2022_01_28-Baltruschat-GNN_Interpretability.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d58d114a-078e-46da-a337-4e8c727fdef2",
   "metadata": {},
   "source": [
    "# GCN Interpretability\n",
    "\n",
    "### _Opening the black box_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca7ce4b4-782e-447f-bf53-7065192f74b2",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "**Author:** Marcel Baltruschat (<a href=\"https://github.com/mrcblt\">@GitHub</a>)<br>\n",
    "**Date:** 28.01.2022<br>\n",
    "**License:** MIT\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a312aa95-11a2-4fc8-b2ae-534a9189c8bb",
   "metadata": {},
   "source": [
    "### Installation with Conda"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c69d9be1-5ebc-4332-af61-3fabdd9c6fe9",
   "metadata": {},
   "source": [
    "```\n",
    "conda create -n expl_gcn -c pyg -c pytorch -c conda-forge \\\n",
    "    python=3.9 pytorch=1.10 pyg captum jupyterlab matplotlib rdkit ipywidgets\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a0a5b6e-1a00-4588-8c8a-b1b2156514e4",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "\n",
    "### Imports and Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "addd9c18-8890-454f-9283-4ee2917a5974",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict as ddict\n",
    "from statistics import mean\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from captum.attr import IntegratedGradients\n",
    "from IPython.display import SVG, HTML\n",
    "from ipywidgets import interact\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import BondType\n",
    "from rdkit.Chem.Draw import MolDraw2DSVG, PrepareMolForDrawing\n",
    "from torch.nn import Linear\n",
    "from torch_geometric.datasets import TUDataset\n",
    "from torch_geometric.nn.models import GNNExplainer\n",
    "from torch_geometric.nn import GraphConv, global_mean_pool\n",
    "from torch_geometric.utils import remove_self_loops"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec0351e4-80c6-4d40-aa23-b01cb6f50e92",
   "metadata": {
    "tags": []
   },
   "source": [
    "---\n",
    "## Loading Example GCN Model and Dataset\n",
    "\n",
    "##### The example model was generated by Son Ha ([Jupyter Notebook](https://github.com/czodrowskilab/5minfame/blob/95a4177a1e027587be74e3004cd1d5b6995eff4e/2022-01-21-Ha-PyG_Graph_Classification.ipynb)). The model was saved at the end of the notebook with the following code:\n",
    "```python\n",
    "torch.save(model, 'mutag_gcn_model.pt')\n",
    "```\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "73c48b38-1067-455f-abfb-cc53b1953d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = TUDataset(name='MUTAG', root='/tmp', use_node_attr=True)\n",
    "\n",
    "# Get the same ording as during training\n",
    "torch.manual_seed(12345)\n",
    "\n",
    "dataset = dataset.shuffle()\n",
    "test_dataset = dataset[150:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4567d565-6e5d-49ba-83c5-ba9941a958eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: MUTAG\n",
      "====================\n",
      "Number of graphs: 188\n",
      "Number of node features: 7\n",
      "Number of edge features: 4\n",
      "Number of classes: 2\n",
      "Size of train dataset: 150\n",
      "Size of test dataset: 38\n"
     ]
    }
   ],
   "source": [
    "print(f'Dataset: {dataset.name}')\n",
    "print('====================')\n",
    "print(f'Number of graphs: {len(dataset)}')\n",
    "print(f'Number of node features: {dataset.num_features}')\n",
    "print(f'Number of edge features: {dataset.num_edge_features}')\n",
    "print(f'Number of classes: {dataset.num_classes}')\n",
    "print(f'Size of train dataset: {len(dataset) - len(test_dataset)}')\n",
    "print(f'Size of test dataset: {len(test_dataset)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91641a7f-5352-465a-b602-6001af221ac4",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "#### Model Class by Son Ha\n",
    "\n",
    "##### The class is needed for loading the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a3a44fe1-5d31-4c2d-8c67-498fb76905be",
   "metadata": {},
   "outputs": [],
   "source": [
    "class GNN(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels):\n",
    "        super(GNN, self).__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.conv1 = GraphConv(dataset.num_node_features, hidden_channels)\n",
    "        self.conv2 = GraphConv(hidden_channels, hidden_channels)\n",
    "        self.conv3 = GraphConv(hidden_channels, hidden_channels)\n",
    "        self.lin = Linear(hidden_channels, dataset.num_classes)\n",
    "\n",
    "    def forward(self, x, edge_index, batch, edge_weight=None):\n",
    "        x = self.conv1(x, edge_index, edge_weight)\n",
    "        x = x.relu()\n",
    "        x = self.conv2(x, edge_index, edge_weight)\n",
    "        x = x.relu()\n",
    "        x = self.conv3(x, edge_index, edge_weight)\n",
    "        x = global_mean_pool(x, batch)\n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        x = self.lin(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "abe00285-3c9f-4fac-837c-bfb2e75402b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GNN(\n",
       "  (conv1): GraphConv(7, 64)\n",
       "  (conv2): GraphConv(64, 64)\n",
       "  (conv3): GraphConv(64, 64)\n",
       "  (lin): Linear(in_features=64, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = torch.load('mutag_gcn_model.pt')\n",
    "model.eval()\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60aa8ae5-7961-4517-8ebc-000f80e84a37",
   "metadata": {},
   "source": [
    "---\n",
    "## Look into the Model<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "241c383c-e3e0-4150-8919-996f093c478c",
   "metadata": {},
   "source": [
    "### Utils"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71200293-3efe-4b6c-ae9f-333226591b2b",
   "metadata": {},
   "source": [
    "The information about the performed one-hot encoding was extracted from the `README.txt` file included in the [dataset zip file](https://www.chrsmrrs.com/graphkerneldatasets/MUTAG.zip)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "408128ee-4333-4ad4-a94f-e3fc732fd59e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pt = Chem.GetPeriodicTable()\n",
    "\n",
    "# One-hot encoded bond types as edge features\n",
    "edge_ref = [BondType.AROMATIC,  # 0\n",
    "            BondType.SINGLE,    # 1\n",
    "            BondType.DOUBLE,    # 2\n",
    "            BondType.TRIPLE]    # 3\n",
    "\n",
    "# One-hot encoded elements as node features\n",
    "node_ref = [pt.GetAtomicNumber('C'),   # 0\n",
    "            pt.GetAtomicNumber('N'),   # 1\n",
    "            pt.GetAtomicNumber('O'),   # 2\n",
    "            pt.GetAtomicNumber('F'),   # 3\n",
    "            pt.GetAtomicNumber('I'),   # 4\n",
    "            pt.GetAtomicNumber('Cl'),  # 5\n",
    "            pt.GetAtomicNumber('Br')]  # 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf2fce3a-ada2-4e55-ad48-e8660ee514a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph_to_mol(data, node_ref, edge_ref):\n",
    "    \"\"\"\n",
    "    Converts a PyTorch graph to a RDKit molecule. Since no further information about\n",
    "    other properties of the molecule like charges were provided, the resulting molecule\n",
    "    may be chemically invalid.\n",
    "    \"\"\"\n",
    "    if data.has_self_loops():\n",
    "        data = data.clone()\n",
    "        data.edge_index, data.edge_attr = remove_self_loops(data.edge_index, data.edge_attr)\n",
    "    mol = Chem.RWMol()\n",
    "    for node in data.x:\n",
    "        mol.AddAtom(Chem.Atom(node_ref[node.argmax()]))\n",
    "    seen = ddict(list)\n",
    "    for i, (begin, end, attr) in enumerate(zip(*data.edge_index, data.edge_attr)):\n",
    "        edge = tuple(sorted((int(begin), int(end))))\n",
    "        if edge not in seen:\n",
    "            mol.AddBond(*edge, edge_ref[attr.argmax()])\n",
    "        seen[edge].append(str(i))\n",
    "    for (begin, end), ix in seen.items():\n",
    "        # Saving of the original edge_index ids for later usage\n",
    "        mol.GetBondBetweenAtoms(begin, end).SetProp('edge_ix', ','.join(ix))\n",
    "    mol.UpdatePropertyCache(strict=False)\n",
    "    return mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d8174a52-b861-4688-a3b1-868104a316d8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def visualize_in_molecule(mol, edge_mask, node_mask,\n",
    "                          threshold=0.0, edge_func=mean, size=(300, 300),\n",
    "                          kekulize=False, add_atom_numbers=False, alpha_offset=0.2,\n",
    "                          rgb_color=(1.0, 0.0, 0.0)):\n",
    "    \"\"\"\n",
    "    Returns an IPython SVG object containing the drawn molecule with atoms and\n",
    "    bonds highlighted based on the specified parameters.\n",
    "    \"\"\"\n",
    "    d2d = MolDraw2DSVG(*size)\n",
    "    if not kekulize:\n",
    "        d2d.drawOptions().prepareMolsBeforeDrawing = False\n",
    "        mol = PrepareMolForDrawing(mol, kekulize=False)\n",
    "    d2d.drawOptions().addAtomIndices = add_atom_numbers\n",
    "    atoms = {}\n",
    "    if node_mask is not None:\n",
    "        for a in mol.GetAtoms():\n",
    "            if node_mask[a.GetIdx()] > threshold:\n",
    "                atoms[a.GetIdx()] = (*rgb_color, float(node_mask[a.GetIdx()]) - alpha_offset)\n",
    "    bonds = {}\n",
    "    if edge_mask is not None:\n",
    "        for b in mol.GetBonds():\n",
    "            eix = b.GetProp('edge_ix').split(',')\n",
    "            mask = edge_func([float(edge_mask[int(i)]) for i in eix])\n",
    "            if mask > threshold:\n",
    "                bonds[b.GetIdx()] = (*rgb_color, mask - alpha_offset)\n",
    "    d2d.DrawMolecule(mol,\n",
    "                     highlightAtoms=atoms.keys(), highlightBonds=bonds.keys(),\n",
    "                     highlightAtomColors=atoms, highlightBondColors=bonds)\n",
    "    d2d.FinishDrawing()\n",
    "    return SVG(d2d.GetDrawingText())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b41a8770-fc84-4fd6-9691-59763c08f44c",
   "metadata": {
    "tags": []
   },
   "source": [
    "<br>\n",
    "\n",
    "### GNN Explainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb18d0d1-d3be-4216-a9dd-31c2f493be0a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "expl = GNNExplainer(model, epochs=300, log=False, return_type='raw', lr=0.01, feat_mask_type='scalar')\n",
    "node_masks = []\n",
    "edge_masks = []\n",
    "molecules = []\n",
    "for data in test_dataset:\n",
    "    nm, em = expl.explain_graph(data.x, data.edge_index)\n",
    "    node_masks.append(nm)\n",
    "    edge_masks.append(em)\n",
    "    molecules.append(graph_to_mol(data, node_ref, edge_ref))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cae03f9-01c5-4671-94a9-1e71d6eaaf30",
   "metadata": {},
   "source": [
    "#### Looking into the first test data entry\n",
    "\n",
    "##### Visualizing the importancies in the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "81baa6b8-01ac-4893-96f6-f87c4d538bc3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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guz/2WDU8S/ULRl5nVcP1R0i5gDc14xe/F/JacpGFja+e4X45OTn89NNPzJo1i23btsljvbzo0KEDf//9Nz4+Pnz44YfUrVsXKOkckg5sBrrg/j2iDaTTiRVohZSD9Js4ke++/bZ4lj4wMJBDhw5x6NAh5s+fX/zYJk2aMG3aNPr37++awStVA2k5llLKY8UjtbdBrh6IAwUhrzEZKYU4m7S0NN58803Gjx9fvGkKSPu675CyAU8uBzmdL/KavkHC8Kni4uJ45pln6N69O48++ijbtm0jICCAu+++m88//5xdu3bh5eXFgw8+SK9evYofZwJuQ2qWGwCbOPfX3ZUMpBQkA9mFcRhQH4iNjWXkyJEEBQXh6+tLUFAQ7dq146GHHuLaa6+laVPZEP3QoUOsWLHCZeNXqiaqCRMYSqlaKAupt87CcTsYVkT8n3+S8s8/FObmEhwdTdNrrqmWems7X6RzSDbSli+mnPvs2bOHjz76iB9++KF405RbbrmFyKLSli+R8GUqeq6awgt5TRnIa7yroIDffvuNTz/9lFWrVhXfr0mTJgwfPpyhQ4cSEBDA4MGDyc7OplevXjzwwANlnjcI6QNegGxFvxFZ4OhuJyY2m42/167F2q4d3evU4VZKdwd54IEH+Pnnn8nNzcVisbB9+3YKCwuZPXs2UVFRnDx5koMHD9K6dWtXvQSlaiQN10opj3QYCdau7nqx89NPS/171aRJtLzxRq6cNQufoOqZUw9CAmQCJeHaMAyWLl3KRx99xLJly4rv2759e8aMGUPfvn0B6WX9ESWz1o6utbYlJ5Pzn/+QP38+tvh4TP7+mGNi8L78cgJffLFaj2VCTj5yjh3juY8+4r0vviA1NRUAs9nM5Zdfzh133EGfPn0wm+VC7dNPP8327dupW7cur7/+OhZL+acbYcAY4AOk/nojElzd5SqJFVi1ahV/33QTdZs149Gvv6Z7dHSp+7Rp04arrrqKhQsXcumll7Jz5052797N9ddfz+zZs2nbti0dOmizPqWqm4ZrpZRHykAu17uqHja0ZUv6vPwyTa+5hjpNm5KblsbhpUtZMXEicd9+i2G1cu3331fLsezdMTKRGuLvv/+eWbNmFW+YYjabufLKK7nrrrvo1q0bJlNJhF6DlA0U4vhgWLBqFRkDB2Kkp2Np1w6f667DyMjAun07uTNnVnu4Bvn/z3r4YQ7/9RcFQKN69Rg2bBjDhg2jUaPS/T4WLlzI7NmzAXjllVdo0ODsexdGAOOQgL0ZCdnRQBNcW1OZjixeDGrfnrpNm1Jw4ABTBg+m/TffEBNT+trG/fffz8KFC1m+fDlz587lscceIy4ujiFDhvDBBx/Qu3dvV7wEpWo0DddKKY9UgMzeuarMoe2IEaX+7R0YSNvbbqNx//581qEDe3/4gcSVK4mqhvBiRl5rakoKDVu1Ijs7G7PZjLe3NzExMXTq1Inw8HBWrVrFwYMHiYqKomHDhjRs2JB1vr4UIIv/HDlrbUtMlGCdl0fQd9/he8MNpT5fsHatQ45rArwGDoSAAIb861+8PmBAqV7Xp/rrr78AGDduHP369avQ84cA9yI166FIqE0F2uL8WWwrcqJ0FFm42CokhCk//MCEm25i79693HDDDcydO7dUmUenTp3o06cPy5Yt44cffuD777/nrrvuYu3atYwYMYKZM2dy/fXXO2zMNuSqSSHyM2tGvhe9gfL3y1TK85nsPT4r/UCTaVu7du3a2VdfK6WUMy0H3kR6QMc64PlfM5nOuonM2Sx79FE2vPwyF06eTK+zbDRTUfuL/rzuyBEeatmS3KIx+fn5ERoaire39xkfmxEeTlq3bvi8+SYBZ7lfVWXccQf5n35KwBtv4P9//+ew45QnB1nYNwJ47yz3O378OJs2baJv377FZSIVZSCz1z8Accj/SXTRzdGzVAayKDUOCfStgEuQtoG+QEpKCrfeeis7duwgPDycuXPnEhtb8lOxevVqbrnlFnx8fFi5ciUhISE89NBDxV1DnnzySe65555SVzzOhxVZeJtQdItHepEXICHbhpwMmYtudYDGlHwdo4s+ppQ7iI2NZfv27dsNw6j0W4zOXCulPJIJ9+3VHNqqFQDZSUnV8nwG8lrDGzQgLi6OF154ge+++46CggKsVis9e/akW7dupKenk5SURGJiIklJSeTk5HDy+HGsixdjOXECIqq6x2P5bGlp5M+diykkBL+773bIMc7GCwnYm85xv/Dw8PNuOWdCFjU2RwL2OiTsrkK6ikRR/TPZBciumonI90AMEqxvKvrTrl69enz77bcMHTqUzZs3M2TIED755BMuuugigOLvj/Xr1/Ptt98ybtw43nrrLSIjI/nwww95/vnnSUpKYvLkyWesQT8TGzKbvwLYg5QuZZz2Z0HRfe0/r/YpvQBkMXLQKX/WBboDF1H1jaGUchUN10opj+SNzH7ZXD2QcuSlpQHgXU0LGm2UXEpv0KABI0eO5IILLmDlypUsXbqUDRs2sHfvXh544AGmT5+Ot7c3hmFw6MQJOiUmkh8SgreDgjVA4YoVkJeH1xVXgLc3ed98Q+Hy5RgFBVjatsX3llswn6O+uSosyKzpXqqv7/mZBCMz5B2BRciOjolIsA9EQnYY8n91PmxIIE1Cyj/CgJbI5jAXAldS/jqDsLAwvv76a0aMGMHff//NLbfcwltvvcW//vUvTCYT06ZNK25NCFKn/8wzz9CoUSOeffZZPv74Y5KTk3nttdfw8zt3X5RMYC2wEpmlTgRSisYfXHSLLPrTl9InwwZSJpKN/H9lFL3eHCRg7wB+RxaQ9gZa474n0kqVR8O1UsojeVMSqtyJYRjsLVrIWL+aWvJZkUWN9sD25JNPcvDgQZo0acIDDzzAggUL2LNnD9OmTWP27Nk899xz9O7dm0OhoZhCQ7Hg2AV41qLyQHODBpzs04fCU9rgAWQ/8QRBH3+M7803O+T4ZiR8WYEtSCBzJPssdkekPGRV0XGTkS42O5FAeeqsbADyhmv/f7CXSeRSMsubgXTA8UFmw3sAzZCNbbpw7hrlOnXqMHfuXMaPH8+CBQu45557SE5OZsyYMbRv355vv/2WP/74g6uvvpr//ve/xMbGcvfddxMZGcmDDz7IggULSE1NZdasWQQHl3+KkgnMQ2bu7bPqWUiQ7lT0Os8VhE3I93JI0c3OipS/JCK15XuBv5HSkQFFXwMN2coT6CYySimPFIG8kWdRcpnZWXJSU9kxezaFRT2l7fIzM/lz3DiS16whIDKSFqct6jtf2YA/8poBJkyYQN26dTl06BDvv/8+kZGRjB8/nrp163Lw4EHuv/9+QMoWnLHo01Y0U583ezaFmzcT+OGHhKWkELp/P36PPAJZWWSOGEHh5s0OG4P9RGuPw45QlgkpExkOPAncDlyB1EO3QxZA5iBBcS2yTmBJ0W0ZEsq3IYHSFyn76AX0A64HHgYeBHpS8cV/fn5+vP/++4wePRrDMJg8eTLPPPMMNptc49m4cSObNm1i6ilrAa699lo+//xzgoOD+fvvv7nttttIT08v89z/AP8BfqRkxjoSKeFogczcVyX8WoB6SEjvjPxcbwAWIx1bZiEnIEq5O13QqJTySIXAY8BSoBtV3+Bj//z5rJ02rfjfyWvWgMlE5IUXFn/swkmTiBk0iJMHDvBxTAw+deoQfsEFBDdpQl56Okc3bCD32DF8Q0O5bt48oi6+uIqjkoCxND+frtnZPBcSQmTRorOsrCw+/PBDZs2aRV5eHiaTiWuuuYYGDRoQExPDjTfeyFvA40jNqyM7W2Q9+ii5L78MQOC77+I3dmypz2fcfDP533yDz/DhBH/2mUPGkInMhr6EdPdwpWxk9vUwEkAPIwEaZLbaoGS23R8pJWmELOhrBIRT9RlawzB4++23mT59OgDXXXcdb7zxBkePHqVXr14UFhYyf/58unbtWvyYbdu2cdttt5GWlsYFF1zAnDlzqFu3LpnAt8gJwi7k+6kVpWedHaWQkq9jU6RE5kZ0Fls5ni5oVErVOl5AQ+SSewZVD9c5KSkSqE9lGKU+lpOSAoBf3bp0e+wxklev5kRcHCn//IPJYiEkJoZ2d95Jl4cfJui0Hsvn62RuLnsHDCA+NZV//Pxo2LAhkZGRxbdx48bx559/snbtWn788UcCAgK46667yMnJIcffv3gxpCOZ7CUEZjO+I0eW+bzv6NHkf/MNhUWt8BwyBiS0Vr63S/ULQEJgy1M+Zq8ztnfO8Ea+hx11VcFkMnHffffRsGFDHnroIX766SdSUlL4+OOPufHGG/nqq6947bXX+OSTT4ofExsby9dff83QoUPZsWMHN998M5O/+IJFDRqwBziAhH9n9vn2QkJ1XUraIKYhix5vRWbLlXI3OnOtlPJYXwGfF/29uSsH4kCJVivLRo6EjRsJP8v9cnNzOXbsWHGbvjp16nDX+vW80bgxVhwbQvJmzyZz5EhMUVGEHz5c5vOFO3Zwol078Pambn6+Q8aQhQTVZ4BHHXIEz7V8+XJGjx5NRkYGbdq04fnnn+emm27CMAwWLVpEu3btSt1/3759DBs2jANJSViaNqX1ggUYQUG0wbGLRc/FhrT3S0B+3jshVymcMYOuah+duVZK1UqNkTf76ml4556yLRYu+uwzhuXlcdGRIyQnJ5OUlERSUhLJyckcOXKElJQU9uzZw/Hjx4sfl5WVhTU9HRo3dvgYLV26AGCkpWEYRpl+ycaxYwCYqql7SnnsM/RVvYJRE11yySX88MMPDB8+nF27dnHfffdx8cUXs3z5ct544w3eeeedUvdv3rw5D3zzDROGDuX4wYOwfz9dO3Rw+SItMyWz2FuQzWkKkIDtuF44SlWehmullMdqgmw6sQu55F7TfqHZNw9pDbT09aVJkyY0adKk+PNHjhzhiy++YNOmTRQWFhIVFUVgYCDXXHMNt99+O7+3bFlcLuFIXh06YI6JwbZ/P4Vr1uBd1F/ZrqCoHMTrlPpeR9FwXSIzM5Nt27bRvHlz2rVrx7x587jtttvYvXs3R48eJTc3l59++olHH32U5s1Lrv38Caxs3Jimv/1G7IEDdIiNdXmwPlUQUnO9Gela8j/g/5AFpEq5g5r2XqSUqkUaITNZe5C2YNVT5ew+0ov+rE/pXSi3b9/OrFmz+PXXX7FapRlhdHQ0I0aM4IYbbiCoaIZ4Pc7rBe7/2GNk3XsvWQ88QJ1ffsFc1Fe7cP16cv/7XwB873XcUkMb8lpDHXYEzzNlyhQ+/1wKpyIjI+nYsSNXXXUVFouFrVu3kpubS2FhIa+//jqvvvoqAKuB75G+3Y2DgmjSvr2rhn9WfkhHkX+AjcC7wH04f0t6pcqj4Vop5bFMSNuz7UhP3ChqVgeBw8hr6knJxiG7du1i6NChxaG6e/fu3H777fTv37/M7nrtKWlR5+iFjb5jxlCwaBH5X39Neps2ePXujZGZSeHKlZCfj++YMfjedJNDjm1Q0nKwg0OO4Jmuvvpq1q5dS1xcHMnJySQnJ7Nw4UJAavTz8/PJz8/nvffeY/z48fi2bs2XSLCuh1wZcmc+SK/xf5CA/RlwDzXrd4DyTBqulVIerTvSd3cPMtNbU7ZMzkNeT1vkBMIuNDSUDh060LhxY26//XZiY8+81qYN0urtBDKz68h+1yazmaAvvySvXz9yP/iAgj//BJMJr+7d8bv3Xnxvv91hx7YhgSqQ0tuC13ZXXHEFV1xxBVlZWWzbto1NmzaxefNmNm/eTFxcHF5eXpw8eZK8vDxmvvoqMW+/TRzydYxx9eAryA85odqIXLVYjfQKV8qVNFwrpTyaH7It9D6kt3BNCdeJyOzhBchufXYNGjQovtR/Ll7I7HUqUpPu6M1kTGYzfuPH4zd+vIOPVJr9tXXA8a/REwUGBnLhhRdy4Sk927Ozs9m2bRsLFy5k1apVdH/0UVYAx5CdIT1p9jcQ2clyJ/ATclJ5ts46SjmahmullMe7GPgLOIhsJuLpdZf5SAeUjpSetT4fnZCdAd1tm/jqZEXezDq7eByeJCAggB49etCjRw8OAq8gC4NbIz24PU0jIAW5gjUXLQ9RruVOC4CVUuq8RCG7NMYgAcEZC/gcKQ6ZtW6FzDxXRSckeBZWdVBuzN4pppOrB+KBCoEvke+5UDy3pZ0JKaGKR2qwV7t0NKq203CtlKoRbkJ2xLMgb7CeKgXZcbIlMJyqlzlciiz8slIzZ6/tr8uXqs/y10Zrgd1IOUjLc9zX3fkj5SG7gF+p2SeUyr1puFZK1Qh1kIDdBtnBLdO1wzkv+chl7TbA1UB0NTxnY+BKJHzmVcPzuZtc5LUNoOa1YnQ0A1iJ/Lw0wTPLQU7XCPk+P4z0wVbKFTRcK6VqjK7I4sameF55iIEEa/sixquq8blHUxKuHb2hjDMZyAmJH/IaVeUcAPYjXWkanPWensOElIklIicOSrmChmulVI1hAm5GapW9kYDtKWHyIJBN9ZWDnOoyoDlSl5xfjc/ravnIa2oB9HXxWDzRCmThbD1qxqy1XSTSIWc3ErKVcjYN10qpGqUOcAfSlu3Yjh2s/O03tw/YCUAy0h3kZqqnHORUZmAUMnudi+eccJyNgbwWP+S16ZtZ5WQgC/+SkJleZ8g5fpx36tdnpsnEx23bOuw4vkBd5LXp7LVyBf19pJSqcS4Aem/axJ5Bg9g0ahRbjxxx20AZX3TrBAzBcYvyhgEhRX+vCbXXeciVilDktanKWQ8cQU5Ogp10zCWPPEJOaqpTjhWFhOt16MJG5XwarpVSNU5CQgIzRo7EJzeXZv37k1+/Pjtwrxpsg5KNbzoD/wKucODx6gKTkR7gOXh25xAr8hoCgSnUnI2DnOkAkIbzWu8dWrSI7Z98QocxY5xyvBAkVJ9EQrZSzqThWilVo5w8eZI77riDo0eP0vGCC/j4f/+jm8mEFZmty3D1AJFguBkJN52RUpBrcfymF3cBfZDZyiw8szzEQMbuB/QDRrp0NJ7L3lHHGbPWBTk5/HHvvdRt147uEyY44YjysxSM/Lx7cmtO5Zl0h0alVI1RUFDA2LFj2blzJw0aNODTTz8lKjiYCOBTZIHTZuSScVOcP7tgIDPVB5CWYS2RYN3TScc3A28gpSfJSGmFn5OOXV3sHU/qAq+iM0TnIwvpa52Fc8L16qlTSd+7l1v++guzt/OWTgYhJxAJTjuiUkJ/LymlagTDMHjyySdZunQp/v7+fPLJJ0RFyVKtNsATyOxwN2Q2az1yydhZ7LPVyUh99RXA4zgvWNs1BZ6hpDzEk+pRC5ExBwFTkdeiKi+Bktl/R8+wpWzezPr//pfYUaOIvvRSBx+tNPvMtYZr5Ww6c62UqhHeeecdPv/8c0wmE//73//o2LFjqc8HIl1EOgNzkVnsLcgbcBQyE1rdZRkGcAKZrT6OdAFpCQxGZo8dXQZyJqOBBcBCSkoDqrP1nyNYkbEGIicmWg5y/hKQ0Bnk4OMYNhu/jxmDb2gol774ooOPVpZ95joJOTHTwKOcRb/XlFIeb/HixTz33HMAPPvss1x55ZXl3i87O5uX7rmHA/HxjP79dzZ6e5OM9JiOAxoiPXJ9qzieQqQTQyKyiDIK6b0dC9yC8xaRnYkZ+BgJ+X8jQcudA7YVGaM/0AP4CL3sWhVHkJlrR4frjW+8QfLatQz4+GP869Z18NHK8kNOcHOQvteRTh+Bqq00XCulPFp8fDz33XcfhmEwYsQI7rrrrjL3sdlsfPfddzz++OPs27cPgNe2bGFw166sBFYhQTgRCZv+SPAILvozkDMHT/sCu0wkAGYW/TsE2bglEtk18mKc10+4IuoA3yClMlsomcl0tzeFQuRr6of0Lp+L81rH1VT5yNc10IHHyIiPZ8XTTxPdty+xd97pwCOdmQn5fi6kZm2epNyfu/0eVUqpCsvLy2PMmDGkp6fTqVMnpk2bVuY+GzZs4KmnnmLdunWkp6cD4OfnR+vWrQlCwuXVwCZgGTKDbQ/KJ5BL6PbNSizIG7YJmZG2IgvsvCkJ4k2L/h6NlH70wH0XDdYFfgJuAjYgrzkQ8HHloE6Rj5yoBCC18t8gY1ZVU4B8/zpy9n/R+PHY8vO5/J13HHiUczMjr9WT1hYoz6fhWinlsSZNmsTmzZsJDQ3l/fffx9e3pKAjOTmZ6dOnM3fuXECCuHdRp4JGjRoRFFRyUdwLCW/dkPZ48afdTiAB20pJr2xz0c0PCdWNkUDduOhWD9fVVFdGPeBHZCOWlUiYzUcCratKL2zIVvCFyInKxcAcSjbBUVVjQ664OPL7c9+8efiGhrJo3LhSHy/MzQUg49Ah5vbrB8D18+bhE+SYIhUT8lo1XCtn0nCtlPJIX331FZ999hkmk4l33nmH6GjZNDwvL4933nmHV199lezsbAB69uzJqlWrsFgs+Pn50bhx4zM+b1jRzb4c0kACdxoy41dQ9DHvolsdPCdIn0koErBfAl6jZNbeFbPY9tlqHyAceAiY4IJx1GQWSmZ0HSkvPZ2EJUvK/VxhTk7x52yFjou+BvJandcAUCkN10opD7Rt2zYef/xxACZMmEDfvn2LP3fbbbexbNkyALp168a///1v7rvvPsxmM5deeinLly8vDuIVYUJCXnh1vgA35AM8BQwC/g+pw7bPYp9aEuMIBnJVIJeS2eoOSE/uzg46Zm3mjePD9SNG+VsUnThwgA9jYghr04ZRO3c6cATCirxWDTvKmXTBtVLKo5w4cYK77rqLvLw8rrjiCh588MFSn69Tpw4xMTG8/fbbzJs3j48++oi0tDRiY2Pp0KEDwFlnrmu7zsCfwETkhMKC1KCfpGQDl+piFD3nyaJjWIqOORFYhAZrR/FHAnZNX+RnIFeavJDXrJSz6MmcUsotHT16lJycHJo2LdkqxGazcf/993Po0CGaNGnCG2+8gdlceo7g448/Lv77K6+8wh9//IGPjw9vv/02r7zyCkClZq5rI/ss9vXA28D3SPjNQ2qhfZE3D3t5QUVntA1KFoIWIOHOHnyCgSHAvUjLQuU4Ucg6gaOuHoiDZVGy2FgXwipn0nCtlHJL119/PYmJiezYsQN/f5l3ev311/njjz/w9fXlgw8+ICTkzEvclixZwgsvvADAf/7zHy644ALi4+MBnbmuqFjgLeBZ4AukN/Y+JBTbF3iCvJGY9+zBlJmJV5cupZ7DXvJhpWRRmVfRzd6ucDQwlJpfeuMuopHAuRfHL2x0JfsGSY2oua9RuScN10opt3Py5EkOHDgAyLbmAEuXLuWll14C4IUXXqB9+/ZnfHxCQgJjx47FMAyGDx/O8OHDiz8OOnNdWXWROuzxSMnIT0jrwp3IDHQhkDV2LEZ6Ol7Ll2MKLOmgbEJmt32RBZLeQFuk5OM6oD9an+hsUUg3GBtyolTVTZMqI6RZszPWY1c3++ZI+tOunE3DtVLK7dhDcHh4OAEBARw+fJhx48YVh+Vbb731jI/Nz8/nrrvuIi0tjY4dOxbPXhuGwYkTJzCZTKVKTVTFmZGtx68o+ncusA3YaLXyeGoqBcBFhYVYkVDtiyyGbAR0AlK/+YZ9ixfz5muv4eWlbz+u4oNsbhSEBFBnhmtnygSaIK0xlXIm/e2mlHI7p84wFxQUMHbs2OKwPH369LM+9qmnnmLjxo2Ehoby0UcfFfe+NplMzJw5E5vNRlhYmMNfQ23gh/QGb5uby2tFH/vJz6/csGaz2Wh4332kp6cTEhTEf/7zH+cNVJXRGAnXJ4EIF4/FEWyUlIXozLVyNr0ap5RyO6eG65kzZ7Jx40ZCQkJ4//33yc3N5aWXXuKNN94o87i5c+fyySefFPe+btKkSanP33TTTdxyyy1OeQ21SU5ODiAnMD4+5Xek3rJlC/n50p9izpw5/PPPP84anipHK6Q/+1GqtwOMu0hBTh7qFd2UciYN10opt2MP12azuThEv/DCCyxevJjevXvzyiuv8Prrr2OzlXTq3b59OxMmTADg3//+N5dffrnzB15L5Rbtuufn54fJVP7SsYULF+Lt7Y3JZCI/P5+JEyeSlZXlzGGqU3QEGiIdX467eCyOkIjUlvdCFzMq59NwrZSqdgZVmw1LSEjAMAwWLVqEzWajV69evP766zz++OOkpaXRunVrPv300+I2fCdOnODOO+8kNzeX/v378+9//7taXoeqmFPDdXkMw+D333/H29ubunXr4u3tTXx8PM8995wzh6lO4Q1ciATQwy4eS3XLBHKABkBPF49F1U5ac62UOi8FyOxQPJBQdLNvEX5qyzVvpOVaNFLn2Rh5Qz/bdtYJCQlkZmaSnZ2Nt7c3K1euxGQyERISwsSJE7n99tuLF8TZe18fOHCA6Oho/ve//2GxWBzwitWZ2LeZt7dMPN2uXbuIj48nICAAs9lMZGQkKSkpfP/99/Tt25cBAwY4c7iqSG9ks579SBitKRutJCILNjsDdVw7FFVLabhWSlVYBrAG+AdIQjZpyCz6eAbSPcK+SQiUbDLiiywsst8CkEvSHYGLkPB9ql27dhUHtrCwMLy8vLjjjjuYMGFCmcWIb7zxBr/++is+Pj589NFHuljRBc41c71w4UIAevXqxd9//83JkycZM2YM7733HpMnT6ZTp05ERkY6bbxK1EV6me9DAmkL1w6nWhQgdeTdkZMHpVxBw7VS6qwMZGZrBRKqjwDJSJcBMyWBORqZ+bIHapCgbUNmxTKQN729SPiuA2wEfkNC9sVASyA3J6d4s5eAgACuuOIKpk6dSps2bcqMbdmyZcyYMQOAGTNm0Llz5+p86aqC7AsVz7SY8ffffwdkY6D169eTm5vL0KFDWblyJVu3buWJJ57gww8/LLPbpnK8S5Cfw/XIbG/g2e/u9vYhJw0xyAZFSrmChmulVLkMYAOyach+ZGYrGWm/FgW0RmakK7JYKIDS2w/nAenI7Pde5A1xDdAU6OPtTUhICIZh8MknnzBw4MByF8kdOHCAu+++G5vNxrBhwxgxYsT5vVBVbcr7f9q/fz9xcXF4eXlx5ZVX8vbbb3P48GGSkpJ46aWXuOGGG1i9ejXff/89N954owtGXbu1RdopHgN2AV3w3AWAx5HXcSEwBM99Hcrz6TSBUqqME8CHwPvA78A65HJrB6ArMsPlx/m/efkii406I5uLWJGZs0XAbC8vph49yq6UFAYNGlRuYEtLS2PYsGGkpaXRuXNn/vOf/5yxS4VyPOMsO+7ZZ6179epFcHAwDRo0AODo0aM0a9aMZ555BovFUlwGpJzLBNyMXDUyIWsoPFEhcnLQGtn1U2etlSvpzLVSqpiBBOnvgDjgILIAsS2O+2URhPTcjUFmsjcCqSYTB4HBlG2llZ+fz5133snevXuJjo7ms88+O2Otr3IOe7gu7wQnKSkJgEGDBgFQr550HU5JSQGkVOTqq6/W/0MXqoPM9B5HrlbVxfPKQ/YCoUAbYKBrh6KUhmullDgJzEXC9S4kaHfBeW+yXkiQjyg6/mqkdGQTMBQIQ0Lcww8/zKpVqwgODmbOnDnUr1/fSSNUZ3K2cP3II4/Qv39/+vTpA5QN13DmhZDKeboiiwCPAzuRK0qeEhBSKCkHGcrZOxEp5Qye8rOjlHKgFOB/wBbgANAECbquKLTwR97YDyOz2MeQWu97gc9efpmvv/4ai8XChx9+SNu2bV0wQnW6s4Xr4OBgLr300uJ/lxeulevZy0MOIIuPtyFlYO5eO5qGnIx3AC5Dy0GUe9BwrVQtdxgJ1puQWStnzlafiQnpPlIXeZPPBe775huWvvQSZuDFF1+kX79+LhyhKk9F6t41XLuvOsAYZH3FBmA70A73DdgnKBnjJcC/XDscpYq568+MUsoJkoG3kTfSE8gCQ1cH61P5I2PKyM/n10cfJQe44/77uf322107MFXK2WauT2cv4zl69KhDx6TOTxPgbqQ9pgFspWRTKHdyHBlbG6RX/gg00Cj3oTPXStVSxymZsc7CfWssvYBO3t4cuuMOvH188H3iCY4CWmntmXTm2v21BO4BPgA2F90uwD12cDSQtqAHkBnr3sBI3PN3l6q99PtRqVqoEGmztwlZyOiuwdrOy2TiX1OnFr/Rvwc8irT0U65XmZlre7hOT0+noKAAb29vh45NnZ9WwH3Au0hN83qgGdAI1/WPzgF2I2UrnYA+wDBk4yql3IleRVGqFvoV2IG0vusAeEK88ULGat/sYr5rh6NOUZlwHRoaipeXnMqlpqY6dFyqapoA/wYuR9ZiHEFOyHOcPA77bPV6IAToiZSBDEeDtXJPGq6VqmUOIRvD7KZkl0VP4Y3UWO4BFiO9uJXrVSZcm0wmLQ3xIOFIp547kNrmMCTkHkRmkB3JQNpxbkbCdSekI8hEZNZat41S7sqdrwQrpapZIfAFEkpDkJ7SniYMGfce4Eu0PMQdVCZcg5SGJCUlabj2ECaktrkt8BUye30QWIN09GkEBFN9YbcQmSVPRHZvbYSUpPwL6QqioVq5Ow3XStUi9nKQY0APF4+lKppTstnNfGR3OeU6Z9v+vDz2mWvtGOJZ7LPYG4ClwD4kAG9DNm6JRNr5BVL5y+L5SH/tY8BR5OS/edFzdkNmrOtW+RUo5RwarpWqJdKBPykpB/GEOusz8ULKQ3YAfyGzWdo9xHPY2/HpzLXnMSFhtxsQD6xAwvYRJBQnAHlIwA4GggC/osfZA3deRgaZKSmYmzcnEwnVhUX3DSl67qbABSkpXFWvHrp/p/I0WnOtVC2xGlnA6IdnloOcLgx5804GVrp4LLWdfYFiYWHFOiLrzHXN0BjZbvwZYBQwGOgH9EJmnf2Qk/pDwH6klCsO+Ovpp/lr1ChOJiQQgfTU7gMMAG4BHgGOP/441zdowM3/+hc2m82ZL0upKtOZa6VqASuwCrmE28TFY6lOjZA367XAQOTStHK+gIAAAHJyKtZHok6dOgBkZWU5bEzKeQKRUN0PWYR4HJnBtt+ykJnpAmRGb57VSrLNRvedO7kmOppo5Gf51LUTG9avB+DXX3/lkUceYebMmZjNOh+oPIOGa6VqgS3INud51Ky6xTDkzTwJ2Ii06FLOV9lw7e8v25FkZ2c7bEzKNUzI75i6SHePcjVqxC+bNtEmIYFLz3AXe5C2Wq18/PHHFBQU8Prrr2OxaPM95f40XCtVC6xAZq2jqFot2JH16zn4++8cWbuWpDVryEpMxOLrywO5uRV+jm+uuIL4RYsAGJuURGBk5HmPx4S8pkRgOXAh2knAFSoblu1hXMN17RQdHQ1AQkLCGe+TmJiIl5cXZrMZq9XKnDlzyM/P55133ikuQ1LKXel3qFI13FFgJ5BC1TuErJk2jb0//njej982a5YEa5MJKtlh4kwika2Q9yGz89HV8qyqMiobljVc126NGzcGzhyu8/PzSUxMxNvbGy8vLxo3bsyRI0f45ptvsFqtvPvuu7qzp3JrWsCkVA23D1lUVAeqvOq+Ya9e9Jw8mcE//8zY5ORKPTY7JYWlEybQ9KqrqNOk+iq/vZHykDRgb7U9q6oMe1jOzc2t0OKzwMBAQMN1bdWoUSPgzOE6ISEBm81GUFAQXl5eHD58mBdffBFvb2++//57Ro8eTX5+vjOHrFSlaLhWqoZLQFpdBVXDc/V47DF6T51K82uvJbBBg0o9dslDD1GQlcVlb79dDSMpLRh5jWe+yKwcyR6uoWJ11zpzXbvZy0JOnDhBRkZGmc8fPHgQgJiYGPr06VN8388//xxfX1/mzZvHyJEjK91fXSln0XCtVA2XAGQiAdRVDvz2GzvnzKHnU08R2qJFtT9/EBKu46v9mVVF+Pj4FC9Aq0hg1nBdu/n7+xMeHg6UP3t94MABAJo2bcrgwYMBmDdvHldeeSVz5szB19eXX3/9lcOHDzttzEpVhoZrpWowK7LYLwPXheuC7GwW3Xsv4W3b0n3iRIccIxhp93UU6YiinMtkMlWqY8ip99XZx9qpSVFpmH2W+lSHDh0CJFxfc801AGzYsIHU1FQuu+wy/vrrLz777LPiGXCl3I2Ga6VqsGRk1hqqXm99vlZOmsTJAwe47J13sPg4phO1D1J7nYUsalTOV5mOIfb7Wq1WrZ2tpVoUXcHat29fmc/ZZ66bNWtGZGQkHTp0wDAM/vzzTwDatm3LoEGDnDZWpSpLw7VSNVgSEq6DcE2LuiMbNrDxtddoN3Ikjfv1c+ixgpDXmuTQo6gzqUyphz1cV/T+quaxh+u4uLgyn2tQtJ6jV69eAFxxxRUALFy40EmjU6pqNFwrVYPlITujuaLnps1q5Y8xY/ANDeXSl192+PG8kNeqZSGuUZlwbbFY8PPzq/D9Vc1jD9d795bt8fP888+ze/duunTpAsBVV10FwJ9//onVanXeIJU6TxqularBCgEbrvlB3/jqqxzdsIE+L76If0SEw49nRl5rgcOPpMqjG8moyoiJicFkMnH8+HGOHz9e6nNms5l69eoV/7t79+6EhISQlpbGhg0bnD1UpSpNw7VSNVghsj24K37Q9/38M5hMbP/kE77u16/ULauoR/bPQ4bwdb9+HF6+vMrHMyOvtbDKz6TOR2W3QK/s/VXN4u/vX9zvury661N5eXlx2WWXAVoaojyDhmulajAvpNb63Nt6OIhhcHjpUhKWLCl1s+ZJ8UbSqlUkLFlCTmpqlQ9lQ16rbjvrGvawnJWVVan768x17XW20pDTXXnllQD88ccfDh2TUtVB34eUqsG8KCmXcLab//rrjJ/7sFkzTh48yNikJAIjI6vlePbyF90U2TXsfYuPHTtWofvba6515rr2atmyJUuWLKlQuL788ssB+Oeffzh69Cj169d39PCUOm86c61UDeaHBOzaUIdsX7jp6+qB1FIRRXX1KSkpFbq/vb+1yeSKPjbKHZytY8jp6tevT+fOnQGdvVbuT8O1UjVYJCUt6qpjq4598+fzxUUXFd8ArPn5pT62b/78ajhS5dm3eG/okqMr+wI0Ddeqouzh+sCBAxXqAmJvyafhWrk7LQtRqgazh2szkAv4n/3u55STkkLymjWlP2gYpT6WU8FwVZ3ykZnrQKCR04+ugOLL9KkVrJ/XcK0iIyPx9/cnJyeH+Ph4mjVrdtb7X3XVVbz88sv8+eefFBYW4uWlEUa5J/3OVKoGswBRSMDOoOrhOvbOO4m9886qDou7inZgqy72WesGaFmIq9jLQo4ePVqh++u258psNtOiRQu2bt3K3r17zxmuu3btSlhYGGlpaaxbt46Liq6eKeVutCxEqRoumpJwXVPZd6GMdvVAajF7WUhaWlqFLvHrzLWCytVdWywWbcmnPIKGa6VquMZAMDU7XGcgr7GxqwdSi4WFhWE2mzEMo0IdQ3TmWkHl2vFBSUu+v87SjUgpV9NwrVQN1xwIQwJorovH4ggFQBoQjrxW5Rpms7nSHUNAZ65ru8rMXAP07t0bgM2bN2uPdOW2NFwrVcPVA9oW/Znk4rE4QhIlwVoXM7pWZTqGaFmIAul1bTabSUlJqdD3TXR0NFFRUVitVtavX++EESpVeRqulaoFLkYWNibhwt0aHcBAXlMU8ho1prmWhmtVWQEBAbRs2RKQ2ehzMZlMxQsZ15zeuUgpN6HhWqlaoD0yq+sHVH2jcfdxHAnUUUAXF49FVW4jGQ3Xyq5Dhw4AbNmypUL379mzJwCrV6922JiUqgoN10rVAhagFxJCE108luqUiJw0XAj4uHgsquzM9Y4dO7j88sv58ccfz/gYDdeqY8eOQMVmroHimeu1a9dWqDONUs6m4VqpWqIXsqlMLuD8bV6q33FkkWYk0NvFY6mtNm/ezKRJk0hKkmp+e7i2byQzZ84cjh49yp49e8o8VruFKDv7zPXevXsrtEixXbt2BAYGkpGRwY4dOxw9PKUqTcO1UrVECHAF0AbYg3TZ8FSFwG6gNdAPWaypnO+3337jhx9+YMKECVit1lIbyVitVhYvXgxA3759yzxWy0KUXb169YiMjMRms7Ft27Zz3t/Ly4sLL7wQ0Lpr5Z40XCtVi1wFXABEIAHbU+1FThbaAINcPJbabMSIEQQFBbF582beffddoqKiAEhMTGTdunWcOHGCsLAwunXr5uKRKndnLw3RumtVE2i4VqoW8QKGAa2Ak3hmechx4BjQEhiK1lq7UsOGDZk0aRIA7777LmlpaQCkp6czb948AC677DLM5rJvNYWFhYDMQipV2UWN9rprDdfKHWm4VqqWaQxcSUl5SJ5rh1Mp+ZSUg1wGtHDtcBQwcOBABg0ahM1mY8qUKYSGhmIYBr/99hsAV1xxRbmPy82VLY38/PycNlblvuwz11u3bq3QIsXu3btjsVhISEggMbEmLdNWNYGGa6VqoauAdkj3kM14Rv11IbAFKWnRchD38vTTTxMVFcXhw4dJT08nNzeXlJQUgoKCii/fny4nJwcAf39/Zw5VualmzZoRGBhIbm5uhbZCDwwMLJ7t1tlr5W40XCtVC3kBdwOdgVAktBa6cDznUghsBQKBTsBYtBzEnQQFBTFjxgzMZjNHjhwhJSWF/Px8+vXrh7e3d7mPsc9ca7hWAGazuTgsV7Qln9ZdK3el4VqpWioMuBcJq0HAP0jZhbspQGbXfZCx3ot2B3FHXbt25a677sLb25u8vDwKCgrOWBJitVrJz5fvNi0LUXbnu5mMdgxR7kbDtVK1WH3gPqAbErb/ATJdOaDTZAObkPDfDRiHbBqj3NP48eOLt7K22WxcfPHF5d4vL6+k0l9nrpXdqZvJVKQPun1R49atWyvUH1spZ9FwrVQt1xC4H+iBBNdNwEHA5sIxGUA8sAE5AeiOjLGJC8ekzs3Ly4s33niDsLAwBgwYgOHnx0bgV+BHYC7wNfBtTg5ZFC2m9fV13YCVW2nbti0Wi4XU1FSOHDlyzvs3bNiQ+vXrY7PZdDMZ5Va0B5JSigjgQST4/A3sBFKRhYNBTh5LNrALCdhdgK7ArUhtuHJfVqR2f2vHjoxMSmIr8n+Xi9TMG0U3AFtBAamA2d+fLiYTrYD2QCzQoehPi7NfgHI5Pz8/LrjgArZu3cq6deu49tprz/mY2NhYjh49yrZt27SfunIbGq6VUgAEA6OQuuZvgTikTCQamdEuf1la9SkEEoFDyAx1S+B6oCege/i5r1TkpOxL5GpDXtEtv+hmQt5oTJT8P9oaNMA0eDC2mBj2A4eBlYBv0a0Z0o/9JqRcSdUePXv2ZOvWraxatapC4bp9+/YsXry4Qjs7KuUsGq6VUsVMSG1zKyQwrQP2AauRRYRRQJ1qPmYmEq5SkF0XuxTdbkWDlbsygPXA50jJx8miWwElAbkOsgjVQjknRyYTvPACBjLjbQ/jmcgGQanI1YvXgIHAbchJn55k1XwXXXQRH374IevWraOgoOCM3WbsYmNjATRcK7ei4VopVUYdYDQStBchATsJ2JKby8mvv6ZV375EN2mCL5UPPAYSptKLnjMHiETqqpsB/Yv+rkHKPcUBTyInXhlIIPZCrnwEULmFPPZZbS+kzSJIrX8WcALZjXMWciWlF/Ac0LSqL0C5tdatWxMWFkZaWhpbtmyha9euZ73/qeHaMAxMJv3NoVxPw7VSqlwmpA92J6RUYwXw5S+/kPTdd+zOyuLIvfcCUpMdXHTzR2Yq7QHLhsxM5iJBzB7GbEiAjwIaIDPVFwMxaKh2V1bgA2Q2+Sjy/xiALDitziWJZkq+n/KQ75kEYCGy2HYCcDu6Gr+mMpvNXHTRRSxYsIDVq1efM1y3atUKLy8v0tPTSUpKIioqykkjVerMNFwrpc7KhMwWNgU2//MPx4ELmjYlFAlY9sCciIRoKyWdRsxI2PZDQnhk0Z+BSJeSTsBFSJBS7isOeAyZrU5F3jiicPwbiL3EJLTouPuBZ5BSlBfQWeyaqmfPnsXhevz48We9r6+vL61atWLHjh1s27ZNw7VyCxqulVIVkpubS9z27dQDnu3cmXpAMrKILaHoz3Sk7ta+nbp30S0EWRjZuOhPZwQzVT0+R8ox7LPV4cjJkTOvMHghVzgyke+5RcC1wBTgRieOQznHhRdeiNlsZv/+/Rw5coQGDRqc9f6xsbHF4frKK6900iiVOjN9f1NKVcj27duxWq3Ur1+fyMhITEhQjj7tfvfffz/Lli1j2bJlBAfrnLSnMoA3gVeAIzhvtvpMTJSUHtlnsSciJ3R3uWhMyjGCg4OJjY1ly5YtrF69msGDB5/1/rGxsXzzzTcV3tlRKUfTsjWlVIX8888/AHTu3PmMi4Z27NjBu+++y5YtW3jttdecODpVnQyk7GImMlMciNRWu8NsjH0W2x9ZEPsc8LpLR6QcoVevXgCsXr36nPdt3749oB1DlPvQcK2UqhB7uO7UqVO5nzcMgyeffBKr1QrAzz//XKEtjJX7eR14DwnWdZCaZ3daaGpC2jQGIwH7VWSxpao57Fub21vynY29Y0hcXBx5eXkOH5tS56LhWil1TseOHSM+Ph6TyUTHjh3Lvc9PP/3EypUri/994MAB1qxZ46whqmryCdIR5AhSK1/dfc2rk318ycAMpDe7qhlatmxJ3bp1yc3NZdOmTQAcP36cl19+mb1795a6b2RkJGFhYVitVnbt2uWK4SpVioZrpdQ52d/cWrZsSVBQ2Q3Rs7KymDJlCoZhEBAQQGBgICaTiY8++sjZQ1VVsA0JqUeQri7uHKztQpCylSPIAse9Z7238hQmk6l49nrVqlUAzJ8/nx9//JGff/65zH11MxnlTjRcK6XOafv27QB06NCh3M+/9tprJCUlUb9+fQICAoiOlmWOv/zyC0eOHHHaONX5K0Da7aVQ0uHFU4QiLR+PAo8j7SCV57OHa/sVsISEBABCQ0PL3FfrrpU70XCtlDone7hu165dmc/t37+fd955B4DbbrsNk8lEdHQ0PXr0oLCwkM8//9ypY1Xn521gM5AN1MW9aqzPxYSMOQNYC+j1Es+UkpLC8OHDeeONN7DZbPTo0QOz2czBgwc5fPgwSUlJAOX2sr7gggsA2Llzp1PHrFR5NFwrpc7qxIkTxW9qbdq0KfP5SZMmUVBQQP/+/WnWrBkAERERjBo1CoBPP/2UwsJCp41XVd424B2kxV04MgvsabyQRY6pyAJHLQ/xPNnZ2SQkJDB37lxmzJiBv78/nTt3BmDp0qUkJiYC5YfrFi1aALBv3z6njVepM9FwrZQ6qx07dgDQpEmTMvXWf/75J3/88Qfe3t5Mnz6d1NRUQML1tddeS926dUlKSuK3335z+rhVxViRUooUZDfEQNcOp0qCkJBtLw/RXjWepWnTpkyePBmz2cyvv/7KtGnTuPTSSwFYtGgRKSkpwNnD9aFDh87ZXUQpR9NwrZQ6K/tlVvtl11PZy0XuvfdeWrRoweHDhwFo1KgRPj4+DB8+HIBvv/3WSaNVlbUY2ApkIbPWnuzU8pD1gPaq8TyXX345zz77LBaLhT/++INVq1ZhMpnYvHkzubm5+Pr6EhYWVuZxDRo0wN/fH6vVysGDB10wcqVKaLhWSp2Vfea6vHA9ZswYfvzxR5544gkA4uPjAWjcuDEAd999Nz169KBHjx5OGq2qrDnASaRntCeWg5zOC5l9P4ls3a48T9++fZk+fTre3t6sWbOG7Oxs8vLyOHHihOwOW84mViaTiebNmwOyDkQpV9JwrZQ6o7y8vOKesm3bti3zeV9fX3r27InZLL9KDh06BJSE6/r16/Pzzz8zbtw4J41YVcYBYCkya122waLnCgYygd+RFn3K81xyySU8//zzeHt7k52dTXx8POnp6eWWhNjZS0NO74OtlLNpuFZKnVFcXBxWq5Xw8HDq169/1vsahlE8c92kSRNnDE9V0RdIsPZF2u/VFD5Ft5PAXBePRZ2/iy66iBdffJF69ephtVrJyckpt88+yNqB0ObNKQT+2LuXH4HvgV+ARUgnnFxnDVzVel6uHoBSyn3ZS0Latm1b7qXYU6WmppKXl4fZbD7r7JJyDznAd0gALVvBWj2y//qLw/37n/N+4VOnUnfy5Go9djDy2r4ExqFvdp6qe/fuvPrqqwwePJgTJ05gtVoxgP1IYN6CdLvZDiS3aEEW8Mvhw6wqeryp6GYB/IBWQPvTbjWhHEq5F/19o5Q6I3u4Lq+/9ensJSGRkZF4e9ekedCaaQXSVcMA/B10DK/ISIJHjiz/k1YrGZ99BoB/nz7VfuwA4DiQiPS+7l3tR1DO0qVLFz7++GPe/+gjGo0fz/XIItw8IP+UP63XXot5/nyM224jo+ixRtGtsOjPRGAVcmXDF4gChgG3AGe/NqdUxWm4VkqVyzCM4k4h5dVbn+70xYzKvW1GQokfjtswxqdtWyJnzSr3c1kLFpDx2Wd4NW6Mf9++1X5sE/LacpHZTQ3XnmsfsOryy9l9+eWsQLrB5FBS/hOA7NLpHRqK6Ysvyn0OAykdyS+6ZSInX6nILPgbwFXAcKAnnrWJknI/Gq6VUuU6dOgQ2dnZ+Pn5FW8OczZab+1ZtiHh2tdFx7fPWgcPH47J7JjlPz5IkNrqkGdXjpYKPAvMR9YGZCAhOZjKb3ZkQgKPFxLGKXqu7KLnPY6sQZgHdASeB2Kr/ApUbaXhWilVLntJSOvWrbFYzv02pjPXnsNAAmc+UMcFx7dlZZH5448ABI8Y4bDj+CChTMO1ZzGQhYhTgHggDbkKEUb1XmkxIW0bA5GfhQykbOQkMASp1b+PmrXYVzmHdgtRSpXrbP2ty6Ph2nMkAceQOlRXBIfM777DyMrCt0sXfGMdNz/oAxQACUC6w46iqlMq8H9Ft11I4I1E6qH9cWAJE7IBUSOkfCQe+C9wA3KVR6nK0HCtlCpXZcP16T2ulfuyz1p745o3geKSkNtvd+hxLEW3AjQgeYKlwDXAt8gJoC/QEAm+zmIB6iE13CnI4schwLvIjLpSFaFlIUqpMk6ePElKSgoArVq1Ouf9DcMgISEB0HDtCQ7julnrwuRkshctAouF4GHDHH48byRcH3b4kVRVzAP+jYRqK9AA54bq0wUiJSjHkVnsGUh3nafQWUl1bvo9opQq4+DBgwA0aNCAgICAc9wbUlJStMe1B8kDbLimI0LGnDlgtRJw5ZV4RUY6/HhmZMZRNxBxX18DDyEnQGacP1t9JvZZ7GAgGfgAeBL52VHqbHTmWilVhj1cV6RLCJTUWzds2FB7XHuAXCRwuiRcO6kkxM6Ehmt3Ng94gpIykHDcrw1eHST0H0E6ivgDk3G/cSr3oeFaKVXGgQMHAGjatGmF7r9//35AS0I8hatqR/N37CBv40ZMQUEEXX+9045r30hEuZelSClIMu4brO2CkLEdAT4GQpDZdqXKo2UhSqkyKjtzXdnFj8q1fCmZ0XWmk59+CkDQkCGYK1BuVB0M5I3OVf28VflSKamx9sK9g7VdILLQ8Qiy6cxKl45GuTMN10qpUmw223mH64psk65cz94r2Jnh2jAMqbcG6jipJARKyl/8nHZEdS4G8AyyUNAKROD+wdouGCkLOYbUX2e5djjKTWm4VkqVkpSUREFBAb6+vjRo0KBCj9m+fTug4dpTRCCzhYVOPGbusmUUHjyIJSoK/8suc9pxC5CFaRFOO6I6l/nIJjHpeFawtgtDFgXvAf7j4rEo96ThWilVin3WukmTJpgrsC318ePHSU5OBqBt27YOHZuqHu0p2RrcWbPXJ4sWMtZx4Hbnp7MhJxC+yGtWrpcKTC36Mxj36ApSWWZkw5ljwOdoeYgqS8O1UqoU+2LGypaENGvWjMDAQAeNSlWnZkiwMSMzu45my8sj85tvAMdud366AmSGPgLpm6xcbypSDmJDFgV6Kn8ggJLykBzXDke5GQ3XSqlS7DPXFe0UoiUhnscMtENmdPOdcTxfX1ocP04rw8C3Y0cnHFHkITOjsXhe6UFNtA8pB0nDM8tBTmcvD9kL/OTisSj3oq34lFKlVHbmWsO1Z2oP/IGEgyAXj8VR8vHckpAsYAuwq+jvechMvG/RLQR5XW3wnNKKOUAmsrjUU8Z8NmakB3YGUh5yC55/wqCqh4ZrpVSx7Oxsjh49ClR+5lrb8HmW9khIS3P1QBzEvnFMMPJac3JyyMjIIDMzk4yMjDJ/z8jIwDAMbr31Vho2bOj0sW4E1gKbgH+AOKRe3ErZPt0mJNhZkKAaC3QquvVByn7cTQ7wHRJEw108luoUiCzM3Ir8v3Vx5WCU29BwrZQqdujQIQDq1q1LUNC55zOtVis7d+4EdOba0/RBZj9TkVnRmtYH2r4jYxjw7QMPMHH5curXr09UVBT+/v5nfNyhQ4ecFq4zkK2/Pwa2ITPTVkpCtQkJ0CZKz4gaSM2yFTiB1P2uLLqvN9AfGA1chfu8yf8MpBT9vSa1RbQgtdf22WsN1wrc5+dOKeUGKrsz44EDB8jLy8Pf37/Cj1HuIQwYCMxCgkFNC9cZyKz1DUB8djYFBQUcPnyYo0eP0rRpUzp06ECrVq0ICQkhODiYoKAgIiIiKlwOVRU7gI+AucisZy4SqL2Kbn5Ff55rUdSpIbuw6HmykFZ3i4DGwEjgdqB+Nb+GyjCAzyj5P3FU6UTazJnkLl9O/pYtWI8exZabi1dkJP79+hE6cSK+sbEOOW4wcuLwC7K4sSbNzKvzowsalVLFzrfeum3bthVq26fcy21IMMhGAlpNYQ+awcAw4LXXXqN///6Eh4fj4+NDvXr1OHbsGHv27CEgIIBevXrRo0cPYmJiMJkcVzWbDtwHXAK8AxxGvvbeyFUE+wYlPlTszdk+s+2DzJ7WKbqZgJPATqQ7R2fgVZzb1/xU+5GyiRykjMJR0p5/nuwFCzCHh+N/+eUEDhqEyc+PjNmzie/alawFCxxyXF/k/+EE8JtDjqA8jb4bKqWKJSYmAhAdHV2h++tiRs/WCeiAzJRmungs1SkTCZs9gdZAYGAgU6ZM4ZJLLqFdu3YUFhbi6+vLsWPH+P7775k8eTILFiwgJ8dxDdUWAhcDs5GQXYgEzTrI17+63oztZQqhSOjLRWZVpwDXIIHb2TYji0t9isbnKA1//JGYtDQar1lDw+++o+F339F01y7qvf025Odz9O67MayOOY30R8qrtjjk2ZWn0XCtlCpmX8yoOzPWDiZgOCUdD2yuHU61sFFSfjD8lI+Hh4dz3333ER4ejr+/P61atWLYsGHUr1+frKws5s2bx9NPP81PP/1EZmb1nWqkI7PVQ5FFivYZ9SBkxtpR8+QmJFwHI+E9A1iB1GO/inNnsbdS0hbRkfwvvhizX9mK7pBx4/Bu2RJrYiIFu3Y55Nj2TZk0XCvQcK2UKmKz2UhNTQWgfv2KVWhquPZ81yK1ud7IZW1Pl4aEyebAlad9LioqinvuuQcvLy+2bt1KYmIiTz/9NKNHjyYqKorc3Fx+++03Jk2axHfffceJE1X7imykZLb6BDJrWwfnLnayh+wQpPQnFZnFHlT0d2fYSsnMtctYiubMfRwzCh9KtkTPc8gRlCfRcK2UAmQbc6vVisViITz83Ety0tLSiI+PB7QNnyfzB55FNvXIwLODQWZKCicefRSvDz9kUlYW3uXcp1WrVowcORKTycSSJUtYtGgR3bp148knn2Ts2LE0adKE/Px8Fi1axOTJk/nyyy85fvx4pceyHLge2WDEPlsdgOv6IJuR2fJTZ7EHAQkOPq4V2E5Jz3FXODl7NgW7duHdujXezZs75Bj2ri65SG9yVbtptxClFFBSEhIREVGhxYl///03AC1btiQ0NNSRQ1MOdjkwBGkllgo0xPNmXmzA8eRkLBs2ELRhA2/Mm0faiBFcd911eHuXjtldu3blxIkTfPPNN/z444+0atWKmJgYOnXqRMeOHdmxYwcLFixg3759LFu2jBUrVtCzZ0+uuuqqCl3V+RMpSTmOdMqwLzJ0NfssthcSsLciAfsnwFG9fvYjNfA2nBc40l56ifxt27BlZVGwYwf527ZhiYqiwZw5mBy08NpESWnIVsB5+5Aqd6ThWikFlITripaErF27FoCePXs6bEzKeZ5GeiVvsVpJy8igroedMKUB/h06EPPcc8S++y6Jhw7x9ttv89133zF69Gguu+yyUp1A+vfvT05ODsuWLcPLq+St0GQy0a5dOy644ALi4uJYsGABu3btYtWqVaxevZpu3boxaNCgM/6crAbuQII1OLb13PmyIOPKROrAb0Ta91VspUXlJCD9ux1ZX3667N9+I2fRouJ/ezVuTINPP8WvWzeHHtcbqWWPd+hRlCfwtMkJpZSDaLiu3UKAfx87humRR0gfPZqMpCRXD6nCMpGWduHA2xddxKwPPuDf//43devWJTk5meeff55x48axfv16DKNkr8OBAwfy/PPP07hx4zLPaTKZaNWqFQ888AATJkygffv2GIbBunXrmDZtGnPnziUjI6PUY7Yjrf/stcxBuF+wtrMH7Dykg8jNyGx2dctFZu+d+XVo9McftDQMYtLSaLR0Kd5t2nC4Xz+OP/ecQ49rRmboPbm0SlUPDddKKaBynULy8vLYuHEjAD169HDouJRzJCcn89uTT9Lw0CH8w8M5HhKC4xrTVZ9sZJa4AXA3cBFgsVgYOHAgs2fPZvTo0QQEBLBnzx4mTpzIAw88wOrVq4tDdkX6WsfExDBu3Dgef/xxYmNjsdlsLFmyhGeeeYZffvmFvLw88oFxwBEkYLlzsLYzIwE7F9m6+5lKPv7EiRMcOXLkrPfJw/nh2s4SGop/nz5E/fILvt26cXzSJHKLytkcwYS8Vg3XSsO1Ugqo3Mz1pk2bKCgooF69ek7Z0U451qFDh3jiiSc4evQo/Rs25NYZM6gfEEAKuHXAzkZmiesDtwCPnfZ5Pz8/hg8fzqeffsqQIUPw8fFh+/btPPXUU9x7770sXbq01Ey2nWEYTJ8+nalTp5KXVxKVGjduzPjx43nwwQdp0qQJeXl5zJ8/nylTpvDg8uX8Y7ORj2cEazsL0m87C/gEWFLBx23evJmbb76ZYcOGkZube8b7uUN7R5O3N0G33gqGQdbPPzv8eDVpQyZ1fjRcK6WAyoVre0nIhRde6NAd7ZTj7d69m6eeeor09HSaNWvGjOef55169bgKqIdsQJLt4jGWJ5OSYH0t8DxnfkMLDQ3lvvvuY86cOdx66634+fkRFxfH1KlTueuuu1i0aBHWUzYXMQyDzZs3s3TpUqZPn17qcwCtW7dm4sSJjB49mrp167L/5Ek++eILMn/4gYCzjMNdeSOL8bKABzl3eciqVat48MEHyczMJDY2Fl/fM/cB8aVkRteVLBERAFhTUhx2DPsMfdlO26q28bTfAUopB7BarZXqcX1quFaea9u2bTzzzDNkZmbSpk0bpk2bRmhoKD7I9tzXIeUWqciCQVcHJCjqCoKMJxKZsX6diq3ODwsLY+zYsXzxxRfcfvvtBAYGcvDgQZ5//nnuvPNOFixYQGFhIWazmUmTJuHt7c3KlSt5/fXXy8xwm0wmunXrxuOTJ7Pj5pspiIzEEhHh2l7OVRCALMbbx9nLQxYuXMjEiRPJy8ujd+/evPTSS+WeYB87dozly5ez6IMPOPnEExQ4aOvxispZInPy3i1aOOwY9nDtqpaDyn1otxClVKke12FhYWe9r81m03BdA2zbto1p06aRl5dHp06dePzxx/E7ZXc7HyS0hgJfIAE7EemH7arwkAscQ964ooA7gaeo/CxRnTp1uPPOO7n55pv58ccf+frrr0lMTOTll1/mk08+YdiwYVx99dU89dRTTJ06lXnz5hEREcHtt99e5rne8fLiYL9+GP36EVzF1+dKJqQ8JBMpD7kR2QDnVF9//TWvvPIKAAMGDOCpp57Cy8uLEydOsHPnTnbs2MGOHTvYuXMnKUUzxNlIe7qC9HSMa65xWLlMzrJlFCYmEnTjjZhO6f5iFBRw4n//I+PTTzH5+0t5iIMUIn3jIxx2BOUpNFwrVQudPHmSPXv20LVrV0wmU3FJSL169c7Z43rv3r2kp6fj5+dH+/btnTFcVc1ODdZdunThiSeeKNMLGqQe9zmgDzKbeQhZsBeMhG5nFQTZkG3EM5GOIDHAdGQr76oIDAzktttuY8iQIcybN4+vvvqKlJQUXn/9dT777DNuvvlm7rrrLj744ANmzZpFREQE11xzTfHj84D3kHIKfzz/UrC9PCQHeIOScG0YBh999BEffvghAIMGDaJHjx68/PLLbNiwgYSEslvRmM1mmjZtSut27XivbVtSLrsMK44LHQV793J01ChSIiLw69YNc926WFNTyd+yBWtSEiY/P+rPmoV3OZ1hqks+0nUn1mFHUJ5Cw7VStdDs2bNZtGgR999/P5dddlmZeuukpCQ++OADBg0aRNeuXUs9ds2aNYBsxFFeIFPubevWrUyfPv2cwdouPz+fHW+9RfeEBC586SXmm83Fs9jByOI9R4VKKxJcTyLBrxEyo/oUEmKqi5+fHzfddBPXXXcdCxYs4Msvv+To0aO8++67+Pr6EhERQXx8PDNnziQsLIyLLroIkM1XjiDlAJ5aDnI6P+TrvQg4CDS22ZgxYwZffPEFmZmZREZGMm/ePObPn1/qcY0bN6Zt27ZccMEFXHDBBbRu3Rp/f38A1gF/IeHTUaHDv29fwp58kpwlS8jbvBlraiomHx+8mzUj6KabCHngAXxatnTQ0eV7tRD5PtBwrTRcK1ULRUdHAzBv3jz69+9ffAm3fv36GIbBW2+9xbZt24iOji4TrrW/teeqTLDOz8/n119/5X//+x9r167FMAyW3XUXA9u1YwpwGFn4lo6UEwRTfQEzHwl42UjYqws0AaYBl1XTMcrj4+PD4MGDGTRoEL///jvffvst+/fvJzc3l6NHj5KXl8dDDz3EBx98QPv27fkIKVWxL9qrCSxIMMgFZgH7Ro3i+++/xzAMfH19OXHiRPFVqwsvvJCuXbvSsWNHgoPPXBTTHliF/L8GOGjc3jEx1HVwH+uzyUe+/xtTvSd+yjNpuFaqFrr88suZM2cO+/fvZ8+ePRw/LvvJhYeHs2LFCrZt24a3tzfXXnttmcfaw7X2t/YsFQ3W+fn5xbO3ycnJ7NixA8MwqFu3Li1btqQdcAnwPbJd+h4kCCcjs8t+SNj0QYLauUKngcz65SEBJReZAQxC6qrbAbcBg5EQ70hZWVkcPHiQtLQ0bDYb/fv3p169eqxdu5b8/HyysrLYvXs3gwYNYvb69ayNiKAAxwVGV/FFSkPmAE2PHMEwDPz8/AgMDMRiseDj48OBAwcwDIPjx49z8OBB2rdvT8eO5W/63Z6SbiQ1lT1cd3D1QJRb0HCtVC0UHBzMJZdcwuLFi4s3wQAICAhg1qxZANx0003Uq1ev1OOOHj3KgQMHMJlMdO/e3dnDVuepIsH61FB97NgxDMMgKSmJ4OBgcnJyuOSSS/DxkbnpYGSL79uBNUjI/h2Zyc5DwnY+Eqx9KNn62h60DaSOupCSDTd8kFAXggTra4DhQDecMyucl5fHiBEjOHnyZLmfb9asGampqaSmppKXl8fsrCzyIiLwxjFlMUZ2NgULF5L/888U/v03tgMHMKxWLC1b4nPjjfg/8gimoCAHHFn+v7KRkpep8+fz8rZtxMfHs2fPHuLi4tizZw8ZGRls3LiRZcuW4evri5+fHy+88AIDBw4s83ztkf/b47huQxlHy0NOsnQVigIN10rVWgMHDmTx4sWsWLGCpk2bArBhwwaOHTtG/fr1uf7668s8xj5r3a5dO+rUqePM4arztHXrVqZNm0Z+fj5du3bl8ccfLxOsf/31Vz755BOOHTsGyMLW8PBwDMMgPT2dhg0b0rKcelUTsiPiRUgQ+wPYWnSLQ2ah85EQbVDSys+EvPnYSz58gdZIMGkPXIH02HYmLy8vWrduTUJCAmFhYYSEhBAWFkZoaCihoaHFfw8ODqZ+gwZ0DwkpDlSOkDdnDlljxgBgiY3F++qrMU6epHDlSnKeeYb8L76gzpIlmCvQOrOy7O3k8oB5FgtDO3YsNSttGAZHjx5l1KhR7Nu3j5ycHLKzs5k+fToffvghLVu2pEWLFrRo0YKWLVtyQXQ0URZLcc90R1+BcLZC5Hs9AseWLSnPoeFaqVqqZcuWtGzZkri4OLZt2wbI5hCBgYGMHj26eJbyVKtWrQK0JMRTVCRYJycnM3PmTEBC9bBhwwgMDGTGjBmYzWa6du3KgQMHaN68+VmP1QCZabbLBXYhQfsIEtRyKQluvkBDJEy3xvW9gS0WC//5z38qdN8DSJ9tR3a/MPn44DtuHP4PP4ylVavij9uSkjg5aBDWjRvJeughgufMccjxvZAg/E95YzOZaNCgAe+99x6LFy9m7969xMXFkZiYyPHjx1m5ciULFy4kMzOTjIwMvLy8uGPhQva1a0cGNS9cZyKvqTfguC7aypNouFaqFrv66qt58803iYuLw9vbmyZNmtClS5cz9q9eUrQRQ58+fZw5THUedu/ezfTp088arEEWsd59990EBwdzxRVXcPDgQR5++GEAhgwZwrp16wBoUcnNN/yATkW3muYfZLbSC8eVOPjecQe+d9xR5uPmhg0JfOstTvbuTf5332Hk52Mq50S4qryQk4dk4CiyE+bpoqOjuf3228nKymLTpk2sWbOG5cuXs3v3brKyskhLSwOgsLCQy/Py+BY5KbHXJ9cEBlIOdfrJpardNFwrVYv16dOHjz/+mKwsWWrUqlUr7rrrrnJ3XEtMTCQuLg6z2cwll1zi7KGqSoiPjy/uY925c+czBmuQfsS33HILIJsJTZ48mby8PLp3787IkSP5/vvvAc45c12bbEKCp8VFx/fqVHTKkpeHcewYpoYNq/0YJuT1FSKv98qij+fn57N371527tzJrl272LlzJ4cOHSq1g2VoaCiFhYVEREQQHh7Oiy++SN8uXbgK2ZAoAykHqgmykSDVCClnUgo0XCtVq/n4+NC7d2/+/PNPAK6//noaNWpU7n3ts9ZdunQhJESbTbmrlJQUpk6dSmZmJq1bt+axxx6rUD/y/Px8pkyZQmpqKtHR0Tz55JMkJCRgGAYhISGEh4c7YfSeYRMSOl3V5d26b5/8xdsbkwP/XyxAfnw8czZtYvOuXezatYt9+/ZhtVrL3Dc6OpouXbrQvHlz5s6di5eXF8HBwbzwwgu0a9cOkJndeUiP9FBcd3JSXQxk8W4wMBQNVKqEfi8oVctdffXVzJgxg5CQEIYOHXrG+/31118A9OvXzzkDU5V28uRJpk6dyrFjx4iOjubpp58utaX5mRiGwcyZM9m5cydBQUFMmzaNoKAg9hWFuJiYmHKvZtRGBiXh+txfWcfIfe01ALyvvhqTr+Oq1c07d5I3fjy/GAYHTvl4aGgobdq0oW3btrRp04Y2bdoQHh5OQkICEyZM4MiRI9StW5eXX36ZZs2aFT+uJ9Kq7iTSOcTZi1arWyby/RCBhGul7DRcK1XLpCDh4DDSyzaveXNuW7CA0LAwvvL3JxLoiCw2s8cpm83G0qVLAbj00ktdMGp1Lrm5uUybNo3Dhw8TERHBM888c9aNPU715Zdf8ueff2KxWJg8eXLx1Qv75kKtTllQV9tlAyeQVoKumHnN/+UX8j78ELy9CZg2zaHHMkdEwAUX4OPvz22nhOl69eqVOdnas2cPEydOJD09nejoaF566SUiIyNL3ccEPA8MARKQvteeurixAKkfjwQmUn5Nuqq9NFwrVYPlAcuRBVibgM3IJdlCpGa0uEqyvXRnNSE9e72QWSX7grSQrVs5npZGcFBQmR0blesVFhbywgsvEBcXR3BwMFOmTCEiIqJCj12xYgUff/wxAPfddx+dO3cu/txVV11Fbm4uQ4YMccSwPVKuC49duGMHmSNGgGEQ8NJLJbXXDmKJiMDy1ls0Bsac5X579uzhoYceIjs7m1atWvHiiy8SGhpa7n3bA+OAmciJvh+eVx5iAMeAOsiGSrqQUZ1Ow7VSNdAh4BNkc4+jSJi236xIgLbvnnfqxh72zT2syLbW+5EaSevSpeQD9S++mH3e3rRx2itR52Kz2XjttdfYtGkTfn5+TJo06Yx186fbu3cvL7zwAgCDBw8usyOnvZOIKpFH6X7dzmJNSCDj6qsx0tLwe+QR/B980OHHNCGv9VwnFEuXLiU7O5tOnTrx3HPPERh49vno/wMWIVuie2J5SCbyezISeAHHbCKkPJuGa6VqCCuwGPgY2S0vt+hmQxZeeSG9hCvSPsygdBjPq1sXGxB3yy1cgszWjEJ20XPVoi4ltdIffPABy5cvx2Kx8Pjjj1e4hOPUziBdu3bl3nvvdfBo1fmypaaSceWV2A4dwnfUKAJeftnVQypl2LBhtGvXjm7dupXbH/903kgovRGIR2qwPWVLqjxk4qEBUg7S2KWjUe5Kw7VSNcBW4AGk9MMeqr0Af6SfbGVn2EzIG6A9OAcOG0b+ddeRFxjIceA3YAnQEngV2TxBOd/XX3/NggULMJlMPPTQQ3SqYJlAbm4uU6ZMISUlpXjho8XiaRfnXcOX0ld7HD17bWRkcPKaa7Du3InPkCEEvv++0xaX2l/fuRZuBgQE0KtXr0o9d3vgYSRkJyOzv47ZzL365CNXAusibfe0HESdiV7NUMqD5QMvIT1o1yALrUDaXIVQOghUhQnwDQykTtFzm5HZpi3AYOBJZKGXcp7FixfzxRdfADBmzJgK9x4vLCzk2WefLe4M8uyzzxIU5O6xxn04s0OIkZfHycGDsa5bh/eAAQR98QUmJ54E2ctfHNWP5B7gLmQWOA1Z4Oiu8pGdRsOQK3dvogFKnZl+byjlobYCA5DV96lI+UYosvrekW+/FiCg6Fgm5E3xbaAvsNKBx1Uldu3axdtvvw3AjTfeyDXXXFOhxxmGwcsvv8y6devw9fVl+vTpREdHO3KoNU4AcuJqRn7mHMWwWskcNozCxYvx6tOH4O++c8hOjGdj74gS5aDnNwFPAcOQgH0c2WDG3eRREqx7AO8hVwWVOhMtC1HKA70PTEZmj/OQQH0+5R9VYb+Mm4/MOG1FZrEfQmaytSuyY6SkpDBjxgwKCwu56KKLGD68YhenDcPgnXfeKdVyz765h6o4E9JB5zASrh31Jpr75pvkF+2OaY6IIGv8+HLvF/Dyy9IyzwHsW7w7sieJGZkg8ANmISE2Hwmyrp79M5DFi+lIKcjFyO9e3UJLnYuGa6U8iAH8B3gZKQGxUFKm4So+yC+SbGQWeybSpuolPK/FljsxDKNMbW1ubi7PP/88J06coFmzZjz44IMVrr/94osv+OGHHwB49NFH6dGjR3UPudboBCxEwqejSiaMtLTiv9tDdnn8p0wBB4VrKxJ6uzjk2UuYgWeQ0PoGMoOdhARaV23UU4hcEbQhs+q9k5O5Y/t26vTvD7qhkjoHV58YKqUqyEDegF5CgrUvsu2uO/wQ22exA5CxzQLG49jL5jXZ9u3bue2221i8eHHxxwzD4NVXX+XAgQOEhoby5JNPVmj3RYBffvmFWbNmATB+/Hguu+wyRwy71uiEnDg68vs7YMoU6hrGOW+WU3ZArE4GJTPzju2mLUzIAsfPio4XivTBPo4EXGcxkNKUJCTYNwemAXsHD+bm667jmmuuYfXq1RiGcbanUbWcO7wvK6Uq4CXgLSS8+iFB1t3mT/yQEpWTwNfABE7ZqEZV2MqVK8nNzeXgwYPFH5szZw5r1qzBy8uLxx9/nHr1KtYdeNmyZbxWtF32bbfdxvXXX++IIdcqnZHQWUjN/f4uRE4gInHu7oO9gV+QhY5RSMBPRH6nOLTGHSlvS0bCdQNkofh84A4gPCwMkJ/N8ePHM2bMGNauXashW5VLw7VSHuAD4EXkDcYf915M40tJwJ6NzPqoyjlw4AAATZs2BWSTjm+++QaQXRTbtKnYNj7//PMPM2bMwDAMBg4cyMiRIx0y3tqmKVITbEFCaE3kjHrrMwlEfm/YZ7EjkLUlh5FSjbxqPFYhcDwjg/i4OE4UFhJMyWz1Z0CTovtdd911BAQEYDabSUxMZPPmzdx///3cc889rF+/vhpHpGoCDddKubntyOJFeymIOwdrO19KSkTeAJa6djgexTCM4nDdrFkzdu/ezZtvvgnAkCFD6NevX4WeZ8+ePTzzzDMUFhZyySWX8MADDzitP3JNZwL+hXyfV2fQcxcG8rp8kUXKrtIb+BW5atcHaIQE/hSkbCMdWetRmSsIViAH+d10FJkVL0xOJvTwYVrv388TyO6Rd1A6ILVq1Yro6GgCAgKIioqiadOm+Pj4sGnTJsaPH8+4cePYsGFDVV+yqiE0XCvlxgqQzWHsixc9IVjb+SGLHTORDiLu2GLLHaWmppKVlYXFYsHPz48ZM2ZQUFDAhRdeyIgRIyr0HPv27ePJJ58kJyeHTp068fjjj2M266/76jQaCZ8FOLcm2BkKkBOI+rg2XIP8zhsK/AR8g4TeGGTxo70+OhFIQDqNHEdC94miWzqy0DoFmflOKPpYITIBEA1cXa8e/wZmJiVxb35+uWUwrVq1wtvbm3r16uHl5UVCQgJTp07l5ptvxtvbmw0bNjBu3Dh++eUXR3wZlIfR37ZKubE3gA3ILFIQ7ldjfS6ByJvYXuBZF4/F0WxUT8iyz1pHRUUxc+ZM0tPTadq0KQ8++CAbNmxg4sSJjBo1imPHjpX7+H379vHYY49x4sQJWrduzdSpUyu0JbWqnA7AhcgupjVt9joPOTkejuO6oVSWCegK/BdYhsxm3430149BasODkEkI+2JMKyW7TPoD9ZCSnh7ACGAqstvsV6GhXBUSgtkwiI+PL/f4jRs3xsfHBz8/Py699FJsNhuvvPIK99xzD99++y033ngj4eHhuiGTArQVn1JuazvSci8DCameeCZsQt7wMpAOIv8CLnXlgKooD9iJ9PS23xKKPp5fdB8vJJjUB2KRbZ7bF/29Im+79kWMycnJFBQUEBgYyMCBA3nmmWeIi4sDIDAwsNzHnh6sX3jhBQICAs7rtapzGw2sQq7O+OF5J7/lsSInxHWAO107lDOqC9xWdAPIBXYhP4/xyM9jHiWtBH2Rum37z2F5faqbNm3K5s2bSUxMpEmTJnh7e5f6vMViKS7Tuuqqqzhw4ADx8fG8+uqrTJo0iYkTJzJx4kQHvFrliTRcK+WGbMCDlJSDePK8ozely0NW4rretecjDel8Mg95A7cHafuf9npPe82nPWDFAeuQ1+6LfB1ikA4EtyGXo8uzf/9+UlNTyc/Px9/fn6CgIN555x0AfH19GThwIIMHDyYkpHREKC9Y6yyaY10HPI10mcjHfWZ5qyIXeR2XI7O8nsAPWfhYlcWXYWFhBAcHk5GRQUJCAjExMWXu07JlS3bv3k18fDyTJ09m7NixzJs3j8svv5zevXtX4eiqpvHEyTClarzlwEbkjc4Ty0FOF4jUce4HfnTxWCrCAP5BWgleDEwB/gIOIK26MpH/k2BkhjoSaRsWVfT3BsjsmAVZPHUUOITMcs4E+iGXtBdTtr3Yli1b2L9/P0lJSWRlZZGZmYmvry833HAD7733HnfccUe5wXrixIkarJ3MFxiLfH/n4Pm11wXISYI/cL+Lx+IK9u48hw8fprCwbB+Yli1bAhAXF0fHjh0ZNmwYAM8//zwZGbqqRJXQcK2UG/oIebP2pWb8kJqQ2aVc4GMXj+VsDKQ7wWDgBuATYB9yBSEAaIh0LKiPbHIRgMxIeyFB2lL0d28koIQgdZ6Nim51kO4G8cB3yGX3y4uOY0V2YFyxYgU2m43AwECaN29eKlTXqVOnzJjtwfrkyZMarF3gfmTG1Af5v/VU9j7PgcBI5KSytqlbty5BQUFYrVYSEhLKfN4ervfv3w/APffcQ+PGjUlJSSnuJa8U1Iz3baVqlEQk4NkXFdUUfsis2DpkVtjdHEOC0r3AEopadCHhuCFyBcGL87+KYEHCuH2m24TMgq8HJgG3ALsKC7HZbAQEBPDwww/z/vvvnzFUgwZrd+ADvIlcxSikpPbe02Qj39/NkYV+tZV99johIYGCgoJSn+vUqROxsbFceOGFAPj5+TFp0iRMJhM///wzq1atcvp4lXvScK2Um/kUmbW2z4LWFGYkiLjb7LWB7MI2AKmtTkJmnqOQhVOOqKP1RjYhaYQE7qNIoB8aFMR9q1axau1axo4de8ZQDaWDdZs2bTRYu1AH4BHkBCwbzysPsZeDBAKvIScKtVVERATBwcFYrdZSO6SChOmPPvqIRx55pPhjnTp14tZbbwWkPCQzM9Op41XuScO1Um4kn5JwXdVZ64L168l+4QVODBnCsUaNSDGZSPE787Pm/fQTJ0eO5HiHDqRGRJDi7U1q/fqcGDiQvPnzqzga4YfMyH+HLBR0tePIbPV9yGLFTKReOhQ5uXE0MxJkIpH/+4PA65GR3Bsezr6zPO70YD1jxgwN1i72MCXlIZl4zrboVkqXg/R17XDcgn0xY2JiIrm5uee8/7hx44iOjubo0aNaHqIADddKuZW1yMypjap3CMmeNo2sJ54g//vvsSUmnvP+ubNnk/fppwB49+yJ7403YmnenPwFCzh57bVkTZ5cxRGVlFVkAn9U+dmq5jBSivEN8jX3RUKuKzqzeCHlJ3UomcW+mfLLZ7Zv386ECRM0WLsZH+Ad5OTMjGcEbBvSJtPebaM2l4OcKiwsjLCwsFK7pZ7NqeUhP/30E6tXr3b8IJVb03CtlBvZhFyi9abqHUK8e/UiYPJk6vz8M3WTk895/4CnnqJuSgrhW7YQMn8+db78krDVqwldvRpTUBDZ06dTuH17lcZkQl5bAa6tu94L3Fo0hhOUzFa7siuLvSd4JFI6sxfZ6GLFKffZsGEDjz32GJmZmcTGxmqwdjPtgC+Qnsrg3gHbigRrX6AtcpJZm8tBTmefvT5y5AhZWVnnvH/nzp255ZZbAHjuuee0PKSW03CtlBvZhCyKqo6ShIDHHiNw6lR8r70Wc4MG57y/d5cumOvWLfvxnj3xHToUDIOCv/6q8ri8kNe4qcrPdH7igduBHUh9bAPcq4+4F7Lo0Ya07xuDXNFYvnw5Tz/9NHl5eXTr1k2DtZu6CJgNhBf9OwP3C9j2YO0DtAS+RX4OVIng4GAiIuQ0qSKz1yDlIY0aNeLo0aO88cYbDhydcncarpVyI/Zw7XYLGS1Fcb8attG2IK9xC2V7PDvaUSRY70Zmhxvghl9r5BdzvaK/HwaGLlzIY9OmUVhYSJ8+fXj22WfxO0v9vHKty5CZ4Ejk//Ik8j3vagay5uEkUgrSAfgFz9ksxtnss9epqamcPHnynPf39/fn6aefBuCHH35gzZo1Dh2fcl8arpVyEyeQTVasuFfgK9y8mbyvvgJvb3wuv7zKz2ehpKduXJWfreKsSJu97UXHro9zFi2eLxMSsK3HjxP/3/+y1TC4dMAAnnzyyTJbMyv3czHwA9ACCbIZyJUSV81i25AylVyk/ONiZNfRRi4ajycICAggMjISKOltfS5du3bl5ptvBuDFF18kP99TmzOqqtBwrZSb2IwEQDOu/cHM+/lnTt55JyeHDyetTx/SunTByMkh+L33sJSzJXBlmSgpDdlc5WeruPeBv5FZuwa4d7C2MwH1QkKgf39Mo0eT+sgjWCyeMHIF0AWpmb8D2VDIivNnse2z1SeQ7/kIZOHifEpqw9WZNWvWDJPJRHp6OmlpFetxNH78eCIiIkhISODzzz938AiVO9JwrZSbSKIkXLtS4aZN5H3yCXlz5lC4fDn4+BD02mv43nFHtR3Dgsykld0DrTSr1cqGDRuYM2cOqamp5328PcCryEYxobjXlYFzsVgsRD7+OLlDh/KjycRCVw9IVUoo8Bay0LElJbPYmcjCXkfNZNtDdQYls9W9gcXAg3jWz4Ar+fr60qiRzO/v23e2BpklAgICePDBBwH4+OOPSa7AgnJVs7j6fVwpVSQHeUN0ZccKgMCnn6aeYRCRk0PYli34jR5N5rhxnBw8GKMaL3Ha3/zLfNww2LdvH7NmzWLs2LE8//zzfPfdd/zxx/k177MCE4EUJFAEnveIXccX6SRyDJgMpLt0NOp8DKBkFjsU+V7MQmayc6m+jWesSPlJOiW7vNYDpiA7v7atpuPUJk2aNMFisZCZmUlKSkqFHnPllVfSpUsX8vLytPd1LaThWik3kYd7hGs7k58fXu3bE/zWW/jffz/58+aRU00r4E3Iaz11e4bU1FS+//57Hn74YSZOnMi8efM4ceIEderUYeDAgfzrX/86r2O9j2y5noV0cHCXr29lhSDlBAfQfsSeKhSZxV4OjKdkh84CwyBt0iTSX3qJHGRDoYqEbQMJ03lIoD5ZdDOQnukXIKH6H+AhdLb6fHl7e9O4cWNAaq8N49zXG0wmExMmTMBsNvPnn3+ydu1aRw9TuRFTRb5Jyn2gybStXbt27bZt21bNQ1KqdnoXeBS5VOyIfrMpJhP4+lKvAjuOna7g779Jv/BCvPv0IXTp0iqPJQsJuROA5rNns2TJEhITE/H19cXLywtvb2969OhB37596dy583nXGacjC7f2I2HD0xvX5SEdT6KQxXKdXDoaVVWZwNfAmzt3svHKKzGFh+O7aROFSGg2ISVUJkqfFBpI+LZ32/EqulmQPvKXAaOBK9FAXV2sVitr1qyhoKCAFi1aEB0dXaHHzZw5k6+++opmzZrx2Wef6WJkDxIbG8v27du3G4YRW9nH6s+dUm7Cj5IZXXdjLur3aqvgJdFzMZDLZsc3beKDJ58s/nhQUBCNGzcmNjYWk8nEoUOHMJlMNGvWjLCwMEymys07f4tss27CM8tBTucL+COh7HM0XHu6IGAU4LtxI48A0W3b0gNpybkHikO2QenfCybk58eC/N6IRb4XOgF9gGZOGn9tYrFYiImJYffu3Rw4cID69evjU4HWpGPGjGHhwoUcOHCAuXPnMnz4cCeMVrmahmul3IQ9XLujgiVLALC0aFFtz2kCGrdrR9++fdmwYQO5ublYrVbMZjMJCQkkJCSwYkXJ/oTBwcE0a9aMpk2bFv8ZHR19xpkgGxJAM5ArAe76ta2sYKR+fB7wJFJqoDzbxg0b8AZu7tqVx4o+loX0gt9d9PeEvXvxCgmhbkQEvkiZUHugDTJbrRyvYcOGJCUlkZGRwb59+2jb9twV7MHBwfzf//0f06ZN4/3332fAgAHFm9OomkvDtVJuIgqZjXL2xioAtqNHyf30U/zuugtzaGipz+X//juZEycC4DdqVLUcz4rsDtfE25tHP/+cVatW8cEHH5CWlkZ+fj6xsbHExsZy+PBhDh48SGJiIhkZGWzZsoUtW7YUP4/FYqFRo0bExMTQpk0b2rRpQ+PGjTGbzaxAthDPp2RDlprAB5mxTEc2KrnbpaNR1WHjxo0AdOnSpfhjgchujxcBO3fu5PrrryciIoIlS5ZoO0YXatWqFRs2bODIkSNERUVRp06dcz5m4MCBfPfdd2zbto0333yTKVOmOH6gyqW05lopN5EOtEY6QoRR9dXGefPnkz1tWvG/C9esAZMJrwsvLP5YwKRJ+A4ahPXAAY7HxIC/P97du2OOjsbIysK6ezfWnTsB8H/4YYJmzqziqOTy9nHkNS6npHtBRkYGs2bNYknRLHmDBg0YN24c7du3Jz8/n4SEBA4cOFB8O3jwIFlZWWWe39/fn9atW7OkTRvW9+6NOTqasCqP+sxyV68m/aWXyF2xAuuxY5iDg/Ht0oU648YRdNNNDjlmJjKb2R1YhK5M92SZmZm0a9cOwzDYsGED9eqVPhU0DINHH32UH374AT8/P2bPnk3Xrl1dNFoFsGvXLpKTkwkKCqJr164VKlfbsWMHo0aNwjAM3n33XTp37uz4gaoq0ZprpWqAUGQb4nSk1rKqG40bKSkSqEt90Cj1MaOohtpcvz6BL75IwV9/UbhtGwXr1oHNhrlhQ3yHDsXvnnvw6deviiMS9oVaAUjfX7vg4GDuv/9+Lr74Yt59912OHDnClClTuPLKKxk5ciTNmzenefPmp7wUg2PHjnHw4EH27NnD7t272b17Nzk5OazdtInVmzaR89tvRH/4YbWMuzyZX3/NkaFDwWbDt3t3/Pv1ozAxkZy//iLnzz/Je+wx6r7wQrUfNwCpJd8HrEIWbSrPtGnTJgzDIDo6ukywBli7di07i05wfXx8+PPPPzVcu1jz5s1JTU0lMzOTpKQkoqKizvmYCy64gMGDB/PDDz/w0ksvMXv2bL0CUYNpuFbKjXQCtlE9pSF+d96J3513Vui+poAAAh59FB59tBqOfHb27d3bU/4voK5du/Lqq6/y6aefsnDhQn7//XdCQkK49dZbSUxMJDIyEovFgslkIiIigoiICLp16waAzWbj0KFDfL5rF8v27MG7eXOH1aMahYWk3Hcf2Gw0+PJLgm69tfhzuatWkXjZZaS/+CJ1xozBuxpr1UFmqv2RVobr0HDtycorCbHLzc3lyy+/pKCggAYNGmC1Wtm6dSvJycnF23Ir5/P29qZZs2bExcWxf/9+6tWrV6EuIOPGjWPRokXExcXx/fffc5ODrmwp19OriUq5kU6UbA1eUxXk52M5coSOZylJ8/f3Z+zYsUydOpVevXrRtWtXVqxYwSOPPMLjjz9+xp3SzGYzzZo1w3fAAHz+7//wHzjQUS+D/J07saWk4N22balgDeDXqxf+AwaAYZC3fr1Dju+LtObb6pBnV86yYcMGoPxwPW/ePNLS0vDz8yt15Wbx4sVOHaMqKyoqisDAQAoLC9m/f3+FHhMaGsq9994LwPvvv092drYjh6hcSMO1Um6kM7Ly35HbIruSAeT9978UPPAAq++6ixkzZvDtt9+yZcsWcnJyytw/NjaWf//737Ru3ZpmzZoRHBzMoUOHeOqpp/jiiy8oKCgo9zhbkYWMVS2tORuTr2+F7mcOD3fI8X2Q17iVmvm9UhsYhlE8c316qceRI0f49ddfAQneFouFnj17ArBs2TLyq3G3VFV5JpOJVq1aARR3EKmIG264gSZNmpCens6cOXMcOUTlQhqulXIjPYEGyA9mTXzrLARo1w4vX1/CsrL4559/mDt3LtOnT2fUqFFMmDCBN954g59++olNmzaRnp5e/Njo6Gj++9//0rt3b2w2Gz/88AMTJ05k165dpY5hIIEzD8eGa+/mzfFq3pyCnTvJnDu31OdyV60i57ff8IqJwf/SSx1zfOTrebTopjxPYmIiqampeHl50b59+1KfmzNnDoWFhXTo0IGQkBAAOnToQL169cjOzmb16tWuGLI6RUhICPXr1wcgLi6uQo+xWCzFs9efffYZaWlpDhufch2tuVbKjfgAI4AXkHrais2Neo5cwH/wYO649loePXSIXbt2sWfPHnbt2kVKSgrx8fHEx8ezfPny4seEhITQtGlTmjRpQtOmTbnhhhvo2bMns2bNIjExkWeeeYYBAwYwbNgw/Pz8OIx0I7G3+3MUk8VC/VmzSP7Xvzhy662kv/QS3i1aUJiURO7y5fheeCENPv0UUwU2mjgfZiRg22evGzjkKMqRdu/eDUCLFi3wPeVKyD///MOmTZuwWCwMHz6c9957D5Cygssuu4yvvvqKRYsW0adPn0pvrKSqV4sWLTh27BgnT56scC18//79adu2LTt37mTWrFk8/PDDThipciYN10q5mTuA14BsZGaypvyQ2pAgGAbcXbTbWUxMDFdffTUAaWlp7N+/v7jN3qFDh0hKSuLEiRNs3ryZzZs3Fz+XxWKhXr16FBQUcPjwYWbNmsVvv/3GfffdR0GfPhQgwdPRscO/Tx+iliwh+YYbyFu3jrx16wAwBQcTcMUVWCrQRaAq7OF6F3C5Q4+kHME+22kvL7CbN28eAAMGDCAyMpITJ04AcqLZqlUrvv32Ww4ePMi+fftoUc2LZVXl+Pj40LRpU/bt28e+ffuIiIjAy+vsv7XNZjP33Xcf999/P99++y3Dhg3TBao1TE1531aqxogGBiBbd+dRc35I7WUanYGyS7cgLCyMsLCwUrWneXl5xMfHF4ftAwcOcOjQIbKzs0lOTsbLywtvb2/279/P/v37WblyJWM+/hjb5Zc7peYt44svSBk1Ct+LLqLBl1/iExtLYWIiJ15+mbTp08letIhGS5ZgqkAngfNhQU5adFmUZ9q7dy9AmYDcpUsXQkNDue6667DZbKXCdXBwML169WLZsmUcOnRIw7UbiI6OJjk5mezsbPbu3UubNm3O+ZgePXrQrVs31q9fz/vvv8+kSZOcMFLlLDXlfVupGmUUMB/pee2P5y+OMJCSkCDktVV0RtnX15eWLVvSsmVJR2zDMNi1axcLFixg+fLlFBQUEB4eTm5uLr6+vgQ2aIBRiWOcr/w9ezg6ciSWBg1oOH8+5sBAAHxataLeu+9SmJRE9s8/k/Hxx9QZO9YhYzBRtEjUIc+uHM0+c33q9zfAoEGDiv+ekpJCYWEhXl5ehIXJdkgjRoygffv2xS0olWuZTCbatGnDxo0bSU5Opl69eoSfYyGzyWTivvvuY/To0fzyyy+MGDGCmJgYJ41YOZqnv2crVSP1BToCfshOfJ4uG5llbQIMOc/nyMnJ4a+//mLatGlMmTKF1atX4+XlRatWrbjzzjuZM2cOGzZsILJ9e6eE68wvv4SCAgKuvro4WJ8q6JZbZNx//eXgkdTMxa+1gT1cn232OSkpCaC4vzvISWfPnj3PWX6gnKdOnTpER0cDsoNjYeG5G6rGxsbSt29fbDYb7777rqOHqJxIfzKVckNm4HXgKiAVmZn01MWNBcj4Q4GZyEx8Ze3bt4+pU6eSm5tb/LF27drRp08fLrroIgICAoo/bq+1dnR7OmtCAgDmOnXK/bz949bjxx08Escu3FSOkZaWxrFjx4CKheuGDRs6ZVzq/MXExHDs2DFycnIqXB5y7733smzZMhYvXsy2bduIja30TtvKDenMtVJuqgPwMFJKkYXU1noaA8gEAoHhnP+iu9zcXPLy8mjUqBFDhw7lzTff5JlnnuGyyy4rFaxBTkKcEa4tRQuQ7IsYT5f3998AeDdr5rAx2GfoPfXEqzaz11s3atSozPfwqRITEwEN157AbDYXB+rk5GSOV+DEunnz5gws2uzqnXfecej4lPNouFbKjT2E7Nroi2eWh9jLQZoBz1bhedq1a8dnn33Gf//7X2644Qbq1at3xvsGIb/YHH0yEjh4MAC5S5dy4rQ3xdzVq0l/5RW5nwO3OLYir7VsUYpyd2dazHg6nbn2LCEhIcXlIbt3765QeciYMWPw9vbm77//Zu3atY4eonICDddKuTEf4A2gDtKWr+wehu4rr+gWhJSDhFTx+by8vCrU07cNztnl0rdrV0ImTAAgdfx4DrVvT/Itt3D4kks4fPHFGFlZ1Bk7loArrnDYGOy7UJ774rNyN2dazHg6DdeeJyYmBn9/f/Ly8opPouzsV+FOFRkZyZAhshrl7bffxjB0z1VPp+FaKTfXAZgE1MnNJev998lav97VQzqnfGSmvQ5wL87twRwFhCMLShy90C/ipZdo8N13+F91FdbkZLK+/5787dvx69uX+p9/Tj0HLlKyISdcPkD7c9xXuZ+KhOvMzMzibbW1D7LnOFN5iGEYrFu3jg0bNpQJ0KNGjcLf358dO3bwlxMWQSvH0nCtlAe4u6CA1o8/jvnDD8n59FNyz/0Ql8lH6qyDgaFUrRzkfJiQsOmDc7poBN1wA1G//UZMaiotCgqIOX6cRn/+SfBttzn0uAXICUQDoL5Dj6QcoSKdQpKTkwEIDw/Hz8/PKeNS1SMkJOT/2TvP8Kiqrg3fU9IrJJTQO0oHRYqIvSB27FhQsDdEVPAFEVDs2FFURN9PURERC+gLYgEBaQKK9E4IIb1nJpmZ8/1YczIJCSHJ9GTf1zVXYDJzzp7JKc9e+1lr0bJlS8BlDzEYDGiaRklJCcXFFdchGzVqxI033gjA3LlzVfQ6yFHiWqEIcBwOB89On472558khYURM3o0RQSmRcSKS1hfiXSa9MdFxpfi2l/oTXlU1Dr4KCkp4dChQ0D1kWuVzBjclLeH7Nu3D4AoZ9nOwsLKWTQ33ngjERER7Ny5kzVr1vh0rArPUq/FdSGwAfg/4DXgeWAqEkl7EXgdmA9sRTVhUAQmmqYxc+ZMli5ditlsZv6LL/JYz57EI01Z8gmMKiJ6VRDdCnIz8CHiffYHPan/4roESXRV4jr4OHDgAA6Hg5iYmGqTc5XfOrgxmUxl9pCjR4+SnZ1drbiOj48v817PmTNHRa+DmHpV5/ow8Cuwzfk4hIhmC+JNdOBKcDIiy8ehSKOOcKAj0A3ojnhEq++vpFB4nw8//JAFCxZgMBiYMmUKgwYOZBBybE5HxHUOkjTor1rHpYiwNgONgPuAZ/DvzL0fEInUCC/FfyLfWziQlYt4oL9/h6KoA+X91tUl6SpxHZzYbDYKCwuJjY0ts4ccOXKEHTt2lP0tqxLXACNHjuSrr75i69atbNiwgf79XWd4PrDd+dgG7ARyEZ1TglRm0vVMO+BURNN0A1ri/cZaChdBL67twErgS2AVcpMvxlWpwIREd0KRA0u/4WvO9+YCx5zPHXBuIwK5aV0E3IB0ylMHpcLXzJ8/nzlz5gDw+OOPc+GFF5b97kFgMPAw8C9y3FuRkmy+ErUaEqkuce63A7IadLaP9l8dTYALkOtCPvVvolyIXNc6AQP8PBZF7altpZAWLVp4fUwKz7Fr1y7S09OJjIykbdu2tGvXjuzsbIqKisqsPgUFBVW+NyEhgauuuor58+fz0UcfEd+/P18Ba5CAYQkSMLQg13wbci0+PnC4CfgRl9iOR1b0rgTOpf4FHAKNoBXXWcBCYAFwEIne5SHCOAKJ5IVRsw+oIQeofrBmIoI7FfgOmfVdDwynbt3lFIra8ssvvzBz5kwA7rnnnrKlwvL0A5YDLwFv44pihyEXU5OXxubAdXEPQaLVtyHR6hgv7bMujASWAEeRG0t98cDpFpw45DPWl8/VkKhJMqPNZiMtLQ1Qketgo1mzZmVievv27URGRpKYmMjhw4cpLi4mJyeHxMRE7HZ7WUv78txw663MWbiQBX/9xc9//421Vy8KkGuuGbm+hyE6x4yIaT0A6HA+9ABjvvOnGdiF3DNaAdcAI5CEaIXnCTpxrQHfAK8i4jcbmcnFIcsgdZmNGZzvC0HEQSJyEOcA+xGhvRnxkE4BBtV9+ArFSdm6dSvPPPMMANdddx2jRo064WvDgEcLCohYtoyFl1/OTrOZYuTYDUEuwno7cHcoPwHVayvHAm2RfIZz3Ny+NxgEdEYm4oUElvB3hxLk5tkYuUEqgo+aRK5TU1PRNI2IiAji4tytEq/wJQkJCQwYMIAjR46QnJxMUVERhw4doqCgoGzSFBUVVWYd0SkG5gILmjYl46qryFmwgPzDh4nt1YvGSHCvpkGT8oFADVey+RFE0+wFPkCi2PcCXdz90IoKBFXQIxXxc05C/EZZSESqA7IM7MlljnCguXPbkc59/wXcjSREBmO3PEXgc+TIEcaPH09JSQlDhgzh0UcfPaEnU9M0Vq5cyeDBg5kxejSXvPgi/wdcigivUOQ4zYGyqEdNG6uUF9MF5bZhdm77XOB94E8CU1iDXNxuRkR1Ad5vh+4r8pGI1eW435hH4X2ys7N5+umn2b59OyDnrd5YRBfXNputUvJaeb91TZonKQILs9lM27ZtGTBgAG3btsVkMhEdHU1WVhYlJSUcPHiwrIY5wEZkhXwmYukoevRRGr33Hh2HDaMFcs7XdTXSgGiaRETTNEZW+ncjRR1uBGYj13yFZwiKyHX5aPVhRFQ3RpajvX3JMTn3EwekI5HsT4DVyDL4QC/vX9FwyM/PZ9y4ceTk5NClSxemT5+O0Vj1/Pfo0aO89dZbLF26lOTkZACaJiRwKSKu9yDH6efI+VLqfBQjUU+T83H8+aPnIuhttc3ORxgSqb4WGIV494KBa5CbVTYy0Yj273DcRk/QboxYQhSBz4oVK5g7dy5//fUXP/zwQ5ldAKBNmzZkZGRw+eWX065dOz7//POy96lkxvqB2WymXbt2tGrViuTkZDRNY9u2bVgsFvbt20fjli15C6lqloZco5sA0WYzhr59PT4eAxJwiEGuJxlIFHsm8AuSKK+i2O4T8OLaCkwEfkK8kwagDb6vjGBEvEkxSBQ7H7gLiWQ/iEp4VLhHaWkpEyZM4ODBgzRt2pSZM2cSEVHZ4V9aWsoXX3zBvHnzKC4uJjk5GaPRSGJiIt26dSt7XSfkIjkR+A2xNW1xPjKRCIXd+Vo9XqYfw+HIhSEO6I0k9PYCziP4IqVxwFikBGcars8WjDiQv10jZJKjSvAFB2eeeSYhISFs2bKFLVu2lDWDady4MSEhIbzwwgskJydXSlpUyYz1C11kt2zZkkaNGrFp0ybSmzbleqT6xzEkMbwt3suXOZ4wpKNtPmIXyUei2PcAY3w4jvpIQN9nCoFHEHGQiu+i1dURiXi705HqIrOQiiNPEWQeG0XAoGkaL7zwAhs3biQyMpKZM2eSmJhY6XUbN27kjTfe4MiRI4C02O3YsSMpKSkkJSXRuHHlmhiRUBbNBhHSR4C/gWRciYkarqzyZkAf5CJfHyaNo5DJ+a9IFL8Jwfm5cpELdnvgP34ei6LmJCYmctlll/HNN9/w3//+l6uuuqrs+U2bNvHll18C8NRTT1V4n2ogUz8JCQmhd+/epPTuzVNAClCE2FCj/DAeA7IqGYnLi/0aIvhfRAS4ovYErLguREz2q5CIUwvkjx8I6FHsMMSm8imylDMdJbAVtWfu3LksXrwYo9HIjBkzKiU5FRUV8eqrr/Lbb78BEvG6+OKLmTdvHmFhYbRs2RKj0UhCQsJJ92VAMsVbef5jBCwm5CZxGTIhDkZ7iBUZdxLwHMG3gtDQuf322/nmm2/49ttv6d27NwBNmjRh8uTJgCQun3baaWWvt9lsZZPoVq0a0tnaMJgPPIvohxAkYOfvKLEZVxQ7GViMiP7X8I/oD3YCUgtakYj1KiRC3IrAEdbliUdEdjLwNfAC9SdpSuEb/ve///H+++8DUst64MDKLv4lS5bw22+/YTAYuOaaa5g9ezYrV67EYDBw0UUXERYmsYVGjRr5dOzBRAfgMSShJwfxnwcL5e0g1yHJpIrgol+/fnTv3h2r1crSpUsByMnJYcuWLURHRzNx4sQKrz948CA2m+2kHRwVwcfXSFGEQ8hKYRL+F9Y6ehS7BWLDXY70UlAdrGtPwIlrDZdPNA3pKhTuzwGdhBigKSKwP0UqKCgUNWHz5s08++yzgHTluvrqq6t83bnnnsvIkSN57733eOCBB/j8889JTk4uazYAEBkZWeblVFTNKCQBOQ65tgRDZrwDGWsYyg4SzBgMBm677TYAVq5cicPhYPPmzQCMGzeukoDet28fAB06dFCVQuoR/0PyP5KRaHBTAtOiFolor2PA78DjBMf1MpAIOHG9CPFHpiKzp2CQC7FIRCwZeBfY6t/hKIKAQ4cO8cQTT1BaWsq5557LAw88cMLXJiQkcOedd9KpUyc2btzIwoULARg/fjxWq8QUqvJbKypiQnIkeiI3tjRcSZ2BiAPJ5DchwvojlB0kmLnqqquIiYkhKyurrBxb586dueOOOyq9Vi/VV12TGUVwsR0JHCYjNagTCUxhrROBaLBURJO95t/hBB0BJa5TgVdwJS8GohXkRMQjB+MxYDJqGUVxYnJzcxk3bhx5eXl069aNKVOmnLDkXnny8vJ48cUXAbjyyis544wzyMrKApS4rimJwH+BU3Cdr4EYkXEgljgD0BqYg4xZEbxERkZy/fXX43A4KC0txWg0Mm3aNMzmyqlPSlzXL0qBpxGrhYnAjVgfTySSaHkUKRX4l3+HE1QEjLjWEB/SYef/g9E92hRpVrEVeM/PY1EEJg6Hg8mTJ5OcnExSUhKvvPJKjewcmqbx2muvkZmZSevWrbnnnnsAlLiuAy2Aebgi2McIrMmwDRmTCUl0mgt4vtqtwh/ceuut2O2yXjJ48GCGDBlS6TX5+fmkp6cD0L59e5+OT+EdPkDKoBYgeVrBIKx1onFdJ6cgxRsUJydgxPUixGedhcyUgung0zEhJ84x5Ib4j3+HowhAPvjgA9atW0d4eDivvPJKjUXxsmXLWLFiBSaTiaeeeqosibFLly7ExsYyePBgbw673tEG+BLoh6w6pSGNZvyZkKwhmfpHkYhRZ+AzoL8fx6TwLB07dqRfv35ERkby0ksvVfkaPWrdokULIiODaf1WURXbkVysVCQAF7Al2qqhCVI5ZDvwlp/HEiwExN85m4p2EF83iPEk0cgNMhXp4PgVATSDUfiVP/74g7lz5wIwceLEGi/5pqam8tZbckkbNWoUXbq4+mf17NmThQsXqqSnOtAMKYk1DViIVOQ4CiTg+9quNuf+Hc5xnQm8hFhCFMFJKXI8HUFqGRcjf+c7V6/GoGmsNxjYjAiXls5HDBWTGRXBjW4HOYbkj8X4dzh1xoQEPVMRe8j5wGnVvkMREOL6G+SP5iA47SDH0xRpk74d+BNQMUVFcnIyzzzzDCA1bS+++OIavc/hcPD8889TVFREjx49uPHGGyu9RgnruhMHvApcguRKHECi2NFIorK3S2Q5kKXiXOf+mgFPALegJuXBhg2xBO5BBPUxpDZ5PvI3LkH+3g7knDUhx1ckIrqikfvflr17yQGaKb910PMN0rArH7F4BTNRzkc6EgydR3A6DHyF38W1A4nu5uD/7ouewoTcKHOQyJgS1w0bi8XChAkTKCgooGfPnjz88MM1fu8XX3zB1q1biYyMZOLEiTVKfFTUngsR+8U05IaYjUQbIxDRE4Znr00liOAqdG5bj1a/iFhWFMFDNrAOWI8cM+nI3zYfuRdEI+JZn6wZEAuQAxHk+c73WZDj7VBGBg7gx86dyUXKR3akftwbGxIaYj3LQlbD/C62PEATYB9ied2K5K0oqsbvf+8/gINIQlGwLplURRySnPk7sjSoGtg2TPTW5nv27KFRo0bMmDGDkJCQGr139+7dfPzxxwA8+OCDNG/e3IsjVcQDM5FOju8CGxGRlIkImxhkaddM7YWOhpT9szq3WYpEgZKALsCdwPWoaHUwsRtYDWxDVl5TkL9rM6TxWW0nZXZEaEfcdhvp2dlsbdWKdGADYg8aiEwAg9k22ZD4C9iBq7V5fUCfLOYigUMlrk+M38X1fGTmH0f9urGEOR85SEemB/06GoW/WLhwIT/99BNGo5Hnnnuuxt3WrFYrM2bMwG63M3ToUC666CIvj1Shc57zsQ1JKPwOiT7p9g0NETj6IwSngCopwRAaiuZ8jQ2JUOsP/X16JPMiYCQimlRUMngoAL5FRO9hxP4Rgyz7J1D3v6UJmeDFd+8OyKpGCjLJ2w3sRLoWX4t0HFUENvOR+78v7GW+JB45Ln9Cut7G+3MwAYxfxXUysBLIA9q6ua2ijRvJX7aM4nXrKFy7FltKCoawMHpZLFW+fksNfKrR555Lx19+qfOY4pGb8tfA3aiIQ0Nj69atvPaalN5/4IEH6NevX43f+/7773Po0CESEhJ49NFHla/aD3QDngOeRKwi3yN5FEVIBLoEEUA2oHTJEuwTJ2J69VVMF1yAAbm4hiIR6sbIZLszcAFwI/UnmtWQ+BsR1ruRvJpmSMUZb9T0iEKOl/ZIHsBuRMinAWcBw1D3lEAlA1iGTMZb+XksnkZfvctCzoXb/TucgMWv4noFsgwWjvsXiWPTp5P37bc1fn2j2098SOQtXow9I4Oos85ya0zRyIXwGOJRUtm1DYfs7GwmTpyIzWbj3HPP5eabb67xe3///XcWLVoEwOOPP05sbKyXRqmoCbHIDeR2REjvQfyG+iMZOLx9Oxk2G7GbN9PiggsIRxKbe5R7sGMHV196KU3POYdH5swBNWEKGspHq3cg9o/e+MbKaEZqszdF/K7rkIjoTlQUO1D5DvkbheJ+l+naBg4BSpKTSXv2WfKWLMF27BimhARiLrqI5s88Q2i7dm6Nx4AEDnOABShxfSL8Kq634UricJeoQYOI6N2byP79iejfn20n8ae2cXpZj8eek0POF18A0OiWW9wakwE5sYqRiJcS1w0Du93OpEmTSE9Pp23btkyePLnGkedDhw6V1b+94YYb6N9fVTkOJMxIp8RTEGGj8054OLOAmywWJp3gvS9//z2ZmZl89913fP3111x77bUneKUikEhFOmTuRKLVrZCkU1/bGM2IP7+JcyzpSPDmCmCoj8eiqJ6NyKqWJyZftQ0cFm/dyr7zzsOWnk5o+/bEXnYZ1j17yP7kE3IXLaLTypVE9HTPLR2DBA0PIsdhzcyODYuAENee6C3X9MknPbAVyPnqKzSrlciBAwnr3Nnt7YUjS8jb3N6SIliYPXs2GzduJCIighdffLHGjSCKioqYMmUKFouFPn36MHr0aC+PVOEp9C6bxcUn7l+WkZGBwWCgtLSU1157jVatWjFw4EBfDVFRBw4BHyF2kFx8F62ujkbA6cBeJGnOjgRwLkJ59wMBDZe2iffA9moTONQ0jUMjR2JLT6fxnXfSavZsDGaReemvvUbKuHEcuukmuvz9NwY3Kk8Zkai8rm3OrvOW6i9+yyEsRmrKWnF/2cSTZH/6KQCNbr3VI9sLQ06y7R7ZmiLQWb9+Pf/9738BmDRpEu1quASnaRovv/wyhw4dIjExkcmTJ2My1ac0mPpNRISsv1lOsFSraRrbtm0jPDycyMhICgoKmDp1KgcPHvTlMBW14BDStvovxGffD/8Lax0z0BXx7W8CFgNL8G+HUYVwDKkwVIpnmlE1ffJJmk+dSuxllxHSrFm1ry1ctQrL339jatSIFq+/XiasAZo8+igR/ftj+fdf8n74we1x6dpGBQ6rxm/ieifyhzESACVLnJQcOkThypUYQkKIv+EGj2xTj1wfQJaJFPWX/Px8pk2bBsDVV1/N+eefX+P3LliwgBUrVmA2m5kyZQrx8fFeGqXCG5wscn348GFyc3OJjY2lQ4cOxMfHU1hYyIQJE8jLy/PlUBU14CgSsd6C+Ox7ETj3qfK0RSwqW4ClwK/+HY4CV9Q6FN8LrOKNGwGIOP10TDGVp4LRZ0uMuTY2kxMRjhLX1eE3cb0d+cMEVNT6s89A04gZNgxzQoJHtmlGvmQLMqFQ1E80TePFF18kPT2d1q1b16pRzJYtW3j//fcBuP/+++nWrZu3hqnwErq4PlHkesuWLQCceuqpmEwmOnXqRPPmzTly5AiTJ0+mtLTUZ2NVVE8+4rHeglSE6UFgl1JriQjsv4EfEL+vwn/o4tof2sZRKCE8U6Oqe12bGosJt9h5PXIHXVyrVfmq8Zu4zkCWTWrWTsM3eNoSohOCRD8yPbpVRSCxdOlSfv75Z4xGI1OnTi2zCZyMjIwMpk2bhsPh4IILLuCKK67w8kgV3uBktpC///4bgDPPPBOAAwcO8OyzzxIREcGmTZt444030DS1qO9vNKTs4k5EZPcksIW1TiukLOBWXJUqFP5hD7Ja7QlLSG0xO/solJ7AbqY/X3LggNv7CkM0XDrSq0RREb+JaytyIQuUBIyiv/7Cum0bpvh4Yi+/3KPbNiKf1erRrSoChdTUVF5++WUARo8eXePIs81mY9q0aeTk5NChQwfGjRun6lkHKbq4Liys2vylR67POeccYmNjKS0txW63M2XKFAwGA9999x1Llizx2XgVVbMZifweAE4lMK0gJ6Idcq/ZAyxE+a/9RQHS2t4fk7KooVI3pmj9eizbKho27AUF5CxYAIAjP9/tfRkR/eZAWV6rwm/iWu9YFihSQo9ax113HcYwz8459QNQiev6h8PhYOrUqRQUFNCjRw9GjRpV4/fOnj2bf//9l6ioKJ555hnCPHzcKXxHXFwcALm5uZV+l56eTmpqKkajkR49enDKKacA0t5+8ODB3H///QBs364WWP1JPhL13Ym0G4/273BqjQFJcjyITBI2+HU0DRdd2/hDXIV37UrciBHgcLD/yisp+PVX7AUFFG/ezP7hw7Hr1yc3KoWUR9c2JR7ZWv0imCbmXkOz2z1W2/pEGAiciYSi9miaRkpKCklJSRjLXZjmzZvHpk2biIiIYOrUqTWu8LF8+XIWLlwIwIQJE2jZsqVXxq3wDXoCak5ODpqmVViB0C0hnTp1IjIykg4dOrBu3Tr27dsHSD3zgQMH0vwktfkV3kO3g+xBxEIb/w6nzkQiEewdiP+6M6o9ta8pxb+Bw9YffogtM5PC335j73nnlT1vjIwkacYMjj7xxAk92bXFgHxWJa4r47fIdTguu4S/KVi+HNvRo4S0bet2V8aqcOBqKKMITv744w9GjBjBnDlzyp7btWsX7733HgCPPvpojQXy/v37efXVVwEYOXIkgwcP9vyAFT6lkfNmVVpaWqliSFRUFCCWEICOHTsCsHfv3rLXtG3bVq1c+JHtSMm9A0j0N5gDIa1w2UPcL7imqC0huESnPzDFx9Pxl19o/+OPNHn8cRrffTfNZ8yg67ZthPfoAUB49+4e2Zc+iXC3w3Z9xG+R61BcSwr+piyR8ZZbvOJ5VQdg8KMv2WdnS+qG1WplypQp2Gw2zj77bC6voU8/MzOTp556CqvVymmnnVYrG4kicAkPDycsLAyr1Up2dnaFxkEDBw7k22+/LbOOlBfXx0e5Ff5hNVLXOongs4McjwHp5LgJ+AdJNvNMnFJREwJB2xgMBmIvuYTYSy6p8HzGm28CEO2c6LuLbn9R2qYyfotct0T+IP5eTnAUFZH7zTeAdywh+pJJKPKZFcFJRkYGAAnOEo2zZs1i//79NG7cmIkTJ9ZIIFksFiZPnkxaWhotW7Zk0qRJFSwmiuCmvDXkeBo1alT2t27bti0mk4mCggLS09N9OEJFVaQhPus06s81OgpXi+p1fh5LQyMKEVaBEDgsjy0ri6xPPsEQGkqj2293e3sOXOK6Zj2IGxZ+i1x3w9XhxxP+pLzFizk2fXqF57SSEnaXay/cbPJkYocPr/Ca3EWLcBQUENG/P+HORCNPolevjQQ6eXzrCl+RlZUFQGJiIuvWrePLL78EpAtjTRq+OBwOZsyYwc6dO4mLi+P5558nNjbWm0NW+Ji4uDiOHTtWZVJjeUJCQmjTpg379+9n3759NG3a1EcjVFTFWqRpTCPql3WvBZLcuB44H5Vg5Ss64dI2/sC6axfm5s0xlbu/lKalcfD667FnZtLsmWcIbdXK/f0gFpgEPLcyUohMdLOd27ciVVfCnI82uKriBDp+O986IjM8AyJA3V1WsKWnU7R2bcUnNa3Cc7YqokTlLSHeQG/v3pnAqumtqB165DoqKornnnsOgBEjRjB48GDWrFnDF198wbXXXstZJ/Dsz549m1WrVhESEsK0adNUAmM9RPdd69ah6ujYsSP79+9n7969DCwXAFD4lhKkqsZR6l/wIxHxXR9F6l/38etoGg6nIvd894vdCbUNHGbPm0faSy8RefrphLRsiS0ri8KVK9GKi2k0ahTNJk/2yLj0RjndqFtw1Iocl9vKPQ46n7chQVfdt66X/QtFVmS6At2R77oHIrgDDb+JazPiC9uHq1WoOzQeNYrGdfCvdvBybVkLMuNSPfeCm8xMaQG0dOlSjh07RosWLbjuuuuYMGECv/32GwDt2rWrUlx/++23LHDWF50wYQI9nEklivpFdbaQ4+nYsSM///xzhaRGhe/ZDKQiN3Fv+ZJTN25k/7JlHF23jpS1aylIScEUFsbjJ2g45CkMiIf8CPAnSlz7im6I6LQi1gl3o6y1DRxGn3cexZs3U7xxI0Vr12KMjibqrLNIvPde4q6+2s3RuNADh6fW8n2Hga+ARUgDGovzUT5SbUaOX73ohYar5J+erLsc0VYRQC/gBuBi/NO8pyr8ulLUDfiN+l3/2YKUQlLiOnhxOBxkZWVRVFTEL7/8gslkok+fPtxxxx0UFxdjNBq56aabGDNmTKX3/vnnn7z11luANJg5x0OJJIrAo7biGlDi2s/sQm7wzfFehZBV06ez+9tvvbT16klCbC8H8F/XwIZGEjJRC0HEoLtWo9oGDqOHDiXa2UzGm1iAOGqmbRzASuBL4A8gD+kiake+n3AkkTiM6kWpnsNmde4/y/nvY8gK1CvAVcB1SK16f+JXcd0dmXWkI0tY9S1n3o4cABHIZ1V4Hw1ZBv0bWXI6jOtE1HB5t5oiy0k9gA5U300rLy8Pm83GkSNHyupc6930evfuzRNPPFEmlsqzZ88epk+fjqZpDBs2jJtuusljn1MReOi2kNqI60OHDlFaWkpIiDKN+YMjyPJ9My/uo+WgQTTt3Zuk/v1J6t+ft3xYzzwMEXmFQArQ3md7brgYEMG5HZd1or6hR5Frsiq/AZiKRJtzEGEdgWg+3RpcUwy47uG6o9wG5CL3/WNI1Z+PgSuAx5EJgD/wq7g+F2iMfCG6CK1P6AdRZ8QCo/AOu5HOan8jpaeykBO/hMreLb2ZjwmxIoUgHq5uQE/gAmAQFU/4jIwM0tPTsVqtJCcn07lzZ+Lj43nooYcYNmxYlRU/0tPTeeqpp7BYLPTr14+xY8eqkmv1HL3UXk3EdWJiIjExMeTn53Pw4EE6dapvjt/ApxC5VhTi3fJ7A5980otbPzkxSEvuIyhx7Sv6Af9DRF+8f4fiFfKRSUNLJFBVFUXAm8BnSCWeQkTotsWzpfvMSFJlY+c+coAM537/BCYB53hwf7UZl9+IBYYhM+oc6pe41pATqwniBVKyyrOUAkuB/wPWICey1fm8DRHN+qN8d0xdaNuRyU8prtnuL8AHSLLErcA1yI1p165dZSXTWrRowbXXXst99913wmofRUVF/Oc//yEzM5O2bdsyZcoUzGaVq1/fqY0txGAw0KFDB7Zs2cLevXuVuPYDRxDRGU79rqQRjYihZH8PpAFxBTALEZX10Y6Ti1hfRlC1ttkATEE6haYhx2B7ql8hdheDcz/RQDGSS5EPPAhcCTyBb6PYfr+mXA8sAPYjosjvA/IQxcjSSSJwmZ/HUp9IRwT1F7hujsXIxSscEcO6oK4JGnLc6ZHuY8hE71/gReSk7OOsUdyuXTs+/fRTevbsecLt2e12pk+fzt69e2nUqBEzZswgOjrY21IoakJtbCEg1hBdXCt8j24JifH3QLxMDFKF4Yi/B9KAaAachwjLHLxrO/IGmqaR9+23RPTrR2ibNhV+Z0GCUvHA8emRDuA1xJahOxKaI/YPXxKBRMgzkXyDeUjuwYvAaT4ag9+1bDegN3IQ5iLh/fpADnLwXUrwd/wKBDRgIeLdOobcFO3ISduMuh/IBlwR7ihkZluE/P2ykUh23KBBjD98mMejowmvpumLpmm8+eabrFu3jrCwMJ577jma+9BfqfAveuS6JqX4QCU1+psUZHLeEMR1ARKY0BuaKbzP9cBPiLhLxLtRW09j+ecfUp54gpAWLejw448YI1y+ghzkPnkRFfWaDZgMfINM5KIRgeuvz21EnAPRSBS7ELgPeBWoumCu5/fvd25AlhhyEMEU7Fhx+Yuu9/NY6gPHgDHAWGAvrmhTEvIde3KGaEROxmaIh8uKXCjejY3lSqORf6p579y5c/nhhx8wGAw89dRTdO3a1YMjUwQ6jRs3BqRso8Nx8v5sbdu2BSAlJcWr41JUTSGupKz6jC6mbUjgQOEbzkDyrSLwXM1rXxHWuTMhLVpQmpJCxqxZZc/bkc8ST0VtUwqMA75G7EdNkHtoIEwo9Ci2huiHh4FlPtiv3yPXILUJP0DEtV4WKVjRkFlSAmKiV/Kq7ujR6mmIwM1DRHUM3vewl89KLkKWl9YiZX7uQ07Q8hGgBQsW8NlnnwEwduxYhgwZ4uURKgKN5s2bYzKZKCkpISsri8TExJO+HuDYsWM4HI4qE2MV3sOGZ+oQBwMG5LPa/D2QBoQBKQm3DalkEUNgiM2aYIyIoNmkSSTffz9Zc+YQd/XVhHXoQAYSfOpqs/HQOedQUFBA/zPOYHvr1uxq3Ji8xo1p3Lgx5saNsTZujDkhAWNcnN+T+Y1Ix9JUxCL1BPAW4M27dECI6zDgGWAUsoRSiO89Op4iGzmp2iBZqoq6YQcmIN7qLERoN8U/XS4jkWM0BxH5M5GanR8jKy5Lly7l3XffBaSW9WWXKZd9Q8RkMtGsWTNSUlJITk4+qbhOTEzEbDZjs9nIyMhQbdB9TEMS1yaUuPYH1yI5ZXmI9TXJv8OpFdHnn0/0OedQ8NtvHJs6lYSPPybfYKA98HBpKbceOoTFYuHz777D3rYtpQkJhCEWpIJy2zE3aULLd97B5OfcIwMSuE1FChiMA97He82VAua6chpSoaEZYgMIRnuIFRGCzZH6isGWxBAoWIH7cZXwCcd/wlrHhKxGxCFR7NWInen71at5+eWXAbj22mtVLesGjt7WviZWD6PRSLNmcpVITU316rgUldFbKmsne2E9QMPV8U7hO8KQldfmSOJ9QfUvDygMBgPNJk/GGBZG4Zo1HFmyhGbAjcDZERFMmTIFU0wMFocDq8lE45tuIv7ii4ns35/Qjh0xxceDwYBWUgL2wFB0usA2IIHcCXjvbxIQkWudh5CIYD7BZw/RkElBYyRL+Ar/DidoKQXuRRJBspDIcCCVaIxERH4GsG7bNm6fNo3uDgeXXXQR9957r9+XvxT+pUWLFgAcOVKz2gzNmzfnyJEjHD16lF69enlzaIrjMCNi8+Tu+ODHjnzWgLrhNxB6Iqvy7yBR0wiCxx4S2ro1CffcQ+qbb+KYMYMOZ5/NWGcEevjttzNp/XrYvJmos86ixa23Vnq/5hTVBlPgfGIDYhE5gPTImAk87YX9BNRENgKxhzRFfK41y7n3P7qw1u0gT6PqWtcFO5K0+D9EWDcmsIS1TgiSsFG4ciX5paWkDB7M6PHjlbBWlEWuayOuQUWu/UEIcgMMjJia99Br+5vw7+pfQ+Y+pBtwFKIVAm21pDQ1lZz588n76SeK/vqLkuRkHFYrAIaBA7Hn5KDt3UvHN94gChn/i2YzPPgghsaNMWzdinXXrkrbNZhMASWsdYxI8PYY8BXSK8PTBNxE9jQkgv064osx4r/2lTVBQ6KYFqAdMJ0TdyxSVM9zwCLEdtGYwG4bawaa3nQTad26kTdwIPeYTHxJAJ5QCp9SW3GdlCQuzKNHj3ptTIqq0dsvF/p7IF6mEEm+jkSVhfUXuj3kViShLh0J0ARKOCb95ZfJ/f77Ss8bYmMpBQwFBWg2G/NefJHbzzuPnEGDWArk9+hBo/POw/LLL2S8/TYtXn8dQ5AkZkcijQxTkRK/C/Ds+RGQ38JdwB1Aa+QgzPXvcE6IhoyvABnrNKSlu6L2rAbmIBOVeAJbWOuExsbS7KyzyAsJ4U9gtr8HpPA7tfFcgytyrcS172mJq3uhN9mzeDH/HTiw7AFgLymp8NyexYu9tn+9lncLAkfMNUR6IhqhFTLhySRwItiNbr2V2GHDiOzXj9BWrTCGhqIBJXl5GPPyiIiPx2w0YrFYeO7ll3kBEaWNgGZjxmCMisK6Zw/5S5b494PUkkSkHOduJKDrSQIy0GYAHkMSAD5H6iY6ENEVCBeHgj/+oPivv7Ddcw8lYWG0Af6D8lnXlUKkNE42YgOJ9O9wakUIsrKShXSmuhDo4tcRKfxJec91TcrrKVuI/2iJiM58XAl/3qAoPZ2UtWsrPqlpFZ4rSk/30t5dfQFaem0PippyBfL3eB44jBx3ifhf10T06UPLN94o+3+RpnEkL49maWl0T0/njrQ0DuzcycaNGzl18mQ+RexUjQFDfDyNb7+djFmzyPrkE6KGDJFkxiDAiBSeOIqU/b0X+Xt4goAU1yAfejISWZiDCOxixHLh70HnrVxJzvbthPfvT4dBg7j8hx94/L77+PXSS5k+fboqqVVLXgD2IDPIYKywEokcm5nAeOQk9fcxqvAPeq3r0tLSGpXX020haWlp2O12TAHoT6yvNEXuL0bk/PXWpL7XqFH0GjXKS1s/OflIEw0lrgODkYiofgHRNXbkWAwUG0E+cMxgoHlcHGfFxfFW585lXUztSNfpbJzC2vl8zKWXkr90KdY9e8ieN4/E++/3w8jrhl6kIAe5d9/toe0Gyt+zSgzAo0hUswMiWA4gNSP9sZyiIQIqt1UrQoDm+/bxGlD4yy8cO3aMTz75hCFDhjBp0iQOHDjghxEGH6uB/yInayMC/IA8AQZk7EXARuA9/w5H4UdMJlNZNLom1pCEhATMZjN2u52MjAxvD09RDhNSd1iPXtdHNMQWEosS14HELcCzyKTHjviw/d09045EcNMRC9ElyL0sptxrViC5cCVU9CcbjEYSH3xQ2qTXoDttoBGPiOsFeC7BOeC1jAG4HWkmMgT5o2ciB4EvC+JbSkvZ88kn5G7ZQlLHjrQGrtm3j3OB66+/noiICBwOBykpKcyfP59LLrmEhx9+mH/+qbphtoY0JFmGeH3uReomXwUMAy4DRgA3AxOB/0OEW31KvrEDT+GygwSDz/pEmHDZQ15HIhKKhkltyvGVr3WtfNe+pzVyY83y8zi8RTZyXY3Bc8vdCs9wFZKn0w/526QgfR38IU0LkMClAQlk3o/cx46/J3+BiNA4KovHsC5daPP55yQ88IA3h+oVopEJw2Hgdw9tM2hWr7siTUXmIAfkMWA/MiOPR7JxPY2GLBfmAPnJyRiXLydixQrGPvAAfwEZBw9it9vp1asXzZo14/Dhw8THxxMTE0NBQQH/+9//+N///segQYO4++676TZoEN8bDKwEtiMXPgvSNMWCiE0HLv+f/tDbcIc7f7ZFkiMuBwbgf79WXfkFsYNYCa6a5iciEok+5CHH6pP+HY7CT7Rs2ZL169fXqmLIkSNHlO/aD/QDfgPWIjfXUL+OxvOkIAGpfgTvfaI+MxD4Gqm1/BWu9txNkEo23v6blSDBymLkHtwDSbrsWcVrDyAl6/KQymhVYQw9+RnkKCoif+lS8r7/nqL16yk5cADsdkI7dSJuxAiajBtXqZtjyeHD8vq1aylatw7rzp2gaXRas4YoZ5KwO+hV6XKAL5FeJe4SNOIaxBdzL1KR4xngb+TLOIxcFONxeejcwQ7klpSQ8f33GJo1o/HAgTRt145GvXqR9PffpC1YQHh4OBaLhSNHjtCmTRu6du2K1WrF4XBQUFDAxIkT2b59O99//z2/r1nD92vWYDr/fKJnzSIfEdM2XMI5Evlj6IIaRGQ7kBOgADkJ7MiyzAbEH9QJ6Zh0BRWXb4KB/0M+VyRBsIRSAwzI8ZeHnKBj8c6kTxHY1LXWtYpc+56WQEdkkn8UCVzUFyxIAOcURMQpApNopDfGBUhJuD2INSMN0TRxeLbpjIasgOcgojoOiVaPQupxn+ie9T/k3haBe/XSs+fNI/muuwAI796dmEsuwZGXR+Hq1RybMoWczz+n4++/E1IuXyX3669JefRRN/Z6cuKRCcSfuPpsuENQapquwDzgE8Q20QWXZ2YfsiSfgQi3Uqr3Z2uIeM1DDubDzm3k79uH+dtviXz/fdqMH89jS5ey4NZbaRobS3JyMnl5eQDs3bsXgJ49exIeHk7fvn0BmDVrFj3HjaPLzz9TeNtt5EdEkHXwIJnIwZmEXNTbIEl8cchMNdL5e71qRjTyR24BtEdmjI0R0X0I8Sw/jcy0nnE+FwwcQPxbRdSv2qvhyN/mGPCjn8ei8A+6uD58+HCNXq/bQtLS0rw2JsWJGYhcX48SOKXRPMFRJFGuKxIJVQQ2g5Eo9gNALySSbEH0yFHkXllXy4iGrBBnISv+Gch9tyNiT/k/Th4M+hcR41F1HIOOITSUhPvu45Rdu+i6dSvt5s+nw08/ccrOnUT07Yt1xw5Sxo6t8J7QDh1IfPRR2sybxym7dxN19tlujqIyIUiA0wps88D2gipyXR4DcLrzkY5EcRcgy2AWXLN2CzKDCOHEUWEjLttFI+fPLl27csrNN5O1eDFFWVn8+tlnrFu0iM6dO7N+/XqOHj1KTEwM+/fv59xzz6VXr158/fXXNG7cmI7du7P633958OmnCZk9m8L//Ie4xx4jFhHM7iz1mJ2PKMSnlYecKGnIktIPwCNIRnIgz5w+Q2bPYQTxQVgFBuRvU4BcsK7y62gU/qBjx44A7N69u0avj3eWrdIn7Arf0hMR17uR1cH64E12IIKsFypqHUxEA+MQz/NPyAroP0ivjzRc1iVdr4RqGsaCAkJiYjDg6sbpwGU31a2nRkR/JCHC/SrgWiTvoCZsc24n3s3P2Pi222h8222Vng9JSqLlO++wZ/BgchcuxFFSUmYzibviCuKu8H6x43Dk+9qG5Pi5Q73QNU2Ae4AxyOxqO/LlbEeWWHQLhn7QgRxoBuRAjUKi393KPToZDBguuYTS889n1apVLF68mGPHjrF582aOHj1KTk4OqampZQmLPXuKS+mPf/8lav58iq6/HntyMs2d4zOHez5dz4hr2ciC3Bh2I1nIPyPdItt4fK/uY0H8ZQW4333TunEjxcuWUbJuHda1a7GnpEBYGO0slkqv1RwOrKtWUfT991h+/x3b/v04cnMxt2pF+IUXEvfkk4S0b+/miOR4SkWsO9uQ40nRcOjUqRMGg4GsrCwyMzNJSEio9vWxsbEA5OYGarus+k0IEqTZg/hdEwh+f3IKsvrZAjjVz2NR1J5wRPxeCWwF5iN2hTQqCuZj06ZR8uWXhH33HYZOncoCiEZE24Tj6nYchXiqrwQupnaWRT2AV1LL99WWiN69AdCsVuyZmRidpUp9RRguce0u9UJc65iQmXqvcs9ZgZ3Ickj5GVwYcvC1QJZGTuRpCgkJ4ZxzzmHo0KFs3LiR77//HofDQXZ2NjabjV9//RWLxUJM166kh4ZSkJdHuNFIxLJlNAsJ8UkFDANyIW2JRLKTkSok/xKYUewtyMnqwP0KITnTp1P87bc1eq1t3z5Shw4FwNSyJWGDB2MwGrGuW0fB7NkUzptHsyVLCB/i3pzVhBxfxUjmsRLXDYvw8HDatm3LgQMH2LVrF4MGDar29XFxMsVU4tp/nIkkNaYi1rpg9l4XIba7PohdUFVOD14MyMqKnmCYgQg/PXg4f/t2cmw2Wu7fT3SnToArj6sdcu8JWb+eXvHxnNe5c52PBT1qHYp3tYR13z4ADCEhmBq763quPeGIhtrugW3VK3FdFWFUFNt1xWg00r9/f04//XS2bdvGl19+yZdffkl+fj5/pKczrXVrLN27Y9u0idC//ybp8st9Hv0wIJHgSMTzq0exNyMF691JQvAk/+Ba3nL3OwofNIjQ3r0J69+fsP79Ody8mrojBgPhF19M/FNPEe4U2SCz5Mx776Xg449JHzmSVnv2YAhx79sKQz5j1YUYFfWdzp0711hc65FrZQvxH7FIUng2UvI0Efe9pf5AQ4JJrYHewBn+HY7CwyQCQ50PgFRNYxPwjsHAuYjwLS9+k5OTOf+WW4iKimL58uU0atSoTvvdhgQnvZ2gn+HsEhlzySUYw3xfDkC/b6ciTgB3CKSAZlBgMBjo3r0706ZN48svv2Tc++/zn9at2QFogweLH7qkxK/LiiFIFDsOiWJ/CzyMnByBwD9Ioqknyl7FPfkkjaZOJfKyyzA1q76/Y0jHjjT/6acKwhrAEBZG43ffxRAXh/3QIayrV7s9rhCUuG7IdO3aFYCdO3ee9LXlI9eaVp9S6oKLvkjJunYg13O/jqZuHEEqSnVC+iQEu71FUT0OZ8MWo8GAmcqC7q+//sLhcJCfn8+7775b5+vLETx3zz4ReUuWkDVnDoaQEJpPn+7FPZ0YExJxLkWsVe6gxLUb2Pv0Ye7557MX8XS3v+ce2n7xBbFXX+3voWFA/NjNkcSW/wEPIks7/qZ85DpQMIaHE9KlCwC2GnTWOxmhyAl6GImGKRoWXZzHUk2SGnVx7XA4KCysT22iggsDcDUiTA0ET+UlHd0OcgowHEnOV9RvdLFsNFYt5TZv3ozZbC77988//1yn/VgRG6e3BKNl+3YO3XILaBpJL79c5r32BwZcxS7cQYnrOrINKZlzAIlwtADMYWFE9O2L4QQHuj+IQsZ2DPgVaWziqfaedaEAKQVUSuDYVAA0ux3bwYMAmKqzltQQIzILLkW874qGhR653rNnD3Z79WdcaGgo4c6EZ+W79i+xSHOuU5BVv2ApjmhFghZtUXaQhkRZ5LoKzZGRkcH+/fsJDw+ndevWWK1WvvrqK1LqEDyy4mpu52lKkpPZd8kl2LOzaTJuHE0eecQLe6k5etUVd1f6A0cFBhEZSLH1fcgMJ4nA/iL1utqpwGLgFT+OZTsiOHXxGSgUfvEFjrQ0jE2aED54sEe2qUevt3pka4pgokWLFkRERFBSUsJB56StOvTotfJd+59+wNmahu3ll/lq4ED279/v7yFVSynSUC0RyS+6HmUHaSjokWuDofJffMOGDQC0bt2aNm3akJSURGlpKe+//z42m612+3F/qFViy8hg34UXUnroEI3uuIOkV/ypToTy5ZrdIZA1YUCiIUmC+5HZXKALa51IpFnNUeC/wHo/jUPvMhlIwtp2+DBZzqL18dOmYfBQIoUJ+axZHtmaIpgwGo1l1pCa+K5VOb7AwQAcffdddr7xBo7kZHYVFpLh70GdACuSsB6HVAcZg0TfFQ2D6sT1+vVyl+/ZsycGg4EBAwYQFRXFwYMH+e6772q1nzBcEV1PYc/PZ9+wYVh37CDummto/cEHVX4OX+NAPqu7KiAYdGFA8RPiX85E/MyBJBJPRjRiE0lDujoW1eK9drud0tJSt8ege779fwoJjsJC0q6+GkdGBpFXXUXsvfd6bNv6xSgQfO4K36OL6127dp30taocX+CwcOFCnp8xgwjghilTGNijBzuQlb9AohgR1o2B04DRuN+yWRFcnMgWkpmZyb59+zAYDJx++ukAWCwWRo0aBcAPP/zAnj17arwfT4trh9XKgSuvpHjDBmIuvpg2n3+OwRQYakq3vyhx7UMygBmIfzke75el8QaJiJdoJ/B6Dd+Tm5vLmjVrWLNmzUn9oyfDm96t2qKVlpI2YgQlGzcSNmQIifPmeXT7Slw3bHTftS6uNU1j3rx5rFixotJrVTm+wGDFihWMGzcOgLvvvpsP7rqL85Co8AEk18bdRCd30ZDqDRuRfJrTkSZq1ddKUtRHThS51i0hXbp0oU0baSWXkZHB6aefzuDBg9E0jffff5+SkpodzYm4qmi4PWa7nUM33UTBr78SddZZtFu4sKwTo79xIMUpQpBmUu5Q7+tcewrdDnIQ+QMEa4TAhFyEU5EW5BdQffJLWloa27dvx+FwEBcXd8Ks5BNhs9koLi6muLgYi8VCanExpSUlOFq2BD8UidfRHA7Sb7kFy//+R0jv3jT9/nuMERFe2VdgzMcVvqZz586AS1z/888/PPfcc3Ts2JGhx5WDVJFr/7N161buuusubDYbV111FZMmTcKIlLSLR6x1exBLXRek866v0QMjJYjg7wHchLKCNFROVC1EF9f9+/cv6xCbmSmVm2+55RZ27txJeno6mZmZJNWgC+KpSIMVT9ijMt5+m9xvvgHAnJhI8v33V/m6Fq+8gjkxEYDSo0c5UK4Km2Wb9FBMHjMGY3Q0ALHDh9Ns8mS3xlaCCOt4ZOLqDkpc15A1wFLEDtKKwIi81pVIxB6SjkTiv6Hqz3Po0CH27t0LQGJiIt26dTuhJ6q0tJTCwkIKCgooKCigsLCQoqKiSokThTjb0IeE+FVcZ95/P0Xz52Pu0oXmS5diio/3+D70CL0vunQq/E9JSQkvvfQSgwcP5rzzziuzhaSkpJCfn1/mgWzXrl2l9ypx7V/S0tIYNWoUhYWFnHnmmcycObNMsBiQIMQpwAIkKXsXcv3shG9KimpI3d39SA+DTsAwYDDBfS9SeIby9as1TSuzhJx22mllpfhyc3MpLS0lIiKCyZMnc/To0RoJa5BOj+F4ZuXZnu0qTquL7Kpo/swz4BTXmtVK0dq1lV5j+ddViyvslFPcGJVze4gj4VTcP6+UuK4hnyP1imMJTjvI8SQiy5w7kUhM+ei1pmns2rWrrGRPq1at6NSpEwaDAU3TKCoqqiCiCwoKsFpPbH4IDQ0lIiJCSgJFRBASEUFxE3/EfYTsp56iYPZsTG3a0HzZMkxNm3plPw7kBFPiumGwf/9+Pv/8c+bPn8/777/PwIEDad68OampqezcuZONGzcCcNppp1V6r7KF+A+r1cro0aNJTU2lc+fOfPjhh4RWsUzdCuj800/EtG9PfNeu7EFapjdBolwxeF7oliBJ6EeRa0kfJFp9Le4vWyuCn8jISIAK9fENBgN33303ZrOZ+Ph4NE0jJCSE0tJScnJyaNKkCXFxcWUT+prQCkmaNSPHpDsaqPkzz4hwrgWh7drR2wcNtqzI/bqbB7alxHUNSAF+R3rOt/bzWDyFCbkZ5AJf4BLXdrudrVu3kpWVhaZptGjRgvDwcHbs2FEmpk/U5SkiIoKoqCiio6OJjo4mMjKS8PBwTOUSFcKQgzcb/3ivc2fOJPf55zE1b07zn3/G7PSjeQMb8lnbeW0PikCiS5cuXHHFFXz33Xc8+uijfPHFF/To0YPU1FQ2b97Mpk2bgKrFdbRzaTM/P9+nY27oaJrGE088waZNm4iLi2Pu3LnExMRUet3mzZt57LHH+P3330lMTGRDairfAnsR4fsvEsFuATTFPSuYhtxrUpBl+AQkkpYEnIeKVitc6NeNgoKCCs8PGDCg7N8Gg4HIyEhyc3OxWOpWvdmAHIO78E0bdH9hQXSREtc+4itEhIYRWF0F3UVvj74cqSASU1zMqlWrSEtLo6ioiKZNm3L06NFK7zOZTGUCuryYNtUg27c9UrXEiCtxwB2KFi8m5/hWqSUlpAwcWPbf+MmTiRw+HOvmzWSPHw+AuX17cp57rsptxowZQ/iQIW6NS8PVhbKnW1tSBAsGg4FnnnmGgwcPsmXLFh544AGGDRvGzz//zIoVK8jLyyMiIoJTTz210nsjnH7/miYYKWrHrl27GDt2LI8//jjnnntu2fOzZ8/m66+/xmQyMXv27EqWnV27dvHSSy/x448/kp6eDoiFpxVwP3L9/BPYguSxHAF2I7a7GOdDr9JUVbaKnvCcjzTYync+jIhQHwB0BAYiNazVDVtRHl1cn6yza0REBLm5uRQXF9d5X92QSmlFiHaob9hxRa4rX6FrjzpXT0IJ8DUiroM1ifFEhDkfuchnjJw/vyzpISoqCovFQlhYGM2bNycuLq5MROvd5OqCEeiOtBbWkwfcwZ6eTsnxXixNq/Cc3XlTdOTkgDPqbl2zBuuaNVVuM/ycc9wW1zZkth8JdHZrS4pgIiwsjLfeeovrr7+e/fv38/PPP6NpGhs2bMBgMNC7d+8qJ6FhztrqdY0sKapn4cKF/P333/zyyy9l4vqXX37hOecE+5lnnmFIuXP+8OHDzJw5k6+//hpN08jJyQEgJCSEiy++GJDzu7XzcSlSveNPJFChi+UsJAm+BLnZmnBVEXLg6pYbjQjx5oifOhaxgAxEPNYKRVWcKHJ9PPo92x1xfQ4wB/H9B1qvCk+Qh0yCu+CZc06J65OwHIlI2JAv3h2KN24kf9kyiteto2jtWmwpKRjCwuhxkhuqw2ol8623yP3iC6y7doHDgbllS6KGDKHZtGmEtKz7oRCHWDQWAA81akRBQQGRkZFlUemQkBByc3NxOBzY7XY0TcNkMhESUlEWa5rGV199hdFopHfv3nTs2PGElUV6Id9rCe5/pzGjRhHjrN15MiLOOYd2PvBtgZQsCkVm+/XtIqSonoSEBN555x1GjhzJzp07ycrKwmazkZCQUKUlBFw3PyWuvYNe07d9+/YA7N69mwceeABN07j55pvL6v+mp6fzxhtv8Nlnn5UlY7ds2RKbzYbFYiEiIqKsxGJ5ooChwFmIoD5y3KMAud45nA8jIrL1nIzmyA1dfyShbs6Kk1NTca2vjLlzfemDRHSPIUK0UZ23FHhoQA5i6boBz9iu1Pl7EtYiF8ZY3P/C06ZPJ+/bb2v1HltaGvsvuADLP/9gbt6c6AsuAKBkzx6yP/qIRnfc4Za4jkZOlqNAzyuuYHhpKXl5eRUeNpuNnJycsuiN2Wxm0KBBZVnIANu3b2fatGll/4+MjKRHjx707t2bXr160atXr7KSQD0Q4VmfU7f0qLyyhDRMTjnlFF588UUeeeQRiouLsVqtREVFnVRcV5cYrKg7u3fvBqBTp07k5uZyxx13kJ+fz4ABA3juuecwGAzs2LGDK6+8kqIiaa911llncd555zFt2jTMZjMdOnQgLS2trMRiVRgQj3QCEkQA1427GJl025AJtxlXPV11I1bUhdqKa3ci1wbgeqRxURpSrq6+eP/1hnpNkVUoT6DO6ZOwDTG5x3tgW5GDBhHeuzcR/fsT0b8/O5o3r/b1msPBgSuvxPLPPzT5z39o9swzGMoJ2pJ9+zDGulfhVO9EZEWScjqGhJCQkFAmhPXqIOXFttlsrlSSr2vXrjz22GP88ccf/PPPPxQVFbFu3TrWrVsHSCep2NhY4uPjGXbrrYSOHEkp9XN5SUOOmUZAbz+PReE/LrjgAh555BEmTZqE1WqlpKSE3r2rPiKULcR7lJaWcuDAAUAi1/feey8HDhygVatWvP/++2WrcAUFBZSUlNC3b18mTJhA06ZNufzyy9E0jZEjR/L1118DItBrgwG5FtSnSJ8iMPCluAYRnq8j4roI91eeA4VcRONdhuc+kxLX1WBFklOseCY7tsmTT9bq9dkff0zxn38SO2IEzZ99ttLvQzt08MCoZFnSgkwkrjjudwaDgaioKKKioqqtiWkymbjjjju44447sNvtrFu3jiVLlrB69Wp27NhR5uUG+Pfffzlj5Eh+Rupe17cGCHot0EbAxX4ei8K/3HXXXSxbtozvv/+euLi4sgi1A6k0sQu5SR2NiCAVyLZa+Rq50PdAdd3zBPv378dutxMdHc2cOXNYuXIlkZGRzJ07l5SUFMaOHcvmzZuZP38+O3fuJDQ0lNzcXC677DIKCgo444wzuOeee/jss88ICQkp63inUPgbX3quQYTn5UgibzaSUxTs0esSRIc0QyLznkKJ62rYjYhO8M8XlTV7NgCJjz3m1f2EIdaXbXV4r8ViYc+ePezatYudO3eya9cudu3aVaEZRkxMDPn5+TgcDqKjo5kyZQqJSPJPJt6pD+tPChG7zQjqz8xeUTcMBgOffPIJ9z7+OB2uu47/IMuqfyNJb3ZkIqZFRsq1xuHgdk3DaDBgQry4fco9BuCZVbRARsPlUS5FJiIhyDU4ktoHOnS/dWxsLB9++CEAEyZM4I033uCHH34AJFHR4XAQGhqKzWbjvvvuqxDd/ueffwCJfJe3wykU/iQqSu4wvvBc69wIfIlYnfIJ7uCYhthiGyP5Eh09uG11lagG3RISju/Fnz0/n+INGzDGxBA5YACFa9aQ/9132LOyCGnThtgrryS8Rw+P7Etva7oDV7LN8WiaRkpKSpl41sX0oUOHcDgclV5vNBrp1KkTrVu35rfffqNFixY0bdqUt99+m169emFFZorZyHfsncbjvseGyxJyi5/HovAvpcBiYE5kJCvfeQc7IqZtuES1bokyxMZiePBBiIjAajCUVZPIB/YB3zlfGwlcA4wG+hH8k1INmWAfAg47H4eQCaod+Q70evgm56Mp0Aap0tEGaXBR3fVj9+7d2O129uzZQ1hYGJ06dWLq1KnY7XYMBgNXXXUV48ePp23btgA8++yzrFy5koiICObMmUNCQkKZZ7s6v7VC4Wt8bQsB6dtwD/AakqwbSfAKyRzkGtMS+I+Htx2s34lP2InnLCG1xbptGzgchHbqRMrDD5P1zjsVfn9s8mQSx48n6aWX3N5XCM4bucPB+tRUDMnJJCcnc/jw4bKf+/fvP2EtzUaNGtG1a1e6dOlS9ujYsSPr169n3Lhx2O12OnfuzHvvvUdLZ/JlGLIEMxOJUtUXcV2IfJYBSEkfRcMjBfgY+ARJFC5xPnRxGIJMaPWKEWVcX3FRUoMKgtyKHF8fA/OAvojIHkHwnT8ZwB9IwngOles8W5DvR5986JMNA7IapJeu02tIt0ciT32pXN5z586d5OTk4HA4sFqt7N+/H4CLLrqIxx9/vELd8S+++II5c+YA8MYbb9Ctm7ST0N9TW7+1QuFN/CGuAcYAvyLn6jGkJnuwTfRLkIl9a+BRPF/yUonratCXbf3ROMaenQ2A5Z9/sGzaROL48SQ88ADG6GjyFi0i5ZFHyHj5ZUI7dCDh3nvd2pcBcLz/PvtnzWKkzXbCdt1ms5mOHTvSpUuXCmI6ISGhUoLjV199xfTp03E4HPTv35833nijrMWzzs3AbCSZwF+TGE9iR8RPInCrn8ei8D3FwHTgfee/9dJroYgQrLow5YnRS7XpF2ldbJcglXZWAxuAKcDzwHUE9g3OgawGrgT+wVWlSE+MikZWfNog0bCqvq8SRIQXIDfGg8gKwQ6kkUsSMAgYgpyHAD///DMlJSUYjUZiY2MZNGgQEyZMqFS5Zf369UycOBGAxx57jGHDhpX9bsCAAfz6668MHz7c3a9BofAYvvZc65iBaYhFZB/BZw/R7SCNgDORa6enUeK6GvTEtNreFD2BZne2F7DZiLvpJpJefrnsd43HjEGzWkl58EHSn3vObXENoO3ZAzYbRrOZdq1a0arco3Xr1rRp06bGfsPXX3+9zNt4+eWXM23atEp1sUFuotcjkbhsZLnXH9+1p8hBIog9gEv8OxSFj/kTeABXgqIREdUheE7wlhfbDkRUFiE2iruARchSbaAlQWrAeuAHJBEqBbmxxSFLzI2o+Xkfivgjyzf0KkF6EexA8mT2AMuQUngjgIyMDAD69OnDK6+8wtChQysFA44cOcLdd9+NzWZj+PDhPPzwwxV+f+WVV3LllVfWcJQKhW/wZZ3r4+lKRXuI3pQuGMjEZQeZind0hxLX1VCCy+/na0wxMWX/bnznnZV+3+iOO0h56CFKk5Ox7tlDmJvLleEvvkjCE0/wUePGDDlB85eakJycXCas77//fu67775KN7Ly/AdYAWxFonHxdd6zfylCjpeWwCu433lSERzo0ep3kWPAhkywvP33NyI3slAkCFCAiOs1wIsEThQ7F/gcEdd7kHE2B07Dc1aWUFwe7CxEvO9D/Ns7gKnffUfU7t3cMWpUldeioqIiRo8eTUZGBt27d2fmzJknbIClUAQSuri2Wq3YbLYTBr88bQvRGYNcc1YgE+fW+GelvzZkIVqjDTAR73VAVeK6Gsovx/qakHbtXP92JtqUxxgZiblJE2xpadjS0twW1xgMmBMT3T4xWrRowcMPP0yXLl0455xzTvr6WGRJ+zYkmhVB8Mx+dexI1DoBuB/VOKahsAMYiStabcb3lW8MiH87BBH6yUgU+3vEchXpw7GURwPWIZ1f9yD2jdZAd7xX1758A5ciJIq9FsgZMoQ+Q4aQicsqomOz2bj//vv5999/SUhIYM6cOURG+utbUyhqh14tBKCwsJC4uLgqX+dpW4iOGXgLEdlrketPKwJXYGc7H22AcYA316KUuK6GMCRC5A9xHdqmDaaEBOyZmdizsir9XnM4sDs7Jhqds1d30JOF3BW2RqORu+++u1bvOQfxbn1C8NlD9O5rEcgy9MPVvlpRX/gLuBaJkpbim2h1dZgQ37Iexf4GafQwH7Ff+JIC4FMkWr0TOUf64NuylJHI+ZiCeLEzgAOITWSI8zWapjFx4kSWL19OWFgYc+bMKUu4ViiCAbPZTHh4OBaLhYKCghOKa29FrkECZO8CdyJlRg8jAjuQgmQaErHORYT1Pch4vUmwaBi/EIF8QZULzfmG2MsvB6Dg118r/a5o9Wq0khIMERGEnXKKW/vRM/GN+K/qwCSgAyJQMvHPhKYu5CHiKgFlB2korEGaLR1BVi2iCYy/ux7FjkSi2H8gHccqT829Rw7Swe1nYBNyXvTFP/XeDciSbz/kproWye/4Drm+zJw5ky+++AKj0ci77757wtb0CkUgUxPftS6urVZrlaVz3SUB+AipkpWICOxcAuM+bkcSp3UryCPAQz7YrxLX1dABl6fRHyQ+/jiYTGS8/DLFmzaVPW9LSyPlkUcAaHTnnRhD3VuEsTl/hgGVDSi+IRZ4B5f/KYvAODGrIx9Zfm6CZE57puq4IpDZjCThpiET0igC7yJqRsZlQSLs1yHRZG+TjiQ3rUNsIL2Rm5m/vd/6qlIiEsVeBDz82We8/vrrAMyYMYMLL7zQX8NTKNyiNuIaRGB7gwRgDnAeErnOQgIQpV7ZW83IR1asTEi5zv8Ad+Oba5KyhVRDdyQS5KnIT97ixaRNn17hOa2khD0DB5b9v+nkycQ6yz2Fd+tG0muvcfThh9k7aBCRgwZhjI6maNUq7NnZhPfrR/Pnn3d7XHoZvI74dymnH+ITvROZaWYhVQH8fXOuijyk7F4TYALivVXUb/YgVpA0ZOIXRWAem+CyiRQiEdubEf+zt7yQ2Yj3ciNiwegDJyzp6Q8MSOAgFPhz+3YOPfUU4cCEsWMZOVKdvYrgpSbi2mw2YzabsdlsFBcXVxDbniQG+AD4P+BtxJZ1ELlPxuK766UdyeGyIOU5eyKJ5918tH9Q4rpaTkUuxnpHNXcTcezp6RSvXVvxSU2r8Jw9Pb3CrxMfeoiwrl3JeOUVitatQ7NYCO3YkYSxY2kyfjxGDyTf6OLalwfeiRgKzELKmqUiN+oEAic6qCHLXRbEGz4WuM+fA1L4BBsS8TiCK2IdqMJaR+/oWIQ0fHgVyY73NAXIqtNmXMI6UBOakoA8u519kZG0vu46Th03zt9DUijcQhfXeXl5gCQ2rlu3jqFDh2IyuVRLeHg4BQUFHi3HVxVG4HbkXv40kntxFLlvNkJsdN66dtoQa1oOIvQ7IMmWd+P7a5IS19UQgyxrHkbElLu+wUajRtFo1Kjaj+Oii4i56CI3935iLMisMhDENcBFwFyk6oFeE7cR/o+ElSIROhBhPQkZo6L+8xbSsKUE794cPI0ZOW+KEXE9HLFIeAoNiVJtQibDvQlcYa3TtUcPorZuJdlk4jskoq0sXYpgoaSkhMLCQho1agRAYqLUwElLSwPg+eefZ/78+bz00ktcddVVZe+LiIigoKDAK0mNVdEeKVKgR7EzkNXoNCTJOh7PCFANub7lICt10YgtpTu+j1aXJ1ACggFLN+Tm5C/ftbfRCKzItc4QpD5ub+QkzEKErT+SSzXEu5WG+DfbI0lbSlg3DHYALyARYL1teTARioy5ACkVWeLBba9FJh37EYHq7wlwTWllMtEEKaP4OfK3VSiCgYceeoizzz6bzZs3A5RVuDly5AglJSX8+OOPALRu3brC+/Qa75rmu2wmPYr9PfAYktzcEglU7UdWArMQUWyv4TY1XF1q0xFPdSpy7ekAXIrkfnyBfzVNsN0nfE4fZGnVFwlB/sCCROFikI5LgUQf4CekbE5zRFjrPipfUYqcwEVItPoSTWOJzcbVPhyDwn/YEEGah1wsA6EqSF2IQI7lLUhVG0+Qg/i4dyIrfP6oCOIO7ZCI1y5goX+HolDUmGbNmmGxWHj88ccpKiqiRYsWAKSkpLB69Wry8/Np0qQJffv2rfA+XVRX19TNWzQHHkTu568hArgDEmUuRaLae5HmTynIPTcDqRyW4fz/McRFsAfpSpuHaJemSJT6HuBrZNV7GP6/VitxfRKGI5YEG74Vdb4iF1miGU5gRp0ikfakXyBiOx6JYB9DZrveiGTry0wZuKLVHRBRknnppZx3yil8/vnnXilppAgs3sFlB4kgeOwgx2Okoj1kq5vb05CI7x7n/1u5uT1/YEICCvuQDnP/+nc4CkWNGD9+PElJSRw8eJAXXnihQuT6p59+AuDiiy+u1GXUn+JaJxQRvnORCe0U4FbgTKAz0IKKFZjsuLpkhyBarD0ipochuVkzkdKf/wG6+Ohz1ATluT4J8cAlyLJDDjIDqy/YkIh8e+AGP4/lZAxCZr0vAfOQWWsB8jeJcj7cnanaNI28X3/FctppGOPiiEaSKc9HvFstgJmZmRQVFTFjxgw2bdrEfffdR/v27d3csyIQsSJewWKC0w5yPKFIlKgI8ZDPdmNbuh3kEDLpDdZJRyzQDOnmOA+5Qav+jIpAJjY2lhdffJHbbruNL774gq5dZc05OTmZo0ePAjBs2LBK7wsEcV2ezs6HTh6wHbHhZSPXXysyCQ5zPtoghSY64r1Or54i2O8XPuEGJLpbQM19QcFAHiJK+yIHbKATCTwD/On82Re5MYJEmI8iy0h5yCpDdXFlB65udlnI5Cnl99/Jv/lmjPfdRxek2PyvSO3OFs736UttmZmZ/PXXX4wfP55PPvnE6xnYCt/zPbJC4sD/S4yeIgyJwi9CVmbqggNYjAjS1gSfHeR42iETqL3ASv8ORaGoEQMHDuSOO+4A4K233sJut5ORkUFOTg6JiYn069ev0nsCTVwfTyzShOZ2pArXk0i1kf8A45HGL1ci0elAF9agxHWN6Ikk60Qhwq0+oJeUiyfwo9bH0xi4F/gNyUa+GlmWTkAijHbks6U4H0cR8Zzq/PdR5/PZiNDQl5uSOnYk1Ggk7Jdf+L9du5iERPXL06ZNG5o2bUqrVq0oKSnBbrezaNEiHn74YTZu3OjVz63wLR8iE7BQgjcyezxm5KJfiLQorwv/AMnObQSjHeR4TEhE7AiwCv915FUoasO4cePo3LkzWVlZ5OfnU1hYiM1m4+KLL65Qgk8n0MV1fUOJ6xpgAG7E5fetD9HrXOSmkoSUvgtGTEg3qI+QUmDzEfvGLUB/5MbfFOnM1tj5SEQK2rdEJkwjgKcQkb6hdWtuvuQSwoHP5sypcp9xcXG0bt2asLAwEhISuPDCC2nSpAnp6ek8++yzvPzyy2RnZ1f5XkXwsBVZISkl8EvL1Ra96+xc6nYtW4lMTpOoPzeQRMQucxj3/egKhS8ICwvjlVdewWw2U1RUREFBATabjUsuuaTK1ytx7Vvqy7XR46xatYrBgwfzyy+/AHAZIsaiEQtCMFOC2CeaIZUQ/NmV0VPEIUkR9yA+2d+RphY/Ad8BXyHiexGwBBFO6xBhPg64EPHTjxkzBoD58+eTm5tbeT9xcZjNZvr06QPAunXreP7557nyyisxGo2sXr2aCRMm+LTckcLzfIRrVcObF0nbn39ScOWV5CYmkhMeTl6XLhRPmoRW5L3icCFIdPYAsLyW701DEv/SEHFdX9ADDSkoa4gieDj11FMZO3YsZrOkz4WHh3P66adX+Volrn2LEtcn4L333iMzM5ODBw8CEu15xmaDF14g+5JLyHY+H2xoyI0xHqklfb1fR+Nd4pCs4n7AYER8n47YfJpT9VL/oEGD6NatG8XFxcybN6/yNuPiAGjbti0tW7YkLy+P7777jlGjRvHyyy/Ts2dPWrdurS5gQYwDyWQvwbtR65LPPqNgyBBs332HsV07Qi69FM1iwfrcc+QPHoyWn++V/eqZ9yVIKb2aYLfbyc/P5w/Ehx6Pd6sL2SwW1k6Zwv916cK74eF81KIFy++8k4LkZK/tszlS8mur86dCEQyMHj26LKn+1FNPrdISAkpc+5qgEdd5SHb6z0jk8Rsk4WgpUkbpEJ7zyiUnJ7Nu3ToMBkPZEktOTg6v3nkn2ty5GPfvJ7OgICjtIbnI99QSmEYQHQA+wmAwlEWvP/roI+z2in/l2NhYAAoKChg9ejQAixcv5siRI3To0IFp06YxadIk3w5a4VH2I1VoHHgvccaRnEzRmDFgtxPx0UfEbNhA1MKFxO7eTch11+HYsoXiJ57w0t7Fe21DVneqw2638+2333LFFVdw4YUXsvTAAY7iSvD1BjaLhUXnn8/6adMoLSig/ZVXEtO6NdvnzuXLfv3I3bvXK/uNQCbkacjKlkIRDJhMJr5euJDrbruNW95+m1lI8t/tSD7V1cB1wE+axirgTYOBhUhVDpvfRl3/CchSfMXA38A252M74oUrQQ4GzfkAEYdGJMIUB5yCdOU5FWnz27IO+1+0aBEgUcykpCR2797NvffeS3JyMkmRkXR+5RX2dO9OGieOgAYiuh2kFdItqS7fTUPgmmuuYfr06Rw+fJilS5dWKGukR65zc3Pp27cv/fv3Z/369Xz++eeMHz/eX0NWeJAtiBfZhPfO7ZKPPwaLBfOFFxLmzPoHMISFEfHOO5QuXkzJnDmEP/ssxoQEj+/fhHzGPUj30Zjjfu9wOFi2bBnvvfde2epd46QkimNisCAJwN5i44wZpK5eTfNBg7hi6VJCo6MB2DRzJqsee4zld97JNb//7pV9JyB5NQe8snWFwnPkIQHG1cC29u1J//BD1iH5FBZcNaIdyHXMEheHVljIf2NiWICsPEUi1Td6IZU4gqFqWLAQUOJ6L+KL/QERgfpBYnH+2+x8GHDd9PSDR//9LuB/yIETAZyBzN7OpWYf1uFw8M033wAispYvX8748eMpKiqiVatWvPfee5R07sxNwEGknFUigS+wS5Fs+ATgLGQmq6ia8PBwbr31Vt58800++OCDKsV1Tk4OACNHjmT9+vWsXr2aQ4cO0aZNG38MWeFBNuMS197C7qwsYz7nnEq/MzZpgqlbN+wbNmBbsoTQW2/1+P6NyDXLjgQyznQ+r2kaK1asYNasWezevRuA+Ph47rjjDjpcfz2zwsKIwnvXO3tpKX+/9RYAZ7/zTpmwBug7bhw7PvmElBUrSNu4kaannebx/ccgq6CHcTWvUCgCie3Al0g5zCxkcmzBlXwdhuif8lpJA2yPPYY9Px97bCw5iGYCWalbAfwfUt72BqTIQX3IxfInfhfXpUgt4S+RBLNc50NDDpBwJIkwnOpvdnq/eV2M6+I8DfgDidaOAK7BVRu5KjZu3EhycjJRUVEcPHiQt5wX+gEDBvDmm28SHx8PSJ3lychF2IRUoghU7EiiTiwyQ30RZQc5GaNGjeKdd95h9erVbNu2jW7dugHSejY8PJxo502/bdu2DB48mNWrVzN//nwVva4HbELOGW9eHLXCQgAMjaqOARsayxXFvmULeEFcg1y3dGvIYE1j7dq1zJo1i61bpV5GVFQUt912GzfffDNRUVF8TdVRbk9y9I8/sObkENexI02Oa98M0Onaa8n8+2/2f/+9V8R1JHIfyUMCJ008vgeFovY4kOT8z5DzVddJRuS+HoeI4Wrv6/Hx8nCiIfpL7/ewH9FLa5Hk3quR7omJnvsYDQq/iuvfgBlIpCAHuXBHIeXTIqld1MCAq4tPnPO5UuQAPIIk4ewF3kdmZo8gke3j0aPWUVFRZcJ65MiR3HTTTSxcuJDffvsNu93Ou+++S0FsLC8gAtuBRIUDLdKhR6yjELvM+8g4FdXTokULhg8fznfffcdbb73Fu+++C8hx8eabbxIW5prXX3/99axevVpFr+sBDsQWYsO7kRtDE5FtjhMkRuvPOw4c8NoYdGvIZmDo0KH8/fff8rzJROvWrUlMTGTlypVs2LCB8PBwtoaHc7RXL3qMHOm1MWVs2QJAkyqaYJR/PtP5Ok9jRK6VBch1XYlrhb85grQJ/wMJGhYiAcfmSNCxrprDgES6Q5EJsw2ZVKYiIvsg8DUwEbjUjf00VPwSwMxFuu48CPyFHDxmpFOW3lveE3/IEGTW1QGJLOchHsMPEVvE8S0/iouLWbx4MceOHePgwYNYLBZ69uzJypUrueyyy3jxxRdZu3YtW7dupbCwkFs1jbE2G62RAz6dwGpAYEWaPcQgFTI+xLuJSPWNhx9+GBAPvr5EDtCkSZOyxEZwRa81TWP+/Pk+H6fCc+Qik3wN714czWefDUDJ55+jlZRU+J3tzz9x7NwJ4LWKISDi2gEc1DR27txJaWkppaWlaJpGUVERBw4cYOvWraxfv56VK1eyddkyDr72GuHFxV4bU/6hQwBEtaq6PU2083n9dd4gBjkGvLcHheLkOJAV/auRqPVhRAi3Q4R1BJ4VvGZEJ7VDAnC5SM7beKRjYl07ujZUfB65/g14FokipyNR5hZ490ZmQC6YMYgIPoZcPEcBI3FFsb/55hv279+PxWLBarWSkJDAP//8A4DZbOaMM87gzDPPpFmzZsybN49PP/2U/Px8xv3yC7PatCEFOQGa4d0yVSdDQ5JyspHJRS8kYl2f6tL6gh49enDppZeyZMkSZs6cWRa9rgoVva4fWHElS3szUhM6ciSW555DO3SIwiuvJOKVVzC2aYNt1SqK7roLzGaw2cDo3fiHBpQYDKxbt463336bZcuWUVJSgqZpJCQkcMkll9C9e3eySkr42GLhYIcOxEZUtebnGUoLCgAIiYys8vfmqKgKr/MGUYiwOOa1PSgU1aNHq1cix6ERaI1vGloZkMh4FOLpPoRUZ/sLiWIPQ0Wxa4LPItelyMHyILIMmYVUq2jiy0EgB0xb5OA4gCuKvQuYOXMmFosFo9FI06ZNadu2LSNGjGDq1KlMnTqVtm3bsnTpUmbPns2sWbNIS0ujtLSUy5DGJT2R+q9HkFmeP6LYVkTgFyItfa9Aug8qYV03xo0bB1SOXh+Pil7XD7wXk62IISqK6B9+wNCmDbaffiK/Rw9yY2MpHDYMjEbCnMfdiTzZHhkD4PjsM/6+9lo2btzICy+8wK+//sr9999PkyZNyMrKYt68ecyePRsb0Paaa4jp39+7N1a9+dKJavH6oDmTbpcp9fqeFIrKbEb6T/yErDzHIlrJ151iDUgEuxWy6v8vUmXsJQJrhT5Q8YmuLUaiw58jgjYMEbjei39UjwlZVmmOeIs2AncC/S+/nPbt2/Pkk0/y2WefMXr0aPLz8/nqq6/4/PPP+eeff8jLy2Ovs85qaGgoV199NW3atOEc4FvEz90WSao8jHx2X/TqcyATFt0G0g05Cd4ksJMtAx09eq1pGjNnzqz2tddfLy159Oi1IvjwZV9NU8+exO7YQeQnnxD60EOE3ncfEe+9R+xWVwNuU/fuXh2DtnIl+b/9xj333MPAgQNZsmQJ99xzD4sXL+a+++4jLi6OQ4cO8crUqSy9+mpKjxzx6nhCYiRdstSZ8Hk8NmfnypByVUQ8jW6XKTnZCxUKD7MauAsJ9hUg0ep4/BspDnOOIwLRNB8BT0NQ9vnwJV4X14VIi+3/IRUrkvB9tPpE6FFsK3Iw/zVlCv+3cydTp05l9erVLF68mJSUFEwmEz179uSss84iIiKCU089lZiYGHr06MFVV11Vtr14RNC+gSuKfRQ5IPXmLZ6mBLHX7EeEfBvgKqTN9+Wo5RtPoKLXDQdf27kMERGE3nYbkW++SeSsWYTdcw+GmBhsP/8MVF2qz1NogPGVV2g9cSKNGzfmwIEDjB07lv79+/Pll19y6623snjxYsaOHUt8QgJFKSmUejHBEiDGaacqPEEnRr1DY4wXbVd66TIVnVP4kjXIyv4+5Nhrhe+j1SdCj2InIgG8L4GnUOdIdXhV41qBR5EaiseQpY0ob+6wDpiQg9iB+MAfRuq+Xn755QwfPpzx48czd+5cTjvtNP744w+MRiMtWrSgXbt2xMTEMHDgwErbvBARt6OQpjaNEY/3fkQIl/d11gUHMqtNRoQ7yMxyEK5otcpy9xwqet1wKJ9978sodnlsv/+O/a+/MHbvjvnMM0/+BjcwRETQcdw4Nm3axLRp02jatClHjhxh4sSJ9O3blzlz5nDNNdfw38WLOf+bb4jy8ngSe/cGIP2vv6r8vf58Qq9eXhuDA7kx+r1OraLBsAXRHgeR64+389DqSiySU5aC+LBn4L/rZKDjtb+fA3gSqWGtC2t/2UBOhhFXFY19wANAyKmncuedd3L66aczd+5cPv74YzRN47zzzuPUU0/FZDIxaNAgwsOrjnU1AqYi7dqfBgYgAhjEk70PEcfpiPAuQb6z8geqhiy9FCPJianIybcXsYDEAJ0Qf9ZHwHeoaLW3UNHrhkEssgxqwPtRGdvmzWi2ig2IbX/9ReHNN4PBQISzFKi3KP3jD2zt27OySRNOOeUU3njjDcxmM3FxcVgsFvbs2cO4ceNo1qwZ//f228S1bev17yTpzDMJjYsjd+9e0jdtqvT7PQsWANDussu8NgZdXId4bQ8KhYujyOr+Aef/kwjse3g0Ui45BfgU+NivowlcvCauP0cM+UcR4RqowlpHF9g2RLxOANJzcpgyZQq//vorBoOBO++8kzFjxrBmzRoAzj333JNuNx6JYC9GkievRaLZbXF5qfKQCPRepFTgHmC38+c+xBduRaJqTRFBfTqSXPAjYkMZTGDOdOsLPXr0YNiwYSp6Xc8xI5YuPanNmxSPHUteixYUXHQRhTffTP7gwRT074+WlkbE7NmE1OD64g4OhwOD3Y4JKUOamZlJSkoKOTk5GI1GHA4Hdrud4uJifvrmG0IQ4enN78UUGkqvBx8E4PcHH6zgvd40cyaZf/9N0pAhNOvf32tjKEGEddX1ShQKz6EhDekOIgm0gS6sdWKQFfkUZKV8j3+HE5B4ZeXrECL4UhGPTrBcpHSBfRDYuH8/Vz3/PM0zM4mKiuKxxx6jd+/e/PHHHxQXF9OkSRO61yLZyAgMcT5syMG4rdxjFxWj14ZyjyZIgmL5h7J9+J5x48bx448/smjRIsaNG0fnzp2rfF35ro1fffUVjz32mI9HqnCH3kjDBm+L69BbbqHk00+xb96MlpODoUkTQm68kbDHH8fcp4+X9w7a0KFE7d3Lx/n5nF5URFFREatWreKDDz5g586dREdHExkZyVVXXcULL7zAK0iQpABXoy5vcPqkSRz++WdSV6/m/zp3psVZZ5F/8CDH1q4lPCGB8+fO9eLeZSWxEWIXVCi8ydfA78jKdGuCK0AWBxQhOm8S0j2yui7aDQ2Pi2sHYoNIQb5ob16EvYEZiQ4nz55NdmYm7Vu25MWJE0lKkmJ2v/32GwBnn302hhOVi6rBPk5xPq5xPudADlQLEqU2IZHqMAI/6t9Q6NmzJ8OGDePHH3+scd3rVatWcd1116m610FEH+T8s3p5P2FjxhA2ZoyX91I1esKeOTycs8PD2bduHc899xyrVq0CIDo6mrvvvpsHH3yQxs5W7K1xNVjx5nXdHB7O1b/+ysbnn2fXvHnsW7SI8EaNOOX22xkwfToxrVuffCNuoFdp8O5eFA2dFOBlRJw2JnCSF2uKAdFKB5GKa3MB/1zNAhOPT5Q+B/5E2pk3JziWOI4nBoi87DK44goKXniBRKewdjgcbN68GaiZJaQ2GBEvUyLiT2+O2EaUsA4s6uK9/uqrr3w1PIUH6ItMgO3U32QdO3LNaZqSwoM33sill17KqlWrCA0N5e677+avv/7i6aefLhPWIJWIohFx7W3MEREMmDaNW/fs4X6rlTtTU7ng44+9LqxLkUlVNEpcK7yHhuRkHUE0UrxfR1N3zMgq+jHgHZQ9pDweFdfJVLSDBHNCSMshQ7DffjvbIiOZ43zOYDAwYsQIbr/9dpo3b+7X8Sn8gx69ro33etWqVcp7HUR0xVU1pL6WmrIhN0bzG2/w888/YzKZuPXWW1m3bh0zZsygadOmld6jR6691xvR/xQgFa0SCbzKVor6wzJcdpBmBGcQUicGibqnAs/7eSyBhEfF9f8hM5hgtIMcT/kZ2f8hFTsMBgM33XQTV1xxhV/HpvAvJ4pef/XVV2W2IZDo9aBBg9A0jW+//dbXw1TUETNwCXLDqI+NRDTkc4UCd997LxMmTGDNmjW89tprtGp1YqexHrm2UH+7F+ajotYK7zMPqfgVR/DZQY5HzwvLRVwLKnoteExcFwI/IHaQxgT3TEwnxvkzHanKoVBA1dHr3NxcHnroIe666y60ci2a9SZDK1asIDc31x/DVdSB0bjEdX2zhtiQ63MicG/79owfP54OHTqc9H0xiGWtEVLBqL6h4Vp1PcXPY1HUX3YD65FVknj/DsVj6NV18pAGMwoPiusliAiF4KkOcjIMyMwyBzlg6ttNVlF3jo9eh4ZK/CE/P5/8fJcrtUuXLnTu3BmbzcbSpUv9MlZF7RmC2EPM1L/odQmSKH2r82dtOAupqJRC/bse5jh/NkdKnSoU3uBLRIRGUr8aFcUj0evvkWBrQ8cj4loD5iP+oXjqR9RaJw45UP5FOjcqFFAxej1t2jQiIiKIjJRpZWZmZoXXXuZsePHjjz9it3u7wJvCExiQzPcw6lf02o5ErsOQ+vu1pT8iPjVcYrS+kIJMHAZS+0mHQlETCpAV/lzqT9RaRy++kIH09WjoeERcbwa2I6XkYt3cVtHGjRx74QUOXHMN/7ZsyRaDgb9P0AVRp/TIEZLvu4/tHTrwd1gY/0RHs6tfP9JefhmH1b2CWiZkOTQHtdyhgGPHjlFcXAzAf/7zH8xmM8uWLeOXX34hISEBqCyuBw8eTHx8PNnZ2axevdrnY1bUjRtxWcNs1b0wiNC91ucD7evw/nCk22wS0iCsvmBFgkPNkei8QuENyq/wu1MJzFFURO6iRSSPHs3uXr34NzaWrVFR7O7dm2PTpmEvOHHacfZ//8ueM87g3+hotjVuzP5LL6XQA/el41f6GzoeEdc/IjOxGNwvIn5s+nRSJ04k95tvsKWknPT11l272NmnD5nvvQcGA7GXX07UWWdh3b2bo088wb4LLkArdS/9Jh75fD9TfxN5FCcnNTWV008/ndNPP52vv/6ajh07Mnr0aACefvrpMnGdkZFR4X1ms5lhw4YB8MMPP/h20Io6EwvcjgjKYoI/em1Drl/hSLvlunIWIkKzkOTG+kAKkpR1KvLZFApv8CcSvY7FvRX+nHnzOHT11WR/9BGaw0HMJZcQddZZlOzfT9qUKezt3x9bWuXMiJRx40i+/XYsW7cSfcEFRJxxBgXLlrFv6FByv/nGjREJschK/y7k+tCQ8Yi4/he5+XjCax01aBDNnn6a9t9/T7fU1JO+/uiECdgzMkh48EFO2bWLdgsW0OHHHzll715CO3em8I8/yP70U7fGFIbcWAtRmbANmdjYWE455RQyMzN54IEHuPHGGxkxYgQJCQns2bOH7OxsoHLkGuCiiy7CbDaza9euSvWxS4C9wG/IkuE3iM3qa+A74CdgE3KxCnaBF2z8B+iAeCODWUhqyDU6HLgJcKdKfxLSJTYJuR4G+zFZhIjr1sBQP49FUb/ZhqySVL8Wf3IMoaE0vu8+uuzaRZetW2kzfz7tf/qJLjt3Et63L9YdO0gZO7bCewp++YXM117DlJBA5y1baLtoEe1/+okOK1ZgMJlIvuMO7M57WF0xIcmNVuSzNmTcFtc2JPvVgvsHDEDTJ5+k+dSpxF52GSHNmp309QUrVgDQbNIkDCZX3DykaVMS75f4TNH69W6NyYAIbAtif1E0TCIjI1myZAkTJ04kNDSU33//ncsvv5x+/fqhaRq7du1C07RKkWuA+Ph4zjpLFpzn/fADvyAdrZ4BHkC6mr4GzALeAz4E3gfeBd5G6oc+5ny8ASxCcgDqi10hUIkG3kKWcEsJ3u/bglzs2+CZWrTXAZ0QwR7MlUM0YCfyvZwG9PbvcBT1mBykaYwV9z39jW67jZazZhHWuXOF50OSkmjxzjsA5C1ciKPElY6d8eqrADSdNKnC+6IGDaLxvffiyM0l66OP3ByZ6EALSly7La73IhFdDf80jTGEnfwwNZXrMlZXwlGzMQWEhITwyCOP8PvvvzN06FCsVivLli2jpKSE4uJiCgoKqoxc24G2w4ezB5i7ahWv5ufzKeLBWw38g3jxihAR50CEnAXJLN+LLCn+CiwAZiMi6UlEaDf0JThvcjZwJxCuaRTu34/DEVytZXQ7SCQyMYv3wDaTgMuALsix6e1W8d4i2fmzExLRr0/J+IrAQo9ah+C+fbY6InrLFFGzWrE770UOi4WC5csBiLv22krv0Z/L//57t/cfhtJK4AFx/S+uZQ5/XJhiLrwQgLTnnkMrd9MrTUsjY9YsMJtpNHKk2/tRszFFedq3b8+XX37JO++8Q0JCAkajkdLSUiwWC7t27Sp7XQ5SmmgC8EXHjhSccQY5EREc0zRMQCskYjYA6A50BjoiyWYdkJt+V6AfMNj571hEhP+LdPmaAzyBRL23EfzL9IHIMw4HkU8+iTZoEAWvvRY037EDOVZ0O8hFHtz2BUAPpMPcboLvuCsCDiHn1AikfrdC4S10ce3tSjQl+/YBYAgJKQssWnfsQLNaMTVpQkgVjaIi+vUDwPK3+zXRdK30r9tbCm7cLrO4Hc9ZQupC0vPPU7xhAxlvvUXe4sVE9OuHo7CQwpUrMSUk0H7RIsJPPdXt/ei2kN1IFCiYW7srPIPBYGDEiBGcd955TJ8+nffeew+r1cqmTZtwIC1uFyJVFY4ix0/HCRNo5nAQaap97MKIJA3rFSwcQKZz2/udj9XI0vYoJEFL4T52u51Jjz6KY/58jAYD9OlDMWIVCeRIpwNZVQxFmqJ4ujWxEamVnQysQ3zLLT28D29hB3bgsoMM9O9wFA2AnfhGK2W88QYA0ZdcgtG5sl966BBAlcIawBgVhTE+Hnt2Nvb8fEwxMVW+ribo5UtTkeBSfJ23FNy4Hbk+gqu8kz8ISUqi4++/E33hhZTs20fuggXk//gjjsJCos85h7Bu3TyzH+RmZcFVSkehAGjUqBEzZ85kwYIFtG/fnstuvJEXEU/1ekT8tgDOANobDHUS1lVhRAR0L+dDQxIflwFTgF8IvmhioFFaWsoDDzzA/PnzCTeZeGLWLOLOPx87ci0I1O9XF9ZmZAVkEd65ySUBVyAJjgcJDv+1A4mqRSC2FmUHUfiCPGRS501LSN6SJWTPmYMhJIRm06eXPe9wluYzRp647IQxKqrCa+uKETmf7ED+SV5bn3E7cm1FbjD+ujgV//03+4cPB5OJdt9+S/TQoTgKC8lZsICjEyeS/9NPdFq1irCOHd3ajwE5aDSC11+o8C6XXHop71x6KQuBLcjMvT2ybO7t8yMKsZO0QFZX1iJ1ezeioth1paSkhHvvvZclS5YQEhLCe++9x/Dhw+mJJKEWQEBGsMsL6zaIsG7txf2dj3j+HUiSrRFpIR6IOJDVVgPQEylJqOwgCl+gT8Y91hb7+O1v307yLbeAptH85ZfLvNcAmuYMAxiquVJpngsVKK3kIXHtwHsHTHVopaUcvO46SlNS6LxhA5F9+wJgio+nySOPgN1OymOPkTp5Mm3nzXN7fwbUAaOomiKkqsdfSI3PMKAvvrdLRSCi4SiSJJmJ2EXGIL5tRc2wWq3ceeedLF++nNDQUD766CMuuOACAG5G/r73INGoQuR792ZEqqaUIoI/FPHrf+P86U0MSPWQYlxRYRuBVy/ajozNgFin7gPa+XNAigZFKd5b6SpNTubAJZdgz84mcdw4Eh95pMLvdZuHo/DEjckdRUUAGKOj3R6P0koe0MT+XBYt/PNPrLt2EdqhQ5mwLk/c9dcDUPDbbx7bp4bcQBQKnVzgJWAlsBXxnXbHf3kIBiSC3dc5tg2I8F/lp/EEG6WlpYwZM4bly5cTHh7Op59+WiasdUYA84CmiGWsENcqnj/QExf1fgO9kfro3hbWOgbEf30RYlHaBxwgcK6VRchqUghyXtyPWEIUCl9hwjsrXLaMDPZfeCGlhw7R6I47aP7KK5VeE9KmDSAivCochYU4cnIwxse75bfW0d0Mbkdvgxi3xXUYrlmKr9EPFFNs1U3X9eftWZ4pVKYv6fhLNCkCD11Yr0U8p72QiF0g2ATCkSh2JCIs3kca1ShOjN1u56GHHmLZsmWEhYUxb948hg6turXIRcA3KSmcV1JCNBKZKkQipL6kFLGo6O2HH0X+zr5OLjQCI4HLkVWSXCQHwD0Hp3toSMLlJsSq0g94BKkQolD4Em9oJXt+PgeGDcO6Ywex11xDyw8+wFCF9SOsa1cMYWHY09OrFNjFf/0FQESvXh4Zly6uG7JWcltchzo34o8IRUhzWXi07tyJPb+ydV5vHhParp1H9qcfMP5K3lQEFgXATMQKkoII6yi/jqgyBiR6GY/4YT9G6mUrKqNpGk888QSLFi0iJCSEuXPnMnjw4Cpft3HjRkaMGMHQjh3JOPtsXke89XoUuxjvimwNl5jXo9W9gP8B0/DvqsnVwL1IBY7myMTuIL6/R+jR6jSgD3AhUhKznY/HoVCAWMc8qZUcVisHr7yS4g0biL74Ylp//nmFRnrlMUZEEHXeeQDkLlhQ6ff6czGXXeb2uPTV/YYeiHRbXDdBQv+l7o+l1kQOGoS5aVMchYUcefBBHFaXw6c0JYWURx8Fqi6aXlv0zmxmwP2WNIpgpxR4E0kYPIIImwi/jujEGJDEykaIbeUD50+FC03TmDp1Kp999hlGo5FZs2ZxnvNmpGO321myZAk33HADN9xwA0uWLMHhcGAA7kDKIF6IdHU0IMK3AM96LR2I/aQAcimkXgAAHHNJREFUSZAKwRWtXgH099B+3MGAlLebDFyKRItzkOhxBt5f5SxBLCmbkGv1QGA0koSqrt0Kf9EeCcx5woes2e0cvukmCn/9lcizzqLtwoUYQ6sP+zUZNw6AtGefxbp7d9nzhWvWkDV7NsbYWBqNHu322EoQnRRDw06kd9sScwoyO/FUyZW8xYs5Vq6EDIBWUsLuga5KpM0mTyZ2+HCM4eG0mj2bA9ddR/Z//0vB8uVEnH46juJiitaswZGfT0S/fjSdMMHtcVmQZZ12SJRI0bD5HolYH0SsF4E+QzcgJdl2Ic0M5gJTESHYkPjhhx9ISkritNNOq/D8zJkzee+998r+ffnll5f9rqCggAULFvB///d/HD16FLvdzrFjxwCIiIjg1ltvBVyVOX5DGvv8iFw3rEh0We/MZsJVrupkaEgE3I5M8G3O7UQgQnEk0j3SV97q2hCDiNq+wJdIYu1BYA9Swi8Jz60CaogNJQWpXJKIRKv7IQmoSlQr/E035D6R4YFtZb79NnnffAOAOTGRI/ffX+Xrkl55BXOi1O6JvuACEh55hMw33mB3nz7EXHghjpISCpYtA4eD1p99htkD3az1Rjmn4J9CF4GC2+JaP2A8VfvZlp5O0dq1FZ/UtArP2dJde4u76io6r1tH+iuvULhiBflLlmAIDSW0c2fir7+eJmPHYoxwP6aod6H0TNVsRTBzAPgBKXnXicCzgpwIA9IBchMisj8H7vLriHzLjh07GDNmDJ07d2blypVlz8+ePZuXX34ZgGeffZYbb7wRgOzsbD744AO++uorCp1Z9vHx8VitVlq0aEFGRgZNmjSpINQNwLnOxxHEhvNfpHqLLo71CkvlRXZ5oa3hEtX660yIqNbtH2OAawn8ib4exe6CTDpWIwL4CNJ4pjGQgAjx2pY0tCMR/DzgmPP/LZBjvCvStr5vLbepUHiLbrgarLhbvtienV32b11kV0WzZ56BRFdhzBavv05Enz5kvv02+cuWYQgJIfr882k6aRJRQ4a4MSIXeqOc7h7ZWvDitrjuihwwDuTG4e4GG48aReNRo2r1nsi+fWn72Wdu7rl6LMgNQInrhk0pEvXdh7QhD9R6vifCiAidrUh1k9MRAdIQ+PNPcZu3Ktel7LPPPmPKlCkAPPnkk4wZM6bsdzNmzGDx4sUAdOzYkdtuu40VK1awfPlyoqOjcTgchISE0LNnzyr31xL4D/A40thnPbDZ+cjBFZGGilYJXXCHIaK6NRKF7QMMRcRqsAnGGCTRcRjy+VcgE7xUxBO9D/kuYpDVlCjkXqL3F3A4f1+CCOryNcZjkFWZZkijprMInk6RioZDa8TCZcIVrKsrzZ55RoRzHWg0ahSNaqmxaoMVOX8bulZyW1xHIlaJg4gAra/LzBZESLnfSF0RzHyP1MrNREROMBKDJJrtBj5FIn319bwtz3pngrMeaf72228ZP348APfffz9jx46t8Prhw4djMpkYPnw4gwcP5rnnnmP58uWYzWZuu+02Zs+eTdeuXYmspusZiPVhuPMBIqQPICJzFyISi5GJWzgiquMQu1EfJLJbXzAjE7rTkSoea5Hv4ggimPOdPzMRMV0+OUqP3scgEeooJPLdGrku9ydw8x4UCgMiOHfjvrgOVPTa1mqV30NlCLsjF8li6udNuhS5wIejSjg1ZJKpaAcJ8e9w3KItLnvIV0hCXn1HF9f9+/dn2bJlPPDAA2iaxu23387kyZMrlbA655xzOOeccwB47bXX+OKLLzAYDDz//PNs2bIFgL5V1Nc/GXqCaXt3Pkw9oJXzAXJ9TQUOAYcRm0cJshpqR8413RbTCvG3t0ZWjxSKYKEXsBSZQMb5eSzeoAg5Txsj52hDxiPi+hzga0R8JBJ8S5YnIwe5iJ9B8PhrFZ5nGRJhiyb47CDHY0Qi1tsQH+w1BM7FXqNqYeVOE4Zjx45x6NAhDAYDDoeD0aNHY7PZGDFiBM8//zwWi4WMjAxat67cKPz999/ngw8+AOCZZ55h+PDhfPLJJwD06dOnjiNSlMeIRKNbINU9FIr6yFVIv4EMJGgXzAGaqshB7iNX0bCTGcGD4roVEm3Ip35FExxIFnpr4AY/j0XhPwqQBKyj1B9rUCyyjJ6G+K/dr3Bae4qQaOXBcj/TnL9z4GrcpDckaI1E3fVHM2omuDds2ABA+/bteeCBBygpKeGiiy5iwoQJvPDCC3zyySfk5eWxZMmSCtHozz77jNdffx2AJ554guuuuw6LxcL27duBukWuFQpFw6QNMAQR17kEf5CmPKWIfTYJuM7PYwkEPCKuzUg74L24orz1hQLEM9kayT5XNExWI8vWoYjns76QhEwYfkdqEvsi2pAP/AGsce5brwet+22LqLrRQiiyaqAnven/7o5M8LtyYqG9YcMGNE3j0KFD2Gw2OnToQHR0NIMHD6a0VKr0d+rUiaSkpLL3LFq0iOeeew6ABx54gFHOJKBt27Zht9tp2rRphdcrFArFybgB+BVZ6W9M/Ynw5iLX4yEoSwh4sPX7NcBsJOqk1zmsD+Qg3e2uRZalFQ0PDSkjdhQRo/XJ9pSIVGpIRrrZeSsOqyGT79+ADchEJRUpoxaGSywnItYrvUyd/t7yzVPykSh3IXIB245MftohInsglUvUrVu3jtzcXEwmE6GhoezZs4d9+/YBMGDAAO655x4uvvhiTM4OZ0uXLmXSpEkA3H777dxfro5s8+bNad68Oddee22VrYYVCoXiRAxFVt3SkOtZfQhG6iv8rYAb/TyWQMFj4ropcD5ywGQj1QiCnWLE+xmPTB4UDZNtiF2hAPczoDM2buTIsmWkrVtH+tq1FKWkYAoL4w6LpcrXFxw+zKHvvyd97VrS1q0jd+dO0DSuWLOGpgPdd6cakXP1KBJN8Ya43gx8h4jro4ioDkcmKqdyct+hXo7NjAjvZs7nNURgpyINffYglVy+BgYjvr8ooKSkhBUrVlBaWorNZiMiIgKz2cxll13GfffdV8na8ccffzB+/HgcDgfXXnstTzzxRAUR3aJFC3755Ze6fRkKhaJBY0JsE3sQe0g0wR+9zkGCJG2RyYPCg+Ia4Fbgf0gnriICv8FBdWiIhzwRKaFVn7xRitrxFzJpbIL7qxebpk/n4Lff1vj1B77+mj8ffdTNvVZPc+QzbsNVO9gTFCKNalYi0fEs5DvsgWeqChmc2+mERK3TkbJue5GKE5uQa1LB+vUUFRUB0KxZM8aMGcOYMWOqTF7csGEDDz/8MDabjWHDhjFlyhQVnVYoFB7lRuALZOUuk+BuE25FAqptgAcJ/omCp/CouO6NHDQfIdGkdgTvF52JfDntgXF+HovCvxxArAiecNc2HTSIxr1706R/fxL792de8+rXeGI6dKDHo4+WvX7lmDGk/v67B0biIhw51guRCP0pHtjmJqSG9m5EWCcBA/DwBaccZuc+miPLk3pUKB0Y3L8/Fw0bRucOHXj22WeJja16IXbr1q3cd999WCwWzj77bF544YUym4hCoVB4ihjgGeAe5JobTXDWaNeDkI0R58Ll/h1OQOHxe91YJFkpH7mxNav21YGJBVnmaAs8BTTy62gU/sSGq8GFJ6KtvZ98slavb3vFFbS94goP7Ll6opHP6K64LgLmIdHqXYitqie+SwI1IDaufshn+QvIDg2l+w8/cDsn9jf++++/3HXXXRQWFnLGGWfw2muvERJS3wplKRSKQOEspBDE/yECtQ3BF4zMRq65bYAp1K98JHfx+N8yCpiKiOp85GYbTGhI1D0RqZ5woX+Ho/AzR5CILtSfJN2qiMElrutKHvAK0mhnIyLY++Kf6ipGZNWpO5KsuRp4DRH9x7NlyxbuvPNOcnNz6d27N++88w7h4fWxf5pCoQgkngA6IpWQMv08ltqi20GaAxMIbmuLN/DKROkMxB7SDElgKvXGTryAvsSh20Ge8u9wFAHAQWSSGE39npWXj1zXhSzgJeBPxAbSAzmH/B2JiUWi2KFINZQ5SIc0nQ0bNjB69Gjy8/M57bTT+PDDD4mKUq2iFAqF99HtIc2Q62+2X0dTc0qRwFMicAH+6ZEQ6Hjt3jcW8WDHI5Ejm7d25CE0xKNpAVoiB7yygyj0KiGesIQEMtFIhP4YktRYG3KBV4H1QApy3gdSeSm9G2VjRGB/BiwH1qxZw913301RUREDBw7k/fffV8JaoVD4lLOAh5AydtnICmAgo1sl45HAxXTqd+CprnhNXEcB7yF+y2gCW2BryJJMAXKAT0U1jFEIOcjyVzAmm9SGEOQCWYqI5ZpSCLyOJDCmIcI6EL8rAxJJbwr8DbyyYgW3OZMXhw4dyqxZs4iICMSRKxSK+s49wB1IYC8Due8EIqWIlotGtN37iMhWVMarq7ZNkcYypyJi+xCBZxHREFGQj3RhnIDUx1UoQI5XB/63N/gCE2Cn5ueoBsxF/NXJiBUk0H3pbYBGmsbKiRPJKCnhjPPP580331Qea4VC4TcMwHjgdlwR7CzkGhsoWJESp7FIEOVDlM+6OrxVGauM1sDHyMzsH0RgNyUwWkiXIsvgdqQyyCTger+OSBFoNCRxbUA+a03F9Z/AWsRj3YvAjFgfjwHoaDBw+Pbb0YqKaPPggxjNXr8MKhQKRbUYgYmINnoXsdgVIXop1I/j0hChn4N4rPshEWslrKvHJ3eVVojAfgDp1paKWDCa4p+W4hria0oH4oAWwDRgmB/GolAEEgZq5p/LQZog7EIm0MHkSTcAQ+6+m41I6/QfUKtVCoXC/xgQ/3UL4EVEYB8GEhCt4mtvsxUJQBqRVb+LENtsvI/HEYz4LCDXBKnn+CDQwbnjA4jI9iV6lmsWIvrPBeajhLWiakKQY9Xh74H4AD1Cf7IZt7WkhOc2bGDDxo04kPMo2AhBOjvuBhbjXglChUKh8CQjgG+AS5Drax6iW0p8tH8HkoeWjNhAuiNJ66+jhHVN8el6aBjwMNLJZzKwFYli5yCVOSLx3sys1LmfXGQG2B6JpN+Kf6LniuAgFBGcdn8PxAfo4vr4Jcjc3Fw2bNjA2rVr5bF5M7lWKzZg+Nq1GNq29f1gPUAikjy0B/GOT8LHF0SFQqE4AS0R+8UCpH+AHsUOQwRuFJ7XSyWIRspDOvfq0erJyPVSUXP8ci/pDnyOJDt+hJj30xC7Rjwifj0heDWkmkEu4l2KRZawT0PKx7T3wD4U9ZvGyEWmtuXpgg2r82coUHz0KO8uWsSBAwdYt24dO3bsQNNcqTX5gCEhgZYXX0zCSdq3BzodocweshoY6t/hKBQKRRlGJA/sTKSPwK+InslCNFMcomvc6SXrQPRRDnIf0HVSe8SiMgxVaq8u+C1Qo0exr0RsGd8iB0sOshwRhYiacOdrayK2NWTmZUXqVRcgB0UjIAkYjByo59AwEtQU7tMWSTA54qHtHVq8mE3Tp1d4zl5SwrcDB5b9v+/kybQZPhyAoqNHWXb11WW/y9m2DYCVY8Zgjhanc5vhw+k7ebJb4ypAzrkWwKB+/UhLS8NgMBAaGkpoaCidO3fmzDPPpMmAAfx5xhkc6NCBAQZD0J9HIciy6xHgd6TmrLqRKBSKQKIl8AYSvf4K+BrXqv9BRM/oWqm8Zjr+WuZANJIF0Un6IwwR6q2QMsQ3AoNQOskd/L4K2hZ4HPFi/wR8idhFCpADIA85GEKQg8aMK+lKcz4cuA4S/SALR9pyNkME/HXOfSkUtaEdru6FGu4LL0t6Oulr11Z8UtMqPGdJTy/7t91qrfx6IPvff8v+HX/KKXUej8Nm4+B332Ht14/odu1oC/Tp04elS5eiaRp2u52QkBAOHz7M1q1bCW3alOyzzyapHghrnWbIDWoPsB/JCVEoFIpAowXwCHAfsAwJTG7GFVC0IlFtK677lX7P0vOGQnCJ8Fjnv5siSd3XOvehcB9D+eXeWr3RYPi3W7du3f4td5P3BBoirv8EtiHLtUdxHTx25CApf+AYkQMkDBFCpwDdkCLn5yIHkUJRFxzIxG8lUm4u0r/D8TjHVq9m+XXX4YiKos+77/Ls+edznqbx+uuv8+yzz1JSUkJMTAx2ux3NYKAAMA8axFXffFOvzqtdiCXmRuBOP49FoVAoakohsBPRS9uAf5FiEXZcAUhwaaVGiD4q/0hCrdhVRffu3dm2bds2TdO61/a9fo9cH48BEcU9yz2XhRw0OxC/pwWJZpuRG2IEEpXujkQa60tETeF/jLhKzRVQ/8R14mmn0XTQIJLXrGHL7bez5umnOe+uu3j00Ufp0qULDz74IMXFxbRv355ut97Kb/v3E3HBBW4J6/SNGzmybBnp69aRtnYtRSkpmMLCuNNiqfZ9u/77X7a9/TbZ27ZhCg2l6cCB9J00iWaDB7sxGiEJuSmtR1a5AqEOv0KhUJyMKKT2dL9yz1kQraSv6IcgWikc/5T0a4gEXORaoQg0PkfqtFuALv4dilfILy3l56eeonjePOKBm2++mRkzZhASEsI///zDbbfdxtGjR7HGx9Nk7lx6DxpEIzf2t/Sqqzj47bcVnjuZuF4zbhxbX3sNU0QErS66CLvFwpHly0HTOP+rr2hfzpdeVzYj3sYHgAvc3ppCoVAoghl3ItcqyKtQnIT+iC83A7D5eSzeIDk7m7ZXX83wp57CaDQyb948brrpJrKzs+nZsyc//fQTp/TtiyUnh/3XXUfGZ5+5tb+mgwbR7+mnuej77xmZmnrS1x/55Re2vvYaYQkJjNiyhYsWLWLYTz9x+YoVGEwmVtxxB9bsbLfGBFJqKhvxXisUCoVCUVeUuFYoTkJH5yMe6VZVn7ADf993H9uvu441r75K165dsdvt/PHHHwwfPpw9e/bQrFkzJn/zDc2vvBKDzcbaxx4jy40Vqz5PPslpU6fS9rLLiGzW7KSv/+fVVwHoO2kScZ07lz3fbNAgTr33Xkpyc9n50Ud1Ho+Obv1RDWUUCoVC4Q5KXCsUJ8GAlG9MQkoh1c1IFZikAc2uv574Fi1wWK1s374dh8NBVlYWGzZs4IwzzuDTTz8lNTycju+9R4enn6blBRcQ1cI3OeU2i4WU5csBaH/ttZV+rz936Pvv3d5XNFLvNR1JElIoFAqFoi4oca1Q1ICBSGlHA1JbtD6gIZV4et5wA7OWL+eFF15gwIABhIaGlpXhy83NZdy4cRwECg0GTr3/fs799FPCGrnjuq45uTt2YLdaCW/ShOhWlRutJ/aTNJ6sv/92e19mpOJQISp6rVAoFIq6E3DVQhSKQCQM6ZK1D0hGLCLBnnG9e8UKkpctI23dOtZt24bmcJT9rkmTJoSFhZGYmMio0aP5F7FMRPt4jAWHDgEQVYWwBgiJiiI0Ph5rdjYl+fmExrhX5yMGlzWkm1tbUigUCkVDRYlrhaKGnA/8hnTGOoZEsoOVjB07WH3TTZiRUpYRQNu2benfvz9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      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=150)\n",
    "expl.visualize_subgraph(-1, test_dataset[0].edge_index, edge_masks[0], seed=13, node_alpha=node_masks[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b07488bd-12aa-4a3a-90f7-01ce8284f5de",
   "metadata": {},
   "source": [
    "#### Not quite easy to interpret... Try to show it as molecules:\n",
    "\n",
    "##### The molecules might be chemically invalid due to missing information in the graphs about e.g. charge states, ...\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f34780-f2b1-4ca4-9d2d-7885da788ba6",
   "metadata": {},
   "source": [
    "#### Nodes only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "16c62a3d-79c9-4cd6-8270-38a90cfe5560",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize_in_molecule(molecules[0], None, node_masks[0], 0.7, size=(500, 250), add_atom_numbers=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b49b3c-fc05-4522-86aa-8999ef58fa97",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "#### Problem: How to deal with the two edges between two nodes?\n",
    "\n",
    "#### `edge_func=min`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "84eb6f0b-fece-482c-9c5d-17aec667f3b6",
   "metadata": {},
   "outputs": [
    {
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       "<ellipse cx=\"274.0\" cy=\"138.9\" rx=\"11.1\" ry=\"11.1\" style=\"fill:#FF0000A8;fill-rule:evenodd;stroke:#FF0000A8;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"273.8\" cy=\"194.4\" rx=\"11.1\" ry=\"11.1\" style=\"fill:#FF0000BC;fill-rule:evenodd;stroke:#FF0000BC;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"370.1\" cy=\"139.2\" rx=\"11.1\" ry=\"11.1\" style=\"fill:#FF0000AB;fill-rule:evenodd;stroke:#FF0000AB;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"322.1\" cy=\"111.3\" rx=\"11.1\" ry=\"11.1\" style=\"fill:#FF0000B7;fill-rule:evenodd;stroke:#FF0000B7;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"418.2\" cy=\"111.6\" rx=\"11.1\" ry=\"11.1\" style=\"fill:#FF00009E;fill-rule:evenodd;stroke:#FF00009E;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"466.2\" cy=\"139.6\" rx=\"11.1\" ry=\"11.4\" style=\"fill:#FF00009D;fill-rule:evenodd;stroke:#FF00009D;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"425.5\" cy=\"56.3\" rx=\"20.2\" ry=\"11.4\" style=\"fill:#FF00009D;fill-rule:evenodd;stroke:#FF00009D;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"81.8\" cy=\"138.4\" rx=\"11.1\" ry=\"11.1\" style=\"fill:#FF00009C;fill-rule:evenodd;stroke:#FF00009C;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"33.8\" cy=\"110.6\" rx=\"11.1\" ry=\"11.4\" style=\"fill:#FF00009E;fill-rule:evenodd;stroke:#FF00009E;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<ellipse cx=\"88.7\" cy=\"194.0\" rx=\"20.2\" ry=\"11.4\" style=\"fill:#FF00009D;fill-rule:evenodd;stroke:#FF00009D;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-0 atom-0 atom-1\" d=\"M 225.7,222.0 L 177.7,194.1\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-0 atom-0 atom-1\" d=\"M 224.1,208.2 L 190.5,188.7\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-1 atom-0 atom-9\" d=\"M 225.7,222.0 L 273.8,194.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-1 atom-0 atom-9\" d=\"M 227.4,208.3 L 261.1,188.9\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-2 atom-1 atom-2\" d=\"M 177.7,194.1 L 177.9,138.7\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-2 atom-1 atom-2\" d=\"M 188.8,185.8 L 189.0,147.0\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-3 atom-2 atom-3\" d=\"M 177.9,138.7 L 129.9,110.8\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-3 atom-2 atom-3\" d=\"M 176.3,124.9 L 142.7,105.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-4 atom-2 atom-7\" d=\"M 177.9,138.7 L 226.0,111.1\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-4 atom-2 atom-7\" d=\"M 190.6,144.1 L 224.3,124.8\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-5 atom-3 atom-4\" d=\"M 129.9,110.8 L 130.1,55.3\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-5 atom-3 atom-4\" d=\"M 141.0,102.5 L 141.2,63.6\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-6 atom-3 atom-19\" d=\"M 129.9,110.8 L 109.5,122.5\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-6 atom-3 atom-19\" d=\"M 109.5,122.5 L 89.1,134.2\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-7 atom-4 atom-5\" d=\"M 130.1,55.3 L 178.2,27.7\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-7 atom-4 atom-5\" d=\"M 142.8,60.8 L 176.5,41.5\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-8 atom-5 atom-6\" d=\"M 178.2,27.7 L 226.2,55.6\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-8 atom-5 atom-6\" d=\"M 179.8,41.5 L 213.4,61.0\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-9 atom-6 atom-7\" d=\"M 226.2,55.6 L 226.0,111.1\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-9 atom-6 atom-7\" d=\"M 215.1,63.9 L 214.9,102.7\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-10 atom-6 atom-15\" d=\"M 226.2,55.6 L 274.3,28.0\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-10 atom-6 atom-15\" d=\"M 238.9,61.1 L 272.6,41.7\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-11 atom-7 atom-8\" d=\"M 226.0,111.1 L 274.0,138.9\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-11 atom-7 atom-8\" d=\"M 227.6,124.8 L 261.2,144.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-12 atom-8 atom-9\" d=\"M 274.0,138.9 L 273.8,194.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-12 atom-8 atom-9\" d=\"M 262.9,147.2 L 262.7,186.1\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-13 atom-8 atom-13\" d=\"M 274.0,138.9 L 322.1,111.3\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-13 atom-8 atom-13\" d=\"M 286.7,144.4 L 320.4,125.1\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-14 atom-9 atom-10\" d=\"M 273.8,194.4 L 321.8,222.3\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-14 atom-9 atom-10\" d=\"M 286.6,189.0 L 320.2,208.5\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-15 atom-10 atom-11\" d=\"M 321.8,222.3 L 369.9,194.7\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-15 atom-10 atom-11\" d=\"M 323.5,208.5 L 357.2,189.2\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-16 atom-11 atom-12\" d=\"M 369.9,194.7 L 370.1,139.2\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-16 atom-11 atom-12\" d=\"M 358.8,186.4 L 359.0,147.5\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-17 atom-12 atom-13\" d=\"M 370.1,139.2 L 322.1,111.3\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-17 atom-12 atom-13\" d=\"M 357.3,144.6 L 323.7,125.1\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-18 atom-12 atom-16\" d=\"M 370.1,139.2 L 390.5,127.5\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-18 atom-12 atom-16\" d=\"M 390.5,127.5 L 410.9,115.8\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-19 atom-13 atom-14\" d=\"M 322.1,111.3 L 322.3,55.9\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-19 atom-13 atom-14\" d=\"M 311.0,103.0 L 311.2,64.2\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-20 atom-14 atom-15\" d=\"M 322.3,55.9 L 274.3,28.0\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-20 atom-14 atom-15\" d=\"M 309.5,61.3 L 275.9,41.8\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;stroke-dasharray:6,6\"/>\n",
       "<path class=\"bond-21 atom-16 atom-17\" d=\"M 422.8,120.7 L 438.9,130.1\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-21 atom-16 atom-17\" d=\"M 438.9,130.1 L 455.0,139.4\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-21 atom-16 atom-17\" d=\"M 428.3,111.1 L 444.5,120.5\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-21 atom-16 atom-17\" d=\"M 444.5,120.5 L 460.6,129.9\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-22 atom-16 atom-18\" d=\"M 418.2,102.4 L 418.3,83.9\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-22 atom-16 atom-18\" d=\"M 418.3,83.9 L 418.4,65.5\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-23 atom-19 atom-20\" d=\"M 77.2,129.3 L 61.1,119.9\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-23 atom-19 atom-20\" d=\"M 61.1,119.9 L 45.0,110.6\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-23 atom-19 atom-20\" d=\"M 71.7,138.9 L 55.5,129.5\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-23 atom-19 atom-20\" d=\"M 55.5,129.5 L 39.4,120.1\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-24 atom-19 atom-21\" d=\"M 81.8,147.6 L 81.7,166.2\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"bond-24 atom-19 atom-21\" d=\"M 81.7,166.2 L 81.6,184.7\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
       "<path class=\"atom-16\" d=\"M 414.7 103.8 L 419.9 112.1 Q 420.4 112.9, 421.2 114.4 Q 422.0 115.9, 422.1 116.0 L 422.1 103.8 L 424.2 103.8 L 424.2 119.5 L 422.0 119.5 L 416.5 110.4 Q 415.8 109.3, 415.2 108.1 Q 414.5 106.9, 414.3 106.5 L 414.3 119.5 L 412.3 119.5 L 412.3 103.8 L 414.7 103.8 \" fill=\"#0000FF\"/>\n",
       "<path class=\"atom-17\" d=\"M 459.0 139.6 Q 459.0 135.8, 460.8 133.7 Q 462.7 131.6, 466.2 131.6 Q 469.7 131.6, 471.5 133.7 Q 473.4 135.8, 473.4 139.6 Q 473.4 143.4, 471.5 145.6 Q 469.6 147.7, 466.2 147.7 Q 462.7 147.7, 460.8 145.6 Q 459.0 143.4, 459.0 139.6 M 466.2 145.9 Q 468.6 145.9, 469.9 144.3 Q 471.2 142.7, 471.2 139.6 Q 471.2 136.5, 469.9 134.9 Q 468.6 133.4, 466.2 133.4 Q 463.8 133.4, 462.5 134.9 Q 461.2 136.5, 461.2 139.6 Q 461.2 142.7, 462.5 144.3 Q 463.8 145.9, 466.2 145.9 \" fill=\"#FF0000\"/>\n",
       "<path class=\"atom-18\" d=\"M 411.2 56.2 Q 411.2 52.4, 413.0 50.3 Q 414.9 48.2, 418.4 48.2 Q 421.9 48.2, 423.7 50.3 Q 425.6 52.4, 425.6 56.2 Q 425.6 60.0, 423.7 62.2 Q 421.8 64.3, 418.4 64.3 Q 414.9 64.3, 413.0 62.2 Q 411.2 60.0, 411.2 56.2 M 418.4 62.6 Q 420.8 62.6, 422.1 61.0 Q 423.4 59.4, 423.4 56.2 Q 423.4 53.1, 422.1 51.6 Q 420.8 50.0, 418.4 50.0 Q 416.0 50.0, 414.7 51.5 Q 413.4 53.1, 413.4 56.2 Q 413.4 59.4, 414.7 61.0 Q 416.0 62.6, 418.4 62.6 \" fill=\"#FF0000\"/>\n",
       "<path class=\"atom-18\" d=\"M 427.5 48.4 L 429.6 48.4 L 429.6 55.1 L 437.6 55.1 L 437.6 48.4 L 439.8 48.4 L 439.8 64.1 L 437.6 64.1 L 437.6 56.8 L 429.6 56.8 L 429.6 64.1 L 427.5 64.1 L 427.5 48.4 \" fill=\"#FF0000\"/>\n",
       "<path class=\"atom-19\" d=\"M 78.3 130.5 L 83.5 138.8 Q 84.0 139.7, 84.8 141.1 Q 85.6 142.6, 85.7 142.7 L 85.7 130.5 L 87.7 130.5 L 87.7 146.2 L 85.6 146.2 L 80.1 137.1 Q 79.4 136.1, 78.7 134.8 Q 78.1 133.6, 77.9 133.2 L 77.9 146.2 L 75.8 146.2 L 75.8 130.5 L 78.3 130.5 \" fill=\"#0000FF\"/>\n",
       "<path class=\"atom-20\" d=\"M 26.6 110.5 Q 26.6 106.7, 28.5 104.6 Q 30.3 102.5, 33.8 102.5 Q 37.3 102.5, 39.2 104.6 Q 41.0 106.7, 41.0 110.5 Q 41.0 114.3, 39.1 116.5 Q 37.3 118.7, 33.8 118.7 Q 30.4 118.7, 28.5 116.5 Q 26.6 114.4, 26.6 110.5 M 33.8 116.9 Q 36.2 116.9, 37.5 115.3 Q 38.8 113.7, 38.8 110.5 Q 38.8 107.4, 37.5 105.9 Q 36.2 104.3, 33.8 104.3 Q 31.4 104.3, 30.1 105.9 Q 28.8 107.4, 28.8 110.5 Q 28.8 113.7, 30.1 115.3 Q 31.4 116.9, 33.8 116.9 \" fill=\"#FF0000\"/>\n",
       "<path class=\"atom-21\" d=\"M 74.4 193.9 Q 74.4 190.1, 76.3 188.0 Q 78.1 185.9, 81.6 185.9 Q 85.1 185.9, 87.0 188.0 Q 88.8 190.1, 88.8 193.9 Q 88.8 197.7, 86.9 199.9 Q 85.1 202.0, 81.6 202.0 Q 78.2 202.0, 76.3 199.9 Q 74.4 197.7, 74.4 193.9 M 81.6 200.3 Q 84.0 200.3, 85.3 198.7 Q 86.6 197.0, 86.6 193.9 Q 86.6 190.8, 85.3 189.3 Q 84.0 187.7, 81.6 187.7 Q 79.2 187.7, 77.9 189.2 Q 76.6 190.8, 76.6 193.9 Q 76.6 197.1, 77.9 198.7 Q 79.2 200.3, 81.6 200.3 \" fill=\"#FF0000\"/>\n",
       "<path class=\"atom-21\" d=\"M 90.7 186.1 L 92.9 186.1 L 92.9 192.8 L 100.9 192.8 L 100.9 186.1 L 103.0 186.1 L 103.0 201.8 L 100.9 201.8 L 100.9 194.5 L 92.9 194.5 L 92.9 201.8 L 90.7 201.8 L 90.7 186.1 \" fill=\"#FF0000\"/>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize_in_molecule(molecules[0], edge_masks[0], node_masks[0], 0.7, size=(500, 250), edge_func=min)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28cd7693-a532-46ef-a4d0-d784b3c6ffd4",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "#### `edge_func=max`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4a17c3e5-58fb-4202-b35f-28cd1c66bddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<path class=\"atom-21\" d=\"M 74.4 193.9 Q 74.4 190.1, 76.3 188.0 Q 78.1 185.9, 81.6 185.9 Q 85.1 185.9, 87.0 188.0 Q 88.8 190.1, 88.8 193.9 Q 88.8 197.7, 86.9 199.9 Q 85.1 202.0, 81.6 202.0 Q 78.2 202.0, 76.3 199.9 Q 74.4 197.7, 74.4 193.9 M 81.6 200.3 Q 84.0 200.3, 85.3 198.7 Q 86.6 197.0, 86.6 193.9 Q 86.6 190.8, 85.3 189.3 Q 84.0 187.7, 81.6 187.7 Q 79.2 187.7, 77.9 189.2 Q 76.6 190.8, 76.6 193.9 Q 76.6 197.1, 77.9 198.7 Q 79.2 200.3, 81.6 200.3 \" fill=\"#FF0000\"/>\n",
       "<path class=\"atom-21\" d=\"M 90.7 186.1 L 92.9 186.1 L 92.9 192.8 L 100.9 192.8 L 100.9 186.1 L 103.0 186.1 L 103.0 201.8 L 100.9 201.8 L 100.9 194.5 L 92.9 194.5 L 92.9 201.8 L 90.7 201.8 L 90.7 186.1 \" fill=\"#FF0000\"/>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize_in_molecule(molecules[0], edge_masks[0], node_masks[0], 0.7, size=(500, 250), edge_func=max)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f428c6a2-fc25-4f34-90a5-9ca69e98e088",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "#### `edge_func=mean`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7fbc2cd6-0174-4833-a3d3-6b13b8b06753",
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize_in_molecule(molecules[0], edge_masks[0], node_masks[0], 0.7, size=(500, 250), edge_func=mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf0b397c-f598-4c5e-92f9-68e93deb397e",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "---\n",
    "### PyTorch Captum\n",
    "\n",
    "##### Code was adapted from a [Google Colab notebook](https://colab.research.google.com/drive/1fLJbFPz0yMCQg81DdCP5I8jXw9LoggKO?usp=sharing) from PyG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "daf39773-5ba8-4472-a870-1396d17193f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_forward(edge_mask, data, model):\n",
    "    batch = torch.zeros(data.x.shape[0], dtype=int)\n",
    "    out = model(data.x, data.edge_index, batch, edge_mask)\n",
    "    return out\n",
    "\n",
    "\n",
    "def explain(data, model, target=0):\n",
    "    input_mask = torch.ones(data.edge_index.shape[1]).requires_grad_(True)\n",
    "    ig = IntegratedGradients(model_forward)\n",
    "    mask = ig.attribute(input_mask, target=target,\n",
    "                        additional_forward_args=(data, model),\n",
    "                        internal_batch_size=data.edge_index.shape[1])\n",
    "    edge_mask = np.abs(mask.detach().numpy())\n",
    "    if edge_mask.max() > 0:  # avoid division by zero\n",
    "        edge_mask = edge_mask / edge_mask.max()\n",
    "    return edge_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8f560cc9-909a-4034-9bbd-d34e7c93022f",
   "metadata": {},
   "outputs": [],
   "source": [
    "capt_edge_masks = []\n",
    "for data in test_dataset:\n",
    "    capt_edge_masks.append(explain(data, model, target=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "81bb25fb-8c34-46d1-93d4-8e96a3f70de1",
   "metadata": {},
   "outputs": [
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       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize_in_molecule(molecules[0], edge_masks[0], None, 0.6, size=(500, 250))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f977533f-2138-42a4-97d4-56833df848b8",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "---\n",
    "### Interactive Widget for Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "508a0ac0-c77f-41fe-bc27-818f78d57fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_both(test_data_id):\n",
    "    expl = visualize_in_molecule(molecules[test_data_id], edge_masks[test_data_id], node_masks[test_data_id], threshold=0.7, size=(500, 300))\n",
    "    capt = visualize_in_molecule(molecules[test_data_id], capt_edge_masks[test_data_id], None, threshold=0.6, size=(500, 300))\n",
    "    print(f'\\nLeft: GNNExplainer (Threshold: 0.7)\\nRight: PyTorch Captum (Threshold: 0.6)\\nTrue Class: {int(test_dataset[test_data_id].y[0])}\\n')\n",
    "    return HTML('<div style=\"white-space: nowrap\">{}{}</div>'.format(expl.data, capt.data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "db459f36-5ca0-4afd-9090-9ff96f05e329",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce4f0873f0854320a426fc62ca12b6e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='test_data_id', options=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(show_both, test_data_id=range(len(test_dataset)));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d065d5-9437-48fd-a3ad-81df7fb16e20",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "---\n",
    "### Resources\n",
    "- https://chrsmrrs.github.io/datasets/docs/datasets/ (Datasets)\n",
    "- https://pytorch-geometric.readthedocs.io/en/latest/index.html (PyG)\n",
    "- https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html (PyG Colab Notebooks)\n",
    "- https://www.youtube.com/c/DeepFindr (Nice YouTube channel about (Explainable) AI)\n",
    "- https://captum.ai/ (PyTorch Captum)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65b2a6c0-443c-4da6-815c-6a92a8a961aa",
   "metadata": {},
   "source": [
    "### Disclaimer\n",
    "The configurations for modelling as well as the preprocessing might be suboptimal for the specific task and dataset. Since the key point of this notebook is to show an example of how to interpret a trained GCN, configuration and dataset optimisation was neglected. Also the shown molecules might not be chemically correct due to missing information about e.g. charge states, etc."
   ]
  }
 ],
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   "pygments_lexer": "ipython3",
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      "model_name": "LayoutModel",
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       ],
       "description": "test_data_id",
       "index": 0,
       "layout": "IPY_MODEL_e5d917d6e4f24f72a014d6ff649ae3f7",
       "style": "IPY_MODEL_59ce0b799ce146d08954d62f49cb7f55"
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       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "\nLeft: GNNExplainer (Threshold: 0.7)\nRight: PyTorch Captum (Threshold: 0.6)\nTrue Class: 1\n\n"
        },
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