test_interpretation_eval.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/moleculegraph38/lib/python3.8/site-packages/openbabel/__init__.py:14: UserWarning: \"import openbabel\" is deprecated, instead use \"from openbabel import openbabel\"\n",
      "  warnings.warn('\"import openbabel\" is deprecated, instead use \"from openbabel import openbabel\"')\n"
     ]
    }
   ],
   "source": [
    "# My library\n",
    "from molgraph.dataset import *\n",
    "from molgraph.graphmodel import *\n",
    "from molgraph.training import *\n",
    "from molgraph.testing import *\n",
    "from molgraph.visualize import *\n",
    "from molgraph.interpret import *\n",
    "from molgraph.experiment import *\n",
    "# General library\n",
    "import argparse\n",
    "import numpy as np\n",
    "from sklearn.metrics import jaccard_score\n",
    "# pytorch\n",
    "import torch\n",
    "import pytorch_lightning as pl\n",
    "\n",
    "from IPython.display import SVG\n",
    "\n",
    "# Ensure that all operations are deterministic on GPU (if used) for reproducibility\n",
    "torch.backends.cudnn.determinstic = True\n",
    "torch.backends.cudnn.benchmark = False"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Namespace(batch_normalize=True, batch_size=256, class_number=1, device=device(type='cpu'), dropout=0.4, edge_dim=32, experiment_number='001', file='l05_3mr', fold=5, gpu=-1, grad_norm=1.0, graphtask='classification', heads=1, hidden_channels=256, in_channels=256, lr=5e-05, lr_schedule=True, model='GIN', mol_embedding=256, num_epochs=300, num_layers=3, num_layers_reduced=3, num_layers_self=3, num_layers_self_reduced=3, out_channels=256, patience=30, reduced=[], schema='A', seed=42, smiles='SMILES', splitting='splits', splitting_seed=42, task='label_full', vocab_len=100, weight_decay=5e-05)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parser = ArgumentParser()\n",
    "args = parser.getArgument('''\n",
    "--file 3mr\n",
    "--model GIN\n",
    "--schema A\n",
    "--reduced \n",
    "--mol_embedding 256\n",
    "--batch_normalize\n",
    "--fold 5\n",
    "'''.split())\n",
    "\n",
    "args"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Function: getDataset()\n",
      "number of valid smiles: 2877\n",
      "pos_weight: 1.5415194346289753\n",
      "Function: generateDatasetSplitting()\n",
      "Fold: 5\n",
      "Fold Number: 0\n",
      "-- Datasets Train: 1841\n",
      "-- Datasets Val: 460\n",
      "-- Datasets Test:  576\n",
      "-- Total: 2877\n",
      "Fold Number: 1\n",
      "-- Datasets Train: 1841\n",
      "-- Datasets Val: 460\n",
      "-- Datasets Test:  576\n",
      "-- Total: 2877\n",
      "Fold Number: 2\n",
      "-- Datasets Train: 1841\n",
      "-- Datasets Val: 460\n",
      "-- Datasets Test:  576\n",
      "-- Total: 2877\n",
      "Fold Number: 3\n",
      "-- Datasets Train: 1841\n",
      "-- Datasets Val: 460\n",
      "-- Datasets Test:  576\n",
      "-- Total: 2877\n",
      "Fold Number: 4\n",
      "-- Datasets Train: 1840\n",
      "-- Datasets Val: 461\n",
      "-- Datasets Test:  576\n",
      "-- Total: 2877\n",
      "Function: generateGraphDataset()\n",
      "Datasets graph:  2877\n"
     ]
    }
   ],
   "source": [
    "file = args.file\n",
    "smiles = args.smiles \n",
    "task = args.task\n",
    "splitting = args.splitting \n",
    "splitting_fold = args.fold\n",
    "splitting_seed = args.splitting_seed\n",
    "\n",
    "# get validated dataset\n",
    "datasets = getDataset(file, smiles, task, splitting)\n",
    "# compute positive weight for classification\n",
    "if args.graphtask == 'classification':\n",
    "    args.pos_weight = getPosWeight(datasets)\n",
    "    print('pos_weight:', args.pos_weight)\n",
    "# generate dataset splitting\n",
    "datasets_splitted = generateDatasetSplitting(file, splitting, splitting_fold, splitting_seed)\n",
    "# generate all graph dataset\n",
    "datasets_graph = generateGraphDataset(file)\n",
    "# generate all reduced graph dataset\n",
    "dict_reducedgraph = dict()\n",
    "for g in args.reduced:\n",
    "    if g == 'substructure':\n",
    "        for i in range(splitting_fold):\n",
    "            vocab_file = file+'_'+str(i)\n",
    "            if not os.path.exists('vocab/'+vocab_file+'.txt'):\n",
    "                generateVocabTrain(file, splitting_seed, splitting_fold, vocab_len=args.vocab_len)\n",
    "            dict_reducedgraph[g] = generateReducedGraphDict(file, g, vocab_file=vocab_file)\n",
    "    else:\n",
    "        dict_reducedgraph[g] = generateReducedGraphDict(file, g)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "args_test = dict()\n",
    "\n",
    "# Load model\n",
    "ts = \"2023-Apr-29-17:24:38\"\n",
    "reduced_list = '_'.join(args.reduced)\n",
    "args_test['log_folder_name'] = os.path.join(*[args.file, args.model+'_'+args.schema+'_'+reduced_list, f\"{ts}\"])\n",
    "args_test['exp_name'] = args.experiment_number\n",
    "args_test['fold_number'] = 2\n",
    "args_test['seed'] = args.seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Namespace(batch_normalize=True, batch_size=16, class_number=1, device=device(type='cpu'), dropout=0.4, edge_dim=32, experiment_number='001', file='l05_3mr', fold=5, gpu=-1, grad_norm=1.0, graphtask='classification', heads=1, hidden_channels=256, in_channels=256, lr=5e-05, lr_schedule=True, model='GIN', mol_embedding=256, num_epochs=300, num_layers=4, num_layers_reduced=3, num_layers_self=3, num_layers_self_reduced=3, out_channels=256, patience=30, pos_weight=1.5415194346289753, reduced=[], schema='A', seed=42, smiles='SMILES', splitting='splits', splitting_seed=42, task='label_full', vocab_len=100, weight_decay=5e-05)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_bin = torch.load('./dataset/'+args_test['log_folder_name']+'/checkpoints/training_args.bin')\n",
    "\n",
    "args.batch_size = training_bin.batch_size\n",
    "args.num_layers = training_bin.num_layers\n",
    "args.num_layers_reduced = training_bin.num_layers_reduced\n",
    "args.in_channels = training_bin.in_channels\n",
    "args.hidden_channels = training_bin.hidden_channels\n",
    "args.out_channels = training_bin.out_channels\n",
    "args.edge_dim = training_bin.edge_dim\n",
    "args.num_layers_self = training_bin.num_layers_self\n",
    "args.num_layers_self_reduced = training_bin.num_layers_self_reduced\n",
    "args.dropout = training_bin.dropout\n",
    "args.lr = training_bin.lr\n",
    "args.weight_decay = training_bin.weight_decay\n",
    "\n",
    "args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2877\n",
      "PairData(smiles='COc1cccc(C2OC2C(=O)c2cc3c(cc2N)OCO3)c1', x_g=[23, 79], edge_index_g=[2, 52], edge_attr_g=[52, 10], y=[1, 1])\n"
     ]
    }
   ],
   "source": [
    "# test with dataset\n",
    "# test_loader, datasets_test =  generateDataLoaderTesting(datasets_graph[1], args.batch_size)\n",
    "# test_loader, datasets_test =  generateDataLoaderTesting([datasets_graph[1][360]], 1)\n",
    "test_loader, datasets_test =  generateDataLoaderTesting(args.file, 1)\n",
    "sample_to_test = datasets_test[8]\n",
    "\n",
    "all_dataset_test = datasets_test\n",
    "\n",
    "# all dataset\n",
    "all_dataset = datasets_graph\n",
    "# all_dataset = {d.smiles: d for d in datasets_test}\n",
    "print(len(all_dataset))\n",
    "\n",
    "test_loader, datasets_test =  generateDataLoaderListing([sample_to_test], 1)\n",
    "\n",
    "molecule_test = datasets_test[0]\n",
    "print(molecule_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # test with t = sample\n",
    "molecule_test = [constructGraph('Cc1cccc(OCC(=O)Nc2cccc3ccccc23)c1', 1)]\n",
    "test_loader = DataLoader(molecule_test, batch_size=1, shuffle=True, follow_batch=['x_g', 'x_r'])\n",
    "molecule_test = molecule_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNN_Combine(\n",
      "  (node_feature_graph): NodeLinear(\n",
      "    (lin1): Linear(in_features=79, out_features=256, bias=True)\n",
      "    (bn): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (edge_feature_graph): EdgeLinear(\n",
      "    (lin1): Linear(in_features=10, out_features=32, bias=True)\n",
      "    (bn): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (GNN_Graph): GNN_Graph(\n",
      "    (convs): ModuleList(\n",
      "      (0): GINEConv(nn=Sequential(\n",
      "        (0): GIN_Sequential(\n",
      "          (lin1): Linear(in_features=256, out_features=256, bias=True)\n",
      "          (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (leakyrelu1): LeakyReLU(negative_slope=0.01)\n",
      "          (lin2): Linear(in_features=256, out_features=256, bias=True)\n",
      "        )\n",
      "      ))\n",
      "      (1): GINEConv(nn=Sequential(\n",
      "        (0): GIN_Sequential(\n",
      "          (lin1): Linear(in_features=256, out_features=256, bias=True)\n",
      "          (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (leakyrelu1): LeakyReLU(negative_slope=0.01)\n",
      "          (lin2): Linear(in_features=256, out_features=256, bias=True)\n",
      "        )\n",
      "      ))\n",
      "      (2): GINEConv(nn=Sequential(\n",
      "        (0): GIN_Sequential(\n",
      "          (lin1): Linear(in_features=256, out_features=256, bias=True)\n",
      "          (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (leakyrelu1): LeakyReLU(negative_slope=0.01)\n",
      "          (lin2): Linear(in_features=256, out_features=256, bias=True)\n",
      "        )\n",
      "      ))\n",
      "      (3): GINEConv(nn=Sequential(\n",
      "        (0): GIN_Sequential(\n",
      "          (lin1): Linear(in_features=256, out_features=256, bias=True)\n",
      "          (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (leakyrelu1): LeakyReLU(negative_slope=0.01)\n",
      "          (lin2): Linear(in_features=256, out_features=256, bias=True)\n",
      "        )\n",
      "      ))\n",
      "    )\n",
      "    (bns): ModuleList(\n",
      "      (0): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (3): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (atom_grus): ModuleList(\n",
      "      (0): GRUCell(256, 256)\n",
      "      (1): GRUCell(256, 256)\n",
      "      (2): GRUCell(256, 256)\n",
      "      (3): GRUCell(256, 256)\n",
      "    )\n",
      "    (mol_conv): GATv2Conv(256, 256, heads=1)\n",
      "    (mol_bns): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (mol_gru): GRUCell(256, 256)\n",
      "    (lin): Linear(in_features=256, out_features=256, bias=True)\n",
      "  )\n",
      "  (classification): ClassificationLayer(\n",
      "    (lin1): Linear(in_features=256, out_features=128, bias=True)\n",
      "    (bn1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (lin2): Linear(in_features=128, out_features=64, bias=True)\n",
      "    (bn2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (lin3): Linear(in_features=64, out_features=1, bias=True)\n",
      "  )\n",
      ")\n",
      "y_test: tensor([[1]], dtype=torch.int32)\n",
      "y_pred: tensor([[0.0007357551512940]])\n",
      "y_pred > 0.5: tensor([[False]])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[False]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tester = Tester(args, args_test)\n",
    "# tester.test(test_loader, return_attention_weights=True)\n",
    "tester.test_single(test_loader, return_attention_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# att = tester.getAttention()\n",
    "att_mol = tester.getAttentionMol()\n",
    "if 'atom' in att_mol:\n",
    "    if len(args.reduced) >= 1:\n",
    "        sample_att = (att_mol['atom'], att_mol[args.reduced[0]])\n",
    "    else:\n",
    "        sample_att = (att_mol['atom'], None)\n",
    "else:\n",
    "    sample_att = (None, att_mol[args.reduced[0]])\n",
    "sample_graph = molecule_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[16:18:17] The new font size 1 is below the current minimum (6).\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x1000 with 3 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_attentions(args, sample_graph, sample_att)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Compound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7faba091c430>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Chem.MolFromSmiles(molecule_test.smiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from locale import normalize\n",
    "from rdkit import Chem\n",
    "smiles = sample_graph.smiles\n",
    "mol = Chem.MolFromSmiles(smiles)\n",
    "\n",
    "if args.schema in ['A']:\n",
    "    reduced_graph, cliques, edges = getReducedGraph(args, ['atom'], smiles, normalize=False)\n",
    "else:\n",
    "    reduced_graph, cliques, edges = getReducedGraph(args, args.reduced, smiles, normalize=False)\n",
    "# print(reduced_graph.node_features)\n",
    "\n",
    "sample_att_g, sample_att_r = sample_att\n",
    "if args.schema in ['A', 'R_N', 'AR', 'AR_0', 'AR_N']:\n",
    "    mask_graph_g = mask_graph(sample_att_g)\n",
    "if args.schema in ['R', 'R_0', 'R_N', 'AR', 'AR_0', 'AR_N']:\n",
    "    mask_graph_r = mask_reduced(sample_att_r)\n",
    "if not args.schema in ['A']:\n",
    "    mask_graph_x = mask_rtog(smiles, cliques, mask_graph_r)\n",
    "    if args.schema in ['AR', 'AR_0', 'AR_N']:\n",
    "        mask_graph_x = mask_gandr(mask_graph_g, mask_graph_x)\n",
    "        display_interpret_weight(mol, None, None, mask_graph_x, None, scale=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "if args.schema in ['A']:\n",
    "    feature_importance = dict()\n",
    "    for i, f in enumerate(reduced_graph.node_features):\n",
    "        f_tuple = tuple(f)\n",
    "        if f_tuple in feature_importance:\n",
    "            feature_importance[f_tuple].append(mask_graph_g['atom'][i])\n",
    "        else:\n",
    "            feature_importance[f_tuple] = [mask_graph_g['atom'][i]]\n",
    "elif args.schema in ['R', 'R_0', 'R_N', 'AR', 'AR_0', 'AR_N']:\n",
    "    feature_importance = dict()\n",
    "    for i, f in enumerate(reduced_graph.node_features):\n",
    "        f_tuple = tuple(f)\n",
    "        if f_tuple in feature_importance:\n",
    "            feature_importance[f_tuple].append(mask_graph_r['atom'][i])\n",
    "        else:\n",
    "            feature_importance[f_tuple] = [mask_graph_r['atom'][i]]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "from sklearn.metrics import roc_auc_score\n",
    "def pearson_r_score(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n",
    "    \"\"\"Pearson's r for linear correlation.\"\"\"\n",
    "    r, _ = scipy.stats.pearsonr(y_true, y_pred)\n",
    "    return r[0] if hasattr(r, 'ndim') and r.ndim == 1 else r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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86erqhoeHv379mm6Noly9etXW1hbXCg8Pjy+//JJK23Jxcbl9+zbdAkUpKiry9/fH7jl48OCDBw9++eWXeMpIR0cnLCysrq6Obo1MgdioLMnOzp49ezb+27C1tb1y5Qq+n5eX5+3tje+PHDnyjz/+oFenCCI2Wl+PLCyQigpKSWGijbLZ7KFDh+Ifpre39/Pnz/H9J0+eUGneZmZmMTEx9OqkEBY2fPjw2NhYfF8gEMTExBgbG+Moz9fXlyGhtHArpaGhERwcXFtbix8VFhb6+vriyTFTU9OYmBgmh9IKg9iobKisrAwODsb9tf79+0dGRra0tIiUiY+Pt7Nr3SbN09Pz4cOHtEjF8Pno2DG0ZQtCbWwUIXTuHAJAkyYhS8tWG929G0VGInoj6fZaKWESEhLGjBmDy7i7u9+/f1/xOinq6+vDw8NxTisOk5uamsSWYU4o3V4rJcydO3cmTpyIy4wfPz45OVnxOhkFsdHuwuPxoqKi8J7B6urq/v7+5eXl7RXmcrmRkZF4Kp/FYkkuLD9u30YuLggAqamhnBwxNooQmjcPASBVVQSAHj1CmpoIANnaInHeJXekaaUo+Hx+TEwMTtuka1S6s5GmcMRqbm5OSygtTStFgX/IgwYNYlooTQvERrvF1atXR48eTQ14PXjwoG2ZQ4cO3bp1S/hOp0xBLGZmZjjHe+HChZ16Y1ER8vVFKioIAA0ejGJikEAg3kbz85GODgJojUbj45GdXeulpydSWCTdqVZKmMrKys8++wzvnmdgYJAXHa24WPrWLc/p03GtcHV1TUtLk/J9dIXS0lTI5ubm+fPni3hr23CbgaPSCoDYaBdpb8CrbTFcyby9vV+8eCH8SKTxj4uLk/Kra2pqrKysOiu4uRlFRiI9PQSANDRQcDB6M94l3kYRQtu3/2ujCCEuF0VGIn19BIBYLBQcLPcxU2laKcnk5ubOmzfPw8ICaWqikSORvEel3zRT37q5dW3oEEd5OA1WAaG09K3Ujz/+SPX08/LyhB9J+bfQgyE22mmkGfCiaGhoiIiIwEsVxc5vigxFPXv2rEMBubm5kyZNKi0traiokFIzm906WQSAvL2RyHhXVBQ6dUrMu5qa0JEjKCoKNTf/e7OiAgUHIzU1BID690eRkaiTkbRUyPYvsyouDo0Y0frvnzcPve0CsqGxEe3YgXR1EQDS1uaGhzc0NHT5wzgcTlhYmIaGBg6lIyIimoV/BzKiU60UHo/COzGzWKzg4OCqt1vRhIQEe3t7/GkzZsygd1RawRAb7QRdnlqVPL+JJ0b79u1LTYzW1NRI+LTc3NyJEyf6+vqOGzdu5cqVkuOd7Gw0e3argchwZDMzE02bJvuPRZ1spTqBXGNpyc1UV8nNzZ07dy6V4HHp0iWZfCx6u5UaMWKE9K1URUWFhO4/jm0VFkozh95lo7du3Vq3bt3GjRsfP37c2fempqZOmjQJ1zwXF5cuzE6mpKQIz2+KpAoKp+mZmJhERUXx+XzJH8jj8aysrDIyMsQ+LS+vCQhoDRuNjNDhw7IPG8+cQRYWre6xeDHKz6/szqcpIgGoU7F0TQ16/hyVlCAer90yws2UoyNKTJSxYITi4+NHjRolqwQPmbRSmZmZ06ZNa28yqsuh9NOnTz/77LPAwEB8WVdXFxAQMGnSpDlz5qSkpHRWpCLpRTZ6/fp1W1vbc+fORUdHm5iYSI74hHn16pWscuXwyJcEp0hPT3d1dcUVdNy4cUlJSW0/hMfjUSGAnZ1d28xtasBr3LhydXXk74/klw7Q2IgiIpCeHrKxydDS0hLOMewU3W+lOoHkWLqhAe3Zg6ytWwsAoL590ZIlKCvrrWKVlYoY3UAIietQV1dXd/ZDZN5KsdlsaplJ2/Eo4VDa2tq6w1C6pqbG19d3+/btkydPxnfCw8P9/f0FAkFSUlIXJgMUSS+yUT6fT43muLi4iHUoERobGyMiInAesra2dlhYWNc8QgTJ85sCgSA2NtbCwgJX98WLF+fn5wu//ddff3V1dY2MjFy5cqWbm5tI0HrlyhVqtYyv786cnO7r7ZjCQhQSsptqaU6dOiV9SyPDVqpziMTSxcUIIfTPP8jREQEgAwO0YgX64gsUHIzGjGmdmDt9uvW9Bw+ifv1axwfWrlXM+gThDrWhoWGnEjxEekKyaqXaJuqLRCedDaUzMjIoG92wYYOPj49AILh37x6xUcbB5XKNjY1fvXoluZhIYys2D7k7SE4VbGhoCA8Pb2/tXWZmZlRU1JUrV4SXlnZ5wEtWpKamTp48GQtwdna+efOm5PJyaqU6ARVLGxig8nIkECAPj9bxTRFnjI5G6upIQwPdu4cQQp99hgCQhwfqfPJANxHuUDs6Ol6/fl1yeQW0UpLHozoVSgvbaGVlpbm5uY2NzciRI9sbuWIIvdFGg4OD/f39JRS4e/eucE1NlMOAF4XkVEHhuakhQ4a09zcgw2mZurq6+Pj4n3/+ucNmRiw4lMZHmIkNpSnk3Up1gsLC1n79X38hAGRpicROsm/ejADQggUIIVRZiWQ34dMFpPnp4VYKZ4ngVkquq+DT0tIkjEdJGUoL2+jWrVsDAgKePXu2YcOGUaNG1dfXy098N+ldNsrn8zdv3uzl5dWey3Q/Mb4LdDi/eefOnQkTJojtkcl2wCs/P9/S0vLTTz/ds2fPkSNHuvw5OJTGCxx1dHTCw8MbGxupp4pspTrHqlUIAH31lfin5eWIxUJqaqjz45LyQDiWF1n5jhBis9nU1idtc5blRIeNaIehtLCNWltbUysXRo0axcDdWyh6kY1yOJy5c+euXLmyrKyMw+GIOGlDQ8PWrVupZZpt0+IUIE/C/KbYtXcyH/Dy8/Pbt29fNz+EoqCggAqljY2NY2Ji8vLyfHx8cO9PYa1UJ7C1RQDo7SVnb4GHTRMSFKipA0T2YYqKikpPT6esysnJSfGtlOTxKPR2KD1+/PgbN27g+1evXt27d6+1tXVsbCyHwwkMDFy6dGl6evqJEyeGDBki/Zyw4ulFNhoTE+MphPDUYV5eHnUmmpeXV25uLl0iJacKVldXh4aGYqvV0tLCDmVmZvbLL7/IZMBr+PDh586dCw8Pj4iIkFUrsmvXLmhD//79FdxKSQWeNSooaLfA/PkIAP3vfwrUJBXUrqDwZmPQgQMHHjlypMOcOfkheTyqsbFxx44d+FBCFRUVHJYePHgw7A2FhYXNzc0//fRTUFBQRETEy5cv6fqHSEMvslEJuLm5AYC6uvrBgwfp1oJQR/ObT548mTdv3oABA2Q+4NWnT5+VK1devHgxJCRk6tSpXfiEhoYGjhA8Hu/ixYs4LNLV1QUAHO/b2NjISrMswZsISBgVef99BIB++kmBmqQFd6gNDAz69+//4YcfdiEjSh5cv37d0dER1+QJEyaIdJju3r1rYGCAO/h0KZQJxEYRQmjkyJEAsGrVKrqF/IvktXd4H3VbW1vZfqm5uTmerGhpaVFTU+vCoP7BgwepeF9PTy8pKQnb6IIFCzgczrNnz3JycphroyYmCEDSUlFPTwSAfv9dgZo6h42NDQDkKCbNTTqEx6Pw0L/w8L2/v38PsFFyFhMAAB5hpJJ1mACLxQoJCcnLy/Pz8+Pz+QcOHLCzs8MnO1FQhz7JCjc3t99//x0AEhMTjY2NqYPgpScoKCg+Pj4+Pv748eMmJiZURj0AGBgYULsHMJSRIwEAJBychR9ZWytIT49AVVV15cqVeXl5oaGh6urqp06d8vHxoZ46OzsDADUioaQQGwUAwNup4f8DwIULF5YsWXL69GlaRQEADBo06OjRo3jSYMmSJV3wtU6xe/fuixcv2tnZffzxx6dOnRI+xLiz7N+/PygoSI1p54tKBu9ux2aLf5qaCsXFYGwMb1Y3MJ+AgIBhw4ZdvXqVbiGgr6+/b9++Bw8eeHt779ixg245MoYcsCyG3Nzcs2fPDhs2jG4hreD51ubmZnl/kZmZWVJSEpfLxbNYXaa2tvbcuXMPHjyQlTAFsWYNfPUV/PwzfPYZODm99ailBTZsAAD45BOQdSdAfpSWlj5//ryhoYFuIa3gE3ToViF7lKZCEKhcAgDAcSJCSB5f1E0PBYAjR4588MEHOPFbRKpclXcXc3PYsQN4PJg1C379Fbjc1vs5OTB/PiQmwqhRrWYqZzIyMnx8fKg1aZimpiY3NzfhcZIeAKPrg9QQGwXoKb9LhtDS0nL48OFPP/2UbiFdIiwMdu6EmhpYtgwMDGDECDAxgVGj4MoVmDQJEhJAW1veEhoaGk6dOjV16tT8/Hzh+zt27Bg7dmxBQYGE95KaTAvERgky5syZM66urkOGDKFbSFfZsgVycmD7dnBzg379YORI8PODixfh1i0wNlbA9+vq6kZGRk6dOlX4ZlZWFpvNXr9+vQIEEDoLGRsVA2nSu4Orq+ucOXPoVtE9hg2DL7+kW8S/8Pl8Pz+/AwcO4PW1BKZBbJQgY6il3ARZsX//fktLSycnp6qqKoFAUFdXh5fSdwjzAwLmK5QG0qlXSnpG5SNISW5ublVV1ZIlS9asWVNVVbVly5b2SpKKQQskGgUglY/AMNhsdmZmZnl5+ZEjR955553o6Gh8n8Ph2Nvbf/fdd/TKI4hAbFQMxFVliIOmZrKbm7q5Ob4cqKqa7OamYmREryrmM3jw4E2bNonc1NXV3bt3Ly16CBIgNkqQL+ZcrnliIrwZyzNAaGJiYuuyS6ZSU1Pz7NmzsWPH4ks+n5+WljZ27NjuZ9RKyYIFC8Te19TUXLFihWI0EKSHjI0SCP/S0tLy+eefjx07dunSpfjO8ePHnZyc3N3di4qK6NXWI+kZPT9io0pJz6h8DERdXX3VqlXXr1+n7kycODE1NZXaZpjhKNOasR4EsVEAUtvkCt7fhPrZilwyDwcHB+E9WWxtbUm2JkEyxEbFQFyVQCBID7FRAoFA6BbERpUSEi8TegY9oyYTGwXoKb9LgkxYt27d3LlzCwsLnZ2db9269csvvzg7O7948WLBggXffvst3eo6gNRkWiB5owQ5o2xTTDt37vzyzb4kffr0cXZ2pnZaUbq5JuKqioHYqBh6RuVLSko6c+YMi8X66KOPRo8eDQAVFRXR0dGTJk3CJ6ESxIJ3mxZGeMNsAqEtxEZ7Jrdv3w4ICPj666/r6+s9PDyys7Pv3r371VdfcblcPp9PbJRAkCHERpWSDuPlyZMnp6Sk4O3UDh06dP/+fXzo8c6dOxWnkkDoiJ7R8yNTTAA95XcpAvbQlpaWFy9eMOd4PoJc6ZE1mfn0umi0srKysrJypMStMXpSXfzPf/7j5eVlYWFBm4KZM4HDARar9XLoUOBwlOhwTaWmJ9VkJtOLbLS2tnbjxo1sNtva2johIYFuOXJHIBBs3br1yZMn58+fp1OHujokJ8Ply/D8OTQ2Qv/+MH48fPAB6OvTqYpAkB29KCjQ1tb28/OLjY2lW4gi4HA43t7excXFJ06caGhoaG5uRghVVVU1NTU1NTXV1NQoSEdJCbi6wrx58MMPkJwML15AXBxs2gQ2NrBrl4I0EAhyphfZKIvFonaQVHY67KxduXKFx+O9evXKx8dnyZIlcXFxNTU1S5YsSU1NvXPnzscff6wIlfX14O4OyckwezbcuwfV1VBQABwOHD8OurqwdSvs2aMIGQQG0zOGHXpRp14CzP9dIoRev36to6MjZXkfHx8fHx+Rm/Hx8bLWJZHduyEvDzw84NIlUH9T03R04MMPYdQomDIFtm2DZctASfagUwqYX5OfPXtmYmIifU1WCnpRNCo9lpaWnp6ew4cPp1tIK+np6VOnTg0ODqZbSGfg8+HoUQCAffv+9VCK8eNhxQrg8eDNKUMEefDee++FhYVJnlBVGA0NDdu2bRs9evQ333xDtxYZQ6JRMSxatGjRokV0qwAAKC4u/uKLL06dOoUQevnypfQn69LPw4dQWQlmZuDoKL7Ae+/B8eOQmKhYWT2cpqYmAGhubsaXy0NbDYwAAA0vSURBVJcvp1VOKwKB4OTJk5s2bSopKVFVVS0rK6MeVVdXU/9XXnpXNBoQELB27dp79+7NnDkzPz+fut/S0gIAAoGANmVt4HK53333na2t7cmTJ1ksVnBw8KNHj0Q8lFGCRXn2DADAxqbdAra2AABPnypIT++goKAAAB4/fky3kH9JS0ubOnXq6tWrS0pKXFxcbt68eejQIeppSkoKADx8+JA+gbIA9SZqamo4b2hpaUEICQSC2NjY/v37m5ubm5mZ/fnnn3RrRAghNps9dOhQ/Avy9vZ+9uyZ8NPc3NwZM2YYGBhoaGj4+/uXlZXRpVMSx48jALRwYbsFSksRANLSUqCmno+NjQ0AqKurHz16VCAQ0CumqKjI399fVVUVAAYPHhwVFcXn86mnzc3NW7ZswYO5CyXUE2Wgd9moCCkpKRMnTsRu1a9fP/zi3Xffffr0KV2ScnJyqP2EbGxs4uLihJ9WVlYGBQWpq6sDgK6urpqaGgAYGhp+//33PB6PLs3i+fVXBIDmzWu3QH4+AkCGhgrUpPQkJCR4e3vv2LEDX/7xxx/j3nD58mWEUHx8PLUN1fTp0+/du0eLzubm5sjISNx50tDQCA4Orq2tFS7w+++/U8dbmZqalpSUIIQePXr08ccfW1tb4zIhISHUv2748OGFhYU0/Euko5faaHFxMdVOmpiYREVFNTU1RUZG9u3bFwBwJ7q6ulqRkiorK4ODg7FF9u/fPzIyUtgZeTxeVFSUkZERjjVwEJqTk+Pl5YXrorW1Nf5DYgo3biAANHp0uwUSExEAsrdXoCbl5uHDhxs2bFi/fr2fnx++ExERsXfvXpFiXC73yJEjAwcOBABVVVVfX99//vlHkTrZbDZlkd7e3s+fPxd+KhIoXLx4kXr05ZdfXrlyRatNByUvL2/MmDG0B9cS6HU2yuVyKbvE7WRNTQ31tLy8PDg4mIryIiMjcd9frmCLHDBgAGWR5eXlwgUSEhLGjBmDa567u/v9+/eFn7LZbGrJvKen56NHj+QtWCrq6pC6OlJXR6Wl4gvs2IEA0BtHIEjJqVOnKBsNCQk5efJkXV1d22JVVVVhYWF4i79+/fpFREQ0NTXJW1t2dvbs2bNxVbS1tb1y5YrwU8mBAkVbG/X39z927JgcdXeb3mWjwo7TdsyRIiMjY+rUqbjY2LFjb9y4IT9JV69etbe3x9/l4eGRlZUl/PTly5e+vr74qZmZWUxMjNgPEW4baAmlxbNoEQJAW7aIeVRbi0xNEQC6fl3hspQbYRtdv379lClTvLy8rK2tk5OT2xbOy8vz9vbG9WfEiBGxsbFyUoUtEscf2CKF448OAwVhRGy0rKzMwsLi9evXclIuE3qLjQr3f21sbKSZSmKz2ZaWlu31TbrPkydPFi9ejD9/+PDhIlW8vr4+PDwcj3Pp6uqGh4d3WJNoCaUl8eAB0tRELBY6efKt+1VVaO5cBIDmzqVJmRIjbKMUv/zyy7Rp09p7S3x8vJ2dHdVfefDggQz1SNOXogKFGTNmiAQKbRGx0fDw8F27dslQsDxguo1WVVVlZmY2NDR0+RM4HA7VlTAwMGivKyGWxsbGiIgIvB26trZ2WFiYyEh518AWiTtc2CKFO1w4ecDMzAwAVFRUFi9eXFBQIP2HC4fSTk5OiYmJ3RfcdX79FWloIADk4oLCwtCuXeijj9CAAQgAjR2LmJljwGyEbZQaLrx586aDg4OEd3G5XOnjQem5evUqPlgB96VEDFpyoNAewjba1NRkZWUlE6lyhdE2eubMGSsrKx8fHysrq7S0tM6+Xey0TBdkvHr1ytfXF2dmmJqaxsTEdHm0WyAQxMTEGBsbY4v09fXFc5QUqampkydPxjXPxcXl1q1bXfsieYfSneD+fbRoEdLVRQCt/w0bhnbtQt1oGnstx44dW7NmzZQpU6Kiorhcrpub2/bt2//3v/+NHTv28OHDHb69bde7ywkejx8/piyy7XCB5EChPW7cuBEVFcVisaKiorKzsxFCUVFRgYGBXVOoSJhro83NzSYmJjk5OQihX3/91cPDo1Nv72xXokNSU1MnTZrUHYMTzq8aP368yGCW5CS7LiCnULqLcLmooAA9eoQYH1kwmW+//TbiDVwut7S09OjRo3v27OlUbczOzhaeK+9srnSHfSnJgYIEsI1isI3++eefTM5zomCujWZnZw8fPhy/rq+v19LSkjIG7FpXQhpEqsjixYtfvnwpzRslx7MdJtl1B+GvHjx4cHdCaUJPQmSJhzS50nw+PyYmZtCgQdBOKpVIqHH79m25yWcWzLXRpKQkFxcX6lJDQ6O+vl7yW7owLdMFOvUtbUNCkfQUyUl2sqL7oTSh54Hbb+EED+HkPxEyMjKofSanTJmSkZEh/LSoqEhWA1/KCHNtNDc3d9iwYfh1bW2trq6uhMLd6Up0jYKCAmlSkV69eqWrq4st8sWLF8KPJCfZyZwuh9KEnk3bpShiEzwyMzNVVVXbWiQOFHBfiv6xI5pgro3yeDwzMzM8pnnq1Clvb+/2StLYlbh27RqVGN/e2rtjx46JZJ5KTrKTK4oJ2AlKR3p6+pQpU3BNbi9X+sKFCyI5M4rpSzEf5tooejPd/O6771paWmZmZrYtINyVoGvgDw8YSbn2Tk55J51FylCa0KvAmXbU6Ydt+0/C3L17d9q0abiko6MjzXl1dMNoG0UINTY25ubmtk3LkOu0TBfAa+80NDSg/bV3kpPsFI80obSs+Ouvv1xdXSdMmPD+++9zOBz5fRGhmzQ0NFCj+To6Om1H82nsSzEWptuoWBjblWhv7Z3kJDsaaRtKl7a3BL57mJiY4K0AQkJCNm/eLI+vIMiQwsJC4SmjEydO8Pn8169fU30pFotFV1+KgSiZjSp4WqZrXLp0ydraGoscOHCgnZ0dXkOlr6+/b98+fEgno8ALvVgsFgAYGBjs37+/mymrbRkyZMiZM2cQQv7+/hEREbL9cIKcuHHjBjU7r6uri2sIAHh5eeGEbgJGyWx069atStGVwGOg1M6PALB8+XIF71fWWfLy8ubNmwcA7u7uMv/ws2fPqqurjxkzZvLkyUz+xRFE4PP5J06cUH9zmlafPn2Y05diDiqIwYcItgUP3ISGhlK7LDOZ3NzcgIAAAPjkk0+WLVtGtxypuHTpkqWlJTWGKxOqq6sdHBwuXryoqqq6cePG4cOHHzhwQIafT5A3RUVF//3vf9XU1LZt24YT+AjCKJmNEpSRO3fu+Pv7Z2VlAUBaWpqvr29ubi7doggEmUFOBiXIHXt7+8bGxr179zo6Oh48ePCdd96hWxGBIEtINNpLaWxsPH/+fHl5ubu7u4ODAwC8fv36woUL//zzz/Tp052cnGT7dRwO58yZM+Xl5c7Ozl5eXngKmEDoGaht27aNbg09DYRQRkbG8+fPjYyMqMnNjIyMZ8+eGRoa4txSeuHxeFOmTFFRUTE1NQ0MDHR0dDQzM5s2bRqPxzM3Nw8KCrKzs6OOCZAJ2traLi4ubm5uI0aMIB6qRCQnJ+fl5VGbmJSVlbHZ7ISEBG1tbbywmACkUy9zmpqaPD09+/fvb2houGbNmuTkZH19fW9vb11dXUNDw5UrVyYmJpqbm9MrksViHT9+HG8kWFNTc/78eXd39+joaHynoaHh3LlzM2fOpFckgV5KS0t9fHyqqqqMjY09PT0B4Pnz525ubmvWrLGwsMjJycGdGAIQG5U5zc3N69atW7RoEQAsW7bst99+8/X1/eyzz3Au0apVq/78889PPvmEbplAbcZaUVGBtz5re4fQmzEyMmKz2ampqd988w2+s2fPntDQ0LVr19IrjIGo0i2gp6Gvr489FADU1dU1NTV1dHTmzZtXV1eXnJyclpZGbQDBBB49enTmzBk/Pz/qTm5u7s8//+zv70+jKgITUFVVFcltSk5O7tu3b1BQUGhoaGFhIV3CGAixUXmRk5OTlJS0YMECfHn06NGNGzdaWFjgQ5aYQF5e3qJFi6iNeAHg6dOnCxcuPHbs2ODBg+nVRmAgZWVl6enpy5YtMzEx8fLyIrPT/0Jr8n+PpaCgwNra+q+//hK5HxISEh4eTociUdhstp2dXUpKCnXn0qVLdnZ2Ys/pJfRarl27hh0TIWRjY4P30xEIBJqamhUVFbRKYxAkGpU9f//995w5cw4fPjxr1iwA4HK58fHx+BGLxXr9+jWt6gAACgoK1q1bp6KiEhgY6OzsHBAQUFxcHBISAgBBQUHOzs4fffQR3RoJjGPWrFknT54EgLi4OCMjIwMDA7oVMQWSNypjkpOTp02bNnLkSNwvnjdv3po1a1asWFFdXa2jo1NcXHz58mXm9OsJBAksXLjw+fPnTU1Nffr0OX/+vK6ubkBAQFZWlpaWVlRUFHU+I4HYqIzh8Xj19fXUJZ5iAoCioiKE0JAhQ+iTRiAQ5AKxUQKBQOgWZGyUQCAQugWxUQKBQOgWxEYJBAKhWxAbJRAIhG5BbJRAIBC6xf8DMdw4LhTwn48AAAG+elRYdHJka2l0UEtMIHJka2l0IDIwMjMuMDMuMgAAeJx7v2/tPQYg4GWAACYglgBiSSBuYGRjSADSjMxsDhpAmpmFzSEDRDMzwgXQFLAzgBUwAXVCBDggNBObgglIISNcB1wCXSGmVXgYELWcDAogMzEdBVXHzcDIwMiUwcTEnMDMwsDMmsHEyqbAxq7AzqHBxM7JwMnFwsjJzcDNo8DDq8HEw6fAx5/BxC+QICCYwSQolCAknMEkLMLAKpogKsYgKp7BJM6YIMyXIAIKKjZGcVFWoKP5BQSFhPnEtRiB9kJDkkFihvq8A8t0Nu8HcWaVNB34aZgGZqtXNx3guXnOFsQOnOB7oIxJyh7Ezpnre8Ca8SyY/VFS7ADP9otgNaV5e/ebxUHUtArI7nfQuQIWF78js7+yKhVs5mO+RrtvOyFq2Jc12P2OgZjzSrPf3oHrOlj9s69/7beJSoPF193+a3+rC6KGO8/MweHFTbAajWozhx+HUsBmOrzLdXB13wRmH1o9ycEiHSJusnSSg8OxO2D1nFdyHaJSIGa+O5brsHsHxMyrPGIHLK9B3MZutXf/0wsQc6TKfA9Y/ICwxQAtAnPzcSA87AAAAft6VFh0TU9MIHJka2l0IDIwMjMuMDMuMgAAeJx9VMtu3DAMvPsr9AMRNBT1OvSQ3U2CIIgXaLf9h977/ygpW5YCyLFXhM0dayRyRovR6+ft4+8/c1x0WxZj3De/Uor5451zy6fRB3N5eXtfzfXxfGmZ6/33+vhliA0F+Ubur9jnx/2zZWCu5oltYfLemydYX0p28ol19erf0oZ0KSZFOptLoSnSN2QKgnQWDm4KZAV6S0CpwOgQp8DQgKTcOAdGBZL1HPxOTTwFJrPqbjmVRu3na8w6o/wP3zbj59TF3DcgHfWJc25IiyRNgVxMlZtPgNiBIAHiGyAJOSyQuc4IF+a7gfYGtmRu1GHebXCdsWTfqM+A2huyObhGfbbruAPrZmp9TiqJJEhvE1JUpGqSTsi1O2xD9tzmpLkmUXYkxX2Z6UTmbiePrUJpXkqCea3A0CqU5tRETZSqym2VZ+TVOirLfNjxjP4wjzugeb6Al/X2xfPbKXC5r7d+CuhN3epydBjf/cw6umshI3RvstyxOxAyUvcZZOTuJpJRumcgrxidgRpE+K9vPzAYYcvToHjU4Adp66sod9AwaiYMYmUNiIMqWQPSoD7WgDyojDWgDGpiDTTKBhoIgz6kLNL9QQfQQH5oN9cMD13lenAfCV2xTh7Gbo690/d2+svz8h/dlCvU2tgexwAAAWx6VFh0U01JTEVTIHJka2l0IDIwMjMuMDMuMgAAeJxNkL1qxDAQhF8lpQ98QrOr3w2BA0NIdZcmVUgRTMpwIRykuYfPypKR3NjfsLM7nmXFuk7LYZ3Oy/R0Obwvp9PLhwqXDVday0PPh20ID/fp6Ex2xDwfYTjnND+qYmOIqliTcqamRM+zNbBQZkNALhwsQhNILdgFMuw8Vwc5FWBczM3CyvoG7xMcmqI72t2gJmvIkw1xM7kmgFRAE2CA5OK2xBfMydk671NlpjpemEzyto2XA4X1Zr1YQrCJ0H+vZZA6nPGJXZsgqgKFuiKm6gjtZKzo28VIrQhtoi4ohq2JtBceW3l2FxLNh/nzdv1+/b3+iDXl83y9fRkr6AChDiTcgcV1cOI7eAkdgsQOUVKHJLlDFgwRoBnGEBAMMUCCIQhYMESBEwxh4AVDHATBEAhRMERCEgyhkIWGVGSFhlSk3YzlkNCQinj+e9M/zPd/TLfOUYS1XAQAAAAASUVORK5CYII=",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7faba09de430>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "IPythonConsole.drawOptions.addAtomIndices = True\n",
    "mollll = Chem.MolFromSmiles('Cc1cc(C)c(NC(=O)[C@@H](C)OC(=O)c2ccccc2F)c(C)c1')\n",
    "mollll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Code to identify fragments in molecules.\"\"\"\n",
    "import abc\n",
    "import functools\n",
    "import itertools\n",
    "from typing import Any, Iterator, List, Optional, Sequence, Text, Tuple\n",
    "\n",
    "import numpy as np\n",
    "from rdkit import Chem\n",
    "\n",
    "\n",
    "def _sparse_to_dense(sparse_indices: List[int], max_value) -> np.ndarray:\n",
    "    dense_array = np.zeros([max_value], dtype=np.bool_)\n",
    "    dense_array[sparse_indices] = 1\n",
    "    return dense_array\n",
    "\n",
    "\n",
    "def _nonnull_powerset(iterable) -> Iterator[Tuple[Any]]:\n",
    "    \"\"\"Returns powerset of iterable, minus the empty set.\"\"\"\n",
    "    s = list(iterable)\n",
    "    return itertools.chain.from_iterable(\n",
    "        itertools.combinations(s, r) for r in range(1, len(s) + 1))\n",
    "\n",
    "\n",
    "def _reduce_logical_or(arrays: Sequence[np.ndarray], array_size) -> np.ndarray:\n",
    "    initial_value = np.zeros(array_size, dtype=np.bool_)\n",
    "    return functools.reduce(np.logical_or, arrays, initial_value)\n",
    "\n",
    "\n",
    "def _reduce_logical_and(\n",
    "        arrays: Sequence[np.ndarray], array_size) -> np.ndarray:\n",
    "    initial_value = np.ones(array_size, dtype=np.bool_)\n",
    "    return functools.reduce(np.logical_and, arrays, initial_value)\n",
    "\n",
    "\n",
    "class AbstractFragmentRule(metaclass=abc.ABCMeta):\n",
    "    \"\"\"Class encapsulating fragment identification logic.\"\"\"\n",
    "\n",
    "    @abc.abstractmethod\n",
    "    def match(self, mol: Chem.Mol) -> List[np.ndarray]:\n",
    "        \"\"\"Return a list of all possible masking arrays.\n",
    "\n",
    "        For example, if the matching rule is \"Nitrogen\", and the molecule is NCCN:\n",
    "        >>> BasicFragmentRule('nitrogen', -'N').match(Chem.MolFromSmiles('NCCN')\n",
    "        ...\n",
    "        [np.ndarray([1, 0, 0, 0]),\n",
    "         np.ndarray([1, 0, 0, 1]),\n",
    "         np.ndarray([0, 0, 0, 1])]\n",
    "\n",
    "        This is because there are many possible correct answers when trying to\n",
    "        answer the question, \"Why did you think there was a N in this molecule?\".\n",
    "\n",
    "        Args:\n",
    "          mol: Molecule to test for fragments.\n",
    "\n",
    "        Returns: List of legal fragment masks.\n",
    "        \"\"\"\n",
    "\n",
    "\n",
    "class BasicFragmentRule(AbstractFragmentRule):\n",
    "    \"\"\"Single fragment identifier.\"\"\"\n",
    "\n",
    "    def __init__(self, label: Text, smarts: Text,\n",
    "                 description: Optional[Text] = None):\n",
    "        self.label = label\n",
    "        self.smarts = smarts\n",
    "        self.pattern = Chem.MolFromSmarts(smarts)\n",
    "        self.description = description\n",
    "        # Update properties, sometimes rings or valence info is not set.\n",
    "        self.pattern.UpdatePropertyCache()\n",
    "        Chem.rdmolops.FastFindRings(self.pattern)\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"Gets size of matched fragment.\"\"\"\n",
    "        return self.pattern.GetNumAtoms()\n",
    "\n",
    "    def substruct_matches(self, mol: Chem.Mol) -> List[List[int]]:\n",
    "        \"\"\"Gets all matching substructs as a list of sparse indice lists.\"\"\"\n",
    "        return list(map(list, mol.GetSubstructMatches(self.pattern)))\n",
    "\n",
    "    def match(self, mol: Chem.Mol) -> List[np.ndarray]:\n",
    "        matches = self.substruct_matches(mol)\n",
    "        mol_size = mol.GetNumAtoms()\n",
    "        dense_matches = [_sparse_to_dense(index_list, mol_size)\n",
    "                         for index_list in matches]\n",
    "        all_matches = [_reduce_logical_or(match_set, mol_size)\n",
    "                       for match_set in _nonnull_powerset(dense_matches)]\n",
    "        return all_matches\n",
    "\n",
    "\n",
    "class CompositeRule(AbstractFragmentRule):\n",
    "    \"\"\"Multiple fragment identifier.\"\"\"\n",
    "\n",
    "    def __init__(self, rule_type: Text,\n",
    "                 fragment_logics: List[AbstractFragmentRule]):\n",
    "        self.rule_type = rule_type\n",
    "        self.fragment_logics = fragment_logics\n",
    "\n",
    "    @classmethod\n",
    "    def or_(cls, fragment_logics):\n",
    "        return cls('OR', fragment_logics)\n",
    "\n",
    "    @classmethod\n",
    "    def and_(cls, fragment_logics):\n",
    "        return cls('AND', fragment_logics)\n",
    "\n",
    "    @property\n",
    "    def label(self):\n",
    "        rule_str = {'OR': ' | ', 'AND': ' & '}[self.rule_type]\n",
    "        labels = [rule.label for rule in self.fragment_logics]\n",
    "        return f'({rule_str.join(labels)})'\n",
    "\n",
    "    def match(self, mol: Chem.Mol) -> List[np.ndarray]:\n",
    "        subrule_matches = [logic.match(mol) for logic in self.fragment_logics]\n",
    "        mol_size = mol.GetNumAtoms()\n",
    "        composite_matches = []\n",
    "        for combination in itertools.product(*subrule_matches):\n",
    "            if self.rule_type == 'OR':\n",
    "                for match_subset in _nonnull_powerset(combination):\n",
    "                    composite_matches.append(\n",
    "                        _reduce_logical_or(\n",
    "                            match_subset, mol_size))\n",
    "            elif self.rule_type == 'AND':\n",
    "                composite_matches.append(\n",
    "                    _reduce_logical_or(\n",
    "                        combination, mol_size))\n",
    "        return composite_matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "_frag_rule = BasicFragmentRule\n",
    "def _and_rules(rules): return CompositeRule('AND', rules)\n",
    "def _or_rules(rules): return CompositeRule('OR', rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AromaticDetection:\n",
    "    def __init__(self, aromatic=True):\n",
    "        self.aromatic = aromatic\n",
    "\n",
    "    def match(self, mol):\n",
    "        atom_aromaticity = []\n",
    "        for atom in mol.GetAtoms():\n",
    "            if atom.GetIsAromatic():\n",
    "                atom_aromaticity.append(1)\n",
    "            else:\n",
    "                atom_aromaticity.append(0)\n",
    "                \n",
    "        return [atom_aromaticity]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmr_dataset = _and_rules(\n",
    "            [_frag_rule('tmr', '*1**1')])\n",
    "\n",
    "aromatic_dataset = _and_rules(\n",
    "            [_frag_rule('aromatic', '[a!r0]')])\n",
    "             \n",
    "logic6_dataset = _and_rules(\n",
    "            [_frag_rule('flouride', '[FX1]'),\n",
    "             _frag_rule('carbonyl', '[CX3]=O')])\n",
    "\n",
    "\n",
    "logic7_dataset = _and_rules([\n",
    "            _frag_rule('unbranched alkane', '[R0;D2,D1][R0;D2][R0;D2,D1]'),\n",
    "            _frag_rule('carbonyl', '[CX3]=O')\n",
    "        ])\n",
    "logic9_dataset = _and_rules([\n",
    "            _frag_rule('amine', '[NX3;H2]'),\n",
    "            _frag_rule('ether2', '[OD2](C)C'),\n",
    "            _frag_rule('benzene', '[cX3]1[cX3H][cX3H][cX3H][cX3H][cX3H]1')\n",
    "        ])\n",
    "# logic14_dataset = _and_rules([\n",
    "#             _or_rules([_frag_rule('[OX2H]', '[OX2H]'), _frag_rule('[OD2](C)(C)', '[OD2](C)(C)')]),\n",
    "#             _frag_rule('[CX3]=O', '[CX3]=O'),\n",
    "#             _frag_rule('[CX2]#[CX2]', '[CX2]#[CX2]')\n",
    "#         ])\n",
    "logic14_dataset = _and_rules([_frag_rule('[CX3]=O', '[CX3]=O')])\n",
    "\n",
    "logic14_dataset_or = _and_rules([_frag_rule('[OD2](C)(C)', '[OD2](C)(C)')])\n",
    "\n",
    "logic14_dataset_false = _and_rules([_frag_rule('[CX2]#[CX2]', '[CX2]#[CX2]')])\n",
    "\n",
    "logic14_dataset_or_false = _and_rules([_frag_rule('[OX2H]', '[OX2H]')])\n",
    "\n",
    "or_case = False\n",
    "false_case = False\n",
    "if args.file == 'logic6':\n",
    "    logic_dataset = logic6_dataset\n",
    "elif args.file == 'logic7':\n",
    "    logic_dataset = logic7_dataset\n",
    "elif args.file == 'logic9':\n",
    "    logic_dataset = logic9_dataset\n",
    "elif args.file == 'logic14':\n",
    "    logic_dataset = logic14_dataset\n",
    "    logic_dataset_or = logic14_dataset_or\n",
    "    or_case = True\n",
    "    false_case = True\n",
    "    logic_dataset_false = logic14_dataset_false\n",
    "    logic_dataset_or_false = logic14_dataset_or_false\n",
    "elif args.file == '3mr':\n",
    "    logic_dataset = tmr_dataset\n",
    "elif args.file == 'aromaticity':\n",
    "    logic_dataset = AromaticDetection()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNN_Combine(\n",
      "  (node_feature_graph): NodeLinear(\n",
      "    (lin1): Linear(in_features=79, out_features=256, bias=True)\n",
      "    (bn): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (edge_feature_graph): EdgeLinear(\n",
      "    (lin1): Linear(in_features=10, out_features=32, bias=True)\n",
      "    (bn): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (GNN_Graph): GNN_Graph(\n",
      "    (convs): ModuleList(\n",
      "      (0): GINEConv(nn=Sequential(\n",
      "        (0): GIN_Sequential(\n",
      "          (lin1): Linear(in_features=256, out_features=256, bias=True)\n",
      "          (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (leakyrelu1): LeakyReLU(negative_slope=0.01)\n",
      "          (lin2): Linear(in_features=256, out_features=256, bias=True)\n",
      "        )\n",
      "      ))\n",
      "      (1): GINEConv(nn=Sequential(\n",
      "        (0): GIN_Sequential(\n",
      "          (lin1): Linear(in_features=256, out_features=256, bias=True)\n",
      "          (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (leakyrelu1): LeakyReLU(negative_slope=0.01)\n",
      "          (lin2): Linear(in_features=256, out_features=256, bias=True)\n",
      "        )\n",
      "      ))\n",
      "      (2): GINEConv(nn=Sequential(\n",
      "        (0): GIN_Sequential(\n",
      "          (lin1): Linear(in_features=256, out_features=256, bias=True)\n",
      "          (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (leakyrelu1): LeakyReLU(negative_slope=0.01)\n",
      "          (lin2): Linear(in_features=256, out_features=256, bias=True)\n",
      "        )\n",
      "      ))\n",
      "      (3): GINEConv(nn=Sequential(\n",
      "        (0): GIN_Sequential(\n",
      "          (lin1): Linear(in_features=256, out_features=256, bias=True)\n",
      "          (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (leakyrelu1): LeakyReLU(negative_slope=0.01)\n",
      "          (lin2): Linear(in_features=256, out_features=256, bias=True)\n",
      "        )\n",
      "      ))\n",
      "    )\n",
      "    (bns): ModuleList(\n",
      "      (0): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (3): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (atom_grus): ModuleList(\n",
      "      (0): GRUCell(256, 256)\n",
      "      (1): GRUCell(256, 256)\n",
      "      (2): GRUCell(256, 256)\n",
      "      (3): GRUCell(256, 256)\n",
      "    )\n",
      "    (mol_conv): GATv2Conv(256, 256, heads=1)\n",
      "    (mol_bns): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (mol_gru): GRUCell(256, 256)\n",
      "    (lin): Linear(in_features=256, out_features=256, bias=True)\n",
      "  )\n",
      "  (classification): ClassificationLayer(\n",
      "    (lin1): Linear(in_features=256, out_features=128, bias=True)\n",
      "    (bn1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (lin2): Linear(in_features=128, out_features=64, bias=True)\n",
      "    (bn2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (lin3): Linear(in_features=64, out_features=1, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "feature_importance_all = dict()\n",
    "feature_importance_smiles_all = dict()\n",
    "feature_importance = dict()\n",
    "feature_importance_smiles = dict()\n",
    "feature_max = dict()\n",
    "feature_outlier = dict()\n",
    "feature_topk = dict()\n",
    "\n",
    "wrong_prediction = dict()\n",
    "\n",
    "auroc = list()\n",
    "acc = list()\n",
    "jac = list()\n",
    "\n",
    "count = 0 \n",
    "tester = Tester(args, args_test)\n",
    "\n",
    "TP, FP, FN, TN = 0, 0, 0, 0\n",
    "\n",
    "b = False\n",
    "count_outlier = 0"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run all dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/2877 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/2877 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "non_of_condition 0\n",
      "no_important_in_groundtruth 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "non_of_condition = 0\n",
    "no_important_in_groundtruth = 0\n",
    "\n",
    "for d in tqdm(all_dataset):\n",
    "# for d in tqdm(all_dataset_test):\n",
    "# for d in ['CCOC(=O)C1=C(C)N(C)C(=O)N(C)[C@@H]1c1ccccc1']:\n",
    "    \n",
    "    # data loader\n",
    "    test_loader, datasets_test =  generateDataLoaderListing([all_dataset[d]], 1)\n",
    "    # test_loader, datasets_test =  generateDataLoaderListing([d], 1)\n",
    "    molecule_test = datasets_test[0]\n",
    "\n",
    "    # testing\n",
    "    # print(datasets_test)\n",
    "    try:\n",
    "        predicted = tester.test_single(test_loader, return_attention_weights=True, print_result=False)\n",
    "    except:\n",
    "        predicted = None\n",
    "\n",
    "    if predicted != molecule_test.y:\n",
    "        wrong_prediction[molecule_test.smiles] = {'TRUE': molecule_test.y, 'PREDICTED': predicted}\n",
    "        # print(molecule_test.smiles, \"TRUE:\", molecule_test.y, \"PREDICTED:\", predicted)\n",
    "\n",
    "    # print(predicted)\n",
    "    # if predicted == 1 or args.task == 'graphtask':\n",
    "    if predicted is not None:\n",
    "        count += 1\n",
    "\n",
    "        # attention result\n",
    "        att_mol = tester.getAttentionMol()\n",
    "        sample_att = att_mol\n",
    "        sample_graph = molecule_test\n",
    "        if 'atom' in sample_att:\n",
    "            sample_att_g = sample_att['atom']\n",
    "        else:\n",
    "            sample_att_g = None\n",
    "        if len(args.reduced) != 0:\n",
    "            sample_att_r = sample_att[args.reduced[0]]\n",
    "        else:\n",
    "            sample_att_r = None\n",
    "        # sample_att_g, sample_att_r = sample_att\n",
    "        if args.schema in ['A', 'R_N', 'AR', 'AR_0', 'AR_N']:\n",
    "            mask_graph_g = mask_graph(sample_att_g)\n",
    "        if args.schema in ['R', 'R_0', 'R_N', 'AR', 'AR_0', 'AR_N']:\n",
    "            mask_graph_r = mask_reduced(sample_att_r)\n",
    "\n",
    "        # molecule\n",
    "        smiles = sample_graph.smiles\n",
    "        mol = Chem.MolFromSmiles(smiles)\n",
    "\n",
    "        # reduced graph\n",
    "        if args.schema in ['A']:\n",
    "            reduced_graph, cliques, edges = getReducedGraph(args, ['atom'], smiles, normalize=False)\n",
    "        else:\n",
    "            reduced_graph, cliques, edges = getReducedGraph(args, args.reduced, smiles, normalize=False)\n",
    "\n",
    "        # record importance\n",
    "        mask_graph_x = None\n",
    "        if args.schema in ['A']:\n",
    "            mask_graph_x = mask_graph_g\n",
    "        elif args.schema in ['R', 'R_0', 'R_N', 'AR', 'AR_0', 'AR_N']:\n",
    "            mask_graph_x = mask_graph_r\n",
    "\n",
    "        # # finding outlier\n",
    "        # q3, q1 = np.percentile(list(mask_graph_x['atom'].values()), [75, 25])\n",
    "        # iqr = q3 - q1\n",
    "        # outlier = q3 + 1.5*iqr\n",
    "        # # finding top-k\n",
    "        # k = 0.25\n",
    "        # topk_position = len(list(mask_graph_x['atom'].values()))*0.25\n",
    "        # topk_sorted = sorted(list(mask_graph_x['atom'].values()), reverse=True)\n",
    "        # topk = topk_sorted[math.ceil(topk_position)]\n",
    "        # # finding max\n",
    "        # f_max = None\n",
    "        # f_max_value = 0\n",
    "\n",
    "        # for i, f in enumerate(reduced_graph.node_features):\n",
    "        #     if args.reduced != 'substructure':\n",
    "        #         f_tuple = getImportanceFeatures(args.reduced, f)\n",
    "        #     else:\n",
    "        #         f_tuple = getImportanceFeatures(args.reduced, reduced_graph.cliques_smiles[i])\n",
    "\n",
    "        #     # all\n",
    "        #     if f_tuple in feature_importance_all:\n",
    "        #         feature_importance_all[f_tuple].append(mask_graph_x['atom'][i])\n",
    "        #         feature_importance_smiles_all[f_tuple].append(smiles)\n",
    "        #     else:\n",
    "        #         feature_importance_all[f_tuple] = [mask_graph_x['atom'][i]]\n",
    "        #         feature_importance_smiles_all[f_tuple] = [smiles]\n",
    "\n",
    "        #     if predicted == 1 or args.graphtask == 'regression':\n",
    "\n",
    "        #         # for class\n",
    "        #         if f_tuple in feature_importance:\n",
    "        #             feature_importance[f_tuple].append(mask_graph_x['atom'][i])\n",
    "        #             feature_importance_smiles[f_tuple].append(smiles)\n",
    "        #         else:\n",
    "        #             feature_importance[f_tuple] = [mask_graph_x['atom'][i]]\n",
    "        #             feature_importance_smiles[f_tuple] = [smiles]\n",
    "        #         # max\n",
    "        #         if mask_graph_x['atom'][i] > f_max_value:\n",
    "        #             f_max_value = mask_graph_x['atom'][i]\n",
    "        #             f_max = f_tuple\n",
    "        #         # outlier\n",
    "        #         if mask_graph_x['atom'][i] >= outlier and outlier != 0:\n",
    "        #             if f_tuple in feature_outlier:\n",
    "        #                 feature_outlier[f_tuple].append(smiles)\n",
    "        #             else:\n",
    "        #                 feature_outlier[f_tuple] = [smiles]\n",
    "        #         # topk\n",
    "        #         if mask_graph_x['atom'][i] >= topk and topk != 0:\n",
    "        #             if f_tuple in feature_topk:\n",
    "        #                 feature_topk[f_tuple].append(smiles)\n",
    "        #             else:\n",
    "        #                 feature_topk[f_tuple] = [smiles]\n",
    "\n",
    "        if predicted == 1 or args.graphtask == 'regression':\n",
    "\n",
    "            # max\n",
    "            # if f_max in feature_max:\n",
    "            #     feature_max[f_max].append(smiles)\n",
    "            # else:\n",
    "            #     feature_max[f_max] = [smiles]\n",
    "\n",
    "            if not args.schema in ['A']:\n",
    "                mask_graph_x = mask_rtog(smiles, cliques, mask_graph_r)\n",
    "                if args.schema in ['AR', 'AR_0', 'AR_N']:\n",
    "                    mask_graph_x = mask_gandr(mask_graph_g, mask_graph_x)\n",
    "                    # display_interpret_weight(mol, None, None, mask_graph_x, None, scale=True)\n",
    "\n",
    "            \n",
    "            if predicted == molecule_test.y or args.graphtask == 'regression':\n",
    "                TP += 1\n",
    "                att_groundtruth = np.array(logic_dataset.match(mol), dtype=int)\n",
    "\n",
    "                if or_case:\n",
    "                    att_groundtruth_or = np.array(logic_dataset_or.match(mol), dtype=int)\n",
    "                    att_more = list()\n",
    "                    for att in att_groundtruth:\n",
    "                        for att_or in att_groundtruth_or:\n",
    "                            or_list = list(att | att_or)\n",
    "                            att_more.append(or_list)\n",
    "                    if len(att_more) > 0:\n",
    "                        att_groundtruth = np.concatenate((att_groundtruth, att_more))\n",
    "\n",
    "                if len(att_groundtruth) == 1 and not att_groundtruth[0].any():\n",
    "                    no_important_in_groundtruth += 1\n",
    "                    # print(\"no important atoms in att_groundtruth\")\n",
    "                    continue\n",
    "\n",
    "                if len(att_groundtruth)== 0:\n",
    "                    non_of_condition += 1\n",
    "                    # print(att_groundtruth)\n",
    "                    # print(smiles)\n",
    "                    # print(att_groundtruth_x[d])\n",
    "                    continue\n",
    "\n",
    "                assert len(list(mask_graph_x['atom'].values())) == len(att_groundtruth[0])\n",
    "                # print(list(mask_graph_x['atom'].values()))\n",
    "                # print(att_groundtruth)\n",
    "\n",
    "                list_roc = list()\n",
    "                list_acc = list()\n",
    "                list_jac = list()\n",
    "                temp_index = list()\n",
    "                temp_weight = list()\n",
    "                # print(att_groundtruth)\n",
    "                for t in att_groundtruth:\n",
    "                    try:\n",
    "                        if sum(t) == 0:\n",
    "                            list_roc.append(0)\n",
    "                        else:\n",
    "                            attention_prob = list(mask_graph_x['atom'].values())\n",
    "                            roc = roc_auc_score(t, attention_prob)\n",
    "                            list_roc.append(roc)\n",
    "\n",
    "                            # print('Ground truth:', t)\n",
    "                            # print('Model attrib:', np.sum(attention_prob), attention_prob)\n",
    "                            # print('ROC:', roc)\n",
    "                            \n",
    "                            # accuracy\n",
    "                            attention_prob_acc = [a > 0.5 for a in attention_prob]\n",
    "                            # print(attention_prob_acc)\n",
    "                            attention_prob_acc_score = accuracy_score(t, attention_prob_acc)\n",
    "                            list_acc.append(attention_prob_acc_score)\n",
    "\n",
    "                            # print('ACC', attention_prob_acc_score)\n",
    "                            # # method I: plt\n",
    "                            # fpr, tpr, threshold = metrics.roc_curve(t, attention_prob)\n",
    "                            # print(fpr, tpr, threshold)\n",
    "                            # roc_auc = metrics.auc(fpr, tpr)\n",
    "                            # fig = plt.figure(figsize=(5, 5), dpi=150)\n",
    "                            # plt.title('Receiver Operating Characteristic (ROC)')\n",
    "                            # plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "                            # plt.legend(loc = 'lower right')\n",
    "                            # plt.plot([0, 1], [0, 1], 'r--')\n",
    "                            # plt.xlim([0, 1])\n",
    "                            # plt.ylim([0, 1])\n",
    "                            # plt.ylabel('True Positive Rate (TPR)')\n",
    "                            # plt.xlabel('False Positive Rate (FPR)')\n",
    "                            # plt.show()\n",
    "\n",
    "                            attention_prob_jac_score = jaccard_score(t, attention_prob_acc)\n",
    "                            list_jac.append(attention_prob_jac_score)\n",
    "\n",
    "                            # print('JAC', attention_prob_jac_score)\n",
    "                            \n",
    "                    except:\n",
    "                        pass\n",
    "\n",
    "                    for tti, tt in enumerate(t):\n",
    "                        temp_index.append(tti)\n",
    "                        temp_weight.append(tt)\n",
    "\n",
    "                    # print(d)\n",
    "                    # print(temp_weight)\n",
    "                    # print(temp_index)\n",
    "                    # plot_attentions_groundtruth(args, molecule_test, [[[temp_index,temp_index],temp_weight],None])\n",
    "\n",
    "                    temp_index = list()\n",
    "                    temp_weight = list()\n",
    "\n",
    "                # print('MAX:', max(list_roc))    \n",
    "                if len(list_roc) > 0: auroc.append(max(list_roc))\n",
    "                if len(list_acc) > 0: acc.append(max(list_acc))\n",
    "                if len(list_jac) > 0: jac.append(max(list_jac))\n",
    "\n",
    "            else:\n",
    "                FP+=1\n",
    "        \n",
    "        elif predicted == 0 and false_case:\n",
    "            \n",
    "            # max\n",
    "            # if f_max in feature_max:\n",
    "            #     feature_max[f_max].append(smiles)\n",
    "            # else:\n",
    "            #     feature_max[f_max] = [smiles]\n",
    "\n",
    "            if not args.schema in ['A']:\n",
    "                mask_graph_x = mask_rtog(smiles, cliques, mask_graph_r)\n",
    "                if args.schema in ['AR', 'AR_0', 'AR_N']:\n",
    "                    mask_graph_x = mask_gandr(mask_graph_g, mask_graph_x)\n",
    "                    # display_interpret_weight(mol, None, None, mask_graph_x, None, scale=True)\n",
    "\n",
    "            \n",
    "            if predicted == molecule_test.y:\n",
    "                TN+=1\n",
    "                att_groundtruth_false = np.array(logic_dataset_false.match(mol), dtype=int)\n",
    "\n",
    "                if or_case:\n",
    "                    att_groundtruth_or_false = np.array(logic_dataset_or_false.match(mol), dtype=int)\n",
    "                    att_more = list()\n",
    "                    for att in att_groundtruth_false:\n",
    "                        for att_or in att_groundtruth_or_false:\n",
    "                            or_list = list(att | att_or)\n",
    "                            att_more.append(or_list)\n",
    "                    if len(att_more) > 0:\n",
    "                        att_groundtruth_false = np.concatenate((att_groundtruth_false, att_more))\n",
    "\n",
    "                if len(att_groundtruth) == 1 and not att_groundtruth[0].any():\n",
    "                    no_important_in_groundtruth += 1\n",
    "                    # print(\"no important atoms in att_groundtruth\")\n",
    "                    continue\n",
    "\n",
    "                if len(att_groundtruth_false)== 0:\n",
    "                    non_of_condition += 1\n",
    "                    # print(att_groundtruth_false)\n",
    "                    # print(smiles)\n",
    "                    # print(att_groundtruth_x[d])\n",
    "                    continue\n",
    "\n",
    "                assert len(list(mask_graph_x['atom'].values())) == len(att_groundtruth_false[0])\n",
    "                # print(list(mask_graph_x['atom'].values()))\n",
    "                # print(att_groundtruth_false)\n",
    "\n",
    "                list_roc = list()\n",
    "                list_acc = list()\n",
    "                list_jac = list()\n",
    "                temp_index = list()\n",
    "                temp_weight = list()\n",
    "                # print(att_groundtruth_false)\n",
    "                for t in att_groundtruth_false:\n",
    "                    try:\n",
    "                        if sum(t) == 0:\n",
    "                            list_roc.append(0)\n",
    "                        else:\n",
    "                            attention_prob = list(mask_graph_x['atom'].values())\n",
    "                            roc = roc_auc_score(t, attention_prob)\n",
    "                            list_roc.append(roc)\n",
    "\n",
    "                            # print('Ground truth:', t)\n",
    "                            # print('Model attrib:', np.sum(attention_prob), attention_prob)\n",
    "                            # print('ROC:', roc)\n",
    "\n",
    "                            # accuracy\n",
    "                            attention_prob_acc = [a > 0.5 for a in attention_prob]\n",
    "                            # print(attention_prob_acc)\n",
    "                            attention_prob_acc_score = accuracy_score(t, attention_prob_acc)\n",
    "                            list_acc.append(attention_prob_acc_score)\n",
    "\n",
    "                            # print('ACC', attention_prob_acc_score)\n",
    "                            # # method I: plt\n",
    "                            # fpr, tpr, threshold = metrics.roc_curve(t, attention_prob)\n",
    "                            # print(fpr, tpr, threshold)\n",
    "                            # roc_auc = metrics.auc(fpr, tpr)\n",
    "                            # fig = plt.figure(figsize=(5, 5), dpi=150)\n",
    "                            # plt.title('Receiver Operating Characteristic (ROC)')\n",
    "                            # plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "                            # plt.legend(loc = 'lower right')\n",
    "                            # plt.plot([0, 1], [0, 1], 'r--')\n",
    "                            # plt.xlim([0, 1])\n",
    "                            # plt.ylim([0, 1])\n",
    "                            # plt.ylabel('True Positive Rate (TPR)')\n",
    "                            # plt.xlabel('False Positive Rate (FPR)')\n",
    "                            # plt.show()\n",
    "\n",
    "                            attention_prob_jac_score = jaccard_score(t, attention_prob_acc)\n",
    "                            list_jac.append(attention_prob_jac_score)\n",
    "\n",
    "                            # print('JAC', attention_prob_jac_score)\n",
    "                            \n",
    "                    except:\n",
    "                        pass\n",
    "\n",
    "                    for tti, tt in enumerate(t):\n",
    "                        temp_index.append(tti)\n",
    "                        temp_weight.append(tt)\n",
    "\n",
    "                    # print(d)\n",
    "                    # print(temp_weight)\n",
    "                    # print(temp_index)\n",
    "                    # plot_attentions_groundtruth(args, molecule_test, [[[temp_index,temp_index],temp_weight],None])\n",
    "\n",
    "                    temp_index = list()\n",
    "                    temp_weight = list()\n",
    "\n",
    "                # print('MAX:', max(list_roc))    \n",
    "                if len(list_roc) > 0: auroc.append(max(list_roc))\n",
    "                if len(list_acc) > 0: acc.append(max(list_acc))\n",
    "                if len(list_jac) > 0: jac.append(max(list_jac))\n",
    "            else:\n",
    "                FN+=1\n",
    "\n",
    "        elif predicted == 0:\n",
    "            if predicted == molecule_test.y:\n",
    "                TN+=1\n",
    "            else:\n",
    "                FN+=1\n",
    "\n",
    "    break\n",
    "\n",
    "print('non_of_condition', non_of_condition)\n",
    "print('no_important_in_groundtruth', no_important_in_groundtruth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP 1\n",
      "FP 0\n",
      "TN 0\n",
      "FN 0\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print('TP', TP)\n",
    "print('FP', FP)\n",
    "print('TN', TN)\n",
    "print('FN', FN)\n",
    "print(TP+FP+TN+FN)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AUROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NUM: 1\n",
      "AVG: 1.0\n",
      "std: 0.0\n"
     ]
    }
   ],
   "source": [
    "print('NUM:', len(auroc))\n",
    "print('AVG:', np.average(auroc))\n",
    "print('std:', np.std(auroc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AVG: 1.0\n",
      "std: 0.0\n"
     ]
    }
   ],
   "source": [
    "print('AVG:', np.average(acc))\n",
    "print('std:', np.std(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NUM: 1\n",
      "AVG: 1.0\n",
      "std: 0.0\n"
     ]
    }
   ],
   "source": [
    "print('NUM:', len(jac))\n",
    "print('AVG:', np.average(jac))\n",
    "print('std:', np.std(jac))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 \t 1.0 +/- 0.0 // 1.0 +/- 0.0 // 1.0 +/- 0.0\n"
     ]
    }
   ],
   "source": [
    "print(len(auroc), '\\t', np.average(auroc), '+/-', np.std(auroc), '//', np.average(acc), '+/-', np.std(acc), '//', np.average(jac), '+/-', np.std(jac))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNN_Combine(\n",
      "  (node_feature_graph): NodeLinear(\n",
      "    (lin1): Linear(in_features=79, out_features=256, bias=True)\n",
      "    (bn): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (edge_feature_graph): EdgeLinear(\n",
      "    (lin1): Linear(in_features=10, out_features=32, bias=True)\n",
      "    (bn): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (GNN_Graph): GNN_Graph(\n",
      "    (convs): ModuleList(\n",
      "      (0): GINEConv(nn=Sequential(\n",
      "        (0): GIN_Sequential(\n",
      "          (lin1): Linear(in_features=256, out_features=256, bias=True)\n",
      "          (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (leakyrelu1): LeakyReLU(negative_slope=0.01)\n",
      "          (lin2): Linear(in_features=256, out_features=256, bias=True)\n",
      "        )\n",
      "      ))\n",
      "      (1): GINEConv(nn=Sequential(\n",
      "        (0): GIN_Sequential(\n",
      "          (lin1): Linear(in_features=256, out_features=256, bias=True)\n",
      "          (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (leakyrelu1): LeakyReLU(negative_slope=0.01)\n",
      "          (lin2): Linear(in_features=256, out_features=256, bias=True)\n",
      "        )\n",
      "      ))\n",
      "      (2): GINEConv(nn=Sequential(\n",
      "        (0): GIN_Sequential(\n",
      "          (lin1): Linear(in_features=256, out_features=256, bias=True)\n",
      "          (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (leakyrelu1): LeakyReLU(negative_slope=0.01)\n",
      "          (lin2): Linear(in_features=256, out_features=256, bias=True)\n",
      "        )\n",
      "      ))\n",
      "      (3): GINEConv(nn=Sequential(\n",
      "        (0): GIN_Sequential(\n",
      "          (lin1): Linear(in_features=256, out_features=256, bias=True)\n",
      "          (bn1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (leakyrelu1): LeakyReLU(negative_slope=0.01)\n",
      "          (lin2): Linear(in_features=256, out_features=256, bias=True)\n",
      "        )\n",
      "      ))\n",
      "    )\n",
      "    (bns): ModuleList(\n",
      "      (0): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (3): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (atom_grus): ModuleList(\n",
      "      (0): GRUCell(256, 256)\n",
      "      (1): GRUCell(256, 256)\n",
      "      (2): GRUCell(256, 256)\n",
      "      (3): GRUCell(256, 256)\n",
      "    )\n",
      "    (mol_conv): GATv2Conv(256, 256, heads=1)\n",
      "    (mol_bns): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (mol_gru): GRUCell(256, 256)\n",
      "    (lin): Linear(in_features=256, out_features=256, bias=True)\n",
      "  )\n",
      "  (classification): ClassificationLayer(\n",
      "    (lin1): Linear(in_features=256, out_features=128, bias=True)\n",
      "    (bn1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (lin2): Linear(in_features=128, out_features=64, bias=True)\n",
      "    (bn2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (lin3): Linear(in_features=64, out_features=1, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/6 [00:00<?, ?it/s]"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7faba0f61430>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[16:20:29] The new font size 1 is below the current minimum (6).\n",
      "[16:20:29] The new font size 1 is below the current minimum (6).\n",
      "[16:20:30] The new font size 1 is below the current minimum (6).\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 3000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 3000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 3000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.003711370255521207, 0.0009200008851212443, 0.0035910908917371476, 0.0018415125150383488, 0.0006480963327797629, 0.016619642677880714, 0.6850625991683195, 0.025146742788356588, 1.0000000000000002, 0.6403106822350974, 0.018192557985796595, 0.002485730817002693, 0.0007340084530777363, 0.0016249031175083056, 0.003828583051653428, 0.0009714669074115806, 0.0005435423107952309, 0.0004938313797125948, 0.0, 0.0032452007932333576, 0.0032452007932333576]\n",
      "accurary 1.0\n",
      "jaccard 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[16:20:31] The new font size 1 is below the current minimum (6).\n",
      "[16:20:31] The new font size 1 is below the current minimum (6).\n",
      "  0%|          | 0/6 [00:02<?, ?it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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iIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiAaUwLyIiIiISUArzIiIiIiIBpTAvIiIiIhJQCvMiIiIiIgGlMC8iIiIiElAK8yIiIiIiARVq6AGISLBZa2u+nW1vN5X+v1oGTE23i4iICKAwLyJ1EA/wFTHd4luLxcdisdaW3xK/1WCqxHmTuGXr7QYwxsHgxP9rDRabCPTGKNiLiIhUx9gdTauJiEB5SPfxbTy0x//r77L7i4d8B8c48fdN/H0RERHZSmFeRKpV8aPB4uNZD1se4qsrm9kdDA4GcIyLY1zN2ouIiKAwLyI/UXkG3rMxfGyjrF53jIuLG5+tN0Y19iIiskdSmBeR+Gy7BR8f38bwrE/lpauNOSY75aU4rgmBZutFRGQPozAvsgfbGuI9PN/Dx2voIe0U14RwTai8xl6BXkREmj6FeZE90NZ6eItno3g22CG+MoNJhHrQLL2IiDRtCvMieyBr4yE+Zj1ooAWtu5qDg+uEcXAU6EVEpMlSmBfZg8T7wPt4fgwv4CU1yTGEKkpvFOhFRKQJUpgX2UNYa/GtR8xGG6y9ZENxcAk5YdXSi4hIk6MwL7IHiJfVxIjZaEMPpcE4OPFAX74JlYiISFOgMC/SxFlridkono019FAanMEQclJURy8iIk2G9kYXacIqZuQV5OMslpgf2ePKjEREpOlSmBdporYG+T23tKY6FYHetz56YVJERIIu1NADEJH6Z7H4+LWvkbcWtmzB5G2E0lKIxcBxIDUV27w5NMuJf1wfJSrFxZi167DhELRoARkZtb+GtZC3EYq3bP1cZmb8ejWM0cfHs1FCJqUOAxcREWk8FOZFmiBrLTG/lkE+Px/3zbcxY7/DmfYjZkMebNkCKSnY5rnYPvvgDxuCf/aZ2C6d6x7oyyI4EyfhvPYGzhdfQXY2sSsvw//lWbW/1urVhP50F86P0xPt8v2TTiB2842QWnNQ96yHIUbIhOvwIERERBoHhXmRJqaivMbiJ3eC52F+mEb41jsw4ydAURGkpEBaGoRcKC3DLF2KmTMH5733sf95jdgN1+Kfekr8uGT4PhQU4Ez+HvfJZ3AmTYF16zC+j3VdnLnzkx3tVrEY7sv/xn3lP/FXEADjedh+fcEmdzXPxnCsow43IiISWArzIk2MxU9+wau1mAmTCF9yBWb2nHg5zaEH4538M2z/ftgWuZjSUszc+TiffIrz2ReYqdMIX34V0bIy/F+dGy+72QEzYRKhO+7GGfstlEUgMxPbex+YNbvOj9NMn0HooUegrAz/5BNxZs6GxUtqdQ1L/IlPiBSU5UVEJIgU5kWakHh5TS061xQWEbprFM7sOdjUVLxLLiJ25eXQtUuijMYCHHIw3umn4r71DqHrboT8AkL3jyHatw928H47LLkx+fk4EydjO3TE//nP8I8+EjIzCR99Ql0eZPz+H/wLrN+A3WdvvMsvxbn6utpfi3i5jWt8sGpXKSIiwaMwL9KE+OV/kjvYx33l3zhjv4t/ePoviN13d7x05qeh1hhokYv363MhP5/Qn+7CzJ2H++zzxMbcD1lZNd/V0UcS+eR97D57Q3oauC6sXFWXhxgvr/nXSzjvfgA52XgjR8Rr+HeC58cIOVoMKyIiwaPWlCJNhLUWa31Itof6pk04H30CpaXY3ObErri0+iBfWTiMd9ovsPsNAmtxPvoEs2pVfLa8Jikp2KGDITsLQqG6L561FrN0GaEnnoHiYvyjjsD7+cnxJwc7wcNX73kREQkkhXmRJsS3XtLHmmUrMLNmYwD/wAOwPXsmF7I7d8I/8nAIhTArVmKmzYh/3lpYuw6zYCFmyVIoKanbg6hJcQmhu++DhQuhTWtif7wJWuTWw4UtfpKLZkVERBoThXmRJqKit3xyB1vM2rWYFSvjH/beG3KykzvXcfAHDYTUVAzgTJkS/3wsRuihR0j5+emEf3MhZvrM2j+Imngezptv4bz7PoRCxC67BDugf/30vCe+cFiz8yIiEjSqmRdpAqy1tQ+imzZhSkuxoRC2bVsIJ99v3XbqCOH4jw+zfGXFIOJPEOYviLe3rM+ZeWsxixYTevQJ2LwZ/8jD4/X7SXTSSf4ubLxCSWtgRUQkQBTmRZoIu6O69aoHYzZvjr8fCsV3X63NDHdODjjlder5+fH/Og7+gP5w/LHYFi3qqfylXMzDffQJzJQfoHlzvBFXwc5sXFWNipl59ZsXEZEgUZgXaTJqOTNvKma161BaYu3W8ypmx10X76Lf4f36PHCdeOCvD9bifPY57r//B4B/+qn4Rx1Rr0FeREQkqBTmRZqIWpXZGINt1iz+fjQWL4uxNvmAnF8Q39UVIDc3cU2a5cTf6ou1mMVLCP15NGzciO3Xl9jIEcnvPFubu6LSExQREZGA0AJYkSai1uUhuc2xGekYz8OsXQfRaPL3tXx54njbqWPt7rc2PA/nny9iJk6GrEy8iy7Adu4cf+Lh+1vfqpQYWfBtNZ+vWfzrp9l+EREJFoV5kSajFkHUGGy7tthOneIfzpoDBQXJnRvzcL7/AUrLwBj84UPrMNYkh7lgEaEnnsZEIlBcTOiue0nt2YfULr2qvKUcdgwsWgyA89qbpO7dj5Qjj8PMqE1HHQV5EREJHpXZiDQRxphaVYnYjh2wvfeBufNwJk3CLF6CbdVqh6U2ZsUKnLHfYTwP26Uztm+fnRx5Ddatgy3F8fv1LWzatMNTTEkJlJRgc3JqfrVh2XLIyoQWLeLnlf8REREJEoV5kSbAGIOxtS2zycU/6QScjz/FbMon9OiTRJ99EtLTth/oIxGcN9/CTPkB6zh4PzsB2779LluMavv2JvrS37fW52+HWbOW0H2jMWvW4B12CN7FF0KLXGy3rtWfsGkToXvvx2zaRPSB+6BTJ4yrIC8iIsGjMC/SRBgMDi4+Se4CawzeGafhvvpfzOdf4rzxFqFOHYhdcRl0qCagb96M8/pbhEbdD2Vl2H164V3wf5CRHr/d83C+/gYzbwE2IwP/6COh/V4796BatsQ/9ZQdP5SFi+Dxp2AN0Llz/JzU1O2f0KwZ/mm/IDRiJOGLLyf28IOYvgPir26IiIgEiMK8SBPiGAffJhnmAXJyiN56M+ElSzGLFuM+8TTOuIl4Z56OP2QQtGoNJSWYGTNw33oX58uvYFM+ZGfh3XAddt8BW0O/5+H+40XcF1/B7tWOyIt/x1aEeWsxc+fHa+0rbCwvmbF+/Pov/ztxkz98KLZH952b8a/pXMfBP/ZoYmP+TGjkzYSvGQlPPw09eqrlpYiIBIrCvEgTYUx8Zt7YWK3aVNpDDiL69+cI3XVvvBZ+7LeExo0H1y0PthY8HzwPXAc7bAix667B/8XP48ckw7c4n35G6Pqbqr/tzXdw3n4v8anoE4/Ew/yuZAz+8ccRy84mfN1N8Jv/gzFjYP/963VnWRERkV1JYV6kCTEmXmxja1FqgzHYgw4k+txTuO9/iPP1WMz4CZg1ayESiQf2Frn4g/fDHnoI3qk/x/bsGd8YqjLXxfvtb/APORibkYHdu1el+wHbqhV234HJjatli+SOK2dTU/H798Pk5MTr5JMJ4+WP3T/kYOzDf8H84Sq46ir4619h4EDN0IuISCAYW6s94EWksfOtT8Qvo04bIHkelJZCcQnEopgtxdjUVEhNgbQ0yMioNGNfC9ZCWRmUlCR3fEZGfGOoZO/H86BoC/he/LyMjKTPdU2IECHMlCkwciQUFsLo0XD44cm/8iAiItJAFOZFmhhrLTEbxbOxhh5Ko2cwhJ0UHFMe2mfOhEsugQ0b4Ikn4OijG3aAIiIiO6DCUJEmxpj4bLOjf9475JoQpvLXqW9fePJJ6NoVrr4a3nsPYrFa7SQrIiKyO+m3vUiTE9/8KOSE2WN3NU0igDvGjYf5yuU4xsCAAfDcc9CjB1x5Jbz+usK8iIg0WgrzIk1QxULYkNkD17hHIrhPPYvz9rvxWvpqgriDQ8iEq3+uYwx07BjvbDNwINxyC7zxRnw3WYV6ERFpZBTmRZqq8nKbkAk39Eh2r8JCzMxZhEeMxH3hn/EQXomDQ8hJKX/9YjuvXBgTn5l/5hk45BAYMQJeeGGba4mIiDQ0hXmRJspgMMaUB/o9aIa+ZUtit9+Kf/CBhO68B+eV/5TP0FcE+XDia1MjY6BtW7jvPjj0ULjzTnjxxXi7Ts3Qi4hII6FuNiJNXMU/cc/GiNkYdWpZGTTWwrr1hO69H/ftd4ldezX2wt8RymqeXJD/6bU2bIC7747Xz19zDVx+ebz9pYiISAPbg6brRPZMFcE1ZEIYa4j50VrtEBtIxkDbNsTu/BMAofsegKiFa67BhGtZdmQMtG4No0bFg/0DD8Q/f8UVkJ5ezwMXERGpHYV5kT2GwcEl7DjE/Ch+srvEBlnz5nDHHZCRjXn0UfD9+Kx6VlbtN77KyoqX2mRnw8MPQ3FxvH1lTo52ixURkQajMC+yBzHGgIWwk4JnY3g21mRn6d2K1pMt22DuuCNeFjNmDGzcCLfdFg/ntWEM5ObGnxykpMDjj8cD/S23xAO9iIhIA1CYF9nDVJTduIRwjEvMRvFtcGfpK56KmMR/DSETwqncQz4jA669Nt6N5m9/g9RUuO662s+qGwPhcPxaoVB8gyljYOTIeNDXDL2IiOxmCvMie6jELL1JwRofz4/h4RPUBbIGJzEbD9Xk6uzs+Ix8mzZw//2wfn18UWurVrW8IxO/1o03xgP8qFGQlxf/b+vW9fNgREREkqQwL7IHq5i5NrgYx8HBx7ceno018MiS55SHeMe45Ztl1dA7Pi0NLr0UysrgoYfi5TK33177WfWKGfqLLorvNnv//fGPb7st3s5SM/QiIrKbKMyLCBAP9k75H9eEykO9B/iNbq4+3l4yPk6nfLuMpNtNpqTAVVdBy5Zw111QUAD33AMdOtQ+hKekwGWXQbt2cP31eEVFFN53H83bt6/lIxIREakb9ZkXkWpZLFjw8bHWx7d+A3fAMbjGxWB2PAufDN+P7+p6++1wxBEwejTstVfdruV5eC+9xDsPP8yH/fpxy6hRdO7cue5jExERSZLCvIjsUMWPCYvFUh7syxfN7ppuOPGYvjW4O/HZeAzx/9VDGYu18d1cX3stXh5zwAHxGfquXetUJmMjET775BMuueoqBgwYwJgxY+jevXvtNqgSERGpJYV5EUlaxWx91c9YfOuXh/ryaG8rIr6tcmTVEF7+UaJu3yR2ZzU42wT2XRKKK378vftuvGf8PvvAY49Bz551vJzlww8/ZMSIEXTo0IGnnnqKXr16KdCLiMguozAvIvWmctjfdsb+p00kKz4qD/MNGXhjMXjnHbjpJujTJ77La69edZqhj0ajfPbZZ1x99dV0796dMWPG0KdPHwV6ERHZJRTmRUQgXkP/7bfxbjc5OfDMMzBwYB0v5TNx4kQuvvhi0tPTee655xgwYIACvYiI1DunoQcgItIoOA4cfDD85S9QWAjXXAPTpsVLcWo55+E4DsOGDeOxxx4jEonwhz/8gSlTpqC5ExERqW+amRcRqczzYOpUuPjieJnNY4/FF8fWYVbd8zxmzZrFBRdcQDQa5dlnn2XYsGE4juZRRESkfug3iohIZa4LgwfD009DKARXXAHjx8fLcGp9KZf+/fvz9NNPk5OTw6WXXsrYsWPx63AtERGR6ijMi4j8lDHxQP/MM5CZCZddhv38c2wdQrgxhsGDB/PXv/6V7OxsLr/8cr766is8ryF79ouISFOhMC8iUh3XjS+A/dvfoEULVtxyC19/9hmxWKzWl3Ich7333pvnn3+eTp06cdFFF/HJJ58QjUZ3wcBFRGRPojAvIrI9xkCPHkQee4xnO3XiyhEj+Pjjj+s0q26MoUePHjzyyCP07NmTa665hg8++GCXzdBba7d5q9jsy7MxYjZGzI9Weovh2fibb/1qzxcRkcZHC2BFRHbAWsu6tWu5+JJL+P7773n44Yc55ZRTCIfDdbrWmjVruOKKK5g0aRKPPPIIJ510EqmpqfUyTrD45Rt4+dbiWw9L3Wv0E7vw4pRv6LV1cy8REWl4CvMiIkmw1rJo0SJuuukmpkyZwp///GdOO+00QqFQrYOttZalS5dy4403MnHiRO655x7OPvvsWj85qPzj27cePh6+tTsV3nckHu4dwME1bqVNv+K3iojI7qUwLyKSJGstGzZs4Morr2Ts2LHcc889nHfeeaSkpNTpWhs3buTqq6/ms88+4/777+fss88mLS0tqXMrZuA9P1b+XkP8KI/P0oeMiykP9pqxFxHZvVQzLyKSJGMMrVq14uGHH+b444/n1ltv5aWXXqKkpKRO12rRogWjR4/mF7/4BTfffDN///vfKS0t3e45lnjdu2djRPwIUb8MH6+BgnxiRERtlKhfhmej8bIezRGJiOw2oYYegIhIkBhjaNu2Lffffz+hUIhbb72VwsJCLr744qRm1X96rXbt2nHvvffiui533XUXkUhkm2tVzMR75YtXGy68b5/FErMxsDEc4xIiHC/BMSRKcUREpP4pzIuI1JIxhpYtW/Lggw+SmprK3Xffjed5XHTRRWRnZ9f6Wrm5udx7771kZ2dzzz33UFpayqWXXkpOTg7WWjy8RDlNEPjWI2I9XBPCpfZrCkREJHkK8yIidWCMISsriz/96U+kpaXxwAMPEIlEuPLKK8nIyKj1tZo1a8YNN9yAMYYxY8ZQWlrKyBtGEkpx8Wzte9s3BhVtLkNOCAdXoV5EZBdQmBcRqaOKGfq77rqL9PR0xowZQ3FxMddccw25ubm1vl6LFi249dZbyczM5PHHH2NLSRHXXHsVzXNzAxuELT5RP0LIhHAJB/ZxiIg0VgrzIiI7wRhDWloa1157LSkpKTzyyCN4nscNN9xAdnZ2rcNrRkYGl//hMpyww18efIiySBl/uuO2Ws/2NzYxG8PHJmrpFepFROqHwryISD1o1qwZ1113HWlpaTz44INs3ryZO++8s1Yz9NZaYjZCRnY6l11xCY5j+OD9D9m0cVPgwzzEa+mj1ifspIB1FOhFROqB+syLiNSj4uJi/va3vzFq1CjOPPNMbr31Vtq0abPD4FoR5D3rVblWUWERrVq3wnGaTidhg0PYCSd2lRURkbrTzLyISD3KyMjgwgsvJC0tjdtuu43CwkIeeughcmuoe7fWErUR/EpBvuJaTWFG/qcq6ujDTiqg1pUiIjuj6Uz1iIg0Eunp6fzmN7/h3nvv5YsvvuDqq69m+fLl1W6mZK1NbLa0J7FYon5EG0yJiOwkldmIiOwipaWlvP3224wYMYKDDz6Yxx57rErJjbW2vI48UqvrVvzYrvivMfW3oLTyNWtzTuK8io2ikjzfNS4hk6JyGxGROlKYFxHZhTzP46233uKmm25i77335uGHH6ZHjx4YY/CtT9QvTXo/V9/3Wb16NSuWr2DN6rVs2rQJL+bRolUL2rVtS/ee3WnVqhWu69Z6nNZa1qxew8yZMwHo1q0bXbt1rfFanuexcsVK5s+bz+rVaygsLCQnJ4d2e7WjW7eudOrciXA4vMP7DpkUQo6qPkVE6kJhXkRkF4tEInz00UdceeWVDBgwgEcffZSuXbtus+B1e6y15Ofn859X/8sbr73B/HkLWL16dZUSlbbt2rLvoH057fRfcN6vzyMc3nFP94rzN27cyEcffMxL/3qZyZMmE4vF+L8LfsNd99xJVnZWteeVlJTyr3/8i9f++xpTf5hGYWFh4vbs7Gx69+3NyT8/icuvuIzMrMwaxxJfEBufnVf9vIhI7WgqRERkF0tJSeFnP/sZaWlpXH311Vx44YU89fSTdO7WCePsOHDPnjWbm0bezDdfj6W0tJT09HQ6dupIRkYGxhhKS0rYsCGPjz/8mG+/+ZbZs+dw0y030bx5s2pDtLWWSCTC0iVL+eD9D/nbX19gxfIVFBcXJ47Jy9uI5/vVjmnjxo3cetNtvPrKv/E8j7bt2iZm4cvKyli7di2TJkzix6k/snzZcu6+926aN2+2/ceIj2djhAijLC8iUjtaACsishu4rstRRx3F448/zvr167n00suYOWPmDheAbtq4iRuvv5lPPv6UsrIyBgwcwB13385/XnuVjz79gI8//5DX3vofD4y5nyFDB1NSUsL7737A/HnzarzuwoWLOPuMX3LzDbcwf958cnNzOfKoI2nVqmWN55WWlvLs08/x6iv/JhQKceHvL+TV/77Mp19+zNjxX/PRpx/wyOMP06dvH8rKyvjff17j888+3+Hj9K2HLf8jIiLJ08y8iMhu4rouhx9+OE8+9SSXXHwx1107kn//9xWa5zav9vhoNMqzzzzH1199DcCgwYN47vln2Kf3PoRCW398t27dmt59ejN8/+GMGf0Q5/7qlwzcd2CNpS1ZWZl07dqVvv36ccovfs6gwYMo3LyZP1x+FRs25G33vEhZhE0bN5GTk8MZZ53On+78E82a5STuq03bNpx+xmkUbylm5LU3kJ+fz7fffMvPTjqRtLS07V7XYjU7LyJSBwrzIiK7keu67H/gcJ7+61PMmjm72pr0CosWLuLN198iEonQslVL7rt/FH379a02pDuOQ99+ffjrC8/usLuNMYaOHTvyn9fjs+sVx86fNx/H1PyCbXZONvfeP4rTzzyNzl26VAnylceyT++9admqBYWFhaxZvQYvtuO1Ab71wOx4wayIiGylMC8ishtZa7H4DB02lKHDhta4kdSsmbOZPWs2AEcedSSDBg3aYUhPtsWj4zikpKTUevzGGEKhEAcceECNxxUWFlFSUgpATrMcHHfHVZ0Wi299XFP7bjwiInsqhXkRkd3I4mOJh+maeJ7H5EmTiUQipKenc9jhh5LTLKfW97d0yVJmzZqN53n079+PLl277PKe7iUlJXz0wUesX7eezKxMDjn0YFJTU5M61+IBCvMiIsnSAlgRkd3ItxaSWOTpeT7zyhexZmdn0617tzqF8I8/+oTzfvkrzjrtbD54/4N633G1YsMo3/fxPI+lS5YyZvRDvPSvlwA46aSfccxxx+zwyUvieljtCisiUguamRcR2U3ivVqqb/e4zbHWJ698IWpaetoOu8w0lFgsxiN/eZQJ4yeyeuUqli1bTkFBPpmZWVx+5WXcdMuNtGjRIunrWWvBWLQKVkQkOQrzIiK7S20mnC1sKYr3fXddt8ZOMDUZMnQwN99yE9FYlGHDh9V7iY3v+4z7dhzvv/dBlc936dqKDh061KqOH0g0p1SUFxFJjsK8iEhjZCA1LV5n7vs+0Wi0TpcZPGQwg4cMrs+RVREOh7n19lu55LJLKCsrY8H8Bbz77ntMmTSFu26/m8mTpnDvn++hU+dOu7xWX0RkT6QwLyKyWyU3PW+MoXnz5gBEIhEKCjbvwjHVnTGGQfvtW+Vzv7/0IkbdfS/PPvUcb7z2Bp07d+L2u/6U9CLY2r2EISKyZ9MCWBGR3Sr51pFdunYGYEvRFlauWNEoF4ZWlNFUfsvKyuL3l1zE3vvsje/7vPvOuxQVFdXmqrtsvCIiTY3CvIjI7lKLjOq6LkOGDsFxHAoLC/l+yg9EyiK7bmz1rGvXrnTq1BGIb35VuLkwyTMV5EVEakNhXkRkNzHlf5I61hj69e9Lx04dsNby6SefsXLVykYxO1/RjrKmsRhjEhtF+b7F83a8AyzU7mskIiIK8yIiu5Uxyf3YNcbQp28fjjr6KBzHYc7sOTz6l8coLKx5hrui33uFSFmEgoICCvILKCsr2+knA9Zali9bzpjRD7F27dpqr2etZcniJSxbuhyAtu3akp2T3IZXWiQrIlI7CvMiIrtRbWaes7KyuPzKy+ncpTO+7/P3v/2D22+9g1mzZm8Ton3fZ9HCRbzw/N8Z9+24xOfHjh3Ldddcz5VXXMXXX32z02F+48aN3H7rHYy6616u+cMIpv4wdZtr5ufn8+QTTzNn9mwATjzxBLKzs5K6vmMcBXoRkVpQNxsRkd0ovkjUwdrkyk769evL3aPu4obrb2TN6jU8/9zf+OD9Dxk2fBj9+vUlt0UumzZu4sdpPzJt6jTWrl3HqWecyv4H7k84HGbhgkW89r/XKS0p5cCDDuDoY45KXPvbsd/y/HMvUFJSAkBRYSGLFy8G4Ltvv+Oi315EKBzGAIcefiiXXXEpGekZ7NN7H1JSU3j3nff44fsfOPiQg+nTrw/NmjVj5YqVfPnFV0z/cTolJaXsu+9AfnfRBUn1yTcYHNzaf1FFRPZgCvMiIruRweAaFz/JMG+M4dTTf0GrVq145C+P8s3XX7N0yVKWLlnK/35yrOM4tO/Qnk6dOlJSUkI4HCYrO4tOnTpRVlpKdnZ2leNXLF/Be++8y+ZqFqeuWL6CFctXJMaQkZkJxHejHXHdNbRs2YLHH3uSBfMX8MrLr25zfnp6OkcedQR33HU7Q4YNSWq23UmyBElERLYytjGsphIR2YNYa4n4ZVj8Wp2zZvUapk6dxqQJk5g27Uc2rN9ALBajWbMcevTqydChQ+jbry99+/Uhszx8r1+/nhXLV+D7Pp06d6J169aJYP3D9z/w+mtvUlpaWuN9G2DI0CH88tyzE5+LxWLMnjWbiRMm8c3X37B61RoKCwtp0SKXXvvszVFHHcF+g/ejQ8cOSZfNpDipGFRmIyJSGwrzIiK7mbUWz3rEbO1bTVprE4tcK358G2NwjIPjOole78nwfR/fT+4JhTEG161aAlPR0cbzPKxvsdj4WBwH13VrFcpd4xIyKQryIiK1pDAvItIAfOsT8yP4Sc7OV/5R3dQCrwFCTiquUb28iEhtqUBRRKQBGGNwnTDJbpK0fNlyvv3mW4qLi3ftwBqAY0I4+nUkIlIn+ukpItIA4p1bnKRmo621fPTRx/zutxexetXq3TC63cfgEDLhJvdqg4jI7qIwLyLSUAyETGiH7RhLS0uZOWMWubm5dOnaZTcNbtczGEJOuKGHISISaArzIiINJL55VDzQGhwsUN0ipk0bNzHjx+kcfMhBhEJNp6NwyAnjqHuNiMhOUZgXEWlAxsR3hA07YZxq9oa11rJp0ybmzp3HgQcf2CBj3BVCJoxD7TreiIjIthTmRUQaWDzQO4SdVEw1Gyf9OG06WOjfv38DjK7+hZwwrgklu/ZXRERqoDAvItIIVPSKD5vwNp1dxo8bT+8+vcnNbR7omWyDIWxScAklXpEQEZGdozAvItKIxGfoU3DLF8VGo1G++3YcA/YdQPPc5g07uFqqvAbAKX9cjlFpjYhIfWo6K6lERJqAeNA1hJwUjI0xd+E8Nm3aRL9+fUlNTW3o4dWaAVwTwjUhnGpKiEREZOcozIuINELGGFxCTJ0yjfT0dPr17xu4GW2nvIe8Y9SxRkRkV1GYFxFppDzPY/LkyTTLacbA/vvi4OLjNfSwdsiUb4blmvivGAV5EZFdR2FeRKSR2rBhA/Pnz6dv377kNmsBgI+HZ2P41m/g0W3LYMrLaVwMRiFeRGQ3UAGjiEgjtWLFCubPn8/RRx8d7/5iDA4uYZNK2EnFNaFG0BEm3oUnZMKkOGnxMRkFeRGR3UUz8yIijZC1lqVLl7J27VqGDRuW+HxFSHZxcXCwJoSPj7U+nvWofg/Z+ufg4Bg3Xg+vXVxFRBqMwryISCPkeR7jxo2jV69etGvXrtpjKnq1x2fnXUImjMXi2RjW+lhsebTfmYC/tR+8Y0x5Cc3WvWoV4kVEGpbCvIhIIxSLxfjuu+8YOnQo6enpNR5rMIndVA0Gx6TEY7ylPND7WFsR6yt3f9/6nqny/xUXi8f2ikCv4C4i0vgozIuINEIbN25k7ty5nH/++XXqL18R8OPB3AFDIuAncXIjqMUXEZFkKMyLiDRC48aNo1mzZvTu3bveZsQrz+CLiEjToG42IiKNjLWW8ePH06ZNG7p166byFhER2S6FeRGRRqawsJCpU6fSo0cPOnbs2NDDERGRRkxhXkSkkVm0aBFr1qxh2LBhhEKqhhQRke1TmBcRaUSstcydO5d169Zx6KGHNvRwRESkkVOYFxFpRGKxGLNmzSI9PZ199tmnoYcjIiKNnMK8iEgjUlxczOTJk9l///1JSUlp6OGIiEgjpzAvItKIFBUVMXnyZA499FDVy4uIyA4pzIuINCJz584lFovRt29ftaQUEZEdUpgXEWlEPv/8c3r27Ennzp0V5kVEZIcU5kVEGgnP8xg/fjzdu3enbdu2DT0cEREJAIV5EZFGYvHixaxatYpBgwaRlZXV0MMREZEAUJgXEWkkZsyYQWlpKUOGDFGJjYiIJEVhXkSkEfB9nxkzZmCtZb/99mvo4YiISEAozIuINAIFBQVMmzaNfv360aJFi4YejoiIBITCvIhIA7PWsmnTJmbPns3BBx/c0MMREZEAUZgXEWkEli9fzpIlSzj00EMbeigiIhIgCvMiIo3ApEmT6NChA+3bt9fiVxERSZrCvIhIA7PW8sUXX7DvvvuSm5vb0MMREZEAUZgXEWlgBQUFzJw5k/79+5OTk9PQwxERkQBRmBcRaWA//PADxhj69++P67oNPRwREQkQhXkRkQZkrWXChAlkZGQwcODAhh6OiIgEjMK8iEgDKi4uZubMmbRt25YuXbo09HBERCRgFOZFRBrQypUrmTt3LoceeijhcLihhyMiIgGjMC8i0kCstaxevZolS5ZosygREakThXkRkQZirWX69OkYYxgwYEBDD0dERAJIYV5EpIHEYjG+++47Bg8eTGZmZkMPR0REAkhhXkSkgUQiESZMmMCwYcNIT09v6OGIiEgAKcyLiDSQuXPnsnnzZgYOHEgoFGro4YiISAApzIuINJDx48fTpk0bunfvjjGmoYcjIiIBpKkgEdnjWSzYKh8lmJ++Z8Cw88E7FosxduxYOnbsSM+ePXf6eiIismdSmBeRPYK1turH+PjWT0T3eKCPf2SgmkBvMIkbTPyPcXB++gJnkmF/zZo1LFmyhP3335+cnJy6PiwREdnDKcyLSJNVEeAtFmt9PDys9ROf2+552/u4mhsqgrtjXBzjYOzWcF9T6cy8efNYu3Ythx12mEpsRESkzhTmRaTJsdZisfjWx7cePt6uu6/yhO/ZGJ6Nz+Ib4sHewcUYs81Mve/7zJ07l4KCAoYNG7bLxiYiIk2fwryINAkVs/A+Pp4fw+LXOPu+y8YBWDx864GN4RiDa8KJchxjDKWlpUydOpVevXrRunXr3T5GERFpOhTmRSTQKhav+jaGZz18/IYeUiUW31p8W4aDg2tCOLgUFxczYcIEjj32WLWkFBGRnaLWlCISSNZarI2X0kT9CFEbbWRBviofn6iNEPXLWL9hHQsXLmTo0KG4rtvQQxMRkQDTlJCIBI4t/+PZKJ7ddfXwu4KPz9fffk2bNq3p0bNHQw9HREQCTmFeRAIlPhvvEbPRBqmJ31nWWsaNHUfX7t1o1bYlPh6Ojc/Oq6uNiIjUlspsRCQQ4u0lLTEbJWojgQzyAEVFRUydOo3evfehVeuWRP0IMRtl276XIiIiO6aZeRFp9CpaTcb8SKOui0/G7Fmz2bKliEH77ZtY/OrZGBZLiDCO0RyLiIgkT781RKRRa0pB3lrLjBkziZRFGTxkcJXbfOvFH6P1t9mtVkREZHsU5kWk0YoH+Xi3mqAHeQDP89iYt4kOnTqw9z57b3O7X/5Yg1pCJCIiu5+xmgISkUbKWkvUL2sSQR7iO7+uWrmK/PwC+vXvu90Frw4uYScMGC2KFRGRGinMi0ijFF/sGglc68n64hiXsElRmBcRkRppAayINDrWWrzyHV2TPb6C53kUbymmaMsWcnKyycjIqBKIaxuOK1/bWps4f0fXqdjUqri4mM2bCwmFQuQ2b04ovPXHbk3X8K2HRwyXkAK9iIhsl8K8iDQ6FlverjGJY61l3bp1jP9uPO+9+z7fjf2OwqIifN/HdVzatW/HMccew5lnnU7ffn1JSUlJfhzWsmnjJr6f8j3//MeLLJi/gH0HDeT+B+8nJye72nN83ycvL49PP/6UV156ldmzZlNaVobBkNsilyOOPJxfnX8eg/bbj5SUcI3379lYvLuNdRToRUSkWiqzEZFGxWKJehF8djwr7/s+48dNYPSfR/PF519SVla23WM7durIjTffwLm/OofMzMyax2AteRvy+PDDj3jv7ff45ONP2bJlCwDNc5szbcYPtGnbptrzfvh+KqPuHsWnH39GJBKp9vp7tW/Hnffcydlnn0VqWmqNY4nXz6vcRkREqqeZeRFpNCwW3/pJBXlrLWO/+ZZrrhzBnNlzcF2X7j268/NTTmbfQfvSPLc5Gzdu5PNPP+fTTz5j5YqV3H7rHaSmpvKr88/Dcapv5rVlyxZe++/rPP3kMyyYv4Di4mIyMzNJTU2t8cmCtZYlS5ZyxaV/YNrUaaSlp3HScT/jF6edQvcePSgtLeV///kf//vPa6xetYY//fF22rZty7HHHVNzuQ0ePj4u7o6/gCIissdRmBeRxsPGS0uSsWbNWv54863MnjWbtLQ0zj7nLG657RY6d+5U5bhzzv0lkydN5rZb/oTruvTo2aPG8FxWVsb7777P0qVL2XfQQIYOG8qRRx/JM089y/vvvr/d8zzP4x8v/JMZ02eQmZnJdSOvZcT111Qp6zno4APp3qM7fx51P2tWr+HpJ57miCMP32Hpj2ejOMbBoNl5ERGpSmU2ItIoVGwOFfFLkzr28Uef4OYbb8GLeRx/wvE8+/zTtG7Tutqgbq1l4YKFuK5Ll65dtjsrX3Hs1B+msmXLFrp06cJe7ffCdV1+938X8erLr263zMbzPObOmcvLL76C7/vccPMNNGuWs8141q5ZywnH/ow5s+eQm9ucaTOn0rpN6x08YkOKk4JjNDsvIiJVaWZeRBoNP8nuNWvWrOH1/72BF/PIyMjgtjtu3W6Qh3jXmB49eyTer4kxhkH7DUrq2Mpc16VP3z7ccfftiY+rO79N2zb07duHObPnUFxSwqpVq5II8/HyI4MWwoqISFUK8yLSaCQb5hfMW8CKFSsAGDJ0CD171Vw6A9sG84rWkZVvT7btZE33EQrV/GPVGENaWtrWcSR57XjdvIiISFXbf61ZRGQ3s0lEW2stS5cuY/269QAMHTaE9PT0Wt/XyhUrGbbfcHp06cV5v/x1rc+vq2g0yrLlywFITUmhXdu2SZ1nrU/y0V9ERPYUmpkXkUbB4icd5vPy8hKdZbp06UI4VHO/9up4nsfaNevIy8sjLy+v1ufX1axZs1m6eCkAAwYOJLdFblLn2fI/YLUQVkREEhTmRQLAYreZlE2Eu/JSkcpBOBH2TPy9iv+vrLHVXie7Ft/3fIoKiwAIhUJkZGX89KElxXVd9mrfjrS0VFq3alX7C9SStZbS0lL+++p/Wb1mNeFwmHPOO5twOPknItba+ENtXH91IiLSgBTmRRqpiu4uFlv+vo+1Pj5+LS5S9UODk2hxGP9T/n4jCPbJzMpXHOfb+NfAGINj6jb+Dh07MPH7CYmPd8fX4JOPP+Xvf/sHXszj6GOP5oQTT6jV/arIRkREfkphXqSR2DozbfFtfKMgvzzE19t94OOVB2EsiTDvGINjQpVm9GmAUo7koqrjOGRlZgHx+vPS0tL4jHUtw/jufAJjrWXhwkWMuute8vLyaN2mNddeP4L2HdrXchyK8yIiUpXCvEgDqyiV8a2Ph5d0R5f6uW8fC/gWsDEc4+Dg4uDAbm+DmNx9OY5DbotcwuEw0WiUFctXEIvFalWusjtZa5n+43RGXnsD03+cTm5uLnePuovDjzisxn731Wv4V1BERKRxUTcbkQZQ0RbRtz6eHyPqR4jayG4N8tXxrU/MRon6EWI2im+9bVo4NjRjDB06dqBFixYAzJw5K7EYtjGp+LrNmzuP60eM5Juvx5KVncWI66/hzLPPrEOQV5QXEZFtKcyL7GYVtfCejRLxy+KhuR5LaepDfHwxIuVPMuL1+rs20Jta/Djq1asnHTt1BODrr75h5cpVu2pYdRaLxfjg/Q85/7z/Y+w335KTk8Ntt9/KVVdfSUZG7VtpQuNbtCwiIg1PYV5kN7I2HpKjfhkxG6Px10DH6/cjiZn65NpH1oVjtu24sz2du3TmsCMOxXEcCvILeObJZ4hEIrtkXHVRUlLCP//+T668/Cqm/zidTp06cs99d3HJpReTmpZap1Bevp2VAr2IiFShmnmR3aCiG01FIA6e+JMQ33qEnDAO7i5YJBu/WjJPFRzH4XcXXsD//vMay5ct55WXXqVLl86c/9vfkJvbvErgtdbiez6bCwuJRCK0bt0Kx3EoLS3lh+9/IBqN0qxZcwbuO2Cng7K1llWrVvHwmEf41z9fpKiwiH79+/HQI2M44MD9SUlJqfO1KxYri4iIVGZsYyqGFWmCKmbjPRvbZbPau5trXEImpV5nia21xGwUz8aSOt73fP796r+5aeTNrFu3nnA4zHEnHMsZZ55O5y5dyMnJjm8MtXYds2fN5uOPPiE9PZ2nnnmCNm3bsHTJUg4+4FDyNuRx6GGH8tFnHyQeTzQaZdx346vM9o8Z/RBffv4lWVlZPP3cUzRr3gyA3Nxc+vXvSzgcZsrk77n9tjv48vMvAejeoztXXv0HevbqWe1jMMbQrVs3uvfotsPH65oQIRPWzLyIiFShMC+yi1T806pNQA0SB4eQk1JvfeorXr2I+MkvZi3eUswbr7/BbX+8nbVr1uL7PhkZGbRq3YqM9HQ83yc/P5+NeRvxfZ/OXTrz4iv/ZOiwoSxdspRDDzyMDXl5HHrooXz46fuJx1GQX8DRRxzLhg0bEvdVkF9AaWkpxhhalc/uA+x/wP489MgY0tLSuPqKq3n9tTcSf/eZmZlkZWdtd/yu63LV1Vdy9bVXYa2lpKSEUChEOLxtaA+bVFzHTfprIyIiewaV2YjsAhWLXGN+FJ+G7VCzq/j4RP1IvOzG7nwbS2MMWAcHJ+kFwRmZGZz7q3M54MAD+NvzLzB+3ARWrVxFSXEJmzdvJhwKkZmZSadOHenbry/n/upcBgwcAEB2Tg6/vfACthRtoUfP7j8ZDGRkZJCZmZn4VOX3K0tPT8MxBmt9MjIz6NZ9x7PsFVzXJZwSb6lZVFjErbfcRjgc5o+33UJui9xKw4lv9iUiIvJTmpkXqWfxf1KWaBMO8pUZHMJOOF7TvbM151g83yNma7+Y1fd91q9bz5IlSygsLKK0pJSUlDA5zXJo264dHTt2IBQKJTXGWCzGjOkziMZ2/IpKdnY23bt1wziGxYsWU7B5c9JjNkD79u1p36E9xcUl/P35v/PAn0fzy/N+ycgbr6Nly5YYYwiZMCGncfbRFxGRhqUwL1KPKjaA2lOCfIV6DfTWEvXLGl27zt0hEonwykuvcsdtd3L8Ccdx93130aZ1G1KcNNXKN3Hxnx2V3v/JexUqLxJPdDiq/JG+T0T2OCqzEalPFmI2tkcFeYjvJBv1o6Q4Kezs1kbGGFwTwq80O2+tpbS0lFkzZ7Ng/gJOO+PUneoM01ilpKRw7q/OIRaLMeruezGO4c477qRTx84NPTTZBay1+PhY68efvNr4vyUfm1hDUjODqehzZEziybSDgzFOva1nEZHGTWFepJ5U7lpTp3O9+BMA13UD+Qu4ovVmiJ3vcuMYl2hJlIKiAubNnc9nn37O559+xvr1G8jIyGC/wYPYe5+962nkjUtKSgq/Ov88srOzuGnkLZSWlPHwXx6mTZs2gfy+kLiKmXdbHtc9P/6kv2J9zU+m22txVRufOtg6mU9FyDeA64RwTQiH8n0c6r2lrIg0NJXZiNQDi8W38QWhyW4EFYvFWLxoMbNnzWH16lVs2piPb31atmxJ5y6dGTCgPx06dqhTgPN9nxUrVjJxwkRCbog+fXuzT+99tnv88uXLmTRx8g43XkpLTeXnv/g5rrv9riohE8Y1ydWmV1bRzWX+/PnMmDGD78Z9x9ixY1m8aBGdu3Rh4MABDB0+lEMPP5S+ffvUOIamwPrwxn/f5I9//CP7778/f/7zn+nSpYsCfcBs3fHZw7ex8s3iqjuQnX1Rq0YGh5Bxccrf4jP5+l4SaQoU5kXqQW3qvK21rFi+gr8++zwfffgxS5cspaCgINHO0BhDy5Yt6NmrFxdc+FtOP/M0MjMzdxjiKmb3N2zI4+UXX+adt95hxvSZuK7LOeedw8OPPbTdc199+d/ccP2NFBUW1Xgf3bt3Y+yEb0hLS9vuMQZD2EndYfeVivFGo1HmzJnDZ599xpdffsnSpUtZu3YtnTp14rDDDuPwow6jS9fOdOrcmays6jvKNDUGh5ATJhaJ8e6773LNNddw0EEHMWbMGDp0qNsTPNl9ts7C+8T8+Kt1jWkNiIuL64QImRCqtRcJPpXZiNQDz3pJ/bL2PZ+JEydyxaVXMmf2HHzfp1mzZvQf0I/mzeM7lxYUFLBk8VLGjxvP1B9+YMb0Gdx2x61kZ2dv97pbtmxh2tQfee+d93j15X+zdu3aRNkOQF6lfunVWbduHfmb8jGOoXv37mRkpFd73KD9Bu1wRtwv3y3WmPA2M3/WWoqLi1m2bBnz5s3j66+/5osvvmDZsmW0a9eOnj178utf/5rjjjuOPn36xLvPOPFe/U1lw60dMRhCTggHh9TUVE477TTS0tK45ppruOiii3jqqafo2rWrwlcjFa+D94j50e3PwjcwDw/P94gQIWxC8U5J9dBeVkQahsK8yE6qqJVP5rhFixZx681/Yvas2VhrOejgA7nsiksZMHAArdu0xhhD3oY8vp/yPU898Qzjx43n008+ZcR119QY5idPmswVl17JwgULcV2Xnr16Mnz/Ybz80it4sZoX4/q+z6aNm4hGo/To2YP7H/wzvbazY2lGRgah0I5/bPjWw1oXiM/Oe57HrFmz+Oabb/juu++YPXs2CxYsoGvXrhx00EGMHDmS3r1707t3b9LTqz6RsNYSAqJ1aFcZRCETxmHrugnHcTjuuOMYM2YM11xzDSNGjODBBx+kR48eCl+NRMWrahULwYOz27MlaqPEPI+QEyJMfFG5vq9EgkVhXmSn2Hhwxd9hyWssFuPxR59g3HfjcByHE352Ag8+9ACdu3RO7CYKkJubS/ce3TngwAN48omnOfOsM2jbrm2No+jTtw89enTnmOOO5sSfncCQoUMxBv777//tMMyXlZWRl7cRgNatWtFr715069Y1ycdfDWspKt5CwcbNLFuynM8//5xPP/2UFStWkJaWRocOHTj99NM5/vjjy18FyCAtbfutF+PdOVzCpDTpGfr4jHxKeSeSn+z+Gg5z0kknkZWVxaWXXspFF13EX//6VwX6RqDi+zFmo0T9SCC/P235BnAeMcJOKi6uFsqKBIjCvMhOsJZ4R4okjl21chWv/+91fN+na9cu/PHWm+m6ndDsOA6dOnfivvtHJRXWWrduzX9e/zepqamJz23ZsiWpx1BWVsamjfEw36x5MzIzM5I676ei0Shz58xl8qQpTJwwkanfT2X+/AV069aNoUOHcsEFFzBo0CAGDhxYZZzJMMbgGJewNeW76jae+uP64JTXyDtm+yVMruty+OGH8/jjj3Pddddx6aWX8pe//IX+/fsr0DeQitaSUT9Spy5WjY2PT5lfQtiECTk735VKRHYPhXmRnRDvUuEnNX/16SefsWlTPgBHHXMUAwcNrPH42vwiNcbUOiBXKCstY+PGTUBFmE9+kWk0GmXmjJl8/OHHfP7ZF6xevYbSkhJatW7FMccdzYNjxtCjew9atmyZ1CLemsR7ZruEHINno3i2afTyd028dWAys6Cu63LUUUfxzDPP8Lvf/Y7LLruMv/71r+y9995VXt2RXc9ai0eMiFcWyNn4mkRtFN+3hLfzSpGINC4K8yJ1lOgPncQvct/3mTh+Ip7nYYzh5FNOrnVrRWsts2fN5tux3+G6LgccuD99+/Wt4+i3Ki0rY2P5zHxubi7p6ekUF5dQWLgZx3Fo1qwZ4XC42l/oq1ev5uILL8Fa6NOnN6efcToHHnwAfcpbR4acMCFT/bl1ZTCETAqO8Yj5sSQ21mmc4h1rQji1LGkIhUIceOCBvPDCC1x11VX89re/5fHHH2fw4MEK9LuJtZaojRDzI00sxm/l2Ri+55HipuHiquRGpBFTmBfZCdYmFyRLSkpYvmIF1lrC4TC9a+j5XpNvvh7LNVeOIC0tjT+Pvq9ewnykrIxNmzbhOA4rV67kjtvuZMqU79myZQuOMbRs1ZJjjj2a004/jVatW1UJjO3bt+exJx8jp1kOHTt0ICs7q0pw3xVBO7Ew1LqEHad8oy6PZPv7NwaJ2fg69vo2xjB8+HCefPJJLrroIq688kqeeuop9t13X82i7kIVLSejtoyoH23o4exyFkvEKyXFSa3T3hEisntoGkdkJyRbu71lyxZKiksAyMnJIbMR9UsvLStj08ZN+L7P++9+wKMPP8aUyVOYP3c+kyZO5t233+P6ETdwykmn8vmnn+P7Wx9zKBRi+P7D6N17H7Jzsrf5Zb8rt7EwJr5VfciEt4aNRjx7aDC4JkSKkxZ/tYKd27THdV2GDh3KP/7xD4wx/OY3v2HixIlVWpJKPbMQ8feMIF/BYinzSwPUoUdkz6OZeZGdkOzMfCQSIRaLL5BLS9vxhkrbc9jhh/HE04/jui7D9x9Wp2v8VLt2bbngwguYOGECg4cMpv+A/rRs2QLXdVm+fAVvvfEWY7/5lmlTp3HjyJt59X+v0LPn1i4qDTVbV/mJQrwTTBhr3fKdNr1GFDxMYudNU8/1x8YYBg4cyGOPPcYf/vAHrrjiCh555BEOOuggzaLWM2stMRslZvecIF9ZxC8jtbyzlL63RBoX7QArUkfWWiJ+WVKlJGvXruWcM89l/LgJtGrdiu+nTaZ1m9a7dHxbtmyhY9vOlJaWcuZZZ/CvV/5Z7XHWWiJlEUpKS0hLSyc1Nd7FwlqLtZaCggL+POp+Hn/0Cay1/N8F/8fDjz2U1ILbZHeDTZa1lsLCQtatW8eCBQt47733aNGiBTfeeCMZGRmJMgiI97r3Em1Dd++Puficu4PruDg48c/swgBkrWXOnDn8/ve/Jy8vj+eff57hw4cntSeA7FjFXhJlfmlDD6VBOTikumn1/qRURHaOftKL7JTkQmJGegYpKfHwW7i5kNKysl05qFoxxpCalkpqWuo2nzfGkJuby9UjruK9d95j4cJF/DDle5YtXUavvXvttjGWlpYyY8YMJkyYwPjx45kwYQJr1qyhd+/enHrqqVtfJYivJAXidemOdbFYrPXx8fGtv8sWzBocHOPglHfdMbs4wFe5b2Po3bs3Tz/9NNdeey0XX3wxDz30EEcffXStF1rLtiw+Eb92m5ZZa8nPz2fpkqXkb8onGo2R0yyHzp070bZd251arGytZdnSZWzZsoXOnTuTmZVcpyjP81i4YCFrVq+lrKyM5rnN6d69Gy1atkjqfL/865DqpNV57CJS/xTmRXaDzKxM2nfYC4iX3CyYv4BOnTo28KiSl9Msh/0G78fChYvILyhgw4a8pML8jjbSqo7v+5SVlbF582YWL17MO++8w9dff83atfEA0qFDB377299yxBFH0LlzZ1q2bElaWvXhIrHA1Dg41oKp6D9UEfC98q5EYBJPzMx2ZvKrVribRMmBU7X+vYE22zHG0K9fP5566il+//vfc8UVV/D0009z+OGHa4Z+J1hryzeDSu5JYFlZGbNmzOKlf73C119+TUFBAdFIJN7qMSVMdk42g4cM5pLLL2bgvgNISUmun7u1ltLSUpYsXsq/X/43b7/5NqWlZew7aCAv//elGs+NRCKM/248Tz3xDNOnTae4uBjf80lJTSE3tzmnnXEq5//2fPZq3x7HqXksno0Rs1FC1G+XKhGpO/2EF9kJZrvBryrHcRgyZHB8R1bP4+MPP+aIIw8PzC9Dx3ESi3YjZRFKS5MrNzCV/n9HtmzZwpw5c5gxYwbjxo3j888/Z+3atfTs2ZN+/fpx/vnnc/jhh9OjR48qs83Jfg3jxxnAYmw84LuEqgyvcqvRrdH+p49m2/trLH+Pxhi6d+/Oc889x8iRI7nkkkt48MEHOemkkwiHww09vECKh9fkNoTaXLCZ5575K3958GHyy/eUSE9Pp1XrVoTDIQoKNjNvzjzmzZnHJx99wrUjR3Dp5Zds86rYTxUWFjHu2+/44L0P+d9/XmNT+b4QEF9QX5OysjJe+tfL3HbTnygoKMBxHFq3aU12Thbr121g1cpVzJ41h88+/YInn308qSfpMT+K67oY9KqPSGOgMC+yE4xxIMlFsEcfczTp6ekUFRXx6SefcvFlv6dr164NHgQrauMrymqqE41GWblyFQCZmRnk5GTv9H3GYjHKyspYuHAh7777Lt999x2LFy9mw4YNdO3albPOOouDDjqInj170rFjRzIyMurla1W5FKfa2wKuItA/9NBDXH755Vx33XWEQiFOPPFEldzUUsWi12SOKy0t5dmnn+PB+8dQVFhEZmYm5/76HI4/8Xjatm2DG3IpKixi/LgJ/ONv/2TxosX85cGHGTpsCAcfevB2r7148RLuuu0uvv7ya9av34AbcmnfoT2FhYUUbi7c4bi+/uobHrh3NAUFBeQ0y+Gaa6/m8CMPIzUtjdWrVvG3517g4w8/Ydy34xh15708/sxjZGVl1XhdHx/Pj2Ec1c6LNAYK8yI7wTFu0tu49+jVg5//4ue8+vKrzJk9lwfuG83do+6iZauW2/2FGIvFMMbgum58oWokSmlJCRhIS0ur866vFay1rFu3ji8//4pDDjuY9u3bVzuWH6dNZ/x34wHo3qM73Xt0T+r6jtkaHq21bNmyhSVLljB37lzGjh3Lhx9+yIYNG+jQoQPdunXjkksu4dhjj6Vbt26Ew+H47J/CQq0ZY+jSpQvPPvss119/PZdddhkPPfQQP//5z7dbkiRVxXd3jiW90/AXn33JXx58mKLCIrp07cKfH7yPE048nlB4a392ay0HHHQAp595GjePvIVh+w9j8JDBNV63WbMc1q/fQKcunTj2hOP4+S9O5oAD9+eXp5/D+HETajy3cHMhTz3+NCuWr6B5bnPue+BezvnVLwmF4mPad9BADj7kYC6+8FLefetdPv7oE95+8x3OOe+XO6zpj5SX2tS+kE5E6pvCvEgdGWPi5RpJCofD/P6Si5g0cRIL5i/g1ZdfJRaLce3Ia9l7715Vfnl6nsfMmbN4439vcPwJx3HAQQcA8OUXX/K35/5GOCXMb/7vfI474Tig+n7ulT9nt3NMaWkpfx51P//654scffRRjLh+BIOH7JcoyYjFYkwcP5E//fF2ioqKSElJ4Zfn/pLmzZvX+FitteULQB0ikQizZ8/m008/ZcqUKcyYMYNly5bRo0cPTjjhBIYPH87AgQPp3r07aWlpCu/1xBhDhw4deOCBB7j++usZOXIknudx1llnaYY+GZakg7zv+zz71HMU5BeQlpbGFVddzvEnHkc4pWppU8UT867duvLkc0+Qnp5Oenp6jddu0aIF9z4wihYtctlrr71wQy6+7ye1gHbp0qV8/eXXABx40AH8/NSTq5RbGWPIzsnmsisuYfKESaxZs5ZPPvqUk35+Es2a1Vy+Q/mTHbd8zwQRaTgK8yI7wWBwjIufxC99YwzDhg9l1J/v4cbrb2LJ4iW8/OIrfPTBx/Qf0I8BAweQlZXF+vXrmfrDVBYtXERBwWbWrl3H0OFDCYVCLFm8hLffeoe0tDQOPezQKtcfP24Cjz/6ROLjWCxGJBIpv208vz73N4nbzjzrdH5x2i/wPZ81a9YQi8Z45+13+fqrb+jRszt9+/UlPT2duXPmMm3ajxTkF5CZmckFF17AaWecut3A7fs+xcXFbFi/gcWLlvDt19/ywQcfsm7dOjIzM+nYsSO/+c1vOOqoo+jatSuZmZmkpqYqwO8ixhg6derEY489xsiRI7nuuuvwPI8zzjhDM/Q7YEmuxAZg6vdTmTJ5CgDde3bn/N+eT0pKynaPN8bQokWLpMcycN8BSR9b2ScffUppSSmhUIiTfv4zmjVrVu1Y+g/oz6DBg/jw/Y8Y9+041q9bR041m8D9VMx6uDasyXmRBqYwL7ITjAHHOvgkN4MXCoX4+Skn0ywnh2efeY6PP/yE9evX88XnX/LF519WOdZxHHrt3Ytee/dM/FLNzMygXbt2pKalkpGZUeX4FStW8Pr/Xq/2fleuWFnltn79+nLKqaeQkZnBw4/+hQEDBvCfV//D3LnzmDL5e6ZM/r7K+T179eTiS3/Pr3/zq+3OJFprmTljJk88+iRTpnzPsmXL2LvX3hx00EEMHDiQAw88kL333rvGkCP1zxhD27Ztuf/++7nzzju58cYbKSoq4oILLtDfRQ38JNfCAEyZ/D3FW4oBOP6E48iqww7PhYWFzJ+3AIA2bVrTsR66XU39fioAbshl2P7DtxvOc1vk0mvvXnz84SesWrmKtWvW0qNnjx1ev6LVqxbCijQshXmRnRKfmTe12OrcGMNhhx/GwEEDWbhgEd98/Q1zZs9h3dp1xGIxmjdvTpeuXdhv8CCGDB0Sf2m9vCzi5FNOZvgB++MYQ6vWrapct1evnlxx1RXl/VoqRrdVRZtICwzbf1hiwWvbdm257oZrOedXv2Tq99MY9904lixZiud5dOrUkQMPOoChw4bSuUvnHYa/oqItrFi5klNP+wVHH3UMPbr3oFWrViqfaQT22msv7rnnHnzf54477sB1Xc4///ydXnfRVCX7BN1ay7w58xKvgg0dPrRO9zdr5mx+c+7/4TiGX//m1/zx9lvqdJ0K0WiUxYsWA5Camkqnztt/cuA4Dp27dCIUDhEpizB//gIOOuSgHd6HLd+7ofLaGBHZ/RTmRXaSweCaUK22eTdOfDOmIUMHM2To9hfA/TQAN2/efLv16vsO2pd9B+2b9BgqS01NpVu3bnTr1o3Tzjg1qbFUd/sBB+7P2++9ScgJE3aS658tu0/Lli259957yc7O5tZbbyU/P5/LLruMzMzazyQ3dcnWy0ciEfLz8xMdobp07Vyn+ysrK2PlipUYx5Cfn1+na1RWUlxCSUm8hWxubu4On4i3bNmSkOsSAfI25CV9P/E9G6z+rYs0IIV5kZ2U2DyoFrPzlc+trzHUhx21qExmHMYYXCekX+6NkDGGVq1accsttxCNRhk9ejSO43DVVVfVunOQxVazAbIt/1P+vt36+fLdtCp16zfbLpxsoA23fir+2JL7txyNRBM7OqempRIO1+1JbMuWLTnp5J/FN//q37fW5/9UaVkpnhd/QpLMqy+paWnxVrvAlqItSd+PtVY18yINTGFepB4YDCETJmprt+V7Y7K5YDNvvvEWx594HG3btq1zGA+ZEA5136pedr0WLVpw1113kZ2dzejRo4lEIlx++eVkZ+940WN8x1yb2EXXYhO10ztUbT42ODg4xsFQaTfdhgz2tXhObhyDW95Zxvf8+NekDjPVffr25l///icArrPzZSuOs/XJme/v+O/G9/3E0zA3lPz913YCQ0Tqn8K8SD2omJ13cZN+eb6xmT59BjfdcDPdunWlbdu2dbqGwcExmpVv7IwxNG/enJEjRwIwZswYIpEIN998c6IHeYWtLU0tfnmNdNLhPSkWHy/eEcqSaGnq2K0BPz7o3RfuaxNQU1JSSC3vDBSJRBKlLbXlOE69LkhOS0tLrLUpLi7e4ROUkuISrB8/KCuz5k2jRKRxUZgXqSfGGEKE8a2tx6Cz+0wYN4HOXTrTtl3dZuUNhrATVpAPkObNm3PTTTeRkpLC008/TSwW47rrriMnJyexWtrHx7Ox8u4uu34WNj7j78UXoNr4E0TXCeHYeKjfHd9fBpP0Iw2FQrRt1xbHdfA9n3lz5zF02JBdOr5kZGSk07x5vBVlQX4BW7ZsIS19++1I16xZQywW3wCvfYe9dssYRaR+6LVwkfpkTHzhZwD/aU2eNJkunTvTuk3rOpxtCDnhRJmEBEd2djbXXnstV199NU8//TR33303RVuK8PwYUb+MqF9Wvo9Cw5RTWHxifoSoX0bMRvGtV+0GaPXJmOS/i40x9OvXJ1GX/t3Y73bdwGrBcRx67t0TiO85MX/+gu0eG4vGWLpkKbFYjHA4nPQOz7B7nlyJSM2ClzhEGrGKet+wE6xdEfPz81m4cBHde3bf4e6u24qvF3Co3QJKaTyys7P5wx/+wIgRI3jppZe4447b2ViwsVGVjNnyHUejfqQ81Pu7NNTX5nt5/wP3Jyc7vmPqF599yaqVq3bVsGqlYufoWCzGd2O/2+7Xa+3atcybMw9rLb327knrNq2TfvxBnLgQaWr0r1CknsVn9ZxAzdDPnzefkpISBgwYkNQ28RXiC39DuEZBPsistaSlp3H5VZdx0x9v5OWXXuHWm28lL2/jLp8Fr62KUB/xy/BsDFuLzZ1qw61F7/Su3bpywkknALBq5Soe/ctjFBYW7pJx1cbhRxxOi5Yt8DyP9995n5UrVm5zjO/7TBg/kak/TMMYwyGHHbLNHhbbVz59oX/7Ig0qGElDJGCCFOittcyft4BoNErffsm3xKvo4ONqwWtgVfQIj9koURshNS2F/7vgN1w7cgRvvfk299w5ii1bihtdoI+rGPeuKb1xarmr6QUX/R/dunclFovx8r9e5rGHH6dwcyGet3Vs1lp8z6esrIzp06Yza+asxG1Llyzlnjvu4Z47R/HRBx9Xuba1Fs/zE2++v/VVCYvF96u/rU3b1px8yskYY5g29Uf+9twLFBUVxTsSWYvneSxZvISHxzxCYWEhbdq04cSTTtzuLs/bfo0MjmncP99E9gRaACuyi1QE3BQnhZiNNqqShco8z2PO7DmEQiF699knqXO2PlHRrFxQ2fKF2jE/il9pwXZqaioXX/p7cnJyGHX3vZSWlvCnO//EXnu1a5R/1771iFof14Rx67HUK95Jx2zT2aastIw1a9aQ0yyH3NxcIP5vfdB+g7jltpu59eY/sXbNWh64dzQfffAxp572C/r270NaWjplZWUsmD+fL7/4mq8+/4qfnXwijz/zGJmZmSxbupzRfx6DMYZLLr+Y4088LnGfW7Zs4R8v/CvR+94Cq1evAWDjhjyefuKZxLFdu3Xl2OOPISUlhfT0dC665EKmTZ3GtB+m8dDovzBv7jx+furPycnJZvasOfzr7y8yf/58wuEw551/LkcefUTSX8OKr5GINCyFeZFdKN4v2xAiBQePmI02ur7MhYWFLFywkD59epOWtv1uFxVCJpRoP6lf5MFkbbwdZMzf9vvRGENqairn/uocsJbbb7uTWDTGgw+PplmzZo0y0FssMRvBEiJEuJ7aWMZLyCJ+BGst69au45uvxvLNV98wb+48fnPB+Zx3/nmJo0OhEKefdTqO43Dv3fexaOFipkyawpRJU0hJTSG9PMyXlsZbVzquw7p16xIdZCifYTeO2eZVhoL8Am689sZqR7l69RpuvO6mxMc/O/lEDj/iMFJSUsqfZOzL3ffeyQ3X3sSc2XN48/W3eOuNt3EcJ7GpVGZmJmefexbXXH81odCOY0H5FmB6VU6kkVCYF9kNKvrQh42D18hm6fPzC5g7dy7nnHdOjccZHEJOGAdHv8ADzFqLb2NEbYyaOtSkpKRw7q/PxQ25jLr7Pq69+jruuudOOnTs0Gj//uM19JawE8buRKCPxWJs2rSJpcuX8u24sbzz1rvMmzuXcDiFFi1bcOihhzBo8KBtzktNTeXMX57JAQcdwEcffMz7737A8mXLiUTK8DyfcDhEVnY2e++zN6edcSoHHLg/2dnZAPTt15eX//sSxhi6de9a5brhcJj9Bg9KqpSoe4/uOO7W0hfHcTjiqCP475v/5o3X3uT9d95n06ZNeJ5Pamoqvfvsw3nnn8chhx1CRkZy5TVQ3jLUKEKINAbGNs5iSJEmKx6mPLyKXtoNPJYpU77nxGN+xn9ee5Ujjtr2JXaDwTUhzcI1AdZaPOvh2UjSrw9Fo1HefP0tbhp5M/sfMJy/PPYX2rZts0vHubMc3HgZWC2+X6215OXlMXHiRCZOnMikSZOYPHkyoXCI4fsPY78h+zF8/2EMHDSQnJycpK4di8XYuHEjmzbmE41GyMzMpE3bNmRkZDTIvyVrLdFIlI2bNhIpi9C8eXOysrNqtei9QoqTSshoXwmRxkBPq0V2s4pZese4iVBv8Rqs+GbWjFnkNMuhQ6eOVceZCPFu/CP90g60itKa2gR5iM8Kn3r6L/B8n5f/9TKxaHSXjbG++OUlbSG2HzZ936e4uJhNmzYxdepU3nzzTaZMmcLmzZsxxjBkyBAeeughBu03iOatm5Gdk5XYUTVZoVCINm3a0KZNwz/5qZi3C6eEadeu3U5dyzGuntyLNCKamRdpQFs7UvjxmXrr79bdY621/OGyK1m8aDF//9cLtG3bFmMc3PInG6BNYZqCeNcan6gfqdOaDWvj9dyRSJS0tNTAfE/E26ZuDfTWWgoKCpg5cybTpk1j7NixfPXVV0QiEfr378+AAQM44ogjOOSQQ2jVqlXivKiNEPUjDflQdoq1lrVr1zFh3ASOOOpwmjVrtlPXS3XSFOZFGhHNzIs0oIpfhgYXg4M1NhHsrfV3+WJZay0/fD+V4cOHkdu8xdZWmvWygFAaDQvRaha77vC08haGEK+9Tk/f8QLpZK8LtX+iWNvzYn4Mz4+XlsyZM4d3332XcePGsXjxYvLz8+nXrx8jRoxg6NCh9OjRg3bt2hEOh7e5TogwnvHKd8INpqnfT+WWG27hxJ+dwE233UTLli3rFMbDakcr0ugozIs0EhVbyFtbPituwMdP9NCOBzG7kwF/6yYvBsOaVStZv249/fr2Jys9WyG+CbLl/diTfcXHWsvGvI0sXbqUNWvWkrchj9KSUnJb5NK6dSu6de9G+w7tk+p6Up28DXlM/3E6pWVldO7cib332Tupa23eXMj0H39kc8Fm2u21F/v03puMjIxqx19SUsKypcuYO2ce3439jo8/+oS8vDw6depEz549ueGGGzj66KNp3z7+OByn5kXdBkOKk0rEK8FvZN2oknXEUYdz0x9v5P57R7Nw4WLuHHUHAwb2r1Uod02IkJOyC0cpInWhMC/SqBgq/251cXGNGw/wNl6Os/X9ysG+asQ3Vf7fVLqu2frHGGbNnE1qaio9e/bUTFsTFK+T95OaUa4IwZ998hn/+seLzJg+g+XLV1RqnQjNmjej/4D+nPizE7jgwgvIzW2+w++bitn0LVu28NGHH/OfV//LN19/Q/GWYk782Qk8+ewTiX7t1Z1XWlrK2G/G8urL/+HzTz8nLy+PocOG8PhTj22zyZm1llmzZvOXBx9m+o8/snTpMnr06MHPTzmZoUOGse+++9K1a1dSU1N3+PWozBiDYx3CTiplfhk1dQFqjIwxpKWlcd7555HTrBl33nYn1155LaPuv4dh+w+LP7nfwd+jYxxSnBRtEiXSCCnMiwRAvF99vByn/BOJUJ/kBbaZcbfWMnXqVNLS0ujdu3f9DlgaDc/Gkno1Jy8vj3vuHMXLL71C4eZCUlNT2WuvduQ0a4brupSVlbFp40a++/Y7Jk2YxLjvxvHYk4/Rrl3b7QbBWCzGkiVL+frLr3nm6WdZOH8hxcVbd5TdsGFDotd5ZdZaVq1cxaRJk3nq8af54fsfEjuXxs/Lo6ysrNr79D2P0tISzjjrDI497hi6dO1CdlY2GalZ8U2O6vik1RiDS4gUxxLxIwQt0EO8VOrkU06iS5fO3HLjrVx60eXc8qeb+cWpp5CSuv0Zd4NDiknDKMiLNEoK8yIBVRHw66q0tJTZs2fTunVrOnbsuOMTJFBqMyu/ZcsWHrx/DM8981d836dz50787ve/49jjjqFnr56kpaWxMW8jM2bM5MV/vshbb7zNN1+PZcK4CfzitFO2e938/HzOP/c3/DjtR6y1tG3XloMOPpAFCxaweNGS7Z4XjUS54bobefed94hEIjRr1ozDDj+UvLyNzJg+Y7vnGWPo178f/3jx79t0nvHxcNi5MGqMIWziNfVBDPTGGFzXZdDgQTzxzGP88abbuPn6W9iYt5FfnX8eWdlZ25wTn5FPje8voRI8kUZJYV5kD7Vu3TqWLl3K0KFDq130J8Hn29iOj/F9PvnoE17810v4vk/7Du156rmnOOTQg0lJ2Tpb226vdrRt15ZB++3LPr33oUOH9hx7/DE1znRnZmbSqXNH2rRtw+lnnsb+B+xPWmoq11w1osYwbxxD9x7dGb7/cE4/4zQOPvRgmjdvxgN/Hl1jmAe22zPdsx4hE2KnngHHRxfvr+4YojaCb3df96n61LVbV/7y6BgeeegRRv/5QZYsXsL1N15Hi5YtEiV5Dg4pTlr8FQ0FeZFGS2FeZA+1evVqli5dypVXXtnQQ5FdwuIlETS3bNnCv1/5D3kb8sjMyuSGm67nsMMPrXZRqjGGli1bctMtNyY+rklaWhr/fOmfpKRsbQ9ZWFi4w3KNUCjEbXfciuM4iRn20tJSHKd2fd6rsuWBfudLRSpKbhzjUOaXBbLLjTGGNm3bcMc9d9C2bVsef/RJVq1cxW133kaPnt0JuymETTixxkZEGi8VwInsgay1rFq1iry8PPbdd9+GHo7sAp71SKYMJH9TPl9+8RUAvXvvw/EnHF9jd5mKxZLJ1J7HF16mJrrFJFuvbowhJSWFUChUq/N2xMenvrZWiXeEckh10ggHtMNLxdf54ssv5v4xf2bSxElc84cRzJs1nzDhnVpjICK7j8K8yB7I8zymTJlCt27daNWqVUMPR+qZrUUL0xnTZ5Cfn48xhmHDh9Gla5da319eXh4ff/QJ777zHtN/nF7totbGYGuL1/phjMExhrBJIc1NL98tOXjS09M57fRT+ce//kFpcSnnnn0eH3744XYXGYtI46IwL7IH8jyPqVOn0r9//1q36ZMAsCQ9Az137lwAXNelf//a9R2vMGvmbC74ze8467Szefbp5xptCEy0dq1X8VcOXBMi1UlLLBYNEheXtFA6hx58GM8++yw9evTgiiuu4O9//zuxWKzeXs0QkV0jWD9xRKRelJWV8cMPPzBo0CCF+SYoPv+c3MLM9es3APGFo+07tt+Vw2ocdmEwNcYhZMKkuumNPtQbDC4uqU4aqW46rgnhOvEndH/729845phjuOOOOxg1ahSbNm1SoBdpxLQAVmQPtGDBAmKxGL169arzTp7SmCUfvLYUFcffMZBZzY6qyejSpTPXj7yO4pJiBg/er1F3R6ooQdpVizordlc2JoxrQvjWI2ZjSa9h2NUMhpAJ4ZgQrnG3eSXGGEO7du24//776datG4888ghLlixh1KhRtG+/BzzZEwkg/RYX2QNNnjyZ1q1b06VL7eujJRiSLSdJS0utOKHO5TGdu3RmxPXX1OncBmHZ+Q6VO1Cxy7IhXoJT0fM/5kdrtaah/sbiEDZhHOOWfxy/ZXtatGjBNddcQ+vWrbnvvvu4+OKLeeCBB+jdu/d223+KSMPQv0iRPYy1lokTJ9KmTRuFeaF5bi4Q/77YsGFDA4+m6anoxuOUl+CkuRmkuRmkOKnl4TpU7asElrrP4zs4uCZUXvKTFr9PJ15Ks7U70I6fzaSnp/O73/2OZ555hlWrVvHrX/+ajz/+WCU3Io2MwrzIHiY/P5/FixfTtWtXWrRo0dDDkQbWrVtXIL4oeu6ceXtGUGuAbouJmfryYB92Ugg7qaQ6qaS66aQ56aSUt7kMmXC8BIaK0G2qXIdE93cH17iETJiwk0Kqk0aam06qm0aqk0qKk0rIbG0xWfFKQdJjLt8x9phjjuGJJ54gKyuLa665hjfeeINIJLJnfK+IBIDCvMgeZvHixaxdu5ahQ4fq5fImK/nQtt/g+CJo3/eZNHESmzbl79qhNbjGswVSRbB3jYvjuIRMiLBJIcVJJc1JJ8PNrPSWRYabRXr5x+luJuluBqlOevksf0p8EasJxUtp6rFHvDGGAw44gJdeeonhw4fzhz/8gdGjR1NYWFgv1xeRnaPf5CJ7EGsty5YtY+PGjey3334NPRzZRSrmbpPRuk1rBg+Jfy/MmDGTCePGN+kZ1x3VijeUyjP3O/O2y8ZnDJ06dWLMmDGcddZZPProo9x3331s2LChSX+/iASBwrzIHsT3febPn4/v+/Tr16+hhyO7THzBYzJycnI48+wzSUtLY93adYwZ/ReWLF5S4znW2ir9x2OxGJs3b6Ygv4CSkpJGG+4qSlOk7lq1asW9997LbbfdxksvvcTvfvc7Fi5c2Gj/zkX2BPqpJrIHKS0tZcaMGfTv35+0tLSGHo7sIrWpjXZdl1+cdgqHHHYIAN+O/ZZrr7me78Z+RzQarXKstZa1a9by6sv/5s3X30p8fsH8Bdw08hauvOIqXn3530QiVc9rDOINbJxGVGQTTMYYMjMzueyyyxg9ejRz587l4osvZsKECfh+cnsb1Ja1lqJIMYsLVjB9wzwmrv6RsSumMGH1NH5cP5cF+cvYXFaErycUsocyVk+nRfYYGzZs4Nhjj+W4445j1KhR6jHfhPnWJ+qXJtURxVrL9GnTueh3FzNr5iystbRp24aBAwcwbPgwWrVuxZaiIubOmcfkyZNZuWIlnTp34suxX5CVlcU3X4/lnLPOZWPeRi66+ELuf/DPZJT3rF8wfyGP/OURNmzIAyAWjTJp0mTWrllLy1Yt2f+A4aSkxNtj9u3Xh1v/9EeMMaxatYrHH3mCxeWvEniex4zpM1i8aDFZWVkMHT6U5s2bA/E2io8/9WiNZSYWCJswISdEYyyzCaJoNMr333/PH/7wB4qLixk9ejTHHnvsTu0zYK0l4kfZXFbEssLVTFrzIzPWz6ckVkqZF6XUKyPiRfGtj2MMYSdMWiiVVDeFVDdM7xbdGb7XQLo360yz1CxS3ZRdWn4k0hjoN7nIHiQ/P58FCxZw880347puQw9HdqGKkpJkdoI1xjBg3wH865V/8vgjj/P2m++wZvUa1qxew8cffbLNsW3atKb/gP7k5+eTlZVFamoqHTt2JCszkxYtcquEp02bNvHRhx+zfNnybe43b0Me77/7QZWPKxQVFvHF518y9Yep25xXVFTEl59/mfi4Q4cOO36MGBzjoiBff8LhMMOHD+fll1/mhhtu4Pe//z2333475557LtnZ2bW+Xn7pZqasm8mM9fP4csVENpcVEfO9WvXkn5W3kLcXfk5GKI1DOgxlYJt9GNq2P63ScxXqpcnSzLzIHuStt97isssu47XXXuPAAw9s6OHILmStxbceURup1XmFhUVMmzqViRMm8f3k71m1ajWlJSVk52TTqXMnBg8ZzH6DB7FP733IzY0HpMLCQhYtXEQsFqN169Z07NQx0Slp2bLlvPjPF5PqktOjR3cuuexijDGsX7eel196hRUrVu7wvObNmnHLbTfXGNYcXMKOZml3BWstK1eu5N577+W1117jwgsv5I9//CMZGRk7/HpbaymOlfLl8ol8tXwC0zfMpyRWUi8lMwZDqptC75bdObTDEI7veihZKZk4+h6QJkZhXmQPcsMNN/Ddd9/xr3/9i27dujX0cGQXsliwlqgfwU9idr7Kudbi+37iDcpn+p143/HadE6x1uJ5XlLHGmNwnHhLxYoxJPsrqmJc2xN2UnGov3aNUpW1li1btvDoo4/y5JNPcuSRR3LHHXfQvXv3ar/m1lpKvTKmrpvDi7PeYu6mJUS82j3xrI0UJ0znnL34dd9TGNZuIFnhHT/REAkKhXmRPYTv+xx33HG0adOGZ599lqysrIYekuxi1lo8GyNmG9+C1N1Js/K7T3FxMa+//jo33XQT/fr149FHH2Xvvfeu8rW31rI5UsQ/ZrzBFysmsLEkv8673dZWTkoWh3QYyu8GnE7r9Pimefq+kKBTNxuRPcTKlStZvXo1ffr0ITMzs6GHI7uBMSa+iRB77voIA/FFr8pru0VGRgbnnHMOzz//PBs3buTcc8/l008/JRqNlpd++czMW8Dd457kzYWfkrcbgzzA5kgRHy75mj99+yjjVk/Ft7umA4/I7qQwL7KHmDdvHpFIhAEDBmgmag9ijNmjO7g4JqyWlLtZKBTiuOOO4/nnn6dVq1ZcfPHFvPjii3iex6Q107l/4rNMWjOdmJ9c+VV9863P7LyFPDjpeT5bNo6oH2uQcYjUF3WzEdkDWGuZO3cu0WiUAQMGNPRwZDczOIRMaI8rt3FwCRnNyjcEYwwDBgzgqaee4q677uL222/nq7njKR6cwlp/U6061OwKFsuGkk08OfVltkRLOLn7kYRdRSIJJs3Mi+wBysrKmD17Ns2bN6dr164NPRzZzYwB14TiwXYP4RiHkBPvd65Z+YZhjKF79+48+uijHHzKEbzy1D/55tkPKdtU3NBDS9hYWsAz017l82XjVHIjgaUwL7IHKCgoYMGCBQwePDjRMlD2JKa8fj5c3mu9aTMYQiaMY9S9pjFYHdtA2rF70fEXfdk4dRWLXvyekjWFSXcq2tWKY6X8febrTFozvdGMSaQ29FtdZA+Qn5/PrFmzOOiggxp6KNLAwiYFtwkHegcn3rlGv94anLWW9SUbeWHGaywqWUn7E/ah10XDKVq6idmPjKVwft6OL7KbrCpazwvTX2fp5lUK9BI4+mkn0sRZa1m9ejV5eXmql9/DVfSHD5kU3CZYcmOIl9Y4puae87L7/G/eR0xcMx2LxTiG3H33oucFw3DCLpvnrW/o4SVYLLM3LuTFWW/hqdxGAkZhXmQPMHnyZDp16kSbNm0UcgSAkAkTMmGCvDrUlr8BuMbVjHwjYq3lh3Wz+HjJ2Crh2BhDsz5t6Hvtoex1TK8GHOG2LJZxq6fy5fIJqp+XQNFPPZE9wMSJE9lnn33Iyclp6KFII5CYoXfChJ0UnAD/Kqiojw+ZFNXINyKbyjbzv/kfs6m0YJvbjGNIaZ6Om9b4Xh0qjGzhzfmfsmbLhoYeikjSGt+/JJEmLtGSraayTFN/HTgikQhTp07ltNNOU5iXbTg4GCcFz8bwbLD6bbvlZTUGhfjGxFrLj+vn8v2aGTtsQGk9H680RnRzGaUbthArLMUCqc3TSW2TRTgrBSc1VOu/X+v5RIsilK0vomRtEQApuemkt8smnJ2KCW3/e2bOxkVMWD2NX/Q8GscE94mu7DkU5kV2MWtteYCP//eni6uq9lsuj/C2/L1EqDfxP3UILPPmzaOsrIzevXsTCumfvFRV8T0VIoxr3PJQ3zCb+STL4OAaN1H3ryDfuJR5Ud6Y/wklsbLtHmOtJbKphLxJy9kwYTmFC/PwI1W/78I5qTTv3452R/YkZ59WOOHkFm5HN5ey7tulrP5sPiWrC8Ev/xlrICU3gzYHdWGvY3uR1jqr2iqziB8f/9GdDyQnNSvpxy3SUPSbXaSeVA7pFotvPXx8sFujfM3T8dVdFLYGeTDWie9ladwqM/c1hZkZM2aQmppKr169FHqkWlufOLoYHBzj4fnl378NvLlPZQaDa0KJBa7qH984LcxfxrxNi7e7MZS1ltJ1RSz8+xQKZq3Fj3gYx+CEXUw4PhNuo/GZ9fXfLWXzvPV0OXMgbQ7pinG3P1NurSVWGGHpf6ez9ptF+GUexjU4aSFwDH7UI7KpmJXvz6FkbRHdf70fqa0zq/25uHrLeqasnckRnYbr56Y0egrzIjupYubdWh8fH996iV9iFb/Kdu5XQcX1AXw8ABvFYHCMG68Ttk61M/e+7/PDDz+QmZlJ7969d2oUsmcwxuBYF8dxsfjEbAxr/QbbsTP+fe3gGBcXt+KTCvKNlGd9PlzyNcXR0mpvt9ZSuraI+c9OoGD2OjCQ2TWXtod3J3fgXqS2zACgdP0W8iYuZ93YxZSsLmTF+3PI3XcvUpqn13j/qz6Zx+rP5gOQ1b0FHX62Dy3264BxHbYs3cSqD+eyYfIK8iYvJ5QZpufvhmGqmfEv8yJ8vmwcB7Xfj9RQyk5+VUR2LYV5kVqy2ERK9/Hxy2cwLdt2P9iVccNiidkY2Hjds2Mc3PKZVYiHssLCQhYuXEinTp1o0aLFLhyNNCUVTwoNLmHjYK3Ft375q027pwQn/j1d/mRVNfGBsaF4E4vyV2z/yZ9vWf3ZgkRbymZ92tD9/MFkdsmt8nec2bEZGe1zyNmnNas/nU/bw7oTzkmr8b4jG4tZ+9UisBBulkaP3wwhu1crjBO/bs7erUlrk0W0KEL+9DVsmLicvY7pRXaPltVeb1nhapYVrqZXbpc6fCVEdh+FeZFaqAjyvvXwbAyL3yiKECw+nvXxbAwHB9cJ4ViX1atXs2TJEs444wzt/Cp1UvGKT7zExYXy73rfeuUz9nUsIfvpfWDAGBwqAnz5EwqF+MCwWBbmL2N54ertHlNWHritZ0lrk0nXX+67TZCvYJx4G8vsHi1wUkKJUL49+bPWES2IvyLQaninKkG+QkrzdDqc1Jv8mWvxSqKsH7eUrO4tqr3/VUXrWFywnJ7NO+v7UBo1hXmRJMVnJ+Mh3q9mFr4hVPfrJf5qQQTHuKxes4ply5YxfPjw3T42aVq2hhmDC4ldZONlZvHXpsprwSrNyla7vDvxvolfON4aUzXwgWetZUXRGgojRds9ZuOUlcS2RABoPrB9PHDXEJSNY3DTwkndf+naImws/rO5WZ82231pNKtrLqkt0inbUEzR4k14xVFCmduW0kT9KEs3ryJmPcJNcJM1aTr03SlSg8RMPD6eH200IT4ZMS/K/IXzicVi7NN7H6y1ml2SeheftXfjxV2mahnajs6TpiXixVhRuGa7f/3Wt2xekJfoLtNqWMfat5wsXzxblleMcQwZHZoRzk4tv/7WV0rd1O3HGyfskr5XDmUbiokWlRHJL6k2zPvWMn/TEiJelLCjuCSNl747RWpiLZ6NxWvTA8bzPKZMnkLfvn1IzQjj2RguIS0elF2qojOO7HmifpQlBSu2u3tqbEuEsg1bAHDSQmR0bFan+1n98XxWvj8HNz3MPn84iJaDOwDxEhrjGqxv2bKygNz92ld7vnEcUprF6++9kiixosh272tJwUqifvB+/sueRUW0ItWoKKmJ2kgggzzEw/y0qT/St38/UlJSiNkoUT8SL4uwjaHSX0SakqgfY2XRuu3e7pXF8KPxBdTh7NSk+8Zvw1R6qySrewvclPgc5fpxy4gWlG79WVf+c8+P+RQt3sjm+fEdXm3Mx49t/xXXvNICPIV5aeQ0My/yE5WDfJBtLtjMvLnz+NX555GSGn8J2ccj6vuEnDCOdVXqICL1xrc+RdFiLNtZzxPxEjXtbkqozq/gNNunNdazOGGXtNaZic9nd29Bs35tyZu4nC1LNjH38e/Y69hepLbOBBvvdpM3ZSUbJi3HK47GT7LUOLlh8SmNBft3gTR9CvMildjyspqgbWtfnZkzZ5GRkU6XLp2rdLKxWGJ+lJCDAr2I1BtrbY0lKcY1UN5dxvp+nRogGWNoOawTLYd1qu5GOp/en0h+CYXzN5A/cy2b528gpXk61rdEC0rxox5pbbNIa53JlqX5mJCDE9r+KwSOcSiJVd8zX6SxUJmNSDmbqI+PNtgGOfVp+o8/0qZtW7p03bZHssUS9aPl7QWD/1hFpOFZwPO97U64uykhnFA8dsSKo1i/fn/2GGPI7NScPlcfQudT+5HRIYdQVgpeaRTr+6R3yOH/2Tvr+DrKtA1f78wcSU7cpbE2TV2oUMfdvcCiiy/uzuLOAot+LLCwuEvx4tCWtlB3TdskbdyTIzPzfn9MkmqSkzTaztVfIc2ZM/OeOSP3PO/z3E/aiUMYdN0UXLFWRF9xqqju5sW8QOC302xsejh2ZN7Ghu2F/J7C2H3HEhsXR5+0Ps0sIQlIPw7htApjbWxsbHYDAWiK2mx0XvU4mlxjAtU+AtW+pkLUDhuDInDFhJJ+6nCSDs3BV1KLUR9AdTsISQ7HEe4iUO3DW2wV4moeZ4vNqEwpcat2B1ibno19B7fZ65HScsnek4Q8wPgJ4xk/YXyry+ky0OD5bXfZtLGxaT9CCFyqs3kx71AJ7RNJ+aLNYEoqlxfhaaejTVBjiQ7BFR2y02v+Si/1W6oAcMWG4mjhgcKUJiGOjn3gsLHpaOw0G5u9HiuHfM8rcBJCNP1tCSmtHHrZ8MfGxsamPahCJdIV3vwCQhA9MgWhWtKjeGYugWpfF43OQkpJ8cwNyICJUAUxI1Na7CzrUDVcdmTepodjR+Zt9mqs9JoAJrJVYwUpJdKU1NTUMHv2bGbPmsPGjZuorKwkNDSEtLQ0JkyawOQpk/B4PEEJ6e3WLSV+v59ZM2Yx4/eZpKSmcOTRR5CcnNz6Z9ANNm7cyE8//sySRUsoKirCMA2SkpIYNXoUhx1xGPHxcajqrnNDTUwMqaPhsD3CbWxs2oVD0UgLTya/prDZZcL7xeLJiKJmbRk1ueUU/bae5EP7I7RdzwxuW9MjhLD+LWnqNozStutsXV4lJXM2ApYvffSI5BbfnxAa29Tt2Mamp2KLeZu9lkYLSkMaQS1fXV3N++9+wEsvvsTKFavQdX07z3ZFUVCfVBkwcADXXHs1J596Em5369OzhmGQl5fHb7/+zjtvvsPMGbPw+/1ERkaQkpLcopiXUrJ2zVpeeflVXnvldWpqajCMrUWtQghUVSVnQA6vvv4KI0YOb34cUkcRKir2jcvGxqbtOFUHWZGpzNmyqNnGUapbI/3Eoaz+zxwClV42fLAIw6+TMCkTV0xoU9QeCWbAwFtcQ/WaUsKzYwlJiQCg9K88KpcVojhUEqZk4UmLalq/qZsEKuvRGnzsG4W66TeoWlPCxg8XU19QheLWSDtpaIv58gJBdlQ6TtWWSjY9G/sItdmraWwI1VJcR0pJTXUNjzz0KC8+9yJ1dfUoikJKagqjx4wmJiaa2tpali5ZxqqVq1iyaAk3XncTPp+X8/5+3na2kDuSuz6Xd95+lx+m/8DcOX/i929N99F1A7Ml/2Mp2bBhA1ddfjW//PwrpmkSFxfHhInjyeqbhWma5G3KY+7cP0lPTyMza2dXmx0xTB1FsXPnbWxs2o5D0UgJS0BB0FwbJiEEUUMS6XP0QDZ8uBjDq7PxwyWUL9hMWFY07qRwVIdKoNpHXUEVtRvKqcurJP2kYaSdMAQEVK4opuCbVaghGhEDE5rEvJSS+s1VrHtjHs7oEEJTIy03m1o/dQVVVC4rxFtUi9AUkvbvS9zYtBZTbASQHZWBU3F0+L6yselIbDFvs9diSAPZ7C1nK36/nxdf+D/+7/mXqKurJz4hnhtuup6TTzmJyKhIVFXFNE28Xi+//vwrd995D1u2FLJpYx66ruN0Np9v+dS/nua1V1/H5XKRmZXJsGFDCfWE8sbrb7Y6roryCu645U5++flXVFXltKmncOMtN5KWnta0zUAgQFVVFaZhEhnZeqGZiYGJYfvP29jYtBkhBBkRqcSGRFNYV9LscqrbQcoRA3BEuMn/egX1W6qpWllM1apiK90GgTQl0jARisAR5cYM6FZqjRCoLg0t3IXq3mp12Ujpn/lUrS7B9OkIRUEoVmqO1K0ceWdsKIlTskg7bjCKu2UJFOoIIT0iGUXY5YU2PRtbzNvslTSm2ASz3MoVK3nphf9QW1tLbFws99x3N+ecd/ZO+ecej4fjTzyelNQUVq9azcmnntyikAc4+ZSTCAkNYeTIkYwbvy9p6Wl8/NEnrYp50zT56suvmf7d95imyZFHHcFj/3qMuPi47ZZzuVyEhYW1+jm3xZAGip0jamNj0w6yIvvQJzyRLXUlLc54Kg6VhP2yiBycSMXizVSvK8NXWode5wcT1BANZ5SbkORIooYmEpYZjVAVpJQkTMwgPDsWoQg8GdHbrTfpwL44I91UrS7GW1SD6TdQXBqOcBdhWTFEDU7Akxmz00PArkgPT6ZvZJod2LDp8dhi3mavQyKbItDB8MlHn5KXl4eqqpx86smcOvWUZgtJFUVh7L5jGbvv2BbTaxqZMGkC48aPw+F0bC3uCoLa2lqmfTaNqqoqklOSufiyi4mNiw3qva1hShMpJMKuhLWxsWkjEc4wRsQPZEHRilbrkYQQuOM9JB7Yj/hJmZg+HVO3ZkuFqlgNnVwaCJoEtRCC0D6RhO7C0lIIgTMqhMQD+xI/MR3DqyNNiVAEikNFDXG0mFaz3boQ9ItKJ9kT38Y9YGPT9dhi3mbvQ9JiLvq26LrOZ59+DkBERAR/O+tMPB5Pi+8JRsQ3omlau87CsrJyfvrxJwAGDR7IvuPGdmD0yJq1sKeWbWxs2ooQgkMyJvLRqu+o9FcH/R7VpVnCvYPGoLodqO5Wct0b7wO7uHa6NReHZU5GVexZSpuejy3mbfZKZDNOCzuyaVMeRYVFAMQnxDNi5PA2i+a6ujpeeelV1q5bR2pKCuecfzaJiYltHvO2rF2zlqqqahRFYdCgQURGRmIYBoGAjmla0TAhBA6HA1Vte/67iYnEjs7b2Ni0ncTQOManjGD6hpnNutr0BAyfTv43q4gbl0ZIUvjW6D+CgTF9GRCT1c0jtLEJDlvM2+yVBJtis37d+iaHmaFDh7SaA78rfD4fn336GTNnzGLY8GEcd8Kxuy3mlyxeAlizAP369WXFipV8/unnTP/2e9auXYvf5ycpOYlDDj2Yo489mgkTx+NwtMGRodHL2dbyNjY2bURTVI7KOoA/tyyl1Fve3cNplvrCGkpmbaB4Ri5ZZ44kamgSikPF4wjh5JzD7WZRNr0GW8zb7HW0pctpdXU1pmlFlnYsLm3XtoNM72mNstLSpvXNmDGL1197g8WLFmOaZlPufVlZGcuWLuOjDz/mzn/ewdnnnhV0ClBjN1g7Mm9jY9NWhBAMiMlkUuoovlj3U4+NznvSo8i+cF82fbKUNa/+SfKh/Uk5NId9+gxmWFyOXfhq02uwxbzNXkjwgtrv8zUJcJfb1a6thYeH8+TT/6KyqgqPJ5S09LR2rWdbqqprAKvh1LTPpxETE8OpU09h/wP2Jz09jYqKCr6f/gOfffI5+Xn53HfP/SQlJ3HoYYe0Iae/Yx48bGxs9j5CNDenDzya+UVL2VS9pUPWafh1ApVeHJFuVOfuyxchBOHZseT8YwJ5ny+j4JuVkO9lwuMXEO4I7YAR29h0DbaYt9nraItEDQkNbXI/qKuta9f2NE1j2Ihh7Xpvc8iG2QIhBBMmTODm225iyn6TrYLaBo4/8XgGDhrI/fc8QH5ePm+/+TbjJ4wLym++LbMXNjY2NjsihCA1LIHTBhzFcwvewqv7dnudtRsqWPf6X2RfMJawrJgOGGVDbVGYk4xThhGeGUPZtLXcf+VdmLd5OeaYY9pkaGBj013YR6mNTQtERUY2uRlsLtjczaPZSli45R2vaRpnnXMm+x+w/3ZCHrCsNE85iX1GjQRg7pw/KS0pDXIL9vSyjY3N7nNw+gSOyJyCtpuuMFJKAtU+ajaUo4Z0fEdWh8vJaaedynvvvEdkZCRXXnkl//73v6moqOiw9Egbm87CFvM2ex1tkanZ/bNxuaz0muXLl1NfX985g2ojCQkJgHWD8/p8KLvwThZCkJiYSHp6OgBbtmwJevy2lLexsdldhBB4HCGcPfh4JqWORtnNK4u/vB4t1NHhYl5BMDSuP+cOO4n9xk3m9ddf529/+xsPP/wwl112GStWrOjQ7dnYdDS2mLfZCwn+hpKQmEBGpiWGy8rK+XPunz0iSpMzIAewOsGuX5fb7JgM08AwLOce0zDbOHZb0u9NSCm3+2tKE0Pq6Gag4a9/m591DGlYDca2fZ+dnmWzA0II4kNjuGDoKQyM7df+/hWmxFdWhzMqBKF23LVJIEgJS+CS4VPJjEhFCEFSUhK33347DzzwAH/++ScXX3wxP/zwA4FAoEdc/21sdsQW8zZ7HZZDS3A3A0VROP3M0xFCUF1VzZv/e5uqqqrOHWAQDBo8iJjYGEzT5K8//6K8bNf2b5sLNrNu3XoAUvukEuoJrqhLCMV2stlLsIS4iYkl3v3Sh8+sx296CZh+dBlo+Ktv87OfgOnDb3rxmfUETD+G1BvEvWmLepudyIhI4box5zM2aShqOwS9lOArrsUZG4pQO0a6KEJhUGxfbhx7IYPj+m/nXhMaGsr555/PRx99hMfj4ZxzzuHpp5+msrKyQ7ZtY9OR2GLeZq9EacOhf+hhhzBg4ABM0+TLaV/yzlvvouv6LiM0jRFKwzCaXpdSYppm09+OiOxER0cxecokAJYtWcaM32c0WWg2YhgG07+b3uRJP2bsGGJjY4NavwDblm0PZmv03cCQAQKmJc51GbAEeRvXZ2KgywAB02cJezOwXdTexkYIQU50JlfscxZjk4a36RoMgJT4yzsuMq+gMDCmL1eNOpeRCYNQdrjeCSFQFIVhw4bx0ksvcdJJJ/Hoo49y8803s27dOvu4tulR2GLeZq9kxwt3cwgh6NuvL9ffdB2xsbGUlZVx1+138c877mbRwkUUFRVRVVVNdXU1RYVFrFy5iheff5FzzjyXkoZi06qqKk4/5Qz6ZfTnmCOOZe3adU3rN02T0pJSNm7cyMaNG9m0cVNTkaopTYqLi5te27hxE35/AACPx8OZZ51JfHwcxcXF3H7rnXz3zXQKCjZTVlbGpk2bePN/b3HvP++nrq6OlJQU/nb2mURERAA0PHA07/0ssFuY74lIGtNhTAKmH7/pR5c6JluPheDnrXaNiYkudfymD136kXTMA6zNnkF6eAo37Xshpw44gkhneNDvk1LiLanDFROy25H5EM3NkX33487x/2BQTN8WAxdCCNLS0njkkUd48skn+f7775k6dSozZszA59t9hx4bm45ASPsqa7MXYkiDgBn8hdjr9fLCsy/yyEOPUllZiRCChIQEho0YRnx8HEIICrcUsXLlCvI25SOE4OPPPuKIow6nvLycU044lZkzZjF02FDeeOd/DBw4AID6+noeefBRvv3mu6ZtVZRXkJubi6IoZGZmEhFpCXAhBG++8z/69usLWA2t/vXYkzz+6BPouk54eDjDRwwnNjaG/PwCli5ZitfrxeVycftdt3PNdVc1dYGtq6vjvXfe54CDDiAzM2OHm5nApbjtyPweSGMevCH1LtumQKAKFUVo7c+Xttnj0E2db3N/55PV01lXuQndbLkrt17rZ85Vn5F1xkiSDs5u1/VJQdAnPJnjsw/imH4HEaK1rXeIYRjMmDGDBx54gJUrV3LVVVdx6aWXEhpqe9LbdC+2z7zNXonS8GfbiGRLuN1uLr38EnIG5PDsv59j0cJFVFZW8tMPP2GaBkIoaJqK0+UitU8qk6dMYsBAq0hVURTi4uNJTU0hMTEBxzYWkqZpsnnzZlatWrXd9hpz27cUbmFLodVwRRECv9/ftEx4eDjXXn8NHk8or77yGoVbCvlj1h8YhoGqqrhcLnJy+nPp5Zdy9rlnNQl5gI0bNvLkE0/x4fsf8tQzT9Ivu1+Tn3Kbp79tejxSSisVxtSRQR7zHbZtJLrUUaSJqjis40tg12Ts5WiKxhGZUxiZMIjP1vzA9NwZVPiqMeSuRb2/oh6hKmhhrjYLeUUoRLjCmJQyitMGHElaeBKqaPvso6qqTJ48mVdffZUHHniAxx57jBUrVnDHHXfQp08f25PeptuwI/M2eyUSiWEa6NLf+sKN72k4VQKBAIsXLWHevHkU5BVQWVWJ0+kkPj6e/v37M3bcGBITExFCIITYZYpB481I13XmzplLbu6GVrcvgCOOOpKoqK1NnxpzkvPy8vn1519Ys3otlZWVRERGkDMgh4MPOYjExMTttgnWQ8TPP/3M7bfeiZDw8OMPM3HSBDTNgUNxoqDYkfk9BCmttJeujMa3hCYcqEKzjy8bgCYXpHUVeUzfMIM/Ni8gr3pLw4PnVsoWFrDmlbnkXDyOqKFJQa1bFSqJobFMSBnJwRkTGBRjuens7rEnpaS+vp7333+fBx98kNjYWO6++24OPfRQW9DbdAu2mLfZa5FS4je9e63zhmEYLFq4mKuvuIbq6moeefxhDjv0MFyanWKzJ2Bd2iUBM4CBFe3sKd+qKjQ0oQHCPtZsmgiYOhurClhTsZHvN8xkRdlaAoaOV/dR8NMa8r9awYB/TGi2+6tA4NKcOIRG36g0Ds2cSE50FlkRfXBpzg4fr2EY/PLLL9x1110UFRVx0003ccYZZxAaGmof1zZdii3mbfZaGt08Am2IzvdEGk/h9tw8pJRsyN3A7bfewR+zZnPvvfcy9bSphIWFdfQwbboYU1pFrpKt7jQ9SV4oQsUhHAg7j95mB6SU6NKgLlDPyrL1LChazptP/Ze1c5Zz9J2n44oPQzd1VEXFoThwKCohmpsETywj4gcyKKYfEU4PqqIiOvGBsfHau3nzZu6//34+/fRT7j7tNC686iqUrCzoIYLeF/BTWlVOeU0lZdUV1Hvr0U0DwzSsmhZFQVM1PO5QYiNjiPKEExsRjabamdi9BVvM2+zVWDcNf7N5mr2B9evW887b73LqaafQP6d/m98vpaSgoIAH73mIL774kiuvvJKbbroJh8NhR5d6IY1pC7oZwKRnH9eqUNGEFTG1jzWb5pBScsEFF7Audz2vvfM6Lo8bXRqoQsWpOnCrLjyOkG49hqqqqvj89dcZ/txzDMvMRNx9N+y7L3Rx2k2jpKv11rFw7TJ+XfwHazdvYEtZMXnFm9lcVkRNfQ0BQ0c3DASgqRoOzUGkJ5zUuGRSYhNJjk1gUFo2U4aNY0hmDg7Vqrmyz9OeiS3mbfZ6rAimr1em20gp+fab7zj3rPP44efpDB02tF3rUVCoqazjscce4/XXX2fq1KnccsstJCQk2BfvXoaUkoDp7/FCvhEr5cZ+cLRpHr/fz/HHH4/H4+H999/vkXnpUkqkrsPcuSjXXQdbtsDdd8PJJ0NYWKdH6aWUlFSVsXLjWqb9MZ3PZn1HWXUFFTVV6LpVLxPsPa7xXHRqDmLCo0iOSeDU/Y/hiLEHkpWURkRouH2+9jDsORSbvR6BQFOcBEw/9DJBb5omq1euJszjITEpsV3rsD6/g5iYGG6//Xb69OnD/fffT1FREY8++iipqakdPGqbzkJKiSF7fkR+WwypW8egcLS+sM1eSXl5OTU1NQwYMKBHCnmwBLBwOGDCBHjzTXjsMbjxRliwwPp/SkqnCHopJcWVZXw5+3umzZrO9/N+p85Xj9GK1Wdr6wQrPWdzWRGby4pYvH4FT370MkeMPYATJh3BIaOmEB7i6aiPYbOb2GLeZq9HCIEiFTThQJcBepOgNwyThQsXMXjoEFyutnkmW1hCvjFvOSwsjAsvvJCIiAjuvfdeLrnkEh5//HFycnJ67E3UxqKxBkTvIa41bcGQOopUEEKxLSttdqKsrIza2lrS0tK6eyitIwT06wePPAKDBsG//gVLl8Ljj8PgwaBpHSLqpZRU19fwy8I/eOCdf7Ni4xqq62oxZedYzwYMnaKKEt74/iO+mv0j4weN4rYzr2Rkv8G4nbZpQndj351tbLAEvZW/q/UqKWEYOgvmz2fo0CG4Xe42vVcgcAgHCup2AsrpdPK3v/2Nl156iU2bNnH22Wfz888/2108ezyyVwp52OpF34ueo226kNLSUmpqakhPT+/uoQSHEBAZCVdcAa+8An4/nHgi/Pe/0AFdY00pmbtqIX9//AbOfPAKZi+fT2VtdacJ+W1pTOf5cvb3HH37Odz88kOsKci17w/djC3mbWwasAS9hioc9Czfj+apqKggP7+A/jnZ2zWFCgZNOFCEulNERQiBoigccMABPPvsszgcDq6++mp++OEHDMOwL9o9EKuQO/iGUI39CUzTxDAMTNNs+l13YUoDE/v4stmZ8vJy6urq6NOnT3cPJXiEsKLwhxwCL74IY8bAPffAffdBURG04ziXUlLnree1b9/j0idv4ZPfv6bGW9sJgw9iLEB5TSUvTPsfFzxxPd/P+w3d0O3zt5uw02xsbLZBCIGKhhAKuunv8UWxixYuJjw8nMzMzKCfPxQUK7WmlcZQQggmT57MG2+8wc0338yFF17IbbfdxrnnntvOlB6bzkJiBuXIJKWkpKSU3PW55ObmUlxYRG1dHeHh4SQlJZGdk01WVmaH+WSvWrmK1avXNN3goyIjGT12NCEhIbtcXjcDOJW2d+a02XOxjtkSamtrycjI6O7htB0hYOBAePllK0r/1FMwZw48+CCMGgVq8Md7SVU5j733As9+9ir1/t2P8HcEuqHz++K5nPvoNVx78kVcduw5hNm59F2OLeZtbHagMYfeoTjRZaBLpi7by5JFS4iJiSElNSUo8aUKFVU4UIL09hZC0K9fP5588kluu+027rnnHrxeLxdeeCEhId1rBWdjYRW9GrSUoyKlxOfz8cP0H3j15ddYvHgx+Xn5mObWY1vTNPrn9GfipAlcc/3V9OvXr93fr5SSosIibrv5dr768usmMT9q9D689+G79EnbdYRVIjGk3vBAbR9bNlaRf0lJCS6Xi6ioqO4eTvsQAiIirLSbtDR44AG46CK480449lhwOFrMo5dSkleymQfffobXvnsfbw8R8o1IJJvLirjnjSep9dZz3ckXER4aZp/DXYgt5m1sdoF1EVJw4MJAx5B6j4vSG4bBwoWLiE+Ib3X6WaCgKipqO0/5Pn368NRTT/H444/z8MMPk5uby6233kp8fHy71mfTMciGP6Y0kDQ/OVNZWcW/Hv8Xr7z0KmVlZTgcDpKTk4mMisTh0PD7A1RUVLBq5SpWrljJn3P/5N/PPc2YsWPaVfgcCOj87/U3+OH7HwkNDUFVNaqqqoJ6r4mJHZu3aSQQCJCfn0+fPn1Q2xDF7pE4HFbu/D77wE03wVVXwbx5cM01EBfXrKDfXFbIdS/ew7RZ3+ML9Cwhvy213joeff95quqqueusa4gKi+zuIe012GLexqYZBAIEqGgoQkGXOmYPai5VWlJKfl4ew4YPwxPW/LRmW6Pxu0IIQWxsLP/85z+JiIjg6aefprq6mn/+85+9K491T0Na4tds4UHTMAxeevElnnz8KXRdJzEpkbPPOYtjjzuG/gP6ExYWRlVlFStXruTdt9/jg/c+ZPWqNcya+Qcj9xmJ0+ls87CWLV3KC8+9iM/n49jjjsHn8/HtN98F9V5TmpjCRMF2trGxxPzmzZtJT0/fMyK9qgp9+8J//gMvvADPPgurV8Ntt8GIETsJ+i1lRdz+6qN8NvM7AnqgmwYdPPU+L898+ioedyi3n3kVbqedktkV2GLexqYVhBAIVBwomMLAMHXMIAsNO5NNmzZRVlbO8JHDd3mTEyg4GnLjO0oTuVwurrnmGrKysrj11lspLCzkscceIycnZ8+40fZCTKk3+/VKKfn9txn85/9eRtd1IiMjefLpJzjyqCNxuV1N31lsXCwTYicwbPgw+uf0R1EUzjrnb20uqgaoqanhkYceY3PBZhISErjhput57tnn27AGiSktMW9reRtd1ykoKGDUqFF7jj2uEBATA9ddB8OHw803w1lnWd70Bx4ILhcIQU19HU99/Arv/zqtVwj5RnTD4LnPXyMpJoGLjjoTp2b3kOhs9pAzw8am8xFCoKDiUFw4hBOlm5MBNm7YRFlZGfvsM7LpdwLLkcehOHEqrqYi146KcAohCAkJYerUqbz44ousX7+ec845hzlz5mAYPWfWYm/CbME9or6+nk8++oT8vHxUVeXSf1zCsccfiztkZ19oIQRhYWFccdXl/OOKy4iMjGzzA1ogEODN/73F999Nx+VycfW1VzF46OB2fCb7WLKxaEyzycjI2PMCBiEhcMwx8O67lqg/7zy4/34oK0NKyds/fsKTH71Enbe+u0faZiqqK3n4nWf5ffFs2+GmC7DFvI1NGxBCWI43iiWYHYoLVWhdng5gGAa5uZa3b79+/WAbEb+t5WRn3fyEEBx88MG88MILmKbJpZdeyvfff28L+i7GKs5u/kZZVFTMzBmzkFKSkZHOsccf22LeceMxs+1xs62NZUv2lVJK1q5Zy39e/A81NbVMnDSBU6ae0q4852AtNm32fPx+P4WFhaSmpu45kfltEcIS8k8+CeeeazneXH01S7//kuc++y/+XhSR3xYJ5Jdu4d43n6aiJrh6GZv2sweeGTY2XYMVqbc6xzoVNw7hbBD2Cp2dH+D3+Vm2dBnDhg4jwhOJS3FvL+K74OFC0zQmTZrEe++9R3JyMn//+9957733qKur6/Rt21i0lO5lOcoUsnrVagBG7DOC7P5td6ipra3lm6++4e033+GrL77G7/fvcjmv18fDDz7CsmXLiYuL5YabbyCtGdeaYOgJqWw23U9hYSGqqu75xfYJCZYH/fPPI5cswTH1dPrNWYjD7L1RbSkls1fM4+lPXsYX2PV1w6ZjsHPmbWx2g22FkSo0FClBNDqMSCRWNNMSJu2/KFvyXEERCkJAjb+OVStWM3HiRDTV0W3Tz0IIsrKyePbZZ7n33nu56aabKCoq4rLLLsPpdO550+I9CCtC3rLg3bhhI16vF0VRSM/IIDw8vM3bKS0p5f57H2DeX/MZNnwYEydN2KnPgJSSb776hq+++BqAk089mSn7Tdmt719KE0Qvdy+x2W02bdpEVFQUYWF7uNWhEOByIU84gYX1FZRcfQVPrSgnOTOM11JCqVN3EXuVEgGkew32qQ6Q4jMIMyR1qmCLU2FRmIM1oVrDVUK0L8bUsI2+9QZjqvwk+0zcUlKpClZ6HPwV7qBSE1s/ww54/T7e+3kax088nJH9huzZ32E3Yot5G5sOxLpQWXFxISRIdZsLqGVu2Sjwt/7G+qlxwcaoemOEvWFt22wD6mrrWLlyJVdccUW3Tz03Cvqnn36ae++9l4cffpiCggJuvvlmYmJi7It3J9La42FJSSkAqmZFNtt1rAiBqmloDX93FARSSlYsX8G/Hn+S6upqRowczpVXX4Gm7Z4Q72lWsDbdw/r164mNjW3Xg2hvxOv3cf38r1k8JJLrc6tJ8Rm7PBNchmRCpZ9L82s4sMxPiCnRpESRIAXoQlCvCOZGOHiuTxi/xDip3dUDQUtISf86nes31nBssZcIvWEbgCHALwR5bpWn08N4OzGEWm3X1/o1Bbn8b/qHDMsaiKbasrMzsPeqjU0nIXaKhDTK9N136Vi1ahWappGZmdntYh4sQR8ZGcldd91FSkoKjz32GCUlJdx3332kpATX0MqmHbSid+tqrZQnRSiEuNtnERcdHcU/Lr+MwsJC4uPjd+requs6L7/0CvP+mkdERASXXX4ZGZkdUKxoa3kbYMOGDcTExOw1Yv6nhTNZsHYZZQ6F27IjCDEk9TuIcLchuWJTDTdvqCEuYBnTFjkUVnkc1KiCUEPSr14nxWdyVKmPKRV+/p3m4bGMcCodwd8vUn0mL66o5KByHwawMlRjYbiDWlWQ5DeZWOFjSK3OUysriAqYPJvm2WmsYHWJfeenT7nulItJi0/ZzT1ksytsMW9j0wv566+/SE9P73F5pJGRkVx++eXExMRw9913U1JSwjPPPENaWlqPeOjYs2hd7YaEuK0lpcTXTK57a0RERHD6mVN3PQIp+fnHX3jzjbcwTZODDzmIk0450Yrg7yZ2ZN4GYOPGjXuNmK/3efls5rdNBaOmEDtFuzVTcs7mOm7NrSZal2xxKjyVHsYXcW7KHAoBAZqESN3k0DIfN26ooY/X4JqNtXgVweMZ4XiV1lNuFCm5ML+WKRU+TOCdpBD+2TeCUodCQIEQQzKmKsCTqyoZWKdzeV4tv0a7mB2x62625TWVvPfz51x/yiV2cKcTsO+uNja9kLlz55KRkUFCQkJ3D2UnXC4XZ599Ni+++CIbNmzgrLPOYtasWZimXdDY8bQseKNjYgDL/ai8rLzDLeI2bNjIww8+QlVlFampKdx2562EhYV16DZs9l50XScvL4/Y2Ni94rhalbeOGUvmNrhU7QIpGV4T4JYGIb86VOW8IdE8mhHGUo/GZpdKiVNli0tlZajGc308nDE0mqVhGqGm5JqNNUyu8BFMICDBbzKx0o8mYZNb5f6scNaFqFQ6FOpUhVKHwvRYF6+khuITVt7+vpX+Zp8R/IEA0//6lbLqivbuHpsWsCPzNja9jOrqatatW8eUKVOIjOx57bKFEGiaxuGHH054eDhXXXUV//jHP3jmmWeYNGkSiqL0qMiMRLY/pUPQJc5BzW68lW2nZ6ThcDgIBAJs2rSJuro6PJ7muwW3BZ/Pz5v/e5O//vwLgOEjhrNq1WrWr8/dbrmAP0B+XgEAFRWVfD/9B2LjYtl33FgSExObXX/POUJsuouKigrq6+tJSEhol8Vpb0JKybotG1lTkNvsMgI4fUs9mV6DegWeSQvjh2gXclfXUyGQwOxIJ49lhPP88gpidcmF+XX8EONq9ZIXakgidBMBFLgUqlVl+4i7EJjABrdGvSpw65Jkv2Hl0zezzjUFuSzfuIZJQ8b0qHvAnoAt5m1sehlr1qyhrq6OYcOG9egbnKIoTJo0iTfeeIPbb7+dc845hzvvvJOzzjprJzeUrqRRvMuGP2B5p1u/l9v8ftv3NEpnAcJyFmr8t9j2p66+QQlafBBJTEwkq28Wq1auYumSZWzauImBgwZ2yKarKit556138fl8AHz7zXd8P/2HXS6r6zoA69et58p/XIUQgg8+eZ/Djzis2fVL+16/17NlyxZ0XadPn/ZbnPYWdMPg10Wz0Vvo1RGtS6YW1iOATW6NN5NC0ZWWTxRDCN5PDOHyTTWMqwpwYLmPgbU6y8Na7srqVURTwWyq1yAuYFDg2l7QCylJ8Bu4TOuKWehUW3xI2FRUwNLclUwcPNoW8x2MLeZtbHoZa9euxe/3M3Bgx4iyzkQIwZAhQ3jqqae4+eabueuuu/D5fFx44YU4HJ1vqblj1N3EREqz4f+yTc2J5HbrM7b+ErZ22pUCRago22QwdtpnFCBauHMKIYhPiGfs2DGsWrmKVStXMeP3meQMyOmQ+gVFUUhITGgS6s0iJaWlpdTW1uFwOIiPj0fVVNytPNAJOwt0r6e4uBjDMEhKSuruoXQ6hmkwY+lcDLN5MT+sOkCkbl2zZkQ5qWjGPWZHfAK+jXUzripAqCHZpzrA8jAHDlMyqcJPms+gXhF8G+uiWrPOuzKHYEOIilkOaT6TazbWcF1OFBXbqMaYgMnBZT5CTNjkUpkT4WjxiiqlZO3mXAJ6AJez+wI6eyK2mLex6UWYpsmqVaswTZPBgwd393CCJiMjg2eeeYbHH3+chx56iPz8fK699lpiY2M7RexuFd2Wx78hDaQ0O62o0rIbtX42pN7UF0BVVBRpNRHr6M+5o2UpbO3W2tjFNTw8nONPPI5vv/mWkpJSHn/0CcaMHcPQYUOandWRUuL3+5tmTwzDoLq6Gj2go2kaEZERKIpCdEw0H336AabR8gORz+fjhmtv5NNPPmPAwAG8/N//kJKSTERkRIvvU4Qt5vd2GiPzqamp3T2UTiegB1jbQooNwOC6AK6GC81f4S1H1ndkboS1vNuUDKq1usqGGJJL8ms5ttjLFpfCgvC4JjHvVQT/SfFwYJmPLK/BWVvqGVqr80ZyKN/GuKjSFC7Lq+WYEi+lDoVHM8P4K8K5y+LXRnTTYPG6Ffh0vy3mOxhbzNvY9CLq6upYs2YN6enpREdHd/dwgkYIQUJCAvfccw/h4eE8++yzlJSUcO+997aYN90erIZdJqY0MKXRLa4oVqKOgWkalqwXKipqUwS/o1CEgiEtAZ63KY/ff5tBXFwsBx96cJOgP+SwQzjp5BN56f9eJnd9LpdceAnXXHs1Rx97NOERWx1CpJQUbinkt19/Y9OmPK665ko0TaO0tJTHHn6ctWvWkpmVyb3330NYeBiKohDTUGDbEl6vF1eDLabDoRETE01cfFzrn83Omt/rKSoqwjAMUlL2fDvDwoqSFqPyADF+E6XhclbgaluK5RaXigGoWOk6oqEZlMuUeExJiCGtpoeNCMEfkQ6uz4nk7nVVDK3RGVsVYExVJetDVNa7NSZV+lgfovFYRhivJ7ee8gOwYtMa/IFAm8Zu0zq2mLex6UVUVlayZs0axowZ091DaRdut5vrrruOhIQEHn74YcrLy7nvvvvIycnZvW6hDZF4ExPD1DGbLcHqeiQSQ+qYUkegoCmOphSS9n5mKSX19fVUVVXy28zf+PSTT1kwfwHVVdUcfOjBHHDQAU2pNCEhIdx8280UF5fw5RdfsXDBIq656jqe+fdzjBg5nKTkJAzDYP3a9SxatJgtmzcTExvLYYcfxtBhQ6ivq2fmjJlNHWADXXAj3tWsg83ehWEYFBcXo6oqcXGtP/z1dkqryludjfKYsknM16ltOD+EICDAp0CoaUXkVQleVfBGciizI53UqIJi5/YPCAqQ61ap0hQkNKX1ZNQb9K23mlmF6yZhhiTElFRb3RJbHEp5TVWrDy02bccW8zY2vQQpJZWVlaxevZrLLrusu4fTLoQQhIaGcvHFF5Oamsr111/PBRdcwNNPP82oUaPaJW6ltFJpTKljyJ57k2js/us3fahCRRUayLZF6g3DYN26dcyfP58ffviBH374gYqKCgYNHsiRRx3JIYcezJh9x+zk856cksxTzz7J0GFDeeftd1m/bj3z/prHvL/mbbecEIK4+DjGjN1aoOZwOMjIzKS2to6srEyUtnaRbAeK6LmF3TZdg9frpbCwkLS0tA7pW9DTqaqtad6SsoF6RViF4dKKqAeNBFWC07SuQz5FYAjQgU/i3bt8iyIlB5b5eHl5BRleg9+inNyfZc3kHVPiZXyln+HVAZL9Jo+trmTfSj+3ZUewwa22KOi9fm+HW+Ta2GLexqZXkZ+fT01NDQMGDOjuobSbRpF4xBFHWFHjm2/m8ssv56GHHmK//fYL2qGn8Yagy0CDiO89NwhDGpjSRBEqGlYu665EvZQSwzCora1l7ty5fPTRR8yZM4e8vDySkpI4+eSTOeSwQ8jsl05ycjKqqu5yPY1pTtffdB1HH3c0y5YuY/Yfc9hcUEBNTS0hbjfJKSmM3GcEAwcNZMDAnKZoaFx8HPc9cA/19fW4Q9yEedrm962qKscdfyxZWZmkpKRsl9rTHLaYt6mvr6e4uJi0tLTuHkqXEDACmK2I3CpNaSowjQ20pW+HJFI3UQETqNHEru0smxaXDKzVeWBtFeleg6UejetzIvkz3AEIfol2keY12K/cx/UbaxhUq3NKUT1FToUb+0eit7BqwzR60ZW692CLeRubXsRff/1FZmYmMTExvd7aS9M0DjzwQN555x0uvfRSzj33XB577DGOP/543O5dR4saaYzG62agTY40PYmt6TcmmqKhyK1C3O/3k5+fz+LFi/nll1/49NNP8fl8ZGVlccghh3DkkUey77774nQ6UVQFvxlcIxiXy8Xw4cMYNmwYp552ynaNvBShoKhKU659I06nk37Z/dr9OTVN44STTuD4E4+3ttOKk46CgtIdNp82PYr6+noKCwsZNmxYdw+lS/C4Q1FbOTfWhKj4FYHLkAyraVu628jqAAIr1WZNSMvSTwGOKvEytiqAT4FXUj3MC9/a2dUvBGtDVNaFhDIvwsH7i8roX29w7uY6HssIp8Dd/MO4U3N2Y2+OPRdbzNvY9CLmz59Pv3799pjW5oqikJ2dzQsvvMCdd97JjTfeSFlZGeeddx5ut7v5aLXU0aVOb4rGN4fEJGD6UYVGXVU9v//+O1999RULFixg+fLlZGRkcPrppzNp0iSGDx9OamrqdvtFSokqlKBTjCyxDorS+Zd/KSW6rqPrerPf544oouVpepu9A6/Xu1dF5sNDwrY/PxoKVAVgWt3pWBzmwKsIwg3JxAo/DgmBIE4VARxY7gcsl5rFYS2f+6q0xL8CVGgKC8McmDuekw1NqRaGOZkT6aR/fT0hhqRfvd6imA91haDY53eHY4t5G5teQiAQYOHChRx33HF7jJgHS1z279+fF198kTvuuIN7772XwsJCrrnmGqKiopqWayxy1aW/R+fGtwWfz0dhYRFr16zl808/58fvfyIQCBAXF8fkyZN5/PHHycnJITw8vHlffgEKGoY06YkPNz/9+DNPPfEU9z5wL6NG79NiZF4gUIVqR+72cqSU1NXVUVRURGZmZncPp0tIjk3AMEyElMQHTAbV6JxWVE+BS+HhjHAMIchzq/wa5eTkYi8D63QOLPfxXYyr5YdfKRlTFWBCpSXmV3oczAt3tjoeR8OlxBStPDAIrO6w1o9N1pnNkRqXtFfUQHQ19h61sekl5ObmUltbS3Z2Nk5n6xfj3oQQgqioKB566CGSk5P597//TWlpKbfddpvVMEY0RHllAHMPEPJFRUXM+H0ms2fN5tdffmXN6rX0z8nmyGOO5MADDmDyxClBp1JZ1pcKKgpGD3LxaSQmJgafz8cVl17Bw48/zJT9JjdbF6EKFdvFxgagvLycQCDQ4da1PZWo0HCGCRdD8us4sbiefSv9rPA4mBXpQZNWmzoTeDXVw2FlPiJ0ybUba1gRqrGxhaLTZL/JLbnVRAdM/AL+kxKKr5UadlPA+hDrHI3UJUNrA8yMcu4cncdys2lM+Qkoglx387JSEQpDswbicuxZ96+egC3mbWx6CUuXLsXtdpOdnd3dQ+kUhBBERERw7bXXNnnSb9q0iWeeeYaU1BQM9F4t5KWUrF2zlqee/Ddz/phNRUUlHo+HY447mocfe5i+fbOIi4/D7XKjKiogkTI4+0qBQFUcGGbHROd3bD7VXoQQjBq9Dy/+5wUuv/QKrr7iah594lEOPuSgnaJzCorl8GNreRtgw4YNxMXFERISsufWT0gJdXWwdi2uDz7gjd/W4aitY2G4xiWDopgV6WKLS8Fo+vyC36KcvJEUykUFtRxU5uO5lRXclB3JhhC1we1GIKTEbUKKz+D+tVUcVeIF4LN4N19s417j0U1u2lDD5Ao/pQ6F63IiyXNbfvQ/R7u4KL+WaF3yj021LAxzMi/Cga/BflJISaQuuTC/lhENYv7XKCebWvC/V4RgYFo/nFrbGl7ZtI4t5m1segFSShYtWoTb7SYnJ6e7h9OpeDwe/v73vxMXF8cdd9zBOeecw0OPPMDwfYa3WjzZ06mrrSN3XS77H7A/Bx50ABMnTyQyMrLp9UbRYkgDpEBTgr/pCQSa0NDl7vvAe71evv/ueyIiItjvgP12S0wpikJ2/2xeevUlbr7+Zq647Eoeeewhjj726KYus4Al5Bt659rY5ObmkpiYSEhISHcPpeOREgoL4fffYdo0+OorRHw85ZPGc92WhcwI17Y6wmx77gmoVgUPZYURrZtMLazn6BIfo6tK+C7WzbxwBzWqINSwoulHlPpI91oBkG9jXdzdN4Jih9K0Tk3CsJoAB5b72OxUCDVMrLZSghlRTl5K9XDtxhqG1up8vKiUdxJDWNywjZiAyeGlPo4s8eKSsCpU48Gs8Baj/iEuNwPT+qGptvTsaOw9amPTC/B6vaxdu5bY2Fgr7WQPR9M0jj32WOLi4rjsH5dx2aWX8/SzTzJm7JheK+iFEAwYNID/vPp/xMfHo2q7tpFsxEBHSIGKFlx0XljLWp77uzeDUVZWxpNPPM2EiePZ74D9dmtdjWPLyEjnsX89yq03386tN99ObW0dp585FYfDgSo0FNHy/rDpmTRaxBoNXZcNU1IbqKPGX0ddwIshDZyqgzBnKOFOD07VgSoUFKGitDDzs2nTJuLj41t1tuo1SAm6DkVF8Omn8MEHsHIlpKbCPffA5MmU1hWz4OEr0Wurml+PEOS5NW7qH0GpQ+GMLfXE+03O2VzHOZvBEFYBK1ipOVWqYFq8m3uzIljt2V7yScCvCOoVqzC2KY1GQLWm8HhGOKaAi/NriQuYXLOpFr+w3uM2rcZTAQF/hju4pX8kcyKcLebv56T2ZVB6f/s87wRsMW9j0wsoKioiNzeXsWPH7jXFQw6Hg4mTJvL6m69x+613cP7ZF3DvA/dwzHHbR3R3pLE7ak1NLX6/H6fTSXh4WNBuKi0RCATwer14PJ5dPlRIKfH5fBhG82I6IjICn98H/q2/czldaI6dv1dd6lZOPMEJXSEEDpz4pa/dlp1SSkqKSlixfAXX3nBNu9bR3NjS0tN45vmnufO2u7j9ljuoqKjg73//OzGRsfYNvpchpaQ2UM+mqs3kVW1hWckaFhWuYmNlAT7Db3UzFViuLELBlCZSQnRIOP1jMhmRMJDsmAxSwxNJCY+3Cp8bjgEpJevXryc9PX3PiMxXVMCCBfDFF/Duu+B0woQJcNNNMGUKeDwIIRhUsoVxg/bhuz9/aXWVeS6Va3Iieb6Ph+NKvIyt8hMTMHGZ4BdQqSnMD3cwLd7N4jDHLotYqzXBZQMjua5/JIaAEuf217RSh+CffSP4X3IoxxZ7GVMVIEY30aSkXhGsD9H4LtbFz9EuqlTRopAXQjAkM4f+qX3buPNsgmHvUAU2Nr0YKSXFxcXk5+dz0UUXdfdwugQpJRKJic6QoYP511OPc9std/Daq68zZf/JJCQk7Ly8lCxcsIjvvv2O5cuWU15Wjtfrxe12Exsby9hxYzn6mKNIS7es7oIVj1JKTNNkxfKVvPXGW+Tm5jJg4AAuvuQiklOSd1r+sUeeYNXKlW36vH+/8O8ceNABu9o6utRxCoW2JJM7FAcB0285ALWDFStWoGoq2bvhL78rhBBER0dz933/JCoqikcffoyaylpuueWWDnnYsulcGqPwZd5Kftv4F3PyF7K8ZC35NUXWedJKB1OA2kAd+VWF/LphLk7VSf+YDAbF9eOgrPEMi8/BqVqpZXl5eYwePbrFB/ceS6OjS1UVfPMNfPyxlVLjdsP558Mhh8DYsRAaut3bkmMTmDRkDD8vmIlfbyVdTlhdXJeHOVge5kAzJeGGxG1KfArUqAp+IVq8bEghKHeolDeXzScEurAccFZ6HChSEmJIHBJqVWE9IAR5znpcIZyy3zE4HXa+fGdgi3kbm17A5s2bKSwsZNSoUd09lC7DlDomJkII+vbry3MvPENFRQWxsbHbLWdF8XL595P/5pOPP6W8zHLBUFUVTdPQdR3DMPj4o094/pkXuOb6q/nb2We2On1vmiaVlZUsX7aCV/7zKj98/wMlxSUYhkFGRjrHHX/cTmJeSsnPP/7MzBkz2/RZDzzowGbEvOVDb0gdVTiCyicXQoBUcCjOdgv6X3/5jSFDh3RKczIhBPHx8dx+5+2Eh0Xw4gsvUl9fzy233EJ0dLQt6HsohmmwuaaYnzbM5uPl31FcV0697m3XuiRWWk697mVR0UqWFK/im7W/MSAui7OHHU+yEkNtbS2JiYm9byayrg7WrYPp0+GVV6CmBjIy4MEHLREfHw8Oxy5FsKqonH7A8fz3m/fJLdzUps3qiqBc6dxzxxSCWq1928hOzeKgkZM6eEQ2jfSys8TGZu/DNE2WLVtGXFzcXmPTJhsi0o0IIYiLjyMuPm6nZXPX53LNFdcw/bvvAUhKTmL/A/Zj6LChhIeFUV1Tw4rlK5j+3fesWbOGW268FYdD46xzzmo2/940TT5470M+/+xzfvj+RyorKgEr9ccwjIa26zuLZCEEJ59yEqNG79Pi5zNNk3l/zeePWX8QERFBbGxMi8sb0kRpcJcJhkZBrynONnfJ1XWdObPnMmW/KURGRbb+hnagoBAVHs0N199ATHQMDz74INXV1dx7773ExcXZgr6HUReo58vVP/PJyu9ZW74R3exYVylTSip91czJX8TSotX09SegYxKfEN97joXaWvj5Z/j2W6uoVdfhmGPg6KNh//0hLCyoKHb/1CxOnnIUT378n+06NPdmQlxurjrx74S49pD6hx6ILeZtbHo4hmGwYMEChg4d2junnNuIxOrwGgwV5RU88tBj/PjDTyiKwsRJE3jokQfJGZBDeEQ4QgiklNTU1LBo0WJuv/l2ioqKSO2T2qpI+PD9j/jxhx+JjIxkv/2ncPAhB7F48VJeeemVFt930aUXtnoTrqqs4oLzLwRg+Ihh7Dtu3xaXlw1FrYLgrSKFECgNEXrd9GMGKejXrVtPRUUFg4cM6pTjTRUqmnCCgLCwMC6++GJcLhf33Xcf1dXVPPnkk7ag7yGY0mR12QZeXfARs/LmUxuo7/Rt1gbqmbFsLjVGHWt8+VR4q4h0hfe840FKCASguBh++QX++19YvRo8HrjsMjjqKMjKsv7dRs477FQ+n/Udq/PXd8LAuxZFCA7eZzKHjd6/532HexC2mLex6eEYhsH8+fM56aST9g4xH2TurZSSn378iY8++AjDMBgzdjQvvPQ8/bL7bXfTEEIQHh7OxIkTePm1lzENg/45LTsqCCG44urLOeGk49n/gP1I7ZOKYRg89MDDLY5JCIEjiJzQZUuX8ctPv6JpGqdOPZWk5NYdiqxUm7Y1VbIi9OBQXOhSb3hIajnt5s+5f+J2uxkydEiH3nyt7q4aqtjencflcnH++ecTFRXFbbfdxiWXXMIjjzxCdna2ffPvRny6n183zuW5uW+RX1MU1DnZUfjL6zAw+SjvR7y/a1w6+nSyovr0nOPB64W5c+GHH+C996wC10mTrFSaI46A6GhruXaMVwjBgLR+XHnC+dz538eorKvu2LF3MTHh0fzj2HNIiU3sOd/fHogt5m1sejjFxcUUFRXRv3//oIRib6ax7DWYtJBAIMAnH31KTU0NYWFhnHv+uWRlZTV7wxBCBF3QKYRg/w6wZNwVuq7z31dew+/3k5aexvEnHBfUTU4iMaVEIfh0G9ha6KuhoQilxbQb0zRZtGARoaGh5AzoH/Q2WkMRKprQEELZKe9fCIHT6eSkk07C6XRyzTXXcMUVV/DSSy+Rnp5uC4BuwKf7+XTVD7wy/wPK6ivbXUjdXvRKL0IVKB6NXzbOocJXxdX7nsvguH7dcjw0NVGrq0P89hu8/Tb88Ycl6k88EU4+GYYNs0R8B4zPoTn428En8dXsn/j2r5+bCo97Gw7NwTmHnsLBo6bY53En0zsNm21s9iLmz59PVFQUmZmZe/4FURJ0BLCmpoYffvgRgNQ+qRx+5OGoWvPdB3eFaZrUVNdQWVFJTU1Nl+Sozpr5B7/88iuKonDe+efusg6gOUzan6sshEBBwam40IQDsYvLf0mJZUk5ZOhgYmJazuMPBgUFh3DhEE4EOwv5bXE4HBx33HG8/PLLFBcXc8YZZzB79uwWbT5tOp76gJe3l07j6dmvUVpf0eVCXkqJv8KLcCho4S5002D+luXc+sMTzC9c3qUzBGAFDZYuXcp7L71E2WGHIc87DzZuhGuvtQT9v/4F++0HMTEdIuQbiQ6L5PFL7mRAn451lOoqNFXl4H0mcd0pF+Owm0R1OraYt7HpwTR2fo2JiSE1NbW7h9MFSGSQN+vVq1ZTU10DQGZmJqmpKW3eWmlpKfff+wAXX3AJjz/6BGWlZW1eR1uor/fy5RdfUVxUTEZmOocdcVibmmDtrpARDU16VKHhUJw4FCcKWx+AtmwuZEPuRsaNH7dbD46qUC0Rr7hQFKVpu62+T1U5+OCDeeaZZ6itreXyyy9n3rx5vTYy2ZuQUuI3Anyx+if+t/AzfMbudxJuD6bfwKjxo4W7UDTr3DClSUFNEU/+8V+WF6/t1OOhMQpvGAYzZszglltu4W9/+xvX3303awcPhuefh3fesfLiU1NB0zpUxDcihGBgWj/uO/9GMhJ637W/X0om9557g51e00XYYt7GpgdjGAaLFi0iMTGRPn36dPdwOh0JQRdqbtywqSmSPnDQgHZ1hq2rq+eXn3/h88+mMeO3mdTXd16Bn5SSdWvX8fknnyOl5LDDD2PI0MFtutFZ6TG7L2SEECgNXTgdihOn4kYVDvI25ZGfn8+kSRPbtj6sdWmKE5fiRhNOFNEg4tuQ4w9YhcwTJ/Lf//6X6Ohozj//fL777jsCge4Rl3sLEvhu7QyenfsWVf6abhuH6dXR6/y4Yrf3YJdIVpau57FZL7O5pqhTtq3rOgUFBXz++eecfPLJTJ06lW+++YYjjjiCr7/9lhFPPw0nnQQpKZ0i4HdEVVWOn3AYd519LckxCa2/oYeQkZDK/13zMKP7D7eFfBdhi3kbmx5MUVERBQUF5OTkELpDg5G9ncrKShqFbVR0VLvWoaoqcXFxJCUlERsbg6q2LU2nrUz7fBrr168nIiKCU6ee2q7ulh2Z9tDojqMIBWEKFi9YQnJyMulpmWjCgSYcTUWrO/7VhKMhuu/C2fC3KS8+yEh8s+MSglGjRvHCCy/Qp08fLr/8cj777DM7Qt+JrChZx0vz36MmUNet4zC8AYzaAI6Yna93hjRZVrKWlxd8RI2/48YppWTZsmU89dRTnHnmmZx77rnU1NRwxx138Pnnn/PQQw8xYsQIXA2dWrsSh+bg3ENP5Y6/XU1KbM+3Ju6fmsXzVz3E5KH7tivAYtM+7ESmXs7ONzfrVi+RW7vQbYfAagon2JUrhv0U3bPYuHEjpaWljBw5sruH0unIhj/BYhhG0yHeXhGemJjAcy8+i9fnIzQkhPiE+HatJxhKikt4839vATBx0gTG7jumXevpLDlrGAazZs1izJgxeEI8aIpj6/ex40ZF4/8693qRnZ3NM888w80338xNN92EYRiccMIJOJ1O+1rVQUgpKa4r478LP2JLTXF3DwehKLhTwglJjdjl64Y0+W7t7/SPTufUwUeiKe07903TpKamhuXLl/PGG2/w/fffU19fz/jx47nzzjsZNmwY8fHxPUKQqqrK+YdPJT4qlhtfup8NhXndPaSd0FSNfikZ/Ofax5gweBRqO78Xm/Zhi/lehiXSG1IRGlveS7Np+j2oG/12CzWUpTW4TFh/lDb5Wdt0DlJKNmzYQFlZ2d7R+VU2/ScowsPDmo7Rmpr2pQU4HA7SM9Lb9d62YJom77/3AXmb8nC73Vx0yYXtdyaSsi3ulEFTU1PD/Pnzufnmm5u644qtqr1bsByIsnn++ee5/fbbue666ygsLOTSSy/F6XR2z6D2MEwp+Xrtr/y68U+MLi4u3RXOuFAyzh/d4jL1upcPln/DmOShZMdktC1VTUo2btzIjz/+yLRp0/jxxx/Jzs7mtNNO49RTT2Xw4MFNdR49iRCXm1OmHE1CVBy3/OdB5q1ZjF/vGalnboeLYyccyj/PvpbBGTk9bt/tDdhivoezbeTdlAYmVvfJxvh7B2wBEwmNF3HZIO8bGs4oQm26oe+tJ+huT+2L9kUwDcNg/fr1OBwOMjMzd28MvYZd7yfTNNF1q5GUw+FACEFsbGzTMZm3Kc+yjuuBx6iUks2bt/DVF1/h8/nY/4D9GDmq5Q6x3cHixYtRVZWcnJweEY1sRAhBUlIS999/PwCPPPIIQgguvPBC3G53j/zOexNbaov5cPl36GZwjdpg+2tid+3/jVWbeWfpl9wx5bJWr69SSnRdJy8vj7feeouvvvqKtWvXMnjwYJ555hnGjRtH37590bSeLYmEEEweOpbXbnqSpz5+mf9N/5B6X30X+w1tRREKUWERXHPShVxw5BkkxyTY52M30bOP3L0Y2SDYpbQ6Pxq7YUnX5m1jImWDDZ4MoDQUtymoe0XEvnH2Y2vCktzm+9j+QapxLmTbm4lAgNhmhqMpqanhv0HuP6/Xy6JFi6xczb2gWRQCxA53perqatauWcvy5Sv4ctqXJKek8M977iQsLIwhQ4fgdDkJBAIsX7acmpoawsPDu2fsLSCl5Pdff2fWrD8IDQ3l5FNPJmF32tR30vn322+/kZycTP/+LTfU6i6SkpJ44oknSE5O5oEHHiAvL49bb72VqKio7h5ar0U3Dd5b+hWFraXXSIk0Jf7yegLlXoz6AIZPR9EUVLeGIyYUZ0wIiqN9qRVSSvQqH/7SOvRaP4ZPRyDQPE4cMSE4Y0ObnG22fc9vm/5iYeFK9kka1Ox6S0pK+PPPP/n000/57LPPiI6OZtKkSTzyyCOMHj0at9vdox5eW0NVVHL69OXJy/7JASMm8Mo37/Lroj/wBwJdZiMqEER6wjlo1GSuOfECxg8ajaaqPfK6sbdgi/kexLYi0pQGhjSCap7T2ZiYmNJESB1FqKioTR7Ve8LJu/Psh9kg2s2gL47bLtf4PdL4ACZhazqTQEjFKjjcpv58V/vR5/OxYsUKDj300F51s2k3jenZUpKfX8D0b6fz6y+/8ufcPykuLmHc+HEcPWpkU358XFwc/XP6s2DeAjYXbGHeX/PZb/+e15wkEAjw1htvU19Xz+DBgzj8yMPb/X1ufTTsWPx+P/Pnzyc5OZm0tLQOX39HIIQgKiqKW265BSEEL730EgC33XYbERERHf69BzMj19OOtbYgpWRd+UZm5y9sNr2mcR/4S+sp+S2XmlXF+Ipq0Wv8YFrpXopLw50URlj/OOIPyMIZH3yRqJQS06tT+scmKucX4N1Sg17lRRrWdpUQDXdCGJHDk4g7sC+O8O2DGhXeKr5b9zuD4vri1lzbjbm0tJRPPvmEL7/8kj/++IPY2FiuvvpqDj30UIYPH940w9cbEULgdro5bf9jmTRkLO/9/DmvffcBawpy8fq9nbddwKE5mTB4NOcdfirHTziM6PCoTtueTfDYYr6H0BjxNaSOKY0ub9QRDI3jM6SOgoqmaCCVhkBh77sobjv7YUi9zQWYbdxaQzoTgNEQgRbWw5FQQe4cta+qqmLNmjXceOONne6y0p1IKamurqawsJC5c+fy4Ucf8Oeff6FpGolJiZx97tkcfezRpKX1wePxNAlhh9PB6WdMZfHCxRQVFfHWG28xYuRwIiMje9RNetbMWfz+2+8AnHbGaaSl7Z7FaGeI+Q0bNrBx40aOPvrodjnsdCUej4dbb72VxMREHn30UcrKyrj77rtJSUnZre9922DK1lk4c4fZOLHVAQhl67+tXLpOLwjuSExp8kf+QnIr85tfSELloi1s/nQZ9ZurwZQoLhVnlBvhUJGmiVmvU7ehgrqNlVQtLSTj3FGEZkYjlNZTX/zFteR/tJTKxVuQAROlIcqvaArSlJh+g/qCatypETtF5hs/w8xN8zi2/4EMiutHbW0tq1evZtq0abz99tv4/X769+/PM888w/777090dPQe1UVbCEFqXBJXn3QB5xx2Cl/N/pH/ff8ha/NzyS8txDCN3U4TVYTAoTlIT+jDkMwcLjzyDCYOHkOEJ8wucu1B2GK+ByDlVpHcE0X8rjAxCJhmQ6ReQ+lB4qk1pJQNtQdGQ/Fw1+/zxiSdxu9doKDukMq0fPlyQkND99iW9oZhsHr1ambOnMnPP//MrFmzKCsrY59RI/n7heczcdJE9hm1D2Fhu470qarKEUcezntvv8f8+Qv4+KNP6J+Tw1XXXNFsWlIgEKCstIz4BMulwuv1snTJMmpqqomMimLgwAFNxZ8dQX19Pf977Q28Xi9JSUmcctopu23Z2BmsXbuWoqIiJkyY0OOPNSEEoaGhXHbZZTgcDu677z7q6up47LHHSElpe+OwRhMBQ+oND/aNhgItvqkJRSgoqChCQRVau7z1u4PaQD2/bJiL0ULX49r1ZWx6eyGB8nqEKggbnED0mFRCM6JQPU6k38BXUkvlgs2U/5mPd3M1JTM20CclAtXdsryQAZOCT5dTMa8AFEH4wHhiJqTh6RuLFubADJgEyuup21CBp18MasiuRfjmmhL+3LCIFb8t4uuvv+arr74iJCSEo48+muOOO47x48cTGhra44/r9iKEQFM14iNjOfewUzl1v2P4a/Uifl00m0XrlzNr2V8UlltpVAFDb1XcK0JBUzWEgMzENCYMHs3I7MFMGTqOYVmDcPTwuoK9Fftb6UYaIz96QzS+t7F1JsFEExqKsJ7Se+JFs/ECZqUv6UE3JuoqrOPASmUSQkFD46+//iItLY34+M6zS+xKTNPE5/NRVlbG7Nmzeffdd1m4cCH19fWkpKRw9tlnc8wxx5CUkkhkdASK2rqjRHZ2NtfecA3XXHUdZaVlPPrQoyxbsoTzLjifgYMG4NAse0Wfz8fCBYt49+13KdxSxIefvk9YWBglJSXcdcc/WbJ4CePHj+Nf//5XUydZwzAoKyvD5/Nb4zcMqqqqm14rKiwiL8+KaipCkJySvN14pZT8OfcvZvw+E4CTTjmxXV1qt0XphEu2aZqsXLmSurq6XuWapCgK5513HuHh4dx1111cffXVPPDAA0Hl/DdeDwypo8vAbj3Um9JscBcDQQBFKDgUR1Nn3Z54PQTYXF3MitK1zX5uf0U9m6etJFBeD4ogdlIGyccNQotwbfeZXA0pNiHpUdSsLCHx8P4ozpYjtlJKyv/Mp3LRZgDCB8aTce4oHFHu7SL6zugQQrOid/l+aUj0Gh9Viwq54983Iip0YmNjueGGGzj66KNJS0vbKwukQ90hTBk2jomDx1BZV01pZTmFFcUszV3J8k1rWJa7msKKYrx+H16/ryFlx0WoK4TUuCSGZQ2if2oWQzNziI2IITYimvBQD4rYC1I9ezG2mO8mGqPxuuwZ1lK7g8QkIP1W6o1w9LioVGMk3jB7nojfEWt638BnGsyZO5uMjAwSEnpP579d4ff7yc3NZc6cOfz+++9Mnz6d2tpaRowYwVlnncWBBx7IuHHjmpwkJCZ+008wbk2qpnLyqSdTUVHJE4/+iw0bNvDuO+/z/nsf0qdPH+IT4ggEdPLz8ikrK0NKSWhoKEuXLGPc+H0xDJOy0lKKCosoKyvHNLY+VBcVFnHu2efz2y+/7bTdgvwCTjj2pKZ/ezweNm3ZsF2Kis/n47NPPmPTxk2kpqZywonH72Yhs2jqqtqR1NfX89dffzF8uJWi1FsQQuB0OjnjjDOIiYnhyiuv5OKLL+a5555jyJAhzb5PSoneJOI7NogisSL7hqGjCg2HsER9TxSUfxQsJGDs+vNLKaleXkzt2lIAwrJjSTlpCGroznnmQggUl0rclEziJmeA0nqRv17lo2z2JkyfgSPKTcqJg3FE71p47/g7KSW+4lpKf9tAxbx8zIBJ7MBk7r/pbk455iQiIiJ2+b69DVVViQmPIiY8iuzUTCYNGdvmdezt+7A3YYv5LqYxIhSQfoyGG8mecrqY0kCXEk1xNNlbdieNua6dcdPubCorK9mwYSOHHHIwnrBQ67jpJTm5jTZwlZWV/Pbbb3z66acsWbKEgoIC+vTpw4UXXsjkyZPJzs4mMTFx52JQqaAgrBqDIFAUhXPOO5shQwbz+mv/45uvv6WyopLNmzdTUFBgNUkTCp4wD2PHjuXkU06kX3ZfAMI8Ho465iiGDhtKzoAcPB7P1hULywYzmLQbl2tnz/OS4hK+/uobXG4X4yaMY5/R++zWOaE0uCN1NPX19SxYsICjjjqq19VmNO7PQw45hKeffpqbbrqJq6++mkcffZSRI0dud2xZD/UmuulHl8HbMLaXxvonTTgarok9xwlMSsmfBYub7kE7YvoNqpcXY3p1FKdKzIQ01JDmC0aF1YmQYO5mUkp8hTXUb6oAIHxwAu6ksDbtG73aR8X8AiKHJRG1bx9i0uLpO2lwr3oY7Up6ynFn03nYYr4LaUqrMQM9PkLcXkxM/KYPh3B2a0SqN9YhbMua1Wvw1nsZPHQwhrByeTUc0AMekprD6/Wyfv16li1bxldffcX06dMxTZPs7GwOO+wwpk6dyoABA3A6nS02ZRFCoCoapukPetsul4sJkyYwdtxYqquqWbZsOXmb8qiqqiQ0NJTk5GQGDRlEXFwcmqY1bTsmNoZbb7+lyaN+WzEbFxfHcy8+Q11d623jFaHsFHWPjonmw0/eb1rXdg8K7aCzis22bNnCunXrGD9+fK8T841omsYRRxxBSkoKF110EWeddRbPP/88++23H4qiNF0PfKaPzuuhuzMSaQVuDB2H4rKcwHrA+Vvlq6G0vqLZ102/Qe36MgCccR48WTGtFrTutA7dwJtfhV7jR3FphKZHNaXf1G+uRq/2ozhVwvrFoLgaZuWkbPp6WtqeJzOagXccYFlhKoKAMJm/ZRkHZo5r0xhtbPYUbDHfRTRGiQNmoKm4qvsv6Z2HLgNo0OWCvnHmQ5cBjC6IvnUGUkrWrVtHIOBnwMAcwMrLDUg/WsO0fXdH6bctoiopKeH777/np59+Yu7cuaxbt44BAwZw3nnnMX78eEaNGkVCQkKb7BgVVBSUNj30CiFwOBzExMYwecqkoN/TXKOY3W3W5fF4GDR41/7XbUVB6bRzae7cuSQmJpKWltYjhGZ7URSF4cOH8/zzz3PjjTdy1VVX8fjjj3PwwQehY6A3Phx2w0e0ghxeHIrTeijv5vO3qK4Mr978w7L0G/hLrIdYLdyJM7rtReFGXYDNn6+gankx7kQPfS8fjyvOA6bEu9mqPVHcGs5YD9JvULm8mLrccgIVVo6+MzqU8IHxhGZFIbapnxFCgCpQ1W1mXZAU1pb22MZxNjadjS3mu4BGq7OA6e8RvvFdQWNEyoFzuy6ynbrNxrQa09+rZz6klKxZtRoJDBg4cOvvkejSj4qGigMpZJcLAiklXq+X4uJiFixYwCeffMIvv/yClJLY2FiOOOIIjjvuOAYMGEBYWNhueTlb0fkAXRlJ7YkIQFU6xxNbSsnPP/9M//79SU1N7fD1dzWKojB69Ghef/11Lr30Ui699FIefOQBjjzmiG7v7imR+E0/piJx4uyW87eRLTXFVPqqmn1drwsgdesaqoY4UJpxkmkRCaZuIgMGZsDcpo8EVlEtVvS9Pq+SLV+uoG5jJWbAsPzrARRB0fQ1RI1JJeWEQTjC3S0+iNUHvNT46wh37d4MmI1Nb8QW812BBH0vEvLbossADikQovOn7yXmdjMfvZWamlrWrllH3359CQ8P2+41CehSR0JThK8rME2TLVu2MGfOHH744Qdmz57NqlWrGDZsGGeccQZTpkxh3333JTo6ukNEp+XjraIKs9fOsHQUqtAaPM07nurqahYvXszkyZOJjY3tlG10NYqikJ6ezvMvPM8NN1zPjdffRL2vnuNPPK4HdFK2gg0IiVNxddv0bJWvBr/RvPmC6dt6zinO9s0IKS6N6NGphPSJQAt3bbWWlBLDa61fr/ax+YsVmH6D0PQo3MnhqCEaRm2A6pXFBCq8lM7YgFAEKScORvPsXJvSSMDU8eo+W8zb7JXYYr6TsZwTenekeHewIuUBNEV0WgFYYy2CNfPR+6O4dbW1lJeXM27cvs0u0yhwO2vKXkrLzrGyspLFixfzySef8Ouvv1JbW0tERASHHXYYTz31FJmZmcTHx2+Xh95RCASacDTYBu6d54/lX97x+7aRFStWUFNTwz777LNHNdMBSO6TxKNPPcpdt93FnbfeRWVFJWefd1aH9hFoL7oMoEiBhrNb0kLqdV+L/vKKY5sUFsNsV/qK4lKJnZS+zS+2sW1tiPpLU+KKCSXxiP6ED4y3imxVgdRN6vOryPtgMXXryimbvYnwQfFEjWq+MVjA0PGbvd8dzsamPdhivhOxmpEYzToG7ArTNCnIL2DtGquJS21tHaqqEhkVSWZmBgMHDsS5C+eMoMYjJZs2bmLxoiV4PKEMGjKIxMTEVt+zZcsWFi9aQuGWQgxDJyw8nLS0NIYMHUxYWFiL7wcrX9SQOproHLGwNYWp9wt5gLj4OB7716OtduLcUdB3BIZhkJeXx9y5c5k1axbffPMNeXl5DB48mKOPPpqDDjqIKVOmNAmirhAiDsWxR32/wSIQTS4oncXKFSvQAwHGjBnTadvoaqRsKDpFJzExgceffIx/3nEPD9zzIPX19Zx3wblN9oXdScAMIBQFlc57WGsO3Wy5y/i2DZpMn440TITWttnVxtz2nV+whD6AI8JN2hnDCRsQt/0+0FQ8/WJIOjyHDa/Pw6gLUDGvgKiRybteJ9aMpW72LtcyG5uOwhbzncRWW8TgUgRM02RzwWbefOMtvpz2FQX5+VRUVOL1elFUBU+oh8SkRMaMHc0ll13MqNGjgnKekFJaDW6Kivn4w0/44L33WbN6LZ4wDzffehMXXPT3Zt/n9/l55+13eeuNt1m1chXl5eWYpklISAjx8XGMHTeWK6++kn1GjWx1LIbUrU6JsuOK+Brbr+sysEcJPU3T6JfdL6hlm7rHtrM4stFGsq6ujtmzZ/PJJ58wf/58NmzYgMfj4bjjjuPQQw8lJyeHjIyMLs87FkKAVNCEA13696BvuWUaZyU61c7Q72f/pUt5Mj6enP79O2cbXYzlWmOgN0RohRCER4Rzxz9vIyzcw1NPPE3A7+eKFroEd9lYG3Lo3WrX1BRti1NtuWu34tLQwl3o1T702gB6lQ9nTGgHbV1s7Q6rWB71Ox3jDbONoRlRaGFOjLoA3i01SFPSXMamQ3HgUGxJY7N3Yh/5nYhli9h6eoBpmvz048/ccO2NrF61GsMwCAkNITU1lTCPB8M0KS0tZc3qNaxauYpvv/6WJ556ghNPPqHFqfG6ujoWL17Ct19/y/9ee4Mtm7dgNDQJqa6upqy0rNn3VlZU8shDj/L8sy/g9/sJCXGT1TeLkJAQKioqyMvLJzd3AzN/n8VLr/4fBx50QOv7w9QbHE066MbVIOTb6iHv8/koLiqmrr6eQCCA2+UiMiqS6OiY7RwS2kNdXR31dfVEREYElXrSWFBaUlJCdXUN0jQJCQ0lLi6O8PDgvJcNGbByzGXwtpWGYbBp0yaWL1/O119/zddff01NTQ1ZWVmMHj2aBx54gLFjxxIaGoqqdq+dXmP+vIZzj3tw2xWNEflOd4IqKyN9/nzShg+Hbs8l7xgsgezb7hgRQhATG8Md/7ydxMQEIiMj2+Ss1JnIBitfl9K1nUpDHCGoQgN27WijOFRC0iKpXlaEv6wO7+YaHNEhHVQPA64Ea0bX9BsEKrzNLqu41Kb0HKOu5RQah6rhUvesVDEbm2CxxXwnIZFBFe5JKVm6ZCm33Xw7K5avQFEE+x+wH6f/7XSGDRtGZFQkhm6wZfNmfv31N1587v8oKSnlPy+9zAEH7d9id9Bvvv6W226+nU0bNyGlJCEhgZwB/fnt199bHJOu63z80Se8+vJ/8fv9JCUncdMtNzJh4gTCwjwUF5fwzVff8PST/yYvL4/777mfgQMHkJyS3OJ6TUxMaVqtb3bzpmBlyQdfHCmlpKKikq+/+prff/mdNWvWUlNTjc/nJzQ0lPj4OEaNHsXUM6eS3RAVD2aMjRaNfn+AX376mU8++Ywtm7cwaNBAbrvz1mbTkBpnTH784Sc++uBj1q1dR3l5OVKahIWF0yetDwcfchCnTT0VT5inxbFIJIYZQFF2Lci2tZGsq6tj5syZTJ8+nVmzZrFkyRL69OnDcccdx+TJkxk9ejSpqak9zm98q6Df82Zitkc02Y92qriTEioqYPlyxNSpnbedLkTKrU5WO+45IQQul4tLL78UKWWPqg+wOoHraF2YbhPtCsetOanx1+70WmNH17DsWKqXF6FX+aheUUTYgNg2p9rsEkXgTg4HRWB6A3gLGyLuO/rKSwhU+pABKyCmhbWcXupQNEIdLacm2tjsqdhivhMIVsgDVJRX8Pij/2LJ4iVomsZpp5/KI48/TExMzHbRowEDc5gwaQJjxozhrTff5uZbb2zVfaJv3yzCw8M5+ZSTOPb4Y5kwcTy5ubkceuDhLY+pooL//N/LVFVVER8fx2NPPMIJJ53QlGKR3T+bESOHoxsGT//raebNm8+nH3/KJf+4pNWIly51nB3hbCPBCLLYKeAPMGf2HG695XaWLl5Kfb1li+ZyudA0Db/fj9/vZ/p33/PG/97krrvv5MSTTyAkpOVIlOXwUsjsWX/w8v+9wty5f1JbW4tpmixfupzrbrx2l2JeSklVZRVPPPYvXnn5VcrLynG73URFR6EIhby8fObOmcvXX33NN19/w/0P3kf/nP4tj2UXdQlSSgKBACUlJaxevZpvvvmGadOmUVdXR3h4OPvttx933303w4cPJyIiAqeze4rxgqVR0DuEskfavCooXds9efVqqKyE0aM7f1udjPVwb7Sa1rhtmphpmgQCAQKBAAKB0+XslELuYNDNQNMDdGem3Oi6Tm1tLVTpKMUBqjcU4d1SQ11uOUIR9Dl9uJUvrwgihiZQNnsTvsIaSn7NJWJIImE5cS02c5JmQ6dqIawggtnQBEoAytYgjjslAleCB9+WGqqXFRE7Ph0t0rXdvpemSfWyIgLVPgDCsltuXBXmDMWt7RkzTDY2bcUW852BlEjZutCQUrJ06TK++eobTNNk7L5juP3O24iNjd3lDcXpdHLYEYdy8KEHBeXfPXTYUL7+7ktiYrc+GBQVFbU6ro0bNrJ40WIAxo0fxxFH7uzRHBISwkknn8DHH35E7voN/Pbr75z+t9OJjo5u+TNjItk9f+XGh6VgHIJM0+Tnn3/m2iuvY+3adQAMGz6MQw47mMzMTEJDQ6isrOKvP/9i+nffs3HDRm6+4RZUVeW0009tdh9XVFTw8Yef8N477/HHrNn4/X6cTmfTw0FrkeP33/uAZ55+Fq/Py9ChQzjv/HMZOWofNE1j/br1vPfOe/zw/Y988fmXhISE8J9XX8LpbDkyZUgdVaqAoKysjLlz5/Ljjz8ye/Zs5s+fT3Z2Noceeij7778/48aNIykpqUeL910hhOWK5FCcGDLQpuLynowqVFThQBFdmP4xcyYMGACxsVbuQ29Ggm7qDdeWlqmtrWX2rNksXLCI/Lx8qqurURSFmJgYsvtnM3m/SWT3zw763PD7/Uz77AuWL1ve6rL7HbAfU/abvNO6TSyjBA2tQ+0qdV0nPz+fTZs2sWrVKtauXUtubi5r161lycpl1NXW4YwJwRXvITQ9qimlRQhBSFoUsZPSKfh0OUZdgI1vLSD5mIFEjUy2OrluK7wNy32mZk0ZsRPTUd0aps+gfPYmvFtq0MKdxO2XhRZmBQxcCR7CB8bjK6yhZnUpJb/lknBYNuo2nWArFxdS/PM6pN9Ai3AROSJ5O0ecbVGFQnpEcq+7ntnYdBS2mO9wrPSPYK0ov5j2JVVVVTidTo474TgyMjNavCCpqhp0CoSmacTFxwW17LasW7sOwzBQFIWhw4biCdvZt1cIQXpGOjkDBpC7fgNLlyyloqKyVTEPjcWwDtp912oocguGLVsKuf3WO1m7dh1ut5uzzv0bV11zFRkZ6U0PRFJKqqurmTt7LrfdcjvR0TEMHDSgxe/B5/Px1RdfsXjREvr2y2L/A/fn6GOO5sXnX+SrL75ucUx+v5///N/LeL1e4uJiefrZp9h33L5ND0z7jhvLlP2ncNbpZ/HHrNl89eXXrFi+guEjhjezO6wIfHVVNevW5DLts2lMnz6dyspKVFVl4sSJ3HrrrQwePJiEhARcLlevv+kpQkHgRAgDw+y9aTeNha6KULvWc9w0YcYMGDoUoqK6cMOdQzCzoYFAgN9+/o1n//088+fNp6K8gkBg+9m90NBQUvukcubZZ3LBxecH1TehprqGd996l6+//KbVcSYkJDBlv8m7fE03/Whq22/JhmHg9Xrx+/3U1tayceNGFi9ezKJFi1iwYAHl5eUYhoHP50NVVQYMGMCBBxxI/IF9WeMoRA3RUF0ailtDaFsfJhVNIW7/LOrzq6j4qwDflho2vb2Qkl/XEz4w3iqIVQT+snpq15RSX1CF6TNwxYcSOSwJ06dTPq+A6mVFuBLDiB7bpylVRnVpxB+QRfWyInxFtRR+u4qa1SV4+seihTioza2gekUReqUP4VCJm5yJJ6v578KtudgnaXCb952NzZ6CLeY7GCm3z1FueVnJjN9mABAWFsbBhxzc5lxlwzBYvGgJ69etw+V2M3rMaBITm8+jD3ZcYAn2lvK1o6OjSU1JAWDjxk1UV1UF5UcsMZGyfcFAKRtj+60/LBmGwYvPv8jSxUsBOPGk43ngofsJDw/fboxCCCIiIjjokIN46903iYiIID4hvsV1JyQkcM3113DjLTcwfMTwJqvGt998p9Vx5eXlkZ+fD8CgwYOYMHHCTuNJSUnm6GOP5o9Zs/F6vSyYv3AnMS+lpKS4hLlz/2Tu7Ll8+813rFm9huzsbMaPH89+++3H4Ycf3vSA1dsF/E4IUKWKoijWTI1s2W6vZyEaovFa5zrWNMeWLbBuHRx/PARhL9vTaa2Owuv18t4773P7TbdTUVFpNZbKSGPw0CHExsQ0NUVbsngpq1et5r5/3kfepjzuf/hewsPDW9x2XV0d1VXVAGT1zWLIsCGozaQbjhq9T7PrMbGCFJpo/rYspcTv97Nlyxby8/PJz89n/fr1LFu2jHXr1rF8+XK8Xi99+vQhNTWV7OxssrOzGThwIAMHDiQrKwuPxwrOfLH6J+7//YUW7RzVEAdppw/HnRxO6e8b8JfUUbOqlJpVpTsvrAhCksObctwRll+94lQt3/odDnF3SgTp54wi/+Ml1OVWUL28mOrlxdsto0W4iJucQeJROQhn8/dGp+pgYFzfZl+3sdnTscV8JxBsVL6qqoq8TXkAhHpCye4fnB3htgQCAd556x3+++prJMQn8MwL/95tMZ+cYjXmME2TtWusKP2uHjJUVbXyvBUFr9dLVWXz7cG3RUp2q5V5sPUIeZvy+PnHnzFNk4iIcK6+7poW/aWFEGT3zw5q3UIIJk+ZFNSyLdLw8LejmBNCbN3nEoxtbriGYbBk8RI++ehTZs2cxZo1a1GE4LAjDuOW225i2JAR9M3q2+3We52NsPzrEIDAgRQquqlj0pNTbwSqUBpEfBflxu+K+fMtB5vBg3t9io2UcrvzY0dM0+SXn37hwXsfoqKiEk3TOO+Cczn7vLPJ6ptJeHg4UkrKy8pZuWIVjz/yBL/+/CtbNm8O6hpVV1dHdU0NAEccdTi33HFLsxauHk9L9o6yKVWusUC+MaK+bt06FixYwKpVq1i0aBGlpaWUl5dTUVGBEIKBAwcycuRIzjnnHDIyMkhMTCQ2NpbY2NhmZ+KGJwzEqTpaFPNCCLQwF4mH9SdiSCJ168qoXl2Kv7gWoz4AqsAR5sLdJxJP32hC06JwJ1kPh2qok+TjBhF/cD8Up4ojwr3TusP6x5J1wRiqlhZRtbQQX0kdpt9AC3MSlh1L5PAkKyLfQhdagWBk4iBCtO5vBmZj013YYr4TCCZfHmiY5rWswWJiYwgNbbuPr5SS+nov1VXVhLhD0PXghG5L9M/JJjMrk/Xr1vPLz7/w048/c+BBB2wn6AP+ALNm/cHsP2Y3fd6q6uqmMVVXVeMP+NE0DUVRUFUVTdVQVAWH5miIrLcvP9gMcv8uW7ac9etzATjgoAPpl932yI3f76ewsAjTNAkNDSEuLm63BVhiYiLhYWGUlZaxbNlyZs6YyYSJE7bbv16vj1kz/wCsqf999hnZ9NrGDZu46vKrMQyDocOGcc311zBu/DjCw8PQNA2H4mywndtbEA16VMGhOK1SSGn0qEi9QKBsI+KhG2dKpIQ5c8DjgWHDumcMHYj1PTd/TaioqODxR/5Ffl4+ISEh/OOqy7jp1hsJDQ3d7jtITEokITGB/jnZfPfNdxx1zFG7TDHckdraOqoqq1AUhaSkRGJiWk/N2ZbGbsulJWWUlZRRVV7N2jVrWbJkCUuWLGHlypXouk5cXBwxMTEkJCQwadIkRo0axbBhw8jKysLpdKKq1iyVEMHN9MSFRjOxzz78sP6PVs8TxaESmhFFaHoUcftlbT/73FDwum2+PVhpOqHpUS2uVygCV3wYcft7iJ2SaR2b27y2bdFsczhUjcP6TUZTepYDl41NV7I33fG7CBm0gKirq8c0rWU97RDyYOXFH37kYcTGRuMJC6Nv36x2rWdboqOjOefcs3nkoUfJXZ/LrTfdxiWXXcy48fuiNhRo/vbLb0z7bBrr1q1vep9saA+u6zrP/Ps5/pwzF4fTiaoqaJqGpmmoqorT6cShOXG73NbPDgdutxuHw4HL5Wr6XaPbjNttLed0OnE4NVDB7XKhOR04HU5cLmt5p8uJx+MhLCwM0zTZuGEj5WXlAIwcOaJdbdw3btzEDdfcQHV1NZP3m8zd9/5zt/evx+Ph6GOO4vnnXqSsrIybrr+Fq6+9iuNOOBa3242Uku+/m87MGTNRFIWTTz2JwUO25oOmZ6Rx/4P3kdonlbT0tJ1s9kxp7GVi3mJrpF5FoCCFiiktO9TuitYrQkVBQRFqU/Fut1NdDcuXQ58+kNyynWxPx0q7M1u85i5ZtIS/5v4FwOgxo7j40ouaUk12RAhBckoy5/793KDHUF9XR011DU6Xk8joqDYJ+SWLlvDZJ5+zft16Cgo2U5CXT1FRMaGhoeTk5DBkyBBOOOEE+vTpQ1paGqmpqSQnJ3dI87YQh4t9U0bw68Y/8RutO4MJIba60uz21rddcUNhezPFrdumfe6KftFpZEen73lphDY2bWDvu+P3IJyurVaAPt+um3e0hqZpHHHk4Rx22KFWDnEH+IM7HA4uuvRCCgoKeOvNt1myeAk3Xn8TIW7LqrHRyrFvv74MGz6MpUuWYpom4RFWbqkQgshIK++8vq4efyBAdXUNPp+XQECnrrYWpFV42nihNk2z6d/b/m7b17b+bGI2LiclprTszySSS/9xCdffeJ3V9bawCMMwcDgcpKWntWvf1NbUMnv2HCrKK4iPbzmPvi1cde1V5OZuYPp33zN/3nwuv/QKnn7yaU48+USSkhJ54N6H8Pl8nH/B+dx2563bCXZVVZmy/5Rm191TotHdiSWcLVGvigbrQmlYwk+2LP5aovFdzU/4N0bhVUvAs320skeQnw9r1sBpp0EP6yfQHlqbqfty2lf4/dYs4SlTTyElNaVN65dSYpompmE2XWMb3cGklNTW1lFdXY3L7SImCAOAbdm0KY/vvp1OiNtNzoAcTp16CqP3GUPfTCtNrjG40Rhx70gUoTC+zwiGxGczf0vrTjxdjpQEqv14N1cTlh0D6s6f36FoTOwzivTItn2nNjZ7GraY7wRavuFvJTw8HKWh42hlZQW6buBwtO0racyt7sgmP0IIYmNjeeiRBxkwcABfTPuSFcuWU15egdvtZuCgAUyaPInTz5zKf199vckjP7whH13TNK646vIWt6GYKoZu+Tzruo7P58MwDPx+f5P3c+Nr2/7O66/HH/Dh8/nRdb3h93503SAQCDB6zCjAEv3VDWk/TqezVc/45ggJDWH4iOFUV1WT1W/3Zz1gqxPQfQ/ex6a8PBYvXExtbS3z5y1g/rwFTQ47Bx18EHfcdRtJyUlt3ILcbfvPPYXG77wxzQWsNDgT2STqJY0PkGZQEn/rGkVTtN36WUGxwow9d99LCQUFkJsLk3ftqtKbkK0Uwxu6wYL5CwErSHHQIQe2+TpgmiZfTvuKmb/PxOPxcMrUUxg0eGDT61VVVXi9XiIiIoiKjsLr9bJlcyE1NTVERISTkNi8g9SU/SYzZt/RxMTENF3DNeHAqXSN41RKeAL7Z+zL0uI1QUXnu5rKRZvZ8sVKsq+eaDWa2oHY0CiO7X+gnWJjs9dji/lOQEBQoiAmJpro6GhKikuoqqomPy+PzKzMTh5d8HjCPFxy2cWcctrJVFZW4W+wNouIiCA2Lha/P0BRYRFSSlJSkonYwSWmJVRVxelwERISfMc+iUQ3A0F21t1+era9N8bMzAz++/ormKbZprG2PDbJ+nXrefyRx8ldl0t0TDSHHn4oG3M3sGrlaqqqqtB1nT/n/snll13JbXfcyj6jRgbdgl4GfQTunQihoAJSKNs8eW8bq5fb/BcarZcaRXzTerb9b0+KvLeEacKSJRASAv37d/doOoSWIvNV1VWUlJQAEBcX144HY0vM//bLb7zw7IvExsUyZuzoJjEvpaS4yHJg8fq8vPbK69x/94OUlBQTCOg4HA7i4mI5/W+nc/KpJxEdE73deRwWHkZY+PZuQsHWBHUEAsEx2Qfy5epfWF2W22XbDQohCOkTiVAF1SuLcSWFbXeeORSNEwccQmpE279TG5s9DVvMdzg73vKbR9M0Ro8exepVq61GJn/M7lFiHqwxJiYmkpiYuNNrG3I3sH6d1Yipf07/pjSboOhk7aOqSlPxmt/vx+vzBmWbuSNOp7PN0/KtUVpSypWXX82P3/9IYlIiTz79BEcfezRIWLxoMZ9/No0fvv+RRQsX8dUXX7F65Spe/d8rjBk7Jsgt2Ik2wdCYY7/NvxqQTU0rm34QO7yvt2IY8PvvVtfXZvLGexONMyvNUVNd0+QlHxsf264ZTCGENbsXGkJISAiqtnUd0pSUl1t1OVWVVXzx+ZdERUcRHm7V7WzYsIF1a9fx15/z+OLzL3jkiYcZOGhgi9ehru5sHOUO5+JRp/HA7y9S4Q3OkayrCOkTiSM6hJpVpcSMT0N1b003HBLfn+NyDu7tZkw2Nh2CLeY7AYES9AX50MMP5f33PqC+rp5vv/mOI446goiIiB4f6TNNk0ULF7Nq5WpUVWX02NFEBd18pvPTEFRVbWr4EggEKC4sxjTNDk1Hag9SSt5/7/2m/gIXXXIRxx5/bFNB2+ixoxmxzwjOOe9s/vX4k/z3lddYs2Ytzz79HM/937PNFu7tSM8+eno6YluNv2cRCMCff8LZZ0M7CsJ7G4FAoKkwv7UOys2hKArnXXAehxx+CE6HgwGDtqbYKKrC8SceT011LT6fl0lTJpGSkkxISAj+QIClS5bx8v+9zML5C/nph5+55857+e8brxIS2jGzfB2BEIKxycM4IGNfpq36EaMLZwZaQ9EUIgYnUDYnD39pPe4UDSEE8aExnDn0GOJCo3v3w7WNTQdhi/lOQBFK0BfEyftNZvSY0cydM5cvp33JxEkTOO/v57XoVrBt+oiUEl3XMQzD6ibp0LpEsObn5fPkE09SX19PWloaRx55xE6uKs2hWBnMbd5mWy7aQgiy+mYRFRVFeXk5ixYuwufztcv+syOpqKjgpx9/wefzkZCQwEmnnLjT96VpGv2y+3HHP+9gxm8zWLlyFYsWLWb9+lyGDh3S6jaCnxuy2etYsQJqa2H4cOgAR5SeTkhICGpDV1Wr8L7t61AUhf452fTP2bkHhRCCQYMHcs8DlsuVqm71Q5dSMmr0Phx6+MH8/ewL+f3X3/nph5/5/LNpTD3jtGa31x2zamHOUP4x+kxyK/JZWLiiR83tRY5IZsvXq6jPr8KdEo5D0Th54GEckDGuqQ7GxmZvxz4TOhpB0EJVCEFychIXXHg+kZERVFVV88B9D/Haq69TWVm5nbtLo6PCurXr+PdTz1BaWgY0No16l0svuoybrr+Z5Uu3uhLs+P5tnWKApsK/Xb4mJZs3b7YiW9uOwTBZtnQZt99yBwsXLALguOOPZdSYUW2YTRCdrjWFEPTt17cpR/bXX36loqKiczcaBJYndSUAcfFxhLZQmBsW5mHAoAEA1NfXUdNQ0Ns6dqyqt7GrczHYv21izhyIj4esrF7fLCoYPGEetAZTgeKiEnRj9/tw7IgQosl6d8dOzoqikJyczBVX/wNVVamrq2PWjFkE/C0Vm3a9kBZCEBMSyWWjzyAjMqVHXT8cUW7cyeFUryhGkYL9M8YydchRzXbZtbHZG9nzQzNdTKNrhpAiqOiGw+Fg6plTWbt2HS889wJbNm/hputv5qUX/8PBhxxE/5xsnE4n+fkFzPtzHrNm/UFVZRXh4eH8/cLzMQyDP+f+xXvvvE9CQgLHHH8MQ4cPBaxOodM++4Jvvv6maXulJaVNr3368WesXbO26bVzzz+HiZMmNhVo/uOSy6mrq2Pc+HGkpKbg9/tZvGgJM377neLiElRV5cCDD+T6m65r0xS2qqjtlpsKKgbB3ZAHDBjApMkTWbF8Bfn5BfznxZe55fabu7UzqtvtapodqCivoK6urtlcfj2gU1JsfV8ul7uNswo96XZs0xyNYnyrq47ZZJ9p/X7rN9n0swDLN0dBNFxrgrLANAyYORNSUqBv2xuo9UbCw8NJT09jxbIVVFZWsmb1WkaMHN7l48jun01CYgKbCzazZfMWqmuqiYmJ2eWy3XXmCiHYJ2kQV+57Nk/Pfp2NVZu7aSTbo7g0IgYlUD47j8mJ+3DtuPMId/b+eg8bm47EFvOdgBCiIdUmuEY1brebm2+7mcSkRF59+b8sW7qMxYsWs3jR4l2uO30bz3QhBC63i7CwMCsKtU3KhmmazJs3j/+99sZO6zFNk/nz5jN/3vym302aPKlJzM+ft4DFi5dQVlrG3Dl/7vT+mJgYzjrnb1x6+aVtcoiwEmzanwakCAEyOLcWh0Pj7xecz+efTaOosIiXX3qF1D6pnHnWmYSEuHcpfGqqaygrKyM9Ix2Aysoq/pj1B4FAgKSkJEa3aQZiZ6KiohgwcADTv/uegoICPv34U2669aZdpkbNmTOXBfMXAJCekU56enpQ27Cnnns+jc2OTGkipdFgirnrY1ru+LMEAwMwGn4hGhpTKSioTZaZO1FYCGvXwpgxEHR9S8+mtTNRCMGYsWP47pvpBAIBvv36W4aPGNalNUmNEXqtoXDW6pHR4ju6ZFy7QlVU9k8fi9/w8+zctyioLur2lBtFVfD0jaHqj3wmiEEkhXVcvw8bmz0FW8x3Ao1NY4IV8wAeTygXXXIhRx9zFDN+n8kvP//CqpWrKCkpQVFUEhLiGThoIPvtvx8jR40ko0HYOZ1OrrvhGs7/+7lomrad84qiKIweM5rzguhmKIRoygkVQnDwoQfx/kfv8uW0r1iyZCnlZWWEhISQlJzMxEkTGD9hPP1zsoMuyGwak9jdfH6BggjSERyGjRjG9Tdex/33PkBJSQl33HonP3z/I+eedw6DhgzC7XJhSsnmggJ+/flXpn3+JTkD+vPwow8RGRVJ3qZNXHvVdVRWVHLk0Ufwn1dfalq3russW7KMgL51yrys1Iqk+31+Fi1YRGRUJABh4eFkZmbicjn529l/4+MPP2bTpjye/fdzCEVh6hmnER1lFex6fT5mzpjJPXfdS11dHR6Ph7+ddWbTulpjdx6WbDqPpsZn0sCQOjLoo7jVNWNiYEoDpI4iBBqOpnS/JuG6Zg0UF8OECXtQio01K9GS4DzqmCN58vGnqK+v54vPvuT4E48jZ0BOhwj6xtRDKSWqpu5ynVJKigqLKSq0LCzj4+MIC2v+utndD+NCCA7OnECI5ubFv95lZen6bhX0bs3JuAkH8tvMWv74aQanHnNS0Da9NjZ7C0K2OenSJhiklASkr82ewcF+HcHciNrz1W673tbe39aboUDgUFzNRw6DQEqJIQ10GVzHXCklXq+Xt954m3899iTr169vei0kJITIyAh8fj8VFRVI0/q8I0YO57U3/svAQQNZuGARRxx6JBXlFRx/wnG888HbTWMvLytn5NBRFBUVtTqOKftN5pXXXiYtPQ0pJZ9+/Bl33XEXa9esQ0pJfEI8AwYOsFKq8vJZu2Ytuq6TkJjAJZdezI233BBUgbFANDScsW92PQVJY6diA13qXSaMFBRURWuI1gOvvAI33wx//QWZmV0yhs5GShOf6Wux90RNTQ1XXnYVH773EUIRnDr1VB5+/MFmOzpLKamrq8flcqJpGlJKKsorqKquRlUUYmNjm9xoDMPg048+o7y8nDPPOoNQz86pcNVV1Vxx6ZV89MHHeDyhPPviM5wy9ZRmr4GacOJUnF06e7ArpJTkVubz1OzXmVuwCF83NJVK8MRyXM5BnD30eG64+joWL17M+++/T2pqapePxcamJ2NH5jsRVWiYQYrORjryAr676+rom4kqWkgBCJbGfOEgaxKEEISEhHDOeWczdOgQPvrwY7795js2bdyEaZqUlZUjBLhdbmJjYzn4kIM4Zeop9ElLA6wo2sWXXERdfT3Dhg3dad0utwt3SOsWf07n1puzEIKjjz2K1D4pvPbf/zHt02lUVVYxe9Zs63VF4PF4GD9xPJddfimTJ09q0d1oW6yZjz0l6tq7sUS8FTfXzYCVC0/XfTsmJqbpRxUqqt9ALFmCSE2FXfSM6L20bnPr8Xi4+NKLWDBvIatXrebjDz6mvr6OK66+ghEjhqM5HAhhecbX1NQw/bvvmfbpNG669UaGjxyOYRj877U3+PC9j4iKiuTWO29h4uSJAPzy06/cetNt1NTUsGD+Ai6+9CKy+2ejaipSSgryC3ju38/z5RdfATB23L4cduRhLV4Duzsy34gQgszIVG6bdAmfr/qRd5d9RZW3usPmk1rCqTroG5XGxaOmMj51BE7VwaGHHsrXX3/NsmXLSElJ6faHHRubnoQdme9EpJToMriOpXs6CooVle+gqW1DBtDbuF+llBiGgd/vJz8vn5KSEqqrqgn1hJKUnExKSjIOh2M7V4rG98DW3NfG10zTpCC/oOn1lnC53cTFxW4nyhttRaurq1m9ajVbNm/BMAxi42Lpl92PhIQEHA5H0PvMmvlwWkWRtqDvdqQ00aWOKfXtMuK745sRZRU4jzsZMWoUPP30HmNL2dgV2m/6WlzONE1m/DaD66++gRXLVyKlxOF00Dcri6zsvrhdLoqKili5YhUV5RXous5V113FAw/fh67r3HbT7U0dYF/8z/McecyRAKxYvoLLL7mSeX/OQ9d1XC4XGZnpJCcn4/P7WbZkGdXV1QhFsO+4ffn3808zcNCAFs9ptxqCItpvEtDRSCkxpcnK0vW8ueRz5hYspsJbhdkJ0kERCv2i0jg4awKnDTmSCOfWrq+lpaUMHz6cSy+9lFtuuSVoK2Qbm70BW8x3MlKa+E1ftxcRdTcOxWVF1DsommJKg4Dp3+v367aoQkNTHD1GBOytSGk50+gyYOWx9wQ2bMQ1ahzmU0+inHMeiN2cIetBGNLAZ9S3ei0wTZOF8xfy1pvv8MG7H1BaWrpTzbEQgojICCZPmcR5F5zHEUcdjmEYPPPUs7z/zvtERUVxx923M2nKJMD6rtesXsN773zAm6+/SX5e/k7piTGxMZxy2ilcfOmF5AzMaTHfWxEqLsXdY6LzO1Ljr+OvzUv5Zu2vzMybT33A16basOZwqg7iQ2M4Mns/Ds4cT9/odDRl+9ofv9/POeecQ2lpKR999BERERG7vV0bmz0FW8x3Mo2uFfpeLDw14UAVWoeKB4nEMHV02fV5nJ1JY75uIOAnPDw86AZgjfUIPVUE7C1YQn5rWk1PQfn4vREf/AABAABJREFUMxzXXI///bcQ4yc2PfTtCYJeSonPqG9w+AliWZ+PkuIS5s9bwMoVKyktKUUIQWxcLAMHDWDYiGFER0fj8Xia9k99XT1enxdFKIR6Qrez4pVSEggEKC0pY9HCRSxZvISSkhI8Hg8DBg5g7L5jSExKxO3etYPWtmjC0VDz0nO/Fykl9bqP4tpSvl33O7PzF1JaX0FRbRl+I4AQNBu1t44566cwZyiJnliSwxI4MnsKo5OHEuEMQ1N2XUhsmiZvvvkmV155JXPmzGHAgAGd+0FtbHoRtpjvAtpatLknoQoNTQSfKtIWpJQETF+PEk27i5SSN//3FsuWLePGm29o1ot6Rzrjgcmm7fTUGSPt+psRf80j8PrLkJGBgoKmOPeYh7+A6W811abnI3CrblTRu1KgvLqPdeWbWFG6jsKaUkrry9lSU0KFr5r6gBfdNHBpTjyOEOJCo0n0xBITEkV6ZAqD4/qRFBYX9HE4b948Tj/9dK6++mouv/zyTv5kNja9h9511eilCCFQUQHHHhdJbglFqJ0m5BvRFAcB6W+Xc09PpaysjG+++pZLLrskKDGvCtUW8t1MY0S+Jwp5/H6UOX8ic7IhwSp+NRtmDxyKw0q56eWpWarQEPTAfd8GFLF7PTi6C7fmYnB8NoPjszFMA6/ux2f4CBg6ujSQUqIIBU1RcapO3JoTl9o+t56srCyGDh3Kl19+ycUXX2znzdvYNGCL+S7CEvQaIBoEfe+96QRDY0S+MxFCgFTQhBNd+vaYPZqT05/NBQXU1TbfHbYRBRVNOPcc2/BeikQSMAM9UkyKNWuhrAxz5Ahwb+1+bGIQkODA2esNkAQCTVgP9r0Vhwi+i3ZPRVVUPM4QPIR0yvqjoqKYPHkyL7300v+zd9+BbVXn/8c/514Nr+y9QzYJ2SQECHtDofPXDYW2lE1pWS0tlNECpWxKyyid3w7asnchzJAQZiCLbDLIIjvxknTv+f1xJceB2JYc29K136/WxLGlqyNHlj469znP0YIFCzRmTMvv5gsUotZxjjUkjDFyjatoM4fcfKtdWtPcs8XGBLtfuq3oZ9qzVy8lkylt/OSTei8XlEpk7nfI01iIBWV0wSZQhch8vEaKxWTHjfnMZlE1G1iF/MxW8NwaCe0ZBldu0MGGd+X1MsbosMMOU0VFhd56663QP26BpkKYb2HGBLvDxtLdXVqTTGvE5p6R38MNpzu5hH9myxijdu3K1LNXTy1evKTOy2VqnlvLIsawygT5Qm4/6x95uBLTnpV/wOQ9fj9lC2uxbmMFpRzhfFNPF6rsjR07Vr169dL06dO1c+fOfA8HKAitK02GRCbQR51Y6BY71SXoIx8Ldpts4bZ3mUDrym35NxLNoKSkRD179tSypcv2+H2CfOGwsgUd5CVJrit17iTF6n6z6/kpWesXZJlQtowxipqoTMhe1iImwpqXHLiuq+OOO05vvfWWNm3axOw8IGrm8yyo83Tkygvp7JhRcHo786Ykny9IxkiuIjLGUcpPFmzZQ0NKSkvUp09vLVq06DPfq70WgRf//MpsCpdVALZWSiSkTZtlysulZHohfDQq27691KmjFI1+pgwmxwFJO8ulzenbsDY4frt2UtcuwUZRdRzflyfPusFC/VA/rIxiblwJryoUb0x2bfQW6h96izv++ON1xx13aN68eRowYEC+hwPkHWE+jzJP4K5x5VgjL1O/GoIXIUk1Ib5wZohNOtC7Mo4prE17clBSUqKevXtp2vMv1CyArf2mqTB+1vDlZ/f4SqXkvPiSnP9Nk/POezKrV0s7dkjGyLZrJztoH9mxY+R98fOykyYGM+m5SqXkvPSK3H/9R2b2+0GdvO/JlrWTBg2Ud+hUed/+prTPwDoDvWeTck34uqnUZoyRa4OznoXeqtLIKOYUhe5MQr4ZY9S/f3/tt99+evrpp3XiiSfme0hA3hHmC4QxjlwFC2QLOdQbBSVCrnFlmnBH16ZmjFFUMXnGk1egXUbqEo1G1bt3L+3YsVObN21W9249FHEiBf3zbmuCLe4bCPLWSmvXKfLrm+X+5yHpk42S40glxVIkKsnKbPhE5qMV0osvyf3PQ/LOOE2p884JZuqz+be2VtqxQ+7d9yry+/uktWuloiIpHpccI7Npk7RqlSKvz5D7/DQlb/yV7EFT9njsTMlQmBeSSsHvfkRRWeMrWcCtgIMySxa9Nka3bt00ZcoUPf300yovL1dZWVm+hwTkFWG+gBhjZO2uGW/fevLlyS+AWlZHTk2Iz5yHL+QXISMTLIy1rhzHkW9TShV6bXOaMUZ9+vSW67r6eNVa9enRr+bryD+b/l+DYf6TjYqef5Gcp56RrJUdPkz+KZ+Tf8Ak2R49JOvLrFkr5+VX5Tz7P5mPVsj95Y3S+g1K/fp6qbQki8FYub+/T5HrrpeM5H/xFHknHC87ZLBUXCSzdJncRx+X8+TTMjNnKfrDHyvxzONSt257PJxvPbk2EvJSm0DUicv6KsC9PYxi6UYB/E43TiwW00EHHaSHH35YM2bM0LHHHpvvIQF5RZgvMLWf3IPyG1fW+PKtL896LVwHbhQxQQ26U1NKE64Xn0yJilFUjnGVsqmCL70xMurTu5+ikag+XvWxzKRw/cxbPRuE3nrfYPu+Ivf9IQjyvi//yMOVuvYXshPHB7XrmUNZK/+kE+R87f8pct4PZebNl/u3f8iOGyvvjNMaLrnZtEnu3/8leZ68H3xPqV/8TOrSpWbm3U6cIP+oIxT1fTkPPSozb4Gcl16V/9Uv13HXfPnygoXsIftdry3zPBp1YlIm0Fvl/enLyChCkN9rxhhNnjxZJSUlevXVV3X00UfLcShXQtvFo7+gmXRnGEeuiSjmxBV3ihU1welZJ13q0kS3JJOeeY+YqGJOkeJOsLW4YxwZ4yjvr4R7IQj1jqImplj6fuW7VtWq9tZhQb/8zM9+YP+BikQiWr58ef4GiDoFb6zrZt7/QM4f/yp5nuzgQUree7fs5P13C/LBBY1UVCT/4AOVvOe3sn37SOXlcu/7Q7DZU0OdOrp0UfL//qTUVT9T6qeX7hbka47ftau8b3wtPXBPzpy5dR7OKniDEaKqtHo5xlHMiQcbMuU5PAeLXeNBxx2C/F7r27evJk6cqFmzZmndunX5Hg6QV4T5EDA1oT7dgtGJpF8UYoo6sZrPIyaqiImmA3gQ9oM5dSf9P7emVCbTFSU4RlxRJ65Y+vOIE00HeFMzs90aZO5Pph910BM/ltct1B0ZuSZa8+8YcYIX+p49eyoWi2n16tXy/XB25Wm9dn8b9hmeJ+eFF2XWrZOMkfe906V+fesPk8bIjhsj/8tflBxHZu58ObPebDjMO47smNHyrrhM6tUrq8BqG5jBLPQzV7kyJugYE3fieXsuc+Qo7halz3S2jufTfDPG6MQTT9TcuXP10Ucf0aISbRphPsQywdQ1rlwnEnzUDunpcB5Lh/WoE0uH/lhN6A+u49aE97YiWMgb/OyCF/qioE9+ZmFvM73oB+cHXLkmqrgTV8wpSveZ3n0hXHFxsfr27as1a9aovLy8WcaCxsmsYanzEbJ9u5x33pVJJKR+feUfdkiw8LUhRUXyjztG6tJZJpGQ89KrkucFgX7bdpklS2WWLZe2bfvsdY2pO8hXV8v53wvB57GY/Cl73jyq5v6FtKVrfTI7xBa5xUGgbqFQH8zGRxV3i9nhtRlMnTpVxhjNnDmTSQ+0aYT5VuTTM/jZfLR1u34Wu0qZYjVvfHY/05FLWY7JnA+pfRYkcxbFjSnqRGte3Pf072CM0ZAhQ/TJJ59o257CG/KmocXoZtt2mcVLg8sO6C/17JldiYcxsvuOkO3QIfjre7MlLwgoztPPKHbylxT90lflPPVslgO10vbtcv/4F7kPPSqjYDdYe+jUhq/aWupsatm1+3ZRzZvoZrut9HqjuFusqBMPJktayRnOQtKhQwdNnTpVzzzzjFKpcDQ4AJoDC2CBTwlKi1zJuLufujV7ijg2va5uD4G89tfMni9Tn6FDh2ratGnaunWr+vbtm9N10TwynWzqVVUVtISUZLt0CTZtyvb4PXsErSuldK/4IMybrdtkFi+Riopk6ntzt2WrnJdellm3XmbVapnpr8uZt0CyVt5XvqjU1VdKJQ13ybHWl0zrnOvJ7BbtGlcReUr6yYYXNGd77PR+EFEnvRNtI37vkb2SkhIdeuihuuaaa7RmzRrts88++R4SkBeEeaAeu8+a7/6y/OkY39Qv2sOHD9f69eu1devWJj0u9oLNYtY6lZIqKoLPi4ukWCz740ciUmlpcAtVVVJVpVRcJNu/n7zjjpFiMdn+/eq8ulm7VpFrr5eZO2/X1yR5Jxyr1M9+Kjt0SHZ19a1wZr62zO+1Y13FHbemx75v/XQZVXbtgDNn4DJlPJmdsAnxLcNxHE2cOFGdO3fWc889p7PPPjvfQwLygjAPNFJzv1gPHDhQ27dv15YtW2p2gkUY1KpftzX/yV7m4kY1x/GPOFz+/hODv7ere4Mc26GD/BOOk0aPkqmokFmwUPpohZxpLyv6/bOV+skl8k/5XIMtL1t7mM/I/E4Fa2histbWnH3JfK5PnY0xtf4nU+tv/H62OGOMRo4cqQEDBuill17SGWecoXg8nu9hAS2OMA8UqHbt2qlHjx5atmwZYb6QNJRzo8HsujZtliorpepE8PdsJJNSebmMJFtcEuzmakxQepMuv6lX715KXfeLoF7eWqk6IefFlxS56hqZd95V9JwLlOjTW3bypL28k61T7e5d1jTQopPZ94LQvn17HXHEEfrnP/+phQsXasyYMfkeEtDiWmdRJNAKRKNR9evXT8uWLaNTQyFpIL/ZoiLZLl2Ci27aJO3Ykf2h166rKdGxfXrnXrduTFCqE40G5T3tyuSf8rmgVt5xpE2b5T7w56AUqP4D5Xa7rVCDDQX4GRUEY4yOO+44rVmzRvPnz6dFJdokwjxQoGKxmAYOHKglS5YQ5sOkQwfZYUMkSWb5Cpm1axvuFy9J1srMm1+zwNXff6LkNsFTtDHyDzpQdtAgGWuDBbHrN9R/FYIqQmTEiBHq37+/XnnlFVVVVeV7OECLI8wDBSoWi2nAgAFaunQpYb5QZJNx25XJHzdONhaTWbNGzvSZNV1p6lVdLefFl6XNW2RjMfmHHtxgbXvWohGpY9DyUlVVMhWV9V+eLI8QicViOu644/Tiiy+yLwfaJMI8UKAyZTbr16/nBapAmPSWYvVyXfnHHiXbp7estYrce7/MipX1z85bK/Pue3L/+7Dk+7LjxsoeMLnhzjPWShs+CbrXpFJ138bOcpmPPw4+LymWLau/hj+XPRWAfHNdV4ceeqg++eQTzZ07N9/DAVocz9hAAevRo4eKi4u1fPnyfA8FaSaLOnY7ej95p30rqFP/aIUiF10SBO49zdCnUjJvvaPoRZdKqz+WSkqU+v4ZsoP2CcK8tTILF8v52z/k/ONBmUWLd11340ZFL75csVO+Iue/DwcLaD9t505F7vuDtHadrDHyx4yWunerd/wOLw0IEWOMhg8frhEjRujRRx/N93CAFkc3G6BAGWPUrVs3lZWVafny5dp///3zPSQoM2ttVG+rE8eRd+Z35cycJeeFF+X87wVFV62W982vyz/iUNn+/SXry3y0Qu4TT8t55LEgpDuO/K98Sf5Xv7xbiY155VVFf3SJVFSk1A2/lDdsaPCNneUyb78jrVyp6CU/kff6TPnHHSM7eJAUjcosWiLnoYflPvJYcPluXeWd8Z16y3eYlUcY9enTR6NHj9bMmTO1detWdezYMd9DAloMYR4oYD169FD79u21ePHihi+MFpHpK95gL/YePZT83Z2K3PAbuQ89LDNnriI//0UQpJ10YPb9oDzG96WePeR96xtKXfbjz7SyNJ4nU52QNY7kebu+MaC/kn++X5Err5Ez4w2599wv9w9/+uzxjZEdvZ9SV10hO65W676qaumTT6S+fWpKepxWuvMrWrdYLKajjjpKL7zwgt58800de+yx+R4S0GII80AB69q1q9q1a6fly5fTa76AOMaRZxtY1GqMNHCAUr+5Xv4Rh8l9fprMq6/tXj/vOLLDh8k/+ED5nz8lWPS6h570/oFTlLzxV1IkIv/AA2oNxJGdPEnJP90n9z8Py330cZmFi6TNW4Ig365Mdvhw+UcfIe8rX5Ydte+uWXnfl/PMs3L/+BelrrlSdvw4yRg5xslmk1igoBhjdMghhyiZTOqtt97SUUcdJbepFpADBc5YmrICBcv3fX3jG9/Q9u3b9dBDD6mkpCTfQ2rzMjuDJvwcWuD5vlRVJZVXyJSXBxtKGSN17SxbWiqV1Nog6rM3uOtDCi7j7GH2PJWSdu6UqhMy1dVSKiVbUiLFY8EbhGh09+NbK7NosaLnXijt2KnkTddLhx2mqBtndr4N2j0K1P5812Om0CcTfN/X1772NZWXl+svf/mLunWrf20I0FrwjA0UMMdxtM8++2jbtm3atGlTvocD7dol1FEOs36OEwT2bl1lBw6QnThedsK4oHa+SxepuLjuzjWZ8O66u5fofFokInXsKPXoLtu/X7CAtmcPqVOnYAOpTx/fGNnhw5S881apKK7ohT+WO/PNhrv1oFWwsrLWl2dTSvlJpWxSSZtQ0q9Wwk8okf4z6Vcr6SeUskml/KQ8m5K1fsNlZnlgjNHJJ5+st99+W+vWrWMDKbQZhHmgwA0ePFjbt2/XJ598ku+hIMNIrhOE+bDHBbvvCCXvuk3q3l3uRT+SXnopu774CB1r0wHeTynpVStRK6h7NiXfevLlyyoI61Z++n9eEPptUkk/kQ75iV3BvoBC84QJExSPxzVjxox8DwVoMYR5oMANHTpU27Zt04YN9e/aiZaTmZnPaXa+UDmO7Jgx8v9wn9Sxk3TOOdKLL2a3ay1CwVor33pK2aQSfpWSNpEO7Y37N7by5VsvHeyr5dmkfOvlPdQbY9S7d28dcMABeuSRRxo1nuBn5cuznjzfk+engj+tVxD3EdgTwjxQ4PbZZx/t3LlTGzdu5IWkgBhj5DqRVlGUYhwjZ5/B0m23Sf36SRdfHAR63yfUh1hmJj4zo+7ZVJOfSbKyStlU+vjJ9G3m7zHToUMHTZo0SQsXLtSqVavqvFxQZhR8eNZTwqtWZapCVV6lqlLpDy/998zXvKrga6kKJbxqeb5Xcwyem5FPhHmgwHXr1k2lpaVavXq1vNptCZF3jhy5JvxNwSImKmMcmdGjpQcekHr1Cmbon3mGMB9S1lr58tIz56lmr3HPhPqEXy1f+ZvBdl1XU6dOVSQS0bRp0/Y4jqDUKPjZVKR2qjJVXvNz8mwqGH9NqZFNfx6cjfCsV3M/K71yVXg709dl1h75Q5gHCpzjOBo4cKBWrlypVCqV7+GgFmOMXBMJ9UZLronIkRt0KjFG6t9fuv12adAg6dJLpWnTmKEPkcwscWY2vqUXqlr5terp8zNjPXbsWHXp0kUzZsxQVVXQdarm5+InVe0HM+xN8fOx1irpJ1TlVaraq1TKz//ZCbQ94X0FAtoIx3E0dOhQLV++XIlEIt/DwacYYxR1YjIhLLjZ45kFY6Rhw6R77pEGD5bOP1967DEWxYZAZh45mZ5lzqdMd5x8dL0pLS3VMccco3fffVcrVqyoWTNQmS6bSfnN8bMJzkwEpTiVeT07gbaHMA8UONd1NWjQIK1YsULJZDLfw8GnZHaEjZhoqAK9kVHEiaY3ifrUuB1HGjhQuusuaejQoIb+uecI9AUsUwOe9IPFrYUgWHSbkN/QBmtNzBijk046SUuXLtXChQvTM/GV8m1LlClaeTalqlRl8GaGQI8WQJgHCpzjOBo0aJBWrVql6urqfA8He2CMkWPcdKAPA6OIE5NpaHOoAQOkO++Uxo+Xfvxj6eGHg82pUGAyQb5atkCCfIZvfaVsosVbWA4ePFj77LOPXnjxeVUlKvNQbmSV8Kpr3kQQ6tGcCPNACPTs2VOu62r16tX5HgrqsCvQF3bJjZGjqBOTI6fhcRoj7bOP9LvfSfvuK110kfTooxILsQuKb20QmAt01wPf+kralj2rWNquRIcfeZiee/a5mrr5fMjM0geLagvz3wfhR5gHCpwxRh06dFDXrl21dOnSfA8H9agJ9E5hltw4mSC/p9Kauhgjde8u3XKLdOCB0s9+FtTQp1Isii0AQWvFZIuXsuQq6ASTavYZ6swiV7m+Juw/Xtu2bde8ufOb9TYb4stXtVfFDD2aDWEeCIFOnTqpe/fu+vDDD/M9FDTAmGBDqagTl2MKZ1Mp10RqFurm/EYjM0N/zz3ShAnShRdK//wnM/QFwEu3SwyDVDNvLpXpGV/tV8uX1djxY9W7T289/cTTeQ/RvvVrZuiBpkaYB0KgY8eO6tKli5YuXZr3FyU0LJihdxQ1sbwvjM2U1dT0ks92Rv4zBzJS587Sr38tHXywdOWV0iOPMEOfJ1aZnUrDtSg+ZVNSM5SbWBv0g0941bLpsxT9B/TXsOFD9c7b72rz5i1Nfpu5srKq9qqZoUeTI8wDIdChQwf16NFDS5cupdd8iGT60EedeLoffQvedrrDTsyJ7eojv9cHNcEOsXfdJR12WNCH/m9/k+iy1PKsgh1XQ1aHbeUr1cTlNpmWnNVe9W4z39FoVMcef6xWfPSR5s+dXxAB2k/vNgs0JcI8EAKRSER9+/ZVeXm5Nm7cmO/hIAeZWfqIiSpSE+qbL9YbBbdV8wZib2bj93gD6Rr6m2+Wpk4NaugffFBiD4QWE+zu6oemvObTfOs1bdcdq/RGVUl9etb/0MMOUddu3VReXt50t7eXUjalpM/vC5oOYR4IiYEDByqZTGrNmjX5HgoaIZild2uCdsRE5ahpauqNMnX6McWcWLCra1OH+E/r2lX6zW+kE06QrroqmKGndWqLyfemUHvDKqhtb6qZcl++kn5Skkl/7NKjZw/99R9/1iGHTW2S22oqST8pz6fcBk2DMA+ExD777KNEIqGPP/4430PBXjAmWICaWZAad4oUrQngboMtI4Pg7sgxbnAME1csfYygnKaZQ/yuOyL17Cndfrt01FHBDP1f/0qgbwFBvXw4Z+Uz/CaqnbfWT5et7PlYjuOoX/9+Ki0tbZnfiyxZ+czOo8lEGr4IgELQv39/JZNJrV27VtbagnphQm52/7czcuXIMTbIIyZTBV37v7XnGzNR3+T/MWCMVFYmXXddsGvsddfJGiOdeqoUi+V/fK2QteEP8lLwuPasr0hDG5fVd4x095psyo2stfI8T6lUStXV1dqyeYt831fnLl1UVBRXJBKR62a3tiSVSgW7cX/6/YNR+ndYn/me67qKxqK7HT9oKRptujUtaLMI80BI9OnTR5FIRKtXr5bv+3Ldwml7iL1nZNKJvRA71NcjM0N/662yrqv1N96o91IpHXnGGYrH4/keXStkW017Q9+mZO3eBdmgvKbuGX5rraqqqjRvzjw9+sjjeuXFV7R48WKlkkGZUiQS0eAhg/S5Uz6nk04+UfuO2lfRaLTe2/z73/6hm2+8RV4OrVmPPf5YXXfDNWrXrt2usSmo9Y+7xVkfB9gTwjwQEpFIRP369dPatWtVVVWl0tLSfA8JCBgjlZbKXnutppWV6Rc33KBLjdF3vvMdxeNxZh2bSKZrS6FvEJUtP31/Gvv21ZdX79oBa62qq6t1x6136v/+8netXLEyqFE3u94yJxIJffD+HM2dM08P/ech/eyqK/SFL3+h3sfsju07tHrV6pw6i23auFG+/9k3HZ715MuX20TrZ9A2EeaBkDDGaNiwYVq1apXKy8sbFeZ3X2yVXVM7U+u/tV8Egd0YI9O1q06+8kq9lUzqmmuu0c6dO3XuueequJiZxyZhP/07HHbBGxOnkaU2Kb/+lqjl5eW6/tobdP89f1BVVZW6de+mI48+Qp//4uc1fMQwua6r5cuW69mnn9MTjz2phR8u0lU/u1qDhgzW2HFj6jzucScepz59+zT4bzHngzm67Te3yxijESNHqKzss8/ZVsGOtY7TQmtd0CoR5oEQGTp0qGbNmqXKysqsLh9spBJsppL5XLK1vl7/i5FJd4cwMjJGks187qT/bMRuomi1jDFq3769rrnmGqVSKd10002Kx+P63ve+R6BvIq2lxCbDNnJ23re+PL/un4XneXr80Sf0f3/9u6qqqtSrV0/deMuN+twpJykWi9VcbvCQwTry6CN19LFH67abb9dRRx+pQYP2qfe2hw4doqFDh9R/v6zVyy++LM/z1L1Hd335/32pztJI3/p7dYYCIMwDITJ06FB9/PHHKi8v320RrJWtKRu1srLWly+v5nR8YzeWqQn/wV9qf6Mm6DvGSXdhSbeFY/a+zWvfvr2uvfZalZSU6MYbb1R1dbXOOecclZSU5HtooddaSmx2sbsWjeYgE4Drsm3rNv3lgb9oy+YtKi0t1Y8v/ZFO/vzn9lgP7ziOjj72KI0dP1adOnVskrUeixct1jNPPStJOuGk4zV02NB67osX7Fq7F4uB0bYR5oEQ6d+/vyorK2s2jsqEeCs/vbV7E2/GUo9M0PesX1O36ta0VnSZtW/DjDHq1KmTrr32Wvm+rxtuuEGe5+m8885jrcdeCtuOrw3xrZ9zkM+05qzvZzF3zlzNeuNNSdLkKZP0la9/RZFI3ZEnEomoV6+euQ2kDslkUv/6+4Pa+MlGderUSaedcZocp+6gnj53KocuZWgkwjwQEsYYlZWVqXv37lq2bJkOOWSqfD8I0tmUzLSETJs4o1S6D7ork97OgheptsUYo3g8riuuuEKO4+j222+X67o6++yzVVJS0mKPh7rrmuueDuax2pKscp6atw2/qZnx+kx5nifXdXXo4YeqS5cuOf27ZlpZVldXy8goGos22OUmc70VH63QtOenKZVK6fAjD9OwYUMbvG3fepIhkqFxOKcDhEhpaan69u2j+QvmqdqvUtIm5Kv+0835EOzwmFLCr1bSr06fRi6sMaL5ZWbor7vuOp1++um66aabdNttt6mioqJZbzfoxe7XvLlM2YQSfrWq/cpaH1U1nyf8KiX9hFI2KT99lstaW4CP2eYfj+/72rx5s7Zv397styWpUdMQ2Wya9c5b70iS3IirKQdNadQbtP/++yEN7DVIQ/oP0x/ufSC7sflW055/Ue+//4E6de6kr3zty+rQsUOD12t95VNoSbwNBArcrnp4q3hxTD169tCSpUtDs3FUEI4ScmwwU++YYBFYGMaOvWeMUSwW0+WXXy5JuvvuuxWPx3XOOec02a6ctdeMZNaKBDXV2QWkIFB6kpU8m0ov+XbkOI4c69bsAZDvsrGmjvK136zs2LFD77z1jma8PlNz5szVyad8Tt/89jea/fe0sUf363mjZa3Vhg2fSArq4QcM6J/z8TMz81VVVUp5qazbUFZWVervf/2HvJSn4SOG69DDDs3qZ9gaNgJD/hDmgUJngyf6lE0qXhxT7z699crLr4YmzGcEIcuTI1cRE5EsrdjaikyXm5///OeKxWK65ZZbtGPHDl122WUqKyvb+xuwkidPvp+S3wRrRjLh3vdrlYylXy5bw2M2lUpp29ZtWrdunT74YI6eeeoZvfP2u/I8TyUlJRqx7wj1bKL68YY05g1KQ2WF1dXVqqqskhS8AWvfvn3Ot2GMUefOnTVu/FhFIhF1796t4XFZq2effk7z5s6T4zo64/unZzUrL7XEORe0ZoR5oEDZdAtJzyZrtiuPxqLq1buXtmzZoq1bt6lz5055HmXufOspaX25JiJXkVYRjtAwY4xKSkp02WWXKZVK6Z577lE8HtcFF1zQqLAlZcppvPTGO80zs5kpGfOtl/fH7N6cGfB9X5988onefvNtvfPOu/pg9gd6++135KU8jR03Rl/88hc0afIkjRs3Vn379W2xHaYbf5/qjr/GpFvpZi7ZiHIpY4xOOOl4nXDS8VlfZ9u2bXrwHw8qkUho5H4jdcyxR+fwWCHOo/EI80AByoSUlE3uNgNljFGv3r0UcV2tXrUqlGFeSm+UYpOy8uUqWtOzHq1fcXGxLr/8cpWWluquu+5SdXW1LrnkkpwCfRDOrJLpkN0SQSjzmPWsp4iJyjGFe2bJWqtUKqXy8nJt2rhJs96YpScff0qLFi1S+c5yGcfRuPHjdM11V2vi/hPUvUd3dezYUdFoNA/3qemLl2KxWM2+BlZW27dvV8dOHZv4VnZnrdXM19/Q22+9o0gkoq9946vqlOPzM73m0ViEeaDABLPxKaXsnnc37NOnjyLRiD5avkJjxta9S2EYeDaob446MUkOL2RtQKYr0yWXXCLf93XHHXcolUrpsssuU8eOHRu8vrVWvjyl/FSLtWHd7fblK2mrFVE0L7P0RqbOEpNEIqGVK1dp/rz5ev+99zV9+uua/e5stWvfTqNH76cTTjxBBx48RQdMOUCdO3fedcw8vikJbrtpb98Yoz59++rtt96R7/latnSZ+jeibj4XFRWVeuS/j+iTDZ9o5Kh9dfyJx+V8doPnPzQWYR4oEJlFfMHsX92LrXr16inXjWj16tUtOLrmY2WV9BOKOFE56f70aN2MMYpEIrrgggvkOI7uuOMOua5bM0Nf12OgrjNW+ZA5sxRRrEUXxxo5NWcjPM9TMpnUio9W6PXpM/TitBe1fNlHWrd2rVw3oqmHHqzvnHGahg4dqoEDB6hL1y719jvPh8b/3IzqOyMzbvxYPfbIY/I8T+++854OO+KwZn1uWblypZ59+jk5jqPDjzxcQ7NoR7k7nvfQeIR5oFBYKWUTNfXxdendu7cmTJxQcxq5NcgE+qiJyTEuM1RtgDFG7dq10yWXXCLXdXXrrbeqsrJSP//5z/c4Q7/rjFVKhVJfHGzSllBE0RYL9BXlFVr58UotXrhIs954U6+9+poWLVysbt26qv+A/jr6mKN03PHHasy4MSoqKpLrpjdwK9A3yZlOQblep74zFJJ02BGHqqioSFVVVXr+2ef1rdO+pR49uu/laPfM93398b4/asuWLerUqaPO+N7pWfWkr60w/3UQFoR5oABYu6setyElpSX69c03ql27JugCUmCSNqmoFTP0bYQxRtFoVOedd56MMbrlllsUj8d1ySWXqGPHjjWPgV0z8oUT5DN86yklKZqeoW8qtRdtJpNJzZs3T6+//rpee+01LV6yWMuWLlPvPr005cApOvOsMzVs2FANHT5UHTpk1z2lMJhg7UGOPzgjyTGOvHp6sw8dPlSTp0zSqy+/pvff/0BPPf6UTjvj1Hp3gW1ooeyenpOstVq6ZKme/98LkqSjjz1ag4cMzu6O1OKYwjpjgnAhzAN5lplxrK+0pjZjjLp169rMo8oXq5SfVNQxtK5sQ0pLS3XhhRcqHo/rhhtu0Pbt23XttdeqU6dOu5XWFFqQzwgCfVIR7f0CUt/3tW3bNm3atEkLFy7U888/r2nTpmnbtm0qKSlR3759ddJJJ+rYE47VoMGDVFJSrKKiolD+rjjpfv65MzLGqffh0L59e/3gnB/ow/kfasOGT3TtL65T+/btdMxxx6h9h91Luay12rFjh5YvXa4ePXuoR88ekqQli5folZdeleM4mnTAJI0es99nbsdLeXrs4ce1asUqde7cWd/6zrcUieYerTL7bwCNQZgH8sjKypefdZCvuZ612rhxk1auWKmtW7fK93117NhB/QcMULduXRtdF5uoTmjlqpVauWKVEomEykpL1X/gAPXp0zvrxVypVEqLFy3W+vUblEwm1blzJw0aNEgdO3XMKnAEJTdJxZx4o+4Dwikej+uss86StVY33nijioqK9JOf/ESdOncqiBr5hmQ2m2rsotjNmzdr9uzZmj17tmbNmqXZs2dr7dq1GjFihI444ghNmDBB48aN06hRoyTXz+osXqFrbEcgY4I3AvUe23F07PHH6PtnfV833fAbbdq4SReee5GOO+FYHXLYIcEbodIS7di+Qx8t/0hvzHhDr7z0ir512rd01bVXylqrN2e9pR9d8GNFohFd+6tr9hjm165bpyefeEqJREIHH3qwxo0f16j75BLmsRcI80A+WauUn8gqqFhrVVVVpfdnv68///GvmvXGLO3YsUOJRELWWsViMXXo0F4HHXywzjzre9p35L5ZtZqz1qqyslIvPD9N991zv5YtWaby8p3yfF+RSERlZWU6eOpBuvCiCzR8xPA6T1NXVVXr1Vde0T1336v58xeosrJSvu8rHourS9fO+urXv6Zvn/pNde/RveExyVfKNs1MJ8IjHo/rzDPPVDwe1zXXXKPq6mr97Kor1KFT4/rQt7SUTckYR04DZ5U8z1N5ebm2b9+uN998Uy+++KJmzJih7du3K5lMaujQofr+97+vI444Qj179lTXrl0Vj8drjhmcyWsNYb6xEcTUrK2p77mzuLhY5/3wXBnH0QP3PaANGzbov/9+SE89+bTKysoUiUSUTCZVvrNcVVVVihfFNfu92fJ9X8YYua6reFFc0Wh0j8971lq9+tKrev+991VaWqovffkLjWoXnGnNy3MdGsvYxuymAGCv2XRJSbaz8ps3b9bNN92iB+57QNu375AklZWVqkvXYCZ+65at2rJliySpZ6+euuwnl+r7P/hevQuxrLXatGmzrv/lDfrzA39WZWWlXNdV9x7dVVJSrC1btmrzps2SpMFDBus3t96k444/9jMz/xUVFfrDfQ/o2l9cp/Ly8ppjxONxbVi/QRUVFXJdV4cfebjuvue3WW6vbhR1YnJEuU1bk0qldO999+q6a6/TKV88RVde9TN1y2IHzkLgyFHUiX/mMet5ntatW6f58+frgw8+0PTp0zVr1iz5vq/hw4dr9OjRmjp1qg466CD17du35vp11Wkn/Oq8tOZsKo5cRZ1Yo3+3rbWq9Mrl11M3n7mc7/v64P0P9J8H/6u578/VmjVrtWPHDvmep5LSUnXt2kWDhwzWQVMP0kknn6iu6TLGxYsW6+UXX5HjOjrggMkaPXb0bsf2fV9XXP5zzX7nPfXu01t33XOnSktLc74vrnFV5BYHpUNAIxDmgTwIdnf1lcxiVt5aq507durGG36t3931O1VVVatTp0467fRTdeRRR6hrt25yHKOtW7fp9emv64H7/6i1a9aqV+9e+ue//6EDpkyu89i+7+uGX92om399ixKJhCZOmqjTz/iORu03SkVFcW3ZvEX//MeD+veD/1ZVZZXGjBmtp59/Wl26dN7tGI8/+rh+eMGPtGH9BnXp0kWXXH6xDjr4IEWjEa1csVK/++3v9dqr0yVJ3/nud3T7nbcqHm+4jKauYITWzVqr8opy/e3//qprr75WX/7qV3TlL36uDh3CMUMfMVEZ6yiVSmnz5s2aPn26/ve//2n+/Pn6+OOPVVVVpSlTpujwww/XxIkTNWDAAPXq1UuRSHYlOpl1BEmbkFU4O6HUdK7ai9/taq9KST+R1WWtDVp5fvLJRm3etEkVFZWy1lc8XqQOHdqrW/duOQdxa63Wrl0rL+WrqCiurt26Nur+RJ2YYjzPYS9QZgPkg820tcuuvOaRhx/Rfffcr6qqag0bPkx3/e5OHXTwgTVt5zKXO+TQqfrcKZ/TVT/7hY459miNnzCu3mNv2bJVL77wkjzP0zHHHaP7H7hXXbp2qTnla63VAVMOUDKZ0D///i/Nn79Aj/z3EX3/rO/VHGPr1q266467tWH9BnXr3k233XGrPv/FU2pOS48bP04HHnygvvblr+uNmbP0xKNP6PgTjtMpnz+5wRcvP1hRIJenqjanqDim0844VfGiuBYvWqz6HirWWm3btk3r1q7Xzp07lEgkFY/H1L59e/Xp20fFxcV7NQO8bt06VVRUqmuXLmrXvl2da1ISiYTWrlmr5cuWa/6cD/X000/r/fffV7t27dS/f39NnDhRl1xyiQ488EB17ty5JrznPDYTzGw71pWv8JXbOHL3OshLUsSJZh3mM3sb9OrVU7169dyr2619zN69e+/dMWQUdSgnxN5hZh5oYdba9CLP6qzCfEVFhY498ni98/Y76tixg35z6036+je/Xmftuu/72rx5i9q1K2tw9tvzPM2fN1+vvPyqDjv8UO03er89vqi8/OLLOuHYk2SM0bdO/abue+Demsu9Pn2Gjj/6BHmep//3tf+n393728/McPm+r0ceelTn/OBc7dixQ98787v69c03ZjUT5ppIMNPJi12bEZSRVAWlaKmUksmUiouLPnMZSdq4caP+8+B/9fKLL2vFipXasmWLEtXVKiouVpcunbXvvvvqi1/+go486kgVFWfX9aX2sR9/9Ak98tAj2r59u8ZPGK+rr/uFOnXq9JnLz35vtu6/9w/6cMFCLfxwocralemwww7T5EmTtd9++2nkyJHq2rXxi9M/M0alu/z4iQJfGvxZMSfeJN1brLWq8ipzbiBQSCImorjb+DebgMTMPJAXvvWz7s7xxsxZmjtnrqRglvtLX/5Svb2SHcdR165dsjq267rab/R+GjlqZL0zhEOGDanZgOWTTzYqmUwpFgtq8Z956hmlUilFo1F94Yun7DGgO46jSZP31+gxozXj9Rma8fpMbfxkY1Zh3re+rLFsJNWGeDZV8/sRiUT2+Hj3PE/Tnn9Rv7jyF/pwwUJVV1crEomoXft2ikQi2rZtmz5e/bHee3e2nnj8CX35/31F111/jbp0qft3w1qr6qpqrVy5Us88/aweuP+PWrVylaqqqiRJO7bvqPn803zfasH8D7X/5Im66porNXLffdWlY7easwJNHdaCpo6uHBMJVZiNmEgj21HWcTwnKs8LdsQNH6OoG8v3INAKEOaBPPBzePGd+fpMJZNJSdIJJ52gktKSnG4r00N58aIlMsaoR88e6tNn16nhTNeG+pndwlVmdtFaq/fem13z9UkHTKrzCD179dSQoYM14/UZWr5suTZv3qL+A/pn1dkmWOjH4rC2wFrbYOmItVYvvvCizjvnfH28+uP0m8VJOuHE4zVqv1FBy8EdO7RwwUI9/tgTeu/d9/TXP/9VQ4YM0kUXX1Tn7Hh5eblu/NWv9cjDj2r5suWy1qpLly6Kx+Patm1bneMxxmjsuDF65n9PqajWGYRgBrr5HrfGGEUUSU8OFP5iWEeOXNO41p17krn/KeOG6g1NRsSJsEEemgRhHsgDP8tZJGut5s2dV9Mqbf/J+zfq9j6Y/YFOP+27ch1H3z/r+7r08ktyuv7yZcuUqE7IOEYDBvRXJBKE/x3bd2jd2nWSpM5dOtc76xmLxdSvfz9FIhFVVVVpxYoVGjd+bFa3nylN4kWv9bPy692J01qrpUuX6aYbf6OPV3+sSCSib37rG7rymp+rZ8+eu60jSaVS+srXvqJfXfMrrVu3TiedfFK9j6F4LK633nwrKG077hid/PmTNX7CeP38iiv10rSX6h33ns4geNaTacQOp7kxijjRrFvc5ouRUWQvutfUeVxjFHPiqvJSBXzvP8uRo6hp2l2D0XYR5oEWFsygZfeyU1VVpa3pGUHHcbJs6bjn42RmMOubYdwT3/f10H8fDnrZR2M6+tija763Y2fQ515SVptVdevWTa7rKpVKadPGTdmPwXpyG92TGmERdGmpvwTN9309+/SzmjnjDRljdPyJx+uXN/5SXdMLt2uLRCIaPHiQbrvrVrmOq7J2ZfWGyUg0ojt+e7vKysrUu09vGWOUSCRUXFRU53Xqvz++mrvdjDFGjnVqBfrCY9JtZoM3NU3/w3BM0PUq4Vc3+bGbS8SNBptmkebRBHh1BFpYLkvOq6qqlEwEJTZFxUX11srXp1v3bjrp5BPlOI5G7Dsi6+tZazXngzl6/rkXJEkjR+2rAw+aUmt81el6Vakoi8BTVFQk4wQvXuXl5dmPoyAjCpqD30C5SDKZ1CMPPSLf99W1axd95/RT1bXLZ4N8hjFGHTt2zOq2jTE5/X40xKb3eDZq3t09g0DvyjVReTZZYL8twZkD04z7RRgT3IZnvVCU20RMVFHT9Gcp0HYR5oEWlkswdRy3Jvx6npfTG4HaRo8ZrX88+HdJyqI+PmCt1caNG/WbG2/W8mXL1alzJ1182cVq335Xr2/H2fUC7fsN1+x6nldzUiLbcaRHk8NlEWa2gU2ANm/erNmz35ckDRo8SIcfcXjN70i2qqqqtHzZciWTKXXo2F59+/bN8fGYnWA/CdsipRTGSK6CevSUnyyIN8CZ0pqW2PjNyCjuFqnKq5RfwLvjuiaimNvwHhtALlhRBrS47F9ki4uLFIsF3Q4S1Yk6O2nUxxgjx3EUi8UUi8WyCi3WBi0Bf3vHb/XUk0/JdV1945tf17HHHrNbKU1RUZFcN/h7RXlFg3etoqKiZsfGsrKyrO9D/mMJWoZtMIR+uGBhzdmqkaNGqqxd9o+jjDUfr9H551yoU795mu66/beqrKhs1GizURPom13QMceRG9Sm5/nlPdjwreV2cDbGBIHeiRfsTqqOcRRzg0XRzMqjKRXmIx5oxXKpkYxGo+rZs2fNBk6LFi5qxpEFrLXavm27bvjljbr7rt8pmUzp+BOO08+uukLt2rfb7bKdOnZUcXHQXWfdunVKeXWf4rbWat3adUqlgsv06t0r6zHxstc2ZBN5169bXxOOhwwd0qjbqa5OaOnSpVq0cJHWfLxGnt98M7ktPUMeBHpHMSfWJL3cG8M1kfTOzS0bWo0xcoyruFMkp8DijWNcxd3CGxdaBx5VQEvL4bXNGKNx48bKcRxZazVj+ozmG1fajh07dP0vb9Cdd9ylqqpqHX/Ccbr5tt+oU6dOn3lhLiou0oCBwaLcnTvLtWrV6jqPW1FRoZUrV8r3/JqdObNHnG8Lsgm+27dvrwnztUu+ctGuXZmOPuZonfi5EzVh/wmKRqKNOk428lHuEvS1D7qlRFtwlj4zG5/Z5C0fizuD+vlIEJwLZIY+E+Sbsi0nUFthPNKBNiR4Yc3+Cf3QIw6tWVz6v+f+p/Xr1zfLuHzf19Kly3TRBT/Svffcp0R1Ql/44ud16x231NsP/sADD5QUtAGc/trreywpsNZq1arVNWcW9h05Qp06dsz6ha1QT5ujiWVRjmKM2fXr08jylV69euk3t/5a9z1wj8465we79YZvcnncZD0zUx1LB2ynmcK1kaOIE1PUiRdM33THuCpyi/PeBSviRFXEjDyaGY8uoIXlOl81fPhwHXn0kZKkBfM/1B/ufUCVlbnXztfH8zy99upr+t53vqd//eNBFcXjOvvcs3T3vb9Vv/796n1xPvb4Y1VaWqpUKqVHHnpEGz/Z+JnL+L6vl6a9pPnzFsh1XR1y6CHq2rVr1uNrrhCC8Gnfvn3Nb9DWHNusZrgRV506dVKXLl3Url27Bluq7o18r/fItIMMSl+KFDUxOSay17PmRkaucRV1Yoo5cblyJaOCCPJSpobeUZFbrJgTb/GzBE66fj/uFDVrJx9AIswDeZHLae9YLKrv/+B76t2nt6qqqnTv7+/Vnx74kyoqKtIdboK4YK2V53mqqqzS22++XbODpbVWS5cs07W/uE7XXf1LTXthWs2xrbUqLy/X//31/3T+ORdq1htvqlOnTrru+uv005/9RGVlZfJ9X57n1Xz4/u6b+vQf0F/HHX+sJOnNN97UX/78V1VUVNTctpfyNHfuPN19591KJpPq1aunTjjpeEVj2Zc2BLW3WV8crViPnj1qutcsW7q8hRaXNl4hPGyDspvgw3Uiipqook5cURMLSj8aHGUwBZHZwTXqxBV14opkrp85fkHc210y44o6MRVFShRpoVn6iBNVPFJSs0kWQR7NjdaUQB64jis/y0V3xhgdfsRhuuTSH+vaq3+pTz7ZqJ9edoUeefhRnfL5kzV8xHDFYzFVVFZqyaLFev5/L2j6a6/rO2ecpt/cepNc19VHy5fr1zcEn/8w+UMddfRRkqSNn2zUz6+4Uv/6x4NKJBKKxWI66ugjVVFRob//3z/2OJ527drpmGOPVu8+vSVJZWWlOveCczVv3nwt/HChrv3FdZo/b75OOPEElZSUaPbs2frbn/+mFStWKh6P63tnfk9TDpySfYmNnJxLkxBSxjQ4lT1q1EgVFRUpmUjq/dnva9u2bVn3kc+LAgxymeBtFXS/kQneWGfq+2vX+Zv0fz8d1MMUUDMba8XdYkWtp6SfkGe9Jl3PYOSkz1REg4XHJrdmB8DeIMwDLcwYI2Od9Itpdi8msVhMp3/vdMk4uv2W27Ry5SpNf3W6pr86XUVFRYoXxVVVWaXq6mAHRNd1tXbNWvm+L9d1ZZXeXdP3d6vhffvtd/Twfx+p2cU1kUjoP//+r/7z7//WOZb+/ftpyNAhNWHeGKMpUw7QL6+/Vpdf+lMtW7pM//z7v/Svfzwox3FqNpVq3769Tjv9VJ1z/tk5lTUws9V2ZPOv3L5DB40ZO0avv/a6Vq5cqdenz9CJJ51QsI+RQg50n/6ZFfJY91bmvromIse48qwn33pK2WRNu9zGcIyriInINa4cUxjrBdD2EOaBPMjUm6Zy2K2wqKhI3//Bd3XscUfrsUcf1/PPPa+1a9epurpa1lpFu0XVrl07jRkzWl/6yheDLh3RYMZt7Ngx+td//yljjIYMGVxzzLKyUo0ZO0YVFRVZj6NXr54qKyvd7WtuxNUJJ52g4SOG6z///q+ee+Y5bdu6Tb71VVRUrP1Gj9Kp3/m2phw4JaudYnc7dhPU9yIcsvl3jsWi+ua3vqE333hTmzdt1j2/u1f7jR6l/v3rXqSdTzx2C48xRhETkbWuoorJyq/ZPTZTHhhcMJj7MEbBGaP048sxQbmRa9yaf99CfOyh7TC20AsOgVbKt76SfnWjT/UmE0lt2rxJW7ZslZdKqaysTN27d1dxSXFeX1istaquTmjL5s1KpVLq0LGjyspKG7XI0DEu2563IdZaJfyqBn8nVny0Qmd+9wd67dXpchxHX//m13TV1VfuseuStVarVq7S6lWrdcCBB8h1XW3ZskVPP/mMKioqtM+gfXToYYfUbM72adXV1frmV7+lp596RiNGDNfTzz+lXr2y3yMh4sRarFYbe8/KKvi/zfztM6VGPB+h0PAMA+RJpm2cl8PsfG3RWLChVM+ePZt4ZHvHGKOionhOm0LVcSRCUBtkjCNr619P0q9/P11+xeX6aPkKrVq1Sv/+1380d848nXjSCTry6CPVs2dPJZMJLV2yTC+9+JKmvfCiHMfRtFeeV+fOnbVh/QZdd80vtX79en3+C6do8uRJNWF++7bteuXlV1VVFewKm0qltGbN2uB723foyceeVMdOHSVJXbt106GHHVLPrsqFtygU9TNBsTv/bggVZuaBPLLWV8JPyKrxNZutVcRE2WSljbHWyrMppWwyq8u+Pv11/fKa6zVz5kwlqhP1Xr5fv776+4N/16TJ+2vB/A91wrEnav269fril76g39//O3Xo0EGS9OGCD3XCsSdp3dp1DY5h6iEH68lnn1A8Ht/j942CHu88hgE0J6a9gLwyipioUjaRl50iC1WmJpUQ1LZkeoNn86tgjNGBBx2o+/54r6a9ME1PPvaU3n77be3YviOoeTZScVGxBg8ZpGOOPUZHHHWERu03UpLUs2cPXXPd1aqoKNegwYN3W8cRjUbVr1+/rNZ29OzZs97HqFN7gysAaCbMzAN5lstsZFtgZBRNb/JCmG97rLVK+tXya52tSiVTMo6R4+x58x3f95VKpeR5nrZt3aYdO3aqqCiujp06KhaLKRKJ7Hbd3RY5aveOSb7vK5lMZtW/3nEcRaPROh+nMScetCkEgGZEmAcKgLVWKZtsdP18a2FkFHFicoxDzWoblvKTNW9urbV69eVX9eK0l3TBD89X127Z7xycT0ZGMaeIN6QAmh07wAKFwOyqEW+raoK8CPJtnVOr5Z/neXpx2kt6+KFHVFxSnOeRZa8t/y4DaFmEeaAAZEpK2mqgN+m1A472XEaBtiXYhyH4Pdi6davmzp2rcePHqqSkJM8jy46RwwZCAFoMYR4oJOkZ+oiJtpnZ6UyNPOEHNUwwO+/I0eZNmzVv7nwdctghWV/dWqtpL7yoac9Pq9kVuSXV3kwIAJpb25sCBAqYkZE1Vq4icoyjpJ9s1W0rXRNJ95JnsSt2yQRhx7hatnSZNm/arEmTJmV9fWutbr35NnXr1lWTJk+qs3Vkc3BEJyYALYuZeaDAZEpujBxFnVirLLvJlNVETFTGUFqDz8psqvbGjFkaMmyIunXrmvXjZPOmzVqyaLH2HTlSpWWlzTzS3UWcaIveHgC0vpQAtBKZ4BJRVI5xlfJTsqp/Z8wwcE0kmLkUPbhRP9/z9fJLr2jcuLHq1LlT1td766235UYiGjduTD27sza9oDyON6cAWhYz80ABy8zSu8ZVzInWhIUwcuQq6sSCha7p2XjqilGfNWvWaM2aNdpv1H5ZL3611urdd95VcXGx9h25bzOPcBfXuJTXAMgLZuaBkDDGyFVErnHlWU+e9UJQT2+CGmInIkeOgsl4wg6y88Ybbygej2vC+ImKOXElbaLB62zbtl1zPpijgQMHqG+/vi0wyuCNasTECPIA8iKcU3xAm5SupTeOIk5UMSeuqBOTI1eFVq8S1MRHFHVi6bp/l5l45MT3fb311lsqLS3VqFGj5BhXERNVQ4/1NWvWaOmSZZp6yFQ5TvO9xNn0hyNXEafuXWABoLkxMw+ElDFGjnXlOK6srHzrybeebM3/WjbiGwULFoOWgkZ0qMHe2LRpk+bPn6+RI0eqS5cuNWemjIxSNqE9bV1urdXqVau1cuVKHXzIwc06PqOg206UGXkAecbMPBBiwUy9qdlkJ+rEFXXiijgxRUxkt500m/y25cg1riImopgTV8wpqrXxE4sAsXdWrVqlxYsX64gjjqiZYc90uIk48T2uHfF9X++8/Y46d+6sQYMHNev4IiaqqIk1620AQDaYmQdagdrB2aTr1G167tKaoCDAysra2n/6NZepdSSp1td2FcYE5T2m5s9d3yW0o6lZa7Vy5UqtXbtWBx544G7fMyZ4fEedmFI2Kd/u6vCUSqU0a+YsTdx/goqLi5plbDW7FbPJGYACQZgHWqlM0MjE8d2C+15mEEIMmpPneZoxY4YGDx6s3r17f+b7mcd01MTkG0+eTcm3vnbs2Kl33nlXl11+qYqKmjbMB2e/XLkm6CPPrwCAQkGYB9oI+rojLFKplGbOnKmJEyfWGcozbyhdBZ2SPHma8/4cxaIxDRs+tAn7y2dCvEsPeQAFiZp5AEBB2bhxo+bPn6/JkydnNcNujCPXRPT6qzPUu3dvDR82Yq/2Y8iUqkVM0DWKshoAhYyZeQBAQZk1a5Y6dOig4cOHZ91esqKiQu+++64GDhyofQYMUtSJButCMutDbO0+T7XtWvsRhHgjk144TngHEAaEeQBAwbDWavr06erVq5cGDx6c9fWWLVumjz/+WN/+9rcVj8fTX921EFxGewjyNd+q+S8bmwEIG8I8AKBgbN26VbNnz9aQIUP2uPh1T6y1WrhwodavX69DDz10t+99utMTALQ21MwDAArG0qVLtWHDBk2ZMiXrRaypVErz589XLBbTiBEjmnmEAFBYCPMAgIJgrdWCBQu0YcMGHXbYYVlfr6KiQjNnztTBBx+skpKSZhwhABQewjwAoCAkk0nNnTtXnTt31sCBA7O+Xnl5uWbPnp3TbD4AtBaEeQBAQSgvL9cbb7yhQw89VNFoNOvrzZ07V6lUSuPGjaMDDYA2hzAPACgI27Zt0wcffKCDDjoopxn2l19+WYMHD1afPn0I8wDaHMI8AKAgvPvuuyopKdHw4cOzDuXJZFIzZszQ0KFD1atXr2YeIQAUHsI8AKAgTJs2TcOGDVO/fv2yDvNLlizRunXrNGHCBBa/AmiTCPMAgLyrrKzUu+++q6FDh6p79+5ZXcdaq7lz5yqRSGjixImU2ABokwjzAIC8mz9/vjZv3qxJkyYpFotldR3P8/Tee+/JdV2NHz++mUcIAIWJMA8AyKvMDHt1dbUmTJiQ9Qz71q1bNX/+fI0bN05lZWXNPEoAKEyEeQBAXvmJhKrmzNF+HTtqv/32y+o61lpt2rRJ77//vo466qhmHiEAFC7CPAAgr9wtW/SDOXP0n0MPzbrERpKWLVumzZs3a//992/G0QFAYSPMAwDyx1pp40aZOXNUPGmSclnC+tprr2no0KHq0aMHi18BtFmEeQBAfs2fL1VVSbksYvV9lc+Zo8MPOECdOnVqvrEBQIGL5HsAAIA27rXXpFGjpM6dpWxn2D/+WJctXy7/uOPoLw+gTSPMAwDyJ5mUXn1VOuywIMxnycycqT7V1cGbAIeTzADaLp4BAQD5s2yZtHlzEMrj8eyu4/vSu+9KJSXSyJHNOz4AKHCEeQBA/sycGYTysWOzL7HZtEmaOzd4A5DlbrEA0FoR5gEA+ZFKSW+/LbVvL+27b/bXW71aWrJEOvTQ7N8AAEArRZgHAOTHhg3SwoXSuHFBoM+GtdKKFdKaNdLBBzfr8AAgDAjzAID8WLVKWrxYOvLI7GfYPS8ozdlnH6lPn+YdHwCEAGEeANDyrA0Wv27ZIk2alP31UqkgzO+/v1RU1HzjA4CQIMwDAFpeKhX0lx89WuraNfvrbdokzZsnTZ5MmAcAEeYBAPmQmWGfODG3UP7mm0F9/bBh9JcHABHmAQD5sHp1sJB14sTs+8tbK73yitS7tzR0aPOODwBCgjAPAGh5M2ZI3boFoTzbxa/btklz5kiDB0u9ejXv+AAgJAjzAICW5fvS9OlBIB8yJPvrLVkirVsnHXigFIk03/gAIEQI8wCAlrVhg7RggTRyZDA7nw1rpQ8/DK576KHNOz4ACBHCPACgZS1ZEoTyAw/MvsQmmZTmzpU6d5YGDmzW4QFAmHCeEgDQcnw/mJXfvFk65JDPfNvKSrbms5qvaucOmZkzpUMPkWLRYKbeSEZZvhkAgFaKMA8AaDmJhPT++7L9+kk9ewahXJIvX9Z68tNhPojywfesrMz2TYrNmaPUqd+QNZ6MtZI1MsaRI2e3UG+yne0HgFaAMA8AaDnl5dIbb0iHHybflTxbLWvtbvPwe2LeelsqLpI/Ynh69t4LvmEzs/NGjnHlGjfzHoBQD6BNoGYeANAirKz8TzbILlqk1AETlTSefOs3GOQlyXn1NflDBsv26a1PV9YEbwV8eTaphF+lpJ+QZz1Z2/BxASDsmJkHADSrzMy7Z1PSzNflduksf9A+2R+gslLOm+/IH72f1KN7gxf35cm3nnw5cm1EjnGprwfQahHmAQDNIiiHsfKsp5RNSbKKvPii7LChsn36ZN3Jxiz4UNqyRXb/CVIslvXt+9aXr4QcuYooIsmh9AZAq0OZDQCgyVlrZa1V0k8oZZOSrLRtm5x582WHDZW6dsn2QDLzFkiJhPwJ47JvZVmLbz0l/YT8dOkN5TcAWhPCPACgSVlr5ctT0q+WL7/m62bOPGnHTvkHTJJcN7uDJRJyZr8vlZbK7jeq8WOSVdImlPLTbywAoJWgzAYA0ESsrJU8m5Jnk7tHZmvlzJsvVSdkx4/PfoZ961aZefPlT9o/pxKbunhKyVqrqKKSDGU3AEKPmXkAQJMIgnxSqU8HeUmqrJR5/wOpW1fZIYOyPqD5ZKOcufPkH3lYk40zU3ZjRckNgPAjzAMA9pq1QbeaYKHrHmzeEoTyqQdnX2IjySxcHMzmjxndRCMN+PLTJTcAEG6U2QAA9oq1Vr716w7yktS1i5JX/SzY9TUHzquvyY7aV7Zrl0Ytfq2PL08pm1REUVpXAggtwjwAoNFs+n8pm1C9C0vjcdmjjgg+zzaUp1JyXp0u/5CpUsdOez3WPfFsSkaSmw70ABA2lNkAABrPKl0j30DtuTG7PrL10QqZbdtkR+0rFcX3bpz1SFmv5k0JAIQNYR4A0CiZXV196zXL8Y3jyPvOt+UfNKXJS2x2Z5XyE3SsBBBKxrKUHwCQo0wnmKArjN/Ahe2uP7duk1m3Ttq2XSoplu3YUerdW4qkF8V+OrRnrttQmM9cLpWSWbtOWrdO8m3QPWdA/12LbvdwHKugwiZqYnKMS7tKAKFCzTwAIHc2aPHYYJD3fWnTZjkvviz3wX/LeXe2VFEp+Z5kHMl1ZPv1k3/CsfK+8iXZEcN37yefTbD2fWnderlPPCX3z3+TWf6RlEoFg4xEZPv3l/fNr8n75tekbt3qDPQpm1LMZN9pBwAKATPzAICcWWuV8KvqrzO3Vub9DxS5/iY5Tz4tk0gEX47HpXZlUlW1TEVFEMYl+fsMlHfxRfK+/Q2prCzrsZgFCxX98aUyL78qk0z3uG/XLpjt37ZdxvdlHUf+V7+i5C+vlgYOqPNYUROT6zDPBSA8CPMAgJx5NqWkn6j7AtbKLF6i6NdPlZk7T3Jd2XFj5Z36zaAGvqhI8jyZj9fIeehhuY88Lm3ZKrVvJ+/ii5S65EfZ7fi6cZOip39fznPPS0Vx+Z87Sd6p3wxKaxxHZs5cRa6/SWb+fMmNyDv/HKWu+0Vw+3vgyFXUiVFqAyA0CPMAgJxYWSW9avn1ldh8slHRcy+U8+jjkuPIO/cspX72E6lL5+D7xuxW5+5Me0mRiy+XWbhIKitT8j//kH/MUQ2OxXnqGUV/cJ60fZu8C89X6qordr0JSN+GefPtIPAvXiLbpbOq33xdGtC/jiMaxZygdh4AwoBuNgCAnFjbQBtHa+W89Iqcl16WrJV/zFFK/eQSKbPxU2bWO/N5NCr/2KOV+sXPgiC+c6fce+6Xtm9vcCz+AZOVvPsOpS67RKnzzg6u/6nbsBPGyT/8UFljanairWfw8q0v5rkAhAVhHgCQE2v9+sP8znK5Dz0ibd0mde8m77yzpe7d6z+o48g/7lj5xx0jI8l58y05b73T8GC6dpH/+c/J++llUq+ee14wG43KjtlPcpxgpn7V6vrvX0OLegGggBDmAQBZs9bWX14jSRUVMrPeDC4/ZLD88WOz60pTVir/yMNkIxFpw4ag5Ca9OFabNsl8tEJmxUqpqnr36xkTLHat7zaMs2uH12i03mH4Db1ZAYACQpgHAOSkoaBrVq+WWbNWMkb+4EENz8pnuK7skCFS504yni8zb75UHSyyjdzwG8WmHKLYIUfJvP9BbgP2fZlFi4M3Bo4TjKkeBHkAYUKYBwDkwDZYT27WrguCszGy/YOuMlkfvUtn2XbtguNs+ETyUsHnO3ZKGzdJmzZJyWRuQ16/Qc4778r4VurTW3bfEdmMhLp5AKFAM10AQNZsrf/Wadu24E9jgn7yuSgqqulGY3bs2NWDfvhQ6dCpQYlMh/bZH8/z5Dz7P5k582QdR95XviR17NDg1ay1WVUGAUC+EeYBADlpsAzFrdXWMdfZbWtrrmNdV5lCd+9HF8r70YW5HUuSmTdfkV/fLG3fLjtqpLxvfT2r/vWU2gAIC8I8AKBpZWa+rZXJor3kbiqrpPROsWrXbleJTq7T5NZK69YpcvUvZZYslYqK5F14nux+o3I/FgAUMGrmAQBNyvbtK0UiwcLTpcuklJf1dc2GDTJbt6aP00eKNmLOyVqZpcsUPe+Hcp56Riork3fJRfJO/WYwruxGkvvtAkAeEOYBADkxDQRd27271K+vjBS0kly3LrsDe14wi75tu2w0GixUjTZcErPrhoMSHfPRCkUu/ancJ5+RIhF5Z31fqQvOa7AlZW1EeQBhQZgHAGTNpP9Xr7JSeYcfKmsks2ixnNdn7uoXX5+tW+U8/ayM58n27yd/9CjJySFW+77MS68o+q3T5Tz1jGy7MqUu+3Gws2znTrmV11CKAyAkCPMAgNw0FHSLi+V/7kSpcxeZLVvl3nt/0FayPtbKefIZOa/PlDVG9sApsqP3y35MlZVyHvyPomeeI/PmW1LPHkpdf528S38slZTkHM6NjAyBHkAIGEsjXQBAlqy1StmkPJuq/4Lbtyvyo0vl/u0fweZR3/6Gktf+QurZI1jUmgnK1koVFXKee0GRy66Q+egjqWcPJZ58RHbc2JrDOf97Xub9OVLElff/viL17bPr+us3KHLzbXL/+ndp2zbZEcOVuvUm+YccnFXnmk8zMoo5cRnDfBeAwkeYBwDkwMrzPSVtooGLWZlVqxQ97Xsyr88MNpAaN1beN74qO2qkbLsymURSWrdezvPT5D70iLRjh9Stq1K/vEbe6afu1uIyevb5cv7wJykeV+K5J2WnHhSU1bzzniJXXCnnpVdkJNkuXeSd9i3ZIYNUV+W77dZV/pe+UOfQXeMqYmLMzAMIBVpTAgByYGSMI2NN/b3YjZHt10/JO29V5Orr5Dz3gsy77yny/gdS166yxUUyKU/aulXauVNyXdl+feVdc5W8L39h9171Svecj0aDj0wdvefJfeRROS+/WhPbzaZNitx2Z733wJ8wXol6wryRwwpYAKFBmAcA5CRYAuvIqoGWk8bIjt5Pyf/7s5zHnpT7+JMyS5ZI27bLVFXLRlypbx/ZPr3lH3yg/G9/U3bggF295WvxTv2m/CkHSK4jO2TwruP36SM79aCctnjyhw2r9945xml4kS8AFAjKbAAAOUv5KaUaKrX5tOqEzKpVwWLYysqgh3y7drL9+0udOuY+iHS9vSorc7teJCJ13PPtOXIUdeKU2AAIDcI8ACBn1vpK+NX1l9qEUNSJyTWctAYQHizVBwA0gpFr3IYvFiJGjhy1rvsEoPUjzAMAcmaMkWMirap9Y4QZeQAh1HqehQEALcrIKNJKZucd48gxLrXyAEKHMA8AaBRjjBxF5LSCQB8xUYI8gFAizAMA9krEREPdyjEYPy+HAMKJZy8AQKMZY2SMUcRE8z2URnGNK9dEmJUHEFqEeQDAXjEycowbukDvyFHExAjyAEKNpfsAgL1mjJGbfklJ2WSeR9MwR44iTkwhrg4CAEmEeQBAEwlLoHfMrhn5MNf6A4BEmAcANKFMoDcyStlkwe0Q65pIUA5kRJAH0CoYa21hPdMCAELPyspaXyk/KV9+vocjKehaw2JXAK0NYR4A0CwyLy+eTcmzqbzN0mcW5xoZgjyAVocwDwBoVtZaWfnyrCfPplrsdh05cp2IHLGzK4DWizAPAGh2VlbB/608m5JvfdlmKb8xcmRqQrwkgjyAVo0wDwBocb715VtPVn462O/dS5EjR45xZYwjRw4BHkCbQZgHAORF7dl6yQYBX76s9WuivZF2+zy4tJFjjIwcOcZJz8UH3yXEA2hrCPMAgLzLBPs9fj2tzlaStJkE0IYR5gEAAICQcvI9AAAAAACNQ5gHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIQUYR4AAAAIKcI8AAAAEFKEeQAAACCkCPMAAABASBHmAQAAgJAizAMAAAAhRZgHAAAAQoowDwAAAIRUJN8DABB+1tbzvTq+buo7oGng+wAAQBJhHkAjZMK7rfV3m/7TT38j871MKK8d6o0ka4IvmvQXTPrrTvpzW+u6hmQPAMAeGWvrm1MDgMBugT39ud/Mzx6ZYG9M8OGIYA8AQG2EeQB7VHv23beS76fDfB7HlJm5dx1m7QEAkAjzAD6l9uy7byXPFmb9ulEQ6mvP3gMA0NYQ5gHsNgvv+bvKaDJfK+ScnJmtd5xd7bkI9gCAtoIwD7RxmVr4T4f4MHKM5Jr0bD2BHgDQBhDmgTYq85vv2SDIt6YnArdWXT2hHgDQmhHmgTYoUxOfamUh/tMi6VBP33oAQGtFmAfaGJueiU+1kd98x0gRZukBAK0UYR5oIzK18Sm/+fvDFxqjINBTSw8AaG0I80Ab0FbKahoScYJ6egI9AKC1cBq+CIAwI8jvkkqXFzGFAQBoLQjzQCtGkP8szw86+BDoAQCtAWEeaKVq18iTW3eXWTdAoAcAhF0k3wMA0PRs+iPl5RjkrZUSCWnzZpmdO6VkIvh6NCZbViZ17izFYk1TdJ5KSRs2yCSqZdt3kDp2DLZxzXW8VVXSJxt2fc0YqVdvKVL/01vKl6LOrqsAABBGhHmgNUqX1vi5XCeRkDNjhpzHH5Uz5wNp5QqZ7duDw7VvL/XrL3//SfKPO0H+IYc0GJbrY5Yvk/PwQ3KfelLaulX+1EOUuuHXUmlpzsdy/+9vcu+/d9cX4nElHnpE6t6j3utZSUlfirk53yQAAAWDbjZAK+T5QVDNirXS+vWK3HSj3P/+R1q3NpghLy6RounAnkxKlZXBZbt3l/ft05S65LJgpj6bae30DLpZtkzu3/4i58knZFZ8JFNVJUnyx09Q4n/Tgtn5bFkrM3+eYp//nLRypRSNBmcViopVvWCR1LdvVodxM33omZ0HAIQQM/NAK2NtjhtCbd6s6MUXyfn3g5IxskOHyT/uePmHHirbs5ckyaz5WM7MGXIee0xm+TK5d94ubdig1C23Sh06NpyE162Ve9edijxwv7R5s+S6svsMkjasr5n9z9mWzYrcdKO0apXsqP2kbt1kXnox58N4VnLFDrEAgHAizAOtiLU5dmpJJOT++Y9yHn8suP4BByh5+12y+42W4vFdx5Xkf+4UOSedrMhVP5eZOVPug/+SnThR3jnnNXw71dVy3pgpGUf+KV+Qf/Ip8seNV/TC82Rmzgguk0ua9n05zzwt58knpJJSeT84S8677zY6kHu+ZJidBwCEEGEeaGW8bIO8tTIL5st94H6pqkp235FK/v6+YJZ7T6k2FpN/2OFK3vU7Rb9wspzVq+Tefqv840+QHTS4/tvqP0CpO34r26NHUEoTiQRnAYqLa40n23sY1NxHrr1a2r5d/he+KO9bpwZ1/o3kpzv/kOUBAGFDa0qglbB210e2V3Bee01m6VIpGpX3/74qO3xE/dPTxsiOHi3/m98Kwu/atXKefqrhG3Uc2dGjpe7dg244zh6mwbNJ0tZKlZVy77xd5qOPpM6dlbr0cql9+yyuXM9hRatKAEA4EeaBViTrWXlJ8jw5016Q8X2pSxf5xx4XLCJtiDHyTvm81KVrsKj1rbeknTuC7+3cKbNsqczSJUFtfFOnY9+X89ijcv/9oBSJyDvvAtmx45rq0AAAhA5hHmhFcsrOnidn9nvB9dq3lx0xIrvrGSPbvYfsoEEykpxlS4PgLsmZ/pqiX/6iYiefKPfvf8tt8NlYu0aRW2+WNm6UP3myUqedvltt/94gywMAwoiaeaAVySmQlpdL27YGpeqdu0jtO2R/3bIy2e7dg883b5apqAiOs3OnzNIlMpWVMps25TKahmvmk0lF7rpT5v3ZUmmpvB9fKvXvn9ttNMBX0NkGAICwIMwDrUTOBS3bt9XUlth27XNr5RKLSiUlkiRTXh70d5dke/aUf/QxUiIhf8jQXEdUN9+X88Lzcv/6FxlrlfraN+Qff0LuO8Y2wLIKFgAQMoR5oLXINc3XDsK51rbbWrfnmJo3Anb/SUrec39wvFx3c60rRFsrrV0r9647pE0b5Y8cKe+ss/dqB9o6sQAWABAyhHmglcg5h7bvUBPozfatQWjOdnY+kZDKdwa3W1oWdKiRpKKi4KMx6roDvq/IA/fLmfaCVFy8a9FrMzSFJ8sDAMKGBbBAK5Fzti0uljp1DibEt2yVtm7N/rZ27pDZsCH4S5cusumSm72yp/FbK/PuO3IfuF/G9+UfeLC8r3w1uLO+v/tHTRK3Mtamv5ZbPGfTKABA2DAzD7RVrit/4v5yV66Q2b5NZv482YOnNnw9a6U1a4L2k5LskCFSly7NM8YdOxS56w5p7VpJkjPjdcXHjtrzZbdtC/6sqlJsyv6SG1Hqip/JO/vc5hkbAAAFgDAPtBIm/ZH1XLTryj/iCLmPPSJt3iznpRflTT6g4V7z1pf7zNMyW7fKFhfLnzhJKsmxPj5b5eXShg3BTLskU1khVVY0fL0NG2q669SpslJm7RrZ/gNq6u85VQkACBvCPNCKGJNDZYkx8qceKn/YcDkfLpD7nwfln3yK7Jixddeb+L7M3Lly//ZXSZLt20/eSZ9rmvqUPY27Uyelfn6VvDPPavDq7v33BnX10ZhSd9wl26Wz7KjRddyWlfO/5xS59mqlfn6V/M+dLEWjlNkAAEKHMA+0Io6R/BzCvB0+TN6pp8lcd43M/PmKXPwjpX5zi+zoMZ/tFuN5Mq9PV/Sqn0vr1srGYvIuuFDq12/XIZctlfPSS1IqJX/iRNmJ++9d0I/HZQ+emtXZBnfa89I0Sa4r74QTpT596r28P/kA2X1HKvLD8+VVVcn/0pekkkYu3gUAIE8I80Ar4uSam2Nxed/9vpzXp8t59hk5r76q6HdOlX/c8fKPO162b19Jklm5Qs60aXIee0Rm+XIpEpH//74q72vf2C2sm3ffVeRHF8pUVir1syuVmrj/rtvasUPOc8/KJJO7Lr9+XfDn5s1yH/pvTe9627u3/AOmNL4zjlT/mwhjpJ49lfz1bxS58gpFfvZT2USVdOq3d3XmAQAgBAjzQCth0gXzOc3OS1KXLkree78iv7xW7iMPyyyYL3fhh3LvvmtXL3rflzwvqOHp1VveqacpdfElUsdO2Y/vkw2KXnDu7l1zPC/4c8VHipx/Ts2X/RNOlH/vH/YuzDc4ICP17q3UrXfI/OQyOT/9SVCPf/rpUllZ890uAABNiDAPtCamEWHeGKlnL6Vu+LX8k06W8/STcmbNklmyOFhAaoxUWio7dJj8ww6Xf8yx8g89TIrHP3MoO3GiUnfeHZTZjB+/+/dicfn77SezY/dFqXsaqt1nUM6bQtn+A2T33182Fs9+dt0YqWMH+dddJ6d9mcwvfylVV0vnnde8byQAAGgixtpct34EUMislZJ+joG+9pWrqqTKSimRkKmsDMJ2cXEQkEtLgz8bUwfvedKO7dmt0I1Ggw45Tg79ZcrLpUS1JCN16JDTdWOu5JTvlK67Tvrzn6Xzz5d++EOpffvsbx8AgDxgZh5oZYyR3Fxn52tfubg4+FAT74jqujmV5eSstDT4yFb6zkWc9H5VZWXSFVcEf951V/Cm5rLLgjcGAAAUKMI80Ao56UDvcd6tXo6R3NoT+B06SD/6UXBm4PbbgzKjX/4yCPj0rQQAFCDCPNBKRRzJNrbcJux8Pyjncd06L2LMrm/vltPLyoISm3btpOuvl1Ip6ec/l3r2JNADAAoOYR5ohTKZM+JICS+/Y2lx1sq8/bac556Vd/4FUseOewzhrhPMzO8xnhcXS2ecESzCveqqoB7/ttvqPBYAAPnC7uVAK2YkRd06AmtrZa3MsqWK/PF+RX98kbR2zWcW3UacoAyp3p9LSUkQ6G++WXr11aDDzcqVOWyxCwBA86ObDdDKWRuU2qT8Jl7QWsgqK+U8/F9Ff3q5vCOPUurG30g9ekjG7Ary2b7Dqa6WHnssqKWfMiVYHNurFzP0AICCQJgH2gBrgyCf9NpQoE+l5DzxmCKXXSo7erRSd/5Wbr++ch2Tew5PpaQnn5Quv1zaZx/p7ruDP3NpnQkAQDPglQhoA0x6M6moG/zZJkQi8j93ilI33yIzf55iF54vd+kSmca8nYlEpM99TrrzTmn5cuncc6UPP6TkBgCQd8zMA22MTZfc+LZtzNK7XkqRV1+SzjlHpn9/6Q9/kAYObNysuudJL78sXXCB1LlzcKxhw5ihBwDkDa9AQBsUcYKP1jxLb5S+n7GIdNRRMvfeK23ZIp1+ujR7dtC+MleuKx1xhHT//VIiESyQnTWrcccCAKAJMDMPtFGZ33zPSl7IF8dmxp55b+KmN4MyqrVO1fOkmTOl739f6tQpmFUfObJxC1l9X3rzTemss4KA/4c/SOPGMUMPAGhxvPIAbZRJd3Rx07X0DbZqLHBGwX2I1TrrsFtOd13poIOkv/41CN3f/nYQ7r1GNOJ3HOmAA6S//EXq0iWY7Z82LVgoCwBAC2JmHoCkYHbbpmfpw1ZP76YX+H4mwO+J7wdlNmedFQT5u+4KQn5jZuitlRYsCI61cWNwrKOOom0lAKDFMDMPQNKukpSII8VcKVrfDqkFwFFQShN365iJr/OKjjR+vPSnPwU7un73u8Gi1mQy90EYI+27r/TAA9Lw4bLnnaedzz+vRCKR+7EAAGgEwjyAGplAb9I151EnKMGJFEiwd9JvNjLjijq7xpvTZLgx0qhRu/rFn3uu9OyzjSu5MUYaNkz21lu1btw4XfKTn+jRRx8VJz0BAC2BMA+gTpn+9LUDdMyRIiZ48mjOcG+Unn03wW1nzhbULG7d2xs3RhoxIgj0AwdKP/yh9L//NW6GXpL22Uexm27S9mHDdPnll+vBBx9UIpEg1AMAmhVhHkBWMsG+ZtbeDQJ2JmRHnN1r1zNhP/NRc5xPfWQu76Y/MmU+MXf3swKZ227ScnRjpEGDpHvukQ4+WDr7bOmf/wzaTuZ8KKPO/fvrlttu09FHH62LL75Yf/jDH5RiUSwAoBmxABZAk/n0s0k2Ty67Bf181vGsXi1dfLE0Y4Z0003SV78adMBphHXr1umyyy7TtGnTdPXVV+v0009XJBKRYWEsAKCJEeYBQAreiaxfL11zjfTYY9IVVwSLY0tKGnEoq02bNunGG2/UP//5T1100UU699xzVVpa2gwDBwC0ZZF8DwAACoIxUs+e0rXXBgthf/WroI3leecFHXBymFU3xqhr1676+c9/rkQioZtvvlm+7+vHP/4xM/QAgCZFmAeA2rp2la6/Xiork379a6mqSjrnnODvOYbwDh066JprrlGHDh101113qbq6WhdeeKE6dOhAoAcANAnCPADUZkwQ6K+6Sioulm6+Wdq5Myi7KSrK8VBGnTp10k9+8hM5jqPf/va3qqio0C9+8QsVFxc30x0AALQlhHkA2JOOHaXLLgvC/R/+IOv7wd/btct5Vr20tFSXXHKJ4vG4fv/730uSLrvsMnXq1IkZegDAXiHMA0Bd2reXfvpTqVMnVd55p2ZaqwkXX6xOnTvnfKiysjJdfPHFateunX71q19p8+bNuuGGG9SlS5dmGDgAoK0gzANAXYyRSktlzzlHH0i66K67dHRFha699lqVlZXlNKtujFE8HteZZ56pRCKhW265RbFYTFdffbW6dOnS5DP0e+pTZj/1vT21MjPp/3x6f4Ca73MiAQAKCmEeABpSXKzx552nH3booGuvvVbbt2/Xtddeq969e+ccwuPxuC644AJ169ZNV155pbZu3aobbrhBffv23etAXzukW7vrT99m1/N/T4zSG4al/6wd8gn2AJB/hHkAaIAxRvGiIp122mnyPE9XX321UqmUbr/9dnXs2DHnGfpYLKZvfetbSiQSuvrqq/XTn/5Ut9xyi7p3757TsWrPvlsFoT3z0VRq3hCk/5IJ97V3780g3ANAy3PyPQAACItoNKrTTz9dN954o6ZPn67zzjtPK1asUGP23nMcR6eddppuu+02zZo1S+eff76WLl2a9bEyM++elRJe8JHymzbI7/F2FdyGZ6WkH9xu0q91NoBtCAGgRbEDLADkyPM8Pfzww7rkkks0fvx4/fa3v1Xfvn0bdSxrbc2xRo4cqbvvvlsDBw6s5/K7Zt+9Anv2dozkpmfrmaUHgJbBzDwA5Mh1XX3xi1/Ub37zGy1YsEDnn3++Fi9eLN/3cz6WMUaf//zndeedd+qjjz7S+eefr/nz539mhj4T4pN+8FFoQV7aNb6EH5wlYKYeAJofM/MA0AiZp85p06bpvPPOU9++fXXnnXdq1KhRjT7e9OnT9YMf/ECdO3fWPffco9GjR+8qpynQAF8fR5LrMFMPAM2JmXkAaARjjIwxOvLII3XLLbdo7dq1+tGPfqRFixY1qobeGKODDjpId9xxh3bu3KmLLrpIs2fPludbJb3wBXkpPVPvMUsPAM2JmXkA2AvWWllr9eabb+qCCy5QNBrVrbfeqgMOOKBRrSZ939cHH3ygM888UynP06233akpBx0czp1iM68u6e43EWbpAaDJEeYBoAlYa/X666/rvPPOU1FRke677z6NGTMm5xAe1MZbTZ8xU5f8+EfyPU93/PZ3mrj/pHAG+k+JOMEiWYlQDwBNgTIbAGgCmTKZBx54QPF4XN/73vf00ksv5bQoNrPINeUbTZo8Rb+/7w/q1LmL3nrzTXmpVDOOvuWk0otjAQBNg5l5AGhic+fO1VlnnaUtW7bo7rvv1uGHH57VrLqX6QJT62vr1q5V+w4dVFJS0nwDzgPXBLP0zM4DwN4hzANAE/N9X3PmzNGll16qdevW6aabbtLRRx+tSGTPm27vmpHfPci3dplAn6mpBwDkjjIbAGhijuNozJgxuu+++9SnTx+dc845evLJJ+ssubFqe0FeCjr0eFZt744DQBMizANAMzDGaMCAAbr99ts1ZswY/eQnP9ETTzyhVCq1W+tKa6WUl3uezXTR8X1f1vcb1Q6zvuM25nq+7wfjyeH6nh+cleAcMQA0DmU2ANCMrLVav369LrroIs2YMUPXXHONTj31VEUikSDI57gZVPnOnfroo4+0ds3H2rhpo7Zv367SklJ179FDffr01T6DBqm4uLhRY62qqtKiRQu1csUKderUSWPHjlNZu3b13rfy8nItXrRQK1eu1Pr162SMUbeu3dS7Tx8NGTpUnTt3afB2jaSoG7StBADkhjAPAM3MWqvVq1fr4osv1htvvKEbbrhBX/3qV+W6ESV9k9WsvOd5enPWG/q/v/5Fb7wxQ8uXLVdlZUXN94uLizVo8BAddtjhOvPsczR8+AhJqnfhbebp31qr2bPf04P/+Luee+4ZLVmyRN26ddO///uoJk2eXOf1Pvxwge664za98tJLWrlyhVLpjjuu66pnz146YMqBOue883XggQfJcd16xxIxwW6xLIgFgNwQ5gGgBVhrtWHDBl155ZV67rnndNlll+nU07+nWLyowetWVJTrz3/6o66/7hpt2bJFktSpc2d16thJ0VhUXsrT1q1btGnzZslaDR+xr26/825NPeSQOgO0tVabNm3S7Pfe1V///Ce9+OIL2rplizzPkyRFozE99ez/dMihh33mur7v6/n/PaefXnaJFi1aqLKyMnXr3l3FxSUykrZu26p1a9cpmUyoX7/+uvPu3+vY445vsKNPjNl5AMjZnlsrAACalDFGPXr00E033SRrra699lp51tF3zviu4vF4nddLpVJ66D//1q+uvVqbN29WcUmJvv6Nb+nkUz6vffcdqXbt26myskoLF8zXM08/pb/+5U9aMH+eHn/sYR08dWqdAdrzPN11x226847bVFVZqeLiYk3cf3+lUp7efefteu/L6lWr9JPLLtGHC+Zr1H6jdfGll+mgg6aqR8+ekqRFCxfq7//3V91/7++1cuUK3fCr63TYYYerqIHyH8+XjEtnGwDIBWEeAFpQhw4d9Ktf/UrFJSW65eZfq3379vp/X/u6XNfd4+VXrlihG6//lTZv3qx27dvrsst/qjPPOkcdOnTY7XJ9+/bVlIMO1r6jRunD+fN14UU/rncm3HVd9e7TR2PGjNUhhx6mkz53svoPGKi//+0v6TBf90nbDRvWyxijvn376c7f/k5TDjxIjrOrn8KYsWM1aNDVmv3eu3r1lZe1YP48LZg/X+MnTqz3Z5NZCEupDQBkjzAPAC3IGKOu3brpul/9WoMGD9PoMWN2C8Kf9q9//l3Lli2V4zj64he/rLPOOVdlZXtelFpSUqLvfu9MWWtljKk3zBtjdMZ3v6/vn3lWze0bY+o9S5Axcf9JeurZ/2nlihUaP2HiHsdfWlam8eMn6NVXXlYqldLadWs0XvWHeasg0BsR6AEgW4R5AGhBwQZRRvGiuM4862w5jlNn6K6sqNBTTzwuSYrH4zrt9DPUrl37Oo+dOU42u81KUiwWy3H0u26nV6/e6tWrd72X+2TjJ5KCvvsd2nfM6ti+DTaTAgBkhzAPAC3Mt5Jk6iytyVi0aJHWrVsnSRo0eIj2n/TZzjIN8TxP0197VTt27FBZWZmmHHiQiooaXnS7N5Xr1lotWDBfr73yiiRpn30Gacy4cVld16clAwDkhDAPAC0s28C6YsVHqki3n9xv9OgGw/+eJBIJXXTh+fpwwXwNHz5Czzw/rcEZ9UD2qbp2i8tkMqmXX3pRd95+q9as+Vhdu3bVxZf9RGVlZTkcL/iTUhsAaBhhHgAK1LZtW5VKJiVJffr0zbp8pmlkf1sfvD9b9/z+bq1csUIrVnykDevXq6KiQmPGjtNVV1+rI486Oqdb9iXl/rYFANomwjwAtKBcqkgqKytr+r6XlpY26vYikYjOPvc8bfzkE3Xt2rXOxbN7Y926dXrqySf0yYYNNV9zXVeDBw9WaWlpg4txP81a0Z8SALJEmAeAAhWLxWo6xSSqqxt1jGg0qrPOPrcR18z+bcfEifvrr3//p6qrqrVly2a9/tpreu7ZZ/TIww9p5ozX9bOrrtZp3zmjUWVCAID6EeYBoEC1a9dOkUjwNL1x40YV6obdXbp21WGHHVHz969+7Rv64IP3ddb3v6sP3p+tq352hfbff5JGjxmbx1ECQOtUd3NjAEBe9erVW/F055lFixbK9/08j2jPMmU0mQ/HcTR27DidfU5wRmDr1i168onHC/bNCACEGWEeAFpQLqXgw0fsq04dO0mS5sz5QGvXrmmeQe3R3hetTznwYEWjUaVSKS38cEFN/X/z3zIAtB2EeQAoUJ07d9YBBx4oSdq5Y4ce+s9/WnB2vu5ZdGvtbh91Mc6uha8pz8t67LSkBIDsEeYBoIU5WYZVY4zO+N6ZateunVKplP7y5z/qlZdfqjcUW2vled5uvd937NihbVu3aseOHTm8Gah7kKlUSk8+8bge+u9/lEy3ztyTWTNnKJFIyHVd9e8/QNFoNLtbNgR6AMgWYR4AWli2YV6SxowZq9O/+33FYjEtWvihLr34Ij3y8H9VXr7zM5etqKjQtBf+pz89cL+2bdsmSUomk7r+umt0wXln65fXXl3z9b3x6isv6/xzztKPf3iB/nDfPdq+fftu3/c8T2+/9ZbuuvN2SUFbzRNP+lxW7SmNKLMBgFwYy4okAGgxVpLvS8ksJ8ittVrz8ce69OKL9OQTjyuZTKpTp04aNnyEpkw5UH3795ckLVuyRO+++46WLV2qlJfS08++oLHjxqmqqkoHHbD/HneATSaTuv6X1+rDDxfU3N6SxYs0d84cGWM0deqh6tKtqySpuKhYV11zrQYO3EdLlyzRt7/5NX3w/vsqLi7WfqNH68CDDla/fv3l+57efecdTZ/+qj5evVqRSEQ/OPsc/eqGmxSLxRq8vxEjuQ4z8wCQLVpTAkALMgpm5h0j+VlMpRhj1LtPH/3unvs1aPBgPfrww1q2bKlmvTFTs96Y+ZnLl5aVady48Uokqmuu36tXb1WU71Sv3r136/Xu+75ee/UVvT79tc8cx1qr1157pebvZWVl+uGPfixJGjR4sP70l7/plt/cpMcee6TOsfTt20+nnX6Gzr/gh1mV2BgR4gEgV8zMA0ALs5I8X0rlsJbVWquqqiotWvih3n7rTc164w2tXLlC27dtU7yoSL169dbIUaM0+YApGjFiX/Xp21eu68r3fS1YMF9VlZUqKirWsOHDFI0GM+SpVEr33/t7LVu+rMHbj8fiOue8C9SnT5+a8WzZskVvv/WmXnvlZc2fP0+ffLJBjuOqT58+mrj/ZB089RCNHjNGJSUlWd1HR1LUJdADQC4I8wCQB9ZKCS+XfVYz17O7Frn6fs31HWPkuK4cx8mqNj1zLN/3s+7/7rrubsfOXM9Ld6rJ/L0xY5GkqBOU2AAAskeYB4A8sFbybPaz87WfqnMJyGHBrDwANA5zIACQD0ZyTfadbTzP0/Tpr2npkiUFuxNsYxlJEV6NAKBRePoEgDzILPaMONm1Yqyurtb555ylv/31z0qlUs09vBblGnrLA0BjEeYBII8ygb4hK1eu0Ccb1mvUfvtlvflSGDi0ogSAvUKYB4A8yrSqzAT6uhYxvT79NXXv0UODBw9pNTXzjgkWvQIAGo8+8wCQZ8ZIriQZKbWHNG+t1VtvzlKvXr3Vf8DAFh5d86g5I2HY8RUA9gZzIgBQAEy63MTdQ7Jdv36dPlywQCP2HamuXbu2/OCamEnPyBuCPADsNcI8ABSIzGz1p2voly5dos2bN2nS5MmhL7FxCfIA0KQoswGAApIpuTFO0IPet1YLP/xQW7du1ZQDD8738HKWqRoyCoJ8hMWuANCkmJkHgAJjjOQ4wSZKyUSV5nzwvnr16q2+ffrke2iN4iiYjSfIA0DTY2YeAApQpg99dUW53n/vXR188MFyI26+h5WzSKb1pAjyANAcmJkHgAK2efMmzZs3T4ccMlWxiBuaOvNM28kIQR4AmhUz8wBQwGbPnq2ysjINGTxYESeoQfd8yaurIX2eOSaojXfY0RUAWgRhHgAK2LRp0zRixAj17t076GRj0x1vFAR6P/2RT5mNrzLlNBJBHgBaCmU2AFCgqqqq9O6772ro0KHq1q2bpHRLR7OrjWU0vVA24jTQ6tGq7u1lGyHTnab27Tu1xgYAaBnMzANAgZo/f762bdumCRMmKBaL7fEyxuyalXHddGa36TIcWyvDGzU6zNeebXcyJTSfGgMAID8I8wBQoN5//315nqcJEybUu1mUqfnPrs9dpbN7rTKcTJa3tUJ97Xxvan1ian0ts8EToR0ACg9hHgAKUCKR0Jw5c1RUVKSRI0c26hg1wb5WCLc1/2ngugR3AAgFwjwAFKBNmzZp7ty5mjJlioqLi5vsuLVn8QEA4ccCWAAoMNZabdy4UQsXLtTUqVPzPRwAQAEjzANAAVqyZIm2bt2qyZMn53soAIACRpgHgAJjrdWMGTM0bNgwde7cud7FrwCAto0wDwAFxvM8vfLKK5owYYI6dOiQ7+EAAAoYYR4ACszatWu1atUq7bfffiopKcn3cAAABYwwDwAFZtasWSotLdWoUaMosQEA1IswDwAFxPd9vfHGG+rQoUOj+8sDANoOwjwAFJCtW7dq4cKFGjx4sLp165bv4QAAChxhHgAKyIoVK7Rs2TIdfvjhcl0338MBABQ4wjwAFAhrrVauXKmPP/5YBx54YL6HAwAIAcI8ABSIVCqld955Rz169NDAgQPzPRwAQAgQ5gGgQCSTSc2cOVOTJ09WUVFRvocDAAgBwjwAFIjy8nLNnj1bkyZNUiwWy/dwAAAhQJgHgALx9ttvKxaLaeTIkXIcnp4BAA3j1QIACoC1VjNnzlTv3r01YMAANosCAGSFMA8ABaCqqkpvvPGG9tlnH/Xv3z/fwwEAhARhHgAKwEcffaT169dr3LhxLH4FAGSNMA8AeWat1YIFC7R582ZNnTo138MBAIQIYR4A8szzPM2fP1/WWo0ZMybfwwEAhAhhHgDyLNOScvz48SorK8v3cAAAIUKYB4A827Fjh9555x0dfvjhtKQEAOSEVw0AyLOVK1dq06ZNGjt2LC0pAQA5IcwDQJ69/PLLGjRokPr160eYBwDkhDAPAHlkrdWMGTM0dOhQde/ePd/DAQCEDGEeAPJo7dq1Wrx4sfbbbz916NAh38MBAIQMYR4A8uiDDz6Q53maMGECi18BADnjlQMA8sRaq/fff1+SNG7cuPwOBgAQSoR5AMiT7du364MPPlD//v3Vt2/ffA8HABBChHkAyJONGzdq4cKFOvjgg/M9FABASBHmAUCStbt/+LU+Pv29prk9qzVr1mjx4sU64ogjmuagAIA2J5LvAQBAS/h0CLfpr9X+s/Y3P53ZM+3fTa2/m1p/7rrgp/5e53isPvjgA7Vv31777LNPtncDAIDdEOYBtGqZEG8VzLJ7/q6/N+Y4u74Q/FE73Dtm99Od9e3/ZK3VSy+9pAkTJqhDhw5sFgUAaBTKbAC0SjYd3FO+lPCCj5SfnolvytvRrjcKu91W+vbrKsuprKzU22+/rTFjxqhdu3ZNOCIAQFvCzDyAVqH2DLznp2vd8zWW9Bg8BTP3rhPM2mfKciRp7ty58jxPo0aNUiTCUzEAoHF4BQEQepmFqV76o5BYBTP2RpJrJMcJPp8xY4Y6dOigUaNGUWIDAGg0wjyA0MosXM33THw2rILSG+NJXjKh92bPVpcuXTRo0KB8Dw0AEGLUzAMIndoz8Ukv+LOQg3xtVtKKVau0ePESTT7gAJWUlOR7SACAEGNmHkCoZGbjU+nZ+LCx1mrVqlVavWqVDphysFK+kevsXk8PAEC2CPMAQiOzmVOmK00YWWu1aNFClZfv1IT9J8mzku9JESc4VUqgBwDkgjIbAKFQU1YT4iAvSclkUm+9OUv7jR6j9u3bS9p1psFrwh1mAQBtA2EeQEHL1Men0j3jw666ulpvzpqlyZMPUCwWq/l6TTtLAj0AIAeEeQAFLzNr3Rrs2LFDktX+kw9QNBrd7Xs1M/T1bDYFAEBtxlpeMgAUpkxpTWuYkc+orKzUooUfql///urcuUudl4tmNpqihh4AUA/CPICClFnsmmxFQT5XBHoAQEPoZgOgIGVKTrK+fHpewlqr6upqbdu2VbFYXO3bt5frOgqaP6pRu63WPnbmGA0dJ3PZVCqlrVu3KplMqH37DiopKam5bkPHSPlSzM15uACANoQwD6Dg2BzaT1prVVlZqYUfLtBjjzysl19+SSs++kiel5IxRiWlpRo/YaK+8c1v66CDp6pz585ZB/rMG4NVq1bqP//6p5566gl179FTZ519ro4/4cQ6r1ddXa3Z772rhx/6j/737LPatHmTrLWKRWMaPmKEvvXt03TMccerW7du9Y4l84Ym4jA7DwDYM8psABSUXMprrLXasH69fnvXHfr73/6itWvX1nnZ4uISnXDSSbr+xps0YMDABo+dTCT09jtv65GH/qtnn3lKSxYvlrVWkUhEP//FNbr8J1fs8Xrbtm3V/2fvvOOjqNY//JyZLem9AAkt9F4VpAmo2BV7u16vYkOxgmIXBVTs2HvXe392VMSGCErvvXfSe8+WmfP7Y7MhgZRNSEhCzvP5bMruzJl3Z2dnvuedt7z3ztu89spsUlIqt8dms3Hm2efw0uxXiYuLr9EWqwa6KlegUCgUikpQnnmFQtHk8LVyTUFBAfdNvps533+H0+kkIiKCU4YN58yzzqFNXBxOp5PNmzbyw5zv2bZtK99/+w26pvPCy7OJjo6pdEwpJRvWr+O5WU+zdOkSUlNSsFqtBAcHk5efX6UtUkpKSkp47ZXZvPTCcxQXF9OhQ0euu/4GBg4aTHBICOvWrOH9995h86aNzP3xB8LCwnj5ldcJCAiocX9oUnnnFQqFQnE0SswrFIomhVnqma+JkpIS3njtlTIh37t3H5557gVGjjq1QsnH8RddzMTb7+CZp2bwy89zGTJ0KP7+1Yvn3bt3M+/nubRt147RY8YyZMgp+Pn7cfedk3A5nVWut3/fPt57522KiooYdeponnvhZXr17l0WSjN06CmMOe00rrz0YrZv38av8+bx96KFnHnW2T7tE9UhVqFQKBRHosS8QqFoMnhLUda8nGTH9u3874vPcTqdxMTEMPPpWYw97fRKY9AjIiJ46OFHueZf/6Znr14VmjVVxtjTTueL/31FXHxbOnTsSGBgIIsX/11jrH2r1q2Z8dQz/PTjHCbfN5XeffoctU6XLl257vobePjBqWRkpLNu7RpOP2Mcul59pqspPZVtFAqFQqEojxLzCoWiySDxrVmSaZr88cdvbN++DV3Xufpf1zJq9JgqlxdCEBYeTlh4eI1jCyEIDQ1l3Fln16ryjXe9q675F5ddcSVWq7XS9TVNo0/ffoSHR5CVlUlSUiJOhwN/H0Jt1AlboVAoFEeirg0KhaJJIPF4n30Jl3e73fzz9yKklISHhzPq1DH4+flVu86Rwrq6cpN1KV9ZfozqPP9CCCwWCxaLpXT7vr1n8OwfXXnnFQqFQlEOVR9BoVA0DaRvXnnwiPl1a9cAEB4RQa9evWu9uZKSEh57+EE6dYhn8IA+LFu6pNZj1AUpJZmZGeTl5SKEIDY2tsawH8+Kvu8fhUKhULQclGdeoVA0GXzVqkWFhWRnZQEQGBhE69at67AxSW5eLinJyTgdTpzVJLbWJy6Xk78XLaKkpITwcM9ExOulrwlZ+lDOeYVCoVB4UWJeoWgGVOWRLR+iceQiXsHnjRg5UgA2taoovsbLA+Tl5ZWFxwQGBmIpV73GV4QQhIWFExcXR1h4BDa7vdZj1BYpJbt27uSbr78EoHuPHpwyfIRvK4vSz1ipeYVCoVCUQ4l5haIJIyWYpb+9wt2Xso1VIfCIeE0ApdVRROkLzUkfGqZR9remaXWKcbf7+fHE9Jk8MX1mfZpWLWlpqTz+6MNkpKfj7+/P7ZPuJCam8nr3laLCbBQKhUJxBErMKxRNhPJeaW9dcV8TQn3eBoc7rHoRwpM8o4lypQ8bSdz7+l5DgkPKBHxRYSGGYdRY2vFI6prkWleKigp59+23mD//DzRd5/Irr+LMs8+plR0qzEahUCgUR6ISYBWKRsYrsKUEtwlOA1ympxTh8XDEemu7u0q37TbBNJt2smVQcDBBQcEAFBYWkpaW2sgWVU9hYSFvvPYqzz87C0dJCRdccCEPPfI4gYGBtRpHiXiFQqFQHIkS8wpFI1AWNiM9wtllgvM4Cvgq7eKwsHeZYJiH7Twe+CpWLRYLPXr2BCA3N5fdu3aVxdA3JaSU5Ofn8/abr/PcrKdxOEoYMvQUnpj+FO3atavT3QEl6BUKhUJRHiXmFYrjjFdymtIj4F3mscXBNxRmOW+9KRte1Hvj+X3BYrEwYuQohBCkp6ezcOEC3G53wxlXB6SUpKamMPW+e5n+xDQKCwsZd+bZvPHWu3Tp2rVugyolr1AoFIojUGJeoTiOlPfEu5p4KIsXyWF7TRr2zoGvWlXXdU47/QxatW6N2+3im6++alLeedM0Wb9uHXfefhsff/QhLpeTy6+8ipdmv0r3Hj3q3pSKpleFSKFQKBSNixLzCsVxoHxMfFP1xNeEKcFlgGHUrmupz9Qi6VYIQd9+/Tnv/AvQdZ1tW7cw9b572bd3Ly6X6yhRL6WkqKiQgwcP4nK5ADAMg507d7Bo4V8sXbKYnJzsY34LUkocJSX8Mu9nrrv2Kn6e+yNhoaHcO+U+nnn2BRI6dTqm8TUl5BUKhUJxBKqajUJxHDBLhXwz1PBH4ZaeWHprqSugvjzFgtJSmT5OFIKCgrjv/gfYvGkTSxb/w++//cpFF57LVVf/i0GDTyIyKgqLrpOXl8e+fftY9NcCFvw5n/c/+oSRo07F6XDw2uyXeeftN4mMjOLz/33JqaPHAB7P+p7du9m3f2/Z9jZv2oRhGEgp2bNrF3/88Vup3YLeffoSExNDZmYmb7z2Cm+98RrZ2dlYLBbOv3A8w4aPZMOGdZW+j8DAIAYMGIifn1+N71mJeYVCoVAciRLzCkUD4XUOG6Xi90QQ8l5kqZfeonlu79WboC/1zvuyr4QQxLdtx/MvvsxjjzzEooUL2b5tG09Oe4yYmBiCQ0LQNJ2iokIyMjIoLirCarXx+2+/MnLUqaWDgKikTr1pGHzz9Ze8+cZrZc+5nE6cDgdSSr795it+/XUe4An5eeW1Nznn3PNY8s/fvPTCc5SUlHjGMU3mzZ3LL/N+rvJ9dOnSlU8+/y+tW7fBMAyKiooIDAxE0yreOFVCXqFQKBSVocS8QtGAGKbHk30i4o2lt2igUz+CXgjQNE9egW/LC/oPGMh//+9r5sz5jq/+738cPHCAnJwcMjMyEEJgt/vRoUNH2rVrx+ixp3Htv68DQLdYGHXqGCwWK4GBgcTFx1cwxGazERgYdPi5QAgLjzjKBl3XsVg8p1IJtImLP2qZ6vD39y+bTKxYvoyHHrifRx6bxugxYyvUzteVmFcoFApFJQjZVDLGFIoThDKP/Aks5I/EIkDX6kfQS+mpoFPbXSelpLCwkP379pKenk5RUSFCaAT4+xMZHU27tu0IDgnxKflUSknioUMkpyTXuKxAkNCpE+Hh4WRmZrJ3755a2e3v50/Xbt2w2Wxs3bqF22+9mZzsbF554y2GDx+BEAIB2HSV/KpQKBSKo1FiXqGoZ7yJrkYL+2ZZNI/3+FgFp5QtayJUHikl27dt46EH72ffnj08Nes5Tj/jDPxs1nrZt4qmjSz7Ue5/HxBH/KMOE4WiZaHEvEJRz7hNz6MlYtVKk1iPUU14a9yXPzuZpsnBAwdYtXIF/foPoFPnznUu8djU2btnD3feMZHdu3bx7HMvcMlFF56w77Ul4xXvksO9HLzPl//7yHWOPBK8JUuF9zVRMZdFHToKxYmNEvMKRT0h5WERWvt1JaZpYpomuq57Qiua4RVYAFa9fpI1HS43ObkFZGRksGDBfH7/7Vd2bN9GUVERd909mVsm3lYhpvxEQkrJrp07ePjBqWzdspkXXniBs88+G4vF0iyPC0VFr7tXvB8p4CtV6pWNU/NiZcsI4fk+auU89uoQUihOLJSYVyjqCVna0dXXb5RpmqSlprJly2b27t1DTnY2xcXFhISE0iauDb169aFzly5YrdY62CLJzclh5coV5GRnE9+2LYMGn4TNZqt0+aKiIv5e+BfZOTnVjqtpGqcMG0bbtu2qXkZ4PPR1EQyGYXDo0CE2btzIhg0bmP/nAtasXk1IaAjdu/eg/4CBnDp6DIMGn0SIj/HvzRdJalIiU++fwrJly5g+fTpXX331CTuBOZGR5bzvZmnztaMXKv3dgIe0wJPb4vXan9BfH4WiBaHEvEJRD9QmztvTwKiI//33c/73xefs3rWLtLRUDMMoWyYoKIj27Ttwxplncc/k+4iOjq5RuHq9+0VFRfy14E8+eO8d1q5ZTUFBAd179OS7H34iOjqm0nUPHjzARRecx57du6rdRnBwCO9+8BHjzjyr2uV8iZ/32ut2u8nKymL+/PnMnz+frVu3cujQITRNY9SoUYwYNZo+ffvRtm07oqKj0LSWIWZ1zZNYvH//PiZPnszq1at54YUXuPDCC8vu3iiaLt4rqyk9+TNNqVGct6eD9wFK2CsUzRlVmlKhqAckvgv5/fv28ejDD/DDnO9xOp3Y7XY6de5MZGQUNpuN4uJiDh44wJYtm9m2bSsbN6zn+Rdn06179yoFnMvlYu/ePcz/43c++fADtmzZjNPpLHs9NTWlwmThSBwlDhIPHaSkpIS4+HhiqhD90TExxMTG1rgvDBM03fPPkSa73W6Sk5PZvXs3a9as4bfffmPNmjUEBQXRrl07hg4dyplnnsmQIUMIDg5G03UMU2C2oLQ+XXiEvBDQvn17Xn/9dR599FHuvvtu0tPTueGGG6q8y6JoXLzhMk25UZyktP+FPOyt906RlahXKJofSswrFMeI1yvvC06nk2dnPcX3332L2+2mffsO3Hrb7Ywecxqt27TGz8+PgoICtmzezP+++Ixvv/madevWsnPnDrp261almE9OTuKRhx5g3tyfcLvdREVFc+ro0axbu5bdNXjbPXcKCsnLy8NmszFx4iQuuuTSSpe1Wi1VevcrjIlHzOhCekIMpCQ9PZ2FCxeyePFiNmzYwJYtWxBCMHLkSB588EEGDhxIjx49iIqKqtAwSUoQOriNKsITTjA04bmzcTh5UdCqVSumT59OUVER06dPx2azcd1116FV0vBK0Tg0ZU98dUg8kw6TwyE4oES9QtGcUGJeoThGyipRUH24q5SSH+Z8z38//xy32023bt155fU3GTZsOHq5xMbQ0DDatInjlGHD6d6jJx06JnDGuDOrFW1RUVF069Ydm9XGWWefw8hTTyUyMorr/31NjWIeICUlxZN8a7HQs1cvOiYk1G4nlEPguVOQnpVJeloyq1au5Oeff2b9+vVomkZISAhDhgxhypQp9OvXj7CwMPz9/asUpqK0JaxV9yQXNxeRVBf0I4R8eWJjY3nhhReYPn0606ZNo7CwkAkTJlRoOqVoHLwx8e5mfHwaeOL59dKeEZXdVVMoFE0TJeYVimNAllaj8MVjnJ+fxxeffUpJSTG6rjPl/gcYderoSpcVQhAUFMTk+6b6VNkmICCQRx9/AovFUsGrXf7v6khOSgI83UxjWrXyaZ0j8Sb0rli+jFWrVrJ61Uo2rF+H1Wpl6NCh3HjjjZx00kkMHDiQyMjIWglQ76JW7cSt4V9TnoHXQz9jxgyklMyYMQO3280dd9xR1oFWcfyRTTykxle8h50hwTQ8x2P58pYKhaLpoq4ACsUx4pa+FaDYvGkTW7dsBqBnr16cdfY5Na7jqxgHjimGOjn5sJhvVQsxb5omSYmJ/P33Qn76YQ7bt22jsKgQXdcZMvQUbrrpJvr26UOrVrGEhoYecyUWUeq51k4A8eRFUPqefKwuEhYWxhNPPIHNZuPFF19E0zRuvPFGAgIClIf+OHOiNoiTeO6Cee8UqUZUCkXTRol5heIY8aUelJSSXbt2kpaWCsCYsacTHBJS621lZmbw808/UVJSQvsOHWoMv/GVpMREAPzsfkRGRuFyucjLy8XldBEcElKlUHQ4HLz80vP8MOd7evbsxXkXXMjoMWMZOGiwJ3lVgE2nXkWmKG2IY9Wbd1gDHA5p8NYD9wUhBFFRUcycORN/f39mzpxJTk4O999/P/7+/g1qr+IwXm98cz7+asKQIE0l6BWKpo4S8wrFMeBrYVfDMEhKTKS4uBhN0+pcPz7xUCIP3D+ZrKwszjv/As4Yd2atx6iMlJRkAHSLzuuvvcLK5ctJS0vF7XYTHBxM/wEDueyKK+nVq3eFkA4/Pz8m3n4HV151De3atycyMqqC913iUx+cWuPtdmnVmmeIg0ZpsmEda30LIQgMDGTq1KkYhsHbb7+N3W7njjvuUB76Bsb7nT/RhbwXU4LL8EyeQYXdKBRNESXmFYpjwNfqKoZhkJ2dDYDdbic8PKJJCa7kZI+YT0xMZNpjj2C32bHZbRQXFVFcXMyCP+fz/rvv8ODDj3D9hBsJDvbcVRBCkJDQCRI6Vfl+pATZUF69Ui+9TS+N9a2qIU8TobyIh2MXRqGhoTz++OOEhYUxe/ZssrKyePjhhwkNDT1mWxWVI/GI2xag48vwvucyQd+o1igUiiNRYl6hOAZ8vaCbpklJcTEAFosVm9VaJzEfHx/Psy+8hKPEQbv27Wu9flX85/obCAwMJCo6muHDRxIbG4u/vz/5+fksXLiAr7/8kpycbGY9PZOQkFCu/fd16KUeep/eRwO454/sd6cLgabX0GWzkdCEJ6Smrp74qvB66O+++25KSkp46623sNvtPPDAA8pDX8+U98i3JCHvxVutx6qVfp3VoaVQNBmUmFcojgUfr+qaJrD7+QFgGG5cbnedNhcRGck1//p3ndatjuuun8Cll1+JrusEBASUJd5KKTnvggsZe9rp3HfvPSQnJ/HUzCc586yzad2mjU9j16fwkVLicDjIysri4MGDLFmyhLVr1zJ58mT69evniT0HdP1w8ypv2dDjjTe23xsT732uIfD392fq1KmEhoby8ssvU1RUxIMPPljrqkGK6mkpoTVV4Q1ps/iel69QKI4DSswrFMcBXdMJDg4GPEmjBfn5SCmbhNASQmCxWCoNzRBCEBAQwIXjL+aP33/jow/eJ/FQIsuXLWX8xZccNxsNw+DQoUOsXLmSxYsXs3r1ajZs2EBoaCijR4+uUPWnrNkSni603tKB3n4ADSXGvEmsWmlIUX174avddqmH/s4770RKyXPPPYfL5WL69Okq5Kae8HZMrdU6hkFi4iESExMpyMvDYrUSHhFBp06dCQoKOqbvv6OkhN27dxEQGEhcmzisPlSzklJSWFjI7l07ycjIQAhBZGQU3bp3x6/U2VDje5IgZGkZ1Tpbr1Ao6hMl5hWKY6G0oVFN6BYLcXHx2O12HA4Hu3fvwuVyHVM5yeOJxWJhxIhRfPLRh0ikT42ovNT2gi+lxOVyUVRUREpKCkuWLOHHH39k+/btlJSUYLPZGD58OJMnT6ZHjx7ExMQQUk1lIFEa4iJLBYj34zLl4WY/3t9eWyv7SI98H6IS0S5KfzSWyLFYLNx22234+fnx7LPPIqXkkUceISYmpklMHJsj3mPD1y7PUkry8/L4888/+OiD99m9axeFhYW4XC40TWCz24mJieWiiy/h0suvoF279j6XoDVNk7y8PDasX8cbr73K2rWrsdvtTLrjLm6+9bZqbcrNzeH7777lw/ffIykpEUeJAyHAbvejY0IC111/AxdcMJ6gUqdDdRhm/eV9KBSKY0eJeYXiGPD1OiaEIKFTJ6Kio0k8dIi/Fy2kuLio2Yh54HApTSnJy8ur9/HdbjcHDhxgy5YtLF++nCVLlrBy5UoiIiLo06cPV1xxBaNGjeLkk08mICCgbD1fRWp5wS0B3ftPOWS5P8oLelFu2aq21hREjRACf39/Jk6ciJ+fH48//jh5eXk8++yzxMbGNrZ5zRbDxzh5KSVbt25h2qMP8+sv83A6nQBERkYSGhqG2+0mIzODQwcPsmb1Kr76v/8x+7U3GDL0lGqPY28/hwUL5vPfLz5n6eJ/KCkpATy9IdLS06u1Kysriyn33sW3X3+F0+nEbrcTE9sKpCQlJYWDBw+wZPE/rJ+0jgcffpSwsLDq3yeH4+cVCkXjo8S8QnEM1Ea/9erVm44dE0g8dIi1a9awasUKxp5+RqN7TKWUZSE/VVekkezft7dsudo0lhKVhJtIKTFNE5fLRVpaGosXL2bu3Lls376dlJQUrFYrw4YN44033qBbt2506NCBqKioettX5cX5Uc9X8VpzQQiBrutce+21uN1uZs6cyaOPPsqTTz5JbGxsox9vzQ2Jb+E1UkpSkpOZfPedLPxrAQBdu3Xn2n9fx9BThhEUFIxhGqSlpjL3px/4/LNP2bhxA7Oensmbb79Hq9atqxz3t1/n8dSM6WzdspmiomKsNivt23cgOTkJwzCqtcswDN547RW++epLXC4XvXr34Z7JU+jevQcAW7Zs5pmnZrBn924+eP9dOnZMYMJNN9dYOtd7Z0sdTgpF46PEvEJxDNTmQhYZFcVV1/yL5cuW4nCU8NijD9MmLo5u3XtUeZvdG3Li9eAbhkFhQQFSSixWa71ULHG73fz2yzyiY2IYOGhwhTryXlJTU5nz/XdIKQkMDGTEyFE+jV3eMu97SU5OZvfu3axYsYIFCxawatUqwsPD6dChA6NHj2bcuHEMGTIEf39/dF2vVRdcxWFsNhs33XQTYWFhTJ06lczMTF566SXatWvX2KY1G7weaF8oKChg1jNP8feiRQCcceZZvP7G27Ru0wZN08q+p1JKxp52OiNGncp/P/uUe6fcT0wNd01CQ8PIzspi0OCTOGXYcM4+51xM0+TySy8iKzOz2nVXrljO5599isvlokvXrnz+vy/p0qVr2eR9wMBBdOvWg1tvuoGtW7fwxuuvctrpp9O5dJnqcEuwKkGvUDQ6SswrFMeAt3qKL7fghRCMv+gSvv36K+b/8Tvr161l0m23MuX+Bzhj3JkVmy1JSXJSEnPn/ohA8J8bJmC1Wjl48CDTn3iMgvx8Bp90MlPuf6DCOkdS/hmvB748hmHw45zvmTL5biIjI7l90l1cdsUVpWEsniv0oYMHeebpmaxZvQqAs84+l85dutb8hqVECJCm5GBSIosXL+avv/5iy5YtbNu2DT8/P4YMGcKMGTPo1asXvXr1IiKiadXfb854PfSXXXYZJSUlPPbYYzz00EPMmjWLNm3aqP3sA1L63uF565bN/PTDHAzDTddu3Zj2xAzi4uOP2s9CCOx2OxdddAmnn34GERGR1U5YhRD0HzCAjz79nISEToSFhyOEYN3aNYgabiG53W4WLfyLQwcPYLVaufGmW+jUqXOF7WmaxsBBg7j8yqt4asaT7Nyxnb8XLfLpO35krolCoWgclJhXKI4RXXg8VL4QHh7OU888xwNTp7DorwUsWfwP/7n2ajp36cqAAQOJiY2luLiYTRs3snnTRrKzs+jdpy9jTzudTp07k5ebyy8/zyUrKwvTrOgyzMzM4LFHHiI3N7fsudUrV3hey8hg0m23llWs6NW7D7fdNonAoCDS09JwuVxs2riRe+++kxeff5ZevfsQGxtLekY6K5YvIyU5GdM0GTP2NB546JEqK19IKXE6nWRnZ5GclMTK5UuZ/8fvrFmzBpvNRmRkJEOGDGHq1KkMHjyYwMDACqUwFfWPpmlcc801REREMHnyZG6//Xaef/55OnWqutGXomIVpJowTZNff5lHUlIiFouFyy6/kt59+lS7f+12O3Z7tE+2+PsHMGjwST5afpj8/Dz+/nsRhmGQ0KkzI0aOquA08GKxWLhw/MW88vKLZGdn891333D9hBtrHN9bIcpbyUmhUDQOSswrFMeAEKBpQPVhq+WWF/Tt14/X33ibN994lW+++oqkpETWrF5V5vkuT3R0NIMHn4Tdzw6A1WolJrYVNpud8IiICssWFRUx98cfSUtLPWqc4uJifv7px7L/s7OyuP6GGwkLD+eGm24mLCKc9999hxUrlrNz5w527txRYf3w8HDOu+BCptz/AF27dqvy/TmdTmY8OY2VK5azccN6wsLCGDx4MHfffTd9+/bl5JNPVqUSjzNCCKxWK+effz5SSiZPnsw999zD7NmzSUhIaGzzmjS+eOXBc4dryT//IKUkPCKCESNH1RhzfiSmaZKYeIi0tDQsFgsdOyZUW6XJF4qLitm+bSvgaTjXrn2HKicYnbt0ISYmluzsbLZv3UpBQUFZOd1q7S6tEqVQKBoPJeYVimPEW56wNvXLO3TsyLQnZ3LDhJtZtWoFy5ctJT0tjby8fIKCgoiPj6dX7z6cPGQIHTp2JCjIc1FN6NSJr7+bg2EYBAUFVRgzODiEG268ifz8/DK7jiy36H2uS9euBAZ6KsJYrVYuvewKTh09ht27dvHn/D/YtXMHOTk5REZG0bdfP0aPGUtCQqfDFW2qQNM0du3cSYeOHbn11omcNHggkZGRhISEKO97IyOE4JxzzsFms3Hvvfdy1113MWvWLHr06KE89FXgaxdht9vNps0bAQgJDqFb9+613pbL6eTD99/jk48/JCw0jJdeeY2Ro06t9TjlKSgoIDkpCYDIyCjCw8OrXFbXdTomJLB9+zaKi4s5eGA/PXv1rnEbvk54FApFw6HEvEJRD+i1FPPeZkzdunenW/fu1XZ1LS+07HY7nTp1rnS5sLAwHpv2pO9GlEPXdVq1ak1sbCuGDR9Rox1VYbFY+Px/X6IJgU2nNMmuTiYpGgCr1cpZZ52Fv78/t956KzfffDNvvfUWvXvXLNpaGt7+A76Qn59PUWEhAAEBAcTG+l7tqWx7UpKbk0PioUMUFRbhcDhqPcaR5Obl4na7EUIQEVl9bD5QlohrGG6yc7J9sxsVN69QNDbKVaZQHCPerp9aHa5m3ooS1T3qc6yaxvYmyNbVDiEEmqaha0IJ+SaKEIIRI0Ywe/ZscnNzueeee9i4cWOlCdQ14U0QLf8wJZimpza7YXqqwZR/eJ83K1m3qXh5a2tGQUF+2f4LCAys010oTdfp1bsP5553PuPOOovoaN/i6avDO8EQQuDn51fj99jf3x/whPwUFxX7vqEm8rkpFC0VJeYVinpACLBozds7ZRgG33/3DWvXrK6TsPOiAbpG894ZJzgWi4UzzjiDDz/8kPT0dCZMmMCaNWt8/tzLRLv01GB3meAwPA+nAU7T85yrEjHvfd5plFvHK/RLJwKNLux9THz1YtEteA94t+Gu0/fHarXy7/9czxf/9zXvvvchffr2q/UYR1I+2dWsoR49gOFtc1taCclXlJZXKBoXJeYVinpCUCpimymZGRk8N+sZvv7qy2MaR9c8dymUlm/aaJrGwIEDeeWVV3C73dx9992sW7fuqBKmFbzuJriMUkFuHBbrtQkxqwxZOilwm6UTAeOw+G80r30tthccElLm9S4uKsLlctV6c0IILBYLNpsNq81WLzkmgYGBgOeOW1FxUY2TDK8nX9O0Cl2WFQpF06YZSw+FomkhhCd2vrlWdti5cwcFBfn06z+gzgmRFq1u4UaKxkHTNIYPH87HH3+My+XixhtvZPHixcDhmHFvB1Svx92oRcnGumJyWNw7Sj355nEW9bX5CgQE+BMd4wmLKSos4tChgw1kVe0ICwvH3z8AKSUZGRk1TjK8dlstFqJjYo6HiQqFoh5QYl6hqEe84TbNUdDu3rWLwsJC+vWr2+1970RGxck3L3Rdp3fv3rzxxhvYbDZuv/12Fi9egmGanrCYUi95o4VSlE4evOE5xnEQ9LU9hnVdp3//AQDk5uawedPGBrCq9vgH+NO2tONvRno66elpVS5bUlzMvn17Ac+dhri4eJ+3o77yCkXjosS8QtEANDdB73I62blzBwEBAXRM6FTr9TVvzkAzes+Kwwgh6NevH2+//TahoWFMumMS8+f/hcttNKl4aLPUW+8yPEm0jR5bX4quWxg99jQ0TSM3N5f5f/xBUVFRY5tFYEAgffv2BeDggQPs2b27ylCb9evXkZ3tqWBz0slDsNlsPm9Hfe8VisZFiXmFop4RpfHi1mYk6PMLCti6ZTMDBw6qdbMbXXjeq6L5IiUITadnrz689e77hIWFM/HWm1iw4E8MHxInjzcmh+P2zQYS9N7+ET4tKwSnnjqGHj17YZom33z1JX8tmH9MieT1QWBQEKNOHY2fnx+HDh3kl5/nVhpqU1xczFf/9z+KCguxWCxcdNElPm+jmZziFIoTGnUJVigaAK+nyqo1/Rh6KSUFBfns2L6dAYMG12pdvZxHXnnnmh9lyaWUhtNIQYeOCbzw8it07JjAA/dN5s/5fzRJQQ+HRb3RQPH0vh7SQggSOnXiqquvwc/Pn4yMdB59+CEW/rUAR0kJpnm4/ZSUErfbTV5uLr//9isFBQUAuN0ufpk3lyenPcpzs55m9+5dFbYhpcQwjLJHxTHNCq95JxGapnHq6DF06twFKSWffvoxC/9agNvtLkt0djqd/Dz3R77/7lsMw+Ckk4cw6KSTfN9H6nuvUDQ6Qja260ChOIHxfrsMWRoW0LjmVIqUktWrVnLm6WP4ds5PjDp1dI0JsN7KPSpGvnkjS49LtzzyeUlKcjITb7mRLZs388LLr3DOOeeiW5pun0ENsOieY7M+jsnyFXYqPi/JyckmJyeHuLj4CuEohYWF3HHbrXz91f/hcrkICQnltDPO4Jxzz6N16zbouk5OTg4b1q3lhx/msHfPbl5/8x2uvPoaSkqKeeShB3jz9dcIj4jgo08+5/QzxpWNvXbNahb/83fZ/4mJibz3ztsUFRVy+hnjOGPcmYBnYjFy1Kn07de/zN6PP/qA+yffS35+HlFR0Vz9r2sZPmIEpmGyePHffPThBxTk59O6dRtefuU1zrvgQp+r6XjvQKrzgELReDTdM7NCcQLgvcDpgNBKm+U0qkWVs2H9emJiY4mJja1RyGuArqvyk80Z7yTTW9v9SIQQtGrdmudfnM1DU+/joQfuAyk5+9zz0HW9ztWOGhITz90Fi1Z6y/kYj08hPOMIpCdW3+1m06aN/LNoIf/8/TcORwkvv/o6HTp0LFsnICCAGU8/Q2RkJJ98/CF5ebl8983XfP/tNwQEBKDrFoqKCnG73QDY7X4kJSV6VpaUectlJbU+F/61gAen3leprb/9+gu//fpLqd2CF15+pUzMCyG48qprSElO5vVXZ5ORkc4rL7/Ia6+87NlvpR7+2NhWPPr4E5x1zrk+C3nvxKkJHg4KRYtCiXmF4jjgFQaafrgud1Py0i9btoSuXbsRERFZ9UICLOVKb6rrd/NFUnocemeWlXyYQgg6de7Mq2+8xd133s6Ue++mpKSE8RdfUuu8iuOFxBN2Y9E8E+i6HqRSSoqLi8nIyGD7zt38/PNcFvw5n6zMLPwD/ElI6MS5551PeHhEhfWEELRu3YYnZz7NFVdfw//99wtWLl9GVlYWTpezdJLUisjIKEaMHMm5511A7959ALDabPznhhs5dfRYbDYb/UrFuJeYmFj6DxhYo+2aJo7qHmu327ln8n2MPe0MPv34Q1auXEFRURFCeJJkR489jSuuvIqevXrX6rNVE3qFommgwmwUiuOMt363UYVX9HjjcrkYMfQkho8cydOznsdutx+1jC48YTX1FcKgaDy8FWF8bfQkpSQpMZGp901m2dIlPPPcC1w4/qImK+i9WDXQatmV2TAMduzYwapVq1i6dCkrV65kx44dJHTqxKDBJzNkyFAGDBxE9x49sPgYclRcXEx6Whr5+XlomkZIaBgxMTFYLJZGucMhpaSoqJDs7GyEEISHR+Dv718nW2x680nyVyhOZJRnXqE4znivfRYNtNIa2o3pqd+/bx95+Xl07dr9qHJ0SsSfOJQPralNx1YhBG3i4njuxZd4/JGHObB/X4PYV9+4Tc8Frqp4biklLpeLwsJCkpOT+eOPP/jll1/Ys2cPxcXFREZGMmbMGGbNmkXHhM6ERkTVSfT6+/vTrn37+nlTx4jXdxcYGERgYNAxjaXXcqKkUCgaDiXmFYpGwKsHNDxiQ+IJeTAaQdRv3LgBXdPp0bMnQgg0QdnDe7FWQv7EoLZC3osQglatWvPqG28hhPDZK92YeEOJrHpF0WmaJvv27WPr1q0sX76cRYsWsWbNGlq1akWvXr24/vrrGTFiBIMHD64wuXWZok77rqkgpWTdurVkZ2UxctSpx3RnRdD0q3QpFC2Jpn9GVihOYES5+HPhTdwrraJxPLz1Ukq2bd2CbtHp2b0bNm/jJxULe0LhrcxS27AuryfX+9tms9VbaIiUsk5jeW3xZV0pJU63RJhucnNzWLRoEb///jtr164lKSkJi8XCqFGjuOmmm+jVqxft2rUjPDy80rF1UbeJUFPB6XTy7ddf8f233zD5vqlccdXV+Pn51ekzKCtH2wB2KhSK2qPEvELRRPBeHCWeRFNK/y4Lw5GHnzum7XB4ElFUVMDO7dtoGx9PfFzrMjsUJw7eOvJGLcooORwOkhIT2b9/H5mZGWRnZ2O1WImKiiKubVs6dOhIaGhonYSgy+Vi75497Ny5g+DgYHr26kVkZFSNYxmGQeKhQ2zevAm73U5CQifad+hQ6XqmaZKRns6BA/tZvXoVi/76k3/+/puAgADatm3LyJEjOe200xg+fDgBAQFldxqqs0ErDTmrzX5sSthsNu6dcj/Z2Vk89shD7Nmzm7vuuZfIyKhajWMRKvFVoWhqKDGvUDQxyusJbxfK8kL+SFFflcgvf7Et86KV/vYK+qSsTPbv38eIESOaZLlBRf3ga48DKSX79+/j/Xff4e9Ff7Ft2zZyc3LKXtd1nY4dE+jXfwC3TLyN4SNGIoSo8djxllzct28vn3z0Ib/+Mo8tmzcRGBjIa2+8zUWXXFrlegApycl8+eX/+HHO96xZvQpd17n1ttuZ9uRMdF2vsI5pmrz/3jv8OOd7dmzfTlFxEUOHnsLUqVMZMGAAPXr0INaHEqxHIkTpBVM0jcT12iKEICwsjBkznyEuLp43X3+NzMxMHnz4EeLi4n3aH94JjTpVKBRNCyXmFYpmQPlwHK9KL6tD5cuFtRJPmpSS1NRUdu/ezf33318vdiqaHqaP4TUul4tFfy3ggan3sW3rFtxuNxGRkXTp0hV/f3/M0k7BiUmJ7N69i+XLlvDgI4/xr2uvOypx2ouUkry8XLZt3caHH7zL77/+SlpaalmddafTWdYB9UiKiorYt3cP//e///L1V/9H4qFDOByOstezsrKobIoihKCkuJioqGiuuOpqRo0aTVh4OMGBAdit2jFNWkVpx2NZx9yDxkYIQWhYGPdOuZ+EhE48/tjD7Nq5g5dffZ2uXbsdNTEqjyY8FYKUS16haHooMa9QNFPqwzu2f/9+ioqK6Nmz57EPpmhySB+FvJSS9evWcvedk9i1ayc2m42zzj6HG268iX79BxITE4NhGBw4sJ8/fv+ND99/j21bt/Dt118x/qJLiIiIqHRc0zT59uuvePjBB8jOzsJqs9G7T18iIiJY/M8/OBwlVdq0Yvky7rx9Ijt37iired+lS1cWLVpIYRUTAPAI1om331Fpcyspj/17I0pFbVUNt5o6QgjsdjuXXHY54RHhPPbIw9x0/XU8/uQMxp52eqWC3ivklUdeoWiaKDGvULRQTNNk9erVdOnShdDQ0MY2R9EASOmbB9npdDLjyWns2rUTi8XCNdf+m4cffZw2beIqCOIePXrSuXMXRo4cxXvvvsPNt9xa7bGjaRpR0dF06dqVoacM46yzz6Fzly6sWb2aNWtWVyvmo6KiiIuLY+SoUzn73PPo2asXWZmZrL/04mrFPFBltR1DgqgnQW/R8CSrN9MYeovFwmmnjyMuri333DWJO267lekzn+b8C8djt9vLPnfd+16VkFcomixKzCsULRTTNFmxYgX9+vUjMDCwsc1R1DNSgq8685+/F7F0yRIAevfuw+NPzCAmJqbSkBSr1Uq//gN49fU3geqTRoUQnHX2uZx9znkVPL5bt2ypURv27NWbOXN/qVBC0eFwHJMQ91aIqg9d6o2hF6VJsdU0022y6LpOz169eP+jT5j11Eym3HsXO3fu4LZJdxIeFoquCU8JSpXwqlA0aZSYVyhaKEVFRWzbto2zzz4bPz+/xjZH0QD44jU2DIP5f/xOQUE+ANde958qhbyX2sSd17WeuaZpVcbi1xVvArmkfkJGhAAd0PS61/BvbIQQtG3bjidmzCQ0LIy33nyd9LQ0Zs6cQXhY3SoWKRSK44sS8wpFC2XDhg1YLBa6deuGpmmNbY6inpH4VsEmOzub7du2YpomMTGxnDpmbJ22t3btGpIOHUJoGsNHjGyyoVsmpf0c6glRWk/WqpXW8vexclBTIzIikkcee5wunTvx3LOzSDx0gBkzZtCnTx8l6BWKJo66gisULZR169YRFhZG+/bt1cX6BET6qChzsrNJTU0FoEPHutePf+WlF7n04gu54rKL2bd3T63XP140hPdciFIvvfB0nG2O3VF1TRAaFMCECTfw/PPPs3XrViZOnMjKlSvLSosqFIqmiRLzCkULxDAM1qxZQ2RkJB07dmxscxQNgK/Sq7ikuCzEJioq6oQPuWpITeoV9RYNbKWivinreoHHRpte2tUVsFosXHDBBXz66afY7Xb+85//8OWXX+J0OhvbXIVCUQUqzEahaIFkZGSwb98+VcnmBMZX0ep2uXA6PELNz88PXa/bZWH8xZfQpWtXNE0jtlXrOo1xIlChOZtemohcWsayqfi2NTzlJjWtYkdoL7quM3ToUN555x3uu+8+7rnnHtLS0pgwYQIBAQGNYbJCoagGJeYVihbIwYMHSU1N5aqrrmpsUxQNhK/CUdd1LFbPpcDlcmGaRp22d+H4i7hw/EV1WvdExSuUheZJlDU5LOyPtx1wWMB7b8nXFE3VqVMnXnvtNZ5//nlmzZrFoUOHmDJlClFRUSo0T6FoQigxr1C0MKSU7N+/n9TUVIYMGdLY5igaGbvdr8zbmpubq8IpGgCv7tWkR0xb8Ah7KQ/3Aqhvfa+Jw7+FqBhT66sOF0LQpk0bZs2aRevWrZk9ezYHDhxg+vTpdOrUSQl6haKJoGLmFYoWhmEY7Ny5E39/f9q3b9/Y5igaCF9lVkhICJGRUQAcPHCAgvwClezYQHhj6r3JsrrmiVW36p64dWvp/7oo9aJT/edYWgIerfxY2uEYeGvpWLqouO3a2SywWq3ccccdPPvssyxbtoxJkyaxfv16TLOZdsxSKE4wlJhXKFoYDoeDNWvWMGDAAOx2e2Obo2gofBRtUVFRJJR6WQ8c2M+WzZsa1i5FGd4wHE0cDoHxdly1lhP59ioetvKTgFLRrmmHx6uLeK/UTiHw9/fnqquu4rPPPiMjI4PLL7+c3377DYfDcewbUCgUx4QS8wpFC8PhcLBx40b69+9fZdt7RfPH15O71WbjlFOG4+/vj2EYfPrJx7hO4FCbphwYUhZjfyyPBrRP0zROOeUU3nvvPTp37sztt9/OF198oUKzFIpGRol5haKFkZ6ezsGDB+nXr58S8ycwtfHInn3ueXRMSADgrwXz+eCD9ygpKa5yeSklhmFUCLMoKioiNyeH3NxcDKNuSbTHA60pq/lmgKZp9OvXj48++oiRI0fy4IMPMmPGDLKzs1V4lkLRSKgruULRwli9ejUxMTHEx8erBLYTGG88tS/yKiwsjCn3P8Ck226lsKCAZ2ZOJyc7mxtvuoWo6OgKyzqdTrZs3sSSxYs5/YxxdO3WDYBPPvqQJYv/Qdd1pj05g/YdOtT3WzomvPtBifljRwhBTEwML730El26dOGVV14hOTmZxx9/nLi4uAY5rximSUZWASmZeRQUllBQ5MDlNgjwt+HvZyMyNJDW0aEEBdjVeU3R4lBiXqFoYaxYsYK2bdvSunXLrQXeUtCE72UQzzv/Qnbt3Mnrr71Camoqzz7zFN998zWDTjqJLl264u8fQFJSIitXLGfXrp1kZ2UB0KlzZ3RdZ/mypXz15f+wWK3cfe9k2tOhbOxPPvqQX+b9jCyV1KmpKRQUFADw9puvM+/nuSA8de5vuPEmRo48FYA/fv+ND99/D1N67gDk5+WTWbrd+X/8wTVXXVEm3K686poaS2Nq1F8cuQLCw8O55557aN26NU8++SQHDx7k1VdfJSEhAV3X6zyuaUoKix1k5hSwbtsh5v29iW27kylxuikucVFY4sDhNJDSxKLr2G0WggLs2KwWwkMCGD6wE2NO7kaXDrGEBPpht1mUwFec0Cgxr1C0IBwOB2vXrqVDhw60atWqsc1RNDC+inkhBIGBgdz/wEN06NCRd95+k/Xr1rJhw3o2bFh/1PJWm43evXrjH+CJs9d1ncioKNq1a4/FYsFms1VYfuPGDXz/3TeVhmGsWbOaNWtWAxAUFMQZ484se23vnt18/903lVZN2b9vL/v37S37f+DAQdW/Rxo+prwl4u/vz7///W/i4uJ48MEHufrqq5k+fTrjxo1D02oXySulZOf+NJas28Nvi7cwf+lWSpxunC53rcb5e/Uunv/gd+Jiwzh3dB9OHdyVYQMSCAsOUKJecUIipApyUyhaDLt372b8+PFcffXVPPjgg41tjqKBkRJchqemua+Ypsm+vXtZt24tixb+xZ7du8jMzPQI9shIevXuw8BBg+jdpy8dOnQsE+579+4hKzMTIQTde/Ss0Cn0hznf88/fi8o881Vht9m56OJLGTR4MABLlyzm++++LfPMl+fIEKKzzzmXsWNPr3Z8q+Yp4aiof0zTZNWqVUyePJmUlBSefvppLrzwQiyWmr3ihmmSnJbLZz+tYO5fG9i8KwmH041ZD/JE0wSRYYEM7NmO/4wfxpnDe+FnV556xYmFEvMKRQti7ty53Hnnnbz55puMGzeusc1RNDDehkSuOpQDL5/k6r1MCCHQNK0shMJXQWSaBqbp26VG07Qyj65pmj7XMi+/XqWv4yn1qDRcw+FtSPfEE0/w66+/MnHiRO68805CQ0OrXD4tK5+5Czfy7Pu/kpSWi8vdcMnTwQF+DB/YiUnXjGHkoM7YrCo4QXFioI5khaKFIKVk9+7dlJSU0Lt378Y2R3Ec8Hb+9DURtuK6ot6qHWmaTi0jLkrXq16g1wblkW94hBB06NCB5557jtDQUF599VVyc3N56KGHCA8PrzD5k1KyaWcSM9/+mfnLtlFQ1PD16vOLSvh18RY27Uzi5stHMuGS4YSHqNAbRfNHeeYVihZCUVER9957L2vXrmXx4sWqLGUL4Vi88ycK3kZMqJj544KUkoKCAj7++GNeeOEFBg4cyIwZM+jevTsAxQ4Xvy/ZwiOz57DnYEa9hNPUFn+7lVNP6srMu8bTPaEVmipzpGjGKF+FQtFCyMvLY9u2bQwePLjevJ2Kpo+3w6jeQrWKwOOVV8mvxw8hBMHBwdx2223MmjWLTZs2cfPNN7NixQqcLjdv/Pcv7nrqS3YdSG8UIQ+lE4qlW7np8c/4Z80uDB/DuRSKpojyzCsULQApJTt37uTUU0/lmWee4d///re6tdzCkBKcpud3S8KieSYy6nBvHNxuN8uXL2fKlCkUFhbSZfC5LN3lIju/pLFNA0DXBB3ionj90asYOaiL8tArmiXKPadQtBD27t1LUVERvXr1amxTFI2EVWtZ3mldKCHf2FgsFoYNG8aHH31CgcvGd5+9QvreVUjD2dimAWCYkt0H05n45Bes2LjX50RthaIpocS8QtFCWL16NR07diQiIkJ55Vsg3jATSws563vj5NWh3vhICat3ZFESMQQtNAF3ykqMtDVIw9FkbhUdSMrk4dlz2Lk/tdJ+CApFU6aFnNYVCsXixYvp1q0bERERjW2KopHwxs9b9RPbQ6+EfNPBNCVL1+3hqXfmkV0ksLQZgSW6L+7MrbgOLkS6ChrbRMDjoV+xYS9PvjmXvIKmEQKkUPiKEvMKRQugsLCQHTt20LlzZ4KDgxvbHEUj4i1XaTlBQ26UkG86SClJz87nmffmsTcxEwCh6ejRfbG0GYYsycIsyWlcI8vhNkzmLtzIR98vVQmximaFEvMKRQtgy5YtGIZBz549yxr+KFouFTz0zVz0egMivCFESsg3HaSEz35czt+rd1UIXRGaFT28C9aE89CC2jSihUfjcLr56PslLF+/V4XbKJoNSswrFC2ATZs2YbFYyuo8KxReQW/Tmn8cvSbAoqtk16bG1j3JvPPlIhxO91GvCaGh2YIQWtNzLmzfm8I7X/1NcYmrsU1RKHxCdY1RKBoBXxw+9SVKDMNg8+bN+Pv706VLl/oZVHHCIATogNDALZtMPqJPeGvIW5SIb3I4nG4+nrOMpLTcGpeVpgGmE+kqRDoLwHCAZkFYgxC2INDtCFG720hSSpAGGA7MkmxwFZSOGYywBYPFDyEqn8VKCT8v2sS67QcZ1r+Tz9tUKBoLJeYVigZGysOhAF6hVKaXyr3mpex6JQ/HNHsrkdRFsGRlZbFz50569Oih4uUVleKNo7cJMCQY5tHHZVNDEydu3H9zR0rJpp2JzFu0EbdRdey5lCayJAsjZxdmzh6kMxdkxeWFPRQttCN6eDeEX6TPlbikMxcjYxNmzu4jkmwFwh6GHtUbPbwLwuJf6foFhSW8/sVfDOzRDj+71adtKhSNRTO/uapQNB2krPhwm+AywOX97f3b9LzmNj2eUOOIh7vc697lves7DXAbYJoVt1UdGRkZ7N+/n8GDB6uSlIoqEaXebb00lt7SRDumemP9rdphm9Vh3bQwTcmiVTvZn5RV5TJSSsz8Q7j2/4GRugbpyPa8ICyg+4FmBaEjHbkYaetx7f8DWZxeYxy7lBKzJAv3ob8x0jd4hLzQQbeVjimQjmzcyctwp6xCmpWH0khg7daDbNiRqGLnFU0e5ZlXKI4R73nelIcfZZ740t/HqjWkdywJZrmBtdK4Z43KPfdSSpKTk9m3bx+nnHLKMVqhaAmU3QXSPOE3hvRMHhuztofAc5zrWsW7VYqmSbHDyac/LMflNip9XUqJWZCI68B8cBeB0NCC26OHdUYLjgfdDtJEFmdiZO/AyNmFLMnEnbERa9wo0KvxlBsO3MnLMfMPeMYNaosldhDCPxrwTCDc6euRhUkYGRsR9jAsUb0rPaAOJmfx57JtDOzZFosqHKBowigxr1DUgfLhMoZXwFfivGlovWGWevM1ygn7chuVUrJz5078/Pxo3759A1ujOJHwahsd0HWPmDerOdbrffscvlNwLGFmiuPP5l3JHEqt3CsvpQRXIUbaWo+QR6BH9MTS6iSENaDCsiKoNSIgGuEfgVmUhiVmIKIaIS+lxCxOx8zb71k/IBZL/Eg0W2jZwaOHJSDsoTj3zAVXPkbWVvTQ9ghbyFHjGabJsvV7yMkrJio8qI57Q6FoeFSYjUJRCySHQ1u84S+G2fhJgyYeUe8yPaE4RundAdM0Wb58OX369CEwMLBxjVQ0S8qH31hKq9/YdE+8uuYV2fWxHTwXJF14QmhspaE03gmqEvLNAykl8/7eRFFx1ZVgjMIkzIJEALRgj+ecKmLXhWZBj+yJNX4Uwh5W4/bN3L2exFeho0f0QJQT8mVj+oVjiezhsdeRg1mQVGkojZSwbMNe0rPzVaiNokmjxLxC4SMSj1fSZYLTrBhO05gcKaYkh+P1nW6DVatW0adPH/z9K79YKhS+4hX23uRTm14qukuFt7fOuy4qPryCvPxzltIxrOXHsXjG0lU8fLOlxOFi886kapouSczcPR7BXVpvXlgDq83nEUJHaFafcn5kiTf2XkMLbFXpOkIIRGCsJ5zHdGEWZxyVeOulqNjJ9j2pNW5XoWhMlJhXKGpAloYWuEuTUM2moOB9QALJKekkJSXTvUcPbHZ7Y5ukOAHxinvdK+TLiXrvw6pVFPvll9O1w553pdubPykZeWTmFla9gDSQhR5xLCz+iMDWtZ6xSWlgFmVg5CdiFqYhjcN3AWS5TCWhVRVJLBCWAITFE9YjHXlQRSKs2zBZvnFvrexTKI43KmZeoaiGMiHfDEr1VcbaNasJCQmhXYdOGKYnU1aJJkVDIsp+KFoiyem51daWl84CpLvE84/F31PzvbYYTtxJizELDiH8IrG2PwPhHwmAsAaWnqtNTEcOelXja1ZPhRtAuouq9MxLKVm/7VDtbVQojiPKM69QVEKFuPhmKuSllKxfv46w8HDatW/viakvV9ZSoVAo6pvUrHyyqvPMGw68Z1RhDaiycVONVOHN14LaeP4wDcycnUjDUS7eXSKlRJpuzPyDSGde2bJViXmAxLTsutmoUBwnlGdeoTiCMiHfzLphHonT4WDD+nW0atWatm3bAZ5LqMssDW9AxSMrFIr6pajYgcNZdfKrNJyH/9FsdduIsKCFdkTYwz3x9hY/z9MC9OD2GH6RyJJMjKwdYBpoEd0Rup8nxMeR42lSlX+wnICv/kTvdpuUOFz4+9XRXoWigVFiXqEohyyt495cw2rKk5ySTEpKCiNHnYrNVvEi5DZBejtoKkGvUCjqCbfbqLbrK+U98dV4w6tD6FYsUX0qewWsAVhan4zr0D+e0pPZOzDy9iN0P6R0g6sYkAj/aE94jasANEu1J0IhBEUlTiXmFU0WJeYVilK88fGuxuyOU48kJSaSlZXJSScPqfR1QwKmEvQKhaL+MEyJrKZKgNDLJeIbJZ4Tbz2egITQ0EI6YEsIxZ22BlmUijRcSNOJEDoExqKHdUHzj8J1aCHSVYDQbRUnGZXgdFXeAEuhaAooMa9QcDi0xn2CCHmA+Pi23D7pToYMHVrlMkrQKxSK+kTXNTRNq7I0pbAGgqaDYSBdBUjT5RHT9YgQGsI/Emu70zwJt658ME2ExQ/hF4bQrJ7a8kZJqU3BIKqXQ372arrOKhSNjBLzCgWHY8mbe2hNedq1b88tE2+vcTlDgpAqhl6hUBw7NouOxaJhOKvwjOh2hD0MWZSGdDuQxZmIoNYNYosQGsIeAvaK3V2llEhHLrg8HWiFPcwTalMFpmkSoEJsFE0YVc1G0eKRzbj0ZE0IIXxqtOJuQk2wFApF8yU4yL964SsEWkh7z9/uYoy8vUjTfXyM8yLdGLm7AQnWQLTA2GqrqQb427BZ9eNlnUJRa5RnXtGikdLjmTZKVWxNsldKSXFxMdu2bWXhgj/ZvXs3WZkZaJpGTGwrTjr5ZE4dPZaYmBh0XfdJSJcf2+VysW/vXr7//ltsVhujx46lX7/+1Y4jpcQ0TXJzc/jn779ZtnQxycnJFBYUEBwSQt9+/Rl72ul069b9qETY8himp3mPRHnoFQpF3WgVGUJ0RBDZeUVVLCHQg9thZG4FVwFm9k5kSHsIbFP1eU6WawUlRGmpyfKuB9+cFp6hJGZ+EmZBEgCafyTCP7rak16HuEifxlYoGgsl5hUtGonvcfIul4u//pzPyy+9wLKlS3A4HJimWVbDWAjBu2/rhEdEMOmOu7ju+gnExMTUeJGRUpKZkcH69ev44vNP+eXnueTmepqu3DN5Cv369a92/fz8PL747DPeeuM19uzZjWEYmKXxqkII/u+/X+Dn58eLs1/l39ddX+U4JuCWYFFCXqFQ1JE2MaG0jg5lx760Sl8XQoB/FHpkT4zUVUhXAa79f2CJG44W2MbTSKrcOVMaTmRJNmZRGnpEd9CtYLoxsrYhHTkIayB6RDewBpZbx+WpZ39EHXvpdmDmH8CdvNzT8dXijyX2pGo6xYKuCQb36nDsO0ahaECUmFe0WKT0eKOheo+8lBKn08nXX/0fjz/yMImJnm6AERERDBg4iPj4tpimyd69e1i9aiXpaWk889QM9u/fx6znXiAoqOoOhznZ2cz96Ue++fpLli9fRlZmZoXXjWpKvEkpycvL5aUXnmf2Sy9QUlKCn58fQ4YOpUfP3gQGBpKZmcG6NWvIzMykf/8BNe4TwwRdVw08FQpF3YiOCCYyNKjaZYRmwRLZyyPSc3Z6BP3Bv9ACYhEBMZ4kWUC6CpEl2cjiNKSrCM0vDBHcFqQbM3dPaQfYCLSQ9ofXkRIzbx9G5haEX7inw6xmA6MYsyjd45E3SkC3Y4kZiAiIqtYrr2kag3u3r7f9o1A0BErMK1os3hAbT1xJ9cv+teBPHnnwAVJSkgkMDOTSy6/gvvsfJDo6GovVU+XA6XSydctmnn3mKRYu/IukxESKi4urFfO//jqPB6dOoaCwkKjIKAYMGEjvPn157523KCysposi4Ha7+fjDD3ntlZdxOp306duPGU89w5AhQ7HZ7Z6KEoZBUWEheXl5tG3Xzqf9YpieKm0q1EahUNQWq0XnlP4J/LhwA67qyjla/LHGjcCwh2BkewS9mX8A8g8eLhMpTUCC0BHWIKThKF1ZgG4D3Q90e8WykoYTI28fZmEyFBwqfU14xpGmZyx7GHrsYPSwTogaUgejI4LpGB91DHtEoWh4lJhXtEgqCPkaKC4q4pmnZpCSkozNZuPGm27hwUceJTQ0rMJyAQEBnDJsOG++/R7ffvMVF4y/iKio6GrHPvnkofznhhtp36FDmZBPTkrks08+qlHMJyUm8vqrsyksLKRz5y68PPs1hg4bhqZVvDgFBAQQFV29HeXxJsIqLa9QKOrC6af0YMbbP5PrKq5yGSFKGzy1GoIW2gkz/yBmSRa4izxdYoWG0G2eDq/+UWiBcQi/cM/KmhVLzABkRPeyZcrQbVhiB2H6R2EWpYKrGCmNw2MFxKAFxSPsYTWGQAohGDGoM1FhQbXKf1IojjdKzCtaHF79bvqoWBf+tYAN69cD0KtPH+697/6jhHx5Ylu14pZbb0O31Pz1at+hAw898hh2u73WF4uff/6JAwf2o+s6V13zL04eOvQoIV8XJJ59I1DeeYVCUXs6xEUyoHtb/lq5o+aFhUALiEb4R6GbLjDdpR504fGqaxYQlgrnR6HpiMBWVQwnEH4RCHs4uuksHU8eHkuz+nyu9bNZGNYvgeBAP5+WVygaC1WaUtHykJ5kT1/KMBqGwbx5cyku9lRm+M/1E4iOjql2HSGET0IePPGYfn5+tRbybpeLOd99C4Ddz4/LLr8Si4/b9AXDl52jUCgUlWCzWrj6vJOx23w/JwkhSr3nAQhbkMeLbvFH1EJ8Hz2e3TOOLcgzrm6rZKyqb9F2bBvF2KHd0TTl1VA0bZRnXtEikT6K1ZycbPbu2YuUEn//AIaPGFmn7c35/lv++vNPLFYrN0y4kR49e9VpHC8pqSkkJ3lKq7Vv3564+HhM08TtdmEYBlJKhNDQdR2LxVJrj72v+0ehUCiORAgY0rcj/brFs3Ljvibbv8LTPCoHM38/enh3T0OrUrGvaxpjTu5GgoqXVzQDlJhXtEhMH68umRkZZGSkA9AxoSPhYeF18hItXbKEt996Az8/P04/44xjFvP79+0jvyAfgK5du5OWmsqSxf/ww5zvWLd2LTk5OURGRtK3f38uu+wKTh0zloiIiNrVvUfFzSsUitojhKBzu2guPK0fG3ckUuxwNbZJVSKLM3CnbcDMO4il9VDwj0QIjeiIIG694lR0XQUwKJo+SswrWiS+ep5LShw4HCUAhIWGY7HW8StTukFZrvnJsZCfn4/L6blAZmVlMum2W1i6ZDFFRUVlgj03N4c9e3Yz//ffuOKqq5kx8xlCw8JqZ7JS8wqFog5omsZV55zM/35excYdiY1tTqUIIdBCO2LVrLhTluM6MB9LzABsUd25+ryTadsqvLFNVCh8Qol5haIa3G43bren1bjVZkUTdfPS3DLxds674EI0TaNHj57HbFdxcRFuw2PX0iWLCQoKYvBJJ3PmWefQs5fH67982VK++/ZrdmzfzkcfvE90dAwPP/KYz/H8TfXWuEKhaB60igrhoZvP5pZpn5FXUFIvY0q3A0wnWAMrNISqK0KzoIW0x+oXjjt5Ge7kpXRubeecUzphs+r1YLFC0fAoMa9ocdRGpNpsNmw2GwAlJSUYpo/tYo+gY0ICHRMS6rRuZUgpy7z9MbGxTHtiBheMv4jQ0NCyZc4862zOPe8Crr7yUg4eOMAXn3/KZZdfccwhPgqFQuELQgjGDOnGJWcM4LMfV+ByV1N33keMnJ2YuXuxtjsNrAH1YGVpsqw9FGv8qWh520nbvYKnn3yQ6dOnM2BAzc32FIrGRgWDKRTV4O/vj7+/PwCZmRm4nM5GtsiDn59/mYf91NFjuOyKKysIefBcoAYMHMgVV14NQEZ6BitWLPd9I8o1r1AojpGQQD+m3DCOAT3aHvNYUkqkMw/pzPc0japnAgKDuX/KZN555x1SUlK4/vrr+frrrykuLvY4UBSKJooS84oWR2kvQJ+IjokhJsZTz3j//v1kZmY0iZN6WFgYdu8dg+IStCoSWzVNY9CgwZ7lSopJSU72fSMqXl6hUBwjQgg6xkXx7JRL6N6x1bH1rpAG0lWMsPojRP2GwNhtVi49cxC3XT2Wi8ZfyH//+1+6devGbbfdxowZM0hNTa3X7SkU9YkS84oWia/Xk6CgILp07Yqm6bicThYs+LNB7fKVuLh4AgODANi7d0+14T8ulydRVkqJYfh+m1tp+ZaHN3rL+zAlGCa4Sx8u4/DfhlnaLfiIh0JxJEIIBvVqzyO3nkOHuMi6D2S6wChBWIPrtaOdRdc4pV9HHrn1HKIjgj3VeDp35pVXXmHSpEl8+OGH3HHHHWzatAmzjqGWCkVDosS8okXi63VA0zTGX3wJQUGeduGffvwRiYmHGtAy34iLj6dDx44AbN+2lX1791a6nGEYLF2yGICAgADatm3n8zZU99eWg1e4m9Ij1B2G5+E0wFVOzBuynLA3Pa97l3Obh8dQol5xJBZdY/zp/Xn6novp0j6mTucXabiQ7mKELaRebJKAn93K6af04M3Hr6Ftq8Olh4UQxMTE8NBDD/Huu++ya9cuLrroIubMmUNhYWG9bF+hqC+UmFe0SGpz4A8YMJDhI0YBsGvnDl547lmysjKrDLeRUmKaZgUPTvnn6iNMx2KxcNY55wLgcDj47NOPcTqdFcaWUrJjx3bmzPkOgMjIKAaffLLP21Ba/sSlvCfdKOd1d5UK9qp7YlYxHp71XOW894apvPWKiuiaxvmj+/LU3RfRs1Ob2p9kTBcYDoQtqF7ssVl0xp/Wnxfuv4z2bY7uwyGE8JxrzzqLDz74gL59+zJp0iSeeuop8vLymkTIpUIBSswrWii16c5ts9l45LHH6d6jB06nkw/ee4ebJ1zPsiVLSExMJDc3h4KCfDIzM9mzezc//TiH6/99DQv+nF92sn9qxpN06hBPz26d+OuviqE6BQX5HDiwv+yRlJRUFjaTn5fHwQMHyl7Ly8stG/Oiiy+hV+/emKbJRx++z+yXXmDf3r1kZWaSmprKgvl/MGniLSQnJaHrOldfcy1du3Yr2663U+yx7h9F88L7iRvmYc+7V8B7EdR9Mlde2DuNiqE4CoWmCc4e2YvPn72BM07pQYB/LRJZTY9nnmP0zGtCEBsZzP0TxvHi1MvoGB9ZbUM9Xdfp378/7733HjfffDOffPIJ//nPf9i8eXNZ6WKFojERUk0tFS0QKT1Cw9eD3+1289uvv/DAfZPZuXMHACEhofTs3Yu2bdtht9vJyc5hx/Zt7N27B5fLxT2T72PakzOw2WxMvW8yr7z8Ina7nf999S1nnX1O2dhzvv+WWU/NLLPF6XSyfdtWDMMgJiaWNnFxZcvePulOrrrmX+i6juF28/333zF1yr0kJh5C13V69e5N+/YdKCosYs2aVWRnZ6PrOmeceSZvvfM+sbGeZF7DMJg39yfCwsMZNnwEmlZxXm/RQBcq1OZEQ0qP0DaOs7jWAF3zTBLVMaUAz53D9OwC3vv6H97/5h9SM/IwqmnNLaXEzD+Ia+9cbN2uRPOrW0MnXdMY2r8jd117GuOG9cRuq12F7pKSEn7++Wcef/xxNE3j4Ycf5tJLLz3qHKpQHE9UnXlFi0UTHlHjCxaLhTPPOpu4uDhefOE5/l74F7m5uaxasYLlS5cCHu+N3W4nNDSMPv36cdrpZ6DrnooLoaGhxMXFY7Pb8PPzqzB2Tk4OO3ZsryCu7HbPMgUFBezYvr3s+aysrLK/dYuF886/gJDgYKY99gg7d+1ky+YtbNywAYTAZrMRFRXFpZdfwd33TCEmJqZs3dzcHL795mvWrV3Dw489znnnX4jdbgc8Hlkluk4sZKnn3RvXfrwxAdP0TBAtGiBUGFdLRwhBTEQw9153OuOG9WTW+7+wdN0esnKLqg5fcRWCbkdotZcuNotOXGw41144hKvPPZl2rY8Oq/EFPz8/xo8fT0JCAtOmTeO+++5j9+7d3HjjjURFRdVpTIXiWFGeeUWLxJvw56plYQJv7Pv+/ftYumQx+/ftIzvbI7DDwsLp3KULffv2p2u3bmWeGiHEURen8if8Xbt2stLH+u/9+g+ge/ceFbxAUkpyc3NZtnQJa9esJjMjA91ioWPHBIYNH07vPn0QQquwTSklKSnJ3Df5Hv5euJD7H3yI62+4kYCAgDLBpa5JJwbeY91tNo3WAUKAVSsN5VHHmALP+cjpMvhnzS4+/3E5/6zZRUpGXoUmU1JKjJSVGHl7sSWcj/ChYZQQYNF1+naN44xhPfnX+UNIaBtV+tqxHXxSSnJycpg9ezZvv/02Q4cO5cknn6R3795K0CuOO0rMK1osUh6u0NESkVKSnJTEs888xQ8/fM+tE29n4m23ExkeUmXdekXzovykVdJ0vOECz4RR3QFSlEdKSX5hCdv3pbJk7W6++mU1uw9l4HC6cTgcOPYvQLoKsXUYB7q90jF0XSPAz4bdZmH0SV05d3QfBvZoR8f4KKyW+q1ND1BcXMw333zDzJkzCQ0N5eGHH+asM8/Eaqv/plYKRVUoMa9o0ZjSU32jOX8JpJRIKesUsymlxOl08vKLz/PmG69x/vnn88S0abRp06YBLFUcT7zx8eU98k1NN1uVoFdUgpQSU0rcbpO0rHwWr9nFqk17+eiVRwkOi6brkItwmRqmaWKz6tgsFuw2CyFBfnRuH8OoQV3o0zWOQH87ukUrvQvUMAeZ9/y7fft2pkyZwrq1a/ly6lSGX3klxMQ0mYO7qMRJVm4hOXnFZOcV4nC6cbtNDNNECIGua1gtGsGBfoSHBBAeEkBYSAC6ygVoFigxr2jRlBc8zRW3280br71Cp86dOfOss7FYrLVaX0pJcUkxn374AbOeeYrRo0czffp0EhIS1O3iZkpzOq6VoFf4gtPppE+fPpx73oXcde99GCYYpsRmteBnsxAc6Ie/n7XRzllSShITE5n3/vtc8P77xI4aBU8+CR07HveD2yvrcguKWbVxH0vX7+VAchYpGXkkpeWQmpFHUYkLl9uNYZgITWDRdWxWnfCQAFpHh9ImJoxW0aH07tyGEYM6kxAfhUU/HDqqaFooMa9o8TT3cJuCggIG9O3JzbdM5J7J92Gx1D45TADScPL7b79yzz33EBMTw1tvvUWvXr3KkngVzYOmFiNfE4LSGHqt6d05UDQdcnJy6Ny5M48//jiTJk1qkoJSSonpcKB9/z1i+nSwWuHZZ2HUKLDbG1zUm6ZJamY+W/ek8NWvq/h71U6y84rJKyjGMDwz+8rOCZXdufOYKvCzWQgPDaBtbDhXnXcyIwZ2pkNcJAF+tib5GbRUVDUbRYtHCE/ZPNNsnrWw9+/bR0lxCe07dKiz8NY1sFhsnHPOOQQEBPDggw8yYcIEZs2axamnnqoEfTOhfNWa5nIoSzwx/VblnVdUQ1JSEjabjcjI6mvCNyZCCHQ/P7jsMujUCZ54Am64AW67De68E4Lqp9nVkUgpOZiSzQ8L1jP3r42s2LiPEocLU5r4MkWubAnPtVBS7HBRnJZLUloua7YeJCYimPNG9+G80X0ZMbAzfvba3QlWNAwqGEqhoJx3sLENqQMb1q8jPCKcdu3a1+kiZxGekoHgKa85evRoPv74YwICArjlllv48ccfcTqd9Wy1oqFoTkLei+Rwx1iFojIOHjyIn58fUVFRjW1Kzeg6DB4Mb73lEfZvvAF33QU7d3q8RvWEaUqy84r48LslXHr32zz+6o8sXLWTohInpqz/lHeX2yAxLYd3v/6HGx/7lJse/5RNO5NwOF2qG24jo8S8QoHHIygAq978BP3atWuIiIikXfv2tV5XL70rUeE5Xad79+588sknDBkyhNtuu4133nmH/Pz8erJY0RB44+Qbo458fdCcbVc0PAcPHiQgIKB5iHnwXFTi4uDpp+GZZ2DJEo+w//VXqIeusYZh8sfSrVxz3/tMfvZrNu5IpKjEeVxEtWlKUjPy+Pa3tZx/22vMfHseKRl5Db5dRdUoMa9QlCKEJxGvOXnoi4uL2bVzBzGxsURHx9S8QjnK15M/0qEvhKBdu3Y899xzjB8/nieffJKXXnqJkpIS5YFpgnjDa4xaOP28PRMMw8AwDEzTbPTP1mikplaKpk9iYiJ+fn5EREQ0tim+IwT4+cFVV3m89BERnnCbt9+G/Pw63YqSUpKTX8SLn/zBPc98yV8rd1DicDWA8TVjSklKRh4vfzKf26f/l6Xr92AYjX8eaYmomHmF4giE8HjoG6tbZm1IPHSI1NRUzjn3PJ8TXwUeb7xew4xFCEHr1q2ZNWsW8fHxvP7666SmpvLggw8SFxfXZONWWyqGj+E1DoeDA/v3s3fvHpKSksjKzAAgJiaWtu3a0alzZ+Li4uvl8y0uLmbF8mVld3WEEHTr1p3OXbpUuryJ5zunGkopyiOl5MCBA83LM18eXfckwX71lSeOfvp0WLkSHn3UU+2mFuUf9yVm8vjrPzJn/nqcrmP38NcHLrfBr4s3s3FnIo9NPJfLzhykYumPM0rMKxRH4A25sWgegdSUq9wkJh4iKyuTPn36+iS+6tKsJzg4mLvuuouoqCgef/xxUlNTmT17Nm3atFGCvokgqfk4lVKyf/8+Pnj/Xeb//hs7tm+noKCgwjIRkZH07duPa/99HRdedAkBAQF1/oyllPw5/w8mXP9vcnNyANA0jWlPzuC+qQ9WuZ5herSPQuHF4XCQmZlJWFgYgYGBjW1O3RDC45mfORN69vSE3hw8CNOmwfDhNQp6KSVbdifz5Btz+WnhBswm5mkyTUliajYPvPgd+YUl/OeiYfjbG69UaEtDhdkoFFXgFb5NNezG463aT25ODv36D6hxeV2ATa9bTe/AwED+85//MHv2bLZs2cK1117LmjVrMAyj5pUVDUpZ8mg1yxiGwerVq7jyskt46fnnWLN6DbpuoXPnLvTp248+ffvRqVNnXE4nC/9awJ2Tbmfm9GkUFRbW2a6kpESmP/EYuTk5tG7dulbvx5QqGVZxmOzsbHJzc2nfvm5J/k0GISA4GCZMgP/7P3A64V//gvffh9zcKg96KSUbdyZy76yv+HnRxiYn5L1ICVm5hTz+2o+89vkCihsp/KclosS8QlEF3lhyrTTspqawlOONy+Vi5/btREVFEx1Tdby8Nw+gqvh4X7HZbFx66aW8+eabZGdnc+ONN/L333+r+MjGpobEUSkl27Zt5e5Jt7F+3VoMw2Ds6afz2htv8cPPv7Dwn6Us+mcpP8z9hedfnM1JJw+hsLCAX+bNIzsnu04mFRUV8ebrr7Fl82bCwsKYdOfdtepQXJvYf8WJT05ODnl5ebRt27axTakfrFY4+WT47DMYPx4eegjuvx8OHap08X2Jmdz//Df8s2YXrmbw5SgocvDMu7/w8fdLmkwo0ImOEvMKRQ14Bb1FO+zZbgo4HA62bNlM3379sViOjksof2dB0+onBlnTNEaNGsV7771HdHQ0t912G99//z1O5/GpoqA4GrM0+bWqj9fpdDL7pRdZu3YtmqZx4fiLeO31N7nkssvp2DEBf39//Pz9SejUiX/9+zreee9Dbr3tdl5/6x1at25Ta3uklCxdvJjPP/0Y0zS59LIrGD1mbK08qibNr7ymouHIyckhPz+fdu3aNbYp9YcQ0L69p0vstGnwxx9w3XWwfDm4XCAlUnqqxsx852eWrN2DYcomeZe4MoodLp59/ze+m7+urGGVouFQYl6h8JGy8pVa0/DUO0pK2LRpIwMGDkTXD6e/lJ94eG2sT1OFEAwcOJD333+ffv36MWnSJN58800cDkc9bkXhC94qNtWxa9dOvv/2a0zTIKFTZx5+dBodOiZUKq41TaNrt268+PKrDB16Sp2ahWVlZTL9ycdJSUmha9du3HHXPQQHh9R6nCYaSaBoBLKzs8nLy6NDhw6NbUr9ExICt94KH37o+UJfdhm89x4Ue7q2vvTJH/zv51W43EazEfJeUjPzmPbaj2zZndzYppzwKDGvUNQCb5iKXk4wWxoppj49I5309HS6deuOrmll4TRWzVOtxmtrQ4SYCiFo27Ytzz77LGeffTbPPvssL7/8Mnl5ecpDf5ypSfT+Mu9nioqKABh35pn07NWrWi+5EKLsAR5Pu7eMZU3lK91uNx998D5rVq9C13Um3n4HXbp2rf2bonSiog6lFo+UkpycHAoKCoiLi2tsc+ofITwZ3yNHespXjh4NM2bAtGksmbeYL+aubLaebSnhQFIWs97/lfzCksY254RGiXmFoo54PfXexFKvJ1wTx0fcr1uzmtjYWNq1jcNuEZ5wGnHYruNBfHw8L7/8MhMnTmT27NlMnjyZlJSU47R1BVQv5t1uFyuXL8MwDIQQXHTxpbWKXfeye9cuvvjsU/7338/Zvm1rpctIabJo4V+88/abuA2DCy4cz2WXX1H3ajh1WktxomGaJsnJyYSEhBAaGtrY5jQcQkDXrh5B/8ADyK++wu+6a+l4aBe6bJ5iHjy16H9bvIVPf1iO260KJjQUqjSlQnEMlNcpAtD0w6EP3t9mPXW29E4SvDH8mzasIz4ujtiY6EaryS2EICgoiMmTJxMQEMALL7yAy+XiiSeeoF27ds278sQJQE52DmmpqYCnxGjHhE51GmfRooXcfutN2Gw2Zj49i+49eh61TEFBIW+89goH9u+nbdt23HzrbYSGhdXZdllDLoCiZeB2u0lKSqJNmzZ1CvtqVggBAQG4b76FfzJchD4/izcP/MnTsYP5PqwTblHJRFxKdCQJjlz6FmcQ6y7CzzTI162kWgJZExDNIWtQxW3UFumJ1e9RklW2DYs0ydH92OwXwUb/SAo1a5Vj5xeW8OkPyzhvdB/atgpX14UGQIl5haKe8XrGvfq9fGx9eZFf9lz5f8p51UXpg0o87aZpsHbtWtq3b09MNZVsjhd+fn5MmjSJzp07M2XKFK677jpeeukl+vfvr07cDUhNk8SCggKKSzy3tyOjorDbbXXajqZpWCwWrFYrWiWCyjAMPvn4Q379ZR66rvOfGyYwYuSoY/rslWdeAZ5j69ChQ7Rr165Od5WaI6m5Rdy9Pp+iDmfxWPJywg3H0d8HKQk2XQwvSOLO9HX0Lc7ELt1YpERDYiAwhEaBZuXvoDa8FdWHVQGxOEXtJkRCSvoVp3N3+jrG5h0kwHRjwUQABgKHprPfFsIzsYP4NaQDxVrlsnLz7iS+nLeKydefUad9oqgeJeYVigaiMh0jyn4cG0lJSaSnpzN69Gj8/f2PfcBjRAiBzWbj/PPPx8/Pj6lTp3LTTTfxwgsvMGLEiBPfo9ZEcTqduN2e0nD+fv5olXn2fGDwSScx4+lZ6LrOsGHDj3p9w/r1vPXGa7jdbgafdBI33XKrzx2JFYrqcLvdJCYmMnDgwBZzHvlxwXp2J2bitgVzW9sx6EiMI767waaTacnL+VfWNkJMFyaQaA1ijz2EEmEhxHTSqSSXWHcRl+Xs4tSCRJ6KHcynET0o0n3vztrNkc07B/6kT0kmLjS2+EWw2T8Cp9CJdxYwtCiFvsUZvH7wLx5pfQqfRXTHpR39OblcBp//tIIbLhlORGgzbfzVhFFnW4WiGbJr1y5KSkro1atXY5tSASEEp512Gh999BF33HEHEydO5Omnn+acc87BalXtvY83Vpu1rGypw+nArENGqRCC3r370Lt3n6Ne8yYnvvbKy+zds4fIyCjunTKVyMioY7bdswFUnE0LxyvmL7jgghYh5vMLS/jmt7W43Z44eZemc2TrJbvp5t7UtdyQuQWbNNhpD2V29AAWBbUhT7dhCA2rNAgznFyWvZMbMzYR7S7msZQVlGgWPo3ojunDxF6XJnenraNXSSZuBK9H9+XNqD5kW/wwEASaLsYUHOLJpKW0cxVwT/o6/gqOZ68tpFJv1qHUHOYu3Mi/zh+i7tjWMy3jnpVCcYKxc+dOHA4HffocLbAaG13X6du3L5999hk9e/Zk4sSJfPLJJxQUFDS2aSccNUnzgIBA7HY/ALKzssq89PXJj3O+5+uvvwTgiiuv4oxxZ6oLtaLecDgcpKWlER8f3yLCbP5evZOdB9KqfF1IyVl5+5mQtRm7NFjrH83VHc7i/cie7LSHkWoNJMPiT7I1iK32cGa2Oonb2o0h0RpIuOHg0eQVdHHk+GRLe2c+g4pSEcAuexjPxg7igC2YfN1GkW4l3eLPN2Gd+S6sEwaCzo4c+hRnVDleYXEJf63YQUGRKmNc3yjPvELRzHC5XOzevRs/Pz/at2/f2OZUird05ezZs5kxYwaPPvooycnJ3HPPPQQEBDQpsSfLftSNxnwrNW06PDyciMhIwNN4JykpkVatWtXb9vfv28fLLz2P0+EgICCQ9h06sODP+Uctl5yUVFbictvWLfz4wxyCgoIYM/a06jfQdA4TRSORkpKC1WolPPzET5w0TJONOxLJyK7a8RFgurkyewdR7hLSLf48HzuILX6RVcR1CgwEv4S0p2dkbx5LWU5rdyH/ytrGo61PqfHkFWo48Dc99e3324JxCP2Iqg+e8ffaQ3ELDYs0aOMqqnI8KWHDjkMcTMmmR0KrE/7zPJ4oMa9QNDPy8vLYvn07AwYMwGarW0Lj8UAIQZs2bXj++eeJjo7m1VdfJTExkccee4xWrRr3RO6NNjkyIbns7yqaMZVPSC4rASorlgNtStcnq9XKgAED+XXez5imyW+/zGPgwEH1Nv6PP8xhz+7dABQVFfLwgw9U+v69deoB/vffL/jqy/+jS9eurF63qd5sUZyY7N27l/Dw8BO7LGUpOXnFrNt2CFlNZntbVz5n5u0HYI1/DL8Ft8Os4aTjFjofRfZkQuZm2roKGJd3gHejenPAVn0ztwLNhqM0/r2jM48Q03lUvL2QklauInRpYgIp1upzuHbsS2PPwXR6JNSfU0GhwmwUimZHfn4+e/bsoX///o1tSo0IIQgMDOS+++5j2rRpfP/990yZMoWkUk/t8cDbfEiWlgh1m56HywSX4Xm4Dc9zRukyJqVC/4iHied1Q5YbwzuOeXhsU1bcbkNR08RBCMG4M88qm/TN/fEHUpKT623f+wf407ZtO9q170C79h1oExdH6zZHP2JiYsvWCQkJOeo5haIqDh48SGhoKCEhte8i3NzILShm/fZD1ea2DCpMwy4NTASLg1r7nMyap9tYFBSHAKLdxXR25ALgZ7o5K3cfV2dt4/ycPVjNw7Xgk60BpFoCkEAnRy63ZmwkyHCW84ZI2rryGVGQhAXJbnsoG/yrz5eRUrJzfxqmavFcryjPvELRjJBSkp2dzd69exk0qP48rA1NcHAwEyZMICYmhoceeohrr72WV199le7duzdYUlu5602ZSIeGKXno9fCX34DA04lXK1entL699kcOV16ke+989O3XnzGnnc4vP89l48YNvDr7Je5/8CFCQkIrvTsipSxtMgW6bkFKicPhoKAgH/BMzrwVlK6+5lrGX3RJjTOWPXt2M2bUcCRwy8TbmXTHXTV+7lol70/R8ti3bx/h4eGEHUPPguaAlJKCwhKS0nKqXa5/cXppWUhY5x/t8/hOobHRPwqytxNuOGjvzAMg2HByf+pq+hVnsNcewl/B8bjwfDcLNCuvR/elb3E6EYaDu9LWMbwgiY8jerA4qA1OoXFf6hqGFiZzyBrEzNiT2F9F8qsXt9tg3dZDmKakBeQzHzeUmFcomhlbt24lMDCQ+Pj4ZhVzaLPZuPDCCwkPD+fee+/l+uuvZ9asWYwePbpe30eFZl2mx5teLQ20CyUeLz14BL0mQKf+Bb0GGFJSXFTEhg3r2bJlM2eedQ5xcXGApwfAPfdOYduWLezdu4c333iNoqIibpl4G126dEUvV0LS6XSyaeMGfvtlHuPOOpsBpSE5/yxayBuvv4rFauXf1/2H886/EICAgAACAgJqtDE7O9vzGUtJYGAgUdE1i5BmdGgrGpADBw60GM98amZejR7rKHcxAFIIUqw1f/e8mAjSLP6YgE0aBJd62AXgJw0CpBs/040o7+4Qgl9D2vNIm1OYkrqWBGcuIwqTOaUwhZ32UNItAQwrTGaTfyRPxZ7E3NAOR5XQPBIJbN6ThCFNrCg1X18oMa9QNDNWr15Nly5dmmUMqcViYfTo0bz//vvcdddd3HLLLbz44ouMGzcOq9V6bE2GSkW84Q1zqT+zjxlvF2ADj7fe20isrm/X6z0vKChg774DzPvlF378YQ5pqakEBgXSqVPnMjEvhGDI0FOY8dQzTJl8N8lJSbz37tv8Mu9nevTsSfcePQkMDCQzM4ONGzawb+9eUlNTyMvPp2+//ui6zoGDB5n381xsNhujRp1aPzulBoRQgr6l43Q6SUlJoVu3bgQGnvi1yTNzCtG06g/6IPNwocoirRblfoXAqem4hIZdmvibbjQkBZqVN6L7EOsqIlv3o0RUlIUS2GcLpVjTMRHk6VYsUtLVkUN3Rw4ST6JsoOnCzzQo1Gr+4mbnFDWtE/QJgBLzCkUzQkrJypUrm62YB0830YEDB/LJJ5/w6KOPMnHiRKZOncqNN96In59frcfzhriYpTHvTfka4fXWG8Ij6Oviqc/Ly2PNmjWsWLGC33//nRUrVxITE8Pgk07m6mv+xelnnEmHjh0rrGOz2bjokksJCw/n1dkvsXTJYvbt28u+fXuZ9/Pco5bt2asXsbGHY9rDwsLo1r0HNpuV8IiIOr772lGDplG0ADIzMykqKiIuLq5FlKXMLSiuMcemfIdVu1mLUrNSYjFNLFIiAYfw9HEt0ix8Ft690lV0aXJxzi5eOrSICMPBj6EdeTW6HxFuB+Py93NSYSo9SrJo78zntYMLOKUwmSdaDyFT96v2xFbkcB63nKmWghLzCkUzIjc3lwMHDnDmmWf6FN7QVBFCkJCQwLPPPsvjjz/OU089RWFhIXfccQf+/v4+eei9VWe8Sa3N6dIgJbglmAIs2uHqOJVhmiYul4tDhw7x/fffM3/+fLZv305hYSHDhg3jldmv0LN3XzokdCIoKKjKfSeEYPSYsfTs1ZutWzazYvkydmzfTkZGOqY0CQ8Lp0vXbvTr159uPXrQoUNHLBZPzPzoMWPp2rUbQog6lbaMiIjg/gceAgmnVNJB9mhbVby8AjIyMnC5XLRp06ZZhRTWFZfboKYzWY5uB0BIiDB8r9cugBDTiYbELTQKdUv1ngQpGVKYwiMpK4kwHKwIiGVqm+Hss4cCkt9C2tHemcfp+Qe4PX0jHZx5/DtrK/ttIbwQM6BaWwzDqPZ1Re1RYl6haEZs3rwZTdPo1q3bCeGp8paubN++PbNnz2b//v088sgjtG7dutqLd/mQGqM5qfgjMCU4DY+gL++l9yY679q1i5UrV/Ltt9+yceNGYmNj6datGw8//DDjxo0jJiYGi8WCIQWGrFnsaJpGq1atiI2NZdSpozFNs8xDJoRA07Sy48q7/4UQREREEHEMHvmIyEgefvTxCuNWh37i6zaFD6SlpeFwOGjbtm1jm3JcCPK31/j92OoXUdoYWdKzJIvFQW18GtsqTXqWZCGAXM1GkjWo2uX9pMHFObvp4sghX7PyanQ/9pWVshSUaBa228PZaQ9njX8s/7f3Z6KNEq7P3MJ7Ub3ILZ10VIbdZlUxdPWMEvMKRTNBSsnWrVux2WwkJCQ0tjn1ghCCsLAwJk+eTEBAAM8//zy5ubk899xzVQp6r5B3Gc3LG18dblNiAgKT7du28ttvv7Fo0SLWrVuHw+FgzJgxXHnllZx00kl07dr16LsXEmrj7BJCoOt6g1USKo8sTc612e3oul6jWBGUhtioa32LJyMjA6fTSZs2vgnW5k5w4BFhhqUJqlB6rhOC1QExSEBDMrQwhQ8ie9aYdApglwZDClOQQJbFj7011Jj3N910d3jEf6I1iJ32sKMFuBCYwPLAWDb7RzK6IJEQ00lbZz65/lWL+QA/m/p61zNKzCsUzQTTNNm8eTP+/v507ty5sc2pV/z9/Zk0aRLx8fFMmzaNCRMm8NRTT9G/f/8K4k8207CaqjBNg5ycXJISD/HP34uY8/137N61k4CAAHr06MG0adMYO3YskZGRZQK+MjHsFcBNsXRzSUkJ90+5l7DwcO6ZfB8RERHVCnrd25Tr+JmoaIJIKUlPT8flchEfH9/Y5hwXWkeHYhgmujSJdRXRpziTq7O3My+kPf8X3hUJ7LCHsTYghkFFaZxUmEK/4gzW+EfXGDJzasEh+hRnArDWP5qdfmHV2iKQWErv2hlCVDthkAgKSpNxhZTYZPU1xNq2rv4coKg9SswrFM2E3Nxc9u7dS6dOnU64Mm1CCOx2O5dffjmRkZHcfffd3HTTTcyePZthw4YhhKdgmrdZU3OnpKSErVs289eCP1m+bBnLli1B0zROPnkIDzz0CMOHnUKvnj2xWHz0nAtPlRyzCYaiSinp0q0bLz43i5zsbKY/9Qzh4eGVLisATVN34BUe50V6ejp2u73K4+VEQghBVIg/AylgeNo2LsjdQzdHDisCYsm2+CGQSAT5uo3/C+tKr+JMEpx53Jq+gSnxI8mrJqylszOXKalrsGBSLCx8FNkTl6j+3OIUOolWTwWh1q5CEhy5bPaLqPTLGW446F6SDUCJZilbrzJ0TaN/t3h0leFerygxr1A0E9LS0jh48CCXXXZZY5vSYAghGDNmDO+99x5Tp07l1ltv5YknnuC8885Ds9iavZAvKSnhv198xn8//4ykpERcLhe9e/dh5lOzOOnkIURFRxMeHo4mBELz3InwRdgKPPXmdc2TR1AfSCmRUh5zbkZAQAA33zIRu83Gi88/h9Pl5IknZ9KqkjAqXVMeeYUHh8NBSkoK8fHxJ0R+UJWYJuTnw7ZttH/nPT7aNgfd6WRxUBueanUSa/1jyLLYMUs94waCr8M7M7rgEGfn7ePSnF04NAvPxw4k1RJASalI99SPd9O9JJvpSUsZWJSGG8Enkd1ZHng4iT3MXcL05KV0duSSZA3kjvjRFOlWCjUrfwfFcUHuHiIMB/emrWG7Xzh7bSG4hGfGLaQk0ijhzrR1tHPlI4E/g+NJs1RdnEHTBD07tz6xP9NGQIl5haIZIKUkNTWVAwcOcPLJJze2OQ2KrusMGTKETz/9lDvuuIPbb7+dzKxsLrvyavz8/BvbvGNC0zQ2rF9H6zZtOP/C8YwbdxZdunatkGwKh0tYWko7yPoicIXwnNBNUWNDVp84sH8/C/9awBnjzqT1McYs+/n5cePNtxIVFc0jDz3AnZMmMuu5F0no1KlsGa20XKfyyivAM/FNSUmhQ4cOJ2ZIhmnC/v2waBF8+SUsXYq1a1e2njSaaSk2NtrLJZyXf/9CkGIJ4JE2pxBmODilMJkbMjczJv8g84PbsdkvAoemE2o46V2SyTm5+4gwSjAQ/C+8Ky/EDKS4nFfeJk0GFaXTvzidPbZQLKVt9qSAH0ITGFaQxDXZ2xlSlMp3e37i27BObPWLwCF0YtzFnJe7lxGFSVilyQa/KF6MGVBtCGRQgJ2u7WPV97yeUWJeoWgGSCk5cOAALpfrhIuXrwwhBG3btuWVV15h+owZPPnEE2Rm53DjTTcTGFh1+cWmjs1m44GHHiEgILDaMpJwWNBba9lgyiLAVQ9i/q8FfzL7pRfo1bv3MYt5IQQWi4XxF1+CBB5/9CGm3Hs3z7/0Mh07JqAJoSrYNGM8d3HAMEwM08RtmGTlFlJY7KCgyFM+McDPRmiQPyHB/tgsOpomsOh6lU2SHA4HaWlp9OrV63i+lYZFSnC5PCL+iy9g7lzYuxcGDICXXkIMH87+5QfY+coP4KymhrwQbLOHc2f8qdyXtobzcvfSwZnPTZmbPN2vEeilktpAkKXb+TyiOy/GDCD1iBAYCZQInWKhU6LpHE65FWRZ/Hiy9RBcQufi3F20c+YzOW0tDqHjFlpZx1i30FgQHM+TrYawq7JE2bIRoV/3eBUz3wAoMa9QNANM02TNmjV0796d4ODgxjbnuCCEoG27drzw0itERs3gxeee5dDBg0x98GGio6OrXE9KidvtpiA/n+KSYkAQGBBAcEjIMd/alVJSUFCA3W6vsmOt2+3G6ay6KUpQkOfzKyoqKntO0zT8/PyOGq9M0PtothCecBuLdmy5BYZhsGzpYqKio+nYsf4qJ+m6zsWXXEp0dDQP3D+FG667lmeefZ6Rw0/xhBap63uzwjRN0rML2LEvlb2HMliybg+rN+8nMTUHw5RHNP4SmKaJ0ATtWkfQr1s8wwd2onPbGBLaRtE6OrTC8V9SUkJycjIdO3Zs/sJPSkhJgXXrPCL+998hMhJGj4aXXoJBg8BmQwjBKAJo32Yx2/elVj+mEGzxi+CmdmMZUJTOWXn76VucQajhxCpNHJpOtm5nWWAr5oW0Z489DHcl9/gyLf5c3vEcrNLALbSyRFYvh6xB3B0/irei+3BBzh56lmQRZjjQkBRrFnbYw5gf3JZ/gtpQLKqvXa/pGgN7tiMupnk2PGzKKDGvUDQDDMNgw4YNdO/eHbu96kSnEwlP5RqB3c+PKfc/QEREBG+9+TpnnX0Op58xrpLlJWlpqfz5xx8sXvwPKcnJFBYWgBCEBIfQrXt3xp52OiNGjvKpRGL5cQGys7OZ++MP/PbbL/j7+XP5lVdx2ulnHDXOsqVL+PjDDyhxlPj8XmNiYnn+xZcrfc2UnsRfX7vFitLOslLUvQZ/VmYmu3fvpnPnLoTVY/KhtyTmqaPH8PyLLzP5nruYfPedvP76awwZMqQ0R6CZC7cTHK8Xftf+NL7+bQ2rt+xn5cZ9ZOcVYZoS04cYr435iWzelcT/fl5JaLA/g3q15+Q+HbhwbD96dvLkUjgcDtLT05tvJRspPY/UVPjxR/jmG1izBtq1g8mTYexY6NsXrBXFc7eOsfTtFl+zmAcQAjc6KwNbsTKwFTbTIMh0YpMmJcJCgW7FLaqvDWUKQYa1mvBFIXAJnY3+UWz0j0KTJgGmGx1JoWbBje8Z61FhQZwzsrfvif0Kn1FiXqFoBpSUlLB582bOPvts/Pz8al7hBECWVa4RBAcHM/H2Oxgz9jS6dju69XhhYSHff/sNzz/7DAcO7Ke4uBgAa+mF0ul0ov+k887bbzJ+/MVMfehhOnSo2ePncDjYv38f8//4nXfffpN9e/eWjd2ufXvGnnb6UWPs27eP7779xjOR8JGEhE5VinnwJLVqpdc/n+PnNaCOTbX27NlNcnIy/77u+gZJVBNCMGz4CN7/4EOm3jeZm266iWeeeYazzjrruNS+V9SN4hInW/ek8O5XfzN/2TZSMvJKu5bWHtOUmEgycwr5bfEWFqzYzrtf/8O5o/rw7wuHkpuaiKZp1d6Fa5JICbm5sGsXfP01fPWV5/k+feDdd2HECAgLA12vVARbdJ0Jlwxn3t+bykKUfMWp6WRpDZtXZAqNAt1W6/UE0LtLG4b07Vj/RimUmFcomgM7d+5ESknHjh1bhNiR8mgRarPZ6Nuv/1HLFhYW8NYbr/PsM0+Tl5eLxWJh9NjTGDxoMLGtWoOAQwcP8teCP9m0cQOfffoxGRnpvPPeh0RERlaxfcnWrVv44rNP+fGHOezauQPTNLFYSm8jS4lZRS3l7t17cMvE23A6q78QFxUV8fmnn+BwOOiQUP0FzluW0wI+l3upq6CXUrJnzx4yM9I5echQ31esJTaLxqAB/XnzzTe5/fbbufPOO3nxxRc577zzWsQx3pyQUrIvMZOPvlvCx3OWkpFdiGHWb2kpl8sgNSOPj75bwo9/radHZAGRkZEEBAQ0j7s1UkJGBixYAHPmwPz5nlCaK66AceNg+PCjvPBVMahXe04b2p0fFmyoMlyvuRHgb+OOa8ag66qKTUOgxLxC0QxYt24dkZGRtGvXrrFNaXAklYv5SpeVkt9+/ZXnZj1DXl4urVu34f4HH+Kiiy4hIjISi8WCEAKn00li4iE+eO9dPv7oAzp07IithnClLZs28cnHH+J2u+ndpw9nn30u7Tt05JGHHiArK7PK9QYMHEjffn1rrCjz3Tdf88Vnn6LrOjdMuLnG92qaeOJnam8YVN8AAJUASURBVIFX0AsT3D5qApfTyaYN64mNbUVcA4Q4CMpV6RHQpUsX3njjDR566CGmTJlCXl4el19+eYsJJ2vKSClxON38uXwbT745l227U3C4qknMrAdMKUnPKiB56yr8nQZrtiXRsVMX/O2+CeHjipRQUgJJSTBvHnz8MaSlQVwczJgBY8ZAfDzU8lgO9Ldx7QVDWb5hLykZeQ1k/PFD1zQuGNOPk5VXvsFQYl6haOJIKVmzZg1RUVEtQszjo5AHTyOtmdOnkZubQ2BgIE/OeIorr77mqJh4m81Ghw4deWzak4w78ywGDT4Jf/+qb0cLIThl2HCmPTmDfv3606t3H+x2O1s2b/J456tB1/UaPcuZmZl8/dWXlJSUcMqw4YwYOapG72OZd74OTkpdA016BH1NXWKLS0pYuXIFQ04Zhs1HT6KvaIBFL+3wWlalR9C5c2feeust7rnnHu677z7y8/OZMGGCEvSNTHZeES989DufzFlGRrbvYWPHipQS05FHoVvnrme+YVdyMbdePorYqJCm4aWXEnJyYPlyT0Wab7/1CPbTToMLLoAzzjgs4OtgrxCCsUO6cflZg3nz/xbicjXBbnC1oG2rcG68dARhwf5N4/M7AVFiXqFo4hQWFrJnzx7i4uKIiopqbHOOCzUJTvBc8Bf8OZ99e/cCcMqw4Vx0yaVVim0hBFarlZGjTvXJhrj4eG6YcJPPNvuKlJI1q1exYvky7HY7F1w4nsgqwn2OxJS+N5LyUn5ZS2lSbHWNpQoLC9i8aSOXX3EVeg0TF59twDOh0MsE/JE2CiIiInjmmWfw9/fn6aefxu12M2HChOYTZnECIaUkNTOfGW/P5bMfluFwHn8xKZ0FCGsAhSUGb3zxF3sOZvDi1MuIDAtslONBSok0TUR2NuKPP+CTT2DTJggIgIkTPaE0vXpBYNXdT2uDv5+NSdeM5tfFm9m+14dk2CaK3WbhpstGcHKfE7RfQBNBiXmFoolz4MABUlNTGTt2bIuIJZalj5owDIO/FsynqKgIIQT/uvY6AgKq7jxYFSUlJThKSkBAQEBgWdJsQ+FwOPjsk49JT0+jd58+nHfBhT5/rlJ69k1dLone66gFT+6d26x80rR+3VqsVhvde/Q45ouvwBNOY9Eq2lC5fYLWrVvz4osvEh4ezowZM0hKSuKBBx4gLCzsmOxQ1I6DKdlMefZrfv57E0Z9tRSuFRJcBQj/SNAs5Bc5+H7+OrLzinhp6mV0ahd9XIVhYWEhmzZtYs+CBVz24YdYCgth8GCYNQvOOgtCQkDzvaqLr8THhvPMvRdzx8z/ciglp17HPh7YrDrnntqHGy8doSrYNDBKzCsUTRgpJYcOHSI7O5s+ffo0tjnHBV+88uApFbl/3z6klISGhdG7jvtnznff8u03X6PrGnffO6VBkz4Bdu/exa+/zkMIwRnjziIhoZPvZTIpJ+jroBtE6Q+Bp3a9xBOLb0jvBEqybOlS2rVvT+vWbeosmAQeL7xX3/g6ihCCwMBAHnroIex2O2+//TZCCKZMmUJEhGo009BIKcnJK2L6m3OZu3CjT2UmGwTDAdJAWPwRwjMTdLkNFq3aycOz5/DaI1cSHdFw/Ta8SafFxcUsWLCAr7/+moULF+IvJeOuvJLIUaNg2DAIbdh66UIIRp/clcn/OYNpr/1EbkFxnSfzjcGAHu145JZzCA5sGRXYGhMl5hWKJs6+ffvIy8ujX79+jW3KccFXMZ+fl0dWVhYAbePbEhQcXCext23rVn6Y8x0Wi4XLr7y61uvXBrfbzfvvvkNebi7h4eHcMOHGWpd+rC95JYRnMF3z5NWaEhwuN8uXLaFTp060qUXXV+9e10oFvPcd1VV7BwcH8+CDDxIbG8vMmTNJTk7m6aefpnXr1nUbUFEjUkpyC4qZ9vpP/N+8VY0n5AHpKvT8Ya14p83pcvPLP5t5ePYcXpx6GUEB9Z9T4XA4OHToEIsXL+att97i4MGDtGnThttvv53x48cTFh8PNlu9e+Grws9m5fqLhpOSmc9b/1tIbn7xcdnusSCA7p1a8+rDV9ItoZWahB8HlJhXKJowTqeT7du3Ex8fT0RERGObc1zwVUM4HI6ymu9BQUFYLXULjwkOCaZV69ZYdAv+DVzDf+uWzfzx269IKblw/MXEt619QrOsR9dc+WusLiDxwD7S09I488wzCQ7yCClZ9qPydb3JrOWTWo/dLoGfnx8333wzNpuNJ554gvvuu48ZM2bQoUOH+tmI4ij+O3clX/y0vM614+sLj5gXCMvRYXNOl5sf/lxP/+7xTLhkBHZb/cgYp9PJ6tWr+emnn5gzZw6pqamcccYZ3H777ZxxxhlERx/f0J7y2G0WpvzndATwZhMX9EJA327xvDT1cnp3qfvdPUXtUGK+mVOZ8JFVPO+l/K32o15T37smhbdZVP/+/VtGvHwtnIGmNDFLa11rVTRg8YXr/nMD511wIQJB6zYN5/l1uVzM/ekn9u3bS2xsK8ZffEmdqrU0pL908+bNFBcXc/JJJ5XFuVc3eWjo84XFYuHaa68lMjKSe+65hzvuuIOXX36ZhIQEJRLqESklqzfv55XP/qSg2NnY5oBmQQTEIGxBlb6cW1DMCx/+Tp+ucYwY2LnOx4JhGGRnZ7N69Wree+891qxZA8AFF1zA5ZdfTo8ePQgNDW0Sx1pQgB/3Xnc6UWFBzHz7Z7LzihrbpKOwWnT6dW/La49cSe/OSsgfT5SYb2Z4vWTlY2dluXJzdbnQa+W8akLWv5dNUXeKi4vZtGkTU6ZMaZBOnM0Zm81W1g23uLgYw6hb/evIqCgiG7hKkJSSlORkvvy/L3C73QwfMYIhQ4c2qYudYRhs3LgRwzAYMGBA2fONbaLdbmf8+PH4+fkxefJkJkyYwKuvvkrv3r2b1P5rzmRkF/Dch7+xP6nq/gnHEy2wDVpg9WFeKRl5PPPur/zvhba1jsk2DIPNmzezcOFC/vvf/7J161YGDx7MnXfeycUXX0x8aX+FpnZ8BQf6ccvlI4mJDObpd+axY39aIyUoH01wgJ0LT+vP47edR1xsWJPbdyc6Ssw3ccp7Ks1S0W5SOw9mTZTFKJf+9gp5TRwW+l5a4vfzmPd1LRIAjyQ5OZnc3Fy6du3aosW8lBLDMHC7XdhsdjRNw9/Pn8DSMnAZ6emUlJQgpWyyF5E//viN7du2YbfbufjSywgJadjkudqSn5/P1q1b6datW5OrHiOE4PTTT+f111/njjvuYNKkSbz00kv079+/RX8v6gPTNPl9yVYWrtxZq3OdN0nUc6e3Yb5z1Y1qSsnS9bv5v3mrmHDJ8Bq/96Zp4nQ62bJlC5988gnz588nLS2N008/nalTpzJ48GDatGn63mSLRWf8af3p3C6ap9/5hd+WbsHhaNhGXtWhaxoRYQE8fMs5XHLGwEYrHdrSUWK+CeP1uJu1aKJTL9ulordfiNLKFKUJcyf697T83Q+oeAekQjURKt4JOXLSU/5uh1fQi3Kv+8KqVato06ZNs7jI1AtHvEWXy0liYiLbtm5l8T9/s3TJYt54+126du1GZFQUbUubaCUlJZJ46BCdO3dpBKNrJicnh3fffgvTNOnVuw9nn3Nek/s8s7OzWb9+Pf/617+anG0AVquV0aNH89lnn3Hrrbdy9dVX8/bbbzN8+PAaG3kpqia3oITZn/1JXo1x2BJpupHOAnAVIk0XmG7QrAjdjrAFgzWgrPpMbZGmgXQXgjMfaThLx9YRuh/CHgKW/2/vvuPbKq8Gjv+eeyV57zh7773IJglhhBloyyqjUFbZlFVogQJllVE2lN1S3pZRVlv2DiGb7B2ynOEktmPHe0m693n/uJLtJB6y4yE55/v5OHEc6eqRfKV77rnnOc+h2y6v8PHOZ0s4btJg+nav/eqabdvs3LmTH3/8kTfffJMFCxbQs2dPzjjjDM477zwGDRqE2+0Oy32+Lm6XyejBPXjtgYv49+dL+efHi1m5YVerznVQCtKS4zl56jCuu2AGIwZ0wzTlxLqtyCdgGKk50czSTsu4cLiApnX1UvDBoN6oY/GXSFQzG6WpPoHSuv6ypYP/r+a/q7ZZ44cGgHL+VjVeQ6j9ddRas2LFCrp3737ELBYVfOFKSkpY8uOPfPrJxyxftpQNG9bTtUtXTjltVlUv+ejoaIYNH4Hb48Hn9fL5559yzIxj23L0tdJa8/WXX7Bl8yYMw+DXl1xaVR7UFC3xltNak5OTw44dO5gwYUILPELzUEoxcuRIXnzxRW699Vauu+46HnvsMU466aRmn1MSapY6kj8DtdZ8OW8dmzKy6/6s09r5XCzdi71/I3b5PnRlEdjB2noFrhiM6BSMxN6YaUOdAD/Udqva6Snvz12DXZqFrsh3WlMGR+SKwYhOw0juj5k25JCAfsWGXcxdupleXVMxA1dptNZordm2bRsffvghn3zyCevWrWPkyJE89NBDTJ8+nUGDBkVUAH8wpRSJ8TFccfZUpo0bwPtfLOOfHy8mK7cQbzOvGBvcN4IJqehoNzPGD+TC0ydy8tRhxMXISs1tTYL5MBEMHO3A6oxt1xSsflbgKoGhqpeIj9TPw+DBOnjlo6Hg/XDZALr672Dm3qyZta9RkuP3+1m1ahWDBg1q98G81+slJyeHrVu38vEnn/LpJ59QUlpCSnIKo8eM5a6772H0mLHExyfg8XgA52B2xhk/47lnniJr717e//c7XHjhRYwYOSqsDtJ5ubm8/967FBcXM/aocRx3/AmHNb6WemoLFy6kS5cu9OzZ+A47rckwDEaPHs0//vEPrr76aq6++mqeeuopZs2a1aQJxTUFPxMOuCpXy+dC8L1qqMCKvMGfh89uF5Li0kre+vRHKry+2m+gNdr2YeWuxb9vJfgD2XszGuVJBGWAttD+SuyS3dilWdhl2bi6TgZ3w61itbaxS/fi3z0PXZ4HaHDFOJl4ZTrbtrzYpVkYSb1r3UZZhZc3/reAM2eOIT42iry8PNavX88777zDxx9/THR0NJMnT+a+++5j9OjRJCYmtqtmAkopBvXuxO1XnMRlZx3Ne18u46PZq9m6ax+5+SXYtn3YpaKmUng8Lvp0S2PEwG5cec40RgzqTnxMFIYRYTt9OyXBfBjQunqJ9XAN4g9ma7AtJxA1jciaMBsuJ041y5kU1SdI4LyWwcWiBg4ceFiZ3HCWl5fH/PnzWbhwIXPmzGHdunX06tWL444/gYmTJjNl6jS6detWZ1DQr/8Azjnnlzz37NNkZWVxzx/v4rHHn2DAwNqzblprCgsLMU2ThARn0ZmMjG3s3LEDpRRDhw6jQ3p6sz0/rTWrVq3ghx++x+12c8qpp9GzV6/DDuZb4r02b948hg0bRkpKSlidDNVGKUWPHj144YUXuPvuu7n55pspKCjgwgsvbNJ7Jfg+rHllruE7VX9bNb8okOgI85evytJ129m4LavO/9dorLx1+LOXgu0Dw4OZ3B8jqTcqKglluJ1gu3wfdv4W7JJd2EXbsRN6YqYOrvextdboyqJAIJ8LhgsjqQ9myiBUdArK8DjlNt5C7MpCzMTedZbwrNq4mwVL1rAnYy3//e9/mTdvHn379uWqq67ipJNOYuzYse0qgD+YUgqP20XXjsnceNHxXPqLKSxcuY0la7ezfMMulq3bQX5hGSjw+6wGj3mmYWCaCtMw6N8rnYkj+zJ8QFdmjB9Ev57puKScJuxIMN+GgkFlXcuqRwJLg2U5y7UHPyrD9UCmA1lxy275LHxjaapfS6XApWDz5s14vV6GDRvW1sNrFlprfD4fJSUlbN68mXfffZfvv/+evLw8XC4Xxx13HH/84x8ZMnQoKWmdiIqODimovPra61m5cgXz583lm2++4oLzzuHGm25l2vRjiE9IwDAM/H4fe/bs4cvPP+OzTz/htzfdwplnnY1Sig/fe4/nnnsal2ny/Isvc/Ipp1Vtu6SkmMKCwqp9JTs7G8t2LmEXFxWxe3dmVYCRnJREfMKBq1L6/X7++X9vUFhQQPcePTjzrLNxuz1Nfg1rzrtoTqWlpaxcuZJzzz037Ca/1kUpRa9evXjyySe5/fbbufvuuykrK+M3v/lNSAF9MFvpD5Q0Hs7nQc0TAANn4SwzzEsR/X6LpWt3sHdfYZ230eX78GevcAJ5MxpXl4mYKQMPKKNRgIpOxUzoiT97GZhuzMTeIYxAY+1b6QTyysDsMBJXxzFgRlVv2xUNngSM+O613F2jtVPDX5KzjSsu+QisSoYMGcJf//pXpkyZQteuXY/I+RSJ8TGcNHUYx08aTH5xGfmFZezJKWDd1r1s3p7Nhq1Z7C8qpbzCh9fnxzQNojxuYqPd9OqaxpB+XRjUuxODenciNTmOtKQ4YmM8YX+SfyQ78vbyMBGJ2fj6+G2wlRPUh9vbva0mEjeV1uDVmvUbN+Hz+RgxYkRbD+mwaK3Jzs5m7dq1zJkzh9mzZ7Nu3ToGDBjA+PHjOfHEE5k+fTppaWlV9/HZKqQTXKUUffr25fEnn+GeP97JN19/ybq1a7nyiktJTk6me/ceRMfEkJeby65dO/H7/SilWDBvLqfNmkV0dAwlJSVkZ2XhcrmoqKg8YPv/eP3v/P53t1T1s6/p+Wef4flnn6n692OPP8kNN958wG3WrlnD559+AsApp85iyNDDOzE7uLtUc1m9ejWWZTFy5MiIymAqpejQoQNPPvkkjzzyCA888ABFRUVcf/31JCXV3i2opa/M2TgnBxaBUkTC88plSVkli1ZlYNXxRtO2hT93HVhOaY3ZYRhm2lAUh156UMqpm3d1Ozr4k4ZLbCrysQq3ObeO6Yir01iUUcvKqrVeYbPRpVlY+zdiFW1HmdHEDhvDEw/cxoknHIfb7a4e1xHM5TJJT0kgPSWBAb06csz4gY3expH+GkYKCeZbWVU2yA7UaRN+wW9T2Rp8gSx9OEyQrZl5C5NWvCHzVnrZtm0rCYmJdOrcreq5RMLnarCNZGVlJWvXruXTTz9l9uzZZGZmopRi5syZ3HbbbQwdOpQePXoQFRV1wAFDB0oVQr1apZRi5KhRPPP8X3n/3//mvXffYdOmnygtLWPjxg1Vt3G5XPTs1YtfnHkWZ519blWGfNSYMfzq4l9jGuYh9eIu0yQqKqqqDV99TPPAj1O/388H77+LZVmkpqbyq4t+HdoTqodhNP8+EJxoHRUVxZAhQyLu4K2UIiUlhT/84Q94PB6eeeYZiouLuffee4mucXWn5hwZfyskUYJXXRWBz8Sq8bbwA4eopKySH9dsr3Pf1t4idFm28w9XDGbqoZNPD6CUE+iHQGuNXbwzMNEVXKmDobZAvh52WQ52eQ6ujmMxEroT22sIPfoOq5pTIw4Uae9r0ThKh3KUEs2iqqzGCvSKD/y8Pb7FXMFa+jZ6csFsvBUmHYEaa9++HH5z2aV0SO/Aq3/7B4ZSVSdJ4fqZ7Pf7ycnJYcOGDcybN4+PP/6YjIwMevTowVFHHcVpp53G8ccfT2xsbNWl77oOMFqD12pcwKW1xrZtfD4fW7duYevmzezL3QcaUtNSGTx4KD169iQ6OhrTNKse27Ksqsy7aZoH9C3Py8sjJzsrpHF06tT5gKsLfr+fPbt3U1JaQlRUFD179qrKGDaFocDdAu+pyspKrr32WjZs2MB3330X0fMzysrKeOGFF6omxN5777106dIFpVTV50FbXZ0Lzi8Kl/mCi1dncOIVT9fZ+cQqzMC381uwKjGSB+DucSzKbNz+qy0vuiwHrW2UKwYV0wGlFNry4cv8Hjt/E7hi8fQ7AxWd6rwntUYH0lx1BqBao7WF00nAhQIS4mN47q7z+OUp4xo1RiHaA8nMt5KDA3lon0F8kF8DuvXr6IOnplYg+xaJtNYUFRWRkbGN40+Y6fwM8NlOQOAKXLdv6/0nmAeoqKhg2bJlfPbZZyxZsoTVq1fj8XiYOXMmt956KyNGjGDw4MGNql1VgcCnMb9DpRSmaWKaJsOGDWfYsOEh3S94n9qkpaUdEKA3RvBKQHMxW+gXnpOTw7Zt2xg7dmxEB/IAsbGxXHPNNSQmJnLPPfdQUlLC008/TVpaB/zB0q02euNYGrQFLjM8ym627MyhrhdDa432lYDltJ80olPBaHz5lfYW49v5LdpfjpHYG3fvk5z/sCrBVwKA8iSgTA/4y7BKMrFLc9C2F6VcqOhkjPgezoRYqH7RlEKpAz9Pysoryc0vCeuF44RoKRLMt4JAXHtAIH8k8NtAjQ4trcUXwROKg/Jyc8nctZMxY8Ye8HNLg7YDAT1tExBYlkVeXh7bt2/n008/5aOPPiI3N5ekpCSGDx/Oiy++yOTJk0lKSiImJqbJB1ZTBeqPm3f4ESm4vkNLlNhkZmayefNmrr322ubdeBuJi4vjkksuIS4ujnvvvZcrrvgNjz7+JD179cZo4oJGzcWmRikibRvQr9uyF6+vrpVDdSCQD3yQumNp2lmQs9AU2nK+gj+1fWi/U2KjlImVvwkrb4OzaJRdY0zKBHM5rk5jnTIfs/4SmryCEnx+C49bQhtxZJE9vjUEssRHYlDi1067tpbKKtakdfsI5AHWr19HSmoqnbt0PiQYtrVzYugyoTVX5C0tLWXp0qXMnz+fH374gaVLl5Kens60adOYMmUK06dPp1evXs06gdI0nMmERzJFy5asbdu2jeLiYsaMGdMyD9AGPB4P5557LgmJSdx8803ccN01/OXJpxk0aHDIdd0tJVhL7zbb9upadm7dXWycBTECQbUyUMps0km5csVgdhgBtg8VnVpj83ZVcG+X78MuywLlwojthIpKBmWgvcXYJXvAX4Z/74+AwuwwvO66faUoLq3Ab9l4ml7NJkREkmC+helg3XY7CDCbKjgJrKX6Y0MgkG9HVz4ytm1j5MjRJCTW3pHDpkZAT/MHBVpr/H4/paWl7Ny5k08++YTPPvuM7Oxs/H4/U6dO5ZVXXmHUqFF07tyZ2NjYZr+0rZSTvXQ1stymvTFbsEOUZVksXryYwYMHk5qa2vAdIoTWYLrczDzpFJ55zsPvb7uF666+kuf++hKDBg9u8449wSu1bVlyU1RSUc//KmdBKAjUsAf6+TZ2oK5YXJ3HcUirB207XwDawojvhqvTOCeQN93ObW0fdvEufHsWgK8Uf85yp7+9J7H2x9JQWu7FPpIPtuKIJcF8Cwq2P/M34rPFWVY9m82bNpGdnUVJcYmzbHNSIj169GTI0KHExsY1eUz79uWwbOkSDMNk0ODB9OrVu8Hx7N+/n3Vr17Br50683kpi4+Lo2rUbQ4cNIzU1reEWZDivgbslA/l2duXj2utuoKioqN4Ay8YJcl3NmLWt+n2vW8fcuXOZPXs2P/74I926dWP8+PFce+21zJgxg86dq68YtGR9qlLOvAutIqOtaHNzBfqVt9RLbFkWS5csYfz48cTGxrbMg7QRnwXKMDj+hJm88PJr3H7rzVxx2cU88tgTTJ02/YCJzm0hWHLjaaPziso6S2zAqY8MpredkpumTDdwPhtquZcynBIaQMV2wt3jWHDHH/hZYrgwkgfgqtjv9K/3lWEXbsdIH1nrY2k0lT6/BPPiiCTBfAupmogZYoSptSY3N5f3/v0O//ngPXbs2EF+fj4VFU6P37i4ODqkpzN8+AiuuuY6jp46Dbfb3XAgHejwUVRUyP/++1/e+/fbrF61CsM0uPjXl/LAQw/XeT/Lsvjmq694/rmn2bhxA7n79uHz+YiOjiY1NY0hQ4dy483O4jwNjSXY492kOQNP5+9IXnSrNkopOnXuTKfOnRu8bbDNXlO7nAT3j8rKSrZs2cInn3zCN998w44dOygtLWXKlCm8+OKLjBgxgr59+xIXF9cmk8tchpPIa0+/54aYqmUDeQBXTg73Z2bS6cor201LP31A60mFUjBhwkSefPpZbrzhOm69+be88trrjBl7VBuPtLrkpjlPyEMVE1V3LYpSoMwYUC7QfnRlkVMWo5opZFAmGIGOVoYL6ijjUUphJPSA7GWARlfm1zNmRUyUGyNc2gUJ0YokmG9Blg4tW6y1ZsXy5dx4w7WsWL4My7KIio6mS5cuJCUloTUU5Oezc8cOtm7ZwvezZ3PHXX/k6muvIyqq7u4TXq+XjRvW8+233/B/r/+dLVs24/dXZ2Oys+texru8vJy/vfoyf37wAQoK8nG5XPTq3Zv4+ARKS0vI3LWLr7/6khXLl/OnBx7k0suuaDDIs2ynIUJzftRaTVgIyrIscvfto7ikmMqKSjxRHuLi4klPTz+s1oHgvOZFhYUkJCbg8USFFPj6/X5yc/dRWFiIz+vD4/GQmpZKSkpqSOUATTlRsm2bgoIC1q9fz8KFC/nvf//Lxo0b6dKlCyNGjOD888/njDPOICUlBZfL1aZZzEC3OtxG+5kT0ZBg16KWDvDMBQs4PioK+vZFtXGmujlULcZ30D6ilGLsUeP42z/+j5f++jxJYbTKrRWcV0TrBvTJifVdiVGoqCRwx4C3GLt0r1NDbzRPyKBMD8oV4zSH8JWibV/dxwXDg3PU0Girsq5bARAXE4XZ2h0XhAgDEsy3oFCCTK0127Zu5a47bmfZ0iVorRkzdiyXXHYFo0aNJi0tDa0hJyebxYsX8dJfn2fXrp289urLnHn2OfTo0bPObS/5cTE3/fZ6Nqxfh23bpKSkMGr0GL6f/V29i+BorVm0cD5P/OVRCgrySUlJ5cqrrmbWGT8jOSWFosJCfpjzPU8+/hj79u3jkYceZOrUaQwaPKT+5xp4TZqjRjTYIagxVz58Ph/fffs1333zDRs2rCc/P5/ysjKiY2JISkpi6LDhnHnW2YyfMBGXyxVSIB58HbXWrFu7hrff/Bfr16+ne4/uXH3NdQwfUcclYa3RWrNxw3reevNfrFi+jLy8PLyVlVUncmPHjedXv7qYXr17h3yiVNeE2OA4Lcti48aNfPrppyxatIilS5fi8/k4/vjjueKKKxg9ejRDhw4lKiqqwefemoLPKVg/354D+tYK5NEafvwRlZ4Offq08IO1nro+E5RSDBo0mIcfezzs9m/LhtYu4+/eKcXpv3/QDPOq8rmoZJQnEe0tRlfkYZfnOG0im2PHNKNQnnjAaV+JvxztSax129pbTNV1FjOmzk1qW5OUEINbgnlxBJJgvgUEg9ZQluOqqKjg+eeeYe4PczAMg5knnszTzz1P9+49DsjKDhg4kImTJjNt+jE89cRfuPLKq+nSpWu92+7dpw/R0VGccuosTj7lVI6fORPDMBg6sB+WVftCIeAEfH997lmys7OJiYnhD3fexZVXX3vASp0jRo4iJSWV66+9ij17dvPSiy/wlyeearCXuG2Dbo5Jm41Yit22bTZuWM+jDz/EF59/TklJMbZt4/FE4fG48fv9VFZWMuf72bz5r//jN1dexc233EZySkr9pUO2TVFREWtWr+L//vE6X335BXl5uViWRXJyMqeeNqvOYN62bT54713uuftOMnftwjAMkpNTiIqKYt++faxZvYqvv/6K9//9Dk8+8xzTj5lR72tb1SHjoOOYbdvs37+fPXv28P333/Phhx+yfft2YmNjGThwII888gjTpk0jLS2NmJiYNq8jbojCeY7BFZTbm1ZdbK2oCFavdgL5bt1a4QFbXiifCcFe+sFSQq/Xi2VZuFwuPB4PhmG0eilZVblNC3e40Vrj9XopLS2lQ5zG5cujoqQAuzwXXZGHEdcZV0enq5EyozBTBuAv3Qvaxtq7FNUrGTwJ9S7m5NxZVT3egSurOAtBKWVgxHfDyt8Mtg+rcBuu2I4c/Oy17cMq2FJ1fyO+S53PzTQN0pLiJDMvjkgSzLcEHXrmMHPXLt7799tYlsXAgYO457776dmzV60fli6Xi6OOGsfrb/wLj8fT4AGna9duvP+fj0lPT68K0vbv39/g/XL37WPxooUA9OzVm19dfMkhi8m43W5OOe00hg4bzupVK1ny42J27txB37796t22JtAUAZp81AoeHkJ9jbdnZHDt1Vfy4+JFaK3p3r0HJ550MsOGjyAxMZGysjI2b97E559+wtatW3j26acAxe/vuIu4uNonG/v9fj7/7FPef+/ffPHZpxQVFWEYBtHR0ZSVlTnjrGd8q1et5I4/3Mae3btJSUnlqmuuZcrRU0lOSWF/Xi5ffPYZb775T376aSN3/uE2Xn/jXwweMrTBeQl2oA1oZWUlK1asYMGCBcyZM4dFixYRExPD9OnTOf/88znmmGPo16/fYZcVtbaaGXrViBO6cKeg9Vf4zciArCyYNav108ItIFhu1hCtNTt2bOfHxYtZu2Y1+/fnUVFeQUxsLB07dmT8hIlMmDiJlAZO5mvKz9/PSy++gN/nq/d2cfHxnHPOL+nR89ArqrZuWsOY+mitKSsrIzMzk23btrF161Y2b97Mrl27WL/hJ4o3byFYUqPc8ShXjdIbpTCT+mMXbMMu3oldloU/c47TnSb20Ja52vI5LSb9lRhJfVCG6bSVzF1b1ZrSTB1StW0jvjsqKgldnou1fyNGXBeMhJ7O/bTTn97KW4tdvMO5S3QKRnzdJ50JcdF0SU+SBaPEEUmC+RYQ7GITis8//5S8vDwnK3/SyYwYMbLeDyPDMEK+RKyUolOnTqENpIadO3ZQWenUJg4dOpSUlJRab5eQkMikyZNZvWoluzN3sT0jo8FgHpwD7mHNUWpEEFdcXMwD99/Lj4sXAXD8CTP50/0PMmTosKp2ilprKisrufBXF/PwQ/ezccMGxk+YWO+EQK01y5Yu4YvPPiU5JYWZJ57MqbNmsT0jg0f+/GC9Y7Isi3fefousvVkYhsGdf7yb31x1TdXvVWvNlClTiY6J4fnnnmHtmjV88/XXDBxUe0u94CTW0tJSsvfu4duvv+Cjjz5i586dVFZWMnz4cB566CGmTp1K165diY+PD/sMfEOCXW4Mw+mUFMllN6aqbj/ZanGI1rB1K+zbB5Mnt9KDthwd2Afq2w201uTl5fLWv/7J3157lay9eykuLjqg5NAwDFJSU53Eyp/uZ+rUabhCOOHdsX0Hjz38EBUV9bV7hO7du3PyyafUPj6c59DY/SBYQlhZWUllZSXFxcWsX7+eDRs2sGLFCtavX09FRUXVFcjg4m4//8VZvP3tVrKKNMrwOPXwBy/KZHpwdZmAz6pEl2VjF+/EW5GHEdsZI64TuGJR2sau2I9dloWuLER5EnDHdEBFJ6P95Vi5a8GqwEjsXR3MA7hiMDuOwb9rDvjL8e2ajRHfFSO2M2gbuyQTuzQLbB+4YnB1OgrMuueI9eicwqA+jT/eCdEeSDDfzHSI5TXObTXz584FnCXlZ51+RqOWvA9uY/OmTaxbuwaXy8XI0aMbbDfZ4DZrHBLj4xPqvF1UVBT9+vVHKUVeXh579+4NaSntUF+fuscXWvCmtebzTz/h888+RWvNyJGjeOHlV+nRo+cBY1RKER0dzajRo3n62b9SUVFB7wZqiF0uFxddfAkTJ01m0uQpJAcm1L3+99cafP6FhQVs3vQTtm3RpUsXTj7ltANO0JRSxCckcNLJp/DWm/8kJzubFcuXYtv2AcG81prS0lLWr1vLwoULmPP9bBbMm0daWipjx47lzDPPZObMmQwYMKBV2ki2tuBTcavqrj6RFNMbOEF88MS2VX81fj+sXQseDwypf65LpGgoK797dya3/+4WPvnof4GuXDGMGz+B3n36EBcbR2lpCdu3b2f1qpUsXDCfSy6+kKeeeZ5Zp5/R4BWsnJzsqpOCadOPIS0trdbbdercma5d684uWyEssKe1pqSkhMzMTLKystixYwebN29m8+bN/PTTT2zbto2YmBi6d+9Ot27dmDx5MgMGDGD48OH06dOH3r17Y5omJWWV5Jrv8vZnS+ps56iUgpiOuHsejz97KXbRDvCVYhduxS7ceugdDBfKjKZmaY0yXGjtCkzqOXDbZlJfsHxYOSvQ3kLsgq3YBQduV0Wl4Oo8DiOpX70Xczt3SKRP9w71v3hCtFMSzLeAULOEpaWl7NixHXBWLBwydGijH0trzRdffMaD9/+J+Lh4/vzoY4cdzHfs1BkzcFKxZcvmqnrSgymlSExMwuPxUFlZSUF+fmjBPNXlIE0R6utbUlLC119/SWFBAVHR0Vx17XV069a9zvEppejStf55CDVv269/f/r17x/qsOuktX3I66aUcmp3A4cvv2VVnQXZts2ePbv58vPP+OTjj9i6dSv5+fsZM2YsDz/2F8aNHcuA/n1JSmr/l5yDTy/YwtGOgNIbhRPEm20RxAeVl8OKFTBuHITZZNDG0oGuYfUlCbxeL48/+gj/+8+H2LZNn759ufnW2zj++BPo1LkL0VFRVFRUkJWdxZdffM6zTz1JcXER+fkNlyWCs35HcCLpffc/xNDhw2u9nWEYxMfHN/hcjMDVNsuysCyLwsJC1q1bx4YNG1i9ejUZGRnk5uZSVFREQUEBycnJDBs2jF/84hcMGjSIHj160KFDBzp06EBycnKtn9+xMR5GD+7Bu18sw7brnkOllIKoZNzdpmGnDsYu3YsuzUL7ypysueFCeRJQ0R0w4jpjxHQAt1OeqDwJuHudgNY2ynXo5FVluDBTB2PEpmMVZgS2WwJaozwJGAndndKbmLS6V34F3C6To4/qj8ctIY04Msme3wJCzTwXFxVV9ZFPSk6uNwteH29lJcVFRdi2jc9bf81mKHr06M6QoUNZtGAB69at5f333uXMs84+oOzEsiw2rF/PF198hi9QJ1paWoJt2xiGQWlpKeVlZZguF6ZpYJqBvw0Tl8uFNupYTKQBwcvpocjPz6+q/R88eAhTjp7a6PISv99PXl4uFRWVuN1uOnbs2OirJweLj0+gY6dOKKXIysris08/4drrbsB90Ou7evUqiooKMU2TceMmYASy8qWlpfzl0Uf4Yc5sBg8ZyvU33Mips2bRsWMnXC4XLsNwlopv33H8IYKlN6YZaAsbZguJGcr5atMgPqi0FJYsgVtvbR/18vX8orXWfP7Zp7z37jvYtk33Hj342+v/x8RJkwOTMZ1fRFx8PH3j+nHV1dcyZMhQysrKOOnkU0JqD5u9Nwvb1kRFR9O9Rw+Skmpfubm+MRYXF7EvZx+F+bnkZGexadMmli9fzqZNm8jIyCAmJoa0tDTS09Pp2LEjxx57LCNHjmTYsGF06dIFwzAwTbPqM66hkxBDKY4e259+PdPZuK3uNsVV23JFY8R3d+rWqw5ywb+Dkz0C6YfglUDTg6qnzh1wauRj0nHFdAhst5ZtNvBc4mOjOHvm2HpvI0R7JsF8Cwg1K1hRUVHVVSY2JrZJWVSlFJMmT+EPd9yFJyqKESNr757SGKbp4uqrr2Pd2rUUFRZy/713s3PHdk6YeRJxcXHs3p3J/Hlz+ezTT1i1ckXV5WVbVx9RP3jvXf7z4fu4XG5Ml4nb5cI0XbhcJm63B7fbJDoqCrfbTVRUFFFRUbhcLqKjo3G73Xg8nlp/5vFEoUwXUdExge4TUXg8HtweN1FR0URHR1cdSAvy97Nj+3YAevbsSbeu3Rr9Ghfk5/PInx9i9aqV9OrVm0cff5L09PTDen09Hg8nnngy77/7b0pLS3ny8b9gWRbnX3gRnTt1AqXYvj2Dd956k/LyciZOmswpp55adZCOi4vjqquv5eJLLmXo0KHExBzYL9rmgMPhEaPmpGqXclaNDU4KbqvON4rqqwZtUk5Tl4wMJ6AfNsyZeBDh6jvBLy0t5asvPic/31kv49rrf8uEiZNqPbFXSmGaJsced3yjHn/fvhy0tklNTcXViOywbdv858MPWLRwAZm7drI7UDqTn7+fTp06MWzYME444QT69+9Pt27d6NatGz179iQ5Ofmw570opRjSrzND+nZuMJiveR8nyD6sh27UdoNtfOv67DaUYtpR/encIbF5ByVEBJFgvgWEGje4Pe6qD2Sv19uIex5o8pSjmThxktN9oJmybLPOOIPt2zN47pmnycjYxkMP3MdTTz6OoQz8fh8VFRWkd+zIxEmTWbZsKT6vl/i4+KrV9+Li4ujYqRMV5RV4vV5KSkrxeivxer2Ul5dhW07IGfygDl6iDv67ri+75u31obeffswMXnjpFTxRUWRnZ+P1elFK0blLV+ITGn/lo9LrZe2a1SyYP4/9gT7wzeG008/gyquv5dWXXyInJ5v7/3QPL734V37+8zOZdswMnnnqCdasWc2EiZN45vkX6Ne/uu7dMAyGDhtW7/Y1TreXIyqaP4hSTl26oZwPumBQH0wqNuXd1tBJUnDyYjB4D4ZbYRHA1zRnjtOSskePMBxc49X3u8zJzmbRooVorRkydCinnHJaowNhrbWz4J7WqEAGPPh+1FqTnZONbdukp6fjcoXeIcq2bZb8uJgf5nxPt27dOebY4xgzZgzjjhpDaorTqtZJYjTcvawpoj1urjxnGt8u2khRSf2Td9uGRnsLwfJCTHqtr0FifDQXzJpIdD0r2grR3kkw30JCiaPiYuOqPviLigrx+fwN3ONQwUxScwXxwW3GxcVz86230advX95+81+sW7eWfTk5mKZJ7z59mTR5Mr8482w2b/qJJT8uxjAMEhITq+oazzrnXM4659y6H0NbaMuH3+/H5/Ph9Xrx+/0H/O3z+ao6MAT7QVd6fVT6rKqrGj5f9e28lV569upVVe9fUFAAOMFvQkI9vZHr4XG7GThwEKWlpfTu0+eAUpjD4fF4+N1tvycnO5t33n6TyspKdu3cyXPPPs3zzz2D1ppevXpz/0N/ZuTIUY0euz7CA/mgmi+bGSjD0frASdTBOmUa6IYCwXUoq79Xqpa/mz9x2bxsG+bPh759IcQ5IuGsvlIqp4NNHtsztgEwdNhwevTs2ej3U05ODm/96//Yu3cvffr05deXXkZsrHNFzOfzUZCfD0BaWgdM0yQ/P5/cffvw+bykpqXRsWOnWk8gTNPk1tt+z51/vOeAzyi32fQ5RY01cVQfJo/qx9cL1mMfbneCFmBlL0f7SnD3PAHch65ae/TY/hw9pl+7nx8kRH0kmG9DiUlJdAiUbJSVl7M9I4NRo0e37aBq8Hg8nH3OLznu+BMoKCigoqICpRQJ8QmkdeiA2+3i+9nfYVkWaWlpdOp0aO/huhimidvduBOQ4Iqv/hAKobXW6BqFtE29JJ2alsqf7n+QyspKXG43aampTdrOwcrKynjpxb/y7Tdf4fF4mHz0VOJi4/hx8UIKCguprKggK2svv7v5Jv5w513MmnUGnhqLdjVIgvk6BYNtQwUC8xrxS81Q5uD/q60KoOa/IyaWyMqCHTvg5z+HxMgvTWgo/szO3ktZWZmTiOjdpyoIb4z8/Xm89eY/WbtmDVOOnsp5F1xYtZ2SkmJKS0sDj5XNDdddzcb16ykrL8O2baKiohkxYiSXXfEbphw9lZiY6omgSqlay/Za82Q82uPmqnOnsWLDTnL2F7fOg4ZMYcR3w7d3MbqyAFwxB3wGpiXHcflZR5OWXPt6IEIcKSSYbwGhfgYbhsG4o8bxw/ezsfx+5nz/XVgF8+CMsUOHdDp0OPSAk5+fz8oVywHo1LkL3bv3CHm7rXGcSkxyAhXbtikpKQ6p087BTNNFeseOzToup07+MR5/7BGUUlz/25u4484/EhMby/aMbXz55Rd8+flnLJg/j7VrVvPb664hJzuHK6++JuQrMOGXXwtPNevsa35bZ5lSuGfdQ7FmjdPNZvz4dlEv39C+nr/fyZqbLhepqalNm5tkGERHxxATG0t0dPQBJ26lpWVUBvrLr1+3lp07tpOSkoonykNRYSG7du5k86af+PyzT7jplt9x8623kdBAyV9rv39nTBjIBbMm8MLbc/A24QpxS1Lx3UD7sMtyMOM6E3wHGobi9BkjOXFK/YvpCXEkkGC+BSgVWkcbpRQzTzqJZ55+Esuy+OLzzzj/wovo0KFDRHw47dq5g8WBxZgGDhpEn759Q75vazy7tDRn5Vvbtsndl0tFRcUBWbG2oLVm9aqV/N8/Xsfr9XLyKadx/W9vIjYuDqUUffv15+prruPMs87m/ffe5aEH7mP//v08+cRj/OwXZ9I11NaZLfw82ruDg/x2Q2vYsAG8XgizxEGTNfBZW1npBNqGUrjdTSuT6969B088/SylJSUkJSUTF1fdXrJDhw784c4/8v5779K3f38mTpxU1Q6ysLCQOd/P5oXnn6WgoIC/PvcMXbt145JLL6+/K1YrRvNKKWKiPVx5zjQ+/2EtP23Pbr0HD4EyozBiO2OXZGJ2GA7KQCkYPbgH110wo2qelhBHsshPy4Shxny2jBk7jmNmHAvAooULePXlFxtcRTA42TP4vd/vp6Ki4oDuOC0tP38/D//5QYqLioiJieHcc89r1OXrppyrNPYu6R3T6dHDWTZ9584dZO3d0/gHbWa2bbNg/jwyM3cRExPDabNOp1OgTWWQs3JvZ666+lpOPXUWANlZWSxaOD/0B5Ljm6hNaSmsXu1MfO3eva1H0ywainuDE99t26a8vKxJjxEXF8eECRM59rjjGXvUUQcsIhUTE8NJp5zKi6+8xu//cCfHHX8CR40bz6jRY5g2/RjuuOtuXnrt73Tp0oWioiJee+Vldu3ceVjPqSX07pbGE78/h24dk9vg0ethuDASezmrwfqd31/HtAT+cMVJDO3XNSISX0K0NAnmm1sjL8MnJMRz1dXX0rlzF8rLy3n+2ad59uknycvLrbWby949e3jlpRfYumVL1c++/OJzrv7N5dx0w3UsXFAd8NV2/wMuGdTSDabmSUJu7j7KysoO+f/dmZk8eP99fPn5ZwBMnTadE048KeQP1aZ1mK++b0i3CyxoNXzECAA2/fQTGzduPGDp9rZg2zbZ2U7mKzo6hvSOtXdoAGeV2dFjxgDO72Nfzr6QH0cOb5FHU72CdGO+GiU/38nMT5kSQUX+hyc5OQVw1ozIzc2t6pzVnJRSuN3uA7rcBH/ucrk4/viZHH/CiQCsX7eOHdsz2vyz6GBKKaaM6ceNFx1HSmLj5xW0FKUURnQqyozCKtmN22Vw3fkzOHnacMnKCxEgZTbNrGZni1A+qg3D5ORTT+PWnTt4+KEH2L9/Pw8/9ABvv/UmM449juHDhxMTG0t2VhYrV6xgwfx55OblctMtt3LPvfcDsGH9Ov79zlvExcdz9NRpTJ02vWr78+fN5Z9v/KPq38HOMAALFszj6t9cXvV/J596Gj/7+S8AyMvL43e33MTqVauYfswMugeyeFu2bGbO7O/YvXs3lmUxbvwE7r3vwXpXNTzkOR/G529jXtvk5GROPvU0Zn/3LQUF+bzy0otMnDSZ1GaaxNoUSilSUpzgotJbSV5eXtVCWwfTWrN3796q+yUH7hfa4yARfQSoOck22GEn2G0n+DdU7/PBX2nNSbzqoJ/X/WAa9u2DjRvhzjub94m0oYZ28y5dupKSkkJ+fj7btm2lqLCwUe+l5hAXF8fQ4cPxeDx4vV4ytmdwTD23b6u3bkyUm9+cM42C4jKee/N7ikvDo12lik5FRSUTVbmHGy+6gat/eQxuV+QvdiZEc5FgvgUED7KhLlTj8Xi46prrSEpK5uWXXmDF8uX8tHEDP23cUOvtO3fpQkx0zAH3j4+PJy4+/oDLv1prNm/exP+98Xqt29m6ZQtbt2yp+nenzp0542c/RynF9oxtLJg/j107d7Jh/bpD7hsXF89pp5/O7277PcNHjGzUpc7DabkWPFkKJallGAYnnXwqb7/5LxbMn8fXX33Bk48/xo033UKH9Noz4r7AgXbAgIEopaioKGfF8uXk5eWRkJDAhImTDqvu3jRNhg0fQVx8PKUlJXzy0f+YdfrPSK9lPNu3Z/DNV18CEBMby1HjxoX0GAoO6+qHaB3BBa2CqxrXt0sfvN5mcP8PfsYonLmshg4E+HX98tesgeho6N//sMcfNuo5u1dKkZKaQr/+A1i65EfWrFrFjh3bSUpObtXyDKUUbpe76jEbvDrQhm/e6Cg3N/96JhWVfv7x34XsLyxtu8EEKFc0ZnxnOsUVcvqUXiTERbf1kIQIKxLMtwClwDTAthqxgJTbzfkX/opjjz+BHxcv4rtvvmbDhvXk5OSgbZsO6ekMGDiIo4+eysTJk+nZs1fVgeGCX13ECTNPwjAMOnfpXGMcioEDB3HJpZfX9bAHOOqocVXbHDJ0GG+/+wFff/kFS35czL6cHNweDx3S0xk/fgJTpx/DoEGDSW7kQTEYaDT5ONrIMqbu3bvzx3v+xCUXXUBOTg5/fe4ZFi1cwMW/vpSjp04jLt5paVZUWMjCBfP55OOP2LdvH/96699079GD/PwCHn/sURYtnM+AgYN4+933q4J527bZnpFBfkF+1ePt3LHDmcdgWWzduoVly5YC4HKZ9OvXn/j4BCZOnMRxxx3PJx9/xLfffM2tN/+W399xF127dMV0mViWxdatW3n4wftZv34dpmly9jnn0rNnr5Ces1x5Dk81F6uy7IYD+EZtO7BNi8Cqs0b1SfMB77W5c51VX1NT202ZTUPPolPHTkw5eirLly1l+/YMPnj/XYYMHYanmdaM0Frj9XrrXdjJ6/WybdtWvF4vpmk2+F5u699MXIyHP/zmZDqmJvL461+RW1DSZmNRCpITY5l1wrl89M/H2bJpA+OPGi218kLUoHS4Fe61E1qDz65/mfG679vwnUL5IGvqr7bmyoaHO4aDuY0GMochsDV4GzHP17Isvv7qSx7584Ms+XFxVVbM4/GQlJSM1pqCwgL8Ph8AqampvPK3f3DarNPZvXs3l1x0AfPm/sDgwUP45POv6BYoOSovL+eyX1/Ef//zQYNjSEvrwHsf/pfJU45Ga81PGzdw8403MPeHOViWRXx8PAMHDSYlJYWiwkLWrVtLWVkZUVHRnH3Oudxz3/306BHaYjfN8RqL5hVcmMq2Q79i1xxcRo1SHG8ljB0LJ54IjzwCUVGtN5AWZNnOZ21dtNb8uHgRF11wHrt27SQhIYEXX3mN08/4eZ0Bvdaa4qIiEpOSAGdhqNx9+/D6vERFRZOenl7VJjY7O5u/v/Yqx51wAuPGjT+kfazWmkULF3DVby5n86afGD16DG+9+z69e/ep8/3sNpwTsrZmWTYff7+KP7/yBRu27sVvNf98g/oopRg5sCu3XDKTk48ewtSjJzN16lQef/zxJq0XIER7JZn5FuQyGhd0BjVXxuFwt9PcmQ9DNU+QqXBmbod6WDFNkxNmnki/fv358IP3+Oh//+WnjRvx+30UFhaAUrhMk+SkJCZMnMTZ5/ySCRMmAhAfH89ZZ5/D6LFj6dSx0yFzAzweD9EhlN1ERUdV1cUrpRg0eAjPv/Ay7/77Hd56859k7trJ2jWrq3rhu9xuRo0ew6WXXc4vzjqb9PSOIf0+quqoJZAPC1XlMDb4deuv5eW3nX3BpcDYuhWKi1FDhkAzZaXDQUPzaJRSjD1qHJdefgUPPXAfxcXF3P67W1i3di2XX3ElaR06OO9NrbFsm4yMbfz3ww/YnZnJ408+TUxsLLszM7nn7jvZunkzI0eP4dG/PEFiYiLl5eU89cRjvPryy7z777e45LIrOPeX55OSkoJSCsvvZ8nSJdx95x/YsnkTcXFxnHfBhXTr1r3O93PwPRwOTNNg1oxR9O3Rkcf+9iWf/bCGikpf4ydeN0FCbBTHTRrMrZfO5KhhvdC2zYknnsg333xDUVGRBPNC1CCZ+RakdfVB/EincJYob46DVLDGuL5sXO3309i2jd/vJzs7i+y9WeQX5BMdFU1KWiq9e/UmKjoal8tVFXgH7+ME2c6E5ZpXLoIdfxpiGAbp6R2Jjq6u9Qxuu7Kigq1bt7Jr107KyspIiI+nV+8+9O7TB4/HjVJGyCdWrkBGL0xigSNaMBvvtw6seW+r343rjb9jPvYo6o03YNKkNhpF89MavHbD82hKSop5/LFHee2Vl8nLy8UwDBITExk8dCidOnXGtiy2b89gy+bNVFZWkt6xI2/88y2OmXEsGzes56ILz6taAfb9/3xESkoKPp+PN/7xd/7yyMPs3LkD0zRJSEigX/8BJCcns2fPbrZnZFBeXk5cXByXXf4b/vTAQ/UGokqBxwivE3KtNeUVPj77YQ1//88CFq/KoLzS2+xBvVIQG+1hxMBuXP3L6fzsuNFEeVwopdBa880333DWWWfx0UcfMWPGjOZ9cCEimGTmW1BV7XwTy23ak+YMMJVyMvONyc4791OYpllVsxpKDXrwPnX9X3p601eHDW47Ni6OESNHMmLkyCZvCwK10o2cUyBahtZOOY3/oB20zX43lgWrVkNsLNagwRjBibPtZGcJpeFAfHwCt/7udkaNHs0Lzz/Hkh8XU1BQwKIFCw64nWma9OzVi5//4qyqz4io6GgGDByEUgb9+vev+kxwu9386qJf079ff1564a98++3XFBQUsGzpkqrtKaUYMXIUF19yKRdd9OsGM8rhkpWvSSlFbIyHs086iomj+vLl/HW889kSlqzdjt9vYx/GAU7j7IfRHjfD+3fl0jOncNzEwfTsknpA60mlFH369GHgwIF88cUXHHPMMVI3L0SAZOZbga3B14jJsO2NqZyMcXN+7jY1Ox8JvF4vJSUlxMfHN2qSnisw6VGOb21LB4L41qyNb1BWFu7Lfo3u3Bn/3/6By1TtZl8JtvAMtaTRtm2Ki4rYlbmLRQsXsDszk5KSEmJiYujWrTtjxo6ld58+JCYmERUV5ZTLWBYlJSVYloXL5SI+Pv6AdrJaa0pLSsjKymLB/HlkZGyjvLycTp06MWr0GEaNHk1iYhIul6vBANRjVnftCleWZVNUWsH6rXt457MlLFu3k5y8YnL2F+O3bJSizgDfCDwxw1CkpybQKS2RUYO7cdEZkxnStzNJCTGYtbTqBaioqOCWW25h4cKFzJ07t1EtkYVozyQz3woUTqDVHgPPhhgtEMhDdXbebEQL0Eixds1qXnv1Za66+lpGjR4T0n1MCeTDQlgG8oDauweVkYF15tmglHPFwACTyN9nVKBgPtQrdYZhkJScTFJyMsOHjwjpMUzTJCkwGbb2MSjiExLon5BA/wEDQtpmrWNT4R/Ig1NLn5IYy9Fj+jNldD/yCkpZs2k3azbvJjuviOzcInbs2U9xWQUlpZVV5TNJCTF07ZhM904ppKfEM2JgN0YO6k5qclxVkF+f6OhopkyZwpdffsmyZcs45pj6uvULceSQYL4VBANP9xEW0Bs4gXxLMg3Q7ayMyfJbzP72W44/4cSQgnmFM8Ex3AOA9i5cA3m0Ru3cidq7B12jVt5vgzKc92l72HeMQEljJAvHEpuGKKXokBLPsRMHMWPCQHx+i/IKH2UVXvyWhT9Qa2aaBh63SbTHTWyMB7fLbFKZzLRp01BKMW/ePKZOnVpnGaQQRxIJ5ltJzYDeb7f/kpuqjDwtFygEM3Iuw5lkGOHH8Sq9+/TB5/OyZ/fuqu42dVE4+5RoW1o7E93DLpAHsCyMpUvQvXqju3Q94L98tjMxPdLDIaWc52ARuZ+tVXNeIjCgD1JK4XG78LhdJCU0fXG9+vTs2ZNRo0axcOFC8vLy6Nix6fOWhGgvJAxoRUodGOS2V60RyAcFX9P2lJxJSU0lNi6OrKy9+P3+Om8XLN86rEW4xGELzt9o5RbcobNt1Pbt6HHjndVfD+K3qleijWRKHd7q0m0t0gP51nTqqaeycuVK9uzZ0+T1VIRoTyQz38qqMvSmk6FvT+UhUD0JE1r3wFT1mraDicaGYdC//wB2Z2ZSXlaGu5Za3WAgLz3lw8PBXWvCituN76VXwO+HWjqpaJzxt4crPKbhNO4J/joi5a2hCI9FoiLF9OnT8fv9LFq0iFGjRrX1cIRoc/Lx0QaC2WS3EdmZpJqC5R7B7FJrB5hVr6kZOQfwuiil6Nu3H3v37qG8vPzQ/0cC+XARrJMP6xNIpSAuDpKS6txhbN0+svMQmessSCAfOqUUaWlpTJgwgc8//7xqRW8hjmSSmW9DKlCOYhK5WfrgaoXBia5tGVwGH7o9XPUYMHAQX3zxGWVlpQf8vDVLmET9qsprQt3PbAsKC1EFhVBZ4aSQXS50dAykpEBCwuH/Uv1+yMtFFRaCz+fMCo2OQaelNbh9v+20RYxkwdp5HUFdrkwlJTaNlZSUxLRp03jqqafYs2cPPXr0aOshCdGmJJhvY8GlyN2Gc/Cxwj3LV4OhqruohMuBqObrGZadRULUr39/svbupbS0rGoSrGlI15pwE9L+pTXs2on54QcYCxegfvoJtT8PKiohLhbdIR17+HDsGcdhz5oFqWmN/yVrDZmZmO/9G+OrLzE2b4biYudkoVNH7NFjsU8/A/u0WRAVVfsmcD5/zDBbfbSxgkmSSOhy1VKte9s70zQZP348sbGxzJ49m4svvrithyREm5JFo8JI8DcRzkF9MCNccxJvOB6IdOCP4MTESLoQq7VmzerVzDxuOv96+11OPPFEXIaSspowY+sQFiry+TA++RjXQw+gNm4Ar9cJpqOjnb6QtgUVFVBZCfHx2GPG4n/yafSIkaHP6rZt1JrVuK+/FrVyhTNxJDYGTJcT0ZaVOVn65BSsK6/Ef8ddEFf7YjuKQHZeRV6pSk3BhaTCebE+A3BFwAJR4aqgoIBZs2YxYMAAXnrpJaLqOEkV4kggmfkwEvxAN3GO48E6Vlu3/QHJUNVf4RzEB6nAHwZgmE5Ab4XB6xgKpRTx8fF06dKVbVs24T7lxKrnI8JDMItdL78f4/13cf/2eigogMRE7NN/hj3jWOwhQyAmBlVYgFq7DuOLzzDm/oAx9wfcF1+I77kX0NOmO2Uy9Q5Eozasx33Vb1DLlqL79cc673z0xEnoDh1QZWWoRYsw//F31JbNmM8/h+7cFeuqq8HtrvV52YEFmCJ5fwu2rXWb4RnQKySQP1xJSUnMmDGDzz//nIyMDAYPHtzWQxKizUgwH4aqgnoFhq4+wAa/WpOpqidaRuKBp+q1DCyOE7zqEe7i42Lp0qULGdu2RuTr3t7pht6LWqNWrcL16CNOIJ+UhP/Pj2Cd80tITq76hWqA42dinXUWrmefwXzlJdSGDbjvvRvv+/+BDh3qH4hlYXz1FWrDevTAgfgffwp75ongclVvf+o0dP9+uG/6LSorC+PTj7HOPRc6dqp9kzoyFy86WNU6FIE5NDpMWtzUvLIp7+umU0pxyimn8MILL/DTTz8xaNCgJi1CJUR7IHPow1ywHt0MdL+JMqu7xtTMkh/243DgZFaP6TxWsGtKpJd4BE9GXCrwvFTb7/yaAzOGqsZrn5wYR48e3dmwYUNbDU/UI3i1rM543luJ+ffXUOvWQmws/jv/iPXrS52Jrge/kZSC7j3w33sf1nnng1KohQsw3/pXw+1lTBPr8ivwPfdX/Pfch33SyVWBfM3b2FOnY48Y6Tzctq2o4uI6N9meCi9rLtZnGLR5IB/sYtZeVt1ta8OGDaNbt27Mnj0bn8/X1sMRos20dTwjQhDM4FQF9oZz+dgd/Dvwvcuo7vN+QFmMqm7daKjq7gkuI7CNwHY8NbZT837t5aBzwGtY43Uz27A+WFF9olbzJC02NpbOnTuzfft2LKuhwmzRmrSursmu0/58zI//59x+wECss8+tc+Ip4OyU8fFYN97sdJ2xbcz/fAj5+fUPRimnfOeiX2Of+8sGynICe7mqP6oNXglsL8KlFXDwfR7piZFwEhMTw3HHHcc333xDRUVFWw9HiDYjwXwEUzWDcyPwdXCQboAn8BX8d82gP3g/o50F7g0JniDVvBJRM7BvqZchePXDVM7vpK6rH6Zp0r17dyoqKsjOzm6h0Yim0tS/nxjLl0JODiiFPeNY6N49tO326Yt97HHOtrdvR21Y7/yHzwe7dqG2boFdu5y2ljXV9+bVGrV5E2rzT87VhOHD0cnJ9Y4jHObpNLvgZ2Mrr0WhcB7TFQZXBtobj8fDjBkzyMrKYu3atW09HCHajATz7cjBGfxQvo50tV3x8Bx0tcNUjc+m1SxbOuAEq8YVlWALwNp+F0opunbtisvlYseOHc35lEUzaKgUxVi+HGzbCebHT2h4ImuQ2409brzzff5+jK1bnO+zs3DfeD2e00/FfdMNcND6A3WybdS6tbgefRhj+3ZI64B1wa+ccp966Ko/2o/g56OpnPd4zY5cLfV4wURB1WJ6Lfh4RyKlFEOGDKFXr158+umnbT0cIdqMTIAV4iBVB93Akbdm4FZbfKOp/SCtDvpHYw/k3bp1qwrmp0yZ0sh7i5agaaBWPmj3bmfHMQx0796hP4Bponv1cr4vL4e8PGc7Ph9q507U5s2ouPi6Z3FrjVq1ErV0CSo/H7VyBcaypahdu7B79MS6+17sU08LoUtO6EOORMHFpQyzun1tcz1lRY2rnUjSpKX16dOHYcOGMW/ePAoKCkhu4KqTEO2RBPNCNKDmwfjg43LNQL65j9ndu3fH5XKxffv2Zt6yaLIQy09UYUHgG6emvVFi49BuN8rng5ISJ8MfE4s9ZQq6c2d0v/61tpUMMj79BNef7jnwh506Yd14M9ZZZzs97hsQPGFpz3Howa2ANc5LbRPCnIia26F6oq1RM9svmfhW4fF4mDlzJvfddx8rVqzg2GOPbeshCdHqJJgX4jC05MG6Y8eOREdHs2vXLizLwgx1ESHRskKJ8mpmvhvbHqbm7YM1WOnp+P90P/j8TiAfE1P33YcNxzrvfJStYc9ujPXrIDcX1yMPoTZuwH/vn6BTZ0kZc+AVOIUzN9ik+ldcM6jXgbObmifvqsZ9QV7StqCUYsaMGdx2220sX76c6dOny2elOOJIMC9EmPJ4PPTu3ZusrCyKiopIaaDOWYQPHewlrzUUFjbuzqUlKJ8PbZoQnxBI+xqQmhbS3e3Tz8A+bVbgHzbkZOP684OYb72J+Y+/gwL/I39p/BWDI8DBwblWVEfzBwfqknkPG126dGHChAnMmTOHyy67TD4rxRFHJsAKEaaUUvTr14/c3FyKioraejiiEXTXboFvNCojI/Q7WhbG9sCE55gYdIe0Rs68VoG+q27nKyoq0MP+fuxp08Hvx/zwA9S2re2roXwLqbepQFsPTlQxDINTTz2VxYsXs3//frTs2+III8G8EGFs0KBB5OTkkN9Qv3ERVvS48U7NhtYYPy5yMuSh8PlQPy5yvk9NRfcfcPiDUQo6dXJaZHo8qLw81KqVh79dIcKEUorx48fjdruZO3duWw9HiFYnwbwQYWzAgAHs27ePwsaWaoiWEWKLUnvkKOjUCaU1xvz5TnebUDa/PQNj3lxnAmrPXuhBgw9vvFUbVk47SsOpJVb76z85lKyziCRKKXr06MHo0aP56KOP2no4QrQ6CeaFCGM9e/akvLycffv2yaXjMBBykJuaivWLM9FKoX7aiPn2m1BZWffttYaSEswnn4DiYjBNrHN/CaG02fP5UMuWQlFR3aUzloXavh38zpL3Or1D/duUMhIRYdLS0pg4cSJr165l586dbT0cIVqVBPNChLHY2Fi6du3Ktm3bsEMt1RAtKqQVgj0erIsvQQ8ejCovx/XUExhvvwVlZbXfPi8P18MPYr7/LmiNnnI01rnnVV8GKCnG+PQTjH/9H8Znn4DX6/zc58P473/wnHs27ltudOrzDw7otY1atBDj4/+B34/u2Ak9akyDz1GISGKaJscccwx+v58ffvihrYcjRKuSbjZChDGPx0PPnj2rgnlpuRYego1q6ruBHjkS6+bfoW65EZWbi/uO27HmzsE+4+fYY8ZAfAIqNxe1bAnmO29jzPkeSkvR/frjv+OuA1dpzc3F9dADqNWr0KNG4z16Gng8zgqvG9dDTjbGO2/j3rQJ67wLsMePh9RUKC7GmD8P82+votavB5cL68yznIWs6qkXkhWiRSQaO3YsiYmJLF68mHPOOYfoENZUEKI9kGBeiDDm8Xjo06cPmzZtksx8GDGUs3JovdxurIsuBtPAfOJxp9zmn/+H+fZbTu26wjkjsCzw+yEqCj1pMv6HH8WecvShveq9XlRlJdrrrT6T8HiwfnszYOD6+6uoRQtx/bjY6WijlNNW0fI7j5GYiHX2Ofj/eDfEx1dvNzcXPG5ISj7g+QkRaeLi4jjppJP47rvv2LNnD3379m3rIQnRKiSYFyKMud1uevbsyezZsyWYDxPB1T5D4nJhXfAr7FGjMf/3H4xvv0WtWgVlpdW3SUrGnjgR+8STsU+bhe7b99C0eGoa/htvQuXkQMdO1YtGKQVJSVh/uAM9cSLGB+9hzJmD2rMbKirA5XJWjZ02HfvEk7HOOOOAoJ3ycly33gS9++D/w50QE4NSSspsRMQ6+eSTefHFF8nIyKBPnz4oucQkjgASzAsRxlwuFz169CA/P5+CggJi6ln5U7QeRYjZeXAC6lGj8Q8ZCr+9GcrLUfn7oaQEkpLRyUkQEwuxseCq4yM5MRH7gl9V/9s46HTC7cY+YSb21GlOXb7PhyorRUdFQ5QHYuOcE4CD7+fxoMceheuJv4DXi/8Pd2KmJiFV80eeUOfXh3NsrJRi4MCB9OnTh6+++opjjz1WgnlxRJBgXogwppQiPT2duLg4MjIy6NKlS1sPSRBYmynUYD7I43G+kpPRjf09BheDqo9hOCcEsbFA9cKl9XK5sH5zFbhcuB5+COX3Y9x7Nyo5GQno2z+tnf1E6xrf4/xRc/8JLp5F4G8j2O0oDLsepaSkMG3aND7//HPuu+8+qZsXRwTpZiNEmOvYsSMJCQls3bq1rYciajAC5TZVAVCkiovDuvw3+G+/A+Odt+Dee52rBtIKtd0JBu22Br8NXgt8gS+/DVbg/2yq92uN829LO1/B+3kt8FvO7YPbDQcxMTFMnDiRvLw8Vq1a1dbDEaJVSGZeiDDXsWNHEhMT2bx5c1sPRRzENJzFXcMkjmm6mBjsq69Gx8VgPPiAE5n98Y/QsWN411WIkAWD+GDAftjbIxDgW85JrWE4f9fM4rcFpRQTJkwgLS2NL774ggkTJjSq1CZ4UlLXSxTckrwtRDiRYF6IMNehQwcSEhLIyMhAay01oGEiOBE25Nr5MGdGR6N+fTFo28nOe73wxBMQFyeRS4SqGZj67Xr208P89do4J7UKcBnVl/zbarfp27cv/fv3Z8mSJeTn55Oamlrr7WpeTbCpcZUBDojmNTWC+MAfwXkz6qBSI3mriLYgZTZChLn4+Hi6du1KVlYWJSUlbT0cUZMCVxjWDTeWgXOVQcXEwGWXwUMPweefwy23QHZ2+NRQiEYJBvFeq3VOODXgs52vtiw/c7vdnH766axfv54NGzYcsnp2XeVGln1gQF/zOdQsOQpe4fAFS45s577B+QdCtDYJ5oUIc0op+vTpQ1FREbm5uW09HFFDsKTAjOBPUsVB44+Kgosugvvvh48+grvugoICiVIiRDCgDAaqVhv82mztBMe23Xa7zYwZMygsLGT16tVVbX2DQXwwEPcFXp/DHaLW4NfVcw/CbR6BaP8i+BAkxJGjf//+FBUVkZOT09ZDEQcLdLYxIzQ9bxrV5QJVoqPhggvg4Yfh66/hxhshJ0eik0igawTTbfjrCmbp/W0U0Hfs2JFJkybx5ZdfUllZWXWC47UbKDk6DMF5BN5AUC+ZetFaJJgXIgL069ePoqIi9u3b19ZDEQcJZuddRuStnBo8Cam1ztfjqQ7oZ8+G22+XkpswFwxYfWE0KTvYAae1d5vo6GhOOOEEFixYQF7efvzB8p9WGodV44RK3jKipUkwL0QE6Nu3L6WlpWRnZx9S/ynCRyQF9KbhjLfegv+oKDj3XHj0USegv/FGyM2V6CQM1Qzkw01bBPSGYTB27FhiYmKYPeeHNik3auurE+LIIcG8EBEgKSmJ1NRUMjMz8fv9bT0cUYtgV4tICOhNw5m4G9KiP243nHMOPP44LFkC118PO3ZIdBJmwjWQDwr2qW+93UYxcPAQBg4azCcff4zVhp+bwRp9uxnq84WojQTzQkQAwzDo27cvO3fuxOfztfVwRB2Cq2O6jfCtoXc1JpAPcrvh5z+Hxx6D+fPhttsgL08C+jBQc7JruKs5ObQlBSe5Jqd0YMzYo/jpp43s3LWzZR+0AVXlT/KWES1AgnkhIoBhGAwYMIBt27bh9XrbejgiBK5AGUu4xPSK6pOMg3tjh8TjcQL6556D1avh8sth2zaJTsKAP4xq5BvS0mMNBvKWDcowOOnkU8jN3ceSH39s8xJF3UbzB0T7J8G8EBHAMAz69OnDrl27JJiPACoQMJthMjHWVOA2a+la01guF8ya5WToly93+tDv3SvRSRsJZuUjadEyTaAne0t0k6lxlSK4+VGjxxAXF8/ypUuorKxs/gdtJLtGQC9vG9FcJJgXIgIYhkG/fv3YvXs3ZWVlbT0cEaKaZTfuNsjSG4Eg3lVb+8mmcrvhtNPgxRfhp5/gyith+3anqbhoVcFFoSKNpZu/3Ca4qNPBr0dsbCwnnnQyc3+YQ35+fptn56F6/oAQzUWCeSEigFKKtLQ0YmNj2bFjR1sPRzRCVZbeAI/ZOpl6s8YJRJ2tJw+HywUnnwxPP+0E9NdfD1u3SqqxFWldvepoJGr2YFbXXsKjlOKUU0/D7fGERWY+qKV63YsjkwTzQkSI5ORkOnbsyJYtW9p6KKKJgqU3bgM8BwfaNdeOb+x2cT7MXTVPGIwWCOJrcrlg5kx49lnYtAmuuw527ZIMfSvQga4okRwMNmd5ULBOvq7tHX30VP711r/p1q1r8zxgMwmWG0Xwr1GECQnmhYgQKSkppKen89NPP7X1UMRhCGbqgwtNeQJfLqPGaqwNbQPndsGafLfpfJmqehGrVinpMU046SR46SXIyoIrroD16yVD3wraQ5vD5mxVWVe5kVKK6JgYuvfogdvtQbXoGW7j2ASuUET6L1K0OVdbD0AIEZqkpCQ6dOjAli1b0FqH1UFJNF7NX59STmYlGNgcfGwP/rvmbzz4fZvvBoYBM2bAU0/BVVehb7gBXn8devWSfbQFWe3gAkgwK304e0mw3Ci022r8fj9+v5+ysjIK8vNxu90kp6QQFRWFy+XCMIwG91utNT6fr9G9611uNy6X64DtW7ZzTizE4ZBgXogIkZiYSJcuXVi0aBGVlZVER0e39ZBEMwse4yMuBDZNOPZYePVV9G23seTGG4l94AGGDR+OYcgF4ObWXpK5wc426jBKwjQN199rrSksLGTxooX898MPmD9/Hrt27kRr5yzAExXFUUeN42c//wUzTzqZvn371RvQa625+647+O+HHzRqrFdfex3X//Ym3G73AeP3a2fth4h734uwIcG8EBHCMAy6d+9OZWUlOTk59OzZs62HJEQ1w4Dp06l4+mnuu+UWSm+4gZdffplBgwZJhr4ZhRK8RpLDeSrBVpT1bUNrzZ49u3n04Yf44L332L8/D+CAfbLS6+X72d8xf95cJrz3Lo898RRjxoytd7/dn5fHzp2Na0ZQWFBQazcd2wbdEhPVxRFDgnkhIkifPn3w+XxkZmY2KZg/+DgSyoH04OOLHHBEnUyT6ClTuOOJJ7jpppu47LLLePnllxk6dCim1BI0j3bWn9w+zFKbhk5scnNzufLyS5kz53u0bdO7Tx9OP/1nnHb6GXTv3gOvz8tPGzbwwXvv8vXXX7Fg/jzuvfsu/vrCy/Ts1avWbSqluOqaazn5lFPrf262zWeffcK777xNbFwcg4YMxeU6NOzS1Cg5ks9X0QQSzAsRQXr16oXf72fPnj0h3T54gKj5Nxz6fV0OrstWgT8MakyylMvDogbDMDj66KN57rnnuPbaa7nmmmt4/vnnGTVqlGTom0HN92570dRAtqHXory8jOeeeYof5nyP5fdz1LjxPPXMc4wdexRmjaB6yJChnHTKqbzx+t945+23uPjiS0jv2LHO7SqlGDd+AuPGT6h3fAUFBfzttVexbZsRI0YwffoxdZad2brtF5cTkUuKGYWIIDWD+YMv1wZXFAxOCPNZ4LWcv3220+0huFhJMBvWUEwQvE2wjVxwmXSf7Ww7uP1gz2RZ1VCAE9BPnjyZF198kbKyMq6++mo2bNiALW0rD1t7qZevqanPp77Wllpr1qxZw4cfvIff7yc9PZ1HHnucceMnHBDIB8XExHDJZVfw5jvvcebZ5xATE9PEUVVbsngRCxfMw+VyceZZ59Cla92tMdtT6ZRofRLMCxFBOnXqRExMDJmZmfgDnRR0jUDbZ0NlIHi3QgzYD0cw0Pfb1YG9VTOwb8HHFuFNKcWkSZN4+eWXUUpxySWXsHjxYgnoD1c7fFM1JQHQUJ99y7L4/rvv2LZ1K4ZhcMGvLmbc+An1Xh2Kjo6mW7duzVISVlFRwcsvv4jX66V79x6ce975DU4Gb4e/WtFKJJgXIoIYhkHv3r3Zu3cv5eXlWIGMe83seFuycTozeAMnFLYt2fojmVKKcePG8cwzz1BRUcENN9zA+vXrWzWgr3nFKtSvcBXGQzssTXpeDfyuvF4vPy5ehNaalJRUpk6bRlRUVOMeQmu83kpKS0spKyvFsqyQ77fkx8UsW7IEgHPPO4/U1LQG79fWn98ickkwL0SEGTx4MLsyM8kvKj0gAx9u7BpXCmpm68WRRSnF+PHjef3114mLi+PXv/41ixYtCjkwaopgLXVVeZhdvS/W9eW1q69ohe3+2krvdZ/PR05ODpWVla3waE17Tg3dx+fzsWrVSgDS09MZPHhoo+ds+Hw+7r7rTrp3TmdA314sXLggpPuVl5XxwXvvkpOTTf/+A/jFmWeHlO0Pu/1NRAwJ5oWIAFW18Bp69+nH3r1ZlJaWtfWwQubXgRKcCMh+iuanlGLs2LE888wzGIbBDTfcwIoVK2pt09dUVRO9D5rX4bOd/a+hrGcw+PcH7hu82mXZkZG1byqtNVprbNsmc9cuPvzgff5w+++46YZr2bJ5c7P+jurSlHmfDZXxWZafvNx9AMTFx9Oxngmt9fH5vFRUlFNRXo4dwgmo1podO3bwxeefAXDMjGMZOGhwSCcS7XD3Eq1EutkIEeaCB61gGU3/gYPYu3cPJaUlEbUSbPA5WIDLqO6II44MSilGjRrFK6+8wk033cTll1/Oiy++yIQJE2pt19cYwfdIVUa9GcarqZ4wbgCm4XQb0bSP7k0VFRXsz8tjz549/DBnNt99+w0//bQRj8dDYmISkyZPITYutlXG0hJBbFlZGT6fDwC3201MbOOfi1KK7t17MHrMWGJiYohPSAjpfu/++2127txBSmoqF/zqopAn07bHk0XROpRujdNuIUSTBLPxlh3MPGrWrV3LjGlTePvd9zlh5okRE8wfzFROgKSQoP5IEuwycu2111JSUsKzzz7LtGnTmrwfBzPxrVFuZihnvzXaaIEfjTMPxdfEKQd+v58tmzezbOkSVixfxvLly1i3dg0d0jsyYsQIJk2ewqjRYxg1ajQpqamt9tliKHA3chVYf2C+UF327NnNoH698fv9TJo8ha++mY3b4zn8wTZg7969nDBjGtu2beW0WWfwznsfhHyyqoAoSbGKJpDdRogwdHA2PkgpRWxcLF26dmHb1i0w88Q2G+PhsjTYlmTpjzRKKUaMGMGLL77Iddddx3XXXcdrr73GuHHjGtVFpGZZTGtlpII1+KZy9lto3f22MQ+ltaaiooLS0hJ2Z2byxeefMef72ezenUl5WTmJSUlMm34Mt9x6G0OHDSclJYWk5OQGO65EiujoGDxRUfj9fnxeL2VlZSS1cDBv2zYffvAeu3dnEhUVxRVXXtXozjjt5cqPaF0SzAsRZhoKUmJj4+jarTubNv3U6mNrbhony+gKZunlKHZEUEoxfPhwXnrpJW655RYuu+wynnzySU444YSQJwpauv7MbEsK5xPRkpISftq4gY0bN7Bk8WLmzPmezMxd9OrViyFDhnLiSSczafIURo8Ze0B3l7a8wtcSiyW5XCZdunRh65YtFJcUk5W1l6Tk5OZ/oBp2Z2by3w8/oLKykhNPPoWjxo2P2CunIrJIMC9EmNCBPxrKNsbGxNClcxe2bd3aiqNrWX4N2m6bbKdoG0ophgwZwpNPPsmVV17JzTffzGuvvcakSZPqzQ5rXb0AWlsKXjkLBvSttRJycOVlOzBx1bIsysrKWLd2Dd9+8zVLly5hx/bt5O7LoUvXbpx66mlMPnoqffv1o0f3HsQnJIRdgNkSo3G73AwfPoKtW7awP28/W7dsCXkialPYts2PixexYvky4uLimDXrDFJTUxu9nfD6zYhIIcG8EOEixGxjTGws4ydMZNWqFa0zrlZiaSAQHGktAf2RIBjQv/rqq9x+++1cfvnlPPnkk8ycObPWOmMdaHcaLv24g1eWTCNwMG2Ffda2Lfbn5ZOxfTsbNqxn3g8/8MMP31NaWkrXLl3p1bs3V11zLccedzy9evXG7XZXXe0ItyA+SCka/do1dHNPVBTTph/Dp598TF5eLt988zXHHn88MTEtM6m3vLyc1159mdLSUsaMPYpZZ/ys0SU2YfrrERFAJsAKEQaCpTWhTGzTWlNcXAxak5CYGLYH6KYK1iO3s6clGrB582auvvpqdu/ezfPPP8/xxx9ftW8Hj1LhFMgfzGU4+25z7rfBw7PWmsLCQpYvX863337LqtVr2LhhA/v35zF8xEimTp3OmKOOYvCQIfTq2Yuo6OjmG0QLMwC32fjXzQ60u61vd1i+fBmXXHQhmzf9ROcuXfj3ex8yfsLEej8zg6+5UqrWtpy13Vdrzfx5P3DGaadQXl7O/Q/8md/9/g+N/mw2lfNaCNFYEswL0cbaYiJfuGuJwEiEN601mzdv5ne/+x0bN27k2Wef5fjjj8ftdodNaU1D3Mbhd7rRWuP3+9m/fz9ZWVmsXLmSTz/9lCWB1UQTExMZMGAgJ5x4EkdPm07H9I7ExMbidrsj8sS+qSfvWjvrCNS3S3i9Xp564nH+/OB9eL1eRowcyVPPPM+YsUcRExNzwOtl2zb79+8nY9tWBg8ZQkJCIpZlsXjRQtauWYPL7eLkU06ja9euhzxOeXk511x5Oe/++x26de/Od3Pm0aNHz8Y9IZz9x2wf849FK5NgXog2FspBqTZlZWVs2bKZgvx8ysvLiYuPp0vnLvTu06fRl3eDbNsmOyuL7dszKCoqwuVy0blzF/r17090iNk+27bJyspix/YMiouLiY2NpUfPnnTv3qNR42qOwEhEnoyMDG666SbWr1/Pk08+yWmzZmFr1WaTXRtD4WRWmzKh07Isdu7cycqVK1m8eDErVqxg5cqVaK0ZN24cEyZMYOTIkUycOJHOXbrgs9tH1Hc4AazXavhKTUF+PldecSmffPwRWmu6duvGabPOYMrRR9OlS1eio6PZv38/GzduYN4Pc1i6ZAlPPP0sZ59zLl6vlz/cfisv/vV5YmNj+c9HnzL9mBmHPMa8uXO57JJfsTszk5tu+R33/On+AyYXh8rTxH1HCKmZF6IN6UZm5P1+Pzt37OCtN//Jl198TnZ2Ft5KL5blx+VyExsXy+AhQ7no4kuYeeKJxMbGNZitC06iW7t2DX979WV++P57iouL8Pl8KGUQExNDv/79+c2VV3PyqafVuQCKZVns2rWTp574C3O+n01RobMNl8tFfEI8Y8Yexe2/v4PBg4fgcrsbfq524PJ7iK+NaB/69OnDk08+yU033cTNN9+M2xPFtGOOxXQ1vM+0teCk2IZ6pmut8QbaJe7Zs4d58+bx9ddfs27dOiorKzFNk3HjxvHMM88wcuRI0tPTSUlJqZpHoAPtMcP9SkVDFIcXvBqq4WA+KTmZxx5/itS0NP73nw/Zu2cPr73yEu+8/SaxMTEYpom30lv1mRefkMDqVSs586yzAWcibXR0NNHR0bVOzK6oqOCTj//H7sxMevbsxWmzTsfThBaYTZg2IEQVycwL0UYauwBMeXk5n3z0P+69+04yMjIA8Hg8pHXoQGxsLKWlpeRkZ2PbNh6Ph0suvZzf3/lHunTpUm9Ab9s2b/3rn9x7913s2bMbpRRJycmkpKTg8/rYu3cPlmURHR3N7++4i+uu/y0JiYmHbGPuD3P43S03sW7tGrTWpKSkkJKSSnFxEfv2Ocuq9+zVi0f/8iSzTj8jpIVUpH7+yKS1ZseOHdx+++38uGQJDzz0CL8486zDXim2tdRWJqa1prKykoyMDNatW8fixYtZsmQJy5cvp2PHjgwdOpSxY8dy9NFHM2nSJOLj46vue/D7N7gOhddqpSfUQlwKzMM4Ybd1aK+B1hqfz8c3X3/FJx9/xE8bN5CdnUVpaSmGMoiLj6NLl64MGjyEY2Ycy6mnzSI6Ohrbtlm0YAFr167G5XZzyqmn0bVrtwO2vX//fm6+8Xr27N7N0VOnccedf2zSnAX5rBOHQ4J5IdpAsA1lqBP6bNvmg/fe5fbbbiFr7148Hg8zTzqZCy+8iG7duxMVFU1FRTnr1q3l9b+9xorly4iKiuaOu/7ITbf8rs4gSGvND3O+58rLL2Xnzh107NiRq669nilTjiY5ORm/38+C+fN58vHHyM7OIi2tAy+8/CqzTj/jgCzVvn05/Pz001i+bClRUdH8/BdncvEll5KamkZpSQnvv/dv/vXP/6OkpJiBgwbx7gf/ZeDAQSHV+HpMWSX2SKQ1bMvI4MYbb2Tt2jU8/+LLnHBCZCySpgC3obEsv7Pq6pYtfP/993z77bfs2LGDrKwskpKSmDp1KjNnzmTAgAH07NmTtLS0kOveg1f1/Lr6MSPN4ZaV6MAk2FArsJwrIpVkZ2WTn59PRWUFSimio6NJTU0lPb1jo8tj/H4/e/fuASAhPoHklJRGPguHy6heEVuIxoqMNIcQ7Y2uXk0yFFu2bOauO24na6+z8Mnd9/yJX196OXFxcTU6fmjGT5jI6Wf8jAfvvw+05rzzL6y3Tr2yspIf5nxPQUE+vfv04aVX/sbUadMwDLNqu6PHjCUlNYXbbrmJvLxc3nj9b5ww80RiY2OrHvdvr77CyhXLUUrx60sv46GHH60am9aasePG0blLFx564D42/fQTzzz5BM+98FJINfRWjf7z4sjSvWdvnnr2rzz5+KO4zPoPV36fj+ycHHL35VBRUYHWmpiYGNI7dqRTp85NnkcCTinFnj27iYqKIi01jajo6Dq7mpSUFLNj+w62Z2xhwby5fPPNN+zZs4dOnTrRr18/zj77bKZPn85RRx2F2+3G5XI1beKqAsMAFaHZeVczBa6G4VzhDIVSiqioaHr26kXPXr2a4dHB5XI1abLrAeMicCWnWUYkjkSSmReiDTSmX7Zt29zzxzt56om/YNs2l//mSh57/KmqYLo2RYWFoCAxMamBcWiysrKYO+d7TJfJrNN/Vmtmau/evfxs1imsWb2K9PSOrFn/U9Vqivv25XD2L37Gj4sX0bFTJ76bM49+/fofso3c3FxmHjedjRs20L1HD77+dg69+/Rp+AVAJoYdaQ5e4bW0tJSYmJhDapZ1YOGkpUt+5L13/82a1avYvXs3ZaWl2NomPj6e7j16Mn78BC741UUMCnHRoJotIdeuXcM/3/gHixctJDY2ll9fchnn/PK8Q652lZeX88H77/L5Z5+yYf16MnftZOjQoUyePJmJEycycOBABg8eXO/7trE04Lcir3b+cCYKHyyUFpXhzqXAJS0pxWGQzLwQbUATelZ+e0YGs7/7Btu2SU5O5rc33tJgQJCYVH8QH6SUonPnzpx97i/RWteZvezUqRPp6ekA5OfnU1pWVhXMr1q5kp07tgMwc+aJdOvWrdZtpKamcuZZ5/DnB+9n//79/DDn+5CDeVtLMH+ksWpkW+Pi4g75f601ubn7+Otzz/K3V19m//792LZNbGwssbGxKKXI2pvFls2bmfvDHN579x0eevhRZp1+Bh5P3aUUtm1TWFjAurVreeP1v/PVl1+Ql5eLZTkp8AkTJ2HXkgp2uVzs3LGDkpISLrn0Mo47/gR69exBQlwMUVFRLdI2UuFkuO0ICmaDY26ul0PhlKdEQrej2iicqwtCHA4J5oVoA41Z+Gbr1i3szswE4Oip0+jcpUujH8+yLNavW4vX5yMmOpohQ4dVBRdKqQYDjZoLqJimgSuQRtJas2N7Bvn5+QAcNW4CHnftnRyUUkyecjSmaVJRXs7GDeuxbbvWDhEHC9eFgkTL0DQcnFZUVHDfPXfzr3++QWVlJQkJCZw26wymHXMMXbt2xTBM9uXkMG/eXD756L9sz8jgT/fcTd9+/Rk1anSdZTLLly3lqSf+wldffklJSTGmaZLesSP78/Lwer11jsftdnPTLb87oOtJUxdEapTAxMlQJ9K3NVMFWs420/aUcl5nReSc0NQkdfKiOUgwL0Qr07p6RcuGb6vZnZlJXl4eAMNHjAy533tNpaWlXHPVFWRlZdGvX38+/+rbRmUK9+XksD8whm7dexAd7bSn9Pv97M7MpLKykuiYGLp07YqqIzhXSgVa7KWSm7uP3Xt2U1paSkJCQoOPH3zNZBLskcFqIDDVWvPh++/x1pv/pLKyku49evCn+x9k1uk/I7HGqshaa372izM5/Yyfcf+f7uFnP/8FfXrXfzWouLiYZUuX0LVrV6YdcwyzZp1BUVERd/z+Nvbs2V3vfQ++YmYHvoLBZksItnc0jYZft7YWHGdzv4+D2f5IOaEJMpQsjieahwTzQrSBUIN527bJz9+P3+9HKUW37t1xh9Cj/ZDHs2327tnLnj27iYuLr3WZ8rrHqlm0aCGZgasDxx1/QlVdvWX5KSwqBJxSiPj4+HpPEqKjo0lOSSY3dx8lJSVUVlSEFszjZOdNOei1e8EVkeuTk53NKy+/SHl5OVFRUdx73wNccOFFwIFtHJVSxMfHc8qppzFq1GjnZLOeK1FKKcaNG8+7H/yXfv0HVAXn8+fNbfIE2qq3Wgvuu0oFDubh2Hs+MB7DcPrvtxRDhdZ3PpzI55loLhLMC9HKQikhCLJtm9LSUsCpyY2uo4tGQ1xuNyfMnEleXh7duvdo1DYKCwv58IP32L8/jw7p6Zxy6qlVi6JYlk1FeUXV+Bo60TBNV1W9sreyEr/fH/I4IugYLQ5DQ+8PrTVLlvzI1i1bAJg85WhmnX5Gvft08EQ4FAmJiYwcNTr0ATfAtp1e6i1NBcptsMMvoK8aGy2ThQ5u02VEzmRYl5IVrkXzkWBeiDCmlKrKCNq2jW3ZaK0bHdDHxsby/IuvVN031PtblsV7777Dh++/h2EYnHnWOUybPuOAensjsBa71hrdQI84rXXV5EHDMFpkUqCIbA1lVi3LYu2a1ezfn4fL5eLMs85usGtTbbKzs8nJzkZrTa9evUhMSmqR/bE1Kz/CMaA3jOo2lC39dnf6+zvlNmHy9GtlBsqNhGgusjsJEcYMw6haCdKyLErLShtVIhOklMLtduPxeHC73Q0GLcGg+7tvv+EvjzyMz+djyNCh3HjzLQeUxRiGQUyMUz/v8/rw+nz1btfv91NZ4WTyo6KicDWhZEi0X8GVTetTUVHB1q1bnFWGU1Pp269/SJOoD/afD9/nkosv4KILf8mSHxc3bcBhKBjQh8PaDKZyguvWykCrwOMcECiHWVRv1FjpVXIZorlIZl6IMKaUokN6OtExMVSUl7M9Yxs+n5eoqMZPgm0M27b57NNP+P1tt5CZmcmAAQN58unn6NOn7wG3c7vdpKV1QClFSUkxhYUFdV450FpTVlZKXl4uAMkpKcTGxrTo8xARqIHgy+/zkZvr7EOpqamkdejQpIfJy81l008/4ff7KS4ubtI2QtXa8z2UAhMnK+5rxIT7Znt8nIC1LdrJBp87Ydiu0gic3EgQL5qbBPNChDGlFD169CQ9PZ1dO3eyfNkyysvKWzSY93m9fP75Z9x682/J3LWLPn378vhTzzB5ytGHBOmmadKte3diY2MpLS1le8Z2LMs6ZEGdoF27dlFUVIRpmvTo0bOqK04o5Ph3ZGgo7rQsi9KSEgCiPFFEN/G9MHDgIE49bRZ+y6JLE9q9hrtg5tejnS43/lYK6IMlJK1RVlOXYECvAgF9OCTnTdW8/fWFqEmCeSFaWfAgF2q2bOCgQfTu3YddO3eydOkSNmxYz+QpR7fI2IqLi3jtlZd59umnyMnJZuCgQTz97F+ZfsyMOrt59B8wkLQOHSgtLWXhwvlcW3lDrcG8bdvM/vYbAOLi4hk1uvZe33WRY6AAQFV3rHEmyzYtVJt1+hkcd8JMQJMQ33BHpcPRlsFksOzEwAnq7RBKmRr9GAc9TjgErMH+8+FQQ+9qoZacQgSFQVWdEEeexnymp6d35Bdnno1puqisqODPD95PTk52s45Ha01Gxjbu+eOd3H3XHWRnZzHzxJN4459vM+PY4+ptyzd8+AgGDBgIwJzZ37F+3dpab7d9ewYfffRfALp06cLUqdNDHl9bZvlEeDFNk7jAPJLKioqqbkqNFRMbS1paGmlpHfC00Aqt4UKp6tIXt9l8JTCK6rp4dyCQD6ez7qqrE2bbtIEMTsiVya6ipckuJkQra+zEJ6UUZ559DmPGjgVg7twf+NM9fyRz1y78fn/VhNjgpFWf18vuzEzmzf2hagn6iooKnnriL9z/p7t54a/PVXWU0VpjWRZLly7hmiuv4O+vvYrL5eKCCy/iiaeeZcTIkdi2jWVZB3zV7FoTHx/PeedfgMvloqysjIf//CA7d+6oegzbtsnJzuaJxx5lX04OSinOPe98UlJTW+w1E+2Xy+WmQ5pTJ19QUMD+/XlNmhR+pAm+h4ITMN2GE+QGA/tQ3l7BBaqCJwVV968xoTPc3qbBcblqnnC09GMSOMkxqyf/yueXaElSZiNEGzAVWIR+6bdjx448/Ohf+O1117Bx4wbeeP3vfP/dd5x/4a8YNWo0iUlJWJZFZuYu5s39ga++/IK+ffvx2utv0K9ffyorKnjhr8+xZ/duBg4azFVXXwtARXk5b7/9Jn+6549VgfaYsUcxYOBAPv/sk1rHopRi4qTJjBs/oepnPz/zbD7+6H989uknfPn5Z/zy7DP59SWX0qt3H/bl5PCvf77Bgvnz0Foz/ZgZXHrZFY1+vYQAiImJYdDgwRiGQW7uPjZuWN/g1aO2Fo67bzDwDgaeQcHPJK2pGriilucQhoF7fYJlN4bplBpZISxO1ujHoMYqt4EfRNJrJCKXBPNCtJHG1M0rpZg0eTLPPP8C9959F4sWLiAjYxt/fvB+XC4XcXFx+P0WpaUlVbdPT0+npEaXDm1rpxe8rs6qZ+dk83//eJ19OTnObbRm+bKlLF+2tN6x3HvfAwcE83Fxcdz/4MMYhsnHH/2PlSuWs3LFckzTxLare+PPOPY4Hvzzo3Tq3LlRZQ1t0RVDtI2GftWmaTJ4yFBSUlPJy83l44/+x8WXXEpCQmKrjK+9OfhtqA75pv0IPldDgREI5m2chb0OJ64Prj4bvMIhWXjR2iSYF6KNmKpxmSGXy83RU6fxznsfMvvbb/jk449Yt24tlZWV+P0+XKaLmNg+9OnTh5NOPoWTTzmNTp07AxAbF8cLL79KRUUFCQkJVX253W43/fsPoLKyMuRxGIZB584Hdv9QSjFw0CBefu3vnPqfD/jvfz5ke0YGXp8Pl8tF9+7dOfW00znnl+eRmpraqL7gkpU/goT4u540eQrDh49gzvezWTB/Hm/9659cevlvqlYmDjcS3IWXYNbcIFB2YzrBvB1o49nQ53LwqkYwgK/5cyHagtJSbChEm9DaWXq8qa2QbdsmPz+fgvx8yspKiYqOpkOHdFJSUtp8Mp9lWRQU5FNSUkJMTCypqal1tqusj6K67lQcGfx2aP3B33/v31xx2SVUVlTQpUtX/njPnzjvgguJiYk5ZP/3+XysWrWSlORk+vUfAMDyZUtZsXw5tm1zwokn0rt3nzrfN/Pm/sBlv76IXbt2ctvv7+CP9/ypUScOUaYEepFEB/44ODgK/grldynCjWTmhWhDpuFc4m0KwzAC3TjSmndQzcA0TdLSOpCW1rQFfYJCnZgn2o9Qr9mceuosLr30cv7xj7+zd+8e7vzDbXz6ycecedbZDBk6lOSUFPbn5bFs2VK+/OJz1q5ZzXkXXMjd99yH2+3mm6+/4vHHHsHn9/NG5zfp3btP1bbXr1/H+nXrqiZ6/7RxA2VlZQBs3LCeD957F5fLhWEYjBw9uqqbU20k8Is8Uu8uIo0E80K0keCELFM5k7HEgQxkkZUjUbCEoaG3RExsLPc98BA9e/Xi5ZdeYMf27Xz26cd89unHtd7e5XKRtXcvRYWFpHXogM/no7y8HL/fj+W3DrjtR//7L39+4D58Pt8h2/n4o//x8Uf/AyAqOpo/P/JYvcG8lIkJIVqaBPNCtKFgyzTbCo9VCsOJ9GY+MgXb+DVUAKqUIjEpiWuuu4FJk6fwxeef8c3XX/HTxg1Ytg2BSddpHdIZPXo0p5w2ixnHHkdScjIAJ518Ch06dMC2bUaOHn3AtpOTkunVqzd+v7/eMXg8HhIT6l9wSlX9IYQQLUNq5oVoYzpQm+mTgL6Ky3AympKVPzJZgfkkQVprfF4vbo+n1rr24HoJlmVRWVlBfn4+Pp+PhIREEhMTMU0Tl8uFUqp69Vitq/rTB38W/Nvv9zcYyAe5XK4654MYOHM+ZD8WQrQkCeaFCBMHBzBHKlNJec2RTmvw1ji5tW2bxx75M/369ednvzgzbLvWHMylwDQlMS+EaFlyIVuIMGHgrFB4JAsG8kLULLMqKiritVdfZvv2jLBeHOpghiGBvBCi5clhU4gwEVxq/UgN6F01MvKSlT+yBd8LwbfCiuXLMA1nsahICeZN6YYihGglR2jYIER4qgroj7BL8y4jsAT6kfSkRb2UcjLbaM2PixeRmJTI4CFDQr5/Tk4O77z9FtszMlpukHVQyARuIUTrkY8bIcJNICPpMdt/WzuFcyWivT9P0XgKZ78oKytlxYrldOvW44Be8A35aeMG7vj971i/fm3LDbIOZqC8Rk5OhRCtQYJ5IcJMMAgItq1srzXkpqpe3VVKa0RdsnbvYueO7UyYNAm32x3SfWzbZvOmTVRUVDBy5OiWHeBBDCWdmIQQrUv6zAsRzhSYgGE6S9zb7aD3lIGTuTQCwY4EPaJumoyMbezcsYNjjz0u5Hv5fD7mzfuBMWPHkpSc1ILjO5BCOjEJIVpfO835CdE+BLP0wYmx7hpBcKQJBjpus7o+XoIeUR/Lsli3bh1RUVEMGjTQ6VUZwgmtz+djyY+LOeqo8URFRbf4OKG6Tl52aSFEa5PMvBARQgVq6Q3lZOgtHRmZ+mCQY0omXjSS1+tl3rx5TJo0iaT4OAwjtH1+65bN7M/LY/SYsSGX5hyu4D4u+7cQorVJZl6ICBLMZptGdaY+XLOBZuBqgkcy8aKJysvLWbJkCZMmTSIqyoMrxCtTCxbMp3PnLvTr16/WFWObS/C8wiV18kKINiSZeSEilAoEEIYGbTqrZlra+btmNUJrxRcq8GUEAq7g40qAI5pq7dq12LbNsGHDME0TFThB9NvOvl4by7JYuGA+3bp3p3ef0LvfNEWwdEwCeSFEW5LMvBARrmqBnRqZcLcRWISpBReuUVR37qh63EA7TelQI5rD7Nmz6dmz5yEZ9vq6PO3encnWrVsYMXIkSUnJLTa2mm1VZT8XQrQlycwL0U7UDCjMQBecYJaeGt/X/Bsank9Ys192MOMe/Lf00hYtxev18uOPP9KzZ0969OhR9fPg/mYCKpClr97HNVs2O/XyEyZObrESGyO4WjGy/wsh2p4E80K0Y6pGZr4qaG+G4EMCGNHStm7dSmZmJhdeeCExMTGH/H9wQrinRttWW2vWr1tLaWkpkyZPafYx1WyrKu8BIUS4kGBeiCOEBB8iUmit2bRpEwUFBUycOLHODHvwxy7DOVmtKC9n9apVDBgwkNTU1GYbT3B+ipTUCCHCkdTMCyGECCuWZbF69Wq01owdO7bB2wfnjZSXFrNs6Y8ce+yxuEzjsC5CBeeEuA3wSG28ECKMSWZeCCFEWCkpKWHFihWMGzeO2NjYkO+3d+9edu7cyaSJE4h2G2ic8puD54scLFj7rgL/CK7nIMG7ECISSGZeCCFEWCkqKmLFihVMnz4dwwj9MDVv3jy6detGr169UEpVTVR1GdUdl2r7cgc7QAW6QAXXRRBCiEggmXkhhBBhZevWreTl5TFu3LiQO9Jorfnuu+8YMmQIXbt2PeD/am5CYnQhRHsjmXkhhBBhZe7cufTv35/OnTuHHMzn5uayefNmhgwZQnJycssOUAghwogE80IIIcKGZVnMnj2bYcOG0blz55Dvt2LFCioqKpgwYQKmabbgCIUQIrxIMC+EECJs7N69m8zMTIYNG0Z8fHxI99Fas2bNGizLYvTo0S07QCGECDMSzAshhAgbS5cuRWvN+PHjQy6xKS4uZuXKlfTu3ZuePXu28AiFECK8SDAvhBAiLFiWxYoVKzAMg1GjRoV8v9zcXNatW8f06dNDPgEQQoj2QoJ5IYQQYaGgoIB169YxbNgw0tLSQrqP1pq9e/eyZcsWpk2b1sIjFEKI8CPBvBBCiLCQlZXFunXrOO6440K+j9aaxYsX06FDB/r16yeZeSHEEUeCeSGEEG1Oa01mZia7d+9m0qRJjbrfvHnzGDlyJElJSS04QiGECE8SzAshhGhzWmvmzp1Lv3796NatW6Mmvy5dupSxY8eSmJjYwqMUQojwI8G8EEKINmfbNvPnz2fUqFEkJCSEfL9Vq1ahlGLYsGG4XLKouRDiyCPBvBBCiDZXuW8f0Vu2MGPcOOLi4kK6j9aaBQsWkJiYyIgRI1p4hEIIEZ4kjSGEEKLNxS5ezMfx8dgjR2IYoeWZysrKWLlyJV27dqV3794tO0AhhAhTkpkXQgjRtrRGLVyIKzYWT9++Id8tMzOTrVu3MmXKFDweTwsOUAghwpcE80IIIdpWfj6sXg39+kH37qHdR2vK9+4loaiIExrRylIIIdobCeaFEEK0rZ07YdcuOPpoCLHEBttm8Jo1vKgUYwcNatnxCSFEGJOaeSGEEG1Ha9i8GXbvhhkzQr+f10v0t98yeOhQCHHCrBBCtEeSmRdCCNF2/H6nxKZDB+jVK/T7VVbC0qUwYQJERbXc+IQQIsxJMC+EEKLtVFbCvHlOiU10dOj3W7XKOREYORJMs+XGJ4QQYU6CeSGEEG2nqMjJzE+cCI3pSDN3LnTtCn36QIirxQohRHskwbwQQoi2s2wZxMTA4MGhB+VeLyxaBD17Nq40Rwgh2iEJ5oUQQrSd2bOhd+/GZdi3bYPMTBg/HmJjW3R4QggR7iSYF0II0TbKymDFCieQ79o1tPtoDZs2Ob3pJ06UEhshxBFPgnkhhBBtY+NGyM6GyZPB7Q7tPpYFa9Y4f48b17LjE0KICCDBvBBCiNantRPMFxY67SVDzbCXlsLKlTB6NMTHt+QIhRAiIkgwL4QQovX5fE57yZgYGDYs9PsVFTn95Y89NvTVYoUQoh2TT0IhhBCtr6jIqZefMqVxLSkzMiAvD8aOlXp5IYRAgnkhhBCtTWsoKHAy89OnNy7D/sMP0K8fdOsmwbwQQiDBvBBCiLawdq0ziXXUqNDvY1lOK8shQ6Bz55YbmxBCRBAJ5oUQQrS+uXNh0CDo2DH0DPvu3U5/+WHDZPKrEEIEuNp6AEIIIY4wPh/MmeO0luzY8ZD/1jrw90E/V0uXgd8f6C9vgJZKGyGEkGBeCCFE69q6FfLz0cOHQ3Q0aCdwt7UTyAeDeF0jmte2jWv1akyl8A4fibIhGMcbyvkKkgBfCHEkkWBeCCFE61qyBK0U+qhx2LbC0odm4Q9RkI9atRp76DB0WvoBgb6lncBeKTBV4HskqBdCHBmkZl4IIUSr0ID2+dArVqJjYvENGYY/lEAeUPv2oTasxz5mRp3btjX4bPBZ4LerM/1CCNGeSWZeCCFEiwoG1LYGKycX17p12GPGouNCnMSqNWRmojJ3YU+e0vDNcbL1tuWU35hGoCRHVZfmCCFEeyGZeSGEEC1GB758tvOl92Y5Gfbjjg+9DkZrjHnz0L37oBvRXz4Y1Hst5++QLgEIIUSEkcy8EEKIZhfMxlsaLDsQR2uN2rYNCgvRY8eGvjHbxlgwDz1yFCQkNmk8fhtsBS5D6umFEO2LZOaFEEK0CL/tfFUlxG0b44fv0cOGo9PTQ99Qbi7G+nXYY486rP7ydiBLL7X0Qoj2RIJ5IYQQzUYHAmWfHShtqcm2MRYswB49GkKtlweMZUvR0THowYPBOPzDVnBsEtALIdoDCeaFEEI0q2D2+2Bqx3bU7kz0qDEQExPaxmwbY8F8SE7GHjas2cbol4BeCNFOSDAvhBCiWQRbQ9YVH6vFi9BJSdhDhoRetF5UhFq3Ft27N3Tt1lxDBSSgF0K0DzIBVgghxGHTurq3e13sGcfhT0xCNyLDrnbsQG3fjnXpZWCazTDSA/ltUIaT2ZJJsUKISCTBvBBCiMOidaCve0MZ7q5dsbt2bdSG1batTn/56TMOZ4j18tvgNqUHvRAiMkmZjRBCiCbTgSDeb4dwY6Wqv0JhWU6JTVoHdK9ehzXO+mgCXXdCXI1WCCHCiQTzQgghmiQY+FqhBPJNNXAg1vU3QFxcCz5IYHVaWVhKCBGBlNYy9UcIIUTjBctrQsrKBw81Ph9kZ6Ny90FlJSQloTt3hqTkurP2wfs2lNEP3q6szFmcqrgIYmPR3btDalq9VwU0TnbLY0rtvBAiskjNvBBCiCYLKSvv9aI2bsD4338xP/ofKjMTLL8TfBsGxMZijxyFdf6F2MceB+npB/aTDyW69npRa9dgvvYqxrdfowoKnMEZCqKjsSdOwrr2euxJkyE6utZt6sDzMQ0J6IUQkUMy80IIIZokuMJrvSrKMf/vDVyPP4bKyABAK+Ws5OqJgpJiVGWl8/OoKOwZx+K/7wH02KNCj6grKjD//Q6u+++FnTtRgHa7ITERystRZWXO9jt1wv+HO7Guua7OzjgKZzKsIcG8ECJCSDAvhBCi0YKrvNbZwUZrqKzEfOF5XI/8GfLzISkJ6+xzsH9+JrpHDyegrqjAWLgQ861/oZYvQ3m92KNG43vlNfSYsSGV1hhffI77ikth3z50r97Yv7oI6/QzIDYWSksxP3gf87VXnDF0SMf3/ofYR0+tc5NuwwnmJTsvhIgEEswLIYRoFE1ggSirvhtpjK+/wn3RBbB/P3rAAPyPPIZ92unVJTRKVde5FxXhuu9ezNdehYpy7NNOx/fSK9CpU/2DsW1cv7kM81//RPfug+/1N9CTJlfXx2sNto35+GO4/ngnKIX1q4vxv/wquN21btJQTkAvwbwQIhJINxshhBCNo0E3VF7j92M++zRq/36IjcW64y7sU05zsvE1J6IGv09MxP/7P2CffDIAxrdfY8yf2/DyrEph3Xwr/gf+jP/Bh9CTpzgnCzW3b5pYvzwPunVDaY2x6SfIzq5zkw32yxdCiDAiwbwQQohGayjgVfPnYyxd4tz26GlYvzy/zky4cwcFHTthXXUNpKaiyssx//E6WPWl/5376WHDsW79HfZZ59R9u6Qk9ICBzvclJaiC/Ho3KwG9ECJSSDAvhBCi0epNzGuNsfRHKC52SnLOPKv+QD5IKeyJk9BduzmtIpcthZIS5/+8XtididqeATnZBwb5gez7ARn5Qzde/X+GUecE2KrnJ8G8ECJCSDAvhBCiURqMcysqUBs3OgF4fDz2yJGhbzw+Hh28fUkpxob1AKjtGXjO/gWeyRNw3fY7KCho3KDLy2H7duf7lBR0Wod6by6xvBAiUkgwL4QQolEazFqXlaEK8p0WkenpEJ/QqNmkdu8+gW8syM5yvvdbqP35qNxcVFFhw7X0BzHWrEbt2I42DOfkokMDwbxu9EMIIUSbkEWjhBBCNC+f18mEA8TEgLuRh5rEROdvrVFFxc63cbFYEyagunfHHj4itLKdoJJizBefB9uGhESs8y88cFEqIYSIYBLMCyGEaJQGM9ZKgTKqb9zYDLddoyI/WNvesxf+/3uzkRvCWVDqxRcxZs9GGQb+8893FqQKgcZZREoIIcKZBPNCCCGal8cDsTHO92Vl4PM16u6qsDDwjYEOZumb0vTdtjG+/QbXX5+FsjLsceOxrr2+wcmvQggRSSSYF0II0bxi49DpHZ3Mdna2s/Kq1iEH5GrTT843ponu2bNpY/BWYnz5Je5rrkJlZ2EPGozvyafRQ4fJalBCiHZFigaFEEI0SoOhcFQUeuBAiIpCVVRgrF4Z+saLilAbNjjfJyej+/Vv3OC0dhas+uB93LfeDNlZ6N598D/8aPXKsCGSkF8IEQkkmBdCCNEoocTD9tRpkJwCgPnO29X94uu9k1MWo/buQQH28Sc4E2hDpTUUFGA+9SSu66+F7Rno0WPwvvIa9qmnhb4dIYSIIBLMCyGEaJRQMtZ69BjsyZOd2y9fhvnOW+D313MHDTnZmG+8DoWF6KQkrF+e17iuM7t24fr973A9eB+UlmLPOBbfy6+ijz3OqZNvTFZeSTWOECIySM28EEKIRgkpyDUM/LfchrFoEWTtxfXIn9GJSdizTofY2OqNaO10r8nLw3XvPRjffA2GgXX2OdgTJ1bfLi8X88MPoSAf3X8A9sknQ0ysc3+fD7V0Ce47/4BavAiiorAuuwL/7b+Hnr2a9hybdC8hhGh9SmtZFkMIIUTotAavHUKLSq8X859v4Lr7Lti3D2JisM6/APvY46BLN7THjSopQa1bi/neu6ilS1C2jXXMDHyv/A369KkK5tX69Xh+cQZs24o96wx8r/7NWfjJ58N87hlcTz0JWXud8pyxR2Fd9Ot6e9HbxxyDHjykzv93G2DKtWshRASQzLwQQohGMxX4GwrmPR5ngSa3G9ddd8K+HMy//w3z3X9Dcgra5UKVl0NerlOCEx2NddwJ+B948IBAHgCl0G4Xyu0GV43WksXFuJ56ApWVVfUjY/kyjOXL6h2a75XXsOoJ5g1JzQshIoQE80IIIRot5GA3NhbrwouwJk3G9c7bqB9+QOVkoUpKUV4vOi4Wuo1C9+vnlNaceDLExR1Sy6O7dMH/4MNQXIzu0QPi453/ME3ssUehiosaNX7dqfPhPzchhAgDUmYjhBCi0bQGnw12Y44gWsP+/ajdmVBY6GTjY2PR6enQvTt4oho/ENsO9LG3G75tTfEJEB1d63+5DOfKg0yAFUJEAgnmhRBCNJrWYGnwNzKGDncGThWPZOeFEJFCpvcIIYRoNKUC2eu2HkgzM5QE8kKIyCLBvBBCiCZrTx1fFO3r+QghjgzysSWEEKJJgtn59nIgkUBeCBGJ5KNLCCHEYWkPQbChZNKrECIytYOPYCGEEG1FBWrMXRF8NFEExi+BvBAiAkXwx68QQohwUFVuEwyGI6xHmstw4niJ5YUQkUiCeSGEEM3CZUReJ5jgmKW8RggRqSSYF0IIcdhqlttESkDvkjp5IUQ7IMG8EEKIZqOIjEWXXIYzcVcCeSFEpJNgXgghRLNRgYWk3IaT9Q434Tw2IYRoCqW1jrCpSkIIIcJd8MhiafDbbTuWoGAZkEIy8kKI9kOCeSGEEC1Ga6e5jd8Gu42ONgonEx/shy+BvBCiPZFgXgghRIvT2gnm/Xbrdq4MBvGSjRdCtFcSzAshhGgVwaONrcGyoaWqb5RyJoRJEC+EOBJIMC+EEKLVBTP1NqCbKbAPtpk0lATxQogjhwTzQggh2kzwCKQ5MMBHH1qOowK3C8bowcWegh10gj+XIF4IcSSRYF4IIUSbq+9IVPO/6orTJYAXQhypXG09ACGEEKK+YFzidCGEqJssGiWEEEIIIUSEkmBeCCGEEEKICCXBvBBCCCGEEBFKgnkhhBBCCCEilATzQgghhBBCRCgJ5oUQQgghhIhQEswLIYQQQggRoSSYF0IIIYQQIkJJMC+EEEIIIUSEkmBeCCGEEEKICCXBvBBCCCGEEBFKgnkhhBBCCCEilATzQgghhBBCRCgJ5oUQQgghhIhQEswLIYQQQggRoSSYF0IIIYQQIkJJMC+EEEIIIUSEkmBeCCGEEEKICCXBvBBCCCGEEBFKgnkhhBBCCCEilATzQgghhBBCRCgJ5oUQQgghhIhQEswLIYQQQggRoSSYF0IIIYQQIkJJMC+EEEIIIUSEkmBeCCGEEEKICCXBvBBCCCGEEBFKgnkhhBBCCCEilATzQgghhBBCRCgJ5oUQQgghhIhQEswLIYQQQggRoSSYF0IIIYQQIkJJMC+EEEIIIUSEkmBeCCGEEEKICCXBvBBCCCGEEBFKgnkhhBBCCCEilATzQgghhBBCRCgJ5oUQQgghhIhQEswLIYQQQggRoSSYF0IIIYQQIkJJMC+EEEIIIUSEkmBeCCGEEEKICCXBvBBCCCGEEBFKgnkhhBBCCCEilATzQgghhBBCRCgJ5oUQQgghhIhQEswLIYQQQggRoSSYF0IIIYQQIkJJMC+EEEIIIUSEkmBeCCGEEEKICCXBvBBCCCGEEBFKgnkhhBBCCCEilATzQgghhBBCRCgJ5oUQQgghhIhQEswLIYQQQggRoSSYF0IIIYQQIkJJMC+EEEIIIUSEkmBeCCGEEEKICCXBvBBCCCGEEBFKgnkhhBBCCCEilATzQgghhBBCRCgJ5oUQQgghhIhQEswLIYQQQggRoSSYF0IIIYQQIkJJMC+EEEIIIUSEkmBeCCGEEEKICCXBvBBCCCGEEBFKgnkhhBBCCCEilATzQgghhBBCRCgJ5oUQQgghhIhQEswLIYQQQggRoSSYF0IIIYQQIkJJMC+EEEIIIUSE+n+XCZ5fwhXebgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count = 0 \n",
    "tester = Tester(args, args_test)\n",
    "\n",
    "b = False\n",
    "count_outlier = 0\n",
    "\n",
    "testing_list_dict = {\n",
    "'logic6': \n",
    "['O=C(Nc1nc(-c2ccc(OC(F)F)cc2)cs1)c1ccccc1',\n",
    "'Cc1nc(C(F)(F)F)ccc1C(=O)NNC(=O)c1ccccc1F',\n",
    "'O=C(NCC[NH+]1CCCCC1)[C@@H]1COc2ccc(F)cc2C1',\n",
    "'O=C(N[C@H]1C[C@@H]1c1ccccc1F)c1c(Cl)ccc(Cl)c1Cl'],\n",
    "'logic7': \n",
    "['CCOC(=O)c1sc(NC(=O)/C=C/c2ccc(F)cc2)nc1C',\n",
    "'CCOC(=O)[C@@H](C)N1C(=O)S/C(=C\\c2cc(Br)c(O)c(OC)c2)C1=O',\n",
    "'O=C([O-])CCCCCN1C(=O)/C(=C\\c2ccncc2)SC1=S',\n",
    "# 'O=C([O-])/C=C/CC[C@@H]1SC[C@H]2NC(=O)N[C@@H]12', # error\n",
    "'O=C(CC[NH+]1CCN(CCO)CC1)Nc1ccc(Br)cc1',\n",
    "'CCCNC(=O)C[NH+]1CCC(NC(=O)/C=C/c2csc(C)n2)CC1',\n",
    "'CCOC(=O)C1=C(C)C(C(=O)Nc2ccc(F)cc2F)=N[C@H]1C',\n",
    "'CC/C=C(\\C)C(=O)N1CSC[C@@H]1C(=O)N1CCCSCC1',\n",
    "'COCC1=C[C@@H](CNC(=O)[C@@H]2c3cc(OC)ccc3N(C)[C@@H]2C)N=N1',\n",
    "'O=C1OCCN1Cc1ccc(/N=C(\\O)NCCc2ccccc2)cc1'],\n",
    "'logic9': \n",
    "['NC(=O)NC(=O)OCCSc1nnc(Nc2ccccc2)s1',\n",
    "'C[C@H]1C[C@H]([NH2+]N)CCN1C(=O)OCc1ccccc1',\n",
    "'CCn1cc([C@H]2OCC[C@@H]2C[NH2+][C@H](C(N)=O)c2ccccc2)cn1',\n",
    "'CC1(C)C[C@@H]([C@@H](CC(N)=O)c2ccccc2)CCO1',\n",
    "'COC(=O)c1ccc(N2CC[NH+](Cc3ccccc3)CC2)c(N)c1'],\n",
    "'logic14': \n",
    "['Cc1cccc(OCC(=O)Nc2cccc3ccccc23)c1',\n",
    "'CCOC(=O)/C(=N/Nc1ccccc1)S(=O)(=O)c1ccccc1',\n",
    "'O=C1O[C@H](c2ccccc2)[C@@](O)(c2ccccc2)C12CCCC2',\n",
    "'CC(C)C[C@H](NC(=O)C[NH3+])C(=O)N[C@@H](Cc1ccccc1)C(=O)[O-]',\n",
    "'CC(C)CCN1C(=O)c2ccc(C(=O)O[C@@H]3CCOC3=O)cc2C1=O',\n",
    "'Clc1ccc(-c2onc3ccc(C#Cc4ccccc4)cc23)cc1', #False\n",
    "'Cn1c(=O)[nH]c(=O)c2c1ncn2CC(=O)N/N=C\\c1ccccc1O', #False\n",
    "'CNC(=O)[C@H]1CCC[N@@H+]1CC#Cc1ccc(C(F)(F)F)cc1', #False\n",
    "'CCn1c(C[NH+](C)C)nc2c(O)c(Cl)cc(Cl)c2c1=O', #False\n",
    "'CC[NH+](CC)CCN1C(=O)C(O)=C(C(C)=O)[C@@H]1c1ccc(F)cc1'], #False\n",
    "'3mr': \n",
    "['CC(=O)OC1CC2(C)OC2CCC(C=O)=CC2C1C2(C)C',\n",
    "'CCOC(=O)C1OC1C(=O)NC(CC(C)C)C(=O)NCCC(C)C',\n",
    "'CC1CC2OC2CC1COC(=O)C1CC2OC2CC1C',\n",
    "'CCCn1c(=O)c2nc(C3CC4CC3C3OC43)[nH]c2n(CCC)c1=O',\n",
    "'C=C1CC(OC(=O)C2(C)CO2)C2C(=C)C(=O)OC2C2C(=C)C(O)CC12',\n",
    "'CC1=C(O)C(=O)C2OC2C1=O'],\n",
    "'aromaticity': \n",
    "['CCSc1ncc(Cl)c(C(=O)Nc2cccc(Cl)c2)n1', #12\n",
    " 'N=c1sccn1CC(=O)NC(=O)NC1CCCC1', #5\n",
    " 'CCc1nnc(NC(=O)CSc2nc3cccnc3n2C2CCCCC2)s1', #14\n",
    "'Cc1nnc(SCC(=O)NC2CCCCCC2)n1N', #5\n",
    "'N=c1c2c(-c3ccccc3)c(-c3ccccc3)n(Cc3ccco3)c2ncn1Cc1ccccc1', #32\n",
    "'CN(C)CN1CSC(=S)N(CN(C)C)C1'] #0\n",
    "}\n",
    "\n",
    "testing_list = testing_list_dict[args.file]\n",
    "\n",
    "for d in tqdm(testing_list):\n",
    "    # data loader\n",
    "    test_loader, datasets_test =  generateDataLoaderListing([all_dataset[d]], 1)\n",
    "    molecule_test = datasets_test[0]\n",
    "\n",
    "    # testing\n",
    "    # print(datasets_test)\n",
    "    try:\n",
    "        predicted = tester.test_single(test_loader, return_attention_weights=True, print_result=False)\n",
    "    except:\n",
    "        predicted = None\n",
    "\n",
    "    if predicted != molecule_test.y:\n",
    "        print(molecule_test.smiles, \"TRUE:\", molecule_test.y, \"PREDICTED:\", predicted)\n",
    "\n",
    "    # if predicted == 1 or args.task == 'graphtask':\n",
    "    if predicted is not None:\n",
    "        count += 1\n",
    "\n",
    "        # attention result\n",
    "        att_mol = tester.getAttentionMol()\n",
    "        sample_att = att_mol\n",
    "        sample_graph = molecule_test\n",
    "        if 'atom' in sample_att:\n",
    "            sample_att_g = sample_att['atom']\n",
    "        else:\n",
    "            sample_att_g = None\n",
    "        if len(args.reduced) != 0:\n",
    "            sample_att_r = sample_att[args.reduced[0]]\n",
    "        else:\n",
    "            sample_att_r = None\n",
    "        # sample_att_g, sample_att_r = sample_att\n",
    "        if args.schema in ['A', 'R_N', 'AR', 'AR_0', 'AR_N']:\n",
    "            mask_graph_g = mask_graph(sample_att_g)\n",
    "        if args.schema in ['R', 'R_0', 'R_N', 'AR', 'AR_0', 'AR_N']:\n",
    "            mask_graph_r = mask_reduced(sample_att_r)\n",
    "\n",
    "        # molecule\n",
    "        smiles = sample_graph.smiles\n",
    "        mol = Chem.MolFromSmiles(smiles)\n",
    "        display(mol)\n",
    "\n",
    "        # reduced graph\n",
    "        if args.schema in ['A']:\n",
    "            reduced_graph, cliques, edges = getReducedGraph(args, ['atom'], smiles, normalize=False)\n",
    "        else:\n",
    "            reduced_graph, cliques, edges = getReducedGraph(args, args.reduced, smiles, normalize=False)\n",
    "\n",
    "        # record importance\n",
    "        mask_graph_x = None\n",
    "        if args.schema in ['A']:\n",
    "            mask_graph_x = mask_graph_g\n",
    "        elif args.schema in ['R', 'R_0', 'R_N', 'AR', 'AR_0', 'AR_N']:\n",
    "            mask_graph_x = mask_graph_r\n",
    "\n",
    "        if predicted == 1 or args.graphtask == 'regression':\n",
    "\n",
    "            if not args.schema in ['A']:\n",
    "                mask_graph_x = mask_rtog(smiles, cliques, mask_graph_r)\n",
    "                if args.schema in ['AR', 'AR_0', 'AR_N']:\n",
    "                    mask_graph_x = mask_gandr(mask_graph_g, mask_graph_x)\n",
    "                    display_interpret_weight(mol, cliques, edges, mask_graph_g, mask_graph_r, scale=True)\n",
    "            \n",
    "            # display_interpret_weight(mol, None, None, mask_graph_x, None, scale=True)\n",
    "            \n",
    "            if predicted == molecule_test.y or args.graphtask == 'regression':\n",
    "                att_groundtruth = np.array(logic_dataset.match(mol), dtype=int)\n",
    "\n",
    "                if or_case:\n",
    "                    att_groundtruth_or = np.array(logic_dataset_or.match(mol), dtype=int)\n",
    "                    att_more = list()\n",
    "                    for att in att_groundtruth:\n",
    "                        for att_or in att_groundtruth_or:\n",
    "                            or_list = list(att | att_or)\n",
    "                            att_more.append(or_list)\n",
    "                    if len(att_more) > 0:\n",
    "                        att_groundtruth = np.concatenate((att_groundtruth, att_more))\n",
    "\n",
    "                if len(att_groundtruth)== 0:\n",
    "                    print(att_groundtruth)\n",
    "                    print(smiles)\n",
    "                    # print(att_groundtruth_x[d])\n",
    "                    continue\n",
    "                \n",
    "                assert len(list(mask_graph_x['atom'].values())) == len(att_groundtruth[0])\n",
    "                # print(list(mask_graph_x['atom'].values()))\n",
    "                # print(att_groundtruth)\n",
    "\n",
    "                finding_max = -999\n",
    "                finding_max_selected = list()\n",
    "                list_roc = list()\n",
    "                list_acc = list()\n",
    "                list_jac = list()\n",
    "                temp_index = list()\n",
    "                temp_weight = list()\n",
    "                # print(att_groundtruth)\n",
    "                attention_prob = list(mask_graph_x['atom'].values())\n",
    "\n",
    "                for t in att_groundtruth:\n",
    "                    try:\n",
    "                        if sum(t) == 0:\n",
    "                            list_roc.append(0)\n",
    "                        else:\n",
    "                            roc = roc_auc_score(t, attention_prob)\n",
    "                            list_roc.append(roc)\n",
    "                            # print('Ground truth:', t)\n",
    "                            # print('Model attrib:', np.sum(attention_prob), attention_prob)\n",
    "                            # print('ROC:', roc)\n",
    "                            # accuracy\n",
    "                            attention_prob_acc = [a > 0.5 for a in attention_prob]\n",
    "                            # print(attention_prob_acc)\n",
    "                            attention_prob_acc_score = accuracy_score(t, attention_prob_acc)\n",
    "                            list_acc.append(attention_prob_acc_score)\n",
    "                            # print('ACC', attention_prob_acc_score)\n",
    "                            # # method I: plt\n",
    "                            # fpr, tpr, threshold = metrics.roc_curve(t, attention_prob)\n",
    "                            # print(fpr, tpr, threshold)\n",
    "                            # roc_auc = metrics.auc(fpr, tpr)\n",
    "                            # fig = plt.figure(figsize=(5, 5), dpi=150)\n",
    "                            # plt.title('Receiver Operating Characteristic (ROC)')\n",
    "                            # plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "                            # plt.legend(loc = 'lower right')\n",
    "                            # plt.plot([0, 1], [0, 1], 'r--')\n",
    "                            # plt.xlim([0, 1])\n",
    "                            # plt.ylim([0, 1])\n",
    "                            # plt.ylabel('True Positive Rate (TPR)')\n",
    "                            # plt.xlabel('False Positive Rate (FPR)')\n",
    "                            # plt.show()\n",
    "                            attention_prob_jac_score = jaccard_score(t, attention_prob_acc)\n",
    "                            list_jac.append(attention_prob_jac_score)\n",
    "                            # print('JAC', attention_prob_jac_score)\n",
    "                            \n",
    "                    except:\n",
    "                        pass\n",
    "\n",
    "                    for tti, tt in enumerate(t):\n",
    "                        temp_index.append(tti)\n",
    "                        temp_weight.append(tt)\n",
    "\n",
    "                    if roc > finding_max:\n",
    "                        finding_max_selected = (temp_index, temp_weight)\n",
    "                        finding_max = roc\n",
    "\n",
    "                    # print(d)\n",
    "                    # print(temp_weight)\n",
    "                    # print(temp_index)\n",
    "                    # print(finding_max)\n",
    "                    plot_attentions_groundtruth(args, molecule_test, [[[temp_index,temp_index],temp_weight],None])\n",
    "\n",
    "                    temp_index = list()\n",
    "                    temp_weight = list()\n",
    "\n",
    "                # print('MAX:', max(list_roc))\n",
    "                if len(finding_max_selected) > 0: max_index, max_weight = finding_max_selected\n",
    "                # plot_attentions_groundtruth(args, molecule_test, [[[max_index,max_index],max_weight],None])\n",
    "\n",
    "                fpr, tpr, threshold = metrics.roc_curve(max_weight, attention_prob)\n",
    "                # print(fpr, tpr, threshold)\n",
    "                roc_auc = metrics.auc(fpr, tpr)\n",
    "                fig = plt.figure(figsize=(5, 5), dpi=150)\n",
    "                plt.title('Receiver Operating Characteristic (ROC)')\n",
    "                plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "                plt.legend(loc = 'lower right')\n",
    "                plt.plot([0, 1], [0, 1], 'r--')\n",
    "                plt.xlim([0, 1])\n",
    "                plt.ylim([0, 1])\n",
    "                plt.ylabel('True Positive Rate (TPR)')\n",
    "                plt.xlabel('False Positive Rate (FPR)')\n",
    "                plt.show()\n",
    "                print(attention_prob)\n",
    "                if len(list_roc) > 0 and len(list_acc) > 0: print('accurary', list_acc[list_roc.index(max(list_roc))])\n",
    "                if len(list_roc) > 0 and len(list_jac) > 0: print('jaccard', list_jac[list_roc.index(max(list_roc))])\n",
    "                if len(list_roc) > 0 and len(list_acc) > 0 and len(list_jac) > 0:\n",
    "                    plot_attentions_groundtruth_compare(mol, mask_graph_x, molecule_test, [[[max_index,max_index],max_weight],None], finding_max)\n",
    "\n",
    "                # auroc.append(max(list_roc))\n",
    "        \n",
    "        elif predicted == 0 and false_case:\n",
    "            \n",
    "            if not args.schema in ['A']:\n",
    "                mask_graph_x = mask_rtog(smiles, cliques, mask_graph_r)\n",
    "                if args.schema in ['AR', 'AR_0', 'AR_N']:\n",
    "                    mask_graph_x = mask_gandr(mask_graph_g, mask_graph_x)\n",
    "            \n",
    "            # display_interpret_weight(mol, None, None, mask_graph_x, None, scale=True)\n",
    "            \n",
    "            if predicted == molecule_test.y:\n",
    "                att_groundtruth_false = np.array(logic_dataset_false.match(mol), dtype=int)\n",
    "\n",
    "                if or_case:\n",
    "                    att_groundtruth_or_false = np.array(logic_dataset_or_false.match(mol), dtype=int)\n",
    "                    att_more = list()\n",
    "                    for att in att_groundtruth_false:\n",
    "                        for att_or in att_groundtruth_or_false:\n",
    "                            or_list = list(att | att_or)\n",
    "                            att_more.append(or_list)\n",
    "                    if len(att_more) > 0:\n",
    "                        att_groundtruth_false = np.concatenate((att_groundtruth_false, att_more))\n",
    "\n",
    "                if len(att_groundtruth_false)== 0:\n",
    "                    print(att_groundtruth_false)\n",
    "                    print(smiles)\n",
    "                    # print(att_groundtruth_x[d])\n",
    "                    continue\n",
    "                \n",
    "                assert len(list(mask_graph_x['atom'].values())) == len(att_groundtruth_false[0])\n",
    "                # print(list(mask_graph_x['atom'].values()))\n",
    "                # print(att_groundtruth_false)\n",
    "\n",
    "                finding_max = -999\n",
    "                finding_max_selected = list()\n",
    "                list_roc = list()\n",
    "                list_acc = list()\n",
    "                list_jac = list()\n",
    "                temp_index = list()\n",
    "                temp_weight = list()\n",
    "                # print(att_groundtruth_false)\n",
    "                attention_prob = list(mask_graph_x['atom'].values())\n",
    "\n",
    "                for t in att_groundtruth_false:\n",
    "                    try:\n",
    "                        if sum(t) == 0:\n",
    "                            list_roc.append(0)\n",
    "                        else:\n",
    "                            roc = roc_auc_score(t, attention_prob)\n",
    "                            list_roc.append(roc)\n",
    "                            # print('Ground truth:', t)\n",
    "                            # print('Model attrib:', np.sum(attention_prob), attention_prob)\n",
    "                            # print('ROC:', roc)\n",
    "                            # accuracy\n",
    "                            attention_prob_acc = [a > 0.5 for a in attention_prob]\n",
    "                            # print(attention_prob_acc)\n",
    "                            attention_prob_acc_score = accuracy_score(t, attention_prob_acc)\n",
    "                            list_acc.append(attention_prob_acc_score)\n",
    "                            # print('ACC', attention_prob_acc_score)\n",
    "                            # # method I: plt\n",
    "                            # fpr, tpr, threshold = metrics.roc_curve(t, attention_prob)\n",
    "                            # print(fpr, tpr, threshold)\n",
    "                            # roc_auc = metrics.auc(fpr, tpr)\n",
    "                            # fig = plt.figure(figsize=(5, 5), dpi=150)\n",
    "                            # plt.title('Receiver Operating Characteristic (ROC)')\n",
    "                            # plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "                            # plt.legend(loc = 'lower right')\n",
    "                            # plt.plot([0, 1], [0, 1], 'r--')\n",
    "                            # plt.xlim([0, 1])\n",
    "                            # plt.ylim([0, 1])\n",
    "                            # plt.ylabel('True Positive Rate (TPR)')\n",
    "                            # plt.xlabel('False Positive Rate (FPR)')\n",
    "                            # plt.show()\n",
    "                            attention_prob_jac_score = jaccard_score(t, attention_prob_acc)\n",
    "                            list_jac.append(attention_prob_jac_score)\n",
    "                            # print('JAC', attention_prob_jac_score)\n",
    "                            \n",
    "                    except:\n",
    "                        pass\n",
    "\n",
    "                    for tti, tt in enumerate(t):\n",
    "                        temp_index.append(tti)\n",
    "                        temp_weight.append(tt)\n",
    "\n",
    "                    if roc > finding_max:\n",
    "                        finding_max_selected = (temp_index, temp_weight)\n",
    "                        finding_max = roc\n",
    "\n",
    "                    # print(d)\n",
    "                    # print(temp_weight)\n",
    "                    # print(temp_index)\n",
    "                    # print(finding_max)\n",
    "                    # plot_attentions_groundtruth(args, molecule_test, [[[temp_index,temp_index],temp_weight],None])\n",
    "\n",
    "                    temp_index = list()\n",
    "                    temp_weight = list()\n",
    "\n",
    "                # print('MAX:', max(list_roc))\n",
    "                if len(finding_max_selected) > 0: max_index, max_weight = finding_max_selected\n",
    "                # plot_attentions_groundtruth(args, molecule_test, [[[max_index,max_index],max_weight],None])\n",
    "\n",
    "                fpr, tpr, threshold = metrics.roc_curve(max_weight, attention_prob)\n",
    "                # print(fpr, tpr, threshold)\n",
    "                roc_auc = metrics.auc(fpr, tpr)\n",
    "                fig = plt.figure(figsize=(5, 5), dpi=150)\n",
    "                plt.title('Receiver Operating Characteristic (ROC)')\n",
    "                plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "                plt.legend(loc = 'lower right')\n",
    "                plt.plot([0, 1], [0, 1], 'r--')\n",
    "                plt.xlim([0, 1])\n",
    "                plt.ylim([0, 1])\n",
    "                plt.ylabel('True Positive Rate (TPR)')\n",
    "                plt.xlabel('False Positive Rate (FPR)')\n",
    "                plt.show()\n",
    "                if len(list_roc) > 0 and len(list_acc) > 0: print('accurary', list_acc[list_roc.index(max(list_roc))])\n",
    "                if len(list_roc) > 0 and len(list_jac) > 0: print('jaccard', list_jac[list_roc.index(max(list_roc))])\n",
    "                if len(list_roc) > 0 and len(list_acc) > 0 and len(list_jac) > 0:\n",
    "                    plot_attentions_groundtruth_compare(mol, mask_graph_x, molecule_test, [[[max_index,max_index],max_weight],None], finding_max)\n",
    "\n",
    "                # auroc.append(max(list_roc))\n",
    "            \n",
    "        else:\n",
    "            display(mol)\n",
    "                \n",
    "\n",
    "    break\n"
   ]
  }
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