpiEmQDgMwYdy8PdWnyNEzeNuj0s3Y+Uqzpo5677kfDOjbUr8xqJ2ZrD8Mw0cPI4U2xn1qaM4XxOqAt1KsUmntbu9UXP6WTj4imXva6dSPND6CtteZQvvFOzG6wFmiFIwSwmE2fTc/u2WvXtLzt58mb//6Rcwxy23jy+5M1/xwmzFcbGh1j2V7nmzfMJfnz1kX9f4GAZw+sM8hNCX/sjBRa7V5IJ5KkxesW6cgsqMt+XnXa/JZrX0lYL8dSA7/516/VVPG2L7HD1eCFCVZFsfZa0kps4T+57cyycXoLa3NdWpvB5GQ9cT+15s/WCcA0HALKIod6JOAHhWSF9LNpWX2FFvWplrz1NLw9g/R/6Q5Ei54bPo8w3crvE397DvnUzeYFo405KsGQOdtBe5wuwF9OWW8vKAfo+xcPw+I5+j4VwNQeXzz9/3PT5CxdTrSufp3xmMBikQf6+23D+M4WNKLoGEW9HGQBMjBVApTSCyiYETr7jrDvj95jV+9dFnuHdxwObpnOKppb5UFCtJfG2b7CEnhfroSTa8USyvkWs5xyXaAbscF4lr0S7WlMuO2bzjcj3j//xHf43/8FP/ip+fv8UNveWJ8py5OV+6fEkA6YsKvRFFn58FMFFcq5gUalWOmeIQk4taL68DUZ5nggDY07meChwxKCHhuRHwiDaB2RmQhgmw/LwFLqGUCgGo04jXQWwYr0119edox6nG9/oQbCOF8lTKUet+iDmy8vj9eqMOZGYUOuWRbkIQViqmNkCy1PXO8OR8ydOLJV+rbvObe2/ymcOH/PTiXW7ZS36qfpdvdC9w5hbDNdgGy0U/k/Yhacxth1WBQnusCsN9NjdYS4XNDKyXesyVc1/qrRPS80Vb8/jJPnEj+a5N7Y9k/0pE5QmIfPUAjFNiehsq1dKY7KvTu30cQYz0XsGCjkpqOE6UC2YH0SbydJy+p4AooRCFnQB1QrBoLIQqcl4H1MxR1I4YFA9ODvji4hVePTzncwf3+MzsPi/bMw71jgMtsftCS2FUK8WetuxpeJUOjaOYba6Q8UDiyPPguAiG8+QKd98d8cTtcbc55mGzz0mz4Gw7Y7OrcJ0hNBbViEuBdgrdCiijewZVr3aj292HafxJANHvN9pgmBmJhbpgccHw7uqYfldQzHqWdUuR5nGfiPpZ5e+Dpu8NrjPEtRUXiE4lxy01xmBT9f5zbgOurGFRJ/VNGQmF1BRMq6hWivIM5k8C9akAXmNMGYf5ibp6cfhSs37JsH1B0bzgUKUnOI1qk228Q9bnSRw6Jd4PfXANQ57c3LC0R3s0R4no0AkxXieHm0EVnXJj3U968+ZlO1lCCmk7Yi4bKCzNjQJfKtavRV77ufd4+GuvMHsiFs6qroi7Hbr32K0AgPWsG0gw3yttLSvqzYeBofR9jExyzkMpJcCz/CHxcddJvSAr5pQa3IKAwYY7dj0s5+jVVlS6PhBXa3lObnWjlKjoYpRFN8axID6A0YygQi6UT09PCOKCMnGAygB2tJazTxp0F6lOWpQL1I+2VKcGc7ImLmpCbWTOa4XyFt37qwX7gNQOXaA7KtncMYQKzE7cZ4R0IWu8ECTSfLcJbCgn8z69nnZq0r5EDXUvs5MaXHkZWd5vsWc76QGa8qxhG1ksBOQLUr+JzkNI+YdObxTDaNU6BRcWBcWqB1Vx+YaiXIE/2kN3bqyN6vF8o3USaIgFK8DlxwyblwWIeefpMe99zHJQfjTcx/60jnzc16Hhvve81R9zrz/Co9nTOwKa3zz7OF/7O5/mlV++R+w6tj//Onf/bcPyHcXtz2/oDkuaW54X7AXb6LlphODQx+++rgkQa4Y+0OehpjrTUtcJIeX7heTxztEdKraxo1aWSlkcfqh3FcpQIbdlsUUfPX2M/IPLzxGj4j95/Ff4rb/zU7z6myeEeYXuA8XGo1uP3zesXzTYHdz6nUshazgvgLi/lhOm60I1Pap3QqCPEbOKvPb3ey7e3uOffe5zPPqLe/y5o3f4VP2A22aFVoGH7ibvdTcAuGkvuWHXvGrP+YT1VMr+qbkeTAqWY3KwiFPyEEBSPo/OVbI3DHUVY8Z1nbxOqrS2GFTorj7ee1Rd4T/5CvadR7L+mWvH8frf2Vnx+giQXbt8bSkud+jzFeHmAaoP2KYiFqI0pk2EKyN7glJiz50dnFUv4jzVe3HySzXL3NotakVcVkQD9XmkWAchIClwC0230AP5LtjcEijvFxKj5Z/i0iWEa+WU7Butxm4FLzr8VsfsLaml4tNnqatBeT44AGRAOttxMyEfTWtY10cGnPMebxVuBvoLB7gFtEfiXjndI4bnqARIWwH0faWl5uEjZ0/2xAHNK1abmv/q/M/xN49++0/kWvng9t02jkmhjleTwzgFYuTAzx/KAesXCjcXoC6zL5w3eOuJUVEZxwuzFXu2YeMqTrs5v92/SRssXerV46K5wriOyWrHhWpQEvsgPaB7b/BBDcBoBp4HZXQOWAd1NGNyms9tAt8VXKke5gKANRLg+aio7KiGzuB0JkZk4LjtLZVxuFByVG75heXbfOnsJVZtleLBeOXazFZsMb3G9b5hSgcRhkZFiPFKT+gQFToqNn1Jqy1Vkuq2wdBHw9pXnLolx3bNLbtiT+9Y6JY93XFLO+basIfiFeMIXOA5p02FtVUsOPdztrHisdvnneYm7+6OOe9mnDczPKIIjzGi0rlob0ZCOS5kuguoVj5TzAFZwlZyYDotLqMUoUhMrcJIsfn6hvSjGqknHUqS+eos0hxrwtxLwlZKcSMUamBVBqsIRlPvb1G+xFWKw/lOCk9ofIRa91jt2a8a+mDYLxs6L8SLunA0gF5L4rl47PHfstgmDDW86swRtaK5HfnFN7/OV87v8A53aA8Nu1sl1WnB8n6B7iNFLry7MATcxXnD0ZMNoS7wi4J+aTn9tOX+0RH9bQMHcBN1tc+cilIU0xHdSR9lerGJX/mah/0B99ojnnZLHmz3OdkuWO8qvFf43hB6A50kx9qRwF9h/AvpZVpMHBPlrEAb1C9Mkn4PJikClQe7kU3JVwz2tjnRzvYbkBOo9LsRy5Bg5H6LLMp5zTNtYsyaZNkUY1rY5VzrTtwPiKKW7paafik9htxcLND6Wz32pGD26Ai7i0OvbBWkMBZ1UjR2cjx1r5g96njpTLP7asHeW0mx+iFxD/iojbyJtrHnInQ88iVP/D5NLNAE5rrl0Gz5x5c/zi//+s/zqV+7RO1a4mLGw79+m/bljpf+kWF+V1Sh25elAFOdwZ5+vsIzB+kZgM62Rpmluo49J17xyEu/0X95+Qnsk4JoerGgyQm+1kPyvv8t+M///i9ht0qsmc8itoFiG7G7wK1UJNK9R7mx4qgyQ7BPNmG5D1gOTqzYtqxetagAN/6wHay6lZPkP+aEOff8gas/0/1Rj33C0oGAAPXjhptbT3lZsbs95/HNGfcPbqKXPfNFy42F2CZ+e3GTT5aP+YyOV8gEH+bhFnEkwqhrfVhTvHSlz33eVgYyDgN4MtSapkVDda3onmLZrL6Z/pQnyGvaXcQ+viC471B9zgx9m9wwul4UF0UhqgqlcLf26fYVt36/QzdpXuzasRCWlQsZOI4BohAiYvGc90xAcDRa1skZos5+3ud7bgIT8teUJKkoUKsN5fk+uzs1y5MKlXsn5cfntTOzwLtuZBFPD8flitnTO6xf1VfA5ivEKEY8bTjPk9uuvuDzvtQHHPkNryPcz3mfH2At/481nrHfzoq15wDSH+QKv27d/YNYFa7z5rMKuk6KaR9jajeg2AbPW27OF3cf4w/Wr/Clkxd59OCQ8kFBsVYcbKSIXl16TBevxLRRS6ILeQ2QtS4nJ24O/ZKJRZhMAdMKKOCdpes1btmzXDbsdiX/+e/9Zd769E3+2sFX+bXzH+efv/NxulUJXqGcTtawgVgK4TY4jWoMpp0UlTMwbRBAugyQ8kKlBWWJQcHUESKrlJxOgLT0kIaskI6iuhvQCXX1mvhOas3pc6bAcxw/43hQx8+TFaD5spnGez/KUeuePdNQ6R5D+K5KP4VcNzFZJIWoRpGUHp+rlCIENSx3Jh2X3hnuXRzweL3kK8sX+OT+E35i8R43zJobZs1Xmpd40u3ReosmclDsMCpKD90o+bZP4M+QA0ycvbQKhCg2x+NtkYAa+jFrG7F9oKgdXa9RuZ3PZH98hmuTz/Fzpkbe+6K+dvdzzm8+vNoxANNRpRZjM0XoEaK0y/H+ZI1PpJRgxvxBlBMZ0BBFaigsfiZE/mgiFycV5w/2+aP9Fzg++DFuzjfcrNfcqVbcLi85MDsOzYZ93XCot9TKsad79rSiiA6t1KA6Aokjb5mKYx0JdHha4JIQI/4gDiTzUy9q63Mv/x71Bzzp9njcLrloZ1y0NeumYtsU+M4Qtwaz/fDFVs9r7fD9jEz2MCpSqMDM9GxcxdqVdMFy/3QfpWBW9yyLjhgVO1cM7nu9N3SdJYSUx64KiksjOdxUGT1R6k/jsOmcml7uoUiE6hLcLK2RUdTR5aVieTdQn3tpy9X6Yd4BCZCW/raDPaXVNMeGi49pmhf9SOK5LDCNljy2G6+13O4lx6z5Mw05twOzU2gP29tGcuOd5MPFNiSVtOxrKiulo3yH3I9XpT7HaJ1azylso7Bbj2o6+jsHNEeK5iZ85i+8zf/0xd/kP7b/AW6maA81i7qE3Q7Ve2YnAV8ojuY7+miGeAL4rrbcU8LPddX0R3Fka1pAYm2QODljUjo5ICUlGzBY36K0gJ9FAdZIz2jvxVkoRgF7UksfpZQU0mNE3zgmnJw+m/tdq+VdJSjLf0opifKSbS9KoS92xKpIykeFm0eKS4U93Yh6uHGw7ST/LcyVdkZS4Jd8dugVmor7YVHTLzTFNqIvIqYHVyUnAouQI/S0LpRA6XoEG2Ii0OXrO6ui7Vqx922YnQaqs57idIfaNOLMBKjFTL6/81cJr0oJCQDkjXPLJJ0IA9ktMn/HEEW1lpRrKAHX3WFFcZIUkEnllkcGF6LJ4IJi84qnenFLc1rjnOGh3+cnedY168/GD2dk++ixh/T+4NK30C0BzW9cfJIv/cqneeOfnkPT0v7Eqzz885bqRHH7Czs2r9RsbxsW78Ld/gY/VQoBO9eXPsjQKCpVEAjc74+w61SPyOIDkLlYV7gk6H/iW06D5b47oIkFl77mqdvn1C047Re03nLR16y6msuu4uG3b1A9svwDd5OXfqcR8LD34D1mZ9GNY/fmnItPR+78FuK+lupNUevRnj+NOAHmYl0Keb4o0/7jOPjaiupizrcu3+TLn3qZl14447W9M25Wa0LUbHyJVYF9e8jM9NyrTri1+Dq3zQeGyD4aI4FTubaRsZZnnGezZffzVMuZiBSixBHOo6xG7drBZW54hjasXp+z399Ev3X/2de6/vd36sEVQBHEIRKIux2qWxBnhcQbvWL2JAkmnE8Aux5qkjkWMZt+bPOQ6pcD+AtQl/i54CN2JyTxaMA0IZHqhCibQWlfqHGf2CWiatpHsvANBaoH20hddvGgpzppMBe75AyiR4A+Aehk4oC9BkBPz8P0tuvA9JQcppWAzwjBcXmX5LCmRkcRcxWMRguYHQpp3YGGfq4JRrH3hyW+BiK06wX/L/VzfOynnvA/XL7HXImw8o8LUH/gKy7b9chBzoWEdOf1alwAdd9SnwW2Lxj6vdF2CCQZbrXF6MhlW3NfH1DbOeftjNZbZlY28dbbEYSeWEeDWJ25cLXjkE+9ofPjB1CZNO/y/QFiVkhn5j8pD/YKpVNDdZMAJRWHPtFax0Gh3DkzgMFqcgxK64bPbXXg6XqG1iFZYVsOzUb69G5qisIzK+X7TtcFH9RQdNDXsvL8/UwqPFgzbhbZWio/zwVN5wXUX6lIG6z0ClNiUdXEgm1s6eMWwwbwzLXQYAtlmGE40JqjxIAPbOnjCnjK/fk3eKs/5t3+Bl9YvT706D5rZjS9pXOW/tixvWmpLgO+VphdoAgR00uPLikoqRGYzsHrVBFthYU5nP4YrwIbP6IxqERUUgdrKaBHHa8k9L5Q2BTrBQvBaJazli7s4WtYFB07X1JpsQabWlZqpHiDcQOxoe0L9tqYbIcC9Uk6VlZR7CLlRUd7o6I/CDzc7fF0vUB5hdv3rI4izakl2oLZ04Bpi2SRPgnuY8QECe7teUv5eIPu9rDbipNP3mD3RsH+rOGo3vHa4oyNk0XImECX2aS5WB8VJ8ka/lG7R+MLGlewaUq61hKDInYanEb1qXeDV+ggi/hURZjXG+2hOlHJMl0S92nP1VwEGD5HGIsC2ifihuNKAh6S/VIGpjModJ0lLudPkhFfpeJqsvUYEqNUTBNWe0T1ksiYFtRMEh03AzePuH3PnZfOuTiYsX3FSp/ti0KKvR7qxwYVYP4ocvx7hq/eukMoIutXKuaPeuaPOszpWhLHP7Pv/r5G7iF93x1x6Wu2oWIbKi78DB81f+/zP8vH/z8d5r0nMJ9x8VM38f+dc8o/PKQ+7fCzAu0CwUq/+Kil0Py8EQisguOur3ji9+mjpQkF9/sjHvX73N0e8e7qiMfnS/qLivKpZe89ngEv5LqQxPX4K1uOvqnRO+mpFUqb+mVGWU9bP7KnlRIiSiGWZtFqUTB7D1ZLLSOTVbRi/WrN9o7i6I8i1bun8gG8HwKiIUmYJguZHZ/7XidAOtoxQQ6lST3uPLrx7N3tqFaW7YWmX1rcwtIsar59uETNHY+O9/ilw2M+Yc+p1EcjYfCpqBh12i+mqr4cL3npF6l6NQCZWbWSXUOuOoeMrz8NDwYbyGeK6GNdPr9+uYrEzY5pL7dhZGB4Ciz3TnoQ6RQkW0MsLGc/Nqe8jFSPt0Jw2OyITSNqDBhfP/erxoCK2C30+1FsA5NaYBgh0h0UbF8MAl437bMq6mufO8YoczkzX9P7ZQsk+2RFvHMTf2M5qqUTsUNsxePYh8eY59p30/XMH3coXzPtKX0lREvnZ2rJeQWYTufw+1FL5+cNYPhHSCUNPBPTwlVAOg+fAgnNd+8ZfT39+V7N1673kP5uo1aKWhm20dPFODgNfbnb44u7N/j104/zRw9foH06o35sOXwqxLhyE7DbgN3J/Jr2ms+uLqoHiINrULASj7iZYvOSKDGjyf3UQfUCECinRPHWKfxWswqKonIoDf/tlz/LP64/jXdaeod2agRo+2Q7WYoiW7UGs9Fj3KXGeDckMFqVYcyfEtGXoEZWbgI/o1fg0u0hPbSaqKx1fBZ8zq/5fkPlC+rZuwYwegpaI8dGrLrVM9fehwGHWJqGuW6ptbTzMc/J/Z43lIpD3guSD8aocEFcqyRvhRAUrjdoI65GAN6LwvPt3TFvPznmy8cv8pdvf4vPzO7ThII92zDTGo/GEPDpKnNeD8rCEEcF6/MAQxdGVVxIn8vqkBTUgdvzFZV1rJYVp+dL3KoYeigLsKWev85OTvP1PXCqTJXnPf845n3V9JGYcoLYSGwVSumLa7pxrmXgcKrcDlYRkpJiJJuN6olQKvqF2OzLtRwJlzNO7804MTf4ahWJladYdtR1z+GsYb9qeHF2yb7dcbtccdOuODRbFrrlUG+Z6x6DAKmHGopJQFAoTa3H2OgYeN0GYJ3+QRMdbQwUKJoYOA+abbSc+AWnfjnEoR+28YNQSoeoMcpzs1pzYHbc3R7ReMtps8C1lnrZsqxbcQlzYtO96wpcEju4XvK2uDMUlwazvdYHPYPS05hsEpfloeIkx7SS1/pUN1O9olgp6tNIfRqYP+4wOyd5/6SYOKiYkuNc7nO7u2FYv6ZoXu1QRUCflMktRxSeplNjTJFy3mDilVhTWtBIrcM0CrtlsOi2u0RybIQAq/swXHdXVNEhopNTkqiT5IBo5JrTrUI3ArJ3xyW+EkDyZw7v8rvb14XkX8o+GKtyINOWq8Dl65YX6x07Xw79pD9In2iTnCiEBPThI2J8r2MgrEy4pkop4rRAnFrzQIrxDRKHZ0DUuaEtlOod0ZixWO6c2H6ncxi9Z/O5l5l/MeUH8GyB/DlDxcgztE8fCTODudzArCLuzaUO6pQ43W0bYlWMLk4m559SE5Q8spd5lQCHQeUJ+Dv7RKOoLgJ2J7cVCrp9M/YDzUQjm1z4jFy/SjGCDBoiEd1r6Qe6Fhv9o6814nDXduKkNoDJI2iWj+tA4vbx6m0ZuI5y3VDY8brW408309hzzcE3A8Eq3MxQpP6fw3qQRiyMgAtWCcAAFBeaZlajGmnN+P8++Xk++cI/4g07/67n7s/GD2bsYsddF7jrDjn1SzaholCOUnn+YPsq/+TXf5KP/6M1+uEJ7rXb3P/LFb6KvPprO4rTLZd/fkZzKzJ/oFLuJCBzn5z3rjv2XR8hwdc+Rk5Dxxc2b2C3DHb6g3JPiVV8eQH/m0d/hXc3RzzY7HN6uaBbl6itobgQ9436NJOVpHXhfOX5eHAo1wnIt3PEyqAvtsR5JbWr2nL5hkY3kYOvnDNVsl4RPqQxAHuJpCKwjaxPYSb9r2fvrXn5rGTzjZqTz7zAvZduUhy0zGYdB7OGedFxWFWU2rHzBX+ufpubJn4k3Pg+yPDEgRCsprluqs2gtdRKvB8D6pHlOj4+TALufB58HF02QgJ5tYG2RftIe2vO7Bt+nD8wkmy+E9CabpPaiUK5gCYp4W/fYABugeJcU6Q2b+LimMQ2TvYq5YPsD9skqglhdHmc1np0Tb+0ScwWCVVy4EjtO3Wfvp+S68IkADpYaRUajMI2QVqRdCkO8lHcgjc95nw7iHpiYYc9Kn8P5RKx6Horhnxs88j7RT5ez3MRHFTPasR3lLi9ai8ubfJaCb/Q6soeE41KLjbgatk/UDB7MrqGFBvFysz5h6/8BP+D5be/7z7sH7yqq1OyPwWiFVJQmMZxQQoMvoTMrC9WicE/EyZ9CIpdWxIjdKWwGudFR+MKWm+GhNpH9UxSnm3JYvpd/qnh9pCU0c5pAaFB2Psx/7y6wER/9W+lEFDajGCz1C0lkR/A8Bgprb/y+Yae0jokRjhDzyzS7623PHH7wrTdlqhFRz3fXVF55wKDSVbhbX9VThSCkkIEqQ9Zb8n9qPNz8ueSYypK7cxO76Nh5WuKhNQZAo0qOAkzPA2HOA60QUfJpkJS8lkMGkOlC/ro+VRR83G7I8zu8m/Pv86TUPGl5lW+tH2Fr17cYd1VhBuKzSsHqLvST7fQAqQqL3ag2Woqe+arpMxSWoOTXsq69wRlri5UH4IR6jDMfd0qdnegvIi0F4byQpTT2S6h2Ik1VT9XdAeKN/Yu+TY3aY8UpXY8bpfyOioyMz19MNSmJ6TNwKrAUb0V27t1xX5gsFOwTRyK3NVJT7Ca8zcL9JEg4evTOXtvGbp9aF/ssVtFPwf3imFzZ8bikVimBKMotg69E2DKVwaMQj9smX3rhNm7ltu/U/PwLx2yVnC2hD/6xIvcvLkS28DeDH2YtVeoxvB0veD+7IDaOB7t9ui94XQ7o+8sIVl1q1SwzCBMVi5k5WBWMUytJAcLybSCDQlYBnQyoJyLAiEO7QSySnpQZADKQdHHxHhShIndbkhMKLuVBwersK1sNnFgE8kaEywQFaaPop5IiUy3NNg2sdt3imajOf1cYO+FFVYHysLRXFaojRE199KjTCSeGFDw9HMVN7/Usn5tj+625+FLAZxh8bbl+OAW84M5avU99F3913xkVfSJl/YDhxruuoI/al/mm80dzvs5T7sFj7Z7PLrYo//mHp/85S32y2/D0QHdq0c8+VlN/2gJNzz3/42K2180zN9bs/+W5uCtnrNPi7PEfSdqlSYWrELNJlR8vXmRb21v8QdPXuTsdIl5WFGeK+aPIrMTT3nhmPeBN2JEhRbldjS3ZwMIHZNydQC9tBIwehMkCO/B7PoxyItRyD359xzAWA19UkkDfk+SAr+oMTuHn1l2twrOPiW95m7+07sCRq63wp5PQZU8OQzr+NDPSinpOWPF4idbwcRC2Ny6D/ja4Bb2CqnD7hjs78sLhT+1uNry9FXDk4/vA+fylh8Ba6VQBUg9U5URdVgeMUoMEr3GF3qw+FFeEXsJhGNeB13abq4VNFVaC/O4IrCbPFZN7tcOqvMgiYRWz9hhjfZBXhTRgGp76XVjzVC8cjeW7G4rjr/mIBWCYtMKgJ2+YJyQF+QmSTKKDbTHCBO0HS3g82P7pSYc9uizakx4vst4Blz3wr6mKFDbhuLS0R2U2IdmnK9oKchNP2e2J3zO69uTHaab4WaT95r+qia3vV9t7to5+5MYcVJA/rCP51lyTxXSWYX5Ye7v2MdIwA+qpG+7gt/afZzfOPsEX37yAqtHS6pHlv1TRXUeKdcCRptGivZCHDID0UhsrjXRx2EdzYDDAD5EcEtpDwMjAKt6RWyV1BW2Ct0r0ODOStxxZLHXsGoXxPs15VbRLyVv00kJLdaTilgocAa904MdZSQVactJ32gTIRF1I0L0zXZeo9UD8mQ3ybsSCB1tGAGPCFd6mMHzr5vpXEhqpQEAn6LMz5kzompSg6qcCLGICSxSV9fMH9E4NFt8FPD32G54wAGQ1KEJeM5/+3x7ypGn6r7pcq61wDL5/qJ04iKWnMJiVAQvf2sdeHC2z9+9/CkOFp/i4wcn/NLxl2liyeN+n8fdXnIws/SpPzSIErHzVhTQxEEhne8L0UAYCdYuGNkqorTY0kRmtueg3HE82/JoucdmV9KvKuK5pexGME+FcXoNI/+eT78Th6RhOvhJzD95fM4pplbVo6ozsLshNn22YeKOlI7rxPlqeH0mgKNiIHz4GOVaUgJQ5x6lvmbsq15pwlnBVkU25oD3TOQrQYmTQO0xM0dd98yrjpeWl9yZXfJCdcnSNBybDYdmyy17yUJ1FCpgcOxpz1xJj8Q6Efia6JirkqWqmKtIHz1zrbidwShaoCXw9HtSV/2wxlUl/p+MalqrgFWeF8tzLtyc825GiErI3CaynLUYFWmdpfOG3hmcl/7R3hkBpLcCSNuNtCbIc/VKC5ZMWHheXDABhH0JsRAXLQC7U5gd1CeRxSNPedmjW49yQibNbaUw4sIGCfCzim7P0BxpNi8r2lvJnnJrsY0aLfKjGvKI7ByRCZRRT5ZznwDpnfRcN23ENFLgLLbJHrNL9tw+Dn2tsy2y8qI0UlNr5UA6KApltTg5dQ6MoV/InCxWigs343Gzh4pC6C5WEKsEPvQe5aFfQG16XNSUf8wFfbp2fVSHOEhJmwxyr+ec76U+0ldaLhktyGuMYtcNqOWcwdJTKwGha3EYil0PSl8BNh7/XMFrD2+h37k/5n8xDg5LA1n5ec5GJAApFf1DKbU+1XSwJ5/DNDB7HEQdB1LM11rA38KgKoNpPXrbSb/sad6bQWlrCKXGdBHThKFmU2yu5hbSliEV4iuFqxW+njhhaAGuVYRiFZmdBBb3dtjHlwJetL3kyIsZuf8nRo+K5/y5QhgB5+t5S516c+bzpAW0zsRuFSLNUbKBvfR0ezrVwxIgPWmDCFwBpEMhjn37b0F7ViRwQfNr7id46a+c87++8QWWqv4gU+3Pxvc5pvWLNvacBsd9fyBOfaGgiQVGBU78jL/ztZ/m439rg713Akrx9GeWmJ+64PDv7GEuO8KsYPdCwC8C7sJSqx4fIx6X+iWXw57eR08TPdsYaaLiPJRsQ8UmlmxCxXvdDb65u82vfvPTHDRxtP/Vsq5Ea1C94/YXG/7w8z+J7j37IXIQI5AI5IFB8DD07Q2BWFr0xZawFBGN8p6YiO6xtITKsL1d0twKfPxv79AXGzlY2amhd1fFD3lUAj6rXUesygS0idhCrJU1uvMs7jXYXcXmnqE9XtDsz7m86TH7PUcHGyrraJYFm1gAUdoh/BDmwg9jqC6v96OCFiC7HapMjsnAchYJwrMAaXoeCfBkVhNPziAKwV8VGr9rmD3pWL9cMbsOQl9XYT8PkJ7cl0kGetcT5iXt8ZLyyQ7dOkzjWd416CY53A3At4gplE+EpU2L2jZX9sOY98TcysJo+oUWoqpVKe6X/DvX+qcttkImMGVFdBSswW495UWHXneothstuo0hzqphP8jXUt7X4mI21s+sGRTOV47RkODlE3sN20zXWzRSx8ptFKMGs5WkW/eR6oKRHKbV5HFpD7nynQVrKk4cZuvo9wraQyO5TSlOWn3umfR9jA+ulM6seCOA7XgHciKSdVviPEqSF2H2VBQx7aHi8lOSIGs9Mru73rIxxaDq9UFTlS196q07VUr7lOSFoIdkJKa/Zb2TntEh9Y7OYDRx8vuVLyX3oVMB5TrlGgGArQ1XrpPK+qFfdGapayXqVmW8MMNTAv7tezeZ7TXsVR1PVgt+78HLBBR7VUt/JIttYTxdV+KG/tJyPHPxQH5X0rM3AdL5ODg5/KKaHlThalBH68RC7YNh66QHtIt6UJ43oYCCwSrODImoY64Ce6ocmA+BmFhXfuhP0UePUYpjY3jNGn6uvM9u7x3+6Njw65tP84+ffpo/fGNG1CXluaJvIu1eRdQV2kmyn8HDYhcpzx268wKGGiVWsr3YQ4RC4/aq5OP/IUgc8pyJCtMhKhCjqM6hOo1Ul5FukRjzqQDXHit2dwJvLE745vxN3ELmybqvON3N2XYFh7OGV5bn3KqF1Z57qGsVqYwTlUtgAHGm/Zrs1uGWiQH7uOLpzSX1fsvmFSuFyqAoL2D2JKRex7J4mq3DpNcKlQBNsZCEJMwrYmUIVlM8WXPr9wrp/9QF+i9Ytrdv4g4Udl8WFGHWCZu5ay0nzYJSe063M5quoNmVYufYa9kkfepFlXoN5sLT0OfZC/icE/t8LHMhSbdy/xUlRcyMJgGZTS46pULUtDAw1Ev7OJ7HuSQl2TbcV+l7pf6OwSlMG9ExoDtJ0l2tx55DUT5fvxzZTDpZmumkCC9PDdvmkF0At+dZvmPxhTwvnpTsXvC0NzyL98zwXe1GwScaPvviA7SKnH1izpO/uOD+poYHe39iU/tHOXyyHMvBuo+BQBzWoUKZ4bYpi3Ea3Gs0GkVACDV99HgibQz81+tP8rfv/xznuxlP3z1E7/f8jz77Bbah5P7ugLurQ85Wc9pVhb607H9T89q/WmEenMJiTv/KDe7+9RnhtR00hvk3C6pzKcb4hVgGmTZQrCL/x3/1S/wf/N/AnhQUl4rZ48jsNMg65yPHIXLsAypsGZQFPgMOEiQoHwmlSYmyoTxVo6WL85KsGjMonHXjku28AqsJhQGdE1KNLzWhHJPsmJLsTL7wpRIrmkqIZP1+ZPk2vPz3HyZ2Iah5TVaZDoy+KYPViIo21pZQGGKpBysYYCykza6F+0rYhdXlaInvk7OAtYr2jpaem4n1+5FgsVYBXXqMCRgbsJOWH3l4r+k7K21GnCY4Reg1qhMyl+6VWOkm8n1ew5RPIdgUqM5F+7yVq8nPVCDVLdRPG0kglIZwDfQ16ZyqBNjGCLmXdGGGYtP6NSFKzO9uRlYqEL1HFaknXYyosiBOLbNjoLyUuCsWFpX7Wlsrrx0CrpJAe/Y4SjD/vJ7Tgwr7fcBLpUS9kVQh1cMN2zf2Ccs5er1lcGnJm01+fWPAhGdY2Uop1Nkl5cUxbj6Jv1U63lMr4Suf42rOMNwcn3/7dxrZUnOqlh4+w/Tntff/MI3iWtF4aqNqJgyKbN9t1HfXMF0vHHyvyufrr3+97JFbLYA4YfYRVsFwHmp+f/c6/+DxZ/nae3dQjyqqU83BigRGe1GPdQGzy4EIqf/hOLeiUkPsm/sCxqFoL4lvuVYUK013I9k2p+s9FhFvIrHTAso1CtVD4RW9LtkoeOHFMx7ZA8xXa8xO0d6QYo3OSUQE3ehk1ZcAkipZbJsIVUDZcKVHdMwK5yAkRClYRWKVr3Mm6GAGO+Kgoh7uz7F0ehw+LWL58Azkq8ljhudPXms6skI6g+X5MyjGnC+OxMcf9fBpDcq5WCYRWxUISkgaKi0YarJwTF3EMicsj9wGaqzRp2OiIt7r8TW8GlqEut7w8O4xT073+dbFDX786BGfXd7ndrnicbdHFyxbJzl7JmnnnFLGVftcYFBmb5Orkk4AduMtpZG+1C7Z7u7VLXXZc2EDTbeASyPLc+YxTFSoeVz/O0g9kcGoZjJfpk4k2sXx3Gf1gyYVbCLdvsL3YFZxKDiB5Dc5dlIhYnpp0aT71KPUCM4TrFyLWYWnnKxT2gvYDSm/qJNLU84vrCx40UPwBt9pNhclG7XgiTpkcOnQEV15bOmpqp69uuXWbMONasOd6pIDu2NpGm7ZSw71llJ55rploRzH2lMoRYGmQPrQjrbgikI9r7fGh2f8SaqmZ6bnll3xpdUrbHuZo7ttSVX3lMbTB03TFfio6DorYLRXhNagWkNxqSkv1KiITrHZdF4OOWeK84fer2nOSeydXTDkLrtVVCeweOwpzx1245LltZK42uoEHiebRZNtNTVuodne1DS3FN1RyqnOLMopzG4k4mTlJ6S826R2M6nlTI4l7VpU1XYrxUnpQy5kK92FRO5IL5qKwMP3Ts53uCCtga4XoD1oJ7Gvaj1xVtLP5TMu7kfu7w64aGeU55HVG1CdK0JdyMfuHaFUdIeyXk4JOtetuL8bye2jDkgDBCJdIsLQO5hJnkZM8bvWEmPbRMy81kcaGPLK6VC9k76vVUnc7Yjeo61FWUt5Cf1xTfVOevCk33Tu65lHnMZlAdmLJySF4umWcLiH8sl+uw3YLSzvdcRdg9rfG9R6WQVndj163UoekXs3O48yWmzKjUFVJd2+lXk4gLUKVydb00rqT3mdD0rcZ2wrNt/Vpad62mHPGwEZspo5fd+YiKxxltoDJSB8AJYnyreo1LAGKO8FZJ8I9Ye40DKAN5mgGLShuOxw8xK7lXy9nyuW91xSXUP+ggOYaFLNVMl+Va6FIDJ/Ks93M8X2ZcU31rc5PXLMJvH5h530/YMYuQY1Hcl3a2gbNn3M9BhNbw/EK/UqYKhjbWPHfRd55Je8ai9pouGuu8E32hc4dQv6aHjSLXmwO+BLb73Mp/5vnQDSVcnFz97h9C906G/voV7R2N2Sxd0N5bnm4Itw+plIqTzv+Z5VKHjil5z4Jff7Q9a+5n5zyN3NIXfPDtmez7BPC6ozRXUamT8NVKfiNvBGhPYoEaAyUTvN4zirKM4baSeX3S8SADa1zc/kJACS2jUsZ2TXvlAUouQvLf1ByeXrlotPwKu/4ijeeii5e1pLssIUGG6bEsij1cRyRqzMIHbLhK04kDXkGpidBbTTFGuYPbH42nJ2q0ZFWPxcx6t2Sx9LjFL0kefWGj9Ko4/iOgegqlLWnPQ9YlVe3ZPttVpLJhpNifoqtQcJsk53rxxj7j+EoEeCv1aoLohwzhjoHbEuZd0u7NjDGJ4FWvOI8QqpQW0bzOkFs4cC4Ma6wO48++861Go7pgE+tUlI32Nwz3BSh4rPIzZ4jz+YDUS6UMh3zK04lY9irYGs4YM7jcpr7fChB8Ga8pN9whhRRCd77uF4a52IVqmOmmu6TPbLdJ3FaTHEqLTmqyvnT/WeqPQISidwubjsOf6qFkLTLnL4zXZwQRj2JMbXjyYJhypD/aRDt47dizO2t2txf6qknVgsAztfcBEiezoA5pk18nmn9nnjA4PSeu7S3pisqlNwl28DEhAseofd7YLZU8X8sWSSzbEhFnECSMsx9E6LzXMhgZEPinVfiuI4DQGlRzA6ptvy60zB6AgEpyH30IpqSASu9Cu7UtSY3Db4K3Ll+2kdxE4tKZFBbNLybSapo60KdEHU3tu+IPaaRd1hdCBGRfNwwVuzG8yLnrrs8clqezjOKg62G889DwmYluPCAFrn/tJqwiK+Hn67oHFK03pLqe3AXN+GklK5ZAfmIKSEz/QU0dHHQJFAAFmgM0gdhkLdNnjmyeqiUIZPF44w/wbv7h1z/9Y+Z3EPtyikD1EqFGiHAHQJKOzbiKsKqguN3Xlh7uCJpUzTUGg2L0miXGy+c3LxQxmpIIgH3YHppNdfcQnVRcS2QZLD1BctakW3Hwk3O14oL+mOAqES5UAfDKtdRbMtUcBhXTEzYuMXtKdRcQCmVatHdU2MUhAMUlzRnYdgqU8Dulc8PX2R7kYgHne4jZWezclO2ldif223fgjUJbgAt1eIy8HaDarGUBr80ZxQappjy+xxz/xbZ8zf0YRZgZ/JuQmlwc0NmzuG7XrBO6+WqMoTu2wNlVQryTpsAKOnPbi4WrDPagQVBEDLcyhaRiD4eUMB+tni/9QycyhSeUmyJTEXMPt6sTLXT6NRg9LBdAGd2IS+UhP78oirNdpFylWgvOyH/gx261nc1+xuKXZvdNi54/Abiuq8x80M7YHBzTTdLU97GCkvkkJ7A31QfGxxQh8NB0XDa8szFqbjnZeP/3jz+EM4pkFfBqSlaKaGjW7aUzjfln9uY0cbA02MCTioOA9zNqHiP/n1/y6f+H86+s/NeOEk8ORnK377hTc439Xs2pJmXcGlxe40y7uKW7+7EUDaaOK85v5fntMfBMzdmvpcQDPbpOvRB/be80Sj2H+3p/yH0u9E916KNl1SCyRrLpUrxnk5C/HqPI2RYDW+FiVxedmjOkcsrQQosxJ3WBOMpt8ziTTB2ONQJVtWTbIFVIQy/Z5qjlKclb5UpoV6HTBdxG5lzurOo3a9MNCtSUGOlgB1ElgqF6QIcp31LScRhQD3UqTIxd0cECGbbY5LU9FBd0pwylYPVoY+7XUfNqDt/YayAWMCtvBY66kLN8QLCnE76YOmqyw+KNre4pzBdYbQGXyviY0mKqkT6T7toXmtDAzAdF7LButSGACnQUkWUp/xx5cEP33gZGgFSqOyGj4Vf4AhGQyLGc2RYu9ukB5vIUhBKAfhOfDOCcBQeBKGaHWZeiwYw9Bv2khwLr2jgEZTn8UhefqeRk5UnYOmRS0EiC42c9zRjHLbDMzV6YipiPRcW3MgrtbMnga2L6krIMeVoRhUUn8K6pw/lDEtGE97SeccbHqWflQY4hSY3kbDv9h+kr9z72d55+3bzO4WzFtRzxTrSLXyFCsvNqtO1v2BTJktG1Uu3GYgIw5JprQ5UEMvLB2hvPQs3tP0e2o8ICnGyZdxvx+JVhwmJFYydH3FI3/A3sGO1acU1VsVxVrT7Qd8AvCynau4wyRldBWSMlrU0YOBQlBCLIxI/NlpdJP2HBsnoPN0M4NBQT3tO53AtWEE9Sz4nEe6XWkJ6GJgkrfl15sUB6br4bXrVLmcg0gc96MehZIcOESNR+xkP4h993ca3muMCcPPGFUiNoMxEdcnENJK32ednpOP09lqzu+0r/Le3iFv7J2MbYWS4rkLYmecb7veZisPuV0PROkQFQFFHwzrrqLzRlo+dZZ2VUGrMTtNsU1AARKfE9LPvO/lPS2f5zzt0rUwqjSvfaBhemaAP+1pZvKaQeKhfgFEjW1S/9NM5jPX3h812MhGFCM5LEq+k4jDhLFvqfKQ23+rUl5Pt2rIaaJV+CoSCmmBFm2EMqKKMIgDTLJjb9uCti04XS1QKmKtFxcm66mtY1m2zG3HQdFwXG64U4jSel/vODRb9vSOPd1RK89cSTuC2x98qn1kR4iaQnn6aPja+W0K47lsKkCObessTYrJQlD0rRWHvV6jtwa7lpYJZmJUlYkTU9LRFJzO/cvzXMo9CYVMIcpou0Xsus889dMO3XnJTbMNowZf6EHRkmM8X2v6uaZfKvp9hS8jplGwG+MV08nn8RWSH/tkFZ4Iqrk9n/JqsOsu1lLnyJb22Y517LWYFFKp2Kv6MBSSgaGNEDkW1DrtW8me04TUZ9rj9quBGGu3Urt65+kxL951XH7CsH0B3MxgU8HWFwp36JgZKQx8UOtun9YlmQdj7eyjPLIFr3YwOFZZI+TMSXE8F79h4nYFDMpeEFApkxcKJYXxugYuRlAoiqX87lZBlWP/CfiKUePLT4DX4e+8/sUovUB7Ryws7sZC3L80zJ8EscWOSbWZAZP0wqqX5+G8xO9aQfDEGIZcJMxrQqEw3dgT9PoUiTblzPm6zGKHEPGFwtcGU4vFrfTBdYNrGVoP5NloEig/VUcz5rsZXJbjKo6E2a1M4sF8bK+CQNGooS3Xwds96xettAk7D+idG9zQrqikSyGiS54uborKB/zM0h5Z2n2NqxVhJteO/2hP/z+R8TyQeQpKTkFmACaK5yyEqFRBiH54fht7PJFT7/n/rT/L5y9fZ+tKvnT/Jf5nn/0Njs2GR/0B99tDHrdLTpoFD8738V/Z443fcNjHF1BY1j9+mwd/SWEqD6GQ+DVdB3YH1YWnXBX8777177LrC9a7iuasxp5b6ieK8jJSbKRmdaOP3OoiKvTiJNkFdOsHcpFbFkObwoEckfrLQ5rPgUHBL+p/Q9RiFx+1IlRmVOnbdE3layzP0SKJD+aKfgm3vhiYf/0p1NVIkBnawClxUs2HXiuoSgG2U50sZFBaTVIQra7UmXQXKbYB08saVl1I24jNiwb185G5UgOxORDQH3G9tCE5aOV+xToJDnL7tLIY2wbAsLYD4z4Cz9b3nEefrynWu6GSFGMcCE72dMOiTw562oiiPb+24JfyWXIcDuP7BITElt0u8t5i7bhHKYXZdNjzHeHsfKj7SOw/2Scm/67UdbSSo6MkkXbzQuZYDIRkaT2MfAjUGMPnf/k+7SY13KQiF5W0G1TRcdKSAUhAdZDjE+IVhfSgeFZq4oSRXj5CqE2Km+JwfYizhhr2jBEgj8wetcwfpNxhm2zMA5jGjWTJtE+5vQqMwu4UvtJcvCnOiG4mMawI9QArrYQydvk8Us8HHR8YlF4sm4FlDQxgqE0grTUT1pAOrGcdq9UhN7/spEBugSpZhDqNNkHA5F4SVasDVeFw3rBNCuT8PjCywadANGQgPN3n5ffo9ahagaFw8szIwWdIG3gqmsSYgCo12neP4HMcGOKVdVgVKIyn1G4IZnOivu0LzNxxc74Z7ivONSenSxYvnFJbx2VTse2L4RhmG/IrH1M+zAA+T49/jGqsPccx+BtY9VwtFnRBwOguWIyK7AisdX3FTnFP7zA6ch4i4FJBMBJSk0TNdMOWDXgVFccp4tRoDIpbZsfNYs3RfAe34bxc4NYW1WtRJ6Tiuvai6tCzZJVTKupzhd0FjFWoXmwOQ6np5yoxzD8EkZNGim8JdA5FKvhvBJA2TcQ0XgDOUg9saF3IGQs3e2JQXHQ1pR6VdEYHHqz2ee/igFuLDa8tz9izLRftDBc1pk0W6HEMrqNO9uD71dAzpz6PLB8qfKHYvFDT70F7HGhuKtxcANfZiUf3shmrxIZSPmBaj68MvjKEvWJQ+WTWzva2JtiC8rREbVvMaYvJPRrKglhY5u8WhLpg/focFTXlpR/AMhVC+l0Qk6glsQxWEnSxTBoLNzACLTolNlEx2vINuyFJbTrG87pjKGxllbX8Lte9dnEoIoCwwLWHfiZJiqi4GUBx08Xx8ygJqHQUhaerDb6UYxssaB+ZJbsLkgo2KnALg2mgvAT/sKDfs1y+ptlXYNogCtEaKAK6t8yf+ARKRi4vKs77Ob+w/zZNLGhDQaV7bpW5QcSfrqFRzHU5JAK5lQCMm18g0oaOdew58YpVLDj1Sx67PR65A9a+FlcIACNA7/67jtmjhqc/veDpesHl/T2KC8P8QvqlVWeRoz+6wDw8AyAWltM/d4vNx3tu/7rl8Ktr7JNLuc9o4rwizEUpHawS4sGDdphnKtva5cT6OSNO2OkqRkJh8JXGLWReNTcK+sWhJNdtwNea3ZEmWugOJOB3s0gogQimlV5cJvV8qy4C1UWgvOjF3tvHFPSN66maFI6usMHNs3sTiRQDzwaUynuiN2i8XJ5KXmNQ2iUVuCQOUsEVFZuSSzSzMzvQbcSUCpzm1C/p0/r/UbBWyiQ+ldxUikRmK4y0/yi0J0RFbx0+aBrraXtLowt6E/E6+ZcEnUhncQRR9PuAomFSoIdBdDj00GwjcbMde+lcV5VmBnIiIRAnSupUEHI3xH5r8aCVwLuL0kt610gw3HVD8St6P6olEghstwEVR9U1xgxM7BhC2k9TqwStrr7G+40hGM5V/TTXug41n0GM2FVHe3NGkRKEAQDLSc93CKhjjOA91UWy73heGPLDxLfSpfcjee8fwMhx6HV19IfpGvdAG+HvXPwc/+Uf/jn0OzPqRgkxsRljhPLCoRuJrwBZE3ufCD1pTwhy8Q6xl5MiZo7rw8KgtMJ4nwr/UJ8Fuf5TcSWrSE0rf/tFwC0jKmiKSyn2mFbh1gXbInDnzjmPOOTwt0vqJ5rzn3DSy95J2c2XYg8eKy/9RyftBsLQ31elgrKSeL4RomGYhSHPe26f6Dxh4+S2AUDOrw1kkeawiI2vpSa5Wmapizw4gy5KFNnX3l67ST6o5PML2VF9KNKJPLQK1MqlntIhKac/+BUw7S8NAjJ7Zzhc7uh9UnwmkNr1E/J3spKOyYI9Rghes9uV3HWHPNksuLXYDPkuQJfabUku7HHR0HmDD3qw8y6MH3Lixkmu23lD7w27rmBzWRM7g2o0utFYL6CZ7tQYe+f4PIN9GYRmjPOH/ucBaT3F5Hl5nuU6f8ahQyoqJQXN1D1GRSEs+FLRLwRY1slBYGrXB5JDBAPKplwwFV8Hx4PJa4ekoI42ESJ6hl69+bGZdRMSUSQUithEggU/08QyEOvU4ifFkmXphnqMUhGjIz4oOmfovWbVlkPdBMCaQGkd86JnYTvmtuPV+Rlz3XFgt+zphr/5gWfdD2f8IHpKt6lXUxMKTtdzXj06p+0LjJXe7E1v6XtD8BrvNLHT4GXdM42ivJS1f9pDWj5s+vtaODGdu+T5l8nVWuZ7sYb548DscY/dupFEbYWc6JPDUO7xl1vhoFSar6KeDEbW/hAgammrFczoTqciBC3zaiiwFqKUVinnNTvJIXQnubFtIqaPA8lW+4DqwgCMDGD0YN89KQxftXEY844ERqsg6l1fW3yl6PfTZ46avrG4Wcp1bnr6PUudbEiDBbPXY7VcOC6d09wK5LuNTJIBhjYiH9Vxpad0iFfztqSKvuLS8ry8DkbgICsedwHtPHG9fuZxy/c6+j075AxX8lulxtcayGYj4HFFVdk7sVsNAXthhgL53lsb9MnlWOv0qdbkQ9rHvajw+l4Wzek51+IU5vcqqcn0CmycKN3kYQOIldbp4fZE+BiIQrnVoBvjuiFfyptRBv0zwJ/BAWMGhWhWkGagwc/L4bsNZPXp66fPq10gFJrytGOZPk+xFofJDBLm/S9qhXYBFay04wO6g4JuX9Rybj6pr1WBxluaP2PRPndMldLTn9dVs6KMFvFWG3v6GGhi4DxoVrHg87tP8Z/9yr/D0ZcV2xcV5Q5++6WP8fr8lCfdktN2wdPtgtOLBbwz5+XfcMy/9hh8oHvjFk9+Wmwj48Oa6nRSi68ty/cCysPetwO7v/UCdhc5dkIi0r2TemcbUgvNMKwDOrdUSOIjQNbviJDxImOeDsSyIFQWv5BA3Zd6sDpGyd+5TUm3J/GLrydiiBRbaQd2I/WIci3tKYqNo3w41jJjtrI3ZryWrtWlhMArZJAMwMEY1w2ipHzNI+uO3UHsRmtmEWgZbs9WH8LmJd//0B1jnQXg9jFur8K+dyLK5cLKGvx+dZYQxj081UKIkdjFsZYUQ2qZllqiPT5BnxfSxq3vkhV2XiflfZT3qTauBoL1QHLwQdbarL7Oa2py41Cd7Bmcr4hdj6qrcX/pHVhLTISGDGgP9t65lzaQyVtuYWStz7l1zPH71LYbBnU0Mo99ylmVl/zfdHKsojFQ8uwxzfte+qyqd/I5MzkrK5eL7IoZRkV0On4REtlCD+D5ILRL9SwVxlgTGGIzvdrIHqo19L2IN/xVKp99/UW6GzOiVrTHlotPQr8XiEWgPDWDC5QuPAFF8x0Vgh9sfGBQeq9ur/Rw7py9kvw4L0ByYQKFDrywv+K98pCoBWAKk3dyvcHCACAHlRmL8q93hsL6ZxTRV9XR6QSE0ao7eiWFkKCugtKQJnlioOUxsMnikMwOdkXpvceHyvcEUTYtio5F0aJVpNSemempjOOyrwWANnCq5hjrmdsOq0Ut1c0i8aykfEku4KYr2LUlR8stxni2XcF1a08FQz/rzo0HcgpMx/QzOxIMALYSW/TCeHzURC9M9d3EuqDybugFpgkYFSijpwuGbfDcMTt6FVkli8+5Uuwp2Sh6PAaxlrsCVhPZ04rXyqcclK+yKDpK63nMPnFnCJ1GpWJA8BFtkiXiHvR7in5pqc4DpreUl34IHk0HxSYMSdqPdJgxyA5FWowacHPwO43dOszOE0pRHoRCUZ4rdvOKX37vJ4k7g5p5dn1Brw2V9bjC03QFhfWs1zXWSC/pPdtidKBzBt0hisPc7yAJztxM4+Z6CAzsJmLXntJF7C7SHmqqMykKNjeF2W12QTbySXFbETFbh2493WHF5gXL8n4/sGjMzlOsY+oTXwgYbbT00jGSkMS6QLUO4wLLb4Pe9pI4gDwu91aoSwkmCrG1WL8+x54LA9vNNKtXJwvHZFGNg+xPfmTwWqzLFIo4MtKDgMz52s6gMioOwYoUFOIIZLuIbSFm+79EVFFpExHL9JzYSM8hX0hBYFqskmJToD8oxE48xGShLGuPaSPzh4q2UaxfD6xfV8wfFLTHEX/UQ1BUZwLgAJTrgNloLvqaPdNwrNY0oSB8IGOMj854v17BmYGaH+PwnPqWVVQ89Au+2r7Bu+0NLl3NytWs+orauEGhU2jPv/XZr/Av/udvMv8nS3RfoTxcPllSPbGYBpbvBfbf2lE8OJceKYs51BXt68d89j/6Mpu/+zlu/NYjuFgRnUOVJXQdOuzj9+oUcEthRu+cJJ157mQlQVY7TIJkMqNNKVHIlJpuX9MeKJpjRfOixxy31LOOEBTbJwv0VloHlOeK+mmkPgvMH7bYp2vpnTLt4+L8GBhpRWxaAfmusQ4z01u51HtsYoEjn9MImKjjFZs35ZK7xUQJoYFoDMaJQjoWY/A0ntRcnEh/X2dLKoVbljgtjbwu3Fz6NKKuspU/pCP0RorHMaDTHl5ZR6ElNgCJKUCKY40v6L2h9ZbWG7ZtyW5X0hcFcWcGJZXZiuJNBTkVV9a/oXrDCEjnYmiAxYOOuN7IWmbMYHFHCBLUGyNrdNcPAbXSqVJfV8R5LX2CTgL2QnpIqcsNMe/LObhO80CC4qtzyG4cxIJQWozWxMO98TEqFeJ1pDqT/UIN6o6kMGhbnlFQT1TP0fuBrasKK9eqUuhth6/m+OMF9p70n4uFRVWlFMRAiltajcUlGAHxWc3s2yvMLxyLuigf15znFFFcQDIYkqfokAynz3f15mcU1SPb9jn3R9nj3nf2Z4xOTf4GPqzbRH3FLlB+Tj/q85TR3y39uf5V++c5OHyH9zBK0cVIAI61pYmeVYisouU/vffv8Ftf+BR2qyXGMNIjtl/GRBoENzfU62Rf7yOhtqlnrZCigMEWb7pH5OKKCpF+YSQW2Tl5ThS19OxhyfrjDr3TAzFP97ImmEb6POV/KiSAz2k6U/I47HN0Y8X6r9bc+Ltz7OcNF5+C/sjhFei5w1qPMXGSd6VjqhWuN8RWXwGdQxmJc4cuPbE3YpcdgCKOcy8/3ikG+2wTxxOl8nUG0UlxQgDoiEpOU0OMOuQ3wNSe20S5KqIS0msnPxUM1rREWR8z2Ckkye8ymX4IQ6zrw+BYpYnfc091EUHEK7mrSyTwn731Hve3BzzZLXh6saQwnsPjHQ9P97HW4xMlJLqskhNAW+uIc5rLdk7TFSzqjmXVYnVg1xdEoPVSD5jZnrnt6IKlTX2me2/ogmHXF2zakt22wrcGeiWuT06J/XUnANrY33kEavO+JYBvCgumqXwGpPPaqrKF5PjcaVw+Pu8qgTW7FeTSjOnk/vZIFDy6E6X2AOJli+OgBoBQ+dyDcfzs2a1mJL8zONXktToXa/O6rXu5TGImT0TJLXwre2ModSI8R1wZsIueetYxK3txYrM9MSpaL4QAraK0+QIhDUQl56MrOFVCMPuDRy8NrnCF9fzNT39P0+8HPqYls+cpYaWOkXLyawrY4XmTwlluu1brnm2oKKznsqvoe0NZepzTg1V3jIq4M4mII20UygtRNefYSw9W8JM1ZbrR5/ObSNWhFPvD7FZRXkjrr+WDHrtx6KYHrQlWE0tx2XK1GedRBOUiOgoR1s3H3FLIUeM8i0bWPePHHHVQR1tZGwfebqOwW4VNim27jdJDuhfStGkTIN0HdC/2rlOC62DRnetMU4ANkoopDipT1TupISS1z+629LrVrRBTvnlyE3u/ghjQtxs+cecp57/5qqhhZyXbFzSv3jq7ct7hWfvuPpgr80EPxB+G5/4giA8/zCGtEWT9GByIlCK8chvz+GyIyYdccFosn56nKYnAaImNe4ffJUuABLoqYyh//23KsiDsGnRRopZziWGi2GCPIEB6H0P6DFF6Saf3EndEUe6p0wvijUNiYbD3TwnnF/Jxc7/TDExpJHhL31XAZY0qjNyfcgC3VxCMXDTBjOvzFQA6AQ1ST4roKO0UtJO4S0UIpUEtK3QjdrQCTo+x+JAHJxv9qHUiZkzsURP4l9VwuulRUdTSoZY9dciLr9vMDjWowOy9NVFr9LYF78VhbLuTukLOW7RGvXQD9iuiVpx/omL9asQdOMzKMHuS6ogm0PjiAzkM/Os0ch3qer6R28SRxBLZpruNjh7PIx+47/Z4p7/Je90xPjlynPQLylPN4pHj4C3P+SdKGl/wT+59ksvVnOAUcWdZftPy8j+9xJysIEbC8R73/s0ZbhG5/a/g4KsruZ6tIewtcEczZk96/EyzeNgOYq6sfB5yjiyKiIgLXkRavKX9JGqZm2FZ0C8tKkS6gxK9LNCNEB+2L9U0h1L3bG5Imze39FAGcBqzURSXGruRFq7lE0/9tKc426G2bQLLJmuNUkPtKG62UlMrrOwfmRifeqqjJd4aCE0hpjwq1Z7Q6FRvmlr1AyNJ0IrLkh7WHUW/NPjK0i8Vx+WWJkaWUc7rn5ahHaiDPdl3rWbz6pLtLcPtB2cDOBtD2hvyOcrg8LTPtPdSM3Lu2RoMUnuJ1qCqSsQJbSu1FKXHPSatj9k1EucTOD2JHYwenShSDTP2ffpToToDVQkhEJoGVchaHxPBO/Y9alZfLQZcIWGZ4bMAxFklxLcp6BxJc5Sr+4bKjxFyYLcvjzUNzB85wWiMwh1WKCevb1ctcVaiduIoKKKI5KzR9TCviVWJnxXozqGfXqK0FncCL4I/fCDWQgqJRqNbRyiM1BY6N8ZfXT+St1wibPkg5yO9Z2xacvs7lRwQYu+kdqYVsXyF3c0CFYWcq50SsvrMwWkiRhbS0nnVVz9cULo0Hq8iPvVSduFqN8ecCHdOWNKrpqI7CKxeteg+0t6IsDNEG6hmvTBPOwOdJvaKra0wC7Gmdr0ZbKpHZbC8TwiSsQ0AdYDo9QhIR8afURI5uS1/zknFK6+JmvRgNQT7Esw9exxiVBTGUxifgt8giXfU2KjoEkOzNn06JnC7XvO4WbK5u0ecB4ozw6Yv2a8kuOs6iw+a0jzbaxIYksmqcAMoPSjIgxz7EDTocMW+2ytJyIwOkBjtSkmBzE4WWquq4feQLtguGm6YNT2GJkrReaGlSGdQbENPEwM3zYxt7IaiYkgHeq5KCjwvF2csi5YuWD57/JAQFWcXC9ymkMJTnj8+qRtSEau5o6kfGWZPIr401Gce5SLVpdhF+epDUF01EaUjMTGNbScgY3coyVx5qdIiLiBn1IrlPekPe7K7RRGgV9A5wy4ULOsWXyrWm1rmvtNsmpKHm33KvVNq07PtS+kH5WUjzYWX3H81FGPhTjuxnxNGmNgHL9/zBKt4dFgJKN2lXiExEkuN6gWk1psW3TrUsqDbE4DMmmQn2Yek3AU/s+jOEY2RdT9Igd8dzLAXO0I99iVrXj3ArnqK+6di82qMxEH7c1BKEnClKFY95aMV+EBzdFt6S02Yn6TCEUaY4NkidQrEDH8nlpDy6WcQdly2MtZ+ZDgBQz84sXSUTVwUz3J/VMI0V2q0GpfCkDDTp8WIfH8oNP3CUKwFFBSLqPFxmY2FgrDn6Fcl/tUGoyCclpSXEnSpKAx6u7E83u7xqD/g9fIpC93RR8OenvjH/SkY017SIQoYrdFchB0XwXPXzbnb3+Z3t6/zoNnnvJuz6ipK7dn0YnU/LzouuhmF9hyWOwAeNXt87qX7vPPfO+bCGfqHe8zfKrj9uz2zb5+jzleyaTepf5VSuDsHPPxfNrz3Kz/Jx//e49T3NhJfvk13PKd8cEk8v8SeFqi+prkzI5QaXeihYOpmBjdLCWmaM24uNvHNjYhbRsLco2ov82oH9lJRXihmTyM3vxypn0Ts0xa12hD7hwK65f6/mXWXg67c+yexCuPSXgnO4q2D8XkgQagLgw13LKS/r2q6EWTMbMUpyy89F1ICPWFTqrYHG8CneGE6RadJSVZlA4MFDQzsWF0YdKkpLjXv7o45P9AcGz+0kvhQjwDRK+kb7Q2LqpP9OWpWfU1lHCEGrE52m9FRasfM9nTBUKT2IFsT6GyBtxaCEfvZjqGIrSZB9MAMTusKaQ5mYNpetGMy+Dywbkr6CikJ6XuZb84TSrGNKy8T0cjHIVmYArnXR4zJuj4GSYgDxCpZwqtJ7zmVe2yC2TrpR62UEJkmPfDet5d0GsqYpN6w8toxoi7WFKs9fG2xuXhaFpJE+TBh5CbK7nOOjep67Br8MWOSAqiR2fUdxzOA8/XHv9/f1+PDD1I/uvaY73zEfnTDfJDv8kMcXbouaqV45B17WnEaSv7jd/77fOWbL2M6ha8j3GhZ7DUoYHU5wz+qWKMotobSasymFycBF4hWQGSz6cYWDLUdElhyURWGOGd7U1Odym1iKRypTyLtcbb3AuVUig0kHsnXebAZaJNCjq8NoVececViv+HBLzl05fn0y494c3lCpXv2bUOhPIV29MGyDanHqi+kx932gKfrBatNjd+kAmolgVbIgGZWMXeTv6cjKkHctNynTIpjE7lYlWEEV4MiODUBqOPoiOXUSEIGVNSQ1M9ZaZv332gYVNFXBNgqfrDr6Ac8atXjUfTR4NGJJByHNksf1LI+ZoJ3ImxrHalKxy8e/BFvzW7xL0/f5MnZHnXZ8x997J/wu3de53Pzu/xvf//fxfWWoZ83yLGO0pZLG4/3mm1b0DojLSZ6g/ea0BroRzBbOQHuoo3EuQenh9uIYPukgs6tKKLE2FcA4qRyznM4MsbN13tIy+NTjpDyAu1zf10m/8brKMftuSXQQNqa1AWCHXuOu7mCOgHVCeQORXangfZmIBz2snXYMPTRzuTw6JVcH11CJCPoNrkMpLxBuQTKBwa3m+nnGcBqpwh9el2jCEYT1oaNrVjbCGXA1B5bOKwNzMqe0jp6ZylS65DKOo5n20EMEKJiWbSD9fFHXTH6QUaICpuusz9cv8SuKWjagu6iQh81YqgQlNSqUo1JOYXdaoqVwq4lz1apxjQSK6/mo/Jm499RydzyybLd9LJ2V+eB2YnHrntRPidA2s9sUlTrIYbPQDghE5lGm8aBxDH5B+O1pR2ShxaMdt3pdtVPAOkdmJ2Q2k0vPdMHQLoTddOQM+S+0RkwHA7ySDSUzx1SHmzGuD/HZm0PVlyhXA1HX/fsbmrp771RbG9p3rzzlFcW55yUrwrgqBTrj3l+fL76vkFlrcIV0sJHdfRpPurlglgWxFnJe790wCv/Lej1TorceYQwAgEZDMhARIyDO5AAwAZVlmMxO0aJw5M6TlcVypoRlA1hUELnOUKI0IbRhWsAIdKc0UkFN6vR24bYamLfiwqusONnc07idmNSr0+ZC0NfbKPHeVhY3FyPdSQz7gOkaxHGfSY/ZndTo1IdrVwziBFCafHLEtUFzKodgle92sm8LiystgKqzApo+9SaxUv7unaXcg5ReIbSJsWbANMqptw5IABD76Sfbrp29G4n56ZpxZFMa8mNJv3aY+8gBlRV4T/1Iu2RtOTr9iFUESpPaBOJJUq6E+KfjNrtT+Poox/U0YGIxYhDX3Scho7TYLnvDvhG+wLvdUc8aPZxwQxOpS5q9mxLHzWf/MW3+Mr8Y9z+omb1BpjNkvOzBeZRxeHbcPitnvrth1J7cQ6qknv/1hH+c2te+i8qFr9/T0gaR4fEbYPuekxtcfN62GdioRPoHAcrbYCodCK0ZWfKsW6sgsQ03VLRHira40h/EIglqNoTm5L6XgFKXB7rp5HDbznKsw77+BIu1jLn0rqM1mKtrXMspuQ7KSWt3zLgmNegEFCLOYRAvLhEzeeojVwr9HpQSgvRN4w1qN6Ji0NvRdxRTIDM9PrDCIzrQL7faqrHgfb2jGANF/0MH6FS9iMhfPggQyuJuf2tAwjSck1iYKDtiAeLAT8C0rFLa23Xy9qS94HBYU9LbWjaNxnI/ZzVckFcb8SlzrnBKlx5qZSo3svaaRRRW8AKCTOv8b0bSTlKiWp+MRvWSlK9RiFW5GG3Q1WVrOs+fe703VEKdo3sSera98zzodof8IBMTsrurDkfkPvG+6OGbqnY3onSijQoti/UzJ5Glvc85UWPW1h0F/DzAje3NDf3MW2k2Hh8qdF9oHq8Aw2b15YUK4cxivbH7tAe2aE+5Oq0zzhYPOopT3ao3mNcQK13cHJO3O1kPwBiCAPoDMj+KUEtVCVqplFmYuOulDy/7SAE3ELs++0ucPFxTfj0mgJwJzN0p/CzdLoby6qrWYWaq0Xe7318YFD6oGwIjMnLzXkKYlMUXmo3qHyyzfbixzvu7R9T3Stx+475u5ZoLLsX3QA+Kq/AabyCvu4lwY1SwM0sfUhzJr13thbLdt0DUz7mhXUEpXMmewWwUukFp4XBXMF9n/XHJ0vxrJYGYerWxnNQ7DAqyjEoGtpgKFSgMp667im12GoVF9IjFuB8O+OFxSWLusMle62p5dZ0ZKC60GK31XTFeEzIamkI6TOqtAn6oJDWJkoKGokt3SuTzpNMXHtFpZLB5R2GyBO3x6qYcai3vFlc8kfdDR67PV4uzgD4i3rHU++pFEm5No42Ohaq41a55ounr/KZw0e8tn+GVpFTPce1k+kXRc2gkg18KDRNBOVF9dHtGXQvfU6LteN7mLo/sKFskKKNlQJH7iutnLCT+4VGdwbTepy1RAP1qWd3y6KcojuMVI8NZ8Ue1V7Lsm45mDXs1y13Hx9Rzju25zMees1+1WCVZ9WWlCtgwu4UJUzE6ijxfVo8XS3KGrtTKBeHoEC7SH0aWd73mEbmY9QKX2hMYioTQPWO6smW+c2CfqkpNjoF8aKWjlbjZxrdpR4RGlTr8Qf12CvWJjuVwrC5U1DNNfZiNrB/wrxi98qCaKBYeRZ3BTh0xwuKe6fMHzlWr1mCUeic8AKZ1Zf7y00Vabk4NRR6wph85N+nm692TO5LiYmR4pOr5DjrPo5KiCjH2JdSkPK5PUF6rcHKLxWFo4LZY1FE9ks7WIn7Ul6nuRkhKm78nmL/2wEVd7xratwiyrnzUFx2hEo2tvKi4N6TQ+7dPOTN6jFdKmLWg6zkT8fIwaD0r1esQ8tbzvKt/gW+2bzAt7a3eNIuuWxrGmcxWlw6AoplKUqeTV9ideCyrXlwuc9mW+Ge1sweCOFl/4Hn9uMdZnUuymIYgi5V1+Ac/uWbfOvfXxDfjXzi/7sWRv/ZJfH2MetPHFA/6eDpKYRIf3vJ6tVKggfFYGHkZlJkdLOY5paoAIq1qBCOvyL9bYpVwG4cqvVj8SmrEGIcejbHxQzlqzGoL5JFTXYdUAzXalQwWMMm1d5QJEgsxall9xWQODH5hpGtcRgLvM+wt7IFzxRcvg4eXgdCpwD3lUkQBmW1m2l0r/jq+W2+ffOIN4v1hx+QBvTaEpym93IcC+PBggmaMrFddN53o6I2bkhkbTBYHZjZnnXZsy4rdmVJTwVa7CNVUNicn3kJ6GNUV8IaFRnXTBcxF5txr54mbPlcZwvrrDyOMSXHlbBS5wVEKFb90FtN+qkplNEDS386roDfIRUgI4QESg82fpkVbREQateLmjs7bSQgXXpQq/d9D6WUzJ2iFBZoJmwYk9jflmraa2+qHFEaEoj1TEgYA6rtqM4j7Y3JMZ64cXynumhm2Obfv0cx5NXXmQ413jace3Xt/j/eW/1rOfoIhSK5MsBDb/i/PvpFvnr3BfTGEG51KB0JvWF9OaOa9Rwdrbmwgbao2T1VlBeFtEkIoIIXRrgmXU9h7DEYI25RUlx2ZIa29gHTRdrj1Muw9YPSur4INOdGrLk7UbNlB5iYYyKQNThALCXeCHUgVoE3XjrhJ4/uw6uSw31y9ohb9pJSeQrlBhv1Lhr6aCmUo4kF9/sjvmnvcFDu8bDa57Sc06wrycMi4DQDCSYKgBNNyrUGW5r0uxaAeXoBCLEmooee1HHI/WKQCR1DHHO+oEb7bQWkHqgZ3JuCMc8ARtPr8E905vzxxkN3QK16at1Tq55C5b3hg326keM15sy53uKD5mvNi3xje5unuyWuN5yv51yGGZ+b3+VvPfwFnDPjtpzyRkKuTQnIrTMBGoZcPfQatTGYRouNsR8L/G4WcWUGAtI8cIqQiAA+xtH2OIwAsfaST+X1ccjp0/zIzkXywun51xZE6Rk+kkenymXt5LrIBcqsrL7y2JQH+PT6Uctx8Yxq7mgYwb25Z77fYE1gWbfUdmzpVWhPqf2VPuGNL9i5gk1X4oNm1xV0naHrLLFPTgROoRudegEyEGynvVmvuGEEherk2vBO4QtDX4jityoNs7KnT4532llpHcZIfLc6DK5CHwaixg9zPNjtU5aerpUWY826HENSl1T9QaEbRXGpKC6FhD61k89D5mya9wNRUP4OqS+FxDdp/d6Iy1F96rCrbsjVRU2p8fUIKgx90l2Y2M/zjKX81RpYuimHc14ULqGIqcdncnfpBJAu1gmMbuQ7FruA7mMC6BIg7cSxTmWl9HVAU00/77ieDwtTbhmWhw8oLQBdVNDcCpRf9KxeS844Fs4/G/jFo3tsfIWbKVRZEkrL/KU1B8VusO3+TmrP77SeTlXWH+VhVFqXDveIVYHqHN1Bqov4lFlP4/G8UWQFe9dLDpCVhF03nLfoXMoPzAA0xKZBFVri76YdLVR9GGxHo1LjWdGi/CcESISIYZ7ki857yRP6NH2nDlshEnc7Wbu2reQfXT+CJQDY4e84q/DJsRAmcfL0upjclwlA7WHal7zYXs+eGqqLgO6EgGECuMMaP7eEQlGdFPiZFXvtJwUqRppbNcQa20gf6uaGTYAgInKwMDvzzO9uhvxLbzvUybmAAykXG7a7lJMro0VROpz0lNtY+d5xu5NzpTXdfkE/k72wPQ6Egx6cxqY+82jwnbTXEGBh0q7pzwY+BvroB5Cyj56L0HEaDN/qb/DN9gXeaW6wcjUn7WIQRYDU1LWK1KZn5aoBI6k+dcn9OzXmpGD9m7e49W5k725LcbpFX26lDVZRQFWy+ukX2PzMjlu/PKd+tJL61Csv0t1aUpztiA+foBqH6QLdvqUzhZAnEuAcrJx7XwAKXCUq534v5QUpRtNubENUrCPL+5FyFbBbj9k5VN+iWk+szBUy0gAe7i1EgQkpj1YDkBxSXSgczMmW+wOJySuiklqPajohwC8Xsh7l3J28p8Zxj4GxZhCCrANBj/3Zr4+8xiXieXaRiLW8h3YR08A3zm/x1q0DXrJNakj60Q+G2hikvVSXg1chmaEgtt3QOgoYxSU+XFHZ4v1YY8nW19dIZ3moVkRmEVB7SwGI21YEafPZSCpwMbVi8EJY8H6sH4U4rP0q7R9T94+h3VtZiNNddn0fnFqSrVEiQMVEUhp8LbQaWloQY3JsHQOoK6GCIrVyGJ2QZJ9IhIsCfMI1o4JQQXdg0a1l756nPA90h6W42RR5PutkbW8wbYW9aPGVYnejxDYSW55/StMvJfj3c3Ej0CtL9/WSm61HdUF6wNcVej5j6P9tzNXj5D1xOZfrKUY51s85b3QiKsniDzeTdaHYgPvKEuUQQeVein29glbjomYTKn5ooPR+uSNEPRRPZ6a/Yo8UUPQTVlCImoNqh3tJ87i5gb0w7L8d0C5SnVq6fflSOQb0RtN1Y4HZu0kVIUUQ3uu0HslGOvSOdjoVfdRYDLmesE5Zq1p+GRjZKk3SlDyImic9PSpCkAvCeYPRjpiSttr03Cw3vFSdU2hHiJqVqXnSLdm4SgrMhaNQHqukR2J51ODXc7rWMrcdi7JjYyeBxfuMmBTPy6qbgNKSOcdJVipqaIVOdnchRlmkVRS6B1KgaLHDebMqDOzSIYlWjrfbW3z+9HVu1mt+bPGIw70/4F9uPsGvPvg0f+n22/zV/a/Qs+YkVLyZbEhBCg2oQKE0x6ZhaVvefXTMUbXllfn5QGxY2wrnxsw6xrEAbHQg7Dsap9C9Fntsp5hFsM2EZPAjHNoIASDaQNRSFNQuovsUcBppDm8asXLQRom1yv3A+hU9JJTLr5asP6U4AWZVz17d8vKtcwDu93KJdt7QYWj7gtkqju3SVa6CC3CaGdgqRgHEjCx6dhcggPGRUOqRZR0j3aHMv2LjhuRksN+KkfJCADI3M9JzI0oyjAtyzQ0HRA8JRjSKWBX4pAIiRhaP+gHMJvWviJW8pu6SHTkCUoRSY2cVoZoUHKdjkmyr1E9Q5es2FZBiItVNN5ZcEx3UhElRHRVU59K7y9WG9sgMgdxUEZGVGsEoQjkmMCiGIqjuZR8UlbTCzQ2mCwSjklpHPouoZsHXkfk9OPz6llAbiHD4dWiONH4mRTjdefy8wDSOYhVHi0Wgj5YmFAOh5E/TaGPPKjge+YKv9y/y26uPc6855MluyfmuJgRNVThMshzc9gX0BT4omq5gezGjvFdQXijmjyMHTx3FqqM42cimvGsk4IopTMlBcO5NYi1Pf2afcLvlzj8qMRc7wv4McwbqfMXymwr14EQsh157mdNP11x8Sogqdq2wGyhXsHc3YHeRYu2FVBPjlYKOcmEMzHLgZI2AgtYkFYQhzsvBhgkYEvXsdiCquCA2YJUo8ZQPqMZP1M7XQOiQ2iFMb/Pp8ZlVZw2xKJ4NYCYgNjAWN3IwdP2xSR3xjADV5D4u8lpTFmy0BhUj/ULjZpFdV3Du58B66C3+YR7735C2Cm5h6Rclp3s1auaYLVvmVc+i7CiMpTIOqwIuJbE5jtJEFkUrvTyN59J4VirS25JohOCkIuKKcY2kM5yCCSBptxAvx55Nw7mGMYjN5zCpDaIPMhcLCfC7gwLbyLqEBrVt5DHwLAHhOSPGiNp1qABuZimNvlK4jIWRddUrVNvBrEbtGgGmjRlaQVwfVxiwWktRy+jR2aCwuNv7rF8uCVaxOFigH5+Nah2tUsFHKjUxK6enn7130LQsHnkuPz6ZfwnIGH7/TnnsZE+7gqVcf056nUHRyXOe85znfVfl9Y94GL77HPl+x/e7GxolwHRq08f/5dEv8k+/9GOoyqNutYS1JQZFcWbQDvp5ydmtgo+/8oR3zRG70yXFtsA2FcWTDWFWyNzqPbG0QzElr9WbFwr2d07W8rTESxsTaI4Ny3dH0ll90rGsK/qZEkvVLg5zKscz0UB7ANuXoTv22MOO24drbi/W/BvH32Kuu6QM04SoedQfAtBHg1ZhOEceNfweouZWuWJmOvaKlvvFAQ/snhBrO4tbFagmf3iJXa5cC5nUq+NI6szqaCW3GxOfbev+PBAaGEjHefvxE0A6W3qn+HhQ4abraSCPfEhwiLWvCVrj0c/kYx90XG05xaA21zrwje1tvvDwFfbqlsPDDU1X8E5zk3d3x/zh3RcJrUFX2VIone/WiOq3CMPrS+sssfWOQRTcGTQOBYR5kJ7kRUDPHctlK2Tp/BnT6+T+xz6B3BFSPNcTgriKmKTqdUn1bUyQHtkJEFcqEtLvMcgcEiwltfPqtewhvRrnSUBU9CHF7C4X4sQ5STlkT8vuSGl+5BZk2jDMmVCQXJci2CjHBMmxe28ojEcrab21LFqOyw1HxZYDs2OuWzyaPprh+rrwMy7cjJ0vuXQVl92M83bGti/YtiV9L+r00CfFdb4eYLzGcgwQFHSiqG47Q19JT97S+isEe5WU+NnqWyO1gu917n1Yxh9HKVsklsHn7tznS49fpNcV9onkxX4h+a5utbTKuVAUq0ixkfVDyNJXX28gN6sJiBwSSdkwFCXNDsp1ZPbEUV50mG2fes9qwszi6hT7D70XRaWcQe/c+iEUarCIv24TP9jMG4gmDkSKYBMYEeV6MK3CbiRnKdaRcpN6kXYR03i0j6g+AdJ9GK26vR9tuqe5RCY45rwCRmDR+bF35bR1EAjgEeCFH3/M/e0d+cwXJc0rHZ/62ENeLC94a3dLrMadY/fCjFcO7w3Hvh/qkmN9Mo/nzekMZOdxHdD+qNp5i1OcEG70tklzJoxxdn7c0Js5FaJz2540YgZ9YACKY4goA9EHlDXiRLSYiUp62iYqpHkS1dX8NluKDgCHtDfKecfgAJYAKZX6OGMQFXbXSxxeBVTTSo6SgXRAbGInIHVZJFcCNcYkkxpQMCNBIhoGm1ZfR6KVvaA10B1oygtNsY7MTgIqONzcJNt8iLqmn6dcytcUFy3dnqFfiHNOt1Ss3hCCuq8DsQwQoX5oudPOqZ82BKtRJgH4U3Jw6o2trBUFYrI1VomoDoznqXfCdulEldrPZY0wXaS41JhdielkzfCziGkUtIbWGy5DDVzrG/6vwXi/lnF5FMqwix2nwXHfzfj87rO81x3x7e0xT3ZLtn0xtIPQKtKnOnduzXW6m3OxmdGc1RQnlr134OhxYPZoh9l0qG2L2rXiCgCDs1f/8jF3/wYsvjTj6MtnqF1HjJHm9UMhqV6sCbuGWBnWL5f0C7n2XK1wC5nDvpK137QKu1aUKxFDLB4G7DZgGy82332K85TUkfSuH1q4KR/EPS9GlE8ufIUhWguqGgRI2b1jiMmnAHGM6MYxOCZkIHlCiCF/92TvH+2zNR4hlaqrdaf0+kMNK/8NY+0r70/XCTkhEOdVeu3IyWrBvf6IvnqP6jvMiY/S8DG5nFxuiTMhDZlGbM7zOhvzuuO9qKdTn+EBjJ6KDWKEvr/aoxrS2is20WoxR/U9cVbJOV2t034ylxpfL3GX6vr0ftO5MpKigJGYpM2A1+V2mhmEHezBJ3UblWtLSTTx3PqU0uAdvrb4coz9pwRVEMKsCmokApqRCBiKiJ+FwZ3ZA26h0L2iOteY1uArhatTG55CgrNoZD67uaF46jFtJBwq2n1NsY3YLZRnGjT0S023L9fW+o3I8mFJdZpazVmNinvSPzYdqwjj8emd4C5GoVoncyAfq9wWTytUawdHQjczdAeK6jxidkIUrs4F59rd0jTHcXBq670Q2H2M2O8jZPrAoLRRqc+VildUPX3UuGBovMVFg0sJptWBx9u9pGyGvXegOpcC5tE3PFFL/5vtLU17pGiM9O5RE8srlaL6gfmdEt+YLLwJkHuXKTcWKwbWap5M15TS11XSV2K0a/N1XNuuqoBL7Tgqt9wqV7xZPabWPed+jlaBR+0+592MxlkO6oYfmz3g3u6QUEbeuHnGN09rotcUSpjJAjjLZtaFZ89mjNKz2epAZRywuPK5rovOpv2kAXxK1nVC6PI5yqPRNm2enjZYjCrRKvDPH3+Cd75xB73f07xR8B8efoHP1Pf5Z/qTfPH0Vf4nR79FH0NKqsf+nkaJvXeRFvP7zSHm3Zr3jg75yYP7MtGDKBs3bZlU8aKOz58fwClDuBHZmBL9jhAPfGVwtaLY/OgBOK2lqBbSvMxCVe2SqpZIqNTY16PQBC/s4+o8Sj+OW575fcOtX7c8+QszqhcdZ9sZ+7OGl5cX9EHT9hatoihCm4LDy1FhE+1kvjMmwxmY9ZXCzRV6qbG7SH0mbOf6LAzARXsgm77dehQJaEh9cfyiEsBjmyyFTboGrSgwi1U/BDIZTCBGun2b2EKaft9QXrgBeI5VAUGSz2i0JNlBeu0I41QWanc4Z/Vy7hExKbSnwqsAWKKqGJKbdP0LGz1escaeJiQxbSa+VLJRe0n4u0OLq7RsNoWcy2jESiwrpUmJf7BjcXTav04na3XbyPv3C0UoSopN2nAKhU8K7Oam9PYpv2qx51vc8QLVexb3O6pLw/pFg5sr/KJE9wE3L6RXlFfs24YmFDShYBtGC/4P+/DXKs7P7x0tbNTz4PjH2zf4J+ef4d31EZtOeuBlNceybjmoGmrT83i7x5OHB9TfLpk/jNx84Ckve1TfY7a9BP1dLwzUVNABhCVYFmJL1nUSUM9nslEXltOfCcy+WrP/zRWq69EbUYLG9Qa12cjjyxJ1ueb2bxXc+ryRRDwN1fQSDHcpwM/sQj0hZxSWOK+IlSEUhlDoNM9T4cmIZX/+BwyAc1QK0zoBOzK4GAJ2u00HMw7MwSEgz8HcJDl4xnI5BasxB3WTPsFjAq+vgs+TzWhQSU+B66kSYaqoyOrYMl3vLogtWWnx81IIJx6ijdxabCiUp4mOudIfelh6ed8PwairhT3pZ5Z2WbKbRZ7WHl15yspRFo6qcBTGU1s3uJioVNi2OlAXDlf3xKBxUYlDnRLFtO7S2shYeMkWVQpZQ6uVFwY+XA1+QoDMvA9RgNx8O6AyKaEscHNNsZpYd7edBMZTJu13GarpkqVwmic+DCzPmHpcmV3qEzWrU2DdE5tWHj9l6V4f+tq8zIl1YfG1ZXdLU6wi7mhGeXI59kbPCUt+7kSdMTB0AZyjPO8lMxlDLXncFIT7TsF5HM/J9zoGQG0a506Atve9n2dC3B/J+Cik+QWQS7P/dPsJ/vk7Hx9AVb+2zN4r8FWUvENJYU89qHhvfshLxxd8++aM9tzQnRUUT8DPCtqbJfP30rqc11+g3yuSu0aa/2meiU2q2INlcCMaheoCs6eOYqZHdwSlyDbVu2NDc0Oxux3Rr254+XDN8WwrRFjT8c3tbU7aOTaBT40b7TzLRI6ZmT7lG2MvTiHOJAUvkcJ45mVPpwN12dOUjt15jWrMWPzVEabgtBK77qFVURytuYEBVCQi5DunBFhMrlhqUrdAj/M8s7almJsLV5PrcXqdKcgtDdLDf+RjrmVNvvAz1r4GRgeNP+4IKZ/cr1tK7eh7y1949Wv81OJd/v7J5zjpF6z7Sqymy2zJF8ntrXTpidm+WEdsKfNBwgEhTpEIktGK8jLWAbPsmc9bjuc7Dqvdle8RorDqtYpoIlb7SR9gietK7YectFBhqDNY7TksdsPrBBSzdNx8WlUMI6nI8aA0AAEAAElEQVRrF0p8VHTB0gfpbd15y84VrPuSdVPR9RbvFb43gw25zLUMXqf8Ltlqa6dQaZsUhWrELSJmlq6V3tLp8fsWRo6rS+SPfF6P7Zp93TDXLYe6YU85mqjZRjsQQZpoaWKBj5pTv+TEL3nQHfKkW3LRC2B9sl2w64qh/7HvjJA8TL6eFCq7EqRjLDWVZGMY1UAMmCOEcML3Toj4qIypNXmuZ4WoOSx3vLl4yh+qF2SvdHLOdacH8rJpBLQ1jcQ9KuWiVy7TISdkWF/yfiyxvNxmd5FyJaTv6qQZ2leFsiDUBl+Z0VY1WYTrPgz7AEpyUFFI53+Tz6EYbesNgyo62kn9K4g6utiopI4ThXSxi9hdQHcR3QdMK8RZ7YKQ0f1EHe2kD+QASMRr/6YggUpxWYrHsJZYlWInrRHAxcxAwc/cuMflLz7lt975GOrBjMNXV3zu6B4rX7OwKQYsC84/YXmx2g2288D72nh/VAHm73U0UWGaCOeXqOJYajOaMa41auzZ6gNxu5WWTd5fIanmEXsnDkgxpjg/3xHAyGIY57X0U15vUHsLIOWvWZXVdsS2Hcmr5loUmOP/5M4Um0byaGvHXASGuEkVNqmCJ7mr0mPMnskSWonddqkGV7xnRro28zWV601+FvGLgN5pYhnxC+j3FHajsY1C94b2MAtroE893U0X8ZWm7BwqQr8cwYvyXFGdRdzcCFhoYftiYPWapVgZMApnaoy5OZBvlY8juSP11hWFXpi0e4lDzQKjBZB2TgRVtYgqlFeUl9Lnd/Gg4+KNktXH0jHIvJKPRHT+gxnT2lSuS+Xb1qHlS/2cX19/mt+7fIWHm30aZ3FeY3QkRJgVjnnRsV82NL7gW09v0L6zx/y+Zu9dzwuXnuq0QbW9WL1vd6iiIDYNqGsNM1It5J1/b8bs1oqX/4u0dhpN3O6Yfek94mZL8B5945gHv7DP2U97zFpTnWiKtbQ/KC895clOwGAm9YBmdBiIIVlup1oUZSHXzH4tav7KSIuhqVtBqiGZxkldOER060fHs0xYynm68yP5G8b5fF1xaw0UtdSWjBn3kfyekHrPT/6+4qKQroHJ/VeAyKkbQ75mkliiO7Ds7iis9QR0cmkMfPirTN99FCqJrLZih656R/Wep75zcyTvDPWNidPC80YGOotC9gUfBKCejrYlHh+Iiv5yI3O1bZMmVI2EBBjPU3aDEYbyFdKS+HrrQTUNjEIJm9S93o8gdH7uxO1PKUUMiQynUr0nKYpj1GlNVgPJ+2o9RY0xVQakExEwYz9qLvmS2hpiEfBGEVs9OCuZRuqYvkoExZRnmS7iZhrlPeWlY3ejlF7NQZTKhROiYLAKtQcEcHc6moOS+sk4t0MtbEM1uV5ynBbrIu0jyDUOcn3GOF4vmaCRnu8riX333t4we1Kyu12wu6lxM4VpJR4OJaCgD5omjnWE63X963X/9xsfGJQ+Ljb00dAGy6qvWbuKzicwKxU2rBLQudSOUntq0/OF3/kk+2+J7W+3byi2gWDA7jzVoy3Lb3pCXdDeqjn99Izti5FQB9RhJ/0BcsHBRAjZwo0Jcx5ULyc9W7SpPsn8MwFzWsSIacIPAXuu3kFMjHpSv7KYejR7r1HKCws6KlpveLjZ5/F2j7vlEZ9Xrw8JXOcNF23NvOhZbWvOVzP+7/1f4nQ9pzrTPF0vJMDpKt598YhHK7FSi7Vil1Sxzo86kgzOGxWFeV3I5/BeE5Llt9iLyZUj12qQuB+NigozVdikny6pp/tguOhmzKxcVSEKc9tHzYOzfXSjoasIryu+7eb8WPmQv3z7W/xXX/zz/L1bP8N/cPivOA9zoGEdWgqlaaLnIkT+sLvJ/+ntX+LxP3+Jg3uRVXOL+Wsdx8WGw4MtbxU3ubs6lIQ6fZ7cK1ypiLVeSN+3Gi7LkuXbFhWhOdSDt/+PcmiTAQNZlLSPQw+yUIhVit3Jha07YaEZBd2+oVxF+rkiWMP6jcjB1+D4dw3n7QHmzo61itzngHnRU+hAbXoukR5+xSZIYhsYQAfF6DogoJEaVLu+gm5feoT4qmB24ljclcKoikIWaQ8N2zsli3uSHCvniXVJf1CKbW4X0L2XxVcr3Cz1olGK6mkqEpVii+rmluZIoxID2a59Cmy02IVnJVwC34JV0n+3E7BduUBUmn6/oNuX72CbsfAf03+D1V4G4odEm2TRnQsGCcSbhHzKR2yf2EoKirUwBZWzaBOFhR3V0MNBeSjaOGxIugd9pbcckzVFJXJARHtFt1C0B4qqhPrEC/h/aOj2oH2lR19aFo/EZikUmpOfmXP+Ew6z0SzeE1a+Twrq7QsFoYDiScGZm/PN9g6amPraf/QCp0Acwr029vgY2caet1zJP9t8hn/29FOcNzMuGwHdjYqU1jMretZtyf1v3+DyGwX77wbmD1o+6XpU7EYFcu+vFE4AVGZgZlZmlZJq55MMhjEYKyyf/C932AdnIxt7tR56bmAtan8+9NPVJ5cSIBkz9JaKdUGYCbgrhAvp0Z4Lv9JLMFyzF42DVStBlMNiqcPQb0W5a6zFEMeEYApATx8z/QkSlDk3BuqTflxD37EpGzWxUyNhtN7LiYEdg79nVNKDhUwY/87voSHUBf1hha90+u6pOGClOKi8FNJCFdkrG1ahFjsi+g+9hbfu5VzYnawN1YXClwLGBqvxlRQn+r3Iuo5cLjxq5qn//+z92a9tWXbeif1ms5rdneb2EXGjzYbJRqRISkWpCqqSBRgwVIbhlwJs2CgbNmC/G37xP+A/wH7yiwEZcFlwoSwIltzRplSSigRFMplKZiaZXWT0Ebc57e5WMxs/jDnXWvvcE5mRSRYzIlETuPecs5u1117NnGOMb3zfN++YVd1QY5kVPUZL2a20njhvaXSgVyVR2yS9KK/NoYwAlQmwCoCB8sql4Dy8UHwSUFgDQYL7ocgYICm6xFmFLxSzbWL8tz0ESTaH5MTcfk4GJjZA36McYjORATpjpXkoeUKZXZKAtwVKa1SfVGL2P0YiKAPSWhKY6Jzsk1KoWT0qW3joVgVlUkaQLtyMYt3Y3k3w23vsukW7elDFmCYxB8D0zWMQJx+R160br78VSGNMkF7YznRbt33OLc9/nsbnzU86j0rBs2D5xx/9bbqLmsWDLbsPlhx/3xAt+JqRbebAtAr/gyWzv3NG8XBP92RJe6yZzUtCadg8MlQXBfaqFX9pF3BHNft7Ftvm+ToBDEqhW0+xRcAJQHeOUBegFboLFC4O3p5uVbC7b2nuaNZfCizfuOS37j1l50rmtqPzlk+2R1zta0JUtJ2l26V7OoNYOlLWPUXhKa2jLhzzosclyd/M/NSIJ23rLZ0zbPcV9443fPXuM95dnPLk6bGAlnsLJnlGJ0lgpeLQ0B6DNNVqGwZ2a4xaGNRJFkw3+rCpGCBMcrYM/OR80JP8hBnZg4YDlZw8pvPkz3vct9eAgKu9NXzbv3JgseSUHhq0s8e0T4CiVhEX5fnOic+ztWGYai93M/6geYNZ1XHeL/iz3avMTM+fPnvMvOj57dff449+9DqhM6jWEOeO2OuxsUWD0hHfG5RJuVlqIkbHwR86FBFVBsrSDQyhvSsGaWiA1tmhyQp1CBRlFa3OW7QK2KhxKuAymBujqJAlYFHy7sRojYpKO6qUl2kVWelm2K5WkVqLLHqh/ACUGyJ9NFy52RBDt8Gy96XIbboCFzWbrmLXF5xfLaiqnrYtUo+7oiwdxqQG8/T5Pmh6FfFRWASttXTesPUlTVVgiJjigkI5CgI9ilIkqDDpfXPlyHJ4XyouxHN8rgfQuo+a61ixDjWXfsGln/Nxd8J5v+C6r9m5gm1fse3LoRk9H++svjZl1+c8PCj1QvP6L9qYAtJ9NJyU++H6A4k1tVOUl0piVCtMEbuPwrCPEptOWZfEQ8lHRV7LR/9BIthGZFHrc0dx1UqeojWhNITK4OZmaILWXcSkxm2VmK9RZy9QsTnJIFpUaog/hnnPJkC6BF/FIYDQrcjt252iSMy5Yity3aZPoHQfBBB3Ad05icNdZkmHgUEn7LlcQB6b1A/+ZWabl7gzzmvirMQdz+iXluqswV5tCYXGF9I88L969Lv8X+rf4T/vf4vT+Z4v1U/55vZVXq4uCSV0r93j+ms9d8odrZfGE2BQAbzpi36brPcvmne6S4ixNNFEaYbueh78SQIw0z91vSW2HaFpyJY4Qz5wIy/QORf0iQFXWGLvJA4/OYLrNTw9I+ykFsRuTzxaotoO1TIAP0rPxnwzjcHzsjQD2BBjRM3notBVlyLp68RTWvVJTtaY8ZqCkQWX6z3WEvYb1HKBPyoJhdwXNt0f2SfU1UJCgAQ2JBUBgOq5Zn+/g2UkXhcwDwRlCH2UutTOo10cJFm1j6hW8nc3k/hu8WFDt5wNMUh7L9IfARG6u6JMsnzlmuv2hKN3LHbrBAR0AbXxibzBmNvLAUpNdWoE7GOEXE1JgH1Mx7I9lbrk8uOeaCzr1zRPf8dSnSlmT6E7ljr6ti15u32Am19gMbTRMVc/Wcnzizam/tAOfyDR3EZHoQwu1aM2sed3d6/xr66+ytvre5zvxEi19wajIvOqo9CBi92MDz88Yfm25frdwPL9Pa/2AdVdyfzeOwHh2k5IDT6pBeSdSk3dmKTq5Rzbf+8N3Czy+v++wD45l/OdfHLDZos+PkI5YR4/+ic/5KV/NrGpgoNGb5SSekthpfY6Xw72EKEY7wHtUy0p11HdaNegw8T2Lc/5B+zWOHrF53+DQl8YAeCpzUOeazIgnvPtzG6dqrdNPkeA6Bz8T+Z1rcd9yupn6XPicka/lFxM+4BupTYdS9mH8sphWsPxrOFJf8wuvMNcj0BbHj+JVf95HIFUayxLmXPbDtZbTv94XLdV4+HpmRxbY0Z1o08jGoQwsvrLErou4RCG2PVSIzSG8PxMVB0S8UenGOCgVmjt4Rw39X5O2xyG1gzS3XUp13Y+59lPORMl8vUGUp/SL9amYtuhjpb0KzPEaD6ptOYav6tGxRpR1+AAlC6uNf6Vnrru2VU1qEjoDEFHVBRsZNrwpHshvflSDXgFQPl8x3xlaFcG7SJHb8PmVUV7B7GKVXIoqvdLTC9+8ZDWBwV6fwj0x4myZpziZjEStRlrdalpTdUVartDVRXNqWb/KPC9/9kMs+qxxXY4XZtnM46+a+lXoBvFJx+d8n8s/gP+o6/+Z7xkXoSWP+v98plB6Ws3ow3CcG1ckTqc5URnz2SAWjms9oNvgnSWRmwKdFWIKcBXhHmB2UbMtqPuA3f9nNUHhnal2b46ozsKhHmAMoiPWFDyLyIeP5ktTUoYegSMA7mYUuIwgnU5cRiTAxVSlc8noFohnQTkQkYqwiew1AdFiIbCiPTYVSsd7QqG7myQoNj1Bre3uCORG4vzyEwHwmmPvii43M/oOqG6CptZ/LokkBgTxxgVvde4oLlQs8FvOz+vpj+Jg9x4pgLECfvcmoBNhSUftBR10rlzqYO9C5agO7qmQKc54jtPHvF/qv4D3po9B+Dll89pg6VLB/cyBJqouQoF327f4L/45Lf48x++zOo7Jctz+fz6HP7g/C3+O/e/xblb8qi+pvOGp6zYdQVmkCyV76x1xJggHmZHPbuXNPVTTXklidPPe6hcYFMTuffMzEXkmatrhJVbCOjqZgbbiJxKsRNv4+3LcP1luPOtyOm3Fee2hscdrbNDw0cuTqmdQfc9zhp0FBmFg2KzyjuWwNg4Mkt8Dft7imAsZl9Snu+JhaHYOHyt6VbpM1JyGbV0yZg2oH3ALax4iCZgrVtp+rnG7oX5nD2l8z2mXUp+vTCnQ6Ew7RjA5OQaxtdmIFn7SHtiZeLuSQzR/P3GYqKaAAEHxcbUvS4sIoZjlH/PCX97R/ZBQCI9nEM3U9hdFHUBUrKSpPp8JQF+uZ4k4qQOeDVeA1GJR0mxFwBEpNYUsZfup1iAMoH6iaa4arj49VOu39C4ZcSsDcVW0S+ErREVhFIAfN2DXSs+aY7ogqXUYhHwResCD0Q0il3oMEpx7lu+3t3j++0j/vjqDT7cHovXXm+prCdE2O4r+o8WzD/SzJ5EHq8DdtsJ0D+RD45GgpZY2lQ4idCn7vyySKCudFLHphVQLS8meZH2HrXdY3LSXFhU7jCzVqTLjDB8lUos6+Wc/v6SmAKFoSs1jvepAJR+fDyDz5Gxwy0VoAYv6XiYDDAFhDOT9aYszU2vl5uBZe/GpCABeJgxyKN3cozSdmLalvJBfIPqYpDfORg3Aekf87wKiZXTeezWEXUxKAlkYE5p6TDMwF8XbPIugdkXIGEOpTpQbMixUMwAcpD5SDzqFWCJ1uLqmr4At4B+GTmbiSwqOg5ATxynnyF+mc6NQ0yThu4jxXkqHOUu5OFJPQbsmQE/8ZbJiWF/OkveS+J9ri7XEtzGMHRZqluSGJUllnLS4BzFBrrFmHRGrcEofG0JFqpLNYDMsbDEZY1/fBf7fE1478PbPyMB0sPn+QAxoI6P2L91l+1LJYuPUswzU4SjOfr51YtJ0s3vEILsX5YF3zaY/QlhFYfC9HgsD4/7C/sZJ89PcutPfUxNLTtefM34uvT7bc9z+PfPc/x1gND+MzL2f9woleJ/++E/5AfvPaA4bmm/d0ThU6LKmHcAQ5Nrf8fxztkdfvvx+/zB1ZcxbUF1NWf20ZbVh8KA07VYK0StufhqTXtHcf9PW1FDKc3YONF5Zs+T9UfuTknzvG49fm5p75Xs7hq2ryjaLzW8/PCSEx1oveGHF3fpvaFpClxnhXnspZMbLTmV3asRzHUK3dV4DbsCNjbiltIoTBFGAMZEbOWwhcf1hn5T8tGmZH+/oCocd+8KM/sHH90nei1S3cN1m+JLr8U716lBbSjnd9FntqoaGM/yJg5vrHj4U8GYEyYgJhgYZMN7JelO2uZBc8jPebzb3eOeXbMyewzh1nguIPlRZraaBE6D5HcZSNRJwcl7+ZLeSyUjBs0fNG/Ic4kZbGaej86OR0loz8B+zo2vBIhBQxGEYJDVyuLheVNBESKjWheJeYsAntm7ePg+UbF11QBaZ4AopEDaBaRJdJJX94lxPEjMh3FbRkXaEDE3xPtFkj6d5KEeYNBE+vR5WkWOzZ5K9wNrPedd68Rc76Ph361eoQuWZdGycyXrrmLfF/Re0zkrxzakxi6lKayjsk481/oKFw2tF3Uyj2IbSgrlOVItRQLKSwKFiiIGhyilTX/2UQ/KZCWelW64b9bCrq6FXd0nlvU2VDRBvEKf9ys2vmLrKva+oPGWLghY3k9kjF3QX9Am18N75ieBkiFqPtwdA/C4vuD72weczvfsj0riviar7NmNANF2Hyl2cbSHmlxmuREZGOaTqEUFIiT/aO1EKa3ceMrLHrMRQDrMS5GOTN60UUvuqJwA0jk3EDBaDz6hQ5FUjQVSkeZO0ryFyLe6eSTMAhipdZmNprxW4g+Y5LqLnQDRpo2pCT2M9j99GD1AEyA9gA8w5iVyUBkOwk0grevBWsKdFds3VmxeMrR35Bi//K9lIXUzafj6//7wq9wptgD81pvv8aDe8H/4/t+jsJ7/yZsfsn8Y+MH/sOB3fu0HAkhP2NF5nsz39Xi+bwk8bjx08zVftLxaT5iual4L0GMtR998jtq3kvt25Shvaq3Ex/laTjKeByMVlJVB2O1djzJGcmQfiMYMjaLRJyWm25qT8/YnsZkKQebk3Ewak9VDBtOmqkc35cdvNlhPRvQBvVoC4GpRHdDp9bneFKwaGG+QgIYUe0n9BmJrqE/3OOvxzkDl8X0lxJNKD+qBuXYVlXi12r3cJ/ZyT7GrB9B6+a40HIYSirVBe3AfnHL8VOqIoTJpTY3EWSFNINP7aMhxELDoIAmcnjLJgdTxEf0C2vuBq69qOG7RNlKVjnZVQqywe2G9rZ8u+b2TX+J/fvJt5kpTKfuFBOB+0tCo4TsZ9A2GtOIiNJx5xR82b/CN7Wu8tz3l4+0RTVegtdSkQ9CsL2bsPi5YfAjL54GTfcTuW8zeieJk8kyOWqPKQghESg11lZjZmlPbtMSGV7pg+e2nfPXjJfrdJzCvyR6/hIAyZvQcbzvUfEY4WYl/u5U81dc25aJqzFEnl4mAzhETvEB+qRZ1IMGdCRB+ktdPrd5g+DlYNxzcl5NFMhMTMvheFqlOVwh4mID7nOsoHyT3v0XC+yeOW+ae3JTja40zBmIxyCfrLqRmYDlMTShoYkBHR0GkUp8ZLvtcji4KDofRwozVGvpOrNdgPJ+zGqW1gLyJdTwcyZvHNNciY5D5KtVa8mtV76CuCJvt4eyUz39aI2JhR8u+vM0fV0fMzQY/7jV5P6bqd7fJdzsna1pd0a70UMfPGEaW2o5GkUPkA0sUrfAaymvYfDwnvL5msWjoeouuerqyoFtZIelN14kUQ2pPsl7NKrIBXwo5x6fa4exJHD9PiWy57sShwdc6kXYEY4LxOv9M48atJbYQcpx8CWHm0QtHUTqUEosipSJ61Usz4SWYTrOPBbwOV8Hw0l8idfjMd9k6MaO71EI2tx06JcCl8YMv8cz0Q0BodRBQugnJf3AMsvMEEGork3QUWduoYH7m0c4QSkW3snTH0B8HKYxEpMBgI/S54J/ApyTfnefdzKQ8YKClYqBCXiTNBUrA2UmxAg7Z0oOvtNLCWgaMHgsC08A3+2QpHaDXGB3oLmo49hQmUNQOVyVwQzEwsFUCZTMQDWNcF4KibS1xyijOHzgBsBUMiqgx6hS/BLQOQ8FCqTiA0RkEXscKZyWo91H8wW3pcPehqByLusMF8Zr75dlHPH7tnLlueeKXfL99xKVf8M3dq/zhszd490f3mb1fcHIOs3M5Z75SFJvItz58if/k0R/TRzv4OIWoeRKXOG8SMUuKK1nWPGjpzG8eRppYU15pbHODufRzGFoHKf5Y/4IvpAoi2dO0hvrS42ojUtd50jCG6jKgvKY+V7Snkc1jzfHbnuO/MFz3x8zfuB4kYRovXY/F9eh9I51q6dynBDlOJk0iKOQ1uouoWgkYahQqlJzsHbr3qBAp1g5fibyDMK9BeY/di98zIaIqg59ZglV0S83+gUzSpq8otoHqsh88r6p1ELB70oHnU2f34GlLSrRRKCf3fwZ5g1E0p0qC5e4w0R+KlNNjnm/6XHdyqcia/L5uqiCqtOj5ErpjSfbbY4PpIqZNwHOX55aI1wLua0i+0AIS6Dz3pC4o7VOHoZd5Lljx8zZNoD21RA37+wXbx1IsKN6r0B6e/dacVPeifqoodpFgI9dvgVvC0XsG3UnBoPCRpjM82y/pvKE2bph3P+8jB/+BSCAQkM7Ut3vLN5ov86eb13lvd8plI/YHPijavmD7dEH9sWX+RCTvqms33Euh1LSpoUD3cZCRy0WdzCTTVg+SQspHok7BfQ5UciKeu0qNlmS7soSjmQTPye8tajWC4BHs2QaaFqwhlEnSN0TpCMwx2sA6RWTwpt2mB0Wd9PoJ4DwyVm9JAm5jQH9aUDIFs5USee6UEMQsm1aIZ6/edKO3mJY+4kFqL8bbAemfduTv0Xv0rkdXBl+LRcPQpVhA1EmZQceDQuOUaf95HXYXhmByKBgqmYczkJjXjyzzEwMULoKSYFeaYszoYVnkABWy/6XdpWJLWv9JyZUa/ku+093Eyw0OWRE3g/Ys65QbNZSiPyqw+3zt+sGTfShyfVownADdgZURI8VWbCwGwC11a2Z7hWIzue7TNd2dlMTymOJ6Q7y6/vQDP2xTocoZ/sEJu0eFHCNFCuQVblVRnk3ufZBCwqeAmnn/tfPYPfRH6Tin432zEeDm+EvXONX482bccdCcxrg/09d8wWqsP7fRA7+/f4l/99Er4BX9RYWaRdRW4euxeOkWMfk3KvzSM7u752jecNYsOHqwYX9+wv65YfYRzN/f0J/WhMJgWk8sNN2JFEVNMzYATdkN1WWQ2CgisnXpuvQzy+5hwfUbmt0bPXbZU5eO66YiBE3XGbwzIr3cKwhJ4r8RnyuCwnSj5L9pBWCfNj1EowjXouzhlhKTxKQMEEJBh+RdRVKquiwWnJ5uaPuCl+9dcX465+xs+QIgHZwmdlrAzwh0N8CkRg/Xc8xZapjkcgcM6TG+iwAWgo5jJ7vJMZl4BuftHEjdfg5GHw3P3YpdqJjrljYUtMFIbq09ZL9fBeFGA7NPeVy2QoopjyIzP7NvcwTfFNKcAOA0vgXfFWCTTKlJSXRuFIBxPvE6RW2JkZgUyw6kinUcGnYBWm+xKgx/AweM1OwbDSNoGHKSHjUhhoHpHFCDB2yIhqAiEwyGPgjQHCZrUJb5nuKTPSb5P4bhfR5NTY8h0oYiMaql4XOVPKAN8p5vXD3m11cfElCsfc3a1ex9wZP9ik1XDQz2nE9nQF72X9N4y5N2Jd6ThaHWPcGsqVVPrRyFClQJlE5FiheGTw9qFagnIHytHLVyw2sysO5RNLWA1V00NFFsfzJgvfMVZ/0iMYf1C167v4jDRc0n6xV7V/ByfcnOFTxaXHOxm9H0MwGlUpHS7iPVlYC1EqMwSudOck9gmKNz8VJ5yRW1g2IrDZh6J3FTqIqBIS2xoRJ1HRdSDpML82pUFivUwIqW/g01sqN1jhchVBFfJUBaRVSrUb3IDxcb0O0ItNsmJMnuBEj3AjgOilMT3+hpM+wLYPRtoGFi78V6jru3ZPtyxfUbRuwlXGTxvpLjoXWKzcA7wz/90d/gK3ef8drigm+cPSb8yzvs/t4VXyqfEqrAq196xnGx56Kbyzw5nT/ghd9vG3/Z5z9vQyMqChl4Felch9pz2KhsjDAKrR2lueHFon0M47mfPmaMvMd5ySM3W2nShpENB2PsDmD1IUgFw9okO6/GfdBacu0MJhx8/uS6+rTmwxigEN/M7sgk+WoGKyyU1K8GGdV8LxmGZo9iEynOLG6luXO0o+1FGWZflqLwhMQnA6idmHChgF4byqMZepPAeg2uUFRXks+4WapXa6ifS63KzTR26wmleJCq1gmoKIt+PsGH3/1mKTQ/nxTbcE728bRDF4HZrBvqy7r0hCJinylqFLtKLDjXwVMbkS3+IuTYP+34NMu4NvY8846vty/zzf2r/HB7nw82JzTODrZx+22NeXvG7Ini3iZSbgLFTuqdBGSeroSBLNaiqbEnRFRhoJNGadU78WNXSu6hm/WbxGY1z69hMUvNJXIm1HI+gLmq6+FqTVzO8ce15NJa1oSDnqxUy8w1o6GOHCaP5aaj3GCV/00BQ6a1rDg2c0/vyeEzb5lTrJF6UwZG82i7pLomPvWxLAYP67/0yMct5HMhNV4BBRODNdX1kiPX0JRXK3vQ6PNFHbfy+KqKOK9FSS6dSzVYuKU4f7hO/OE6kPyd5WAi5MZk7RDz9dH1kJUo41j3p+thJqB01EpY8mpCLMpNxD/u3Kf14aceN9eSAMoa/NGMfqUGtQxRgmWw6vRJDTXXUPI6MTQoeqjONcVbnpP5nt4bdl3BflNRbCP1uSNYsS02fWJIR2mGKC86dDvWU13CakIh+f/iI7FJjak5PSsYDDhGbeiXGtNqVC/HRbubC8OPH0OYk5rYAHytUDNRZ87NOM2+wDcGfWWpzwUcb+5J7aN1lrMwA8LP3Mz0mUHpTV8NiWSdPMe0GT2hihQQahUlCdWBlW2Hgod2MuGZNojs4aYlVjIpRaXws4LzX7Z0R5GT7xn6hWL1oWP2XNHPFe2JprkrXzAH2tFCLCLBBHSnUT2Dp64UXXgxmUu5biCOwDSpqOHzZE4qSqakGy0BvRKQN6qIRW4yayShnibcMSoBi+oevzSsylYSgXsNpfHMZh3XtqSyjst+TlE6QhTmtw96LCikbenUWR5TEUJku8ev9MJtm+lSWp6NceyoVyoOjG4Vxy7SUvvBs7JQgSO757de/YBSO06KPUvT8lJ5xcoIy+pRccVctfyb7Vf5xz/4bfGz+tGS+rniZCdSUCpI52Fmp6oA+r0ZzW8WHJstKzSGiAtm8PjKoHyJxyTQ3HmDNZ5Z2XMeFfxgNgbcP8ehlEjXU4BPd5LuweWiXQHbVxTlRma2WGjszkn3WpRkMBSR6gKKa9i+Grl+07B6N1BeK56rI9pHO+Jc/JNCVBQbOfFTz4C0N/J/kOpQsGoASzObWDmZj6MWhvD+pZr5R3t8ZaQbvAmHE1nvsOtu+NPsxPO5Xxr29xT7RwHdKdavalbvwexjN+xDsQkiI+ENbllg92EAYzCKiB6LWFECOZUAXYB+KSCU7vOCyAuyp+rGJSBdquPvg9dXLtan90ctxUnx3dX4WZJ6tNCXshioKMeIIJ3k2gngqWJiuHs5v1niRjsBz0fAA5SLuJUhKpilRSgq2L6k6Y4DsQrYS8P2lYC602Ler5k9Vbg57B/K9v29Dt8ISFe16Rz1EdNKUa0LwnTYufIv7T/41zWyXJLFsI8dn3j4J1e/zR+dvT7IDJbJh+/8/RNWP7TcP4uUG4/uJYBsToVxNrVmyI0Z9aX4cCoXMI3D7FKgOwTcMbGCheUcqzIlvozJgFL4eSkdpzKBjp8zTL56lNUGCB61bykuLLHQg5xfThwPup2zL3Sc7NPwAZPfb/HMfcEXeqI4MHSqTmWRpt2Hk+7CWJXCPq0t0eqhA0+5gOriILNHYeUz5jXuZEa0CrPtMefdTx8UDnI8N0BAQPUe03r6aEQqJxUKfKmw+yQpuNGcN/MvHosnyrlWA6tSSQyiVJJkZASZ88hAwDBPJrBAjcD2oMzA4dwXTfrM/Hh6n+miNBP0/eihDrcA06ngkqUZrQUtHa3dkWH+JL0vXYux68dr9SYDe/g+aihaCYgXKTaR7qXDIC0aNXiql9eTe8YaKdBq2D2sWG0foDbbyRvj8F2EMS0MC2Ut8fSI7avzdBzTMUoecv1RQZlY4QfHIXeLK3UYYxlp2sB5YfBPQLyxG31SiPu0UEUdnu/PVPu8cc65+RF68seN62fU/f4Mn/PfDJqo+D9/8ju0+0KAN6eIVUBfWnQnCgYARMR3cNlztGyYVx1GRZ5tF7x5es6/u79gf1bS3Z1RfbQZGnJ12+OrGl+B3abTk+u46doIVlNddPjajGtND7vXF1x8xbJ9zcNxQ1k5gtf0naVrC0KviY2BALrVKCcxj25HEFr3k59R4hspXEn39sHaakTdpV9KJ7lyJPUXiVPcXK4tf12wm1U0FzWr1xp+/d5H/MvLr0w2lNYeJ4C0cjnxl+ZJ5dRBrJdVAnQGrdNrh9gxNx6HNB/aOJKn8xzo1dCkOIDXZnIPfU7GPbvm3C150h9xbHc8bZfsXElt+gGU1QPL2NJ7cytgMrKjBfCPidUcIxDFXxglHsPRMjRjq9ZQXmj6VSQouVaGY6TluEcrrB+fgWuvRAY4r2sKlJH9NFo8x7P3uFUBrQIumgNQutRyoWXAdqo4hhpBoeE96TGdJEEyw1ergE4sYjOZ5AQUnjClgUJ5iNJMbgh4BEBuQpHeowWUTipEhfJ00VKrjnt2zfP9knCseFBc87g8p1ASt3wwv8NFP2fjK676GRfNnL0r6L0RJniKa0PUNL7gsp9hCNTKEaLmxOyY65YihuEGTCtx8uKDECOBOIDS2eyriZZdtEMeEOIo8z393gvVpe+UX6foED+4dagxk23/oo8+GK43MzbbmrOTJeu+5q3Vc7kGiwghsSV1Yqf0kkeYPrHgjMIn5bAcTxyQH6L8oongobj2FNft6PNX2pEhnaQVdR9ErtvFkSmW8tbMjg5GDcv56G2oBq/DbB/mKwh1hMqjtpbyXKO8+GLbrVjKCBgdMY0/8I5WvR+lulNz6oFlzwtA5SRWuzmUIi5mNI+PuHyrYPsqdC91mNpRfWPOgz9eYy43EASI1z28cv+Si92MJ7sVy6Ll44sjTp8FFkdr7pgNAEdVw7qvh4YPOJwvbvqiZ+uJPDKB5hdx5AbXLG0aH95BbfaofbIOKezgKTn4PGd22c3zaBLDMZ3/GCLZNSl2nQAZMRBTzBCnllAhSCPzlO08GQf5bB42AQ7TvDyPm6D58IXVi885R1wtaE71YK3m6vHYZEWBkR2d7qPU2EGhMHvBg4+rBldqtl3JbjbDtJpi4+hsMVTSTRfQrSih6danJnZDP1P0S5kf5k899fNG6l29vCarJQSr6Y8LupUBDfUn/ZBjT+v7wyWr9dC0kh/PjYtijaVgVuMWEV0EjAmU1kmj/9mc+sOCk+8KE/vsFUWYe3Z9yRNfcqI9NnnqvkCn+wUYt4Em77rIP1v/Jn9y9RpX7YzrriImgt31kyWLtwtOnksTT3XtUC4SrVhquFrAt8F2oZd6oGll3bDbBPwVSS1MK6CQa9YKOShmP3CAGPHHM3xtBzB5kN9N4DcR7FWLPr+Ut9iEe3j5bN1NpLRvAs1T8sNQl5qAz5P7cagrpf0aSA9DU5Yan8tjukYM97ImzqrRoktLnU5tG9RmJ2BYlt9OdaefadysMyXgXLmA2fW4uSUaTT/TQ418UDX0YjGz94VgrehfCF/pIp8ia2TO0WJn5hc15noDvSPOKjlXWQp7WO/DOBdPR8ikBS25hbWyNmiR3o5tB/WSLE0flZYGqK5DzYT5r0xSyLOpdBGlWPWC8uN05JpnVtFT6fvcpvAxbabIhIjpSABsd1ziZiT7VYWbxcE2KyrBB15o2DYMxyRYaezuvaHzhudXS/pP5sw/1gQbsZsetyyEcFMrfKFENbFJ1+XZGn93BUoaqJo7iuoyMv+kp1h3SaZb6mK+0viTBBwXin6RsgStmCU12p8U1sj6Oh7P4fHSorLPthZSqlKR3eWM4mnB6n3F0XuO2Scb9NWO9a/d5/JrGqrA5W7GO909/m71ZLBD+GnHZwaltYqUaSUvk/yzVR6jIlYFKu3QKrB1FS5q7pQ77hRbTMvQRWAbPy6g3qOvWrI0g+5Kjt6Z0Zwolh90vPsfF/RHVoAfB4uPIvNno5SRLwWo7laa7lhYhzFfIAGiFz+gAajKxyfKazSpKJyLeCHJXKcix/AYEFUkJJ9F0MI6Rg9g9DCZKUm++8TyXdUtIWg+uDyhfmmLtZ51U1EVchyXZcsn6X0RkcwKQVjZU6a0d/aAGR29GjtJ8mOJaS3yIOlryu7S9zKZWhOYlz2F8Wy7tIAmILwPmsqLjFdjCtau4lF9zWmxow+GuenYhZJzt+BZt+JfvPdl9h8vqZ8YTANqBlVHkjMUT17tsuxVpFtqfKWonyqu/JzH5Rl9tBybHa8nSfBPmhXrrqYLhlJ7AorW2cET+3i24/xqgeni58JTOkt3axXpMyjtU/U/Fb67kyjHxEmB0XQB03q6Yyvg7S4QrEFZqJ8rdo8i5aVi8dRz/F3DpZ2xuSuy64URf8E4ULKGHRmu2QxEk7ovtE+doCoVHnMwbmD7UFNcF/havAty57XODM6AsOoKk+SzI3bd055Y3ALCkUM/L3BzCeBVH0TaWzF08eSOHrTCtLKNUBiUjdJNmPY9KjCpcSUHeVmu2xcK08XxPr6xpkwn38yAGYgWY22LoaNcy/WjooDeupN90A7cXIrNyon/lvbyuvIqFUIDYkMwKXDmrnnThQRWKzCRUGq6laI7Uri6oDtSFNuIm0GxUXQFqNd3xHVJ3BbEVWB9DOa0ZbloBtlN9awk6oiv9PAddBcHTz0XNE2wLyTgn8cx+F2h2MeO39vf4Z9f/AbvrO8O8+fVvubi28ecfA8ebwJRefq5ZvOSESAg3VsiuQPVVaS+8FRnLfaqga4fZacn8tMHflm5U9OmazCDzpNAW/deuvv6xADNoDaIzApI0ab38OycsNuhtUFPt5s+cvB/nibmQ6dzHAP9W9jQMBaDon5xP6dDimfp8d5xIHNjpbM2zCtCBqITM0O+rxQd9E78srs3H9DcL6Wjz8tcVZ21mHWHanqReflZulhvMtInx8lsO8pC0y01bq6SbE4KCBNgURgvhdO/ig7av66RpmSQuQUj0vVRT8+xvOamtLOabCM3OgzNOsObc9e/OgAs4zRvjFBfelgLkDvIcg+bUMnDSY/nKCcjIUDQ+JO5dBc3ojag2i4VqCalvtQUETOYDRLvPbxP/8od7LM1nF9CkO7Rqy+XxEWN2qV40OghGSj2YXLta9qHy8EWoXk4Y75/TPjBO1IUS/5TmamtqpLoHOHeKfvXVjLnB7mPtU+STDpJ8FWFSBsWKiVlSr576vydgtXRuSGxKtdJ6SOtS1GP5y9YMA2EkhvrddqOYgCRh9/z4yqOTQpqPJcH701s0em1MvxM+zKc+J9/uHQwsuuFmeS5P23Kbybz32eZC8yN+bK75T27qLivFU0M/NP1r/OdTx4R9wblNLGIqLn4/eX7LepImAcWD7bMyp6mtzy7WOFbA73mqG559PIFT6/us3laUFwWmG2Hn4nahN73HP0o0C9yQcSLLH7yoDLblmiMFJdcYP94weWXLNtXIn7l0IsepaFvrUhheyXWRk5hElio+wTKOpH7OmgUTmvoAKakx02aX5QfC7blGuKZfO9+oSSu9fJ7VIruNKI6RfNsBiby51eP+I8f/Rn/Sn+Z4JMCVJLrziOzcgff6DRPhklmqoIaCwI3GytSjgfjfZf3V76bSNQONk1MXsON+PDnPArleb16Th8Nz9yK965P2XUF81IAodJ4KutEctqPUKPRgd5rrj9ZUX9isQkgLuLhnAEM51n8zBjmpWikoOJm4GdAo4emhcwyl+OYjt2kEKMiaOmbFbk3r+g6K/mKFlsZFzSO0RM7j4Ci9ZZC+5EtfQNUmjJ2Q4wHr5sC2FplYNvgbnyGj4qCMADUQ7wRJZcH8fImCENmrrsEWGnS4aQNBVfMODbiPfyj3T2YM8h916ofQOqVbvAonrkjnvZHvLe/w8e7I2JU7F3BnoJV2XLVz4bPbqLYkpyYHcFoYEuhQpKOlPmxIBKUp4+eLjHAfQLWVrojxJ4Okd4OaPxEY9pHTR9LfNSE9J0L5YRVScCowCNznT4zDHn3z3P0P2XnyM19/jQPYa0ilXasXYXfW+K15QcP77HpSmam56Wja75br+hXinKdlJfSnJhz5Vzl1S6iGo/u/ODRqUIkGE1IMoyqi5jWo/cusY6j+HpWVvLgFG/ZnT+w9FEuEGaW9qSgW+mhOToUamjQHViaBbi5NFX7CmkgsZFYBeg19ceGWEhMYndiP2WbLNktIJrkAIkd3bnUmCfXzwF4GOPoDZwtWXKDbVKZUVaskygL/IMTrt9asH5Vs3nLYY57uC5YfX3Og3+7xrz7JOVlliwbe/3PXoIZXPx715zXC+qqp18oHs7WPPMrybGjGvzmb4LQt+XD09d/2utueql34TOXST8XYx+7sanEGKLV6HlNDEEYaVcbqetYg6qrxNLMTaY5Yb2xUa0kLzAaRSE+tkmCNe72cLREz+eE3U5eF6Lkn4Udm5utGXPxaf4LL+ayU9Dr0xS/bgAQaprHy04Tuw539yHdiRpiGTeTeCDHDSE1dEwbPnPsbVooNrDelnx//5DVNyoWHwdejqBz0/teo3tRMOiONHEhcqart9uxluAlhz3+kWP24RZ3XOEWBjeTepmbaXzJEAOJZ6mhPB+vvXyVqhhHOeacPw+xf7KIC6Ariy5EDtbsFf2TGvtUM/vejHsf7jFXl6jNnth1tL/2KsGWEBTn2zl/0b3ELxVij2S/4GDcbcPHcMAA38SWP2yO+M/P/kPe25zSeosLmk1TsX/7iDvfVLyyDRA9vlS4mWL3sBDFMp1Uhpw0Uc+eO6rzFrNuwXlU24l/b10N10PUShikMUIMxKWAz1EpzK4b1gYAs+ulVmM0emJ/SA+6cejLDX69QRuDnY0sV6ml5nmZFyXgYbRwu4Xd/AIomO/XnAvfds+CNDDl+zaB0bEsCIuaMLMjgzykpquASJHfPaE/ndHelZqT6WXNLa56iqfr2z/rx41JnUnUEeS7qd5TbHrcwtA9UPhZzr3k/JlWppdKO+r0OX2UBo0v8iiAYjs514P9Qrqmeidy6kBu5sdLDXRYFz7Fd2tQxxtICp00Ou126HmNXi0Jl1dyrUclNdq8FuTP/7RzejNP/7Tr78e957Z9zcMYYtvK/VxKbkSUNcBP6jkHa4RiIqc91u5MA7vvHbH5+Jg3/nCDbq6JhWH7eI5uHdW2ozwXNU03t3QnlubUEMo5y6Yn1AXmqqHYRsprxen3OoqrlubhDDfTuEoNkt4xsbmHZnYNKmhmT0jrwI+PncU3fKz/DjWA0mALqU8c/8jjPpmz+LijfHI91tadJ242RB8Iv/lAVKw7zW5d8e39Y64W73Ks659p5fjM0VapHXPbDR3HhZYO6MpIklzpHqsDe1+ysi0r27AyDabJhfLkU9N5Ccq1MNQAYasZxfyjhurSYlrP7EmF7hmlVYDyWoJm7YRpUJ/LZOPmUsRujzW+ygH5WJwY5HsHVFf+DcA044UVYwKyYWQteAGkAxqlAjGK1E3UiXUMCaAEH+wwF/qg6XtDty5Z3NlzMmvwUaSRTx6t2XQVs1mH9zp5VKjRL3rClM4jJvmW3PkOjEWa/FoF6IhCyX55hdIyEfig2XUFpc0Sb2rYT4DKCFu6TEz4D3YnvBfv8GS35OxqQX9RUz0zVGeKxWVkAfRLOcZuAdu3eopVB99csHo/jJ2aaUhCJTJipfL00VLrnvtqTajFI+wj4Kqb4ZI/kNHirdU6y5P1Cv3eTCS0PgdM6To1F3ROE21KWPuI6RTRCNhZXoj3sm08biaFxuJsR1Vo3FxmtOra0x4ZZk9lZrn+ciSUltmzwEv/JTz/jTntr4nkzvxpwNc6JcYME7MwoXOFCQoVxKPUCFBuEushlGOhaf9AEVWFipH6Qjr63LLErJPcUGGJxgzyXXbfgdEsfWB3b8X+NcXig+R3bBR+USQ1hOQrrRT9USkghouU6z51FkoA354W2K2AG6GU76RbTygt3bGA2EOHUmLn5MQi+4DkIuaUJJyfHzrcQxzkzNEIW1FBX4s8uGuheZC8I+Y5U4FYRObvW+kob6OwDFPCkKU9ssxHKMDuzZD0RKMkwGmge8zgO7SfK4oNzJ9CfNeg4jwFtHHYbihm+HKOMTBLMr3BRlwt851pBWS4aGbcm2+l+1vFLwQoncdF2PP7zX3+7+e/yYe7YzZ9ybPLJfGdBYuPFMdXIr95/bqhXzI0dpi9JACLJ4HZsw57LaobJFke2u72IMcYCZBz8QRSYcWjmLw+A7jOS/CSgNeDBT6A2vdDA5R0m4vHD8HLe9yEa5IBZ63GztH8WP49/7xNImkyDsjwk9cPrDY96TAvCylCFIZYCLM833uh0CMaFNP1FxU4UK1DbfeU+5byA2kaU11/CPBbK9v+aZnSNzsZYeyET+Cn3XQsPlG4mUmdxl7UVCrNeluwbiue9Eeso+LOF6GLO8p6PJzJHGvkOWlySAbMZZzKD/6++bqDz0hxlkzIabu5QScmUGofoE0Iwk0W/hSIJvv95GREjrFbltLI0zhwQSTrIb3uxvZgSCCUMfh7R3SnJfaqGIpKdutwswI/L7G9H+6TzHI0zVjoJIyNTroTAL55acns7IS428ux0nrwxIt9jzpa0d2f0x2ZgYGTj73Kt49G7pXNTu7d6RyRv/sNAF8OhsM2026D8VdS0gLj2jU8PjnvAzhmbgGhMztRT147PfEDu2MC1N2Wp30OQemb4+YdfBNA/qsYP2mmMkCtxJrnPBj+5Op12m0JPtn71J7FqmFXltIUmeOQueP+akPnDecXC8K2QPXy/NPrJa/dueDJ3Y79/ZrlxxXVJxvMXqAuvd6zfL9i/7CSYrwXoFq1kieptkOVBW4+Y/vmgus3NNtXApx26fpVxF5Jw44TaVbdZlBaiVx/kOtPAOoJGO1yLBiTn3QcLi89iSVNk9R2QiQYlWTCxns0lPKvvFD4GbQ1LO7vAFjpBm0Cbi8FT7xC5eOZczOvXlQ8yYAq6WdmR+fLIuVwI7g8uS/y7ejVYVw4uTdREksqgADa/fxvkF2oMCow1y2vly2l8WxCRdtbsVQymsbZ0aM52R0BbHY19tJgdylGnrK/Jr/n7x0s+JqhuTI30fg6NQToiJ9DKMPBMVf5OOVt9QrdKYxSwzUVL0ravaVfSjO1NWEA1LPErlEhAYOHPtJTBvVNSW9NfEFS+gWZ7giF9gevEWlzAZd1jIQEdGsVCEnGO283S4lfuxqrA7Xu0UTmphVJ7GjYhZJ7sw1PmhX3qg19NLShoEp08Vo5NIGF7nijfMYje8Wb1TPWqxlP+iOetEdcdDMBz4Nm7wsBgtNN4NNMpVVgoTpWuqcgYpQa5slaQRMDfUTAaSUM8alcN0Cp/ABQlcpT4vFK0Ud7wKIOCci+zMSm9PjXfvwl+4UdTjQZebJboRqD3SreOz9lVnWEqPit0/f54fF91NWM9hQWH40F3Sz/Kwo3alQt00qUxlyQuNiC3Sf1MBcx2wT8KUWsNKEuEvsFYScHBtJGlmXsj0raOwXbh5ruCBYfR2lM19L8HJK0uC/UKPlo5Z7Wjdg1qGRzZ3rprzOdqLTZVpQLTeuH2txgb+TCC5KtYxwWR3DC+zHf0lpeY4wUnBMrzt0/YvP6jMuvaJpHHr3qCRclp9/WPPjjNfqdTyRvKhNIowVwv/etlvXjir7sab3lV+9/wp8dn7IqGt7v7k4mdG6dN24b09dNH/v01/+U+c3nYBTKCOhmGRoMUAq/rDDbTgBq51NsLkxKVVfjec3NBXlMmdTGoLRGVxUhe0h3Hbp3xKqC3S69JxD3e5RdjtsLavgMAbwmee9tI+fMkzF4KE9zlIPXT86X96iiYP+oxiewQaZwCTxEbSAOCgM59r4pga97mP2w5OidwOm3LlD7jlgX9HfnhNKIFH/viVZTXmpCZfC1lpx7ZrE+Ul15lNfoNrJ/vGB/V9QJXZ1ZeRLz+Go8LHanWH5oRLZ1chhizsnyVw0pFk3gdFZRiFUBRQG94/G/aDC7Xurv+w7VtMSmJTpH7PpkJwaq1ex3JX++f5l/OH+fSkWKLzgY92kjM/ku/I5/3dzjn579Fu9s7tA6y9OLFfH9OYsPFKcbiT3XrxpcLfVI5aRJs1hH6svA7GlHcb6TGon34qULB009NC2DhZXRYh+nFGFRc/XLK9pjRbmOnPy5Q++61BSESN63buzknQyVa1gJQFTtBBCGsbHoJoP5YCPqsOY0HTfvr/zYbY9n1uqsktpQVUi9qTTSaF7qw/XORYwP6EZIEKGsMK1n8aNrqUFl9TWthUByc58/bT/hxYaVZEspXdAaveuon8lrQiG1WbEwCkRt2TbJyiS9v1IFX/ThgfZIs7BCMouFhVmFO51RNL0c70JsQgdST2GTmmM65/4wts7Adsxqj2WBioHoPbF30EHcNaiqQllL6HohT3bdKFnvw3Cep9eYWCTGF8/1dPy4ZvQQpXHwtmv14MB41GzG/r4WvNHK8hKDxGFhYi2XCW/TvCqaVM5JpDa7USw/8pjrRJAC6trSPJoTrKLY+NTQ5Jl/7EW9Y25wxzPBQRpDuRHlBDc3bF9a0i2l2chX0vibQekh/9URtEjSz58UQpD7cUxzSJYvalKDSnOGt2LHcXXN6jtnw7wSu25kmntP9MKGF/Vrg59FgjG8vztlFyPHP/bTP318ZlC6C5Zj1VAYSSpLLb5HIt3tB/mqSjtmpmNlGua6G/xghaEoRWfVOZTzqfNADcUY0ybvz95z91s93cqgokwU9fNOOlHTpKZDTBJ4juppZFYnGaRK0x0ZkdOYiya6rxhA5ymTkjABphVSMDFRyEEo0cUnycJJG7YA03oM2JwSj2cS2AyS2CoV8c6IKXgZ6HvDsmxFBqStubPY8fHlEdkf2vvkAzZlSd84B9GPN1TMfl8qybGNz8g2SHXiFNfFqHBOEUKBC1pkp5F9NzpIt33QnO3nPA1LrjYzussKszaUl5rFpXQRlevRd6g7kePt5iJxY88tfms4ehaH4hdKugBDoYZukj5YatXjU3C10C0+appQ0KauVJEjFhbo082Sy4sF+mnF8dvSnGC6Wwrgf80jS7YXxtNOui11H1GlEvXQIN7S3V5TbgJubtBNhd32+NqkoFc6lvuZojqXxXP7OBKspj6PrN6Ba33C+nHLm897/EwfyHcdjMRwNj0HHT7BMhbrUlG+vSfVq4d/4tjdt7Ifm046plJyqLyXwCgvICFgtor5M8/2uWX2PBCMSgxxMOs2LWJyHnMwrbuA6kKazOW8m31IPoniKe1mhuK6Y3dqBx+uUAlLRjXp631KjjgU1jIrbUBzxucP2GMpCIlmkqwUcOcbmuO3O65fLzn77YDdilw3UUCQYBnZmzoV9TwYT/IzBm1l7tB9xFeK7q6nfcVTv1fia1lkdS/HLCpJTFDZm0INgLtdR6rrQLEJ2MazfVSyfL/BXjb0ixPWTcmqElDopizZ53VoNG3s+cP2rjCkN3fYdCWfvH+H2XsF1blIDO/vK7qTmGQ9hVlen0UWn3jKyw676YbkiiRxR1pfgMPgIwdTKk36ZmQmvBDU5CBoSM7TQn0b0znHZyGilkuRRNZSIFRTFvFQ1Jkk/XncBkzDrRLdwCFTOv/M313rxH4WYDpUVgKZUhjR2QPr5pB5ORC1QZmALax4zOz3w37H1BWv6grq6mdnSd/43jkQGraV5MiK6w67VajWo3dyjcfCcnx6widvnPDenVP6ky9GwUh5CRjRaV0Oo+85mQqtOAAcc8PR8NDNY33z0CeA59YpID0uSZfnU+XvlJZ/8Za1NZ0vtzAUO5F0HPykYXxPLlzCyFg2GrVY0J3WaX5TqBggaHTn8XXEzy1mZ4bmj2AkXrBbl5gWCrxIb/lKp++paO8UVI/uot7+QK5Po+W11qKUont8h/ZUEkq7k0TYzUW2S/sRfAuzQoDQPJcolY4HgDSoRO+lozsnaXjsXuKoCIJq5maA6fpzAxibgswih5uTm3hQFMPE4bHBjmUKMOv4gn8rN6YHVPzc4tGf0nT9M27rxY3dZE/3N/6+DQj3MdLFyF90D/nhxT1oE6uyDMxWLcezhq05GhLCaCNF5VgUHY2bE/ZWQAAl53N/WdMcFZze2XD1qGT3iRW29OVOmqRCwF42zBK4EZWSJkDXg/eEkwXN/RnrVy2b16C748CmJrTUCBmCIvqUGWffaJ+aQKdgdBeHxwYg2ss1abrEBEzJtu5GKxVpekqAdZKPzXNXeyL5UXklId/+lYCae/72y++x9wXP3UpylV6PgCaSX2VVKuVHq4Zo4tDslV44FF9jvgeQ98hkmtfH9PrUcJmB7qjyic7Aelp3QkT38ho5Nj/pCvuvf2gV2IYKgDtmw516x6YtsSagkle0YAN6AKZDkByu3xfUG5Xi1Nvj5GljdrTgZmmezuuDlxg4lFFk0G0KqlWEdF7i9LwAsVSEWgkjNLHWdaNQXuNDwcX2SGIoG1FFwFaO5aIZlZ+0/DQqpJz0EFTSxJHZqEZgOktUH7AatTzf+k8ra2hcjOgYEiArssohQhtE9tomtrRHo2McmMY7X2FMHOS979cb/t3zV3ArTY9JCmlWAGpV0EfDTrecmB217nlkr3ijeM6r5ZxPymOeuxXP++UBC7MNljYU7JSnVDWL0IKGIgYKhNZukHlKI8wXOSZhuF2yn6xHUSs3ANJ9FM/sPhr5TjAwpAMCZvuo6VNJqPncrhp/uaGJA3O+DwXP1ktUK/lY88kC/XJk70t+Z/U2771xh99/9jV0Jzl8c2oot0EafGKUJvBpr6rVQ/2KENGpPgXpHnNhkCwNpR0sSohSz9KpphEN+NLSnlj2dzXNPcXuTUnoZ8+LIZfPqjhRgVsIsBW1gM6mYay9KSlWunl+LlJuI6YJmDYIQ7pLQHSW605F6RfkunMuFSNYO8ZCGdjMYHVZEFYz2gcLLr9csnsZ2oe9+BN+XHP3m5E7Xz+Dj5+NUp5ZYtVHUchJjdp3FzvWbcV/79E3+YM3vsKRbfjO7uXxuMfczJLIIz9Jt3IyBr/5G9fI8PwX8D7QaEoV2LyimX3tIcW6p9g19KtCmPCpwTTWhVji7FJxJXdpgiiuTIYSv8L0ARqqSuJ/J6BX3O4lzrd2ALDjdoc6WqWczifQ9EYeCy82Wt94/oWcGeSaMxwAageANOm5Uho6QhlFcSWnrfl31BDzDHXhKfiAEG9CFWmPNftXltTPGvT1nvLplt1rR5guEJ0mGC2kqU5AZDcvEvEEirUUC9oTQ3Mq6p4qCugtQEhEOUWo4sB6i1bhFga7ZVxvX/DWZqjiR5Nk/hMBQlcGXRVwtaH60TO5p7UeFQ7ycQRUatiNFnxr+N7mAds7gXlSF/lZvEE/zyN/n01o+P32zlCLer5ZcP10yfxHBdVFRMVIc1fRr1K83EF1AfVZYHbmKK47zHUj99AUXLvJSs6qETDUakbAVdPPlGAVDXL+ux7ltICEQUhBWbHyYIQg6gV1xaBQkE/VNCfK94nRh3WoWxjStw6VLeISyScTHxLRI1otEsxGifpHoUWBLNWfhs2kz9C9JpqIigYTIqrpMGnNiZutzCnWSh0tkxd/lpHnm0m9LBqFQqP3jvqp7NPQZG81US85fzrn/XunXAaYqx6NplJfLMWMmyMAm8ca09+l2HhmH22JVtMvLEVmtBtFrGvJQZtc25nEADeuF+UDRDuC2MkjXfmQ4oRAbBpUXYmaXddLEuGReSgpOeA8lHqUrs+M6+n8nkaun8kfuf451pvk/Z9CkLipmgngPf7eMe2dmAhgasiHcn3uJhANJKsUJrkQ9EvJoa9fN7Qnd5k/89RPW4qna0wzwy0LdOOJk3uCKM2BfmakQeKkFvIIsH1gaU8FwwwVhCIOIPi0zhQLwSiE1CYEv/CTCixaDXXGYd1AFGJjYVF1DW0n92NVoXJ8plIdM9XETCt1heABp/hgc8I26J+ZK/SZ77J1V3FS7ljaTqSwEiBdaYchUGjPpq/QKnBqdxTaSedwkJteus40ZnKtyOSWLrpUzNS9R19uKUsDlCKDcb6VziOl4HhBLDS66WWCTp1CZttgkk9DVZeEmcgiNXcKNi8b3Dx96KRAOyQEKEIqeGRv6Zg1FWMcij0kADkGOTkxRkJQQ1FQqYjWkRg0JsmKWeuJNXhn+PhaQOimKyisT2C0XJzjfT9hVEU13o8RYWxHhDUQ5Hl0HJnVShhZuVgT0z57rwketAkpp8g9YgGloPWWfVPQX9YU54byWpjQx3soNwHTOVSI2J1HNx7de/yikGLUJsldFKB7WVhNm8537sJIxzukbsQrP2OuW3RKpXsMC93ysLiiUJ6Z6XnarGi85Wx/xOWPTjn+nmZ2FiivPcXGSVfvz3lk+XZdRNZ2nARMIhVSSuApXnwK28rs1h+XVM/3mMYTjcwwuo+YNCGW18K27laweayoLiKLjxTdukbFhix9cmvXTwYCHBgfh0lUZNUZZFF1HymuNVk+NFh5TvUZ4NPDhK/iZCJP0qzVlac6N5TXfvA6NY0bpEBUK0EUCCijnPjWRNTwU/eBaESKVTtPv7AEq2lXGt3mfUoT/7TIP/0d+XtIzmuVvJ0ZCvySlY8FuIE5GKULSveR+kwxexY4/eYl+mJDcXFMc++YYpNZRZF+oceCX0zFTJ+kvNM+qPQ4TvYFBcWlZDkP/6Rn88jiaxLDTkawAsKErPKgksxmLwuFbTy+MvhSiYzN+SXl5gjvhTmTD8UXgSmtUeyi5/eufoWvP32VfVew+2DJ6Z9rdB9pTxXbx+AW4ldenymxbnjSUZ230kG6b8cNhiiBUO7kLgsJfKYSLzmYmvgpH4wpwAupyzK9fwC8b/kyuWCjxW9ZuUL8nG7KIU0Dq5sg9C3joMNtmsRPknrxwi5E5qk0qYvOJH85NQRX4nXHARg2bDrJ48v9r5MsvcEfVRTringtwEh0DmUtajEnzutP3e+faUwTODWRkkkj1JaYZP517wUQbTQz07PSnj6qz30Xq4ABqXhn1HA9DA1yuTY5BNr536dcI/HFpwZ8Jh/K/FgqcBBlrjLbThoMbhu3sdhjKmKFAFWJqxWLD1qi1ui2l23lZHgCSGOMJCF5u4XIxasQcUc1ZV0T+x7dOsIs0C8NxaVOjYujsoXZdaMfldH4eSHNiH0g6CSTWVspEpfFWGTRmu7LL7F5XKE91OeO8tlefL3vzCRRjnFQc/GzApO9sPNcko/Hp/S/RR8o1g7t7dikNAVt0r/pYyJFH4eEJhbJu9IggBwIAGQiygS0kZhS6SA/FWgdhphTp6ZC+X18LI/8tw+3TWD/zbg5sir+v92+xfNnIhGqikC9bLmz3LEougHsDDYSi0hRyP3kvJF4vJjcnL3mo7Njfuu19/nDuyu2L1dUlzMWG5H2i7MKFQL2uiFqjeodsbL44wVubtm+XLJ7qOiO5KbWrSaGSKwUxUyKBq6X+T96JZKmFgGnC/Fp1h0SMyYA1nRR4pZAAlFuJP1OGoElxgQ3M4MMbUiS980dPTR01M/l5/4fXvMPX/sB/6/v/zKvzc757uYh/+Xzr9BfVWSWrXJS+AyV3BQqpFxLc3gP3By3PRzl/cqn75tyuzw/TtUJdK9SvEZiB0/i5FZymJ/3WOm9MFijZh1mLIuWqnAUOuCNovdmuN8LneWVU/OnU8kLTf7lxtOYrwc1eSyHIHnu0YjqVyN+4Zx02DL5kkWxhZJ8UuOdHhr1IG3LiAe16lU6x6ko3oh/rfbp9GkIuuDKzohFAr2LSLnoqKue41nDsmxfiGOnsryjb6zkFvpGHPXjYmBNTI3oSeEsMoLbaSE2Kg71jaBGVqXVYWi8r3XPSbHnajujDQUejUdj0kJRaGEnN7HgOszoomGrKmrVU+uON8rnvFqesQ0VT/oTzt2CXShpM6gdChpVsA0VmkCjegoCgSCK0TGiU5ykkXMrR0M+16MGWe+hgqY8JgUGAlqHAZzOw6jwC+clfbNRNzfvljrQBYtzGt3L9Vo9M3T3DB/ujumPDf/pg/+K81+f8+7vvkFzT5rEw1OFrUYw2fQRuwvYrRsaV0Np0HuH6TtCbVPhNhJnxcCyBqSGodRAtPC1eD02p5bNK5r9g0goI/rRnn/w1g/41z/6EsoXY0yf7mVfKfqFfL9iC+VVpNyEpL4l57OfK/YPpH5kd2D3YfC9Nbt+BKMTsJL3GRjj88m/GKOoUg0xUiQ2LUprYl3i7q1Yvzlj/apm+7pDzT1qY1n8sOD+11tm3/6QsNmSLWNUXUuc13biZ+0MbmHRPrLpSs4ulzx/ZcnrX35KiIpvnr8COttXydUvSgt/OVZniOoFIPqL5jndR0+hYPerDR/cqTj9c8u9ZwXFJi2IewEJ3LwkVAa7KUS9MsYBEH0hT/YR1Tupw8aIqkoInpi2Gbdb1NEKVVdSxAZC18v8ZPRY4zVG8vU8QrJdTNeV8pNaU3r+hfFpufMtQEQsLM09hS8T6JruGaUlFs+AQ7akOMiV03BzuQ83r0N7t6C4Lpg/XXDyZ5fM37uW+7EwGKUIRZJL9xFfSpNKv7KUlz3Byn3t5ikHUBIL5Xgl6igqN5nokNSgQqkPwAOJcw7rWZnUIFO+zGehNMJcLAvwQfL4yXlVA9AIqvfoXgggYW94f33COmhOtE9rxi9m/vCR9/y/L/8Gf/rsMbu2pP/zI+68JzW55q6iP45EEzA7xewZrD5yVBc99rJB7drhfsCHBKjd1g2obv+ZfjfrhuN3SqmFdgG9bcb6awiDAqZq3QjaTepasSoFMGJSQxhIFTdqSf5TktmhzvRj6k9pn3JNJlQixe2LTIQ4rDkFM16TU5k47SEYyUGiVthZibpaE6+uGXzhC4ua18kXXf/YetlPHENdD3LTE4AKYVA4CGXy0C005VWPvapFEfhG/GCUxt/WtP8FGD5C8yCwfT0w+7DglesSs++xe49a78A5VF3S3xFGb3nRyLqQMbxbaqdSd4oH15vSGuWDSHj3DtUnL/Wqgs02kT2lfqJmBrox1oipYUrpePs1HCORCXHgNg/pH8esvm0oRfPSnP4oYnZJASzhBwNJIJWEB8KASvWbdMsrl0DjSnIqXyuaB4rdA0v5quXetwzV9z7BVKV4d1eFNO7NS0JhxM6vD9IspaA8bwjFTObeRtaCmKxbc26bwXnTx6Eh2HQS1/lSf2r5cDiUw/cTnCJYlfJEUSbIIPR0zojT86EVuLxuiypbtJrn6wVXqbn6ZxmfGZQ+LhuWtmNhWgFbdcAgUle17nnardAq8qBYszINWgXaUAxFgGCEoq59KcHvpIipfBgYAjEBYnrvMMljQQ2m6zJhh6OZXHjOiV9DnrBSJ5hpOsyVIlYFdlNTbGs2L0vHQfYJCYXEXyogksYp11YuScvdYL0IwJv+UJLw6RiHhD0v9kUhYPNQh1QRa1PXw74cKgLeS4f7IMk9letWkejV8Bwx/e7kotGlBwuhNdLRmLsa9XihZfRBFVJB8y6D3WGI9y7fO2H2oWHxcWTRRGwT0K1D+yRtE0YP735pJYFZp4XYR6okBRu1orkr4Kpv1eAzdlCkjQys1Kftilr1lJPkHYMk+2bHsd1RaceH+xMu1nMWH2iO3nMUG4fqU9fY5yBPmBaFY5J9CEaKfqYTKUN5IbQnCrtTlNtAt9IoV1FedagY6ZfiL20bKXRmvxvdy/v2D6SgVqzh8ksV1bWcF53kpKcjJkkilRIMAZ4ioAd5vdycP/84Ad+vWOZPvcg3JsBikOWNcfARiGUh92qMVE93nPzQUD9vBNBqPXrbEmq5v5WT5FbkUEjAmXSS4iPKpIJiiJi9Q8VI2QW6U5H7rq4Dzakerp9BAnVy3ocAnXGhEBmmMfEfXyyvDZqhozSkn7qH+iJw9N016v0nUFfozZ7V+yuCTZ6JWg2gs3IMnotRCZgcLHRHoxoAUYoDposc/0Bx9G5L+cmabnmX3Vw8aUKW4Zh0XOX715egaikoS9ehYnbm4WoNXY/ZB5wz7NpyZMl9TkcO4nJ36u/uXuO764ecnS9Z/UnNaRNxc8X1W5EwlyCo/sRw57uexQd7kTPdJyqTSfLUOSlLUtmyeMrcjJ0sa9OgJUtbA0PjxVR+OoGicu94eW4ozkyC/fx+q4dATfkgcjgZQAtj4CTXo5/sy1jIGZL1aSenRu631CUqMmAF7b2K6rzD1wY3lyaFfC3LNaNeAJ+nQdR0DI0ZE7aazFsRdVoSi3vYswV6s5Ng3RrZn5zYTDsOp8Wrm+OWTschoBy64uUc5HkrA+2h0ENCHq0iM+t0owe/H/0FSJZz046wfaUIoyIMot4xB4OjVPN4PfDCWjeAL9PHSAHjpOlHkd6bL1mPSM99lgRv2JdcHAr444UkkVd7aTjKgHSSa5IPSfeDydrVQZC+ppWEQmvcsqCsSmHjeymU+0qPSUZquBLZ4XRfKXlc/KBl/00TMPuA3oisENoAItkX7xyzebUiKiguPeWzPXot0oJFIfdPKNUAvIXSDLL7wzFVSarotiTHmMH/V/cKb5Kqw+RGu1nciml9CtmSoRAAM5ZBwE8bhPlsIloHjIkYIyo21nhMKsLaJDGbpWatCsPvGZTJxXidZHI/b4XV3MSrh7//+vfv5mf6GFloxdt9zZ9dvozaWVj1GBs4mgtgBozMXo0UxnUQWeK8TNgwxuUm4s5rZm/2FLWjOy7Z3zNUF3Ps842o0qTOcgXCjq5nbF+p2d+T+LpYR6pz8DPF/r7C1xFnLQ0kj2aR7TadSLXmnCuDLbfiTFEaE1W6p8ZmxpCsV5C5VytMI5YxbqZFYedI0d6B+pni9AeOzSPD9d9r+J9+5U84tVt+1/wSH+xPcUHz3Q8eYjYavwxpzVFEHcb1KTOjlRxLdJoPdZoz/WQCzOlXUOBJPsdqlOEevxqZYZ2fN60k99ozNnUif+fmwp/3+FH7gC/XTwC49HOe7FdcbuY8OrlmaUXCtnXi1ZxHaR0hKtTOEBU09wJhFsbjOQGQiaC6sVgR6gCFvFZpabgu7+/4W4/f54352eCrrIlsfEUbLO/u7vDe9SlnF0ui0yibCinaE4MRIDoixfXEWAfG860k8PClyNGpoAjPCrY2sjHHYoO06ilnPfeOttybbQ/mtZu+sXn/gAMGdZbdnUp/W+1Bg456WBNzTLr3BaV2AyCdmdNOmQRid2x8NWxzZRpW84b3dqe8Nr8AxIMwRLHJKpTM120oCEr2eUtFGUTau1SeO2bD3XqDIbKNJd9pXqFPn5uvAaMCZfD0yrBQPXPtKFQc6gvZ87CJkUKJ5zQq0kSFjmrwWBagOhwyo5VOx2mU+M6y3Zk5/Ys6ipQMl6Uj9wfrHtrO8qPnd7l8MOdXqg/5T1/+A/43X3qZ+v2S+izia9i+LNe4eDNHip3CLjTFNkiznFXYIjGQ1i55RKcmnDaIp2aOc5CGbbcUz2g301x+BcKXdhSlw3vN33/z+7w1e87vXf+y1KsmjdExSe6jwG5h9lwYfHbdo3zAz4sUWxn8peRZts0WKLK/uDD6SmZAOsf3Eyb0CyMGlLKyL94Dhnh6RPdgyeZxydWXNM1DJ3Ld24LV24aX/s0a88MP5SrzXgrVxkhz1r4lxojqAtFAX0n+f7Wd4bYFf3b1MoXxbHzFk6vV4dyWxk8b57ygMKZe9JT+oo1CCYT48P4Vv/61j/j/md/g9DtzQiG5lN7vRYK794TKoPe9NHLn+HfayJ1jpJBZ/FpyvsIKM3rfEL2AD7FpUMulSEM7h9JKCvCzapCGRWuJeWCM9yf5YLbBG8YkRtOzGaqqDgHrXK8K4ZAppxSUBc1bd9m/4jBbsYiMVr6ay9apcQQcpjUYX4vlnVsIIB1txJcCNnQnsH9JUZ8vmX39XbTRwvgzGp3tfzKLNEbQWuywPtSsvp+auqfgf24GgEOAp3e0r9+hX6ZcKsU/Yt035sxZtjtP176UBjU3NxSzArOzB3nNcKyzL3i6500n8VkoFM8uVnzkV7xVNKI49PlKH/5Kho+B/8fmV/nm+Suc/8VdFu9rZteR7UuK5kEgVoHymWH5geLk+x3FdYuaEG8GVQgY1AdQYVIzYrx+p3UmrUeQFyBEqk82hzs33IN6TJLCuM2sOqCSf3Osy/GzpnP2TeDOTx7LvuyDlD/j9sqCWCfQLCmA+Jkd8uZB1XGouZLuHYWfEJuzGsABETCA6VUiYkWah3OqwqAvt6hG6nyxLqXelL/3tI50UxFheszyMXB+YO0eHHelBi/j7NsdSi1N6krqAboP2K3ilfkl62h5rApRoEn33BdZNcDPA/+jv/sH/OPv/Db9nxYUF3vUvBTySduh2h67bsW/fN1Ik7Q1EhOYBDa/II0+qV+mWqayJtVkFNEH4maDWq1EaS4xqGlb4slqxPdiRGXP4lRzPDx/qQaYrRhzXJXUFYHEyC5evEayNzYMDRy5dhlXC57+dkE0PpF7xHo1x1n5us11+kGBNtmKEiEWqXkpEQxCEYiFzKX9kaLYVtz/oEZtdkTn5RrsHabpZM04IESB2jXMn14w+2E1SKQfNItMFUHDZM4pLM1b9wnVaIk19KVO8l5gXDeSyq9YvUotLixL9BUpLrCDIorYU1rAyHOQLGBkX3QL+8uaP9q/xW9Xb3+2i/LG+Myg9FG5x6ZvWSV6SZGku3PyMtcdc9MO3kq7xDPXfnJitRJGwARYkAKPTDqDvEPbU6z10MEXjR66kqLV4ySVLuQh0EmLOWny0b2jVDCrZtLFli6y2EihO/vvECWJ9rVckDHI87GI0tmhI9GllE1HYbPYIIEXkGUSuzD5+yBGkglYKWG8gADTISqCSwB1VENFZWBFA8qkAk2S7A5Op04SNQQq+TkpikUp5qhI6I0UDkBkvZWi2ZXEi5IHf6ioL3rsTqSbsl/jwMqLkjDlG8XsxEc1zkqwGrtNTI1FgfKpcz351Q3gTv4OUeTyoo70wUjym1r3DTH97dA60EXDvWJNiIpX7hzx/t0l/VyjO40NCbQoPj8Lg1ZSkB46w0jHwQFJtjwUAlraVo5lf2QpNj1m5/CFIEcxB6k9w8xXpUTSV9Degc4JoFlsREJ78LOajOHY52sjZuaLQjuZKINVeB8xlUgwHr0bJJF1aXKryonHQBg9cp2Xe3fbMP/AiP/08UykTyDJtxToXQc+YhqHWxaDB7NySVqi0Ogu+1m5BD4YdvdtSpoF3FcudYsWChPihOU8BkJDTpmAfAHX8wQ8en4NoADyvgzOZ+C7v1OjTt5k96Ckn+eOvtTAYtTgKQ0MUt4DqJz8HsZJHurzEfiOWtE9WtEt1eBTNNRavUwphzJWeRFB/D+0on4mHhXiZ68InaGrzMCa+7yC07m70MfAD92ef/7813n7+V1mf15TXkd2Lyl2j6V4oZ9UnPyFYvlRT/1MGh66u3PsxmLOU+Cup2uHluBnmszdBDimwWp+f0oa4zR4yX0YYQIt5UAod8JOA/+YpHFTEH9wL062O3iy5O1lYNeI75QEYPKdlI9yLyU/HbVrwBps54hG4WvD/n5BP0vyY5OEYPRbV6OfbUq4x2alye+K0Ws0sSpywBlVYqSuquQ3FUd5v/zF8vGeHucfB3hOChDx5jmBwfMnWPEeClaNzO/UUS6e6lJ0/shZ3ix+/ooZP2kM3oODukUcksKI4kB6OQPU3AJQ5+1N1tX8vuF4TtbbiPydG8S0RwpGt+6kGpNVz0HSqpIcY/twjq8kidXrHXG3k9f16TrIXa5KCTO6S0x770Xqz6hBcvImA8LVqSlx4t1uWiT+01oSzNTEGKyiO7Ic/XCLOVvDvpEOW+egLPCP73P1lSW+UKze76iebERdIUbZ94sNVaFp7gvIoHyK9aoS5fYMEmyp0CV7KrHOAOgnmXDV9ug2WzCkczEc0jiwFvK9GsqY5JEisRJQyFYeW3iMCcKCBkrrKa0b5G0L7Sm1RyvxZ7UqDN6pVgUq7QYAGhj8SbWKGMLgU/p5GgMg/VewLR8jN2eCm9stbtxHt/W7+xj5Zvsq3/3wITiFsYHZvOWk3jO3HY2XamaoGIABqwPLouWoblnPe/rLiuLSUKwVbhZxy8i/+Ve/xm/++9/jj67fYt0WaFdzFCP22Rq0JiwrYmFYvy73WDACeqgi0i8VvpaGwsVH4qeOMmhnBFhOMZ72wop1VcoL0hySG/qygowKiSntxnhfewEpsv+k+L/J/dovLduHhu5YgI+j9zym1fQrxbO/aflv/3f/iN9Zvc0r9oLvdY8A+Obzl3m4XGPfqaUoVQTBmAskP0kqTlnFARsTMIo032Z57pzX5IslkuI8NXpGZwbwpKFTeTA7hW1Ad7K+5fVD7sXDeO42FsBf9wgo3m3v8bC44q7ZcLado7++4pNiRfNGx5dff8KikKKdi3q41ztnMTstzZEnjmLeYW2gLlPRQEsDiw+as/Ml8bJgAP5TLir9TJEHxxu+snjKm9VTjkzDSouFx6VfcOnnwuL1BfOipzIOo8Mgl32+nXP58RFmbZIihGx4iGtzDcAi858GclMWDIz3eF3QNoZPeou/ozitZR9yc81tbOgpgzpLNMvfOcgef5/6UmcyfYhKPJ6VNAOFHDgxNlD0KajyQeOV5uXlNZ9sV9yvNqy0n8gIK3ptROpbC8Bb4DEEOiwhKhoVKZRnpfcUumdBx6/UH9KEkiYWSTJV5MO3saRGiU1a1DTJB7ZWI7aRR9rjlE+PILOOil5pTIyYZDpu8Ph0cjQCUGdAXFjjn//Y6ieNadMCMDBhQ5rHF1VHk2KkUELcWdT3ay6+uuAv2pd4rThHbQ3NSz3Nq5HVt0vuf6OnWPeoKM36zV3L5hVDe2oIRUQ7RXsaiEc9r/yzkvq8x/bZPiji54Xki7WhPdZ0R2OzUX/q+epXP+K15QVvzp5zbHes9J7/25PfZvZeIbGDGpvp+oXkk9VlZPFJYPHeBnOxJW6F+WSXC8JqQX9vjt0ZmjsG2wj7TvL6BEgnksgASE8ZMjnmucFEVUWRXudRVUU4PWLzlWMuv2LYPwyE+w1F6Qgfznnp38Lpv3qbsNlK3tM7oveEq2vsw/vS9LrfS2HThYFx50u5+eyZ5Yfn93jp6Jr/6oO3aJ4Li8iFEbT5WZvvblNm+HHPf95HHz0B2LYlvzR/wu/OJEaw217Wv64H06DXDSaBUmrfCmg8AQiGkYrdQ+E7g5q95KgxiJJP7HppLC5L8SoO6TV1VkzSuKN6kMtVvU8y8ekzp76xIQ7Agyjt+cO85ab91lSFaniN5vnfKFHBD81p+dRqn5o7cr1GjbWcaKVWKaojAkhHm2IVE0VBw8L6ccH8W8UIDJCWijyHTusSbScy9ecXP/H8Za/OGCPq8enAZMt5e2a0yYvH5/LriEJqCIUwuPM9Oj2fcZqvhQhe4kohaSj6TcH7/V2oP/y5NIz+dYxvdI7/z9Nf4f0npxz9SIDXy6+Bu9OhGsPRX1iO3vXMnrbopieUFpPINqrpiIUlZkYnE3BuqrQH47U6ZTMbM9RDhhpUDjcKO86/BzWlsb4y1DSndadpI8kQL4/368HItZaqSPsuGECkHJ5XrUNrjbs7ozuyuFrh6rEhd1TrUEONaWjWZbxep3XabB8UCmn6yMpLUdcUlREL137SIOUyE+HGMb3t+0y/b2pkH+J6ffjaqBShNKIuWKjRSoNUa+7h4/3xoFAjMdXnL4f+ceMmo7tOh+JesaYoPL6qoE+19xBkjugdunGiate7gZBGjFL3D3FsnMnKFzD8nZuDYtsJGJ1szmLXo5xDL2b4q2t5S9fJIddCPPXHc/z9BaoP6L1Lyi1Rft6socJQwyWRXKNzss2qlH3J9dn0+zDyvqc5e/elOzT3Amgwe0WxHV+a1aWmTT/5uhcl14jdS+w2qE4pyavyWhOQRsa7yxlmsxPQfALiq6xUlh4b1AJ3e1hv0trrb6/b3RzWov1dorJiZToZQqq7WXMl3Q/yt0/Yha8tNs83w/ylQN+AjEMQ6+UGiAm0bw3f2r4MJ/91g9J2lNWqtCOkJKnSPVduzpFtOLY75rqjVG6QutJO5OFMG2Ru6cJ4kd0cfjK59g7VjqA0Wg9gQrAaCoXtPXFeEedVOrlyUw1S31EkZ3TnKa8cm5cM7UlKgCNMcy4VYPZRpDtKHtQ2JbUpGDmQPcsSjDZOVSlSwi0Xt6BM8kFKgbIBYwK5TT1LocVOp07zeAiwZYA6KGKS9ldestDYSFf80F3npOM82rSNPt8Y6SLvtHTEx4gLCvW8ZPGRZv6kFQmn7B0QGJhqQy7fB8K8SBLsSromrHR66c6nInFeyGT/VdpOsIqYpOS0FxZeBj1ysoyS7m3x+TKUyrPQHXfMllBofun4KR9/+YhnrCivSmy6+P3Prg7wVz5Cbg5IAWPUEe1k0fU1Q1G6O1aYVlNfBnypcIuC4qrFbh0+WnytBzBW94q4lI5Hu4/0c9g/EHZtd8Tgj3PT+yePgcGZJOtUIPlgQezkXLpKAu/+SOHmmvqDvQRCZUF/T/Tu9d6hN8lzyCW2ZwIFM0io+gk47iNhYVCtQXu596IqRkCillvAl4r6uSTByonPfH86w81hdiaFVbuT68bNU/OIY+y2ky83EoFSYSH7Gwz+eflfXgPsWKSIliFR6RYKV5d0R4rNa/Lmox9MCpwujqxsA96mOWKy7azAkGUTfSHSaqGA9eNSJJ1qNTBUTZv2O4z3/gC2T8+lke+vWzd0t6oIsdeD/H8Gpj/PIxB53x2xcRX7y5o7iXngFhE1F7qTXwbWbxhQlubOAtukRoQ7llllKc53hwUTGJjTBz7MN8ZUfih3rUatx25lGIJVpUJq6pkE+BOw+9MK2EMXZppDh6EUsbRgEuM+AVqh1BIUpySzWDvKj65E/QNEciv9rhYz1Omc5m5BcyIso2A4AJyzTOcoy3L49+HOpjXQSjFulF8RNqusRxZdGUzjxSsLJ5uZyr3e6PB7QR5qcmxfaA7ITS+521sjjWOlHrpXQynzBpDAXajPFJf9jLMw55ez2fzneQQEcJkwo6WQo8YuYFJ9QcUXCg4ZOJiOaQPLaJEh24zp3A5MuSEhFInF28bAbL4pl5c9qpQaGnG6l4+pfvhUEo1UPDkYKdlWRhO9R89qUHqIC1Rg9LbSoHrxQxyaMpDfi3UcpPojiBTf/UpsJgLobSuSX/kzewf3HnD9pSX9XLH60FE+3aJ2jTQ1pgY71XTYyz12WSSZo9RQVlnUJhVwjJF/mU2g0w01ZRClxN3uoDvmYC0gjslLXv+DFdBQGIoRPXMY66nrnsJ4SusxiRVYGE9lHLXpBxB6ZnoK7bFKmkGzWlFuDtVETLowCuUHgCF7jH6exs30/ubftwHGP2ncfM9PArtvek4DvO8r/mT9OvG8JM6kWeDOXADpbV/RBZMS1Tg0/PigebJf8cGzU+KHM44+UMyfBsq1p1tprt/UhBLeX5+gqkDzyAGWYObUDyp0G/EzPShdqCDKSyqKzUe5icP9CySLkDj8fUD0ClCn6zw39Nw89colpZ3cSJh/lJp+VYrEfq1xM2mAjBoWTzz3vtnh5obzXympziPNvcjRbz/n31/9gFeLMwyRja/x7y24fg2u1jNMLwl7tWrpmkLyGZ/2yeSJarIs5KbeDErnMaQLanhNLigPS1CW6E4/TSvfLZTCMh+Inyn3i1ria4nXf8LF8tcwXiou+bg/4crPqXVPTE2pRPDzAvOGND+4YNi7gj5d4etdJYWRKqJLj7WBqnBUhXyp2jpmiWl9YeeSr+Q1JR9vJ+vv4+Ulx3ZHOUmOfRRm7TrUPG2X9N4wLzruVDvulFsK5Tktdjzvl/y+fZNnT47xfQo8gsQWqhffXmnaTAUcleIyolwLQ+4LqtV4X3BRzDkqW7TxfyVqDxnUnkr0CiCt0SbSRw3e0qdmaSmQCHM6j0J5HJqHs2vevrjD3hdUxh1MYn0w0nweoEgXV48RCe30HddKWHNNTACpbjmxu+ExAafHjebXyT4EyaGjSHlP57qxLiyf5VHJTkwSjxAVJvlNa8SLNwPUuXmpUJ+DG+KvaNyUY/bpGli7SlTtykjoFG4h9ZmT7wX+yY9+nf/1136XT9wxdqNxR46/+dZ7NG8VfPgfHrNZ18TrUiTreyUKTwsHOmILz93VnrN3TwGpoeweL1i/YgmVNLCpIFLb+/tQ/cYFjxY73lid8986+XP6aPlRe5/Xq+cUyvFRf8o3v/UGR2tws6QMESUWC6U0Ci0/8szf22KeXgjo6ByxbaUe1rQUgDqpqWMklBqTGpCmqoUvANLZOxokxhvUcNLV5XtRjDla0b18wuVXZmxfUezf6iTe3Viqd2pe+Rdr9HtPCLs9sevAWgFeAH10RFzMUJs9sXepGTKIcoWLXL1pubPcsX3/iMv7C/6Tt/6Uf/TJ36E8M/SPJTf6y8wLISrc5Ma9bVufN5WZnzSMUhRACDJvRxPpTqVgpvuAzsyrtkO3aU7J59al2mm2ssmj7YguMdyUhr4jNq0AqLnO2Du59qoKdjKPDaBGys39TJj1Vim0UiijRiIEDHXboYE55YZKKZGCLYvba8c3ZVy1pn39Lq6Ge/9WM38q81l3ZNg91MLmVGOMPgDTyVpHt5n5htixJEudLK9KjKzfMDwsC1GBStaRw7iZ91al1M/yNZ4Upl4YE+BFJULF2Hg+xj3TPOOmlV9uSFd9lJp4Bjez+tX0c5yTezsI2y0aMI3C7Q3f2z+iX77HTP0lPH0/p6ONPe+7O5zv58TrErsT72i3cpi5KKbsH2p0Z0BVECv6uaLcVsw+2qO1Hu0OXsh/NWOxcHzuQKUvEyJy/Wm4XhILPjP/481ze+M6z5fQpO6VvZ0P6lVWpzqieFj7MrO1QbeB+sNrIUCkuYEEKqo7x/hyQXOiCQW4edrmBKgLUxTpZv1Jg+rl71AkULqXmDAUUuuGpN6jCryzmL1YhOrGjccxs7pvfu9PGS+QTabHUCmRHy+NsEJTjSkaNSid2h2c7ee02SYGIdZ8kVnSPZI/9dHIEl8pwvGc9l5F8WQudhqp0WgIZpOSglxfZpyf88hgtPNDrSRMYgWlVWpOcmLvsVigtntEXS+gB597TzRaVGNdpHQB1UUBd9EJXwiHn6vUQB7Ij8Usp/+Thtai5lFXbF62rN5WnLztKNYNKGjuFGxfModkg7xeZDKQjUO93teJaGBknynSfKuk5tudQPPynPlHz+S7FFmqY3KfTsH2wooVZfsZKyH5/ogR1YeB/TwcrpQr57rvrZtI64b2CLaXVHEHS7xcN/dBzl9ukAmjXWroFK5V/OD6Pu3LPYX66SkHnxmUXlgpDhrCwMIotBvAxWO7447ZUieWdKHEazrrnJtWiiC66VGdO+jIUyGxkQ1DF4YKE8D3xoUnoJbGz4qBWRON+AzqXY/eteMElgJu0zhMW410e9L9ldaPYGH1gWP7yLJ9RRGsdLxqlzuDEvNZIe3VrUJ5PXqgDRetHv4GhvdGA86MBWflJElXgcFf5OA9MDAlB1ZBLiY08gLtOSiE+pIEdin6VRilcPI8qiKx18yeaRafBEzjBRTss2SzXEBRQaisLFg4+lWR2E2M9P8oALOwgKXLaPAoVRCMop/LwmP3EXsthbRgFV0Qb7R8gktEnowovlaL1F3eRwn6fufxu3x854inmyVNVxCjGlgAP8+RpdH6oCGOgHT2SS52UQzqCynOuHlk91Bh93LcmrsyMRWXTQKVCnRIgYeJVNd+KA4WO6i+L5LW0UB1FYZjPR35/BwAZ5P63tRD0OwVttE0naZdGZYpKQ3LivZEurmrC41ZF4JraCUTZQbv2g60Rm/aYXFSVXEQKCkXsI0ntuIf7eaGbqUlwFtrQpP8d1Y161crZmdxmBxViPhCPMrdInWwdVLoGbyB0vcjx2/xMGhXIR4AveK3pwaASym59/ulwjSyXdMqinViD/Uk5r94g5OOQwa0pcApHVmmTV2sWn762RiwdUfC2pHjrwbFhrHLcTxd+b4W2XPY37P4UuFnBUVZEPeeYt2DsXhnyBYCL1wMn4ORA7mQvuA73X0e1mu+1RpUhOZE4eaBe/evubyeY58byktFP4fdAynMu0WkOleU1wabGcW3dPLLIqwOZU7y80oNcj4ZAB1YuznQz38HpGUsD3MjEEPO4wvgtIZo7RD0ZgA6WoVLfnGhkGYSnwJhn6V8raK+0JRPNHHXHCSsqq7pXzpl/XrN7r4eWHq5AWJsuJiiE/K8HJcUPGTZWTX+PTRzNGpkWevcPKTFwzlIUBh7NRwjBYktmtsH4wvHSI6JOpgPDrqC8zwSQppfzJA4+VIk630p/1RkAA7Kq8iz/ZI+2i+EfPenjgwiI9fnME8NIUVaI4eJ7vYxdHPCYdB5Y0rQfRwY+C+MgXkwDfxTASo3c6QYZ3+/oDhfovb7cXsTtgBGD6CuClH8ApXC7h1uVsq9m+XCvBR63HwsQskTimIXpeBDmietMPCKjae86lHX20lToEIdH9E8WtIvFNV1oDprhgYPFaLEnLmBZd9iNz3hdGyoi8k7O/ogIP1NhnpmTQ8xnnQTmzYH5+rwkE/iy2hScamQhEVVnqJ0wmgsHKXxzIoek9jOpfbMbUdlHKV2FCowM11qBHUYFQbg2RAHdaJsqWMIsq1085lPy0R+zsPw1yfd/VnSuyYW/PnFI0yjCacd87rlqGpofMFFkwOAScElMYCfrpfwwYzjH8D8mTTB6j6gvKW81Pgazr7xAJYBlo7mEahgxW/wWmKRYEkSijH5PjPkO4PNQmosVD4yeMW7mDrYGYuUpLlFM3i3xdQUpbuA9tKFTlaimMRm3UpsQ+rLQH0muYGfaS6+VrN7pNi/3nHv9wv6ZaTpLW8Uz4ec8B/94Hc4+Qs4f2BRZyU6Je6LWYvWkbYpiN6girFBlyiNNAQ1gtGRw67uHGtlD+k8TaTNqB6RL/fjsXK1rK2+lmYQkelW8tqYYsEI0Y+F3J/nuG/X9NGy9jXnbkld9uxzA04LpfEcFw1tsLio2buCEBXdvmDWQb9kkOH2SUZ6kPjXgV2v8XuLDtLhPzZtKlSviWXgQb2mVj0ezTb5gjVBANILtyBERWG8eLlGYdceFVu+XD3hzeoZ+/sFX4+Kprc0+1Iar70itobYqTFWT03Yg8ScEZY7wGDT0WraTUW4qw59pW+CREOqPwLO+TXT1wY0IUYc+oXnXTToEAEL2uGCISQmv55sQ8BdYRQXKhCCpvEWn5jWGQAtlB/9rgMEJe8Jk0A/H9987+xCxX17zV29Y6U7mmjYhYouXZyZqV0in+1THWqYQ+OoFmEmcUT+xSNSxSHNtxqFiQo/WD6MiWLxC8CSBgGgp8OoODR+uaBpezvI84Yqomae2fPA+TdP+NGbD3i9eo52YErP4/klX5k9ZX23Fgnp9ohn7ZLWWSrreGl2xZFtOO8WPGlWtG/fo9h09EcFF1+1bH6pY366pyocvTds/+IYX0f+o5ff4buXD3ljdsbfn7/DP13/qtxnynHulvze868x+1CIAqFgdFQp5P6pLyKzj/eY82sBokGkNrONUduit3v0vMACvSnEp9SnfwMTaQJIp3h+aDgM6fHMxtQKpQviyYrm5SOu3iy5+gq4Oz269MSzivtfV5x8b4t+55OkqiP7FDvxmNR374jsbIziQ5ykPkUJQxqj9o8CLxU9/jxgnxe8VFzyyr1Lnn5nTihHq7y/DJv5iy7XfXNoNE0E5zTP+5Xc0yFSbF6smWWbNVERcnL8szrStIF710PwSfkoQN8Tul4A6amFT98J8JDOc+w6aXZIjGztxT5A9xNG5JTlOB357yG3CYdNsxkMvw0URGKlh3/cMf+LJ9K8agyL1ZLyVx9y+aWCfpk2m4CGYKLUdRxixVcla51CiEbKSCEoGslpu3vQvXJK+c4zqS1MmKo3JW6xBap3hKZJTbnp8ds8sw/OTxzUAmCsY01BaVCHlk5RgLVy7aU2l89rDJCv9SiA6gDiJDJMKCT2M43ind3dH7tvX+SxCz3XvmZW9KhWJSXLSHmnoap6NmcV1YWQis5+2UACnuYfW6ozK3XzJi3AUQ318HxiBoJEBpymtZLccGFu1J9gmNxVnzGBG9fRBC/MQxpP5X6NVvydgxVgOpMefKUTs/Ow7oSCYhupnll4tpM1QykB7FZL9q+fsH5VrE91D24x1pxiiuMmvXKyi7kONf1aiTwVHJh0/wQLcZdy/lTAME1SRghSR1d5yopxXPhuqzVlhvrkeI3S9oePZzW+DEj7cmRsZ4KIaSObpuJDf8wvs+YXZahOscuMvojIde/E53iY21NdNWb7Qq2kiQlEunlowJnYnQUvsUeqnwDja4KoosS2RZ0coeqKuN/LetH10pSWyKN254Up3fSDJLfszCQOmZ57M7mntDmsm96yrgyPKSVrVFlw9G5H9XQLP3xf8u2yoFqtsH/jpYN1QuIeBsU7Iol4GAn1BJC2YsWWlblwGj8PXH6pYPHtetKYdMtakR+zRvZv30CO6n/CWkHGUMMta4ZiUNCN6dxHk+u/ccijbRMpNh571UiTwVTuOwPUSdlEKSU1syiqa5jUFN4pPjw/pomeJT/9+Myg9Fx3B2B0/n3ja+aJ2brQrSReUVMmNkdmJJvGi1/xOhUK80SRg2CtpDsgT+QhyUkYAzMtJ9J5VNejdz0+Ffkzo9lX4hNoC41Nk32wcvOYbUc0mvkzj5sJ2HpAwS9lwbF7TzSW/as9qvYjGKy4wWACZZLvs88FyhsFluFq4CD5z39Hq1AJrBw9Ixg613MhavBM8wrtR7lx3SuRXs0+u+mx2fNAc0fR3U1FhyKAEWk8UwT8pqA+i5Tr8eb1qxpCxM9TX7hVyU8OCqPpVhq7j3KsEzCge5HaVJ2Xwk+h6I5lQg9WghtfSzHF7qG89kStcHWk83aQF9OJxSPyZjl5DtSq575dUyhPrXvuFFteXczYeynEVObn38lttRzDrpdVOS/yMf3TTqQd3CIXyhR+FmlOhS0dge7IokKF2YtntpsZXJmubZ0lF+V49nMlYDRQbtPCcTOfuq2rNxcoUsFSRWkaiFb8AmdnSQ56WaFaj77asXCBWFppIAHxdygs7mSGblMgaypZONpu9IEI4kuufOrQDAGzTd6hGkxToFJEJH7MBjR0p5Vc2/s0ufnkO6dT4SowdIx+av6ZQYB0XKSjPD013p5kNn8Gf4NVQwOI7iP1U2HQKC/n0JeKfqEOtjNFHzIbKQZSoS2ig0qSkRkAV6NceAKxYwblMnM7y2ikQnPe/2DEf8XXhnI2k7kwyD0dgnQRf/pB+fkPH4MwMoj8/tWXuVdtKC40uoPd646Hr53z1dOn/AUPaX6tI/6bU9wc+uOIO3YQFKd/rikvWplzfFr8h67iSbAKL3aTJp+nQdY+A6KTjlLpJEvNNQ6U9gzKEfnQ6tFDOhbyd15jfJ08kItRHj9YmRdD8h4JVpoZBkazHu/fqMHNNaa/w+p7Bn12LcWCsqB79S5nvzZj/1ACoAxChiyDmX05xdZN/p5eD/lwZDZSTMX/IEV+/CgFPkhm56aJkHY0RJSTRgIpYuQGpsQOd8mbyh0WM2N5oyNwCvDHifx5kuyORZLuTslClrKVuVWCqKjh7Sf34HXYhJZTM//pL8rP28hxz2S+ysGkwNa3zOt5qHydTuKN6eOp2Kz7SOzEU/rAfw1GGSYfhMmSGdB5k3WJLxWmAzfT7B+vmO87ePJ8bBqcJg6Qko/Ufa01ZtMRHpSyFiZpb4I0LvbzdN/6PBdCsQvCrMmSUbOK6tyJHPfVRuQerZXgOUa6t+6zeaWk2EZW7+zQ21YKYiBzZozyucZA02LPtvRH5dBIxEQ6fJRhy39r0GHID8iSl72TZiPG+X9YG9LPCGN3bQKkbSl+loXxzAthSs9sT6ndAEovbCvs6BRLz01HpeVvQ0yAdEjPuwF4zo9l1rROAPUv+vhJpeWbzOibsEsAzv2S5+uFyLCayJ35Hk3k2XbJ9bbGGIGdxBc8+YAD+31JcS3rcHukCdZi91JcKXYR08PJDz3Xr1uufi2gVz3tvYRsx7HZLgN14xyfrQ7GmDI350huJeuBxAspuXTCNFMToDoXphWSe3itibXI1jWn0uyhPNgmUGxkP9ojTf+KYfcw0t33nLx0wZdPLjkqGr7+7q9gGth/7wT9G4E+Gv4X3/wfs/rPjimvHedXBfUzAeOVi2gFx4s9zzorgDSyTzED0ZkBnY9BVAmIVkO+c8AIT3ml7jI7WrYXiixdKQXm3CEuFlKHibqeFHI/D2S4Jha8Wp7x1B2xCyWn9Z5NLd8zFJHa9MxMj9WenSswOmCAuLcSJ84ChQlY60V5wcjPRdExtx3XbQ2dHuOFfIyDsD1Z+SGv76NhGyq2oaKPRiwAoubNxZnMN0mdoVCepWl4ZK/QKvBri4/Y3xWJ70+2R1zsZnSdpdeRqLMax+Q85EZtFcfYPI7nWm0NvTeDVcFtI0tm33ws/zzwoI5ZYltmiwxgy+stkuCKxLYmEvwIdGdJbZsahLa+pLSO82bBcdkQEgsZwCr/ovd11Ewp+X0UiW+vNB5NGwo8mt5Y5rploTpWWpRgPIomFphJa03gsNFmCkxrxn5AgyyrZVoDfPou8p6Y2NLpeA3rShzVzL7gI0yS5BBF+n5lW7pg2WzrsXk/QlE5dKt5+Eeef/TG3+F/+Zv/mvqZYvtl+K3lu5yYHdtQCZN8AX20wzn5SvkEj+KfX/9Nfu87X+Pl9zzKR9aPC5rf3PEbr3zMf//hn1IozzO34n93/Q+IW8tbs+d87+oBAcW5L/j+/gFv1Gf8y6tf5mmz5PtP72PCBBAoxvy2Po8sPuqwTy6J1xuJ3awR2eQpYLjeYOoStahSDiWy3SozpHMdLstaZkA6xINCb1bAwRj8S3dYv7Xk6i3N7mWPOu1QQVF/e8a9bzmWf/YJ4exCis9KQS8Ig75zAquFrEe7hti2Al6mpkCx3YvsX9GUjze4II345ZWi1j1HVcPTCLN5UtFJRYHPymi+OR/8JED7i8aU1ih6FH1nhSRUpsbEH3yIqmtCttQh5W9lMTIkAUIUFSSTKvBaiyRqFEnRuBd90wxIq7qCvhcQumnFV7osoW1Hpp02qN5hLxu5hjo3XG8DQz9bZB18GT3E2THINXmbZd3BSM2x5dvPsM+eE3Rqku164r5hvtli9m/w9G9V9MuIcqkRO9VntFP0y5gUjQKqCCgdBRNLDAXfST5w8cszHr6fwBtrDtmwU3ZmWYhfalaIuo0lTX6rGs6D3vUQ67HOp3JMyNiEGFOMlWLD8tph1x1m04pdU5x83vRzTTp33hMKk+pdDLWvs2bx44/zF3i87zV/tnuVme2Zf6RpT6F/teV/8NVv8F9872/y8lee8ez6IdWZorsT8EcevTVUV0K0UT6BpjezjkSCmwar0ZqREJFrTtNaldZDzUqIXnFQn8yWZgdS1In1HI0+kJ+W2tJYe8qWgoPVYLJUmaq+oqC5qwjmhNPKYp9eyWfdP2Hz5hHnX7P0R1FIep0QROQ7TSTtM4aRQbmb02VElHiSco5vNLrLhDw12KhKLhTQTouKk9aDVWtWahV7O8GBDobWw3HOxIcp+WR4rxHZ7lBKvc5XGlepUXlVMZAg1h8ecfnLC5p4wVyVX2iWdB66FYXjed2BWqDPrqnP18TrtcyRwY+1qMKK2l2+Vn0A38kclpoXBrZyBlNdTk7lWOlZTTQ9oZE1HufRRyv8fi9N000Dx0tQCn29o0x4w0A4mtYUczNC/tzkGz7MubfNqbc0vMcYBZ/QQiqYfftDQmpaIogNRTw7Z/6HO+z+DZ78do2bTIXRxJSLyzzplkEUkm1E5ZpAVqKKimgDsdDsXjb4+8eY59fDvsWpJWTe3wxYp3U5Ovdive6W7wRy6+nGiUR+kuLOWBJAUGMDU1YwFtW1pNR52aKv96j9jXUDxoYmpQbMVpU10eikOMaghtBe1fSfhbF+y/jMoLRRgVr3gyRgrXp2qdP32O5Y6HYohGX184VuU3FbglvVJWlt5w9PhE8+tbmwmb9074jLWdK3l0ldtd3oT1iZVHRQ+FoNAJOvbd5pfGXQTU+0imLjqK7Ew4eokqw4uCDAYb+wsjDPHNWsH+TPsiyuSReaNalDMwW0KnXegkj5TR/Pr4tR4YN0r0ckkI5REYLCe50aVaWrPPQir4CXgo3qRPpSd6lYnQC03G0EMvcW28jq3YZ+MQMbwCWEziui0wQdMRtNdS0Lx+7lmurSDQtsvzTioWtH1lbUArKozS03fIRoNf1Cs7+v2D0O2I2ifi5AerGOzM4i5ZXD7jxubkDpoZs8J711LoRgRD4sGowKGNVxV0nB6ymrwTdx2pH+8xxGSxHCpyJL9qiRIotEjcUu0DoDpZw/X0PzAGwjTLB+ofBlyfJ9kVFXtcE0iVU8yz7TUnwsN6mY5kVuVPsXb/op40MFiEkCSCVAOiaJB+0j3kgAZPcRu+lpHs1xM83ynS3mfENMTSIqSWtEq+mOClS66MSnyFF8nOSLYkQ1vUjbZO8VH1H7xKYLEb1usBeG4v6Cfm6TxLthf89iG/leKjAA0qaLuCox8pPk0kCyz4V/GKozg/esuvH8cFDGYzN0xBbCci62o8+dSMyQPKDVWCQ2HDCXDgqlYVQPQMk23EzYOVJwmyQTTALDXISbbE9FKaSGRDw3LWxfLqnfrWC7ExmsTqNMkLnir4lt9rOOQGQTWr5z8ZBfuxNZ/UiO573Hl/zW/Q/41vlLXG9r/qM3fsDv3T/BLwPqqEMrWHx9xvKDBj+3mG0vx83qcQbo3Ri89C82q8R5DTpKsaYYwVRyR2m6XzObTGk1+ANFZI6LZuJzXOqx89SqEcy1GawYgVR5Lu3H8FgcZcPS/a2dPHe2MDSnp8yeH6NipFtodo80u5dE+SKUIUn3R6gmwY+KaDu5eED8afP1rqSBgVyATc1ToTPQK1wpTVzGMrAhZKnXqChs6eAkKNROWgyIURLtfNy9F2+ayVCJnRrLQhIz2ZnD85M7fDMgXSh8kUFpacoIldwmxSZSXUW6b865/ls1/RegcKpCHDFnM/GWVowy3kAW+IYbIEmau4cLfvJcTEC28kMecOu8pyLYvRvB1JQMv8Ca1hIUD+yDJKcYS5uSNQHPupXBvHpK3TviJ09FjigVoGJWuslS4UniTjcusYYnX8B5dKfoVymoHnyEIqYJ4/uVQm32VJ8oAaSbiWy7NnDvhM3LEo/On3To6/2YxGQWEAzFAADVtJjGExcG3QT0rpPr9zYp+glLY+zADdD32CYtOMPaPx7/oalq+Dui0z+rA6UVme7KOua2QxOx2idmdM9cdyLnnaxyauUoVAKulUuAs2xc1IliAqhTIZLxuakU7Odp+Amj4OCxn3LcRnbtgbkSNlV3yzanV38f4U93r+O9JlSBsvCsioYuWFpniEHh0cM5DQaR6AWqumd7P2D30jQl0tABu3fMziSWmz1pgJrNawa17PF5HcjyjAacEVDuQAIPxoaqNDGIhHcCWqN0OJtO7hutpFIkTUXjvKFdTMUpRaxkDbt6wwx2J1FBow2+hn4Z6e556nt7fvnBU06rHSvbcGQbat3zb3/1NRb/ekl7At9pX+Efvf93Wfxfjzn540/Yv3UXs5NFz+6hnUHTW5QzRK8kL/JaAOlIKlaN518FhW7S9/RqAJ8H5SgYkup8XwUrsZwvhbmR4yztgfT9shWLzsXXLn1vowZVkZ/nyODWI3vJM3fEsmjpjmW+9bOUe2ov6zjSFNt7g9lIfohXuM4M+WppPZWKlNoxMz1GB5Qbj2vUEeW0NKd5UIXIVK9DzS5UNKEYbLqWpsGowKndccduWJmGWvXDXHOi92gVebU8Y7csWfsaqwJ90KyjIniNy40H+ZyDXJtZbSwpbUUbUd24nyGxvklNwLd5vn4aKD1lUobpDYUAxZklmYHp7DftgsFqAZY1cg5CVAQV8UpRJIB9VvZc7Ga8vLB47QcWtYuGNltfpM/K28lzcyDSY/FoAf1RrH2Nj4qFrljrjhO940S3aOKQJxviAJCDzJVZbSID0+M1lZpv1AhOkxrC83byeci2RoMP9ee40TV8ylp2G5nkkC0v57kNhtr0uHUhc4xT2J1mXnf0qxX1s4Z7v7fg//noVym2Ee80rxZnNLFgkT66Vj21GpUBa+V5x53yR+evs/pWiU3ejLuXFV956Sl//953+dv1u/ywv8v33CNmy5Zdupf/1t33uGc3fOiP0Uoa9b9/fZ9dX9Cc18yS4sPQJKjFxmb2PFB9spVmu7JAzeqxiN/1UMjCEtsOfb1FFQbdGrRLfp2pmRQfpLkvjUGuW2kGqeHEvo6rBf7uksuvLrh+S9E8cqiZg2cVR+9o7n99R/nDTwhX1wJIJ2lJXVWo1XJgRrFvJI5TGn20Er/pGAcGbSgUv/TgKefNHDeTOfrd9h4/Or9DKMUPHFJuk87xZ7lmP22u+LQRf8Lzn7cRiNQqEnrNB/tTzMzRnVTUxhCv18KGNCPLLdQWk+TUFQzNCDFfE0ZYWxmcVl1F2DcoY1BGo+Yz2CtUZuQ6JxLem+1gN5YJR2rbSN6RLKyEeawFJOh7CdjyMEKEUCm3HL9gCgLUJE+Zjij7HjdbyUuyX2oIqMWcuNtTf+cDlo/e5OJrarCSQIHqpQEsVAFKAaR1ajxUk3xaG48xkcuvFtz7xgnm4/MRYJiw8eR7aMHbymJsooVhsrr5HZRS6fh7zOWGxSfVWHsoRjurnKPbrRc7lsZj9j1q1wpo1492b7cdo0Fit65xJ5I7SWOjxFqbvuQzOpl+4cY7/R2+ffUSH1wdc/Re4KN/EHj44IqlaXEfz3n08kd8+PIp/cqi73Yorzj5I8vqXelA9rNC5KXxqYkuoppOrjU3KgqorFhhjdRTq3L0+QapqxaGAyUjn3LNiqEu4guxNMtS0yHZtw2As+UQfE51p6zoJI/LdT7KwZNq+KJE2R2vmD1fEJUoR+4eKtr7SUW1kNiFmZf7QUnuM70vctwZghpy3KyOlJ9z/3/y/ivGti1Lz8S+Medy20XEieOvz5t50yeLVcWqItkkQXWTQAuU2DINAXqRID3otSEBehH0IkCQngQIkiAIAiQBYgPdDYkNdIvdIpuuaZosbzKz0l5/77HhI7ZbZs6phzHnWmvHOddklsmTpXlwEBHbrL32MnOOMf7x/39rCXPo1hlma+K+jJUjVZ1WvNVGC++R4MCB1J0SGuvm2TpTVOEgz7TeNB35e2qBNJIYY50pHssESLtSeg/ebBWYnHju/kvhR3/lHuvJB1Q2tff9/A4X9Ny/t7nFzdmapbkBeUZYbRRgxCpO55QwF3Kd92XsZ+wcwQGu0ffE+U6yDCZVVEHRcyMiqpJnLdLoWh02G2QxjwBwiHVDP8xHbbdDMupJBUkefKw+4D0ScgRdW0Ji03+W+kTEKvCesNqoiov3GtvojveEiuJ7H7N3+03OvmKUEBSVkxUDELpZIBShZ0eLDdgsMYn7rwGZpz2wrF6dsneizPsduW4YwOj4WL9eNM2uHP2nnmSHuVwzOap6dc6Q0RON0vbFB4rzDhv9u03jkE2j68Y2Kd+GIZcQoVdOTMMHJMto5xmJACM+3r+NqrX8NONzg9KGsONT5zC0wTK3W27aZS/bbfDaaSue3HYKorW+L2b2nZgjdk1IF9/YnyEVEyPSr7TdLL5fX7I9zGIH/PDtfWH6oqJE8M5XiuyICZSXnnYeKeexxmMbsGuhnRlsHfCtxZfdEKwGwRqP81pQuVxXND/eo7gY+ZSlIlKct1QClZ49CwxAmqRFIRViBXxsMvJDQSWBVMFCt+fpKj94cxmQHH1fp4l3kqn3hUBntPCQsmIT8LUl30oPnimjQCBKViQgoJsoCIZAOxWyWkFrV9r+/KsXhcNNMpq50E3BTx2+yTAtVGeebBOoThtMrSxpcQZvRdlA14IdS4jSZtr8gFfmgMGzsFvenBzxoL5B7TOWlH3x4Gc52ijxu9qUmDp2Z6XulBTkeijPE+gfJ7IqsHxVWLwPWR1wpbC+XzF9tKV4sqK7OcHlhtwre9LFINREvba0bY/sSkDDUKTLE1MXbW6wujATQn992a3rQQLpPI9/LaN+qUU2MyYP91h86Nl7f0v2VDt7pHVMHi7xZYafZNh1h9QtJOUD0MS1boeFIUp17Qwn5MdrbJVR355y8eWFFgkjOzoVU1U6XzBOi6519Dg0zYhFnLFTrLT16O8Qg7Kg2w5RXkK8SoSXl57VfRuVHFKQSM+idhV9l9H1Gkh/j8bALrGSVNZ5eC4RPNJc80zOnOaCeO7Sfe8tqoQQk7RmT7+7uzHDHp0MtgAjduSL7in92EHdZnz39B4H79Q8+dWKX7z5mIlpmBc1Hz+9w39x/C1MHjA3ahazLcsf3ODGj1L3XApI9GeSkJdtoxJVTfNcUJqmRYpcwenc9t2nPh/AZu3y0g5UCaoaoZI+0oOkPovNElnsQus7UtHA3w5JwOBVFQZgWsKgppAYzuMhgXZfaBeG5Sva/dDNAr5yhKR4kXlCZ4hTJaE1SGfU2y4V7jtdE54BJgv1yPIzD7OOrGqx1uMrwbcGb6wG7X3nrh7rZGPhGy3eifd6TJ2PXmMx8b3uJw36XJYNxFGvjQHB2mHeMpqc+SwC0qXQVboOuWpQGMiXQb3zPl4ye1Txf/ubf5W/+tZ/iItB8AvbyTqS6RYX59sRBB0S2DmS8t4BoOO8EEZ/98+lkWLH8Mxp74ddtRq4j4PhFOwmoHrMkvZhCOAhWnWonUIwsL6bYzc3yM4vVAYORnGdJ6y1qzbkGSzX0Haq+FDGWM6Isic2WtjsA+amVV+nVWwKnFQqNbdea4AemxzJMt3HxYzlF2/QToXZE0dxtBoaHGFIZMZZglF2Q7ZsCLYkv2xUDtyHyAQyunaJ9IF4SO8zo8ecJ9t6XTjG63466GkheM45EwmU1lFm6h2dfKOTNGxpOqa26b2iEyN6zIy2Md4e1Ik68mc4wBFoSLH7CwBOX4/efhoQ+vMMFyA3hrUfjklq7Cf+bOOlf+4L/vDyvj6RBSZlwzyvWbaQW0ebeZxTa5+s1jjMWI1aq7xjuejIlzmzJ478qiNbNph1g10VuFmOWTeU55b8ckJzN/rsdvT3d9/kFItJvT1I0DkdGKxDOqLEHXFOSbFnlLd2MsRgAUynFkbdPAcXaGOTyfp+oDjXfKK+4ZG7NbduXHE4WXN3csVhvlJAU3yvlpWL42988Uf89t//RZo94f/+wb/B1X9yn5f+xYd941EKRewWurmj3ua0K/WUliI20nmGoCiB7ZG1a+oY87ZRTrJmx64oKYW4UotqO1YW8VJP3nUI+MgOltjdLtGuLnV3vwigdM/CNB2V0flAnNrsuGnoZflT7uSDsO0y8qXGA9lGcCcldZVT555VVmFzz5NqQZF1mqc0EguRQMagxOUFMZ5VV3LRTXup6KlpuJGtuGmX+KA5/9TULMyGQhzbkNP6jEtTUgS9x+Z2y9oXeLTx2hqPsR4xsRFhvIaZoP9HTQmaCMfCU7Y7L3waAJ1+/zRgqgeHR+9LTeMKOsefCI3PesazMYMseAZsXE4mnirrOOumNDEfVUa0qKd0+hyRnoHujTwzP2uLn4mM9EAbHG1wrLo5rc2ASyrpmPZzfoh+0PQS3oyAabhmiTCaWxOLerwHaT8dQiUO9wI0fH+e8Vz/38+x75l4Gp+xyGvMyvYN/6YTpmXD6n5G9XjNzd8+452v3mc2EcSEyIoO2NiMoYoluqbmEZD+7uZVfvDBfe49UMs6nwmblzu+tveYvzh5h22wfH/7Mr91+joHsw0hCGtf8OdnH+KCcNTtcSe/4u31HdZtzvlqgr2yKiXcqyypBVRxEZg8qTFXK8JsQpiWdLMScR67LCMT1WmctK0JqxUyrTBlDp3W26RzQ60tFXb14OrPxJQRo0298ynNKwecf7Hk4kvg7tWICZijgsPvCLf+1RPCg8d4Y3qPaMkzLW7vLfAHC2Rb449Pkbu3CPdUIjh0HpbKwFVLmEC2Dry1eMrvtq/iSqE+9HxveZ/lozl2Gqiy7jNz3uuS/5+H9fzzBkJfH3VoaQOwsfyT3/86GLVBE6N3usym/fkW71UTIc9iA7Ed7qAegIjM6lyL5DKfIZG9JVWJ5Llut+uUEbfdKlvaWpVt7TrIpkOdVkS9zsfkJJEesE3yr1IWyjCOQIUkEAQ+FXgIIUTpUY8UhSoqgbIpo92QX61ZfLBldW9KfXPwWpagNnsh11zbZB5rE8gSRmCbp2ky/O2GyzdnHFyslbyRjZhvI4lWADKLlKUC5t1z6hSj/U/fMyzXFB9pvhTKwa5ynMso2ctpHWRkzxTaNnqEx3xmJz/2eq6LAn/nBptbeQ8QpvrZpsnxIWA/LyDyczTO3ZTWWzY/OuBw2XH48gV3ZkusaPP/7//ml1SF5k7N4cGSi9+9xeKjESiWFPda+ms5XC318Xbw6Q7G6LnMM2Qy0eetEnF8bnty3bjpQEJAFnkPQLuCaPemMb4fqe/tkBuMNlSMHwvp76SOI/H3pKYXYzE3M7QLYfWKIQHVPvd6H3Qak9uNgUudH7Q2qWqtNhJ4+v+xfplykryEdi/QHXTYeasx3CLgbKaNHEHvmdQMYTq93kxjsCslOkmjdeawrZU5mu1CVyHaA0jXqeJGNqozJcwokktcJD4kQDo1PPkC8kuYPXHM3rnELNf8Rz/8Jf72r/wet64rOPwcjipiXf/4N7/J9OUl00Lw8ylyfgF5ROTbZsDoRIZ8Lm0k1oP6xqXg+nMhWTaoZoSggFRV6vWSZYSuI9QNsqCfl0PXYZq2VxJgBBgnhnTIYq0wWF0fOjfEJdOJrl2ROS92VFl4nr2DyKAK2HWq5mGMrg2pzmVtv06E5YrFeyvWdxasXgayIY8MNtDNh7qsyT3GeLJsWC/65coLbtZy9fKExQ9LnafT/sCgEDJWRCgzTFVCXT+/+ep5wwfC5RXFw4y8iutFVPSUkCwGUDW1dYu0kSg8wmmC97vS3detKMQoKWw+w9+Y0+wrmTVEFrbWH2D9U3pifW5QOo3kWVf7nDZYXslOWdgNbdQ+sVH6KQXtysxUsCb5gIboIbkjEZS62WCY9FEgTMb+I5GdZTcOM7cRwBko6b6IoGxAPRQFullGtmzpJrm+r7UDozTeA/mSKE8ZlJ0s0DnTMyFC7KgWYLMqufsHgb33NIjuLyIBX9gINKWkWuVQtZAifeFoAKbjd4rAVl9c9mlhUdnms68amtuuBzyAXqo4/Z6OXTdF5dhEiw4h0+NgLzKqEz0XPtOCmM/1JjBdIIHq3VT/DgLNvpA9jcBKlB9RNqihaD1dpZKuxQVgtLKTrQOT4w677rRzzwXcrOi/0zRrdj0OgxZNW4iyYQ4vhjaWLnPpuJ1d0gbLcbug9jn2OqDzMxop6Ul+2un8qo+rBhi2Ddhaz0sqsnSTQH0g2COV3WmnhnavwF7WZCfq/1TfqvQzIiM6saOHDpsYTI/l7kLg7CsZ9Y3A3tswPXG9PweE6CMb0tu1JugC9c2K5obHTjtC6VgXGc2NjMsvTCnPp0yfeqZPavKnS+1Ky4dgmCJ1o3qk7XqGdR+Qu7QPpv8b72GS976x5ZXvAeR+38Louvb63xfoohBz6WcQmPFj8Z7rmYnEunwMxFJhM7GivdXHe4ZybNbYAZTDUHC9Pq6Dzj1ebJ59bOfv/iaO31N6/EDntEy/R7YOPSDiy9iutXMbvdjJ9EO3YK+q+fDHd/nqx09p/uY97pZXtMGybguqR5biEi6+3lHljqtVxY0fQHGlTSriAj43GO+hiyzd5VoZmTFxJsqy7AznoNaARqBX3wi5LtauGti5yVsmiIKiPUhgExA9nLOhG3VXKjQlAPp4TADiHEwKVCQoI64TVbTwKIOhlp7JJZ1gN1FK3tuokhATgi7EQho6vzRqPyFOm8BM55F2FyTuphk+NzR7lmavYHO7ZHvbY+9tsFVHZwLeWpygRSgnahvRaKJg6vgZTafyLnUTuxcHFinWPnvsfVCwr3OESRk74CH4JKeuvtvJc9sV2rXqYtIQDBRXsPdhx/TtUzi7pHgkfPgPv8TpFy23rEpQvtDjOWzQT31tYt4+b4573lti9/Mn1tMCSNNp8SjKFAH609pnA8/Ibkkgs6y2TI6mrO/mqizR6H6uXqmYlF8k/50fQ9tqUjJKGEPTIhdXEDzStCM/q1i03GzJ1pDv17hJjgXMlWPyZIs9Wep+JMa1sZpEZJkW1pyDScXy63dYvmSZHnmm71/2YLK03bAG9f5IA8hOCNjTpcp8b+pYxMqHxKXQ4yBA8DYy3q+djOC1SSrsHvsAJM/fPjwMYLY6d3tr+uJn3WUYArOsYWK1o3hc8J7aumdkfRLgUpkWEyV2HWYnDk8gRgJGqt6o62c3ru/BnwQmmItQiVAHz1UQKglUIrigseY2CK/YnA+6jpl43mlvc7yZ6+WRe/aqmr2sxkqgyju2TY6I0OUBX+q5y3OHlcDZxYzJOwXzBx3V8VZVAeoWqVtM57DHl4SyoJ1l5Cton5bkl4ZsQ2/VYTuQXCWDgSitxYjhG4Mi0SY9IF576r/ZVUIeJcOzlRbQFMQ2Ua3KYzedxo/zjCBw+F04+msNX3vzIb92+D4eYe0K5lbvuamt8cEwNQ0Lu+n9hQGWrwqTpwHzf7jF/d9+Wzvli5x2Tzuni0toF0DpaS9KqkcZ3TTQ3W5JzZLSGehk+I6x0AXqa1hcKXPBtuBy6Ga6LndTcGVQby8Z3tdbGyUl0iiz3vdkCH1Br40NjZ8W0/1pj1w6tr6gkpZFvkU6obiE+qaCaZnxtEGbUHwQWmeU3ZWBtJB1gt9anSvR777OKtZW81obGdK6HshO85oxgdpnXHYVG1cwy2osnpmpWdgNl36C80IxsgYYD5WFNv28lfY3sw5jklzMtTeN418YAGozPPd55XNDkB3WZIjgsjq9xHsqMqavN0VfH73XNJYMt+tTjYLORdaRiYIWW5cPABhCF4zKdccOmB4s9+BE52dD6BuE8ijr7RCV9Q45W5/ThowmWGam5p695EAaSqH3pnah79F/ptHHsPvcJwugp9coK9wQ5cpfgJjqOiM6NWD9tJLK6TyYyBI3jVBcRiWsACeXM5o/77n5BxCqjDf/X5c8/sv7+HXG//7Dv8FBqXYOpVX1AReEjcs53s756PwA5wzzb5dUZw3FWc3xL8y5/eoxf3P/D/lBc59/dv4VTuoZmy7nr9x9l3/q3mLrc466RW+F91Jxxn/68bd48uiA4nGOf7WmCSAnhd4Pc8f0+znT4478bIO/sWDz0oz6wNKVmj/kqynT/Qn5e4+jT7ACcmZTqxTz7kEZ4tMxgzIWkkEBQn9rn9Ubc/XH/mJLvl8Tzitm72Xc/6/W5N//UOOlXP2EpSqRqlLWURmBy8dHSFVR/8qXefoXSpZvdhTHlvv/qmPytlqryLYhW3dUFxnfOX+J0nasXgnc/fIRb5/fYvFOxvJbNbO8ecYP+nly/c/7/ZPGzzsgnUYTDGZruPX7wuq+UFx1fZwfEuusyBXIcdEn9HrTJqk8KQMY4D0igpnPtHBflkpEINfft7We+yLXRvBN/CxjtEEisqQlFcFTU4QxCkw4p6/zoWdKJxWu5+YpMOQqad990OvWmP5nD7SMCFH5g1OmTyrqm1qXlS42Bpahtzw0ZheUzuLvzhuax1PKE0swXkHjcW4w2s+hFqyNHboBN4AwzxuJudh1cH6pgE+eax6T8prxuYr18947Wsxzm8R7wpZkhL057a05m3sV28PRfngQLzRd9nOhRPbTjAO7JjeOm99WRa7D6Ya35k95d3MLaYXFe4blKwHvDEcf3eD+dwPZ1uELi2lcZDOjFgjbRhsfmrYHowfF18gyTbWpttSm7/0poRjqT64wSgirBiu1rhrs3sasZ607DeAzifxgUIZ/iqlGoDNeaznSqgrRWIHIdJHME0kwpo1seSc9Qcd0gWyrtgqmST+d/l+3Cvy2XW8FEaLFA0boDqa0+wWbm5bVyznrlzxyuyafN7Qm13qTMRGUVosw08Xaa+uQ1UbnmrZR9nlibY6HI9aZ9L4SEZhWav2a7j8hypybvu6cakyppnHwbsvkXa0xhbpm8l++wo9/4S7fLJ7y886UzkXriYffFc7f2ifbeEKVad3eOs0ZfIjNaqqOp+tCrBGNYgRtZvLg2PGQlumEsI73RVkooJzqKc6rGkYXn9vWWrNNdcGRmp10rlfy6Mkrae7sot9xlhGqGMu4iB+O14FPGlH9I7RtbLbK9DukZp5xHdkY7MMTZo+mrO9FZj/x1i5QBQGrKpXWelUPiDVeY1TFzgdhuy0JZ4Wy8mcFdgxKj0giPZZo0KaKqoB1NoDYnzTSscujEsfpBaYs+mvfWrNbg4wKlkkhp2+SSjYe46avpPiUZX0DQphWNHf32N4pqfeiOiwhzjNAEFbhJ4aXgZ/EU9rWKgGIweC4igH0zNT9aww+JjbqCeww+FxReZ2oRBflto1MFRe7p+PFN2ZKp79TR2fdwOjCyY8N2VVOyC2uynpvaZX61K6jBAK7UhfcbmIoT1VusfeIjaxM2+jEVKw0eQdwziDin+nGDJ2hWHrs5XY04cWTPxyMPiFNhdHe48DvvkbSIpZ+7xk6ApnFlznbm/u0B6InPRVSHBFkjJvr6Nl7YzBau4QC04eG2SOH6QJNJZGdIf1k7fLorRBvOOMg2wRsPXz/xBzMV9pJY1ygPPcUV0J5Iazv6f7ll40uKE4XaWkdJlPA57XJGblOafFEx27tEAsbkceS4/CY2D1uObRLPcbi2b4A1AZrYtEl80r6MCiwkmmF2jhlsorX49hN1WcbNLDY3g6IN0xOPLYNNAuDuTOjeLrCXmywi4LlS+pNmG8CWL2+YQBIQ7qG0L99LrQzmH39jLNXJ6x/MGH/HU95PpQkevloq+ferh2nXylhUeNb7XSwixbZryHzXNU555c52UVFcTEh22qhcP/9jOqDc93YyJdKtsqc9tOKUFoFriNTOViLrzJ89IDvJiYWJCOIfy3WSLhrkqtsS2X0Zy7sFBPHaoD93yLDTxMZzJ7ex7mdKUPWboMyo+0ABrtSdsBkZaSnjcf/IxA9fXgY/4yM2p6W9byC3LW/03dNI8namDYwf+R0Ls0y2mnkOwT51PX3Zz06HBmWNjhumjW3JktWv3Mfjk/Jr+4ztzVLV/LRkxuUovdEdmkxHy3YOwssPmrwmXrM+Moq0CqCbBq4uFI/sjE7OvrXXh/BOWSzBR8wIdBVe+rHkwntzA4yPoV2TYZMpdf7onYCnBPYnGS44xyb1C7Somy2EqVMIrhcp6J7BJLrMPJwiqDy1mmw3yqgbJoo6Z/WwLYbvHHHUtkuJfZ2CNbT39A/lsXHp1FuJxzus31lj/M3p6xehfBqTT5raUWXQPEGvGhDjVNg2C5r9YHbbockbOeER/+TFGj5ACYGPuj6Js7jMzVpCZGxDsSuYAVXXC6xwUy7Vw/ebpm8cwKn57ou1h2v/r1T/rd/+7/O//HV/5xSMkpSY9yL2dU6NLcklnQYgtDrr42NNJ+3RrYzbz5nPhAfkLp7fokh+bYZ6BnTsAOiS9tRHK8QP2V7K+/9pX0mbG/mFK+9BB89il5DpmcqALDZ6vUQRowEp13kNC35ZcBMGoKdYDYqO2ePr6KvTWQVJAlJMUipPmgUOfXrhyxf0sa46rjBrLeDt1Tc73GTox6fUeFgs9UC2biZxTkg3y2kGYv6jT47bO2HmA6GQoAM/1OTlbSCrDPaznDpDXWbMasa6iif6IIoS9qqRHcbbM9atARyOgrjKaJqUSHDut6OkoBCakzcqTEgrX+/mPfHH+dwQBsClcDKBxaiSXkbAek2wFQC69CyMBqLPmkPWLc5wRvEBvbKLR5h1RUqH5yGHxbqSdHSOcN0tmV5r2D9yGK6kvLIaWLtPVK7vvBvukBxrs1VqRDkKo3ZQNeYxIz2XqCi7z4Wr8zjrE5NpSn4IUp3o7F7ZQiXgDGYuoPO001zXGVxZWwqdbovrhRmPyr40eQOlW356uIJL5dn6m8bDGdxxx75fR5u9nm6XvDg+AD/pOLujzyLD9ZkH5+oekHdIM7r/Rj3rdnXfCo/tVRHsL0jdI1R9kUXgeRO+phnULwRso3GzaaLBaUC2hm4ScBVWpgDBaFNS19M7VVyMlSaMzUx2jinmt24UQLPxJ0/i3Hi5ty0yygRXPNqdTbEHRmURhu9u7i+FcaxbXKNXUt9Tf894k/xgu3obWVSLJOmA/EgbfyMoGDqxhU8WO/zxvyUXBwPmxvczi6pfc5ZN6MNln2/UbDabDjMltw0G7bBcuUrTrs5a1cwsW0PHBmjViPhmlS77kScLPsGX+m/QzDhGbbjZ43Epr7Omk0S3SYVa+NIwLAktjRG97kHmQ2NG21bBqnvSdaSGc+qLSgisGwk0CbmtPhddrbR1/sgfcO7jUAraLOOD4ZtyPsaypWb9FZp27Dmtt0wlUAh0surjgFnK8qitkgPXg/PKZDd+0ULg4R3Ok4RkH4RPKW7a6D0uGD1eVjR1/3GdxoHvFoX5Muh6L8+nnD3SyesXrvJ/McXmPWWl/7Blju/Paer7vLUDvY9wcTGoSaQrR13rHD8zZLZE09+XtPNcq6+AF+ZX7HyJW9v7/JovYeRwP3pJbfyK15ZnHNczzlvJzQ+443JCd9ZvszTt29SnVjqO47pvEYksAK4zJG1ZXIcKE9aQmbY3p2yvWExDoql5keuEFavVCy2t7BPz6Bt8E2jLNam0uIl0LOkE6AFGmt5R2hbBcKmE7o7e5x8Y8bVG9DcacEGwocz7v5B4PD3juHojLBa6yadNvdJWSBTbVSSjUpC+lfucvRL+xz/iuPu60+ZSyC8Jpyc3+GV/9L0TYPSKdP8hz94mS9/5SHVV8+5O13yvX/9JourwGx/84kNep82T3we0PlFsIb7o4yJFFSyJmSBybFj8ZEjP91Akatf5kqZw2Zvro3CY2ubnik/sOUEIM9Jku5a21BFpZ6xmA2AcYj5qMxnKtHeKGgVxkBtpsm00A3sXpP37N3+NTHnHchLA2DeM39HDGp9XepI831cPy66SwQL/dEJB99bcPHFfVzpMV4iS9ojhUqxGqNgQwhCkXWEIFx8sM/+DyyvftQx+fhCGZiiigA7ILGMPITT9y4LBWSS33Y67jDkW30+NgAsCjZrw6qyn0c5jYnH3o++e4hswijVLslX2xrCbEJ7Y8r6XkmzMLTTQX0nKQUWZ3C1HEkg/xkYF37DXPQ7vZUfs2xKDn/3FID3z/b52/e+zd998Is9gai4EIoPS6bHSsjpCS7Rz9kua+RqTVitd31fx4zLVJshzot1DZdgRHCzHFcZ6j3bW5W5SmvErtCmy77mZFH2cul7skDfxNmAaY2ylhvT15kk1u+N0/qh1vM90gVsrfWlRBqT1mn8nuxVvZIH+2uqZ3TaAbAKfrfORCw9jF8vhuzolAyYiOFwf87mS7e4fL3i/GsBc6smZJ4uyxCnMoKmFvIVZMtW1cvWm6FRKjJKQ5J5Tnm8tYgFvFFAb7NRLAUIk0JrG0Z6NnpXRtJJrDHZGm78qGP6h48GZYWy5P4/PeJ/8av/Hd74a/9X/mL1x3k1/umPNih5ZnLUUp0ZyuONYjQi+Mulzl+iNgzq15wIiCMCQxrG9Mob/Tzm0HihLLReOZsqLpBlyoxuWlWJ2NYq632lpIOwrWE+iTahkPzBRZmhuj6MmqakLJWFnWU6727qQWHDh2caFpLyX79GjOXCYWBFpzUmqJQ3IuA9/vKKve+dcv7WbepDxem8BT/xkHkk89jMkY1kuxMgvdoUmB/NOHwXpkfd4I+dLCxGjUPP4IPoemGqStdAN3rvuPHluSe7ITwjt+12asKSJTDe6TqbGg/S56S1I31GnhPmE7rDGfVhwfaGVStkFENJ85RpYfLYfKLFzmeNzw1KJ3ZFJS1tsFSm5W5+od8/ZOSyW7BzGAqcgr+SZHg16JE8V2mRJJXoHaFTeZk+GALY1rErJkSZ0BRsaPediT4ntjc6D4Q8I0xL7fIQNbXvJjbKFXtCJhRXnnZqBzA3dpp3VZQGbocT7b3sNLSJBGhTUWpUtB2xb4Ixw4Xl0e7v9Jr0eAixuyc+3icHfgCkU1eKcztSfhCLKWne97pgSQeu0G4OYgE02FgjbQ3ViYIf3cTQzrWQoz65AwAnHVQngXzl+8fKs673gTRtUH/u8xqMYDeO0qcCl6G+oQGl1E59YkTBGmn15gV4pThlGwyF+D75zVF5St15lX830U96ZqAJFhNyDtDEZ+l+9iuEEZXVtMb3Reg+gIhzhsowBi2wrYRuFpsFvBbX1ve1K2x65NXT71aOz+dUT9YUJxuye7myB50WHl0xNCEkUBlCD0yLg3wFTWd59c4ZR5OGJ3fn7H8/Z++jDmnDTheMCRAyYfVqwBQOV1stHkXpd2Mcs9mWyY0rFqV6m13UFacXMy4/mDL/+A7TJ47qtCO/VC9PqRtCVdAdVOqHUupkp7Iugi9k8BUN9Cx908YCS/yOEkIvCRGCAnntPPrLRj9AYgFyZ8juY5LAkEDv90iAroqBWz14QWuDRgSTYYcd3eezQ/2sL24lb1eEfmFJzNlPynHH+XFflIWdClOwqaHBkK2iaoQ1NHMFpZUt/mIn0R2OdWi5ZQMfXx1w63cvQAyLDzx/cPEyr83OAA0MqxMoLwLTx03vhxlEcLnpFQNk2yFXK/UgGzMfQeeb9lpXWQSqQwiw3SLBk+UZ7f4B7dxSL6RvGAkGLXjnATdRJpbOrRosm0bIaqOSoo2CBLYJKi/aaQOP6bT7Nvk8mSSvNZLYCs/rfHteF3gayQ/eqVxOH0CBBh79tZkm8tF702PB94tG6Dp4ckx1dMb9bxu6N+5y9OdnnH8tYG/VeBvoYqXOxqJ/ftlizpdafEqJl0jfWdgXLcbFgky3IVY/PzTaASl5FpMF2zPWk8ePz5UJ5zM9vgfvxmShaQZPrixDPn7Ed/6jb/C//h9c8r+88y8pJaPDYV9k0C2kSeLanDEa/RrP4DH9mZsdMcueN0yHSt1fD2Kf18ARYmAvQ1wFYJZbim0Lss/6ToHPFNwKVli/vsdsvdXYrWkJbdMnixghtEPSbpugXdVWGxOLq4DLHN3EUl6uaF6/hd202Loddeii+1NEBmDb4l++xfkXS7qpsP9eR3a2GQBpY2Bid9nS/QEeFYmSak/6HCB0btcSA0he29GUc6dgZLcO8UKIJy6Y2HIQmxJ31g0PZm0IreAbwzrLqOcFF5njoqqoijm3pyvmWY0p1DJnTUkujqmtKUXZdatQakNolOwGqOLnGwbQGuiZi6Bx+U+bLPxJjs9i8f2kwwJFPM/bYMjFUyG0hAhIDyD1lTdU4jntZsP7M2UynjZT1l3BJG+jt62wyRVECqWnzDo6V3Aw2bLcn7C+V2HaDLstKTatytanIRXt3GoBMMbzq1cDrvLqS4VeG7YR7HZgDPd+yD5aGmy0GSRfhr5Zrlh6srWnm8QG3HlOEMhDoJvnXLyR4QthcydgN8LB257i0tFVhuo00L094f35IZVVyfjjds6Hqxu8d3aTqx8fUB0Z5g885bnn9VWHXa3IHp5qHieCzCbQdnQvH7J6OWisO4Vu7jFLy+SpUFwFmn1BaqMKIC3YKCdtOnolEo0F2QGjuypubxbwRcx/EpOjiduBQS0iA1cFBaWhV9IIEnRa86oAMnJo+pmPK1dxYFd9rl2aFl9ozJvAWRvZx+neruucKqh3t2kj4J5r/DIUNJNii35OYkpjwNeG3GlRelK2ypINQu0yapdxt7hk7Ysdyf/a52A3VKKy7gWuZ/iufMlJO+O0nXHRVCzbgtYZvDd63E3om777hS4qxfSKTwkbcwKl5lc943l0vIyEHTnv683jCYB+3uPXf1fgXF/XBUPyogbwIhoLoexviH7ewVBYlRDedhldYSlM1wOhrbdgwATNc5PfdM+ejtekD7ZnJ5tg8OIxsZHI4HAYbcLu9liZkjZkHNo1h6ajiAB0atH+PL0VUTRrYFHHzl+PvDAM6TSekWp/TqPBT7o9L9Ifb3GqQOUq3U71JOOlb17w/T93h73fW+P3Z8i6Jn//icaf48JorFeFuoYb+6zfugkGynOHPVuxeu027cJzupnyd49+me8e3ePqeIadOH71jff5nYvXWbUlD5f7zIqG4+WMb9v7XF5NmX9gafYD+69ccGe+5Gw7ITvwnHdzygc51ZnDrhuaW1O2NzONxUKg3jc676+gWQjmjTlz55B1LLSPG/LSSIqFaNE/ROlGKQvCYkZ7d4/LNydcfDnKdQch/7jg1X9UUzy8gKfHWlg2Br+tMVWJObyh7KfOweWS4BybX/4CH/+bOeVbl3xp/7Kfy6ZZw2//uT3M3gJ3dq4gZqv3yY1vW1ZvFvz1V97mv3j3qxx8H1YvCXfmK+rus8uX43s/KSd8nmvk53l4ArlAyAOuMsy+94Sw2arUqXNDbO81X5PkAZCujVSHheE6ieB0n9slYNR77dXsSUbx2G1r9ZpGASSTFPS8p5frTQX5xM5qWs3virzfh2TNBagUeGTpATtg9PDlNS8WEeUOhAh69N9Z5X2lKAjOYT56zOzRHhfTaMVVecgCJvdkmeuZb5OiZbUtCL+3z2u/0zL77gO1ExJB5jO6O/vYVfRNlWG/JASVQLexDl7l4KcK/IyZtZ/EmA5egTbfDTlHDwLG79SNnnNuUKoqS62pR0ah35/SzQuag4x6YXsluESucJXm25NTT77yXH7955sZOh7J4is1abUYHv3+Pd568kO4e4vuxwv+3o1v0XlDeSpMHykzePq0UVWriGEod0rIm0YZvMtlD5CGptmpy4TEODQyqJOJEJYrxDnsrGJ9p6CdCvWNqA5XDXZvvgyYWnMBu4VsK2RrJTjYOpDVAbuJfuK1w9ZuV8o9WavtHIhrmZaYnbrT9VKq5r2jeXbMoBzXnXbec+26GQNjF0smv7Nk+oclBz++y9NfnrD8WoOZtXSNEMSQXyrpzR5d6D0W/HCfdPqzb6AKXlX4jEBRRHA6XvN1Ha9/iy8KfDawpLsq2cTpl95/zzH/zmMFTBM+FTw8PuaL/485/9O7/z3+9S/83ed82Z+fYUVoZ1qvmbx3hlxcEfbmAP26IFkWmxFinCoyNFw8BwAVUaJZAnNDqr0m64IEKPcYnVe1u/k0Nm3ERrmxqoMRUtukjOfGRBzILEhkcTetkh7SvsT7PDi9RiQ3Q2NV2ufMDlhiHCEEje/S3z4gNirnAeHjxyw+uMn2psbbPo914SxgMj80mFpPbh1G4PjjA27+luXW719iPnyq0uhliXv9brRTiOtnpp+RyCrYiPt1XhtgJirhLQ27JKB0bMb1rOhj368L4/v7msJAr6qhOz68Li+UPDxqOPOzCd1BRbuXs7mZ0eyrp32qTSQJ/PI8KF4xSUSIn3wN+dyg9DbkUQ5QfaTu5hfMTK1yXTIwpJOvncOwb2qafZXPLrfdsFg77c4YS04Dg5E59JO7rFV3Pi3gCmIPgZIGT9GLpNUiptk2/fM2z7DTSouDZU6zX5CtHdlGJVshFjisglTKUosU9RELUZsSwqAM/Cn0xAFMi9/FSw9MA58vc+w7FsePxaQ91mgTWCZKmIxy3KYvxKifmjJE80vbM559pu/NtspCyJxXFkYTPcxqpzJ/ouBhdlXjZgXBCLZ20XcV6FRKI28dZtmQVRniilh8HToFRULfXRYylU4Zy3cboBIPdLTi+6SguQYueAxeWqameSEKq6koYo0fgZPxv6GX8RYvA+t8qx4hqdfCTQLbmwZbG4orTRab/QzTTSgfLzn44ZL1K1MNHiOomtgePtNznpjA4vQcBmNZn0344GTK4s6St77+gB8V98lXGbPHAxgmAUzn2d4s6PY8tKb/EiHmDiHQF3Y6byiM47W9M97cP+HyfsXJZsrDDw+ZfFwy+7hg8VFBvmxxVUY71/uo2YtMBKfbzNdaQA0CBGVG+kzIWh+v5QGITqCNeo2of4krIDN8JrtFmz1iICkR1/AAIXrR6zkRp9KQiV3jyoG5k46T/jI8FsbAdwKp0zUQY7Ve5jmBRdfW9LEk+Lg4asafJxCssL2lDQvFcYAsU8Z9eqEM3/NFG6XkrH2DRTh2GWe/e5vD1RM2v/Yl1ncMv/fDN3jy6gJ5UrL3vosArza+eJG+USGdA1s7zMUyJpqhl4IfS8gk5nSfIDuvXYAp+e06eHpCdmvO+k5Gu6dMNZ88l6Oke3liYgIANqoV5GuH3SoQZdctdtUg6+0gQxY7Y8fBQwrogvfRDzc26/ysGb0xSAl1h33nEfcubzB7coNHf7nCvLxRWfNWAfvpY8hPVoT1dre7L35n37QxqB0pWCQZNeh9pTWR8chqA3mGnwjeRgmrPEkpDYod8w88s3fOCeu1JgvpuMag86V/fMp//Od+iX/vb/5z5tpG86d19D736IPNpOXsATvECZ/8PgWmP7Ebsn8h/TyT5pTrdTXbhJ4hAQzNHN4/48/UbzaCxqHr9NhPc/CB4vEVZjtlc7/CFQpMtzND88oh2cUGc7aMHzqSbor3ZL7ymMYPcnw4qjPHSZvB/Yxsc4ftzZy9HyQfOBs7PDV2DG2r19Sdmxz/woLNbWHvfc/8B6c6D5SFenI5R8CqjBggQTQRSFYOyQcpSTlNSk12krRhSlpSkuUDSIxlrt23dtVguinjRSPE27sXLpDY6DSy+xAnBLGEtaU1cGEnnNvAY7mp8oGlIys6FrMts6JlXtTM85pb5YrDfMXCbpnbbS/1PTM1U1NTiefcT3t/aSu+j9PzEVj9sxzXdW7+uEFpB71P9UuZ4djBlYebMeHbBvjIFbzb3OHcTXmrfMxRs6CwygQyxnPVllw0Fdsuo+ksTSyCSy+BrIXPv/TS+2xczvk/uM+931hj1m2fg3Sv3ibkluzJBd3NGe1EuHgrsP+VE+4vrgDYdDnnm4qrVUUrpSb5gLQKREO8bLyuS+1C1M8qSlm7KpAvLba2vTR1cyAc/EAozw31geHsV1uQwK+89T6PV3s8PLjHvV83zJ60rO7mVEfC5Y9u8Jvc4N3f/wq21sawu8uGu+YKs6zh9EL9raKnW8i1Ax5jCOcXyI0DHv61OcEGikttLLIrw+RImD5Vyb9sbckvhWwjZCvNQYJozMVE669JAchnIIXGut2E3spB3LVQSui9Vp/1lh5ypvTagAK1fhof8iDdzx6IaEPG1hcszAaPSqa7ShviPim3n05r1ncqspVK89lam+W8TYovGpj6QnBlTBwthMKDH2yvXBkoMkfjLY23lLZj43JOIlP+g+Y2Hzc3WHUlpVHwuvY5d/NzDsyabbA4hIXZsJdtOWrmdMHig8qAus7gR1LhOyMwLFh+dH+ZQD5pdxiRSaI7jef6Cgf1iP40b+mfdiQZb0Og8dkAajv9rpnxbLu83w8XGdUJPO+C/m6CsqnT47l1CvyLx0f9TkvAx7XGITQhAw9PgVUoaO0lC9OykMDCZGyjp7dBWdIAOZr4uE+pV7zI4xlQWuS5j3/e9/ePCTROZSmUVRbiHCt85+OXmf7iGcvfucf8u4/xe9MBJOtlIkfMnTwjABdv5Oy976g+voIQWL5kMY3n8ck+Dz64SX6SsTjWZqIf/ZOvUqwC4gK5g6s9Q+WhPhBmqCKE/cYl9/cuKYyjKSzLULK4uaJ77wbZyuEmOav7qlpjOo3xFh/p5NhN1D9ze2DIXlow+ehJXzcTH4ai7Ki5lMzo484jVYm/e8jVlxacvWVZf6nBlA77ccXN7wYOf+cYHjwhRB9hyTNMkWMXczjY03jv4oqw2eLrGrOYa3E49aF4g7Va67moJ9y5dcnVr73O9D87IVxdYY9Lsv2SvQ9h+f++yz++fY/F48DkxHH0bwQy8/limMSM/nlnP/+kwwJk0b5jqWxDSYy1zWa3Afo6U9d/xrFNdk3BDdO5GM3BxfRxOvOp5t0RcE5+nyF9ZmJ6JdZPAkOCRGUwtK57PZeO+fX1Mzp+XUjbv8acEyMD4yzuz8HbNVevlbR7QePukTeoNZF0Avjv7PPqP16Rv/+EXj0peGg7fGGRUCCbNpIgzFAjNv1OaaxUlXo/bGvNLyI40EvIpgb61IRv9fj27Gk/ipaFATxIzOiYeEiWEeZT/KLCTXKa/Zx2pqBcIl2oR7HGDLNHnunTluJkQzcvIM8wKKD7oqqPfd7R4ZhKQRscdej4R8tvUh0LR//OV2j2BbuBH//hywDc+9D360F9kPd11WTlRABztVWGdGL5B611hDFom9RoQrzmUv0JCHWDPbvCtjPahdAuogLQxJOtDLMHatuSbQL5ylFctNhlg1lulG3Zdn2zRj9ijUm9flNn3y7o/EIM7whNQ/nt93n54h4P/T5XX29g0RGaHNMJ1dFWPbrHIPpYzQPivRDJOT5A0+gxTrUmq/L3sqlhVuAqtW1Nst2qpASTp4H5u7pWSZI5TveuNRQfnPDkRy/BL/wpHqM/gVGKwU89PjfIxRV+tcZUpRKyzKge6XdjxWcaf3afjKlWWthHqnZ1o03/IsO1aq3mj4CUpcYOTlXFQmqMQoZYK9VrkgIFDMTNEHrFp2F/TD+vP3c8Jw7u5bp3HvTaTNRpQ4lkGfs/XnL+5T26SFYi84hNa4VDJFBGtvTxhwfc+5eGG79zBOeX9E1RTYtpHG6vwl5sCNMSaV2/Xkjn1Qe6Z5yKrhfz2bBetFHx8Pr3NKPzZzzPANLjY+CDgijpuWzYlkTp/VAVhLLA7Vc0eznt3KhN2IQeewyZKszmy8D8oWfytAEDZ1+uyH/KOtPnBqXbYLHSceUrfDC9bLcyXZWdoUUvlRizIdBiaPYDq7sZ+aXBtkrLZ+PVlzYdEIlSAKNgqA+So+l2iHKQwZkoR2JVJsCHXopI0klJVP7gobGYtlP2zLZF9pThk68D9b6eKBObB12pbDfTCt4L3hkkmgXvNLMFee7FPR4ymthED9QAVF9n9sFwoaTf089UxGYAsHqp2FioSsO2IXpE07MIUrdLeSZkWwX9iqWnPId82amEx6rpJdbT5wZr1bO1bgh5hnV6EUvTqa90lYGLnSKbVovAkivTsED9U6KPuEq0qlxzsycc2iWVBFwAQxjUba4dUvWX9rRowcNENnUlDf4FWWh9EMq84zIbUMlgBoDAW5V8Ea8FuPJUC3BuEjCdEDqob3mafWHxnmXxsAMC9UFGu9hn+vGaxe89IuQZq6/e7iXp0/aCkR0v43rPMnsY6CYF9d2Oq4cLlm4PWXRs7gizx8O+iw94azj/oiVMa9haKPSCMrknzx1F5sgzR5V1vVTdsi2pbEthOuZFw73XTjlaLNi8XHDx5YLypCRba0CVbRVktE3oWcq+ELqJHpduIrQTwUf1AeMUMN7pOYi1NNNBtoL6UIGrsQe7fiH6vxWYkR6MTsVI4wbQxrR6zwSjwYl4lc8JGSRXgp55I6N9SgXPMaN5DEDHwqpKPz/n/btfbec7JDA+NTSYFpYvC/KtS47zBXvfA/KMZk+QBErHC+BFrDulDlUjwjvtbcTDg//GPdb3A65ylI8yHl/dYe89g2076n2LbULPOpaYAKfzaS7WhIvLfj2AlIBGwHd8Tvr5NHY9RpAsOA/bmvx4CW9UNAvopsqMxgYmDyyzR4H5w4biaIM9vVQwdiwL0yfUKnHzTIIQ5c0GZrIb3n+9k/QnHM9IZn/iCz9fUCBlodt8cszi5BwJb/DoL07gToubevJLw+xho6BEO+omTAxp52MDgNUixEgKJ7SdFgLSZ+UZ2Fy7WNc5zAptQMiTv48y4kKm9/reu2s4OtNjFpOMvsO/qpCLJfnRDVbeUJuWiRTP/Y4vyrgu4d1LOz9nOfvjJGzYxg/ebtfHWGpdImK+s9Pa6RqsVTC9bshOV1SZob6Z05WqYtBNLXabx05WgxSD1JdET6Fs7TCt30nQs41juykId4TpUcbkuEEuRglpuofSdX/zBldfPWR7Sz1n5x9tVVJ+PhkaAmFQahkX3fqGENHvPakI1uDnE9y8wNQdWfSXJs0V4+O0I28RE6NN068FgzRw5LkbSN5ewWoX/PWRuk0JKmmcQLnQCq3JOF0WnEX2hrGuX5fLvKOwjmnecFBumNmGRb5lP9uwbzdME0htWiyByjT68wXwlH7e+OOGynPgicu58J7/fPkLvF4c88vlA97v9nm/uc2HzU0e13s82ezhbhs20RfW1RYvllVTUFhHaYdFpco7unuWw8VKPfec5Z99+EXqbcH+KtDNMto7JV0lNAtlQCAweTIhZHD5BTj82jG/eudD9rINH28POGXGWZjgne2vr8T2DdeCYp9Dc6cDA3UAM+kwViXJJ4s1F5czwtOS4pUV2e/NQITVS8Le4YrVquKsnvK3Xvou/58gXD66x61vd1TnqlIweaosu4PvX2Afn6lsXvKGS81UeYaUZZ+44r1aOVjL+it3qG8EinMFXkwLdivaPb1Vr+t8GXCTyPzY6Gt8Qa9Ok7wdQe+BBET7jMGOKE1Tgd4TOVkRjZt5Tbonx/HXODeX4d57Afo0cAirCEoD/U9ljAdy43a8kK3xvLR3yaMvwmpdUp8X2LXBNNI3K5tGGelyGZu9ohy8twYJkC81Ru5mgctVxXvhkNZZpkXLsil5uNqntB2nzYzzZsJlXWEkMM0bCuP45v5DtlVOEZvVz9001goChenIjUqfOmcIrYHO9PLcSb1I/yDG1IMnmS8De7Nt/30TsGQCJO/oMVP6+rjuL/u85z/tPSHG1deZ1T6iDI2z+GCwJrBtlVle2ZbGW4qg8t9dZEsnKewEUvsk1Rd3L1k0JNZ0eqy9tmDU5Kx8yZW0uGDYM1vu2iVWHHmqxYjgk7qJCHkAdz25Rj8+zWxjtvSLPnpv788jxfwJr0kN5cFozWdsz2TenTD5pSUP/rrhy++U2mSXRaZbUjiKIAQhIEVB/cXbiA+U5/p8qErmDxyzx8Lq7kTrSk1UtNh6iosOu261idYI2SrHdIF8nbG9Ybj4VseXDy6obMvW5WRGVTmcF0LMTZsbBV1soK73haO/AHaTMX0k5KtAvlJltWbPMq1KuLoieZsKjIrAMbaKNTOZTeleucn5WzMuviTU91sIMPnOhIO3HXvfPSF8/KhXezJVqYDnbKY1ubZTNaXNVoGtSYUYw+TDC+7+5g3Ozvb54KUF5lbNbLbl1nzFoqx58K2ML/zuPfz5BWGzwa5bfCbMH3RMj4Rs5Tj9esn89jmgc4D7DHJCYsR/3uvlJ3ndizysCGQeMFG5SGuoUuTa0A0M4HIYiv9pjK8L4rKbFJTC0GwsfmThlDw9jSHUDRICpixxy5UyECfV8FnjOqiJRZMelB7mKvVDj80fUa1ox//zei4cYt3X+4EVvvN0AN8pe3tSQddRvntE/muv0txykCvzzWbKeCvzDucNRx/e4K1/uFbFhEh+UlUBtRfKlg3tQUXeOtg0ysbL7e73TPuQK/NM8kxtKZtW1ZnsCBzxo2OTwId0ftIxibLdfVOxSB+vhTzDzUu6RUmzn+HzKF0cN+ULaOcah02OA5OTjumPTuD0HKkqwsE98knbW0D8WeFMWxE+6gx/uHyJ5dcaZvfO2VxOCe/PqJ5YyjNwhdYlbY02Um4DoPVs03ryVYtcLvGb7VDrcG5g7MIuIOQDoWt7FTnJM5Wzv1oy/WjF8bf2tf40d9i1Ye9tlF35/qNRPBt/ZpkCzuOcNoKJYiNzNLFUf1aA9PW60/WGhpSXi8F88JiX/gk8sPusvrHFl4HywmMfn+GbVu+xaFmXcnGxRuWbIYKXZsB74nnQdRkFRTdbpJ2pRWlpVI2vjHhPC3sfdZiTy3j/WF3XUt1AFCA0L2bK/BMNHwJhorlecF6Zwdta55dr50jSHJ1IBSMZet3YNWLK8CGEEGuzttG/YSAKydAUJFUJF/G5ulEP8D4OeQ5ONl5n0ngeAJ2ahbwfFEDifiYp7521zzm1d0xNU6mRKcRjVCiL0D46ZfZwwflXAiFX7EtsIMuijav1ZNbx+NENXv4nwv5vPVDyTNqvqPJoLlZs796mWjcKQCecLNWtRsB0EIHcEqYaQ0ltCaZVMkV/AlIjyu55oEigc5wHxu8x0Uo5mP64qFy3IZQFochx+xXtIqfZs729zdg1N0l1l2dqs1n98DFYQ/vyIT6D6pmOgc83PjconZKqtSvZz9Zsfd771Bl8ZFL7/rVGPEduhtvvOP9Khm0m7P/YKUCc54MkS+dU/5xCJ6B0gGL3nEAshmqRUPJMF829Od2Btrr7MkM6T3a5VVnItlOfwBTgtK12zISAXXd0t0qyteuljnq2YoYCqQ5Nor36gqbdCUEwSar503CB66ByHOKvLSbj115/L3FiGJelwhiYBpOAZyDJH/tcME4ILvSAl90I5al2XJnGY7cdZq2ez7SdSn1Y03f/hiSLE1QOnTxTGcwmTvaZJTirALsLvecp0HulBhursy6AMbR7OfWNjNUbjpt2pb5Wok+75xxLIwFDwOFoQ6YAtSjrB9hhWv+sRioelNbhq/EEuvu/l0YPUCwD7TL6pY5UeEIeuHoz0O5lzD/yFCu9zlavzij2SoqnK+bfe0rz8g3aRYarEqs5MCYH5muPz4QbPxBWFxmr1zx+6sieFkyeKtg3sHKF+jBjezPo7Tbv1FM6ydi1lloCWfTUcd5Q2Za9cosRz9blGELPSnDRtxkD1bmnuPSYNnZmhVSYJ3Z2B+w2kIt6i7pCJVWyrRYJXJ5YHOle0gObbQKN0+NnumeLiWNWcgKjd86ZC3gr1AeqAmDP1Z8RFJBOLNkx0H9dGjd9RpL7vn7evdVD6IsILsT3Pw+QYHQ/99fC+PMCbF5yvDRf83B/pooPk5JuFnpQOnj4Y0Ww/gjjeletSolZHnUNv7l6k1/6t37ArXLJP/3oLVYPFgDMPzKID1y+lvW+mfma3r/eNAHbeLJlC6fnvcxKSgb6oDwlaKOAFVDf6ZiMEhMCqUp4esL06ICzr2W4hScUnul7OS/9izXF248Ghmjnhu7kcRehFXp5rec1F6W/oZekeWYbP8XoA6s/6kgd2qDf06j31PxfvM1rV1/g/b+VE242lGcZk7ePVALOasCXihz4MLDQQY9/CmDHRYERa1ryQsH85RqzNyVIqV7Sva+33g/77zuyB6e6f0WuxzhJlZlMO27nk14ZJJcXNHUe+zQDfTfoJ4zx3POZLOn+PTK6Bp/dvKn9s1JeENf8a0nj6PpM6jQheRAafY+0HfmTSwgLuKUqKojgqgyZTZD1pvcx6yWdYvGm/14+gLXYTUe3Kgm3PfmyIz9a4W/uYc6XmnSKWrEE7+HGPsuv3eTiTbVkWXwU9yOzA+BuRa/lWiUGe2moxG7KrDZHWoufqwxnuyho9jOytcWeFr0HYu/VNu48v3ZepB5A6dT8pGtPIHgZ5v8ooRuuxS791J1Cw54dK1roqC1swZmAk5wWWAtggzI7YhEtyx153lFmjkneUsZGssrq/4ltmduaiW35y89eCX+q43q68seNCSZFyt/cfoHfuXqdf/zjr/KX33yH9jDj91ev8bunr3K8nDEpWo5OF7y1eMrGpXVDCF5oOsskb1kUWsRdtYO3tPOGy1VFsyrIjnOMh+VrcPVGTrvvkFnLfH/D4WTLtss4v5zSHVUU99b8wq2HlKblSb3HZTNh1RZ03gw3vVWJam0qTUUujZ/8LPDqG8fcm12yn2+pveX9y5vk1vE/fvW/4h+cfoN/cfVVbu8t6boZLof6puNrh8es9wruTy/5SvWI9f2Cv/OF2xy8YylPGqZZiQRDOxO2d6fMTq6gyHWtqTU/kDzv5cFSt3uIOU54+Q5PfzFHukB1HCIoojmP6TQ3ER8oVoF2byjy+CKypInfN8qaw9DUpz/VD60Hkl2S/44Xj1EwW5xEy5QhziVi/enxNJL/tHTKNPxZj4XZsg2F5tLRLz4x3+mnNo/F977FuXHcW1xRTzes9go6pz71bZPhGlVhsCtLtlbmTWqcnD3WGKu86Lh6JWMDNOclZ08n2LVhOfeE3IMN2MrxYLKP90LbZIgE8qKjzDsy4zhvp9Q+46xRudbUuNpG9rBEwBOffMRlsJ+K+QLxWu9tqhyEmy3zUteLHaBIdoHp6z6x1+W6nwdMp+2FIM+AUJ03Oz7Q4/ekbW1dRhcD+jLruFxVdN6wjv7zyZPaSMD7YVuGEEFqbXpPqhVGQl9Paa/tT1K6MOJpARuUQX3U7XFlJjiEddhw2zZMRcgxz/GSHopzniH37sHyBPiP3nf9+78I4ycBpD9tG+lcBqvNK7YJsd5iWHwgPH1ln7d+8WNOf+kVDv/Bj+nlEGFgu6Vi5yt3ufhCwfyxQ1pPKC1SO/Z+cA4hMH04VYua3KjSnBHsuu1fC5BtHbiA6Syrl4XqYMuqLbDGU8Tumtmk4QeXd5hdqFXa9oY2PHSF2m/9d//Kb/IbR29wtL4PormybQLtRFSm8/hU5++mhVJUPSY1DYpopelgj+b+AWdfrbh4C7rDBqktez+w3Pv1K+zHR7jTM31PBMDM3mKQXHaOcLnUouetQ0IZ5S/bDlltWHy7ZfJkn/W9ktXdCZu7E959ac781ormwLP++j2mfxgIm40qjkxzjHPIlccXlotvtHzjxhmdN8/c98891/9/CEgbpC8JmU5jbCnyAQxCzzc+DKpA1+qRfX6ZpIgNuwA2KCAHz7DMgnOaY9ctzKawXGkTsh8YbqOi6rC9kadlCEGL8MlDNoxek8bYd3k8RmCDgkrxGjcSG+qsEpkiSBCWKxYfeFZvgMnVGzTLHJnVufnkeMHL/0jIfviRknWqirDZDCpXPmCOL+BGhZsVGBGtrY6PpzHDOmcgVJnOE5NCa6ipNnwdsA9B85fUmBtB/1SvDUktVISQGUKR4aYZ7TSjm5o+D/R2sMYSrzWuydPA7FGjuf1FbFjJVT69m1mKUuPeFzav/gmH3heWX9+8jpHAf/8Xf5PvXr7Eyfk8NjwKzQE0+1F5ooYsuu/4XGuW5Zkne3qpDZuRsY8PQwOELbTZIUnggqp7RW/1MLpXQtNi3n9EebbP5vUWqS03f1+49U8/1EbPxDq1ZmAyPpPDh75eG7zTeTiN59WZfhaM9+vetmWUI55MAIv5+Cmv/H3He7ND2n3P/ttKPJEi1wanZkQqKdSTPTVipBqgdNposnOMRVmuQQSz2iJ+QjMzvWe3L2D+QWD69tlgR5AA0yzTukU/P/4pH7M/gWFEsFVHMBkSPZvDdqvXarLtwAyA7jXgeYcxPcKx+tihvx9ic0DC4EC3neYuF5n+Rd4rV4S6hkn5/HrX8z43/Uw4Yvrs4PtmofF4BrCNrw+piQGG5usQm+2id3XfGOEcB2/XnH2jgFztaMcs6cw6VnXB4a/n7P/6h1Fl0GkjmI+4WfCE80uy7SH1nRnVwytCken+GggmNj9GJUNxjmCtviYzSJmr/WFqzEprhvdKwrj2XXfOWVXRNy4Z06sIIqK2x7nFT1RNupvZQU0jkiy7KpEJNa4oLwLzjxvKt5+oaoQ1SFniJhndRJj+lBjd5wal53bLcbugMi0Ls8EkVjRD0uSCoSWjEYcNQhsyyD3dSw1HecH6zh63vltSfnCKLLuhYwENcFMnTCAGG7EjrT/AVRkB6Rnd4YzN3RJvo9yug+o0VwnqToFS03Tqbdy0/UJvVzVFYbSTvtUOMkmFFSF2Q6l3HECIxaI+icZoMeOTQIFxwAWfXVQeB2afsE1lV9OzA/rO/vQzFmpsE2gW0if1WKATiguhWHqV5V42g9+z04k+7M8JuVWfX6P+nhjB1B1uloMLZFc15ko790Nme8a3tA5fZFBlKucdgy4AV2mAlBKoq9eEr3/jPe7aWGiI/5tP6HZVprQ2OJi++8pEefif/SohsehQZS2h9ASxEYSO8nPQS3kDvQR1vtQO5nZPZSDEqX+gv79ltZ9ByJk9FCbHHSGDbmrhzoziGPJH59jtnO2tim5mMF3sBI4Aps+id3sD8weebC0sX88icBM9QLMoj50Jm5sGN9djm+UdTVv038tmnjxzZNZhY6f2frHtixU2a9i6nKJzFGVH11p8YfFW1DdUwOdGJfGj96gJYLIolWYUTEmFKJ8LvoOsjhd1BGz7Ux2GgDrJNCZg+Pr8l1gYPSDQqxRoka+bpvszdv+IsnHSeRqz3Xtf0DEoTXy9sIMHJx8/GFg+6VrY8ReFnQaTEOI58fRy/Gkb5rChcVYDZ2Pws1wlNGR0fF68+hGgicAmNPzvnv4Nvn3yEn/pzns8qRdUeccyNjE0+xBuaFE528bECz3P4gWZBiZHMRnogdEATRMDCvUoNpMKqaoBlI5zVFhvCHWN39YaKKBrSdhsmbx7SvYrd+HNDfYHc17+Z2vydx71QFpoGnqfppTowhD8hwBhlNSLaMWvaWNjTuxSuy4PA3/yCcL19eT6WpS+Q9MSXK1duLFAUf3wMXdvv8qTv1hw40ct4fQ8dh2OAGnimlwWSB6LUpkGdhKDWr9c9Z2IgP6+3WrC4QPmYoU9nPQePyEW4atj2Pv2MaFpomXHbgc9xhJWK8xGE+dCPKXk1KF9sT2lr41PkvDW+Ub63z9PfDeeo64P0wWNp5JM3id5mKXhPMHGD61K/HyihaZtq92U3iB1Q/74AlvreiQ+0BzkuNJQeQ+bGncwx16sCFcrTSqbjlDEeykyH8y2w15McTc68pM1IbdcfmWf/e/G2C1dp0XO8uu3OPtyRjeDvXcC8x+cIm2Hn093j2WSJPK+l9YP1sBiSigyfJnpWlVaXKl+5srGHMVuRoE4nCO01xjT45FA6ZDsOujtHcSoOkiydwgmPHuOxo9JILjdRSWIH+b4dDGkp50QWksXMjoJbAWuRB9Xfw7dJla7eo0NGOP53/y5Tz/9f9LjT5qYeuoLfmvzBf7Oh7/G0z+4y+JD4V9cfpX337zJ04s59ckEszFcTj14OG7mnDcTXBDyaUuWO/YnW0IQHi8XOC+styX1Jsc8LXhaRO3niUdeXVNWLS/vX/D6/JSb+QqAW/kSh3DWzmjvW/5e9k28F86aCU+2C5J3b+s0xzHG4zJPQIuJwaMSloLeixZC5bg3u+Tfe+kfMpOWHzT3+D9f/nU+Oj6gfcXyVw7e5jcPX2eStTw91PgwNUz8tVtv87Rd8B8+/VUK01HcXbO5OWfyyDF/7wqf7xEEzr+YE+w9Zt8/IpxdROZFzM/aBr/VogOzGWIN/uU7PP2LB7T7gekDoTpzyox2+h8YiuEuxDh1WOeD1XXfecWkk2e0z4OqLtnQX/+m01hZPfboLWF8occp3WN9HBbjr3FjaFKbMq0C0ine+1mPhd1w2s05d1MO7BqHIey1NC5nx/4pDhutjjZdTutNX4Ms844sc3SFJcwEf1NoG4v/uKI60jUWYHLcYbeeYDPt/G8Nk0eW4hx8bnGVpZuqrOR6MlIgCdBmgXXl+aGzfFweULcZy1VFAPLcaUrr9KB6b/C11QaLNLX1HZ+AY1emPsbii70NeVQpGIPIJI9pQp+LjBm+6j390+WHqekkjef6T2N2LphJ3mrvu7NkRhXjumAxPVvZ92C2kbADeO8A5nb389L7a59hJLB2JVZ8lE8XtiGndZY23OTcbNmGcw5MzaHpqMTsMKbHI2EjdsAk9eMl4F4wUO7TGgqeByA+//XXzimCCaEHpbURVm1FlBknNN8pubhTcfK3tkyOv8D0e48Jyy7GIjnsL3C3F7SLnPWdDNPB5PF2AI3QmMPPSq0pdR2mMer3GdBaiV40WjPZQHOz4uL1jK4K5N9e8HQ25/jNJV+/95iDQmsm9emEWyee7aGlmata2OZu4ODrJ/yt/T/g737nF7n/PUd9YKj3DZNjj2TQ3ZxhP86HQmnm6X0eQWP/Owuu3trn8g3L8k1HsIHpOwWHP3Ts/c5H+NNzXNP0TbEymyKLeR9LhquVEkycx+zv4W4uNP+vO8Sp9Y5sGrKjK/YfnLJXFrT3Fly9UnL6zX3yrXD6tYLq0QJ5sMFcLMmjD2jIDL7QOcQHobCOTXfdBOTZa2KspPB5AefPA3b/XAwn2G2KsyN7uch78o9aWcUYO+bUO+DDuBnTMdQp3TXANf4cvy8Y1HJpf0+BobZTVt6k6kHUHfYb6PUYAVgttZoeoO4/M6mMjaVKd76zMqQT2CCWHhQziwWyNx/qB60CUcF59t5Z8fivTRAbsDYx3xzbJufgNwr2f/chTCZRbtsh0ykkKyGj4E5+smb70gIWOflFg13V/fdUAg8RRAYIYAVfZiNw4Nr52wGi4yGXtIbGn1ZwhelrW6n+mNQZm7kZ5rgmkK8D+dIzebhEPn6ix7f3Ex1q7UHUUqKUzw0RvPDDE3jiNvwHH/8Kpe3484sP+YHcRUSJNW4S6CaBbC3RQgQICuSbVmPP6cOOcHmlUt3RMzp0EcSaTpHpFJlPFeSJ17N0DplMCOt1BKebeG1qk/Sd37zi8s0Ze+8Ybv3LB4TLK2Q6GVQCXKyUR2BWv0yqKQo9Y15kaDxPzOG8eH7jxp/k+Ky6U5xvQqNMWikLeHjEy/98xuVrBdkHT/typq+1viNFoXNXVakaRyJBwJDj1w2wJmxivakTaIyqeFwuyS/n+JdzXKXkB1PDjR9t1Fs5zW1GIggZu2aL/BNxmZ+3YRGyzJGvvDb4R1a45MWgQgh67SSJ+DQiseJ6s9IzTftppDpTmr/j9R6Sde92q/FD9P6maRHnh7kQ6NXs+n14/vd6hqCTvosxzxJCgMSoJ96/SeWxVzvIciRJZsfGJaQmOE/x4ALT3CHYgDEBawNWAtYE6jaj/u4Br/yL4+H6gf6e7B9zjvzhBe1bN6nvLyiOteYl3quUd2YGvMIYPe6iOUcosuF+ir7fO+rLo2aBMJbnHjWDKaYhivNZtU8MmSppBCs9eU68AtHtTD8v20BxpUo/0wdrVXJoWrzT+jsYbWoK0M2g+pyEmuvjJ1pxrHhuZZfMTENlGqooIeUxzIzKbOXSUUX9NWdq6AzBBNxhy1VlcZOSxZ277L23Jnt8rrrzO10MI821vgsgaGdMlhGmFW5/QjvPdsA+gHZhabEqW9YFxBdRwszrIq160QQR2v1MF+/Rx4nXoqStoXGCWF2sAmhXqQS9LzyfOVHtMJdgALFHRdNUQJVUoB0XcJ63/cSaiMXJnuFtiHLdkfUpcR/jLuRLlSl2uYFJDlOVTO0mlmahyU2w9JN1ouhXx0GZCAH2PjDkISDrGGgl75PMsL09AYHyeBul9oRuUeBKQzu31Avh4stw9xce8zduf58Lb9k3DhcUkG4xfcd2G0+qCyobM5Yv24Ycj6EJlq1/NiH50x7J16gwDjPpgJyxTOB1MCGBBsWlyhauRWj3iI0VKgMRCsf6NUNzw9B+kDF7rAXpuspw1YLqKMecr5ieLfEHMzb3pnq5xHvBuIB02uVuusD8MVTnhmYuKvcd/cDFB1xh2B4KYaasl3pVYHK/E0Nk1mEjSA1xu6KNAhPb0nrLZV2RW6fMXaPFwnzpaWemB5GDTROj7mOIsmWmDdqglEt/Lacifn/o0i/xmGYbaPZ0m+F5NHvidtJ7kvxTBIC91U5I00RVhHJgs5mOgcE+AqN7ps1oP8af1f8eWS0hntOhCMczSUdIBdKgf2jTggzsamBzW9jfW7GuCyhV1qOb5yr1c32aeAGTaU/go87z9//wG+STlvuvXLByJZsmJ7vUTv9mnrzsoTMSZTwFU6d5XMjWDk7ONcFNCQH07GiZaUIQqmKYY2OXseQZbHKMiAZAzqmEmQicnDH7+A7HbxS8/OvtAEhfX1AT2zIlcGMJLRgYvKIFfIpR8hD9aIEheXCfEGH9SY5ryh10nQZdWUYvewYarG637H/vnK68weTtY308SsQkhrSZVDCp1Fc0s/QqG7FzjxAw81kvExSSP9g2JRo5YbMlW7b4rMSX8d4TOHi7QS6Xus0w8g/Lsiih6JCy1OQvNqG3wb2QntJ/1PETNRxef20sVJja7R7HNJLE0fXizigZCJnFT1SNxnZe1VU6p56y2wZ7fEmRGXxp6aaGbm7pDibYzNAdlJh1HQtDotJENoxYDII0HXYjhLuObq/CblpsowoaMqnw8wqpu1jAzGgXgXwpTJ+2yHJNmFaaeLSeflKMLIL+WxgDZUG3XxFyg8sNvlDwz5V6zdgmDImP97E7PW6hcxBGagmjRCd4r1LBEifzuM6lvqrUfDwGnnfPgQyPSypABUVX4nZ2ZG4TKJ0es/GJ0RojeWzw7N8nBIfa0vyUycKf5ri+rF/PK13oSSfA8PtVyJhJx+9s3+Df//DXePjuLW68p1LR83czPvZ3kEYo1locNBcZ7VfWrLqC082Uus3Icsd8UpMZz/FyxvnJHOoYUNiA23PMb6949eCcw3LN/eqCG9maOqJ9uTiuXMVpN2Pjcja+oPUWkUDXZjxdR4WQyMAGLYR7p+shRq04BCEUsfCbBZXjDPD26S14CXLxLOyGbZfhH0z5j+//Ev/D+/+Kr959yqorcBOQlUpo//77r/L+wSFnT/YAyGYt3omCvrkleFVS8tZSW9jctEwOZtrTkLqyfYBppWuwNYRJQfCw/OIeq5dh9rFQnXoFpLtYqA4KRAcJ2ukd2AGIxYHUGi8FC76EbhKL2lnQuSJIH7tJB6YVtXgRItMa3DTgMwWy+6TajS6QMADQg1y+7oTPhybSn+WwKFt27Uv27JaZqcmrDh9znUxcH3/DALi4qGKk7gJBmcLe0Aa1oSqKjqLoWN7KyFY51UmI0r069/kckpezadUWiC10nfTgfhDTnwO7EUyjj2/3clYTT8gDZqOv2c48SEA606tCiJNh/hs34aRaSmqAQu/LMOuYV/Wzctqw89jzwOde5lsG0Pp54xm/4hFYnHyH7bX3j99TGEcXjMqqm0DdZczyhsK6KPMd9yOCoACd0UJSYkhbFAjOjaX24ZnPSL8bCXgZyZHH67rF0rqMJmQ4hCuzpbWXHNqW6bVjk9jS4/bIHqAm7LClX1QZ7z8uJqsPQmGc3vdBa0O29TFfFRYfeZ784W2+/Bc+4O2//jp3py9THdWYzuNLS30jZ33T0s6F6iyw90GtKjEu1ojyEhhyz0Cs1xhRIDo20JlNq01yhaU+zKkP4eZ3A5PjjmZhOPJz3ilu8RfufcRJPSO7sJjO0c6MNpd4oZ17vnL4FCMe+7hk9mANMmF5X+/fbOPpphlZnuE3Wz2zOw2eBr8/5+rL+5y9Zdnc8wQJLH6Uced3t5Q/eIC/WgJorpVn8WdOmJTIcqOAS9MiRY6Zq3+0uVgjkyISHnQ+DqnBMMad9rJhcmyZf6iN1oNimLKlZVXApCBkBXbbcePbBT9c3OXN+8fkxtF6u3OP/EldLz9vo6dwNBqvDw2pmf6Mkrh4BaafIcVczwd64sznyFvTNb+tkb2geV5dKyg+nfTgg9Z5RsBDqpkmNl1i6o32rVfTtM/J89K+jdnXEZC28xnh1bu0exXiAtnJEmnaHvizyxq7nGNueIwJPUv6/OEeX/x+PXj42tToGi0ik7St85inZ1QidHsVbpLhK9vnw6ZRwEF82AluJdDXEgJ6LPqG5FgL87kZSjsJVIAh1o+ED19Gie5ErnCoZWPQOaA82pIdXyl451ysUZv+WIWu0+NrtUl3WjZYkT8TntJaGwg8dCWPzvb46t2nPG33eO/sJvb7M/YeBbaHekBdFTAddGgtzjghW4NZBrKLyGBOgFus7ZhJhbl1SChyVRXbAYesKg2BNtM7p00aRpVfsw+fcvc3v8Ds4zXh8mpQFwu6Mg/NQ+ZZXCBJFTt61SIgvuc55+wFsb3s62Tie0XC6t1jipNYL0oy2k7rPImQSDq+sd6kRBGvzFWribZxTmtUCXgU9bjPLjaYbqrxrlGlgPzReb8dlTMOQxNMlqnaW2Z3lVB/jkeeOyVuiqh6g/fgXWSUx7nVOULb9k0Tnzp6qeuwu2akhpprcQZtpyBr3SCzqRKIGm3ukc7FxqbQK+8lxnBPYrjW5Bw/5Nn96v2xXR+D9fszYhePWfyhazFlibl5SJhNlPm7bVRluSwI6w1SNxRnwuZewFhlSZvYwHR8dMDr/7pDzi4HyCIew4CHOq4fRQ6XSyYfF2xeXVDfnSlhdN2Ba1U5I67gYYwFpmNqJR6fXQWL8RqBAZ+N1g0ju/BAD07TA+BBVJm0qwRvpa8xlZeBbOOpjmqyp5dac3O+J7VKVM9M58nngs/DT008+Nyg9NoX3MquOLCqZ5HjYiIDBk8lrQbFqCSvJdAGFwFcASeYRtjc9WxvwfbmnP33SqonG+zjs6GoPz5yacIFpfpPJ3Q359SHJd3EDAcujj4BEF2kE+hr3DA5B1FQrF6oTrpRC18llXTQTayCaLGrGxO0OCSBASiXTy4Up4LleD1IZ2d0Iw0gXQxSrgeBIpEJmoo7oyJPLA5ol0Tc/ySHZ/Vv0ymoGzL9u50IzawgWGgWQjdTH9XkZUHpyEpHXnTMqqbXxrcnOdWJkK9zJEzIN9qxKy4oC3qScfVaRnkeyC9VKsOVsL6b4zNheygs3/D82q/8kP/JvX/Gg+4GH3UH5PkplYTIhtY02QXZKQKM2dBJto4AFvNCyHe3TguNWnSWnl2bQOIQa9p9sSMW5IzTwk9xoZ0pzZ5+l3aVK8Mp87T7gavXla0wfaKFPl8I21sVVQiYqy3mcsO06ej2J7SLDF8oyAtol1jcj/Lcka9iQSdTGRpvBVcZmgP10AkhStWb0EuPpg7C1P1vCLTBsGdrMuOweHKjLGpjvLLCALvVIJyZynK7QqcZ00Vv6Q7ItIHCtEGB6TqM2M8aFKbCGewWMk2r96LPYpEyST6OgV+JRcl4DIi3rxYgVWbe1sQiRKCbDZ+V3t8D0n1hbtje8yW9Q3/exYIvdq9RcfoZfVFUhu81bFeZ7Mbpvdzc8NwpG6wJlLdbgilpZxlExpsLBiF8KkvyZz0eugXZwwL5UsOFm/D987s0P9pj793oa7RW1YBuEmL8LbQGbK7HrLgI5KfryJodBfWAKXJksUBmE8Kk3A1ifGJOqlSNiOCNIWxr/GaLmVSEbc3h91e4csbk994ZGL3jQCCPLKGxl0/vFxQT+jErGoZg7VrHqnxWkPc5xh+LdDdoESmxwEEDptQ1by3m6Jxbv+Xh5Hz43NQpnmXIdAJ5ro0AUT452OgfnTr4aj1nvWROqw0F4pwWSLoOc75C/FwBkgzyK5j+6GinA3/cyZpAVKnKmASmArX/swVKX29k+7SXxvmoV04Jw3TmM8gutxqGiAxMgZ3A3g/XcRx9F2ueKZBb6XWSHW01ieucvr5z5I/OCJOS7KpQH7ODAnev0qD2PW08DE2DrATj/JB0B0FWG4orQcoOVxXkR0umDwyhtLjJgss3Z1QnLSffKlVVwcDsoWf6w6d63WUWXNAkC1SGqMjIztfQaVIS5hPqO1NcYYYCaYyNxEVPyVScKaN8dxrWqPRSBzAq4I2YIUm9Q2NEdkAXn4XeR3W8PvU/+/MMOyxoo7HmzroWwvC6oOs1EtSv1QRs4fGd9NYOL+iS8Ny71DPknJ4h/rMxdkqSs5XAKhjqYLllW9oAH3V7HNo1/2z1FX5p8h7/lx//Vc4e7TH9KKO40gC8OoH8ytDsa+MpAZob8IW7J6y7gtOLGe2ygFbYmCnHnRBsQKYd07sbXjk45/X5KQCvVmc9+FyZFo9w0U3ovKUNhsZnPetw63KWbcmtxYqrvKRxFms8rbdsWi3SV3mHr4StL/CiBaUE1ElrQAKmdNjMc/Z0wT+6+ib/oxu/yU2z4tZ0xXl9i+987zXae7/Br954n//042/hM9jcFbqpJ/u4ZPlxiZkG7EbI38upjgPzR7reNQcl9Z4q/UyOPV1lOPlzC8QtyLYBG+W3fSa46E/YTbSRtVlAtoLyzJOv/RCDhhE4DeA1zkMi86QJQ5yzL7SLQDdL94I2o5lGep9hGTFqmz2NF30Z8IXv0TUJssu8jZLeMsqRxhehMrbDC3GjtMGSS8fal7hgWJgNedHRbQU3HwGx8bpyUWbaSsClxBCD89JfezZa7wBUB1s2HsqznOIq0E2j0lKOSpi36jGpTaT0Xt49U99pXmsa9aGUDtyp5sztQh9HtKHbZ/G8G+nz36Gpcwx+yPBYjH8lCOVeHadG2Slu+CB97vd5PKM/6fmePYk887oxIJt8pcd/e7QhOTOOprMUxmGtZ7UtuD1b7mw//Z4amDuvHXRZfNzHbbexE9YF6Zm9vSc1kBnXN2gnr+lSuj63THPl2pc4hIYrbpuaSiIY/ROOF0W6+5nGgc8AGZ99/bMrTSYOHwyzrOnVtkyrdRjjHDhhcuS589sF3z94mS//6kc8+tqCj09mmMtM588rYfYocOPtFlN7pFWwGlDlOGI80U9nAVqvVmegc6PXxrtghe2dCdsbwp3f65AOuqlh+qjm9UfwgTng0f4FtcuYPNV4wGfS547FuTZGXPmK/FLIH18wNUI7nbC5Zdj7oKPdy5hMJrCJKkVdF2N2IVQlF9884OkvC+5WjawyXvlHwt6/eoew3eKboUlXcvWO9rf2YbWF0wv8eoNUFeZwNhROM6tF5qsNocx7hk9iBIWqBAPiHOXxhttnNRjBrBvM5RrmM/1yzuOtxWcGV1nmjxz1H07Y3MqxhX/GM/qnAaGvM6N/3oFsKzELEr0Gk7SpwCDhHRWvpFAAR8bM49QBOAZ500jeldfkXfVlA8u6B3a818bSyBDVOMDv5sGjJvKdzxrn2l2gb2IFQvYJpeskPRv3PUR7Kbl1SHNzSjvLkBAQNyWLfs4Ast5Sngr+jaD1LuOVJf2djOLhiW6rbjTn9Ilp5xWsEKMqNtst8sFDiv093O193CRXoLjQWrVUVsFp57W2NW5sTcznCB70wEMk+RDPJwx17v7wuRDB6BGhIUB55Zh8eIU5u4xqZV4lnttuYNwmqXRrBm9qoCuFea7H5s+Sp/SVr6jPKr7TvMS333+Zvd+uePV312THS+qX97l6rWD5iqGba23cFzEeMsL0aUDOr/CtNtcn2V9TlZgbB4T5dGAnJkn21PUlQqhU0tu0Dd6pmhJth9/W3PiXH2lO7MNAEhhf48+A0fIsMOfDIPP9ooDPzxvJSoBYY+g6VehbrpH1pr9WcU5BrzxDikKbz4u8V3Tt2aSRMUqeDfPP1TIqnOm1LoBcLMm2h3ovdrD/XkNYrdUmINZFwlbJlWQZiFFPahg1Tf78DkegyjtMM/igS5YN80HCz6IcOl1sYEoS3GOW9Lg5KRFsxo1EcfTno++rUEKOoNd6L8PuXGz60fqtXJ9xPomoOR7ps1OsIjLgateY3b3cNwzfRQzmxgHta7dxkwzTOPITkERgMgJNy+QoUH8zqMWD8RSZo3OW2Q9LZt97oJuK1zWRREW0qkufQ9chHz5i4qG+p7GOKy2Udve7WtEceoQDJlzzOgita4f02EVI64sMa8oYcwg2NjHF3bJNwk01bsjXgeqopnhwpnYF0ZYxjNePNA91w7F3E4Mrnl/j+Tzjc4PSPhju5ee68zHSTolQ8QmG1qtQQJJec4qeZ0uDrYXNncD2Zs7s44z9/ZLieI2stsimHk6i0QIDRpCqpLu9x/ZWpV7QRrCx+NH7gY2AW2Vkjk5iD/oIbaGTmemGidu0+r+dGfU78+pVJXmUe70+x4+690JuaW5NMbUjP1oSiozVG3MNJgJMjhtcbqgeLfFVRnOjAlAPw60nWzny07VKY2aqHy/bVn2c4/73EzDwvHwxHYMQQelUowgC65cCq1dFGQhTRz5vsJnHO8E3GTSKuHXbjG6dsZFKpR5zh3mlpfZT1rctpivIzkutDBropuoTXd/QbuFunuNzaOewum/Y3gwUX73gf/7V/5J/d/EjrnzgR8093ioekxPIiR3gxLVbAkXwvZy3R8jFkweHE6EKLW3I2IacPPzsTeAar3JtwQmhMYxV3YLIsJAFhiYH9HsSVMbb5wriY8BvbOxc0XumO+xYVhZvDZOjQHnpcRNNYLNFQX60xpxckm8a7HZGs1/gi9SBGrQ7O80V3bNBTbMwdPtOyTnxfIMmadqkFFkX6HkqbIeVQGYcuWiRJBPPJGtZZznGeLXtjfGWeHCF0OxLZDgHiqV23aD1sx4YsE3qjkrvDX1hMQg7Erfi6EEA00bfzdFXS0F5kmXsmc+iUjwKHsRzkdFLQEo3nujp5wyfpCAlFuts2GHDp9f3jxmVBOnvxQhISNDPMJ30n7kj7x2/bNqmLwR3o8Uaz2t7ZyzbkkBJszCQdYgJmBBiUh1GO/OzHW4UsBi00cQ2wvZkwv/zt/4Se98t+MKvL8kenEKeUb92yMnXK7a3hW4aaOce0wo+ymvOHgXk9EIX+BRoxg43mc/gYIEvi5FRnjIysdL7ZYWqUGA6BmOhU3CVPMd+733ufzDVzswsg6YmeScDGkSMf78e8CcgL3hNnkEbqiJjupdVSt2sQN8p+pMOMfxxeUrLdBI7BgdpbZIvd/KFfnI8rMMpgc0y9QOaVCq3U+R9Ia7ftnjt/LbKGJHUvdokkDp2yJYFcrVSSSGrjIq9DxzhajkkLAnUjkGr+nx3WhxoWoIJWKGX7+bPTAo9jGdu7efd6mH089o8loovfbAb5Zt23h5CtDK5tt3kXQbPyivJULSXTY3pHLkLQIUvhPK40QQxy+hVb5pWmxlcZHF0DnHgneHsKyX333dcfGlGceWZPFghPnDyrZLtTWVBzh4I+2/rIhPKQotDEVAPaKe/m2aEYo5dtXSLgvWdgvpAWHzY6bpgtSFKutAH7f1IbOvneO194ugbD9M6NtznO7hIktROv48/N6191hMai2ztAJhFCW7i2kYXAfiozEGMNXwEMF8EkO3TRnvtcK6DxRAoxPeHZ5xnbIPBhFSAdsyMpwieOgLS/6t3/5vcn17y2x+9yr/9pTvU//om8w7Kc21wM10gqwPtxGBaTbjamXD5NcdrszO+d3YX18VjlyuT+O7hJbcmK25XS0qjANDENJy2Mz7a3tB9lMCyK1h3RQSRDF2wCj7FoWxWQ24ds7LpGa6GwLyoWbfa9CQCedlhJgHnjNqhNBYz1waJ4IUsdzAP/MNHX+UwW/Fm+YRvHTzk+7depvo45//03n+NzHie/vgWEwPVEcweSj8neCsUS20OzJeO/KpDnMe2ouC9KLsqX/teQcBbcHmU9jIq6dWzkTsozyFfaXyarJP6ZtmgDZAh02YQH5WopNMEuJlCN6W3skHUPsnUJsZ50jOefabqM74Igz+0GYDo3pfYa9wgjtF+QLgmruRjc4p6VP/Rr+k/6miCpTItV97TBqsKZFXDpRPGyOxYktbG+NsH6a+5kIDbXBWQOmdoG1UAKG9s2dzNmD/0bA+MNrvG5j/Tak5i20CbR5nvGFpKbIBWb2/dH9vGBsouxrVxiTENgALTIQs7+z544sT4PjVoIn0u4CvP3my7c2yS97MPgonA8Pg4XAefnwdmfhqADZ8MxO74TxMtiKJ6VHquyBwXF1P84QCM7bCcr31+4zV3SoxqH2SH9ekRGp+xcbkyqUfe051Ri6RWLA5DLg4bn6/Ri3zrC9rsnNt2w5RAMcqf0qrfsylHv48lvF+Exu/PGs8DnY3457zy2vsQNi6P1gGDkp6EAB3gA/OPHPf+6YTzf/4qsy6w6FJjDoiPihAObFRo2dzOCaL3RWq0No3HbjtM4wagIskoWgulYfXqlIs3LM1B4OwbQph46CC7mLD3Htz9Dc873es0h467T+J14AJSw/yhgw/g6N+cc9TtYWsIZxdk0wrxE+pDqM8t5aWL0slB/X2zTBv2phVX37jJybeEkAcO/3XBnd84Rx6fEDZblU+NqkgynRD25honPjzWRj0fkKpSCdZrClVJOQnnkabrAcUeQLFG1XA631+A0jqNN2OTq59VEIEe4wL5pWP2yPL0bEF+y+ncFz3bG2+fAZifN8ae839mpLpHI+XfEmuv6vkJSXUKY/S8mQhMR9/VnXzgeey3MRDRA8aml0HFSJ8f9vKpdYPk0Tu060bbjAtuyiGSEgvsAhupQG9j3p0aartuNycf7xMMPqGAmU5p7+7jc9Pb17V7BXY56a0dw9WSyVFgBRjjKazj6fEeX/qDNbLagDHIbNoDh1JVkSnrI9BhtPE8eMLZBWa5woiQowAFk4pQ5PhFhS+zXja1ryFdqykkQDoYGcAF6BU9gb4e7HPB1oHqpCVbNtgHx+rxGXQ/e0nnnn0ow+cFodeTisC0r0qN08TjQiD7OVBW+qwxN1pzv/ITZu/lvPr/Xaod5UYbrMkyqh+uqb7vuXn3kCd/cZ/la6npUY97ddwSVqtBSp5IipjN8AeqfqTzukAGwdvYfB+fck7nu/kc4wO+rvFxLvanZ72lWe/t7UPPXn/GBk4S06YbnrOjOpV/dl38XOOTPNo/bXyWesL1OldS6POBUDdKOIzqgWG50vpQO/b1LaAsCGVBqLK+c1nGIGQmmvubqVpzdB1+taGXaiYnbLeU5x1QkC+h/FAbjFUZ0YBrhn2LNT8xuap7ZD8t7/PFGRZhVjSqVrfeaJNQkfdrQG+zEBnmfbPSdQWN62tDJDQ8w4ruLRfMcK7i60MQPdbRVxoYbEUgJhwDqPpM/WVUR5XRuqZfNN4P+WfDm2H0Hcx8jr99QHOjwBda7BcfyLeNgrJlSegc02PHmdeG3zxzGAmcXcx49feaYb+Sv/PIA15Szcs7rQ/7AB88oHqQwe1D3MGU9qAckWuJjXsMas875wIS01lfH5uSYkPTAEqnQzrGQkUJg7n0ysjBQLH0LN5dYx+daj08rdswYLJ5NigdJguD2OAQykKB7ipg+elUNj43KP2l8jGghSITZ1lLIDctFo9DMKikXCUOF6KndPTHUw+vtBASfRugviEc7ZcUFwXzxx3V0y1muVVJoG2tRfjFFHc4Z3N/QjvThdy2oQefBom2JPsbBsuq2HnWs4sZuvn64DSCYqYR2ukASmMCIbJH00Z8nOx72ZXM0h1UfPBvF8weCi/9k5rlFxY8+G+1iA341rD3+xOWX/C89XcytnemfPxvWWwtyFtL6tMJ+WnJ/X+dMX3vku7GhM3dgvkHa2WQdU6/nzBkjul+HOFQ2So+L8PjyjQIPeCWL4WwyXBXlm7UtS4GLR6kwif0hJwQBHl9xcV0QrNv8fk+i3eXSOe1I6IU9t7XHbt6OWN7J9C+UnPz7jn/szf+Jf/t+QdUkuFCzgUNL+enHJgGK9rBnQNtCq4YsWPGVC/x2FhoSR7TefjZAw+FcbF4YFWmPkk3mziPpkklgqPD5abgmG0D5ZkWhLa3hOamiwW0QU7QTxzLr3WsX8nY+7GlOvN4a3CVwRVzsoOK/OkV9vEZ1UWJO5jipjmusghBA+4Y4CZZ+9QNs3xFkOrZapyxHmuV/WzNAEpn4snEk4uC0qmAkqUoOQjFuaG87OhmtgdEmj0tKHYzoZsIxZWQryL7O7JuxId4T6tHjnohaiU1WIbmklhkNJ3KNgLXiv76WTbKC4aMQVLeaBHf53pOXBuLcV2I3ZD6eGKHJOa2eo1qMSKd38SMTp+pnUj6mHi9CU0jfUF0XO9KrObE/tENsOMPrwVYmB+umWQtf37/Y/6DH/8yr21btjcMtnT9uQpem1b85yjG/GkPH1kck8eBL/z7R9pok7ogY3JZ/vgxL70ttK/d4vgXpixfFdw04EoozoXZw4bQRZ+o1NlsrcpG39jXYDV1mEXf7ZCrj7nIoJhB5lUypuuQ6IssxmhxaLXWhLdthq7BNuyC0580xqzJ6wmcDwTCEJiPn97xSwnPBv/j59PvZsQavj6uJxSfdTmkwCJJkEeJ5VDXu4lE8oVBCxyyv6eJtgh+f6o/c4vZdn0SJs4pQ8IYmFWEqsQYg3dOPZU6DWwlywh0zB5suXhzRn4Fix9daDLdRtZ2CoRjcpaKGyKizQiRldUGR/ZnEJD+iUdae4iBqqDFF+AT/aSfo9SiPwdA+pnnrg/nEadKKllktvjCEu7fgQ8eaOEyyW5B3+wQug7TgPeG5esB2dQsPthgWo9Z16xv73P1JYddGrK1sP9uS/bglFDmur0k4dU5RDymFkyhn7388pxmLrQLDb4nJ0Z99sZfxwct8vqgUnmj7uA0VPbpk28oM9ab3pECjmhgOmTX1o0+YUjvicGbubJMH8ailRmtZbYPRUneud7q+uRmHrPXanPY9aLECwYwXD+ShjAwINE4sAelE9NTPG08YCuvTGkjgd/afIEPlisdRgABAABJREFUfniPD/ZuYh+W/GftNzl8Emj2IsCTaUxlOk3qfCGELSxfFe6+dkpmHGdXU/w6AwN21rK3WHM4WbNXbChNRxsMq66kNhkn9QxDYOsyMjP4+l4HqoBeQjjJLudFjQumV9lJrJwy61TeOzPMywZrPBebisvlhOAEmztsHpiUDXYSOL6c8fcef4s3Fi9x1kz7uOHhj26TLQ0HH6oaTHXhsdsIMEu81huNrY2L13umEpHpWmsXtmdEqw0JvSxkD/I6zZnyWlUG8nUEpNNlFuiVB/Rvva5t4zXZD+AycBV0s4CbKiBOJzuAcshU7UljMPBFZEYnsDTIAEbH/eqB6fjR4zxpPJJSjlof/ZQX8h/j2IaCGQ0Ls+HcTZmamsPJmmVHn5el4YPp89fG633QN5CaWPQJQhdVwvKiw3tDlnmat9Zcns6YPfZsbhlcqTZNphOMC3RltHGKsXOff4I2a8d5KySrlb7JZviZcp/rDVLB+iGXjc0DplHmiqsCvgrM7y2psqGYEyLgno7AdbZ08plOw0jovaEz6/vX7GDjI+A4BMHE94uE3pO684bCDI26CTz2CNYbll4TkFVXsD/Zcv5or2c8a2NveOZzu3iuti6nMF3Poq59Rh4/y0RJ78ebBcebOY2zFNZRZh25cewVWxZZxsQ2moeJYKWITHvdxsqXnLspJ9klr2VnLExLrmkghSgPZfucRitL6M/Xdenyn8X4LOZzOidpGF0Yd54fj3HjwdZlmifEXFNSc1sIem22nsV7m74QGBLgIFFS16CWBI1Xb9dc13nbqHWWaTy2dpg2FnENGltI9JKNhcPVXcPVV1tVvyqdCgZ6cLOO86IADDe/E6gPLNVJh5sYukqBqNkHS9ys4LKuOO3mqjBW10jdEATqQ8f2zFJcaWNuYvsEMUgGzUv7LO9b7BZufD9w47sXyIOnhLrplalkWiogPVMQL6w24J3WxjIbLYBs37DYK870wFfYLWaPrjstKLNTbA1R/SYYVcsJ1tDNMnxhMI2nvPS0ZyXcUrWItJamxpVPu4Z0Pf6zPXy8xrOqpatKiigxHZxTb9YowR6ayBQe5QNiDcHbAWiGT26Aju8Ta0H8kJeOAAO/Wqs3ZwQ8wuWVArqJWZVlwzUhMrCxEus6Ma6djxaOdmDm9SSdWMOKksj9d0kg9p2b1LdK6j2Dz4TJqcPnhm5/Qn65UnakCPMHHafrnMV0i5FA9cOK/IMPdGOxMK+MStnxDN0ZIQxgSGo+6TqI8vfm5DMYZM871uM46jpD9rmb0H0QiACIjJ9kLNm9k+enBvBY8zISqEOHFcH+GblrFmaD3YKstr2Efa8Ah55f8/iE+//Fmqs/d4fjb2Rs7zmKc0P13jE+KvQpoFcgiwXcOsBPczCC2XaYbUe3V+FLS7ZqkU1LyC1kBtk0Oo+GgDRNlI83A6szXbOiAe6Yvb7Dtkzjuuf3dQDoeTnrp4FEaR8+73je9sf1pOuvszGBTbUnq8BWGPkAB+f6n2Y6gekEv5jgpoXWgQuhvlliGsUgTB2ojjbItlOVtP0ZpnO6VkVlPpoWyTKqjy7IvnabGz9ukdWml1/vR1kOx8FawnyK359iKrdDtgF+LiXtD8oNV8VNAIJz+NVG7Q17hUhPcHENb1pCYuwnAPIaGP2JNoR+AJWT9zrJSjE+7pcrzMH+sDas11ojAiUEPE8+PDVPpX2Jc6zOy0YtfssCqRtIjUNdN5zjUUOJbiOSm6xF7t9he2fW24r6XGgXOWYzx262/XaqJzXhsoLFlsI66i6j/O6EyYdHw9qUFD1S3TTaTgD0CtCJAARwdIo9Oh2ql9E/vpesn5T4MofMqBd0zL9wEFvOooKI0cNi2WnMAlQNKOXNPmDXLWbbYi7XhOVqp44doI+Dd8/rc9YNI4gt+se8VULmNnj2GJrkPIHyuRfL7viJPKXHw17L4nNxO77AJoHX8SCk/2P56dSlbjo9iMt7GduDGfl6yuTJluxsjSw3uMM5IbdaXCkU4U/kQBgKDn2trwczh587uFWgB3B7da5Y0NAu2MAnFvHGQW8MwttZhi897dziJznL+5biA8PkcWRkNwFfeWWTCbi5eqr44wmTRxk+D2wOLZOHls3dgs2hIV9WTNbNM2nh85YLn+kFl5gMxPkgEIs2/fFQmV/TKbChxQVR0ERbz/Vn9HELXqJ6pCG/s6G5aTieTchXE6onG0DPXTsVrl4Xtnc7Xn7zmH/31d/lr09/yOuZo5JcJVXFk8tI9j3AeYCcQCXQMrBnxh3aqaDnRStmLgLU/gUMkrwdsX37ws2oQNN3ONLfF7YNFJdoYbA0dJMwFKBiYwSA3+u4+KqleWiZPQoUS0+zZ/WcZ/tkVzX2dEn20RHZpMIdzlU+KFfPuIAG+D7T4l19I6O+6RE73iktjpv4P3lJi4S+4GrE979nxvdd6SEIzhmmlzpZdpUWMV2BSgJm9B6AzUI0KfZ+RzY1TZZJhkjlt0Nkrunkm7y5TKvbeqYgFn9PPQtaNJPI0tH3pGKbODAO8itdhFwVZS4iYyMYfb0rBo/DMSM6Fdz6KSF5gIadXRn+TtcCsVFkbAOQGCOB3q/PZ3B/seTl6Tn383M2VxWwot0DY9XHDrRxUPj0WPNnNcz1GSslranQ0zkkg+A9+Ucn3Gkc5fmc028K7YHHNlA8XULygk6yP5MJZj7Dl4UmBGgRSUJQn8QyJz/baiGptNqBb7UkJVWlAetmw/MYo39mx0/a8ZwknWP3bnBOGRNZBnmGuzHDVznSOmW93YwKIAcZ4qE6asiPlwoeWgjzKbLeDEFqv1+G7GRFvpxRnnvMcqtd292IET5mS4PKqM0mhCpDCk8lQi5/Rs7jJ50n2f3Zd0imfocIviTQIL1WAoT1tu8o3dn6dYB6BEZDDKbHHZdG1GunuVaUSfd112nMAxSZYX1/wtVrN7jVtHBypvFGmWsTSJTnouvINgG3tdhXNtRv3aN8R6W5r37hLpdf9oRph1wUTB8FJh9fQWaVjbNt4jEQKAt8kVHfmbC+m7O9IfgCuonO3eKE7b5hvukwjbKkjdOitKk7pFVPa7Jr3cGpG1fSIr77HKCWFPAsEMOw1gzHUE9Wf5pHzGaRQPBQnhlufi+x24lrmowUOgapviTdt3w1o/lmoxYcO4XfT4lnf0bDXVsXZsbjArSo9LBHcPE4e4RKHDMjnHvIBX63vse7zW1+bfoO/8nHv8DibUuzr8V1czTRhrYIfpmOnhXrrTYoNHvC7JeOeXP/hD88vU99WWLWVq0AZmBNYNPl1N0eTyXggqHzBiOBy21FmXW9l28C0Lbx9z5WIqi1iQw+wIXp2LpcWeHW9cBXYRwHleNre4/ZzzZMTYND+N7yJX7n8St4byiyjkneMS9qbASz/+m7b+EeTjVkN3D4HUO+Uia0reM1Hr1S+2YGq9dSFz0Ikxx3athzkSXrc4amhzhNmA711+uCynrXobdU0TxqdN0lRkOMv7K1eql20xxXCa6KEoml15jXS7S+EXwJwcZGwOQtnUbK13qmrYziJ+njqEDMg6JiDbuhrjZJS7qHfvb3x8zUXPmKhdmyDQXbkONCbJIW1L4oDk8CWnfB1evjOsnAe6GsWi6/2lGd2qgUEZ8MGq/7DLqJ9PZWHj2+QTReTvvjoz0RQMhR0k4/zwWSStC4gVP8roIUxBwpKRjNO2alzultvN9+Esbu83yi/zjAVfmkzoY4cuvABtZtzrzY9bIOIcqpG08TLFuXUdmOxmfxdQqc1y7aHEng/ctDnp4t1NKpM5B5smlHWbUcztYcVmtuV0uwDaV0CobHe3QbMmwIeDEcdXt4DAdmzW27Yt84XBjyhjS09+Cn94H7kxrPA6Gvj2dY8tdes3NNCP1ktnU5MuuiclVkorRuyE2sxa6b4UAZYsOrRZwh5EZ71wRCPgCyxg3FvGBEZRkl61ktwQquNBEI17yzPNjStRnhqIRWCKUnlB5zb8vpfsbB7xXMHjnyZUc7L/A5ZBswZ1dIO+VkOWXtC0yLMu6AZk+4/+UjnrR3mD8S/LTo1Yek0IY+Vxomx57bv7che+cR9MVXQYpCAcWq1L/rRv1oo8JRD0anRsNYGA4QbYvScTMKJrbqG5kmpZBYWD4u1GkbeRZ9qDXWbOcZ9Y0s1g0s0yPP5GFG86btm8CcN+SxseT6HPBZKgrXx8+7fHcaee5o50blcolF6qZRUDp6iCa5694WyzkgNQN7nguSwjNNm4kNpw3YESQwBtpWJXCtVV/ppkXyfFiYEqMaduoCfTN3enzEuuvfmwDCUcP09SHW0t6eYxrP4oOGkBu6iVXrijYjm1aw2UCWUVw0sKzgFqzbnMMfjACM5zSqfq4xblj/PON5r82u5bWfx9f7pxliEGPw1vbruhGhDY5S8k9/78/JcGFoznze6Jsh6ob5d48w9S2e/rJa3YTLq0FaWpRVLrMJblbiywy7aekWJet7Bc3caG18VrL42FEdb6F2UGY61ZWF+ul2URY3y34yMPhPa3wWS3qsIpgeSgCcf/Y5YPeeSHWda58TmgYznYK1+HmFmxaKnZQZV6+VHP0FT3FWUN/uyJYZd39jxuyjNWbbKhliNkG2W/BrXc/aVmtK6y2LB47Jg2XfWBA61+83kwo/m+DnBb60nH+xop0L1eTipz6EL8rIxTLNGs6rKNUvRhUjIwA6BpGDc9F6R9nsSd5cX5DY+OHZOfETG5hkOMd5lG9PbPjpFHd+PgK9U/PaiMBwXbHuWuPSYO8XGx2SJ3lSgQyjufzaECO6DwdTmoOsb8bOV0GtF6Y5tix0f0Wt8IrjGeElIbeO48sZN992avl2bb5/RtVyfBzGfz9z78fm1qbVxrHlWstG1ugzn7SmpPXxeSPtW1pPe8XKP4JM2PX7P0SspPRUsYZoxeCCfxYL+ITxU4PSaVjUP7p4TjqzA1wH6P32UlEhaBKbOuwlKGDVLARXTphMMoojq0lA58nWDl8MB72PHa991yADZtT/fe1eSUTcMUCoTBkGOfD+PpAIiI3eGwFdvGd5PyO/UPCqnef4AvbehcPvXoII65cnnLYKUPs87nsRqJ5kLN4PbG8NHlzbfaOg4YGlemwx6+2nptQSUHZHo2Cat9IzLndY5DDIOsdiASaxzSUWrWKCEIsIYYSkBgOTaU34RsPJ5QH3z1uW9zLqm8L6vue1bz7k33npD/iVybvcthsODRgx6D+txOZANerY3sbPygm0AZpg+oTyupRaLp42GCxB2ZcvALUh7WthHJSOZDw/AAMyyKGOgAJtHJAecDadMqaDCNvb0M1DX7gG1B86D4R5x+ZrLetXM6YfZdz6bkc31SCrm0zJ5wV22WCfnGPef4Sd///I+7NYy7bsTA/7xpyr2905J070cfsuOzKZSTKLZGX1VFEqS2VBroItw7AFw4ZgwzAM2w8C/CbYT342rAdZgCzIkC0ZLktWyVCVpWpYxWIyi1Qyk9nfvDdvE3GjP91uVzPn9MOYc+21T5y4N26yyIzMmkAgztlnN2uvNdecY4x//P8/ob11CfGa4LnCRG89z/wlg9tv9GNMPLwekNaCZ2Z9X1TNxFGajtJ0O0zpJCvng+CXGcVZTBBcgEywDVSPtbjoqiifXUA7E8Qb8pXHthGITrKp8f6S/r6EnQYSr+eMID1zLDHS9QSrbLh4bRJIAK8vlJnjc73P8Vpks7WQz1VGEkMv963HHPB52Hr1sL0uvU9oeriT/v+e5ZPWvfS6IDpNfDzQ+HCSMDSxsBfi8b48PebN8QP9Wk5w05JmLzYNWI9zJjagqBff8zhsVK7oE4FzUi4EDZLCpsbefsil0xVwjcdfMuRz4OGxsmsjW1FiB1m4tIfbKwm5wa467aoH6ssli1uW4qxgcq8hO6nBg59WCniWhXb+b+oepFPp4ifZkecfe67H09iun3YM5dCCdsD3XtuxEOX3xrhpAS4QRhndKOP4MznNPsiXTwlB2P9bU2bOY1cafIYqx0zGSNepCkqSp8oyODlj/OAy1eOOkABLMZrgELSQkQpgRU67P2JzrVRlk7zGwE8kFfOzMH7i21oGgGg98EhO999FBZO0X6XnWas+WWnvEmKBJlz8+sh2kHWNPRKKcc78xZJHX73GlX/stVhptPgRxOh92HUKdHRCnjvu/tkxV0c36caG489YwuEGVhn5XNh/t8YcL1SOv4usijzDTyo2N8esr2jxstnTtT5J/5pG/VKzOlCctJiV+qnLpu2LYeIDtsh0/w5Bpat6rz3fM7ufvEAe225jynTud0CXPh7TQFcSUDOsl8ffTRZwZaCZWYLRPSolY2mvGG4ppgtRMjR6WjqDmN1163lX4BvGf4bQx3tDxvRjJ3yzfoGvjj7kR/V1/tPbv8T11065c/uQy8sYWw/OuWm3MUE70Ua55kBoZoHmxYZfv/oRx82I4+WIbNzRRdatW+ac2hEANrJOrdkyLle1FujKrMNY13tEpya+TGL8ZDyZuL4orr97ZUtmgcq2eCORPem5MZrzcnnEzK45tAs8hpnZYAi8fXqVxln9TAl84dJ9Pje9iw/CD85KspMMcWqRYpsQVVj0c31h+vOSwOc+LrJbMDqBkT0zedgMHeWZbb39DFvrvCOkeExla9M+r3JhqCpQE8iWLd00p51Cc+i0yS8pNMVcIOQBb/xAtp5tHByvj3gBtwWf9bPi42Hw9OEES7FknxRugejnpWcjMaRB8+qVLyltt22CjCPJWCeWaAhyYY54/pYfWvFcunXK8uZlja9SzG1gfTVeh9jEEUT/Tx7sxglm0GAjfnuPSUhrVfx7yvMlPZnUVqod/k7i5+q1DoWnHDdUWUfrTc8yfpqH9Hkg/ryPc3puek5iL180hs+XnYX82UZuHHbSstiUlNZRZe3OsaXG3iTTDboeIFuf6TTO6oo7H14mf5hR1dLn8d0oZzUq2VzJ8YeCEc9+YcjzJYj6UrsY/xgCzggblzH3FYd2ERuBFlTiyGXrdvPzPC4CGLtgdB0OhmLUAgVm3cWGMrUfGLJEko1aL33rPaaz0AgmM72CVzl3Ucbeb9fefBA7WdnWBVzQJh3nKY8LFncmmFqojvXJbqSAcStQHmw4+SXBrjNG9wPdSOdLdRyB3KZldTJTi4hl0GLzpGL+muffePEP+XfP/hz+Dyb4wpIlj0Mx4Dyj23NGHwbCh3eV/ZkYatMJMpv23tBhsSJsNhq/7031Cxm1SunBZD3hW4bT4JyF1NjX279sX5Osxnqg2koE7w0+N7iR6W205q+BbQyjB4G7Dw64fvWUadFQd9mOZUYanyTZ//M6POCc0TpIYmNFFlxIBWn0Z0m/R8nV4GLL4Hkv0cG4CAAGtsX1COKGrkMiSxERBakjmNCDCsb0zcb95wW/41WaGiUAwkDaeCewPd9cbi0yqgi5oXy0Bg+bG2Ncacg2XlllVa4hhgj2rCY7HWvj4arilbdPL/yKO96qQ5AhNXFfdD7+OOO8otqfxBgyzJ0jWweONxoDm+eQAPSTDit+J669SHVOc2YHpwsm3+24Zm5gN17rEjAgRlSEyYhuWkRZeMPp6xUP/0ILTWD/exmLP7ti9faYW79TUDxeE2yGNJ3W4stCa1C97/FznqQ9bZyrufTs2VQ7M0IC2USEkJii58c5D3sZj5QlWioLPRhhebNgeVPIrm6w9yba6PXimuX7Y8SPmdxeIXWrtabRiLDe9MckQFhtmL63RFa1slATo3U2wU0qmqsj6oOM9aHaTq6/uKasWvbHmyeP92dsuBCY5RuamWVSFiTlkuCcEsT6RqW2By1DkvKGASj9MWvQxzXuRJUHKXLCstV50nV6nU9OVL69abXmN1BD3FnXk5KG87qfpceHNcrUBJckys/LeMu5BqNYh+pmBfnCM1p3dKMMV2pu3I2sNi8tllp/Xq4pT7YKbvVxxd7bc23YS/voBefhCYD6fMfwRbW4ZFMRJfrDUDr9aVL7Q1WC3QMYnEf/z64JJh6bWEPIs74JvB0kUQmYfpbxzKC0Fd9Ld/c/D/7vnxcPJEktD7uw+wR2UGOINbptF3ZMhLtSPXoIE4pHSxBRGZG16VmQvfZ6Kvqdv8jpnKXDS2Bh+jUMintePaZdJUidULEtvSDEOTDEmIII7nDC8iVh+mFgdUtY3VBQ2tYB+/AUjMFcjYtf7XQzivdOfdXx8Ir+vPdDiy8yXCXM7nTUewrGh9jluHMeL1gTbBP6Do/+edAXUXuWBNv/e2+vOI+F2MXuJO4h2+pOt8nZBGF/tmb+lTlHR1NO/sqGr77+Dv/61a/zgj1lZlqaYDg0fT6HQejQ7mwrQiVtLyFWicejBUmH0GLwQWhiEFRF9n0CqE08cCee4jnINVISVNqOfNSCRG/AQH+9hj+Hc4/1nDVRpsnoEdiNsLliaQ48fhovno0XrIvIa+VZvdHwwSuG8fuW0SOjYLABV1rsKCdbNLDckN85UimMIidUBSG3rF+YsXrBY0qHWD2KrWT3FpS2xpMbT2k7Cut6L+nkYZYbt8OULu9nlGce0wSsD4hX2pgrRQFTUX/HVBhtZ4LPDPlK5c6A2Dih//s4N43TAsuWIbMtkLrIlk5qB/0Qev8EX6jPuSsDrlLJU5V3FnynrKlsqa93Jb1MqiuDsqQzXQ+2y5z0BbnUAIOgoLSE6FkaPRLTcQ0qpTL4HngtKPbWAl76+7Ober4w/Yh9u2buKzCB5qCg2/NU5skb4Dm4JZ66+ezUhkwMOiLwGFwEmovYQXd0wuE/aclXN/W1q1X/UrFWO1THFd1ehRtlei5zw9EXxsxfgfF94frfeJ8/e/nH/Ef/+V/ixb8n5GfRl9NaKEF8pRJmyUPlAra0iHBRJ+ifyDj/GZ8Erj4h1X3B1X8WgHb4PucBt9QVHP2+JM80UK1KZZTGQ56/qKIszb5KsX75+j0+M33A36n+PIuXR+x/v8WPct3/9ibIZqPScc4RNrUGpU2rthC1UwDaGPXrkigRaA3tzUvMXx2xuWxYX4X2jTU2cxSZx8a1tA7tz01X986Q3f97Se3hNLgAJA4SrU7cRWgqTzKl+8cHxaXBc0JmPj6YTcWotkXWQnl/QXUz5/RNw/T2IdWdMz3OqtSkfDJC1nXfXOecUH9+wyNfESw0+3HvXxmKUygfLKFttZA5KgnjkubKmPpSxuqqYXOY7B5UocM2+n++CBRLr75rD86QTaPHudmAsdolnWnBwE+KJ5MhY2Lx9OKRitA73ZD9awdxsAkDqW562ckhWCISaA88J29afBGbJAfgz87nxr3EdFBfjj6BFxRnnwbGPC/DkdjRCkrbuEG6GNA2wfBht8//9+iLfOnmHR61U+69f5nv3HiR7CgnEdiDhXYSqB7r7wlk3VzW+KHZD7QHjs+/epdJVvOwnjKpGq7tLTibldRtzvJkRNdY6jYjs448qskYUSZhUihJsroJsB7nrTI7B4B01jf1xXgJlegGZU1bCbTekhvHa6OHOISjbkoTMiyemd3w1f0fUfuM++sZpVXJ3+vlGa8XD7l6a84/Gn+Gf/D2W7i6UlnXBuxmq0LT5yuxKVWiVUnyDNY4Zmtz5AaAtHFgGp1fdqM2J1kd5Wk7DVRUMlvvAeN8j3T1AEwXMK3DrBq6q5U26+VhN/kDktJMsq7pm5i9bBWfBrHTMJfcaXiOQ+DJezE9FmNwcdIrdj0P48CuOHFjZnbNUTdlL9/Q7IUdTzsX86T0r/MG582FAJzEZGOrqBpo2ozpqGb5mYbJ20VvW+OqrX9isvtJagNpXQshWexI78+pUt/RO9wQbWwG/zxsVYJQifbheibgq4CMOyajumc+WrPlvIbtU/X3+GZJvcDCE9L5aTyt0XnnOX0gf/EwomyMITM9AV5Jmv/64Rn3H++zKRuqrO0/O31u5w2FdRSmZtkVOKfXLBOV759mNUf1mB9+cIPqw1wtGWK+Ip1anmVLS9tWUb1B8MEwsQ0Og8X3x2TF07rt/pUYx23IODArDu2GsQSeIkL7czvSPYPoNc1zh88Es2kIRRb3ES36Sacegbty1BZZNxqTiiiImhnEeapm4JObpHuTN2wEotMwjcOsVVWmPPWM71iytdZxfAau0SYqQkZtSor9WgGNmKN2FfhcCBONne1Rzkf1PsVci7XrFyYcvHXEl0bv8+qVz3OUTxUQTp68wWtj6OlZf0xSlQTnMZMJMlYP3CTXHZbLbWyZwOjzY8jEST8PgOchOAH0SoM79jBRHh0DwRpVvcqEbK22At3VlvpuwfSOo/hxxcPMw6U5Vdbh3ZM53D+PwyBsQqA+rbRJyHtlSffqZN2WLd15fNNijOnZcurvGufy00BQu7tY9s9PuXT0hQ0+KDAwHiN1rZ8d5XkBbXK2RouAab4kiemUYA5zkRCiZU/ywzZbICDVg9P3bBpkeo38sdYPPvrNQ8JfPmZ+d8ar/xkq/zspybKM0LaYxYrq8aHWIN+bYo4+1M89V1f+RPbbcPjAp8Z0zWAe9564/wzHRflb+kyjCg6uFIKztMEzNc8iuvqzMSxhWyNPYwiGiijDNzPIZESYLxh/7R1dy/t8sEWMINMJ7V6leWEmnLw2UYuglVVluAz1Jz+D1bUMn42pHm4I1iJ5hkxUOc5vavXcvWhN/eOOi2pAn9Tk8MdprEhqdkMf7GHDiWiTTHDEuX3utT4qNxQFVKWSH6qMYIWTN0vEw+hhYN0Zqg2YjSFMhMk9hyuFzbWK6r42CIib6v42rOHVSnwBlAG8P8NdP2D+xozNgeCLmK90kM9h/bCkuQbl3mJ7mIRnZn0+T8OjjdPNVKL6iUG7lJJ3uY2AsO4RPet2WP8Z+gtfNM7NlZ3mpQuIDGFTI/szJMt1D2kbgim3DPpswPx122MlgtL9tU1e7CY2CUZJ8qGncy+TP7z/4/0R9meID1QfzZG6IzfC4jOXEAftLCOfVdhHUWK+7ageBU5aS+Mso9s55uHJdn8asrsTsH7+HA1VBIY2JxeNdB/1+4LXPcWcW8jS+6Sm8PMKG/1JV+Wxfh/zYcAo/5g60cfW/XTPD1ZtZczKsvSBSyb039M/I0LxEzOlFZD2PTDdYHq2dD7M8GNSLC5K1QbZgjKDIgPE7nyh974laJe/1B1+XCKtI1t1sfOS6O+zBZ/Oy4I9waCOCUIfnCWgNp5s0ykpVTzaiZ/HOTaMP4zHp47qEFi8VLG50eEqSzd1nGIxDcxfMRTzm5F9LbDXsn5hQjMzhKJD1ga7Nj0rMlsFzl4f0exBPbe4AtpZQfXojFDl24Q+DP5P5zGtE1F2r6/zpOfGhtp0bvWLpL+nJEK23zURK5xsIWETaFclR/dH+JFj9N8+5t/6zG9z2epifc/t0bDgsqnZBDg0BblYurjr5GLJQ9fPEQPMjDD3gVWw5OLJ8bRi8N7QxJWjwOt8Clu/qyJ43HNUZA1BGV5tLLwk+dQdvGD4u/Q1sfjH+HwXKBZo10kw1Ab8KEmLBMi9FuhMUKC68KxfFJp9Q3VkGN8VRkcdfj/Hl5Y8t5iztcpudU592venLF7M8NNW1znjVXXLerLMYUXlujOrhdXcOirbUkSWdGJKW/EqLSiBzDjWTU71SLCbxKxXf7okxd3Pvfg9EfWjo9KzkEdJSKIsaXoPfNgWsARdUIlFrQ4o42c1A1AgfkYwysx2JVEmMkpFCiQpe0z0NKyiWsAAkPbFNoB9YrqFbeF22wkf+vf2PZid7sfEZA5b/DGqFaTrr1552+KdG3n27ZqJ0UKdrDLaCYRJR5l3vZdWYqI8r2GSEb/1F4Me5OxZmV1H6L2co49G3TD7/duEzWYr85Y6Smcz/KUZrsoQpxIrx58dc+WbC9bXZnT/wgnXRnO+t7jB1W942j1LczBh+u4cPy0wmyhPN5kQTk61UzAlxM+jhNKzjI9LjD/uNedHCoCSxLbfJsUyqpDJGDdTme5ghdPXKvJV4ODbx2wOL/OVf/k7XC/nvLe6rKzW2nHyhT1mP15DbuiqEfl6qslY2ymjs67BWuydR+oJ08bO+sN9msMxy1sly5vajJYtA3YTyBdCc1TQFoHZCyfkYrBifi68r56oq8vu/08A0j0AsG1oCYO4yLbsFDZ6hoL3u55VsC0ipmQk+h7uHEtKUlISnYo2zUCKD/T9FysOvpvR7O1x8lbBoZtRPFgSipzuypSQCdmpjWt/wLWWl28c8cHxDSa3DfmZ4EY52VKojj0ht3QvXyP74AHkE9a3phx9Pqcb6ffM1vR+t8U8MHtvQzavMUdz9SzKc8JqTYiMIf3+qVnQIZ3fsn/Sdxz65D1l2KS0LUFlr4BgtMlolw29vW6JGd3zHOMS5L0g+w2bmWCygHfnQGtD/3sI0u8nYgNF5vE+4P3P1jpmCVhx2pwYDE3cXC0BF8Hqt5sb/OMfvMWHV3+Xs65idDvj7734GUwUAZAAXRkliHPoJtBO9Rq0kzi/xp4rL5/wxYOPWHYll8sle/mG3DiK/Y61y/lwdomj5VgJihIZjXGU1uEKBZ8fPNojKzpeunKiqjHG9Wzowna9hHdpO0a27VnTpe0ikOXJxdEGy162YWwavrN8ga/dfYXVpuCVy8f8qze/yS+Ud1jtl7xdXONBPdP3NB2P3ZRX84f8m9f+IZ+b3uX/Nvs1Vs0lsk0gW4F0XpszGh89UHXO+Cg1m/Zkn0vv2RxkG9eYFrLNFoy2TYjyzZERHUIPthgXehUb8QrkBGu0KWbTYU6WSNPiyoPoIRwbaNM8HYLTsJMXShAtXqXc8dzYUayRbVgRiPebp1flGuafCVA3zwkofeSmjONkrkTn2Mi2dPsOk/nYFLEFo4cs6afLd2/XifS/6wyLdcne5SXr+7ky5QtVD7KbeH5cOl9sJd9jHO8zaGf0Xtw+U7Uh0+l9Z1pUGh22DeTDPaodvJ8FX3mYdEz311R51zMQbASZnyan6wff3Q++n2cXNL6INf1x45PYlemz0v+JBX1zcsZHdw4prGNHWh1Jolisu5x1m9M4Swjq+S0SKPOOxzLm0emU4v2S+orDXKk1L7OerrVsVjmyyMhWQvhgzIPOYK4GqqwlE88kqykHPtUJpM7FsfIFd9tLzP2Iy3bBMpxx2awYm23R7nnwkD4/PlaKmyev1ZMe1E9eb+cNfuAh3uxZRm2HOL8lAUQrM6nbrUcsaJGxc708dZKPlGgnEvIIsg1A2d34KSV3QeWOc0u29oweGrI6DPLWQDsRsrUgIac7y5h+pDF5EKgve+7d9JTHe4w/OGP0UPid269z2AW4csiDX875H7zyLR52e7wwPuXBGEKm+Y1kWfSLjPG9MZpzBY+ZVoT9KaFu4PhMJZdDQEaVxubOw3yBTCf99wnWbAvY6Tumgqjz26KnVY9oXY/Vbi41rOycK0//czex+EwYP25opiWSebqRqnVc/o7nZDPm3ms5N146oox2GB87n57h/n/+7oJPP1yA4l5GMe+0uTf4/vqEdmuBJK3BNy1+vUayvM8JnwBez40n/hbzi8SskxRfG1H22xikKPDzOWG1hlmcH10HZCrpmhSLQOszJu+fI1mUaO0G8vopL03MtCzbAhTBY2cz3NV97P0T2hcv67wB/nu/8XX+8ztf5do3WkKWk80mhLsPkDxn8pHnaDHihX/od2P/lA9FBpyg+1cCHFT90MAFZIGd8Uk5ejKU/ykMbcy1uEnB+orQrkoeOmFm3M9FXg2QS4crBg+k65uuiwGCIzRuO6d8ICyXSJbho+KYjEa4q/u4sSprNfsZ5alTctHdnPVNx/SO5+yo4vqPHdN355y9NcN0JdmixU1ych+Q8QiJDfqcZ3T+SYw/6fqWESjyXSAu35IEwoDlut0jzQ4oFkLAlCUhs7hpiS8M6ys54uHgnQ121XH61lTjzEYI9yr2f+dd/LVLHH/xALspyc8auv2KfG+G29T0PupGCJsNMhoRXrrOw1+9RLMvFKeBYqGNttnak887stOam393RfvCAev/nXlmUO15HZvg+ObjW9qolOo0xoJ3qkSZZbExKfp6J6uHQm+YENd1/EBt4Px6doHqwK6ShCpghNik5hdL7FitGN3JCX65xuSFsqm7LjY4JUAg7ILMaYgBtBkqHd9W4S7d33G+JZn8EHrPZSly/N6I4t4ctz/i9r9yyFt/7R0+/N4lXvw7Qj7vcJOcbFzhj06gadl/d82DVY6bGa7+YVTssEaltods6aFizEAhZHvssrv+DMcOYcj19e/tue52nzvcNkR0Dbvo2sT9JaRar0g/Dz51Q0q8fyXWzKTuCFbwI8+pz3l5EL/7ZzQH+tSrYGJJJ9nuNC6S7wbYSq6x/T9siwTiw+BnSM2O4gdAUGa1u04E0zhsbfDWqM8wWtQLEkGqVI2AbYdaBH1SEpyKtUNUMHlcJ6k0cbG7fAfdpS+e6H/C6pohPxLKE8GuLJsXWkYf5Kxe6VhfN5hGuPLNgM08H/6WAeuRtQEDowfCja+tOP7MiHYmrG4K5TFka08Qw+ZyRvXj3dN5cVFGmeUmHn8P4KXvNwCpxQ++TzwNwQ0ArbSZxMLOkJlA0MJVcZQzeaXhuJtg8UyC5VZ+3BcQLYE66A1jRcgiwGxEGJsOCwoiIGyk6w+niHPLi6gfeRxD/8EETH9c1/uf1uiLHgjjsuVkyBCIoweoZTCL0nlPOZxHgVZ0UyxPghbunGFzTYHpEJsI5BzTyl5eEy4ZFlcy1ldzyuOcbAWTex5XGPJKvU7MyRKA5Rt7nHw2kI3j9bGRGW0VgLZm6xc9yltGWUsVi6ql0UJrbjq91mJ6Ge/l0Yib9z229gQBV1m9H61+PxOTUGf0sb5RwoIb6YkolvrcPq8UeiB3qKgnIYAXbBvogmw9A028x41Kd/sC2nEEncde2Tm2R4lJwLQbe9qJJdtE/+gsFunK+KF+cPFkUDsdSkz211u9S0MCvwOEOGdDPO70/fvGkIA2wbjt99AT4ln5gompcRiKR5bNYWC8t2FcNiw2ZVTcMh8rTfinPS5iS28VHGKQ0PuSuLgxDraixNB1fusblTyNbY7MJrRT9ZjxuWF5PWd6Vwvf5fGUlw8fM80a3j7bpzpqyY/WLF+dsXpxSvm4VranN5hxhaxWvVTMT3X8BLLT5zvwwk+QNw7fIzzlGEIIGnQYS8gz7VzNNVHI6sDs/RXy+ITpRwfK/AuWd04vc+mjU2S1YZpdY/VCxeSDlQaik5EC3E30A25aZGR77x8ZjajfvM7yZkEz0722OkrsPAVFpnc8V74VcCPD5t/Ua/fzKt/9zKNP8thZL037Keb2efluk+80lCijMsYLw0R+xydH5blTLGEfHHPt9w3Hn5vSTjOyRYFZqv+OXbRI67B1ACf4zrBXbiALFKch+uNauhGUJx31lRHrKxmHywNlCNWeYh4o5uoBVJx5inmLXXXYx3P1SGwaLcTlGX6+0OMLXud0SqD7uCf0oN3OOUldt09ZJ5QFpec+sD2HwZyzVbioaDWo5SYpYzFgEkM3CxHP2X2tmBD7CQ3JN7rrTL+37X6FP+FixKcc7bnF6r1uj8t2yYFp2CDcc1MqaZmIov0NhtrnVO+U/PaXP8f3jm+QL+DxO4cUETTT+FQB1HpfaKeBbkQETYEgeC+8celRX7wujIJga5fTessPj6/ypSsfsRwXHNXjHlwyhF72tfWGs3WFeVjQ5TlcOeGFySk+iILPxpGL70HnBBDlUWlm47VIk8CyES1WPP+fu1/ig99+mWt/0HHlqOb4My/xf/7rB/yvvvD3eal4TBssR82E6+UZ+9mKQjo82sD5Z0Y/5pUvPOLfGf8V7ttbTG4L44dgGp3PW1AQusrgSvWSDga6cZT0jkBhvtRcytZBWddNkocPMRdIjYIxeBn+3jf86nwzrcOcrpDFCkIgn3dqJ5N7dmKnKOUsCaROOZuEbaOHj/dGyrWHIPUwRUv/p7zF6/uKYwBODybf8xE2UUnLxudYCTzs9rYe0nHNaINVT8SnjKS6kEZiDg+/nveCjazrps1oD502CEQZdZeZbXHDbxnNiVFuYgOAz9DYPqTCk/7vY8FXvCCd5ou9FHtiS6dcPFdWaLABWzjKzMXGTG2M9UG9mJN899CbfQjMp78PgfeLxpC1/LS/Pyswez7e7oFxE3pp9eGwxuO84aOTPVYnI2gNUps+FQmFRyYdwQncbJldW5BbR9NlhCBkuYMxdJmnzTPMxuAXOQ+zGYV1XK6W2sAQLD4YHLrWGAJWPD7Yfv60wdIEy9KW3MhOKfCU4j6Vd/ef1ngWkPnTPt8j/frfdZbVVct+niGLtRYWnd+CUEZUltF7zTsBymJbTITILvS7xc+wXQvNANhSYkRiIAm4gGk81akuaqbRPNnn8X5rUKbwQiiP1vhSLT0wgZdef8jyxg3yxRhxUP9gj/mLwubSDdwXF0zthnfra8y7ElfEz47+pclPsy+kBq9y3XmGPD4h1A2hrrXQWBTazOe8sm+CgU3dW/noSY3nrAfp1GO4H9ZElR1dLIKPz015YYwr9bzoufKFZX2oDIkxau0XVponukrI5479d8G4nMd7E25cml/YpPBJs/r5ioz+2QwPZOunfLOhzLS1SB56wEZfHJ5gQj8xLmDvpiK/gtJGmaXO6ee1TV+49psaW5aq3te2ev7LQgHn9F4x5kaMMvqTtHjTIHWmMW9q/kzgdFXuMi+LHDPfEPYmYIRuHPhrL/2Abx6/wPS2yuBXy9h0EkGM6Uc1D+6MGd1bPvmdk3pSvM/7xt30s1ygpPQEwPwJs/Gic78DRIQL3vMnGBex9yKYIq3n4F3Ho8mM06+UuOB+Lm4SFzyVdEoYMReQOMKw+Mj2noieu6Hr+oYOMxnTVFlsvtQGmvy4o7w3Z3z9MvVliQqOQnncIh/eYzwrOXutYv+dDtO4XmKa+aKXl/4TGR/DXr1Q++vj2K7PMga5dN8Av7NfSjzPfsuI9ckXOM7tIocixxeGbmxpx8L0TotdNJizNfnZrI+dbA1htUbueiZXJqyv5hQPl5iQqRJbkWtzlXOILSLr1LB+cUYwWiuvjjryswZ7tERWGz32plX1ECO8vv/oHKhmdgi3Pyvjo/cvc3OT4pQo2R302vRs6d4eQZUuZCD1vAOqPgsJJq5X/d4Qgjb/i6hct3PapFTkW7Z0123rMjuKFKa3gQjit+tgJFUKVtfyJjIEbJSJ9wqCS/o/vV98vYzHULfQdvjS4kaBdZfzm1/+Ll9/95c4/F6AtSNUpe5h3pHdP0UW11jsl1z6cKHz2z1lXZYn76ed+07OYYywew+m75lHC4yLapypyaNXvJALAOb0vHP3YwgQLgCkn+G77ByLVTuMzYGADbzTXuWLxQnAp2ro+FSgtLkAeFaAOvTsaEPYSST7QoN/CkM6JrXSgU+yi22UnEyM6MwogyXTIF66sEvBD1tPahiARvFm65vwI2AbIhhG77GsBRHjQs8aUBmnCxbjlPjG9yPApe/B3gdrmlnOR+OM4gzauaXbc9EnV+D2CK41hNow+0FON1F2tFm1lPOS5sBiOtj/cUv5aEN9uaKdWcLonHRKYLcYE0c3VomjIPSA3/nXJTZAMLIF11JB1G+fN3zucPhcJ5dp4bMHDyiNFtMqo8WUyrbkBBwKOueyu2xbhByV8c7jBZyIwRvXS3hbAqU4CLtgNOjcSoWD83/7aY1MPCKBaVnzuFRmsK3ZSnSb7YLTyw1K/G8wX/VZWhw0Tovx4sF0hvpQ6GaeUHhlS0d2rInd3lnuyPYdYa+mfsHic8f8/pjpj3NGDzNsXVI9LqkvZTz6JYPcUtnb9B62Z0X7Xm4yt47cKEt6CEjbWGgdjlVXUNzNKU+1wUCg93PW70DsTQlI73muBcZgtcAVJtpUkS89EqRfCxIYrydq9+fUyBIyekDax4De50IbmVJuNJCLTCc8oL93evLdSOX7fYl6SGehB5W1azcW+gfF3V6ZYXgzpvsq98puT08xAZ8AA49+97QpuDQhtnNaPEgWuNscUPuc9zeXyZcqQ3owWTPOW5w3tM5go5Sfc88PIDcEpi2+97HcBgRhm2DF7jx9PKL1yUujLGG12kmi/d6YkGuxotmz5GtPeX8Fj47Ze+8ye8WaTBx3T/d48aRGfnyHUfkKp5+ZkC+tFpeCWiNIUVzcfQe7AczPwPik5OHjOt+fOvqOSqMdhVVOyA3dyNJVhulHDfbxglA3TO6sVYrSdDx4tMdh/Qh/dEy+N2HxYkm7V5AtO0KVYUYjZBkbAhJzPoKb7uYhR5/XfS9bQbHwlCeaNJjFRpMGr/LNzQv7kG2v3887MB3OXeOPbUSIa5Hpwm7x5OPGE0Gn9AXDXu0jqc30cmfxualgk7pGOwe1esHbByccWGFztVKFg7XKaDNfIlWJbfb1zSPoIY304Pree47FC7Y/nmKhiYZ0jvLOKVdOVUUn5Fbl3zdaTKU9x9wW6SXoU3GWriMM5LsRiYyi3RMb0vd9SuDebwFy7n8gSLggILvglEu00sh8D3r4IP02tONRCn1SkZ6X5Ei7zv7Ue2w+aZyP335/9TpfGN1hVtwnx/Nhe5kDu6LKjoAIyCGMHgT+8ORFPnq8z6yF8UeGbqJxuzZkCr4IdFPoRsR9H8xG52nIAq+MjxibhpX31D6j8RlXiiXfPr7J0fcus/yzj/js9D73iz0e1RPOmorCOkogE8dZU7KcV+RrQVphXpf8+uUj2mApTdfHSLk4StP2xw9QSdc/Vvstg+BBM+OHP7jFW393SX77MXQdVz8qMN0t/uP9r/A/e/m3GZuaK+WCS/mKhauwBA7tgnE8ny9kx/yPXvoa/8VvfpE//KPX2XyQMb6v3s+m0zzI5YLPBFeBK/rAE+n0n3FBAenIjE6AvjhlP0v0N5cA0sXH+jgmYLrYhR1Z0qZ1yHLdf8/8rMFnpXpJD5v9TIwHz6kLiNPcERI4mvLJi+dVP6sGz01g9DDnlEDvOf48yXcXopY481ApqCghNlJC523PlB4OYQtAf9wIkU2t6kiBulbZ+2yhsVRq0MSgTQMmrl1BtGmyU7licfQWCSmPkUgschUkJqRY9D1sPO2WfqHUJoiYx2e6bp1vunmWJSwE0WO8YKT3S+fl4xjQ4Rme83Gj/4zMs2pyRnnbP24k4LzhwWLK6uFE9zYL7LdgPSZaKHmv4PPsypq9qu7ltztnEdH4Pis7nAn4LEM2hnZR8Hg0prQd46yhCwZDYARgwAbPKhT993JiqENG7XNWvqQNGTOz5rJdAh3F83IjPON4FubrRcNF8MF7ob6E+lYenaqslvfKeh4C1LAFqpN3IPQgay+Z2LldcG84rEHYssKCNUjnsHWF35i4vmoNQf3Wo3T1KjYGLRu68Zh2TzCN8OEHV7gCLF4qaScwuSPMX/P4UeBXbn3EcTdhbBpO69G2BubDbiFRDHJ4gN+fII9OYb7A15EhNR6p1GoIanNSltvvfa5gLG6ghhUCkg/+bg0ht30cKQPGaRjGWX0zuoI8vjBsrgh2A64wNPsaF0qAZmKwm0Axd4weCIt5Sbu/JDc/W/P3T2J4Anm8FO04NYgahnKnwTmk1TVKJU0H9QgzkPX8SQBQMZAXGB9wCwV3lS09wpQlfrMh1HUEGAyhc4iNDOjU1NE3uPmtopH3qqoVmymkKBRwSMxL2D1m55GzBd1LV3Gl1fgHw8lmhM90DlWP9d6XIieEQH5vzt6PRqqQ0H+fwVxOa0Gat0MG9UVKa/8slLf/Wct3XzD6vLBzmMWa6Q8dixuHT9T6flZHqkXl4vvGuX6kOeMDGL9dH2MTT6gbBZJ6xQFDmI57BYzmIMPWnvLOKdx9QPGLhxqjhoBdGLKTmrCpKe4cU1y9TnOQk5/GGmlVKDDomt3GkJ+l8USDg+lrAD2wmAiECbjrOkLHdo8crDMKjOa4cYErDO3EIB6yjcPePyG0LeWx5nzioTiV3g6u+vEj5i/dork6UQ/vcane33UdrQX02oa6pjhuuPxoQ3b/ROsUbasNMNF+IDQNMhnR7GUc5Gt+1kcbAqMPcyV4dk/e172CRpb1610iA6n9ptk2zFxUB3nKXhGGa3JqPrC2l3UPdY0UcQ1eO8J6jZSRnd11cX+KDYLEtbZvbABct3M8smkgO9de6oPWB2B3vopBxhWsakJVgofm0POrhx/wo+VVTI1aYq0hVLmeG6cKgMWxYZlNMGcPtu9X5Ftw+ry39EWNGeclvD9upCbwj1Pj8ErevXDfEbPdW5NNCmyl0NNzPu1IbGvR/DBfwfjHOd/79Vv8q5PjTy11/+ye0hekiEMv6dSBPPRcGhaedgDp4UggTvy5T45zIau1oBSybQAggw7UnmkaNdu275OKGBrkJ/ahpHvDxlf1EzmBhip/7YpBsSSkD9p2b/dgorVc/cYKu+mQpiN7vObVkwrTecIfCL7UbDw/2TD9sKDdz7Ebj92sCJkgrUecY/ajOeM7OQjYZYO0jtGmpZiUunjkdqfIuWNUjBamu5EWWZTlvT2XEB8fnN/zgHT6nkl+r78ukT3hrQLS4pTtbByMbMtpN6aSFmcME1tTicOjftGl5H1nUR49PktyKunIEUrJYvBsyUPLfdexCVZ9pCWQx9Z79Qt7cu49D1JjyT8QYJI3yo41ZlfWOV0qYeeaAP0535G3RoFV8YFsHbD3wLTCxhmaq55eYl22RaZ00bSR0/MbL73H+lbO75VvUF9STcbirKS+HPAvrymKTrvvMy0EWQlYs/VCVC9pR2EcmfgdL+k8+nwDsQNfOK0rRg8EGz3Tfab3T/IsTEMCPZsf2On58JnWA7J1BK2z/mup51enjLNUBAvp/EX5+2B2ARpXRk/okS7idqmqBRC980YK8EsEiX0R53oeCEVk5yTWjmFbTDvnD6rXNexeay+IVXWEHawiytAHLwRnBkVS2Sbk8VxkK33/eVfRecN7i0PdMPeC+liKp8pU+spJXLueMzZcAqYv9PGBbSKA67uhpSw0yYydcn6+0PXeqK+k5BndKNfGJAP1nuHS91fY4zm+aagebdjLaka2ZflwjFk8JjhH9sEDyuuv0OxllF3AugB5hpQFsl7vBg9xfKoO0UHg3X83+PSb/EXByZ9msmKip9i5hyXLCNHHOxiJiUKguL+E41OV4Xm8oPYWi0ful3pNReDuQ6ZXJ2wOC/JFiy8zzEgLXKGukbLsv6dMxtSHpTZovdNQ3j1D5istiqUgVtST3PgZPr/EldGi95HueP6kxjTO0CJnOBeH7IDMXhmBAXb3ihi/hBj/9HYlhh4g0A8aWh/o59pWsOsLIlQzCO5TvJS6MNNjsQAZ4meZCEylAmP6zG0RdvD+KelJrOq6IbtzxHg9oz0cEUaaHN39m29y/etnFCctZlliN8K3i1vYRouR7UiYnjqmHwnZxpHNG04+O6W+uUf54TGyqbFLZTj10pvE5DfK0YlzW0+9JOPVP1GPPYQIrnmPaWOBKRWDhgWp4dQayI9JTHr03KYKMP1eESzYtcHNZPscAWPC+ZAgNklu1/P+77LbJHPeOzp4Q9uyu7H27/vTj5eGI8V677ZXeK+5wr/39/8Kv/TlH/O/f+U/Y+4L/sPbv8GlcsX/5oW/SxsyznzFv/NP/zLXF4Hv/9NX8SNPcRboJoJdK7Dgqsi8zOO+H0Da6LG8jvdG7vnB2XV+cf8jKtOy7EpGtuWoGfPh/UsUc8Pvf/Ayv/gLH/ELkzssqoq5q3YsS75z9yYHv1vSjYR8Gah+ueNSvqT2OaVpaf02rVq4qv/ZBYM3hqNOpU+TvO7ClfwX3/tFXvnbgezduwNZscClPzrl7N+9wf/jf/lrfOngNtfyOW2wjG0dz2POxudMTK0Ak13zxf2P+PCVSzz2lwDL9LbGkuIDWYBubLCt4GNTXZKvTjLcaaR4VHxQUkPjFZx2ei/1gjNRrttnBp9bTN1hnMPnBnMW/UitRYqC+nKFq4ICzanxNxCLeOcmSfJ7dqmRcfA3YcDi1YdCjIN6oHkARqfmyB6AjsDpTpP0T3mcuDFXszmVabBZ4MSNGdkGiTYwbTA7TOnkcf5x4zxYLaLS/sZoQ6qzAbvWtdZnkC9tjKWtAs1lUCubUhs7u4PYYWpDD1SbjezESn0z6bC6MMjxpVMWtWpDgbvZUkVJ/NI6Wq/KPy4IhXU7xz/8Pjvr5tPY0T9BTHwRMG0k9HWeiz4rfY7NPItVydXJcoctvWwLTu7uUTy2tC/VZEXHqGrZH20oM5XcHmUttcs43VS0zmIkUGSqXlW3GXnu1IfaB6Twyqp2wmpZcZR3TPOawrieUT700jYS5d/jdXJxLm1CzmG04MIuKaT51OfrT3J8Wmb0+TGUUU/DxFpO6y2XZivuvTCiORxR3RNtZBNRu6tWlVG0cTIVU7zGBVFaUvKcsGl2i6KZ7ZmdO6OTrRylD0gLWIPZtOQh9NYhNpIxbGkRbzHOkG0CstzgXp6xeLXjN3/lu/y9732Wyb2Ox7+o0qZ2rbZRt954yI1qztuLa3ztndfgNGeSgJjk8WkMMh4RpmNoWuSDe4TgIcsw00nPpu6ByrzY3uPx+4UyFmC9J0SP7cRC69V0YAs8pxgr7jEh+UUOO+iC7jVumtGNDcvXWka3c7qJpZsEihNtiulGuu+4Un8fvZ+zvFpwOF5vrQ3Yll36t79gjrhzc+iTJOGf95Hqbq4KtBNttkz+0f0IHl/XKnca2YrBOWUfJ/bikOF2biSW8tCvVYyoV6wRJLMESkigdNthsgxGI6hrZUuPKpXRjYCQRJuufs5lGRJ9QCXP8E2rgEC698YjnctNq6AUxNzFAE5Zjgezvl5mWunVR8pTj+mg2cuQtqCItj4Al364j1lstu+3owIlTzbEDRjUEnbP1476WPBPvt9FY/geFz33vPfwRR2o54/544b6AfXrnHQdoSxY3QwcmgYrPx+e0h6tK/tsUKs5f24SMA2w1jkgRb61MEs5caXnxJWWzb5h/70GHh3hFkvKoxbpCq13NmCaDl/X+I/uMbp5wPFnRuyvHKERrXMVeb/WPsHfTo0kO8f3Mdfz/PMveG7PXv5JxkVNGcP3MqIWFnlGKHN8YfGjrK9zSOfJFg2yVHKBqnHE90rS+Jm+vpsWqq5ZqCpm/nCJe/gIe+0q2Rpao/XibE2/37oP7zB6fJ2jz5dc+3qj1yDKh++QTKwl+957uu6lRqvgByzhTn+uGxY3LX91/zuY56yu9GnHPAjV42gnO7Rb6JnPTmP6VI+zVtnFPTCs50mA1EAUEgCbJKGf1kAzbGAT0flR19HeoVH2dFHAeoNvWiQ1Ig29ra3hiUsQfPSYTlYOUZ69qgZ7wScMo82B3axU1YMs8HL5mN++/yam0xpzdQShyFRWfrUmhED1SMjPst3asbVbCe9zLO+LasyDk/LEedL3M2jxL/TKIX1cOVTQOM+g/qRmsvR+/fvYT96Xnnro0q8HZlVz+N8cc+k7lkd/c6pv38cIz3b/PDMo7bTHE0ugipJ6iSV9PgFSUDGwCTlkgdAlQIet5278PRUMfBFBz06ZPcbp87pR6oYIkGkHpak7fGEV2PXq0TpczGVwcsMFMjRJaq6vG0Y5tyx4bG04/YKL4FMgeS2bCAIaCXQS39dAtmi2E2n4ewjYBX0Ak51syE42el3MdhEWF6DrsDFgFO9jN3ogO1HJu2BMz8weMkdTUabdDzAXzBEUZ1HK3LMFz88Vc1TGXHogLwzeN0nehfj+UZEMJF6jDlY3lGHi0fNRmaaXcWZQt80uELiw8ebrcBhMH0CPBeZemIdcu9mCKGMaaDG0wTAzrbKkn5MkobRbhp6RQL5f4x/a2Okci2BpzY7ZUVJq6MFottcxNQvo3xSAEBcYPQiUx8K8ztnc6vQ5NgLK1mNi50zyF/6Dey/ivWF8uOLqK0tenh3x3tllHpxOya2PzZ1RVjKC0dYoyJnHosY4ayiMY5I1TG3NXrZhZjeMTd0XhnKx1D7n3oeHvPJuu2VHxyKfSpHFez5LxceAcer7ZiTgYwIO0I20WFUs4vcJ2hDRjYTyNPSgr54zBamzjXpvaTOLTj5XQDvVRgqA8R3L1W80jN9+RChzzn7hkAe/aule6JStYaAbBbK5EPJAsCrBjdDLGm4vdDywFJNZr3LqgkqSAcEE8rIjySmGoOCDScU0L3iv0qsE8DYQ4r0iXo99ctf393kZfd9MC92eY9kUUNBfs84bGmc/0U/rpzEWocZIRjdC5Uc2moCqh0WAGCD1ybGLFeOUEBsDsaM7+ABXL+NLi2kcj35hzP57HfndE/zDx2AM5oP7nLQjJllNdpoha/VEc8cnjD84ZP7WHr4wSGeQIlPJL8A3LbYYtND6MGAFDDpno9TQTmK4030WH08e2ex23u5IJ+0ELYO/DbqvQxflz7B90k6e9wDtzjGAfmYqGiVv3PS+Q6/c+JoerAsB2TSa3GcZbDZbFjOo1FpZ4HNlbxx9wfLS311ijs/wiyVmfwZ5xg+Pr3F4fcX4roHZRPfiuqb44V3Ofus1Jh962r0COypUwruutYs+z7QQtl4z+r23Gf0efZC0W3wOSGE1YDWG+Ys5/8a1b1KHlpEUF+45z9tQCUd4Iv8cdi8Olh2NmWT7WnjCzz418w23xiDSg+DPNM4nsCmRDPRglcsN5vzcT8Wu5OWT4puu25V6Cg3maE6WGcymw08rZXHH+VecGvI5zA8yfBHINvH7BXqmZjBCNxLcKH7OQBpzCJQHtOEoWLNNblJH6DmmUH/v9V73g+/WnwrRAlNInaj+yeLQzrkL24shQeNJLwQvmNz1sUAALgKRf+LxnMRGnzTaYPl/Hv8af+eDz1FmjnwuvP3oCg9fnLD0Je++e53x5RXuBYNDWPkSaottoXosrF7QwnRqmHSjsFWcCQp8pfhLuhhzjgNXb5zy6vQxVezUNOJpfcbd1R48LGn2PX6d4xFWvqANlsq0vUpPZVpeu/qY23tT9t91tBPDtKh7IPq0G/es6CTZ3XqLw+CDcNZV1F7ff2S0kPrNoxfI3x0xun0U9xeHVBV+puD19MM13/rmq0y/UvOlvQ/xXlSiV4Q2ZGxCjg2eTYjsR2+1WTJobmPb7f0jIdoDNVvbEwVv47wfhjsReAa0WDkApIdD95ktS1ofE/W0Xm1ik40WNTaHlm7mekC694MOkGS2U84k8fcdFaf0fzrO9D4+guvDuDt+N+Pi84QB83vwGY4dMP6nNQ7simWcc4d2wcTUPCpnSKYAZOd1Xq19Qeu3+1xqjvXOIhLIo4S08xJZtmGnJiICXWvVCqg12OTJHs/FYNnC1kLWAxD0+XowaAN3qY0g6bndKNDeaNRawAvUiVKdipkBbEByT152TEc1mfVs2oyms2RR5jqxnF0QnDeao8jWY/oiZnh6Tets39zj4//GeKz4HqRMPw9fa+NEa50lE48XfW7KR0XUI94BXTA9kz15OG9cztVLc+6+cxWubsEsH4R1q+uDdEJWaOw2Px2xWFTkRUeWecq8ZZR3HJ1M+aWXbvPy5JhlV3LaVizakkerCesmp23iCa8ceME7Yb6qOK1GTLKGKmupvd2qZxiHAdYupxNLZhze6XlMrOlTN2HfLnkhP+bArKliA/LP+7ASsPst85cryjszjYGc7xvUaFr9lxr1hqfEa+3mPKM0tFumpn6I0QIvxGKgVyZPZpG2Q2oHhXodms5rXB4BW1sbfBYoThSICwbsfsu/dPhH8Hn4/c/9EvWhzvv562j+Cnzr6BZ3//AG0wfC/I0OX8Q1LsU+zmnxNuZXWznvmIedjwUHbBhtfhwCEdr8l5jQ/YgM3J0hoqSQQSFzZ4QAkYTSTA3V4Qr3KMPlQjcJ5GcGV4CpkgqbNgPaBto228rof4rxk6ojPK/DYHABgoXpRy3h9Gzgvyyo74LZsuAGrz3/u1hVEZKqBB8UyC6KHZAirCOLMM9hU2NGFYwqKHPMfK4yrV2LPzlFphPMdIpfrnDHp9jYiEyeaSOr91uVtMbjVqsIWuh7sNaKoZmMYTYlVAXkFnu2IgwUWST5erYd9aGCDXaj1/psVXFt5TFtoN63hMwQZhOIoPTo+/f0TZJXevK7TfmzIcYdYTefTnN6QJzq07MQoPHR0xR6/84heDCQMu7vv+RPPxznfz8PJpwHJLzZvZ+TKlk65uAJHaoWNaloXjzEF5bmipKMXAhP5Ko/a2MdGgyGqzYQbtR0V2dkxyvoCo1Rl8vd+o6R/vfQtH3wFHzAHh7QHlRICMxfzFi8JFz+jsr/mi9+RmPspTZ9mk7wVYa9egV3dAzAoz/fYlzJ/o+c/m0yUXWvaFGa/HH7YUTrZUa20sQAIr2SBdDnDiRZbNDcF0gKBKHrdA8o1HphB5zWQqXG8wMg0BcZbpzjS0s7yagPDN1IaCdCN9K8yhfaBBMKH1WQoLdubc3WNscLSIV0+1SPhRtfW5N/9/2epRucR6LFm6ss3djw8FcD1SPD4dcazHTC2VdeIF+nJnBh/MBjrl6GzhGWS+79hqW50XDphyXZssNWJT2YlwDDgRT7+eZzyTK9F4BwuM/Zm4Frdt4zPi+yJ/xZGA/diPqSKEnRqNJEaBp6yedYc3XzOYjBFHkP9nsfEOu3oHPwSJZr40/TENqge0Wm/sK9H/QQpE7ew7GhSJoWv9HkI6zWyGyKKXJ802qD0GSiTX9xX+j3HGOiwrEC6pJlqmIAek0j213yHDMZEdpOUxcXGwpD6P2xe1a48/jK4kqLWRluN4cEYHLP4XOhG1nER31fo/fd4fcbsnm7XY8T0JveMyVcCaC2WzKHdF1MYAfNHWHgtz5o9nqibnWhfHfQ2tRwnG88CX43JuubQeLxPwvxqcc6BzLhoN91U2sTYNPSXD7kt/a/jUH6e+VZGdPPDErnMRo34nHIEwlLkl72ETWyieEq5zz1hj+G7f9anLv4s32RaQ03FkWC3QJ2vZTu4P0SsHd+JDnwHYnFQP8eaRTXVnRthm+fdKAaMqUvbFPvN4iLv8vTRs+AeuIP28JwOtaU4yePbJ8lXwt9zhPv8kTh+nyicO4rCPT+X+deb1rwY/XLM4S+81CLZNuzZZCnyqiaC75nJYZSHKe+pA2WmWloB50VufgdMPp58L/qvCEznkw8he2YTdecVmPsZgAanB+pBnfBfXD+58SIM10gXwUmdyFfZmyuWtorLTLu6DpLlrkemHbOaAEmdvudrive8VeYb0qs9RSRHS0SIkCtr7MSIivaU5iOwjhK2zGyDaXpeilKZUt3tGQY8XiEve/mZMtN7+Ws80d2vpM2iuj3Eb+N602c1D42RrhScNHHsGf9We1WKpYqH+kK2RYdO01Ifa5samdFi9Slfq/L3xQuf+MU89FD/GKJFAX7J3Oy1Uvc+Ysl3dVWJWMzDayI79szopMHdbp2ol8oAdEiAYnye2K10BUyyHOnxWOfcpUQYz59jnNatPNBoBHMyvRyktla77PxrOYHp9e4VK4wogC/jB3zVcVyU1BkLl5rLUrOqvrZJ++f0Dgvn5yjXnbdJOCrArtpEBeDoeChHfjOOHdhd1uSEhNrCaV2TzYH6imdLTvCfIGMKg00rx9yZ7mmNB3FsRBmY4wIYT6HD+7Bm3vaXe8tkjzS8lyDBIjg8vDDBwu5ZLqhh1iMEhMTV0vv1zGUHU8jbfpJQkZk22GYALEEOqakHLZBXF5qZ2Eq/JSZsiqMyuKF0ioLNxO6yuIqgyu1eONyUUXCXFQN4BwYahr14s3WgeqxY/yjx4RHmjxh7baYkWX40uILw/J6LLA6j398hLlxDXd5RntQsW5WHDVjsmVQD5YihweP8WdzioXn9K0ps/fW+HFBNhnDyenwQu+er3jtd1hYIspmiZ24x78Q+EJ5h1JyrBja4J4/WPq8rJtJsYhsr4WR3lYDiGtQ/HHw+Pl9JcT32n7W7r4dIhDz1C7NYcNFYkkPihZBu/F6ZQJys03YLwCozneNSpLjS4B722LWbWQiWUZHUSYTKE50LuYnFp+BbbVwpIBTbHhqPZN7jmzl1P9w+Pn2ApBaRJPvLCN0m4vPATBkhutivr0n+78P/z8fwPdx5FPikjQpnSDlII+JzU8/aeP6z+r4Tv0if+s7X2b0nRHHVzzVXNi8N+Mfv/lZXiyOKO9lbFYzTn5pzMTU5OKQUUdXlepVawPNvtptuErZnNlKZU2p2Fp7GH2uM+BmnmuTBVNbU5qWhasUtIlg1PRDQzuB9dgwd1W0IjJY8ayi5t+GnJvjM37w2QZbF7QzeLNY81F9wMg2tEHBoC5YWm/JjaN2mQJCwdC4jMw4Om+5Wi3wQfjg3iFX3gmYoznBO2Q0on35Cu0sJ1t2ZIuGa78nfOeVG/z6/o/xssuY9RiWvmTlS07cmJNuzKrJsRsTpV/9FnT1Qe1U4u/iwg5mOGTyyA6bOETQeJCAp5qssLN2B2sIhUEarwW9TCXzw96ExYsGRq2ClTHWUkB6yHBOi11c9nz6DHogWmPKxKRmlx09AKJV3WH3PdJ7m1RfMfQM0p/mOLArznxFGzJO/DjaYjluXDll3eQc5CvaYKldps3RH9OEqMzei/1Ut0Q0h9tsbRkS+N+fiWF+LtufU4HRi14rsdtz3O05pgfrvhmz6yzWbu0I+uMzahFU5qraJGg+NczxhmBRsjHY/R7SH+/QW/onHeEphYME7n7cSEBYYR1h5Gi8pTCOLhhaZ/HeIJXDbjKaByPGLyo72XtDs87xZUfXGZouwy0zPlrsc1yPmeQNh+WSG9UZr06PqF3G3fUe9xdTVpuSptY6Rb0seJBPuTGbA9CIpbIdpe3wTqjROVPHdaNDmdh4XdOG68kmyzkwKzA//XziT2J4pFdAzK3TvP2NiiBXydeBvbfn2ONljIMuaD5NwwSCP1cIhC3YNJQmTfWdVPhOTGLnkabFLtHF1KkSjQRtJLAbbSzMz+oYo8FoXPNq/ojfuvQd/vA3b9He24dOKC+vyb1wvBjjvj/jxX/UsnghZ7nXEmwWY7jtXRQ22+sreRZ9HaNv4YUMTbsFp1NDYAQuer/oBMh5lCFt2AluetXDc8qGWt/Sz/RFlK2vVPmtGQXaseArR3bP4ItozRFj22A1j2nqJ8uZP8mKcB7Uft4UyJ5lWIEk2OLXm8iALrZz0whish3QQIHcTEGZPIMySt+Oyl4dqbk6pny4wmeGszdn7H/nBLl9V8EkazSvcZ7uxj7ZsdozJbZ16DqkaRFrMAnMqOstEy+OHW/Z9Fhi0jmVd/VtZNNtItN0udqCAqmAbwxhXGpePDKUx4GTdkzbZIiD4vGabqSNf2Fcxlgj9OAc0OeavXKSDJigmZKHQiz0K5nIxTjJQ9upnP9aVb56SdpB7r8j4Tq85xIwen7NSa/7NGOghtVPDuhzvx6A6zrEKbu1G1tk0jH+2Zv6F45cLAZDjiM4If/wMbQt/tol2lv7FB8cEY5PtrLN5oK1JK3dea5S8NawfEHwn12w+PaIgweHPPryAZP7HflC67bFCZhNB6MK+dzrfPgvjLly/RFnr15h730T6b7nKhYpJx+yJId1pQQ+h2irEAE4yrxvpAiJmJCUKzKj63Fm1BohV1KfK62CblW09Sm0TtRV+r+LCpLAEwqsSfmyPI5xmFfgwDT0DbApLuwbRl3oyYlZ7bHrlh21DCNQlmrx1npcYcmurWmuCvMvXmP8wYTFTcv0nsM2UByL1jesxe9POPrNV8g+f4YNwuralP0fNVEi3ewqRZwfaR00RmuTUdlJFitm717mql0D40815563MTMN+UJJX2FcaU2k7XrVi155LQKiPpFFrEVwu41Nkus5jQoVCcAe1gsxZqeG2BN9zj/XiB5H0+o57zr8eo2xFplOdN1Me4Jz2yYMMTofXbPj9y5GkKaFskD293RfCwHWkWgT94nggzblbWrcC1fUjioP2KioerwYc23tYQ3t1JIvwV/ZR368IEhN9f5JPCa/vYf7hpaoiJPW+rT+er8lb6Q9LgQF3klrcQT1E/FhKFX+JzmeVZFzUAOTpLBBrBHvjen2KlbXSw7M6lN5Safx7PLdz+A1NASmU7/ds+b6F+V8qQDR7uWUj50yozu/fdPzgPIgsE1jlzm0+/ydovBg3Lx0xslqxOnpmORnpm87KJa4J6Vath/0CRfifIdd/33Dzv9PPOf8SwQF0uIin3x4L/zI4XeXBE4P3lPYFm7O11uFnkltWsj2GnLxO3PCIeR6KNhn6Ig4L4WRi2FmOk49NLGCe94LrMUoM/950NqDnjmfRZnrg9GG47EnLC3Bod2HJiY1ASAkJHbrLw1br86njfjHfKHgkWkN6y6nuWLI9ppYPw94b8gy17MNArFLv8kJQcisJ7N6zZJUdxqJLV2Yjsp2jKKX9NTWjG3D2NZU0lKZlompOXMVY9Pw3eMbHPyowzQON8p2VAqG92Uvv+gjSBKkB6bFRNAii6ynSoHm4et9Ab5mV4oeDXZ8p4CFj+z/bqzneO8duPp7R4QPPsL38k4Gf7Ri8t2CvRdf4Oi6Vj6D18K2+OhRFxkdmLB7GxoF9BOQIBIQo2x1Y1QaUQSKyGDuC2dxDqeYLgSh7SyuMdAYspXKkCbVBoBx2bBsip7t4AvIqpa2tRAyWuMx1vcsiyq/2Bv5pzlysVTS0c08fpxjmgrGlSZsqw3hbL77gvOgcHwsOIcZVRqsdp7FCwWLl+DKtwJ+vsC8+SrtJfVO++io4HK1JF+i0jx7E1itCJsaCbC6ljG97TG5BkhizZMyW8PfBx1uPWCcnzMmSsxoa0Cy3UQy+mb34LO1SGn6rtRe3m7AbNYmIwWcfa5JYju1uEITCASamTZg+BxCBj4fLCgM9oIU759TZjRR+rmdKvu0mQn5ap/8+JRQb1nSKreT48uMYGDxshB+Yc7mD0ZMRiNOf/Ump69Zrn2j5uyjGR9OLpGvgMzQjUryY5X5W18yuJEwez8WrIZe4ueu+7NITMlixeie4fWsAaqPfe5PfUQmvQQFQHoJbmHLkE57s5Gd5oEk2w0JJKZvYpMQtkonT1Tt9T/TPAPjKQHSO69/Mk6RuNaGskDqZpcN5IdFSb9l3Qx/T3JRISCLNeJn1Fcr7NpTHSnQNf5I6Maa2Nraq5Rl9BUsNg3T7y5x+5MdK5ceRI7d0ySPw1hEkhDUC/J8h3NqCjnP2unZgWmjMah051NOX3rbYSw66JIM0RcWJ1qj9qJF7aAitsOQ8efYEr0f/5d3/jzTPxgxu+1Ynag8afVY+I9f+RX+u299A1ML0/eF31l8hv/W3rew4jFZYHNZek+4Zj/gStQ32oNdSYwb9B5xo4C3RLUTCCPXx5LDptqRbXhz7xG/M3mB0cNA9wv1luWIULuCOlZ4HYZ5W/Krb73HDy5dQ7wwyRoe1NMonetpfMaqKzirK+Z1QecsnTMqvesM1aghBKE50AXPfFSx/3a0KBDBX95j/uqIYCCbWcZ34dIfnfCjL12ifcMyNg2PuilVaHFBaIKlDSX3233e21zm3fllFqcjqlNRmco6NpWI3lM98SXo7wzjz9j8kf4mqSFlMMT5PoENiBZg098igOlK7ZrPJyNCkROKnMVb+yxf62BjtYDl6GW7pZMdwJl0fIFtESzJeScA2mucaNoUU25fqwz53TmX1Lf0CfEjYu70PGQUD7oZlWnJpWPjc6wEruen/PLlOyxdgcNw1oxoBwBiGDADn4Xxp4IQAlFVydbb9b1frga/hxiwDmqR+pz+/KUmVL3PZNwxKRvWjcoUpWbZ3rPZeHLrCEGwA4WnzDrqNqfprDZZngOkDfTe0RIfe8KD+hPAo+Hfz7Okd89ReOK9zkugXzQy4yltRzmt2XQZRbFdb4zxmNzTjQOju5bmmiXLPN4HWicEb+h8vDadMF+X+CA0zrLuch7ZKYfliivlguvlGfVexvurQx6tpzycT1gvSpbLitOipcw6Op/RBfUgT/nyyLZKGohrmYlscIfB4mmDZe4qVr5kkxXY7Phjz+efxngW1uvHXfeLPNjT44nl3nSW5lrHo6uCtEJxOma86VS+2wziIud6QEmGrOnzcZO1TzKqcakYAEaBD3xQP0NABvE2poC0pkWFCrNqlAlmhHqT80/Xr5OL43/x5m/zf1z8S5RVy2evPOB7D66zeX/Ga/9VTfnOAx598WUO9pesZKRrvbEKaKR8xAeVSU4s6UHx9In4+yKWNBrLJwnU/i8mcB7QD4l9F3+XAD7TwnIIYFqn+XmuYIo4WH00JVSezdUMAlRHgdV1oRtHcopTpQbTgF/kuCuys1b9JNLbH3fv/ywNN/UKxkYQQGTgGx0L/BQFMpsq463IIc/w44Jmv2R5M2fxgmFz1eMmnuKxpfzFE4q/fcDsw5ajLxjy1R6Th0dIHdlpMcZvZznZMcrGS2y4TmWMzahCyrJncQbnntx/h8D0+Sb1yF6V+aIH0MOmVsa1V/UvyYWwqVm/eJPVlYx8HSjmgdO2wnVGGxlWDTDRudbF/CQMQIN0HDGHCLMRochwVUY3yehGhnYS607R6jBtzaaBfB0ojzvyeUt2uoF1reD5J43z9YcnWJl/zLbrbUesfrek0plpo7fWGgyXDxfkIrT/TIyxf7oj1ZstovXGtu1Zl/OXSqZymSJZxcEOQCOiDQdEey0SSCbQXPLsj2vu/sUR+epqr8I6euTxuWDrAJ3DHU756C/tYb98ikjQRtrc6EcM8+QnPMm3nupSVbHBc1s7Is8I1uJLS8gt7V4RQWaDz4V2JJG4prUDXypOkFRXe/ubGDsP64+mQVV0Yi6QSDOAKtq2qjrkrcbWpgt9/Vfj89DXfqXz+vfOYzcKDIoLmNPltkkjnmsZ7DHeCsZ6ZqOa2/9iyfXfmTG951Qdo1VlWNMF/Lji6MuXePibDdfHGx6fTKn3DNI6fPTtTsptF67mqSHdGCVSDM7//A3PTXveiPxnbxyYDldCcebg6EQbhuEJiwZJcs7DxjpA8my7hzgHJsPMpnpfTMfgPWG1QaoSvz9Fbt/VF8acvAcvxyPCuMKcnuF6hrxTOe/JBAO4+ZywXuscT2z/1KAzXA+j1PoQkAZUvcM5Pd5ZASLaIOS8elDrF9c9yFrcuKCdZoRMyBaqpFRvou3sUU0zG2tcUmT6GSHA42OVOhfZAtNp/eic3qf5QKmgH3bbwOe0YVF6D6qtmoGUcX/surhW/zELQ08obpzbVz7t+/tAIGxfV5U0VyaRHCVMpAUyDIIn9KrInzQ+lad08pB2seMInp4IuyAYvAanFx3L+Sz33N/C4O/zF3PKxxt8YTGpUJqaDBKTOHW4kR5/yvsOpMhSADEsKIqDUdbCGM7ORuotu42j6Tv7nzYSoyAVkT+hsD703rlwDArS6RiH/4eU2IdY3E4bAdvjTODz0IvyQnB6UJzZuaxCz8I2HcymazLjolS7nrw2ZFTSYQWMfAxLml3Q2mCiNKKnEGXgq4Sdvt6h3mK5eH2sZ2j/9MtIud0Gax5hltfIfoObV30BDdhu8p3sntiwnXtPMKdTMSgEvBXd+DtluUzueYpTYXOcsXhd6MYdedWRFx3eCy1Rxs+6nvgyBCyHRZXMKFt6lLVUtmWcKRg9yWrGRqW7k2x3Yizp9zW0wfLRf3OTV4800E7yqucDKwUP4mYWpRZVfiN+8ThZXQQVukqwNVFyErKNdk13pej86rYy4SmwQgKulCjbHTj8Nlz+3fuEj+5vD8Q53aiKAv/4mOu/M+Lk85fwBy1kHjcWTBsLdoVHslSA2PkyeqsY34PS1m5/FhGsTezlQRdykiP0wiZK8Ll5jl0YsqWhONPr3Y2gPIX1VcMLk4U2PYjj7nKP5iBQlsqO71qLi4XuroWmsayzTynP8Cc0zrOlQdcps1EpvDCpaK5PseuO7O2NgkUpKHyaJwlosJMZzLpjeUs4+NJDFt+6zP63Cpav73P/z1he+v9tcB+Oebu8im2VdeDGOfnZmLCpaUfC6oaw915QKeoyxyQZsgSEDgFSSUBxYi3n9DIrw2ObTLRo1XXRb05BV8myXtqu72DNLH5S0k1y6ks57URopoIvtiCz/gt9skuIBeR469okbRyHaVUySpz+nBhivZxpUhiJTLl0b+m1QYNGAbvqBklrDAJzq8lPBCvqS56/+uqP+Id/5pd544+mmC7QHOi+Uzy2fLB3yBXoFU3cjcv4cU5zkCwj1H8eH7vr07l8iqfJUOY8SfhIURAmIxDYN1UMeOwzS8T8qY4QvaJCtBqJCi/epOYDTcKCRR/PZKvqAnEv2MYtaquxtUkIcX9Ie7wC1TE4t+w2Cj1xbLFLajiG63cqymQxzgqRCTkuNcgfqADo62TLoDnvyZwknYaxWtDvazIhW3tNbp3QrhW4N63v2dquUh9DqWNnUt0MCldaMN0BqdNnZ9EWIMu0eWQwhioF2++czoN54rl980qSSup/Hz6RJx+Lj0unEt7J6uF5nK5/GuP025e59Dhg60B1nLrqA2c/mvH2zWuEDOwm8Nv33uDXJu/yUXsJt8pop3FOd6JycRakS7GVFl98ro0argqELPQxMh7mTclhtuxZqGm8MnrMH331AY++d4VfvHWPqa1pg2XlVE6581Yta4KwaFUFIzMK5HywuERuHY+7nFWb0znLuslZrwr8MqOXsYsA7LzSAsc96+i8oTgRsqOl7jFVSXtQ0ZV6fzcTwV4qGM83zN6F9zaX+dLkQ2DKxudsQgEeVr7kfrvH7dUB9+czOMvJF5BtQu8Fr/MvaNaXbhMXtre/R9cqu82vdvaC9JAIZGn+Sp8XmVaLnG6UUR9kWgiTq7iRxZXC/EWLVDX5nYJuFLZe0bEgtisdHn/3eh6kZVeeO4HT3VaO+wmZ7zh6Zalh+J1Y9IM86Kc97nf77Ns1E1PjMRx1Y27lx5SxE/OoGdP4jCLaubhn6J6/KM3eLscGu5HdXC8MlqSw3Xp6EDrlgjKIUypVMPKZkFcdrTN0zig7OKoywXaZ9fG4nQcRlebOrad1nrrNGRVbicokQ46hV1wb1hwuqj98mszwIhnw4VAG9uD5hAs/0wchEwcW9iYbVnXBXqFgTG4dRWYw1tFcdYy+k7E6rqhuzuM50rzNO9PnQ11ncV5wPqPpMkQCp3XFUTnm5uiMm9Up/8MbX6MJln8yf4vfe/AK9x/us6wL/FjovOnZ9JlR+fG1y5VBL8oqr32GF0MbUg6vKg9tsLQheyYiwk97/CQs1uH1bpxlfWfK6IGNzZ3qn94djMg6h6w20Ha77E1jdmPSJ05TBK97lRiLSGxYhYub/2y69oKsG423u0FklgrIBrrHFf/Bj3+DG9M5l8slzhn2Rxtthno8ZnrXkC0aQpnT7gWujTYsbfwcozez5jK6D0kW2UsphvPxuw4b8tJxn4+VEnCzU6PS5+1Y53lRNqnEeFSG8W2qS2ntIBjB56LqcO9blq84mr1ANrdRJUXXnSGBwriA2RhcnO/nr/PHjZ9FJvTHDY8nB0LmlbU4HoN3+DNVZ0iWVeQ5Zm9G+8pVTt8Yc/qm0L255hdf/Ig/f+l9rmRzrmZzDuyS31+9zn/0zlf4/NX7vG0PMG2gGwdO3siYfqtUgMMkCy6HaYPKGV86gOOTKNPqlR0togByViowEFLhPsTGjy1rUdLjwatSmlcSAKCNHc4RmpbgHHYSc8WgDRVSldz9akbI4NJ3hHztOa1HhNbQjRRg2FyyFGedMqKTzU/CpESUJT4dsb454fEv5DT7od/zfOExtWh8aaLtWyd9g5Y221nsOmdye8Lhd2tG39kQ3ECCeTiGjGnj2WFR/7EmxLmidXpPMdsFRpThLm1HOzU8+pLwP3/199mEwFieO+2xTz0SMOJQZcNw+QBfZZhVg60D7TSjnIwJZ4vdvO+8B7i1hCJHfKwfVZ6X909wLxnu/KUDLn8zkC1a8qlV+0Ix+FnF6Wem1L+24Fevf8T3H19j9l6MRY2ojYPdNir19gYQfdVFG5n2Z7Q3ZtQHOetDSzsTXMU29zfbOFgbNeltPNMSl622qja9Mk7YAswAPQgdAeq+0cKFHbAZNH/I562+xnl9r7M1qalE0j7Yub4+JsnjuW23ksqwve9F9wHTeKpjx8N7Y37lV29zt6p5T65z+Q8so2NPM1EVwNU1i/g9Hv1K4DfeepdFV9L8p9f0Pi0yZWMPLFMvvKeiTHXY1FpbLgqkKgizMX/pz32bUp4ZKntuR44StvJlR1hvNAZI8zsRZxIA7BxSaMOcX280N82yvn4uCSyeTQjjkodfuUQ7EQ5/0LA5zDh50/DK3zbInYc6XxIhwTmVZt+vyB6UsFhGC0HdG2QyQcYjTASp/WK5ZV0PY4+gRKVtjclvLSB9wDct1DWmaRCv4HNoWq3/nPdcz1Q1s9m3mDZQHsPG54TO0I0N47cXyEsjvYe6WEfqpbSlt0vc8WhONc3ObX2xMz2+0LOqo+lxF+X64+PSZYTNZqvG/TSS68fV9eCJst4nPv9ZRwhoAh3zuiI26RhV6wwC7eQnf/tnvtOM+F6qGej9pIe/p45cK+r7q+wGr92/FxXt4s9Btovn8HFXaBH79LNw8E7RM18IQYukMSjVBVRf/ER8KeeKu+kzz10wLVBoZ9PxZsQb+495P1zWv8ViSd/tHc4d6yeN85NjKC8Zvbd2WD+wBavNILhP9aEtjqf1riJWWFJRWvsXBgEHO4B0AqV7cDoVZyL4QZxzw3Pls4CtdRO4NNpg2TKlXYiS0QRyVLL340/H7gmxIrQhYIBD07AJGWehZE80qVdgWt/TfiKt+E9/+CAx+XdMpxsWVYmpBWO1dt2zotN1GDYSsC3I6YODx9LzByPN22wTGN8PiLfUB5bmSoa/VJPnDmNUp6Bzti+6lBGUPp+AiYQo2e2orHpKl6br/yVAujJtz+JYek1q/tHxW+z9iF5+Nc3HHXWCeF89AboPsAOJqs0p2QyZgrP5Ql9v2oDJEkM0kK/iy2X7L5goNzMKjB4Kh3949AQgHXxQVk86p3fuMb5zmcVMwAZCEVRONQtI5rF2u97tMJ5FWdGKM0aWtKT1R3qgP0mop9f6ILTOslkV+EWOXViKMyFbakdiO4PmIDD7EI5+Ed6cPeSoGTPNGr790U26mWdqHUXWUducprGEyMZ2rcW75y95aINjZlr1+Fw1yGqDOE/95ozNQcHV+dVtR90FQ0FIs5XvQudPfRj4qzd+zN/5mxXZ+vO0Y4M4wRcGuxbmd/aYjgU/yshO1jSvXuXkt17i5DMaXPvCYBr1jpPRCBbL3Q9OwVqSFO8aQhgEI2hyH0YlYVTQXBrRTtVvp5kK3UQUIMl0XifP0+2eFcGURj1etAkjAsqNznnbnFs7TMA2CuTkK087NpgObO0RDz4XUiOLJhO7a2W2dJjWa9dq68AFxDlk3ajcTd2ol0tkUfS7UQqkRMhXHSEznLYjvvxXv88fVJ/l8Nsq4dTOLG4UKH444vEveaZ3Ksp7C1Yv7/HgKznt51fMpmu6b82wy1bfM8+hW287WIfefBfJuAEk1YPOsf+b9/oOvIuaIZ6L0TfSBZ0+fsvECnF/97ng8tTVrCBzKv73YMoQsElzyOk8S3WdJH+bGhl8/sTR7I5z56vvhE2dnPEY+88FPd7CIlWh1yx5M+2wlekT0yfA4gFrenJ7hdQtflzgs5J83tHuZZjOxoaj6CUteo7aSxU5aKKx2apfDL5AfAxlKw2YDlIWhHOgdN+IMgSxZfBdht/L++26EBtV9LNkx55CXzO8VqG/f83a4Pfin7xK3/7zOPZ/ALbVbvlirt7MBJjcFr736Dqg8f+9Dw/5xs1XeG91GbO0dKMYO8U5Lg6yleYJ7VTPsy8ViA65Fgt7UHhlufPogM31nMNiwcoXlKZj7XJycfyv3/iv+ftXPsdL1TG5OB40M9aRlr3oCh5tphytxzSd5fR0jLlb4YvAO1cLZtM188UIt8pUor0R7MqQDXus4qX2K/VYOykmhI3l4AxksSJ4R6gKFrdK2plEa5JAOzG0l8fsv9fxR8e3+Et7P+DITli5UmXFDZy6MXc3e9xfzVgsK+xS2edagIrnrPXxftpuCkFQEl9A535AZfmhn8Ma38U5nhjSZnu/SCxIEQLdtGDxQsHquq5Lq+slXaVrlRuBeZSTLSI4EZnSSWZ9OJ5gRNdbdkYv0z0otvWvi0yVXm0iFtiEGCOmPdnEZseR0M702H7a45XiEbebyzxo97iZn/Bq8YgH3R6/ffdNHj+eYnPP3mzFlbE2gRoJtD3Aa3YY0xcBMsPahjGBZlUwSnVAHxkvHbu5elzW9En0oLQvQlQ1Uh9BaYVQePbHG3Lr8ZmjbgVjtnEwqCJT7xEd42ZlTXvGRcvJYoTLBGt003saYPQ0Se2nAVG9D3VizgoXKm717ysB71OzYvSTjnWNC4HwQYy/X234cHnQq1eVtqN1ltlkw6mzrF6wjD/IaK9kqmwVLZVcBPLbSu2Y1nVBljnGRUuVdfggnNYVx5sRt4sDvn16izemD/nq7G3+xqXfZ/5WxT84+zz/+P7rAFyq1jQ+wwdlbK9cQZW1mCB0KHFgDZRR4SGNhSupfdZbFvw8DR+kv57p9/Edywt/XxWbpPW4vYJuZOFwQl43W2YQbD08I5tyh32ahgvq05yKdl5FDHtbAtP1kqthtdHYO7IUKQtk2ShQEQLBR2BxXRP2Jip7emY4/cYVHk8vI9c2+Npyuq44Xo7AC6YDN8mRtqIbB66OFtyJxQYx21nfM8J6/9AYp6V/qUAdQYI+Dkr/7GDRHsR3W+WfwT5j2YmdfBYL4mEr4y1GonIUdGNLvvKMHgjrayrZXT0UIGxVZ1LIleoo7dZ//lnHUGkijZ91j2mDIRdBNpbJD+5BZK6t/vybnL2ScfamY/+VU/61V7/FvzT7B9zK1lq3E8EilJJRStZ7p3Y4Lptv8//Kv8zX//AtLrdQPF6RL0rOPt8S/u4ITs8iMKDzq7x9gizXUJU61430RXZf19FTU+dAr1Tk/fZek+RjrTG3ZGXP5g/rtQIZw1w8z7bNFNA3udu1sLnhKOeBfO44rSvwQlcKfpSzviLsvQfd5SnZsX6HNP9DZjn9levc/YuBMHbI3Me8XVXtsoXVvCvEeztAyNMeqfsjJtAddJzuCfWlklceX8Z8cHcLEJxj/UHMSTwKTF80zoMLT3hI7/49hF3JmF4BIQHfRlRefDSifu0qy5uGyYfwQX3Igck49Q1j87O/F3g873dCuF9Bc4apG8KkYvajBb7K8JdmyPFp9JmNbPW+UUDBTMnUSi01Vt/8+5b3v/UGh/+dO0x/qeajK4csvzehehQUNC7g7NU9+OUzfvXmHb7+Tz7HW//BMcdfCrQzSzZvFIzKs60KX/DbwPbaJR7/6mXqS+rfrPUesHWgOA3IScqRiQQer/VWS1SbCFFRyKvKmdV5apwqINl1h2m6fq02a60DDZu8pfd6d1tgD7Z5xXik4HLXKSkj7Sux5joc6q3e9j/3wGgcIqL7TrQQ2xxarv9u4N7ffoP3/qZw8MIZx3sj5u9VVI+E0UPP8eeEs9+s+eKte/ze1z/LZ/6vp+zdaLn71SJ96FYNcTcI3q4XcX83k5E2AuQ5ZBnv/utX+T/d/PfpKMkirvGz6CcNMDMZmxdausqSj0fQtL2yRR/TpPOTrDryAnPzGu2NGaevVpy9LjSvbvjcy/f4V67/EX9t8j3++3/0P+H69D2++0cvs/+eoR0L8sun1F8bMboXVVQiSWdHiSB4VbmI9bzgHP74GBmNkPFYiSfJ2mFg6xg6VZURUTVJtUN0PUgtdrcO7h8f9fuBFIUykBNI7lXK/PhzY4KBvfdaEHhUTwltVGzZNDRTw+hhqx7wudpFyGRMaFsNRdpNb3WBD7grMzbXRyyvW+pLQjsLW6JRBr7a7RaRVsiWhtE9YXzfM71dkx+t4KMHkXFdbNeHND5uHor55H3iolrpJxJY4usikYkQ+uYwLu3RTi3rS4bmt864bpUpbft19Nlqs5+6/SMxpu1TOoaT7JZH+ufsxOwpsU1gksQE4YJcMsm5ATT7Gfnc0ezl2Mb3i2uSbdMPVXmNp7Kkh0zh+Nj559gaFpuSL754h6/Z13AxkTifHId07OcTkgHQDGwvXv/Cp1z0Cx4PMvAbTUXpMChQCyplWPm+AAP0jDYiO6KXKU/ffXg4qbZkBvXTCISkAsTwNc1eoLQdmfE7iWyBoxSH/QRmOCgInTrX9BQZLEINVAITaTnxIxoxffNDkoN3CB6heapp85/u8EHwUYg6M57DyYqzyRS/EkItWyYvEVwwEFJGFa/l8NrBNt9Kv+yC2gpAqPdwYPahI58b1rVl01Y0l2vM2GNsUCaF0bPsvPR7v4mAqYmFltw4CtuRGUdhOoz46B2t/tGVaalEAWlLoJKWh36Pr7/zKi/dcxeuNen+6L9QKvTvPInYUCI4E+/lKIXkSiFboaBh6uoz9P4mw8YKbyMgnSvId/B2Bz++s+3AS8WB6K+SgAq/3rD3vmPxhkEyR8g9oTaYwpHlrveBHkqsJjA6MUASIG2TVzSQWZVCtxJijVeT5U2bsV4XhLOC/MyQLYR8qYGmq6Ic6cxRLIT89SVvje7zu/UbzLIN7XGFzFrKvNP3TUUw72mbjIDg3Sffe39aIwGE2mHYgoXucEIGSN1SHnUEk6t/WpZpoHqBn8XOtYvBahD1kvna/Vf55Vu3+d1/+U0uf10oTuDxL5R0E8/+dy3zVwNnr42YvS+cvVpx8jm48sUH7BU1j3/8Eoffix2d1kSGtPo090mbcxpgH+xBkdNenlBfzmmmhnrPRMlsYuME27UZyJYJiIoF0Ha3yK5SR/TrflrTe4az3+1KTY+B3lu29uSLjt7nMyh4J63Tf53XoNP5bbcebAPxJBOTgEfQTvdSu9dDXQ8KAlEyKgTwwvR9y++N3uDqrRMu/eIjzlZXmdwO3P4XhPxUmH4YePzLgft/pmT/nYzHXzSEzy741z/7Ddpg+S8//1VuPt6oDNYwKUjeVk/bI+Pxy3iMZJbmjRv8jRd/u08WPOH585OGJ9c9rzJWYmyU0433cqYqEW7g55QYIf12l0CCCOoQtgCBdkhLbGrQ3xW4+6RAMwaNEeCQYVcxcS42ofcs1zmu3mO2LpG6VfnJlMDWTS8NNfwcyfNtEL8/BcB+9JgwKmmvTTCdzuN80SEO5i9m2CYnWzqNswTaSUZxr4VHJ7teXMM5k2SR0rlPf8t3m0p2n+92k6YeYJdtoG6MForPgxmxKKANfikII14k1AbCCb4IFMeGOvqwkpo1n59l+09tlHP1C08SnLYN+EwoTgMP3zugMLon5o8y/pPv/Qrdo4p8uQUz7UZB2xQ/uVHowehtvBGDWA+mMZSPDO16zLdfvsWty8dsfM7MbsjF4YLhW6uX+Oz4Po/aKW2wOAxrl3PaVLx/eomjB3vkD3NdpwU9HiuEs4qVqchbIU/XMq71fWOJ0eMRt70/g5SYFqrjtN4GNq8d0k6E6ig22uQaQ9aXciYfrvjhD2+weTnnleIR7zdXqH3OaTfm9uYSHy4ucboa4TpDnsDwWLQXF+K/xPZLHvayzY0TMHE+1417S9/wFO1XUqNNsIKbFrhSO6VHjzoOflhjj1dI2+FnI/woRzpVPXj4K1O1Yhqwm/vG5HjeTMcTTGjY7p+9TOD5W9GqN15q5AkWukKbfFy1q0TiSm38MjXk51xEfhrDB8NhtmAaMlz0CW+D5devv8/6Ss7I6pr67uIyp+vx4HUxN0pWUyKxZvqkp7Q2iGvOHTa2b17aaaY5l6+n0eeDUQ7SlwGysP1j4XpJ7uHnhWD6mJkIGg1BQdB+aGu0EbTzBus1vh4CRkMwMQRRbZRzMt/p34WA9VMW2vTcVLMYHtf595GLOiGGnxE/P1k7DRsEcusx1tFeaclPC+qPJmQvn6nVj7OMy7a/hlWh0vwC1G1G01nKvCOPeYVHaLzl7cU1zroRL1bHvFY+4Lf2vs1fmP2AfzT/LH94/CKrtsAazyTXwt/Qh3z3fOhemotn7QrWKHP6pz0+icX6tGva//0C0HE4xnnL8a/OuW1n3PzdDeW7D5EfnVAc7BP2JvjpGCMSC/JafJNGAefkPcm5ImiSe5XEPBrKYPZJuCXMJhACzc0D7LrFPjoDt1EZ5U4XOvHaXRc2G9ifkm0C7SXHX/jy93mwnvHD77xIPjcs2il2r+F/+tXf5t/f/GVu/ddrpHWE6zVXiiXZSnTtT+zTTD2k+1gp+RfqFxhYEek6H4Z/i5Lmve2QgZ4pYyOwHJ8/VAxMP/vc4Cq1VDH1tmsrqQMFKzRTQ3XsVG70RHBjbcIf329p9gvaWXzv2NwbCjBuuwame/CTxkXPG25/P4ss6jq0VGL5m3/h9/hbl7/M6zcf8dbee7w5esCt/Jgb2SlX7ZKr1rNvCtqQaQ1OpCeMrEPTk0s2oVNFFqD6SGNkN44N4pVTb2WIdRWDWJD5UmW723YLMlvYAfqCB0cPFiXpXoqCvgk0qXVF5lxqaNV12G+Vl2BrDZT8pEcl1VFg/ZpjfSmnethwtqqgS0CAo3oc2FzJmdzu1Ju61SYUcY5QlSxuGewiYI5VVcQmBmqn8039coPGojF+sU0gqzVmsmuVLTa1Izs7Q+4+7sGQfgxyjJT79sD0hWNXTe6JfPlcI3ffHJx+hW0jr0jvGxuWK8rv3+Hm6RXWL064ls9pg86Rn4fhQuAPNy8yuW0gz5D5itNfvobpoDzWWontAdXBC40G8MmDNog2aJ69mjN/DV78ew2bf+8m9/8syGHD4s2W1S1LcSzUVx3Z1TWhyfje//3zvPrtDY9/9RJHXwxc/z1Ray0Rrb14r+QLLOCQ6ZTNjRkSAqOHmiNlS98Dysb5rbJdCH2eL63T2k+ynwhB95Pz88JH1mfMf0PntvuT29aMNJXdAoJiTWysj3M1SdInpmibMJOwvYfjvpEAxC1zeTt3Jcuir71gNi2+sBx/DsQJN75uuPEPDavrh/CKp7niaA7h7HNozn1c8s4fvMFL3205/fw+D/4M+MxvCX1pj/a+b6zdOR99Y0wg1A2SZSy/eJOv/rVvcd3+7LOkAVbBUezXVHcbrcWI2aqk5BlhUtEejjh9tWT5grB+uWXv2oLPXnnAL+19m5eLRxzYFdfsnKt2zaExGLH4AOsuJ5sbgnEUi0A23nDy1h7VN7dAZlIDMMs1GJCqQtYbXddjjSw4B5tUexzUYn3Q5ho3mMcplpHIbI9xi85RduxWelJaYvXGY8IImxf2ePyXa4r3SsYPLUFg3pVqtSbgL02jzWFGtWqhyAmLdE85bbyqJrTX9zl9fcTiJaGdqlqbEj6TYhs6Vw1IG+OqWK8IhactPe2BMH9TeDyvmHw44uY/CPDwWD9nOD6pMeK8BcTTnjMcz8ykdrEInVQPdR92s4rNvmH+Gnz28mPyiPOlJo5nVbH8ie62pwHSPtE84zC9dsQFbyLbusaQ+duPCFgJUD42rA8t5UnLZpoRNqKm5IOTqIDfU760bP8Few4sSx9GBBe6wGZd8NnqrsohtwVJnm73NWzZOE983vnM/imjL1anxD48WcAW6YGNdI7E6y8+Azf22EoBHkU/ti8dSpXvANLDAoRsc+xg0Yg80DOvgtn+DNBe8kzzyJyVjlwcRQQwC/HkMbj9tMNgyPEgMDYdy9Cx8iUzsyGPVIohMP08DN8XHhSQzsRzdbTg/qUZ9XKKrbVrGRgwn4nnPwaiKZ89N/d3fo/XtMv0/frkLhNsGyhPPdlaGD0UlrdGrK8VZIcb8qKjSF34Xnr2rokS1DYeczr20jjyASA9tnXPjs6l6yX7K/H8zvGb7H+9ojhdb5kpuTx5u8fvfJ5B3Z+TyJLpC5KxuJ9Y06bThVtZpAnIG7yv2T43WDj4oWf6jdu4ptFNMHlNDDrc+w6somDv7TkPvrKPG3dIpqyPsurI804LX36btKaiWAKjRdT7zkTWh/OCCUJh1SMpM57WG9ZNzqbOaeYFZpFRnhiyFWSqpkV9Sb1pfBWQVjBN4M+99GMO7Io7y32MeKr7GfmX54zzViXKrMealrrTCZTkvJ+34Qmc+AICbK4WTE/W+NkIWzvG9z1kRjvOmtOnvEHsnpYo3RvAZ4bRw8Cj715h+UbBS68+4nZ9jen7lvmrjuqepTr2bC4bjn4BmtmI088Frn32IV+99mP2sg3/ybWXkW85hgHzDrCVAtdL+yy+cIXVFUuvKIHuE3Ro44RPRfSwLaa7MJAk3QLM0oWt905AwckEUgZi0IwyQbtt1zYAziPRV042zZP7RZdkBuPz63q7B/iwK4sWGR7brmlRxmv//EHAkiT6fKCbZlSPAnwv52F7ienNBd1nV6z8mDB2ZHctplPfxNXnN2yuFoxeP+WrL/yYhSt5sJmxuaLfN6Tznj7vfLJw3uc3Pa/rCAcz3v+XR/z12R8BozjXfFRoeb7ug6GlQd8gBlufpuCRIHhrtjGKVTAqAc6+UO888SgDVNC9WiCUHlN1BGcIrYFWMBujAHXH7rx+2kiSccNrAhrwRw/ZkGVbnC8TjBi6zGBygylzXVtrhxhRYHvAFuK81GwI6kkNhHGJLwziAm6cYWtHEKgvCaa1VBE4cbkQSqG+uUfZdrBc7x7rObUZ4OI5dO57B6OF5J3XXhTDDO+V4fcxg9deFPolpnQR96+1hUnXP/7P5Qi6n6tlgcHlgisVPMxWEpM4wdZC98GY6QNlHfi++X0bs7tKAentfRWTPaT/PbFru33Hzeq0B/ysePYzLarsxyrg1NY8bifM24rbywPunc1YPB6TPcqx68jsHdx/2Ua2cd0Q4APND/rHB57IAbK1AuvZOq53RY4rDPkysLphWL7gKU6EcRR8caVl8r7lD5cv8xvTdwBY+YLHzZS76z3ONiVdZ1TtJcVJaQ/yARO9A3ubn1QIGOZNsVEqKRSI00anIEIorTLcgJAZXGVjk6SuVdnakx/X2HmtzL7jU3zXIfOKrCwIVUF3dY9urMC8OHrJwB6gjoBzkg5M+2nPpkuxX7+ebuNHoGdCu0poJ4P8ReglwG0N2RIkRLuLLjzhP/3TGE2wHNgVBs+JG7P0JQtXUfuM07biYT2l84bjzQgXpbGV6aev75ub0Mc/jmWcG9+rbJg4H4FdifNhviKQZEl9pvtRyCNrMQ5bKiO3dQYXY+cnmjjjMQ8thBJb0UigyBytszjvETE7zbTp9efH8LFP2ul6r/iPYzz3rOgnX6/HH54AQ0UCXVBlqrrThLn1FhOvRW4dLghl2UEQ6sOc8tiwPKgoDqNNj3WIGKqiVVA6SnCf/94uCKs2Z5oHWm+5v5np+Y83wavFI/7a/re4VpzxjdOXebyZUHcZuXU0Pot2WYIPjsx4Om/j74bSaHOylcDa/XyAEcPRq90NmiIOpmvOfiXwwd6MF/+ra4y+0xJOz1RVa28SFX0yKHMt9OetqvU07TZ+Hg6rBmUyHmnBEp6IJ0JmWb11mXZqqWeC+IrD7wjZ3WNtkk0g1TAOj8CDqdVO4gfff4Fr/9TQ7AnzVxvcJuPl4hHXPvuQ+toB5YMlJt6fpqGXze4B5/ieT8Q9NsarkUEdBj/vMKZhh3QRMhOVfwypCT0QY6O0vlhDyM02foWtvYKJny26LiHQjaXPu7oRdBOrMUA6ZENPpJCWJ9a8ixoSPokJ/eR7fOzTn7uRR7nl//HhP+HP/frbfLG4x6ExjE2OweDxZJR0OAyG8oJ8qRQF59rgWHmtBzkvbK47bG2pL5e6Pz8qkM3ptgAeC9ChbSF4/HwV5V4jUy2BUmkMm0ATCJH29kFja2Ji9sBVJlHFzChlZfC+ktjXufqQ0xrW14WDd4XNvQnihPVVw967GaMjx/rQMls1W//PeFxmseLaH4xwpcG2MS/v/JaIFGMkXFTgS/+30Y8eVPq/rhXgGwB5fcPvoKFl52+DvOWZQOeP+/tFI6lVxXMroMBU22I+esjmi3u8Xj7AiDyzF+jzPDyBFsf9bp/6IHD2hUtM3yvoKsP4QUs2r3X+jUbaTGFiHeVcE0WvIBeDg27iefyFgvI4cPkbsL6mkseLN1vMy3OYV9jvTSlOoTryzF8uOX0T3L5DguljazGxHhNinSYYWK+pfvyI8m6xrff0TUSxdjUEW4dEg/44h/vHBc1RF/y+AxrH9xZr+/sj1VSfGNYqIzn9/VwzBCG2c5+f897rZ0zGylA2BjzYdYsbVQQDJ29kZKvA9LZDOsv6huBfW/Pq9ce8//UXyc+EYh6oDyzzlwz+co15WOBLixxtJZ5D+Bi7VO9hOkEuHzB/64Db/6Lwb1/9J5z4juv2kyTnnv9RiuHq/oJQjGlfvc7qVsXqqmH5EjTXW67fOuHW9BFfmjzm1eoxr5f3eSk74YZ1fWNKalpqQ4EVYeVbJkXLh48OKM40N87njqWzLF9gl90bPdxZb/RdRGITQmKaxXmWSDFidokqg7nax12JzR+B6Z5Y4D3eBwgtkuU694POM9pd9rVxAbwSQWytn7FoS/CxgbnK49wyjG+7XkmyXxecxx+Ouf9rYxaveIL1sb4hSCtYBzRCj91xLs+CXr1rODl9qTGVdN2WNPHHGZ8QyOzsyx83jNG9Lr1nvIa+sGSbwMEPhfFf0dqeH2RkyVbkk8Yzg9I+GJXjjizp82MIFMYwAYv/eHx2kPD2wPTwb2ixYvJRYHVDkO979QIxgWzjyRfbIksYFgbhwqJi8rOEQbI9+LDUrd/Vlhv2lOmopl4OErMgvZ/JRay+/nND2ILDnwTQRmb10/b9MAjSIAbgcc/yhcrKZJnrv08qXO902Ul/+NtCw/AcpMeHbKz0owUf/VEQYK9VmefEopUEWnpyQbuKf4KRi6XF4bzv2dL3/IgxNRUh+oFsCwrPQzG3GHgG+yB4ETLxHE5X3JlV+GWOb0Tj9eHUBE20EjAdE6s+cYtPSouWj38Pcb2VKLEXDL33YFYnaV/D5syyujFmdbWFfRiXbc9gSID0+cQsgesJYM/FRUn+J+/3D9vLfO3t13np/Q7Tui3o8jHXRAG3rddpektBO00RlXeSWMQNBlypXacStHBoWukXbnGA1XMTLHgLxRz2vz9XuQ4AazFZhq9rTWqC3wmKJMtgsSZfHODSLpEFirwjs44QhE623njey06BbQhIG9H5mRgSrTdsuoxVk7NcVPhFTnZiyZcq121a3fDaGdSXPaEImI2Qzw3BdrxYHeOC0DjLu6dXyJawV9XkxuG8obCOxlllimTuY4uQP81hEOa+ItjA4oZl+j3H+pU9urGhOHPqEb0aQ/S7ekKyashijAGHryzZOnDpu8Ly9IA7ny2ZvDRnIVqcK+Y6P6pHcPprDScvBm5dO+EvXv8RC1fyBycv40q2rLGL1qzIku6uzlgfWpVNih3QtlaWn3QB07iegZbAZcIwUY0BtxsAzOfB7/SY99silA/aHZc60VKnatDu1tC0TySrO1KCKbF15xKD/rTGz+sfiMyG4VON0e8Qg0bxgW5kmL+qwefeDzLWp/uEV9Zs3qiR0xzTwtlrQvUI6iwwfuOUP3PjQ46bMd9Z7PPgZEqxEGRV917bT4QHic19wXFLniHTCadfuspf/a1v8EpW4Al4fCy4PH9s6b5IB/p9Y8EusRdpQTqjvpwbwbSGtlXg1+dCqAJu6pSVEISs7JDUFGM9e+MNl6o1zhtql7HplFm1rgs2J9WWyXDRkG3QL8TiozHbZEJE5d7nG+xJIM8sochwo5yQGQUURxm+0Kso0wCuioD7YM57kJjs+lwDWFPmuElON81pZirXaNpAN8rUkmJP13yf2cjG0XVzfTXHbvbIP2h2i1oXjZ7h/0mdpdvnhFiI7d9xyJYegtdPiwGBnS7LpJpTOoLJyM8sbemR3A+e+8/XMF3A5xKvt7B4weBKyJf0RWxfgN0oUypfBTaXhWADrog1bRtBsnLY8U7vId3LpUaA1hdg95rosWoYm0aBaQIrXzC1GxauYuNz1r7gnbMrfHj/EjwqKZZCttwq3wg6F0MW47G46PTrZ4xhjKcHTvtDjEV4U+vzi9Oul090peAK4eyLDVnVsZ7liNN1tZ3lXPphx9+78xZvvXmf027MWVfxqJnwYDlltSnVl9ZJnyfYxmPc1tstRJaDBKKpr4mqU1smtdm0W6WNtgPnCVWBy6f40uIq9WSs9wxdpddm9kFD9d5jwtkcmhafJNZGURfbOfxszPLFCttEu4qkBhJj2m0T1yBpD/F5wo6VjYSAt0I3VgC6q9hKafaMJXbUSSR64pkugt0uDHKjn/5NeGC1OcJjmJiGjdM5Oe9K7i33oviV0DlLFxsQh8zh4bhIllb/oAmgjYWTYJXp3jdep3ww7OaJ6Z/PYwElAdIJlDaBomy1EdOZ/nikV0qTHeaziX9LktgArbOMy4aT5aiPZ4cg8TBnScD6s0rtDkHo4c+G7Rw4PxJ4+ywj+TcXxvVs5LrLGOeNgtLG4a0wKlpEAsubhvDOiOxuyXLUMhnVdM7GXhE9tsK6HVWrdOzeWTLjqZ2Wb3LjmHclD5oZhsAmFLyQH/Fbk+/yevGQ//ejX+G9s0PK0FHabsveNpDh6YKhcRmZcfgg2uJn3M/1vpTOQQjCrKyZlTXzX9rwfnmFF6uXmX7nPmGxggePlU22NyVMR3gRbRhd1ZjVBkALqMPivxEwGYwq/GyksU+KGRJgkBkefyFncyUw+UgVHDbXRkyOFqo2E5nLSU1JbyGNS/beNrz7mctM3s8YPW5Z3cj53K37fO/DG/xXx1/gX3vxW/yHX/ktbv6uJy9a2mDIFzE3EdGYyBhtQu06gvfaRJi8EePo7fIiM5rIEhxKNw4f87my3PplJ6puaIPuNmby1mgs17j+nAgS/QjjuhGf7kpo9lUJcPGSsLxlqa91ZKd2R/lQ9zrp15J/nocLgVws163nteyIXHQPzsXiglerPAm9wtRwpIywfy6OVYATP6J1FvY6Fm8ExvcttobqHaOs6KGUexpRIlWKfCsF7xzi7Lagf0GhPAzycrHKIPVNiyny3cK8tU9IterrIpM/17w9P7GsbjlcaZm+a1nd8szfcFz6wdYOrL830jHEptniR3d1nmcZoW6e/Kwk951GbOwO8Rh2jikxmIbeuedHn//7rRLBpwShL6oL7PyewJvh8SXp25HKF598Bl4vHkQ59599QM7j8fG8HH7lAXeuXGb2oz3Ew/ROwKwaQpERxhWs1zwBXPSN8nF98h5vBWYdi5cFcWqXU5wFshUsXzG8dvmId/wVJrc1YWnHwuqG0LzQgKjfNJ1DMjtghG4bEkLTEh48Uonjc6SCp4623T4nrdFDlnJPcDg3RxLu/pTGCH0sbB/vVQy2zQ19/eqTGsNTQ0YCudPvRQFVGRuXLPgYn04ci1cyRvcNxVzBZ3HCyS3L9dGcd0eBgx/oXnH2qmF9wxGckK1RJY4ustFly/C+CJqW8ZjVV17h9l+xfPnXf8T/9sbX+Gx+xsxksb70vFWWPt0YS8G/9cZ/yf/h3/7rvHbwmC+Nj3i5POKl4jEvZUe8lLXsm2JnX7BS9T8n9cvEfHUh0BK4MTnj7jdvUB5pDmkbz6bNaPe8kr+SWoxVX+rgPCxWSppxTueBc4QYN4eoeEzwhM736zlGtr7RaUR1gSRB3ntiG6PvIXm8Z21/xbfe5VvQO/+woLmiNmL5wnG8GSFOc3FTd4wfNBy/pQ2GYSg5H3223SRXef2NkG0MdqM5bbJDsnUYkJdiI7RT3Ma0AdNo7cvWUenSeY2b7jxQr+0/LiB9wQjn5cCfdfjt/gzoOaxr8nunHMxr2ksjDgvNZduwVU9ug+NZXLKeGZR2SI9yu8iIHiaEbay6eHYlq4qi6yWwU8FIteBjI1IsGkAMLrtYFBy8t20Dq5va6S8u0OwJxUIvZBjIte0wVlLwHz0ae4+xNBI4e27zCQL2KGcZCt649IjTRUXXZFvAMCZz3diQn6nMBshWInNYJE0XrT+W+LzU1RSL/hJBufS6kA8mYDfwiIjH7arQs6SvXD+jc4Y6SkX7ZAOUijGpuCBspbhj4QdD9Pyll2ULFpXfcfo5BF3sVW4QJrMNB/ma0rQ7wHQlrveTvijY1WB4txMjlcpTIFxJBgZW3rFvWhoWnLgxB/mJNjoEic0OoZ9vP81RZS3JT7rzNnqRwbXxnEezCd1RjskHbJBY+Em1n52GgPj7sMYSoC/GJ6lWUK9F9dlTRonv57gCZ9M7geqRsL5WsHjV4q6vmIzqWFvfZQiIqBe8Ed9L4SW/8Fw6CnFY2fqHt1i+dvYG0z8qKU7W0cvZ7rJt0ne7YAzV7xKrOi3SPosSX17vZ1cKchp2CpVaFNNitov3dJJjHL/vMR/cJRiDGVW6Yaw3ypj2Xe9JDHFjqmtM3ZItt7EbmWdUaGDX+4/1jSjbNS9JEFqzLRo1nd1hVZ+tKup1TjguKE4N+UIlyW2j7LzlLWhutkjukccFxYkhP9Nzt5+tVIHAeO6dzKhqOKjWWoyzjtxq8SvPHNlzlIifZ6kuQs3ST5Fxx8kXhOm9Q7pKWZHFw3X0P9akj67r5YRCo12pfTAJ6gu76WheHHP/z3mygwZ/v2L8zRHtrIJ9ZSqffvH/z96fxlqWpeeZ2LPW2tOZ7hg3boyZkXNWZQ2sKrKqSIpDUzQ10VBrbhluNQz7hw0YsA2j7Xa7YcCGf7V/+F/LE9AQJLTUEKQWJUoUJUriWFSxyJqrsjKzMjMyI2OOO55xD2st//jW2mefEzcyMotkVZSkBVzce8/ZZ5999tl7re/73u993xr/cxMm1zfxpebpZ+4zzEr+6TuvMD7pwThl8y7ohRTO27nahuA1BgBNQ/rWHc7fyqVI1AGP5YA6nc+xczWO7twPj+1C8/YMEHYlmF8+p1xIPDrHAYh3S/fx7nt2k4z1Y4lSaTEZCh24qsilc9UYCRRLS93X1C/Oef7iPRLteOdom/k8I+tXlAvDxs/f4+Wte3zhxjP4Uhajf/3qS6jTlHSs0A6yY78EC7V0uKvQPKL7ffmsnWNUkW3iPGprk/EnLzD9T0/4z8//Grkahq2e4IShjTO6MUdnaAFedOUkkK096UyRzgX0qTYU5Syl3NH4UYNJHFnakKcNg6zi2uiQp3qHaDwTmzO1cr5O64LDc30ac/7hxLBVB3DLCRlWr8GQfKrGie+hc3BaCSCXB3+3NJGf3GD7GS7T4qtmlNisxMKhovWOjZ6DbjsTdmy6nO+jSkvdV/gUZhc8RSJF2wi0ZafgdY/tkyHqdCrzhu0kn/HaivdDPN+xgzsynWMXvNOBHSSFWZEWfnTRbHmuAvjZylzCksq5tm3YpN509O5oml4CW4Ep7tauh3WgRa1eMh46qN3qWGchto+Hxqoz9/8DGMqFposEqqGmGRDY8YAWL0CzEFA6rpdeiW+0T0VRxCdebnuNeEy30t0SQ6g6NoB4XOEpU0eWLEGjGNPcrjY5bQpeCpTk47rPW+Nd3rm+R3qQkExUKyEdC+CRnRXBvC6gGkf8itrbS3Uei13RDpJJuA6GPZSDyVOKc+dPWdQJk0XC/IolOzFUG4bhewtuvrnD3+9/hiuDY0qbcGuyyWSeYxuNbTQ0WtjhAdxvAVetl2w5L01VqnYoGxLhspYPVNWSsDaNeMJZC2WOSRPKvYKTZ1KaME3373k235yT3jrCHxzhm6ZlSujNEWrQF2uOLKXeLmh6GjP3BCXqZSwYgZvOuVyq4cj9bzMBoG0uj8XvABdA7vky79GND8A3rSpJjFHj+8gB8MhY9fs9jHIsXMbUiaTqrpH8Z2AqiS/tco2L4Ivy0tQVVX2igg/IxzIB9G0ag7Maa7UA0mG4THJyKmHtY6FVhFGd3wnYzItMfuYkLzFeGqprjU89w15JZY2Aq+FYuupC3eG9kpAqXJtN+GzOKwZFxWSeo3VFYhyN1Sv2tV3JXRc+v6T/S6Ax3og23Oupdjiv2v9RAjhrJP9pvH4I9I5S3ZUz5KZpaxpK+VYtTisfmlykCaDxmp3ejJNpj8Npn95mLe/jRTkpC82uDBdMntbk13P0V0fMP+3p5RWNlRbgzaRpP2MdHktDHpInTXuMtTXMm1SOQzly3eBQTGxBnUv89TfO/w5/33yWt8a7lDaR46bbICCfu3Gm/QyJcuTmCZAPWBvNWt6/3pSw3ogR/19v2qg711vjpIlikFW4Fw+4ke6wu3eJ3W+M0W/exI8nqKZBz3v4QY9mZ4Af5aQHRlg31uJZWuFEuWF5zi/jcy1xhh0VuExTbXvMsxPS14a4DOqBxo36mAcnEqMEdijGtHO2N8K4u380wmxIk7fNYb8Y8+3jp/jt117guU89YPbRBffKHkmyINcN2cS38Xbb6BulkGOcFOP/Lhs6ANhdMHpFthva4ESsTcLf1nOWGodXoK0LFhDycm+khqD8Uv2wKWQfugHVKOyGRV2Z09QJW8M5k5PtVqEtAirK0jbjdH+vj3Xgej3EM7pjqfODD5U+9Ih+0Nu691A+bpSmrx6tgBBXGOudNPt6z9il3Ky36Wc1+g9yrv3lN/m6u8bwTcO5b8xFvjeCnJ3c2VuH2T+Pn05BqdanFaXwRYYfFAIcT0s4PIH5ovUXjQ3oPjQ+KGNaJimdeMZ3QLeV3MV5bD9FWejdU0wvae5/wmAq6N3T9H7mPtXGLtnYSv2sl2LGM/nCmwa0wTtLCzrX9dkXg7Utg1QNB9J0uznADuQcJ3dP2oZ0P57AfNEy7ZY+qKw2gzRN6/W60gQLtMpx4TO2o8se7OYlxgQf1QzSdFnvSBOZ14YD/HQuAGhVoZMEv7eFeXnMVVPSU0ubkB/mIYoACXvJmJe37/GnL73Ke5/e5tfffIG7eZ/e3XMoB9WmwqXn2Xm1ZvDldyWenZUCdqlwPqsaX6QkC09yJ8MlnsU5yE4lZ5lclZjqW29fIh9UHH/U07+l0TX07nn6t1NcBsVhhaobUeKII35HHWbkCvMYVus3ZzUcdEZbd4pNDo9ozj5zmuso1nnvV1mvcV9ngWWhkaTdbm3Ee3lFOSCCjYtSmliMQi0a8geGwdc1xZFjug+zCxqz8Jy+4NAnKb/37lP4nYr5uYLN65bhLU/vV6ag4fgjSSCKWEhTvLVSF473QKgR+Oef4t0/t8n+z9zkP3/mb/NscsjVRNNTGdBvgdgnTYHvceOhuR/4c/0JP/8jf6ddI5YjCz+P318kgYxdw32X8OLwHm+/+iIPfrpCfSEjP/FM7ml6d81DDUkYA2UJvULm81BnlPpr59r0rq2JRkn3tkbakYRXqShnuNlMgOnw8pY5HdeGumbFUzxKf3tPPUzo3VfUTzWcXiu48LsL3rm/iZlryi2FHUo9rRkobD8lmT/cnJTdOODpfzQTTC/k0OvNGdFmMTbzRW91qXcHr/auUkFd49NUQHvnl9L50KldLd+jVb5cx2LCdity+mEdUfH8dEckZ3TZ6SDHEe+bDqFJNbKecHyKXlRMX9nkM8PrjEKDQ7x/EvXBarQfGJSOzMkMt+qTxBLMioCXCR3GtU9Wk4b1+DAmvWcNBc4ojBO5U5/BfL9HfuootxPKTYMuHaaWDgNbnFEI7L5vN8luH+8UisP/2kJ2rPn92bM823/AN9KLFEWNc5rZWC5Ot9Hw4GMZ2VPD4CXiW0m+pVzeskClXGB1BLDXVFKUia+LsuG69kT5OinAyg5m+9KFAbKPpifMStVv+Nz+O3zx7tNUTdi/J0iU04LwrYp6PAeaFgRdLT5I0uPUsgDXbDj0Qi502/eMspqeqSiC13CU7gYwSrVdEbGb5lFjZUJsNecNhQKnPBbHSNU4veCOHbCr5wGMNkyfEABipeNeOQpT47xms5hzeeeEt2cZjc3QjZJ8LvjRyLUtYLVX4lccx0rTQCxiItvLBssfbxAv5pDftTKHCpKFY3hT0buvKTdHnF4d4i4u2NycMcxDEdQv5cfjyHVDHnylAWwr15FQ6JqvzK7x67/xCa58q0bXtpVzVN6H5oml3Lw0QajO9aWWn+GMe1834bpNZAOXQrmh6B1I84m2gfQUr+0AZNgc8mPY/v37qCyj/OQzTK5kuATSmWf47hzzjbeWEzKyaOEcfj6n98AzDpL4KnhxZ0kDTqO1SOvKFy6FKx38o1vJbiUFrFjwOpr1mM9y7DglPTQtGJ3MBXi3uWL8jEOdL8nThrpMcFsNbpIyvOWY7xgKVbPwKYsmoTzsUT1v2SsmjOucyiU0TjPKS8omoXaaYB//xI0Uw66Z8Oln3yXTlt9NX2DwjmbzTfGJVXXwpNrZRE1mksA+qhKgQZUNugwSduMUvVeSPT8l9Ypm3MeWhqevPOBj27f5HfUMp+Me77x3DjVOyE4U/akiHcsc3PrsxKKR73T0A946SbjHk8d/0PXu0PWx7jt3Vmf5ymddfT4mnD4k6qqbiHcChQ+6v5XxqM5rpaRzNZGgUHkBh/Jv9jj4taeoNhWzpxxuo8HnCrNR0U+lCLs1nHF3usX8O1ts3VD07zuy0wabK2bnjci7pgnkucjpe4/u9wUMqetW6kdk2DI4v8ODH9vhwc9W/NlXvsbfOPfbXEryH8pkQYr+nbtVLf8XGUIBp1XjSSdKuoNPpAmu3NaUGymLjZpiWJEaS8/UXEyP6euSOviROi8KBTOb89vq/EPJ6/pQsfmjG7gisUjL3g/FUb8oIbIGjEanKfQKVGnxRRKsHEJCnSxlyV2mg9WCar1fUfKZTeNDg5Gi6alWprnZstRlEvyxfWi6kw7W4uoG/Vdncm03FmUsPk8lMYgfrNOk6OuzfetXvxst69JZ93NIKMQzElq5+ch+Z62G1AEjlfZ4p/B9i3Ka9FRR9Qwqtw/FrZ4OsByXTI+wiUIgF/+XbcL8FTY/C5iOwJVeQUl/cCPaldZ9Tbmp2pjHFr71XDYloETC0+bBN7pwqwtdW9juAo1KWNJxajQBeDOepk44rAc827uP9Zp3Fjt88/Ai1ml2L07ZTObMbcp7x1ukDxLSSZDADzF0q0IUv55Os+AZvQTyuTqxTuyBVYqlqlEt3eBiT+GpthznUklYJx58Yak2DdlEgfMM3tW8c2GbXlJTNgmH0z5VJamcazSqEYBxXQ7Mq8BO6yoXWCds6LoR31LogBbLRFYBqqxxiaLcBFPD8JZj460Z5u07uNlcPl+WSgEvS1F5LvdmnuGGfZq+fOmmDufA+1YYxcc5Mdx7TU+k3EWFJ8S/oYk2WrwoB2YuzZmmInjSn/1d+JXzoJ7IYGnqcsZWEr3dZMKWlm7zRIv0s9GOqkkoO82P0UM1YkhdEMZ5Rd0+H+YIp8DAYiEt5i6JFh5eQgSvllLe2q80NLtMvNFIPGgvdjcu5N2FbRszIxj0qHnoUXNUlCMH0NpT1il5upRw7QKM3dc6r1a8pQnvEdWM1s/JCvgcgGkQv+WkE+932ebr7/l+DO0keGPH/UcGtfOKRDusCfny9pRjD9l7Gf6NEbPnJySJnMeTeUGWyHbHkx7lSUF6kKCsNOaonZKNjTnnhxKbljahcobTpmhB55v1NgNdihR8k62yxQNQ3sScZvml47zHKUXtnow8+49yxHPQlXFvrTicpp/W7F474oHeoulvsJcZ0rfu4MYT/KJELRaYPKHe6eOKTLLjXPwMfVUtYwLfCQS6oK3SVFspdV+TTBWz05zdiaMaauq+ot4pMIeny/1ovcKsjHOfPU3JJ4p03OBVQu01vVsG5Q1vvHieZy494O2jS1zpLTiue6SzDoOtlcGUJjxA4qMuGB0A6Qikx+ETvRpLrtfQfKhdtQC1f2gb1cT3D4+nGpfpoKwT9xs2qSCdKnyq22YT55XY3jja4kckVHzY8UFA5ydRgez9RsyH/jB5kQlFb62ECHJiBxRJgz/xfO21p/jcj7zBVx+8TPb2PVxVoUaiUkbTtMVztX+OxdM7KBvs3QJ5KK7pNuQDpuyTHQ1Ij+bo6Rwf7rU2Z45yqRHE7YKugRGqjF6VugdmFwsW58SiI5nD5JWS3ls5vbue+7e22LikOc005a5n9xsSf/nYGLGkjsrvsy4UHzyAAxjvsxS7O6TczamGBuVhY1LCrJQYKzJNI0C4DuiFvGud2XxWQ7vkxcvz0z6mFSrN5HdoAAhPLn/HmC5Nl4260LIMxy9u8uee/T36QSHthy6/PmNEQlShan7rref4jcOPondK7DRFbUpUYsqozOA5eiGld2sX9c5tIlFBZOOTpYy1gd5dxfkvzVns5Rx8zJCd+rbmqcYJpVOktWLnOzXZUUVyNENN5hz95BVsHtedDomgrpfA9Jr0NfDYPP59yQ9n1Ye6vr3wEMv+zNetvcf6e7ZNTvF16RrTXivJXZ1vmzMwBj+fy2NFjqoalPc0Q4ntN75+n/rz+8yGit79YB2TePyDHmqrAg2D9+aYkzk8OIbNIU2+ERNfAJKrl+Remc3xG0NOPrXH7Z9xfOpjb/N/2f9nfK64wZWkhyNtwbQ4/l24B+DxTUmPG9Ks5LB4auDYFRS6xhZSOz96xbP9LUVynLDzWpiro9pjvLbSQECqa7wxrac9xkhjUgBlY7O/txYaT6ucAa0Kh1+U7f8yJ6ZyD3XqnSo20p3R3KOQ5niz8Oh7OfUAqs2M0R+kzC56xi82bL8h9pPJbNUymAjaKg9lJXZ0sT60zkJWGu8fsaZoadjyOhfFtLieOCdzsrWyPxUacI2mBe07n0d23cVbOzHoGcoHK4D0GccsrHSW5zu+d1hfcB6/WOCrGpX49nsdX9W8kN9Bo1dikcdhgnF8YFB65nKcisi+sKWN9xSqQQfmamRQG0TK1iKJdIuwEYoPoRNbxYRXq9Uvuz0rUowwtUfVmtOnDFtvVsKY6CnyXC+l3tbkKFowTEsBtJXuVqvgnXzLqn0/kdODf3zz4/yZS9/m4xduc2uyyXiR0x+VNI2GAubPwryUApAwWUPCEIsA3cIV0mXuc4+qFGYRuzulcKatWm4bOv/bU+DAJyJh5HKPzx0YT39rziv7d/jI4Bb/pny+Zcy257hTEI0gefx/nbmBpvXldQlScLAKl8jEb/seNTbYgWO7mNM3FX1dBoZ0TYajUB5xrHn05G2U+J9Zv4qgdSd85z1aKYyHgXY4Ku7YIQtvKJQFGqb+yZCTWQWlPc7rFszdzmcc7445XGyRzJLADBJiAR4552E+65KeIgjdvZq9opUAX95JtIB2+1j8Qyoyci1PnHhb14ryqMfJfs7pXsn25lQ6w714iTXOiA+cV7jARo/NBpU3DALg8Y9vfpztb0EybXC5eUgGZmV046f15PWsEa7TyD4S1pwA3dApCId9e03LJN96U+am8uVLnDybUQ8U9RCKAzBlwcatbdy9ByKFEboJvfeopiEbO2gUJA5thPkcWQn1mkyi80q8CDvFrthtvyjlurSNwR1n5AcCPiQLMHMvxdUExs9b1HZFf7CgaQz6VgGZXAjpxDE7bxiZBe9VO3KNLTTmwoyBqaidSPctmlBUDOfGai0eGk/gGLseo7TkcnFM9THDl3tPo6sc1IawKbWi7itM6Rm9PcW8dx/KaumPAyuBczPQ5A802697lM+49TMZPvUkpwaGlpN5wTe5KAXS05ThWwnb321IJpb0tKIZZRy+nK8G+loTpTvWfZ+A9wd117d91PPdbmjrHn7+/bZfSxbOTB4eB4x/mBE7+hqRkonsCJtJQejcl49BKSbPjJjup8wuZrjccyPZ4vrdXZrSkN3KGF2H4a2a/lvHqPGM+uo5jl4YLPdflpKIZKnIhwUgnCRBDfosnj/PvU/nNJ8d89NPf5W/tPP7fDo/ZqQzFr6hr34IRJXejyV9xpA1WbXNaaZyZGNL71BjM0Xd1yx2c8qdjFt7PeZVSr1veLp3wMX0mMvpIQZP5Q2Hdshvf4D3lDd2nXuh05XaHUkCiW27OqNsEo1Fjafo0J1PnuF7OT41+NTgUo3LDLYwuEIac5bSRg7l5NoS5qzM+boElKcZOHQVVDFyT6nALBQnzyT0bvRR07kUV+pGfB/jnFE3+DxtgTffKe4+FKasn6PYcfq4OTUmJe3r/CMXOaWl6a8aebJThcsTmh1ZF5Tu7MezCkx3f52177bauzyOhwqpca3iycDjbCrNa+WWoh4JCO0SxAe9gqjQY3NoBsLQ9Ilv13ufePFnshJ7t6C0Jzyu2g+qrApMarDjlDeO9/jcxltMbM6X7jzF6RvbeA1fykv+o73XmduU6WGPYrH0om7jjvgea7/jc2p9DvasSsGH4M5H9cQIDmvVqj65BHpJjfUKZTx+Zih3PL0H4ArDzms1Nz5tuD3eoGqMNMt6JcFlo9GlWuYfHsyiEVUQQM1K8acDWvC5K8cXi75pAliJ77RG5RmuX+C1ojjwbNxoGHz1Ju50vCK/p/JsWbirarzRuK0Bi/0e1UhuOt14+f5REBppbfgIPrDx6qFayVV0LXLcydSTLPzyUo8xdACuu3H0unrPuriSesyS/f0et+pt+rrifHLKlp5x6grqgLSk2lE78Vi2bUwqr1v/X+tYoFCt5QyAc9KN7L3CVkbumQx8tMwxqgV5uo3KEECfLEh2p07sI7THO4NyiqRoqK1uWad/GCDHe0WWNMzLjEYbssS28fZZKhDrY92DOcbsZ7FqtYr+ystYPmJwUUXKfcD37Y7UWKpGPKWt0ygjdRKnfCs7nieQ7DkeMCK9nVHdGFDtlQxGC4Z5xaTMmC0yysMe/XcTRu868hOHSxTznYLplR6vXxmxd+mYzWKB84ppk9MzNc4oTpo+JPDq9BL3F8P22NalzOP/UemrZX2/Ty7/pIxHeYN3RzdPW//tEeZs92rNk4arz93n7u6Id88PufRbV+i9ehtflvjZHHXjLlmzh+ulEl9ojZotpMkVK/OfNi3gK76ADhpp2mt6EseN3nHkRyn5SU1TaJo+LHYSss2BLNSJFpubk7FYKKQGZWXu3Hg9Ye+rC9IHM5pBzsKmbFx39A4avvXjF/hLz3yV67d3SY3lO4f7bJw0qDrIgregSgdgjkA0LBvtIgDdxq9qdbvQEOuN+EgLI5S27tZt1uvGVz4umEYaFl0aFA2DAQ8IGN30w3pRQ3FPU88G+IHndJjSn0t87JU00TgjDW1pmCfOtC74gKPLkn5SbbG+H8MojfGKVDksin5acbKh2PtCwpcmL3LhWw53dByYkVFaWEkuVze4UZ96ZMJ35FdUk+LwSogF5W6G7Sckk4LkuECfTCS3iHWAACoo59rrxwcGskh8PzwHVCPN9GML9IOM/IHGZI5yxzF8V5EeJIyfsfSvTDCNXLsqKi21B7cGHpw11uoCosgp17ME23GRdpJfRdDEdXItILJj24bzmP+vg9QtCN9hpkdP1siWjmB01ze+60MMywbEyFB0DtUrUEnCwSuG/2T7i63g7Q9l4/faiD7rn8xvcmHnlMNXL5B/p08y9RQnjuy4CXYyHpdqdGXRJ9OlNHAZwK+qljxTpTQ9RbntSe9PSCYV851NTA029yRjg8092Z1USGe1FzWhkzHeyPwfbXOwASBLkpYhCRJPrzRRd5nFhJpYugbhrF+rDylYrv2/Vod6aKbT6uHXrDNA11/zfscDK6B2q8oXP0dQ8FC13It24FjsGtRswehGSXGUMD9n0PNwbW7XpG8XQTq5Qc0Wsk4P9yi3VZuP3f1zz3D8ssdlnuKeYX6p4doLt/mvrnyRn+i9xdVEk6qc0tfhY/8Q1JV+AMMoaVo0KGoPC5eSKsv0Epg7OVc+dYv3Fpfo3VOMvnMS2NFrgHAAS/XujsxDKqhoABS5gLpZ2qq0qLpBnU4EAA3Xq+oAnCu10HrZxLryvFLL+yvWrMKxVEOxSUmninrkOXoxJZ0KISR9fsZie0Q2dtQDhcuNXBdBCl5wnM77Oy/r00N1ALt6L2SpMMS1xvdybE/iSH00kfy8aVr1inZdaJs9/PK+Pqvu/Ag7B/WYGnW7zsQGnDRpWe6taqnuxIzxM9YVPthXUuRMrzU8m0zQfG8qGx8YlJ66HKsVVUiSYwBZ6RrjHbVf7kp8p+WxLFnzJlLLH7/2t+r8D/ExuYDTsWLylGfjXY0pPTZXNL2QjAeWcezEIwY/RqQho7Tx+uwZAcD4dzyOdOy5853zfHN0zOXimE+MbuJQvDnb49XDfeZVCkNoCtMWAAjBa5JanFMCTNUaX0khQBWWJG9w1lDPzWpktp5UaXmsbXSwCl1Y8rxhZzTlo9t3+ZHRuxSqZuZyZsc9Rg34hNb7CwhSmbSFvMjqaOum4XkfiqUu9QJ8Gyn4+VTQe9W3+LnG9y17vQl9XVHomkJXpKohV5ZCKVKl0e+zRGk01Rog/ahh4veuHANVcex67JkpqXIUNCse5j/I0U1+xOdMwOndfEqx2/CagqPpDupI4HrvWMrVd675tmAWv79urdt3rlUl+yBez5GpECfItXvIGSkCDu55egdQPtAsdnocbxc82G3YuXDC3mBKkdTikRzeNEqzGxwDXXKn3uKX7v0Ik1+9wP4bU/BSII2d2z4UVdv7SIs/VLcQuNIoccaQAEKeFKkLJZKNqRJvaeeFBeRj8E9gQ0PvvQnzZ3c4ejGl3KI9Lpcpyg1NdWWHdDrHLUpJbGJHU92QH9WoOkX1G7QWT22RrkuotaexSuJ/7Vrpzy5e0VhN1SRU0wwqjZlqegeadCzsaGXl3pztKxYXLJtXT9DaMVvk2O8O6d1XzC570gmk45pqI6VQNd+aXCQ1FtXAixfuk+uajVRR2oREORZRii9IND6JybMJnda//dZz5EXNxc1Tzp8/4e6LOzSDBLMQFmW1Id6GNh+yeziR4GK96zNMtU0u0r69+zXZq+9x70efo95uSMeKZJIw3io4Hfdwpyn5A0P/nmf42hFqMscdHJK+9AzVRo5qrPjXtD7nwoBXdi05hceDzo9iG8ex3i36iK7VR40uU3qls3WdLb36og93zOvH5wK71PkVieLynMdnCea9+wydo7hf0PT7AiY8B80kBatIx4r8WDznVVnjFwuqnYzyXEhEnMenGTAXtnTssB8MmH38Mrd+MuHS52/xP7v4Df786OucM4ZCJVifSmMTvO968ySND3trto1K1hF9+kzpMCXkxw39+xpbKKqBZn5ul9+4uEN9rmb7/JgXd+/zbP8Br/RvUqj6YQD2caMLTDdLINdrhQqs+ZggC0OoRikBvTzI800D0xlaa0gSTCJMAl+kpIMMWyTCok7E0sQWupX2juteOoZqW2M3LG5u6N/WND1FteUk8Z9qZtc2GLwaCgZNg1pU+CITeTpfC1heN8LsDudy9bPq5WNKtUXO9jzEAmpkP60PrYLCQtjUq7YwK/uIvyXuUUC97UinhuxIgU9pNi0+caiEZXB2FratWAFH4nK5UkcLb/kkrgXdUQ+EGVbuSDyUnXiRzgtNqy4Dl3mafvCMjl9RrQJj1gug2yiRPzS0gK9yishsXg+z9dhw8/Y2J5f7fPX4CqdvbLP5uqIpFN8dXuCpwRGzJkOVBl1LQTzKda+wcB1tI+2ZAHV8T4ew29euB+fAIeCxasJaoDVNrsB4UmMxjZcGUa+wuzWL3YzmlqF//RR3usmBG0r8N00ELNRemusIeczMkz9YYKaVdHI3QeI+dF23Y0320sdu7Tjvp5nIb+/10bXn/JdO0e/dX7KjQ5d7KxEZ5b2GfZrdAfMLBeVI41L5/C4JBaWMIN/PSo6iHPTvBo/rttGge1Jpm5C9iYxq2a7LuH6sw88TdovMbM5eMmak5yv5TeWSAEiLBL/WHms11gro/LCUv25BXPkJX0m9bLAUxnRgQhvx516R24k5RASZon+08eiko1YQvpe8qINst8a5CIb7tRp/vIl9+7/Yfa2eB+chSyzzEurGrNjpnPWVdgHW5T4Uem0O/DBAVQTAxdrogwPicRRZzXQuza8mKCtpROobQIdG1l5ac+niEXezTZLrBc39HDVa8AsXX+UfXv8k5XFBdt8wuOUZvVOSHkxRi4pRkVGdHzJ+Kuf+j+3gnzlilArQIA3GGgscNQPemuwyr1NSsxpPu5BsRmssdFTBk8LjkzDW5brP+g7XH1tf+9bB6Ha7tefj2hmZ1HubE+afXnBweI7LNweoMhO528NjeO8OZmcLuz3CZwmaQm6JRYlvrIB0jYWqRjVSlPVZih1mKAe9A4tZWOrThPS4ZIAA0i5V3P/sNuWOIjv2bL1ZUUzn+PkCvMeUDlvA5X96F45Osc9dxO5XzJqM3v2a4uYpp2/v8qlXrvOvLrzE4bTP8a0NthYlUU67lYvtAtKtLOTS17m1AwJaH2nVaRJHt8poMo/ETnvVsmHlBLdfTLsPAknEpaoF8ggsVa9FHWWeaKoNee/sGIoHsDinqUpRHlONb1ncXi1JGV11gw/LhBbf+9XHP2xDyr9LI1WGgSrp64qNbMEbVzzP/f1TNt/OyN68J7hrkQtQZ4xcA+G6cX3JeZULKpAtELD6HsKihqancWmGyw3JIEdPS2k4nc6Wfs5ai/85rDA0z2rSVhaKQcXFyw+48aXL2LlBK5ifV+QPIP/JMX/tuS/zr+6+BHpjCdC1Bba1cQbTS5kATmBR85LkJMVrha5TqVdVjcRdAHmOVgo3nQk5Ah4G6MJnlJ2H66+35sSpFWizzMnbx8P/3UbDuB9rJZeP92DdSI40m4uiDYB1lB+7zNM/8w4fyxRPtCXWhxwaiTWeThL+T8//M/63h/8Jo3/eY/vVCXqyQI1n+LrGT6YAqCzDNc0KUENoHgVQdSNKjhcbqosb5N+5Se9oxOSS2BCkJwrzyQnJl0fMLllmewm910K+UBRiT9CV83V+FfCJozsHJ6F5qtsgsM7IXL9G68eA1LAKlnUl+ONzdvU9VpjRa0xr6FzHXe9o55fPdX2ngwS4rxvU/p68/zysVXhUqZjte/zmkGRcUm0lHH0EdCMWT6MXJyS/s83BZyzzSwP6N+/jraUZZlSbPnYWcfAZy49/4g1G6YK3xuf49M4NPjN4mwvJCTOfcKNpGOiGTS01Jvh3oxnjj2PEmlvlteCAylE/VbL9hZyrP3XE/BMp9S/toY9OcX5VfTLWhlRRUD+1t1RYTTqxRMduN1re5ocDknuncDoWwDYqPKSp1GtdYA77wC7u5rRW5NvbGmp83Ekdsh4gCskn0PTg9KM1m99M6d/1HE5y0h2Fcorxiw37f9D5HJEB7kINKTb9PEJVAyDKbqssw+5s4HoJ1XYmWKaHYSPqJKo2qMVCVEgiCzzm1Vp/Tw3VZ8p1LzuZpQkmWj5E1vl6Q4F1so4QatJdWfUspb64zdPP3WNHf+9s/A8h392QYlsftgy7IvEb/za4lildeSMeSus5cygYtB6DGjAK7zpyYVomHpAiRu+OYvInZkwv9OgdOsZXDPNdTe8AsuMmAHRBMMgAqgNIG1pG4TqTocX9OqC1qT0bb2i+WDzPJz/yDu/oHTbTBU/1Dvn5577NrXqLB/WIuU0ZNwUnVcGD+ZDxIqdq5AvV2ou3Yk+tEPKStMEktjMnC5KojcUYjzGOLGnYHczY7405l0+4mJ2wn56wl5yyq6do5Ri7gpv1Nr96/1Nkt9MVBvoK4A/CeNbdYpp8Ie25D+fbpeBTD5lDZTaoRznqeYpLICka9vNThmbRsqQL1VAoR64MuUrfl6Zfe8vMK3bWHm/9axDf6do7Ku8pvXiVb+qSm9U2AFtBxtusV/9+AKPbYR5/Mt3gvGYjWbCRLOjt1fz2PKduhmTHoYMyFjDj/R6K0HGCVgG4b2vj4bcP/6j4Eh9yRUeb1MUXxFw+ei6ZWvyZ+vctvQMpwi62UiYXdzna2SbZWXB+e8yV0TEXilNyJcC/85rr4+f5tesvob+0wcXfn6Nrh0s1qnbSTR2PT3XAabM8cL92PcbHuqyWOFo2tJLfLgVbiOSjeAV29hkku4o7nnJ/wPHzKbMLvi2m5cfy2V0G8/2c9P4mejym9UoInYrJSYmqslCIk4OKxZA0eMBJPt8ppnlPbQ3WauraUM8ykvspyVzkodOJJ5kLaN4MFPPznvJqyfbuBKW8ANJvDBnegOll8bwsDjxm0VBveI5tn7dOzsl1lsLzo/sk2jFQJYmyJM6Satted3+YzvA/zqHRvJAeiC/3723yYLIJDvZPHOncim9Y7du5uvegEsnsGHR0At7oP60clNuO2YWM7Fses1C4iXSm5oeKnZ1T7h5sQs/iUkNxaFGTucjFWEu116MeheD4DBltHxm7adrKbPu6eWi7lXGWFFdnSAL5PgHu42SFY4J6hiTgsoqyNu8+LqBe377Tieu9h/lc/Ea3N3HDPqpuSBaephfuv/kcVQ7RlaXpwfyZip+9ep3vDM/z8vY9fsN9lL2vWPLDEj+eogZ9jp5PsdmSgXv6M89y+pQwAbdeh/FVhfr0CX/1+d/h50ff5KV0zkhnaHo4HAlm2TjzQ5I0r7BFImjyQUY3uI2sE+9FBjtR6NpTHFnSuWdwR1GNUqrNXb6ys8PXXrzMN89fYr8Yv7+axaPGurTPGWo2MchXKgCzWbrs4nReOiiBFvhNEnSSoE8SkkQ6MX2e4hONHaTUg4RqZIQ9kcm87xPPtWfucfP3L3Hxd+ag4cafLKiHnmrLM7mQ0H/DoGbBrygAbz7P0DFpaOzDygTQHmcbqEcGtVu9x866N1sJ7zDaRsCzAOmV9/T4vqXa0hT3FfmRwmuDyxU+C8CiieDQEsSJ+Hj7nt3goAW8Y4HWf+j+lO/3iEomNvdkJ2LNAWFeyb2wpAuPL2wbH7UsYyVNk6oOMt0gTPp43kJMpTxLdjXgtcc0Cn+UcrPc4vrBDhd/xzN8/ZjjT2yxuJPw1rVdekkt7xHAUN2R6G7Z0OG5FbB6/RKL26wXYcNlJjmQD0yVEHf0JDBqnMZoJ0Xy1JH2asrtVJLXuiE7MlThs+pKQQqgWuUmU0N+atGzerkWdaTO5IADGBGLY+E+bplx1qJMhhoNsJsDmp4hO64wd4+XgHQi3dQqMUsfyLpGZX3Ky5ssdhPKTfkCoh+0DfmqKUUSLTtlea4bv1SyYnl+dRNiYi2NCy0YoZe5XRv3duLO9x1P2D3x0d5Npi7nerWHUY7DZkhfl2g8k0XeMnaXHs0d8MXq9h5xoaFTnkNYzZ2/tXGYzEk/a2hqbWW4YSUOJtjaoD3KeHQq12Q7vTQab3zLbFZKAHMfkhXfiZvjsb6fbHf7ebyiyGpmi4za6lbKOm4fv+azYl/vxebgUQBmfE339/sN3Yn711nYjxobWcm9eoPGawZpRW0NrRe1DsVpJTLs/bSi2D/g/mBAfdpDK8+n+tf5teJlpvcT+ncU/fs16YOJyOwbDY0le++IneMevYMBd3/sHLc+63hx+x4ApUtIteX6bI9Jla8A9+uf3xHOQZDyRgmwrb+XytcPeDwOkO4sI+3jLTM2PDavk7YJuUgbZjWoeSlz26AHyS4cHONPxhjvcaMBLktQST8UFBuZc9NkGUMZjd3IqQfic6krJwoWtUNXVupUCqqR4vBTltGFMUc3NkhnKfk3g8pF49CNMOXFa1FTD1OGWzOmdUZSOlRZk9/XjG2Pn9x7C4B/9IWfQtczkePOU2FwrwMRsOoVrVSbH7cjsqLj+hDsV+j+Hxu4zgKku99TB/xe7p/WJ9oW0rDUjKSp26WKfCw2fc0gNHt73zbCK7c6nX8vgHT8vzsfea9IzA/fffBHNXKVsqkrnkoPOJ+PSZ8fg9akX3kTF2VFFwgw7bw0T1iL3jtHPUyW9iax5hUaOFfqPxpokLpSorC5wWYaM0wx8wI9GYis9+k4SHgL6zfKTUepVw9y32kplpfbiqpMeevGHum1GSykeW/2iTlumvJje3epvWFep2TrS8U6S/ox0qO+buB0LIBkWZFGn+lF1dYZVK+Qz79YLMPJAPCpJEHnucRQyWpZ/iGrrw6QAEgTYWRXx2Pv1B/8Q02HEhOqIPHtL+8zeXGT937B83M/8m3+q4v/HOuzll3s8D8kmfajR6wvA/xHxSl/78f/P/ztl36CX/2lz/L0r4A5OpVrKk3wdSMMRR+sqwJ7WQUwX9UNPk0wC9C9hmaQkJ6OaQrFyUsWXSnSRlHfHJBVoKzi8GOKnS9luMkUs7VBM1AyN7aKdOE9lW5V49x8garinLwGJD2CwNB6srcffG0SXMtlVbDkaodlRdL9THnvdXWAsxrYurZ4ddO5n5YNuO3vcA8cfeYc04uava+W5LdOobEMb2hOP1bhE405GDP+/CbnPnGXyb/Zx6YweXWbwUjhM8ftn0h4/isGV5bM93PqLSf5j1KYmeb33nla8JWs4TfK5/lW/yLXBofsZWNeKm5zITlh30wY6ZJRAKfND4FazPd7GKXRXmOUZepyjuoBF/eP6b2+xR/8k49RbXiufXuBOzhc9ThXCpTGzxeozQ2qrQyXqYBXqRXcoju8UtiiR7KVk0y2SE4X6INT/Gwu60G4hlWw/Its6a4nstKiYOPX5nWlNUeftKTbC7KvDElmivS5OacvaC58QWHuZIyfcZx+1PJnPvUN3vg7L7e5MrYJoK2T+6arNPaoEVWsKpGct4XUvJpcYWIDSVvj1auqGXFEP+cVhnZHzjs2zqi130VoPor5f5IsGdDh/4eUEQLg7Jtmzcte432w/0oSabJKEu5+fsD/7qlfkUP6HteNDwxKD3QZfKXlgxcd0ArogNKSeE19ikOTJ02smbWZQGSIRrAqdrl3iwqSJMuE7RLxlZ1qx+QpKI4gPwqFq0xhewZTLgs7sYhqU5HCaxmcILKIsSs/FJfkNavF4vzEsfGdhK/X1xhePeXlc/d4d77D146v0E9k4k+05UpxzLXeAcV2HdillpnLOGwGjJuCw6rPpM5XkiOtPP2kYpSWZLqhZ2q2kxnn0jFbZsZAl2gcDk3lDQbfPna9FqDqTrPJr9z5GG985zL9SfBgXDuH0CmaKWno8BkPV8+UgGc+dZA6dGbpD0q08tSNoT7N8LljOBCgdaBLCl0FUNqSKihU8ljWWukbFt60wU4Er2PAYL0nWtU7YOENFkXtNQNdcr/ZoDZJ67X2pAytPIly5LrBKI9Rti0CXCqO+cj+Hb5ydI10nLbzWZsLxYDdybUJLIFpYIU5DS0YG8HbyHhvXx9vNL8s9MGyizhZgLKe1HqSuaf/QFH3NLMLA+7s9bi1t8Xm1oxBLn6l0yrj/s0ttr6esvNqiXIemwd/ASsFdBd9ODsLS2RJR6Ca7mdui8vLxCSei/aY1fIz2lQti8SdRMYZAZ3zseX06ZRymyCVA9qq1pcVoCkUblSgez3phuyw43TVoJuQxAYGg2+SlpkCoXkkDOcV1hqqKhE/6FmCmRiK+yJtnMyDx2HtqTY0s31PdalmtD1DKc90nlPdHrB9XQq11bmG9NjQOxC5TJt77tabzKpUjiWTa6vQq9IkqXLCcAAqa7CPk5j+AQyHY08r/m+f+if8l4u/yLl/kzG6UZEdzlFl3cre+SZ6Lln8dCbBTOz6RQIMFeSEdeNhs6HcyKBpuPjbJYvdlMOPKvo/f4/P713nHx9+gp//6Hf4rcGzmH+TL7s2s4zJxRRdy/qiGrtMPs4CcZWWhDJ2Ez9iPOwDtQ74vo/XDzzMCj/zTUIApPQqeBbm0fX3/LAd/t0uVqWUBDFJImzTRSnyMrXHjyy2n2LyvD1u2/P8+U99lVvzTQ6Oh/yJa7/DV69cRtkB+mSGe3qfZpQxverk3ANozc2f83zylTfZyOa8/unzvDw84RfOfZuPFzcoVMOhA0dFXxksHufroMihyVVCg33yE4cIdrIMvh/7Ek/LkFbWo4PncgukVgFECnOpSyCbePJTz+A2uNeGvDcY8uaW4tp77+Ee+Ub67L/jcXdle5ogXRa6s0VSKLDvUEu2hPNgK1RRLBMD75beOLMQvBsjxZjEYJKEzHuK/S0OPrGBT8BlHjLHi5v3qL5ykfTeGHU64dr0HO/9yQ0W5zyzi4ryyhbFa3M5xticlQh41wbUvtOAojRttbTro6jVKjtonYXgloDdStEqsoLC+hFOCGd1XSntUZml3vfoKhM1gaNQiMuDV1YU0gkgq48gUHf9jBdJXFe1f+R7xveVz/Hh5oQ/rlFtgE/kcJuhxOhNH7z2uBzswOGTMGc3SpjRHtKxFplmpFNeWNUeUwZlFuNbxaBuvtGVRk/HmldPLtD/lREbX3gLX1bsTGbU/cu8c/Uc58+foBd62SDnVnOFNkbpgNIrvztD2fgdda6BUI+KAFC8llRtqQcKPMzqjI18IUBgryEvaqb7FdUgw/dzhu/A0ZZCzwWIjiryKsQ9/Tue/KCk2e6B9zSDhN5balW6O173EXwI939MXpXSqM0R9cUtqq2U/KDEvHlbfOQHfWEvRQ/pyMZpLKrImbxynsOXk/ZaVk6UD3r3PEnpsenqOTOVX5Hv1t1GGiXxmzCkaa2KYgEjAhntfdONrd9nPJZJ/X0elTdMXc7EFuS65tn8HpU3XJ/sUH1lG5t7sVgqXGvjBMg97RUqcS3gCcspyrvgRaZAJ9L4rFRDreSE+fA9+E7zx4oHNWHe0qCNXZkCLUDmAkta5EO9C0Cfdp1jUSvH1GU9P9xAI6BQkdUsqpTamhYkfL8Rd2O0awHr+F7rvtJmDZjtsrjXmZMiyrD0nv4go5fUaONZNAnb+Sx8hs4EAuTKUzvTejdf2Tyhv3ufB/Mhf/fe5wHIjhX9+47sqJI4rFlKsIoHX0X/9QX7bpcbW+fY/PiCdHBMoi3OKt6bbomCV2iuXffiXvFYVkvwVualJ+wG+R5HF5Du/n6IQd353pNwviaLnAu/PxZ2dJaiqhq/swnnd1FVDfMFKs/EoiRPQPVR8wBEJQZvDL6XYvsJ1SjFpQqzcNKQZD3mcEx1eZPZ+YSjj0Kz2TDYmzF/fYvL/9ax8cV3BejTqmVymoXn/k9fYPdrp5w8l/Hi7ju8eXiOC6cLcI7Bbc+dZpP/4tyX+J+++efZfsMupbjzUCTWSGwUvQK9p6Nbv5Tq7rA3I5MpPu7XCpheBasAF4q0PLwexnpFfE9l5f1EISUo1TiPrqDapq0T1CNRCkpm8n7pRN7fZZ3mQBdCsD9kg3YXkIb3rzH/uzq6LMGRzngpPeDO8B3eO7/F2597gYvfNVBVAqTmOX6xWOaM2uC2h8Jya3xnPQ7qJw70SjwVFwXEukWDMxqvFE0/RecGNcoxgx6qrNCTGX5RtsBhqxS2NuoBXNk74t3bO/BOH1143LBhNFrAaMHBYsAXq2uczgq2F/WSdbpuyRPHY2S8JbeZi8SzDwzQbo7e9UENjYBKKcmDomVVt4YTQb31vN5oomVSu2lH0all1a3EnEpAGePRG0OmH7vAwUdSJs82vPjSLT679Tr/xea3+ZnimFz1goyxofRNW6/9YR4ahcaAsljveT61/B/3fp2P/vVb/L9+9KcY/LfXGH3hbflOjMFXtQDRRQ5Ki3x3ZPUWMoemE0+WN0wuFvT7PZpCUVycwFc3UA4u/C6cPAO9O5rZKwsmH9ml/857VE/tsNj1JHPb5pQqz9CM2hxZwF/FijLeOgv+jHqfWmtoeKzt3OOG69jZPWqs3XsqXXqZd2sEZ8qPh+GtFTUFwKXLRtnBLUf9kwsWF4f0ZiXHH2vIx3027npsARf/bcXtz+cUt1L8R8ec/vjTjH5txskzBrDo8RRf5OQHmqru0eSe2ngmhePBYMSt0QbnhxMONgZcKw64kh2wpWdcS4/Z0QIc5urJsAt9kkaqDIWSXHLmMoqkoR4kXP2XY7xRmO/ehCxbyssHJVeqUtb70WDZuOZDLOA4M2eLZD1pas6whShpJCdzUdKYLZYS0irUcHzH6iHWR9eadQAhSsw1P/qJG/yBukr2+0MmJwXFoWFyGfIjmF0q+asf/zLPFfd4dfAx0lAr9t6LZUkrpf1+64MK9e1QLypL9HhBYjRZrkkyIZnosl7eT0mC2tzAT6ZBRdJ0Eqil2qvUvTrom9Ztba19b1hKpK8RTh4iX9iOkoH3MhfGtTHey3WoI6QZfjZDGUP54gX6v3CXn++/hSNhXWnjg6oOfGBQOg4brqSuvJjlYVmtyGRNIwUiAE0PjVBEiKzdWHdfsnjlRbr2NAc9/HnL/JyhOHToJjAhU/HMFU+sUOAq5Ec8klnJSCLDwXfoqN0YxCtQDRSHHt0YFg+2+YPtTdzAkg4rhgPxfOqnFQ8WQzJt2cjmDJOKnq7IdcPILDiXjnmmB7VLSHXTevTWAWjua2EudDuvx7bgsBliUZQuZeFSRmbBzGWcND1uzrd4Z7zN/aMRzf2C/ECYRS5hyTzvXgtqWeRsC56JlyJfLOAEdo4yHpU40qxpk9imNqieBQ+XN0/om5K+LhmoikEApQulVkzNHzUW3rHwaQted1nVGi33RCiKpN4GD2lYYNgzYypvqAJQ/SQwpRPtSALqm5uGRFuS0JgQmzdSZfnc9nXuPTXi7tE+2YlaXuuhGAQQgdoIInu1LBB1NDlbwLptPIgFJdW9hlXMzsBLbSg+Z/PAerMeU3lM5Ugn0L8H2npcYmh6m7hUUXrIK8+zJw2mnLXBggKR20iUsPBi40dMLLt/t2DMao1D0fns3eGX13GUc3S5XNMqeJC6RAraLoVkCi5RTC8qbCHFaV1B775c3zZX+AZ0CvVGTpFnMJmGwLNqJ3rVSAHOOc28SvGsevV1ffqsU5Rlip0n6NOE/FSTTqRRBi0MIF17Zuc10yset1+SFwIoz8uM+taAre8omp4inXhGryf07zoGb5/K4mIVd6oNhnnFjXfPgfE8KIc8179Pqhts50S6cC81yQeb8L/fI8HQ1/Bz/ff4P3z2n/Nf86fxv5mxe7JALwIrOsiuxA7odniREm07u6wkn8nc0xstqLZyMJrsuGS+l+Iyz1966iv81sELNOOU/fyUq7vHVPnFkGSEIOqisNnje7SL8EM6kgJoKa8flknqDnVGufJxndVneUqvB//rntbdpKSbCERZlz8qemQIcpqPXGN8rcfwxoL0/gSA7NSSDSrqYUFWlqgkQdk+9Zbj5d5tfu2dl/Dv9Hn7I3t8/Pwtrg9ept7f4OTZgmzq0FdmJN8cEr1U0HBc9pg2GTu9GVp5vjJ5iveqHfbTU17I77ClZ4x0xaa2jLRZSZKTH4Ye7i4g3fn9qKE8UsyIDJaoPLGyUVCACdYk6XwJ6nil0I0nC9e4z94nqfKOleAxdnvGayyuOSDSPd2OSWsD2KGW906QlsMFADoGdHHfcRvTOaYYIKcJ5s4RvSsDFrsJzcDz7LV7vHa8j3Kee39ij70vJpjDCRe/kHL3x3rMz3sOP5Jz4XALfedAiq5a0wwz0noA9w5Zl06Kx7LSSBKD8ziPrif9j/nORKmEVj5ZQOJu0q3FgzWs5TpxlHsWm4lMtDcSn7m0452cOXS6BIGUAgJTT+nIlgSthX0X/z+rGcWsMCx/8MNlyGc1SIyqVPu/zUN8ahU6MgWUNHylp8Ki0hWkU4kF6pEiAvJtI1ywoFmymVWbfygL7x1ucfXVmcgXjoa4YZ/+A8vk3YzDfICpVGAyCOgZ47KoNgQsQev4uO8UWFluI4fv2xhNmnLFrzqCA3JSRJoVB+NFzk4xI89ryjIlSxrssKQe5th+yvC25ehjkveYUgleoUHVkMwVpnS41OAyTbmVMDuvSRabZDeOaGX5W9aCWnaOR+Z0ksDOiHpvRNMzpJOG5OahFMwaBKDZGMqcUFZyX0XfxCzFzC3FgRHblcCe041vrWtif10E+1tJWN8psAKRHa2bZU7YJMvvcl2BR8Va99rjPwzjbr1Frms2zaz1knZeZLuTOQxuEkAcjc00thfsbXrQ9D22cNjEQ+KXgHUcHlTmKHoVWdLgvUJrJw0Sbinz7awkJloH+e+gqiXTYwS0ZSqvylTyi8xSN8L6FdeP+F3GBGXtUM5gS581tPKkaUPTGBLtELXSDzZ/dXPqM72k3+cYHgdAfxBlIq2kiXuyyGFw9nG3Ps5InaR2hsYZzvfHfOv+Bco6IZ9DOrGYWbhhuso4WCm8JYb+u6dsf3OHN/f22L42o2dqjuse4zKnSJoVi5+2IeYRYwWY/gGPx0lzP2776Bv9QfYV/8+S4AeuYL5ISR6c4vNccpRFKTHF1kjAiSBvqGclPk1E8S9Nlh66RkHjMKcVvVnTylHa3OASDZt9yu2UckuhK8/g7ZTtXx0yuDHB3D8RNpAcPBEI7h06Dl82bG7kHH3M8XODA7767lUuzk7wiSEbO37/5Br/2ca3+dpbV3j+oEIvhK0W/a291pCaZRNe9IduC5TLdQpYYUjH0c7VgUEthWUfZGlhhUUd/w7xaduoFaW/43sYiaOShWc68vTuapoC6g1h0SoHulYkC9dKdsdaiPKsMKQ/CDj9ftu0TQv2hyDH+CMeXZZTgmHHGD5XXGd6Ludv/tx5Do9fYuf37gsokKWoYR9fZLiB+GPWG+KdHtd8ac6T9SBeJ+2IDWgRxHZgGlHY9ImA0z43+KSHWaToLJVm9vGkzUl8zFkiszPUIl/auks/rXjv69fID+H02ZRx0kdnltOjPjQaNdeo+rA9HLHj6TSpv08u3zKdjXhn+0r8QOlagK0zl7tARpoKMzYC0t63MrTL89OtAyj8WcfTqb22HqKRQdiR8lZKcfTjl5n/9WP+4rVv8NnBm3w6e8CmznC4FoDLldRpjVI/NBZZ7zccntpbciWWdwZFqg1/cfgGn//EW/zFv/y/oho9x9brU8zRDF1W0tScGPzJqcxx3mNfforJ0z22/uAevQPLUW2Y7ynY3ebgk56+9uT3RNFh9MaY8dVN0rFnWmlu/bTm+XefZ3w1pxl6knFQLspS3PltXD9D1RZzOBE1lCxY4URGfKKluSiSgLSS7TrDJ2tr2uNqQGtPf2AVt/fZh0vlOm578Kxf5kY+/u/b9QDrMdOaZCFqkboO16p15Cdyf00vpMz2L/D0c7e5/XsXcRn0HjjyG8eoz+3Tu+2ZfERx80/C83eeYXrNUtwx+EWJ29kItmeBrJhDM1S4UnNUaRqnsV4zbXL0yEEKx26GoQT+AzB91tAoRkqzl5xyPh2Tm4YHzycM/+C+nPPZTHLcWHvyFpyWmvugT7ORBxyic13Eaa0l5IU8wgn464Kst0s19UaKzzSmn6HHOaqsYL4Qu5TY/GMFpfRdvk+s6cQaViId7v/2u8+wtT3l6CVpOGkKz+JqTXKQ8tSFQ57KD7hdbbHYTei/7paqHLAKRiu90ozbjq58fW1Fknu2wAC9KjQNei8NTeE41aAnsSTAyelDgHprmxVVOLv163V7h7OaU+I5aEkn71OzjjWCSB7LUtSgj08T/FPnuf+pDapfPOb//vyvsKUFVhZlig8fO30oUNr5pUx3TJq7UspdoDBuF5OftliwngupGPxGECvIuwRw1XuRonOJIr+rKZ9fMD+foGtNfupIZk5ALAM21wGgVcFzujNJd4tLdvVYlO/cBzGgDcFxdirycu5dRbWRsjifcJr3OM49vnCo3JJkDaPBgo2iJDcNmbFspAsGSUmqXJvkauVWEtoIUsfHLJrGGaY2Y25TTquCWZ0xrTLGs5y6SnDzhOQoIZko0lCoiZ5sLvhDL4vgy8fRXgB6E4qeiUMlAkIb49pChNaePG1QyjOe9DCJpZc1WKt5aXSXvq7IlKVQNbmy5ArSNTDa4c8MZBYeFj6FDm/LdArFJnhSG2SxSr1c1iMclbek6pA7zShIhn8AZuH3YWjlSbUl00vQKtcNqbb0dcXYFgzTBZ8+d4Nf3t/Glhm6XvpWRnC6K2cfx4rMkadlQnsUKpTuVJjL6P7EfSFePV6HAm2Y+CMgDXKvCAsvyCJ6SGaOyFTWlcOUIgOpQXyCQlIh3g9rLGnDsjAcfuTDdH53Plc3J5SCbwhWPIEtI0VolwSARINPRCEBJd2K032DLTwoSGZSrLaFYrEL5aUaVWl67xlMmZK/24ejY7y1eOfFjwKk6OlUsALTWKtpGoO3CpNIMc97JVLdVYKbpCTHhuxEkZ0KOzqdemwmjTCLXc34eYveLtkciNfbvEzh1RHbNzyLXUV26tl+bU4yLlEz6SDz/Zx0rJg2OVvFnJtjg9touD7ZITcNV/IjCl1jlKN2JigtLBUrntSRovjrG99l68f/If/NxZ/lnacucum3CvKvz5Aqdxihc7hNNOPc4paTtFlYjHGUI48qCvTxFFMNsFdK9pMTvnP7POY04W65wVODI761cZlB04j3bZIw33dsfUdJstd28rlVkCokiDG4eayn9Pqiv779urz3+wHQ7SbLbVQXJDtLzuWMbtbHdbius7vja5Q8SbWdcfyCxpuC7dMFalGTHyxIEs9i2zBwHl+WlOd6nH/2AItm/u6IwX3F7x8+xSubt/nKSwmjdzRNH3SjuLx7wum9ATiH62dkB4Z3m318LmtpVtT0i6q1rzgcDLiYHXMtvU9tJtS+ZseIdLdW/oemi7tN9j5IzufDNOk8qlO0W2+cw9HK3bo0AnMxkJftTImwez7QQYbryAcfoBCsRtn8tiDelbcO3eMYIweYJKJyEOXIuhlBvJ9jEF1XS6m6xLB49hzF2wdkpw2QYAeOy4NjfutrL3NtbKkGGp8bOK5Jbx8zei+n3FbM9z3Tp4eM7h+3x2YLg+mlEgqdqYBw9hfhsk4DzPt1mQegvm26ipehW8abbcNfe3qXKj1egxo0NNqjylB0SAVQ0j2xeMnzhixpMHoJXijlMUq8SSPQkWiHCbElnA04JMo9FJP/IIdLRekIhPkaWd8uk3OgrAIrEt2CaAJe1laXetKJwiw8vifMYJd7kYXWCFtax1hHLeOnsCvlYX5YkN68g08T3PYGiysjkomldyfheD8j6zC9ZD/L/7uKLm1xdS1/iI9F1Zf2emAJ0EqhK2wbGgVdCsoppvOMxSghSyxNY+ilDVliGY82aPoJ2VFFMumJt9oCYZMnCCgttnhUWynZcU1TKMotRT1IRFbShVa2yLSJn0EryEWGUw16NLtDmqEAzOmDidg1FPly7apq8U8tpbjWsiJCISIbO0wlKg8RUIjNiir4F/sWqAjnJjYMx0baTmNjBKjPmkPPwhb+kIS57/vo67JVw6mt4a3yPKmyPLfxgHc/usviXkYyVeSHkpf2Dl3LLrG5ou5rbC7S+LaQeyn+9olH9yybfQG5XEeW1nlwTmO9NGXqwDT2yOPdZhaphQbrmsqAB5NYmlrm/2hHJRuvzjPeRxDIP3Y9Mtphnaaf1RyV6SOB7JbpzCro2GUBd+e7zm145liX89bKS7b6PVxM54cT3r6/Q+POjlGELb7aKDRvUrbzGWliOXlvk14d5exVG5e1KjlBgUQpBfOS7dcWnD5bMLuSofMxp3UhrPDHMMzP+vxPqh3QWeMsIPpx23TH+uetGvm+EmNp7vfAHQs7sYxMSgGcVBmUY4wWGUOtpAEwTQT0XQsfVG3DBOgxcwGBXWrIj2pGGjbf9mQnDflrt3CTqfA4lGoBN+WECpydNNieZnoxY3T1hKEpseMUtMYNpdj77nibW1Yx/FaOambLgqOSnB0TfodiqO82uirVgh5x3fSPsheK1yPQytGujegvHZnQ3fWw21TUrRV4Ba7w5Iceu6+wI0tTGLyRmDbWL2LdQXYQ55fl7z/s8H+E+/phGhrVsqUdnhTD1cTxZwevMvronP+H+gUml/fZfNthSoc3CmckD3GhiVY3Ht1IfcmUIIuFknKfYtWKyvtW1a5VjkGhAkDlFWAULjN4k6OKBF1kovwym0sMss7E9HApPyHXDe/k1+gdWsbXEtKbGTgY3lPkxx5tfSvPD7QNGiv19Eex4LqAbyA6tKMDRq/IxuogNetcC0gDotZm7bK5r/0g3Vw/BKCtwlN4LjKkQbxAgzewN1oUHSqHd07kal/W/K1P/G0+lsbPlGGUIiVdqds2Ysz574h8d4h12tq0pqbGoLhkLP/XH/3H/H/3forX3z1HdndA764iO5Hzs/X6DPP17wIwv1Bw8oxm6yua4sGC5qRAjaRh+plXbvL2zXMUFtKxR08X9O5vUA0V6YOUC5+6w+2fvUg9BJc16PECvMcOMsrdgnLboCwki0GwHQ1zmWLFZjOqUKKkKXdl/GGnqg+SGq5t85DLh1pu06pK0cFfOrXvuE1+kgaVS4/NQo2gsSTThnKRoi4rZs/U/MLWXU5uX2J+Dga3lzXBZA7NrT4vfOI9bv3kU/QvnTD83REArpdg5iyVRT2gFLZWWJ0wTQoOtEPjGSSSywx0icGTqorUN2j0D02t6fsxjNL0dcoLyYTD3k2+nV/ktacdfjTAHx2LZHsEVNtGyhDHZBk+1cvmNR+ujVj/J+bZnQtNEXIdv4wPEo0fZagiCRhFDzWZCdFJq/W0e7mrANZ67/FG43dlzj66P5J1KXXYoWN7b8x8I2OnmPLV8VOMm1yUzGAJAJ81T3fJD2Gs+Kz7sE5NJqimQZ1O2lobsGxoCkxsfxYhSutlM1OarqxDrcJGp5Yb97uyDnXJGnrZtNQei+3Y3mkjcV5dofo9qpcucfhSwfga+Gtzfva5r/NfXvhVtrSm9pCrD813bscHfqX1GgtkBJa0Ev9orRzO6xVAOsp6W68pTC0FokaBXpXgbQFplhNXK+/jYyLm28mrd8+zeEZR7jp0rXGJJssU+YlFlw4zd1RbCYtthe0pmoIQHIFqEEnf4HES/cskmw3vHQtZHXAwsliUl0JAOgsHGYL7apRicxgPBpzkHpd78bbLRAY7ySxZ1jDIK4x2pOHLb5x06EzLjLJMcNbgao1vYjFOoyol/hRThWlCjNS5w3wEAY1v/44LV7uNlmKnT73IIWogcejEYRJHklrSxJIllixpGC9yTo77oGC0MWdYlByOBzy9e8SzvftsmVnrJZ0pR6aUAASdIPYsJnTpG75dn2PPjDmre6LLstZoCpVQKPGhlscsF4wFxtRPCAAXAWmNJ42M6QBIgzQdRA/2F3r3uHLxkPdOz5PMzHIhD9hXbKxogWWWf3dIPqsjftfx9eGxVpVA0cogei2Payf3Uz3QUjCsfVskVE1sCFHg5DldS0ey3cgw0xqcx+UG1TiIYEhk9CkVEs+14qFaJrbRL/tRwc9Kg4gjyI5KwqMb2ZcztMoAykO5RZusJnORN2t6imbg0L0Gpw31pmaxo3HbQ9Qtg68bKSIE8EG5mCELQ8RZjbdLKcIISFeLFD9JSE8M+aEAy8lMkuR06mh6hvl5xewpAaSLoiZPG8o6QX17xNZrjuOXNDbzXPzCguzGgXRDLSphjiKfwaGY1pkwtnLL8bzg3WSbnq7YSBbkqlmRamycWXorPkFDmk5gGBbcvzC8x5955b/jN57d5X/z1P+El04voN+9Kx1uWehwq2pUnuPHY5Eg0Ro16jF/bpfe20fkD+aUZUq1Z3E7I/S9I6qh5q997A9Y+Ize7w4pjjx/8NIV/uxT3+Z3XlLsfPU8XH8PLuyh9xekv19I11eWQtNIsd0YqBtcWYq/UFyc16VSoF24W5boumzSWufaQwDwozpSu/vvBmV6dX8ohU9MCOzCPW50K2WK1tIFuJYMt3/Dw513IQDxRqPqhnTcUDxIl6y2xqJP58ynm1SvwLlf60OWcufHM/7X177It6aX2f6Gov+g4fVvXeFzP32d6ccW4Au232iY7hvOpRX2nvj+ukyaOsxCPMR8klD3Mo56BaejHvf6Qx5sDHh6eMikX7CfnnAhOWbmJ2zqSnx/SFYShq7s3JMyuoD0B+5EDtMkTQwsaQuFK80+IaAX8EauGd0s517lgLI64w0eMUKw6kG6MKENpEHev5UDwiyD3MgKAkko02LZsRqLLevdnCCPOYdPE6YXU9LTEenRgmRegPZULmHjOwn5wSnVZkK1XVCcSDfr6I0x5WiD8TNw9GLC8I0hajxD1Y2A9Ytm9f26TS7t+6vlvaVUp4nRr9x/Xquz1+DO83gBSJffFcQmMcmOZG1RxoNTKONQ/Qaf69bTVSUuEEqUNEVFRiPx9IekLQBFSxeKJfhyFgjRff2TAErbnsTeyipwhPU3NEzamC/ItrqRa95lnnpESFADEDeiVU1xuV+JnZSVuF/FRoG2ExCyBwl+MkX1+4xf2GS+oxneaugdOiZHyfL+CvlA11dahXsugtPtb5bbxKG6ku+dw/DhehALEo/XMlfb8Bnqcc7BcEBiHP2iYiNfsJPP+L2t8zRDQ35vyuBmn3JHQGkdmlN1DdlY4rjpviF/UJIspCism7AGmGQJSHfuX5UJ888N+9hhRrWZkUwb0runqHkprLokgbqWuKksV5NtENbHqIfLdQsciCRrpwjlhD3V5iuKtkFxyXRXLWCxHk+uYARq+brunLfOoO6e//Z7+sHfBivDojm2fQDJs7RYQj3Tf8DtpzdwTykmVc7BeMDJJENNEsxUk50qkpn4c6dTyE5d69FuU0W5rZk87djcmNJPZTF3XuGSBuv0Cqhrg5c5QPR2hk7jdLiXjFf4Si+bRZxefonQ3hAdFTh5uANuxzlsNTRRGL2U1DbaUeQ103lGnjYk2oWaon+kN3T8mm3n2ONob2sv6m6RNRxB8PiZz2RXh8fOAji7DUHSgO7Z6014051jXOf0kroFyrsjno/aGYxyZMZyUhV8fv86v/zWDjZTVCNNMs/QpTSXqcbKkccYsbFgNOnBlK3XCg4+3WcvnzCucjJj2/NjtHskYBuPu8ukfhKA6e+FGb3y+s5jcV9RHt6dcX3EYbSnSBoGN4ywMWNxzugg4ZrC3bEobRSFFPiiqpM2aNOJ47sNqfHxoM5kgvpRbgz+dIy3FteN+0P+g7WoRSUsMu9JZjkHn4A/feW77CRT0kOD72Us9vvMzmsKa/g/v/Mfs/tqja5se58C+DQUGLsgdEfpZiVGNSGvN1ryg3jNeXCZboHFLvttOXerFox2RrWxa7f4HL+kVtEEQElTuZ5piiPH/Lwh3aioB31cqkgmnSJ2+8WDrlSr+rD+fX/Y0WXY//s4ujlUzK00Cc+kBfvmFi//yN/iHz/zaf7J9Y8x/e4mxQMtzXE2/DhPsgj2KolcA6Z0MucqJdexXV43LUAB7TW1XErC851Y3mUJLk/QVY7uF5LjjKeossRVgWzgYTOZ4ZCacT3QlOcbitsJKOjfd2x/8bYAE9P5w7mJd48Go6GtCXigZZJ18/fOaPN/E/KNOC8o3VoZtUw0WD6uVdvkR1W3DTDx9Sqww0XBJrx22McXokKm5pU0Dy5KVL8HSrG4WnEtqTAqe//c2a/WcH+YR4JhjURM7R1aKY6t47n0Hv/Lp3+Dty6c5+vjy3zjzkVOjnpQaxa7A668nqMuXGGxpSW20gpzMKF3a8j8Ss34uZJLaUXx3YLZRdh806Hqhv69htNnU3p3FcOs5P5PiHxZ/8sjaYRQCttPqTaMEG8MNIVZjXXV8vdKbHvW0vi4Seuh/ITVeXT9+bU4uWW1dvOszjZtbtR5ri3Xh3leNuy8ZYjvlYOk9LJ+pAalFenBFHVnB//pUz6zf4dff/sF+oDLw9yRGEwJ83OK4buK9FMW/ZNHTKcFl9+YtvXqdOZpPG1zrPKSb/tEYVXK2PRQypMnu2KvqisMnkIdUyhHig3KAU9WbekHORIM50yPT+d3ON75Ntc/ssOtP3WBS//KoF5/C90rlsxaY1CDPmwMqLd6rZWJCjhDbIRTyPcaf+JYKml5UdboxBY+0TSJRuUGPcrRkwFqUaImM5jNpeGOzhwcm5dCbj7cmPNjF27w6999gd1fK5hdUFRbnsksZ9Cr+M69fQDqKmGUwLrqhUoSWSu68+TautEqzsg/UvNyDtycVlZca2l+jJ7uUQFz3Ss6ED9UnoU1JNROGsvS3lGtgspZKutHPFZo7SS6SmleswSmI9M92hb0cm79mQtMPz/jhYt3+Qu7b/GLo6/xSpaQKkPte+3n/cOoa3xgUNqhg2yywwYQ2nRaZOzaQVgUFiVJpBbASKQTfLjwWE52sTAR/m4fkwgGl0gRI5t40tsZzeWSepyjnKIeKqqRIhsL+9MZRTKXQhUF2MLTJFKc0o1C1cuJWIXirbJgKlDBt7Hb3dMkMYgOr1sj9RUHSynl6LHjNdg8weYSkDUpHPQCyzP4BKpQeNMNmFqR1lJQ0mtNEUsZc1qpoha81MIaWRZykEJXCtFj0Gsvcm6BFa0CGzpJLWkqMt2NNRydDHBWoRNH3q9J04adwYxplVFkNT+z9wZ7yWnLko4g7FlTtFGa0tc478PFanngKr42e5q/sPEVwDwU6Lxf4FN713KrB6rhvusx9dkjt/9+DY0k8om26OAp3TcCAFivGduCfvTe1jX/48tf5++WP8r0ZIdkrpZdY90AIxawob3m6GzWdpmpIOMd7pvYUAGr+4uyohEMbiWwwrUeFwa8BETex6KhXx6PE1kllyfCovEel5nWByJef8t7V60EUt2x7pe9AsCz3L4F2cO96A3SgatDwTIBPYemJ/e4rsRjMpl76qFs17+lmRQZ6USjS2GPLPbE/8WfjAX4bBpJ0jsdfN0kNsksJnFUVUI1yTBHCdmRJj8W33lTEdiKnukFw8nz0OyXpEVD0atItONo3Md8c8jGdc/9TyvsqOHSv1JkNw6WPnHWgs/Ej2MBc5vy9q1z5HNFPizxXnG06PEa+7yyeZv99BQT7hmLxiUlSaxEPmHDKN22oZy4ir7K+PneMf/Pn/p7/O+zv0L2xvNsveEY3ixJ74zRswVudwNzJ8MeHcvievUCdz6b8swNg5ouqE52GV0Y02z3sBeGHP+ZKblu+Ae3Ps2FL07Aed56dYfiWo1/acr9z+2wd+M21eVN0qyif3fp3dE8fZ5qK6fpaczCkU7CJBy7Uo1IxkSgSR6TTSKTa71P5pHMgkeMFTmzyFToPu+hC5KtPr72P9Al55y5TbzP16bdOI+YUppRsrHH1F6kpWYlqm4wd3J6Lx9jr+xx8vyAvR+/zUnT50t3n6I4duSHNTtfLdA/7bl4/piD0T7OKObnFfemQ7bvSIewT5Sw+jzoWliQyoJtDNYrJlZxu3NsiyIlVWKDoZkhnZe0TVFPdMLQKbDEol/rU6tW58WHgOs4l+plsW8J4qg2HlDOt3PuCjmr60XT3bcLRZdErXZ+GrMsjngf1iH/8PWn1VJ6Lz4XPeC1gl6O6vhg+rrr2yYAsUpMW6RJ5p5mmJLdnJHMPKrWzJqM0Q0rfrt9hW4S8n6OmpWY4wnbr2fUox6LPc/iyga91+ZQN6RHc/SJdKCuKAZ0ZPlW/LrCubG5aeXDzvy8kUG+9pxLlMRZMVb0qi38+iil2y00AN4ZabrSwRIjnsJKY9HU8euKYEu0XFlbKzuHv1JhWC7H/pFMwx/Y8CEeD5YcsZFONQpdyvXtEonLYRnzukzmrHqgcLnIbMVTopooBxb3Fd4jFEpdptr3zg9CkjjssdgWKeR6qEkWjnSa0PRDnN6VFuvOoeH/swoybUE+PBZ5oSttDWGfmtBE5EVSNa4rqtQcnfZJEsvF7VOGacm5fEIzcthUoWrL8LalHhpM6dsCCw7SWYgLgzRacShs6fS0CgWC0NAUGi98nolModb41GA3sraInB5M4XQiyazzIl9by36idKV8HrkvVFHQDPLWQiB6hOJZ9oN0zpcL+VUEnV0iDYht/0AsynVA53VGHbCSiHygntXufcTKbfMDGwJaanJdM9JzCiVcHtgDJKdIjWVvY4IbSTG8toZFnbAoU5rK4EqDKo0oDHjJ/3xm6e3N2B9OsIH5bJTsb91T2WlRXOgCd13gt1Ga2moaq1GVxucuvB7JE2JN/gN+ZrHKVe0XEIFo63R7TnpZTVUnzKuUYbG0vOqCil3G9HLffgWUjNvZDiBpOmCyDduqR3QBO5QIX5zF2MbjWAW0Uy2AcPezPGpEpYuySZj6jOeGD3jhozd5e3wVlxlsljMC9LzBTCsoK2HqahWYuQqqmuHNiut3tlFI43uRNCvv+76y3agWmH4ibgge3wyw3kDwQY76LKBbK0/tlJzOoCBQWUN23Il7AiCtskxitCjTW5YSm1U13i4ebgD1rlW9kf2I358P3rMrPqDGoPK8BaKUUlJYdWKToCuLSzT9257Fxxd8cnADi8aUimq3x+x8wuRpx0A7Xv2tZ7k6LYWhTYgru8zmTuyzIgMb1y/viepsdGNV3VF6Uyr4BPs2HpXYXrdxrdcsC5+tL3Xn/WP+5EMc5cGUnt5dRTJ3eGUoehUuG0hOcurRpVuutWEdlmavh7/bPyzT+YmKm35Ao8sS7OuMz2SOa7v/lh8fvsF/v/9Zfvv150lu5uSHYrOirWob7HQdGu+URqdSyzUKTOVogbaw7xacdh7t/aqtRydHVjEPTzSun0GRoJVCzdP28jaVEEO2kymu8LhEc/mZB9yenwekYYvGouYLvLMQZEe7jdytFPZaThYf9zbkNtatMtXU8l7vvGj5dwAg2mb2yFyLgEKcQ/IcpbXkTl0bpgAouNkclaWo4UDiuDRBHRy3gKcvKzm3SSLrxKDHhUtHDFUqlomdhW49f47/P9F59Qcc6+C6w2HxHFrHfdvjO9VFXp1f4rXTfW6ON1nMM7AKMzG4FPyVfU5f3BDLvbHHpwY9ntG/5WleKbm0c8I3377MuRueBz/qGL6nqa7shMZZR/FA88bt87x06S6v3drn8ldDLtwrgj2N1Fm61M6u9SHQXlPd+PcPO9QZ6hYPTXdr/wt+4h/Oh8K2D4HSbe3j4feSJzrxSR1VOKXZQh2dsvHmLtlHFxyVffq/OcRl8T099e6A/NBz8CnH7lc13/ruZc5dOMV9pUdy9wYYHXIZUd9yacBHfKdxCk2TJUyTnHGRc1j1OcoGbOg5W3pGoeakusEohTkT8fj3cxilcd5y0fT4s4O30dcc/79f/Cmub17k/Jc3yQ9LXLApsbl8D01PCZENyU91Q8ACY44cRvzD+7VGh3DdRTKf9ctrTKnwfgWmDDYP/V6Y311Q9mpawpHXoKxjVJTsZFMu7x3D8R7pTHPn8+COCsY3BqRjWdMGU+g9CD7wsR72KAXNbv03gPJ4hwqV8HVv9Qic+66fM8g90LVuBFSRo9JU9hFj0KYRlTOLqBQ2jcSVUZVwvoBesQTLlZbXaLM8F7EBco38FPffnDtH8afv8S8+/rc4bwbU3pKqvN30j0pJ4MN7SoeZcl3Ge70SYNE4r8VzV0sgIqzjZWEIOhNgBHYDO3pll14CXWWhd1dxumuotx1mYTBANZKLHRUYE8myuJ5Ml136zcDjczkO5dQSYPbgMoS1EYNcJ9utHKdb3hxLr7rlZ25lKhrQlSed0ILIEQxs7zYVi8qrE3cXlHeGVg5ZCjYCQke5u1bi3ITHIhCdSKGT1KOMa/enE0eSiPSttZrZtJAadGpJswZjHMOibL09D+Z9vFd8/PwtXipuY5QLLOk6ALJgEN8RWAYv1jsSDA7fBgLXmyHvldsUyj1S3vusIV7TMgFY70mVBJqntvhAr//jHLlpgoSsANLd5Dg2bGjlsIhqwKX0mJ++/Ca/dGsTczMGwPLrrLwnJmrQqaF16gUeRSuZFQuxHZB4hb0TsrdYK6d733klnuJr1zN07sdWzjW8p6EF3pbyXJ2fbjGRmBCv7r89xrVLQcDu1cKQSxWmCp7oJtzbC898T8AcXSmyk9D8ogSYzsae9ERLkruQ+7npa1SvF/yIoq+EXspsIl7Szotsdxqk66uTnOQwIT9WZMeimGBKOSc2VUwvGOYXPM1+RT6owjXgmZcZ5ptDivue45fB7VVs/X7O5jfvS3dvFwAKIxt7vnzjCumNHNvz9IOEK8BpWfDm5BxNX3M5P8YqTV/L+5UPafk8eSMNi/KJq/h0fof/+rP/gH/10kf5zRvPcefOgPzBHulEvoidV4f0/7V4Gc8v9al2XOtdVtxMeerFYw4uPc3hRxV/+aXf45++9woP3trh5YP7+H7O1muw+FMpP/H02/zutY+x++JTHL6UMx8riltj6TA2mmorZ7aXUA/AlBpTS9fqCgNLrV7TsbByJiMLHg7kz7i3Vp5f78g+8/Vq9f/udr6zJiHHqDvrWzeJWILS6zfect20uQTy4jOvaPqpzPONZXhdcfmzx9z+9DUOP2X5Gxde5++++Rkmd4Y8d1hjJhU733Z84/QSnzl3g1/e2WO+q5lfrZm/s83ezVvSARiTsYUAqyokC1JM1zhS5onjMO1TmIZUOUZm0a5Bxldk3mGUwhGS+id0rHy/YV5XAfD1SadRp30BrWTdysPWt0VFRZj+V2S+Oh59WrHSdNAWAXXL/qdppBuyU3hQaSp+PJ0iZssU7n4O1wF1W2lueU2bWColiWGaoJpGCkuJgSpI/jYWlQtonp00lFsp2Z1wLI1i3qQMbszA+sD4dG0RFCB775DR/gXKbcP4ckrxdiqMopMp1PXKscobevAdv7fOufGJoelr8gfN8jVrzSVR3nx9vzYFX1ioNapWLQMYz1IYJq6XuGXhQSlI5EtSYY5XppPwtxm9/MRtWtZTvKcVoPySCRjWTxWqtcsc74+gkvFHMMx8WWzUVZhLDZhSkUzjHCCFxXooSj+6WRYVFucdPvGYucZpj2pEUQgnSa7v5AC6Dvtpi/HQeyCJpSsysf7JoBpqBvcakqk0u7W5QDtfrgZjyxxgGTupmDArHirAtIoGIe7xhJzBCBuPRLfsYVUr7HGGBTb377GZLsTuZ1TjdQZKUdyvSC73hKHkaJsfTOWpe4rhrRq9aNDzmpHzmKNZ6HrW+DzD5xnKe+woF//pVLfxWjK3JEcz1Hgm62RVL8GX9QIsyHyRZfh+gStMWyxQ3i3zGwerQEj43VHXaS/PGBeqtbX2rLFeJ3rUdo9YHh6SH/wBjZFZkKqGLCgs1WHiyDuS3o0Tt2PrdAt29rOaIpU5K+YgiXakxpJpu8Iq7m7jfWAId3L3xukWvG2cJg3s2vY1TUJtNeUik0bPnm9VHnzM0zvn/w8738TjHvUXTBcZtTWkJoBsPPqr/rDjIcYtas2OLMzNjwCoXOdIIjhd2oQ0azhd5Ayz8gMfS20Nb07O8XPnX+MffLLHwWu7uFTj0oJ06shPcpJxjZlVxKYxkHjCpRp1ZHjQH7ARbIO6n/H9QOn389H+QQ37GFDas/xc3WaDs0b3u1th03YaIlRoiHAeDk8GnDuJa7IOjV0K3y9EKawWZrT3q82dKlvKKfqmAadRHfWSGDMpo/FW7kNljLwuzVCjAW4Y6hvzCj+dyTZNE+ZdzeBuw7GxpKrhpNnAJZ6TaxmzS4ri2VPmVcq5r3vxj040auHahtpWGbBbQ4wNd3KAq/UDQvHXOrzSkGicVku5ZWIepNrXQ5jPA2tavIVFCt9lYTLvANHSHE9rm1cceYojjw7AZaId1kjOnU0dyazB5aatBfignraiihAaa77XsVau+A+jMxyeTZ3xM8UxVy/+C14e3OWf7b/CzbfOUdxJyMYsY1QtdRLdgG8CEOEVKN0y4rQSskM83wpWQGhghdEFIR6LjQlG4zZ6AkQkBsoKs/CiVojD55ajlzV/9cIb/L1vnsfuNMz3MvywBw/KVQ/obiNr1yorSGILMcO1YEOXMb2SS0WJbQjzdDcHjLlVmFNCDUgN+vhBD7vdp9rKKbcMxYOa3mt38bOZfN6maX1ElVL4y+epN3so61CNQ789FiVAowXA6PXaxt/FxSEf3X6Nvs5aFcp/X0YXmF74hrHzHLicm802b5d7vD3d5eZ4k8PjAf4oI5nqoOQG02ujoFji0RbsMEcfTdj+7oKjV/qc9EsG38ypRlJ/17XH9oyoRKSeagTZN/rc2xySfqtP/50HAPhcGg2SuVtVi4U2/pXHVifkbpPmhxrrZYb12DfWJR6xfZvjQLtetP+vYCNtkXplIn2ozhF31QkalQ+yykZDXbP9esVb7+6A9ly6Z7n/GU0yUXKO+wn9B5aDrYZqsyC/mfHAb/L8F+f40wlqUyS8k4Uo2NpGYW04oKAup1NpBK+rhGmVMalzTpoep4mQ3/q+ZoTcK0+iEt8PckQyyHkz4E8P3uHy80f8t6Of4neee47ed4ekk4CjxVNufZuTRwsrkDVBN2G9DgxmpT0EBbWHmhncap4h1+ByvXepxm8UqEGOnqShgXMBCwSgNkbUNOqGItFsJnMuDE55Z+MCygrO0LuZoBshnm6+WZGeLHBF2lGziDfnGQlkvEbic5GssVI7kkb0lYan1qc6sqPjfnwbJ7YqG02z9KCOIPOZX5ISm8r1xievUInB17XU/PqFbBfqvGpRQllJvl9XVFsZP7r3Hfp/zDL2H1y++4wZsPYJWjlhT68977ywPbRyeCMXYjIHPNh8WTACeYxQuFDw0GTr49quID/x9G6kzJ+uWVyE/IEhmUkRqel76m0nhZtGo08SkqkiGwtbIpmpdpJ0Kdjct0FtXJ6jVGBkRa8WdRGPuk6Hx3JCDkWnEFRHeSFnQnGwy7RYW3Q8LMHneA1G4DmRYBvlA1uUpWR3IgVqn3TQhgBQozw6FVAtSRxNo6nLhLLMUMZjEofWDq8Ued5wdeuYrWyOQ3Fc9pg3Kal2XByc8jNbrwNQeRM6hxpM6LZ/1IhdNA5P7S1fmz/LnfnofV/THeJl4lovEA2gFCkw0jVTX7/fy78vIwlgdK5rkYCLKgLKBUlvuaqc11glnm0/MniXLz79NPfH58mOw7URi3aa1ruwHXHe6/wdr6M2r4sArvMdwKOTY8ZOVNWVw49Mbf/Q/Ra7jtugSIeEvXOc8f19ooOc7LIDur121yQXz6yHdJ5feTiA63G4ZNls4g0ti9wWoBpRR8hPpACaTgMD3AojqunTqh/UPY3v5RLMh2TCxXs2FPydlZYJkziaxlDNUtKDhPxQPIjSSfDkVlCOFIsdxfSqw2/UpIUsDGlqmc4zuD5g613P5KrCXpuTvVlw4TcP8e/dkS6mXI4lynD41FAcW7KvDkmmcPJKQ5E2LaOjdpp7sxFaeYZJydAswBGkHnOe5GG9I1WGiSsZe8WhLUhVw4v9O+irnjt7I9493eboZEAzT9B1xvDLm/itEbO9hPxA4YqUZDxndN0zTEu++xHFxc/dRivP/RvbjK4LwKUax+bbJa9PzvOL577Gbz7zPA8+OeL4o478RoY6mciCn4pUq0iNqlbuPrK8ur43sLwmfSxkxeRgPdg/K/jvPrZWZF8BNYh/ryYHj6wn+uX13T6klmvQOhgtf/uzKy7dBCIAgdErLPps77664O5kxOHnK37ypTc5qIZM3xsxuGnI3nuAWlSYxPCd+/v8zEtvMLpyyvzONlv7Y/jVHfEmTVPpYF3I+7gksBo7LNNGaeo0ZZwUHCYNmWnYSjfEe1PVZEiTVK48Doc+wxbiSRjJSRnYiWI5oqSuGABJHv4eYm3EIzERmBQAAQAASURBVEzkzrXnzbLQ0Z2L2+82XkdWCkeAAM9JsgyIrV3K4ispDrW+7iaFxIQuS7MMjuPf6xLwcTgPOgRSQY4bENlHkGNOOwF9moAOgfWgh2osk8sZKBgMi3Zd/O4bF3m5mqCsZfPVE3xq5LxFwN1ahu/MqHtDym1FeXWb4vrBqi+PUrTlrnUwOj4WwHObqiB3qZefq8siWh+hqNT0FMOdGfNZjp0bWJjWfoXAkY33u8uiJ5isIT6VD9s+ZkLCpf1KMRsPPjROrtR/1cOS3EsVQVm0nxQwOo5kJo0oLvPYnm8bLs1C1BOKA0/Th3JbjtssFLqWc1ltOEwpJ0FkoKXBVAf7DpdCnUmDWlRB0jVkpxIv2AzyEwtJgh2kzM8pZs/W1O+lZBNN776n3lAhiSbId/uVolFsYnOx+SfmFSZcc2EdsVlMuuX1TQ6mjowA2d4XqYRatcUWvv28PlH4xPONG5f4n3/iC9yrRxKT9GSf6eGM4jDH1NIUYc2yCSWde7KjEn08Ae9J5xWqbvBG4/IU109pekbiNxPOU2kxswBGT+b4uUiLqTRFjQYwGuAPj6TZIzKAlKwLOktRRYHXGr2wpLVbXqShqUVA5yDPFxhzS0B6dT1cqkOph9ZKed/OY2etY++3Xn4v230fxkCXpKrB4KkCYmSUa/OIOkptr4FvEUQT4YpVD2GHYmAkV5o36QowF8FqrWy73yR46wHLvxUr7GWtwE4TkjAX+djc9ojzuC6j2z2G1knE0zYqrwOLUV4boG6WoHR8y1aeufNYN8XuMr0fknf2S7lqOr/X398+5hrp7jvuY2ETtgdzbt3Z5srmyfvvIB5v+KxHZZ8H9ZC/8vSX+e+aH+Mk36Ta0KQTQ3ZsSKcp6ayQInntcKmm2tBMLmncZkWaWoqk+VAsz8iUfpJG/P4eN75XqfF4fso6ITEOox1GeW7d2ebiP03Y/PaRrP8+FDGVCh6tQUo9AlhapA6VS5YKLL1CrsOmEUnGWMRPEsn7kjCHao3b3miLry41rQKNWlTL92ka9KzCZQnZ4YKtfz7kn139BJ/auEG133A0Mlx4+S7bxZzrv/IMe+9OBKSa10Hdyaw2OYb5NzZbeaNXi79R8Szo3nqniZr3yjrZl1tuK42ICpfEJIklOzrk2LQsaZZqUnEuD3O+sh5T+nad84k0yxgra7opPT7VS/Wq9mZf9Wd/nET744aHJy5uelJGrMv1VMZHUs/V7a/wucF3+Xvbn+c33n6O+fUB+aHYSvhE0YSGAq+C97TRJFrAO4mVnBCFlG/9z9szf5aEdke+Xa4dhTMhp0lFTS2Zy/Naefrbc/oXTjmsB+hGsbl3ytG1bdwgxxxpfOmlMbDd/yrQIP6cIfBODFEeFUIurY0A1UAs+rd1nSjV2lWWidLbAEWO3d9kfqHg9GrCfN9T7Tf0d2bsDme8d7hB8aWnuPo/3MS+d1uAhV4PvbOB3dng/mc3UA62vluSHUxxNsi46gTSTBjWNlokZXx64532O3xSc+Y/ztFgqb1j4cUu5U6zyZ1yg4PFgNNpgZsmpBNNMpO6oq5hEfye05nUGG0vIQWyG0fsfv0i9zdG7B57Tp8DM9H0DsQPGcDMUuaXLIN3DPfvbPL0V2vU6VQaGrJEpO5nNoBh3Zrpcn6U/8MHaO0Sv7fP/75T4botwkN1rWU9oq1PtTiOX+6/W1/q1pQeAUo/8hiNIX/nkM1vXWTytGN6UVOfqxjcyEiPFm1dxTcZ42ctPvH030rJ3ngXby0+S8EL6B8bn5Tv5IzR4skqvFU0VlO5hNIl1N5Q+0RsawIpTghy/wGYjiM2ehil2dU9frqoePbyL/Ovt5/nH1z7NK++eoXiTkI6DdhGq5pBsPWQ8MolClML6UCKf04wB7xAFOt10TMIA8Aylgl4hDcahgUqS1CJEcnr6RzqCtc0UNckKuViesyNbJvXt6T+4DcqzM2ceuhRdyF/9xB/9wHmuatLmeyuj/NDDVTLx1t57u7oND359ce7dpGRPBI+r0iFe3xZyjoTY7ao3ANBzSeoajQNKC25+WIh65TRsk8TVQo15EF+wIYan3OtbY0yGu8M5bbhI4NbstkfIwHoQ1PrXKct3aKwXryQbOfxCFJbr8i1BSOAqq48xRSqjSVQ5AOZ5nHd6iLVIsWc4Q3wScriYs3iokNVWiaV1ONzizYenVh8bqmtoioNqtaYsSadLrvxtVXQ0PpG29xjC79MasMfHlogWFmFtqBLRSAoEn2ole0Ad2El6YIN3Q6oliWglgWYGJi7dCnD6UOxbinj7YWlajykDmU82oQbWHuMEYlu24hHdWVTbOpQysu2ymGMY9AvOTeckmrbdnUfVz2cV9TO0EtqXty4x7XiAXvJKTOX0w9FkwpNXw4VrRR6japglKb2ltLX9FTG67Xn3xy8CEDKsvjwuCF+J2B9g0GRhkV6Szusn32gffxxDt25aM0ZNAyHonZJW0RZ+AyD469c/Qp/p/4xpl/fkeJsxAUioLzWCLE+fOe5bsMGflmQ8XSuuQ7jmiiVF18TfpSSe0yp2Hmq2o6l9nmWE344AXL9GrXiD/jQ8XaCqu59tfJZ1j9gjMcC8wm1LPDaTIDneqjaxpHigSedy31iM0V+LCdxdEO8hKYXFTaHpg9uo4fSGhUWCpHO7Z5zT5panFNUxznpQULvriI/CWy94JU4Pa+ZXVCUVypMEbzPjSNJHIt5RvJan423PCcvQP3MnOy1Htd+6QjevCHva6WVx8cCRSKdlWbuOPe1mtOnEzYujNnMhfEwb2ShqZ3m/nyIVvt8ZHhH5l+v2Up/8PfE+40Gy8zVjL1j4RPu2RHfnF/lzdkeb5zscf90yOKoQE8MaSUTb3P1HKfP9KkHisF7nnojI7lt2frunC/fuMLuj93jz136Br929yMAZMeeZm8DXVt0aXntwXn+swsT/tTLr/Ivb3+KdH/Ozhf6kiAmpvVXM5V8r0v7hqXM8sr8HICudqiHpbbl8bV/z1jfHmrECMH8mUnDevE8HGMX7O7KQa/8bp/vvj68T7fzPMZJiRSRdONRUyt/13Enivz6AZPfu8wv/OJX+MjgNv/NN34avVNRfLUQVt2oj88SpvcG5B+p+anLb/HPTl/BVylXvzaTYChL8Sp0sNYipeYyOWivQCdK8vlSU5UJkzLjNC04rAdsJjNGZs5Al4yCTcQfGWXqj2HoRSXnJTWoRAdlCQmYW5WJWACMihKPGSsNDbGBx8egPMQecd43ppWZa18fOzdhWUzpjij9U9rAzu5c90qzbONbPzAJZolSySokBkoJEF0F7zRtVrf3ns0358KcnVXt5ytuJ8tkd1bihz18blBlkBzyGn00YePdhINBwWw/Jb+bo47H8hmaUAzWShDE9c/q/Qro7HUApT/E8N5jC8XT20c8yAeMi5zFLMMtEvxcYxZ6yZpGYsclA9Tjg41MV/HGay9xs2ZF7UYFiq0Uqmlv2jNlK+PjXnypn7gR4mAXGjN0qTBzUTjJJg7lNNWGsKejBDcKdCFgf7TCAWloSRaIBLwKEpKdn+hJaEpamwCVGGw/wRZgBjXVlqEeaLKxQzVGgHIjr3dGAJsWmI7xGjJfLtcHcIkOjaTyuVyKyHN5AacbD6pRyzUn7Xi3Bwl4sSjx6GENHg7qAV87vEwzT5Zfft2QTRwuUWSVxy0U83OaxZaWJtztHF0NUbXFp4Z6tIHLtBxv/AqcJz2t0WWDXjRQN6jxFF9JcqOKAj/s40YFeI+p6tanSnXiQPJcElutZD+mUxTQCpLO3BYLBh0VEtrn+EDz4Mrl/qg61weof3UbtZ6EYb2Wvh40aZDyMniSjs9zdzgPzknDq1Fe7vOOL7RRjiZcqPGxrtx1/N95JcBP52SsM4Wd8hjlqRqDOTVy/62fv07s3j7k3986oPt8F5yOQymRwM4SSxOA+dikGbc8a+/dvazLQJ/lGR2Zla0Hc2cHHxSs7YKjjTdcGJxyc36Oypoz3/Os/3tJTe0M3zq5iN7y/KfP/R6/tvEyb9w+z2ySspgamT8WItvsDVSbDn+uYnt7wiujMbWV9bUKPh5tM8P7HPuTBkh/P0eaiNS69woLqKOU4TvTZcEvAkwgHt6BbQOd+MmI74Dqgk8qFP1iATPKI0av2MBK0eNpkJe0KK1QRS7z5KSj+GItajJHD6T5eHC75ktffJH7nxiyd/kYox2jrOTbX3uap79WC5s7ej0b1arYrIyYv/hO3BgugzZvDxZg2jk8KjIEHjqHUmTWAqy4qJQBkXDSKvytH0OsecSQ0Uudz/cVzdBgC0+GrMWmEiDTa9Wuu3Ef3Sb2hxp3HoHgnPVoW/8O98wHxFH+vRq5WkpJO28Z6pyfLWpeuvirfHH7En/r4k/wzeuXyK4XZKegK6nLuCjfHdRxdKICYy7EZI2TemITAy3omjI8chlxXmpoRuGSFOOlVjMLWr+v7N9BK8+/fONlbD/EL1ahFtEHvhPLtIGde+g9gKXlUJLINnGO8HrJeA5zho+eqlrRgtWBleb6Oc0oZ76fcfKsYfJ8zSdfepM/e/4bfCK/waYuxc9WwS9/8iX+pv3zXPp/3wWTogd97PktTp8bMD8vKoG6cuKjCqg8F5a0kbnH5xk4x+yi4sd6b1F7HYg/nn+fQDajNKVrqL2n9AnHrs+DesTd+QaH0z7VLEXPDWYhuYKuwdRyzSZlkNiOI02gqtn52inja5scftLihw297+ak4wW6shitSMcZPFUy31EUr/fov34HX1aoYR+faLGLiteWpZ2jl3hBvDYJj8eJ8uHP99DcujYeCwr7tW3OChjWwee117ZWZLG2pJfrwQca3su6lSaoNIWy5vyXZ4yfKTj5VInSnnTqUYFp2vRT1NxQXJziveL8LxVy3/UKfC9DWwc1eKVlObbh3l//bN14L5DM9Jl1/X+/7pkPO55Jh/y10XU+U7zD3974cf7ljZeYvLtB744JiqWSS1stTQHexOZyAFHP0FrjrZcapJLrQWpbvoNlnH1NCc6wfMIlGqUSVKLBFeg8Q01movziPaVNuJwe0TNXqDbAFoqf+8hr/PrRx3A9x/BdI7mtFRs5tIKoEqP1I5umgLMBaXg4DgsEER/WEtUq0y6zIZWlqzWt+ERUKczz5d8Q4kwjbOjZHEz0rK7xVY3e2cKnCX53Q9K22wdB0jvWvUO82ytQacr0ouaV/Ga7bvxhfKPfb3xwpnRggIqvtKf2pvOYSHUDbac3CJM61w0kvmUXZBOHbjR1KV++S2OgGrpWuoX1tkDRCW61sNk23oTiXsr0qqfebSC3DDYWAiSVKc5J54t3CpU5yBx2EK6lRkOtSMaGZK7agq6uFNRh0gqSQM0gMJQLhy8cWIWzCpUJc0MK+7Ss6SgB7qMUo1OdbtLwGYz44LaBmOssQDoWvIJUcWRDG0/0iUaLP7ROBWCuxhl4FV6jUcahjSft1dKw5DRJatkZztgIAFftDGWTMPNpK9HmvGK/P+bZ/gPOpRN2EpHOPbRDtsyMTFkWPm2/Xwc473FKOu660hYaAau/U5f8zfs/x3fu7fO5K+986AvUdhY95z0WkQ3fegKKrKly6OAF1i3yPGoUqqL2CU9nD/ixC+/yL25tYm4kYMAaYdwsJevD7wg2dB/rjAiiAa3UtgANLO8f3wEuOs/5NrkMshdBwrv1oQbOzMhUvEZjMZ1V6cW4206hdnnAy8Ap+lyvAGjx5d6j/NL/WoAy2U45SGaexTl5f1NBfhqKcKVvO7BN5UI3nMPmmnogx+gyg1lh+gTpMKtaz3WAap6SHiYUB4rs1JNORWKn7ivmu5rpUw47bMDJnKCNJ0kcVZlg3ugzfNdz/CL4F6aYtwdc/bUp6tYDGA6W/hawQhcx04pqO6MpNLNL8OzmCRvZgibINTbhOls0CQeLAfezERfyEwr9g1cOeL9Re0vtLVPvWHjFd6p9btXbvD0/x/XJjgDSxwXm1EiH6kwClclTffHxmcr5F68ZRXrriMFvXuZP/C++gfOaN27sA9A7ctQbKaYUgP/0KMF6zWdHb/HmZ87x+tsXGL01laSg3wMlgKtqYtdzvGbDfbF+S6u135ydCKxI48GZMkkrz3c7Sx8RvJ8NVncCs3WA2a/td/39olLC2hDAR7VS0V4pSZyS0NY4X3Dpt0sO/kcDrptz+HcGZM+N6T9IaXYHsgY1juGbCVs/N+VTw3fofazin/zy50lv3hTvvZ1NvFEkC9daW1gvTDoTGtC8UehS40rDokqZVDmTJmNiCxYuZeFTFt5Q47Dek6gn1P+qbiTQDAXHyFiJvkku0cuioYZWbPmM+TNKCyliMZGV73fJHvRLefY8QwWZOaxtpXaVkYBa5Lo7nfwRqI2vqRv57uPQ6mxMuqXrhScD+zh2X/o0ab0UlQkFnJaNrcluHePjNYZch/07oagaZb812GEmwFdZybVW1qQ3j+mdP8/8nKbe6ZMdnkjR17ll4QgkYA8SRm3hqMjbgrHXoKpm2YEaG7O6n3F9eIfN4NnhAzLdcJL1OEp7zPKMKs2wKhG/4wBM60YKnbEAoZvwFWrVSh163Y39tMR+Jvy0TWVLn0oP7QTRLqUPSTE8nAv9wEaMIUJsr4KljinFasOlsrZLgRppPvMC8ArIDC5fXvqqATNfznWmkkKoKb1s78GroF6UKvEz1Jqmp3G5TPoud5RbiciGhqVZ2xB/GIUNXportjrI/lwqx2YzWulwn3iavkiPC+UP+f5LTXas2PquSAG6NDiVOd9a8Lieg8Kysz1ltz/ljcl53rm9C5VuZU5Vo0gnlqYvdhPzXc3RJ0SZKr9nKLcz9PMZg7uWZOZa9RizsJjSYeY1el6DcyJvVtUyPzgnxaBegRv0cMMMl2h040S6tqxWkle0QRW5FD5BEnenIDDmvNIi+6oIahFhfg+d8l2A2of1po2Dz4oh6fzvu/Hv6iZdkOJx4POTwJYWdrTB4FtAeuEytHJkOha7fQuWWKdaQDfVbl3dFIDSJtTOkyi3CkqzVFfQSjhTrjM5nJXPRJnv8WmP4kizOO/C+qMeeQKXynuyL639ajrQeY+zptblc4o8bWjKTEDx1LfAdJRtfhwbct1zOr6my+R+SOI55nbvw6Tuju7ziRL59Hx3zklZsNtbNo0+Klf0oYHIKEdpE751cpGd3Sn/8YWv8geDp3lnssPd8YiqMS2zd6MoOdebtI0LiyZl7tOz5a7XHlthrfPkMaVhtYlg/ei6DQrfy4jfcWZsu+/JIqe4p9GzWgBoraVSZjuxhLVLj9emWS6srT1d3K6hbYTrqq80DWKOGW6QUIDE11A5/HwBxuCCZK+KyjOTKXpjgOul5AcLrvzrPreOr2A/OqGeZBy/m7H/hiM9qUKDXRPUMIIS16PW/zYn7zzW2sIECy/nw3ysl0FG1zJXB3UPFYhCqjMnq+Vz8n+oXYRmM61kjY3nJ6o8lSODzaQpxWgBhmLDcKu2oTr7a3ehHrpWHtW4tz7cmq2Rc09gTvEDHrW3S2ULXAtSXzR9fnFwwEef/of8xt4L/P3Ln+Gtt/bpvZuSzAIY7X1QvQvNg0auKW09utZ449Faoa1DNR4fZiUBvBBwonMtt9es7RAqlCI7tdxY7LCRLPjI6A6/cfcFiq/0WZzzTOc5w+safXQaciLXUfJQRHntrox317pHpSn46CUdVJmsa3ML7yV/Umna2qWQJrh+hs0N9SilHmqqkabaUNgMzKnhG+9e4tZkg1d27/DR4S0+XrzHpeSEnxu8zj/68++g//tN3OlYmHRVQ3ZqOfcNh/7/s/dnQbdk2X0f9ttDDmf6pjvfulV1u6qru4Ye0EA3CZAgCBAUB4Ck7bBCQVohibLlSYpwhP0sh8N+8KvDLwo7wqFQmFLIFE2RIgiDYpMYBBDoAUM3eq55uvO933ymzNx7+2HtnZnnfN8dqrqq6za6VsS95zvn5MmTJ3Pn3mut/1r//zJg7x4L+ODj/NSPXWJh8PFzNc/YCk/eSix6AoafDGpiF4R9cxHgMBTsuxH36hH7ywHTeY6aWWFlqkAvu/jBVLGjP8ZtQYHbHmFu7mHuHrDz3QnX/7rDFg40zC8UKBdwuaKZCONVs1/y5FcquHNPxsigkPjVdc0xMXADeuM6zaO9sHql0ahvD7t873Wp1JwEphNojoyvbs3oNXKkQq54/CeaOfq27vhFUM4Pc8xcKISzt+5w4atPcvPnLXqn4viKwtstgobZRYXanDO/O2TrTy3Dl68Lxf14RDPMVwrC+8e/8hs10tSnQ5TkdEL9H/P7q5s/LkH042Er2vPR1xnrkpdyx396/vf465vf4p898dN8+fVPU78xIt+T/C4KkXNQsaAoxpO6kYJzXYfYCCp/a+clrsS3YyRJPKxcoh6bnwq0DBjBaMgUXheozKIzC3XDtMpxKDLlqLY8/lzFs8M7/LaC8cVjjq5usv39Afa16P/F+VX11oewNrZVzGEp1St4epDFokcFMnf3u8ETW1+i6PYBVeSy9hgDZ7aZPbPN9KKVtWQgjDJP/Is7cPMuYbnsmlKslcLxnS0Of+Yy2bE0neR3Z4TFglBVKGu7giZr43mzHD/l+Ux+RKbKlrb9w7BHBqUFdkz0xK4FpIEWkD71C7QTjTy6JEE295hKUY+Eyttb1XanrU+afYdTN/K+j9V1+RHoN6C5lTG/YJkGxXhrxlPnd1k2lr3pgOUix0cxuaR9hfVgock9DYBTqFqj51qqo5Rq4wizUBgPHJtIbyQdDCkx5eNvk4RjvFF0aH+rrlipAkVJ57Mbekm6OYWKVYPt79Wh7ZohSzQASo7TRQq8WuOWARcTl0p7Aavjv6Ks2RnNTlTEH1cFLgidpNUS1G7mC84UU0Z2yZlsyo6dUuiafTekDoZNM2fhs3bBy/QcFxSuRS7juYqDtA6OWai43gT+n3f/Mr/3zjM0jWYzm1O+h4EsA18SDonkIJI7tDrWH6XpSK2XFi+/jkQBmW7IlCNXjQCHHhYh4zOj6/zp05e5t3seU8Vu6dgJBN2YCWnsxMfVjl55aMFmZIy3FN3ps6G3DyT5oBRolzpDJRmufHdv9RNJKtGCO3odMKqlk026UZJYVB2dd7p3+5eqG+qtE9aGj202PW1ABKXlicsVPtJyFoee2QUDAbKjgCvkuOtCgIWmFNp+rSE/aFDOcHzZyG/MDdZooVTSutNerTXEQo+6Nqi9nOKeaEgLXbeimihmFxWzy46QB1TZzW+D4ZKqspjvjdh8zbP7ksI8d8xymnPpjz3Zjf0WiA7zeVxUOtCGYUnIrXSllrB8oubC4AgApyUxc1xLlfyysczrjOvzDbayGTt22lYFP44mgYBjFoS2e7cZc7va4EasUF3OMtRCy3w7V5i5zPf1MGobR6qdoBEtzKMp5//wiN//O5/gU1t3sNdzoWvfW6Ir0StWjad4Z8AiZJyzh/yV8z/g7d95CrO3K4GoNQSjMEvXUomKFmbnXAO0nVvt36w5Gied7bDuiDykQnW9a7l9rfcV61WubXFHW2Ub2jm6r/fWUin1Evmy/4cECbHTTQUviaDU0QcU7+7zJ3/0Sa59+g7Kw3y/pLhXoRrRtVIBxtc8PmgmZs6t5YStHwTRUQ8eX+YtTZ8cTFzj6zgXJSpvBziFc9IN1ngphvPo2FWmqGJh1ONsynUdwcTqdRUTBSqC0yF2UYfMtHMsCbDpIQ7KhZZSceU7IvCSGC2UkwIdtz1CFxl6URFmc9Qy6lv6IB2RqVMgKHGIl5U4r2l8NQ4fKYYfaj7IvNqCwKrrRLZaErkxiauymNQtcukaH+T4MkNXjmCE4nl0q0EfLyQpNCjQx0tmT44IakTx9lJA7MyiFhWTN2csN8fMLhVku5vSNZCqTUNaE9fGfJ7htofo44owyOQcxiKC9BvaTu9Wl7o31lQE7y2cz4+YNkUbxFrjmRrPXBX4pWn9Nx8CeNUVjjRdYK98WhOJHdRy47ayFSa0HcJBh7ZQUcWiRaVkDkh5blQ4cd3eC6Xrh2UJ1FUO9EK1/o5ySMLSqF6XctweAX1NpWQbRSuzk7oaXCHv6UoSSXYuiWwgsrrEfVvpYvGZwhUBYxy+yVmcDWy9Ctkx1GNhY6lHqqX9Djbg8+jDjzx6XDMcLRnkNZkRytyBrbGxYNFqT2lqMuWpg6bxhoWz3JhucHd0ngtfD91aEmTeC1kg5J7hxoIL4yP+ytkf8F+98UXCzELpgKgr5Rx2KsVo+5/M2f/5BX/umbfwQXHteJPr13cYvJGTH2nszFPs1tijJWpexarvRuakGJyH2JGnhgOpoC4zQpGhGo+d16jZUqhre4UjQmdpha4/3iNKSQdDYkkIsXvOW9UC0j75jzo90q6zDwgnH2j9Yb0OZLf+9fo4VCfdzo/KDGFFBqsOFocSqQqdNMmUFCfG9TABJkoFMuNW7u1Et53bmsI2XRxIwOokLSSU3T6EVgv5NOA1dVLvz0vMTaGM7yr4wvs6if0O6dO6qfsgsY5/Z8ZRO0NuHVZ7mh5gtH7sKfbtuz79bU18f/0z6f31zpkH2YnEZQSzK294YueA20fjFVD6fuaR89zv2P6D3Wf4/Na7/NLm97k3HvP29hmOmhIbB3QTDMdNzmE1YNrk7Tk5rTP7x01TOtmDCr7b8PYhReH3MxeZBRSwezwkfHuDc99tUM6hqlp8DN35vtIpg1ApOkewdtU3SKwsSf9VqbYAsLW6Icwjv3AqkEvd1y5qUSeZFaDVqK4q9PEMyg2C0Qxuzrj0+wXzV4agoNivyfcrdONR8xrlHKHICJnuOmDWiuxUCPgImLdxS5ybVQsQh1hEKX6l8qJfLscf5+3ok6YYykdpL/nOeJHiGtzPSygfxDfSq8dmqoAbK+xUMb014sxRwCy6eMGv+cDB0BXtIGDyepHJQxsFg8J79fgU7/0YmEazDHULTGs0z2QZ5yYv89wnbvJr21/gNzZfpHp3yOCWxPgiGRJapj2UFANoG2JjgTBoaR26WNb51WVmvQorOr06dlnnBzVvzXZ4aeMGLmjevrHDhXdjQeqnG8krtVUMUd8zJf+1AlxsVnARgHbCrpfoT50TWn6thRY2z4ThQEc9XK3xZUbILW6Q4XONK8QXcoXEd6YKFHvibxa7mub2gIONkt8+s83Xzj/F5y9e5wubb3M52+d/dvmr/INP/i3MV/dQyyX6YMoghHZuUNM5frmUY9dafLRUKLismT5/jp95/g0mOl9ht/xJA9kcgRpFHQxLn9EEI2uu17HTSlybJN2TphDtQmwik7nPlxkmz2CxZPOPbzHfucjhc5b50xW3hhnKQb3pKS8dsbg54vLvwPAHt6VjdzTEDTJh6ovjNtX2dU0RdIFczyd+EIvQibzT+vunvL3uEpyIGNfnzF4eqa8D3z5v4xlWcmltI8SJgzo5KatgpLhpWKIWS8KyYvvrNzHVBW78hZLl83MWn1KERrN99ojF3Qln/shw7qu7hKMpKE0YDfClyH35TEVpCdVKQaZCYm8hZJ4scwyymrFdsmmFiW+kKkrlyJRgER8mGPfjbFLsEch6esMaxVgXfKk44OK53+GF0XX+680vce3dHYrrGdmhkmlWSd41yQ95cbfkXxNZGlUgRFZUrZFOeh8/m8ZgXwsoWr9eNoiTKBJWEwtWow+mHByXHLkBhW4IF5e8eOUmX9u7ilkoRkXF9Eol/hMQjEGtaTevA9JAt4b06bL7b5/CWNP/u6Xs9rEzO8k9BA1lQXNxi9mlAYdPGWaXA+HJOZ+8eJ1PbdzmYn7I9eUW/+KJL3DlN7cY/t4PUJklLJbCHnDxDMefmHDn84bNVzUbb8zRhzN8YuTRWk5+lkluAHBbQ4ZPHjGMa/yHeQ+8Z/ruZAmkBtruadGXlkcCuLRN6vCNDoiqpevN1AqXS5VY0pk+AUrFfHrXEkHbKRC0aMjlx57sWNFcLzh8zvLU5/a5NDzk/DDjoBqwPy9pnGG+yKjnWay2oHu0gWAdbuBwyeuJIHC2a9rKKRU6umJtBZBWa1WUQUvCOOlVCyUpJOoiVNKCjonFIFdBNZGiMESHrYnJGacI1kuHjI2aE07FDJ08js7NyG2DjTSPzsvNXjlD7XQb8Bsd2sTZKKsY2orNbM6ZbMp2NmXHTFmEjBvVFply+Fg9YuLrWXBkusGhJIGBLPCpSw1kcjr2S353cZZf3/s8/8Nbn2Q5yxhtLBjqikK9vyGnlSRzNWBQ1B8ip/2jWhG7F/p6ZH096QRIZ8qh8dTBth2tO/aYX33iO/zDRcHyB5vYI+m2abul0xzVd1QC4jDFt9qEWn9bHRM8PnTdmjH5ve5reGQ7AfpVS2uZHtOHOu2nGNT29AC9lUWk7e5KQUY8nhPJwXSPhzbf3oHT61Qwqbs7XWot80SxG0T7swa7gPENh505CFBtWg6eMRy9UDHYWhC+vcHZP4Xs2DG4q1huaZqBIU8Aj9ESTHtQtWrnmXq/ZHhLUxxIN1M1UVSbisXZgNuoIQuYwrX4hFJwvDdk8FrO8Gbg7k+DujSnriw//+lX+Ep5leXGZc7+8Rhz8x5qYxyp2aTjSGlN8FL96rOSey8ZPvvJtxhEPUBNYGwrrPIsnGVhMuZNxt5yyI3FJsNh9YCR+tFZ0p5wIVCHwMxn3HSb1MFy2AzYX0iFaphbzFLFf5KQyA9ljtSNANRJpzMMC9TRFPv2bfZ/51m+/wsyZxb7CjOXSrJmq8DMHeO3YOEzRnrJ79x9jjPfdbB/BINSgkujUXUMdnWv6vNEgnQ9AbLuVHxw56ylP7pfMAAQQHvf3hutlkq8r5TzJzXi+vdYn1opWb/q0MebLzpFnd5RDDBmCy79buDaZBv1xBJ9N8cXsYshOnAuV9xsNsmU4yu/9RLPfudAKvEGA3EIm6gFnQpDnASCPs0NoTvudUvANNCuQ4+t9YAWFR1poK3gFE2yINfKapnvjSJ4mZtCUF1xEqvza1vEFAIB1epVJwuAGyawt0TNB6hlJVqFVXXqsA2RHkiF0KPoFfC17Qo6rS0PSJ3SK+tPOgdGiV51T0uNEPAbQ0kK5VYAeefxuUIvodirI0hupJvTe5YTgzeK4m0kWWQNKgT0/pTBvSHzHc3i0pjBq/P7XI8uQYsSDUSNFCvJPNPzLXQ35tt7aSXA8Chl8QY2zZwLxWELSBSmwWiP95rKBHytW01oAvJ3iGtPYtSJ7CB99pxWHiZ+RvU6qlu6b0WvmFEcBhVBa6UDKqWe1CnsDx+BSadM9JtijJeS1a4QkLn1mbVs3+o7N7KtWSIyOk66q+U8RYmdWrYzlSQ5VZBOrKRtWY8iXZaWItM8dzRBUU/kmLLjwPQyNBueMGowuScvanLrGOQ1g6xmYGs28gUXy0OGUcun0FJ8qJWnjrpkqWhx4TMOm5Ibi00K40SOqEnXTsXgXEBpgMlgyUa24O3lDlVjY9FBN9crH9DzGh2T/0rDD+6e52B/CAcZw5uGyVue0c0ae1RhjpdQ1XIf9cfwfNEGzUprAZgjM4Ka16i6EQB7WdFS0KZOiJh8bbsAQQDpOKcLDXkqWlQdq06vkHGFdSft5jQk8b3aw5YE9QjbfATmpKVcYq5YzGq1w0W2nKoxAkw3Gu+EzloBtXUY49E64LxikNfkUTrofkBlbhxWdUxZHt2yPzXe4FM3XATDj26P2bihaEpWMpxKhZPJzGinAc794+h3u4ZeAfK6i6VVILeOamFpnJF7iA58fyBFOL24KR7Pyiz+Q3bcakJ7rlIXulWeKogU1nL5aHFv6swROna5LgdVydd3n2a2mXMpP+ClwbvkynG93uZuPebt+Q53FyOWzrbdoIWRSbV5DxUej2undC/1c/8O8/e57wRighT6Nq9OuPiNhuG7U0lgZqJTS99Hi10t7T60gLWiG4u8bw0Ej8J2vkbfH/KhK0YmFSsaVOxolh+VQOpYTWM0oW4IswV6MaTZGuC1wiw943cW4r9UDr1sULUUHYUyi4XjejW+1qkQvus67n4QLVua5B+CJIHXb8i0bR8kid1J3ijpfF6J/VWXB4zd14klCXrTSdxO11IMXu5qzMJSHHppTInb9pnfgkLyavexBDana/4wa4v6PrZTrQ/OGGDmHQ7fdstpDNtRY/TT536bF4fX+ZeXX+SPXr5KfiMjP1CYRUxuNXE8xBr9oASMQENoeoweyUdJB3Ef8AEkRjIHC25MN/jpzXd4ZX6e7J0CU3mKe4GjZYbdgFDmqONVkCGBCWKxEr6OLDKxaETFgpNQN+hBiRqPCIOCZnOwIsskx6ZENiXeD8oHzAIpisrEl5civYA/Eipud9tSv73J185O+Pqlp7l05oC/dul73P7ikEtfV4SqhqNjdBMZG+qGMJ0JcB41RVWWtXNXKHNu/3TG//Hyb60A0j+JutK+Nw/qmK+12ktj19o93+Y0U8FMj02ynmSYyQA1m8Pte1z4gxI73+LOz2q4OsMFxeZowcH+kHNf02x95R0pRMozwmgQQWm9ssC1bDXrx9GfG/vP1/NSD1vuTw3438P7/W3C6nuSs+lA6RPTbJ/2e93WcglJPq4+MyKfL+HwiHB4xMYfzEFd5dov5qidCpU59u6N2flKxvnfuwvXbkm+amebameIyyVWcYUA0y5TNIWiGSqaATQDKS42o4bRYMlOOeNSecBZe8SOOWaiKyYqUEbc4mNA+nQTxgUf/da4LihNE2rGuuAlrXhm4xVeeu4a//LCZ/j1sy9x/MYm5W0d870KH3MTyq36Cm2OK7JpePQKpXfwq9jBI7nxCsk1ZZb6oGDqpdnrpSdvcK445je/+QJqGJguc8LCYKZzyDKJg4Nvu5iV53RQOnjAdHH9ie9fv8FD+3oqeCI2ramybOUe3Cin2sw4fMqy/0LgqReu86vnXuel4TXOmGOetPtsamn++k/+7d/iV7b/d7z4vW383V3Ju26MmV8ZMz0v8ksqBOzhgnB03Mpytccf8+MAiwsD/uKVb5Epg8ezDH5FwuODtPdE350pAaNdzITrRN8dNKi+2rSYD4pCNygd2ond25iAI2DmvqXqa0ot1WOaCHJ1iWkFPS031TqmPmrZNlYS2nYWGF4zfGf4BNvnj7i8ccil4SEvbN7i9nLM9eNNZsOMqrHUtaFeWEIlHSxAS43YdnZ7RXPZ0cppaulQVkuDmWqh91is/uZgFSFRcveSanLs4ukGpzra5RABwKB6ifieNnUDPteEkWOwsWA8WOK8VMj7oDDaUzV2pWIcoHKagMFqzzAm0SbZgqGt2crmXCwO2LZTAGY+52494dXZeXzQDExFoRu27YyhWVIHQ6lrSl1RqppaUHO5Bohj4wk0oeatpuIr86f5B9d+jlffvABeYUY1ZdZQ6Galkua0Cf5+AuoGqVRKKOB7U338cCzpTpjYMQ2SkEgMAgmQzlWDSdu2Hd+eS9k+P3v5Lf7V7guYuVQ0SgXpaiDeJlRSMpvuzUBvfuviyA7Y6r8eEEc//p3GYEjYVXw/URe3wV4MRlvarBRYJgrLPiDdjnVWAOkTVEv9Y+r/UHrbRQpvmTdU11nVwGJHwJvsOGCnAoLUY8vh04bFF6f83FNv0wTNq3nNbnWG83/kyGaiLy2VDV2w7yNtLl5AgqYx6JnGRB0k6ZiCaiPgNhpU6Qi1xtcaZaUAx3tF+VZOfgQHn4ZwYYl6e8D2y/D7N1/k4ku3mf2Plrz801uc/doGW6/MyK5BmC1guZSORefwxYCDT2S4F4+5UB4x0NVK90ZtzUqXiwuag7rkwA24kB0+dMz+qC0lGGsciwCLYJn6ghvVJtfnG8yqDFcbdOyS1hUtPauAo1KhqptI4VJ53CBD5xnheMqV3zrmlSfOUj57xDJMmF8sCUpRjTXKW+oNxZEf8J2jK7zz61d56o+u4RcL1IWzUU9adUn+JtKDJ0qivqOw5pmrZi1rsTaVrVN6P1TLJ31Od4B0AuFPbrR2LKF7XbWVTo9gWq0cp470aAJw6JbCO/SD/0GBahyhqtj45m0OnrnE6Jduc2u+ze0vFBR7eawCh8PnHa/Pz/HlN57nyX9doW7clWq982dE58UFVASJEl1/+n7pDo6PJmCMxyjp8JJ5tUHH+fSxDxMS9bWKnBCnZLuk0CdNnr3uAJ9AyI6usC3O6F+/ADJKVQtoqiBAlSti0r40MMlRYYiZi4asWtYCUM/mUiDTK1oIVS06NtBSjbd2mq500k1LTnXW6UG3BQ2ZbcHtUOaEzFDtDLDHAurpZfQrMik4sgfLVv+dqo6OMiy2NBtF1nY1hzxDVTXDG3PmOyOml3LKd3KUiw6aMdI11B8skXJPNx7lHK4wrfzDSvHA+uP6tYu/0aEYmooNO4+Uuw4fFLNljvcKp4MwmcTHlrkH8TVTIWTo00cmwDqtzyGxCiX69tBKxqAVoQm9ZFhoQWuVfNtUIPoRW5tATtRqybfR0u0snVbCjOIKeU8twUTf2huwS9GRTnOlnBeh7jbLII9VwCy9VGJbuV/MUuNyBVnWjgejPSGT4k9XiK50vRP45HM3eGnrhhQWqsBQVy3oPHMFHsXYLFj6jCNXstcMmTYFB3XJYVWydMJoMqsyqspSV5ZwkGOONBtvyDqiAsJMQHSrcw9OUdqGs8UxX7l1NZ404NgKa0s0tajRhWV029P80QDvBly85RneqrDHM/TxArWIBWupE09K0lv/J9Q1WIvK87YyGudRCcBuv0x1AHTSKkxFG705DqtjwG/wuZEOhQROm0TZveojpgKW1mfs+4Xp9ROD6P7vnZqYWN/uMcPfUgGDQ3HkS0ol574t/vZaYr8gzCFNY/BNN6G5RpI1ygSazLU6zLltGGZ1C1QmS8Bpph0uKGzQUvjbFtlKEJvA1qNFQXktIz8ILLe6kxn6U4riBA12+9Ypr59KqbsGYmsVqL3GIvep1p5FbVe6v/u2DjCvdwirtffSd6wfg0fdN13fL0SWR48lArs9ykerPctKaB1qZ8iMWznmlc7s3nMVC5tqL6D2cVXwx7tPspmfZTNftCD4tMnZWw7b+D91ks+bTOLT+2iR97+/fzwJmH6/Xcc/jpZyKPfuTrj0x4HRG0eix6wVmDgfhiSLFcdJZjs6b2Oi792btrSUz9PP2/WQztA4Eu1v2w3dn0dTp0yyVAyqpFBUHc8wmaHZLPBGC9C1FM1D5aSANxQWN8oj05nqxfipUEiRqlpTsZZ8CVEeIvpuEUBWTWjn8NQV3TJb9IoUJVaPoGICrANt0Vl6rcsldImLYCHUsr2uRRe4vBsoDGRTJzFalLpZ9eVibjEWdPTHe+qQTq89iMK+f7r7LA4f26olXVWQPN5QS/6q3zGXKL6v2DH/q83r/MroZf7r7c/zj9/+ArdfOUt5R5MdxeIII8X/LdOeBpSW+VAl8EGL35LoW330X/oAXfRtgtGY2ZSbd7cZPrnkzaMd8n1FM1AM7zp2bw3w20FYmo6mUgzSlyPJM2EqIua90lcYI8WMTnTl9aBEbW7gt8a4YRZ99JikS0UQBMzcYTSiua5pGQZ1HPfKyiOLQDaTe8hnMLijqK4PubU14D/fPI9+0mO2t3B37xHqRphroM0fCXNTPNikY68Uy0sTzv6lG/xcMadfHaPRq/S7PwGWKU1GQ6lqRnop+W7TYK2nTgyl0YJBiia05ALFJZKckTNKip9dgNu7qDeucXa2ZOPtLe58bkS1Bf5gyDPfWFC8+jZhOoPgpUt6Y0AzXgV1Vtgket3F653RK01La/ZIndL9Tdanvgf42aducyJn2+WAT/XBU0ptPUe1/lSLxFI9Meh6Eztf4KczMIbJ77zCC18f0lzaxo0y8nf3YO8g6uYa9GjM8qkdqi0bj0fRlCriTCIbW4+h3gjUmw49qdnenHJpcsiz4zs8M7jDk9k9LtojNrVjqA2ZMtifsOKNR7FE+S+4juk1IcnrfeAyw/CXyoYX8z/gZ8ev8k8u/gy//d1Pk93KKPZUW2AuXXIhrgUd5qAbRF5XRRwg7VgAD1kn7kPp3RZ+E/OqTcdaaPelANgHxec2r/HbN5/jzNctu5/3VJVl9IbF7M8kRt8/XM19pVg66UYn1gMSZiMxc2gaWum49Yq3XqGjMkb8wdgdHQYFzaSgGWVUG5blhsiQNmPIDuGtV89zY2+Db5y/wme3rvNUcY9n89tcNIdMdM2ff/517j3xFPbWHYnxncfOHOPrinJPMbhbo48XcnxaoQelbJcKmmLh+fElw69uf5NMGZbBUweHxXwoa8Z7alutgyVTDRmOGkON6ai7Q+xeUiKDLdSaugWlWwc1zj7BdpOYWQbMoiFp0rhCOqhberd0ADH51FbFxd31/86Owd7LOB6VvO01b4VtlJJKqDOjGeeHR1jtWbiMaZ2zaCzzKmNRZTS1oakMhC4xGCQSkb+9EvrEYUMzBKUDNbSBOEBwWoDrSqNqha6U6OG5uGF88Jkk5rwFX3goPKZ02Kwhy7pk7zrl0HSR0zQmBvh9h1rTxG8YFhXjvGK7mDGxS7ayGWO7ZGwWGAILn3GvHvHto8ssmozcNOTakWmpmm+CIQuOZbBkQRZvFxQLnzPFUeoah2qpU11o+MN6yLcWT/Lluy/w3Xcv4Q4zOZhIP26052x2hMeTqez+4HNPlyABWgnIdjFhsnwMuqSBTnOiPU7XgtJ9au/+9g6FD9KNMDFzfnHr+7x5dYfX5pfJd6O3EZOrhLUkXXJg4/7Se20BA3SABHG8tvdISLsWlkVDpEOOXdU67TDERSGCwHSV5Ok7WoCkFxiGmFhc0ZZWq45WG3imY+p9pfyGqF0VuteTkyMas5Afym+sNsFOwc5Dm8CenzUc/8ycX33uu4zNkhvLTT5z7ga//9KQ6pWc/NBhE/1X6iJViTY3/g4vCT/dCINDMxRd+WbcJa2D01IxaaUrpZlZyndy8kOYPhEITywwb5Vc+e2a8toRo5sbXKsv8Hf+ra+irwR+9xPP8sq72wzeeZLsSOjHtROq0OnlQPniHr90+U027IKhqdoEVNL0W/oME5NWjTfMmpzdasRTxb0fbkB/CNbqTgWhTJqGnIXPqIOhCYbaGXxtMJGaJVE265pI58yKwxqsxheGMBrA8RT71m2ufPkp3vnVAdsv7vLO1ibZrpynZiPw9Kev8Ws3P8drX3+K537jHuHwSLSkM4sbSXUcKTGy5nOuMAS061d8KYFtD6BS6r9/IrHxCHmOlQCjvelXv7/30gnHXrbp7cOfjC/65myqTulZXKcFeJPKOVPVMJvB0ZQn/9ltrtXnyf78lNFf2eNgOsB7xYWtI54ZHPPP/+Cneeo3PMWfvgl1hd7axA1zQq5bpoUQuyq8iR2ULcWS6LGqzFNkDeN8ydhWTMyCUktAWSpHrhQG1WpjPW4WctsBzvfz4WLnYdux4iUpqUPsUA+qAyITnZePc5damz/jXAxE9hWZT9O5DkZRj227LmSHDfnrt/AhoGKHQNLEwfY6dayBhxEyJE3pVHXZdgMBIVLAG01Yemgc1eUNXKmxM+nmMbtTmu0hQcHgbkBNF6sOvNYMbzccPJNRnx2T3diXMWsNIc8w944Z3is5umypLm1QvLGMiax43tbHt1aiq+s8zchglr3gon9tJKt58vfG5LGdwR8eXGXhLI03NEFTO8NxnVPVFue0ANJx0VMxESdLUM+/TMnT9FV9/y+9nwDsPi9VYkwIqo2XgiK+rnprd3ikuefDtqBA17GjucfOEgy4gcwBKgLQkIBm+Tt1Uhf7AbOEZkCPpQXwYBfSIW0XXkBpH2iMoR7rtuuazOJjMjAAelLjF5bZOc3G2w16btAEKm+psMxdRh3pt4+qkmmdUznDsrYsK0u1yAgzi5lqsiONPZaiufwosH3oMXOhg9SukipvF3CDCEbnuRxDoyi35syOCnxQXMwPUSqQ2wa1MBT3NNmsbsejqmp0lTN+9YDJD2Jw7IPoPmst3c2pOKRxrYRAaBphPQBUWUJm265AVTeyH+elQ8jaTtMqWewyb4HqNHdpjS8tPq7TPtMxwaqi3JHqqLp7oMWJAkbNgxer/nvqlNfoxs7DB+N9Emg/YkuxQh0sVbCcs4dtBf/A1B1DcJwTZCmIdLWRdSEERO6CjEXyUSM1YZnXFFlDaRtKWzOMzDvy3R5jq6jPLAXPPoJWPhh250MOX9ti+3qgGaxNIBEEUjoI3aoWv7g9zghGa30SlD5N17lPu+u8JkRf18VtJ+WSveMh89pSZk1HpLQOsAa1MiRCbzvnhf68Tw++flypOxztT4C0Sb+5bwnEF79ct/vcLmZczzfYWwzYGcwiXbpa+d50HnwPUPPQdksPswoXNEd1yUE1ONFp3v/9Rvu1ptzV7zp53GFluxSYPS7A9IOOX4oHODGG3qvdvTNh4xsFk9cOcKNc8lLzGlU1rW/W12gNeRbn2Kx7Xfe6kZPkhxzkKsAcWWiCVsJEo1TnYzVA0wiobSWZqUzW+SCFIVQ14eAQNVuQVdv4YS7vN17ilFgUlAqCgovHl+LsVOSoFSH6e94KTWbL0JRyBiDjwShouq7qtH37k1KqIbGR9HIALc12bzjFPpZ27k9pEm8jTWoj3aNm6SliUXLnDwR8obtu7vTPhJU4q50bw8k8WrfNyfEiy1noXb7HYHF4zKzfXNI3o7q+2/42LngumAH/ydb3+JXxt/n/PvEz/Pq7L3HvlTOUtzV2qggNeCPFg6YSX07HcabrEIEJkfLCQ8hNSy+Pj4wz6REZ58NvDNj64oy7xyOKg0A2DZS3l2SHQ5qrC5YXxpT7R3Iv1I1oaVoj93KWdetH0whokGeQ5aimEV3PjTFuVOJL2xWyBDrmsn4M76TAHtUrConjX8ei3ZTz1k0g1Aq7COTHiuEtaAaaZqBYvHiF4ps1/nhKWIheqIoghlwEE7VDZQ7yowE3fq7gH3zqHzILNROlkCx9QKN+IrSk+1aojJF2bIUl5+wh5/MJF4YTidWWGY0XOU8Q6QAiMJzmLJ8hPo8GPdAEs8GwcYTrt+D6LfK7e1x5edB+X5jN8fM5Ks9RZ7ZZPn2Gast2uVK1Nk7ocq1A5x/3np/YhtX315si2v1+AJf5ZAf0ye9/4PtwEpTubZfyFc1ANJv8lQGD7DL5G7fxu3vCqjZfoPcPMKlAxDm5Py+cZfrcWeZnrWAsRnLdzVDhCmiGIp3qNhx2VHNh65jzo2OeGd/lfHbEM8VtAaTNjIlWFMqQYVaKNz62ztbPyYMKXIqI+5w3I/768IAvFb/Bb25/l39y+wsdg8a+5ATQStaCyIhqquijZF2DElHaQUX/SqWeihNMhPEAkkYzCBue1uADw5uKg2YIwLXFFtdfO8elw8D4DUNzqUeFarSsA851DRhGo2ikWSLFyyBzhvddQVPUgk5SEGoyjgw70U/MRSrLDXN8YWnGWS9u7pr+7DJgamHUKO+Au2aoJ2Ne2xjxvTNXGJyfcfXMLj+19S6fGtzkc5Nr/MMvPs/lb5qWpSF/V5MNoqzDoiYcyTqiko/rPAxiziD6p/svBn62vAOMKFT2oXVJw3sApc19IvxE3Z36pB3+xMwXYxySTk2qfujTBCsHduGkk2GOJDNyuSAuJjT6k3AfzEpJJYI4LXauWCwstgfuLp0Az7CJtY5RUXFmMONMOWXhhAo36bRWjaFuDE2jhe47JQmj45sOpE/d2y4uaZtRQ1CBpk0gQr9TRan4GM9PSky6xvRohuK5N37FqZZH1Y6ZjdGCYVazUSwY2oqdfMZAV5zJpm1Xx14z4rvHl6WqOl6ffuDsgyLTrqUzcWgWPqNQDU5r6mBJGemFz5jhOCLnZrPFDxaX+PKt53n3zjbNQY5qlJwD3f1Gqz2lqleoYx5kCZB2vcXL9wQpHgfwIQHSfWA6dU0LlX2qGhIXcBHLpl1Q5ECpawyBl7ZusH91wG61Q364mqTrU1u1ubeTOaGVv9OivuKXxCGs6ALClkpby8BVkuWSzqqQ3lekcs72Pu47Rb0vOVUP8BTnqn/P9gtKVj4Wutf6XTXDOx6XSfLWLmSBcrnGDTR7L8Dnn36Xo6Zk6S2vHZylsA1Pnt9l9/IT7Bz5SJcZF5RY3bRCA+Yik0EeqDbBF4GQdzqeNBGQzqP29GHO4O2MbAbTKx5/piZ/dcDFr9QMXrmDnwyEKhSY+5xPDm7zHz/z2xxdHfCD2UXenW1xY7qB85qz5YIvjne5Uu61DBSJRr9UjpnPKXSD1R6rHblumLuMw2rA3H14i8QHYVopDL4t5ABhFVCxErodXGk8RFupdtMyeFxpaDYHZNMR/u4uk2/e5FJ5mZs/v0V5ccbkSZkPM+PYnw+49dUneObLx3DrLtQNamOCK3N8YU4de/LF8Tv7SXP5IfLY111euw/6tlJUcsr+H2inJd7j6+8riX5ah/WDdKzXjkOFgMo0ajLELCpxcu7u8sSXDfd2d7j9xRK2apQOvHtrm1tvXeSTvzEje+ceYbkkhEA4s4Eb57hCNIPSOu+zWIiWtH4KcGUgFJ6saBgVFTvFjJ1syo49ZkvPGKmaQkGGBNmPqzZW6r4H1uZLpFO/kWr2fjKFyHTRdqkHSN044ujEJDZeOqnbiTWZbKljkQ8mdv+70FFDa1qnVw4tHkA/wRG7iQmhpfIRcOo+iE+aW507/f2VExOkEnoZqfm9Q82X+AsTUFAcRt1b6EBu7ylvzdh/dpPF2Zzslu4At8zC0lHcq5mdMyzOZBRvSeBxX/YAYyI1paYeauy0d9zJwdJKEr73+70I+PjNW5dxbnUBbGpDUxtCnYoS0uKc1tuwcp+3SdU0TFRYZRORrdrDk3OiOsA6BFYdgvjRJs5ja/TuH5UpR9R9ptWNTgkfAayF+UgdgR6rjjVBQbACSqdEdjDSxYAK0mXdo/kmpGIMzWLHML2kWZwJDO7KOZL3xL8uBjXz2lBtyrkcvat5eXiJV8rz8j1Oi/ZlpdEzTbGrKHcD5RzG0ReRhKQXGtUIPOvKSxDcu8668cIWkcZmLJJTNeyMZiwWGYvGYpTnS+fe5ut3npLfVJPoiWTMayXASRqr6X6J96tKnc8+dP5OIydO5XkPcFZyrzVOnqd5J8tk3853gfkpJmCFAasFkM5XAWmha0/03XJd7gtIt4Ok/wWnjKG1oX7ymFa3vR/I/aD3fpRmIt1zHQxnzDFTX1CqGqM9T5Z7fNM8wbzKBOPyHeAaEtNX6/gjv8cpQm1xc3AqsDAFKutA6jxrGOQ1pZWCL6s8Q1u12tKNl47bu7Mhu+9uMXlXAGIp5OhlECPofBrAc5olfeik/bpu0iW8ul2yBN6OyorpIiczXiQSwv31sN+PtftUSgp91mwdsG1iRJriwHQcjdcS++Y1y9riSs1QVyzeh7/epwh/VHtcgOUf1k76N2L98fZ+wGiAeZWRX8/ZfrXG511KzOfSFalqFyVWeoU41kBa09Up3S/r8UB/2mzzRsnfWmWfaBd2G3Ws02uRMUZ5TyAXHdv5Ep2A4jJrabp9YnyqPCFPX67a9T8VlreH204m0c+no2ENqSg3Vy2Q0sbw0Ydtgba47xX5oV789EBAo80FBHTt8cYIU1Yta2sqoId0HWgZlryGEAthThsH76XbeX0u+7hT+oc3T6AOjkJZPpkp/sPtr/HF0ev8t2e+yO+99Qz1GyPyfYWdqTYf4I3C6NBRuaIJVsBas3RAb/1QPWbA5LsoxZnv1kx9IR1v80B5t0LXDhRsbsw4fHqH8pXUkZD8fd3tU+vopwijgRqNhC5bKUKRSUGI1ijXAyD6AON6PsOJs9EWGJsITrvQkzUJsqbH365reTRLyW3XE0OxvQnHwnSpjGmT0WlOUYUUs4U84/Zf2OZzf/P77OiKWYBhCDicME8+xnHzh2FGaXxwFEqzqR0XzSGLPKceWYyS+PqOHVOZEl8YXK6wC5GS0r3GiTYHE6ApLEHvMHIO9g8Js7l0rkM7llRZwNkdlle2mV7KY7xCjxVg7UD7Q2jdP17LTa1YP1bs563S2w/Kz35Qdr9jWnl+8ov7a1D7PP6rxiWb+jw54Pf2hUrfCdMIWYYalHDhLEef2uLwqqEeSu7W5fLoBw4KRzGq2B4uuTA+4lx5zKXigEv5AZezPbbMlIvmmB3tKJVGK0Wp7GOdW/pxtUJl7Bj4W6N3ef7KDf7x+It8+drz7L66Q3FXkx1LQ5PTEGJco3XoSYcGggooG+NrpdCIfIqKubLkC4VerhZiiKR1nH81g9uemc8ZmyW35hOK21LZXu4G9huNHgnrjApBisdTjN0C01IEpKqa4L0UfNeN6DjHwvEQgsjlGSNryJktfGQTk6a4+LusMFTiQ5u/CIaOZcbF/IiBUINdRgnjA3B3LMubG/xge8zL5y/w5PldLg0POXyh5srWJu7GTZjNUYB2Q1nvFkv8YtEVlocghVdaSb4gBBaXJ/zCz36HbV3+SMbGI4PSVUjdqhqnOhowo7xoYEXTEfFKmtNGebTxbVIpiZi39NVpkk2LqZcqCLOUz3ijRY8jEwqGoFULUrXUFnQTmGrAzIGlgQmxYpuVasnZrGAxz9k/luoIEwPbYVHxxOSAyhtqb1g2lrt21KP9kX04p4U2zauYZCR2uaiWCjwEUIYVoCV1ybSdoD3rUzYqpVCpWjNWZFvr2urszDqM9mRajvuJ0QFb2Zwz+THbdsqxKzlyJW8tdjiqS6x2LFzGvcWoraZ2XpMZ4UoQyrHA0kXdBFMLeBSjpzqYlkquxrDwOd9ePMnr83P80d0r3Lq1BYcWMxeKNV9E3WyF6BlqT2YcI7181OEGdCB0H4zu64E8bpY6pE8zj2bmMikSwONilDr1OUd1ybnRMbvnRrhliZnH65/y4ynJlP7u2zoOsf78ft0icbj2yAs6cNaLY9xSGyfnqa8Vrbt90P+XDqvvEK0lGgOdA7ICot/n0ib9QeWEwstvGOwx2BlRLxJm5wxbL91Dq8Abh2d4bvMON/cmTEYLntw44N3Lge2X6QW+XibgSJ0W0rlqFL42qCzgrIDRyketR+KBR/rV+jCnuJlR7MPR1UA4v8RcKzn3Dcfw7UOaC5tMnxhw62cVn/zC2zxd3qPQNR6ND5oXh9d5otjn7niMVoEdO227iI3yK+Bt6sLPtCOjuzdN7Jb2QTHz+X0u9kdnRmmpxEWRKchxLYOA1U4CAR1isjqQ9MTEWY8FEpEyCaJuYQZB5ehqA31wSDg4Yvur15m8scm7vzzhzidK9KAhHOac/Zrm6h/eg3duxAMy+MkAN5JKtJUAMlo3btVKENAPBtY7pded/bbo4wG+7P2qWR9mj0oFfsonV+aHFFw9dN8pOEi/Xyv0cgTzuSQAbt3l7L8+YuePN5g/OcGVmuyooXzzJhwc42czAPTWJsszQ5qhiSCGgEIuF3YU0fhRNEOoxx43cWSTis3xgoujQ54c7vFUcY9z9pAzZspE14yUFjou9eFQyXwgFrs7MKpLOAKp+6ZNtilJULRV9kpB22XSgdYtHtB4TBP30+vkD1rJ+qIiiEU3v6oAXrOSJHWFliRrAqJ9TIAmQDqCVmFYoKdzUIHQUUvQdlBGDURlrTjgVU0YlgSrBXiPVHIAymiqi5s0pWZ0d9EmN4M1NCODt5DvNx3Ils6XUujjJcrD4dOG0Zsl5mAK3uPzDJVnZPembLyh2X2hYPzmGLN7DAvpdEjAnFKqlXBQtVAXeQt2Fk9Y6hTNLMEY9HwpHUwmUjFZ2/12LQVSu29vEAaupe0jXnbl1jSF3Op9f6J7WcVArPc87atba+/z/olB0r3VP6aP2nTTaWejZC0PRsarcrKuF4chUlVrqi2FizGRdrJNPZbqd58hNN8lZEeyL5fJNanHhvkZxfRK3C7zBBtYbpmuyMIEjveGnD1/yMZwwf6751A+sPO9ms03tLAdxVhDuYCuG7mXXFh5PQHMyoeVQhPVFpb0TrxHWBCslusRqRazY8XF0SH3joccTksWPuNifij6g4VHO5EtEaaEWF1S1XEsNwJ+pzGe6PiXq/QGqROon3xtLQHSWnVV3OkeNJqwQtMt++l35vncRCBaC+NFlhgw+h3SXXKgXesfthSe8n448UfvvbjvFBf2izpP3dFjkHNa+CzqSEvh3tSVbNgFpap5Kr9LaRuO5gV13XGdCwgT48c+UJziTmT+kXECTC1eBZYKlhoOTYhFlg6lA4NR1QLB3iuWiwx1s2DjXY1ZhlhAlnyb0MW10dZBIdV7P73X7j+cVkwFRod2u761nwdy23DkC5a1ZVhUbVzbB6YDDwZlfVDSkH+fwCMEReO16HSf0kl9qq29nLqmJ0XFnaMRmsCsydGqo0l/VDD9/QLSP+6gmkmOC/fv9u53mvet3z2dPls70+5LqcDhnTHnXgaz8G2hql4mekZE5ifNtdFHUiHqTcuO2sK9+GXpy9v1oKV3lB8EoZd6S4VvUQ5BZVbWJq0FAOuxtCjnhSVKaylAs6YF0kNmhAGH6KO7gGqEBUy3bDEST7vYaaybuFZB170ct/WZbqXyXK5biYy0/y4XJ8Wkyf80lW9ZeRKVuIrzfl96JrG1EbrvVlGTmiDH3C/41bVHNR4/tCJzFuMX3QQWZzRq1KzML/0u6X7n8/rY8GvSdyoegPca7yUZ7n23v4/tvZtoUMv5thgumQFny0Oev/Qv+N2tZ/gHZ36W11+5yOC6JTtWeAe6ClFbtrejkJqapFhhRVs05YDTwqc1wzcP+OrBJ6julVIIGeWBgoLLG4e8evUM562RLrfT4nFjJCYejwTI2BjJfKAUIbdRhsq38RuwUuitNITevK1icW1Ay+Y+oJKfmOK4dG+lnJuS32yUaK8qD34yjHFWLTFJUB31a5RcwhqOX9jB/+oe/5uLv02NoiSwDA1FBNvgJ4e2O5lGMVQ5RtfkqmaobjLSSy5kB5wvjnhjfIa3J9vsHQ1ZHOeomcHMdCcr51TL6Jd8x8UZi8/OM7i1gb03FVr1lCPKLH48YH5lxPSiYbmVWIOIcTttXBdibNd3i1aA6NSNpSQbdl+XoM3VrG5zwhXo57ce0U7uY21SPG04rc+b63nq/muhJ2Ua42ezgOXmgMm5K+SHl7DHtUg5aIUrLcvtjNl5zfEVqM41ZJtLirJmI6+ZFEvOlFMmdsmF4pDz+SEX7QE75pgtM2Oiara0J1OKocoolBR0JCYB+Mm7Rz4s65/H1G37uVzz9Nk/4BcmP+C/3Pk5vvrmVdzrA/JD1eYFmgFopyJbmtx0QUX/AfFNUJ3WNDE+B1ZzvEnKJOXYrGZwz7Fbj7gy2uVgWVLek8+We556v0Rtefwwx2jTymwBPb/NRrlDLYXeSSbORFbAumnnaTUZw5kt3EbZFZX0c3uNFwmvWGioko/VMmnI/ewz1cbRZhkIM3ktP1C4m4rlrQHXti7z5vlzqIGjfvIM+sbNTuZhvpAmjrqRLunoy+rRUArRnSeUOYTAjZ8v+H9c+hdoOvaHD9MeGZT2kY7b49vuaKNWq//73dQ66U4TsNbRmFQJJhVw2sQ5O3V26EBTGnQtPdfKBVTlscER5nKBbG5agNrl8i9Vk3WAGmRTMFNNs2nI8khR18sHa+3xXuOWVii4Y5A/nRXc3Zu09GNZ5vjcpevYCDYaFbgx2+C4KjiYlyyXljx3eK+oKyuaXlnUC2x01x2jgtDNON0lQNJiEeRvnXmMdejYMW2Mb3U0fVDsjGZsF0L99anxbUpdtx257yx22K1G/ODwPPvzAUZ7cuMobINRHqs980Y6L0rbMLA1jRdHxgWNDxJ0N0Fj492xXw/Zymbt9bxRb/Hy4iJ3l2PuLMd8+83LcJSRHWiKpcJnQbrbBr4FpNEBZTzGBAa2ZsvM3vMkL+MtYFC4VIWeLuhHbLU3FNFjboHEBCCqk4kMh267H6ChCgaP5us3nyQExc984m3+0F1FvZuja+n4SYCXihUyK7FsIEl7d4t4zEW1RRpt1aj8a/ejaOU/UyddQAK8lChGSTIxOWCJcqzVxIl/e9vL+MUAs9WEPcUJUqH73tQo1lZe01F4Q1yQSkko5wcxOPZC210cOga35rjCcO9nNH//yW/zT9/8HJl1XJttUh8UzK2M/fyTh/A7Q8wyBvdZJoBEWUgVtkHOeZBAlHgOVKLxjzrzibY7zC3jVy12AQcvOPTOEvvGgCf/1ZL82gF+VLL74ojF3z7gP37uKxS6boHkhc/I4oUbm4UA1XECy4xQvNfeSscKiqGuIt37Aodi6TNqRH/QaE9tDQd1yVvzs48+eH/EptGUSjHUNRM9ZzubspktGBYV0yKnLix+IWCUz+jGvpK1QhIWqgUD/EixPDdkcHwO7u7ib9/FHBxx9caIMCyFWk8paBx+/0Cqv5yDS+dYXhy3OrupEi19F3DSie8nzfsJp54zcaqlTU/b5hGmr/XPdR1dj5YQfRj4tF4/c9+8aAgrAVIz0Phsg9J5uH0Pf3AE4QDu3mP4rmiShGUlwXGkq9HbWyyev8Rix7aV7y6TBLfQKkVQehxoRh61VTGZLLgwOebKaJ+rg3s8Xdzlyewe58yULd0wUZqxLqIkxOPAnfEA07RjJtnDOv3uZ/1E3WmmQsBUiWIjrQFr39sCFun4VkHmkLqyU5dkCNIJmX7DqVyIsUO6LGlpKZFjDVaj6rivxhE2xswvFlJU5IN0JfkAmaUpNXYWE8PeE+paEkNZTLZ4T7HvmZ/V+IFFLzLU8RyVurN9IN9fUhzkHH9izMbRAtW4llpp5djSOc2Tpu/6TaFkv+l+917OUepMMDE5rGLHr9UnC8HWk6HZ2gYnqtfXzm36fCqChJP3dvsdpyQqFGAelcv4R2PZMSvJaF2DWkp1sG4gP4oFkUPRlU5AmIpdwj5DquDzyKigpOtaeZHcqDYUh8bgyoBPTCcJFDWBeiQJwpYtxsPhtOTZ83e5M44MLEaRHTaYhUNXriue69O4pufrnXIg817l2vG/gtAlQCqT1GAwGrWoKPYDZ4spRdaIzhWKY1fggoKBoymFVqsdp6moYh0USfctrAbS66ZljTxhPjlnoeuO7oPYNlZ5G0PIEiitu+7o2FXXB6MTIL2yprbr6tr3vx/3/pQp6URy7zRLY+Ajtn03lOJEc8y+k4LpUgnT1Xl7xHObd5jVGXvLUcumZawXwGRphMUnxZWJkQHpKGj1LT3STdAoVBOTPAGUzyTZ7QZtXKo8DLwwGuhatPi8kRgk+fcrsewpQHKy9df7wNFptk5P3X+eNKbzvGG+zCgyYQ/qg5JtfjMBT719qnQ8CbDsbdvfzsdAyWmNVqfcI2vHqVU4ocdcmobjuqC0NU3TFfan/b2XTuaHdUo/bF+ngd+nfeYEnfdjbuk473e8qfjAnfJ+4wxqrsnmcWw0vutcyQ3Ka9ru4ahFqLyX6R7Tzbl9dok1NqWgZaFLgBQAWe9YVJdglGRkBKSt6WRfYkF4cKEtbkzgmCstzcjGDuVuzvNWkppm6bpCWtXF68rJOpLigI56PBZHBtoYXwqh1ErdWwtI94GSfhwVP9/mufrP6cUfvXVA+XjcjULXvtu/j+c/UoevykBIHKHMKhgN3Hd+6W9zP7A6WWK/OQ3Y/tgezYTWW1MHJzm9ECiU5dks54q9xk89+4/45+c/z3/z+heYvrYpetNO4VVoWVZcLt3CIuemCTqgnXTV06TxJKCDcgE/LlC143e//xwbr1hM7SQOWTbYqWIjW7C8XLfFiSqzxG6g3j0MlAUUQqsaiqztZEudeJK7Unir0evxgwsdSA4ds0D7Aigvs3oLUiRafw2hSR108miiHI0vLdm5M4SjYwEVUhGxMajRkFDkLK5uc+0va/7DT3yDQ19CAztmxkQ1bGkBpn9SLVMGjWKgYKwazphdnsvu8dnyHa6Pt3lz+yzXltvcWk7Yr4bsLwYcL3OR/YjFKs6nosBA1RiOnynIDobY45HQzytiYx24QaA64wlljc4dxnryokFrHy95V4gnxBOr67Ru3wtSpBXNrMmIpLU7+SL9Yqz1bfrbvRe7X+NVstI0D3z/vfgwyWfJtWPhLAfVQNhsERYaoz2lbZiYhk8OjrhUHDA2S4zyjI0UdE7MnIv2QDTEVcNQOUZaUShNhiFTxam4xGOeUfqxtb50ax0cmYKzZsQvD2Z8/so/57/feYb/4uxf4K23zjF4KyM7ilruFoIK6EYKlXSNFNrmRCa4gFJe7p8GotPX+T6x4C30ioZQinx3wfXZJi+NrnG8KMinIvtlp47iVol7bsZyu2BU5K3kVWrEUH1mGxMbO+Zzec9a6aD2AXJpyvHbG7iJFD20xUwuiFRLYs2QxJtgNVqJP5aY7hJTTdOtCX0/KK88TCGbKsI1RfV2znJbWFw3vz3EzxcEV+EXS2HYgJjzS/6lR9kggLQxLK9s8su/+kdcMZkUM2E/9AKNR16V9BoADRJcoToAet0ExFYY42kiUNXSt0HXqZksLsjt+0ZAauU8qgnYo2VblR+MphlZAagLjYtAtVJg5oHsQLM8a7CZk85mp1tnU6cAVkWq3iCPbeBuBEytK8vXXrvaHZ4OlIOKMq+5uHEk1N9NRhM1uJzX7C0GLGtLE78vObJCuaa770/V4MjCYmO3dmEcW8WccbZkZJds2AXn80N80NyoNnnz+Az/6vqncV7TOE2T9qkC1jhMogGPVd5o6chIi5nzmnkjyV0fFFZ7Gq8lcDa1ULa5jIGp2a1GvHJ0nrvzEYeLgqN7I7JbGfmBYhSDDG9FczdopPujUsIkmUmnuzJSlDDOlmzpGe8n22QehzaG+5iX0HSFtrtP3d231G0unxNK9CM3YDHPcU5zbzLiuSdv8XJzkfLdXKqBUzXe/UzRnnsBUSWATFXHCWzrOjtpqbkTvtUmZVJQqeMbrALUgV5ldEoqpmPoJxcflGyMP0elPx5gwdDqgqgAxaEkkV2hKA495d0KGs/s6pCf++zLLL1lUWVsDhbcPJpg9y2LQc5uOWR7NKceT8iOnJwXrUldblJlFQPi/nybAtvIhoANQt95mLP5usYb2H+xQQ0d2feHXPmdOfbenFBm3P3iBvZ/coe/fenlFUC6b1pWSGpM6+glcBpoqeFdvMitZl3smHZeR6fNY5Wn/iAEYz4EM9H5K3Ds6IaFPRA5gDHUQWNU4GZQ1KoADD5TWCvVkcaEtkigG9syDquRxmdCmyRAk8bvHxBu30EXhawlVS3OQd0QPvEE88vj2CXHyvhL9wT0kiopt9JPoqfXY+tVeMA4/2FA6ZWEPdAWcLyHqXA9P3wC5PYntzn9nlTt9ycQf7mpcfkO46pGp863qpZg/XgKWUaYzoRa6eknOPr0NseXDK5Q7X3tIkW3KwJ+6NCjmvFkwfZwzuXRAefLI66Wd7mc7XHeHLGl52zqmpFWjFWOUYpMmVOTjY+lfVAZLbVKvbj6XnzwoU2mpgQj/QIlVJzbI9hqTZcsCZ42JEtaTY3HDyIoHB186RCK39t+Nog+jimS0xPBVHHeBUBzuJ0R9UAzvNPEameDnTvQmqZU2HlAL5JIUDx3WSpokPl/fl7TjDPMsZGEko8AmgY9ryn3HMeXDaOtIbbfLWpMdy3ioxvlsgb0k0opQRw169rzkXR3Eu0xUmhJgN4S31rQazfVw6bpE+O5d+P7U6574OSNvL7ZY3aLmCVtkZ2di9Zf6poSPXlhT6hHEIysBSrpPxtO+B4hC3GdlDnFFwJUh9zLda17J8ALmO2HJa4Aco8pHFnmeGJ4wPeGEuvoSuQ+VEDGQfKhiKe78ejYxSYdzwFVxxuiN77cKKfayHCDTv893ZvZUU+vvWkoDgK5bsito/Gamcs5bEoaZzCFww3SOExFJyGCyqmzT6927nl/EpTu09Kn5/1jhtXEaQRG+hrSAkZLl17II2X3afrRMd7rM4qsdH6s+4v0Xl+zR2gmfbCdclvd972PwByaoZpz5Ad4NDvmGIAjX2II/I3tb3Gl3OPro6d5Z3+L4+MSV4tWfWLsagtXkoOdGLzi06ACWKRwuICQaVRM8CiHaFNGU16SO34E9ViKPqDzyYMVlpv3AtY8CIhuvzd1Ove262tPu6AwKpAZxzJkLGtLXi7bzut1T9vH7aG7zFqFtkNabuUoZRU78aADMWtnTiRLJZ72K8/lMfnxiiYY0Y9GMc6WWOu4Oxvy9OYe07o4kVD+IOx+5/Z+gO26Bu+Dtv0o7LRbvn98ujdW7jdu0pjpm1KJMU5h5gImhZ7ECanoOoByQYr0VBB2Cy3MLwKWxpyR9926nm4GmwKKuEbo1ee9gxFfoi0iip2YvUKmkPalIyjtgsy9RhEy3fqEQcXjTedP0TFTkBKY0VfxXbFpS7etwGvdzc+xAF03IYLZXSFuCzDHHENbFB+LfGXHnOiI7rqi02e7Y2jpv53CLlbvj0RLLnIQsUBGJyYOeqyI6sQ46Nt6PeXp3c/dZzsWitOpwT+2RzcpHhY2gjo4miCSfp/NDS+c+RZ/Z+Mb/JNP/DT/9M3PcfTqFuUdKVCF0PoRyivsUubvkGjdTfLVAjQB8OilQ82WjL+zQ1NCNdaoCyXlnSXZNB6QDqj5smU+Uqa758RC7HQt8LldYRJTzrfapHLfPeJ87kPbQa16sVRfz502pxeLg10qFBEQI1hN/cQOejZBR/mWoDW+MPgyox5bZhcy7DH8F9/6OZ5/4hP8zfPf5vniOpk5YhE8zs8plWGsfjS0rI+LrQCPSjNUOVkwbGrPU2hezO4yK28x9YFZMCyC4ciXIq2CoQ42Sus1rQ8w9QUbesEiJIlGTR26vF6uHC5ojnxJqerIFti0jUx903QsiS7o9u/+dvfLMadtdNyvW3Ns17/LcBK0fuj5e0ji9mHfuf5+srSuaxXIWpZUJU06QWMIFMpxFDrpE0Nof0OpXPubhyqQK4ULoe2AFqYGS4K9LI8xu96fYUvsmQBjXeKCZxnXgR1T8G+P3+avPv86v/nUVf7zp3+et14/z+AdSzYjMiyGlmUxaASktoHQgGoMoY74WmRoiWl2KQ6KsW875JXC3trn29cu8cXttzi8OeHiIpDv17EQveTCziGHT19g8M4E9fb1rmEjUXcnFhstLHqhV6ioYkETGyNckRGyWLiUmhysJuQdO2JryS9z4XQ2jUZBlWJsuW/MIjKOKhUB7UA2g9FtqAcaLp1HvfUuofHCxlM3HbtGCEKBD5BnKOepntji9b+r+X+d/9cceNjUOQ0OwofLHPDIoPTCZ61utA6+1cNdB6Y9gna1GtNBtzn8fnV8AqSDip2RfUfXItXckdqy81oTHZAswmZaETKDKy0+1zQDATSagSI/UizmFldKZYM2Ut0avO50/0IXzCjbC2gC0tUMLRIQAgSnmLuCOQV7e2Ne12fROqBTV7PxbA/nTArR5xJ67NXJ1irRg02vWS2AUhM0lbNU3nBQldyZj5hXGbUzqBiAA1RNT/Oon6DUUvUbgm8dZxerpJbO4tb0o7UKWC2LX24cVskx3VuMOFiWTJc581mB38/J9jX2WDEWtu8uuRSr5pWLHP+J+x5IlfPpe0pTM9EVnvyRK5BEy0F0MzWaOjS99z56c2gKGrLocCRHwNB1Sfcdh9Qd63tH71C4RuOXhluHE3760jscPlFw+/gc+W4XHEplMys0wakSb73buNugCxwDPVrYmBhst0+3WAoqdfd5egC1fGeXXGzHQa/IJO2/H2yeAMPS/KvpQA3V+w3xM97EBKeWoDY/9vKaFcoKQsCXlr3nDf/jzbd5a34WY+S+298fMdxTTDctfhuuTPZ5dfMC+YHIA7RJ2VQhlLplUzCQOqRz34LRqIA5zJi8KZXZR886zFaFeW3Apd9fkl3bJ5QFx89usv+LC/7nV77Fjj1mETKWITsVnNYqYFYSFB4Xu+5PMx1pvXWQjnyvFJnyFKZZAbQfN9OIPovRCs+SnDuM9JJSNWxlc17Nz3FjuMHhaMBimmGODHYOZhE1QqNOqIBr7bRMMzA0gwtsvDxA39qV5I4x4hhUQtmrJmP82U2On91gvq3xee++6CfI26RSfL6ePJcfsmInAOu+nbKf9fdW9nOfz3cbnfLaA2x9OKyDYwKOqJOvndhR99gWqXioNizV5BIbr21g9mfoqEmqIs1rGBZUZ0ccPZVz/KSiHgeaSQM2oEqHzRsGZc2wqNgu5+wUM54Y7HM2O+KCPeCMPeacOWKiakbakwFDbaTAoVfl/dgHFh/G4a3N8+2fKiVJQ5dAUXRVoqp3D/lO/oTMIvQ1sQO4jzI4h6odfqOk7RaGFZq67rgUfmNIM86xh4uVt4IxqGoGg5L5hRK78Jilo55kNEMtki1I17ydBfRUPq+KgjAa4Cel+H+zJdlRQzOwzM5ZilsKPy5bymSUgrqhvLPg6MqI6ZNDNu8eSeIpdkuvdDYB1XYuvmjtugAhJahCIAwKMAPUohIQsGlEP8iLnlHL7oDqupnTKVmn627WbuLT5pSVHciFa4tS1kFuFf9TCKBy6lzyHiaOH4Flx6Glr8uOA+WeQ9ceV2qWm4b5GS3nNEiwpRpQmdCE+ljU4nPwNgWoAjSELOCz0LosKp3rCNjJ6VH4POAmhVRcZ57RcMnOaMaGFXp6FNi5wxUGVbuW9hFox0T6O0TqLjfKqEd2JcD0mWq1ppNeeTvH+oBZSBd+yKT0stxteHu6LXGB9tytxtyYbTCvMoqiZj4uuu9XKq5zNYnhQOUZ1E5ed05eWwel18dCet7zh/qJ2ZDug0Tjn9uVuMvlParuVleUFmDoA9Era+GD1sd1C6v3xWm+7g9ljwHWMNJLSl2z70acMceM9FLig6Cp0Yz0kj8/eo1L+T6vbZ7nG7tXuH64wey4wC+s+Kgmnah4D5gYHJDWhd5JC9EnMEqmQktL19tz+aUjIShcLufd5cI+kKRsHmYdWCjDqA8q3k//NXUsJ1AxgYg6FlaXVu5Hax3L2pJZ18bIp1mfJlyrsAJS9rurVXwe0mMQSnDcatSaxcL21InUgtRGikm1Ek3qRZPF54Fzkym3D8ccVAMyfTI38MPaewWkT/v84wa63Y+Ou31/7bX2evVMqYDrjXutut/aeI2dS16pBXCDjARvJEbUVfQrTASnYze1cqFdu0Nk8FixBADLgcmxuJgYXQOlg9Wdfq2RNSUBU6rHyqRSQaJFdKQL086Ny03N4owAZPlhoNz32JnDFVp+g+l8QwKt5ERQXYdO6r4hRB3nmPQMUfOWXm6gBZ8DtOxmbadP3Kw3x7fF8OuyYOv+j4nyD8vYCJNyAlrh8gRK0wLSQYs/YGx3/z8q1fZp90yap/psDvIz32MA9rG1lkCIPiABAgwBkSpX8+kM/rc7X+cLwzf5/5z983z19auYd0oGdxRqGvAtHiUMajqq8uhaE4yM2bYRaFJi5xWDu4G7v1jhswJ3U2OqDLMIvLp/luLdnDCTzjb6FNzJeoV4fZpu8cO7wSsxlW8L+B5qPnZQa7VaANh7X1z6uDb1O67jfewyI/eJLuVeiiwC6VE3gY3XFYvDAd+/+TQvP3GOv3j1Df7e2a9QZntS20IQebXHPYb+kE2j0BGkzJRhEDyb2uHiJLIMx2ilqEPCOcQyVPu6b/cFixBis5IAow5WQFIPuBAwSp1ID5i4TwAfQvt3ny3UoNCRufb036PjcZ70ifTKN6of+tq7tYnWrN1Deu0X3u+YXehy/bTHL0ypPub+M6WZhYoMRaZ0PAeBOni00q28p3TCS/48HU9fG9oTfuLH/Edp6+e+Wwfk2u1oza+M3uL8M0f8izOf5TcvPsfROxsMrhuyqfgnKhYpmRq8A6MVygRQ3VqgY+4LBygNjSfRd0uxuYLGUf7pkL1PDxm+bdHOo5eNNGJkgbODKd+9Cue/bqCqOsa+tDYkLMFFbetBCVmOGg8Jg0KA50I+0+bg6LFsSrAR0w4JZ6GTFwvI71XyRPwRWYd0oO0GhxD9N/HtgpV1ACCbeerzE/KDTdy9vVVWnyTTpYVunBContjmzV8p+Xe/9HsALAIMibilknnpw2KnfGRQuu7xBjs0eRCUIFXBJDBastYah8IH3eoVw8mERHJ++xWnKSAICkzUFAFxCFqKx6jlppa1JBFrJ/TeU4Pv6epUE8vSDQjDBpP7jqo4HYKO3xdoNZ2VDp1DGy/ASqV5BK3T7/GxrR7rUSYwPSolUDUCVqfqTRBK7tQhfSK48qrt6JYDCdEXks/XynZBvZeAKu2rbyEo8qg57YLCN/YEFZrVguo0XjN3GU2kJJkuchbHBRxainuGcin63LqGvvZF0JIQXME0FAQb2n+knFYM3AvtyB5C+3GaGXVK1PKYWAIYu+7osEJtsg5AGiWAdRU6YLpUtXTmLw3TO0O+k13kc+duULzoePdbF8n3NXhOdHa2prpYsA8st5clhBaQ7ify1gHp9Dwo1XVdaEXqrkiLeTcGukrldfCuD+71x0eyRJmZdHcTCUN7TPF3eivdlABmAdmho56YqO0ScAOLzzSbf+EWd+sJt5djBrmADtk7BeN3PIuzmqc39/jc5Bp/evF5Nl9fO4kxiPBZ7PbXQeaimJhWXkkwqgLluznFLlQbMHuqQY9rxl8Zcu5P5mT3pvjNIQefnjD9dw74+8/8CZluqINlohfUwbQ64v3x4dCrdDhBnCyUb0Hm2lu8ElAujbWkO1gHQxGFY5f+8aVjMlIajFWGHQ1D1bCp7zHRcy5k+1wt7/H2xg5vT7e5Ox+zNxswn+csFxYquQdUZLMQKnnZr2oUyx3NYmuTyTsjslmDnjdtl4HPNNWmZbEtWj7VpjA7eOu78d9SXnIS1OlupLXna6+vT1H97qFTgKITyb80X/Y+f8LijXq/tMgJyqc1yt7TYt+V3YfVKabrOFj9/hDpq+ZLw/FVy97zE+x8gplHsCkTZ6/agGo70OzUjHbmnB0sODOYMbQV2/mMsVmync0Ym0Wr73PGHDPRFaVyDFWgVIpSGTQ2BnA66pOdviY89gF2H9D6YewRdxGUiknA+PUgDjniuGriHJ4ZtNGEuhfE9hOqVU2wSqo+j6cymPqgdOpSVho3zATktRpdCdhICNI5lFnqi1vUI83gdo0rDMttQ1MqBrflu1ymKCrfUvCH8RC3PaQZZujKkc2W6MrRjALHVzRbP0ha2E3sPJAksj6uKHeHzM9oxmcmmJt7QrcUk0x41/7GxVbUjlvXsI6/322PqDfyVoPRHleo4zkcHEfqpXibu1MSnSdv9Qe+EKLftLJBm/yFE4mz3ueUUqe7S48Z2GCqIE29NeSHDjtv8EbTDLRQdmfiF5hlaHXRUYF6oETOo0jzTGj1JZVThEyiPl3R0UYj10U1McUX44BmZDFLyPKGSblkki95Y3oGe2CBCl1JwUQKDt0ox+emk37ojQ9JuKcvoy2c0lUgO24w8xo9r+nrqqMUvrS0+s7Wkh1W/Om7T/DJi3eY15Ybsw1uHG5QVYYsc4TcRz8vOUlBxrFzUnThvWiex+Oiv23flOrYDmA1OIWuKzqB0Uk72mqhscw0TSlJUZ9HX9B0PuEK4LDmE0LPL4yvPQw3W3+79Rffi63fh7148HG4PYZ6SR0sBk+ppS25DhajPAufofFsmRmfym+yZaY8VdzjB5sX+c7+Ja7vbbI4jM5y0pDWETQzoc2itjFCihOc7tH30hYV94uXfA4QWtYmnyH3Wf+aPsAv6VsIXSfyw6zVnD7FpLMZrPEsltItbbRvqZrfK7ja77BOwDfQPq7XX7kYN2gVMJGCMxWSpHg774mh+qA4Pzzixt4G+/OSC+Pjlf2dpoe8bu9VU/r9WP88/DhoUbf07OHBk0hyV+T3yfXRKLwXpoBU8Ooz8VnS/O1jrCsagbJ/7YVBLPRzcsqglO/8uxBiB2V3TMJa0+Wv2t8Q/REBwRGfR6duHg+OjtrRCaW1z6XbNMlozc9Z6lG8NxUstxX12LD1mnSAt0wegZYZrP3udOp0RyPeJmzTMcZja99PN0SaP1t91dUbpV9EvPIaXR6g+5LenwZ81LFu8wHtWtM/hkQzHuJStxqznDaGT+bM1jYIEvP39cjb1x+DdeLH0RIYnei7LaZNatdBYg4dg/AzesBfHexz9Ynf4L8ZfYl/ffZT3HrtrAASx0hTQRyfUgQn+VbJF0dgui18UGy9MufOz+YsrlboOic/NpgK7r65w4XvR3/Je6EzTUwzfVPdPdFaYh9Irpzv388POBF9av/7BOSJ3UrGvfyG/ncHSbF3TVsq+V4dnavIDUhH+eh6ID9U1NfH/M7dFzn6fMHfOfcNfnH4JsXjP8V/JNblqyLTXxC67zRvChDq2jGslawrCXA1vmasO1pogGVoMFrFBitHHzCFDrxN+Q0XPD7mXlNOYz2/0S/wOHH8D3j/gzS7NoYenn+5D5jV248LPq7T5sR77TnvgcwDJdfEK9+e376GfdKHTudV/wh8qY/t0SyNlzq49lrV1Ex0zi+UR3w6+22+NH6DXzv3eb62eZX6ekFxT6SHdIxxWvpup2JcExsltciAKB2EQSPFvUZ1bHeZ5ew3a77xV68wuBVxgOjD6aViaCv8U3NSEbjyvcaNnuwKIHTd21uEMsdnhpBbfGakoCn0/CwgSZKo4DscJVlizkgW5SKg57+l2F4pwS2VQtVdo0lwwjgTjEI1AVdowuYEPZvj54uOvtt76ejWCozBTwbc/akhn/25V/j58cvccgPOmTkz7yjjtcoUkbHqg8+1PjKKkakGj16hkwBaaln5O1buqAYXdFvlrdaS8m38EJ1ScXRDpzGTaPJWqMQilSJSjRAaCKNCHHzv0QsHswqjFPbQUN4x2PmI413D7IKmmQR87iH3XVe0Y9XTCArfqK5JQAWh9dahe+7jaIiBvcp9e8ghdsQEH/vGFRHkln0rHVhpV0rjWUcaNGj1rL1XeKfxTpJmTSPn2FpPlvkVp1ucZwHFAeZVhjWO3Doy7amjfrTRolHdeM2szphXGbPjgrCfk+8ain0YTCVwcRktdaE3EFAQO6HbxEY6bTq04CKNitTdoQVmjBa64YzwyItBV9HUr3RS+LCqL/1Rm+mB0etaGwmENHjppEa6WQScdhhEJzjPG5Z1CV6zd2/C9eGM5zbvcPPJCXU9xs7EKfU5rYZ0slbqLF0TtfpPJdoKOB3IWH+tt5/UNZwqnNvXe/vvJ37XO15Oc85bZoR0G0A3dvrjSiEaEnHetLNYiOKl6sfOpaPt+ImcnzpzvS0AcF6hCZS3FYO7NT43vLhxk4lZsDwj6HewaiUS7ajA0mQfH7UkvdXMkB8o8j2oNmFxURzK8R8POPfNOdnNA8KwYP/5Cbd+ueHvPf0dztqj9vo7NJlyJ851f3z0n+vYLa0jMO3jidaq67E3EdHPlAMNWlWPLX13srSADcgZa81E14z0nItmxkv5TY6GObc3Jtxstrhdb3DQDDhsSuYuYxH/+aAojaAUjTc0QSQMDpYle8scH5RoLIbQds0naYTzeYVRvmWnAEn0WeXa11b1dmS+TJo76xSNiRLyNE2evqZ8nzmhe3/1eSoyeJglSvfTLFvTPXTvcTyclvRM+0jvCe2UW3k/0w21t9TBMDRLfNBs2ykznzPUFblqqILljDnGKE+panbMMTmeQrkTXdAmUrytA9CPAjh7wuOpBdTrPPyhLQRWLnUqqFSdIyvgLF1M11IPR2AuJftSx0rUJ1wFqrSAaCCazEAocjg4ijqzvTGckpVGEzKNyxVhy2LmGjtzLS222hwxv1QKZtIEZmcMix2Ny7vfhpYOCKpakrUbA6qtQmj9k8/UeLyF2WVPM8mx+0vpMIoMOxiNqhuGtyrmZ0tmTwyY3N7vOhn6TBnWUI8UxWGQ37ueeVSKeiNncTYTn88H8qOMfN9ip3OwFm9it5U+CaCtTw8n1sX1r9MnN2qB6tAVGKx/PhWmKTgFZXv8gnDlIZt6spkA0tWmZX5GU4+U0HlHuuCkaRm00HnXG140olORjo8alA6R34hBqQAHgiyYCnStaIvhFFSblqBhezLj7ED4HN/YO0N2EBmZaulibiYZvjDUE0sz0LhMjkU70cBWLmCWgfygJtudyUF7L0FvYhXIYqgVA+KgNRglXXex4htr0IsG/eYmO0/PuDcbsb8YMJ2WeKckACde2jRGtTrZoQcyiIySIoyHWUiOIx0YDXKvGN1JJmVG6FMLIx1rRaRRjdXZ3qjWJ1zvjH4gKN17vTum047z4T/lQXbilgi91x6DcMIHzb4b8kS2Bwgg7VBkvW6XmS+43Uw4cCOezu/w3NZN/vrWt3j90gX+0bs/zbU7W/ilgUYJOJ3GRvKt+8+BkMXelCZ0NLXpugXwA0n0qAZCTgvGSeFxHI/vcWpp2cnglIsSl6AU/4XVzlYQuat5bcmMx0SK5aq2bUHqad9HBH37NN794vAEamoVhCipN0DdKZIJy9q0x6KUAOTOOApnsFZOctJWrLyJfmuDMZ7amRXWMng0UPphmtIPstP0pP+s2HuhGz8VkEzxrZfuEpySvJJLeR8BfVAKB+il6sWpqYO5o+dWLr2u2m5nKXjVsl8VTkw30pHcA8S8dG4rLVc9JMBYCStH6ogEqMeaxbbo7Q5uCzh39LTm8GnP8I4hm/pekUm3bshpC+33t+BbTJb6rFs7JGnaK3jrnfMVf6RbStrn7SlPxxC6OUb71ak3FaqjhGVEqZiE1R0TR8prALLmJ5az9cRUur79/Z82VE7rlo7FKOuU8H8276AfjQnQ59ExKEnxXB8sSoBTgeWlDP5P577Bv7/9Ff6z83+ZX/veZ2neLCnvSP4m2JQPU20+KtUBKSUFjX5ziL19SHnjIl/6G9/m96YvYBcGswiMXzNsfWtXmI6WS9Rk0n24XzysY2I/sQUk8z1/TKnV9+5jbZFKr+BjHXwAkmSp5F1TcWGaa3zAZdKg1bIfKkR6w3c3oI4a602hyI4lNtn8nuGbx89x7fObXHzugM/me5TGYR4L7skfra3TeANt0UShshO5hnW94fVOQUMH1Fht2uKn9NksFmLUwVGok9qsfTaB9nEt5/GgY3rYb3xcbB0oP+0YRYP+pCWa5z6O0H4+VmtoRNZNpN38fff1sGN8HM/dnzXrF3akx3Rv1cHhcFy2BX93fId/a/hr/KPt5/mv3/4S1185R3nTkB9FJlUt4KxuxH8wbSE7gEb5IPhLE18KXZ4r5BnD1/f43qsXuTgXBreQxXVoT3zzp8/voppxLzZWqzm9NIdnFnd2A2/1ipyD8qGTYFkvYFI9+m7VA6f76wt08i46+Z8h5rEAD9o58dGiPjUN2Crik1phFg1+XGB2ttBHx1FuMRZjFQWqLPBbY/Zf2uToqqcJhu8vL3E522NT38AR2ny4DuFEMcoHZY9O3x0k4T/US0AAFE3XKW2UpwoGF1IVi+eiPeD1+TkGec1RqphX4pkGHR0KpfBWEkvaxQVVIRXatrsgwSXHQ0fqE90mMIPWBK07PTcAFxi9M2Vwq6OZW24ZlpuGakNRj6EZefxAqutQAbLkgK79+LZCUq16pYG2wzo9bzu+U3DfB2+d6vYRdVDa/bgIHTpDYoFETg9No1fohCD0wGj5jqRXjfFtxWjVGKYup64trtHU8wx1bMj3DPkh5MeBIjlxMTkdfy6mDm3xQBvMKCVVu7Z3fAlMTMkom24u0JnHWoc1jnP5ESO9CjCsmz/F5e9TlAxVzpJ6hSrlozSdbtBTdEH6yYYOfNTEq9t2yWaqYWO44I6XCS/MDG/vbnOmnPKpC3f49iyHmzl21jnefWuBXdqcNW1zeVAtVVff2g6RUwO03n57gXdbKKLoUTKqlcSjbNv93VYGpf22w19JoYfvXk77aDfXMdmpiNTdEhAEA9mxwx4L4HzwrOLJUpJ4VvmW7r7cDUIHu+l5qrjHsSsJWzUqiJZ8+3ONxpV6NXHa82BUrSj2NPkeuBIWFxyhdIxeyTn/9RnZnWMIgeW5Ibd/NvCrn/0WV8u7cu0j3cXM5wI6PmDUJn3phNJ7Oj0YF1H72tsTHbmmB64OzOlJucfN0hxQqIwz2rDdo4Bchl3qcJeaQBUCdYAa0YSr0Y+UXFtPUlXo9v50KPI1kNjQAc9yfKv7P6n5sxJTnmrZ2vP7E0uebg9zotcpkk6zdZc6UUa9l330LdFQvRdb/x2JripRVJUqW6F3ehAlzKMECR8WpcwPZf35pj+v/jC2ksiIycTIcBGUasGHBE63VfWwql/jZfXwmcGY7tyFEFCud7Ubh146/KRE35XkKDr05Bfia3Ef2gVcLtXmygVspL5bnh2w2NQMdh2LMxnHVzTLnd4xFhm6DuSHQo+tyoJqM6cZSmDhrQKj0YsaO1XYZ47Zf3bM+T+Yxq5T2kpWgPz2MeXlnIOrltHrI/SezNfSPS6JYD/McaUiu+5WgwOjW31HX+i2MMtbRbVhaAYDJnvjLkHm4jr5MOfkYcP4tPERg5eVJO/6Z9L6mfzsx9hMFWhKRTXR1OOCxbaiHiuaIdgZbadzM1K4AqqNgCvFV1eNQlcpQS6BaDpnetk5RLpW6Cb6KmbdCYLFjgDgL23sYrVjYGp+cPs8tpYxq47n5Llhea6kGVjKuxWDWS3jK3a7tYUgdYNqnNB0pc5nkPEDLXNHsJqkQRiMxkyX7X0WjEZVDdvfg9EvV4yLJbcPx7hjCyZQK4taasyyXg2ItYlgt5FKbYh/p7bXtd+ekL5exzaRgly4MBXBGGEAsBqfGyk0SWB0nnQ8k2a0arui+0WK6TxDz7ciPe9pKMKJMf9AALn32g9jq53SHz3ccM+N2TIzNJ4MKVKrfIFDs/AZu36MVoHz9pAz9piJFnmDo2bA0/kd/tNn/znXn97md/c/xR+8e5X5fhlbPZFuaWhjVOWlY1nFjmqhRZUYUDm5nj7zqKDwxkOBXMuWRUn+ScF5aEFepTsQp8/mleJX6OLYkD7Tu7Dea6wRCarGxWLqHi2vDwofqbSrRh4nowVHx4OVc6njMTkEVHbekFuHQ7qs+3rOIagWeA5Kjju9H2L1/CqA3YHIjZf0qA8K1Ric1xybnNI2NFlFaerW11w4y9Uzu3zvlSeYj6cUpmm7shPFt0/nrQdSp8LAxqei1Ucfq0qFleVm3Tde17WWYzl924/CTjDKnQYg9jpaH6W7u72OyLj0qXDPxwJl2zVSSE6KWAQYY9YWOCZKGxAZK9IiRDfnqs7hC1pJQwWsdCG3x2WSNm1Ae6EIl1hVt/N9otMWkFaorKcXDYO7nsE9F6msFdWG4hMvXefum1fYfN2Lu1bFnE7squx8iuQP9TqpA5HBkBgbqxW67lNzB23+qB/Ir76XwOS+RFgLasc1RPkQKbxBxS4gNMJQEr8/3QI6agp7i8xpuutmDjGBtTImlDDH9ReTFTkB6IoGQicd0D0/5Xd/bI9s9wOg1t/rdzR+wpb8Xy/8Pn9/59/wD174OX7tlc+gXh2RHyiyIxkbPpOusGwe8LFbOt2nxe2as3/qOP7lgv/gF36X/+rsl/DvDnnqv1+ibtyWrjFrTwINicGgzFeKOVKHW0tdCR2d90rRx6pmdCutlDqhYdVXD0hiId6X7bBs/Tx5SPmrVkM+pO+L+7PErunufvI2sgfZwOia4vDgAv/r6/8Bf/Uz3+P/cOHLPJN1HaWPZQz9I7JW8/zU1x9u/bG8/ol18O1+n33Yaz/u9sP8pkKtZ9ZW95vOrOm99n6+/8/ieX8c7UH3Vb+wAOCMHvAfbb7Or7zwPX7z6U/yX77753n725co72jsVKGtsGioKLUpEgYKVUjReFBALtto56EJKOcJgxx9e4/83jnufg42XofjJwcUB45y13NtusmisZR7R4TRkLBYSsyd9KRTrG80IRaf66YHSEdZOeU65+FkUXbPBwlI00jfQmK6FerxdlsdYYO4ziiNMEc3XdFi+m6fGymu3yqBc+37ybdLhY5BwcarileOnuFbT1/hs8++y89uv8EXh69z0R5xTjeUKrQSiqmw4IMq5HhkULpUFTW21y1d44PGKBc7omVw5bGFpw6WKhiaYKjdKQMvgUA9ap+OOjhWRSeHOfrl+K5SMy347YVOgUAInR5P1P/RlSQbB3c85a7o0rhC4QpFPdJUkwhSD6MucibadMF6Ok2uNQuqQwL7lrYPSMVtP0nTd9QDHaDdjp3eYFNdwB58bBXR8uEu3umC/qYxVMtMOqxrLd9dacxMkx1q8grKCuy8O2e6XgMO6cDBsHbM6xqsKx0QirZbuvutq3RumVonL340S/CzACJONFAIJ3QsPgordU2mmxOgVfqlPnVH04HQEM+Fku5pHzRXN3e5nZ9BVTLQF3cG/AlX+Oyl63zqqZu8rC/A9UK0FE4JBlc6jmNOPOF8IVIdS2GBvJaScWn707pF0uttZXPofa7daG1yPeXYunt7dbsUGLYx7sq4kiBTtH+FurvYa2JHs6I8blDLmupsyeCndil0Te0tVjuUChwsS4ojx/x8zieeuA7A3XrMeGtGUMPu9/oAmaWaRFA60s4HHbuvloryjiabQjWB+ZMNqlZMvptz+X84EoCjyKkub3DtF3P+p3/xKzxR7LVB7swXDPUSQ6AOQre2DkzfL/Gje9zmpa6pg8GjBJjub4cAhQ5NoZtT9/W4W7/qcaDyjooH397n7TyAxzykW8StneOkRZ8YFvqaPen19cG7roPzXgHcR9XRedTPvx9b/87Tjuk9zaPvI0f5MM2j9arXj+19WEvlG5MeafKPl1v50CYXVX8CV0QfilWQCqRTh9B1QDcNZlbTjHO0tUIX3Ae3Q1xgUrdQ0yXP2y4c76knFu0kmXh0xbLcCVTnGsyhUBw141wolfcFcAl5JjSOPoi2sAuE3KKmCwa3AwsVOHoazn4jw2cCqKkgutOA6AXdazh+Imf29Abjg2nn8GsBnZuRdEDn+xW9jKY82A5gNFVou2xBiibbTlIt/lSw6pSAY/Ueex8qJiuFX+ufXykKSwnjxzyvFBTUI0U1EdDZDULs0BK/utoSDVtXxOS4li6YEEE2SZ53fo0KXSc0SGJuxa81xOLX7vy5TOFz2K8G5NpxZWMfY3xHyeg8ZlpRBgn0aHxHDy9csELhFSnAfJmJ3nJLvYpoUid/u/Ho41WddUA0yonJ+MZR7jlePzrDmXLKrYMJqtaEEPAK7Exj5s1JoDkBJUaDzrrkar/jJ1mKrVJy00baysxG0EMAaVcYgtVC+2XX9TxZAaNXivrW/cL42gdq78P9P+2YVNzX41DEUaoag8co6V4+cgOOfAnAkRtwIdvHEChVLaW6QbPvh4xioXipa57Pb/DkuXu8NL7O7+89w7tHW+weDmkWGWGZFgSkuCNS3CXGMhXjVW9icaSCoLq/2xg0xZ+JWeh9Wr8LsQ9Yp9e6outVrePTgMfRcMnu4ZDN8QKlPc6vAslJazjTp3sk/SY351f9w76lJS/pTgOxxjy0n02Tr/OaJtMtMO2DYpwtGezMuXU44ZNn7jJvMjLjWDgrslp0+38UexyA4w/THqYpncbFenf9adv3NYJXdyL+iPJRL9p0CT1dSzFdcqlafWfNis+UGFJCBJ3bIhcv3DSpCCm9t95VGaKWX78QPPToIU/Ez7Fz+PiSYXEWBnfBzhuCVjTB0Ayl2KEeQz3UFPuuBbB8yqeRcgeqlyMIbWKy/a4EMsdg/YT/se5rpLUgpHOSzh30bxr5ztD6raeaij6V6e0zrfGpSDADN/RkkZWwL793v6IGdZ/7JrlPBEXbN9FnbPyzfbs9tmaU4oqt+Y/O/B4vDq/zTy5+ge+8+gTFtZxiH8wiYBbQlOJ82KUM1EYbss0Rk+/e49v/5pPkv+D4qSff5Y/f/RTFzal0i4HohGqz6jOFICBDYjFwnqT5vlJ82gOc22r1+NC/h9u5glPu6WSqDybEffTnAL0KVqfceAIW2oaRnt66zGe0flnQ0uiR3cn4rdeeY2SX/N3tr/KFwj0Q9PvYPraP7WP7KM1iuGwL/vb4Na4+c4d/svVFvvz6p6neHFHeU9iZQteBJjKIZXOJc/xQoXMlOZzg8SqqkSuNshoGJfmBwv25Q46YEN7UaCfz6PXdTZobQ7Znr8lB9LqsT1ivyG/l5XX9n/66ACtA86m7XV8z1uTzglIdu0aP8QYV5UfjXmSD7s9uB92jqQLZVKGvBfKDnO/f+gTfe/oitz894W9sfotJdo9MBZahocB+4Pnb9yRCukLbLXwhLehWB0sWuYUTdTdA7Q1N1Elukxc9h7et1oyVqRIwxzcjbYlK1EWC0XRdoV4cVrWSlF1NxqggyaDQ6m8ozDx2JrSJFkm2uEJRjRTNSFOPoBlp0eixAZ8LUN1R+/YC9NC72C0a2CXIVkzFAa16mqPpI6gukPaxCyM5MqFLLnkvWki+0QLUOwVLjT005AuFXorToStx1iTgIurusXIt0iGdrkPY21avPoZTnqNDm+RQvQp5rULsFFU/VDXF49Ef3Zn8ppiMQPUqzHX7mguGTLnILBAilbci3XoOzSdHd/jaxlXUrUIC1qBZ3BvwxuAMn9i6x5ULe7zjdtDv5pJ47VsEltM8E0CqZ5ITapVUBDVdUNh/DGr177Z7pf0XeuA0J8Z0+u4VO2Xct8Fpf0JUvW3Xgm5vY9IfATjyg4pmlMkYrh0hM0wvWi5tHPLq7Dxb2ZzGG6z27B6OuOhg/1nNL23eYuZzlt4yKipUGKAqmaxVBBTqYbzHbJdk0zMtlbjHUI9hcdmhGsXGK4aLfxAB6TyjOj/ixs+VvPCXXuN8foghsAgZdTDt2BCtwJMoQT/h0nYAB9/qjSe67sQgUAfT7sfEooY6CDXg0luGie/0x8hSRXT/OXRUSEnT573YCRaGR1gzH6TT88FUTX7wKNGD9ILkmB/ab/3BgwVrlvTC+vYxCP0B2Hp2LOkY9qrqW+c0VfWTEnLiW7SV/Gkd6DvSaWx53QHTVYMvBqgsIzgnr/cvbwhgDT52D+kmFvlZ6erRQbpjlRN9m+UO1BNPub1gOR+hGk+zKbrN+nAuXUXWEIwEGkl3OOQWfa9icM9zWFnchYZ6o0A7j8s0+Ax7PG/B5OywRtc5h09axt8xLetL8nlcaUS/eFp3v6N3joNKFMVxwQpdhw6NExkZFX1Tf9otdTIJ/l5NKdqk8robpDTid4UODD3BOvM+QLwP046eMjRDcIX4pmauMDU0JSzPhhZoTtdIV0LPDbTnut0mJuZUg4DRGrwRv6UZiM9r5moloa48NEN5f95k5Ln4aM5p2sbMWrQG9XTZAseEQChywsjiypiwjInEdoxo+U5de3TlUc5JYWzVCAVXkArt9n6tG1pNZ+8xC8f1vU0+ffU23wW5dh5wCrNU6EXTBrmqF3Su6KXHYz21tUvrLmhWSsBoq1sw2me67Yz2WUebKv+6Lpx0LdrEqFp7jQ5A+GHtvuwA72knJ5+fFp9/VFbqGq08U19QB5HDqIJlQ8/JrBP5C4SZaeEzpiGnCoaJnlOHLpS/aI4xgzfZNDNG55f83uGn+KO7V7h9dwO/sFAj18sEsL6NVYONAFECn3WgL13V74JumRnaYRRWLpLqx7cPMAUrOtBKhRVQ8VFB1zKvOTwcsKgto16X82mfP63TOMXdre7wKWBoK6Pbe6/tCE/gqNd4D43TVFqksnwedemCxqqGJ7YPeOPmWRZOdLD1I3b49o99PXa4n93vHPTf/7Ns6fffr7s8dURLgZMUI/voswjbBniju7g4dUr3wGOgo52Or7UuBDHhqHtg0zqoG7uedR3lHhR4q1sASgWi5IN83huFK2Uunl4JuGGgGikGRvSww8jix46b+xPIAtVEUe4hBXa9Dp5WSgvawnZ01Ka1SZOXFYCtlYHRq6/3i+IE+JLPr/zN6lzbrcW9JG5/g5hv8FkE1UJv+7jfoMAVijDwGONxTrfNHqeO7QRYxzlt3dqfEXqfT5I49/nMx/bhWtKaHqqMZzLFZfsGny3f4de3f4p/feXTXHvzLMO3LNkx2IXkulyAoDRGB9ykJDva46l/ueSbi+epx4FLf+BRN263cYyK+aDW0jgvs8hywwq9voyh3phdW+xSc8V6LBF/UNxGse58rGu8QzfOu0Kx1c/5rAOiE+icnqOECSIVdQQrhZgukznPHeZ8494Vni3vcNl+h0tmVTLrY/vYPraP7XGxlNc4owf8fLng4rnf5nPjd/jHZ3+al1+/xOCtjPxA8kVKQz0QJjXdBAGZDbhcS3ODQqRyncUA2y837P2cx31iyuJ4jFkK/ufeHnHm20BdrVY89uf0FMubFIOvTewPAZ0fZitgNKf83W635keFVrAvsr1wYs1JJv5YQDuFXXhhr26EuW55MOS/m/8Ub37yDP/epT/gS+V1hqprHvsg5R8eGZSe+QKtAqWqyLSLoItrg2EXVBS/7rpEAZqgcT4u6smRVcmx7zm96Z9ec4aVah3QZAoILoiGBrS6QJi1E5MoGEnvqzagkAAkYBcOpo0MUqMoSyMgdS5JmGagqIeKeqJphrA84wk2SABvPTSp+jwec6V7v6UH8PWP3ks0377cAtlyjnTmCMm59qAyT3Cx+9kpONboWpEthJLFziR4MrV0Euk6HVJoKan63T3rlabJ+sml1aRTRwWTqu9S8YC3vU72XiKjBbzjl0l3cHcN18Go06zfJS37MEjrr0B1j4MlLWkXTDvmgbYoA7p7gRUt1u79T5a32NyccXSrkG4FAnqm2TsYsVEu+PTWbbQKvH10geLuGtDUS6z1H9skoY9OvQsrCdm2U7q/qx5YfaKDOvS26Vn7Mx4lu7e2TaLy7x97+j0ytuS5bgL6uEIXRhYbH/CFZXFOcXc24ubRhL/x5PeoIg9bdZQTDMyerXhmcKcFcodZTVAKU7k2eRuMwZWKoBMrguhTDu4oyruBo6uwvNSAh83vZFz4SgSkM4svMm7/dMkn/tobvLhxg2NXUlpJHGa6YqSX7LshM18AUOj6RKf0uq40IMU9SU9QuY7KO3ZLy8lL86yS5KW3bNr5Qy7A42mnBUAueDwnNejfb1FLXzZgHbA5DSTtb+ODO7HNw47hhwW2HzY3winAU/+9nlbLB3VM78c0auU4fxKpwXxmOnqdsOrLAC0tjwpIp07yg1rAWGbI1S6a+HcPdBLAUnRzgtUoIj1R9IGUixpkXnynNnGSdjUewvFUuqCtlXU2VWQahZotcMUmfmuCurmI2/R+i/MEa2iGRpIgSomEQvTfZpdK6pFieMdRTTTNSAr9sqzBzWSNWm5ZsmlALSqwlmajJBhZOpuBxRUK7Sx6Y8TwZgXXBpjLC+qNDDNXuFKTHfdkDJTCHi7YeKvk3mcM0xfOMfrmdcKohMYTSsty04iG8XRByKzoZ4cgOsAe/Nh25y+ArkPn4YeovRhYoZHuvr/7cx2oS4mmh3VOt9u1L6xt4InjpdteN+vZLrpk12OQWF3uhHjOxIf1Flz0K1UjIHL/vPR/W7+A0ltiAV5oCziVAxu7ptNs0/o+yZcJ3WuVM+Sm4XuHFynzmuUAARCcQx1O6euM+ws7HH56k2qc9P2S3y1+iqmEKswshO7ezGsBoxuHqpvuful35KdAVikwhuywwn9/DFfTj1SxKCEwuI0AC17+hRAgeLkXi3z1fkx/e9/tH1paeunq1hIXWY3PTFugG4zCFbEzut9xE2OA9SLClW59Hj7E1Apqc8r2Dxuia9+38rm+r9rb90c/6h9sLijqULDwGXWwTMycDT1nxxyTR3alPFYB7YchR37ARM/JlGPqC3Yie9k0xuQjveT5/CafP3eNWztjfuPi5/i11z7D4riQ4RdjVV00BK8IUwsDF+NTiUO7Gu+A0gJg+6B6clqdNnTarm/rz+EkWNTfQivpNlZqVWN5veO132ZmtHSF2tyxXGSUWbNGv907nlO+U75XtKRTd6V0S8t762B0oh5PXbchKNB+pcY2UYzLfgS0ttrTBM2V0T5vmW1uHG7w7M5dZk1OZlaL907TmH4Qbff9wOX1fbyXzurHoQt7/XelsZHeWyN3ObG91kJB36f4Tp/XCpa1wszpgNH08djw0AwNeLBz1+os69p3c2vqSlS6zSnhQjvfBiM+nXK+m4eMaP6pxgvNY6Bl8xPgS3wn0VLuxpTyQYryYgHUYlsz+ew99g9GzM8P2HxT/L75GUO2MWV5b8BwrmgGkgMylcfUHqeNpN5CkN8C6Mik43LdzvO6pi1UD6Y7N0nXGWK+qgl4LfJ7iRa8BdViYZSK8izeKlkr403ljWopw5NePcTnUSpOBaHc9plamc+9EbmPo6cDZtjQ1Oa+94G4a/35hFM7tINCAMqUvwOCT80i8PAkx8f2YdrM1xTK8jO54Zmdr/E3J3/KP7vwBf7p2c8x/96EwW1FNg00A4XKwVeQjSw2sxSv3ubq7oSQW8ztfUIsOlTrIEL090JmCUXW0uoDtEBzjNfWYyhgpSC4S4T2X2PlHunHg2HtY33a/ARG+7UQWooh6dgs+znclLM1keEmNnv4XApCyTxLZ3h7ucM7gyFDNWdbr0phfGwf28f2sX1Utq7lnrATi+Fp67gyeY0/98wb/PNzn+cfnfsCs+9vUcZ1IBjpq1BB4TKZI5URgBqtCY3IzNldy/gHe9z8wVn+2i//MV/m09SHY4KG4Q048ye7BOehrlF53q0Za/H2abrRcDLnd9o2j2r94sD11+U4Tv/cimzEiTc5ERT1WQHz/YD+Ts63d5/hP/tCwb935Sv8yugNzigL6A9Ug/2RQekjX1IqCfaMqqmCjTTe4tRmyrHwGUaFtmPaqIBLQawOKxVfbWVmf7FXseY/bqdbaF8W3n5M1p6wVAmgIOg1DbXQ7St1NCi/1iUEopmmxaHXlccuHGoWIpAXpGsgkw4CNF3XQGapxgqXK1wpC309CoQsgrWWqFfdHffK8wCq0r0uZiUJuUUu1R21/MsPQ9vlrGOFRxsAxf2E9riE8lCSbtJdonwgMYP5ROVCl5QLsbO2f23aAC09N72KO5M6WeNvtKL1FxIY3UtOeP/gSu2HWZ9e9nGg7O5bEdF/HzSZWusIDKIX3a9sT12tqbgDwKOZ6AV/+cqr/LO3fwYz1zIOPLi7Ba/PLqCfCVwcHdJ8SnM9nKfY05LIzWknkqBkfKzcV4qoFQcUCm8CdkFbgJFofHyauBOA7TsAW8Zhl0D0VoLPpG2eNLa86QXP/YLT5Byn92KACXQaVmkcxmroYKX6WTfy2uimgAf1yJIf1PjMUG/keAOL3zzH0adqiqcb6RiuLfZuxt3PKr70/A+og5GiATSFaVgCPtcyb+SZaCyMaSlCzb5h8pbcR/vPQ7PhoFFc+DeanT+5Jx1TxtDsjLj2C0PO/dJ1fmb7bcZmgQ+61Y/OgH03XClUAAGhfdBRh9x340c7ctXgkO7nTDXo+DeIrnGha7Ig8+zS27gvub9y3bQFQX8W7DRNnvT6+9nXw/b7KN/9fr/zw/r8ez3G9X0+CvD9sH08iv2kd0brRdM5kSE89GyoNcdRKODiEw8rRYkthdwprxHnW6slUZo6dVJVZw/cVpGOWJ7c5xrXUrznJgX2lpYuzX4nQExCmsoTojSIagK6krWxmuQCFC4Dfkuhlwoz1cxmBdlcxe5MRbEf6Ykzixva6N8o6rHGZQAZelli5jX54YDFOc3xRcPGO3IsaulWAbk6UOzV4A2HT1oGb00k6TTI8Llhsa0Y3vGiCZwSqfE3KS+gvrfdNUlJ2aCQLtMISksBGKvFfj1nX8X3WoA0+cHrt2Had3sNeXAudA18k/bDU7ZTEFsSH7CzH42l8xB0aJPNqTNNV6otrEzFdd52f3cMRXGMVbS/KRXcKd+BkvLG6jlNRZXKwcFswJnBjHcPNiVWiUACQGgaKdJQGs5ts7wwYjkRVqUERisnwLRQSDrM0mMWIjGiljHx6ZIDfsqF0b37LW679Qq8/hfPcGHziDf3BqilRmVeCjaqevXziUZ8HfBuf2sszk2d1EYTMoMvLCEzEpdFNgNvVVuQm+KJrkOP1q88YQ8bUu/DdX/PQHVvm/69EOLzUzG9U4Lxj8puNZsMdcVEzxnpIya6KzI89CUjvcSjqYJhEcSxz5XDB80oysSYxN6kl2yZGZu65pyxTPQhf3PjT/l3fuZrfGt5hX+1+yK///KzBKfw0wxUQE9qlAkCUEfARmsBo40JaO3xXoqjfTArMlLrgPR7mWJCUO2y5lOHWuiKtpMEen/7dXNesT2ZcW9/zLzKmAwW7WfXdat1zEv0LQHdbWex19ADtjs96R5td1D4titTrwKkXsV/GSEoaq85M5hROcvELrm8c8jbN3fYHY4obX0qCP0o9qDPpNjgtO7q97O/P0um26lSYRPBVEggrMTILWaZmilaSY80qcSPtfMu9113u+aIEOeb3nyt0hq/St0bjOqovkPaTuTpghKJixe27/GtRSH5p0yDDdSTeO8utQDLRropTRWPz8ed9fJjLfBM73el5oN+4aJWK/F8Rxcc78u+P0m3BqfnIf3s0IFsgfZW69h9CC0dsfim/e+kPR/LLYXbrMn06bFM+7HUIQ0rDaz937tuJ7bj9LnnY/twLcXjmTIMyQFYhpqhyvhMXvOZc1/jf7nz+/zfrv4Sv/XOc0x/sMnwpiI7DtQjxfGljOxwhHllD+7tovMMX9VtUZ+yVvRB0wWvGynaG5W40sr93rOwHh+180B83qfYhpU4i95jO/57rAMtC03L0tS7D3XvMz0/LOVn+0ykKb+3mq/twGg39LDRMNpY8MT4gEv5QZvD/LhL+mP72D62x9X689OAnOOw5IVc88KZb/Hvbv4h/+9P/Dn+u7c+y8Er2wxuaOwMvBGHQTdgF7EY3kQWYQfVpQ2Kt3fZeAWmv5jzf/6pX+P/vvFXuPnGGT7x3zp47R0AWS/g1AAnZDGePs1FeK+d0w/pgu7+Xl+bWH1dr32ux9jT4rEROwRZN1zWrUviC3e5ktG7iutHV/i/fPJv8b3P/jH/i51/w9M2J1PZj15TGqT788iXbUDse95cWtBcUCut3I03beCWACjl1pJGquenE09siCeirS5Vq4lVmxzdnvZLNElMhUiFTLfAa8VKSjjuWzuPWgbplkSCDm818We2CQ1TeXymMY3HxGPKjqOWh1FtRVoH4kZKv9RpoFVLLwgxmVV1iS3tiIk23wHVfVqoZCm+Sd3LKUhi9bwqT1u9mtjdWqptTj7vnJ+T9Hw+gnYrtN2m93w9WdUL+oAIyHaA/KMAIn0Q2uNx8UfW7wNM+TAsgYun/Q2R0p5GaL5PSX6ZSFVXB8NTxS7lpSnLd8eYhcIVQTqvDg1v3d4hu+g4O5iy/+QR8zAhO9AtaLuOh69YnHRCAIzsF1R7DybHNXXQ+4CA4pkA0haphE6BpfK0UWXSf0yBfAuQp+9OlztWYaf31PqJOOWY09jyBspdyRq0NLQKmqFGBZi84zl6IcROfAHLlYfFcws+Nb5NoWuGuuLc6IiDesDbXOoAmCzHx0VEOcjuWcrbCjwszgWarQa10Gy8atj6/iEsKwEhioy7nxvgvnDEi9s3yZRj08w5cINTx0F/vLQd9L2CHhkrptUdT8A1CBjtVm4soe7WKqBx1AmaDP5kYcRPqD0KC8NHYafRlH+U9qM4hgd9xwdZXfc4m64erPV+snpxdX5UsAo616BOA7d6Sc2kYxuUagG2xNCSvkPoG+O+fbcPScKuXpcQAjQNduZoRpYszwSsg15ntRId3lnTArVBK8y8pt4saUrIph3gZ2egnGYxyhkdSnGgy4VuG+cIgwJXaszCR6kV0QFebhjyQ4s9XGKnEGaG2SXF6JZsq2onAFzoOsXtcYWpSxbnFIsnxpTXjmnGGT7TLLcV268IEK7q+JsiUBi0xucal6vVAqqYGA25pSlNm1gOca1T8TEQ1+cUF/QvbecOnbz8D8p9pqRUerq+n9M+q7p1+nGoXTJz1fmMSsBo6TaWc+fyk77muknRQ+xQp7fNKQBk66PQbROMfO9ikTFvMg4OhgxGlVB/d+KxJMaA5aUJs3NWiteWAbsMmArs3GPmAkbr2qFqh1pKZ7Sqe/f+A5C60KEkqMaz8fqcH7xxib/4wqtcG29RW4s2nvzId538/f2mpOrKCYpxjzUCRBsjxbm5lfgm13gj4zsBF95KUJril5Y2dS15un5ufyTW/57TXMgeIL1yf9xn+/798H503j9o2zFTMtUwiiw7rYxLnJyTNMwiZJSqZqSXrW840QuOfMlECxi7CBmHvmQRNLu+IQPOmSn3/IAXi2u8eOkav7D9Mv/q3gu8ebDD/uEQ5zS+MqBDlF8CpT1aCyCtdZCkTGI4ShjUfcDo07qk103HQvQAHRgcbUUPuncB1/M1zisy41EqkBc1dW2oMkNm/Int2++5j6k+gBt1qel9Jqwc1+oxrnzepy5qqBqDC4rSNhSmYa8asFNOuTWYcOdoxLNn79F4vdIJfT/a8fT6/brIT7M+ML1+RR53EPrkGHpvx7t+XU61lkUjSFd1nPt1A/ggRczRtPMiT9IrPEuMNomJZr17coXlL61NyT+LnZKJQScYFdcvAaOFZlK1/p9Kms+xS9gHxXiw5KAYx8Sioh5Lp76uBIj2GZHiN+6n1xGeOjFdv0s8zfcxf9X+RlJcrjqQuc0/hbZDs/WTYs6j9ZlUj80lScL47jyuyPuFLreQjrffbS0F8rDcDuhB053ccDK7kAps2t/1kGnpMet9+Im2+8WHGo2N95gnsKU9//tzv8Vf3vgB//Dcl/jD156meKOkvAOUgeMnB2ze3UQ5Bz4IbXc0ZYzELyZ16yhCkeNGuTAlqDS+1apvcVq+U/XvLVgFEFY/expwvZJP7e2vLfzof7fqXms1pXs52WBjI1EGLg+x2SMQRg3ZuGJnY8bF0RGfGt/mSn6PM3r5E8lk9rF9bB/bj4+tSwKOVYEnUAfHltb8+1tf4zODd/n/nf8cv/fWMyzeGFHuKrIj8dNcLlid0eLjmKWnGRoK5zj/lV1+9/nP4H5e8ezmPW41ZymvHQqjmHOyVsBqIXkI0nWdZysSDw+ysM7qvG5rPmT7ufV990Hn+LnQ4p1pm44VsS1mUj0gOq0davVvb1WL+yVc0WexceB2zq+/8RIAf2/rq7yUn2QTfb/2yKC0D5oaqL3FKM9QV/9/9v7s+5Isu+/DPvuciLjDb8yxqrKquqqr0RPQQAPNAQJIkRQpWaJkP8gPfvGT/xc/evndXsvLL172smRZgy1KoiiaIEGRokiAABsN9IDu6u6asnL8TXeKOGf74QxxIu79ZWZ1N7qyq+9eK/N3742IE9OJE/vs7/5+d6hrGr1lp4KnZ4w6DE6FTk3Iro5OZpLpSdmPg7mCFPXR4n9blPQieJCzKmMj2XHW5J8WQVhProlVWnb4IyMgMajFBfjaN2ZQhNzXEdBLcsgSapjQKbYIJvYZpJG93CTWQX+DTaeBhRoZ3Jn9XU4O4t8szVIEF9My00X2dwzehfpI8XgL6e5wvkVnLZyadMyUzk9yfISepZIdJI3r6jBgPjr2lGXeqsWhPxEIUQLS/iWbMfh4Qy26gxUrGAw1Lj8rXmUg1WzwrLTmXv2Ev/XW9/jvN19B3wvyOdIJdg3yZwf86dMJX//VH/LOrUf80HiuvndC89SA7+95CfyWGZk++tWSHiq0B6Ul9M0kEeTr8JuN9cgTg984DdLxQr63ximeqDjgerlNLWQf8h5jcPmZ41YR8M+ynAbqB1f4SR0UDHyY+K9ODPUlHP3gCjMJw9jK1RijtLc6/so7P+R2fUEtjgOz5vXqCf9UvjjYj0wa2qMqXj9h8jicw+XnlO7UYa4sN74p3Po3F9iH52GicjTl8a8dcva7K/5XX/hT7tYXTE37QsGdsq50UFzt+4FXw0KDzHcKNKb6gW4UkTdorh+91orWW9ZafWZA6ZcNvH0R+6Qg+E8Lmv+k1+UX8dr+optvoptVZMAPTHU3wyZZfl+nAV7RXTz5UqI3rWcCUIykoKT0WZqp/ElkOWttqM4m6GoVBvHUP2LNNVWlOluyeesYJg16tdg+D4IUZTu1oU6tgDlsuLpXo0ZorhymU6qVMnkK3VzwE8vhBx43DbLf1dNFOPb5BF8Lk8uObmoxXXiXuQa6qUXamslTZf3I0h55NoeGg4suAPZGBvUTzdkCszlmdVM5f7umeVKzvlHTzgRfQ/14hVoTQERrQi1rkaDQcWjpprE2kdMc3LSb4Hd1c1u8E2MOVnJTRoHe4fW65n6PQOadAOv40S0xStgCcdPulZcDgKsWDIDONPnxNWHiOKFPvpQIVndFwLvwf9P1kZHfkYGD5C+PtpEW7Bqu1kHBRp80bGoHNX0iRxwvxdRsTqrQDzahdmG18gGcXnbYRWRGdz7UjS4kvzXJfJXUq10y2ylQ33mq8xW3f/8G775+k6/cu8/GWb7zrTeYfxhAR7yi63XoCGmiXCaqGNOD0U3I4PaNDaz/xmRWtFZEEFqGMo+Fz9/LVI7mHuly/mzmols2bvdFVOdzX0jJF8Wx78K3tFz3U7Zb1eXgu0MC6xmYSpsBaYNnKm0GrQEaPCvxzKVjXTz8ByasVYtwYhyGBWu1zE3Hvzf/Dl9qPuKjOyf84/Mv80/f/zzLVY3rwniWgen8T0MNNt/XUE4X9UVrSAO5DvOu2H7yozOI+gKdy8Rjc97QVI62tazbGms2GCFLeXsVnDf5+/iY+vZCLWjoX706Wq+X7t7dTtme9wZV4elixnyywanhxmTBneNLPnh0wqPlnBvTFyu9cx2j+rq6ybteO+Pl17X1Msy03Q6J5dJ+UubqFvs+g6Ra+FC9kpdK8IFyHEmH20rJfEkxpdTNYtk4acvgVdhPkvKm85nQIGHngxsXpLvDg+jrUFLObOCP33udX3n1AY8ObsTkcqE9UHwnTC+E5lxZ3xC6qVBfprhUaEM6QAqlEiEnuKuEd4Ovo2phJExkwoIO3wsDKeMMWIf3XfKZJJ1zii1p4U8NHJR+7DYJlNdwzdK7302E9anQ3dnQNA7vTB+aGCRspA9x3NmZnbS7j5TL82j1MjwUv6Tm1NPF8hUVNpf3Mgi1GF6xFX979hFfeP3v8Y9ufJn/9JVv8OF37zD7MDiX81dPqJ+eo4sl0jToZhNABmuH6lR1hU5runkV4r8GxA8T9ICt2PSYvVz29T7Gymgb6X8fxWMHwHMiNZWx21xWhaGKZa393wr8xEPtMVPHdNpyMN1wc7bg3sEZr0+f8tXZB7xdP+QojoG/LMnie9vb3n7xLCXOtOpiiVeDQZhIiLPdq4Sb9j5faf57fvPoK/wXt77O9398h+a9huljwa4i4U+igrMYZAL+5ABzdsXbf++Af/X4a/gG3vijDh6f9Tv3PpR122E6sT3Y/By3dKecd2l2uHw3S5pe3SavR++/Doil15SBSMlQpVJy+Z4xwdfqCbDgpmG9q7Mp//LR5/jS9CNu2+/xmp0/+5xe0F4YlG7VUse/Hgn1TuknkSsaprLJbL8EwHTe4CJgtcWULhiUvUPaTwjCRGAYbQsXK240rtGo5aRC8wUubSB/Aj14Hf+q6R3QJOOUHQobnPnB67qUzyxueHIyxIfC4U56oC2DxtoXYU9g8yCDLgeNi9/LiVTRgcrAXF6eQEDtv49Z0fkaJEZKkam3JRFjhscy/m1sadKepau3V3lhs/lggxzsywBOl6zX8txMBKlrcYWCgCm2M9QM60rX4virR9/n6Vsz/tnVr1A/CvUs6wvh9M8d/Bvh3xzd47c//y5fuPmQb3/esH73iOaxKfonYdI8SPQgBxf7ey9BtsIP77tvwoDjK3Ad2KY/PrsZJQfFCWdm/adJufST+LLvlf1pnBhR9sOcwRPXqa5AlmuY1Zi1y2zp9lA4et9hf/wx88NbeISrtqGpOl75wn1+4/h95mbDsVlyZJe8393g/upo6x6uT2yuEb85JtQ5vdFintTc/kPh5h88wVwuAmvvZMbD3zjg8e9s+He/9B0O7RorgaF85ub5PqdEhTHDeYtZrz4HHVu1ecxM/cZcgx4khrURH1QoTOhTc7veuf7LbmOg9GcxIfp5T6p+USZxL9txfhp1rn/eprWJ2YrPcUSvszLAAX3Qk9KfGdWZUTCtjyCS7Z1V7WtTl+2pFcQINDWsViRFiizRLYJYi1yuEH+En0+Ri6seUEuS4N5jVi22sfgmvBe6qWVzGOSO60uHdJ564RE1LKxQXwjNeYefmOAbLdcggps3qAhuYnsGuEbgdyJsbEO1VJrzwFzqZsQa1uE80VijUSRIHsf3y/K2sL41pZsKy9uG5hzMug0vuCRzbEKbvrG5dExOpIxdNiRtedQE0NQTlpkCAJWtQFV/f9L5jG1cM26n7XJ/0vu2OMbBfhOA+xN2w5+l5bIi0bQqTkkIAHRkTefjTnMI169X+hLih6emcf3cbgFgE9sxnWIf1SxerbErQ7eqqTw9gJD6tbWIC8kU1TpIdVfLWDd67ZBlGxIaSmA4KRekflVayaQrnmeMCbKSTcXp91b84E/ucvsv/4DLzYTD71uqp8uYPDG+oBEMjwkVJCmxJNNtTawRbaISVASfo1xXDnwW9aK3Aqrp+64b+lP2qR148dYKQ2bR7mcngcyDhhLQsWvHsmObT9E2aoM6jgg1jkZclut+6uYcmA03q0u8Gh65Ga9XT7jShpVWNDga8Tzw85z42irMJcyfGoGpeFoMq1gi5sv1Ob81ueK3pz/mP7pxm//rh3+d7zy6w+X5DBEfgWiPMR4rikZgWssE8OK+iOhPDBRCvlUhSeQF27HG0zmL80JlPSLQthZtQvzAGh/iFM9pb8yqTV0is6SvOaYEsvfrACo5CV41ANOXmymts9gDpasNd+cXfPDwlA8/PuWVty/Y+D4c8yw57xdlN/80LOiXhUHt/XDcfBH2/bNs6x7nwS3UawZ6cgOSaz9DGCulSErLvlfyTUwsTRXZK2I0JI2L9KB2ikll5nGcJ6c2CoYx9H6ddIqkYKGEpK3pQ6X7/pzl7Rpt+jIjWoOuLM0Z1AtlcyxReURIko1qBKyCi8cW422phvMYSAvJ59uPUACre58yx+QGcap4DilbLwZIS0W/ZCl+l8MGHVCFBpXgG/kK1ifC6o5ipy7fxzEgPbZr69s/9+VDPzDt7VMzKybfqyTpnYDqWixVTNT9Ug1vHP8J3/jSu/zje1/h//Hdv8Ti35wwezihfnwLfvxhYL1VFVJXwWdSDSzqqkLrCj+t8ZOgkJSex0HiWjkfg53x0sEYsXMZOc6VQOcBKQhINaHL31PMLpCcNBBJLGgd4lg68UjtMY2jqR3TpmU+2XBjuuTm5IpXJhfcri95pT7jTnXOO9Vj7lilFoNFPpNz8L3tbW+fLTMR3CgZunPT0KpjKhVfqg2vHH+Lf3v+Hf7hq1/lP3vtN7n/J3c5+LGJY3nEGH1QVG5vzpmcXTH5wUPefHqEn1bU7z1Cl6uQwNQ0qHOYJpSQGCeWa217H2EU69tyS64DndP3UrmmbCNhgkUp5LC/UVtFXKQEpctkqQE7uli3THDSYt2guANaKb4COwnqtD/a3OJ7zcecmgWHMh3fpk9sLwxKT0zLwk3wCHPTBilaCbWsNlEbeqUNVtYZbJtKS2U8rrWBRawheGc3Qcs9AVC7su/zBSrqeZiW4DxHp59KetY1gOsnA2q3I3sJdC5TZHOMqrypMSNVE9PI9450kisyEbBO6+dgTnkeBWBuWqjQwfmmzIPeA5dt54Xt70GGvLhMJbhPdF52bMfI1xg4TkUmRJ9R0f9NAatQPzp23F2+SzwWv7ZoHUCzSzPhSTtnpUFioRa72/FRn2XaPB4rwko7vCpGJAPTXpX2JUpZLcFHWxxXLbFOsJrMkjaifS3hqCzglSzJ91dPf8Cf3HmV5aNT7EaYPFYOfnSFfXzJ03fu8aPbN7g7v+DXXvmIP+osbnWAXe14hsaDYPzu6zCRVCdgYhZyJMWpjTI/NXSHipkJEhMnUlA+TThDJrNkADlnQjMaFFMguAwIxySK0ikPG/b9LB3z/OMUEDDhees82MBum320QhdL7h71DJN50/LVk/sc2hVT2fBqdYZD+OH6No+XcxpTjheO9jC05RvFHXjMYYue1xx/33DjT84wV8sASB9NuXh7zpOve77+znt8fvaQR+0BtTgmpmXl68CGV4NVzyjmvtPaQnvdITlxIQHSKcGnNCM+M/LLWuZODNOtSPUvhu0nQb+c9kt538c+Ar2/kS35N8U2ff2XbSmeJLmT2zMB5NWuWC8FSpVhuZME6MWagjpt4NKCL+TtEshbWViusCuPO55Q3YfM1BQJtdk6h3lySb1q0WmN1pbV7UPcRKgfK/V5UHioFg7pFO4amjPFtJ7lSU290FDTzQYgulo43CTIC7s6XJN6oWwODa4W6oVi19A8CeO4XbYRBYhoSckuj/7L5hQuXw8o6PqWcuubGgA9CAxTW4CIEhK46kVQtUlSnQDV0qEm1MEuWQ2Dd+/YB2MYhA0Lh1+3fGHlme/2rIpji+/daPUdx/Fpmi9K4wDUl2SmcwaZI3AsvvdLS2wp+RXG9duVEuuU/kfx3GXWdNzm+M+Fs7eDOo0uLNVSMKsNAxqkDdLwEw9m40Pd6EWLWXcBtOhcL2efQGiRIPuVjgVyf9Qyw8+Uvwtqw8Wxlxu+8J867v+rz1OtlHs/vgjPRpTSx9rQz5N8vhG0rqCp8bMaP6l6VnQtuMZkIDqURpF+DjIOho4tzRPi513Lf5a2VRa7eO7C963HZpjvseN4xvOVlwWITmbwWAy1dJkZnUq+XPgZt6pLXrdnPPJznroDDswaI54pbfYDDXAgG1ZaYVEm8To4FKdKLXBAx0YNKzUcGMUi3LEVv2se8/ob/zXmTeW/ufh1/sHHX+FHD2+wWdfUTYc3iveSGdPOSciPMqFms3Mmf4frAcSgdhfneRrYy8b4wS0zohjrngtCatw+gM/hcZhMWi6fzrmynpuHi1BSLLZXsqWN9AOrj8CyQdg4Q1N1dM7mOVvrbAFkQtfZeN4hH6RkkwcQGtQLnYtApwkAf9taHl/NMaKcTpb8ymsf850/fYN3n97k1aMLAKrI5HYjQNaguWSa8ybXt9716I2Xpe/pfISejT5mp6e/Py0A/DJYmTAw/p6qOJlIEtBKAqhaCE71ktxxTmvT3DapS8RrR7jfOclayWBvtkh40Bi3Kn08b00oI+cCOB5KzwUQ2biouqHksiimhcMPO6Di8a/NMfOOdj7B1YKvFHtpmT30QYlPezW0vp4gcbDXwbtRLXjpVc+Ikua+itfJp/E1+IS9YmD//i2vXfiQ2kq7i+D92A+OvlbIPOyXa/Fu9DVsjoTFa0r7Sqglrc9g02f3L/63U85966W3dWD9C+dlcaB+CWzX/HD8WwCn+98mMaQ9kYrfnrR8sfqX/Du/9S1+/0tf5v/29b/C4z+4zav/0zGTDy6RdZiL6HyCO5zQHgZVpFDGZOjvZGn61NfHcdrSd0rrF7XgM9FnVPKwZKCF51Oz7HaIz4E2iloN2a42/BOr2MpjrGdSO5qq43CyYVa1HDUrprbjZnPFoV1zo77iyKy4WV1ybFbctJccScuR8UxFODINE3mRiNXe9ra3vb0clhKTxhaY1GHJbXvAXDa8dfJN/u7hN/mf3n6b/+Of/R3Ov3/CwfuGahHiXqaFq3sN1cUx5rs/xjw9w9QNul7j1+s8zzbzeZh3p/iQ8+ikxt0+wk2rnfE9YEiOHbw3ZNulSDjl4Le0Tb9yKqUyJJFKbnsnudQMlw/IqOU6Et5JCYhOGImvwR16OGw5OVrw2vyMu/U5c7POasY/rb04U9pXsTauZypdZvUFZmB/oVq1AZjJjEBBnelfztG/y0CV06F0ClDK9AxMglOfHf0cwIpB0zwhEEqQbJcNAlsw1HLK+5OtwEVgfI+c7dExhh0Mv/fn26/StyN9FKbssONA4vjvDtu5nRl+7zurDGW5Exg96swZkDbXHEvMoM876SQ4VF1gjHovLF3DKm58nXRtkuYpa0lbJO8r1ZJOAZZP2xI4mMBEG+n/5XiSnwXtk2My0xUfFAjiLDjJeP/V137EP/j4kNmf1Bz/cIN9fAnrDW/+N494d/Yqk7/ZcVSv+Rtv/zn/sP0S/HCKaQUSJlnM965jYukU7EbCpJV+IqoWuiNPdXNFe1VTLRrMRnLftZtedq8EkMNzPXTCUxA5qyJQBIzj40rRbRCiZFjf146/vwyZq5MKs+4wrcPNpyBQf3QGN0/5y7d+xMS0HDVrGtNxWi+oxXFqF0yl5f3uBpduwsZZmiRf6z0Yw/pE8I1HZw6pFL+2HH+n4t4/fIQs1/iTAwAuPn/Ah3/D81tf+wFfP3mfVi236ivWWnHppkxNy8I3GZQu72tpOWlBPK2v8NKzp2vThUBiTPppsXl9h2SmtVOTg1MT02LUMDcb5uYXkym9t719lk2rHuAMfz/h9olxSXImpQiO0jvCQqwDEwOmTRico3BDYEgWjONxpqWKYFczqqcX6CrQVEMQV1DnQ33mzlGfb1jfnFDPp7DehBHKu55tAMjVEukcmzdvsLgTWMfNpceer9DKYK3h/HMNi1cCKCxecY0wf9AFkG0WMi7ri5blq+FztVYmTzvUCE9/pcalhFUX5KC1Atl0ASBM1zsyV3XS4Jt0vZSLtwW7DPJNRz+4CqvHWr0hcza9mAK7u7pyRXJVSFY0G49OLOLBLhVTJFOFbcPnvqbis78nG782niu3HffjJkSJT4ZyogwnMy+DNRfFOz9OgJKfkBI2BwB1XDaYwBXzCKT4nIPf5CTJ/IwUAH8CvKdPlKeXE/S0AydMHgOdG/QBjMFuQpDfrrrgiyw2oa+la1tXMalByIB0cvpKX3/wPA/HBDWCViEBz88q7KLl9Fvn+GmFOVsExj/00vlNjUyn6MEMnda4eYOf2MCKjrXQUyJtqhNdBkPLpMDMoPtJuspfdPfahRNct3xXH3lGu7nfvARWS8gmabXi1Cx46udcuFmoNS2buE4oSVNLx8JPmEqb/c0LrVhpFcpnIbTx2bCAjfPMWqAWT6vwbjfh7WrDodTMjeXIrJkI/MfH/5q/fvBt/uDVz/N7j7/Inz++zbqtYq1kM0gaUAVrFWMczpkITod5fapFPQQGg5w1CRyNLOzEsu4i4CoSADFr/ICh7PJjGdbxmtSzwu9WFNs4ui6UEEtS1Il97VVCzd3imDZdGLwr65k1LYtNTdfZDMCnY0u1ogMIL/hYCzoKc4Tj8j2jWokgb9w3HrrOcLFusMZzY7Lg1ltPePTDG9z5ylXP6qaXHZe47cb3x2yNpx6xqZ03mRDgExBfrCMwOGcXr0W6JiUYvUsK/NMwM5JaHzPVdzHXXySRIa3nnQksZKe4iQkyveqjwp0GxYzoTyVgOpWQUonDudPhGJOGetWYgB0BZZFcJm4QHyqyB6SLfa0EwV3vQ6bTXd8Q6oXl6P2OH713jLm55vL1kLznG8/8A8v00Yb2yPaEieJSpWPfHcwMB2Ucgelt+jFSvPYhNklAtULejlz6ThwZCE9tEn8XU27Xx9USGJ/8lwQQIoJrhM2RsLoD7d0N1bTL6gVlosEgeWlX3qsS4ojZERj5SjtAbokX7yUIO+3tBc1gmBvL15o19+wf8lu//i7/3ed+nf/yq7+BefcG84+EaqE57pVsAEjDYJ6V1AUHoHIZJzW7P/ta++C/1R6AtuF3ao9UHlMpVd3RVB5rPJV1TOsOK8qk6qiNY15tqIxnZtsY71oyNS0ndsmhXWXf4NismJs1B7JhKo6pOCYC00jwqaUaMMz3tre97e2zZoEEKbwpDjv7AW/++n/Cf/nGN/jvf/AVlj84ZPaRUF8Gwujq7pyD+0f4x0/Qq2WPC6YSXNYEJyCRGMLkBzetcBPTxwK2sqpHzOZoGawuY3kwVNhIyws/LaxT/p7a67cdrzOY7+ffZKjGIclni6o0DfiJ4ivFzxSdOOqDlpOjJW+dPOZzsye8Wp1xIAUL4qe0F68pjQxkZWvIjE+HwRJkbBNAZyLLz4gGJy8FQXOQKdw0E7M1w8s6TJqLBOawbnLqo/M6kDYiTg7oJYE0OrsJnO4bYiszYWfiYyHNElgW/UpJwjG3l/6O2yk7RflznrmS/+bj39UJB22NOurIdmVGDMDl/FeG2RGlU1UwoAfSztU1QHzcNkyctZ9gFZMD74WLbkKrQ6mFsRlMZkiHrP4gFW9UaXGYeB9eBkA6WervHsmuXZDu7kIwQNKz0TGEq0PwpPUmB5k2amm14jcO3+N7n7/N5T+7x+SjK9i06GyCPD7jjX84492D1/nSv/UuM7vhq298xLf8a9Q/nmC7PhCyi22VWEV2FbLBfZRxhNCvq6sweXRzwVYeN3Gh5uCMUFc61tRMwVzSZNJpX9tA+8mmj4zmMKlkAEyLS+v2bYXgtOSsUbsOwLPWFdIFUEQ2LZuTI5pzhfWGp3/tc3xp+i942B2xcZa3Dx7x+ckDTu2CU7vg3E/5cXsTh+Fq1XDc+p7RfXyAmxUThpXl9JsVr/zz83DNpw1qheVrB3z0byvvfOkjPnfwhEs3YW77AGFKxGm9DWOhegyKQXMCQmmpTrRH8GoxMXFhYgLjxeJZa02rdkvC20dAOve7+Hdu1zSfkZrSe9vbZ8l6gHD3e2sMIAJDNZcou0gMjCbgKNUghFgCxEZZ3kbAh1qBad0UeAk1AkcBl8I/6OY1VVUB2wku2raINZjLFdyaovNpAMlM3Ng5mEzC7y7ISbbz4GJWV0pz1sGmRagx6y6zZZtzl69Bfb4JfkRT4yc2+1umVerzjubRiuUbBwGAraE7EMxGIxMUdFIjq5idlehrsT61a/pb0B0qbqrMPjKYqzWZ3pavi4Ax+IkNkseSArMBKEnMdV8b7Fpp6P2yBKwCA1+sBKHLQNYYONvKkn0esBa38Tb4Stn/Ku7xy0YLFQcpEVKgl+pOPsLouqWSN/l6jnzwwWtS0zyAnlE7Ah9FwzMjLtQoNw8aeH2JW1RMzkbvbB9k4M0mMNbMskNa17Pxq1A/PdRxNv38IT23BegADJ7l/oIUywDfGOwa8B5Zd9irVZAHj/Le1BVS1zCb4o/nuIMARnezyPSpoyx3kU2dJp7pmm5NgtNxXO+ib53Lz8x2+avP+P48oHmw3jVTl5cNkG61opYuKO4gXPkJF27GSmvu2HMAnvpZri1dSxfKaIljpRUnEoLXbfy9EUfxSAE9MO0IXaHBc+GVkyqoUh0ZYaXKHSPcsxveqf6Er89+yP904wv800e/wo/OTtl0FW1r8c5gKodIAFqrymOtR9VkIDcxoseWgCQFnDNoAfwmsCg8Qv3vY5ajMZ6cY1rec1Gmsw2bTYXzhrrqAyYeQAXnw/NnI4O5BDI3naXrLJtNhe9CQr228cGxiql8YKrZcL7lMZVgNPH408Clcd/eGVabmivrOarXfOHGQx5+fMyPHt/gC3ceDgBhHwFj6BnUJdBsRDOj2hof4ykMQOb0Pdz/IUg9iezxl0Wu+y/CxmzpreWV4Ose9A1xKPoayEXgsIzbZIJCWjeaaJTCLsf3ctws3/XSl11JAcKU9Jb8uxxkjNv7OkhXm9YwfdQx/dCyrGvaowB2SSdBfabr42Y57pMPMsbdEiunnJ/HmJeKkpO9kiJhPA/j4vtE+ncW0DOn0/Uqzl2NxHOLiz290hrFNSiOtUyoag9gcwqbk1AfNz1P6iEHX0a3uawpvWv5lj3vOfgMPyefNTMIU6mosEwrx4F5yqu3/ke+8Zd+yD/94hf5/fc/z6OPD6meVtgVgdhRWArQB9JN/DH57Vn6lEDASSC0VTQxmuMyqTymDu+KKr4zmqqjqRxWlNo6ZlXLvNrQGMfMthxUa2a2ZWpaDu0qx96n0maFlCZ+PzJLptIxl45GPLXEd7sINaHeNgi1TDCYfG2S/VIqlu1tb3v7pbBaLK3CTCxv2I437JJXb/9jfnX+Af/t3a/xR9/5HJMPaiZPYHXLMrt1jFwtUNfH08VaxFoQkwltofEKnTXBR6kKn29Ljjs1VPhz7MD7iuTA4e8j/C4uy+tKgeftUu0o1GsHah2m9+MS7uKbqIZcK37iofGYiWM6azmYbrg5W3Dv4IwvzB/w+ckD3qwfcWQcNc1Pd6OifaKa0k4NrVgWvsGgTE2LEc/a1xjxzM0mgyRH1oNGiawUoLOpDka8Xq535rUCH53+PH8YBdeCwxomeEK4cLh+chsCkMQgqYJTyq6hoxt/nQ3khEerDurZpexJ23eUNCHJxz/ufGMrOxzbn8O2svP3wTGXQaZxZ82dtnf0M+A82q6U5dYYxPIxs2/72Iub4whM2XQONkxojA0XcdE1WzV2x5ZqBIQ2Eps4IJ+tlnWlDcNQy6djJSDt1GDQPmlDHCuticLd2Bgl3QamfQatIQSmblaXfOPmj/mH89djLUwNAcnphOrxFXf+YMp33rrL6194yp3pJa/cOeP+2W3wBtPKFtOqNDXQnCvTp46ruxXtUXxmNEhnNWegYlhMZ2BChkyqNeUasG0MfEcTFzKqvfRS91IGlkf7JuwqZpiPgsmjAbM5C8Az0wbTulzba3XDMnsUNPPOPm84skvutyesXcXd5oJb1SWnZsGpWfFALV4Na1+xWjZxkh4OrLt5EOTKKwUP8x9W3P2Xl5jzJTqfoMaweOOAh1+reO1XPuLLJx/TecvtySUXbsqln3BcrXBqWEQlidQvzAsAxEmOL6lOhFrU/XZOzUB1ovwt9Z30+RdVuntve/usmz1fhQ9JVhqGyizuOe+ymKGpsc4xEF5/iZEpgiRGcGVyDdn2sOprFtL7JeL1Wkakm5og4b1YDPYv1qBdB7MZcn6F+BP84RR7fhV9HhMkvyuLHkxh0+EOJ6xvWEwH9ZUyuX+ZWcjmYsXhB3PAMnm0wh0kyeI1OIfWVQ4OTx+sqc5XyHKDzidc3rNBTUNgcwSH78FqDt0cNjcmTC9X4TobAWPBebqjCW6u2TfRWmECR+/F6580WCGCeAbfWDbHFd3UZP9nCH72THS1/TLbDn1e18iWf5ck1xMoXdpOmeEXAaVrep+5vLejwPHLAsJBDxrvBJ3jXCDFrKU8r3gdBzWnKbanv25pu1KlpUxydY1w409h8U7LWoX5xyCdi8mtAi74ZmbVImsXfDJrglR2ZULd5jrKvsNu+f1yImr7pIaxYoHagiVWVeCm1E+WyLJjoBV86wbd6Rw3r+lmFjcz+NTXbFEjuvi3NaFN1/Ca/rBzvvET9p0XYvuX648ST5/3DJQs+d2sxWu2L5d/yuYQDmKgeaOWp36OEc+pWXBkVhzIhittmEZJpFD6x7DSilarMMdgWE4oBag3qligFsHEpF+A27blzFtadbTqOZSalW64Uo9Xz23T8DsTx6/Wf8yXJx/yvVuv8s+ffp4//uBeYA13FmM8de1y7eQkY10C0iWgXFcus4BVBazPALSHDBQns0YzOWHMjC3B6ARcOxUq49kAy01Nbfsrkra3RjPg3bOPlc4ZLhcT9IcHTJ8I04dKcxkGKV9BNxO6ubC4p2xOHeagRUwAyCUep1cJYDYEgILwyBobfXsVnDMsNzVXk4bDes2X3/6Qb3/7dc6Pp9ydX+BVOKzXdN7SqaHzhsp4Om/Y+IrWWWobarolZrmqxETXUHapMa5nnxcvlSRhnu6HoZf3bp3N12lXrtwvmpXg/JgRDiBG6WbgpiG5LY/HhsimleE4KWS/BHqgtk/+60HqPOan7STOj9Hh8tiOryRUcigSDFPCd2JmY2BzIIHBbGF9o6JeQPe4ykHF+sLQnPuQMLfRQexIx2N5PJ+Av8fzJ773Cr8hlC7ptxevoYxW9IuMC+9w08V4QU6ADMsg+Tr9+y00VPzVCECnEFBczVeCm8LmVFi+7uCwpaoc6k0Y8n1xnceW/IIxQaVc5Rny3+mi6cvkNO3tWtsCWJVcEtAA96zjnYP3+d3pD/k7J/f4V2+9zb96/Dk+OD9muWzwrQkKjxo2EOvDc50d0TBmGOOxJiQmhelZYDY38d3WWEdjHLV1TG0AnCfGMbMbJqbj0K6Z23UksDjmZs3UtEylDaookeFcR4ZzLZ4azcI7hl79ZBJrQRsqrAhmVGd1DzrvbW97+2W2IOkNc2lY+A2fryz3jr7LN2bv8g9ufI3/z/tf4+Nv3wExzO/MmZ0fI5tNiEW5yJwz0sftvIemDqU9Dxr8xOCa5LgMsY5sBVt6Cw8scbqSDW1G6xdt97Ekej/ODLfZhfOlcl0+leKt+piYVgoTj2kcddMxaToOp2vmdcuNyYIbzYJXJ+e8Vj/l7eYhr9pzbtuWAzH5Gv+09sKg9NxsMjCdwJdWLSYCcimTqyUwqJ0aNlRU4kMtDEnBO8kTNSBLH2lkT+bM/nISQJTsLh387CEU/5LTrJCY08OsVAYd4zrLMkQl85e+s4yDhmXAT0ftawLI86y4ON50eEV7fWcugljjjrzj2Mcs6a1jSNc/TbjKzmyK/ZrieEycVJtR8Cxfb4nSVwJeYr3p2LGtYhpHVXd4L2zc8ztsWU/aqeJwWJEcMPGEgEqrntVLwpZOgHRZEc3ig8w9bQYUU83hXWbEc+FmbNTiMRyZFb999Of851/9K7z2DyRk5aw3UNfIxYKTf+2x61v83v/2V/jaax9ya7bAfPEB9//kLuaseC7iPcvdRsEuoVqF+pgH9zu6c8PqhqGbCd6E+zl9rNSXFWrAdKGdHCi15I6YgsIqSTY2JA64SdjG1yGIU19pL3tk+8xng4bnPilhJFA6Jq4cfhSkMf2sBtXAQprWXL1mePPvPcSfHrG+oTTiqE1HYxx363P+cPEWt6tL7tVPuPIN769P6bzFr2xMlhC0cyxem6A1YGD+5w2v/94V9vEVOmtAhKu3D/nwrwt3fvU+Xzn9mAO7xoiycA1eA5h86SZb9/O6+1yOnT6BzWpoEzAdN0uM6VYsq0gnTG22arESmNi16UJCEMo8Zs/ubW97e7lMlpF1nMp0FOU61JpBkC4DW2n9vCDWM3b9d8VAZJomwFpjLdvE/AnrMvQ/iEFTYiA1ATYGfCPowRR5akPdWolAuEj4rArOUV11bE4aZh+ZwJA2AljoHLJYQ2VZvD6nnYVg4OSJQxYRnG87xHlm71/RPK0xi5b2dILdKObsKhzuas3kvoREpPUGuVzAdMLijVusb5T1tMO7qZsJ6zuOq1drJverwJZOrNLKsrzbBCe86uuzmYXl4MNwbyQlBogEFqoqflKxvGXppjJMBqC/hoP77If/IBxfexAlKEf344Wlkseuzi7Xp/CBd4HSsGMi9GlacudDzC8HrkvfeAw2jwPqogzL/KTLmfweHS4DAuMrs9tiUwZmjzyrf37CwQaaR2fEorChZpQqZt1illE6u6nRicVP65AdHRnRpXLBTpmt0g/fUWMqrZvKoAQZrSbIeXezXKNTG0M3q2gPTJY39ZVkZs/2XEC29pN3/Sygtvj6XND2Ocu31CCet/6OPn/tMehonRLzKD+P3KOtPvIp24WbBXAZz0YtTQxWN+JY+AkYuPBTLJqB5yOz4sJPOTJhbG3V0KplKl0sDwRzCa8Nm2U7g3rTVVxei+eHneGNymBFOIpBbIdiRVjohhMz5d+fn/FXJh/zHx/9Md999Qb/1ZNv8D9++DarTfBRAyhMBH5NeG5iAL+0JJ+dtsn1naN1RS1l1UJ+2wfZ8GnTZoZ02D6C8UoGpE9mK+aTDU+vZghQWxfWFcUVoKzzJr9SH/z4Bkffqbj3py3zdx8iq02fBLJah/IUVQWTBp3UdDcPePKlGedfgM0rHVL78Fp3AolhLYQxpxPUxbiCCwn4rcD5/IjvH94OAaAHlo/OXuW9V28GoKwND3GYX4O4kHBsNqEGnllLVuUQD6alV6DSACSaTflO0sH64hWN8ss2EgTqpPIRFev4j34GHfuntJRsMK59PbYEOG8nLkiWhycB05DX8xV0U0PdeezahblsbTCtzwl8Qj+GDcZuiD5UBKcj8NwnAw7jSfmdn4FqQnKhglYGVwGNbo11vjaoCUB1NxOqZRjnu6nQzcJ9744UuxKas1BmKzyMpepfbDT6TBkYzzGp8FAJ4A2Dc86J5sT5vh1dgwJAFkeO4akR1GsRyyO/GweSyTaUyksgd0gGCwo47SGsT4XVHQ9HLbbyPSCdjj2NO+P3/fPeMxLjARGYToklO52lZwDbe3v5LJUJrMXiVDgxUwxCh+OOhX9//jF/e/YRH9345zzwc76/ucsP17e5dBPWvooJP71aXVKoqMUxMUFKOxFRatMN2MwlwBxKXqbvjgZPLZ6puDytsEJQH43s5olUUTESwGKlymCzLzqhQbBi8rl6NP/2vOuSbA9a721ve/usm1NPh2MiFZ6gxPvlesOXb/5r/pfHf8R/9+av8Z/96Ld475U73HzlNW7+4QS5WKCrNahHJhN0NsHPJ7ijKW4aymR1c5MVXoGsfjOYd+eYTzkvLw5ORsvjbwloTpZktUvcLuElarVX90gM6FLFI8W+bI/P2coxbRyTumVadzTWcdKsmFcbTpslR9WKm9UVh3bFLXvJkV1yZFacmhWnpuNIDHMTFDh+7qD0x5sjILyYk3RtYkx6BKuehW9y3dOFn8Q6qIKkbOEYAFDpbxxEBzlmh4Z/DNjH0Dv7JcMhLCgOchTcQWXbhyyCXrvq7EkEkDVlyiaZQTNqI21fBPxK4LYEwFM9mjzR0GGHHGZBFOdsRn/TsV8TXEwdeiA3lX5PGRNlpzej5cWD0d+n/pjTswWAT5tLntP4+IBgQGqPzXJulqntcn3c5DiNrf/NhKQU0iS0D5IkexmqoKw1PD6lVHNyYDcxuASw0h7EzFn5eDwJaKxCfa94RVsNMtD3fuUBV1+8zeG3Pg4AgQ+MLlmsOPzTR1z9D3f59n9wlzdPn3JndsX9V9fo5YxYwnoY0LUhaAFg10p7YPNzdHDfsTk0rG6Gmai4AFyXAQ5xQbr7uudNYn/Qos8hMdicnqeChZ/W8U1xjFWUYI0y/JOHG7AGaR1aB9BWJzXLVz3LN4/zIXxz+QZzs+HW9Ir3Nzf4xx//Cl+/+T736ic87I5ZRmUHc16h0iKtQ6YTVicGXyvVmeX2NzuqJ4sgx2EMi7cO+PgbhvkXnvLF0wdMbBfq9uHwhPsYmCo909nYcHFa3y9PlhjNK18zjTcig8hKZkx7L7k+oJGQ3ODjBbTxe0gGUrwXFq7hbnOe29zb3vb2cpnW0c1KgHMBSmNMrl2ba9BmCR8pEsR6CengI5kMhGW5SBNke3MphYGPIbm24CC5buAwB8faHTTUlUXXa6gqEpAuNgLQ1mKvNqzuHKLTBmnbwCiyAs4h55f4V2+xPgkMY9NB83STjyUBwGa1wVytoAqSw/XCo1eLEPi/XCBXS9T7IFMM+NNDLl+rBr5TYrqKAicty9tTtKnCO8NKrKUd3m0pwS75OZPHBnu+6e+FBplmtRbxnu6gYnMcSlDsYmDKwIkL/0wXlGVM/E0NuFrCey5e5xC0jcf/IqDYDn+vPJbB9SgB6fHE55q2Pg3LLOb0QxH0zh+ecawDMHrH+lu4hWcAHiSfJjOpLLz2+1eYTYc5X4Za0c6H/jxpskS3TmrcxOKm8fvIJx/MCbJ/LaMJ6ag8EL1vLz4qRsVzUiO4SZ3PNwHQKbGvnORuJ5imA2H4dxywv+46J3B6fC3H6z+nD+fk3sFv168/OLZnrZ/6kO7o5y+wj/LZGyeYfBo2NaE29IWfstKaN+vHuSTLkVkGeW8cTfQNW7U4EVZac1MWtBrqSFuUIxNkPldqmYgLLOnixk2kwojjvvNMRVmpsFbHNK5yoZ65CA/cmmns0wvfhqRzgd+cPOXVO7/HXz36c/5seY9/9fhzfHhxxGZTDWpIhxymyBrWoGqWJK8TiO28CUpL6XuxPoB3AYAiLl9eTAPA6yU813G9BN6i8DTOYZsnhvvNcUiUVukB3LX0Y1AH84+Vt97rmL/7AD78OOx3FZPJjAFrwYcEFUSQuqJ6UHH3vTm3/viY7niSgTVUMa6Nx6JFDeJ0nD0ooJXBzSqgobq6wixbuqNJ9hGCdHIAEMUpdD7MX9qur2/nIwAZ1VJyAoj3/fJUmkLCO1rLUhVeg8oJBGWRcNFfvOP+BVsCpNMT6jREVBLTPa0DvUz5rjaI26Ra2qkWOIR3tG0ElmHc1EqQDeGaS4wFJVA6xaJGcaTyt+RnpXhS7gOVDJjWQC6hlQ/dyiBZO4z74RkMMpHEz+SyLG6q6HGLkwrf2JhobiLTOs7907vGyKDtLGYY+5svAPUBqcMUY6YU73AlA/HB90znzPBdUfhQab6f3mtaSZY9EI2guEA7EzYnwuZU8addAAa9xPIe8V2eG40Nj78nS2zXeNy5rnR6gex6r8V7uQekf/FsDLa26nLMbSI1a21pUV61wi17xdvV92ln32ellpVaHOHdOmgTj0VDbAaNnzUyniMIjlJLz2TOwDNJShtMWJqZzaWVQHNV9Ml0Prtin+WyMeicvo+vxx6M3tve9vZZt1ZdrCsdEkU7HFUcRadSceY3vGmF/93JH/O7X/ku/+9X/jJ//8tf4emXb3Hw/i2mT5L/3s+9kSE4nEh3vd8kffylDBPFhPRB4rhhC2hOyzSxmMcYXlQv1joCzZVH6qDWJDYkAjcRfzOiWOOZNy2V8Uyr8PewXnNgNxzVKw7tmpNqQS2OU7sIyh3ScmxWMaGq48i0zCWobdViqGmwIvla/qzshUHpHy9vYERpTEctnipSG9KL+My47OhbPJXxPSiT/NQuOucpcJb+eQ2Al9OcDZrkDTNImtrwDAIvJVJaSmjnZWPbETwqTZFB50rM7dRumf0wznTQ8ndJmaIMlaaLQ9vqsEWwqmx/sN6uQCP9ejkTV8rf6Duz2bEsPhjlvsLvmveXd6/hfEanHh6e1I5VjFWM9SE73XqOmyVTcQUbetvDT1mAPl+wUGO6VYdDWUfnyqnyMkBwK1+HPp6Yq+Iii8GEDEch1xlO5jTWYpdehrmUaDaRJdFqxV++8yP+/tdfZf6jKabtAjBtwmxNlmvu/rMnvPvaTT78Lc8Xbj7klVtn3P9gSnMueaAsg9Rh/qWY1ofag5VgW8UslekThyh0E8nyDipgiiz7QbbPaHCFcUC07/uukWFQNh5LX08qBmPrJO8fMr6ri8hia10AClRxjUXurXj/b82oz4X2zoZvXtzja0cf8Pb8ET9a3uTBxSGb04or3/C4O6D1lto46ouYRuE8OpuwvhECP0fvwvwH5+G4K0N3OuHRVyv40iXv3HjMcRVYyF6FFoslDPQpczYl4gA5EaFVm8HqBCa7yGKxxaShZDenGtNODU4Mtely/0D8YOhP7TtMzsJt9YWH873tbW8/J/PHswEQhYmyjELPjM7vbunlf0e+QfJrfB2kgpJkdE7WSwl9Itj1btWEXYl44ff+3e+mltraEIiHwBAbbSeLNegh7sYB1eUCVIO8t7WICKtXD4LEtoHmwmOvtmtUy3KNNiHo084N848icG0tmmpVp0Dw4Zzl6wdsTvrjyPLLU5jdV9a/4dmcKt1BTQXIsg3SytOazbGglc/vMDrh4D1FnCNHphPLCfCzms2x7SWxpV8tt1GCcTEWmt+LmTnVb9/f5/578hmfabuCpSP3KcuAD/pM8b30uV8GexE55+Icn1Nx5/o24t8MjOYgelhkIjCd6l2aZYssVuhqFRLiDub4m0e0JzO6uY2JcyZnRafA+4B1O5oDjH2fcg5inI58/uH8IvhTaf6Rtit8+q02+7YH515elnG9q9G1fe6lHrvuWwCybq/zE9hPy85+nuV5jV4/Lv48LfhwwaezKCutaZ1lpTWuEurI6V1pDQqb6F9u1LLwdWZPP/VzAB65Q6bS4thEhpenjQ66iR33g+5mbuNfr+Z8qbnPU3+Dj7oTanE5WXYqLSutedAd0foKK54LN+Vxe8DaVZytp1y+d8zsg1CuIVuaP6RnQwtVBB8SXVPid5rfB5Zn34RtyYNvqY4gPmwb6tj2AC6QE7Pq8xVEkLGvi6v5uRAXAFtzsYIn5+hiEZU/4ns5/W2LGWd6PzmPrjeYJ2c0RkDMECgGUB/k/yC/Q/P71NqgOmIkv2sB6raDlMjmNbch1kAsfxGStaqwz7iftL6W+yjeoaT9eA3nWFh/TC8X8ta5PpE7vXLLJ/W5YHT861Liwmi5xLHUVwGYdpOgPpEA4NSXy9iPRNUan/oGBIC11fw5l1zLgSF6VnIJXqeDIPVnxdcmJzek/aV3Q0q0kxaqZQhmVgtY3VUmBxv8rOXqoGFhlWrSoT+eg1fmH26X9erlxRWTkp4iE2cw9sZ3mK/Dvk0XjtPVQrXuEy7S3D/M9wXThgd+Z1JT6aekWIMDE6+NGqGdmQxIuwOPxHhSFtNWGUh3Z7Zz9s3S81b4D/H3LFgkDANcsd3BuyWB65/+K2JvP6F1UXXRYgesLkNQBwE4ASosHS7HHL1uRxpNVBzJ3wvYGWCXhHZpzwODnfoBsDxe/1nL0m/lOokEdB04vbe97W1vvywWxnwTx1llLjYQHhV+tV7x6u1/zN8++Rb/zRtf559+8Hk+/PEJ9VNDdSW9cmwKGSUwmu2/AwwvAc1x3u5jedsQryMq+KXlSc0PqDzYCDRHsNlaT1WFchGTumNadUyqjoNqQ2M7KvFMbMfMtlRR1WNiOuZmQx3/TiUkLU9Ny1zC31AuIih4pFIR05hUFRKqKiZSb73XrBhadT8zaPqFUYyDakMlntq47OBbCRljadKw9lUAqQ2su1BnOskai+/rzqRgXcl4Fh9AabFBriowBEymX6olO9VlR/hJAhNbmu2FbWV3pqCSEDKzR8GfvM7WPoadMx9rEfzdCoQMglPD/T/TIc4BrGFAa9zeLlB6V8BMje5mZaegnpdBMA7RwJK2ZHkAEY0l8ISq8pzWS+rYlkFYaxm5IILPPjiCxe8bVeJcj1W8oC0Wr8Lbz7gkPw/zxWSmlGz2kbFQfk9mxAeQOgHsBOnvEIjyA5Dyc5PHHP7uAy6/dZPjP1zECxwvhjWYxxd87u/V/Li7yY9+x3H34JKPXlvh1lNMK4OECBPrN9t1kHVDJCcIr08szYXj4P017XFFe2DpJqGOkydO3uKENGVL98A0eRKZB13ICQpl4sMgkyhZGuA9IbEhjkjNGQH48ArGBPDAGLqZ5e1XPuaNd55yf3nEO4ePeNrOeNge8vrkCX989jo3D0I91N97+hWMKGsf+kt9RQaEuluHbE6Uwx8a7vzhJeIC8L169YCHv9Hgv3HBl+8+4JXZORPTsvZ1kI9XxUuQ/0tJN5VxHNp1TsrxMRM/SW3beN8XvqH1tk9AGA0+4xrT6d4lprQpBjuvoc9Mo9S3Z9jn9ra3vb0c5uuosV36HfERz+/sAohO0oekv0lxJQXcI0tybGG5hHIpWwuv+Zy+ZzBM6GYWmhqu4nLntlBBWW+oLx3rW1OqD6sQtI/BDr1xzPK2xUeGzvRxhyw3bJkL0qihJq9Qna9AYu1qYhB/2kDboocnXLxe4ZrimOP7qz0Qjt7reApsbnlWt2qOnixJBdjcvKY9ApJ/AtiF4fDDDmmLSK0EBjoG3KxmfdSXgBkwkOn3XSZ8KX2QW6OvlN+LhT+V4qBpu+f6sKU/CFkJZbDc9MdQRu53+anXJTX+PO15QOkWc3UXcPi88yjBaPprnf96Yi3rkIm8OWmoRahEQA/xsxo3b2iPKtzE9KWFkt9TJCTkQ/Klf98/38n3KZcD+K1yP+l6DNstmfXjZeP1PzHAOm5rvHicQLAD6P6ktgWEj/bxEzOpr9tu15BYPHsvA1P6m8s3OO+mtGo5tGu+u7xLp5aH64OcED6zLWftjI2zbGJmRALk1i7UGr5YTagrx9nljKbpqIzPtYK9F7rO4H1wzt3KQhuB6qXBz3yQid70LEbp0qAFdgOmlfB5HcZ3tVBdKW++55h9eBZKLqj2Uvne92ze+E8Se7dLk3oJ350L43+6HxGUHVgJxHpFE8BqDCISvkfAUKwJAHJkOidGsTRNViiB2A3auJ6N8yxrEWv79mLCVQZ6S8DXK31tDYracwapEloXylwIEhRIynXz+9cEHVdjw/nZMJCImAgqF49rLqnhCfJk/bxy0J9LSWSICeh2cF0lSe9pSJgeHNOnaErPjIbdQ37Jot7ZhspA0juxY10CJ9P7UuhltzX4WS4CxFnGO4LTuZxcPEhvJTwnEJnIsTwK/ba74ikSTmCQTOEboVpGOV4bk6Dq/j0inXLwIbnGuZuAXRrWT6ZQK2++8Yg3Dp/yzsFD3v/8Kd99eoezh69m+fBUt7mMpwGRoRyOs1RJSz5FtfLhPHS43ZgMkhItU6LIIMGyuFEDAgThc2Ii+Rq6OaxvQHvqoPYZbNYSRC4B5JQZVoDRfR+A1IuSPHeW/k4nD0GBYXSPInKdj3Fvv3i2S160FpuJMKXktcUwkZpWHUaGctnPkij9Wcpiv8i2aR2n/loWtFN/LTi+t73tbW+fdUvjX6sOg8RyDh6PxnIJkRgR/ffb1vG75jHv3Pn/8b84/Tf8kze/zB88fpMf3r+Ff9pgVgI+JPkJRckRE5ylPrYHmdhZRWfPaPwHYn1kNSt17TDGh3JHokyilPakCmUiGutoTMfUBoB5ZltmdsPcbpibDRPTRrB5w9S0oaSpdNS4XEoCoMEzEcdUPFYih5SQSDUXGxOs4nuwYEGnd+B1779QcvfZZSNe1F4clLYbKuOirKwOMlNrcRlUq4xnbjZcuglehcZ0mMrnG1VOAHIgIk7gTOehi05sCubEWrUDuUIK+SGJ38sJ5ki+esDyLJkIO67fINirRVvsXj8vk/48dgLCOVA1PLbx+juZDzL6m9vd0dY1oHMJbufsDduvOwAaR5OFELyTvlZiDOgFnfooKVgrWvsgI2BCJmu7qaibjvlkw3G1CjJwBIepLvIqPKExI0odH4iQpahxHh4GjwMUp8pKfS9x+ClbYCBoBgwTMOjVBGd3FG1LrFqH6cFqITOrw7AQJYZMy79z77v8V1//XY6+1SC6DrWlnQMT6h5X959y7/drfnh4G/t1z6u3z/jwrGH6oQ1jS2IpxGCom0A3D4CBWrA+gNNuYkCrUFP6YUt3YFmfJGYxeSLrm36elibwW33U7gicjvt7+Xscr/tgrzB94pHIllMr4fhroTu0HFcbGtPx5sETXp2c8Svz+3x/eYc/u3yNebXhlekFf/jwdVabmi/eegDACqjPNQfH1rcnVAvh9jdXmMUGfzilPW54+OsNV7++4jfufsznDh7nsW18D72GgDKQpbpbEuBs8hzWRTDZqWHla7xKZtiXietJ/t2r7RnZkV2dmNKpfrlTE/uPMDebvJ5/1gC1t73t7dOzFNkblCRJHzQ4uWgv7SgE2c4ETJPG8RBYtOueYdkH/+jZHwnkhhcOpKU23ETQ2QTO+yD3lnWO+mzN+q0D9HAGjzch4G4M61eOaA9CYmG19lQXhUR2aTYEv/0kuKGy6QKI0HVBwtvazO5qb0x7lrQU8cf4ThKneCfozLE+rjnuPH5ahQSkuaWbFsikCvW5oT5f9mAJ0dergl/i5hVuVgCGJXgsQ0Ar3ctSxccjg+8ZNB4HYl8QlC4ty2gW+87v38K/zsvKdl4Sv+mT2icGpPOGcfUCiM6/F+Cqt7A5jkzoeYXakBTrJoKrZSizPTqG8rfSpy7ZY+OEvLHPNGhn/Hd8r8enOGhj+zo9F6S+DuC9Zp3nArh6TRvlKjsOaSyP/yJ23X5eKNmjBKQ/fUya/+L/9LcGdWtNp30iqUsSbv3vYV4W1k/AGQonXfjtaOVDTXIp1ots4cwubtcDOelcWsITEkHLUhMexHtoA2gsSeJeFW1bWK/RNiYbGxPkrkfnKHFM18j4zbLSiXXqfQCCiz6WVTrSXHzE8pXEMk6KZmXOflX1gO74WBLgnQDmZt6fbwESS3oHqvZgcQKA029dF9owNkhfiyBVlRnI4VyrfJwBPPdQjXx27/I+0vaBWVvsM5+A6Y/La5+MJjK8b8nG32N7gckd9hfa8QN8/WWwEj8sLQHS4zrSg20TAJkt9hNR1JvBeyEnobkkq20i45eemJDaHblXAwnqtCftazeHeWzRrzUA1+nZTAmIyffTymSQ2kep68QoTu8TNwtzejyYhcWuhI8PD0OMDuHN2RPO5lMu168GMD3WGPRWsEVcLDN/kjukqf54eBaCwppGJbPABBffq52ZTVAtyPGzMkZUxq3ovyeQPwHfahJbPazsGqE98sjUZZZ04TSP7vH4no97wfCx7psoTj4B3Nd1pZfgHbG3n8yeBSZfV4M5bfOiDLCfFQP5ee18EhnuPSt6b3vb2y+zpTFwkHgkhvHonsb7UNbBca9S7tgH/HrzMQ9uTPizN17jO6vXeHdxi/vLIy43DZ2zVNZtJT3aqKSaSsxMbEgOrqLKdGV8Vp2e2I6Z2QRpbdNiUOZ2zVS6IKNtWmrpaMRlVdSptFmZt45KvUfGD0pG1GKwSFYDcTELz0q9U8njme+R51zjCvsze9e8MCjdhkgb1vTytAmY9iL5BoRauZJBmjoyqSXS3tvDFD0JPmC1AnEe6dLEVzHFZFjrkGnsa5PZCWIKGe0isDS2PvAXA767gjPjgJ/IdnsJNHtOUG8rqJTOMzvmPYg3lkd6LjBd7D9nnl4TeMzfRwDzAHAeB8jS/iTJoKUIJ5guSj8lUJri2tIHZKUTVAzqNMhUTxwS+8XcbGIORthZGgBczvL2WKpBfZWUueILYLfFU4sO5HNeFgu1fiVKeAsT8aEGzRiELszR15wpmdfhu+GN5gnzv/SQ5T87Zf4nH0Jdh4z+zqGzCbLa0Lz/lLf/v8d87+QWv/0b3+PBrUP048Mh+0RAK7i6B6azTB/7EHhtAvtBVHHTMAG36476osNsDKYLcmK+EtoDg5tKnlCWwfVdbJ+03ywb1sd5tvphn7ihSCc055FF0dRhUuw6fN1w+Zrlq7NzrCitt3ywPuWr8w/p1PLtp3f5W69+F6/CP/r21/CHHZvTJxjxrFxNvSgm306588ct1dM1iNAd1Dz6tQlXv7Hi3t2nfO7gMTPb0nnDxlcYlKpIyFmH2Xkc3/oIjikiHIlJnaS2O2/ydxtfJElJIn0PfSIEi5yaKOkdgiOldd4Uz1XPut/b3vb2kll+v/bM5wFojBTv8uE7AEA6j/FgkgxoBFyCTGTaLq6sSuTxDPa9U6XiGl/ENUJ7Y0bzuEZX6zAGj8177IMzzBtzFp8/5eD8Cu06/N0bnL81wU0E6WD6xGEvVn2dy8JSre317SmmVWTd9n6fagBANp7NF17lyRcnGYz1o3eOr2F5q0LPDeZ4w+p2g583ARQRYX1q8VNFY9aqWRpO/txjz9cBBPHaB/Ljv/WJpZsXx1q83zK4XIDUOZip4T2LYwBIk9jTMnr3+aKdF7Vy37Dl/43/XusffoqmY5Bo7HK/gK++3ejwc+pzA0C6AEzFk+tniouss0YQtVnFKQXwSyZuWZd6fGylj59+L+PoW78Xvnp/7kV7BSN7J2g78ONl67ed38fY1A4m9HD5jg76M+5Dz2VGP8tGQMKLtrXFov+U7d7fey98SPcxAbYpcaZ8JrZogNIvNxLAYgiJP2Wb5TZudOOt6X9L7Rsz3CZv64aAa9eBmCApDb0c9bjvpH1YAtibmM0Q5KltZCQnwDhKXGcA1oxfWOnYinMpQWqNgHPZho2sYhO3c34IWifguWzPK6jrfy+XF9LZIRE4DDgapV8D9pUeaI0JwvHYEiCeWM8mJmJhA9A/sBEgnfYrklVB8rIIfA8ukfPhPo2OX2xxbc3LVQIovepSXWkT40/FkJoDgumvFP2jBKR9dBySihuln5R8qqiklYBkX0mMOfVqNaLgMVkiPtWY9jbUOk7AbgaxhVBaxUSuru/rjCc/MJEaTOsRHwBsX4ekpm5ayHlLSKBa3xDsBhb3PCpgVyFGM30oyMdHvHfvkB9NX+FfnLTghVcehuuWS27R+34lJitesZlBnQbHcG26mem3KeJAKvFRi+9K04VxtWQMlYl0JfjubWSVG3CmP2fx0B6CP3IY60N3T11XUyMgJlzooIw+GhcKZcO+Q2mxrqSHc3hweZzrt8nLrsnR3Nsvpu1B273tbW97++zbi471kwjathpKPtRWuWMd71Q/5K/N3uX940Meu0M+7o5Z+xAbW41KZ9rMLE5ljrsAIouPYHL4W0vHVHpms41ExiaWCC2ltA3QiFAjmJFvH9Q9JoPfthKx5PmlH35S+1m29YlmIIn12apl6ep4MAF0dNozpvFByjsB1cb6PigaAz2pzmwHoBa10SHvFOl8BKqDTmGoO6X4xsRgYPCSBwCyDqYYwXyU2rOAGwZsBkBxaUIvu7QjWvjMgN4gQDT8bRys2hWcupYpMVg3OtpSBKzK/ZZM6QT+FYD0gDVdHocZnlvIko3XoU3f2Q4emhDQM62AF7x4dBI08OvaMZtsaKzL9XGT7ZKccZEZDeS60j4DboE57V6idFVTTH49QptqTJsuD0YJKAzJGkFi2anNg1b/N56nmnxv0vP2H7/1x/xf/jd/na/+H+YhcG8EXa2RKx+YABdXNMs17/ynr/Iv3Bf5wlc/4M8/njH7yOZ+lfrY5rWWs6ZGvm2YPvVUS8VNhM2hyeB0e1hRLR31eYvpPL4yQcXg1Rmb48jAVo2Zy/T1xKP1sl7Dv2EhIblBiklrmjvG46w2MHm0DoGvehZo5MaAVy7eCmD9aX2VJbJ/7/EXebQ6AODB5pC//8e/xqv/Au7/jmXZ1VjjebKaBeAj1q6ffrjArDb4gwmruwc8/HrD5VfXfPmN+3zl5D4bX7F0/T1OEtppnm7j7xPTYkRZ+AaLpx0oAEi+p2vfD7UpmcfGqLdHMlMa7ftCZkZjaL3kpJ9ktTjmZh0A+qKG9d72treXzEQG4+BWzegyKJ4ChUIoaZICmJ4QMItszJwcBKVq4ZZfs1MRZuyrjECP9qimmU1hFUsolEFvCOPxekPztOPyjYb5ySGyWLF87ZBuTq4NWp+14Z21K3Ew7m9zbINcZWTgkRhbovjbN7h4a0p7FIKuWhXvl+K9s7op1I8N7oayvhHqStdPV6gVuomg1of3lkB1IRx8uOnrSZegjvPorGZz2NcNLu9Lee1yYFz64HnytfL60gdlS+B6ZwLXNXad37V1bKNbtBMYZXu9X0h7jhuYGFgZdCwSKgfNJH84PnNqJMepQwA+vv9t305mVCWGfam+NPbl8wHF/Y19eh0tG80D8rhQ7O+Z12DXvX2eyzwGoT+plPXW9tvLx8z+rcPcsc3zbPBcPGefvwimyxVAABNLpmsJaD7LMigdWMqYAmRO7OKidvGYcQz0oGUJjKd+Xkpqp2PWOIY610sQJ3nurFg2GqxS3WVsRvBkIEVNv12SNjYJ+H3GgFkyoks57GdJUouBpr8m+drbZthOOub0PTLE8zWsNCp7mJAlla5/MmN6QH6LshnbT+un5d4X5xH+6kDquzi2sj0JTNYwtu0YEFQzO9c3FX5WhThL657fx37OZrLMtuB9CGaU8tsJaA6vcYmAdOH7xN9LG4PXZczEWzAaAehUas6kF0Qc6z0hcpbvCbnWYPLz+l0Wtc6F4bOT7pPt720a74OEdfzdgrSawWA3CYD0+qbi72ww94PqVxdjOLNHiq+F9tDQtg2YwK42bUhEN04CXju+1ZLekzo4ltyPdDhuJ/C4bCepsuXzGcWaBnk1yW8qgHII17KbwuZUkdrHWtHBwc3ds8j02smSHrOetV8WZMCH/jYZoB5ZQuHzO3/HOnvb2972tre97e0zYQnQbTVKXIswkZq5OG6aBa6+YqEfFaVlh7H3Njo0IWYfVgoxfqXOgDUDZnP4LbCbHRpZzjW2cJp2MZyTfVYSrF4YlLaieDW0BFAosQcxjs5bKuMy2NJpRauWGkdtHNZ6NjY4/FU3BK3cJLATjDPYTZAICv88Zh0zqBWk9TnTVGN2ZMZ/ckbjMBgLRSBq19xsMJFj67NK79huB5lGE51xkHcUaNoFOu8MGu4KTOXfZIv9fB0oncHotF5aZobHVwZKJR5U3rcPE4SMJyuD/eXJnAn31jeKNh4z66gnXZ4QTqpYZH0LhN6OEvoCdCsB6fQ38qpfKkuAZV9DOBxvAgktmgHpXr5bclZMCTQmGXwIoGOSaP6dr/w57379y9z4vXcDk8DEIEnXBfY0MH33Ebf/59f43sFdzI0N3dkUu4mTyTjBbu7XdDPl0dfh8EeWk+877EpJjH4VAau4xiDOopUJ8n6qQcI79q1BcsTOZ4u+j43/xn4kngEwnaQv7Rrs1ToDBeI92lT4SUV3t2ViOta+4ka1YO0rVq7m6WLGzYMFv//jd7jxL2uqpUcbz9pVTOi4Wjcc+hSsDsEkP2/wjeXsnZrLX2l5+42H3J5ecdVNOK1D27U4Omx4yQixbkK4v5U4JvHhaNUOgmpGlJWv8700owiA074mddmHDJrZ2EGSW/K/sJ7Nbc3tJten3rOk97a3l9RKhjRkcHScVJdXTwy+NM6mZYbwY/YJiuDrrvF257HsHq8hjf/hczc3+MMpcn7xzPOqHy7QNyds7h5SXU1Y3rJZgry+UuxVOwyuj/dpLe1MmDzxQQEEwrtNFD2cs/zcEcu7ASAeg4olA7Wbw/SRsPqSZ3PqWN+oqR8uwNqiZqFCJ0wfC/VZBIBKJl8Egtyspi3qSad9DM5Ad/t8SVI8rVO+I4XRfaLf5lmWYqbPtB33fidD+nnt/LxsfBw7rsGgJM+O/rNL0WjX92uvb+EbF65+v7kZ7ndb3Uh2XtNrgem4XblsyB4bzx2G8xNl+D2dY2L1MT6W6wDbn7YPfNLtZccx/AUF9z8xEF36pJ+2RTZvzkstQWYItZaj9ZLQSs+YjcBrZlgXDN4oKZ1/S78nCWoILF0ILNvisPJ+gK1yDs6TJbCLY8tm7bDdAnhNILdk5rL0zOHUnhFECoDYmDwvKA4wLuuX6xicLj+LDBjkKoJO7GA9X6cslLiLOiTbq43v7Jggllm1CcSLpYaADDYCGXhLoF9qOz23AYQ0WVrddJrZs/ldr8PaxGWbYR9stVuat0TJZOJxQzsTNkeCacG2ZKnql8USIF2S9EEKAJrdoGT+PFwXGH6GQXJbuKajl72QfZpQYiWst0utothJ+JumhOm6a2grMam16hUAczksEz77WLrONRJjMEFS29dhrrx5pcVaZfJYMA42NuyvWimz+2A2Rf9QHxIRJDLB45ic3x2h+f4f9CVH4vGFPln0K6UvjRfn87k287PejekSpf3Y+FyltoD2SOhOuiixLvm+lrk6gwYTeDzemergePGSOC39+3J03nndQTvsbW9729ve9ra3XwKrJSjfpiJEBmEiFRMJsOkJSqu769wklV9bTMoNNgLQJlIUt2P2SWJ8F6N5F5Hzk9ovAnD9wqC0QWnV4H1FbRxOZRtoweAUOm8zS9CgVNazITnkwcfzMdNUpQenummoU2M6xXQW01aI157hyPVBB9EgI74zKFMEn/oNimXPCubqaL2S8SS9Lzx2wAdA8uD7NfuV8Xblb/1kZQw4l5OG68DorYDk+Dx1O+BW1o5G0/0ig4duqgGMrjWD0diQ2YqAd4bpbBNAadtxaheDOtLPMzeaaXoNmSM+AtPb1cp+/rYFKo6srxkdgOr0PQHTz7Oybvvfufmn/O//1hc5+dYp5vFFZJP5mJEfgzibllt/dIZxx9z/Oy3dgWLXsf/FyWl1GYIm7Ynn6k1PN7Ucv+uZ32/xE4NrTJiY2xAoifk+uJmhm0Y5s/DTAMQobZCIkfpmtHHtKuPI9a3VhoX1pWLOrnpWR5w4r2/W1PMVDzeBFd1FhsPaVRm4Xdw/4O4PWtY3KsxBR+sNEwuX5zNONgouTqyNwR02PPnilLMvKW+9/YAvnXzMxHR5XAvyGo7WOLyXkJmvBvBMTJfXLe9TUoioxbH2VS5tML6v5Tatt7QF2Fyu5zA9Azq+qHwcew/tCoPHxbbcvqb03vb20pmvzcAXyEwUAVSiJGQfkOuBKxn4ALlkgh3VmWbsV0j/eRyoLqwMdhOZVckfcxOhO57SfFyhbRuC2WNgsKowj54y//iQy9cbxDdsjvt1Zg9azGI9ZICNTGc1vobqqourROhtOmH1zi3O3qnppr2fIx58Q/ZNsiykwPxjzwqQw46LN6cc/jkxwA+pFmR1YTl+12FWXc9aU0VtAkVgc6OhPSjuRTrW4ril+GEsTZmZVaVPVt4j+vuS6kQ+0563/Jp1xz7fM/3cn7c9x30bS0YLur3N2K8YLS8lt3ftLz9D8R4M7mnhb8fc2/738TUt5gGDmtXl9d9xbGVbg1d33DaNCXkcKNbJSRCewPwb9bGBK7GLVT84mPFiGS0ebrCV+Du6N+P+PJ6z/awZzAOVCHhhAKGUZX/uM/jzsCiBTarZGpNNB0ByYZLYsEmW2JpyYcGYhpzBnUBdCGNfZXsA2Bq0siSgNoO3JUM6S02neW1x4QyotX1/E4mgm/RjYQTnKN53Wpl+G08GZ0Mb9O/KxNxM761k5fsrzl2G46AMk1+N4GsG815Xy3CMzNexn1OnxN3U38T3DFlNcxjZ3b/L53UgARzHGbXg61Dywsb6vDuniGn9NLZoTByP85pBmY7RvL4c79I/XyW/Qq899k/TesltAM0AdWJEl0zo6+S7k6Xfe/lugoqf7WNL+f4WCWnJzyhBWbVgNv34rKJ9PfhiDA5AcPIJgCjzTQKgo0x3Aoihvye+gs1hqCmd+oTpQtzl6vMdx7evuPz+CUc/8qxuGdxxBx+HhOzmzGNcRXtkqK5CH5HSxfT9/korgejU/9Ll9FYwTvv+ayRfDxNVDdG+JnRiWAelH0Z9U4NMuQnPnq+gWoNtlW4qrG8qMnW7naetTpLGl/geLE8Uhu//8ecyPjHeR/peEltehvfE3va2t73tbW97+wu1VHvaqd/CnAxCLXYnwLwL60oM5wAM98t3AdAlEJ2Wl7/tOs7Pin2imtJLV++si5vqnvooRZtAknSjmsqxEDCbfjIUJLVjA9IHaLSKwYIaZBqzOuM2Qca7D1glwBToJ87PmlSNJqrjoNJw3WLiHr9fxzwZ+M35s+xer1zXFJ+31u0DTfn6JFC6AKc1nVcRBNtZO3oUtLlu8jkOkrkIRruJZkBaK8VPfO+kV/GzUaT4N6kcNhZ3PzLLgQxBsgAyDx8yp/1vu+S6Ny+JxFhmqcaLVYvLdYEH60VGNGozQ9qXRQulkGyOQCT0rOm5WXNg1vzt3/wW/+pv/gb3/oFDLpf9Drr4IBmDeXjGzT90rG7e4vJzoU1x/SRTDVQLwa4sm1Nl9YqjPTEs7k84fN/TXLi8vm8M4j2m9bRHNd0sBHcG8tyxTwwsB6CKYIwOtxkw0Hwf1JFOqK88enaOHB2RC3FWhm5qsH92yB9+98t0Rwq31vyVd37IUb1iMa1RFarTDatbU9YnQjPpEKC2Dp40VIvINKkM7Y0pZ5+f8Pi3PLfffswXTx5waNc4DHOzyXLcoRa4ZAbzWg1WNDOkk4Q4hDrPoa64DsDttI6NdSJatRhVXGTXt2oz0JwA7ZL5XLKkIbCuK+k4Mqv4u8ky33vb295eLstlR6IPEQLIcaHSM0TiuJiDywVzLLCxiGyaoS8i5bu/BLLLICCjz6UVgHQyNVHCu65htQpJUIOTCuxn7RzTDy+5eu0G7UEvw1itlebJKpRgiECGdKOsUhHaoyZIfV/F+pnOwcGc7u4JF6/XGRzOuFtU/ID+fYZCdQWTJ452VSFGuXpdUWuR1lEvPGZjcBNhdl+Yf7gM78wE9pQgy7RmfWJCXeixLEt5fcrv0r/OJL8PZbhd4feNEwB/Lvl1Gnf189rfJ7TnSkY/C9jUHb+V2zyj6S3QRvrnaaCyNAKZdyaZwqBPjCX1t46X3s8eH08+rMGcZfu803PxMwVVt2Lz4x9G9+sn6U8/ZR9MSQgyvvfX9YUd+5Udv32qdu+VwVdfW9QYqPr6tQP5X2t6wCvVrE1lHkjvitFcFzL7VpQMiKX+nSSD08XJwH0e4IaH7Evff/QeKRNv0rbeSp/Ek/q67bcZJBpBBn+3xs/yPpZM4XKePRpnx8/iIPE6LYrPVJmEIgLS9tcIDeBZSqBPYPVgyC8Z0eVhFPGLPqkp3J8c6+h8DxomYBPI4LuVfBySyntEmfYtJuuOcVWcgg/l0dQY/LTCrLqwbvf8ZOmfpz3rtTBmRO8yEd0JVqfvxijdZHsOqyIICYTutxswiyHft2w+pvSkRIqSLZ++5+ejSJaIJeZS33BNAGu7WUig8DYodIXydnD4yiWXF1MOf2yoF471KdjDDjV1bsO0gIdqGfpIALv74y6TjzLBIY0XaXz1/Usmldwzse8jfXJlqqstaM/GV1D6ZIfBGG1Gx5LY5yjdFLqDQpZbBUW3WdLE75ou7o5BSodftxxnP/ptNLY8KxFub3vb2972tre9ffZtCCoHs8X/u2wMKO+yT7pszKRO+/msANMvDEonQNqrsPY2sD3jzM2pxMLgvax3ZRxtBHAq63KgZ30KBx8EacfExkwTW7thUMcvOfFhkhrq5IRJ3VCCKkWDdk0CCYtie8OJyxBkloFj3k/s+kl6OUHOQanUDxIQN5bckx1/YUtGe/DZyPXbm2ICXG473ufo3Mfr7mTRiA6A75AkEIPhlcZMeIUkb6VkKVKxYdYhVmmajq61PHzvlOmtJafTJQdmvZVNsovtnADpEoz2O2amL8PUecxMteIzUJ3Y0BmQLix9t8U5loBiArYTiNlIh8PwjeMf8o/+6hdZfueE+beWfT2zkknhHGxa7v7LK3x1EFhlEC6YDIM+zZngpobNKx0Xp9BNa2YPheY87L+bCrMnDi6gPTS4hr7PCzsD9ioh8DK2rT4Kw3pU0geDmguPOo+YOAs1Bl8Z1MLRuyExZfGq4UomVF/wHDQbVq5m2dV87u5jPvjiG5gNWOupraMxHfWF5OCBO2i4eHPC0y/D0evn/PqtDzmtF9TiWPiGta8yIG1UI7jsM8hcmcigjoByApwDgB2Y1IkxHfpCSNqxUgDMKjmZJ4HSaGTfm36bBFRv4sBoUGppmZp2+yLvbW97e+ksMV9S4M37gvXi+rG7LM+hVWQIRZ/GW+kB6SKYB1EhhmFgNbOqx7Ha/K6P24z9GPrPq1uW+WwCFxc9Szox7VTRtkWaGnnwlOnTY9YnVQQ6YPKRxz6+jEF0kwPq6V2llQVV1jcrTAvmcom2LTiHu33M0y8fsrotW8eldV9OJL3HxMPhh57JoxWcz9HjFn9vhZ9WVJuO6aOOajHBz4Ub3+4wiw3Euqea2IWRzd0dTViU5UJRAADqMUlEQVSfmt63gS1wI/8tY6Dp/ac9yDLwJRNwkYiHZZB2bON79oz1tkCaEqwZfc7A/stgnzC4uwtwHgORW9tcJ/detjkGrixDZlQJ2G0xMIffB/XEx/tNLr0f/r5FzhptX4JMJTj3QuzjdPxjZ/lF+9c1tgV0vUB7W0k04zaf59DvOt8XuQajNgaKFC+ZXX3hNHyIxxnYkoFJ6av4e/KZCylnb3sweQCUlu+D1I/yPLUYE0qAeFeShKbPOlQCYOTDx99NnJeneWw6nrBfHTy34/uRQdtibDWd5uQslaK9tEpUGunB92FZjF1lAJIEd9hXyXItrlPeAVlWO4cDXJRD7nxuf7hfZQtB8zqYq0n57jcy+A5A24U20jZlLeqkPlIs01SbG/r1SkuS6S7WjvaKWIOxdruW+EtiJRDpvSkku2W0XnGPVTIbOoDSPTO6Z1aH3+q6Y3Wzo5vX1AvN7HFxAfwVVdzEBJa06fumaTX7CGYT1Mp8LZh1zwr2TZizmjb0NZ9kuLWYh3dgvMdNTfYPu0kAo7uZhOQ4ieNBPAe7hlVnmXxnFlRfOsVNYX6wwtlZSGqMzH/RsD6AVnE8qQop+fh8h4Rw8lhuXDzPricjgAxUBvK11367rKRGfJYJcQTT9c+9GrIsuY3XMTCmwzO0viH4aYpt7HBafB8fy05NYkeX74WtDK/iN+K2xfs9r1u2n2JdXsDJS/vu2Nve9ra3ve1tbz9bSyzpJK2dfvs0QOBngdy7GNe/iPbCoPTK1TTGZYCkEh8BlgBUWxtrTntLHcHqBNCkGqyugeXrHbMHlvqRZ3VqQQMYnSQtgWGQjzRH7NlC+fcoC5az5Udzup2BjjKQGP/m4NDI5xywEMbrZ/A2guHST3gH7TPcbgxCj7O3t7Lay3XLYPR4W93+bednKOS9tW83rWPC72GCBFgNQHRuS/tgrVGwiolMab+s8Jc1S9Mw+bji8BLMq11mf15XoB225bq3lhNkuxNLun7m2j8fSzWHa+MiWOgy4FwC0UldwCHU4phKABR7YLOili6zaEtpZ4vP4Pcte8n/+tf+Nf/1X/kd3v7RDDkPbDWKWnMAslxT/3jD64uWh9844eo1wW7o72lazwVgur6sWd9QVl9aId9Y8+RiCmc1OuuY/6CmOQ/DRF/7iqFUXTHZLGW/BpO98taPnkEtgrniYfbREpk0aF3lqISosjo1rG/B8u0NR7eu+NXTp7wyOee8m3FYr2m95cZkweNvPObpR0fcmWyYVS2V8UwfCr4x2NZz8fqEh7+lHHz+jH/r3rvcqq9yUsDcbFj7KrOfPYLFYwUq4zmMM/y2iPiVjGqATi3LtuG4WgZGtZgMUDvC58p4Vr4OYycej6XT8K9Sl/vMzG6weFpv6dRwVK2Y2Zbb1WVk0vcs6Xac7r+3ve3tU7duPo70R0ZZBWBzID4HzFOpEh+D+zGQmH2Mwbg5+l6ADSWjeivZjuF3XzB40jJR2Nw7YXJxFQBjMSHoXYVxWQC8R9uW+XsLFrePaI8Es4GD91Zh/3WFtF1gTE8nISpcV/hZjZ9ULG4bDj72yKaF4yPWb93i7POTEJiMAdkxo850QXXHbgIbaH7fcfjDBfZqw/T+Ef6VBe26YnFvyvH5iupyQ3M2YfLEMn14iay7cGyVDYwxr8HnqcM2myNy4HgMHpfXMy8bACRg1gzkZ0P0u7j2Ovy75aeO3KSBItBovV3MwRBMHq6XGY2jPvBp2U4W4bNcwB3LtpjDz9vneN2ib6WY9piBm3x9hS3A7kWu5diP12q8fHyzn3MS+gLr/LQ2Ps9nzaOusWeBzFtgPtvfdyU2PnN/L9APnsvG/5Rtc2hykk1C4kJt2cCatJsI7PikHhbfE65/3k2sR1yyepPl90x6Z2gCeGN5LFPchEKNbAvgHYG54iIAmxBEp4j3fWJVG29mVKSQTcoqiu12rvfznQ+gaVFTukzcyolNZSLuNfd1UAs72agMRaprnep5b61TgsDO9SWF4jaaampLnN3Gz6TP+bgNg/rdIv22ANaiRe1tANbrwTENzqWsI15sE/yDHeeRzjXNFTNbO16jcruXyBKIHEDmHe+M0W+93HfPtB3WpE6fBfAYA/agZX2jZvJEqDe+8DdCQKha+gEbWivACUYVHLHOeByzUiI1DGJaQcI6tJdqgSPQHgTAO5XGMp3SHgiuiQkYG3KcKcm8243i/+yQO99y1Bcd4mF9amk01AZPx7g5FKaPlOkTHxM74jUrZbnjMy0q2d90tYRjjdcgjdemG447muNvgTCik1B+z7aajzeX40sYcyV0U8FNAgFFroRqHfavBtoDYX3LQ+37ABxAcQ+HHaT4ew1bfqcNwGuGY+UApJZ+3RJ939ve9ra3ve1tb595KwFpiAq/6rZ+v87GzObngcrXrfOTMq5/keyFQemyDqpXocOAr5iYDo+Euq6Fw9Z5S2UCuFIZHzIjp4CEzM1uZvBNcLqbK8/04zXdQYWbGlwTsk5dIzkgJErMXA3th0zs8Fk1BV3TRJLMuhSnA0bNmGWkhcOfty3lw4r28nYZFI6TjDJD+howeAxslyB1edxp/+P9bcmiycgHL9bLznUCny2oSaxy6dnPqb00z85gdAShDajEz/lvWE+sohuDrC1shPpCqBZhEhQSDZSLzwn/4Vt/yg+ubtHgiguQDq8vIm9FMjAdGNWBLe1VcSitKi19vOwTxqz+QqwWx8R0EZjuBqxo+7yoXTQjyiSC1EkCHwlAZy2OqWmztDfAF6Yfc/g7D3j63Vvc+B+X0LZQ10NgOmbCm7Mrbny7YXlnTncQJE61YijBbUNgf/pI8GdTNscTjr7ylNffOqMynsefm/PRN+9y9K4Jst7XjRjKMBAZuwsJDNkREFZiLMqArzTP+czZog/eWBPq0omwOYGbf+0jvnH7x9xtLnjSzjnvZixdTecNJ82St+aPeXV6zp8cvMad2SVP1zMu2wnVUrFrz9nbU86+BDe/+Iiv3f6QVyfnAdTVnq1uRDHo4Lqn+w29bHvLMIEgbOvxajHiWfuata/z7ynoU4sbjKc9w5pY+sDmEgi4pk90QOnUxlrX4X5vtNquPb23ve3tpTGNgcrkgyQAOtfKtET1F+0BsYKB1vsQOwCsLR8jSB1mQDotg22fgWK7vFLfNAqbk4rJbAqrdVQpKmqTFsCAeXrF9OkB7YGlvlKqp4vQ5Kyhu32IWXcBrFDFzWvaoxo3M7ipUF866Bx6PGV1u6Y9DEHealUcV+EDpXeQXSrzh56DHy2oHpyjlWXyFC46i3rh4k3L0XfDyR186GkufWBJR+nxBEiHfSjaVLSzsG+zYfudRe8KlYD0QP7VQ73QYcATRVxfv3YMID8PLBuD0teC5EWbJSidkyjzCTxzdz8X+yRg8q5tdsaffxLMsfBNxoB0Xl746lvblru/7rr+rK/3JznPHf1rq2b0eLl/9vLnAtLja/gT2CdNOPgs2NEPl/39SsBPIdFtNm6boTuuN+21V1CCwfiG9wHgLd8j3g/WzyBtAqWt2Q3ulgBr3kf0cSOAKkbAWPAlKL0DsO13jmb1J431q81WuwPgF4Zgenkdyt9FMqCczycBhiKh1EMJRBsT/tmgDKJV8d7LxxH+ifPxPkVActDO+J0t/fKUeGBMlHWWrJaSmdBpvXytiepkkt+n2S8w/T7U7Nh3YpsnMNF5fGNx8yrfQ9O9vA9dKcX9PCvrTG9LPsc4lg9xhqpytEceNxWaK0jM3ZxgXbwXVIr3fb5/qcycDt8TCagOB5/XTTXPxQUWsV2HObh4xXTQzeJyH7cfzZ/tKszX60sXGNoW/ASmdcf5cQSIbVSsOQvzXvHD99OuRLY+PhV2qFYCyzy9H10Ym9I5JQXD9Dm1k8vPlM2bqIpYQTcNrHHpQgJNkq13TZjju4NnZTSNjpsd359lu3ywZOM4xa51f9leSnvb2972tre9/ZLaLrC3FvtCbOmfFCj+rADMP4m9MChdGc/G9yBMYx2bSL9Iv2U2YJSkrYxj6SKnNQKe0w8rbv7JBeubE9Aqy5LVj65ofnCFziYwaXBHE7qDGl+HWoBuYvBVAlll4EAap1meOFsOxEmW3esnCMNzGwRvI/tINbYr28u3gGT67wMGdBkEHstQxkDDNkBNL+1dnsd4nfK4dLhe+k0j6znJCsbpQ894rcIsS6t+YiEOiHWjExMaA2IUXVnslcGsAwjdnMH0iWfy1GFaz+a0YnNgaA8lSD0T6ux6FeamBZKWdK+LH4DpYKlgvBXJcY4SkG4VUsmz9iWYG9TGYcVTm46pdBk83FV33RWdLoGfNY5G+ujxSuuwnpoMfNbS0YjDqWGlNUd2xd9941v833/rb3Dy7WPMg6cBkI5BDNUQclRVWCypP4DJ0xlPXvfgbZDykpil7ABPlqyslmBXwlJP+bP5Sci6Fpg8Mhk8GU/mBjGCFw1GSh90zJKmCU/1IBdXQwAmBoLaE+WvvfJ95mbDw/aQs3aGU+HpZsad6SWtD1LaN+oFXz75mDvNBT+yN/nu0ztUASPh4i1Bv3DJb9z+gNemZ0yky+DymP1sRidjC/TBRqUI/4x6Eq32YHOoQe0jm17pfF8DOkl55+2KQn0tgWQX+pbBoMzteiANXoLae9vb3l4uS2VGxPeSkIn1koKVGZD2wZcQRkBA3H7AHM4qLYU/pPktT2wltj+MO45ZIoklkxfH8dk1gj+cImfn7KSqWBuO/WrJ/P0V66M5s8cOWW1AhO5kwvlbU+qFp75wVFcd3UFFe2RZ3jKogeoiyHZrbWlnBrtRWAdgtfR9ct1BDeVf5g86pvcX2IfnsGnhcE5zpviNxdQ+1JWeBP/z5HtXyKYLLOkEJnRuEPz3TRh3q6souZlvYHG+5TsufS5XjeUncpkXA+IManU7aatgII7tWe/REBzmWhA0sLZ067ivS0r4RbBdctVj3+NZ619rO+5hXiT9vxcG869ZR/NzNl5/fODwTMWln8Tv3drF2IkbH9Kzd/IiMtlb9jwJ8Z9gH7sk3Z9rCWB5CeYPY6v+9EcRiJUMwiagdMDoDT/0n0v2bskwhgDyWjsETAu2bi/bHAFY5/tjoAfwxI78S7E9cJtA2nzsEWC1Bqq4XpKirivUGCQB5JCB33BOvj/+AtxVK6GdBNCKBDA3HY5qLm+RtxEGx6bW9POOQmY4sVkHSe2RIZrrcps+Ub6bSA9a6guOxwW7tBwLNM7FtOqBOwiAYmLA200PBA7m+qN30WAKUICE6W+ab6USaXYN7ZHQHob9BCZ9sd1L8IwEUFkwRuNt163l29tAGmB6ue7h8tTdvDcYo7hjx/pGTXMuVOtwYxTCdbXxOqfcipIpnGIxPr5zK5PjN1nxIMVfSuJD9PF8FZVfut478zFpXFy472H/fam6eulxlyb0YUeMjymzuuXRqUcNdJG1XC98Pmatpa9Nny6R9v0+14VOCj1ptYIdvatfaHzWTATQffRrs4KgEVxNZIMHQFoFmkX0s+L+3URY3/Yw8UEq2w7936EDW/weDuKT99edGX2jdqVYb29729ve9ra3vf3S288aOP5lBqJLe2FQuvOGzls8gkExBLnhBKhU4ulizelKwm8bX3HZTmi9yaBTcw7mBx8wf3hI9cZNuoMK8Yo7mWEmdXCMW4e9WGMfX4VMbmvRWYM7aPCNwU0svg4yQGnymaQdgVy3J7FqJCoyDTJfk/Nc2CA4JQwmeVu1pspg1cgyg7tYPgaZy/qRg3Vke/sxM3qrprQdtqG2j0Bn0I8ibm367fLEwWpgUCtQaZCnWljsSrBLoVpBfaFMnirNhWPyaA1G6A4qNkeWzVFFOxc2pyHr984frvETw/t/95Sjek1Nr8tfZpiM60p7PF6hVU8tJtTdlSDv/TI+soFRG84hMWn9oL5wAKR3A9UmgJ8RjofAsK6ly2zYUJu6xWG4cFOMKJ+fPODf/zt/wD+5/5d44z95CoBuWmQ+RZwLwLQxqPewXHHzT5Y8/s2G1b2Oyf0Ku6JnJadghwllk9TA5JEw+ygGozzYteaJpG9C8GKQEJHY+ePn51lzuSKwkvqvr2DyFHSzyRKxEAJDvjZ0h56rbsLH7ginwvlmxucOHvNH91/nlVcveHv+iP/Xd36TN2895ddOP+S1+ik3qiv+5w8+x40rz+KVGvmNc/7mmz/g87OHoW/FB6c2Dhel3xJgbHD5niYJ+gRcG0Kt5xpHiyWoH+qAOV2LpwWs9Az6VKc6mVeh04IZHX/rQWdDqh49MR0zu2Eq3aDedIiIMIpK7W1ve3sZrFr46FuUwG+SdkzBvh4oCAE+xbjAgkqgdmJCJzOxrqjECOJAKEHiPq6RBy7lwrcYIpAZWOIVdzihbpowLidQwPsBQIAx1B895eigoj7fBGaZCOsbNcvbwrqzbI4ts48DSOwmQjeH+gqqs2WQMpUgUVsv49gXpWvV9Eo04pR64Zk82lB/fIGsN2AMejCDyjJ96tBOsDPH5sixujtl+tECc74MAEk6v7ZPBlMbwBWtDfVSs0zlThCxBLXSMZWgtCcwmFLgujJ9YLkM7sa2Buo9RRtDds54ue5MwtxqN72jhWGd5JchxvqJA8jb20jRb7dK91wDMo+tZLsP/G4KP7s83mdcuzKQv7Vs+/Cf0U6/5k5W808JFj231vYziGr9SmUDu6/3YPXx8ueB1Fvj0XOO5xcckAagqRFjAoicxqT4N6NoZcJAMfaSQOOSBZzB4dFnILFpBzL1lRm8XyACb3G+WpbX2gJtJbIzq95nd3W/3uTCY9peprhaa0788bUMSk9kdmg8rvRsloDaQP0hHysZKMxMex0vj5/LBNs0zy7m6GNmaUqIao8Ck9U3PUhnWsnA9MB8aMe0YDrZigVIMSanJLWUGD6s8Rtr8jr6Z+26v/GY0oCTfQjVfE7pfWo3ZFWQwNQdtvEyPCeJ6bwLXAbwXga/J9Da++11Q3vbbRsDZt6xeM1Snwvzh30NZHERRHWQ1eIyW51+YI/9PydCANKF2aVWJsee0v0VAkO4rDEtPjwfahOLmP5e0r/PTafUC2VzaGkuXEh0mIQkaq0VXxm6qVAtQx/ytcTa0LKdzEDffrIAFMekQCGD474OQPog3hSTLPI4Eln8yc9J7Oh2LnTzXrbcrkNcAYk+ngR/0J0kaX8G/Tj8i19KaZ/8st55u683KTa6btsx0P0SPA9729ve9ra3ve1tb59F+wSgdACkvYbJ1cpVVCbMqqa2xavJrFhMAFbWVFy0E5w3gbGLYNbgzy4wm5a6slSTBvEeP6npDpsgYxWlkOyiRS6XyNUSWa2RH18hdQ2TBqzF3TzE1xY3q+gObHaAA4jVSwiZ6OCnLOEExGWm5g4HPbGsS9N+XrkFII+tnOCmv1o68iP2c7nfMhhWTr77OtZF20K8trvb6rN0g/SVKGgdAWgT6xx5MOvIWBaYfFDRPIXmXJk9dkwfbKgeXKDTGncwwU8t7XFNe2RDnd+bgRlt1yHp4PiHHdMfPcXdPGDjLDebBVPxAXAmynPvkm8jMKPT37JutCWApsnq4a351MwjoVZ0+r6DuToGpNN3j2dDRSNdrhttxEcAtMu12H2UZl5pndnVf/34O/wPv/sl1n/4Ks2fvoc0dWAZWJuZ0xIlVpsPnnL03XtcfLFjc9NRPzVUC8l9qQyCiw+1OgeZ9U2YnKUEDyieHYrnYhR02Qo0jibXxDmfmih7ZpTDDwI4IVVVgCZR6tYqrRpO6iUfrY4BOK5WKLB0Na1aNouGd7tbvH34mA/bU25UV6yWDeKV87csv/HaB9ybPmVu16x92CYD0xF0dkX6gxHFisegODWZxZxA4yz1rX6wXbp3dcGeN5Fd3WK3pMHT8v679Ek/akMdatsWNa0rasIEPvU3t3XB97a3vX3aVq2CwzEGiBNjMUsgSojupUB8ZkaLoD7BU4WzYiUHmsUr6iQH8XqwQYeIWBx3Q2KRXg+yKSABoPWNhdk0SGyrIrE8BBBqSnuPWAurNdMPLnKNUJ01rI8tvob2EFavdYivMB1ZvcauFLlaouqRztNcOMzG50QkX4drYtoQxLUrT32xwVyukMUKKovWFdQVqNI82SCrCXoIMu+4ujth9kPXM/ZcF0FdDdsaiYB0hRqhWnrspr9XptXdzFEd/U2XV8FsgsSuWkG9x0awRPyYLV0kG4zaeKaNWO1wfRt9TUgZxHI/bXsh4HO8/thPL0CnXWznreu4C/xne7vc/gg82por7Nhma59FzDwlAn4S+6TS2c9vb0cbzzmmZxHJdra5o72t/vnsXT57h7v2+Rkw99Yrgc1rBazgbWBD+ir5v3HF0cXzVgZgUA8+EreFDBwXc8is1kEPVu5i2AIZiOoVK+KhlKC27Vm4WoGbhgQK0wqTxxa7Vtw0vKPsUrMSUz6WCJaalv4dpsNSBIN5cnEdRPt3SgL50vgwSBpK40U76tfan19eT4t3sxHEe8QLdtWD+4nNHOpqD+9Lkl+2LdfX+S5B0rhv2+oAoE/vPtP6ndtJlPEmMnMH55Vk2FPd8L64MtJ5tLb42mI25UXu2/20LYHR6d/YtsFnycB0KQgwbDSUOOnZ0oKtHN3NDatbU6qlobkKSRQpeUFUY1/tAdfQVvg3iBfFd7pEWW98n+CHSq9iogGYTXWX08GmvpL6sLSDQ0fidt0sKAgiQOOZ2A4qTzezYKBaxXs/VmjxRLWe/sLkY0jdo4r+Z1wnx9OKe5BjYNFf1fzs9TWls/Kg7Z9z04HZhDrT6TfXBB8Rq4ElbYrnafCclkDxTwBIj32gsVNUIux5m8JJ+/Qfib3tbW9729ve9ra3z5y9OCgdQWeIgIwBPHQRMOnrTRs2LgIwCKsuaUWR2SeYWKvm8VPEBBDNThqsCEwnaFOjkwp3OMFUBnSObyrqDwiTrM7BpsW+v8J4TwVMqwo9mAUg9KShPbQhK7sWNodhIpyc/LFUlaB5YpEB48Fkg7RinnDkCfqu6MoYGE5tj2tHCxk0hqLdETN6a930XfrJ604wu+onVWpBJ73OmDjBbAzSCs25MH2ouIlh+li5/fsfhOswn6CTGrVC+8oxm9OaxW3L5iRktZoWJo+V43dDENY4pT53NA+vkLZjczrhIJ7CVMg1o5GUajy0spZ0/k0Eo4ojd7m47stlLiZsjAHpBBQO5bsjCEoAHzfxMUzM6SYC0kFCupCMJvzmNExE/8N3/oT/9nf+Ld76+BQuFqG+dDV6pI2B5Yrb39zQzRpW9xztcUhGMJue+WxSvXbt+1pSFi+zvE3bB7KAvi+X36MNng3p+/e4b/tU59oJB+9dIVU1ZH9AkCprPKf1ksebA7798C6vHZ9Ti+PWQdDmfrg+5PB0wcWDQ87aKbU5CmDyVYW3wuKtjq8efhRrOoe26yiN7hGseFrtAeMESCe2dAKsvQo1LrCrtRgT1Wc5bxOvSBUzYDa+oorjZOct1jw/Kj+Q9dYgKT636wHzPgDdJh7bHpTe295eNpM2JtqYnrWUrXyhJcA4BUGdIpacTDZoUzXmdfVB5xxkVDDOD4KOfdBd+vVHJUgG7cfAoulCOzqbIOsNumnReNCS5FS7rn/vPHiCTBp02tAdT1mfhsbXtzxmbYb1lx00F4qu1kEGfLFi+qABVXxtg59YCTjFrh2+Mthli3lyERqoqwBIJyDHWOxVS3U+w51Y6knH5nSKtB06qZDWBYa392GbyOamsujEhnvTaWSo6TazmSJmmYP9owunIJ3P5wdg1x5f9Yz3fD/G96G4t+M2hyts/z5oQot2FTDgpmbok37Kdh0D/dr1rwEhnwVObl/H0XUu762E5aVy0TiYv72D0Xfd2kV+7vItG4Ozz7vVu/b7UwbGn8ta3mUj0HkL0NsB8A13en1zu47pk7I0nyc5vmUvIbjw9CuHfRJyAnlt8H139ps0po9YxgH86tcbMH7TOyFuUwJR0IOrJSBc+u2ujsxVCcndJRib2jZOg8pFHd5bpg0KW7bVPA7aVgsmcDGn0ABYie9rzWaZbJ/q9kovnaxg2pDQRGJtDs5ft+thA+J8YDIntYvWZfWyfqV0cBpZzz6wyY3p3yUuvE/S+3LQfmKyp/XGfVQkSLAn4Fs11LduWzDxgnjX19FOMuxjmb+0TlX1LN0x4915BnXAISQ3EU85ybwDOWF8x3X7eVtiM48Z0f0Ko/Mk1p0uwdNrTkNTm8ZjrdIcrVneaagXBuOESkFtSFRLY2SS8i7Z58FfMTHGpAPyQb6GQuhDEhVw4n2q1pqfw/z8tP2cO/hymp/rlOhgUFQM7UH/nppUHWbiaOcN9SIqvmh4zkp2flkGRjQUegnqMNFniH5iPhcbZOsHz3qev8vgAqfnNUncJwDcdJKTUKTrE99DW0J7LGxOPXQm/F4pFOoC2zePfA75+85g3MiM7m5zMEiO/OZiDi4vso+97W1ve9vb3va2t719InthULp1sX508q4JgPS6qzJDurEOg9KpoRHPxllaZ5lWHVp5zNoGx9Y5dLlEN5segHIuTKg2GzAWM2lCvdwoZWbqCn96GAK1zvWTvDShsyGIKa2jOdsweejw0xo3sSxeqVneNrhpn8ltXJSs6gIYliUbIU4EyIGBQQBjBCA/jw3aA82SJ9d5ec4kLdal+J4C1Ql0tpCA6LxtAUr7xICuFCpFGo9UHms9tvKowmZVU70/Yf6hxMl/qFlVrRRxHtsqV1+5g5sYupnQzgU3EXwN9aUyOfMcfOQD+0vBbNLfLshVLdaBweQc3dywijXFa5HIkw7ztFoCCO3xONWQkBDNEsC4VgNA/QlJNT9XC+zZKqoDDIMFQ+Zrz4RNnxOzNiWkpzrGCei04mlyMTGYmBZLAE5/3N7ky/OP+O5/8H1+fPEOr/39+0jX9YEU5/rPVcXsTz/iNfcKD35zwtXrnvbYU13FAEakyGf5toj+l1JfauJEkn7+tzU/2/Vb+Xvq0/E3IMrGg596pvct9qMn4biTbKEPoIibGOqDFRbPP/jmV6ke1PDb58ztml+/8QEP14csXc1vvvI+/+Txl3i0OuCoWnN/c0z1uKKbKffefsDt+oKzbs7CN7RqmZsNU9PSqu0BadPfi1CqQAMjXkPSgMey9lVmS9fiQn/FDsaKrCxBqBNdFfdybGXfCPsaSr5PTMfEdBlQd2lwimB0Aqb3tre9vVwWZH0VfKzyXEZJi0B4rivd9YH0MBz7IZidAotjKV9hECQUeqCgl1fsA+1S7t/ptUBQCsZLZWG9DrUVyzqmxhZBbQfOIZ1jebehm0c/5bilfr8Jhx8ZbeJg9qAN4LYxsFpjP34K1mKKOqPShcC9f/0m7qDB3A/JVzpvUGvD8VlLdzqhPagCk6h2bBY1N+8HsEJWbQAPkt9Y1CNVK/hJFVl6BRtth+ORcfwiYF+Cyn3tR+3BlE14gUoGsvv7GO6TfrIsu7RrXwTBSxAi3f/8Ux9UzcmZn7KNX4VmxMrbkp3nGuBRubbfPm/7su63koL1KShfXKfxd4a/j37aeYx5vvCcY91yqf4i7tUYG3uR9Z913LvuwfPkuceLf0598mVmWM8eFGzV1FcMjJOLEui6BeT7MH4Nki18v2yrpjSEOVtaBjvZtIP3VVTBQKQvgaCjtsv1ow9P53IN7FwfuwRb03dfDAyj+tj5t/SuyXLi6a8Jv5VKHoMLVLQjhVqXtQGgLWp4ZwC4mIeE6+X676lUUkr0KtrXtH56t+2ykR8gIiE5C6Cuwz6k6Y+7bftjG7dZXqOULJbqe6si3vc+xTS8h6XtQq3vdJ5l33gJAGmIh6MSLv8uFuszt5VrH3VJy1TAG1zUcPPHHe1Bw/RReABdHea9STo9NEyO/eRDKse3+Oz6SkItdk/PsE6xoLid2WgoRTcLktvdNM7Di/HQdOTfMqBMiNu4idDNwD6peLA4wK8q7FppLnwu89H7CKNrkJRbSlayxOP2imskH7e3YItrkH3LpPZSdMekzpDY1yaW+5Iu+ETGhWMP56ZsDoXlHUUPugBKmwhIw+C4ghyCDO9D+vuiYLEv+lDJui7fzampws9Kv+vLoGm/t73tbW9729ve9vYZsxcGpf3Io11HBnSSSkqyto11UXLWsPEWVWFWtdD44JxWgqT6hGmC6Ry+mNyFHTRBkrHIDDbLIF2bJ1up7tZsgp9WdMcT1qc16xODaRW7CRnZZR0prQJ461R6xrQLEmPJ+ceDT1mpKYgxcoSvA6XHYHT4rZcyym2lYNhIQi2cHwOwGtJ6OpRfi99LIJpKMbXDVp666TAm1J1VoOssSb6pm8HmNWhvOKQVDn5sqS5DcLBaQL30NBee6SMfsnitYFqPWTvMxoVgxqaY1NZVCDzUVa7d2E3DwZ9WC2xmDMfJiOpg5uVUYy1mxSJ5vTJWtU4xCkJc/NO21Oe9EVpnsfF8DIFhO8l1hIedxMUbaGOt6CFgHfq70wDZz1nTanjWGunYaMVKa7wa1r7mb936Dv/nv3GHxQ9uMv+2R1abrSz5xGab/uARt+UWdjnh6nOKr/vgsFYEmbDYP43rEzh8ZEinmnBjy/KWkPvmLlm6sj9TrKsm9N/JU7aDScaglcE1Ql07/uDJm9z+pzWXbwjHzYq52fDl+Ud8//JrHNerwEKedlysJ0xOOj5aHtGcCe0BfOnkIWtfU5uOhZvQeosTg5XAPk/JAFPTZqZzuqdOzaBedGJMJza1FXJNakgMZhuUJFBq07Ozy4QECP0lAcudGlxkU3dqMvA9IdS9Tv3Hxm0s+0ny3vb2MluvEJhAydHvyZL8puvln1FBnSKULNsAFiTwM4+ZxuRgp6/C+JIBiiKYmI8lBqFV6SVAB8ejaGWCtGcMmJfLMIQgugSZavE+lJGIAfn2IByPT26ckwE4Jx7qsxU0sfjFxqOrNVJVIYie2NeRiZ0DwFWFHs5wR1PMukOrCndQs7pZc3nPsrnZcftwxdm7Bxz9YBEC/st1CMYn5pwtAvAioXarRnC0xAqSLGqyNGz74hra8fr9fdZYiiaP9nGbIajN0KHZAcgOQOtSpjX93bGJpj6jRT94wdjtX7Rt1YAeyXPLKPi7FVQvQblxMHmX6Y59pltdXL/EHBP6a6ZGdpOrduzyWhAkrf88x/V59+fncf/GJ/FJXYwd1/qTgtI/E0LaGLTV3Z9fFjv41kf9F5GQeB3LC+REmkAbjYBuAQZDv2ysW5xA1IKR2+8mqJblOtTXgZEJwC32p+N9l+Bwaj/OBUm1sinA26IERFgQ5qmSANK4nXZdSF6vbASufR8DgB6QLWtvy2h8r2xQ1pAgw60J1DWExKZSscKA1jbKDpuiDncYC3xtQokGr3m7XCZhbAVYnMDI1Fb4kSGbXQJQ562QanmPxz0kyCmXtrPdfFkDW1Z88ClWNyymU5pzj2mjEkolw/fEy/Z8RPlugWc4UBQo8ei3rdU0tkm4r97gPZjahfmrC/EjXwveCmIiiJsV9jTHf8qkkTJ5MNSYlsBUrgiy1ND3S43+AmEfLkpk51rSudF4Wml/NrxHqrVHNIDZk0eG+/dPqR9UTM491cr39d3TbpMEvylZ3mGZqPbrx2PwVa/QIzvG9EgyD22YVBs6rJtK5qXPNqmtWcm1tJMqhJsKblY0rgTw2OrwJfuzZClvsaGLz1t+hsT+Mlq2t73tbW9729ve9ra3n4m9OCgdnbgEkiTAZGIcRpRlF5iEne+XL7sQbJxVLXYSGLW+AplN0bYLclvOozGDGhMBaPVhwrtYhKBnVSHW4lerMGG1NgQsIzNUFkustdiHFnPvJqvTOd1c2ByHiVaYSAZH3voiOJosOvqePjgmPjjk0vWBPej/DsDnXVYA0vm7DIG7/E/G20QAz6TPGifPxX6t4msFC1p7sIrUHlOF9AEx2scLAFXBWs9k3uLecWwkLG8A/8MDTr/rmD5MVNhwzmbd5Uz5zDTqCtkygKZGZ5Pw2QYAUaIkZjsPoN2N+goTD8bnQMGof+FpU+BD0m+95LcDrrRiKg4HrF4CVmhipiYVgcRkJQKSE9pnbQ4Qmbd9EMcV9bZ9BExXKmzUYmJ03KIc2hWtWmpx/M23v8c/+p3f5J33psjVMsxeU9DFufycsFwx++7HNGenfFgfsXg9sJRVYx+TUPPdTQNhwbRkuTDx4BqJk9XRSYyDwsJ2TKOQp81JGamfC2CU5lxDUkMpaRcBgxSE+d6Hd/nCt664/NxhAP/x/Or0ff6f67/MsquZVS1N0+G8YFA+vDqmvoT2UHhj+pSFbzi0K+Z2jY0lBnxkSSeQeSIdtekyiJzqTuc60ujWCZodUZzEsPYIjenovMVEOfAESoc2PRZh4/ua1alcghGlMqnOuMt9rhZH6xvqQuvNvAy6rHvb294GVoLJW78L4UUXAWBxiqwdWAmBc9Uo3xgBCNcHwkugUpzG0SZ937Hvoq5wGQiXBEaYFMSUAIwrQXo8BhP94RSzadHlEjQWBIQh6JHAa+c5+GDD4u6E5esOXZuwT9sfW3OhmMWmB2CIgEVioLVtL2k6baifLGG9QedT/MEErS2sO7qTCVevTjh7x7D+2pK/+6U/5Z998DZv/MOW6mwZEudMZPYloCcGZXXWBJa0D8kAY0BYWtfXSLSmeMfFa4UGZvv4fiSwP4HCrgi6StEXZFuafdxPBkzJEpCO2yeJzXJ5Oh5XG/zExJqQYZ2xJPmnYeMECB1jLyP/sGf89+/erdfdGIQcB9HHLmNUQwrtFm5M4aNq6URfh0yX+3zO963Nk09Ufn+WjeYcu9p/XhvPBYzHbe5ars9Y/gJt7pK9L23MnH+e7ZTRf9Z56vPvzc/b9OyczPZNydhGcpmELMGcNyjA57RdwS4e3IICsJW8TXym4vo9qNx/1qrMuJEhIFxuE5erFSLpNAC7Np6DC2xNteUgGb+b/jnLZSu0OD/ATyy+NmE+nhinhapBAMUiOBznFCW7NTBK4xhYzD8CMzTMbfLFkKjmYfq/uZ0mlDwK5R36tnyl1z7Hg5r0Zdxg1Ad79mz44CZpQWxHw5wM4hysTHInztNS7KIrfIAk+ezC8S9f9diVMPuoyvM5rfr5XrnPl8GMjfNhLz0Z34cZl3rBGEVSCbl4EQUQ0UFyk/rhC0OMD0C38STQGwPtiQcJSdjNuaObGXwjub632vg3JRmYoV8TGg/fExEC4jodgz6qJqjRdZOw7+SXiaZ7qbntLOfdKr4Jfa9aeA7WMDkzHL7XZCIG0CuBVIJKeD68TQlxAkmSW4pjihbm+oWkeBfG5ARck4DnKNWvhhDvaaFe+EKFpGBNK6jXXoofWN0wLO4pfu5yHCBdv54Brf1n0WFiYFo3sahLi+NQf0/ixd3lBJeruQBCS2JSC/3nl+i52Nve9ra3ve1tb3v7rNgLg9Jjm1Vtlu1uXWBEp5rTtXF03rBxFhFlXm1omq6vKS2Cdi1mMgmOsAOpI/Pa2gBSe82yWqGOYQvqUTEBmAZkNgsTgq6DtkU3LdXBDLuZ4RDqdZhw+UqxFwWgW2YgjyeSxWQxTXaVIhimbAWSBpYnvEX7yRcuJh0lKF2C0RT/EiCtlh6ktsErDhNJHTrdXlAneC94J7guBBTUh8lbuGhhveT8a2s4fVc4+rPHaBPrLrZdCKK2XZgBWtPLpafg83QS7mNdQduhswatbQgyR/Z0eyisXcWRWVFjaXEFA9pjsZkhDQWLOl6QjaZ1g7VqMjPUPy/y9nOwta8yEF0bR+stXoQ6wgMLN8ks3FYtU9Pi0XwOF34KBJDZacXcbKJ8s2Fu1hyZFRduFli5mKJ+cap9rExMy5fn99n8u9/kf774dT73n2+Qi6s+WFQF1Fm7Lgb8PfbDx7z+X1+y+NJtHv56TXsc+pivQudMwQm15GSONCnOk+x0+YUscR+SOPr7lfosOdEj9ulYXz4Ew2KiysYwv9/CpkUPZmF7kQA8aGBoO2fggym+WrK+4zJQvPAT3vvOXW6/85iTZsnhbM3t+RWPN3Puf3zC3cee+3/Nc1It8zVMtvJ1lu2ujYv3ssvXObObUUwBCls8a19RmXAv8tgnIUknANweohx35y1LV4dlpmMSo0utBinwpaszY77zdtC/N85ywYQn7Zxb9RUeYU2NQbl0oQ+Nz2tve9vby2Fm8wK6Ht6japG1y0wu03roAgMZGEipJrbZIJjfSUz+2Q7OJSA7jbl4ekKbl77+YQKmrelloes4BnoCW61gyon3YEwATsKPGbyYfnDByWnF1duAVbxVjJccXK0WGvaZwA7i/pKEaRX/Rva4di6Xc5HWodOK9uaM5e2aq3uG5aue2XTDHz26R/dPbtI8OAPnh4CwNfkaBqA5gs0ZqN0RdUwATPk9saGTP8uOoGjZnI5+1PEKO7ZJu9MdbZdsR6W/juVy3yc9mTWh7mUBsHyq9gJg7hDY0YFvvqOJntV1jRT02GUcJNdJ/yyVvz1r+xdhTb3QpS5W2mpyB0D8vHV2Ee1fdP/X7fOZy19gneeB0FvS6p+0j74IyLx1DC/Dg9CbnJ5EZaAwxgVQ14aEExGIterHdY+1MoOEk37uGVi9YYzrJYMTmFYygAOg2zebxv6UNB3mqoENmebQ+XMCh02cG2icdzfk+2JXoV1fpx2EdV0DbqoDQMyuIwDY9cCeb8BbxW6kB73j4Ud3HV+HOYz4QgUtXjI3ge4gJHKXILHaMK5IWw425Nq4auMc24Vj0omiVWzACxLZrzquVasFkOUkXncdxhzcUKo5HJvkcc7XBbgY29Ko5JYSf/M0LP1WjJGZ3Rr/JtA6gYR2rbEuOKiL5bzGyWwvgZW1pJOc9+4Vd71/U6ffsSyxpUuwWhTfKO3cMAPMxocgWezniWWcmeoa+39698Q4UaliktjT6W8AccN99pa+PnphJtZTz/eviEeBYjZ9fws1mmOd9sjmzolbsbZ7Pr5CAVBN+pxiSsV1SIouqe94jYB2/1mt0NWR4W1DUoPdhD513e1I/S9JfK9vCO2xCyp/pY9z3T1T6cftkhV/3TspAcmSfii+6+ghSuZ7/xric6sgO1++e9vb3va2t73tbW97+2nthUFpFwHoSdUFAMQH+eIsYWs8lXg6DWw/g7LpKm7OFsxsy6Tu8JuQrSt1jVQ16nwAmBM7EgIgHWW+dLMBMZimDsBzjKJq16HO9cHGVAvKWmSxwrbQTXsfN01a04QwTLx6L3Zc07lkw5Syh6mGDgwn4lkGSXa3V07wUj3FQS3pcv38Xft1Ru2l4IE4iSwmQmC5U1RMXLcArMc1dBSkAl1ZqD3zh1H2+WIRgiGXV3GipeG+VFUvvRYlNbWpSYC1VgZtqpDJ3lrEGHRSc/F5z2w94denP8ZKqK+buMMOxRSS3TWWuRhaHGv1mSEd2NIhLlOLD8kGeOb5pn565qOkc4vFeRMASfr6w4kxneS8d5lFA3u26I+BTRvOzxHknJ2ageRzsqlssNbz28ff51/81c+x/KObzP90A+vIeo+10aSq+oQCY5DVhtn7l5zOjrl6xbK6GwI64V8IDNk1fb+WEEQaSNAzfGZ2xjF2TPoSQ9DbINeHKNIK1cL1oIcx4aZbQWuDa2I9dBdYE3Ky4SAWuV5pzfRjy+VrEziCk+mKyniebmbIk4ZqrdQ31tTiWPiGqW4z2BMTuY5RmSSPDZEFHZMLSmujvIIRxWFyHWq0R3zS8JEAdBPZ2WtfD0DvWvyAV5+A7VQKofPK0jcsfEstLrO8XQS997a3vb2k1l2DkkVLQUFxXUgKS6YufE9gcfJ3CqlUKQBTkSIqvQNwkcRwS0FDL+x4pRT7z//FwKjmd4mWxwK9MkcCl41Bzq+Yvz9j+tEB69sSAu6k4CpMzn32+/A+A84ZmCkA8MxwjuCrVgY3tWxOKy7vWZZ3FZ06Fk9nuG+ecO+PN0F2vJSzDRdh8FmTJHkKYI7NCOoLadd0zlsBT3Ze8+uvK1kq+oXWf0bbSTJ8wOSO24Ua2SHBwUdW08tgn1jeOWDrxbUeASfJ1y5AnrEa0najDJWM/gLsk+aKfWJgddf2z1nnuUzqHQDy89jZz9znLgD6E4PWz2j/RY5hRxsvE/AG8Oiv36Ms9RRAVsl+t0Rmf5kU3a9HrOFKBiGBzNLsa0v3882QoDw8hkH/SeBsub84diewOgNc4+cvzRPiPvJxFeoErKG6At9ITmw1XQDZ0vNuYh1aNwkS09VC8zH1pQlCm0E+WHrgrVB58hW4gik9ltFPcsZ5XErAd8H0DMcVgP4B0Ou3n7sMTpZtSgT1XALGNUtr9+OPZjZ47p/ps4JxvmeSUy7X0brxe1QcEO+DLxHZ7NKFOf9WDXCKbV8CRY0ssZ2+qpRT0uG4PQIyM5gd/w5e1YR7+f9n78+aLVmy80DsW+4esfcZM/Nm3qHurapbAwooEAAJEAQ4oJtDk7KW1FK3XmQymUwvMr3oN0mmx5bUJuODZDKZRImiWt0tkk2RIIAiCkNNqOkOOZ9z9hAR7ksPay13j9hxzsmsunXzEBbLLPPsvSPCw8M9wsN9fev7Ftt2Y1cD4IbRnxLimhA2yiaGTB3M95Ia0r7Xto+U3yduVOHqXrDczdXzlH1MVb1Hz7H1ZR344ZTF7GV8ECZvmUvkXNFUmM42X3ERRWlH61YY9VzyROszY2v1nJKL5RpTBUYnDTIP+wKMT+cZuStc+d4fE/b3GbxOI5/Cdf1Zt+m44HF71wHzEpBJWu9q3oZqv5us9uvdiWi+xRZbbLHFFltssb989try3fbZocjZ2m+pctwN7OCdrMauYgsixnAMPPizCO570HoloLNzwgwCwBnBGVDLlOUcWJzyPhSa4qRU4NS99QDDO/cQNRq7DqQE5h029eI2R1/rRPQgx+J1NplwW7m2bQw2H54//83/eCRtnGuhF8O6T1lFVRVhKAuayuKSquPr82vk9ouvOqyfPML6Tz4CXyrL1o5tWgH9Lb+jOrZp3wkwrRaPA/rjADpv0ZyuMJwE8Dri0fEV3nI79OwyExqASHVrXRIzepK84j0YkRk9Dv3lDRJ6/bW/Q8xQezbWvs+5iU0W2hi2Ju9c5wHOgDRFeCT0HBBBOKYeLUVEEHr2GZDOASCQtkvscJGOkNjhrXCJ/9k3/hX+t//wP8LXnz9A+OFjcchXQR4ZOAAAIrgnL3HeR6yen+HlrsXuLcJwDMQjRnSWR3y8QB45pVy1WFUzRxVQL2Ixvu/MuayyZyAgbAjhYg9ugrDiKtlACwBZ/fkaJz+VZ+P+vSu81V7hMq7xR1cf4PgjxtNfakUufrXB2vf46eYeVo8dYpPw4TtPM+Bc5/iWfNDSByvXj3I0p+oeM2A6aZ84YqQknsIpaz8D0wASyX0gv6siAFsZCb5qPPuciPW+YWFNQ5jWl0MLB8Z52OJYAXmTHQdKzunFFlvs7hjFqQd8OgkQpyMNqk6SEqhTB6OCv2wM3ywnXA26CYXJa2VFmyvo75PXZZb/vo5WWYN/McF1EbTtsnwreSepV8CariOUOpmiinMIjy/w7n/b4MVXG+ze1neGsmpWj/ciqe19ySut6RpEEpwlKKmVgCrLs52OG/T3V9g+Cti95bB7hzGcJtDe4d4feDz81g7txxfAECXdyJzT3TkgePDKj52YE2PSfJIWODDo3Cy40dyrlt2UPtF+4AmgbacyuXKHeef/SLKbR9/nypM6qGN6spkiC1iRWKTa/z2xEfBi8w9gZt47+Ww/ve4U8cApfkN9UNXlJrtl+4HPfQoS3lL8rJ+8/o1mzvEqNin3ENB9TQf9LeXNbr/tmINz1Iu9uYd5rgPvjol6EOelrxsIKWiKHZtXc2kba48UMAZ168AMA1QzOGkn079VOQJa8eg3AcwwPra2CnSblpdliBNrcIxut1QUqEC33AYs70FAwFd933DQ1FD7WN6nxvJM9o70IoFclT/KK+2c1nXmPql/d278fpy8cy3NRp1OI+f5HrVNOfeo/KkqgKWNsPd7Vopzh/snvr7cuq7AWM6dGdz3enkOPERwPw5chgX+2w14B0BpA6EBHfJnnDom0U1Q4BFj3DHvzZS3o/6NAYJKNgPAKqI/Ddg89DgG4Hd2/zI4qUy8gbfKSiaw5OWux9vpuhflGa6fGzcQXOWJcwMfPK/sCmDMAZI9hUpKDtdX7RAlGI0TIXmVzfYAFMwGA9zUdbLjit8nB1oAopDggWabFIgWuXFLJ0dcAdI21Z1775CA6L6TgncPCcM9pe8Prkh357FkxoHHcwWjcqRVbT/arn0M1mAEG3/KZ/s+rfPs58UWW2yxxRZbbLHFPjN7LVA6KMicuDi+jNFnv9vnmByCSzgKPRITYnLYP0oYfkjA/XPQk+eSK1BBM/Ie2O+lUJXtni6OMiDtvch9pwTmCFqt4N55hO7Lb2H7qEVcUZEKnzhoZo3LvrKwubktRk5BA43nJq8GRhvAXEmOjfJLjRjTfJh7yo6r9imR65OJfH3NJtNdTe7JAG0moJGQ86sPB3zCK7yb3sXqe4/BLy6k7UMArVrJ8Wjyz0SSR7pRJ3TXg1fH2D1scfGBR3cPaF80AAH+bIO/eu/HuO9SBgJrv0a6xrFl6xpjSQPGlmYkZgUG3zwoHVzMz4RHyqDnLjVITBkk7NkL+AkFOillJm7UvNCOGJFJ2bqDSnSHDEgL+OjQYMigZw+PXWpF9jke48PVY/zDv/f7+H9vfgtf/cc93NMLcTTU+T4B+eudOFReXmG969C8OMXmi8d48WHA9lhzpwXN/xQlz1nOOWY+EMYBe8kZEwO1U0q36z2cVlzWlroQXT0l0NVOVBP2fXG+6A2QPOHkx4wHf77D9u0Wb59c4UHY4Pu7h/gn3/sm7m0YuJSV9tr3+OWTT/B0f4LVM2DznsNvnX8qgL/r4MECCkPA34Yi1q7PoLH1WWSXQeuaJe0r7dsIYciDkzKhJWighwLRECdAHAX1OAyK6BdmvWzPwLSxpLVxB3a47CXJnAHjKzeMQOmpQ3uxxRa7AzZ9Lqe5bfN+ScHXomiRbTp+139zDlIag9D1ftW7dpqPus5rzInFdacOcdcLW5u2HWi7l/dJfW6uBnyrp1nwoO0ex//uI4TN23jy62vsHxDiCnA7wO2GcowFTqWkErRpXKZzSK1DXAf0Zw32Dzy2jxz2D4DhiOGvHE5+THj79zcIz7fjdCNzwIIXdZfMJLI54HVmDLQhid8+OC2zAlg0EPBQipgPypLfkXMs1saEQ0BgAkyP9yfAk7KehFHO3km3mFSoc0jBlZyQb9om1TjwBfPN3w/KqMEwd7j9IDCuBgYIYFSqSLbLjKzqQZ1uwG3qAL1r97luLKjrPTrpdIdx2xysRa455labtlW96TZAmmf667b+vHX7zec86AsN7L12O27vm8/bzr+7kQ82JqSUczLX4wFFCdDBkCQQpconTymVsc/eISlJcI4F/AAyTlWAZTYDV+19Y/vHyRrCbCYQaaRO4b3M6TPgWilsXHNMDkKvfACkabt4VNf80gLIwWldbR9y4zUiMZdt9k6ocm3rBkkPYe9TC5CybY6A6EG7/fx4YefSNhT1Csr9xnUe7updVN7lLrd5DkjLbNeq7et0GfbXudFcAAAwlIA46oq7R9J9tHJcE+TYrh+3xesGnvyizNjSrgwsdc2MET1X27zNbt1KzntcBjIw7UJCPGLsHxDc4LFGBFgBXWU1S65nAgZlJTMLgF6Juxhz2j7Ln4olnwDy+lll4Ik5K/vN4bEyxytXawA2Rc7vqxzUYUFoltrDorqolEkRRUVhyqCux0dPGFYOqYHKkANuX/xQXqXfrwvCy9eRpK7D2qF7oFL4g8vv49Ih05d31SC2H83se3BCHNwYB4zpuWMqn8ayrl5sscUWW2yxxRb7xdorg9KMApSwgiUGqADQ38QSS37p4BLO2y02Q4t9F8Dv7PH8G0doL97G2b/uwdstXHMsUbtXVxXoLOVQCCLbrQxp1zZgZnA/SE7q42O4oyPgnYfYfPUBtg89hmMS2a946Jyac0zlyb7NU6u5O2YWF6NFRj2xnXOAKfA2AqSp+lvJc7PjMfDsqmN1Ej2SXdNzEtM4n5VJRNnio6of1fsRw4eE1DvQWcTlNxN2j1a4/+0P8PY/fwJ69lL28w6870DHa1ncqFOa+gF8vMbw1glefO0In/5NcYa4HhhOCMNZwsPzDf7+2R9jnaWMpTIdMzyARtsygpV5T2gYVd5pjP7GmxYfb8BWbhAwU4HNlesl7zEwVhHQzpuT8U4TcF2kmRmJHa7SSgI6btBYbSgiKcjas8fvnn0Pf/p33sHTP38fD/9FB9qiMKZrB0xikUfVwAL/6QscR0YKp+Dg0Z8Bw5oRW3GQpSDsNtdRZk4DcmumKqc0UK0RSW9Dawq9J2u2ddIcdKvnxYlmOfWg4IlJFJpT/vJ9j68dXQAAfrS5j92TI9xnwF84DEkGj0fhAld9i/aC8eybwP2wUSC3L3mhtS8azQsOCIt62ie1GYMZnBCcMNwBILiExJS/N1X+6Wl/58Adpny+DCy7iEG/1/msQcBOc1IPySM2hFO/r/JX8+z5FltssTtmc87eyJL/uGZCAYUhbVY7nGsH98hhXfYzAJqGCtSopB5zfZhR55k24IP6CHQ9qOvl3T8HTNTXZO8XYzoPAzgmtD96ivurR3jx9Rab9wixAeJ5C7c/ErCbuSivGFBhqUGcAzcO6SigPw3ozjx29x36U6A/S+DAOP6hw3v//ALu5bYAKd4L83xqzgFNQFoJKJ3zFc/Z1Hl5UyzcHLPsGjD5daSD64CBWWC6cqASA4isCiSk8rNyH7Dlpr0rYMPUbgBCD5SHMN+Gec48LQ/ANJh0CirdatPdb5mO5nXETfv8DA5vHl/G+Bx02C63Af6/cJuc75VY0AeBHa9/ztsCG16b7f0LNv9nPxp9p6apUln1BZxOUebzTSvpr5hLGgTg8H0BBWrruT8AtqBvcmVcOQBoLVhI5rw5KClXejLnrJ6xrK5lv2egdFAQT+viquDzuk76l3wZdMmZikZF5SRfUj8QydrU3lVWv+AlkFqBaAvUyQFaJjnuJAd3WgV553gnDFhrMw3sqafaNzHI2Smj1JksO42fR0LJX03lOwDEhkb+ApMtZw9N2VWepVqWPfsf9HcLDPYdZ6ZsOU4AQpDkmM7VuiPPBqO6nWvm7EHEydzBNP+qm4DYo+kMizJKXDFSS+jPCL7TYIe9BmkzQAOQVsL8dYklIA3AgRJBrqLVmw83oryLTFkgX+5B3e2eR5bp9vuoktpWlq2Z9d5ljPPFe9lugLbTtB45Z3aVP7rUT+S2AVFjcL0wo+V9W4Bsq1sNoNfXYIB7d04YTi2PjDbJlBn9WRhVIHT1W/YJzr2hD/rubvmdFltsscUWW2yxxf6y2SuD0gRkxnNmRFPF6GNSXygjJpdBaU+MTzZn6DYt2uMOwy9t8dPVGrv7X8L9P9ug+fFToO/hHtwHDQOw30u+whjBXSdMEkdw6xVYHYxuvQIdrUH3ztF98ABX768kgtMJm/NgEl45tKYXNbdtBF7bpNu8P5Nj8kIxg8LXlEGTvxUgnTyX4wyM9sAsa5rKxP+A3TOJhM2MasfKkIYA0yh/Qyvyw8EPiO8mPH6b8Ph37+Pszx7h3ncjmquI2DqsnnUS6U2EeBSwebfB1fsO23cZ8d09OBH80waUCP2DiHe//BR/7wt/jr/SPkMP0vzRQNS/CRhJJfcmd1xdTmRlS+sFmXR3c0dCVxuKOHZdzkUcQZlZa9uL/LaCoOzgNDe2N68BhH27pl5yRCNhxw32qTk4Z4SDQ8l7DACNE1k2AbEd/v47f4b//H9wD+tnD3Hy7U8ld9iqHUm5oe8BDuL4X7dS3rMLnP/+BqffO8bF105x+UWP3UPA5L44iLR0zlNXe4eZirxg3T21k1jv+bAlyfF2nsBtgr/0OHpS5ZJvG2F4BQf2ThbXHtg+ZOwfNLj4SsKXj57hMq7wk8t7oF7ye62eEbZDg4erK1ykNX7w04f44ouI/i3g2HfoU4B3PAKda0C6tPEhMO01P7jlcgYcVrrCtlzSfUWBnAskMMBcmoJz4EIPYNBGHZI/YEm3fsCQHDr26FLALjbYxYCuDbjXbOGRbg1eWGyxxd6MTR28nOhwey01ncGDVORL/TXPtu1vstqASI2kmB3w2ekOZGUKYgMJhNlSJFH19yHJb1FZdoCMzzGCc95oAuDBzLJvzSgD5B3TNKC1A4aI9e9/H6uP3sbFN+/hxVc9Pv7tI6yerXHy0YD2eQc3JDmv5tXmlUdqA+JRQDxy6I8d9ucO+7cI+7cYcc1YPXV4+EcRp999BtrshZ19sZF3muWkrvJdc/AKSDdIq1Dkad01YK2BuY6lqblio2s+yQOnbHYwj79n0zzVuQ3jzHmnfVy3KzDn187XiAQgOJkiclW3CNDAkpf0Ddusn5ev+WxWX36amWfkefWcN3/mdK4Cfez7TTbxmb+Kr/o2H/s0t+2tZc5c2vSYKV7zuvm7b8Pep3nQfyEA1iTwRWRXX+PwGeD7wO4G7paNTk8UFNWAzCEWIDXGkvMVClypzDMFL+OatVnwYBv39V1CfQR7S1dg83YF57wpK2gKCV3zcrDxvAR0xJVDnWsZ1bOTgdXqu0gdCwBm5cYWIynyFJDz15Jh3va64wKi1UElBeRDqa/TNUqjgbJ5Z2A4ZgwnDG4Y7HgczE26/uayf1aAsu2DpUBiuCNZb3EieZdHu+jJg+dQCiXOa24D35wqlaVhfDwPOijld7b+HajUJ7C8q6PsZ0zbbIlUxtnyWANh4xC2GC+yCYhrOZ3vqrHiVZ6fz8OYDkBjQF+JDFEtmQOorwOk7VgABLqW/J9OI9ILh+EY2ASH9iWhvUhoNgl+lzQPuJf7M1DGmvN8Ip+j5Pi2oLDyjqre6xkkRZn/zZi897jktI5J2gF6T9s70Kt/rilBEJQABB49uxIkIfdqzkWdgwD02WwIsZVjfCdS3fkauALqY/WszryDmQDfM7ZvOVx+yOBVkvs3sMxLO4cMTucLrupTfx4VjJGv7eCFVT/bONw+Ap15sg+jPC9zZS+22GKLLbbYYost9nPbK4PSgMzPhmSs18Karr8bYzC4hOOmQ588LvdtnmO3qx7bBwEvvx7Qn53g3oMW659s4Pa9OBMBAaeVcUoAKATQ+Rn4/ETkqIjAq4C4bhCPvEoCUZbLq0FisglrPck0s/3qOWn13XLZ5sU319smn2tmM3A4Ia9+nzKkD1jUDuIEHUU+V1GofjIzz191oVMvtMkA6SrnkNVbFwRpcPIZAHqRUrr4lR6739nj7GSHPnpc/PQMzXOPsJH9+hNGWiVwy+BeFhPxPKI52+OX336Kr58/xt89+3auYq83QETxwXbgAuMRoVPAGhgD0pFJ2cMlj3m8A6sDkd0uK/x9ag7YqpFFettDJKAdRZHbpgRHCSsFtAFg7Xp4Yuy4wS41o/zTZokdehSGdc8efWqz1LSV8z/65T/Af/Gf/g5+6eI+2j/9qSxygy9ybl5Z0uYcVyYDDRHuYofzP4k4+36D7sEKF19s0J8S4lqcPTkqH8iMLKC6p+sFIipHE5CdTGnFSA3gdg7HHxGOPt7J9q4HtQ2YJG8bN4XJtnpCOPpoB/eew7Hr8KP9A2y6Bn4reembS+Cqb7HyA/7Fs6/i6NtrsI9o7u1w7DrsUoOLuM5tBAiwb1Lq1r5m1p4m3W3KEJZf2lNCX3m/fOXtqVMazJlIfcv+Tp+NmjFtnx0xWhfRuohdbNBFCXLoo891PfEdgjvMbb3YYou9eTuQMzTGqlmvzLepRDePx+Zr8z/PMq8LSM3m1TaQoj6ukg0dSX+HAEYQgDh4CWwyMLMCVIlI1GuGCAoYS46b/CuzSKKenoCeX+D8jxlhc47nX2/QnxFergPCNmD9LMLvGa4z2VIgrRz6E4/+iNCfEPpzVe3oCOvHhAd/OuDop5tSz30PPlqBhgjGcNhmWbbVHMgKLN/kgK+Z6xPgspa7rNvmVViyuQx/y7htBbkKEL8OOVQwyq7J9anMhfU8NWv+TdnPLJ1sDvDaEZ0L1T+mBHDb65DGc5XbXp8HfXrLMVmF6TVsltV8A1g/JYHNne+AOX1LHW6X1r65wM+dif2KNqq3gih3yTa/+l6lIkQCBjXCgDSwaAwm6RjZIoNGAAqT1obzHnD78fXbOGJBNSnI/tYmh8HQKHL09Tq1Ak5rUDqfR9cErqfxWluDJUxpyZZNpsJk12JBFXlabs89U94OVHWs90MZZ1wneXs5olSCqjrZmGIgt6o/5fEmUq5H3LkMdDkDhSfP+ugZsCC0OngXACuw7Kv7kIAiqVy3JUkbSptyyRWu4HNmTVvb6v3tBmTlOL9jhN3hi8kCB3xftr1SUMcbMK7bxV6FVT1pGjk0W8jh8eYjASD91SQMx4xwJc9fauQZc4NTmXyG62X+IEEd2jdE4mephxrSelVm+aQpqVqY1ssAbBfHx1tFXRTgODVynZbjuhRMApJ7qcvo/eYwukkl7ce4jDzOeCCuSHNISx19B8k5b8WQnM/uFbuPkgLipd7l3dmdOGzfJgznlUb5lEwx11fTz3P71eNL/VtllDQYxT7P3Sv1PcZ6sa8zoVtsscUWW2yxxRZb7LXslUFpR4w+OTjizMCoc0jXZnPBte+xGRrsugbkGc4xUpLZalwDm3cZ/UmDk4fnOPvhHk0bQJdb0K4D73Ygk/AKAbxusfviOZgk6tpvBrAnhKsBrk/oTwKGI0JckThLdZGYgWJbeNb1NHC4rjQqJzKVfyZDxNVv0O8WHSoNVeaw9XlGAHhVZl7IO53zEiZS3lWlqZxj1ipmNAgq162f81+tk0Z5x8GBBwd0Dm7rQAMhnkSsH+zw2x/8EP/gwbdx5rf437/1u/jDH32A7fMWbuck4jww0CRQk3B8tse75xd4++gSAPBLR5/gK+EZehbgrwdQ8377CeB6nfCwSXZHBeg8LKf03ZAq7lNAD2CHBnsuj1NUuW4zA9MTKF+rAKIq+0wCckcm7FKDnucfTctDbcevXA8kzVuNws5+K1zhb//an+Nbv/Wr+MLlQ/ifPAGSK9Koln8MGOUw5baR56ePoP2A1bZH+6zBcNpgOPaIK1cWql7+Dmt11uvzFFcY3e+xxZj5D9mvfUFoXhLOfxDhL/ZSN6B4GTS/HekiOVwxUuPx5XeeIIHwsl8juAR2jLgS54EFzXzn6SMcfSpR2W8/EKlvTwl7DnDgDOIba33KjDbA2H7PYLRtR0ldEOGQZQf1uwUimHlipJkFsCeGV0/gPgFJc0S7yhO3ckPOV20geAJhFwNedGuElUiJL7bYYnfQwnhsOQAhk0hAjnJYmlTrHHvTfp8BXDPY0ARYrmF2rjCuazCiAqGZXHHS67kpsTDnul7+GXA+yXGaJWXZVRMM3d4PJU2EBT69vMLxn/VoX5xj84UVLt/32J4Bu4dB2V3Ic50UkIOhWIGT9WPg+JOE44/38C87uH0vgHmMYO8EkDbmIKWKXVi3IbLyCxIX8Hyura8zY1e/prOSSSRCkebzLx4w673JsKu8psm8V+VZrlLW9C21XGgBkua8vm/GbgM+ZwHfKXA0mQePnOCYSNTPVmLy97rtdfl8/fapvQogfUOmELE4Pg+lSdvx+H6RNcp0XJjuf/MpX4n5fMeBaFuvlR9w50GFy/fDaI3IJOBTagRgzM9xHcTsBDDLa1Db7rRPEuC8LvQTjRjIgIE05XyY3OM0zNzm1drXRdk/r6VtnV1L/7LI/Wa2s4LZLnK+H1lB3vo6rZ4uogDiEEBwlBKoOk+9ns8qAQocDis6VEOwpX6s6jxtA01vYVPs2DiRPlZw0lQb5oIe6t9SoHzMqAqxGisIkjaDARp4xAifBhWQpdyoFbAOnkvOayja9yXfsFlMJd1FHaRsKT3esDGLH8N8Tln5wynzXL8DuHE8vvbVVx1bB7y7kDDci3AfhbxPbKWQFBxcBMJGc7sDRXUjAIiUf7eyUzUvsqWdLPuE4VwrB9T3iOVvzkEIEQd9zMHl+ZWxlFMj52MvdcnPtt2rer/W9zzr8WBZ23cnck1uALwGtdhz7iI0j3W59hRIFAqqZ3UKFHfnhP2jBLRJmP8GlMeJ06xqu3H/2ssfKE4+HPb93K07HTMSJOf2wX56Ui5+xHL+xRZbbLHFFltsscU+a3stUNryRjcugYiRksssVkDmegZSD8lhYI+rvkXXBbiQwEzYX67gNk4XpYR4BFx+QNjfX2P9tMX6yQnWj3dwzy6B5y91cZRAMaH9dIvtByfYP3BoXzi0zzv4TQcfGeFSgLPdWw2GtUw2c7T4NQvuIkGE8YSzmkiPQO2IEavZ9jWHgC0eps4rA67r73IOLmVU5x2xqCfnks6YcYQSsjRnBqRdYUfX18cJ4EQgz0jbANp4+L0CbCcJ9z94if/xV/8N/rPz30fPDv+bx38X3/7kXcQnK/hOI/QDY/Vwiy89fI6j0GNIDvsY8NHVOd47eYmvrT4BIKSwHsJ8tvn/xDec/SQjmLnyexsg2ECkkyMI3bUw9udnPXsBB1MzUgpYux6JCT38Qf7i2iIIDYCeAyIc1uiR4PQ7Sb7oGa+lAJ4CZj7uz9BQFGY1OewU+r8XNvgb936Ap//pMX7KH+KD/8tG2G5enfVJJQXUeZ/BEEDY1Jq3jWIEtkC76dBCF8AGbpgcmTnNCVo2630s21PrdB/dzpLLyu8i/LaHu9hJPZqQJQO58cBA8heA3wGbLxB2D4/wP3z4F2go4un+GG+fXGH7YYvtj86wfwAcAdjHgOcfn+G9S8bzX3H4O49+iF1q0LgBp36HPgUkEBpKs7mfLViiZuMby91Y0lLJohoBcpnx3ieT3y5sZgdG4yTowFjQiT2SMqYdJTRESCRpDwYNAHJgHPkejhJaN2QmODNhNzTYx4Dj0OMk7F/r3l1sscU+H+MJKJ1z7eV3eMxjJUYyrO4QWAYO5Z4dxgxlqBPUmNFEJWbGvl9TV0rqmGYBpOnllTj1+35yTnV6GnvaeXBMoFrFxXKdGjvZkzjAYwJtdmj+osP50yMc//QEw0nA5u2AYU2IayqBTAz4LbDeMFYvGM1VRPNygL/s4Db7LFs+OmdKgG/k9x4CPnsnUrjBS38EV+Z+U5CdpB2S9htpwMCIWV457TPAr+83mshjj0CImgHvBFigmn2E8o6dynZLflLCmKJY9Wl2BrPI91o9q3qILO8d9K5OqpSfjwpEnbK+6qlRHWyRv99ymQeg9vQxnUzZZhnH9a33Ks16Cwh9UMb0exxjRcQYyddeCw4bqDXjwJ+C2NPvsyB1vctNAMDcpmnXzOx7wHid7nNN+dde/wQcAcqa7Gdm7X/G1mzG6zpjKJrsre9kewns1AuiyTpVjRJK/tddyuOYKItxkRq2/a2fJ9vsd4pcxr0MEKfxuAiMttsagfo4fg9GlmNjGo+/zBJUpLltkZKM2wCo67O8eQ3EsgY95fQX9bvQ6mtpHJgPUmGMQF0qgT/sXclVbdt1bM/nug68nQYzVUFCsDYD9N2cJFc2c06RYdvq3Nuk18nMWaFEdpvc2HatKYmCidafew0ssxzi+brrd4krv90UkPU5GqcyX3GeM1ANACnKO5y8KMLJpZTnojBoBV3MXUXWdeV9npI6cfQ4Wkds3/U4+y5AUXJyg4DhSBgEyQPEDmEr4HQ8KoPWsHZoLk3xRQY816V8X4uiH8HvEoa1y7mWxVdERZ48SUBFxl/1Gbd7Nq4ciBkq/FXAaFMamEsNQiqnr/u5Hpn1PRw5DEfCnPad7O47yR8dG3vHQp8VjJ57Six8CA0qSZ4kX7XON4Yjwv4tIJ3pxU7S2My+QOt96s9U7T95V9QBLVQfk+cTNleDjFP5HkG+PlMnHI2pXJ1jscUWW2yxxRZbbLHPzF4ZlN4NAa0vErFXXQvvJJfqkBy8ynYDMoe7v97CgfFyt0a8bNCc79E0A4ZPTrF+QujPGDTIhJgd0J0D/YnD5h0Hv2uwenGCo8cP0bzsEZ5vgM0O/vELnFxskB6cYvfOGvuHKzQrj3Cxh3t+hfYqILxsMJyv0J8GmWCvCMMR4CocyRieNUg9lQEzyzJeSSLS8z56oQZGH8iITefXSctytrjgInk2ilQ3QE//mpM3M7ErkLk+x4xcd76GKppYFnEE9AT3MqDt5LqGewPe/dIz/KP3/wT/6OxbiCD8r5/8h/gvf/x1PPvoHBgIdN7j7P4G754J87RPHi92a1zQCsGVhe3Xjh/jK+EJmix/PG6KCELPDpEZLSWs9Do8CYs46srRAdizRweHNUU0SNixx5oiHrot3rS9HNaFAa0d7pixiW0GKD2SMGeTR+MiknrCGghYL/tInmOR7BaWbc3eNXC0BlCjRkMkkAKsY6bsPjVwlPC3H34P//gfnOPZJ+/iwf/vU9C+E9ZbCIXpFSPgSu66qQOeKscLDQkcFCDRCyCTo00p71Mbt6FE9ivYAXNIMYsDyNh0CeLIsmP1dqUBGL6xwZfefoZ3m5e4iGthmLuE//mv/Ev8q3e/jIt+DU8JT/YnOPphA+KE3Zc7vNNeqNS2DHfWXmsahKkOl5n7jhJ69tKe7DT/OZXfgMyYdhQBBzSIiAo0W17nxA59xXQeKuDbZLkHk+jmIktv24fkESjiOPQKWEecNntc9StEdhL0o6zwp/tjrNyAla910hdbbLG7YKnxI8YSe1KWrL7u+5idj7VMd2aVoRqDLYprAkIfsCOngJsG91wHONn4LFKgqQAQCiLfaubkrpU48sknYC60/t6BLrdoNjuEJqB9dop4HDAcBZGC1Nmp2zOazVBY0fW7KRYmNKDO2mluaztn2yCetKXKnQAlh8xSlaFcecTWKdMvwe1jBVBr0Z7AjQP1Bs7LC4uVBW2M6NKnGDn7R0yckTz3eNKU5XIhf3k6wZyw9EoFpY+T5am1QMw3bLcBuCNA2uYeoDxfnh5vgQGHAKvtgOJ8foXzj46pyroJHL2tyFc656QgxvicxmS7djtmQFbDWar79hdqPKnTTdd9S31yTO0ctnLTOV7hOlmDQm5lq39O9uCffS8H+ZA9r17njcPh3I7r37ISRQUqqnoELC91zYQ1C6GAkpMUBACKIsfcsXacgZz1sfbdeQm+cQ5k16KKGxlcjUmUNqxYBWXZynX67qpPXdfZ2qkCamvLoK0FxFo9qnctgi/KTFZ/7zTASdvU6bswSNgqDbGoO03fIXMpGaZjew7QTdJHdi11oBVNhJ+T6jOpmkpWudLfRLHK5bzilJK07wzYn9/r1bVnxZA7Akgf2FxOhHq8NB/UjNyyAb0Hpr8zIMBnfh8z+vsR3XnA6rnKZgdCNMk3cgowO/i9zJlSkHvKd1wxnHkUQJKaahzuD8c2ihY0ovVy+n7zEjBR52gHAUmD64SBTaMguCy4ZfdaBrvL+VIguF7q7xrKkvC+03XuII1jygYlCIZGXUGpYMXJK+t6kOtJDWF/n9Cf2hhTtb31YfV+vtGmUWoVY1ryagv7vO7b0bvcWCOT97sZT31sth9X51xsscUWW2yxxRZb7DOzVwalgwLQkSlLKRtzGsAIkC77J1xsVjnCsesCnMoz+q1M/sUpCyBK5GlcKUh67LC/1yJsG7SXa4Qto73oxTE4JKw/2SEeByAy4nGLeNQgXHagPqJ5ukV47hBPWwwnAf2pgNOpEXkhxyxsYZs4z4DINTg9YmTY5zwxnyyWJ9Hq9e/CqDbQGQWUVuC5yK9xcUDW4HOejE9+q89nnrPKg0aae4g7D0SC2znQAKSjhPT2gHsPrvArjz7Br55+hLXr8Y+f/TZ+sr2HZ/tjtCHi7Q+e5wAEALjsVmh8RB89mAnBCwP0tO3wcH2Fnj2epGOcuRfYMaGB5IrutfpeV0UmzZ0gQLS5JVL1d6Xs1MSEfc08vgMhq/sUMvAOiOSzAY4RDg31GYQGSm5iAzdT0fDKYKixpJPqdSV2WWo6gdGnMJKdFslvNwKsPUn+avv9P/nwW/jf/Yd/C83mIc7+8BMAfZXr02fHiDgxuMjLGusZKTtwROJLQWmnIEnt9IkVAKGWy7OE4rUzBxgxF8S5XD1wWpbvGe++9RL//S/8EU79Dj/t7iGyw9p3+NrqE/zHX/pDfL9/hH+z+RD/r4++gaNPGMkD733hGR6FC7yIx8Ky1zZZuR4NqSw2Un6IhcXskGbur569tL1uM/a7Mfn3HDJj3uS3h+SxcteDxX3ycJQkr7U9D0wY2GHtorKkRbr7yPdYhQH7IYD1PktM2PQtLprVCNhebLHF7oallRfWcWRAQdQ8QvaxjIeujLEjMuyEnQbgdilm29cZ6OyqOYM4KFkBVGGF2SQoyTshRZBzSPdPQZdboOtuv1CnrC9gDEwbO0z3yYCFXUMSh7n/9AU8gLYKcKqZcdnhXjvvuUyGsuSld+W9ZWWsV4jnK+zeXsF1jOZS2NZEDGj+xZFz2xFS49CfOnUyE4In+G0U+Wxtv7j2iEcezcUAt4/XzP0mP5o8ugGu3h0qjQCHZSWAnILdfvx7ZvmBRZKyknJlA1/uCCANzIOMo+2GOVmbAGXOTeN9yn7z5xkFkVafb7NpmXNpJaeA8E3lEhf86PqdMDrJATt7yuae1okn+xhYcU15ts+ozKkU8c9it4H1N4DNB3W6pi6jNZu2W172zAAOd0m+fs6yGoWbqEB5B/i2ek+ogoOBxaZ+BJSxcXqtSQFqPynbyrCUEQZUko7TTpWR5saNet5f/9XjmKiofQQnyhPOFB0qYHYUpGPvqzIeT5nNWemBbb0g9RNFJuRtNeAV1w79kfgAxgoLKOtx+82pb8Db+YWx7gYF3Frk50okusV3gclzN6rzVNkhM14LcJjLtbze064aipQ5B92eqnGQUdIbQH73e2hKDIbfF7l1a+fkRR5eQMfq+bmLS4kZUJAsXQWNhwlmKipxDHAS0HL0Oyb7g0cB/M4zYpuweT/Bdw5hx5WPRmPNEmFYA4CD36Us8d1cRKSV3PuypgYSXLm/vfyeGmWxk4K+juAGLkxmC74bxo90lgDXIAphY5frpererxnA8rfkJAfLfUQ7BaB7yTvOmRwhz2EK9l4o7qdRV1RjrqUcAATw9j2jOyPsHzLScQKGnzMKaNZZBwGiE0RyLwPQt5Q1Nx+wz/Yc2Fyc6c6lqVhsscUWW2yxxRb7y2CvBUobMy+mMqm0yNQhegRvABsrE9Cj2wgzxTlG3wW0vURhhp3kmhUZbwCD+k+VBZEaYDgB0orQn3mAgebSw3eMZqtyZLFM3ikxhtNWJIS6COojwssd/KVDc9Egrj36M49hrQB1C0SbmJuzqv5bgcs5R1e9L+TzbM6ZeuI6dZZZ2RVjOju/9Dvs3FNPkpXjuZRLXKJ7jSUNXaB1DhxJchwlKrJK5wOOznc4P97hnZNLnDc7nIQ9Pu3O8Lw/wqfbU2yHBjsFv4KPaJQJH1nyOhkz2juB746bHvfaLX7r/Ic49Ts8iad4319IDmWKyvY9nNF37AQUpMKoThAmsIcwqcHABiEzhiPuRk5pk623/MN235sZCG05oxMTGhcL41bBSDu+54BNatGzh9PfIzvJXcwu7ydlTr4rY9ryJUPB05497oUt/rO/+a/xf+LfxlcvHmL9/SfZucVNkOj7fsBBTmdg7OyC3oIxFscYJ/CQxiwuy12XneGuUgGgIvlXS9pZdP4161XXybmPXYeWBlzFFfZDwFdOn+KfPPs1dPc9fnP9I5y4Pf7zT34H771g7B4QfufhT3Ds9rhIa0DZzo0brg1qEDDZl7Y1ZjvF3A/mBfIQYNoCD0xuW44DGkoYVMLbfrd72J4lAaADAiUMldfK2NJyjyStQ8Jp2GfQmpiQokefHC77Fdolr/Rii905G449KDq4nuFikhcdQ8DNQVhS4rj3I3Y0gKwckaVPDTO4BTQaATDMcF3l1SQqrGzd16m6RQoOaeWBkyYDwW1KcFdbcBVINGfGemMkGeODL3UeMb0L2JGZ2Hn8V+9ytEAoZZKZcxcJJbVEUdo4kLu29BTM4OM1hgdH2L7TYn/u4DsJvGoB+G0vIIAHcg5iZvghAZ4wHLeILdAfOcTWofGE9nkHiozhpMHm3Qb9KeHkI2D9qTAAadB6Geg8seSdMOkMUE8sBEeG/Vf6MKuVjOeZaQrU+DFracTMBoqMJmEU3PDGbAqEXtNWxlofOdmvsYNc3KA8RzcHc57OTOflwKG6wNy55v3hdsJbgfKfVyp6CgYcsLen0+wacFO8ZY5JPbU6H/ccSHXj+DPXtnO7ZeDgsCwCVc/Q7WUd7DN3zEFfVGDoXXgm3nsbAMAK4ErKAZ+Bq6xAVL0jWMfyuHI5yDrntzWC78BwnbAvecLgte95bNAhaaRGQCVXbD7O+nh670GAzhxAUgO7CvTm59nO5av72EDWep09XRPUwKt+Zg1oryXZDXQDxNfQnzNSy2VdPwH5cvHGTLWcxbomp47AgcEr3TmR1pdKqq2qjlSdgCslMxCK4tngivpJSFLmQML4XBk6rQ/uQKBBx/WWNShY62D7JIBM0jqSyLd3BNepHHMGLCl/jiv53SSgy3OJu2MHz7e+F/WymSG+jiw/U3xTc/foHGiZAemqH10TgXciroY1XEdoNiKzn/KzBvTHkq985YBh7SSAIXoJWBgYce0wrKXQsBPfFZywh+OK8pLS71nk+r2SPagwjl1fjblVHni7hjohS2Yw6/NpASX5edD3VAoCgPs9I7UuM7T9VqT+49rl84gsOOfy6uAfe14lLQDkmkikuwFgf+6weY/QPRiAUO7Z6fOS/Vk3sZHtuHoeM+lTmuv32iz9nf3z1WcrL98f1bxyscUWW2yxxRZbbLFfiL0yKE1UmHz5e5o4xlQ6xxh826EB7zwQkvgJewdSuSLaIy9AR04jdRSkVsDrnFcryGTXViGUPPxOJvLNVqI7w1WE6yVnlCcC7Xu4fQ+32SN4h+Zpg7QOGE4ChlOP/sihPyE9R4mMLgBxVS+byE8kvGtn2ayjq/7dFqS69inAsp5LF65c52UkCAhdn9QX8PmAKQ2Adx7UE6gnuF6lKNcJ7mGHRw8u8Oj4Cmvf50CDp/tjfOfFQ+w6ubiTVYdVGBQYc8KIV2amga6bvsE6DDhqerCCrUPy+LB9jPebZ/h+9wgbDjimYQQmm0Um7NlLeQy0SGW/3LhAAxbwV5vAgNy74EOaWkMx3/uSU/gQPM+gMQo72m68CM1PreAyEsrxlfx05KmXBvl3p+BpUmD7zO+wSw0eNZf463/1O/jWp7+ML+3vo/nRE3ATRM6bWcBpTcqVJb+y1d4oWXiaJJjJvmYg2hws1f4YUlYVvJWBo8yHqfmOsR8C7vsNnsdjbFVD7f31c/yLJ1/Bd47exW+uf4R/u/0ywo9XcH3C1QeErx09xot4koHhxg3aT+M7qA4OyLmhWZj51t4G9lvjrFw/yvntrT8BvQ/k99yfkECGQX9LGnCQmDCgyHHb/WP7mzlKOPI9NkORoLX9L7sV1n6S93WxxRZ74yZzGQJ7yVvougQXE2jP4DYgrULJQ2xqE4CwgDSAh/ZVwIkF/NQ2leVWCVC2nHqV+oWwTSvQMjjQfgAHJ/LZxyID6vcVxfIVHHO1dCoPg9TBWHzGcC47owQt6V8Fytk7oKUx09r+1sBzXY5tU0CahggOHtwEpDYgtcIUP/l4AEVG2EX4S2V/O5F7JAPrAS2Ds6MV6hyOa4e09uDBoTsP2N9zSCvJ7WjSmSILbI5hvR4qdc4+U3KAU4CZCkBEKs2ZgWY7TgEnDpXjGYAxmuxVJIpDlWPaLilg5Cx+k3YgWTojFzvrm9bpbs2YlgLmzsEZmLbvwqZVpYLbmmFuPn9DHWtf9tz2n9mmbTXddgAYzxRxUz2m64dpvGcc70OM8RyNcSj3OwckTk7J2mAGQI/35xtZabcxrOfmmTzXmW/+Ucj29Dcf6HqRFJAq69/65sqsZQXFYqvAYjX/rpm5rgN8p6DjtAksUDlUx+f1KEADdIyar3NmT06exWmgdq6PA9ix5HpVICquuaynI+D6srieu28p0ZgRrOuR1HJOt0URcLGAX8MRYziLspbmyc2MCpDUOuX1eeLS9o4O2o8MFI5u8jsmgQ46HhvYr43mBsjcwLEEniW9fgZw4co4xcj7goG04nztJrcM3dfAR6fp0Vwv6yc3ETsxeenYIucfH7Fs78qzkV//ZZyYTgNmg1KuGfMO2NJlINIyVVkuAZwIrkkYHgzYPWoQfsgSXDjIMxNb6de4ArrkkBp59/Z7h9XzCBcZ3Issdn9MiC2Jmn6SYBFh2zNcRM697PqShxlAUX6AVZNz4In0F4+uN5MoqrkbVb61HHTiAQyynZ2r/FxC9uCOASfnkqCUmXF60ubmM3NR2okdYfuIsH8r6fNUfDuYXFf+/YY+NHnuOuiDq0Dw0bE3Wf2y5snn8QnHnz+TF/piiy222GKLLbbYYrW9Migdk0PU3NGk4GQEEBPBa9SvgdTrMKCPHp9enoAGgjuLiJGAzsFwExdVTmqQuV5SOSoaVCbLpJJQJtAmqxVbZVIfA4CwgAHADRadKouw5hIIu4TmKsHvE/xOGNTNyw7tCyA1DsNxwHDiMRyJvFdcqYPPHAJNqcNoYW4LAFSL7Tknli7Qcw5pr8d46IRfj/dcWNIWdZ3kdxg4buBsIs2PpIvXKBHUbi8gtN/JgiXqIj2tErhhxJ3HJ5/cw+PmDO2qx6oZ8uKMFExlABe7FS6wQvBRFd0IV7HNkcfMwPnxDl302G4lt+92swIn4K/dfx9faR6j54Dv9G/jd1Y/xo4d1pSQIMzoVDVSYkIiGgHWAJSVKkCt5ABOWEOYqAl0J+S7XVUHA+wlR7HcKD37DObbPjVw2WAYgda95og2qz9nQLQ6903gtJwv4iJKnuVdavCb936ET/72GT795At4/wefCjtaWWci2ZrG4EadY85Nfq/zhFaWHcFSERAzCKnIkE2Pve4ccpHlYwSudi3u+yv8qHsLF71IyH/QPsOj9dv47ePvYcce//XTr+PoI3HQDx/ucOw6vIhHWLmSm9nunZrVXgPVCSxs/BQKyKzH1Cz0ui+gjGkvGqt5cRtcrHJQyz4WuFBb/d2Abju23j+4KP8oYZ9CTqnQJ3cAVi+22GJv3jKAGCSAJzUelBwaCCCZGsp5+4gFCJWxEwAzXJ8AY8nZmDiNypq8CihOtkWbR0geT+IEpJTZePG4wXDs0Z17xEYkFykSXM+gPkr+0qn064xZLlTuOrDz8iZom0OHJrMobowO1neEMacVlGZH5dz2/rhOXYM0z2ZMQNsgnazBKw+KjPb5AL+LAn4nIK2DMMgTpD2YgTYIS7ENiGsvrD+U/IqxJWwfyaRwf98hHgF+q+f2mt6iEXCBvfR3XGk5CioPRy7PJ9nJnLMOXhyOyvyTzYGsINE0DcwYrJp2BkYACwfxvlK8+47VGnQ/8AtDL/saVZXDwlCA+2qOTlNhkRng9KZp5vT8s6zlaVP/vNPW2oEOXMNinjnO7gWagmXXA47ZbgGYpX3HFzrFLqaBEHm6o/W3Ndy1BczZzC4ZjJ4DNKbAud0HdyTCtT+hEVs4x6tSBXLqOtDGBaIC0qYweVYGkW92PQsore+YvJ1lG4hk/Z0ZmboDC/g2Ao5ym2mwjr6zxvN7/T2VPLijuFZnYKqcKGwjkpdrcEMqklnGCJ/cOyNVBYamn6gCf7QObpB3nKmNpFUQVYqYDtYaJdcya1s6WJoFGzvsuk1ynGIC9aoKYvU1S5qu4xpjDcIirSM3vuw/xKImgupap2ktrM0tP7Stqyw1Uy3jPkRZ700DvExlxNZ+daqNO8QQnQYNYHQZJAF89rsNxLbvDAB6XYAKJy0LUMlv+efWA7ZfJLQvA8KeBEju9VSRgHVh6g9HQL8nrJ/I/eT6BDeIdPywJgzHDikQjj9JaDYSNcEeGaBOjbynCmiNHGxR34vT900OFKqeg5r0YeUYyMyO4Jwwol3HqnIGEDQvc+Ic5OIGCaqUsZLzOaQO2l6+BMbZO6E/EdnueJok+IJYSRaE8VhkYHPtWKv6bNJPo89UAGp5x1kKk6qcat/Z8vS+kUANzmWVCuqujrHYYosttthiiy222GdrrwxKm5QzUMAcZhLmDyUkFgnvozYhUMJ2aHB1sQYHhg8RKTogUXaAsK5Tc8RoRJXvSBZ/KVA1EbdIUskVmxplNwdZjKcGiA2AVvbvQdi9BRB7AasHizRGjnT1nSwsUigTfTNzVJADOOBwQmtz06njaepbqRxrfM0+B16k0aSbgDjZN9F4Iq3R09JGQLwnIDSvE2gV4UNCaCKaZtDgb+1Hl+CdBhgkJ5LrySGqg2iIDsPgMXTSaanzQOdAnUN8cY5wRWhfsrCavsjgo4TLYYWGIk7cHh/399GvfqLS0gVg7lnO1VBCr6sYr0BgbQkFrG6QsNZO2bHD6sCj+GasSGuX+iQmRBTWa4MI54S1HNnhIrUiB615n2tGtYHuxnYe/absZ0Du73qbrQaNeWvnv4wrrNyAM7/Di+EIv/PoB/jO/2SD7zVfx/v/z6egrgeaVXZacPCzrCUAqMFkGlKWmmUQSm5pVic/owYOqC5j7m/S1a867/Vi5Fw6Huw+PkGDiE1q8aI7wlHocd9f4X/13j/FN5ot/g8v/wr+6A8+xNtPGVfvefzO136Anj1Wrsex6+CREOFUHr30FYAKsE6I5LC3aJS6rzXAIE762yyzoSGS2wMkn7Q8V37Eqo4seaedMuqlDpyluVsfcRy6wqaHgNWeGMehw5B8kfBmkQK/7Fbz/bbYYou9MaOojm3Nfckkzn92hOHYZ1DahgcbWmq2X+1wpMTCpq3PMQWBupTPCQBemdY189ZYvfHIozvz6I+F6WPMKmGgEbgN15GOxpYYWDVA2whQYY7yWEl51zbohTqLuoM41Gvms7Yfa31HznPmMbig7UsJ4FUDXjdIx43I3xLg9ykDD7l9iUS+ceDROQ3Ak34pDuX+BIitE+WeVpy8/SnQ3Xe4+NJRkdU0xRAuTEcDmVxXPifPBZAzZqCldDFMwupjajpV/ewclKxv5buAWSOPdZGMvQtgw1Rqu2Z+A+VZqJ3KahljmICJs6xK1mcPVPpbC5nLzzwu8ObyKU2WBNO1wEwZr/YgvbrNnnPSLrmeXP6Otk+/39KuB+1wWORBndLMhWfmOnAQ35iZtNp/c0ukgxLrfaqxz851cF11nOcdeCTe+Zcv5AMzEBnc+KJwEbmAnwc00erzkA7eGxRjBi2zIgcwAlo5+NG4SkMczfcPAlFtbJ+ev94XkLHdxn5TfPBOyk9JxmJmGfcTA8NQjqvLrc+l6iG1MgfVAGxdD/stBHgDa4HC7J+uQ7QcXwVCsZdrYO+zslQOnBri9bm66xQLk/GOLAhX32nUBGDfyXVreWxtYXW1/TWAipzPQWDZ6vaOqaRacg4YBknBka89gZpG2rIOOqu23wWr8zyPcj8D2QciqcskIFqGDGsTA1Ax3j8XWN+38lJNVc5jU/gDMWgdcfVFD9cT2gsgbBmrlwkpEGIneZRjS+jOgasPCKuXAaunfSW/L+NNXBGGY+DKO4QNYf0swUWgP1LA2sscrNmqX8zAaZ2vpaDBgvUjofL7FrhCsRwzCgjRdBgpyJyG2MF3SVOjUA66YF/yJ5OyvQc9R+0rI555b5K0w+6UsH2XMZwl9U/Zs6dzEUflXWVlmBR97vtbBmYe71//ftMxVs/sS8vtg3w/5X0m75XFFltsscUWW2yxxT5bew2mdGFEA2VuZqBOSgLJBJfgXUIXPdI2qHQ3Z8daPGLwlSwZUqNzP80XCCDnsgpboNlXC2jNSSWfSQBoEuDa2CNxRQWwbuwYZTtTvf6wiHQaRaCP8lxROS8HiOx4ks8jR5DtZ3+nuaWqBstsb2VIZ6lu+1vnnbI1uU/5eABFdip75nRNSgX4C0FA0BAivC7WiGQhxwqsJSbs+0YDox1SIqQof1kn6tx50N7BbR2aK4LfAu1LRnMFNJsE1zPCNmHzdsDuHUI8YQSXsKaIt8NL/KB7hKdxjWPXozP2LooEeFTQ2VUNFiuQzgBpD5Hw9lSC5v3BSujNWNToiiy5TGVBWwBPzpLQtVnO6IPfDWg+OFeR57bv19E8TD585QYMyeFxOs25iX/l/GN8+/fewadXD/DO//cpaLMDt404pmq7ydHDrLeg7uNUAsxzjvhnyztdR1UkADGOgO9RQMgcA1tZGe0Th3+3/wANRdxrt2hdxEU8wv32I/y3+4f4v336V3D6fY9mG/Hkrzp8ePwUESR5pBVwduAROGxmbeM1h3NPHmvXwyTUfdXO9tkrGmB96CnlZ0uY8yn3+4AxC94To09F6rvuNwAIFLHSHNF5jNXjV04A6y557GNhS8e5xfliiy32Ri0r/ts7XNkzWYrZy7Odgbg8DFfOuRH4dMtzzoAb3AiYIfYjYLt+dUTNbajZITRIkPOcJZ6u0KzX4P4V0gN4B1414gfedwIgxFhAB6DIdbvJODwMQHIVIKH7xyQsL3PA35LbmoNXQLoFB8oy2A5QZRBkFh8AqVeQiScHJ/80j7cB0vszwnBCGI50bmmBiixztyEAcV21K4skq0mk5v4nZFZiVtepgPDRfNJSs9SgdS3LCZT5anV/Ada31f0T7sZ86Tp7pVdXfX18OxA62mSgpLUp41CZ5bbTzwCjt9VpeobX7oXRcz9T6AyQXk9v7P67cbp8Cyh94/lv+u2W7QwBmGarZHEqXD2jk+J40g7yG/0MjXw3jH70MSiEMjYasGnBODruZWCxBoeqOTOrGoZ+GY27c03DFUBK3oGZ5TcWBaUM+F6XOgE4HMvtmOBlXeFcZv8yEdAkAXSZJTBW6wfvC5hrZUxZ+G5SHytTZL0KKO6clOV0TG8m6xvmDIpx47I6L3sHbh2SF0ULDi6/f21tQ3EiiQyMgiBM1S03xwQDrQE/iurH0PQVBj5aHe19TFEDBgzba5wG5rvyLKgahzHZrSwJhEtVWSm/O8CA62eCEO4GJl2sBgut7tNddMxgaIqzWpq7Hk+q/W0/Nv9PHQVDDI5OyAkMDPcH7B42SIGwcrL+oyRKEOZH8ntCd4+xv0dorhyIJW+z7wkDyRzCVPi6ewQ3OOzvE66+mIB3duAnK5z8UAIEReGAEHacc35DqzglRmS2f7WtTutBth+VOemw1hRvnvI9bYGLWRZb0wnIPVkFT6Zxc1p6EQHmCfuHQPdWAjep9F0VYDC6DjtVrZjxsyxn50DqURDC5NxTu4kgsthiiy222GKLLbbYL8ReGZQGBDxhBT9aPwbZkgKeqyDMwG7woN4BpzKTJgBoEvpTh/Y5we9lEpsaKCOlLDLZkUhWAppvRxbWLjKSOQs9kBpCbGQhlzzgd7YYVPZ0EE+kgdRRnYkpcHYMjvCpyYSUHZDOB1CTgIsGfuNgOarqyf1ogXTNRNqAaA4M1PKLjuWfZ5BnOAWnnZPrDyHBOflO2geRCV738Y7hXRoBWzYF76PHEEV2PTFhGDxSknxKAkL7vBBjJiAReOfhNg7NhUNzAaxeMMKWRQK9S/C7BNclpFYct7LQRWYG7VPAjj3uO9G0/CSe4SvuKfbs0VBSdrTcL52yVoEBO/gMxtaMaQ+R7DbJ5QRgzx7HNNxwp34+lvMCM+Aco2ePBjGzlIFK1ls9fb1KPK+rfMTRGNMsubmvPR+L1Lkdkyo6laOYzxEh24TxG7ByA/ZDg8ZHHPsOL4Yj/N6Xv4d/9ve/gfXz+zj/g8fqEGqAri9On6mDYs6JmyBMMyIgOJXmGzKLWha3vmJ5pMIymAO9gdnfKDHWnxL+zz/9q/iH7/wJvnryBNvY4kU8xg+H+/gvL76Jb337S3j7sXiGT77xHO82L+X+UmDYkzhinALPUwlta1cDphuKsDx7CTTqRz/x3GZZedIHIfsBD6W63WHS7ooNL/sGl/K9YLmnDXRuXMTKDzht9ojJoYfPageLLbbY3bIakGQC/MDwXUJsncxn1NlnjNcsh0qV088+01iCGMDBd2CSUxgAOzf+bnOXqn4yD1PFGnOeD8BwEhCOVsCrgNLOIa1bUBtA3oG2e3DfC/BgQAsZUCAggciVx5J/GkBmWNs7Zw6gMGBiwqbjdYN4tpY5ygEDtwqAJAEciJEZOdw49McBUdO5DMcC2PcnZV6ZAV4tuM7lLNeDEcM3S29q/6Z61m1zx6pfyjbKx+TrBeediIqDuESJVtPY2gn/szh4f4F2Gyu5uszxczAq5Pbysz96bt/bHM5zoMct7Xjrdb3mK/rgfLf145w/HvqY2D052efADz/dPgNSv1a9rmtnQsVgnznM8CTGITBJyM97Pn91fYfYAhWlgFet3+dt3gNNA163AuaumpJnXgHJzJiuANnUhsKoBmBMR7ZUBpBxbm5encdDTRtRwFD9o+vtOsBJgpX0vcVjIEvKRE6BlRoBqZKnnKLLxjRTRnMDH46XKO+15Cd9P1FRsGBvy4tsMuUyXku6r+GIkFRFzcquz5mlz/X+kXeDqbApAB2Lf0EA36pOM8/DNDikvia7Xyki55S24PmR8nQEXCc5o/2uvFuSF7no1EJyadf3PAOuJ7hO29jqoHWWc+qySOtt6nEj0PyuPBe11eNXBarP7prbQyW5WQPuqQasq32r7znmQd/BPJD4gdqE7QcDunse3UtC89Jj/UyenbCTf0ePE1zv0J0Bu/se6+cRTKT5vRn9ufTD0cfAcELYvUW4/OUe5+9c4r/74R/jXz7+EB89+wCUJGiRVK2lvdRAAwv61meJZSAt8x/SZqmkugHA9wVkdoNI+vfHhO7UYT1I8IMdn6N+nDz3luOadBiaC4yKrfy4vycM6f6+AtKASHarol99/x/0bZ7rzPfpdcakgTh239ZzoLJT+VwFHeSHZ3rOu3j/L7bYYosttthii/0ltFcGpU0uycDny8GDiBF8REwCprRhwMP1FboYcLlZAQz4JiEqGGq5klNTnKbEAAWABwFKbeJuC9MsZZlkcu4TA7uY80oZgMaeEFcOHChHHYeriOZygNsPSK3HcByQVg79scOwdhiOoE5HWdzFVS21qJPUSML4bBMGp/n4CAVUZowdTZ5BbQQ5AZlDiGiaiN22RRocnBcwOTQR3ic0PuLhyQZfPHmOF90aH2/OMjBlIJNXgJqVjdtU/RETISaPlBy6wWMYPPptA956NM89mgtCcwEcv5BoXUqM7SOHi68A8TzCX3r4PdC+IDQXjGYjLOiw7eH2koubYsp5Es0JIfJPDsORy6x0JOAn23u4SGvcd1t8sX2Kp/EUX2mEsWqr9AYJPTmcYECPNAKhTQK5oYSdylX3GeTT3+4QS7pmsSYm9PAHIKQBnLvUYJNaHLsOK9fnnMU9e+xTyLLbTQUwOygQOpJ+dnnfyMrypTGwGeHgeAyI9uxxOayQmHDW7PB7X/8O/j//8Jug+Ahnf/IMmV0wkrm01Xllth8AihHsBExIQehf1FeekNqJFtNYtq+WnptKsU6MInD0JOF73/4C9m9/B19ePcVPu3vo2ePfbr+Mf/zHv4n7fxTQbCIuvuzxt97/fpa9zjmktZ0aFAayBRBYW9oz3fN4aDSw2vpk1L/XMN5Fol37ghL6Co0QxrZIdVvgQhc9Os0Tbfd4DUgPacy2Xvsel26FXseJOZB9scUWe7Nm+UENYHG9gM6pKYDoyKlsZlOMmwC1ORDKAASa/lYdUzkWyZyBHmVeRcJipCQ5DodHZwhEoIurwpgmB0lCeChTmlYhT4tI5UFH8smeJQiqlnmtc2gaIK25N/M+wCG4wiwSq84DTQCvGsnj3JCCCVwkz51dHxSQZiRHIsntCakl9EcO/ankQxyOpJ1Siwz4MpAZPSmUeaA4/SvHdjJAnEs7T7qMUTl67dJYwISa/Wz71eUzUSXbzZNtKAGTgAZX3Z33wyHwWTvE6dr9fqbyJ89B/vkWadCDvKOzYOfr2QHAOy3gOnC23n8GELj1vPV+tznfb2tznhmrbjl+mhJmlIMYM/dDNb4xUNZYFZYwlYw2MCaXN73m0f46Ll0vNvS527P/6GsaaC2A6LCmDIAagCuArq7BPCGtkNUbRs+7mqSrAuAk3YAbANI80TZ2+A6j/NB1/5oscH6HaCC3pR/I4CYwfs70e2oUOPW6prd+NHA0keSrrYEqWyrSGHzL56jBSNvf2qkT5qftKwpqjP4sgdc1QosyHjLkfTQdHy1gnPRzpBw8jlQBWbc8C6ZSlgMm9DgiIEXKcsZEDHKyPCLPSFGlpDVllts5aTNI+8TjBG4TqJUXEycqcsSDA+0d/L4AdQY0u6F6Nyko5/eUQekMSN+NZXZpPzVpSyovY6DkfwbGg1PN0K3NmNGjE5X9MuOaUfqeAI4EOh4Q24TtiUN3z2E4dghbwuoZI+yF0Xz0JGF3XxjDYe/gBiFVNFfA6pk8BEdPI7q9w/6B9P/LJyf4w3vv48Fqgx+eJoRLh9gCFGwccGgvE3xXUtuZCkYdYAAiWHwy+3LpyZMyujnPQykR4lrGGkpUBalAAk+8gM3GBpfyMR43Scruj0RNZvu2AtKBMU41h/H7K9+X+i6o+1CfSblPx89ZZkJX4zbVx9V/J/16cC/MvSdG5VTPeb19scUWW2yxxRZbbLHPzF4LlO6jgh/JYUhOFlG6nYhzfuIuefSbtqSfGpxOTstEOjUAzMHngOx9SJzzLZoUETth92aJIZW/REolgpxZwFqvebiI4DYdaLMDdnt459BYhVqJRuc2gINDaj3SyiOuPIZjh2El7Ji4ArqzRiKmdUEd16wR2LrIJmQAW3LmWDQuhGGZKC+oTEZb2pBAJCDmxX6Fn+AeXuzX2HTCaB2iR69gvvxzSL0DRwf/LEgk9F5A/NVzwHWM0y0QdgnNZYLfR/jtHm43CCgYGbTvkO6f4Ohxg9OfBMSW4PuI2BB8xwhXsThOU3Gco3GZuQ5f2Loi8SkLFjiAIuHJ7gQde3Rw+FLzBP/V5a/gK81jnLldBqZNtntNER2c5MqF5Jg2wC8yoUFCq+xWT5wlwD34zkgV10xYYT4nDAqmGyO3No+EY7+HB2MPyiCygZdhsr/lizYw0iSf83Ym+JmmMPluR4xtbJAgoOaWG9xvthiSw1vtBr/6zR/hz59/iKNPjhE+eVlA6Vpu1Sym8nvlSafIyrJLYAJ2XzhFuBoQnu+K4zemIkFoee30L0EYbbUkIZOMC+bMJJYo9NPvevzJb72Ldx68hCPGi+EI//rZlxC+fYzjT0RK7eKrEW+3l4jssHY9GmXVJ9j9k6qc38Y0TwoAC8jiMo1QpLv3HEbA9KhZ2B1IfAOFsZ7YIbg4kpxPGXQuDjSpByNQRKjKqgHpIvmd4Enk8r0C2vEOgQ6LLbaYmjkNvTBd/D5iWAtoaqyxWuI5M1/MMigwdqZZqhOg8rVRAXyAysc3jZmp/H+AOKqzSnQq464E/QHdgxbsz9AwCzAdJ+NglaOTYkRaB3Ab8tyMdiRsaED+msTsJJdn/mypJOr8pNPUDvWx3oNXAem41eAoc9aqg5WELcWOAKfMPc01bUB0bAubbzhWmU11vhpLDhGgoYzZFCk78Q3YYQ1sRNLLmoi6jBirXraTG/vPXU9IDRfZWCvPnM/VP9J7hlK5LywtTVbMTJay5o74VafMV5S+/Jmndge4w/gHA9yu2/8A0Jv8cABSY4x/TJ+p2XNMLDv67RyzyjE3FDC3bXodk+8z+MzNNlVmnoI5BmZW36f5nG9r2xv7wtq1wgbY6lEzdSfXcmP3EoCk+WnviMDM7qFThS/knPWyrkIBfoFq0Jf16HDCSI00go0B9l6hnhC2MobHFYMGGVusXACjzzmXNwBjG4/uaydrYUs/AF/YuFMiIpOC0ha448blyH6c3zHjQJLSLwfBBvaPq3uRqrpO5eptuYKyr/zjceFz4KWZAb6EQ8b9dc+g3rR53LB71PwgDLDmLyZAgHHbPhXM0vF7dCp9h4xzKFfXom1IkfL+lloi75P/Ho67dyL2m5FB6CK1bdt0rWhtzcqI1n68KeZotI04t18OZqkag8y/E+UFzVHmDVglAafXHuGlSKmHDQkjOYnK3O6B+JPWW1XY6ginP9R1b5K80eyA5nEDtyf8Md7H+cMrpJUy5yFzM/ZA7ADeSP3cIMGCVlXKc0S9vgqMrucFXM0JKAF+zxjWhOHI5f4OW87PU2pkW9imEghTPWfs1dfggd5ySN9L4gtLdn693x2DmESVZrpWtXavguvY5ite5dhr4Nn+uWkZ1Q1SRXre9GiPHqzpPWPbqrZdbLHFFltsscUWW+yztVfPKc2UJ4WJBSSO0WnaKwFGjO132a2AnQN7cTVxpNEEEcBI0hBAtTCESn/piouU1eIJbkgl4p4ZNCTQILmpqB/GTksDv1KSHFxDlBk5M7DvgKuNzDNjhCMH8k7l0wJ41YJX4lRNjVcGjUdqnTKEVZKskQjTHE3uRSosrkJZZBMQJbX2aGLLBERiDAR0DnjGgN/J5D0NEt3eKmtc8grJAsINjLCL8vs2wvVRgOc+grZ7oOslNyOAnJssJWDVgo/X+PHfO8NwApHmfi7S3Kwyy+J8Lat3yUsluZJSM/VMSdRt9FpndTw82xzhIh3hvt/ghDpsUosfDw/wa+1PsOOAxvXYKWM0IeW8vI5UppuKVLdId0OAaQA9XAat53Iuf97WqOfXmKwRBA/CPgYEF3Huu8x6NUDzVAFpY0j3ySuTlgFOOf/xNG/0LjXCxGaf92koYs/CrJU6yDkaiujJY5taODD2KcBRQusHIAHb2GQm7q+ef4Tzv7vDt3/6TXzhn+6yzGo6buF2A2DPlXeg/SCMNECeJ72vkOS5NInuF799ipOPPU4v9rqwJGUX1Hp3XP4FCMtNdcGy9CADFBNIkq4CAO5/d8C/+Hdfx9FvCFvv+xdv4Uf/5n3c/5Hcq/2Jw70Pn8GTgP/HrkNLQ5ZI95TQs1fAWMBkye2tYC9c7gcASCRBFCsa8CIdAQBWrhN2tPZRAmGfAk79Hg6MnfZtp/d50HzWR64DIIz1SJT7oP7bugFrP2DlBkQ2yXDGAOj3MVh9GvboWo+n22N0w2tlY1hsscU+JzMHu+s0l7Qvuf7s/VscourGNqez5gWsGWhTRjQzCqitUy07hgxcGFVo/DU2KOwZA0xH9Sd091v0Zw/RvjxHeLbJ8w3ebAVoXq1kLjakUkfvQatWxvNdN/YG94O8E7Ks9+SkFqhkQVJAOd7eP8HLfO2oRTxukFoPEOBiAm2TMKEbj3jk0R03+p0wrEWeuz8jBU6Q2XZ+J3/Dtsi6uit1wPrStjXrx1iH7AHECiRzgK8ZedW9AAI4MdyeNBeobvdcQDgFEmrgLANCViSV31gBE2E9VQzFKSjzpm0OMTgAkavdX6HOB3K603t4Gr13G1B6AO5OUSGMQeTDzbeSDafSxPOA7/XHT3OJM2HsqOfDOkwzxNwGRh2cY1IeRdzIwpe53G1tOz3osJy8z3VtMqOgcKPZ+Mm4lTX/eVid5gFAifs8xFiKYkMUaecsVsGlDCbFT20sUYbstNwpkxbVOXLguL2LUs5oU46z585h1G+EcXmW4sl+l+UT5fGt7l/2lPuP6jrZLjWIRFUdebw/7BwDF1CZ6sKuQatqMAqYMD5p5Gq41abv06SA6eCAQQG7wMCK5T0eSYKMSPeJwmI2+e0Rq/w6lv+rgGgTn8xdtDToJMbafkaC/0DMqwakSaS6TbZ79nprcBuYGcSnA2y1HwF0v8NwRugfSsq68NKhfUngp0BzJexpJMDtGbEpN4NJYrsInPxICBd+3yJ9u8V6DewfJvgt4egTysSIuJL3elFSUT9Bi5wWxN4pznw51XwxBXn+fSfrZd9TBpf7I4fVywgaGP2ZBxhC0GiAsCsy4E7vw+5UmNSuk3PvHikg7RnU67zU+ouRwekcJEEQFnRtNocxf1nuRho/R/oc3jrG6yDInssx03NO+7v+zpPPd2HutNhiiy222GKLLfaXzF4LxTDGb567V3NCUtZeYofLfQsaCHyq3rlqQpmdpYwR64OpsAbYyeTZa44rgsg3iXPDWJacAWnUObYM6BpiZuYQSaQlyGUpSCLKeQTBCTwkyZm4YcBJ3CwRwZGT784BQZk/3oGbIOycJkguXS8AcGocuHEY5d+yXFu6CEFmf+rpLTfzXurrOnHsUh/FybvvBGDve2EombM2RfkbAuAql5oykeDUQ+oI1A9Ixy36373Ae/cv8OnFCZ5uW6TeAb3Ig4VLD78h+A7we1ls+L0wVNvLJHmnepHBTA2BNII3R18nxuVmhefxGF9pHiOCcC9s8HF/D99sf4oOHmfolEEszGgoS9pXgPSKAA02RgKEWa2gr0lU93eA3pBACBQz/7ZPHg0NCDkXsPR3D499JQedEmVAWo4UeB6QnNzmVPSUFIhusE9BZL3VC9Gzzw+i5Za2v3VO65Kn2GFIHj07eJBIe2v97zVbuH/0BD/FQ7z/Tz7NIMFwvkZ4vgHt+3I/9wPQBHmW2EmARy27CuCdf/kSIEI8bkExwV91B+zoPHiYrLdz4xxslndaV4E2bqTg8PBfEv6bj38dcc1oXjqcf8xoLyXi++LLhN989LG21Xjx6SnBIeUAABDgzEGgJ++1vzwL0z2CpCzHuIyrHEzQuDH9zQIDRr/B5NSFimKs+sTSF4Dkjq7LcMRYueoeQskl7elQIcAR4zh02LdhYUovtthdtMpB7LuE1DhhudRgtDkOzekNqFLMISB97Tlusqnzerq/A6hTZ6WqpMAcedUchgLQ3WsQ12fwu2O4XQ+3PRXAuS9jYq1wkednw4QybPOvlCQgsGZFA8AwqbQG2YlyDqnOKQFNQDxqEI9CTt3iBoaPjLgKiCuHuHLoToWJaEo9ca3XFAFvoLKCI37P8HsgrqFBiBAGNJf9CrtH/iWVtK0ZPAJ2zzhOrb/VCZtBZQDccAamMyA9kbDMnw0IsnpkcEr2Z88lnyO0nDc/dTqQTBaQv0J6pk12G8gy9TNnlKraZYoiHYAPN5R/7XkmKNwN/u5aUr/8dvPpbmMlZ1WmG8qcTpVfl4k+Avmu3emGbTPO/NtkzGexMr5hG3A7QDHdvSzRMMeCf+NmqgY12AoDc0uTuiBAz3Q8yWI/Oo44y+nKtmZDDnqxfemAoauPpN7bBMWLZoDR0X1igF39eFTbJQCaimx4qvojl0fqOpgDEqvy9BEgnpxD60ambmH3IZcy6vJmH4z6mMxw5kNA+uA5nT6EpZwMSPckktwMyQtdj+eQZ5t6EknyTlTRcqSBE5Av+2Tq89dzheoePwDtqyrWhPE7Z/ndR/N9lKm/h/dJyQ0NAKqWMtltVsb7pu2T72kvgXAIDKaE/mHCcOqwf8uheUkIG4/2hUN7Jf6TZpsQW5HmZpK5heskGLB9DrSXjP09SYcVthIo6HbQoBKtQr4nOc8lY2N53GW/NF0PJuSc644AOJknBUhQYmwlv7Rbq3pYlDzjvpfzFMl3mdc0G86Bfd05MJymMtc5YDBf0340+UwAV8z1MQg93/4juy7gxCJp5uYVB2VU+9Xnn3m/L7bYYosttthiiy3289srg9IC0o5z+xCVxZl3jJNGmIDbfQt4wK8UFHYM7s1LR2PVrGqR7HpkqaHkVUnY69mZlV2kMoQOhXlJBG6C5KsFDlYRNfiMQT5nB5WBvGaWxzAfnIT5MgzAdgsoSG3nJblAkDpKXUzCurbzVhLI1DZSTi2L7AgUgjC6+x4FTNZzA2CaeJXseOelHfpOT6B1c76aTCeQawAiDKctHpy9wHHT4RuPtmj9gC4GDOxwHDq87NbYDQ12Q0A3ePTRY9MFxMEjXjSgvZfcUx1w9AnB72QhRRrFDSak6LFJK3gwdhzwjdXH+JPdF/A8HelvAuq11I+A5Z4dduyxpogVxREYnUDYsUcEZeCvuwOeVZHo5gIUQ1i3K0oCLqMCprPsMme558SFLVubSXlbTuR9ChiSR+MLsJnrQCwgNKf8d5pbOCmVwABSUcmWABI44NTv8Tvv/QX+7791ioffOsfqh8+BdRBW2bqVlu4H8LoVJn4/yMMZvAAYULab3pdu24MbD14HpHUDtx9ASSNRgkfOKV0HkqQE0TKlA9nwDBZEGRfWTxNScGBycJGxfiZASmwdtl/f4531BRqKmVXccRDJbnYjprEEAzhlThM8GEn3kXuv5KT2FNG4mPO8T++DUVtr2QMKPTHCwVWafCKtnuCYqsABIFDKUt+RCyA97ffaWh9x1u5n67bYYou9WUtB2S0694grBaVVZaRmONdYmsmaypdDcOlGs3mVfp5lP07MRZ6wazQgsM7/yiJ3HdcBOA9wfSsBdQPDdQkUE+I6ILUO7dOdFBxTUbK5sc7XbPde3hvMQD+Ags9zPgSPdNQgrT36M4/dPY+4kusNWwGpTW6yP6GRQ34kcam/Ww7U2BJWLxhhC+yOBZy2PiBWVtKEGciehVkdUZjMmurlwCpQutwD1X71FNDYQ4oI1TKWmQFk/Wn7oXwnXyRys7zpm7YpSMnjvrgVOL3tEuYA4mvyGr9OGaPNM2DFTfWeY1rfSrec4DAHJK+pNPbMKcbgE90+jtwGIM/YTXX8mW3usbmm7FvPOTcGzuNYb9a4/NPMRWPQ2ADpWPYPG3m22UEYlZo+wMDofHwFROdz1eXzNX1tw00NNNu/NJbvrtsz1bmg6+K0LvlvNa4C5doMB7LxjhVZzfeaHVvVBXU7aQFusO8WnF7Vi2n8F/XvulOa3NxaTn50eeb4ObP9IgGDANJ+pwplA4E7yu8kYsB1lKXb3UClXRwhEVf3wDU3st0D+t6YgnrTMTf/rf/dESND1a95OdQxzqVftL+r/Xmur/KESydgN72c5kxzgucgAMfgVcJwGhGPPFxH6M8dupeE9VPG+nkU+e0gx7mBsvLKaispqMIWOPqU5PnqgbCzOsrFmrR3CsBwpKoojfweV8isejByTnqKrPMEAcIBwCvRgLXM4VjaoNmUIGuK6nuj0hRMgB8YHAjdGbB7lMBteRexYx1rJCd0foZVwvtmYPmwC268H6e/j4Du6oVo99DPajYgLbbYYosttthiiy32mdorg9KuAqSZJULX8iMzgOAjVmGQfNJdADcJRCzRmjYRrGQMqV4k2W9xPGecrgNSIFB0gOYyFoZmku8Z2EJxbjonrOGo+mbeKxiWyurFK1iWZcGT7utgmmjk3Rhgtty3BiAbcA0ANYBsbGa7noFKXsV6X2MSeT8+1qzvkAFnonKcscabtnxnLtfgCIAHDxHUNnj54QoP2j0SE7ZDg+0guYYdGC/TGo4Yb62vNLfuuPH3Q0CXPLooYPXziyOk6JEigbcedBThPOPsdIuVE2nlE+rh/Qt8Es6x4wbv+ZdSXRqDqw0l9JaXF4w9Sxv5yoEQFVDtyJrnzQNwjhJWbpDrSYxBpbUBkX02ULlBVMBa+ibCYRtlVblyw6gtDGQ2ie06J3UNSmY2tN4DxrRO8DlHNYARs3YfgzClieEUEO+TR+scGkr427/8Xfz+3/lVfOX/6ED7CCSge/sIl184x73v7tB8cgE0QVhxMYE1H5ww64pHgJ0D7Xr44NC3a6SjBk5zwXOA5thUakT9PDSiRCD7KcidNGegI7he7ocUCKvnsp/vWRhqBGzfJfzer3wH94Lcg7kvmNCjybmkJSDAoaFBQOpK29aOcSrzbZ89Eo5dBwfO+wByj/YQFnaWCEdCIpXaroBwCzaYPluW/xsAGh9FQQAYnb/sO77vu+QRKGHte9xfY7HFFrtjZrKKrpfAmf7YjZi2Ip9Y5XSsnd26T84LOHK4HQJ5Vu7I92a/Y/zbuJJyHpOUNPAiA9LKno4r+e57OUFqCXHli5QkFwdmd36iYPUp3KDKNq5cp+8s6I7A4TCnpcmcg5St3JDmXFXnvZe2TQ0hroXtwx4YTiXHqusog3SuA2LLIpOq3lUJFKACAllT6OtgOCYcfcrwO86ymfa6SI3IYmd/tf5jlLYmGLZBI+doZsZPgWig5F8EF9B7oMJMtP70LDKY1zlJHQv4ESspU4Ywa+/A3Ola0znELOB727Gv6TA+AKndTAETKewRS7kGt17VJvu7mQC80XdPh8/ybUB2tT8xj4BGksn0ePcpk3raDjNO/ykIPddfN1Zxivsc7HB4TAFTa+Tp1c43Z4WR+yo31y/erD5Oha7svTEFjevvDmNQWMZEA4Cq4yvcLZ/PyqtyVk+fidwz0/FHxyZX57t2pV7sdKlq956BnjUwzlUfxFK+i8rGVpUIwwltHUASYatlkK4ZFLg1kLwe00nL17jXWan5yTMGoKKET44xYDrh1piS8TlIxt9BlMmE/azS3InBQ2GiZql16xe2dZO0syMS10L2o+jLp/a32LVU4z9NUknU32tAur5n3qhZfuHq3rt1+Kvrnar71+6XA8BzApIeqMpUZcxZSIWRHRjoVdomAtww4iohniTsHxK27zisngU0l8jzCb+X/NHyDBDiSupq8w5AgWcLoiP5Dqjiy5EEvnFgpCBluR6gQVTvwoaAHUA690qBEdd6b20khzQlRtgB3ElAXmwI6ag8SybxLQESwqC+/IJHdx/ozxjccgGibV4y114kc5uDHOjA6D7N/yaBf+OyuMyRZ7dXn3X+/HOxna1Oiy222GKLLbbYYot9pvbKoHQNiBkgbSA1EWPlIxwYm14lob1KbiddiE3s2ojsyWLV2Dp6IlmUKigtDhutQ9QVYq0pbr95L3KPpM7ICIAVqDYp7OypMYoKyz6JwRB57AxgGzDsqEhl23kJhZkNFEBcGnF8vZyA5MBJ813XLOzS2KN9xRNQ2iNvM3MzwHaKQEy4/CLhLWK0PuKqb7EfAlZhwCoM2McAIKFLAUPFuDRJ4YEdgksiN9z0OFvtNd9tEtlqF9Enj7NmjxO3BwCcuR5IwEN/iYt4hG80T3K5HowdPBwYLaXMOm50Rdhkx7y0qWce3TRu9gb6fK3O7ZyI0FaSzsHFDDzDASv1KES43L6JXZblNpDZVfdOYkId5LDX8GinObitvDi5rUwievSbss67FBAUTLeytrHBqd/jrNlh+CtXuPyVBzj90+dALw7/x7+dsH3nCB/8Pzr4F1c5rzSZFHdKVcAEgWKUnOyN5GEf0KDZiwx9OYare5uBGEFDlCc9lnuckjLwWoe0EgAcDPgO2TnKjtAfEzbf3OMrx0/wKFyioSHnkhag3un4kmYDGpJ6CBwIa+q1zSSfdM+iPLByfb7v+hTQuEEY1pSyHLqZIwGv+6pPBm7KdnBm/tcBCXV/2e/pICHsuK87Y90vK+bFFrtzxiqTSAmIa5eZbPV2dgVEmMUMa+fbBBQCzGFfAaRWjs6pUjOdV4y/+l5zLev+h69XlYbU9CexLbkIc911LM65e7mqqwEDecwWBpvVc5RTFOPfp+dhX7WFOi4zSBxFCjW1CdxQdjC7hpBaRXESi2JPQmEgW1E9ZaZzXDP2DwjrJ4xwJc5fJCD6yoFvbW/txlX/1UAMFdDdsZxXJGsrBy5L/fP1EUp9FYjOv5OK2joeAybG2oo0CgLNzl3LjfqGbe7+Le04U0GeAaqnt/RMma+z/UBhaQakPgCEp6D1Aaj3enZQp8ij65yTAD8spK4SYZqH9Vbp7IN1ykz5NyBDs/mZb+m72XtyZsgS9uv8uV+FLX14DN+J5wGArGtrJTIFUmoA11iPNSN4fH/oEpCqcdXW0goUj4KfMtit8+1qjAaV35lotNzMAeQT9vVI/ju/S4o6mu1IkTLwSTk/LnSM0npWdc+sXwUWLRct5TGNCyBt5Wrcq7C5dTxM1RgKVGN05d/IAUTXIFhJy5p5D4+uXf+Och4nufbM3tb6uo5KCgeS9FmmDpXbUa8pAYAyYSlVabsyqDc/xtcMecPzDp99lPf/XXgu5rBLngwBPPEx5Qg9jOdNQHk/Xmd6fx3+eP1XC1DIAQGOS8oM0s9eArKHNiGeOPiNrqET4LcEDiwS89rnAv5KYIo7LulDWFVTkrKS2QNplQCvcxmdz8SGQImQNGd0CnLP+b3Ul6KkakmeRMmFJOjb7rHYyP1tALcbGIjye38iwYG7txnDiQQAlrnQ+DkiLj667Nur78+Zph5tr+ZZI1D5FZ67vF9dxtRu+n2mnxdbbLHFFltsscUW++ztlUHpen6WEmHVRAzJgZkQXELjZTV61bXCCAYEnAYkf5JZJeUzWvPpIhQMOIsEx6GDI+eey+VVk2DvFLTiEehFTsFkZgGMDcRVhvOIKW3Ac20plglpYnVEKUgck5QTgq4+U8G3rdyGBJw21rStUpOuwGvJ8GGYbAemkuEHZkCfMy/DxPSY7ZcGMBP2QxhJR+9jyIBWYkJUr6rkxLWiOf8GANuhQeMjvE9Y+QGnzR672OC83cIjZbZnQwlr1+Gj7m307HBMEeYr7ljySIu/PKHVs3Xs4JAs/Xe2FiKrbOXeNWsoSr5hkiCDIflZeW5HrO0o1xA1H3RipwxbzvsNyR/kF/YgBMS8nx2bJbl1X0DyEDswenaay9gBDmggIKYnxj4GhCZhxQO+9u5j/MWvf4jjH6/gnl3Cb0/Ax4yr3+jx/KdnePhfX6oz3xaaYw8BN5ITO56swMGBCRiOPfw2wF1i3uGc7/ckDIE+AsHl8ikmuD4htV5lSGVRTEmAFDcwrj5w+PWv/hhnfodjt1e5bGFGRwWXs8KptpO0exmbRCGA0SOMgh76kYy3ANF7NLnNpZ0T+lRAZ2NL1wOn9Um0Z6/epr/NAct2nEnA1+aI0UVRMVhsscXuoJFIYzOJdDdXL7aa3ZxBaXuUefy77E/ZcZ2L5/mysqMfQDU05bLHDExx9Cd9OY8YwIx8zswUVgbz6DzmJG2QmV0clMFsChkEZTmXkxvQnHNmjtqOMwNw6nCs5V9rJ6I4dSkDItY2NQPHDQIEWHn1tToDSQAMx4x+TwhXMm812Us7zwEAfRPGVQEyACkwTQdtYWwjJi5yl1a/PGemkkcWKED0BGyZtbsANkztJkczJvcqqv7MO1xTZrV9CijTbQ1xWztNQKmMY71GGbcB5QfTXC88s7I/33qdU3D94JwTEPo20FpOU6/Bbr+Om84/V/6c/z/jDJ/1/XsN9vi5G2tfmDpFNa6SjlmkMrzOlqQ2Da+X1PFwbLZxBYwitIUCdE7Hr8PHS/mNmlsWdAhgEpCDRmV802Nt2QtkpYyaoW15pUfjul4bW0W1WTJYznrRbIdV+amrcZ6gy33zKdj7YApM1moCTsqVfSpAjayhq7H2unvHVDCIrDmQg4ISQEMFyldscfbIAURZgtnqZe+uqJdh/VaPnVaXA8mFUgZVbTMte9Sfd+CZOMgBrddl8ukjOe7r3iG3vFvyPlX54200fjCmZvdVIuE0WMqMKRmDScbvk4jhqMgb9D0pGE3gkOAvvQSXdJTvhbhmAbcJo0A6API7UCZtNl9zwp7uPSGuJS952Ag47XrofEYGBEoM3xEQJbjD96J4I+tsvee8SIPvHxDiETCcJKSV1kUDJKwd63Qjo1eF7XNTf9gx9ZwNKAFgNx177T1gg8s1x8z9NpUhwt14JhZbbLHFFltsscX+stkrg9IxOiVCCgM6MYk0NySf9FHoMbDDxWYlCzYHsIGzNqnL3lP9OUuGyUI7WzWxTF7AM9JFOGlxkpcWyJLB3gkLMyVgiGNZYCt2BPTqCWKa7lQ+E2GEjJKDgNE2U1ZAuc7lWst3E3QhQUWaO8tvp2qSLXmlpX5VHTKL++BCtHFYVty1fLdtN8a05rref+NdvPfhE3iX8HK/zoEEgZJIADsJK3dVqDspSxrACCzrksdR06OPHp9cnmLXNXC6yP/gXsDV/ZXsxw4OjPtug8u4xo+Hc3y9eQYA6Bnocy7kUnZmj0Ka2PJKN5REzptdBqbvgllu6F5zZSe9SbNMc+4qGgUCCItW9vcKwPeAAp1xzJplaZMCSiY46D0EYGCfwWpgLNndKKvag9ElN8pxHfUZCC7iYljjLOzwztEFfvhbL/D8e2d461/v0D7Z4OyPH+D4H36Cx/+9M5z89BFWP3gKigkcvDxztaRl47H94glSQ/DbhNSSyKseBYRG7vGc+x1QCX6AhwHU9YBzoCYAwYmENwAaElxk+D1j0KjxFAiul2jv/tjB/fUX+L23voNj1yHCaZsyei65owFkCfVUAdJOGdLJ7i32cJAc0z179CkgkcPK9QJMo4DH3rw4iiRN831boIL1SwkeGO8HSD5pR5z7ugaxB/0+Ba0zYz4dyoIvtthid8AYcJ04+Uw8IwO4AXm+Y2D0nJzuCJhWRlVV/FgSGsjM4rzPLTErcrw6zDMzt5Ks1ukbDSrP7XGY35QgDC55TcNVaaRT5diUfcu70T4Ye25sCrxmEIZHvsLMoOWqnJ6KmIw5hhNAnTlhIUzpQRh07LgAOBVj3a5v95CxIkJ7wcIQV8aS66mSrETuQ7L5awUaVZcj/ZVzUHJuL/uNegL1NGZGQ4EMBds5MKgvSFQGuI3lx1SAFqcOWS51eNN2W0zhAQg9ee8R6AA/mJY52s44lOuetsPknLOM33r3Oue7lXeL9PW0zNeNraSInDZpvlI4bLxRHWc6/+YqzzriR7/N+PpvIpXl56P+7aa+0zpmnMLwBZ5sn/b3bQACaXswbg9Q+BwsA835hYAKLEOWzs1jbj02GC6Xj89figpF/Q9ybJYBr3JUGxY0rYrdu26o8s1qecQCwuV+jAxyJNfkx+8fY1ib9PeBVLSOh/m+SlUdMqu6Uk7g8btoyvKtwV9mu+7q2a1ZrkAOKicNHBqBY6QNlwBKrmxLpb2FHQsdv8djOJKM4RSLKkd9/bburdnxs8bVdbL6CQyQzE6Scg/VIH42a9c6YEHvK7rp3G/C6mub+x2Y3277WPvMDQpc7Qcc7pO0A68bUAiVeh0DA4FVLXAUtMAAoj6MFrTgGDhKkmZD54bxOAGekZQ5Dc/ja6sD0My3Fqs6Tq6DV4y4AmIkxLWD3xP8XtbRbi3PKA2EsGO4nuD3jLBnTR8g79n9GaE/I3TnQPcgIR0lYXbb9WUlFhyCx3ngxvg6bpuH5GOqtr/lnX1gWWnBxsSZfeYmApUyy3i+ecv5FltsscUWW2yxxRZ7bXtlUDovenWGNkSHGB28T/AuIVDEJrbYX7XFawBo9CgDypKRwuSPRGsCFCWfjZwA2UFISR2GKs917SS2ls+u80mbpWp1ZSCy5V+eOpFuSljEBkLbOUjAX1dt56l3JQFDKvmsjWU9ORcPA2bNWNQZUK/PrdeS4rjeRJIHOyaRLW8aPP6NNd5bfSysaOLMbPcuoYU4uiK7DJ6eNJI/t0s+g6H7IaCLHpe7FbrOo79q4Z8HhCvC/pyR1gk/YuDxO2fYaX7ohhLuuz3u+Q2exlN8o3mGDQvgt6aIyIReAUAzY0EnFIC1UZAxVkDvm7YIh6gBCUPy6FlyMycWmWyggIY5PzAkT7TJZ+dc05Dc01E/D8lyRBP6FDLrOucnVm9CyVcsIHXN/LVtltu4toEdUhJZ6yPfo0sOQdGFrz58ij/+7TO44SHuffsFHv5Rhx/8xj38vW/+Kf75f/Ab+PDlKfynL8qzlnNuEbhx2J+PdWhdz3AmpQ+Mn9PqeO57gBSUbkJmFGaGQJfgPdAfefhOnHNhy3j8m8B/50t/jlO/w5p6yU2up/KaGzpqeyVlT0v/yU5evTEJnOW6HQgJnNszssM+NXCUsKYBa9fnPn0VO2Rkl+fVJPKDi3CU8j0/Yrjrs9m6cbBNF5XF7dI4OGaxxRa7EyY5+CSgRhzi6iysnWFTh10FHABjX+MBADMCJOS75SM92DY1naoZiF0DRlz9NWd1aoujOzXIzvERq1qPiS1GIG+qZamBzJwGk8Tp1c7bmesUslLloGQq+VVdKctbfk4DQ5KedgBCJNSiEqzHmPxrfR01g627J2B82Gq9aza71a0CswWgBAz4kekrjVjfI3nayuS1onNFx1mClgbKQAJM0jyow9oYeBVYazKYOT/1XTfOXXhgs0zpybEHx0x/SOONBzL5kzJuOycxj+SKZ6W1J98PWM7TKtxyfL7fq/1vShE+daTPnnM6jZnWeVL+aCwCCug1Pe/Bia4v8xAJxyGgc82+N7Gqb6zPqIzbEJLPweopso4beazUwO1ZVrP1hw6NJQZcWKbsNa98VZ4dL+BjuY9rMNjKPgD7qveB1E2BcvAo/tz1DA7CtsxKHcRZzht1QNP0/rExXQGvjEvV9aneExRlXM7y5jy+x0Qm28ZPraQFAQ36TjOGurYxByjztZwrt0/vRs9+Dqiy9vYCynPDIyUMGqjkku4quXFdouWlVMWSnr5bwQA5GwtUOhpU3oF1O9p7rGZlV39zeXo+C4y4K4xQNmat/TWgF7qErMc3a6Mp+H4wYGG8fUqYuGkM5un+VccQMmM6K6BY3xDy/Qbtj1xwgKgJGths94sDEFK5Hjt3qurgZvwhmTFN47ZxQFpLWpO4KvnMkQDfEeKWEDZ6ikDozim/J/sTYP8WI60SuNX5a+AxQ7qe20XRVjhor0kf0NSfMx24b7sP5+YBNTCeoykx03e3lG37VIGPdypQY7HFFltsscUWW+wvib0yKN14ARCFMV0tsghofcTACl52OrkeyfdQXlSwE+ekNwy2XiiZs9NqVTlQstNP5b9AJBK/vewzYl+aGRhtst2/CDMQmpz8m4LStc3kqPu57ZoymRmkuX/Tw3NcfrnkIV6FAZ4SIguouh0abPsGm32DGB36LuRFBg9OFoYDgToHt3UIG4KPwGoLhK2cT/rMYbdp8bg/1Ty8EVHB6bfDBT4a7gEAduxxRoOwPgno2SFCpKQzy1f/GDu0h4PHTB+/QauBY8vX7L1kETYgvzHpbZPVZgUQoUzaSobbESMgYUgOWy7y0AZ4ryjmNopMiCCsJqukmo0tZabMoLZz2H517vDzZoefbO8huIT77RZf+vWP8KP4BZz8ZI32RYf2L05w8msd7v+tj3H1h49w9mKTny+T1AYRODic/WgvvzHg+gQOBLcdgCGqmgFjBGiPwOooSgdDBBAwDrYA/C6h2bjsXO3OHc7/2hP8yvFHmt97EKb0KP84S3+QiHr3mdVcQP019Ygk2/YKBEtQRGmjXWrgSRQGVk6UAvI5wIe5oVkkvGWbSKlb4IDd56GqZ0Np/AxUfWr/Rr+DsIsNWhfRuogOiy222F2zsEmggZFCcVyLD1mkmTPL2eY4FchTpyshKgBDZtdmAHIMQLHlrbYyK/Cqlp3mRr+b39Yh58AGKiccyTs+qSiLOTNrFpzN2ywnMyBzvZwP2ldzvWjn0joShF1GkluRuJSbtxtL2mHMiEs2XVBm9yBM6bg+dDQbIJyJZKEAMrk8bdcp67w/Z/gdYfUM2D0kyaVo21Ha28pIQfNcD1SYVEnzR1q7KviRHbyRSj2ByqkqoANb/sYIcJOQc0ozKkesXkN2bjOoTSLrO1T7vUE7ADrT/OfR/lP57amK0FS2erSt/D0AefI5psCF9q+BVVzuJwPVxqfnWZWDg+vI9ZMAihuNyvlzHaaXPQXbZxz1dl43tzy5bVo9s8Q4CIqZAAkHTOrEFatWQI9xgTN9ydXx077K/Wh9M3c/TOpbl1ONlXfKqjqNgN94WHfDWmSHsi9XU2obO20ImXvu6rGoHivrezyfx+kuXOS8RzmuDf+xAyoQdC54YharmlvSXlMv25aZ0pk9jDFobIBrBQbmd2fNlGbKrPGkJ+X8coSMpUmA5fo9h+r8nACOGhSl7y1SMFHULlSKnavjJtc0Cj6wy6/f7wawcdXBk/4btw2V8mfGjykrdATsvUmzutbAIoAR4Kp5oslLMBpi5UMgO5bLoDEFqROKH8UY9NW8B/puzsckjIPSjHhRBTswATAmcd241m/GlmaosiCXczsGBb0We7fbez/PUbROdmxur6qO1ZhQ3xscJFCCW5KmikDsCe6I0N1DDhrp7iWdN3Iuq5a9p54wHq+obE80fm3YfcnVP2vfejd9vkb5361MRklnYj95zqC4bcuAeN32uZLVQ2VmbW+fCXLuRAe+SZufLbbYYosttthiiy322dkrg9IAkJJDSg5OPRvMBCKGdwKkXexXABNoFXU7ZCWaoykpR3/XTscUCBQYiuflyWvZl2G6W+xIJLWJhEmZCNRxAaBrwEsrMZLtvg0YnnOO3GRR2dNQNrTlqM7H/wJWdnUO6uvA9hjBys7efOkUzYdXWl2H4CW39LZvEJnwcrPG5skxmscBzSWh6YDmkotDjZTtOshi2ncps56SJ6RGpMhSZMS9xza2Krstt1dDEW+Hl/ij7RfxIjVYUUQPQgPO8seRCf3YYyeXqh8SE3rNvWyS3m/aLDdxoIjWsUplCzP3amhxv91m9nFwEVfDKh8Xk0hMH/keDcXM6L0cVtimNss1x+SxchEeLGzZSAiugNP7FARAVW+95Z/exgYrJ9Le+xjQJ4/WDWgd0KUwAqS75PGTzT0kEIbEOA4dvnz2FPd/d4t/e/5lfOn/2uD+nzB+/3c/wK+/9RH+8H9JePxfvINH/80nQN+Jc8Z7MDPCkyu5P2MCggc7B+oHIEYJHDGJ/WT0MQJCAGlOdmYGb7egGEErBaWJ4DcANx6pdQibCDcwXn7YYvOfvMT/9Mt/gHv+SkFfoMWQwWT7e89vs8T6tP96EHYcpB/AWLke+9TkYw3g7tljE1v07PEgyPO0Sa3m5k5oXMzH7JMoEmRAGSLJ3bgIT8KItj604AY3AaTn2NSD5gUf2GNQOfauOn6xxRa7W+a7hNS6wjz2AjRlyWgzc3xNGG15M1WSxbaflnHd69CcadmXO2FTsflNo8mpyns/BZXoHpTh7DBmvGECNJjDsy67AsEpAtFj5OBLrThvXVQQWp25ObavdlwaIJ3BKLt2Bib5srPKTuXENtC7vm5UearNz1mD/XWubAP29m8Bq+fA+gmwfYcQ11yyN5jj1XKbmtS25+KX9izsOYuXdIYYIYPy+ZqB4jCfARsK0Mqjtpdc06k4dElAkgxc38X3BE0+z1XxpsDSKSB93fRQm3rKGgVQnOCvapP7PTMRb6jHAbh6y/LgID80YcRsnuvKKSBc//Yq0+Zb2WD0es0kz689j1Mh9nmzoBgAh/c+Zr7j9rY2Ul5Wg7hjj0ENOpOBjTVz1d4PBjTW4I4FHtFh/ztwNe6PG8nY15mVa/9pG03LYuijpmMyJXlHjABz3cys75Tp/VRdx1wf5K65rn8MnJv2dwVKTwNIaJB0DdRU5VpAw2BSvYQazHYgJL1ZTNUjkbBg/V7G2dRwCZKKqIKKSpsnTYYtYLSkHbL9XR2cX1+LBSTYvTq9B/yk/exlN3rpIQN2dR/Xx9X4rNUL+b19Bx6QaV7maxi3SAQeUCY0t+0/KrP67Li0ef29DvwiTAb+ue/1+ejwfq3mFSVqRA9KJKnvDJwFMkg6Ak/zhHLm+uwZnbv2HKSo5/QkgntrLnVKJGB8dVwGhIeq7pPLLNdXTTpfhYWe70dr5+qBGL1jp+NXaQOT5OeKTT+67uk557bZ81Xlx6Y6IOG1XnyLLbbYYosttthii72KvTIo3Q3FE1LLTlre4cSEF5dHQEhwTULqXQl2t91jyacE/b1m0NS5n8bBrbrQU+lscZo6uG7Ge+J0ZV6zrNUBPJuf+ec11rBZA5+njjNOso3ocNsvygyEJwc4wvOvB7x3/yVicthHD+9UYjp6XGzW2D0+wsn3A+59L6F9OSA1lJ3TxCw5gRsJCGCvTPeOMRw5xBXGE/XBYZ8Ceg4KHjusXY819TjzO3yrex+/sfox9omUKZzQqgPVQGmT8q5z6Jr8sskr34W80sa0lZzNImO/Tw22sUFwEa0bFJR0o1tvYI+AKHmkjbWrbRVnwtMzAxfyrHnizDI/ZNWOj+8zYJlGMtC5LqnkVjYAXcBpD0cJ5+9e4qd/5z5Of0D45JP7+OuPfohfe+sj/NPfe4D184c4/aNPBXROSRaMMYHXrdzvQ4Qbuiq4gQDvgKBjST8AgxyLGCV4JEZ5xocId7EFH62Qjhq4XS/regKcI1x9ocGTv8b4D97/Id5pXgKQ3NHWfgYkmyKAbe8VmSj3mHyOkH7yFb2iPhbQPODks5S39YlJqUcWprVHGjGbp31iv3llvicFm+t80UnZ8EOVW3r2nqiY73GR715ssTtnTEBcU87RnLzNZ1Aks+eOM7B56pybc6q96uuQqjITKoTGfq/A8sopnkIBpY0NNgJCKwb46BwV+FD7XfP5rqkfVee2nM/TNmDPmflcAx3sIO+SiRM44w4GzGoObWNGW1tOgwUoIuM5qWXs7xPWT4DVM2D7LjJImB38tRM1YQQiZmexsaRInc1gYYk76wtl/TAdqvzU5zAnNXFh/NSMOGWRleMqZ/Eds5r1//Mcf51NmaYHrFHbNq3PTIPdBHqPyrxlzTFlUh/knH7dNcPc7hNw67b+v639f9bbZ8psvvGcPP79AEe67jpvs1e4/jdlGRi2saFug+qZzuvjWOSyoZECdRPke5TqtXR1PpWfN9UJJhwCyJMxPI/PFjAeMR7fq/s9gRT0ppuZ0tMls74Ts5hSHfRT1ws4uE/GBZXr8XsJnI5rVuUKfUewgLFuICQFswyE4gxIynaGLuVVgtvaIAVklYus4NHrezSofyMK0EUDxmzpGfbyNLilfh9mYHnC4BwBo1UnZ7yzZpHX50iAj0DYMNoLhusZw9ohtlL3f6+sBg6vuR9mx4jJPKGkyGB5T+c+qnw407Jum4Pld+9kp8z6pXITZyCaxn1rv9fPzMjBhvnrntYjf+bcVmzXmssfM5IltQCVc9fzjrq86bXZ7wfRMtfUa8rwBnKgXuZe18VMP9f99bMY0+GYNB0DF1tsscUWW2yxxRb7TO21lh3eJ6QkuaQzyVEBzstuhe7FCrSOOV3zaHHkWPMGUl4A11HUFHURFGWRWBsxHzhv8uKhIFUY5Y4Gym85z/QtXos5B5K/5Zh8XMSIipPrWXs3q0XNz2sZBJ/kkzaz/Nbn93D5YcJbEEassUlzruPeo33sce97CSc/3sFteqTjBhxImFlePKjiSJdO5wBZuDYoi3jouiMSroYWVyw6n5GFXZ/I4VfWP8E/e/mreOgv8Za/REoOaxpUmrvIHycQGkoHwHME5d/mwNvP20RSO8EjZSbrPglL1nJGJ3YjBrixneG1DxIwwCGo+oCBkSYJLkxeMdtHwOmExNKf9W1rrFyvQGdk+eyJMSQJRMis20p+PFDMwSVdJUv9pfvP8fjXe3x8+hYIcm2PVpf4nV/9Lv5V90v40vA2Tv7ksdzXmsedNjthTjcBHLyA1oDsYzL79iyYfLf3oBiVIZHAmw3ACZQSqA3gRupEidGfenz6N4Df/K3v4K+f/wXW1OX72szpyjRN7hMDoPN+lSfQVZ4gA/Gn+zUUhTGdWqyo5IHv2RcgmkpwgfVHbRZIkHNHq8fO2O6lL11mQQeX4KBs+VG9pM+YCcNttKvFFlvsc7e49uiPnLCPK8Bz9JfHPrvM1LXvjsbOutopeQO4O8dMYXWQjpx8FUhXy2bbX3YCSKdG2XHGAq7A4lGeSswAHJPtbrBgQYwdolpeJnxV11EDLQeAvb5S6t9zMVTAjppZPB0xrd2N3UzK4q7PF1eM7pzQXADtC0J3nzObHKnyE0+d1nU72HUbYFQ5fTPTh8u/kYN40j6lQUq/HLCorMwEyV95xywHRBCAxIe37Yw///VOgNGzMwL68kkm313e/XrAYYIH3FbFQ/D1lvXAdPMU05iWOQPI1ExbdjNDwm1O/AOgHPNrjmv2l4Oq/a8DjUb7yz5TEuH155z56abb/A6CC7Yuvq5uNbiY8zjnm+5wjZzbrBr73IT9WjOutZiR5furfmbsPqLJtvozdPuIvQwwqALVcQ0bl3Q8RB7Ts3qIXW8N5trStGZJc2kvsARRu54EUB5Km4AJfq/s5SCqX8YAdwNk/csQMBnio3CDsopZtpOrpOmrnNYgIPYAecoS566nEghQg9LVNTEwYkqb7Po0xUHJD00C3E3fpdOOnLQJJaC5YBw9TWhfRoSLHm43IJ406O632D6a8We8aZsDN+s5kTGI6Zr9J++BDBbXDOTpCFkzlKfHj+o2+W5lj+rJ1+8/Ped0zmB/CShS4IfVzeDs9KV5sB9V90WdAxpjRrKdu07z8AqRYwfv7GlfzM1P81xLU9tUjGlOPK4XYxx0V88d575j8rtt0zLzMzyjdkJ2j9zB98Ziiy222GKLLbbYv+/2yqC004kfM5CirHpciGiDzFRf7lag3sGdDIidO5zEG95SM4PyJFCkoW3hRcwlstr2rT+rHbAIpqDvFKT+RVgNOueVdG0zdfy5z0nzn2fq1b9/D3i0zyzK4EpuaaerhrAlrJ73cJsebrOH6xRQbQPSUUBs21Gkuzk/XITIPlV3kUW17jT5pKeETpvAg7GNLZ7EU6xJoNYOHp5TZq5KvVLO0ZswzpFsct9j+tGbMQMHa5nmletx5AMcJVwNqxGj1QBnEBRwJgzcIIHQYkBjTOUJiO2Ysiz39NwjuWcWCfQheaz8kFnSNft3VEa1WhvYwzGPmNStizgOHd46IoSvJWz7gPOwwzY2eG/9Ev/BX/9j/Ff8q/jC0ds4+/4G7tkl0ARw28hzyCyS3UmfC/tnzyUR0Kr2agarHdC0oBBATQPe7OBiAp+fIJ6vcfGVI3z8dxh/7298C3/z/LsAgE1a4czvEFFyNWeQusrx7pQlZveagdNT8DlO7q2aLW0y633yeUy030ZAtDHkMZaat0AD2W4BAmkkU+9I2NdjtjXle6ju7/q3Rb57scXung1HhNTQAYg6B7SNiKzXONlqqVUDCm4CYGogN5+jctqaA95YxmDNv+nK/hmYdeVvZoUZWJDKdw7IuavZ5n263dqBqqBEVue65OxEATHmHJf1NRmogqq+mLRhvn5xLJLOTY0pNiq/lrfOwAlyjm4B5IHhVECg9gXQgrB/wEgrht/SQZ1HYEKUGlr7jcD4VADw8X1CxTFcsShrB23J5YgxKGFsqOocd9KxWknTmyLSTWzaVwGoD4I8ap84V0EZ9tP0eGOPVhvHTNPDg14XOL8NED4YH2b2P6jT6IDx75z/mxYyLu92EPj6HaZMTymToSrGANEhg3z6VceZTLKbXvfMWrCe5k5BPAE6CHQd0/EuGBccK78HeLK9Ak5GoDQBIxYn7DfKoO617Vjf2/aO4VLEqEzbZgE1BjBrf9Zr+yxBXp3HnqUCrs83hEnxjvrI6kelWGunGpCeZRMrkOx6wO+lbPZ67AD4HQBV/UpB1dxY0oqZ7La1n723xgAv5X7LzHMnqSlS0ntTj3WWKkODC1jTMxAr8GXXOWpL/W5juwV4aacRE3gmqJ6sbqkCy7U81wOrF4yjj/YIF3tQHwFmuG6AGxI4rOc6581bbpdrANc6R7Dtd/AyqPavQee5d2XVD3lQuq1+9SRrGiA2/VzXy7bNjU/T4+17PYe5ZWyvj50Cz4TJ9dbHEkrO6JvONTefnT7D0+pU7UlOAyxGk7jq2BllgZHVQR7Xje/KhmYdjEZjVsIBCzwXM1enxRZbbLHFFltsscV+bntlUJqVjWeANCDM6XUQAHO3a0qOmqgLK3UA5oksm/OMND+xTvrqqF/DphKP1hJl4VbVidStZDmnTbb658kp/bpWl5f1xuhwn5wo8XMw5wFEwHtcfrDC6dkLND6ijx5EnPMJ99EJjp4AvxngugG02wvL1Ts4ZsATXJ+UMU1lrWd9FSDR4pO+MVnqCIKvJJWPfIef9Pdx5rbwlOCZ4ZDks874G8g91SLl/NEmr2xlzkkivymTPNcCNJ76PY58h4E9rgYBXBsXCwNaZZtFSlvbiEWmWWTV3UHZCSR9oaHKPbucjzjvh1J++UwHeYpbL3UZJlLPQ3JwxOgrlnRyCV0MSEw4anowgGPfKcva4d3VBb75az/En+6+jGF9igffBtymAxoC+kFzSIscd1YccARAZOUlTxZn+W54D2oaoAlI58fo760Qnu9AT14iHbd49s0TPP4txj/4G9/C75x/L7OWDYytr9Pr6jRmuW6R9jYGdA1IZ+Y0OyS9Bz14RKQ6BKYb7FNAo+iNAdNmkv95Pve59WlkQoMERxiByzXwXAPNdRDBdF8izgHjiy222N2x1FRzF3WGj2zy7gQqUKLaB6jABXVQW3aQKevT/KIHP14Hcti8y/ZTcDNLiPN4/wx2Vexnc9IzYX5mOXW21s5A2HXR9cAJlf3MDARgfa3keppTMgM2kwMNkLafbX6qJ6DEwpKu62LtpPVOK0ZaEcJGAw9aHjtDk9VXOyzPicu8lwkj8D+3vV3PLe0mdO+x1Cb7Scebg70u6y+b8QTEvi34c0a1aA6kzn7oKbh7TfHTZ+5WkHrO8T4p73WA7rkgjhE5bWasufV++AwUnrLcM8YgxLX73wbgTG06fk4DdbgCLPNz8grlfo5GzDAmcT3eyrhYXggjlrQGV/B0XM7POYsCg44n0yVoLgfVccC4vVDa0lJ3UfVwWH3ye2IGSM7BU7ZPBUrPBV7k4Caqhr3qneOq89u4P2I6Vu1nZbkB8B0hbKWs1EhAtQG07OU6HZCDkfL63uLNtbwcAJEAqsFLO18VrGXX7mJhSY+BzolV/Zx/0lRaDBopH9SBMTZ+5dQV9i5J5XPdRq5jHD3uZY0V9YISA0MP/5Kx/qx9JT+LTRc10xzTZgdRFBO7bhCdmRPl/W0SNQWxMTnmJrCSy/0zIlbMPCMHda+/55v/mvPUh7Gl/JhDgIED1RWe/Mu/VYMJzcwrDk48/nwQ3DSd1ObiKQfVjd4Nt4371j9WVr4GKjmpr+0X20yTa67e8VrB0Rzw7ridFltsscUWW2yxxf7S2CuD0ikpKJ0cnMoIB5+w8gO65DF0AWhTBYLxeJKrCyNb3Eoep/FaYRQ5rBGM4wVz/YUE6IooDiiicU7pGpA27619nnOyGFD2ulYf4x2KtHY1m/0sAWm2FeY1M2RHADxovcLl+w4PjnY4aTq8TGuR+U3CyxyiB2sUNSXObFW2nL9dDxcTWgD7h2sI3Un6LWl+TItezbmyksgIRzg0NGSguecATx3eaV/iB9tH+FLzFA1F2YeKTLVDQsceJ26PViW8RdrbYccC8lqu6jdtJuncsxemMRiNi1jxIE4IBUCNsZyU8Wws8MhjmfKb8kkDyHLcKwcACUPyCC5imJyjZukGBcANoA4UMSSPjsMI2ExMuYz63PsY4F3CZdcgJodPuzP85ulf4EU8wg93b+FXzz/C8d/s8K/f/jI2757h0R92OPrRhTx7jgDfiHx3TNZolaNHwej1CnzvFOlkhXgc0J8GXL0bcPUFwurFEU5/co5n3/A4/ruf4n/xpT/Eo3ABANhxg/t+M8olbXLddVsaQO2RhIEMl8HoNAKb4yiYQljTVm655xwkh/heJQIaZbE3FDNwnCZOkNIH4wCCHkBAzHLsZlH7q9X+nSuz/s3NepsXW2yxu2Bu4OyES94cnmPn8ghQqpzpsqs67RIVwNUca6XobDUzzoaFpMfJ7wVwzedxk+8GjNbblEGWHeHmZMcY5KFqrlcD2faZq/3doNMzYAQsW75oqhzR7Ph6cZgacLJyJvNLDlwYgwONnK3GdmNCzg9q5WT/p9VLnf39qRzQvgCIHYbj4njN7UvIOVFHCkBTp2k+rspnmoGQ4ritHecZ2FbHsTEMKRrjiHB4c8y33xs1S+WByin8uvWc3hjVHH/KAhOw6+YTUNUF14HHNwK89vzUm+v7cQ43mHmF38YYPyhieg777RY8pz7+ACh8jZuGZxqsBuJepaT6easqcb3ZM1YVMMvC5WodGe/Yg8Ayzqf8HBSQupbZzvsmA3LGZdRGBqbaWHMbEG99ZCB4NZblcdPuD67qpJ/r91iWE68AI0LZz3wCc+x+8wOMmPJzz5x+dioFboDwFLQlyD3oOsBv5V4ZBpXeTnrsQKCAzKoGUNa4KGO4jbu5fWsQfHL+0g+ST9rYyvW7ag4Us3t0FJRi+1k7pMPfDsrT/Zzm/q7r2myA9vEG1A/gVQANCYiDpGEaIsKzDd64zS1pRmyFa46p5hyjYK7Zdqp+TBMk9WAgnA6+M9/nXlxVmpNr6zc6jcpp1+XV+93kTqrLtvLqoLT6b95h7rfJuad1v82IM7B7bT2t7Lk61N8Zhdl90zvX5kQofTHXRRTlec7PhJ2jmmtmvNvmWJYCZbHFFltsscUWW2yxz9ReGZQOIaLvZfeUHEIzIPgI7xJ23Qq89aCjKJHUJp8USSbPoTjLysIOiA3B9wwkIPmycDJAK2zSKL+hyGIxmEhyHkcH6gnwDowgzEyS74hJmJcpgZFEZ3pqmss2A9XAYQ7p6fY5MzCbbeUOZOA7Ko0nVE09lfx+FTMQHlCAjzUalDBiYXsPDANovUb3pYe4/KUB7/iI7dCMQC1PjOClbjJBF/YqNwE5P7A5R7oB7YsOw0mD3nv4DuAVwH4sVckOoIFw2a/Q0IA19ehZrrtnjz4e4Wvtp3jcn+EqrfAwXAqIyAIIOiQkODyJp9jxFr3bSRkg9BwQQXgeT9DQgIf+8tXa7RdoQwXiJnY4CXvskjJoKeI0dOiTxz557KIA6gJAltzAiTiDkykJCGwS3FJukWjex6CyzoXxCwB98ll2O1DK5Vu98jZl5QowS7n8fZQ+mspD72PApm9AAFZhQMOEf/vkfbzbvsSHq8c4Pu7w4/0DvH/0Au/9lW/hW++/hx985T289W/ewtHThPXTDv6qh9v2QNeDYgKvGrD34JVHPGoQjwM2bwds33boT0QWdThP4HaAOxrgjjs8ePgE//GD7+Od5uWo/Rv06NnD66oywd0arOBImPkYAcCHx9T5pa2t6nPlnOzsNQCBdVthTTv1ho0l1t0I/E/a1ybPPpX7Nsnxmtlu9WFlXDtIIBAtoPRii905y8F3Cu46Y/ZWllnUrvjiDMAkIDshiVglsQUmmmWnQZlf5pdzlWNNpwuo8llmoJQwZh0rGJBCvZ0yU8z1BSyxIdSGtpwTEyhOw5rJDJQchVxdf+UkNJY2xeIfJSakCkRmxweAuYG3blAmnNWBKmDe649M4pzU9rEsGbk5ayCEhF2X5VmDXEN3j7F6RmheSp2HI4wcupa7NH+eaXORNK58wMmc0qXNRvq61xlLHyFCGJIozlRzuL4KU/VN2M9VrduY0bW882uk0Dkgd9l9mMutN+LgOTwARqenvu37pE0OwK7DXebbsXoWDwHnyfdpYO4NYP91doDL2LrvuvrNXee0Pq8JCEyndXlcURnvg+t8w5bHex1fWRUwSOWfRfqZc6qFAuhyftYPC9X203s2ody8Nt5RlABw1jV2bZaLGtC/FjRO43Pk7Tp+J1+PZeNrk4KlXm4o3+18QFnW5t90uQsel2P1dn15T9RtI78xUpC1avuC4TsgtlA5Yoh8d6eFOfnN78oSITVVvex69P2WQa2kuab130hhRPf3ewvotvrpe0zHJrmuCsijqo9TeRfYtbuhes9WQCRB/S4KyGXp8FT+sgOOP+5FstsRaK8d0YSfLTD/F2XTgcTmOgdsXypsZNuvfl9O5yLXDZrTiJxaCryuym0RU7meVj7GL48MeFbBZqnad3qe+tqm+9Vjpe5/MH+w/SbvjwNgnaq/hHEe6Ve9LezabeKVkeAbyuDJZ5J5Cw00e511QEHddSO1BMyo7lTzK5AEKFr/1sGT9XlyFya6tdsXW2yxxRZbbLHFFnt9e2VQuusCWIGP2HkM5NFHj5f7NV5u1gATXEhIicaTOmAkIZUdmCSL4SzjZQvINJ7vj5x3KukNB+RExcCtTiYinaD/IszA5uuA61+UBNZINrw6r8lwEeHFLx3h6NEFYnLolZl74IDyCcMR0J8FhGce1A8CHlqbegc4B4oMt48InhCjK2ulQLMLjVa9GxdpjcgOJ26PM7/FVVrh2HV4EY/xdnipYLNDgsOaenTssaYeCQ4d+8yKbilmZjUAXKXVZ9CIP58liOx2LbG8T0FYuMRo3ZBZ0V21Tw38GgAp4CRhnwK65DMYXdvATsBJDzgWRrqA00WO21gWdrwB0okJAxyGqIxdXbDWoHdiQkxOpKBJpLxjKjmm7bc/fPk+vvDoOc7cFl9eJXx7+wU4ivjle5/g7d+8xB+88z6e/eQEZ989wulPWqyf9PDbAdRHxOMWmy+ssLtP6M8I/SkQjxhxnZCOI9zxgNPTHd463uKvvvVjvNe+xLHf476XqP1danDs9mgpomOPTVoBDjkft1nHAd5k4SvG820WVYrbgP/p77XVwLQDSx0q0Nrp4GVM+Cnob+XbfVQD0sasru+Xuk5TBj2AV3caLLbYYp+buYERm/K8UuIClAJjRpntUznI2L7bIVMn4xyQxerYt3lXNc/i+ti6PFM8sTzQXB1Tq6LE4nyX+lfbpzkva6BuMj4ZOH8gtYvKN0wqTa2Mvwx0ZMCBEFtluw0kErW141G9inYON1RA9sTDaOw9quqcm1d9q+wZ7AG/E+ZbWskx/RnQvATCFWE40vIDl2s3x3ZmO1PuDIr2WZh7hRk3M7eqndwTZzSTOdC5tHv9T/vldVivb8TmQM+5Of5kv5tIbfX3vBZ4FXB6yuA2uWRg3K4z5wcwdop/Vs3+GuUYDpJxAb4dTzlgTs9MLF43gEDSL/EByH9tefWYUYNuN1jd1jXDNZdj5d7ReVI91o7kuWuQ1dbKWcKbFWRHHqNGNsGCXOS8tsv40xxYVdVp9B1SJ7DlWOYxkxvjew2Tuudy6u2o+t/Aa7b1fsVKrsqya8sgk7Gk63ddLJL+xhZ2yoLObZz3Lb+DANfLXN15ZMWOnLLbIQPWmTGuZdAADXag0leMcRAXVe2h12DvHumsIpuerzcHdBmDnIvaSUQOFmMPcOBxe9Vsbv3d7xhhEw/H2jr92SvGy98JY0hnJH59iWXiUcBcNseH+amvPT+Vv3NzKznR/DhU9W8tZV2PfSPw+to6jD8XtvVMfWowN0dXYPw+c/ZM6c0fq/1mUsYcjK2vO9YevAMmA1r1vM8ddzCeAQd9R6ZAo8fR9DRc5qKjQNHqWVpsscUWW2yxxRZb7LO1VwalAfPPyIwuDQ5dF/CCCdvLFaBA1pCcTFajTc5pJN0mjj35XC/awCpxWU1MjcGSJ4KJwY0rxwAjxxK7mchI5wSonUpzvwZb4sCuA5pvyxt9ndz2Z2G1ZLjzQPB48Q3gC+eXKs0s2732k/wDfBPRnzO2DwPWnzagfacR+uYFLotUigy/V0Zn46V/NBLdFjlz/kRPCY5Kzuh7YYNnw0kltUyIOb+yyH73HJDICTsVDEeip7bWv1OA8E2YMV5XbsDKD5ndHFzM7NeV6sAZuzUyoWeHRsHkpDmRk/6+iyEDlA4CbBr42SlT2qXyrCXnFKyWY1q3ByBAZaCEXcWC7mJQIFtkvMEOwUn5+xjQR48+OTROHsw+uZyDuoseQ3QIPuHJ7gT/bvM+fvPkL3Dfb/DNo59i7XrsUoOLtMYvn36Cjz88x8e/dYbnuyP84Mk9DBcr0OCAkwGrkys0zYDgEo5DxL3VDm8fXeIk7HHke3yweoZ7fosTt0dkwonr0LNHxx4NRWzSChsAa9fjvt9kWXczT0n6hlyW7rbfwUAPnwHjesAw6e7EPvebANTzrOqGIvYp5LzWa9ePwOuGInoAqOTTzRITVpoGoa/zWlcmrHgNKDDm9XSfKphgTt57scUWe/PmhgoIrZxrI/BkEoyXH/eadaZTJHPY5+PyHAqFgcbj8lOQ9AE2RanZwX7PmeGFYTxHS17f7RVbx3Jw2jXUCjjG3kqh1C07+Crnn0mkAsJGSyaVXc0RZZs0RA5IZIjUqvqLXS/zvtQUR70bSv5Ok+UGgEic65WJS1pWzSIzpnluW1IHZi9gTArKMlRW4HAkhTWXwPoJYfsOl/7UOhl4L2lOoKlnII5vhgDeLcPylIrcLIEbqXNmsWuZCAwerHMBcgR4LjKTQTqYOgcOCXwcZUrXv/nUJwfAZz2nvmVuPiV3XbvfdJ6uoI6d/1ayG1V1qe5jtoiPiZM8B2+M6lC2AWOndj39GN1rt9iojGlb0Pi6RuMGlTVVvV/CuIzcblyOo8S3t9kNdadYNs6Vcx2ruc6lfFjo4blHKQhuqJ+xpW+r9+dpLirgmCrAEchgSAGACyCdgRJSAHemXEYZfyUohUv/1+Mz48bnqgZgD1mJM/dGVW5tU7Ba/toDIgMtGxs5J4/GfH8RZHy1sZ5tzK9Y2Hb/5mAqRmpoVJYbNPhJc2FIjCuDQ1nXlkAxRh1IlgHvqOVEAMwyHhvgraA0uHID5PGk+lyriUzbKYOGmtpskHef2zt97wsDPI//uS1K20Dfc34P+K2+gGu/SLXevxNG///2zqZJbiM5w29WAd09Q1If1EqKle1YrQ/27sHhm///f/DBx3XEhr3hVawkiuTMdANV6UNmViXQaHJIkeJQm08Eg9Pd6EKhgEYV8s0PVlsSdYHYez8A3dHL3q/uM7toGb0N+5sB+LE6FPAx97T+jGWUNOsffni2HMfW6zm35qFKi9rM7EXSBLCl5Hfve0GayvK69fvkzMvIZu+AZse8+oGbA0OLinZzEE1Js/FxF6T9eF6ar9p1Clm7pioOgsV9zlg4D5jjoY90b03b+fSemdzXbP1glvcbq/Xexpup3SvWzoK+rTZXJnbZZbBwvAmCIAiCIAjeHW+QvrvidJIIThoquBDmacB0HMC3A7AX8QqVgFnTahvr9DjoRg7De4Eb9oDeFsArIxMTyYM4kTMe0flDlb1nwvTrxONX4dsn6pHJVrt6ndoMwBuJ0fcRy5M7VkDSlFuqxiKf188eo3x7hzEXTCVrF6mlbiSS9N2Prk748YsdXv7DDtffHXD18ijHMpeewtsMSiRjjsrIpwpQkjRtydU9qyKUvax7HGjqIjIYJxWYn+YX+J/j5/ixXONJumsRoPYdQARCi5IuAEbOTXx8RKdFFOuH4mYescsFIyQF9lWWvps4PdeMWYu47dIsDgFgTJyQ9Twcy4Bjze01YLWos0bQSpGzlBlDKk14nKsIypWppQYHetSsRUk3QbsOImIz4ZDnRS1loEfekv3t0kVbz4gYpRJ+utvjP3/4Bs+mK/z7kz/jm/EHZDAONOE6HfE0v8Tv999hepJRkPDDbx/hpuwwccZ1PuE6nRbpx0cquE7HFu38KB2RqIpjAnot8ap1ygGpJ31XRxSS9yzCeKQZd7yTqG7uEeeVCXc8arS91NnOVLCnimPVWuWcXFrz1N6z970gfaxDG9uqeRZviuzX9tG8yZM4gxR1QgDEcWEdOW2p1gd3bQ9UUYmQWM+n9m9IVc5nFUE6CIIHito9xejFMEHL7HvryI1Lws9y/STiAjMAlwLVxGhbT7U0ovodtjWWGWKBhdOg7/Na4ALUEH6S7/AAF9217FsdXDt6TCIy9HZNMOekKcJdGm2r92cReRahw9lsw2b8p348CW1tZgJ2s2O66CNyfc4mjFv/m1GUxGBtu567kbOO3MQDmvXcElD2YsTMd8Dux4Tp0yri9WkpPphowEn2V3e1G54HMbxbPcl6pevJsYoAbf1LDBqrqvK8iABKWSLx8yDldMqcQcTY7XV9Mr+usOwDhXvqYDnly4vzXgZjv35/jfji228/UcJyjc6LL5wJxltRavcRxFcdWe7O6wPrfeLCODihoDm2cH9vLV6/rfH90rFtRty+siHtFzQu/ZLw42gOOPft+5ts+wsg974uSLfkP15sYTRH4J6OXmtPbw2TnWO731d3rsmda7svrh+d/f1b+yKRg33D9pl7vWxk+XcTSltks7MFJAYzSRYN1vu9/52yu5as/zoX9GdQVqFY1+cDtfG1f/56B2vWjso6n2iJMHMacf7ZXkD054Y0AjvNur3+wKSWNCGdJPpaBHdnD7k0Ttr24nzUfushnevzUb5Is40Hw5KIiRhOZ2MDBoZbltTdi/09oB+DUW2xgOVYrJz7NlmL1/67bRs00ZPntBI5V/tl951Fe1v7vrDtmnXb+veiBnSFWOrWY+CxNY1zZNn2UKGWB5/h9qFrre0sJe7PN5272m61xICbh+xe1/rg+wKdf7Vffi3Y7htb47GevNzEffa9LVubWxcv1s3pHnNWEARBEARB8FbcW5SeJhU2K4ErIY3yZFdPYiWkzCgvRmCsoIkkwmNlJCCXZspHyXgWC1413JnRtHmRzz2FlXyH1CvZPW3bgnO9yF5HTP9cWqorhgjdFwx+7zpKuj2drtpNBBpH/N9/fIp//PIvuJ1GjLmIKKabTC6q8np/wvz0Bi/+mZCPI34zPcH+L8+BlKS+tAlrtYJTBmtda5oZGRWcEqgCx1kejIlEvBPB+RY7KihIuFPh77N8g0fphMqE/7r9Bn+8+l+ceMAn6RZ3dcST4bZFUL+se2QwCgi1piZaj6lgEUr1gZg5Y1g5N1T36FKYmhA9arT4SAUDkkTRcsZUM2pN7ZJs0bTtsiJN5Sz7MTHSONWMU8nY5YIEbp/ZdhYVb1ha6dmNn4mjRJKocdLvlSo1r0tNKJW0n3J8d/OAZ6fD4thNjJ24tKhhAPhy+Ak7VzPchGUReyVN+5rKqQnNkh5bU3CTRC9nrq1e+UgzKhISKjIxaiWNUhZRHFwlRTxrD3QMrM9HjBLJ76Kire9Wf9vXgbY+jVgadXw0dLZIeBWgJOqdUSlhTGUR9dwFa/0HOjtnhp1TAGB3s1yneg+C4OFhUVcmLInRrRtPWyCKN/Tzyq7vDfpYGuesJvIi4sq+pwljWrZ/M8DN3VB/ZtNbRU3mkxjCFwL3KsLEH+vi2C1CE/K/CdEmNsN9f2FANNU3QwTdJNHJRJAITDXiWlQ0a3QPJydouwhKE5cXApwdpltO2XhYpLrVZrUa29ZHHiSlNycpRTG8IAy3hDQRyqOCsgeqRjz7qCkR0hm0lxBoIhGXmSWNd0qaOJkJwzijlj7/5lyx282olZCzrDrMOYmIMebSsxpVmUvHXFBrQs4f3qHvbWiCNC2NxvLhhsjzDmltk11Tfb++D8ttl9fTa7lP/61Nd+2+6je3aHulxbS3179T//v/QJZ4/7t0CdN/3ajgSgwVnfvgt2jbmSUa1z3D9gxhy4nCX4eL99qEQG4e2h7fRRvkaqq6a91rf5tp0wnqvHz+mQnS3mnB1sacCLxyrALcHOce873omszJygldkj6+98e2T0fSetTc51JASjwUQnFzm6Tg7uIWWPafJpkX08Qyl6opIOl8SdzrSVsN6+Ugo93XLp2D9ltHH6s0McbnhOGlvK47gDOhHOS8pplAkxsbFelTAXYvCug0d1uIP//ewf9D46+/Mw+5V3yPTFm80F5rE91uNL/iJFxq41K7b4BPs93+XkcAVw0o2Jp33mxnro+8PP7N7V/RFl/4/NJ37DrfSE++cGy0Y1NngTNnzJbpR3a0cDCBzBf2gtCfk9u9bNXv9v31sbVo++17aRAEQRAEQfBuuLcoXUtCHgpqYnAlfUbQRa0+wGEmwGoMuzRLNNMiTRA7L2OsF4q6ALT60Vbfzx7CpU4UgzP1yJuEywtrq59FtPS4/jkisX9YS1meQPOFBxpKPbL5voK479tWRPf6YbEymAtACZQT5m+e4od/q3iaC7ZS+paaxKOaCUgVh3HG/PkdXvwuY3y+w9ObKwzPbkG1qqGctGYXN7tHTbRMj2iGBT28kUoTpAv3KNPCkpJ7SBV/PT7Bb3fXyGDc0YxMFd+XxyI8u/rRRSNln6Tblhq54h2L/G+BCY2GiZe97q9E47bPtdb0SAWP8xE3dYdTkkjpuzIuonoTuKVvnjlh8KKlRu4mZjw/HTCzpNleiM1VRPPEEmENvFq49FG7/vpogVkEIMmof3Yl6bZ/d/09Pss32p+OnPO3Pz+VU6szbrWhQdJuRq8PfSlafn2cxaWJT0gojJYW3nxjqkZE97b7eTMj2U3dATivX+33M6CL2wVJo7XTMh27nmfpfwIYm+m7z8dFo6JTRamp15IOguBBY0KWNy6vDYxWi7JHCaPNq/Y9X3VAhIpu4OOkEV9Jo8MWBl3ZvtXHa+sqIJ+498HvMwE82DYAFyDfWSpyauKA7ydUZCaGRHCbXc/9b2ms2Ym7NLs2VjT/QnVqrDsRgZF6Qo80MVLRetGW7lxrR9vf7VyYYA+IULw4UdreSNI/rQstQrcOkArjYGhaSwCZQUMFDRXT04T5+x2GlwQkxvBYZpmkYjCp41RKDCIRmE1AZhWnbQ6bixzgYTeh1oTTnMEsQvRhnFtyHgA4ziJA2z4A+WwcZ9yeRpzmAaWk9tmDYiu70QV8lHD/Ps5qXK4PswudJgKs9rHuw6tqW18wyHuBr0V63YNLp6RlU7DXaanPtMgx+3yrLd8fv6G8eR79tiXmbPXrHv3vny834NXgrT/vdbz5Yl/W94qPfTnUahEXSf1szwAAVKRmJHsO8yLuzPqSLl+Tbq7pYj8vROWtcW6CKLD9m0Efd7mvU3Mkt7JOVt7Bb2dtn93vbd5zc1bT2p0AfdYVtRl0h62ebr5FQ899vkyzzmVZo4abLUIabg5bWrvZ5s1WF1qjj2F1pGdLqa6fSxeQTrbvXvM6T7yYg86cWdz/XYBDz/DhzmW23wcBM2lE9klF6SN1gd61TzOknnTxIeDa4YcgRHvu6xXTxsxtvxamX+fBs47abdut/n8Tn67VGuwSVPVHVsVWZRlQmjhaNR08sIwwNszhw+6BW330x2vXEffrau3k9fbpMnofNutgt+uRlr+Btg5ezVP+HlWdSK3X/SJiOvXTbPctvz0TNk1//rdH2veFc9bH6ccXBEEQBEHwUfBGNaXLnCX1FAPVFo72fyFZEBe3utVFbyoi2FlUja/71YygVh9Lt0kzo2YRRJEsPZc9aKrnvI+IXpOop51WmjD9c6Klt1J/23u/9APdQrzWgcsjfvzDY3z+7d9wM40ShVnT4tnIBEjzIM2p4mp/wumLAc+/PWD/7IDHzEg3J/GuTjLmci4ral6u6pthg3tEw4FOTZQzMtWWdvmQJrycd7gpe1znI+7qiOt0xJ+OX+Hr8Rm+HJ7L9nWHiQfsqCwE6a3o2l+aurIATTWLgIwumBZ9MvIGJqkf3dN671Nu6bXXdYftHM0uUtfScs+6PxOrTzx04TpRE5klDXhP6115WX/YoqmrRnZT7qLrmAt2qSCniiFVHPKEb66e4TfjC3w1/iTHuHIQsGMuoFZH/E3xjgwA2rF1gbl/Zg4KFcvfZt0Qr6vWb66qhhTOWhO6ilOBu65MfPbnueqxXRKmPXYuJpYo7b0WufOR2uv2X9smCKipOSys9xUEwcPCRFi/PmjCtDO4yS2JWymORRSyMyCaGN3rRKoh3RnfetkT6m1UE26xNLKbgR1oxkmq0ChmSRFuRlMq3NYCzYjojrWJ5j4ajNCijb0YLWMiEYH55EQlXn632Rs1OhtEqMzgESJQDyLy0twjls0g3URnM/COOuC6XqV11DABlBhlSkhDlSVmUpHYzc1Jja1FU2FTqkga3ZyvKm4HxvzdHnRMqFeEcd8HRCKcUxOfzRnNO4ZtOZBJNhM0UblUWR/MtkaYrUxLauIzkazvKhOmKWOe84OMlD4TkDfWDQuRN60uky3b95tOhxsi9Va/TNzdWtnI8kCFHkuJcGEJdCZC0eq1vrWpo3DffvH56rUY2W3DS2PKTay/dFw/l3V9b1o/f61emkjdHF3/DpY2VsfY15PuTtlo0bqwFNUMcUgekj5foHuonDWu/9tYbozppkOQPi+3zXl5qmqmfh1qBgsAyM5Bud3zbV7xJoKz844mHtn+04Sz7ARnvwN9z0cEt/YsepqopTyXdgl0YgxHZ3vQ+ZRoWZLK5laLWLf/ba619xbzYlGHL1DLRqK640I8W4tsi2Px4ttalKbl99LESHOfS9PJzcPc200zkI4FF0Xoh5TG21+n9ne7ENf3azobx7M2/OuE7fbX+3/d/Wf92cZ9fNvho5dTa/sxMdqnH7DF4uL4z/tpGWAuHsu6r+46ahllWnuX54zFNmc/4NccY0UT19d99am8t1Lst7/XmXnYDfliLtWGa9+WtTzK2eVjQ64/ULvPWnYHW3//PcxDQRAEQRAEvzT3FqUZQLkb2sKMqxi8bIGHBGBXgVNaGDjbgxyj1T0ifYA78wIG5IGwao0kNTA2w4RFT2uH2qJyXVNa29msL/0uMVGY63I/WwvXS8aCn0tb4JP0Z8j48V+Af/3kJ/zp+6d4fDieGTtzqiCmRbpHANgdZtx9PeP5P40Yb/bYA0g3J/BukAd65pahPE2SipkG6pE3xHopMCYecMcJN3WH63TCgU44pAnPyxWe1wP2NDfD64EmrR884b9vv8AhTXiS74AK/K08xk3doWTCIU2oLKmvrc7wQ8HGuDBp3d+kxmb53CJlAYnStczP+zQDA3Cs+UxUbKKyXvS+3rC91+saS13rQcVVVICZmnjZUj0nsYz4VNNAjwbOqWJ0BvKrYcJnu1t8Mt7hyXCHT/Mtvh6f4VE64pAm/HX+BD5O2mpjv4kgfbadKiKZansY9WNjEdO976+Onrea5ZYKvgnPLnrf/y9t0ZlYLBHb4hBwSBNezPu2bVr3sTkLpLP+t/b0CTsxXYyUTk29Wh+T/H7tHK7rUwdB8DCwCLIemaKCC5ZJaZuRuhndzo3GadbfuzrpdcO/s5g5wzYznMBDKurqZtUijLEUxlwfMHZDP7GKDnlp+JdJnxaicnI+Oy1KfND/s/SXCjWhIE1A2fdtLyTB6H23QyeADxWs5WTIG3QBDKOPTpZsP1xlTiR9fy02j2PBrHWYiRg5VyldoSKwRTkDQCVJuZ1zlfrNKgYfrk64+4qR/nyFeRxRhyqf6balJKTEqMRNKLY2rP0xF9mmEuaSW4kNIknxXVw5jSFJtHVKjFISTqfBtZlw2E1yTOiC+seEiUvNgM7ritLrL2y850WgrY/XBnUTl/W71mwTzNeCjrO1d2H6chfP+nKf0/Ka7c+WAE0Fw5k43RxaWJdC7zFS8nxsV683op57Cn9X//RVjXzkyD2VQAnN+adFiq+EGRGo9bO5gik1UXvZKLoTEdBKEMgLar8nsnliSzzTdAxb1xZnnRPICTjmmKROU5K5g/VcUt+/HlPL3gE0wbpamSizFbjHXOlTH4v+nouUdvuwNhJL6nPOMh5p5ib0U5uXe1S0RUmv7RRdmOYmoMGJ8NKvPuf6KGliRmJq2TwI+rOrfUhMePN2EXNYs3MGoGUKaaeziOBMszgvpbKch6lqhPjEoLkCQz5P380MKvXMieSDYWOaVEx8Xbf0GuMt0XrNOnL6QluwjHyAnOeMLrQC9+rT1jZngrTf3tpdR3tfwqf6t8O6Tx9J144+ct+vsdp9A5crpq36x8Qybut9m73Q7kGutrqJy97Rghavtw9gUUalvbk6Dnsv6xp0cl1y99T1/bX5A/j71QP5WQRBEARBEPyaIOaH5BYbBEEQBEEQBEEQBEEQBEEQBEEQBEEQ/Jr4yKtwBUEQBEEQBEEQBEEQBEEQBEEQBEEQBA+ZEKWDIAiCIAiCIAiCIAiCIAiCIAiCIAiC90aI0kEQBEEQBEEQBEEQBEEQBEEQBEEQBMF7I0TpIAiCIAiCIAiCIAiCIAiCIAiCIAiC4L0RonQQBEEQBEEQBEEQBEEQBEEQBEEQBEHw3ghROgiCIAiCIAiCIAiCIAiCIAiCIAiCIHhvhCgdBEEQBEEQBEEQBEEQBEEQBEEQBEEQvDdClA6CIAiCIAiCIAiCIAiCIAiCIAiCIAjeGyFKB0EQBEEQBEEQBEEQBEEQBEEQBEEQBO+N/wd7wkQ9heEIIQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 2500x1000 with 10 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, axes = plt.subplots(1, 10, figsize=(25, 10))\n",
+ "for i in range(10):\n",
+ " axes[i].imshow(X[i])\n",
+ " axes[i].set_title(y_brand[i] + ', ' + y_model[i] + ', ' + y_date[i]) # Show brand, model, and date as title\n",
+ " axes[i].axis('off') # Hide axis\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5a6cc569-104d-410b-b5ba-50251f428e33",
+ "metadata": {},
+ "source": [
+ "## Feature engineering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "25a5601b-d6ff-4ca7-a906-8e585e7881dc",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "ename": "MemoryError",
+ "evalue": "Unable to allocate 14.4 GiB for an array with shape (64467, 300, 200, 1) and data type float32",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m X \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfloat32\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m255.0\u001b[39m\n\u001b[0;32m 3\u001b[0m X \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m150\u001b[39m, \u001b[38;5;241m100\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n",
+ "\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 14.4 GiB for an array with shape (64467, 300, 200, 1) and data type float32"
+ ]
+ }
+ ],
+ "source": [
+ "X = np.array(X)\n",
+ "X = X.astype('float32') / 255.0\n",
+ "X = X.reshape(-1, 300, 200, 1)\n",
+ "\n",
+ "y_brand_encoded = pd.get_dummies(pd.DataFrame(y_brand))\n",
+ "y_model_encoded = pd.get_dummies(pd.DataFrame(y_model))\n",
+ "y_date_encoded = pd.get_dummies(pd.DataFrame(y_date))\n",
+ "\n",
+ "y = np.concatenate([y_brand_encoded, y_model_encoded, y_date_encoded], axis=1)\n",
+ "\n",
+ "X.shape, y.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "66c9455e-52ab-4ccf-89c9-0af023e9e807",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((51573, 300, 200, 1), (12894, 300, 200, 1), (51573, 392), (12894, 392))"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=.2)\n",
+ "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "036b9b64-1703-48dd-9e88-8ef99569b0f1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/12\n",
+ "\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1s/step - accuracy: 0.0000e+00 - loss: 17.7596"
+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[9], line 41\u001b[0m\n\u001b[0;32m 38\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcategorical_crossentropy\u001b[39m\u001b[38;5;124m'\u001b[39m, optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrmsprop\u001b[39m\u001b[38;5;124m'\u001b[39m, metrics\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m 40\u001b[0m \u001b[38;5;66;03m# Fit the model using the data generator\u001b[39;00m\n\u001b[1;32m---> 41\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdatagen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 42\u001b[0m \u001b[43m \u001b[49m\u001b[43msteps_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Number of batches per epoch\u001b[39;49;00m\n\u001b[0;32m 43\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 44\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 45\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32mc:\\Users\\nawar\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+ "File \u001b[1;32mc:\\Users\\nawar\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:339\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[0;32m 328\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_eval_epoch_iterator\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 329\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_eval_epoch_iterator \u001b[38;5;241m=\u001b[39m TFEpochIterator(\n\u001b[0;32m 330\u001b[0m x\u001b[38;5;241m=\u001b[39mval_x,\n\u001b[0;32m 331\u001b[0m y\u001b[38;5;241m=\u001b[39mval_y,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 337\u001b[0m shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m 338\u001b[0m )\n\u001b[1;32m--> 339\u001b[0m val_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 340\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mval_x\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 341\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mval_y\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 342\u001b[0m \u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mval_sample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 343\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_batch_size\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 344\u001b[0m \u001b[43m \u001b[49m\u001b[43msteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 345\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 346\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 347\u001b[0m \u001b[43m \u001b[49m\u001b[43m_use_cached_eval_dataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 348\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 349\u001b[0m val_logs \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 350\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m name: val \u001b[38;5;28;01mfor\u001b[39;00m name, val \u001b[38;5;129;01min\u001b[39;00m val_logs\u001b[38;5;241m.\u001b[39mitems()\n\u001b[0;32m 351\u001b[0m }\n\u001b[0;32m 352\u001b[0m epoch_logs\u001b[38;5;241m.\u001b[39mupdate(val_logs)\n",
+ "File \u001b[1;32mc:\\Users\\nawar\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+ "File \u001b[1;32mc:\\Users\\nawar\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:425\u001b[0m, in \u001b[0;36mTensorFlowTrainer.evaluate\u001b[1;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, return_dict, **kwargs)\u001b[0m\n\u001b[0;32m 423\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[0;32m 424\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_test_batch_begin(step)\n\u001b[1;32m--> 425\u001b[0m logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtest_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 426\u001b[0m logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pythonify_logs(logs)\n\u001b[0;32m 427\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_test_batch_end(step, logs)\n",
+ "File \u001b[1;32mc:\\Users\\nawar\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 152\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
+ "File \u001b[1;32mc:\\Users\\nawar\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 833\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m 835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m 836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
+ "File \u001b[1;32mc:\\Users\\nawar\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m 876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[0;32m 877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[1;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[0;32m 880\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[0;32m 882\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 883\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+ "File \u001b[1;32mc:\\Users\\nawar\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[1;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[0;32m 137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[0;32m 140\u001b[0m \u001b[43m \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[0;32m 141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32mc:\\Users\\nawar\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[0;32m 1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m 1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m 1320\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m 1321\u001b[0m \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1322\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m 1324\u001b[0m args,\n\u001b[0;32m 1325\u001b[0m possible_gradient_type,\n\u001b[0;32m 1326\u001b[0m executing_eagerly)\n\u001b[0;32m 1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
+ "File \u001b[1;32mc:\\Users\\nawar\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 216\u001b[0m flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 217\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
+ "File \u001b[1;32mc:\\Users\\nawar\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m 249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[0;32m 250\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[1;32m--> 251\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 252\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 253\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 254\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 255\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 256\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 257\u001b[0m outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[0;32m 258\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 259\u001b[0m \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[0;32m 260\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[0;32m 261\u001b[0m )\n",
+ "File \u001b[1;32mc:\\Users\\nawar\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\context.py:1500\u001b[0m, in \u001b[0;36mContext.call_function\u001b[1;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[0;32m 1498\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[0;32m 1499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1500\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1501\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1502\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1503\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1504\u001b[0m \u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1505\u001b[0m \u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1506\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1507\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1508\u001b[0m outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m 1509\u001b[0m name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m 1510\u001b[0m num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1514\u001b[0m cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[0;32m 1515\u001b[0m )\n",
+ "File \u001b[1;32mc:\\Users\\nawar\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m 51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 52\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 54\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 56\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "\n",
+ "# Create an image data generator with data augmentation\n",
+ "datagen = ImageDataGenerator(\n",
+ " rotation_range=10,\n",
+ " width_shift_range=0.1,\n",
+ " height_shift_range=0.1,\n",
+ " shear_range=0.1,\n",
+ " zoom_range=0.1,\n",
+ " horizontal_flip=True,\n",
+ " vertical_flip=True,\n",
+ " fill_mode='nearest'\n",
+ ")\n",
+ "\n",
+ "input_shape = X_train.shape[1:]\n",
+ "model = models.Sequential()\n",
+ "model.add(layers.Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n",
+ " activation ='relu', input_shape = X_train.shape[1:]))\n",
+ "model.add(layers.Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n",
+ " activation ='relu'))\n",
+ "model.add(layers.MaxPool2D(pool_size=(2,2)))\n",
+ "model.add(layers.Dropout(0.25))\n",
+ "\n",
+ "\n",
+ "model.add(layers.Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n",
+ " activation ='relu'))\n",
+ "model.add(layers.Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n",
+ " activation ='relu'))\n",
+ "model.add(layers.MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
+ "model.add(layers.Dropout(0.25))\n",
+ "\n",
+ "\n",
+ "model.add(layers.Flatten())\n",
+ "model.add(layers.Dense(256, activation = \"relu\"))\n",
+ "model.add(layers.Dropout(0.5))\n",
+ "model.add(layers.Dense(3, activation = \"softmax\"))\n",
+ "model.add(layers.Dense(392, activation=\"softmax\"))\n",
+ "model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
+ "\n",
+ "# Fit the model using the data generator\n",
+ "history = model.fit(datagen.flow(X_train, y_train, batch_size=64),\n",
+ " steps_per_epoch=len(X_train) // 64, # Number of batches per epoch\n",
+ " epochs=12,\n",
+ " validation_data=(X_test, y_test),\n",
+ " verbose=1)"
+ ]
+ }
+ ],
+ "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": "3.10.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/mlmodel/Final/SCRAPPED_model_in_py.py b/mlmodel/Final/SCRAPPED_model_in_py.py
new file mode 100644
index 0000000..cc842be
--- /dev/null
+++ b/mlmodel/Final/SCRAPPED_model_in_py.py
@@ -0,0 +1,176 @@
+# %%
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import glob
+import cv2
+import os
+
+import keras
+from keras import models, layers
+from keras.optimizers import Adam
+from keras.layers import BatchNormalization
+from keras.regularizers import l2
+from keras.callbacks import ReduceLROnPlateau
+from sklearn.model_selection import train_test_split
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+print("Loading data...")
+
+# Load and preprocess the dataset
+images = glob.glob('../Dataset/*.jpg')
+data = pd.DataFrame(images, columns=['src'])
+data['brand'] = data['src'].apply(lambda x: x.split('_')[0].split('\\')[-1])
+
+print("Data loaded successfully!")
+print("Preprocessing data...")
+print("Filtering dataset for balance...")
+
+# Filter dataset for balance
+brand_counts = data['brand'].value_counts()
+brands_to_remove = brand_counts[brand_counts < 1000].index
+data_filtered = data[~data['brand'].isin(brands_to_remove)]
+
+data_balanced = pd.DataFrame(columns=data.columns)
+for brand in data_filtered['brand'].unique():
+ samples_brand = data_filtered[data_filtered['brand'] == brand]
+ if len(samples_brand) > 1000:
+ samples_to_keep = samples_brand.sample(n=1000, random_state=42)
+ data_balanced = pd.concat([data_balanced, samples_to_keep], ignore_index=True)
+ else:
+ data_balanced = pd.concat([data_balanced, samples_brand], ignore_index=True)
+
+data = data_balanced
+
+print("Dataset filtered for balance successfully!")
+print("Brands in dataset: ", data['brand'].value_counts())
+print("Preprocessing images...")
+
+# Preprocess and resize images
+resized_dir = '../ResizedImages'
+# force delete the directory if it already exists
+if os.path.exists(resized_dir):
+ import shutil
+ print("Deleting existing resized images...")
+ shutil.rmtree(resized_dir)
+
+os.makedirs(resized_dir, exist_ok=True)
+
+print("Resizing images...")
+
+def preprocess_and_resize(src):
+ img = cv2.imread(src, cv2.IMREAD_COLOR)
+ dst = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ resized_image = cv2.resize(dst, dsize=(100, 150), interpolation=cv2.INTER_AREA)
+ return resized_image
+
+X, y = [], []
+
+for i in range(len(data)):
+ src = data.loc[i, 'src']
+ resized_path = os.path.join(resized_dir, os.path.basename(src))
+ if not os.path.exists(resized_path):
+ resized_image = preprocess_and_resize(src)
+ X.append(resized_image)
+ y.append(data.loc[i, 'brand'])
+ cv2.imwrite(resized_path, resized_image)
+
+resized_data = pd.DataFrame({
+ 'src': [os.path.join(resized_dir, os.path.basename(src)) for src in data['src']],
+ 'brand': data['brand']
+})
+
+print("Images preprocessed and resized successfully!")
+print("Splitting data into training and testing sets...")
+
+X = np.array(X).astype('float32') / 255.0
+X = X.reshape(-1, 150, 100, 1)
+y = pd.get_dummies(pd.DataFrame(y), columns=[0]).values
+
+# Split data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+print("Data split successfully!")
+print("Training model...")
+
+# %%
+from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
+
+datagen = ImageDataGenerator(
+ rotation_range=15,
+ width_shift_range=0.1,
+ height_shift_range=0.1,
+ shear_range=0.1,
+ zoom_range=0.1,
+ horizontal_flip=True,
+ vertical_flip=False,
+ fill_mode='nearest'
+)
+
+
+model = models.Sequential([
+ layers.Conv2D(32, (3, 3), activation='relu', input_shape=X_train.shape[1:]),
+ layers.BatchNormalization(),
+ layers.MaxPooling2D((2, 2)),
+ layers.Conv2D(64, (3, 3), activation='relu'),
+ layers.BatchNormalization(),
+ layers.MaxPooling2D((2, 2)),
+ layers.Conv2D(128, (3, 3), activation='relu'),
+ layers.BatchNormalization(),
+ layers.MaxPooling2D((2, 2)),
+ layers.Flatten(),
+ layers.Dropout(0.5),
+ layers.Dense(512, activation='relu', kernel_regularizer=l2(0.001)),
+ layers.BatchNormalization(),
+ layers.Dense(24, activation='softmax')
+])
+
+# Compile the model
+model.compile(optimizer=Adam(learning_rate=0.0001),
+ loss='categorical_crossentropy',
+ metrics=['accuracy'])
+
+# Learning rate reduction callback
+reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.00001)
+checkpoint_callback = ModelCheckpoint('saved_model.keras', monitor='val_loss', save_best_only=True)
+
+# Early stopping callback
+early_stopping = EarlyStopping(monitor='val_loss', patience=10)
+
+# Train the model
+history = model.fit(
+ datagen.flow(X_train, y_train, batch_size=256),
+ epochs=100,
+ validation_data=(X_test, y_test),
+ callbacks=[reduce_lr, checkpoint_callback, early_stopping],
+)
+
+# Save the model
+model.save('complex_model.keras')
+
+# %%
+
+# Define model architecture
+model = models.Sequential([
+ layers.Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=X_train.shape[1:]),
+ layers.BatchNormalization(),
+ layers.MaxPooling2D((2, 2)),
+ layers.Conv2D(128, (3, 3), activation='relu', padding='same'),
+ layers.BatchNormalization(),
+ layers.MaxPooling2D((2, 2)),
+ layers.Conv2D(256, (3, 3), activation='relu', padding='same'),
+ layers.BatchNormalization(),
+ layers.MaxPooling2D((2, 2)),
+ layers.Conv2D(512, (3, 3), activation='relu', padding='same'),
+ layers.BatchNormalization(),
+ layers.MaxPooling2D((2, 2)),
+ layers.Flatten(),
+ layers.Dropout(0.5),
+ layers.Dense(1024, activation='relu', kernel_regularizer=l2(0.001)),
+ layers.BatchNormalization(),
+ layers.Dropout(0.5),
+ layers.Dense(512, activation='relu', kernel_regularizer=l2(0.001)),
+ layers.BatchNormalization(),
+ layers.Dense(24, activation='softmax')
+])
\ No newline at end of file
--
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