{ "cells": [ { "cell_type": "markdown", "id": "7793101d-312a-404b-9ff1-6fb7a32b0914", "metadata": { "tags": [] }, "source": [ "# Transient Background Modeling in CDS\n", "\n", "This notebook demonstrates a background-modeling method for transients. Traditionally, one models the background by fitting the light curve before and after the burst. However, that approach only uses the count information after projecting to the `Time` axis. `cosipy` operates in Compton Data Space (CDS), so the background must be modeled in CDS as well. We therefore need a different approach that preserves the multi-dimensional structure of the data.\n", "\n", "## Overview\n", "\n", "The general idea is the same:\n", "\n", "1. Define background windows before and after the burst.\n", "2. Sum the data in those background windows and scale the result to the burst interval.\n", "\n", "We provide two scaling methods: `duration` and `fitting`.\n", "\n", "## Scaling Methods\n", "\n", "### `duration`\n", "\n", "1. Convert the summed background model into a unit-rate background (i.e., a 1 s background).\n", "2. Multiply by the burst duration so the expected total counts scale to the burst interval.\n", "\n", "### `fitting`\n", "**This will be supported in the future.**\n", "\n", "1. Fit the burst background level in a light curve using the background windows.\n", "2. Use the fitted result to estimate the number of background counts in the burst interval.\n", "3. Scale the summed background model to match the fitted counts.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "4e8581d7-558c-405a-af36-f5b8eccaa424", "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/io/package_data.py:4: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", " import pkg_resources\n" ] }, { "data": { "text/html": [ "
22:21:08 WARNING The naima package is not available. Models that depend on it will not be functions.py:43\n", " available \n", "\n" ], "text/plain": [ "\u001b[38;5;46m22:21:08\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The naima package is not available. Models that depend on it will not be \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=870555;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=214329;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#43\u001b\\\u001b[2m43\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mavailable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
WARNING The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it functions.py:65\n", " will not be available. \n", "\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The GSL library or the pygsl wrapper cannot be loaded. Models that depend on it \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=636155;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py\u001b\\\u001b[2mfunctions.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=47159;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/astromodels/functions/functions_1D/functions.py#65\u001b\\\u001b[2m65\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mwill not be available. \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:21:09 WARNING The ebltable package is not available. Models that depend on it will not be absorption.py:33\n", " available \n", "\n" ], "text/plain": [ "\u001b[38;5;46m22:21:09\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The ebltable package is not available. Models that depend on it will not be \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=171809;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py\u001b\\\u001b[2mabsorption.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=617466;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/astromodels/functions/functions_1D/absorption.py#33\u001b\\\u001b[2m33\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mavailable \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
INFO Starting 3ML! __init__.py:39\n", "\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;49mINFO \u001b[0m \u001b[1;38;5;251m Starting 3ML! \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=518591;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=98183;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py#39\u001b\\\u001b[2m39\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
WARNING WARNINGs here are NOT errors __init__.py:40\n", "\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m WARNINGs here are \u001b[0m\u001b[1;31mNOT\u001b[0m\u001b[1;38;5;251m errors \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=954403;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=303688;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py#40\u001b\\\u001b[2m40\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
WARNING but are inform you about optional packages that can be installed __init__.py:41\n", "\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m but are inform you about optional packages that can be installed \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=631489;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=297654;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py#41\u001b\\\u001b[2m41\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
WARNING to disable these messages, turn off start_warning in your config file __init__.py:44\n", "\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m \u001b[0m\u001b[1;31m to disable these messages, turn off start_warning in your config file\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=887349;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=49331;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
WARNING no display variable set. using backend for graphics without display (agg) __init__.py:50\n", "\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m no display variable set. using backend for graphics without display \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;38;5;251magg\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=56791;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=243834;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py#50\u001b\\\u001b[2m50\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:21:09 WARNING ROOT minimizer not available minimization.py:1345\n", "\n" ], "text/plain": [ "\u001b[38;5;46m22:21:09\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m ROOT minimizer not available \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=336782;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=88609;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1345\u001b\\\u001b[2m1345\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
WARNING Multinest minimizer not available minimization.py:1357\n", "\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Multinest minimizer not available \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=960876;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=524743;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1357\u001b\\\u001b[2m1357\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
WARNING PyGMO is not available minimization.py:1369\n", "\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m PyGMO is not available \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=966337;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/minimizer/minimization.py\u001b\\\u001b[2mminimization.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=44696;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/minimizer/minimization.py#1369\u001b\\\u001b[2m1369\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:21:10 WARNING The cthreeML package is not installed. You will not be able to use plugins which __init__.py:94\n", " require the C/C++ interface (currently HAWC) \n", "\n" ], "text/plain": [ "\u001b[38;5;46m22:21:10\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m The cthreeML package is not installed. You will not be able to use plugins which \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=732039;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=44431;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py#94\u001b\\\u001b[2m94\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mrequire the C/C++ interface \u001b[0m\u001b[1;38;5;251m(\u001b[0m\u001b[1;38;5;251mcurrently HAWC\u001b[0m\u001b[1;38;5;251m)\u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
WARNING Could not import plugin FermiLATLike.py. Do you have the relative instrument __init__.py:144\n", " software installed and configured? \n", "\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin FermiLATLike.py. Do you have the relative instrument \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=291878;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=198214;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251msoftware installed and configured? \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
WARNING Could not import plugin HAWCLike.py. Do you have the relative instrument __init__.py:144\n", " software installed and configured? \n", "\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Could not import plugin HAWCLike.py. Do you have the relative instrument \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=617541;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=83956;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py#144\u001b\\\u001b[2m144\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251msoftware installed and configured? \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:21:11 WARNING No fermitools installed lat_transient_builder.py:44\n", "\n" ], "text/plain": [ "\u001b[38;5;46m22:21:11\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m No fermitools installed \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=301673;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py\u001b\\\u001b[2mlat_transient_builder.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=793142;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/utils/data_builders/fermi/lat_transient_builder.py#44\u001b\\\u001b[2m44\u001b[0m\u001b]8;;\u001b\\\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:21:11 WARNING Env. variable MKL_NUM_THREADS is not set. Please set it to 1 for optimal __init__.py:387\n", " performances in 3ML \n", "\n" ], "text/plain": [ "\u001b[38;5;46m22:21:11\u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable MKL_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=534420;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=4047;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mperformances in 3ML \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
WARNING Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to 1 for optimal __init__.py:387\n", " performances in 3ML \n", "\n" ], "text/plain": [ "\u001b[38;5;46m \u001b[0m\u001b[38;5;46m \u001b[0m\u001b[38;5;134mWARNING \u001b[0m \u001b[1;38;5;251m Env. variable NUMEXPR_NUM_THREADS is not set. Please set it to \u001b[0m\u001b[1;37m1\u001b[0m\u001b[1;38;5;251m for optimal \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b]8;id=650082;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=732364;file:///home/sheng2/Applications/conda_envs/cosipy_yong_dev/lib/python3.10/site-packages/threeML/__init__.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[38;5;46m \u001b[0m \u001b[1;38;5;251mperformances in 3ML \u001b[0m\u001b[1;38;5;251m \u001b[0m\u001b[2m \u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pathlib import Path\n", "from histpy import Histogram\n", "from cosipy import TransientBackgroundEstimation\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "b937a9e4-05d4-4055-b86d-8599241a0739", "metadata": {}, "source": [ "## Load and examine data" ] }, { "cell_type": "code", "execution_count": 2, "id": "6d8d767c-f840-4dd3-860f-1865f7078110", "metadata": { "tags": [] }, "outputs": [], "source": [ "data_dir = Path(\"\") # Current directory by default. Modify if you want a different path" ] }, { "cell_type": "code", "execution_count": 4, "id": "9bd49ee1-d39e-4ec6-8543-8e0da0619f79", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Read the data\n", "\n", "file_name = \"GRB_bn110605183_with_bkg.hdf5\"\n", "\n", "data = Histogram.open(data_dir / file_name)" ] }, { "cell_type": "code", "execution_count": 5, "id": "e8a4a251-89c1-496c-ad3e-068bf84112ba", "metadata": { "tags": [] }, "outputs": [], "source": [ "# initialize background estimator\n", "\n", "estimator = TransientBackgroundEstimation(data)" ] }, { "cell_type": "code", "execution_count": 6, "id": "b74a32d9-6f8b-4022-b8c5-10c76d1ee077", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABTYAAAKyCAYAAADvrIF4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAACLX0lEQVR4nOzdeXxU5d3///eQjWwEQoCwyCrgQgRsFNlT72pJoVRUFrHgRu+KeGMt8nPrraKIVVs3rLa1WsRWjURsUaT025ZNwSVAJGyaAgYoi2wJ2Rcyvz+4Z5yZzIRZzsyck7yej4cPyTlzzrnOmXOdc+ZzPtd12ex2u10AAAAAAAAAYCFtol0AAAAAAAAAAAgUgU0AAAAAAAAAlkNgEwAAAAAAAIDlENgEAAAAAAAAYDkENgEAAAAAAABYDoFNAAAAAAAAAJZDYBMAAAAAAACA5RDYBAAAAAAAAGA5BDYNVFNToy+//FI1NTXRLgoAAAAAAADQohHYNFBJSYl+8pOfqKSkJNpFAQAAAAAAAFo0ApsAAAAAAAAALIfAJgAAAAAAAADLIbAJAAAAAAAAwHIIbAIAAAAAAACwHAKbAAAAAAAAACyHwCYAAAAAAAAAyyGwCQAAAAAAAMByCGwCAAAAAAAAsJzYaBfAm7y8PG3btk179+5VaWmp6urqlJ6eriFDhmjatGnq16+f2+dfe+01LVmyxOf6pk+frttvv93rvKKiIi1dulQ7d+5UQ0ODevXqpWuvvVbjxo0zcpcAAAAAAACc7Ha76uvr1djYGO2iAGHTpk0bxcXFyWazhWX9pgxs/ulPf1J1dbX69eunvn37SpK+/vprrV69Wv/85z+1cOFCjRgxoslyWVlZ6t69e5PpAwcO9LqdtWvXasGCBWpsbNTgwYOVlpamzZs3a9GiRdqzZ4/mzJlj7I4BAAAAAIBWra6uTt98842qqqp05syZaBcHCLuYmBglJSWpc+fOio+PN3TdpgxsLlq0SAMGDFBCQoLb9Pfee0/PPvusnnrqKeXn5ys21r34EyZMUG5url/bOH36tJ588kmdOXNGjz32mMaOHStJOnnypObMmaO8vDyNGDFCQ4cONWanAAAAAABAq1ZVVaUDBw4oJiZGHTp0UGJiomJiYsKWzQZEk91u15kzZ1RdXa2ysjJ9/fXX6tGjh5KSkgzbhikDm1lZWV6nT5o0Se+8847+85//qKSkpEmT9EB88MEHqqys1KhRo5xBTUlKT0/X7Nmz9Ytf/EJ5eXkENgEAAAAAgCGOHz+uuLg49erVSzExMdEuDhARKSkpSk9PV0lJiY4fP66ePXsatm7LDR7kqPie2ZqB2rRpkyQpJyenybzhw4crPj5eBQUFqq2tDWk7AAAAAAAADQ0NqqysVHp6OkFNtDoxMTFKT09XZWWlGhoaDFuvKTM2fVm9erUOHDigHj16qEePHk3mb9myRcXFxaqrq1OnTp10xRVX+Oxfc8+ePZKkAQMGNJkXFxenvn37avfu3Tp48GBImaEAAAAAAACOYI5nt3tAa+E49xsaGkJOWHQwdWDzrbfe0r59+1RTU6OSkhLt27dPGRkZevjhh72+3Vi9erXb36+++qrGjh2r+++/3639fmVlpSoqKiRJnTp18rrtTp06affu3Tpy5AiBTQAAAAAAYAj600RrFY5z39SBzc8++0ybN292/p2ZmakHHnigSRZmjx49dMcdd+iKK65Qly5dVF5eri+++EK//e1vtW7dOjU2Nurxxx93fr66utr5b19vStq2bSvpbMe+vhw/flwnTpxw/l1SUhLYDgIAAAAAAAAIiqkDm88++6wkqby8XHv37tXrr7+uuXPnatasWZo5c6bzc1dffbXbcomJibrqqqs0dOhQ3XLLLdqwYYN27Nihiy++2NDyrVixQkuWLDF0nQAAAAAAAADOzRKDB6Wmpmrw4MF66qmnNHDgQL366qvatWvXOZfLyMhQbm6uJOnTTz91Tk9MTHT+29fgQDU1NZLU7BD0EydO1CuvvOL87xe/+IVf+wMAAAAAAAAgNJYIbDrExsbqyiuvlN1u18aNG/1axjHIkGuT8eTkZKWkpEiSjh075nU5x/TMzEyf687IyNDAgQOd//Xq1cuvMgEAAAAAAOBsv4v+9r24ZMkS2Ww2PfLIIyFv18h1IXosFdiUpLS0NElSaWmpX58vLy+X5J6lKck5INBXX33VZJmGhgbt3btX8fHxXkdfBwAAAAAAQOuydu1a2Ww23XzzzdEuCv6P5QKbhYWFkqRu3bqd87N2u10bNmyQJA0YMMBt3vDhwyWdPSk9bdy4UXV1dcrOzvY5uBAAAAAAAAAiZ9KkSdq1a5fuvPPOaBcFJmG6wGZRUZE+/fRTNTY2uk1vaGjQu+++q7///e9KSEjQlVdeKels5uby5cubjF5eVVWlX//619q5c6fS09M1ZswYt/kTJkxQcnKyPvroI61bt845/dSpU3r55ZclSVOnTg3HLgIAAAAAACBAaWlpuuCCC5SRkRHtosAkTBfYPHjwoObPn68f/ehHuueee/Too49q3rx5mjx5sp5//nnFxsbq/vvvV5cuXSRJ1dXVeu655zRp0iTdddddevTRR/Xzn/9cU6dO1YoVK5SSkqLHHntMbdu2ddtOu3btdO+996pNmzZ66KGHdNddd+mhhx7SjTfeqP/85z+aMmWKhg4dGo1DAAAAAAAAAA/N9Yt5/PhxzZ49W926dVNiYqIGDRqk3/zmN7Lb7bLZbOrdu7fP9e7fv1/Tp09Xp06dlJiYqOzsbL3//vtun7n55pv13e9+V5L0+uuvO/sGDbSfzr/97W+aOHGiunTpooSEBJ133nmaMGGC3n33Xednvv76a9lsNuXk5HhdxyOPPCKbzaYlS5a4Te/du7dsNpvsdrsWL16swYMHKykpSUOGDNHy5ctls9maTeKbN2+ebDabXnjhBbfpVVVVeuKJJzR06FClpKQoJSVFV1xxhV5//XW/9ztcYqNdAE+DBw/WjBkzVFhYqD179qisrExxcXHKzMxUTk6OrrvuOrd+L9PS0jR9+nTt3LlTBw8e1I4dO9SmTRt17dpV48aN05QpU9SpUyev28rJydHixYu1dOlS7dixQw0NDerdu7cmTZrkHE0dAAAAAAAA5nX8+HGNGDFCxcXF6tatmyZOnKhTp07p7rvvVnFxcbPLfv3117rsssuUmpqq//qv/9L+/fu1adMmXXPNNVq1apWuvvpqSdKoUaN05MgRrV69Wv369dOoUaOc6xgyZIhf5Zw3b56eeeYZtWnTRsOHD1fPnj116NAhffzxxzp48KCuu+66oI+Bq9tvv11//OMfNXbsWF144YWqq6vT+PHjlZaWpvfff18VFRXOQbUdGhsb9fbbbysmJkbTpk1zTv/mm2901VVXadu2bcrMzNTYsWOdg3rffPPNKigo0OLFiw0pdzBMF9js1q2bfvKTn/j9+aSkJN1+++1Bby8rK0tPP/100MsDAAAAAAAgeu677z4VFxdr4sSJysvLc7ba3bJli7MrQ19ef/11zZs3T0899ZTatDnbsPm5557T3XffrYULFzoDm7NmzdL555+v1atXa9SoUU2yJc/lT3/6k5555hl169ZNK1eudAuGVldX66OPPgpofc1Zvny5tm7dqosvvtht+vXXX69XX31V7733nmbMmOE2b82aNTp06JDGjRunzp07O6ffcsst2rZtm+666y49+eSTzrFojh49qgkTJujFF1/U+PHjNW7cOMPKHwjTBTYBAAAAAABao788+LGqymqjXQy/JKUl6JrHR0a7GKqoqNCf//xnxcTE6Pnnn3frivDSSy/VnXfeqccff9zn8n369NGiRYucQU1JuvPOO/Xoo4/qk08+UV1dneLj40Mu56JFiyRJzzzzTJMMz8TERF111VUhb8Ph3nvvbRLUlKQf//jHevXVV/XnP/+5SWDzz3/+syTpxhtvdE4rLCzUhx9+qMsuu8yZaerQpUsX/f73v9ell16ql19+mcAmAAAAYGZ7PzmszfnFqq9pUFzbWGVP7q8+w7pGu1gAgBakqqxWVSetEdg0i82bN6umpkZXXHGF1340p06d2mxgMycnp0ngMjY2Vn369NGWLVt04sQJde0a2v3+0KFD2rVrl9q3b68pU6aEtC5/TJw40ev0MWPGqEePHvrnP/+pb775xpmZWVNTo3fffVfJycmaNGmS8/N///vfJUnXXHONW1DTwdHn5meffRaGvfAPgU0AAADAD5vzi1V2qPL//qpVwbJiApsAAEMlpSVEuwh+M0tZDx8+LEk677zzvM7v2bNns8u7juPiKjU1VZJUWxt6oPnAgQOSpL59+8pms4W8vnPxtc9t2rTRDTfcoKefflp5eXn6n//5H0nSBx98oNOnT2v69OlKTk52fv7rr7+WJD344IN68MEHfW6vpqbGuMIHiMAmAAAA4If6moZm/wYAIFRmaNrd2njLRDS7xsbGZue7Nsf39OMf/1hPP/203nzzTWdg01szdNftjBo1Sv369QulyGFDYBMAAAAAAACW5Ggm7siK9ORreiQ5skn37t0ru91+zqxNR9P4iooKr/ND2adLLrlEgwYN0ieffKK9e/eqQ4cO+vDDD9WpUyfnQEkOjmzWa665RvPmzQt6m+FkvbA0AAAAAAAAIOk73/mO2rZtq4KCAu3fv7/J/HfeecewbTkCjg0NgbXa6Natmy688EKVlpZq2bJl5/x8RkaGYmNjtW/fvibbqq+v17p16wLavidHZuabb76p/Px81dXVaerUqYqNdc9/dAxo9N5774W0vXAisAkAAAAAAABLSklJ0Y033qiGhgbdddddbn1ifvHFF1q8eLFh2+rWrZsk6csvvwx42fvuu0+S9POf/1zbtm1zm1dTU6P/9//+n/Pv+Ph4DR8+XCdPntRvfvMb5/SGhgbNmzdP+/btC6b4TtOnT5fNZtObb77psxm6JA0bNkxXXXWVPv74Y82ZM0enT59u8pkvvvhCf/vb30IqTyhoig4AAAAAAICouuKKK3zOmzVrlmbNmuVz/i9/+UutW7dOf/nLX9SvXz+NGjVKpaWl+te//qWf/vSnevHFF5uMfB6M3r1765JLLlFBQYEuv/xyXXzxxYqJidHEiRN9jkTuMHPmTBUUFGjx4sW69NJLNXz4cJ133nk6fPiwCgsL1atXLxUWFjo//9BDD+n73/++fvaznykvL0+ZmZnavHmzqqqqdNNNN+n1118Pej969uyp0aNHa/369dq1a5f69evn8/j/6U9/0rhx4/TSSy/pzTff1JAhQ9StWzeVlZVp27ZtOnDggO666y6NGzcu6PKEgsAmAAAA4MPeTw5rc36x6msaVH0q9FFRAQCAd59++qnPeecKmmVkZGjjxo363//9X/31r3/VX/7yF/Xt21dPP/20rr32Wr344ovq2LGjIeV89913NX/+fG3YsEGbN29WY2OjevTocc7ApiS98MIL+t73vqeXXnpJn3/+uT777DN17txZo0aN0q233ur22e9973tasWKFFixYoC1btig5OVnf+9739OSTT2rJkiUh78eNN96o9evXO//tS+fOnbVx40a98sorevvtt7V161Zt3LhRXbp0Ud++fTV37lxNmzYt5PIEy2a32+1R23oL8+WXX+onP/mJXnnlFQ0cODDaxQEAAECIlt2zXmWHKr3OS0pP0PQXr4xwiQAAVlVTU6N9+/apT58+zY5aDWO9/fbbuuGGG3T77bfr5ZdfjnZxWrVw1AH62AQAAAB8qK8522G/zXY2kJnWLTnKJQIAAN5s3ry5ybTCwkLNnz9fkvTjH/840kVCBNAUHQAAADiHxA7fZme+eee/VHWSZukAAJjJyJEjlZmZqQsvvFDt2rXTvn37nE3F77zzTo0cOTLaRUQYENgEAAAAAACApd1///368MMPVVBQoNLSUqWkpGjMmDGaNWtWs31IwtoIbAIAAAAAAMDSHn74YT388MPRLgYijD42AQAAAAAAAFgOgU0AAAAAAAAAlkNgEwAAAAAAAIDlENgEAAAAAACIELvdHu0iAFERjnOfwCYAAAAAAECYxcaeHb+5trY2yiUBosNx7jvqghEIbAIAAAAAAIRZbGyskpOTdfLkSZ05cybaxQEi6syZMzp58qSSk5MNDWwatyYAAAAAAAD4lJGRoQMHDmjfvn1KS0tTYmKiYmJiZLPZol00wHB2u11nzpxRdXW1ysrK1NjYqK5duxq6DQKbAAAAAAAAEZCUlKQ+ffrom2++0alTp3T8+PFoFwkIu5iYGCUlJalz586Kj483dN0ENgEAAAAAACIkPj5ePXr0kN1uV319vRobG6NdJCBs2rRpo7i4uLBlJRPYBAAAAAAAiDCbzWZ49hrQ2jB4EAAAAAAAAADLIWMTAAAA8LD3k8PanF+s6lO10S4KAAAAfCCwCQAAAHjYnF+sskOVzr/j2vLYDAAAYDY8oQEAAAAe6msaJEk2m9Sua7KyJ/ePcokAAADgicAmAAAA8H88m6AndkjQ5F+NiXKpAAAA4A2BTQAAAOD/0AQdAADAOnhSAwAAAP4PTdABAACsg8AmAAAA4IEm6AAAAObXJtoFAAAAAAAAAIBAEdgEAAAAAAAAYDkENgEAAAAAAABYDoFNAAAAAAAAAJZDYBMAAAAAAACA5RDYBAAAAAAAAGA5BDYBAAAAAAAAWA6BTQAAAAAAAACWQ2ATAAAAAAAAgOUQ2AQAAAAAAABgOQQ2AQAAAAAAAFgOgU0AAAAAAAAAlkNgEwAAAAAAAIDlENgEAAAAAAAAYDkENgEAAAAAAABYDoFNAAAAAAAAAJZDYBMAAAAAAACA5RDYBAAAAAAAAGA5BDYBAAAAAAAAWA6BTQAAAAAAAACWQ2ATAAAAAAAAgOUQ2AQAAAAAAABgOQQ2AQAAAAAAAFgOgU0AAAAAAAAAlkNgEwAAAAAAAIDlENgEAAAAAAAAYDkENgEAAIAgVJ+q1bJ71mvfp4ejXRQAAIBWicAmAAAAEIC4trGSJLtdKjtUqYJlxVEuEQAAQOtEYBMAAAAIQPbk/krrliyb7ezf9TUN0S0QAABAK0VgEwAAAAhAn2FdNflXY5TYISHaRQEAAGjVCGwCAAAAAAAAsBwCmwAAAAAAAAAsh8AmAAAAAAAAAMshsAkAAAAAAADAcghsAgAAAAAAALAcApsAAAAAAAAALIfAJgAAAAAAAADLIbAJAAAAAAAAwHIIbAIAAAAAAACwHAKbAAAAAAAAACyHwCYAAAAAAAAAyyGwCQAAAAAAAMByCGwCAAAAAAAAsJzYaBfAm7y8PG3btk179+5VaWmp6urqlJ6eriFDhmjatGnq16+f1+VWrVql9957TyUlJYqNjdVFF12kmTNnKisry+e2ioqKtHTpUu3cuVMNDQ3q1auXrr32Wo0bNy5cuwcAAAAAAAAgRKYMbP7pT39SdXW1+vXrp759+0qSvv76a61evVr//Oc/tXDhQo0YMcJtmRdeeEH5+flKSEjQZZddprq6OhUUFKigoECPPvqoRo8e3WQ7a9eu1YIFC9TY2KjBgwcrLS1Nmzdv1qJFi7Rnzx7NmTMnIvsLAAAAAAAAIDCmDGwuWrRIAwYMUEJCgtv09957T88++6yeeuop5efnKzb2bPELCgqUn5+vtLQ0vfTSSzrvvPMkSdu3b9ddd92lJ554QkOGDFFqaqpzXadPn9aTTz6pM2fO6LHHHtPYsWMlSSdPntScOXOUl5enESNGaOjQoRHaawAAAAAAAAD+MmUfm1lZWU2CmpI0adIkde/eXSdPnlRJSYlzel5eniRpxowZzqCmJA0aNEgTJ05URUWFVq5c6bauDz74QJWVlRo1apQzqClJ6enpmj17ttt6AQAAAAAAAJiLKQObzYmJiZEkZ7ZmbW2ttm7dKknKyclp8nnHtI0bN7pN37Rpk89lhg8frvj4eBUUFKi2ttagkgMAAAAAAAAwiqUCm6tXr9aBAwfUo0cP9ejRQ5K0f/9+1dXVqX379urcuXOTZQYMGCBJ2rNnj9t0x9+O+a7i4uLUt29f1dXV6eDBg0bvBgAAAAAAAIAQmbKPTYe33npL+/btU01NjUpKSrRv3z5lZGTo4YcfdmZuHj16VJLUqVMnr+tITExUSkqKysvLVVVVpaSkJFVWVqqioqLZ5Tp16qTdu3fryJEjPkdhBwAAAAAAABAdpg5sfvbZZ9q8ebPz78zMTD3wwAMaOHCgc1p1dbUkqW3btj7Xk5iYqIqKCmdg07GMJK99ebqur6qqyud6jx8/rhMnTjj/du33EwAAAAAAAED4mDqw+eyzz0qSysvLtXfvXr3++uuaO3euZs2apZkzZ0a5dNKKFSu0ZMmSaBcDAAAAAAAAaHVMHdh0SE1N1eDBg/XUU09p9uzZevXVV3XZZZfpwgsvVGJioiSppqbG5/KODM2kpCRJci4jnR18yDEQkSvH+hzLeDNx4kSNHDnS+XdJSYkWLlwYwJ4BAAAAAAAACIYlApsOsbGxuvLKK/Xll19q48aNuvDCC9WlSxdJ0rFjx7wuU11drYqKCqWmpjqDlMnJyUpJSVFFRYWOHTum5OTkJss51peZmemzPBkZGcrIyAh1twAAAAAAAAAEyFKjoktSWlqaJKm0tFSS1LNnT8XHx6u0tNRrcPOrr76SpCYDADn+dsx31dDQoL179yo+Pt45+joAAAAAAAAA87BcYLOwsFCS1K1bN0lnB/8ZOnSoJGnNmjVNPr927VpJ0ogRI9ymDx8+3G2+q40bN6qurk7Z2dk+BxcCAAAAAAAAED2mC2wWFRXp008/VWNjo9v0hoYGvfvuu/r73/+uhIQEXXnllc55U6dOlSS98cYbOnDggHP69u3b9f777yslJUXjx493W9+ECROUnJysjz76SOvWrXNOP3XqlF5++WW39QIAAAAAAAAwF9P1sXnw4EE98cQTSktL08CBA9WuXTuVlZVp7969OnHihOLj43X//fc7+9aUpOzsbF1//fXKz8/XbbfdpuzsbNXX16ugoECSdP/99ys1NdVtO+3atdO9996rRx55RA899JCGDBmitLQ0FRQUqKKiQlOmTHFmggIAAAAAAAAwF9MFNgcPHqwZM2aosLBQe/bsUVlZmeLi4pSZmamcnBxdd911Xvu9nDt3rvr376/ly5eroKBAsbGxys7O1syZM5WVleV1Wzk5OVq8eLGWLl2qHTt2qKGhQb1799akSZOUm5sb7l0FAAAAAAAAECTTBTa7deumn/zkJ0Etm5ubG3BAMisrS08//XRQ2wMAAAAAAAAQHabrYxMAAAAAAAAAzoXAJgAAAAAAAADLIbAJAAAAAAAAwHIIbAIAAAAAAACwHAKbAAAAAAAAACyHwCYAAAAAAAAAyyGwCQAAAAAAAMByCGwCAAAAAAAAsBwCmwAAAAAAAAAsh8AmAAAAAAAAAMshsAkAAAAAAADAcghsAgAAAAAAALAcApsAAAAAAAAALIfAJgAAAAAAAADLIbAJAAAAAAAAwHIIbAIAAAAAAACwHAKbAAAAAAAAACyHwCYAAAAAAAAAyyGwCQAAAAAAAMByCGwCAAAAAAAAsBwCmwAAAAAAAAAsh8AmAAAAAAAAAMshsAkAAAAAAADAcghsAgAAAAAAALAcApsAAAAAAAAALIfAJgAAAAAAAADLIbAJAAAAAAAAwHIIbAIAAAAAAACwHAKbAAAAAAAAACyHwCYAAAAAAAAAyyGwCQAAAAAAAMByCGwCAAAAAAAAsBwCmwAAAAAAAAAsh8AmAAAAAAAAAMshsAkAAAAAAADAcghsAgAAAAAAALAcApsAAAAAAAAALIfAJgAAAAAAAADLIbAJAAAAAAAAwHIIbAIAAAAAAACwHAKbAAAAAAAAACyHwCYAAAAAAAAAyyGwCQAAAAAAAMByCGwCAAAAAAAAsBwCmwAAAAAAAAAsh8AmAAAAAAAAAMshsAkAAAAAAADAcghsAgAAAAAAALAcApsAAAAAAAAALIfAJgAAAAAAAADLIbAJAAAAAAAAwHIIbAIAAAAAAACwHAKbAAAAAAAAACyHwCYAAAAAAAAAyyGwCQAAAAAAAMByCGwCAAAAAAAAsBwCmwAAAAAAAAAsh8AmAAAAAAAAAMshsAkAAAAAAADAcghsAgAAAAAAALAcApsAAAAAAAAALIfAJgAAAAAAAADLIbAJAAAAAAAAwHIIbAIAAAAAAACwHAKbAAAAAAAAACyHwCYAAAAAAAAAyyGwCQAAAAAAAMByCGwCAAAAAAAAsBwCmwAAAAAAAAAsh8AmAAAAAAAAAMshsAkAAAAAAADAcghsAgAAAAAAALAcApsAAAAAAAAALIfAJgAAAAAAAADLIbAJAAAAAAAAwHJio10AAAAAINr2fnJYm/OLVX2qNtpFAQAAgJ9MF9isqanR559/ro8//lhFRUU6evSo2rRpo+7du2vs2LGaMmWKkpKS3JZ57bXXtGTJEp/rnD59um6//Xav84qKirR06VLt3LlTDQ0N6tWrl6699lqNGzfOyN0CAACAiW3OL1bZoUrn33FtTfeYDAAAAA+me2L7xz/+oaeeekqS1KtXL40cOVKVlZXavn27XnvtNf3jH//Q4sWL1aFDhybLZmVlqXv37k2mDxw40Ou21q5dqwULFqixsVGDBw9WWlqaNm/erEWLFmnPnj2aM2eOsTsHAAAAU6qvaZAk2WxSu67Jyp7cP8olAgAAwLmYLrAZExOjH/7wh5o8ebJ69+7tnH78+HHde++9Ki4u1uLFi/XQQw81WXbChAnKzc31azunT5/Wk08+qTNnzuixxx7T2LFjJUknT57UnDlzlJeXpxEjRmjo0KGG7BcAAADML7FDgib/aky0iwEAAAA/mG7woNzcXM2fP98tqClJGRkZuvvuuyVJ69evV319fUjb+eCDD1RZWalRo0Y5g5qSlJ6ertmzZ0uS8vLyQtoGAAAAAAAAgPAwXWCzOeeff74kqa6uTmVlZSGta9OmTZKknJycJvOGDx+u+Ph4FRQUqLaWDuQBAAAAAAAAszFdU/TmHDp0SJIUGxurdu3aNZm/ZcsWFRcXq66uTp06ddIVV1zhs3/NPXv2SJIGDBjQZF5cXJz69u2r3bt36+DBg+rXr5+BewEAAAAAAAAgVJYKbObn50uSLr/8csXHxzeZv3r1are/X331VY0dO1b333+/20jqlZWVqqiokCR16tTJ67Y6deqk3bt368iRIz4Dm8ePH9eJEyecf5eUlAS2QwAAAAAAAACCYpnA5qZNm7Ry5UrFxsZq1qxZbvN69OihO+64Q1dccYW6dOmi8vJyffHFF/rtb3+rdevWqbGxUY8//rjz89XV1c5/JyQkeN1e27ZtJUlVVVU+y7RixQotWbIkhL0CAAAAAAAAEAxLBDZLSkq0cOFC2e12zZ4929nXpsPVV1/t9ndiYqKuuuoqDR06VLfccos2bNigHTt26OKLLza0XBMnTtTIkSOblBMAAAAAAABAeJl+8KBjx45p/vz5Ki8v15QpUzR58mS/l83IyFBubq4k6dNPP3VOT0xMdP7b1+BANTU1kuTWhN3b+gcOHOj8r1evXn6XDQAAAAAAAEDwTB3YPH36tObNm6cjR47oBz/4gebMmRPwOnr06CFJbn1hJicnKyUlRdLZwKk3jumZmZkBbxMAAAAAAABAeJk2sFlVVaX58+fr66+/1pgxYzR//nzZbLaA11NeXi7JPUtTknNAoK+++qrJMg0NDdq7d6/i4+OdgVEAAAAAAAAA5mHKwGZdXZ0eeOAB7dq1S5dffrkefvhhxcTEBLweu92uDRs2SJIGDBjgNm/48OGSpLVr1zZZbuPGjaqrq1N2drbPwYUAAAAAAAAARI/pAptnzpzRggULtGXLFl1yySVauHCh4uLifH6+tLRUy5cvbzJ6eVVVlX79619r586dSk9P15gxY9zmT5gwQcnJyfroo4+0bt065/RTp07p5ZdfliRNnTrVwD0DAAAAAAAAYBTTjYq+fPlyZ5ZlWlqannnmGa+fu+OOO9S+fXtVV1frueee0+9+9ztdcMEF6tixo0pLS1VcXKyysjKlpKToscceU9u2bd2Wb9eune6991498sgjeuihhzRkyBClpaWpoKBAFRUVmjJlioYOHRr2/QUAAAAAAAAQONMFNh19YkpyBji9ueWWW9S+fXulpaVp+vTp2rlzpw4ePKgdO3aoTZs26tq1q8aNG6cpU6aoU6dOXteRk5OjxYsXa+nSpdqxY4caGhrUu3dvTZo0yTmaOgAAAAAAAADzMV1g89Zbb9Wtt97q9+eTkpJ0++23B729rKwsPf3000EvDwAAAAAAACDyTNfHJgAAAAAAAACcC4FNAAAAAAAAAJZDYBMAAAAAAACA5RDYBAAAAAAAAGA5BDYBAAAAAAAAWA6BTQAAAAAAAACWQ2ATAAAAAAAAgOUQ2AQAAAAAAABgOQQ2AQAAAAAAAFgOgU0AAAAAAAAAlkNgEwAAAAAAAIDlENgEAAAAAAAAYDkENgEAAAAAAABYDoFNAAAAAAAAAJZDYBMAAAAAAACA5RDYBAAAAAAAAGA5BDYBAAAAAAAAWA6BTQAAAAAAAACWQ2ATAAAAAAAAgOUQ2AQAAAAAAABgOQQ2AQAAAAAAAFgOgU0AAAAAAAAAlkNgEwAAAAAAAIDlENgEAAAAAAAAYDkENgEAAAAAAABYDoFNAAAAAAAAAJZDYBMAAAAAAACA5RDYBAAAAAAAAGA5BDYBAAAAAAAAWA6BTQAAAAAAAACWQ2ATAAAAAAAAgOUQ2AQAAAAAAABgOQQ2AQAAAAAAAFgOgU0AAAAAAAAAlkNgEwAAAAAAAIDlENgEAAAAAAAAYDkENgEAAAAAAABYDoFNAAAAAAAAAJZDYBMAAAAAAACA5RDYBAAAAAAAAGA5BDYBAAAAAAAAWA6BTQAAAAAAAACWE+vPh44ePRryhlJSUpScnBzyegAAAAAAAADAr8DmlClTZLPZQtrQzTffrJtvvjmkdQAAAAAAAACA5GdgU5L69eun888/P+AN2O12rV69OuDlAAAAAAAAAMAXvwObo0ePDjrjksAmAAAAAAAAACP5NXhQhw4dlJiYGPRGQl0eAAAAAAAAAFz5lbH5l7/8JaSNhLo8AAAAAAAAALjyK2MTAAAAAAAAAMyEwCYAAAAAAAAAy/F78CBP9fX12rBhg3bv3q2Kigo1NjZ6/dx9990XdOEAAAAAAAAAwJugAptHjhzRz3/+cx06dEh2u93n52w2G4FNAAAAAAAAAIYLKrC5ePFi/ec//9HVV1+t8ePHq1OnToqJiTG6bAAAAAAAAADgVVCBza1bt+o73/mOHnzwQaPLAwAAAAAAAADnFNTgQY2Njerfv7/RZQEAAAAAAAAAvwQV2LzoootUUlJidFkAAAAAAAAAwC9BBTZ/+tOfasuWLVq7dq3BxQEAAAAAAACAc/Orj80lS5Y0mTZ06FA98sgjGjx4sAYMGKDk5OQmn7HZbLrppptCLiQAAAAAAAAAuPIrsPnHP/7R57zCwkIVFhZ6nUdgEwAAAAAAAEA4+BXYfP7558NdDgAAAAAAAADwm1+BzSFDhoS5GAAAAAAAAADgv6AGDwIAAAAAAACAaPIrY9OX9evXa9WqVSouLlZlZaWSk5M1YMAA5ebmavTo0UaVEQAAAAAAAADcBBXYbGho0GOPPaZ169bJbrcrJiZG7dq108mTJ/Xxxx9r48aNGjt2rP73f/9XsbEhxU4BAAAAAAAAoImgoo5//vOftXbtWg0ePFg/+clPdPHFF6tNmzZqbGzU9u3b9corr2jdunXq27cvo6IDAAAAAAAAMFxQfWx++OGH6tmzp5555hllZWWpTZuzq2nTpo0uueQSPfvss+rRo4c+/PBDQwsLAAAAAAAAAFKQgc0TJ05oxIgRPpuZx8bGauTIkTpx4kRIhQMAAAAAAAAAb4IKbHbu3FnV1dXNfqa6ulpdunQJqlAAAAAAAAAA0JygApsTJkzQmjVrdPz4ca/zjx07pjVr1mjChAkhFQ4AAAAAAAAAvAlq8KDvfve7Kioq0qxZs3T99dfrkksuUYcOHXTq1Clt27ZN+fn5ysrKUk5Ojo4ePeq2LFmcAAAAAAAAAEIVVGBz2rRpstlsstvt+sMf/tBkvt1u18aNG7Vx40a36TabTWvWrAmupAAAAAAAAADwf4IKbH7/+9+XzWYzuiwAAAAAAAAA4JegApsPPPCA0eUAAAAAAAAAAL8FNXgQAAAAAAAAAEQTgU0AAAAAAAAAluNXU/TZs2drwoQJGj9+fFAbCWT5mpoaff755/r4449VVFSko0ePqk2bNurevbvGjh2rKVOmKCkpyeuyq1at0nvvvaeSkhLFxsbqoosu0syZM5WVleVze0VFRVq6dKl27typhoYG9erVS9dee63GjRsX1L4CAAAAAAAACD+/MjZ37typY8eOBb2RQJb/xz/+oQcffFAffvih2rRpo5EjR+qSSy7R4cOH9dprr+m///u/derUqSbLvfDCC3riiSe0b98+fec739GFF16ogoICzZ07Vxs2bPC6rbVr12ru3Ln67LPP1K9fP11++eU6ePCgFi1apN/85jdB7y8AAAAAAACA8PJ78KCtW7cGvZFARlCPiYnRD3/4Q02ePFm9e/d2Tj9+/LjuvfdeFRcXa/HixXrooYec8woKCpSfn6+0tDS99NJLOu+88yRJ27dv11133aUnnnhCQ4YMUWpqqnOZ06dP68knn9SZM2f02GOPaezYsZKkkydPas6cOcrLy9OIESM0dOjQoPcbAAAAAAAAQHj4HdgsLCxUYWFhGItyVm5urnJzc5tMz8jI0N1336077rhD69evV319veLi4iRJeXl5kqQZM2Y4g5qSNGjQIE2cOFHvvvuuVq5cqWnTpjnnffDBB6qsrNSoUaOcQU1JSk9P1+zZs/WLX/xCeXl5BDYBAAAAAAAAE/IrsPn888+HvKHMzMyQ13H++edLkurq6lRWVqaMjAzV1tY6s0lzcnKaLJOTk6N3331XGzdudAtsbtq0yecyw4cPV3x8vAoKClRbW6uEhISQyw4AAAAAAADAOH4FNocMGRLmYvjn0KFDkqTY2Fi1a9dOkrR//37V1dWpffv26ty5c5NlBgwYIEnas2eP23TH3475ruLi4tS3b1/t3r1bBw8eVL9+/QzdDwAAAAAAAACh8WvwILPIz8+XJF1++eWKj4+XJB09elSS1KlTJ6/LJCYmKiUlReXl5aqqqpIkVVZWqqKiotnlHNOPHDli3A4AAAAAAAAAMITffWxG26ZNm7Ry5UrFxsZq1qxZzunV1dWSpLZt2/pcNjExURUVFaqqqlJSUpJzGUk+m5k71ucIhnpz/PhxnThxwvl3SUmJfzsDAAAAAAAAICSWCGyWlJRo4cKFstvtmj17trOvzWhbsWKFlixZEu1iAAAAAAAAAK2O6QObx44d0/z581VeXq4pU6Zo8uTJbvMTExMlSTU1NT7X4cjQTEpKcltGkmpraxUb2/QwONbnWMabiRMnauTIkc6/HQFYAAAAAAAAAOFl6sDm6dOnNW/ePB05ckQ/+MEPNGfOnCaf6dKli6SzAVBvqqurVVFRodTUVGeQMjk5WSkpKaqoqNCxY8eUnJzcZDnH+pobzT0jI0MZGRkB7xcAAAAAAACA0Jh28KCqqirNnz9fX3/9tcaMGaP58+fLZrM1+VzPnj0VHx+v0tJSr8HNr776SpKajGzu+Nsx31VDQ4P27t2r+Ph49ejRw4jdAQAAAAAAAGAgUwY26+rq9MADD2jXrl26/PLL9fDDDysmJsbrZxMSEjR06FBJ0po1a5rMX7t2rSRpxIgRbtOHDx/uNt/Vxo0bVVdXp+zsbJ+DCwEAAAAAAACInqACm3v27NHKlStVWVnpnFZbW6tf//rXuvbaa3XDDTfor3/9a1AFOnPmjBYsWKAtW7bokksu0cKFCxUXF9fsMlOnTpUkvfHGGzpw4IBz+vbt2/X+++8rJSVF48ePd1tmwoQJSk5O1kcffaR169Y5p586dUovv/yy23oBAAAAAAAAmEtQfWwuXbpURUVF+sEPfuCc9vvf/14rVqxQYmKiysrK9Oyzz6pbt2667LLLAlr38uXLtWHDBklSWlqannnmGa+fu+OOO9S+fXtJUnZ2tq6//nrl5+frtttuU3Z2turr61VQUCBJuv/++5Wamuq2fLt27XTvvffqkUce0UMPPaQhQ4YoLS1NBQUFqqio0JQpU5yZoAAAAAAAAADMJajA5q5duzR06FBnn5cNDQ1atWqVLrzwQj3//PMqLy/XrFmzlJ+fH3Bgs7y83PlvR4DTm1tuucUZ2JSkuXPnqn///lq+fLkKCgoUGxur7OxszZw5U1lZWV7XkZOTo8WLF2vp0qXasWOHGhoa1Lt3b02aNEm5ubkBlRsAAAAAAABA5AQV2CwrK1Pnzp2df+/evVuVlZX60Y9+pISEBCUkJGjkyJH65JNPAl73rbfeqltvvTWYYik3NzfggGRWVpaefvrpoLYHAAAAAAAAIDqC6mMzJiZG9fX1zr8LCwtls9ncmm6npaWprKws9BICAAAAAAAAgIegMjYzMzO1detW599r1qxR165dlZmZ6Zx27NgxpaWlhV5CAAAAwMSqTtZq2T3rVV/TIEmKaxur7Mn91WdY1yiXDAAAoGULKrB59dVX6+WXX9ZPf/pTxcXFac+ePZoxY4bbZ/bu3asePXoYUkgAAADAzMoOVbr8VauCZcUENgEAAMIsqKbo1157rXJycvTll1+qqKhIw4YN049//GPn/H379unf//63Lr30UsMKCgAAAJiZzXb2P0nO7E0AAACET1AZm/Hx8VqwYIEqKytls9mUlJTkNr9Dhw569dVX3ZqmAwAAAC1ZYocESWebpgMAACD8gsrYLCws1NGjR5WcnNwkqClJ7du3V2pqqv7973+HXEAAAAAAAAAA8BRUYPNnP/uZVq1a1exnVq9erZ/97GfBrB4AAAAAAAAAmhVUYNNut/v1GZujkyEAAAAAAAAAMFBQgU1/HDx4UMnJyeFaPQAAAAAAAIBWzO/Bg375y1+6/b1hwwYdOXKkyefOnDmjb775Rtu2bdOwYcNCLyEAAAAAAAAAePA7sOnap6bNZtO///1vn4MD2Ww2XXDBBbrzzjtDLyEAAAAAAAAAePA7sJmXlyfpbN+Z06ZN0+TJk3X99dc3+VybNm2UmpqqxMRE40oJAAAAAAAAAC78DmxmZmY6/33fffdpwIABbtMAAAAAAAAAIFL8Dmy6ys3NNbocAAAAgKXEtY2VVOv2d31NQ/QKBAAA0MoEFdh02Llzp3bv3q2Kigo1NjY2mW+z2XTTTTeFsgkAAADAlLIn91fBsmLV1zQorm2ssif316Y3dkW7WAAAAK1GUIHN06dP64EHHtD27dtlt9t9fo7AJgAAAFqqPsO6qs+wrm7TCGwCAABETlCBzRdffFFFRUUaMmSIxo0bp86dOysmJsbosgEAAAAAAACAV0EFNjdt2qQLL7xQzz33nGw2m9FlAgAAAAAAAIBmtQlmodraWg0ePJigJgAAAAAAAICoCCqwef755+vIkSNGlwUAAAAAAAAA/BJUYPPmm2/Wxx9/rB07dhhdHgAAAAAAAAA4p6D62Dx58qSuuOIKzZ07V1dddZX69++v5ORkr58dN25cSAUEAAAAAAAAAE9BBTafeOIJ2Ww22e12rVq1SqtWrWrS36bdbpfNZiOwCQAAAAAAAMBwQQU277vvPqPLAQAAAAAAAAB+CyqwmZuba3Q5AAAAAAAAAMBvQQ0eBAAAAAAAAADRFFTG5tGjR/3+bJcuXYLZBAAAAAAAAAD4FFRgc8qUKU0GC/LGZrNpzZo1wWwCAAAAAAAAAHwKKrD5/e9/32tgs6KiQnv27NHhw4c1ZMgQZWZmhlxAAAAAAAAAAPAUVGDzgQce8DnPbrfr7bff1ltvvaV777036IIBAAAAAAAAgC+GDx5ks9l0ww03qE+fPnrppZeMXj0AAAAAAAAAhG9U9IEDB2rLli3hWj0AAAAAAACAVixsgc3//Oc/OnPmTLhWDwAAAAAAAKAVC6qPTV8aGxt17Ngx/e1vf9PHH3+sSy+91MjVAwAAAAAAAICkIAObY8eO9ToquoPdbldqaqrmzJkTdMEAAAAAAAAAwJegApuDBw/2Gti02WxKTU3VBRdcoB/84Afq0KFDyAUEAAAAAAAAAE9BBTZfeOEFo8sBAAAARNTeTw5rc36x6msaVH2qNtrFAQAAQIAM7WMTAAAAsIrN+cUqO1TpNi2uLY/HAAAAVhHyk1tRUZGKi4tVVVWlpKQk9e/fX1lZWUaUDQAAAAib+poGSZLNJiV2SFBc21hlT+5vyLqrT9Vq2T3rlT25v/oM62rIOgEAAOAu6MBmUVGRfvnLX+o///mPpLMDBjn63ezRo4fuu+8+DRo0yJhSAgAAAGGS2CFB01+80pB1nc34rJXdLpUdqlTBsmICmwAAAGESVGBz3759uueee1RTU6Ps7GwNHTpUHTt21MmTJ7V161Z9/vnnuueee/Tb3/5WvXv3NrjIAAAAgDllT+6vgmXFOn24Unb7t1mhAAAAMF5Qgc0lS5aovr5eTz31lIYNG+Y278Ybb9Snn36q+++/X0uWLNEjjzxiRDkBAAAA0+szrKv6DOuqN+/8l6pOMiARAABAOLUJZqHCwkLl5OQ0CWo6DBs2TDk5Odq6dWtIhQMAAAAAAAAAb4IKbFZWVqpr1+b7CuratasqKyub/QwAAAAAAAAABCOowGbHjh21Y8eOZj+zc+dOdezYMahCAQAAAAAAAEBzggpsjhw5UoWFhfrDH/6g2lr3voNqa2v12muvaevWrRo1apQhhQQAAAAAAAAAV0ENHnTTTTdp06ZN+tOf/qQVK1bowgsvVIcOHXTq1Cnt3r1bpaWl6tatm2666SajywsAAAAAAAAAwQU209LS9PLLL+u3v/2t/vnPf+qTTz5xzouPj1dubq5uv/12tWvXzrCCAgAAAAAAAIBDUIFNSWrfvr3uu+8+3XPPPSopKVFVVZWSkpLUq1cvxcYGvVoAAAAAAAAAOKeQI5CxsbHq16+fEWUBAAAAAAAAAL8ENHjQ0qVL9fvf/14NDQ0+P1NfX6/f//73+tOf/hRy4QAAAAAAAADAG78DmwUFBXrttdfUrl27Zpuax8XFKS0tTX/4wx+0ZcsWQwoJAAAAAAAAAK78DmyuXr1aqampuvbaa8/52UmTJik1NVWrVq0KqXAAAAAAAAAA4I3fgc3t27frO9/5juLj48/52fj4eGVnZ6uoqCikwgEAAAAAAACAN34HNo8fP65u3br5veKuXbvqxIkTQRUKAAAAAAAAAJrjd2CzTZs2zQ4a5KmhoUFt2gQ0NhEAAAAAAAAA+MXvyGPHjh21b98+v1e8b98+ZWRkBFUoAAAAAAAAAGiO34HNSy65RFu2bNHhw4fP+dnDhw9ry5YtGjx4cEiFAwAAAAAAAABv/A5sTpo0SQ0NDXrooYdUWlrq83NlZWV6+OGHdebMGf3oRz8yoowAAAAAAAAA4CbW3w8OHDhQkydP1rJlyzRz5kz96Ec/0tChQ9WpUydJZwcX2rx5s95//32VlpZqypQpGjhwYNgKDgAAAAAAAKD18juwKUlz5sxRfHy83nrrLb3xxht644033Obb7Xa1adNGP/7xjzVr1ixDCwoAAAAAAAAADgEFNm02m/77v/9b48eP14cffqjt27fr5MmTkqT09HRlZWUpNzdX3bt3D0thAQAAAAAAAEAKMLDp0L17d/3kJz8xuiwAAAAAAAAA4Be/Bw8CAAAAAAAAALMgsAkAAAAAAADAcghsAgAAAAAAALAcApsAAAAAAAAALIfAJgAAAAAAAADLIbAJAAAAAAAAwHIIbAIAAAAAAACwHAKbAAAAAAAAACyHwCYAAAAAAAAAyyGwCQAAAAAAAMByCGwCAAAAAAAAsBwCmwAAAAAAAAAsh8AmAAAAAAAAAMuJjXYBvPnyyy/1+eefa9euXdq9e7eOHTsmSVq/fr3Xz7/22mtasmSJz/VNnz5dt99+u9d5RUVFWrp0qXbu3KmGhgb16tVL1157rcaNGxfyfgAAAAAAAAAID1MGNl9//XV99NFHAS+XlZWl7t27N5k+cOBAr59fu3atFixYoMbGRg0ePFhpaWnavHmzFi1apD179mjOnDkBlwEAAAAAAABA+JkysHnxxRerX79+uuCCC3TBBRdo6tSpqqurO+dyEyZMUG5url/bOH36tJ588kmdOXNGjz32mMaOHStJOnnypObMmaO8vDyNGDFCQ4cODWlfAAAAAAAAABjPlIHNG2+8Mezb+OCDD1RZWalRo0Y5g5qSlJ6ertmzZ+sXv/iF8vLyCGwCAAAAAAAAJtRqBw/atGmTJCknJ6fJvOHDhys+Pl4FBQWqra2NcMkAAAAAAAAAnIspMzaDtWXLFhUXF6uurk6dOnXSFVdc4bN/zT179kiSBgwY0GReXFyc+vbtq927d+vgwYPq169fWMsNAAAAAAAAIDAtKrC5evVqt79fffVVjR07Vvfff7+SkpKc0ysrK1VRUSFJ6tSpk9d1derUSbt379aRI0d8BjaPHz+uEydOOP8uKSkJdRcAAAAAAAAA+KFFBDZ79OihO+64Q1dccYW6dOmi8vJyffHFF/rtb3+rdevWqbGxUY8//rjz89XV1c5/JyQkeF1n27ZtJUlVVVU+t7tixQotWbLEmJ0AAAAAAAAA4LcWEdi8+uqr3f5OTEzUVVddpaFDh+qWW27Rhg0btGPHDl188cWGbnfixIkaOXKk8++SkhItXLjQ0G0AAAAAAAAAaKpFDx6UkZGh3NxcSdKnn37qnJ6YmOj8t6/BgWpqaiTJrQm7t/UPHDjQ+V+vXr2MKDYAAAAAAACAc2jRgU3pbDN1SW59YSYnJyslJUWSdOzYMa/LOaZnZmaGuYQAAAAAAAAAAtXiA5vl5eWS3LM0JTkHBPrqq6+aLNPQ0KC9e/cqPj7eGRgFAAAAAAAAYB4tOrBpt9u1YcMGSdKAAQPc5g0fPlyStHbt2ibLbdy4UXV1dcrOzvY5uBAAAAAAAACA6LF8YLO0tFTLly9vMnp5VVWVfv3rX2vnzp1KT0/XmDFj3OZPmDBBycnJ+uijj7Ru3Trn9FOnTunll1+WJE2dOjX8OwAAAAAAAAAgYKYcFX3Tpk16/fXXnX/X19dLkm6//XbntJtuuknDhw9XdXW1nnvuOf3ud7/TBRdcoI4dO6q0tFTFxcUqKytTSkqKHnvsMbVt29ZtG+3atdO9996rRx55RA899JCGDBmitLQ0FRQUqKKiQlOmTNHQoUMjs8MAAAAAAAAAAmLKwGZpaal27tzZZLrrtNLSUklSWlqapk+frp07d+rgwYPasWOH2rRpo65du2rcuHGaMmWKOnXq5HU7OTk5Wrx4sZYuXaodO3aooaFBvXv31qRJk5yjqQMAAAAAAAAwH1MGNnNzc/0OLCYlJbllcgYqKytLTz/9dNDLAwAAAAAAAIg8y/exCQAAAAAAAKD1IbAJAAAAAAAAwHIIbAIAAAAAAACwHAKbAAAAAAAAACyHwCYAAAAAAAAAyyGwCQAAAAAAAMByCGwCAAAAAAAAsBwCmwAAAAAAAAAsh8AmAAAAAAAAAMshsAkAAAAAAADAcghsAgAAAAAAALAcApsAAAAAAAAALIfAJgAAAAAAAADLIbAJAAAAAAAAwHIIbAIAAAAAAACwHAKbAAAAAAAAACyHwCYAAAAAAAAAyyGwCQAAAAAAAMByCGwCAAAAAAAAsBwCmwAAAAAAAAAsh8AmAAAAAAAAAMshsAkAAAAAAADAcghsAgAAAAAAALAcApsAAAAAAAAALCc22gUAAAAAWoO9nxzW5vxi1dc0KK5trLIn91efYV2jXSwAAADLImMTAAAAiIDN+cUqO1SpqpO1KjtUqYJlxdEuEgAAgKUR2AQAAAAioL6modm/AQAAEBgCmwAAAAAAAAAsh8AmAAAAAAAAAMshsAkAAAAAAADAchgVHQAAAAiTqpO1WnbPetXXNKj6VK3Peb5GSWckdQAAAN/I2AQAAADCyDESut3ue56vUdIZSR0AAMA3ApsAAABAmNlsUlJ6gtK6Jfv8jLdR0hlJHQAAwDcCmwAAAECYJXZI0PQXr9TkX41RUnpCtIsDAADQIhDYBAAAAAAAAGA5BDYBAAAAAAAAWA6BTQAAAAAAAACWQ2ATAAAAAAAAgOUQ2AQAAAAAAABgObHRLgAAAAAAqfpUrd6881+Kaxurjj3b6cT+06o+VRvtYgEAAJgWgU0AAADABOx2qepkraRalR2qjHZxAAAATI+m6AAAAECUpXVLVlJ6gmw29+k229l5AAAAaIrAJgAAABBFSekJmvyrMZr+4pVK7JDgNi+xw9l5SekJPpYGAABovQhsAgAAAAAAALAcApsAAAAAAAAALIfAJgAAAAAAAADLIbAJAAAAAAAAwHIIbAIAAAAAAACwHAKbAAAAAAAAACyHwCYAAAAAAAAAyyGwCQAAAAAAAMByCGwCAAAAAAAAsBwCmwAAAAAAAAAsh8AmAAAAAAAAAMshsAkAAAAAAADAcghsAgAAAAAAALCc2GgXAAAAAIikvZ8c1ub8YlWfqo12UQAAABACApsAAABoVTbnF6vsUKXz77i2PBIDAABYEU9xAAAAaFXqaxokSTab1K5rsrIn949yiQAAABAMApsAAABolRI7JGjyr8ZEuxgAAAAIEoMHAQAAAAAAALAcApsAAAAAAAAALIfAJgAAAAAAAADLIbAJAAAAAAAAwHIIbAIAAAAGi2sb2+zfRi8HAADQGhHYBAAAAAyWPbm/0rolKyk9QWndkpU9uX9YlwMAAGiNeAUMAAAAGKzPsK7qM6xrxJYDAABojcjYBAAAAAAAAGA5BDYBAAAAAAAAWA6BTQAAAAAAAACWQ2ATAAAAAAAAgOUQ2AQAAAAAAABgOQQ2AQAAAAAAAFgOgU0AAAAAAAAAlkNgEwAAAAAAAIDlxEa7AN58+eWX+vzzz7Vr1y7t3r1bx44dkyStX7++2eVWrVql9957TyUlJYqNjdVFF12kmTNnKisry+cyRUVFWrp0qXbu3KmGhgb16tVL1157rcaNG2foPgEAAAAAAAAwjikDm6+//ro++uijgJZ54YUXlJ+fr4SEBF122WWqq6tTQUGBCgoK9Oijj2r06NFNllm7dq0WLFigxsZGDR48WGlpadq8ebMWLVqkPXv2aM6cOUbtEgAAABCy6lO1evPOfymubayyJ/dXn2Fdo10kAACAqDFlYPPiiy9Wv379dMEFF+iCCy7Q1KlTVVdX5/PzBQUFys/PV1paml566SWdd955kqTt27frrrvu0hNPPKEhQ4YoNTXVuczp06f15JNP6syZM3rsscc0duxYSdLJkyc1Z84c5eXlacSIERo6dGh4dxYAAAA4h7i2sZJqZbdLVSdrJdWqYFkxgU0AANCqmbKPzRtvvFG33XabRo4cqY4dO57z83l5eZKkGTNmOIOakjRo0CBNnDhRFRUVWrlypdsyH3zwgSorKzVq1ChnUFOS0tPTNXv2bLf1AgAAANGUPbm/0rolKyk9QTbb2Wn1NQ3RLRQAAECUmTKwGYja2lpt3bpVkpSTk9NkvmPaxo0b3aZv2rTJ5zLDhw9XfHy8CgoKVFtba2h5AQAAgED1GdZVk381RtNfvFKJHRKiXRwAAABTsHxgc//+/aqrq1P79u3VuXPnJvMHDBggSdqzZ4/bdMffjvmu4uLi1LdvX9XV1engwYNhKDUAAAAAAACAUFg+sHn06FFJUqdOnbzOT0xMVEpKisrLy1VVVSVJqqysVEVFRbPLOaYfOXLE6CIDAAAAAAAACJEpBw8KRHV1tSSpbdu2Pj+TmJioiooKVVVVKSkpybmMJCUkeG/K41ifIxjqzfHjx3XixAnn3yUlJQGVHQAAAAAAAEBwLB/YjKYVK1ZoyZIl0S4GAAAALMQxwrn73wAAAAiU5Z+iEhMTJUk1NTU+P+PI0ExKSnJbRjo7+FBsbNPD4FifYxlvJk6cqJEjRzr/Likp0cKFCwMoPQAAAFqb7Mn9VbCsWPU1DYprG6vsyf2jXSQAAABLsnxgs0uXLpKkY8eOeZ1fXV2tiooKpaamOoOUycnJSklJUUVFhY4dO6bk5OQmyznWl5mZ6XPbGRkZysjICHUXAAAA0Ir0GdZVfYZ1jXYxAAAALM/ygwf17NlT8fHxKi0t9Rrc/OqrryRJ/fr1c5vu+Nsx31VDQ4P27t2r+Ph49ejRIwylBgAAAAAAABAKywc2ExISNHToUEnSmjVrmsxfu3atJGnEiBFu04cPH+4239XGjRtVV1en7Oxsn4MLAQAAAAAAAIgeywc2JWnq1KmSpDfeeEMHDhxwTt++fbvef/99paSkaPz48W7LTJgwQcnJyfroo4+0bt065/RTp07p5ZdfdlsvAAAAAAAAAHMxZR+bmzZt0uuvv+78u76+XpJ0++23O6fddNNNzqzL7OxsXX/99crPz9dtt92m7Oxs1dfXq6CgQJJ0//33KzU11W0b7dq107333qtHHnlEDz30kIYMGaK0tDQVFBSooqJCU6ZMcWaCAgAAAAAAADAXUwY2S0tLtXPnzibTXaeVlpa6zZs7d6769++v5cuXq6CgQLGxscrOztbMmTOVlZXldTs5OTlavHixli5dqh07dqihoUG9e/fWpEmTlJuba+g+AQAAAAAAADCOKQObubm5QQUWg1kuKytLTz/9dMDbAgAAAAAAABA9LaKPTQAAAAAAAACtC4FNAAAAAAAAAJZDYBMAAAAAAACA5RDYBAAAAAAAAGA5BDYBAAAAAAAAWA6BTQAAAAAAAACWQ2ATAAAAAAAAgOUQ2AQAAAAAAABgOQQ2AQAAAAAAAFgOgU0AAAAAAAAAlkNgEwAAAAAAAIDlENgEAAAAAAAAYDkENgEAAAAAAABYDoFNAAAAAAAAAJZDYBMAAAAAAACA5RDYBAAAAAAAAGA5BDYBAAAAAAAAWA6BTQAAAAAAAACWQ2ATAAAAAAAAgOUQ2AQAAAAAAABgOQQ2AQAAAAAAAFhObLQLAAAAABhl7yeHtTm/WPU1DYprG6vsyf3VZ1jXaBcLAAAAYUDGJgAAAFqMzfnFKjtUqaqTtSo7VKmCZcXRLhIAAADChIxNAJLIcAEAtAz1NQ3N/g0AAICWg4xNAJLIcAEAAAAAANZCxiYASa07w8U1W1USGatAC0edR2tASwzgW9QHAGi5yNgE0Oq5ZquSsQq0fNR5tAa0xAC+RX0AgJaLjE0ArZ4ja8tmO/u33d66MlatiMwLhII6j9bA7C0xyJxGJJm9PgD+4hkYaIqMTQD4P4kdEpTYISHaxYAfyLyAEajzQPSQOQ0AgeMZGGiKwCYAwHLIvAAAa3PNnHZkT3MtB4Dm8QwMNEVTdAAwCM3qAAAIjCNruupkbZRLAgAArIjAJgAYxNE05Fu1KlhWTGATAAAAAIAwILAJAAZhQBIAzWmpHf631P0CAADece+HmdDHJgAYjAFJAHjTUjv8b6n7BQAAvOPeDzMhYxNAWNDfJMyGcxLR1lI7/G+p+wUAALxrCfd+sk5bDjI2AYSF61s83uTBDDgnAQAAAEhknbYkZGwCEdAaM8XobxJmwzkJAAAAQGoZWac4i4xNIAJac6YY/U3CbDgnAQAAAKBlIGMTCAPP/jrKDlVKMjZTjD5BAAAAAABAa0ZgEwgDR4bmWbXO6Y4ssaqTtV6WCm0bBcuKCWwCAAAAAIBWg8Am/PaXBz9WVdm3ATmyBH2LRP8c9AnScpB923rx3QOIlOpTtVp2z3quM4DJ8CwAAKEhsAm/VZXVemQatq4sQc+Hjo492+nE/tMt9iGk+lSt3rzzXxHbt3A91Flh4KaWnn1rhe8gWlr6d4/g8UMXRrPb5ezjm3PJPy29HnJ/Ngd/ngVa+rkIAKFg8CD4LSktQUnpZ/9z9BXZmrIEXQcAKjtUqb2fHHb7u6UNBmS3K6L75nl8jdqmFQZuaunZt1b4DqKlpX/3CF64rolondK6JbfKZ7dQtfR6yP3ZHPx5Fmjp5yIAhIKMTfjtmsdHOv/95p3/MqSfSLNzfTtafar5/W1JPxTSuiU799mIgY78EUyAx59MA8c8IwduQmBa8ndgtgwKs5UHwSPoDaMkpSdo8q/GtJpnNyO19HrYku/PLU1LPxdDxfMP0LqRsQk0w/XtqN0e7dJEhuMH0PQXr3QOdmRWgWQaJHZIMP3+tHQt8TswWwaF2coDADC/lnh/RuvC8w/QupGxCUO01Ldkrm+yEzskKK5trEsfOAgnfwY5INPA+qzev5fZMij8KU+0r9e+th/tc4GBVcwt2uet2Xk7PgiMt2tQtDNcOe+D53nsov1dGq3q5Nl7lj/nhj/312jfg0NltucxAJFFxiYM0dLfkiV2SND0F6/U5F+NUVI6b7QjwXWQg3Mh08C66N8r8qJ9vfa1/WidC3Ftz77jDeSag8iL9nlrdhyf0Hm7BkUb32vwPI9dS+TvueHP/ZXnMQBWRsYmQuLIcPF8YOAtWetj5JvxtG7JOn240pmB6WtE+nP1e9oShJqt4evYedbRaL2Zby7rlkyV8IhGVkNz/RU7tu/tXCg7VOl3Rkqwsif3V8GyYrdrjr+snuFiJWTjNK+lHJ9oZk57uwZFm5W+V7Pds818rMKhuf31p4UTraCA6OO5MnhkbCIonhkugFFvxh19fLpmYPoakd7xo8NxPrZEoWZr+Dp2jjfyZnkz7y3rlkyVliOQ/oo9z4VwnwN9hnVtcs3xFxkugDHMlDlNK5DgcM82P3/Obc5/IHp4rgxey40GIKw8M1yAcL7V9bVum01q1zVZ2ZP7a9Mbu8K2fSMEm8kQarZGc8fO8eBafarW65v5aL81tFKmCppnVH/F0ToHfNXfUDNc6NfTndkyvqzAcQ5Zvf/AUDKnoyUa56uZrxncs2Emrn2QSmS+mQX3ee8cx+X04bPPxmROB46MTQQllAwXwCiJHc5md1rhhmi2TAZHv7HTX7zSZz3mrSGMZtX+is9Vf4PNcDFDdpqZmO06aWYtreWMFZ8ro3G+cs0A/MczrPlwn/fOcVwcCWPtuiZb6n5oBmRsIuys+GbGUebW0IejP8ycIWAVVsxkCDUbzSz1yGzlYNRi6wlH/fXsS9jBLOdrNFjxOhktRrecseKzWrRF+nz1dc2ANVDHoiPQZ1i+p/CKxHXTit+h628uq7RGNBsyNhF2Vnwz4/nWxLUPx5bcn6MnM/V5hegJNhutuXoUSWYrh5WuhQgPb30JO3ier4A3Rmc4cn0yt+auGbAG6lh0BPoMy/dkfVb+Dq3UGtFsWk+EBlHT3JsZs75R8fbWxMGRJeEos5WagHn2mejgaxRzK/Z51dJZKXvW6LeP1adq9ead//J6vjZ3XIIth9HXp2hno0UjEzDa2Ydmvcc0x/N8tdI9Btbg7XoZ7esTjGOW656VnleCFcix9qxTpw9XOo8PzMOs10LXc601tugIhBW+Q4l+X41GYBNR5XijclatCpYVOyu358NCx57tdGL/aa8PD+F6iHO8NXHVZ1hXt3W/eee//O60P65tbNgurv50ku1+vF15L79jXx37GI2H1GBv3v4EVKIddAmGa/asWW+EnsfVtR6FEti02/V/da3p9+XPcQm0HM1dn3xxrYdme2DxXf+N5zgHPLcX6WzZYL5Ds3Ccr67XX0dg3yznlRWvoa3Z2fpX67xe/vP5QiWl7/L5ctMqrPBj0ejn1Oae+cxy3bPC80qoQjnWrb01VKQC8OH87RVJkXyGixazvJQJl6bfobWeS82OpuiIqubeqHimke/95LDPtHIzp5yndUtWUnqC0rolh/2t7Lk6yXbNBEpKT1BSeoKz75nmRKNJuus2g2mS6U/zY7M0UfZXWrdk5/dl5oe0cBxXRz3ydr6G67gE+8bXrNci1/qf1i05rNvy9gAeiWugJ7O+tQ+E67XQbOcVzeatJXty/yZ133FOWZkVBroLx3Oqr302w3XPKs8roQr2WLeW49OccP52i+Rvr0jx/A0X7ue4aDDz73kjuH6Hrb3+h4O5f8WjVTtXRXedH8yDRSTe8Dv6RHLlyBQLV/ajP51kO0YmlvzLOI1Gk3TXJv+SAm7270/zYyt11Ow4l1y/L19vNqPdXKW5rhwcHNkm/pTPtR55nq/ejksojD52pw9X+mw+Hy2emYDh4O0c8HWdC+RcCFUktxWI5rpZcL0WVp+qNVW3IL6azbeGJqhW5GiFse/Tw03OKSvzNtBd2aFKU2XPhyvYGOzgfuHkeV/meuDO6OeWYHm2MIl0WYyqE54tB5r77dUSBPobzh9myZRsrrsGs107Qjlmjj5fzfK7oKUgsIlWK1rp4J5NwYzeZjgulp5N0iPBs8m/FNwN3J/mx0Y1lY40X02gzNJcxVtXDq5cy2iWpkJGH7vmms+3Buc6BxwieS54biuaXO8Hvs4T12thtH8I++L4npfds96ZwdnSm6BamRXOqWB4Pv+E0hzbUTfNzsw/kMP9vIvQudYRq/J8bov2fd2KzNJ9hSczXzvMesxaM2o+wsbsfW95e8MfyUzE1jggj+db+2hlFvqTtdVchoEZ3mxWn6pVtceDqONccj23EzskGDrIldGZF2bLljXy2KV1S24xGVGREMlzobls4kjylo1pNoFc74y+vzkyWR3/RuS4HnvJ/1YtZrg/ehPouehroEjXe6AVeX4/4QyIttTn3Wi3ivFHJL/naPOnlZAVRfI7NEP3FZ7SuiW7XTvMdm/xPEaurQQkc/b13NLRxybCxip9byV2SHC+8Y6EPsO6avKvxkR0m9Hmq49O175UIt3Ppa8+IP3pTzSafcAE0veoo7nK5F+NadIXT6DH2ah+Vj23265rsib/aozpbvyuxy4pPfC66mgKNf3FK1tVXQ9ENM8FR4ZhtM87x/3AzOdJINc7o+9vjkxW13sEIsP12Adyr2spfaS51k3Xe6jVB3zx/H7CqaU+73p7djWbSH7PZmGW+7pRWuN36OB4hna9dljh3mL2vp5bOjI2ERau2WTN9b3V0pg9SzVafL2195YdF8nzwtvbXX8yDAJ5s+k434160xps36Oe2SeBHmejMi9CLUcg/KmP0exjqrVfJ4w6F8z2Ft8sjDouoWRynCvrr7mMFEfGsysjs8/hm+exd2QT+9PfmRkzf4zgeQ88fdh856E/rVHM8n1E+rrtOKfPdQ3xp1yBtuyIxm+DSH7P3IP9F8ixMktdNYto3FuCObcj0RI0EuOEWBGBTYSF6xtMRxaOt763Whoz9PNilr4KHeLaxp6zj07XjrAjyVv/f0b1J+rZt5RRgu171NtywWw31OMSajkC4W99jFYfU2bN9IgUo84F+jnyLprHpWnfoQ7u5fAso4O3wR8cWlKfkGbk7djTd+q31yvXY2FGVgn8R/L65HlON3cNCaRc/g7iYobfBuHEPdh/HCtrCeb7ikS/x9EaJ8TsWtaVFabg+qbfNQsnkn3tRGsERqP7efH3TZHrm2gz9FXoWZ7WyPN8R+SZud8lz+ukVX6MmlFLzRALVaj9P4WSZeSZWS7J64jufFfW4Ov5zVvWiLcfc56fc4hkBptRz4X+tpqIZh+Hni2lXMsT7ZYCjnJ4Zrya5VoQjvuJmZ9FjNCa7sHN/S4LJNvX19+RLrPRPFtBWT2L0KzndrTGCTE7ApsIieeokWndkn1mWURyZO1oZxX4OxLwufjzpshbdkU0A5vNZdq0JtEYSb418Lzm+JP5YFR9NIq3OsJ5gkgI5A1/04wA/wWbWQ5z8nU/85Y14s25zqVIZLAZ9Vzo77ntKxs5VN7ugZ4/aB33PNdyhVKfjWSWckSD2Z5FELjmfpeZNRsz0uUy4zFoqSKRHWolBDYRkkj2l+cvz1HUHKw4QqBZ3xTBndH9aErBZVc4+rMLNiMjmv1NeiuHt/2I5jXH25voliDQTCaz9Kdllgwkq/H3Db9nllFrDUbAN29ZI+f6nOdAMpG4lvp6LgyncG3H2z3Qn5fZwdTnQLPT/MF1BVbW3O+yYH6zRaKFYTR/S5rld6vr87sUuT4po9WCtLUisImQGN1fnuuFJ5gfq45MqEi+PY8kR+DKKoFZowWTrRcJwfSj6TqohrdzPZA+mXz1ZxfM8fG3joS7uYmvfY9kH53eeL6JNrvmBmvz7AfW3zfrZslKCFfmjz8BbH9eZgT7wz/cAn3D7y37ywyC/cFAQDx0nue/v+dUIP1pG3WPae650IovvL3dAwNppRNIfQ40O+1cXOucZzmaq8/hfJlmxXPALJq7lgbznUU7CORrf0L9bdocx/PXP58vVFq3c3fr4ZgXCm9BPs9t+Vsfo3kf9dwPB1/HLpJ9Ugb7fI3QtIl2AQBPZYcqVXWy1vB+Cc3y1igUjsBVa33DnT25v9K6JSspPUFp3czRT1Fat2Rntoo3vh5AHN+lr3PdNavhXPvqelwc/0Xi+DjqqlGDgXk7Vmb4nn19h1a4pjQ3WJvjvHGcv/7uj1kyyT3riJHOdW778zLD8cPfyDqCb+tjsAMROr4Xx3XXLC/IrMDz2IdbuOuPZx2FO6Oy01zPG8965099Due1lHOgKX+viZ7XUm/zAvnOoj24bHP7E47fpp6/Hxzb8HYuGv3bp7ltSf7Xx2iPIeC6H83tj4PNpoCfeYMR7PM1QsPTHEzJ0VyJQTW+5RhsxDEAg5UF22Q62tl6nprLBHHwbDbWsWc7ndh/2usbRm/nuj99MpnhuBhx0/bWxC6Y/TI6I8uzXI7vyewZX+dqftlS+oENd0ah57FzPa7eODIdPOuzkQ+2jm24Xk+s0lF/qNkvoQ5E2NIH8nAIx/UpmoPi1dc0GJ65Z9Yfm2bJiDKKtwGXAhlY1HPa6cOVzhYvrusL5lww+hywapc1wQxC2ly3AoG+AI1UdxHNZYKfq5uEQH+burbM8jwXfP1+8NatR7jGLvDVhci5vgvPrkWi/VvdtYuTc/1Gdm1dEI5uxBxayvO11RDYhCm5NlfiouB+U7Py8TCyybRVBBJ0tPJ3awSjArSBNOP3h2e5HEErM79g8CfojsA5jqtn4PLbc6w2rNlsns2bItm0ykihHB+jfjC09IE8XK9P/nRl4s9no/1jzSzdYISbt242jH5O8mcQIqM0d28P5pzyfIY027lg5i5rvH3PoQ5CGurLxUg/r5zrGuJrfwL9bep+nvp3LkRyMBhv2wrkuzDLb/VAy+H5HIWWg6boQAvmeBu179PD0S6KpOg1mUbrEkgz/mB4nsdGN4M+l72fHNaye9brzTv/Zar6HQmObItQBskKVPWp2ibb8tYthmfTo3A41zbMmoHmjc2mqNSf1sDz+uRvVyZmvx+bpRsMX4y6Prnew8L1vTT3vYczkylUns+QZm/q6e8AMuG8n1mlfjtE8rdLqOeNIyvTs867nqdmP0dbm0g8q3lqblBUf5jt97xZtdw0KaAVM2unxWZoMo3WI1wZWd7O40i+rW4tWUu+hDuTyZO3zDdf17I+w7p6bYZulGhnzBnJLNkeLU2gzRa5LxvLyLofyGBLgfL2vZ/NKDNvJpO3c9vK1w7XZ/VwCTUbM5LM+tvFG18t0KSzQU3XY27mc9Ssg7KGU7Seozxb+fgT6LZSnTCDln/2Aq1QqH2QBaKljzBr1f6S0HIFk7XkT/10ZB5Yoa9G176dwlknHf2OSU37yPLFV5+sCB/XPgkl8/d/i8B5+05DzYIJF7P0PRcof/pQjeQzXyDbsuKzmre+R610vgSjuf5jA/ntEu1+aD2/O4fmzj2zXack733bI3xc+/b25wVDJH/PtwQENoEWKJC3UaG+rTO6P0MzMnN/SQgPI8/jaL4R95YR4rn9ppkH5s8CDWcmk0OwHfZ7ZkKZOVvDLIy+D8G6PM8FB1+BtmCyYMLNqtnI/jw7RrKuBfp8abVntWi3/oiG5vqPDeS3S6D90Pq6rgQrkEx3f57DoiGubSwZ+xHm2pLMn8BmS2qlEwnRr1UAJDXNOIlrGxuRi1iob+taywizDqcPh/+B3oxvdc/FNdvPqjdfz1FBjRJqHTtXNlpzx7650Wi9fcYxoqRncCDa2REtQSSOm+sorFb6noy8DzkGRGgN2U8tkee50LFnO53Yf7rZrLZAs2BaGyOvBb5Gjg7H9SaU58tIPKtFQiD3Xis8h3kbUTuY561A1xPNlhT+PIdFSriec1saK7VGTEpLcPt/a0ZgEzCJpm8fI3MxNeptXUsfYdYhnG9co/VW14g32a7ZflYUbHaeP0KtY+fKkGnu2PuzbdfP+HorHOmMOKOzK6LJ33ptRGavt/6+rMDI+5BrJm9ryHIwSx9pRtVZf84Fb6MVO67fjn4i3csVXmb5DjyF85nCccwdfQqH87mluedLfzJ8zfJ9BCOQe6+VnsOManXh73qi2ZLCLFmR4XzObWlCeeaN9PXmmsdHRnR7ZmbdK72HuXPnqrCw0Of8p59+WsOGDWsyfdWqVXrvvfdUUlKi2NhYXXTRRZo5c6aysrLCWFpIob/dNdubyWD3x9EvkOPtsmOUtnB2Jo7gBNPfXiCi9VY31DfZjuPiyPZD8Lz1E+YrG801kyncx95bdoTneWJkVmdL6qfS33odatai6/XJwerHDudmlj7SzFJnz3U8wvHsaJbvwJPRzxTeruvRzkbzJ8PXqD4PI5nF5e23QXP9tpr9OSzU/nBdny/M8rvPTByjZoezf/Rgz38rfne+MtMl78eBTFhzaDGBTYexY8cqMTGxyfSMjIwm01544QXl5+crISFBl112merq6lRQUKCCggI9+uijGj16dCSK3Or4MwqgP287jHozGWqfTEa8EXe9YLbrevbiaIULf2vS3JtOo7I1ovVWN5Q32a7HpTVkR0WCr37CmstMiNSxb67vuED7vGpONLIrwpV15W+9DqX+N3d9ol62bGbJBjJL37K+joe3voTDvc1oC7ZcgWRBRnvfg9l+sKORR7pPec/fBr6etazyHBbKsXM/9ubcv2iI5KjZwZ7/Vv7uHNnirvXK8zikdUsmE9YkWlxg84477lDXrueu0AUFBcrPz1daWppeeuklnXfeeZKk7du366677tITTzyhIUOGKDU1NdxFbnW8vd11fcPqz9uOQN5M+vNmxdEnUzBvFI16Wx3OPqIcb/LM+rBjpGiMkmrWbA1YnxkGwvCXUX1nRUtrrMeeGcKeWcDnWsYK/U8Bkve+hOFdKFmQVhDsaOTR6FPebP3Xh9oyI9j9sdKzUCRFctTsYM//c5XJbC0wz8Xo60Br+o0ebi0usOmvvLw8SdKMGTOcQU1JGjRokCZOnKh3331XK1eu1LRp06JVxBYr1Le7gbyZrD5Vq3+9UOg2zdubFddAYqBvo8LRR6VRgU3PN3lGMfsP20iP0h7tjAWjmbW/sJbMrH1K+tP0yPOhLBIjlodDS6vH/nLNpPB2n/BW/43KzAUixZ++hKPNLANWtPRrYaijkUeyT/lwbCuQZzzPl1/+3COaO39bS3/8kRKNUbON/g7N1jesv9ffUI9DuH6jt2Ztol2AaKitrdXWrVslSTk5OU3mO6Zt3LgxgqWCkRw3Wc838mndvL9Z8XZT9/XZaPMsa3MPJNmT+yutW/I51xGMskOVqjr5bdaD2X7Y2mxNv8NAjl0gn21pHOdNUnqCaetBS+N5zAMVrvPV0eTGWz13vc7yUNay2Gw6Z/335zOAFUXrft/c9Rbh1ZqOdaDPeI7nfdf7vLfrv+szARnR/mnNvzUkOc9Dx/gS4eZvV3dGXYOb+34d9TBS+94atLjas3LlSp0+fVo2m03nnXeeRo8erS5durh9Zv/+/aqrq1P79u3VuXPnJusYMGCAJGnPnj0RKTOM56t5uK830N6aIJr1bXUgzSUdb/L2fXo4LE0szdzk1NubtECOXWtslurQ0rM1zCjU/umMPl+9DVzg2eTGswkUWg5/sm6tmpkLeBONwR9cm/VK32YKma35cWvga/ArqzWT9YcRz3jerv/BNvH3JRLH3rENx78jzduzm9HdkUVTc8c0Un3DBnJtN3qQ2OaezYPJto1Gd2tW0uICm0uXLnX7+6WXXtJNN92km266yTnt6NGjkqROnTp5XUdiYqJSUlJUXl6uqqoqJSUlha/ACImv5hSB3rStFMgJpqzh2j+r/bAN5DhY6ZwAwnG++hq4wHObZm3WCQD+aG7wrXDyNuCa5P16i/DyvIcuu2e9M3vWn2ayZu1OJljB7E+oTfw9tx3OJspNt+E5LzK8HbOWFNiMxEvv5r6vQAZ+DccgQP48mwfa/Veku1uzkhZzNAYPHqwJEyZo0KBB6tixo7755hutXbtWS5cu1auvvqqkpCRNnjxZklRdXS1Jatu2rc/1JSYmqqKiotnA5vHjx3XixAnn3yUlJQbuEfzRmrPqACDcrJg55Ojz00plBloqMku8qz5Vq2p9m6GZ2CFBkvUH5gmVWTIkAx1sylfGp1VFc38iMdCXZ3apQ2uvf0byzH408hwyIsPeLDGEYMphxWfzSGgxgc3bbrvN7e/zzjtPM2bM0MCBA3XPPffoj3/8oyZOnKiEhATDtrlixQotWbLEsPUhcFbOqmvuDQ2DtwAwAyt19O/ZEXvBsuJoF8kUvN1PzjVKKfcchMq1PrpPg+SeyUSGZmSy9ALh+vvCkb3p4O08DrU7Gfd1R//536j9CXXb59pusMcnHJl0+Ja3TEmjziGjMuzNEkMIphxWejaPpBY/eNDll1+uCy64QBUVFdq5c6eks9mYklRTU+NzOUdWZ3PN0CdOnKhXXnnF+d8vfvELA0uOlq65zrsZvAUAAuPZEfu5gnethb/3E+45MJLrecd55Y7j0pTr+WK2wTRCeSavPlUbUNYyz//nFqnjY/XvYu8nh+mP0WCB1mdEVqt49dCjRw/t3r3b2WzcMZjQsWPHvH6+urpaFRUVSk1NbTawmZGRoYyMDOMLDDct9Y1Zc29ozPIWyUpa6nliJXwH1mfl75A+P73zdj85m80a3r6l0LqZ+Tkmkte5SPTj1hIEkqUXacGcy8FmLJu53kRDNOuP1b8Lzz58rfQ8ZzbRboFg5WfzSGrxGZuSVF5eLunbPjV79uyp+Ph4lZaWeg1ufvXVV5Kkfv36Ra6Q8Mnqb8wQGZwn0cd3YLxIvx1uSd8hb9Z9a0nfMxCoSJ7/1LXWiYxlY1B/AuPoY3zfp4edrVZsNgV97Bx93i67Z72pXjZEWrTrM/XAPy0+3FtaWqpt27ZJkgYMGCBJSkhI0NChQ/Xpp59qzZo1mjJlitsya9eulSSNGDEiomWFd1Z/Y4bI4DyJPit9B2Z/+xmtt8NW+g598XbsvH+m9QolA8n9b8B6Inmdi8S2qJvm0xLupUYI9dzkOPqnuT7Gg+mP0Wx93kZbtM/DaG/fKlpExmZRUZE2bNigM2fOuE0/fPiwHnzwQVVXV2vkyJHq3Lmzc97UqVMlSW+88YYOHDjgnL59+3a9//77SklJ0fjx4yOzAwCAiDL7289ovx22Mm/Hru8VXU39fVuBWesMmblo7cxaNwHOzcjw7GP89OHKkO6LZu7zFvClRbzSO3jwoJ544gmlp6drwIABSklJ0dGjR/Xll1+qrq5Offr00f/3//1/bstkZ2fr+uuvV35+vm677TZlZ2ervr5eBQUFkqT7779fqamp0dgdAECQ/H2QM/vbT7OXz8w4duFhtuMaaFbz3k8Oa3N+MUHQFoAMRXdmq5uAA+dmZDiO87J71qvsUGXIrX3C0edtKNdt7tvwR4t4Erjwwgt1zTXXaOfOndq9e7fKy8uVmJio/v37KycnR9dcc40SEhKaLDd37lz1799fy5cvV0FBgWJjY5Wdna2ZM2cqKysrCnsCAOZm1h+U0e7YG+Fl1vMO0ZM9ub8KlhU7+xGLaxvbbDYQAykYJ9r10fW7P9f3DgCtRaD3xUgK5rod6LN9tO9NiK4W8W337t1bP//5z4NaNjc3V7m5uQaXCABaJrP+oDTzwxxCZ9bzDtETaCaQ60AK7brSJDIU0a6PZIEBQFNmvjYGU7ZAn+2jfW9CdLWIwCaAyOBNGMz60GTWcsEYfL/uuBYHL5iBFOCO+ohw4voGQAr8XsO9qXVrEYMHAYgMOgEHgOjjWhwYBhgCrIPrGwAgULwCA+A33oQBQPRZ8VocjSws+t4FrMeK1zcAQHSRsQkAAICwikYWlus2yf4CAABomXhtDQBAiOgTDGheNLKwyPwC0JrwLAKj+XNOhXrecd7CCGRsAgAQIvoEAwAA0cSzCIzmzzkV6nnHeQsjEA4HACBErTkzjDftAABEX7ieRbjPR47ZjrU/51So511rfoaGccjYBAAAQeNNOwAALRf3+cjhWAPB4XULAAAIGm/aAQCIjGhk9HGfjxyONRAcMjYBAAAAADA5MvoAoCkyNgEAAAAAMDky+gCgKTI2AQAAAAAAAFgOgU0AAAAAAAAAlkNgEwAAAAAAAIDlENgEAAAAAAAAYDkENgEAAAAAAABYDoFNAAAAAAAAAJZDYBMAAAAAAACA5RDYBAAAAAAAAGA5BDYBAAAAAAAAWA6BTQAAAAAAAACWQ2ATAAAAAAAAgOUQ2AQAAAAAAABgOQQ2AQAAAAAAAFgOgU0AAAAAAAAAlkNgEwAAAAAAAIDlENgEAAAAAAAAYDkENgEAAAAAAABYDoFNAAAAAAAAAJZDYBMAAAAAAACA5RDYBAAAAAAAAGA5BDYBAAAAAAAAWA6BTQAAAAAAAACWQ2ATAAAAAAAAgOUQ2AQAAAAAAABgObHRLkBLUltbK0kqKSmJckkAAAAAAAAA6+rVq5fatm3b7GcIbBroyJEjkqSFCxdGuSQAAAAAAACAdb3yyisaOHBgs5+x2e12e4TK0+KVlpbqs88+U9euXRUfHx/t4qCVKSkp0cKFC/WLX/xCvXr1inZxgFaJeghEH/UQiD7qIRB91EO0BGRsRlj79u119dVXR7sYaOV69ep1zjcaAMKLeghEH/UQiD7qIRB91EO0dAweBAAAAAAAAMByCGwCAAAAAAAAsBwCm0AL0bFjR918883q2LFjtIsCtFrUQyD6qIdA9FEPgeijHqK1YPAgAAAAAAAAAJZDxiYAAAAAAAAAyyGwCQAAAAAAAMByCGwCAAAAAAAAsJzYaBcAgFRTU6PPP/9cH3/8sYqKinT06FG1adNG3bt319ixYzVlyhQlJSV5XXbVqlV67733VFJSotjYWF100UWaOXOmsrKyfG6vqKhIS5cu1c6dO9XQ0KBevXrp2muv1bhx48K1i4Al5OXladu2bdq7d69KS0tVV1en9PR0DRkyRNOmTVO/fv28Lkc9BMKjrKxMM2bMUGlpqbp376633nrL52eph4Bx5s6dq8LCQp/zn376aQ0bNqzJdOohYKzS0lL9+c9/1saNG3X06FElJCQoMzNT3/nOd3THHXc0+fzHH3+st99+W8XFxZKkAQMG6IYbbtDw4cN9bmPfvn364x//qK1bt6q6ulrdu3fX+PHjdf3116tNG3LhYH4MHgSYwAcffKCnnnpKktSrVy/17dtXlZWV2r59u6qqqtSzZ08tXrxYHTp0cFvuhRdeUH5+vhISEnTZZZeprq5OmzdvliQ9+uijGj16dJNtrV27VgsWLFBjY6MGDx6stLQ0bd68WRUVFZo6darmzJkT/h0GTOqHP/yhqqur1a9fP2VkZEiSvv76ax04cECxsbFauHChRowY4bYM9RAIn0WLFmn16tWy2+3NBjaph4CxHIHNsWPHKjExscn8qVOnNnnZRz0EjPXll1/qnnvuUVlZmfr06aM+ffqosrJSJSUlOnbsmNasWeP2+XfeeUcvvviiYmJilJ2drbi4OH3++eeqra3VXXfdpeuuu67JNrZv3667775btbW1uvDCC5WZmakvvvhCJ0+e1He/+1098sgjstlskdplIDh2AFH34Ycf2p966in7vn373KYfO3bMfuutt9pHjx5tX7Bggdu8zz//3D569Gj7hAkT7Pv373dOLyoqsl955ZX23Nxc++nTp92WKSsrs48bN84+evRo+9q1a53TT5w4YZ82bZp99OjR9i1bthi/g4BFbNu2zV5TU9Nk+vLly+2jR4+2/+hHP7LX19c7p1MPgfApKCiwjx492v7UU0/ZR48ebZ82bZrXz1EPAeP9z//8j3306NH2Q4cO+fV56iFgrFOnTtknTJhgv+qqq+wbNmxoMn/Hjh1uf5eUlNhzcnLs//Vf/2UvKipyTt+/f799woQJ9pycHPuBAwfclqmvr7dPmTLFPnr0aHteXp5zemVlpf3222+3jx492v7hhx8avGeA8cgrBkwgNzdX8+fPV+/evd2mZ2Rk6O6775YkrV+/XvX19c55eXl5kqQZM2bovPPOc04fNGiQJk6cqIqKCq1cudJtfR988IEqKys1atQojR071jk9PT1ds2fPdlsv0BplZWUpISGhyfRJkyape/fuOnnypEpKSpzTqYdAeNTW1upXv/qVevfurWnTpjX7WeohEH3UQ8BYr732msrKyjR79myNGjWqyfyLLrrI7e/8/HydOXNGEydO1KBBg5zTzzvvPM2YMUNnzpxRfn6+2zLr16/X4cOHdf7552vKlCnO6UlJSfrZz34mSXr77bcN3CsgPAhsAiZ3/vnnS5Lq6upUVlYm6ewPvq1bt0qScnJymizjmLZx40a36Zs2bfK5zPDhwxUfH6+CggLV1tYaVHqg5YiJiZEkxcae7Z6aegiEzx//+EcdOnRI8+bNc9Y5b6iHQPRRDwFj1dbW6u9//7sSExP1gx/8wK9lmqtPjhcHvuqg64sFh4EDB6pbt27at2+fDh8+HEjxgYgjsAmY3KFDhySdDaa0a9dOkrR//37V1dWpffv26ty5c5NlBgwYIEnas2eP23TH3475ruLi4tS3b1/V1dXp4MGDhu4DYHWrV6/WgQMH1KNHD/Xo0UMS9RAIlz179igvL0+5ubkaPHhws5+lHgLhtXLlSj3zzDN69tlnlZ+fr6NHjzb5DPUQMNbu3btVVVWl/v37KyEhQZ988olefPFFPfPMM3rnnXd0/Phxt8+Xl5c762b//v2brK9Lly5KS0vTkSNHVFlZ6ZzeXB10ne5ZdwGzYVR0wOQcTQYuv/xyxcfHS5LzxtWpUyevyyQmJiolJUXl5eWqqqpSUlKSKisrVVFR0exynTp10u7du3XkyBGfoz8DrcFbb72lffv2qaamRiUlJdq3b58yMjL08MMPOzM3qYeA8RobG/Xkk08qJSXF2RS1OdRDILyWLl3q9vdLL72km266STfddJNzGvUQMNbXX38tSWrfvr0eeOABffTRR27zX3nlFd1777363ve+J0n65ptvJEmpqaleB/uSpM6dO6usrMytPjnqrrcXEtK3ddPbCw3ATAhsAia2adMmrVy5UrGxsZo1a5ZzenV1tSSpbdu2PpdNTExURUWF8wHSsYwkr30Iuq6vqqrKiOIDlvXZZ585R3KVpMzMTD3wwAMaOHCgcxr1EDDeu+++q927d+v+++9XWlraOT9PPQTCY/DgwZowYYIGDRqkjh076ptvvtHatWu1dOlSvfrqq0pKStLkyZMlUQ8Bo5WXl0uSPv74Y8XExOjuu+/Wd7/7XdXU1Gj58uV6++23tWjRIvXq1Uv9+/d31pHm6qC3+uSoh9RBWB1N0QGTKikp0cKFC2W32zV79mxnX5sAwu/ZZ5/V+vXrtXLlSi1evFg9evTQ3Llzm2SuADDO0aNH9Yc//EFDhgxRbm5utIsDtGq33Xabrr76anXr1k0JCQnOAUgef/xxSWf7waXvSyA87Ha7JOnMmTO69dZbNWnSJLVv316ZmZm644479N3vflcNDQ166623olxSwBwIbAImdOzYMc2fP1/l5eWaMmWK8424g6OJQU1Njc91ON7AJSUluS0jyeeDqGN9jmWA1i41NVWDBw/WU089pYEDB+rVV1/Vrl27JFEPAaM9++yzamho0Lx58/xehnoIRNbll1+uCy64QBUVFdq5c6ck6iFgNNf64W3wIMfLvy+++ELSt3WkuTrorT45tkMdhNXRFB0wmdOnT2vevHk6cuSIfvCDH2jOnDlNPtOlSxdJZwOg3lRXV6uiokKpqanOG1FycrJSUlJUUVGhY8eOKTk5uclyjvVlZmYatTtAixAbG6srr7xSX375pTZu3KgLL7yQeggYbOPGjUpJSdGvf/1rt+l1dXWSztaNuXPnSpIefvhhdezYkXoIREGPHj20e/dunThxQhLPpYDRHHWqbdu2at++fZP5jjpx6tQpSd/2kVleXq7q6mqv/Ww6+uF0rU9dunRReXm5vvnmG6/92DrqoKM8gFmRsQmYSFVVlebPn6+vv/5aY8aM0fz582Wz2Zp8rmfPnoqPj1dpaanXh8ivvvpKkprcoBx/O+a7amho0N69exUfH+8c9RnAtxz9/ZWWlkqiHgLhUFFRocLCQrf/HFlhdXV1zmmOYCf1EIg8R/9/jv73qIeAsRyjkdfW1jrvd64cddARwExNTXUGH4uLi5t8/ujRoyorK1NmZqbbS4Tm6qDrdAbvgtkR2ARMoq6uTg888IB27dqlyy+/3G30ZU8JCQkaOnSoJGnNmjVN5q9du1aSNGLECLfpw4cPd5vvauPGjaqrq1N2drbPDqSB1qywsFCS1K1bN0nUQ8Bo69ev9/pfXl6eJKl79+7OaV27dpVEPQQirbS0VNu2bZP0bfCFeggYq0uXLjr//PNlt9udz5+uHNP69+/vnNZcfVq3bp0k33XQMd/VV199pUOHDqlPnz7Oey5gVgQ2ARM4c+aMFixYoC1btuiSSy7RwoULFRcX1+wyU6dOlSS98cYbOnDggHP69u3b9f777yslJUXjx493W2bChAlKTk7WRx995HYDO3XqlF5++WW39QKtTVFRkT799FM1Nja6TW9oaNC7776rv//970pISNCVV17pnEc9BKKPeggYq6ioSBs2bNCZM2fcph8+fFgPPvigqqurNXLkSGfzV4l6CBjthhtukCS99NJLOn78uHN6cXGx84Xfj370I+f066+/XjExMVqxYoV27NjhnH7gwAG98cYbiomJ0fXXX++2jTFjxqhr167697//rXfeecc5vbq6Ws8++6wkadq0acbvHGAwm90x5BaAqFm2bJkWL14sSRo9erTXfoYk6Y477nDrZ+WFF15Qfn6+2rZtq+zsbNXX16ugoECS9Oijj2r06NFN1rF27Vo98sgjstvtGjJkiNLS0lRQUKCKigpNmTJFd955p/E7CFjAqlWr9MQTTygtLU0DBw5Uu3btVFZWpr179+rEiROKj4/XAw884BbYlKiHQLgdPnxYU6dOVffu3X2OAEs9BIzjuB+mp6drwIABSklJ0dGjR/Xll1+qrq5Offr00XPPPacOHTq4LUc9BIy1aNEi/e1vf1NKSooGDRqk2tpa7dixQ3V1dfrhD3+o+fPnu33+nXfe0YsvvqiYmBhlZ2crLi5On3/+uWpra3XXXXfpuuuua7KNoqIi/fznP1dtba0uuugidenSRdu2bdOJEyeUk5OjBQsWeO0aDTATApuACbz22mtasmTJOT+Xl5fXpCnAqlWrtHz5cpWUlCg2NlYXX3yxZs6cqaysLJ/rKSoq0tKlS7Vjxw41NDSod+/emjRpknOEPaA1OnTokFauXKnCwkIdOnRIZWVliouLU2Zmpi699FJdd911Pvv5oh4C4eNPYFOiHgJG+frrr7V8+XLt3LlT33zzjcrLy5WYmKhevXopJydH11xzjc/m4dRDwDh2u10ffPCBVqxYoZKSEtlsNvXt21cTJ070WT8+/vhjvfXWW86+Nvv376/p06c3aYbuat++fXrttddUWFio6upqde/eXePHj9f111+vNm1o5AvzI7AJAAAAAAAAwHIIvwMAAAAAAACwHAKbAAAAAAAAACyHwCYAAAAAAAAAyyGwCQAAAAAAAMByCGwCAAAAAAAAsBwCmwAAAAAAAAAsh8AmAAAAAAAAAMshsAkAAAAAAADAcmKjXQAAAAAAAAAg2o4fP678/Hzt2rVLu3fvVnV1tZ5//nkNHTrU73UUFBTojTfe0N69e3XmzBn16NFD1113nb7//e/7XGbbtm268847JUkrVqxQ+/btnfP279+vv/71r9q5c6eKi4tVV1envLw8de3atcl6amtrtWzZMq1evVpHjhxRamqqBg0apFtuuUV9+vRx++znn3+uJUuW6KuvvlJcXJy+853v6I477vC63qqqKr3++utas2aNTpw4obS0NF188cV68MEH1bZtW7+PjcPBgwf1u9/9Tps3b1Z9fb0GDBig2267TZdeemnA6yJjEwAAAFE3d+5cjRkzJtrFOKdVq1ZpzJgxzv8eeeSRoNbz29/+1m09r732mrEFBQAAATtw4IDefPNNHTt2TH379g14+Y8++kjz5s1TfX29br75Zs2aNUsJCQl6/PHH9c4773hdprGxUc8//7wSExO9zt+xY4feffddVVVVqVevXs1u/7HHHtOrr76qoUOH6q677tLEiRP1xRdfaPbs2Tpy5Ijzcxs3btT8+fNVX1+vn/70p5o6daoKCwt15513qrS01G2dFRUVuvPOO/Xhhx/qe9/7nn7+85/ruuuuU11dnerr6wM7QJKOHj2q2bNnq6ioSDfccIP++7//W9XV1Zo3b54KCwsDXh8ZmwAAADBUoAHK9evXh6kk4TNq1Cidf/75Qf3okaTs7GzFx8fryJEj+tvf/mZw6QAAgDdz585VZmamHnjgAa/zBw4cqA8++EDt2rXT2rVr9dBDDwW0/uXLl6tjx4567rnnFB8fL0maOHGiZsyYoVWrVmnKlClNlnn//ff1zTffaPz48crPz28yf+TIkfrwww+VlJSkt956S8XFxV63fezYMa1fv/7/b+/Og6ou9ziOvyGOiALKpoEsajCmZGLhUgjjOFqKhho1g9iooTQmKDo20yCZjWSO45hlGoqgQmFiLmMumAM6kluRK+qMlYpLgoKaQICynPsHw+96Qu9F0Zt0P6+/Ds95fr/v8zv8c+ZznoWIiAimTJlitD///PNMnz6d3Nxco/7y5ctxd3dn2bJlmEwmo86kSZP4+uuvjdmjAMnJyRQVFZGSkoKHh4fRPnbs2Af6bBpkZGRQXl5OWloa3t7eALz22mu89dZbLF26lJSUlAe6n4JNEREREXmkJkyY0Khtw4YNlJeX3/M9gISEBKqqqh7vwB6h4OBghg0b9tDXBwYGEhgYyNGjRxVsioiIPCHatGnTrOsrKipwcHAwQk0AGxsb2rVrd8/+paWlpKSkEBUVxc2bN+/Zx9HRscm1AZycnCzaXVxcALC1tTVqFhQUMGbMGCPUBPD19cXHx4fdu3cbwWZZWRk7duwgPDwcDw8PqqurMZvNFs93twsXLpCSksKRI0e4ffs2Xbp0Yfz48QwYMMDoc+LECfz8/IxQE6B169YEBQWxefNmLl26hJeXV5OeGRRsioiIiMgjFhUV1aht586dlJeX3/M9gI4dOz7uYYmIiIg8VgEBAaxdu5aUlBSGDh2KlZUV2dnZnDlz5p7b16SkpODs7ExYWBhpaWnNqt2pUyfc3NzIzMzE29sbPz8/SkpKjNmZgwYNAuDOnTvAv4POu9na2nL+/HmuX7+Oi4sL+fn53LlzB09PT2bPns2+ffuoq6vD39+fGTNm4OfnZ1x7/vx5YmJicHV1ZezYsbRu3Zo9e/aQkJBAYmKisaKnuroaBweHRrUb9ur85ZdfFGyKiIiISMsybdo0jh07ZrEsPSsri/nz5xMfH4+joyPp6emcO3cOBwcHQkNDiYqKwtramqysLNavX8+lS5do37494eHhjBkzplENs9nMjh072L59u7Ghf+fOnRk1ahTDhw9/JM9RUlJCRkYGhw4dori4mFatWuHs7ExAQACTJ0/G3t7+kdQRERGRJ8/48eMpLCzkq6++Ij09HagP7ObOnUtwcLBF37Nnz7J161YWLFjAU0891ezaNjY2JCYmkpiYSHx8vNHerVs3vvzySyNMdHZ2xt7envz8fIvrb926xYULF4D67zMuLi5cvnwZqF+O7uHhwaxZs/jzzz9Zs2YN06dPJy0tDVdXVwCWLFlChw4dSE5ONmZ0jh49mpiYGGNvcQAvLy9OnDhBRUWFxQzZEydOAPVL6h/ouR+ot4iIiIjI/1hubi55eXkEBwfTs2dPDh48SHp6OmazGXt7e9LT0xkwYAABAQHk5uaSlJSEk5MTQ4cONe5hNptJTEwkOzsbT09PBg8ejMlkIi8vjwULFlBQUEBMTEyzxllVVUVMTAxFRUX06dOHkJAQqqurKSwsZNeuXURERCjYFBER+R+pqamhvLy8UVt1dXWjA3IcHR2xtm7++domkwkvLy8GDhxISEgItbW1bN26lY8//phPP/0Uf39/o+/nn39Ov3796Nu3b7PrNnBwcMDX15eBAwfSo0cPfv/9dzIyMpgzZw6LFi3C1tYWa2trwsLCWLt2LStWrCA0NJSKigqSkpKMw4Bu374NQGVlpXHvxYsXG0Gkn58f7777Lps3byY6OprS0lKOHDlCVFQUFRUVxrJ4gL59+7Jq1SqKi4txc3Nj1KhRHDhwgDlz5hAdHY2dnR2bN2/mzJkzFrWbSsGmiIiIiDzRfvzxR5YtW0b37t2B+qXuY8aM4dtvv6VNmzakpqYam9lHREQQGRnJunXrLILNbdu2kZ2dTWhoKO+99x42NvVfg6urq5k9ezaZmZkMHjyYbt26PfQ4Dx8+TGFhIW+++SZTp061eK+iosKoKSIiIo9ffn4+cXFxjdpPnjxJTk6ORVtmZibu7u7NrvnZZ59x+vRpUlJSjKB00KBBjBs3jiVLlrBixQoAcnJyOHnyZLOXn9+tvLycqVOnEhERQUREhNH+7LPPMm3aNLKyshg1ahQAEydO5NatW3zzzTdkZGQA0KdPH4YPH86WLVuME9obZl4GBQVZzK709/fH3d2dkydPAnD58mXMZjOpqamkpqbec3w3b97Ezc2N/v37ExcXR3JyMpMmTQLql9FHR0eTlJR039Ph70ffrkRERETkifbKK68YoSbUb+z/0ksvsWPHDiIjIy1O6OzYsSM9e/bk+PHj1NTUGGHipk2bsLOzY8aMGRYBo8lkIjo6mgMHDpCdnd2sYLPBvfasau5hBCIiIvJgfH19+fTTTy3ali1bhrOzc6Mta5ydnZtdr7q6mu3btxMZGWkx+9PGxoZ+/fqxefNmqqurMZlMJCUlMXDgQGxsbCgsLAQwZpdeu3aNmpoaY4l3U+3du5cbN24QFBRk0R4QEEDbtm3Jz883gk2TycT7779PdHQ0ly5dwtnZGS8vL+bOnYu1tTWdOnUCMMbw1wOJGtoaxmw2m4H6H5jvNwPV09PTeB0eHk5oaChnz57FZDLh6+vL9u3bAR5of01QsCkiIiIiTzhfX99GbQ0nfN7vvdraWmNmQFVVFefOncPV1dWYlXC32tpaAC5evNiscfbq1QsXFxcyMjL47bffePnllwkICMDHxwcrK6tm3VtEREQejIODA4GBgY3aXFxcGrU/Crdu3aK2ttb4XnG32tpa6urqqKurA+rDy+zsbLKzsxv1nTRpEr6+vqxateqB6jecqt5Qo4HZbKauru6e43J2djZC3draWo4ePUr37t2NH2QbfvAtKSlpdG1JSYlxsnnDj8w2NjZN/mzt7Ox47rnnjL8PHz6Mra0tPXv2bNL1DRRsioiIiMgTrW3bto3aGjbZ/0/v1dTUAFBWVobZbKa4uJg1a9bct05VVVWzxmlvb8/y5ctJTU3lwIEDHDp0CIAOHTowduxYRo8e3az7i4iIyJPj6tWrVFVV4ePjA9TPYLS3t+eHH35g4sSJmEwmoH47mv379+Pt7W2s6pg3b16j++Xk5LB7924SEhJwc3N74PE0zIjMyckhKirKaN+3bx+VlZUWJ5jfy7p167h+/brF8n1vb298fX3Zt28ff/zxB+3btwfgp59+4tq1a4SHhxvP3rt3b7777jtef/31RrNN7772XvLz88nNzWXkyJEPvB+5gk0RERER+UdrCD+7devGypUrH2utjh07MmvWLOrq6jh79ix5eXls3LiRxYsX4+DgwODBgx9rfREREWmehn0vCwoKAPj++++NE7vHjx9v9Js3bx7Hjh0jNzcXqP9hNSIigpSUFCZPnsyrr75KXV0d27dvp7i4mA8++MC49q8npAP8+uuvAPTr188iBCwvL2fjxo0Axp6WmzZtwt7eHnt7eyNcDAoKokuXLqSlpXH16lXj8KBNmzbh4uLC8OHDjXvu2rWLvXv30qtXL+zs7Pj555/Zs2cPI0aMYODAgRbjio2NZebMmcTGxhIWFkZ5eTnr16/Hy8uLkSNHGv1mzJhBTEwMb7/9NiNGjMDDw4MbN25w6tQpiouLWb16NQBFRUXMmTOHoKAgnJ2dKSgoYMuWLXTt2pV33nmnif+lf1OwKSIiIiL/aG3atMHHx4cLFy5QVlaGg4PDY69pbW2Nn58ffn5++Pv7M3XqVPbv369gU0RE5An318NvduzYYby+O9i8l3HjxuHu7s6GDRtYs2YN1dXVPPPMM8ydO7dRYNhUZWVljcaUmZkJwNNPP20EmyaTiaVLl5KWlsbBgwfJycnBzs6O4OBgoqOjLcJST09PSktLSUtL4/bt23h7ezNz5kzCwsIa1X/hhRdYuHAhqampJCcn07p1a4KDg5k8ebLFHuKdO3dm5cqVrF69mqysLEpLS3FycsLPz8/ic2vbti0uLi5s2rSJsrIyXF1dCQ8PZ9y4cQ+1J7mCTRERERH5x3vjjTdYtGgRCxcuJD4+vtGJm1euXMHKyqpZJ6KeP3+edu3aNTqAoGHPq4aTRUVEROTvsWTJkv/ap2EG5sPea8iQIQwZMuSBxgUQFRVlsYS8gbu7e5PH5ODgQGxsLLGxsf+xX48ePfjiiy+aPLbAwMAm7Z3p4eFBQkLCfx3jJ5980uTa/42CTRERERH5xwsLC+PUqVPs3LmT/Px8AgMDcXFx4ebNm1y8eJHTp0/z4YcfNivYzMvLIykpiZ49e+Ll5YWjoyNXrlxh//79tGrVSntsioiIiDxiCjZFRERE5B/PysqKWbNm0b9/f7Zt28aBAweorKzEyckJT09PpkyZwosvvtisGn379qWoqIjjx4+Tm5tLZWUlrq6uDBo0iMjISDp37vxoHkZEREREALAym83mv3sQIiIiIiItQVZWFvPnzyc+Pp5hw4Y1+35Hjx4lLi6OCRMm3HP5mYiIiIjcn/XfPQARERERkZZm/vz5hISE8NFHHz3U9cuXLyckJIS4uLhHOzARERGR/yNaii4iIiIi0kS+vr5MmDDB+Ltr164PdZ/AwECLw4R69+7d3KGJiIiI/N/RUnQRERERERERERFpcbQUXURERERERERERFocBZsiIiIiIiIiIiLS4ijYFBERERERERERkRZHwaaIiIiIiIiIiIi0OAo2RUREREREREREpMVRsCkiIiIiIiIiIiItjoJNERERERERERERaXEUbIqIiIiIiIiIiEiLo2BTREREREREREREWpx/Aciktq6N69LnAAAAAElFTkSuQmCC", "text/plain": [ "