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apache / madlib-site / 1b386479a81f3b2a9e92716cdb3e5819e277191c / . / community-artifacts / Deep-learning / Train-multiple-models / MADlib-Keras-model-selection-CNN-cifar10-v1.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Selection for CNN Using Keras and MADlib on CIFAR-10\n",
"\n",
"E2E classification example using MADlib calling a Keras CNN for different hyperparameters and model architectures on the CIFAR-10 dataset.\n",
"\n",
"The CIFAR-10 dataset consists of 60,000 32x32 colour images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images.\n",
"https://www.cs.toronto.edu/~kriz/cifar.html\n",
"\n",
"## Table of contents \n",
"\n",
"<a href=\"#setup\">0. Setup</a>\n",
"\n",
"<a href=\"#load_dataset\">1. Load dataset into table</a>\n",
"\n",
"<a href=\"#distr\">2. Setup distribution rules and call preprocessor</a>\n",
"\n",
"<a href=\"#arch\">3. Define and load model architectures</a>\n",
"\n",
"<a href=\"#mst\">4. Define and load model selection tuples</a>\n",
"\n",
"<a href=\"#train\">5. Train</a>\n",
"\n",
"<a href=\"#plot\">6. Plot results</a>\n",
"\n",
"<a href=\"#predict\">7. Inference</a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"setup\"></a>\n",
"# 0. Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"%load_ext sql"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Greenplum Database 5.x on GCP - via tunnel\n",
"%sql postgresql://gpadmin@localhost:8000/cifar_places\n",
" \n",
"# PostgreSQL local\n",
"#%sql postgresql://fmcquillan@localhost:5432/madlib"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>version</th>\n",
" </tr>\n",
" <tr>\n",
" <td>MADlib version: 1.17-dev, git revision: rc/1.16-rc1-95-gc62dfe7, cmake configuration time: Tue Mar 17 16:53:55 UTC 2020, build type: RelWithDebInfo, build system: Linux-2.6.32-754.6.3.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(u'MADlib version: 1.17-dev, git revision: rc/1.16-rc1-95-gc62dfe7, cmake configuration time: Tue Mar 17 16:53:55 UTC 2020, build type: RelWithDebInfo, build system: Linux-2.6.32-754.6.3.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7',)]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select madlib.version();\n",
"#%sql select version();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import libraries"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from __future__ import print_function\n",
"import keras\n",
"from keras.datasets import cifar10\n",
"from keras.preprocessing.image import ImageDataGenerator\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
"from keras.layers import Conv2D, MaxPooling2D\n",
"import os"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Others needed in this workbook"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import sys\n",
"import os\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"load_dataset\"></a>\n",
"# 1. Load dataset into table\n",
"\n",
"PXF can be used to load image data into Greenplum database.\n",
"\n",
"But for this demo, we will get the dataset from Keras and use the script called madlib_image_loader.py located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning .\n",
"\n",
"If the script is not in the same folder as the notebook, you can use the following lines to import it."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(1, '/Users/fmcquillan/workspace/madlib-site/community-artifacts/Deep-learning')\n",
"from madlib_image_loader import ImageLoader, DbCredentials\n",
"\n",
"# Specify database credentials, for connecting to db\n",
"db_creds = DbCredentials(db_name='cifar_places',\n",
" user='gpadmin',\n",
" host='localhost',\n",
" port='8000',\n",
" password='')\n",
"\n",
"#db_creds = DbCredentials(db_name='cifar_places',\n",
"# user='fmcquillan',\n",
"# host='localhost',\n",
"# port='5432',\n",
"# password='')\n",
"\n",
"# Initialize ImageLoader (increase num_workers to run faster)\n",
"iloader = ImageLoader(num_workers=5, db_creds=db_creds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load dataset into tables"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load dataset into np array\n",
"(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
"\n",
"%sql DROP TABLE IF EXISTS cifar10_train, cifar10_val;\n",
"\n",
"# Save images to temporary directories and load into database\n",
"iloader.load_dataset_from_np(x_train, y_train, 'cifar10_train', append=False)\n",
"iloader.load_dataset_from_np(x_test, y_test, 'cifar10_val', append=False)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
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"<table>\n",
" <tr>\n",
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" <tr>\n",
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"</table>"
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"[(50000L,)]"
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},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"%sql SELECT COUNT(*) FROM cifar10_train;"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
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{
"data": {
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"[(10000L,)]"
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"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT COUNT(*) FROM cifar10_val;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"distr\"></a>\n",
"# 2. Setup distribution rules and call preprocessor\n",
"\n",
"In this example we will train on 4 VMs with 4 segments/VM and 4 GPUs/VM (i.e., 16 workers).\n",
"\n",
"First get the GPU configuration in the cluster using the MADlib helper function `gpu_configuration`:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"20 rows affected.\n"
]
},
{
"data": {
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" <tr>\n",
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" <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>gpsix0</td>\n",
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" <tr>\n",
" <td>gpsix1</td>\n",
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"</table>"
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]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS host_gpu_mapping_tf;\n",
"SELECT * FROM madlib.gpu_configuration('host_gpu_mapping_tf');\n",
"SELECT * FROM host_gpu_mapping_tf ORDER BY hostname, gpu_descr;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Review the Greenplum segments in the `gp_segment_configuration` table:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"21 rows affected.\n"
]
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" <tr>\n",
" <td>14</td>\n",
" <td>12</td>\n",
" <td>p</td>\n",
" <td>p</td>\n",
" <td>n</td>\n",
" <td>u</td>\n",
" <td>40000</td>\n",
" <td>gpsix3</td>\n",
" <td>gpsix3</td>\n",
" <td>/data/primary0/gpseg12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>13</td>\n",
" <td>p</td>\n",
" <td>p</td>\n",
" <td>n</td>\n",
" <td>u</td>\n",
" <td>40001</td>\n",
" <td>gpsix3</td>\n",
" <td>gpsix3</td>\n",
" <td>/data/primary1/gpseg13</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>14</td>\n",
" <td>p</td>\n",
" <td>p</td>\n",
" <td>n</td>\n",
" <td>u</td>\n",
" <td>40002</td>\n",
" <td>gpsix3</td>\n",
" <td>gpsix3</td>\n",
" <td>/data/primary2/gpseg14</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>15</td>\n",
" <td>p</td>\n",
" <td>p</td>\n",
" <td>n</td>\n",
" <td>u</td>\n",
" <td>40003</td>\n",
" <td>gpsix3</td>\n",
" <td>gpsix3</td>\n",
" <td>/data/primary3/gpseg15</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>16</td>\n",
" <td>p</td>\n",
" <td>p</td>\n",
" <td>n</td>\n",
" <td>u</td>\n",
" <td>40000</td>\n",
" <td>gpsix4</td>\n",
" <td>gpsix4</td>\n",
" <td>/data/primary0/gpseg16</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>17</td>\n",
" <td>p</td>\n",
" <td>p</td>\n",
" <td>n</td>\n",
" <td>u</td>\n",
" <td>40001</td>\n",
" <td>gpsix4</td>\n",
" <td>gpsix4</td>\n",
" <td>/data/primary1/gpseg17</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>18</td>\n",
" <td>p</td>\n",
" <td>p</td>\n",
" <td>n</td>\n",
" <td>u</td>\n",
" <td>40002</td>\n",
" <td>gpsix4</td>\n",
" <td>gpsix4</td>\n",
" <td>/data/primary2/gpseg18</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>19</td>\n",
" <td>p</td>\n",
" <td>p</td>\n",
" <td>n</td>\n",
" <td>u</td>\n",
" <td>40003</td>\n",
" <td>gpsix4</td>\n",
" <td>gpsix4</td>\n",
" <td>/data/primary3/gpseg19</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(1, -1, u'p', u'p', u'n', u'u', 5432, u'gpsix0', u'gpsix0', u'/data/master/gpseg-1'),\n",
" (2, 0, u'p', u'p', u'n', u'u', 40000, u'gpsix0', u'gpsix0', u'/data/primary0/gpseg0'),\n",
" (3, 1, u'p', u'p', u'n', u'u', 40001, u'gpsix0', u'gpsix0', u'/data/primary1/gpseg1'),\n",
" (4, 2, u'p', u'p', u'n', u'u', 40002, u'gpsix0', u'gpsix0', u'/data/primary2/gpseg2'),\n",
" (5, 3, u'p', u'p', u'n', u'u', 40003, u'gpsix0', u'gpsix0', u'/data/primary3/gpseg3'),\n",
" (6, 4, u'p', u'p', u'n', u'u', 40000, u'gpsix1', u'gpsix1', u'/data/primary0/gpseg4'),\n",
" (7, 5, u'p', u'p', u'n', u'u', 40001, u'gpsix1', u'gpsix1', u'/data/primary1/gpseg5'),\n",
" (8, 6, u'p', u'p', u'n', u'u', 40002, u'gpsix1', u'gpsix1', u'/data/primary2/gpseg6'),\n",
" (9, 7, u'p', u'p', u'n', u'u', 40003, u'gpsix1', u'gpsix1', u'/data/primary3/gpseg7'),\n",
" (10, 8, u'p', u'p', u'n', u'u', 40000, u'gpsix2', u'gpsix2', u'/data/primary0/gpseg8'),\n",
" (11, 9, u'p', u'p', u'n', u'u', 40001, u'gpsix2', u'gpsix2', u'/data/primary1/gpseg9'),\n",
" (12, 10, u'p', u'p', u'n', u'u', 40002, u'gpsix2', u'gpsix2', u'/data/primary2/gpseg10'),\n",
" (13, 11, u'p', u'p', u'n', u'u', 40003, u'gpsix2', u'gpsix2', u'/data/primary3/gpseg11'),\n",
" (14, 12, u'p', u'p', u'n', u'u', 40000, u'gpsix3', u'gpsix3', u'/data/primary0/gpseg12'),\n",
" (15, 13, u'p', u'p', u'n', u'u', 40001, u'gpsix3', u'gpsix3', u'/data/primary1/gpseg13'),\n",
" (16, 14, u'p', u'p', u'n', u'u', 40002, u'gpsix3', u'gpsix3', u'/data/primary2/gpseg14'),\n",
" (17, 15, u'p', u'p', u'n', u'u', 40003, u'gpsix3', u'gpsix3', u'/data/primary3/gpseg15'),\n",
" (18, 16, u'p', u'p', u'n', u'u', 40000, u'gpsix4', u'gpsix4', u'/data/primary0/gpseg16'),\n",
" (19, 17, u'p', u'p', u'n', u'u', 40001, u'gpsix4', u'gpsix4', u'/data/primary1/gpseg17'),\n",
" (20, 18, u'p', u'p', u'n', u'u', 40002, u'gpsix4', u'gpsix4', u'/data/primary2/gpseg18'),\n",
" (21, 19, u'p', u'p', u'n', u'u', 40003, u'gpsix4', u'gpsix4', u'/data/primary3/gpseg19')]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT * FROM gp_segment_configuration ORDER BY dbid;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now JOIN the above 2 tables to build up various distribution rules, depending on your needs.\n",
"\n",
"We build distribution rules table for 4 VMs:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"16 rows affected.\n",
"16 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>dbid</th>\n",
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"</table>"
],
"text/plain": [
"[(2, u'gpsix0'),\n",
" (3, u'gpsix0'),\n",
" (4, u'gpsix0'),\n",
" (5, u'gpsix0'),\n",
" (6, u'gpsix1'),\n",
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" (12, u'gpsix2'),\n",
" (13, u'gpsix2'),\n",
" (14, u'gpsix3'),\n",
" (15, u'gpsix3'),\n",
" (16, u'gpsix3'),\n",
" (17, u'gpsix3')]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS segments_to_use_4VMs;\n",
"CREATE TABLE segments_to_use_4VMs AS\n",
" SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
" WHERE role='p' AND content>=0 AND hostname!='gpsix4';\n",
"SELECT * FROM segments_to_use_4VMs ORDER BY hostname, dbid;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the preprocessor to generate the packed output table on the segments we want to use for training:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"16 rows affected.\n"
]
},
{
"data": {
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"<table>\n",
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],
"text/plain": [
"[(0, [3125, 32, 32, 3], [3125, 10], 1),\n",
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" (9, [3125, 32, 32, 3], [3125, 10], 13),\n",
" (12, [3125, 32, 32, 3], [3125, 10], 15),\n",
" (14, [3125, 32, 32, 3], [3125, 10], 6),\n",
" (19, [3125, 32, 32, 3], [3125, 10], 12),\n",
" (27, [3125, 32, 32, 3], [3125, 10], 10),\n",
" (28, [3125, 32, 32, 3], [3125, 10], 5),\n",
" (29, [3125, 32, 32, 3], [3125, 10], 8),\n",
" (34, [3125, 32, 32, 3], [3125, 10], 0),\n",
" (55, [3125, 32, 32, 3], [3125, 10], 3),\n",
" (56, [3125, 32, 32, 3], [3125, 10], 2)]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS cifar10_train_packed, cifar10_train_packed_summary;\n",
"\n",
"SELECT madlib.training_preprocessor_dl('cifar10_train', -- Source table\n",
" 'cifar10_train_packed', -- Output table\n",
" 'y', -- Dependent variable\n",
" 'x', -- Independent variable\n",
" NULL, -- Buffer size\n",
" 256.0, -- Normalizing constant\n",
" NULL, -- Number of classes\n",
" 'segments_to_use_4VMs' -- Distribution rules\n",
" );\n",
"\n",
"SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM cifar10_train_packed ORDER BY __dist_key__;"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
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"<table>\n",
" <tr>\n",
" <th>source_table</th>\n",
" <th>output_table</th>\n",
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" <th>dependent_vartype</th>\n",
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" <th>__internal_gpu_config__</th>\n",
" </tr>\n",
" <tr>\n",
" <td>cifar10_train</td>\n",
" <td>cifar10_train_packed</td>\n",
" <td>y</td>\n",
" <td>x</td>\n",
" <td>smallint</td>\n",
" <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
" <td>3125</td>\n",
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"text/plain": [
"[(u'cifar10_train', u'cifar10_train_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 3125, 256.0, 10, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT * FROM cifar10_train_packed_summary;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Same for validation dataset:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"16 rows affected.\n"
]
},
{
"data": {
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" (56, [625, 32, 32, 3], [625, 10], 2)]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS cifar10_val_packed, cifar10_val_packed_summary;\n",
"\n",
"SELECT madlib.validation_preprocessor_dl('cifar10_val', -- Source table\n",
" 'cifar10_val_packed', -- Output table\n",
" 'y', -- Dependent variable\n",
" 'x', -- Independent variable\n",
" 'cifar10_train_packed', -- From training preprocessor step\n",
" NULL, -- Buffer size\n",
" 'segments_to_use_4VMs' -- Distribution rules\n",
" ); \n",
"\n",
"SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM cifar10_val_packed ORDER BY __dist_key__;"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>source_table</th>\n",
" <th>output_table</th>\n",
" <th>dependent_varname</th>\n",
" <th>independent_varname</th>\n",
" <th>dependent_vartype</th>\n",
" <th>class_values</th>\n",
" <th>buffer_size</th>\n",
" <th>normalizing_const</th>\n",
" <th>num_classes</th>\n",
" <th>distribution_rules</th>\n",
" <th>__internal_gpu_config__</th>\n",
" </tr>\n",
" <tr>\n",
" <td>cifar10_val</td>\n",
" <td>cifar10_val_packed</td>\n",
" <td>y</td>\n",
" <td>x</td>\n",
" <td>smallint</td>\n",
" <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
" <td>625</td>\n",
" <td>256.0</td>\n",
" <td>10</td>\n",
" <td>[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]</td>\n",
" <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(u'cifar10_val', u'cifar10_val_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 625, 256.0, 10, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT * FROM cifar10_val_packed_summary;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"arch\"></a>\n",
"# 3. Define and load model architectures\n",
"\n",
"Model architecture from https://keras.io/examples/cifar10_cnn/"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"num_classes = 10"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
"\n",
"WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
"\n",
"WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
"\n",
"WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
"\n",
"WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d_1 (Conv2D) (None, 32, 32, 32) 896 \n",
"_________________________________________________________________\n",
"activation_1 (Activation) (None, 32, 32, 32) 0 \n",
"_________________________________________________________________\n",
"conv2d_2 (Conv2D) (None, 30, 30, 32) 9248 \n",
"_________________________________________________________________\n",
"activation_2 (Activation) (None, 30, 30, 32) 0 \n",
"_________________________________________________________________\n",
"max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32) 0 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 15, 15, 32) 0 \n",
"_________________________________________________________________\n",
"conv2d_3 (Conv2D) (None, 15, 15, 64) 18496 \n",
"_________________________________________________________________\n",
"activation_3 (Activation) (None, 15, 15, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_4 (Conv2D) (None, 13, 13, 64) 36928 \n",
"_________________________________________________________________\n",
"activation_4 (Activation) (None, 13, 13, 64) 0 \n",
"_________________________________________________________________\n",
"max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64) 0 \n",
"_________________________________________________________________\n",
"dropout_2 (Dropout) (None, 6, 6, 64) 0 \n",
"_________________________________________________________________\n",
"flatten_1 (Flatten) (None, 2304) 0 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 512) 1180160 \n",
"_________________________________________________________________\n",
"activation_5 (Activation) (None, 512) 0 \n",
"_________________________________________________________________\n",
"dropout_3 (Dropout) (None, 512) 0 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 10) 5130 \n",
"_________________________________________________________________\n",
"activation_6 (Activation) (None, 10) 0 \n",
"=================================================================\n",
"Total params: 1,250,858\n",
"Trainable params: 1,250,858\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model1 = Sequential()\n",
"\n",
"model1.add(Conv2D(32, (3, 3), padding='same',\n",
" input_shape=x_train.shape[1:]))\n",
"model1.add(Activation('relu'))\n",
"model1.add(Conv2D(32, (3, 3)))\n",
"model1.add(Activation('relu'))\n",
"model1.add(MaxPooling2D(pool_size=(2, 2)))\n",
"model1.add(Dropout(0.25))\n",
"\n",
"model1.add(Conv2D(64, (3, 3), padding='same'))\n",
"model1.add(Activation('relu'))\n",
"model1.add(Conv2D(64, (3, 3)))\n",
"model1.add(Activation('relu'))\n",
"model1.add(MaxPooling2D(pool_size=(2, 2)))\n",
"model1.add(Dropout(0.25))\n",
"\n",
"model1.add(Flatten())\n",
"model1.add(Dense(512))\n",
"model1.add(Activation('relu'))\n",
"model1.add(Dropout(0.5))\n",
"model1.add(Dense(num_classes))\n",
"model1.add(Activation('softmax'))\n",
"\n",
"model1.summary()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4\", \"config\": {\"layers\": [{\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"filters\": 32, \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"batch_input_shape\": [null, 32, 32, 3], \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_1\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_2\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"valid\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 32, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_2\"}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_1\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.25, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_1\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_3\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_3\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_4\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"valid\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_4\"}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_2\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.25, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_2\"}}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_1\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 512, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_5\"}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.5, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_3\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"softmax\", \"trainable\": true, \"name\": \"activation_6\"}}], \"name\": \"sequential_2\"}, \"backend\": \"tensorflow\"}'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model1.to_json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Model architecture from https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
"\n",
"WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d_5 (Conv2D) (None, 32, 32, 32) 896 \n",
"_________________________________________________________________\n",
"batch_normalization_1 (Batch (None, 32, 32, 32) 128 \n",
"_________________________________________________________________\n",
"conv2d_6 (Conv2D) (None, 32, 32, 32) 9248 \n",
"_________________________________________________________________\n",
"batch_normalization_2 (Batch (None, 32, 32, 32) 128 \n",
"_________________________________________________________________\n",
"max_pooling2d_3 (MaxPooling2 (None, 16, 16, 32) 0 \n",
"_________________________________________________________________\n",
"dropout_4 (Dropout) (None, 16, 16, 32) 0 \n",
"_________________________________________________________________\n",
"conv2d_7 (Conv2D) (None, 16, 16, 64) 18496 \n",
"_________________________________________________________________\n",
"batch_normalization_3 (Batch (None, 16, 16, 64) 256 \n",
"_________________________________________________________________\n",
"conv2d_8 (Conv2D) (None, 16, 16, 64) 36928 \n",
"_________________________________________________________________\n",
"batch_normalization_4 (Batch (None, 16, 16, 64) 256 \n",
"_________________________________________________________________\n",
"max_pooling2d_4 (MaxPooling2 (None, 8, 8, 64) 0 \n",
"_________________________________________________________________\n",
"dropout_5 (Dropout) (None, 8, 8, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_9 (Conv2D) (None, 8, 8, 128) 73856 \n",
"_________________________________________________________________\n",
"batch_normalization_5 (Batch (None, 8, 8, 128) 512 \n",
"_________________________________________________________________\n",
"conv2d_10 (Conv2D) (None, 8, 8, 128) 147584 \n",
"_________________________________________________________________\n",
"batch_normalization_6 (Batch (None, 8, 8, 128) 512 \n",
"_________________________________________________________________\n",
"max_pooling2d_5 (MaxPooling2 (None, 4, 4, 128) 0 \n",
"_________________________________________________________________\n",
"dropout_6 (Dropout) (None, 4, 4, 128) 0 \n",
"_________________________________________________________________\n",
"flatten_2 (Flatten) (None, 2048) 0 \n",
"_________________________________________________________________\n",
"dense_3 (Dense) (None, 128) 262272 \n",
"_________________________________________________________________\n",
"batch_normalization_7 (Batch (None, 128) 512 \n",
"_________________________________________________________________\n",
"dropout_7 (Dropout) (None, 128) 0 \n",
"_________________________________________________________________\n",
"dense_4 (Dense) (None, 10) 1290 \n",
"=================================================================\n",
"Total params: 552,874\n",
"Trainable params: 551,722\n",
"Non-trainable params: 1,152\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model2 = Sequential()\n",
"\n",
"model2.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
"model2.add(BatchNormalization())\n",
"model2.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
"model2.add(BatchNormalization())\n",
"model2.add(MaxPooling2D((2, 2)))\n",
"model2.add(Dropout(0.2))\n",
"model2.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
"model2.add(BatchNormalization())\n",
"model2.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
"model2.add(BatchNormalization())\n",
"model2.add(MaxPooling2D((2, 2)))\n",
"model2.add(Dropout(0.3))\n",
"model2.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
"model2.add(BatchNormalization())\n",
"model2.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
"model2.add(BatchNormalization())\n",
"model2.add(MaxPooling2D((2, 2)))\n",
"model2.add(Dropout(0.4))\n",
"model2.add(Flatten())\n",
"model2.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
"model2.add(BatchNormalization())\n",
"model2.add(Dropout(0.5))\n",
"model2.add(Dense(10, activation='softmax'))\n",
"\n",
"model2.summary()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4\", \"config\": {\"layers\": [{\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"filters\": 32, \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"batch_input_shape\": [null, 32, 32, 3], \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_1\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_6\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 32, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_2\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_3\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.2, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_4\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_7\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_3\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_8\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_4\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_4\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.3, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_5\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_9\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_5\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_10\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_6\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_5\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.4, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_6\"}}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_2\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 128, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_7\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.5, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_7\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}], \"name\": \"sequential_3\"}, \"backend\": \"tensorflow\"}'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model2.to_json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another model architecture from https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d_11 (Conv2D) (None, 32, 32, 32) 896 \n",
"_________________________________________________________________\n",
"conv2d_12 (Conv2D) (None, 32, 32, 32) 9248 \n",
"_________________________________________________________________\n",
"max_pooling2d_6 (MaxPooling2 (None, 16, 16, 32) 0 \n",
"_________________________________________________________________\n",
"dropout_8 (Dropout) (None, 16, 16, 32) 0 \n",
"_________________________________________________________________\n",
"conv2d_13 (Conv2D) (None, 16, 16, 64) 18496 \n",
"_________________________________________________________________\n",
"conv2d_14 (Conv2D) (None, 16, 16, 64) 36928 \n",
"_________________________________________________________________\n",
"max_pooling2d_7 (MaxPooling2 (None, 8, 8, 64) 0 \n",
"_________________________________________________________________\n",
"dropout_9 (Dropout) (None, 8, 8, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_15 (Conv2D) (None, 8, 8, 128) 73856 \n",
"_________________________________________________________________\n",
"conv2d_16 (Conv2D) (None, 8, 8, 128) 147584 \n",
"_________________________________________________________________\n",
"max_pooling2d_8 (MaxPooling2 (None, 4, 4, 128) 0 \n",
"_________________________________________________________________\n",
"dropout_10 (Dropout) (None, 4, 4, 128) 0 \n",
"_________________________________________________________________\n",
"flatten_3 (Flatten) (None, 2048) 0 \n",
"_________________________________________________________________\n",
"dense_5 (Dense) (None, 128) 262272 \n",
"_________________________________________________________________\n",
"dropout_11 (Dropout) (None, 128) 0 \n",
"_________________________________________________________________\n",
"dense_6 (Dense) (None, 10) 1290 \n",
"=================================================================\n",
"Total params: 550,570\n",
"Trainable params: 550,570\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model3 = Sequential()\n",
"\n",
"model3.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
"model3.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
"model3.add(MaxPooling2D((2, 2)))\n",
"model3.add(Dropout(0.2))\n",
"model3.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
"model3.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
"model3.add(MaxPooling2D((2, 2)))\n",
"model3.add(Dropout(0.3))\n",
"model3.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
"model3.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
"model3.add(MaxPooling2D((2, 2)))\n",
"model3.add(Dropout(0.4))\n",
"model3.add(Flatten())\n",
"model3.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
"model3.add(Dropout(0.5))\n",
"model3.add(Dense(10, activation='softmax'))\n",
"\n",
"model3.summary()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4\", \"config\": {\"layers\": [{\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_11\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"filters\": 32, \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"batch_input_shape\": [null, 32, 32, 3], \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_12\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 32, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_6\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.2, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_8\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_13\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_14\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_7\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.3, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_9\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_15\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_16\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_8\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.4, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_10\"}}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_3\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"dense_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 128, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.5, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_11\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_6\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}], \"name\": \"sequential_4\"}, \"backend\": \"tensorflow\"}'"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model3.to_json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load into model architecture table using psycopg2"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n"
]
},
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"3 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>model_id</th>\n",
" <th>name</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>CNN from Keras docs for CIFAR-10</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>CNN from Jason Brownlee blog post</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>CNN from Jason Brownlee blog post - no batch normalization</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(1, u'CNN from Keras docs for CIFAR-10'),\n",
" (2, u'CNN from Jason Brownlee blog post'),\n",
" (3, u'CNN from Jason Brownlee blog post - no batch normalization')]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import psycopg2 as p2\n",
"#conn = p2.connect('postgresql://gpadmin@35.239.240.26:5432/madlib')\n",
"#conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib')\n",
"conn = p2.connect('postgresql://gpadmin@localhost:8000/cifar_places')\n",
"cur = conn.cursor()\n",
"\n",
"%sql DROP TABLE IF EXISTS model_arch_library;\n",
"query = \"SELECT madlib.load_keras_model('model_arch_library', %s, NULL, %s)\"\n",
"cur.execute(query,[model1.to_json(), \"CNN from Keras docs for CIFAR-10\"])\n",
"conn.commit()\n",
"\n",
"query = \"SELECT madlib.load_keras_model('model_arch_library', %s, NULL, %s)\"\n",
"cur.execute(query,[model2.to_json(), \"CNN from Jason Brownlee blog post\"])\n",
"conn.commit()\n",
"\n",
"query = \"SELECT madlib.load_keras_model('model_arch_library', %s, NULL, %s)\"\n",
"cur.execute(query,[model3.to_json(), \"CNN from Jason Brownlee blog post - no batch normalization\"])\n",
"conn.commit()\n",
"\n",
"# check model loaded OK\n",
"%sql SELECT model_id, name FROM model_arch_library ORDER BY model_id;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"mst\"></a>\n",
"# 4. Define and load model selection tuples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select the model(s) from the model architecture table that you want to run, along with the compile and fit parameters. Permutations for grid search will be created for the set of model selection parameters will be loaded:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"16 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>mst_key</th>\n",
" <th>model_id</th>\n",
" <th>compile_params</th>\n",
" <th>fit_params</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" </tr>\n",
"</table>"
],
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" (16, 2, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=128,epochs=5')]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS mst_table, mst_table_summary;\n",
"\n",
"SELECT madlib.load_model_selection_table('model_arch_library', -- model architecture table\n",
" 'mst_table', -- model selection table output\n",
" ARRAY[1,2], -- model ids from model architecture table\n",
" ARRAY[ -- compile params \n",
" $$loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']$$,\n",
" $$loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']$$,\n",
" $$loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']$$,\n",
" $$loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']$$\n",
" ],\n",
" ARRAY[ -- fit params\n",
" $$batch_size=64,epochs=5$$, \n",
" $$batch_size=128,epochs=5$$\n",
" ]\n",
" );\n",
" \n",
"SELECT * FROM mst_table ORDER BY mst_key;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the name of the model architecture table that corresponds to the model selection table:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>model_arch_table</th>\n",
" </tr>\n",
" <tr>\n",
" <td>model_arch_library</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(u'model_arch_library',)]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT * FROM mst_table_summary;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"train\"></a>\n",
"# 5. Train\n",
"Train multiple models:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>madlib_keras_fit_multiple_model</th>\n",
" </tr>\n",
" <tr>\n",
" <td></td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[('',)]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS cifar10_multi_model, cifar10_multi_model_summary, cifar10_multi_model_info;\n",
"\n",
"SELECT madlib.madlib_keras_fit_multiple_model('cifar10_train_packed', -- source_table\n",
" 'cifar10_multi_model', -- model_output_table\n",
" 'mst_table', -- model_selection_table\n",
" 10, -- num_iterations\n",
" TRUE, -- use gpus\n",
" 'cifar10_val_packed', -- validation dataset\n",
" 1 -- metrics compute frequency\n",
" );"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"View the model summary:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>source_table</th>\n",
" <th>validation_table</th>\n",
" <th>model</th>\n",
" <th>model_info</th>\n",
" <th>dependent_varname</th>\n",
" <th>independent_varname</th>\n",
" <th>model_arch_table</th>\n",
" <th>num_iterations</th>\n",
" <th>metrics_compute_frequency</th>\n",
" <th>warm_start</th>\n",
" <th>name</th>\n",
" <th>description</th>\n",
" <th>start_training_time</th>\n",
" <th>end_training_time</th>\n",
" <th>madlib_version</th>\n",
" <th>num_classes</th>\n",
" <th>class_values</th>\n",
" <th>dependent_vartype</th>\n",
" <th>normalizing_const</th>\n",
" <th>metrics_iters</th>\n",
" </tr>\n",
" <tr>\n",
" <td>cifar10_train_packed</td>\n",
" <td>cifar10_val_packed</td>\n",
" <td>cifar10_multi_model</td>\n",
" <td>cifar10_multi_model_info</td>\n",
" <td>y</td>\n",
" <td>x</td>\n",
" <td>model_arch_library</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2020-03-24 22:19:52.670763</td>\n",
" <td>2020-03-24 22:48:08.984136</td>\n",
" <td>1.17-dev</td>\n",
" <td>10</td>\n",
" <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
" <td>smallint</td>\n",
" <td>256.0</td>\n",
" <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(u'cifar10_train_packed', u'cifar10_val_packed', u'cifar10_multi_model', u'cifar10_multi_model_info', u'y', u'x', u'model_arch_library', 10, 1, False, None, None, datetime.datetime(2020, 3, 24, 22, 19, 52, 670763), datetime.datetime(2020, 3, 24, 22, 48, 8, 984136), u'1.17-dev', 10, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], u'smallint', 256.0, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT * FROM cifar10_multi_model_summary;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"View performance of each model:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"16 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>mst_key</th>\n",
" <th>model_id</th>\n",
" <th>compile_params</th>\n",
" <th>fit_params</th>\n",
" <th>model_type</th>\n",
" <th>model_size</th>\n",
" <th>metrics_elapsed_time</th>\n",
" <th>metrics_type</th>\n",
" <th>training_metrics_final</th>\n",
" <th>training_loss_final</th>\n",
" <th>training_metrics</th>\n",
" <th>training_loss</th>\n",
" <th>validation_metrics_final</th>\n",
" <th>validation_loss_final</th>\n",
" <th>validation_metrics</th>\n",
" <th>validation_loss</th>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>2159.70019531</td>\n",
" <td>[167.736330986023, 333.425091981888, 500.695713996887, 668.302587985992, 837.287312984467, 1006.06942605972, 1176.67615294456, 1346.787348032, 1518.12181210518, 1690.67020988464]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.957840025425</td>\n",
" <td>0.121589891613</td>\n",
" <td>[0.769860029220581, 0.824000000953674, 0.873139977455139, 0.89300000667572, 0.895640015602112, 0.906700015068054, 0.931919991970062, 0.942839980125427, 0.940620005130768, 0.957840025424957]</td>\n",
" <td>[0.66564804315567, 0.511175155639648, 0.367111206054688, 0.307224303483963, 0.300335615873337, 0.268611431121826, 0.196494549512863, 0.165175542235374, 0.172968357801437, 0.12158989161253]</td>\n",
" <td>0.836000025272</td>\n",
" <td>0.582142531872</td>\n",
" <td>[0.742699980735779, 0.790499985218048, 0.808600008487701, 0.818700015544891, 0.822000026702881, 0.821699976921082, 0.831499993801117, 0.83569997549057, 0.83050000667572, 0.836000025272369]</td>\n",
" <td>[0.731135308742523, 0.629253566265106, 0.586650609970093, 0.561055064201355, 0.600637912750244, 0.576708614826202, 0.567015171051025, 0.560995817184448, 0.577787160873413, 0.582142531871796]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>2159.70019531</td>\n",
" <td>[161.545518875122, 327.271953105927, 493.721956014633, 662.059704065323, 830.583410024643, 999.47811293602, 1170.08322310448, 1339.875207901, 1511.35515999794, 1682.88088107109]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.942120015621</td>\n",
" <td>0.166348025203</td>\n",
" <td>[0.768679976463318, 0.83459997177124, 0.865559995174408, 0.88782000541687, 0.900080025196075, 0.898840010166168, 0.929400026798248, 0.926339983940125, 0.941559970378876, 0.942120015621185]</td>\n",
" <td>[0.659707903862, 0.479000419378281, 0.394761860370636, 0.320042163133621, 0.286971271038055, 0.289592266082764, 0.203219100832939, 0.216255784034729, 0.16890250146389, 0.166348025202751]</td>\n",
" <td>0.834399998188</td>\n",
" <td>0.587312161922</td>\n",
" <td>[0.751600027084351, 0.794099986553192, 0.81029999256134, 0.816500008106232, 0.825600028038025, 0.819199979305267, 0.825200021266937, 0.833199977874756, 0.840300023555756, 0.834399998188019]</td>\n",
" <td>[0.725527286529541, 0.628955543041229, 0.603530406951904, 0.578122794628143, 0.565659284591675, 0.587804615497589, 0.575832903385162, 0.586635053157806, 0.558215022087097, 0.587312161922455]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>2159.70019531</td>\n",
" <td>[159.585074901581, 325.616278886795, 492.079638957977, 660.141077041626, 828.460551977158, 997.682931900024, 1168.2648499012, 1338.03905391693, 1509.35755109787, 1680.94471693039]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.957620024681</td>\n",
" <td>0.123506456614</td>\n",
" <td>[0.780160009860992, 0.830079972743988, 0.864920020103455, 0.880159974098206, 0.907660007476807, 0.922760009765625, 0.930760025978088, 0.94489997625351, 0.953960001468658, 0.957620024681091]</td>\n",
" <td>[0.640457689762115, 0.484953910112381, 0.392606198787689, 0.350569307804108, 0.266773730516434, 0.223709508776665, 0.199900239706039, 0.160074487328529, 0.134055703878403, 0.123506456613541]</td>\n",
" <td>0.833199977875</td>\n",
" <td>0.585961103439</td>\n",
" <td>[0.747500002384186, 0.790600001811981, 0.805899977684021, 0.807099997997284, 0.818899989128113, 0.814999997615814, 0.821300029754639, 0.827600002288818, 0.830999970436096, 0.833199977874756]</td>\n",
" <td>[0.733774244785309, 0.621092319488525, 0.600942432880402, 0.603525757789612, 0.582037806510925, 0.593123733997345, 0.593485474586487, 0.585796058177948, 0.578036189079285, 0.585961103439331]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>2159.70019531</td>\n",
" <td>[164.595175027847, 330.364078044891, 497.435774087906, 665.216387033463, 833.770098924637, 1002.83351397514, 1173.14754009247, 1343.40405297279, 1514.43769907951, 1686.70381498337]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.950980007648</td>\n",
" <td>0.143490716815</td>\n",
" <td>[0.78983998298645, 0.840439975261688, 0.875959992408752, 0.891099989414215, 0.90090000629425, 0.919920027256012, 0.934400022029877, 0.938539981842041, 0.955479979515076, 0.950980007648468]</td>\n",
" <td>[0.613083124160767, 0.471387982368469, 0.363269150257111, 0.319806933403015, 0.286749541759491, 0.234226956963539, 0.19084843993187, 0.177600309252739, 0.131557434797287, 0.143490716814995]</td>\n",
" <td>0.821799993515</td>\n",
" <td>0.595590293407</td>\n",
" <td>[0.754000008106232, 0.79229998588562, 0.801699995994568, 0.812300026416779, 0.819000005722046, 0.819000005722046, 0.823300004005432, 0.829999983310699, 0.836300015449524, 0.821799993515015]</td>\n",
" <td>[0.721113681793213, 0.621586740016937, 0.596519410610199, 0.595609545707703, 0.600475549697876, 0.613331437110901, 0.595634639263153, 0.574845016002655, 0.569734573364258, 0.59559029340744]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>2159.70019531</td>\n",
" <td>[155.881345033646, 321.711883068085, 488.33388209343, 656.33606004715, 824.596586942673, 993.720223903656, 1164.30447506905, 1334.05466103554, 1505.45432305336, 1677.10981488228]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.897319972515</td>\n",
" <td>0.302623033524</td>\n",
" <td>[0.585739970207214, 0.694540023803711, 0.738439977169037, 0.788339972496033, 0.815580010414124, 0.834140002727509, 0.851960003376007, 0.870700001716614, 0.883599996566772, 0.897319972515106]</td>\n",
" <td>[1.1557844877243, 0.861163735389709, 0.741564929485321, 0.606875598430634, 0.530352532863617, 0.474545627832413, 0.425946325063705, 0.374209344387054, 0.336798161268234, 0.30262303352356]</td>\n",
" <td>0.815199971199</td>\n",
" <td>0.553332149982</td>\n",
" <td>[0.588599979877472, 0.692600011825562, 0.732900023460388, 0.767099976539612, 0.781899988651276, 0.791299998760223, 0.802100002765656, 0.805999994277954, 0.810199975967407, 0.815199971199036]</td>\n",
" <td>[1.1479709148407, 0.871322154998779, 0.77245706319809, 0.673963844776154, 0.631623148918152, 0.608054459095001, 0.585181176662445, 0.569720149040222, 0.558107197284698, 0.553332149982452]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>2159.70019531</td>\n",
" <td>[172.771174907684, 338.155246973038, 505.790575027466, 673.744323015213, 842.298762083054, 1011.25688695908, 1182.03339600563, 1352.39258003235, 1523.84475708008, 1696.31324291229]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.889580011368</td>\n",
" <td>0.317797064781</td>\n",
" <td>[0.610719978809357, 0.702679991722107, 0.758080005645752, 0.787779986858368, 0.820479989051819, 0.840399980545044, 0.858399987220764, 0.869899988174438, 0.879320025444031, 0.889580011367798]</td>\n",
" <td>[1.1027592420578, 0.840861320495605, 0.687958598136902, 0.603124439716339, 0.51472145318985, 0.460182726383209, 0.407543867826462, 0.375441372394562, 0.346606284379959, 0.317797064781189]</td>\n",
" <td>0.812399983406</td>\n",
" <td>0.564498662949</td>\n",
" <td>[0.608399987220764, 0.697700023651123, 0.739300012588501, 0.758899986743927, 0.782599985599518, 0.793600022792816, 0.799199998378754, 0.806599974632263, 0.810599982738495, 0.812399983406067]</td>\n",
" <td>[1.10815918445587, 0.86056125164032, 0.739441454410553, 0.684951841831207, 0.629340946674347, 0.604862153530121, 0.582197725772858, 0.574008762836456, 0.559266090393066, 0.564498662948608]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>2159.70019531</td>\n",
" <td>[157.675038099289, 323.760583877563, 489.999938964844, 658.165150880814, 826.338515996933, 995.962205886841, 1166.19140601158, 1335.88773202896, 1507.49120998383, 1679.03732204437]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.865880012512</td>\n",
" <td>0.386452704668</td>\n",
" <td>[0.554459989070892, 0.660399973392487, 0.71670001745224, 0.754540026187897, 0.78276002407074, 0.806519985198975, 0.81879997253418, 0.83898001909256, 0.850740015506744, 0.865880012512207]</td>\n",
" <td>[1.26610136032104, 0.961102485656738, 0.805769920349121, 0.702959299087524, 0.625120878219604, 0.556592226028442, 0.513025283813477, 0.460623860359192, 0.426946371793747, 0.386452704668045]</td>\n",
" <td>0.806100010872</td>\n",
" <td>0.569638252258</td>\n",
" <td>[0.552999973297119, 0.657100021839142, 0.711000025272369, 0.737600028514862, 0.758300006389618, 0.77700001001358, 0.783100008964539, 0.794099986553192, 0.801199972629547, 0.806100010871887]</td>\n",
" <td>[1.26569390296936, 0.972574234008789, 0.835387766361237, 0.750236749649048, 0.694831550121307, 0.648999333381653, 0.622370600700378, 0.599871814250946, 0.581832528114319, 0.569638252258301]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>2159.70019531</td>\n",
" <td>[171.145457983017, 336.337003946304, 504.029928922653, 671.705837965012, 840.568361997604, 1009.40254306793, 1179.99340295792, 1350.53466200829, 1521.91818499565, 1694.26437997818]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.868319988251</td>\n",
" <td>0.377308398485</td>\n",
" <td>[0.549000024795532, 0.662000000476837, 0.71262001991272, 0.75543999671936, 0.783819973468781, 0.801800012588501, 0.819119989871979, 0.837419986724854, 0.854939997196198, 0.868319988250732]</td>\n",
" <td>[1.28990209102631, 0.96648907661438, 0.814616560935974, 0.696749150753021, 0.613618135452271, 0.56260073184967, 0.512484431266785, 0.460049092769623, 0.412180662155151, 0.377308398485184]</td>\n",
" <td>0.803799986839</td>\n",
" <td>0.570892989635</td>\n",
" <td>[0.548399984836578, 0.653999984264374, 0.700100004673004, 0.735899984836578, 0.758899986743927, 0.765999972820282, 0.779900014400482, 0.790300011634827, 0.803600013256073, 0.803799986839294]</td>\n",
" <td>[1.28837704658508, 0.981181442737579, 0.849580764770508, 0.754298150539398, 0.693142831325531, 0.669469833374023, 0.640279293060303, 0.606397569179535, 0.579119145870209, 0.570892989635468]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>4886.20019531</td>\n",
" <td>[165.13839507103, 330.998286962509, 498.064635038376, 665.857858896255, 834.544732093811, 1003.45657801628, 1173.99867010117, 1344.03136301041, 1515.32256889343, 1687.61437892914]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.901880025864</td>\n",
" <td>0.298958897591</td>\n",
" <td>[0.73361998796463, 0.803380012512207, 0.845399975776672, 0.863979995250702, 0.876219987869263, 0.889880001544952, 0.894959986209869, 0.898679971694946, 0.908540010452271, 0.901880025863647]</td>\n",
" <td>[0.760902941226959, 0.566388249397278, 0.448656052350998, 0.407294452190399, 0.36840745806694, 0.333151549100876, 0.314622759819031, 0.304351091384888, 0.28503081202507, 0.298958897590637]</td>\n",
" <td>0.782000005245</td>\n",
" <td>0.749579071999</td>\n",
" <td>[0.707300007343292, 0.752300024032593, 0.762000024318695, 0.773599982261658, 0.780399978160858, 0.786300003528595, 0.784300029277802, 0.784099996089935, 0.788299977779388, 0.782000005245209]</td>\n",
" <td>[0.836693525314331, 0.743824541568756, 0.733612596988678, 0.737680375576019, 0.760832130908966, 0.723663866519928, 0.78948837518692, 0.804735660552979, 0.70469468832016, 0.749579071998596]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>4886.20019531</td>\n",
" <td>[153.837145090103, 319.967153072357, 486.56719994545, 654.53327703476, 822.778059959412, 991.797204971313, 1162.18237304688, 1331.98686599731, 1503.55213904381, 1675.16517400742]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.830699980259</td>\n",
" <td>0.507873356342</td>\n",
" <td>[0.575259983539581, 0.66838002204895, 0.703379988670349, 0.743640005588531, 0.776799976825714, 0.796980023384094, 0.801840007305145, 0.814220011234283, 0.8264200091362, 0.830699980258942]</td>\n",
" <td>[1.19788825511932, 0.950228035449982, 0.852661311626434, 0.741594791412354, 0.646690189838409, 0.594518661499023, 0.582961976528168, 0.546181917190552, 0.528378546237946, 0.507873356342316]</td>\n",
" <td>0.768700003624</td>\n",
" <td>0.684073984623</td>\n",
" <td>[0.57039999961853, 0.656899988651276, 0.682200014591217, 0.707400023937225, 0.735099971294403, 0.744599997997284, 0.745100021362305, 0.754899978637695, 0.758899986743927, 0.768700003623962]</td>\n",
" <td>[1.20627999305725, 0.984861135482788, 0.915386915206909, 0.849376976490021, 0.766118228435516, 0.73919004201889, 0.750212430953979, 0.715151488780975, 0.710894227027893, 0.684073984622955]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.001)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>4886.20019531</td>\n",
" <td>[153.238389015198, 319.147723913193, 485.898155927658, 653.912374973297, 821.91964006424, 990.972142934799, 1161.34698009491, 1331.33741092682, 1502.62626290321, 1674.06854605675]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.856920003891</td>\n",
" <td>0.451146841049</td>\n",
" <td>[0.761780023574829, 0.809880018234253, 0.847639977931976, 0.856180012226105, 0.873279988765717, 0.855459988117218, 0.864660024642944, 0.858539998531342, 0.870779991149902, 0.856920003890991]</td>\n",
" <td>[0.700620949268341, 0.555083990097046, 0.447981059551239, 0.41928830742836, 0.381862819194794, 0.444543987512589, 0.415986865758896, 0.422646254301071, 0.402874350547791, 0.451146841049194]</td>\n",
" <td>0.768199980259</td>\n",
" <td>0.752578496933</td>\n",
" <td>[0.71780002117157, 0.746599972248077, 0.770099997520447, 0.769400000572205, 0.775499999523163, 0.763999998569489, 0.77319997549057, 0.769599974155426, 0.773899972438812, 0.768199980258942]</td>\n",
" <td>[0.833690404891968, 0.785688281059265, 0.77972024679184, 0.768506526947021, 0.768417119979858, 0.789122343063354, 0.87133800983429, 0.791547298431396, 0.893152952194214, 0.752578496932983]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>4886.20019531</td>\n",
" <td>[168.396234035492, 334.120693922043, 501.40336894989, 669.109417915344, 837.907988071442, 1006.88363099098, 1177.267567873, 1347.72842693329, 1518.65019989014, 1691.63165593147]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.902440011501</td>\n",
" <td>0.307300060987</td>\n",
" <td>[0.571120023727417, 0.666620016098022, 0.719539999961853, 0.757499992847443, 0.801400005817413, 0.826300024986267, 0.855199992656708, 0.875140011310577, 0.896160006523132, 0.902440011501312]</td>\n",
" <td>[1.22345066070557, 0.955978691577911, 0.811635494232178, 0.699091494083405, 0.582630336284637, 0.513621211051941, 0.437977999448776, 0.38476750254631, 0.333268582820892, 0.307300060987473]</td>\n",
" <td>0.767599999905</td>\n",
" <td>0.70242357254</td>\n",
" <td>[0.563700020313263, 0.645600020885468, 0.688799977302551, 0.71450001001358, 0.735000014305115, 0.746500015258789, 0.758599996566772, 0.757600009441376, 0.764500021934509, 0.767599999904633]</td>\n",
" <td>[1.24051570892334, 1.01462411880493, 0.90478104352951, 0.8267902135849, 0.766438901424408, 0.734673857688904, 0.706092000007629, 0.708917021751404, 0.692819654941559, 0.702423572540283]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>4886.20019531</td>\n",
" <td>[169.02210688591, 334.716751098633, 502.072870016098, 669.68922495842, 838.805229902267, 1007.46399402618, 1178.16211009026, 1348.57077288628, 1519.57116508484, 1692.27466797829]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.859300017357</td>\n",
" <td>0.428565859795</td>\n",
" <td>[0.532220005989075, 0.620400011539459, 0.674700021743774, 0.71340000629425, 0.747839987277985, 0.780160009860992, 0.801919996738434, 0.826839983463287, 0.849420011043549, 0.859300017356873]</td>\n",
" <td>[1.31418240070343, 1.07741296291351, 0.936273753643036, 0.825335919857025, 0.737494826316833, 0.649210572242737, 0.589954197406769, 0.524990677833557, 0.465339541435242, 0.428565859794617]</td>\n",
" <td>0.756900012493</td>\n",
" <td>0.719096183777</td>\n",
" <td>[0.529799997806549, 0.603900015354156, 0.656199991703033, 0.683700025081635, 0.703100025653839, 0.719600021839142, 0.730199992656708, 0.741400003433228, 0.747500002384186, 0.756900012493134]</td>\n",
" <td>[1.31718754768372, 1.11032629013062, 0.995527803897858, 0.911838352680206, 0.854842782020569, 0.803986191749573, 0.772469937801361, 0.748935461044312, 0.730905950069427, 0.719096183776855]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>4886.20019531</td>\n",
" <td>[162.9407351017, 328.580847024918, 495.239603042603, 663.518301010132, 831.940176010132, 1001.0917840004, 1171.16344308853, 1341.38065099716, 1512.53006696701, 1684.75480604172]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.829819977283</td>\n",
" <td>0.51047205925</td>\n",
" <td>[0.512880027294159, 0.614199995994568, 0.665300011634827, 0.705659985542297, 0.726400017738342, 0.763320028781891, 0.793179988861084, 0.792519986629486, 0.817960023880005, 0.829819977283478]</td>\n",
" <td>[1.3586277961731, 1.10278081893921, 0.960904538631439, 0.845035135746002, 0.784774601459503, 0.686800301074982, 0.603691697120667, 0.598818957805634, 0.545015513896942, 0.510472059249878]</td>\n",
" <td>0.746500015259</td>\n",
" <td>0.735714316368</td>\n",
" <td>[0.512399971485138, 0.601899981498718, 0.643800020217896, 0.679300010204315, 0.689000010490417, 0.714399993419647, 0.736500024795532, 0.735700011253357, 0.744300007820129, 0.746500015258789]</td>\n",
" <td>[1.36217331886292, 1.12988150119781, 1.00909614562988, 0.918819069862366, 0.883056640625, 0.818843603134155, 0.768383204936981, 0.770775377750397, 0.754891037940979, 0.735714316368103]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>4886.20019531</td>\n",
" <td>[162.132050037384, 327.965554952621, 494.437786102295, 662.785852909088, 831.344939947128, 1000.31394791603, 1170.5982530117, 1340.48602604866, 1511.95220208168, 1683.82030010223]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.746420025826</td>\n",
" <td>0.811488032341</td>\n",
" <td>[0.714479982852936, 0.716300010681152, 0.784420013427734, 0.769860029220581, 0.730359971523285, 0.772319972515106, 0.791440010070801, 0.772459983825684, 0.677720010280609, 0.7464200258255]</td>\n",
" <td>[0.831750333309174, 0.864085614681244, 0.660020709037781, 0.700904130935669, 0.816842019557953, 0.718474090099335, 0.635774910449982, 0.702050924301147, 1.18704259395599, 0.811488032341003]</td>\n",
" <td>0.70300000906</td>\n",
" <td>1.0656965971</td>\n",
" <td>[0.679899990558624, 0.676699995994568, 0.729300022125244, 0.713900029659271, 0.696600019931793, 0.724799990653992, 0.736299991607666, 0.736199975013733, 0.661800026893616, 0.703000009059906]</td>\n",
" <td>[0.961720407009125, 1.03706383705139, 0.896331906318665, 0.848886549472809, 0.986389935016632, 0.962011396884918, 0.896553337574005, 0.922386705875397, 1.3008621931076, 1.0656965970993]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']</td>\n",
" <td>batch_size=64,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>4886.20019531</td>\n",
" <td>[165.757286071777, 331.686079978943, 498.744575023651, 666.556679010391, 835.225320100784, 1004.05871200562, 1174.84750103951, 1344.6810259819, 1515.93833899498, 1688.55732393265]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.537280023098</td>\n",
" <td>1.35161483288</td>\n",
" <td>[0.735180020332336, 0.752040028572083, 0.727559983730316, 0.721400022506714, 0.643440008163452, 0.655160009860992, 0.696080029010773, 0.618160009384155, 0.567300021648407, 0.537280023097992]</td>\n",
" <td>[0.76802670955658, 0.751371622085571, 0.805666506290436, 0.850524604320526, 1.10775411128998, 1.05535495281219, 0.927546322345734, 1.24175298213959, 1.2892210483551, 1.35161483287811]</td>\n",
" <td>0.544900000095</td>\n",
" <td>1.33901309967</td>\n",
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" </tr>\n",
"</table>"
],
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" (4, 1, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.0001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=128,epochs=5', u'madlib_keras', 4886.20019531, [162.9407351017, 328.580847024918, 495.239603042603, 663.518301010132, 831.940176010132, 1001.0917840004, 1171.16344308853, 1341.38065099716, 1512.53006696701, 1684.75480604172], [u'accuracy'], 0.829819977283, 0.51047205925, [0.512880027294159, 0.614199995994568, 0.665300011634827, 0.705659985542297, 0.726400017738342, 0.763320028781891, 0.793179988861084, 0.792519986629486, 0.817960023880005, 0.829819977283478], [1.3586277961731, 1.10278081893921, 0.960904538631439, 0.845035135746002, 0.784774601459503, 0.686800301074982, 0.603691697120667, 0.598818957805634, 0.545015513896942, 0.510472059249878], 0.746500015259, 0.735714316368, [0.512399971485138, 0.601899981498718, 0.643800020217896, 0.679300010204315, 0.689000010490417, 0.714399993419647, 0.736500024795532, 0.735700011253357, 0.744300007820129, 0.746500015258789], [1.36217331886292, 1.12988150119781, 1.00909614562988, 0.918819069862366, 0.883056640625, 0.818843603134155, 0.768383204936981, 0.770775377750397, 0.754891037940979, 0.735714316368103]),\n",
" (6, 1, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=128,epochs=5', u'madlib_keras', 4886.20019531, [162.132050037384, 327.965554952621, 494.437786102295, 662.785852909088, 831.344939947128, 1000.31394791603, 1170.5982530117, 1340.48602604866, 1511.95220208168, 1683.82030010223], [u'accuracy'], 0.746420025826, 0.811488032341, [0.714479982852936, 0.716300010681152, 0.784420013427734, 0.769860029220581, 0.730359971523285, 0.772319972515106, 0.791440010070801, 0.772459983825684, 0.677720010280609, 0.7464200258255], [0.831750333309174, 0.864085614681244, 0.660020709037781, 0.700904130935669, 0.816842019557953, 0.718474090099335, 0.635774910449982, 0.702050924301147, 1.18704259395599, 0.811488032341003], 0.70300000906, 1.0656965971, [0.679899990558624, 0.676699995994568, 0.729300022125244, 0.713900029659271, 0.696600019931793, 0.724799990653992, 0.736299991607666, 0.736199975013733, 0.661800026893616, 0.703000009059906], [0.961720407009125, 1.03706383705139, 0.896331906318665, 0.848886549472809, 0.986389935016632, 0.962011396884918, 0.896553337574005, 0.922386705875397, 1.3008621931076, 1.0656965970993]),\n",
" (5, 1, u\"loss='categorical_crossentropy',optimizer='rmsprop(lr=0.001, decay=1e-6)',metrics=['accuracy']\", u'batch_size=64,epochs=5', u'madlib_keras', 4886.20019531, [165.757286071777, 331.686079978943, 498.744575023651, 666.556679010391, 835.225320100784, 1004.05871200562, 1174.84750103951, 1344.6810259819, 1515.93833899498, 1688.55732393265], [u'accuracy'], 0.537280023098, 1.35161483288, [0.735180020332336, 0.752040028572083, 0.727559983730316, 0.721400022506714, 0.643440008163452, 0.655160009860992, 0.696080029010773, 0.618160009384155, 0.567300021648407, 0.537280023097992], [0.76802670955658, 0.751371622085571, 0.805666506290436, 0.850524604320526, 1.10775411128998, 1.05535495281219, 0.927546322345734, 1.24175298213959, 1.2892210483551, 1.35161483287811], 0.544900000095, 1.33901309967, [0.70660001039505, 0.716899991035461, 0.697200000286102, 0.698300004005432, 0.631600022315979, 0.638100028038025, 0.69489997625351, 0.619000017642975, 0.570500016212463, 0.544900000095367], [0.875513792037964, 0.916396498680115, 0.912582337856293, 0.935619235038757, 1.15493774414062, 1.10954606533051, 0.960697770118713, 1.31051886081696, 1.26705503463745, 1.33901309967041])]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT * FROM cifar10_multi_model_info ORDER BY validation_metrics_final DESC;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"plot\"></a>\n",
"# 6. Plot results"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.ticker import MaxNLocator\n",
"from collections import defaultdict\n",
"import pandas as pd\n",
"plt.rcParams.update({'font.size': 12})\n",
"pd.set_option('display.max_colwidth', -1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Training data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"16 rows affected.\n",
"1 rows affected.\n"
]
},
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
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"\n",
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"\n",
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"\n",
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"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
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"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
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"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
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"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"720\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n"
]
}
],
"source": [
"df_results = %sql SELECT * FROM cifar10_multi_model_info ORDER BY training_loss_final ASC LIMIT 100;\n",
"df_results = df_results.DataFrame()\n",
"\n",
"df_summary = %sql SELECT * FROM cifar10_multi_model_summary;\n",
"df_summary = df_summary.DataFrame()\n",
"\n",
"#set up plots\n",
"fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
"fig.legend(ncol=4)\n",
"fig.tight_layout()\n",
"\n",
"ax_metric = axs[0]\n",
"ax_loss = axs[1]\n",
"\n",
"ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
"ax_metric.set_xlabel('Iteration')\n",
"#ax_metric.set_ylabel('Accuracy')\n",
"ax_metric.set_title('Training Accuracy')\n",
"\n",
"ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
"ax_loss.set_xlabel('Iteration')\n",
"#ax_loss.set_ylabel('Cross Entropy Loss')\n",
"ax_loss.set_title('Training Loss (Cross Entropy)')\n",
"\n",
"iters = df_summary['metrics_iters'][0]\n",
"\n",
"for mst_key in df_results['mst_key']:\n",
" df_output_info = %sql SELECT training_metrics,training_loss FROM cifar10_multi_model_info WHERE mst_key = $mst_key\n",
" df_output_info = df_output_info.DataFrame()\n",
" training_metrics = df_output_info['training_metrics'][0]\n",
" training_loss = df_output_info['training_loss'][0]\n",
"\n",
" #ax_metric.plot(iters, training_metrics, label=mst_key, marker='o')\n",
" #ax_metric.plot(iters, training_metrics, marker='o')\n",
" ax_metric.plot(iters, training_metrics)\n",
" \n",
" #ax_loss.plot(iters, training_loss, label=mst_key, marker='o')\n",
" #ax_loss.plot(iters, training_loss, marker='o')\n",
" ax_loss.plot(iters, training_loss)\n",
"\n",
"plt.legend();\n",
"# fig.savefig('./lc_keras_fit.png', dpi = 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Validation data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"16 rows affected.\n",
"1 rows affected.\n"
]
},
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<img 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i0q4O44eE8ZiCN8o6/+ubdJJ+PBwgiXW/nPjzLhxtIvYX6qKqWKlipZjZbRM1EWFRE54kPloNk8sBEc0l1hNaTHWgRUDhtI+ZvwVxkFh0eY75xhjv0QqtFdJ5tH25tzTipa1T69SpksLg8jSPVgfS0V/X7pF012Yc52HmVIiILBr166vOZ3Oz1ZVVTVardZoBOiIK0ly1NTUlLnd7u+vXbs2UnzFofpCcMi0m80IcH7zi4tfxDzxpj+kpjqgr6A+6rWGA9UIH1dPHHiKzdNtkiBcuLjRp1SfRIleSzXeRja8frwEh+beQXkcWXW6ylCyp22Q6Z5C2Vw0SWH477U+R4rBMVGCI5r6JB6Cg2oVTZlB4yL8i5BuGjSoaBiRtNKMJ54K8TTAKMERa33iN5axSxUMlSxG54v+s0iihoFw6XbEQLlcVKmi0YLb0geYpxF0IdKMmnjaz2cy7S4Xck0do2UYopxWC3g6m78npG+CgCAwPgKynoYwmg6CI5Hr6VQQHMSJ6x8PcriecJ2i2wE35QyQTSVCeFwNrmdUK3LzyNgdVKPyMIkbXh6kGCmagoP2C91Ww0sigozy0Cza4Uw8BIfWxlj6z8MHxncg6agFb+d9qJihy6mRMlGCI5I9mQiCg0pUxuMIL9NBcFCFxAMgEkgkpzQ3dbql8AAvkqKJ7dYUsKLgGGMm7ty582eZmZnvqaysDCfKxpy/+/fvn9/T03PvmjVrqMKPWoTgMPI1IHVmMgJMw0rZI0/3ycIyMjOZ2mjBf5hSlu4nkbJykHFnMEijG9ZoX/aaiwq/BG+LAG40FxVK7BjEKtwVg7eg8kSL1pwIgkPvohJJ7slnjuWikkiDTMt4Y2Qe6mNJMHAZGXQSWyS4xiux1uf9OJ9obESaL1p6RaPzRd8+njYwiFh4bAytDo1E+j/rCY542s/7cay5wJAo4ljzFISKHBIf4Sl0x8NQ/i4ICAKzFwFZT+MjOMy0nsZCcMTrohLpE0DJP92FuU6RlOfaFa2Q3OApOE/hqboojKDIiHQtlSaMxRBNeajFRphomthoBIfmosKNMRUr4SWSi0qkfsTSf5JGVL9wraZ7DttGEma8MtMIDk39Eh4rQ9/PsVxUqIqm63l40buoRHLH5iEn3XfpGs45SZcTHiZRYRutSAyO8WYfsxbs3Pm7nJycdy1atGi8+Hgj7lZfX1/R2dn50Nq1a/WBbEc9UQgOA4MgVWY0AmRZGeOAJ98kOJgVhCfhJAH0WVK0TnIzygWD/qbhgRW1HOpGN6zRCA4tEnZ43AOtDWwjI3CHkwTaZppqBZ5E6AtVJ1SfsIQTHNp13Ijrs4ho10fLojJWkFH2jQtNtCCjiSI4uGDTxYjyUgaIihY9mTJAEkeMYM4AVCxa0E2y7JTKjscSx1qfz9DiuXC8GBxWX7SUdUbni/5aLQ97JBmkXtGiJzjiab/2TAbTYlAtGpRUjFC1RFeneH2hZ/SXhjReEBAEIiIg6+kwqT0T11MOaiwEB+uPFWSUMRdoU4QHGY328WFMM96PcSNIqI9XNNtltcFMKnTTYIwEvu6LcHMqO5hanQQC7TAqHqIVugTzubQpWIyoT8cLMqrZVPogo2NhEEv/tYxyn1ez3oyH7VQSHNq4c+xJmkUqWhaVaAqO8YKMMk4Z7cOxgozyIIp2q75wX8CAlZwXvEd40ex1BmTn4SOV3LSxqZiOVh5TXVjGcmMZb3xm/d+F4Jj1QywdnAIEKC+j1J7qDEb1Zioy+jBGKvyC5JcggwQx97ZWuKlkHm1utI1uWKMRHCRPSADwVILt0KdGY6BQLtKR0sQyMCfVGwz8SGmW5qpBVQcjcWuBT8MJDi1yt+amE97vaASHFhCUsk8u7mSyWUgAaX6g0dLEJorg0HwZKWelsRGt0I+VwaFouOjVFJrLEdvO/uhJDo4lTwX0gUljra8RA5Qx8hSFwbVYeFLwuOoyZHS+6PvGdGUkSEia0G9Uy7PODQbzwGvxTfQEB6+Ptf3aM2n4cSw116toaXen4OMqjxAEBAETIyDracgtYCaup5xWsRIcdIcl8c31gS4RWspMpiWl6wg3iPq4FFyLqVTghpVKQ335lGq/7FZVFvwb03PSfgkPtr0KAF0xGYOKJ+tjbSi1Z2j2AtPacqMfqWiHIVyXmeWCdeneqi90k6H9R0JCsxWNEByM4UBChIdnVEGyX1qhzcaYAVTa8sBFS3EaS/+vUu2ccFddEk1UpXCDzk04XbDHK1NJcNDWYuB72qx0o9bUxvo2jkdwsC7djamgYJwMuj7RpmchwUZbifbcWGli6R5OO1CLz0Lihe6/JCt5X7qUhBfalIxpR4xZwmO9RcKZc5UKW77iDQI73vjN+L8LwTHjh1A6YAIEPhzG1I8VOFFjc9lsboq1VKhUBTCrBONgGN2wRiM4eG9+OfMLmdJVpovlgs9NK5/DRZBftuEkATezPNnnfavV6yj75O/5pU7jgxvVcIKDCz1JFH7R/ke3uDCAJcmLaAQH26nlHeeCTcOWizIXcQaN4rUkfvTp0sYLJmXESNBPGS7a9MfVB42KNqW00xv95pyLE9l0+gJTjUAiggoeqh3oE0wDjG3WSqz1SVJx/DgO9M2mccN705+WBhJPaozOF32/aKjQJYnGIg1LGno88eICTuOGpwjEJZzgiLX9+mdqbjFUvBCT8NzvJvgoSxMEAUFgmhGQ9XTmrqecOtp6T3uAdkS0QncSrgk80GBWN26uSQTQBuFmlSQP7Q0eGjDjHNUGLDxEop1Am4EuvVqQUGa+oJsvyXpuMrUAjbR9qM7kCTqVqTwkoHqQAU5JblA5S5cAI4W2D9dLBvEkQRKtMJ4DDwN42MTNLu0Mbkq53tIdgc/nOkhC5n/Vmxi1XTRCiJt5rtXEh/1j34kfCQh9cMpY+n+nSgjxMEeLD8dNNMeCbSchQ1snkjo5HAujWVRo3/FQTSvxZI7jtY+qruEcH+JC1yNiro2tEYKDKg7asHQB4vzlXKR9RbuIB3ycb7SD9ckDtPlOe5tjSxuJtiwJOmJFW4puzHRb570iFX4WaIOy8Fm8Plrh/oI2IYPhsy1SoiAgBIdMDUFg4ghwEeOmll+OZH65QR+r8CSezD5dQbjActHlwkLmlouz0Q3rWAQHn89Tfvo088taew6JCC5cXOwjqSC4cebCRDUDv5gpt6M0kQuQlmkknODgvZlhgzEVKA/kFzuLVm8sgoP1aNhwoadShBgyeBdVD0zdGu72kUiCgwYRDTDizbaGp30LH0MuUFxE2T4y89pixXFgZG2+aGRwgaOBwMWQMTrCpayx1mefSapwMeNJBdvMkwYG7oxlvoT3h6c8JNUY8V2TAtPPls+ijy0NwHCCg/eItf3aczU3J6MxSyb+yZQ7CAKCwExDQNbTmbmeavNMW+/Hm3dUHNDuYSHJQWKLygxu4LS4Tdw0cj3STtJZl64dN6obQR4iUH1BRSpPwakmpWJC72pKpQOl/CTsSZhwfnGtJ0lBu0avghivzfy7tpHms7nRjFaoJKDig4dNPFCgOyzdkml7cXP6B/WAS7veKMHB+twE82CJth3VBSR8eE+SNeHuxbH0n3YfA5CT0KAtx009D2loK9JFl/aMFoh8PKw0gmO8euEB+eMlOEh8MeYc7WseCNFO0du4RggOtpX2680ASFIx3oqmdqYdR6VRuGu53r6lGy7tViqhSQzRZYYxx0hgjOWOq7kFc96SrBqrsI88PKQam8SglCgICMEhU0MQEAQEAUFgKhCgAUbDjAYXVS9SBAFBQBAQBASBmYQAM7TwlN9o9rSZ1Ddpa+wIjHeAZ+SOdMX60BhuLNo9SO5RoUL1DxXZmmuxkWfMuTpCcMy5IZcOCwKCgCAw5QhQYky5J/2geSoSLf3ZlDdMHigICAKCgCAgCMSAAAkOKhy5yaRaU8rcRWCiBAftISqB6DpFZTDds6IVTQXLOC9U00gZAwEhOGR6CAKCgCAgCEwGApQd/06V0F6qujwxNe2Dk/EwuacgIAgIAoKAIDAFCNC9mFnq6OLCrGBS5i4C8RIcdDWhyxRdmOjOMl4cOLrO0L2JhBrdz7UkAHMX+XF6vnPnzjsyMjLet3Tp0kajIAWDQdTV1VX09vb+fe3atWN+tiVNrFFUpZ4gIAgIArMLAX2MGAYUpR+qFlBtdvVUeiMICAKCgCAgCAgCcw2BeAkOxhphwHi+8yDo+0JaJHbq7Nq166akpKRvlZSUeAoKCvSBYSM+KBAIWFpaWvJbWlqsPp/vM2vWrBnzME4IjsSOl9xNEBAEBAFBQBAQBBKDALMQMbAeAxwzlR+DwjG4XryFqiVmUmK2KmYyYHBDKYKAICAICAKCgCAwhQhs2bIlKT09/X+Sk5Pf43Q67UlJSVyfIxa/3x/0er3weDzdfr//n729vd8999xzw9M7j7hWCI4pHEx5lCAgCAgCgoAgIAgYRoAyX57sMLMUSQ6mBpwIwcHo+8xkwEwNQnAYHgapKAgIAoKAICAIJBYBkhyZmZkfCQaDjHXCVMfRyoDFYmkJBoNPrl27lvbAuEUIjnEhkgqCgCAgCAgCgoAgMA0IMEhgvfrc3SoxES/BMU9NT/kdAEzHLQTHNAyoPFIQEAQEAUFAEJhsBITgmGyE5f6CgCAgCAgCgoAgMFEEJkpwPAKgQlWC+ITgmOhwyPWCgCAgCAgCgoA5EZgrBAdzEzOqcqukPzTnRJRWCQKCgCAgCEwrAvR/LQBAIsE7rS2J/PCJEBzXALgPwBkAXlaDxcWj4BBbwoQTQ5okCAgCgoAgYAoETGNHzBWC4xTVh9cUoy+NEAQEAUFAEBAETIoAg3q+bsK2xUtwZAGoBkAFxyfVfjG2hxGCgz7Ber9gHpQ8ZUJspEmCgCAgCAgCgoBZEJh2O2KuEBz0vW3ctWsXiouLzTL40g5BQBAQBAQBQcAUCDQ1NWHNGsbxRLmaGs8U7dI1Il6Cg6mPrwRQBaArRoKD8Tq+HQ6E2BJmmxrSHkFAEBAEBIHpRsBMdsRcITiYy/jo0aNHUVrKH6UIAoKAICAICAKCgIbAsWPHUFZWxv/yn2MmRCYegmMDgK0A3g/gb7o+xavgKKIaVGwJE84OaZIgIAgIAoLAtCJgJjsiFoKDfjWfAfBxNU0b41nQp/VbAPoNIMq0bLcCuEG93g2gFsDvAPyf6hOr3ebPAD4Y5Z7XAvingefpqwjBESNgUl0QEAQEAUFg7iBgJsMkCurxEBxvqnG3aDfoSx2ALQBuUlUdbQZHWmwJg0BJNUFAEBAEBIG5hYCZ7IhYCI6fqwTFQwD+DWA5gFsAPA/g/HGCd5Ic4SkKA3yRzGCQr1SV7FgP4DYAX9ZNA43g4KlLeNkG4HCMU0aMkhgBk+qCgCAgCAgCcwcBMxkmCSQ46JLCGBxjlV8BuNngSIstYRAoqSYICAKCgCAwtxAwkx1hlOA4AcDbAEhuXK0bLhIcvwDwXgB/H2MYTwfwIoCfAficrl4ygBoAuQCyIxAcRts33gwSo2Q8hOTvgoAgIAgIAnMWATMZJnESHPPVg5MDuiwwVwCgnRFe7gewC8APAewH8IbBgRdbwiBQUk0QEAQEAUFgbiFgJjvCKIHwPQBfB7BRVWxoI+YE0K6qMy4ZYxgvAvAkgC8BuD2s3quqz6/i/KsWTcFB5QcjmPdNML2rGCVz6zMmvRUEBAFBQBCIAQEzGSa6ZlPFWaH+nwcqJCvuUP9/CMBfdXWfA3A2gIUAGsbputEYHOG3EVsihjklVQUBQUAQEATmDgJmsiOMEhxMi0Y3FLqVMHaGvrwAYCmAgjGGMAdAPQAfgE8BeEW9F+NskPT4BIC7IhAcvSrB4QFA15RvqNfGOlvEKIkVMakvCAgCgoAgMGcQMJNhEoG0iDQOdHs9RwiOOTNFpaOCgCAgCAgCJkbATHaEUYKD7imFABhBPLww0CgDeDkAkIiIVhjN/PcqGaLVIYHxAQAPh11E2ShPaighZQDTkwF8FkAaACpFnv/YlfMAACAASURBVB5nfMNz10vkcxN/IKRpgoAgIAgIAtOLgJkMk+lFYsyny2GJiQdHmiYICAKCgCBgDIHBwUH09PTA7XYjGKSoMXKxWq1IT09HVlYW+PNYxUx2hFGCgz6tdgD0cQ0vf1FTsFGloeWYj9T/U1QFBpUcjMfBuBufVnPTM0f95nGGpFL1k2X6Ov48VomYu15Suxmb9FJLEBAEBAFBYG4hYCbDxMTIC8Fh4sGRpgkCgoAgIAiMj0BHRweam5thsVjgcDjGJC58Ph88Hg8yMzO1VPJRH2AmO8IowTFRBcdJABhrgwFGf6tDhi4vTP1GSmgxAP84w/InAB8CsExNMRutuig4xp/fUkMQEAQEAUFAEFAQMJNhYuIhEYLDxIMjTRMEBAFBQBAYGwEqNxoaGpCRkYGSkhLYbLYxL6C6o7W1Fe3t7aioqEBqKrfukYuZ7AijBMdEY3D8EcCHAeSrQUn1yPxSTdG2BACVImOVbwOgOuNMVQVidB6LUWIUKaknCAgCgoAgMOcQMJNhYmLwxZYw8eBI0wQBQUAQEATGRoDKjc7OTlRWVo5Lbmh3CgQCqKurU1QcJEVmE8ExXhYVBgDdNAakJEguVGN4tITV+40aZLQKwL5xJubdakpaI2SI/lZilMgnXhAQBAQBQUAQiIKAEByGpobYEoZgkkqCgCAgCAgCZkTg8OHD8Pv9WLiQCceMl/r6etjtdpSXl88qgoMuJm8CeAjA1bqeMW3bL9QYHCQfWOhqwngdNbp6P1WDhH4ZwG2632cD2AuA6WaZhYUuKgwkyndXGIKM4fGyqvJYYXxIlJpilMQImFQXBAQBQUAQmDsICMFhaKzFljAEk1QSBAQBQUAQMCMChw4xwzoUd5NYipHrzGRHGHVRIQaaKwlJjicALAdwKwCmiX0HgIAKFPPPEzX9vfn/1wAwEOnf1GsYZPRGAAvUYKO/Vq9fBeDfamaVOl0WlY+oz6ASZHssgyIER4xoSXVBQBAQBASBOYWAmQwTEwMvBIeJB0eaJggIAoKAIDA2AkaIikh3MHKdmeyIWAgORiFhqtabVFKiDcC9AL4FoE8HRiSCg3+msoN1z1NdVQbVrCg/A/Cg7vpiALcDWKcSEykAjgPYAuAHYcoQo/NYjBKjSEk9QUAQEAQEgTmHgJkMExODL7aEiQdHmiYICAKCgCAgBAcRiIXgmMlzRoySmTx60nZBQBAQBASBSUVACA5D8IotYQgmqSQICAKCgCBgRgSMKDEitdvIdWayI4TgMOPskzYJAoKAICAICAJTiICZDJMp7HasjxKCI1bEpL4gIAgIAoKAaRAwQlQIwWGa4Rq3IWKUjAuRVBAEBAFBQBCYqwgIwWFo5MWWMASTVBIEBAFBQBAwIwJCcJhxVOJvkxgl8WMnVwoCgoAgMDsR8A4Cfg/gzJqd/YuhV0JwGAIr4baEq6YGtuxs2IsZfkyKICAICAKCgCAweQgIwTF52E7HnRNulExHJ+SZgoAgIAgIAnEi4OkHmt4Gjr0BHH8TOP4G0LoPCPqB9GKgYBlQUKV7rwLS8uJ82My7TAgOQ2OWUFui8eab0ff0M8i78UYUfuHzhhoglQQBQUAQEAQEgXgRaGxshMfjwaJFi2CxGI9UUV9fD7vdjvLy8qiPNpMdYbxn8SJpjusSapSYo0vSCkFAEBAEBIGICLh6QmQGSQySGSQ12moBBGMDLDU/jPRQSZD0QiAGwyC2h05PbTMZJtODgKGnJtSWaL3zV2i7804kFRRgyZZnYUlKMtQIqSQICAKCgCAgCMSDQGdnJ5qampCXl4f8/HxYrdZxb9PR0YHm5malfkFBgRAc4yI2dRUSapRMXbPlSYKAICAICAJjIjDYpSoyVFUGCY32/dEvya4ASk4GSleF3h2ZISVHa436vg/oPjw26M7syMRHZumMJT6E4DD0OUuoLeE5chQHzj9fefC83/4GGeecY6gRUkkQEAQEAUFAEIgHgUAggOPHj6Onp0chN6jKsNlsUW/l9/vhdruRkZGBsrKyMVUfZrIjRMERz+yQawQBQUAQEASmHoGBjpGqDJIZnQejtyN3EVCiEhkkNIpXAqm547fb3RdSfIwgPmqAzoaxVSDJGRFcXZYBWeWAgVOS8Rs2eTXMZJhMXi8nfOeEEhxszaEPfxgDL72MjAsuwLxf/mLCDZQbCAKCgCAgCAgC4yHQ39+vkBw+nw8kPaIVkiBOp1NRb4zn0mImO0IIjvFmgPxdEBAEBAFBYOoR6GtVlRmvq24mb46hrLAA+ZUhRYZGaJSsTHzwUAYlbavTER+q6qOjPhTLI1qxpwL5S0erPnIWANboJydTCbqZDJOp7HeMz0o4wdH92OM49sUvAnY7Krc+h6RcAwRcjI2W6oKAICAICAKCwGQjYCY7QgiOyR5tub8gIAgIArMYgT63D8e7BpGSbENpVgqs1jiWld6mkcE/qczoORoZNYsVyF820s2k+CTAkTEmygGPB54DB+CurYWrthbuujq4a+tgcSQj64orkH3NNbAXFcU3Uj430H5A5+aiEh90lQl4o9/T5lCJj7AAp7kLAZt9xHUurx+H2gdQ39qH+rZ+HGjtww3r52PdgsRsiM1kmMQ3CFNyVcIJjoDLhboNGxHo7UXRV7+C3A9+cEo6Ig8RBAQBQUAQEAQSiYCZ7Ig4LNFEQjFl90q4UTJlLZcHCQKCgCAwTQj4/AE097pxrGtQfbmGfj6q/q7H5RtqXXKSFRW5qViQn4aF+WlYkJeGBfmpys9FGU4o3EfPsTA3kzeAvuYoZIYNKFw+0s2k6AQgOS0qIsFAAN4jR0YRGZ6GBsA/hsrCZkP6uecg57rrkHbmmbAkwqXE7wU6Do4mPuj+4ndH7UPAakdP6nwctVegLlCG1weL8EpfAeoDxfBgmPj42iVVuGnj4oTMDjMZJgnp0OTcZFJsiePf+Q667rkXjqVLsfCRh8eVAU9O1+SugoAgIAgIAoJA/AiYyY4QgiP+cZQrBQFBQBCYsQgEg0H0DPqgERXHuwdxtGuYwCCp0dTjQiDGxCPDgAQxz9KGEywHcZL1IFbaGrDS2oDsYHdkzKz2EJmhBf8sOQUoWgHYU6Ji7GtvV4gMRZWhKjLc+/cjODAQ9RpbVpaykXRUVirv3iON6HrwIfg7OoausZeVIfvaa5F99VVKhotEl0GXB0cOVqPr0NvwNFUjuaMW2QP1KPM2ItUSnfjwBa04jGI0JVegJ2Mx8lZdhnUbNyWkeWYyTBLSocm5yaQQHINvv42Ga9+ttHjB/fcj5aQTJ6f1cldBQBAQBAQBQWCSEDCTHSEExyQNstxWEBAEBIHpRMDjC6Cp24Vj3Zr6YjSB0e8ZQ9EQ1vjctGSUZjsVN5TS7BSUZaeghP/PTkFpphOu1gPoqd+J4LHXkdq+B8UD+5AR6IkIgTtoR3WwHHsCC/F2cCEO2BbDnVuF8oJsRfFB5YeiAMlPQ15aMoKDgyBxoZAZdXUhF5PaOvjb26NCbHE44Fi8eASZQUIjqbBg1Ak53Vd6N29G1733YeDVV4fvmZSEjPPOQ85170bqaafFpOoggXS824X61n7Ut/Up73Qr4TtJpUjFggDKLO2otB7BmpQWnORowuLgERS6G5Ds7x9xCUOCBd7xDSRt/GJCppmZDJOEdGhybjIpBAfnysErrlTmdvYN16Pk29+enNbLXQUBQUAQEAQEgUlCwEx2hBAckzTIcltBQBAQBCYLAW6IOvo9ygZaU2CE3EiG/9/a50bQoPqCriUkLPQEhvKz8rsUhdRgjI2h4uoBju4EGncAR14FjuwEXF0RuxtMcsKVtwKt6VWoty/BW/4FeKWvEAfaPYpCRF+sAT/K+tuwoOc4FvQ0Ka9FvU0o7GuHFVE6Y7Egef78UURGcsV8WMZIfRZtbNz1B9F1333ofugh+LuH1Sb2+fOR8+5rkXXVVSMCQfa7fTioxsQIkRn9oTgZrf0Y9I5FIPmQnuJDeb4FJTlBFGQFkZ3uR2aqDw6HB4P+PvR5+tDr6UWvpwe9gx3oHWxXfu7zu9AX8OEbi6/BuzckZjNsJsNksj43CbjvpBAcbFf7n/+Mlh/+CNbMTFRu2wqr05mA5sotBAFBQBAQBASBqUHATHaEEBxTM+byFEFAEBAEDCPAgJIkL0hahBMY2u/cvuhpvcIfVJDhUFUXTpQMKTCGCQyqJKKm/2L6MAbLJJHRSDJjB9BSHTldKrOFMBXrkJvJqlAQTVvSiCaRoPE1N6NnTzVa3tqLvup9CB48gJTjjUjyRQ/K2eHIQENmCRoyi5VXW0E5bIsWYV5xTljcjzRkpYwM0mkYfLViwO1G71NPofPe+zC4a9fQ5QFbEg6fsB5bq07FltQ8tA50AzYXLFYXLLZB9d0FWAdhsblgtbngdHjgSHbDanMjaB2EO9APbyC6K4rRtn5m9WfwsZM+ZrT6mPXMZJgkpEOTc5NJIzh8HR2o23g24POh9Mc/RtZll05OD+SugoAgIAgIAoLAJCBgJjtCCI5JGGC5pSAgCAgCRhEgmbGzoRPP17XilYMdaOwYQHu/x+jlSLHbUJajKS10qotsp6LKKM5ywpEUQyrSEeoMKjR2RFZnMJtJ4Qpg3jr1tRbIWzIq7am/t3fItWQ4g8l+BHTqiPDOWlNTkbRkCVzzFqCjsByHs0qwx1mIfS4bGtr6DeFDl5oFeWrAUyXY6bDbS7ojCd6AFx2DHehyd6GHCglPL9r6u9HQ2Y4jPe1o6etG20A3ut09yG1uxwW723BOdR/S3MNKkqO5wNOnWPHcSRb0p0xsObVarEi3pyMjOQOZyZlIT05Hhj1D+b/+xTra3ysyK1CcVmx4roxV0UyGSUI6NDk3mTSCg809csst6N38NNLOOB3z//jHyemB3FUQEAQEAUFAEOCaU70bJZVVsCWNPISKFxwz2RETs8jiRWDqr5tUo2TquyNPFAQEgZmKANUL+5p78XxtG57f34ZX6tsRTY3BrCNFmSHSoiQrRFgMuY2oBAaVClHVF+OBFIs6IyVHJTLWA+XrgLI1I1KzKmlYDx4cDvqpxsnwHT8evRVJSXAsXDAU8FMJ/rl0KeylpWPGu+ge9OJQe7/iGtLQNoAG9eeD7X3odvXAmtQLi/LqU96V/9u03/XCZu8DbCNjWowHFf+e7A3i9OogLng9gKXHhq/w2IBXl9vw8vpMtCzJRYYjUyElRhEWGlkRgbhITUqNfxyNNH6cOmYyTBLQncm6xaTaEr1btuDIJz8FWCxY8vRmMNitFEFAEBAEBAFBIJEI0A599eH7sf3ev2LVhZfivI98IiG3N5MdIQRHQoZUbiIICAKCQHQEWnvd2L6/Fc/XtWF7XRtaeke6J1gswImlWThzST6qijNUEsOpkBt2mzVx0CrqjF0hVYbmbhIxdoYlpM4gkTGPhMb6kDqDDQXA7CWumhq4a2rgqtkHd0013AcbFHl9tJJUWqIQGU6VxCCRkbxwIazJyeP2z+13o32wHa2DrWgbbFN+5jtf/J3+/1RmxFOCfgeCASeCfidsSEWaPR1ZyRnIS81GYVoWyrJyUZ6dh2xnBjLtmcg43A7H41sReHILgv3DWVsclUuQ/e7rkHXlFbBlZsbTlGm5xkyGybQAYOyhk0pwBH0+1J17Lvytbci/5WYUfPrTxloltQQBQUAQEAQEAQMIeF0uPPmbn6H25e1K7ZLKZbj2W/8De7LDwNVjVzGTHSEEx4SHU24gCAgCgsBIBPRuJ9vq2lB9fHQ2ESoyNlTm46zKApy5OA956RNfXEa0ghFG2+pCZIYSP4OxM/ZGjp0xpM5Q3U2oznBmIuj3w3PosEJgkMhwkcioroGvtTXqkDNIomPpSCKDxIYtI2PENYFgQHEPUYiKgTa0uUKERaQX3UdiLRZYkOvMRX5KvvLKS8lDmi0H8GfA40lHX38K2nscsPjTsCS/AEsKM7G4IA2L8tORkzY+6aK1J9Dfj+4nnlAysLh27x5qpsXpROamTUoGFufJJ0+rOsMIdmYyTIy0d5rqTCrBwT613HEH2u/6vaLeWLz5PzFl7pkmTOSxgoAgIAgIAjMAge6WZjxy+3+j9XCD0toTz70Q5330k0iyTyxmmdZ1M9kRQnDMgAkpTRQEBAFzI6B3O9lW14pXD3aMcjthrIzTF+fhrCX52Lg0H4sL0hO76Z2gOiPAVKy1tYoyw1WtqjNqa5UUrRGL1aooMJxVVXAur4Jj2TLFvcSTk44OV8eQ2kIjLNpd7WgdGFZg8P/+oPE0tVob6MpRkFqAPGfeEHmhkRj69xxnDpKsifErNTr7BvfsUYiO7scfR3BAp+pYtgw511+HzMsvhy093ejtprSemQyTKe14bA+bdIKDWXzqL7lEadX8P/8JaaedFlsLpbYgIAgIAoKAIBCGQOOet/DoT38IV2+PQpyf+8EbseqiyxJqh5rJjhCCQz4CgoAgIAjEgUBLrwsv7G8biqVBNxR9oTfHSWVZCqGxobIAqyuyYwv2OVabqM5gZhPFzWQcdYYzOxQ7g24m89YhWLoavh73KFWG59AhRMsry6CfDhIZVVVwVC1DsHIBGgssqBs4hLrOOuzv2o+m/iZFfTHgG97YG4XVZrGFCIvUkNpCUVyoBAbJDOV3zpAKI5WZWkxe/H196Hn8cSUDi7uaGWdCxZKaiqxLL1FcWFJOOtFUvTCTYWIqYEY2ZtIJDj6u4Yb3YPD11xVCrOz220wMhzRNEBAEBAFBwMwI8ADu9Scfx3N/uQvBQADOjExc8bmvoPyElQlvtpnsCCE4Ej68ckNBQBCYjQjQ7WRHQ4cSR2NbbStqmka7TWhuJyQ0GE+DmTwSUqjOOPZayM2EhAbdTgY7I9yasTOWDxEaweI1cPdYQ8oMRZURcjXxd3REbVZSSckQkWFfVom2sgzsT+1BXfcBhcyo66rD0b6jhrrFQJsFKSGCguREJKUFf5ftyAaziMy2QsPC9fbb6LznXvQ88QSCLtdQF50nnIDs696NrEsvhTUtbdq7bibDZNrBiN6AKSE4uv75Txz/xjdhcThQ+fy2GRXLxcRjJ00TBAQBQWBOIeDzevHMH36N3Vs2K/0umL8AV37xm8gqLJoUHMxkRwjBMSlDLDcVBGYBAn0twEAHkLsISErQRn0GwcLNKUkMpm8lqRHJ7SQ12YbTFuUpsTRIajCGQ9wZTTRs4lRn+HNOhLsvDa4DjXDtq1FiZbjr6hD0REk5ywwmixerZEYV+hcW4lABsC9wbEiVUd9dD18geuDQwtRCVOZUojK7EuUZ5SMIDBIaDluC44rMoPkT3lR/Tw+6H30MXffeq4yLVkhuZF5+GXKuuw7O5cunrYdmMkymDYTxHzwlBIe/rx91GzYo7mHF3/mO4t4kRRAQBAQBQUAQMIpAX2cHHv3J/+B4bY1yydLTzsLFn/ws7E6n0VvEXM9MdoQQHDEPn1wgCMxSBPrbgIbtQMPzwMHngbZ9oY4yjkH+MqBoBVB0AlB4Qug9s3Qoq8ZsQYRuJ8xyomQ72d+GaG4nGqGxen4OkpMmoDzw9ANttUBLDdBaDTTvBY7uHFOdESxbC19KFVx9mXAd64Z73z5FneE9ciTqMDDwpxYrI7B4Po6XOFGbPYDavoOKe8n+zv3o9UYP5Ml0p0uyl4TIDJXQ4HuWI2u2DP2U9YPE2eDrbyhER8+TTyLoHnZtcp68Ejnvvg6Zl2yCNSVlytrEB5nJMJnSjsf2sCkhOJTx+OrX0P3QQ3CuXImF990bWyultiAgCAgCgsCcReD4/n149MffB0kOZr8767r3Y/07r534Adw4iJrJjhCCY85Of+n4nEeA6oxDL4TIDJIaSoaNGIozCyg6USU9SH6cGHKPcJgziGKkntHthMoMkhnR3E5KlWwnBdiwNB9nLs6PKcPG0DPDiYzWfUBLNdB1OHJWE17ozEKwZC3c1iVw9WfB1eKFe/9BJQhooGd0VhbtWfbycoXMSFq6BJ3lWThYBFQntaKue7+izGgZaIk6yAzKuTBroaLIIIGxNGep8nNxWvGkL4wxzLxZU9Xf1YXuRx5RYnV46uuH+mXNyEDWFVcoLixMqzsVxUyGyVT0N85nTBnBMbBjBw69/wNKMxc++siUzYM4cZHLBAFBQBAQBEyAwJ6tz2DzXXfC7/UiOSUFl9zyRSxes35KWmYmO0IIjikZcnmIIGACBAa7gEMvDis0mpnSMjiyYXQpYDDKhRuBBWcBWfNC6oKWPUAzX3tDyo4x3BaQs2BY5aGoPk4MublYbdMOAk/Pq4/3Yvv+kNvJKwc74PEFRrSLbiena24nSwuwKD8GtxPPQAgfRZGhvsYjMjgKjjx4HUvgRTFcA1lwt/nhOngU7gP1gC+yi4glOVnJWsLsJYMLi3CsxIHq3AHsczcqcTIO9xweM0tJWXrZEJGhqTIqsipgtyYmXdi0D/YMaoCi6ti5UyE6ep96CkGvd6j1KatXK6lmMy66CNY5Ii018dBNGcHBOXHg4ovhPXQYuR/6EIq+8mUTwyJNEwQEAUFAEJhOBAJ+P7be/Ue89sQjSjNySkpx5X99E3nzyqesWUJwTBnUQw+aMqNk6rsmTxQEoiDAwJSHXwIObguRGsffikBoJIcCUi7YECI0+LN9HP88nwdor1MJj90h0oPkR++x6EOR5AQKqlTFh87VJb1g0oevpcelKDRIaPDV1jc628nKsqyQSqMyH6cYcTvRiAxNiUEyYwwiw++xwDtgg9eXA6+lGF5vJnyDyfB2e+Ft74GvrT1qBhMCZMvNVVQZgcoKtJWl42AhsDu1A7U9B3Cg+wAGfVFSuQJKAE+9Wwl/prtJmn36A1tO+uDPwAf4OjvR/eBD6LrvPiiZbdRizcpC9juvRPZ118GxaFHCe2YmwyThnUvcDSfFliCZESl2T9tv/xetP/uZ8vmv3PocLHYhHxM3lHInQUAQEARmBwKDvT14/Gc/wuHdbyodWrBqDS699Ytwpk2totpMdoQoOGbH3JZeCAKAuw84/DLQQEJjO3DsDSDoH4kM42mUrQUWaoTGegSSUtA16FXiTXDzr3/vc/tgtVhgtQBWK9/Vny0WxSBXfs93qwUpvm4UDhxA/sB+5ZXXH3rZ/dE334OOPHRnVKIncxl6MivRm1WFvszFsNidQ88KPWfks7TnMhVr6G8W2Kx0NQz93DXgCaVwrWuLmO2kLDtlKDDoGYvzorudRCIySGZ0cuMZUr8wJqjPZYW336a8fANJ8LpT4fVmwMufu9wIDEYJ9Bk+by0WJC9YgKRlleidn4ejJXZU57mwO3BEcTHpcEXPfsKAnouzF49QZdDFhOlWJxz4VD5fU44AN70Dr7yCznvvRe/TzwA6VUfqunUK0ZFx4QWwJicmALCZDJMpB9v4AxNKcGw+tBl3770bNyy/ARcvuHhUK7xNTdj/jvOAQADz7vwlMs4/33hLpaYgIAgIAoLArEeg7XADHv7x99Dd3KT0dd2V1+Cs698P6zSops1kRwjBMeunvnRw1iLAzXfjy2oMDRIar41yHQlabOjLW4mm3LU4kHYK9iatwLEB6wgio73PA18gzFUlQaBZEEC5pRVVlsOosjRimTX0vsDSBJsl8jP9QQsOBktQE5yPmkB56D1YjqPBfAQRX0DPNLqdLGa2kwKcVZk/2u1EITJqQ24lVGJQmcGgn52HEPAHVeKCBEZSSIlBMoPvg3Z4+63ASC+XqOgx7aO9pAT20lIklZbAVlKMnuxkHEn3YL+zG3ssx7Gv9wCO9I0RMNRixfyM+aNUGfPS58E2DQtagqaK3GYMBHxtbehSVR36YLJZ11yN0u99LyHYmckwSUiHJucmCSU4PvPsZ/Bs47M4pfAU/GXTXyK2+PCNN6H/+eeRfu65KP/NryenV3JXQUAQEAQEgRmHQN0rL+Lfv/oJvG4XkpIduPATt2L5mWdPWz/MZEcIwTFt00AeLAjEhkDQM4CB+pfg3r8NSYe3I731TViDw776vJsfVuyzLMQLvuV4wb8COwLL0A/j2RioiMhLcyA/PRkFGQ5kOu0IIsgDRPiDQfBUmVxIQH0P/T8IfyD0+0h/H/m3UD17wIX5vsNYEDiERf4GLAoewpJAA3IQPXhmXzAFdZiHmkAF9gXLUR0IvXow2tWC/Vg5Lxsb1fStq8qzQ9lO9ERGaw2CzdUIHKmG99ixYdJCIy9URYbfbTx2iC0rC0llpQqBYS/R3ksUMqMrx44DGA72yYCfTMPqDYwcQ/2sKEgpGEFkLMlZgsVZi+Gky4+UOYdAMBBA/4svKRlYep99FvP/9EekrU9M8DAzGSYmHtiEEhyvHn8VH/3PR5Xu3nvZvViRt2JU15lp5+hnPwfYbFiy5VnYCwtNDI80TRAQBAQBQWCyEaAt8OI//4GXH/iH8qiMvAJc+V9fR9GiJZP96DHvbyY7QgiOaZ0K8vC5jgAJgX6PH229brT2uUe8d/T0IrPtTczr2YWqwTdwQqAWDsvIzXAgaMHeYAVeCqxQXjsCVehF6ihYc9OSUZDuQH6G+p7uUAiM/LB31rPR/2M6Cn09+lpGBjRlIFSqKfwj42bomxfMnKdkcgkULIe/8AQE8pfDkjMPju5DCDbthW//6/DWV8Pb2ABvS2dIeaFTYQR9BlUhViuSiotGEBcKkUFCg6qMkhJY09LQ4+lR0q6SwGCwT+291xM9DSvjYQylYVUzmDB7SbYzezpGQp45AxDwtbbClp+fMPcjMxkmJoY/oQQHv/+vevQqJVXzlYuvxPfOGq3GCXg82L9hI/zd3Sj8ry8g72MfMzE80jRBQBAQBOJDwOtxK5k/pjpuRHytnb6rPIMDeOLOn+DAzpeVRpRVnYArPv9VpGZNv71oJjtimnYyUz4xEmqUTHnr5YEzFgGmId11qBMH2/pHx7hQCA0PBr2hOBl2+LDScgCnW/cqrzXWWjjD7tJIGQAAIABJREFUCA3Wqw7MV8iMt+wr0ZC+CikZecjPcIwkMNT/k8QgaWFngAoDhQEO3fv2oeWNV3Dk9e0ItLYh2ZaMZGty6F19WS3G7mfgkeNXIfHhHQSYatXTp773M/BF1GsZeoQkhm/QBgSNfc1ZnE6VrFCVFyQvSkPuJCQvkoqKYElKGnqmx+/Bwe6DqO2sHSYyOuvQPNActV1JliQsyFowlIJVIzVK00oTtlEdH1CpIQiMRsBMhomudV8FsBrAGmZLBcDgNwtiGD/KHX6kXj8PUNhf+n9tBfADAPtjuBerJtyW+GftP/Hdl76rZC/afM1m5KXkjWpS0/f/B51//SuSFy3Con89Lt8VMQ6aVBcEBAFzI0Cy9+9f/zya6vdj6Wln4bSrrkPB/Fi+6s3dv0S1rrPpGB65/XtoP3JYueXJF1yCcz90I2xJ5ghAbSY7wpjln6iRmb77JNwomb6uyJPNjAC/pElmbKttxdbaVrxU3w6XN3KAhiT4cJLloEJmnGbdi7XWWqRaRisV2lMXo6NgPVzzzoB14QbkFhQrbiSKy0WchfI2b2MjXNU1cNVUw12zD/17dyPY0hbnHc1/mS0jBfbiQtjLF4ReZaVIUuNhkMSwZWdH3DgEggEc7TsaUmKoqgwqNBp6GsZMw0rSQsleomYt4fvCzIWw28yxEJl/xKSFU4mAmQwTXb8ZqIeRdV9TSQr6sMVi9S4D8EcAL6nkCCMeVwL4CAAHgNMA7I0B54TbEsyAdP795yvKr1tOuQU3rbxpVHNcNTU4+M53Kb+v+MffkXrKKTE0WaoKAoKAIGBuBBgo8/++ePOIRi5ZdzpOu/p6FC1cbO7GT1HrGt58DY///Edw9/fDakvCeR/5BFaePzo49RQ1J+JjzGRHCMExnTNBnj0rEGCmkZcOtGNrbYtCajR2jMwawngQFbmpKEpPwurkwzjFvxvLBt9Aac8bsPsHRmOQvzSUtpWZTirOAiaYSjUwOAh3XR1oJLtrahRSgyqNwECEZyMUL/N4LnCs1AFLaREG/C70e/uVVKSMx2G08ESSrhf6V7o9Han2VPCdv3fanEAiv4XYPHcPLL4BJFVUwr5wGeylZYoSw+ocP24Fs5ToiQz+TPn4WGlYsxxZQ5lLqMhg5hK+pydPbXouo+Mi9QSBSAiYyTDRtY/5cOvV/+8GwA9VLARHtMFeB+BVAL8B8KkYZkTCCQ4++yc7f4I/7fkTClMK8eQ1TypqjvBSf9VVcO+tRva116Dkv/87hiZLVUFAEBAEzI3Aa/9+DFv+/L+Ke0pKVjY6jw0HWl+0ep1CdJQsIV899woPTnf962Fsu/tPCAYDiivK5Z//KuZVnWA6MMxkRyRya2E6oHUNmhSjxMwdlrZNHgL8sqk+3ottda3Yuq8VOw91wOsfufHPT0vGlYuCuCC/HSvtR5B6fAdw+CVl8z2q5C5W07aqqVsziuNuPP3yXTX7QqoMqjP27YPn4EElzWCk4rIDhwr5sqChyILu8hwsW3cB3rH0EqwuXD0iK4fX70XrYCtaBlrQNNCE5v5mxR2D7/wdf24daIUv6DPcfhryhamFKEotQlFaEYpTi0P/TysK/S61CPkp+QnPDkLC4kDXAYXMoIsJSQz+3O5qj9p2pmFdlLVoVPYSBgKVNKyGh1wqmhQBMxkmUSBKJMFB1xX6kt0D4IYYhmRSbAkqxC558BJQLXb7xttx8cLRp3Idd/8Nzd/7Hqypqajc/rzyLkUQEAQEgdmAwCM//j7273gJVWeejU03fx61L7+Alx+4Z8gVg31ccPJqnHbV9SirGh2MeTZgEKkPjEuy+Xd3ovr5LcqfCxcuxpX/9Q1k5heYsstmsiOE4DDlFJFGmQ2BrgEPnq9rUxQadD9p6R12JclDN5bbjuC8vHasT2vGwsBhpHTWwhItqGTOAlWhsRFYcBaQSZs5thL0++FpaFDVGCFVBhUa/rboLiau3DQ0FAB78wbQUGhRSI2mHKAwvRgXVFyACxdciJMLTsZE4mv4A35QBRFOgmgEiEaKuMcIGhqOBNtDkoPkB4mPIUJEJUVIgvB3jA8SXnwBHw73Hh5SZWhERmNvY1Q1igUWzM+cPyroJ1OzShrW2Oap1J45CJjJMJkEgoOSiCwl1BHAMPPfAfAOAB8A8NcYRmlSCA4+/7NbPotnDj8TNWWsv6sLdRs2Iuj1ouSHP0D2O98ZQ7OlqiAgCAgC5kSALtO/vvG9cPX14oKbbsHK8y5SGsrf79/xMl564B9oPXRwqPHlJ6zE6Vdfj3krTjLF4VIwEITH7YcjZThGWyKQ7m1vA4mf5vo65XbLzzoHF3z8FtiT6V1pzmImO0IIDnPOEWnVNCPA1KZvHelSCA2+3mzsQmpwAEstR7DUegTLLI1YmXwUy6xHkO7rit5aSo3pclJy8rBKI7s8pt75+/rhrqUqoyakyuB7XR2CrihBNm02OBYtgmtRCWrzPNiacgivpLegN3X4416WXqaQGnydmH/ihEiNmDrDRSsYVPzNm/qbQgqQMBWIRoL0eftiunWuM3dI9ZFiT0FDd4Oi0vAEPFHvQ+JEn72E7iWLshchJcl4at2YGimVBYEYEfAM+tDdOqi+BpT3HvX/Z1y1BJXrimK8Y+TqZjJMonRoIgqOywA8prsv1Ru30TtkHPAymIFPV4dgv3b06FGUlsZOTI/1rB1NO/CRpxgaBLjnsntwQt5o+fHRz38ePU/8G6nr1qHir39JyLjLTQQBQUAQmE4EWhrq8dcv36o04aM/vwvZxSUjmkObsf61VxVFR9OB0GafhUqO066+ARUnrZpWomP7P+vw5jON2PTxk7BoVWKUFUf3VePRO76Pge4uWCxWbHjvh7D2sndNaz+NzBEz2RFCcBgZMakzJxBo6XEpZMYLNUdx/MCbKHE3oMraiKWWRoXUmGcZKwCnBchdCBSuAAqXq+8rgLzFgMGgkvwS9zU3w1XNoJ+qKmNfDbyHQtGSIxVrejqcVVVwKK9lOFJsxzPWfXjy+LNKYEx9Kc8ox4UVF+KCBRdgRe4K039RMu6HRn7wXVGBaG4x6v+pFjFSSFgw7aoW9JM/L8lZApIiUgSB6USAn3tXvxfdLRqJwfeBIRJjsHdkamh9W9dfvhDrLmVykYkXMxkmUXozEYIjH8AqAGQuqW++HsDjAL4LYCyfOio9vh3enskgOPQpY69YfAW+f9b3R8HQ9/x2NN54o/L7xU89ieSKiokPvNxBEBAEBIFpRIDxJZ77y++RnpePm371p6i2Kb8jGWiTRMex2uqhFjM2x2nXXI+Fq9ZOuV3LNv3pS9vBdbp4USau/tLaCSP51jNP4pk//BYBvw+OtDRcduuXsGAVE4mZv5jJjhCCw/zzRVo4SQh4PB7s2f0GGvbuQP+Rt5Hbf0BRZiywNMFmGSOYZkZpiMQoIpmhEhr5y4Bk4z7RlBm76+tVMkNTZ1TD390dtbfM9OFYvlwlNJbBuXw5bKUleLvtbfzn0H+w+dBmRRWhLwsyFyiuJyQ2qE6YbbEi6OqiJz40V5heTy8qMiuGSI3S9NIpValM0pSV285QBChh7e9265QYgwqh0dPG9wF4XKFU0WOVZKcNWYWpyMxPQVZhCrIKUlC8KAu5JWnjXWro72YyTKI0eCIER/gtKb94C8ADAD4+BkBTpuBgG8ZLGUvXxP3nnQ9fUxPyPvFxFH72s4bGVioJAoKAIGBWBB667f+hfterWLHhXGy6+QvjNpOkQuOetxTXlSN7uSyEStGiJTj1quuwZM2psFjjzzI4bgN0FbqaB/C3b7889Jv3fOdU5BTHtyb7fT4895e78MZT/1Lul1tWjnd+8RvIKSmLpUnTWtdMdoQQHNM6FeThU4JAMAh0NwIt1ehqeBMdDW8iqa0GRe5DcFiin476nTmwFZ2gKjI0VUYVkJITU7P9fX1w7d07rMqoqYFn/37FlzpSsdjtSK5cAmcVyYxlijrDuWwZbFl0IQcY5+L1ltcVQuPpQ0+jZbBlxG3ocqEoNSouwOLsxbOO1IgJfKksCEwRAgF/AL0dJDEGQmoMhbxQSYzWQfijpIvWNy8lw64QF5kFJDBSlZ/5yshLhtXmg9flgtc1CM9g6JVTWorMfMbLnHgxk2ESpTeJJDj4CAYYvQYArdHR+bkjN2LSYnDwcfqUsTevuhkfP3k099Ly85+j/Te/RVJREZY8+wwsNtvEB1/uIAgIAoLANCAQCPjx64++B+6Bflz0ic/gxHMviKkVJDheevAeHH77jaHrCuYvwKlXXY+lp54x6UTH3heOYctfa4aevfqiCpz+rtjT2g70dOOxn/5giLBZvPZUbPr0F+CYYcGkzWRHCMER00dJKpsegb5WoGWvQmagZQ8CzXsRaK5Gkq8/atMH4URn2mIklaxA3sJVsBWryoz0IoA5XmMsZJc99fXoe24r+p57DgOvvQb4I5/Q2rKz4VhOAqMKzuV0M1kOx6KFIMmhLwyWuat51xCpEZ7toyq3SiE0zq84X8n0IUUQEAQSj4DP60dPmyukxGgZdiPh/3vbXQgEQsovpnJDkLFfvAjyPehBMOhV3h2pAThSAUdKAHZHALYkH6xWekl44PO6hwkMlcjwDg7C540cR+bcD30cqzddnpCOmskwmSKC4xEAVzAwPYBWgyBOKsHBNvxk10/wp93RU8Z6Dh/GgQtDQfjK77oL6RvOMth0qSYICAKCgLkQaK7fj7u/GlKifeyXf0BWYXwxpeiyQteVg2/sGupg3rz5OPVd78ayMzbAap0cIviZv1Sj5sXjALcKQSAtKxkf+MGZsFqN7x0Yg+Th2/8bvW2hZYgpcc+45j2TTs5Mxkwwkx1hfAQmA4mpu+ekGyVT1xV5koKAqwdorQGa96hkhkpqDESPk+EJ2nAgWIoG63z48pcrZMbSk9cjv7QSmKCcLeB2Y+DVHejbulV5eRsbRw2UvWJ+SJWhEBlViqsJT+GiuY14A17sOL5DcT959vCz6HR3jrgng9BpgUKZ9UOKICAIxIdAgFmJVEJhoLsPnU1d6GruRndrN/o6etHf1YeBnn64BwZCxIWOsAgiRFyEfsd3KrOMp0qOr8XkXkOBx9ZdflW8txhxnZkMkygdGk/BwS9B+gkeUJilUKG1zICi4YVxOF4FQJ8+ZlUxWibdljjWdwybHtw0ZsrYQx/4IAZefRUZmy7GvJ/+1GjbpZ4gIAgIAqZCYMdjD2Lb3X9EZkERbrzzDxNuG4OQvvzgPTiw85Whe+WUlOLUd12npKC1JSU208nd33pJUWqeuLEMu7eF4t5ddsvJqDghz1Bfal7chqd+83P4PG4kORzY9KnPYelpM5e0NpMdIQSHoSkolaYdgY6DwBt/A46/GSI06HISpQSCFhwKFqI2WI6aYDnqguWKKqOyahU2VJXgxLIs2GJgV6M9x9vcgr5tVGlsRf9LLyHIzY+ukLxIP/tspJ9zNlLXr4ctPX1cGD1+D14+/jL+0/AfbGncomQb0ZeVBSsV95Pz5p+HeRnzxr2fVBAE5jIClL8OdHWB6dZ6O9rQ29aGnrZWdDU1o6etDf2d7XAP9CLgj+6qljD8LBYkO1OQ7HTCnpIa+jlFfTlTYHc6kaz7vV2tq/3OrqvLa2kMJTKmjpkMEx3m7wegRdK8BQBzQd+h/v1QWIrX5wCcDYBRVxvUOj8DQM0znZr5O9o8JwLgfSmTo4LjqRjGOKEEx5tvvok9e/agqqoKq1evHmqGljJ2VcEq/PWS0Vlsux5+GMe/8lVF6bdk21Yk5cTmNhlDf6WqICAICAKThsCDP/wODr6+Eyeccz4u/mTiYgpRFfHKg/ei9pUXhtpOdcj6d74bJ5z9DtiSRqqk4+ngQI9HCTDKcvWX1uDlhw/gaG0XlqwpxEU3cpmJXmibvHDv3Xj14fuVSiR4GG+joCIxQcPj6U8irjGTHSEERyJGVO4xeQg07gBe+iVQ/RiTYo96Tps1H3t8ZagJzENtoBz7gvOwP1iGrMxMnL20AGcvLcRZS/KRlTrxLzPm5Hbt3q24nZDUYFyNEcViQcrKlUg/9xyF2KBKw8gGhIEyXzj6guJ+8lzjc9CnR7XAglMKTxlyPylOK548rOXOgsAMQiCcvOhrD5EX3S2hV19nO1x9neDnNt5isSbDZnfCnkxSIgXO1FQ4M9KQmpmmkhOpsDtITAwTFUOEhEJY8PchMiPRhES8fYp2nZkME10bNdIiUrO3AjgnQl09wXE+gE8AYGh7uqJQp8xjNl77YwB7YsQxoQTHAw88gLfffhuVlZV473vfO9SUESljL70HJ+SPTBkbGBhA3YaNCPT3o+jrX0fu+98XYzekuiAgCAgC04sAlZO/+uj1SjypTZ/+PFZsfEfCG9TWeAivPHQfqJQA4/Ex73d+AdZfcY0S7yMpmZx5fOXA6y148n93w2a34safbETdzmY883/VsCZZ8OEfnQVnWuR9B+ON/OsXtyvEDsv8E1fi0s98GamZoTh7M7mYyY4QgmMmz6TZ2vaAH9j3BPDiL4HGYZnZYEoxdjlPw/PdBdg1WILa4Dz0IKSKsNssWL8wd4jUWFqUbohcGA9CBgjt3/5CiNR4/nn429tHXMI0rWkbzgopNTZuRFKusbSjDCa3/eh2bG7YjK1HtmLAN6z+sFqsWFO0RiE1qNQoTE1MEMHx+ip/FwTMgsAQeUHVRXsbFPKCKgyqL5pb0dfRhsHeWMmLZFisGbBY05V3WNPhTM1Bak420nMykVWQieyiLOSU5CCvNBsZeemwzqEAjmYyTMwyDyO0I6EEx1tvvYUHH3wQSUlJ+PKXvwy7GnuJcZyufuxq1HXWIVrK2OPf/Ba67r8fjhXLsejBB00MmTRNEBAEBIHRCByv24e/fyOUNeWmX/8ZGXnM5j05pePYEYXoqN7+3NChR3pOLtZdcTVOOv9i2JMdMT94+/11ePOZRpRWZuNdX1gNr9uvKDr4vvH6pTjpnNEqa7bj4du/h85jR5Tnrd50Bc5+/0dnja1hJjtCCI6Yp7RcMGkIePqBN/4OvPQroPPg0GM6MpfjF4MX4+7eU+DDsP/c/NxUnLOMKo0CnLYoD2mOxPjWuQ8eDAUI3boVAzt3Ar6R/vTJixaprifnIHX1KaMCgkbDZ8A7gG1HtynuJyQ3SHJoxWaxYV3xOoXUeMf8dyA/ZfK+6Cdt/OTGgoABBKKRFyQxqLwgoTHQQ/Ji/NSpw48bTV4kJWchLStXOa3JLixAVlE2MnKdSM91IiPXgfRsp3LyIiWEgJkMExOPSUIJjoGBAdx2221Kd6ngoJJDKw/UPoDvvPQd2K12bL5mM/JSRvp0D77xBhquv0GpvvDBB+BcwbAiUgQBQUAQmBkIvPLw/dj+j/9DdnEJPvrzu6ak0V1Nx8Hn7t32DKggYUnNysbay6/CyRdsUtSWRsv9P9iBlkO9WHNxBU57ZyhzyrN/qUb1i8dRWJGBa7+6bsSt6l/fgX/9/HZ4BgeUWCDnf+zTMWeNMdq26apnJjtCCI7pmgXy3GEEepuBV38H7PwDMDgcSPNY4Ub8oOsCPNbDrCAWJW7Gxsp8nLOsUCE1FuTHl2s6HPqgx6MQGb1UaTBA6KHDI6rQz5kxNKjSSDt7I6zzSuHyu0DXEuXlU9+1/6vvLp8LjKlBImNn806F1GB9rSRZknBq6alKTI1zy89FjlP8qOVjMbMR0JMXfe3t6G1vRW9Hu6K8YMwLupAMdE+cvLDaMpCSkYv03HxkFRUguyB7mLhQCAwnHKlJCVFxzewRMd56Mxkmxls95TUTSnCw9b///e9x5MgRrF+/HpdccslQh8ZLGUuVR/2llykZu3Le9z4Uf+PrUw6GPFAQEAQEgXgR+Of3v4lDb72Ok867CBfexBBLU1d6Wlvw6iP3Y/eWzfCrh5gpGZlYc+k7seqiy8ZNz0qVxl2f24ZgIIjLbj4ZFSeGCOhjdV146I7XlJ+v/+Z65JWlg9/VjLWx/d6/Km4yaTm5uOLzX0Pp0qqp6/AUPclMdoQQHFM06PKYCAgwWOiLdwJv3wf4Q2kQgzYH6ksvx7dbN2J7V0jFQGLjXaeU4eZzlwyRGvzCYOrUWIgG1iXhwHe0dyFr1wEUvHEYRbuPw+4eqdLozUhCTVUadi9z4O0FVvQmeUPX+lwIMhdUnIWncWeUnqEoNc4pPwdZjpnvcxcnFHLZDEaAn7+e1mYwYjlfzQf2o7PpGPq7Jk5e0H0k2UnCIg+ZVF4UUHnhQHpOiLhIz3UgLdsBm23uqi8CLh987S7YspJhS4/fh1g/Bc1kmJj4o5FwgmPr1q3YsmULcnJycOutt44g5bSUsQUpBXjqmqcUNYe+tP/hD2i5/cewZmWhcttWWB2xy6xNjLU0TRAQBGYpAn6fF3d+5Hr43G5ccusXsfxMxoee+kLF6I7HHsDbTz81lI7dmZaO1ZdciVM2XQ7+HKkcqenAIz97Qwlb/bE7NsChxvmjbXT3t15GT+sgVp1fjvWXleOp3/4c+156XrlNyZJluOILX1Psm9lYzGRHCMExG2eYmfvEID/1zwEv3Qnsf3qopcHUPOwtuxZfPXIa3uoMGexMdPLOU8rwgTPz8ErbE3i8/nElVWo8RIMlGMTCJmDN/gBO2R/EEiYHDCv7S4BdS6x4bbEFDcVA0BLfx8Npc8KR5IDD6gi92xyoyKzA+RXn4+x5ZyMjOcPMIyRtEwRGIOD3BdDScByN1TVo3l+HtiP16Gk5BB9dysYso91GSF7YbJlIzclFVkEBMvOzVOWFE+k5jiEXEkdKYtzNZupQ0kgK9HoUEkN5dQzC1+GCX/050B8iZHOuqkTa+sQEHjaTYWLicUs4wUHcf/e73yldvvnmm5GfP+yeqE8Ze9vG27Bp4aYR0PhaW1F3zrmA34+yn/4EmZtG/t3EOErTBAFBYA4jcLRmL+759pcUBD7+27+A8TCms/Bwhilr39z8hEK6sDBA+CkXX441l14Jqjv0Zce/DuLVxw4qCg0qNfRl5xMH8cqjB5GcOghH0pNoPRRyuT/h7PNx/sc+NaHAptOJkZFnm8mOiG8HZ6SX5qqTcKPEXN2bAa3xeYA9D4YUG81vDzU4mLsYr5e9D185sAK1HSF/OBIbV64qw8WrPdja9DCebHgS3kDsaRyzfQ6sOmTBqv0BnFjrRmbfSJ9+j9OGoysKcHxlKdpWzQdysxUyQnmpxMTQ/20OOJOcSLYlgwTGiHfd73nCZiRzygwYMWniHECAMsv+Ljf6Ol3oU97d6G7pQFtjPXpaD2Gw+wi8rmNAMBqZYYclqRBWWzEstlwleKczLVuJe0HyQlNc8F35OceJ1KxkWBOQpnmmD0/QF4Cvk+SFC/4OlchoV4mMDheC3vGzv2S8oxxZFy5ICBRmMkwS0qHJuUnCbYlAIIA77rgD/f39uOiii3D66aePaPnntnwOTx9+GicXnIy7L7l7VK8aP/Vp9D37LNI2bMD8u0JEiRRBQBAQBMyMwMsP3IMX7rsbOaXz8JGf/tY0TR3o6cauxx/C60/9C15XKE4eU7avuvASrL3sXUq8DpZHf/46Gqs7ceLZZTj7hmUj2t/b4cKfv3QvPH2PA8FBWKxWnPOBjylkyWzfH5jJjhCCwzQfq1nakMEuYNefgVd+C/QeHyY25p+Bl4tvwNf3zEN9e+hLhIKJy1cW4OTlh/Hs0YfwVttbQ/UzkzNxVeVVOCn/pCECIhLRYD3aDN/zr2Bw2/ZQgFDvSGIkuaIC6eecg/RzzkbqmjWwTCBF1CwdMenWLECACgD3gC9EXnS50d85TGKECA238jdX/wACvmYE/c0I+JqU92CgOwoCNtjshXBmzENG/nzkli5E3rxyZOSlKC4jGoFhdzAT5+wsxFV7sYfj/Z+uJF5VfeHtdMFPQqNzEL5ON/w9biVrneLyZuG7+vOId+D/s3cdYE5VW3elJ5NM7wWmMzD0JkWKqHSsqIgN7Ipd7L7/2d6zPXt9KqKioj67iAoCUqS3oQ1MZXrP1CSTnv/bJ8lMMkwnGW6Ge/jCzSTnnrLOSbLvunuvLVCKIAqUQxQogShABiERRAFSFp4SGBkCf3/PeIRxyTDh8O7xOMFBc/3pp5+QkZGB5ORkXH/99W7T7yplbNPGjSi56272A5qyaSMk0dEcho8fGo8AjwCPAPDtc0+g6OhhJuxJYptcK81NjTjw+y84+PsaUFpXKmKpDCNnzsGYeZfh6+eOsWwpM29Ox6DxrV6UZBMcWv8bNn7yAWCzQiRW4LLHnkT88FFcm6JXxsMlO6InBAcFPN9H3kQA6JZRNYD/AfgngK58lQlICmS6FwDJftP55AOUDYBuOXzGbDr3MgHAvwHQkd7bAeAxABm9WBWvGCW9GMfZc0pdIbDrfeDg54BRY5+3QARr+iXYGroIzx6QI7/Gvm2I2Jg1XIHYgRnYVPoL1PrWVKyDggfhmsHXYF7SPCjEp6obM4HQAwfsWU82b4axoMAdY7EYfuPH2bOeTJ8OWWLi2bMG/Ez7JQIkatWsMTGvCydZ0UJi1NNrRvae2ejuAWCzmWCzVDNCw2qpgI2IDWtt+xgJhFAFRyM4JhGRSamIG5KGuMEpkPnJ+wWmdLf86NGjOHToENRqdYdEhSuJwcWJt3fHv7fj5JJh0ts59MF5XrEljh07hm+//RYikYili5W6EO9dpYy1mUwsTIVSmIfffx/C7rijD2Dgu+AR4BHgEegdAmaTCe/euIhpXiy4/1GkTZrau4b64Cy9VoODf6zBgbU/g55TEYolEIiGQiwfjyUvzmY3d6jQvDatfB9HNq13XPKEQeZ/CW56ZT4U/p7RyuqDKZ9WF1yyI3pCcLzpICh+BPA7gCEASPaWlFMuBNCZPy2RI1sATHaQGbsoM4+D7KDgJcqT9qgLqhMBbAZQCuAdx+t3A4hwtNEa49C9pfCKUdK9rs+yWiX7gZ2DqHiFAAAgAElEQVRvA5k/M/aSFakK1tHXY0PA5Xhxlw751U5iw4YpQzVQhu/C7sotMNvsceWUMvWCgRdg8eDFGBs59hSXLrNaDc2WrSzjiXb7dlg1DgLFAbUoNBSqadOYp4by3MkQqdoXCTrLVoafrg8gYLVYoW0wthAXruEjzAuDvDHqDbBaOhe6tdkssFnUdiLDUgmBrQpmU3XrZ7INFsHRsYhKGYSo5FRGaEQkJEIi6x9khnOqZrMZOTk57E45HSk0wNfLrFmzMHky/ayefuGSYXL6s/FaC16xJZqbm1m6WCIzFi9ejLQ0d5dn15Sx669Yf0oa8cqXXkbtJ59AMnAgktf90e/doL22unzDPAI8Al5HoCTzKL55hu5XA3d++EVL2IfXOz6NDii1a8b637BvzQ8g7w57EWHEhTNxziVXMl2NX159HmXZx9k7KeMnobLoHJiMIky5MhUjLxhwGr37zqlcsiO6S3AMBUCkApEbC12gJoLjLUrhDmB1J0tAQaXkgfEGgAdc6hGldQIAqcvYA5vsZQ8Ayp9DJAqRHFRiAdDOIXJkVg+X2ytGSQ/H0H+rWy1A1u924dCina3z9I+BdcId+EM2G69tq0RuldOTw4SxQwtgUm7DyUZy4rGXEHkIFqYuxFVpVyFK6eLyZTKh+chRaHfuYMSG/sgRlmrJtcjT01tCT+TDhrGYN77wCPgCAo3qZhQcVqPgSA1Ks+tgNXc/S49YIoQyWAqprBGwVTO9DNLNaFIXw2pxzwzkxCIgPNJBZKQgKnkQIpOSIfPzTMplruFNF4ylpaXMU4M8NuhC0llkMhmGDh3KwgKEFgET9bRqTLBoTLA1mWBtNNpfcwh62n8sBRDYI0rsz1v+B4R+EoiDZBAHyCAOlEEUJGd/i+i5n12bx/lgZ3fwd2fvOeN3nefSHX+x2DOCrFwyTLi2j1zG43FbgvYoLDZ8+vlnKCwsxLhx47BgwQI3CChl7MzvZqLB0IC7Rt2FO0a6e2kYcnKQf9HF7Jz4z1fBb/x4DkPID41HgEfgbEZgx7ersfO71QiNG4ilr77nU1CY9Hp886+VqMzdDNh0bOxCkQhSPyX0DuJj8lXXYuJli7D5q2xkbitD2AAVFj3pLkTqU5PuwWC5ZEd0l+D4FwBKsj7N4bHhnC7d4qN4AvLOaE3gfioYswH8AYAkc//T5m0iM4i8oAeVFAA5AFYCuLlN3Y8B3AiAjIx28mB0uAoeN0p6sN79t6pRBxxaDex8D6jNa51n1HBYJ96N3zERb2wqRI6D2BCI6zF40BE0iP9Gk6k1zn9o6FBcM+QazE6YzfQ1bFYryGDT7twJ3c5dTEvDqnWPghL4+UE5eZI99GTadEgiybmHLzwC3EfAarWhqqARJw/XoOBwDWrL2o/wk/mJmbYFZRdhxyD7EWhgJEZDVSFqivJQeTKvRQyr7eyVQcGITE5lhIadzEiBX0D/T03c0NCAw4cPM2KjpqamBRYiBhLjEpAenowEazisFXqYq3Sw6tong9zwFAkgDpZDFCKHmB6hdFSwI70mlPq29giXDBMOf4o9aks0biiEdm8lVJOjcUhUiA0bNiAwMBD333//KV4Yr+9/HSuPrkRHKWNPLloE/aHDCLz0UsS8+AKHIeSHxiPAI3A2I0DeG+TFMWr2Alxwk2+F1BEh/emj26Ft0CJxWBVKMjdAU2sPqycx0nl3L0fKeApCACryG/D9y/vZ86ueHI/wAZ7Ry+Ly3uGSHdFdgmOdIwyFwkrs+XNay3YAgwCEdwJ6MIB8ClECsAzAbkeIyhIH6UE7/CPH+aTRQd4gtwJY0aZNeo00O+j2xtoeLLJHjZIe9Ns/q2qqgD0fAXtXAM0uMfwpM+3EhnYQ3tyUg+xK8tiwQeSXj7j4A6jDQdgckUxioRiz4mcxYmNE2AiYSkrshMauXdDu2g1L7anaANKEBCinTGGeGn7njIeQFwjtn/urH87KqDej+HgtIzQKj6rR3OQufitXSRA/LBQJw8MQGqtkRAaJdWrq1KjIy0FlXo79mJ8LvaapXYQoX7uTzHAeVcGhZ427utFoxPHjxxmpkZ9PPzetJVQehEHiWCQ1hEJhknS4wwQykYO4kEMUqnAjMkRBMgj6cfYXLhkmHP4K8KgtUfdjDrS7KyBNCIDtsii8//77bOrLli1DRIQ7aV+uKcecH+bAarOivZSxdV9/g4qnn4ZAoUDqtq18aCaHNxE/NB6BsxUBk9HA9DcsZjMufvAJpE7wTIhlX+HZUK3DF/9HgQRg6WEDI2Q4tvlPlOdkY/zFlzOvFGchMmT107tRX6nDiBlxmLqILpX7d+GSHdFdgoPCU+jXNrKdpSGh0SsB0K1FYydLRyoyRFi4rjBZ6jeQiLjLecsBvOLwCCGtD9dCXiJEbJDQaWf50Igmc6XKaNwHyFU5JobsE770CoGqE/YwlMP/AywOnkskBUYsgnXCMqyrDsabG3NwoqIJEBghCTyIkOg90LVEGYHFDl816CosDD0fsowcaHcRqbEbplJnJFLryMQREVBOmgi/iZPYURLVGrbSq/HzJ/EI9CECXYWehMQoGaGRMCIMEfEqaGproC4tYiSGk9Sg3OztFdLHIG+MFu+MpFQERkadNWSGExPS0SC3/kMHM5B5PBNGl6xJcpsEyZYopFqiEWpTOQJK7GcKFGJIY1WQxKogjVJCRN4YoQoI/cRnHYZOLLlkmPThx7SnXXmU4Gg+UQv1p8dYzFPUkxPw1ofvoLGxETNnzsS55557ytg6SxlraWpCzpSpsBkMiHruWQRfSWYZX3gEeAR4BLiDQNHRQ/j2uSdZdoFlH30JhX8AdwbXjZGc2FmOjZ8dh1Qhxi2vTu3ypseBdYXY+WMe5EoJlr50LkTi/h0+zyU7orsEB8Uf0G2vVmqqdSOsAkB5zchLo76T/TEawD8cnhykx0G6G5QbiLQ2LgHwp+Pc/wPwLIALAGxq0975ADY6dDxIz6Oj8jSAp9q+yRMc3fj0tq1C8cEnt9qJjRy7MjArimBg/C2wjrsF64uANzZkM2JDIFFDGrwTitADsMAen0ZlgmoYrjOOwaA8PfS798CQk3vKYISBgVCecw78Jk2EcuIkSBMTztqLjV6sFH/KGUaAsptUFjQyLw3S01CXuoeeCEUCRCX5IXyABX7+Oug1lagtLUFtWQnqystgNrZ1jrNPSCSRICI+ySXUJBXBMbEQCn07JKK3y2UzWVGZVYKMgxk4VpSFJlMrzkKbAAOtYYzUGGANhZD+KcWQxPpDGuMgNGJVEAXL+O+WNgvAJcOkt3ujD87zKMFBe7ns2Z2gY/CVg7CpbA/279+PhIQELF269JTpuKaM/Wr+VxgWNsytTukjj6DxlzVQjBqFhK+/6gM4+C54BHgEeAS6j8D2bz7Hrh++QXh8Im54+e3un8iRmn99fhyZ28uZx+2Cu0d2OSoShf/s8e1MNnDO7cOQPLp/h9NzyY7oLsFxuh4cwx3CoSQw+l+XHUEhL0dJowVAMgALAN6Do8uPTB9UsJiAYz8CO94GKg63dhiSBExcBtvIxVifq8GbG3KQWV4PkTIX0uAdEPtnsbAUidmGoWViXFyXgKEFVghO5AFtshYI5HL4jR3b4qUhHzIYAtHZedHWByvKd+EFBFpCT46oUXikhoWeMNFAmxZWSy3E4kYoA7UQCOqhb6pCk5qya3dcSKwqdEA8opIcAqDJqQgbMBAiccdhFV6YFmeatBotMJVrYSrVoLGoFicKs3FCW4gqYauGDw023BrASI0UeSwC4kLsnhnMQ8MfokApT2Z0Y0W5ZJh0Y7hnqopHCQ6aRM2qTOgz1VAMC0X1OCG+/vprCIVCPPLII5DL3TMZ0XfLFWuuQHZdNi5KugjPT33eDQcK7yxyECNJv62FLCnpTOHE98sjwCPAI3AKAl/98xGUZWVizLxLMGMJqQ74Vln99C7UVegw8dIkjJ2T0K3Br3n7EIqOqZm37vxlI7p1jq9W4pId0V2C43Q1OEgwlMRBwxyipK5rRxQepYAlcVHyFOE1OM7kztY3APs/BXZ/ADS6hI0MmAhMvge2QXOwIUvNPDaOVVRBErgfkuBdkEiqkFQODC+0YUyxBCnFFohMbYT7xGIoRoyAcuJERmrIR47kdTTO5FrzffcKgaZaPfPSOJlRgeITBbAY1bBZamG11rIjbPWwWdv3xnB2KFMqERITh5CYAQiJpWMcOwZGREHkoawYvZrcGTzJaiAyQwNjiYYRGsYyDYxVGpQIapEjKkeRsAYWQWtqVxXkSAtMwPDkIYhMjWOEhiiAIiX50hsEuGSY9Gb8fXSOxwkO7b4K1H2XA4FUhLDHxuClV15mKYyvuuoqpKennzKtH3J+wFM7noJEKEHblLEk0J03azbTtAq95WZEPPRQH8HCd8MjwCPAI9A5ApSB5J2brmYZ3i55+P+QMm6CT0HW3GTEyof/ZmO+bPkYxKS6Jv/seCq5+6uw7qOjLJxlyQuToQzsv3YKl+yI7hIcXWVR2Qpgbic7lQgSSu1KWhhVbeqRqhaJjFKoCt3+57OonImPfH0RsOu/wIFVgNEhYigQAkMuthMbsWOx6UQV3tiQg6PVuZAGbUeieT9GFBkwrNCGoUU2+LVzTScbPLiF0FCMHQeRqn+mozwTS8b32TcI6BoakLs/CyczslGRVwhdQyUjMmxW8iLoJKWrQIDA8IgW8oKRGTFxLLzELzDorPYqsOrNMJZqYCISg46lGphrmlvgVAuaGKmRK6qAXtAqyCoRipE2MAWjxo9B0pAUdqebL55BgEuGiWdm5JVWPE5wWDRGlP97N9v7YTcNw/92rWEiuaNHj8Yll1D0rnvRm/W48LsLO0wZW/3uu6h5+x2IwsKQ+tcmCCRnp/eXV1afb5RHgEeg1wgUHDqA75//JwQCIZZ9vBokjO5LJT+jGr//9wiEYgFufX0axJLueZybTRaWecWgM2PywhSMntme2oMvIdHxWLlkR3SX4KAQk0MAfgSw0GVq9wB4y6HB8YXjdQo1oV/UEy71XgdwP4BHAbzs8jrRX5kAyA+TsrBQiAqVvQDSHKRHmeM1MiyoTUore2EPt4LHjZIe9s/d6qUH7GEomT8DNgf8EiUw5npg4p2wBcXjr6wqvL4hC9UlGzG2aRtGllcwUiO4neyWkoEDWwgNvwkTIA4hqRW+8AhwGwGrxYKG6lZNjJoiEvssRH1VGSzG9tO4OmcklsrcvDDsnhlxCIqOgUTaf5n67q6oVWdi3hjMK8NJZqj1p5yugwF5okrkSCtQa3PPFJOYmIiRI0diyJAhkMl4TLuLfU/qcckw6cm4+7iuV2yJqvcyYCxqgnJSNI6HV2H9+vVQqVRYvnx5u0SoW8rYhesgEbWSGCTYnXvhTFDQd9x778H//Bl9DBHfHY8AjwCPwKkIbFv9Kfb8/B0TSL/uhc5kFLmJ3vbvc5HxZxGikgKx8JGxPRrk1q+ycGRLKUhcnrKvUMr6/li4ZEf0BGFnKAmRHL8BGALgXgCUJpbEP52+wwUA4kmo3mXx6O8DDiHSLx3n0JUvBWBREBOJjb7nUp/yBv0FoASAU4WGyBTyACFpcSJbelK8YpT0ZACcqktaGNl/2IVDC2n5HMU/GjjnNmDcjbDJg7Blbw7+/PIHhJ/cjJGVxYhqaHUPd54iDAuFatJkO6kxcQIksbGcmio/GB4BVwQMOh3qyuzCnrVlpagtK2ZCn/UVZSxtWWdFKPaHf2g0IhLiETs4EaFxdo8M/5BQCHhPAgadRWtqJTIc3hmW2lPJjBacQ6UoCahHlqEEBXUldv0SRwkNDWWkxogRIxAU1D1XUH639x4BLhkmvZ+F18/0ii3R+FcxGtcVgFIRi29OxLvvvssmcvvttyM6OvqUSbmmjH1p6kuYl0QJ5lpL0U03Q7tjB/xnXoi4t31PyM/rq8h3wCPAI9DnCKx+cjnKc7MwdsFlOO/6m/u8/9Pt8LuX9qHyZCNGzxqIyZdTsEH3S1VhI759YR874YrHxiEywbeyx3R3plyyI3pCcJAvDnlh3OYgJWoAfAPgnwA0LpNvj+Cgt8mzg+pSdhQiKpoBZAAgGu+HdsCbBIBCYyhIi6xeyrzyuIMo6S7WznpeMUp6OghO1C/aBfx8N6DOaR1O5DBg0t2wJM6B7sAhZP/xF+q3b0FMTeUpQ9YrRMDoYRhw3jz4T54MaXJyv2UiObFe/CB6jABdJDepa+wkhjNLiYPI0NTVdtGeCAJhEASiEAjFIYzASBiRisHnDkH4gNAej6W/n0CEhv5ELXsYi5tgqe9Ae0QAiMMUTCdDHKNEtbQJxypycOx4JgyG1nNIVHHYsGGM2IiLi+O/W/pwA3HJMOnDafe0K6/YEqZKLSpfp3tAQPg9o/D+tx+jvr4e559/PqZNm9buGB/c/CD+LPwTI8JH4Mt5dN+otTSsXYuy5Q8BYjFSt2yGOJT/7urpQvP1eQR4BDyHgLFZx/Q3SCfoskefQtKY8Z5rvA9aMhktWPHAVlgtNsxbNgKJI0hSsvuF7NKvn9uD2jIthk2PxfTFFKTQ/wqX7IieEBy+vBJeMUp8DpDqbGDFhYDBnoHAGj8DzcFzoSs0QLtrF3SHD0NgcUYJ2WdnFAPZA0QQjB2OUfNuQPI5MyE4S0UQfW69z6IBk/dF6YlM5O3fzR4NlRWdzl4sVQKCYNhsdjKDPYQhkPuHIGF4OBKGh2Hg0FDIFOKzCMXuTdVU08yyPjQfV8NY0HiqDAmRGRF+jiwmjmwm0So06Bpx+PBhHDp0CLW1rUQTuWqmpqYyUiMtLQ1i/vulewvh4VpcMkw8PDVPNucVW4KM34r/7AN5OwXMjMc2/RHs3bsXAwYMwM03t3+nc1/FPty4jrTbgbYpY60GA3KmToO1sRERjz6K0BtPTTnrSVD4tngEeAR4BDpDIP/gXvz44jPM2/Wuj7+GzI+SaPpOKc2uw0+vHWQDvvmVqZCreq5tlLGhCNu/y4XMT4ylL53bbQ0P30EJ4JIdwRMcvrRzTmesWjWw4gJYKgtQXxIJrXUUdEezYdO7u49bBEBeNHAkQYDSQZGYOncpLh6yECqpb4kBnQ5U/Lm+gQCFmxQc2o+8fbtx8uA+6LWujmRgP6RBkVHwD4uBUBSMZo0KDTVyO6khVLRMMjjKjxEalMIrKikAQhEvXOm6A2xWG/POcJIa5ipyvmstAokQstRgyFOCWHpWSbQSQqldfIu8MzIzMxmpUVBAzn2tJSoqipEaw4cPZ3oDfDmzCHDJMDmzSHTau1cIDuqxfk0eNNvLIIlToWGWEqtXr2YeTA8//DD82rkY6CplbMWzz6Ju9VeQpiQjac0a3huKw5uKHxqPQH9HYMsXK7FvzQ+IShmEa//9ms9Nd99vBdj9Sz6Co5W45qneZX/RNRrx6WPbQTbVrFuGInUcBTP0r8IlO4InOPrX3mp/NmYDsOpSWHJ3ovCvcBjq3O9KF4UJcCQBOJogQOYAIVJiJuKuMUsxKWYShJRJhS88AhxBoLGmCnn79zBSo/jYEZZuzLWQeFXy2AkIjEpHQ7UChcfqoS5xJz6EQgGiU4OYi2H88FAERfjWnYS+WAqr0QJDTj3z0qDwE6umNZMJ9S/0l0AxJBTyISGM2BC4qIlTisuTJ08iIyMDx48fh9lF24SIDCI0Ro0ahcjI/vfj3hdr460+uGSYeGuOHmjXOwSHzQZ9Xj1qVhxlQwx7eAxeef919tlZuHAh+8y0V5wpY8VCMf684k+EKVrdppuPHkPBFVew0xL+9w1L0c4XHgEeAR6BM4HAF48/gMr8HIy/5ApMu8b3PMrWvJ2BomO1SJ8agxnXUtLP3pW17x1GweEaDBwagovuGdW7Rjh8FpfsCJ7g4PBG8cjQSLTvp2Ww7v8KRZtD0VwjhVUoxLah/shIbGKkRoNSAKFNgfNjF+DBCTdiQMAAj3TNN8IjcLoI0F3KqpN59tCTfXtQVZDn1qRILMaAoSMQM2gMxPJk1JQC5Epo0LoTH+QSGD8slHlpDEwPgcyv5+6FpzsXrp9vaTJCf7zWTmrk1ANmd1FhcaQfFOmh7EGeGpTT3bVUV1czUoPCUJqaWrOgiEQiDB48mJEaSUlJoL/5wj0EuGSYcA+dlhF5luA4+AWw5yMm7m0bsRhlz+2CTW9B0OUp+Cl7I3Jzc5nI7uWXX94uJK4pY5eNWoY7R97ZUo++O09eehkMWVkIunoRop9+msOw8kPjEeAR6K8IGHRavHvTYthsVix8/BkkjOpZBpIzjYvVasPHD26FUW/BhUuHIG3iqcLP3R1j/sFq/P7BEVASlRuePxeq4P6VFY5LdgRPcHR3V/pqvW2vwrr+WZRsDYG2Ug6rAHj7IiG2D7V7ZkitMbgydRHuOWcRlKRLwBcegTOMgNlkQsmxw8glT439u6FRk55xa5Gr/DFw2FiowgbDaIhBRV4zNHWniltS6En88DAkjghlab340BP3haULIHOVDs2ZtdCTnkZxk7uehhCQJQRCTqTGkBCIQ1vDeqgl8tSgH7OcnBxkZ2ejvLzcrQPSDyBSIz09HQqF+7lneIvx3beDAJcMEw4vkGcJju9uBo5+BwyYANy8HurVx9F8uIZ5RuUN1uD3339n4SkPPfQQhB1kanpj/xv4+OjHzHtj/cL1bilja1etQuXzL0CoUiF121YI+c8hh7cWPzQegf6JANlxP738HIQiEe5a+TWkct+yB2pKmvDNv/ayxbn+X5MQENb78VvMVhamoteYMPHSJIydQ4lE+0/hkh3BExz9Z1+dOpNjP8H2zRKU7ghGU4n9A/nhHCE2jBZCaR6B20YuxdIxF3RoOPVnaPi5cQuBZk0T09Gg0BPS1TA2u+s8BEZEIzxhBETSZNRXB6Gh6tTUowp/CWLTghFHj8HBCAznQ0/arrLNYoOhoKHFU8OidsdRIBNBnhbMvDTkg4IhbOPpotPpkJeXx0gNurtMf7sWSudKuhr0CAmhTOB88RUEuGSYcBgzzxIc+VuAVRfbp7tsN3RlIaj9OgsQC6G4Jw1vv/8Oe+uWW25hWYXaK5Qydu4Pc2GxWdA2Zay5rg4506YDJhNi/vMyAi+6iMPQ8kPjEeAR6I8IbF71Efav/Rkxg4Zg8XP/8bkpHtlcgq1fZ0MZKMWSF889bT2jv/+Xg0ObihEU6Ydrnp5w2u1xCVAu2RE8wcGlneHJsZTuh23lfJRvl6GhwH6h98UMIX6ZKESQeSo2LXkbEjHvKu5JyPm2eoZAfUU589DI3beLZUCh9GEtRSBAaGwylCHkpTEQdVUyCOD+dSWRiRAzKMhBaIQgNEZ5SthEz0bUP2tb9Wbos+vspMaJWtia3cN3RIEyyNNDGKkhSwyEQNyqu8OyO1RUMEKDHiUlJaDXXEtoaCjLgkJhKAMHDvQJwpRcZY3GGuj1ZSBfUaVfMsTis1volEuGCYc/iZ4lOOg77+0xQN1JYOJdsE57BmX/2gVYgdAb0rFi42qo1WpMnz4dM2bM6BCWzlLGltx7H5rWr4ffxImI//QTDkPLD41HgEegPyKw6tF7UV2QjwmXLcKUq6/3uSmuX3EUOfuqkDI2ArNvHXba43f1CFn4yFjmYdxfCpfsCJ7g6C+7ynUeDSWwfXg+Krc2oy7HbrT/OEmMr84DBPpk/HbVF4gLPruN+f647FyfExEY5bnZDj2N3VCXFLkNWSSRIigqDQJxMrQNUbDZ3D0whGIBopMCmXdG3OAQhMf7Q8RnPGl32c31BhZ20ny8Foa8esDiTkqQhgaFnVD4CWU9oWwNzqLX65Gfn9/ipeGqp0F1KI1rQkICIzXowUVPDbNZC72hDAZ9GSMx2MNQzo4GfTl7brO5C6fK5XFQqdKgUg6Ckh3T4OeXCKHw7NBr4ZJhwuHvMs8SHDTRba8CG58FFCHA8hOoXpkFQ34DlOOjsFuZh127diEmJga33XZbh7C4poxdPW81hoe3ipJqtmxB8e13sHOTN2yANC6Ww/DyQ+MR4BHoTwiQd+57t1wD2Gy44h//Qvxw3xLWpBs6nz2+A9p6A6YuSsWIGZ7RKPzm33tQU6xB+pQYzLiu96KlXNsrXLIjeIKDa7vjdMdj0AAr56B6QwFqjvmz1jaMkePDWSZYTWH4z+QVmDc05XR74c/nEegWAiajAUVHMljoCWU/0TXUu50nVQRAETgIRv0AWBEHgcDlYlIARAz0txMaaSGISgmExJF+tFudn0WV6EfYVKa1kxqZavbcnT0SQJYcBEV6COSDQyEOahW2onNJINTppVFUVMT0NVwLhZ44CQ0iN6RS6RlD12o1w2isciMu7KRFK5lhNjd4ZHy0H5V+SS2EByNAVGmQyaL7lVspgcUlw8Qji+edRjxPcDRVAK+lAzYLcMVKNNVNQMPakyxTUfOicHz+xedsJqTD0VE6ZfoMX7nmSmTVZWFB0gK8MPWFltnbzGbknn8BzFVVCLvrLoTfc7d3kOFb5RHgEeARaINAzp4d+OXV50GC8Hd98g0kUt8S1WxUN+PzJ3eyWV31xHiED7RfV51uoRAVClWRykVY+vKUfmPbcsmO4AmO092lXDrfagG+vhbqX7aiKsPu8rRvZAD+M1cLq1WBy6JewnPzzufSiPmx9EMEiMTIO0CpXPeg8PBBmI3uAqAyZSQgTIQNCRCI3C8USRjUrqERwsJP5Mru3T03GtXQanOh1eVBq81hF78ikR8LOxCL/NlRxJ6rIBb7uzx3/C3yh1Ao9amLVpvZyu70sqwnmbWwNLjjLPQTQ55GXhohkKcGQyhvTQ9tNBpRUFDQQmrU17sTTyRoGB8f30JqhIWF9Qk2TPjU3OTifeFOXJBHhsFYCRtdDHajCARiRkbI5dGQy2LYUSanY0zL39SWRpMNrTYbGm0WNBr7w2JxTy/s2p1IpIJKNYh5eTi9PYj4kEh819WUS4ZJN5b2TIIxFykAACAASURBVFXxPMFBM/nqGiBrLZB0HkzzvkblK/vY/ELuGIbXv3wPJpMJl156KRPt7aj8mPMj/rnjn2gvZWzVq69B/dFHEMdEI2XDBgg6ECw9U6Dy/fII8Aj0TwQ2ffIBDv6xBnFDhmHR0y/63CSzdldgwyeZkMhFuOXVqR4Tq2/WGPHpo9thtdhw4Y3pSJsQ5XPYtDdgLtkRPMHRL7aUYxLrnkTdFytRsTeIvZA3PBxPzquFRSBEvOle/HzTTRDzLv39acU5MRe6KK0tLWkJPSnLOcHcEVuKQAiJLA42QSKEkmQIRfb9SYVSZDlDTojYULp4FrSdHPVjNFYzAsNOZuTaj9pcmEy1p40F3a13EiKMDOmAEBGJ/R1EiTtpwkgULxMlVp0J+qw6O6mRVQebwf1CXxQqh2IIpXINgTQ+EAJR61c8xfKTMCh5apw8eRIWi/u5/v7+LYQGpXOVyTx/p8VqNcJgqITe4XHREkLS4n1R3imx0HaRJZIQyGVEWhCJ4Upc2J9LpUTM9FxriPaawVDeQnY4yQ+tNu+U0BbXMclkUVAqUx2hLnZvDz+/FIhEnsfytDd8mwa4ZJh4em4ebM87BEf2OmD1VfZh3puBik/UMFc3w//8AfhNvRNZWVkYOnQorrzyyg6nQiljZ343E/WGerRNGWs4eRL5c+excweu/BjKyZM9CAnfFI8AjwCPQPsIfPbQXagpLsSkKxZj8pXX+hxMm1dn4djWUgxID8HF93o2vIbSxVLaWLKBL7l/tM9h096AuWRH8ARHv9hS5KrxCRrfe4xlTAEEUA+Nxb3zKmASCyCuuwzrbnoCEQHy/jJbfh5nGAGrxYKyrOPI3b8b+ft3o668zG1EAvKGECVAJEmCUJIIgdCexYc8MmLTgpiHBhEagRGKUzwDnBeXTvKCERoOMsNsbux05nJ5LJTKFMjlA2CzGpk3gNmigdmsYRfO7G/Hc29BaCdK7CRIC1Hi+ndHXiSMUCE9DPK0EDpwEcBCeho5DTBk19tTuVroa5skV+1Haaw/5GmhzFtDEkHnk0ioABaLFRRukptLWU9yoVY7SSD71z7pblAqV2foSWRk5Gl5adi9L+pbQ0eY7oUzbKSc6WEYjFVwz0Xb8SqQR43d+8LpbWEnLZgHhsMbQyTqfbq23qy/1WqCTneSeXpoydODvD402dDriztsjggWhSKhVdvD4fmhUAx0rFVvRuL5c7hkmHh+dh5r0TsEh8UMvDEcaCoDpi5HvfF6aLaUQBKlRPEUK3799VfI5XI8/PDDEIk6Juw6SxlbcO11aN6/HwHz5yP21Vc8BgjfEI8AjwCPQHsI6Bob8P6tdlLjqn8+jwFDR/gcUF89uxu1ZVqcc1Eixs9P9Oj4Cw7XYO17h8lcs6efDe1be8ajk3E0xiU7gic4vLHCfd1m/mZoXlqM4q2BgE0AfdpA3LqgFAapAKa6Cfho3guYOii8r0fF99fPEDA261Bw+CDT08g/uA/6JneyQSBU2T006CEmPQ0xxJTpJIUIDXv61rA4VUumE8pkodeXOrwwXL0y8rq4iy+AQjGA3SlX+qUwQoMefiwThrJbqFPfFovWjfAg4sNJiFjYcw3MFgchwp67EyTeJkq6NZFeV7KTIHZxUSJTqCH31+wkiPM1+9HtNarfIk4qgMlUB6v11PS9HQ2RvCuIqGgJGXEhLojIkEhCT4tw6TU0vTiR9gIRcSy8xRHmQl4fhElHRShUQEV72EF4kLcHhbvIpGG9GMHpn8Ilw+T0Z+O1FrxDcNBwN/0b2PoyoIqC4fIdqP4wk01CsSwNb698jz2/8cYbWehYR8U1ZeyLU1/E/KT5LVXrv/8B5U8+CYFUitRtWyEK9N1wKq+tLt8wjwCPgMcQyNr5N35940WIJBLcvfIbiM+gdldvJqXXmvDx8m3s1EseGM1sWE8Wq8WKTx/fgeZGo1cIFE+OtbttccmO4AmO7q4aV+tVZ0P3r1ko2iCHzSKALSkOt11Sgwa5GWZtMm5OeR4PzUrn6uj5cXEcgabaGuTvJz2N3Sg6eggWs3uKUYEoAiIHqSEQhUMkFiIyMcDuoTE4GJEJARCKbGhuLmoJJ7GHlxChkQ+rtblDBOx3vuPtBAYjMlIdREYSRCJueCORfoPFomtDlNhJESJE7ESJixdJ27+JNDE1wmLVcXwn9Gx4dPHeGjLSGj7S6n0RBaGQ+2EbPZu1e217SFVNq7eHg/wgIsRqdddLcT2Twm4Y2aEc1BLqQnu/u+Rdb8fMJcOkt3Pog/O8R3DUFQJvjmQeTrZFq1H+bQisOjOCLk7GqoyfUFVVhSlTpuDCCy/sdJotKWPDRuDL+V+21LVqtcieOg02nQ5RT/0TwYsX9wFcfBc8AjwCZysCG1a8h0N//sY8N8iDw9dKwZEarH33MIRCAW55fRoksp6Hu3Y15+3f5yLjzyIEhMlx3XOTfOamTkfz4pIdwRMcXe0+Lr+vVaP5hRko+skAq1kIYWwkll9tQaG0HlZjKNJt/4evbzkfIuHZssxcXizfGltDVSU2fPweCjL2txm4EELxAOalIZImQyDyR/gAf8Zsx6T5Izi2HgbTSTedDJ2OiAxjJ0SGBH5+Ce14ZCT0q4tgq94MU6UOpgotzHSs1LK/rRoTbLDCKtbDKmoGBFZyxII4VAZZSiBkyYEQR/tBILDRpQ+0Wg2KigpRVFiIkpJikGAoBDaHf4UNMpkUcXGxLPwkNjYaCoUC5LFiDw2xtTyntuxaKfTM6vLc+bqVvUN12JE9b33Nfp79NXouFge2kBr03DX1rG/tfu+OlkgxIvwotKXV2yMLOl0hQOvQQVHIBzq8PezEB3l7+CkojW2reOzpjJxLhsnpzMPL53qP4KCBf34ZkLcJGDQXteJnoDtQBVlqEDIGVmD79u2gMLI777yz0ynur9yPpX8sZXXapowte+JJNPzwA+TDhiHxu2+9DBXfPI8Aj8DZjMAnD96J2tJiTL7qWkxa6HuE6s4f83BgXSEiEgJw5WPjvLKU6jINvn52D2v7suWjEZPqWS8Rrwy6k0a5ZEecLVe+3jVK+noHUX9mAwyvzUPhF8WwGEUQhQXjpVvDsQf5sFnkkFbdjz/uWogIf27c6T4TEPF99hwBm9WKfb+uwfZvVsFidtxlFsiYjoZIkgKhJB7B0SrEpmsRMrAW8qByO6Ghy2XaBDabu4eH6whIU4HCSNp6ZJAWgVDYvWwpPZ9R359hNVhgrnIQGBU6mKp0MFdqYWnomOShUYpC5JBE+DFCQz4kFJIwezwmpWwtLS1tyXhSXl5+yqSioqJatDRiY2M7jdPve0T4HruDgMWit4e5MH0PIj9I3yOLpcTtqMQPvA0pKY92p/ku63DJMOlysGeugndtiWM/Ad8uAQRCNM/dBvUPdYBIAOOSaKxabU8X++CDDyIgIKBDBDpLGavbvx+F117Hzk38+WfI0wadOST5nnkEeAT6LQLa+jr89/br2fwWPfMS4gYP9bm5/vDKfpTnNmDkhQMw5YpUr43/2xf2oqqwCYMnReGCJb7tcc8lO4InOLy2Zb3YMLk+r1yKwnd2wtwsgihAia8eHIPvDDthswmhL74RqxZfh8kpZyaW24sz55v2EgJkFOfuzcbGle9CW5fPehGIFVBFDUNgnAyhCfVQBFcAkmIYjMWdpumk8ASl0klk2MNK6EG6Gb3JaOGlKZ92szaTBaaqZuaJYffIsJMalrqOww+oU1GgDOJIP0jYQ8mO4gg/CF3cH7VaLfLySBw0h2U+aW52D+WRSqVITk5mpEZKSkqnFzynPVG+gTOKAOl4OFPXEulhFzfNYTo16UNeQXT0ZR4ZH5cME49MyDuNeJfgMBuB14YAuhpYpz2Bso3nAhYbAhen4p3fPoHBYMDFF1+MMWPGdDq7jlLG0vd8/py5MBYWImTJEkQ+/ph3UOJb5RHgETirETixfQvWvvUfiGUy3L3ya4jEvnUTy2yy4KMHtsJqtmHu7cORNNp7OoZHt5Rgy1fZTLPuxpfOhVTuGa/MM7EBuWRH8ATHmdgBp9mnac1zKHx6FUxaMYRyCXb+81K82vQja1VfcQnuHrsE913oPbbxNIfPn84hBIx6M7J3l2PPr1/BZF4PRZgWilA9/MIBeZCGhUt0VEQiVQt54eqVQdoLdnHK/lFsZivMNXYiw0QeGZU65qFhVjd3mhBE6C9pJTBcyAxhBz9edXV1OHz4MCM1SkpKTgEvPDy8xUuDwk/EYt/9EewfO+PMzYIuVEmgVywOgETS8d38noyQS4ZJT8bdx3W9S3DQZNb/A9jxNhAUj2r/L2DIaYDf6AhsEBxCZmYmhgwZgkWLFnU6bbeUsSOX4c5RrWEtNR9+hOrXXoMoOBipWzYz0VG+8AjwCPAIeBKBPz98B4c3/oH4EaNxxZPPebLpPmmrPLceP7xygPV148tT4Bfgve9JEjP99NHtsJitOP+GIRgyObpP5uiNTrhkR/AEhzdW2Ittmnd9iaL7noKhQQKBRIiSf9+BB+o+ZD0a6yZivOoWfHbTObzuhhfXwJebdl4YlRcdRGH2HjQ2ZEAakAepqmOvA9JTcHphuGYukcmi+pXOgs1iY6RFW48Mc40esJIGRftF6CeG2OGJIYnyY6layUNDpOz6joXJZMLx48dx8OBBnDx50q0DIjCSkpJavDSCg307NtOXPzdnw9i5ZJhwGG/vExzV2cC74xkEzeM+hvrvSNB3TMV8GX5Z8wvIe+uRRx7pkuB888CbWHFkBcIUYVi/cD0kIvv3kamyCrkzZlDsG2LfehMBs2ZxGG5+aDwCPAK+iMDK+29DXXkZplx9AyZcdpXPTYG0N0iDIyjSD9c+M9Hr41+34ihy91UhJjUIly3v3EPP64M5jQ64ZEfwBMdpLGRfn2rJ+RtFS2+EXi1mGR3NLz6CpQ3vwGg1wqxNgbLuDvx+73kI9+/f2Qn6Gndf7c9qNUGry4OmKRNNmkw0NWaioSETNjS1OyXSm6SsJQEBw+CvSoe/fzpUqsGQSsP7F5FhtcFSq28JKWEeGRReUq1j7uAdFYFc1L5HhkrSY3xIR+PAgQM4cuQI9PrW1Kr+/v7sDi2FniQkJEAi6Zok8dX9yY+bWwhwyTDhFjJuo/E+wUHdrZwLFO2ANfVilB25jQ1AcX0S3v72I/Z8yZIlSExM7BSmCm0F5nw/BxabBW1Txhbdfju0W7ZCNX06BnzwXw7DzQ+NR4BHwNcQoOx7H95pFzpe/NwriBk02NemgLXvHkLBETXzpiCvCm+XomNqrHn7EOuGsqkEhts12HytcMmO4AkOH9k91so8FF89H7pyAShdgvKZh3CD7XOo9WpYDWFoLlyGL286H5OSQ31kRvwwPYkApSKlOH1GZDRlQqOhRw5stvaFLSnrTnOtFM1qOWz6MIyacRNSRizweipKT865q7bIW8VSb7CHlDjDSyi0pEoHm6nj0BuBVGj3yIjwA/PIcHhnCAOkPSYyXMdIOhpEaBCxUVFR0fKWUCjEoEGDWFw96WqIRJ5PRdYVVvz7PAJcMkxcVuNxAHQ7ayzpYgKgVDMJPVitWAA3AJgDgBQ1KZ6nAMBvAF4EoO5BW1TV4wSH3mJluXP8RC5hfRlfAT/dAQglqPL/FsZKMVTT4vBN8XoQOTp58mTM6obnRUcpYxvXrUfpffcBQiFS/voLksiIHsLAV+cR4BHgEWgfgcxtf+H3d16FRK7AXR9/BZGPhdParDZ8/NA2GHRmnH/DYAyZTF/73i1Wqw2rntgBbb0B4+YnYMJFSd7t0Eutc8mO4AkOLy2yJ5u1aepRcuU0aE6aWLOhj9yBu8L/xonaEyxjirbgLjw4/VzccwGvu+FJ3LnYFl20U1YFO4lxnB2J1GhuJru//WIxKKGvHwh9XSx01SZoK0qhrxODcpGOmr0AU69ZAqncN9nitjMmvQzt7nLoMqoZsWEzWjpeRrEQkggFIzCY6GeUndQQBckg8FBqZcqAUlBQwEJQKH7eYmkdT1hYGEaPHo2RI0dCpVJxcbvxYzqLEOCSYeICO7lU1QKgYGgiORp7SHDcAeBNAGsB/A0w97VzANDtRWIZKRaklW3ser09SnC8lF+OT0prsDwhCrcOcBGxM+qAVwcDhgY0JyyH+sQMiMMVyBzdiK1bt4L0eO66664uR+uaMvbLeV9iRPgIdo7NaETO9PNgqatD+PIHEXbrrV22xVfgEeAR4BHoDgLr/vsmjv71JxJHjcXljz/TnVM4Vcc1dSuFp1CYSl+UnT/l4cAfhfAPkeP6f03ymB3aF2N39sElO4InOPpy5XvRl81kRNni6Wg8Ws/OjrjtCjw3ugkbizayjCnNRTdicuwkfHbjORB66KKsF8PkT/ECAjabBTpdASMwNE3HW7wzTKaObzrKpLGw6hOhLohAY1k09HUDYW4OhlReD4t+PbR1pWykQZHRmH3HfYhLH+aFkfd9k6SfodtficaNRbA0tNETEQkgCVe06mRQ1hIiNULkXvsBaWhoQEZGBiM26uvtn10qFHIybNgwRmyQUKhAcLZ8Bff9nuB77BkCXDJMXEZOt7HsaZ2AowCICeyJBwflJqQvzLYkxi0AKN7jVQAP9QApjxIcj2QVY1WZGql+Mmw9Z7D798Ha5cDeFbAFpqC08nXKawXTtVH47Psv2XDvu+8+dKXLQ4T4Vb9exW6GzE+az0JVnKXi+edRt+pzSBMSkPT7b/x3UQ82AV+VR4BHoGMEVtxzMxqqKjHt2hsx/uKFPgfV0a2l2LI6Cwp/CRMY7Ss7rb5Shy+f2sXwuvj+URgwOMTnsOOSHXG2WNceNUr6aseRcVJxy3zUb7eLD4Zdfi6+umIEPjpij8OljCkhlvOw9t6pCFPxuht9tS7e6Mdi0UOjzWrVy2g6Do3mBKxW9/Sgzr4FAjET/iStDJVqCIxN8Ti5V4W8fc1MidlZYlJVEIsOInfPb7CS94BAgLHzLsG5i66DRCZ3m0qT3oScKg1yKpuQVaFBTlUTLFYbZqRFYPbQKAwM7RsWuyf4kith8+FqNP5ZCLPaoWUhFEA5LhKylCDmlSEOlUPg6v7dkw56UNdsNiMrK4uRGpTilT6/zkJkBpEaQ4cOhUzGf1Z7ACtftY8Q4JJh0sGUe0NwdISev8MbZJ0jfKW7KHvUljjapMOF+7JZ3z+PTsGEIBdPrvLDwAdT2Xs1oteg1w6C/9x4/HfXVyxt9Pz58zF+vF2MtLPSUcpY/YkTOHmpPcVw/Oov4ddF6tmu+uHf5xHgEeARaKyuwkd338SAuPb51xGV7Hue5X9+cgzZuytZalhKEduX5fuX96MivwGDJkRi5o3Ez/tW4ZIdwRMcHN47VQ8thfrX3WyEIeelYO/Dt+Hxv59gfxtrJ8FUdQm+unUiJiTxuhscXsZThmY01trDS1r0Mo5Dq80DWCT2qYXSsZLYJ4l+OsU/idwwG0XI3lMJYpvVJZqWE6UKMQZPikJkfDN2/O8DqEuK7HsoJg6z77wPwQmDkFulQVZlk53MYEcNSuvbJ1OcDQ+JDsCcoVGYMywKgyJVfcZqt4cJywaTWYvGPwtY6lZWBGDpFAMuGAhxaN+F3FRVVTFdDUrxqtM5xgJAqVSy8BMiNsilnC88AlxGgEuGSQc4eZLgINW4TACrSLOzB+viUYKD+p27LxsHm3S4IjIY76THuw/lg+lAeQYMwQtQXX4HpImB2BaWw7R8SLfnmmuu6XLoBosBM7+diTpDHZa1SRl7cuEV0B87hsCFlyPm3//usi2+Ao8AjwCPQGcIHN28AevefwNShR/T3xD6oKbYqid3oEmtx7lXpGDUhQP7dMEz/y7DX1+cgFgixNKXp0CmEPdp/6fbGZfsCJ7gON3V9NL5NS88iurPfmGtB44KQdUbb+HmP2+zZ0zRpKC5+EY8PDsdd81I8dII+GY9gYDFYkBd3Q40NGa0eGcYDB2HfMukkVAxImOI45gOhYJCGVoF6Cg+8NiWUpzYXQGTvlXTIXygP4ZNj0XiiGDsXfMNdv3yE2olgaiThkKaPgm60HjkVmtRVKuDi3PBKdOM8JdhUKQ/e+jNFqw/VokajXvYR2KYknl1ENkxIjawz8KjiNgw5NajYX0hTMWt2WAUw0IRMDOe6Wn0RaHMJ8eOHWPERmmpPeyHCrkypqSkMMFQugDhBUP7YjW67oMEvKqaDIzEY4+6ZpQ5nlttNiSFqZASoUJyuJIdQ89CjzguGSZ9QHD8D8CVAC4AsKmTHUSeHvRwlkjSA6HPfEyMZ4TnVpep8WBWMWRCATImD0WwxMWg3bcS+PUB2ERylGk/g02ohPoyFX5c+zNLE/voo492K9OSM2VsqDwUf17xZ0vK2NrVq1H57HMQ+vkhddtWCJV98/3Z9SeWr8EjwCPgiwj8/u5ryNy6CUljxuOyR5/yuSlo6vT47PEdbNxXPDYOkQmkS913xdhsxieP/A2zyYoZ1w1G+hTP/M701Qy4ZEfwBEdfrXoP+qn98A1UvvYBO8M/RQrhJ//D4k23oVZfC5sxHJqTd2J6Sjw+WTq+zy4sezD8s76q2ayFunYLqqvWoUb9FywWbTuYCODnl+TwyhgCFUvLOgRSaVi7+FHYSf7BahzZUoLy3IaWOiKJEIljwxEwIgQ1IhsOHD+JXfszUW5RoEESCJsLMdK24RCllHliOMkM+1GFID+pW1UKUzlYVIc/jlbgj2MVKKlz9/SIDpRjVnokZg+LwjkJIRB7KRzEUNCAhnWFMJ5snb88LRgBsxIgjfW+SCeRK0VFRSwEhcgNk8ku+kuFYuHJU2PUqFEICOjZD2JFgx4782uwM0+NQ8UNUEhFIJIpMkCOyAAZIvzliAiw/02vB/tJ+c99m82sN1laCAtGXNQRkaFHab2OERqEsamTFMBtPxvBfhIkhztJD/uRHrFBin6LPZcMkw5+RDzlwbEcwCsAPgRwexc/WE8DOMVK9yTBobVYMHL7MWgsVjybEoPbBrhkNNE3Aq+mASYd6q13QWOcC/mlA/HOH5+wYV933XWMUO2quKaMfWHqC1iQtICdYmloQM7UaUx0NPr55xF0uT1khS88AjwCPAI9RYBspI/uuglN6mpMv/5mjFvge98nOfsqsX7FMYilQtzy+jSIvGTPdoatM0QmKikQCx8hbW3fKVyyI3iCg2P7puHb1Sj7v+fYqJRxNoR+8QOW7PknsuqyILD6oSl/GSIVcVh775Sz8i4jx5arZTgmUwNqajahqvoP1NZug9Xq6vEgRID/MPj7D231zlClQSTqWtOiUd2MY9vKcHx7GbRNRtQLbYzI0ASIYAiTotJqQUGtttOLtwC5GGlR/kglAiNChUFRdu+M3ui20A/YsbJGrDtWwQgP0uxwLXRhODM9knl2nJsSBpn49FOeGks1aFxfAH1WXUtX5KodODsesoRAr2+JpqYmHDp0iBEbanWrwCvdQU1PT2fERnx8PCjda3cKecPsylczQoMe+TXtEWAdtyQRCRCukiHChQDpz0QI7bl6nanF+6KVwGj1wqjRtJ8OuT0UaY/GBisQE6hgR/JmyqvWIK9Kg7IGh45LB/DLxEIkhbd6ejhJEPJokktOf693Z/94qw6XDJMO5ugJgoPERYnYoDSxZH23spTtd+p1Dw7q9tGsYnzWkdjoT3cBGV/ALE1DReOrUIwIw4+67SgpKcGECRMwd+7cbm2J5ZuXY33hegwPG47V81e3nFP64HI0/vYbFOPGIuGLL7rVFl+JR4BHgEegLQL1FeX4+D57RqbrXnwTkYnJPgfS1q+zcWRzCWLTgnHpA6PPyPiLT9TilzcyWN/XPD0BwVG+41nHJTuCJzjOyPZtv9Om9X+g5L4HABugCDch7pPP8GDRN/ir+C8IIIK28EZAn4qvb5uI8Qm+p67LIag9MhSjsQbV1RsYqVFXtxM2m7mlXYFAgpDgSQiPmIPwsAs69MxobyBmsxV795Zhy/YSZJY0oIZIDaEVtSIbzJ18YiVWI0KMdYiVmTBt8hiMGZLAiA266+8tFWi6MCSyY93RChwqafWsoHmpZGLMGEwCpZE4Ly2C/d2TYqrUMvHQ5qOtpIIkToXA2QlMQNRbc6IxUjrXnJwcRmpkZ2e7CYaSazqRGpQNRaHoWuujQWfCrpOthAZpnrQtflIRzkkMYZ9ruqCncIrKRj0qGw2objKgqqlnHgjUPhEh5P0RzrxB7J4g7OjwBOGKR4jZYkVlk8HugcE8L04NI9F1lu7XBUyRUICoADnztIgJkjMCIzbIjz2PC1YgOlABZSf7UGswI79aywgP0qmhBz0vUHdOIlIynAHBfm5hLvaQl1M9onryGejLulwyTDqY9+kSHKR8twLAegCXAGiTbqlbaHtcg4N6PaZpxgV7s9gAfhqdgomuYqPFe4CPZ7L3Kg1vMKIjZ7oBm7duQUhICO69995uDfxA5QEs+cMuN+KaMlazfTuKbybeByybiiwxsVvt8ZV4BHgEeARcETiyaT3Wf/AW5EoVlq1YDUE3b/pwCcVv/r0HNcUajJufgAkXURKvvi8knr/qHzugqTVgzJx4TLrUd4giLtkRPMHR93u33R61O3ag+NZbQOkuZcFGxL/5b7wtqsDHRz9m9fXll8JUPxGPzhmMO8/znc3OEXg9Ngy9vhzV1etQVb0e9fV73YRBhUIZQkOmMVIjLPR8SCSdhyrQhWx5gx7ZDpFPIjOOnKxDUVMzOrsfLZcIkRrhj+QwBSSlx6E/ugMhploE2vSYvPBqjL/kCojEPSMTPAEQXZiud3h27C2ohbU1iQikYiGmpYYx3Y4Lh0QiWOkeBuPav1ndzNK96g5WMbKPijjSD4GzEiBPD/EqsVFTU8NIDfLY0GhavVOIyBgxYgQjNqKiojqFS2MwY+/JWuzMV2NHXg3zeGmreUKeAOMSgjEpKRSTksMwIi4Qkk5cIUlDoTnjWwAAIABJREFUok5nbCE+iACpchAgRH54mwghMoQ8H3pDKumMZkZeUGhTmSNshB0dZEZFo55l6+lOUUhEDtKCCAwFIy3sZIbdGyPSX+aVECmTxYriWp2D8NDayY9qDfKrNGgytBKb7c0hTCVlXh9OwsMZ7hIdIOdUuAuXDJMO9sLpEBxOcmMDZeCjn9Tu7Ld26niF4KB+nGKjCyOD8a6r2Ch9ebw3Eag+AY15HurNy2C6NByf/fE1G94999yD0NCuhcbp92bRr4twvPa4W8pYm8WC3JkzYS4rR+httyHiwQd6CQ1/Go8Aj8DZjMBvb7+C439vRsr4ibjkoX/4HBSGZjM+fnArs9cuvncUBqSfuRvJu3/Jx77fCqAMkuGG5ydzylbobGG5ZEfwBAcHPoLNGRkoXHI9bAYzpAEmxD+9FL+njcQTjowp1vpzoS2/CDPSwvHxkrNTd4PCId7elINGvQkSoRBikQBioZDdpSbNB7FQwC4Q274uEdL79jr25/Y6zjbYOY7XWVuOtlueCwWwmGugbToATdNeGJpzIBJaIBLYH1KxDGGh4xERNgXhoRMhkyhPGZNQAFRrDCxTSVZFE0u/yo6VnV8ckcN7fKACwxKCkBYV4NDKUCEu2A8lRw9h/YdvgVJyUaFUXLPvuA9hAxM4sKPBREk3ZFYy746/c2vcQmjoLvvEpBCWkWXW0CimLUHF0mBA46YiaPdWwsmOUIpXEg9VjAiHgID0QjEajcjMzGSCoaSx4VqSkpKYYGhaWlqHYn7NRgv2F9YxHY0deWocLmk45YKd9tPoAcGYlEyERihGDwzySPhOWzhOIUIa7d4fRIC0HBv1bD/2RJOC+mnrEeL0AHF6hBAObt4XDXaPjDpdV1EArbMIVUrdCAwiL+xeGPZHUC9JFi9sG9ak09uGwluI8HB6fNCRMO+sEFmTHKG0a31Q2ItD5yMhVMkIwb4uXDJMOph7VwQHyd1T3B+lpHLddEsB0J2CvwBcBKDzdFGdA+81gmN1uRoPnrCLjR6cPBQhrmKjO98D1j0Oq0CJ8ubP4DcpESuyf4RWq8WcOXMwceLEbm0X15Sx6xeuR7ifPbtT9Vtvo+a99yCOiEDKX5sg8MHMB90CgK/EI8Aj4BUE6LfwwzuXQFNXixlLb8OYucQj+1YpOqbGmrcPgTwySX9DKu/7G4VOxBqqm/HF/+1kf150z0gMHNo1ic0FtLlkR3jnioELKLuPwWtGyelOVZ+VhcJrroZVq4fEz4z4e6fg2Nx7cdP6m2GymiAzDUFN7nWIDlRi7b1TQcKQZ1OhC+WnfjmGtYfLfXba9GXZWdYSoQ0ItgoQZhEiUijCqNRQzJg6AMPTQk+5G23QabHli5U4snEdw0MkkWDyldcyMSeupuMiUuqvE1WM7PjrRDWaTa2ZX2gOo2MDMV0iw8TiZsRZ7F9JokAZS/fqNzYCAi+IPNGPMQkFkrcGpVwkksNZAgMDmVgoeWsEBQWdsu8MZgsyiuoZmUFeGvTcaHFP8UskzvDYQEx2EBrj4kOYeChXijeJkI7mSERiVKA9fMSVtGB6GA4Cw9d1LFzn3qQ3IY/CXdqQH4VqXaceK7R3Bob4MeKDCBAiP5j3R4QKAXIJ64L27wmtHgPkUqg8oHNDbXLJMHHB8XrieR1/3wOAfgBfdfxdCOBzl7qbAUwHQDEWBY7Xycr+EUAjgEfaITfITeunHnwuvWZLkNjoqO3H0GSx4pmUGNzuKjaqVQOvDQYsRtQaH4AhaD52pZYwT7Pk5GRcfz3B1HVxTRl758g7sWzUMnaSsaQEeRfaw2AGfPgBVNOmdd0YX4NHgEeAR8CBQG1ZKT55wK7ZfMN/3kE4R2629WSBnF4TlJHwqifG9+RUr9T98dUDKMupR8q4CMy+ZZhX+vB0o1yyI3iCw9Or24P2jIWFKLh6ESx1DRDJLUi4fiBqbl6BxeuWsIwpSkE0Kk7cBjH88M3tEzE2/sy5S/VgWh6pSgb8zxlleGbNsZY7wFNTwzB/eDTMVhsobp+OdBeanpvcXrPCTK9brae+b7G11mXP7XXJBd1oaobBpIXBpGdtW21CmK0iWGwiWG1ix7H3HxlyQIgLVCDcJoRcbUSIAQi1CBFiFSA6PgBDp8UidXwkJB1cCJ88uA/rP3oHGnUNwzh60GDmtREaO8AjmPdFI5TtYmt2NcvGsjGzEg16d/f+FKEIs1LCsWBmMobEBfYqJKKzedAdz8OHDzNio6rK7v1ChdK5Dh48mJEa5LXhKhhK++twaUOLKOi+wlroTe6EBpFY6dEBLYQGaWn4Oy5G+wJXb/XhJEKcHiBVLp4gpBHiDJUhjxCpSNhKXLiQFhRGQgQGaYDQxfvZXoxmK4pq7WEuRIC4an10pTUSoJRA4i9Fk1wArVyEf4xPwJ1D4zwCKZcME5cJOUmL9ua4BcB57dR1JTjazYLicg6RJD1xe/MawUFjeiy7BJ+W1iDFT4Zt5wx2//777ibg6PcwWNNRbXwZ6gV++HHDGvbdRelipdLu3fx468Bb+OjIR6CUseuvWA+pyH5e4ZKl0O3eDf/ZsxH35hse2VN8IzwCPAJnBwKH/vwdG1a8C4V/AO788Auf1N/46bUDKM2ux4gZcZi6aNAZX7jjO8qxadVxiMRCLH3pXMiV9hscXC5csiPOFmvTq0ZJbzabqbwchYsXw1RRCaHUivhLZbA8uBbXb70POXU5kItUqMm6AzZTGJ6YNxi3TTt7dDcopeOTPx7BxhP2C1DKAvKPBem4cmycRy94bTYLGhoOMpFQSumqN5S5LaVcHouI8DkID5+FwMAxEAiEoAs+JynCiBEXgsRJvDDSxYVcMRgt0BRp0HioFuo8upFoL5TilQiNYdNiO821rddosHnVRzi2ZSM7TyyVYcrVN2D03AUQCrnjFdDdz4GV8NhehrotxTigN2ALTNgGM9ROwQ1HQ/GhfiyMhdLPjooL6nUMotVqRX5+PgtBOXHiBOhvZ4mIiGAhKKSv4ednz2pDehDHyxuZfgZlOdlzshbadkQuKaXu5OQwTEwKZSE3bdPrdheP/lCPCEkqvdHo6A/z98QcnJo8ToHT7Ip6nCwrg05dBqWxDmFoQKig0f5AA8IEjdiZeCWeuulOT3TPVQ8Oj8zNg4141ZZwFRv9cXQKJrmKjeZvBlaRNipQYXgfwhnn4P2dXzJvnsWLF7Mwuu4U15Sxz095HhclU9QO0PDLLyh75FFAIkHq1i0QBwd3pzm+Do8AjwCPAH594yVk7dyGQRPOxUUPPu5ziFjMVqx4YCvMJitm3zoMKWNd0nWfodkY9WZ88uh2mA0WTF88CMOme+ZmhjenwxMc3kS3/ba9apT0dDrm2loUXnMNjAWFEIitiJ9tgOzRP3Df0XexuXgzRAIRjCW3QNeYiAsGR+CjG8b1+uKup2M7k/XJUPt6bzGeX3u8RbhvVnok/nXpMJb5wRPFajWhvn6PndSo/hNGY7Vbs35+SYgIn82EQv1VQ0/7gk1dpsGv7xxiasjOEhTph6FTYzB4UnSXjGzu3l2MFdfW21OkxqUPw6zb70VwFG1p3yo2kxWa3eVo2lwMq8YeIi+QiqCaEgPllFgcqm5iqWfJu6O41j1MnrJ/kEApER6UbYS0VLoqdXV1yMjIYN4ajY2txJJMJmMZUIjYoIwoVLIrNS2EBqVwbWzjWUJ1ksKUmJgcyrw0iNToTZrdrsbMv9+PESASyKgBtNWAtsZxpOf2v23aaugaK6FvqoJYVwOVoR4iuHsKtUXnxPCHMXihZ8TcuGSYcHgXeN2WmLc/Gwcadbg8MhjvuYqNEjH79migrgBN5kvRHPMAfpXbdYPGjRuHBQsWdBu29lLGWpubkTN1GqwaDSKfeAIhN3Qv7KXbnfIVeQR4BPolAmS7//f266FrqMcFN92JUbPn+9w8K0424PuX9rNxL33xXCbuyYWy8bNMnNhZgYh4f1z5+JkPm+kKEy7ZEbwHR1er5eH3LY2NzBXUcPw4BEIbBpxXD+VDX+P1uoNYeXQl6y1AczVKi0exWPW19045K+4MF6l1eOyHw0zXgAqJDT5zyVAWknK6d4UtFgNq6/5mXhrVNRthNte7rapKlY6I8FmM1FApUz224lWFjVjz1iHotSYmkJk0MgxDp8ciLi24yznpGhvw16cf4sR28sIGJDI5pl17I0bOnOtzrn82ixW6/VUsMwoJibIiFkA1MQb+58VBpHJ3raYfy8zyRqw7VsnSz7ZNq0pCk5SJhciOKalhcOo2NDc3o7y8nD3y8vKY14ZriY+PZ6QGhaKUNppaNDR25amh1p6at4Y+f0RmTE4JxaSkMKYfwRceATcELKY2ZIUrcdHOc3Pv9C1tEMCsCIZIFQGhMhxwPtLmAskzPLIoXDJMPDIh7zTidYLjq3I1HjhRDKlAgIxz24iNbn0F2PQcLLYAlBs/Q/4FQmz6ezNIN+j+++/v8nfFCUlHKWPLn3oa9d98A1laGhJ/+rHb7XkHar5VHgEeAV9AQF1ShE+X2/V8lr76PkLjfCds2onvwT+LsOP7XASEyXH9vyZzBvaynDr8+OpBNp6r/3kOQmNUnBlbewPhkh3BExx9uFXoDknRzbfg/9m7Drimrvb9ZBIChD1FEHCDAi5UUNxWrVpHXbXO2lo7vu7d+nXXtl//3bu1ah1171m34gIFBBWZMmRvEiDz/zvnBgyKmkBCbjDvTwwk5557zntOkvc+932fp/biRYCjgW9UGRyWfo4djs545zRzF86PPxbJl4dTZY+NSwehj1/7ThMl5QCrYrLwxYGURvLJyWE+WD4xuFWEqkqlFKVlx1FUtB+lpcegUkmbrLREEg4Pj7FwdxsDsbiBw854myE/rYJmbsjrVLB1EGDi82Fw7+ig1wlSzpzC4T9/Qm1VJbMneoVhzJPPwdHDU6/j2dKIaHnXJhaj6tANKEu1qoxcDuz6e0Iywo8SiepjGcU1FOwgmR0JOU3BKREP6OqghC9K4FybBwGn6R1ve3t7ShjqGdgDV0vVjdKtzSlckCwRIttKyk6I0klHF6ZkxWoPkAdIlkVdRbMZFjTToqao6WukbQtNxhOjSOCEEvIjdG58VNi6wtPFB4Fuvgjx9IOTkxdg6wLwTMvozqbApIUubYvDTA5w6JKN/jfIB0v9dFKlqwug+aonOBoVSuWvo2rkZKw6uYnOe9myZSAld/qYrmTs+IDxWDF0BT2sNiEBWTNn0d87bdkM2+BgfbqztrF6wOqBB9gDlw7sxpE/f4bY0QlLf1ljkcDo3p8SkZlQgm4DvTBqQU/WrCb5rCZqKlUldQgb7YfIaZ1ZM7bmBsKmOMIKcLTRVlHL5chd9gykp04RDnz4DKyA4+zFuBQ+HYsPMIopQfZ9EX9hKmFnwDsTeuCJIYFtNDrznIaQ6722OQEXs5mLBHKB+fEjvTCqZ8su5BWKSpSUHKHlJ2VlJ6FW68o0cuHk1B8eHg/B3W00RCJvk00652oZyIelUq6maW6TXwiDs5fdfc9HylAIsJF6Loa2FdqKEf34YvQaMcaivjDIB3LdlVJUHrwBZaGMmTcHEId7UGUUvqvtfX2h26CmpqYxM+NqVj7O5MhwTSpCodoB5M52g3Ghhg+3CsGOCoT5OkLj3BGZUj7OZJQht/zOO+ckS4iUmjRIt5ISlNZmCxk0MWtj83hAUQcUJQP5iUBBIk35v1U2UgKo9Ze1bTIBLh8Qu2mzK5hHjZ0bigXOSNLY44JSjNMKEW7ynVAqcEItj8kIEvO4GOxkj2EuDoh2dqAEk+bYh2wKTMyzMfQ6q8kBDjKKN6/nYuXdyEbXzwFS9qBOFYbqzj9idcl+VFdXY/To0YiMjNRrEqTR9rTtePf0u+Bz+Dgw/QA8xB6UzyNj4kTI09LhPGcOvN57V+/+rA2tHrB64MH0wM6vPqFxa7dBQ/DwC69bnBPI596fr55CXY0Cwx7rhuAhHVg1hwt7MnF+VyZsJUIs+HQwuHqUaJtrAmyKI6wARxvsAo1SibyXXkb1wYP0bJ59K+Dy8DDkTViBOfsep4opvnadkHl5IerqbWj6/W/z+polyG0Dd1DFkl9PZOCbw6kgigLEZvXviDfH94CjrWEswXJ5CeXSKCo+gPLyM9BobqlycDgCuDgPoqUn7m6jIBSaXkc6M6EY+39Lglqpoaluk18Ih8Tt3hf0VPLx1DEc+etX1NVUU38EhPXFqCXPQuLm3hZLYpRzkHnUp1ag8mAWFLlEfZEx215ukIzyg8Dz/iAPCdTJB2RDqQn5nTzXnNVDgHJbX2RrXHC9iije3HsahKxWF9Do6uHwQHDbGGVxLbWT2gqg4DIDZDQAGsUpgKapVPFdpydyulUOYtcAXpASEd3ftX+TtlwuiuoVOFFejWNl1fSxSN5UKYh86fZ2sMUwFwkFNPo5iiHk3p9TxtRLwKbAxNRzbUX/bQJwXK2pxfALKXSYW8M6Y7CzTlpyyn5g/Uz6WoHqT5zvK8DF+Ivo1KkTFixYoPfUdCVjl4YuxTNhz9BjS/9ciaLPPwdXIkGXkyfAtdEv007vE1sbWj1g9UC78YBGrcaPT85FXXUVRj3xDC2jtjQrL5Bi3X/P0WHPXh4BF+/7x6ptOcfqsjqsfjuG3BvHhGW90am3W1ue3qBzsSmOsAIcBi2d4Y3Jmz//nXdRuXUrPdi9dxXchgdA+vgWzD28FGkVaZAIHSEofAFZBbaUd2Pv80PgKDbsQt/wkZnniOSblXhtcyKSbzKkjx1dbPHZ1N6I7Kz/G7au7iaKiw9SUKOiIhbQIeLjckVwdR0Kd/excHMdAYFA0mYTTb1QiEMrr4CUZjh7iTHpP+Gwd753cFhTVopDv/+AjLjzdJw2dnYYPv9J9Bw6wqIArvqsSlQeyII88xaZp6ibMyRjOkHY4c6aQQKGEOJPAmToAhokW6M5I7Kt7u7u8Pb2psSg5NHT07NRGrG6ToGjKcU4kFyAo9eKQOQ27YQ8SkhKMjRI2UkPb4lVprTN3g1mOFF1gRbESGianXG3oTh4A96hgFtXwN6jSeYF5bgg2Rj8+0tv1qrUOF8pxbGyKhwvq8YVqbYcS+e8HWwEiCYZGi4OGOLsABeBactNWuJ9NgUmLRl/Gx3TJgAHmcuEuOuIq5JhiocTfgrWUbJVKaH5vxBwavJRpZyJzGFLsO3UHipt/dprr0Ek0p8rqDnJWGVJCVKHDQeUSnT46n+QjB/fRq61nsbqAasHLM0DxTcysfq15+iwF/7fL3DxYVf2gz7+vHL6Jo6uuUZJ/xd9GcXK2HvH15eQe60cgeHuGPdUL32mZZY2bIojrACHCbcAuYgr+uwzlK1aTc/i2qMaHpF2UD1xEP+J+wLHc4/T9NBQ/ms4liiBgMfBpqWDEdbRyYSjMk/X9UoVvjuchp+Pp4PIqXI4wILBnfDq2G4QC+8d7Gs0atTUXENp2UkUFx9AVVVCk0nwePZwcxtOJV0JuMHjtT13Av2A/PsaRVjdOtpj4nNhEEvufnFE9kbysX9xbPXvqJcx/CBB/QZi1BPLYO/sYp5FasFZ5Xk1qDqYhboURuWFmDDAEY5j/WHTyZH+TeZaUVHRJCuDABsymbZ85bbzkkCdgBcExGgANEhtuUCgH+hXp1DRkhQiMytgcSpfC9xtPYTZUEB5JgNi5Cfcys6QMrLSzZpLEODdG/DqrX0MBexblh1F9vNVaR3N0CCAxrnKGtSpGZncBiNlJ5FO9gyoYcayE0M2DJsCE0PG3cZt2wzg2JBfihe0ZKOXBgfDVfd78shHwIkvoNK4oCRkB365up3KX8+YMQM9e+pfP343ydicZ55FzeHDsIuMhN8fv7exi62ns3rA6gFL8cDFvTtwdNVvNG598qdVrAQH7ufLBqUSkhlBMiTYaCnnCvDvyivg8jhYsCIStreR87NlzGyKI6wAhwl3RfH3P6Dk++/pGZw6S+EVIQdn0V58lX8MK5NX0ucf9n4e648wUpXvPtwTi6MCTDgi83R9Mbscr29ORGoRc3c+0N0OX0zvjb7+d7+Qr6vLR1nZaap+Qh4VirImgxcInOHmNopKurq4DAaXa7402oTDOTi1KZWOzzNAgonPhcLmHhk4VSVFOPTbD8iKZySpRA4SjFj4FLoPHmoxXw6KQiklD61NYlRviAl87SEZ4w+Zq6YRzGgoNSEKJ80Zj8ejYEZDVgYBNAiYweez7w63ed49D/hZiUoJKSnRLTEhJSf1tzKFmniI8GC492AyMxoADa8QwEY/gt+7eZuUnRwvZwCNu5WdhDqIKY/GUBaVnRiye9gUmBgy7jZu22YAh0ylRlhMEqqUaiwP8sHTumSj5Teg+SYUHGhQyn8fuzs6IzMzE+Hh4Zg8ebJBLnnl+Cs4kHUAwa7BWD9hPf0Oqj5yhHKGkTsRnQ//C4FWTtugjq2NrR6weqDde2DHlx8h7cJZ9IgahvHPvWKR8yUknpXFtRg0NQh9xhhfdMAYTlHIVfjrtVNUuCBqRheEjmCnUg2b4ggrwGGMnddMH2WrVqHw08/oKxJ/GSUV5cxcje1CDSX2IjbBfwa2/dsP9Uo1xgZ74ue57Yt3o1auwv8OpuCP05n0piuPy8FTQwPx/MgujdKeDa5TKmtQXnEOZWUMoCGTpd/hVZGND1xppsZYODlFgEsuZsxo5E5u3L4bOLeTkSPt0M0J45/uDaGo+XGR9on/7seJtX9Crr3g7zowCiMXLaXs05ZgytJaVP2bDVl8Ec3MqOTIUO4kR1VHDYrl5RTYqK/XJXe9NSsCWnh5eTUpMyFlJwTksJrVA5DLgMJkoECnxKTwCqBqfj9BYAcQ8KIxK6M34NED4Lce7CRlJyQzgwAa9yo7oYAGi8tODNlVbApMDBl3G7dtM4CDzOut67n4M68EQbY2OBXRvQkArv5jIrg5J1CrikDs4Ddx+NxxENWol19+2SCg/FLRJczbN4+68e/xfyPUPRQahQKpw0dAVVICt+efg/syRgLSalYPWD1g9UCDByj/xhNzUCetwZinnqeE+JZm0sp6/PX6aTrsaa/1hVcgk3nMRiNZ4ldO3aRZ4jPfHsDGIdKS8w4daJkS+e+mOQdpBThM4P2KLVuQ/zYj+2rvU0flYDmj38PFrsOx+OBiKNVKDPKOROrlR5FZXAdfZ1vsIbwbBhJsmmDoRuvyTHop3tiaiBulTBkC4T8gWRshHZgPD7VaierqRJSSLI2yU6iqim9CEErakNITZ+eBcHGJgqtLFGxtOxkUuBltMs10RC7uz25Px8UD2fRV/16ueGhJCPjC5i/WK4sKcPCXb5GdlEjbE0Bj5OKn0TVCf9Z7U87nfn0rymtxY38ycpIzUYoqlHCrUcqthgLNkzWScpKGEpOGRzc3NyuYcT9HPyivy8qaZmWQcpPSVEBzF6ZYIpPaWGJCsjNCAZdAgGsccIy8nwl3RgOgcbayBvW3lZ3YactOCKBBgA1y0WkOtRNTbRE2BSammqMR+m1TgEOXbHRLWBAinXUykZK3AZsWQKPhIrvXZqxMOkmn99RTT9HPXn1NVzJ2XMA4fD70c3po4RdfoOyPPyHo2BFBB/aDwwIiXH3nZG1n9YDVA6b3QGFmOv5+4z/0RIu//R1Onl6mP6mRz5B+sQj7f00CT8DFkq+G0ke2WkFGJbZ8zmR+z3ynP9x8W5eZaop5simOsAIcRl7hqv0HkPfSS+QKHmKPenSMLgW3zxzkjnwLc/Y+hvL6cgQ5BsG39lXsTqigvBublw5GaDvh3SBkj5/uu4Z155gLfyGPi+dGdMZT0YFQynMay06I4olS2VQdg8PhQSIJo4CGi0skJA6hZs/SaG57EBLRk/9cx+XjefTloD4eGL2oJ3j8Oz8YVUolEg7txan1q6GoZ8gHSSrf8AVPwtah7QhQW7LNs7KykJxwGbnXs1FcUwolp/mLT6FQ2CQrgwTXrq6ulPTOagZ6gKh+qFUMuSXPBuAJaJq4xRpJ3aq6eSeYUcl8PjRrEt+mJSYE2JB0MIof1BoNSuRK3KxXIL9ejrx6BeKrZLT8pLgZtZMwB3EjOWhfCTvUTky1F9gUmJhqjkbot00BDjLeh+OuI7ZKhkc8nPCzLtmosh7qz7qCq6xAtWgx/hD5Ua6jESNGYOjQoQZNtTnJ2Pr0dGRMeJj247dqFewi2HnH0KCJWhtbPWD1gNE8ELt7G46v+QMObu5Y8v2fFgn4n9qYioQjOfDp4oQpL/cxmm9M0REBo4naS0WhDL1H+GLIjK6mOE2r+mRTHGHBkbNBa9AmQUnNyZPIIXWrCgVErkr4DSsGr/Ng1Mz6G48fXEwVU5xsnDDb90t8vruETuC/E3tiQWT74N0g6hVvbbuM/ErmQj7U1x5vjKyBE4dkaZxGXV3uHYsmFgdSMMPFOQrOzhHg89mHSOoOWq1SUzLRa2cK6NPdB3lh+Nzud+hSkw+itAtncHLdKpTnM0CInbMLldHq3C/CoM3b1o2lUikO7NuPxKTLd5zahi+Ej68PvLVKJoQ7w9nZ2QpmtGaRKnKAKzsAckc2j6gC6RqHKbkgYEcD6KH7yBcBPGHzbe71Gu1P1Eyf5FykP93XGp6zAUhZ2N0AF7UaKMu4VWLSQAAqu8XTcsfc3Lo0LTEhmRnilpHsEvCiVKFEXt0t8CK/XoGbdXKQRwJmFNQroCCgy12MqJ2Q7IxoFwminO1ZqXbSmq12r2PZFJiYao5G6LdNYgndcf6TX4b/XMuGkMPBxcHBcNMhG1VufAX8K79BqfbEvrDPEZcYDz8/PyxatMigqd5NMjZr5izUJiTAcfIk+KxYYVCf1sZWD1g90L49sG3F+8i4eAHB0SPx0LIXLXKymz69gKIb1ej7kD8GPhLE+jnE7c/C2e0ZENkLsOCzyGZvrJpzEmyKI6wAh5F2giwuDtmLn4Cmrg42zhr4DysAz6sTVIsP4vlz7+N3IMdIAAAgAElEQVRE7gnwuXy83ecbvLVeCrlSjXEhXvjxsT4WiXrquq1cKseHu69g6yXmQt6Gp8bMnucxxGsDuLfd9RcIXODiPLgxS0MkYghWLcFUSjUO/XkFJKWNWK9hBEHtAg636dso79oVnFi7EjevX2WmxeGg1/DRGPrYIojs75RMZcvcCShz+fJl7Nu9F7VyBqRyUzugA9cNHXsGICC6J1w8XC1+v7LC35V5t0CNXEYi2DKMAC7NgB8EFKnMAeTNy/xS0ITwY1C+DG2JiWcwINRPb74BvCCZFwSwoI80C+PW3wS8kN8DvLjdv0TphAAagWIbSgxKgI1ACyo7qVPWgcfhQUCyfIxgbApMjDAdU3XR5gCHLtnoe0E+WKZDNqopuAbOzwxgnt75U6xJK6Kfz0Qu1tbW1iAfNEjGuohccGj6IQh5QpRv3IiC95aDIxKhy8kT4Dmw+waEQRO2NrZ6wOqBFntArVLhh8WzIa+VUXCDgByWZvI6JX5/6SRIVvbDz4bCP8SV9VOoKa/H6rdOU15DIhdLZGPZZGyKI6wAhxF2Rm1yMrLnL4C6pgYCRy78h92EgNTKLv4XX2btxKorq+hZ3uz/Hn7f646MEin8XMTY/XwUJCLjBKdGmIbBXZAL4u2xF/HB3jyU1zK18N2cUzE/eD08xUyGCpcrhKNjv0ZAw8G+JzgcyytdUMpVtE7vhlY1pM9YgvYGNrnYL83Lwan1qyijdIMFhPfDkDkL4O7XyWD/tuUBJLV51+YdSM/NpKcVaHgYoO6C/oMjIBnWEVxb8xK6tqUvTHauqvxboEbOrT1Czyd2BXpMAnpOAuw8GGJNpfy2R/Jc/d1fU8kBZZ3OcaQtea6Zx8Z+dF7TNM+nYrA/hA6AV6+msqxu3ZhskWbMlOCFt40APjZCkMcOIubRR/sj4fMsCqwj3E3Jpck4n38e5/LPgZBDfjXsK0R3jDZ4iZo7gE2BiVEmZJpO2hzgINN4+3ou/sgroQDc6dvIRhUrhkJQm4Ba2xH4Ut4HKpUK06dPR0hIiEEeKJQWYuyWsVBpVPgk6hNMDJoIVU0NUqOG0Bs3Xu+/D+eZMwzq09rY6gESJ5JSXb6eMu9Wj1mGBwrSrmPt2y/RwS754U9I3DwsY+A6o8y5VoadX8cDHOCJ/w25p/ohmya367t4ZCeXgY2ytmyKI6wARyt3bX1GBm48Nheq8nLwHfjwj86DUMIB5m7BVlUZlscsp2eY13MestNGYVfCTcpLseXpwejly1623ru5pb6+iJKCpuVdwLenXHGhoAdtKuLV4dGuOzDUNwYSh+6NZSdOTv3B44la6WXzHk5Q3r0/JSIvpYIOJGJyIPqNuwVYSCvKEbNpLS4fOQjCKk3MM7ALoucuRMdgdmpqN3hUrVbj7IkYHD1+DAqNkj7tr3LDiKCB6DApBHwXy1478+4cANWFwNWdTPnJjRgAOuURts4MqBE8Beg0BOCZGUQi3B93ACgGgCr2Hkx2hnMAQTap63XBi/w6UibClIvoZmIYmnlhy+Wig4gBKrxthAxgQf/W/m4jgKWBF83tU1rmVpFGwQzyE1sYixpF0wyZx3s+jtf6v2aUbc6mwMQoEzJNJ2YBOHTJRjeHBSFKh2y0fucvsLn4GjQaPjZ1+ghXbhQiNDQUU6ZMMdgDzUnG3nz9DVTu2AHb0FB0+meDwX1aD3iwPXB+x2acXPcX/HuHI3LGXHh36fZgO6SdzL5hXR09vfDEt79b5KzO787Ehd2ZcO1gj1nvWg7HUGpsIQ7+nkyzx0mZiljS/M0jcywKm+IIK8DRih0gz83Djcceg7KwEDw7IfyH5sLGUQlM/AaxHUKw5NASqpgypMMQDLJ/Be9uZ0oWPpgcjHmD2H1Hv8EtKpUM5eXnUFbOqJ3U1KTiTH5/rL82FTIlk14e6pGGl4aUoFvH/nB2GQwboVsrvMquQ+ukCuz+PgGFmVV0YFGPdkHoSEZ/mqTmEZKl2F3bGglEHT08ETV7ProNjGI963xBXj62b9iCgmom28ZWI8QQ+94InxIJ287O7FoISxpNTZEW1NgOZJ1qCmqInIAeDzOgRkA0QyJq4SZVqXChUop0WX0T8IIAGeTHkLKRu4EXBMgg5SQkA8PRwjIvDFnevJo8CmaczT9LMzVK6+7kL/GX+CPCKwIR3hEY4DUATmRPGcHYFJgYYTqm6sIsAAeZzMS4VFyokt5BNqqurgK+7A4uR4oMn4VYfdMJYrEYr7zyisHcSM1JxkrPnUf2/PnUn4G7d8Gmc2dT+dbabzvzACFW/2XpfNTLpI0zC+zTH4MffQyegdZ9ZMnLveXT5ciKj0PI8NEYu5RRUrE02/H1JeReK0dIdAdEz7Yc4E2pUFFp23qZEpHTOyNslB9rXM+mOMIKcLRwWyiKinBj7uNQZGeDayuE35A82LoogEHPImfwUszZMwcV9RXo7NQZb4b9gLm/JVDejQm9vPH9nHDWpkZrNCpUVSdRMIMQg1ZWXoRGo6BeKqtzwuorM3G5JJj+7WCjxlsP+WBmRLjBgVQL3d6mh8mq5CCpYCU5NTSFjZCJ9oz0oemWJFvjzOZ1kFUyWR0iBwkGTZuF0NHjwOOz+6JVoVDgyJb9OHftItTajILuHF+MGjkKroP97+AUaVOnW+rJpCXA1V1A8lYG1NCVO7VxbApq3KVUw1KmTrIykmpqqaTqsbJqCm7oA2I0gBcNZSMNmRcPCnjR3PqW1pbifAFTckJ+cmvuJGL2sPWgYEbDj5edaaT42BSYsPi9YDaAo4FsVMDh4NJtZKO1XyyArXQb5AJ/fKIgmRscLFmyBB06dDDIlc1JxpKsxPSxD0GRkwOXRYvg+dqrBvVpbfzgeiDp6CEc+Pkb8Ph8uPr6oygrvdEZnfsPwuAZj7G+fPfBXb27z5zEwD8smkVv7I179mX0HDLc4txEBAN+e+kklPUqjF7cE137m+Z71VSOOb4+BUnH8+DiY0ezT9giWc+mOMIQgIPkHBOY7ikAJP2gGMBGAO8BuAXPNr+a/wXA1Go0byQ3Xveq8F7tybfrlwZuGqMGJaqKCtyYNx/116+DIxTAb0ghxO51QLfxqJnyM+bun4/0ynQ42zjj11GrsHRlFrJKZfB3FWP3c1FwYBnvRm0tkW9lAI2y8hgolZVN3KvW8HC+5FGsTYqATMFwbRCC1PcnB8PDoX2WMBAin53fXEJ5gQxcLgejFvZE534eSDt/BifX31JG4Qtt0HfCZPSfNA02Yv0IEw3cu0Ztnhp7FXv27UGFikl1l2jEGN09CsFTBoArMnOJhFFn2gadycq0oMY2IPMEoMthYSMBuk9gMjUChzEqJxZshNiTSKkSUONEeTXKFE35OsiXQ4CtDS0VoXwXWt4LH9GtspH2nHmh79LWyGsQVxhHMzTOFZxDannqHYc6CB1oZkYDoBEgCWiT4IVNgYm+/jRDO6PGEgWZlUiPK4Jvd5f7EtzVqtQIi0lGpVKFd4N88IwO2ah03yHYnZtO3bFJ8iSSq+wwbNgw+mOo7UjbgXdOvwM+h48D0w/AQ+yBkp9+QvE334Ln6ooux46CY+VTMNStD2T7dW+/jPy0FHSPjMb4516hynIxG9eiJOcG4w8Oh2a7Dnp0Dlw7MJmxVmO/BwiB/vp3GaDzyZ/+goOL5WVtF92owqZPGdW6eZ8MhoOFlWMXZlVh82fM+B99sx88/CWs2DhsiiMMATi+AfA8gG0A9gEg5AvPATgJYBQpt76HdwkRQXNkBOQ58i4hfU7VOb4B4CC6Q0z+/C2LA6CVp9B7PY0alBDirdylT0MWH4+O0TWwdy+jpHqq+Xvw7Ok3cCrvFFVM+W30b1h5hIs9ifkQ8rnY+vRghHQwP++GQlGF8vIzKCsnoMYp1NZm3+FIW1s/uLgMgZQzGCuO2ON8FgN6uNnb4MPJwRjXy1tv51taw6qSWpDUtaqSOnD5HDy0JAQCmyKqjJJ//Zr2e5mL4GGjMHjGHIv4cJeWVGHfup1IKktjxq/hoI9TN4ycNQ5ib/PvSYvZIwTUuLaH4dTIONYU1CDkmt3HM6BG0AiLBjVI2cmZCimOl1XRLI1UWf0dS+QnEmolVR0Q5WQPR4EVILvdSXKVHAnFCQygkX8OSSVJlMRR10Q8EcI9wimgMdB7ILq7dAePywDJbWlsCkzact4GnsuoscSeHxKQdbkUnft6YOyS+5OCvpOai99zSxBgK0RMRI9G4EtZVgf115EQctOQ4zgEf1T2o9kbJIvDUCOSsWM2j0FZXRme6v0Ung1/For8fKSNGAlC3e/74w9wGDHC0G6t7R8wDxRlZWDN6+SSAZi5/DP49mT2N8kISjl7Cmc2rUPZTSZjjRDP94iKxsDps+HsZTnKeg/YkjZO99y2jTi1YTWcvX2w6OtfLdINCYdzcGpTKuxdbDD/k0iLmwPJttvw4XmU3ZSiV3QHDGVJiQ2b4gh9AQ5Sk3BZC0RM09kJBOD4FsBjANa1YIf8QgBAAA8D2KNzfAPAEQAgqwX93n6IUYMS0rm69CZqV4yFnTgbsPcClhzB59fXYs2VNfTcHwz+ADUl4Xh3RzL9+6NHQjB3oL8RptK6LnJyViE17RNotISSDb3x+Y5a+dZIqngitPHFytOZ+PJgCuoUDHY1tU8HvPdwTziJ2UNo0zpv3Hl0eYEUO76Oh7SiHnwhF1HT3ZB6bjvSY2+pXpAaUqKM4tbR/Ot5v/lrlGpc2n4a/14+BRmHuUh15zpi4tgJ8Ivoer/Dra8TD9RWACl7gaStQMZRQM2QsVIT2AHdxjGgRudRgMAyM5pI2cnl28pOFLdJrjrwuJTcMNqFkVTtZGvZWSmm2NwqtQpXy642cmgQToM6FSO53GBE2rWXW6/GDI1Q91AqyWluY1NgYm5f3OP8Ro0lrsbcxJHV18C34WHxl1HgC+4NbF2T1mLY+RQ6vNvJRis/Ww7Huq+h5thghWYx6mFDeTjsWyBN3pxkbPYTSyA9dQr2I0ei4w/fs3iJrENjgwf+/f0HJBzaB5cOHbHgfz/ekYWmVqtw7dRxnNm8HhWF+XTIHC4XwdGjaLmvxN3yVDnY4Pe2GMOmj95B9uV49B75EEY/+WxbnNLo59j/62WkXyxGl/6eGLOYKbu3NLt0MBsxW9NgI+ZjwYrI+35/tMX82BRH6AtwfESUygAM1WZsNPiJRPOEBe04gPEGOo/k898EQNgbScmL7m0tXYCjDIAMgM5VhYFnAowalFClgdWTgewzAN8WWLgXW6SZ+O8ZMmxgQfACjPV+AlN/jIFcpcbEUB98OyusTdKM7+WZ0tLjiE9YTEkPORwBHB37wNUligIaDg7B4HCY4Op6YTVe25yI+ByGX8LbUYRPpvTC8O7t+wunOKcau76NR221AnxBLbz8ryE97nijMopXUBcMnbsIHXv2MngDtvUBBN0tvpSNfXv2IVNVQE/PBw9DuvZH1KOjwLPebb/3ktRVAin7mEyNtMOAmuGhoSYQA10fYkCNLqMBgW1bL69RzpenU3Zy8i5lJ30kYi2gIUG4gxh8rr5fGUYZIus7Ie+zzMrMxgyNC4UXUC2vvmPcXZ27NmZo9PXsCzsCjLHM2BSYsMw1usMxaixRWyPHytdOQ6PWYPyy3gjoff9U70kXU3G+UorJHk74JfgWWXnl7iQ4XBgBLqce+7ijcU4dQpVUiKKKodacZGzV3r3Ie+llgMejZSp8d3dDu7W2f0A8QAjYf146H4q6WgyfvwR9xk++68wJn8OVE0dwdusGVBUX0XZcHh+9RoxBxNQZFpEh+4AsK52mSqnA9wtnQSmvx4TnX6XlR5Zm5HubkHQSnr3o2V0REu1raVOg45VW1mPVmzH0+4NkAJJMQHMbm+IIfaPVA9oyFDGA23OVTwMgt4IN/bZbAGAlSW4A8O5ti9IAcJBI0UELfpwH8KG2PMbQNTRqUEKlH9dMAYqSgRlrcMHFG08efBJKjRLRvtH4cNCXmPz9GWSXyRDgZoedz0aanXdDJsvChdgpUCqrIHHojfDwNeDz7Zv4UaFS4+dj6fjuSBoFZog9FuGHN8Z1N/v4DV1wQ9sXZFRStZQ6qQxQXYSyLg4qBbPVnTy9ETV7HroSZRSOvm8ZQ0dgvPbyghrE/HMYp8sSoeAwuKG/gzcmzZ4KVx9D36bGGxfre6qvBlL2a0GNQ4BKfmvIBMjsOlYLaowBhOSj0LJMqlQhpqKmkUujubITf5GwMUMj0lp20uwCF0gLGgENonRSVMsE5brma+/bCGj09+oPV1tX1m8WNgUmLHaWcWMJANv/7xLyUsrRfZAXRs7ved+pbywow/NXs0HIRi8O7gl3IUNfVp9dBeWvC2HH/xelQj98J5+GkJAQTJ/OcHMYaq8efxX7s/ajp2tPbJiwARq5HKlDo6GurITHq6/CdfEiQ7u0tn9APEAyN0gGB18gxFM/r4ZIjywicuF8+cghnNv2D2rKGPUonkCA0FHjMOCRR2HnZFV2Y8P2yb2WjH+Wv06HsvSXNRa5LpXFMvz9LpOVTQg6iUyspdqeHxORlVgCv2BXTHzOcDDb2PNmUxyh79UaKU8h0JBnM84gRKOPAiD5yjpXBPd1G+HuIIVPQQAyb2v9gpbjIwZAOQCi30OeI8QP5Fv1r/v0TkAR8tNgZNwX8/Ly4ONjpPq+uiog/QhyOvbF7L2zUVlfSRVT1oxbg9c2pWDv5QLKu7F9WSR6+piX/EWplCI2bhqk0lQIBK4Y0H8HRKKmHBpJeZV4dXMiruYzcqiEEPWzqb0xKIj9gfl9d9p9GuSmlGP3D5dQXxUPVf1ZaNQkYQiwdZBg4LTZCB39EOuVUch41TIFsnZfxsGkEyjgatVduEKMHT4aYVH9LAKcae1aGnx8fQ1wXQtqpBJQQwe/5YuYDA2aqTEWsLGsL0FSdpJYzaidEIJQonZye9mJhM9FlBNTdkJ+rGUnd+6g8rryJkon2dV3cha5ilybKJ10sDdMvcLgfWuCA9gUmJhgesbq0ugAx+VjuTix4Tps7PhY+HkUeDxC2Xt30yUbfSfQG8/6M2EZuYtX8vGfcFe9RP/+GY+hQuSHV199FTye4Zwu8UXxeHzf47QvEteEeYSh4MOPUL52LYRBQVQy1hIAf2MtvLUf/TxA7o7//cYLVDElOHokHlpGqPT0N6VcjsR/9+Hc9k2NKnWEzD1s7ARK5i6WWDnD9Pem8Vue2bKeEsWS0qOFX/1k/BO0QY/XzuTj8KqrENry8cT/hli0cmD6pSLs/yWJ8PVi3ieRsHc2b+kwm+IIfQEOou1EbhM0J7a7GgD5FiTwKnNVdX8jgAVhazyszQy5/xEAudJOIoqcAAjdMiMD0bw1q8JiVIADoKnIc/fORUZlBlxELlg3YR3+TVRg+U6Gd4OUdcyJMK8+MfmySUp6DkXF+8Dh8BEetgbOzgMavVanUOHbw6n45UQGVGoNSAb6osgAvDymG2yFhgdF+iwkm9pkJhZjz3dbIa85CY2a2b6MMsojWmUU9t+p16g0qDqbi5P/HsdFdTrUHA2dR7B/N4x7dGKLarDZtEZGH4tcClw/wGRqpB4ElDocCTybW6AGydiw0cVJjT4So3dIy060gEZzZSc8DtDHwa4xSyPMWnZyxxrIFDKqdEKlWwvOIaUsBRqtnHJDY3uBPfp59aOkoBFeEQhyCrL4iz02BSZGf2MYr0OjAxxEsWvVmyQRFpj0nzB07OFy39Hqko2ejugBrjazsGxTChwuT4OAm4ML6I09GIlFixbBz8/wOITEDrP2zMKV0isY12kcPo/+HHVXriBzKkPD1umfDbBtQfnLfSdnbWDRHiCqKUQ9hdjsD7+ET9fuLZoPkSCNP7AH53duQV01c+NNILJFn3GT0O/hKXplhbToxNaD7umBjR+8hZzkRISOmYBRi5+2SG8dXXMVV07nU+Wqh581f9ZDa5yoUqppuU2dVIFBU4LQZ6x5uQHZFEfoC3AYO4Pjc616ymwAGwxYXCI1S8CLsQAO3uM4k2dwKNVKPHvkWZzOO00VU/4Y8wf4ikBM+ykGCpUGk8N88PVM8/NuZGX9jPSML6irunZdjo6+8xrdFnejjHJtpBczKr9dPOzx+fTeCPd7MFIBz20/hZhNq6BWagmuOFyEjBiNwdPnwN7FMjJX6lLLkbI9DseqL6Gcy6yjRGSPiVMmoUs3K4lo42aXy4C0QwyoQcANBZOlQ40QPAaNBEKmMtwaIvNmXBnweQhSdnK6ooZKtxJgo7myk062QkQ7M8Sgkc4OkPDbP3BpiA8VKgUSSxIZQCP/HBKLE2m5oa4JucJGpROidkLS9snnfnsyNgUmLPar0QEOMtfNK2JRmFmFkKEdED2H3P+5t6VI6xB9nlH02hQahCEuDBBbm1yC+nWfwknwO+o5NvhSswQDh4zAyJEj79dls683Jxmb8cgU1F+7BqcZM+D9wfst6td6UPv1wIGfv0HS0UNw9+uExz//rtXAL+HzuLhvF2J3b0W9lIlxbMR29CYU4fawEbP/JlR7WW2SXfP9oplQKRSY+OIbtGzbEm3df8+ivECGgY8Eou9Dt3iMLHEuZMwn/7mOxKO5cPIUY85/I1r9nmuNH9gUR+gLcBiTg4NEhUQbijySPN479Qfv7t352vIUQ1VbjB6UrDi/An9f/ZuO9MPIDzG8wwQ8/N1J5JTVItDdDruejYKdjXkDYF1SUW+vaejRYwXd+DK5Ep/vT8GqM1lE9Y0SBz49LAjPjugMmwfg4qc0Nwf7fvgZhRkJjTvNv3c/DJ+/CK6+ht/pas2HQUuPVZTUonjXdZxOj0UyLwfgkH8cDOjbHyPGjISNjXnT1Fo6L6Mep6gF0v5lQA3CraFggiNqXAEj5UrKT4gKiq2TUU9tqs5U2rKTE2XVOFZehdhKWbNlJ0OI2olW8cTfqnbSuByklDCtIg1p5WnMY0UakkuTUausbbJkXA4Xwa7BjWUnYe5hEJGSpXZsbApMWOxmo8cSZK4XD97Ama3pEEuEWPBZpF4p05MvpuJcpRSTPJzwq5ZsVC1XoeCD/fDmzQOHo8Q2jEGh10gsXbq0RS5tTjK2bPUaFH7yCbh2duhy6iS4tpZJstwih1gPuqcH6qQ1+GXpfEpAOXLxMoSNMVR74O7dk77j9uzAxb3bIa9lPq9F9g7oN3Eqwh96GEKRdR+aenuSzA2SwUHs6d/WWmS5UG21HH++eorOYcrLfeDTxTJiv3utLRFI2PjxBdpk2mt94RVovjIuNsUR+gIc91NROQFgnJ5vrikAtgL4RsuroedhtFnDOEZpy1v0PdaoQcnNmpt4ZMcjNCheGLIQL/Z5EUv/jsOB5ELYEN6NZyLRw9u8d4Flshu4EPsIJRV1cOiFvn3+AY9ng9isMry4MZ4CMcSCfSQ0ayPYx3xvCH0XsbXtCHFVzOZ1uHzkICiyA8IV2QETnnsagX3CWtt9mxyvrlOi6kgOrsYk4DTvGmo4THmFh4s7Jk97BB06WF7tv1EdRzI10g8DV3YwKihynUo2csc9cDgDanQfD9haRqZSbp0cDKBRjZNl1ShX6gpOAaTspK/ErjFLI9RadoIaeQ0FL9Ir0huBDPJ3SW3JXbcb4VAi2Rmk5ISUnzgIzVeepFGpoK6uhqqmBuqqKqiqqqGuqWYeq6ugqq6hjw6jR0Pcr59R3kJsCkyMMiHTdGLUWKJhiBVFMqx9jyG9m/pqX3gH3f/7eFNBGZ5rhmy0ZGUSxBlvQMw7iRvwwUrMxEsvvQSJpGUxyXeXvsOvib/SMtxD0w+BWyVF2tBoaBQK+Kz4DI6T766QYZolsPbKVg9c2r8LR1b+AoGNiJKLmiK7ora6CrG7tuLi/l1Q1jP3R20ljhgweTpCx4yHQGi9uWOq/XF641qc3bIebn6dMP8Ly5SKzogvxr6fL4PL52DJ/w1lhbSqMdbrn4/PoySnBj2H+GD4Yy0rCzPGONgUR+gLcBBdTHK7exsBiHSc8ByAb7UcHEw6A0MaSvg6mPzJO203gAkAegMgpS+3G0l7IBp6lbe9QHg34qnGKcPB0fSW271XxuhBybWya9iUsglvRbyFVTHZ+GD3FTqCFdN6YWZ/82YBMKSi0yGVXteSim6HjY03/j57A+/vugKlWkMJUP8zsgueHBoIwX1IzYyx6c3ZR71MhthdWxC7Z3vjFyKH6wT3wLGY8fYs2IgZFno2GyGQk8UWouDAdcTIk5HOK6TD5XF5iB4WjcjIyBYRybF5znqPrbaC4dK4uhNI/RfQvRtPpI8Dh2lBjQmA+P717Xqf10QNaxrUTrRcGmmyO5PcAkjZiYsEw5wdMNjZ/oEtOyF8GUSmNbUilYIZDY9E6eRe5mPnQ3kzCKjR3aU7BngPgJvt/SU69V1ydX09A1DcBkg0C1QQAKOaABfVzGNVFdQynRKqe5zU443X4bqACJK13tgUmLR+NibrweixRMNIN3x4HqV5NQgb1RGR07vcdwKEbDQ8JhkVShV0yUZrzuWjdsdmuAvfoX18j3kYPGkh+vTpc98+m2tAJGMf2vIQLdv6OOpjTAqahNwXXkT1/v0QhfZGp3XrwGkBiWmLBmM9iLUeIJwtq155BqW52eg1cizGPEkuD0xnssoKnN+xGQkH90KpYPQNiNJKxJQZ6DXyIfAF7I/rTOcd0/S8YfnryLuWjPBxEzFiwVOmOYmJez29JQ3xh7JplgPJdmgvlnA4B6c2pUIo4lGyar6ZOBTZFEfoC3CQPfAdgGe1IMdercrJ8wAIO9YIIuKg3ShZRIQDNGn+DiPBAaGgjwMQcZeNRfKFiKrKdgBXdVRUngBAZAwIb8cmAzelyYKS+JwKPPozw7sxNbwD/jcj1Kz1T5RUNPl5FBXtbSQVtbXvi/d2JGFjLKkMArp62uPHx/qgs4f57lAauH4tak5kxxL/3Y8zWzagtkqLl3FswRcNQkBYNMYtC+CX10AAACAASURBVIPATB8ChkyoPqMS5bvScLUwHWcFqajnKOjh/n7+mDhpItzcjHdhZsi4zNq2pgi4tge4ugvIPAGoGZ8Q03D5kPtFQdp9EqRdxkFu6wKVBiDlHeRHedvvapDnyGtMG93fG9uDOZa0YV6/1f72Pkl+hT5tmjtXjUqNxGoZHaOuOfJ5iHK2pzwaQ50d8KCVnZBUeQJkNJSXNGRm5NXk3UEAqus3D1sPdHbu3AhmEECDABt2AoKhN2/kM1QtlTGZEo2ZE1W3AIhG4OIWINGYaaEFKoikprGNa28PrsQBPHuHxkfHKVMgGTvGKKdiU2BilAmZphOTxRLnd2fiwu5MSNxEmPvhIL3iiHdTc/FbbgkIz06MlmxUVVmP/E/Pwku4BHxuIWLQFzk9nsLMmTNb7JHXjr+GfVn7GiVjZefOI1sLrLm/+CLcnnqyxX1bD2wfHsi7dgUblr9GJzP306/hGdi5TSZGMnOJ4srlw/uhUjLcSfaubhg4ZSZCho+yCAW8NnFUK09CSF+/XzgLapUSk15+C10GDG5lj+Y5vIHvKHyMHwZPbZs92hYzra2RU7JRtUqD0Yt6ousAr7Y47R3nYFMcYQjAQZjpiFQr+SYjrCwk1/cfAO/dpmhyL4CDFG99rO3jt7t4n+SX/aAFQHy1oAY5FwFSCDnp+RasmkmCkkqZAuO/PYm8iloEudthJwt4N7Ju/IL0dOImoGuX9yCQzMRTf8chIYdRCBnfywtfTA81Oz9IC9ZQ70PIBcr1s6dxav0qVBQyBKJcnhAcQTj4on4ICvfFmMXB4AnuLcen9wlN1FBZVofKfZkoSsrBKf415PHK6JkIv8aYMWMQHh4OLpfdczCqayqygau7GVAj+4w2mYs5g4YvQp7vUBz2iMav4j5I11g28Ri/oezExYFmaYRKxOBplRKM6lOWdUYIP29U3WhSVkLADCLNqtY0YOh3DpqkzzeAF+Sx4XdHmztT/cnngyIvD7ILsai9GAf5jeymGRTV1YD67udqkcv4fPAoQCEBz8EBXAcH5lEXsHCQMM9LdF53kDB/29mZ/C45mwKTFvm4bQ4ySSxBhk6yN0gWB7EZb/eHe8f734C4Lq3DUC3Z6MbQIAzVko0WfncJooI/4ShYDSls8b1gGV554+0WZ/k1Kxn7wQcoX7ce4PHgv2YNxH3C22YFrGdhpQf2fv8/XD15FJ6BXTD30/9r8zFWlRTj3NZ/kHTsENQqpoxT4u6JQdNmoefQEeBas4xatSY3EuOx+eN3QPRIl/2+Drb29/98atUJTXCwQq7C7y+eoCDA+GW9EdC7fd0cJKU3pATHt7szJr9gns9jNsURhgAcJthubdal0YMSEiQ/uSYOh64UQiTgYsczUejmZd43fGnpCcQnLKIXft5eU1Fj+waWrb2Ekpp6qpH8yphuWDbM8uUM77Vrcq8k4fjaP1GQdp0243C4cOnYHzWVoeBw7dF1gCdGzu8BLovLcghRXPWxHFSeyEayJgdx/HQoOcwFV48ePTB+/Hg4OJh3r7XZO7c4hSk9IcBGPqlQu2UKoQOSvIdis3Mk1tv3gYzXcpIxAijwORwqt0h+54FDAQXyHOG4IL+TxyZtyHNo2oZ5nWl36zidNtr2t7dh2jPn7u0gxmAnezi0Y8JfokKVU53TpKyEEH8ScON2BRPdNZcIJRS8aAAwujh3oRkZBOC4m2nUatSnpkEWF4va2DjI4uKgLGRKvPQ1jkh0GzBBgAp7cBsACPsGYOK257VABsfWVq878vqOxxTt2BSYmGJ+RurT6LFEw7hITLF2+VlUFtWi3/hOiJgUqNeQH7mYirOVUkx0d8JvIYwiQNW/N1Dz7yV4ixaCAzU2YgL6z/8YAQEBevV5e6PmJGNJGVbWjJmoT0kB38cbgdu2ged4f+6QFg3AehCrPUB4MX55ej5V1xjz1PPoNcI4WWUtmXRlUQHN2r1y4gjIZz8xZ28fDJo2G90ih4LLtaqItcSvpzasxrltG+HeKRDzVhBmAsuzvOvl2P7VJTrwxV8Ogci+fZUxZSaWYO+PibR+Yt7Hg+Hg0vbE6GyKI6wARwvfo7+fzMBHe0gFDShJ54x+hBbEfMaQik6BUlkJe/teSFH+Dx/sTqF8GxIRH9/MDsfwbh7mG6CJz0zqPk+s+wsZcbcSfAL7RoAnjESOlg0meIgPomd304uh3sTDbbZ7yrORUEyzNoqrS3FScBUl3GralgAaBNggAEe7NkL+SoAMkqVBfkoYoKrBpDYuOO45FH87DsYpp3DIucLG1xx4XEQ62yPK2QEDHe0oQHAnYNEAVjQFLNq1T804OZJ1QcpICHiRXpmO1HKGK4OUm8jVdy/jICUkDRwZjWCGUxfKk0GUoO5lhPywLjmZAhkyAmhcvAh15e2UTqDZErZ9wiHq3oNemNFMiUagoiHDQkIzLzjCW/vMjO406anZFJiYdKKt69xkAAcZ1pltabh4IBsuPnaY/d7dqnibTmBzQRmevZpNwdFLg4PhLhRAnleDou8uwVXwIWx555AOP6QP/h/N/Gup7UzfibdPvQ0+h4/90/bD084T9enpyJz+KDS1tZTwtsO339z3/dnS81uPY68HYndvw/E1f0BoK8bSn1dDIGr7C6vbvVOen4czm9fj6unjjaTyRCVv0PQ56BoxGJwHKfvVCFtn3buvIP/6NfSdMBnD5i0xQo9t30Xs3iyc25kBZ287zFmu3+dr24+y5WdUqdRY9cZp1FYrEDEpAP3GtwzQbvkIADbFEVaAowUrSUpShn1xlPJuTOvjS3k3zGkqlQyxsdNRI00BuO7YV/g1Nl8qpUMifBu/Pt4PndzuXnNuzrG39txUGWXTWiQd/RcabQq7d5duiJy1AEknNMhKZFQTCHHb4GmdWRt81WdXoXJXBmQ5FbjIz8RlXjY0HIaIoV+/fhg1ahRELAgaWrtezR6vVgHZZ4Fr2vKTypwmzUpsPbHDdQh2uQ7FeccQqAlxKFHA4XDQz9EOQ53tKS8FyXwgksdWa3sPkDu8hNizQXq14ZEAGbdLsOqOTsQTUSBDF8wggIaXnZfe71VCyFmbkMCAGXFxqI2Ph6aOURfSNZ67G8R9+1HVEXG/vrDp0sXkZR9tvxItPyObApOWz8LkR5oU4CjMrAKpESc2578RcPa6//d2nUqNMC3Z6NuB3njO3xP0/fjZeQiqT8JN+CHtb7XLK5j3/LstdpBcJcfozaNRVleGJ3s/iefCGRLJii1bkf/22/R3r+XvwXk2oUmz2oPiAbLXVr64FARQCBs7ASMXPc2qqZObXzGb1uH6WUYalJi7fwAGP/oYgvpF6P09w6pJtfFg5HW1+GER4d9Q4ZHX3kVQX8sEB3Z9F4/s5DKzK42YcvlOb05F/L85kLjbYu4HA9t8f7MpjnhQrgaMHpTsT8rHbyczsWbxAIiFRPjFPKZLKlpR74K/rn+Ey/kM0dJDwV74ckYo7G3MNz5TeaVeJsWFnVuoLjrRXCdG0hCjZs9Hp9AIKgOVe62cPt//4QD0n9Cpzd/o+sydEMJV7s+C7FIRbnLLKNdGFZcRCCLkoRMnToS/P+HsbWemlDPkoKT8hJCFyprKd2aI/bDLdQj2ug9Fgn03WvdJPqx62dvSDI2hLvYY4GgPMYtLjdrZit0xHQJcnLl5Bkeyj+BE7gmU1zPvt+ZMyBUiwDGAAhm0rMQxiJJ/drDvAC7HMB4ZVUUFzcqggEZsLOquXAG05HK65xb4+UHct28joEH+vl/2R3tfs3vNj02BCYvXweixhO5cSRbf6rdjUFNej4GPBKLvQ0zJyf3svdQ8/JpbDH+REGcG9qCZa+Xb0yA9mwtP24UQaMpwHBEIfWEjnJwIj3vLTFcy9uD0g7Dh2VAw5earr6Fq926a6dRp4z8QdTefTGHLZmY9qqUeyE5KxKYPCb0eMO+L7+Hup9+eben5WnpcUVYGBTrSYxk5ZmKELyRyxmPoFNbX+t1wD8dmxcdhy6fLacn3M3+uh434/sBrS9fJVMep1Rr88dIJyOtUGLWgB7oN9DbVqczary6X05SX+8CnS8s/71syETbFEVaAoyUrqD2GfLGbO2C+ceMXpKV/jtTyAPya/DzKZLx2zbdBlFESDu2nWtyk7pOY2NGJph2Suk+lAtjzfQLy05mUdJK1ET7avLK9zW0xjUKF6hN5lGujTlGPc/xUXOdrCVG5XERFRWHIkCEQtCepM7kUSDvMlJ5cPwDUNy0bSLDvir1uQ7HXbQhS7ZggiagDkOyMIc4OtPzERdD+wLpWfAS1+aHldeU4nnucghoE3KhTNc2UIOnrnRw7NWZldHFiODI6OnQEn9uytVMUFGjBjAuojYujfBp3GIcDm65dG8EM2z59IfBsvyV5plh4NgUmppifkfo0KcBBxnjin+u4fDQXHv4OePTN/noNW5ds9J/QIES7OKA2pQylK5PhwF8NR/5GVMEOKeO2on/EQL36bK5Rc5KxpB1REcqcOg2K7GwIAwIQsGUzuGLLJnpusZMesAN3fb0C18+chE/XHpj94Resn31BeipiNv6NzHgi5siYd9fuiJwxF34h5lVBZKvzSPn3hR2bzUYgawy/lORW45+PLtCuHv9oECRuLedsM8Z4TNnHpk8voOhGNboP9sbIeW1b1s6mOMIKcJhyl5m479LSk7gUvwjHcwdhfcqjUKq5cBDx8e2scAzv3r6C++aUUfg2Nug/cSr6PTyF1n4SmaRd3yagOLuakuwQvo2QoR1MvAqGdU/mUXu5BJV7M6GsqEMmtwhnhNdRC4aPwNfXl2ZteHp6GtYxW1vXljNgxtVd0KQdBkfJZKcQU4OD85IQmqWxz20IckTecBXwackJATSILKqfLRFVspo5PUDIQI9mH8WRnCO4VHSpiZoJj8NDP89+GO43HAO8BqCTpBMEvJYTd5H3hzwzC7JYBswgWRpE8eQOEwhgGxwMcf9+sCVZGuHhVoLDVm4SNgUmrZyKKQ83OcCRl1KO7f/HEOHN+0R/orgGstGH3R3xe0gANAo1bn54BlzFTXjbPEH7O+azDMOe/LRV/rldMrbhJk/t5SRkzZkDKBRwnDoVPp8QwTyrtWcPSCvK8euyhVQ69KFlLyI4eqTFTDcv5SoFOrKTEhrH7NszhAIdvj1CLGYebTHQtW+/RIn7+02ciui5RMjA8uzysVyc2HAddo5CzP8s0uw3p03pwYa58m14WLgiEkJRy24utWSMbIojrABHS1aQBcfU1mbj9NlpWJU0BifzGD3qLh72+HVePwS0Q76NM1vWI2bjWjpPQg5FsjVI1oa9M6OeIK2sx46v41GeL6UkokQppVuEeXSg77Y9CPFbxe50yDOrIEUdTgtTkM1lSjOEQiFGjhyJ/v37W770a3UhkLIH6iu7gKwT4KqZkiliCg4PJ5360iyN/W5RkNm6YZCjPS05IaBGdzsRTa+2mvk8QECGq2VXaZYGATUIMaiu2fJtEdUhCsM7DsdQ36FoTopV39FrlErUXUtBbVxsIyGoqpThD9I1jlgMcVgYbPv1pTwatr17gWvbfu/A6Os/Y7ZjU2BizHkZuS+TAxxqlRorXz+NuhoFoh7tgtCR+hGYbykowzNastGLg4LhYSNAyZorqEsuhb3kTTjJL+M6pzMC3jrbqszA5iRjG3xc+tdfKPpsBf3T54vP4ThxopHdb+2OTR44t30TTq1fBZGdPZ78eRUEQsu7IZFz5TJO//M38q4lN7rWv3c4BToIn9uDbvUyGX5YPIsq0kx9478ICO9nkS45+HsSUmOL0LmvB8Yuad8AVp1UgZWvn4JaqaHXQt0HtV05DpviiAflSsLkQUlbvuMJqejBU/PxRUw0MioZltyxwZ7434ywdsm3ceXkUez7/n90np1C+2DY/CVw7XAr6KsqraXgRlVxLbg8DsY+EYLAcPe2XJJ7nktVLUfVwRuQxhbQeuWrvDxcEKZDoWEu/Lt06YIJEya0qjba7JMtvwH11V2oTd4Bcd4FcMAQpBKTcW1w1GUALT855joInV29aHYGKT0Jl4ghtLKZm335FGoF4grjKKhxNOcoJQzVNSLDSgCNEX4jEOEdQWvvW2JEWrIuMbFR4aT20iWopdI7uuI5OTWCGYQQlNT0c9pTuVZLnGfiY9gUmJh4qq3p3iSxBLl40FV1OLLmKq6ezqf106SOWh8jZKPhMckoV6rQQDYqjS1E+ebr4AuPw4v7Bc2auzF1HwJ6D9Kny2bbkO+w2XtmI7k0GaP8RuH/hv9fYzvyWu7Sp1Fz/DgtUQnYthXC9sgh1WLvtZ8DyZ794z9LUFlUaNHKGmRFyL69cTkeMf/8jfy0lMZFCuzTn8abzl7kbf9gWsbFC9i24n36+fTsnxtotrSlGVnfVW/GQFpRjyEzu6D3cP1AY0ubp+54D/yWhLS4InTo6oRHXtLvO8QY82VTHGEFOIyxom3YB3mjbjq2HB8f7Y5KuSMlXnx5TFcsG9YZ3HaoIJGTnIjNH79HUyBJfeTUN98Hj38r3aqiUIYdX1+ipGw8ARfjlvaCf7BrG67IvU9Vm1SCss2p0NQpUc6pwSnxdRSqGDJGsViMcePGISQkxPLS5Yica3EKKi5vo5kaLqW37n6QuVXy7HHQdRAtP8n3HYoIN3cMcWHkW+35Vh16NmxQmUKGU3mnaJYGIQmtljOSxA3mL/HHiI4jKKjRy60XeFzD101VXQ0CYjQonBBwg8i43m58H29G4YSSgvaFMDDQKuPXxpuETYGJztTfBECis74ACJp/g+DcBrrmKQBDtX10AUBYbVsa+xgV4Ig/sAeJh/cjdPR4hI4e1zitrMsl2PNDIh3lwhVREEv0kylenpqHX3TIRjVSBfI/PgdoFHCzfQwijQwpvjPR7YlfDXRh0+a7M3bjzZNkaYCPIj/C5M6TGxsoy8qQ+cgUKIuKIOrZE/4b1oP7AMgst8qhFnhwA/EkGfqCr35qctPJAqdDh0zi68xLsTSjoygrnT7n6OmFBV/+CP4DuoePrfkDcbu30WyWOR8xNxotzchN0DVvn6HDnvFWf7j7OVjaFAwe742kUuz+nim/mvvhIDi6t03GK5viiJZ+yRvsbDMfYNSgxJxz+W7v3/j6pANUGj7shWp8O2cARnRvJ3wNtzm2NC8H6999BfVSKYh++awPPqepkA1G2IJ3fBOP2io5BDY8THimNzp0dTbn8jSem9Q/V+zJgPRsPlRQI0GUjQROJlRaKduwsDCMGTOGghwWYxoNKrJjURi/Fc5p++BRndlk6MUCZ+xzi8J57+EQBQ1DpJsLzdRwF7ack8FifGOEgRaeikfalpNQ1quofCn5AY8LDlGKIX9zmeeYH/I88wi+9jXySI7hcsGhv/OZNnzSlk/7qFXXIaMmC9erM5BZnQU5RwkVF/RHzdXAV9IRwW7BCHYNhofYoxF4a6izb6we4pBrL+3XB/mdA3j4S2CjrG4EM2Rxsai/lgKo1Xd4RxgU1AhmEFBD0IFdXDlGWE6L64JNgYmO80gqWBmAi1qAgjBLGwpwZAEgqDchtiAgiS9bAI59P3yFKyeOoEP3npj1/ueN01Yp1Pjz1ZOU8X/YY90QPES/90eqtA5Dzl+j/TSQjRb9lAD5jSooXL5DgOwAKrnOcHwng35OtNTUGjWeP/I8JRwm2Vxrx69FN5db6fzSc+eRvWABuWKEy/z58HzzjZaeynocSz2w48uPkXbhDDr27IUZy1vH68K2KRKgI/V8DPZ88wW9uUYkZQdNfzDlj9e88R8UZaZjwCOPYsjs+WxbKr3Gk3KuAP+uvAKBiIcnvhraLm8G3+4Iohqz+s3TkFbK0W9CJ0RMDNTLV61txKY4wgpwtHY12+j4eqUKb2w8jG2JzN3Pjo5S/LV4HII82icSKauswLp3Xqbpj3ZOzhQ5lrjfIk4tzKrCrm/jUS9TwkbMx8TnwuAZIGmj1bj3aRTFMpStuwZFvpRybRx0uIxSBaP4QiT6CIloUFAQK8Z6v0FIFXJcu3oUiuSdCLhxCJ51hU0OybXxxGGPaBQEPgTvzlEY4upElU/MrS50v3mx4XUSRJXdlCLtbC5Sj15HpfIWeMeG8Rk6BgJuDIx5Fzz1bRkaPB5EPXpQQIPh0OgLvgvDnWM19niATYGJjldIVJah/TsJAHmTGApwkPbZlNcY2A1gAlsAjqzES9jy8bt0ek989wccPW7drDj4RzJSLxTCL9iFfr/paw1koxPcHfFHSACqjuWgan8WauxT0V35Iu2m8pE1cAybpG+XzbarrK/EzN0zkVeTR1WSNjy8ARLhre/g4m+/Q8mPP9JjfX/6EQ7Dh7fqfNaD2eOB6rIS/PbMIsrLMOH5V9E9Mpo9gzPiSE6sXYkLO7eALxDSLBXd96cRT8ParupqavDDE7MpUDnt7Q/RqXc4a8d6r4EdW3sNySdvwq+nCyY+r/9nqUVOVmfQZ7al4+KBG3BwEVHlGMJPaGpjUxxh+tma2pv69W/RGRyFVXV4avUZxOfK6GwH+GTitycWwFFs2RdEd1s6hbwem95/i9ZCEqWUmcs/g1cQySxm7GZqOXb/kAhFnQq2DgJM+k843HzZ4QvpxUJUbE+DRq6GlFOHfU6XUVFbRS/4Bw0ahGHDhlFCUbZbdXUpzu1+H2EZO+CmqGgy3FSxPy77joKi28Po0SUCIQ5iKzGonguqUWuofFdGfBHSLxWjsuiWqgzpQqiSwdZGQ1NlCY2Jhv6n/Z0+0mfoa8zzt/5mnme4T+iD9neSXqFpkpFP0y+Ydnc8r8ue0pChwTwyPTd8ZTB9NByvIpwcHC66pG6CX8kZ2PbuzYAZ/frBNjQMPHs7PT1kbWYuD7ApMLmLD1oKcOh2xyqAQ61W4denF4CoUUTNmoeIKTMax5p+sQj7f02ivFKLvoiCjVi/TLitheVYduUG+ByAkI06V8hR+H8X6WcJx/Zp+GpyUeI9DG5P7Wj1VrtaehVz986FXC2nHD3fDP+mEdwmBMI3FixAbWwcCKdOwI7tELQXdbBWe86yOzizeT1iNq2FrcQRT/74F/jtlB9JXivDyheXoqa8DJ37D8TkV96x7IUzcPRpF85ix5cfgcvjU/4NgUhkYA/saL7+g3P0ZlLEpAD0G8/wFj4IVl4gxbr/nqNTnfxCGHy7m/7GEpviCCvAwfJdHnejHEvXxKK4Rg4O1Jja9Rg+nPUSxGL9UlZZPr07hkfuCOz6+jOknouhee/kC6Vzv4jGdtnJpdj382UoFWrYO9tg8gvhcPI0f5mHul6Fih1pkF0somOtdVBhryge5dUV4PP5mD17tmVkbaiUKIj5FaITn8FJUdno9+uOPZAfOBb2IZMR3CkMIlIaYTW9PEBUEW6mVSLjUjEyE4opX4yuiWpL4FGRjC4ju6HL0kfBa0FJj1wlx/mC81TOlZCEFtcWM6fQaMBTA94CdwzzicIQj0EIcw4BXw2QCxDCh6GRK6BRKpjftT9oeI3+rW3X8Dp9Tc5waWjbXSzuiAyZD8RiDq33FNhZZiCk14K200ZsCkzu4uJ2B3CQeR5b/Rvi9uyAi48vvUvckP2mqFfhj1dOgpSrjFrYU29VMEI22udMMsoUKrwV6I3n/DxQ8EUsVGV1yHP/BxHVa6ACH7xXrwN2reer2pa6De/FvEeX7MW+L2JRyC0ZSUV+PuXjUFVWQty/P/z+WsmU0VnNYj2gVqnw23OLUVNagv6TpmHoYwstdi76DPza6ePY8+0XtCnhgAsII3RAD4Yd/etXXNy3Ez7demL2B7dK6Cxp9kRR5I+XT9IhP/JiODp0Y0cZe1v5cMvnsSjIqKLfH+R7xNTGpjjCCnCYerVb0f+6c9lYvjMJCpUGtvxaLOn1NxaOeQPOzgNb0Su7Dz3+95+I3bWVDnL4gifRZ9ytNFpygXjg9ySoVRpI3G0x+T9hkLi1DXHOvbwmz5eibN1VKIuZu/GKziLsrIlBeUW5ZYEbaYdRued1OGplQat5YqSEPYWug+ZD4vbgoN7GeIcoFSrkXivXgholIF+yumYnzYd78SW4lyTAe2APeL39JgTehkl5EVJQShKafQQn805CqmiqRhLkGEQJQsmdVcKrweWYDpQiJF5r3z0LUvcZPacbQoa2TwDWGHuDrX2wKTC5i4/MAXCQGlDdOlBSQ3IxLy8PPj7GUVYozEjD32++QKc899Ov4RnYuXH6e39KRGZCCQLD3CmBtr62PC0Pv+QUw08kxNmBPVC1OwM1p28ix60Q4TVPQwQ5lKM+BD/qeX27vGe75THL8f/sXQVYVNkX/8EMXdKdgiIigtjd3d21Kuq6ru2usfauHfu3de1uXV27E0RFQEAQ6e6OCf7fvU8QkxmYgTc45/v225G5ce65770579xzfr9zIefoM2Zv571oZNKopH3WnTuInvoz/bfBL9Ng+DPzWS6yaYHQF564sHYFVf6nLXtQw0S83y1ZWzXJpjy1/HdEB/hD19QMo9dtq7YZK5/vzaG505AUGY6m/YegxZBRsrZ1VN9w32Rc2e5LcTcmbG4NJeUfK8D65mEM7h19C66SIsatbQlltY8kDdLYUDb5EfIAhzR2uIJjEryNpZcCcNyLlA0DphrxmOa6B61cPWBlWX2j5T43/sPtf5iaXbduvdB+LAG/Z4QEN67t8QdJ8dc11aDBDY0a5aOqrOD2lHQnP3w5nvFIvxwK8IsAjgIU2hriTMA1pKUxwY3hw4fDzq5ywH3Kva6kYAhuLAQn5AYdglAJXjLvBYdeK1DXRNxy93JrIfMdC/P5IMjV732S6P9JCVVp0ddXgF7wXei9uw+NvEQoWVjAZPEiaLYRvX45IScB96LuUeYTkrHBFzJUw0QI6KerkStlPmln1Q6EBaUy5c6hQAQ+iYOWvipGLG8KjjzLpzLNX+G52OSYfGMxVRHgWApgyef6SDLAQX5HDsyeitSYHNqSDgAAIABJREFUqC/oNt8+i8OtA4HUOR2/vhUF0xZF3uXmo6UnAzZ6or4dmqYKkbzXDwXgIVV1KRrhNQp0bKEy4xWDEFxByefnY/TV0QhMDQShlD7d6zQFKS6W+FV/Iu3wYQpsan3wAM3mkItsWoBQhhLqUGsXNwxcyAQ6qrskR4bj0PzpFHPk81Ky6rr23MwM7Jg4gi5v0OJVlMVQFuXp+Xd4eT2SYvQNnN9QFpdQIZ0L8vg4MO8RzXpvN8oRTi0kE5j/llJs8iMq/stWIdNXWmeZweBIzMzH5CMv8DKSwT1wM/LFT85HYGveFU5O66steOP7V89xYc0KFBUJUbNhE/SevQCKH2gpY9+l49JmHwj4QhhYaqL3r65Q06xaHAshoX09G4I8v2S6Txw9VXB7m+HY9TOyE9zITQXur0HR871Q+PCi/FjHFVfcf8PcZp2gqyTdSG+l3f1SnCg/m4cw32Qa1IgKSKXXaLGQ9wYzhxqwrqkKjduHIbhxgflKSQn648fDYLIHFNW+n4FEXn7eZ7ynWRrkP/8U8o73UZQVldHMrBnN0mhj2QYGagZSXO33hyaUzceWPqPQHx3G1oFj0+p9sldlhpbSxGxyTL6xxKoIcEg9g4Os1fP8KTw6cYgCak/acaDkt49kfu2f+4hmRnX1cEZNt49Bg7Iug36vQvA0PQcEbHRvHWvErniGonwBvAxvoH/W30z38TcAq48loGWN+b3vo7KiKOgoySxrYNQAe7vshZIigxsiLCxE+NChKAgIBNfYGLYXzoOr+2OlilfEtmzpm5mUSMtTyEO+96wFcGjSnC2qSV2P4lIyggs3buNOaBsYSn3Oqpwg2PMx/t34FzhcLn7efxJKylV7oFheW5xb9wJxoRlw7WiJFgM/YvmVdzxZ7Hdz3xsEeyXAtKYO+s+VbokVm/wIeYCDRVcrwduYcuQFErOYGv3+DrfRzeYSdLSc4O5+ChxO9axrTwgLxckl88EryIexnQOGLPmrBMyIAAOdW/+CsqXoGKlhwFx3qGlVbXCjMCoLKceDaE0zETUXAyh2MMLB44eRns5gbrA6c0PAA7z3AXf/BPKZQFqYqjlW1JyC+g0H4RcbYzlo6HeeCwRDg2BpEJDQ2JB0mlVULAQQ0LKOHuzcDGHjVAP5l04jacvfEOYyAMHqjRvDZMkfUPkOi45AKIBvsm9JUCMyi8nkKhbCVNDaojUtP2lh1gLqSlWPQVOsWzHzA8HFGbakyQ9Bx8ain5AKqcImx+QbC6mKAMfnqkjlsISwhe0lL47AF2wFl/72ocHTWo2N0Wl8XZH3+HxCGqZ8ABt90awuuOdCkfc6Cf4mCXBJ/x1mSESR6wgo9GWyJiUh96PuY9qdaXSo0U6jMbfR3JJhC8LCEDZgIIpyc6HZrh0stm+rtgc2krAlG8d4fPIwnp07CQ1dPUzcuo++/P4oUpCbg30zPEAY/mo1bYleM6s39fHtfTvhc/0yLJycKdC/LAopFd4z8wGE/CJa4kdK/X5EiQpMxaUtPnTpI5Y1lSpuIZv8CHmAgyVXOylH+eMig7ehqcLB1Ab/orbWNSgp6aFRwwtQU6ueNe1ZKck4tnAWRanWMjDEiFUb6SkWEfIiSQByyP8JW8qAeQ2hY1h1mBvkND37UQwyroUDgiKAq4gavezAq6WKgwcP0uCGkpISDW7Y2rIUsyLkJnB9AZAcTG2cydHAJuvROG8zGP9zdkArvepJO1zR2zw9MZdmaZBSqYQwhvK3WLgqHFjXZYIa1s4GUFHjIs/HB3HLlqMgMJA24+jpwXj+PGj37v1Vpz6Xl4tncc9wP/o+LUFJzU/9ZA5TDVOapUGCGg2MG5ScjFZ0XZLunxKTjRMrvOiwnSfUhUPDj7SXkp5LPp5kLcAmx+QbK6u2AQ6y3hNL5iEmKABOrduj28+zSkzg/yAG94+9hbIqh5apcLiiYekUCIVwe8KAjf5ua4qJ6QpIPfEWKdwcRHF3oSfuoIirBoU5wYCq5CjW/375N/b47aH6b2izAZ1tOpesJePiRcTOZ14MjRcsgN5o2azrl+ydJxujCfh87Pl5HGX8kWVMhopYO+DBHVzdtpEOMXDRSljXq76Uo6RsLiU6Es0GDkfzQcMrYrYq60uyv8+vf0nnJ0xUVX04WlWGIIdwhxY+oe9S7l2t0bRvTampwiY/Qh7gkNo2izZwIV+Ipf++AQEUJVLTUANzm92AUv5JKChw4Op6EHq6zUQbTMZaFeTm4uSSeRTESFlNHcNWrIOBJYMbQOrGyIOJvDCRumNCcZSVWkADHIZWlf8SLsjhIe10MPKDmBdPrqEa9EfUQbZK4SfBjREjRsDGhoW4FYlBwI2FwLtbVH8hFHHYtCfW2YyHrYEFdjtbw1SlajNj2HT5kmBWSkxOSVCDXIelRUWdC1sXAxrUIBkb3A/AVYQtIHHjJqSfOsXQtCoooMaQwTCaORMcHZ1PxiAp3Q+iH9D/nsc/B0/4KRBpLd1aNKBBMDUc9Rxl5rSTsByRYJC+uSaGLGokM3qz6fqrCl3Y5Jh8Y/1lBTisSJIUgFCC9fyNMVhFE1tax9c3r+LW3m1QUlXDlN2HoaTCZGzmZBTgwG+PKU9zz1/qw7qu6MwnS9/FYGdUEixVlfHUxR7xqzxpxtn5GvcxJX8zlMAHem4CGn5kPqnotUcy0DxuecAzzhPqXHWc6HkCtjofA/4kwEECHQpKSrA+cRxqdUXPSqmobvL+5bcAYba7tPFPKCgoYsLWvdA2EL1cqvyzsqsn8QtOLJmP2LcB0DO3xOi1f4PDFY2+mV0r+b42JEtlx6SRtBHJ3iBZHLIoL66F49mF9zRjgWQu/Mjieek9vP8Lp9iFo/9sLrXsWjb5EfIARxVe8QRvY8rRlyClKUQ6ORljVnNfxEauov92cFhUbUFFyWnAhbXLEf76JRQ5HEq/VRwNJ7R4/271QczbdHoT9pjmQj+/vB5B7ULqyOq1s6Avl5UBZFjwPgOpJ4IgyCyk86u7G6NGn5rIyMnEgQMHkJGRQTM3WBncIDgb9/4Cnv8DFDGgl556DfGb7VQEataEh4UhFtU0g5Lij/Io+PYNTxz/hPBMmqUR6pOEzA+sOMU91LWVaYojue7MatX45Nojjg9x2hPXroMglQmCqdSpA9OlS6BWnwHnIgEMn0SfkqAGwdYoLVwFLs3OaGvZlmZrWGhZVOHTqfxTJ0Zk4vRf3nSA7lNdaCBILuy3AJsck1LWIkf8xWi5vwAgUdgNH74nPwiHS7W9B4Ag9pK36fBSf+8FoBghj3jttQEs/vA9qdHbKsbuSKVEhcyfl52FnZNGQSjgo8f0uXBs8RF8mJRpxr3LgFNLM7Qb6SiyuqXBRo+72MH5bDjI79lT03A4pG2HKwIAU1fA477IY4rSMCUvBYMvD0ZibiIIm9OxHsdKSumEOTm0VKUwPBzK1tawOXsWHE0NUYaVt6lCC5xZtRgRvq9g16AR+s3/Ane3CjWr3KkTw9/jyG8zKF5c65Hj0ahX/8pVoBJme/v0IS5vXgOukjLF3+AqyWYQ5/K214jwS0GdFqZoP6pOJViOvVNkJOXiyOJnVMFe0+vDykn0QLk4q2KTH/GjvNVIzSkRZ+NLt30ZyeBtJGQyeBszO9bCCLcEvH5NWFKEMDHuAyenDdXy9JO8DN7asw2+t6/RtXeZ/Cuc23Win8lLJgHECfFOpP8mYIU6huo4v/4FPRAvLRo6ynBuYw6nluYgL5+SFqJL1t0oZN6KoKdnCsoc1OhnDw03IwokyurgBr8QeL4XuL8ayM+gpsnQtsYMKw9c1WsODS4Hmxyt0NuohqTNJlPjCQVCiqNBghok6yAngwliFYu2gSrs3IxQ080QxjbaUPhKIKggNBTxy5Yj14spzVDU0IDhr9OhO3w40vkMlSspPXkS8wRZvKxPxieMAy3NW1JMjeZmzaGlXPnZSdLYsH//54PIN6kwsiHI5e7V8jkmDbtV5ZhsckxK2aE4aPE105C38rZfaft5gOMAgDHfsC0JkoiTcidVX+LCupUI9X72xUvk69tReHQ6hJZqjl3TUqzTt/6v3uFJeja6G+hgS7oyMq68R4R6KoKEl/ETTjFm8XgAmEqWJYEEc8ddGwd+ER/dbbtjdavVJc+B/MBAhA8egiIeDzp9esNszZqqvPTlc5dhgbT4WOz7dRJt1XfeH6jp3viHtlkxPgXJthq/aSc09aTzslhVRiaZZCSjjDCnEAYVWRTiv/8z5yHF72s/ug7qNJeDnhcHyh0aGqHzBOlk5bDJj5AHOKrgzj1B8TbeoFAghJYKF5uGuKKFLQ/PvfuCx0uDlmZduLufBIdTdXgT0jSL18UzeHiM+Jz4opbz0ZkQvL4VxXzX1w4u7SxxYqUXPU0nDCrN+9vD/34MBXksDngochVQq6ExzeowspZMLTHJ1kg9GYSCUCY4oGSqAb3hjlAyVP8iuDFy5EhYW1cuJec394cYJfg6U46S8o42K1LRxmnHiZit3Q08RSXU1lDFP842sFevnqC1ZV27BHgqKjAN718lUgaUgpyPVKukr56ZBs3SIEENUmah8A0aRWFeHpJ37ETK/v0Aj8mI1+raBdlTBuNBgT/N1PBN8kURiY6Vkjp6ddDKohXaWLSBs4EzFBVEq6kva11s+j7uXTrOfah97T3dFZZOemxST67LVyzAJseExRsk1QBH8LNH+HfTaigoKmLyrsNQ12bK2jJT8nB44VP6ud9sN5g5iM5AciEhDZMDIgiLObxq10TRFh8Ugo8jag8wpegADJEKNJoA9ChOjJGc9Y8GHsVqLwagcGGThRjqOLRk8NQjR5GwciX9t+nqv1Cjb1/JTSwfSaIWeHB0P55fOgstfUNanlLMcCfRSWRosPzsbOyb6YG8zAyaaUUyrqqT7Js5GWmx0WgxZBT10WVRSuOBEdr6GkbsAWOvKnsGPonFnUNBFMdp7JoWUNWQfGYOm/wIeYCjEq80grex/PIbHHnG4G3YGWpgz+iGsNHjwPvFYGRnB0BJSfcDqKhspqeXZc7i1DfSjvwwdP9lTskLpM+tSDw+w7yU12tjjlZDa+HBiWAa0CBBjMG/N6IvnMUOH/l7wKNYGqEtFhM7HbiQ8pUG5S9fyQ9OQ+rJtxDmMC+tGs1MUaO7HRSUFJGamkoxN0hZirKyMi1LYU1wIyGAARB9f5cxh4Ii0uqPxii9IfAWMA/3gca6WFPbAhocTllbVa2+L8zjI8I/hWZpkP/zCphynWIhmQYkoEFKUEi9ZlmSde8eElasBC8mhjblmxniyVAnnNZ9h4TchE+6q3HV0NS0Kc3SaGXeCsYaPwbw5oWNLxETnE6pcvvNblCWSeXfV7EF2OSYVLEpvje9VAMc/MJCWvtemJeL9uMnw61LzxJdTv35HEmRWfT3rdWQWiKbqDTY6G+2Jhh6Lhr8xDxcNwqAReZldMEDQEUHmB0EKJf97BN5YhJcLyrC/AfzcTX8KriKXBzsehAuhi50CPJd9LRfkH37NhTU1WF75gxU7FgKzi3OoqtZWz6Ph91TxiAvKxPNB49AswHDqtkKy7ccv7s3cGMnQ7csyzgVn68+OzUFu6YwCW9Dl6+DeW3ZLO0oBmdW01bGuDUt5FmkAArz+dg//zH4BQK0GV4bzq0lT17BJj9CHuAo37NN7F6JWfmYeuQlvD/gbXSsY4yNQ+rTDI43AbOQkHCJARWtfwB6etWTWzzmbSBOr1gAAY8Hc8e6FIW6uLYv5HkCCMUkEXJ63mWiM2KC0kAo8og061cTDbp8mSXBKxQgxCsBvnejKChksZSnfKVIIETmzQhk3YumwyiocqE30AFqzgyGAAlukLKUzMxMGtwgmRtWVgTXroolJ5mhfH2xn9T4MMrYtcPNhvMxKUkDeUIhlBUUsMLBHKPN9H+YB31ediHCXifToAahySJUYcVCkjIIjoadqxHsXA2gqStaNgsvLg4Jf/6JrJsMWKuAq4CLTTk427QIPKWPj1NzTXMa0CBZGg1NGkKFI5sc8hW5sktTk5EABwl0yIW9FmCTY8JeK0GqAQ6y7us7t8D/7k2YOtTG8JUfsyq8r4bD8+J7aOoyIHHfyiz7mu2WvYvBjqgkWKgq4Xq6GvLux+BNjTi8zn+B2dgNDoRA352Aq+RfXglD1LArw0Awh4zVjXGq1ymQ0jz6/ExPx/t+/cGPi6OYRTYnjkNR5cd7VrL4ekfQ4/u48vc6mlU0adv+aleOUV7bFwmFOL54LuLevYWBlQ1Grd5C8eRkXQIf3cN//1sProoKpu07IbMgqqTUPdgrgR5cdfWoJ+vbIjH9bx8IQNCzeFo+POi3hhIbt3ggNvkR8gCHxLf3ywFfRaZhcim8jRkdHTC9vQOto42M3IeQdx9ARe0XwspKcmjmlbA0kacgNZzHF82hpwC6puaUMUVNiykniQ5Kxb//ew2hoAim9jogKe0CvhDHl3shJ72Agor2nd3gu3XH5DQoNjgdvveiEebzafkKoaokp17fK1/hp+Uj9XgQCiMZjARlKy3oDXUEV4958WVlcIPgbHjtBu6vBQqYUhro24PXaSUWKzjjQGwK/ZO5ihL2OtvCTVuyp3Mib34lN4x+mwbv/8Lo9VAat4VkARHGE5KlYVvfAGqaouO28AryELBrPRT/OQVuAZMx5GujgH86KyJOXwEcBQ7cjNxoUIP8Z6djJ9YLSCWbqFKmI/fk2bUvKK2ulZMeek2vvpR6lWJQKU/CJsdEykutyPBSD3BE+vvSgwAi47fshq4JmRJIi8/BsaWe9PPA3xpSTCBRJTQ3Hy08g2jzg8bGqHvoHdIUcnBW5RkG4gqcEQxYNQfGXxV1SLHavU9/j6FXhiKPn4dmps2wo+MOcBSZl8HcFy8QMWo0IBRCd+RImCxaKNbY8sbStcDJZb8hOsAf9o2aoc8c+d6UtnZ8aAiOLpxFGdPajZmIBt37SHczKmH0G7v+ht+dG7B2ccPAhSsqYUbpTHFowRNkpeaj5SAH1O9gKZ1JZHDUmOA0XNj4imo+7I8mtCRbksImP0Ie4JDkzn5lrJPPI7H4AoO3ofkBb4OwpRBJTX0Cn9djUVQkqNagoiSocXzxHKTFxdKgBjmVqmHCAP4kR2dROtjCfAF0TTXQf04DWhd2a38A3nrGg6usiCGLGotVP0fqlWn5yuPYT/AVTOy0KabH5+UreW+SkXo6BEX5zIurZhsL6HS2hgKHwUZISUmhZSmsydwgb+1v/wNuLAJSPzBxqOoAbX9HVL3RmBgYA5+sXKp7ez0tbHWyhp4SV8pXetUPT16oCdMOOeUsDmxwVTiwcdanWUGEXlFZTXQ7pOen41HsIwTdv4D6+5/BIpEpa0nXAA52UMQbN120/ICl0cysGXRImrdcPrFAuG8yrmz3pX8T96VMbsrKtQCbHJPKXblYs0k9wEFOhndPG4/slGQ0GzgczQcNL1Hw2NJnSIvPpdmMJKtRHBnw6h0ep2ejq742/ryYAEJ9frqGFwzy32A0zjFD/fwcMBS9/EWc+a+FXcPcBwxWgYeLB6a5TSvpnrxjB5K2MOn+Ftu2QqtDB3GGlreVkgVSYqJwYNYUOvqABcthU19eavi5qW/u3koB85XV1DF+8y5o1BAdH0dK21ahYf+ZPhHpCXFoOWwMmvQdVKGxqqozCWyQAAeRQb83lBg2X1WtR5LzEvDVI388RWZyPtw6WaH5AHtJDg82+RHyAIdEt/bjYF/D29g9qiHsjRgMiby86BJQUU1NJzR0P1UtQUVJ/eaZlYsQE/QGHCUlDFr8Z0lNHwlEkBPe3IxCys08YJ47tPRUKaPF1V1+1E5thtWCc5vy4ZF8LF+JBgEcKhZSvlK3tTmcmpmC9ygG2U9i6VeKGkrQG1wLqrU/AiKS4AYpS8nKyqJlKaNGjYKlZRVGg+P9geu/A2EPmOUocIBGP9Hgxp08Ln4OiEAaXwByY8+1NcEMa2MofgMkU0qXfpUMSzA2bh0IoCUpRAyttNCopy0sHXXBVRYtbZQESELSQ0poXEMjfDDsLh8dXjOlLaT4x6u5LvLG9UPz2p1Qz6BeySlklSxaBiYlNj256jlSorNp1kz3KUz9vVzYZwE2OSbss06JRlIPcJCZikEddU3NMG7TrpJssGcXQ/HiagR0jNQwYllTsbLESoON3k5WhaZ3Ep7ph+FNTihmcQ5BS5AKNJsGdJEea8IarzU4EniEGnNbh200241IkUCAyPE/IdfTE4o6OrC7cB5KpnLWg6q+D+4e3IOX/12EjrEJftq8m5apyOVTC5ADvH0zPJCfnYW6bTqg69SZMmuizOQk7PmZMDmCHkSSMjlZlOKSd3LANXFjKyh+OKyUxbVIQ+fnV8Lg9W8YZZ8c81dzidqHTX6EPMAhhauH4G38fPQlnoen0dE71jHCxiGu0FZlEGsFgrwfAlSUnET9t3UDreEk0nPGb6jdrCX9nJ/Dw7l1L+hplLIqB/3nulMA0dzMQpxY4Ym8LB5lXuj1S32xnLivbSctXwlJh+/dz8pXCA4DVwF2KoowqaULvaG1wdH+WP9bOrihoqJCMTeqLLiRnQTcXQm8PPQRZ8O+I9B5FQSGtbEhPB6bwhMoX4eeEgc7nGzQRq96UI6WdYumxGbj2i5/pCcwWStOLUwpQC1XqezARj4/H17xXiVBjbicOJpu2tavCCPvCKGdx8yeW9MMxkv/gEWjNmWpI//+Mwu8e5GI63v86V+HLm5cAhQsNxS7LMAmx4RdlvlEm0oJcCRFhOHQvF+YF41VG2Bqz7xoEJBRAjZK76U/GkPfjDkwEUVKg43OUtfG8PMxiOQm4wb3NVrDC+3xGFDXB2YFAVzRy/dEmbu4DU/Aw/jr4+GT5ANtZW2Kx0HwiojwEhIR1rcvBGlpUHN3h/XBA1Dgip5xJ44e8rZlW4BXWIDdk8cgPycbrYaPReM+A8vu9IO2IJSqhFqV3pcyDMz55v5tXNu+Ccpqavj5nxMyiyny4Phb+N2PgYWjLvrMcPtBr8pvL7s0K1ePn11gU4/BGZSEsMmPkAc4JLGjpcbwiUrH5MMvEJ+ZT//6awcH+h/B2yBCXrYDAmYjPuEiyRmAmysBFW0hYS3YMdyjE4fhef4kVab0DyS/UIBLW3wQF5pB2VF6/+IK89q61DZXd/rRU3hSSjDsj8Yigz+KumJyY/ucDkHQ62TwSrF3Gttqw6W9BWq6GVEKpeTkZFqWQjI3SHCDZG5YWJQvk0RU3b7ajl8AeO4EHqwHCjKZJga1gC5/Ag6dkFLIp1kb99IY7BB3bXXsrmsDc1XpOKgVWosUOod4J+DO4SCKCk2upTZDa8OpJVOz/i2Jz4mnAY370ffhFeeFfAFzrxKxTCrC1Jsc1IwopP9W0NSE0YwZ0B02FArVAEBMCltQ5pBCYRFOLPekwUxp8q+XqYi8wXctwCbHhMVbVSkBDrL+g3OnITkyHG5de6H9OA9qEvIbSehiSQp24162aNRDPNaR5e9isT0qERYqSjh7JQ1CAR9H1B9CXZiBWfgHCiRPbdABoG4/qW1BQk4CBl8ejNT8VDjpO+FQt0MlIMzZ9+8jymMy8zM3dQoMp0+Xmh7ygb9vgeKXXUUOFx47DkBdRw4S/S2LCYUCHF0wC4lhoTCyqYkRf22USSrda9s34839W7B1a4j+vy2V2VvkxEovmjXaqIcNGveyk9l1SFPxC5teIeZtmsRBWNnkR8gDHBK8gk49j8KiC/4leBsbBtdHl7omn8wQGbUfISEM97uD/QJYWf0kQQ3YM1RpCi2XDl3RceLPNBODvOxc28UEMYh0nlAXBASUSNCzONw+EEg/dxznhNpNPrVdRVcnLBQg/VIocr0TICgqQgxHEREcDlKTPhzTA1DXUYaduy6ev7+J7Pz0qgtuEBCJoMsMzkZaOLN0NV2g7QKg4TiAo4SXGTmY+CYcMQUMne1P5gZYYm8G5R8gjVQgEOLpuVC8vh1F106YBQhS9teA9wRCAXyTfUuyNILTgj+5lBQVFNFIux6GPFWExeWXJMWKfq/dvTuMfpsPJSOjil56P3z/t8/icIvc2wrAiKVNRaLi/eGNVskGYJNjUslLF2e6SgtweF08g4fHDkBNWwceOw6C8yGb4dHpEPrcM7DUxJCFjcXRHe9zC9Dck/mN3R6viMavM3BD/w0ic+LhoX0Xppk+QM32wKjzYo0rbuNncc/gcdMDwiIhBtYaiCXNlpQMkbBmLVL37wcUFGC1fz80mjYRd3h5ewlY4NjiOYgLDkLtZq3Qc8Z8CYxYvYeIDQ6iWHNEOvw0Fa6du8vcgvdM+wmZSQloPXI8GvXqL3P6E4UL8vjYO+sBSDpz719dKbC8XL60AME4JFiHihwFjFvTEqqaTIVBRYVNfoQ8wFHR3STcwnwhVlwOwOFnEXQ0OwMN7B7tDnujT0sEUtOewsdnDAUVNTbujbpOGytcfiEB9SU+RISvD86tXgKhQEBBqfrNX0JT3cjp04MTwRQAlEhpdGNyIkVOeQnYKAGE7DrJWaK24cXnIOVYEPiJTBmDam1d6A6qRXE34t6lw/dONKUTLQanLIIQPI1UdBzSAPUaSxaEp0yDx/kC1xcA4Q+ZpopcoNFEoM08QF2P2nFfTDKWvosFr6gI6hxFbKxtib7Gsg1uVaZdPjQgZUyk5IGUHREhaYidf6oLNa2PWSsZBRl4EvuEZmk8jnmM9AKmbbGQ9OgW5i0ojat7sBBZazeBHxtHv1a2tobxH4uh2aJ6ZlaJamdJthMKhDi65BkFtnJsbooOo+tIcnj5WBKwAJscEwksR1pDVFqAg9bDTxtPS+bIaSo5VSVCfq/OrX9JP49c0Qw6hmpirXXgq3d4lJ6NjorKWH01BQFqMXhSFAQXlRj0LzhF8taAX18Dul/Ssos1URmN9/rtxZaXW2irFS1WoK99X/q5qLAQ4cO4nU+cAAAgAElEQVRHIN/fH1xDQ9heOA+uvr4kp5aPVYYFSpdIEdw0K2c5dpIoF821HZvx5t4tqGpoYtzmXVDXlh3g8YzEBOz9hTlwHfnXZhjbVbLfK4qBRWgT8SYFl//3GgqKCpiwsRWUVeVlbl8zG8Eo3D/vEXj5ArQa4kAJGCQhbPIj5AGOCu5oUlYBph59UYK30cHRCJuGfsTbKB4+Ly/mA6hoKjQ166Ch++lqCSpKUmqP/zEPhXm5MLSywZBla6GiztCTel8NpwwXRFw7WqLFQAfGoREW4dLfPogOSoOalhKlLir9slqRLSLBgJzn8Ui/9B7gCwFFBeh0tYFmS3P6ACwtEe9icX7vPXAy9KFY9DGaSctX2lmgZgOmfEVqkpUA3FkBvCIgbB/qZxy6AJ1XliDb5/AFmP02ChcSmRd2B3UV/ONsi1oaDJ1tdZf49xk0AygngykhadDVGk1625WUgPkl+WH/m/24E3kHgiImE6NY7GvYl9C41jesj6K4BMSv+hPZd+7QJgrKytD3mAT9CROgqPIRi6W627Sy1vfmYQzuHX1L92rE8qbQNhDvxayy9PxR52GTY8LiPai0AAexwallvyMqwA+OLdqgx3SGgYT8Xh747THFq2re3x5una3EMlcJ2CiAf+9lQ6UwB6dVnkIRQixUPwZObhLQeh7QXrqUoCR749c7v+Je9D1aonK0+1HU1mOwRgojIxHWrz+EOTnQaN0Kljt3ygEuxdrlijW+9c8OvL5xBbqm5hi3aadED5sqphm7e+dmpGPfTA8U5OSgXvvO6OwhOyVW/ndv4vrOLVDR0MDUvcdkssSGXB3FQMwEaH7wgkbsvmCqWLu7hwMR8DiOgvJLylZs8iPkAY4KXGCf421Mb2+PGR1rlbxsFQ8tEOTjxYvByMp+Ay63Bho3ugA1NclEyyqgvsS7Zqel4tii2chKToKmrh6Gr9oILX0GvCbwSRzuHGJSYx0aGaPTOKeSAAMB/3x4kikb6Da5HuxcDSWimzCfj7Tz75D3OomOx9FVgd4wR6hYaX8xflJSEsXcyM7OhqqyGlq59ESUTw6t4ysWgjjs3MYcdVuZU/RhiQkvH3i2HXi4ASj8MJ+hI4NmT4BEP0hwTj5+8g9DSG4B/Us/oxpYX9sSGtyywTQlpmsVDUQCVSTzh6RnCwVFUFLloOMYJ5rtQxzlRzGPsM9/H14kvCjRUFlRGY1NG9MsjVYWrUrA7Ip4PKQePIikbdtRlMeUJ2m0aAGTPxbT7A25SMcCAp4Qhxc/RU56AZxbm6PNcNlEaJeOdap+VDY5JlVvjW9qUKkBDr87N3Bj19/gqqhgyu4jUFZlgoL3jr3FmwcxINTnA+YxmR2iSqFQCLcnAUjh8TE1Xojxr3NwRscL6QVZGGcVBevIM4CWGTDDD+BI9/STZNoNvTwU0dnRsNSyxImeJyj4KJGMK1cQO5tJ+TeaNw/64xl2B7lI1wKF+XnYNXk0CvPy0GbUT2jYU3p4LNJdSdWM/urav7izfxctsRq+cn0JQHDVaCP6rFe3bkDAw7uo2bAJ+s5dLHpHlrU8v+Elze4lmHqtBkuH8pplSy63OgQH0fNSKOo0M0WtJiYSCWSyyY+QBzjKeWmc8v6At8EXQkOZQ1lSPsfbIEMzoKJzEJ9woVqDivLy83Fy2W9IeP8OSiqqGLJsDYxta1LrkpSxK9t86ckTARPtNa0+OEpMJgRhvji50gt8nhCOTU3QYaxTOXfk026F0VlIOR4EQQoDIKlWzwC6/R2gqPalw0aCG4QKNicnB6qqqhg9ejTMzMzo3tHylbukfCWZ6k+E1KzZNzSiKV1fw3wQeQGkHibgInBzMZAeyXRT0wPaLQDcCc7GR13Jqdust1HIFQihpKCApfZmGG9uIJEHksj6VlFDkkp3/+hbkJpBIrqmGujm4QxNQ2VcCbuCA/4HEJoRWqJdHb06GFN3DNpZtoO6EpM9VCy53t6IX7YMBSHv6J9ICrTxgt+h1bXrD2HLKtrCkmlf34nCo1MhFBB29MrmlB5aLuywAJscE3ZY5KtaVGqAgzBY7PQYBQGPh27TZsOpVTuqVFRAKs16JDJ2dQux76MVobHYFpkIsyIFnL+RCW+t9/DjhcHRWBVDE/5iFj78FFCri9S3Iig1CCP/G4kCQQF9Zm9pt6XkWRy7aBEyzpwFuFzYHD8GtXr1pK7Pjz6B7+3ruLn7f+AoKVHsFzWtLw+EfnQbfW/9pDT7yG+/IikyHCY1HSjdKtvpdYmvu/vncchOSUbb0RPh3qOPTG6xgC/EnpkPQA5Tukx0hr27HD+tsjeSTX6EPMBRjt2PSc9Du/X3KPaGLcHbGOUOB+OvU3KWBhW1t/8d1lYTyjGjZLrweTzEBL5BWlwMrefVMWLAPSsqBEH64vpVeP/CCwoKiug7fzHs3JjUsMSITJzf+IqyXOhbaKL/7AaUIYUIqcsntcQJYZkUJHLoH02g8pUAhDj6kQd19uNYZFwNAwRFAFcBNXrWhMY3opOJiYk0c+Pz4MbncxKMEJJBEPAollLcFku5y1difYBrvwORT5ihCM5Gk8lA6zkMmOgHIadty97F4p8YBpTVXEUJe+raoIGOhjhmkdm2GUl5uLrLrySThpQJNR5mgQsR53A04CgS8xJL1tbcrDnG1h2LpqZNvwhW8NPSkLhuPTLOnftgb0XojhgBw1+ng6MpOtWizBqSJYqTYNXhhU8oDXT9DpYUh0cu7LAAmxwTdljkq1pUaoCDaHBp458I8XxC8awGLFhOlSIgy/vnPkJBLh+th9ZCvbbiMXyF5Rag2Qew0c0vcmGTmohrykzAZLG1NzgRD4HaPYBhxyplK86HnMcfT/6gc810n4nxzuPpZ2FuLsIGDUZhaCiULC1he+4sOFo/Bv15pRj+K5Mc+X0mEt6H0GAaCarJRXwLRAe9wcklDDBrp0m/wKWD9AOF4mv5sUdafCz2/TqJ/mHUmr9hZCObzCOkhPnsWiaLd+yaFtDQkR+gVOS6KE9fNvkR8gBHeXYQwGnvKFz1j8emIa7QUfs6+uynoKK9UNdpU6WfEmcmJSLMxxvvX3kjyt8XvAImo4Egsrt27YUm/QZDTbNiDsOdA7vw6uq/dNzS6NEZSbn0YUNeZrT0VDFgnvsnJ00vroXj2QUGk6P3DFdYOlYM7ViQw0PamWDkB6bSMbmGarQkRdns6y+wogY3Sl8ihOI2+HkCzer4vHylbmtSvmL2/YdqVjxwewXgc/Qjzkbt7kCnFYDBp6BOMfmFmPQmHC8yGWDUtrpa2OZkDX1l6aYNl/OWkHi3cL9kivJMnHiCl+Lc0wjPDa/hdMhp5PBymOtYgYOutl1pYMNRz/ELHYqEQqSfPYuk9RsgyMig36vWqweTpUugVreuxHWWD1i2BV5ej8DT86HgKiti9KrmEsPbKXtmeYvvWYBNjgmLd6rSAxwhz5/i0vpV9PDAY+dBaNRgAuC3DgTg7bN4CrLcZ4ab2CYb5PMOD9Oy0SZVgDXPs3BE/RH4Qj7GNdKG9fNlgAIHmBUAaEmWzexbii55sgTnQs6BsFrt7bwXjUyYQ5L8t8EIHzSIgo9qd+8Gsw0bKt2PEtu4MtqBZOAe+X0G1X7osrUwd5RMRq2MmqNCav+3dQMCH96FqpY2xm/eVWE/u0LKlNHZ9/Y13Ny9FaqaWpi65yjrM06+tZxXNyLx5Nw7aBuqYdSKZtI0mXzsb1iATX6EPMBRgcuUZAsQ6tOvSX5+LLye9wGPV7mgojRLI+gNwnxeIOyVN1JjGBrNElFQgIqaOgpymRdEAijUpN8QuHXpCa6y+LgSL/+7iLsH99Cx3Hv2Q9tRDAozAUA7t+4FyAm8igYXA+a6Q9fkY9ZBcnQWTv/lTfEUyOkTOYWqiBSEZyCVlKR8AJ9Ub2CEGn3soajydXyK0sENNTU1WpZiamoqsgpM+UoGfO9GfVm+4v6hfMW2VGonLw94ug14uBH48HIOIyegy59ATSbtuLTcT83ClIBwpPIEBNMes2yMMcvGBJxvXG8iKy4DDUkp0PP/wvH8ShjFWlXWVERs0+e4kHeMOuBE1LhqGOAwAKOcRsFMk7xzfCn5b98ifuky5L16Rb9U1NKC0ayZqDF4MBQ41R+3hK1bXZjPx6EFT2jgyr2rNZr2ZUrZ5FK1FmCTY1K1lvju7JUe4CC/6bs8RoGUq5ROHyesX1d3+tHg7/i14tP8XUxMg8ebCCgWAZfvZ8NX1R/hBfFwcaqN/mELgLxUoMMfQKvKOcXP5+dj9NXRCEwNhJ6qHk73Og0jdSbFPO3ECfosJ2K6cgVqDBzI4ktEdlW7sft/8Lt9HQaW1hi9bqs8kFSBrSSYdPtnelAsk/qduqPjhKkVGE26Xa/8vQ5Bj+/DvlEz9JkjXXBhaa7kvx2+CHudLNFyd2nqWx3HZpMfIQ9wSOEKo6CiLwcjK6tyQEUzkxMR9uoFDWpE+vmUZGkUL41EZUl6q62rO/2/kqoqXl79F14XTlO2EyLahkZoOWQURWsXtV7w3fNnuLhhFaWxc2jSHL1m/Eb78goEuLDxJRIjsijWBjldMq35kS6L1MedXu2NlJhs6BipYciixlBSLt8LJ3kZzrofhcybEYCQMGEo0sCGhvu3y29IcINgbuTm5qI8wY3PLxlavvIgBgEPPy1fMbIh7CvmsFfzBOf2YiDjA86Guj7QfhHgNvoLEDdhURE2hSdgfXg85VHR5XJo1kZ7/R+jDpaU/5CTyQi/FGrmXL0UnLXZghwVJvtCX1UfI+qMwODag6Gj8nUKNoK8n7R1G1IPHSK53Mz13bsXjOfNA9eAAb2VS9VawOtyGJ5fDqNgsSSLQ1VDMhzsVbsq2Z6dTY4Jiy1Z6QEOYoube7bC99Y1GNs5YORfm6h5SDbhP3Megl8oRPvRdVCnuegBetK/NNiox7sCtIx4j8fctxSHan79DCh4bgd0bYBfXgGKUmQPK7XZUVlRGHJ5CLIKs+Bm5IZ/uvwDJUUliocV8+sMZN24AQVVVdieOQ0Ve9mksWTrtU0OvXZNHkP9x/bjPODWtRdbVZUZvV5cuYB7h/ZSwNGRf25iJfUqubcIqGxOehrajfVAg26yue9kHfvmPKIl5O1GOsKp5dcPvmTm4pFRRdnkR8gDHBK+iCioaOAcxMdLD1RUwCdZGgElWRop0R9enEuthThCtm7uNKhhYk+YXb4MIORmZuDZuRN4feM/EGAkIkY2NdF65DhY13P9rmXiQ0MoqCi/oICiRA9a8ieUlFVobfDVHX6I8E8hz3R09fiSFeXphVC8vBZBv+8/1x0mduXjChdkFSL15FsUvGMoU5VMNKA3whFKhp8CS5ZeSEJCAsXckFRwo/TYxOEM8WbKV5KjPrKvqCmmoZ76NdTTvAHV5qOA1nMB1S/XnMrjY1pABO6kZtFhXbXUscfZBpaq4mfWSPiyrpThSFYPOZHMTGbKqPyNH+KJzXkIFQWw0bahwKG9availILfkrzXrxEzdx54kcw9oWxrC5Mlf0CjadNKWYN8EtEsQJyQQwufUA72xr1s0aiHrWgd5a2kZgE2OSZSW2TFB66SAEd0oD9OLv2Naj924w7omzMsbNd2+yH0ZRJsXAzQY6qL2KsrBhs1zhfi6P1knFFlMKEm9WsDs/N9mfFGXwLs2og9dnk73I+6j2l3ptHuJENvXqN59DMpMSTUsbzYWKg4OMDm9Ckoqv4Y9OjltaU4/XyuX8HtfTsoY8/knYegov5j4HyJYyNx2wr4fByePx3ERzet5Yhhy9aKfIAo7lzlbZ8SE4UDs6bQ7mPWbYWBlU15h6rSfmnxOTi21JPqMHxpk08yxqtUsR9scjb5EfIAh4QvvqioAwgOWUFHtbf/DdZWEyUyQ2ZyEsJJ2YmPNyL8XoOXz9BbFouqhiasS2VpFNfpijI5ARh6dPwQgp89Kmlu4+qO1iPGwfArDzuC63F04SwQzm8CVEpQotV1atBTlruHgyglLBFCA0noIEsLAQEipSuEQKRBV2s0K2d6en5IGg1uCLMZwE+Npqao0cMOCh/YWb627s+DG2PGjIGJieTri2n5yuPH8L3oifdZLigCE1xSUlFAvbaWqN/B6gua2VeZuZjgH4aYAmY9Y80NsMzeDCqVdHImynUizTZ+TyLx4FgIwFcAX6EQD2qeQrDhc7gaumKs81iKrk9qs78lRXw+knftQvL2HTRrQ0FZGQZTJkPvp5+gWI7SK2muVT42YwGCw0HwOEgJG8niUFb9MbBl2Lr/bHJM2GojAFUS4CBYQnunTwD57W3afwhaDBlFTRT8PB43/wkAh6uI8etbin0PfQ42mlj4DKmFmWjVqhU6hK8BojwB5wHAwH2VuiV/v/wbe/yY0tf1bdajiw0D0pj76hUiRo6iz/gaQ4fAdOnSStWruk5GfJZD835BcmQ4nNt1QpfJv1bXpVb6uiL9fXF6xQI6b9epM1G3TYdK1+F7E/rc+A+3/9kONW0dSkX9rbJ7Vin9FWUIAcDdI0E0G5Q8C2V1HWy3c1n6scmPkAc4ytotMb5PS3uGVz6jUVQkgLFRT9Stu7ncNxmJ/Ma+DaDgoCSwkRwV8YUmxnb2TNmJa0OYkiyNCuIKxIW8xf0j+yiGBxUFBfowbjF4JLT0mdR+Ugd84o95NCJN8DuGrVhfcprkeek9vP8Lp+0adrdBk96fIjGT0pWTq7yQkZhHGVUG/daQOmbiSJGgCJm3IpB1L4riMyiocqA7oBbU632/9CA+Ph6HDh2imRvq6uoUc0MawQ0I+MD9NcDD9UCREFkwh7/RKvgF69HTaiKkbIekz7l1sqLsMQdjU/BHSAwKi4qgpqiIDY6W6G/8kUlFHPvIWtvk7BScPHAP8GfWm6mSjOu198HFsRbGOY+jacplSWFUFGLnzkOeD8MCQE73zNavh2rtiuG6lDWv/PuKWYDg9BBGFUIR3by/Pdw6W1VsQHnvClmATY5JhRYi3c5VEuAgS3p04hA8z5+ihwo//b2X+haFeXz8M/chhPwidJ5QFw4NxWdGG+zzDg/SstE6kYehAf7wVYigv42Tm2gBF6cCHGVg9ltAvWIg4OJsi0AogMctD3jGeUKdq47jPY/DTofxJ5J370HSxo30s/nmzdDuym6GCnHWXVVtY4MDcXzxXDr9iFUbadavXCRngcub1+Dt04f0IHDcpp0gB5JskX83raaHm7WatkSvmUyWmCzK7QMBCHoWD9v6Bug+RfxsNllcMxt1ZpMfIQ9wSOgK+RRU1BEN3U+Dw/l2qcTXps1KSS4pO4n096HgRKWFBBRsXBpQileCpSFOloaoyySR/NAXXnh4dD9SY6NpN66SMhr06EO5sa9sWYdI/9dQ5HAxcNEKWDoxvPQEg+L+sbf0s2NzU7Qf5fhFcOfBiWD43YuGIkcBg35vBAML8R7y/PQCCiRaGJFJ51Gy1IL+MEdw9b6fpkqCG6QsJS8vjwY3SOaGsbH4jmCZNkwNA85NBKKfM01N6gED/gEMa6Mgl0fX/vp2dAnNLLFDSm1NHLVRRJoWB/bqKtjrbANHDbUyp5L1BqTW+vDz48i/agDjLCYlMko3EGqd0zCqwfASZ/Z76yTXasb5C0hYuZLSCRLRGzMGhrNmQlFFTg8mC9fIw1PB8L0TDTVtZYxe2QzccmLxyMJa2a4jmxwTFtuqygIc5FDhwGwGqHDo8nUwr12Hfr687TXFLLJ3N0KXic5im+5SYjpl61IsKsLehzHwKvKmY8yePgVauxoCBZlAl7+AZpULkpian4pB/w5CYm4iaurUxLEex6CupA6SzRI1YSJynjyhwNG2589D2eLTTFGxjfCDd7i2fRPe3L9NS5RHri7/wdwPbsZvLp/49vtnTqb4Jg269Ua7sQwla1UL8aF2TBqJvMyMTxgQq1qv8sx/ePFTZCblyQ9LymM8CfZhkx8hD3BIYGMZUNEhyMryB5dbA40bXYCaGlMj+z2hWRrBgZTthACEkvTAz8XItiZsXRvSTA1Th9oVztIoS6fi7wkmh9+dG3hy+igtRSHCUVKCgMeUUBB+dMKTToSguV/b5UfLTqzq6qP71HrgcD7NzIgKSsWlzcwJe9O+dnDvKl6dX2FsNpL3+kGYy7BoaLY2h05nGyiUkQFSKcENsnDfk8CVOUAhg5+BZtMYBHrupy/ahEGCpNI9vx6BwizGlkIFIMNBA2MG1oGlVfUGE32T/Ab73+yHn28oOgaPgTqPWa+wQQKGDO8AI00GNb8sEaSnI27JUmRdv06bcg0NYbr6L2i2aFFWV/n3LLJAdlo+iGNCTqBbDakFl3YWLNLux1KFTY4Jiy1fZQEOYpPD839FYnjoJ6wMAY9jaWkoV4WDn9a3BFdJPMBuAjba4EkAknl8THyXD7W42ygU8tCnTx+4xRwCvPcBhnWAqU9pVmdlik+iD8ZdGwd+ER/dbLthTas19OCEn5yM9337QZCcDLX69WF95DAUlORAxeXZm/zsbAoyyecVotPEaXDp2LU8w8j7lGEBr4tn8PDYAYrBMWrN318t/65sI5J3joNzGbyb0tg+la1HRefLySjAgfmP6TAD5pUf16+iesj7A2zyIyr316rqdl9qTgkDKjoX8fHnCRElXF33Q1+v5TdXmpWajHCflzSoEeFHsjSYk+diIcBOxVgaJKghjSwNcbahMD8P3v+eh+eFUxDymeACSa/rOHEaajVtgYSwTFzY9AqEGcXIWgt9Zrp9UQdckMfHieWeyE4rgImdNvrNcYeiouiXXhFfiMStr8CLz4WiOhe6g2tDzbHsdNm4uDhaliLVzI38DODyLMD/DGNWTROg3w6gZvtvmvnfxHTMfhMBh3d5aB6Yjxq5wpK2JL3OvZsNjG2qT6CD3COPYh7hwJsD8IrzgktcWzSN6A1Fgk2iIkSHsY5wdBP9xZac3MX+9jv4iYnUblqdOsFk+TJwdX+Msh5x7l9ZaHvvaBDePIyl5VojVzQTu2xNFtYoCzqyyTFhsb2k5kuIsmbvy+dx//A/IMxok3cdAoerhLzsQuyf+4geMHSf6gJbF/GZolaGxmJrZCKM84SY4e2JCGECnJycMLilA7C7LaPaTzcBy8aiqCnRNkcDj2K112o65oImCzDMcRj9nP34MaJ+mkA/60+cCKPZsyQ6748y2Mv/LuLuwT1QVlODx46DUFYTL/P4R7FTRddJyAEOzv0FabHRMHesiyFLV5e7hL2iuhT3f3XtX9zZv4u+Z3jsPFTl+pR3Xe9eJOL6Hn9a/j1xU2u5D1FeQ0qgH5v8CNHfMiWw8CocQmpOSVTUQQSHLKdLs685H9bWn6aekSyNuOAgCg5KghpJX8nSMLSxgx0pO3F1h5mDY6VlaYi6H4GP7uG//63/0JxcMoTAFDC0tkdubiMI+KbQNlTDgLnuX4BnknbFtXFcZUUMWdgYNYzF+wHNuBGOrDtRgAJgNNUVypZaZapeOrihoaFBy1KMjETLDihz8OIGkc+AsxM/0r/W7g703gpo6H91CJ6wCAS1fnd0Ev3eVEUJOx2toPM2Gy+uRSA94WOwy9JJDw27WcPMQXZf2nkCHq6GX6WBjZC0EHAFymgbOgz2KQ3o+vXMNdB9cj3ofIf1prQhhYWFSNq4CakHDtA/K6irw2ThAuj07y+zP8wiX2vVuGFmch6O/PEMhPK53ShHOLWQ07tVxXazyTGpivWLOKfUfAlR5s9OTcHuqeNQVCRE33mLUdO9Ce12YdNLxLxNh2MzE3QY4yTKUJ+0Cc8rQNNngfRvs32ikZPhDRUVFcybNw+cPW2BeF/AbSTQZ5vYY1e0AwmQz38wn/6WcBW5OND1AOob1qfDJm7YiJQ9DBip5d690Gwpz+ATx97EtoRBg5Qj1+/UDR0n/CxOd3lbMS0Q/volzv75B+3V/Zc5qNPyQ/BQzHEk1fzShj8R4vUEji3aoMd0BoNFFqW41NXMoQb6zWb8S7lUjQXY5EfIAxwVuAbS0jzxymcUBRU1MuoB57pb6IsWcULCXr9gsjR8v5GlUc+VwdJwdYembtnZCBVQs0JdowP8cWbVIpBAjWVdF7QbMwGPTx1DqPezknGV1BzQZ/ZUWNdz+GIuUr5CqD+JtB5aC/Xain5ST/oURmchcbsPIAS02lhAp1vZdJIkuEEwN/Lz8yGV4AYBEn2wFniwjgKJgqsGdFkFNBz/zRTeuIJCTPKPwPPMHGqLVrqa2O5kDUNlJq1WKCzC+1dJ8L4ajpTojxSzpvY6NKPDyklPZl7iswuzcTbkLA4HHEZCbgJdn06eIfq+nwa1zBr037WaGKMtofQVEXMhPziYAokWvGVwXkhastm6tVC2kgNTVugGZ0nnWwcC8PZZPA2UjljaBIqflbixRM1qrQabHBMWG7pKAxzELqdXLkKknw9qNWuFXjPmU1MRavKHJ4MpI9G4tS2/KBEVxZ7FYKPNEwvgEniVdhk7dixsEm8C/80BlNQZsFFVbeSkF+DF9QhY1tErV8aIKPqUbpPLy8WwK8PwPuM9jNWNcarXKeip6qGIx6OsKoQenKOvD7sL52m5olxEswDx704uY4AlSdmEkc2nwPCijSJvJY4FioMKGrp6GLdxJ1TUxTvwE2eu77UlWDbbJ45AfnYWOk2aBpcOsluadOrP50iKzIJ7N2s07VNTUiaSj1MOC7DJj5AHOMqxgaRLaVBRDXVHmGktR4SvP8XSSAp//8Wohta2FEeDBDVMHRzB4bKfEpFE9Y8vmkOZU/TMLTFs+TqoampS5Pbjy88jJeI6igTxdK2krtClQxc0Gzi8pKwmL6sQx5d7Ii+LBwtHXfSe7goFMUtTEv73CvyEXHCN1GH8i9t3aWCJHuTmImUpUgtuUCDRSUC0F7PHxvWAgQyQ6LfkYWoWJgdEIIXHlPjMtDbGHFsTcL5Sz0xOVCL8UygbDSn/KRZDKy368LarbxLyLZMAACAASURBVCiWDct5eZerGwGDI+nEp9+eRhaPwSIh1K59OaNg6uUOQUERLU1qMcgB9dqaixSwIT/CaUeOIHH9BhQVFgKKijCYMoVSwCrIwD1ULkP+gJ0oh/0yT5oc1nGcE2o3kTx98w9oVrGWzCbHRCzFK7dxlQc4CBgkAYUk4N+Tdx8GKWslWDYHf39CLdF7hissRSjh/NxspHRy4gew0Z+9n4KXm4gWLVqgU6vGwPraAD8P6LkZSUYDcWW7Lw1yEKDsIYsaQ89UQ+q7QIIbwy4PQy4/F01Nm2Jnx53gKHJQGB2DsH79IMzKgkbz5rDcu4f6I3Ip2wJX/l6HoMf3YWpfG8NXbSi7g7xFhS1AqJ73z5oCfmEBGvbqjzYjx1d4zPIMkBj+HofnT6ddx2/ZDV0T2cycJLh2e2c+oCV6PX+pD+u6X8+gLo+N5H3EtwCb/Ah5gEP8/QMBFX36YAhiA6OQHa2L3Dg9FOR+ynhC6hitXVwpQKiNawNo6YlfF1sO1STWJTczA8cWzUZGQjylthq+cgOlpxPwhbi89TWig9JoyYhruwIEPTqH9IQ4OreSiip9aLv37Is7h0JpVoKyGhdDFzeGVhlsJ58rL25pyufBDXL6ZCjJ05zXBEh0dplAosXrIOBta8PisS0ykRb11OBy8L86VuhkoFPmPpFAR8zbNHhfjaD/LxZdUw24d7WGQ0Mj1pxyh6aH0jKUy+8vgy9kgjhqXDX0s+sH96huCLmTSv+mrqOMrhOdYWrPZHGUJbzERMQtWIicR4+Ya8vSEmZr10DdrWzq2LLGln/PPguQGlpSS0uu8WGLG7M2kMc+y0lGIzY5JpJZkVRGqfIAR0FuLnZ6jKIvSF0m/wrndp3oQs+s8aZBcec25mgz7NsB929ZhZRQuj30Q7JQiB7v42EZ9YyWdU6dOhU4PwV4fQxh6kNwI2YE+AUM5TkRkmXYb1aDSrlfr4Vfw9z7TCq9h4sHprkxAImZ164jZsYM+tlw9iwYTJwolc2vToMSH2/3lDE0O7fLlBlwbtuxOi2P1Wt5dvYEHp86QsvRR6/9H/QtKj8T9cWVi7h3aA809Q0wadt+kQ6c2GjUqMBUXNriQ99HJmxsDRU19h8es9GOktKJTX6EPMBRjl3NyYzBLg8PWp1QWgytbGDjxjCemNWqIxNZGl9bPq+wAKdXLKTYIVxlFQxe8ieN8JMa+Zv7AxDynCk7aD+6Duo0NwUBT3p98xqenj2O/Cwm60BFXRsCNAZH2Rkdx9WFY1NTsSz9SWlKWwvodP1+aUrp4IampibF3JBYcIMAiZLAht9pZg2axkDfHYB9h2+uKSgnD9MCIuGfzQS+6mupYU9dG1ipiU9fGv8+Ay+uhiPcL6VkPm0DVTToYk3tSoCVKltIAOZl4kvs99+P+9H3S6YnacMEBK6v+UB4HY1CVCATnCFOMKEw1NARbf1Zt24hbtFiELYUIgRnw3jBAnA0pX9SWNm2lM/HWCA5OgsnVzIUy109nFHTTcKYOXJDf9cCbHJMWLxVVR7gILa5vGUt3j55ACvn+hi0eBU118vrEXh6PpQGksf+1aJcAYeVITHYGp0Eg3w++ntegSKKMGPGDOikB+D1th14nDWGgqlr6qnAtaMVHp0KoXO3HVEbdVtVDlXrGq81OBJ4hM67rcM2tLZoTT/HLV2K9BMnAQ6HsqrIA+Hfv4ueXzqLB0f30wwgj50H6eGUXCrHAvzCQhyc8zM9GLRydsHARasqPcBwYd0KhHp7UjZEwoooq+L173s8vxIOfQtNDF1U+SDIsmo3aenNJj9CHuAo5y6fXDYfCe9DYOPC4GiQoIaWvmxlaXxt6aQk4PLmNQj2fEzxJHrP+h0OjZvTpk/OvsOrm5H0c5PedmjY/VOq14LcHBAqLBIZFvAKaTtldUN0m+qBmg2biPwAJ6wpn5SmTHf7Lh1sTEwMDh8+TMtSJB7cIECi5yYC6cy6UQaQqLCoCPtikimYaIGwCCT0MN3aGLNsjKFcwbTZpKgsvLgagdBXicU4r9CooQK3TlZwamUmMp5FOS952k0gFOBu1F0a2PBN9i0ZykrLCmPqjkHvmr2RGVOIa7v8kZWaT7+v394SzQbUFKkuXJiTg4TVq5F+mmGlUdTRgemyZdDu2qUiasv7yogFSOp7uG8yDCw1MXhBI5GfGTKyPFarySbHhMWGYkWAI/SFFy6sXU5/oydt308zRNMTc3H0DwYbq7xUiRF5BWjyAWy01xtvmCdHo3v3HsgN0aJMR0SMa6Sh2+89abD65v43CPZMgIo6F8OWNBE5gF2R/SXg1eOvj4dPkg+0lbVxsudJWGhZQJifj/BBg1EQEgIlMzPYXjgPjnb1YSOriM0+70v8vH0zPOgLtlu3Xmg/1kOSw8vHEsEC7189x/nVy2jLnjN+Q+1m32ZfFGE4sZoIhQJs/2k4iM/eefJ01GvXWaz+bGp8cfMrmlFer405Wpcjc41Na6kOurDJj5AHOMp5RRG6V3VtHUrTVp2ERPRJZJ9I29ET4N6jL/38+nYUHp1mTmucW5MHSa2vvnyQk/1z6+4jyu8qBIVvSkxDaLFIraGpQ9mpsxnXw5F1VzTWFBLcIJgbBQUFNLhBylIMDCQQaKJAousYMFERgUTjC3j4NTAS99MY/AkrVWVsrWOFxjU0JXqJELyCl9ci8NYrgWbVEFHTUkL9Dpao18aClgRJWvL5+bgUegkH3xxEZNaHYA8AFwMXjHMeh3aW7Wg9dMDjWDw4HkxLmQhrTvtRdeDQyFgkdfJ8fREzdy54Ecz46s2awmz1aigZi9ZfpEnkjVhtgfiwDJxd84Lq2ONnF9jUk8C9zOoVs0c5Njkm7LHKF5qwIsBBygp2Th5NMyZbjxyPRr36U0VPrPBCSkw2XDtZocUA+3KZceDDADziF6JOcira+j2GCa8h+GlM5p296iN0MDwI7lw/cnoBgrN1dOkzFOTwadlk5wnO5ZpT3E4JOQkYfHkwUvNTUUevDg53PwwVjgoK3r1D2MBBKMrPh1bnzjDfslkeJP2KcQn4PQGPJzJ2w/YqKZEQd8+rY/vza5fj/QsvWiYybuMOKKuqVcoyE96/w5HfmZKuCf/bCx0j2cS8EgiE2DvrIS2Z6/xTXZF9zUox8g86CZv8CHECHOQw+ldS+giAHN0TrstTAAjnEUMN8W1ZCmDJd74nhfufRwrIm/AaAG1IIgDJwPwwxp1yXDescErKoXeldnl98ypu7WVo4Fy79ED7cZOpcxDinYAb/7yhWQO29Q3Q1aMeBYv8mvjdi8aDE8H0q+b9auD9y4sI92FeWIjUatoSrYaNQQ2Tr5esiFOaEh0dTTM3JB7cSAtn6F/FABIlAG3z3kYhjc/UJg810cMKB3NocTlS20NCr/nqRiQCn8TRgAIREtxwaWdBsyZUNSsefEvPT8eJtydwPOg4dSaLpY1FGxrYaGDUgF4jAp4QD04FI+DDKZ+OkRq6edSDvnnZwZ0iPh/Ju3cjedt2QCCAgpISDGfNgt6Y0XKwOKldPewd+NKWV7S0ycROG/3nustfUL6xVYR56VvP4fLsLpsck/LoX0l9WONL3N63Az7Xr4AAmJM6fiJel8Pw/HIYSAnjyBXNynXvXIpKwaR3UdDN4mPSzQQo85iXroYdjdD4TTsoFBUC/XYB9YfSv5PfnzuHGIrZntPqw9q5ckD+POM8MenmJAiLhBjgMABLmxM3E0g/c4aWNxIxWboUukOHVNKlITvTXNr4J0I8n4AcPA1dRtxsuVSFBdIT4nFg9hQIeDw07juI+saVIc//PYcHR/ZB29AIE7fuq4wppTJHYkQmTv/lTcce81dzaOrKy6ykYmgxBmWTHyFOgGMLybYHcB4A4RCrA+AXAA8BEHSizxApPrGICzns/YqNyN8IYhQZkzmCYITw/BCaChL42AwgAwBBjSLHA90A3BLD3qQpa5wSMfWutOaE/eX8mmUgqYt2DRqhz5xFFAApJjgNl/72gZBfBBM7HfSZ4QruN6g9SYrsyZVe4BcKKQsCYUMgQk4LSGZIYngo/bcih4v6nbuhaf+hNAumWMQpTSkd3NDS0qKYGxLJ3BATSDSLL8CCkGicjmewJvSUOFhX2xI9DEUD0pTEBudkFMDnZiT8H8RQ2xPhqnDg3MqMnuSJintRWpe0/DTs9duL08GnkUfQ88mYilz0tOuJsXXHomaNj1RcpBTl2i4/JEYwmSs2LgZ070UBeyqMjqb0r3mvXtG+Kg72MFu3DqqOjpIwjXwMGbRAbEgazm9groc+M91gUVtXBlchXZVJmRrJ4uo+1aVc9/fXtGOTYyJd61VodNb4ErHBQTi+eA5dzJh1W2FgZUOzN0gWB5HBCxvB0FJL7MUSsNGup7zR5Wk2NAqKUAQhXLoZonWf+sCpMUDABcCqOTCeoZIlWZskTTzmbToFEielKkoq0gvsl14Q+Y3a8pK4psDy5svRz6Ef1Sd29hxk/vcfFFRUYHPqFFRr1xLbDtW1Q3ZaKvb8PA5CgQDdf5mDOi3bVtelysS6Hp86imdnj1O/eMz6bdAzkz6WzbnVSxH2yht123RE16lMJocsSnFmOXnujP6TKaWXS9VagE1+hKgBjroA/D4EIgaUMh8JcPwNYASAY+Uw6y4Ak0jgH8CVUv1JZgiZxx2Az4e/k6NgUvNACvvJ2w+Tmy+asMYpEU3dym1F6KJOLJkPXn4ejGxqYsiy1TRVjjhL59a/pLSwuibq9DRVVePrWQHkNPH8+pcggJiauiqUNUVF/WNbEjghdGQPTxxCVjJJ/iHZBupo3GcgGvToAyVlFZSUpigCRlNcofwN5+zz4AYpS9HXr+CpUTmARJ+lZ+OXwEhE5TN4I+30tLDZ0QrGKhXPnCjPFZCXXQjfO9HwvRtN94wIh6tIgWDdOltB26Ds9MdcXi6let3nvw/ZvGw6hqaSJgbXHowRdUbASP1T4MeooFTc2PsG+dk8imJNsFncu1iXCXBHnND/s3cVYFFtXXQBQzcSgpSgKBZ2d9fz2d2K3d397H52d9ez9elvBwa22NIpIA1T/N8+FxSVmBlm4OKb/X1+wnDviX3u3LvvPnutFXPqH4QtWADi3SAz790L1uPHQ1NXNiJSRXykPqdgeODE8scI+RCDIiXM0XasWjUn46rRPfbUqiesaqpYZWs0UxIsgE+BCY+vUt7EEnQP3TF6EONRoOdone592cs98XDERCQxjiy6H8tr7x6E4vKu19CQAkk6QJLRM1Sp647mzZsDH/8H7G3HNTniEWBZnP34NSyRJVaokrB8YwfU6sh9rmqj6o3R10bjesB1BlHZ13IfSlqUhCQuDp/bd4AoIAA6rq4oevQINA0MVD2cAtH+/ROHcefwXugZm2Dwxt0QaOdPvFIgnJUHgyRS/13jhiE2IgzOHhXRfupchSqvZB0qJbbWD+gKYVISmg8bi9L1sibLl7XN/DqONtY+PomAW1UbNOlPr6lqy28P8CmOkDXBsQDAdABEV00VG+lG9UAk7UAyCi3ldCzJIRBrFcluEOQlXXeMPqc27wD4+ZtHdYfziOMyrcJD1i55E5TIOuC8Oi4u8guTg42PioRxISt0X7AcRhaFGEHk8aWPmdY9sbITaZlJoaxfkNMZ3GncbUaVh0Mpi0ynQOzRTy6dhdfJw0hJe7El/GH1xh1h/tAYmqmaMK7vANPmPxKYpjcWEBDAYClCoRBUuaGU5MbPRKJuLYA/1wGGmeP/Sf512edQrEuTf9XT1MBMVzv0L2Kp0geTrNdESpIYL28EMt6UpDgRO41K2ekhULG5E8wL/6pEQvKuJz+cxManGxGRxCWgjLWN0b9sf3Qt0RVGOj9CTSiQJnjM/VMfmf64rqGAYSAdS+WcaCJllJA5cxF38SLrR8vKEnYLF8GoTt6RbMnqS/Vx+eMB/1eROPP3M9a5ooSJ+TNy1fZKVXJ0X6aEoqmVPjpMrgR9I0Jw5t74FJjkfjYqa4FXsUT67i89uz3XbWeQvrsnPrB7s4WdIbrNolBJNqN7OqkREMSFLNJYEwfrGKOa30NUgggjR44EpFJgrQdHul1zJNCUQkPOHp3/DK/Tn1lyu9OUyrBylL96RLaR/nhUrDAWXc50QWB8IByMHXCo9SFGPpr04gV8u3UHxGKYdeoI2/nzFWn+tzqHyCW3j/JEbEQ4Kv/RnvGiqS3/PfDh4X38s5z7LrWZMB3Fq9RQ2aBC3r9lMT+Z5/qdMLG0UllfqmyY7lc7J99BUqwQ9bqXYNyAast/D/ApjpA1wXEpDYZCKfCUn1xIiQiq/5P3W9IXwE4A9K3mAJOc0Tf7LgDSPuNYkL4bCb5fpr0DUgiTYyl5FZTIMW6VHipMSmSVGxF+n6Gjr4+u85aBpG6TE0SsciM6JAHaelpoP6EiLO2zDlao0uPIoocMxlKmXhHUk4HJOCk+Dl4nj+DpxTNMh53MVNsKFV2aofzMTtDU/rXENWNyw8TEhMFSclW58QuRqB7Q7C+g8gDGTp+ZvUtIxvDXfniRJv9a1kgf60s5wc2Qf9g/kVDCODFI+YYSVcw0gGIVrVGphRNbU3pIXPW/ysp8fWN92SE6mjro7t4dA8sOhKnudwhRuj+oOuTqHh98esIlQiiQbT6ojEwVIgn37yN48hSIwzipYaPGjVjgKTBXwxBU+mUvYI3TdXls8SMGe3IqWwith3sUsBkof7hUoUUErLQ7T/w6lPgxs1berjSfAhPle09pLfIqlogKDsLOsZwCRufZi+BQqiwyEvV2n1Mt04T2z94QiyT435433yTgqXJqv70EtywFcIwMRcuX9zFq1ChYWFgAN5YB1xYABpbAOB9AwCXYqHrj8F8PWdxAz4SOUyorlSMmuxV8E/UGPc/3RIokBfUd6mNNgzXQ1NBE5I6dCF+6lJ1qt2I5TFu1UtqFUBAbyqjc0X/1Zpjbql8K+bCOjJx/8RzGV0e8GET8qirZXq9TR3H74G6Y2dhiwNqtfJi+QmPIqBpFFeOy8L0p1JH6JLk8wKc4QtYEB8FTqDY9MzkDgpN0Ivg8AK5WXzajSpBaaXwb3JYBZwRNIY3IYQA2/tQUkToQTGURgGnZdENv4xnfyGnc3qS4YWdH8YnaqEyNZOaIe4N2fdpPmcPK4yjQOb3mKSsP19TSwB8jPWBfMvNqjPSg5ujiR4gMjGc7il1mVJULfxsTHoZrqzbi4yeOKIjMsYwHY4a3Kfqd5yEkJAQ7d+5klRuU3KDKDRZsKWpEJHpiEBDgxbVgUxbosA2wzpz7gR5A24O+YMHHYCRLUylPgJGO1phQtHCu5V8VnYKs51Ep+5v7IaAqm9gvnHQrmZmbADetT+Cu5Cr7XQMaTOZ1ePnhsDXKnAQ2KjgBFza/YCXJZAR/IUUdQSYJqYzjkwqFiFi1GlE7KacJaBgYoPC0qTDt0IEXVS+y+lJ9XN554NPTCFzYRI8eMMnYvNoRzrsZyt6TWCjBP6ufMgiglrYmg+0QJ5IyjU+BiTLnpeS2eJXgoLntnzYWoR/fo2yjZmg6aCRT1to97S5Laldv64JKWVRDpvuFlFDOb3zBri12T69ly3ZEj//7HqN0k6CRmoruXpfRuWF9VKtWDYgJAlaX4dTFOu0GSnNKa2TBH74yqCpZ7U7FmbJXXtnJ9ycx6y5x3gNjKo7BgLIDGKdYwJAhSLh5C5qGhkw6Vsch78aUV3OXtZ9Ty+bj4yMvFmN1mkl7iGrjiweiQ4Kwe8JwtuFXvUM31OpMyH/l2/GFs+D7zBtlGzZF08FEq1gwLZ3cmCSqByyvkyMsumDOsuCNmk9xhKwJDmKHJKCeYybu3gOgF0HoCYop43KQQsobAPRmRQSlGY3aojYHAPiZ3pcApTQWYpXKjhknU9UWdYKDczPbtd++AaSaQtZk0EiUa9QMxKNxeetLhmljnw8oBbcq2ctH3f/nIx5f8GMFD+0mVIKtq3xBd7pqSlRSKF5K7yEklFNgIXOv0wC1u/SCwNAIW7ZswdevX5WT3Hh+BDg3HkghdBSA6sOBxrOJmTPTy5fkX8e+8ce1KI5E0yFN/rWakuVfZfzuKHyYVCLF+0fhuHfuHRLCuaoZskCTd5BUCMOAZl1RwiJrGd8Pj8NZ5QZJcmkKNFC3ixtK1bbLMUGR8v49giZOQsob+soDeuXKocjSJdBxzhyGpPAE1Sf+Vh6gF7VDCx6AkmquFayYetN/0cgPl9LvyxpAc88ycK34IxeOMvzCp8BEGfNRURu8S3B4XziNa7u2QNfAEEM274VAR4cpmZGimbWTMTpNrZKlK+i7dW7DMy7xrQHUaOeKCk0c2T09/uNXVHv/CZG6mqjk+wbdtMXo2bMn19aBLsC7ixzZaN9zhIH81sf1/W/w6lYwI7ruPrsaIx7NK5tzdw6Ovz/Oqje2NtmKqrZVIY6MxOe27SCOiIBe2bJw3r8PGjrKgXXl1byU0U/slwhsGzEAqalStB4zBSVqqCGhyvCrMtu4dXA3Hpw6Ci1tbfRdviFLtUFF+6Tkyfr+XSFKSS7wBLP/2+sDnzsh6gpPRS8GFZ3HpzhC1gSHsis4qGaQ1FO6kXT7T35WV3Co6MJLbzZdIop+z0hOduvwexYUkdXsUIwFOtkZlcKeWPqYcTBUbObEgiN57AfVFBsDWI8oD7+XT5jiypcAP9aUlkAAXUdXhAn0IdDVg6enJ2xsMiskkqFnRiQ6AXhBRUeEj7AB2m4Eiv1KskRJIHoYnAuOwNw3vohPSYGWRIyWZgYYZmcBXYkEYpEQYpEIEvpfKIRELIJYmPa7SMikv9I/L1KiFErWIsXj/LOQ+BCsf7oepz+cgXNUGVQMagqrhO+7WTZFTRgxHcn8UYCbbpQYuXfqE1NqYW4z10XzQWVBx2dn5MPoffsRvnw5UlNSWBBsOWQwLIcOZVKwalN7ICcPvH+YJlFND4tZ1RivwH/N7hx7j6dXAti0a3UshvKNs78vK+ofPgUmis4hD87jXYIj4Ws0Ng/tw6oV2oybhuLVaiLwbTT+WcUpEZG6QGZJhoDXUbi45QWEyRIItDUZSZ9Lhe9I41RJKqbve4QdjtowSElC30f/w5RJE6FDyYH3V4D9aXzzdScCDb+jiVMSRTgwxwuJsUI4ly3ElH4yPk9UuUYEUel1vhd8onxgoWeBI62PwMbQBgSN9O/Xn3Z3YNGvH2wmT1LlMHjZdjpfi4GpGQZt2AktgfoZzLeFEiUnY+e4oYiLjIBLpapoN4mrSFKWBb31waFZ9OoFDN60B0bmuaiCVtagFGxn/+z7rJJYlio1BbtQn6aAB/gUR8ia4FAmB4eANo1JdRIAAQB/5vRQc3AocFHJesrHx144tZQj2ypRow5ajZrIICoZSUKprJTKS7Mz4nc48tdDdoMh7BuRilHptDz2g2rKsPLQSeP5ICKsVzeu4u7hfSBJM7JUTS2UaNgMVRo2YYkFSh6wBAL9LOQSDT9/zpIL6Z9/DYHY9x4kKckQp2pColcIYhNnSKSpPyYpMrQjz1xkObbnotWwcSkmy6FKPSYmJYZJvh7wOQChlEOROZs4Y3SFMSge74HHF/0YJCndLB2MWFkzBbtEZnh520sEveOKswib3WxgaegbZ78DJgoPR8j0GUi4xXESa9vbw27pUhhUVCtiKHVxf/PGqKrswJz7iAlPgls1GzTp999iSidFpFuHuaq2sg3sUadzcZW9LPIpMOHxZc27BAf56sSi2QxuWrxqTbQZPw2UlN456Q7j06rduTg8Gv4IyyBJcaryoOogIhFvNawcrJ1+TVg/P+qDZoWSkaqhgWYv72N6s4Zwc0uTXD0/EXiwhVuqznuAUn9+Wzaq9qOqI7JmnmVQrJLyK46yukYC4wLR+WxnxAnjUN6qPHY03wFtTW2Er1mDyI2b2GkOmzfBqF7+bjjk5TVOkGSShqV4qlq7zqjdtXdedq/uSw4PvLt/G2dWLWZntJs8Gy4Vs67AkqNZdmi6go65nT36r+K+CwXRCFa3Y+JtzkcTKsKumFlBnMZvOWY+xRGyJjhyUlG5CaCFjKtFGmMnsoGZkFzDlxxUVKoDSCNPkKlXXgYlMo1ciQfRjvqeSSPxxd8Xdm7uDINJ5axv74fgyi4f1hPJDjbtXzpHPBsF3RR8E09Hp6mVsyUhzWwK6dAUSAHjBg4wbfYrXOH1y5c4telv6ESGQIPY23lmmlpa0NLWYTJrVFIo0Nbh/teh/79/TiSupFLjWLY8Os34zjqv6ukki5OZ5Ov2F9sRJ+LgNVb6VhhWfhjaFmsLgSblGDkLfh+NR+d9EeAT/e0zMxsDiFIk3whKSWq2+p8u0NTKPpEVd/UqQmbMhCSaa8u0bVvYzJgOLaMflVhUPX91+7+HB9KxtlRU1GNedZhaKY9Yk88eIg4SksGjCrmiHpYMokNqSKoyPgUmGeY4FUDFNMn4ogCotE8RbBu90Y1Nk5gnbOIZANQ2h8eU3XgZS/jcuobz61awischm/dBz8gI6SXcdsXN0G48uZBEUFJx9/gHprBFRslsSm4YmWcOI0l8HoEeb/1wL41sdLa+BK3SiTolImBPW8DvNqBtCAz8F7DhEpAUa5zb8Bx+LyJZAoWgKhll42V3t2JH3gy8ieFXh7OTe7r3xOSqk5EqFsOvdx8keXtDy8wMTvv2QrdY3m84KDaj3J31/uE9nF7+FyNPH7h2G0ytFayCzd0w1GfL4AFGsL1gBvxfPoOpTWEGVaGYUhl2dP501q5HkxZoPJD7fhRES+fnIqi056q6OXLAFcQ5FtQx8ymOkDVaIvAzafadTCMBTff9SABr0zg49qV9SDgFqn3jAPe/2lkARGVdDgDHIPerHQXQPi2w4bQCAXo7IoJRqvggooBUOS4AXgYlcoxfKYcGv/PBwZlceVqPv1aicDE3+L+OxLl1z1ngU8TNDH+MLJ9jJUbgmyhGlK9FoQAAIABJREFUeEdW7U8XVG4hX7z5MzTFZmQFaAh+fGmOiopivBvJyckoYm2NorrAi6uXISXlEzINDXbT/5ZUYEmGtN910pINEEMQ8RJayV8g0JBCYGgOLbfG0DK1SUtKcMcLdLS5ck2BAGe/JuJ0VAJEWgIWLPZztkX7ItbQ1tHlzqE+BVxCgxIcstjnJ48YQzZZh+nz4VxOtVUMJPl6+uNpBkcJTwznvjzaRox0rYd7D+gLspb7DfONxeMLvvj8jHKMnGnraqFRH/cccf/SxESELVqMr0fp6wtomprCdu4cmDRvLoub1MeoPZCpByQSKfbPvM+kq0vVskWDXu6/vafCPsfi1EpviEVSWDuboO24CtDWke1+o6hz+BSYZJgDPeepjI+YKytlkJWXZ5qU2FiZJmd/AIA9gHFpyZKqABLkaIyXsQSVtm8c1JNh65sMGoFyjZrD98UXnFv/nHFr9FtSGwIdTfy74zV8n3P3dudylmjSvxR09L4nun/2gzRZjP2bHmKihz6Ddwz1uY9Zw4Z8ryJK+AJsqQ/EBADmzoDnNcCAK3uPjUzCwbleEAulKF23COp3z5rfSQ7/y3zoWu+12PqCU4lYXm85mjk3gygkBJ/bd2DJdy1LSzjt2Q1dF6J2+73t+KLZTKGjaPlKaD917u892d9gdpGBAdgzaQSo8qZW556o3qFrrmdFVc7r+3Vhlc6tx0xmFdwF1dJhm8T5134iPRbUxhcP8CmOkDXBQb77O02elZIc54kDEgBR8JJMbEPaHEhzMGlNOnGClL8YBQcE5H9M78bZLAil1R8AEAFYlRbUeFKVblpyhCAz8hgvgxJ5JqCMYy9uWMWgH9bOrui5eDW+BMTj5ApvtktfqIghIwnV1c862KExpCSJcWi+F+KjUhgHA0nI5rSj//PYYy76Iu56AKAJWGeApqQfR0op27dvR1hYGAwNDTF48GBGLkqQE0pwUCKDkgvZ4nrlJBJ9nyb/+jxN/rVMmvxrCSXIv1JG/ui8aQh4/YLz/aJVDBakbKN+rgVcY5Kvn2I+seapNLdrya4YVHYQzPRkL+Mj6d8nl/2RGJuCOl3ccpQaTHrxAsETJkLox3GnGFSvDrvFi6BdOHuSWmX7oKC3R6pCRhaFWHJNbd898PJGIG4cfMcqxnrOr5GnxIV5vQ4kA3t86SMkxYlgYqmHDpMqw8BEOTt42c2FT4FJhnHS2yd3MwMI80AbHfJk1C3TEhm0OULwV0laW38AOA1gOoCFcqwxb2MJquCgSg579zLoMmcxSD1r+8RbECVLUL2dCz48CmfPfDKPxg6o2b6YTBVBwdueo7G9CFG6Wqjk9wa7WjWCldV3rg6EPAO2NwPESYBLfaDHcSLPYv08veKPO8c+sJ/pRUReEnI51uWXQyVSCQZfGQyvEC8YCAxwsPVBuJi6INnHB359+0EaEwOBlRWc9u75rQmvY8JDsW2UJ0tQ/TlhBopVoQJotfHdAzf27cCjMycg0NFFv5UbmXxsbizw9UscnjuFNUFkxIZmpAtRMO3YkkegTYCKzRxRo91/owqroKwUn+IIeRIctH1EyiWD0gIM2gY4DIBYcLinJmfZJThI2pW0qaiNnASYKYFCQDQCSlJ0Rzs4tA1+RYGF5m1QosBcFDolOT4em4f0ZtlbKk1zLl8fx5c9RlKskJFGUhBN/+dkpKLx5m4IIyUjSViCMchjwoA4hG94yupvMoOm0Ev6qVOn8OzZM5bA6N27N4oWpcpkGe1nIlFDa6AdEYn+LNbDtUf97Qz6gvkfg5GUJv863NEak5Qs/xry4S0OTB/P+mw5aiLclUw4+iT8CVY9XgX6n4wkX1u7tMbwCsNRxEh1WvepEgkit25FxLr1gFjMyEOtxo6FRd8+KkniyHgVFMjDnlw8g//t3IxSdRuixXDaYFZbugdIvnrvjHtIjBEyLgpS8PkdjTgTji99zLiNdA0F6DCxUo7JRWX5gU+BSRZzUiTBMTAt1iCIyt6f2iVFNqoIJfl5WY23sQTt0NNOPZnn+h0wsbTG5e2vQES9WgINSMSpDHpat6sbytSV/ZkQfycIC30CscNVl5GN7jZORZ1aNX/014tjwHESvqM00gigGSdBSlwgx5Y8RoR/HCMIJrlnrZ+qNWV1vCLHRSVHofOZzghLDIOrqSsOtDoAA20DJL18Bf9+/SCNi4PAxoZLcjiqhrxXkXEr85x0ZQ5KnHuu2yFz5akyx6BuS34PCJMSsXPsEMabks6tI38r38+4e/QA7h07gEL2jui7YkNumsrXc4n/b9uYm6zqnOB1VImmNv54gE9xhDwJDv54UP6R8DYokX8qip3hff4fXNu9Fdp6+uizfBvO/P2aEfeRhnT7CZVkUif4/PwLzm94zgZQp0txlGsgn548g6asfQJxeCIENgbIDJry6NEjnD1LKCagadOmqFnzp0Aqu+n7ewEnBgJfObUPuDUH/lwPGGZ+AwxLEWFMBvlXez1t/O3uhBoqkn89s3IR3nndYfjXfqs2KYXF/OPXj6xigyo30q12kdoYU3FMtpKvil1FP54lDAxE8KTJDNNMplPMFUWWL4deyZLKaP4/1Qbtsu2aMBxiUpsB0HfFRhSyl+/79bs7LH03mMiMey2oAUPTnBOyBcknlMQ5veYpI/yll8A2Y8rnKXkanwKTLNZNkQTH5rQNFWLN5koJvtv+NCU3YtfMuEmT3WXD21iCytlJTSUx5itqd+uDam07seoJ+t6w+7OeFlO/cigln3KCOCoZ3msf4c+6hoxstHfEZyztTFRqP9nlmcBdQiwT898WwKML+5GSG0cXPWRcMtXauDClrry0ZxHP0PdiXxB0s0XRFlhSZwnbPEl6/hz+/QdAGh8Pga0tl+SwJ/TS72Ok7LZlWD92TdTo2A01O/X4fSb3H5iJz50bOL92GZtph2nz4OzBcekoYlS9QVUc5Zu1QqP+QxVpghfnBL2Nxqk0hagBK+pAz1CtBsSLhUkbBJ/iCHWCg09XhorGQlUKu8YPQ1RQAMo0aIa4mBqsvEueIDopXoiD8x6wig9S0vhzdPkciUh/nk5O0JTAwEDs2LEDUqkUpUqVQqdOnWRTDCBejlvLgRtLgVQJINADmi4AqgxkXB2Z2fmIr5jwNgBRIq5iuaONORa62cNEoDqce1RwEHaNH8rk/Br0HYyKLahKWjELTQjFxmcbcerDKUhTOXRYmUJlMLbSWFS1JVi56oyup9jTpxE6bz6kCRx83bxnT1hPGA9NvczJ6lQ3moLfMvmTOFpoBzbdStdrhObDiDpAbekeICjdnul3mbIPSViTlPXvYqRmcXnHKwYjIGs6sDSKV85bIkA+BSZZrKsiCQ4iE21NqDkAST+1my5XT+QQnFTNr2YMgP6lGy2Kd1BQEOzsKNfBL7u2awu8L5yGRRFHeDSbjHunPn5jK6vezhWVmhF6WH4LW/0YfYqk4KmlARyjwnCrdX3o6v6UYJRKgP2dgI9XuWdw/4uAHcc3dfvYezy7EsBijq4z5a/8lH/EP55BCmKLHixiH06tOhXd3buznxOfPEHAgIEg/ijtIkVYkkObh+uq6Pzf3ruNs6sXQ0NDk1X1GBdS73Yr6sv8OI9igyPzprLEhLltEfRZvk6hjTGRMIXxb0jEYvwxbircqtXKj+kopc9H5z/D6/RntilL0vFq45cH+BRHqBMc/Lo2VDKaQJ+XODyHw96VbjgOHwnFoAE0H1QGrhVyxvXRTZYk3z56R7BdoK6zqsmNgc8JmpKQkIDNmzcjNjYWlpaW8PT0/DWAysw70X7AiUFAwH3urzZlgA7bAOvMiQjjxRLMeB+EQ6Gc/KyZQAtLSzigjbXs/BS5WaQr29bj2b8XoG9sggFrt0HXQD6ID0m+7ni5g6mjpEi43X4nEyeMqjAKTZyayJYQysUEJDExCJkzB3EXLrJWiKjNbtFCGNUpuIRVuXCHUk71uX0d5/9eztqiUtT3D+6yMuIBa7bmGnerlAHyqJFHF3zh9c8nCHS10OevmtAz+j12b+6d/ADvS9xOe432rqjYVLEX0dwsFZ8CkyzmoUiC42oaRxhlrn+W4poHYCYAegvnWLN/NYLFcriPDMbXBEfox/fYP208BAaNINAlyjKwKs2URDGT/W4xmPtMXiNJ9xOvAjG1ghHjcjhopYsGZTNB9iRFA1sbAlGfAJMiwKDrgJE1hMliHJzHcXcVKWGGP8dUUPmzKuMcKYaZfGsyLny+wNTDdjXfBQ8rD3ZI4uPH8PcchFRKcjg4cEmO34Q76uj8afB/+Ryulauh7US61NVW0DwQ4e+LvZNHsY2xOt37ouqfHeWeAimnkIIK2dCt+2FgYip3G3w54czap/B/HYXSdexQv4e6Wpgv65I+Dj7FEeoEB9+uDhWMh16e6CXKxqUYUoQdQDjvKq2cUfUP2djD3z0IZezrZA17u8O9pq1co0wVSRH2d9bQFKrY2Lt3Lz5//gwdHR2W3PiBxCyr3p4fBc6NA1JI9Q9A9WFAo9mAduZVBA9jEjDitR/8koXs8LrmRljj7ghbXdUT+KVPgfCU20d7MihC9Q7dUKuzbCWjlMw46HOQscLHCrn5FtIrxCRf2xVvx8hEVW0J970QPGUKxKGhrCujRo1gO38eBBbylTyrepwFqf2kuFjsHDcUSbExKFalBlqNnoTtIwcw3G35Zq3RqP+QgjQdlY+VSI73TLsLYZIYlVs5o5qM9zCVDywXHby8GYQbB96yFogboW43tzx9+eNjYJKFOxVJcPynKjio0nLXpOOQSrnqn+JVbFhC4fq+t4w3q/+KOgqp8aT4xyJk4zM0qaeDOF1dtEqJwfbmRI+WiYX7ANsaA8J4wLEG0Ps0IND5ruoCMFWukjXkiyNy8RVjpyaKEtHtXDdGwG2tb40dzXewzQGyhAcPEDBoMFKTk6Ht5AinPXuhbZPz5k9ux6TK86lidOfYwayL9lPmoGiFyqrsTt22Cj2QXpmlravH4M3yVuLcObwX908chpWjM3ovW6fCkaq2aeLd2DbuJiNObtyvFEpUU5PYq9bj8reuTnDI77PcnsFb3GxuJ5bT+fQCRbhciUiESq374dUdc1a90WdhLZlIReOjU5hqCu0AEZlPy6Fl5Q6+Yy5+Rtz1wCxVU65cuYLbt2+zqRAspXTp0tlPKzkWOD8BeE4ctwCISLTtRqB45kSiImkqVviGYq1fGNvC09XUwAwXOwywt4RmFhCWnPyam7+nP2zoYTVg7dZs2ayJCf7MpzNM8pVgKWy62oboV7ofepXqxQjTVG1SoRARa9YgasdOtnunoa8Pm6lTYCYrhEjVAyzA7V/cuBqvrl+Bjr4++q7cCGMLSzw6exI39m5nEsZUVmxgmjfVRQXFjV6nP+HReV+2M93rr5o5Kj/xeV4k2Xl+43PGT+BcthBaDCkrtyqVsubHp8AkizkpkuD4z3BwfA1PZLKwRFBLpoknGLR+NETJqdg58TYj5aMKDqrkkNcIQhWy0AuzbGNx1sUahsIU+DSuAh2tLNTAfM4Ch9OS95X7A61JDA+sEvTD43CGm+8+pxr0jfNuc4H6p+RG93PdkSBKYBsEm5ts/sZVlXD/PgIGD0FqSgp0ihZlErKkslJQ7fre7Xh89iRMrGwwYO0WaGqqDn5bUH1UUMadkpiAHWMGMy4VknclmVd57OCsSQh++xoVW7RBg76k8VAwLSIgDkf+esgGTzxcJpb6BXMiv/Go+RRHqCs4fuMLjaaW/rKko28At1pT8eFxNOyKm6Hd+JzJiqis8+y6Z/B/FcVKwQnvJq9cYU7QFB8fHxw+zCUqatSogWbNmmW/IgEPgONEJMrJkeZEJPohMRnDX/vhWRwHvy5tpId17k5wN8q/G2NKYiK2jxoISj55NG2FxgN+JXwi398KusWUUT585bjxqLS2a4mu8CznCQu9vKmaSPnwAUETJyHFx4eNQa9sWdgtXQJdeZRtfvPvmKLTy1g2SqRfRP5FJkxOwtbh/ZEcH4eqbTuhTrc+inbxW55HHBy7p9+FOEWC6m1dUKl53pIWKsup4X6xTKZbLJTCytGY3ZO1dfPvJYRPgUkWPlYkwZGTigqV82WOZ8x8ELzcLAl+/xXnNz1HSoKYSSknx5yDVPQGHWcsgFPZ8ji95gkCfKLhVs0GTfrlsIGQhfOjjr2D9ytf9KxjxbitVtqboXvxbL571xYBN0gIj1hQVgOV+yEhJgUH5nixCizafaVd2Ly2FxEvMPTqUBDc01jHGJsab0I5q3JsGPF37iBw6DCkCoXQcXXlkhyFCuX1EHPdn1goZBtb9Ayp3bU3qrXrnOs21Q3krwde3biKixu4RGGnmQvhWIa7ZnMyUXIy1vXvCqlEXOBlgp9fC8Stw+9gaKaLPotqyr3ZmpOv1H/PvQf4FEeoExy5X0/etsAkUMcNRXRwIMo1aoHPr8uw0q563UvIJBOXsXSa8XVUlK9kMydoSmRkJLZs2YKUlBQ4OTkxSVgtrSwCfCIwu0lEoktkIhKlue8OjsTcD0Hf5F+Hpcm/6mpmseuUhytJRHBUdkhcCyTZRQRS6Uas75TYeBz2nXSylUsrDC8/HA7GqlfWSBWJkPTiBeJv3ETUrl1sRwuamig0yBNWw4czKVi15c4DRPq1Z+IIfA0Nga1bSXSbu/QHWd10STdKTA7asBO6Boa56/A3O/vO8Q94+q8/S7z2/qtmviYGFHFt7JckJgebGCtkfEYdJlfKd1UYPgUmWfg0pwQH6XxSSRvJv4rS2qAteMqGvwBAklwcqzRADM+n0zg4FsixhrxLcLy5H4Jre99AKkmFvrE2Wgwph+u7FiD4nQ9K12+M5kPHIP1ZrqMvQP9ltRWSak16FYkve1+hc+UUfC5khfIaYlysnw3sQSoFjvQC3pwFCELZ9yzgWB2vbgXh+n4OktVmdHk4uOdNsj7jGr+Pfo9B/w7Cl6Qv0BfoY13Ddd/IueNv3kTg8BGg56Bu8eJwpCSHubkcl0j+H+pz6xrOr1vB4otBG3ZlWyWa/6NVj0AWDxAHx6HZk9n3mqReey1ZCy2BIMdTfZ8/wfG/ZrKk5PBtB6FnZJTjOXw94NK2l4yIu1hlazQbWIavw/xPj4tPcYQ6wfEbX4oBr57jyLxpbIYN+s3FvVMxTPmk35JaOZaGxkQk4tCCh2yX1K2qDZr0l3/XJztoilAoxLZt2xAeHg4jIyMMHjwYxsYZyep/WpiLU4H7adrd1qWBjtuzJBINTxFh7JsAXI3iuCqK6HLyrzXN+XNjF4tE2DVuCGLCw+BWvTb+GDsFn2M+Y633Wlzxv/Jt8jXtajLJV/dC8mwyyndRUzJI6OuLhDt3kXD3LhK9vL6po1BLxC5PVRsGlSrJ17D66Cw9cPvQXnidPMwC0F6L18DS8ced0KT4OGwd1g+ilORvko9qd373AO0E751+DxKxFLU7FYdHI9Un/pTlf+JAOrHsMaJDEzmZ7omVYGGb/wksPgUmGXzdi3iU034fSWqnAFak/U6Ji70Zjr0OgIghigLwzfD5eADE4kt/P0iPBAD0WQCAKnJIxFKTvElwEGzE68wnPL7AVTOaFzZA6xEerGz76aVzuLpjI4O+DdmyD8IkYNeUO0xRpfVIDziVlr8qQSqUIHjePWywDMEOj+LQSE2FV41ScNTPRq45JY7j44h4w0FJB11HqrEdq1wK+RgDEyt9dJtZFQKdvK9c8o/1h+dlTwQnBENHUwcr6q9AfYf6zJdx164hcNRogJIcJUvCceeOApXkODR7EoLevGZqGaSakZ9G92hS7SPVC7WkZ+5WItz3E/ZNGYPUVCnq9RqAyq0zkWv+qYtbB3fjwamjsC7qymKNgmps03LKHSTECFGnixvKNfi9JJ0L6rr8PG4+xRHqBMfvclVlMo+za5bi7d2bsC1eAhaOfZkKimNpC/wxsny2syas7qm0AIRKwUjWTd4HU3bQFCaLeeIEXrx4AU1NTfTt2xeOjrT5loU93g2cGcX9sUIvoOXyLIlEL0R8xfg8ln9V9BLKqJ6R2NUdx+P+hYRkbgGUKlSKJTZq2NVQtPlszxNHRyPx3j3E3+WSGuLgkF+OpxJd4wb1UWjwYGhll3xSyQh/30a/ECv6lNGQSiSo1q4Laneld7hf7fqebXh87hTj4Bi4bju0dbJ5kfh93ZXlzG4eeocX1wNhaKqDngtqQKCd9y9J8rpdIpLizN9PEfTuKzQFGmgzqjyKuPFjd5hPgUkGv6YnLTJz9Q0A3BspZ1klOOhvfQGQ7jJJwlLm+ywAkhbjdHllN14kOMRCCa7u9mF8FmQO7uZo5lkGugZcdV1ibAw2D+nN7jGE1yfc/onljxHyIQalatuhQU/F1Ae+7HqF1+/fom8dWyTq6mOYnQVmlcjm2U2DifwIbG0AJMcAdhWBfhcQFSHB4b8esKqTis2dUKOtq+wroMQjideKkhy+sb4QaAiwsM5CtCjagvUQd+UKAseMBcRi6JZyh9POndAy5b/6xJcAP+yeMJzNIR2ipESXydUUU+Db8hIfn0RQAQGsnU3gUMoCjqUKwcbZON/4huSaBM8OvrJ9I55dPseSl/1WbYaRefYVUAdmjEfI+7eo1Lod6vcawLPZyD4cqnrcO+MeO6Hz9CqwcshmQ1T2ZtVHKtkDfIoj1AkOJS8uX5ojMqLNQ/sy3F3jASNx74wuKLiWhb3c+7If7p2gKl/gj5EecJRzt4eDpnhDHJ4EgY0BbEZWgIbgOyzEy8sLFy5cYO03b94c1atXz9ptvreBPX8CUjHHt9H1AJAJWRbJv876EIQDIZz8q6lAC0vc7NHWhh8vD5lNMDY5BlsnDgHC4xBikYxL1cLgYOLAJF+bOjeFpobyoDTSlBQkeXuzZAZVaiS/5lRxMppWoUIwrFEDhjVrwrBmjd9GKo8v30kaB5WZHpw1kQUc5rZ26L10HQQ6mRPtxUV9wfaRA5l2fcP+Q1ChWWs+TSXfxxIXlYx9M++xlyRZYXf5OWgK9q/sfI13D8LYMJr0LwW3qvxhgedTYJKf65RD3/me4CBYExHT0q44Gckl1unqBq2fCD9PLp2HT48fwKVSVbSbNAtPr/jjzrEPDMbSd0ltaGrKH/7Fe4Ug4uRbjCwZhkdOJUBPqNnF7DDI3ip7PPz7K8CBTnQDBDy6A203wOvMZ0YWTOOgF5ZCRfKnwjIyKRJDrgzBm6g30IAGZtWYhY5unBRn7KXLCBo3DpBIoFemDBx3bIeWiQmPL0/gfzs348nFMzArbIv+qzb/AH3M64FTAo6IZTMzHT0t2Je0SEt4WKgJI2VcHKru3DlmMONwc6/TAC1HUDFa5iZMSmT8GxR3tJs8Gy4VqWCtYNpbr1D2/NTW08LAlXUVun8VzJkXrFHzKY6Q/wlXsHydPtp8D0ry2m0P/jmGWwd2Mex+wwFLcW3fR4a77besdraqA5FB8Tiy6CGk4lSUrlsE9bvThpd8FnPhM+JuZK6a4u/vj127doGkYcuUKYMOHTpkHRhFfQa2NgSSogDrUsCAy4Dur1nbRzEJjEg0Xf61tpkR1ro7wk4vbxnaZfWSUCLEoTeHmOSrQWAymj7kJP0sejZAz5ajoK2Ve44LeqClvHv3HXby6BHHpZHBNHR1YVC5MpfQqFUTum5u+RoMyeq/gnxceuk4zUEWorDLW/7Gi6uXYGJljf6rt8iEuS3I/pF37P/b6wOfOyGMx6LH/Oq/vOTJ254qj7//z8dvcAI+kqPyKTBR5Trksu18jSXo+Xx2/TPER6UwNbRaHYoxeJZGJmpgb+/dwtnVSxgMbvCmPRALdb7tgLYbXwF2xeVP/ktiUhCy6AGO67/AloqlEWnEVTT8aW2GlSUcYCjIporq9mrgymzO/c0XQ1xpEA4veMhUX2yKmqDDxEoMQpsfRtLrw68Mx9OIp6z7CZUnoE9pjtw59vx5BE2YCEil0PMoB8ft26HFUx4DgjRuHtIHpLpRt2d/VPmjfX64k/VJULwDc+4jKU7ENskIUhDwOgr+PlGIDkn4ZVymVvpwLMUlPIqUMIeOXs78Evk2uXzu+PnVS/h3y99sFF3mLIa9e+Z8FJ+fPMKJxXOgoaGJ4TsOFmgur+v73+DVrWB2jfwxKvsq9Hxenv9093yKI/LnaZL3y5+vQUleT5debklS6mtYCCo0/wOJCdXh9zISLuWtmAxhVkZYyWNLHuFLQDxMLPXQZUZVuR8yP0BTGjrAtOl3boG4uDhGKkr/W1tbY+DAgdDJYvcaJAW7vQmH3TUoBHheA8zTodjcDEj+daVvKNZkkH+d5mILT3urfJF/zWmdSfL1/OfzWPdkHcP9kulr6aPbC3dIfSOZRjkRR2koSIIqCgv7ltBIuHcPksjIX4ZEpbZGtWqxpIZ+xYrQ1FXDHnJaN2X9nSoydo0bCmFS0jfyv5zajg4Nxs4xQxjmtvmwsShdr1FOp/yn/k7SmAdm32cyq7JUp+WXczISKxJEoH6PErxjgOdTYJJf6yRDv/kWS/i9imS74UQULtDVQtMBpVG0nGWWQyYi402DerL7TaMBw1C+aUscWfgQEf5xKNfQHnU6u8kw3V8PCfv7CZ6EvsYt3U+4XbIi3lpxBNklDPWwo4wzXA30Mm+XvqSkgPbyGKChBfQ6gSCRB06tesKOr9vVDWXr5x+uPlGUiNHXRuN+yH02niEeQzDMYxj7nsacOYvgyZNZkkO/QgU4bN0KLaP858352dEvr/2LS5vWsET4oI27YWCSf5Caq7tf4829UEYA3W12NZaETjeqvgvwiWIJj4A3UUz9J6NRVU9hV1NGQEsJD1KZUqTiSKELvACcRDH+gZkTEPrhHYsbey5ewxKZP9uNfTvw6MwJFHYtjh4LOQWWgmoH53khKjgB1doUReWWRLOkNj56gE9xhDrBwccrJJdj8nv+FMf+msFa6TpvDU6v8QdkAPQPAAAgAElEQVTxahBGt1ilrJVQvE5/YiWjtDPUfnxF2BYzk2skGaEp2oUNYD3iOzRFIpFgz5498PPzg66uLjw9PWFpmUVwRoopB7oAH/7l2Nf7nAGcfuSi+MjkX/3xNC6RjdHdUA8bSuWv/GtWzqLS9DvBd5gyyrvod+wwwvt2KtEJg8oNgjg4GvunETwcaDFiPErVaSCT3yXxCUh8+AAJd+8x6InwIwcrymgCW1sGN2FVGjVqQGCR94z1Mk3mP3DQP8sX4MPD+9A3MUW/VZugbyQbhjSdS8eiiAP6Ll+vcALsd3XxvzteMdiHmY0BC6T5FgjTi+m59c9BpJDEgdRqWDleYs/5FJjw+FrNlwQHySPePvKOJfKIF6vV8HIyYdAvbliNVzeuwM7NHd3mL2PPd3rOG5nrovdCxWQWY6/4IeLKRxzX90JCagqCy1TCBUsHiFMBYy1NRujd3CqLF2thIrCjKRD6AtA3Z6SjV88k4c3dEFZ63n12dTa2/LIUSQom3piIawHX2BB6uvfExCoTGVz066lTCJk6DbQI+pUrwXHLFmgakGgPf2z/9HHspbdkrXpoNWpivg2MEhen13LVMDklrig2paRbwOtI+L+OQtinWBavZjTigLN3N2cJD9rBNzLPIomWbzPO+45pnffPGM+uxwZ9B6NiCxKG+tH2TR2LsE/vUaVNB9Tt0S/vB6mkHqkaaPv4W6y1tmMrsAoftfHTA3yKI9QJDn5eI7ka1ZmVi/DO6w7sSpRCuSajmSQbZdEJnqKdBVt5mG8sky2kILxCU0fUbF9M7jH8AE0ZXgE6GTC1ly5dwr17HEFQly5d4O6ejSrIxWnA/fVc/3+uByr0/DYWShbsCY7EnA/BSJJKKReDIQ5WmOJiCz7Iv/7stLCEMMy4M+PbrhD9vblzc4ysMBKOJt/J2aiUmEqKCYpAxFGCTKRYU8ViJL98+Y0YNOnpM0aAltE0DQ1hUK1aGo9GTegUdebdTrHcF9ZvcML7B3dxesVCNpOWoybCvRaJPchmxJy+dzJHsttmwnQUr6Ia4lnZRsO/o2hXh3Z3yJoOLI3ilTnIFx8sIiAOJ5d7Q5QigaWDEdqNryh3VVxezYNPgUlezVmBfvI0wSGVSHH72Ae8uBbIhko72ZQgoySHLOb34imOLeA2Owas3Qap1BgH53LflU5TK8PaSX4+CWFQPML/foIAzS+4pPOMteXcpAVWwRBhQu55NMrRGpNdbKGVCXQGX/2BLfWBxEjApgySu57DgYUvGJTBpYIVWgzOuspUljnn9hiRVIRZd2bh7CfioQXaFWuH2TVmQ0tTC1+PH0fIdM6fBlWrwmHzJmjq6+e2S6WcH/b5I/ZNGc3ayg62oJTOsmmE7nV0P46LTIatqym758kDPRImiRH0LpolOyhREhOR9Etv5raGcKTqjtIWsCtulmVcq+q55nf76RBWgqL3X72ZEZKnG8GU1vfvxqo/20+di6LlC64Knu/zLzi34TnbvBi4uu5/dr3z+3qTpX8+xRHqBIcsK1aAjkn4Go0tw4hcVMKqAd49MkXQ26/ZSr0SIzuVrpJsIUl5dZ5aBVra8hFcpvjHImLjMyZDZ/wTNOXVq1c4evQo82Lt2rXRuHHjrD3qvQc4TWqAAGqMAJr99e3YCCEn/3ol8rv86xp3R9Q2l20nPK+X8V7wPUy5NQVRyRzxabXC1TC20liUtvxVcpegCARfoHWr39sTlVr9CUrmiPz9OWJQ+nffC9K4uB+noaUF/XLlvvFo6JctC41MkiN5PXd1f989QIEGrW18dBScy1dC+ymEiZXv1ks4WsLTUqlp979Wyn3+774eFza/wKcnEShUxBBdpleVK6BWlW+oDPv4kkdM1o52pTtOrizzi6mqxpRdu3wKTPJj/jL2mWcJDnrRu7TtFfxfcVBDgpg27leKbVbIalKphMlN072nVueeqN6hK+NFoGe9ouol9FwKXfwAkhgh7jkH4FXoOwgEAnQc4IkpIbG4H8PxK9Q1N8LGUs4opJMJl8LnWxx5OKmGlWqLt06LcWWnDzuPYLQ01/w0aaoUC70W4vDbw2wYTZ2aYnGdxYwfK/rwEYTO5rhEqDrSfsMGaOrlf0XBv1vX4fmVi2CVfis25Nsz4vbR93h2NYCpRHWdURXmhXMH5aEERzqcJfBNFITJnNJculE/dsXMvpGVElmtvM/X/LzWctM3qSUR4WhyQvwvsNePj71waul8Bl0ZvuMQdPT4kYhTZL73Tn6A9yV/xtVDz1G18dcDfIoj5Iuy+evTnEaWZ0FJTgNR9d+9Th7B7UN7oGdkjJ6Lt2DfrIcs6UAlrc5lM4eE3D7yHs/+F8Cyox2nVGa7RPIYg6as9YY4Igk/Q1MiIiKwdetWCIVCFC1aFD179oRWJlhB1p/vnTTFFBFQvBnQ7eA3xZRLX2Iw7k0AIkXcDlF7G3MsKl4Eptr8I6Ki4GjL8y3Y8HQDUpEKEx0TzK81Hw0cGmT74L26YyOIhFJXVw9/2JeAyMsLoqCgX5ZCx9n5W0KDdpHUEq7yXK15f2y6rJtAV5dBTEyt5VfOCHrzGodmT2KDz2/pv7z3YM49UokzJWnJWg4ti6Ie+fuClJIkxolljxlmmNQC2k+slG8qETl7jzuCT4GJrGPOh+PyJJag5Ni59c8QGcQlC6iqkqRU5dkJT/fN9b3b8fjsSZjb2aPfyo3w+ucTHl/0Y5Cu7nOqKfQyGH3qAxLuh0Bqq4tjktuIjY2Fg4MDevbpi4W+odgcEMG6L6KrjW1liqKCSSZQDq8twAUORpHacBbOPG3EduwpGUhQs/wmmaREzmrv1djxcgcbY+0itbGy/kroC/QRdeAAwubNZ58b1q4N+/Xr8pXPitQyNg3pA1FyEhr0HYSKLdrkw1cDCP0cwyqBKeas1sYFlVt+52BTxoCooonUg4iolK6VcN9YBtvKaPomOlx1BxGWulvAwISfZPPK8Ae18fTyeVzdvoE1RzA0gqORXd+zFY/P/QNbt5LoPn+5srrLl3boWRryMQblGzugVsfi+TIGdaeyeYBPcYQ6wSHbmhWIo4h4aPtoT8SEh7EKAPMizUHZdF0DAfotrc1UVH62oLfR30i+FCXv+XrhM+LTVVMyQFNSUlJYcuPLly8wMTHB4MGDYWiYRTY/o2KKlTunmKJnggSJhMFR9gZzu1gmAk0scXNAO57Kv0YnR2PqramMc4OsTKEyWFF/BeyMKC7+1aRCIZKePGUVGpF37+BSagIkWppwDYtGiVCu8kPLzOw7j0bNmtC2y7ytAnGR/scGGfTWh0tMpKaiXs/+qJwLVvtDsycj6M0rOJbxQKeZ3yub/mMuzXK6Z9c9Y2TK1k7GLFGbX7t4RNZMYwl8E82Sxq1HecChJP+5b/gUmPD4mlZ5goNe4M5tfI6kWCG7fur1KIFStRS/52eEuBHRoKbABkcXPWIu7jqrKgrZyS/PmvI5BhGbn7M2vtbRw7GH59jPTZo0Qa1atXAqLBrj3gYgUSKFjoYGFrnZo4ddoR+Xld5MT48AnuwDEX/FtDyMQ3t1IRZJmeJGnS6KkaAq+9rZ9mIb1nivYc1WsqmEdQ3XwUjHCFF79iJsIQc7NKxXF/Z//w3NrEjTlT2on9p79u8FXNm2HgJtHaaYo5cPKi9036MkMyV1qYqi07TKKle1Im4Gus9ShYf/60hOXegnI2hgOlmpnauZ3NXJKl66XDdPVVr7p41D+OePsC7qih4LV0JTUwt7Jo9ChO8nVGvXGbW79s51P/nVgFgkwdaxN5myIx+qu/LLDwWlXz7FEeoEh4JXDXFVKLKbomB3Mp3m+/Qxji/iSif7rtyIa/vCWLabWPsb9Cz5SxtUAnto/gPQbpG1M8m0VZSb/C4raArtfhAs5fXr19DU1ET//v1hb58FQ/oviin/A8yd4R2bgBGv/fEpiXto1TQzwt/ujijCU/nXZxHPMOHGBIQmhHLBY4mujKBMR+v7DgL5JeX9+2+wk8SHj5Ca9B1j+s7GHB8KW4CKkNvXaQarBg2h5+6uJpaU6RvAr4MkYhH2Th6NyEB/WDunBR5ZVS/JMPR0yTc6tPtfK2BbTH4JZxm6KbCHhH5K2z0E8McoDziW+umFKg9mRt/v/+32wZv73D2gUV93lKxumwc9574LPgUmuZ+NylpQaYLjw+NwXNn1GhKRlG1MNB9UBva5TI7RNbl7wnB2H6rY8k/U7z0Qe6ffY8/9qn8URZVWiikSRB1+i8Qn4dDQ1YK3RwQeP3/CqjOHDBkCKysrvElIwoAXvviY9vzubmuBhcXtoaeVYaNFnALsagUEPgR0TeBd4hTuXY5hROcdJ1VmJel8sINvDjLIClnpQqWxqfEmmOmZIXLnLoQvWcI+N2rYEParV0Ejj5MctL77poxBuO9HlK7XGM2HjckXlz089xkPznwGoS8pwawIv0tuBk5+IMlhLtkRBdq8EwulPzQp0NaEnZv5Nzla88IG+ZYIz81cfz43+J0PDs7kqqEaDxwGtxp1sGFgd7ax0nH6AjiVK7iyqsEfvjIeK7L+y2pD3/j3rshR5nWRH23xKY5QJzgUvAIOzfdC/NcUGBjrsBI4KoujL17G3+lnfWNt9ndBFuSeCnaf6Wn/LP8LHx7eg32pMmg+bDb2zeRIPf8cUz7TIOl/e33gcyeEZbS7TK8iN1byR2iKIaxHlIdGWpXI3bt3cfnyZdZ/q1atUKVKlcynSoopB7sC7y+nKaachtihBtb6h2GFbygkqYC2hgamutgyMlFNObkLlOnfrNqiB+uBNwew/NFyiKViVsI6t+ZctCjagp0ijo5Gws2baUmNexBHcOW7GU23RAkGO9GuXAmHDm5HUlwsyjVujiaeI/JiCuo+VOCB+ycO487hvUyDnnZVbFzkJ+7NOCy6zvZOGc12ZYpVqY4/J3Bkd2r77oFTq7wZ55BtMVO0n5D3pGoPznzCw3O+bEC5eXnMjzXlU2CSH/OXsU+VJDjou+19yQ/3T31iwzCx0kfr4eXkfiZnNYd06CqREA7euBt3T3xiPAm0u02cNYqYJEGEsJWPIU0QQbOEMY7EXMfXr19hZ2eHAQMGsGRHrFiC0T7+uPAlhnVRzlgf28sUhUPGTYrYEI50ND4UEosSOPp1DSKDk1DI3ogRoWplTIgoMlAlnXP642nMvDMTBEEtZlYMW5psgZWBFSK3bUP48hWsF+MmjVFk5co85cEK+fAWB6aPZ/13m78cdm6/bmYpyQVZNkNVG4f/egCpJJU3MAJKElLSm5GV+kQxpZafjeBQ6VAWqrLTM9JWtatU1n66YhLB00kx5fLmtdDUEmDEzkPQ1s1/jhhFJ/74oi+7LxKkrsfc6oo2oz4vjzzApzhCneBQcNG3T7iF5HiRzGeTBBqX8PieEDFIS37QZ5QgSU+O0LHyllfHR0Viy/B+IJgKqTQkxjmxmwIlV/osrvWLdKLviy9MupCsdqfi8GjkIPNc0g/MCpri6+uL3bt3M5JMDw8PtG3bNuv5XJoO3FvHNdlmHXzdO2PEaz88iuXkX0ukyb+WNuInQVK8MB6z787GZT8umeNq6spwui5mLuz3BK8HCBw6FNJEbj7pJrC2/sajweRbM0jmPrl4Bv/buZlVbRBZmIVdFpUvcq+Y+oS88kBUcBD2TBoBiUiESq3asl1TZRgp7ZDiDhldG4XsvyvxKKP9gt4GkdD9s5qTJ2w3vgLsiuednJzP3RD8bw9HlFiypi0a9iop9308P/3Pp8AkP/2QQ99KT3BQaf/1fW++Vf1Qco5KsfWNlLdTGRsRjq0j+rOpdZg6FzpGrt92RXvOrwFTK8Wer4nPwhF18C1rN76xMQ7dPsV+btiwIerWrct+pjhgnX84Fn0KAe2nW2hrMfLRehYZuL4CHgK7WgISIcIK98axZ+0Yj0ON9q6o2NSJN5fDFb8rmHhzItvIcDB2wNamW1HEqAi+bNqEiNUcjMW4eXMUWb4MGoK84Qe7tGkNXl77F1ZORdFrydo8v+eQpOvJ5Y8R+ikWJpZ66DqrGi9VLhJjhaDnA3F3EIdHYozwx+tKA6zqhGRoCdJi42LCm+SaLF8AEhjYOXYIiNRcoKMLsTAFRUqWRte5XLxQUO3s+mfwexEJ91r0TM1GfbGgTvA3Gzef4gh1gkPBi4tkrOiGmRQn5P6PFSIxTvTtM/qdsKSKGFVUsMqPnKpDTLShZ6DNoDL3jh/E3SP7oW9sgkEbd+PYYm9GUJYZlpUSMyTjReMuUsIMf46uIDfcJitoCpGNbd68GQkJCbCxsWE7OTpZlWx67+UwuBQEVR+OQx7jMeN9EBIknN887S0xzcUO+jzZwfl5Ld9Fv8O46+PgF+vH/tTapTVmVp8JA22OUC3h3j0EDB2G1ORkaBgYwLBKFRjWqskSGzqurlkGIgRt2DluKGLCQlG8Wk20GTdNkctIfU4+eYDBs+ZNQ8DrF0z2t8/y9UpjMCe8LSmyRIcEo1TdhmgxfFw+zZKf3ZLviZCMgm3amWszKm9Kc2mH8Ozfz0DBvoO7OVqN8ChQwTGtJp8CE35eXWxUSk1w0LOYFICC339ljZeoXhgNepRUCU/A4TlTEOjzEu51GqD5sHHYNeUOi1tqdiiGCk0US5TS9y1yz2sk+0RB01AbTz0i8cD7IYOlEucWxQDpdjMqDkNe+yJKJGHy7lOK2mKkk/X3qswM8cBNow148cEWBCkgwlETS8USMKq4ju4E3cGYa2OQLEmGtYE1S3K4mLogYt16fFnHbdaYtGwJu6VLVJ7kIPWMzUP6sJfZRgOGoXzTlqqYcrZtEkE9EdWTZVUtnOeDyqFDum6p6iS9uoO+f1TxkdFoo9G+hDnKNXRg/xcE875wGtd2bfk21OoduqFW5x4FYeiZjpGoAGgzOSVRjIa93eFes2DAPQusw5UwcD7FEeoEhxIWNLMmmMRniiQt4SFKS4B8T4awxEiG5MjP0leyDotIyPSMtZEYEwqJKBaF7CxRuFgxvLnHYcBrdSqGIm7mHIzGSJtxbFza+hKE9aUbeNeZVWFSSL7gIStoilgsZpUbAQEB0NPTw6BBg2BhkQW5XgbFFKFrEwwrvxhnv8SzMdvoCEDyr/Ut+IG/zWwt/vnwDxbcX8CCHG1NbUytNhUdi3f8lrSIv30HgcOHIzUlBdoODnDavUsuctA3d27g3NplrOv8KjuV9RpUH/ejB2g3jXbVyEgStmgF5cqaPb96Cf9u+ZtV+AxYsxWm1t9fItRrAWSsTiNJOVXj+L8ExuPE8scQJUsYuV77CRWho583u7fKXG8+BSbKnJeS21JagoOC96OLH30rnSfViUotnFS2A//86kX8u2UdK1cfumUf7hz3xatbwSjsYooOkxSHc4ljUhhUJTVFAm0PCxyOuIqoqCgULlwYnp6eP6imBSYLMeDlZzyL43inmluaYK27E0wEadK35ycCD7ZAKNXHgYR9SEjQZDvqrUd6qMwvilwf3mHeGH51OOJF8TDXNcfmJptR0qIkItasQeSmzaxJkzZ/wG7RImjkgncpp7F5XziDa7s2szUlclFdg0zUanJqJBd/j41MwsF5DyBOkRToHXaxUALiemDVHa+jWPLjm2kAFZs5McghX+BSWS2ZVCLBPoKx+nMwyc6zFsKhdLlcrHD+nhoZFM94Asl6zKsOM+u8vb7zd/YFs3c+xRHqBAdPriFiCk5KrwBh1SA/V4dQckTEKkbkgcb8MD0NsIoPYp4ma9CrpELs7F/Pf0b8zUBAUwPWw8tDpwjHwn7hwgV4eXmxn7t37w43tyxY0KN9ga0NgcRIJFi4oYnHOnyS6rLzWlmZYlkJB1jwUP6VxpcsTsbiB4tx/P1xNl4qTyWVFCIeS7f4W7cQOHwEUoVCaDs5wmn3bmgXlk8alKBG+6aNZczY9u5l0Hn2Il4FeDz52vBuGFQmShUWtLNWslY9tBrFEX8p08QiEbaPGgiCpZVv1gqN+g9VZvMFvi1KLhOb/5eAeDiXs0SrYaoL8OKjU3BsySMkfE2BoZkuOk6uBCPzgol35lNgwuOLUGkJDpqj/6tIXNzykj2Li1dWbaIyOT4emwb3hEQsRsuRE2BoURZn1j5jru67pBYMTblnsCIW7xWCryc/sFOTW5tj35Vj7Of69euzfxktWSJllZr7QjhlNBd9XWwv4wx3gqFKRMCetoDfbXwS1cWFyLHsmCYDSsGtinzPUEXmIc85ryNfY8i/QxCdEg1jbWOsb7we5a3KI2LlSkRu3caaMm3XDrZ/LVAJSXhG8thyjZqjyaC85eui/qlqjRICtIFGlTZ6hgWXwyLj2tP9nKryXlwPRLgfx91BiUC6DuXdEJTnmlLGsVSldWTeNFbN7bluBwR5THqrjDmkt/HyZhBuHHjLIPz9ltRSx8DKdK6K2uJTHKFOcKhokVXZLGmBJ8WnwWFihbh16Agi/EJhau0M+9JV8ck7gsFjtHW12P+0U/SzUeDfcmhZuW8YP0BTGjnCtAmHj33+/DlOnDjBfq5Xrx4aNGiQuQuYYkpTIMIHCbpmaOCxEf76djDU0sRfxYugS2ELucekSl9nbDsgNgDjbozDm6g3XPDmUB8Lai2Aqa7pt8Pib9xA4IiRSBWJoOPsDEeq3MhQpivPWP2eP8WxvzgiyXZTZsOlQhZErfI0qj5WpR6gqhuqvtEzNGJKRoZmqiltfXzuFK7v2cZkAQeu266yflTqLBU2/tE7nL04knWZURWW9vJLYeY0PFKhOrHCG5GB8awajio3LO0z8Ark1ADP/s6nwIRnrsk4HKUmOKhh2rDIK3LDdCLyouUr4c+Js7Fz0m1W/l2vmxvK1FOc64lijIitLyD8HAMtM108KxeF+w/uM6jKwIEDGfHoz3YgOBJT3wciRZoKfU1NrCrpgLYk/57whSMdjQnAhfg5+BTvwSC73edU590L9Kevn+B52RPhSeGMXHx1g9WoYVsD4UuXIWrnTjZls04dUXjuXKUnOQLfvMLh2ZNZHz0Xrc41ibW837m390NwZRfHOdR8cBm4VrCWtwneHy+RSOH1zyc8uezPxkrKRgSVcClvxeuxfwnwY7BYgsgWZPt3xyu8exAG1wpWaD64bEGeyn9m7HyKI9QJjgJ+2cV+icC2EQOQmipF6zFTYG5X7pvGfZcZVZjGfXIiQWREXFUI4waRwLWiNXT05CujzgqaEhYWhm3btkEkEsHV1RU9evRggc0vxhRTugHvL0GkIUCncitx38wDVUwMsa6UI5z0Fd9BUvUyXvW7ihl3ZrCSVC0NLYyuOBp9S/f9IRkT979rCBw9GqDkhosLHHfthLZ17h4wx/6aCb/nT2Dp6IxeS9YwfXO18dMDGWVcmw4ZhbINmqpsoMLkJGwd3h/J8XGo+mdH1OneV2V9FcSG6YWLeIaiQxNRrLI1mg0so9RpUOBLJM1U0kwcSK1HlMsXWVplTopPgYky56XktpSe4FDy+LJt7r3XXZxeuZC9bA/ZtAd3TwTjrVco7Eua488xFXI1FNGXJISt9gbEUuhVt8ahwH/x5csXWFtbM7iqIBPSzaexiQyyEpTCVZUOsrfCTFc7aIc9B7Y3Q3yKPg5EbYBIostbCERgXCBLcgTGBzK46rK6y9DQsSHCFi1C9J69bF5mXbug8OzZSt28Ob9uBXxuXUNh1+LosXBVrtZO3pOJv+3A3PtISRDDpYIVWvzmL59+ryJxdddrVmVNVra+PWp2cIVAWx2PyXvtyHP8nml3mZy1okII8vSlPlY5HuBTHKFOcChnTfOtlbtH9+PesYMg+bdBG3bi/ilfPL0SANL3ppJBedVYsptIZtCU5ORkbNmyhWFuTU1NGbGYQRY40NRLM6Bx72/WxVi3SThi1woTnAtjpKMNBJr8vBRFUhFWP16NPa/3sHFb6Vthad2lqFz4R16FuKtXEThmLJfcKOYKp507IbDKfZY/7NMH7JvK6do3HzYWpes1yrdrTd1x1h4QJSdj14RhILUCh1Jl0WnWQqV+9zLrmb739P3X0Tdg331dA0P1EmXwAL24Xdn5GsRo2H12NaVJblJp9rV9b5jENpmiUD++LRafAhO++SbDeAp0gkMsFGLTkF5ISUhAg76DYWZbHRc2vWBJuv5La+e6kiTuRgBiLviy75ywgzX2njvEVFTq1KmDRo0yf3ZFCsUY9toPN6I5KEB1U0NsKe0M63f/AMcH4HlCC9yKG8T+1nZcBcYpxjcLTwzHoMuD8DHmI9sAmV9rPiMdD5s/H9EHDrLhmvfoAZsZ05XyXCAZ+c1D+zCVrqaDR6FsQ9Ul0zPz9eVtL/H+UTiraKA4MzfwJr6tZVbjIdjKvztfMRlyMpJYpsQ5yZeqTfkeoMQGJTjISC6aFG7Uxn8P8CmO4OdbpfLXsEAHJVm5gwiFSPqNsPi0i1u7ax/smX4XhAsnQqQqrYoqzZOZQVOkUikOHz6Mt2/fMiIxUkzJrBSVBhH9YDfMz49i49lUpBP2lB3PqjYqmvD3pSwsIYxJwj0Jf8LGXbVwVSypuwSW+pY/+DX28mUEjRsPiMXQLV6cVW4IChVSmu/TYQ/Gllbov2pzgcZUKs0pPGvo+t7teHz2JLS0tdF76TpY2BVR+QiT4uNYFYcoOQm1u/ZGtXadVd5nQeqAoHz753ghNiIJJasXRqO+pZQy/EfnP8Pr9GfWVuWWziByyN/B+BSY8NifBT6WuLx5LV787zIKF3ND59nLsGPCLYiFUqWoFKRKUhG+4SlEQfEQWOnjZdlo3L57h73UU3xgb585DEaSmopln0Ox2i+MLT0RjW8t7YyqD5ZAemcdTkQtRJioBHuZ7DqjqkpUZnJ7zUUnR2PIlSEgbg6y6dWmo4tbZ4TOmYuvR46wzyz69Ib1lCm5TnI8OnsSN/ZuZ8ltqsTR1ss73p/Pz7/g/IbnbD6/S3JX1rUnlazHF3zx8Cg2RnQAACAASURBVOxnpKYCAl0t1O9eAiWq8YsfRtb58Pm49w/DcHn7K+Zjz5V1mECC2vjvAT7FEeoEB/+vlyxH+OGRF/5ZNp/9fcDabUiM1cPJFd7s9x5zqysts5wqkiBs7ROII5KgbWvIiEU1BJq4desWrl69yvpr06YNKlasmOlYbz69jOr/dINOqhhXLarhUpMtmF3cAYbpzOk8XIN7wfcw5dYURCVHsdF5lvXE8PLDofUTRCT24kUEjZ8ASCTQLVECjjt3QJCVcoyC8/waFsr0zaUSMer1GoDKrdsp2JL6NFV4gKps9k8bx2BitTr3RPUOXVXRTaZtpidW9E1M4bluO2PTV9t3D7y+HcyqLWiHuue86rmWm/xWFUKSntUoaeKe65cVvqwXnwITvvgkk3EU+AQHyVcfmTuVTa3/6s3wOhOFj94RSiPkFQbHI3zdE0AK6Ne3w+FPlxAeHg5LS0tW4amtnTUR5aUvMRjx2g9xEikEGsBcl8Lof2MYIt98wNHI5ZBCgCqtnFH1D34mFeOF8UxdxTuci8MIyjqgdH+EzJyJmOMcR5lF//6wnjhB4fsGVcRQPBAdEpTnJNMpSWIcnOvFSJUJ1tRmdHmF58Hj73iOQwt+H43L218zP5BRAr1OVze5Yd85dvQfPuDmwbd4cSNIKfC5/7Ab83zqfIoj1AmOPF9+5XV4YvEcEO7fqVwFdJw+n7ENE+uwlaMxOk9THiHl1/OfEH8ziFNNGVEeOnZG+PjxI/bt28fKTymxQQmOny1GJMaK/7N3FWBRZl34ZYbulA7BQEwERbG7Y+3u7jXX1l3XXXt17dZf11jbtbuxUFTCQhDp7hhm+J9zP1BMZmCAD5jzPP7/AjfOPffOfOc795z3fXIfky/1hZEoHm+17BDQ7z+0sLSWnxHkPJIkS4Ktz7Zi49ONyEIWdFV18UejP9DYqvFXMyWcPYvgGTO54EaVKrDZuQPKBoWTPnt11xY8OX8a6to6GL5uGwOxVEjxW4CyqPbPncrYboysbBhOilC56JDkKXtr+8ThjBmh+dDRcG7bqfiNwiMNxJkS7Jt/j2W1VW1syW7b8isf/GJw+m8vSMRZsKxsgE4Ta0KoXHpulfjkmOR3j4qgX4kPcBBD17YJw5EYHYn6PfrC2K4FLu3wYWd52MqGcnlJiz8fgMTrQcxnkPSxwO4T+0AZn+7u7mjd+sflFP4p6Rj24h38ktPYdvY3EGLFzUG4H+gOz+TuEAiVGHCwoTk/sz9TM1Px8/WfcSf4DtN/eLXhmFRrIsLmzEX8yZPsd0ajRsHk5yn5Cg68f/EM//42h40zaMV6mNjYFcGx56a4/s9LeN8MhrKKAH0WuEHPRKPI5ubbRKlJGbi6xxcBzzk2IMouajOyWqEAWvNt7UWhz8ElDxiAN58DmkVhh5I2B5/8CEWAo6Sdnmx94yPCsX3SCFCeXOepc+DgWg+7Zt1hiOzu3SrAubWNXFaWHpiAyM1eQBagk82aEh8fjy1btiAlJQXm5uYYNmzYV7cyd2OTMOu5D7beH4Uqye+QqKKHjOGXYWT2HepYuWhbsEEoxXT2rdm4E8I5JtWMqjEKWAvtrxHg40//h5BZswCJBOpOTiy4IdTXL5gCP+idEh+H7ZNGsnIEBahkoZlZ5oFzUoWpY59fV8CychWZxyhoh0tb1+PZlfOgEqbha7dB+A0wv4LOUZL7P7v2AbcOvYJAWQkDf3OHtoHsYMbRIUk4tsITxJxiaKHFGFPUNIsukFUU9ueTY1IU683nHCU+wEHrvvnPbjw8eQT6puYY8OdG7Jx5G5LMLLQeUVUudLUMkHytJzKjUqFipQ0fpzjcuHmDmZz8BRubH/snyWIxpvsF4XgEh3fQThKCLXfH4lDYH0gQm8HcQRc/TXNhmVl8FJFYhFm3ZuFS4CWmXu/KvTHbdRbCfpmDhP/+Y78zHjcOJpMmyqz+6b+W4dW9W7CoVAV9f1shc//8dqCsheOruHJd9+4V4NxKPj5mfvXhQz+64PO6EoR7x9+ywDcFCRv2rMCC6fLEv+PDWotSB8oU2j71JnvvoCwh6yqGRTm9Yq4CWIBPfgQ/nw4FMO53upYKpyT32m4f/B/uHz/E6CFHbtiF4Jfx7HaRZNBSd+gYFjxV/VulKWJIsGvXLgQHB0NDQ4OlnOrnerHPkEhYLe3GwFDsejEXrWPuQaKkDKVBx6FU/ussCPlvdf5G9Ir0wrTr0xCewtUA93Xsi+mu06EqVP1qQLqFCZk9hwtuVKsGmx3bIdT7RBWbPw3y7pUDKknUoMPWbYWO4edYIHmPoGghTwtQkJGARTPT01GzVXu0HDFOnsNLPVZcWCh2ThnNSmQUQLRfmy0zQ4y98+4xBqmaza3RsFdFqW1LDZPj03Fk2SMkxaRDU08VPWa5yuX7VSYliqAxnxyTIlhufqcoFb5E1PsA7JkxgXvW/bYSnhfTEfgiWq6MQ+nv4hG5hcNq0G5rg4N+5xAWFgZDQ0OMGTMGqqpfP1tzbwq9PO4IjsKiN8HIzAK6xd7G7IeHcSp2EWvWtH9lVG1U+FhH+T0omZJMLLq7CCffclkbnew7YbHbAkRQkOPsOfY744kTYDJ+vNRTJMfFYuu4oaxctd34qXBq3FzqvgVpSN+hdKMeH5GKcrY66D7TRYGJkMugEYEJuLDtBRKiuKwjojUlfJLSFgQvyBmSpS99F/233osFMEesbiSXrDJZ5le0zb8F+ORHKAIc+d/HYutJ6ejbxg8FPezcfuqNhn0GMgorP48wmFfQQ7fpLnLR7VulKf/99x8ePXrExh8wYAAqVKjwca5XyWkY7xOI50mpmP92E8Z/OMj9rdM6wGWwXHSS9yDkRO333Y9Vj1YhMysTmsqaWOy+GG3Lt/3mVHHHjiN07lyWOaNeswZstm2DULdo0J2JGnTHpJGgbA5CTSf0dIUUjwXo3FCJWMDTx9A2MMSQ1ZuKlcUkB4jW0NIaQ1ZuYDSQCvlkAc+Lgbh37C1LrR74uzs0dX/8cpXTMyMtk+EaRQUlMbCzbtNqsxLA0ih8ckx4bN9SEeAg++6dORGRge8YjoN55S649j8/qKgJWZmKvOgvY0+8QbJHKJRUBFDqb43th3ezUhU3Nze0a9dOqm2+H5eEkd4BiMjIxPSAXajppYdXaU2hpipG398a85rBg0pelz9cznwMkhY2LbDMfSkiZ8xG4sWL7HcmP/8M49EcS0xecv/Ev7h9YA8rVR21aTdUVGXPRstrjm/9nTIUPC8EQiBQQs85dRRlGN8wEmUdXN/vhzePIthfdYzUWUaUWfnCv/zKz57yuY/Hibd4fD5Q7uX2fF5zadGNT36EIsBRAk9VDpc9lJQw8u8d0NQ3wq4Zt5GRJkbjPpUYR3dB5VulKU+fPsWJEyfY0M2aNUOTJk3YfzPQq+Ao/Po2BGmSLPQOO4e1L//kVKg3Dmj7R0HVKZT+BAi24O6Cj2mkFfQrsJIUe71vA5jFHTmC0PkLWHBDo2ZNWG/fBqFO0b7sPL14Fld2bISSkgCDV26AkRV/8UwKZdN4MqjvnRs4u45LD+48bQ4q1nUvVs3oRYVeWPiiT7Ea4xuTU6CCGKbSkzNRu60t6nd1yFNFYmE5u+k5u9mmm6QO42rAtpr82JHyVKCIG/DJMSnipcsyXakJcDw8fQw39+2Eho4uBq3Yjr1zPBgzBJ1zuxryyQ6UpGUifM1jiOMzoGavB1/HOFy9epXZe8iQIbCzkw4/IjxdhFHeAXgQl4itXksR/mog0rN0UNFJiNaTOD+Er0L+0YanG7Dl2RamYn3z+ljTcAViZs5F0mUOpJ1AR42GD//hEgg7ZcfkkaDMQZcOXdB00MgiWXLk+0T8++cjZEmyShVrVGEYj/aa6MOpJDJTJGEBIbeu9nBuacPbcqrCsENBx6RLhZDXcajR3AqNevG3rL2g6yyN/fnkRygCHCXwhB35fT4Cnz1BeWdXdPtlEfyfROLcFo7LfsifDaS+nfze0llpytonXP1sNmtKWGQ4duzYgczMTFSqVAl9+vSBQCBARLoIU/ze42oMx2HfIdUX2x5PgkCcAVRoCfQ9BAiVeWflV7GvMPX6VAQmBDLdiLN+fr350FT5Nqd57KHDCFu4kLXVqF0b1lu3QKhd9ECflL2ze9pYUFlChTr10GX6PN7ZtrQrRPSshGKfmhDPqz04vmwx/D0fwtS+IvovXa2oAf7iID488w4PTr+DiroQg353h7rW9zE0yFElQD2fWyFsFL6nw8vjM8cnx0Qe6ymkMUpNgCMxJoqVO1BU46dZC/HsuhDBr+Lg6G6OFoPkhyWU6heD6N3ebDv0ujrgwLP/QGeNSlvHjh0LNTXpshBEkiwsfhuMAwHvsf3mTvhE9uOe3UMtYeuWf/DgQjonXw27+8VurHq8iv3euZwz/m68BgnT5iHp+nX2u3K/zILRkCHfVYeyBY/+wfkgQ9dshqFFwS+y8lo7BXmPLHsMCnIYmGmi91x+UvTmtY6i/jthNl3Y5o3Y0GQ2tU1VQ7QY7FRg37yo11Ec84lFEmybehP0/wTaWsGlXHGooZgznxbgkx+hCHDkcxOLqxtRhu4gcFEAXWbMRwVXN1b79+ZxBKydDNF5Uq0Cq/ZlaYrYQMhARePi4mBgYIBRo0Yx/I3zkfGY+vI9YkRiNudonRQsvDoQgpQowLgyMOISoM6/9LyTb05iiccSpInToCJQwWy32ehRscd3XwhjDxxA2OJf2Ro1XF1gvZmCG8WH4P7y3m389xeXIVNcwJYFPmQleIALm9fixbVLUNXQwJBVm6BjJJ/bzoKaJPilLw4umMGG6TF3CWxrFPy7oKA68al/eooIe+fcZZludTuVR50O5b+r3uPzAfA44c/+Lm3GB5/Wmh9d+OSY5Ef/IupTagIcZC9i4yBWjsrujWFToy9uHXoNNS1lDFveUK4YCzEH/ZDyNBJKakIIB9ti2/6dEIvFcHV1RceOHWXaumPhsVj3+B4mXPBHeLoTNFViMeCPtlApxmeytAv499W/+O3eb4yhzdHQEZub/I3k6QuQfPMWG8J03jwYDuj/cTjK2oj68B7vn3vh+dULiP7wHtZO1dFrYdFkxVJZCpWnQAmsPM+8QuEBqUtrw5LSTpQhxu1Dr+BzJ5SpTPhNrYY6wcpRAZj5oz0M84/H0eWPWZMhyxrwugStpJzFotSTT36EIsBRlDsvh7lu/bMbD04egbahEUau34lMURYrT6F0uOaDHFHF/WvGD1mmzV2aotvSBtrNrXHgwAG8fv0aysrKGDFiBGNrWPAmGPtDY9jQ+spCrCqvjw6negERPoCGATDyKmDIL676tMw0/PHgDxx7zfHRW2pbYnXT1XAycvquiWL27Uf4kiXcA6puXVhv3gSB5rezPGSxc0Ha0u3yP3OnIuzta1g6OqH3omWK2/qCGFSGvrkp+poPGwPnNrI55zJMla+mhxb9gg++L2BTrQZ6zl+arzFKc6d7J97C83wg1DSVGRizqvrX2WWvHoYx2kySinVMmVPKV7YGee4VnxwTea5LzmOVqgAHBWopYKusqoYBy7bj4K8cS0bnKbVgLccXMXGyCOGrH0GSnAl1JyP42cfg8uXLbK6BAwfCwSHvkrHc++iblIpdpw/D/oYpxFCFuflzdJg3AWpCoZy3W/7DnfU/izm350CcJUZ5vfLY0ng9RNMXI/nuXSKNgNa0nxFf3gbvX3jhvfczlimYWzr9/Asq1Wsof8W+GDEuPIUBi9JNevUmlmjcl/9ZMoVulHxM8PphOK7t94MoTcwCRa7t7Bj1qUCowMn6ljlz8LJ0TTQw8Lf6+bC4oktxWoBPfoQiwFGcJ0HGucWZIpZSSiCTxF/v3rM/Xj0Iw6WdPowCkW5dCoLa/K3SlBu3b+J6dgpl165dISlfEeN9A/EuNYNp38hAG2srWcLixFDg1TlAoAwMPAGUbyTj6gq3+fuE96wk5WXsSzZRU+umWNJgCfTUvp9hErN3L8KXcjclmvXqwXrTRgg0+MH7nvtFu+vM+XBwcStcAypGR2ZGBvbOnIDY0BCYV6yMPr8uh0DAL4f63dPHOJadxtxvySqmp0I+WSA1MYNlcVBAuH43B9RubfuZeYgK8eTap4wy06KiPsuIE6qUDUeUT44Jj89sqQpwpKckY/OogcgUZTAGJp97eogISEC1JpZoIucX2pSnEYg5yD1/9ftWwqFH/yEoKAi6uroYN24c1NVlY36LF2Xi6Jo9SPUvDyWIgTpe6NJ/EizVpQMQLs4zdj3oOmNty5BkoLzAAjMMhyJ8/78IT01CmurXpXN0oWVTrSYrS65cv1GhX2gQ3saJNU8YDgLRavdd6KZgsijAgYmLSMHF7d6s1IeEyABaD68KbQPZznwBVCgxXc9sfIaAZ1FwrGeGFkO+f/lYYhZUxhTlkx+hCHCUoMOXU5pAAJMjN+xkqfFnNngh4Hk0ytc0RvuxNQq0mrgz/ki6FQwIlFBuQi0EJodi/34O/bt2nTp47eSC1YFhEGcBqkpKmOtgjpFWJhBcXgjcWcvN3fEvwHVogfSQd+fLgZcx/858JImSIFQSYnLtyRhSdcgPnYToXbsRsWwZU0XLvT6sNmzgTXAjxz5Uj0t1uUZWNhi04m/evWzLex+Le7w7h/4Hj2OHIBAKMeDPtTCxkQ4gryj1puyefb9MQUTAWzi41kPXGQqMli/tf/vf1/C6EgQNHRWGxaGsygWpYkKTcWzFY6SnZLJ6824zXH6I01GU+1oUc/HJMSmK9eZzjlIV4CAbnF7zJ1553IZtDWfYu45gJQmUTj/kjwZyzVyi76boPT5I84uBQFsFyoPtsHXPdobr5ezsjC5dusi8JaKMTOyfdRLJqQYwVn6LC62UMbRRDzQyLFrwb1kUJwynD97P8fDBRbx86gHdpK+zyFQyxbC0c4BDm/YssGFgblHoQY3ca/C+FYzr+7lgVMcJNUs1uLIse1eQtuJMCfts0bOHhDCgWgyuIjdA34Loxpe+FFjbOeM20pJFaDbAEU4NC5aRzpd1lSU9+ORHKAIcJejk/fvbXJa2aO9SFz/NXMC+BHbNvA2JOIvRUVV0Nc33ar4sTRG76DLcjbS0NGjY2uNGdTd4Jqay8R211LHRyRZO2hrA03+AE2O5ed3GAu2y2VPyrYn8OookIvz1+C/s9dnLBjXRMMGKJivgYvpjGt3o7dsRsZIDA9Nq2BBW6/+GQMbbJfmt4vsjRQT443+/TGYgcW3GTEa1Zq2KYtoyOUfU+wBma4lYDLefeqFhn0G8tUNujBZi2jG2/jxLgbeKF5FiyXHp2DvvLsvSaNS7Imo0s0ZKQgaOLHuExOg0aOiqosdMF+ga8yNbq4jMwoAfLS0taTr6Hw5dVSFfWqDUBTjePLqPkyt+Y8xcvX/diBOrX7E1d5/pAjN7+WJoZcalM1aVrHQxNGuXwyubWJw/f57N169fPwZgLquE+Ibh+FqupMxZbz9+btIDA6rWxgSbckUaFPie3kTvHuznw5WcvPACPbcZXU0uEQkliDYWo0mdDrA6dxeqnl5Md4s//4BePgI/stowd/uk2HQcWOzBsIoq1TVFq2FVCzKcou8XFqDshMt7fBijF0nN5tYsm1CoXDYyBX90IOiS4cDi+9z3wSI3GJgVH9ad4uDmzwJ88iMUAY787WGR94oNDcbOKaPZvIR4bl+7Dnxuh+DaPj8oqwowbEUjxmGfH/myNMVgdFXs2rMbIaGheGdTAbcdqiNFwj2QR1uZYLa9OdSpfvD9fWBPR4AYUxxaAP0O84YxJTw5HDNuzsCTCK6muK5ZXSxrvAzGGj8GhIzashWRa9awPlpNGsNq3ToIpER5z4/tC9rn7PpV8L11DdpGxhj21xaoqEqHSF/QectSfwJ6O7BwJkJf+UHfzByDVqzntZ0lEjF2Tx0H+s5watQM7SZMK0vbJdVaiSHF+2YwS7/uPa8uTq97iojARPZd+tO02ihnqyvVOKWpEZ8cEx7btdQFOKj0dfPoQUhLSkSTgcPx5okFooOTUauVDRp0ryD3rUjyCEHcibdsXKOhVXHw7kkEBgZCR0eHlaoQgLmscn3XI3jfT4CyUipcrVehZ93f0dTUDGur2EBHOX9+kaw65LTPFIkQ9volAl94IcjbC6GvX7LAeG4RKivDolIVlp2RZaOP2W/+QHRGDLRUtLC+3goYz9mI1KdPAYEAFsuXQ69jh/yqI1M/yrI5t/k53nlFQV1bhb1kamjzv+RHpkXyoHFSbBou7vBG6BsOX8XERgdtRlaFnknx4rsVt2lyMocou2XYyoa8CFAWt01K2vx88iMUAY4Scnpu7NuJR6ePMYDPEX9vZ+UIJ/96gg9+sQwIj+r58itflqace3gFd59742alWnhnwqWImamqYF0VGzTOSf2Mew9sbQYwxpRKwIjLvGFMuRdyD7/c+gUxaRwI6sjqIzG+1ngI88BLiNq0CZFr17E+2k2bwnLdWghU+f1wj48Ix66fR4PoYxv3H4o6nbvn9xgo+n3HAk8vnsWVHRvZX3vO/505pXyX51cv4uKWdVASCDB87VbolTPju8pFql9CVCr2LfAApcTqGKojMSYNSkpAu7E1UL4GP1hxitQglLKhyOCQxuSlLsBBi768fQO8Lp1DufIOqNxgMh6eCYCusToG/FZf7i8Z9JmL3PoMGQEJEOqrQXWIPTbv2AKRSIQaNWqgW7du0uzDZ22IIemf+beQkgzYqT1EZsW7GO20CA6a6thZvTwqaxUe1gEFlCPe+X/M0KBsjcyM9M/0o+wYU4cK7NlhU7UmLByrfBYkD4gPwMhLIxGWHAY1oRr+cv0dlgt2Iu3ZMxbksFy1Errt2slsF1k7EBsfsfKRtBrmhEp1Fc8NWW0obXui4KXP2aNzASB0WaIvb9bfkfnzZVUu7/bBS48wuZTcl1UbFve6+eRHKAIcxX0apJifbgS2jh2M1MQENOg1APW690FyfDr2/HKHZTq2H5d/p/zL0pTXBlHYePs+rlV2Rooad5PS0UQPKypbw0Alu1Y0PRHY0QaI8OYYU0ZcAYxkQ0GXYtkyN5FkSbDl2RZserqJ0bARgOjShkvR2KpxnmNFrt+AqPXrWTvtFi1guWY174MbOYu6tmcbPM+ehJqWFkas2wF1be0816toIJ0FEmOisHvqWGSkpqJq05ZoO3aKdB2LuRXdym6fNBJJ0VGo2boDWg7PLiMrZr34NP2VPT7wuxf2UaXGfSqhelMrPqlYpLrwyTEp0oXLNlmpDHDQS/nBhTOZJbrMWIkL27kKpd7z6sDYSv54FqLIFISv9QQys6DtboHX5jE4c+YMm7NPnz5wdHSUbVcA5H45b6O/HEerVMcam/7QFAqwxtEaXcoZyDzmtzowLBGibn3xjGVoBPk8R3py8ldNqTSQAhrW9M+pGtQ0f5xuH5oUyoIcgQmBUBYoY5nzAlRcuB9p3t6AUMh8Et3WreWyhm8NQiXP/yzyQGqiiGFudBhfQ+7BrUJTvgQP/MEvhhEFUJkkiVMDczTsXQkq2dhQJXhpUqkukWQh3D8egS+i8eJmMMPAcu9WAc6tbaTqr2jELwvwyY9QBDj4dTa+qY3vnRs4u24Fu40dtWEXo4h9di2I46zXVMbQZQ3zhfT/ZWlKYldjjLv9CM8sOHpXbaEASytZoaepwacHnUQCHOoPvDybzZhyHCifdwChsM0cmxaL2bdm407IHTZVdePqWNlkJSy0fwxSRM5K1N9/I2rjJtZPp1VLWK5aBSWeZ27ktmdKQjx2TBrBXsIpg4MyORQiHwucXPk73jy8Bw1dPQxdvQkaOiWndOHxmZO4vncbhCoqjFJaS18+Dr58LFv8oxANIjn0FCR2bmUD90JIxy/+VUqvAZ8cE+m1LvKWpTLAQc/B7RNHICEyHHW79kSATyUkRKbCtYMd3DoVDt17wvUgJJwPYNSZxqNr4NCNE3j37h20tLQwfvx4aMpIx05rOLPhGXtR0hTEoK/xJMxyWYRDunXZIRltbYL59hZQFsju9sZHhLGARg6OBjHZfSn6puawrlaDC2o4Vc/X921UahRGXRqF17GvIVAS4NdqM1H9t6NI9/UFlJVhtfYv6LRoUSiHPifgS6XOxJpCmW0KKRoLUHDjym4fvPfhso4NLbQYrp6RRem8rEpNysB77xj2WX3vE/0RjyTH2n3m14WRZelce9GcqOKbhU9+hOzf9MVnt4LMXKKdkkOLf8EHnxeoUKc+ukyfy+xwdPkjhPknoEoDczQfWCVfton7zx9JtznWlKBB5THCPwDRGtwtg6uOBjZUtYOtxheYDpeIMeUvbr6OawDXYfmaW56dnkY8xfQb0xGeEs6G7evYF9Ndp0NV+OPyEnKIIv9ai+gtW1g/nTZtYLlyBZRUvqZpk6e+hTEWsXsQywe9zA77ayt0jU0KY5oyNebrB3dxatVStub2E6ejSsOmJWr9orQ0bJ0wDGmJCajTpQca9xtSovQvCmXfeUWCQEerNrKUK2NEUegu7zn45JjIe21yHK9E+xI/skMOS5SuiSmcms7C08sf2ItW3wWFQ0GeJZYgYsNTiEKSoVxOA+qDHbBxyyZkZGSgatWq6Nmzp8zblhCdykAKMzMkqKpxHo3L/YOfG+7GoUyu7Ky+vha2VrWDyTeoWHNPlhwXi/fez/D+OYejQaWgX4qWgSFsqnIBDfqna1JOZn2/1SE+PR7jLo/Ds6hn7M9zHCfC7fczSH/1imVy6LRuBYPevaHp5ia3DAt6yTy9zovNV9Yz2eSyifkYhEq3nlx+j/sn/EFZDcoqAjTsVZExiRDgbEkWdpEYlITAF1EsqBH2LoGV5eQW+q6hzKEKLuXKJAZWSd7f3LrzyY8o2Z8a6U9EiXVKooODWIo8SffZi2FXywVUP/6/effY7zpPrgXrKobSWyK7ZXpAPCK3y/e2iQAAIABJREFUPGOUr0dammC1IAViJQEEEgnGm+nhFyd7CL/8Un16ADgxhhuh7mig/XKZ55VnB/rS3O+7H6serUJmViY0lTWx2H0x2pZvm+c0LLixahWit+9gbXXbt2NgXkrKX1O25TkYDxrQy+yOySNBjhmxqRCrikLyb4H0lBTsnjoGSbExsKtZG91mLy6RTsa9owdw9/B+qGpoYOSGXVDXUtyK5P9UlO6efHJMeGzpEutL5GXT3L5Gq9ELcOtwEuvSf3E96Jt+Aj8kvKeXd2/i+bWLoOeOipo6VNTUuP9XV4fyFz9//Ls6tfu8LWLFiNv7CkKoQL+FHd4ax+DUqVNsXgpwUKBDVnl6+T3uHHnDunUz/AVm5hKsbrkfK8I4FjjCE9tezQ6uep9KRtKSk9glUk6GBpWgfClUAmrtVAM21TkcDUNLq0J7JiSLkjHp6iQ8CHvA1JhiPxTNVtzgghzZomprC/1evaDX7ScoG+Q/Oy8jLRMHf3vAGKTMK+jhp6m1y3ywV9YzJ8/2Yf7xuLjdm+FCkVRwLcewOVQ1SpZvSufqg28sArKDGinxXAlOjlAAx8rRALbVjWFT1RC6RrKDC8vT7oqx5GMBPvkRigCHfPa00EahFHNKNdcrZ4rha7exMhXPC4GMT5voDIf82QACGVMuJRliRKx7gg9JaVjkooVH2twx0EtJxO/meuhRu8bX6/mMMaU50O/fYmVMScpIwoK7C3Ap8BL3ENCvgFVNV8FeL+90WgpuRCxfgZhdu1hf3Y4dGR1bSQ1u5GwWgcQRWBwBmg1a8beCHrQAn8orOzfh6YUzUFZVw5BVG0osSGdaUhK2jh8KUVoqo7YliluFKCzwLQvwyTHh8Q6V2gAH2Xzf7CkI93+DGi3aIvhtLSTHZ6BeV3u4tLVDRmoKCLyY/JHE6Ei5b5ESBCxAkoksZEokUBIqw9zSCmqamh+DJ7kDKV8GS3J+Fqqo4dq+t4gNzYC+SgR6Gs2FqmMznGu1GZP8PiBJLIF6ZgamKyWjRngAgryfIdz/LbKyJJ+/gKmpwcqx6scMDRO78gzcvagkXZyOaden4caHG2zKYRUHYGiEI+IOHULqE44djoQyTin71KB3L2i4usocdLl9+DW8rgZBoKyEPvPqKqg5i2qDfzAP4aEQQ6L/E+5zRoC/bUZW43VmA/nVVPZJGRr0L+R1HCR0g5pLaB221YxhW90IlpX0oaxSdJ8nHmxrmVCBT36EIsDB4yOXmZGBLWMHM/q23C8nB5c8QPSHJAaIR+mEsgqVphx5E44/ndSRpMIdAaeQd5hkqIGu7b6R/UCMKduaA8mRgFFFjjFFQ1/WaeXW/mXMS0y7MY2BcZF0su+EefXmQVMlb4otFtz480/E7NnL+up16QzzpUuhJCz5X7R0s7Zn+nhGD+rg6oauM+bLzeZlaaCQV744sGAmCJyh8YBhqNNJdlR/Ptkrh4GJ8ENGbtjJXhYUorDAlxbgk2PC490p1QGOHNweyvSq2mI+vG+Fw8hCCVaVgvD04pmPYJoURK9YrwFMyztAlJ4OUXoaMtPTWEZHzs/0O1Ea97eP/9LSIRFnFvn2KoFS/oUQaOgiRkkItcQ4KEs+p24VCIm61RHWVHZSvSbMK1SCULl4y1VFEhHm3pqLcwHnmM26V+yOufXmQvzmHeIOHUb8qVOQJCZ+tKeqgwMMevWEXpcuEOrn7aOFvYvH0eWPWbmAW2d7uLa3K/K9UUz4bQuQr0pU5rf/fQNxpgQCoRLq/+SAmi2sZQ5iFZaNM0ViBL+K44Iaz6OQEMVlneQI6WxeQR921Y1Y+QllgpX0cpvCsmVpGZdPfoQiwMHjU+Vz6xrOrV8FgVCIURt3M9CqmNBkVmNK0m2GC8wd9GRaQdS7OEy/9xrnzbkHt4YoHU39PNFQSxVDhgyB8MsX/fQkYGcbIPwFoK4PjLxarIwpJ96cwBKPJaDbDVWBKma7zWYPfWm+NOmBEf77UsTu28cFN376CeZLfisVwY2cQ/Dq/h2cXv0H+7H34mXsBkoh0luA2Ef2/TIFUUGBMLGzx4Cla9jnryQLldlsnzCMUQk3GzIatdt1KsnLUeheSBbgk2NSSEuUx7ClOsBBJY5bxgxm2QwuHQbh6aUnEGf4AOCCAZTRRiWQLh26Qt80fxSi9D2UO+BB/53yLhpRJ3whzhJBtaYBQoTR8PL0hJJEgsoVK0BfVze7T3quQEquYEp2YOVLetYfbXgWlBBmYoEYmwro1dAdLWq7sAwSvolYIsZvHr/h6OujTLWKBhXxq/uvqGZcDZKUFCScO4/YQ4c4StlsUVJTg27bttDv3RsazrW+6R/RS/PhpQ8RE5LMAB17znGFUCjg2/LLvD5RHxJxYZs3y44goWBB88FVoKH9Y4y5wjIclc7kZGkQAwzh3eQWTT1VFsygf9aOhiWutKaw7FZWxuWTH6EIcPD41BFtG9G3VXJrgE5TZzNN75/2x6MzAdA2VMOgJe4y10qOPuWFkzpc2lilxBjUe34f5dRUMHr0aOjofEEHR4wphwcCfv9xjCkDjgH2TYrFYhniDPx+/3cce32MzW+pbYnVTVfDychJKn2yJBKEL1mC2H8OsPZ6PbrD/NdfWclPaRIK4vwzbxrC3ryCRaUq6PPrcqmCP6XJBgVZy/3jh3H74F5W5tPv91Uwc6hYkOF40/fStvV4dvk8dIxMMHzd1mK/meSNYRSKfLQAnxyTXNtCX9AEKDSafHsAlLN9GMACAF9zc369n6YAFgPoAID+m3iBjwNYCOBrKoy8z0OpDnDQ8vfPncqeH7lFRV0Lrh27oFabDtDUle1SJW+Tci1ij79G8v0wKKkIUG6yM45cOomXL19CXV0d48aNg65u3gxW9JwXZaSzTJKUhGScWvsQKfHJMNIIRl2VzRAJNCFqMB0aVo7wNLLC7A+xSBFLIFQCZpU3xwSbchDwENCRnuvrn67H9ufbIcmSMIaV/lX6Y0KtCR8zV9N8fVmgI+HUaRb4yBG1ihVZoIOyVYW5fLwH/73Dw//egZbb4xdXXpc/SHuGSms7wrO4dfAV/Dw4WnMtfTW0Hu4Ei4r5x16R1lYSsQSEC0JBjYDn0Swg9pkoAWbldbODGsYwttZW+JzSGrcUtuOTH6EIcPD0gNENMpUbkPSYuwS2NWqBvbwuus8iucQRTVzRskh4bApcPV9CJFBCu9hI2Dy7wx7mlLlha2v79VCXFwO3V3O/77AaqDNclunk2nbZg2XY58tlXjSzboYlDZdAVzVvh4fak9MTtvhXVrtKQsBcZosWlrrgRo7Bg3ye4/BiLiDWZfo8VKhTT657UVoHo9KePTMmQCwSwaVDFzQdNLLULDUuLBQ7p4xmN7Ntxk5BtaYtS83aFAuRjwX45JjkWtFaAJOygxKUp0+UYRMB3AJAh/jz68PPTUG0FoTSSEEJosp6AaBadrDEG0ADAJ/eBKUzY6kMcNAz0v/JIzw6fQwffMlMnKhoGCALzrByckf3mYX7HJGkZSJ8zWOI4zOg5qAH9T7lsXHjRqSmpqJSpUro27evzC9OAc+jGHUsSQuzfXDEUUDfFhh1HdA0xJuUNIz2DoB3Epda39RAB3872eTJsiLdUZF/q+eRz7Hw3kJGI0tCFz0L6i2Au6X7x8kkycmIP3MGcQcPIc2Hsm84UVJXh26H9oyBJcXInmVvEEZCrVY2aFDGKbLlv1OFM+JLj1BcP/AKmeliFpiq07E8XNrZyYzDl5d2RFtLzDqBz4nGNQYZqZ+XlalpKsOmKpelQQChxZVNktc6FH8vegvwyY9QBDiKfv+lmvHqri14cv409M3MMWzNFvYyHvk+kT2USHrNqQMTmy8yLvIYecWt11iVmQwtkRh9PM5CRSJGmzZtUL9+/a97eh0EjtOlGTGmjALar5BK78JodD/0PkZcHMGGHlJ1CKa6TJXa0WHBjYULEffvEdZfv28fmM2fX2qDGzn2P/bnIrx78giGltYYvGJ9iS+zKIxzlXtMCh7++9tcBjhHdH+DV26AqnrpQvU+s24F/O7cgKGFFYas2ljqPwOFfWZK2/h8ckyybUv1dc+zgxvdc9mbAhzriOADwD8/2AfiM6fsj34AuNQ9Tvpm9yOQoiUy7mOpCnBQSZ7v7RsssPEZcwi9PWVlwbndYPh6GLGXqSHLGkJTt3DT4lN9oxG9h3spN+heEf7qkTh6lCvN6NKlC5ydnWXcLuDCthd48zgC6hpK6Kc/ChpZEUD5xkDfQ4CqJtLEEix8E4w9IdFs7HKqytjoZIuGBrL5VzIrls8OhMux68UubPbaDPpvks4OnTHDdQb0qYw4l6Q+f4G4w4cQ/98ZZKVyLDJUmvPEfR7iVM2ga6SGPgvrQUW1ZJdh5tOUJbJbbFgyLmz3Zjh8JJaV9dFqaFWW1ZFfIYrayKBElqFBmRoRgV/TuBpZabOAhl01I5iW14VAUc6UX3OX6n588iMUAQ4eHjWqSaU62PSUZDTuPxR1OnO+3d2jb/Dk0nsG1NNvkWwc6OKsLLhe8kKoClAz6A3q+79gFGw9evT4OlgQ9ADY3QEQZwD2zYD+R4qNMSUxIxHdTnVDWHIYqhtXx952e6FM5TJSSJZYjNB58xF/nDKSAYP+/WE6b67UwREppuBtk8jAd9g7axJzUluNmogaLdrwVlc+KPbi2iVc2EyXxcBPvyyEvXMdPqglVx0i3wdg74wJnEM8dQ4qun269ZPrRIrBSqQF+OSYZBuQgg9zATTOztjIsSsBJdDbKNFLtP+Bsb0IsoAyutl73SehshfKsw4B4CDjZpWKAAfRYD+7ch6eZ08iKYZ7sSexreHM/A1iSyEqWCun6oiPbQtRmhjNBjjCqSEtv3Al+oAfUr0ioaQuhOnPtXHswin4+PhATU2Nlaro6clWIpMcn84yX+kWunLldLSM78MtwNIV6HcI0DJmP56MiMV0vyAkiiWgAzLVzgw/25lCyMOSFdLXP94fi+8uhmeEJ9PfUN0Qs+vORhu7Nl/5OOLERMSfPs2ASd8kmeN1xZ6sT23fzbBrUgX6xMCSD0rewj0JitG/ZwEC97x79C2eX//AmmjoqKDFECfYVjWS2mjpqZkI8olBING4escgNeELGlc1IayJxjUbT0PbgH/4NFIvVtGwyCzAJz9ClgBHQWth2XcwgDkAugKwAkDwz5QPSfW0lHKaI7sBDP7OjtA3M3cdL72UKKfkxfXLuLDpLwiVlTFq0x5W80oR1r1z7yIpNp2lpdXtWF761QM4GxiFYf7cl2Hf+5fgoK2BkSNHMqfhM4kLArY1y2ZMqZDNmFL4dX7fW8zc23Nx6u0pqAvVcbjTYZTXk27dLLgxZw7iT55iQxsMGgjT2bPLRHAjx5bnNqyGz82r0DYwxLC1WxXsGd85ZCnxcdj18xikJSehsntjdJw8U6bPVklqfHzZYvh7PoSpfQX0X7qmTH0eStI+FYeufHJMstd/IbsMheix0r+wyR2CkQJg8gNb+dGFfLbf8WWzGHosZPePksHeJcqX+HJdBDjsee4UvC6eZbSvJJQdWrl+IxbYKGfH0az7P3mI438uBqVuVG44C4EvMlhKeqeJNWUwVf6aipMyEL76MSQpmVB3MoJ6NxtWqpKSkgIHBwcMGDBA5u8t71vBuL7/JVOoc5tgWHtRElAWYGjPXeAYcXGugNR0jPIOwLNELtuhgb42y+YwVSteNpXvWZLwOI68OoLVj1cjWcRhIzSxasJY5cy0vgaBTYhKxYFF95CZCVhE3IejD8cmR6JerRoLdOh16ACBZt6MdPnbXUUveVqAaGSv/s8X6SlcCYlzKxu4dbX/JlgsZanGhqYg4EUU3r+IRuibeEgkn9O46pXTgB3RuFYzgkVFfQhVShdGnTxtrxjr2xbgkx8hS4CjILWwZAkCebgOQBvADgCEYkWh+BoAyJE5mMtcOQGOgd8w4U0A72U8XCXKKSGQyNDXLz972Qp9E4djK7lIPWVvGJjRpZT00vOaN25BBOuYcHR4fg+9e/dGlSpUzpxLGGNKWyD8OS8YU64EXsGU61OYgnQz0a8KZRrnLVmZmQiZPQcJp0+zxoZDhqDcrJkyO0V5z8TvFgmREdg5ZRRjz2jYdzDcunK3Ngr53AI5pRtqWloYunozYysqrcIocOfPYMvrPvc32NWQPeW7tNqmrK+LT45J9l5QeQoFKAgc9EshoFH6QqMI/edXj59aUm0DcTzTIX+aa4BaAJ5k/+wCgHuwfluoTiF3rQLp4hkcHAwLi8LPZpDXmYwODsKj08fhe+sqex6QKKupoXrz1nBp3xV65T43MbUhivrUhHg4NekJ/2fWjKZy2MpGUNOQLoOyILqnPIlAzCEuIGHY3xEBwkgcPkxbDnTq1AkuLrRt0gtdEB1f5YnQt/HQNdFA315RUD45AshMAzSNuHIVay5rL10iwZK3Idj2gYt7GakoY4OTDZoaSof5Jb1W8mtJGa4Ewn49iFxsQEtFC1NqT0Gvyr0YICkJveD+97cXw1QgpoveP1dB2qVziD10EBlv3n5URqCtDb3OnRgwqXrlyvJTUjFSoVggIToVl3Z4I8w/gY1P5SOth1eFrrEGRBliBL+MzaZxjQYxoOQWgbISLCvqwzY7qEHZ4QpRWKAgFuCTHyFtgKOgtbBkL8rQIBT0ugBC8zBgToBDWv3y2o8SE+BgpQUz6XYB6LVgKeNkJ7l54CWe3whmCMW955IJpRf/lHS43/dlHdq+8EDlpFhMnz4dKiq5biVyM6YoCYGBx4uNMYX0jE6NZqUpMWkxqGdeD1tabfn4oP7RyllwY+YsJJw9y5oZjRgOk2nTylxwI8dG1/dux+MzJ6CmqYXh67ZBQ4e/Tpr0J1p+Ld89fYxjfxChAtB69CTm8Jd2ObT4F3zwecG+W+g7RiEKC5AF+OSYZO8IvXXRQ8rmGztEV890AULRyO+xoTTKvlShcShSTtmi5MsQNgelAtLY1Ob2D07AomzGlc+alJQAR/BLXzw8dRRvH3l81J+eAc7tOqFW6w4/fB7k4IAZWdshLa0HiFa05VAnVHbLHz2sLJ8yehmP3u2NtJexEGirwGyqC46dPYkXL15AVVUVY8eOhYGBbIFoYn849PsDBqxZu40N6rvGAP/0BlJjAGUNoMcOwJHIdjg5FxmHKX5BiM8UgxzRSbammGFnBmWBvNxSWSySd1uy2cXAi1h6fynzm0icyzljkfsi2OvZw88jFFd2c35gu9HVYe/MJT9Rv1RPT8bAknj+ArIyPsULNWrWZIEO3XZtIdAoXZhUeVu05LQQiyV4cOodPC8EMqVVNZQZs0nw6ziIRZ/jMBNWh211I1bOYuVoAFX1wg9YlhxLKjQtqAX45EdI+01d0FpYqqGlellCQ/8727Eg5+J7COY5AQ4KPdPtCaHp/AgtPa89KTEBjss7NsHr4hkYWFhh6OpN7MWcaJp2/3IHqYki1P/JAbXbfIPx5AcWmP88ENuiYqGfmoZeD86jVo0a6NaNLrZyyZVfgVuruF90WAXU4UA9i0PogTv52mRcC7oGHRUdHOty7Jvpll/qliUSIXjGTCSeP8/+ZDRqFEx+nlJmgxtkg9TEBGyfOIKlI7t0/AlNBxYfE05xnKUfzUlUgrunj0dCZDisnKqh14I/ysRZCXj6GEezgzp9f1sJi0qOfNsahT7FYAE+OSbZyy9oBgcNQ1keBEia81YuBrA9OzPkJwBUc8HRbHxbSlwGBwFrv/V8yAIbIS8/sWjomZrBtWM3VG3SXKpyRcoipWxSEjuXiQjzV2EvxfRyXBSSGZeG8NWeyMoQQ9PFFOodrFipSlJSEuzs7DBo0CAIZKR5v3/KH4/OBjD1if2hXlNVmFztC8TS75SAdssBt1EflxeUloEx3gF4nMC5qm56WtjkZAsL9cIFWy2IfePT47Hy0UqceHOCDaMiUMGoCuOgfKQS0pMz4eBsgrbf2cPM2FjEnzzJsDoy3r37qIZAVxd6nTvDoHcvEO2sQvhpAWI+ubzLh70r5AhByJg56H2kcTWy1CoTfg4/d6j0a8UnP0LaAEdBa2H/BDArG3uD3pzbASDYZuK6+hUAx//5SXICHITRQQ4GhZSpNGUegPv5OCIlIsBBL1ybxwxiL6NNBg6Ha0fyv8CAgE6t4zJsB/5eH7pG0kfSiePd+cZzxCtlwc3fG85Br9GvXz9Gu/ZRnh0GjmVTYtYZCXRYmQ8Ty68LPZjn3yGAe2Bpw6Xo5NApz8FZcGPadCRevMjaGo0dA5NJkxRf5PSBOX4Ytw/uZZguw/7aylhCFALc2LeTsQeQXQatWM/YRcqCUABx3+wpiHj3Fg6ubug6g/usKaRsW4BPjkn2ThTU78jZUPI16K2cfAmqe4jIpo+l0hUqk5WFKpa3vkSmiBhRruHRqWOICeHwtkgIb6dO5x6o6FYfAoH0bBn0PUEljkQxbe/SBiH+VaGsIsCwVY2KjHUj6V4I4k5y5RPGw6shUBKBAwc4Qpz27dujbl3ZslkJnJFoYz/4xX60T0VnPbiJfode1BXud+4TgZa/AtnBE5EkC3/4h2JjEB0bwFBFiLWONmhlLBvYaVF/u9wLuYfF9xYjOCkYLV8NRoXo2lBWV8KAxe7Q0vsx4wbtfcqDh4g7dAgJly4Bok8vzBouLizQodOmDQRfYrgV9SIV831lAQLVvXf8LYOYISwNaydDqGvxE0OmtGzfjVeROPkkGNPaVIalvvTvZ6Vl/bnXwSc/QtoAR0FvUojGgoBFI7ODGhspiwoAXQ9QyugwALtyGYkCIvT3x9lo53TLQimmBDxBqOmX8zgYJe7WhdZDyOUXt6yDUEUFozft+Zg+enWvL3zvhsLMXg/dZ8pWe/pPSBSmvvwAZYkE/T3Ow0AoYOUpysrZaWlBD7MZU9IB+6ZA/6PFxphCNghJCmGlKQSY1cq2FVY1WZVnkIJSKj9MnYqky5yDYjxhAkwmjC+N3x35WhOx8uycPAoEMFe1SQu0HfdzvsYpTZ3C/d9g/5ypyMqSwL1Xf9TvTsyRZUdeedzG6TX0NQtGI2xsQ9WDCinLFuCTY5K9D3lljtKlB12WyCqUzRGUnVXaUsbOvAtwENua16VzDDw0OZYrTSCxq+WCOp26w7pq9Tyfod+zwd1//8G9I/9A28AIYqXBRMr1WXmDjLaTuTmjr9z6DBkBCRAaqMF0igtOnj0FLy8vVmI7ZswYGBlJzxxBCtDLe8CzKNw74Y/YUA6Yk/BFqpq+gKtoBTSF8UDVbkDXTYDKJ+aIS1HxmOz3HjEiSgICxlqbYI69BVR4WrJCOqaIUrDl1H6oXuJAVK87HIBrkwqY6DwRmirS4S1kxsQwJrrYw4chCvwEfyfU04Ne166shEXNXjrwd5kPgKKDwgI8t8DDgBj033YfGWIJ3Mob4uCoevn+vuX5UqVSj09+hLQBjoLWwlJAogWBcwMgZMucIj8qoqTfEfKNZR5lKJQXR2kMRO2WV45ciayb3T/nZ4S9fY0qDZui/cTp7DBR/dzOmbcZxVmj3pVQo5n0t8z0IG/t4YfnaemoERYG95cejEee+OSZMMaU5kByBGBU/IwphAg+4uIIPAx7CCN1IxzvchwG6j+us5VkZCB48hQkXbvGlmQ8aSJMxo2T6oNYlho9u3IBl7b+zVDxBy3/GyZl+IVWIhaz1GsKchhZ2WDgsrUQKpetGw6JRIzdU8chNjQYVRo1Q/sJXCq6QsquBfjkmGTvAmVdENUrXZBwXOmcEEgVlZ0QBkdO9ie9wdGHmJhTfiRU9kqA5j2yfRLuwSG98CbAkRgTBc+zp/Ds8jlkpHKsH8SI4tigCep06gYT24K/dMaGhbDgOIll1WGIDtFHJTdTtBpK91JFI6LIFISv9QQys6DdwAJqLS1YqUpiYiJsbGwwZMgQmUtVSHNikHjpEYYHp/0ZOx2JsjATtdSPwlnrJFTL1wZ67wM0ifyPk5C0DIz1CcT9eC4wUltXE5udbGGj8eOMiKKx1NezEBXogcX3kRyXjhijIByuuJJV4lhoWWBB/QVoYNlAatWo9CnFwwOxhw4j8coVMCqWbNGsW5cxsOi0agWBKn/Ld6RerKKhwgJSWOBtZBK6b7qLuJRPGU7Lu9dArzrWUvQunU345EdIG+AoaAYH0Vl0BPB7dplJ7p3dA2AQACcAHALS94WyPIYAIGhnYmH5npS4DA562aK0cZLei5fBypFzIN55ReLspuf0XoohyxpCU1f6h4dnfDLae1IVEND1yQ2YJcRi4MCBjGoNGcnAzjZAGDGm6AEjrgLGFYr1E/c/n/9h+cPlTIf1zdejiXWTH+ojSU9H8KTJSLpB8C6Ayc8/w3j0p/rZYl0Mzyanl/o908ez1GX72nXw0ywOWLMsCoGuEvgqSZ/Fy2HpSF89ZU9eXLuEC5vXspei4Wu3Qq9c4YMHlj0rl5wV88kxyWU1wuyakB3kIORouiAhLC+iiW2e61KEQBQInCq3T0OMbQ+y+xKgANUUUKoWpUHOpQrIfOxOsQc4oj+8x8PTx+B76zokYu4lU0VNHdVbtIFL+y5yL0H8Z+40hL55CcsqDRAd5sYADIetaAihctFRSCZce48EAlBUAkzG1sT79HDs37+frb1NmzaoX79+PraS60JlKy9uBOPRuQCGUUGirhQPV+0jqGbtD+GgQ4D+J5zbTEkWVgaEYW1gOFUBQE9ZiDWO1mhvop9vHQqr4/X9fvC+FQJlVQF6zKuN4+GHsenpJmRIuDvGTvadMLPOTOiry6Z7ZmQk4o4dRxxldQQHf1RfaGAAvW4/waBXL6jayoYVV1g2UIyrsEBhWCAqKR0/bbyDoJhUGGuroqqFHqhURU9DBZenNoGJDj+DnoVhi9xj8smPkDbAUdBa2E0AxmTfvKz/wsA5+BwUSr6bh/HprYyyM6Rpm3uoYndK8jpUl7aux7MalM/1AAAgAElEQVQr59mN8uCVGz6mOF3c/gKvH0UwtOMuU2SjdJzoE4h/w2Nhk5CKdk8uQEtTE9OmTYOQoiWHBwJ+/wHEmDLgKODQLC8VC/Xvb+PeotfpXuzB271id4b8/SOh4MaHCRORfIvIeYBy06fBaETxAaMWqnHkNPjrh/dwaiXFGIFeC/+AtVPRgMXJSX25DBMfEY7d08chMz0dNVu1R8sRZTfbR5wpwvZJI5EUHVXmbSGXw1XCB+GTY5LLlAQaQZF/ilxTHRVxdx4CsCAbfDyn6bcCHHQbQBco9QCYZ2NtPASwOpuaPj87Viy+BGVjBvt5M+BQf09aAieaevpwbtsJNVu3h4Z2bjbb/Czt232enD8NYlRRVdeEksYIKEEZnSbWhE1V2UpDCqJRlliCiPVPIQpNhnI5TZhOcsbps//B09OTldtSqYqxsXFBpgBlOzy5GAivK0HIzOAw7XUEEahrfAaVRk2FwOpz/+tGTCLG+wQiSsQFRYZbGmNBBQuoyQh8WiClf9A5+FUsTqzm2JAb9KiAWi25IE1AfAAW3VuEx+FUAQ4YqhtiVp1ZaFe+ncyp9ZTVkXznDmNgSbp2HRBz5TskWu71od+rN3RaNIdSbsa+wlqwYlyFBYrIAqkZYvTZ5gGvoDioqwhwaFR9WOhroOXqG4hPFaFzTQus6yvb+1oRqV7o0/DJj5A2wFHQWtihAHYCWAbgly8sTCmm/bPLTt7kYf2ctpRq8Im4O+8tKxanJG+1uBYEKrp5zGCI0lLRbMgo1G7Xmf1elC7Gzhm32MO22UBHODWgZUgn0RmZqH3XG+lZWejo5wur8JdwdXVFx44dgSu/AbeygUTbrwTqZgOMSje03FuJJCIMODsAPtE+sNS2xNHORxmP+/dEkpaGD+PGI/kuFw8rN3MmjIbREVPIjyxATvKBBTMQ+soP5hUqo++SlTI7NCXZwrT+438uAlHDahkYMpYios8ty+J59iSu7dnGcH9Grt8JLX3ZqBfLsu1K29r55Jjw2LZF6kvQC+SbRx4ssEGsJjmib2bOGFGciBFFtXBvClMS4rF59ECQLuXseyEh1gpOjSzQrH/Rsi9lfEhExIanDDxRp4UN1BubsVKV+Ph4WFpaYvjw4fkqVfnyrBFI46MzAfC5HQxJNnefoUoQ6newgG2blp89M8PTRRjnE4g7cUT0B9TQ1sCWqnYor1m4e5LX5yMzQ4yDSx4gPiIV5Wx10H2WKwS5sEKoHPjo66NY/Wg1kkSc7o2tGmOe2zyYa1MsUHYRhYcj7uhRxP17BJmhoR8HEBobQ797dxgNHQKhvmyZIrJroeihsEDhWkAsycKYfY9xyScc9JHaMtAVrZxM2aSHHwZh5lGOlGv30DpoWrnsAfrzyY+QNsBR0FpY8pqJoDkBAD0VuW9U7laFaigox43KTkjojYPCwITLkVsoHEZk7hTYkDWnvEidElk/XgQQdnn7BiirqGL05r1Q16bMWuD1w3Bc3OHNALCGLm8oExLy+sBwLPEPhZZIgj4eZ6AiEbNaVbsUL+AwVQSBo4IlSthilo1PN2KT1yYoQQk72+yEq5nrdzWSpKYiaNw4pNyjowCYzv4FhoMHF/MKSs70H3xf4NAiLsbYeeocVHRzLznKF1BTvzs3cGbdijK59u+Zjpibtk0YxuiE63Tujsb9FYHCAh6zEtudT44Jj41YJL5EZkYGfG5dxaPTxxlOTo6YVaiEup17wKGOm0yMKAW15/Fli1nmiIltTSQmtICGjgormc390lzQOaTpH3fuHZJufACESjCd6IyglHDs3buXdW3ZsiUaNmwozTBStYmLSMH9w0/x5sUnV9TcNB31B7nD3OETg4o4Kwt/BYRjVUAYKB6iLRRglaM1upQrvmAxsWh4Xghk+9NzTh0YW3E+5ZcSnhyO3+//jmtBHBSNprImJteejD6OfSBQyl8JUpZYjKRbtxjVLCsfzo4SKZuZwXLFcmjWqSOV/RWNFBbgmwXokmzxaR/svstRTf/apSoG1f8E0E5/77PVA/ffxcDKQAMXf24MTdVsQge+LaaQ9OGTHyFtgINMUZBaWOpPKaZbAHhnZ3NQ+ujY7CAH4XNw/J5ALQDnABCJNwU/CM2JWFSIaYWeH60B3JZxb4rEKZFRJ9acUTb+MgURAW+/Yrg4s/EZQ/u2q2GMDuNqSD08PXDrefiCONxbvY+Cw7vb0NbWxtSfp0CwoS4Q8xYo3xgYcAwQFi+44ouoFyx7Q5wlxpCqQzDN9ftgh5KUFASNHYeU+xxTsOm8eTAcQMk/CpHFAseX/wr/xw9gYG6JIas2QiCUnjpQlnn41DY1KRG7p45FSnwcHFzrocv0uWUqe+VHe+Fx9CDuHN4HVQ0NjNywC+pa33aG+bSfCl3kbwE+OSbyX53cRixUXyItKQlel84yRhT6rsoRwk0iRhTLKlWL5XvL984NnF23gj0rVLRHQUmggZ+m1YZFxaK9kc8SiRH+lycyo9OgYq2DcmNr4uy5s3j48CGEQiFGjx6NcuXke2sa4eMPj12XEZT4CaOMfLJ6Xe1hZPHpu/JObCLL5gjP4EpWBlkYYXEFS2gI8xcoyO+JjXyfiH//fARioHFtbwe3zvY/HIp80EuBl7D0/lJEp0VzTrhJLVYm7KDPsa/kV0ShoYj7919E796DrJQURr1rPGY0jMeNg1IOk19+B1f0U1igiC2w/ZY/lpzhoCJHNbbHnPYECfW5EPBou79uMVaV0Y3tMfsbbYpY7SKdjk9+hCwBjoLUwuYYuBuAmdmc9BSsuAdgcTZgWE4bQrqja1YK85IzQaTClO9GIeY/pEBJ/9ZmFqpTUpDTE/bmFfbPncqG6PvbClhU4j4wacki7Jp5GxJxFloNd0KlOtIDABKd2cDnhKkGjPa4C6X0CLi5uaGdgwD4pxen7jgPoNzXH86CrEXWvmmZaeh5uicCEgJQQb8CDnY8CDXht1M7WXBj1GikPHrEpjFbuAAGfcsWtaes9v1e+6j3Adg7cxKjSG01cgJqtGwrr6F5O86Fzevw4tpFqKhrsNIUHaOC1WvzdqH5UIxeqraOH8pK5Br0Hoh63XrnYxRFl5JuAT45Jjy2ZaH4EglRkaByMWK7os8hCQUTiFHNteNPxU7jTFTjm0YNZLrpW3ZAWkpl1GhuhUa9KhX5VqX7xyFyK+HeA3od7KFa1xibN29GbGwszM3NMWLECBbskKukJ+LD1jm496oaIkTZJH5KgGM9M9TtZA8dQ45ONjJDhEm+73EtJpH97KSljq3V7FBB8xPdrFz1+mIwiVjCghtRQUkwMNNE77l1IVSRLsASnx6PVY9W4fgbIi0CVAQqGFljJEZUGwGVAl6Epb97h5Bp05Hm48PG1nB2hsWKFVC1IvJEhSgswH8LnHseinH/eDKq7A7VzfF3X+fvZrCtvfwaay6/glCghFMTGjAA0rIifPIjZAlwlOT9KRSnRB4GyXnxMraxY/SdSgQACsDnTgiu/c+PoV8PW9EIKmrSP7D7e/njSkwCXKLSUcebkmGAYcOGwebaBODdDcC+GTCIEmSKV5Y9WIZ9vvugrKSMfzr8gypG3w64UMojAYrmUMGaLV4Mg97ZgZriXUKJnf38pr/gff0yw6IY/tdWqKgXjQNWHAYL8n6Gw7/OYVM3HzqagfIp5HML3Ni3E49OH4OGji5GbtjJWBkUUrYswCfHhMeWl6svQcFmYkSh8jliuiKhICwFnQmLS9fYhDemOLdhNXxuXoVeOQeki7pA20ANg5a6F0tGSezx10i+HwYlFQFMp9RGcFIEdu0ikj2gWbNmaNLkxwxs+TKqWISs0z/jrccb3E/sjzgx93JObDLVmlrCpa0tNLRVIcnKwob3EfjzXSjEWYCmUIDllazQw+wT3Wy+5peiE5WlUHkKsc10m+7yWSmNFN1ZE49QDyy+uxgfkj6wn+nyibI5appQInX+RZKRgcg1fyEme58EOjow/3UxdNu1y/+gip4KCxSBBR4HxqDftvtIz5TA1dYA+0a4QV3l++9k6ZlidFh3G28iklDDSg/HxzVgwY6yIHzyI8qGxblMkODg4GBYWEgP1FnYhzE9JRmbxwxijA7Nh42Bcxuq1OHk1NonCPKNRQXXcmgzoprUqgSmprPyFKIvG+v1DllxXtDV1cWUvq0g2JJdn9r/CFCxldRjFkbD+6H3MeIix3oy0XkiRtX4Pr1r2NKliN37P9bWdMF8GPbrVxgqlakx6cZw55RREItEaNhnENx+Kp0BI6pl3ztzIqtjJ2DVPr8tL9La9ZJyqJJiY7B94nB2HnIDHZcU/RV6FtwCfHJMCr6aQhtBrr7EwYUzEezH3WoTI0rt9l1Qs1U7XpaJBXh54uhSIq8BVHWHQyDUQ8/Zrihnq1toxv7ewJK0TIStfgxJQgbUKujDeHg1XLhwAR4eHgxodNSoUTAzkz7rVeoF0PXtjeUQX1sGv9TmeJAyACmZ3PpV1YVwbm2Dmi1s2IXUg7gkjPEJREi6iP29j5khfq9kCS15Z5dkKx8XnsKARcUiCao3tULjPvnPrknNTGV0snt89oAASQkfrV+VfpjkPAmaKppSm+tbDZNu3UbI7NkQRxEhEqDXozvM5syBQLNg4xZIKUVnhQW+Y4F3UcnotvEOYlNEKG+shWNj3WGgRQgLP5aHATHouZmKFIAFHZ0wrGH5vLqUir/zyY9QBDiK8Ug9vXAGV3ZugrKaGsZs3vuR0SElIQO7Z91mqVDtxlSHfS3pb3F+fROCjUERME2VYPD9q0hQSoK7uztap54CnvwPMKoIjH/AaiGLSxIzEtHtVDeEJYehhnEN7Gm3B8qCbwPxxOzbj/AlROIDGA4bBtOZM4pL7VI3b86tvaqGJoav2wZN3dKXRkfYEoQxQeneA/74Cya2ZeMhk5/DSkDHBHisY2SC4eu2QqhcvPg8+VmDok/+LcAnxyT/qyj0nnINcLx5dB839+2Ea6ducGrUDMqqeTvOhb7C70xAGSZbxw1BclwstI2bIlNcG7Xb2qJ+14LhNOR3Pak+0YjeywWHDLpXhGotI1aqEh0dDVNTU4wcOZJRyBaKPNkPnJ4EkViAZ+J+8Ezqiow0ulYCNHRVUae9HWOaiZdIMNn3PS5FE74+UEmTSlZs4ahFldfyE8LbOLHmCUJex7HMmr4L3aCqXvC1e0d7Y9HdRfCL8WPKmmuZY0H9BWhoWTAw18yoKITMnoPkW7fYuKrly8Ny1UqoO8nKHyA/G5amkXxCEqCjrgxrQ0XQqCD7Gp2Uju6b7iIgOgVGWqo4Ns4dtkbSM+/NPvYcBx68h6aqEJemNoGlvnw/9wVZW2H15ZMfoQhwFNYu5zEuATvRzTKlqFZr1hptxkz62OP59Q+4efAVVDWUMWx5Q6lrKFPFEkYNG5spxpDXiVAPucLGHD2gG8wPtATE6UCH1UCd4cW0am7aubfn4tTbU1AXquPfTv/CTu8TCnFuxRKvX2d0sITCrdOqFSzX/gWlYgzMFKvRCmFyAt7cMWkE0pOT4dKhC5oOKl66YHkvMSooEP+bNRkScSbqdu2JRn0VbDs/snFceBh2Th7FsFnajJmMas2KN8tL3udBMd6PLcAnx4THeyXXAAdRr5KUlOfa9b3b8PjMSWjomkIi6AdNPTX0/MX1IwZFUe9b9D++SH0WBSV1ZZhNdUFIfDh27tzJwNsbN26M5s2bF55Kb64AhwcDGYlIUzaHp8UmPHuixDIoSHRNNFCvsz0captga3AUlviHIDML0BAo4fdKVuhrZii38h7vW8G4vp+jEe44oSZsqxnJbd0iiQh7vfcyprt08iEBdLDvgFl1ZsFAPf9MMXT2Y/buRcSq1YBIBCUVFZhMmwrDQYNKzOdBbkaW40Bbb77F0rN+UFUWYF0fZ7StVgiZTHLUl69DpYnE6LvNA0/ex0FNWYADo+qhto1s5z0+VYSWq28gMjEdLRzLYftgV7l95vlqNz75EYoARzGdkpBXvjgwn8tG6P/7ahD9W44cW/EYoW/j4ehujhaDpAcCPRQag8l+76EsycLSO97wl7yBgYEBJtVMg9L1pYC6PjDVB1CVPgIpb/NcCbyCKdensGHnuM1BX8dvA4Wm+foioP8AhrytXr06bPfugUCj9Ec/5W3vvMZ7cPIIbv2zG0JlZQxdswV65Tg+75Iu5DwdXDgL9DnTNzPHoBXroaL6bQDbkr5Weep/9u+V8L19HQYWVhiyaoOinEeexuX5WHxyTHhsKrkGOHi8zm+qFu7/Bvtmc89vLeNBEIuNGZgl4T2oaxd9xpc4KQPhVKqSkgmNqkYwGuiES5cu4c6dO+xFggBHLS0LEcgy7DmwvyeQGAoI/s/eVcdHdXTRk5W4O1FIIARJAgSXQj+kaHEN7m4t1haKtLgUWigOwZ3i7g4JEEISIBDi7rqe7zfzcEKySXY3L/DmH9rs6Jn3du/cufccIbJb/osHYTURcjuORuCSYuloiEZdXZFkr4PRwZFU3Y6U7jZmWOLmAEOB8vxqBW1KdpoYe+fdhUQkh1sDG7QeUkMtj1VEZgSN5vBLYIjezXTMMKP+DLSv1L5Uh7a8oCBKQCoJZ6Q3Db5rBrtFiyCwUJ2TRi2AsKxT4tRbeu45/r366t3MCO3D/M410b+hM8tmy+7pyBX5GLf7Ic4GxYPQIv7r411iR9HJJ7EYv+cRXfA6nzpo71GB3Ysv5ezYZEdwDo5SbmZJm59dtwpB1y7BuqIr+i/+690PRFaqCDt+uU277TTRC07Vlf+Sb+v3Ao+zcvFDnBRez0l6Sh6aNW6AlgETgZxEoOkUoNXckk651O1S8lJoakqqKBWNKjTC+tbrC9RalyYkILxnL8gSEyGwq4BK+/dDYKV8mk6pJ/oNdSCViOmtfXZqCg2Rbjf+yzK95QkWIrV4cfM6OuWes/+EU83SEaSVp7WXZq4kosx32njm+2fqLLg1aFKa7ri25QgBNhkmLIbtm3ZwkEMUkdtOjY2GW+N2iA6tDoUsH7YuJug8uRYE2qU7rJdk33MeJiDtwAva1NynGoTuJti4cSOSkpJgZWVFpWPVlqpCBs2IBnb1AJIY+Uj8bzbSqozG3eOvEfYo6d2S7KuawqNjRSwUZ+BUUgb9u6ueDjbUcEZNo5KlEpD9OP1vIMKfJFMHU7+5DSjRqboK4eM4EnoEK/1WIkvKKMWQdJXZDWfDzrDk/HaKnBwQrrWMw0don3xLS9gtXgzDptzvjzJ7SQ7kv/33lKZDkNKmug0SssQIiGKkpie2rIIpraqUyhGlzDy+ljoLTgZjy01GibK0/BnkHR3m64fLzxJhbaRDU1VM9DTvDNbU3rDJjuAcHJra9Q/GIbKMGwi5qFSCVsPHUVKxt+Xh+QjcOfIKekZCDF7cBDwlNdQfZeainT/zI7/ifhJC827R/578vS1Mr8wAtPjA5EDARI23GYVgSV7yiVcm4mrUVRgJjXCk8xHYGnweOkd+6ML7D4A4JAQ8Q0M479kNXbeSk2WVwfaWuyEDr5zH+fVrQFzVAxavhnVFl3K3hg8nnJEYT2VwJXm5qNG8FdqOZW4cuaIcAkeXzkeY/31YV3KlvCVvlZ2Ua83VKq8IsMkwYTGG37SDg+zL3SP7cWv/ThiYmuF/w5biwrYQEFbzip6WaDeqptI2i6r2mNgWyduCIH6RBp6RELZTvBGXlojNmzfTVJUmTZqgdWs1p9vlpQP7+wPhDKcEvAcD7VcgPjIHd4++QswL5qBJikttK0Q3NMOCtBRI8vOhw9PCvMr2GGRnUezv2lC/BJzfHET7bT2sOtzqaSYdITE3EQvvLcSlSCYNWk+gh0l1JqFP1T7g80ru5Mo8fRpxc36HIjub9ms+ZAisp0yGFou5aVT1HJe0H4lMgSn7H+NUYBztonddR/zZtSYkcgXG7n6Iq88ZJ1ufeo74o0tNCJQ8U5R0PuW93bZbrzHvBMPtM7RJJczpVHpemOi0XLReeR15Ujl8Gjjhz64e5R2mL86fTXYE5+Aog8fs4ZnjuLKdSHPqYfR6XxCSx7flwMIHSIrMQs3m9mjet6rSsyNEVvvjU1ElS47J958gUBABSwsLjBPuhxYJo6zZHeixVen+VF3xaOhRzLnNMLAvarYIHV3eK8a8HYvKwY4dh+xr1wA+H44bN8CwCefBV/VefNofIY8jfDAp0ZGoVMsb3WbNU/eQKumfzJvcJCZFhiM54jX9NyniNY1GoUaXkTGGrFpP/+WK8gjEvniGvbN/pg26/zIfFb3qKN+Yq1luEWCTYcJiEL95BwdxIG+ewCigdf91ATJTLHFjfyj9f0Ks2aJf1WIf1Eu737I0ERJWPUS+RA79ujYw7+GGy5cv4/r163QuQ4cOhaOjY2mHKby9TAwcGw8EHmDqVfmB2lz52gaIDE6l8q0p0czBXYunBZt6VvjHKR8hPEYeuJOVKVa4O8JYyZQVUbYUe+bdRV6WFM4eFugw1lPjuF+MuIg/7/2J5DxGEcXTyhPzGs1DZbPKJcZaEh2D2J9/Rt7jx7QP3Ro1KAGpdsWCudpKPNBX0DBHLMPoXf64EcrgP+o7F8xs5/7uOZDKFSBEl4f8GcnfVtVs8Hff2tArg0ir8gD3uaB4iidJL/uhhg3W+XirTN51840w/HGKifI6NLoR6lZUv2x0WWDOJjuCc3Bo+AkgNwq+P4+jh0nPlm3ReiQTDk4Kkfna/ftd+t9df64Du8qmSs0uVSqj5KIiRT5mBeVBmnQN2VoidPayQO2A35g+hl8GHLyV6k/VlWKyY9D9eHfkSHPQ2rk1VjRfUeAPcfwffyJt1y46vO38eTDr9XVKl6oaX1X0R9j8jy1bQLvqOXshnGp6qqJblfWRm5lBnRckhYL8mxQRjpSYSCprWlAR6uii/cRpqFy3gcrm8C11dGDeLEQFB8Kxugd6/b7oW1r6N7tWNhkmLN6Eb97BQfbmrbzt27TGO0df4uE5Jjy+XsdKqN9R82pV2bdjkX6c4R8gsrGCSkbYtGkTEhISYGFhgdGjR0MoVHNoODkZXZoP3FzJPMIVagE+BwFDaxClExJxce94GDKTRfRjvpCHOA8j+DprQaTNg7OuNjbUqIhaxkWnrFzaHoxnd+Mh1OWj75wGZUb0miHOwCr/VTgcepiuiSjijfAYgeEew6HNL1m6TL5MhuR165C8fgMlmdfS14ftb7/BpGsXjTtx2PpdlJ4rwZDtDygJJikz2rpjTIvPFY3ImWP5+edYe4V5N7ydzbBlUF2Y6pdsb9iKR2nn9SgyjZKKiqQK1HYyxZ7hDVXqCJLJFeiy7haexmSiirUhTk1sRolgv7bCJjuCc3Bo+OmKfhaE/b/PoKOS8G8bl/ee7genXuP+iddU5mvgn42pl1+Zsi4yEfNfxcJQAfheicEFwQPabFalp9B5fQFwqA8Mv6BMVyqvQ3I2h50bRompLHQtcLTz0QKZt1N37ETCwoV0fIvhw2D9M3ODzBXNIEB+BCkp5/Ng2LhUgc/ClWViSMhlUqTGxrxxYrx3aBBpwi8VEgFl5VwRlk6VYOVUkUrBWjo5Q1uXI6Ut6dMTHvAQhxcyEVd9FyyDnZvyZMclHZNrV7YIsMkwKVskCh2dc3AAVE6ayEprafHQ6/eFsHevgUu+IXh+N56C18KnKmo002w6LHEgJG14AklEJvjmurCZXAcJKYnUyaFQKNCwYUO0bdtWM4+W31bg1E9AvgIwdQJ8DgNWTKqtXKZA8M1YEHuPRF/QosvHtao6uF1ZB1pCHua42mG4g+UXf4Mjg1NwYk0Abdq8rxtqNnfQzLoKGeV+3H3MvTMXUVlRtJariSvmNp6LWta1Sjy3nPv3ETt9BmTxzHNl3L49bOfNBd/IqMR9fg0NEzJFGLjlPp4nZFESzD+7eKBfA6dCl7adpF6cDKbRCZWtDeE7tP43IVuqzH5HpOSg27rbSMmRwNlCH0fGNIaFoepJ6Z/GZODHf25CkQ/81NoNE1pWUWZ65aoOm+wI5U7Q5QreAifLGqPk9D8rEHLjCj1E9l+06t1kyQFz77x7SIvPRa3WTmjSXbkQP0V+PhrdDUGESILekRI0Cw3EU0EUKpvz0T91BWhybM/tQI2uZbKLRF5smd8yOvbalmvxncN3n80j6/IVRI8fz8jBtmkD+79WcTJhZbBbMc+C6c0cKR0nz0TVRqXTui9qCcRpQaMx3kRlkDSTlJhoKutaYNHSgpmtHXVgEEeG5Zt/ja2sy8QZU9T6yvPn5Pto9y9TQFQTXLzro+t0xtnBla8XATYZJixGmTW2RFliJJNKsfe3n5EY/gqGFpYYuGQNtPUNcXrtE5qOQQ5d7UZ7oJKXZsnBpYm5SFj9EJDnw7CpPUw7uuDatWu4cuUKhWvIkCFwdtaQosTzs8ChIYA0l1Gw67sPcG70btskIhkCLkXh0YVISEVMmkquPg+Xq+vicSUd/GBtglXuTjATCj7aatJu34L7yEoRoUJlE3SdWkfpyzB1PzMimYjKyfoG+UKeL4cWtKhS3sQ6E2EgLJl6nzw9HXGzZyPrwkU6faG9PU1Z0atVcseJunFQZ//kMN5/yz1EpeZByNfCX71ro4Oncsocp57EUb4Ows9hY6xDnRzutt92Cm9ajgTd/72NsOQcmOkLcXhMY7hYGaptC/88FYxNN17T6I2zk5qpdSy1LaKQjtlkR3AODg0+AXlZmdgwZhANq289cgI8W/7wbvTk6Czs/4OJvOg5qy6snZX70rmckol+T8Jou4M3s3FXdgM5WmIMd3gFh+jjgIkjMPExwP/4R1ITy36V/gq9TvSCRCFB9yrdqTf/00IkwiL6D0B+Xh50PT0ZOVhdXU1MjxujAAT+W/YHXvndpdKqg1f8S+VjS1uIMZwaE/UuKoNyZkSGIzfjPfHap2PoGBi8cWSQaIyKsHauBAtHJ5DUE65oBoEX927hxCE034gAACAASURBVEomPWXQsn/oPnDl60WATYYJi1HmHBxvNodE2u2aOQlSsQiudRui88+/QiqW49iqR0iMyKLpF50n10YFVxONbmfm5Uhkno8AtACrMV4Q2BtQwtG4uDiYmZlhzJgx0NYUaWWMP7CnN5CTBPB1gG4bPrtsysuSwP9MBAKvR1NFGlKSjXi44qGPLFd9bKxZCd4m750DNw68wJPL0eALeOj9Wz2Y2ZbMcaDOTQlOCaaSsiGpDOcAIZSfUW8GWjq1LNFlBHG4p+8/gIRFi5AvFlOONqsJE2AxYji0+CUnNVUnBuroOyQuEwO33kdSlhh6Qj42DPDGd27FcyLeeZWCkTv8kCWWwUhXgM0D66KBi/JqjepYV1n1KZLK0X/zPfhFpFGHw94RDeDtrF5ujFyJjBKOxqTnoZGLBfaMaFCid6KsMCtqXDbZEZyDo6jdUuHn/qf+w9Udmymp6ChCLvpBCP3bHFYTaz34zGuo9AM/4EkYLqRkokEuMPtmJE7qPIQOxJgp9IWWNAdovQBoMlGFq1CuK6lCCp9TPvQHzt7QHod/PPyZB18aF4fwXr0hS0qC0M4OFQ/sh8DSUrkBuFpqQYBww/j+PB75+Qq0HDYWtdq0V3ocYoRkp6UgOSL8XVQGidAgRKD5CkWB/ZAQZzM7+3dRGUx6SUUYWXw5PFfpCXEVS4UA2bNtP41FWmw0qjVtgfYTuLSxUgHK8sZsMkxYDBXn4Phgc4jUPZG8J+V/Q0ahdttOyM2U4Mgyf2Qk5UFHX4Bu07xhXkFzh/B8uQKJfz+GND4HAht92EyojaTUZGzYsAFyuRz169dH+/bK/66V+llMfQ3s7gGkvCT0osAPfwKNxn3WbWZyHu6ffI3n9+Jp4C0pMeZ8XPXSh08jZ4xxtELi60wcXuZPP2/Q2QV127HX6SxTyLAjeAfWPV4HsVxM11PPth51dFQ1V55A/0OgxKGhiPnpZ4hfMIqB+vXrw27ZUghtbEq9TWzvwC88lXJuZIlkVGZ06+B6lE+jJCU4NhODt91HYpaYHuxX966Fdh7KRYGUZDw2tlEo8jFh7yOqPkMiztb2q4P2GsLgyrNEupekLOvhiZ511UyArMENYJMdwTk4NLTx5PC3beoYeljwatMBrYaNeTcy+Wznr3eQlSpC3Q4V0aCTcjKdkXliNLgbQn8Llz7Og25KEIIF0WhjFIrGWScBEhI4NQjQK9mXYGmgWft4LdYHrKchitvaboO3zccEp/LsHET4+ED8/Dl4RkaouHcPdCorl5ZTmnlxbYtG4Nz6NXh65Tz0TUwxbM2mArkspBIxUqOjkBgR9t6hERkOUVbmFwfQNTKG9SdcGeYOjhBqqz7XsehVcjWUQeDp1Ys49y+RiuVh6OqNMLXRjAyhMnPj6qgWATYZJqpdmUp74xwcn8B55p8VCL5xhUb79ftzJZUZz0jKxeGl/pRjwtBcB92n1aXcYpoqkugsJK59TB0Bxq2cYNzKGTdv3sTFi0yaw8CBA+HiopydpZI556YCe/sCUQyJPBqMYRwdBUiqpsRk4+6xMIQ/YZQxSHlpK0RuY0s0u5OBjLhcWDgY0khffjmQ/IzMjMSSB0twPfo6XQuxCbtV6YYJtSfAQq/4kQMKkQiJy5Yjbfdu2h/fxAQVFv4Jo5YtVbJVbOzk6vNEqu5BCDCtjXSwc1gDVLUtHQ9JVGouBm27j7CkHHrAn9+5JgY01FD6FgtAXnQ6BBuuM9Hvv3WohuHNNPh9AGD8noc4+SQOpvpCXJzaHJZq4PwoC5jZZEdwDg4NPQFRQU9wYP4vzI/r0r/pjfXbEh+WQY0BUvr+3kDp244/XsXin8hE2ICHo+fTcFDnFkQQYYbuPuiKEoB6I4AOyzW0wvfDBCYFYsCZATQHc0iNIZhad+pHcyAM2VHjxiHn2nVAIIDTxg0waNxY4/PkBiwYgayUZGydNBIyqQSNe/qgRouWVLXkvYLJa6TFxdIoj4IKj8+HuZ3DG7JPhvSTcGYYmJkrHZnE7Q07ECCkr1smjkRWShK8WrdDq+Gf3zyyY6bcLEqLAJsMk9KuRY3tOQfHJ+BK8nKxc+YkpMfHwczOAQMW/QWhri4SIzLx38pHNG3Fwt4AXX+qAx19NauYfDC39NNhyL4eA/C1YDOxNniWuti6dStiYmJgYmKC4cOHw0iTZJXSPODISCDkODPLap2AbpsAYcFk2LEv03HryEskhn1yaaAF9JypfBqzGt+FYnV9K+YWlj1YhlcZjJqHodAQozxHwaeaD4T84j8XWZcvI+6XX0E4Okgx69cX1tOnf3UpzscDYjF1/2PIFPmUAHPXsAZwNC9aZUeZzUnNkWDo9gd4HMVgOOF/lTG1tdtXb6ftvBOO2ceC6JoHNXLG3B9raHzNiVkitFpxDZkiGbrUssNffWors2Wsr8MmO4JzcGjocTm5eime376OClWqot8fhPzzfbm+/wUCr0RTr3yf3+orNSORXIE6d4KQKpVjXBLQ5nEEzmg/QjWEojdOMn1MeAhYfC4bpdQAJayUJ8ujvBvhmeGobFoZ+zrugw7JPX1TSLRKwoIFSNuzl/6lwh8LYNqjRwlH45qpC4Hre7bjwbFDRXZPojzeppUQngySXmJu7wiBuuX4ipwZV0FVCDw8cxxXtm8EXyjE8L+3wNBMvTmqqpo310/xEGCTYVK8mWu0NufgKABuQka857efKUF0jRat0HbMZForKjgVJ/8JAAkHt6tiik4TvSAQaoYzQSGRU8JReYoI2o5GlI8jJTUF69evh0wmg42NDQYPHgw9PQ2qbZFUzfO/AXfXMigShTtCPmpQcCQDsZfCA1Nw/tALyBIZaVn/anoYO9gTdT/g5dDoG1CKwUjayoHnB7AuYB2IvCwpTkZO+KnuT/je8ftiHzKlCQmInTETuXeZyBidKlVgt2I5dN0YxZryXnbejcCcY0+p8om7rRF2DKsPayPV8pARTohxux/iyvMkClevug5Y2NUDgnIQHVSS/b0YnICRO/2okkmrajaUx4SvpGJlScYrrM3e+5GYdSSQVtkxtH6x+VRUPR9V9McmO4JzcKhiR4voIzczAxtGD6I//j+MmYyaLVq9a0F++LfPvIW8TAkadnGBd1vlcioPxqdiQkgkCAXkySvZCFEE47kgFqO0j6GCJAxwawv026+B1X08xOL7i7E7ZDfVQt/bYS/czd0/qpDq64uERYvp3yxGjID1Tx9Hd2h8wtyABSIgys7G1skjQYhxSeHxBbBwcPxMwcTAVPPpT9yWaRYBQiK4adxQ+izU7dQNzfsP1ewEuNE0ggCbDBONLLhkg3AOji/g5n/qGK7u2EQ/JXw9hLeHFMIpcXFbMP1v1zpWaDO8JngaOlCIXqUjeRNzgDDp6AKjpvZ48eIF9u3bR6VjHR0dMWDAAM2Rjr7F7s464ByJ6M0HzF2B/ocA8y+HyBM78drNKGx7FodLLtrQFvLh61EJ35mXLk2hZK9A6VsR5wZRW9n3bB+N9CWlQYUGmF5vOtzMiuecyJfLkbJlK5JWrwbkcmjp6MBm5gyY9ulTbIdJ6Vemmh6IY2vtlZdYfp7hGiFcG1sH1YOJmiKgpHIFfjkSiIP+0XS8lu7W+KdfHehpa8YZqRrUiu7lSXQ6em+4izypHF4OJtg3slGZrpG813023sX98FQ4muvh/OTmZTqfohEsugab7AjOwVH0fpW6xoPjh3F99zbo6BtQctEPlSCinqXi+F+P6RgD/mgEY0vlbhM6+L+Af2Yu2kEb884lYbfuTZgjGqOwh5nvwOOAS/NSz704HdyNu4sR50fQJhNrT8QIT+a/35asS5cQPX4CiDvaqG1b2K9cwcnBFgdgDddNi4tBYngYLOwdaeixKhRVNLwEbjgVIXD3yH7c2r8TQl09jFy7DbqG6pNRU9GUuW6KiQCbDJNiTl2T1TkHxxfQJoey/5bOR9jDB9DW00P/xauprDcpj85H4vYRQrIJeLRwQLPeVTR2+Ew7Eoqc+/HQEvJgM8UbAnNdBAYG4vDhw3Q+rq6u6Nu3LwQqUAwr1oMYfIxJWZGJAH1LoN8BwOFjrrJP+wvPE6PX41eIFEmgraWF9TWc0d7KtFjDsqlyWHoYlvotBUlfIYWnxUNPt54YW2sszHWLFymYFxBACUil0cwh3bBVS1RYsAACs/J1CUMOvX+eDsGWm6/pOpq7WeHf/nWgr116RbvC9p68vyvOv8A/V5j3tLaTKXWqmBlos+mRKfFcCOdI13W3kJwtoc6EI2OawMpIc7xAX5r4y8QstF99k0r3jm7uipntPr4ULvGCy6ghm+wIzsGh5oeAKBFsnTKK5qcShnHCNP5hubIzBMG34mBTyRg9ZtRVajZPsnLRxo/x7G59qYDZ60ic036MrjgDLzwDbGoCo2+CMgdpqGRKMtHtWDck5CbA08oTvm19aRTH25L3NAgRAxg5WD0vLzj5bv/qciU1BDU3DIeAxhEQ5WRj07ghkOTloUmv/mjYvY/G58ANqF4E2GSYqHelpeqdc3AUAh+JVt05fQKy01Jh41IFfRcsBV8gBDk83Tr0EgGXomjr4kSrlmq3ACjyZIhf5Q9FpgQ6lU1hOawmda48ePAAp06dot1Xr14dPXr0AI/HK+1wxWsfeQ/Y2xvISwMEekDPbUDVdoX2ESeWUCdHaK6Y0ItglbsTetkWzxlQvEmqvzYhICX8HCS1mRQjoRFGe41GX/e+xeLnkGdnI37efGSeOEH7EdjYwG7pUhg0UC71W/0rLXwEmVyBmUcCcehNJEUnLzus6OlFlU40VXbcCcfvx4NoWoyLlQFNnXAwUw3nh6bW8Ok4GblSdPv3Fl4l5VAFmiNjG8PVij2XNKsuvMDqS6E0VebE+KaobmdcVlCVelw22RGaOwGXGrZSdVBmRklE4GMc+uM3OvlBy9fC0vE9S7FcpsC26TchzpWhaa8q8PqfclJBU59FYk9cKtx0tLH7eAquC4MRzw/FFGwBDwqg81qgdv9SAVbcxr/e/BXHXx2HnkAPBzsdhLPx+3VKY2PxundvyJOSIXRwQMX9+yCwKD57dnHnxNXnEOAQUB0Cb3lZiBrOyH+2UiJBrnw9CLDJMGExqmVmS7AYk4+mRgnVF/xKIzW9O3ZFiwHD6Of5inxc2BqEUL9E+v8tB1WDeyPNSFPmBaUgZSeTJmPWww0GdRlZ0Rs3buDSpUv0v+vUqYNOnTppLLLkHWjJocCu7kB6BKDFA9ovA+oNL3S7UyQy9H3yCk+y8mi9P6rYY7iDVXl5RAqcp1Qhxf5n+yk/R5Yki9apaFwRP9f9Gd85fFesfck4dow6OhS5ufSiz2LUSFiNGwctFnODiaRyKlt6ITiBrr1/QyfM+7FmmfBDnA6Mw+R9j2lUgY2xDnyH1oe7bfk8dItlcgzYch/3X6dCm8/DruENUL8SuxyCZI7tVt+gijZejqY4MqZxmey7Kr5A2GRHcA4OVexoIX2cWLUYL+7ehF3V6ug7f+lHNYkM2Kl1T2igxaDFTWBgUnS4VLpUhtq3g5CnyMfvcn20vxiH3bo30BTX8B3uM6GOU4IAoeYOHxcjLmLK1Sl0bb82+BV93N/f7hKPekQ/H6pbTuVg9+2FjqtmiU/VvMVc9xwC3wQCOelp2DR+KORSKb4fNAJ12nf+Jtb9rSySTYYJizFXqYNDoRDjdfg6VHQeBT6/fN+Sfrhntw7swt3D++ifus2ci0q1mehUuVSBE/8EIOZ5GrR4Wugw1hPONTVz2ZGyJwR5T5KhpSuA7U/e4Btp08iSCxcu4Pbt23R+TZo0QevWrTX/+GUnAnt6A7EPmbGbTAZa/g4UElGSRQ5uT8JwNyOHNplRyRaTnW2K5QjQ/EKLHjFNlIa1j9fi4IuDULxRamts15jyc7iaKm87SiIiaMqK6OlTOiiJHCYEpNoODkVPQsM1skRSjNzhjzthKXRkNqiZ3A1LwQhfP2SJZTDSFWDTwLpo6KKZd1VV8JN0n8n7H4Mo0ZDyd9/aIFExbCz3wlLQeyNDlju3U3UMbvJeaZON8/3SnNhkR3AODjU+OeRAsHHsYCjkcrQb/xOqN/v+o9HObwlC6IME2Fc1Q5cpykkEbYhKxO8vY2HI5+GcnwTxyZG4ov0QU7EJ+hABzWcA3zNytJooyXnJNDUlTZyGRhUaYUPrDe9+YKkc7JixyLlxg5GD3bwJBg0bamJa3BgcAhwCakDg4uZ1CLhwGoYWlhi+ZhMNP+fK14EAmwwTFiOqMgcHkdkOCBiGhORbsLZoCE/PTeB/oDjGYgyKnBqxeQ7Mn4WYZ8HQMzLGwKV/w9CcORxJ8mQ4suIhUqKzIdDmocuUOjRFV91FniVB/Ep/5OfJoFfDAub9q1FbhTg5jh8/jkePHtEptGrVCk2bNlX3dD7vX5IDHBoGvDjDfFazB9BlHSD48sVXnlyB4U/DcSmVIQMf42iFOa525d7JQdYSmhZK01buxN2ha+Nr8dGrai+M9RoLU13leEfyJRIkrVmDlM1baB88Q0PYzp0Lk44dNL+/XxgxJVuMwdseIDCGUZX5rUM1DG/2ZcJZTU48JC4Tg7fdR0KmmEY//NWnFtp7aCbqShXrXHr2GdZdZWSJCbcF4bhgc5l5+An2PYiCgTYfF6Y2h52pcpyMbFoTm+wIzsGhxifj3n8HcXOvL3QNjTDqX18ItN+T9UglcmyddhMysRwtfKqiRjP7ImeiyM9H03vPEJYnxiATE0w4EI0rwqcw4V/Ej7gI8LWByU8BIyb8Ut2FGAYTL0/E1eirMNI2wpEfj8DWwJYOSz6LnzcP6fsYJZcKf/4J0+7d1D0lrn8OAQ4BNSKQkRiPLZNGgnALtRk9ER7ft1HjaFzXmkSATYaJJtddzLFU5uAg4x65cwALz4nQtcoptK9pAi+PteDxvg6nYWZyEuXjIPw9TjU90f3XBeDxGFWGnAwxDi/1R1aKCLqGQnSf5g1TG/VHsOT4JyDtIMNfZtDAFqadK9NIEqKocujQIQQHM2ksHTt2RN26ynGiFfP5Kby6XAacmQ74MQdyVGwG9N4F6H35QC9RKKii3rHEdNrEp4I5llZ1BF+DHGwqxeCDzogdeS36Gpb7LUdEZgT9xFjbmJKQEmeHUMl3JfvWLcTOnEnTpEkx6doVtr/9Cp6BgbqmrlS/sel5GLDlHuWGIPwLi7t5oGdd5VLVlRpABZWi03IxaOt9OkfySM37sQYGNlJO7VEFw5e4iz33IvHLUUZByaeBE/7ownDvsLkQrpCWK69SIlQiYbtpoDfr5/wpnmyyI9i926p7ElVqlCgzLXIA2DJpBDISE+DdoTNaDPxYUSTULwHnNwdRubQhy5pC16Boo+Zqaib6BITR4U+IDGB1LQa7da9jBLbBGqmAVz+g67/KTE8ldY6GHsWc23NoX4ubLUYHl/de8ZRt25G4ZAn9zGLUKFhPmaySMblOOAQ4BMoWgdP/rEDIjSswq2CPwSvXvTu0lO2suNFLiwCbDJPSrkWN7VVqS3ReewsBUczB1NEoGsPrxWNAq1/B+4CgW41rUXvXoQ/u4PjyP+k4TXoPQMNuvd+NmZ6QS50cohwpjC110W2at1JpuqWZNDkwpx99SVVVSNGvYw2z7m7Q4mtBJpNhz549CAtjbCxCOlqzZs3SDFeytoTd8eYq4NI8pr2VO+BzCDD98sFXnp+PGc+jsSuOSXH40doU/1RzgramSVNLtuIiW0nlUux5tgcbAjYgS8rwc7iYuGBavWloaq9ctI0sNRWxs2Yh59p12l7b2Rl2K1dAr0aNIsdXR4VXSdkYsPkeYjNElET0n7610aYGc0HItpKWI8FQ3wd4FMl8V4373hU/t6nK2sP3lWeJGL7DD3JFPv7nbo2NA7wh4GuOqLU0+0fSaSbuZaLJ1vevg7Y1y0/EDJkzm+wIzsFRmiexkLbhj/1xeNHvtMbglf9Sqc0Py5n1gQh7nARnDwt0HOel1CwGB4bhbHImGpsYYO25NLzMikK49kkMwBGm/agbQAVPpfoqbaWY7Bh0P94dOdIctHFug+XNl7/7ssu8cAExEydRkjHj9u1gt3w5JwdbWsC59hwCLEEgOSoCvj+Po7PpNGUm3BoqZ2CyZPrcNL6AAJsMExZvkkodHK+Tc7D83HOcCox7t+RatmmY36MDPB3Kl7zll/bs0tZ/8fjcKWoD9P59Mezdq7+rGv86A8dWPYJMooCloyG6/lQH2rrql8PMOBGG7NtMXr6epyXMe1eFFp8HsViMnTt3Ijo6miqqEPnYKlWqlM3j+OQg8N8YQCEFDG0Bn4OF2nfEeTP/VSz+jUqi8/2fuRE216wE/XJysFMG5FRRKv559A8Ohx5+x89BHBzE0UEcHkUVglHazp1IXLYc+VIpIBTCesoUmA8epFEb9WlMBo2KSMmR0HSETYPqorGrZVHTL9PP8yRyjN/zEJeeMSTBPb0dsKibB+scBwTbXhvuIFciR017Y+wf2QgGOur9TlHlxpBndMj2B7j6PIkSvJJUFWPdoi/AVTmH0vTFJjuCc3CUZicLaXts+Z94+eAOHKrXpD/qHxZxngzbpt0EUVFpNaQ6qjYo2msbLZKg/p1gopGCddbWqL/zFS4JA9GYvwFVEM6EMg4+qabVfNwtIX4adm4Y/BL8YKlnSVNTzHQZYywvMBARAwYiXySCXu3acNq+DTydoslTNTJxbhAOAQ4BlSDw37IFeOV3D9YVXdF/8V+svclRyWK/kU7YZJiwGHKVOjjervNRZBrmHr2AgLj3IfOdPCtg2g/ucLJQf+qGOvGWSSTY8+tUJEWGw8jSCgOX/A1dw/cSjeGByTj9byBVWXFwN0PH8V7gq1kWkxwiMs+FI+tqNF26bjVzWPSrBi0hD3l5edi2bRsSExMhEAgwcOBAODk5qROiL/f9+gawzwcQZwDahkAvX6Byqy/WJ+taE5GIRa8Zh1lDEwPs9HSBkYBJDfpayvPU51j6YCnux9+nSxJoCSi5PZGWNdExKXKZopAQSkAqeROtY9CkCewWL4LASv1KNIS8c7ivH7LFMpjpC6lCiaeDcpwiRS5MzRWIjO2vR59ivx8j90yiI/7pVxv62uxwIJB0mq7rbiMpSwx7Uz0cHdcY1kaaE1xQFfxRqblos+o68qRyDGjojAVdyiCSrISLYZMdwTk4SriJhTXLTk3BxnFDaJ56+4nTUK1J84+qP7sTh0u+IRAIeTQ9RZkbi0VhcVgdkQAbbQEuJOsg5040zuoewxhsY/rusxdwb6+G1Xze5Y6gHVjmt4x+sLblWirhRYo0Jgav+/Rh5GAdHRk5WHN2yTFpBCBuEA6BrxyB2BfPsHf2z3SV3WfNQ8Va3l/5ir/+5bHJMGEx2mpxcJD1Eh6IPVdWY/0dA0RnM5xcQr4WfBo4U1UFC8Pye1GQEhOFXbMmQyYWo0r9xug0ddZHTtGQ27G4vOMZXXOVejZoPaQ65cZQZyHOgKzLUci8wHA76FQxhcWA6uBp85GVlYWtW7ciLS0NOjo6GDx4MCpUKKNQ8cQQYFcPIDMa0OIDzacDzX4C+F++1d0anYRfQmPoujyN9LDX0xUWLDmEqmpPyf5djrqM5Q+WIzqbcVQR58a4WuPQ060nBEWkeREJ2YRFi5F+8CBty7ewoE4Ow2bNVDXFz/ohErDj9jyERKZABRNd7BzWAJWt3zv71DawCjsmuK+88AJ/X35Je63laIqtg+vB3OA9x6AKh1O6q4w8KXr8exuhidkw1hXg8JjGqGJjpHR7tlXcdD0Mf54Oobwnh0Y3hrdz+YjoY5Mdod5fEPY8MWozSgpaIpFHIzJphD18JCEX/UR7+8Sax4gMToVrHWu0HVm0Z06sUKDO7WCkSGX4yckGPnsjEJobBX3tNaiHJ1CYVgRv4kPgDYGXOmF/lf4KvU70gkQhQfcq3TG38Vw6nDwri5GDDQ0Fz9iYkYN1KTpkUJ1z5frmEOAQUB8CB+b/gqigJwVGqalvVK5ndSHAJsNEXWtUQb9qtSWIssrT4Fn471EEjr7sgFQRc0FgqCPAyO9cMLxZJdbclhYXy6dXLuDc+tW0WcthY1GrzccXMn6nw3HvOMN/Uau1E5p0r1zcIUpUP+tGNDJOvaZttSsZw3JwDfB0BNS5sWXLFmRnZ8PAwABDhw6FhUUZyWRmxgF7egLxDGkiKngBXTcA1tW+uOaD8amY/CwS8nygir4ODtRyRQWdsj2ElmiDimgkkUuwK2QXNj7ZSFOmSalsWpmmrRB52aJK5tlziJszB4pMRonGfNAgWP00FbwPRAGK6kOZz488jMa0Q08oL4SLpQF2Dm9AowzKa9l5JxxzjgeRTHS4WBnAd0h9OJqXTbQZcRiRlB8is0ucwsRxVN4kbT99Dki0DOFoCorNRFUbI5yY0JRytbC9sMmO4BwcKn5aFAo5Nk8YjqzkJNTt1A3N+w/9aIS8LAm2zbhFwzHbjfKAS+2iQ+KOJKRhbHAE+FrALVt7CLY/wzXhHfTiL4EQMqDtEqDhaBWv5PPuCNGTz2kfhKSGwN7QHod/PAwDoQHNZYwaNRo5REteKITT5s0waFBf7fPhBuAQ4BAoOwTCnzzC4T9n0wn0mb8M9lW/bGyX3Sy5kZVFgE2GibJzLoN6anVwkPXk58vxNGgKYuLO4XJUM5yJ6IwsMWPYWhnpYFLLKuhdzxHCcsatQG5+T/+9HM9uXQNfKITPnyth5Vzp3RaSz6/ve4Gn15jIgyY9KqNWK82khmTfjUX6f4ycpNDRCFZDaoCnL6RpKiRdhaStmJiYUCcH+bdMijQPuPwHcGcteUoAIin8v9+ARuO+eLl1Jikdo4IiIMnPh6OuNg54uaKSfvmNBCoM9+S8ZMrPcST0CPIJPgBaOLTAT3V/QkWTwlU/pLGxiJk2HXn+/rSdTvVqsF++Ajou75/P0uz51puvMf8kZOVJvAAAIABJREFUo9BDeCG2D6kPy3IckfUWi7NP4zBx32MakWJtpEPXVd1O/ZLPH+4F+d6YeiAARx8x3xur+9RC51pFq1KWZj811fZJdDq6rL0FRT4w7YeqGPe9Zpy+pVkfm+wIzsFRmp0soG3Yowc4uphhvx761waqNPBheXotGtf2voC2Lp+mpwiERedGdvIPxYPMHHS0MsHSZ1Kk+8Xgte5ytMRNyAUG4E97DuioPxSL/HhseLIBWtDC9rbbUcemDiMH+/tcpB84QJdZYdEimHbtomJUue44BDgE2IYAefd3/zIVCWGhcKlTD11nMKTKXCmfCLDJMGExgmp3cJC1KxRSBD4dj+Tki8iR6uFexjwceqIPsYywcIHeABODt21N23LFfyPOzcXOmRORkRAPc3tH9F+4CkLd9znyCkU+zm16irBHDFFm62HV4VavaI4yVTwvOX4JSDv8gvoOhBUMYDmsJviG2pRw1NfXF1KpFJaWlhgyZAiN6CizEnGbIR9NC2em4NgQ6LIOsHAtcErXU7Mw+Olr5MoVsNYWYL+XK6oZlt/IgaJwD0kJwZIHS+CfwDgrSKpKP/d+GOU1ikrMfqnky2RIXr8ByevWkRcQWnp6VErWpFu3Er9j5Ddy1YUXWPMmnaNBJXNsHlQXRuWINLIovO8RTpEdfsgSyWCkI8DGgXXRyFVzkU4rzj9/ly5TXpwARWH64ecLTgZjy83XNHrj3OTvUMmyDL97lJg4m+wIzsGhxIYVp8pb8j2nml7oOZuRR/uwHFnuj7iXGXBvaIuWg9+ziX9pjKdZuWjlx+i2H/JwQaV/nuKFJBze2tNgjGzI6o+GoD0jx6rO8iTpCQaeGQh5vhxDag7BVO+pdLiULVuRuIzh47AYMxrWkyapcxpc3xwCHAIsQiD03m0cX7mQzmjgsn9g5VT4TRmLps5N5RME2GSYsHhzNOLgIOuXy8V48mQkUtNuMr+vDsuw54krDvlH0xs9Ukj++6x27mjgorkDRWn3Jv7lC+ydMw0KuRwe/2uDNqMmftSlTCrHiTUBiA1NB4+vhY4TvODorhkur9yAJKTufw4CsMBaD1bDPcE31qbSsbt374ZcLoednR0lHtX9wDFTWkyK3V6cDVyYA/htYZoK9YHW84G6w4ACpGH9MnLg8yQMGTI5TAV87PFyQR1jdh+Uio3JBw2IY+Fi5EWs8FsBovhHipmOGcbXHk9Tq/mFpHPn+vsjZto0yGIZolbDFi1gM3MGtCsW77eNOOvmngjCjjsMx0urajaUkFNXiUvN0qy9LNo+i8/E4K0PEJ8pgjafh1W9a6GDp/o5a/Y/iMSMw0zaVt/6jljY1aPEzqiywE2ZMXPEMko4GpOeh8auFtg9vAGr18gmO4JzcCjzhClZJyslGZvGDQXJo+04eSaqNvpYPjE7TQTfWbdpb+RH27lG0UbJtOdR2BmbQnMozxlaIXVnCF5o++J/vINQQAu8SQGAmbOSMyxZtTxZHuXdCM8Mp7mN+zvuhzZfG5nnziPmjUPDuEMH2C1fxuoXr2Sr51pxCHAIfAkBQqS8/aexSI2NhnuT5ugwcRoHVjlFgE2GCYsh1JiDg2Agl+fi0eMhyMjwA8BDzZprkK5oimXnnuFiCCPXSEpLd2tMb+uOqrbqj+RUxd74nTiCa7u20q46TJoO98YMUfnbIsqR4uiKh0iNzYFQl4+uU+vAykkza8sLTkHK7hAQ8gqBhS4sR3hAYKqLkJAQHDhwgEatVqxYET4+PhB+wq+mCmyK1cfLS8Cx8UAWI3kLlxZA57WAicNn3QRn56F3wCskSWQw4PPg61EJTc00g2mx1qTCymK5GDuDd1J+DmLHklLFrApm1JuBBhUafHEkeUYG4ub8jqxz55g6QiHM+/WD5dgx4CuRoiSVK/DzwQAce8zsS7c69lja3ZN1kqoqhJoewAkPxsvEbEqM+XvH6hjcRDUpPgXN89qLJAzd/oBymjR3s8KWQXW/WnwvP0vA0O3kNwBY0dML3b0/f79VuZel6YtNdgTn4CjNTn7S9vbBPbhzaA/0TUwxct028AUfs1w/uhCJ24dfQtdQiMFLmoBfRA5thlSGWreDkadQ4M8q9uhyIwVpATHQ0x0DeyQgw74FTEYcU+EKCu5q0b1F2PNsDw3129thL9zN3ZEXEICIgYOQLxZDr04dOG3bysnBqn0nuAE4BNiHQNC1Szi7bhWdGElTIekqXCl/CLDJMGExehp1cBAcZLIsPHo0EJlZT6ClJYCnx3pYWn4PEhq++OwzPIpMp3AR0ZHudRwwpbUb7FhOXkgco0eWzEP4Y39o6+ljwJI1MLX5OBWFXAgdXuqP7DQx9Iy10WO6N4wtNZNaIXqRhpSdwciXKsA31YHVcA8ILPXw6NEjHDvG2Fxubm7o3bs3+Pyi04zV+jznpQNnZwIBe5lhdIyBtouBWv1AT5oflLBcMXo+fokYsRQ6PC1srFERP1iWEaeIWkH5uPOk3CSsebQGx14ee8fP8T/H/1F+DifjgnleqMrOufNIXL4c0mhGpYVvagrLCeNh1rs3tAQFS6PmSeQYu9sfV54zaVZDmlTE7A7VwVOzKpAG4fziUOm5Eup0ePjmO2lsC1eaSqf1yXNY2rkGxWag1/o7yJHIUb2CMQ6MbkSJmL/mMm73Q5wKjKPSwhenNmetqhab7AjOwaGiN4KEW26aMAzZKcmo37kHmvUb/FnPBxc9QGJEFmp8Z48W/aoWOfLm6CT8FhoDfT4Pj+q6I3vRA8TmX0U9wVLaVjrgJISu6pO0ImPcib2DkRdG0vEm1ZmE4R7DIYmOQXjv3pCnpEDo5MTIwZqVDwmjIkHnKnAIcAgUCwHy3bd/3izEPg+GrpExBi5ZAyMLy2L1wVUuewTYZJiUPRpfnIHGHRz0t16ajocP+yE75zl4PG14eW6BuXljGk1wLigeS88+R1gyoyChI+BhcJOKGNu8Mkz0vywlWtYY52akY8f0CchJT4NtZTf0mbcU/E8OjSSCg6T1inNlMLHWQ/dp3tAz0owSiDgsHcnbg5EvkYNnpA2rER4QWuvjzp07OPfmZt/T0xNdunQBr4C0EI3jG3ISODkZyGEO1qjaHuj4F2Bk89FUYkQSGsnxMldMievXuDuhu61mUoA0jsknAwalBGHJ/SV4lPiIfiLkCdG/Wn+M9BwJQ+2C5VoVYjFSd+xAyvoNUOQw75h2ZVfYzJjxmaQskSod7vsAD8LTaL2prd2oxLOqD/hljWNh4xMHz4S9j3AxJIFW6+HtgEXdPFRGihybnoeu624hIVMMOxNdHB3XBDbG73l82IxNaeaWmClCy5XXKNdJt9r2WNm7Vmm6U1tbNtkRnINDRdv80u8eji1bQHsbtmbzZ7cR6Qm52P37Xfp5159qw65K4Q4BYrg0u/+M/ggNtLPAXJEOUvc+h1hnBly1gpCqVxHm0x9/5qFX0XJoN5mSTHQ71g0JuQnwsvKixKJa2bkI79cPkpevwDMxYeRgK6kvDE2V6+H64hDgEFAPApnJSdg5YyJE2Vmwd6+BXnMWglfWN5vqWepX2yubDBMWg1wmDg6Ch1iSjIcP+yI3Nwx8vj5q1doOUxNvChUJiT/gF4W/LoYiKUtM/2aiJ8S4710xsFFF1ub9RwQ+xiGixJSfj3o/dsd3PkM+2/rYl+k4vvox5FIFrJ2N0GVqHQh1NBM1IY7MRPLWIOSLZOAZCCnxqLadIS5fvozr16/TudavXx/t2rVjxyE2Jxk4OQUIOc7gqGcOdFwJ1Oj6Ea5JEin6BoThaXYeyCFgoZsDhth/G05p6hQMP4eV/isRl8PwbJjrmmNi7YnoUrnLF/k5ZMnJSFq9BumHDtHnlRSD75pRR4eOqyt97wZuvY+QuEwaODPvxxr03fsWC5E4nX3sKfbej6LLb1HVCut86pRa4jpTJKWRG8/isyih6aExjctNWp4qnoPd9yLw69GntKtdwxqgaRX2vbNssiM4B4cqnjoARxbPxetHfnD2rI0evzKOjg+L3+nXuHf8NQxMdTBoYWNoFRGudiM1Cz0DGNmyK/WqwurIa2QGP0ZF3bHgIR+xDefBru1kFc2+4G5+ufELToSdgJ5ADwc7HYSTnh2iRo1Czu07jBzsls0wqM/Jwap1E7jOOQTKCQKv/O/hv6XMd1/D7n3QpFf/cjJzbpoEATYZJh/sCNFHJczVowCQ0wK5niaSXXMAMNephRdyLUtYLPu+aU9O/4S1eyMAX0Zvs1ilzBwcZJYiURz8H/aBSBQNPt8QdershrFRzXcLyJXIsOXGa2y4HoZssYz+ndxyTm1TFV1r24PPwjD5m/t24N5RRoWt+6x5qFiLcdp8WMIeJ+HshkB6rnSuaYF2YzyKTPEt1q4WUlkSk43kLYFQ5MqgpSuA1bCaEDoY4syZM7h//z5t2bx5c3z//feqGrJ0/RCQAg8Bp38CRBlMXzW7A+2XA/rvIzVICvSAwNe4n8G8Rr+6VMAE54+jPUo3EXa3FslE8A3yxZanW97xc5D06+n1pqOe7ZfTLEXPniFh0WLk3rvHLJDPh7BrD0zS8UZQDg8CnhZW9PL6aqRKS7qLVEHmYijWXAqlXXg5mmLb4HowNyhZBBZx4g7Z9gA3XyZTjH2H1keTyuw74JcUL2XaEeLaXhvuwC8iDc4W+lRVhW2ktWyyIzgHhzJPVRF1MpMSaXoK+fX9ceovqNKg8UctyIu+d/59pMXlwKuVI5r2qFLkqEMDX+N0cgYamhjgSLVKiP3jLmS8v1CRfwFZMITerFAIdPSL7KekFS5EXMDUq4xSym8NfkOvqr0QN3s2Mg4dpn+zW7IYJp07l7R7rh2HAIfAV4jA1R2b4H/qGI0sI45eZw92hlF+hdCXeklsMkw+WMzqNw6KowDOAKgGYAKAG0SYgCiqFrJw4hy5BoD8IBNnBgmhJD+axNlBPPMk13NGMYErUwcHmWteXiT8H/aFWBwPodAMdWrvhqHhxymvKdliKp1IbvykcsaH425rhBlt3eltKptC5uUyGQ6QFLcXIZS/bODSv2Fg+nmE69PrMbi25zmzlka2+N/AahpbhzQhB0mbA6HIkkJLhw/LwTUgdDbC0aNHERjIqDi0bdsWDRs2LObjpMbqmXHA8QnAywvMIIY2wI9/A24/vBuUSMcOe/oaV1Kz6N/GO1lTRwebng81IkS7TshJwOqHq+ll3tvS2rk1VQp0MCqYzJHY9NmXLyNh6VJIIyJpsyyhHg5Ub4MOv03A9x726p52uel/190IzDn2lCo/EXlr4phwNC/e2YXgPe3QE6ogRQrbiTbVuTmhCVlov+YG/V4nHCeEXJpNhU12BOfgUMGTcWv/Ttw9sp/+KI9YS8hFPya7SYnJxr4FjKe/x8y6sKn4ZS1uUidWJEHdO8HUcltf3Rmto8RIP/QEljoDoKMlQpBtd9QYzTCQq6Mk5yWj67GuSBeno4ldE/zb6l+kbN6MpBUr6XCWY8fCaiKxMbnCIcAhwCHwHgG5TIq9s6cjISy00MMKhxn7EGCTYfIGnRoAyOmRODe6f4AY+fFZA8AHwJ5CkGwEgMiW/QVgygf1yBXiMxKZDsC0mDtR5g4OMt+cnFc0kkMqTYW2tiW86+yDvv7nqaKRKblYfv45jge8Udkg0VUu5pjZrhqVmGVLIZdEO2ZMgDgnB04etdDjl/nQKoDX4t6JMPidCqfT9m7rjIZdXDW2BGlyHpI3BUKeIYaWkAeLgdUhdDHG/v378eIFCQoC5eOoVYtFTl0SzfFwB3DuF0CSzWBVuz/wwyJAl7FDJQoFxgZH4GQSE+1BUqIXuzmAp2JiSI1tVAkHCkwKxJIHSxCQFEB7IPwcA6sPxFCPoTDWLthmf/QqEQdmr0K3J2dhKBPRdkRO1nrGdCov+y05igqD/ezTeEzc9wgSmQJWRjrYPqQeatgx5LZEKSon5yWys18gJ+cFeHxdVKo4Djyezrsu/7r4gqbfkTKllRsmtSr6kriEj0G5aLby/HOsufySRrKcnNgU7raFnyk1uSg22RGcg6OUO09uHzaNH4qctFQ06NobTfsM+KzHO/+9wsOzETC20kP/+Q2L/NJbEhaHVREJsNIWwL9RdWRsC4IibBtshFsgBR+RPc7BtaZ6lAqIp3Ti5Ym4Gn0VRtpGOPrjUejdeIyYyUw6jHHHjrBbtrTINZQSVq45hwCHQDlFID0hnvJxSPJyCz2slNPlfbXTZpNh8gbkP0jkPACiIUoiNt4WwiiX8iY6o30hG0Kuqs8CmA5g2Sf1yI0DuWYt7lUrKxwcZC1ZWSF4+MgHMlkGdHQqwLvOfujpFbycpzEZWHzmGQ3vfls6eFTAzz9URSVLA1Y80y/u3cKJlYvoXAhJOyFr/7QQ++TqrmcIvsVwJ3zXxw0eLTQnmShLEyGJODlSRYBACxY+1SCobIxdu3YhIiKC2kW9evVCtWok0IhFJS0CODYOCH/zGpk4MnKyLs3pJOX5+fj5eRT2xqXS/+9mY4bV7k4QsjClSZ2okufr9OvTWOW/inLPkaLN08Z3Dt+hg0sHNHNoBh0+c/C+GZqMkTv9kCuRo6JAinVSP2id/I+c2OnnBo0bwXrGTOhWdVPnlMtN3/fCEjBix0NkihTQF8oxq9k9uBreRp6I8HR8nCloY90RNWqsgpYWj0ZtEMldUnp6O2BpD89v/vwhksrRfvUNSixNHNWHxzRmTfohm+wIzsFRyq+H0Pu3cXzFQhqSPeLvLTC2sv6oR/KFuWv2HWQmi1C3fUU0+NGl0BGJN937TjDVKp/ibIOfrS0Qt/AOzITDYKCVhMf8WvD45bLapMmOhh7FnNskvRlY0mwJvs+0Q8SgwYwcrLc3IwerXbIculJCzTXnEOAQKCcIPL9zEyf/Wkxn27TPQDTo2quczPzbnSabDJM3u3DuTRoKiWdmmDPfl1tEqROAVSE7RvIcwojSKoCxAEjSPOlr0Bunx2gAm4q546xxcJB5Z2QGUAlZuTwbenpONJJDR+fLPArXXyRRR0dwXCZdNrkB7FvfCRNbVqE3q2VdLm5ei4ALZ2j0Rp95S2Dn9rmjQCFX4Mz6QIQHpoAwZP4wvCYqe39sd6lzHSSCg6SryJLyqDaveZ+q4LkZwdfXF3FxcdQ28/HxgYtL4baeOudYYN8KBXB/I3Dxd+BNtAHqjwJazQW09aHIz8fcl7HYGM2osLS2MKYysnp8kun1bZVcaS7l59gWtO0dPwdBwEhohNYVW8MSDfH3KQUkcsDBTI8SPla0NIA4NBQJi5cg5xb5eiLazTyY9uxJI54FFhbfBIj5+XKaRped8wI52S+Yf3NCkZv7GlGZVvjr4RikiU0h0JJhuMdO1LNlFG1IJBpx1GZlMSlfTk7DkZA/AoO33YdMkY9mVSyxdXA9lamxlPfNuPMqBX03McIV8zuzh9CWTXYE5+Ao5VN+eOEchAc8RKXaddFt5tzPeot/nYHDS/zp3/vMqQ8Lu4KlqN42/C8hDaODI6h814OG1WH8KBmiE3tgqc2Q912uthj/6z2mlLMuuHl0VjS6H++OXFkufqj4A/50mYSI3n0gT02FtrMznPft5eRg1YI81ymHwNeHwIeHlV6/L4KDO8k44ApbEWCTYfIGI2LpkpNrQSd2wkrZk6iikij7QjAlOuqb3zhD3lYjhAMDAfynxF4YkXPNB/XIXB7GxMTAzo74Osq+pKXdx+OAIVAoRDAwqEI5ObS1v3yYIkR1J57EYtm554hOy6ML0NfmY3gzF4z8zgWGOh+n2GpyhVKJGHt+mYrkKBLxao0BS9ZA1+Bzm0kqkePYqkdIeJ0JnkALP06sBXs3zUnVy7MlSN78FNL4HOpkMevpBlQ1xLZt25CcnAyhUIhBgwbBwUFz0SVK71NyKHB0NBDjxzQxdwW6rgcc61PZ4ZXhCVgWHk8/amxqiB0elWAo0IxqjdJr0FBF4ui4FHkJp16fwt3Yu5DnM9EZpCikxjCU1cOKdkPQxOl9VAHBMOf6dSQsWQpJGPGvAjxDQ1iOGQ2zAQO+mgtCsk6xOO4TRwZxZryEQvGpP/r9hmVIHbDSfySiM02hhXz83NIAI5p70u8s0ufzF3MRE7ML0VkVsNR/BnIkPMofdHB0Ixjpslf2WkOP5EfDTD8UgAN+0fQ7++LU5rA1KXu5XDbZEZyDoxRPZUZiPDZPHEHJRTtPm43KdRt81tvNA6EIuBwFC3sD9Jn9+eefNujyMBR3M3LQ3tIEWz0qIXF9AAxixsKA/xQv4QzewP/UcjOgyFdg6Lmh8E/wh6WeJQ632IG0IWMgefUKfCIHu38fzS3kCocAhwCHgDIIkMPK3l9/QlJkOAwtLDFwyRroGbEnV1SZNXxLddhkmLzBnciIEYvWqYB92AGA5IOSU216IftUm/Bkv4nkIHwchHdjHOGpBEBYst8wMH6xB3Jr8funn7LJwUHmlpJyAwFPRiI/XwIjwxqoXXsXhMLC3zWxTI7ddyPx9+VQpOVK6RItDbVpNEefek7QFpTNzX1KdCR2zZoCmUQMt4ZN0XHyjAJD0kXZUhxe5o/0hFxo6wnQ7ec6sLAv/AJJle+zIleKpK1PIY3Opk4O0y6VIXfXx9atW5GRkQE9PT0MGTIE1taaiy5Ren1yGXB7NXBlETmpA1o8oPFE4PtfAIEONkUlYfbLGNpdLSN97PFygbmw7BxfSq9LjRUJN92ci7txNeYc+HqM/Onb4mriSlNY2ru0h70hkyaWL5Uibd9+JP3zDxQZDL+J0NER1tOnwahVq3KVZiGRpHzmyCCcGSRy7EuFx9OFgUFlGBq4wcDQjfnXwA06OrbIyJNiuK8fVQMhZXRzV8xoW5ViQiJArt77CVPP1EWa2AzWhsCxCf9DBRM9Ne5u+ew6PVeCliuuISVHgh9q2GDDgLplvhA22RGcg6MUj8ONvb64/99BGJpbYMQ/W8Hjf+zlJjclvrNuITdDggadXVC3XeEOgpDsPHz/gGEJP+DlisY8baQs2Q8bHYbQ86BOX3SbvlYt6SkkHG+533I6ztrvVsN53k7k3r0LLSIHu20r9OuW/YtTiq3imnIIcAiUAQIpMVHYPWsKpGIRXOrUQ5fpc8qVYVcGkJXZkGwyTN6AUNoIDg8AhGuDEIyu/wBYkqbylFysAiAsle+vZT9Hn/URHG+nnJR0AYFPx9EDgrFxbdSu5QuBoGh+jUyRFBuvhWHzzTCIpIwoDZEgnPZDVRCejrIgSnxy6RwubPybzqX1iPHwbNW2wPciMyUPh5f6UxvLwFQH3ad7w8hcc7eYCpEMyduDIAlnUn5MOrpA7K5DIzlycnJgZGSEoUOHwsxMc9ElxfoCiX/KRHMkMGkBsK7ORHNU8MK+uBRMfRZFye6rGuhiv5crbHW+zRt0Elmw5OxzrL9GfK5AvcpyNKsdhQuRZxGeyZDevi21rGpRZweJgjbTNYM8PR1J69Yhbc9eQMZIN+vXqwebWTOhW716sbZL3ZVlsiyaTkKcF0xqyQv631IpoTwquGhpCSjBMXFeECeGoSHjyNDTc4SW1pcjfwiPxIS9j3AhmOE66VbHHku6e4L8vdf62wiJz4YuX4RfGq5Fp2ZLYGaqHt5BdWOq7v6PPY7BpH2P6TAbBnjjhxq26h6y0P7ZZEdwDo4SPgpELWDj2CHIzUhHox590bgnIXT/uEQ/T6NhlKT0X9AIJlaFeyCnP4/CjtgUVNbXwY367si+EQPe+UkwEFxEEsxxv+7f6NCxYwln/OVmL9NeovfJ3pAoJOhRpTtGnJIi4/AR2oAQipp06qTyMbkOOQQ4BL4NBIKvX8aZtYwCU4uBw+Hdocu3sfBytko2GSZvoCstBweRGhtCAhPekJJ+uCPk9DweQGUAzKlFucIqDo5PpxwffxxBwUTePR9mpg3h5bUFfL5yB/6ETBFVKjjgFwU50XQE4Olggpnt3NHYlUCouUIOlCdXL8WLOzcgEGrDZ9EqWDo6FziB5OgsHF3+EBKRHGa2+ug2zRu6Bpo7iCskcqTsCIb4JRNIZNzGGTnVhNi+fTvEYjF1bhAnB3F2sLLIJMD1pcCNlQBJweAJgOYzgKZTcDIlB2OCIyDNz4ezrjYO1HKFs17Zc7VoEkfyLvz2XyD23meiNtrVtMVffWpBR8CnKRXBKcE4GXYSZ8PPgkR5vC0CLQEa2zdGh0od8L3T9+BFxiNx6VJkX73KVNHSgkm3rrCaNAlCDUf5yOUi5Oa+eqdc8pYvQyR+r7b0OcZa1Gnx1pHxNiqDODd4vJLx8snkCsw+FoS99xmp3eZuVpRulHAFEX6gaQ0PobLhNQgEJqjrfYBGhHDlYwTIMzho2wOKma2xLi5M/a5MU3nYZEdwDo4Svi1vSfQIy++ItVthZPG5AXBl9zME34iFdUVj9JxZeARElkwOr9tBILrkCyrbY4SjFRJXX4Zlak/wtGQ4gZbwHLIKzs4F/8iXcBmQyqXwOe2DkNQQOBg6YGtiB6SvXku7s5wwHlbjSDQvVzgEOAQ4BEqOwNl1fyHo2kXw+AL0mb8EFSpXLXlnXEu1IMAmw+TNAotSUblOzhuFgEEcJG3ecHgkflLvXxIZ/SZVhQmbVK6w2sFBlhAbexAhz2bS1VhYNIenx78fSS4WtcyXidlYevYZzr+5WX178CCOjmoVNJdiJs7NwY7pE5GZlAALByf4LFwJoU7BzhpymXTi78dQyPJh62KCzpNrQaCtOd6IfKkCKbtDIHrGqJAYfe+IdHct7Ny5EzKZjKapkHQVkrbC2hLtD/w3GkhmJG9hVxvosh5X+HYY+vQ18hT5sNUWYn8tVxrR8S0UksY1dX8ATgUyqj296zpiYTePAhUr5Ao57sXfw6mwU5S3I+f/7F0HeFRF2z3bS/qmJ6QCCSkQCEhHQKQKCIiCiOW3V2yIfvb6iQ27nxVRilSVLoKF3hMSUkgjvfdll89XAAAgAElEQVTNJtt3/2fmbhokZLPZJDdw53n22TYzd+bcu3fnnvu+5+jrmyCSCWWYEjiFRnYMvmhC5fsfQJuRSb/ny+Vwf/BBKO65G3ypfXE1mQxQq3MuSy9paMglKiLt7kKSRkL0fFqnlwyAQECC3+xbyAX6p39lNNnANvZO3FLmRAFnzi6ETlcBqcQPI0ZsvaKIsn1H1nd6y69qwNSPD9IIvLvHBOGNm6N7bfBsWkdwBIeNh8GWt15CXlICQoePxPwVjOtIy2I0mPDj80egrTdg/K0DETMl4Ipb+qGgHC9lFELG5+Pc2EjIa/Vo+PgZuIg2oAFSfOf4NJ54ZgX4bXjD2zgF2uyL+C/wTeI34IGHteKHIX6DCQt1uXkufFeu7JXw1K7Mh2vLIcAhwD4E9BoN1v3nKVQVFcDFyxtLV37apngg+0Z+7YyITQsTC+okxYT4A/4G4JYWe4LkbH5m0eBYZ/mcpJqQ2/YXWtT7GADxN38ewPstPncFkAKAXE0QF5YrpahcegCwnuAgA87P/wnpGW/SsXt6Tkd01GfgkzvznShnc6vw7p4LTXnyPB4wf6g/npkWhn5u9r/QaWtoxRlp2PjaCpiMRpqmQtJV2isZZ0rx5w/J1HEyJMYDMx6MBr8HHUDMBhOqNqVBfZ65i+84zg9l4UZs3LgRJpOJCo7eddddELPZhU6vBv56CzjxFWPdSSxRp7yCU4PuwtLkXCgNJihEAmwY0h9DnXvmGOjEIWu3qvVaA87l1+B//2Y1WSs/NDEUL8wYZNWaWGPQ4N+CfynZcaTwCAwmJjWFFIVUgZkB0zA7WQrx6m0wVjM6FCI/P3gtfxZOM2datY32JqvXK3Ex+2PU1JxCff1FqsvTXhGJ3JiIDEtaSaNORkf6PXYDukVHG07m0UgZEjxGdICemcrY6yqV5xEXvwRGYwMcHSMwPPYXCIUsjYbqDmCs7PObg1l4d+8FEhiEXx8Zi2GBvZMWx6Z1BEdwWHnwtKymVtXhu8fuhV6jxvznX6O55ZeWnPMV2P1lIhWfuufdcTQ/tL1CGMzrT11ARoMWS33d8eGgACj3Z8LhyBQIeDU4jOtQP3o5ZsxoOw/VhinQJonlibhr711UGfppySyM/e8fMOt0VG8jYPUPV43as634cO04BDgE7IcAERslDgkGvQ5ho8Zh9tMvdGkhZ7+RcT0RBNi0MGmxRxpTSQjJsQcA8Q1dBoD4MN7Q4jYkSYQn4Y0t1zTkfZxFiHS9pQ0RGX0AABHEIuGJ5EquM6VPEBxkQjk5XyPr4gd0bj7e8xAZ+QFIxGlnClmbHEgtw3t/XACJ7CBFLODj7rFBeHTSALg52Baa3pkxnNq+FYc3rKFNZj/1AsLHjG+3ORF0J8LupERO8MOkJYxwYU8Vs9GM6q3paIhnAoYcRvmgoL8G237dRt/3798ft99+O4TCzpFNPTX+pu3kHAF+fxSoIXf6iczvWKRP/Qjzc02o1BvgKODj58GhGOvWc6Ku3YlBmVJDibzTOVU4m1uN5CJlU5oW2e7zMwbhkUmEQ+18qdXWYl/OPuzJ3kNF/FuWMKE/HoxzQ+C+pCZ9DllsLNXnkA0m/G7nCrGNTkpaBo2moFVDgcDBkloysFnw0zEcYpF7j/4+OppNQn4NimvVVEei5e+2svIgEhIfoPpCCrdxiIn53ua0mI7G0Fe/1xtNmPvFUaQWK6nrzM4nxveKpS6b1hE9d+bv3aPG7osSbUMD0k8eQdTEKeDzLw+F3P9jMtJPlsI/zBXznom94uyPVNdh4TkmDfjAiDBEOcqgfO8duGg+gBF8fIL7cNt9TyIg4MpRIJ2BWG1Q47adt1GBpJGmIKz4tgqm6mrqlBK88RcIXMlNLq5wCHAIcAjYD4HEA39g/3df0A5vvP9RxEydZb/OuZ66hACbFiYtJkL+XEkUxoMWUoLcHt8EgIRNtpTwb4vgIN2QqxJSd4olVYX4ohJFtk8AMEJTnSt2X0t0bvOdq511cRVycpiUUz+/xRgU/rZNFzQkV35bXAFW7U9HqZKxgHSSCjF+gAdc5SI4y0RwsTxcZeKm142fkbp8vm3LTbPJhG3vvobcxHhI5A7UOpZEgbVXjv+Wibh9TE7/yDkhuO6mkM6B1sXaZpMZNb9nov4UY7UqH+aFrBAldu/ZTd9HRkZi4cKFdo/G7eKwL2+urQP+fAU4+yPzncgBZZNew0yMQ6HOACmfh++igjHVw8Xum+7ODglpl1WuwumcZkIjt7Lhsk0SXizc24k6fMwbxjijdLUUq4op0UFsZzOqGSKOFO8qMx496oCIJEaslhQSRe35zDMQebd/rDfWJXPKz1+NzKz3YTYbQBxMAgPvh4vzUEpsSKVELLhz5GZX52rv9kXFW5GaSoLxAB+feYiM+NCmc5m9x8Wm/ghBNO+ro8TYEytmhFMSuqcLm9YRtv3j9DRiXd9ejy5KDDojVj93BHqtEROXhCP6+iufHO9Pysau8lpc5+yAncMHQlekAr4eDzE/G4kYhL9cFuOpp56y64/53ZPvYsOFDXDRCvDdVg8gr5CSGtQO1s46H13ffVwPHAIcAlcDAmQhtvvT95F2/DAEIhGWvP0RvIJDr4ap9fk5sGlhwmIwe3Qt0VUcyO8tI/Md5OczF6kBAfdi4IAXbV5LqHVG/Hgsm4bu12maw+47Gie5WHSWNpMglPiQX/JeJoKrhSRpJEwIeeIoEVIx959XPEGffQeGY9Hr70HQThQEmfNfP6Ui7QRDMEy6IxxRE+xzgdrRPFtecNbuugjVUUa0UTbYA8n9yvH3P3/T97GxsZgzZ47N+8HacdilXsYBYMcTQB0zF03wJCwOehon4AohD/giIgjzvHsnHN6a+REdjaTCWkponLFEaDTaIrdsLxXxMTTAFSOCFBgR7IbYIDd6zHZXSa9OpykshPAoqWeO1ahcE+4+YEZwGSPyy5NK4f7A/XC/917w29Fv0eurkZKyAhWVzLFFtDOioz6Fo+PVp3OVnf05LmYTbhoICnoEA/ov767d02f7fX1HMtYcy4FEyMefT1+PIPeOnbTsOVk2rSM4gsOee9bSV+bZMuz7Lonesfi/98dD6tj+SbJEq8fw48kwmoGvIoOwwNsNqk1b4Jh6P+3tW9yOkHG3YOrUqXYb6bGiY3ho/0MQGM34324/uCbnM3awa36EfPhwu22H64hDgEOAQ+BSBEj027oXnkRNaTHcfP2xdOUnEEtZLL53jexCNi1MWAx5nyI4CI7kgv9C2ssoKtpIYQ0Ofhz9Q4lzru2lul6HjafzUVjTgFq1ATUNOijVetS2eFiMWGzfiKWlgM+Ds1QIOd8EfWURpEYt/Py8MWDQwFZRIy2jSJwkApxcm4aSC9Xg83iY+fBghMQQuZWeKwR35Z+5qPuHcd+QhLsh3rcQx04cp+/Hjh1L13U9mUJj8+zV1cDeF4BE5hgySZzxXvjT+NR1Mh3/++H9cKdfzzrttDeX2gY9zuZVNREaCQW10BkuF9R0dxBTIqOR0Ijyc4FY2PNRDiazCXGlcTSq48+cP1GnqcXkRDMWHzTB1RJYYvRwhd/yFXCbezN4LXT4amrOICn5SWi1DEHi63srwsNe7RYxUJuPHTs2ZM5lL6GoiATxAeFhb6Jfv8sdLO24yT7XlUprwNRVB1Fcq6ERdmvvG9mj5xg2rSM4gqMbDt8/vjmPrPhyBEW7Y/bjMVfcwgfZxfgopxTuIiHixkZCzONB+/ZMSI3HkQc/rMYiPPjgg/DzI+uqrheSD7hgxwKU1Zfipb9cEXOa8bf2+/BDuMy+qesb4HrgEOAQ4BDoAIHSi5nY8PJymIwGREyYjJmPPdOjf8LcDrocATYtTFi8f/ocwUGwJLnrKSnPoaR0O4W2f/8VCA56qNtgNpnMUOkMIBebhPQg5EfNJQRITQPzeUtShBAldVoDDbG2RyGXq1ITUZPlwdfHEZ5uUkqKeDpK0M9NRsVSAxRy+tpB0j26GMq/8yjRQYq4vwuOe2Yj/lw8fT9lyhRMmDDBHlPtmT5SdgC7ngYaGCHV494T8UDoU6gQK/BKfz88FujVM+OwbIVc8BZUq3Emt5nQSC9tmbnWPJxQD4dWhEaIhwPr/nN0Rh0VJSWRHSez/sWsI2rMPmWGyCKDXBmqgPzZxxEz+Vbk531LoxnIb5u4m4SHvwVfn6vfgp04w5w//4glYoWPIYO/gqen/W4A9+gB3E0bO5BSivt/PkN7/3hRDOYP69dNW2L3OqIzBAf5r3gSAPlXJAJd5QA2W/Jbm/2QrgwjEfh6EQD5FRLE6wAkWfo4fEnTUQDeAUCeyd/dMQDE+4zkz3a29NiiRKc20PQU4qJy4z0RCB/t2+5Y9SYzRhxPRqnOgGWBXnixvx+0SQkQb5kIHs+MzbgJJYrReOKJJ+x2In7r+FvYnL4Ztx3nY+G/jLqy55PL4PHII53FlKvPIcAhwCFgMwJxe3fgnzXf0vbTH3kK0ZNutLkvrmHXEeAIDqsw7LG1hFWj6UQlcmGQlLwM5eXEPRcIG/gqAgLu7kQPPVOVkCMk/YUQHzVqXSsCpFqlxckDB1CprIdR4gTXgdGo05mbSBRCjtha3OSiJrKDEh8WAqSRCJF1wXa27nAhandfpEMTBjnhkFsaUi6k0vezZ8/GiBEjbB12z7dTlQO7nwZSd9JtK8UueGbAM9jlOQlPBXnj+ZDWApH2HCDRgrlQUkdTTU7nMiknjZowLbcj5PMQ7e+C64LdMNyScuLh2L7Qvz3HaK++VDoVDuQdwOHT2xC1KQ5jLjRHoZyOEiB9lgHh/gaEKQZhcPTnkMt7VmvGXvO0pR/iqBIXvxRKZQK1wI4dtg4uLlfWOrRlO325zSPrzmJvUgkUDmIceGYife6JwqZ1RGcIjk8t6uVEzXyvRc2c2LURYoKsTNs3VWZQJYrm/xL3LAA/ACBm20SdaAgA8o/LxL4xZbSlbiFxMrV8RvzBCD08FsD5Tu6oHluUpJ0oxoE1qRCI+Lj3/fEQy9q/K7CjrAYPJueAMEcnx0QiQCqG9suHICnfiFq44FPcg3ETJlKW3x6F5PnN/HUmRp3X4ckdzO5ymTcPvu/+124Eij3GyfXBIcAhcPUjQO6+bf/wHWSdOQGhRIKl//0E7v3sJ6R89SNo3xmyaWFi35nZtbceW0vYddSWzkwmHRLPPwziSkBKxKCV8PO7tTs21W191paV4OcVy6BTNyA4JhYLXni9KWyfXAArLeRIY2RIcYkKB3ddpJEhJpkA3oMVqNQaUFDdQO/+t5W+0NbgPRzFNOKjOfKDiQAh7/1dZZCKLhebb9mP6kQxFR8lhd9PjgOOybiYw5Aet9xyCwbb4JrRbSB31DEJsTm/BdizHNDU0trbvG7ESwOexPzgULwz0J+mBnW1NNq1NrqbxOVWo153uauzk0RINTMaCQ2ipdEVQqqr47Z3+/KGchze/S2cv94M/0LmxqROCOwcyUPSjIGYGjEXs0Jmwdex/Ruq9h5Tb/en01XgzNlboVbngdjdjhi+5ZoieTrCv1SpwY0fHaTnvVti++Gj266cTdBRf9Z+z6Z1hLVnoCgLqdCeHz1JgtrQAQCECCGRHyMBFHdQ9xSAQRYShZAcpBCVKEJ5nwAwzVqwLfV6bFGy8/ME5CVXov8wT8x46Mo2TwviM3GsRoXpHs74aXAozA3VML8/CHxosA/X4ziG45FHHoG3FSrK1uBBhEVP/7Uer28wQmQA5CNHIvD778Bjsy+7NRPj6nAIcAj0SQSI5fba55ehrqIcHgFBWPLfVRCJ+9adtj4JfBuDZtPChMWY9thaorswMBo1OJdwL2pqTlJX3aioj+HjPae7Ntct/RKR4l2fvEf7vn7pvbhuzoIrbqcsV4nfV8VT4Xd3fwfMfzYWErkIJFqkQqVFfrW6ifAgxEd+FfO+sEYNPRFIs6J4OTFpL40pLwGU/GAIED9XGdV3qD9bSm1kaUyyjwR/yBNQUFRIHVUWL16MsLAwK7bEoirKIkaANPMAHVSJ2B3Phq2AW+RMfDIoECSSojOlI7vWxr78XKQYEaxoIjTCfZxAtFqu1kKir7JzPkfOxS+hP86D5w4h5CT+HUCVI/DLRD4ODeYh1mcEbgq9CdOCpsFF0rfcbWzZdw0NOZTk0OurIJUGUJJDIulZrR1bxt1TbdaeyMUrv5MkCWDD/aMwdkD36+SwaR1h7RnhbQAvkf8SS8RG4/6RAiAiDuR2wJX8/kg7Uof41xNfe6K6SR6XezMBxNeG+CetBnDfJQcCifz4PyIZQc6lnThIemRRolbpsGbFUfqnOePBaPSPbT8f8UK9GpNOpdEpbIwJxSSFM/Q734fo7DvQmyX4kHcfnD374dFHH7VLdEWFugIzts3AS2vqMagAEIeEIPiXDZwdbCcOIq4qhwCHgP0RKEpPxcbXngexgxwyZQamPkiC9bjS0wiwaWHS03PvxPZ6ZC3RifHYVNVgUCH+3D1QKuPB4wkwOPrLPpfH/ue3n+P8X/vAFwiw+M334Tvgyq4ReSmV2P1FIl2f+Q10xZxlMRB2EHVhNJlRVqehkR75VUzERxMBUtOAohoNSJ2OCglm8HGWUrLDF3woclTwAQ/ezkKkyi9AU10MsUiIO++8E0F9zcWORHPE/QTzvpfA0zH6F+t8bsKRkS/gk5hoSAVtC3d21q71umDG3YQQGyRi5lopGm0JkpOfRk0Nue8LODsNQWToStSv24eKH74HT6enn1/0AX6aIkBqIA9CvhCjfEdhjO8YjPYdjYFuA8Hv4zax7e3vWmUC4uLugMmkhpNTFGKHbYBQSBIFuELOdbd+cxxnc6sR7C7HH09d32GkWVdRY9M6wlqCg6SQkDQUOQDGBL25HCXpnETK4QrArARADIyJ9gaxB5kJgMTzESLjTXI+bNH2dks0yAMAvr+kT/IZSdqeDYAxFbeu9MiiJOlQIQ5uSINIKqDpKcIr5Gz+J70APxZWIEQmxtFREeCbjDCujIZAX4w4jMAOTMCkSZPowx5l1ZlV2P/vaqz6ngnvC1z9AxzGkmwfrnAIcAhwCPQuAqe2b8XhDWvoIG5a9hwGjZvYuwO6BrfOpoUJi+HvkbVET8xfr1ciLv4OqFQp4PHEiBnyLdzd+47gpV6rwfoXn0FlQR5cvH1w58pPIZFf2RIx7WQJDvyYQuHtH+uJafdHU7c7WwtJiSlRXkqAqJFPoj+q1SiuVcMK/gM8mCGHDs4CPWLDAjHQ350RQCU6IAo5JUdYH6FQnQPz74+Cl0suCYB8iQ++H/46VtxwGxyEAlhr10rsLUmKSSOhMSzQjQrDXoulouIfpKQ+B2IFS9ftAfehf//l4PMZPQV9URHKVn0M5a5dTfCcHiTET5PMKHNrPq4VUkUT4UGIDz/H1qYFZoMBZp2OPkz0Wc+81zc+M9+1fDD1LHUb67Wso2/sq0V/LftsUdekb67Dl0ohCQuDJDwM0vBw+locGgr+FSLNKyr+RkIikYc0QaGYgJgh34HPvzaPmUt/J2kldbjps8MwmMx4fPIALJ/evfbBbFpHWHtmJ5oXJBzBu42TDBEaJUmcJK6YSQ67vJDUFkJuEGFSQmp8RQSlATxLrJ8B3AuAMWpnPvvQEhFCtD5aFhIlQogNciQz6nRtFycA5NFYyLjjCgsL7eZG0tZmf18Vh8L0GoSP8sGN/xfZ7uBUBiOGHkuGymjCGwP88FCAF8znfwdv290wm3n4nHcPquCKxx9/HB4eXQ8pqtHUYNq2aVi0V4VZZ8wQBQai/x97W9lNXQFL7isOAQ4BDoFuRYBEb/y68nXkJMRBLJNh6cpP4eZjH+eobh34VdQ5mxYmLIb1qiE4CMY6XSXOxi1BQ0Mm+Hwphg5dAzfX61gMf+uhleflYMOLz8Cg1yF8zATc9OSKDiNe4//Mw7FfGS2MwRP9MWFxWIdtbAVEbzShuIYQIEz0ByE+GqNB8svrUdago9kqHRWS6kHSXDydJLiUj+GhxTL+khX9pQv8S2UxWrUlCUuXtm/Vdesv25LY4JlNmKb6HbfWrIbEcjmwVjwb253vRWKJrl271uFUP4OJ0Ogtu9aO9kFPfm8y6ZF18UPk5TH3eIVCV0RFfgAPjxvaHEZDfDxKV66EJiGRfm8WClAS5o46dQ0lIYgLi9AA+kweYpMAUhMfQiPA1xsBU0cSij05+za2JRRCEhICCSE8WhAfQm/vpt9uYeFGaiFLiq/PLYiIeK/bfte9jEanN//hvjR88U8mTRnbvWwCSEpXdxU2rSOsJTiyLCklgW2A8jOAOwG4AahpBzSSoEeUMomiUkQLIoS0IZ9pLBob5Ff2iiWqg9T/+5L+yK/7LwDExP2TK+yg1wG8dun33UlwqKq1+OnFozS38qbHhiB4cPvExJrCCryQXgAZn4f4sVFwFQlh/GIKBBVnUGiKwnf8aVR3g+hv2KN8Hv85fjz7Db79wgQHjRley5+F+/0kkIYrHAIcAhwC7ECgobYGPz+/DPXVVfAK6Y/b3/oQQhF3F6an9g6bFiY9NWcbtnNVERxk/lptKc7GLaZifQKBI4YN+xkuzj0jSGcD/pc1Sdi/Bwe+J/fMgGkPLcPgG64s0UZSI45uzUTCX/m0zaibQzFiJpGH6/lSl1GN5J+TUKw3IEuiw1EUoUYvgEYgh17sjIp6Jv2gr5X+vEJ8JPoaQ/nk0gHIMvnia+Mc/GEcCU8PT7QkNNho19qbeKvVBUhKfhJKJWMY6eIyAtFRH0MqvTLhT24SKHfvQdlHH8FQ0pkMfutnS/T6mh4iUfNr+rkIfFGL78lnbdQh7Uk0Bu2nsX6LekZlLbTpGdCmpdGHsZYRsW2r8F1cIKXRHgzxUekYjzxsglkCBAc/jv6h5FKRKxq9ETM/PYzsinrEBrpi68NjuxS5diVE2bSOsJbg6GoEB/GTImklxPb15UvA+QnAXQBIyAMREe2TERzkz/LIlgxIHIT4v/fHQ3CFvMNJp9OQVq/BEl8FVg0KBArPAt8xzOxG3hJcMHvbzR9dqVNi+tbpGH5Wicd2mwCRCAP//QdCd3fuV88hwCHAIcAqBPKTE7HlrZdhNpswbOYc3HAPCdbjSk8gwKaFSU/M18ZtXHUEB8FBrS7E2bhF0GqLIRS6IDZ2A5wcic47+wshLHZ+/C4yTh6DUCzB0nc7dmMym8zY/2MKMk6X0glOvnMQIsf1TsSYNk+JitXJMGsMqJCrsEcQB51eT6N3b196F2r0/Cb9j6r61kHSRP6isZgviQVp+R2pc1mkyCUVLv3+8vbNNTrqm3zPMxswIPdHzCldAzEY+14NX4xkv0nQR9+KyGFz4Cy5drQ0rPkllZXtQ+qFF2AwKKkAcHDQIwgJeRJ8fvtujJf2a1KrUbPtVxhKiluQEQyhYBbyUayrREZ9DtJUWUhXZUPDN0Av5EEvAAwCUPIhzCsKMX7DERswir4WSqT02oFnB2cca3BoOqbNZhjKyqFNZ8gOTVo6Q3xcvAgY2raENvMAo4cZen8zXGMmw2PELTTVRdSv3zUdtX4sswJLvifC0sBb86Jx52hibGr/wqZ1hLUER1c1OP4H4GEAxFa20fa1EdlGfY5xAI4B6JMaHFtWnkFZjhKRE/ww+Y72FwbHa1SYH8+ER/45IgxDnOQwb7kPvOStaDAF4n0+UQPnYdmyZVAoFF0++r5N/BYkguOdtWYMLDDCedZM+K9a1eV+uQ44BDgEOAS6A4FjWzbg+FbGlGvu8pcw8Lox3bEZrs9LEGDTwoTFO+eqJDgI3g0N2TSSg9gvikQKDI/dCAeH/izeFc1D06hUWPvCMijLy+ARGIwl73zUoRuT0WDCri8SUHChGjw+D7MeHozgIV1PCbYFMF2RChU/nIep3oAiaS328eNhNBnh6+uLu+++G1Ip0fPvm+VM2kmoDn6AUSUHITM1EzTVQmec7jcV2ujbMCRyEoLkfXeOXd0zRqMWmVnvoqBgLe1KJHJHVNQquCvGd7XrK7ZXG9SIL43HiZITOFF0AheqLuBSosxZ7Ez1O4hYKXkEOAX0ONFx6SRI2o02O+cy4sNQVtbufHlyOSQDB0AaRqI9wiEND6P6HgKXq99tphGU5VsSsPVsAYit8oFnJ8Lb2f6/OTatI6wlODpyUTlkEQ5t7+AizifEFYX4er1wSSUiMEpsZgcCIFf+fc5Fpba8AeteIe61wLynh8E/nGTetF0eSs7B9rIaxDrLsWd4GKAsgvnjwZTtPmlahL18P6oT8uCDD3b5xNagb8D0bdPhlF+Nj36wiIuuWQOH0aO63DfXAYcAhwCHQHcgYDIZsfWtl5Gfch4SBwfc9d7ncPZs35GqO8ZwLfbJpoUJi/G/agkOgrlKlUaFR4mooUTiQ0kOmSyAxbujeWgt3Zhipt2EG+/rOMVXpzFQ+9jyvDoIRXzc/PQw+IT2zgWPvrQe5d8nwVSnQ664EgcECSDRKcRVZenSpRD1sXQ9g6EONTWnUVV9DOqGXBilY1Bf2ADX1B2ILD8NAZp1H3KlvjjkPwPqqIUY3n8YhjnLwe/haIHeOsgJsZiU9CTqVMl0CG5uYxAV+XGv2J1Wa6pxquQUThQzhEeBquAyWPwc/DDajyE7RvqMhLuMPdHghupqmt6iTj2PwuPfAjk1EBXxwbtCppfQx4fR9WhBfIiDg2l6zdVWqut1mLLqIEgk2MxoH/xv6XC7T5FN6whrCY7BABIAELHQW1ogQiIyPrNocDQ6oRDKnxwZF1rUI1f8uQBI3BUJb2C8pABfi+hoIYCW0q6nLe9J3SJLXbKwIH0SryTi6NKZ0q2LkjN7c3By+0XIXcS4+91x7eY2lWr1GH48GQYz8HlEIG71UQAH3gCOrILR7IKP+Q9AZTZi2rRpGGsHh5M1SWvw0dmP8MB+YOoZA8iPNnTvnl5nXzuz47i6HAIcAtceAqqqSqrHoVbWwvriMTgAACAASURBVDdsEBa9thICofVhutceYl2fMZsWJl2fTbf10K1riW4bdSc6VirPIy5+KYxGFaTSAAyP/QVSKVmqsb+c/G0zjmwksnDA3GdexMBRHTvFNSh12Pb+GSgrNDTF+JbnhsPN58puLN2FhKFCjfLvz8NYo0WGuAQH+cxFb1hYGBYtWgSBgJgPsrOQKIRaZRyqq4+juuoYlHWJMJuZG2uNRSBwgI/PfLg7TUNF8jE4Jm9DQA3JTG8uCY5h2Oc3HfUR8zE2YAAmuDlB3k7KNzuRsH5UJSU7cCHtZRiN9QD4CA15EsHBj1DrZjaU/Lp8nCw+SQmPU8WnUK1l3FxalnC3cCa6w280Yr1iIRcRs83eL1pdBc6cWQhNQz6k1a4IkzxFCQ8NTXdJhz6f0eBpqxByQ9y/vyXKozniQ+Dh0eevn36LL8DTm8jlPPDdXSMwNbIt7xDb9x+b1hHWEhxktp8DeNxCcuyxiIUuA0A8oYiARCMdmwOAJPdc2jcJSfgGADljk2gO4qJCKHbyz0n0Of5sASn5V/oHAKEPyXZJIWQK2RMklYXZO9aXbl2U/PLmSVQV1SPmhgCMv40EorRdVuWU4P3sEihEAsSNiYLUqIF5VSR4mmrkG+bhB2EIbfj000/DpYthUxqDBjO2zYBKWYHVX/EhUuvhtWIF3O8lwTRc4RDgEOAQYDcC2efO4td3Ga3o625eiOuX3MPuAffx0bFpYcJiKLt1LcGWedfUnEH8uXtgMqkhl4ciNvYXSMS9k77RGUyI0OLWd15BXlJCp6K/asoa8OsHZ6Gu08NRIcEtz42AoxsxBuz5YqjWMCRHpQZJonycEKTTQQwZMgTz5s0Dn8/v+UG1sUVCXtTVJaOq6hiqq4+hpvYMTCZtq5rkQt3ZOQZSiR/KKw7AZCJ+Akxxcx2Nfv3uhJupH8rjNkGevBWu9Y33MwEj+DjsFosdPtNQN2AWJvr6Yaq7M7wlff/OutGoRnr6mygqJiaUgETsjaioT+DmNpIV+7atQZjMJqRXp9PIDkJ4nC09C42xeX+SNkK+EDGeMU3pLNEe0fSz3iokOubM2VtpRJpMFogRw7dAbDmPGVX10GYQTY90aNPTm4gPU11du8MVKBSWaA+S3sIQH5IB/UGsbftKIVFhd60+hcMZFfB1kWL/MxPhKLHfPmLTOqIzBAehFJ8CQIgKIjldAWATgFdbRGSQfdwewUG+IwITK4g7l4UQOQ7gDQtJcunxQRKvSWoMyacg6kZEn+M/xO7VhgOp2xYllYUqbHyLBJUAC58fAe8Q5zaHRzyIrzuRgmKtHo8HeuHl/n7AmR+BXU/BbBbiN/OLSOTXISAgAPfdd58NU2zdZEPqBrx76l1MOc/DQ7v0NNxqwKGDELq1nz7T5Y1yHXAIcAhwCNgRgUMb1uD09q20xwX/eQMhQ+0fUmnH4fbprti0MGExkN22lmDbnKuqjiIh8X6YTDo4Og5C7LD1EIlc2TbMy8ajqq7CzyueoNFffuGRWPTau+BbEflQlquk6Sp6rREKPwcsWB4Libx3LqaNSi0lOQxlapwVXUS8IJvO87rrrsOsWbN65S4yuTCqb8ik0Rkk7aSm5iRIGsqlhRwrbm5joXAbC1fX6yAUOtIqen0tiou3oaBwHdRqEtDNFJIK5e93O/z8boOkJAvKuA0Qp26HVNfsntHAl2Cf+zhs856G6sCJuNFLgekeLohwkPYKFl35EahU6UhKXob6+gzajbv7RERGfACxmD2pHtbMT2fUIaE8AceLjtMoj6TKJBASpGVxFDlihM8ISniM8R2DEJeQHt9ftbVxNCKNkG/OTkMQG7seAkHbUSbkGDcUF0NDhEwtTi4k4kOXnQMYW0cjNc2Tz6fR8STNRTZ4CGQxQyCNjARfxl4B3dzKekz7+BC0BhPuGRuM1+dGWbPLrarDpnVEZwgOqybH0krdtig5sT0LZ/fmwtlDiqVvjWn3x7u7vAb3JeXQsJYToyMQJBUDX44CKtKgMkzB5+IR0Jp0mDFjBkaPHt0lGPVGPWb+OhOlDaX43yYXuF+shPNNN8H/ow+71C/XmEOAQ4BDoCcRMBoM2PTGCyhOvwCZkzPuev9zOCr61kKwJ/HqyrbYtDDpyjy6uW23rSW6edw2dV9R8Q8Szz8Ms9lALw6IhaxQ6GRTXz3ZqGX01+gFizBu0Z1WbT4/pYoKj5pMZvgNdMWcZTEQinonXcCo0qHihyToilU4LkxHipDRQxgwYADmzp0LZ+e2b6ZZNVErKxF3HRKdQQgNknqi05Vf1pLcGW8kNNzcRnd4oU4csqqqDqOgYB0qKkmgNuPOwuOJ4OU1A/38l8LFIRq8rL+gO7cRgow/IDA2i5NWilyw3fMGbPWeilKPGEz3dMU0D2eMdXWEmCXRLW3BSy6ei4u3Ii39dRrJwuMJ0b//cgQG3Acejx1ROVYeFm1WI46NZ0rOMPodxSeQXcuQci2Ll8yLESz1G41RPqPg7WDf9Ij2xl9evh+J5x+l99Xd3SdjyOCvO+dMo9VCl5XV7OJCIz7SYawg9/nbKEIhta+VxhDCIwayITEQBwexysXlf/9m4b0/LoBI3fz26DgMDbAPec2mdQRHcHThF01OWOtePQFluRrDZwRh9Lz2FccXxmfiSI2KhtitHRIKZB4A1jFyJgn6N/GbiMlte/bZZ+Hk1LUFxNb0rXjj+BsIKePhvR8YdZ3An3+Cw0j2hr91YTdwTTkEOASuYgSIM8La55dBU69CQORgLHzlbfD5vXPRcRXDDDYtTFiM8zVFcJD9UFq2h4ogkosDV5frMHTo6nbvgLJpvx1ctxpndv5Krpxx68tvIzA6xqrhpZ0swYEfU2jd0GGemP5AdLu6alZ12IVKpgY9yn9Mhi5fiaPCNFwQErk6UFeV2bNnIzo6ugu9X95Up6tEdfUJC6FxDGp13mWVxGJPGp1BxDAJsSGT+ds8BrU6H4WFG1BYtBkGQ01TP46OkejXbyl8vOdCoNcBKTtgStwEXs4R8FoY3mZL/fGr943Y5jUVZU5BmKxwxnQPZ9zg7gyFyH5h9zZP0NLQYFAhLe1VlJRut+w/f0RHfQoXl2Fd7bpVe3JNUmswolCrh5dYCE9x70QgkUGV1Jc06XcQwqNCfTkZEOoS2pTOQiI9nMRdu/a5EpgFBeuRlk4SDgA/v0UYFP5Ol6NJDBUVTHpLWjo0qSnQJCRCl9scndRyPHxnZ8iGDGEeJMpjyJBejajXG02Y8/kRXCipQ4SPA3Y8cT1EdtC6YdM6giM4unB6Kc1RYuvKM7SHxa+MhLs/E4p3aUmv1+D6U4zm6vohoZji7gysXQBk/QWNcQi28pcik1eM4OBg3HNP1/LMDSYDZv82G4WqQrx1PADh/2ZDHBqK0N27uvxj7gJUXFMOAQ4BDgGbEcg8fQLbPyQZi8CYhUsw9tYlNvfFNWwbATYtTFi8j645goPsi+LiX5GS+hzdLQq38YiJ+RZ8fu9oVFh7bBgNemx8dQVKsjLg4Kag0V9yZ+scUs4dyMPRrcTUD4ie6I/rF4f12vrJpDGgYk0ydDlKZPNLcVSaDo3FbpUQHCRlRS63TdiRXHg3Op2QSA2VqqU3AIM0idhxdR3FkBqKsXCQD7A7FkajBqVlu2hUR13d+aZdLBQ6w9d3Ifr53wG5PBioLQSStsJMyI5SRoC1scQ7DcJWr6nY7nUDqsQKjHRxoGks5BEq771jta4uBeeTnoBaTbL3AU/PaYgYtBIikXXHYss5EgKj2mBEvkaHAo0O+Wodfd34IJ/VGZk0EQEP9IbqHb7ulPAR9KIrDRl3Vk0Wjewg6SynS0+jXk+EVZuLgCcA0ewgzixRHlGIco+Ct9zbrsdaZtaHyM39H91oaMhTCAkh0o72LcTJRXP+PNQJiVAnJEB9/jxMtc3pVi23JgoKpNEdNMqDkB7h4eCJiTxl9xfy29939hc8t9MN942qx1M33QUBv+uUAJvWEV2fTffvB3tsoVsWJUe2ZiDhQD7N17z91fatV19KL8APhRU0LeX46Ajwy9OAr5j6pbqX8L1UB73JgJtuuonmWHal7MzaiRePvAipDvj5fyKgQQ2vF56HexeJk66MiWvLIcAhwCHQVQT+WfMt4vbusNyRfQeB0UO62iXXvgUCbFqYsHjHdMtagsXzbRpayzugHh43YnD0F+Dze+8OsTWY1ZQUY+0Ly6BTqxEybATmr3jV6jDxo1szcO4A47Qwam4IRsxiROB7o5h0RlStT4UmrRoN0OKw9ALyqQweaMTvzTffTFNXOipEh6C2Np6J0GjH6YQQV64uI5i0E8VYODlF9airR60yAQUFP6O0dA/M5ubUFHfF9VSUlGhWUJcRQnAkbgbObwGUTGQLKQaeAIdch2Or9zT84TEeDQIZBsglmOZOyA5njHBx6JGLfXJRX1i4HhmZ71AdGx5PjIEDX6QpOLx2yAbSpkrPEBiXEheN7+stBEZH+7rl934SERb7KrDE1x39SHp8Lxe9SY/kimQcLz5ORUsTyxNhMBsuG5W71L2J7CCEByE+PGS2ix0TfFNSl6Ok5He6rYhB78HPb2G3okGEj3U5uVAnJkCTmAj1uQSa3gLD5fMl5IY0IgKyoTE0woMQHyJ/f7uSPAZDPQoK1iI37zsaNaU2SODq4IaxY/61y/mcTesIjuCw8dA2m8z46cVjqK/RXvHPr95gxNBjyZRVfbW/Hx4N9AJ2PgmcXQO9yRcnDW/jgDiJHsDLly+Hg4Pt9mRGkxHzd8ynuW/LCiIxfm0iZQMHHjoIgat98qtshItrxiHAIcAh0CUEDHpyR/Y5lF7MZO7IvvcZ5C7cea1LoLZozKaFib3m1A39XLMEB8EyN+97ZGa+S2H19pqNqKhVPXrxa8v+TD16EHs++4A2nXTX/Rh+0zyruiFrvANrUpB+qpTWn7x0ECLHk93fO4WMp+FsKWr2ZMOk1iNNUIQT4gwYLFas5ObY1KlTIW5xB7i108lxi9NJa+cL6nTiNIRGZ5AoDWfnYRAIei/ioRFdki5TVLSFkgQabbO7CrEu7ue/BH5+t0IkcgNMJiDvGJC4CUjeDmhbiJMKZNjjPh7bvKfikNtwGHlC6mJIoqinu7tgksIJjkL7pzvq9UqkXvgPysv/oNORyYIwOPpzkNSbCr3BEoGhbyYxWkRiqMl8rCgOAj4CpOJWD0JekM8IoXGsRoX1xZU4XK1q6o1c8E1WOGGpnzumurtAZIc79lYMtcMqDfoGnCll9DsSyhJwoeoCdJYopUsbe8m9aHQHeZCIj0j3SLhJrTdPIGRTQsL9qKo+Ss9dMUO+o6RZTxaTRgNNSgolO9SE9EhMgKGouM0hCNzdm9JaCOEhHTwYAse2swWuNAejsYFGSBFiQ6+volX5fBkC+t2JwMD7O9TOsRYfNq0jOILD2r12Sb2ijGr89lE8/fSON0fD1avtEMG1RRV4Lq0AUj4PcWOjoNArgVURgEGDav1D2CUKRRZKEBoairvuusvG0TDN/sj5A88dZMJIN/8eDKRmwnnuHPi//36X+uUacwhwCHAIsAGBlndkg2NiseCF162+I8uG8bN5DGxamLAYp2ua4CD75WL2Z8jO/pTuIpksGHJ5ELUBlUh96bNU6guJxBdSqQ9r0lj2ff0pkv7ZD75AiCVvfwjv0I6jHcj8jAYTdn+VCCI+Sm66z3x4MEJiPHv18CTio7W7s9EQXwYlrwEHRSko5TMX9QqFG2bNioVEmk0jNKqp04nysvE2O52MsTiddJ/2QVfBIiRNRcXf9OKsqvpIU3ck0oSQbESrw9nZEs2n1wAZfzJkB3luIU5aLXbDVs8bqF7HOadBNBJQzONhnJsjpnm4YJq7M/y7GN1AIgQuViXgUOoqFOrMqIAXVPLrUC+NRYHWSNNK1CZGVLWj4ijgI5AQFjKGtOgnaX5N3rsKBVbd2c9Ra7GhqBIbS6pQpmuOGiAaHYt8FLjDzx3Bst4ntFriQSI8SEoLifJIrmQexKKWpOC3Vfwd/SnR0RjlEaGIgIuk/RQg4v5zNm4JVKoUqicUO2wDnJ2JuWfvFX1ZGRPh0ZjakpQEc0PD5QMix23/0CbxUpLaIhkwADxh25ozxJKY6Nzk5H4Dvb6S9kd+OySSKCjowSbbXHvNnE3rCI7gsHGv/rshDcmHCuEV5IRb/9N2Wgk52U05nYaUeg09kXwaEQgc/gj4602YeI7IU3+PdbIzlIEnqtixsbE2jgbUnmnhzoXIqM7AbeYRWLjyBO0raP06yIdz1oo2A8s15BDgEGAVAheOHsRuyx3ZCUvuwcibuzfElFWT78bBsGlh0o3T7GrX1zzBQdY1mVnvIS/vuw6xFIs9mskPqR+kEl9ILM9SqR9dXPeEg4Reo8G6/zyFqqICuHr7YunKTyGxUrdCpzFQ+9jyvDoIRHzc/NQw+PbvvH5Ch2B1soImsxo1v2Wivr4AGR6HUe2eCBfXEkgk6st6YpxOxljEQYnTie1h/p0cpl2r19dfpBEdRcVbYTQ2RyY4Ow+lF2xeXrOao0/U1UDKdiaNJfdoq3EUOQZhg8cUbPG6EbktRFKjHWXUkYXodgxxlF1GIJjMZpTrGiMwWqeR5KmJLoYaWrN1jiguQgFDXEhFbUZikO/bS2WxBVS9yYz9lbVYV1SJf6rqWki1AuNdHWlUx0xPF0hY6kRDbGnJ9U0j4UHIj8yaTBgtEUyXYhLoFNhEeBDyg5AejuLmyAetthRnziyk0UHk9zBi+FbIZAG2QNstbcxGI7SZWVAnnKNRHkTAVJuZCZgvJ8h4cjlkUVGMjofFtYXv4YzCol+Qm/tNk/sRny+Gv/8dCAp8CBJJ9xC1bFpHcASHDYem0WjCmuePQqPSY9zCARh6Y2CbvZyqUWFuPCNUtXd4GIbJhcCnQ4C6YtQZFiDedBP+FieBz+fjueeeg6wLvsn/5P2DZf8so9valDIZvO37IR7QH6E7d9r1JGkDXFwTDgEOAQ4BuyKw/9svkPjXHzR6Y9Hr78E/PMKu/V+LnbFpYcJi/K95goPsGyp2WHMCqrpUaLTF0GiKoLU8t2Ul2t7+JNagEokPjfpoigJpIkKYz4jIpT0u9MpyLmLDy8/CqNcjbPR4zHzsGQitFPRrUOqw7YOz1DFPIhdiwXPDofC1PZ24K8d3K6eTqmNQay53OtFpZdBogxERMR9BgTO65HTSlbF2V1uiI0AcSQoL1kFVn9a0GZFIAT+/2+Dvt6T1nGvygPNbmciO8tZCqhfdY7De/QZsdJ+ESnFzyqOPWIQp7k7ggccIehJhT60OWisjMFwEQKBM1kxeyJqjMPpJRHDpRZcXMp9fiivxS3EVirSM0yIpJH3nVh8Flvq6Y6CDtLt2n9361Rg0SKtOa4r0SKlMoZEf5lb0DbM5sh+DXYKb0luInkeARITkhLtgMNRCLg/B8NjNEIsVdhufvTsyqlTQJCU1R3kkJrZrVWt040EXbIQu2AR9qBCeoxciJOxxSCTda83LpnUER3DYcATqtUYk/J2PzDNlmP34EDi6tX0ieCQ5B7+V1WCokxx/jAgDErcAv94PM/go0XyPfbIy5JhLMXDgQNxxxx02jIRpQhYbS3YvQVJlEia7j8Zjr8bB1NAA7xf/A0UX015sHhTXkEOAQ4BDoJsQ0Ou02PDiM6jIz4WThyfufO8zyBzZG2bdTTDYtVs2LUzsOjH7dsYRHB3gSYQsyd1RjaaYkh9aTVEzCUI/KwIJEbe2CAQOINEeDBHCRIEw75ufrdWMiN+3C3+v/ppu2tXHF1PufQQk1c2aUlvegG3vn4W6Tg9HNwluWTG83bVfe/2RtRoRziT58CR03GCsZ14b6mE0qZln+l3bjwZ1DlSq1Mu6F/Cd4KCMgDh/ALJrnXBORxwqeBAIBJgyZQpGjx5Nb6RdbYXgWVN7hoomlpfvg7lJqJIPD48bqCipwm1cM0FG7n6XnAfOE3HSrfRmY2Mx8YXI8B2P9R5TsNZpJNSCK1/gEzKAaF74COohUf4DN2M2PFGOCI+hGB/+OFwl7P8/MprN+LtSSbU69lcqYWwRHDDKxYFGdcz2dIXMDvahPXXsET0PouHRMtIjR8k42Fxa+Dw+gh194WnKRz+RARFuAzBn9HrIWxBdPTVuW7ZDjn9DURF1a2lIiEftmX9hTCsAr61MHoEAkvAwi57HUBrtIQ4OtnuKL5vWERzBYctRZUWbcp0escdSoDeb8cmgACz2UQDfTQaK4qERTURR3dNYLztCw6vmz5+PmBjrPNrb2vTRwqN4+MDD9Kv1+nsg+vB78CQSRlzUpfdDKa2Ai6vCIcAhwCHQKQQqC/Kx7sWnYNBq0X/EaNy8/CW73Ont1CCuospsWpiwGFa7Exwkr1zIbzt/msU4dGlohOAgBEhj1EdTFIiFANFoSlo5aHS0MZHInYkCaUF8NBEhRBNE7EkFBckFwbEtG3Dq980wGY202/AxE6j4qKPCvdVmTCb9JURDPSqLKnD01ySYzGo4eZgRc6Mn+AItDJSQIOSE2vLcPknRfBHe0aza/76l04mbYgycHKMInUFFSGv3ZqNIXUG1Oer4TLpKUFAQ5s2bBzc368UYbR9d77TUastQWLQRhYW/QKcraxqEXB5KbWZ9fW+h0UBNxWQEcg4zNx5JKouumXQziRyQHjQNv3pPRbbPKPRrEYnRKOTpIOAhN/drXMz+BEQnhGg5hIe/BV8f60Rsewel9rdaotVjU3EVJTvyNM3uNSRV5hZvN0p2RDrK2DZsq8ZTp6tjSA+LpkdSRRIKVAVtthWAh4FuYYiyCJiSSI8w1zCIBOx0jCKiqcXF25CT8xUjxmsAxEVCeFQNg0OhJ/RJGdDl5rY5V76zM2SDB0M+ehQ8HnjAKiw7qsSmdQRHcHS0t2z8/pOcEqzMLoGbUEDFRWWFp4DV02lvZdoPkMJzw7/iZMqwk/QUqdS2cDDyh333H3cjviye+ke//E0NVed1uflm+L230sbRc804BDgEOATYj0Dywb/wx1cf04FOvudBxM6cy/5Bs3SEbFqYsBQiMiy7Ehxkwb3i0Aq8M/4dDPUayuJp9+zQzGYTdPoqJvrDQnpoLREhNB2GkCP0ItY6wUYeT0hDsxujPvRqA4oyz0OrrgZfaIJQAjgonCCS8S0kRUOnCBZ7oEMiVQQCGb1QZl6T59YPscgdrm4j4eIc267TCRUh3ZONmrginBJm4IKQcSAh7iozZ87E0KFDr2oimBBT5RX7aVRHTc2ppl1DsPTxvplGdTg6hrfeZXo1kP4Ho9dBxElbilnKPYBBs4CIuUDIREAohlZbjpSUZ6kTBymOjhHUJYWkOfT1QnRGjlSrsK64EnvLa+lN2sYyzElOiY55Xq5w6Ab3mZ7ErlZbS6M8SFoLOQ8nlJ5EubbtyDIRX4QwQnpYrGrJc3/X/r1KTJPjvKTkN2TnfAmNhiFrCInr63MLgoMfg0zWrwlOQ3U1NOfPM6kt1LUlEabaZrch+ejRCFrzo13gZ9M6giM47LJLW3diMJkx6kQKCrV6PBLgidcG+AOb7gRSd8DgGI2SinexX56EXFMZBg0ahMWLF9s8itMlp3Hvvntp+9VBL8Hx4Tfo66ANGyCPHWZzv1xDDgEOAQ6BvoDA3i9XIeXQ39Qh4fa3PoBP/4F9YdisGyObFiasA6d5QHYjOMjNidt2LcKFqlR4yjyxec5meMj6pvhjb+wvcueS3LVv1v9g0l8YIoQhRkhuvb0LsVaEWQJNnRAmgwRiiSPc/TwgbCQmhHII+ISocICAvraQFOR1E4nhQOvzBXLmmS+1u9iqJrMGNb9n4mJVPg6LUqHmMXflw8PDMWfOHDjaYDVpbyy7uz+VKg0FhetQUvI7jcZpLK6uI6koqafnNPD5l9yZb6gCkn9jyI58Rqy/qUhcoA2ORZYkHaVOGpgEPPj7L8XAAS+ywlrX3nhW6AzYXFKF9UWVyFJrm7onFrULvN1wh687YpwuF2O19zh6qr/TKa/hRPYG5Ov4qBKFIbuhAWXq5migVoeCQIJBikGtnFsCnAIgFdp2s9raOZpMBpSU/o6c7C+b9HcIseHjPY8SG8TVqqNC/nt0OTkW15YESAYOhNvtt3fUzKrv2bSO4AgOq3ZZ5yrtLa/B/yXlgIB7fHQEgjUlwGdDAbMJNZIXUVl7HU1Poc4nCxciOjq6cxtoUfuBPx+g3tFDPYfivWPBqN26lR6sITu2X9Usvc2AcQ05BDgErioEdBo11v3naVQXFcDF2wd3UoeE3hEA7MvAsmlhwmIc7UtwrN6BVN5b4Am0iPUaju+nfwdyt5Ar9kGAiFG2lQZjMuuaoiTMRgHyElNRdCEDRj0fJoMAwdGjMGTKbMgcPFpEVBDSglzMCejgEv7Kx5EtGfR11AQ/TFwSzro1l1lvgvLffJT/m4Wj/AvIFjAXa3KZHHNvnktvsF0LhaREkTD+gsL1aGi42DRlsdgL/v63w99vMSQSr8uhIOKkqbvozUlz3gnwWkQMGfk86IOvg3Tog0DYdEDqfNVCSS6IT9TWU6JjZ3lNK6FV4jxDrGZJGotzH4/qIJFjyclPo7RsF92XkREfQuAyjonyIHa1lhSXKk1Vu/vaS+aFfk79mIej5dnymhDYtgomkzSokpIdyM75HGp1Y8oJHz4+cxES/DhroofYtI7gCI5uOCUtOpeFg9V1uEHhhA0x/YF9LwHHv4BZ7oPCqq+RLijDIVEqRCIRTU8hoYO2lITyBCzds5Q2/d/oj+Bx+3+ob7L3yy9DsdR20VJbxsK14RDgEOAQ6C0EWjkkjJmA2U+usHkh0Vtz6O3tsmlh0ttYXGH7diM4yDZ+OZWHV/7cCFnAWrrJpRFL8fzI51k8/at3aEXpqTjw3Zcoz2MECeUurph0530YNH5Su+eSY9syEb+fcTEZOScE193EzhQFfVkD7VW+KgAAIABJREFUqn5NR2peBo6J0qCzqBAOjRmKGTNn2Jwi3deOBur+U32MESWt+AuAiU6BpDCRaI5+/nfC1fW6y/Y3iQZKO/soJNkn4Vmhg6LWAF5Lu06BGAidxKSxhM8CHFrrufQ1nK403mq9AdtKq6nd7IV6TVNVGZ+HuV6MVscIZ3mf/f8lIsnnzt1LXaLIcTE0ZjUUinFN8yTHUGlDaRPZ0ShmSlJeOipSgRT+jv6U/CDRHi1JED9HP8iEl2ucEGKjtHQ3JTaayTkevL3nICT4CTg4hHa02R79nk3rCI7gsPOuz2rQYNxJxobq58EhmObEB1ZFAlol1P2eQGXmdOxzSES+sRxRUVG49dZbbR7BY389hkMFh6i/89d181H65pvgSaWMuKjz1csm2wwY15BDgEPgqkUgYf8eHPj+Kzq/qQ88jiE3zrhq59odE2PTwqQ75menPu1KcECrQtzqZbi7QQx4MPn87014D7NCZ9lpuFw3nUGACI/G7d2BY5vXQ69lLt4Co4fghnsfgbt/wGVdmU1m/PVTKtJOltDvJt0RjqgJ/p3ZZI/VJRdmDWfLULg7Gf8azqNIUE237eLojHm3zEdICDvJme4CiKQ2FRRuQFHRJuj1zXfkHR3C4d9vKdXrEAodUFHxD1JSn4Nez+AVGHAf+vvdB37GX0DKDiDrb8DYnL4BEuETPI4hOwbdBDiTU8bVV8jxFKdsoFodv5fWQG1iyCJSwuRSLPVTYKGPAopetMO1FXW9XomzcYtQX58OgcARw2N/gZNTZLvdNZIehapCFNQVIL8un4qYktfkUamptGooJFWxkfQgRIizuRLmmr/haCyAE98MPo8HL69ZCAl5Ao4O7EzFZdM6giM4rDrsrK/0akYhvi0op97XJ0ZHQHDqW2DvCpiFMpTy10Kl5GO99Aj1ab7tttsQGdn+j+ZKW02tTMVtu26jVT6Z+DFCnvoS2gsX4LJgAfz++471A+ZqcghwCHAIXAUIkEXGrk/eQ/qJIxCKxFjy31XwDAy+CmbWM1Ng08KkZ2Zs01bsR3CQC4I1s4C848iURuJmJ2/wHbMg4knwy+z1CFdcIoRo03C5RrYgUFdZgX9++hYZJ4/R5kTf57q5t2DUgtsgEktadWk0mrDny0TkpVSBxwNmPDQYoUM9bdlsj7QhIqQ1uy/iTGIcTgkzYeQxF6ajRo7CjVNvpJHF11Ihd+xLy/bSqA6l8lzT1MmFrZvrSFRU/k0/EwpdERX5AbWfbVWIMCURJk3dCaT/CeiJRW+L0m8kEDEHiJwLuF2d/0d1BiN+s0R1JKoY5x5SJHweZnm40BSWca6OfSqqg0TtnDm7EFptCUgq04jhWyGT2UZeEuvaRvKjifhQMURIYV0hdKZm15r2fnsiHh/+jn4IcunfOv3FsR/8nfzbjP7ojd8xm9YRHMFhxyOg3mjEsGPJUBpMeCnUF08EeACfDweqs2EMX4rihMW4ICjEEdEFmpZC0lNs/TN55t9nsD93Pwa4DsC64NeQt4gRiAnetBGyLljO2hEOrisOAQ4BDoEeRUDbUI+1zy9DbVkpFH79sPTdTyCy0aGqRwfOgo2xaWHCAjjaG4L9CA6yhaRfofr8ETj4aHBYFotH3A3gi2vgJfXHb/M3w1nMRWL25rFwMf40/l79NT2fkOLi5Y0b7n0YocOuazUsncaA7R/Hoyy3DgIRHzc/ORS+A1x7c+gdbluTVYPsbefwtyoO5Xwlre/hrMCCxQvh53d1Rh10BIpSeZ6KkpaW7gQhPhqLi8sIREd9TG2Ir1iIG0vWP1SzA2l7AM0laQs+Q5jIDkJ4eF2d+ieJdQ00feXX0mqojM1RHaEyCZb4KrDIVwFPcd8g0YhILYnkIBoucvkAjBi+CSKRfX/XRIuxvKGckh2pxX8gtWgvihuqUGngodLAh9Jk3WU60fe4VPOjMRWGfMfn8Ts6/O3yPZvWEdYhZ5dp92on9l2UtDMVIsDzbFo+ZS3jxkTB/eKfwEaGeKgdtBV156TY45iAIkMFhgwZggULFtgESmZ1JubvmE/bvn/9+4j5/ghqt/0KSXg4Qn7/rU+xpDYBwDXiEOAQ4BBoB4GSzHT88uoKmIwGRE28ETMefYrDygoE2LQwsWK4vVXFbmsJEnFU9sGHqFq9Gu4RdfCKqcNPslH4wKscPL4BIzzH4oeZ/+uxhWlvAcr27ZJUlZO/bcHpHdvoOYWUgaPGYvLdD8LJvdn1Rl2nw7b3z6K2XA2JXIj5y2Ph7ufI6ukREdKaf3Jx+PBhxPOzYeaZwQcP14+dgAlTJkIgYARVr7VC0lGKireirGwv3BUTqTsFny/sHAxGPZB9iInsuLALqC9v3d4jjCE6COHhG0OEQDrXP8trkxu+O8pqqDDpGWWzg42QB0z3cMGdfu643s2Jpl2wuVRXn0D8uf+jttGE6Bo29Ge7OuaQ/4GKir+Qnf0Z6lTJTVB4eNyI0JAnIZAGo0hV1JTy0jL9hUSFaFumR7UDpEQgadL+aCJBLAKoJBVGLpLbbRewaR3B7iPLbpDb17u+rWGRg3TqmXQkqdRY6O2GLyKDgDWzgZzDMA+YiuLs5VDVq/CL9ChNT7n99tupXZct5flDz2NP9h4EOQfh1xvW4eKkG2BWq+H96itQLFliS5dcGw4BDgEOgasGgbO7t+Pfn7+j85nx6NOImjjlqplbd02ETQuT7pqjHfq1K8FR8vobqNm0iQ7LO7YWirB6vOg4Bjs9C+ln90Q8iGdHPmGHYXNddBWBysJ8/PX9V8hPOU+7EkllGHfbHRg2Yw74FiKAkBvbPjgLtVIHRzcJFjw3HE6K7rWN7Oq8SHt9eQPSN53Gn2UnUctnLkZ9Xbxwy9Lb4OHJWRd3GWOTEcg/yWh2EMJDWdC6S9fA5sgOktLC75m77V2el5UdpKrUWF9cia0l1agxGJtakVR+EtWx2FcBX4ltZgtWDqFL1UpLdyEp+Unah6fnDAyO/qzJTcnWjsk1Y2Xlv7iY/Snq6phzCike7jcgJGQZnJ0Hd9g1if6oUFcwWh8tND8aX5erLyHV2ulxpM9I/DD9hw63Z00FNq0jOILDmj1mRZ0ztfWYHcdYhu2JHYjYhizgmwn0vW7yzyjbq0CyIB/HRelUsXr58uUQCjvJCAPIVeZi7u9zqcXsW+PewvUn61H61tvgyWSMuKiTkxWj5apwCHAIcAhcvQiQxcP2D99G1pmTEEmkuOPdj9sUCbx6Eej8zNi0MOn86Hushd0IDjJis8GAgmVPQvX33yC+8v5jq+AcoMFtihFIdSkDzDysmvgppoZM7rEJchtqHwFyXkk98i8Orv0BDbU1tKJnUAhuvP9R+IVF0PfleXX4bVUc9Boj3HwdsGB5LKQO7A/JJ3OrPV2EA3v+RBIYG0ohBJgyfjJGTxnHRQbb64dB3FeK4hiigxAeVVmte3b0BgbNZjQ7gsYBAvYfO9ZCoyZ6NeU1VJj0eE2zVgmhc250d6YOLDconCHks+/SNC/vB2Rk/pdOtV+/uxE28BWbfhPkd1ZVdYgSG0plQhN07u4TERLyJFycY6yFs8N6aoOaany01P1oFD4lnzVGf0wJnIJPJn/SYX/WVGDTOoJ9R5E1CHa+jl0XJW1t/vGUXGwtrcYQRxn2jQgDb/tjwLn1gOcgVHn+jIa4Mux2OodifSWGDh2KefPmdX4WAF49+ip+y/yNhhvtmLcDBfNvhTY9HS4Lb4Hf22/b1CfXiEOAQ4BD4GpDQF2nxM/PL4OqsoKKjd7+zkeXCQRebXPuynzYtDDpyjy6ua3d1xImtRp59/wf1AkJ4Al5CLy+HCIvHab4DEa1rBYCyPH7vC0Idgns5qlx3VuLgEalwpGNPyPhwF7AYhc6eMp0TFhyD2SOTii4UIWdnyfAZDTDt78L5j45FEJx30j3MNbrkbT5OP7MOYp6HqNDEeTih/l33QpXdzdrIeLqWYMAOXbKUhmyg+h2lCa1biVzY2xnSRoLsaEVsT8ayJppkzqZDRqavrK5pBqVeib1ixQZnw9nIR8OAgEcBXzIBZbX9LPGh6DVa8em71q3IfWlfJ5NRERb80jPeBv5+T/SrwYMeAFBgQ9YO11QYqP6KC5e/ARKZXxTO4ViAkJDlsHFJdbqvuxRkdwkr1RXUvJDLBAjyj3KHt2CTesIjuCwwy4t1+kx/FgKdGYzVoUHYImTAfgkGjDqYJ71CYr2hEOlqafpKaQsXboUAwYM6PSWSb7V7F9nw2A24JXRr2B2w0Dk3s6kpARv2QzZ4I5Dmjq9Ua4BhwCHAIdAH0Wg4EIyNr/xH5hNJsRMnUXvtHKlbQTYtDBh8T6yO8FB5mqorqb/5bqcHPClfARPLkGNwoyZfqHQC7VwEQTiz0Vb7JorzWKM+8zQijPTcOC7r1CWw9yFlzk5Y+Kd9yHy+huQebYMf/6QDJiBkBgPzHgwGnxB30k9qEktxZ5tO5FuYNIpxBBi+pgpGD59TJ/ZP31uoJVZFrJjJ1B4pvXwxU5A2DRGt2PAVEDCbn0Xa7HXmUz4o0JJyY6D1XXWNrO6noAHCxlyOflBCBRCpFDixEKqkNfM55d/J+cDuWnPobp8F91+VOTH8PGZe8WxEGKjuvo4LmZ/gtras0113dzGUo0NV9cRVs+lL1Rk0zqCIzjscMR8nluKdy4Ww0UoQPzYKMgPvQccXAnIFFDPPILKDRdxXpiHk8IMyOVyPPvsszaJN7194m1sStsEL5kX9t6yFxUvvYba33+HJDICIdu22Y2ltAMkXBccAhwCHAKsQODkb5vp3VZSZj/1AsLHjGfFuNg2CDYtTNiGTYvxdAvBQfrXFRQi5/bFMJZXQOgkQPDkQpxTSHGvjyfAM2OQ40RsXvA59z/PsoPDZDTi3J+7cXTTWujUjEVmv4hoTLnvERRm8HFkM5O6HDneD5PuCO9T+89sMCFu62HsTz0CDU9P5zHQKQBzl94CJ2/7ukmwbLf2/nBqC4ALu5k0lrxjgLnZkQRCKdB/CpPGEjYdIJEeV0HJU2uRotKACJTWG03UhaXxdUPL94bm78jnTN3/Z+884KOq0jb+n57eQxJSCAmhNwFFFKSIFMviWlaxi12xrm7Rrd+6TVfX7lpWXRW7YkO6dAWl95IekpDeJmX69zt3JhWESTJJbsI57t0ZZk59zkny3ue+7/M6sLu6BwQ9DkyuOvw0FsL8IggxBBFu0DElPJhZUSGkBrg9bSoqtiihKJWVW5omFhY2kZSBDxAeflb3TLabR1GTHSEJjk5uvsPlYuLm/RxtsHFHQjR/To6Ef4+AulKY8jBlxVdSv6uEL4O3U2yrYPz48VxyySXtHrW4rpg5n87B5rTx6zN/zfz4Szhy3lRcFguxf/oT4Vdf1e4+ZQOJgERAItDXERDeG5/+/Y/k7N6B0T+A6//5HGExsX192e1en5oMkxaTF4+8hbrbHcJRUUgcAB+JaE2gOYj7xKv9E/DHkwAh/KLbG+DeZQSHmGfD/v3kXHc9zro6TJFaBkzN552oMJ6KdGtrzUu8i8dnSC+kdh/ubmhgLi9j7duvc+j7DcpoQnh0/MU/R2s4k12r3Wlmz7wombMuSemG2fh2iIrsYr54/zOyLceUjgNcJuZMmM6oiyaiUaFegm9Xr4LezCVwaInbuyNzHTjdZJP7oOlh4FS3Z4fQ7giK7voJi9AahxXsDWC3HP8q0uU2fd6yjnh/ijYiDKffCIgdCTEjIDTRqwwzwlNCeNELskMhPOwOWpIfjZ+f/Ds3seK+HJjtTlrQSl7jmmxyMZ6tDGv4ksEcRIdTycCSkvIAEeF92wNKTXaEJDi8PrInrriitIob9mQpX343cRgphz4Gob+hNeC8ZyeFz+RQba/lQ9N3Sp0bb7yRgQMHtnvUJ358gnf2v0OEXwTLLl9G/fufUvTXv6IJCCBt/Xp0QYHt7lM2kAhIBCQCpwMCtZUVvPPr+xCvMSlpzP/LE+j07b237dtIqckwaYH0s8B9wGJgKSCUHEVaEXEXORNOan+OBsTVtojPHvH02d5c7V1KcIiJmjdtIu+OO8FuJ6C/hoRz8/lVbAwrgky4XFp+N+5Zrh49rW8fxl68uuxd21n935epLCpUVhES1Y+IpAs5lhWh/HvqNUMYeV58r1uh0+nkhy/Ws3rXBmy4M2GM8E9m7vx5BCX1DQ+CXrEp9ZVweLlbsyN9lZswaCwaLSRNcmt29BvaTDLYfopYaPzcG6KiDZEhYq+6o5hC3USHuBTSYyT0GwbGrr/nEaRJg7ORNGlNflTUl7Iv43lq7Fbs+mickVezqqKeElvr2+pg6pgWbuTiuFSmR4YQou8dWjwd3Vo12RGS4OjoLnrazd+VwZryGqaFB/PBmBT4z2S3UNDoq6hLfZzy9w+xy5DDj7p0goKCeOihh9C2MwWUEIIR3hsNjgYeHP8gN4+4mayf/QzLkXTCrrySuL/8XydXIZtLBCQCEoG+jUDu3l18/PjvFFHA8RfNY9oN3guE9W1k3KtTk2HiwVuonon8eYLcuLzFHgiC4zngWuC9DuzNK8DtImIJWNLO9l1OcIj5VH3xBQW//o0yteCBDsInFnNNfH8yhVClI5i3Z7/PGfED2jl1Wb27ELBbrfzwxcf88PnHOOxuAcXAiGHYHeei1YUw5/ZRpJzRDU/au2DBpfklfLboIwrq3CkoQ1z+zBkxjaGXTkDbS4RUuwCWnunSWusmOUQYiyA9rL7XsOjwwnRGlFAavcnzKt63/Hfj5yYw+DfXEwSOuIcqOej2Ejlh0UBESjPhoRAgI0Gk29V0321tTc0Btm2/GofDjF4fgtVeQzYpbGcCu3RTyHTGtZq9QaNhUlggs6JCuSAyhAH+pg7Dq9aGarIjuu8k9OxudIlRklVnYdKWA8rK/jdqILOrd8LbHsGZ29dSutJIw4FyvgjZRom1krPOOosLL7yw3Ug8s+0Z/rv3v4QYQ1hxxQo0ew6Rc42w7SD5k0/wH+kb9dt2T0w2kAhIBCQCvQiBTR8tYvOn7yszvvRXvyd1/MReNPuunaqaDBPPSkVasMeA8zweG40AiADnMmAd0N4/qOKxXwFQ7Ql5cT+K9r50iS1xouFLX3uNkqeeVr6KGGbFOr6SK/vHUavToLcNZMXV7xMtPTe937keqFlekM/qN14md89OZXSN1oDOdDbGwAnMe2A8/dN6p46F8ObY8PUa1m3fhHDi17hgjCGVmT+fQ9CI3knc9MDx8O2QIiQkc63bs+PQUqgrb00oiNCPExIMJ/m83W08ZIXOBO18kHscGA4blKVD0T44tsf9KoiPGrdn1AmLKaTZ20MhPUa5vT26UJC1vHwTO3fdgsvlDhsKDh6paGxERk6jwGJjZVk1y0ur2FRhVkJoWpahgX7MjgplVmQIZ4QEoO0ucsZhB3MRVBdATQH4hUHKVJ+cRzXZEZLg6MSW/ik9n//klRBvMvDDpOHo3p8Ph5cqLmLOq7+i4K9bqHLW8rHpe2WUBQsWkJTUvlRvVZYqZn0yizp7HXePvZu7xtxFwa9/TdUXX+I3YgQDP/2kEyuQTSUCEgGJwOmDgBAE/Pgvj3H0wF78goIVPY6QKGmQixOgJsPEcyKXe8JQAgB3vsrmIlKSDQbau3k3ASLPnyBPft+Bk99tBIdwjy7669+oePddZZr9xpnZN8bKwph+uDQQbp/Gihuewc/Qt12eO7BHqmoi9vHQd+sVfQ4RIieKRhtJQMRsrnz0Z0TG995sGEUFx/hk0UeU1JYr64pwBjEn+VxSrxiHLqTvPZ1W1cE62WQab6S764a5O4GpLXMTHQrhIa49UCy8Pdr+iWiclPD2GOghPkY1EyBhAzpPwniGKC1dQ+Gxz4iNvZSoyBknFBKutTuULDHLS6tZVVbdKjWu6CbKoOeCqBCF7DgvIljJ7tKhIsKRBGlRXegmMKrz3aSQeFX+XeAmN1qK1g6eA9d82KHh2jZSkx0hCY4ObqkQrxn33T4q7Q5+OzCO+4PN8Px4lJxgv3iH2tqzqPj0CDuM2WzTZhASEsIDDzzQ7vCUl3e+zEu7XiLIEMTyK5YTWOd0i4tarcT+358J/8UvOrgC2UwiIBGQCJx+CNSUl/L2r+6joaaa/kOGc9Uf/64IAp7uRU2GiWcvRHhKPyDmBHsjhEavBMRd1E/5MZ9oS4V2x7lAKuAWzzp5EeqeboVPdxFz2Z6fn0///oLr6NricjjIf/AhalasUAbqf04l743W81K4+8n/UN1tfHjNvWil0GPXboQPerfU1bLxg3fYtWIJgvQQxRQ0mqv++ADRSeKY985it9v5dukqvt+2WVFl0Lo0nEka584+j+Cz46UIae/c1t41a+GRoHh7COLDQ34cE94ewlnvJ4pIuxsz3B3aouh7NHp7tPx13zUwiOQU26vrEBqOgvA4XNdCR0X8XtBqmBzmzsgirjiT0T2Rhuo2ZEUb4kKst044N7ajCI0T4b1x1TvtaPTTVdVkR0iCo4Nb+n5hGQ8ezEPEVG0/ZzjRqx6FH151x4Ddt5OSN/djOVLJZyE/Um6tZtKkScyePbtdo5mtZmZ9Oosaaw23jbqN+8bdR/n//kfR3/+BNjCQtPXrlFdZJAISAYmARMB7BDJ3/Mjif/xZaXDWpVcyZf6N3jfuozXVZJh4IM7wZDk5kdujyPt7PSDUDSu93JIhwEFgtcczxJtmJ8zE0l0Eh5ig02Ih95ZbqN+6DXRaEs4r5rejQ1kX4I/LqefiqL/xj0vmerMWWUcFCBRlprP0pecoy8tUZqPR+TPt+gWcMXs2ms669ffg+nJzc/nsg0+orBPRXxDrDGNm1JkkXTEGY//e66XSg5DKoTuLgAjTaQxtEcSHID2KD5zE20P8RWn09hjZnMklLNln3h4nWlJOXQPr8nPYnZ9OSWkO/SylxFlK6G8pVl6T7eXEWoox2U6VOKxN74HREBwHIfEQ0r/NFe/+zsfhO2qyIyTB0YEfIMG+z952mN019VwWE85LA0Ph6eEgDt/sv+EYeRuFf9tCBWY+NbnzH996660kJCS0a7TX97zOs9ufxV/vr2ROCTeFk3nRxVgzMwm7+iri/iRsL1kkAhIBiYBEoL0IrHv3DbZ+9ZnS7Iw5lzDl2pswGE9ft2o1GSaevfS1B8cTnuwp84EPvDwvPerB0ThHR1UV2ddeizU9A62/gahpBVw/Ioo8gwGnNZwHR7zEreeM9HJJslpPI+B0Oli36DO2L/kAXG7X+ri0oVxw+0Kik0Q25N5ZLBYLK75ZzrZd25UFGFw6znYMZtykCYTOHIDWpO+dC5Oz7jsICG+P8gy3p4cgPBoJEBHC8VPFGAT9hjcTHkoml+HgF3JqXJyOZr2LxhCRxnCRptCRwpOTLm1GcWh02AL7YQxNQBvaH4Lbkhfiszi3cGs3FzXZEZLg6MDml9vs/Gz7EdLrLHw1Lo0z9/4XVv4exA/BQ/sx76il8osMtvllsYNMwsLCuP/++08Yl/VTw9fZ6pTMKRWWCm4YfgOPnPkIdVu3knOdeGgFAxd/ht8wkTFPFomAREAiIBFoLwIiu8Hif/6ZnN07lKaRCUlcdN8jRA9ofxrv9o6txvpqMkw8+PhSg0PcWR0FxKvI0flTAdun2ppu0+BoOxFbQQHZ86/BXlSEPsQPx/n53DA4mnqtFkdtGs/PeJHzh7ZW7T/VYuT3PYvA3nVHWPn6KzitwrFIaHNoGXfhPM658hqMfv49O7lOjH7kyBG++OxzzPXuJ86JjijG6AeSet5IQs9NkNlWOoGtbNpFCAhvj+L9HtLDE+oivD1apuFtO7TQ8RChLSLERbyvK23WuWild9EOLWsh0OrxtnAG96fAFMVuTRjr7SHs0oRTYIqmxBiOU6PDX6tlWkSwot0hsrJEGw1dBI733arJjpAEh/f71qqm8OL4saqWM4NNaJ47A6ryYOJdMPcfFP9nF5bsKj4N+ZFKaw2TJ09m5syZ7Rrpnf3v8MSPT2DUGhXvjeiAaPIf+RXVX32F3+jRDPzIN4Iw7ZqUrCwRkAhIBPoQAuJJ6g+ff8J3Hy/C5XSi0+uZcs3NjJt7Sa92F+/IFqnJMPHM/1RZVNYD3sZm/BwQ7jrPAg90BB9Pmx4jOMT4DYcOk3PttTjNZoz9Ajgyq5BHEiOVqbkqZvDhL/6PEf1DO7E82bS7Edi9Jo+1767GXrcal9MtQhoUGcWMm25n0JmT2vVgrLvnfrLx6urqWPL11+zbv7+pms6lJU4bQWraIIZOHU1MXGy7denUtEY5lz6OgPC+KPN4e7TU9qgWXHkHitD9OC5URHhfeMJFxGtAxAlT3Yp7TvFQfUVZtaLdIe4/nS2mIG7mx4UEMCsyVNHtEBlaND0gMqsmO0ISHB04o62a7FsMHwthdg3ctx27tj/H/vEjpZoaPjf9oFS94447iIvz/smKxWFh7qdzKakv4eohV/PY2Y9hr6ggfeo0RVw07q+PE3b55Z2duWwvEZAISAQkAkBh+iG+ee5fVBa5U9AljxnH7LseICg84rTBR02GiQf0UcAuYDHQ8g/evcBzHg0Od4oRt2ioeHzlfhR+fPkauAgYDYjQl46WHiU4xKRrN28h77bbcNls+CcG8vHcEt6OdLtKG8sW8NWCO+kf1nuf/nd0Y3pzu+8/z2Db0gwcDVtxWH/A5bQry0kZdyYzbr6D0H6xvXZ5e/bsYeP6jRSVFB23hgCjHylpg0hJTSElJUXxdpZFIqB6BOoroGh/s6ipCHURYSeB/VoQGEL3QuhftCAwvAlp8XLxZVY7q8vdZMfa8hrMjpZ0ByT6GZWMLCIN7dlhgRi7Sd9HTXaEJDi8PEw/We2/syBvCwy5COa/R836o1R9k8WP/pnscmURGRnJwoUL28WkfXjwQx7f8jh6jZ5vLvuGuKA4yt58i+J//hNtUJBbXDRAZM6TRSIgEZAISAR8gYC1oZ6BcDMuAAAgAElEQVQ1b73K3jUrle78g0OYdef9DJow0Rfdq74PNRkmLcB6HljoITm+AURc5n2ASBM7Q2hweupmAwPcTxqOK4KUyAW2AZ3dzB4nOMTqqr/5hvyHfqksNHBwAH+cU83WQD+0Dj1RNY+y+I5LCfHreXdl1R96lUxQPJ399u0DHPz+GE5HJSFhWyjN3afMTm80cfZlVzHhkp+j0/fePTWbzWTsO8LhLXvJKcvHrGmdOUKsNSIigtTUVIXsSE5Oxt9fEnUqOaJyGipGwOJ08n2lmRWl1awoq+Jog63VbIN1WqZHulPQzogMIcLQdVo4arIjJMHRmUN7dBu8Lmws4KYlkDyZoud3YM2v4eOQLVRbaznvvPOYMcNTx4uxbE4bF312EYW1hVyWdhl/PufPSkqxzAsvwpqVRfg184n9wx+86ElWkQhIBCQCEoH2InB480ZWvvoCDbVmpemYC+Yy9fpbMJj82ttVr6qvJsOkBXAif68IKbldONYApYCIzxR/BN0b5C4nIzgeBf7q6eO1Tm6KKggOsYayt96i+B//VJbjN9rErbMsFBv0GC1hDNY+zts3T8Gg03ZyubJ5dyHgcDhZ+vIecvaW4cLF6KkWDqz7AHNFuTKFiPhEZt5yF4kjhBNS7y620jrylu4n/eARCrTlFGgrsGrcXiuNRbjXi1TMguwQV2JiInp9192Y9W5E5ewlAm4ExP3igdqGphS0O2rqWkEj/iKcFRrIrKhQZkeFkBrgW7tGTXaEJDg681PxyS2w9xO3yMwdG7CV1lP01DaKNVV8adqq9Hz33XfTr5/3Oc4XH1nMH777A1qNlq8v/ZrEkERqt/xA7o3uNIYDv/gcvyEi250sEgGJgERAItAVCNSUlbL0xafJ27db6T6ifwIX3vcIMQNFJETfLGoyTFSMsGoIDoFR0T+foPzNNxW47JN03DAV7BoNftWpnBfzJ/515Zh2eY+qGPfTYmo2i4MvntlBUVY1Or2WOXcMJmv7EnYs/QqXy+2sNGLq+Zx33QICQnq/1oqtuI7qVTnU7i6hVFOtEB0F/lUcc5bjdLZ2uTcYDAwYMKCJ8BB2tbab3O5Pi8MnF9knESi22FhVVs3ysirWl9dQ73S1Wmeqv4mLokP5bUqcT/5WqMmOkARHR490VT48OxpErOSl/4Gx85Vf1NWrctkSmMEeRzbR0dHcc889Xo9gd9qZ9/k8cmtyuTjlYv4+5e9KW+GKKlxS/ceMIflDb7PbeT2srCgRkAhIBCQCbRAQAqRbv1rMpg/fwelwoNXpmTz/BiZcdGmfFCBVk2Gi4sOoKoJDCOMWPPyIYh+IkjvTxcNnusMY/IrP47ozHuD+mWkqhlNOrS0C9WYrnz25ncqiOoz+ei57eBwOWzGrXntR0QpS9jY4hGnX38Lw82b45Kakp3fBWmCmemUODQfc3io2HJTFWinu36CEsxQVHa/fERgYyMCBAxXCQ4S1hIb2fsKnp/dBjt+3Eah3ONlYUdMkVFpkdXtNTQkP4uOxg3yyeDXZEZLg6OiWrvoTbPy3W1Tmwb24dEaKnt6GraSOD4M3Y7bVMX36dKZOner1CEsyl/CbDb9Bg4bP531OSlgK9vJyjkydBjYbcX/7G2GXCTF4WSQCEgGJgESgOxAoykxnyXNPUlGYrwyXNHI0c+55iOCIqO4YvtvGUJNh0m2Lbv9AqiI4xPSdVit5t91O3ZYtoNOx+hIrrwwzoXW50OVew2MX3cDl4xPav1LZoscQqC6r59MntlFXZSUw1MhlvxpPcLiJXauWseG9t7DWu93ORbjKzFvvIaK/yHzc+4slt1ohOixHKpsWY0oNRTelH/m2EjIzM8nIyKC6uvq4xQq9u8ZwFqnf0fvPglxB1yLgdLnYXVOvaHYMDvDj0phwnwyoJjtCEhwd2VJrHTw9DBoqYfpjMPVXCAa6+LkdHNNU8rVJaJnBvffeq4iMelOcLieXfXEZGVUZzBowi6emPaU0K/vvGxQ/+STa4GC3uKgUXfIGTllHIiARkAj4DAFbQwNr33md3auWKX36BQUz6/Z7SZt4js/G6OmO1GSY9DQWJxlfdQSHmKujpoaca6/DcvgwGn8TL1/ewLeJBoIdUJv9AC/fMI9zBvUtQk7FZ8QnUys9ambxv7ZhbXAQFhPA5Y+Mxy/IgLm8jDX/ew2hFSSKzmBg4s9/wZk/uwK9ofeKkLYEzZJZSdXyHKw5zUSG35BwQmYlY+gfSFlZmUJ2iCsrKwuLxdIK80b9jkbB0oSEBKnf4ZNTKTuRCJwcATXZEZLg6MhpFQTH1jdg21tw81IIiqZqWRY1a4+yOSidvfYcYmNjufPOO73ufVXOKh5c+6BS/5NLPmFIxBC3uOicuVhzcgi/7jpif/eY1/3JihIBiYBEQCLgWwSO/Pg9K155noYat+E9cvospt90G0a/3q/2rybDxLe75tPeVElwiBXaiorInj8fe0EhmtBAfnNVHRmROpIbtOQf+yMf3HkBg2OCfQqG7KxrEcg/VMGXz+/EaXcRMzCEeQ+egcEodHchc/uPrH7jZapLipV/C52gC25fSMKwkV07qW7qXdi/wpOjakU2tqPNesL+IyMJuWAAhphAZSYOh4PCwsImwiM3N/eU+h0xMTF9IrSnm7ZCDiMR8BoBNdkRkuDwettOUNHlAo1GISKOPfEjtooGPgj+njpbPTNnzmTy5Mle9S7aX/X1VRwoP8C0hGk8f77IjCfy3W8m96ablfcDv/wCv8GDvepPVpIISAQkAhKBrkFAPEFd9vIz5OzeoQwQHtefCxc+TOyg3v37WU2GSdfsnE96VS3BIVZnycgg+5prcVZV4YgJ5s6raqkK1DKxyo/DtX/jk3um0C/Et6r5PkFVdvKTCKRvK2b563vBBQNGRTLzpuH4Bbo9NYRn2XefvMe2JZ8j9FhEEaTredfdjH9Q3yCzhH3csL+MqhU52Is8GSE0EDAmmpCZA9BHtSaXrVYrOTk5TYTHT+l3NIaziFep3yF/ACUCvkFATXaEJDh8sKcibrDkpV1KuqtvjG6j9/777yc83LuYpvVH13PParcY6XsXvseo6FHK+6MPPkjN0mX4n3EGye+/54OZyi4kAhIBiYBEoLMIiJuJ7Uu/VOLhHXY7Wp2Oc668ljPnXY5W637C2tuKmgwTFWOnaoJD4Fa3bRu5Ny/AZbVSkxTEXb+ox2rQcGFRFPv8/syHd5xDoEmm21TxGTtuanvWHmX9B4eVzw1+OkZNjWfM+UkEhBiVz4qzM1n56vMcyzii/Ns/JJTpN9zK0MnT+oyngsvpon5PCdUrc7GX1rsx0kLg+FiCz09EH3Zi4s5sNithLEK7Q4S0SP2O3nTy5Vx7GwJqsiMkweGD01P5VQbmTQV8F3KE/dZc4uPjue2227zqWbDT131zHbtLd3NO/3N45YJXlHb2sjKOTJvuFhf9x98Ju/RSr/qTlSQCEgGJgESgexAQNxZCgLQ8P08ZMGH4SObe80tCoqK7ZwI+HEVNhokPl+XrrlRPcIgFV69YQf79D4DLRfYQf35zqRWdBublDSYn8be8ev149Dqtr7GR/XUhArtW57Hly0xEKllRdAYtw8+JY+ysJEIi/RFZn3Yu/4ZNH76Ntd5NAAwYfQYzb7mbsNi4LpxZ93btcrio21GkZCx0VHq0N3QaAs+KJWR6EjoP6XOiWQl72xv9DmHDN3p4JCYmotP1TtK6e3dGjiYRADXZEZLg6OSJFKxy4d+3YK+x8F7QdzTYLcyePZtJkyZ51fPmws3ctsJNhrw15y3Gx4xX3pe+9holTz2NNjSUtHVr0fpJt1KvAJWVJAISAYlANyJgszSwftGb7Fy+RBnVFBjIBbctZMikKd04i84PpSbDpPOr6bIeegXBIVZfvmgRRX95XAFiy1gDT81xEuNwcE7OJJzjHuQv80b2maf7XbbbKuu4odaG8ObY9W0ellp3iketVsPgiTGMmz2A8NhAaspK+fbNV0j/8Xvle73ByNmXX82ES36OTt83REjFulx2J7Vbj1H9bR7OaquyVo1BS+Ck/gRPTUDnCeM52RY26nc0enfk5eUdp9/h5+fH0KFDGT58uEJ66PXS+0llPxZyOipCQE12hCQ4OnkwGjIqKX1tD0e1ZSwz7lR6e/DBB72O6bt52c1sLdqqEBuC4FB+cTudZMyegy0vj/Abrif20Uc7OUvZXCIgEZAISAS6EoGMbT+w/D/PUl9dpQwzYur5zLj5Doz+AV05rM/6VpNh4rNF+b6jXkNwiKUXP/1vyl59VUHhsyk6Ppis4az6BsLyfsbgWXdzx9RU3yMke+xyBKwNdvZvLGDnylxqq9w392gg9Yxoxs9JJjopmPQfN7P6zf9gLitVvo5MSFKI1/ihw7t8ft05gMvmwLy5kJq1eTg9pI/GpCNocjzBU+LR+nlPSJxKv8NkMjFkyBBGjBihkB2GPpK1pjv3S47VtxFQkx0hCY5OnrWKz45Q+8MxNoYd4WBDLklJSSxYsMCrXrcVbeOmZTcpdUVoighREcW8aRN5t9yqvE9Z8jWmVGmEeAWorCQRkAhIBHoQgdrKCpa//AxZO92pwkNjYhUB0v6Dh/bgrLwbWk2GiXcz7pFavYrgEC75hb/5LVVffKGA9fKFWtaM0XJDZTWHixYw7+rbuHi0WJIsvREBh83Jwc2FbF+eQ3VpQ9MSkkZEKERHVIKRTR8tYsfSr3C53CKko2fOYco1N+EXGNQbl/yTc3Za7Ji/K6BmXT6uBrd3i8ZfT/B5CQSd0x+tqf1hJkKv48CBA+zfv18RLm1ZjEajQnYIz45BgwZJsqNPnSa5mI4ioCY7QhIcHd1Fj4tc4d+2YKuz8l7QJix2K3PnzmXixIle9XrnyjvZVLCJ0VGjeffCd5vcRY/e/wA1y5fjP2E8ye++61VfspJEQCIgEZAI9DwC4qZyx7KvWb/oDRw2GxqtlklXzGfipb9QxEjVWtRkmKgVI6BXERwCR5fNRt6dd1G7aRNOrYZ/Xq5hxyAt/yiq4L3qB3nw1puZkByhYsjl1E6FgNPhRGRb2bYsh/KC2qbqcYNCGT83GZNfOStfe4HirAzlu4DQMKbfdLsSRqfR9K3bAGe9nZoNRzFvLMBldeuVaIMMBE9LJGhinBLG0pFSU1PDwYMHFbIjOztbyZ7YWIQnx+DBgxWyIy0tDUF+yCIROB0RUJMd0bd+s/30aeoSo6T+YDllb+0jR1fCSsNu5Q/FQw89RHDwqdNz7S3dy/wl85UZvzDjBaYmTlXe20tKODJ9Btjt9H/yCUIvueR0/BmRa5YISAQkAr0agdLcbJY8/y/Eqyj9hwznwoW/JLRfjCrXpSbDRJUAuSfVJbZEV6/XYa4l54brsew/gM2o5Q/zNRTEung1v5K/O/7A3+++lpTovvVEv6sxVWP/QhMue08pW5fmUJxd3TTFqMQgzpiVSE3RFjZ99C5CN0iU5LHjmXnLXYT2i1Xjcjo1J4fZSs36o5i/KwS723tFCJAGz0gicEIMGn3HiA7RT21tbZNnh8jQ0pbsECRHI9khwlpkkQicLgioyY6QBEcnTl35h4eo21HM+vDDHK7PY+DAgdx4441e9Xjvt/eyNm8tQ8KH8PElHzex6KWvvErJv/+NLjSUQevXoZW/HL3CU1aSCEgEJAJqQ8ButSqpZEVKWVGEHoe4oRg2Zbrapqoq9XPVgdM8oV5JcIjpi4cn2fOvwXb0KOYALY9er8EvyM5z+bU85vcPnl94BZFB8mZMxWfP66mJG+6jhyrYtjSH/EMVTe3CYwMYNimI7J2fkbn9B+VzvdGkeJiNv+hSdH1QQNNRbaF6TZ4SSo7D7XWhi/AjRKTZHdsPjUgv1IlSV1fX5Nkh0tA6nW4yRcFWr1fCVwTZITw8hGCpLBKBvoyAJDi6f3d9bpQ4rQ4KH9+CzWplUeAmbA4bF198MRMmTDjl6g6VH+KKr65Q6j019SlmJc9S3iviorNmKwZIxI03EvPb35yyL1lBIiARkAhIBNSNgNDkWPbSv6mrqlQmOmzyNM6/5S5MAYGqmbiaDBPVgHL8RHxuS3TnWi1ZWeTMvwZHZSVFYRp+d4OWcdTzy2Ma/hz9NC/deRF+BvWGUXUnVn1lrGOZVUroSvZut9ioKIHhRhLSKkj/4WNqK8qVz6KTkrng9nuJSxvSV5beah32igaqV+dSt70IPByEPtqfkJkD8B8VhUbbOaJDDFZfX8+hQ4eUMBaRmUVkaWksItVsamqqIlAqyA5/f/8+ibNc1OmNgJrsiPb8RAt/rvuBO4RnG1ACfAT8QXhsebGlzQFrrSuLtm19I/8E/PEn+nwE+JcX47Ws4nOjpG5PCeWLDpKlK2G1Jzzl4YcfJjDw1Abrw+seZnn2clJCU1g8bzFajdtVzrxhI3m3uVPGpnyzBFNKSjuXKatLBCQCEgGJgBoRqKuuUrKsZG5zPzkNie7H3IW/JGHoCFVMV02GiSoAOfEkfG5LdPda63fuJOemm3E1NJARC3+6Vscd5iqmVITyasoLPHnDVHQ+uNnr7nXJ8U6OQFm+WSE60rcW0Sgf4RfoJDB4B/kH16N8qNEwdtaFTL76BlWRr77cW1tJnUJ01O8qAc9diSE2kJBZA/AbFuEzTZKGhgYOHz7Mvn37SE9Pb0V2aLVahewQnh1CqDQgoHdk2vLlPsi++iYCarIj2kNwPAvcBywGloqHUMC9wAZgJk2c6E9umvhVIuq6c5Y1FxvwYZvPGgmOB4Fm2tldScjTH2jn0fC5UVL27n7q95axNuIQ6XVHlV9W119//SmnlVmVyaWfX4oLF3+f8ncuTrm4qc3Re++lZuUqAs48kwHvvH3KvmQFiYBEQCIgEeg9CAjX8V0rl7Lu7dex26xoNFomXvYLJl0+v8cFSNVkmKh4R31uS/TEWmu+XcPRhQvB6WRHioYnL9fwQmkpxtokVo1/hd/OG9cT05JjdgMClcV17FiRy8HvC3E2hmzoS8D+LbUV+coMgsIjmH7zHaSddY7Pbvi7YWntGsJ2rJaqlTk07CtramdICCJ0VjKmtDCfrttisShkh/DsOHLkCHa7O8uLKILsEOHtguwYOnSoVw9J27VQWVki0I0IqMmO8JbgEI+Y9njIjctbYCUIjueAa4H3ToGhIDj+B7jzop68NBIcAwG3Qlvnik+NEmeDnYLHN2Oz21gUsAm70868efM444wzTjnLRzc8yleZX5EYnMiXl36JXuvO0W0rKiZ9xgxwOOj/r38RevFFp+xLVpAISAQkAhKB3odA2dE8ljz/JCXZmcrkhVu4SCcbFhvXY4tRk2HSYyCcemCf2hKnHq7ralR89BHH/uB2lF0zSsOiOS4+LCzisGUUebNe48Ypg7tucNlzjyNgrmhg56o89m3Ix251KmlkXfYd2Ou/x+mwKvNLGXemEkoXEtWvx+fbVROwHq2hWhAdLbRKjMkhhM5OxjQw1OfDCrJDeHQIskOQHjabeMbrLiJRQXJyshLGIsiOoCAp/OvzDZAddikCarIjvCU4HgceA87zeGE0AiQUcwT9uQ648BSoNRIctwutNRGRcZL6LQkOESBYJzSyOrErPjVKhFK1NbeaHWt/ZFn2RkRsnQhPOVVMXV51Hpd8fgkOl4M/n/NnLku7rGlJpS+/TMmzz6ELD2fQurVoZZqpTmy3bCoRkAhIBNSNgN1mY9OH77D1q8+UiRr8/Dl/wZ0MP2+GT58eeouCmgwTb+fcA/V8akv0wPxbDVny/AuUvvii8tmn52jYdbaDtwuLWGY/l8CrXmPWSLFcWfoyAvU1VnavOapc1no7TkcV9vpvcdqy3L+XTH6c84trGTf3Zz3uZdaV+2DJrqJ6RQ6WzKqmYYQnR9C58filhqLpAm0aq9XaiuwQ/24sguwYMGCA4tkxbNgwr7IzdiU+sm+JgDcIqMmO8JbgWO4JQxGBYpY2i9wECKo/+hSLFwSH0NsQpIhQsRIaHiI05XdA828UdyeNBEcNIHKuCqUeEbj8F094jDc4t6zTJUbJBx98oKgnC8Gga6655pRz+tN3f+LTI58SFxjHkp8vwaAzKG1cDgcZF8zCVlBAxIIFxPxKyIzIIhGQCEgEJAJ9HYGc3TtZ9tLTmD1if4MnTeGCW+/Br5uf3qnJMFHxnneJLdFT6xUhU4W//z1Vn3yqTOG12Vr80+p4vLSct51zGXPry4xNCu+p6clxuxEBQW7sXZ/PzlW51FVbcdqOYKtbAy63xF6/5FQuuH0hsalp3Tir7h1K/DxYMiqpXp6DNU/cfriLxqDFNChM0ejwHxqppJv1dRGeHEKYVHh2CKFS4enRsiQlJTWRHaGhvvcs8fV6ZH+nJwJqsiO8JThEeIrwUYs5wZYJodErAZFfrJl+PL7iFuBjIF3oq3k8Pq7yhL6c08aj4wGPxsd3gMhxJWSdxWfCf3cB8NYpjo4gRcTVWMS8t+fn59O/v2+eSAgBoSeffFIRDrrssssYPXr0Sad0rPYYcz+bq4SzPDbxMa4eenVTffP69eTdLrRbIXXZUozJQsNVFomAREAiIBE4HRCor6lmxSvPk/7j98pygyOjmbvwIRKHj+q25avJMOm2Rbd/oD5FcIjlu+x28u65h9p163Fq4KnLtMyKqOTqGjMvaedzycKnSYyQIojtPyq9s4Xd6uDAd4VsX5FDTWkV9oZNOCy7lMUIr4Kxcy5m8lXXKymv+2oRRIcIWalZdxRrdlWTGGnjeoVWh//QCPyGRWLoH+hzjzuh0SFSzgqyQzxEFfcbLUtiYmIT2REWFtZXt0GuqxcioCY7wluCI0N4qgFJJ8BbqGEKdU1B87tz4HlfHgX+6vHiEK8nK5HAXo8HSKKXIS6t+vMlwbFr1y4WL16s5Ll+5JFHMJlOnj/+b1v+xvsH3yfKP4plly/DpGuun3fPQsyrVxNw9tkMeOtN79GTNSUCEgGJgESgTyAgjOo9365gzf9exS6e3mk0nDXvCs658hp0ere3X1cWNRkmXbnOTvbd5wgOgYezro6cG2+iYc8erHp4fL6Ox7RFjLVY+bfpLhbc/xdCA7r+DHZyb2RzHyLgcDg5vKWI7ctzKM9Px1a7EpfTLcjpHxLOBbfdpYiQ9vXiqLXRcLiChgNlCunhsjSnfhVrF94cwrNDkB1dEcoiyI7s7GwlG4sgO0Qq2pYlPj5eITvEFR4uva36+nlU+/rUZEd4S3D4woPjRPsi/mIKLQ6RGcWb35RCEUuEr8wGVpxko7vcg2PRokWKGrKIjbvqKuGI8tOltL6U2Z/Mxuq08vCEh7lxxI1NlW1FRaTPOF8RF43/99OEzJ2r9vMr5ycRkAhIBCQCXYRAeUE+3zz/JEWZwtkRYlLSuPDeh4noH99FI7q7VZNh0qUL7VznfZLgEJDYy8vJuvpq7Ll51PjBU9cbeLEujwi7i2fCH+Wehb/EpBfRxbKcTgg4nS4yd5SwdWkGRenrFBFSd9Q4xA0ex8X33UtI9Kki1PsGYi6HE0tWtUJ21B8sx1HW2rOiq0NZhMe4IDuEZ8eBAweoqxPyhM0lLi6uieyIjBTPhGWRCHQvAmqyI7wlOHyhwfFTKAslIyEj7I1kt2AGRHiKN1lbWo7nU6NEuIs98cQTOJ1OrrzySkXx+GTlqa1P8da+twg3hSveGwGGZte+khdfpPT5F9BFRJC2dg0aKS7avT+NcjSJgERAIqAyBBx2G999tIgfvvwUXC70JhMzbrqDkdMv8Lk7dOPS1WSYqGw7usyWUNs6rbm5ZIgHNhWVFIfComtMvFiRhdOl5/Wkf3LXzbeg1XprNqptdXI+nUFAeJjl7i9n8+LtFBz4HKc9R+lOozUyfOplzLzlKvSG08fLR+BhL6l3kx0HyrHmVHdrKIsgO3JzcxWyQ1y1tW6tlMYiyA6R2XHUqFGnTIDQmXMh20oEWiKgJjvC279Up8qish7oiOuBEBwVSj6bgSleHJPGecwEVntRv7GKTwkO0WlpaaniMjZp0iSMJyElKhoqmP3pbOrt9dw/7n5uHXVr07SFuGj6zAuwFxYSeest9Hv44XYsSVaVCEgEJAISgb6MQN6+3Xzz4tOYy0qVZQqXcCH05x8sZKx8W9RkmPh2ZT7tzee2hE9n54PO6vfsJfP669A2WMjuB9su0/O7ylzMLj8Wj36Z6y9vzv7mg+FkF70QgfzDFax990uOHV4CLrcXgd4Ux1mXLuCsSyaiM2h74ao6N+WeDGURD1vz8vKUexLh2VFT0yyQKrI8Ck9zQXYMHDgQrfb025vO7axs3R4E1GRHeEtwCKUzoTK0GLi8xWLvBZ7zaHC86/k81aPXcbBFPeEr5Q7ea12eBMRd/a+BJzxf6YHAE2RWEbobO4UmFiDetw5EO/kO9JhR8tz253htz2sEG4NZcfkKgozNea1r1qzh6F13KzNPXb4M44AB7TlHsq5EQCIgEZAI9HEEGsxmVr72Aoc3b1RWGhQewZx7HmLAqLE+XbmaDBOfLsy3nfWYLeHbZZy8N/OGDeTccQdap4vdyRo05zv5RW0h5a4gvpu6iItnTOvO6cixVIrA0YOFrHjlVSoKfvTMUINfyAQmXjafMTNSMZhOz5Aml92JSDvbcKBc8e5wlJ84lMVf6HYMjfBpVpZGskPoBO7du5eWqWdDQkIYO3asckVERKj0VMlp9WYE1GRHeEtwCLyfBxZ6SI5vPFlO7gNEmtgZQqfKsynZgLhTb9n3v4GzgTVArrDRPFlUpgMiu4p4bSQshCSwCFv5HDjQIouKcH0Q7eZ7srG05wz0iFFSba1WtDfMNjN3jrmTe8be02rOeXfehXntWgLPmUTSG2+0Zz2yrkRAIiARkAicJggId+h961bz7ZuvYGuoVwRIJ1z8c8696nqfuYWryTBR8bb2iC3RE3iUf/opRY/9Thl600gd48aZGWetoMAVSXeqEa0AACAASURBVM68xUwaN6YnpiXHVCECBzZt49s3XqLBXOSenTaYwPBZjL9oGqOmJeAXePqErrTdnp4MZRHkhghf2bFjBzk57pCixpKcnKx4dQjvjpN5oavwuMkpqRgBNdkR7SE4BBUrUrXeDog8psJn9kPgD20ympyI4JgHCFeFkYDw5hAKRUcAkWL2aaAlvSnSi7wITAQSPKSGGEsQKcLL44cO7G2PGCWv7HqFF3a+QIA+gBVXrCDU1Jy72lZYSPr5M8HpJP6ZZwiZI3RTZZEISAQkAhIBicCJEag4VsDS55+iMP2QUmHY5GmKAKkvipoME1+sp4v66BFboovWcspuj774LDXP/0ept/rcQK4YkE+ss44MVzy2G79haIpMaX9KEE+TCkI3aOMHH7JtySe4nHZl1VpDGv7hMxk9fQhpE2KISghCc5pruCihLIfKaThYfuKsLKFGxavD11lZysrK2Llzp3K1DGERGSBHjhypkB0iI4tIBSyLRKCjCKjJjjhdTnK3GyV1tjpmfTqLKksVC0Yu4MHxD7Y6LyXPv0Dpiy+ii4oibc23aE4jcaaO/uDIdhIBiYBE4HRHwGG3s/mzD9j69WKuefwpopN8c5OpJsNExXvc7bZET2Ihnj4ffPRBWCx05mHNxXEsCNqFH3b2atKIvGsZcf2ienKKcmyVISBI2OUvP0/+QZF8URQjev/J6Ewj8A8KIH5IOInDwkkYGkFotL/KZt+90+mJUBYRwpKRkaF4dRw6dAghVtpYoqKiFKJjzJgxBAU1h9N3LypytN6MgJrsCElwdNFJenPvmzy97Wn8dH5K5pRI/+aUTS67XfHesBcVEXn77fR7qDX50UVTkt1KBCQCEgGJQB9BoK6qkoBQEdHpm6Imw8Q3K+qSXk4rgkMgKMTQt956FUHf71PikLddN4JrbavQaVwcI5KS2KmknPNzAofMAJO8KeqSU9fLOhXE2IGNa1nz1ms0mKs9s9eh1SegNSSjNQxAo41UCA5BdCQMFYRHOP5Bxl62Ut9NVwllKa5TPDtOmpXFo9th6B/YaW8LkWZ2z549bN++naIiT3iR0BfQaBg8eLBCdqSlpSGESmWRCHiDgJrsCElweLNj7azTYG9QMqeUN5Rz3bDr+PVZQkO1udR8+y1H73brcaSuWokxQUTiyCIRkAhIBCQCEoGeQUBNhknPIODVqKcdwSFQcTY08N0Vs4hML8Gmg+xbJnFx1WdoNULz3V3sGgPOxLMxDp0DaRdA1GBFK0aW0xeB+ppq1i96k31rV+NyNcr0efDQBKLzkB1avSA8/IlKDCJREB7DwokbFIbBePreWDeFshwop+FwBS5Ls6eFQFDXKpQlDE0nM9cUFhYqXh27d++moaFZNSAwMFDx6BDCpP369Tt9D7NcuVcIqMmOOF3++nSrUbLowCL+8cM/MGgNLL1sKTGBMa0ORu4dd1C7bj2B555L0n9f9+rQyEoSAYmAREAiIBHoKgTUZJh01Rp90G+32hI+mK/PumgoK+HHn88iqriBOpMGwz9/Q1n+IYJyv2UshzBoWt+AEZrkJjrENfA8MIrkeLKcjggIoiN37y6yd21XLnP58UkVNboYxbtDJ8gOfRw6g5641FDFw0OQHtEDgtGepvodXoWypIXjr2h3RKAL7rgnjM1mU0JXBNkhQllaFqHRIbw6hGaHn5/f6XiU5ZpPgYCa7AhJcPj4uFodVi787EKK6or4xeBf8PtJv281gi0/n/SZF4DLRfxzzxIya5aPZyC7kwhIBCQCEgGJQPsQUJNh0r6Zd2vt05bgECgXpu8me/58wmqcVIbqGfHx57iiknhv/V6ObPmas2xbmabbRaymovWm6Iww4Fw32TFIeHekSe+Obj226hlMhGKU5+c1kR1H9+/FbrO2maARrSER4dmhhLTowjAF6Ikf7A5lSRwWQWg//06HaKgHFe9n0hjKIsJYRBpaa241NDtSKR0ZEoLwGxKBKSUUU1JIh707qqqqEOlmBdlRUdH8M63X6xk+fLhCdgwYMACtVuv9AmTNPo2AmuwISXD4+Kh9fPhj/u/7/0On0bHksiXEB8W3GqH42Wcpe/k/6KKjSPtWiov6GH7ZnURAIiARkAh0AAE1GSYdmH53NTmtCQ4B8o6Nn+G8+zECrODQQvmgaLSTxtNv6kWssSTx+qYcomqPME27i2m6nUzQHkanqHe0KGEDPN4dsyB5ChgDumv/5DgqQ8ButXL04D5ydu9QSI/SXJGIsXXRaEPdRIdCeCSi0ZgICjeRMEx4d7gFSwNCOu61oDJI2jWdU4WyoNdiSgrGlBqGKTUUY0IwGn37CAkhTJqbm6sQHSLtrPDyaCxhYWFNwqTivSynNwJqsiMkweHDs2hz2rhk8SXkm/OZlzqPxyc/3qp3l81G+ozzsZeUEHnnHfR7QGTdlUUiIBGQCEgEJAI9i4CaDJOeReKko5/2BIdA58sP/0r8394lwNIaq+ogLYUjYslNG8bnmmFkmhMIcVk4V7uXayMPMdGxHUNdcetGOhMkC++OWW7vjshU6d2h4h+Arp6aCF/J2bNTITsE6SHCW1oXrTuERe8RK9XFKJ4ckfGBTYKl/dPCMPrpu3qqqutfCWXJqnKnoE2vxF5Ud9wchVaHMTkEU0qY4uFhFKl7dd4THkKfY9++fQrZcfTo0Vb9p6SkKGTH0KFDMcjMkKo7H90xITXZEZLg8OGOf5nxJY9tfAytRssX874gObR1+r6aVas4uvBe5Y/3oFUrMcS39u7w4VRkVxIBiYBEQCIgEfAaATUZJl5PuvsrSoLDg/nO7M0cWv0pzu+30X/vMfpVtvaTFz4bGf017B4QzuaYgRwOGInNksw1CU7u7J9OYtl3kPeDSNPSehfDBzaHsiRPlt4d3X/GVTOiy+mkODuzKZyl4PABnC3SmioT1fij1ScpmVl0SnYWt1ZHTEqIEsoivDv6JQeja8dNvGoA6OREHGYrlswq95VRib2k/njCw6hTCA+/1FCF9DD0F4SHd7eGJSUlCtEhwlhqa2ub+hb6HKNGjVLIjri4uNMylKiTW9drm6vJjvDuFPdaqJsm3uVGicPp4NIvLiW7Opu5A+fyxHlPHIda7m23U7thA4HnTSHp1Vd7P6pyBRIBiYBEQCLQJxBQk2GiYkC73JZQ8dp/cmp2h530PevJX/U1rs3b6XewGIO9NeFR4we7B2rYPiCYbTGpBEeN5tYxE7jCvxJ9xreQvgrMzakqlcH0fiBIDuHZIfQ7hHeHLKctAtb6OnL37fF4d2yn8ljh8TfsuihPKMsAtPp4NBoDBj9ds37H0AjC4wJOy5tuR7UVS1Yllgw36WEvPQHhYdJhGhiqhLMohEdcIJpTiLs6HA7S09MVsuPw4cOIkJbGEhMToxAdgvAQGVlk6dsIqMmOkASHj87asqxlPLL+EaW3z372GWnhaa16th7NJ+MCt7howosvEHz++T4aWXYjEZAISAQkAhKBziGgJsOkcyvp0taS4PACXpFW9tim1eSvWgKbtxNUWHVcq8wY2JmqYddAP1xDRzJ90ETO0IcyqiyHoIz1cFR4d7TR7ohI8ZAdQrvjXDD4ezEbWaWvIlBZdIyc3e7MLCJLi7W+zQ27Ro9WF+/W71C8OyIVYiMg1NiUjjZhSAQBAWDLzcWak+O+st2vIimAPiYGvxEj8BsxHP8RIzCmpKDR9Y30tfYqS5N3hyA8HOXN6WEbz4zGX9+a8IgJOCnhYTablVSzguwQHh6NRQiRDhkyRCE7UlNT0fURDPvqz1ZH16UmO0ISHB3dxRbtnC4nl395OemV6ZyfdD7PTH/muF6L//0MZa+8ovyyHLR6FRr96Rcf6AOoZRcSAYmAREAi0AUIqMkw6YLl+apLSXB0AEnr0aNUrV9L0eplsG03uoZmkULRXa0J9iRr2JmiYXeqjsikwYyNGMoYh5axpbkkZG5EU9t8s6RMQfHumOLW7kibCYL8kOW0RcBht1OYfogckYp29w6OZRxRHii2LBpdEFqdECoV3h2C8HATZAG1hURUHCK84iDhlUfQO46/0W++4ffHb+hQD+kxAv+RfYf0sFc0eLw73F4ejqo2IjuANrCR8HBreOj7ndgbRmR7yc/PV4iOvXv3YrE09xUcHMyYMWMUsiMyMvK0PbN9ceFqsiMkweGDE/Zt7rfcv+Z+pacPL/6Q4ZHDW/UqxEWPzJiBo6SUqLvvJvq+e30wquxCIiARkAhIBCQCvkFATYZJixUJ9Tvxx/UOQIhaibvcj4A/iPtiL1ceATwKXAokADXAXk8fG7zso7GaJDjaCVjb6i6rlbrt2zGv38Cx1asw5uQe12NuNArZIa6DCRrCgqMZG5zMWJuLMSVZDM/bhbFtbsyI1GayY8BkMPh1cqayeW9DwGW3K14Xwvui+vBhcg/uJf9YPscsdTS0DbNwgVbXD41xIDpBduiFVoQOjctJmLaC2HAr8Ul+hJpzsOzbQ8P+AzirjvdEEhhp/FuTHsLbwyQ8PXrxg0xBUAiPjkb9jobMKpzVbdP5gjbI4E5HK7K0CMIj6vj0vVarlYMHDypkR1ZWVqtjlZSUpBAdIu2syWTqbUdOzrcNAmqyIyTB0cnjKX4JzF8yn31l+5gSP4WXZr50XI/Vy1eQf//9oNW6xUX7CxtJFomAREAiIBGQCKgDATUZJi0QeRa4D1gMLAWGAeIJgSAmZkLb/KPHYTkAWAsEAf8FDgOhwGhgOfBBO9GXBEc7ATtVdduxY6R/vYr0JSuJTd9NkK310/MGA+wdoGFHqpvwKAnTYNAaGOEfy1irg7ElWYypKiGqRdw/en8YeJ5HrFR4dww81TTk970EAZfDga3wGNacbHcYSYuQEuEphN1+3EqEH4fZZKA0OIDS0EDKAv1xtrn70WiNaHWJaBrT0ercKU/1Ri1xg8KIHxxGbLiNwPIMrPv30bBvH/X79v806eHn18rTQ4S5mFJ7L+kh7nWEZkcj4SFenebWnlgCL22IET9BeIgsLamh6CL8WumdVFRUsHPnTuWqakEYiawrI0aMYOzYsQjSQ4S0yNL7EFCTHSEJjk6en435G7lr1V1KL+/MfYex/cYe12Pugluo/e47gqZOJfGV/3RyRNlcIiARkAhIBCQCvkVATYaJZ2UjgD0ecuPyFqsVBMdzwLXAe6dAQRAhwvPjLOB4RcL2QygJjvZj5nWLjMJKFi9ajnnDRsYdO0BaVf5xbQsiYIfHu2N/kgabwW3GJuqDGGuxM7aigDENFgbZbDQpJUSmNZMdA4R2h/Tu8HpTeqCiyJ5iLy52a2Fku4mMxktoZQiv6JMWvR5jQgLGAQMwJg/AIF6VKxlDXCxCFDf/4H63WOmu7ZTm5RzXnc4YDpokd4YWfUJTOIvRX49IQ5swJJz4IWEEuyqx7NuvEB6Nl+OnPD0E6TFkSFN4i9/Ikb2W9FAIj+K61oRH3fHkki7U1CRYKggPfbj7Z08IkQpvDuHVceDAAYRQaWMJCAhg8ODBimaH0OswGo09cArlkB1BQE12hCQ4OrKDnjbiB/yGpTews2QnE2Mn8vrs14/rzZqbS8as2crnCS+9RPCM6Z0YUTaVCEgEJAISAYmA7xFQk2HiWd3jwGPAeR6PjcZFCwu5DFgHXHgSJEQ7UUd4gDwPGDxXXSfQkwRHJ8Dztml+ZT2vrstg6Yb9jCw4yITig4wvPkyItXVUkiA39idq2J7iDmkpFMFIGrdZG4iO0RYrY+vMjG2wMNpiIUhoMhgCWnh3XADhwsmnY0W5yXPZsTqsymVxWLA5bFid7veNnzf+W3ynfO5012/Vzun5zmHF1uK9UsfTn5/OjzHRY5gQM4Ex/cYQaOi9WSkU7EpK3B4YbcQ9hd3savhpHQxlt7RaDPHxHuJCEBnJCpkhiAzhJd2e8BBzeZmi25HjueprqtscCA16Uz9cxHtS0orsLO5wCv9gg5KhJX5IuEJ6hET7YS8opGHv3g6SHsLTI7Vd8+/Y6fVtK5fTTXg0ZHiytGRV4ao/AeER4dcU0iI8PQQBUl9fr+h0CLJD/B1qWfR6PSkpKQrZIa6gIOGMJ4taEVCTHSEJjk6ckh+P/ciC5QuUHt6Y/QZnxp55XG/FTz1N2WuvoY+NVcJT2vNLtxNTk00lAhIBiYBEQCLgNQJqMkw8kxYhJCIMJQBoq3a3CRgMRJ9kgf8Afu3R3rgVmAvKQ/0jwP8B73oNTnNFSXB0ALSONimuaeC/G7N49/sc6i02BlUe5eySQ1xgziIq73gRyepIf3akwOYBFiWsxWJsNnE1LkizWhlrsRBnd2DTgEWjwRoQgTU0EWtILFb/cKwewqItQfFTZIWrrRZIRxfbznY6jY5hEcMYHzNeucbFjCPUJKKver4I8sJVV4fDXEttRSVZmYXE1pSgKzza7ImRnYOz7hRco0aDIS6ujReG2xPDmBCPpgue7AvvkaKsDIXsyN69ncLDBxECpq2LBr0xFpcmAa0hsSkdragTFG5qIjsE6REc4YfAw5Zf0IrwEN4ejsrKE26WxmTCNHSIkrXFncHFQ3oYBEfbO4ogPGyFtVgyWxAelmYvjcZVCM0ORcPDo+NRba/l0KFDypWdna1g17IkJCQwdOhQheyIioo6LdP9qvkEqMmOkARHJ07KrStuZUvhFs7odwb/m/O/437QhJjWkekzcJSVEbVwIdEL7+nEaLKpREAiIBGQCEgEugYBNRkmnhWK8JR+QMwJViyERq8ExGPU45Xv3A2EbocQFhXCpILUEAJZwtf5l4AIfxFPJ948BZrBgLgai5jLdpEdoL/U0uqag3iCXivrrLz1XTZvbsqmqt4dnhBhr+OOwFKmV2fCj5txlJa2aunS6yhJi1IIj5VxZeRGuZq8O7pt4p6BhGaISWfCqDO6L63n1fNefGfQeeq0/K5FfVGnvKGcbUXbOFRxCJG9r21JC09jfL/xjI8dr7xGB5yM/zseBaFvIUgHp9msXA7ltRZnbZt/i89rW3yv1KtRCA2lbW2tiEHwGmaRXdAdQuIOKWl8bxBaDD0sPGmzNFBw+CB5+3aTu283RRlHcLYIpxCLFOKkWkMsaBPQ6gXh0R+Nxp0pMTTan/ihbu8O4ekREOIOt1A8WAoKqFdCW5pDXBwVFSfErbeTHi6HC1uB2U14ZFZhyarGZT0x4WFMCMKQEIwjWk9OXSGH049w5MgRhFhpyxIREdFEdiQmJkrdDq9/4rquoprsCElwdHCfdxbv5Pql1yutX575MpPjJx/XU/WyZeQ/8KBbXPTb1RhiYzs4mmwmEZAISAQkAhKBrkNATYaJZ5UZnpCSpBOs+m1A/AEOB078GBRWAecDmR5x0kbrWLQRnwkf+PhTCJX+Cfhj2/ElwdF15/BkPZstdhZtzuG1DVmUmt1OPSI5xs9GxXJXoovI/dup3bCBuh07oM1NqDMqnKJRcWwfCJn9XOj1RgwOK3pLFfqGCgyWSvS4MHguo8aFwT8CQ9QgDDHDMEUNwWAKVAiKtmRFW4KiiazQGRVBVK3Gt4KJNdYahA0qyI7txdvZX7QbfYOdAAv4W8HfAgEWF4m6KIYYE0nWxxCvCSfQqnETFk0Ehee9h9A4pUdFJ7a93BRMQVAUBYFR5AdFURoWQ/TQQQyfMJxzRyaQGh3UK57GW+vrFP0OQXbk7dtDcVYGrjZkkyA3tIb+aAThYUhEo4tVSBBRIvoHevQ7BOERhimg2StDIT0KCz2kxz4a9rp1PX6S9DAaMSkpa4c3eXuYBg1C0ws8PVwOJ9Z8c1NaWmt2NS7bCUgxDUoqWm3/AIoDasiszSc9P4vqNmFEUrejEz+cPmyqJjtCEhwd3Ni7V93NhvwNjIgcwfsXvX/CX8w5N99M3febCZoxg8SXXuzgSLKZREAiIBGQCEgEuhYBNRkmnpV21oPjK+Bi4K/A79qg9z/gBkDkdD9wEmSlB0fXHrsO9d5gc/DR1jxeWZeJ0OtoLLNHxLBwehrDQ7XUfv89tRs2Yt6wAfuxYx0a54SNBKOi1aHR6ZSHVxqR7UGnc9uA4jOdFo0gNFp8ptRR6on6Ldqd6DPRzjOGYG+U+p7PhNt/o3eF4mFR6yYoTqlX0ZnVGwzogoLQei5dYGDTe21QoPKdJiCQ9FpYe7SWPRV26gx+1OlNGEOCuXhSGhdNGkR6tYONR0qV61CRyNTcusSF+nHuoCimpEUpr1FBvSNlaEOtmaMH9pG3d5fi5VGSm33c2jSC5NLHo9G5PTw0uhjljIgjE50U3BTSIrK1GExN0rhKP8eRHh5vD0d5+Ql3VYTtmIYMwX/0aALGj8N//HgMMSdyguvMofB9W5fdifVoDdacGqz5NViPmpU0tScqLi1URdrI9a8gq76AkhohydRcpG7HqffHVlSMef06atevx/+McUQuuPnUjbyooSY7QhIcXmxY2ypl9WVcvPhizDYzz05/lhlJM47rRYgmZcyeo3wuMqeIDCqySAQkAhIBiYBEQI0IqMkw8eDTWQ2Ol4E7PWllX2iDeaM+x7nAd+3YD6nB0Q6wurqq1e7k8535vLw2g6zSZgHS8wZHs3D6IM4aGKHcIFrT05XMLOYN66nfuu3UWTi6euLd0L/LZMTmr6fO6KJSZ8VsdFJvhDoTymu94A8CA4iOGkBCTBrJ/Ycrr/rgkGZCIzgY7Ul0LuqtDj7ZfpQ3N2aR2QL/wTFB3Do5hZ+N7Y+fofUNu1h6UXWDm+xId18lNW0ldmBYXIhCdkweFKXs44n66QoYHU4HJfUlFJgLKKwtVIRghc6KoivS+J9HF6LtZ+J7Z20DluxirFlFWLOLcJQeT+agpKT1CJYqhEe0myDTuqBfPcTXQXwtrn51uHTOJh2Kpnm4nJjKzYRklhKSVUJoVimh2aWYqk9MCDhjo9CNHUnQ+AlEnDUZ/7Q0NzGn8uKssyleHoLssAnyI9+Mo/L4s1KjqSdXX0quXzmFtjKcbXRxhG6H0OwQ2h2nq26HCD+r370b87p1mNevx7K/mdf3GzOagR9+6JPToCY7QhIcHdzSKksVX2d+zfyh80/oflj05JOU//cN9P3jGLRypZvtl0UiIBGQCEgEJAIqREBNhokHnlNlUVnvEQ79KTTFI6k3gH8Cv2lTSQiMijSzaUB6O7ZDEhztAKu7qjqcLr7ZU8iLa9I5eKz5hvLM5HDumT6IqYM9N5AiHqm+HiWNp8PhvnEUrw4nuJyImwBFO8Ip3otXB0J0ktoyyNuKK/cHyN+By1KLuIdq1D906fwhegTEjMLVbwT4h7v7auq3RV8OJy6nGMfl6d/zqoztcn/Xsp0yT6f7M62WRq8JbWCjV4Xbi6LJw0K8DwxsFaYgbtqPVB5RQloaL6Hn0bYEG4I5I+aMJuHS4ZHDlRCbtqW4uoG3v8/h3S05VNY1p2wVxNKtkwcqxIRyw+5FEXsgPDoE4bHhSClbsspoaBOqYNRrEXs5eVC00vfwuBC0wsulA6UlgZFvzleIjILaAhrfC1LD7jw++0cHhlKa+DfoiC03EVfmR2yZHyF1x+Pp1BrR6RLRiZS0IqRFG6ngZ9dYORaSRX7oYfJDjlASlIdL8xPaJi4XETWQcsxFaqGLtAIYnO/C7wQZdWv9NeQmB1AyKIqaoQm4hqYQFhxNhF+E+/KPINIvUnkfILIOqag4zFaF6LAdFcSHm/RwVjdrc1iwcVRbRo6uhDxtOTZN670Uuh2NZEdf1+0QIrbmjZsUUkOE7rUVtRW/MwInTyZo2lTCLhVyVZ0varIjOvYbovMYdHcP3WqUOK1W0qdOU+Lmou67l+i77+7u9crxJAISAYmAREAi4DUCajJMPJMeBezyiIVe3mIh9wLPeTQ4GjOhpHr0Og62qCe0NnIAkfNxKGD2fBfnER3NB4Z4DZC7YrfaEu2c22lfXdwsrz5QzAtr0tmZ1yzNMio+lHumpzJreGyHb4ybwHXYIX8bHFkB6SuhUBzRNqXfcBg0E9JmQdLZoFNX9guBU1Z1VhPZsfXYVorqio5bhr/en9HRoxXCQ6SmNdqTefu7Ar7clY/N4c5uYdRpufSM/twyOYUhsS31eDt2HC12B9tyKpo8PPbkVzURSY09RgQa3eEsg6KYnBZF/zD/5u3xeGA0EhZNJIbZTWIcqz2mpPb1pogbfIGBKBrxn0b5/6ZX5fPGzzyfn+izlm1MtS6CixyEFDoILrJjrG2dJUS0d2n90encZIcS0qINU8Zx6G2Yo4qp7VdMbXQxDWFVTeMLnRcxjvifKHW2OiprywjILiY+o4qhR10MyXMRdoLENVYdZMTBwUQNBxM0/H97ZwLe2FXe/Z92WZZ3e1bPvidkmQk0CSEzpEDTECC0tIUWKEsgUAqUlq0UCqWFUmiBFmgpO4HS7wP6sa8JzZ6QPcNMJpPMPpPZbcurJMvavue9urJlWbYla/GR/Z7n8XNt+d5zz/2dc6X3/vUuB1Y6EBFEmlx/VviwRI8GWwTJiiH+djoaMmJIq68VtzOTXLWWLTkUs7w8MsJHJrwlFY6TJMUZZz8nnL2W4BF2TPb+aPD62bR+I1svvoCNGzfirUJVnlpykPs69tRTjNyR8dKI7t49Jdmvd+MGK6IguHMXgR3bK56vxSQ7QgWOKqy+wZ/+lNPvfJcVN7nxttvwLJVE8NqUgBJQAkpACZhJwCTDJIfQZ4G32iLHz+xkoW8HpEysxIZmv86UwPc11nPI5HYT8AVgn+3NISUM/gwQkUPyc9xS4myowFEisPnYXQz9+w738bnbDvHrIxPx+ZuWBHnLNRt48cUrcLsq5KI/fA4O/29G8Dh8G4wOTr5kbxOs35URO0T0aJG8tmY14SVeDLkeHseHRBuc3NJpF8loN8nIOgKpTbzi4p3c+OxtdDVVL19Gf3jMmst7DvVYHh4n+8M43EM4Pf04PP04vf20NA3TFBwm5QoxMNZDskgBo7Oh0pK/cwAAIABJREFUkxXBFaxsXGltrd+DK1nh8LK85xD+M3vBF4S2ddC+DtrWQnBpxarxCPfBc2c5sU/yd+y1cniEB6ZWUXG6m8YrtIiXh8PVbE2Mv9HDyi2t40lLW5cGCnrOiNdKf6wfCa8fPLyf0Ud349jzJA1PHKfxTOEczSe6sMQO62eVg1455SxeOSKwiMghYkdW9CjkFZIVSgLuwuMt9+4QrsnBmO3lIcLHMLGnh+mN9XPc1csJZw99zqzenTmbCyerW1awef1Gtu14Bi3dGS8a05tULArff/+4qJE4N1molOo7gSsuHxc1pLzyeIuE4KmfQ+sqWLezIpdqkh1h/uxVBHltv3U5/prXEnngAYLPfx6rPpcf+luZC9JelIASUAJKQAlUioBJhknONUls5zsAESrWAlILVIKFP5jjkSG7TydwyP9+H3gPIB4hIoj8GviwLZKUik8FjlKJzfP+4g0goSu3PXl+fCRLmnxcs2UJz93SxVWbOmn2V8jDwvLueDgjdhy8Fc7umXr1EsKyyfbuWHW5cd4d2QGfHDrLFx/8X35x+D5GOIDLPzVRq3gNbG3fOh7SsmPJDtr84jg195YNIZnOA0NCSJLpqeVFC52xxdvO6uZuukW0yBUwgitY3rgcv9ufOWz4LBy7Z+KnT6pKT9PEo0OEjqzgIeJH9u/W1eCeu9gjD+ahUyctocMqS/vEXkbzqoXIqJzuVhxOO4eHFdIStAbb2OIdL0m7bH0LLVJ9ZJZQnkRfH9HHHiPyyKNEHn3EKllLYqqHS6wjSM+mLp5e18jB1W6eao3SN9bPQGygYMniYlaAz+Giw+GlPS1ln+O0x6K0px10dGymvfsK2lc/m47GZZZYUq53iCV6hEbHc3r0HTvHoXPHOJ46b3l5pB05njSSCsXRwvqWbjav3cSyjSvxrmrG1eI1QvQYO3bM8tAQT43IQw9NySnkWbkyI2js2kng8stJel2EoiH6Rvvo6z9G37Hb6Tv9EKHB4/Q5HWxq2cCNr7q1mCmbdR+T7AgVOGadrtJ2iB09ypHrXmgdtOpLXyR49dWldaB7KwEloASUgBKoMQGTDJMaX3opp1OBoxRaBu277/Qg/3HHYStXRzZ3hgzP7XTwzLVtluBxzdYliJdHxb65lQfnQ7/KiB2Hb4dYnneHr3myd0ezLK/5bVJ+95uSX+P+4/SFJ3IbXL7Rx1UXjhBzH+TRc4+yP7S/oNCwsXXjJMFjaePkCh6zCRilhJB0+jtp8S6FeBsDQ0HO9DWQGGsjFW8jHW+FtIcmn5srNnSMJyxd19mIQ+bl+L1w7G44di8UEjQk98TKyyA5Bv3HYGRqCM/UmXJA80pb/FiT8fwYF0PWWblZZvOCyO1T8r/0Pn2cE4/v4ekn9nDyiceJRSaS6Wb3dbrbx0vSZkJaMnkzXB4nbcsCdKwM0rEiSPvKRmvb2Dr9g7rkqInu2Uv00Ucs0UPED/ESyG+Sv6Fh+3b82y8ldfEWhjcsJZQaJjR4lNDAMfqGTxIKn6NvtJ9QfJhQKkaIFJE55k4p5B2S6yWS9QopJXeIVCVK9EUZPtrHgf0HOHTmCMej56bk7WhONbA61cU63zJWrezGt6oF78og3u4mXM3iFFjdJmkPRMiQiiciakgRi0nrxOUkcsEazl3SzdEL2jjWnqBvNITk2RFRQ3JGztSucLXwpVfeXdLanK4/k+wIFTgqvC7PffwThL72NURB23DrLXWRqbjCCLQ7JaAElIASqDMCJhkmBqNTgcPgySlmaMf7wtz6xDluf+o8Dx4NjeeSyB67srXB8ux47pYlPHtDB42+CuUUEO+Okw9mxA7J3XFWqiDntaUXZbw7Nr4AVv1WTb07Dpwb5it3H+X7u08h1WmkeVwOXnzJCqsiygUrMmER2RaOh/nN+d/w8LmHrdCWvb17iaemZrRc1bSKbe3brIesUnNgTBtCku+BYQ9qJJbgfiucJVOd5dD5TBjCEvq5wrmfK5xP8BzPk6xOn57KXgQNyZey5ipYezWs2A7unIfXsXBG6JCf0FH79+z2OBS49ikn8bVA25rJ3h9ZT5DmbnDNvNZSqSTnjx7JeHfs28Op/fuIx6ZWTnG6O8EpiXUDOJyNOByN4Jz43dcYzIgelvDRSLu99QWmejJJstzYgQMZseORh60H7UTv5LKs1nU6oaE9TkNnjEDnGA1dY7h9UxOiRh0O+l1O+rxBQs1LCAXa6GsIEvL46HM66Yv10y/CSGqUfqeT1BzDRLK5Q/KFj0KiiHiHiDeStEQ8weHfPMWTj+/n4KkjjMQnJy3xpT2sTnWyOtlJd6odX3MgI3asDOLubMDdIT9+nAVYzvT+JB4mkUTECiMSUaL/6cMk73sQ/wP7aN17AndsslfNQCM8tt7Boxsd7FnrIOov7nHeLclokykarbWxhFFnN2tbtvGFG/6ymLfPWfcxyY4ojsisl2T8DjUxSlKxWCa56MAAXe94B51vfpPxYHSASkAJKAEloARMMkwMno2a2BIGX/+CGpo8EN93qJfbn+rhjqfOc2Zw8sOiJNC8fH27JXZcs6UL69v/OT5wTQE3dCbj3SFix+E7Cnh3tMCG52bEDsnd0SxpYyrb5KFKhIAv332UOw/0jHfe0uDhVVes5k+vXMvSZjuMY5ZTx5Ix9vbsHc/jsbtnN9FEdMajShUwir56YXvsHiIH7iBx9B6awxLBNrlF0j4eTm/maHA7jrU72XjJVexYv3Ru5Wil8s3Qaei3BY9cAUR+Hy2c52LSiCQ5Z8uqwqEv4gXim5rANZlIcPbwwfGQltNP7ScRn/C6mZ6XJCPNCh6ZrQgg/kAzze1NtLW6WdIaZ2kwxFLXETwjx2HgaRg+bVUVioddRHu9RHq81jY2WDjEy9vmIrCulYYtqwlc+gw8Gy/E0bYaWtfM7s0y0kPqyZ8ysP8HhE7eT8iRos/lIiTiiMtDqHUFfcEuQh4vfeIhMhqadb1Nx0PEjTZf26TqMSKEyGuBaID4mTgDx/oZGJjsCeFKO1mRamNNqotVyQ4C+DKJXiVdid9tCR3pVhex5iTDwVFCDcOc84Y44zhviRi90RC90V5L1BiI9rH2ZIzth1PsOJRm7UREndWfSEWSCPbRjU4e2+Dg6DJIZ9+LUh7SySCuhJ9lyRgbU4NsSYXoSCbpSKasrT/hYU/8Ym6PX87dqUuIMSHeSWWib954edG310w7mmRHqMBRkSnNdDL4459w+t3vBrebTbffhrurq4K9a1dKQAkoASWgBKpDwCTDpDpXWJFeVeCoCEbzOsmWK739yR7Lu0Nyd0j52dy2piNghbLs2tLFles75vYwXOjSk3F4+sGM2HHwV3CugHfHsotg3S7o3Azt6zM/Tcut0rGlNqlU8sPdpy2PDSnRmm0i4Lz+Oet42Y6VBLzlea6IN8eTfU9agsehgUOMixl2PoxJOTBKvYD8/UVckFATK+TkHggdntJj2hMgvPSZ7PNezC9GNvKd052EE5PZ+T1SjrbdDmfpYtvypsoIWtH+6b0/Bk9aJYpnbYHO6cWP4DJrHSTGxjhz6CkrYeng+bNWwtLI4IC1jQ4NZcoNl9xcuJw+vC4HDa4EzZ4IrZ5B2oIJgi0tBDqW0hDsgiEPiVNhogdPM/rk4Sl5IeS0rq5OAjsuI3DZDhp2XIZ/6xYc7iLWmSTuPXAL7P9RRhSc5FXhyHg7bXsxkY3PJ9TQZIkHknMiG6JhbaN91t/Z3yV3SFpqPZfQGhINrIisoDvaTUekY1zMyHbhTDtowEtDWn58BGRr/239nvZZf0tiqT7PAD2uXpLhHoK951l2ogdf/3nSkT6wc8yEfQ52rw7w8Op2Hu1ezoCnnXQiaIkZsk0lgqxMRbmOx7je9RCXOiev+4F0I7cmL+M255Xs8W7H62+g0eey7u2gz215pzV6XVb1o9ddta4EEtPvapIdoQJHRaY008nxV72ayMMP0/Q7v0P3Z/6tgj1rV0pACSgBJaAEqkfAJMOkeldZds8qcJSNsD46GIzGuVe8O548zx0HeugZnlxi0ud2WiEskrdDRI9V7Zm8BxVp8sCezd1xRLw7pNJxgSZJMq0KHyJ4ZLe2+NHSDU55lJpoofAY37r/ODf/+jiSayPbLl/XzhuuXs/zti6ZNSllRa6v3E4sQUOSgto5NAoIGngaMyEna59jh5xcOinkZzSe5OFj/dx9qMcqSbvv9FTGnUEvm5c2sazFz/IWP8taGljeLNvM31KutmyPnsQYDD5dwPvDDoWJT81/MQWfrAPxishNfJpKZPodOGF5X6QGThANRwgnvEQSHmsbTmZ+H443MJhoIpzwMZp0khCPlDk0t9dHY2sbgeYW/A4nnkgUT18I58lTeIZH8CWS+OJJa+tKp3EEAgQuvcQSO6RkacMll+BsbJz5zGORTLWi/T/OVADJzy8hQuC2l1iCB11bp80rkUglrASpWeFjJlFE9hlLTfaMcafcLIssswQP2XrSpSUqdqed44KHCCIZAUR+PDiSaSJ46WtoYsTbwEjAQyTgJhb04A246U6d5uKhO9ncdxudw/sn8Ur424msv47Utpfg2bCLBr+/pve0SXaEChxzuIkLHRI7fJgj10vVOVj15S8TfM5VFepZu1ECSkAJKAElUF0CJhkm1b3SsnpXgaMsfPV5cCqV5okzQ5bYId4du5+WyhGTr2VDV6MdyrKEZ61rw+eeLC7M+cot744HMrk7Tj2Syf8wdHL27pweO8HlegYaurmrr5kfnfBxMLGEU2nJ0eDhRRcv58bnrOei7pbZ+5vPPSYJGuKhcWTqaGYRNGYbft9IjHslf8fBjOBxOi9cqdDxEsK0tMXH8uaGHBEkRwxp8dMZ9OGaY2JNKxtuuLdw6IvkAhk+M9tlFf6/y5cpDSohMdZWwkayf68m2dBFeGSE0KnznDt6lr6T5xk428twKMTo8CDJZBhSYdLpCKQnC3/FDsidTOLNETws8SOZItjcSktDI61NzXibmnAFm3A2NeEMNo7/7moKIklOnQ1+XINP4jx1F87jt+AcnQizssbRsTEjdMjPih1FJ9EUz62jvSOW6PXE6SH2nRlk35keBmIhnO4RHPLjknLFw9bW5Rqhy+kimG6iMRWkMR2gMe3Dn3LjT4B/dFSSe5CYY2lqb9qdET8QIcRJYzpGkDBNDNLk6KXZF6Zl02aCO67Fvf7qWXO5FDtHc9nPJDtCBY65zGCBY8597GOEbv4GnlWr2PDLX2hy0Qpx1W6UgBJQAkqg+gRMMkyqf7VzPoMKHHNGt3AO7A+PcddBydvRY+WuEM+I3BbwurhqY+d4KdoVrQ2Vvfh4FPqPZx7083/kW/tZQhFSuEi1rMLduWEi3CXrASKeAJ7i8m5U9qJyehs8lVPlpDqCxkxjl3ClI71hHjgS4mR/hLODo1Z+lrNDso0yGi8+1EPEjaVNPlsAKSyESNliz1wefsWbQTw0puT+OJbx3rEEjMnihfVaY9ecQpuEmYh9Qz1RQqfD9J0eoedEP70nzzN0vo9UMkI6LeJHZpsWESQlIkgY0lHS6WLyg0zMTMNYnODoGMHROE3WdoxgbAx3vrpoH+LwuHH6XLhcMZzOMZyeNC5vyto6GwO4VmzGueYSnN0X4mpuxhlsIu4PcHwUDoykeXwwxZ7eGE+eG552jiXx7qYlTVbS3QtXNHPB8ma2rWieVGpaqmmO3HmnVfUk/NDDEM8k3006nYz6/SRWr4Yd20lt3sxYVxfh0VFGRkYYHhgmPDLMSCRCXITNOTS/w0ujp4FGf4DGYCNNLc00dbTQ3NlKsClIMJj5aWhowDmH8LbZhmSSHaECx2yzVcT/JbnowZ27SA0O0vVXf0XnTW8s4ijdRQkoASWgBJSAGQRMMkzMIFJwFCpwGDw58zE0+bZ3z8mB8USle05OLcm4dVmTlbdDQlkuW9M2t4fZIi9uLDbKHQ88zD0PPoiz/yhrHOdY6zjLRncPKzmPMz25GsPUbh0g4S35IS8SBiPhMN4KhuJkTy6CxnjIyT2ZB/b8Jh4aa66cCDlZfklNq8xkhyPih4QvWYJHVvgYjOYIIKOcGYgSHis+zENyRXYFfXYYjHiA5AghzZm/lzT7Kpfzpci1VMpuiXiS/rMRQqdG6Dsl4keY0OkRRvonPDxE4MgIHhFL/IAIvoYxPL4YTsIkwiFGI/3ExqZWhskdS0M8STAas8WPsYz4MYPwUcp1JHEQ8fiJuP1EvQ0QDOJtChJob6Gls422rnY8zeJVEsQlniWNsg0i5XVH7r6HkbvuJH78xORTut0EnvlMgjt3EnzuLrzr1hUObRKPnbN74IkfEtv3E8Khs4wQYCTdyDCdDHnWMOxfz4ijiXA8SXgsSjQVI2mlIC2tSWhVoy8jgqxZu4YXvuj60jqYZm+T7AgVOCowpYM/+hGn3/PeTHLRO27H3dlZgV61CyWgBJSAElACtSFgkmFSmyue01lU4JgTtsVzkOTquOtAJlGpbIdGJwsKTT43V2/utMJZnru5iyVFVimZjeBAZIxvPXCCm+87xvmcfCHPXNPGG65exwsuWIZLkheKh0fW68MqeZr1AjkKySLCDSSxaaGcHyJ++CeXkp12zJJYMzcpaCFBwxucnENjngSN2bhP9//h0XiOAJL1ALGFENsbZCBS2rf0HY3evFCYBpbZOUGyeUHKTQ471+ud7rhYJJ4RO0T4EK+PUyOW90csMoPQlo7iCwzh8YhY2E88dp7o0FnGIhMJcQudLxhsprUlE+LiwUNqNElsaAxH7xmah0/SHgvRkIiRGnOSijtIxp1SiqTSl2z1JwlVLUFj5y4ar3o2rmCw8HlE1Dj1KDzxg0wSVbknc5t4VF1wA1zwUlg5NcxGBLfoYJiB030Mne1nuHeQ4YEhRoaGGYmGiYyNEnWMEXXEiBIn7ZiaWHWVZwk3vv8tFeFgkh1RnZmtCKaKdlJVo+TYK19F9JFHaPrd36X7Xz9d0YFrZ0pACSgBJaAEqk3AJMOk2tdaRv9VtSXKGJceaiCBRDLFY08PZBKVPtVj5fHIb+LmLp4d12zt4tJVbSXnazjWG+ar9x7luw+fJBrPeA1IWMR1z1jGjc9Zx/bVbcWRSaWsMqBTw16OZl6bVLlimi4l9CFb4WV8K8JHK5x8aKLKSf5DnHRnCRr5HhpFVNgo7uqM3Cs6lhwPe5nwBpkQQ+S13pHSwjqa/W7E46OzyYuU+838ZH5vDWT/ztkGPIjoVnay1BIIy0N5eGDMCnEJWd4eGdEjdCZMcobwn3QqSjrZh8MRwuUehHQfY9EeErGZhY9hV5CQt42QVCHxurmy7TzPCzzOltheSKZJxZ0k4w5SKT+prstIdm0n1bSJZCxJaiRManiY5MgwqeERUiMjJEdGJl4bCZOORq38Hv6LLyK4a5clavgv2DZ9qgK51+R+eOKHGVFDRMfc1r7BFjVuABH2yihNnU6kSPSPkugbJdEbYfj8IEOWCDLIyPCI5QHStqqLK998bQkzOP2uJtkRKnCUOaWxgwc58uKXWL2s/tpXabzyyjJ71MOVgBJQAkpACdSWgEmGSW2vvKSzqcBREi7dOZeAPLDeeeA8Uor2nkO9jMQmf4stD6A7N3VZYodsO4K+ggDlAfGhY/18+e4j3Lr/nJWLUpqUfnzFs1bx2qvW0t1WwVASOcHIucI5PyTp6XRVXmaa/kUoaMzlbpCSvueHYlYIjOT/mCSEDEmYTNTy2MmugbmcQwQxEUZaA16abVGkdVwcyQgjk14PeGi1RRMprVspcSSVTFkhLUO9UYZ6R+1tlKG+zO/R4cIeL1nhI5UKkU72kkj2WUKIU5KgztCa2tvpaPbQ4eyhY/QgHd5hOrwRfK4kON2wbmcmQemW66Fp6bQ9peNx0skkTv8MuWukMs2J+ydEjfwEsZ1b4MKXZoSNJReUJWrkDlTeK+KxUWKRMGORiLWN2duxcIT4QJRgawdbXrBzLktnyjEm2REqcJQ5pWc/+o/0f/ObeNasZsPPf67JRcvkqYcrASWgBJRA7QmYZJjU/uqLPqMKHEWj0h1nIjCWSPHw8ZDl2XHHU+c5cG5k0u7ype3F3a1cY+fuuGhlC8l0mp/tPcNX7jlKbq6Pla0NvO6qtbz8Wato8pdWrrLsWZIn60ifLX7Y3h65iU+jocwpVNAoG/V0HcSTKauM8URekKiV+FZyhQxE4wxF45nfI5nt0Gi8LEEkdxxSSaYlxzNkXBjJfW2S58iEZ4nX7Sx4SfJQLqLNvtODmSomUs3kzBCneyO0pBzWT2tSts7xv9vSDtx54SaW8JEKkbIFDxE95Hcr6ekMLegZo9M7QocvkvnxRunYcCG+i18MW18EbWuKm8tkIpMs1/LU+DGEz08+bukzMoKGlLVdsnVKn8IhEYtNFiVEoIjmCBVhESxEvJj8uvW3LWSkxWNkhrb20st42fs+XNw1zbKXSXaEChxlTGlqdDSTXHRoiCXvfhcdN95YRm96qBJQAkpACSiB+SFgkmEyPwSKOqsKHEVh0p1KJSDVOrJix72H+sbDTbL9SP4FqbQhlTyy7dJVrbzx6vVce+FS3HOpwlHqIOeyf7Q/U+pUcnS4FnbIyVzwzMcxUglleDRhCyAZISRXALH+tsWQgaj8P8FgJLNfKclTZ7s2qTY0EUaTCZuRMCsRNfryKhPl9rW6PWBVL7GqmNg/UqlmdCQx4fXRG2Wwd5Rhaxsl3D/h5TJX4aPRPUanL0xHe4COjRfRuf1aOi56Dr5A48TwpPrJ0btI7/sBif0/IzY8SCzlZizlJpZ0EWvZQKxrO2OtW4k5/OMihIgR46JENCNOyN+pZPHJamfjnf2/0+XGFwhY4/Y1NrJ801ae9/o3F3v4jPuZZEeowFHGlA58/weced/7wOPJJBft6CijNz1UCSgBJaAElMD8EDDJMJkfAkWdVQWOojDpTuUQGI0nefDohHeHlCwdfzhxwLUXLrMSh+5Y3Vax8IByxqvHLh4C4i2SFUQmCSGWADIhmuR7jYgniXgtldJyS7JmBY38kqzF9pdMpBgOieAxagkew31RBntGM9veKLFwgnRqlHQq4+Uh3h7ppHh/9M7q8dHg99Hc3koqMsBYZIixpNMSM1IU9lApdsyF9ssVJ7xZkcLeTvzdmBEvAgG89jbzdyOyj9vjrdr7hkl2hAocZay0Y6/4Y6K7d9P8wutY+alPldGTHqoElIASUAJKYP4ImGSYzB+FWc+sAsesiHSHShOQRKISxiI5O15yyUpWd1Qwv0alB6v9KYFpCIhwl+spIpV/Jokl0biVIHeb7Z2xcUkQn9tVE56xaMb7Y1wAyXqAiAByfoDEWK8ldojoMRHqMjmsbNaBOpy4PX5cngY8vgY8/gBe/4QnhT/YiPwEmptoaA4SaA7ibwxaokTW26Ka4sSs4y9iB5PsCBU4ipiwQruMPnWAozfcYP1r9de/TuMVl8+xJz1MCSgBJaAElMD8EjDJMJlfEjOeXQUOgydHh6YElIASqDSBdCpNZGgs4/lhCx9DPVH6T55k4OwpIpFB0qkRHA4vOMQ7wgcOX85WXpeEwaVVq5E8PB6/G6/fNb71Nrjx+lx4ZOt34fW78dhbb0Pm74n93Viv+dy4PJX3JinE2SQ7QgWOOd4JZ//hI/R/61t4165l/c9/VjV3nzkOTw9TAkpACSgBJVA0AZMMk6IHXfsdVeCoPXM9oxJQAkrAWAKJeJJwf5SxWJr4aJKx0UTmJ5r5feK1JPFogrFYkjHZjiaJW/tlXsOuhlSNC3W6HXnihwtLLPG76ewOsuPaIhOnzjI4k+wIFTjmsJJS0WgmuejwMEve8x46Xv+6OfSihygBJaAElIASMIOASYaJGUQKjkIFDoMnR4emBJSAEqhHAuIlEh8TwSMrkNjiiC2SZESTrCAysY8lkMgxlmCSEVMS8dJynaza1sZL/mJ7RbCZZEeowDGHKU3FYgz+4IcMfu97dP/n53G3tc2hFz1ECSgBJaAElIAZBEwyTMwgogKHwfOgQ1MCSkAJKIECBJLJVEYoiSaI53iLTPImsT1I5LX25Y1c+vzVFWFpkh2hAkdFplQ7UQJKQAkoASVQvwRMMkwMpqgeHAZPjg5NCSgBJaAE5o+ASXaEChzztw70zEpACSgBJaAEjCBgkmFiBJDCg1CBw+DJ0aEpASWgBJTA/BEwyY5QgWP+1oGeWQkoASWgBJSAEQRMMkyMAKICh8HToENTAkpACSgB0wiYZEeowGHa6tDxKAEloASUgBKoMQGTDJMaX3opp1MPjlJo6b5KQAkoASWwaAiYZEeowLFolp1eqBJQAkpACSiBwgRMMkwMniMVOAyeHB2aElACSkAJzB8Bk+wIFTjmbx3omZWAElACSkAJGEHAJMPECCCFB6ECh8GTo0NTAkpACSiB+SNgkh2hAsf8rQM9sxJQAkpACSgBIwiYZJgYAUQFDoOnQYemBJSAElACphEwyY5QgcO01aHjUQJKQAkoASVQYwImGSY1vvRSTqceHKXQ0n2VgBJQAkpg0RAwyY4oReBwAn8BvAlYC/QA3wE+CISLmL30NPvIscEC/9sCfBzYBXiBR4EPAbcVca78XdQomQM0PUQJKAEloAQWBwGTDBODiastYfDk6NCUgBJQAkpg/giYZEeUInD8G/B24PvAz4FtwNuAu4HnA6lZkIrAIft+MW+/OPDtvNc2AA8CCeBfgUHgjcAzgOuAX5U4fWqUlAhMd1cCSkAJKIHFQ8Akw8Rg6mpLGDw5OjQloASUgBKYPwIm2RHFChwXAnttceNlOehE4PgM8Ergv4sQOG4GXlsEevEMkfNcBuy29xcvj33AKLAVmM4jpFD3apQUAV13UQJKQAkogcVJwCTDxOAZUFvC4MnRoSkBJaAElMD8ETDJjihW4PgI8H5gp+2FkaXnB/qAO4EXFilw3GSHnIym43aMAAAXuUlEQVRMs3+j3ee9wPPy9vlb4O+By20Pj2JnUY2SYknpfkpACSgBJbDoCJhkmBgMX20JgydHh6YElIASUALzR8AkO6JYgeOXdhhKAIjloRMhYjPQVYTAIfk2RBRx2Tk8JDTlA3YISvbwK4H7gI/a/8vt9gXALcBbgX8vYQrVKCkBlu6qBJSAElACi4uASYaJweTVljB4cnRoSkAJKAElMH8ETLIjihU4JDxlCbC0ADYJJ/lDwAeMzYD1AeC7wCGg2fb4eLkd+vJsIOvRIaEp/wO8Bfh8Xn8X2GEqHwP+ZoZzNQHyk20y7kdPnTrFihVin2hTAkpACSgBJaAEsgRMMkwMnhUVOAyeHB2aElACSkAJzB8Bk+yIYgWOw4AHWF0A2zeAVwNtwECJWEWkyHpqyFaa9CV93gh8Na+/9YCMRRKevmOGc/2dXXFl0i4qcJQ4O7q7ElACSkAJLAoCJhkmBgNXgcPgydGhKQEloASUwPwRMMmOKFbgqIQHRyHiIpqI58YjgHhxSFMPjvlbm3pmJaAElIASWIQETDJMDMavAofBk6NDUwJKQAkogfkjYJIdUazAUYkcHNMRPwpIqVjJ4yFNc3DM39rUMysBJaAElMAiJGCSYWIwfhU4DJ4cHZoSUAJKQAnMHwGT7IhiBY7ZqqjcBVw3B6SScHQYuB+42j5eysH2AjNVUbkCkJwexTY1SoolpfspASWgBJTAoiNgkmFiMHy1JQyeHB2aElACSkAJzB8Bk+yIYgWOi4DfAN+3Q0iy9N4GfMbOm/Ff9osb7HwdT+Yg7rBLv+ZT/2fgXcB7gU/k/FOSkf4+sMM+r/xLhI99dhWXLUC6hClUo6QEWLqrElACSkAJLC4CJhkmBpNXW8LgydGhKQEloASUwPwRMMmOKFbgEFqftcuzisjxM2Ab8Hbb0+K3gZSN9BiwBsjt+9OAeF3cDpywxYoXAtfYnhiyjeZMyUbgQTt0RY4dAt4IiNByPSAhM6W0buDpRx55hGXLlpVynO6rBJSAElACSmDBEzh79iyXXXaZXOcq4OSCv+C5XaDaEnPjpkcpASWgBJTAAidgkh1RisDhsiuX3ASstcNIvg18MKfEq0xdIYHjBrvs6zMA8eZIAgcBKTH7KWC0wJyLgPJPwC7AK2VeAamO8qs5rI/t9vFzOFQPUQJKQAkoASWwaAiI5+Rji+ZqS7tQtSVK46V7KwEloASUwOIjMO92RCkCRz1Pj1RrEXGlJ8fTpNzrWWqLJjKJ58rtrIbH1+u4BVG9jl3HXcMFXsfrRNd4bdeJ8p7M2wl0AY/b3pO1nw3zz6i2xMQc6edabder8q4tb/18UN7FEqjXe7Maa9wYO2KxCBzFLtJS9rNicYGVwOlSDpznfet13IKtXseu467toq9X3rrGa7tOlHfteesZpxKo1/crHXdtV7Pyri1v/XxQ3sUSqNd7s57X+KxzowLHrIim3aFeF3S9jrueb8R6Za7jnvv7w1yPVOZzJTe345T33LjpUZUjoGuwciyL6Ul5F0OpcvvUK2+1OSu3BortqV7XSr2Ou57X+KxrSgWOWRGpwDF3RBU/sl7fRHTcFV8KM3ZYr7zr+cOmXpnruGt7b+rZphLQNVjbVaG8lXexBHStFEuqMvsp78pwLKWXemU+6zWqwDEroml3aALeCXwSGJ57NzU/sl7HLaDqdew67tou83rlrWu8tutEedeet55xKoF6fb/Scdd2NSvv2vLWzwflXSyBer0363mNzzo3KnDMikh3UAJKQAkoASWgBJSAElACSkAJKAEloARMJ6ACh+kzpONTAkpACSgBJaAElIASUAJKQAkoASWgBGYloALHrIh0ByWgBJSAElACSkAJKAEloASUgBJQAkrAdAIqcJg+Qzo+JaAElIASUAJKQAkoASWgBJSAElACSmBWAipwzIpId1ACSkAJKAEloASUgBJQAkpACSgBJaAETCegAkfpM/Q+YAdwGbAOOA6sLb2bmh6xGXgV8DvABsAPHAa+C/wrEK7paIo/2RbggzZvKWXkAU4APwP+GThTfFfzvmcAeNxeM/8OvHXeRzT9ANLT/EvWSdDgccvQ2oG/AV4KdNsVjoS7rKO7DRz73wEfmmFcCXvdGzh0ay28Hfhj+z0wBhwAvgjcDEy3jub7WpYCHwauB+T3s8D37XkYmO/B2ecv9XPmcuCjgGyF+33AXwO7DbkeHcZkAqXOrwn86tWOEHYLxZZQO6I2d0K92RFCRW2J2qyN3LOYbkuU+jmzoOwIFThKvyHEeAwBj9oix1AdCBz/BPw58CPgfiAOXAP8EbAHuAKIlo6i6kc8D3i/PeaTgDzsXQS8DhDulwLnqz6KypzgX4A32QJBPQgcIgbIg2puk3Xz7crgqEova4A7bMZfsR+2W4CLgV8C/7cqZy2vUxmb/OQ3ee3d9oP375d3iqoc7QTuBJ5tixnyviLGt4gdvwV8AnhvVc5cXqdLgAcBEUy/YIuOz7DvzX3AVUCkvFNU5OhSPmfk/VvW/Sngc/bZRUCVa5X52VuREWknlSRQyvxW8rzl9FWvdoRc80KxJdSOKGcFF3dsPdoRcmVqSxQ3v5Xaqx5siVI+ZxacHaECR+lLfT1wxD5MvhmWbzFN9+B4JnAQGMy73I/YAsLbcgzj0onU/og/BL5jP0DJg5TpTTx+5KHqPcAngXoQOOQb+NeaDjZvfCLKyL0oD9j15N1TCLM8fN8EvAj4qYHzcKXtJSAeYH+ZMz4v8KTtSdNq4LhlvH8B/Anwf3LGJ8LMfwN/C8j74ny3Uj5n5L1lK7DNFjlk7CuB/bY4LJ572swiUMr8mjLyhWZHCNd6siXUjqjNnbCQ7AghprZEddZNPdgSpXzOLDg7QgWO8hZ+vQgc012leEOIB4e8Ab65PBQ1PVoeYB8A5BslccEyublscUMeuOVb1aN1JHDIA7Y8sI6YDNge207bo0BCJj5rh3VISJMJ38aXiq8ROG17KYlgkyy1gxrsfy3wC1u0k3Cx3CYflPKALT+mtd8AmwBhnBtCIx4pEoIl3CWMz6Q20+fMRlu8/ipwY96gxYtJvN3EW0XCcLSZSUDtiPmbl3qxJdSOqM0aWUh2hBBTW6J666bebIlFZ0eowFHe4q93w+Q6O5/F38+SB6A8SuUfLTlDxFNGthcAH7fDU+TDyMS8CrlX/C473v9C+8V6ETjkYU94i2HVY4emfKCAF1D5s1uZHkTskpAIyb3xBkDWtoxdPJdkff9XZU5Tk17Ec+ZrtieBeBSY2NpsTzYJG3uLLThKiMprbNFDBNMvGThw8S4R11KJsc5vEnoo19UF9Bo09pk+Z7KeJ28Evpw3ZnlNwsxM9QIyCPG8DkXtiNrhr1dbQu2I2qyRhWRHCDG1Jaq3burNllh0doQKHOUt/no2TOThT8SBZwESg/5UeSiqerR4Psi38tl2DJCH7W9V9azldy5JaGWNyAO2iDLybXw9CBziHSMJaA8BzcALgZfbsfwS02+iR4ckiRRxQ8QYETX+w/Y+eScg4tLrbdGg/Fmtfg9yX0ouCPEkkPViarvafqiW5IPZNgz8KfADQwf9/wDJabI9LwGn5PN5zB6zJJCWHEumtJk+Z2R9S1y+3KM/zxuwvCbhTZL7Jz+fjinXpuPIfEbUQ6hrobmqJztCxl+PtoTaEbV7l1hIdoRQU1uiemun3myJRWdHqMBR3uKvZ8NEBAP5sJeKEx8rD0PVj5ZqGBJjLkagPJi8BPg68G9VP3N5J5DEluIeLrGzkqCzXgSOQlct60SqNIiwJFvT2q/sRHKSH0dyEYzZA8x6GozaIRMp0waeNx7J9i/fDPwv8HzDxyr3oqwHYS5VO8QrQpIZy716A3CrgeMXUUYSckoVqXfYAqQIYBJPKw8SEtYk+9xj0Nhn+pwRDx8RUCWJ4m15Y/5tex1JjhS5Pm1mElA7onbzUo+2hNoRtVsfC8WOEGJqS1R33dSbLbHo7AgVOMq7AerVMPkH+8FEvtWTb/fqrUm26IfsslimijNSlvcbgITRZB+W6lngkAc/8dx4xK7MYNqa+bHtip8VYXLHJwlTxatAwpsk8aLJTZLmSvUUCT0wsepLlp3k75FcG/Lw/J85QLNlDCWnhXigmJg/RBILfgZYZo9bxijhHRK68nvAJXZuIlPWyaL75sUU8DUah9oRNQJd4DSm2xJqR9R2bSwUO0KoqS1R/bVTT7bEorMjVOAo7waoR8MkWytbYvwlKV1uor3yaNT2aClLKUkMV9X2tEWdzQc8bT8AyrfE2SbjlW+PJR/Eh+04/4GiejRjJwmXEE+U3JAEM0YGn7cT5RaqCJSNq5WwD/E0MLW5ASmHLFtZKzFTBwpIUktJYNkJ9OWNM+sdJgkwxVPCxCau9SLSNNnheVJuWgQb8UqR0sImJadddLGzJi6YKo5J7Ygqwi2ia1NtCbUjipi8Cu+yEOwIQaK2RIUXxgzd1YstsejsCBU4yrsJ6s0wyYob8o225CQw3V1/ptmRDMbyACVZok1rUh6zv4hByTf1Ej9fD02Ss0l+BTEGxTXPtCYP2/LQLblO/jpvcCIovdKuniF5RUxt4j3wPTv0KlcYM3G84jYt5UeXAiIO5LaskSihKibn9skds3hziCh5p4GhQYsu+7mJC76KY1I7oopwi+jaVFtC7YgiJq/CuywEO0KQqC1R4YVRQnem2hKLzo5QgaOEVVtg13oyTD5oew18086sXA/ihrxRFCpveA0gsZLiDSGx56Y1CeeQHAT5TaozSPJLKa8pJRylRO8BwwbfUeAbeRmilAKVTO5SqURcH01rkmvjuF1aVR6ss4lQl9tJR0/ZMammjTt3PD8BrgfEbXqvyQMFPm3nsMhfD2KUP2FX4JH1bmKISj5aCaeRcKA/sN9PbjeM/WyfMxKuJ/HWsu6lzK00yf0juVzEK8X0XC6G4a75cGab35oPaIYT1qMdIZdTj7aE2hG1X/kLwY4QampL1H7tyBlNtiVm+5xZcHaEChyl3wSvBtbYh4k7vBf4pP23PGCJgGBak8R/nwNOAJKULl/cOGdoQkDJaC0PqJI8T9iKF4FUOHiF7UL+3LxKCKZxzx9PPeTgkAfXKwB5yJP1IoldpRqDiEpSXUW2UUNB3wR8Adhne3PIvfln9hqSUpm3GDru7AOp8JYcJ5cbPM7s0OQ9UCqNiEEo1YzutZOMSmlSWefyniNinmlN1rM89Mt7i4RcSTiK5DuR95X3A/9oyIBL+ZyRykZyv0p4U7balHw2iXeNhGXJN9TazCJQyvyaMvJ6tSOE30KyJdSOqO4dUc92hNoS1V0bub3Xgy1RyufMgrMjVOAo/WYQr4Fd0xwm7s3y0G1ak4ojr5lhUKaO+4/s5JCS9E++DZZ8ISJ0SHUG8SiQB8J6avVgmIjnyVvs0sHizSHfwEvZ1e8AnwKkGonJTUqAvsfOryBC3q9tzyV5ADe5ZavUiHH1JZMHmjM2SSIq3+iKF5U8TIvwtduu2CGhNiY2Eb0kRE9EPBFPJdeGfHMha1vCbkxppX7OXAl8xBbH5H1Scs28z7Byt6awNWEcpc6vCWOuVztC2C0kW0LtiOrfDfVqRwgZtSWqvz7kDPVgS5T6ObOg7AgVOGpzI+hZlIASUAJKQAkoASWgBJSAElACSkAJKIEqElCBo4pwtWsloASUgBJQAkpACSgBJaAElIASUAJKoDYEVOCoDWc9ixJQAkpACSgBJaAElIASUAJKQAkoASVQRQIqcFQRrnatBJSAElACSkAJKAEloASUgBJQAkpACdSGgAocteGsZ1ECSkAJKAEloASUgBJQAkpACSgBJaAEqkhABY4qwtWulYASUAJKQAkoASWgBJSAElACSkAJKIHaEFCBozac9SxKQAkoASWgBJSAElACSkAJKAEloASUQBUJqMBRRbjatRJQAkpACSgBJaAElIASUAJKQAkoASVQGwIqcNSGs55FCSgBJaAElIASUAJKQAkoASWgBJSAEqgiARU4qghXu1YCSkAJKAEloASUgBJQAkpACSgBJaAEakNABY7acNazKAElUD6B5wK3A68Dvl5+d9qDElACSkAJKAElsIgIqB2xiCZbL3XxElCBY/HOvV754iOQ/WB/N/AvQCvwDuAO+8cEIpcCL7UFjGN5A1LDxIQZ0jEoASWgBJTAYiWgdsRinXm9biVQRwRU4KijydKhKoEyCeQbJmuBo8CHgb8rs+9KHf5a4GvANQVEFyfgBeJAslIn1H6UgBJQAkpACSiBogioHVEUJt1JCSiB+SSgAsd80tdzK4HaEqi1YdIEDJd4iTMJHCV2pbsrASWgBJSAElACFSSgdkQFYWpXSkAJVIeAChzV4aq9KgETCeQaJg/b+Szyx3kcEM+ObHs58DbgEsAF7AX+GfifvAPTwM3AN22PEAk1kXPIOVcA7wSeB6wBGoAj9v4SKpP1xhAvkg8VACf9ivAxXYhKI/AB4I+AbqAfuAX4W0CuJ9tyj5f3vncBG4GzwL8DnzBx0nRMSkAJKAEloAQMIaB2xEQuMLUjDFmUOgwlkE9ABQ5dE0pg8RDINUxEiPhj4NPA94Hv2RhGgB/Yv38EeD/wC+CXQAr4PVtoeKstCmTpicCxzxYwvgTst/8hv/8u8Hn7PIcBj/3atcAXgTfZ+14M/DlwE/CPOX3IMb+eRuCQviTx6FW26CK/bwL+DBgAngmctPvPXv8DwFLgK/Y+rwIuB14J/PfiWQ56pUpACSgBJaAESiKgdkTG5lA7oqRlozsrgdoSUIGjtrz1bEpgPgmU4lq6A3gE+BjwN3mDFgHkt4GVOSEoInBIewHwq7z9xWNjFMjuk/23iCx/YntdnLFfnClEpZAHxxttkUS8St6Tc97rgZ8A/wW8Ok/gkHNtAwbt1wO2p8ch4Mr5nCA9txJQAkpACSgBgwmoHZERONSOMHiR6tCUgAocugaUwOIhUIph8kngL20hoC8P0Uts7wfxwJBQEGkiXvwGkNCUmZokCQ0CkjBUPDtE5JD+fjxHgeNngIyj0w5NyT33Y8AGu1qMeJ9kr1+8Q8QzJbfJ+UXckH60KQEloASUgBJQAlMJqB2RETjUjtC7QwkYTEAFDoMnR4emBCpMoBTDRISD62Y5/5/aAkVW4PiunQcj/zA38NeA7C85L/Lfd14DfGOOAoeEwohgsqrAWMV7Q8JOJBzlfI7A8QZboMk95OuAjEPfEyu86LQ7JaAElIASWDAE1I7ICBxqRyyYJa0XshAJqDG/EGdVr0kJFCZQimHyc9szQkSO6UqySs6NbGhJNsmohJjkt8/YiUq/DUi/IjZIqVcJg/k48DpABAZppYaozEXgyD1fdqwqcOhdowSUgBJQAkpgZgJqR0wkGc3aLWpH6F2jBAwjoAKHYROiw1ECVSSQb5hIRZNjdtUTqWCS27KixAU5yT5nGtpMAodUNdkD7Mrr4M128tFcwUG8KMRouAa4I2//Qjk4fmqHunTYCUNzD3nUDlFpsxOkTleFRY5RgaOKC0+7VgJKQAkogQVBQO0IFTgWxELWi1jYBFTgWNjzq1enBHIJ5BsmIgr0Ap8F3p6H6lnAg3ZFlT8o4MUhYR/nco6ZSeCQczwB7MzZX0q7igCxOc+D42V2NRTZZiu7ZA+bKcmoeIJIGEy2ieeJhNkUSjKqHhx6XygBJaAElIASKJ2A2hEqcJS+avQIJVBjAipw1Bi4nk4JzCOBfMNEhnIQaAE+agsW4ZyEnx8CxLPjcUDya5wGlgOXAS8EJGFots0kcPynXQr2O3aFFRFHXg9I8lIp45orOKwGjgK7gf8AZDzyt5RkKyRw5JaJlRCYu+w8H2+xPTpEqHnaHqR6cMzj4tNTKwEloASUQN0TUDtCBY66X8R6AQufgAocC3+O9QqVQJZAIcPkt4BP29VPsuVS1+Ygk3Kr4t0hQoF4XUj+DBE8fgiIcFGMwCH9fthOQCrihggOXwEesgWPfI8KCVN5ry1UiIBxs52bYzqBQsb1AeDldsnZAeCX9mvHc8aoAofeC0pACSgBJaAE5k5A7QgVOOa+evRIJVAjAipw1Ai0nkYJKAEloASUgBJQAkpACSgBJaAElIASqB6B/w9r190KbF+8GAAAAABJRU5ErkJggg==\" width=\"720\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n"
]
}
],
"source": [
"df_results = %sql SELECT * FROM cifar10_multi_model_info ORDER BY validation_metrics_final DESC LIMIT 100;\n",
"df_results = df_results.DataFrame()\n",
"\n",
"df_summary = %sql SELECT * FROM cifar10_multi_model_summary;\n",
"df_summary = df_summary.DataFrame()\n",
"\n",
"#set up plots\n",
"fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
"fig.legend(ncol=4)\n",
"fig.tight_layout()\n",
"\n",
"ax_metric = axs[0]\n",
"ax_loss = axs[1]\n",
"\n",
"ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
"ax_metric.set_xlabel('Iteration')\n",
"#ax_metric.set_ylabel('Accuracy')\n",
"ax_metric.set_title('Validation Accuracy')\n",
"\n",
"ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
"ax_loss.set_xlabel('Iteration')\n",
"#ax_loss.set_ylabel('Cross Entropy Loss')\n",
"ax_loss.set_title('Validation Loss (Cross Entropy)')\n",
"\n",
"iters = df_summary['metrics_iters'][0]\n",
"\n",
"for mst_key in df_results['mst_key']:\n",
" df_output_info = %sql SELECT validation_metrics,validation_loss FROM cifar10_multi_model_info WHERE mst_key = $mst_key\n",
" df_output_info = df_output_info.DataFrame()\n",
" validation_metrics = df_output_info['validation_metrics'][0]\n",
" validation_loss = df_output_info['validation_loss'][0]\n",
" \n",
" #ax_metric.plot(iters, validation_metrics, label=mst_key, marker='o')\n",
" ax_metric.plot(iters, validation_metrics)\n",
" \n",
" #ax_loss.plot(iters, validation_loss, label=mst_key, marker='o')\n",
" ax_loss.plot(iters, validation_loss)\n",
"\n",
"plt.legend();\n",
"# fig.savefig('./lc_keras_fit.png', dpi = 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot training and validation curves together"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# mst tuple(s) to plot\n",
"mst_key_to_plot = 10\n",
"df_results = %sql SELECT * FROM cifar10_multi_model_info WHERE mst_key = $mst_key_to_plot;\n",
"df_results = df_results.DataFrame()\n",
"\n",
"df_summary = %sql SELECT * FROM cifar10_multi_model_summary;\n",
"df_summary = df_summary.DataFrame()\n",
"\n",
"#set up plots\n",
"fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
"fig.legend(ncol=4)\n",
"fig.tight_layout()\n",
"\n",
"ax_metric = axs[0]\n",
"ax_loss = axs[1]\n",
"\n",
"ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
"ax_metric.set_xlabel('Iteration')\n",
"#ax_metric.set_ylabel('Accuracy')\n",
"ax_metric.set_title('Classification Accuracy')\n",
"\n",
"ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
"ax_loss.set_xlabel('Iteration')\n",
"#ax_loss.set_ylabel('Loss')\n",
"ax_loss.set_title('Cross Entropy Loss')\n",
"\n",
"iters = df_summary['metrics_iters'][0]\n",
"\n",
"for mst_key in df_results['mst_key']:\n",
" \n",
" #train\n",
" df_output_info = %sql SELECT training_metrics,training_loss FROM cifar10_multi_model_info WHERE mst_key = $mst_key\n",
" df_output_info = df_output_info.DataFrame()\n",
" training_metrics = df_output_info['training_metrics'][0]\n",
" training_loss = df_output_info['training_loss'][0]\n",
" \n",
" #test\n",
" df_output_info = %sql SELECT validation_metrics,validation_loss FROM cifar10_multi_model_info WHERE mst_key = $mst_key\n",
" df_output_info = df_output_info.DataFrame()\n",
" validation_metrics = df_output_info['validation_metrics'][0]\n",
" validation_loss = df_output_info['validation_loss'][0]\n",
" \n",
" label_train = str(mst_key) + '-train'\n",
" #ax_metric.plot(iters, training_metrics, label=label_train, marker='x')\n",
" #ax_loss.plot(iters, training_loss, label=label_train, marker='x')\n",
" ax_metric.plot(iters, training_metrics, label=label_train)\n",
" ax_loss.plot(iters, training_loss, label=label_train)\n",
" \n",
" label_test = str(mst_key) + '-test'\n",
" #ax_metric.plot(iters, validation_metrics, label=label_test, marker='o')\n",
" #ax_loss.plot(iters, validation_loss, label=label_test, marker='o')\n",
" ax_metric.plot(iters, validation_metrics, label=label_test)\n",
" ax_loss.plot(iters, validation_loss, label=label_test)\n",
"\n",
"plt.legend();\n",
"# fig.savefig('./lc_keras_fit.png', dpi = 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"predict\"></a>\n",
"# 7. Inference\n",
"\n",
"## 7a. Run predict on the whole validation dataset\n",
"\n",
"Pick a reasonable model from the previous run."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>mst_key</th>\n",
" <th>model_id</th>\n",
" <th>compile_params</th>\n",
" <th>fit_params</th>\n",
" <th>model_type</th>\n",
" <th>model_size</th>\n",
" <th>metrics_elapsed_time</th>\n",
" <th>metrics_type</th>\n",
" <th>training_metrics_final</th>\n",
" <th>training_loss_final</th>\n",
" <th>training_metrics</th>\n",
" <th>training_loss</th>\n",
" <th>validation_metrics_final</th>\n",
" <th>validation_loss_final</th>\n",
" <th>validation_metrics</th>\n",
" <th>validation_loss</th>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']</td>\n",
" <td>batch_size=128,epochs=5</td>\n",
" <td>madlib_keras</td>\n",
" <td>2159.70019531</td>\n",
" <td>[157.675038099289, 323.760583877563, 489.999938964844, 658.165150880814, 826.338515996933, 995.962205886841, 1166.19140601158, 1335.88773202896, 1507.49120998383, 1679.03732204437]</td>\n",
" <td>[u'accuracy']</td>\n",
" <td>0.865880012512</td>\n",
" <td>0.386452704668</td>\n",
" <td>[0.554459989070892, 0.660399973392487, 0.71670001745224, 0.754540026187897, 0.78276002407074, 0.806519985198975, 0.81879997253418, 0.83898001909256, 0.850740015506744, 0.865880012512207]</td>\n",
" <td>[1.26610136032104, 0.961102485656738, 0.805769920349121, 0.702959299087524, 0.625120878219604, 0.556592226028442, 0.513025283813477, 0.460623860359192, 0.426946371793747, 0.386452704668045]</td>\n",
" <td>0.806100010872</td>\n",
" <td>0.569638252258</td>\n",
" <td>[0.552999973297119, 0.657100021839142, 0.711000025272369, 0.737600028514862, 0.758300006389618, 0.77700001001358, 0.783100008964539, 0.794099986553192, 0.801199972629547, 0.806100010871887]</td>\n",
" <td>[1.26569390296936, 0.972574234008789, 0.835387766361237, 0.750236749649048, 0.694831550121307, 0.648999333381653, 0.622370600700378, 0.599871814250946, 0.581832528114319, 0.569638252258301]</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(10, 2, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.0001)',metrics=['accuracy']\", u'batch_size=128,epochs=5', u'madlib_keras', 2159.70019531, [157.675038099289, 323.760583877563, 489.999938964844, 658.165150880814, 826.338515996933, 995.962205886841, 1166.19140601158, 1335.88773202896, 1507.49120998383, 1679.03732204437], [u'accuracy'], 0.865880012512, 0.386452704668, [0.554459989070892, 0.660399973392487, 0.71670001745224, 0.754540026187897, 0.78276002407074, 0.806519985198975, 0.81879997253418, 0.83898001909256, 0.850740015506744, 0.865880012512207], [1.26610136032104, 0.961102485656738, 0.805769920349121, 0.702959299087524, 0.625120878219604, 0.556592226028442, 0.513025283813477, 0.460623860359192, 0.426946371793747, 0.386452704668045], 0.806100010872, 0.569638252258, [0.552999973297119, 0.657100021839142, 0.711000025272369, 0.737600028514862, 0.758300006389618, 0.77700001001358, 0.783100008964539, 0.794099986553192, 0.801199972629547, 0.806100010871887], [1.26569390296936, 0.972574234008789, 0.835387766361237, 0.750236749649048, 0.694831550121307, 0.648999333381653, 0.622370600700378, 0.599871814250946, 0.581832528114319, 0.569638252258301])]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT * FROM cifar10_multi_model_info WHERE mst_key=10;"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"5 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>id</th>\n",
" <th>estimated_y</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(1, 3), (2, 8), (3, 8), (4, 0), (5, 6)]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS cifar10_val_predict;\n",
"\n",
"SELECT madlib.madlib_keras_predict('cifar10_multi_model', -- model\n",
" 'cifar10_val', -- test_table\n",
" 'id', -- id column\n",
" 'x', -- independent var\n",
" 'cifar10_val_predict', -- output table\n",
" 'response', -- prediction type\n",
" TRUE, -- use gpus\n",
" 10 -- mst_key to use\n",
" );\n",
"\n",
"SELECT * FROM cifar10_val_predict ORDER BY id LIMIT 5;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Count missclassifications"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1939</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(1939L,)]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT COUNT(*) FROM cifar10_val_predict JOIN cifar10_val USING (id) \n",
"WHERE cifar10_val_predict.estimated_y != cifar10_val.y;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Accuracy"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>test_accuracy_percent</th>\n",
" </tr>\n",
" <tr>\n",
" <td>80.61</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(Decimal('80.61'),)]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT round(count(*)*100.0/10000.0,2) as test_accuracy_percent from\n",
" (select cifar10_val.y as actual, cifar10_val_predict.estimated_y as predicted\n",
" from cifar10_val_predict inner join cifar10_val\n",
" on cifar10_val.id=cifar10_val_predict.id) q\n",
"WHERE q.actual=q.predicted;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7b. Select a random image from the validation dataset and run predict\n",
"\n",
"Label map"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"label_names = {\n",
" 0 :\"airplane\",\n",
" 1 :\"automobile\",\n",
" 2 :\"bird\",\n",
" 3 :\"cat\",\n",
" 4 :\"deer\",\n",
" 5 :\"dog\",\n",
" 6 :\"frog\",\n",
" 7 :\"horse\",\n",
" 8 :\"ship\",\n",
" 9 :\"truck\"\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pick a random image"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n"
]
},
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS cifar10_val_random;\n",
"CREATE TABLE cifar10_val_random AS\n",
" SELECT * FROM cifar10_val ORDER BY random() LIMIT 1;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Predict"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>id</th>\n",
" <th>prob_0</th>\n",
" <th>prob_1</th>\n",
" <th>prob_2</th>\n",
" <th>prob_3</th>\n",
" <th>prob_4</th>\n",
" <th>prob_5</th>\n",
" <th>prob_6</th>\n",
" <th>prob_7</th>\n",
" <th>prob_8</th>\n",
" <th>prob_9</th>\n",
" </tr>\n",
" <tr>\n",
" <td>4032</td>\n",
" <td>2.626629e-06</td>\n",
" <td>2.7014737e-08</td>\n",
" <td>0.0022699283</td>\n",
" <td>2.0414376e-05</td>\n",
" <td>0.99757725</td>\n",
" <td>1.5412388e-05</td>\n",
" <td>1.7638637e-05</td>\n",
" <td>9.5042335e-05</td>\n",
" <td>1.4481008e-06</td>\n",
" <td>5.417283e-08</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(4032, 2.626629e-06, 2.7014737e-08, 0.0022699283, 2.0414376e-05, 0.99757725, 1.5412388e-05, 1.7638637e-05, 9.5042335e-05, 1.4481008e-06, 5.417283e-08)]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS cifar10_val_random_predict;\n",
"\n",
"SELECT madlib.madlib_keras_predict('cifar10_multi_model', -- model\n",
" 'cifar10_val_random', -- test_table\n",
" 'id', -- id column\n",
" 'x', -- independent var\n",
" 'cifar10_val_random_predict', -- output table\n",
" 'prob', -- prediction type\n",
" TRUE, -- use gpus\n",
" 10 -- mst_key to use\n",
" );\n",
"\n",
"SELECT * FROM cifar10_val_random_predict;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Format output and display"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>feature_vector</th>\n",
" </tr>\n",
" <tr>\n",
" <td>[2.626629e-06, 2.7014737e-08, 0.0022699283, 2.0414376e-05, 0.99757725, 1.5412388e-05, 1.7638637e-05, 9.5042335e-05, 1.4481008e-06, 5.417283e-08]</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[([2.626629e-06, 2.7014737e-08, 0.0022699283, 2.0414376e-05, 0.99757725, 1.5412388e-05, 1.7638637e-05, 9.5042335e-05, 1.4481008e-06, 5.417283e-08],)]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS cifar10_val_random_predict_array, cifar10_val_random_predict_array_summary;\n",
"SELECT madlib.cols2vec(\n",
" 'cifar10_val_random_predict',\n",
" 'cifar10_val_random_predict_array',\n",
" '*',\n",
" 'id'\n",
");\n",
"select * from cifar10_val_random_predict_array;"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x1607c2710>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n",
" \n",
"deer 0.99757725\n",
"bird 0.0022699283\n",
"horse 9.5042335e-05\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x = %sql SELECT x FROM cifar10_val_random;\n",
"x = x.DataFrame().to_numpy()\n",
"import numpy as np\n",
"from matplotlib.pyplot import imshow\n",
"%matplotlib inline\n",
"x_np = np.array(x[0][0], dtype=np.uint8)\n",
"imshow(x_np);\n",
"\n",
"x = %sql SELECT * FROM cifar10_val_random_predict_array;\n",
"x = x.DataFrame().to_numpy()\n",
"x = np.array(x[0][0])\n",
"top_3_prob_label_indices = x.argsort()[-3:][::-1]\n",
"print (\" \");\n",
"for index in top_3_prob_label_indices:\n",
" print (label_names[index], x[index])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
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