{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "66f65bc3",
   "metadata": {},
   "source": [
    "# Termination times"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be0bcd9d",
   "metadata": {},
   "source": [
    "This notebook shows how to use the `corsort` package to determine the average termination time of several sorting algorithms.\n",
    "\n",
    "Here the following sorts will be compared:\n",
    "\n",
    "- Heapsort\n",
    "- Shellsort\n",
    "- Quicksort\n",
    "- Corsort-$\\Delta$\n",
    "- Mergesort\n",
    "- Binary insertion\n",
    "- Ford-Johnson"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfa06d7c",
   "metadata": {},
   "source": [
    "First we load some packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "efc2cf00",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-23T23:53:23.517890Z",
     "start_time": "2023-05-23T23:53:17.171632Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from multiprocess.pool import Pool\n",
    "\n",
    "from corsort import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2e77a64",
   "metadata": {},
   "source": [
    "We populate the list of sorts we want to study. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7aa256cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-23T23:56:52.945537Z",
     "start_time": "2023-05-23T23:56:52.923353Z"
    }
   },
   "outputs": [],
   "source": [
    "sort_list = [JitHeapsort(), SortShell(), SortQuick(),\n",
    "             JitCorsortDeltaMaxRho(), SortMergeTopDown(),\n",
    "             SortBinaryInsertion(), SortFordJohnson()]\n",
    "legends = {'heapsort': 'Heapsort',\n",
    " 'shellsort': 'Shellsort',\n",
    " 'quicksort': 'Quicksort',\n",
    " 'corsort_delta_max_rho': r\"Corsort-$\\rho$\",\n",
    " 'mergesort_top_down': 'Mergesort',\n",
    " 'binary_insertion_sort': 'Binary insertion',\n",
    " 'ford_johnson': \"Ford-Johnson\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0636590f",
   "metadata": {},
   "source": [
    "We decide the values of $n$ to study and the number of trials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd52b412",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T00:04:59.463361Z",
     "start_time": "2023-05-24T00:04:59.449781Z"
    }
   },
   "outputs": [],
   "source": [
    "n_list = [2**i for i in range(3, 12)]\n",
    "nt = 10000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb7aecb8",
   "metadata": {},
   "source": [
    "We now run the experiments, with some multi-processing to speed-up things."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9ff8a9e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T00:04:38.713090Z",
     "start_time": "2023-05-23T23:57:07.726042Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of heapsort for n = 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:11<00:00, 841.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of shellsort for n = 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:04<00:00, 2344.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of quicksort for n = 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:04<00:00, 2355.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of corsort_delta_max_rho for n = 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:16<00:00, 624.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of mergesort_top_down for n = 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:06<00:00, 1666.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of binary_insertion_sort for n = 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:09<00:00, 1011.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of ford_johnson for n = 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:08<00:00, 1164.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of heapsort for n = 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:35<00:00, 285.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of shellsort for n = 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:22<00:00, 451.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of quicksort for n = 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:14<00:00, 667.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of corsort_delta_max_rho for n = 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:56<00:00, 177.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of mergesort_top_down for n = 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:25<00:00, 385.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of binary_insertion_sort for n = 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:29<00:00, 336.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of ford_johnson for n = 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:27<00:00, 362.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of heapsort for n = 32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [01:02<00:00, 159.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of shellsort for n = 32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:42<00:00, 236.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of quicksort for n = 32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:37<00:00, 267.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of corsort_delta_max_rho for n = 32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [01:40<00:00, 99.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of mergesort_top_down for n = 32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:41<00:00, 239.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of binary_insertion_sort for n = 32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:39<00:00, 253.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of ford_johnson for n = 32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:36<00:00, 275.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of heapsort for n = 64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [01:42<00:00, 97.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of shellsort for n = 64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:48<00:00, 206.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of quicksort for n = 64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:39<00:00, 250.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of corsort_delta_max_rho for n = 64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [02:10<00:00, 76.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of mergesort_top_down for n = 64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:44<00:00, 227.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of binary_insertion_sort for n = 64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:39<00:00, 254.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of ford_johnson for n = 64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:33<00:00, 295.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of heapsort for n = 128\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [01:34<00:00, 105.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of shellsort for n = 128\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [01:39<00:00, 100.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of quicksort for n = 128\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [01:04<00:00, 155.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of corsort_delta_max_rho for n = 128\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [02:26<00:00, 68.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of mergesort_top_down for n = 128\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [01:18<00:00, 127.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of binary_insertion_sort for n = 128\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:54<00:00, 181.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of ford_johnson for n = 128\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:41<00:00, 238.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of heapsort for n = 256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [01:37<00:00, 102.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of shellsort for n = 256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [03:26<00:00, 48.48it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of quicksort for n = 256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [02:12<00:00, 75.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of corsort_delta_max_rho for n = 256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [02:16<00:00, 73.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of mergesort_top_down for n = 256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [03:08<00:00, 53.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of binary_insertion_sort for n = 256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [02:03<00:00, 81.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of ford_johnson for n = 256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:57<00:00, 174.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of heapsort for n = 512\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [02:11<00:00, 76.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of shellsort for n = 512\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [06:59<00:00, 23.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of quicksort for n = 512\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [04:57<00:00, 33.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of corsort_delta_max_rho for n = 512\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [17:44<00:00,  9.40it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of mergesort_top_down for n = 512\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [05:43<00:00, 29.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of binary_insertion_sort for n = 512\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [03:53<00:00, 42.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of ford_johnson for n = 512\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [02:39<00:00, 62.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of heapsort for n = 1024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [04:22<00:00, 38.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of shellsort for n = 1024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [13:26<00:00, 12.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of quicksort for n = 1024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [08:52<00:00, 18.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of corsort_delta_max_rho for n = 1024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [2:33:02<00:00,  1.09it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of mergesort_top_down for n = 1024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [10:30<00:00, 15.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of binary_insertion_sort for n = 1024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [07:31<00:00, 22.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of ford_johnson for n = 1024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [04:16<00:00, 38.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of heapsort for n = 2048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [07:55<00:00, 21.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of shellsort for n = 2048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [24:50<00:00,  6.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of quicksort for n = 2048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [16:31<00:00, 10.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of corsort_delta_max_rho for n = 2048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [8:50:53<00:00,  3.19s/it]    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of mergesort_top_down for n = 2048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [04:32<00:00, 36.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of binary_insertion_sort for n = 2048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [03:13<00:00, 51.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate comparisons of ford_johnson for n = 2048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [01:45<00:00, 94.52it/s]\n"
     ]
    }
   ],
   "source": [
    "with Pool() as p:\n",
    "    comparisons = evaluate_comparisons(n_list=n_list, sort_list=sort_list, nt=nt, pool=p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8efd371",
   "metadata": {},
   "source": [
    "We save the raw results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "28436404",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T00:06:10.440095Z",
     "start_time": "2023-05-24T00:06:10.399275Z"
    }
   },
   "outputs": [],
   "source": [
    "import dill as pickle\n",
    "from pathlib import Path\n",
    "\n",
    "fn = Path(f\"terminations_n_{n_list[0]}_N_{n_list[-1]}_nt_{nt}.pkl\")\n",
    "if fn.exists():\n",
    "    with open(fn, 'rb') as f:\n",
    "        comparisons = pickle.load(f)\n",
    "else:\n",
    "    with open(fn, 'wb') as f:\n",
    "        pickle.dump(comparisons, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a895b3",
   "metadata": {},
   "source": [
    "Then we display the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "77b5e814",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T00:06:28.159258Z",
     "start_time": "2023-05-24T00:06:26.860838Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABPoAAANLCAYAAADYS/PyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3xddf3H8dc5d4/spE1H0qQLymqZZRWKbJQlKgIqVUFkSamUIQIFZBXZKCIqU0V/IkMBAWW0rMqWUeiiA2hp0qZJk9x5zvn9cUduRtukzc77+XiU3HvW/d5Lmt77zuf7/RiO4ziIiIiIiIiIiIjIgGb29QBERERERERERERk2ynoExERERERERERGQQU9ImIiIiIiIiIiAwCCvpEREREREREREQGAQV9IiIiIiIiIiIig4CCPhERERERERERkUFAQZ+IiIiIiIiIiMggoKBPRERERERERERkEFDQJyIiIiIiIiIiMggo6BMRERERERERERkE+jTomzdvHkcffTQjR47EMAwee+yxVvsdx2HOnDmMHDmSQCDA9OnT+fDDD1sdE4vFOPfccyktLSUUCnHMMcfw2Wef9eKzEBERERERERGRoay/ZFx9GvQ1NTUxefJk7rzzzg73z507l5tvvpk777yTN954g/Lycg499FA2btyYPWbmzJk8+uijPPzww7z88ss0Njbyta99DcuyeutpiIiIiIiIiIjIENZfMi7DcRxnm59NNzAMg0cffZTjjjsOSCWdI0eOZObMmVx00UVAKtkcPnw4N9xwA2eccQb19fWUlZXx4IMPcuKJJwLwxRdfUFFRwVNPPcXhhx/eV09HRERERERERESGoL7MuNw98oy6waeffsqaNWs47LDDstt8Ph8HHnggr776KmeccQZvvfUWiUSi1TEjR45kp5124tVXX93kixCLxYjFYtn7tm2zfv16SkpKMAyj556UiIiIiIiIiIj0a47jsG7dOoqLizHNlsmwPp8Pn8/X5ev1ZMbVVr8N+tasWQPA8OHDW20fPnw4K1asyB7j9XopKipqd0zm/I5cd911XHnlld08YhERERERERERGayuuOIK5syZ0+XzejLjaqvfBn0ZbSvsHMfZYtXdlo655JJLmDVrVvZ+fX09lZWVrFq1ivz8/G0bsIiIiIiIiIiIDFgNDQ1UVFSwcuVKCgoKstu3ppovV09kXG3126CvvLwcSCWaI0aMyG5fu3ZtNgEtLy8nHo9TV1fXKvFcu3Yt++677yavvalSy/z8fAV9IiIiIiIiIiJCQUFBt+REPZlxtdWnXXc3p7q6mvLycp577rnstng8zksvvZR9grvvvjsej6fVMatXr+aDDz7o0osgIiIiIiIiIiLSE3oz4+rTir7GxkaWLFmSvf/pp5/y7rvvUlxcTGVlJTNnzuTaa69lwoQJTJgwgWuvvZZgMMjJJ58MpJLVH/7wh/z0pz+lpKSE4uJiLrjgAnbeeWcOOeSQvnpaIiIiIiIiIiIyhPSXjKtPg74333yTgw46KHs/s27eqaeeyn333ceFF15IJBLhrLPOoq6ujqlTp/Lss8+Sl5eXPeeWW27B7XbzrW99i0gkwsEHH8x9992Hy+Xq9ecjIiIiIiIiIiJDT3/JuAzHcZzue1oDU0NDAwUFBdTX12927rVlWSQSiV4cmQxlHo9HgbWIiIiIiIhIL+tsTtQf9dtmHP2J4zisWbOGDRs29PVQZIgpLCykvLy8Sx12RERERERERGRoUtDXCZmQb9iwYQSDQYUu0uMcx6G5uZm1a9cCtOrKIyIiIiIiIiLSEQV9W2BZVjbkKykp6evhyBASCASAVCvtYcOGaRqviIiIiIiIiGyW2dcD6O8ya/IFg8E+HokMRZnvO60NKSIiIiIiIiJboqCvkzRdV/qCvu9EREREREREpLMU9ImIiIiIiIiIiAwCWqNPRERERERERGSQcByng20dHNfZczs8rqPrde5xO7It1+vW55G+39A8cJfPUtA3iM2YMYMNGzbw2GOPtdr+4osvctBBB1FXV0dhYWGfjK27GIbBo48+ynHHHdfXQxERERERkTZyP2znfqB2Ojim9bY219mKAKEzwUH7x+noOs5mj+lwHFsYf2fCis4EFZ15ndqPZfPX6Og6W/U6dSLg2dL42+13nHbfJ109J/U4HR2X2dP2uPbX73Tw1cHz62i8W/p+cBynk/8POtrY0aZOhmAdbOvoQbYlCOzkpm5/HpsK+DI++3JdR6MYEBT0yYAUj8fxer19PQwRERGRIS33w6eTsy27P7st55ycT1Nb+mC8qWM391jtrpsd37YHTq0+VLZ53p0Ze0eP1W68nb1WOrxoG3S03Z953JYwwcnZ5mTP6eh5tIy7JZxoG0pkrtPxc6T1gW2fS/vd7U7szAf0dh/2OxMcdCIg20IO0OFYOk4oNn/O5oKqzJLduUcYtP7+MQyj3f6OzuloS7uxtX2cdvdbP2678W5y/E56f/tz2g4lO36n9TVaH7OJkzq+u8ltHW1sd+1NjqFzOlp3vfNj6dz1OndeR8cZObc7OY70hTq7nHxnX88Oz92G/z8dj6Wjx2i/9bmP1nDnsx927qL9kIK+XrS6PsKntU1Ul4YYURDo6+Fkvfrqq1x88cW88cYblJaWcvzxx3PdddcRCoUAeOihh7j11lv55JNPCIVCfOUrX+HWW29l2LBhQEuF4D//+U9+9rOf8cknnzB58mR+97vfsfPOOwOwYsUKzjnnHF5++WXi8ThVVVXceOONHHXUUQC89NJLzJ49m/fee4/i4mJOPfVUfvGLX+B2p75Fp0+fzk477YTX6+WBBx5gxx13ZMWKFQAcf/zxAIwZM4bly5f35ksnIiIi/URuYAFtQozsMZl9rY/d3L62gZKTe95mztnSY9Pq2E4+9hbG2zb4yRxjp2/YOUFPbjjTagyZc7BbBTptj281npygqc1L0+bADs7NfU3anOh0uG3zIVFHwVJb7f5f5GwzjJbTWoUpm/yg7rS6t6mHzQ1IDMNo9z2U2tEShDjtL9vq2MxwWn3g7fhmhx/eW32438R5Lfs7CAJy7hibOLaDQ9s8lrGJsbUcuKUP71vzgb8zwUFnQoO2h3QqfNlCGKVGfN2ntjHGFxsijCwMUBr29fVwBjzHcbAdSNo2lu1g25CwUrctJ3U/u89xSFqp7aljHZLZ49K3039sp+V+q31tjt3ma6aPs9peM+c4K/MYttPq38WBSEFfFzmOQyRhdfm8R976jCue+BDbAdOAK4/ZkRN2H92lawQ8rm7/4f/+++9z+OGHc/XVV/P73/+empoazjnnHM455xzuvfdeIFU9d/XVV7Pddtuxdu1azj//fGbMmMFTTz3V6lqzZ8/mtttuo7y8nJ/97Gccc8wxLFq0CI/Hw9lnn008HmfevHmEQiE++ugjwuEwAJ9//jlHHXUUM2bM4IEHHuDjjz/m9NNPx+/3M2fOnOz177//fs4880xeeeUVHMehpKSEYcOGce+993LEEUfgcrm69bURERHpr1oHNO3DnVa3c4+xN7G9zbnQOijaXOBEzvm5++zsGNsHUbbT+nq203GA5DipwAnAtlvGsrnAKTP27LG0PmhzwVvbYGlT4ZXT6sIdX89xckKjnLAoUyHTEhK1r7Np/WhGtkImdT0HnDYJS04WZbRJX7IBC7TZbuSERa1l3m/mnmO02dfmctlrmbkHZ/YbxiaDk00FTh1t2+KxHTyHTV9r88eKyMD17EdruPOFJdmfm+ccNJ7Ddijv1sfIBF9WTjBl5YRNluNgWZvYbm9pm41lk7M9575lY+U+rp17Px28Zc/NuZ+57ZAOv1rup66ZOi67r114160vn/Qww+lo4v8Q09DQQEFBAfX19eTn57faF41G+fTTT6mursbv99McT7LD5c/0yTg/uupwgt7OZ7MzZszgoYcewu/3t9puWRbRaJS6ujp+8pOfEAgEuPvuu7P7X375ZQ488ECampranQvwxhtvsNdee7Fx40bC4XC2ou/hhx/mxBNPBGD9+vWMHj2a++67j29961vssssunHDCCVxxxRXtrnfppZfyyCOPsHDhwuwbrV//+tdcdNFF1NfXY5om06dPp76+nnfeeafVuYN9jb62338iItL9eiQ06+g6uY+Vs912Ur9Vtm0Hm9SbbDv9ASJzrOWkqqta/ZY5fQ079SRaPR5tx5RzDrnjytxpE+xk4qfWMVf7wClbedQPAycj5+COqolanlGbAMjY9L7Mtdo9drvHUXAkIrI5TjrESlipYClpOSQsm6Sd+trR9mTO9oSVvm87rY5piCT414dr2j3enlVFuE1z02HbpsK6zRwjqSIml2ngNk1ME1xG5raBK33f5TJxtTrOyN53mWbquMxtI3VeyzUyx5rpYwzcppG+RvoxMvuMnH3tjutgX3p722MbIgnO/793saLNrLr1Wx3mRP2dKvoGuYMOOoi77rqr1bYFCxbwne98B4C33nqLJUuW8Mc//jG733EcbNvm008/ZdKkSbzzzjvMmTOHd999l/Xr12Onf6W+cuVKdthhh+x5++yzT/Z2cXEx2223HQsXLgTgJz/5CWeeeSbPPvsshxxyCCeccAK77LILAAsXLmSfffZp9aZ4v/32o7Gxkc8++4zKykoA9thjj+58aUREpAdsKjTLTB3szdDMcjK/cW8JzTL7uzs0sx2HVikYbWfcpdMwh9T0PMdoF0gZmXMMMA2jVWiU2W7k3M+EaS4jvc1oE5q1uW/mTIXrKKwSEZHBIzPVsX0o1nI/E6olLYdEbqjW9n6bkC2zvXXI1vb41sFdR9fsTW8sr+uVxzHT/4ZnAqTsn7bbDAOXaxPb2567qfM7c1wH29oGYLnHuV1G+/Fng7J0mJe5bQzO9xJleT7OOWg8d/zrf309lK2moK+LAh4XH111eJfOWVMf5ZCbX2pV7moa8O9ZB1Je0PkqrYCn61NTQ6EQ48ePb7Xts88+y962bZszzjiDn/zkJ+3OrayspKmpicMOO4zDDjuMhx56iLKyMlauXMnhhx9OPB7f4uNn/uKfdtppHH744Tz55JM8++yzXHfdddx0002ce+65OI7T7gdEptA0d3tmzUAREUnJrC+S+WqnA6feDs3s9DSQ3ADMzqkW657QzMn+l34QmmXPVWgmItLt+vv6aplpm10PxTpXqba56+SGaJu75kCsOPO4DDwuE7dp4E5/9bhMPK5UsORxtd7ubrPdYxokLIdnPmpd0WcAJ+1VSUHAs/WhWidDOVPvB3rEpt7L5i4T0u49bUe3Sb/PdMDO2Q+k3lOm5zPsNKqAK762Az+4pS+e7bZT0NdFhmF0afoswNiyMNd9fWd+9vcPsBwHl2Fw7dd3YmxZuIdG2Xm77bYbH374YbswMOP999+ntraW66+/noqKCgDefPPNDo99/fXXs9V3dXV1LFq0iO233z67v6Kigh//+Mf8+Mc/5pJLLuGee+7h3HPPZYcdduCRRx5pFfi9+uqr5OXlMWrUqM2O3+PxYFldXzNRRKQvZKapZAK5bECXXkslO33TIbtosO20TGGxbJukBYnsQsitA77M7f4QmmXW6cqEYdC2okyhmYiItNd2fbXT9qtm/wll2VCtbYVaNmzbQjjWmemgnQvlUrcHWoyWqeRyZ8O01gGZu03IlgnXcrd7NrHd3UEY59nMdTK3PTnbu7M6bGJ5mF+9sCS7Pv7ZPbBG31CSCbahg2Ctg9u5x+K0bQjV5hfOkLtCSM6X9HvVnIwg+z7STB3T8l4z9b7STL8JNQHTTL2vdKUrD11GqhoxVXHZMl04U72YeV9qpN/rmobBuAKzZ1/YHqSgr5ecuGclB0wsY3ltM1WlwX7Tdfeiiy5i77335uyzz+b0008nFAqxcOFCnnvuOe644w4qKyvxer3ccccd/PjHP+aDDz7g6quv7vBaV111FSUlJQwfPpxLL72U0tLS7Np5M2fO5Mgjj2TixInU1dXx/PPPM2nSJADOOussbr31Vs4991zOOeccPvnkE6644gpmzZqFaW7+L1dVVRX/+c9/2G+//fD5fBQVFXXr6yMikunC1VEgt8nQzs6ZqpJe1DiRbBvIpSrvLFJfU2+cWsK4lFTwZpo5b1rSX1NvQtJrmBiZNzOGQjMREekRjpPqUBlL2sQSFnHLJpawiSVt4kkrtT2ZuW8TS1rpr6nbm97Xsi2SsGiKWTmPCfe8/Cn3vPxpHz7zzsmtMvN0EJClqs/aVqJlju+4Qm2T18y5nxuqtQRorUO2TLXZUHHYDuXsVlnE6g0RRvTTqtDO2FwVG7QO0DKB2qZmcuRWsWXDNyN1x8DIrrubfeycG5n3mZlQzYScYCznK6nwzWVm1u7LrNtnZt+nmkZq9oSZnv6bCdYy1zBbhXYtv2DOPcbocHvHx26LBmPLMxj7KwV9vWhEQaDfBHwZu+yyCy+99BKXXnop06ZNw3Ecxo0bl22qUVZWxn333cfPfvYzbr/9dnbbbTd++ctfcswxx7S71vXXX895553H4sWLmTx5Mk888QRerxdINQA5++yz+eyzz8jPz+eII47glltSdbCjRo3iqaeeYvbs2UyePJni4mJ++MMf8vOf/3yL47/pppuYNWsW99xzD6NGjWL58uXd9+KIyICU2wUtE6hlA7mOQrv0cS1VAqnOZpmQLrdBQmbqqpV+Q2XZTrqaLfX2KLcRQTZ8S/9WMPc3hJn1T3K3DdZ1TkREpGclLbsldLPSIVybAG1LIVxu6JYb3LU9pq9mgxqAx212KhBrW1XWPmRrG6B1XJHW0TXbB3ep7S7T0L/h/Uxp2LdNAV/bKrbM13ZVbLSeHtphFVubY1PXJ9uZPTVZNPf9ZGpnKutKh1adqWIzDAyz5b1lS8Uabe53XMWWG5Z1FKilgrfWx0L7c80hFCr3V+q6S9e67kp7ma67dXV1FBYW9vVwBhV9/8lQkQnbrHQAl7ntpMM4Kx2ytdwm3amtpXLOsp3slJpMdVzmGjap6zrpteMg86aqZa6ASUvYZhqtq+Y6CuUy+0VERNqybId4MhPA5QZt7UO4tiFbR8dsLoTri7XYTAN8bhc+t4nXbeJzm/jcruxtb/q+L7PPY+J1mfg8rvTXlvu+nPOb4kkue/wDcj+hmgb8/tQ9B2xVlnSfTPiW+ZoJ3rJNttLv/1rWEe54qmhmRZPMmmyZqaKZmjZnC1VstA3EaAnGUiEamLRMFU11kyXbACP3PWR2tkZOZZrR5j3p5qrYOjpWusfmcqL+ThV9IiKyVTpqBJEN5NKVc5lALhPOZSrmcte4Sa01l57CmlNhZ0Mq6EvPR0i9bzFareWReqPUvirOZRiYLgMvRvpNlqrmRER6Q39uomA76fBtK6eXbjGoS9jELItYovc7imb4skFbboCWE7x1ELj5Ml83E9Tl3ve6U1VtPfXv6TkHjW+3vlp/+16SLbNbBW7tw7fsciZ2y9TS3GCOnNVMMuEc5FStZW7n/FLWla7OdJmppU1yu8vmVrRlQ7JNTBXtqIot99iemioq0l0U9ImIDBHb2ggimZnSuoVGEJkKOsipmnPSb5TIrYxLf03/JtOVfjNmutuEdir/FxEZENo2UTinEwvgZ9d9S3QmaGtdydZ+XTgrG7hlKulyp7TGk3YvvRKteV1mmwDN3HI1nKdtUNf++LYhnMc1OKaPDpb11QaKramSa9WQgdZVcakGCrnTSNPv/3J/8YqB25UJ49LBXM706dT7w5zjM1NPMyGc2bI0in6hK9Kegj7ZZtOnT0czwEV6RmcbQWSDu801gshMYd2GRhCtp7C2bwTRds0OEREZPGzHIZpultAcT9IUt2iOpb6ubYjy4OsrsgsiOA7c8fwS5i+uBWgJ6joI4friXaTbNDYx3TSnGi5b7dbBdNNsFVz7EK5t9Zv+Tey6bV1fbbDqL1VyLrOlk27ml7Zmel3izLmZdQtdHcy+yJwjIj1DQZ+ISB+ybIfmeJJI3KIplqQpntxkI4iWdedo0wiCVtNZcxfUbdcIwkwtRq1GECIiQ4vjOEQSFs3Zf29aQrrmeLKD8C6ZPbY5bqXux6wuh3LvrtrQ6WO3tO5bKmhztZpumrvOm7ft8R1UzmWOUbW49KStXUvO3kSVXKbhV29VyeX+AlfvEUUGHgV9IiK9KJqwiMQtmhMW9ZE49U1JoskksWRqDTqPabZrBOE2DAx3y9oimW5WqhAQERka2oZ0ucFbUydDukjc6raOqS7TIOh1EfK6CfpchL1uXC6Dd1ZuaHWcAZy6TxXFYW/70K6D+26X2T0DFOmC7qiSw0i1cehKlVyqUs7Yqio5s1UQpyo5EWlNQZ+ISA+xbYfmRPpDVyxJXSROU8wiGrexsPEYqSqEfL8Xr1sfbkREBqMthXTtK+x6N6QLed2t7/vchLwugl43IZ+7w2N9brPDCp9nP1rTronCltboE+msnqiSM9JbOqqSMwwD1yaq5NymgcelKjkR6Z8U9ImIdJNY0qI5llOt15wkmkgSt1Kfznwuk4DXRV6eR1OGREQGgM6GdO0r7PoopMvc93U9pOsOaqIgW5LbGMxKr0Oc2+Ars83JNvXauio5l5GasqoqOREZihT0iYhshWy1XjxJU7SlWi+WsEk6LdV6eX4PPrerr4crIjLkOI5DNGFnp7Rm1kHdUkiXu705nuy2kM40SFfLbTmka7s9s60nQ7ruoiYKQ4eVE85lQ7o2IZ7tZAK7luYPpmngzoRu6XDO72lZR9HjSv3Jhm45FXMuVcmJiGyRgj4RkU6IJVNr6zXFLRoiCeojCSLxDqr1fKrWExHZVm1DurZNI9pWzfVKSNdR1VwnQrrM9oEQ0snQlJnemgnqrPR6dNmv6ds5Z+CQnp6aCd3M1P2Whiipqa1et5mtqstU3LnSjcFyv+rvhohI91HQN4QZhsGjjz7Kcccdt9XXmD59OlOmTOHWW28FoKqqipkzZzJz5sxuGaNIX8it1muOJalrTtAYSxJL2FiOg8sw8Ker9bwufXATkcGjtjHGFxsijNyGaZdbCulytzf2YUjXEsil1qRTSCeDQevKOgfbJjs9NjstNr2OXWY+bKbKzpVuBuZKh3K+dFDncbVU2mXCObNVWNc6xBMRkb6loG8QW7t2LZdddhlPP/00X375JUVFRUyePJk5c+awzz779PXwttqMGTPYsGEDjz32WF8PRQaJ3Gq9jZEEGyIJonGLaNLCwMDnNlPBXljVeiIyeD370RrufGEJjgOGASfvVcmU0YWbDOnahnONfRjSpRpHtHxVSCcDXdsqu9z16+ycKbK246Q7vpKtsstMbXWlq+w8LoOgz8Traul03Lq6zmxVXZe5rb83IiIDk4K+QeyEE04gkUhw//33M3bsWL788kv+85//sH79+r4e2laxLEtvOGSb2XZqYfWmdLXehuYkjbEE0TbVemG/m2KXV99zIjJoxJM265vjrGuMsb4pzrqmeOprY5w1DREWfdmYPdZx4I8LVvLHBSu3+vFMA4LpwC3s62RI53W1qrBTSCeDQdt17HKr7HL3ZarsDFJ/B1OhW+sqO68rFdjlrmdndjAVtm2IJyIiQ4eCvt5U/zmsXwrF46BgVI8+1IYNG3j55Zd58cUXOfDAAwEYM2YMe+21V6vjamtrOf7443nmmWcYNWoUN910E8ccc0x2/0cffcQFF1zAvHnzCIVCHHbYYdxyyy2UlpZ2ahxz5szhD3/4A19++SUlJSV84xvf4Pbbbwegrq6O8847j3/84x/EYjEOPPBAbr/9diZMmADAfffdx8yZM3nooYe48MILWbRoEaeccgoPPPAA0LLY7gsvvMD06dO36fWSwSueTE0fa06vrZep1oslLcip1itVtZ6IDFC241AfSbCuMR3cNcVahXjr0/c3RpNdvnZhwENRyJsN57LVclsI6YJeN36PQjoZfNo2nmi/pl1ulZ2B4ziYhonpaqmyM41UZ9i2VXaZ7W2nw7pVZSciIl2goK+rHAcSzV0/790/wdMXgmODYcKRc2HKyV27hieYmkvTCeFwmHA4zGOPPcbee++Nz9fxOjtXXnklc+fO5cYbb+SOO+7glFNOYcWKFRQXF7N69WoOPPBATj/9dG6++WYikQgXXXQR3/rWt3j++ee3OIa//e1v3HLLLTz88MPsuOOOrFmzhvfeey+7f8aMGSxevJgnnniC/Px8LrroIo466ig++ugjPB4PAM3NzVx33XX87ne/o6SkhPLycqLRKA0NDdx7770AFBcXd+o1kcEvU62XmkqWaF2tZzvprm6pKpHikKr1RKT/a44nU6FdY6oCb11TLCfAS22ra45jdXKurMdlUBzyUhLyURL2Uhz0UhL24nGZ/HbeMnKvYhpwy4lT1EFVBiUnZypsbpWdnbMt8/fKMFIdY2lbZWcYuAwTr6elys7jMvC6XLhcrQO6jta0ExER6QkK+roq0QzXjty2azg2PHVB6k9X/OwL8IY6dajb7ea+++7j9NNP5ze/+Q277bYbBx54IN/+9rfZZZddssfNmDGDk046CYBrr72WO+64g//+978cccQR3HXXXey2225ce+212eP/8Ic/UFFRwaJFi5g4ceJmx7By5UrKy8s55JBD8Hg8VFZWZisKMwHfK6+8wr777gvAH//4RyoqKnjsscf45je/CUAikeDXv/41kydPzl43EAgQi8UoLy/v1Gshg1c8aafX1kuyMZqgrlnVeiIyMCSt1DTalgCvpRovs219U5xIwurU9QygMOjJhnjFoVSAVxLyUhzypb96yfO7N/lLDq/b5FcvLMF2UiHf2QeNV8gnA0LbKjvbaVttB3Y6xk5Ni82psiPdMdZMVdN5fSYe08x2j81sz6x35zbNViFepuOsiIhIf6GgbxA74YQT+OpXv8r8+fN57bXX+Ne//sXcuXP53e9+x4wZMwBahX6hUIi8vDzWrl0LwFtvvcULL7xAOBxud+2lS5duMej75je/ya233srYsWM54ogjOOqoozj66KNxu90sXLgQt9vN1KlTs8eXlJSw3XbbsXDhwuw2r9fbaowydOVW60XiFnXNcTZGW9bWMw2DgKr1RKSPOY5DQzSZnS6bW3m3rjGWDfc2RBKdvmbQ60oHeN7W1Xg5oV5R0IPbZW7T2A/boZzdKotYvSHCiG3ouiuytdpV2aWbUdittjk4tLSMNQDTMDBbNaAw8XtTVXYel4HXbbaqsjONdFDXJrDb1r9DIiIi/YGCvq7yBFOVdV3R8AX8aq9UJV+G4YKzF0B+F6oDPcGuPS7g9/s59NBDOfTQQ7n88ss57bTTuOKKK7JBX2aKbHZYhoFtp8Zp2zZHH300N9xwQ7vrjhgxYouPXVFRwSeffMJzzz3Hv//9b8466yxuvPFGXnrpJRyn4ylGjuO0CmgCgYACmyGqw2q9hEUsYYEBPpcLn8dFScitN+Yi0iuiCSs9bTZnDbxWTS1S02qTnZxG6zYNitIBXjbEC/vahXoBr6uHn1mL0rBPAZ90i7aNJtqtaZfpGEvrKjvDBLfRusrO7WlpPuF1mbhdZqrKzsiZCtsmxFOVnYiIDFUK+rrKMDo9fTardAIcfRv8YyY4VirkO/rW1PZetsMOO/DYY4916tjddtuNRx55hKqqKtzurftWCQQCHHPMMRxzzDGcffbZbL/99rz//vvssMMOJJNJFixYkJ26u27dOhYtWsSkSZM2e02v14tldW4qkwwMjpPuhBvLqdaLJYnGrWy1nt9tEvK6KQ6qWk9EupdlO2xozgnt0kFebpi3vjFGU7zz//bk+92UhH05gV16Cm24JcTLD3gw9fNMBiDHcUjaDgnLJmmlbictm2TmF7mOg8s0W1XZmYaRnQ7rdbdU2Zkm2TXrMlV2LQFe6qv+3RcREek8BX29ZbfvwbiDYf0yKB7b4113161bxze/+U1+8IMfsMsuu5CXl8ebb77J3LlzOfbYYzt1jbPPPpt77rmHk046idmzZ1NaWsqSJUt4+OGHueeee3C5Nl9hcN9992FZFlOnTiUYDPLggw8SCAQYM2YMJSUlHHvssZx++uncfffd5OXlcfHFFzNq1Kgtjq+qqopnnnmGTz75hJKSEgoKCtpVJkr/lrBsmmMWzYkkGyNJ6ppT61DFkzYOTk61nlfVeiKy1RzHoSlmtXShbYyzrrml8i6zbUMkTieL8PC5zVQFXk7lXWoabUuoVxxKNbcQGahsxyFppYO8dIiXsB1sx053kgWPO1VJ5zFN8gImAY+PoNeFJ9OUIh305YZ4qrITERHpeQr6elPBqB4P+DLC4TBTp07llltuYenSpSQSCSoqKjj99NP52c9+1qlrjBw5kldeeYWLLrqIww8/nFgsxpgxYzjiiCMwzS1/gCksLOT6669n1qxZWJbFzjvvzD/+8Q9KSkoAuPfeeznvvPP42te+Rjwe54ADDuCpp57aYmh3+umn8+KLL7LHHnvQ2NjICy+8wPTp0zv1nKT3Zar1muMWzbGcar2EhWW3rdYz9Vt7EemUeNJuaV6RXgsvVYHXelptPGlv+WKkmk8UBb2t1r7raDpt0OvSzykZ8KycaryEnanKs7Ndl13p6a9uV2rdujx/6nvf73GlO8um1r7zuFJTaRXgiYiI9B+Gs6nF0oaQhoYGCgoKqK+vJz8/v9W+aDTKp59+SnV1NX6/v49GKEPVQPz+y63Wa4wms10jY0kbcPC6Uh8U/G5T1Xoi0o7tONQ3J1qFdtlqvMy2xtQvDDor7HPnVN617kpbHEwFeQUBdeeWwaFT02pdJh4zFeJ53SYBj0nQ68bncaWaV7gyYV4q0FO4LSIiQ83mcqL+ThV9IrLVtlith4Hfo2o9EUlpjidzutDG2gR4qW11zQmsTs6j9biMNh1o23elLQ558bl7r5mFSE+znfYhXu60WiBVidfBtNqWaryWjrT6pZuIiMjgoqBPRDotYdmpUC+eqtara44TiVtEkzaO4+Bzu/C7XZQEtbaeyFCSsGzqWnWhbZlOuz6nqUUk0blmFgZQGPS0qrzLDfEy28I+t36BIINOMrsunqbVioiISNcp6BORDjmOQzRh0xRPEolbrG+K0xhLEklY2LaDgYEvPdWnSNV6IoOS4zg0RJOtm1c0pZpZ5K6DVx9JdPqaQa+rw8q73K60RUGvptHKoLTFabWAyzSy02oDXheBdGW8V9NqRUREpBMU9IkI0FKtF4lbbIwmstV6saSN44DXbeJ3uygOqpukSH9V2xjjiw0RRhYGKA37NntsNGGlK+5ah3ZtQ71kJ6fRuk2j9fTZ3K606RCvOOQl4NU0Whm8LDtVfdfxtFoAo8NptSGvG4/b0LRaERER2WYK+kSGoEy1XnM8SXM8vbZeNEk0aZFM2piGma3WKwyamKoWEOn3nv1oDXe+sATHAcOAb+4+mvFl4ZwqvDjrm1uCvKZ456bRAhQEPDlVeC1BXu7tPL9bPytk0Nv0tFoHMDqcVhvyufC5Na1WREREeoeCPpEhIGnZNKWr9RpjCeqaEzTHktlqPY/LJOBxURRQtZ7IQFLXFGdpTSP/+2wDj777RXa748Bf3/xsi+f7PWbLmnfZ0M7baltRSD8XZGjY5LRa2yFdjtduWm3I6yLgcWlarYiIiPQbCvpEBpm21XobInEaIjnVeqaBz+0i4FG1nshA4TgO69Kh3pK1jSytaWTp2ibWN8c3e97oIj8VRaFWIV5xKFWJVxL2EvC4FETIkLHJabW2g2GkKvJyp9XmB1wEval/LzWtVkRERAYKBX0iA1zSsmlOWDTHcqr14kliCRvbAa/LxO8xVa0nMkA4jsPajbGcUK+JZTWNbOig4YUBjC4KMKowwIJP15O7mp5pwNXH7rzFtfpEBouuTqvND6S61WparYiIiAwmCvpEBhDHcYglbZpiLdV6GyNJIkkLy7IxDAOfK12tF1C1nkh/5zgOq+ujqQq9mqZ0pV4jG2PJdseaBlQWBxlbFmZ8WZhxw8JUl4SyzS2e/WgNv3phCbaTOvbsg8Yr5JNBo6NptQnLxtrEtNqgN1ON135arddt4jY1rVZEREQGJwV9skXLly+nurqad955hylTpmzx+BkzZrBhwwYee+yxHh/bYOc4DrbjUNsYI9rQUq0XT9pYtqr1RAYS23H4YkMkW6W3tKaRZTWNHTbFcJsGlSVBxmVCvbIwVaVBfO5Nd6w9bIdydqssYvWGCCM60XVXpD/pcFqtlfo3sN20WpdJgc9NwGt2OK3W6zZxqRpPREREhigFfYPcqlWrmDNnDk8//TS1tbWMGDGC4447jssvv5ySkpJOXaOiooLVq1dTWlraw6Pdel0NI/sjx3FwnFQYYDsOlu1kQ72PV9fjuLyq1hMZICzb4bO65laVestqmogk2od6HpdBVUmIcelAb/ywMGNKglsV3peGfQr4pF9qO602U43X0bRaz2am1XrdqfXzNK1WREREpGMK+gaxZcuWsc8++zBx4kT+/Oc/U11dzYcffsjs2bN5+umnef311ykuLt7idVwuF+Xl5b0w4q0Tj29+Mfr+KlOtZzupUMCyU/cdh/THnhTTMBiWF8Bwe/tyuCKyCUnLZlVdc+tKvdom4km73bFet8nY0kyoF2L8sDAVRUEt6i8DWttptYl0qNfRtFpPdlqtt9202kygp2m1IiIiIltPQV8vWtO0hpUNK6nMr6Q81PPB2dlnn43X6+XZZ58lEAgAUFlZya677sq4ceO49NJLueuuuzAMg0cffZTjjjsue25hYSG33norM2bM6LBa7sMPP+TCCy9k/vz5OI7DlClTuO+++xg3bly7cbz11lsceeSRnHfeeVx66aW89957zJw5kzfffBPDMJgwYQJ33303e+yxBwCPPPIIl19+OUuWLGHEiBGce+65/PSnP81er6qqitNOO40lS5Zkx/3AAw8AsOuuuwJw4IEH8uKLL/bAq7r1bNtpVa2XCvbIVjOYBhiGQe7nfUsfdET6lYRls2Jdc6vut8vXNZGwnHbH+j0mY0tTFXrjylLh3uiioKYUyoCTO602kVOZ13ZabSqkMyn0eTucVutLB3n6OyAiIiLScxT0dZHjOESSkS6f98TSJ7huwXXY2JiYXDL1Eo4Zd0yXrhFwBzr9G+7169fzzDPPcM0112RDvozy8nJOOeUU/vKXv/DrX/+6S2MA+PzzzznggAOYPn06zz//PPn5+bzyyiskk+0Xj3/xxRc57rjjuO666zjzzDMBOOWUU9h111256667cLlcvPvuu3g8HiAVCn7rW99izpw5nHjiibz66qucddZZlJSUMGPGjOx1b7zxRi677DJ+/vOfA3DOOeew11578e9//5sdd9wRr7dvq982Va1np7MAg9Ri+S5VLYj0W7GkxfLadKhXkwr1Vq5rJmm3D/WCXld26m2mUm9EQUCBhgwoCcsmmrCIJuz23WrTnWq9bpOCdKOL3Gm1qa+GptWKiIiI9DEFfV0USUaY+qep23QNG5trFlzDNQuu6dJ5C05eQNAT7NSxixcvxnEcJk2a1OH+SZMmUVdXR01NTZfGAPCrX/2KgoICHn744WxAN3HixHbHPf7443z3u9/l7rvv5qSTTspuX7lyJbNnz2b77bcHYMKECdl9N998MwcffDCXXXZZ9rofffQRN954Y6ug7ytf+QoXXHBB9v7y5csBKCkp6ZNpxo7TEualgr3UWnuZOCBTrefR7DyRfimasFhW28TStalQb1lNIyvXN9NBpkeez824YS2h3riyMOUFfq2bKQOKZTvEkqlQL5a0cEitF+l3uyjL9xL2urPTan0uV6vKPBERERHpvxT0DVGOk/r0ujWVb++++y7Tpk3LhnwdWbBgAf/85z/5v//7P44//vhW+2bNmsVpp53Ggw8+yCGHHMI3v/nN7JTfhQsXcuyxx7Y6fr/99uPWW2/FsixcrlTHycw0377Wdl0iBwcjPQ03Va3X1yMUkbaa48nsWnpLaxpZuraRz+oidJDpURDwtKrSG1cWZlieT5W4MqA4jkMsaRNLpir2LMfBZRj4PCZhv4uKYICwz03A6yLgcWnNSBEREZEBTEFfFwXcARacvKBL53zZ/CXHPXYcNi0Ls5uGyWPHPsbw4PAuPXZnjR8/HsMw+Oijj1qtvZfx8ccfU1ZWRmFhIYZhZIO/jEQiselxBLY8jnHjxlFSUsIf/vAHvvrVr7YKFOfMmcPJJ5/Mk08+ydNPP80VV1zBww8/zPHHH4/jOO0+QLcdG0AoFNriGHpSNuBL2tlpfJqGK9L/NEaTLK1NhXmZdfW+qI92eGxx0Mu4YaFs59txZWFKQl79vZYBJ2nZRNOhXtyywAGf24Xf62J0OEBewEPQ4yLgdeH3uPp6uCIiIiLSjRT0dZFhGJ2ePptRXVDNFftewZWvXYnt2JiGyRX7XEF1QXUPjTI1hfXQQw/l17/+Neeff36rcG7NmjX88Y9/5OyzzwagrKyM1atXZ/cvXryY5ubmTV57l1124f777yeRSGyyqq+0tJS///3vTJ8+nRNPPJG//vWvrY6dOHEiEydO5Pzzz+ekk07i3nvv5fjjj2eHHXbg5ZdfbnWtV199lYkTJ2ar+TqSCRIty9rMq7LtbMdptRi5gSr3RPqL+kgiW6GXqtZrYk1Dx6FeadjH+EyoVxZmbFmY4pA6W8vAYzsOsYRNNGkRS6TW1TNNA7/HRXHIS1HQSyC9pl7A49L6eSIiIiKDnIK+XvL1CV9n35H7smrjKiryKnql6+6dd97Jvvvuy+GHH84vfvELqqur+fDDD5k9ezYTJ07k8ssvB1Lr3d15553svffe2LbNRRddtNlpueeccw533HEH3/72t7nkkksoKCjg9ddfZ6+99mK77bbLHjds2DCef/55DjroIE466SQefvhhEokEs2fP5hvf+AbV1dV89tlnvPHGG5xwwgkA/PSnP2XPPffk6quv5sQTT+S1117jzjvv3GLTkGHDhhEIBPjXv/7F6NGj8fv9FBQUdMOrmJIK+GziVmoNPhPwmEaqq4aI9Lq6pnirJhlLa5qo2Rjr8Njh+T7GZxtlhBk3LExBYNM/40T6s3i6Ui+atEjaDqYBXrdJ0OtmRIGfPL8nG+xpPT0RERGRoUdBXy8qD5X3SsCXMWHCBN544w3mzJnDt771LdauXYvjOHz961/nwQcfJBhMVSbedNNNfP/73+eAAw5g5MiR3Hbbbbz11lubvG5JSQnPP/88s2fP5sADD8TlcjFlyhT222+/dseWl5fz/PPPM336dE455RQeeOAB1q1bx/e+9z2+/PJLSktL+frXv86VV14JwG677cZf//pXLr/8cq6++mpGjBjBVVdd1aoRR0fcbje33347V111FZdffjnTpk3jxRdf3OrXLsO2HRK2TSJpYzkOpmEo4BPpRY7jsC4d6mUaZSytaWJ9U7zD40cW+LPTbjN/wn79UycDk2U76S64FjHLxnHA5zbwu92U5/vJD3gIel0EvW78HlPTzEVEREQEw+loAbQhpqGhgYKCAurr68nPz2+1LxqN8umnn1JdXY3f7++jEXafK664gptvvplnn32WffbZp6+H029Z6QYbCcvGTgd8rj6Y7hSLRlm5YgVG3jAMt6YVyuDmOA41G2PZMG/J2lT32w2R9muGGsDookC2Qi/TMCPoVagnA1O7hhm2g8tMNcwI+VwUBbyE0g0zgl53n/ybJCIiIjJUbC4n6u/0iWiIufLKK6mqqmLBggVMnToV09S0nlyWbZNIr8Fnp7sSauqTSPdzHIc1DVGWrG1s6YC7tpGNsWS7Y00DKouDjE2vpzduWJjqkhABr5oIyMCVsOzs2npxy8LAwOs28XtcVOQFyPOnqvUCXhc+t77XRURERKRzFPQNQd///vf7egj9iuM42Qq+pOVg4+AyTDwuVUuIdAfbcfhiQyQb6i1Lr6vXFG/fPMdlGowpCWan3Y4vC1NVGlTQIQOa7aSm4MYSNjHLwrYd3G4Tv7t1w4yQz4XfrYYZIiIiIrL1FPTJkJUb8CUsBwdwmwYurXEkstUs2+GzuuaWKr2aRpbVNBFJtA/13KZBVWko2yhj/LAwY0qCqqKVAS+3YUbCsjGN1BTcoM/NyKCfsE8NM0RERESkZyjokyHHcRySORV8kKoiUr4n0jVJy2ZVXTNL1zZlu98uq20inrTbHet1m1SXhNKNMlJfK4qCuBVyyADXtmEGgNeVapgxoiDTMMNN0OvC51bDDBERERHpWQr6ZMjIBHzxpI1lK+AT6YqEZbNiXXO2Sm/J2kaWr2siYbXv5+T3mIwtDWdDvXFlYUYXBdU8QAa8TMOMVLWejZ1umOH3mBQEPRQGPIR8boI+NwGPS9/zIiIiItLrFPTJoGc7Dsl0g42kbWNgKOAT2YxY0mLFuub0mnqNLKlpZOW6ZpJ2+1Av6HVlO95mOuCOLAgo4JBBIdMwI5JIN8wwDHzphhnD8n1qmCEiIiIi/Y6CPhm0bNshadvELQfLtjEx8JgmKH8QyYomLD6tbcqGektrGlm5vpkOMj3CPnerKr1xZWHKC/yYSs1lELBsh1iypWGG4zi4XKmGGaV5LQ0zgl4XAY9LU3BFREREpF9S0CeDjm07JGybRNLGchxMw9Bi5yJAczzJspqW9fSWrm3k8w2RDkO9goAnW6mXCvfCDMvzKdyQQcFxHOKWTTRhE0tYJGwbl2GmG2a4sg0zgj4XQY9La0mKiIiIyIChoE8GjZYOuja2Aj4Z4hqjSZbWpsK8VKVeE59viHR4bHHQy7hhoWzn23FlYUpCXoV6Mmi0apiRtMHIaZhR5Cffr4YZIiIiIjI4KOiTAS9p29k1+FILo5t4XPqQJkNHfSSRnXabCvaaWNMQ7fDY0rCvVZXeuLIwxSFvL49YpOe0a5jhOLiMVMOMwqCXwmCqWUbQq4YZIiIiIjL4KOiTAclxnGwFX9JysHFwGSYetz6wyeBW1xzPVuktSVfq1WyMdXjs8HxfqkovHeiNGxamIODp5RGL9KyElQ71EqmKbsMg2zBjeL6fvICboMdNwOvC61aVt4iIiIgMbgr6Brk1a9ZwzTXX8OSTT/L5558zbNgwpkyZwsyZMzn44IP7enjtTJ8+nSlTpnDrrbd2uD8T8MXTAZ8DuE0Dl6ZZySBQ2xjjiw0RRhYGKAl5Wd8UT4V56Sq9JTWNrG+Kd3juyAI/44a1hHpjy0Lk+RXqyeCSaZgRTdjEkhYODm6XScDtoizfS2FADTNEREREZGhT0DeILV++nP3224/CwkLmzp3LLrvsQiKR4JlnnuHss8/m448/3qrrxuNxvN7uneq3pWs6jkPSdkgkbZLpzgEu00Cf4WSwePJ/X3D3vGVk+mIEPC4iCavdcQYwuiiQrdAbVxZmbGmIkE8/zmVwadcww7FxkWqYEfK5GF0UIOxPVeqpYYaIiIiISIrhOE4H/RaHloaGBgoKCqivryc/P7/Vvmg0yqeffkp1dTV+v3+bHiexZg3x5SvwVo3BU16+TdfqjKOOOor//e9/fPLJJ4RCoVb7NmzYQGFhIbFYjNmzZ/Pwww/T0NDAHnvswS233MKee+6ZPXb69OnstNNOeL1eHnjgAXbccUfOPfdcrrzySpYsWUIwGGTXXXfl8ccfzz7Olq7b0TWrq6u5//77W41z2bJljK4Yk6rgsx0MWgd8r77yMhdfOJsP3n+fcDjM7Isu5tzzZvbYa9rbYtEoK1eswMgbhuHWOmqDTSxp8ebyOv69cA1vrtjQbr8BjCkJMjZdpTd+WJjqkhABr6vXxyrS05KWTTSZCvViSRtw8Lld+DwuCoJuCgJegh4XAa8Lv0d/B0RERESk52wuJ+rvVALSRY7j4EQ67ly5ORsee4wvf3EN2DaYJsN/fimFxx3XpWsYgUCnpyGtX7+ef/3rX1xzzTXtQj6AwsJCAC688EIeeeQR7r//fsaMGcPcuXM5/PDDWbJkCcXFxdnj77//fs4880xeeeUV1q9fzwEHHMDcuXM5/vjj2bhxI/Pnzyc3M+7MdXOv6TgOI0aMYNGiRey0007MufJKkpZDYXEJzYkkBgYe00glH2n/evppzjjtB/ziuuvZe+99+ONDD3LxhbP56teOZuy4cV16bUV6S8KyeWdlHfMX17Lg0/UdVu1lXHH0Duw+pniT+0UGKttxiCVsokmLWMJOr7Nq4Pe6KAp5KQi0NMwIelyYapghIiIiItIpquijaxV9dnMzn+y2e5+Mc7u338IMBjt17H//+1+mTp3K3//+d44//vgOj2lqaqKoqIj77ruPk08+GYBEIkFVVRUzZ85k9uzZQKr6rr6+nnfeeQeAt99+m913353ly5czZsyYrbpu22tmTJ8+nZ13mcx1c2/CcmxMI73+XpvPeNFolF12nMTV11zLid8+CQDLshg1vIwbfnkTp874fqdep/5OFX2DQ9Ky+d9n9cxfUsNrS9fRFG8J94bl+dh9TBH/+mANuT+MTQN+f+qelIZ9vT9gkW6W2zAjbtmY6YYZAa+LoqCXsF8NM0RERESk/1BFn/Q7mfx2cxWAS5cuJZFIsN9++2W3eTwe9tprLxYuXNjq2D322CN7e/LkyRx88MHsvPPOHH744Rx22GF84xvfoKioqEvXzb2mZTskLTv11XZwcPC4TB7+85/4ydlnZY979Il/st/++/PiCy8QjUT4xje/ld3ncrlwu934fApGpO9ZtsNHX9Qzb3Etry6tpSGazO4rDnnZf3wpB0woY+LwMIZhMH5YmF+9sATbSYV8Zx80XiGfDEjtG2aAx2Xgd7sYlu9NTcH1ugh63fg9phpmiIiIiIh0IwV9XWQEAmz39ltdOifx5Zcs++rXUtN2M0yTsU/+E8/w4V167M6aMGEChmGwcOFCjtvEFOFNhYGO47Tbljv91+Vy8dxzz/Hqq6/y7LPPcscdd3DppZeyYMECqqurO33dUCiEZdskLIeEZWOnzzON1Dp8AF/92tHsuede2XNGjhoFwLyXXmSnnXfB5WpZp2n5p59SV1fHlCm7bvkFEukBtuPwyZqNzF9cw8tLaqlrTmT3FQQ87De+lGnjS9lhZD5mm78fh+1Qzm6VRazeEGFEYUAhnwwIjuMQS9rEkqmKvaRj4zZSDTPCfhcVwQAhnzvbBVcNM0REREREepaCvi4yDAOjk9NnM3zV1Yy46kpWX35Fdo2+EVddia+6uodGCcXFxRx++OH86le/4ic/+UmHzTjGjx+P1+vl5ZdfbjXF9s0332TmzJmbvb5hGOy3337st99+XH755YwZM4ZHH32UWbNmdeq6DpC0HJrjFrYDLsPA4zLwer3YVsu0xry8PPLy8to9/nvvvksiEW+17a5f/4opu+7K9pMmdfHVEtl6juOwtKaJeelwr2ZjLLsv7HOzz7gSDphQxs6jCrIB9qaUhn0K+KRfyzTMiCYs4pYFDvjcLvxeF6PCAfIDHjXMEBERERHpQwr6eknhN75BaP/9ia9YiXdMZa903f31r3/Nvvvuy1577cVVV13FLrvsQjKZ5LnnnuOuu+5i4cKFnHnmmcyePZvi4mIqKyuZO3cuzc3N/PCHP9zkdRcsWMB//vMfDjvsMIYNG8aCBQuoqalhUjpgC4VCm7zuqTO+TySexLYdrHSFnyenwGPMmDG88cZ/WbF8OaFwmOLiYkyzfQXI+/97D9u2eejBB5g6dW/+/sjf+N1v7+bfL77U7a+jSFuO47BiXTPzl9Qyf3ENq+uj2X0Bj4u9xxYzbUIZUyoK8aiCSQaotg0zHBxM08DvcVEc8lIU9BLwurLVemqYISIiIiLS9xT09SJPeXmvBHwZ1dXVvP3221xzzTX89Kc/ZfXq1ZSVlbH77rtz1113AXD99ddj2zbf/e532bhxI3vssQfPPPNMdr29juTn5zNv3jxuvfVWGhoaGDNmDDfddBNHHnlk9pi219199z14/J9P4gvlk7Bapui2/Vx43vmz+NFpP2D3KbsQiUT46JPFjKmqanXMqpUrWbduHY889gSXXXoJSxYvZqedd+axfzzJrrvu1j0vnkgHPqtrZv7iWuYvqWXV+ubsdq/bZK+qYg6YUMruY4rVTEAGpHi6Ui+atEjaDqaR+t4Oet2MKPCnGmZ4U9NwFWCLiIiIiPRP6rpL17ruSuc5TqqxRiJpk7RT32Yu02Bb111/8h//4Mc/Oo1Vq7/shlH2b+q62/fWNERTa+4trmVZbVN2u9s02KOqiGnjy9izqpiAV9MUZeCwbCfdBdciZtk4DnjdqYYZBQFPagquGmaIiIiIyBClrrsiOWzHIZlusJG0HQy6J+DLeO+9d9lxp52652IiHahtjPFyelruoi8bs9tdpsGUikIOmFDK1OoSQj79CJX+r23DDMtxcBkGPo9JftBNUcBLyOdOT8N1b3EtSRERERER6b/0KVW6TSbgi1s2lu1gAh7TgG7+zPjeu++y0047d+9FZcira47z6pLUtNwPv2jIbjcN2HlUAdMmlLHP2BLyA54+HKXIliUsO7u2XtyyMDDwuk38HhejMw0zvKmGGT63KlFFRERERAYTBX2yzWzbIWHbJJI2luNgGkaPBHwZf/nbIz1zYRlyNkYTvLp0HfMX1/D+5/XYOQsZ7DAinwMmlLLv+FKKgpo2Lf1bPGnTGEsSSSRxu0w1zBARERERGaIU9MlWs2yHpGUTt2xsJ1X5pAXapb9riiVZ8Ok65i+u5Z1VG7By0r2Jw8NMm1DG/uNLKQ37+nCUIluWG+553Cb5fg9jy0Lk+z0EfWqYISIiIiIyFCnoky6zbJtEeg0+O73Wk8elKhHpv6IJi/9+up75S2p4a0VdtvMzwNjSEPtPKGXa+DLKC9RwR/q3TYZ7AQ95Prcq9kREREREhjgFfdIpjuNg2Q4JyyFpZwI+UwGf9FvxpM1bK9Yzb3EtbyxfTyxpZ/dVFAWYNqGMaRNKGV0U7MNRimyZwj0REREREeksBX2yWS0BX6qKzyHVedSjD5bSDyUsm3dXbWD+4hpeX7aeSMLK7htR4Gf/8aUcMKGMMSVBjO5qAy3SAxTuiYiIiIjI1lDQJx1yHIdkOuBLpqc5ukwDZSPS31i2w/uf1zNvcQ2vLV1HYyyZ3Vca9jFtQinTxpcyflhY4Z70awr3RERERERkWynok1ayAV/SJmkr4JP+yXYcPvqigflLanllSS31kUR2X1HQw37pyr3tyvMw9c0r/VjCsmmMJmmOJ/F4UuFeVWmQwqBX4Z6IiIiIiHSZgj4BUsFJMt1gI2k7GCjgk/7FcRwWfdnIvMU1vLKklnVN8ey+PL+b/caVMm1CKTuOLMClcET6sWy4l7DwuA3y/R7GlAYpDHjJ8yvcExERERGRraegb4hLBXw2cSu1Fp8JqfX39DlT+gHHcVhW28T8xbXMX1zD2o2x7L6Q18U+40qYNr6MXUYX4HaZfThSkc1TuCciIiIiIr1BQd8QZdsOCdsmkbSxHAfTMBTwSb+xcn0z8xbX8PLiWj7fEMlu93tMplaXcMCEUnatLMKjcE/6MYV7IiIiIiLS2xT0DWIzZszg/vvv54wzzuA3v/kNkGpckLRszj7nbH7/27s5+Tvf5Z7f/6GPR9ozQj4PD//1bxx97LF9PRTphC82RJi/uIb5i2tZsb45u93rMtmzqohpE8rYfUwRfo+rD0cpsnkK90REREREpC8p6BvkKioqePjhh/nlTTfh9vpJWDbNkQiP/PUvVFRWbvUafI7jYFkWbnf/+xaKx+N4vd6+HoZ0wtqGKC8vqWXe4hqW1jRlt7tNg90qi5g2oZS9qosJevvf95lIhsI9ERERERHpLzTvrRc11kX57JM6GuuivfaYu+66ayrs++vfiCUtDAye+sfjjBo9msmTp2SPcxyHm3/5S3bcbiIlBXlM3WM3Hv37I9n98156iZDPw3PPPsv++0ylKC/EKy+/zMaNG/n+qd+lrKiAsWMquOO2Wzni0IOZ/dNZ2XPj8TiXXnIx46vHUFZUwIH778u8l17K7l+5YgXfOP44Rg0vo6yogD2mTOZfTz+d3T9/3jwO2G8fivJCjB1TwWWX/oxkMpndf8ShBzPrvJ9w0ewLqBxZztFHHcGkieMB+Pa3vkHI58nel763rjHGE+99zuy/vccPH3iTe19dztKaJkwDdqss5LyvTODBH0zlsq/twPTthinkk34pYdnUNcX5fEOEukicgM/FpBF57FFZzO6VRYwpCVEQ9CjkExERERGRXqVP0F3kOA7JuN3l8z5+bTXz/7IIxwHDgGknTmT7fUZ06Rpur4nRiRI8x0k11rBsG8t2OOm7p/LHBx/g5FNOAeCB++7je6fOYP68edlzrrzicp547DFuveNOxo8fz8svz+eHM06ltLSMaQcckD3u5z+7mGuvn0t1dTUFhYVcfOEFvP7aa/z1kUcZNnwYv7jySt595x123mVy9pwzTv8hK1es4P4H/8iIESN44vHHOe7or/Lft95h/IQJnH/eT4jH4zzzn+cJBUN8vHAh4XAYgC8+/5yvH3s03/nu97jn9/ey6JNPOOesH+P3+7n0ssuzj/HHhx7ktB+dwb9feAnHcSguKaFq9Eh+c8/vOPSww3G5NN2zL9VHEry6tJZ5i2r48IsGnPR2A9hpVAHTJpSy77hSCgKevhymyGblVu65XQYFAVXuiYiIiIhI/6Kgr4uScZvfnvfSlg/cDMeBeQ8vYt7Di7p03o9uOxCPb9OBleM4JG2HhGWTtBxsBwzD4Dvf+Q5XXf5zVixfjmEYvP7aq9z/0B+zQV9TUxN33HYrTz3zLFP33geA6rFjee3VV/j97+5pFfRddvkcDj7kEAA2btzIHx98kHsfeJCDvvIVAH5zz+8YX1WZPX7Z0qX831/+wuJlyxkxciQAM2fN4rlnn+HBB+7nyqt/wapVKznu+K+z0047Zx8747d3/4bRoyu4+bbbMQyD7bbfntWrv+CyS3/GJZf+HNNMFaWOHTeOa667vt1rUlhQSHl5eZdeZ+kejdEkry2rZf7iWt77bAO207JvUnke0yaUsd/4UopDmmYt/VfbcC/f71a4JyIiIiIi/ZaCvkEgE/DFk6kKPgCXaWTX3ystLeWII4/ijw89iOM4HHHkUZSWlmbP/3jhR0SjUY4+6shW143H40yeMqXVtt123z17+9NPl5FIJNhjjz2z2woKCpgwcWL2/rvvvoPjOEzeaYdW14nFYpSUlABw1tnncN655/Cffz/HQV85mGOPP56dd94FgE8+/pi99p7aqpJx7333pbGxkc8/+4yKysp245K+0xxPsuDT9cxfXMM7KzeQzEn3xg8LM218KftPKGVYnr8PRymyeQr3RERERERkoFLQ10Vur8mPbjuwS+c0bojx5zmv4+RUNBkGnDRnb8KFvi49di7bcUha6Qo+28bAaBXw5freqTOYNfM8AG6+7fbW17FTU5EfeewJRqar7jJ8vtbjC4ZC2dtO+gm1nU7s5DxR27ZxuVy8/NqCdtNnQ+npuTN+8EMOOfQw/vX0U/zn3//ml3Nv4Lob5nLm2efgOM4mr5+7PRgMIX0jmrB4c0Ud8xbV8NaKOuJWy9T2qpIg0yaUsf/4UkYWBvpwlCKbp3BPREREREQGAwV9XWQYxmanz3akaHiQ6d/Znhf/+DGODYYJ00/ZnqLhwa0ag207JG2buJVai88EPKaZWvBsEw49/HDiiXjq9mGHtdq3/aQd8Pl8rFq1stU03S0ZO3YcHo+HN998g9EVFQA0NDSwdMkS9p+Wus7kyVOwLIuamhr223//TV5rdEUFp/3oDE770Rlc/vNLufcPv+fMs89h+0mTePzRR1sFfgtee428vDxGjhq12fF5PB4s2+r085HOS1g2b62oY/7iWv67fB3RREu4N6owwLQJpUybUEZl8dZ9j4v0BoV7IiIiIiIy2Cjo6yU77DeSyh2KqV8boWBYgHBR16cu2rZDwrZJJG0sx8E0DDymsdmAL8PlcvH2e+9nb+fKy8vjvPNncfHsC7Btm3333Y+GjQ0seO01QuEw3/nu9zq8Zl5eHqd897tcevHFFBUVUzasjGuuugrTbGkaMmHiRE486SRO/8H3uW7uXCZPnsK6dbW8+OKL7LjjThxx5JHM/uksDjv8CCZMmEDdhjpeevEFtt9+EgA/OuPH/OqO2/npzPM448yzWLxoEddcfRXnnjczuz7fpowZU8WLzz/P3vvsi8/no6ioaMsvlGxS0rJ577N65i2uYcGydTTFW0LUYXk+DphQxrQJpVSXhjrVNEakLyjcExERERGRwUxBXy8KF/m3KuCz0g02EpaNnQn4XJsPuTqSn5+/yX2Xz7mSsrIybpo7l3M+XUZBYSFTpuzK7Isu3uw1r5/7S35yzll84/hjycvP5/xZP+Wzz1bh97c8z7vv+T03XHctl1x4IV988TnFJSVMnbo3hx9xBAC2ZTHrvJ/w+eefkZefz6GHHcYNN94EwMhRo/j74//g0ksu4t49d6eouJjvzfg+F13ysy0+3+tumMvFF87m3j/8npGjRrFw0ZLOvEySw7IdPviinvmLa3l1aS0bo8nsvpKQl/3Hl3LAxDImDAsr3JN+S+GeiIiIiIgMFYaTu6DaENXQ0EBBQQH19fXtwrBoNMqnn35KdXV1q/CqNyRtO7sGn207uEyTLRSx9bmmpiYmVI/huhvmcur3f9DXwxnwYtEoK1eswMgbhuHune60tuOwcHUDLy+u5ZWltdQ1J7L7CgMe9htfyrQJpUwakY+pcE/6qYRl0xhL0hxvCfeG5/sV7omIiIiIyBZtLifq71TR1884jpOt4EtaDjYOLsPE4+6fH0rfffcdFn3yCXvssSf1DfVcf801AHz16GP6eGTSFY7jsHhtI/MX1/DyklpqG+PZfXk+N/uMK+GACWXsNKoAlwIS6acy4V4kbuFKh3uVxQGKgj6FeyIiIiIiMiQo6OsnMgFfPB3wOYDbNHANgIqp2265mcWLFuH1epmy6248+/wLlJaW9vWwZAscx2H5uibmL65l/uJa1jREs/sCHhf7jC1h2oRSJlcUbtVUcZHesKlwrzDoJd/vUbgnIiIiIiJDioK+PuY4DsmcCj4Al2kwAPI9AKZM2ZVXXv9vXw9DumBVXTPzF9Uwf0ktn9VFstt9bpO9qouZNqGM3SuL8LoV7kn/pHBPRERERESkYwr6+ojjOCStdAWf7WAwsAI+GVjW1EeZv7iGeYtrWL6uObvd4zLYY0wx0yaUsmdVMX6PazNXEek7mwv38vweTSkXERERERFBQV+vs9MBX8KySdo2BgYe0wB9RpVuVrMxxstLapi/uJbFaxuz212mwa4VhUybUMbeY4sJevVjQPonhXsiIiIiIiJdo0/4vcS2HZK2TTzpYDk2pmHgMU0FfNKt6privLK0lnmLa1m4uiG73TRgl9GFTJtQyj5jS8jze/pwlCKb1jbcy/Mp3BMREREREeksBX09zLIdkpZN3LKxnVTgosYG0p3qIwleW7qO+Utq+ODzeuzUUo8YwA4j85k2oYx9x5VQFPT26ThFNiUT7jXHk7hNkzxV7omIiIiIiGwVBX09xLJtEukpurbjpCr4XPqwKt2jKZbk9WXrmLe4lvc+24CVSfeA7YbnMW1CKfuPL6Uk7OvDUYpsWttwL+x3U1mcp3BPRERERERkGyjo62ZJ286uwWc74FLAJ90kErf47/L1zF9cw1sr6kjmhHtjy0JMG1/G/hNKKc/39+EoRTZN4Z6IiIiIiEjPUtDXDRzHwbJT4V7CcnBINTzw9PMZuiuWL2eH7Sbw6n/fYPLkKX09nHauufoq/vHE47z+xlt9PZQOhXweHv7r3zj62GN77DEcIJa0eGD+Mp79ZD3xpJ3dV1Ec5IAJpUwbX8aookCPjUFkWyjcExERERER6T0K+raB4zgk0wFfMh3wuU0Dox98bv3RaT/gjw8+mL1fXFzMbrvvwS+uu46dd94FgNEVFSxdsYrS0tK+GuZmnXf+LH581tl9PYxNBo5LV6yiqKio2x/PcaA5btEYS7KxKUJ9JMk7K+uIJ21GFPiZNqGMAyaUMqYk1O2PLdIdFO6JiIiIiIj0DQV9WyEb8CXt7PRJVycCvsb1tdSvWU1B+QjCxT0frh162OH85p7fAfDll2u46oor+Mbxx/HJkmUAuFwuysvLe3wc8Xgcr7frjSDC4TDhcLgHRtQ5juNgWdYm93fra+dAc8KiMZqkMZbEdpzsGFymwaGThjO5ahjjykIY/SFJFmlD4Z6IiIiIiEjf6+eTS/sX23GIJyzqNzZRv7GJSCSCnYjhJGIkY1ES0U3/+eC5p3ho5uk8cd1lPDTzdD547qnNHt/RH8dxtjzIHD6fj/LycsrLy5k8eQqzLpjNZ6tWUVNTA6Sm7oZ8Ht57710A5r30EiGfhxeef57995lKaWE+XzlwGos++SR7zWVLl/KtE75OVcUohhUXMm3fvXn+P/9p9biTJo7nhuuu5Uen/YARZSWcfeYZHHn4ocw67yetjlu3bh1FeSFefOGFDsd/zdVXsfeeu2fv/+i0H3DiN07g1ptvZuyYCipGDOf8n5xLIpHIHvPb39zFLjtMojg/TFXFKE759onZfY7jcPMvf8mO202kpCCPqXvsxqN/fyS7P/P8n3v2WfbfZypFeSH+9MeHuPYXV/P+//5HyOch5PPw4AP3A6mpu/94/PHs+R988D5HHn4oJQV5VIwYzjln/pjGxsbNjv/cc87hi/WNfLquiS82RGiIJrAdB7dpUhjwMjzfT0nIy9d3G834YWGFfNKvJCybuuY4n29oZn1THK/bZPvyPPaoKmLPqmKqSsMUBr0K+URERERERHqJKvo6wXEc4kmbpGERjUS4/4yTtvl68x/4LfMf+G2Xzjvtnofx+Leu0UJjYyN/+fOfGDduPCUlJZs99sorLuO6G26ktKyU8845mzPPOJ3/vDgvdZ2mRg4/4gguv/JK/D4/f3zoQb759eN49/0PqaiszF7j1ptv4qJLLuWii38GwJtvvsFPZ57HdXNvxOdLdYL9y5//xIgRIzlw+vROP495L71IeXk5Tz/zHMuWLuV73zmZXSZP5vs/PI2333qTC2adz+/uvY+9996H9XXrefXll3Oe1+U88dhj3HrHnYwfP56XX57PD2ecSmlpGdMOOCB73M9/djHXXj+X6upq/H4/P5l5Ps89+yz/fPpfABQUFLQbV3NzM8cd/TX22msq8159jZq1NZz94zOYNfMn/PZ3f2g1/tJhw/nTo0/y8SdL+OmZ32f0+EmccPKpuEyDsM9N2Ocm4HGBAbGo3e6xRPqSKvdERERERET6LwV9mxFNWHxR10w8aRNLJvG7PXgG0IfYp596kmHFhQA0NTVRPmIEf3v0MUxz84WcV1x5dTb4mnXBhZxw3DFEo1H8fj+77DKZXXaZnHPsVfzj8cd58p//aLWe3oHTD2LmrFnZ+6MrKrjg/Jn88x9PcMI3vgnAgw/cz3e+970uVakVFhVx822343K52G777TniyKN48YUX+P4PT2PVqlWEQiGOPOqr5OXlUTlmDFOm7Jp9/nfcditPPfMsU/feB4DqsWN57dVX+P3v7mkV9F12+RwOPuSQ7P1wOIzbvflpzn/585+IRiLc84d7CYVCsCPcdOttfPPrx3H1NddRWFxGLGETzi/kJ5ddh8vlYv/RYzng4MN4+7X5nHvWjwmmwz2R/iZh2TTFkjTlhHsVRXkUhRTuiYiIiIiI9CcK+jrQHE/yZX2Uz+ujRJojlAJu08RlGpg+H6fd83CXrte0fh0PX3xOq6m3hmny7evuIFS8+eq6XO50JVxnHXDgdG67404A6urW89u7f8PxxxzNvJdfpXLMmE2et9POO2dvl49IhVs1a9dSUVlJU1MT1/7iav711FOsXv0FyWSSSCTCqlWrWl1jt913b3Xf5/Nx4kkn88B993HCN77Je++9y/v/+x9/+b9H6IpJk3bA5XJl7w8vL+fDDz8A4CsHH0JFZSU7bT+RQw47jEMPO5xjjj2OYDDIxws/IhqNcvRRR7a6XjweZ/KUKZsde2csXPgxO+y0Mz5/MLtt9732xrZtXn7zf0zecx/iSZtxE7fD43YTSlfujRszmg8//ICg17WZq4v0vky415xI4jJS4d52RanKvfyAwj0REREREZH+SEFfjo3RBGujG/liQ5TmeJI8n4dheT5obPlAaxhGl6fPFo4cxYE/OIuX7r0Lx7YxTJMDv38mhSNHdfdTaCUUCjFu/Pjs/V13250RZSXc+4ffc8WVV23yPI/Hk72dqbaz7dQU0ksvvoh/P/cc195wA2PHjSPgD3DKSScSj8dbXSMYDNLWjB/8gH323IPPP/uMB++7j+kHfWWzgeOWxpYZn5MeW15eHq8ueIN5L73Ef/79HL+48kquvfpq5r36Wnb8jzz2BCNHjmx1DV+bADUY6lo324ZIkvrmOHHLYfm6JkI+NwnLZl1dMwCW7WAYBm6XQSjgo7o0lG3ckjt+kb7WUbg3UeGeiIiIiIjIgKGgL8e7qzbg9oXID3gYVZgKqpxknK61wOjYpOmHUrHLrtR/uZqC4b3TdbctwzAwTZNIJLLV13jllZf5zve+xzHHHgek1v5buWJFp87daaed2W333bn3D7/nr395mF/ecutWj2NT3G43Xzn4YL5y8MH87OeXMXJYKS+9+AJfOfgQfD4fq1atbDVNtzO8Xu8mu+9atsPajVHGTtyOJ/72Z5qbm4BUUPjem//FNE32mLwj1aUhAl4XcdPcYndmkd6UzKy5p3BPRERERERkwFPQl8PncjG8MNBj1w8Xl/ZqwBeLxVizZg0AG+rquPuuX9PY2MhRX/3aVl9z3LjxPP7Yoxz51a9iGAZXz5mTrZbrjBnf/wGzZp5HMBjMhoXd5eknn+TTT5ex37RpFBUW8cy/nsa2bSZMnEheXh7nnT+Li2dfgG3b7LvvfjRsbGDBa68RCof5zne/t8nrVo4Zw4rly3nvvXcZNWo0eXl52SrAePq5H3X8N7nrpuu57Pyz+PH5F5FsqueXcy7mpFNOYWxlz1ZuinSVwj0REREREZHBSUFfDp97cH24fe7ZZxg3pgJITWuduN12PPTnhzngwAO3+po33PhLfnzG6Rx84AGUlJYy66cXsHFjQ6fP/+aJ3+bCC37Kt078Nv6t7CC8KQWFBTzx+GNc+4uriUajjBs/nvsefIgddtgRgMvnXElZWRk3zZ3LOZ8uo6CwkClTdmX2RRdv9rrHHf91nnjsMY467FA2bNjAb+75Hd/93qkANDQnAAgEgtz10N+4Yc4lnPK1gwmGghx33PFcf+Mvu/U5imwthXsiIiIiIiKDn+HkdogYohoaGigoKGDe+8spLCxstc9JxnE2rqVyzBh83RxMDUWfrVrFpInjmffqa+y66259PZytYttQ2xijIZoK+dymSTJd2WcAZXl+8gPdk6HHolFWrliBkTcMw+3tlmvK0NFRuDc8z6dwT0REREREZDMyOVF9fT35+fl9PZwuUUWf9IpEIsGa1au57OeXstfUqQM25IvELb5siJG0bQygMOilOOTFsh0Slo3HZeJ2KTyRvqPKPRERERERkaFLQZ/0itdefZUjDzuECRMm8tDDD/f1cLrMcWBdY4wNkVQVn8dlMjzfj99jAuB2Gbhdrr4cogxhCvdEREREREQEFPRJLzngwANpiiX6ehhbJZaw+bIhStxKTc8tCHgoDfvUPVf6lMI9ERERERERaUtBn8imOLC+OU5dUxyH1Fp8w/J9BL2q3JO+oXBPRERERERENkdBn0gH4kmbLxtixJIWAHk+D2V5XkwFKdLLOgr3JhTlUaRwT0RERERERNpQ0CfSxobmBOua4jiOg8swKMvzEfbrr4r0HoV7IiIiIiIisjWUXoikJS2HLxuiRBKpKr6g183wPB8uddGVXtIYTdIQi7cL9/L8btwus6+HJyIiIiIiIv2cgj4RoCGSpLYxhu04mIZBadhHfkB/PaR3JC2b2qYYXpfJ+LI8ikMK90RERERERKTrlGTIkGbZDmsbYjTFkwD4PS6G5/vxqIpPekl9JEFjLMmIAj9VpSEKAp6+HpKIiIiIiIgMUCoXkW0y76WXCPk8bNiwYUBcN1dTLMnK9c00xZMYpKr4RhcGFPJJr0hYNl/UR7Adhx1H5rPTqAKFfCIiIiIiIrJNFPQNYj867QeEfJ52f5YuWdInYznxGyf0+uN2xLbhy4YYq+ujWLaDz+2iojhAYdADyvikF9RHEtRsjDKiwM+ulYVUFAfVYENERERERES2mabu9iK7Poa1LoqrxI9Z4OuVxzz0sMP5zT2/a7WtrKysy9eJx+N4vd7uGlaficQtvmyIkbRtDKAw6KU45MVQxiK9IGHZ1DRGCXnd7DS6kPJ8vwI+ERERERER6Taq6Osix3Fw4laX/0QXrGHDzW+z8b6P2HDz20QXrOn6dRyny+P1+XyUl5e3+uNyuZg/bx4H7LcPRXkhxo6p4LJLf0Yymcyed8ShBzPrvJ9w0ewLqBxZztFHHQHAv55+msk77kBJQR5HHnYIK1Ys36rXMRaLccH5MxkzeiTF+WEOOehA3nrzjXbHvfP22+y/z1RKC/P5yoHTWPTJJ9l911x9FXvvuTt/+uNDTJo4nhFlJZz6nVPYuHFj9phH//4Ie+42hZKCPEaVD+erRx5OQ+NGPC6TEQV+fnvbXCaOq6IoL8Tee+7Os888kz13xfLlhHweHn/sUY487BBKC/OZusduLHj9ta16zjJ0OY5DXXOc2sYYowqDTKksYlRhQCGfiIiIiIiIdCtV9HVVwqbuF//dtms40PzkpzQ/+WmXTiv6+V7gdW3bYwNffP45Xz/2aL7z3e9xz+/vZdEnn3DOWT/G7/dz6WWXZ4/740MPctqPzuDfL7yE4zh8tmoVJ5/4TX54+o84/Udn8Pbbb3HJRRdu1RguveRiHnvsUX77uz9QOaaSW276Jcd+7av876OPKS4uzh535RWXcd0NN1JaVsp555zNmWeczn9enJfd/+myZfzziSf426OPsaFuA9895SRuunEuc666mtWrVzPju99hzi+uZe+DjmBDfQNv//c18n1uKouD3Hn7rdx+6y3c/qtfM3nyFB64/z6+dcLxvPnOe4yfMKFlDJdfzrXX38C48eO58orLmfG97/L+Rx/jduuvj2xZLGlR2xgjz+9h59EFDM/zYyrgExERERERkR6gpGKQe/qpJxlWXJi9f9jhRzB+wgRGj67g5ttuxzAMttt+e1av/oLLLv0Zl1z6c0wzVeg5dtw4rrnu+uy5V1z2c6qrxzL3lzdhGAYTt9uODz/4gJt/eWOXxtTU1MTvfns3d//u9xx+RKpS8Fd33c2k/4zn/nvv5fyf/rTlMa+8mmkHHADArAsu5ITjjiEajeL3+wGwbZu7f/d78vLyADjp5FN48YXngatZs3o1yWSSPQ48nLKRFYwYbXLgPnsQTIelt916C7MumM03v3UiAL+49jrmvfQiv7rjdm65/Y7sGM47/3yOOOooAC69/HL2mDKZpUuWsN3223fpecvQkqriSxC3LCqLg1SVhgh69SNXREREREREeo4+dXaVx0xV1nWB3RCn/o53IXfmrQEF507BzO/Cuneers+0PuDA6dx2x53Z+8FQiFnn/YS99p6KkbMw3d777ktjYyOff/YZFZWVAOy2++6trvXJxx+z59S9Wp03dererY7JDRW/fdLJ3P6rX7cb07JlS0kkEuyzz74tT83jYY899uSTTxa2OnannXfO3i4fUQ5Azdq12TGOGVOVDfkAysvLqVlbQzxpU1Qxkan7H8gJh+7PAQcdzJFHHM7XTziBoLeIhoYGVn/xBXvnjAFg73325f33/7fpMZSPSI2hpkZBn2xSNGGxrjFGQdDDduWFDMvzqYpPREREREREepyCvi4yDKPL02ddpQFCx4yl6YllqbDPgNAxY3GVBnpmkDlCoRDjxo9vtc1xnFZhXWYb0Gp7MBjq8JjNee2/b2Zv5+Xnd3hMR4+1qXF5PJ7s7cw+27az29ye1t/ChmGQtCxW1UVwHPjdnx9j+Udv8+q8F7j7rl9x1ZzLeWn+KxSXlGx6DG1a77q3MAaRDNtxqGuKk7QdxpQGqSoJE+iG6fYiIiIiIiIindGvm3Ekk0l+/vOfU11dTSAQYOzYsVx11VWtQhbHcZgzZw4jR44kEAgwffp0Pvzwwz4cdcd8uw+ncNZu5H1/Bwpn7YZv9+F9NpbtJ01iwWuvtwruFrz2Gnl5eYwcNWqz572xoPX6hP/974JW98eNH5/9M2zYsA6vM27ceLxeL6+++kp2WyKR4O2332K77SZtzVMCIGk51EcS2I6D4ziEvG7GlIb4yvQD+PnlV/Daf9/E6/XyxOOPkZ+fz4iRI3ktZwwAC15/je1VqSdbIZqwWF0fwe91scvoArYvz1fIJyIiIiIiMgT0p/yqXwd9N9xwA7/5zW+48847WbhwIXPnzuXGG2/kjjta1k+bO3cuN998M3feeSdvvPEG5eXlHHrooa06r/YXZoEPT3UBZoGvT8fxozN+zGefreKnM8/jk48/5p9PPME1V1/FuefNzK7P15HTTv8Ry5Yt5aLZF7Dok0/4y8N/5qEHH+jy44dCIU770RlcesnFPPvMMyxc+BFnn3kGkeZmTv3+97fqOTVEkqxc30zCSoWXw/L8fLboPW6+8QbefutNVq1cyeOPPUptTQ3bbZ8KE2eeP4ubf3kjf/u/v7Lok0+47NKf8b/33uOsc87dqjHI0GQ7DjUbY9RHEowtDTOlopBh+f521aIiIiIiIiIyOPWn/KpfT9197bXXOPbYY/nqV78KQFVVFX/+8595883U9FDHcbj11lu59NJL+frXvw7A/fffz/Dhw/nTn/7EGWec0Wdj789GjhrF3x//B5dechH37rk7RcXFfG/G97nokp9t9ryKykr+9PBfuWj2Bdxz92/YY889mXPV1Zz5o9O3+Ji2bbfqUnv1Ndfi2Dan/2AGGzduZLfdd+fxfz5JUVFRl5/P6g1RmuJJADwuA7dpkh9wk5eXz8vz53PnHbezsaGBysoxXHfD3GwDkLPOOZeNGzdyyUUXUrN2LdtPmsRfH3m0Vcddkc1pjiepa45THPJSXRqmNOxVwCciIiIiIjLE9Kf8ynA6s/BaH7n++uv5zW9+w7PPPsvEiRN57733OOyww7j11ls56aSTWLZsGePGjePtt99m1113zZ537LHHUlhYyP3339/hdWOxGLFYLHu/oaGBiooK5r2/nMLCwlbHOsk4zsa1VI4Zgy/d6VW67tivfZVx48Zx8223d9s1G2NJajbGsOzUunolYS+FAQ8MopwlFo2ycsUKjLxhGO4uNG6RHmXZDuuaYmDAmOIgFcVBfG5N0xURERERERkMGhoaKCgoYNWqVeTn9B/w+Xz4fO1nafZUfrU1+nVF30UXXUR9fT3bb789LpcLy7K45pprOOmkkwBYs2YNAMOHt17vbvjw4axYsWKT173uuuu48sore27gklVXV8eC115j/ryXOO30H3XLNW0bahpjbIwmAPC5XQzP9+F19+uZ6DJINMWSbIgkKA17qS4NURLu26n4IiIiIiIi0jMqKipa3b/iiiuYM2dOu+N6Kr/aGv066PvLX/7CQw89xJ/+9Cd23HFH3n33XWbOnMnIkSM59dRTs8d1pntrrksuuYRZs2Zl72cq+qT7nfmj03nrrTf5yczz+doxx2zz9SJxiy8bYiRtGwMoCnopCnnRbEnpaZbtUNsYw2XChGFhKoqDCpdFREREREQGsY4q+jrSU/nV1ujXQd/s2bO5+OKL+fa3vw3AzjvvzIoVK7juuus49dRTKS8vB1LJ6IgRI7LnrV27tl1KmmtTpZbS/R7+v791y3UcB9Y1xtgQSVXxeVwmw/P9+D0KWqTnNcaS1EfilOX5GFsapiikadQiIiIiIiKDXX5+fqugb1N6Kr/aGv06JWlubm7XBdblcmXbE1dXV1NeXs5zzz2X3R+Px3nppZfYd999e3Ws0nOiCZtV65uzIV9BwENlcVAhn/Q4y3b4siFKLGmxfXkeu4wuVMgnIiIiIiIirfSn/KpfV/QdffTRXHPNNVRWVrLjjjvyzjvvcPPNN/ODH/wASJU8zpw5k2uvvZYJEyYwYcIErr32WoLBICeffHK3jsWh3/YsGbQcB+qa4tQ1x3EAt2kyLN9H0Dt0mh7o+67vbIwm2BhLMCzPz9jSMAVBT18PSURERERERPqh/pRf9eug74477uCyyy7jrLPOYu3atYwcOZIzzjiDyy+/PHvMhRdeSCQS4ayzzqKuro6pU6fy7LPPkpeX1z2DMN04QDQSwe8PdM81ZYviSZsvG2LEkhYAeX4PZWEf5hAr4otGIjiAYfbrv6qDStKyqWmM4fOYTCrPZ2RhALdriH3jiYiIiIiISKf1i/wqzXAcZ8iXDGXaJs97fzmFhYXt9luN6zGTzZSWleEPBDBQ54eetDGa6mrqOA6mYVAc9hL0DJ0qPkhV8kUjEWprarDdQVzh4r4e0pBQH0nQGEsyosBPVWmIgoCq+ERERERERIaaTE5UX1/fqTX6+hOVCXWCGSrCboK1a2sU8fUgy3HYGEkQt1LZs89tkud3U9swNF91B8ATxAwV9fVQBr1Euoov4HGx48hUFZ/LHJrfdyIiIiIiIjJwKejrBMMwcIWLcexCsJN9PZxBx3EcXl26jv976zNiSQuf28U3dh/NfuNKur3N9EBimG6MoTZXuQ9saI7THE8yojBAdWmIPL+q+ERERERERGRgUtDXBYZpgqmOm92prinOHS8s4Y3ldQDsODKfmQdPpLzA38cjk8EunrSpbYoS8rrZaXQh5fl+VfGJiIiIiIjIgKagT/rMK0tq+dWLS9gYTeI2Db679xiOnTJKYYv0KMdx2BBJEE1YjCoMUlUaIuzTj0IREREREREZ+PTpVnpdYyzJ3fOW8uInNQCMLQ0x69CJjCkJ9fHIZLCLJS1qGmPk+z3sPLqA4Xl+TAXLIiIiIiIiMkgo6JNe9c7KOm77z2LWNcUxDfjG7hV8e88KPC6tRSc9x3Ec6poTxC2LMcWpKr6gVz/+REREREREZHDRJ13pFdGExX2vLufJ91cDMLLAz/mHTmT78oHVploGnmjCYl1jjMKgl+1H5DEszzekm7yIiIiIiIjI4KWgT3rcx2sauOW5RXxRHwXgqzuPYMa+Vfg9rj4emQxmtuNQ1xQnaTtUlYYYUxIi4NX3nIiIiIiIiAxeCvqkxyQsm4ffWMXf3lqF7UBJyMt5B09g18qivh6aDHKRuMX65lQV39iyEGVhVfGJiIiIiIjI4KegT3rEinVN3PzcIpbVNgEwfbsyzpg2jrBf33LSc2zHYV1jHBuHsaVhKkuCqhwVERERERGRIUOpi3Qry3Z4/N3PefD1FSRthzy/m7Onj2e/8aV9PTQZ5JrjSeqa4xSHvFSXhikNe1XFJyIiIiIiIkOKgj7pNmsaotz670V8+EUDAHtWFXHuQRMoCnn7eGQymFm2w7rGGJgwfliYiuIgPreq+ERERERERGToUdAn28xxHJ796Et+//KnRBIWAY+L06ZVc+ik4aqokh7VFEtV8ZXl+aguDVES9vX1kERERERERET6jII+2SZ1TXFuf34xb66oA2DHkfnMPGQi5fn+Ph6ZDGaW7bCuKYZpwMTheVQUB/G6zb4eloiIiIiIiEifUtAnW+2VJbX86sUlbIwmcZsG39tnDMdMHoXLVBWf9JzGaJKGaJzSPB9jS8OaGi4iIiIiIiKSpqBPuqwxmuTueUt5cVENAGPLQsw6ZCJjSkJ9PDIZzJKWzbqmOG6XwXbleYwqCuJxqYpPREREREREJENBn3TJ2yvruP0/i1nXFMc04Ju7V3DinhUKXKRHbYwm2BhLMDzPT3VpmIKgp6+HJCIiIiIiItLvKOiTTokmLO59dTlPvb8agJEFfs4/dCLbl+f38chkMEtYNrWNMXwek0nl+YwsDOBWqCwiIiIiIiLSIQV9skUfr2nglucW8UV9FICv7TyCU/etwu9x9fHIZDCrjyRojCUZUeCnqjREQUBVfCIiIiIiIiKbo6Avh69+GUZ4Bxy3OsZCqprq4TdW8be3VmE7UBLyct7BE9i1sqivhyaDWMKyqdkYI+hzsdOofEYUBNTgRURERERERKQTFPTl8G1YQsATI15QTTI4DIyhO0Vwxbombn5uEctqmwCYvl0ZZxwwjrBP3zLSczY0x2mOJxlRGKC6NESeX1V8IiIiIiIiIp2l1CZHMjAcw4oSqH2PRGgU8fwx2N68vh5Wr7Jsh8ff/ZwHX19B0nbI87s5e/p49htf2tdDk0EsnrSpbYoS8rrZaXQh5fl+VfGJiIiIiIiIdJGCvhyOYWAFSrCtOJ7Gz3BH1xPLG0MibySYg7+yaE19lFv+vYiPVjcAsGdVEeceNIGikLePRyaDleM4bGhOEE1ajCoMUl0aIqSqUREREREREZGtok/UHXBcXhLhkZjxjfjXf4QnWkMsvwrLXwLG4KsychyHZz/6kt+9vIxowibgcXH6tGoOmTQcYxA+X+kfYkmLmsYY+X4PO5cXMDzPj6kqPhEREREREZGtpqBvM2xvHrYniDuyjsDad0nkjSaePwbHHejroXWb9U1x7nh+MW+uqANgx5H5zDxkIuX5akgiPcNxHNY3xUnYNmNKglSVhAh69aNIREREREREZFvp0/WWGC6SwWEYySi++k9xR9YRL6gmERwOpquvR7dNXl5Sy69fWMLGWBK3afC9fcZw7JRRmKrikx4STVisa4xRGPQyqSyfYXk+VY2KiIiIiIiIdBMFfZ3kuP3EwyNxxTbgr30fV3At8YIqbF9hXw+tyxqjSX4zbykvLaoBYGxZiFmHTGRMSaiPRyaDle041DXFSdoOVaUhqkpD+D0DOygXERERERER6W8U9HWFYWD5i7DsBJ7IWtyxOuJ5lSTyRuO4fH09uk55e2Udt/9nMeua4pgGfHOPCk7cowKPy+zrockgFYlbrG9OVfGNLQtRFlYVn4iIiIiIiEhPUNC3NUwPiVA5ZqIJX91i3JFa4gVVJAPD+m2zjmjC4t5Xl/PU+6sBGFUY4PxDJrJdeV4fj0wGK9txWNcYx8ZhbGmYypKgqvhEREREREREepCCvm1ge0LY7gCu6Hr8Ne+RCI8ikTcG2xvu66G18vHqBm7+9yJW10cB+NouIzh1nyqFLtJjmuNJ1jfHKQl5GVsWpiTkVRWfiIiIiIiISA9T0LetDBMrUIphxfBu/CzVrCO/ikR4JJh9+/ImLJs//3clj7z9GbYDpWEv5x08kSkVhX06Lhm8LNthXWMMw4QJw8JUFAfxuRUoi4iIiIiIiPQGBX3dxHH5SIRHYMbq8a/7KDud1/IX98l4ltc2cfO/F/FpbRMAB21Xxo8OGEfYp//l0jOaYkk2ROKUhn1Ul4YoCQ+MdStFREREREREBgulPt3M9hVge8K4I7UEYnUkwhXE8ytx3P5eeXzLdnjs3c956PUVJG2HPL+bs6ePZ7/xpb3y+DL0WLZDbWMMt2kwcXgeo4uCeN1q7iIiIiIiIiLS2xT09QTTRTI0HCMZwVe/FHekllhhNcngcDB6LgBZUx/lln8v4qPVDQDsVVXMOQeNpyjk7bHHlKGtMZqkPhpnWJ6f6tKQvtdERERERERE+pCCvh7kuAPEwyNxxTYQqPkfidAI4vljsH0F3fs4jsMzH37J719ZRjRhE/C4OH1aNYdMGq4GCNIjkpbNuqY4bpfB9uV5jCoK4nGpik9ERERERESkLyno62mGgeUvwrYSeJrW4I6tJ56XatbhuLa9+ml9U5w7nl/MmyvqANhpZD4zD5nI8PzemSosQ09DJMHGWILyfD/VpWEKgp6+HpKIiIiIiIiIoKCv1zguD4lwOWa8EV/dQtzNa4kXVJMMlMJWVt3NX1zDXS8uZWMsicdl8L29qzhmykhMVfFJD0hYNrWNMXwekx1G5DOyMIBbVXwiIiIiIiIi/YaCvl5me8PYngDuyHr8Ne+QCFeQyK/E9oQ6fY2N0QS/eWkZ8xbXADCuLMSsQ7ejsjjYU8OWIa4+kqApnkxV8ZWFyPerik9ERERERESkv1HQ1xcMF8lgGUYyinfjCtyRWuIF1SRC5WBu/n/J2yvquO35xaxvimMa8M09Kvj2HhWqrJIekbBsajbGCPpc7DgynxEFAVymKkZFRERERERE+iMFfX3IcftJhEbgijcQWPcB7kgN8fwqLH9Ru2OjCYs/vPIpT3+wBoBRhQFmHTqRicPzenvYMgQ4jkN9JEFzPMmIwgDVpSHyVMUnIiIiIiIi0q8p6OtrhoHlK8DyhHBHagnE6ojnVZIIj8ZxpxpqLFzdwC3/XsTq+igAR+8ygu/tU4Xf4+rLkcsgFU/a1DZFCfk87Dy6kPJ8P6aq+ERERERERET6PQV9/YXpJhkajploxle3GHdzDU3hKh78IMoj73yO7UBp2MvMgycyuaKwr0crg5DjOGxoThBNWowqDFJdGiLk048IERERERERkYFCn+L7GdsTxHb7Wf5lHdf/exFLG1JVe1/ZbhinHzCWsIIX6QGxpEVtY5w8v5tdygsZludTFZ+IiIiIiIjIAKPUqJ+xbIe/fZLg/vc9JGwo8DrMmmIzdTsfCY/T18OTQcZxHNY3xUnYDpUlAapKQgS9+rEgIiIiIiIiMhDpE30/8sVGm7kLInxYawGw90g3s/byU2I24Vr/EZ5oDbH8Kix/CRiqtpJtE01YrGuKURjwMqksxLA8H4a+r0REREREREQGLAV9/YDjODy1NMFv3o0STULQDWfu5ufwag+GYWCTh+0J4o6sI7D2XRJ5o4nnVeJ4gn09dBmA7HQVn2U7VJWEqCoNqbGLiIiIiIiIyCCgoK+P1UZsbv5vlDdWJwHYpczF7KkBysNm6wMNF8ngMIxkFG/9ctyRdcQLqkgEy8FUSCOdE4lbrG+OURj0MrYsRFlYVXwiIiIiIiIig4WCvj704soEt78ZYWMcPCb8cLKP4yd6MTcTvDhuP4nwCFzxevy1H+AK1pDIr8LyF/bewGXAsR2HdY1xbBzGlYWpKA6qik9ERERERERkkFHQ1wcaYg53vhXhhZWpKr4JRSYX7R1gTEEngxfDwPIVYnlCuCNrccfqiOdVksgbjePy9eDIZSBqjiepa05QEvZSXRqiJORVFZ+IiIiIiIjIIKSgr5e9sTrJTf+NsC7iYBpw8g5eTtnRh9vciuDF9JAMlWMmmvDVLcYdqSFeUE0yMEzNOgTLdljXGMMwYfywEBXFQXxuVfGJiIiIiIiIDFYK+npJJOnw23ej/HNJAoCKvFQV33Yl2x682J4QtjuAK7oef817JMKjSOSNwfaGt/naMjA1xZJsiMQpDfsYWxamOOTt6yGJiIiIiIiISA9T0NcLPqxNMvf1CF80OgAcP9HLD3bx4Xd3Y9WdYWIFSjGsGN6Nn6WadeSPIREeCaan+x5H+jXLdqhtjOE2DSYOz2N0URCv29zyiSIiIiIiIiIy4Cno60Fxy+HBD2L89eM4tgNlQYPZUwPsOrznXnbH5SMRHoEZb8C/biHuSC3x/CqsQEmPPab0D43RJPXROMPy/FSXhihSFZ+IiIiIiIjIkKKgr4cs22Bxw+sRlm2wATi0ysPZu/kJeXtn7Tzbm4/tDuGOriNQs4FEuIJ4fiWO298rjy+9J2nZ1DbF8LpMJo3IZ2RhAI9LVXwiIiIiIiIiQ42Cvm5m2Q7/90mc+9+PkbShwGcwc08/+4/ug+mzpotkcBhGMoKvfinuSC2xwmqSweFgKAgaDBoiCTbGEpTn+6kuDVMQ1DRtERERERERkaFKQV83+mKjzdwFET6stQDYZ5Sb8/f0U+Tv21DNcQeIh0fiim0gUPM/EqERxPPHYPsK+nRcsvUSlk1NYwy/x2SHdBWfW1V8IiIiIiIiIkOagr5u4DgOTy5NcPe7UaJJCLrhrN38HFbtwTB6Z6ruFhkGlr8I20rgaVqDO7aeeN4YEuFROC6t5TaQ1EcSNMWTqSq+shD5flXxiYiIiIiIiIiCvm1WG7G5+b8R3lidquKbPMzF7KkBhof6Z3WV4/KQCJdjxhvx1X2Mu7mGeEEVyUAZ9JdQUjqUsGxqNsYI+lzsODKfEQUBXKb+n4mIiIiIiIhIioK+bfDiygS3vxlhYxw8Jpw22cdxE72YAyAws71hbE8Ad7QOf827JMIVJPIrsT2hvh6atOE4DvWRBM3xJCMKA4wtCxP26a+uiIiIiIiIiLSmtGArNMQc7ngrwosrkwBMKDK5aO8AYwpcfTyyLjJcJAOlGMko3o0rcEdqiRdUkwiVg6lvjf4gnrSpaYwS9nvYeXQh5fl+TFXxiYiIiIiIiEgHlOZ00Rurk/xyQYT1UQfTgFN28HLyjj7cAzh8cdx+EqERuOINBNZ9gDtSQzy/Cstf1NdDG7Icx2FDc4Jo0qKyOMiYkhAhVfGJiIiIiIiIyGYoOeikSMLh7nejPLk0AUBFXqqKb7uSAVbFtymGgeUrwPKEcEdqCcTqiIcrSORV4Lj9fT26ISWasFjXFCff72aX8kKG5flUxSciIiIiIiIiW6SgrxM+qEky9/UIq5scAI6f6OWHu/jwuQdh+GK6SYaGYySa8W1Ykp3OmwwOA6N/NhgZLBzHYX1TnITtMKYkQFVJmIB3kATJIiIiIiIiItLjFPRtRtxyePCDGH/9OI7tQFnQYPbUALsOH/wvm+MJknD7ccU2EKh5j0R4FPH8Smxvfl8PbVBKVfHFKAx62aE0RFmeD2MANHURERERERERkf5j8CdWW2nZBosbXo+wbIMNwKFVHs7ezU/IO4TCF8PE8hdjWwk8jZ/jjq4jlldFIm8kmJ6+Ht2gYKer+CzboaokRFVpCL9HVXwiIiIiIiIi0nUK+tqwbIf/+zjO/R/ESNpQ6DOYuaef/UYP3WDLcXlIhEdgxjfir1uIJ7KWWEE1lr8EVHW21SJxi/XNqSq+sWUhysKq4hMRERERERGRraegL8dH6y0efquZj2otAPYd5Wbmnn6K/FqbDsD25mF7grgj6wisfZdE3mjieZU4nmBfD21AsWyHdU0xHGBcWZiK4qCq+ERERERERERkmynoy/HzlxOYPg9BN5y9u59DqzyqsGrLcJEMDsNIRvHWL8cdWUe8oIpEsBxMhVVb0hxPUtecoCTspbo0RGnY19dDEhEREREREZFBQkFfB66bHmSHUr00m+O4/STCI3DF6/HXvI8rVEMivwrLX9jXQ+uXLNthXWMMw4Txw0JUFAfxuRWMioiIiIiIiEj3UZrVgYTV1yMYIAwDy1eI5QnjjqzFHasjnldJIm80jkuVahmNsST1kTilYR9jy8IUh7x9PSQRERERERERGYQU9LVhGjAyT2vydYnpJhkqx0w04atbjDtSQ7ygmmRg2JBu1mHZDrWNMdymwXbD8xhVFMTr1veWiIiIiIiIiPQMBX05TANm7uGnLKgwZmvYnhC2O4Aruh5/zXskwqNI5I3B9ob7emi9rjGapD4aZ3i+n+rSEIVBVfGJiIiIiIiISM9S0JfjN4f4GDdSgcw2MUysQCmGFcO78bNUs478MSTCI8H09PXoelzSsqltiuF1mUwakc/IwgAel4JjEREREREREel5CvpylAaG7jTT7ua4fCTCIzDjDfjXLcQdqSWeX4UVKOnrofWYjdEEDdEkIwr8VJWEKAgO/mBTRERERERERPoPBX3So2xvPrY7hDu6jkDNBhLhCuL5lThuf18PrVvVNcexbJsdRuQxsjCAW1V8IiIiIiIiItLLFPRJzzNdJIPDMJIRfPXLcEdqiRWmm3WYrr4e3TbbkA75Jo0ooLxgcAWYIiIiIiIiIjJwqOxIeo3jDhAPjwAnQaDmf/jXfYgZq+/rYW2TDc1xkgr5RERERERERKQfUEWf9C7DwPIXYdkJPE2rcUfXEc+vIhEeheMaWI1Q6iMJErbNpBH5CvlEREREREREpM+pok/6hukhER6B7fLhq/uEwNp3cTevBcfp65F1Sn0kQdyymDQinxEFgb4ejoiIiIiIiIiIgj7pW7Y3TCI0HDPZiL/mPXzrF2Immvp6WJulkE9ERERERERE+iNN3ZW+Z7hIBkoxklG8G1fijqwjXlBNIlQOZv/6Fq2PJIhZFpPKFfKJiIiIiIiISP+iij7pNxy3n0R4JBgQWPcBgdr3cUXr+npYWbkh38hChXwiIiIiIiIi0r/0r3IpEcDyFWB5QrgjtQSidcTzKkjkVeC4+67hRX0kQTSZmq6rkE9ERERERERE+iNV9En/ZLpJhoZjeYL46pcSWPs27qY14Ni9PpSGnJBvlEI+EREREREREemnFPRJv+Z4giRCIzDsOIGa9/DXfogZb+i1x2+IJIgo5BMRERERERGRAUBTd6X/MwwsfzG2lcDT9AXu2HpieVUk8kaC6emxh90YTRBJWGw/Ik8hn4iIiIiIiIj0e6rokwHDcXlIhEdgm278dQsJrn0XV6QWHKfbH2tjNEFzPBXyjS4Kdvv1RURERERERES6m4I+GXBsbx6J0HDMeD2Bte/gq/sEI9HcbdfPhHzblSvkExEREREREZGBQ1N3ZWAyXCSDwzCSUbz1y3FHaokXVJMIloPp2urLNkaTOSGfpuuKiIiIiIiIyMChij4Z0By3n0R4BGDj/3/2/jxO1rOu8//f91Z7dfV2us+ac072hLBJGEIcRTLIKM6I2zASlOUhUQcUQkQE+bJEIRF+jAMjiAQQomNABwV1GBRHBiQCA8g4QbIBOVlIcvY+vVTXdt/39fvjumvrvftUd1V3v548mqq6667qq07O6a5+9+dzfU7/izKnvymvem5DzzVXDVWuh7psb0EHR7JyHKenawUAAAAAANhMVPRh+3McRelhRUFBfuWk/NqU6sUL1CgelPHSa3qKuWqouXqoyyYLOjiSI+QDAAAAAADbDhV92DlcX2F+r2I/o/TU/cqe/L/y50+sOqxjrtYO+Q6NEvIBAAAAAIDtiaAPO04c5NUo7JMTzit76p+VPnu33PrckufO1ULN1UJdSsgHAAAAAAC2OVp3sTM5rqLsuOKortTMIwoqZ1QbOqxGYb/kBpKaIV9Dl04WdQEhHwAAAAAA2Oao6MOOZryUGsX9il1XmTP3KHvqLnmVMyoT8gEAAAAAgB2Gij7sCnFqSLGfl189I+exM4qCvbr06OWEfAAAAAAAYMegog+7h+tpLhjTjMnoYu+4LqjcLWfmMSmO+r0yAAAAAACA80ZFH3aNaiPSXC3U4YkxTQxl5FTPSo/9X6m4Txq7SMoO93uJAAAAAAAAG0bQh12hGfJdMJLTvlJWjiMpNyZFDWn2calyVhq9UCodkvxUv5cLAAAAAACwbrTuYsdrhnyHRnLaP5yEfE1eIJUOSH5aOvEt6Xtfl2ZPSMb0bb0AAAAAAAAbQdCHHa0r5CstCPk6pYs28KvPSo99w4Z+tbktXSsAAAAAAMD5oHUXO1Yz5Ds4bEM+d7VY23GlwoQU1qSzx6TyKbt3X3G/5PFPBQAAAAAADDYq+rAjVRuR5qoNHRzO6cDwGkK+Tn5aGj4oOZIe+392YMf82c1aKgAAAAAAQE9QpoQdp9aIbcg3kl9/yNcpMyylClL5tB3WMXxYGjksBdleLhcAAAAAAKAnqOjDjlJrxJqt1nVwZAOVfEtxfam41wZ+p++3wzpmHpPiuCfrBQAAAAAA6BWCPuwYtUasmVbIlzv/kK9TKieVDkpRTXr0G9Lx/ydVp3v4CQAAAAAAAM4PrbvYEephrNlaQ4c2I+RrchwpNyZFdWnmUbtv38hRGwD6qU34hAAAAAAAAGtHRR+2vXoYa6ba0MHh7OaFfJ28lDR0QPIC6cS/2Aq/uVOSMZv8iQEAAAAAAJZHRR+2tXoYa7rS0MGRLQr5OqWLUpCT5s/YvfuGL5DyY5IbSK5ng0A3sJeOs4ULAwAAAAAAuxFBH7atvoZ8Ta4nFSaksCpNHbMfMnaIR+eHn5b8rJ3Y6wUdIWDznOYxrw8vAgAAAAAA7AQEfdiWmiHfgaRd1+t3E7qfkUoH7HVjJBNJcdj+qNWkypS93tni67gdgaBn24L9jA0E/XR3VWDzvGZQCAAAAAAA0IGgD9uODfnqOjCc08GRAQj5FnIcyUlCudXEzUAwuWzMS7UZGxSaWGpmgo7sc3p++9LPJFWCmcWBYPO266s/pY4AAAAAAGCrEfRhW7GDNwY45Fsv11tbu64x3RWCUUNqVKT4VBIKdpzreB2BYFIlGGRtKOinlg4EaRsGAAAAAGDbI+jDttFIQr59pR0S8q2H46y9ZbezQjAOpfqcVJ1eom3YWbyXYJBNWoczHQFgcy/BjoCQ4SIAAAAAAAwcgj5sC40w1nS1rr1DWR3abSHfejVDO6VXPs/EHVWCkRTVpEY5uR3bMM8YrX+4SMdt2oYBAAAAANgyBH0YeI0w1rlKXftKWV0wmifk6xXHtW29Xmrl85YdLnI2qRKU7CaCZv3DRToDQgAAAAAAcF746RoDrTPkOzRKJV9f9GK4SJwMF2k+n8zGhot4AW3DAAAAAAAsg6APAysMTRLyZXRoNCffJeAZeL0YLhKHshWCCYaLAAAAAACwJgR9GEg25KslIV+ekG+n2ZThIm4SNDZbhwNbHchwEQAAAADALkHQh4EThkZThHxo2vBwkarUmGsPF2lylLQid4SCQUbyMgwXAQAAAABsawR9GCjNkG8vIR/W63yGi1SnpfhMx3CR5nM224aTlmQ/newlmGG4CAAAAABg4PDTKAZGGCUh3xAhHzbR+QwXqZeTULBjuEjzOZvVgc1w0M/aCsEg024lZrgIAAAAAGATEfRhIISR0dR8EvKN5RUQ8mEQrHm4SNy9j2BzuEg5CQkXPWdzH0FP8tLdw0X8tBTk7AeVgQAAAACAdeCnSPSdDfnqhHzYvhxX8twNDheZlarnuoeLeIFtD86UpOywFOSlVBL+UQEIAAAAAFgGQR/6qh3ypQn5sDusZbhIVJcaVWnuhDT9PXssyNiqv+yIlBmyoV8qbysAAQAAAAAQQR/6qBnyTRQJ+YAuraEiQ/a2MVJYtR9TD9qKwOa04FRByo3a0K8Z/q2l3RgAAAAAYMWx3ZLJRPbnrdpsv1e0YQR96IvOkO8wIR+wMsdJBntkpWxyLA5t8Fc9J5VPSLFJ9vjL2HbfzHA7/AuytPwCAAAA2JnijoCu63KZ41Gyr3pUl+KGvd46P3nMLEEfsGbNwRsTxYwN+TwCCGDdXN9W86UK7WNhzYZ/s49L5x6W5CQtvzlb9ZcuJnv95W0oCAAAAAD9Ykx7sOGiQG6Z41ESzMUdQV0roIs7HpcEdnIlR5JRcmnsHuuOYy9dL7ntSq4rOYG9bGzfLimCPmwpQj5gE/npZM++kr1tYhv8NSrS2e/a207S8psuStmk5bc56IOWXwAAAABrYcz6Aro46q6gixrJQMLYBnXqqMAzcRLIOQsCOklqBnLNjySo8/zu280Qb6O8sCd/TP1A0Ict0wz59hDyAVvDcZPW3Vz7WLPltzIlzR233zi9lA3/MsN20i8tvwAAAMDOtRmtrq3ALrafoxXQSXKMPbYoiEtu+4HkpBdX2GFDCPqwJaKOkO8IIR/QPyu2/D5mW34dx+71F+TslF9afgEAAID+60mra9jR2trZ7ppU1RmnHdCpGdA53VVyS7W6dlXY8fN+PxH0YdNFkdHZViVfjpAPGDTLtvzOS5Wz9g2F60l+Nmn5Helo+c3bb+wAAAAAlndera5hRyVdZyhnukO7ZqtrM6CTscHbWltdm3vXYVsj6MOmWhjypTwCAWDgLdfy26hIlTN22IeM5KWTlt8RKVuyoV8qZ6sBeYMAAACAnWIjra5x2A7qwoZkwnarq+Lu/egkG9p1ckSrKzaEoA+bphnyjRcI+YBtz/VtNV+6aG8bY9+4hBVp5nvS1IOS69iqvyAn5cakdMFeT+UlL+jr8gEAALBLxUsFdMtU0pm43eoa1dstr3HUbpvt2o8uahfPdaLVFX1E0IdN0RnyHRkn5AN2HMdpt/xmkmMmlhpVqVGWzpy1b34834Z/maF2y2+zWpCWXwAAAKxFs0U1TirlWu2tYffxOLL7T0e1dlC3aC+6qB3YaWHAtlKrq2dDOlpdMeAI+tBzcSwq+YDdyHFt625qiZbf+dPSzGP2mJ+27b3ZERsABvkkAMws/bwAAADYGYxZHM6ZePGxKGl77QzsOqvummHfwmo6x+mooPPara5usHSFHbADEfShp+JYOjNX1XiyJ1/a54snsKut1PI7/Yg0FSXVgVkbEGZHafkFAAAYdCsGdh3HWy2wdVtpFze696ZrBXatMa9JkV0zsPPa4ZzrJoGd13Ef1XTAQgR96BlCPgCrWqrlt9liUZ+T5s8kU359KVgw5ZeWXwAAgN5qBXadoV1z37qO4ysFdq1KuwUVdssFdp0tsAR2QM8R9KEnmiHfWCFNyAdgfVxvcctv1JDCqlQ+1d3yG2TtlN/MUEf4R8svAADY5Vqh2xoCu7DWDu1agybiJQK7ziq7jomv7hJ71hHYAQODoA/nLY6lM+WaxgppHRnPE/IBOH9eYD+6Wn5rdtjH9ENJy6/b0fKbTPlN5eyefx7f3gAAwDbUFdg1Q7uoYwBFxx52KwV2caQlJ8IuGdj53RV3BHbAtsZPQjgvrZAvnyLkA7B5HMcO8PAzkobtsTiyVX/1Oal8WpKx+7YEWSk9JOVGkkEfORsI0vILAAC2ShwvDueWOhY1pDAZOhHWJdNZiRcvEdglVXadQyWa4RyBHQAR9OE8NEO+UUI+AP3gerZ9N5VvH2u1/J6UZh61b269FC2/AABgY1YK7DqPh8lk2JUCOxPaEM50lNktG9ilu6fHEtgBWCOCPmxIHEuny1WN5dM6SsgHYFCs2vIbt1t+00nLbypPyy8AADvdUuHcUm2xYX3B0Ilkmmxz6EQcrxzYdYZzXtAd2Lm8zwCw+Qb+K82jjz6q3/iN39BnPvMZVSoVXXrppfrwhz+spz3taZIkY4xuvvlm3XbbbZqamtIznvEMve9979MTnvCEPq9857KVfFWN5tI6MkbIB2CArdTyW5uV5k7ZY15gz8mUpOxwu+U3yPEbdAAABsmSe9Z1HGseb7bDNodPLBfYybHf603n0ImVArvkPgA7W/nUuh8yKPnVQAd9U1NT+v7v/349+9nP1mc+8xlNTEzou9/9roaHh1vnvPOd79Tv/u7v6qMf/aguvfRSve1tb9MP//AP67777lOxWOzf4neoZsg3krOVfJmAkA/ANrNky2/dVv3NnZCmv2ePBRlb+ZdNWn6DnH2Mn+7PugEA2C7iWJJJgrXmx4LbMguOLTw/bu9hFyVDJ8J6u3VWUTvgM7H9vJ2BXdfAiebQiUByM+0Aj8AOwFLu+R/S/3rXuh4ySPmVY4xZOIdnYLz+9a/XP/7jP+qLX/zikvcbY7R//37deOON+o3f+A1JUq1W0+TkpN7xjnfol37pl9b0eWZmZlQqlfS1z/2VSiPDvVr+jtMO+VI6Ol4g5AOwcxljq/7Cqg0A48j+MOBnpXReyo219/pL5flBAQAwuNYSujWDslWDubhjz7p4QWVdEriZMHlsx4eW+hxGrbCvUzOscxx7XmcVXeegic6KO74PA1gLE0uVc9L8meTjtDR/1g72ax6bOylVzmqmZlT6nVlNT09raGho1afeqvxqLQY66Lvyyiv1b//tv9X3vvc9feELX9CBAwf0ile8QjfccIMk6YEHHtBFF12kb3zjG3rqU5/aetzzn/98DQ8P6/bbb1/yeWu1mmq1Wuv2zMyMDh06RNC3gjiWzs5XNZwl5AOwS8VhO/gLa/aHED9lW36zw1JmuGPQR5aWXwDAYvGC0GzZUE3rC92aIVtXO2tS9WY6K+ekVujWCtqa15v3J1Ndpfb3sq696By12l0dd4XbzeuufUrHXXC88zoAnIc4kqrnbFBXTsK7+SS8K5+RKs3Ls+2vsatoBn2PPPJIV9CXTqeVTi/u8Nms/GojBrp194EHHtD73/9+3XTTTfrN3/xNffWrX9WrXvUqpdNpvfjFL9bx48clSZOTk12Pm5yc1EMPPbTs89566626+eabN3XtO4kx7ZDvCO26AHYr15dSBfvR1Gz5nT0unXvEHmu2/OZG7VAQWn4BYDAtG7otDNbMEucsEbx1hW5Rx+UaQ7fOdtalQjepuzVVZunQzEneq3cFbU4yCKJ5zsKgjdANwACKw6QCb2HlXUeQN39GqkytOcCTHPtL+ty47dLJj9nL5ofjSX/7m2p+7T106FDXo9/ylrforW9966Jn3az8aiMGOuiL41hXX321brnlFknSU5/6VH3rW9/S+9//fr34xS9unecs+IZkjFl0rNMb3vAG3XTTTa3bzYo+LGaMdHquHfJlA8riAaDFS9kPJb/lM7Gt9gur0tlj9rbj2fAvVbDhHy2/ALBYvCA0W1fotlQrqukI3RZUvLX2dOsM3RZUty0buklLB2/nE7otOKdV+UboBmCHisMkrDvb3ULbWYU3f8ZW6a01wHNcu7d2bmzxR37cvg/PjdlzVpuA/YOvlf7O7tG3VEXfki9pk/KrjdhQ0PfII4/owQcf1Pz8vPbs2aMnPOEJy77Y87Fv3z5deeWVXceuuOIK/fmf/7kkae/evZKk48ePa9++fa1zTp48uSgl7bRcqSW6GSOdmavZdt09hHwAsCrHtW27QVbKJseaLb/Vc1L5hP350EvZ8C8zbCf90vILYNC1grLmPmydt82C2wsHKsQdFW5rDd2agduCIK4VuklLVrs1n2ep0KzrOqEbAPRcK8BbZv+7VgXeOXV9/V6J40rZUVt5lx1bugovP27fV/fql+iX/5g0fKX0Oz+toaGhNe3Rt1n51UasOeh76KGH9Ad/8Af62Mc+pkceeUSdW/ulUin9wA/8gH7xF39RP/3TPy3X7U1r5/d///frvvvu6zp2//336/Dhw5Kko0ePau/evfq7v/u7Vo9zvV7XF77wBb3jHe/oyRp2q2bIV8oGOkolHwBs3FItv82qv9nHpHMP2x8g/YwN+7IjtuU3lZOCvN0HEADWo1Udt1Q4Z5YJ6zoGKzQnnZpQisL24IVWtVu0+FIdQVhrmILWELol+7e1Qreg+5yF+78RugHA1osadn+7lfa/a1bgrZXjJlV24wtCu+b1pAqvlwHeeuT3rOv0Qcqv1hT0vfrVr9ZHPvIRPfe5z9Vv/dZv6V/9q3+lAwcOKJvN6uzZs/qXf/kXffGLX9Sb3vQm3XzzzfrIRz6ipz/96ee9uNe85jW69tprdcstt+gFL3iBvvrVr+q2227TbbfdJsmWPN5444265ZZbdMkll+iSSy7RLbfcolwup+uvv/68P/9u1Qz5hpohX4qQDwB6yk8ne/aV7G0TJ4M+5tubBDu+rfpLF+1vMVP5JPzL0fIL7CTNirXlwrdWVdwS97eCuUYSyDU6pqE2q+A6w73m7SXW0czOHHeJDyeZbOovcz/7NwPAthTV23veLbf/3fwZqTq99ud0vCVCuwXhXW7M7pO3g75/DFJ+taapu7/+67+u173uddqzZ/VE83/+z/+p+fl5/czP/ExPFvg//sf/0Bve8AZ9+9vf1tGjR3XTTTe1ppZItp/55ptv1gc+8AFNTU3pGc94ht73vvfpqquuWvPnmJmZUalUYuqukpCvXNNQJtCFhHwA0D9xKDUqNgAM6/ZYq+V3RMqWbMVfKmerAalwAbZGvFylXLxyJV0c24qIuCOQi8L2uSt9aKl/38b+MNWqfFsmnFvyfr5eAMCOFtZW3/9u/oxUm1n7c7r+0vvfLazCywztiABvZnZOpct/UNPT02tq3ZW2Jr9aizUFfTsdQZ/VGfIdHc8rR8gHAIPDGPtb17BiJ/1GoeQ6dsJvkLNVf+lCss/fgna3ZS+1/H0EAdgJVmpTXa5SrrOFtbNKrhXQdQ5r6Az3ktsL31m32lidVcK3FcI5AACkJMBbZf+7+TNSbXbtz+kGtsouP273wFtq/7vcqJQu7ar3hxsJ+gbFeU3dPX36tP7P//k/iqJIT3/607s2FMT2QsgHAAPOcdotv5nk2KKWX9Oxh5W0ZLDXvN386Dqv4/7WnlnuEqGDI7nu8vc7Cz/vRi+1+PZGn2sXvTHd1jY08CGSoqg7kGtWzrX2pGuGcB3DIZrDHrokwxyaf//d5t93T11/7z1PUrD4fqrlAAAb0agsbpstLxHg1efW/pxesPr+d7lxu00M37t2lA0HfX/+53+uX/iFX9Cll16qRqOh++67T+973/v0spe9rJfrw1ZIQr5ixifkA4DtxHGTab259rFWZZHaUypbYYbpOJbc7rq/+ZhmxdKCx6zrOc/nhZlVAsltFFx2rWGp29s4uFzvwIfOc5pBXC8GPjSvr1QZ5wdL3E+1HABgkzXmV9//rnxGapTX/pxeevX97/LjdhDcILxfwJZbc9A3NzenQqE9MfDmm2/WV7/6VV166aWSpE9/+tO64YYbCPq2GyOdJuQDgJ1jp4QXXYHiaiFj83py2Xm8Z8GlUVfItL4X08fg0ll8zF2htbv5OTqr56KOttUND3zorJTTgtBthYEPXdV0vEcBAAyI+vzq+9/Nn7FB31r5mdX3v8uNEuBhVWsO+p72tKfpne98p57//OfbB/q+Tp482Qr6Tpw4oVQqtTmrxOZIQr5C2oZ8+dR5dXIDANA7XYHWDrBUcLnouLRy0Ng8fyuDy1UGPrjN6+mlq+kAAOiHuZPSzKPS0AGpMLG2xxiTVOCtsv9d+bTdOmWt/Eyyz91SIV5HC22Q43snemLNyc7f/u3f6hWveIU++tGP6n3ve5/e85736D/+x/+oKIoUhqFc19VHP/rRTVwqeipp1y2kfV24h5APAIBNtdOCSwAABtW9n5a++J9t5bnjSv/6JunCZ62+/938mfUFeEFu9f3vcmNSKrf6cwE9tOZ058iRI/qf//N/6o477tCznvUsvfrVr9Z3vvMdfec731EURbr88suVyWRWfyL0XxLy5Qn5AAAAAADbmTE2pJt5TDp1j/SV93fcF0tffJf9WKsgv8T+d0tU4QUEeBhM6054rr/+ev3oj/6oXvva1+qHfuiHdNttt+kpT3nKJiwNm6Ij5DtKyAcAAAAAGHRRQ5o7YdtxZx7r+HhUmnlcimqrP0eqsPr+d7kxKchu/usBNtG6Up7PfOYzuvvuu/XkJz9ZH/7wh/X5z39e119/vZ73vOfpt37rt5TN8g9ioBnpbLneCvkKhHwAAAAAgEFQL3eEd0mQN5tczp201XnLcVy7F19+XDr+L4vve8HtUunQ5q4fGBBrTnpe97rX6fbbb9ezn/1s/f7v/75e+tKX6k1vepP+7//9v/qt3/otPeUpT9G73/1u/eiP/uhmrhcblYR8ubRHyAcAAAAA2FrGSJWzy1TlPSZVp1d+vJeWhvYnHwc6ru+Xinvt1HZp8R59P/BrhHzYVRxjWmPZVjQ+Pq6//du/1dOe9jSdPXtW11xzje6///7W/d/61rf0S7/0S7rzzjs3bbGbZWZmRqVSSV/73F+pNDLc7+X0XjPkS3k6sievYpqQDwAAAADQY3EozR7vCPMeb1+ffXz1YReZ0tJB3tABKTu69qm0G5m6C3SYmZ1T6fIf1PT0tIaGhvq9nHVZc+KTy+V07NgxPe1pT9MjjzyyaPDGE57whG0Z8u14hHwAAAAAgF6pzy+uypt9dH0ttkP7peLCMG+/lMr3Zo2FCQI+bIwxUlS3reTb1JpTn1tvvVUvfvGL9apXvUrz8/O6/fbbN3Nd6IXOkG+ckA8AAAAAsIquFtvHF++bVz238uO9tDS0b+mqvMKk5AVb8jKAZZnYDniJ6vYybkhRaO9zJLmp9u1taM3Jz4te9CL9yI/8iB544AFdcsklGh4e3sRl4bwZaWq+rmzKtSFfhpAPAAAAAKCOFtsl9srbaIttMbnMja29xRbYLHGUBHidgV5k73McyUtJbiAFOSk9JKVykp+2QbWfkubr/V3/eVhX+jM2NqaxsbHNWgt6JQn5MoGro+MFQj4AAAAA2G3q8+2ptQur8uZOrN5im9+zdFXe0D4pVdi61wEsJw6TIK9ZlVeX4uTvtePa6lEvZf++NsM8L50EeqnkcpkK09rM1r2OHltTAvTLv/zLeuMb36hDh1afVPOnf/qnCsNQL3rRi857cdiYs2VCPgAAAADY0VottstU5VWmVn78Ui22zaq84l5abDEY4rBdkdf8aIbUjif5ga3MSw/ZQC+VXxDkpSVvd+Uia3q1e/bs0VVXXaVrr71WP/7jP66rr75a+/fvVyaT0dTUlO6++27deeed+vjHP64DBw7otttu2+x1b4o1DiAeaFNzdaUDVxfuIeQDAAAAgG0tDm313VJVeTOPrd5imx5apiqPFlsMkDiUwroUdwR6zXzG9Wxo5wVSdiQJ83LtFttmhZ7r9fc1DBDHrDHdOnnypD784Q/r4x//uP7lX/6l675isajnPOc5+sVf/EU997nP3ZSFbqaZmRmVSiX98f94jyZH9yjnZZVyAqXcQCk3Jd/ZHn9hpubqSgWuLiLkAwAAAIDtoTG/dFXezOPS3PE1ttguEeQN7afFFoPBmI4223rSYtuQmmmU67fDvFTe/r0Nsosr81x3y5bczImmp6c1NDS0ZZ+3F9Yc9HU6d+6cHnroIVUqFY2Pj+uiiy6Ss41/E9D8D/ihv/r/KTeUU6hIjiRfngI3UMpJKe9llfHSSrsppZxAgRvIc7buL9lqpubqCgJXF40XNJQl5AMAAACAgWCMbaNd1F6b3F61xTbV3VbbGebRYotBYczi4RdRo32/F9hptn4gBXkpXbQBnp/prswbkGxpOwd9G0qEhoeHd+TU3byX1XBQat0O41ANE6oSVzUTzSk2Rq4j+Y6vwAmU9dLKuRmlvXS7AtAJtjz0nCoT8gEAAABA3yxqsV1QobemFttlqvJyY7ZyD+g3E7cHX4RJVV4Utu9vDr/w0vbvbarQDvNalXmpgQnzdipSoRX4ri9fvrIdx2JjFBobAE43ZnXaTMmR5DquAsdX4AbKe1ll3Uyr9ddWAG7OH/VUua7Ad3UhIR8AAAAAbJ7GvG2nXWqvvNVabOVIhYmlw7ziPlvdBAyCZpjXqsqrS3Fk73Nkq/K8lBTkkmm2+XY1Xmv4RUCY10ckQ+vkOo6t3lMgee0IMDKxGqahMA51Oppasf23WfmXclPn1f57rtxQ4NmQr0TIBwAAAAAb12yxnX18cZA38+gaWmyDpdtrWy22qa15HcBq4mhBm21dipuTbJ2kKi+V7Je31w6/aIZ4rUCPlvFBRTrUI57jynPSkpvuOr5Z7b/nyg35nqML9xDyAQAAAMCaxKE0d3LpqrzZx6RGZeXH02KL7aI1/KKj1bZZdeq47Tbb9NCCSbadlXlkDdsR/9U22Wa0/zZDvqPjeUI+AAAAAOjUqCxflTd7QjLRCg92FkyxPdAd7NFii0ESh92DL6JGR5jn2cEXbiBlhpefZEuYt+PwX7QP1tX+axz5jttq/40bgXJ+WhePDiuVihQZb6Cm/wIAAADAeZk7aUO5oQN2X7uFjJGq55auypt5TKqcXfn5l22x3Wf3y6PFFoOkVZXXEegZY+9zvaTNNpCyozaIDjJJZV7HJFvX6+9rwJZaU9D31Kc+dc2TZL/xjW+c14J2s2Xbf02kRtzQqcqcYoXak03rWG1K36v7SnmBCl5GBT+njJdSxksp7aaUdrd++i8AAAAAnJd7Py198T8nVUmO9MSfkUqHOtprk2Bv1RbbYrI33hJVeflxWmwxOIzp2C+vOfwitMcdx1bmNcO8XElKJWHewso8l7/TsNYU9P3ET/xE63q1WtXv//7v68orr9Qzn/lMSdJXvvIVfetb39IrXvGKTVnkbuc7nqoNo1JQ0IHhrIoZX8YYNUyoetzQ2casjtemJGPkuq5Sjq+0m1LRzyof5JRxA6VdGwJu1vRfAAAAAFiXOJRmj0vTj0oz35NOf1u6/286TjDSN//7Mg9ersV2n71Oiy0GiTFJgNfobrNt8gI7zdZP2mwzQx3DLzoq8yjmwRqsKfV5y1ve0rr+8pe/XK961av027/924vOeeSRR3q7OkiS5qqhHFetkE+SnGb7r9s96SYykRpxqHoc6njtrMLqKUm2/TflBsq6aRWDnHJeWmkvpYybUto7v+m/AAAAALCkqNHeL2/6UWn6e0m77aM25GvuJ7aSiSukiSu7q/IKe23wAQwKE3dMsU0GYMShZCS7KX9Sleel7eCWVEHyF1bmpQjzcN4cY5rN3WtTKpX09a9/XZdccknX8W9/+9u6+uqrNT093dMFboWZmRmVSiV97NPv1fDwcL+X02WpkG8jwiT8q8cN1eNQsTFyHClwbPtv3suoSPsvAAAAgPWK6jbMm07CvJmOQG/uxMphnp9JqvEOSNkR6Z6/kk1GEo4rvfDjS+/VB2y1rjCvGeglw10cxwZ1bmBDu1RBSuft3/Guyjz2gNwOmjnR9PS0hoaG+r2cdVl3cpTNZnXnnXcuCvruvPNOZTKZni0MvQv5pGT6r+srp/Z/o87236nGrE7WpmSM5LoO7b8AAAAA2sJaO8ybWVCZN3dy9TCvlIR5pYPdl7mx7gqmPZe29+hzXOkHfo2QD1srjmw1Xthsta1LcXOSbRLmeSkb5KWKUiq3RGVesPLnADbRuhObG2+8Uf/pP/0n/dM//ZOuueYaSXaPvj/8wz/Um9/85p4vcLcq10I5jrR/OHPeId9ylmv/jU3cqvyj/RcAAADYJcJae5JtZ5A3nYR5WqEZLMhKQweXCPQO2Gmga+0UuvzHpINPX3nqLnC+4rC7zTZqtMNqx7VVd24gpYeSQC/X3ievuXeeRwEMBtO6/2a+/vWv14UXXqj3vOc9uuOOOyRJV1xxhT760Y/qBS94Qc8XuBuVa6Ekaf9IRkOZrf9NgOu4ynhpZbwF03+T9t9yVNFUY5b2XwAAAGC7Cas2zFtYmTf9qFQ+pZXDvFx3gNcZ6GVHere3WGGCgA/nLw47grzksrlzmeMlrbSB/bubKtiwujPI89OS6/X3NQAbsO49+naiQdqjr1wLZSQdGM5oKDv45b6d7b/1OFQjDmn/BQAAAPqpUVlcmdecbFs+vfJjU/l2eLewMi8zzKAADKaoYUPssGY/JBvStdps80mY17FfXnPvPJfONCy2q/bow+bZbiGfRPsvAAAA0BeN+aQyrzPISy7nVwvzCov3yislH+kSYR4GW1RPAr2qvW4kub4N7rIj9iPIJRV6mWRABj9nYvdYd9AXRZH+y3/5L/qzP/szPfzww6rX6133nz17tmeL2022Y8i3Etp/AQAAgPNUn+/YJ+973e22lVV+7koPtdtrm4Fe83amtDXrB86HMQtCvYY97gU2wMuN2yrTVBLqBTmGYADaQNB3880360Mf+pBuuukmvelNb9Ib3/hGPfjgg/rUpz7FMI4NKtdtu+v+kZ0R8q2E6b8AAABAh3p5QZDXEehVplZ+bKa0dJA3dEDKbK9WM+xyrVCvkgR7jfaEWz8t5Sek7HC7Ui/IMQwDWMa6/2X8yZ/8iT74wQ/qx37sx3TzzTfrhS98oS666CI96UlP0le+8hW96lWv2ox17ljleigT25CvtMNDvuXQ/gsAAIAdrTa7uL22Ge5Vz6382MzwEgMwkst0cStWD/SWidt76YVVOzRDTnsQRnGfDbE7Qz2GYgBrtu6g7/jx43riE58oSSoUCpqenpYk/bt/9+/0pje9qber2+Hm66Hi2Gj/cHbXhnwrof0XAAAA20ZtdnFFXnP/vOr0yo/NjnQEeAe7A71UYWvWD2wGE3cMyahKUWQr9fy0bb8dOmirT7tCPQo3gPOx7qDv4MGDevzxx3XBBRfo4osv1mc/+1l93/d9n772ta8pnU6v/gSQZEO+KAn5hnOEfOux0fbfgp9VgfZfAAAAbFR1ul2R1wzxmrdrMys/Nju6YPBFc7LtfjsRFNju4qgd6IU1yTRDvYzkZ6X8pJQpdgzKyBLqAZtg3SnHT/7kT+rv//7v9YxnPEOvfvWr9cIXvlAf/vCH9fDDD+s1r3nNZqxxxyHk6721tP+eqJ3Vo0u0/xaCrHJexlYA0v4LAACwexkj1aaTIO/R7kEYM4/aqr2V5MYXt9c2rwe5rXkNwFaIwwWhnpEc14Z6QS4JsAsdlXpZpjkDW8QxxpjzeYKvfOUr+tKXvqSLL75YP/7jP96rdW2pmZkZlUolfezT79Xw8PCmfi5CvsHQbP9thoAL239zXjpp/00r4wbKeGnafwEAAHYCY2xlXjO866rM+54djrGS/PiC9tqOyrwguzWvAdhKcdjRftsM9Twb6qWydh/JdEeo52cI9bDtNXOi6elpDQ1tr+FG5923eM011+iaa67pxVp2vFbIVyLk67fV2n/PNeZ0qnZuyfbfvJ9N9v4LlPFSi6oId7Pm7w2M2r8/MEvcv/CcpY61n2uZ+9snrnz/GtbVvNW9vpU/b/fvSFa+fz2f13FcpVxfKTdQyvUVOD4BMwAA62WMnVjbGeR1DsJorBbmTSwO8kpJmOdnVn4ssJ1Fje5QT7KDMPyMHf5SusC2mjf30wv49wAMmg0FfX/8x3+sP/iDP9CxY8f05S9/WYcPH9a73/1uHT16VM9//vN7vcYdYb4eKoyNDpSyGs4TDA2itbb/htVIne2/GTeVTP/NyHVcaQNh1UqhkTGm9Rgj05lrtY8be83IdD2/Oj6/fWz7XtP1/93H7XpMaymdz9u55s51LV5950taOUBbLgxc6tmWev3dZ64jTFzmebVgXU7Huhw57efoCuDa97efzGl/Dqfz8Z3P231/8xzf8eQ5ngLXU87LKOdllPaCdgDoBgocj30mAQC7lzFS5eziwRfN2435FR7sSIWJBZNsOyrzfPYexy4Q1TuGZNTt21nXt6FeZljKjXYPyfBT/V4xgDVY90+I73//+/XmN79ZN954o97+9rcriiJJ0vDwsN797ncT9C2BkG97W23673xU1bnGnGLFSmKeVnjjOO1gyd5jdR4zZvnQSB2P6by39VzOEscWnNu81fX4JSrEln28s/i4o6UrzJrHu5/eaR1wVnkOZ4nPteyfwXpewxLrWv019K+KzhijyERqmEiNONRUY9ZWmCb/c+S2Qr6U6yvrpVtBYDMAbAaCLvtNAgC2M2Ok+TNLBHnJZNuwusKDHak42bFPXkdlXnEfYR52D2MWhHoNe9wLbKiXG7fBXqoj1PP4uRXYrtYd9P3e7/2ePvjBD+onfuIn9Du/8zut41dffbVe+9rX9nRxO8F8PVQjMjowTMi30yzV/gv0guM48h1fvmyIt1BsYoVJCFiJ65oNK4pMZONhY+S7nnzHk+/alvO8l1HWTyvl+K1wMOUG8h2PtmAAQP+Z2IZ5nUFe5yCMlcI8x5UKk4uDvGaY51GBhF2mFeol7betUC9lw+38hJQd7q7U8+gQAXaSdf+LPnbsmJ761KcuOp5Op1Uur7LXxS5T6Qj5Rgj5APSI67hKJa3jSwnjSKEJFZpIc+G8phozik0sGUeOIxsiup5STlIN6KeVdlOtluDm3oC+623xKwMAbHtzJ204N3TAtsY2mVgqn25X5nW12z4mRbXln9NxpeLe7sq8ZrttcR+VR9i9jLH/dlqhXmiP+2n7UdwrZUrdoR7v74Adb91B39GjR/XP//zPOnz4cNfxz3zmM7ryyit7trDtrlIPVY+M9g9nCPkAbCnf9eRr6TdxzaEzYRypYULN16s6UYuSdnJHnuPIl6/A9ZR2U8r6aeW8dFcAaANB2oIBAAvc+2npi+9K9jBxpINX2yqimUeTMK++/GMd14Z2pQOLJ9oWJgnzABO3B2SEVSm2W2jZUC8jFcelzFBHqJcl1AN2qXUHfb/+67+uV77ylapWqzLG6Ktf/ao+9rGP6dZbb9WHPvShzVjjtlNttEO+0TztAgAGx3JDZ5piE6sRh2qYSOWooulwTqGJ1RzOYtuCbRCY8dLKe5nW9Onm3oBBEgQCAHa4qCGd+a508m7psf8rPfjFjjuN9L2vdZ/veHbQRdcAjGZl3l47BACADfGizlAvtltO+xn7kZ+QMsV2qOdnJZdfwAKw1v3d9GUve5nCMNTrXvc6zc/P6/rrr9eBAwf0nve8Rz/7sz+7GWvcVqqNULWQkA/A9uQ6rtJeSkttT75wSMh0Y05natO2GtAYyWkPCQlcX7mOISG+094bMHB9eVQDAsD2Yow0+5h08p72x5lvt/f/Ws5VPy0dekZSmTdBmAcsFIdS2LGnXhzZ0M7P2ACvsFdKF7sr9dhjGcAKHNMa+bl+p0+fVhzHmpiYWP3kATYzM6NSqaSPffq9Gh4e3vDzNEO+fcMZjRHyAdhlmkNCmm3BjTiyQ0LslJAlh4TYakC/NSU4SNqDGRICAH1WnZFONUO9e+316vTi89JD0sQV0vAF0jc/IanjRwvHlV748e69+oDdLA7bgV5YTX5R6tlQL8hK2REpXeio1MsQ6gF90syJpqenNTQ01O/lrMt5/UptfHy8V+vY9mqNqB3y5Qj5AOw+axsSYgeFzIXzOteYVRRHkppDQmwImHJ8ZTy7N2AzCAyag0IYEgIAvRfVkxbcJNg7dY8dkrGQG0jjF0sTV0p7LrcB39CBdhAxckT64n+2e4k5rvQDv0bIh90ranSEejUb6rm+De9SBal0gZTKt4dkBJl+rxjADrHuoO/EiRN67Wtfq7//+7/XyZMntbAgMIqini1uu6g1IlXDWHtLScjHL10AYJH2kJDFvwwxxihMWoIbJlSlUdPJWiiTjAnxHFdBMiQkcH3lvYxyfkZB0gqcYkgIAKyNMXY4xsl7pFP32v31Tn9HipdowS0dlPZcYQO9iSuksYvscI3lXP5j0sGnLz11F9jJonq7Si+sS+oI9TIlKTsqpTom3/pLbZICAL2x7qDvpS99qR5++GG96U1v0r59+3Z9e1VnyDeeJ+QDgI1wHEeBs/wQj+aQkNBEqsR1zYbzCquxvdOY1pAQ3/WUTYaEpL1UKwBkSAiAXas63W69bVbs1WYWn5cespV6E0ml3p4r7ATP9SpMEPBhZ+ucfBs1bHjuBTbUy45J2eGO/fRykk+3F4Ctte6feO6880598Ytf1FOe8pRNWM72QsgHAFtjpSEhkhQmIWAjjlpDQmLFcowkx5XveApcOxU456WV9dJKu4FtB04CwJQbMCQEwPbW1YJ7t72ceXTxeZ0tuM1qveJ+9gIDOhmTVOol7bdRQ5KRvLStyMvvWRzqeUtvXwIAW2ndQd+hQ4cWtevuRoR8ADA4fNeXL1+ZJbbv6xwSUovrKocVNUyU7Bdv5LmuAsdPhoQEyibTgjsDwBRDQgAMms4W3NYU3LW04F6ZtOASSAAtxkhRrSPUC23w7aVsqFeY7A71/Kzk0SUAYDCt+6vTu9/9br3+9a/XBz7wAR05cmQTljT4bMgXaW8pS8gHAANutSEhUVIJGJpQ5bCic405OyTEkRw5rSEhvuMq52WUS6YF2xCQISEAtkizBffk3Ukb7r2rtOAmwd6eyzfWggvsVCbubr+Nkz3m/bRtvy2O238zrUq9rMT3eADbyJqCvpGRka4qhnK5rIsuuki5XE5B0P2D09mzZ3u7wgFDyAcAO4vnePK8lYeENAeFTDVmdbI2Ze+Tkeu48uW1Qr+8l1HGSyvtBa0AsHkfQ0IArFlUt9V5rWq9u6WZxxaf5wXS2CXtSr2JK6TiPlpwgaY4Sir1mqFebP99NEO9/ISUKXZX6rl8vwawva0p6Hv3u9+9ycvYHloh3xDtugCwG7SGhMhX1lu8Q2CzLbgRh60hIZGJk67g5pAQWxGY9dLKumll/XTXlOCUG8h3PNqCgd2q1YJ794IW3HDxuaVDHZV6V9CCC3SKo47JtzVbuec4NtDzM1Jhr5Qudlfq8b0XwA60pqDvJS95iSSp0WjoF3/xF/WmN71JF1544aYubNDUwkiVMNK+oYzGC2lCPgDAqm3BYdIS3IgjzTTKOmOmFZvmkBBHvuMrcD0Fjg0C834mGRLitwaFMCQE2GGq55IW3KRS79S9Um128XmZUrtSb08yCTdd3PLlAgMpDheEekZyXBvoBTlp6ICULnRU6mUI9QDsGuvaoy8IAn3yk5/Um970ps1az0CqhZEqjUh7i4R8AIC1811PvrxVh4Q0TKj5elXHa2dlp4Q48pJqQt/xlHZTyvp2SEizFTjlBgocO0mYakBgQIW1BS2490izy7Tgjl/aMTCDFlygJQ7bgV4r1PNseJcqSKULpFS+PfnWT/NvB8Cutu5hHD/5kz+pT33qU7rppps2Yz0DpzPk21Mk5AMA9MZahoTYEDBSOaroXDin2MQyMnLkyHPcVkVgzsso66XtkBDH7gfoO65cx5XXeSl7STAIbAJjpOnvdVTq3SOd+e7qLbgTV0ijtOACkmy7baMqhRV7KdlBGH7GDpopjUjpBaEeAKDLuoO+iy++WL/927+tL33pS3ra056mfD7fdf+rXvWqni2u35oh3yQhHwBgizWHhCz1I4wxxk4L7hgScqp2TkZGMsbWBDqOXMeRm/zPcZxWyOc7njzHU+B48pPW4cD1ugLBRSGh48qTY9dFWAgkLbgdlXrLtuAOd4d6ey6nBRdoikOpUbEfUT1pv83afyMjRzv208tJ/uKhWQCAxRxjjFnPA44ePbr8kzmOHnjggfNe1FabmZlRqVTSxz79Xg0PD0taEPIV0mJ7JADAdmKMUaxYsTGKTaxYyeUyx23DcNI4vMlhYedtYFtoteDe3d5fb8kW3FTSgnt5e3+94l7aCIGmsJZU61WkKJJc3wZ5mZKUG7WtuKm8FGT6vVIAu1wzJ5qentbQ0FC/l7Mu667oO3bs2GasY6DUwkiVeqiJYpaQDwCwLTmOI0+evPPIF1YKC2umTliIncnEHS2496yhBffKdrXe2EU2uABg29mboV5zbz0vZYO90mEpW7KhXqpA6zoA9NCG34nU63UdO3ZMF110kXx/57yhqYdxK+SbKBLyAQB2L8JC7AqVc+3pt81wrz63+LyuFtwrpT2X0YILdIqjjmCvbo8FGSnI22AvnVTrpfJ23z0AwKZYd0I3Pz+vX/3VX9Xtt98uSbr//vt14YUX6lWvepX279+v17/+9T1f5FYJo1jz9QYhHwAAPUJYiIES1qTT37ZVeq0puI8vPq/Zgtu5t16BFlygS1RvD86Iwvb+etlRKTfW0Yab5d8OAGyhdQd9b3jDG/T//t//0+c//3n9yI/8SOv4c57zHL3lLW/Z1kFfpRHqookMIR8AAAOkV2GhSYLAiLBwdzCxNP1Ie0+9k3fbFlwTLT53+IJ2pd7EFdLohbTgAp2MkaJae3BGHNt22yArFfdL2eF2sMckXADoq3W/g/nUpz6lP/3TP9U111zTNXHvyiuv1He/+92eLm6r7RnKaKKYIeQDAGCHcRxHThKqbTS+6QwLFw43qccNxabWFSISFm6xZgtu5xTcpVpwsyPSns4puLTgAouYuF2tF1Zt0Odn2sFeZiip1stLHqE4AAySdX9VPnXqlCYmJhYdL5fLXcHfdjSep5IPAAAsrTMs3Ki1hIWdx2082HywZGQIC6V2C26zUu/UPdLs8cXntVpwr5QmLqcFF1hOHLar9aJ6uw03XZRGjna04eYkdxt8jQCAXWzdQd/Tn/50ffrTn9av/uqvSlIr3PvgBz+oZz7zmb1d3RbjPR8AANhMgxAWKnm/s1JYmHYDZb20Um6gwPUVuL58x7PXHU++423dL3hbLbgd++ot24J7uHtfPVpwgaWFtfbgjCiy/06CrJSfkHKjHcFept8rBQCs07rf+dx66636kR/5Ed19990Kw1Dvec979K1vfUtf/vKX9YUvfGEz1ggAAIDEZoeFtbiu+aiqU/VziuO41X/sua58+fJcV4HjK+OllHFTSnup3oaBlal2pV6rBbe8+LyFLbgTl9twAkA3Y9qhXqNqj3kpG+yVDkvZUjINt2D33QMAbGvrDvquvfZa/eM//qPe9a536aKLLtJnP/tZfd/3fZ++/OUv64lPfOJmrBEAAAA9tJGwMDKRIhMrjCM1TKhKo6bQREkY6EjGyHNdeWq3Dae9QBk3rUwSBraCwGYYGIVyzny7o1rvbmnuxOJP3tWC25yCO0k7BrCUOLT76jUqUli3x4KM3U+vdFjKFG0LbiovuV5/1woA6LkN9TI88YlP1O23397rtQAAAGBAecn+fyl3+YqfpcPAaVtBaGLlZk9q+NwjGp56RMWph+RNPybHxF3PYeQoLh2UmbhczsSVcieeIGeMFlxgWVG9YxpuZPfXC7JSdmxBG26WcBwAdoF1v2N69rOfrZ/7uZ/Tz/zMz6hUKm3GmgAAALANdYaBXnVauTMPKNv8OHtMXqOy6DH1dFHTIxdoZuQCTY9eoJnSQZl0Xn5SGeg5oTKzDynrppXxgqQy0A4ZscNG/K3dMxDoJ2OkqNYR7MW23TbISkMHkzbcJNjz0/1eLQCgD9Yd9D3xiU/U//f//X/6lV/5FT3vec/Tz//8z+t5z3ueUqnUZqwPAAAAA84Ja8pOPdQO9c48oNT8mUXnxV5KlZHDqoxdqMrYRaqMHVUjN9aqMkpL2iMpNrFCEymMI4Um0nRjTmfNjKKO6j/XceTLk+d68h1XGS+tjJtSdmGbsOO3WokJA7HtmNjuqxdWbDuukQ3wgqxU3C9lhpJqvbzkUfUKAJAcY4xZ/bRucRzrf/2v/6U77rhDn/zkJ+V5nn7mZ35GL3rRi/SsZz1rM9a5qWZmZlQqlXTnlz+s4eHhfi8HAABgcJlY6ZnHW1V62TPfVebc95Zswa0N7UtCPRvsVUv7e9aCuzAMjJLbkYlb04Vdx2ntGeg7rtJeWhk3UM5LEwZiMMVhu1ovqts2XD8rpQtSbqyjDTcnuRsfyAMAWFkzJ5qentbQ0FC/l7MuGwr6OlWrVf31X/+13v72t+ub3/ymoijq1dq2DEEfAACA5M+fVWr2hOrFSYW5UXusMq3sme8mod7yLbiNTKkd6o1eqMrYUcVBdqtfQpfOMLAZBIYmUmximSQMdOTId5ptwq7SSVVgzkt3DQ9pXvcd77wmHgNdwlp7Im4cS45nq/UypQX762X6vVIA2FW2c9B3Xr9SPX78uD7+8Y/rv/23/6a77rpLT3/603u1LgAAAGyh4e/+g/Z/7aNyZGQkVUaOyK/NLt+CO3qkI9S7UI3c6MBt9O86rlKOu+IAkYVh4Fw4r3ON2TWFgRk3pdSCIJAwEMsyph3qNar2mJeywV7pcLK/Xt6Ge97yf2cBAFjJuoO+mZkZ/fmf/7nuuOMOff7zn9eFF16o66+/Xh//+Md18cUXb8YaAQAA0GNuo6LM1MPKTD2k3Mn7NPToN9SM6RxJuakHJSUtuKX9rSo924J7QHK9fi29p9YTBjYnCs+F85puzCpqhYFGjlz5jg0CfdezYaAbKOOllXJ9+a6voKMqMHB9wsCdLg7tvnqNihTW7bEgY/fTKx2WMsX2/nq04QIAemTdQd/k5KRGRkb0ghe8QLfccgtVfAAAAAPOrc8rO/WgMlMPKXv2IWWmHlJ69viqjzv+lP+oqYue1fcW3H5rhoGSpGXyzdjE7fbgOFI5nNd0Eg7aGkklYaArL6kOTLuBsm5qURjYGQoSBm4jUb1jGm7YbsPNji1ow80OXPUrAGDnWHfQ95d/+Zd6znOeI5ffOgEAAAwcrzarzNmHlJ16SJmpB5U9+5BS5VNLntvIjqgyekT1woTG7vusHLW3bjaOq+kL/tWuD/nWynVcuY6rQP6awsDIRCqHFU2bOYUmllphoJNUBiZhoBPYNuFkgEhAGDgYjJGiWkewF9t22yArDR2UssNJG27eTskFAGCLrDvoe+5znytJOnXqlO677z45jqNLL71Ue/bs6fniAAAAsDy/Mq3Mgkq9pfbUk6R6flyVkSOqjhxWdfSwKiOHFWXam0vXhvZr/9dvl2NiGcfVY1e/pDWQA73RFQYuY1EYGFU0HXaEgUZyHKejTdhX2gmU8VLKeCml3GDBRGEbCnqEgefHxB3TcGs2l/XTdvptcb+UGWq34Xq9mSwNAMBGrPu70Pz8vH7lV35Ff/RHf6Q4jiVJnufpxS9+sX7v935PuVyu54sEAADY1YyRXzln22/PPtRqww0q55Y8vVaYUHX0iCojh1UdOaLqyAWK0oUVP8W5i35Qc/uuUmr2pOrFCUK+PllLGGiMaQWBYSsMLCs0kSQjx0haIgxMe4GyXlopN1hyojBhYIc47Aj26pLjSn5zGu5Yuw03lacNFwAwUNYd9L3mNa/RF77wBf31X/+1vv/7v1+SdOedd+pVr3qVfu3Xfk3vf//7e75IAACAXcMYBfNnlDn7YNJ+a6v1/NrM4lPlqD60txXoVUYPqzp8geLUxn7xGuZGCfi2AcdxFDj+qmFgMwgMTaRyXG2FgY49QXIcGwR2tAJnvXS7MtBZPFF4x4aBYa09ETeO7bCZICflJxbsr5fp90oBAFiRY4wxq5/WNj4+rk984hP6oR/6oa7j//t//2+94AUv0KlTS+8BM8hmZmZUKpV055c/rOHh4X4vBwAA7BbGKJg71TEow1769fLiUx1HtaH9tv129LAqI0dUGz6kmOABG9QdBrYHiUQmap4g4zjym2FgEvht+zDQxO1puI2qDT39dDI4Y1TKlpJqvYLddw8AsOs0c6Lp6WkNDQ2t/oABsqHW3cnJyUXHJyYmND8/35NFAQAA7DgmVmr2RCvMa1breY3K4lMdT9XhA6qOHG4Fe9XSQRk29UcP2b3+fPlrrgyMVYnrmg0rrTDQGEmOFoWBgevLkyvP9eTJtiP7ridHjlzHkStHjuPKTW53XZcj13HlSEset49dR7tsHLaDvbBujwUZu59e6bCUKbb312PgIABgm1t30PfMZz5Tb3nLW/RHf/RHymTsb5ArlYpuvvlmPfOZz+z5AgEAALadOFJ69ni7/fbsg8qce0ReWF18quurNnywNSijMnpEtdIBGSqJMADWFwbaqsBKXFc5qsoYIyOjWKZ1vTlMRErmDBtJjkkum2GenT7sqBnq2cDPkeTKsS3HcuU5rlzXkyfbguwmYaMXR/KiupywLtdEch1fTpCVmx6SM7xfTlCQmy7IDXJyXFduEkQ6cU2usbcdpxk2rjNUBACgz9Yd9L3nPe/Rj/zIj+jgwYN68pOfLMdx9M///M/KZDL627/9281YIwAAwOCKQ6WnH+tov31ImXOPyI3qi0/1UqoOH7J76SXVerXSfsllSie2r3YYeP5iE8skwWBso0DFSUjYPGaMUUOhasbIhLFMXFcc1mTCqkxsZFxP8lMyQUFOKicTpCU/I8d1ZOpTUmNKTtlph3lJkNcK9tQ+7jiOPHnyXE+uYysUfflyXPuaPceT67pJpWHymI7Hdx5vDlppVSZ2BInN2wAAnK91fz++6qqr9O1vf1v/7b/9N917770yxuhnf/Zn9aIXvUjZbHYz1ggAADAQnKih9PSjHe23Dyp97nty43DRuZGfUXXkgiTQO6zq6BHVinvtJv8AltQKu5YrootjKarZFtyoYY95gZQp2Im4qYJty/Wzkrf8vzXTER4ame6AseO6rVQMbYBommFj3PU4Je3Lau58nqzdMY6MY+SY7lCxGfQ1X68rV3Lsdc/xWpee69nLJEz0HX9t4aHjLAodO493hpsAgJ1nQ794y2azuuGGG3q9FgAAgIHhhHVlph9R5qwN9DJnH1Jm+lE5zUEFHaIgm0y+Pdxqv60XJyUqdIDzE0fJRNyaFDckx5G8jA30MiU7GTfI2o91ZFfNEGw9j9morjBxiWCxeRmaUI24sWSYuGTrszHt9Zv261pYKdisUmwGfXLUChJdtUNF13Xly28FjEsFiauFhwtDSFqfAWDr0ScCAAB2PbdRVfrcI61ALzv1oNIzj8sx8aJzw1Re1ZEjXe23jcI4oR7QC1HDDs4Ia3bSh+NKfkbKjUrpoXao56f6vdI1W1jFt5k6Q8GVqhUbcUO1uLZklaIxNjU0xsiRrUqUsfsmGntlUZWitDmtz50Vjs3Hd94GACxG0AcAAHYVtz6vzLmH7V56U3ZYRmrmuJxW311bmB7qCPRs+20jN2arigCcH2OSNtykFdcY24brZ6TCqG3H9ZNgz+PHlrVwHXdLqxQ3s/W5GS62gsAkSGxWG/qOrT4MvEC+6ytwAwVOINdtB4EL26EJCwHsBnzHBAAAO5ZXm1Nm6uFWoJc5+6DScyeXPLeRHe6afFsdOawwO0yoB/RKHElR3VbsRcm+ln5a8nNSYZ+UStpw/azk8u9ukG1V63MzOIxNrFhx+7qJVYtriqNYcb19rFWFqO5LwkIAuwlBHwAA2BG86kx76u3UQ7ZSr3xqyXPruTFVR5MqvZFmqFfa4hUDO1zUaO+vZ6KkDTctpYelzFCyv17GVvCR62EJzfZdTxsfYtRZPbjwox7X22Gh2mFh9xPYC8JCANvFhoK+c+fO6ROf+IS++93v6td//dc1Ojqqb3zjG5qcnNSBAwd6vUYAAIAufuVcEuo92LoMKlNLnlvP72lV6NnLCxSli1u8YmAXMEZqVKTGfLK/nmeDvcKElC62q/X8oN8rxS7Siz0SNxIWms7tIJphoeMu+eE5nnzXbweFyQdhIYCNWHfQd9ddd+k5z3mOSqWSHnzwQd1www0aHR3VJz/5ST300EP6oz/6o81YJwAA2I2MkT9/1rbdJlV6mbMPKqhOL3l6rbg32UvvcNKGe4HiVH6LFw3sIq1wryKZ2FboFfYmE3EzNtjzNl6NBQyCzQ4LG3FDtai2qEW5+wnsBWEhgNWsO+i76aab9NKXvlTvfOc7VSy2fxv+oz/6o7r++ut7ujgAALCLGKOgfLq7Um/qIfm12cWnOo5qxX2qjh7paL89pDjI9mHhwC6zsHIvyEiFSSk7bCv3ttFEXGCr9Gr68lJBYc/DQseGhb7rK/CCFYPBpYJDAP217qDva1/7mj7wgQ8sOn7gwAEdP368J4sCAAA7nImVmjvZtadeZuoh+fXy4lMdT9XSflVHDrfbb4cPyfjpPiwc2KXi2IZ74by97WftAI1WuEc7LrAVmqHc+djMsNB1XBsSEhYCfbPuoC+TyWhmZmbR8fvuu0979uzpyaIAAMAOEsdKzR5P2m8ftMHeuYflNSqLT3U91UoHbdttMiyjNnxIxiNEALbcUuFe8YBtyyXcA7atzQwLwzhUPaoTFgJ9tO6g7/nPf75+67d+S3/2Z38myZYgP/zww3r961+vn/7pn+75AgEAwDYSR0rPPNZdqXfuYXlhbfGpXqDq8CFbpTdyWNXRI6oNHZDxNjQrDEAvxLFtyW0G8UFOGjpow71UUfL59wlg+4SFXXsTuq582SnJnuNJTvJ4uXIcpzVRuXm9edxxHLlKJi4vPK/jODAo1v2d+l3vepee97znaWJiQpVKRc961rN0/PhxPfOZz9Tb3/72zVgjAAAYQE4UKj3zqDJnH1J26sEk1HtEbtRYdG7spZK99Jrtt4dVG9onuYQGQN/FUXughiPJz0mlg1JmWEoVCPcAbIpNDQtNqHpYt0NQjFGs9jTk1lRkIzlyJMcOS1EzqzO2oKl52QzymuFe83+dAV9nNaHv+HJcR758OwzF9eyxzhBxQajYfL7mn0fnOa2AsSOQZHAKVrLu79pDQ0O688479bnPfU7f+MY3FMexvu/7vk/Pec5zNmN9AABgADhRQ+lzjyRTb+2QjPT09+TG4aJzIz/TCvNstd4R1Yt7JZc3pcDAiCNbuRdW7e3OcC9dkKisBbANbGbo1TkpWbKhYjMoXHgsjEPVVZeMukLFzvNkZMPEjktHjg0dm7cXVAx2DnBpVhnKsed58lotzL2uWlwYMFK1uL2s+zv4gw8+qCNHjui6667TddddtxlrAgAAfeSENWVaoV5SqTf9mBwTLTo3CnJ2OEar/faw6oUJid80A4OnK9xzbFtu6QIpM2Qr9wj3AKClV5OS1yM2iysPOwPG5mVkIoUmlInNwFQtevK62pupWuyfdX83v/DCC3Xttdfq53/+5/Uf/sN/0Ojo6GasCwAAbAG3UVFm6hE7JCOZfJueeUyOMYvODdMFOyQjCfQqI0fUyI9L/IYXGFzNcK9RtT/gBXnCPQAYUK7jtsO4LUDV4s6sWlz3d/avf/3r+tjHPqa3ve1tevWrX61/+2//rX7u535OP/7jP650Or0ZawQAAD3g1uftkIykUi879ZBSsyfkaHGo18iUVB090tpXrzJyWGFulFAP2A66wj3HhnvDhzvCPaZRAgCoWmyFfcnjO6sWK3OVLfsz6TXHmCV+Zb8Gxhh9/vOf1x133KE///M/VxRF+umf/mn94R/+Ya/XuOlmZmZUKpV055c/rOHh4X4vBwCADfHnzyo1e0L14qSMl0pCvQeVSabfpudOLvm4RnZkQfvtEYXZ4a1dPIDzE4VSWOkO9/LjUrpIuAcA2LXWU7XYDBNlpNnZWf3oFT+q6elpDQ0N9fMlrNuGg75O3/jGN/QLv/ALuuuuuxRFi/fvGXQEfQCAbckY+dVpBeVTGn7gixp54Itd3RJLqefHF7TfHlaU2V5vXgAkorBdued6Uion5cbblXsMwAEAYEPmZud0zdFrtmXQt+FNOR555BF97GMf0x133KFvfvObeuYzn6n3vve9vVwbAAC7nhPWlJo7pVT5lIK5U93Xy6flRvXFj0kua7kxVccvStpvj6g6coGidGFrXwCA3ooaUqMihTU79CaVk4r7pUyRcA8AAKw/6Lvtttv0J3/yJ/rHf/xHXXbZZXrRi16kT33qUzpy5MgmLA8AgB0ujhVUppLg7qRSc6cUlE/bQG/ulPzazIoPN46jMF1UUF183mPPeLnmJy/frJUD2CpRI5mWW5MczwZ6hHsAAGAJ6w76fvu3f1s/+7M/q/e85z16ylOesglLAgBgZ3Hr80rNneyqxEvNnVQwd0rB/Bm58crbXoSpvBr5PaoX7Efren6PGvlR+dUZXfpXr+0aqmEcV/XixGa/NACbpSvc86VUXho62N5zz2UwDgAAWGzdQd/DDz+8Y0YOAwDQC04UKpg/k4R4p5KqvFOtqjyvMb/i42PXUyM/boO7QkeIl1zGqdyKjw9zo3rs6S/V/q/fLsfEMo6rx65+iZ2SC2D7aIZ7jZrk+lK6QLgHAADWZU1B31133aWrrrpKruvqm9/85ornPulJT+rJwgAAGBjGyKvNLgrwmhV6QeWsnFVmWzUyQx1VeRM22EuCvDA7ct6td+cu+kHN7btKqdmTqhcnCPmA7SJqSPWyFNYlL7CBXumQlCraKj7CPQAAsA5rCvqe8pSn6Pjx45qYmNBTnvIUOY6jzmG9zduO42zLqbsAADhh3QZ3HfvjBeWTSs2dVlA+JS+srfj42Et1VOLZ6rx6YSKpyhuX8dOb/hrC3CgBH7AdNMO9qC65ga3cGz5sQz7CPQAAcB7WFPQdO3ZMe/bsaV0HAGDbMbH8yrkkuDvZEebZYC+onlv54XLUyI20qvIW7pkXZoYktrYAsJyonoR7jXa4lztsL1OF9rhsAACA87CmoO/w4cOt6w899JCuvfZa+X73Q8Mw1Je+9KWucwEA2Epuo9K1T15nq21QPi03Dld8fBRkF+2T1wzyGvkxGS/YolcCYEdohnthw7blpotSfqzdlku4BwAAemzdwzie/exn6/HHH9fERPckv+npaT372c+mdRcAsHniSMH82Y798U4qVT5tw725U/Lrcys+3DieGrnRdiXegjAvSuWpygNwfsKaVJ+3IZ+fllJD0sgo4R4AANgS6w76mnvxLXTmzBnl8/meLAoAsEsZI69eTgK8pBJv7nT7+vwZOSZe8SnCdEH1/ERHVd54EuhNqJEbkVxvi14MgF2jGe7FDclLSekhW7mXLkpBjnAPAABsmTUHfT/1Uz8lyQ7eeOlLX6p0ur2peBRFuuuuu3Tttdf2foUAgB3FiRrtffHKp5VKQj3bcntaXqOy4uNj119UidfeK29ccZDdolcCYFcLq1K90g73MsNSboRwDwAA9NWag75SqSTJVvQVi0Vls+0fpFKplK655hrdcMMNvV8hAGB7MUZ+dbprr7zO637lnByZFZ+ikR1u75WX726zDbMlyXG36MUAQMIYW7nXqNiBGkHahnv5pC03yBLuAQCAvltz0PeRj3xEknTkyBG99rWvpU0XAHYxt1G1Qy469sfrrMpzo/qKj4/8TBLcjXdX5eVtVZ7xU1v0SgBgBa1wb16KQhvuZYelXHPPPSqIAQDAYFn3Hn1vectbNmMdAIBBEscKKmeT/fFOdlTl2ZZbvzaz4sON46iRHe2oxJtQvTDeqsqL0kWGXgAYTMYkbbnzkokkP2ODvexo0pab6fcKAQAAlrXuoE+SPvGJT+jP/uzP9PDDD6te767a+MY3vtGThQEANpdbL3dX4nVcD+bPyI1XnqIepvKL9serFyaSqrxRyd3QtxgA2HpLhXv58STcKxDuAQCAbWPdP4X91//6X/XGN75RL3nJS/SXf/mXetnLXqbvfve7+trXvqZXvvKVm7FGAMAGOFGoYP5M9155yWVq7pS8xvyKj49dz4Z3y+yVF6dyW/RKAGATtMK9shTHdo+9/B4pO0K4BwAAtq11B32///u/r9tuu00vfOELdfvtt+t1r3udLrzwQr35zW/W2bNnN2ONAIClGCOvNpsEdydbk2xbVXmVs3LMKkMvMkOtqrzu6rw9amRHJJehFwB2kM5wzxhbuVeYtEM1MkXJT/d7hQAAAOdl3UHfww8/rGuvvVaSlM1mNTs7K0n6+Z//eV1zzTV673vf29sVAsAu4c+fVWr2hOrFSYW5UUmSE9baAy+SIC9o7plXPi0vrK34nLGX6mirbVblTbRuG36oBbDTGWMn5TbmCfcAAMCOt+6gb+/evTpz5owOHz6sw4cP6ytf+Yqe/OQn69ixYzKrVI4AAJY2dvf/0ORdfyFHRkZSvTAhN6wrqJ5b8XFGjhq5kQVVec1JthMKM0MMvQCw+ywM94KMVNhrJ+amixKTvQEAwA617qDvuuuu01//9V/r+77v+/QLv/ALes1rXqNPfOIT+vrXv66f+qmf2ow1AsCO41fOKX/iHvtx/F+Uqky17nMkpedOtm5HQba9T15SldfcJ6+RH5Pxgj68AgAYMEuGe/ukbIlwDwAA7BrrDvpuu+02xXEsSfrlX/5ljY6O6s4779S///f/Xr/8y7/c8wUCwE7g1ueVP3lvEu7drczMY6s+5tGrX6zZQ09XlMpTlQcAS4ljG+6FyXAhP5uEe8NJuMcvQgAAwO6y7qDPdV25HZuzv+AFL9ALXvCCni4KALY7J6wpd/o7yp+4W4UTdysz9VDXYAwjR9WRC1SevFLV4YM68JUPyVHH/Y6ruf1PVpQu9GP5ADC4lgr3igdsuJcqEO4BAIBdbU1B31133bXmJ3zSk5604cUAwLYVh8qeOabCibuVP3GPsme+KzcOu06pDe1TeeIKzU1eofmJy7tCPCcKtf/rt8sxsYzj6rGrX9IayAEAu14c25bcRsXeDnLS0EEpU5JSRclf9++uAQAAdqQ1vSt6ylOeIsdxVh224TiOoijqycIAYKCZWJlzj7RacXOn7l80AbeeG1V58kqVJ65QefIKhbmRZZ/u3EU/qLl9Vyk1e1L14gQhHwDEUbLnXsVuXurnpNJBOy03VSDcAwAAWMKa3iEdO3Zss9cBAIPNGKVmj7cHaJy8R3693HVKmC60Qr3y5JWqFybWtbdemBsl4AOwu8WRrdwLq/Z2Z7iXLkge4R4AAMBK1vRu6fDhw5u9DgAYOP782WSPPRvuBR2TcSUp8jOan7hM5ckrNDdxhWrDByXHXebZAABL6gr3HNuWW7pAygzZyj3CPQAAgDXb0DunP/7jP9Yf/MEf6NixY/ryl7+sw4cP693vfreOHj2q5z//+b1eIwBsCa822zUZNz17ouv+2PU1P35xq2KvMnpEcvkBFADWrRnuNaq2LTcoSKXDUqZIuAcAAHAe1v0u6v3vf7/e/OY368Ybb9Tb3/721p58w8PDeve7303QB2DbcBsV5U7d32rHzZ57uOt+4ziqjBxtBXvz4xfL+Kk+rRYAtrmucM+Rgrw0fLijcs/r9woBAAC2vXUHfb/3e7+nD37wg/qJn/gJ/c7v/E7r+NVXX63Xvva1PV0cAPSSEzWUPf1d5U/eo8KJu5U9c0yO6R4gVC0dsAM0Jq9Qec9lilO5Pq0WAHaAKJTCSne4N3LEhntBnnAPAACgx9Yd9B07dkxPfepTFx1Pp9Mql8tLPAIA+iSOlZl6MNlj727lTn9bbtToOqWe32P32Ju8UuXJyxVlSn1aLADsEFHYrtxzPSmVa4d7qYLkspcpAADAZll30Hf06FH98z//86IBHZ/5zGd05ZVX9mxhALBuxig985jyJ+5OJuPeK69R6TqlkSnZar0J247bKIz3abEAsINEDalRkcKaHUqUyknF/e099wj3AAAAtsS6g75f//Vf1ytf+UpVq1UZY/TVr35VH/vYx3TrrbfqQx/60GasEQCWFcydag3PyJ+8R0F1puv+KMgmoZ79qA3tt+1jAIDzEzWSabk1yfFsoEe4BwAA0FfrDvpe9rKXKQxDve51r9P8/Lyuv/56HThwQO95z3v0sz/7s5uxRgBo8arTyp+4J2nHvUep8qmu+2MvpfnxS1Tee4XmJq5QdeQIP2wCQK90hXu+lMpLQweldDPc4xcpAAAA/bTuoE+SbrjhBt1www06ffq04jjWxMSEJOnRRx/VgQMHerpAALubW59X/uS9yp+0wV5m+tGu+43jaX7swlbFXmXsIhkv6NNqAWAHaoZ7jZrk+lK6QLgHAAAwoDYU9DWNj9u9rY4fP663v/3t+tCHPqRKpbLKowBgeU5YV+70t5N99u5VduqYHGO6zqkMX5AEe1dqfs8lioNsn1YLADtU1JDqZSmsS15gw73SISlVtFV8hHsAAAADac1B37lz5/TKV75Sn/3sZxUEgV7/+tfrV37lV/TWt75V73rXu/SEJzxBf/iHf7iZawWwE8WhsmePtdpxs6e/IzcOu06pFfe2KvbKE5crShf7tFgA2ME623LdJNwbPmyr9gj3AAAAtoU1B32/+Zu/qX/4h3/QS17yEv3N3/yNXvOa1+hv/uZvVK1W9ZnPfEbPetazNnOdAHYKEytz7nutARq5U/fLC6tdpzSyIypPXqG5yStVnrxCYW60T4sFgB0ujpK23KqdlpsuSqULbMiXKkhkewAAANvKmneo//SnP62PfOQjete73qW/+qu/kjFGl156qT73uc9tWch36623ynEc3Xjjja1jxhi99a1v1f79+5XNZvVDP/RD+ta3vrUl6wGwBsYoNXtcI9/53zr4j7+vyz71al30t2/R3n/+uIqP3yUvrCpMFTR96Go9dvWL9e3n3ar7f/w/69FrbtD00e8n5AOAXotjqTYrzZ2SKlN2372Ro9LeJ0qTV0lDe23QR8gHAACwIf3Mr9Zc0ffYY4/pyiuvlCRdeOGFymQyevnLX97zBS3na1/7mm677TY96UlP6jr+zne+U7/7u7+rj370o7r00kv1tre9TT/8wz+s++67T8Ui7X1AP/jzU62KvfzJe5SaP9t1f+SnNb/nstY+e9Xhg7aSBACwOYyRGhWpUZaMpCAnDV8gZUq2io/p5AAAAD3R7/xqzUFfHMcKgvYkS8/zlM/ne7aQlczNzelFL3qRPvjBD+ptb3tb67gxRu9+97v1xje+UT/1Uz8lSbr99ts1OTmpO+64Q7/0S7+0JesDdjuvNqfcyXtVOHG38ifuUXr2eNf9seurMnaRypNXam7yClXGjtoKEgDA5jFGCqt2qIYxUpCRigek7Iit2PP4OgwAANBLg5BfrfkdnjFGL33pS5VOpyVJ1WpVv/zLv7wo7PuLv/iLni2u6ZWvfKV+7Md+TM95znO6/qCOHTum48eP67nPfW7rWDqd1rOe9Sx96UtfWvYPqlarqVartW7PzMz0fM3ATuY2qsqdul/5k7ZqLzP1iBy1J+Max1Fl5Eh7Mu74xTJ+uo8rBoBdJKxJ9Xkpbkh+VipMJuHekOQHqz8eAAAAkhbnRel0upWLLaXX+dVGrDnoe8lLXtJ1++d+7ud6toiVfPzjH9c3vvENfe1rX1t03/HjtmpocnKy6/jk5KQeeuihZZ/z1ltv1c0339zbhQI7mBM1lD3zgG3FPXGPcmcekGOirnOqpQMqT7Qn48apXJ9WCwC7UNSQ6nNSWJf8tJQZlvJjti03yPR7dQAAANvSoUOHum6/5S1v0Vvf+tYlz92M/Goj1hz0feQjH+npJ16LRx55RK9+9av12c9+VpnM8m9SHad7t2hjzKJjnd7whjfopptuat2emZlZ9B8P2NXiWJlzD7VacXOnvi03qnedUs+PJ6GerdoLs6U+LRYAdqkotHvuNWqSF9h23JFxW7nHL1sAAADO2yOPPKKhoaHW7eWq+TYrv9qIgd6c5Z/+6Z908uRJPe1pT2sdi6JI//AP/6D3vve9uu+++yTZZHTfvn2tc06ePLkoJe20WqklsOsYo/TMY8kAjXuUP3mvvMZ81ylhekjlySs0l7TjNgp7+rRYANjF4khqzEuNqh1ilCpIQwelzJC9zqRcAACAnhkaGuoK+pazWfnVRgx00Pdv/s2/0Te/+c2uYy972ct0+eWX6zd+4zd04YUXau/evfq7v/s7PfWpT5Uk1et1feELX9A73vGOfiwZ2DaC8ulWK27+xL0Kque67o+CrMoTl6k8eaXKE1eoVjog9fg3DQCANYhjOzE3nJfkSEFeGjmShHtFyeVrMwAAQD8NUn410EFfsVjUVVdd1XUsn89rbGysdfzGG2/ULbfcoksuuUSXXHKJbrnlFuVyOV1//fX9WDIwsLzqTKtir3DibqXKp7ruj71A8+OXtAZoVEYOS67Xp9UCwC5njA33GvOSjOTnbOVedtiGex5fnwEAAAbFIOVXAx30rcXrXvc6VSoVveIVr9DU1JSe8Yxn6LOf/ayKxWK/lwb0lVufV+7U/a199jLT3+u63ziuKqNHVZ68UnOTV6oyfpGMxzRGAOgbY5KJuWXbohtkpcLejom52/5tGwAAwK61VfmVY4wxPX3GbWhmZkalUkl3fvnDGh4e7vdygA1xwrpyp7+j/Ml7lD9xt7JnH5Rj4q5zKsOHbCvu5BWa33Op4iDbp9UCAFqiulQr28sgYyfmZkdta66f6vfqAAAAdp252Tldc/QaTU9Pr2mPvkHCr4aB7SqOlD37YLLP3t3Knf6O3DjsOqVWnGxNxS1PXKYos72+QAHAjhU1bOVeWJP8tA338qO2ci9YflIbAAAAsBKCPmC7MLHS04+2WnFzJ++TF1a7Tmlkh1vDM8qTV6iRH+vTYgEAi8RREu5VJceXMkVp+LCULkqpPBNzAQAAcN4I+oBBZYxScyfbk3FP3iu/Ntt1SpjKa37ics0l7bj14l4m4wLAIIkjO1CjUbVfn1N5aeiArdxL5ZmYCwAAgJ4i6AMGiF+Zak3GzZ+4R6n5M133x15K5YnL7GTciStUHblActw+rRYAsKTWxNyyZCQFOVu5ly1JqYLk8nUbAAAAm4OgD+gjrzan3Ml7lT95jwon7lF65vGu+2PXU2XsYpUnL1d58kpVRi+U8fhnCwADxxjbklsv2+tBVho6aPfeSxckvnYDAABgC/CuE9hCTlhT7tT9Kpywk3EzUw/LUXvwtZGj6shhlSev0NzklZrfc4mMn+7jigEAKwqrUn1eikPJz0qFSSk7Yltz/aDfqwMAAMAuQ9AH9IA/f1ap2ROqFycV5kZbx50oVPbMA3afvZP3KHvmu3LjqOux1aH9thV38gqVJy5XnMpv9fIBAOsRNaT6nBTW7cTc7IiUG7fDNfjlDAAAAPqIoA84T8Pf/Qft/9pH5cjIyNGpK/+d4iBrw71T98uN6l3n13NjSbB3pcqTlyvMjvRp5QCANYsayVCNmuQFtmJvZCwZqpHt9+oAAAAASQR92G2MkUwkJ47kRg05cSQnbsiJQzlRaC+bHwtuu0vc79VmNXb/Z9WcmejIaOLuv+76lGG62Ar25iauUKOwh8m4ALAdNCfm1iuS60npolQ6ZC9TBYkv5QAAABgwBH3oPWNWDMycJGBz4yRoWyVwWypg67q94Ji76NiCz9OxJ95mKY9dpJkL/pXKk1eoVjpIsAcA20Uc23AvrNjbQUEaPSplhqRUUXL5eg4AAIDBRdDXIZg/Jw0P93sZaxPH7QArbsiJOgKuZQO2NQZhC267y1W8LXNs4R50gy52fRnXl/GSSzeQcT0Zz17GC2637ncDyYQafvDLXUUdxnH1ve9/RddefQCAAWaM1KjYgM8YKcjZyr3MsK3c87x+rxAAAABYE4K+Dpf93c2a+9cv07mLfjCpSouWCbU6A7Z2MOYmgdtqYdiqFWqLzl8QwMUNOWbzq9J6xThuEqDZj9jzu26bVW4vOt8NZLzuwM24nmIvWPfzyvHOu9pufs/l2v/12+WYWMZx9djVLyHkA4BBZ0wyMbechHsZqbBPyo3Yyj2ft0gAAADYfngX28GR0f6vfUT7vv5Hcs12q0pbHHwtqkDrqlBbPQhr3l5c8bZ04NZd+dY+T67b7z+eTXXuoh/U3L6rlJo9qXpxgpAPAAZZWJPq83a4RpCR8hN2am5mSPJT/V4dAAAAcF4I+hZwJDlLhHxGzhJBl59Uka0erpkkXFtrNVu8hnO6wjT2gOurMDdKwAcAgypq2Mq9sCb5aduSmx+zQzWCTL9XBwAAAPQMQd8CxnF07N+8UfXC+IKqNPbnAQBg24jCZKhGVXJ8KVOURg5L6SG7Bx+/HwMAAMAORNDXwcjVY1e/VJXxi/q9FAAAsF5xZMO9RlVyXDtIY+iADffSBcI9AAAA7HgEfR3u++E3K7//SL+XAQAA1soYu+deY94GeUFBGj4sZUs26Nvh+8QCAAAAnQj6OjRyw/1eAgAAWM2iiblZqXRQyg7bibke220AAABgdyLoAwAA20NYtdV7cST5Gamw107MTRclP+j36gAAAIC+I+gDAACDK6pLtbIUN+zE3NyolB2zwzX8dL9XBwAAAAwUgj4AADBYokZ7qIaXssM08mP2MpXt9+oAAACAgUXQBwAA+m/hxNx0USpdYC9TeSbmAgAAAGtA0AcAAPojjm24F1YkOTbQGzkqZYaSibmkewAAAMB6EPQBAICtY4zUqEiNsmQkBTlbuZcp2eo91+33CgEAAIBti6APAABsLmOSibllez3ISIX9Ui6ZmOvxdgQAAADoBd5ZAwCAzRHWpPq8nZwbZKXCpJRNwj0/1e/VAQAAADsOQR8AAOidqCHV56SwYcO8zHAyMbdoK/kAAAAAbBqCPgAAcH6i0O6516hJXiClC9LIuJQesnvwMVMDAAAA2BIEfQAAYP3iyE7MbVQlx7VTcocO2nAvXSDcAwAAAPqAoA8AAKxNHCcTc+dtkBcUpJEjUmZIShUll3QPAAAA6CeCPgAAsDxj2uGejOTnpNJBKTtswz3P6/cKAQAAACQI+gAAQDdjkom5ZduiG2Slwt5kYu6Q5PP2AQAAABhEvFMHAABWVJdqZXsZZKT8uJQdlTJFyU/3e3UAAAAAVkHQBwDAbhY1bFtuWJPcwFbsFZoTczP9Xh0AAACAdSDoAwBgt4kj25YbViXHtxV7pQukdFFK5ZmYCwAAAGxTBH0AAOwGcSw1ylKjKjmODfSK+5OJuQUm5gIAAAA7AEEfAAA7VWtiblkykoKcNHxYypaScM/t9woBAAAA9BBBHwAAO4kxtiW3XrbXg6xUPJBMzC1IHt/6AQAAgJ2Kd/sAAOwEYVWqz0txKPlZqTCZhHtDkh/0e3UAAAAAtgBBHwAA21XUkOpzUliX/LQN9nJjdqgGE3MBAACAXYegDwCA7SQKk6EaNckLbKg3Mm4r91LZfq8OAAAAQB8R9AEAMOjiSGrMJxNzXTtIY+hge2IuA3MBAAAAiKAPAIDBFMd2Ym44b28HBWnkSBLuFSWXdA8AAABAN4I+AAAGhTE23GvMSzKSn7OVe9lhG+55Xr9XCAAAAGCAEfQBANBvjapUL9sW3SArFfZ2TMzlWzUAAACAteGnBwAA+iGqS7WyvQwyUn5cyo5KmaKdoAsAAAAA60TQBwDAVokati03rEluYCv2CsnE3CDT79UBAAAA2OYI+gAA2EwLJ+ami1LpAnuZyjMxFwAAAEDPEPQBANBrcSw1ykm459hAb+RoMjG3wMRcAAAAAJuCoA8AgF5oTcwtS0ZSkJOGL5AyJVu957r9XiEAAACAHY6gDwCAjTJGCpOJucbYffYK+6XciA33PL7NAgAAANg6/AQCAMB6hTWpPm+HawQZKT8h5UZtuOen+r06AAAAALsUQR8AAGsRNWzlXliT/LSUGZbyYzbcY2IuAAAAgAFA0AcAwHKi0E7MDauS40uZojRyWEoP2T34mKkBAAAAYIAQ9AEA0CmObLjXqEqOa6fkDh2w4V66QLgHAAAAYGAR9AEAYIzdc68xb4O8oCANH5ayJRv0MTEXAAAAwDZA0AcA2J0WTczNSqWDdu+9dIGJuQAAAAC2HX6KAQDsLmHVVu/FoeRnpcKklG1OzA36vToAAAAA2DCCPgDAzhc1pPqcFNbtxNzsiJQbt8M1/HS/VwcAAAAAPUHQBwDYmaJGMlSjJnmBrdgbGbdDNVLZfq8OAAAAAHqOoA8AsHM0J+bWK5Lr2XCvdMheppiYCwAAAGBnI+gDAGxvcWzDvbAiyZGCvDR6VMoMSami5JLuAQAAANgdCPoAANuPMVKjYgM+GcnPSUMHpeywDfc8r98rBAAAAIAtR9AHANgejJHCmlQv2xbdICsV9km5ERvu+XxLAwAAALC78VMRAGCwRXWpVraXQUbKj0vZUdua66f6vToAAAAAGBgEfQCAwdOcmBvWJC8lZUpSfsxOzA0y/V4dAAAAAAwkgj4AwGCII9uWG1Ylx5cyRal0QTIxN8/EXAAAAABYBUEfAKB/4lhqlKVGVXIcG+gV9ycTcwtMzAUAAACAdSDoAwBsrdbE3LJkJAU5afgC256bLkqu2+8VAgAAAMC2RNAHANh8xtiW3HrZXg8yUvGAlB2R0gXJ49sRAAAAAJwvfrICAGyesCbV55OJuVmpMJmEe0OSH/R7dQAAAACwoxD0AQB6K2okQzXqkp+SMsPJxNwiE3MBAAAAYBMR9AEAzl8UtodqeCnbjjty2FbuBTkm5gIAAADAFiDoAwBsTBxJjflkYq5rp+QOHbThXrpAuAcAAAAAW4ygDwCwdsbYPfca8zbICwrS8GEpW7JBHxNzAQAAAKBvCPoAACtbNDE3K5UOStlhKVWUPK/fKwQAAAAAiKAPALCcsGqr9+JQ8rNSYW8yMbfIxFwAAAAAO9bZytl+L2HDCPoAAG1RXaqVpbgh+WkpNyplx6RM0d4GAAAAgB3scw9/Tn/wf/6g38vYMII+ANjtokZ7qIaXssM08mP2MpXt9+oAAAAA4LxEcaT5cF7lRlnzjXmVw7LK9XL3sUZZZ6tn9fUTX5eR6feSN4ygDwB2o+bE3HpFcj3bjls6ZC9TTMwFAAAAMDhiE6saVm0o1xHOzTXmWiFd83hncNc8Xgkr/X4JW4agDwB2izi24V7zm1xQkEYvlDJDycRc0j0AAAAAvWeMUT2uLwriWmHdgsq6pYK7XlTZZbyMckFO+SCvXJBTISgo57dvS9Inv/3J8/48/UTQBwA7mTFSo2IDPmOkIGcr9zLDNtxjYi4AAACANQjjsBW8dbbAdoVzKwR3kYnOew2BG7RCubyfb4V2ywV3nffl/Jx8d/UYbDw7rg989QPnvdZ+IegDgJ3GGCmsSfWybdENslJhn5QbkVJFyedLPwAAALDbxCbuanPtbHvtqrJbpgW2HtfPew2u4yrv57uCuFYgt0Rw17zevC/lpXrwJ7Gy6y64ThdnLtbz9LxN/1ybgZ/2AGCnCGtSfd4O1wgyUn5cyo7a1lx/878hAgAAANg8xhhVwsqS1XIL212XaoHt1T51OT/Xrp5bprKudd3vCOuCvNJeWo4z+FsGjWZH+72EDSPoA4DtLGrYyr2wJvlp25KbH7UTc4NMv1cHAAAAIGGMUSNudA+QaAZy4dqGS/Rin7q0l16xkm5hQNd5X9bPynXcHvxpYLMQ9AHAdhNHSbhXlRxfyhSl4cPJxNw8E3MBAACwq52pnNHx8nHtze/VWHasp88dxmF3IBcuHi7RGdwt3M8ujMPzXoPv+iu3uq4Q3OWCte1Th+2L/7oAsB3EsdQoS42q5Dg20Bs6YCv3Unkm5gIAAACSPvfw5/TBuz4oIyNHjm540g267oLrWvc396lbWC3XGdwtFdCV67bCrhbVznuNzX3q1rRHnZ9XPtV931bsU4fti6APAAZVa2JuWTKyE3OHD0vZkp2Y61IyDwAAgN2lETU0H863wrrOy1OVU/qLb/9F61wjo9vuuk2ffuDTqkW1nu5Tl/Wzq7a5Lndfxstsi33qsD0R9AHAIDHGtuTWy5KJ7cTc4gEpOyKlC5LHl20AAABsT1EcdQ2G6AzpWscW3m6ekxxvxI11f95H5x5ddCztpW0Il1qhkm6Z4RK5IMc+dRhY/MQIAIMgjqTajB2u4WelwmQS7g1JftDv1QEAAGCX62x5bQZwzYER842OY0nra+ftSqPSs7bXpqyfbYVuzUvP8fT1E1/vOs+Ro1c85RXal9/XNSmWfeqwU/E3GwD6yRgb8DVqUnZYGt1rh2owMRcAAAA9EptY1bBqQ7pGpRXGdYZ0C0O7hbd71fIqtavpOkO61vWFt5e4XGny6+ce/pw+9M0PKTaxXMfVy5/4cv3AwR/o2dqBQUfQBwD9YIxUn5Pq87Zqb+KIlB2TPK/fKwMAAMAAMcaoFtWW3ZduyWMdl83KOiPTk/UEbrBiCLfaZdbPbmo13XUXXKcn73nypk3dBQYdQR8AbCVj7P579bIdqDF+qZQbl3y+HAMAAOxE9ai+ahi32mVs4p6sxXO81v5zzdCtuf/cwlbYfJBXNsi2quyae9Vth5bXsewYAR92rcH/FwoAO0V9XqrNSqmcNHqRVNgj+al+rwoAAADLCONw1RBuqcESnQMkwjjsyVocOa2wLefnWlNfW7eTUK45MGLh7VyQU+AGTHsFdjiCPgDYbI2qVJ2x++6NHLUBH3vwAQAALOlM5UxP2i6jOGpNcS03OgZEdEx1be5Z1wzlyo324Ij5xrzqcb0nr8mR06qea4Zwzaq65W4vDOnSXpqQDsCqCPoAYLOENRvweYE0fEgqTNhqPgAAACzpcw9/Th+864MyMnLk6IWXv1BPnnhye7+5BYMklrrdDOx6OeE142VW3n+uY5BENsh2BXQ5P6eMn1l2eAQA9BJBHwD0WtSQquckeVJxn1SYlDKFfq8KAACgr8I41ExtRudq5zRdm7aX9Wmdq9rLU/On9J1z32mdb2R0x7136I577zivz5tyU6tOcV3tkpAOwHZB0AcAvRKFScDnSPk9NuRLD0l0WAAAgB0qiiPN1me7w7vksnm9+THbmN3Q58j7eRXTxfWHdMn17TA8AgB6ha94AHC+4si26JpQyo5Lxb1SdpiADwAAbEuxiTVXn1sytFsY3s3UZ2Rk1vzcruOqlCqplC5pOD3cdek5nj76rY92PZ/ruHrns97JBFUAWCOCPgDYqDiWajO2VTc7LBX3S9kRySXhAwAAg8UYo3KjvGRotzC8m65PKzbxmp/bkaOh9JBKqZKGM8P2sjPEy5Q0nLKXhaCwYhts4AX60Dc/pNjEch1XL3/iywn5AGAdCPoAYL2MsQFfoyZlStLoRTbg87x+rwwAAOwixhjNh/NLhnad4V3zemSidT1/MVVshXULq+86rw+lhnq2h911F1ynJ+95ck+m7gLAbkTQBwBrZYxUn5Pq83bvvT2HpdyY5PGlFAAA9IYxRtWoumJ41xniNeLGup4/H+QXhXbLhXf92ttuLDtGwAcAG8RPpwCwFrWyDflSBWnsYjtsww/6vSoAALBN1KLa8qFddVrn6u3wrhbV1vXcWT+7pvCulCop8Hj/AgA7GUEfAKykUbGDNoKcNHqhVNgj+el+rwoAAAyARtTQdH1a56rnFl2eq9nr01W7510lrKzrudNeetnwbmGIl/JSm/QKAQDbDUEfACwlrErVWclPSSNHbAVfKtvvVQEAgE0WxqFmajOrD62oT6vcKK/ruQM3WHW/u+b1jJ/ZpFcIANjJCPoAoFNUl6rTkuPbKbrFSSmd7/eqAADAeYjiSDP1mVX3u5uuTWu2Mbuu5/Zdf/GU2WXCu6yfleM4m/QqAQAg6AMAK2rYgE+OVNgrFSalTLHfqwIAAMuITazZ+uyawruZ+oyMzJqf23M8DaWGNJwZtiFectnVPpscywd5wjsAwMAg6AOwu0VhEvBJyo/bkC9Tkni/DgDAup2pnNHx8nHtze/d0NRUY4zmGnOrhnfnauc0U59RbOI1P7cjR0PpoTUNrSgEBbmOu+71AwDQbwR9AHanOLJDNuJQyo5KQ/ukzLDkkvABALARn3v4c/rgXR+UkZEjRzc86QZdd8F1MsZoPpxfU3g3XZtWZKI1f05HjgqpQte+d8uFd0OpIcI7AMCOR9AHYHeJY6k+K4V1KTssFfdJ2RHJ5Y0/AADrVY/qOl05rWPTx1ohnyQZGd1212367/f/d83V59SIG+t63kJQWHW/u2Z457v8SAMAQBPfFQHsDsZI9TmpPi9lhqSRo7aSz/P6vTIAAAZSFEeaqk3pTOWM/aie0enK6a7rs/WVB1dMVada13N+btWW2eYl4R0AABvDd1AAO1tnwJcqSnsulbLjks+XPwDA7mWM0Wx9tiu8O105rTPVM63rU9WpNQ2wSHtpjaRHdHz+eNdxR45+7epf0+GhwyqlS0p5qc16OQAAIMFPugB2rvq8VJuVUjlp7GI7bMPnhwwAwM5XCSvt8G6JarwzlTNraqf1HE9j2TGNZcbsZcf18ey4xjJjramzn3v4c/rQNz+k2MRyHVcvf+LLdfXeq7fg1QIAgCaCPgA7T6NiAz4/a1t0C3ukINPvVQEA0BNhHC4b3jWvlxvlNT3XcHp4yfBuPDuuseyYSunSmgdYXHfBdXrynief19RdAABwfgj6AOwcYc1O0vUCqXRIKkxKqWy/VwUAwJrFJta52rml98VLbp+rnVvTc+WDfFclXjPEa14fzYz2fC+85ucCAAD9QdAHYPuLGlL1nOT4dopucVJKF/q9KgAAuhhjVG6UlwzvmrfPVs8qMtGqzxW4wZLhXWewl/X5ZRcAALsNQR+A7Stq2Ao+ScpP2oAvM9TfNQEAdq1aVFtxQu2ZyhnVotqqz+M6rkbSI0sHeUmIVwyKchxnC14VAADYTgj6AGw/cSRVpyUT2Qm6Q3ulzLDEzzsAgE0SxZGmalNdlXidU2rPVM5otjG7pucaSg219sXrDO+agd5IekSe623yKwIAADsRQR+A7SOOpdq0FIc22Cvul7IjkkvCBwDYOGOMZuozK06onapOycis+lwZL7NkeNdZmZfymAAPAAA2B0EfgMFnjFSbkRo1KTts9+HLjUru2qYAAgB2t/nG/LITak9XTuts9awacWPV5/Ecb3F4t6AyL+fnaKkFAAB9Q9AHYHAZI9XnpHpFSheliSNSdlTy+NIFALAaUaMd3FU7Wmo7KvMqYWXV53HkaDg93FWJ1wzvmtV4Q+khuQ6/ZAIAAIOLn5YBDB5jpHrZfqQK0vglUm5c8vmSBQC7SWxinaueW3awxZnqGU3Xptf0XPkgv2R41wz2RjOj8l2+zwAAgO2NdzMABkt9XqrNSUFWGr1IKuyRfPYyAoCdxhijucbc4vCuckanq6db++JFJlr1uVJualErbTPAG8/YYxk/swWvCgAAoL8I+gAMhkZVqs5IQUYaOSwVJux1AMC2VA2rS7bSNsO8M9UzqkW1VZ/HdVyNZka7wruF02qLQZF98QAAAETQB6DforpUnZbcQBo+ZAO+VK7fqwKAXelM5YyOl49rb36vxrJjy54XxqHOVs8uGd41Q725xtyaPmcpVVpUhdfZWjuSGWFfPAAAgDUi6APQH1HDBnxypcJe+5Ep9HtVALBrfe7hz+mDd31QRkaOHP3ExT+ho6WjNrhbMKX2XO2cjMyqz5n1s4un1HZU441mRpXy2J4BAACgVwj6AGytKJSq5+z1/LhU3CelhyQ6rgBgy4RxqNOV0zpePq7j5eN6cPpBff57n2/db2T0ye98csXn8F2/XYWX6d4fr3k9F1ChDQAAsJUI+gBsjTiye/DFoZQbswFfdpiADwA2SSNq6FTllB4vP64T5RM6Xj6uE/P28lTllGITr/ocB/IHdGjo0JL74g2lhmipBQAAGDAEfQA2VxxLtRnbqpsdTgK+Uckl4QOA81WP6jo5f7JVmXd8/ngr1DtdOb1ie23KTWlvfq8m85MqpUr6+4f/vut813H1m9f85op79QEAAGCwEPQB2BzGSLVZKaxK6ZI0epGUHZE8r98rA4BtpRpWdWL+RCvA6wzzzlbPrhjmpb209uX3aTI/qb05G+rtze/V3txeDWeGuyryLhy+UB/65ocUm1iu4+rlT3w5IR8AAMA2Q9AHoLeMkepzUn3e7r03foFt1fX4cgMAy6mEFdtaWz6h4/PH29fLxzVVm1rxsVk/u2yYV0qX5Dhrq6C+7oLr9OQ9T17T1F0AAAAMJn7yBtA7tbIN+VJ5aexiKb9H8oN+rwoABkK5UV46zJs/runa9IqPLQSFVpDXbLfdm7fXi0FxzWHeapr77wEAAGB7IugDcP4aFdum62el0Qulwh7JT/d7VQCwpYwxmmvMLVuZN9uYXfHxQ6mhRWHevvw+TeYmVUgVtuhVAAAAYDsj6AOwcWFVqs5KfkoqHZIKk1Iq2+9VAcCmMcZopj7TNcG2+XFi/oTKjfKKjx9OD3eFec2PydykckFui14FAAAAdiqCPgDrF9Wl6rTk+FJxv1SclNL5fq8KAHrCGKNztXPLhnmVsLLi40czo5rMTXYFec0wL+NntuhVAAAAYDci6AOwdlFDqs7Y6/lJqbhXyhT7uyYA2IDYxJqqTi0b5tWi2rKPdeRoLDu2OMzL7dVEfkJpj60LAAAA0B8EfQBWF0dS5ZwkI2XHpKF9UqYk9WbvdwDYFLGJdaZyRsfn7T55j5cfb+2dd6J8Qo24sexjHTnak9uzZJi3J7dHKS+1ha8EAAAAWBuCPgDLiyNbwWdCKTOaBHzDkkvCB2AwRHGk05XTS4Z5J+dPKozDZR/rOq4mshPtoRfJ3nmT+UlN5Cbku7xNAgAAwPbCO1gAixkj1WaksG6DveJeKTcqxZ2SuQAAjXZJREFUuW6/VwZgFwrjUKfmTy2aYnt8/rhOzZ9SZKJlH+s5XqsqbzI/2arKm8xPajw7TpgHAACAHYV3twDajJHqc1J9XsoMSSNHpeyo5Hn9XhmAHa4RNXSycrK9T15SlXe8fFynK6cVm3jZxwZusGKY5zr8kgIAAAC7A0EfgCTgK9uPVEHac6mUHZd8vkQA6J16VO8afNEZ5p2pnJGRWfaxaS/dHebl9rauj2ZGCfMAAAAAEfQBqM9LtVkplZPGLpby45LPJvMANqYaVpcN885Wz6742IyXaQ29WBjmjaRH5DjsDwoAAACshKAP2K0aFTtoI8jaFt3CHinI9HtVALaB+cZ8d5iXXD9RPqGp2tSKj835uWXDvFKqRJgHAAAAnIeBDvpuvfVW/cVf/IXuvfdeZbNZXXvttXrHO96hyy67rHWOMUY333yzbrvtNk1NTekZz3iG3ve+9+kJT3hCH1cODLCwZgM+L5CGL5AKE7aaDwA6zNXnlg3zpuvTKz62GBRbe+U1222bH4WgQJgHAACAHWWQ8quBDvq+8IUv6JWvfKWe/vSnKwxDvfGNb9Rzn/tc3X333crn85Kkd77znfrd3/1dffSjH9Wll16qt73tbfrhH/5h3XfffSoWi31+BcAAiRpS9Zzk+FJxn1SYlDKFfq8KQJ8YYzTbmG1PsO0I846Xj2uuMbfi40up0pJh3mRuUoUUX1sAAACwewxSfuUYY5bf+XrAnDp1ShMTE/rCF76gH/zBH5QxRvv379eNN96o3/iN35Ak1Wo1TU5O6h3veId+6Zd+aU3POzMzo1KppDu//GENDw9v4isA+iAKbcAnR8rvkYqTUnpIoqAG2HHOVM7oePm49ub3aiw7JmOMpuvT7TBv/nhXsDcfzq/4fCPpkWXDvFxAJTAAAAB2prnZOV1z9BpNT09raGho3Y/frPxqLQa6om+h6WnbKjQ6OipJOnbsmI4fP67nPve5rXPS6bSe9axn6Utf+tKyf1C1Wk21Wq11e2ZmZhNXDfRJHNkWXRPaCbpDe6XMMAEfsAMZY/SX3/lL/el9f9qaXDuWGVO5UVY1qq742NHMqA3wcntboV4zzMv47NsJAACA3WthXpROp5VOp1d9XK/yq43YNkGfMUY33XST/vW//te66qqrJEnHjx+XJE1OTnadOzk5qYceemjZ57r11lt18803b95igX6KY6k2bSv5ssNScb+UHZFcEj5gpwjjUA9MP6D7zt6n+8/er3vP3qvZxmzXOWeqZyRJjhyNZce6wrx9+X2azE9qMjeplMeUbQAAAGAphw4d6rr9lre8RW9961tXfEwv86uN2DZB36/8yq/orrvu0p133rnovoWbehtjVtzo+w1veINuuumm1u2ZmZlF//GAbccYqTYjNWpSpiSN7pdyo5Lr9ntlAM7TbH1W90/dr/vO3qf7pu7TA+ceUCNurPq4//Tk/6Rr91+rwAu2YJUAAADAzvLII490te6upZqvl/nVRmyLoO9Xf/VX9Vd/9Vf6h3/4Bx08eLB1fO/evZJsMrpv377W8ZMnTy5KSTuttdQS2BaMkepzUn3e7r2357CUG5O8bfHPG8ACxhg9Xn68K9h7bO6xRecVg6IuHb1Ul41cpr35vfov//RfWm27kuQ6rq4av4qQDwAAANigoaGhde3R1+v8aiMGOgkwxuhXf/VX9clPflKf//zndfTo0a77jx49qr179+rv/u7v9NSnPlWSVK/X9YUvfEHveMc7+rFkYGvVyjbkSxWk8Uuk3Ljk80M9sJ00ooYemH6gFezdP3W/ZuqL947dn9/fCvYuG71M+/L7un77d8OTbtCHvvkhxSaW67h6+RNfrrHs2Fa+FAAAAGBXGqT8aqCDvle+8pW644479Jd/+ZcqFoutnuZSqaRsNivHcXTjjTfqlltu0SWXXKJLLrlEt9xyi3K5nK6//vo+rx7YRI2KHbQR5KTRi6TCuORTpQpsBzP1Gd1/9n7dN2X313tgenEbbuAGurB0YSvYu3T0Ug2lVv5N4nUXXKcn73ly19RdAAAAAJtvkPIrxxhjVj+tP5brU/7IRz6il770pZJsanrzzTfrAx/4gKampvSMZzxD73vf+1obHq7FzMyMSqWS7vzyhzU8PNyDlQObpFFNAr60VNgrFSakgKmYwKBqtuE2W3DvP3u/Hisv0YabKrYq9S4duVQXli6k5RYAAADYRLGJFcahIhMpiiOFJlQUR4pMpNnpWT3/ic/X9PT0mlp3tyq/WouBDvq2CkEfBl5Ul6rTkhtIuT1ScVJK5/u9KgAL1KO6jk0fawd7U/drtj676Lz9hf1dwd7CNlwAAAAAG9MM65YK8YyMmttau44rz/XkOZ5811fgBkr7aWXcjGrzNT3h4BPWHPQNkoFu3QV2vahhAz65SQXfpJQp9ntVABIztZnuabjTDyiMw65zAjfQRcMXtVpwLx25VMUU/44BAACA9Viu+i6MQznGkXFsguc5XvvD9ZQP8kp7aaX9tFJuSr7rt4K95nXf8eW5XutzzXiL98zeLgj6gEEUhUnAJyk/LhX32Ym6FPwAfWOM0WNzj7Uq9e47e58eLz++6LxSqtQ1NONo6ah8l2+3AAAAwELGmGWr7yITSUZy5EiOWsFd8zIbZJX1skr7aQVeIN/xlwzxXMft98vcUvzkAQySOLJ78MWhlBuzAV92mIAP6IN6VNd3z323axruXGNu0XkHCwe7gr3J3CRtuAAAANjVlgzw4lChCRXHcetnXMc48lzbOttsoc17+VYLbeB1VN0llXfNEI/33Esj6AMGQRxLtRm7F192JAn4RiR3d/3mAein6dp019CMB6YfsL9F7BC4gS4evrg9DXfkUhVShT6tGAAAANhasYmX3QPPxEZybMjnOE47vHN8pdyUikHRBnhepju8WxDkEeCdH4I+oJ+MkWqzUliV0iVp9EIpOyp53uqPBbBhsYltG25HsHd8/vii80rpUtfQDNpwAQAAsBOtNIE2jmPbPis7XbZzgEXGyyidttV3KT/Vbpl1Fod42Br8SQP9YIxUn5Pq83bvvfFDUm5c8vgnCWyGZhvufVP3tdpwy43yovMOFg92BXu04QIAAGA7W+8E2uZQiry//gEWGAykCsBWq8/bkC/ISWMXS/k9kh/0e1XAjnKudk73n72/Fewdmz62qA035absNNzRy3TZyGW6ZOQS2nABAAAw8Jr73210Am3Wyyrlp5TyUgyw2IEI+oCt0qjYNl0/K40ctdN0g0y/VwVse7GJ9ejco61KvfvO3qcT8ycWnTecHtalI5e2gr0jpSO0EAAAAGBgrDSBNjax3ftOjoyM3f9umQm0KS+15AALz/UI8HYBfsIBNltYtZN0vZRUOiQVJqVUtt+rAratWlSzbbhJsLdUG64jp9WG2xycMZGboA0XAAAAW26lARYrTaAN3KA1gTbrZbvCu4UttLzPRRNBH7BZooZUOSe5vlTcLxUnpTRtgcB6nauea7Xg3jd1nx6cfnDJNtyLRy5uBXuXjlyqfJDv04oBAACwG6w2wKJp4QTatJtWOkgzgRabgqAP6LWoYSv4JFu9V5yUMkP9XROwTcQm1vdmv9c1NOPk/MlF542kR1oDMy4bvUyHhw7ThgsAAICe6AzwwjhcdYBFcwJt1ssygRZ9x98uoFfiSKpOSyaWsmPS0D4pU2qVYQNYrBpWu6bhfnvq25oP57vOceToUPFQa2+9S0cv1Z7sHn67CQAAgHVZrn12LQMsmECL7YKgD+iF2qwdttEK+IYllxACWOhs9WzXNNyHZh5a1Iab9tK6ePjirmm4uSDXpxUD///2/jTWluu874T/a60a9nDmc8+9vJwnkRookiJFaxY1WKPbgNKwO+iglQSwg3Y6AVowGoGDGLCTL0rHb5DghSHFkt+GgwB5IcSBjW6bkSxFEinJliyT4qUmU6RIcRDJO59z7hn2ULVWf1irqlbVrn3mc/Y++/x/xOWuXbVq1dr77KHqt59nPYQQQgg5DmRReH3dR1/3kehC4GXBF5m4y24bYSMvYBGqkBVoyURA0UfIfkgToHMVUA3g1F1AewmQ/BIgBLAnWy9de6lUDffi5sWBdguNhVI13FtmbuGvoYQQQgghZIBM3vkyz2gDIwwkJEIVIhQhWqqFVtxCI2hYgVdTgTaQnP+OTCYUfYTsle4a0Nuwcm/uJiDmxP/kZNNJOnh2+dlSNdzNZLPURkDg5pmbS9VwTzVP8SSLEEIIIYTAGJMLvEzm+fPi+cJuNppFO2ijETYQyQiRsnPiRSriHHjkRMNXPyG7RafA5hVARcDiHcD0dYziIyeSK5tXSkUzfrb6M2ijS21iFeN186/Lxd7r5piGSwghhBByktFGW4mXFkIv1SkgAGEEAmUj7mIZYz6eRyto2dRaGSJUYS71mE5LSD0UfYTsht6GnY+vvQTM3gQ0pkY9IkKOBD8N9+krT+Ppq0/j0ualgXYLjYU8Bffuhbtx8/TNTMMlhBBCCDlBGGOQmrRIr02tzDPGFbqQKk+dbYdtNMMmWkErF3iRivKCF8z6IGT3UPQRshO0BjavAlIBCy6KT1FekMmlk3TwzPIzeeGMZ64+U5uGe8vMLcX8egs2DZcQQgghhEw2dYUvUp1amSeQF7UIVYjpeBqtqIWGapTSayMZ8QdhQg4Bij5CtqO/CXRWgdYCMHcz0JgZ9YgIOXAubV4aqIZrYEptGqph03BdxN6d83eiGTRHNGJCCCGEEHKYpDodmC9PG51XsQ1liFCGaKom2rGNzCuJPCfzGJVHyNFC0UfIMLQGOssABDB/GzB9Fgj4liHHH200Xlh9oVQN93Ln8kC7U81TpWq4N8/czLlQCCGEEEImBGNMLvF8mQfYufKklHmK7VQ4ZdNsg2Yu8CIV2Sq3MhzxIyGE+NBaEFJH0gE2V4DmvK2o25wb9YgI2TObySaeufpMLvaeufoMOmmn1EZA4NbZW0vVcBebiyMaMSGEEEIIOQhqU2xTW8VWCGEr2IoAkYowE8+gFdgU21AV6bWhDJliS8gxgqKPEB9jgM1lwGibpjtzAxDwFypyvLi0eSkvmvGTqz+pTcNtBs2BariNoDGiERNCCCGEkL2S6GRA5hltYISBhLRRdyJES7XQiltoha2SyMvSbZliS8hkQNFHSEbStZKvMQPM3WKj+fhdR8acVKd48dqLeSXcp688jSudKwPtTjVP5ZVw7164GzdN38Q0XEIIIYSQY4AxZiC9NtU2Kg8GNirP/ZuNZtEO2miEjZLEi5StYksImXz4TifEGKCzAugUmLkRmLsRCKJRj4qQWjb6G6VquM9efXYgDVcKiVtnbi3Nr7fQXBjRiAkhhBBCyHZoo63ESwuhl+oUEHa+vEAFCGWIWMaYj+fRClqIgxiBDEqRefwhlxBC0UdONmkf2LwKRNPA4k1Aa5FRfGRkXN68jNfWX8N17euw2FyEMcam4V59Ohd7L66+WJuGe9f8XbnYu3PuTqbhEkIIIYSMEcYYpCYt0mtTK/OMsed1SioEMkAoQlv0ImyiFbRygZfJvEAGTLElhGwJRR85mRgDdFeBpAdMXw/M3gCEFCNkdHz1xa/i80993k6MDIHbZm/Dcne5Ng13qbmEuxfuzsUe03AJIYQQQkaPX/jCj8ozxgACCIRNrw1ViOl4Gq3IFr7w02sjGbHwBSFkX1D0kZNH2gc2rgJRG1i6HWifYhQfOTISneDS5iWc3ziP8+vncX7jPF5efRnnLp3L2xgYPLfyHIAiDTdLwb1r4S4sNJiGSwghhBAyClKdDsyXp42GMAIQQChDhDJEQzWwGC+iETQQB3Eh8pzMY1QeIeSwoOgjJ4vuNaDfAaavA2ZvBKLmqEdEJpBO0imJPH/50uYlaKN31M8n3/BJfPCWDzINlxBCCCHkiDDGDETl9XXfbbQptqEMEcgAU+GUTbMNmuUKtsrKPkIIGQUUfeRkkCZ2Lr6wASzdBbSWAMlf0cjeMMZgpbdSiDxP6F1Yv4CV3sqW+4cyxJnWGZxpn8Hp1mlMhVP445/8cWnuPSkk3n792yn5CCGEEEIOGD/FNhN6aWqr2AohbAVbYYtczMQzaAU2xTZUYZFmyxRbQsiYQtFHJp/uGtDfBKaWbFXduD3qEZFjQKpTXNy8WBJ5FzYu5MvdtLvl/lPhVC7zSretM5hrzA3MqTffmMcffv8PoY2GFBK//uZfx2Jz8TAfIiGEEELIxJLoZEDmGW1ghIGEtFF3IkRTNdGO22iFrVzk5ZF5MmSKLSHk2EHRRyYXnQIbV4AgBhbvBKZOA5IFC0jBflJsBQQWm4u5vMui87Lldrg7ofyBmz+A+5buK1XdJYQQQggh9RhjBtJrU22j8mBgo/Lcv9loFu2gjUbYKEm8SNkqtoQQMknwU41MJr0NoLdmU3RnbwQaU6MeERkBB5Fi68s7X+otNZcQqoOde2WxuUjBRwghhBDi0EZbiZeWq9hCAMIIBCpAKEPEMsZ8PI9W0MrnyPMj86qZFIQQMslQ9JHJQqd2Lj4ZAAt3AFNnAMW5MyaZLMX2wsaFssxbt6m2nbSz5f7DUmxPt05jvjHPE0NCCCGEkEPEL37R0z300h6MNtDQUELlKbbtsI1m2LQyzwm8TOYFMmCKLSGEOCj6yOTQ3wQ6q0DrFDB3E9CYHvWIyAGx3xTbhcZC7Vx5e0mxJYQQQgghuydLte2lvVKqrRQSgbSReU3VxFJzCe2wPSDzWPiCEEJ2BkUfOf5obaP4hAQWbgemrwMUX9rHiWqKrV/04vzGeax095Zie7p9Gqebpw88xZYQQgghhNSjjS5kXmqj9GAAIURerXYqnMJ0OI1m2ESs4vwfz9kIIWT/0IaQ402/A3RWgNYCMHsT0Jwd9YjIEFKd4tLmpYGIvJ2m2LbDdm1E3pnWGabYEkIIIYQcMalO8wi9nu4hSRM7dx5EXuxiPp7HVDSFRtBApCI0VIMFMAgh5JDhJyw5nhgDbC7b2/lbgenrgYAv51FTl2KbRedd2ryE1KRD990qxfZ06zSmIhZUIYQQQgg5ahKd5BF6vbSXn88pofLKtXPxHNphO4/Mi1SEWMVMtyWEkBFAM0KOH0nXSr7GHDB3M9CaG/GATg7GGKz2Vgci8nabYjuQZuuq2EYqOqJHQgghhBBCMrL58/x0W63tHMjZ/HmxirHYWMyFXibzYhWzEAYhhIwRFH3k+GAM0Fm2c/LN3QzMXA8EFEMHDVNsCSGEEEImE210HqHX0z300z4MDADk8+e1wzZOh6fRClvl+fNkSKFHCCHHAIo+cjxIe8DGVSCeARZvtnPy8Txjz3SSjk2pralku9sU29Ot0yWpxxRbQgghhJDRkhXEyGReohMYYyAgECqbbjsbzWIqmkJTNe38eW4evVCyIAYhhBxnKPrIeGMM0F0F0j4wcwMwdyMQxKMe1dhzECm2S62lgYg8ptgSQgghhIwPiU5K6bZJmkBAQAiRF71YbC5iKphCHMSlCD3On0cIIZMJRR8ZX9K+jeKL2sCp24H2KUbxeVRTbC+sXyhF5u0lxTaLzltoLDDFlhBCCCFkTOindv68LEIv1Tb7IpABAhkgVjHm4jlMRVMDBTF4TkcIIScLij4ynnRWbdGNmbPA7I1A2Bj1iEbCflNs5xvzAxF5TLElhBBCCBk/soIYWYXbvu7nBTFCFSKUIZqqiVPNU2gH7TzVNlYxIhlx/jxCCCEAKPrIuJH2gc2rQNgClu4CWkuAnNyTlroU2wsbF/Ll5e7ylvszxZYQQggh5HihjS7Sbd08ejCAEMJG58kYU+EUpsNpNEM3f55q2IIYivPnEUII2RqKPjI+dK8B/Q4wdRqYvQmIWqMe0Y65vHkZr62/huva12GxuVjaluoUlzuXa+fK202KbbXoBVNsCSGEEELGl1SnebptL7Xz50HYrIus6EWWbtsMmnl0XqxiBJKXaYQQQvYGv0HI6NEpsHkFUA3g1J1A+zQgj4+8+uqLX8Xnn/o8DGwls7edfRumo+l83ryLmxe3TLEFYKvYMsWWEEIIIeTYkeiklG6bCT0lFEIZIlQhTjVPYTqcHpg/jwUxCCGEHDQUfWS0dNeB3jrQXrIVdePjJbVeXH0Rn3vqc/l9A4Nvv/rtgXaBDGxEXqXoxXWt67DUYootIYQQQsg4Y4yxQs8Vw+jpHow2MDAIZIBQhohVjMV4Ea2ohYby5s9TETMwCCGEHBkUfWQ05FF8EbB4BzB1BlDH5xfNV9ZewRef/yK+9tLXare/6/p34U2n3sQUW0IIIYSQY4Q2Oo/Qy6SegQFg50aOZIR22Mbp8DRaYSuP0ItVjFCGLIhBCCFk5FD0kaOnt2Hn42sv2Yq6jelRj2hHGGPww8s/xCPPPYInLjwxtJ0UEn/vDX9vYK4+QgghhBAyHmiji3Tb1KbcGmOnYQmVFXoz0Qymo2k0lTd/XmCFHiGEEDKuUPSRo0NrW1FXKmDhDmD6DKDG/yXYT/v41ivfwiPPPYIXr72Yr3/g9AP4+O0fx4X1C/jDH/whtNGQQuLX3/zrlHyEEEIIIWNAopOiwq3uIU3tvMlC2IIYgQwwH89jKppCI2iU5tBjQQxCCCHHEX57kaOhvwl0VoHWAjB3M9CYGfWItmWlu4KvvPAV/MULf4GV7goAIFYxHr7xYXz0to/i+qnrbcNTwH2n7xtadZcQQgghhBwu1XTb1KQwxiCUIQIZIFaxrXAbTiEOygUxOL0KIYSQSYKijxwuxtgoPghg/jZg+iwQjPfL7qXVl/DI84/gmz//Jvq6D8BWxf3IrR/BB2/+YG0V3MXmIgUfIYQQQsghYozJK9tmabdaawC28FmkIjRVE6eap9AO2nmqbaxiRDLi/HmEEEJOBONtXMjxJukAmytAcx6Yuwlozo16REPRRuPcxXN45LlH8P1L38/X3zF7Bz5++8fxtrNvY/oGIYQQQsgRoI0updv20h5gbLptIANEMsJUOIXpcBrNsJg/r6EaCBXnzyOEEHKyobkgB48xwOYyYLRN0525AQjG86Srm3bxjZe/gUeefwSvrL0CABAQeOi6h/BLt/8S7pq/i7/+EkIIIYQcAqlObXSeS7dNdGILYrj580IZYjaaxVQ0hVbQyoVeto0QQgghg1D0kYMl7QEbV+0cfHO32Gi+MfRkVzpX8Bc/+wt85YWvYK2/BgBoBk28/6b346O3fRSnW6dHPEJCCCGEkMkgmz8vS7tN0gQCAlJKhDJEqELMNGcwFdj58xqqkUs9JdWoh08IIYQcKyj6yMFgDNBZAXQCzNwIzN0ABPGoRzXA8yvP48+f+3P81St/hdTYqmunW6fx0Vs/ivfd9D60wtaIR0gIIYQQcjzRRqOX9tBJO+ilPRhtYGAQyAChDBGrGIvxIlpRK69um0XosSAGIYQQcjBQ9JH9k/ZtwY1oGli8E2gtjlUUnzYaj59/HI889wh+fOXH+fq7F+7GL932S3jrdW/lySUhhBBCyC4wxqCne+imXXSTLhKdQELmEXmnm6fRClu5yGuoBkIZckoUQggh5JCh6CN7xxiguwokPWD6emD2BiBsjHpUOZvJJr7+0tfxxee/iPMb5wEASii8/ezb8fHbP4475u4Y7QAJIYQQQo4JvbSXR+slaQIhBEIZohE0sNBewHQ0jWbQRCNooKEaFHqEEELIiKDoI3sj7du5+KI2sHS7jeKT43FCd3HjIr74sy/iay9+DRvJBgCgHbbxizf/Ij5y60ew0FwY8QgJIYQQQsaXRCd5pF4/7cPAIFQ29XapuYSZaAbNoJmLPWZGEEIIIeMDRR/ZPd1rQL8DTF8HzN4IRM1RjwgA8JOrP8Ejzz2Cv37tr6GNBgCcbZ/Fx277GN5743vRCMYn2pAQQgghZBzQRqOT2Dn1umkXxtg59WIVYzaexVw8V5J6geTlAyGEEDLO8JvaY+Ola4i6MWQjhAxDQLip5lygmhDGW3Y7+W0G2h/d2I+ENLFz8QUN4NTrgPbpkUfxpTrFd177Dh557hE8u/xsvv6eU/fg47d9HPefvp+/MhNCCCGEoCiW0U276KZdpDqFEgqRitAO2zg7dRbtoI1m2ERTNRGqcNRDJoQQQsguoejzOP+dl7ExtQKhFEQYImjFUI0GRBRCRhFEHEKGEUQoSxJvW9EnrSQUfhvpdpFunftXkoootqGuX1GWiQMiUni7Vca3awnZXQP6m8DUkq2qG7d32cHBst5fx1df/Cq++PwXcblzGQAQyADvvuHd+NhtH8MtM7eMdHyEEEIIIaPEGIO+7qOTdtBNrNQTQiBWtljGUnOpNK9erOJRD5kQQgghBwBFn0dzPkazqaCTFCbpwiyvoJemAAAhFUQgARVCxRFUqwnZiCHCQgKKKAKkApyj09rZtAQABIzxDmZcM1O5vxUCpUa+VIQnBgHsW0RmQXACKdBdgQgiiJk7AbEIbEiIjbReKkp4B6rKS395izYAxJBIwdfWX8N/e/6/4esvfR3dtAsAmIlm8KFbPoQP3fohzMVztfsRQgghhEwy/bSfR+r1dR8wQKQixEGMs62zmI6n0QgaVuyxWAYhhBAysVD0VZBRBBkNrjc6hUlS6CSB3thAsnoNMBoGgFASUimIQEE2IgTNJmSrAZlHAkaQcQgh1YGP1xiU5J+pisMh2wABrd39qoh0t6bfBZJNIF4EWosw601gPQVMIfkGB4TBSMLSirLYq5WPlWUD4NnNp/GVi1/EU6vfg3EDvKF5Ez505iN4+9I7EAUxxBqwuZ4AQgyXm56IrI2U3KeIJIQQQgg5bBKd5BVwe0kPEEAoQ8QyxmJzMS+W0QpaLJZBCCGEnDAo+naIkAoiUpDRoAU0OoXupzBpgvTaJvpX1wCTOgmoIIMAMlCQjRiq1YBsxE4ohpCxFYFC7u0ErCTNyovbsEX8oEmBzjWgqYCps66i7u7HZzJzmN1URaJ/35hCTLp/PZPgb1a+ja9e/RJe7r6Y9/um1r34wNxHcFfzjRAQSC4DCZLiEXmycXBQxbadiMitZaBbFgJSWjGoYiCIJGQoIEMBFVIIEkIIIWTvaKPzCridtAMAUEIhVjFmohnMTc2hFbbyaD0WyyCEEEJONjwTOACEVFCxArCFBEwSJKtr6F1ZziPqoERFAjahGjFEZCMAZRTuSwLuiaQD9NaBxqytqhvtfS6+PCWkfDOsdb50LbmGRy/9d3z94lewkqwAACIR4R2L78YHlz6Cs43r9zwmn/2KSJ23NdDGwBigt2pgYOfAkQEgQyBsSqhYQkVW/skATJchhBBCyAB1xTKkkIhVjFbQyotlNIIGWkGLxTIIIYQQMgBF3yGTS8C4RgKmCXSiYZK+lYCXl7O9IAJpowHDAKoRQ7YaULGTgA0XCRiGBycBjQY6qza3deYGoH3Kzjd4hLyy+XN85eIX8e0r30Lf9AEAc+E8PnDqQ3jPqfdhKpg+0OPtVUTuBKMNdALovsHmZgpjUltwJRBQARA0JYKGzKP+ZMB0YEIIIeQkkRXLyKRekiZ5sYxYxVhqLmEqnEIzaKIZNhHJiD8UEkIIIWRbKPpGiFABlEKtBNRJApMkMEmK3soqzOWrMK6orgjcfIBBYIuCNBtQcWTlXxRAxhFkvIvKaUkX6K0B8SwwfQaIpw7sMW6HMQY/vPZ9fOXCF/HDa9/P19/Sug0fWvooHpz/BQTi+L1MhRRQEaCi4oTcaAOdWvnXuWrlHwDIwIq+oCERNKUVfyGgQkH5RwghhEwIiU7QSTq2WEZqf9AMVYhYxTjTPIPpeDovlNEMmpR6hBBCCNkTx8+gnBBkEADB4J/HGA2Tapi+k4BXl2EuplYCimJOQBEqqGYTqt208wG6qsA2JTjKOgO61+zt9PVAewnWPB4+Pd3Dt698C1+5+CW82vk5AEBA4C2zD+IXT38Ud7bvmrgTXCEFlERp3j5jbOSfSQx6qyk6yylgnPwLnfxryFz8yVBAqsl6XgghhJBJI9UpuqmdU6+f9mGMQSADNFQDC/ECZhuzNlLPiT11xFkUhBBCCJlcKPqOGUJIiEBuIwH70L0EaWcZ5sLlYl8XBSiiECoOoEID2ZqFnD8LiTZEmkIesuhb7i/j6xe/gkcvfxVryTUAQEM28O7Fh/GBpQ9jKT59qMcfN4QQUCGAivwzKaAz+XfVRv4JZduqWCJoACry5v2j/COEEEJGQl4swxXMMMZASVssYzqcxvzUPJqhk3pBA6HkvHqEEEIIOTwo+iYIXwJWdZ0xGiaxRUH0+irSlRRGNYBQAy+vQIQhZBhARBHU1BSCVhuiEUPGMWTDVgoW4d5PTF/ceAFfufhF/PXVv0LqUlYXo1P44NKH8a7Fh9FSrX088slCCAER2Kg+NIr1OjHQiUF/XaO7agCkENKl/cYSQRNQoYSMsnn/KP8IIYSQg8QYg57u5RVwtdEQsPPqNYMmrmteh3bUzqP1IjU4PQshhBBCyGFC0XdCsBLQAEZDzc4CjTkgbAKwlYFNP4VJ+tCdDtJra+imKWwOqYRQAWQYQkYx1HQbqtWGiCPIZgMyiiGbDYiaCENtNJ5afRJfufBFPL3243z9ne278IunP4r7Zx+AEkxV2Sl2Lr+yvNOpk3+bGt1rBjCF/JORQNgUtuJvyIq/hBBCyG4pVcBNUxhhEKvYpuA2FzATzqAR2Dn1YhXzO5YQQgghI4ei70RggLQL6NQW2mjOAZ5gE1JBuMrAA5GATgLqfhdpZwPJtRXoVEMIAEJABCFkENiCIK021FQLvQD4TnoOX1t7DBf7FwEAEhJvnX8bfnHpI7itfceRPfJJRyqXtuvVXjFO/umewea6hkEKIZz8C4GwKaFir+JvSPlHCCGEZMUyemkPvbQHoCiWcbp5GtPRdDGvXtCAFHLEIyaEEEIIGYSib9IxKdDvACoE2vNA2N7V7pkElDWVga0E7EP3+0ivreHS6s/xrcaP8detn6Ij7Qly00R4u34z3td4OxbMGciVGEnnMkSWDqz4EjxohBJQSpSkrdG26IfuG2xupjA6cSnCdt6/oCkRxH7aL1jxlxBCyMSSFcvopl0r9QzyefXm43nMxDNohS0WyyCEEELIsYOWZZJJu4BObBRfYxaQB/vnziTgS/FVPCqewDn8BFoYAMCSmcO7+/firZu3I0wEdLKCdb0MA0AoCRmEkEEI0WwgmGpDtlqQcQQZN2xaMCXggSKkgIoAFQ1W/NV9g85yCqPt3Ik2RdhV/G1KqBB59B/lHyGEkOOGNhq9tIdOaqP1tNaQQiJWMabCKcxOzaIdtvMUXBbLIIQQQshxhiZlEjEpkHSs2GufAsIWgIMVNCk0vo9n8ah4HD8Tr+brX2duwsPmQbwBt9n55KbrhteHTlKYXh96ZRX9K5cBbWBg7HyAgZsTsNmAarehWq2iMEjcgGjEEJLpMvslq/irqhV/E6/i77JX8TewFX/DlvDSflnxlxBCyPiQF8twFXATnUBCIg7svHoslkEIIYSQSYeib9LIovjCNtCYAQ74BHYTXXwb38c3xJO4KlYBAMooPIC78bB5EDdgads+hAqhVAjEjYFtJulDJwlML0GyvILe5cuAMYCTgCIIIIMQqt2CbE9BNRsQcWyjARtNiDiiBNwHQggIF8GXYYyBSVGu+Guc/Aut/AsagIq8ef9Y8ZcQQsgRkM2n10k7SFI7LUUoQzSCBhbaC6V59VgsgxBCCCEnAYq+iUED/U0bxddaBKI2DjKK7xKW8Zj4Hv4aP0BX9AEAbdPEu3Av3mXuxwx2N/ffMEQQQgUhMOgArQTsJzD9BL0rV2AuXIQxGgISIpAQkU0HVs0m5PQUVKPhUoFtNCAl4N6wc/mhvuJv35d/ruJvaFOEg6ZAEPnz/vHiihBCyN5JdJJH6vXTPgxMXixjqbmEmWiGxTIIIYQQcuKh6JsE0h6Q9oGoZefiO6AoPgOD5/BzPCoexw/wUxjnaa4zi3jYPIAH8HpEOLp5bHIJ2KyM0xiYNIHp92H6ffQ2N2HOX7DzAUJAhAoitOnAqtWGbLehGk3IOHJFQezcgGR35BV/PbSr+Jt2DPprlYq/kUDYFFCRhIpc2m/Air+EEEIG0UbnFXC7aRfGGAQyQKxizMazmIvnSlIvOOB5iAkhhBBCjis8KzrWZFF8CmjO26IbB/DrdYIUT+JpPCqewMviQr7+9eZWPGwewN24BeKA5/zbDzbiLARqJaCGSRIbDdjrI924CPPqa8iiHUWoIMMAIoyh2i2oqSk3F2BsIwGbDciI8/fslFz+xcW6vOJvz2BzPZN/sBV/A1fxt+Gl/YaUf4QQcpLIimVkVXBTnUIJhUhFaIdtnJ06i3bQRjNsslgGIYQQQsg2UPQdV3QfSHpA2LRRfEG8/T7bsI5N/CWewjfFk1gV6wCA0Ci8FW/Ew+YBnMHivo9x1AghIcIICCOoOgnYzyRgF+naGswrRWGRLApQhBHUVBtBe8pVBG5YEdhoQEa82NiO2oq/mfxLDDpXUxiTQsDJv9DKPxXLXPyx4i8hhEwGxhj0dR+dtINuYqWeEAKxssUylppL+bx6jaCBWO3//IYQQggh5CRB0Xfs0EC/YyP3mvNA3AaE2leP53EFj4on8Df4EfoiAQDMmDbebe7HO3AvpqphchOCEBIiioAoQvUZzCVgvw/d7SBdW0M3fcVulMJWBw5DyCiGnGrlElC5aEAZRoASgJTFP5JTK/+MJ/+WUxidAsbODShDIGjYyD8VIY/+o/wjhJDxpp/280i9vu4DBohUhDiIcbZ1FtPxNBpBw4o91WBENyGEEELIPqHoO07kUXwNoDG3ryg+A4Of4EU8Kh7Hj8XP8vU3mNN42DyAt+BuBAP66+SwpQTUKUw/hUn6SDsbSK6topPadFQICaUUZBghjBtQcQgZxFCxjQxEoIAgsGnGmQhU0qZfSwEoBQh3q06WHBTCVfGtVvx18q+3mqJzNQWEq/gb2Iq/YdPO95fP+6d4kUgIIaMg0UleAbeX9AABhDJELGMsNhfzYhmtoMViGYQQQgghhwRF37HA2Cg+AGjOubn49ibh+kjwOH6Mx8QTeFVcBgAIA7wJd+Bh8wDuwI1jNf/eOCKkgogVENdJQA2d9JD2E/Q7azCbBtJISCUQyBBBGEAKBSUlpAqKyAUhrOgT0gpAoextEFjpFwRAFLjoQLdNeJJQeZJwgqIHhRAQoY3gy7DFV6z8Kyr+WvknAyCIJYImoCJv3j9W/CWEkANFG51XwO2k9hxFCYVYxZiJZjA3NYdW2GKxDEIIIYSQI4ZnXeOOSYB+10XxzQDB3tJor2Ed3xTn8Jc4hzWxCQCITIi34U14j3kLljB/kKM+sQgpoaJGqfCxTlPoNEUvTdBNOk5KBVBGQUUxgjC2UYBSQQoJYQxgDJC6NG2t3T9bR7jAFGnBQniCUAKh8iRhFj3otklZRA/mEYXHRw7a4isYkHc6NdB9g/6GRveaAUwKId0cf5FAkFX8DUUuAAkhhGxPXbEMKSRiFZeKZTSCBlpBC6Hi/LWEEEIIIaOCom9syaL4DNCcBeLpPUXx/RwX8Zh4Ao/jb5GKFAAwb6bxHvMWvB33oInGAY+bVJFKQSoFwNo/YwyMdvJvbQ1dcw0QwrYLAoRRDBVFUCqEjCO37xAyAZjLQA30U6DTgw17c9IwF4TGix5UQCAAyCJ6MAiA0N1KUUQLZpIwizjMJOEYzaWUV/z1MKmBTgzSjkF/TduiH9JG/skQCLOiH1nab8CKv4QQ4ku9JE3yYhmxirHUXMJUOIVm0EQzbLJYBiGEEELImEHRN46Y1Eq+ILKpuruM4tMw+DGex6PicTwjXsrX32LO4mHzAO7F66BwfCK4Jg0hXDEPFQCRvUAyxkCnCXSSYLPbsXJOSigVQAYBgrgBFYRQQQAVhBBZBJ6UsH/KXUhgrW3/viBMU6CXACYThtWdvOhB6dKDs9Th0EUNhi6CUHpiMI8glCOJHhRKQCkB/zo0r/jbN9jcdBV/hav4G9iKv0EsIaMs7Rcs+kEImVi00egkHXTSDvpJHwYGoQoRqxhnmmcwHU/nhTKaQZM/hhBCCCGEjDkUfWOFAZKuFX3xtI3k20UUXxd9fBc/xGPie7gorgIApBG4F6/Dw+YB3IrrD2vgZJ8IIZzIK9KdjNbQaYq010O/swkDA+nm9lNhhDBqQDrxJ4Ng5xdfmWjbTYCoMU4CVgRhLwE63jZjskdU3PjRg5kcDBSgvOhBP6W4lGLsycEDurisrfirDXRq5V/nqpV/gKv4G7iKv80s7Res+EsIObZkxTK6SRepSSEg0FANTIfTmJ+aRztq52JPyZNblIsQQggh5LhC0TcumBRIOoCKgMYCELZ2vOsyruEb4kl8G09hQ3QBAA0T4x24B+8xb8E8Zg5r1OQQEVJCSQkVFvJPu5TffmcT3fV1G4kmpY30C2MEUZSLv1Kxj30Pxsm63QbjZdGDqS5kYJIA3b69n2rA+Du4NOOs2EgWPSiljRbMogaz2zohWI0i3MnDk8IGIFaKfmhX8be7mqKznNrAxsDKvqAhETSK+f5Y8ZcQMm5k0XrdtIte2oM2GpGKEKsYZ9tn8yq4TMElhBBCCJkcKPpGjgHSLqBTIJoCGrPADivTvYBX8ah4AufwE2hhbckpM4v3mAfwNrwJMaJteiDHDSlt0Q6E/nx/GjpN0NtYQ3fdvg6y+f6CqIEgjFzkn0sXPtoB29ut5hmsUooe9G6TBNjsFOuy+Qb9IEJ/HsE8ejCwEYRBJXowq1hclYlSAELYKMtwUP5lFX97qyk6V92hlWsbSwQNW/E3n/eP8o8QckT4c+v1035eMGMqnML81HxeBbcZNBmtRwghhBAyoVD0jZIsik+GQHveRfFtLQVSaHwfz+JR8QR+Jl7J199hbsTD5gG8CbdDcv69E4Od7y8r9uHP95dCJwk63VUYGAghbMqvcvP9hUXKrxy3i72Dih5MDdCvRg+ayk5e9GBWtTirSJylFYcqTycW0v6TWfpxbCWhzWQW6K9rdFcNgKLoRxBLqAYQRP68f5R/hJD9oY22Ui+xYs/AIJTF3HozsY3Wa4UtRusRQgghhJwgKPpGRdoFdAKEU0BjBlDhls030cV38AN8Q3wPV8QqAEAZibfgbjxsHsSNOH0UoybHADvfn43gyzBaQ+sUab+Hfncza2iLfYShrfSbRf6poCj2cZzYa/RgFiFoqtGDm8WchFlKcUYlelAKZSP3vOhBLUNoA/S1RBe2rQgkZCQhY4mwHUC1giLtlxV/CSFb0E/76KZddNIOkjSBFBKRitAO27h+6nq0wzZaQYvReoQQQgghJxyKvqPGaCDZtOm5rUUgamOrKL5LWMY3xPfwHfwQXdEDALRNA+/EfXiXuQ+zmDqigZPjTD7fX+DP96dh0gRpt4v+5gYMAOkq/aooQhDFkK7S74HO9zdOCBe9t9trYj+lOFvup0CnbyN1tYE0phyUKAQMBLRR0JDYNApGCohAQUYKsqEQtgPIWEIq6xCFEsVttixcYRJR9JsXKsmWBQCIwXZw9+vaucX6dhP4tydkjNFG26IZbn49YwwCGaARNHC6eRoz8Uwu9WIVT+bnMyGEEEII2RMUfUdJ2gXSxMq9xowtvFGDgcFz+DkeFU/gB/gpjJt/74xZwHvNA3gr3oAIW0cAErIdUkpARqVgUp2mbr6/DXTXrwEQEFJBBcrO9xdFkEEIqQJIpU7uxaWULrV4d9GDQmsobaAyQWgMTNqDXjfQKwabiZty0ACAgBAGEAZCuQLGMJDS3pfSQAoDIY2rW2KQO8D8dogQhC8G4Qk+t80XhPDbyKIfmRVMyfZ1xVP8djJrK8vr4R1zK0m5k3alxzFMZlYeF2UmGTMSnaCTdPJoPSEEYhWjGTRxduospsKpfG69YIfz+BJCCCGEkJMJzxaPBA30syi+BSCeQl0UX4oUT+IneFQ8gZfE+Xz9680teK95AK/HrRDbzOFHyH6Q+Xx/lmy+P6NTdNeuYdNoCCHzYh9h3IAKQyiVVfpluthQhkQPCthVdc9cVpfEmEqWsRH5OtvQfaJoQMBAaCcAtXE1R4xzbW69sMtSGghhnO8yxUHzg7tOtTuIScvzHOZtTM1+2Xrv1pjio8+gItdcerQBILxUaWPq24mK5MuX/ba+3POPV913K5npLcNf78tMt1/WvioVayVldXw17fKhbiMph7Wr/iNjQR6tl3bQTbxoPdXAUnMJs/GsnVsvaDFajxBCCCGE7BqKvsNG94G0B4RNIJ4DgsEovnVs4q/wfXxTfA8rYh0AEBqFB/FGvNe8BWdx6ogHTYglm+8PCIDIL/aRQCcJNrvLsJbJpvzKwBb7CMLQpv0e1/n+xoSsLskg1aIihQyEAYwWMEZAayBNKlIQnhvKnZaLElRWCqosdViiEIOuMHG2PFKqD8hUhKIvFuHfZtuGyUwASAf3re633TF9kZcdNxOpVZkpUD6eyFYMkZn5fU9abhmZ6Ym/7A+opBeR6f1hlSgqUQsXhZm9f2tlpztWFrEJUZad0j++rNyKou8Jx4/WS9MUECii9dpn0Q7aeTVcRusRQgghhJD9wjPKQ0MD/Y69qGnM2yi+ytXxBVzBo+IJfBc/Ql8kAIBp08a7zX14J+7FFFqjGDghW2LlX1ia789oDZ2mSHs99DubMMbk0YFBGCGIG7bqr6v0ywiVg6fkfkpysF4KZt4qixjUCZD2RB4tWOwry+nALkU4k4JSOc+TS0BTSMLDcjknIUJtQFIOEYtD29T9Q2VZe/t5+2pTiLtMSgphZWi+nIlJ97+qVCzZZFSiEYcJSFdtW6iiGrZ0UtIXkrsVkL5crP1XabOPF60xJi+Y0U1sJdxA2Gi9U81TmI1mc6nXUA1+FhJCCCGEkAOHou8w0H0g6QFhA2jMAUGcbzIw+AlexKPiCfxYPJ+vv8Es4WHzIN6CuxDwz0KOGXmxj9Av9pFCJyl6m5vorK87CSRtReAoRhDGttDHJBf7GFP87M/tpCBgSunDxgA6zSIFRe6Ksii0vO9s3kBRSMEsajB3O160oJ8xS1CO5ht3/AjLrEq1GbbefyHVtN2VgMyO7y9nqd5eVOROBaT03hiZVJTZCxi1AjKBRtck6OkeEpMAUrpovQbOxLNox1NoRC00giZChEAigQQQogst+/mYhC8e3bGr6/gZSQghhBBCdgKN0oHiovgggOaci+KzV9F9JHgCf4tHxRN4VVwCYAMj3oQ78LB5AHfgRs6/RyYKKW01WcCmqxtjYHQKnabora+hq6/Za2uX8htGMVQYQQYu8o/z/Y0NmRMpM0QKelGC2byCaSoAJwX9vUoBXk76SQnIoBIpKG0koZ867Nf1ICPGF2jjlI1bjWaELxPr1nsCMk0H2hmt0dd99NMu+mkfRmsoSAQyxHzYQEu1EKkAURAglhJCXAPEGowx6BsgyV+vFalYSWsuCb5M+GVtlQIgIJSVjUL6tzbtWihVrHNvGFGNnoRwxXo8uQh3bDnMug++4QabiG0a7KBNzT4Daw6o3+o6ylRCCCGETAIUfQeFToCk66L4ZoGgAQC4hnV8S5zDt/AU1sQGACAyIX4Bb8J7zVuwhPlRjpqQI0MIAaFs9B7gz/eXuvn+ugBsNI5N8w0QRK7YRxb5Jyn/xp36eQXrpKDnUDRy15L2gaQnt55XULoCIi5CULkU4oH0YYFSWjE5YewzRDQ1KXppD720j0T3AQiEqoFYzmI+mkIzbCJWDTSCBgKx89MpU5NWbfwXvC4iGo2/TRsgsQVxdE3bUj/unxDeu68yLWS2LguSLJ6vTET6VKVZdXPd87z1PvVSbQ/H2eZvvO/j1EbXDkrE8uYh+5S6FfXHKTUaHEvpyDs4TvV9IHa4j6gdW82xh7ap/7sM/XPt5m+72/V1YxnaxQEcc9fjO6RxDFu/m+dpWPORjM/D1J9X7KvtiPvcedNJfOy7OP7OOz2afY74WHt+rvb8FI/587jX/Xa4T//atd33PSZQ9O0b46L4UIriewUX8ah4Ao/jb5GKFAAwZ6bxHnM/3o43o4XG6IZMyJiQFfuwBT8sRmto7c33BwMppZN/EcK4YaVfELDYxzEnv/4c+BMOfvluNa9gbyfzCroIQakMVIDy9qOYV5CMNTbqLkE/7aGb9mCMhhQSkYoxHU1jOpqyUk810AhiDLUEO2BA8Oyrt4OjTkDuaJ/yip0c6MD3qe3BtTGV+7V7mLo2uxtb0cVonoP6XQ74OCVL7FYNhlvC1DWq9pvtODCmmrbZsWsQdYOqXWePWfv3qRvfsLEM77r+hVg77qEPZsj6/QvMI+9jWNd76eMQBNiOZcmu/MFhSLUDb3h8jn8Yz/1u+/Veljt+2vyX8q6Otccdxe4HKfawj9vTW97bfgPfDztlx8//wT6Pm+vrO+9jzKDo2w8mAfpdOwdfcxY6aODHeB6PiifwjHgxb3aLuQ4PmwdxL+6EAiOSCNmKfL6/wJ/vT0OnCfrdTfQ214t2KoCKIgRRnBf64Hx/k4mfvbjreQW1k4JGuIrE/r7FvILSpQgL4bIggyJSUPpSMJOEnFfw2JIanUs9G60HhCpCLCPMtebQClu52DsplXDrBOS2+xzSWAg5KdQKp2EX3ruNWtlN30O72H8fB/Z49sNOf1w5jC/0g4jkPIrjj/ix77Qtz/HJUSJefHH7RmPKyTh7PXAMkHTs1WNzFt24gb8RT+Mx8QQuiKsAAGEE7sXr8D7zAG7F9SMeLyHHGyklpIyA0J/vz8q/3sYGuuvXAIii0m/cRBCGkIFL+1X8qDtpHMS8gqYrgG3mFZRO+tXNKyh9KejdkqPFGIPEJOg5sadNCiUUIhVhKprCdDiNRmClXhxEEGM10SAhZJKplRZj9EUxPiMhhJxEjD0pt7daw6Rp7X1oXbTN7u/ytrSv1ug+/TQufOtbo34K9gyvfneLSW2qbhBhuRHgm9H38Vd4ChuiCwBomAhvx5vxHvMWLGBmxIMlZDKx8/2pUsGOfL6/NEH32io2YSCc/FNBiCCO3Xx/oS0AwmIfxLHreQWrVYi3mlfQk4KZ7FOuCnHdvIKZOKQU3Dt5tJ7uIUlttF4gQ8QqwunWEtph+8RF6xFCCCEnjeTyZfRffRXh2bMIFhdHPZxtMe7X5pJ42kZwmSGSa8t9/L6rtzXCa+j9nQq3rY7n3dbKt6OM/J0weIa7YwyQdgGd4sVmB482foAnxTPQwk6EvWhm8V7zFvwC7kHDVRklhBwdtfP9GQOdJkiTPvrdTQAGEDblV4ahq/TrIv843x/ZAfVSEBg2r2A+t2BlXkFTmlfQ67s6r6CykYEqqEhBTwhmcvCk0tf9PFrPGA0BiUiFmArbmG7NMFqPEEIIOWaYTBQliV1OkkIeJQmMW59vr7TdfPJJrH3lK/bkSwi03/MexHfdtSPhtJV42lKe7VR4bSHnyA4RAlDKXrvV3EJKiOw227bD+5AS+to1dL7//VE/yn1B0bcTTArd38QPGufx9eZP8bx8Ld90h7kBD5sH8SbcDskLCELGCiv/wtJ8f1nKb9rtor+5YUs3SGlTfsMYQRzn4k8GnO+P7J3SlGfbzCs4tNhIv25eQVk/r2DgRQr6qcKiaFOKMqzcPw5oY9BLuy5aLwFgEMgAkYqw1DqFdjDlxF6MQIbb9kcIIYRMEsaYsghz4qu0nEkzT56ZJBkqzerWD/Tl9zdsfaW/0tgqQu9AI7mMwfpjj2H9sccOrs+j5gDklVCqkGHb3d+rNPNvq212ervdmLM+D5Hk8mW8/Bu/cajHOGwo+rahk67hO+GzeGzqOVyRawAAaSTegrvxsHkAN+HMiEdICNkNtthHBOU5AJvym6K3uYHuxjUb+KcCqEBBRTGCMLZz/QUhpFKUf+TA2VexkWxewa6NFKzOK1g6BlARfcbeL6UNm1IBklJ7aYaKwsN4W2TRer20h9SkEJCIVYR20MJMcwaNsImGihEHMaP1CCFkgjjqtMs8bXKY8EpToN8/GuG1AxE2rO3ERoVlEVxBUMifIIAIgkIOuW2m10P/pZcGuojf8AYECwv2BGcXkmxbAbWL210Lt2wdrz2OlGBxEYu/8RtY+8xnRj2UPUPRN4QrWMFj4Q/xnakX0BF2jp+WaeCduBfvNvdjFlMjHiEh5KDIingAfrEPJ//W19DV12z0lIvyC6MYKoqgVFbpl/P9kaNl58VGvK1+1GC27HbLqhLb7aLYVj3uIcpCA4O+7qKn++inPRgYhDJEKCOcap5CO5zKpV4oOUUGIYSMG8aYsuDyRJRJEivKsnVVGeat7/zgB1j/5jfzqK7mQw8huvnmeuHl91OXyrmTyDbXdmKpiLBcju1AmuXLWdth6/1+s+Xq8arrq2Oqjqd6vB2SR2P5UYFSYulTnzoWc/WRg8Poyi/j2TqgkOLG2M+uyr/Wgw/i9O/+LvD3/ufRDH6fUPR5rF3p4PJCF48GP8D3w5dh3AXNabOAh80DeCvegAhMBSJk0rHFPrJqvTEAr9hHkmCz27FfAlLmKb5B3HBpwgFUEHK+PzJ2lKIGa9k+TWZAFnrrdAJoJwoNKrIwM4oQuSxMkSAxCVKTQJsEQgiEQYBG2MZMeAaNKEIjiNAIYigoiBQQ7losFdpLT7Z9Su8+IYRMArXizL/viTO/XVWcVe8fSFs/Ks3bdhhsfve72Pzudw+l7y3xIrmGCqrDEF51Mm5YH1tJumzdCftezKKxLv/BH1iZIyUW/9f/lZLvkMmiYgHkt3UCrVasoV7ADbS1Z5il80v7+jbFqSayH6bdSW/267go/8os8nlwvPvZCaWyFfPim248zKfsUKHo83go/QCe+uHf4KmHXgYA3GVuxvvMg7gbt0KywDwhJ5raYh9aQ6cp0l4P/c4mDIyb7y+ACiOEUQPSiT/O90cmge1lIVAnDLUxSEwf/bSPfpoABpBCIRAhptQ0mkEDASKEMoQSoT2f6xp0OsCm0YDQ9phZ18Kd2AlAojIfoQSkFJBSQMmsoImoFYKUhYQcPONc6XJAnFUlVibO/GiwqmRzbQYKAuy3bZ1km4Qos0yCZTJqyHImqPTmJno/+clAN81f+AWEp0/Xiq0dC69hEWzD1vNH22PL9Ac/iOb994/tZ9F+qRNoQ6UaMCDghrWvrhOi8qOtAAREptUAAMKtr4q0XKJBQEjvBNK9r0SWCiJtdTmpZJEqnd8qQFXWuZM3kfVVlXfeukLeFSeKQpbXCXj9VNKk09XVw/kDHgEUfR5CSLyr/T78f75zI1Yh0Q2a6MVtvOKqHgpp3K2GVP59b727X96mvfujfpSEkIPCzvcnocIi0le7lN9+ZxPd9XX3XWQj/1QUI4iiXPxJRflHJpNUp+jrBH3dgzb2pDKQARqqgfm4iThoIFYRQhlB7uGL0RiDyg+8+Tqbhmzc/aJdLigpCwk5UHJ51u/j2le/iqt/9Ed5pcvZX/kVtN761vqosUxu+TKsJkpsaNth4myLaLVJE2dDJZofMVYVWmG4bdu8/U7bDlvew9xiw9IuF3/t1yZO1pDDJVhcPJDXzJZCDChJNBtVtkMJh8EotpJYQ/HDahatVrydCokGX6K56DVRiWLLhZcKAKms7MrmAxSykGlC2PeuEG7bzoUZgELESYk8i8OXaNmDrMo4cuAIYw6ypM3xZHV1FbOzs/jRp/4bpuN2aVtf93AtWcW1ZBNraYprWmHNNLBhpmDEHjypGCYA64VhfquGrN9CMPI9Q8josfP92Uq/Ok1hnPSQStmU36iBIIxc5F+WLkzI8cFG6yXopz0k2l5EK6EQyBDNoIlm2EAsY4QqHKu59exJd/W829Scnxv/nNwi3C/YgDvR9c6nvR+XpRCQSmRzaUMpe2Jc+tFbiixrJL9fnE/zi5wMx2idiy/T61mh1euV1pVu+/2Bdej37fpd/kPlGMe6AMB24syP8tpOnB1E2y3k3UlJw7z23//7QNrl9Ac/OOphkRGTz7emdSHO3DrjLcOde5cqlqEi7PaVAjpEotWlhLpoNaGcRFMBIEW50IZUdvswieaOL3YbvVaz7iR8fhwkmSdaWVnBzMzMqIezK3hFWcEYg4sbP0MznEE7nEMoIyxEp7BQuTbRRmMjWcVafxXraRcbWmNdxNiQ0+jJFowR0FpAG+E+W9ybygjoVAApkGLnk4ruBSF0IQnFdrKwEI+FWNxCPJbWH+rDIORYI9wvYn7BDn++v053FQYGQghIqSCV/cKXKsjTgIULERLul/H81ltHyFGRmhT91I/WEwikQqxizMVzaARNRCpEJCNIMb6FarJovvK7Z/fvJaOrctDk1xWpMTA9s3dZCBdZSFk4NhhjiggxX6Bl/yrr9iLSavepEXfHJTpNzsxAtloD6ZUDEitbzmTYQbV1berk3UkRZ8eNSU+7nDS2k2y7knLe2zEXcVkkW/YFmX/hyZIQy8WXCuz5tFI2ek3lX6AjSwEl5Kih6PPQRuOpte9i5p6zWMcGLvRWIC5dhVztQPUUQjGFRjiPdrSIUDUwFc5hKpwb6KebrGO9dxldvYw+1pHEGma2DT2/CAgFrQWMdiLQycDsvtFlQWjy5cp6I5Bq4YQiin5M8YFijIRJAZ0e9jNXEYBKbykSt0uDHt6WUYpkMqid788Y6DSx0X9JD4np2hMhWJGST4wB4eS6hPROaqQ7kZHSndy4kx8hsuXiPn/RIzslj9bTPSSplQpSSIQywkw0g2bYRCQjRMrOr7cXUXbcEVLsXxYOiSTUBkgTA/RrZGH2keCWa2WhG45yacg2Bdn9U7IsC13qsb56Gfr8qwjOXo/g1OJAm1Fh/NTN7aTYXkTaDuQa3PHLxnZMEAIiDK3EiiIrtaq3YTj0H7bYttN90tVVvPK//+8DKZfX/97vUdSQXXNQaZcnnf1Ewfnt85RSJ93yZSALYR8QcaK6HAZOwimIwEWoSm/ZpZEKPxIuW/ZF3lbLnFeREAAUfSWeCh/HjXffkd+XUQhcfxrmeiCB/bcJjSvpeZjlFcgr65Adg9A0EQdzaEYLaIWziIM24qAN4Oai8x6QvNrHeu8yOv2r6OlrSIM+9FQInF6AaMR7H7h3PmUMYFLpPpN9eSjtsltvtLSC0FSlIzyh6LZV1mX3iysMux4aOGynmEcpDkQeDkuDrpGGO0iDhsCupGK/q9DvBAgbCcL40M0qmUCs/Nu+qnc27xiMdmnBBiZJoE3fbXPpCe7/AsUvkVb6uWggaaMMrSBUZTFYihwslsnkMxitZ+fWi1SMuWgOjaCBSEVjH6133MgkW2XtrvrYKu24nxRzBBWyMM3PH0z2W8J3vw781/9fPr+a/NVfg3zo3RBJH0j6EGkCkSaQaQKj+xBJHyLN1vdthFm/D+Pa239Jvmz67tZv17O3demgJTmXjul3axYpto1U27dIy/quW5dFqY34Bxw1NcVKl4TskLGOgsukm1IQKrAiriLUkJ0bDixvI+cIIUcC5+hDkXv9Z//6s/vOvdbrmxAXl6HW+1BphEjOoBEtoB0tQA25KDLGYKO/jM3eFfTSFfRlB7opoBdnIOfGKBe8IhRh4IShk4IuLTlfrkQuVqMP66IU/fu5bDSj+FKokYWqPkqx1w2wudpA9tU6Nb+B5lQPEC4C0d0KZALRF4lum6hsQ3nf/Ba2Talfl/tV7peQAuNOGEuCMJOC2rjZSGx4UB4kVEoNhosIVC692AlCL6VYupPIciQhX4zjjDEG/SHReo0gRjNo2YIZKkJ0QqP1xgmTZvOp9YBeD+j3nBzrFesSmzrqb8uXe65d1ke/3E5vbgKXzo/6Ye4MIQoJFgyTaAFEGEGEAWRVkPkirSrp6tZl4i6KyhFzYcgfQWoY56q7hOyEsYqCkwJQWbo5o+AIOUo4Rx/Jke0m0G5Cwybc9QGso4NLyYswF65ArnQR9CVCMYU4nEM7WkSkmmhH82hH8+XOVoDulQ1sdC+jkyyjb9aRxinMbAs4NWd/wT1K/F+LMumkNA7lq6EapViVhi5KsYhQrE+FrqZAp15U43ZRikYLpLueW1pg7Woba1fb2zc9NDxpiDoxaNsMiETUCEe3vmi3jYys7aM4XmlbzdiQH69m2xZStL798ZOehxUZKrIqVzskO0nNZKAxBibV0P0EiRc1mFtBY/KTRj9yMEsploFyklAORAlWIwnJ4ZFF6yW6j9TY11cgA4QywlzTRuuFKkQk46E/TBH3/khTK8l6mTAr5Fkm0/ztA0Ku33PRbDVCLtsv8eVcbzyKHQQBEDi5FgRAmEWTFdIMoZNhbp3f3gQBEES26l8YAioEVACT9RdEMMoKNKNCGBXAKLdNuf6yioHuA16jiFoxAsi+yUUxfzlSd5tVUkZ2HZ0HoBS/kpm8A9exWzbZ9bi3zv7gaYr1BABTLsnhwSg4QgjZGRR9R4QIAojrTwOlNGCDq+kFYHUNuLwKtWnTgKNgFq1oAa1wDrFqIW61ANxUdNYF0pf6WO9fwWbvKvr6GhLVg54KYU7PQzYbI3qUB0hFKgppI44O/NJzP1GKRqDTUbi83Bzodqbdgwq07Q9WLhoU5xaorgPyyEVjRGkeJmNEqR+47cOja1z7rL8DeJqOL8NEYkUa5rKwXkaWRCJqxOmAEB0WbVnto1i3cS3GyoUpZKebC2dXMb24Xpmj0uwptXy3ZBMO20Ps7F2XS0GdSUJdpBR3sihC9/DqUoqlWx6WUuwu7FmIZGdk0XqJ7qOf9gHYaL1AhpgKp9AMW4hkiEjFiNTxjdazhRHKcqxWvvVqZFptFFwl4q3nC7lC4I18jrYgtJItcrdhZCVYthxFVq5FUWm7vS1vz/ftdND5v/6/5ccmJJq//X9CnjptI0rGVMbnFZThfc+a7MLdFUcxQJp/PgFAOdXZ/yjJngHhPrPyz5nM8Ql4n/9uk3RVlqUo+YFMJGbp2bknlMg/Z/3+8iIx1XWEHENMmpYFm9Z2Ofs3bJv71Vd4FVDtKsG54AghZAdQ9I0YoSQwPwPMz0AD6Lp/13ANevMCxIWrkGsJgjRCJKdtGnC4ACVDzMRnMBOfKfVnzhtsJCs2DThZQV9sIm0JYGEGmJmyxyMFFaEIYFdRir2exOXlLG03w+D6m5cRRbpk2YQTb9ZzuGUn7Jz7yLeb/OTG/cboGpjsFu7iRNrJ2rXbWbth6OyCJ2tXuhXe/UwuohCImWj0haEpBCRQXpdvw076cNt20UcuRatjzPvY6gKo3P74IHDl1VlceXV2yPa6AjZWBA4rbLN1m6360XkhnK2uNe1FrMJO3zzlqEEDOEloC5G4dVsUIilSirNIQVepuKYQyUlIKc7F3pWLkBcvIllcgJhfQCgjzDZnEQcxQhkhVocXrWe0LqLQqtFoXsqp6Q9Gq5Ui2XIRVyPk/D7cfG8jl26ZOAszqWaFG/JUz90KOa+PqEbSBYcn3OLOJrr//z/M51eL/+dfhzp746Ec6yCpr6Ccb913/6W5DXOB6NbDfZ8mtspyIRCN951phyEqL1WDitzLRuscRr6M4dGIMpOIWVtPDvoysrqu3H7yPhPJ3hgm5PLIuOqyu83TUvOO3GvPq0AKF8UP5b6Hw2ol5MCm3AcuatcXb4yCI4SQXUHRN8bIZhO4pQkDmwJs04C7uJS8BFxahljehOpJhKKNRjCHVnwKsWqiHc6hXa0GvAr0rm7aasD9ZfTNGpIohZltAkvzR58GPCFEkcaNN67i5ZdnkAmJG29ctZIPKF1fGHeG7/9/R3gC0BeEQghII6AGpGFxhEwa5rJQmHxrfiudABSpS1nyhKIoj720f/bYxuBcqrj4qkjJipysF4iDwjHrc2BbNapyaF9b9F/TR9KX6G4MFuSRKi3Grv0L+72mlu8HTwaKLSRhqY12F6N6sI0YVjyn3KYUZVOdZ1Ab6CSB9ucctEMtXpeVdGIr+5RLtXG/9pfSiI9PIZJUp+jpHnppDwAw88RTOPWn/w+EMTBCIPiV/wWNh94F00mA/iZMfwXo95EMjVarRsHVpaV6kXG9spBD0h/tEyLE0Ai2kmCLqkKuWBbhkCi4qEbSZdJtgi4ow3e9H+qN90JfPA+5dAZynumXQCHIarbsu+9DjUaEjSbMhlqNHtwuGlH4ItEThIxGHB8GpJsv6bYQdnmkHJDPwJGnrDqJVgg29z2oJBBFkGFZyokgsFFyQgBKFft563I5V11HCCHkUGAxDhxsMY5Ro5dXIS+vQm4ahLqBSM26asBzQ0+2UpNgvXcFnd4V9EppwHNWNpJt6fUker0AUZQUkm+cyD1d8RoQA4Iwa1dEE/onguWowkIVZmLQwMBIt3ZAFg5Kw0IeHugjPZb0uwrPPXkDqpGht9//83yuvvyiTss8fdyfu9J4c1OW1g+0kbVt/D789aP+A2WVtodFHvrr6ttoQGjbj9AQIgWEBpBCwm1TqYs80E5Q1qQUq2BoIZJcDAIQ2kBoDZmmQJJAJImrOlosV2+R2Eqm6LvbJIHoJ0A62M44sSaSBCJNIdMUMtEQ/T7E6spI/1YlpKqJVgu3j1bz20U1ki6MgCiECCLXr7evUpQKhFTIIwuz7xBky0U0YvEDVfZdU4lGRBEBmH317zQaEXDOCEVE4mD2ZSYIC6FaEpKoRCeikFL17cdXMNZGxmXpq9sIO0gBuDkhDdz5mxfFlgs5ty6XbVllaKXsZ2a2Lt/Pfaf5Qq5mnVCcu5UQcrJgMQ4yNsi5GWCumga8BtO5DFy4ArnWh0pCRHIazdBVA5YhZuLTmIlPlzu7AGz0V7DRu5ynAeuGgF6cgpidYRqwRxRpRFFv1MMYTjUyD/AV3vZ4TQdSkI2A1HaNSIu2WTThdinIAHJBaISdl8W4Oe1yaYjKfgORhjt/KONIGKc4c9sVnH9+Adkzd+a2K6WCHPkFjDw6kZxdAFYL4FRlYUkqVuayHJSOcts2/h/UGAmTAkiBgytPsjXCpJAmKf7pvv1nEsi0C6l7UGkfMu1Bpj2o7F/Szdsq3YdM+979nl1O+xDZdv+fOZi/ayeew2ZzCc3Ni2h0l+3KrIBC5Mu0SsRbFNoiCVGNkBsWBRdFpX5LQo4XhISMBaOORjSATWvGYEQivP2EcLf+0CpSMXs8fpp2VQBW5SKcSBSyaCurctHtV05p9p43oyGMdpFvdtlVerNZFPkyauaZc9/qIpN0znLWpKIKIYE4stNRhKFLZ1W2urNSVr6pSiprJuC8dZngYworIYScXCj6TgiiEQM3n4VBUQxkAz1cSl+BuXQFcnkTqisQop1XA46DNlrhLFrhbLmza0BveRMbvSvo9K+ib9aQhinS2QbE6QWmAU8i3nniQacgAwJSC7dObZ2CXJdCnN3mKcj6WKYgz51ew7S6iPRyF2oxhlocfTRtdvGkXDEcO2ce8ugCkaYQWeRamkIkqY1IMymETor73jbbLltX2S+x/ZlUW7GnJYx2ss/YSMbUSBgoGCOhoaAhoWUELQNoGSFVIbQs/qUyhFZh3iaVbll522UII4vPLSMUUqGQYjCd+tCea5NawYgEEikEUkikkEJDSiCQgJICYSCglIBSEiqQkIFAEAiIpIcr557B+TMP2Stco3HDK9/E2Y+/C2pm1qb6exe6onrh665wbRtRXDh7F8TZvGC8cCSEAIPSrbL1wI83EGnoRyi6lfbHKA2dAmmqobUGtCkKRbnbbL39krF9C9hpD/x5PwWQ/7AtZFEQKptnTigBIUOIQEIEAWSgIKPApbcKGwWuJISStgJ9UNwXUrriEBLSiTmZzV/nfVb74tGKwiyqURSp2YQQQogHjcwJRygJceYUcMZGy6QAOgBWcAV6+QWIyytQmwaBbiBSM2hFC2iGc4hUE1HzBqB5Q9FZH0hfTrHRu4LN/hX00mtIZBdmKoBZmoNot0b0KMlYkMk2IBd2u5KFNVGFxbKATO1BBNSxTEFunXsKc1/6Uj6/2vJHPoKN++4tN9J6UJL54sxbj0ysebfl/Qqx5rerrkO+rXKcMZ31wUgJEygYZecMMkEAoxRMoACVLbtbpQC3rIMAqYqhgwhaRUiDCKmMrDBUThIKe2tkiFQoaBFAiwAGbtnJRwMF7cSkcRW669KpjSmioo1QMEJBI6p5UCg+oLeaBu+6txXLQuLnN7wXPz8HAGsH8tz6iFz62YtNWbmfC0VPHMrKfbvd21YjILP7W27PhWT5/tbbvHFJOJFZP+58yqqacWdjzy7EJ4HNDY31tRTtKYVmi5H75PDJijPl0XBAntI6kMLqV2nNQwBtHShjXD2o8iSDEApAKIs3v3KRcllFVqncPHNeQQc3dYMRAgLSfrdAuHX2fWGkyue3K0UqGluYpZCSBkgBk7ifLg1gp5BwP7+b7IfMbJsZEHyZUS0VWUH2MF1xKiVc9KKAUHa9zD+vRP7ZZfcTpR9uROlHHlH6jBwQjRP0eUcIIZMKRR8ZipybBuamoQH03L81rMN0r9o04Gs9lwY8hYZLAw5khOl4CdPxUrmzS8Dmq6vY6F1GN1lBIjaRNgzM/DQwzzRgsgNqogqBA0hBdlLwoFOQjdEQ/Q5ErwvR7UL0Nu1t166T3Y5b34VcW0PjueeKVCRjMPfFL2LmsUchtCkE3bjKNSFggkysWcmWCbXSuky2BcqJtqBWwMFrX+rHb1OSeEEu7YbkqO0ZG2dq/4W1LbIY6e6O+jMwSEyCfpog1SmMkQgQIZINRKIBhRAKEbQW0Gk2ZZNAmhqkqXFRKoDWblkDOgW6XYW1a4OSMG4kUNnr0qXaF/N0eRWtUSz764Y+DgObUu0eVfVRnkT8OcJ8EWi9gRi6Xda0r4u4HBCpVdFaK1K3EJg1+18838dzzxSv5Tvf0MD1N0S7flyTJj/JcEzNvHK1c9C5Qkp2J/vayL5VgfwF5aqke/chgCi0hZOkLaKEwEo6KJULOcisyIOfDqvsV4JSth/lBN6Yvyz9lOg8SjFbZ4p1gP0uMGla3s/4+yHPjc5++izuw51XFKLRfx9X5aI/P2Je3Cor4CJl/pkipbSRjgOisHJfwo6ndEz/c8Y7Por9CCGEbA9FH9k1Io6Am66rpAH3cTl9Fbi8DHF13aYBixbiYA6taBGNYArNcAbNsDKJ5TrQX+3YYiB5GnACM9OAXpqHjOovrQnZEzWysDaq0BiIXg+i18nlHHpdqG5xX2YCr1vIPNnrFPf7+5uzUQBQG5tDt5fk2nYCrC6yLajuVxZwZdmWCbn6aDiwct6WpCZFX/eR6AQAoIRCHERoBW1EMkKkIkjhP4dZqvTO6XZSfPevskT3op83399HFNvIkSxqxriQk+LiUXsXlMaqa2OgdTFBlnZVowG7bANp7BWbMS7CxUXX2l2Euy/tPyHdBZ0AjL1vivBYmCxZv1TJulrVGu7i1933gnqylD6j4aoww2tT3NfaPk7tS01drmCqK/er24dR2p5Ohvx89scdPPvjzp7334v8lEPaCyn2FfkptxClw/aVzmoMS3uXdX3lgrX+WOPIlrKuKu2ksJHncO924c0V50TbZhfY6ChMzUZozoV59ByyeeYGhFy2r7KfE0oWT2Im604Q5ZToo3vwReEWv4CLO0vyZKMNutQwyXABaZxAzOUisp9Fs6lZim1VGZjPbZjLwIL8feTJwOIHDOEqQmc/YAg7R6P75SF/HfnysnL87Bj2+c9Eo11Zko3ZMLN2eQEaSklCyHhA0UcODKEkcHoBOL1QSQO+CrP6IsSlVYgNjVDHiNSMqwY8j1A1MNe8HmheX3SWAPqVFBv9ZWz2LqOXriKRPaRtCZyag5huj+hRkrHHGDtnXK8DWRJxg5F0otupl3a97oFFz5kggIka0HEMEzdgotj+ixswUQMmjgEDNL/z9bKiEQLrv/T3oKfmc+kmlIJWob0IUgpFVGERVzhY2ATI5yQ8AYVNxoEUGkmaoG/6gDGQQiKUEabjaUQqRqRCBPJgf8SIG8Cdd/fx7NMh4CJP77y7j0YTcMlsB44xnjzMJ9rPRKIuiUWDLEKl2AfIZGE5tT6rJClEdnEm8vs2ssOtl9JGjriIHlsNWbk5BYVdL/z9vfsuBU8g61eWjrHVY7bic5gINKWMQ2NMSUhq70K5Kijz/Sv3t9xWIzR15X7t9ppx97oG62uDRWGi2Iou7TINdc3jGv58TZ783BeZ5KyVmcJLIx8iJAW2EJbI1Lu7b/V5sc5bloB0n/k2olTm0VlSCjfvnIBUEjII7bxyYZDPM2fnmFOQgXsPhsruFwT4+U9X8aNvvWr/vKKDe99/I25502IhY6STJNljl1u/58jRUUoTPiIKUViRhL48BPL7+Q9VCfLvFOT7ldv76+yPTPZcJxOPMMZlTbs3T027YXIQ/k22vnCUhRz0t/mveRQ/GCBLsXbbhspHt092zGx9WY5mx/XaZZ8NxYdEISUpJAmZWCj6yJEgZqaBmWkY+GnAG9DdqxAXlyFXu1BJiFC00YwW0I4WEcgIU9EipqLFcmdXgM0L12wacH8FidhAGhuY+TawMMc04ONOmtZIuWGRdF76qy/09ODF6l4wUuZyTmeSLi5LOp1Juzh2Qi9btuuxw+qj6dQUpr765/kcfWsf+CV077h9yMAAIHUZxO5kz0U+CS0gDTCssElOfjZaKWwy4rkKjyMaBonuI9EptEkBCIQyxHQ4hUbQQKxihDITcIfHddenmF9Isbkp0WxqxI1DPdy2UmyvFBGHg3Iwk4bGaJgkGZSMtoNijFmftWMu5F+RHua2Swkp7AT5ArKQinkVSysa8/auXyltcZSSWHR2R4555OvmhsaX/2y5tE4I4OEPzW45V99ByM+B6ExXJwED23e+b1378lgGJWsWtLa9hC1L1FzguvvDnyy7T+qvqF0+TETl1j/+AY/BAE999WU89dWXtx+VJ/2E9JZrpODAulJ0V2V7ZZ3cRdvy+m322a6vyjq5o/5rxrJV/8dQmhYianDcG6s9rF3tYGq+gdZMzfy1h4wfzQh/Odvmy8TKujzqMXtLeVKyWFeWmMikZf43NMjfp05C2jVFOz/9PZd7+TyMKPavSEP/ZZLLQel+RpNZJKUv5ovvz/IUEXX9F6Kyej+LkszGWRpiXbv8u7l8DELI9lD0kZEi4xi48UwpDXgTiU0DvrKcpwEHpoU4nEUrWkQzmM7/ldgA+mtdrHcvuzTgdaRBH3omhjk9Dxkd/UnCiUPrIiLOSTrpSbo8xbUi6UTXRd/1OrbgwwFghChHz5WkXCHtCoHX8KReJukOfs63YXTveQv6t9wBtXIF6ewC9PTM8MbZeR+AgypskkvDg56rULgximK8uVgUNfe9xzfOpCZFT/eQanvJroRCI2igFTQRqgiRrKbjHg1xA4gbByO6R0URyXfwffsSsYhGBGC0F3GnYdIkl4el9rAfCcZ7XwDZNZd3kS0KCZhfRGVyUJTFoRQSwqUzFhGIRfRhJgoHIxT9f/t/rTVbEve9tY2nHl/PA1vufbC9bUGO7EK9/JPGMXgTHxJGa5g0hU61FZ5Jam9TjTTVMKm2cjDVtsKrNrnUNJDQNsTH/eAi7WtNKhgpARnACGn/QZT+AYCGADRghByUna7yrJWZZhf3nRzdwf20n6Lf3dvnjzGASQ9BNp5AhgnA2nXbCcxMkO6i7ZYCs7quTiBJgcsvX8MLP7iSP6bb7z+FM7fN1MvQYZJUZlME7ECY1o3TE2Pj/JlmctOI/Meu3QjGUpRk1t8WEjOXku7Ezghvrk0vStIGSg62GyYHs3XZ/UL6FeRycLvUbSkhAWxc6+LalS5mTjXQnm0U5xUV8Vk65/DGkG+vSMpi/aCsLDWrSksM2YeQQ0CY/NPh5LK6uorZ2Vn82b/+LGZmtri4JmOBubYOXFqGWtcIdOTSgBfRCueHXlhro7HRv4rN3hX00lX0ZRemJWGWZiGmp474EYwpxkD0exUpt4PIul6xTfb2Ny+djw4jL3ouHkx/9SLndEXembgBE0ZHJulOJL7oyKMKRc06uyarfCzgXcblJ1VODOadjl9UYYrUpeNaAZSl47aC5qGl45LjhamIwVpR6C6wjNHeBZgnEQFACPinZvmFQX7R4aUZZ/dd5IWUClkqs5WJyn4vekKwLAq9CEW3rrNpsLFh0J5SaLVVvu0kMTBnXZoiC/cbmLNOFJ9r9m/lzVknRZ5rK4LA/gtDW+E1CIr56aSyFV+lnZZBCGF/aDpmc9RtrPbw5595qvxjkgA+/r/di+Z0WER6lm6HrMsiJ4et99bl4nFYX9V1Ozluqe3O+te7aDvwXAw8Vm9d5bkgR0CNlCwKJ20hPWsiTOUQmTkgOat9i0rk5w7E5s6PWSc+93PMQqpuFXlXkpKVKMn6dcVrvhCMbtmTmMW6QmK+9OMr+P6jP88/nO957w248Q3z9vPUeOOrlZPuRZCNSwxGTlYfZ0kI18nCbB//ZebLQi/CMZeW8KSzAPI5K0Xx98rb1wjLgeOj2GdgfPnQhbfdG19VTObisl5m5rts8Xo4DmSeaGVl5dh5Ikb0kWOHmG4D0+1KNeAN6N4y5MVliNUOVD9AKKbQCOfRjhcRynhoGnDnwpqrBryMvtlAGmuY+Slgce74pAEbA6SJFy1XlnWyIumsvBsUegc2L50KYGIn5gZSW+OylKtJdzVRzAIP405x/oMiqnAXr58RRhUOisHBqEItsnTcBKlJIYVEIEJMR1NoqiZiFSOQQfFEkBNPKaLvgLGCYFAOllOZDWB6XvSGLbIi8usgk79a/csYkX/WFvIPAlhbEVhbyU7gy2Iwl4nCi1D0BGJxMSHzC4CqUMyeM3sR4e/rP5d7f06HyTrjS7sBWWc/X2plnRQQUQw54bJuP7RmIrz1Y7fg8f/2Qh4V+uDHbsnTLoU6AU/CEVArBbcUpPXSVA9ru6UgHSJgd9y2vI8eaAt01nu4/PL6wOOeWWogjNSQx5sVUtrB87ETYeqNB7s5tyEW95kvdygX66It5YD4rBGiA9GqVnKmfY0XflhEhMIAP3j059hc6yGKg4Ex+bKsHB1YrHNfU55gExDCeCLME2/w+ka2jz13zfuD609mT5hr5+4C5f3LYg2FTHOnw/6XjIEXZQmUZGZx2mqK/baQmfnx80PUy8x8+1YysyIny89pcb/YLuws097fqzq+OtlaJzPL4xPedn98/rhtm+ULGziuUPSRiUFGEXDDaZgb/DTgFFfS14CrqxBXr0F1BALTRBy4NOBwBo1gCo1gCsAtRWebQPJiD+s9mwbc02s2DXg6smnAcVw6trl4BWJ5A2auBbG0sPvBp+k26a4VWTew7nDmpbPproWky6LldDQYWeenxe50XjpygvG+TLesgLwVuafzvsTdstTC/WKrKlGF2a0/DnsClBqNRPeRmhRCGEQyRFu1EYcxAhUiUqE96TKAySoOldKSvcfF61lygNgT3IP/XB2eymzybXk6sxdWkUddoLxPdczG5Eq+tN6/LWSed6JvGwDG5KJSwt6XAKDdxYGxoRoC/sWXLASmVDZdWjkRJwSkk3QyiiCCECIMbOq0UhAqsIUnXCVy6arEniRZt19uu28JZ26bHencapOOEGKipemwyND3/E93HejraVjEpJWgZYk4KDQr+w6L6twuMlV7x6u230F06aAo3dkx9RD5OUyKVmXqtqdqbp9Uj5ck/enjF0c9hEOhVlh6cy4OrK/KtdI61IhPDErPfB5HAO7Wl3lFVGKd3PMkX95H5fh5lGDWX7acRUCWJWF53H7/mTAVgDR2GgJRFssQ9WL5lZ8u48nHfnqUf8oDhaKPTDxC2Uq9ODWXVwPuAljFCsz6q7YYyHoClcaI1DSa4QJa0QICGWG2cRazjbNFZxrQr2ps9pex4dKAFUKcmXkzRCRh1jVefOkxqNnEprbWyTuv2mueFntQ89IBNdFydVKuUU6LzeeqawDB0c1LR8i+yH6oFMWJ5G6iCrVJkaYpUmggdem4KkJbtBBKm4orhROFfffPHjAXhOVxoLQ+vy/dGmnKbVG0L/atuSXkkChF3x0WrkKFjayDm/9QQ6fark8NDFz0nYtCSLWtEpvNVQchkLozduNO0I2Tc5ACUAIQys3bJGyEnbuFiz5Etl5qAH2Ifh9IBERXFhcH+YVFkdIsXUSfFFkxFpWnRgtXsMU+l+W0aHvh4+7nEtO/zY47mD5d9HE8ac1EFHxkz2wXGXpQ5MKUv03vGr9a8oAU3VIy1kjRLYSrqel7UKYORoz2NhM88zcXBsZ9672LUIGb+9SUJWYmT4etN7p4zMbsbL3/PJXGmK3X9qy1KlSzMez87wE3xynACNQD5hg/nRR95EQj2i2g3YIGYE/9gXV0oHsvAJeuQq10oPoqTwNuRYuIVAPtaAHtaDByTwiJWxbfBwBIVQIdJ9BTKVKTQJsEWtt/xi0bncDoPoxJYFK73pgUBtr7ZwBhYCRgJAApYQIBEyggCGBCBcQRTBjBNCIXleDSjOTxvVAg5KAxMEh1gsQkNtJICCgRFPPsyRBK2jnJ/H0Gv+PN4KJ3m7k+YUS+XmYhf5X0imKHLeSfcNvkFmIxO5QvDIFSe0L2TEnWmbw0bXHfvVP8NFiT/QIvC8nmJB1UgCCSNvVVBVbYBXZ+QSgJAenknbufp9IKJ+H29jCKqEVTjkw0/n3/4tFAp9W2uhTFaKMWvVlIBZBPEuWRCbwsArEs+3YqGd1tjWQckIYnVDKSyYCRoeNNEX0tIMdUlM6ebg7I4tvuWxr1sHZFST6WKr4X97cUiTXr9yYsh4xhYH29sMzHiyHrTWXcujKGYet3IlNrnwMAqH9Os7GlqYZOjrHlA0UfIbXIKASuPw1zvZ8GrHElPQ+zsgp5eQ2yAzTFAk5P313bhxIBlDqCt5gB0HH/rpU3WcGYIjWpFY3Q0EihTeqW3X+ikIp22cAI/x9gXDSScRFKkICRwi0LL6pC2DQpykYyBqQmRaL70PZb3VbHVU00VAwlA4Qy3N9FbU30ndlyqXJvO1HoliXgmUC3XIkqHKxaXDmq/zArbUvbZOXEpvL0mLqnq3pMUbq3vXzkx8TRkUktb666bC47K+7se2UrWWc/7yVkGOxB1mXFQY7+oRdzAh49O5WM9mJG700ylm4LdiMZ7WZpJaMoqkNXJaPIxO02krFIwSoiHf112XJpjHaQ+d+qmEeS5Cn0tRtrF7Mdh/S31cH8RTN02877G/KdOGSfIJSYXWoBALobe818Gd2F+m4isg5nAPvdfR8djPixGwOcuW0W7//7r8fGSg+t2QjNqQib1w6uaCCAHZ+77PhbZy9fTvnnOOx3rr/pwL/sdtbhoXy/H/RTuM0gN6/18KU//OFOextLKPoI2QVCSYiFOWBhDhrA2sUrWFrX9kTXoY3G8/FTwMy0vWhK7Yk7tI16EFn4oAGEhk0LNALCCAgDyHzZTmIujb1gKv+nIIWyy0JBIbC3IihJCyUCKBFg37VADWzO8x4YlI1OMg6VjRpamK1loy0yOVQ2CncRKuTxko16vQezkUC0Asg2f73eCxo2HTcx9qJACIlQhmjIGKEKEcoQcpx+ft6TKKysGSYLK9tF1dB5XQgARQZ0TTvhbTOVJqa0c30XQ+XgHmVkecA7O+Z2MrKmj2MjJP2IO78IhRNGwhXmADzBkhWMkBIIQ8jQqwqbFZaABAL3gTsmsu64cmwko7GRz2mSRTVqLypC59GbRUGDrK/8kZb6s6sK6WeMP5G6W5/JPYPi4ktky4Wg9KUihIAUEiaLRpTSdpBJSmQi0slmXyBmPbpUb+NWy2Jg+f7538xty2Vmtk7426Sb7L6YI8p4PQ41cdU3kreTL1sql/BDdx/SbMsftcqbtuhwq01D+h9YLWoXd7bvrtvsu4Otd91xw5qWuzjsvj436g693w/vnUqXfR9mH8+bAFqzERZvcPf3KrWH7bLD/rbuYys7v4eu/Y1bDHDHHnaHtvpIpHbNMeoOO/gjx846rD6GqBnivg/chO988W93Nr4xhKKPkH0glhbw4vlv46apt0MKCW00Xlr7NuStN+26L3dajOElNfSWWwEUIcypFYz2Qs9Y2ehEYyYY62Sj9IRjIRulXT9UNioodzvOstEKR53LxtRoGGwjG+uEo7vm3alshBLIKlRu+fBeXsftyV0Qws71+PzVn0Dc2N7bAz5B+Om42mhIIaEQYCqcQqii2nTcicO7Nu101rC+vox2ew6NxlSl4dZnYjs8ndvdpi3W7UlGltZV7OihycjBgQwMQQzZttdjZgJGp+6LIXVpJakVLTqxE1G7psJFX0FKiMD+wCHjBhCEkHl1WAEhAyvuZPZDiHLCr2Yc5Egp3Jh7zfj3DXKplrm1XLIZb//Srb/Ne0MIU3nhZT9UZm3sTtn3uFSZxCr2yL/jpffSF748Q1kEGwOIQjBmr1v/14micEvxQEw2rmxZiPyJEkYDSAt56fbx+zameskuSh8NeRVM9/mQZBLSk3cyj3IEvNnmiwia/HHLXEpkjx/CVR0V0s4t6dLOi6hIYQvB+PIyvy+K59BLqZbZk54fF7m891Ov8z6cYBV5MRnhKmS7xyHL+5WerdIfHeVtQ+4MiBkxrF0F71iD26ordg7T0Ak5WLaMKi41PNBmO5eOB3jQG143h1semMb/8X/trP24QdFHyD7R99yEn138flF1dw+S76CwFZacYNoD29ft3alsdJGMmWz0RaM2gBY2utFdyFjZaK+QC9mYCcdCNkr40Y1FRGMuG/Pletm4bw5ENmap01Y2aqQADOaCG/MxCyFxW3I3Xv3J80iEFZNWORZSMrtv/DXCpXshzSVl3lbYZcBPz9ZAfmFiLwiqKVP5RQK8C4NsnFlkEPyTab+PSptdtC+OXYmmAKyeNTq/IFRCIVQRYhVDSYVAKsi+hk666KCLUmpaPv7iuPX3/efEH2t1/OPDyy//CD/8wdeRXbC/6Z734cYb33h0A9hSlA1Sf541JjLSrR8YvvtRRFTXecjSOl9GZsJG5xF49tZ4UXfZ69TY13yWKisCK+SktLexFXT5dmXTY63sc/v5XwPZMLSwk9EKz/VIXWqzub6C9ZXLaM8uojk1O/AwBmXlzi7sd7ft8N9f5aCzihTLRdqgaMuae7sU8xRlXeVtRbFPvpzbr4Hl3MvUREv6VQQBG1RZRK0hF2xWJGXrJCSKdb6QFvBkUC6Fao7l75b9YOW1LwmvMfxc3Ct+Jersvlso5GIuFO1t/hrI5qzMt2tk4hHGQKeo9Ju9xrw+AO8lI/L23mL+t3Gvnnx7/scT/vc1yvf9dGjvO1IIAZlH7GZisoj+lW49pJOgbg7JQuwK7/Ujy9/r/venLya9YyN/XWZCs+78IX9E+bH8NpP0OiRk3Njx++tgm+2q5UGSTR9wHKHoI+QAEEsLwNLCiD6CxgsrG9Weq5jtXDYOn69lP7KxiGas/NulbAxkOYZxt7JRCIHr49t33H6v2CjHFFqnxfyNJs3Xp0iKNp6kzNrk8hKVNnnkZLXPpNQ2HejTP47tf3ypk4Ll9flFDLyLmyH7Fhff5fuFAKikuXlttUmxuuJXmDP44Q++hp+//GM3V6j36ZSPobJcXC1BVNq7o/kP3bvvX2gV98s6THjN/MblsVT7qPay7VhKUSHbPZ4hYxmILCnG4jW17b2IK2GKC/v81sYL5ccVyApKuNdCJu68uc/gLqKz6q7uihoiLZ7vwRGWx5ityx+P8USS8f4+uWuy+108/1M8/8x38j5uf93bceb6u/LX3vDXqndx76/PooXyxy9gvO3F8529rovXUrbeeKIgX++WrQMRZdda2r/OuQpkItUXa5lPKf5cIhsW8sXs7y4FEHhiTQgI950nnGSVnhTLjmH7LyRs8dwUy3nQWHUM3nuoTs6NE+sry7h26SKmTy2hPTs36uHsiTzt95hQkoQVOZmlX/tS0o96zCOInbzUSNwqnXVe028xCX5VTrp3WP7myz6C8veqMaXP5ex9kX+uuud9c3UF68tXMbWwiNbMrGvrfRb4eN+15feGK2qTvX+ELD7y/CjMTG56xxAQ7rM4k5HVeS298eePxfsMgyi/x0v7eJ/VlcdUd/5QfUzFYrnNXsRodTyEkMmAoo8QMnHsRzY677cNByEbs24MVM/gVv360omWMQY/x/MuLcefr9FJR3eb3zPestOQRVtZrC+F+dhf5O26fSdXHxraRT4ao5GaFAaFiMzTsDNpWBGGqaned+ISCVK9vWQsSc66vt26kU+67WiqaUyH87jWv4rN9BqWl18b9ZDIMea5Z76N55759qiHsQ0VwehdhPuRQuVoH1+ey7JId21KEWv+BX6+vxjcByiOUepnSHsxpP1262TNWLfYf/B4stJ+8HEiO0YWNQr33ZqlslZTPt3t+eefxbN//W1kFvl1b38Xrr/zrszmFLI0O25mhjKB6rUpixlfVJQlcel5zNp4f/st+/QETFmw+H1W968bQ6XNtn0eLOUfko4HQ+WkMXj28e/gu//3f80MIR765f8Rtz/wkPdd60Vb5vsXkZGFaDTQefeV6Ey3n/GWS2PzInjLKfXecWGf+/wHH5H/rmKFpyc7K4dAYUS9+wD8v6Iv6bLXa97Kl4PeDltJRL/jYyFGhz2moqOBx1l8pgDrV69g+fxrmDtzFtOLi6X9Su+W/OFXtpWOUW7sj9V7BOV+DuG9TshOoegjhJBDYLey8fmXn8atyV35XI8/C4bN0Wew5/zhbHegnB4NAVTuZ6nUNppR5JGOqNzP9gHEkD7LfeTHGejT669yqSKFzOclGlcdmaVHA8jncywvV+Z89G/zZV1el903Ns06S8O2Fy4aRWK2Ri/poPPqMu6ZfxeEkDBG49zVRxFdN4UgjPMLFZPlo+bFGfK1eUqaP0cWRH5plD3Q8vb88ecbUd7DFKv9C6Wil7yv4X1Uj2W8rsoXbNXtdrUXsYJiuZSC5w4mhLerd9GO7CIiP7GXgJvfq9TWf+SlK7py4YLSdn/c1een5sIzSxMtmpryYzb+39M75pB+s41Jv4uNtSsDsrjRnkMQhF6KYfk14kf31Kc31qU6Vp7/ynOzO7LXmBl8WGTEGDzz7W/imW9/c9QDGT9KorAiSzwZOlQe5m2Ktjvt0xc79W3Kfebt/Pa+wKwI1ly8btVn/nhQyGPXT5L08crf/qh4rozBd//v/4rXnn0GQRw5oVQje73nqW7sA7K17rGVHlPd8+//bQYfX53QHYjQy/aR5eetLLfssvHXubbOI1ZEU9ZXsVy0FxDClNYXUtjemlJX7ovQvU6ca3Vb8zvlsecnl/ZcruhP5P0a//lw/UkB5AV1ROZ1i+fWGNjnyQgXCW2L7mRS0faRh0jDOkp7/8UfPoVzX34k6xT3f+R/wK33PTAg3/J7W8i5OrlY06j0mAvn5+8z+L72pWvhW0Vlfd3rNevDe125/Ysf9b33my93S4+3/EzUCcxh2+r7qzy/YrDNwDb/NTX0+auO1ztmZZ/6/uof75aPSwisXr6E44owO55RcXJZXV3F7Ows/uxffxYzMzOjHg4h5IRykqruapMi1bY6roGx8+zJELFqIJIhQoQ2BU4DQCYJkcvDTEIiX7bb/OViH3jiEvkZrcimJiv1I4b0Vzl2+QxoojHeCXwJUZViprQt3798jpv3Wt/HYNtcTtaOqyoGDUrjyC5K8hNgd9Lr0meFlDBeZBIk3KT5fjtvMOXzzeKx1dxmpQWATNKJfB/jizp3m8uu0gWVXTbC5Bc/+QWUKwLkrhPyNFmh7AWRyCqOi+IiQSj72nVFSGELUNs2m5ur+NGffhkPLHwAmSx+4spX8OZf/Ria03Olx3cYUQpVkTkoEL1lkwldTxpW2pTTCz35WbfNk5TGaG95sG+/fSZYbWpk5RjV8cEb+8C6Snt/HK79do83G29eJbemfUm+lh4T8nVb7T/4OMrPVa+zgauXXhmQxTPzpxGGsW3nvWfLc9J5z00ubf3HVbxbqsK4tt/8NVWsG95H3oP3tyi3I0dP9XVEyIFSJ8b99TXyFSWpi5K4rd4faOcfy1uuPV6pD3+/8jlJSewOG0/pGDWPt/LYy9uyfga3DUq6ypjqjl8jxwf6Hlhf/HgwePzyc5b9GDLsb1GW8zVjy8dgpejV136O537wFH77T/4CKysrx84TMaKPEELGBNmOgAkVfAaD1XEDEWA6mEaoQoRDquMame1d9DQWlCQiCgkIb3mIjKwTh1tLRlF4tGHtUkDqwSI8lbi5PQnKfJ/qUz/g3w5e/oyGqiw8GTQBPLj4i/l9ISQeXPww8NUUKS7X7zRUdIryy2Gr7dXbmu2iclvcGdxf5NEd3vpq38O2b9P3vrZn1yR10RKlMdSMb7fHHvrcV7YPPHf7O/bmxip+/Gdfxb3z78ll8VNXH8Mb/ocPotmuXCAN+7jYMmJm+KYdtdnhMUsxENnHn64IyPy2HCVrsgI72RdCRZICLs1TA4WU9CJ1fcma7V/px+1ppwGBE83ZuD2BmfeZPyZ/nVsWnuzMx1gR6tkRS22yMeWjKQnoYjzG/jhRHYf/nKHcb7ezgbUfn8dDpz6Sv46+e+lLaN29iChq5u0Gno8aQVz6e1alcv487/Cx5vsWy7XH21Y6+/2Uj+2320p8+9tLx/Of18oPFsXY6rcX+9Ycr+b1WN7mP+ZtnuOBdjXPU+U4e2VLWVx9LPs6EplUGmp61EPYMxMj+j7zmc/g937v9/Dqq6/iTW96E/79v//3eM973jPqYRFCyAnFIDEpEp24ixCBQCg0VROxihGoAIEIDyUy6EjwLpb9ywx/zZEOpy8w81yrJPIMDFZv34AJvfFUh2bKy2Jg26DkE7V9FO2slHQn9dpNAu8ufkVeZdZemOYC04WYibxQhStMIRWEqyZrf531ilkAEPDSU/ILW+/4Wf/+eggUGwcft59qVKzzlsVgG+G1LD8/xrsv8uNnjzu/6PHGkt/mFzz+umzZ1Kwbsk/dtur2vTK0n+06Prz3yLhfrI37+PZDBOC+hYfz+0JI3LfwPuCbKVJcHdm4DpthnxX767DOgG9zgDGWp+6DfmfHNAZmSRefs0LioaWPQmxIoCPq9xeVO6J8yG3HW90mkGWJVv4eu+hjL8fd6u897HGbytO92/ENe31s9zxv+3eo9L3t36LmxwSUz67qPkFN/pVu8n373U1c+Nuf4sbWXTYS3hi8vP40Tr3xDkRho9Jn0e/A/7P/ierWYlt2/GIEpvTay2Vidgow5IuzJJqzpcrxjam2Nn7D4hSn1H9FolYemxGehPXHYyp9C1M+Ts04SkLb37f0f//kDKV2A88hPAnsn2XXyPvy81EeQ1WkD/bpPQfutru+jvhKgJua9+L/wH/FbhkHNzURou8LX/gCPvWpT+Ezn/kM3vWud+EP/uAP8LGPfQw/+tGPcPPNN496eIQQciLw03EBO0dIJEM0VMNG7Ykon5OGHCwmNNg800XzfAzhIkY2z3TLkg/Y9kS75hS6WHSSThvtljWg7bKBgTA6P8m010USUDIXd8gqzKoAUAoQAkZKlPJI3UWBkQrFr/zVK7Zi2c71XVwc2AIBzsNKAUg7Z4/0lv101qJYgusrL/xYbiOPq5DeAfkJ72YK/aXl8kYByA/MQTScUK1eb20lFrHL7TXXPmar7dmFwtC+t9juX6kN3V6zzdtu9nrs6vZhxxi2fQd973ls1b/LwPrK9ur61LhItQreDyMlBj9wdrZt0jnw52UUT2bdMXc+jmr0uYAAOqU3L/E4yc9KnRKPEeCm9t1FGyFw09TrgReB3c9zPUS6k8lnHrjWXd/1buPipiZijr63ve1teOCBB/DZz342X/eGN7wBn/jEJ/DpT3962/05Rx8hhOweA4MkTZCinI6bi70h6bjk8BB9AdWXSEM9KPnqyKpDa2OlnckEnoHW2v7pdJbiZSPpjKvYCQgr7JQCZGBFXlY1U6nCmilVzHki/DlSnEiTngcIJCRg55lz26Q3YXku6SByf5jPB5NV+3PzrRzbaNERon/WgX5yLXep8v4pyFsbox4WOSaYzRTpF6+WVwpAfngOoqng64jCtdb8vFBdV/KOvvwdIn5qok5y9JDj1wnmmgEUgSfVYwwev1ac5j2awW3VodSI1YGeRLmtF3g0sK10nCzozW9rkP9QVOqzCEb2jlMRcd628sohY/DnB6tu6wHqyX7pCAZAen8IRKLUre13i+Nss35gaorK46h9XPnzIrxtNTv6d4ccvW5sA1Ns1I2hNJ7tJ+UogrxEsaJu7LUvMPvCE97rqdSs9JqovC+GPXelTTUv7G3/jpUnxfsoEAD0RoL0lc2B3eX1Tcimi3Pa4nOmfH+bdluOc6frd3iMHT1PW3w2HcYY/JXen7L0Nt3pczts9Zav/70+d6a+efZQUg10Na511/HGf/+xXc3Rt183dVAc+4i+Xq+Hxx9/HL/1W79VWv/hD38Yf/mXf1m7T7fbRbfbze+vrKwAADY6m1DhsX9KCCHkkDBIjPbScYFABIhUiChoIBAhAgQQWiDRBgl6Ix7vycM4Yaf7hbgzWsNoFx2nNQwE8jmZhLTFJ7J0WKlsERQlARVAKAUZCCCQABSkspF5QglIJfOCD9JF0sEtAyjkXl4wwkBIU56oWRZSzoq5cjiQQeW39+p1/T4KUJMalgDz7hBmPYVoK4hmAqyvjXpUxFGd38yUBFB2heXamHxD3j5v520viau8O1PZ15Nf/vW17wzcOnWLRvCTohRNcpeEXu4Cy8XjGHDwtVJeDN9Us1+1XbVS47b7V9JKi03e51l2XyC3X6XDZBPHZ/sU4c15h8X/qxUws0Bil/7vW5XqRPqm+ni3f6y5sKo8riEP2tviLF9N14MFBkTlGKU7gyP0n7zKMfrpNWw+fjHvovngaUQ3ly+yy8Jv8Fhbv3gqo8l+gHIv5IEqnnXUCYVaMzh09a6p7+KAOq87gDdwf5Ooa5tv2PppKzXdzQ9yw5rWrDfrfVz72aWBQc/edxaivbtr/V2McBeNd9hwVwc/2ENPFDt9nXnN0vUEy3/8E6y5iL7MF2XEcYw4jge62IubOiyOvdW6dOkS0jTFmTNnSuvPnDmD1157rXafT3/60/iX//JfDqz/n373Nw9ljIQQQgghhBBCCBkR/27UAyDHlWrK7e/8zu/gd3/3dwfa7cVNHRbHXvRlVFN0jDFD03b++T//5/jN3yykntYaDz74IJ544olDTfV56KGH8N3vfvdA+1xdXcVNN92El156iWnHZM8cxmvzpHPSntPj/HjHdezjMK6jHsNhH4/fw2ScGYf3/KRx0p7T4/x4x3Xsox7XpH0PH9Yx+F1MDoLqa9MYg8uXL2NhYQFSynx9XTSfz27c1GFx7EXfqVOnoJQaMKQXLlwYMKkZdaGWcRxjdnb20MYJAEqpQ/vgmZmZ4Yca2TOH+do8qZy05/Q4P95xHfs4jOuox3DYx+P3MBlnxuE9P2mctOf0OD/ecR37qMc1ad/Dh30MfheT/VD32tyNI9qLmzos5PZNxpsoivDggw/iy1/+cmn9l7/8Zbzzne/ccT//5J/8k4Me2kiOQche4Gvz4Dlpz+lxfrzjOvZxGNdRj+GwjzcOzykhw+Dr8+A5ac/pcX684zr2UY9r0r6Hj+oYhOyF/b42D8pNHQQTUXX3C1/4Aj75yU/iP/yH/4B3vOMd+NznPofPf/7z+OEPf4hbbrll1MM7VLKKwbupBEMIIYSQg4Hfw4QQQsho4XcxGRfGxU0d+9RdAPi7f/fv4vLly/hX/+pf4dVXX8U999yDRx55ZOIlH2BTjn/nd35n2zxxQgghhBw8/B4mhBBCRgu/i8m4MC5uaiIi+gghhBBCCCGEEEIIOekc+zn6CCGEEEIIIYQQQgghFH2EEEIIIYQQQgghhEwEFH2EEEIIIYQQQgghhEwAFH2EEEIIIYQQQgghhEwAFH2EEEIIIYQQQgghhEwAFH0TzN/5O38H8/Pz+JVf+ZVRD4UQQgg5cbz00kt43/vehze+8Y2499578V/+y38Z9ZAIIYSQE8W1a9fw0EMP4f7778eb3/xmfP7znx/1kAg5dIQxxox6EORw+NrXvoa1tTX8x//4H/HHf/zHox4OIYQQcqJ49dVXcf78edx///24cOECHnjgATz99NNot9ujHhohhBByIkjTFN1uF61WCxsbG7jnnnvw3e9+F4uLi6MeGiGHBiP6Jpj3v//9mJ6eHvUwCCGEkBPJ2bNncf/99wMATp8+jYWFBVy5cmW0gyKEEEJOEEoptFotAECn00GapmCsE5l0KPrGlMceewy//Mu/jOuvvx5CCPzpn/7pQJvPfOYzuO2229BoNPDggw/iG9/4xtEPlBBCCJlQDvK7+G/+5m+gtcZNN910yKMmhBBCJoeD+C5eXl7GfffdhxtvvBH/7J/9M5w6deqIRk/IaKDoG1PW19dx33334fd///drt3/hC1/Apz71KfyLf/Ev8L3vfQ/vec978LGPfQwvvvjiEY+UEEIImUwO6rv48uXL+Pt//+/jc5/73FEMmxBCCJkYDuK7eG5uDufOncPzzz+P//yf/zPOnz9/VMMnZCRwjr5jgBACf/Inf4JPfOIT+bq3ve1teOCBB/DZz342X/eGN7wBn/jEJ/DpT386X/f1r38dv//7v885+gghhJB9sNfv4m63iw996EP4R//oH+GTn/zkUQ+bEEIImRj2c12c8Y//8T/GBz7wAfzqr/7qUQyZkJHAiL5jSK/Xw+OPP44Pf/jDpfUf/vCH8Zd/+ZcjGhUhhBByctjJd7ExBv/wH/5DfOADH6DkI4QQQg6YnXwXnz9/HqurqwCA1dVVPPbYY7j77ruPfKyEHCXBqAdAds+lS5eQpinOnDlTWn/mzBm89tpr+f2PfOQjeOKJJ7C+vo4bb7wRf/Inf4KHHnroqIdLCCGETBw7+S7+1re+hS984Qu499578zmF/tN/+k9485vffNTDJYQQQiaOnXwXv/zyy/i1X/s1GGNgjME//af/FPfee+8ohkvIkUHRd4wRQpTuG2NK6770pS8d9ZAIIYSQE8VW38Xvfve7obUexbAIIYSQE8NW38UPPvggnnzyyRGMipDRwdTdY8ipU6eglCpF7wHAhQsXBn7NIIQQQsjBw+9iQgghZLTwu5iQeij6jiFRFOHBBx/El7/85dL6L3/5y3jnO985olERQgghJwd+FxNCCCGjhd/FhNTD1N0xZW1tDc8++2x+//nnn8eTTz6JhYUF3HzzzfjN3/xNfPKTn8Rb3/pWvOMd78DnPvc5vPjii/iN3/iNEY6aEEIImRz4XUwIIYSMFn4XE7J7hDHGjHoQZJCvf/3reP/73z+w/h/8g3+AP/qjPwIAfOYzn8G/+Tf/Bq+++iruuece/Lt/9+/w3ve+94hHSgghhEwm/C4mhBBCRgu/iwnZPRR9hBBCCCGEEEIIIYRMAJyjjxBCCCGEEEIIIYSQCYCijxBCCCGEEEIIIYSQCYCijxBCCCGEEEIIIYSQCYCijxBCCCGEEEIIIYSQCYCijxBCCCGEEEIIIYSQCYCijxBCCCGEEEIIIYSQCYCijxBCCCGEEEIIIYSQCYCijxBCCCGEEEIIIYSQCYCijxBCCCGEEEIIIYSQCYCijxBCCCGEEEIIIYSQCYCijxBCCCGEEEIIIYSQCYCijxBCCCHkhPPss89CCIE///M/xwc/+EG0Wi3cfffd+M53vjPqoRFCCCGEkF1A0UcIIYQQcsI5d+4chBD4t//23+K3f/u3ce7cOdx88834rd/6rVEPjRBCCCGE7AKKPkIIIYSQE865c+cwOzuLL3zhC3j/+9+P173udfjEJz6BixcvjnpohBBCCCFkF1D0EUIIIYSccM6dO4df/uVfxtLSUr7uueeew5133jnCURFCCCGEkN1C0UcIIYQQcsI5d+4c3vGOd5TWfe9738P9998/mgERQgghhJA9QdFHCCGEEHKCWVlZwQsvvIC3vOUtpfVPPvkkRR8hhBBCyDGDoo8QQggh5ARz7tw5KKVw33335eteeOEFXL16laKPEEIIIeSYQdFHCCGEEHKCOXfuHF7/+tej2Wzm6773ve9hbm4Ot9566+gGRgghhBBCdo0wxphRD4IQQgghhBBCCCGEELI/GNFHCCGEEEIIIYQQQsgEQNFHCCGEEEIIIYQQQsgEQNFHCCGEEEIIIYQQQsgEQNFHCCGEEEIIIYQQQsgEQNFHCCGEEEIIIYQQQsgEQNFHCCGEEEIIIYQQQsgEQNFHCCGEEEIIIYQQQsgEQNFHCCGEEEIIIYQQQsgEQNFHCCGEEEIIIYQQQsgEQNFHCCGEEEIIIYQQQsgEQNFHCCGEEEIIIYQQQsgEQNFHCCGEEEIIIYQQQsgE8P8CUYlVYUPIW2IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 10))\n",
    "ax = plt.axes()\n",
    "ax.set_xscale(\"log\")\n",
    "color_dict = auto_colors(sort_list)\n",
    "\n",
    "for name in legends:\n",
    "    stats = comparisons[name]\n",
    "    color = color_dict[name]\n",
    "    n_list = [n for n in stats]\n",
    "    bounds = np.array([entropy_bound(n) for n in n_list])\n",
    "    times = stats.values()\n",
    "    q = np.zeros((5, len(times)), dtype=int)\n",
    "    for i, per in enumerate([2.5, 50, 97.5]):\n",
    "        for j, s in enumerate(times):\n",
    "            q[i, j] = np.percentile(s, per)\n",
    "    q = 100*(q/bounds-1)\n",
    "    ax.plot(n_list, q[1, :], marker='.', label=legends[name], color=color)\n",
    "    ax.fill_between(n_list, q[0, :], q[2, :], alpha=.2, color=color)\n",
    "\n",
    "ax.legend(loc=2)\n",
    "ax.tick_params(labelright=True, right=True)\n",
    "plt.ylabel('Relative overhead (%)')\n",
    "plt.xlabel('$n$')\n",
    "plt.ylim([0, 100])\n",
    "plt.xlim([n_list[0], n_list[-1]])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}